[
  {
    "path": ".github/workflows/format.yml",
    "content": "name: SAM3/ufmt\non:\n  pull_request:\n    branches:\n    - main\njobs:\n  ufmt_check:\n    runs-on: ubuntu-latest\n    steps:\n      - name: Install ruff-api\n        run: pip install ruff-api==0.1.0\n      - name: Check formatting\n        uses: omnilib/ufmt@action-v1\n        with:\n          path: sam3 scripts\n          python-version: \"3.12\"\n          black-version: \"24.2.0\"\n          usort-version: \"1.0.2\"\n"
  },
  {
    "path": ".gitignore",
    "content": "# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n# C extensions\n*.so\n\n# Distribution / packaging\n.Python\nbuild/\ndevelop-eggs/\ndist/\ndownloads/\neggs/\n.eggs/\nlib/\nlib64/\nparts/\nsdist/\nvar/\nwheels/\n*.egg-info/\n.installed.cfg\n*.egg\nMANIFEST\n\n# PyInstaller\n#  Usually these files are written by a python script from a template\n#  before PyInstaller builds the exe, so as to inject date/other infos into it.\n*.manifest\n*.spec\n\n# Installer logs\npip-log.txt\npip-delete-this-directory.txt\n\n# Unit test / coverage reports\nhtmlcov/\n.tox/\n.nox/\n.coverage\n.coverage.*\n.cache\nnosetests.xml\ncoverage.xml\n*.cover\n.hypothesis/\n.pytest_cache/\n\n# Translations\n*.mo\n*.pot\n\n# Django stuff:\n*.log\nlocal_settings.py\ndb.sqlite3\n\n# Flask stuff:\ninstance/\n.webassets-cache\n\n# Scrapy stuff:\n.scrapy\n\n# Sphinx documentation\ndocs/_build/\n\n# PyBuilder\ntarget/\n\n# Jupyter Notebook\n.ipynb_checkpoints\n*-Copy*.ipynb\n\n# IPython\nprofile_default/\nipython_config.py\n\n# pyenv\n.python-version\n\n# celery beat schedule file\ncelerybeat-schedule\n\n# SageMath parsed files\n*.sage.py\n\n# Environments\n.env\n.venv\nenv/\nvenv/\nENV/\nenv.bak/\nvenv.bak/\n\n# Spyder project settings\n.spyderproject\n.spyproject\n\n# Rope project settings\n.ropeproject\n\n# mkdocs documentation\n/site\n\n# mypy\n.mypy_cache/\n.dmypy.json\ndmypy.json\n\n# Pyre type checker\n.pyre/\n\n# PyCharm\n.idea/\n\n# VS Code\n.vscode/\n*.code-workspace\n\n# Model weights and checkpoints\n*.pth\n*.pt\n*.bin\n*.ckpt\n*.safetensors\nweights/\ncheckpoints/\nsam3_logs/\n\n# Data files\n*.h5\n*.hdf5\n*.pkl\n*.pickle\n*.npy\n*.npz\n\n# Logs\nlogs/\nruns/\ntensorboard/\n\n# OS specific\n.DS_Store\nThumbs.db\n\n# BPE vocabulary files\n*.bpe\n*.vocab\n"
  },
  {
    "path": "CODE_OF_CONDUCT.md",
    "content": "# Code of Conduct\n\n## Our Pledge\n\nIn the interest of fostering an open and welcoming environment, we as\ncontributors and maintainers pledge to make participation in our project and\nour community a harassment-free experience for everyone, regardless of age, body\nsize, disability, ethnicity, sex characteristics, gender identity and expression,\nlevel of experience, education, socio-economic status, nationality, personal\nappearance, race, religion, or sexual identity and orientation.\n\n## Our Standards\n\nExamples of behavior that contributes to creating a positive environment\ninclude:\n\n* Using welcoming and inclusive language\n* Being respectful of differing viewpoints and experiences\n* Gracefully accepting constructive criticism\n* Focusing on what is best for the community\n* Showing empathy towards other community members\n\nExamples of unacceptable behavior by participants include:\n\n* The use of sexualized language or imagery and unwelcome sexual attention or\nadvances\n* Trolling, insulting/derogatory comments, and personal or political attacks\n* Public or private harassment\n* Publishing others' private information, such as a physical or electronic\naddress, without explicit permission\n* Other conduct which could reasonably be considered inappropriate in a\nprofessional setting\n\n## Our Responsibilities\n\nProject maintainers are responsible for clarifying the standards of acceptable\nbehavior and are expected to take appropriate and fair corrective action in\nresponse to any instances of unacceptable behavior.\n\nProject maintainers have the right and responsibility to remove, edit, or\nreject comments, commits, code, wiki edits, issues, and other contributions\nthat are not aligned to this Code of Conduct, or to ban temporarily or\npermanently any contributor for other behaviors that they deem inappropriate,\nthreatening, offensive, or harmful.\n\n## Scope\n\nThis Code of Conduct applies within all project spaces, and it also applies when\nan individual is representing the project or its community in public spaces.\nExamples of representing a project or community include using an official\nproject e-mail address, posting via an official social media account, or acting\nas an appointed representative at an online or offline event. Representation of\na project may be further defined and clarified by project maintainers.\n\nThis Code of Conduct also applies outside the project spaces when there is a\nreasonable belief that an individual's behavior may have a negative impact on\nthe project or its community.\n\n## Enforcement\n\nInstances of abusive, harassing, or otherwise unacceptable behavior may be\nreported by contacting the project team at <opensource-conduct@meta.com>. All\ncomplaints will be reviewed and investigated and will result in a response that\nis deemed necessary and appropriate to the circumstances. The project team is\nobligated to maintain confidentiality with regard to the reporter of an incident.\nFurther details of specific enforcement policies may be posted separately.\n\nProject maintainers who do not follow or enforce the Code of Conduct in good\nfaith may face temporary or permanent repercussions as determined by other\nmembers of the project's leadership.\n\n## Attribution\n\nThis Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4,\navailable at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html\n\n[homepage]: https://www.contributor-covenant.org\n\nFor answers to common questions about this code of conduct, see\nhttps://www.contributor-covenant.org/faq\n"
  },
  {
    "path": "CONTRIBUTING.md",
    "content": "# Contributing to sam3\nWe want to make contributing to this project as easy and transparent as\npossible.\n\n## Pull Requests\nWe actively welcome your pull requests.\n\n1. Fork the repo and create your branch from `main`.\n2. If you've added code that should be tested, add tests.\n3. If you've changed APIs, update the documentation.\n4. Make sure your code lints.\n5. If you haven't already, complete the Contributor License Agreement (\"CLA\").\n\n## Contributor License Agreement (\"CLA\")\nIn order to accept your pull request, we need you to submit a CLA. You only need\nto do this once to work on any of Facebook's open source projects.\n\nComplete your CLA here: <https://code.facebook.com/cla>\n\n## Issues\nWe use GitHub issues to track public bugs. Please ensure your description is\nclear and has sufficient instructions to be able to reproduce the issue.\n\nFacebook has a [bounty program](https://www.facebook.com/whitehat/) for the safe\ndisclosure of security bugs. In those cases, please go through the process\noutlined on that page and do not file a public issue.\n\n## License\nBy contributing to sam3, you agree that your contributions will be licensed\nunder the LICENSE file in the root directory of this source tree.\n"
  },
  {
    "path": "LICENSE",
    "content": "SAM License\nLast Updated: November 19, 2025\n\n“Agreement” means the terms and conditions for use, reproduction, distribution and modification of the SAM Materials set forth herein.\n\n\n“SAM Materials” means, collectively, Documentation and the models, software and algorithms, including machine-learning model code, trained model weights, inference-enabling code, training-enabling code, fine-tuning enabling code, and other elements of the foregoing distributed by Meta and made available under this Agreement.\n\n“Documentation” means the specifications, manuals and documentation accompanying\nSAM Materials distributed by Meta.\n\n\n“Licensee” or “you” means you, or your employer or any other person or entity (if you are entering into this Agreement on such person or entity’s behalf), of the age required under applicable laws, rules or regulations to provide legal consent and that has legal authority to bind your employer or such other person or entity if you are entering in this Agreement on their behalf.\n\n\n“Meta” or “we” means Meta Platforms Ireland Limited (if you are located in or, if you are an entity, your principal place of business is in the EEA or Switzerland) or Meta Platforms, Inc. (if you are located outside of the EEA or Switzerland).\n\n\n“Sanctions” means any economic or trade sanctions or restrictions administered or enforced by the United States (including the Office of Foreign Assets Control of the U.S. Department of the Treasury (“OFAC”), the U.S. Department of State and the U.S. Department of Commerce), the United Nations, the European Union, or the United Kingdom.\n\n\n“Trade Controls” means any of the following: Sanctions and applicable export and import controls.\n\nBy using or distributing any portion or element of the SAM Materials, you agree to be bound by this Agreement.\n\n\n1. License Rights and Redistribution.\n\n\na. Grant of Rights. You are granted a non-exclusive, worldwide, non-transferable and royalty-free limited license under Meta’s intellectual property or other rights owned by Meta embodied in the SAM Materials to use, reproduce, distribute, copy, create derivative works of, and make modifications to the SAM Materials.\n\nb. Redistribution and Use.\ni. Distribution of SAM Materials, and any derivative works thereof, are subject to the terms of this Agreement. If you distribute or make the SAM Materials, or any derivative works thereof, available to a third party, you may only do so under the terms of this Agreement and you shall provide a copy of this Agreement with any such SAM Materials.\n\n\nii.  If you submit for publication the results of research you perform on, using, or otherwise in connection with SAM Materials, you must acknowledge the use of SAM Materials in your publication.\n\n\niii. Your use of the SAM Materials must comply with applicable laws and regulations, including Trade Control Laws and applicable privacy and data protection laws.\niv. Your use of the SAM Materials will not involve or encourage others to reverse engineer, decompile or discover the underlying components of the SAM Materials.\nv. You are not the target of Trade Controls and your use of SAM Materials must comply with Trade Controls. You agree not to use, or permit others to use, SAM Materials for any activities subject to the International Traffic in Arms Regulations (ITAR) or end uses prohibited by Trade Controls, including those related to military or warfare purposes, nuclear industries or applications, espionage, or the development or use of guns or illegal weapons.\n2. User Support. Your use of the SAM Materials is done at your own discretion; Meta does not process any information nor provide any service in relation to such use.  Meta is under no obligation to provide any support services for the SAM Materials. Any support provided is “as is”, “with all faults”, and without warranty of any kind.\n\n\n3. Disclaimer of Warranty. UNLESS REQUIRED BY APPLICABLE LAW, THE SAM MATERIALS AND ANY OUTPUT AND RESULTS THEREFROM ARE PROVIDED ON AN “AS IS” BASIS, WITHOUT WARRANTIES OF ANY KIND, AND META DISCLAIMS ALL WARRANTIES OF ANY KIND, BOTH EXPRESS AND IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES OF TITLE, NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY RESPONSIBLE FOR DETERMINING THE APPROPRIATENESS OF USING OR REDISTRIBUTING THE SAM MATERIALS AND ASSUME ANY RISKS ASSOCIATED WITH YOUR USE OF THE SAM MATERIALS AND ANY OUTPUT AND RESULTS.\n\n4. Limitation of Liability. IN NO EVENT WILL META OR ITS AFFILIATES BE LIABLE UNDER ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, TORT, NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, ARISING OUT OF THIS AGREEMENT, FOR ANY LOST PROFITS OR ANY DIRECT OR INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL, EXEMPLARY OR PUNITIVE DAMAGES, EVEN IF META OR ITS AFFILIATES HAVE BEEN ADVISED OF THE POSSIBILITY OF ANY OF THE FOREGOING.\n\n5. Intellectual Property.\n\n\na. Subject to Meta’s ownership of SAM Materials and derivatives made by or for Meta, with respect to any derivative works and modifications of the SAM Materials that are made by you, as between you and Meta, you are and will be the owner of such derivative works and modifications.\n\nb. If you institute litigation or other proceedings against Meta or any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the SAM Materials, outputs or results, or any portion of any of the foregoing, constitutes infringement of intellectual property or other rights owned or licensable by you, then any licenses granted to you under this Agreement shall terminate as of the date such litigation or claim is filed or instituted. You will indemnify and hold harmless Meta from and against any claim by any third party arising out of or related to your use or distribution of the SAM Materials.\n\n6. Term and Termination. The term of this Agreement will commence upon your acceptance of this Agreement or access to the SAM Materials and will continue in full force and effect until terminated in accordance with the terms and conditions herein. Meta may terminate this Agreement if you are in breach of any term or condition of this Agreement. Upon termination of this Agreement, you shall delete and cease use of the SAM Materials. Sections 3, 4 and 7 shall survive the termination of this Agreement.\n\n7. Governing Law and Jurisdiction. This Agreement will be governed and construed under the laws of the State of California without regard to choice of law principles, and the UN Convention on Contracts for the International Sale of Goods does not apply to this Agreement. The courts of California shall have exclusive jurisdiction of any dispute arising out of this Agreement.\n\n\n8. Modifications and Amendments. Meta may modify this Agreement from time to time; provided that they are similar in spirit to the current version of the Agreement, but may differ in detail to address new problems or concerns. All such changes will be effective immediately. Your continued use of the SAM Materials after any modification to this Agreement constitutes your agreement to such modification. Except as provided in this Agreement, no modification or addition to any provision of this Agreement will be binding unless it is in writing and signed by an authorized representative of both you and Meta.\n"
  },
  {
    "path": "MANIFEST.in",
    "content": "include LICENSE\ninclude README.md\nrecursive-include examples *.py\nrecursive-include examples *.ipynb\nrecursive-include examples *.md\nrecursive-include tests *.py\n"
  },
  {
    "path": "README.md",
    "content": "# SAM 3: Segment Anything with Concepts\n\nMeta Superintelligence Labs\n\n[Nicolas Carion](https://www.nicolascarion.com/)\\*,\n[Laura Gustafson](https://scholar.google.com/citations?user=c8IpF9gAAAAJ&hl=en)\\*,\n[Yuan-Ting Hu](https://scholar.google.com/citations?user=E8DVVYQAAAAJ&hl=en)\\*,\n[Shoubhik Debnath](https://scholar.google.com/citations?user=fb6FOfsAAAAJ&hl=en)\\*,\n[Ronghang Hu](https://ronghanghu.com/)\\*,\n[Didac Suris](https://www.didacsuris.com/)\\*,\n[Chaitanya Ryali](https://scholar.google.com/citations?user=4LWx24UAAAAJ&hl=en)\\*,\n[Kalyan Vasudev Alwala](https://scholar.google.co.in/citations?user=m34oaWEAAAAJ&hl=en)\\*,\n[Haitham Khedr](https://hkhedr.com/)\\*, Andrew Huang,\n[Jie Lei](https://jayleicn.github.io/),\n[Tengyu Ma](https://scholar.google.com/citations?user=VeTSl0wAAAAJ&hl=en),\n[Baishan Guo](https://scholar.google.com/citations?user=BC5wDu8AAAAJ&hl=en),\nArpit Kalla, [Markus Marks](https://damaggu.github.io/),\n[Joseph Greer](https://scholar.google.com/citations?user=guL96CkAAAAJ&hl=en),\nMeng Wang, [Peize Sun](https://peizesun.github.io/),\n[Roman Rädle](https://scholar.google.com/citations?user=Tpt57v0AAAAJ&hl=en),\n[Triantafyllos Afouras](https://www.robots.ox.ac.uk/~afourast/),\n[Effrosyni Mavroudi](https://scholar.google.com/citations?user=vYRzGGEAAAAJ&hl=en),\n[Katherine Xu](https://k8xu.github.io/)°,\n[Tsung-Han Wu](https://patrickthwu.com/)°,\n[Yu Zhou](https://yu-bryan-zhou.github.io/)°,\n[Liliane Momeni](https://scholar.google.com/citations?user=Lb-KgVYAAAAJ&hl=en)°,\n[Rishi Hazra](https://rishihazra.github.io/)°,\n[Shuangrui Ding](https://mark12ding.github.io/)°,\n[Sagar Vaze](https://sgvaze.github.io/)°,\n[Francois Porcher](https://scholar.google.com/citations?user=LgHZ8hUAAAAJ&hl=en)°,\n[Feng Li](https://fengli-ust.github.io/)°,\n[Siyuan Li](https://siyuanliii.github.io/)°,\n[Aishwarya Kamath](https://ashkamath.github.io/)°,\n[Ho Kei Cheng](https://hkchengrex.com/)°,\n[Piotr Dollar](https://pdollar.github.io/)†,\n[Nikhila Ravi](https://nikhilaravi.com/)†,\n[Kate Saenko](https://ai.bu.edu/ksaenko.html)†,\n[Pengchuan Zhang](https://pzzhang.github.io/pzzhang/)†,\n[Christoph Feichtenhofer](https://feichtenhofer.github.io/)†\n\n\\* core contributor, ° intern, † project lead, order is random within groups\n\n[[`Paper`](https://ai.meta.com/research/publications/sam-3-segment-anything-with-concepts/)]\n[[`Project`](https://ai.meta.com/sam3)]\n[[`Demo`](https://segment-anything.com/)]\n[[`Blog`](https://ai.meta.com/blog/segment-anything-model-3/)]\n[[`BibTeX`](#citing-sam-3)]\n\n![SAM 3 architecture](assets/model_diagram.png?raw=true) SAM 3 is a unified foundation model for promptable segmentation in images and videos. It can detect, segment, and track objects using text or visual prompts such as points, boxes, and masks. Compared to its predecessor [SAM 2](https://github.com/facebookresearch/sam2), SAM 3 introduces the ability to exhaustively segment all instances of an open-vocabulary concept specified by a short text phrase or exemplars. Unlike prior work, SAM 3 can handle a vastly larger set of open-vocabulary prompts. It achieves 75-80% of human performance on our new [SA-CO benchmark](https://github.com/facebookresearch/sam3?tab=readme-ov-file#sa-co-dataset) which contains 270K unique concepts, over 50 times more than existing benchmarks.\n\nThis breakthrough is driven by an innovative data engine that has automatically annotated over 4 million unique concepts, creating the largest high-quality open-vocabulary segmentation dataset to date. In addition, SAM 3 introduces a new model architecture featuring a presence token that improves discrimination between closely related text prompts (e.g., “a player in white” vs. “a player in red”), as well as a decoupled detector–tracker design that minimizes task interference and scales efficiently with data.\n\n<p align=\"center\">\n  <img src=\"assets/dog.gif\" width=380 />\n  <img src=\"assets/player.gif\" width=380 />\n</p>\n\n## Installation\n\n### Prerequisites\n\n- Python 3.12 or higher\n- PyTorch 2.7 or higher\n- CUDA-compatible GPU with CUDA 12.6 or higher\n\n1. **Create a new Conda environment:**\n\n```bash\nconda create -n sam3 python=3.12\nconda deactivate\nconda activate sam3\n```\n\n2. **Install PyTorch with CUDA support:**\n\n```bash\npip install torch==2.7.0 torchvision torchaudio --index-url https://download.pytorch.org/whl/cu126\n```\n\n3. **Clone the repository and install the package:**\n\n```bash\ngit clone https://github.com/facebookresearch/sam3.git\ncd sam3\npip install -e .\n```\n\n4. **Install additional dependencies for example notebooks or development:**\n\n```bash\n# For running example notebooks\npip install -e \".[notebooks]\"\n\n# For development\npip install -e \".[train,dev]\"\n```\n\n## Getting Started\n\n⚠️ Before using SAM 3, please request access to the checkpoints on the SAM 3\nHugging Face [repo](https://huggingface.co/facebook/sam3). Once accepted, you\nneed to be authenticated to download the checkpoints. You can do this by running\nthe following [steps](https://huggingface.co/docs/huggingface_hub/en/quick-start#authentication)\n(e.g. `hf auth login` after generating an access token.)\n\n### Basic Usage\n\n```python\nimport torch\n#################################### For Image ####################################\nfrom PIL import Image\nfrom sam3.model_builder import build_sam3_image_model\nfrom sam3.model.sam3_image_processor import Sam3Processor\n# Load the model\nmodel = build_sam3_image_model()\nprocessor = Sam3Processor(model)\n# Load an image\nimage = Image.open(\"<YOUR_IMAGE_PATH.jpg>\")\ninference_state = processor.set_image(image)\n# Prompt the model with text\noutput = processor.set_text_prompt(state=inference_state, prompt=\"<YOUR_TEXT_PROMPT>\")\n\n# Get the masks, bounding boxes, and scores\nmasks, boxes, scores = output[\"masks\"], output[\"boxes\"], output[\"scores\"]\n\n#################################### For Video ####################################\n\nfrom sam3.model_builder import build_sam3_video_predictor\n\nvideo_predictor = build_sam3_video_predictor()\nvideo_path = \"<YOUR_VIDEO_PATH>\" # a JPEG folder or an MP4 video file\n# Start a session\nresponse = video_predictor.handle_request(\n    request=dict(\n        type=\"start_session\",\n        resource_path=video_path,\n    )\n)\nresponse = video_predictor.handle_request(\n    request=dict(\n        type=\"add_prompt\",\n        session_id=response[\"session_id\"],\n        frame_index=0, # Arbitrary frame index\n        text=\"<YOUR_TEXT_PROMPT>\",\n    )\n)\noutput = response[\"outputs\"]\n```\n\n## Examples\n\nThe `examples` directory contains notebooks demonstrating how to use SAM3 with\nvarious types of prompts:\n\n- [`sam3_image_predictor_example.ipynb`](examples/sam3_image_predictor_example.ipynb)\n  : Demonstrates how to prompt SAM 3 with text and visual box prompts on images.\n- [`sam3_video_predictor_example.ipynb`](examples/sam3_video_predictor_example.ipynb)\n  : Demonstrates how to prompt SAM 3 with text prompts on videos, and doing\n  further interactive refinements with points.\n- [`sam3_image_batched_inference.ipynb`](examples/sam3_image_batched_inference.ipynb)\n  : Demonstrates how to run batched inference with SAM 3 on images.\n- [`sam3_agent.ipynb`](examples/sam3_agent.ipynb): Demonsterates the use of SAM\n  3 Agent to segment complex text prompt on images.\n- [`saco_gold_silver_vis_example.ipynb`](examples/saco_gold_silver_vis_example.ipynb)\n  : Shows a few examples from SA-Co image evaluation set.\n- [`saco_veval_vis_example.ipynb`](examples/saco_veval_vis_example.ipynb) :\n  Shows a few examples from SA-Co video evaluation set.\n\nThere are additional notebooks in the examples directory that demonstrate how to\nuse SAM 3 for interactive instance segmentation in images and videos (SAM 1/2\ntasks), or as a tool for an MLLM, and how to run evaluations on the SA-Co\ndataset.\n\nTo run the Jupyter notebook examples:\n\n```bash\n# Make sure you have the notebooks dependencies installed\npip install -e \".[notebooks]\"\n\n# Start Jupyter notebook\njupyter notebook examples/sam3_image_predictor_example.ipynb\n```\n\n## Model\n\nSAM 3 consists of a detector and a tracker that share a vision encoder. It has 848M parameters. The\ndetector is a DETR-based model conditioned on text, geometry, and image\nexemplars. The tracker inherits the SAM 2 transformer encoder-decoder\narchitecture, supporting video segmentation and interactive refinement.\n\n## Image Results\n\n<div align=\"center\">\n<table style=\"min-width: 80%; border: 2px solid #ddd; border-collapse: collapse\">\n  <thead>\n    <tr>\n      <th rowspan=\"3\" style=\"border-right: 2px solid #ddd; padding: 12px 20px\">Model</th>\n      <th colspan=\"3\" style=\"text-align: center; border-right: 2px solid #ddd; padding: 12px 20px\">Instance Segmentation</th>\n      <th colspan=\"5\" style=\"text-align: center; padding: 12px 20px\">Box Detection</th>\n    </tr>\n    <tr>\n      <th colspan=\"2\" style=\"text-align: center; border-right: 1px solid #eee; padding: 12px 20px\">LVIS</th>\n      <th style=\"text-align: center; border-right: 2px solid #ddd; padding: 12px 20px\">SA-Co/Gold</th>\n      <th colspan=\"2\" style=\"text-align: center; border-right: 1px solid #eee; padding: 12px 20px\">LVIS</th>\n      <th colspan=\"2\" style=\"text-align: center; border-right: 1px solid #eee; padding: 12px 20px\">COCO</th>\n      <th style=\"text-align: center; padding: 12px 20px\">SA-Co/Gold</th>\n    </tr>\n    <tr>\n      <th style=\"text-align: center; padding: 12px 20px\">cgF1</th>\n      <th style=\"text-align: center; border-right: 1px solid #eee; padding: 12px 20px\">AP</th>\n      <th style=\"text-align: center; border-right: 2px solid #ddd; padding: 12px 20px\">cgF1</th>\n      <th style=\"text-align: center; padding: 12px 20px\">cgF1</th>\n      <th style=\"text-align: center; border-right: 1px solid #eee; padding: 12px 20px\">AP</th>\n      <th style=\"text-align: center; padding: 12px 20px\">AP</th>\n      <th style=\"text-align: center; border-right: 1px solid #eee; padding: 12px 20px\">AP<sub>o</sub>\n</th>\n      <th style=\"text-align: center; padding: 12px 20px\">cgF1</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td style=\"border-right: 2px solid #ddd; padding: 10px 20px\">Human</td>\n      <td style=\"text-align: center; padding: 10px 20px\">-</td>\n      <td style=\"text-align: center; border-right: 1px solid #eee; padding: 10px 20px\">-</td>\n      <td style=\"text-align: center; border-right: 2px solid #ddd; padding: 10px 20px\">72.8</td>\n      <td style=\"text-align: center; padding: 10px 20px\">-</td>\n      <td style=\"text-align: center; border-right: 1px solid #eee; padding: 10px 20px\">-</td>\n      <td style=\"text-align: center; padding: 10px 20px\">-</td>\n      <td style=\"text-align: center; border-right: 1px solid #eee; padding: 10px 20px\">-</td>\n      <td style=\"text-align: center; padding: 10px 20px\">74.0</td>\n    </tr>\n    <tr>\n      <td style=\"border-right: 2px solid #ddd; padding: 10px 20px\">OWLv2*</td>\n      <td style=\"text-align: center; padding: 10px 20px; color: #999\">29.3</td>\n      <td style=\"text-align: center; border-right: 1px solid #eee; padding: 10px 20px; color: #999\">43.4</td>\n      <td style=\"text-align: center; border-right: 2px solid #ddd; padding: 10px 20px\">24.6</td>\n      <td style=\"text-align: center; padding: 10px 20px; color: #999\">30.2</td>\n      <td style=\"text-align: center; border-right: 1px solid #eee; padding: 10px 20px; color: #999\">45.5</td>\n      <td style=\"text-align: center; padding: 10px 20px\">46.1</td>\n      <td style=\"text-align: center; border-right: 1px solid #eee; padding: 10px 20px\">23.9</td>\n      <td style=\"text-align: center; padding: 10px 20px\">24.5</td>\n    </tr>\n    <tr>\n      <td style=\"border-right: 2px solid #ddd; padding: 10px 20px\">DINO-X</td>\n      <td style=\"text-align: center; padding: 10px 20px\">-</td>\n      <td style=\"text-align: center; border-right: 1px solid #eee; padding: 10px 20px\">38.5</td>\n      <td style=\"text-align: center; border-right: 2px solid #ddd; padding: 10px 20px\">21.3</td>\n      <td style=\"text-align: center; padding: 10px 20px\">-</td>\n      <td style=\"text-align: center; border-right: 1px solid #eee; padding: 10px 20px\">52.4</td>\n      <td style=\"text-align: center; padding: 10px 20px\">56.0</td>\n      <td style=\"text-align: center; border-right: 1px solid #eee; padding: 10px 20px\">-</td>\n      <td style=\"text-align: center; padding: 10px 20px\">22.5</td>\n    </tr>\n    <tr>\n      <td style=\"border-right: 2px solid #ddd; padding: 10px 20px\">Gemini 2.5</td>\n      <td style=\"text-align: center; padding: 10px 20px\">13.4</td>\n      <td style=\"text-align: center; border-right: 1px solid #eee; padding: 10px 20px\">-</td>\n      <td style=\"text-align: center; border-right: 2px solid #ddd; padding: 10px 20px\">13.0</td>\n      <td style=\"text-align: center; padding: 10px 20px\">16.1</td>\n      <td style=\"text-align: center; border-right: 1px solid #eee; padding: 10px 20px\">-</td>\n      <td style=\"text-align: center; padding: 10px 20px\">-</td>\n      <td style=\"text-align: center; border-right: 1px solid #eee; padding: 10px 20px\">-</td>\n      <td style=\"text-align: center; padding: 10px 20px\">14.4</td>\n    </tr>\n    <tr style=\"border-top: 2px solid #b19c9cff\">\n      <td style=\"border-right: 2px solid #ddd; padding: 10px 20px\">SAM 3</td>\n      <td style=\"text-align: center; padding: 10px 20px\">37.2</td>\n      <td style=\"text-align: center; border-right: 1px solid #eee; padding: 10px 20px\">48.5</td>\n      <td style=\"text-align: center; border-right: 2px solid #ddd; padding: 10px 20px\">54.1</td>\n      <td style=\"text-align: center; padding: 10px 20px\">40.6</td>\n      <td style=\"text-align: center; border-right: 1px solid #eee; padding: 10px 20px\">53.6</td>\n      <td style=\"text-align: center; padding: 10px 20px\">56.4</td>\n      <td style=\"text-align: center; border-right: 1px solid #eee; padding: 10px 20px\">55.7</td>\n      <td style=\"text-align: center; padding: 10px 20px\">55.7</td>\n    </tr>\n  </tbody>\n</table>\n\n<p style=\"text-align: center; margin-top: 10px; font-size: 0.9em; color: #ddd;\">* Partially trained on LVIS, AP<sub>o</sub> refers to COCO-O accuracy</p>\n\n</div>\n\n## Video Results\n\n<div align=\"center\">\n<table style=\"min-width: 80%; border: 2px solid #ddd; border-collapse: collapse\">\n  <thead>\n    <tr>\n      <th rowspan=\"2\" style=\"border-right: 2px solid #ddd; padding: 12px 20px\">Model</th>\n      <th colspan=\"2\" style=\"text-align: center; border-right: 1px solid #eee; padding: 12px 20px\">SA-V test</th>\n      <th colspan=\"2\" style=\"text-align: center; border-right: 1px solid #eee; padding: 12px 20px\">YT-Temporal-1B test</th>\n      <th colspan=\"2\" style=\"text-align: center; border-right: 1px solid #eee; padding: 12px 20px\">SmartGlasses test</th>\n      <th style=\"text-align: center; border-right: 1px solid #eee; padding: 12px 20px\">LVVIS test</th>\n      <th style=\"text-align: center; padding: 12px 20px\">BURST test</th>\n    </tr>\n    <tr>\n      <th style=\"text-align: center; padding: 12px 20px\">cgF1</th>\n      <th style=\"text-align: center; border-right: 1px solid #eee; padding: 12px 20px\">pHOTA</th>\n      <th style=\"text-align: center; padding: 12px 20px\">cgF1</th>\n      <th style=\"text-align: center; border-right: 1px solid #eee; padding: 12px 20px\">pHOTA</th>\n      <th style=\"text-align: center; padding: 12px 20px\">cgF1</th>\n      <th style=\"text-align: center; border-right: 1px solid #eee; padding: 12px 20px\">pHOTA</th>\n      <th style=\"text-align: center; border-right: 1px solid #eee; padding: 12px 20px\">mAP</th>\n      <th style=\"text-align: center; padding: 12px 20px\">HOTA</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td style=\"border-right: 2px solid #ddd; padding: 10px 20px\">Human</td>\n      <td style=\"text-align: center; padding: 10px 20px\">53.1</td>\n      <td style=\"text-align: center; border-right: 1px solid #eee; padding: 10px 20px\">70.5</td>\n      <td style=\"text-align: center; padding: 10px 20px\">71.2</td>\n      <td style=\"text-align: center; border-right: 1px solid #eee; padding: 10px 20px\">78.4</td>\n      <td style=\"text-align: center; padding: 10px 20px\">58.5</td>\n      <td style=\"text-align: center; border-right: 1px solid #eee; padding: 10px 20px\">72.3</td>\n      <td style=\"text-align: center; border-right: 1px solid #eee; padding: 10px 20px\">-</td>\n      <td style=\"text-align: center; padding: 10px 20px\">-</td>\n    </tr>\n    <tr style=\"border-top: 2px solid #b19c9cff\">\n      <td style=\"border-right: 2px solid #ddd; padding: 10px 20px\">SAM 3</td>\n      <td style=\"text-align: center; padding: 10px 20px\">30.3</td>\n      <td style=\"text-align: center; border-right: 1px solid #eee; padding: 10px 20px\">58.0</td>\n      <td style=\"text-align: center; padding: 10px 20px\">50.8</td>\n      <td style=\"text-align: center; border-right: 1px solid #eee; padding: 10px 20px\">69.9</td>\n      <td style=\"text-align: center; padding: 10px 20px\">36.4</td>\n      <td style=\"text-align: center; border-right: 1px solid #eee; padding: 10px 20px\">63.6</td>\n      <td style=\"text-align: center; border-right: 1px solid #eee; padding: 10px 20px\">36.3</td>\n      <td style=\"text-align: center; padding: 10px 20px\">44.5</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n\n## SA-Co Dataset\n\nWe release 2 image benchmarks, [SA-Co/Gold](scripts/eval/gold/README.md) and\n[SA-Co/Silver](scripts/eval/silver/README.md), and a video benchmark\n[SA-Co/VEval](scripts/eval/veval/README.md). The datasets contain images (or videos) with annotated noun phrases. Each image/video and noun phrase pair is annotated with instance masks and unique IDs of each object matching the phrase. Phrases that have no matching objects (negative prompts) have no masks, shown in red font in the figure. See the linked READMEs for more details on how to download and run evaluations on the datasets.\n\n* HuggingFace host: [SA-Co/Gold](https://huggingface.co/datasets/facebook/SACo-Gold), [SA-Co/Silver](https://huggingface.co/datasets/facebook/SACo-Silver) and [SA-Co/VEval](https://huggingface.co/datasets/facebook/SACo-VEval)\n* Roboflow host: [SA-Co/Gold](https://universe.roboflow.com/sa-co-gold), [SA-Co/Silver](https://universe.roboflow.com/sa-co-silver) and [SA-Co/VEval](https://universe.roboflow.com/sa-co-veval)\n\n![SA-Co dataset](assets/sa_co_dataset.jpg?raw=true)\n\n## Development\n\nTo set up the development environment:\n\n```bash\npip install -e \".[dev,train]\"\n```\n\nTo format the code:\n\n```bash\nufmt format .\n```\n\n## Contributing\n\nSee [contributing](CONTRIBUTING.md) and the\n[code of conduct](CODE_OF_CONDUCT.md).\n\n## License\n\nThis project is licensed under the SAM License - see the [LICENSE](LICENSE) file\nfor details.\n\n## Acknowledgements\n\nWe would like to thank the following people for their contributions to the SAM 3 project: Alex He, Alexander Kirillov,\nAlyssa Newcomb, Ana Paula Kirschner Mofarrej, Andrea Madotto, Andrew Westbury, Ashley Gabriel, Azita Shokpour,\nBen Samples, Bernie Huang, Carleigh Wood, Ching-Feng Yeh, Christian Puhrsch, Claudette Ward, Daniel Bolya,\nDaniel Li, Facundo Figueroa, Fazila Vhora, George Orlin, Hanzi Mao, Helen Klein, Hu Xu, Ida Cheng, Jake Kinney,\nJiale Zhi, Jo Sampaio, Joel Schlosser, Justin Johnson, Kai Brown, Karen Bergan, Karla Martucci, Kenny Lehmann,\nMaddie Mintz, Mallika Malhotra, Matt Ward, Michelle Chan, Michelle Restrepo, Miranda Hartley, Muhammad Maaz,\nNisha Deo, Peter Park, Phillip Thomas, Raghu Nayani, Rene Martinez Doehner, Robbie Adkins, Ross Girshik, Sasha\nMitts, Shashank Jain, Spencer Whitehead, Ty Toledano, Valentin Gabeur, Vincent Cho, Vivian Lee, William Ngan,\nXuehai He, Yael Yungster, Ziqi Pang, Ziyi Dou, Zoe Quake.\n\n## Citing SAM 3\n\nIf you use SAM 3 or the SA-Co dataset in your research, please use the following BibTeX entry.\n\n```bibtex\n@misc{carion2025sam3segmentconcepts,\n      title={SAM 3: Segment Anything with Concepts},\n      author={Nicolas Carion and Laura Gustafson and Yuan-Ting Hu and Shoubhik Debnath and Ronghang Hu and Didac Suris and Chaitanya Ryali and Kalyan Vasudev Alwala and Haitham Khedr and Andrew Huang and Jie Lei and Tengyu Ma and Baishan Guo and Arpit Kalla and Markus Marks and Joseph Greer and Meng Wang and Peize Sun and Roman Rädle and Triantafyllos Afouras and Effrosyni Mavroudi and Katherine Xu and Tsung-Han Wu and Yu Zhou and Liliane Momeni and Rishi Hazra and Shuangrui Ding and Sagar Vaze and Francois Porcher and Feng Li and Siyuan Li and Aishwarya Kamath and Ho Kei Cheng and Piotr Dollár and Nikhila Ravi and Kate Saenko and Pengchuan Zhang and Christoph Feichtenhofer},\n      year={2025},\n      eprint={2511.16719},\n      archivePrefix={arXiv},\n      primaryClass={cs.CV},\n      url={https://arxiv.org/abs/2511.16719},\n}\n```\n"
  },
  {
    "path": "README_TRAIN.md",
    "content": "# Training\n\nThis repository supports finetuning SAM3 models on custom datasets in multi-node setup or local execution. The training script is located at `sam3/train.py` and uses Hydra configuration management to handle complex training setups.\n\n\n## Installation\n\n```bash\ncd sam3\npip install -e \".[train]\"\n```\n\n### Training Script Usage\n\nThe main training script is located at `sam3/train.py`. It uses Hydra configuration management to handle complex training setups.\n\n#### Basic Usage\n\n```bash\n# Example: Train on Roboflow dataset\npython sam3/train/train.py -c configs/roboflow_v100/roboflow_v100_full_ft_100_images.yaml\n# Example: Train on ODinW13 dataset\npython sam3/train/train.py -c configs/odinw13/odinw_text_only_train.yaml\n```\nFollow [`Roboflow 100-VL`](https://github.com/roboflow/rf100-vl/) to download the roboflow 100-vl datasets. Follow [`GLIP`](https://github.com/microsoft/GLIP) to download the ODinW datasets. The data folder should be organized as follows, and put your roboflow_vl_100_root and odinw_data_root in the job configs.\n```\nroboflow_vl_100_root:\n  13-lkc01\n    train\n    valid\n    test\n  2024-frc\n  actions\n  ...\nodinw_data_root:\n  AerialMaritimeDrone\n    large\n      train\n      valid\n      test\n  Aquarium\n  ...\n```\n\n#### Command Line Arguments\n\nThe training script supports several command line arguments:\n\n```bash\npython sam3/train/train.py \\\n    -c CONFIG_NAME \\\n    [--use-cluster 0|1] \\\n    [--partition PARTITION_NAME] \\\n    [--account ACCOUNT_NAME] \\\n    [--qos QOS_NAME] \\\n    [--num-gpus NUM_GPUS] \\\n    [--num-nodes NUM_NODES]\n```\n\n**Arguments:**\n- `-c, --config`: **Required.** Path to the configuration file (e.g., `sam3/train/configs/roboflow_v100_full_ft_100_images.yaml`)\n- `--use-cluster`: Whether to launch on a cluster (0: local, 1: cluster). Default: uses config setting\n- `--partition`: SLURM partition name for cluster execution\n- `--account`: SLURM account name for cluster execution\n- `--qos`: SLURM QOS (Quality of Service) setting\n- `--num-gpus`: Number of GPUs per node. Default: uses config setting\n- `--num-nodes`: Number of nodes for distributed training. Default: uses config setting\n\n#### Local Training Examples\n\n```bash\n# Single GPU training\npython sam3/train/train.py -c configs/roboflow_v100/roboflow_v100_full_ft_100_images.yaml --use-cluster 0 --num-gpus 1\n\n# Multi-GPU training on a single node\npython sam3/train/train.py -c configs/roboflow_v100/roboflow_v100_full_ft_100_images.yaml --use-cluster 0 --num-gpus 4\n\n# Force local execution even if config specifies GPUs\npython sam3/train/train.py -c configs/roboflow_v100/roboflow_v100_full_ft_100_images.yaml --use-cluster 0\n```\n\n#### Cluster Training Examples\n\n```bash\n# Basic cluster training with default settings from config\npython sam3/train/train.py -c configs/roboflow_v100/roboflow_v100_full_ft_100_images.yaml --use-cluster 1\n\n# Cluster training with specific SLURM settings\npython sam3/train/train.py -c configs/roboflow_v100/roboflow_v100_full_ft_100_images.yaml \\\n    --use-cluster 1 \\\n    --partition gpu_partition \\\n    --account my_account \\\n    --qos high_priority \\\n    --num-gpus 8 \\\n    --num-nodes 2\n```\n\n### Configuration Files\n\nTraining configurations are stored in `sam3/train/configs/`. The configuration files use Hydra's YAML format and support:\n\n- **Dataset Configuration**: Data paths, transforms, and loading parameters\n- **Model Configuration**: Architecture settings, checkpoint paths, and model parameters\n- **Training Configuration**: Batch sizes, learning rates, optimization settings\n- **Launcher Configuration**: Distributed training and cluster settings\n- **Logging Configuration**: TensorBoard, experiment tracking, and output directories\n\n#### Key Configuration Sections\n\n```yaml\n# Paths to datasets and checkpoints\npaths:\n  bpe_path: /path/to/bpe/file\n  dataset_root: /path/to/dataset\n  experiment_log_dir: /path/to/logs\n\n# Launcher settings for local/cluster execution\nlauncher:\n  num_nodes: 1\n  gpus_per_node: 2\n  experiment_log_dir: ${paths.experiment_log_dir}\n\n# Cluster execution settings\nsubmitit:\n  use_cluster: True\n  timeout_hour: 72\n  cpus_per_task: 10\n  partition: null\n  account: null\n```\n\n### Monitoring Training\n\nThe training script automatically sets up logging and saves outputs to the experiment directory:\n\n```bash\n# Logs are saved to the experiment_log_dir specified in config\nexperiment_log_dir/\n├── config.yaml              # Original configuration\n├── config_resolved.yaml     # Resolved configuration with all variables expanded\n├── checkpoints/             # Model checkpoints (if skip_checkpointing=False)\n├── tensorboard/             # TensorBoard logs\n├── logs/                    # Text logs\n└── submitit_logs/           # Cluster job logs (if using cluster)\n```\n\nYou can monitor training progress using TensorBoard:\n\n```bash\ntensorboard --logdir /path/to/experiment_log_dir/tensorboard\n```\n\n### Job Arrays for Dataset Sweeps\n\nThe Roboflow and ODinW configuration supports job arrays for training multiple models on different datasets:\n\nThis feature is specifically enabled via,\n```yaml\nsubmitit:\n  job_array:\n    num_tasks: 100\n    task_index: 0\n```\n\nThe configuration includes a complete list of 100 Roboflow supercategories, and the `submitit.job_array.task_index` automatically selects which dataset to use based on the array job index.\n\n```bash\n# Submit job array to train on different Roboflow datasets\n# The job array index selects which dataset from all_roboflow_supercategories\npython sam3/train/train.py -c configs/roboflow_v100/roboflow_v100_full_ft_100_images.yaml \\\n    --use-cluster 1\n```\n\n### Reproduce ODinW13 10-shot results\nRunning the following job will give the results on the ODinW13 seed 300, see `odinw_train.train_file: fewshot_train_shot10_seed300` in the config file.\n```bash\n# Example: Train on ODinW13 dataset\npython sam3/train/train.py -c configs/odinw13/odinw_text_only_train.yaml\n```\nChange `odinw_train.train_file` to `fewshot_train_shot10_seed30` and `fewshot_train_shot10_seed3` to get the results for the other two seeds. Final results are aggregated from the three seeds. Notice that a small number of jobs may diverge during training, in which case we just use the last checkpoint's result before it diverges.\n\n\n### Eval Script Usage\nWith a similar setup as the training config, the training script `sam3/train.py` can also be used for evaluation, too, when setting `trainer.mode = val` in the job config. Run the following job will give the results on the zero-shot results on RF100-VL and ODinW13 datasets.\n```bash\n# Example: Evaluate on Roboflow dataset\npython sam3/train/train.py -c configs/roboflow_v100/roboflow_v100_eval.yaml\n# Example: Evaluate on ODinW13 dataset\npython sam3/train/train.py -c configs/odinw13/odinw_text_only.yaml\n```\n"
  },
  {
    "path": "assets/veval/toy_gt_and_pred/toy_saco_veval_sav_test_eval_res.json",
    "content": "{\"dataset_results\": {\"video_bbox_mAP_50_95\": 0.25926449787835926, \"video_mask_mAP_50_95\": 0.34191419141914187, \"video_bbox_phrase_ap_50_95\": 0.1371423984673839, \"video_bbox_phrase_ap_50\": 0.3000915217572178, \"video_bbox_phrase_ap_75\": 0.08651579443658651, \"video_mask_phrase_ap_50_95\": 0.1935287184180603, \"video_mask_phrase_ap_50\": 0.3513242920930748, \"video_mask_phrase_ap_75\": 0.18776520509193778, \"video_mask_teta\": 50.647, \"video_mask_loc_a\": 50.969, \"video_mask_assoc_a\": 49.191, \"video_mask_cls_a\": 51.781, \"video_mask_loc_re\": 53.646, \"video_mask_loc_pr\": 59.689, \"video_mask_assoc_re\": 53.932, \"video_mask_assoc_pr\": 55.295, \"video_mask_cls_re\": 69.966, \"video_mask_cls_pr\": 51.792, \"video_bbox_all_phrase_HOTA\": 0.45019177261810006, \"video_bbox_all_phrase_DetA\": 0.3065446597162029, \"video_bbox_all_phrase_AssA\": 0.6620514646913795, \"video_bbox_all_phrase_DetRe\": 0.34337909467405614, \"video_bbox_all_phrase_DetPr\": 0.672412162481676, \"video_bbox_all_phrase_AssRe\": 0.7418235522588219, \"video_bbox_all_phrase_AssPr\": 0.7588797497822176, \"video_bbox_all_phrase_LocA\": 0.8264969431924951, \"video_bbox_all_phrase_OWTA\": 0.4767458776352726, \"video_mask_all_phrase_HOTA\": 0.4656079602771566, \"video_mask_all_phrase_DetA\": 0.31732796518831874, \"video_mask_all_phrase_AssA\": 0.6843555199075559, \"video_mask_all_phrase_DetRe\": 0.35291773825479045, \"video_mask_all_phrase_DetPr\": 0.6910909348843808, \"video_mask_all_phrase_AssRe\": 0.7573349011890161, \"video_mask_all_phrase_AssPr\": 0.7716315286320553, \"video_mask_all_phrase_LocA\": 0.8328852506118952, \"video_mask_all_phrase_OWTA\": 0.4913272309590218, \"video_bbox_demo_precision_50_95\": 0.3349983250083749, \"video_bbox_demo_recall_50_95\": 0.20302968778882485, \"video_bbox_demo_f1_50_95\": 0.2527916520815702, \"video_bbox_demo_precision_50\": 0.6499967500162499, \"video_bbox_demo_recall_50\": 0.3939382001872721, \"video_bbox_demo_f1_50\": 0.49051719921648634, \"video_bbox_demo_precision_75\": 0.29999850000749995, \"video_bbox_demo_recall_75\": 0.18181763085566405, \"video_bbox_demo_f1_75\": 0.2263672578625684, \"video_bbox_demo_pmf1_50_95\": 0.34412433298399697, \"video_bbox_demo_ilmcc_50_95\": 0.6546535992794136, \"video_bbox_demo_cgf1_50_95\": 0.22528223318760104, \"video_bbox_demo_pmf1_w0dt_50_95\": 0.29496371398628307, \"video_bbox_demo_cgf1_w0dt_50_95\": 0.19309905701794375, \"video_bbox_demo_positive_micro_f1_50_95\": 0.2527916520815702, \"video_bbox_demo_cgf1_micro_50_95\": 0.1654909649029892, \"video_bbox_demo_pmf1_50\": 0.6499302549834214, \"video_bbox_demo_ilmcc_50\": 0.6546535992794136, \"video_bbox_demo_cgf1_50\": 0.42547918070548385, \"video_bbox_demo_positive_micro_f1_50\": 0.49051719921648634, \"video_bbox_demo_cgf1_micro_50\": 0.3211188499755299, \"video_bbox_demo_pmf1_75\": 0.3666121729955099, \"video_bbox_demo_ilmcc_75\": 0.6546535992794136, \"video_bbox_demo_cgf1_75\": 0.2400039785911576, \"video_bbox_demo_positive_micro_f1_75\": 0.2263672578625684, \"video_bbox_demo_cgf1_micro_75\": 0.14819214011874154, \"video_mask_demo_precision_50_95\": 0.4049979750101249, \"video_mask_demo_recall_50_95\": 0.24545380165514646, \"video_mask_demo_f1_50_95\": 0.30562163642146994, \"video_mask_demo_precision_50\": 0.6999965000174999, \"video_mask_demo_recall_50\": 0.4242411386632161, \"video_mask_demo_f1_50\": 0.5282529055527269, \"video_mask_demo_precision_75\": 0.39999800000999997, \"video_mask_demo_recall_75\": 0.24242350780755206, \"video_mask_demo_f1_75\": 0.3018386687406093, \"video_mask_demo_pmf1_50_95\": 0.42578319170459134, \"video_mask_demo_ilmcc_50_95\": 0.6546535992794136, \"video_mask_demo_cgf1_50_95\": 0.2787404989620873, \"video_mask_demo_pmf1_w0dt_50_95\": 0.3649570214610783, \"video_mask_demo_cgf1_w0dt_50_95\": 0.23892042768178906, \"video_mask_demo_positive_micro_f1_50_95\": 0.30562163642146994, \"video_mask_demo_cgf1_micro_50_95\": 0.2000763043009796, \"video_mask_demo_pmf1_50\": 0.691595879731724, \"video_mask_demo_ilmcc_50\": 0.6546535992794136, \"video_mask_demo_cgf1_50\": 0.4527557319131855, \"video_mask_demo_positive_micro_f1_50\": 0.5282529055527269, \"video_mask_demo_cgf1_micro_50\": 0.34582266594990074, \"video_mask_demo_pmf1_75\": 0.44161046360299766, \"video_mask_demo_ilmcc_75\": 0.6546535992794136, \"video_mask_demo_cgf1_75\": 0.2891018794771529, \"video_mask_demo_positive_micro_f1_75\": 0.3018386687406093, \"video_mask_demo_cgf1_micro_75\": 0.19759977089274647}, \"video_np_results\": [{\"video_id\": 0, \"category_id\": 847, \"bbox_HOTA\": 0.5142922923345529, \"bbox_DetA\": 0.4728133947307452, \"bbox_AssA\": 0.5594614107579609, \"bbox_DetRe\": 0.5232999318999904, \"bbox_DetPr\": 0.6740601503759398, \"bbox_AssRe\": 0.6250872094372617, \"bbox_AssPr\": 0.6825128274969564, \"bbox_LocA\": 0.7656278351108177, \"bbox_OWTA\": 0.5404796613713161, \"mask_HOTA\": 0.5531074184495137, \"mask_DetA\": 0.5015830888530848, \"mask_AssA\": 0.6104397175171865, \"mask_DetRe\": 0.5479132211304601, \"mask_DetPr\": 0.7057644110275689, \"mask_AssRe\": 0.6703397116425474, \"mask_AssPr\": 0.7231025840584305, \"mask_LocA\": 0.7775805526027232, \"mask_OWTA\": 0.5779237956098605, \"bbox_TP_50_95\": 0.8, \"bbox_FP_50_95\": 3.2, \"bbox_FN_50_95\": 3.2, \"bbox_F1_50_95\": 0.2, \"bbox_TP_50\": 2.0, \"bbox_FP_50\": 2.0, \"bbox_FN_50\": 2.0, \"bbox_F1_50\": 0.5, \"bbox_TP_75\": 0.0, \"bbox_FP_75\": 4.0, \"bbox_FN_75\": 4.0, \"bbox_F1_75\": 0.0, \"mask_TP_50_95\": 1.4, \"mask_FP_50_95\": 2.6, \"mask_FN_50_95\": 2.6, \"mask_F1_50_95\": 0.35, \"mask_TP_50\": 3.0, \"mask_FP_50\": 1.0, \"mask_FN_50\": 1.0, \"mask_F1_50\": 0.75, \"mask_TP_75\": 0.0, \"mask_FP_75\": 4.0, \"mask_FN_75\": 4.0, \"mask_F1_75\": 0.0}, {\"video_id\": 0, \"category_id\": 1390, \"bbox_HOTA\": 0.7406733177321637, \"bbox_DetA\": 0.7406733177321637, \"bbox_AssA\": 0.7406733177321637, \"bbox_DetRe\": 0.8079854809437387, \"bbox_DetPr\": 0.8428625520636122, \"bbox_AssRe\": 0.8079854809437387, \"bbox_AssPr\": 0.8428625520636122, \"bbox_LocA\": 0.8823614751992302, \"bbox_OWTA\": 0.7727325543244231, \"mask_HOTA\": 0.8154208948182305, \"mask_DetA\": 0.8154208948182305, \"mask_AssA\": 0.8154208948182304, \"mask_DetRe\": 0.8508166969147005, \"mask_DetPr\": 0.8875425975009466, \"mask_AssRe\": 0.8508166969147005, \"mask_AssPr\": 0.8875425975009466, \"mask_LocA\": 0.8885850303003239, \"mask_OWTA\": 0.8323023646534773, \"bbox_TP_50_95\": 0.6, \"bbox_FP_50_95\": 0.4, \"bbox_FN_50_95\": 0.4, \"bbox_F1_50_95\": 0.6, \"bbox_TP_50\": 1.0, \"bbox_FP_50\": 0.0, \"bbox_FN_50\": 0.0, \"bbox_F1_50\": 1.0, \"bbox_TP_75\": 1.0, \"bbox_FP_75\": 0.0, \"bbox_FN_75\": 0.0, \"bbox_F1_75\": 1.0, \"mask_TP_50_95\": 0.8, \"mask_FP_50_95\": 0.2, \"mask_FN_50_95\": 0.2, \"mask_F1_50_95\": 0.8, \"mask_TP_50\": 1.0, \"mask_FP_50\": 0.0, \"mask_FN_50\": 0.0, \"mask_F1_50\": 1.0, \"mask_TP_75\": 1.0, \"mask_FP_75\": 0.0, \"mask_FN_75\": 0.0, \"mask_F1_75\": 1.0}, {\"video_id\": 0, \"category_id\": 1985, \"bbox_HOTA\": 0.6033247555682881, \"bbox_DetA\": 0.582364339356503, \"bbox_AssA\": 0.6261229142494006, \"bbox_DetRe\": 0.731940716536352, \"bbox_DetPr\": 0.6764452113891285, \"bbox_AssRe\": 0.7595503987929015, \"bbox_AssPr\": 0.7140399793949845, \"bbox_LocA\": 0.8359470620317413, \"bbox_OWTA\": 0.6767785755234768, \"mask_HOTA\": 0.6327138779062043, \"mask_DetA\": 0.607379512629533, \"mask_AssA\": 0.661378852549867, \"mask_DetRe\": 0.7515462714435757, \"mask_DetPr\": 0.6945642795513374, \"mask_AssRe\": 0.7849752407175019, \"mask_AssPr\": 0.7406062552585059, \"mask_LocA\": 0.8390964342806022, \"mask_OWTA\": 0.70480310037791, \"bbox_TP_50_95\": 1.5, \"bbox_FP_50_95\": 2.5, \"bbox_FN_50_95\": 2.5, \"bbox_F1_50_95\": 0.375, \"bbox_TP_50\": 4.0, \"bbox_FP_50\": 0.0, \"bbox_FN_50\": 0.0, \"bbox_F1_50\": 1.0, \"bbox_TP_75\": 1.0, \"bbox_FP_75\": 3.0, \"bbox_FN_75\": 3.0, \"bbox_F1_75\": 0.25, \"mask_TP_50_95\": 1.7, \"mask_FP_50_95\": 2.3, \"mask_FN_50_95\": 2.3, \"mask_F1_50_95\": 0.425, \"mask_TP_50\": 4.0, \"mask_FP_50\": 0.0, \"mask_FN_50\": 0.0, \"mask_F1_50\": 1.0, \"mask_TP_75\": 1.0, \"mask_FP_75\": 3.0, \"mask_FN_75\": 3.0, \"mask_F1_75\": 0.25}, {\"video_id\": 0, \"category_id\": 3802, \"bbox_HOTA\": 0.47649622572399936, \"bbox_DetA\": 0.433826727090632, \"bbox_AssA\": 0.5248504404498464, \"bbox_DetRe\": 0.6931060044477392, \"bbox_DetPr\": 0.49540798304486033, \"bbox_AssRe\": 0.5970635788657445, \"bbox_AssPr\": 0.6521277350832161, \"bbox_LocA\": 0.8179341001135264, \"bbox_OWTA\": 0.6029168695170898, \"mask_HOTA\": 0.48643902599730804, \"mask_DetA\": 0.45408850965718345, \"mask_AssA\": 0.5231400608341074, \"mask_DetRe\": 0.7197924388435877, \"mask_DetPr\": 0.5144825150123632, \"mask_AssRe\": 0.5970712271676157, \"mask_AssPr\": 0.6408536817832409, \"mask_LocA\": 0.8335384601792232, \"mask_OWTA\": 0.613243699873648, \"bbox_TP_50_95\": 1.2, \"bbox_FP_50_95\": 4.8, \"bbox_FN_50_95\": 2.8, \"bbox_F1_50_95\": 0.24000000000000005, \"bbox_TP_50\": 2.0, \"bbox_FP_50\": 4.0, \"bbox_FN_50\": 2.0, \"bbox_F1_50\": 0.4, \"bbox_TP_75\": 1.0, \"bbox_FP_75\": 5.0, \"bbox_FN_75\": 3.0, \"bbox_F1_75\": 0.2, \"mask_TP_50_95\": 1.4, \"mask_FP_50_95\": 4.6, \"mask_FN_50_95\": 2.6, \"mask_F1_50_95\": 0.28, \"mask_TP_50\": 2.0, \"mask_FP_50\": 4.0, \"mask_FN_50\": 2.0, \"mask_F1_50\": 0.4, \"mask_TP_75\": 2.0, \"mask_FP_75\": 4.0, \"mask_FN_75\": 2.0, \"mask_F1_75\": 0.4}, {\"video_id\": 0, \"category_id\": 3827, \"bbox_HOTA\": 0.7782356487108129, \"bbox_DetA\": 0.7707506578022777, \"bbox_AssA\": 0.7864050337062197, \"bbox_DetRe\": 0.8251451746704767, \"bbox_DetPr\": 0.8309663046505151, \"bbox_AssRe\": 0.8370220441715713, \"bbox_AssPr\": 0.8427028310094968, \"bbox_LocA\": 0.8515357797457908, \"bbox_OWTA\": 0.8052026625060156, \"mask_HOTA\": 0.7841937402779418, \"mask_DetA\": 0.7773711399985175, \"mask_AssA\": 0.7919510612320402, \"mask_DetRe\": 0.8283712784588442, \"mask_DetPr\": 0.8342151675485008, \"mask_AssRe\": 0.8407743682610512, \"mask_AssPr\": 0.845985151807301, \"mask_LocA\": 0.851867149412845, \"mask_OWTA\": 0.8096925025911031, \"bbox_TP_50_95\": 2.6, \"bbox_FP_50_95\": 1.4, \"bbox_FN_50_95\": 1.4, \"bbox_F1_50_95\": 0.65, \"bbox_TP_50\": 4.0, \"bbox_FP_50\": 0.0, \"bbox_FN_50\": 0.0, \"bbox_F1_50\": 1.0, \"bbox_TP_75\": 3.0, \"bbox_FP_75\": 1.0, \"bbox_FN_75\": 1.0, \"bbox_F1_75\": 0.75, \"mask_TP_50_95\": 2.8, \"mask_FP_50_95\": 1.2, \"mask_FN_50_95\": 1.2, \"mask_F1_50_95\": 0.7, \"mask_TP_50\": 4.0, \"mask_FP_50\": 0.0, \"mask_FN_50\": 0.0, \"mask_F1_50\": 1.0, \"mask_TP_75\": 4.0, \"mask_FP_75\": 0.0, \"mask_FN_75\": 0.0, \"mask_F1_75\": 1.0}, {\"video_id\": 0, \"category_id\": 49272, \"bbox_HOTA\": 0.0, \"bbox_DetA\": 0.0, \"bbox_AssA\": 0.0, \"bbox_DetRe\": 0.0, \"bbox_DetPr\": 0.0, \"bbox_AssRe\": 0.0, \"bbox_AssPr\": 0.0, \"bbox_LocA\": 1.0, \"bbox_OWTA\": 0.0, \"mask_HOTA\": 0.0, \"mask_DetA\": 0.0, \"mask_AssA\": 0.0, \"mask_DetRe\": 0.0, \"mask_DetPr\": 0.0, \"mask_AssRe\": 0.0, \"mask_AssPr\": 0.0, \"mask_LocA\": 1.0, \"mask_OWTA\": 0.0, \"bbox_TP_50_95\": 0.0, \"bbox_FP_50_95\": 0.0, \"bbox_FN_50_95\": 12.0, \"bbox_F1_50_95\": 0.0, \"bbox_TP_50\": 0.0, \"bbox_FP_50\": 0.0, \"bbox_FN_50\": 12.0, \"bbox_F1_50\": 0.0, \"bbox_TP_75\": 0.0, \"bbox_FP_75\": 0.0, \"bbox_FN_75\": 12.0, \"bbox_F1_75\": 0.0, \"mask_TP_50_95\": 0.0, \"mask_FP_50_95\": 0.0, \"mask_FN_50_95\": 12.0, \"mask_F1_50_95\": 0.0, \"mask_TP_50\": 0.0, \"mask_FP_50\": 0.0, \"mask_FN_50\": 12.0, \"mask_F1_50\": 0.0, \"mask_TP_75\": 0.0, \"mask_FP_75\": 0.0, \"mask_FN_75\": 12.0, \"mask_F1_75\": 0.0}, {\"video_id\": 0, \"category_id\": 49504, \"bbox_HOTA\": 0.0, \"bbox_DetA\": 0.0, \"bbox_AssA\": 0.0, \"bbox_DetRe\": 0.0, \"bbox_DetPr\": 0.0, \"bbox_AssRe\": 0.0, \"bbox_AssPr\": 0.0, \"bbox_LocA\": 1.0, \"bbox_OWTA\": 0.0, \"mask_HOTA\": 0.0, \"mask_DetA\": 0.0, \"mask_AssA\": 0.0, \"mask_DetRe\": 0.0, \"mask_DetPr\": 0.0, \"mask_AssRe\": 0.0, \"mask_AssPr\": 0.0, \"mask_LocA\": 1.0, \"mask_OWTA\": 0.0, \"bbox_TP_50_95\": 0.0, \"bbox_FP_50_95\": 1.0, \"bbox_FN_50_95\": 4.0, \"bbox_F1_50_95\": 0.0, \"bbox_TP_50\": 0.0, \"bbox_FP_50\": 1.0, \"bbox_FN_50\": 4.0, \"bbox_F1_50\": 0.0, \"bbox_TP_75\": 0.0, \"bbox_FP_75\": 1.0, \"bbox_FN_75\": 4.0, \"bbox_F1_75\": 0.0, \"mask_TP_50_95\": 0.0, \"mask_FP_50_95\": 1.0, \"mask_FN_50_95\": 4.0, \"mask_F1_50_95\": 0.0, \"mask_TP_50\": 0.0, \"mask_FP_50\": 1.0, \"mask_FN_50\": 4.0, \"mask_F1_50\": 0.0, \"mask_TP_75\": 0.0, \"mask_FP_75\": 1.0, \"mask_FN_75\": 4.0, \"mask_F1_75\": 0.0}, {\"video_id\": 0, \"category_id\": 50554, \"bbox_TP_50_95\": 0.0, \"bbox_FP_50_95\": 0.0, \"bbox_FN_50_95\": 0.0, \"bbox_F1_50_95\": 1.0, \"bbox_TP_50\": 0.0, \"bbox_FP_50\": 0.0, \"bbox_FN_50\": 0.0, \"bbox_F1_50\": 1.0, \"bbox_TP_75\": 0.0, \"bbox_FP_75\": 0.0, \"bbox_FN_75\": 0.0, \"bbox_F1_75\": 1.0, \"mask_TP_50_95\": 0.0, \"mask_FP_50_95\": 0.0, \"mask_FN_50_95\": 0.0, \"mask_F1_50_95\": 1.0, \"mask_TP_50\": 0.0, \"mask_FP_50\": 0.0, \"mask_FN_50\": 0.0, \"mask_F1_50\": 1.0, \"mask_TP_75\": 0.0, \"mask_FP_75\": 0.0, \"mask_FN_75\": 0.0, \"mask_F1_75\": 1.0}]}"
  },
  {
    "path": "assets/veval/toy_gt_and_pred/toy_saco_veval_sav_test_gt.json",
    "content": "{\"info\": {\"version\": \"v1\", \"date\": \"2025-09-24\", \"description\": \"SA-Co/VEval SA-V Test\"}, \"videos\": [{\"id\": 0, \"video_name\": \"sav_000208\", \"file_names\": [\"sav_000208/00000.jpg\", \"sav_000208/00004.jpg\", \"sav_000208/00008.jpg\", \"sav_000208/00012.jpg\", \"sav_000208/00016.jpg\", \"sav_000208/00020.jpg\", \"sav_000208/00024.jpg\", \"sav_000208/00028.jpg\", \"sav_000208/00032.jpg\", \"sav_000208/00036.jpg\", \"sav_000208/00040.jpg\", \"sav_000208/00044.jpg\", \"sav_000208/00048.jpg\", \"sav_000208/00052.jpg\", \"sav_000208/00056.jpg\", \"sav_000208/00060.jpg\", \"sav_000208/00064.jpg\", \"sav_000208/00068.jpg\", \"sav_000208/00072.jpg\", \"sav_000208/00076.jpg\", \"sav_000208/00080.jpg\", \"sav_000208/00084.jpg\", \"sav_000208/00088.jpg\", \"sav_000208/00092.jpg\", \"sav_000208/00096.jpg\", \"sav_000208/00100.jpg\", \"sav_000208/00104.jpg\", \"sav_000208/00108.jpg\", \"sav_000208/00112.jpg\", \"sav_000208/00116.jpg\", \"sav_000208/00120.jpg\", \"sav_000208/00124.jpg\", \"sav_000208/00128.jpg\", \"sav_000208/00132.jpg\", \"sav_000208/00136.jpg\", \"sav_000208/00140.jpg\", \"sav_000208/00144.jpg\", \"sav_000208/00148.jpg\", \"sav_000208/00152.jpg\", \"sav_000208/00156.jpg\", \"sav_000208/00160.jpg\", \"sav_000208/00164.jpg\", \"sav_000208/00168.jpg\", \"sav_000208/00172.jpg\", \"sav_000208/00176.jpg\", \"sav_000208/00180.jpg\", \"sav_000208/00184.jpg\", \"sav_000208/00188.jpg\", \"sav_000208/00192.jpg\", \"sav_000208/00196.jpg\", \"sav_000208/00200.jpg\", \"sav_000208/00204.jpg\", \"sav_000208/00208.jpg\", \"sav_000208/00212.jpg\", \"sav_000208/00216.jpg\", \"sav_000208/00220.jpg\", \"sav_000208/00224.jpg\", \"sav_000208/00228.jpg\", \"sav_000208/00232.jpg\", \"sav_000208/00236.jpg\", \"sav_000208/00240.jpg\", \"sav_000208/00244.jpg\", \"sav_000208/00248.jpg\", \"sav_000208/00252.jpg\", \"sav_000208/00256.jpg\", \"sav_000208/00260.jpg\", \"sav_000208/00264.jpg\", \"sav_000208/00268.jpg\", \"sav_000208/00272.jpg\", \"sav_000208/00276.jpg\", \"sav_000208/00280.jpg\", \"sav_000208/00284.jpg\", \"sav_000208/00288.jpg\", \"sav_000208/00292.jpg\", \"sav_000208/00296.jpg\", \"sav_000208/00300.jpg\", \"sav_000208/00304.jpg\", \"sav_000208/00308.jpg\", \"sav_000208/00312.jpg\", \"sav_000208/00316.jpg\", \"sav_000208/00320.jpg\", \"sav_000208/00324.jpg\", \"sav_000208/00328.jpg\", \"sav_000208/00332.jpg\", \"sav_000208/00336.jpg\", \"sav_000208/00340.jpg\", \"sav_000208/00344.jpg\", \"sav_000208/00348.jpg\", \"sav_000208/00352.jpg\", \"sav_000208/00356.jpg\", \"sav_000208/00360.jpg\", \"sav_000208/00364.jpg\", \"sav_000208/00368.jpg\", \"sav_000208/00372.jpg\", \"sav_000208/00376.jpg\", \"sav_000208/00380.jpg\", \"sav_000208/00384.jpg\", \"sav_000208/00388.jpg\", \"sav_000208/00392.jpg\", \"sav_000208/00396.jpg\", \"sav_000208/00400.jpg\", \"sav_000208/00404.jpg\", \"sav_000208/00408.jpg\", \"sav_000208/00412.jpg\", \"sav_000208/00416.jpg\", \"sav_000208/00420.jpg\", \"sav_000208/00424.jpg\", \"sav_000208/00428.jpg\", \"sav_000208/00432.jpg\", \"sav_000208/00436.jpg\", \"sav_000208/00440.jpg\", \"sav_000208/00444.jpg\", \"sav_000208/00448.jpg\", \"sav_000208/00452.jpg\", \"sav_000208/00456.jpg\", \"sav_000208/00460.jpg\", \"sav_000208/00464.jpg\", \"sav_000208/00468.jpg\", \"sav_000208/00472.jpg\", \"sav_000208/00476.jpg\", \"sav_000208/00480.jpg\", \"sav_000208/00484.jpg\", \"sav_000208/00488.jpg\", \"sav_000208/00492.jpg\", \"sav_000208/00496.jpg\", \"sav_000208/00500.jpg\", \"sav_000208/00504.jpg\", \"sav_000208/00508.jpg\", \"sav_000208/00512.jpg\", \"sav_000208/00516.jpg\", \"sav_000208/00520.jpg\", \"sav_000208/00524.jpg\", \"sav_000208/00528.jpg\", \"sav_000208/00532.jpg\", \"sav_000208/00536.jpg\", \"sav_000208/00540.jpg\", \"sav_000208/00544.jpg\", \"sav_000208/00548.jpg\", \"sav_000208/00552.jpg\", \"sav_000208/00556.jpg\", \"sav_000208/00560.jpg\", \"sav_000208/00564.jpg\", \"sav_000208/00568.jpg\", \"sav_000208/00572.jpg\", \"sav_000208/00576.jpg\", \"sav_000208/00580.jpg\"], \"height\": 1280, \"width\": 720, \"length\": 146}], \"annotations\": [{\"id\": 167, \"segmentations\": [{\"counts\": \"dPV;1iW1=F5K3M2O2NO1K6M201N2O10001O000O2O010O10O010O100000O1000O100000000O1000001O0000001O00001O1O1O1O100O110O011N1N4M2O1N2N3Mk0UO01N101M3N3H:\\\\Og0]OegW>\", \"size\": [1280, 720]}, {\"counts\": \"VPl:;aW19H5L3L6LO0M3L4N3M3O1O1N3N1O011O001N110O10O0100O0100O1000000000001O0000O100O11O00001O00001O01000O101N1O10000O101N2O2N1N3N7I4eiNlNaU1W1SjNTOlU1_12L4L4^Oc0D;C\\\\``>\", \"size\": [1280, 720]}, {\"counts\": \"]Y^:1jW16N1N001iNNajN3YU18`jNJ^U1;^jNEaU1?\\\\jN@dU1e0WjN[OjU1i0RjNWOmU1l0RjNQOoU1U1liNkNSV1S1TjNgNmU1T1a0J6I8L4O1O1O101N101O001O001O1O001000O01000000O01000000000001O00000000001O00001O00010O1O100O10000O100O2O0O3N1O1N2O4L2O4liNPOmT1T2NN3M3M3I7B>F;D`0\\\\OdP^>\", \"size\": [1280, 720]}, {\"counts\": \"hoa:8X1JPU1c0fjNAWU1d0djN^OYU1h0cjNYOZU1l0bjNUO]U1o0`jNQO_U1S1]jNnNbU1U1[jNmNdU1T1\\\\jNkNcU1W1\\\\jNjNcU1=YkN]OjT1?]kN[OeT1c0]1O2N100O1000001O010O010O010O10O100000000O010000000000001O000O10000000001O01O01O1O0010000O100O100O2O0O102M2O1O1O2O2M1O1O3N3iiNjN[U1T2KO001N1O3L4M4K7J4M5J7kNZiNc0XW1HTWU>\", \"size\": [1280, 720]}, {\"counts\": \"b__:?[W19H8J5L3N3L3N_OjiNXOVV1c0f0N101N2000O0ajNB_S1>elN_OZS1b0hlN\\\\OWS1e0R2O010O10O10O010000O100000000O1000000O01001O0O100000001O001O001O001O10O01O010O1O010O010O10000O10000O10001O00100O001O1O0000001diNUOaU1k0[jN]OaU1b0\\\\jNCcU1]1001O2N3L2N4L6J4L2M5L>A5L5J4L4Loni=\", \"size\": [1280, 720]}, {\"counts\": \"UoW:`0\\\\W17H6K6L2M3OO1B=N3N20O10001O001O1N110O0010O01O10000O01000O10000O10000000000O1000000O2O001O001O001O001O00001O00010O1O010O001O100O100O10O0100O10O10O1001O00000O10000010O10O01fiNhNiU1Y1SjNSOdU1n0XjNXOfU1c1O0O11N1N3N3L3M3M5K4M1N3L<E5K3L4M2N3L4L4MVgc=\", \"size\": [1280, 720]}, {\"counts\": \"UoW:?\\\\W18H7J6K4M2N3N_O`0L5N110O010001O001O00010O1O010O100O1O01000O1000000O10O1000O10000O101O0O101O0O101O001O001O0010O01O00001O0010O01O010O1O100O010O10000O10O010000000O0100001N10000010O1O1O010fiNkNfU1V1VjNROeU1i1M1O0O1000O1O3M3L3N3L4L2N2N4L<D4L3M3L3N2N3L4L3N3LT_X=\", \"size\": [1280, 720]}, {\"counts\": \"[o\\\\:?[W1:E:K3L5L3MN2B?N1N2O2O001O11O001O001O0010O0100O010O0010000O010000000O10000O10000O10000O100000000000001O001O001O001O00001O00100O001O10O00010O0100O100O100O0100000O10O10000001O01O0000000001O2O1N1niNnNTU1R1fjNXOUU1i0gjN]OWU1d0cjNA]U1b10O100O2M3M3M6K4K2N2N2Nb0]O5L3M3L6I8HP_S=\", \"size\": [1280, 720]}, {\"counts\": \"Vo\\\\:>]W1:G9F6K6K6K4L3M3M2O3L2NoNQ1M4O001O1O2OO02O01O01O010O010O01O10O10O1000O0100000000O1000000O10O10O10000O01000O1000001O0000001O00010O001O001O00100O00010O010O10O0100O010O0100000O10001O00000000001O000000010O002N2O1O0niNTOnT1m0ljN[OQU1e0kjNBRU1`0hjNDXU1c10O2N2M3N2M6J3L4M1O4Kc0^O4K4M3L4M3M5J=BdgQ1Of_c:\", \"size\": [1280, 720]}, {\"counts\": \"Qi[:7eW15J5M4VNLdkN5YT13bkNM[T1;^kNG`T1?\\\\kN@cT1f0XkNYOhT1m0SkNSOlT1S1ojNlNQU1[1ijNfNTU1S201N102N3N2M4M3LYNlkNFST16ZlNCdS19llN^OQS1`0]mNUObR1j0Z2O1O1O1O101O001O001O10O010O010000O01000000000O01000O1000O100O10000O10000O10000000O10000000000001O01O01O00000010O01O010O010O010O010O10O10O100000000O01000000001O0000001O00100O1000O12N1^jNTOkS1m0gkNCWT1?akNG`T19ZkNLfT16TkNLPU1`12N3M3L7J4L3L3N3Lj0WO6J4K4M4Jag`<\", \"size\": [1280, 720]}, {\"counts\": \"mX^::aW16L2M3N2O0001N110O001O001O0O2OTONliN2RV17fiNKWV1o0O3M2O1N2M4M2N4L6J8H<F6H5M6I6KN2K5^NWlNmNiS1Q1klN_NTS1]1i1N2O1N2O2N1O1O2O0O101O001O0010O000100O010O10O1000O10000000000O1000000000O10000O10000O10001O000O1000O101O000000001O0001O01O01O01O01O10O10O10O0100O10O1000O1001O000O02O0001O0001O1O100O2O2M4M1gjNjNdS1W1VlNmNlS1S1lkNROVT1Q1^kNVOdT1Q23N2N4L5K6I?^O>fNbQS<\", \"size\": [1280, 720]}, {\"counts\": \"fXW;9cW15L4K5M3NO11O0OO1MViN[OaV1c0?N2OmNN[jN0eU15WjNKhU1;SjNFkU1X1M4M2M4L2O3L4N2M7J9G<D4M:E:G4L2NfMhlNNWS1MZmNEgR19amNSOhN_OnS1Z1kmNgN`R1X1T2O2M2O10001N101O000010O0001O01000O01000O1000O1000O100000000000O100O010O10001N1000000000O100000001O000000001O0001O0010O0001O010O0010O01000O0100000O1000000O0100000010O001O1N110O1O1O100O3O1njN_N_S1c1VlNiNgS1X1PlNnNQT1T1^kN[OcT1P22M3N3M1O3M3L<WN_jN<RV1jNejN=a^\\\\;\", \"size\": [1280, 720]}, {\"counts\": \"Xal;1dW1<I6L4O0O001N1O11O00eN_OZkN`0cT1JWkN5hT11SkNMnT19njNFQU1`0jjN@TU1g0hjNWOXU1o0bjNRO]U1h1O2N002N8ILjjNjMlT1X2TkNUN]T1m1ckNYNUT1i1lkNXNPT1e0]lNPOD=kS1<nlNQOG7WS1f0\\\\nNXOaQ1g0l2O1O1O101O0O101O01O01O0100O00100O10O0100O10000O011O000000O1000O10000O10000O100O10O1000O2O0001O000001O000010O01O000010O01O00100O001O0010O10O010O0100O010000000O100000000000001OO1000010O0001OijNoN^S1T1[lNQOeS1P1TlNUOmS1l0jkN[OWT1f0ckN]O_T1c0]kN_OfT1c0SkN_OPU1g13M4L4L:F6J2N1O3Lc0^O8G5L:Eh_c:\", \"size\": [1280, 720]}, {\"counts\": \"Xif<6WU18llNKmR1>mlNElR1d0olN_OmR1g0^lNL^S1:[lNJdS19YlNIfS19WlNHhS1:XlNEgS13mlN^OVS17_2N1O20O00100O1O101O001O00100O1O100O100O100O100O10000O101N1O10000O1000O01000001O0O10001O001O1O001N10001N101O1O001O001O0010O01O010O1O010O010O100O00100O100O100O10O0100O0100000O1000O10001N10SkNRNgS1o1QlNaNgS1`1TlNgNhS1Z1RlNlNnS1S1nkNQOST1o0ikNTOWT1l0gkNUOZT1Y21N001O1N2O4K5L4L5K2N3M4L`0@3^NPjNn0UV1fNWjNU1^V1L3M6K4K5I8I5JToV:\", \"size\": [1280, 720]}, {\"counts\": \"gV_<a0[W15J7J5M1N01K6M2M31000000O101N101O001O1O1O100O1O1O100O1O100O1000O0100O100O10000O2O000O2O00001O001O001O0O2O00001O1O0O2O1O001O001O100O001O010O1O10O01O100O100O010O10O0100O10O11N10O10O01000000000ejN_NVT1a1fkNfNXT1X1gkNmNVT1T1hkNoNWT1Q1ekNRO\\\\T1m0akNVO_T1k0^kNWOcT1R22O1N2O1N2N3M4K5L3M2N2N3M2N4L6J3M2N4M6]NhiNP1hV1L3N2L6K3M4K3N4HRWb:\", \"size\": [1280, 720]}, {\"counts\": \"Voi;9cW15L4K3M2001N10100001O1O001N2O001O1O1N200O1O1O100O100O10000O10000O101N101O0O2O001N101O001N101O010O1O001O1O001N2O001O1O1O00100O1O1O010O1O100O100O100O010O1O10000O01O100O0100cjN[N^T1e1akN_N\\\\T1a1bkNcN\\\\T1]1bkNeN^T1Z1akNhN_T1W1`kNjNaT1W1[kNlNfT1V21O2N2N2N2M3N3M1O2M3N1O4L4M4K5L1N?B2M2O2N5J5K4L5K6J4K4Jfnc;\", \"size\": [1280, 720]}, {\"counts\": \"ZoZ;4hW16L3N2M2N3N1N2O001O1O1O2O0O1O1O2N1O1O1O2N1O1O2O0O10000O2O1O0O101O1N101O1N2O001N2O001O1N101O1O1O1O1O001O1O010O1O1O00100O001O01O01O010O010O010O011N101N01000O8I4L2N2N1O1O0001O001N1O2N1O2N2N2N2N3M3M3M2O1N3N4K:G6cNeiNg0mV1M3M3N2M2N2N2N1O1O1O3M2NYfV<\", \"size\": [1280, 720]}, {\"counts\": \"V_];6gW14N1O1O1O2N1O1O2O1N1O2M201N1O2O0O2N1O2O1N2O0O1O101N2O1O0O2O1O1O1N2O1O1O001N2O001N2O1O1O1O1O001O001O00001O001O011N101N10O01O2O0O2O3L2O0O3N1N2O0O101O00001O00O2O000O1N3N2N1O2N2N2N2N3N1N7I2N2M3M4M5J6J3M3M4Kb0_OPge<\", \"size\": [1280, 720]}, {\"counts\": \"eWf;6hW13M3N1O2M3N1O2N2N1O2N2O1N101N2N1O1O101N2O001N2N2O0O2O1N2O1O1O001N101O1O1O1O0O101O001O0O2O1O1O1O1O1O00100O1O101N100O1O10000O10000O100O2O1O00000O1000000000O2N1O2N2N2N3N2M4M2N1N3M2M3N1N3M3M2N3M3L4M3M3M4I9FSW^<\", \"size\": [1280, 720]}, {\"counts\": \"eon;7gW13N1O1O2N2N1O2N2N2O0O2N1O2O0O2O1N101N2O1N2O1O0O2O1N101O1N2O1O001O001N10001O001O1O1O1O1O2N1O1O002N2O1N1O101N2O0O101N10000O1000001O000O2O00000001N101N1O3N1N3N2N2M3N2M2O0O2N2O1O1N2O0O2O001N1O2N2N2M4L?A5J5L2JYVT<\", \"size\": [1280, 720]}, {\"counts\": \"`Wf;5iW13M2O2N1O1O2N101N1O2N2O0O2N2O1N1O101O0O2O1O0O2O0O2O001N2O1O001O1O0O2O001O00001O001O002N1O2N1O101N2N101N100O2O1N1O101O0O2O00010O001O000001O001N101N2N2N3N3L2O1N2O1N2N2O2M2N101N10001N2O0O2O1N4L4M2M3N1N2O1N101N2N100O100O2O00O11O1O0010O1O1N4ON010G^hN2gW11101N2LWU`;\", \"size\": [1280, 720]}, {\"counts\": \"fg^;6hW12O1O1O2O0O1O2O0O2N1O2N2O0O2O0O2O001N2O001N101O1N101O1O1O1O001O1O1O001O1O1O00010O001O101N1O101N2N2O4K2O2M102N00001O1O00000001O0O2O1N2O0O2N3N2M2O1N2N3N2M3M3N1N101O2M2O2M2O2M3M100O1O101N2O1N2O0O1O2O0O1O100O100O100Nl]_<\", \"size\": [1280, 720]}, {\"counts\": \"ooU;2iW16N1N1000001O001O1O010O1O100O1O10000O10001O0O101O001N2O001N101O1O1O001O1O1O100O2N1O10O0100O100O101N2O001N5L3M4L2N4L2N1O01N101N3M3M2O1N3M3M3N3M1N4M3L4L2O1N3N1M4L4Kc^g=\", \"size\": [1280, 720]}, {\"counts\": \"mgT;8eW16I5K5M10OOM5N2N2O010O2O00001O01O100O01000O100000000O100000O0101O0O101O000010O01O1O100O010O01O001O1N2H:EQQm>\", \"size\": [1280, 720]}, {\"counts\": \"RPV;9bW1:H5K7Ic0^O6J4L3M3N2N1OPObjN@]U1<jjNBUU17UkNGjT18XkNGiT16]kNFcT19bkNB_T1>b1N101O00100O10000O100O10000000O1000000000000001O0001O01O010O0100O101N2O1O2N001O101O3L4WiNSOWV1a1J20O1N2M5K3N2M9G5K2O1N3M2N101N2N2O1N2N3Le^g=\", \"size\": [1280, 720]}, {\"counts\": \"WPV;5fW1<D`0A9G:H5J4M2O2N2N1O1O1O1OXO\\\\jNUOdU1f0cjNYO]U1?ljN@SU1=RkNAoT1;VkNDiT1:[kNDfT1:^kNCcT1;ckN@]T1`0b1O1O1O100O1O10000O0100000000000O1010O000001O0010O0010O0100000000000102M0001N2O2M2N2N5IR`[>\", \"size\": [1280, 720]}, {\"counts\": \"Sio:1SW1m0K5K6J6K4L5L5K3N1O1O1O1O001OZOXjNUOiU1g0^jNVOaU1h0cjNVO]U1e0ijN[OWU1a0ojN\\\\OQU1a0UkN[OmT1b0[kNWOgT1f0[1O0O2O1O1O1O100O10000O010O10000000001O001O1M5Kgig00YVXO4N2N0002N001O2JQgW>\", \"size\": [1280, 720]}, {\"counts\": \"Rij:9YW1k0YOa0D6M3M20O1O100O2N101M201N2N2O1N4iNiiN>oV1M4L3N4K5L4KYfV11fYiN2N202N1O2ON3N2N1LPiN0nV18H8NN2N1O4K5Jjn]>\", \"size\": [1280, 720]}, {\"counts\": \"Pio::PW1h0K6I7I?B4M3M2N2O1O1O0000O1O2gNfjN@eU12RkNBRU1:\\\\1M3M4L4MlaZ15o]eN5fNMYjN<cU1R1N21O2M2O2M3M5L3L5K3N6I=BnVZ>\", \"size\": [1280, 720]}, {\"counts\": \"RQ[;3RW1>RiNJiV1j0J8J7H9G6K3M2O1O0000oN^jNEbU19djNC\\\\U1;hjNDWU17PkNGPU15VkNIkT14YkNJgT14^kNIcT16`kNH`T17dkNE]T1:e1O0O2O0O2O10000O1000O1000O100001O0001O0001O01O0010O1O10000000001N101O1O3N1^iNA^U1a0ZjNEgU1Z11N1N4M3M4L5K9G5J9H3M3Mjnn=\", \"size\": [1280, 720]}, {\"counts\": \"Uhh;;bW1:chNDeV1P1Ia0A5J4M2N2O0O2OlNajNH_U17djNH[U14jjNKWU13mjNJSU14QkNJoT13WkNJiT15[kNIeT15_kNHaT17dkNE]T19e101O100O1O1O1O010O10000O1000000000001N1O1O3Lihf>\", \"size\": [1280, 720]}, {\"counts\": \"U`Q<=_W1;bhNAjV1R1H`0_O5L3M1O1O2OkN`jNK`U14djNI\\\\U16gjNIWU16mjNHSU15RkNJnT13VkNKjT12\\\\kNLcT13bkNI_T15ekNG\\\\T19e1O2O1O1O010O1O100O10000O10000000O101O000001O00001O01O010O010O10001O1N3O0O3N2M3N3Me0\\\\ON1O2M4L4Ma0^O6J5K3KlV\\\\=\", \"size\": [1280, 720]}, {\"counts\": \"SQo;4kW14K3M4L5[OIYiN>]V1i0I8I3M2M3N1O1OmNZjNOfU1NbjNL^U15ejNH[U18gjNGXU18kjNFUU18PkNF\\\\2]O_o0l0XnNDnT19XkNBiT1<_kNZOeT1e0\\\\1N1O2O001O1O10O0100O10000O100000000O1001O00000001O010O01O010O10001N10O11O1O1O3N2M5L;RiNjNZV1_11N2N2M5Li0UO5LjnZ=\", \"size\": [1280, 720]}, {\"counts\": \"XQo;2jW17K3N2N3L5K8VOl0E3N2N2N010O1NnNVjN3iU1L`jNN_U10fjNMd2[O^o0g0QnNLVU14njNJQU14UkNHkT17ZkNDgT1;`kN\\\\OdT1b0]1O2O001O010N110O1O1O100000O10O101O00000001O00001O001O0010O01O10O010001O0O101O2N1O5Lj0VOO1O1N3M2N3M=mNViN6bW1FhfY=\", \"size\": [1280, 720]}, {\"counts\": \"XiR<6iW11M4M3N2hhNIdV1j0N2]Oc0L3N2O1O100O2NkN\\\\jN1dU1M`jN2^U1NfjNOZU1OljNLTU14RkNGnT17ZkNAhT1?\\\\1O2O001O001O001O00100O100O1O01000O1000001O0001O001O001O1O0010O0100O00101N101O1O3biN\\\\OYU1g0_jNA^U1b100OO2N2N3M2N3M:QO]iNMfW1D_VW=\", \"size\": [1280, 720]}, {\"counts\": \"]YP<1lW14M2O2M2N3N1ihNHeV1:WiNIhV1h0N1O1A?M4M2O2N1000SOniNNPV1LYjN2gU1M]jN0bU10djNJ]U15PkN\\\\OSU1c0U1O101N1O10001N10001O010O1O10O010000O1000000000001O000001O10O01O1O0001O01O100O1niNBaT1>XkNKfT17TkNMkT15PkNNQU13ejN6[U14VjN0kU1R12N1N3N2K6_Oa0G;@QPV=\", \"size\": [1280, 720]}, {\"counts\": \"\\\\Qo;3kW13N1N2O1N2O2N1jhNBiV1?UiNCkV1<ViNEiV1;ViNFgV1j001N2I7I6M4N2N1O2O0O10VOmiNGTV15m0O1O1O2N100O1N3N1O3M4Hnak0MY^TO6K1O2N1O1O1O1O1O100O1O100O1O10O1E=KmnU=\", \"size\": [1280, 720]}, {\"counts\": \"fQo;4iW13O2M3N2L4J6K4O001O0O2O0L4G:M3N1O2OTOmiNMRV12YjNDfU1<_jN\\\\OeU1c0l00O1000O100O110O000000100O1O101O0O2O1O1O1N101O1O01O01O01O001O001O1O001O1O1O1O1O2N2N2N1O2N<nhNSOYV1Z100002M2N2N3L4K5L8^OmgT=\", \"size\": [1280, 720]}, {\"counts\": \"]ag;2mW13L3M6J9H1N100O3N2N1O1N2L4K5M20001N101O0O\\\\OkiNASV1?TjN[OlU1d0g010O100O2O000000010O10O01O010O10O10000O10O02O00000000010O00001O010O001O010O010O10000O101O1O2M4N1N10M3N2N3L3M6DegY=\", \"size\": [1280, 720]}, {\"counts\": \"[al;5hW14M3L3O104K1O2N2N2N1O1MLQiN]OnV1l0M3N1N2O0O2NC`iN]O_V1a0giN\\\\OXV1d0a01O2O1O0010O0000010O00001O010O01O10O100000O100000O20O000000001O000010O010O0100O01000O100O100O110O10O1N2O0O3L4L6CaoU=\", \"size\": [1280, 720]}, {\"counts\": \"Ria<9bW18H6K5M6K6I3L3M3N2N1OAliNVOSV1i0QjNTOoU1k0TjNTOkU1l0d0O1O2O001O0O10010O000010O010O01000001O000000000000O101O01O0010O01O00010O01O100O2O0000010O012N1N3NO1N4L3G9_OkhNOc^n<\", \"size\": [1280, 720]}, {\"counts\": \"ng_=5jW14L3M3M2O2M3L4N1N101O00000000O100001O000000001O00000001O010O00010O01O01000O110O2N1O102M3N2M10O3K4L3L5WOlhN:iW1CQoa<\", \"size\": [1280, 720]}, {\"counts\": \"]Yd<8eW16K2O2N1O2M2O1N2N2L4N2N1N3N2L4M4N1N2OF:M3N20000O1O101N10000000010O010O1O1O100000000O1000O2O0000000010O001O001O01O00010O100000001O00011N2N3N0O2ON5K2M3L`0ZOV__<\", \"size\": [1280, 720]}, {\"counts\": \"RYS=4iW14J6G9K5M3L4K7K5K3M3N1O1N10@`0O100M3N3N1O10001O00000010000O100O1000000000O10000001O000010O0001O01O000010O10O10001O01O01O101O2M3N11OON2M<E3ChhNKa^Z<\", \"size\": [1280, 720]}, {\"counts\": \"nP\\\\=1lW17I5L1D<M4N4L3L5K6J6L2M2O1OZORjN[OnU1d0XjNXOgU1g0^jNTOcU1k0i0O2M200O101O0000010O1O001O10001O000000O1000000O0110O00001O0001O01O01O01O11O000000001O1O102N3L=DM4M3M3L;E5IbWo;\", \"size\": [1280, 720]}, {\"counts\": \"dhZ=1mW16I4M3M3K7J6J6K3M3M2M3N2N1O2O^OliNYOTV1f0PjNWOoU1i0UjNSOmU1l0c00000O101N1000001O01O0001O1O10O0101O0000000000000000O20O000010O01O00010O01O001000000010O01O1O6K2N1OO3I6J6JSXo;\", \"size\": [1280, 720]}, {\"counts\": \"iXn<1oW14K2M2M4M2N200O1M3O1N101N2N2M3M3M3N3N1N2M3M3N101N@jiNYOVV1f0b000O100O1O2O000O101O01O00000100O10O0101O0000000000000000000001O0001O10O01O01O0010000O10010O1O1O2O002N1OO3H8Jc0]OiWo;\", \"size\": [1280, 720]}, {\"counts\": \"\\\\WQ>:dW19G6K2N1O1O1O00000000000010O00001O01O01O100O100O100010O0O10O11O00000001O010O010O001O01O010O01001O01O01O2N101O00N4K5L>@Phg;\", \"size\": [1280, 720]}, {\"counts\": \"hhU=2iW15O1M3M2O001O0010O0010O0O3N3M?B2M2M3N2M2O10EdiNQO]V1m0<00000000O2O0001O01O0O100010O01O01000O10000O10001O0001N1000000001O0010O01O00010O010O0101O01O0001O101O1OO1M5K5J\\\\`k;\", \"size\": [1280, 720]}, {\"counts\": \"ZhZ=2lW14J5J5L5M4M8G5I5L3N2N100OF;N2O1N2O1O1O101O0O101O0010O010O0100O10000000000000000000000001O01O00010O00001000O100O1010O0001O2O1O6J3M00N6@\\\\iNYOgV1`0cPS<\", \"size\": [1280, 720]}, {\"counts\": \"i`o<2kW14M3N1N2O0000OO2N2H9L3O1O6I9G4N2N1O10O1O1^Oa0N3M2N3N2M2O2N1O20O01O00010O0010O1000O01O1O1N2N3N100000001O0000001O01O01O0100000000001O1O0010O11Nc0^O01M3M3M8YOTiNGUW10ZXT<\", \"size\": [1280, 720]}, {\"counts\": \"i`o<7hW12N1N2O0O010N3G8L3O3N4J;F5L3M3N1N2O00]OTjNSOlU1k0ZjNnNiU1P1e0M2O10001N1N20001O1O001O101N101O00001O00000000000001O0O1000000000001O010O1000001O010O1O100O3Nd0\\\\ON2N3M3M8XOQiNG`W1LogV<\", \"size\": [1280, 720]}, {\"counts\": \"mPh<7gW13M2O1O1N1O1O0K7M2N3N4L9F4M3M3O00O1]OhiN^OXV1`0miN\\\\OTV1c0oiNZOQV1f0e0M3N2N2N2N101O1O1O1O1O2O1O1N2O4K2O00[Qn02\\\\nQO8H7M4N100001N100O1000O1002L=DW`2Mm_M0Q`<OcfX;\", \"size\": [1280, 720]}, {\"counts\": \"ePm<7gW12L3J7N3N104M9G1N00000001N2^OXiNJhV13[iNMeV11]iNOcV1O`iNOaV1Km0N3OY`<1g_C0O000\\\\Wk=\", \"size\": [1280, 720]}, {\"counts\": \"Rae<8fW11O1O2N001O1L4J5O2O3M3M>B3L3O100O10O0[OliN@TV1?niN@RV1?QjN]OQV1a0h0M4K4N3M4L4LSXZ>\", \"size\": [1280, 720]}, {\"counts\": \"\\\\gS>1nW13N3L2O1O1O1O1O2N1O00001O1O1O10O01O10O100000RjN]O]T1d0`100O11eiNARU1?jjNDWU1<gjNFYU1:niN@1e0RV1P1102M2N2N2M3M4L<POU`_<\", \"size\": [1280, 720]}, {\"counts\": \"Wa`<2kW14N1O1O2N1N1O2O0O01O100O1O200016J4L1OO1O2N2M3N2N001O2O1N4L2NoU15kiN2O001O1N2O1O1O1O001O1O1O1N2O1O0000000000000001O01O01O00010O011O000000O200O2N4\\\\iN\\\\OcU1a11N2N2N3L2N5J;TOV`_<\", \"size\": [1280, 720]}, {\"counts\": \"QQh<4iW14N1O1M20O0100O1H8K5M2O2N4K:H7J2M3M2O1O\\\\OSjNVOmU1j0VjNTOiU1k0ZjNUOdU1i0_jNWOaU1g0cjNWO\\\\U1g0mjNSORU1k0R1N3N2O001O1O0O2O1O100O1000000O0100000000000000001O01O01O00010O01000O0101O001O1O102M6WiN\\\\OjU1\\\\12N3L3N2N4J=ROQiN4eV^<\", \"size\": [1280, 720]}, {\"counts\": \"ohk<5jW12M2M2O010N1DGRiN;jV1?N1O2M6K6K8I4K3N00\\\\OPjNZOoU1c0XjNZOgU1e0]jNYOaU1g0bjNXO]U1g0fjNWO[U1e0mjNUOTU1i0S1N1O101O1N101O1O00100O100O1000000000000O11O000000001O01O0010O01000O010O10001O2N1O3N3]iNZOdU1a10O3M2N3L3N7H9SOSiN6fW1Eo^Z<\", \"size\": [1280, 720]}, {\"counts\": \"hhf<3kW13M3N0O1O100O1J6N2N3N3L5J9G9J2M3N00AhiNXOVV1e0SjNVOmU1j0VjNTOiU1l0YjNSOfU1m0\\\\jNQOdU1n0h0M2O1O1O101O0O2O00100O1N200O1000000O100000000000000000000010O01O001O10O010000000O2O2N2O1aiNZO`U1g0ZjN\\\\OhU1k0kiNZOVV1W13M2M4M8WOZiNGdf`<\", \"size\": [1280, 720]}, {\"counts\": \"c``<7gW12O1N2O1O1N200ON2N02N3M4M3L5K6J6I6K3O2M2O1N2O1O00@niNUOPV1h0YjNSOfU1m0_jNoN`U1P1fjNkN[U1S1i0N2O1O2O0O2N2O1N10100O1O1O100O10000000000000001O0000001O001O001O101O2M:E_Y6O^fI8F6L5002M5L1O1N2L5LZo\\\\<\", \"size\": [1280, 720]}, {\"counts\": \"h`[<2jW1401N1O1O1N2O1O1O0O2OO2L30O1O1O0O3N2M4M5khNZOgV1R1K6K3M3O2M2O001O1N^ORjNWOlU1i0YjNSOfU1l0h0N1O1O1O101N101N2O001N2O1O1O100O010O100000000O101O000001O00010O01O00100O001O010O1O1O101M4GfiN2UV1:F92N2M5L1O1O1N2N2N]_Z<\", \"size\": [1280, 720]}, {\"counts\": \"iXZ<1jW1500O1O2L3O1O1O0O2OL400O11N001O2M3N2N5J<C6K4L3O1N2N101O1O^OkiN[OTV1d0TjNVOkU1j0ajNmN\\\\U1S1fjNlNYU1S1j0O1O1O2O0O2O1N101O1O001O1O1O100O10000O1000000000001O00001O010O01O01O10O010O100000001O2O2OO00Oh0ZOUOfiNIZW1O9EZg`<\", \"size\": [1280, 720]}, {\"counts\": \"iXU<1lW13M4M2N2O1O00O001O011OO01O10N2O1O3L4L5ihNAgV1o0K3M4L3N1O2O1N2O1O2NYOXjNYOfU1g0^jNVOaU1i0ijNoNVU1Q1ljNnNTU1P1ojNoNPU1P1Q1N10000O2O1O1N2O010O1O100O1O1O100000000O11O01O0000010O001O001O010O1O010001N1liN]OkT1b0RkNBmT1?ojNDQU1<kjNGWU19djNJ^U16^jNLdU14XjNMkU16miNMWV14biNMmV1_O\\\\iN>XW1K9FU_d<\", \"size\": [1280, 720]}, {\"counts\": \"hXU<3hW16M3M10O1O010O0100O00002M2N3N2M3Mb0]O6K3N2N1O2O1O1O1O2M\\\\OVjNVOiU1h0]jNUOcU1i0ijNmNVU1R1mjNmNRU1R1n0O2O001N101N2N101O1O1O1O100O100O10000O10000000000010O0001O010O1O001O010O10O1001O0010fiN@RU1`0gjNE[U1;`jNHbU17[jNKgU15TjNNQV15diNNUW11O2O13Leof<\", \"size\": [1280, 720]}, {\"counts\": \"iPT<2kW17I3N3M01O1O1N11O010O001N2O2M3M5J7I?B3N3N1N2O001O2M@QjNROoU1k0ZjNPOeU1P1cjNiN\\\\U1V1h0N1O1O10001N2O0O2O1O1O1O1O100O1O01000O10001N100000010O00001O00010O1O1O00100000O1000O2O1O100O11PiNKQV13liN0PW12K5001N101Ofof<\", \"size\": [1280, 720]}, {\"counts\": \"d`l;6hW13L300O001N10O0O20000000O1O101M3N3M4K8G8J4K4M3M2O1O001N^OQjNWOnU1h0VjNVOiU1j0[jNSOdU1l0gjNmNVU1R1ljNnNSU1Q1n001N10001N2O001N2O100O1O1O10000O100000000000000000001O010O0010O01O100O1O01000000010O100000TiNFQV1?aiNHfV1e0;E5K2N5J8H[ok<\", \"size\": [1280, 720]}, {\"counts\": \"f`l;6fW15N1N2O1O00O00000010O001N2M4N2M5J8I8H6K3M2N2O1N2OXOUjN]OjU1c0ZjNZOeU1f0^jNXOaU1h0ajNXO]U1g0ijNUOWU1h0SkNQOlT1n0T1O0O1O2O001O1N10100O1O100O100O100000000000000000000010O0001O1O010O1O0100O01001O01O10O01000YiNFfU19SjNNRV1LhiN:eV1:4K5K3N>AZ_n<\", \"size\": [1280, 720]}, {\"counts\": \"fXk;6fW16M2M100000N20O001O010N101M4N5K6I<D4L4M2M3O1N1O2OZOPjN^OoU1`0XjN\\\\OgU1d0]jNZOaU1e0cjNYO\\\\U1g0hjNVOXU1h0mjNUORU1k0RkNPOoT1o0R1O000O2O1N101O1O100O00100O1O100000000000001O01O000001O0010O01O01000000O1000010O1eiN\\\\OWU1f0_jNBbU1`0SjNHnU1U11N3N1N5K7I4L4K6J7I9D\\\\Wm<\", \"size\": [1280, 720]}, {\"counts\": \"`Po;8gW13M1O1N2N00O2O1000O1O1M4L3N5L4L7I7H4L4L3O1N2O0OZORjN^OmU1b0WjN[OhU1e0ZjNZOeU1e0_jNYOaU1e0djNWO\\\\U1h0o0M200O1O101N2O00100O1O1O1O100O2N4M9FmPY>\", \"size\": [1280, 720]}, {\"counts\": \"hXZ<2hW17L101N20N1010O1J6K5M3N204J7I:F5L3M2O1N2O001OYOSjN\\\\OmU1c0YjNYOfU1f0^jNXOaU1f0djNXO[U1e0ljNXOSU1f0U1N1O2N1O3M2N2O3M6J6GRQY>\", \"size\": [1280, 720]}, {\"counts\": \"cX_<7eW15M3M1O1OO2N1000I9L4M4M5K7I5J4L3M3O0O1O1010YOTjNYOlU1f0XjNXOgU1g0]jNVOcU1g0djNUO\\\\U1j0n0N1N4M2N3N4L4L4L5IlhW>\", \"size\": [1280, 720]}, {\"counts\": \"c``<3iW16M1N2N1O10N2OO3B=M4N3M3L5K6K6J30O003M4K3XO`iNKdV1OciNLaV1MdiN1XW1N2Lbob>\", \"size\": [1280, 720]}, {\"counts\": \"hPc<2iW16L4M1000OUOOiiN2TV19biNH^V1m0N4M2M4L5M3L2O1O1NSO`jN]O`U1`0gjN[OZU1`0PkN[OPU1c0X1N1N3N10001O001O1O101O1N3N5IYYU>\", \"size\": [1280, 720]}, {\"counts\": \"dha<6gW13N2N2POLSjN4kU16miNJPV1S103L4M3M2O2M2N00oNejN@[U19ojNDQU18ZkNAfT1>_1N10001O001O1O010O010000000000000O100000O2O000000O10001O3L`ag=\", \"size\": [1280, 720]}, {\"counts\": \"bX_<9dW13ROMniN5nU15iiNMTV1:eiNIYV1o0N4M4K4M2N101O0OmNhjNAXU1;ojNBQU1:\\\\1O1O2O00001O1O10O01000000O10O10000000000000000O11O00001MXba0CV^^O20N2OWWW=\", \"size\": [1280, 720]}, {\"counts\": \"b_[<9eW18ihN@_V1g0[iN_O^V1V1K7H6K2N2N2N2O00nNdjNB[U1:PkN_OPU1?YkNYOhT1e0Z101O001N100010O010000O1000O1001O00O100000000001O1O100O2N10O1O1O00100N2O100O1O3N2N1O3^iN\\\\OaU1g0WjN_OhU1c0QjNBoU1W1101N2N2N4L4M2M9G4M6I4L4L5J5L4KSQT1MQljN0S\\\\f;\", \"size\": [1280, 720]}, {\"counts\": \"dPo;6iW11O2L3O1O2WO4ZiNObV1m0J6J2N3M1O2NSO]jN@cU1=fjN]OZU1a0ljNZOTU1e0QkNWOPU1h0T1N2O1O101N101O001O010O100O10O1000000000000000010O0O10000010O0001O00100O100O101N3N1hiNAkT1a0ojNDQU1`0gjNCZU1a0]jNCdU1\\\\1101N101N2N2N2N3L4M7I:E8I4KboU=\", \"size\": [1280, 720]}, {\"counts\": \"Xal;2mW14L1M201O1WOOaiN4ZV1m0L8G5MXORjN@lU1`0YjN\\\\OgU1b0]jN]OaU1a0hjNYOXU1f0ljNVOUU1g0R101N2O100O101N10001O0O20O0100O10O100000000000001O00010O001O001O01O01O00100SjN]O[T1d0bkN_O]T1c0^kNAbT1?ZkNDgT1=UkNEkT1<RkNFoT1<ljNGTU1b0\\\\jNDeU1[11O1O1O2N2M2O3L3N2M4`NiiNQ1kV1H3L3L8ImfY=\", \"size\": [1280, 720]}, {\"counts\": \"SYf;<cW11O1N2O2L3K5_Oa0M3M201O_OliNWOSV1g0d0M2N3N2O0100O02O000O10001O00001O1O01000O10O0100000000001O00010O0000100O001O01O01O1O010O102giN]OPU1g0gjN^OXU1i0ZjN_OfU1]10O2O1O2M3N2M2O2M4M2M6PObiN3bW1Ci^b=\", \"size\": [1280, 720]}, {\"counts\": \"Xi^;3kW14L3N1OO1O1O20N1001N2L03@e0Hb0@3M1O2N101NWO^jNVOcU1h0j0O2O10O10O1O2N1O10001O001O1O010O100O10O10000000010O00001O0010O01O00001O010O00100O100liN]OiT1g0njN_ORU1c0ijN_OXU1e0_jN_OaU1`12N1O2M2O2M3N2M3N2L9Higc=\", \"size\": [1280, 720]}, {\"counts\": \"WYk;5iW14L2O2N100O100N2N3N2O01O002M5K4L4LYn\\\\1OhQcN7I8hiN^OdT1e0YkN[OgT1f0SkN_OnT1b0njNAQU1b0jjN@WU1h0]jN\\\\OcU1`1010O2N1O2M2N3N2M4M2Ng0XO=AonZ=\", \"size\": [1280, 720]}, {\"counts\": \"XiR<7fW15L3N1O1O1O010O100O11O1O001N1O11O0100O1O2O2LU^U1HP\\\\iN7PT1KnkN6QT1LmkN5ST1LlkN4TT1MkkN3UT1NjkN2VT1OikN1WT10hkN0YT1OgkN1ZT1OekNO^T11akNMbT12_kNMbT12^kN0`T10`kN0aT1NakN1`T1Mia7OQ`H2N100N11M3O1Oe`i<\", \"size\": [1280, 720]}, {\"counts\": \"bia<1gV1b0iiNESV1b0eiNDVV1V1L4L3M2N2OPO^jND`U16ljNETU18RkNEnT19VkNEjT1:\\\\kN@dT1`0^1O100N3O00001O10000O010O1000000000O11O000001O001O0000001O00010O1O100O2niNC^T1>^kNFaT1:]kNHbT1:YkNJgT19SkNIoT19kjNIUU1<cjNH^U1\\\\11O1O2N1O2N2N3M4L6J9G2N2N103K4L4B?Jgnf<\", \"size\": [1280, 720]}, {\"counts\": \"k_`<=_W1<F?A=C3M2N2N2OmNdjNC\\\\U19kjNDTU1:QkNDoT1:WkNBjT1<]1O100O100O2O1O010O10O1000000O11O0000O10000000000000001O1O010O1O2O0O100O10001iiN@kT1a0QkNBoT1`0kjNDUU1>fjND[U1>`jND`U1g0PjN_OQV1W12N1O2N1O2N2O4K8H9G:F4K5KVWm<\", \"size\": [1280, 720]}, {\"counts\": \"X`[<a0VW1<I6L9F4M2O2N1O2N1O3M2N10[OXjNSOfU1g0cjNWO]U1>PkN@oT1>UkN@lT1>XkN@hT1?ZkN^OgT1a0]1N2O001O100O10O010000O10O10001O0001O0000000010O010O10O10O100000001O1O011O00O10[iNG`U1<YjNHiU1<liNHWV1o03M2N8H8H4K4M3K5KonP=\", \"size\": [1280, 720]}, {\"counts\": \"lgW<:cW16L4L4L4K7K6I4K4N0O10N2L3N3M3G8K6M3N1O2N2O001O1O1O010O1000000O10000000001O00001O010O1O010O10O101O00O1000100O1O11ON5JSa21`^M>5J2N001N5LiVW=\", \"size\": [1280, 720]}, {\"counts\": \"nP^<7dW16M2M4N2N0O05@ghNMQW6O>6PPK4O1000100000000O1010O00000000100O1O1000O1000O02O0001O001O100O010100OO3?B10J5J3N4K3N1LkfY=\", \"size\": [1280, 720]}, {\"counts\": \"Z`Q<6hW17H5M4K4L4M4L4K5L3M2O0O1O01N2N1N2O2M3M3L3O2N2M3M2N3L4N3K5HaPm>\", \"size\": [1280, 720]}, {\"counts\": \"bhR<=_W17K5J4L4M3N2N2M2N2OO1L4N3N1J7J5N3N1O2O1N101N2O1O01000O1O01000O1000O10000001O0000010O00001O001000O11MV`Q>\", \"size\": [1280, 720]}, {\"counts\": \"\\\\aQ<5fW1b0]O7M3M2M4M2N2N2ON1E<M3L4K5N2O1N2O2O00001O001000O0100O1000O100000O1000000000001N2O1M4Lh_[>\", \"size\": [1280, 720]}, {\"counts\": \"XPo;;bW17I6K4L4M2M6I5MJ6I7K5L4N2O100O2O0O2O001O1O010O1O010000000000000000000000000001O0100O3LhQ?Hkm@5TiN0iV15nhN0SW1>001N2O0O3N2M8I4HQo_=\", \"size\": [1280, 720]}, {\"counts\": \"^PY<8fW15K6I4M6I4MO2O0N3J6L4N2O1O10001O00100O010O0010O01000000000O100000001O000000010O001O10O010001O0O2O00001O0O5ciNXOYU1k0bjNVO`U1m0RjNROI5UV1`11O2N2M3N2N5J6K4K5K9G6J8HdfT=\", \"size\": [1280, 720]}, {\"counts\": \"_X_<1hW1=G6K6J3M5K00O1M4J6L4O1O1O101O0O101O00100O010O100O1000O1000001O0000001O00001O00100O1O010O100O100O101O1O1O1O3M2N104K8IN1N3M4L6J6I7J6H[oP=\", \"size\": [1280, 720]}, {\"counts\": \"^Ph<=`W17I5K5L4L3M3M10J6H8M3O1O1O101O1O001O1O010O100O10O1000000000001N1000000000000010O0010O10000O10001O0O2O1O1O1O2N100O2O7IM4M4J5L7I4K7GW_i<\", \"size\": [1280, 720]}, {\"counts\": \"\\\\hf<9dW18G7K5K5J5L2N2O1OD<K4M5M2N2O101O001O100O010O100O01000000000O02O00000000000010O01O00100O010O100O10001O001O1O2N3M1O2N101WiNPO^V1Q1:O0O5J3M5H7I\\\\_i<\", \"size\": [1280, 720]}, {\"counts\": \"igP=4iW15L3N2N1O10000O101O0O1000000O1000000000000000000000000000001O010O100O100O100O20O01O2N1O1O2N2N4L3NM5L2L9G8I7F]_n<\", \"size\": [1280, 720]}, {\"counts\": \"UPh<5iW17I8H6J:F4L3M3M2O2O01BciNWO^V1:l0LdiN@`U1=S1M3N5K2NV`2Ofgl02ShPO2M3N2N2N2O2M3M3N4L4L4ViNQO]V1X11O1O2N3M3K7J6I4Lcof<\", \"size\": [1280, 720]}, {\"counts\": \"WXn<=`W16K5K6K3M1OO100K6H7K6M2O1O2O0010OO10010O00010O0100O100O100O100O2O0O100000001O001O1O1O0100O0100O100O101O2N1O1O2N2N3N2UiNPO_V1X11O3M4K4M6H6J7J]oa<\", \"size\": [1280, 720]}, {\"counts\": \"]Qm<1nW13M2O1O001O00001O001O1O1O1OI`hNN^W12801NYoW12fPhN2O1N2O2N3M4L3RiNZO[V1k0]iNYOcV1R100OO106J6J8G8GRWc<\", \"size\": [1280, 720]}, {\"counts\": \"Wik<4jW16L0O2N2O0O10001O0O2O1N1010OM^OUiN6\\\\W1N1O1OPh[1MSXdN1OO_nP=\", \"size\": [1280, 720]}, {\"counts\": \"_Pm<8dW19I5I6M4K4L3M00M4M3K5M3M3N2O2N1000100O010O10O01000O10000000000000001O000000001O00010O010O100O100O100000001OO1O1O2L3JVXm<\", \"size\": [1280, 720]}, {\"counts\": \"^Pm<8cW19I5L4L5J5K5L3N1OG9H8N2L4N2O2O00001O1O10O0100O1O10000O10O100000001N100000010O0010O01O01O01O10O10O101O00002N1O1O1O101N3N108FO8E8I5Gb_d<\", \"size\": [1280, 720]}, {\"counts\": \"n_o<>_W15K6I7J8I4M2M2O2NDoiNmNnU1m0ZjNROeU1g0djNXO[U1d0jjN\\\\OVU1b0T10001O0010O01O1O10O0100O1000000O11N1000000000000010O00010O010O010O010000O100000O100O2O001N2O1N6GXPg<\", \"size\": [1280, 720]}, {\"counts\": \"agV?6W1LYU18_jN0]U15ZjN0fU12WjNOiU11VjN1iU10VjN0kU1OTjN2mU1MSjN3oU1LPjN4RV1JmiN7VV1EliN:nV100N2O002N2M2O1O1O001N2N1O2NSom;\", \"size\": [1280, 720]}, {\"counts\": \"n`c=1oW11YhNO]W1:010O1O1O10O010O1O001000N3N1O0000004L1OmV`00Si_O0O2N1O100N2O1N3M3N2O0O2N5K3M200O1001O0O2N3N4K3L6K5JZoR<\", \"size\": [1280, 720]}, {\"counts\": \"iPa=1nW13M4L3N3M2N3M3M2N1O10O2O005J2O4J5JWh[12hWdN3M2O2M2N1O2O0O2O1O2N0100O3M3M4L8GSVj;\", \"size\": [1280, 720]}, {\"counts\": \"Shd=:eW15K4M3M1O3M;E1O0001[O\\\\iNMdV1JeiN6\\\\V1DiiN;nV101M6IZXY1NhgfN7K2O100O2O000O1001O000O10000O10O01O1O1O3L[^f;\", \"size\": [1280, 720]}, {\"counts\": \"_`c=;`W19J3M4ZiNYOmU1m0liNVORV1o0eiNXOXV1Y1N2N2O\\\\OXjNSOfU1h0`jNXO_U1d0fjN\\\\OYU1a0kjN_OTU1=QkNCnT1:WkNDiT1:[kNDeT1;^kNCcT1;`kNBaT1>`101N2O001O1O00100O100O1000O10O100000000001O0001O01O00100O10000O10000kiNGbT19ZkNMdT14WkN1hT10SkN4mT1NojN5PU1MmjN4SU1OhjN3YU16YjNMjU1T11O1N2O2N2M=D6I9H5J2O3L3M4L4LPfb;\", \"size\": [1280, 720]}, {\"counts\": \"ga^=7hW11M2fN;XjNHfU1;WjNGgU1<UjNGiU1=RjNGkU1X1N3MQOUjNMjU11]jNJcU14ajNK^U12gjNMXU11ljNMSU10SkNOlT1OZkNMfT11^kNMbT12bkNK^T15ekNH\\\\T16hkNGXT19h1O1N1010O01N2O1O010O10000000000000000001O000000001O00010O10O10O10O10iiNHeT17XkN1eT1NXkN6hT1HWkN;iT1BWkN`0iT1_OVkNc0SV11O010O01O1O100O1O2N2O1N4L2NYnh;\", \"size\": [1280, 720]}, {\"counts\": \"XaY=1^W16hhNNTW1a0]O[OhiNi0SV1d0O1000O10000cNgiN0Lm0nV1N101N3M3N5J4L4L3JW^U12jajN3M3diN1bT11[kN2cT11YkN2fT1OXkN3gT10TkN2mT1OojN3RU16ajNM`U1Y12M3N2N2N2N3L4M=C4L7H=D:FR_P<\", \"size\": [1280, 720]}, {\"counts\": \"dPm<3lW11O1M3O1\\\\Oc0L300010O1N1O20O1000O1O2M2M3O1O2O001N110O001O001N2O10O01000000000000000000001O000010O01O010O00100O100O0niNDaT1=ZkNGfT1:VkNIjT19QkNIPU1:ijNIXU1_11O2N2N3M3M3L6B_iNQOhna<\", \"size\": [1280, 720]}, {\"counts\": \"aYd<:eW18I2K;E5NO1UO^iN5[2DdQ15TlN3m1:jQ1CVlN3P2>jQ12UnN0jQ1OVnN4hQ1LXnN5gQ1JYnN9eQ1G[nN:dQ1E]nN9eQ1F]nN7eQ1I[nN6gQ1H[nN9cQ1G]nN3cM_OPT1<^nN=aQ1C`nN=_Q1BcnN?\\\\Q1_OenNb0[Q1]OdnNg0ZQ1VOhnNm0VQ1QOknNP1YQ1iNinNX1PT104G^cQ1VO`]nN0O2N2N4M3N6I3N3M0002L3N3M6Ibof<\", \"size\": [1280, 720]}, {\"counts\": \"nTh<;_V1FSjN3Be0bV1c0M3M4M4hMZNmmNk1nQ1VNRnNn1aQ1[N_nNh1]Q1YNbnNk1ZQ1VNfnNl1mo0hM\\\\oN;h0n1ho0jM`oN8h0o1fo0jMboN6i0P2fo0iMaoN7i0o1ho0iM_oN7k0o1ho0hM]oN8m0o1QQ1QNPoNn1PQ1QNSoNm1nP1RNToNk1mP1SNWoNi1lP1TNXoNh1jP1WNXoNb1fS1M5K7G6I:^OnhNHZT60c[L0igN3O0M4O3NL5M101OoW11ngN3O1N101N20M2O3N001N1000002MUVR=\", \"size\": [1280, 720]}, {\"counts\": \"Te`<2iW1:J2G9N4MJ6N1O4@dhN8iW1I2NWS1:\\\\lN5VmNO^m05kQOd0Pn0_OVQO]1en0eNZQO]1cn0eN^QOZ1`n0hNaQOW1]n0kNeQOS1Yn0oNiQOn0Wn0SOjQOl0Un0UOlQOi0Tn0XOmQOe0Tn0[OnQOc0Rn0ZOTROc0mm0XO[ROd0em0ZOaROb0`m0[OeROb0]m0[OfROa0]m0]OkRO:Ym0DSSOKTm03PSOHSm06^5M2N4M2MiYj04PfUO5L3O1O001[OBeiNa0[V1_OaiNd0`V1]O\\\\iNd0fV1^OTiNe0mV1700001N101O1N2N3L4L4L4M3M2O00h^Z<\", \"size\": [1280, 720]}, {\"counts\": \"jYi<3iW15M2M30OO000UOOjiN1TV16fiNJYV1n0O01O13M<D6K1N2OUO]jN[ObU1a0fjN[O[U1`0kjN^OTU1`0PkN@oT1>UkN@kT1`0[1N1O2O001O1O001O100O100000000000000000O1000001O000001O0010O010O10000000001N101giNAnT1b0ijNEWU1=bjNF_U1^10O2M4M3M4L4L3M8F;BQW^<\", \"size\": [1280, 720]}, {\"counts\": \"QhP=:cW16TiNEjU1b0niNDmU1b0miNBPV1b0liN@RV1d0jiN]OTV1Y1O0OZO`jNoN`U1l0l0M3M3O2M2O1000001O10O01O100000O10O100000000001O00000000001O001O0010O0100O1O10000O1001OO104L2O1`iNXOcU1c1000O2N3M3M4L4L6J4K4M5K3M4L3L5KonW<\", \"size\": [1280, 720]}, {\"counts\": \"m_j<5gW18I5WiNEgU1>UjNGfU1>UjNFgU1`0RjNDlU1?niNBTV1;miNDVV1>liNXO[V1h0>O1M3M3N2O2O001O000010O10O01000O100000000000000000O101O0001O0001O1O001O100O100O1000001N11O01O1O3M3YiNTOTV1]1O01O2L3N3L4M3M7I2N1O4L4K5L1O2M3N3M5KRoW<\", \"size\": [1280, 720]}, {\"counts\": \"kog<9dW16I6K6XiN_OhU1g0PjN_OlU1h0oiNXOQV1Y11G:O1D<N2L3O3N1O101O001O010O10O10O10000000000O02O00000000000000001O010O001O001O10O10O100000001O0010O01O2O2TiNRO0NWV1c10N2N2M6K2N4K6K2N1O5K3M2N2N2M4L3LZg[<\", \"size\": [1280, 720]}, {\"counts\": \"PPc<1kW1:G6WiNAhU1e0RjNBeU1e0VjN^OhU1g0SjN[OjU1j0TjNUOlU1Q1niNmNSV1^12K5O1I7A?M3O2O001O001O0010O10O1000000O10O1000000O2O000000000000010O001O10O010000O10000000001O1O002N1O2ViN^ORV1X11N1O3M4L3M5K4K3N2N3M4L3M2N2N2N2M5IV__<\", \"size\": [1280, 720]}, {\"counts\": \"nWd<6fW18I5J6J7[iNWOkU1n0QjNSOmU1Q1SjNmNkU1Z1:001O1O^OfiNSOfV1i0:O1O2O001O0000100O10O10O100000O10000000000000000000001O001O0010O0100O10O1000O1000001O001O100O2_iNQOlU1d1OO1N3M4L3L5L4L2N1O4L4L3M3M2N3M4JV__<\", \"size\": [1280, 720]}, {\"counts\": \"oog<3hW1:H4J8J6J4aiNQOiU1T1QjNPOlU1b10GQjN`NQV1`1700B>K5N2O2N101O00001O10O010000O100000O10000O1000000000000001O01O1O010O1O00100000000O10001O01O01O011N3N>B1OO2M4L3M4L2N2N2N5K2N2M3N3M1O3MY_Z<\", \"size\": [1280, 720]}, {\"counts\": \"TXn<5gW18H7SiNDlU1b0PjN@lU1g0niN\\\\OoU1j0liNXOSV1[1N10JoiN_NPV1^1:N2F:J6M3O2N101O001O0010O01000000O10O100O011O000000000000000010O0001O10O10O10000O10O100001O1O011N2N3M`0B00M2N3M5K5K2N3M3M3M2N1O3M2N2N2LU_U<\", \"size\": [1280, 720]}, {\"counts\": \"SXS=5gW18ohNJmU1;PjNFnU1?liNERV1?jiNAVV1c0fiN\\\\O\\\\V1f0`iNYOcV1n04L40OH9J6J6L4N2N2NQX;KVhD0ko42SPK3L100O1O1O2N1O1O2N101N1O2O1O1N2O100O100000001O1O100O4TiN[OUV1[1OM2N3M3L7J4L2N1O4L6J3N1N2N2N3KRgQ<\", \"size\": [1280, 720]}, {\"counts\": \"WiZ=3jV11QjN5jU12PjN0nU16kiNMUV16giNJZV19aiNI^V1l01O1H8N24L6J0010lNmiN9TV1DPjN:lV1N3N2Lcfg0JeYXO1N2N3L3N101N2N2N1O2O1O2N1O0010001O001O3N2Me0\\\\O1OO3L3M3M5K2M6K3M2N3M3M2N2N2M3N3LQgg;\", \"size\": [1280, 720]}, {\"counts\": \"Ra^=7iV11niN0nU17liNLPV1<iiNGTV1>iiN\\\\OZV1Q142N3M3M8I5K2NVO]jNYObU1b0gjN[OYU1c0jjN\\\\OUU1b0QkN[OnT1d0VkNVOnT1h0U1O101O1O1O010O100O1000O10O10O1000000000000000010O0000100O010O10O100000000001O00100O2O1RiNBVV1W1OO1M2N5K5K5J5L3M4L3M4L3M2N2Nm^f;\", \"size\": [1280, 720]}, {\"counts\": \"iPW=2mW13L1N3^OMUiN4gV1d0N2N3L4N5K9G3N2N00]OSjNUOkU1i0]jNQOeU1l0h0N2O1O100O2N10001O00100O100000000O10O1000000000000001O000001O001000O01000000000O100001O1O3N3ZiN\\\\OhU1^11N3M3M3M9G6J<D2N2M:CR_P<\", \"size\": [1280, 720]}, {\"counts\": \"\\\\hk<6gW14N0N10010M2N3O0O3N3L`0_O5M2O1O0001J6K5M4L3O1O1O1O1N2O1O1O1O100O010000000000000000O1O010O1O2N1O1O100O2N1O100O2O1O1O10O010000001O10O4YiNmNXV1`1OO2N2M3M<D3M5K7I5K_WY<\", \"size\": [1280, 720]}, {\"counts\": \"TP^<3lW12N0O2O0OO1101O000O3M3M4fhNEiV1j0N3M2M3N1O1ON3M4M201N200O2OO01O100O1O1O100O10O10000000000O10000O100O10000O2O0O1O1O101O00010O1O0010O1000O02O10O>CO0O2N3M4L3SOUiN?YW1M<D3M5K]Wc<\", \"size\": [1280, 720]}, {\"counts\": \"Zhm;6eW15O1O1ON2O1000000O1001O1N2N2ihNBkV1o0I2N2N0O10L4001O100000010O0010O00000010O00010O10O100000000000000000000001O0001O01O01O001O0100O00100000O100002O=B2O0OO2M3M6J8SO\\\\iNMnnU=\", \"size\": [1280, 720]}, {\"counts\": \"YXf;2jW14O1O001N101O00O10O1M3L6RiN^OZV1d0_iND^V1?ZiNIcV1j0O2M2O2N00CjiNROWV1k0?O3O00001O000O110O00001O01O010O0100O10000000000000000000000000010O001O0010O010O100000001O1O2O1N7Ib0_ON3N2M5K3dNfiNk0\\\\W1YOa_]=\", \"size\": [1280, 720]}, {\"counts\": \"_Xa;4kW13NO01O1O00O102K`X63TgI>OO010O3M3N2M5JXn>MlQA6J3M3N4L2N1N2O001O00000000010O0001O001O01O010O10O10000001O010O2N2^iN\\\\OcU1j0jiNCWV1S11N4M4K7I6Ja0_O5J7HYo_=\", \"size\": [1280, 720]}, {\"counts\": \"i`S;2nW12UhN1aW17O100N1000M3O010O1O1000O001E;M3O1O4Ke0[O3N001O@iiNZOWV1a0TjNZOkU1e0ZjNWOfU1h0ajNSO^U1l0k0O1O1O2O0O101O001O1O1O010O2O00000O2O00001O00001O1O1O001O010O001O0010O10O10O1O2O0O2N2O1O1aiN@[U1`0XjNLiU15miN4SV1l03N2M5K8H8H:F4K;Do^g=\", \"size\": [1280, 720]}, {\"counts\": \"iP[;3lW12N001N1O1O0E=L2O2O3L5XiNSOUV1b1J2N2O0O10YOTjNZOmU1e0WjNXOiU1g0[jNWOdU1h0`jNSObU1m0`jNPObU1n0h0O1N2O2N10001O1O1O10000O100000O100000000000000000010O0000010O01000O1001O0001O00010giN[OTU1f0bjND^U1<\\\\jNIeU1X12N3M3M;F3L=B8H9FlVk=\", \"size\": [1280, 720]}, {\"counts\": \"Sa];7fW13N2N1O00O2M111OCKPiN7kV1`0O2M3M4N4L>C5J2O1O1OVOYjN\\\\OgU1b0_jNZOaU1e0cjNXO]U1e0hjNYOXU1f0kjNXOVU1f0R1O101N101N200O00100000000O10000000000O100001O0001O000010O01O100O1001N101O010O1O100O1]iN\\\\OiU1i0eiNC\\\\V1P14K5L2nNViNh0TW1M1N5L3M3M3M4KQ_b=\", \"size\": [1280, 720]}, {\"counts\": \"XYa;3kW14mkNJUP18i30000O00011MB0nhN2mV1c0N1M4N4M4L<E6I3N2N1OVOYjN\\\\OgU1c0^jNYObU1e0bjNXO_U1g0djNWO\\\\U1g0hjNWOXU1h0mjNUORU1k0R1N1O2N101O1O1O100O10000000O0100000000000001O0000010O00100O1O100O10O101O1O1O010000003UiN]OQV1e0ciNE_V1m04K4M4K5K5K5K3L4M2M3Mj^]=\", \"size\": [1280, 720]}, {\"counts\": \"]Qe;2mW13L2N101OO0010O1A>N3O1K5M4N4L`0A2M2N3NWOWjN[OjU1c0\\\\jNYOdU1f0`jNXO`U1f0djNWO\\\\U1g0hjNXOXU1f0kjNWOUU1i0Q1O2O1O0O2O100O100O1000O10O1000000O10000001O000001O000010O01O100O1000000001O002O0O010]iN@eU1`0PjNIQV1R14L5K4M5J7I5K4K5Ke^]=\", \"size\": [1280, 720]}, {\"counts\": \"]aQ<1nW13L4M1N1O1_OOTiNNlV1b001N4L4M3O2M7\\\\iNfNWV1e1K3N2NXOXjNYOhU1e0_jNUObU1j0bjNSO^U1j0hjNSOXU1k0ljNSOUU1k0njNTORU1i0S101N2O1O0010000O10000O0100O1000000000000001O00010O0010O0100O10O1000001O1O011N01001UiNBPV1W14K6J3N3L3M3M1O0YOmhNa0]W1K8Gc^S=\", \"size\": [1280, 720]}, {\"counts\": \"cXd<:bW18I<A6M3O1N3N2M:G5K3MUO]jNZOdU1a0djN\\\\O[U1b0ijN\\\\OWU1b0njN[ORU1e0ojNYORU1f0QkNWOQU1h0S1O200O10O0100O1000O01000O10000000000001O00000000100O0100O010000O11O001O010O100O2O3WiNUOVV1\\\\10N4L3L3M2N5K5L2L3N3M3Mbnf<\", \"size\": [1280, 720]}, {\"counts\": \"bQm<7lV13giNLVV1;fiNCZV1d0aiN\\\\O\\\\V1j099H2N3M2O3MMciNjNWV1k0UjNXOgU1d0`jNZO_U1c0fjN]OYU1`0jjN@UU1?njN^OSU1a0W1N11O001000O10000O10O10O1000O10000010O0000000000010O0100000O01000001O00000001O03M3Od0[O00O1N4L5L5J4L4L3M2N5K5K3MYn\\\\<\", \"size\": [1280, 720]}, {\"counts\": \"g`T=6fW19G7J6J6K:F2O1N2O_OliNWOTV1f0d0N2O2MliN\\\\OQU1c0QkN\\\\OoT1d0V101O001000000O010O10O10000000000000000000010O001O0010O0100O100000000010O01O00100O1XiNVOYV1_1J1ON4L3M4M3L7I4L3M3M2N5K3M3KV^U<\", \"size\": [1280, 720]}, {\"counts\": \"m`T=>c0HRV1`0hiNBTV1d0jiN\\\\OSV1h0kiNXOTV1X111O2O2N3MVOWjN@gU1=djN\\\\O[U1b0kjN_OQU1?QkNAnT1>TkNBkT1>WkNAiT1>\\\\10100O0100000O010O10000000000001O00000000001O010O010O010O101O00001O001O002O0O10f0ZO00N2N3M3M2N3M7I4L3M1O6J1O2N4LQfV<\", \"size\": [1280, 720]}, {\"counts\": \"bPk=6gW15N1N1O2N100O1O1O10001N101O001O10O010000O10000O011O00001O002Nd0]O1OO1O1O1O1N2N2N3M4L3M1O1O2N2N4L4L4LP^Z<\", \"size\": [1280, 720]}, {\"counts\": \"RbU?6iW13N1O1O1O0O01000O100O1O1001O012L8IhmW<\", \"size\": [1280, 720]}, null, {\"counts\": \"ihQ?2lW1:H4M2M3N2N8H6K1O01O1O2M0O1O3N8F8I3L5JmUY<\", \"size\": [1280, 720]}], \"bboxes\": [[286.0, 497.0, 69.0, 77.0], [278.0, 487.0, 70.0, 75.0], [267.0, 471.0, 83.0, 88.0], [270.0, 465.0, 87.0, 94.0], [268.0, 458.0, 98.0, 101.0], [262.0, 443.0, 109.0, 106.0], [262.0, 438.0, 118.0, 114.0], [266.0, 442.0, 118.0, 122.0], [266.0, 430.0, 155.0, 130.0], [265.0, 415.0, 134.0, 139.0], [267.0, 410.0, 143.0, 142.0], [287.0, 405.0, 142.0, 156.0], [304.0, 403.0, 144.0, 157.0], [325.0, 407.0, 133.0, 152.0], [319.0, 419.0, 130.0, 136.0], [302.0, 441.0, 120.0, 122.0], [290.0, 454.0, 117.0, 114.0], [292.0, 459.0, 103.0, 103.0], [299.0, 474.0, 102.0, 99.0], [306.0, 480.0, 103.0, 104.0], [299.0, 475.0, 126.0, 129.0], [293.0, 486.0, 107.0, 100.0], [286.0, 488.0, 82.0, 85.0], [285.0, 477.0, 53.0, 45.0], [286.0, 481.0, 82.0, 80.0], [286.0, 482.0, 66.0, 77.0], [281.0, 485.0, 74.0, 76.0], [277.0, 504.0, 73.0, 73.0], [281.0, 495.0, 72.0, 80.0], [290.0, 488.0, 72.0, 84.0], [301.0, 486.0, 42.0, 83.0], [308.0, 485.0, 69.0, 85.0], [306.0, 489.0, 72.0, 179.0], [306.0, 491.0, 73.0, 185.0], [309.0, 493.0, 72.0, 86.0], [307.0, 495.0, 75.0, 83.0], [306.0, 503.0, 76.0, 71.0], [306.0, 503.0, 77.0, 72.0], [300.0, 502.0, 79.0, 71.0], [304.0, 506.0, 78.0, 65.0], [321.0, 502.0, 68.0, 68.0], [345.0, 502.0, 53.0, 60.0], [323.0, 504.0, 77.0, 66.0], [335.0, 495.0, 70.0, 69.0], [342.0, 487.0, 71.0, 72.0], [341.0, 483.0, 72.0, 68.0], [331.0, 476.0, 82.0, 68.0], [359.0, 473.0, 60.0, 49.0], [337.0, 474.0, 79.0, 65.0], [341.0, 476.0, 69.0, 65.0], [332.0, 477.0, 77.0, 68.0], [332.0, 482.0, 75.0, 69.0], [326.0, 503.0, 95.0, 57.0], [330.0, 517.0, 35.0, 39.0], [324.0, 505.0, 29.0, 54.0], [361.0, 490.0, 39.0, 73.0], [320.0, 489.0, 80.0, 71.0], [326.0, 484.0, 76.0, 70.0], [329.0, 482.0, 75.0, 71.0], [325.0, 481.0, 75.0, 69.0], [320.0, 476.0, 82.0, 70.0], [316.0, 474.0, 88.0, 70.0], [315.0, 474.0, 84.0, 69.0], [311.0, 473.0, 85.0, 73.0], [311.0, 473.0, 83.0, 70.0], [310.0, 474.0, 84.0, 70.0], [304.0, 474.0, 86.0, 69.0], [304.0, 472.0, 84.0, 71.0], [303.0, 473.0, 86.0, 74.0], [306.0, 471.0, 48.0, 71.0], [315.0, 469.0, 39.0, 72.0], [319.0, 472.0, 36.0, 69.0], [320.0, 483.0, 26.0, 57.0], [322.0, 464.0, 35.0, 76.0], [321.0, 461.0, 47.0, 77.0], [319.0, 464.0, 62.0, 79.0], [316.0, 317.0, 105.0, 228.0], [306.0, 473.0, 76.0, 78.0], [304.0, 491.0, 75.0, 76.0], [299.0, 489.0, 73.0, 71.0], [293.0, 490.0, 78.0, 72.0], [303.0, 494.0, 75.0, 67.0], [309.0, 490.0, 83.0, 72.0], [321.0, 495.0, 73.0, 80.0], [320.0, 478.0, 69.0, 75.0], [316.0, 487.0, 70.0, 72.0], [313.0, 491.0, 68.0, 63.0], [318.0, 496.0, 61.0, 64.0], [308.0, 500.0, 30.0, 54.0], [309.0, 500.0, 51.0, 54.0], [308.0, 517.0, 44.0, 60.0], [306.0, 492.0, 68.0, 64.0], [314.0, 497.0, 69.0, 68.0], [319.0, 494.0, 67.0, 57.0], [326.0, 498.0, 66.0, 56.0], [325.0, 494.0, 67.0, 59.0], [333.0, 494.0, 55.0, 55.0], [326.0, 500.0, 68.0, 53.0], [331.0, 492.0, 67.0, 57.0], [330.0, 522.0, 67.0, 46.0], [329.0, 542.0, 57.0, 24.0], [330.0, 497.0, 59.0, 54.0], [330.0, 494.0, 66.0, 59.0], [332.0, 481.0, 62.0, 62.0], [389.0, 495.0, 25.0, 54.0], [348.0, 507.0, 62.0, 52.0], [346.0, 534.0, 71.0, 45.0], [349.0, 513.0, 71.0, 58.0], [348.0, 495.0, 75.0, 86.0], [344.0, 494.0, 74.0, 81.0], [340.0, 477.0, 72.0, 77.0], [330.0, 479.0, 69.0, 72.0], [323.0, 509.0, 71.0, 189.0], [326.0, 513.0, 59.0, 200.0], [320.0, 500.0, 84.0, 211.0], [327.0, 498.0, 74.0, 76.0], [333.0, 484.0, 73.0, 73.0], [328.0, 476.0, 78.0, 77.0], [326.0, 476.0, 77.0, 75.0], [322.0, 477.0, 78.0, 76.0], [323.0, 477.0, 77.0, 71.0], [326.0, 475.0, 78.0, 72.0], [331.0, 481.0, 77.0, 73.0], [335.0, 486.0, 76.0, 68.0], [341.0, 488.0, 78.0, 68.0], [344.0, 480.0, 76.0, 74.0], [338.0, 478.0, 74.0, 76.0], [329.0, 476.0, 76.0, 62.0], [318.0, 473.0, 79.0, 60.0], [305.0, 473.0, 78.0, 67.0], [299.0, 467.0, 77.0, 73.0], [295.0, 468.0, 79.0, 73.0], [284.0, 478.0, 84.0, 73.0], [290.0, 481.0, 75.0, 76.0], [292.0, 481.0, 80.0, 73.0], [295.0, 486.0, 81.0, 192.0], [298.0, 495.0, 78.0, 72.0], [308.0, 497.0, 76.0, 72.0], [323.0, 498.0, 71.0, 72.0], [330.0, 501.0, 72.0, 72.0], [336.0, 504.0, 72.0, 71.0], [336.0, 514.0, 71.0, 69.0], [354.0, 520.0, 50.0, 68.0], [388.0, 573.0, 18.0, 16.0], null, [385.0, 535.0, 20.0, 52.0]], \"areas\": [1425.0, 1422.0, 1884.0, 2542.0, 3172.0, 3996.0, 4502.0, 4593.0, 5025.0, 6046.0, 6772.0, 7205.0, 7398.0, 7512.0, 6844.0, 6084.0, 6129.0, 5580.0, 5379.0, 5095.0, 4735.0, 3783.0, 2547.0, 978.0, 2177.0, 1775.0, 1588.0, 1208.0, 1604.0, 2126.0, 1318.0, 1866.0, 2015.0, 2133.0, 2110.0, 1984.0, 1201.0, 1807.0, 2297.0, 2358.0, 2186.0, 1486.0, 2409.0, 2184.0, 2142.0, 2121.0, 2311.0, 1644.0, 2207.0, 2041.0, 1846.0, 2055.0, 1167.0, 485.0, 802.0, 966.0, 1498.0, 2043.0, 1989.0, 2000.0, 1875.0, 2058.0, 2077.0, 2175.0, 2124.0, 2171.0, 2151.0, 2092.0, 2462.0, 1372.0, 1206.0, 1124.0, 755.0, 1025.0, 1116.0, 1242.0, 2298.0, 2292.0, 2078.0, 2032.0, 1991.0, 1117.0, 624.0, 2162.0, 1885.0, 1704.0, 1144.0, 711.0, 928.0, 932.0, 787.0, 1046.0, 1459.0, 1344.0, 1476.0, 1435.0, 1182.0, 994.0, 1279.0, 516.0, 253.0, 1207.0, 1363.0, 1221.0, 470.0, 588.0, 600.0, 594.0, 1794.0, 1485.0, 1340.0, 1529.0, 1479.0, 1852.0, 2161.0, 2075.0, 2076.0, 2187.0, 2158.0, 2235.0, 2188.0, 2148.0, 2253.0, 1458.0, 1461.0, 2151.0, 2188.0, 1986.0, 2266.0, 2446.0, 2193.0, 1236.0, 1866.0, 2040.0, 2052.0, 2075.0, 2004.0, 2004.0, 1955.0, 1938.0, 1809.0, 1977.0, 1320.0, 210.0, null, 475.0], \"iscrowd\": 0, \"video_id\": 0, \"height\": 1280, \"width\": 720, \"category_id\": 1390, \"noun_phrase\": \"a braid\"}, {\"id\": 864, \"segmentations\": [{\"counts\": \"]df71nW11N3N1J7N1O2M3O007H7J4Kc0^O3M1N2N3N1O0O2O2N001O001O0010O000000000O101O000O10000O2O000O101N2O1N101N1O2O0O2N1O101N2N101N1O2N1O2N1O2N1N3N1O2N1N2O1N3M2O2M2N2N2O2M3M3LcSXa0\", \"size\": [1280, 720]}, {\"counts\": \"]\\\\l61dW15]hNO_W1<N2M4M>B7JG]iNZO^V1X1L8H2M2O00001O1N2O0010O01O000000001O001O00010OO1000001O1N101O001N10001N101N100O2O1N101N100O2N101O0O2O0O2N2N1O2N1O1N2O2N1O2N1N3N1O1N3N1O1N2O2M2O1N3N1M3N3Nb[ma0\", \"size\": [1280, 720]}, {\"counts\": \"gS^53jW14L4M3O001bhNCVW1j0I5K2N2O1N2O6J2N4L3M1N3N2N6J3M2N00001O001O01O0001O01O01O01O01O01O000O101O00001O001O00001O01O001OO2N1O1O1O2N1O1O2N1N2O1O1N3N1O1N2O1O101N1M4M2O1N2N3M2N2O1N2O2N0M4M3M4L5LW\\\\Uc0\", \"size\": [1280, 720]}, {\"counts\": \"kjj31jW1;G7I8H5M4M2M4M5K1N2O001N1O010O2O0O2O002N1O2N3M2M5L2N2N2N002N1O1O1O3M1O5K2M2O1O1O00000000000O100000000000O100O1000O10000000O100000001O000000000O2O0O1O1O2N1M3N3M101O1N2M4M2N2M3N3M2L4L4J6Gb0XOo]bd0\", \"size\": [1280, 720]}, {\"counts\": \"cjQ37YW1c0J6J4M3M4M2L4M3N2N2L4K5M6J3M4M4M1N2N2O1N2N01M2N32N2M3NO100O11O1O1N2O1O001O001O1O1O001O2N1O1O1O1O1O10O0001O00100O2N0010O0000010O001N101O001N2O1N2N2N2L4K6J6J5J7J6K6J7I6I:G7I9@nUde0\", \"size\": [1280, 720]}, {\"counts\": \"UZT3;[W1c0@;I7H5K6K5L4L4M2M4L5L2N4M2M2O2M2O1N2N2O1N2N2O1O3M001N3N001ON2L5M20O1000001O01O000O11N10000001O01O1O0001N11O000100O0101N10O001N2O1M3M3O2N1O2O1N1O1O3L2M4K4L5E;J7I6J6K6I9I5I8Gleae0\", \"size\": [1280, 720]}, {\"counts\": \"Zb_35_W1b0]O`0I6J6J6K5L3M4L3I7N2N2N2N3M2O3L2O2N1N2O000O100000000O2O2N00001O000O101O00001O00000001O01O00001O0001O00001O01O000010O001O001O001N2N2O01O13L3M2N2M3N2M3K5J6K5K6J5L5J6H9I7H7JQfWe0\", \"size\": [1280, 720]}, {\"counts\": \"ci`3k0oV19J6J6J5L4L4K4M4H8L3N1N3N3N2M2N2N1O101O0O101O000O2O1O2N0000001O0O101O00001O001O1O000000100O0000001O01O000010O00001O010O01O0O1O2O1O001O001O100O2M4L3M3K5L5J5K7J5K4J6J7I9G<D8FT^Ve0\", \"size\": [1280, 720]}, {\"counts\": \"VbP3b0ZW16I7K4M3N2N3L3N2N3M4L3M3L4L5L4L4L4L2N2M3N1N2O1O2N2N101N101O1N101O1O0O10001O001O001N20O01N101N2O0001O00000001O00001O0001O010O0O2O0000001O00010O001O0N3N1N3L4L4L4L3L6K4K6L5H7J6I8FS1kNjmbe0\", \"size\": [1280, 720]}, {\"counts\": \"WbW27eW1:H6I6J5L4L3N3L4M3L4L4M3N3M3L3M4M2M2N2N2O0O101N10000O2O1O0O10O03M3N0O2O1N100O2O0O101N1O2O1O0O1000002N0000001N2O1O1O001O00000001O0001O00000000000001O000O1O101N1O2M2N2O2M2N3M3L3N3N3K6I6K5J7H9H:Ec0WOTnQf0\", \"size\": [1280, 720]}, {\"counts\": \"hid17dW18CDmhNe0lV1;K4L3N2N2N101N2N2O1N1O2O1N3N2M2O3L3M2O2M3M2N01O0103L2O000O100O101N101N2O0O101N2O0O101N101N2O1N101O001O001O00001O1N3N002OO01O0000000010O01O00O2O0O101N101N1O1O2N2N2N3M2N3M3L3M4L5K5K6J7I4J8I9Ec0[OS^cf0\", \"size\": [1280, 720]}, {\"counts\": \"ja^15fW1>D9H4M3J5L4N1O1O2O1N3N2M3N2M2O1N3M100O101N4M2M10000O101O001N2O0O2O1N101N2O0O2O0O2O1N2O0O100O3N1O1N101N2O1N2O1O001O000000010O1O3L4M00O2O0O1O1O1O1O10010O000O2N1O2O1N1O2O1N2N2N2N2M4L4M4K6J5J6K5J6I9F?AT^hf0\", \"size\": [1280, 720]}, {\"counts\": \"_Ra19aW1;H5K4L5K5M3M7I3M3N3L4M2M3M2O1N2O1O2N1N2O2N1N101O1O0O2O1O1O00001O1O00000O2O1O1O001O001N10001O000O102M3N2N1O0O2O00000000O11O001OO101N100O1O100O1O101O0O1O1O1O1O2O0O1O1O1O1N2N3M2N3M2N2N3L3N3L5J;F8Gc0ROnUgf0\", \"size\": [1280, 720]}, {\"counts\": \"Xb^1;aW16K:D6N3M2M4M101N2N2N4M2M3N2M3N2M2O1N2O1N104K2O00O10O2O0O2O1N2O1N101N101O1N100O2O1N2O0O2O0O2O1N101N10001O1O1O1O1O0O101O00000000001O01O0000O1001N10000001O01N100O100O2N1O1O2M3M4M3L4K4M4L5K6H8G;G:E<B=Ab]hf0\", \"size\": [1280, 720]}, {\"counts\": \"WRW19[W1`0J4L4L4M2M4M3N2M3N1N3N2L4N2M3N2M3N2M3M3N1O1N2O00000O2O0O101O0O100O101O0O2O1O001N1O2O0O2N2O0O2O001N101O001O1O00001O1N2O2N00000000O10001O0000O11O00000O1O100O2N100O011N1O2N1N3N1N4L4L4L5J6I6J8G=B=Cj0UObUQg0\", \"size\": [1280, 720]}, {\"counts\": \"ejZ18dW1:I5J5L3M2O2M2N2N2N2N2O2M2O2M3N1O001N2O00002N1O1N2O2N001O001O001O1N1000O1002N1N101O001N101O0O2O000O100O101O000000001O0000000000001N10001O00001O00000000001O001O0000O1O1O1O1O100O1O100O1M3M4L4L5K5F?YOm0XOeUlf0\", \"size\": [1280, 720]}, {\"counts\": \"abh1<bW18I5J5L3M2N3M2N2N3N1N101N3N2N2M101O1O001O0O2O1O1N2O1O1O1O0O2O0O2O1O1O0O10O03N1O0O2O001N101O0O101O00000O1000001O00001O0000001O000000000000000000001O00000000001O00O100O100O2O0O1O1O1M2O4I7I7I7F<\\\\Oj0\\\\Okm`f0\", \"size\": [1280, 720]}, {\"counts\": \"RRf17bW1<F9J5K4M3L3N3L3M4M3M2N3M2N3M4L4L4L3M3M3M1N3N3M1N102N2N2O00O10O2O00000O10001O0000001O00000O101O001O00001O00010O000O100001O01O00010O0001O0000001O001N2N101M3M2O2M4L3L5K5L5I:G7I;Ca0@>BgUlf0\", \"size\": [1280, 720]}, {\"counts\": \"QZg1g0PW1<J5K5L4K4M4K4M4L3M3N3M2M3O2M3N2M3N1N2O0O3N1NO110002N000O100000000O2O001O001O0000000001OO2O000001O010O01O0001O0000010O001O00001O001O001O1O1N2N2O2M2N3M3M3M4I6J6J6K5K6J8H6J7J8Ejenf0\", \"size\": [1280, 720]}, {\"counts\": \"cZg1k0PW18J7J3M4K4M4J6L3M3M3N3N1N3M2O2M3N1N3M2O001N100O10001O00000O101O0000001O001O001O001O0001O00O10O20O002N10O00001O00000N21O010O01N101O1O0O2M3N2N2O1N3N2M4L3M3L4K4K6I8J5L5K5J<E:Dmlof0\", \"size\": [1280, 720]}, {\"counts\": \"UZb1c0UW1<H6L5K4L3L5L4J6K5L4M3M3M3M2N2N2N102M2N2O000O101O000O2O00001N10001O1O0000001O0O10100O1O000001O000000000010O001O0001O00001N100O2O001O001O1O1N2N2N2M4M2O3L4L3M3M3I7K5L4J9G8J6H<DeeSg0\", \"size\": [1280, 720]}, {\"counts\": \"TZg15WW1j0H6J5J5L3L5L4L4L4L4L3L4M4N2M3N3L3N2M3N1O2M3NO100001N2O0000O11N2O000000001O001O000000000000010O0000000010O0O10000000001O00001O010N101O00002N2M3N1O2N2M3M3M3L4J5K6J6J7K5G<F8G;AVfnf0\", \"size\": [1280, 720]}, {\"counts\": \"aRZ2c0[W18G6K5K4L3M5J4M4L3O3L3N1N2O2N2M3N2M3N1O1O1O0O2O1O00001O000O100000001O00001O0000000000000001OO2O1O010O0000O101O01O1O010O01O001O1N1N3O2N1O2O0O1O2N1N3N1N2N2N2O1N2N2M4E;F:H;F<AUU]f0\", \"size\": [1280, 720]}, {\"counts\": \"aZo29fW17I7I6J3M4M2M3N2M2O2N1N2O1O1O1O1O001O1N3N1O1O001O1O001O001O1O00001O000000001O002N00N20001O01N101O00001O01O0O10000001O01O0010O01O0O101O001O00000O2O000O2O0O1O1O1O1L3L7J6I6K6J7I?@Q]ee0\", \"size\": [1280, 720]}, {\"counts\": \"Uje3a0]W17K4K4L4M2M3N1O3M1N101O001O001N102N1O1O00001O1O001O1O001OO101O01O1O0000000000000000O11O001O000000000000O100001O00000001O0O101O001O01O01O0O2O0O100O1O001O0O2J6K6L5L4J7J8BjmSe0\", \"size\": [1280, 720]}, {\"counts\": \"iZm33fW1?E8I5J5M3M3L4M3M3M3M2N2N2N2O001O1N2O1N101O1O2O1N1O00001N2O00001O001O00001O1O000000O2O01O0001O0000010O000O10001O0001O01O000000001O00001N101O1O1O1N3M2N2M3N2K5J5M4M4J6I7K<@XUkd0\", \"size\": [1280, 720]}, {\"counts\": \"_bZ3>UW1b0G7K5J5L4L4L3N3L4M3M2M3N2M4N0000001N101O0000001O0O10001O000000001O1O1O00000000001O1O10O00000010N100001O0001O010O01O001O000O2O2N001O1O1O1N3M2N3K5M2M4M3M3L3K5L4M2N3M3M3M5K:C;E]][e0\", \"size\": [1280, 720]}, {\"counts\": \"jak2k0nV1>E6H8M3L4L3M4M3M3M2N3N2M3N1O1N3M2N2N2O2N001O1N10000O100O02O01O000O2O01O000001N110OO1010O1O1O01N10000O2O001O001O001O001O1N2O1O2N1N2O2N1O2N2N3L2O2N1N3M3M2L5J5L5J5L5K5J6J6K9Djeke0\", \"size\": [1280, 720]}, {\"counts\": \"Wjl2e0oV1a0H7J5J5K5J6K5M3M2N3N2M2N3M2O0O01001N2O001O0O100000000O2O0000002N1O1O000000O100010O1O1O001O0001O0O101O001O01O01N10001N2O00002N0O2O6J1O1O2M3N1N3M2N3N2M3N2M3M2M4I7J5I9J;D:C]eke0\", \"size\": [1280, 720]}, {\"counts\": \"ScZ3b0PW1c0E9I6L4L4L4L3N2N2M4L4N2M2N2O1O1O1N3N1O2N0O2O001O001O000000001O001O001O00000O100010O01O000001O000001O001O01O001O0O2O1N2O2M2O1N3N1N3N1O2M3N2M2O1M4M4J5K5L3L5I7L4K6K5K6Gj\\\\`e0\", \"size\": [1280, 720]}, {\"counts\": \"hY^3=WW1c0E:G8I5K5L4L3M3M4L2N3M2N3L4N2N2M3N1N3N1O1O1N1000O02O001O0000000000000000000010O0000001O0O100001O01O001O00001N10000O103M1O1O2N1O1O2M2O2N2N4L4I6I6J6H9I7G8K5F:JQfae0\", \"size\": [1280, 720]}, {\"counts\": \"ga_3R1eV1<G8J6L4M3M3M3L4M2N3M3L4M3M3N2M4L100O101O0O10O0011O001O000O01000000001O00010O000000O2O000000010O00001O001N101O1N20001N1O2N1O2M2M4N2N1O2O0O5K3J5K5H9H7L6J5H7J6J6JlU_e0\", \"size\": [1280, 720]}, {\"counts\": \"bRX3b0SW1`0E9I6J6K4L6K5K5K3N2N3M2M5M2M3N1N3M3N0O2O1O1N10O0101O00000O101O000000000000O1001O01O0001O000001N10001O0100O01O00001O0O2N101O2O02OL4L3O2L4M3M3M3L3M4L4K6K5M4K4L4L4M3K5M3I:\\\\O\\\\]`e0\", \"size\": [1280, 720]}, {\"counts\": \"TZT3m0lV1=F6L5J5L5J5M2M4K5L4L3N2N2N2O0O101N00101O1N2O001O2M2O00001O0O10000O2O000000000001O0000000010O000010O0000001O001O001O0O2O001N2N1O3O01O01M3L4M3L5L3M3M4L3K5L4L3M5L5J5J8G7L8EZUde0\", \"size\": [1280, 720]}, {\"counts\": \"UjV3d0XW19F8K4L5K5J5M3L4J7J5L4L5L3M2N2N100O100O1000000O2O1O2N002M10O010002N1O00001O000000001N11O00000000001O0001O010O01O00001O00001O0010O10N2O0O3N1O1M5J7K3L4K5M5J4L5G9J6F:J7E<AehNLnTde0\", \"size\": [1280, 720]}, {\"counts\": \"Vke3b0YW18H8K4M3M3M2M4L4M2N3L4M2N2N2N2N1O2N4M1N4M1O1O1N2O1O001O1N10001O1O1O00001O00000000001O000000001O01N10000000001O0000010O0O101O0O101O001O1O1O1O1N2O1O1N4L3M2L5J6L3I8L4M3G?_OnlSe0\", \"size\": [1280, 720]}, {\"counts\": \"QSj4;_W1>G9G5K5K3N3L3N2M3M2O2M4M2M3N1O2M2O1O1O1O1N2O00001O001O0000001O00001O00000000001O1OO100O11O1O0000001O001O01O000O00101O1O1O1O010O1O1O1N1O2O001O1O1O1O001N1O2M2N2L6H8I8I6G9I9CkTmc0\", \"size\": [1280, 720]}, {\"counts\": \"oQ^56gW1;G4L3M4M4K3N3L3N3M3L3N2N2N2N3L2O2N2M3N2N2N1N100000000000001O00000000001O001O001O001O001O1O1O001O000000O101O01O01O01O10O001O001O0O2O000010O1O01N100O2O0O10001O0O100O1O1O1O1N2O1N3M2I8J6I7J7K5K6J<_Og]kb0\", \"size\": [1280, 720]}, {\"counts\": \"jQR69eW18H8I8I3M3L4M2N2M4M1O1N2O001N101O2N2N1O2M101O1O1O00001N102N1O001O000000000O101O1O0000000000001O001O0000001O0000000001O000001O01O01O0001O0O0010001O0001O0000O10000O01000O101N101N2K5H8H8I7H8I8L4M4J6HSVVb0\", \"size\": [1280, 720]}, {\"counts\": \"oZV7f0VW1;F8E9J6J5L4M3L4M2N2O1N3M3N1N2N2O0O101N2O001N2O1N3N001N2O001O1O001O001O1O00O1002M2O1O010O1O1O0000001O000O11O000001O000001O01O1O1O02N1O1O2N1O1N2N2M3N2L4M3M3M3M3K5L4K5L5K4M3N2M4J6K:F9H4L8H:F[lVa0\", \"size\": [1280, 720]}, {\"counts\": \"jZl67\\\\W1i0A6L3K5J7J4K6K4N2M3M3N1O2N1O2M3N2N2N2N2O1O1N2O1O0O101O0O102N1O001O000O10001O0O101O00001O000000011N1O00001O001O01O000001O001O01O00001O001O0011O1OO1N2M4L3N3L4L4L3N3L4K5I7K6K6I7I:F7F<B\\\\eda0\", \"size\": [1280, 720]}, {\"counts\": \"YRm5g0PW1=H7K3L5K4M4K4L5K5K5L3N2N2N2N2N2M2O2N2N2N101N101N1O1O102N1O1O1O2N0O2O1O1OO100001O00000000001O0000001O001O00001O01N1000001OO100001O1O001O001N101O1O1O1N2N2N2N3L3M3M4K5L3N3K5J6K6J5K7J7I6J8Ei0XOYU`b0\", \"size\": [1280, 720]}, {\"counts\": \"T[P57cW1=D8H8A?G7N3L4L4K5L5K5L2N2M3N2N2N2N4L3M1O2N2N101O0O101O001O1O1O0O101O1O001O000O2O001O2N1O1O00N21O00000000O20O1O000010O0O100O1O1N3O010O0O2N101N101N10002M2N1O3L3M3N3L4L4M2N2M3N8F5K5L3M4L3L5K5M6Gd0[OPUYc0\", \"size\": [1280, 720]}, {\"counts\": \"eZY3;]W1?C9I6L4M3M3L5L3M4L3N1N2O1O1N2O1O2M2O2N1O1O001N2O1O001O1O1O001O001O0000000000001O1O000010O01O000001O0O100001O1O000010O000O1O1O2O0000010O01OO100O1M3O2N101O0O2N2N2N101O2M2N2M4L3L4L4M2N4M2M4K5L5K4K8GYmnd0\", \"size\": [1280, 720]}, {\"counts\": \"_Yl1=]W1=H4L4M2M3M4L3M3M5K3M3N2M3N2M3N2N1O1O1O2N1O1O2N1O1O1O1O1O1O001O1O1O2N0000O100001O000000100O00000000O2O000O100001O01O000O10000O1O1O101L3OfjNbMTU1_2ljN`MTU1b2401O000O10010N101N1O2M3N2N2N2N2L3O2N2M201M2L4L4L6K4L4M5J<CVfZf0\", \"size\": [1280, 720]}, {\"counts\": \"baY18aW1h0\\\\O7K3L6J4L5L3M3M3M3M3L4M3N3M2N2N2M4M1O2N2N2M2O1O1O1O2N1O1O1O2N1O000O101O1O1O001O00010O1OO1001O10O00000O1010O0000O1O20O1O00O1O2O00000O1O2O0O1O2N101O0O2N101N2O1N3N1N3M2N2O2N2L3K5N2L4N2K6K5J6J6J6K4L7I5L8Bomjf0\", \"size\": [1280, 720]}, {\"counts\": \"`Qf1g0QW1<H9I6I8I6K4L3L4L5K5M2M2O2N2M3N1O2M2O1O2N2N1N3N1O1O2M3N2N2N1N2O1O2N001O001O001O0000000000001O001O01O01O001O0001O0O001000001O001O001O0O2N1O1O2O3M3M1N3M2O1N3N1O1N3L3N3M3M3N1N2N2O1N2O1K4M5L6I5@`0J7J5K5L4K6Ik]^f0\", \"size\": [1280, 720]}, {\"counts\": \"\\\\Xe2T1gV1<D:E:J5K5K4M2N2N3M3M3M3M2N2O1N2O1O2M3N2N00O1O05L2N000O2O001O1OO10O0100001O00001O000001O000002N001OO101O000000O01010O1O0000O2O001O0O1O101O1O2N2M2O1O2M2O2M2N3K4N3N1O2M4L2O1N3M2N1M4K8@`0F9G9I9BZgfe0\", \"size\": [1280, 720]}, {\"counts\": \"Tio3c0mV1BUiNo0_V1?I6K5K4M3M2M4M2N3M3M3N2M2O2M2O2N1N10002M2O002N1O000O100000001O1O1O001O00001O000000001O001O001O00000000000000O110O00010O001O001N1O2N102N1O2N1O2N1O2M2O1N3L3N3N1N3N1N2N3M2M4J5J8H:H6I6J6J8@k^]d0\", \"size\": [1280, 720]}, {\"counts\": \"fjf5f0XW19G5K5K4M3M4L3M2N3N2M4L3N2N2M2O1O2N2M2O1O001O1N3N1O00001O1O1O1O2N001O001O1O0O2O1O010O001O1N110O1O1O001O1O001O00001O0000O101O000O2O0O101O1N2O1O1N1N3N2N3L3N1M4M4L3N2N1N3M3L4K5H8I8H:I;E:@jlhb0\", \"size\": [1280, 720]}, {\"counts\": \"XTS81j_22ShN5J2N101O1O0O1YMCjmN>VR1BimN?lT11O000nMBalN?]S1DalN=^S1DblN<\\\\S1HblN9[S1MojNCZ1a0dS1<UlNEjS1=TlNDlS1=TkNmN22<e0]T1n0akNSO^T1P1_kNQO`T1X2O010O100O1O010O00101N10O10ObNZkNjNN6iT1d0\\\\lNXOdS1;llNCSS1:PmNCTS1;nlNCTS1<llNDTS1<llNCUS1<llNAWS1?jlN_OWS1a0ilN^OXS1b0glN^OZS1a0glN]OZS1d0m131O2O100O1N101O003M2K6Hdn`a0\", \"size\": [1280, 720]}, {\"counts\": \"n[h81oW11O00001O1N101N101O0001N1O11O1O1O0O2O0O100O1L5J7K3L30002N2dNSOljNM3T1PU1BijNb0XU1@ajNd0`U1m01M4M2O1000000O1000001N1O1O2O0O100O100O10O01O1O1O1O3N1N2N1O1N2O7J4K4J7Ah]e`0\", \"size\": [1280, 720]}, {\"counts\": \"jbd83dW1e0C6J4M3L4L3M3M2N4L3M2N1O2N1O1O1O4L3N1O1N3N2N0O2O0O101O00000O101O1OO1000O2O2N001O1O1O000000001N10O10PkNVMjT1m2300001O2N10N100001N11O0O1N21O1O00O1O1000000O1N3N1N2O2N1O2N1O100O2N1O2M5K7H6H7G9H;H7H:CXeh?\", \"size\": [1280, 720]}, {\"counts\": \"Zke83^W1g0C;I5J6J5K5K4M3M4K4M2M3M2N3K4O2N2N3N1O1N2OO0101O3M1N2O1O1O0O2O1O0000001N3N1O1O1O1OO0101O0001O000001O1O001O01O000O2N1O1O1000001O001N100O2N1O1O1O2M3M2O2O1N2N2N3M1O2M3N2M4K8H9F8F<Ea0Bi\\\\l?\", \"size\": [1280, 720]}, {\"counts\": \"_Zj7a0WW1b0B8I7J4L4J6M3L4M3L3M4L3M2N3N3M3N2M2O1N2O1O1N10000O2O001O0O101O000O101O1O1O1O1O1O0O100000010OO2O0001O1O00100O00O100O2M20000100O0001O0O2O0N3N1N4N1N2O1O2M2O1N2N3M2O3K4I8J6I9G<D9Fa0ZO[Ui`0\", \"size\": [1280, 720]}, {\"counts\": \"dbh6;`W1<YOe0G6L4L4K4L5K4L5K4N3M2O2M2N2N2O2M3N1O2M2O001N2O1N10001O0O2O001O1O00000000000O2O00001O00001O10O0O100001O0001O0001O01O00000N3O1N101O001O010O1N2N2N2O2M3N1M4M3M3M3K5J6J6K5K5G8I9F?Bjmja0\", \"size\": [1280, 720]}, {\"counts\": \"[kR5j0QW1:H7J6J5K5K5L2N3M2N3M3N1N2N101O1N3N1O2N1N2O1N2O001O002N2N1O1N101O001OO100001O1O000000002N001O00001O1O1O1O0001O01O00O101N1O1N3O1000OO2O1N2O1N1O2N2N3M3N1O1N3N1O2N2M2O0N3M3J9F;H8I5G9EY\\\\_c0\", \"size\": [1280, 720]}, {\"counts\": \"ZcU33jW17C:G8M4L3N3M2O2M4M4L100O2O0O1O2O000O101O1O0O1000002N0`jNSNmT1m1b0014]jNRNlT1X2fjNSNSU1`2M1O2N001O2N2N00O0201N1O001O1O1O00000000001O1O1OO2N100001O0000O2O010O001N10001O00001O0O3N2M2N110cNWkN^NMm0mT1b0^kN]OdT1b0]kN\\\\OeT1c0[kN]OfT1`0]kN^OdT1a0]kN_OdT1`0\\\\kN_OfT1?\\\\kN^OgT1a0YkN[OlT1c0VkNnN^O0bU1o0W1L4I7M3N2N3M2O3M3N3MgTPe0\", \"size\": [1280, 720]}, {\"counts\": \"ibW2>ZW1;H7G<F8H7K7K2M2N3O0O2N2N102N4K101O1N101OO1N2O05L2N0000001O1N1000O100001O001O001O1O0000O100010O1O00001O01O000O1001O0001O0000001O000O100O20O01O001N2N101O2M2O1N2N2N2N2O100O1M3N1O2L4L3O2O0O2M3K6L6J=DZePf0\", \"size\": [1280, 720]}, {\"counts\": \"^Z[2>ZW1=F:E8K5L5K5K3M4L5L3M3M2M3N3N1O1O2N1N2O1O2N2N2N001O1O1N1000O1O12N001O000000000000O1000010O000000001O000O1001O01O0000001O000O2O0N2O2O001N1010OO2O1O1O1O101M2O2M2N3M3M2N3M3L3M3N2M4M2N2M4J7H7L5K5J7J6JTmle0\", \"size\": [1280, 720]}, {\"counts\": \"jaf2=^W19I6J4L4K5N2M4M2N3M4L3M3M4L3N2M3M2O1N2M3M6K3M3N1N2N3M2N1O2O1N1O100O2O0O2O000O2O0000O100001O0000001O000000O100O100001O00000001O0O1O100O2O01O01N1O2O0O2O0O100O2N1O2N2M2O2O0O2O1M3M3N3L4L4K4K6J6G9C>CRobe0\", \"size\": [1280, 720]}, {\"counts\": \"QiV3?^W16J5J6L3M3M3M4L3L4O1N2N3N2M4L3N4K4M2M3N2N002N2M2O1N2O1O0O101O2M10O01O102M2N2O0O2O0O011N2O1N1000O10O2O0000O1001O001OO100000000O100O11O01I_kNnL`T1X31O100O100O2O0000001N1O2O0O1N3N1O2N1O2N1O2N1N3M3N1N3L5K4H:Ia0ZNbiNh0Q^Qe0\", \"size\": [1280, 720]}, {\"counts\": \"YYW4282XW1c0F4M4L4L4L4M3M3M3M2L5N1N3N2N3M2N3L3M3N2N2M2O1O0O100000O11O0O2O00000O10000O2N100O2O000O101OO1000O101N2O1N2O1O0O2O000000N10100001O1O0000001O00O1O1O1010O00000001O000000001N1O1O2N1O2N2N4L5K5J5K6I:E;E=C`foc0\", \"size\": [1280, 720]}, {\"counts\": \"maQ5g0WW15K7I4M3M2M3M3O1N2N2N2O0O2O1N2N3N1N3N1N2O1N2O1O1N2O2N1N2O001N2O1N101N2OO0100O100O1O10001N3N1N2O001N1O00011O1O1N100000000O1OO101010O2N001O000000O1O1O10001O010O1N2O1O1O2N3L3N3L4M1M3N3L:G7F8F:E<I=D]mWc0\", \"size\": [1280, 720]}, {\"counts\": \"[Xn53mW13L1O2O1N2N2niNORT13ikN5ST1KlkN9PT1IokN:nS1FQlN>lS1BSlNa0kS1@SlNb0kS1_ORlNf0lS1ZOQlNj0nS1WOnkNm0QT1SOmkNo0RT1SOjkNP1VT1UO_kNR1`T1W1000001N10000000001O01O0000001O010N101O1O1O2N1N4M2N3L2O2M3N1N4M3M3K5H9I6E?@f^nc0\", \"size\": [1280, 720]}, {\"counts\": \"XY^3b0\\\\W15I5M3M4M3L3N2O2M2N2M4N1N3N2N2M2O1N3N2N2M3N1N3N1O1N1000000O2O0O100O100O100O100O1000O0100O1O100O1000O010O1O100O1O010001O00O1O1000000O1O001N101O2N1O1000001M2O1O1J6I8F:I8_Oa0EoWZe0\", \"size\": [1280, 720]}, {\"counts\": \"UjQ35cW1>H6I6K4L3N3M2N2N3M2M3N3M3N1N3M2O1N4M1N2O1N10001O1N2O0O3N1N2O0O101O3L2O1N1O10000O010O1O101O1N1000O010O100O10O011O0O2OO10000O10000O1000001O000O1O2O0O100O100O2L3O2N3L4M3M3M4L3M5K4L3M2L8G8I8I4K5M3M2N3L4JhmSe0\", \"size\": [1280, 720]}, {\"counts\": \"kiS24gW18I:E8I6M2M3N2O1N2N3M2N2O2M4M2M3M3N2M3N2M2O1O1N0010000O2O001O0O1O1O100O2N100O100O01O01O010O100O10O010000000O10O100000000000O1000000O2O0O1000000O1O2O000O100O1O1L4M4M5M3L3N2M3N2N2M4M2M3M3M3M3M2N3L3N3L4L4L4L7GWVne0\", \"size\": [1280, 720]}, {\"counts\": \"oj[37gW12O1L6K4K5M2N2N2O1N3M2O1O2O001O0O2N2N1O2M2N200O1O11O00O1N2O2N1O1O1O1O0102L4M2N01O2N1000000O010N1N30O1O1O1O1O1O2L7^Od0^Oc^^f0\", \"size\": [1280, 720]}, {\"counts\": \"lQ_22fW1:CFmhN?oV1a0H5K3M2M3O2M2N2N2N100O103L3M2O2M3M2O1N2M3N3N001O001N1O2N100O2N001O100O100O1O100O101N10O10O101O0O1000001O1O001O000OO20O11N1001O1O1O0000001N2O00100O01O1O101N2N1O3N2N1M2N2O1M4M4L4L4L4K6J5L6H=^O[^je0\", \"size\": [1280, 720]}, {\"counts\": \"lQX33jW15\\\\hNNTW1f0H4M2N3M2M4M2N3M2N2M4M2O2M2N2O1N101N2N1O1O3M3N1N1O2O2M5J5L2O1N100O2N1O1O1O1000000O100O2O0O2O00O0101O0O2O0000001O1O000000000000001O0010O0000000O1O100O20O02N1O1O1N101N200O3L4L3M6J5K3M4K7H9E9E<I6HlUPe0\", \"size\": [1280, 720]}, {\"counts\": \"Yig23fW1<G7H9G7L4M3N1O10000O2N10000O2O0O010000O01O001O00N3N1O1O1O9D?RORgg0McYXO2O001O3M0O2O1O00001O0O10000O2O001O1O1mjN@dR1`0\\\\mNBbR1?\\\\mNDbR1=]mNCcR1>[mNCfR1a0blN^OlN3bT1n0_kNkN2l0^T1W2OO000O2O000O100O2N2O2N4L2M3L5K4L5K6E:D;K7I;DoV_e0\", \"size\": [1280, 720]}, {\"counts\": \"cY[27bW1;G7L3L6K5K3M3M3N2O1O2M2N100O2N1000N2O010O100O001O100O001N2N2N20O0100O001O0N3M3O0O2K400100O010000O1N2G9FXkNG]R18\\\\mN3aR1J^mN;aR1B`mN`0_R1@`mNc0_R1]O`mNd0`R1[O`mNf0`R1ZO_mNg0bR1WO^mNj0bR1UO_mNk0jT12O000O101N100M3O1N2N2N1L5O00010O10000O010O100NON3N300001O0N2M3L5K5M3F:^OTWne0\", \"size\": [1280, 720]}, {\"counts\": \"eaR2:ZW1?K5K4L3N3M2N3M1O2N2O1N2O0O1O101N6K2M3M2O1N1O101O001O0O01O1O0010OO2O0O1N3O10ON2O1O2O0O2N1O2H7O2N1O2M3O1N1O200O1N2N2N2O1N1N3N2N2N2O100O100O2N10RkNCYR1<hmNEWR1:jmNGUR17mmNJSR12PnNMQR11QnNMRU10flof0\", \"size\": [1280, 720]}, {\"counts\": \"Vii16cW1?E5K6L2L4N1O2N1O00O1O1O2O001N110O0N3L4N2M3L3K60O1000O0010O02Nj_2CY30[]M5J4L2O1N1E<I6N3N103L1K5O0L5N3L3003M2NM4M200O100O1O100O1K6I6M3O1O10000O2O000O010O0O2O1N1O20O00001O0000N1O07J5J9I5J6K9]Oj0YOff_f0\", \"size\": [1280, 720]}, {\"counts\": \"WiX24fW19I>D6H6L4M2N2N3M2O2M2N2N2O3L2N2N2O2M2O2M2O1O0O2O1N2O1N2O2M2N3M2O1N1O2N3M010O2N2N1O10O0011N2O1O0O01000O2OO100O1001O00000O02O01O0O1000000000000001O000O100O102N1O1N3M3N2M5L3M2M3N2N3L5K3M2M4L5L6J5J7F8ZOUWne0\", \"size\": [1280, 720]}, {\"counts\": \"gQi29dW1:F:G5G:K3M2N3N1N3M3N1N2N2O1N2N2O1N1O2O0O2N2O0O2O000O1N2O2O1O2M2O1N2N2O1N2O0O1O101N2O0OO1011N2O1N100O101O1O1O1NKakNlL^T1[30O100O10000000O2O010O0000O1001O001N1000000001O1O1O1O1N2O2M3N2N2M3M3N3L4L3M4K4L6J4K6K8H7I7DVVZe0\", \"size\": [1280, 720]}, {\"counts\": \"biV33532K20YV18`iN92B3OUV1P1eiNUO2MXV1a1N3N5J2N5L2M2O1O2M101N2M2O101N00100O1O1O100O2N100O1O1O1O2O0O100O101N100O2O0O100O2O001O1O001O1O00001O1O000000001O100O00001O2M20O1O1O2N2N1O2M3L4N101N1N2N2M3M3M5K5K4L5K5L3N4J8F8I9E7H7L8EP^Ve0\", \"size\": [1280, 720]}, {\"counts\": \"maR21hW1:J9@_ORiNf0iV1=K4M2M4M3M2N3M3M3M3M2N2O000O2O0O1O10O01OO2N2N2N12O0O10O10O100O10O0001O00100O100O11O000O3N2G8L4L5M3M2O0O2O1O002M7I5K100DlhNKXW1=2HfhNFZW18lhNDXW189KfV[g0\", \"size\": [1280, 720]}, {\"counts\": \"_bm1d0XW18J5K3N3L4M3M2N3N2M3N2M3M3M2O0O100O10O0100O100O010O100O01000O10000O10O010000O010O1O100O01O010O1O1N1O2O01000N2O001O1O1O010000O010000O1O10000000000O1000000O2M200010OL4N3N2G:Kdfif0\", \"size\": [1280, 720]}, {\"counts\": \"`ZQ2b0ZW18I5K5L4L5L3M4M2M3M3M2N1O1O100O1O100O100O100O0010O1O010O10O0100O10O010O0100O0010O10O1O10O010O0100000O010O01O01000O100O1O1O1O1O0O200O2O0N2O1O1N2O1O1O1O1O10000O1N2O2K4L5J6L4M5Hefdf0\", \"size\": [1280, 720]}, {\"counts\": \"YaP31ZRd05\\\\U]O4N3M3M200O100O2O1N1O1N2N2N3L3N2N3M2G:I6J7O001O1O001N10001O00001O0000001N10001O0O00O2N12N1N2N2O2O0O001O000O020O1000O00101N2N4L9F6]On0\\\\Ofeke0\", \"size\": [1280, 720]}, {\"counts\": \"aZY31kW18VhNM\\\\W1`0J6L3L4K5M3M3L4M3M3N2M3M3M3N1O1N2N2O1N2N3N0O4M2M2O001N2O000O2O000O101N101N101O001N101O000O1O100O100000000O10001O00O01001O000000O10001O0000O1000000000000000000O100O100O2N101N3N1N3M3M4K5L5I7I6J8I=]Ogemd0\", \"size\": [1280, 720]}, {\"counts\": \"`jo34gW1<E:G5M3L4L3M3N3M3M3M4L3M3M3M3M2N2O1N1O2O1O2M4M1N2O0O2O0O2O0O101O1N100O1O2O0O10001N1O101O0O2O0O2OO10O1000000001O0001N100000000000000000001O00000O0100000001O00000O10001N2N2N2N3M4L2M3L6I8I<C5I8Fg0]OdeYd0\", \"size\": [1280, 720]}, {\"counts\": \"Xai32jW16D=J<E5L4L3N3L8I8H1O1N2O0O2O1N3N1N3M2O1N2O1N101N102N1N2O1N2O0O1O100O1O100O2O000O2O00001O0000000000000000001O000000000000000001O0010O00O2O0O02O0001N101N3M3N4K4K5K5J5K4L8F;E<Al^ld0\", \"size\": [1280, 720]}, {\"counts\": \"eib2<`W19F8J5M3L3M4N1N3M3M3M3M3M4L5L3L3N1N2O1N100O2O0O10000O2O0O100O100O100000O10O1O10O10O0010O0100O0010O010000O0100O10O010O01O1O011N100O2N2N2N4L5K3M3M3M2M4M4K6J6K6J7J<D1M4K4LQnVf0\", \"size\": [1280, 720]}, {\"counts\": \"VR_2;[W1>H6L4M3L5L2N3M2N3M2O3L2O1N3M3N3L3N1N3N2M2O1N100O10O001O0001O01O0000001O0010O1O01O00100O001O1O1N101N101N2O1N2O1O1O1N2N2O1N2O1N2O1O1O2M2N2O1N2O1O1O2N1O1O2O0O2N101M4M2M4J5M5JmVXf0\", \"size\": [1280, 720]}, {\"counts\": \"YY^31oi86i]H2N2O2M3E<F9M3M2N2O0N2N3N1N2N2M4J5O1O2O0O2O1M2N2O1N3M2N2M3M301O001O0O2O000O2O0O2N2O0N2N2O1O1O1O1O1N2M3N3OO01000O10000O100O2M3L:F9H8G?^Oa0YOf0POW^ee0\", \"size\": [1280, 720]}, {\"counts\": \"mYT3l0oV19I6K3M4K4L5K4M3M3M3N3L3N2M3N2M3M2O1O1O0100O0100O10O010O10O0100O10O10O10000000000000000O100000000000000000010O01OO101O0001O01O0010O2O00O02O00O1O100O1O110O2O0M3J6L4I8J6K7I9E9I7H:_OnU_e0\", \"size\": [1280, 720]}, {\"counts\": \"`SS36\\\\W1i0^O:J5L5J4L5L4L3M4L3M4L4M2N3L4M2M4M2O0O2N10O10O101N3NO0100O10001O1O1O001O0O10000000O2O000001O000000000000O2O00010O00001O001O001O0O2O001O1N101O2N1O2N1N3L3N3M3L4J7I:H4L4M4L3K6K7I9Ee\\\\`e0\", \"size\": [1280, 720]}, {\"counts\": \"nke3d0TW1<D<I5L4K5L5K7I3M3N3L3N2N2N2N101N1O2N100O101N10001N101O0000001O00001O000000000000O100001O01O0000000001O01O0010O0001O0O2O001O001O001O1O1O1N2O2N1N3N1N2M4M5K4L3L4L5J5L3L5L4L4L4L=@kknd0\", \"size\": [1280, 720]}, {\"counts\": \"Zko34iW17@<N3M2N2O0O3M2O1O1O1O01O1O0010O010O100O5L4L1O0O1000000000O10O101O0000000000001O0001O0O100010O001O1O0010O0O1N3M201O000100O1000O1O000N3O101O2N2N0O2N2N2O1M3N2N2M2O1O:E6KWlid0\", \"size\": [1280, 720]}, {\"counts\": \"_m^42^W1b0C?D:D>I6J5J5M4L3M3M3N2N2N2N2N2O1N1O101N2O0O10001O0O2O000O101OO2OO2O001O00O1001O00000000010O1OO101O0001O00O2N1O20O10O0O2O001O010M3N2N2N2O2M201N1O2M4L2N4L3L3O2N2N2N2M3M5L2M7I8E:Cd0_OaZSd0\", \"size\": [1280, 720]}, {\"counts\": \"PmR53TW15ZiN0aV14XiN8aV1KZiN;cV1d0M3M3N0O1O3N4K3N2M3N0003M1OO1O1O1O1O10O01O0001O000001O1O01O0000001O002N1N200O100O002N2N1N2O2N1N2O10UN_NUmNa1fT1N2O1OXNcNnlN\\\\1RS1jNilNU1XS1mNflNQ1\\\\S1oNelNn0]S1QOelNl0\\\\S1TOflNi0[S1XOelNg0\\\\S1XOflNf0ZS1ZOglNe0ZS1ZOglNd0[S1ZOflNf0[S1YOflNf0[S1YOelNf0]S1YOdlNe0^S1ZOblNe0]U1O2O0O2O0O101O000O1K[Zic0\", \"size\": [1280, 720]}, {\"counts\": \"TSo4a0YW1;H5L3N1N2O1O2N1N3O0O1O100O100O2O0O10000jjN`NlS1`1SlNbNkS1_1VlNbNhS1^1XlNcNgS1]1XlNdNgS1]1YlNdNfS1\\\\1ZlNdNfS1\\\\1ZlNdNeS1]1[lNcNeS1]1[lNdNdS1\\\\1\\\\lNfNbS1Y1_lNhN`S1X1`lNiN_S1W1alNiN_S1W1alNjN^S1V1blNiN_S1W1alNiN_S1V1blNjN^S1V1blNjN^S1V1blNiN_S1W1alNiN`S1V1`lNkN_S1T1clNkN^S1T1blNlN^S1T1blNmN]S1S1clNnN\\\\S1R1elNmN[S1R1flNnNZS1R1flNmN[S1S1elNmN[S1S1elNmN\\\\S1R1dlNnN\\\\S1R1dlNnN\\\\S1R1elNlN]S1S1clNmN^S1Q1clNPO\\\\S1P1elNPO[S1o0elNQO[S1o0elNRO[S1m0elNSO[S1m0elNRO]S1m0clNSO^S1k0clNUO`S1h0blNTObS1j0l1M2O2N1O2M3N1O1O2M3N1N3M3N1N2MdlPd0\", \"size\": [1280, 720]}, {\"counts\": \"^SY585NSW1k0D4L5L3N1O2N1O2N1O101N2O0O101N100O100O2O0O101O000O101O0000001O0000O1001O00001O0000000001O0000010O000010O0010O001O1O100O1O1O1O101N1O2M3N3M2N1O2N2N2N2N1O2N002N2M4L:VOihN8P[dc0\", \"size\": [1280, 720]}, {\"counts\": \"mc[5`0\\\\W1g0[O7H6J7J5J5L4M3M2N3M3M1O2O1N10001N101O001O1N2O1N101O1O0O101O01O0001O001O00001O0O20O001O01O001O01O00001O000000001O010N101N2O1N2N3M3N1M4J5N3H9M2M1N3VNcjNm0\\\\V1I?A8GPkfc0\", \"size\": [1280, 720]}, {\"counts\": \"ojR5>QW1m0_O9H6J7I8J5K2O2N2N1O2O0O2N2N1O1O101N2O1N1000000O10001O1N1000O1001O1ON200000O3N0001O0000001O000O2O10O0001N1010O01O0O2O1O1N2O1O1O1O1O2N1O4K4M2N2M4L3M3L4J7K4K5L3L5K4M4L<Bllfc0\", \"size\": [1280, 720]}, {\"counts\": \"obQ5f0nV1f0A9H7J6K3L5M3M2N2N2N2N2O2M1O3M2O0O2O1O0O2O001N2O000O1000000000O11O1O00000000O1O2O0O100001O0001O000O2O1N2O001O00010O1O0O3N1N3M2O2N2N2N1O2N2N2N1N4L4K4M2N3M3M4K4L3K6J8H;CeThc0\", \"size\": [1280, 720]}, {\"counts\": \"\\\\ST58dW1h0YO<D8H7J5L3M3M3N1N3M3N1N3N2M3N2M3N2M2O0O100O2O0O11O01O001O0O1000000000000000000001O00000000001O0O101O010O1N101O1O1O1O1N2O2N1O1O1O2M2O1N3N2M3N2N1N3K4M3M4K4M3M3M4L5J5I9FTdec0\", \"size\": [1280, 720]}, {\"counts\": \"jjm4`0]W1d0\\\\O8H8I5L4L3M3M2O2M3N1N3N1O2M3N1O1O1N2O1O0O2O1O1O00000000000000001O0O11O000000000000001O000000000000010O001N101O010O001O1O1O2N0O3N2M3N1O2N1O1N2O2M2N2N2N2M3L4K5K6K4M5J5K6K6Ce\\\\ic0\", \"size\": [1280, 720]}, {\"counts\": \"_b]4m0oV19H6K4M3L4M3M3M2N3N2M4M2N2M3N1O1O0O101O00000O10001O000000000000000000000000000000000000000000001O01O00000O1000001O000000001O001O1N2O1O1O1O1O2N1O1O1N2O2M1O2N2M3K5L4L5J5L6J6J6K4GWUWd0\", \"size\": [1280, 720]}, {\"counts\": \"USQ4284PW1k0C7I7J5L5K3M4M3L3N2M4N1N3M2O1O2M3N1O1N3N1O1O2M3N1O1O1O000O2OO10O11O0001O0000000000000000010O1O00000001O00O0100O20O001O0001O001O0N3N1O2O2N1O1O1O2N1N3M2N2N5J3O2L3M3L4K6K4L6K4K8F9Gh\\\\bd0\", \"size\": [1280, 720]}, {\"counts\": \"njT4<[W1a0A;E:H8I6J7I6K7J4M4K4N1N3M2O0O2N2N2N2N101N2O000O101O000O100000001O0000000O11O00000001O00010O000000O101N1000001O00001O1O001N2N2N2O100O1O1N2O1O2N2M3M3M2M7J5L3K4L5I7J6J4L4L4Ka0_OQUad0\", \"size\": [1280, 720]}, {\"counts\": \"Xio3g0QW1:J9B=H8H6K5L4L4L4L5L5K3M001O2O1N1O10O011N100O10O10001O000O10O100O100000000001O00000001000OO2O0000010O000O101O1O1O0O2N20O010O02N2N2N110N2K5M3M3L4L9G5K5L5K3M4L5J4L4L4L4M[nid0\", \"size\": [1280, 720]}, {\"counts\": \"WYh3o0gV1?E9I7I7J5L4M3M4L4L3M3N2M2O1N100O00101N1000000O1000000000000O1001O0000000000000O10010O00000O2O00001O001O001O0O2O1N2O1O0O2O1O1O3M101N2N1N4M4J5L4M4L2K7H6J6L4L5L4L4K4M5JRfRe0\", \"size\": [1280, 720]}, {\"counts\": \"oie36^W1m0[O>D7J8I3L3L4L5L2N3M3N1N2O1N2O0O2O1O1O1N2O2N1O1N2O00000O01N2O11O00001O1O000000O1O1000001O0001O00O2O002M2N101O010O001O1O1N101N104L3L2O2N1O2N2N2M2O1N3M3M3M3L3N3K5L3L5K5K5L4L6K4LXmnd0\", \"size\": [1280, 720]}, {\"counts\": \"SZh3:aW1`0C8I7H9G7J4L3M3N2M3M2O2N1N2O2M2O2M3N2N1O1N2O1O1N2O1O1O1N2O001O001O0000001OO1O10000001O010O000000O101N101O00010O001O001N2N3N1O1N2O2N1O1O1O1N3N1O1N3N1O1O2N1N2N2N2L5I5L6J6J6J6K5M3K4M5LSehd0\", \"size\": [1280, 720]}, {\"counts\": \"`Rl3=aW19H9G6I6K5K3N2M2N3N2N1N3N2M3N2M2O2N1N101O001O001N101O1O001O1O001O0O2ON2O100001O000000000000O1O1O10001N11O10O00001O0O2O1O1O1N200O1O1O1O001O1O1N2O001O1O1O0O101N1O1O2L5I6L5H8J6M3L4M4L6Gjldd0\", \"size\": [1280, 720]}, {\"counts\": \"jRQ4a0YW1c0@7K5K6J4L4L4L3M4L3M3M3N2M3N2M2O2N2N1N3N2N2N1N2O1O1O1O0000000O2O00000000001O01N100000001O000O11O01O00000000001N101N2O1O100N2O1N2N2M3N2O3N2L2O2N1O2N1N2M4L3N2N3M2H9J6I7I7L4L5K7IZdcd0\", \"size\": [1280, 720]}, {\"counts\": \"fZm3k0oV1<@b0C8K4K5M3M3M3M2M4M2N2O2M2O2M2O1O1N10000O10000O101O000O2O00000000O100O1000010O000000001N1O2O00001O001O01OO2O0O2N2O1N20O01O1O1O1O2N2M3N2M3M3N2M3N2N2M5K5K2N3L5J5L4L5J5L5K5K7Gbdhd0\", \"size\": [1280, 720]}, {\"counts\": \"oQX3d0PW1a0G9D;H6J6K5K5L3M4L5K4M3M4L3M2N3NO1M3O1O01O012N001O0O100000O10000001O000000000001O000001O000001O00000O2O1O001N1000001O0O2O1N2N2O2M2O1O2N002N1N3N1O3L3M4K6K4K4M4L3M4K4K6K3N3M2N7I=A_][e0\", \"size\": [1280, 720]}, {\"counts\": \"oYo2m0jV1>D:G:I7I4M5L3L5L3L4M2N2N1O2N100O2O0O100O10000O10O0100O100010O0000O100001O0O11O00000000000001O0000001O00001O000O2O002M2O2N1O1O001O2N1O1O2M2O1O2N2N2N3K7I5L4J6K4L6H7J6L5K5J6J5K[efe0\", \"size\": [1280, 720]}, {\"counts\": \"ajl2U1fV1>B7K5K5K6K4M4K5K4M2O1N3L3N2OO1O0101N2O0O2O0O10000000O01000001O00000O100001N1O1001O0000001O01N101N101N11O01O0010O00O101N101N200O1O00100O1O1O2M3M3M4M4K3M3M5L4K6I5L4K5J7I5L4M5JdTie0\", \"size\": [1280, 720]}, {\"counts\": \"Rba287NPW1k0YOd0F9H7I6L4L4K6L3M3M3M2N2N1O2N2N1O2O001N100O10O2N1000O10001O00000000O10000000000000000000001O000000001O001O000O2O000O2O00100O1O1N101O1O2N2M3N2N2N1N4M2M6I4L5J5L4M3M4K5K5J5L4M7H9H]mQf0\", \"size\": [1280, 720]}, {\"counts\": \"]YV2l0oV1d0^O7I6K4L4L4L5L3L3M4N2N3M2M3N3L2O1O1O1N1000001O000O101O001O00001OO1O1O10000001O00000000O100O100000001O000000001O00001O001O0O102N1O1O1N3N2M3O0O1O2M3M2N3M4M2M3M2M4K5L3L5L4L3M4L4K6K4K5KmeZf0\", \"size\": [1280, 720]}, {\"counts\": \"Wj_1g0TW1=F9F6J6J6K4M4K4M3M2O2M2O1N2O2M3N1N3N2N1O1O0O2O00000O1000001O00001N10001O0000O1000000001O0001O0000000O10001O0O100001O01O00000O101O00001O001O1O001N2O2N1N3N1O100O1O2M3N1O1N2O1N2N2L4L4L4L5K6J6K4K5K5K7HZUgf0\", \"size\": [1280, 720]}, {\"counts\": \"^YS1;`W1b0_O:F>B:G9I7I5L3L4L4L3N3L101N101O002NN10100O1O001N2O1L3L5M3O1000O0100000000O1N2N2O1O100O100O2O0O100001O1O1O0001N100000010I6M4K40001O00001N2O1O1O2O1N1O1O1N200O1O1O1O1O2O000O1O10O01O1O1O1O3M3L4M3L4L3N2L7GmUQg0\", \"size\": [1280, 720]}, {\"counts\": \"mPc0?VW1e0C8G8J6I5K6K6K4M4K4L4M2M4M3L2O2M3N001O0O2O001N1000000O1000001O0O1000001O0000O1001O000000O11O1O000000O101O0O10001O0001OO101N1O2N2O0O100O2N1O2O0O2N1M4M3M3N3N2M3M2N3M2N3M3M3K5K5L4M3N1L5M2N2M3M4M4J7IjVeg0\", \"size\": [1280, 720]}, {\"counts\": \"j`;2dW1c0ehN\\\\ObV1[1C=G5K3M6J3M4L20M2O10000001O00001OO0L5L3F:AoiNoNTV10]jNNTV12oiNFVV1<f000001O00K60O10OQdP10i[oN701N2O00000O1O1_OEWiNb0gV1<O10000O1_OmNTjNT1lU1mNSjNS1mU1`0O1O1O1O100IUNYjNk1eU18J70000O01O1H7H9O001O1J6L3N3M3L6^OXiN@YW14Zggg0\", \"size\": [1280, 720]}, {\"counts\": \"f8]1bV12L4L4M3L5L3L4L5M3M2N3N3L3N2M4L5L2N1N3N1O1O1O3M1O1O0O101O000O101OO100000O100000O100O1O1N2O1O1O1N2O1O1N3N1N2N2O2M2N2O1O2N1O1O1O1N2N3L3N2N3M2N2N2N2O2M2N2M3M3L3N3N2M3N2M4L3N4L6J7Ii_dh0\", \"size\": [1280, 720]}, {\"counts\": \"\\\\8_2aU10M8J3L4N2M2M3O0O101N3N3M1O001N3N00001O001O1O1O1O00001O000O100000000000000000000000O10001O0O1000001O000O100O1N2M3N201N10001O000O101N100O2N1O2N1O2N1O2O1N2N1N3M4M3M3M2N3M2N5K3L4N2M3M4K4M3M3M4L4K4M4L5L4IV^Uh0\", \"size\": [1280, 720]}, {\"counts\": \"_8V2jU10O010O1O100O1O0O2O0N0002001N101O0010O010O0N201I700O0010O01O001O1@WjNgNjU1U1\\\\jNhNfU1U1_jNgNbU1Z1b0N1010O01O010OO02H7L41000O00100O00010O0N201000O10N2O10O01O1N2IikNOoLJdS15^oN>`P1B`oN?_P1AaoN`0^P1_OcoNa0]P1_OcoN`0^P1_OcoN`0^P1_OcoN?^P1BboN>^P1AboN?_P1AaoN=aP1D^oN?_P1B`oN?_P1AaoN?_P1AaoN?_P1AaoN;cP1E]oN;cP1C_oN<bP1AaoN?_P1@aoN`0_P1AaoN>`P1AaoN>QT101M2O1L4N2O3I_e[h0\", \"size\": [1280, 720]}, {\"counts\": \"dha03jW16Je0\\\\O;F5K4K6K5F8L6K4K5L3L4M3L5L3M4L5J4M3N0O2N100O101O0O10000000000O1000000000000O10000000000O10000000O10000O1]N[lNkNeS1R1alNnME?jS1^1ilNoM_Ob0hS1]1WmNaNlR1\\\\1WmNaNkR1]1XmN`NjR1_1XmN\\\\NlR1b1j1O1O2L3N2O1O2N100O1O1O2M2M2N3K6O001O0O1O2O1N2O0000010O1O1O1O1O1N102N1N2O102L3MPhbg0\", \"size\": [1280, 720]}, {\"counts\": \"cXX16eW18J4L3N2M3L4M3M2O2N2O1N011O1O100O1O100O100O100O10000O1O1000000O101O1O00000O101O00001N2O001OO01000001O1O001O001O100O2O0O1O00001O0010O00000010O00000010O00010O001O001O1O001O1O00100O10002N1O0O2N2O100N2O01OO010O102M2N4L4K8G5K:^OTg_f0\", \"size\": [1280, 720]}, {\"counts\": \"PiS2;bW14F<I9I4M5L1N2M4M30N2O00101N01O000O2M3N2O10OO2O0O2N101gMRN\\\\nN2mMh1dS1XNenNi1YQ1XNgnNi1VQ1[NjnNd1UQ1[NmnNd1RQ1\\\\NQoNb1nP1^NToNa1kP1`NSoNb1mP1]NQoNf1nP1[NonNh1PQ1XNRoNf1nP1[NRoNe1mP1[NToNd1mP1[NSoNd1nP1]NRoNb1nP1^NToN_1mP1aNUoN\\\\1lP1cNZoNOYMP1]S1VOUoNJ^MP1]S1BfnNk0ZQ1VO`nNo0_Q1QOhnNh0XQ1XOjnNf0VQ1ZOjnNYOlMV1ZS1AQoN?oP1BPoN?PQ1@PoN`0PQ1@PoN?QQ1APoN>PQ1BPoN>PQ1FmnN9TQ10bnN0^Q13_nNMaQ13_nNMaQ13`nNL`Q14`nNL`Q14`nNLaQ14^nNLbQ14^nNLbQ14^nNKcQ15^nNJbQ16^nNIcQ18inN[OXQ1e0knNWOUQ1i0lnNUOUQ1l0jnNTOWQ1k0inNUOWQ1k0inNTOYQ1l0fnNTO[Q1k0enNTO]Q1l0bnNTO^Q1m0bnNQO`Q1k0dnNlNfQ1Q1g2N2N1O1O2O1N1O2N2N10011N1O11N2ON2N2M2O102K3L3Nmole0\", \"size\": [1280, 720]}, {\"counts\": \"_PZ26gW15K5K4J8K4I7]NZOWkNGDW1QU1`1K4N1O2N2M3N2M4M2N2M3N2M2O2M3M2O3M2N3L2O1O1N2O1N101O2N1O001N2O0000O1O1O1010O00000000001O000001O00000000001O000O2O0O10010O000O1O2O00001O0O1010O100OM4O1N3N1O2M2NUOQLimNm3WR1SLjmNk3XR1TLjmNi3YS1N1O20OO2L5L2O2L3M4NSOmLolNP3RS1PMPmNn2RT1M4M2N2J7D<G8L4K6I9G:H8I5JooXe0\", \"size\": [1280, 720]}, {\"counts\": \"PVT37hW11O2O001O0O2O1N2N01O01ZkNAkQ1`0TnN@kQ1a0j21N101N101N2fkNYOYQ1g0enN\\\\OYQ1e0VnNNhQ12WnN1gQ1OYnN2eQ1O[nN2dQ10ZnN1eQ10ZnN2dQ1O[nN1eQ10ZnN1dQ11[nN0dQ10\\\\nN1cQ1O]nN2bQ1M_nN4_Q1LanN6^Q1JanN8^Q1IanN8^Q1IanN8^Q1HbnN9]Q1HbnN9]Q1GcnN:\\\\Q1GcnN9]Q1HbnN8^Q1HbnN5SNiNXS1S1dnN8^Q1HbnN8^Q1IanN7_Q1IanN8^Q1IanN8^Q1HanN:^Q1GanN9_Q1GanN9`Q1F`nN:`Q1FanN8`Q1I_nN7aQ1I^nN7cQ1IYnN;gQ1EVnN=kQ1CUnN>kQ1BTnN?kQ1BUnN>jQ1BVnN>jQ1BWnN=iQ1CWnN<jQ1EVnN:kQ1EWnN9iQ1HXnN4jQ1LWnN2jQ1NWnN0kQ1OUnN1kQ10TnN0lQ10TnN0lQ11TnNNmQ11SnN0lQ11SnNOnQ11RnNMoQ13QnNMPR12PnNMRR11omNOQR1OQnN0QR1OomN1SR1MnmN2SR1NlmN2UR1MkmN3WR1LimN3XR1MgmN4ZR1JfmN4]R1LcmN2`R1LamN2aR1O^mNMgR12ZmNKiR15WmNHlR16UmNUO[N>bT1<YmNAjR1?UmNAmR1=SmNDnR1;QmNEQS19olNGRS18olNCWS1;jlNBYS1=glNB[S1=elNB^S1<blNCaS1;`lNDaS1;_lNDcS1;R2O[jNFeS19[lNGhS16XlNJlS12R2O1Odhb00[W^c0\", \"size\": [1280, 720]}, {\"counts\": \"m^d3b0]W1:F;F8H4L3M3L3N1O2N1O2N2N2N2M201N1O1O2N2N1O002N1O2N2N1O2N1O2N1O2N1O1O1O2N1O0000001N1O2O01O00O1O2O01O00001O00001O0000O100001O00O001001O1O0000O12N1O1N101N2O1O2M2O001O2M2N3N0O2N1O2O2M2N101N2O1N1N3O3L4K3N2N2N2M4K4A`0Ia0Cd_Xd0\", \"size\": [1280, 720]}, {\"counts\": \"^im41oW15J3N3L2O000O1O3L3L4O0O100O100O101N2O0001OO2N1O100O100O1O1000O1O1000O10O10J51000O11NmLjN^oNV1aP1kN_oNU1aP1kN_oN3nLl0cS1QO^oN4oLj0dS1RO]oNT1eP1kNZoNV1hS1O1O1O1SOUiNe0kV172N2M11000O0O2O1O1N2O1O3F<F]_bd0\", \"size\": [1280, 720]}, {\"counts\": \"^QT53lW15K4L7G3N4N1O0O2O1N2O1O1O2NO10O011O001N01O10O100010O1O0001O1OO10O01001O000O01000O2O00O2N0100O2OO2O0000O1000O10000O001000O1000N110000000O100O100O0100000O1O002M2L4O3H:I6IPgec0\", \"size\": [1280, 720]}, {\"counts\": \"SgY4511aW1:L2O1O1M3N2N1O2ojNZOfR1h0YmN_O_R1b0`mNC\\\\R1>dmNFVR1;jmNIQR18PnNLjQ15WnN1bQ1N`nN4^Q1KbnN6]Q1JdnN6\\\\Q1HfnN9XQ1GinN9WQ1FjnN;TQ1EmnN;SQ1DnnN;SQ1CnnN>RQ1@PoNa0nP1_OSoNb0lP1]OUoNd0jP1\\\\OVoNd0jP1[OWoNe0iP1[OWoNe0iP1[OWoNf0hP1ZOWoNg0iP1XOXoNh0hP1XOXoNh0gP1XOZoNh0gP1WOYoNi0gP1VO[oNi0eP1WO[oNi0eP1VO\\\\oNk0cP1UO]oNk0cP1TO^oNl0bP1TO^oNl0bP1SO_oNm0aP1RO`oNn0`P1RO_oNo0aP1QO_oNP1`P1nNboNR1^P1kNeoNU1\\\\P1hNfoNX1ZP1fNhoNZ1XP1fNhoNZ1XP1fNhoNZ1XP1fNhoNZ1XP1eNioN[1VP1fNjoNZ1VP1eNkoNZ1VP1fNjoNZ1VP1eNjoN\\\\1WP1bNkoN]1UP1bNloN]1\\\\S1000000000O10O100O100000O10O10O100O010mLcNboN]1aS1O0010N1000O1O2OEfNQjNX1PV1kNniNS1QV1QOniNm0RV1XOkiNf0RV1e0O2N000102M2O1O1O1001M1O2O0:E:G;@QPbc0\", \"size\": [1280, 720]}, {\"counts\": \"nVo2k0TW17K5J9G5J3M2O1N3M2O2M6K3L4M2N1N2O1N2O3L4M2M3N4K4M2M3M2O3L4L2O2M2OO0100O4L2O1N2O1OO10O01001O0O2O1O001N10000000O10000001O1O000000000000O10000001O000000000000O10000000001O00001O00000O10001O000000001N100O101O0O1O1O2L3L5K4N3N1N2O2L3J7K5J6J6K5L5H8M3M4JUiYd0\", \"size\": [1280, 720]}, {\"counts\": \"RbU31oW17I3M0000000O202M2N1O0000001O000000000000000000001O001O00O1001O1O001O000000000O100000000000000^LZOjoNf0VP1\\\\OhoNd0XP1_OeoNa0[P1BaoN?_P1B`oN>`P1C^oN>dP1@[oNa0fP1@UoNc0kP1^ORoNd0nP1]OnnNf0RQ1\\\\OknNf0TQ1B`nNb0`Q1CSnNe0nQ1ZOfmNR1ZR1oNbmNS1_R1mN`mNT1`R1lN_mNT1cR1lN\\\\mNT1dR1_OhlNa0YS1_OglNa0ZS1_OdlNa0]S1_OclNa0^S1_O`lNb0aS1]O\\\\lNg0dS1XO[lNi0eS1XORlNo0PT1_11O101O1N101N1O1O2N1O1N3N1O1N2N3N1M3O2O0O1O1O2N1L4N3M2CfjNPN_U1b1g0L6K5M3F;C?\\\\OgXfd0\", \"size\": [1280, 720]}, {\"counts\": \"bWg13lW1;E3L3N2N100O101N100000O1000bjN[O`S1f0_lNISS17llNOoR11QmN0nR10QmN3mR1LTmN5jR1LUmN6jR1JVmN7iR1IWmN8hR1FZmN;eR1D]mN<bR1D^mN<bR1C_mN>`R1B`mN>_R1CamN=_R1BbmN>^R1AcmN?\\\\R1BdmN>\\\\R1AemN?ZR1BgmN=YR1BimN>UR1ClmN=SR1BnmN>QR1BQnN=oQ1BRnN?mQ1ASnN?mQ1@TnN`0kQ1AUnN?kQ1@VnN`0jQ1_OXnN`0hQ1@XnN?hQ1AYnN`0fQ1_O\\\\nNa0cQ1_O]nNb0bQ1]O_nNc0aQ1]O`nNb0`Q1]OanNc0_Q1]OanNc0_Q1]OanNc0_Q1\\\\ObnNd0^Q1\\\\OcnNc0]Q1]OcnNb0^Q1]OcnNd0\\\\Q1\\\\OenNd0ZQ1\\\\OfnNd0ZQ1[OgnNe0YQ1[OgnNe0YQ1ZOhnNf0XQ1ZOhnNf0XQ1YOinNg0WQ1YOjnNf0VQ1YOknNf0VQ1YOlnNf0UQ1XOlnNh0UQ1TOnnNm0ST1O01O0001N100000001N2O1O2N000O101O000O2O0O10000O101N2O0O100O101N4J`U]f0\", \"size\": [1280, 720]}, {\"counts\": \"RRk1;eW11N2O000O101N2O1aNKPkN6VS1MglNh0E[ObS14nkNX1<gNdS1Y2PlNoMnS1R310O000O100O2O0O2N100O101O000O1000O11N1000000O2O00000000O11N10000000001O0O1000O10000000000000000000O10O1001N01000000000O10O100O10O020O0000O001O2O00O10O1001O0000O1N201M3L5M3K5M4J5K5L6G8H8K7G9I8Ca0ZOf0_OUPme0\", \"size\": [1280, 720]}, {\"counts\": \"S7\\\\2dU1001N2O001O000O2O000O2N1O2O2N1O2M2O1N5L4K3N2N1N3M5K4L2O2M2N2O0O2N2O0O2N102M10O0100O2N2O0O1O011N2O000O10000O100O2O1N010O10001O0O10O1001N2O0000O0101O0000O0101O000O10O10O2O00O100O02O1O1OO10000001O00O1001O001O00O100O10000N2O2N2N3J5M3O2N1O1O1N2N2O2M2N3L5K5L4K5K5I6J8H9H8I5J>^O>EP`hf0\", \"size\": [1280, 720]}, {\"counts\": \"d6\\\\3dT10O2O1N2O2N2N2N2M4M2M3N1O1N2O1O2N2M2O2N1O1O1N2O000000001N100000001N2O1O1N100000O100000O2O0O101O001OO1O1O11O001N10001O00000000O10000000O101O000000O1O110O00O100000010O00O2N1O2O10OO2O1O1O101N2L5L3N3L4M2M3N2M7I7I3N2L7J6I8H7H6E=]Oi0[O_Weg0\", \"size\": [1280, 720]}, {\"counts\": \"X92;S1SV1mNmiNS1SV1mNmiNT1SV1kNmiNV1RV1jNniNV1_V1O1UOViN>kV1AUiN?jV1<0O10O1N21O010O1O3M1TOTiNb0VW1O1O00OO102N2O1_OYnQk0\", \"size\": [1280, 720]}, {\"counts\": \"k5^3bT1001O1O001O1O1O001O1O2M2O1O1O1O1O1O1O1O1O1O1O1O0O2O1O1O010N3N2N1O1O1O001O2N1O1O2OO01O1OO10000O10000001O00O100001O01O00000O10001O000O100O2N1O101O0000001O0O1O2N1O2O0O1O1O2M2O10000O100O101N100000000O1O1N2L4L400O1O1O1O1O1O2N1O1N2N2N2O2N1O2O1O1N2O0N3K5L4L3N3N1O2O0O2M2M4M210O004J6J6H8H7K5FPQaf0\", \"size\": [1280, 720]}, {\"counts\": \"`6k1UV1002N2N1O6J001O1hjNiMeT1W2YkNkMfT1W2VkNlMjT1U2TkNlMlT1X2njNjMQU1d2O001O00000000000O2O0000001O0000000000001O2M200O1O1O001O002N2N001O00001O1O1O1O000000002N1O1O010OO1O100001O2N8H10O01O1O1O1O01O00001O0001O01O000010O000001O01O00001O01O00001O00010O000001O01O0O2N1001OO1N2O1O100O1O1O2O0O1O10O10O001O1O1O1N2O2N1O1J6O1N2O2N1N2O2M2N2N3N1O2M2O2I6N2O102M2O1N2N2N2N2O1N3M3M2O3L3L6K5J8H>_Ojgae0\", \"size\": [1280, 720]}, {\"counts\": \"]73mW11Ne0[O7I:F3N2niNbN^U1_1^jNdNbU1m100000L4O1000O2O1O2N1O000O10O0010O2O1O001O1O00000O010000001O001O00001O0O10O1001O00000000001O1O00O100000000001O1O001OO1N2O10000000001O0000000O100O101O00010O0000000O101O000000010O0000001N1O1N2O101O01O2N010O00O1O10000001O001O001O001O2M3M3O0O4L0000O11N3N001O0O2O00001O1O1O01O001O01O1O0001O0O101O0O2O0001O10O1N2N1O2O01OO2N1N2M3J8K8I4L7H6K5K`0]Oa_Ve0\", \"size\": [1280, 720]}, {\"counts\": \"m^R2a0ZW18K4L6K5K5K5L2N2M5L3M4K7J4L4K5L6J3M3M4L3M3L3N2N2N2N1O1O1O2M2O2N2N1O1O2N1O1O1O001O1O1O2N1O1O1O1O1O1O1O1O1O1O1O2N1O001O000000010O000000000000001O1OO100001O1O00O1O11O1O1O01N1O12N1O00O1O1001O01O0000010O00001O00100O000100O00O2N10010O0N3N101N1O1010O0000N3N10O1000O2O0O1N2N2J6N2L4L4N2N3N1O1O1N3M2N2O2M2N3M2O2M4K6J5I8A>J5N3M3M2N1O2N3L4L6G;G8FP`_c0\", \"size\": [1280, 720]}, {\"counts\": \"og^4b0WW1<H6K4J6L3M3N2M3N2N2M4M4K4M4L5K6I6K:F7I6K3L5L4K6K4K2O1O1N2O0O2O2M3N0O10O02O000O10O01O3N1N101O000O2O0000001O00O1001O1O01O000000000010O000001O00001O0000000O2M21O1O01O000O1001OO2O0O1000010O0O10001O000N3N10000O2O01O01N1O2N11O1O1O01O1O01O01N101N1M4L3O2O000O1O2N1N2N3N1O1N3N1N3M2O2N1M4M2N3L4M3L4M3L4N1O2M4L3M4K4L5K3L5Ia0QOdh_a0\", \"size\": [1280, 720]}, {\"counts\": \"iWP5c0XW18J4L5K5K4N2N2N2M2O2N2N1O2N2N2N3L3N4L8H8I<Cb0^O>C9F5L2M2O2M3N1O2M3N1O0000001O1O1O0000001O002N4L000000000000O10O1000001O00O100001N2O000000000000O10000O1O100000001O01OO10000O2O0O10001O0000001O001N100N3O00001N1O1O2N100O2M2N3N101O000O2O0O1O1O2N100O2O000O2N100O101M3M2O2N1O2N1O2M3M2O2M2O2N2N2M2N2M4M2L5L3M4M2M4L3L5L4L4L4L3N3L3L5L3M4L4L4M3M3L5K4L5KWP^`0\", \"size\": [1280, 720]}, {\"counts\": \"ZSb88hW11N2O1N100N2G9M3N2O00001N2O010M3M3K401O1O100O0KgNeiNY1[V16O001N2L4O1O100O000001O1N2N4M6D;Ji0VO7I]mja0\", \"size\": [1280, 720]}], \"bboxes\": [[197.0, 378.0, 81.0, 73.0], [176.0, 375.0, 85.0, 74.0], [139.0, 361.0, 90.0, 78.0], [98.0, 322.0, 95.0, 99.0], [78.0, 284.0, 88.0, 116.0], [80.0, 276.0, 88.0, 116.0], [89.0, 268.0, 87.0, 113.0], [90.0, 264.0, 87.0, 115.0], [77.0, 280.0, 90.0, 118.0], [57.0, 283.0, 98.0, 117.0], [42.0, 265.0, 99.0, 116.0], [37.0, 272.0, 100.0, 117.0], [39.0, 301.0, 99.0, 110.0], [37.0, 289.0, 100.0, 111.0], [31.0, 284.0, 99.0, 114.0], [34.0, 316.0, 100.0, 90.0], [45.0, 313.0, 98.0, 91.0], [43.0, 274.0, 91.0, 113.0], [44.0, 278.0, 88.0, 107.0], [44.0, 303.0, 87.0, 105.0], [40.0, 280.0, 88.0, 110.0], [44.0, 271.0, 88.0, 112.0], [59.0, 311.0, 87.0, 97.0], [76.0, 328.0, 89.0, 72.0], [94.0, 318.0, 85.0, 62.0], [100.0, 317.0, 86.0, 83.0], [85.0, 295.0, 88.0, 96.0], [73.0, 278.0, 87.0, 108.0], [74.0, 287.0, 86.0, 102.0], [85.0, 312.0, 84.0, 106.0], [88.0, 275.0, 80.0, 106.0], [89.0, 272.0, 81.0, 108.0], [83.0, 289.0, 86.0, 114.0], [80.0, 283.0, 86.0, 110.0], [82.0, 283.0, 85.0, 112.0], [94.0, 325.0, 85.0, 98.0], [123.0, 336.0, 87.0, 90.0], [139.0, 307.0, 98.0, 86.0], [155.0, 302.0, 99.0, 83.0], [184.0, 315.0, 95.0, 119.0], [176.0, 292.0, 92.0, 120.0], [151.0, 276.0, 95.0, 121.0], [128.0, 294.0, 98.0, 128.0], [84.0, 310.0, 99.0, 97.0], [48.0, 286.0, 100.0, 94.0], [33.0, 278.0, 102.0, 120.0], [43.0, 271.0, 102.0, 134.0], [68.0, 233.0, 96.0, 126.0], [102.0, 252.0, 95.0, 115.0], [146.0, 323.0, 93.0, 111.0], [207.0, 292.0, 64.0, 109.0], [224.0, 316.0, 69.0, 78.0], [221.0, 315.0, 95.0, 99.0], [222.0, 309.0, 91.0, 122.0], [200.0, 289.0, 90.0, 122.0], [173.0, 285.0, 90.0, 119.0], [130.0, 336.0, 91.0, 114.0], [81.0, 320.0, 101.0, 113.0], [57.0, 300.0, 99.0, 100.0], [60.0, 294.0, 99.0, 105.0], [69.0, 267.0, 98.0, 115.0], [82.0, 247.0, 100.0, 113.0], [108.0, 261.0, 100.0, 107.0], [129.0, 278.0, 98.0, 112.0], [152.0, 258.0, 57.0, 105.0], [88.0, 258.0, 86.0, 96.0], [78.0, 280.0, 101.0, 103.0], [54.0, 268.0, 104.0, 96.0], [86.0, 302.0, 59.0, 67.0], [63.0, 261.0, 98.0, 111.0], [83.0, 272.0, 99.0, 115.0], [70.0, 248.0, 100.0, 103.0], [60.0, 266.0, 98.0, 105.0], [53.0, 263.0, 78.0, 104.0], [46.0, 257.0, 98.0, 104.0], [58.0, 246.0, 100.0, 114.0], [71.0, 265.0, 103.0, 115.0], [82.0, 260.0, 95.0, 118.0], [53.0, 266.0, 69.0, 85.0], [49.0, 297.0, 87.0, 86.0], [52.0, 296.0, 88.0, 83.0], [77.0, 297.0, 83.0, 96.0], [84.0, 295.0, 100.0, 107.0], [102.0, 290.0, 98.0, 110.0], [97.0, 258.0, 88.0, 106.0], [66.0, 264.0, 85.0, 100.0], [63.0, 280.0, 87.0, 98.0], [88.0, 281.0, 77.0, 115.0], [80.0, 271.0, 90.0, 105.0], [79.0, 309.0, 90.0, 112.0], [94.0, 337.0, 89.0, 104.0], [102.0, 326.0, 85.0, 55.0], [114.0, 357.0, 91.0, 113.0], [130.0, 362.0, 83.0, 109.0], [127.0, 329.0, 80.0, 110.0], [135.0, 340.0, 83.0, 73.0], [137.0, 351.0, 78.0, 109.0], [130.0, 299.0, 85.0, 105.0], [129.0, 309.0, 85.0, 105.0], [131.0, 337.0, 85.0, 104.0], [126.0, 325.0, 87.0, 97.0], [113.0, 315.0, 89.0, 86.0], [103.0, 319.0, 90.0, 108.0], [106.0, 291.0, 88.0, 121.0], [102.0, 247.0, 85.0, 107.0], [96.0, 254.0, 84.0, 106.0], [94.0, 286.0, 89.0, 107.0], [96.0, 300.0, 92.0, 106.0], [99.0, 320.0, 92.0, 88.0], [103.0, 322.0, 89.0, 112.0], [100.0, 302.0, 88.0, 109.0], [83.0, 266.0, 90.0, 113.0], [76.0, 272.0, 88.0, 109.0], [74.0, 300.0, 88.0, 109.0], [65.0, 271.0, 90.0, 113.0], [56.0, 273.0, 92.0, 112.0], [38.0, 297.0, 100.0, 107.0], [28.0, 271.0, 102.0, 111.0], [15.0, 247.0, 99.0, 117.0], [9.0, 221.0, 103.0, 140.0], [0.0, 248.0, 89.0, 119.0], [0.0, 255.0, 101.0, 121.0], [0.0, 226.0, 96.0, 143.0], [14.0, 233.0, 102.0, 127.0], [32.0, 228.0, 112.0, 87.0], [54.0, 209.0, 105.0, 152.0], [59.0, 176.0, 116.0, 165.0], [80.0, 178.0, 127.0, 151.0], [93.0, 215.0, 108.0, 110.0], [126.0, 176.0, 67.0, 145.0], [131.0, 282.0, 85.0, 47.0], [110.0, 195.0, 109.0, 153.0], [76.0, 199.0, 124.0, 143.0], [81.0, 207.0, 109.0, 139.0], [44.0, 225.0, 102.0, 131.0], [47.0, 197.0, 112.0, 148.0], [0.0, 190.0, 137.0, 165.0], [0.0, 198.0, 114.0, 162.0], [0.0, 296.0, 27.0, 52.0], [0.0, 185.0, 143.0, 153.0], [0.0, 204.0, 168.0, 140.0], [0.0, 227.0, 177.0, 95.0], [53.0, 204.0, 168.0, 164.0], [114.0, 204.0, 158.0, 166.0], [128.0, 207.0, 171.0, 177.0], [219.0, 304.0, 44.0, 69.0]], \"areas\": [3812.0, 4185.0, 4663.0, 7116.0, 7491.0, 8004.0, 7843.0, 7915.0, 8171.0, 8763.0, 8282.0, 8346.0, 8325.0, 8333.0, 8573.0, 6950.0, 7021.0, 8059.0, 7522.0, 7292.0, 7655.0, 7967.0, 6709.0, 5118.0, 4434.0, 5707.0, 6599.0, 7352.0, 6978.0, 6982.0, 6894.0, 7103.0, 7630.0, 7568.0, 7438.0, 6738.0, 6385.0, 6453.0, 6589.0, 8507.0, 8394.0, 9137.0, 9715.0, 7551.0, 7347.0, 9335.0, 10420.0, 9922.0, 8782.0, 7759.0, 2847.0, 2588.0, 7254.0, 8669.0, 8701.0, 8609.0, 8163.0, 7526.0, 8142.0, 8232.0, 8912.0, 8935.0, 8565.0, 8323.0, 4201.0, 6272.0, 7485.0, 7127.0, 2742.0, 8093.0, 8643.0, 3600.0, 4074.0, 3560.0, 4508.0, 8740.0, 9117.0, 8341.0, 3729.0, 5417.0, 4988.0, 3834.0, 8633.0, 8578.0, 7524.0, 5974.0, 5262.0, 5361.0, 7259.0, 8177.0, 7527.0, 3324.0, 8208.0, 4163.0, 3937.0, 4702.0, 6801.0, 7223.0, 7164.0, 7114.0, 6782.0, 6437.0, 7817.0, 8589.0, 7340.0, 7205.0, 7387.0, 7478.0, 6226.0, 7742.0, 7563.0, 7930.0, 7826.0, 7929.0, 8302.0, 8293.0, 8802.0, 7099.0, 9110.0, 3692.0, 7606.0, 9674.0, 4347.0, 7466.0, 5948.0, 5641.0, 13821.0, 6431.0, 9218.0, 1969.0, 2850.0, 5701.0, 14595.0, 6037.0, 3790.0, 13592.0, 18383.0, 16235.0, 855.0, 17054.0, 17082.0, 12418.0, 20669.0, 20512.0, 21938.0, 1662.0], \"iscrowd\": 0, \"video_id\": 0, \"height\": 1280, \"width\": 720, \"category_id\": 3827, \"noun_phrase\": \"the face\"}, {\"id\": 865, \"segmentations\": [{\"counts\": \"nX\\\\;1kW1<@=G:H5J6K4L3N2N1O1O1O2N2O1N2O1O1O0O101O00000000000000000000000000O2O000O2O001N2O1N2O1N101O1O2N3L4M2I9F9K;DSob>\", \"size\": [1280, 720]}, {\"counts\": \"iXR;3gW1;G8H6G:K3M3M3N3M5J7J2N2O1N1O101N10000O2N2O0000000000000000000O1001N101O000O2O0O2N2N2N1N4N1N1O2O1O1O2L4N2L3N2M8G:EaWi>\", \"size\": [1280, 720]}, {\"counts\": \"\\\\ho:c0RW1>G8I8J4M3M3M3N1O2N3M2N2N1O2N10000O10000O100O2O000000000001O00000O1O2O00001O00001N10001O1O1N2O1O002N2M2N2O4K3M4L2J9F:WORiN3P_j>\", \"size\": [1280, 720]}, {\"counts\": \"UXm:1gW1;chNFfV1T1E9J3M4M4L2N3M3M1O2N3N1N2O1N101N1000000O1000001O1O0000000001O000000O10O2O00001O001O1O0O2O1O1N2O1O1O1O2M2O1O3L3N2N2M3M5K3M5G9D>EfWd>\", \"size\": [1280, 720]}, {\"counts\": \"nWh:<[W1?D8L4M4K5L4L4L3N2M2O1O2M2O1N102N1O1O1O1N1000000000001O0000000000000001O00001O001O001O1O1O001O0O2O001O0O2O1O1O1O1N2O1O2N1N2O1N3N2N1O2M3N1O2M2N:nN[``>\", \"size\": [1280, 720]}, {\"counts\": \"a__:1iW19]hNGSW1l0G6L3M2O2M3N3L6J3N4K2O1O0O1000001O1N200O1O4K4M1O1N101O001O000001O000O101O001O0010O01O000O2O00001O1N2N2N101O10OO2N2N2O1N101L4M4N1N1O2O00100O0O2N2O2M3N2N1N2^OTiNMPW1Ne0LPP^>\", \"size\": [1280, 720]}, {\"counts\": \"[g`:9bW1`0A9I6K4L4L3M3M3N1O3L3N1N2O1O2N3L2O1O1O1O2N001N101O1O2N00O100001O0000O101O001O001N101O0010O01O001O001O001N101O001N2O1O001O1N2O001O1N2N2O0O2O1O1N2O001N2O1N2N3L4L5K:H9FfWZ>\", \"size\": [1280, 720]}, {\"counts\": \"age:19<mV1a0H7J6K4L4L5M2M5K4M2M2N3N1O1N2O001O1N2O1O1O1N101O001O000000O10000001O00001O00O100000010O001O001O00001N101O0O2O001O1O001O001O1N101O1O1O1O1O1N101O1O1N2O001O1O2N1N2N2N2N2N3M4K5D\\\\iNTOhV1e0>AYhm=\", \"size\": [1280, 720]}, {\"counts\": \"\\\\ok:P1iV1:I9G9J5K4K3N3M2N3M2O1N2N2O2M3N0O2O1O001O00000O10000000001N100000000000000O100000000001O0000001O00001O001O001O001O1N101O001O001O001O1N101O1O0O2O1O1O2N1N2O1O1O1O1N3M2O2N1N2O1N2O2M7G7POP1CYhc=\", \"size\": [1280, 720]}, {\"counts\": \"WgT;;^W1:H`0A<F4L8H4L5K7J2N2N2N3M2N3N1O1N2N2N4M1OO010000000000O011N10000000001O0000000000000000000000000000000001O0000001O00001N101O001O001O001O001O001N2O1O002N1O1O1N2O1O1N2O2N1O1N2O1N2O3L4L3M3L5H8@?G:I<C\\\\`S=\", \"size\": [1280, 720]}, {\"counts\": \"`oi;3iW1e0UO>I7I8I6J6J5J5M2N3M4K01001O3M3M101N2O1N10000O2O1N100000000000000O2O000O2O00000000000001O0001O00000000000000001OO1000000000001N101O001O001O1N101N2O1O1N102M101O1O1N2O1O1O1N2O2M2O1O1N2L8H7J6I6I9G8G:C]P]<\", \"size\": [1280, 720]}, {\"counts\": \"V_`<?YW1c0A8I7K2M5J8I4L4M4L3M2N2N2O1N1O3M3M2O1N101O0O101O00001N101O00001O00000O10001O00000001O00O2O00000000001N100001O00O1001O10O01N101O0O101O1O001O1N101O0O2O1O1O1N2O001N2O1N2O1O2M3N1O1O2M3M2N4K5K5K6G9F9H9@hg`3FbQ^L0ggU8\", \"size\": [1280, 720]}, {\"counts\": \"]Vm<1W10oU2d0PPO8K4L?A6I8I9H5K4L3M2O2M2O1O0O2N2O1N100O1000000000000O101O00001O000000001O0000000O101O00000000001O01O00001O00001O001O001O000O101O10O01N101O001N2O0O2O1O1O1N101O1N2O1O1O0O2N2O1O002M2O1N2N2N2M3F;G:H9E:Fk`W;\", \"size\": [1280, 720]}, {\"counts\": \"eWm<1^o5`0eWKf0A`0B<E8I2N3M3N2L3O001O0O3N001O00001O00000000000O1000010O0000001O0000000O11O01O00001O01O1O001O1O1O01O01O1O001O001O000O2O001O1O001O0O2O1O001N2O001O1O1O1N101N2O1N2N2N2N2O1O0O2N2O1M3N2N2M4^Ob0K5H9J5L\\\\XV;\", \"size\": [1280, 720]}, {\"counts\": \"Z_e<3aW1c0F;E`0B7I5K5J4N6I2N2O1N3N001O1O1O0000001O0000000000001O0O11O0001O00000001O01O01O00001O0010O01O00001O001O1O1O001O001N1O2O001O1O1N101O1O2M101N2O1O1N101O1O0O2O1O1N2M2N3N2O0O2O0O2N3\\\\Od0K5N1N3N1OUhg;\", \"size\": [1280, 720]}, {\"counts\": \"a_l;`0^W1V1iN6K3N2N2M2O100O103L3N1O0O101O01O00000001O0O101O0000000001O0001O10O010O001O001O00010O0O2O001O1O001O1O001N2O000O2O1O1O1O1O2M2O1O1O0O2O1O1O1N2O1O0O2O1O1N2O000O2O001N2]Od0L3N2N2N3M3Jeof<\", \"size\": [1280, 720]}, {\"counts\": \"ioZ;h0UW1d0]O6J6K4M1O1N2O001O0000010O1O0001O010O001O001O01O1O1O01O010O001O1O001O00100O1O001O1O001O1O1O1O1O002N1N101O0O2O1N2N2N1O2O001N2O0O100O2O1N2O001O4K7I3M2N3LPgh=\", \"size\": [1280, 720]}, {\"counts\": \"Z`X;4=3\\\\V1T1F9I5M4M3M2O1O1O1O1O101O0O1O01O010O001O1O001O1O1O0O1N3N101O1O1O001O1O1O1O1O001O1O0O2O1O1O1O1N2O1O1N1N3N1O1O2O0O2O1N1N3I8K5L3O1N3M3M2NX_V>\", \"size\": [1280, 720]}, {\"counts\": \"e`];`0UW1b0E9H6J4M3N1O3OO01O101N1O2O000O10O1O1O001O1O0O101O010O1O1O001O1O1O1O001O100O1N101O1O1N2O0O2O0O2O0O2O00001O2M3M2I6M5L3N2M3KQoX>\", \"size\": [1280, 720]}, {\"counts\": \"cXf;i0nV1;K5J5L4M2N3O2N1O2N2O0O3N3M0OO2O001O0010O010O0100O1O001O1O100O1N2O001O1O1O001O1O001O001O001O001N3K4J6I8L4L3N3M3N8G4LdnS>\", \"size\": [1280, 720]}, {\"counts\": \"oWa;m0nV1c0@5K3N2O1O001O2N1000O01000O010O10O01O0O2O001O10OO2O001O1O0O2O001O1O1O1N101N2O0O1000001O0O3L3N2N2N3N1N3L4L4M2LZg\\\\>\", \"size\": [1280, 720]}, {\"counts\": \"Z`X;g0RW1<G8J4M5K4M2N2N2N101N2N01O001000O010O01O001O0010O01O00100O1O0O2O1O1O1O1O001O1O1N2N2M2O1O2L3M5J5M4N1N7I6GQWi>\", \"size\": [1280, 720]}, {\"counts\": \"WhT;c0ZW18I7J5J8I3M2O1N2O2N1O3M1O2M20O000001O1O010O000010O0010O01O10O0100O001O1N102N1O1O1N2O1O1N2N2N2N2N2N2N3M2O2N8G4K7Hkng>\", \"size\": [1280, 720]}, {\"counts\": \"XXW;8eW1=B7K2O2M3M2N2O1N2O1N2O10000O10O1O010O100000000O1N20000O100O101N10000O2N1O100O2O2M2N2N1O2N1N4J[hP?\", \"size\": [1280, 720]}, {\"counts\": \"a`b;9bW17B>L3L5L5L3L8I3M1O1O2O0O10001N10001O001O0000000000000000000O20O0O101N100O2O1O1O1O2N2N2M3N2M3L4M3L9^O>Jd_`>\", \"size\": [1280, 720]}, {\"counts\": \"cXf;8bW17F:L3M4L4M4M3M5K3L3N2O1N3M2OO1M3M2D=N2N2O1N2O1N2O1O1O1O100O100O10001N100O3N1O2N1O1O2M3N2N2N2M3Nl_`>\", \"size\": [1280, 720]}, {\"counts\": \"fXa;7fW15K4J6G8M5L5K6K3M2N2N1N200O1O2O000O100O1000001N10O100000001O3L4M:F4L2N2M3N2N2M3M3N2O000O1N3M2M4E[_e>\", \"size\": [1280, 720]}, {\"counts\": \"Sh^;4kW1<XhNJPW1f0M3L3M5L2N2N2N1N2O1O00001O0000001O00000000001O001OO010000O10000O00100O1O010O11N101N3M3N2M4M2M3M4L6H:[OkhN0`Vd>\", \"size\": [1280, 720]}, {\"counts\": \"\\\\ik<2ZW12lhN:oV1>K6H7L4M3K40O100O00102M2B>Ie0_O^ob>\", \"size\": [1280, 720]}, {\"counts\": \"ihh;1lW15J6H8H8K4L5M3M3L5L3N1N1N3N2N3M10001N100000000000O011O0001O01N100000001N101O1O1O2M2O2N1N3N2M4L3G9K8Ge_[>\", \"size\": [1280, 720]}, {\"counts\": \"cPT<7fW14L4L4L4J7K5M3L5L6J4L3M1O2N1N2O2N10000O10001O00000001O1O00001O2N=C5K4K4M4L5K4L3M2M3N3L4L4Le^V>\", \"size\": [1280, 720]}, {\"counts\": \"b`[<8eW15L3L4I8K4M4L5K6J6K3M2N2N2N2N100O2N10000O10O1000000000000000000O1000001O1N101O000O2O2M3N1O2M4M2N2M4K9H7\\\\OlWf=\", \"size\": [1280, 720]}, {\"counts\": \"dX_<7hW13M3L3M4K4L5K6K6I8J2N2N1O1O2N100O1O100O10000O1000000O100001N10001O00001O001N101O010O2N1N2O1N3N2M6J6J5Ic0YOU_b=\", \"size\": [1280, 720]}, {\"counts\": \"h``<4hW18I5L4L4L5I8J9H2M3N3M1O101N1O2N1O2O0O1000000O2O1OO100O1O2N1O2O01O01N101O001O001N101N2O1N3N1N4L4M3K5L9]Ob_b=\", \"size\": [1280, 720]}, {\"counts\": \"hPc<3jW16K4L5J8G9I8H4L4M1O2N1O2N100O1O100O2O0O10001N1000O10O0101O00001O001O001O0O2N2N101O002M2O1O2M5J4K6F;AhWa=\", \"size\": [1280, 720]}, {\"counts\": \"g`e<6gW15L5J`0B8G4L3M2N2N3N1O1O100O2N1O100O101N100O1000O01000000000000001O001O1O1O1N1O1O2M2O2N2M3M3M4L4I9E=Ecg^=\", \"size\": [1280, 720]}, {\"counts\": \"h`j<Q1mV14M2K5N2M2O2M2M3O1O2O03M8H4L6J4K2O2N1O1O1O1N2O1N100000001N2N2M2O2M2N2M3O1O2N1000cVf=\", \"size\": [1280, 720]}, {\"counts\": \"oXd<6gW17I9H7J6J3L3N2N2N1N3N1O100N3N1O1O2O000O1000000O11O0O100000000O10000O101N3M3L201N2O1N2N2O1N3M3M2N2M4LYo_=\", \"size\": [1280, 720]}, {\"counts\": \"Sia<<bW14L9G5J5M2N2N1O2N1O2N100O2O0O2O1O0O2O00000O1000000001O0O101O00000O101N101N1O2N2O0O2N2O1N1O2N5J5L3L5K4JmVa=\", \"size\": [1280, 720]}, {\"counts\": \"XQc<7gW14M2N7I4M2M2O2M2O0O2O0O2N2N3M2N2N2O0O10001O0O100000000000000001N100O100O2O001N2O0O2O1N2N2N1O2N2N1O2N3L4L4M3J6HZ^d10[jP:\", \"size\": [1280, 720]}, {\"counts\": \"Vao<9eW14L3N5K4K4L4M1O2N2N1O2O1N2N2N101N2O1O1O001O000000000000000000O2O00000O2N100O100O2O2N1N2N2N2N3M3L3M3L7Go^S=\", \"size\": [1280, 720]}, {\"counts\": \"c`m=2mW13]iN0QU12kiNJj07ZU17djNJ\\\\U17`jNL`U15VjN4jU1ORjN2nU13kiNOUV1n0000000001O000O10000O2O0O100O101N2O1N2N2N2N3M3L4M4L4K5J8FhVh<\", \"size\": [1280, 720]}, {\"counts\": \"\\\\Q\\\\=?^W15L3M2O1O2M2O2M2O2N2N2N1O2N2O0O2O1N101O0000000000000000000010N1000000O101N1O10001O1N2N2N3M2N2N3M3M4J5L6Bmfe<\", \"size\": [1280, 720]}, {\"counts\": \"Wa^=4iW16L3M2M2M5L3M3M2N3M2O2N1O2N2N2N2O1N2O1O0O2O001O00001O001O00001OO100O1000000O2O000O101N2O0O3M2N2N2M4M2N3M3L7J3M4K6Jjn\\\\<\", \"size\": [1280, 720]}, {\"counts\": \"hXl=4iW15L6K4K7H6K3M3N1O2N1O2N1O2N101N10001O0O2O001O01O01O0000000000000000O2O000O100O101N2O1N2N3M2N3N1N3M4L4K6J8GUWT<\", \"size\": [1280, 720]}, {\"counts\": \"cXl=8fW14L4M4L4K5K3M3N2N1O2N2N2N1O2O1N1O2O1N101O0O2O000000001O0001O0O1000001N100O2N1O101N1O2O1M3N2N2N2N3L3N4L3K9I]WT<\", \"size\": [1280, 720]}, {\"counts\": \"^`m=5iW19G4K4M4L4L3N2N2N2N1O2N2O1N2O0O2N101N2O00001O001O000000000001O000O10001N1O101N1O1O2N2O1N2N3M2O2L4M4L5J6IcWT<\", \"size\": [1280, 720]}, {\"counts\": \"UPU>?_W17I4N1N3M2N2N1O2N2N2N2O0O2N2N101N101N2O1O0O2O00000001O000000000O101N100O101N1O101N101N2O1N2N2N3M3L4L4L4K7Gk_k;\", \"size\": [1280, 720]}, {\"counts\": \"^PP>2iW1?D3N2M3N3M2N2N2N2O1N2M3N2N2N101N2O001N2O001N100000001O0000O2O00000O100O1O2O0O1O100O2O0O2N2N2N2N2N3L4K4M4L4KlWo;\", \"size\": [1280, 720]}, {\"counts\": \"]hi=>`W14I6M3N1N3M3N3M2M3N2O1N2N2O0O2O1N2N101O1O001O0000000001O00000000001N1O1O100O2O0O101N2N2N2N3N1N3M2N3M3L4M3MggV<\", \"size\": [1280, 720]}, {\"counts\": \"_Xg=7eW1;C8L3M3M3M3M2O3L3N2N1O2N2O001N101O0O2O001O001O0000000000000001N100O101N100O2O1N2N2O1N2O1N2N3M2O2M3L4L7H7JdWY<\", \"size\": [1280, 720]}, {\"counts\": \"chd=7fW15K6K8D7N3M1N3N2M2O2N2N1O2N2O0O101O000O2O001O001O000000O101O000000000O101N100O2O1N2O1N2O2M2O2M2N2M6J6D<J:GXg[<\", \"size\": [1280, 720]}, {\"counts\": \"iP\\\\=1mW18H5L7H4L3M3M3L3N3M3N2N2O1N101O1N1O2M30O01O01O1O1O1O011M2O1O001O001N3M2O1N2O1N3N2M2O1O1N1O2O0O2N1N3G<FYof<\", \"size\": [1280, 720]}, {\"counts\": \"hXX=3iW16K4M7K6I2N2O1N2N2N1N3O1O001000O010O101N1O1O1O1O1O1O2N1O1O1O1O1O1O1O1O1O1O1O1O1N3N2N1N3M2N2O1Ndfo<\", \"size\": [1280, 720]}, {\"counts\": \"ghZ=6gW17L4K6H7J2N3K4L5N2N2O1O01O010O101N010O01O01O002O0O1O1O1O2N1O1O2M2O1O1N101N2O1O1N101O0O1O2N2O1N2M3N6Gm^i<\", \"size\": [1280, 720]}, {\"counts\": \"bYQ>1oW11N10001N1000001OO01WNKgkN5YT1NekN1ZT11ekNO\\\\T11bkN0^T11`kN0aT10]kN1cT11ZkN1eT10ZkN0fT11XkN0iT10UkN1lT1ORkN1QU1OljN2WU1LfjN6\\\\U1IajN8`U1IYjN=hU1CQjNb0QV1e02N2N3N0O100O4L4I9Fd_d<\", \"size\": [1280, 720]}, {\"counts\": \"nP\\\\=1nW13M3N2N3M2O0001ON2N2O100O1O1N1O2N100O100O1000001TOXO^jNh0aU1[O]jNe0cU1]OZjNd0eU1l000000000000O101O000O101N2O0O2O1N2N3N1N3L5J8G9H^of<\", \"size\": [1280, 720]}, {\"counts\": \"iX]=1jW17L3M3N3L4K5I8K4L2N3N2N2N2M201N1O1O100O2N1000000000000O1000000001O0000001O000O101O1N2N101O1O2M3N2M3M3L8G7GfWc<\", \"size\": [1280, 720]}, {\"counts\": \"ah_=9dW15M1N4I8J6K4L4L3N2M2O2N1O2N1O1O1O2O0O1000000000001O0O100001O0O101O00000O100O2O1N2N2O1O1N2O1N3M4L4J6J<DdWc<\", \"size\": [1280, 720]}, {\"counts\": \"bP\\\\=8eW15M3M1O2M5H7L4M2M3N2N1O2M2O2N101N1O1O2N101N100000001O000O101OO10001O001N10O11O0O1O2O1N2N101N2O2M4L3L4L8G9G;E\\\\Wc<\", \"size\": [1280, 720]}, {\"counts\": \"]`^=8eW16J6K4L5K3M3M3M3N2M2O2M2O2N1O1O2O0O2O000O101O0000000000000000000000O10000O2O0O3N0O1O3M4M2L6J5J7J7Flof<\", \"size\": [1280, 720]}, {\"counts\": \"^h_=3fW1>G6K2M4M2L4N2M2O2M2O2N1N3N1N200O2O0O101O0O2O000000001OO100O11O00O101N100000000O2N101N1O2N1N7J6I6I7ES`d<\", \"size\": [1280, 720]}, {\"counts\": \"U`^=>bW18G3M3L2N3M3N2M2O2M2O2N1O2N1N200O2N101O0O2O1O0000O10O2O000000O10000O2N10000O101O0O2O1N3N1N3N2M3M3M4K5H9EQXc<\", \"size\": [1280, 720]}, {\"counts\": \"YPW=6hW14L4M9G4K3M3N1N3N2M2N3N2N1N2O2N1O101N1O2O000O100O101O001O00O1O2O0000000O10000O2O0O101N2O1N2N2M3M3M4M3L3L5J6GYXh<\", \"size\": [1280, 720]}, {\"counts\": \"X`T=9dW15N4J8H4L2N3N2M3N1N3N1N3N1O1O2N1O100O101N101N10000000000000000000O101O0000000O2O0O1O2N2O0O2M3N3L3M4K4M4K5J:CUhj<\", \"size\": [1280, 720]}, {\"counts\": \"\\\\XS=7fW19H7I4M2M2N3N2M2O2N1O1N3N1O1O2N1O1O2O0O100O1000001O0O10O10010O0O10000O100O2O00001N2N2O0O2O2M1O2N2N3J5I8K6ISXm<\", \"size\": [1280, 720]}, {\"counts\": \"ihT;1\\\\gj1;W`VN5J6K3L4L3M3N2N2M2O2N1O1O2N1O1O2N100O101O0000000000000O1000O1000001N10001O0000001O1N2O1N2O2M2N2N2M2M5H8J>AmoP=\", \"size\": [1280, 720]}, {\"counts\": \"U`j<6iW14K5K5K6J5K4M2M3M3M3N1O2N1O1O100O101N1O1000O11O0O1000O1001O000001O0O100000001N10001O0O2O1O1N2O1N3M2N3M2N3M2J9HU`S=\", \"size\": [1280, 720]}, {\"counts\": \"UXi<7hW14L4L4K5K5K3N2M2O2N3L3N2N1O2N1O1O101O0O10000O10001O000001O00000O100O1001O01N1000001N2O0O3N1O2M2O2M3H9F:H9ERXW=\", \"size\": [1280, 720]}, {\"counts\": \"TPm<4kW13L4L4K4M4M2M4L5J4N2N2O0O2N100O1O2N100O10001O0101O1N2N2N4M3L5K6K2M3M3L6K4I[_g=\", \"size\": [1280, 720]}, {\"counts\": \"U`T=1mW13M3L6J5K5J8I6K4M2M3N2N1O1O1O1O1010O002N3N3L2N200N3M3M3M3M3M2N1O2N2N3M3M4LZo_=\", \"size\": [1280, 720]}, {\"counts\": \"R`Y=6gW16K4K5L8H4K5L3N2N2N2N2N2N100010O1O2N1O2N2N2N2N3N2M3M2N3L3N1O2N2N4L4L2N4KVW\\\\=\", \"size\": [1280, 720]}, {\"counts\": \"S`Y=5hW16J5K5L5N2M3M2000O1N2O1O1O0O1O1O1O1O5K3N1N3M2N3M1NToi=\", \"size\": [1280, 720]}, {\"counts\": \"noV=9bW17H9I8I7J5K4L4M2N2N2M201N10001O001O0001O1O3M3M3M3M2N1O2N2M2O3M1O1O005K3M2N2N1N2O2M3M3M6I^gT=\", \"size\": [1280, 720]}, {\"counts\": \"koQ=4aW1d0A8J8J6K4K3N2N2N3M2N101O1O000O2O1O00000000000000000000000000000002N4L4L6J6I9H7I5TOnhN`0`W1G8HYoZ=\", \"size\": [1280, 720]}, {\"counts\": \"mWn<;YW1`0I5L4L5L4L4K3N3N1N2O1N101O000O1000O10000001O0000000O1001O013L6J4L6I5L2N4L2M5L2N2N3L5J:FY_]=\", \"size\": [1280, 720]}, {\"counts\": \"nog<;_W1<E:H7K4K3N3M2N1O2N1O2O000O2O0000000000000000O1000001O000000000O200O1O001O001O1N3N1N2O2M3M5J5C>E<Go_]=\", \"size\": [1280, 720]}, {\"counts\": \"SX_<c0YW19I4K5L4L4K4M4L3N1O1O2N100O2O0O1000000O010000000010N100O101O0O1000001O0O101O1O0O2O002M2L5K7J4J8H9ETXf=\", \"size\": [1280, 720]}, {\"counts\": \"jXZ<=`W17I6J5K4L4L3N2O1O1N2O3M2N2N1O2N1O100O2O00O1O1000000001O0001O001O001O1O0O1O2O0O2N2O1N2M3M3M2N5K4J:EeWk=\", \"size\": [1280, 720]}, {\"counts\": \"mPT<;_W19H7K5L2N2N2O2N1N3M2O2N1O1O1O1O1O100O100O2O00000000000000000001O001O0O2O0O2O000O2O1N101N2O1M4K7G9F=F_WP>\", \"size\": [1280, 720]}, {\"counts\": \"fhW<;`W1>D6L2M3M3N2L3N3M2O1O1O2N1O100O1O10001O000O1001O0000001O001O000O2O000O2O001N2O0O2N2N2O2L4K6K6I8Ddon=\", \"size\": [1280, 720]}, {\"counts\": \"mX_<3lW14L5L3M2N2M2N1N3N101O0O1O2N10000O1000000O1N201N101N10O01N2O1O1N2N2N2N1O2O0M3M38H4L4K5K5J7I9E<E[gh=\", \"size\": [1280, 720]}, {\"counts\": \"RQm<3mW14L3M2M101O0010O01OO01010O01O0O1001O00000O100000001O00001O00001N1O2O1N2Klfh=\", \"size\": [1280, 720]}, {\"counts\": \"ghk<e0VW17K4L4M3M2N3M4L3N2N1O2N101N1O101O0O10001O00001O000001O001N100O2O001O001O1O1N2M3L4M3N3L4L6J6J9FZW\\\\=\", \"size\": [1280, 720]}, {\"counts\": \"S`j<d0WW18J5L3L5L4K4N3M2N1O002O0O2O0000000O10010O1O001O00000000000001N1O2N2O001O1N3N1O1N2M3L5K5L6K3L9G:CfW\\\\=\", \"size\": [1280, 720]}, {\"counts\": \"ehk<8XW1c0K4K4M4L3M4M2N3M2M3O1N2O0O10001O0O2OO101O01O000000O1001O01O1O1N2O001O1O002N1N3N2M3N2N2M2M4L3J7I:GcgY=\", \"size\": [1280, 720]}, {\"counts\": \"gXi<:]W1:F:L5M1N2N3N1O1O2O2M2N2O0O3NO10000001O0000001O001OO100O2O00010O1O2N001O1O1O1O1N3N1O2M3M3M5J9Cag^=\", \"size\": [1280, 720]}, {\"counts\": \"_Ph<a0^W18G2N3M2N3N2L3M3O1N21O01O003M00000000000010N1000010O001O0O2O10O01O1O1O1O001O2N2N1O2N2M4L7F[_b=\", \"size\": [1280, 720]}, {\"counts\": \"ahf<9`W18M2OO1O1O1O1O101L3NR`o>\", \"size\": [1280, 720]}, {\"counts\": \"Qia<8aW18I7J7M2M2M3O2M2O2N1O00100O2O0O1O2N1000001O001OO10000010O00O101O01O01O1O2N1N6K4K5L2N5K5J6Jnfh=\", \"size\": [1280, 720]}, {\"counts\": \"]Y_<`0YW18J7K4M3M4L2N2N111N100O101O0O100O101N2O0000001O1O001O0O100O01eNniNh0VW1^O;F:ElmX>\", \"size\": [1280, 720]}, {\"counts\": \"gPY<`0XW1:J6J5L3O2M2N3N101N2N1O2N1O2O1N10001N11O0000000000000000000000001N1000003M1N3N2N2M3N2M2N2O1N3M3M3K7E<E]oi=\", \"size\": [1280, 720]}, {\"counts\": \"SY_<6fW16H8I6K6K4L5K3N3M3N2N2O1N101N2O0O2O0O2O000000001O0000O10000O2O000O101O001O1N2O001O1O1N2O2L5L3L3N5K5K4K6GX_b=\", \"size\": [1280, 720]}, {\"counts\": \"khf<6cW1>D8I5L5K4M2O2N2M3N2O1N1O2O0O1O2O000O1000000000O2O0000001O0000000O2O1O1N101O1N2N2O1N2N102M2O2M3M5J6K4L7GWoZ=\", \"size\": [1280, 720]}, {\"counts\": \"XiP=1^W13khN2PW15ihN2QW1b0L4L4L3N1O2N2N1O2N2O1N1000001N101O00100O00O100000000O1000001O0O2N101N2O1N4M1N2O1N3M3M3M4L2N3M4L5I8IlnP=\", \"size\": [1280, 720]}, {\"counts\": \"dPR=98LmV1h0K5J4M3O2N3M1O2N2N2O0O101N1000001O0O101O00000010N100O10001O001O001N2O1O0O2N2O1N2N3N3L3N2M3M4L3L6K5IUWR=\", \"size\": [1280, 720]}, {\"counts\": \"ShU=:eW19H4K4M9QiNPO\\\\V1S1_iNPO`V1Z1N1O00000000000000O11O1O000000001O000000O2O1O001N2N3M1O2O2M3N1N3M3M3M4K5K5K4MSoU=\", \"size\": [1280, 720]}, {\"counts\": \"h`T=8[W1b0E8L4L3M3N2O1O10O01O0010O00010O01O001O00000001O00O10000O1000O100O0010O02O1N2O1N3M2N3N1N2N3M4K4M5J6I9IS_n<\", \"size\": [1280, 720]}, {\"counts\": \"mhU=3gW1<C:G7K5L4M3N2N101M3N1O1O2O0O2O001N101O001O01N1001O000000000001O001O1N101N1O2N2N102M2O1N2O1N3M4L4L3M4L5J5K6JU_i<\", \"size\": [1280, 720]}, {\"counts\": \"\\\\aY=4kW14L2N2N2N2O1N2N2O0O2N2N2O2M2N101N2N1O2N1O100N3N100O001O1O100O010O0001O1O010O1O1O2N2N2M4M3L5M2M5L4K4K8He^i<\", \"size\": [1280, 720]}, null, {\"counts\": \"SiU=3fW1<G5I7K6K3M4N2M3N2O1N1O2O1O0O2O1O0000001O001O001O000000000001O00000O101O0O2O1N2N2N2O1N2N3L4M2N001O4M5I<EQok<\", \"size\": [1280, 720]}, {\"counts\": \"kPW=:`W1:F7L4H8N2N2N2O0O2N2N102M101N2O1N101O1N2O01O01O00001O0001O001N100O2N101O001O1N1O2O1N2N2N3M2N4L3M2N4L4L4K6K5HTWh<\", \"size\": [1280, 720]}, {\"counts\": \"c`Y=8aW1:C;K5L3N3N2N1N3N2O1N2N2N2O1N101O0O1000001O0000000001O00000000001O001O001O1O0O2O2N1N101O1M2O101N1O2N2M4K9ROcPg<\", \"size\": [1280, 720]}, {\"counts\": \"XPa=3iW15O1O2N2N1O3M1O1O2N2M5L100O00010O2N1O1O1O0O2OROUiNh0jV1WO\\\\iNd0cV1\\\\OaiNb0^V1^OdiN`0\\\\V1_OeiNa0YV1AgiN?XV1BhiN>XV1CgiN=XV1DgiN=YV1CgiN=XV1DhiN<WV1DjiN;WV1EiiN;VV1FjiN:UV1FliN9TV1HliN7UV1HliN8SV1HmiN8SV1ImiN7SV1ImiN7SV1HniN7UV1GkiN8XV1FiiN3_V1KciN0`V1Oi001N2O00PPb<\", \"size\": [1280, 720]}, {\"counts\": \"SQk=1nW13M3N1O0100O01O1O1000O1O1O010O1O2N1O1Nnf30RYL2O0O3N2M1O1O2N1O3N1N2O0O2N2O1O000100O2O2M8Gb__<\", \"size\": [1280, 720]}, null, {\"counts\": \"_XQ>;aW15K6M2O4L7I2N1O010O5K100O00001O01O1O1O0010OO2O1N101O2N1O2N1O1N2O1N5K5K2N2N101Omf`<\", \"size\": [1280, 720]}, {\"counts\": \"hPP>6gW1:D7H8K4M4L4L3N3M2N1O1O2M3N1O2O0O10000O10000O10001O0001O0001O01O0O101O2M2N200O1O1N2M3M3M3M3N3M5J4L`0]O[WT<\", \"size\": [1280, 720]}, {\"counts\": \"n`h=1nW15I5J5K5L5J5K6K4M4L3N2N3M2N2M2O1O001O1O100O2O0000000000000000O2O0010O000001O001O0O2O001N2N2M2N3L4L5M2M6KlWY<\", \"size\": [1280, 720]}, {\"counts\": \"jWg=?`W14L3M3N4L2M4M2L3L3O101O0O10001O0O100O1O1O2N1O100O100O10000O10O0100O100O1O1O11O0O1N4K6J5L4L4M4J8G_`_<\", \"size\": [1280, 720]}, {\"counts\": \"[`Y=8fW16K3L8H3M2M4L5L3L3N3N1N3N2N2N2N101N1O2N1O10001O0O100000000000O101O0O2O00001O001O0O1O1O2N2N2M4M2M4M4K5J;Ed0]O^of<\", \"size\": [1280, 720]}, {\"counts\": \"ZPU>7hW13M3N0O2O0O2N2O000O1O2N2N101N2O0O2O0010O2O1N3M5K4L4L\\\\Wm<\", \"size\": [1280, 720]}, null, {\"counts\": \"c`^=7eW15M2M3M4M2O1O1O1O1O1O1000000001O0O110O001O1O00100O01001000O2N9H0O1N11N1N2N1N3N1O3L3M3M3K4Faok<\", \"size\": [1280, 720]}, {\"counts\": \"Pa^=>[W18K5L4L4M4L4L4M3M2M4N1N1O2O1O00O1001N2O10O0O1001O00O1O002O01O2NN2O2O001O1O1O100N3M2N2K6J6K8I6I;EQWh<\", \"size\": [1280, 720]}, {\"counts\": \"d`Y=1jW1<E8F9K5J5L3M4M2N2N101N2N2O0O2O1O0O20N100O100000001O000000000O2O00001O001N2O1O1O1O001O1O1N2O2L7H6G9G:I`_i<\", \"size\": [1280, 720]}, {\"counts\": \"[hU=8`W1=H6J6K5K4M4L3L2O1O2O001N101O1N2O1O001O001O000001O0000000000001O0O2O1N3N001O1O1N2N2O1O1N2O2M2M5I:H6_OjhNOfW1LTWm<\", \"size\": [1280, 720]}, {\"counts\": \"]XS=8]W1>I7J5M3M3M4L2M3N2O0O101O1O0O2O1O1O001O001O1O0000000000010OO101O001O002M101O1O1O1N2N2N2O1N3M2O1N2L7I9[OnhNMgnP=\", \"size\": [1280, 720]}, {\"counts\": \"``o<6^W10chN:SW1`0I7K3L3N2N2N1O2N101O1N2O1O1O0O101O001O01O01O00001OO100000001O1O1N2O1O001O1N102M2O1O2L3N3M2N3L3M3E=Gk_S=\", \"size\": [1280, 720]}, {\"counts\": \"chP=2]W16dhN6TW1`0K5L3L5L101N1O2N101O1N2O1O001O1N101O01O000001O0000000001O001O1O0O2O001O1N101O2M2O1O1N3N1N3M2M4L6^OR`S=\", \"size\": [1280, 720]}, {\"counts\": \"QhU=g0UW16L5K5L3M2N2N1O2N100O2O001O1O1N2O001O001O000000001O001OO101O1O001O001O2N1O001N2N2O1O1N2O1N3N1N3L6K5Jb0]OWWm<\", \"size\": [1280, 720]}, {\"counts\": \"d`Y=3hW1;C;H7K5K3M3N2N1O1O2O0O101O1O0O2O1O001O0000001O0000001O00000001O001O001N2O1O001O1N2O0O2O2N1O1N3N3L3L5K5JaWh<\", \"size\": [1280, 720]}, {\"counts\": \"^h_=?[W19I7J5K5L4M1N2O100O2O0O2O1O001O1N101N100000001O0001O1O1O0000001O001O001O1O1O1O1O2M2O1O1O1N3M2N2N4K5]OR`d<\", \"size\": [1280, 720]}, {\"counts\": \"VPk=d0WW17M2N2N4L5K3M2O001N101O1N1O2O1N1O2O0O101N100O101O0O1001O1O1N3OO01N101O1O1O1O1O1O1O2M2O1N3L6J6J9WOng[<\", \"size\": [1280, 720]}, {\"counts\": \"aXl=6eW19E9L3M4L5K5L2N2M201N101N2O0O2N101O1O001O00001O0000000000000001O1O001O001O1O0O2O2N1O1O1N3N1N3M3M3M4[OVPX<\", \"size\": [1280, 720]}, {\"counts\": \"\\\\`h=a0[W17J5L2N3M2O1N4M2N3M2N2N101O1O1O1N2O000O101O0000001O000001O00001O00001O1O0O2O1O001N2O1N3N1M4M3L6B?Gfo\\\\<\", \"size\": [1280, 720]}, {\"counts\": \"YXb=:bW17K3M3N3M2N3M3M2N1O2N2O1O0O2O001O001O00001O000000001O00000001O0O10001O0O2O1O1O0O2O1N1O3L3M3M3L8Co_d<\", \"size\": [1280, 720]}, {\"counts\": \"YhU=9cW18K2N2M3O1N2O1N1O2N3M1O2O1N101O1O00001O00001O00000000000000O2O000000001O0O2O0O2O0O2N101N1N3M3N2Ld0]O^go<\", \"size\": [1280, 720]}, {\"counts\": \"YPh<=aW15L2N2N2N2O1N2O1N2N1O2N1O2O1N101N1000001O0000001O0000000001O000O1O2M2O1000001O001O1N2O001N2N2N2N;Cd_]=\", \"size\": [1280, 720]}, {\"counts\": \"UP^<2kW16XhNK\\\\W1e0F4N2N2N2N2M3N2N2N2N1O2N1O101O0O1000001O00000000000000000000O1000001O000O101O0O2N2O1N3M2O2M3N2M4M5YOmhN2TWf=\", \"size\": [1280, 720]}, {\"counts\": \"Z`[<1kW18J7I8H3N2N2O1M3O1N101K4O2M2O2N1H8O2N1001O001O0000000000001O00000O101O0O2O1N2O0O2N2N2N3M3M3M4G:CZXk=\", \"size\": [1280, 720]}, {\"counts\": \"_`Q<2hW1<F8J6J3N3L2N3M3N2N2N2N2N2N1O2O0O2O0O2O001O00000000001O00O011O0010OO10000O2O0O2O1O1N3M2M3N3L4J6K5K5HRhR>\", \"size\": [1280, 720]}, {\"counts\": \"_Po;8fW15L3J6K7K2M2O2L4N2M3N2N2O1N2N2O1O0O2O0O2O001O00000000010O000001O0000000O2O001N2O1O1O1N3N2L3M3M3F:M5KjWU>\", \"size\": [1280, 720]}, {\"counts\": \"aXU<5gW18I5I6L4M7I3N1O2M3N2N2O1N2N1O2O001N2O1O000000001O0000000000000001O00001N2O001O1O2M2N3M2N3M3M2N3L5Ea0Bcon=\", \"size\": [1280, 720]}, {\"counts\": \"hhW<2jW16L4L4K5L5I6L4M1O2M2O2M3N2O0O2O1N2O1O001O001O0O101O00000001O00000000001O0O2O001O1O1N2O2M2O1N3N2M3L4L5D=G9I[gh=\", \"size\": [1280, 720]}, {\"counts\": \"kh\\\\<:cW16K5K4K5L3N1N2O2M3M3N2N2O2N0O2O1O001O001N2O000000001O000001OO100001O001O001O001N2O1N3N1N3M3L4M2M4L4L4ZOmhN0e^g=\", \"size\": [1280, 720]}, {\"counts\": \"Qae<6eW1<E5M4K4M2O2M3M3N1O2M3N2O0O2O1O001O1N2O0000001O0000000000010O000000O2O001O1O2M101O2N1O1M4M3M3L4K4M7@]g^=\", \"size\": [1280, 720]}, {\"counts\": \"TiP=8\\\\W1?K3L4M3M5L2N1O2N2N2O0O2O0O2O1N101O001O001O000000001O0O11O00001O001O1N2O001O0O2O2N1O1N3N2N1N3L4L7FVgT=\", \"size\": [1280, 720]}, {\"counts\": \"SaY=<\\\\W1:K4L3N2N4L4M3M101N2O1O1N101O001N2O001N1000100O0000000001O00001O001O1O001N2O1O1O1O1O1O1N2N2N3M7H9@Rok<\", \"size\": [1280, 720]}, {\"counts\": \"Va^=5eW1:F8L3L5L4M2N3M2N2N2O001N2O001N101O001N20O000001O00000001O00001O001O001O1O1O1O1N2O1O1O1O1N3M5K3M2CQiNASVh<\", \"size\": [1280, 720]}, {\"counts\": \"[Y]=4gW18J6K7H7J5K4M2N2N1O101O001O001N101O001O001O00001O00000000O20O0001O0O2O1O001O1O1N4M1O1O1N3N2M4L3L4BoVh<\", \"size\": [1280, 720]}, {\"counts\": \"PRU>4jV1NoiN`0iU1DPjNa0oU1CdiNf0\\\\V1>1000001N2N2N101O001O10O01N2O1O2M2O2M4JenP=\", \"size\": [1280, 720]}, null, null, null], \"bboxes\": [[291.0, 509.0, 55.0, 70.0], [283.0, 498.0, 58.0, 76.0], [281.0, 482.0, 60.0, 80.0], [279.0, 481.0, 66.0, 82.0], [275.0, 483.0, 73.0, 78.0], [268.0, 473.0, 82.0, 83.0], [269.0, 471.0, 84.0, 86.0], [273.0, 474.0, 90.0, 89.0], [278.0, 462.0, 93.0, 100.0], [285.0, 449.0, 99.0, 105.0], [302.0, 451.0, 100.0, 107.0], [320.0, 447.0, 191.0, 311.0], [330.0, 449.0, 102.0, 309.0], [330.0, 455.0, 103.0, 303.0], [324.0, 463.0, 95.0, 93.0], [304.0, 482.0, 90.0, 86.0], [290.0, 496.0, 77.0, 73.0], [288.0, 493.0, 68.0, 76.0], [292.0, 508.0, 62.0, 71.0], [299.0, 508.0, 59.0, 74.0], [295.0, 501.0, 56.0, 68.0], [288.0, 507.0, 53.0, 72.0], [285.0, 507.0, 57.0, 73.0], [287.0, 495.0, 48.0, 48.0], [296.0, 495.0, 52.0, 68.0], [299.0, 496.0, 49.0, 63.0], [295.0, 497.0, 49.0, 63.0], [293.0, 504.0, 53.0, 64.0], [329.0, 513.0, 17.0, 53.0], [301.0, 499.0, 51.0, 65.0], [310.0, 497.0, 46.0, 67.0], [316.0, 496.0, 53.0, 69.0], [319.0, 501.0, 53.0, 63.0], [320.0, 505.0, 52.0, 68.0], [322.0, 505.0, 51.0, 66.0], [324.0, 506.0, 51.0, 66.0], [328.0, 518.0, 41.0, 58.0], [323.0, 517.0, 51.0, 60.0], [321.0, 527.0, 52.0, 54.0], [322.0, 497.0, 99.0, 89.0], [332.0, 531.0, 52.0, 53.0], [356.0, 529.0, 37.0, 54.0], [342.0, 534.0, 53.0, 51.0], [344.0, 523.0, 58.0, 56.0], [355.0, 512.0, 54.0, 55.0], [355.0, 508.0, 54.0, 57.0], [356.0, 503.0, 53.0, 55.0], [362.0, 499.0, 54.0, 56.0], [358.0, 502.0, 55.0, 56.0], [353.0, 501.0, 54.0, 58.0], [351.0, 498.0, 54.0, 60.0], [349.0, 503.0, 54.0, 59.0], [342.0, 510.0, 52.0, 59.0], [339.0, 520.0, 48.0, 44.0], [341.0, 514.0, 51.0, 54.0], [359.0, 501.0, 37.0, 65.0], [342.0, 509.0, 52.0, 56.0], [343.0, 500.0, 54.0, 59.0], [345.0, 497.0, 52.0, 61.0], [342.0, 499.0, 55.0, 61.0], [344.0, 495.0, 50.0, 59.0], [345.0, 494.0, 51.0, 59.0], [344.0, 493.0, 53.0, 60.0], [338.0, 492.0, 55.0, 61.0], [336.0, 492.0, 55.0, 59.0], [335.0, 495.0, 54.0, 57.0], [285.0, 494.0, 101.0, 56.0], [328.0, 490.0, 56.0, 57.0], [327.0, 493.0, 54.0, 56.0], [330.0, 490.0, 38.0, 55.0], [336.0, 486.0, 38.0, 57.0], [340.0, 491.0, 37.0, 54.0], [340.0, 504.0, 26.0, 41.0], [338.0, 480.0, 45.0, 65.0], [334.0, 475.0, 44.0, 68.0], [331.0, 481.0, 45.0, 64.0], [326.0, 486.0, 50.0, 60.0], [319.0, 488.0, 50.0, 65.0], [315.0, 506.0, 50.0, 62.0], [310.0, 506.0, 51.0, 57.0], [313.0, 505.0, 49.0, 59.0], [319.0, 501.0, 48.0, 63.0], [330.0, 545.0, 37.0, 17.0], [329.0, 507.0, 48.0, 64.0], [328.0, 490.0, 49.0, 63.0], [329.0, 498.0, 50.0, 63.0], [327.0, 503.0, 48.0, 56.0], [326.0, 508.0, 46.0, 51.0], [325.0, 513.0, 11.0, 26.0], [321.0, 510.0, 46.0, 53.0], [319.0, 525.0, 35.0, 61.0], [314.0, 503.0, 52.0, 61.0], [319.0, 509.0, 53.0, 64.0], [325.0, 505.0, 53.0, 61.0], [333.0, 514.0, 53.0, 59.0], [334.0, 508.0, 51.0, 60.0], [337.0, 513.0, 45.0, 52.0], [336.0, 510.0, 52.0, 53.0], [337.0, 504.0, 55.0, 61.0], [340.0, 528.0, 52.0, 52.0], null, [337.0, 513.0, 53.0, 58.0], [338.0, 507.0, 55.0, 64.0], [340.0, 495.0, 54.0, 60.0], [346.0, 504.0, 52.0, 50.0], [354.0, 512.0, 46.0, 47.0], null, [359.0, 518.0, 40.0, 48.0], [358.0, 501.0, 51.0, 66.0], [352.0, 500.0, 53.0, 65.0], [351.0, 481.0, 49.0, 62.0], [340.0, 489.0, 54.0, 67.0], [362.0, 511.0, 27.0, 32.0], null, [344.0, 511.0, 46.0, 54.0], [344.0, 514.0, 49.0, 64.0], [340.0, 501.0, 52.0, 62.0], [337.0, 497.0, 52.0, 63.0], [335.0, 498.0, 52.0, 62.0], [332.0, 498.0, 52.0, 64.0], [333.0, 499.0, 51.0, 59.0], [337.0, 497.0, 52.0, 61.0], [340.0, 504.0, 53.0, 58.0], [345.0, 500.0, 51.0, 60.0], [354.0, 498.0, 49.0, 62.0], [355.0, 501.0, 51.0, 59.0], [352.0, 501.0, 50.0, 60.0], [347.0, 504.0, 49.0, 48.0], [337.0, 506.0, 50.0, 43.0], [326.0, 507.0, 50.0, 44.0], [318.0, 494.0, 52.0, 53.0], [316.0, 488.0, 49.0, 60.0], [308.0, 496.0, 51.0, 62.0], [306.0, 501.0, 51.0, 60.0], [311.0, 500.0, 51.0, 59.0], [313.0, 506.0, 54.0, 58.0], [317.0, 515.0, 52.0, 58.0], [324.0, 517.0, 51.0, 58.0], [333.0, 518.0, 50.0, 58.0], [340.0, 521.0, 50.0, 57.0], [344.0, 523.0, 50.0, 56.0], [343.0, 533.0, 50.0, 55.0], [362.0, 541.0, 24.0, 47.0], null, null, null], \"areas\": [3145.0, 3450.0, 3996.0, 4392.0, 4608.0, 4970.0, 5615.0, 6459.0, 7786.0, 8653.0, 8773.0, 8950.0, 8613.0, 7827.0, 6749.0, 5817.0, 3963.0, 3426.0, 3007.0, 2996.0, 2749.0, 2768.0, 3086.0, 1759.0, 2846.0, 1565.0, 2083.0, 2650.0, 635.0, 2566.0, 2053.0, 2897.0, 2587.0, 2682.0, 2593.0, 2662.0, 1179.0, 2374.0, 2181.0, 2045.0, 2145.0, 1433.0, 2090.0, 2330.0, 2281.0, 2304.0, 2235.0, 2322.0, 2315.0, 2443.0, 2528.0, 2448.0, 2012.0, 1253.0, 1711.0, 745.0, 1671.0, 2483.0, 2489.0, 2501.0, 2360.0, 2378.0, 2497.0, 2522.0, 2534.0, 2388.0, 2402.0, 2557.0, 2344.0, 1312.0, 1284.0, 1247.0, 541.0, 1953.0, 2408.0, 2129.0, 2487.0, 2665.0, 2465.0, 2355.0, 2328.0, 1568.0, 486.0, 2455.0, 2439.0, 2501.0, 2176.0, 1888.0, 157.0, 1929.0, 1623.0, 2516.0, 2630.0, 2529.0, 2315.0, 2386.0, 1824.0, 2171.0, 2554.0, 1657.0, null, 2423.0, 2605.0, 2641.0, 1224.0, 483.0, null, 1304.0, 2603.0, 2724.0, 2263.0, 2767.0, 566.0, null, 1166.0, 2490.0, 2489.0, 2576.0, 2556.0, 2615.0, 2413.0, 2544.0, 2498.0, 2492.0, 2353.0, 2447.0, 2434.0, 1887.0, 1715.0, 1743.0, 2260.0, 2182.0, 2457.0, 2381.0, 2374.0, 2357.0, 2387.0, 2326.0, 2331.0, 2278.0, 2230.0, 2211.0, 851.0, null, null, null], \"iscrowd\": 0, \"video_id\": 0, \"height\": 1280, \"width\": 720, \"category_id\": 3827, \"noun_phrase\": \"the face\"}, {\"id\": 866, \"segmentations\": [{\"counts\": \"^UTf03eW1?D8I7J4L4L4L5M2N2N2M201N2N2O000O13M100O0000001O01OO2O0O100N2N200O1bNeiNW1\\\\V151N7J3M2NIlNdiNS1]V1mNdiNS1[V1nNeiNR1ZV1mNgiNR1ZV1nNfiNQ1[V1oNeiNP1gV1N3N1N2O2N1IihNCVW1d0O2ONlhN^OPW17b0G9MmZg3\", \"size\": [1280, 720]}, {\"counts\": \"]m\\\\f0<\\\\W1<F8J5J7L3M3N3M1N3N2O1O1O0O10001O01O001O01O01N10000O101N101N1O100O2N2M201N2O1N1O1O2N2O1O001O2N1O1O2N1O101M2N2M3N2M4IkZ]3\", \"size\": [1280, 720]}, {\"counts\": \"Z]df09bW1;D8J6J6K5K4N3M2M3N2N1O2N2N1O1002N1O0010O000001O000O1O2N1O1O2O1O001N100O2O0O2N101N2N4M0O2O1N3N1N2O1O01O10N3N1O0O1O3M3M1O4L:E`jP3\", \"size\": [1280, 720]}, {\"counts\": \"_]if01eW1>F;F7K5K5K4M3M4M2N2M201N101N2N101O0010O00001O1N1O2N100O2O0O101N2O0O1O2N101O0O2O1N3M102M3M0100O13M1O1O101N1O3L2N1O2N1N3N2NlRm2\", \"size\": [1280, 720]}, {\"counts\": \"TUmf0=_W17H7K6J4L5L5K3L4N2O1N2N1O2N2O001O1O000001O1O0O1000001O0O100O1N3N2O0O2O010N2N2O1N2N2O0O2N101O2N1O00002N3M2N1N2O1N1O2N1O2N2N2O4Hkjf2\", \"size\": [1280, 720]}, {\"counts\": \"PURg0>^W1<E5J7I6L3L5L3N2N1O2N2O0O2N101O01O10O00010O000000O100O2M3N1O2O1O001O0O2O2N1N1O100O2N2O1N2O001N2O2N00100O1N3N2M2O1M2O2O1N2N2L;Gdja2\", \"size\": [1280, 720]}, {\"counts\": \"g\\\\lg0^1\\\\V1:I4L4N1O10000000O10001N10000O2O0O2O1O001O000O2O1N2N1O4M1O2M2N2O1O1O1O1O1N3M3M2M2O1O1O2N1O2N2N2M5K7JbjW2\", \"size\": [1280, 720]}, {\"counts\": \"^mbh02YW1T1^O6J7J3M3kiNZNoU1h130000O1O1O001O10001N101O0O200O1O1M3N3M3N1O1N2N2O1M5K3N2N1O3L3N3L6J4Mbjm1\", \"size\": [1280, 720]}, {\"counts\": \"Tmlh0k0gV1e0E6L4N000000N201O010O0O2O0O201O2M1L4N2O1N101N3M2N1N2O1O3M2M4N0N3N2N3L5Kejh1\", \"size\": [1280, 720]}, {\"counts\": \"^Unh0f0gV1f0F8L3O1000O101O00001O0O1O2N2N3M3N1O1O2N1O1N3M3M1N1O3N2N3M2N3L3N2M5LdRj1\", \"size\": [1280, 720]}, {\"counts\": \"UePi0o0`V1b0K40000001O0O2N1010OO2O1N4M2M2O1N2O1N3L4M2N2M2O2N2N2M3N2M6Kdbl1\", \"size\": [1280, 720]}, {\"counts\": \"flQi03:k0WV1c0N1O1O01O0O2O0010O01N4K5L101O1N3M1O4K3N2N1N3N1O2N2M3M3Ngjm1\", \"size\": [1280, 720]}, {\"counts\": \"`\\\\Yi0d043ZV1o0M0O01N3L4M2O1N2O1N4L4L2N3M4L2N3M1O2M3M3M3Lijm1\", \"size\": [1280, 720]}, {\"counts\": \"b\\\\^i0X1iV11N3NO1N3N2M5K2N2N4M1N3L3N2N1N3M3N2Kjjm1\", \"size\": [1280, 720]}, {\"counts\": \"_]^i06QW1MSiNk0dV1=M01N3N1N8H2YOmhN>[W100O1O1N3N2M3M3IlZP2\", \"size\": [1280, 720]}, {\"counts\": \"hUbi03dW1:C<0N3M4M1O1O0O6IjZU2\", \"size\": [1280, 720]}, null, null, {\"counts\": \"cUnh07aW1=E6L4N20N3N2O0N3N1O2N2N1N4M2M4Mhb`2\", \"size\": [1280, 720]}, {\"counts\": \"\\\\Uih0>YW18M3O2O2I65K2O01NN3O0O3M2N2N1O2M2O2N3L4Lib`2\", \"size\": [1280, 720]}, {\"counts\": \"Q]Vh0916hV1l0G5K5M300O2O01N101O2N1O00001N2O1O1O1O2M3M2M5L4L4M2M2O1N2O0N3M2O2N4Jlja2\", \"size\": [1280, 720]}, {\"counts\": \"jlUg061O`W1j0\\\\O8H5L6K3N2N3M1O2N2N2O0010O1O010O01O001O0000O1O2N1O2N10001O000O2N1O2O0O1O101O1N1O2O001N2O4K3M3N3M1O2N1O1N1O1O2N1O2N1N2O2N3KmZ_2\", \"size\": [1280, 720]}, {\"counts\": \"RUWg0<[W1=H7J6K4K4M4M2M3M3N2N2O1O01O01O001O001O00000001N101O00000O2M2O100O2O0O2O000O2O000O2O0O2O1N2O0O2O1O6J2N1N3N1N2O1N2N2N1O2N1N3N2M5Kkb[2\", \"size\": [1280, 720]}, {\"counts\": \"QmZg0=ZW1<I7J5L4K4M4L3M4M2O0O1O2O00001O1O000010O001O00001O000N2O100O1O2O00001O0O101O000O2O0O2O0O1O2O1O1J`iNkN_V1Z12O1O1N5L3L4L3M2N2N1O1O2N103J6KljW2\", \"size\": [1280, 720]}, {\"counts\": \"nl_g0;_W1;F8J6J5L4L4L4L3N2N2N2O0O2O00000010O1O0010OO100O101N1O1O101O0O10001O0O10001O0O2O0O2O001O0N2O2N2O1O1O010O1N7I3M3N1O001M3N1O2M3M5IQkR2\", \"size\": [1280, 720]}, {\"counts\": \"Secg05dW1>C:I6J5K5L3M4L3N2N1O2O0O20O1O0001O1O000000001O01N1O10000O101N100O100O2O0O2O00001N100O2N101O1N1O2N10100O2N4K3N4K2N2O1M2O2N1O3L4LnRo1\", \"size\": [1280, 720]}, {\"counts\": \"WUag04bW1=G:I6J5L4K5L3N2M3N2N1O2O001O1O01O10O1O0000000000000000N3O000O10001N100O2O0O101N100O2O0O2N101N2N2O00001O2N7H3N1N2O1N2N1O2N1N3M4KPcQ2\", \"size\": [1280, 720]}, {\"counts\": \"mTag0`0ZW1:H7J5L4K5L3M3N3M2N101O0O2O0010O001O0001O00000001N100O100O2O00000O2O000O2N100O2O000O2O000O2N2O1N2O001N2O6I3N2M2O1O0N3N1O2M2N3M7FPcQ2\", \"size\": [1280, 720]}, {\"counts\": \"o\\\\bg0=[W1<H7K4K6J5L3M3N3M2N1O2N101O01O1O000010O0O100001O0000000O2O00000O2O0O1O1O2O001O0O2O001N100O2O2M100O1O2O1N2mNXiNl0oV1N3N2N0O2N1O2M2O2M3M6IoZP2\", \"size\": [1280, 720]}, {\"counts\": \"U]gg02_W1h0D6J6I6L4L2N4M2N2N2O01O0001O1O000O2O01O01O000001O01OO1N2O101N1O1010O00O2O0O2O0O101O1N101N1O2O100O001N2O6I3M3M2M3O1N1N3N2N1N4M4HP[k1\", \"size\": [1280, 720]}, {\"counts\": \"kdmg0=]W1<G7J4K5K5M3L3N2N3M2O001N101O001OO2O000001O1OO101O0O100O2O0000001N10001O0O2N101N1O101O1N101M2O3N1O1N2O6K1M3M1O2O0O101M2O2N2M2N6GQSe1\", \"size\": [1280, 720]}, {\"counts\": \"kTPh0=^W1:H6J7I5K6L3L3N3M2O1N2O0O3N10O1N1001O010O0O20O00000O100O101O000N3O0O101N101O0O101O0O2N1O2M4N1N2O0O8I3M1N2O0O2O000O1N3N1O2N1M5L5HPcb1\", \"size\": [1280, 720]}, {\"counts\": \"k\\\\Qh0d0XW17J6K5K5J7K2M4N1N2N2N2O0O20O100O01O000010O0001O0000O10001N1O101N101N100O101N1O110O0O2N2N103L4M1N3J6M4N0O1O1O0O1O2N1O2M2O1N3M7Gjbb1\", \"size\": [1280, 720]}, {\"counts\": \"n\\\\Qh0d0WW19I6K7I5L3L3N3M2O2M2O0O2N11O1O000100O0010O000001OO10000000001N1O101N10000O2N1O3M5L0O2O1O1N2O0O3M2N2N3M2O2M101N1O2M2O1N3N3L5Jgjc1\", \"size\": [1280, 720]}, {\"counts\": \"PUPh0c0XW19I7J7I5K4M3M2N2N2O1O1N2O1O0001O1O000001O00010O000000000000O101O0O2O001N2O0O102N1N2LhiNaNVV1^170000N102M2O2M2M3N2N3M1N2O2M3M2O2M5K4KeRe1\", \"size\": [1280, 720]}, {\"counts\": \"Uemg0<]W1<G9I6J5K4M3M2M4N1N2O1O1N2O001O10N100O20O001O00001O000000O2O00000O100O2O001N101N2N2O2M2M201O2OOO102M3M3N1N2M4M1N3N1O1N3M3N3L9G`Zf1\", \"size\": [1280, 720]}, {\"counts\": \"odmg0e0WW19H8I5K4L4M3M3M2O1N2O1O1O001O1O0001N100001O000010O0000000000000O2O000O10000O2O0O2O00100O001M2O2O2N1O2L5M1M4M2N2M3M2N3M2N3M4L7IdZf1\", \"size\": [1280, 720]}, {\"counts\": \"Sehg0?[W1:I8H7J4M3M3M2N3M2O1N2O1O1N2O010O00001O01O0O100001O00000001O00000O100O10001N2N101O3L101N1N4L3O1N101O1N3N2M3M2O0O2O0M3N3M3N2M3MhZk1\", \"size\": [1280, 720]}, {\"counts\": \"V]bg06`W1a0F8H7K5J5M2M2N3N1O1N2O1N2O1O0000001O001O0001O01O0000O100000001O000O100O2O0N3O1N4L2M20100N1O2O1N2M2O2N3N2N100O001N1N3L4M4L5J6HfbQ2\", \"size\": [1280, 720]}, {\"counts\": \"P]gg0b0WW1=G7K4K4N2M3M3M2N2N2O1O1O1O1O001O01O01O001OO100O10010O000000001O0O1O002O000O101N2O1N4L2N2N2N2N2O001O2N003L2N2N2N1O2L4L4M4M4K4Ifbl1\", \"size\": [1280, 720]}, {\"counts\": \"PeRh0b0WW1=G7J6J4M3M2N3M2N3M101N200O01O010O01O01O01O001O000O10000001O00000O101OO01O1010O01O0O2N2O102M0N3N2O2M3L3O2O0O1O1M2O2K5J5M5L4L5JgZa1\", \"size\": [1280, 720]}, {\"counts\": \"od\\\\h0c0WW1;H5K9H4M3N1N2M3N3M2O1O001N2O01O10O001O010O00010O000000O1000000O100O2N10000O1O1O2M200O2O1N4M3L2O001M3N2N3N1N2O2OO10N1L5N2L6K9CgZW1\", \"size\": [1280, 720]}, {\"counts\": \"bTnh0n0lV1f0^O6K4L3M101N1010O01O0010O100O0001O00000000000000O1O1O110O000N201O0O2N1N2L4N201N101O100N2O5J4L3K6K3N2O0O2N1O2O1L6KkZm0\", \"size\": [1280, 720]}, {\"counts\": \"j\\\\jh0e0TW1=F6K5L4M2M4M3M2O1N3N000O2O0010O000010O001O001O0000000001O0O10000O2O000O1O1O1O2M2O2O0O2O0O2O1N2O1O1M2M3O2N8I2M200O1O0N3N2M3M5G9JnZh0\", \"size\": [1280, 720]}, {\"counts\": \"kTih01iW1j0VO;F7K5M3M2N2O1N2O2M2O1O1O00001O0001N11O001O1O001OO101O0O100O2O00000O1O2O000O1O2N101N2O0O2O0O2O2N1O0O2M2M4M7J4M1N101N1O2M3M4K5JTci0\", \"size\": [1280, 720]}, {\"counts\": \"edfh0V1]V1`0J5K4O2M2O1N20O01O100O1O001O000001O0O100O2O0O1000010O0000001M2M4N101O1O0O2O1O1N3N0L4N3N2N3M6I3M3N2N2N2O0O2N=AjZW1\", \"size\": [1280, 720]}, {\"counts\": \"h[[h0l0TW1g0XO5K3N1O11O01O1M2O2N1O001O0O0100000000000000O2O000O1000001O0000001N1N3O0O2O1O1M4L3N2O1O1O1O1O1O1N3N2I7J7K3N2N1O2N\\\\S`1\", \"size\": [1280, 720]}, {\"counts\": \"VkSh0d0WW1;H<F6I5L4K4M3M2M2O001O1O001O00O100000O10000001O01O00000000O1000000001O01O000O2O0000001N101O2N1N2O1O1M2O2N2N1O2N3L3H8M2N3M4L6H7H^l^1\", \"size\": [1280, 720]}, {\"counts\": \"WRPh0>aW15K7H:H5K4K4M3L4M4L3M2M3N1O2N0O1000001N11O01O00000O010O011O00000001O01O00O1O100O10010N100O2O1O000O2O1O1N101N1O2N1O2M4D;M4K7J7H5L4J[]a1\", \"size\": [1280, 720]}, {\"counts\": \"Tjng08UW1e0M4L4L3N1M4M4L3M4L3N2N2N3M1N2O1O001O000O10000000O10O10001O000000000000O100000001O001O00000O2O001O1N2O1N101N4L3M1O2K5K5K5L4M4L5L4K6HQ^a1\", \"size\": [1280, 720]}, {\"counts\": \"iYQh06YW1f0F8J5L3N2N3N1O1O2N1N3N1O1O2M5K3N1O1O2N1O1N2O001O1O00000O010001O00000001O00000000O2O000O101O0O2N1O2O0O2O000O2N1N3M3N2M4L9G4K5J4M3L5M3M5Ee^\\\\1\", \"size\": [1280, 720]}, {\"counts\": \"SaWh0>3MnV1h0J4M3M2N2O1N2O1O1N3M3N2M2O2N1O2N1O002M2O1N3N00001O0000000O0100O1000001O01O001OO2O0O1O1O101O00001N101N101O1N2O1O1N1N3M2O2M3L4K6K8H6K4Ke^W1\", \"size\": [1280, 720]}, {\"counts\": \"ZQZh04dW1d0@;H4L3M3N1N2O0O2O1N2N2O2N2N2N1O2M3M2N2O001O0000000O1000001O0000001O000O11O00000O101O000O2O000O2O1O001N2O1O1N101O00001N101N2M4L3J;E9G7J7Jb^R1\", \"size\": [1280, 720]}, {\"counts\": \"WY`h08cW1:I;E6K3M4M2M3N1O1O1O1N3M3L3N2N3N2M3O0O1O1O0O2O001N101O001O0000000000000010O0000001O0O101O0O1O2N1O2O001O0O2O0O2O0O2O1O1N2N2N1N4L3L6A?I9H5K5Genj0\", \"size\": [1280, 720]}, {\"counts\": \"VYeh0:dW16I5K6M3L5L2M4M3L3M3L4N1N3N2N1O2N1N2O1O000O2O000O100000000000O11O00000000000001O00001O00001O00001N101O001O1N2O00001N1000001N1O2M3FgiNiN]V1R1<J8I8G7Jcne0\", \"size\": [1280, 720]}, {\"counts\": \"Wakh0<aW15L5J7K3M3M4M3L3N3L3N2M4L2O2N1N1O2O001N1001O0000O10000O100000001OO2O00000001N100001O00001O001O0O2O001O001O1O1O1N101O001N1O2M2O2M3H9H8K6J7I8Edf?\", \"size\": [1280, 720]}, {\"counts\": \"VQSi05hW1<D8I6K3M3M5L3L3M4L2N4M1N3N1O0O2O001O001O0O101O001O0O0100000000000000001O0000O2O000000000000001O00001N101O001O1N2O1O001N1O2N1O2M3K4J7L4M5K4L5K7H7Gc^4\", \"size\": [1280, 720]}, {\"counts\": \"WaZi03hW1b0A7I7J8H4L4M2M4M1N2O1N101N100O101O0O100000000000000O100000000001O0000000000O1001O001O000O2O00001O1N101O001O1N1O2M3N1L4L4N2N3M3N2N1O2M6K6G6F8Ne^O\", \"size\": [1280, 720]}, {\"counts\": \"Si[i0:dW1=D7H;F4L4M3L4M1N3M2O0O101N1O10000O2O00O2OO1000000O1000001O0001O000000000O100000001O001O00000O2O0O2O001O1O1N1O2M3N2J5M6K5K3M5L100N104K5Ifn1\", \"size\": [1280, 720]}, {\"counts\": \"XaZi01kW1a0A9H:E8H4M4L4L3N1N2O1N100O2O0000000O100001O00000O11N101O000000O101O00000O10000O10001O0O101O000O2O1O1M3N1O2O000O3M2J6M3N5J6J3M4L2N1N108GbV3\", \"size\": [1280, 720]}, {\"counts\": \"Xi[i0;bW1<E6K6J6J5K4L4M1N2N2O1O1N10001N100O1000000O0100000O2O0000000000000001OO1000000000O2O0000000O2O001O1N101O1O001N2O1O0O2M4L5G7N3L2N1O2O5K4G9E8O_^O\", \"size\": [1280, 720]}, {\"counts\": \"bY^i06eW1=C;H6J8I6J3M3M3N0O2O10O010O01O1N10O10000001N10O1001O001OO01000000000O1O100000000000000O2N001O2O0O101O000O2O001O1N2O1N101O1O1N2L5H7M5K20a^O\", \"size\": [1280, 720]}, {\"counts\": \"aYci0f0VW1`0B6J6J4L3N3M110O1O1O1O1N101O00000O20O00000000O10000000000O10O100O1O100001O00000000N2N2O11O00000010N101N2L3N3N10001N101O0O3N1N10f^O\", \"size\": [1280, 720]}, {\"counts\": \"bi[i09cW1g0YO9I6J6J3N3M2N2N2O0O2O001OO2N2N11O1N2O001O00O1000O01O11O001N1000000000O0O2O101O01O0O0101O000N2N2O1O2O1O000O101O1O1O1N3N1O0O2O2N1kN\\\\iNk0fV1RO_iNj0cV1SOfiN;X]4\", \"size\": [1280, 720]}, {\"counts\": \"XbZi0;_W19D:H9M2N1N3G8H8N200O2O00000O100O1000000000000O010000001OO1000000000000O101O0O10006J000O101O001N2O1O1N2O1O1O1M3mN\\\\iNg0gV1UOfiN?]V1]OjiN;RW1FQ^>\", \"size\": [1280, 720]}, {\"counts\": \"dilh0d0RW1=E:L5K5L3M2N2O1N101O2N00000O1O10000O100000000000000O1000000000O10000000000O10O10000000O2O000000001N1001O01N2N100WNoiNc1QV1[NRjNd1UV1O2M5L2N2I7VOViN:nV1BWiN:mV1A\\\\iN7ZW1LPVb0\", \"size\": [1280, 720]}, {\"counts\": \"gYjh0;^W1>[Oa0I6M4K3N2M3N2N2N2O0O2O001O000000O1001O01O0000000000O1O1000000000000000O10O10O1001O000000O2O001O00O01O20O00JTjNYNlU1h1501O1O1N2N7E7K6L2YOZiN1iV1L^iNLgV10h0Ld^c0\", \"size\": [1280, 720]}, {\"counts\": \"hafh0;]W1=^O`0H7L3N3M2N2L3N3N2O1N2O00001N100000000000000O10000000000O2OO100001O000000O1000O1000O1001O01O000O2O001O0O1N3GSjN[NoU1e17O1N101O1O1N2dNbiNR1aV1iNeiNS1hV1YOXiNMPW1K[gi0\", \"size\": [1280, 720]}, {\"counts\": \"fYeh0<^W1:A>F:K3M3N2M3L3O2O1N1O2O0O2O00001N10001N01001O0000000O10001O0000O100001O00O10000000000N1O3N1N20WjNPNbU1Q262OO001N1N2K5N3O0O1O2O1O1O2M2M4M:D:XOYgi0\", \"size\": [1280, 720]}, {\"counts\": \"lQih01eW1>I6E;D=J4M2M3K6M1O2N1O2O0O1O101O00001O001O000000000O10001O0000000000000000000000O100001O00O1O1O20O1OO1N3N1IVjNWNlU1h1601O00001O2L3L4N5K5ZOWiNLlV11aiNBbV1;e0L5Kg^c0\", \"size\": [1280, 720]}, {\"counts\": \"caUi0c0XW19D:H8K5L3N1O2M3L301N101N1000001O0O1000001O000000000O10001OO0100000001OO100O100000000N200KYjNSNeU1m1[jNTNdU1l1]jNRNeU1o151OO1O1010OJTjNXNlU1g1VjNXNkU1g17O2O001O1N3L3M3iN\\\\iNP1mV1[OXiNKmV1O[iNJgV14d0O4Ken6\", \"size\": [1280, 720]}, {\"counts\": \"lY^i0;]W1;D=F9I5L4N2M3N2M3N1O2N1O1O101N1000001N100O11O00001O0001O0000O0100000000000O1000000O100000000000001O00000O101O000O101N1O2N1N3N1N3L4M3K40W_O\", \"size\": [1280, 720]}, {\"counts\": \"kQbi0>\\\\W1:H8I5H7K6K4L4M3N1O2N1O2O0O100O1000000O10001O0000000000O101N0101N1000O10001O000O100000O100001O000O1000001OO10000001O001N1O2N1O2N1OX_O\", \"size\": [1280, 720]}, {\"counts\": \"lQbi0`0ZW1:F9J5H7L4L4M2N3N1O100O2N1000O0100000000O100000000000000000O10O2O00000000O1000001O0O01000000001O000O101O00001O0O101O2L3N102M2I7M20o^O\", \"size\": [1280, 720]}, {\"counts\": \"\\\\bdi0:]W1:mNS1K5N1O2N100O100O10000O1000O1000O10001OO10000000000O2OO1000000000000000O2O00000000000000001N1000001O002N4L2M2L3N3L4I8L3N3\\\\OmhN4VW1LjhN3ne0\", \"size\": [1280, 720]}, {\"counts\": \"Tb_i09cW18I5K5K4N2N2N1O2L3O2M2N3N1N2N2N2O1O1N2O0O2M3N3N1O010O11O00001N100000O11O00000O1000001O0O01000000001O0O10001O000O3M6J3M2N2M4L3L5J5[OmhN9VW1EmhN7UW1HlhN8ie0\", \"size\": [1280, 720]}, {\"counts\": \"bYYi0g0UW18E:I6I6M3M4M2N2M3N1O2N2O000O1000001O000001O00000O1000O0101O00000O1000000000000000001O000O1000001O00000O1000001N1N201N1O1N2M4M3N3L4M1N2nNYiNi0PW1N4L7\\\\O?0e^O\", \"size\": [1280, 720]}, {\"counts\": \"QRSi01cW1f0@:E:J4L4L4N2M3N1O2N1O1O100O1000000O1000000000000000000001O000000O11O0000000O101O0000O010000000001O0O10001O000O1O2N100N200O1N3M3N4K5UO[iN3iV1I\\\\iN2iV1KZiNOkV1OWiNKQW10To6\", \"size\": [1280, 720]}, {\"counts\": \"PZoh0a0WW1<E;H7K3L4L5L3N3M2M3N2O0O2O1O0O101O0O2O000O10000001O0001O00O100O11O00O2O0O2O010OO01O1000001N1M3K6O00001N100O1O2N1O10001N101O1N1O2N2N4L3L?@9G;Dne:\", \"size\": [1280, 720]}, {\"counts\": \"WRih0`0[W18D<H8J4M4M3K4L5M2M3N2N100O101N101O000000001O00O0100000000001N11O0000O10001O00000O2O00010O0O1O2N1001OO1M4N1K501O0O1O101N2O0O2N2N3M3M5L1M6J4M3\\\\OhhN:bW1JPf?\", \"size\": [1280, 720]}, {\"counts\": \"WZjh0d0VW1:E9I7K6K3N3K4M3N2N2N2N101N1O1000000000000000O1000O1000000001O0O10001O00000O101O00001O000O2O001O000O1O1N3N1N2O1O2N1O101N101M4N2M3M4K6K9E5M4Kkm`0\", \"size\": [1280, 720]}, {\"counts\": \"[jQi0d0UW1:H7H8J5L4M4L3N1N3M3N2N1O2O0O10O010000O100000000O01000000000000000001O00000O2O000O10001O001N1010O00O2N1O1O2N1O2L3N3N1O2O1N3M2N3K9I7I5J6FPn;\", \"size\": [1280, 720]}, {\"counts\": \"\\\\b_i0b0WW1;H7H7I7K4M3M2N3M3M2N3N1O2N010O100O100O10O1001O0000O100000000O1000001O000000001N1O10O20O1N1O2O1N101M2O101M3N2N2M3M5K4N2I8K7I:DP^4\", \"size\": [1280, 720]}, {\"counts\": \"Zjei0b0XW1:F:E8K6L5K3M3L4N1N3N1O10O01O1000000O1000000000000O10O101O000000000O100O1000000O2O0O20O10M2O2O0O2O1N1N2N3M3N2M3M3N1N3M4L5K20]^O\", \"size\": [1280, 720]}, {\"counts\": \"[bii0b0UW1>F9H7J6J5L2N3N1O1N2O10O01O1O100O100000000O1000O10000000000000001OO010001O000000001O000O101O0O101M2O1O2N2N1O2N2M2O2N1Oc^O\", \"size\": [1280, 720]}, {\"counts\": \"Vjji0i0TW18H7J5K4K4L4M2N2O1O001O1O1O2O0O10O0101O0000000O10000000O100001O01N100000O10O10001O0000001N10001N100O2O1O1N2N1O2N2M20^^O\", \"size\": [1280, 720]}, {\"counts\": \"_Rgi0=]W1;G7J6K5K3M4L3M2N2O1O1O001O10001N101N0100000O1000000O10010OO100000000O1000000O101O0000001N1000001O1O1O0O2O0O1O2N1O2M2M3L40Z^O\", \"size\": [1280, 720]}, {\"counts\": \"jbdi01dW1?H:G7J4K4M3N2N2M3N0O2N201O0O1N2O100O2N101O002NO01000001ON2O00101O000O100000000O101N101O000000001O001N10001N2N2N2M3M3L4K6I6L30Q^O\", \"size\": [1280, 720]}, {\"counts\": \"_Zhi0`0\\\\W1:H6I6K4L4L4L2N3N2N0O101O1O101O001O0O2O000O1000000O01N102O0001OO100000000O1N3O000O101O00001O00001O001O1N1O2N2N2N1M5H7J5OS^O\", \"size\": [1280, 720]}, {\"counts\": \"ejji03cW1?I5K7I5J6K4L3M2N2N2N2O011O1O1N2O000O2O000O1000O000010001OO1000O1000000000001O000O1000001O1O001O1O00001N101N2O0O2O00W^O\", \"size\": [1280, 720]}, {\"counts\": \"[jji06dW1:J6J7J5J5J6J5M3L3M3M2O1O2N2N2OO0100O100000O1O02O1O0000O1O100001OO100000O100001N100000001O000O1O1O2O000O2N2N2O1N2N10_^O\", \"size\": [1280, 720]}, {\"counts\": \"Ubni0<`W16L4L8H5J5L5K4L4L5L2N2N2N1O1O1O1000000O100O10000000000O0100000000000000000000O1O1001O01N1O101N101N1O1O2O0N3N10e^O\", \"size\": [1280, 720]}, {\"counts\": \"VRVj01kW19E8K5M4K6J5K4K5L3N1O1N2O2N1O1O3N2M10O01VjNQNcU1o171N2O00O001O1M3001O1O0000O11O0000M3O2N100O1000000O2O0000m^O\", \"size\": [1280, 720]}, {\"counts\": \"mabj08bW1:J3M5L6I4L5J6J4N1N2O1O001O1O1O1O1O2O1O0000000O1000O100O01000000000000001N1000T_O\", \"size\": [1280, 720]}, {\"counts\": \"ialj0?\\\\W17L7I5K4K5K4M3M2O2N2M3N010O1O100O100O100O101OO1000O10000000000W_O\", \"size\": [1280, 720]}, {\"counts\": \"iimj08aW1=H5K5K4L4K5L4L3M3M3N2N1O1O010O1O100O2O0O1000000000000000000Y_O\", \"size\": [1280, 720]}, {\"counts\": \"bQoj0c0[W17H6J7H4M5K4M4K4N1O2M200O010O10000O101N1000000000000000000]_O\", \"size\": [1280, 720]}, {\"counts\": \"faQk0>]W19I5H9J5L4I6L5L3M3N2O0N20001N1O100O10001OO100000000000__O\", \"size\": [1280, 720]}, {\"counts\": \"oYkj0b0WW1:G8K5K4M3M2M3N2O1N2O1O1O1O10000O1000O0101O1O1O00O100O1000000000V_O\", \"size\": [1280, 720]}, {\"counts\": \"aZaj0<^W19F:G8J5K5M4M2L4M3N2N2N1O1O10000O101N10000000000000O10001O01O00O100O100000000000h^O\", \"size\": [1280, 720]}, {\"counts\": \"hR[j09aW1:G8A>K5K5L4M2N3M2O1O1O001O00000010O00000000000000O1000000000000O1O1N2O1001O00O1001O0O2O00]^O\", \"size\": [1280, 720]}, {\"counts\": \"jbXj01iW1=E7E;C<L4L3N30O100O1O1OO000000001O000O100O1O10001O0O100000O101OO10000O0O10011M2001N1O1000M[NniNc1QV1]NoiNc1Zd0\", \"size\": [1280, 720]}, {\"counts\": \"jjTj06bW1;G8F9F;J5M4L2M4N2N2N2N101N10000O10000O100000000000O100001O00O100O11O00O100O110O01OO10000O101O000000e^O\", \"size\": [1280, 720]}, {\"counts\": \"TZWj0`0TW1b0@=I6K5L3N2O0O200O1O000O20O010O0001O0000000O2O000O10O1001O0O1000O11O001OO101O0001OO2O1N10O2N100m^O\", \"size\": [1280, 720]}, {\"counts\": \"oQVj0c0TW1:E<J6K4M2N3N2N1O2N010O100000001O1O00O1O10O1000000000001O000000O100O101O0O101O00001O000000001N1000Q_O\", \"size\": [1280, 720]}, {\"counts\": \"jaSj0>YW1<G9E:J6L4M2M3M3O1N1O2O001N2O0000000O2O00O10001O000O100000O2O0000O1000000O100000O100000000000O2O000000\\\\_O\", \"size\": [1280, 720]}, {\"counts\": \"_YRj0a0SW1a0G7J5L4M3L3N3M2N2N2O2M101O0O101O001O01O00000000000000O1000000000000000O1000O100000O1001O0001N1O10O2O000`_O\", \"size\": [1280, 720]}, {\"counts\": \"aiTj0<UW1d0F:I4L5L2M4L3N2O1N3N0O2O1O1N1010O000001O00000000000O1001OO10000000O1000O100000O100001O00O2N0101O000a_O\", \"size\": [1280, 720]}, {\"counts\": \"mPVj0Q1kV18I5L4L4M4L2N2N2N2O2M2O001N110O1O010O1O00000O110O000O100001O0000O100000000000000000000000O10000001c_O\", \"size\": [1280, 720]}, {\"counts\": \"f`Xj0n0nV19I4L4L4M2N3M3M2O2N000O12cNRkN@iT[1\", \"size\": [1280, 720]}, {\"counts\": \"_aXj05VW1l0C8K5L4L3N3L3N2N2O000O2O00O10000000O0100000000000O2O0000O10000000000000001O00O1001N2O001O1N10g_O\", \"size\": [1280, 720]}, {\"counts\": \"kiTj02dW1a0_O=D;K5L4K5L3N2N2N2N2O1N2N2O0010O1O0001O00010O0O100O11N1000000000000O10O100001O01O000000O2OO1000000`_O\", \"size\": [1280, 720]}, {\"counts\": \"mQQj0?TW1`0I7H7K5L3N3L4N0O2N1O100O1000000000O11O1O10O0001OO100O1000000000001O00000O100O2O0O10001O001O0O101N10000000o^O\", \"size\": [1280, 720]}, {\"counts\": \"`Zhi01bW1`0\\\\Od0I7K5J6K3N2M3N2O1N1O2N10001N2O0O2O001O0000000001O1O1O0000000O00101N10000O0O2O1O1O101O0O100O2O0N3N2N100O2O0O2O0O101N10i^O\", \"size\": [1280, 720]}, {\"counts\": \"Wj`i0`0UW1?B<K6K3L4M3N2M3N2N2O1N2O1N101N100O101O00000010O0001O0O10000001O00O11O0O1000O100001O000O01001O000O01OO300O1O0O2N2N1O1O1O2O1N1N2N1O20l^O\", \"size\": [1280, 720]}, {\"counts\": \"[j[i07^W1`0B=C:K6L3M4L3N2M3N2O0O2N101O1O001O001N10000010OO10000010O00O100000000000O011O01O00O10O020O0000O010O200I]jNoMbU1U23O10001N1N200N2O3M2L3N4M3L3N4K4I50e^O\", \"size\": [1280, 720]}, {\"counts\": \"VjVi01eW1d0^O;D;I6L5M2N2M3N2N2N3N000O101O000001O01O0000000O01000000O100O1001OO1O1O1O10000O1O1O1000000O1000001N10O10O10001IliNaNSV1_1liNbNTV1d1100O2N2N1N3L5M3M4QO[iN=kV1[OYiN103dW1Kle5\", \"size\": [1280, 720]}, {\"counts\": \"kYTi0?VW1`0B=I4L5K4N2O2M2N2N2N101N2O00001O000000001N10000000O1O1000000O1O100000000O100O1O2N01000O100000001O0O1000000O1O10010O0O1N3O1N3L5I6L6K6J2VORiN5ZW1IRW8\", \"size\": [1280, 720]}, {\"counts\": \"hiQi0d0oV1`0I7I6L4L3M3N2N2O1N2N2O1N101O1O0000O1001O000O01001O000000O1O100O1O1000000O100O10001N100O10O011O01O0O101O0O10000O1O10001N2N3M3M4J7I7J4M5L5^OghNOhm;\", \"size\": [1280, 720]}, {\"counts\": \"TRnh03VW1j0D<H8J5M2M3N3M1O2N2O1N101O0O2O000000000000O1000O1000O10000000000O100000O100001O00000O10000O100N2O1N2N2OSjNVNfU1j18101NJ6O1001O2ON2M3M3GfiNiN]V1S1`0]ORiNAWW1:=I6Kd^>\", \"size\": [1280, 720]}, {\"counts\": \"ciQi0a0PW1d0D9J6L3N2M4L3O0N3O1N2O1O1N101O001N2O0000O100O100000000O10000000000O100001O00O1O1O10000M3N2L5N10000O1O2N1O100O11O2ON2N2L4N2O2H8nNXiNf0UW1H9Hm^>\", \"size\": [1280, 720]}, {\"counts\": \"_YYi0d0PW1a0E7J6K6L2N3N2N2O2M2N2N101N101O000O2O00000000O11O0000O2N1O11O2N0ON3O10001N1N2N2M30001N1O100O101N10O101OO2O0O2O00100M3N4L4K7I6C=H7KlV8\", \"size\": [1280, 720]}, {\"counts\": \"\\\\i`i0e0TW19H8J7J5K5L3M2N3N3M3M2N2O1N2O0O100O10001OO100O11O001OO1O1O1001ON3N1M3O2O000O10001N100N2N2N201N1000001N101O000O101N2N2N3M2N2N3J6POViN?\\\\W1L10l^O\", \"size\": [1280, 720]}, {\"counts\": \"gYhi07bW1<C<F8I6K6J5M3M3N3L3N2N2N2N2O00000O2O000O10O10000000000N21O10I_jNmMaU1Y20M211N100O2L3L5N10001N1O10001N101O0100ON3N101N2O0O2O1N10\\\\_O\", \"size\": [1280, 720]}, {\"counts\": \"dQQj0a0YW1:F9J6I6K4N2M2N3M4L3N2O1N1O1O100O100O101O1O0O0100O2O000O0100001O0000001O001N100000001N101O01O00O2O0O110O00[_O\", \"size\": [1280, 720]}, {\"counts\": \"kani0?\\\\W19H7J4L5J5L4L4N2M2N2O1N2O2N10001N101O000O100O2O00O010O1O2O000O2OO101O0000O2O00000O101O000O2N11O0001O000O100O10W_O\", \"size\": [1280, 720]}, {\"counts\": \"giei0`0[W1:I4K4J7J5M2L5L4M3N1O2N1O1O2N101N101N100O1000001OO010000100O0000O11O00000O100000001O0000000O2OO100001O000O10010N100O101O0000[_O\", \"size\": [1280, 720]}, {\"counts\": \"fQXi09bW1:G7H6K6K4K6J5L4M3M3N2M201N1O10001N101O001N10O00100O10000O100O2O0O01000O1O100N2N2O2N10000O100O101O0O10O11O00000O10001O0O101N1N3M3N3C<[OPiN8RW1EPiN9TW1BQiN;dW1CfV3\", \"size\": [1280, 720]}, {\"counts\": \"aYjh0=_W18G7H7K6J5K5L4M3M3M3N1O2N10000O2O1O1O00000O100000O1O100000001O0O100O011N1O100N101O101N100O100O1000000O10001O1O0000001N101M2O2M3L5I7K7J;F4BchN2[^c0\", \"size\": [1280, 720]}, {\"counts\": \"Xi]h0?^W16K6I6F9J6J6L3N4K3N2N1O1000001N101O0000000O2OO10000O2O0O100000000O0O3O0O1O1O100O10000O100O101N100O010N31NJkiNaNUV1^1miNbNRV1_1liNaNUV1e10N3N101M2N2N4L3M4L6J6JZWQ1\", \"size\": [1280, 720]}, {\"counts\": \"iX[h02iW18K5K5L2N2N2N3M4M2M3N1O1N3N2N2N4L3M2N2N1O1O2N000000O100001O1O0000O1O100O1000000O100O2O00000O10000O1O1O2N2N2L3N3O2L5L4J9GW_\\\\1\", \"size\": [1280, 720]}, {\"counts\": \"_`Pi0a0]W1:G4L2O2N1O1O001O1O1O1O1O1O1O2N2N2N2N1O1O101M2O2N1O00100OO100O2N1N3M2N3M3M4L6J=Ba0@5L_f]1\", \"size\": [1280, 720]}, {\"counts\": \"aaZi03fW1<F9H6K6J8I3K5N3M2O00000001O0N101O2N2N2N2M3M7I7I5I>Cdfg1\", \"size\": [1280, 720]}, {\"counts\": \"jQ]i0<aW18I1O1O2M4L4K4L4I7M200010O1O1N2N3L4L6L4H>@o^k1\", \"size\": [1280, 720]}, {\"counts\": \"kYci0>SW1d0H5L3K5I5M3O20O001N3N1N3N2M3M7H8I9F9Gbfg1\", \"size\": [1280, 720]}, {\"counts\": \"YZhi08`W17K5K6L6J5H7L3M2012N1O1N3M3N7H8H6K;B[nc1\", \"size\": [1280, 720]}, null, {\"counts\": \"dbni08aW18M101M2F;I7J6M2ON8K<D:EPVe1\", \"size\": [1280, 720]}, {\"counts\": \"`bii0181SW1d0J6N1M3L40O5K1NO11O2N3N3L5M3M2LnUe1\", \"size\": [1280, 720]}, {\"counts\": \"Zjoi07gW14H<H8I4L4NO1N102M3L:F8I8GYeb1\", \"size\": [1280, 720]}, {\"counts\": \"nZmi07eW19G5I5O20O2N2N6J3L3L4K[eg1\", \"size\": [1280, 720]}, {\"counts\": \"Xkei0360PW1h0EWOZiNm0dV141100:G6I5IZmR2\", \"size\": [1280, 720]}, null, null, null, null], \"bboxes\": [[566.0, 649.0, 58.0, 66.0], [573.0, 652.0, 59.0, 64.0], [579.0, 647.0, 63.0, 67.0], [583.0, 646.0, 62.0, 68.0], [586.0, 646.0, 64.0, 66.0], [590.0, 644.0, 64.0, 68.0], [611.0, 649.0, 51.0, 66.0], [629.0, 655.0, 41.0, 63.0], [637.0, 656.0, 37.0, 58.0], [638.0, 656.0, 35.0, 56.0], [640.0, 656.0, 31.0, 53.0], [641.0, 658.0, 29.0, 51.0], [647.0, 653.0, 23.0, 55.0], [651.0, 658.0, 19.0, 45.0], [651.0, 659.0, 17.0, 43.0], [654.0, 675.0, 10.0, 25.0], null, null, [638.0, 671.0, 17.0, 32.0], [634.0, 662.0, 21.0, 38.0], [619.0, 645.0, 35.0, 58.0], [593.0, 644.0, 63.0, 69.0], [594.0, 644.0, 65.0, 66.0], [597.0, 643.0, 65.0, 64.0], [601.0, 641.0, 65.0, 66.0], [604.0, 643.0, 65.0, 65.0], [602.0, 644.0, 65.0, 64.0], [602.0, 643.0, 65.0, 64.0], [603.0, 642.0, 65.0, 64.0], [607.0, 643.0, 65.0, 65.0], [612.0, 643.0, 65.0, 64.0], [614.0, 643.0, 65.0, 67.0], [615.0, 646.0, 64.0, 69.0], [615.0, 650.0, 63.0, 69.0], [614.0, 653.0, 63.0, 67.0], [612.0, 654.0, 64.0, 67.0], [612.0, 654.0, 64.0, 67.0], [608.0, 654.0, 64.0, 66.0], [603.0, 655.0, 64.0, 66.0], [607.0, 654.0, 64.0, 67.0], [616.0, 651.0, 64.0, 70.0], [624.0, 650.0, 64.0, 70.0], [638.0, 646.0, 58.0, 73.0], [635.0, 643.0, 65.0, 70.0], [634.0, 642.0, 65.0, 70.0], [632.0, 640.0, 56.0, 75.0], [623.0, 630.0, 58.0, 71.0], [617.0, 603.0, 65.0, 73.0], [614.0, 573.0, 66.0, 76.0], [613.0, 549.0, 67.0, 73.0], [615.0, 535.0, 69.0, 80.0], [620.0, 532.0, 68.0, 77.0], [622.0, 530.0, 70.0, 76.0], [627.0, 531.0, 71.0, 79.0], [631.0, 532.0, 71.0, 70.0], [636.0, 532.0, 71.0, 69.0], [642.0, 531.0, 74.0, 71.0], [648.0, 532.0, 72.0, 68.0], [649.0, 532.0, 69.0, 69.0], [648.0, 534.0, 69.0, 69.0], [649.0, 535.0, 71.0, 67.0], [651.0, 542.0, 69.0, 67.0], [655.0, 545.0, 65.0, 70.0], [649.0, 541.0, 68.0, 71.0], [648.0, 536.0, 60.0, 68.0], [637.0, 533.0, 68.0, 70.0], [635.0, 530.0, 69.0, 71.0], [632.0, 526.0, 67.0, 74.0], [631.0, 524.0, 68.0, 75.0], [634.0, 526.0, 70.0, 73.0], [644.0, 527.0, 70.0, 73.0], [651.0, 528.0, 69.0, 75.0], [654.0, 532.0, 66.0, 72.0], [654.0, 534.0, 66.0, 68.0], [656.0, 534.0, 64.0, 68.0], [652.0, 533.0, 68.0, 70.0], [647.0, 530.0, 73.0, 74.0], [642.0, 533.0, 72.0, 68.0], [639.0, 538.0, 72.0, 81.0], [634.0, 544.0, 73.0, 78.0], [635.0, 547.0, 71.0, 76.0], [641.0, 546.0, 69.0, 77.0], [652.0, 546.0, 64.0, 77.0], [657.0, 545.0, 63.0, 77.0], [660.0, 549.0, 60.0, 74.0], [661.0, 553.0, 59.0, 70.0], [658.0, 557.0, 62.0, 66.0], [656.0, 560.0, 64.0, 68.0], [659.0, 561.0, 61.0, 68.0], [661.0, 560.0, 59.0, 66.0], [661.0, 553.0, 59.0, 72.0], [664.0, 550.0, 56.0, 71.0], [670.0, 545.0, 50.0, 73.0], [680.0, 539.0, 40.0, 65.0], [688.0, 537.0, 32.0, 66.0], [689.0, 535.0, 31.0, 66.0], [690.0, 531.0, 30.0, 69.0], [692.0, 529.0, 28.0, 72.0], [687.0, 538.0, 33.0, 66.0], [679.0, 551.0, 41.0, 72.0], [674.0, 561.0, 46.0, 71.0], [672.0, 567.0, 48.0, 64.0], [669.0, 553.0, 51.0, 71.0], [671.0, 546.0, 49.0, 73.0], [670.0, 539.0, 50.0, 65.0], [668.0, 531.0, 52.0, 73.0], [667.0, 528.0, 53.0, 73.0], [669.0, 527.0, 51.0, 74.0], [670.0, 523.0, 50.0, 77.0], [672.0, 519.0, 14.0, 64.0], [672.0, 517.0, 48.0, 67.0], [669.0, 526.0, 51.0, 76.0], [666.0, 539.0, 54.0, 68.0], [659.0, 542.0, 61.0, 76.0], [653.0, 542.0, 67.0, 80.0], [649.0, 540.0, 71.0, 80.0], [645.0, 539.0, 70.0, 71.0], [643.0, 535.0, 70.0, 74.0], [641.0, 535.0, 70.0, 73.0], [638.0, 529.0, 70.0, 74.0], [641.0, 525.0, 67.0, 77.0], [647.0, 521.0, 66.0, 77.0], [653.0, 520.0, 67.0, 79.0], [659.0, 520.0, 61.0, 79.0], [666.0, 526.0, 54.0, 77.0], [664.0, 532.0, 56.0, 72.0], [657.0, 527.0, 63.0, 75.0], [646.0, 522.0, 71.0, 76.0], [635.0, 520.0, 70.0, 73.0], [625.0, 519.0, 68.0, 73.0], [623.0, 521.0, 61.0, 63.0], [640.0, 523.0, 43.0, 63.0], [648.0, 531.0, 27.0, 57.0], [650.0, 535.0, 22.0, 52.0], [655.0, 534.0, 20.0, 60.0], [659.0, 540.0, 19.0, 54.0], null, [664.0, 554.0, 13.0, 51.0], [660.0, 569.0, 17.0, 36.0], [665.0, 574.0, 14.0, 39.0], [663.0, 590.0, 12.0, 29.0], [657.0, 586.0, 9.0, 44.0], null, null, null, null], \"areas\": [2511.0, 2691.0, 2842.0, 2896.0, 2934.0, 2972.0, 2321.0, 1652.0, 1474.0, 1318.0, 1094.0, 977.0, 745.0, 495.0, 404.0, 153.0, null, null, 354.0, 456.0, 1364.0, 2939.0, 3004.0, 3001.0, 3002.0, 3012.0, 3000.0, 3003.0, 3025.0, 3017.0, 2957.0, 3001.0, 3031.0, 3050.0, 3095.0, 3141.0, 3350.0, 3146.0, 3107.0, 3160.0, 3411.0, 3263.0, 3141.0, 3355.0, 3364.0, 3142.0, 3126.0, 3927.0, 4015.0, 3934.0, 4261.0, 4185.0, 4217.0, 4290.0, 4026.0, 3932.0, 4189.0, 3927.0, 3844.0, 3735.0, 3832.0, 3834.0, 3985.0, 4016.0, 3343.0, 4044.0, 4142.0, 4189.0, 4221.0, 4165.0, 4134.0, 4479.0, 4226.0, 3946.0, 3771.0, 3610.0, 4486.0, 4156.0, 4587.0, 4525.0, 4355.0, 4293.0, 4006.0, 3981.0, 3863.0, 3686.0, 3471.0, 3389.0, 3351.0, 3243.0, 3549.0, 3425.0, 2713.0, 2166.0, 1760.0, 1694.0, 1782.0, 1674.0, 1880.0, 2590.0, 2830.0, 2430.0, 3232.0, 3242.0, 2922.0, 3390.0, 3526.0, 3413.0, 3513.0, 714.0, 2898.0, 3407.0, 3220.0, 3826.0, 4690.0, 4854.0, 4092.0, 4283.0, 4260.0, 4218.0, 4195.0, 4092.0, 4066.0, 3843.0, 3578.0, 3409.0, 4124.0, 4079.0, 4025.0, 3950.0, 2938.0, 1914.0, 1128.0, 757.0, 829.0, 630.0, null, 364.0, 411.0, 352.0, 216.0, 233.0, null, null, null, null], \"iscrowd\": 0, \"video_id\": 0, \"height\": 1280, \"width\": 720, \"category_id\": 3827, \"noun_phrase\": \"the face\"}, {\"id\": 867, \"segmentations\": [{\"counts\": \"QcZ=;5ISW1j0F7I7I4M2N20N2K5N6E<]OP^Z>\", \"size\": [1280, 720]}, {\"counts\": \"WcP=?YW1=E8J6K5J5L10N1O4K7G:D?]OlUc>\", \"size\": [1280, 720]}, {\"counts\": \"_bP=i0mV1`0E;F;G6K4M2O0000N2M2N9C8H8@b0WOY^_>\", \"size\": [1280, 720]}, {\"counts\": \"RbP=3eW1e0\\\\Oa0B9I8G7K3N2N2N2N2N1O2N1O2N1O2N1O001N10000000O1O2L4L5J8F;^Od0\\\\Oa^k=\", \"size\": [1280, 720]}, {\"counts\": \"ZQ]=d0TW1?G9G7I7K5J5L4L3M2M3M3M4L2N3M3N1O001O000O1000000O10O1000000O01000O10O10O100O010O1N1O2N2K6M2N2K6I7L6H8F;H<SOa_c<\", \"size\": [1280, 720]}, {\"counts\": \"oXR><ZW1>H:H7I6J4L3M4M5J4M3L4N1N2N2N2O1O1O1O1N2O1O000O2O1O1N2O1O1O001O001O1O1O00010N10001O001OO1O10000O1000O1001O0O10O01O1O1O1N2N101N2N101N2M2M4M3N2K6K4L5J6H8F:F<J9AThm:\", \"size\": [1280, 720]}, {\"counts\": \"nob??[W1<G9G6J5M4L5K3M3M3M3M3N1N3N1N3M2N3M3M3N1O2N1O2N1N2O1O1N101O1O1O1O0O2O0010O01O00000000001O00001O01O000000001O00000O101O000O100O1O2O0O2N1O1O2N101N1O1O1O1O2M2O1O1O1N2O1N2N1O2N2O1M3M2N3O1M4K4J7M2J6L5K6L4K7F7EhX`8\", \"size\": [1280, 720]}, {\"counts\": \"U_\\\\a0c0YW1<E:F7K5K4L4L5J5L3N2M3M3M2N3M2N3M3N1N3N1N2O1N101O1O1O001O1O00001O0O11O0001O1N2O001O00001O0010O1O01N1000010O000001O001O001N10001O001N2O0O2O001N2O0O2O001N2O1O1N2O1O1N2M2O2M3M2M4M2M3N2N2M4M2M4M2M4E;I8G8L4J8I6Gj`e6\", \"size\": [1280, 720]}, {\"counts\": \"ifeb02jW1c0^O:F8J5K5I8J4L4L5K4L3M3M3N3L3M3N1N3M2O2N1N2O1O1N2O1O2M2O1O1N2O01O01O001O1O001O1O100O000000000001O001O00001O00001O001N2O0O100O2O1O1O2N1O1N2O1O1O1N2O1O1O1N110O1O1O2M1N3L4M3M3M3L4M2M4M4G8H8^Ob0I9J5Kkhc5\", \"size\": [1280, 720]}, {\"counts\": \"eVac0=^W1d0_O9F9G7K5K5K5K4L4K4N1N2O2M2N2N3N1N2O2M2O2N0O2O1O1O1O1N2O2N1O1O1O001O001O1O1O1O010O000001O0000001O0O2O1O1O001O1O0O2O001N2O00001O2N2N1O1O1N3N1O1O2M2O2M2N2N3M2M4L4L3M4K4K5L4L5@?D=J8GhPQ5\", \"size\": [1280, 720]}, {\"counts\": \"^fmc0R1fV1b0A;H5K5K4M4L3M3L3N2N2N3N7H2N2O0O3N1O1N2O1O1N2O001O001O1O002M2O001O010O0000000000000000O101O001O000O20O000100N2O1O1N2O2N1N2O1O2N1O2N1N2O1O2N1O2N2M3M2O2M3L4K5L4L4K5H8G9I6H7K6L5L5Kkhe4\", \"size\": [1280, 720]}, {\"counts\": \"_^Qd0P1gV1`0C<F7J6J6K4M2M4M3M2N3M2N2N2O2N1N3M2N3N1N2O1O1N2O1N2O2M3N001O1O0O1000001O00000001O00000O10001O001O1O001N101O001O001N2O0O2O1O1N101O1O1N2O2N2N1O2N2N2L3N2N4K3N3L5K4L4J6K6J4J6H8K5H8L3N4L2^OjhN7``_4\", \"size\": [1280, 720]}, {\"counts\": \"eVkc07`W1`1eN?B6K4L4L8H3N2N2N2N3M2N2N2N2N2N1O2N2O1O1O3L2N2O1O1O1N2O2N1N101O000O101O001O00O100001O0000O10000000000O2O00000O2O001O001O000O2O002M2N200O1O1O1N2O1O1O3L2M4M2N2N2N2N2M3M3M4L3M4K5L4H7K4M4L4H8H8I7K5L6L3L8GoXY4\", \"size\": [1280, 720]}, {\"counts\": \"egTc0?RW1Q1QOa0H;E6K4M3M3M4L3N2N3M2M3M3N2N2N2N2N2N2O1N101N2O0O2N101N2O001N101O0O101O0000001O00001O01O01O0000O2O0000001O1O001O1O001O0O2O001O001O1N2O1O1O1O2N1O1O1O010N100O2O010O1O0O2O1O0O2N2N2N2M3M3M3I8J6J5J6kN]jNFhU17\\\\jNBlU19ZjNYORV11biN2ONVW1MohN2]ho4\", \"size\": [1280, 720]}, {\"counts\": \"Z`_b06bW1P1kN?E:B?K4L5I6L4M3M3M4M2N1M4N2O0O2N100O1O2O1N101N1O2O0O100O10000000001N100001O00000001O000001O01O01O00100O1O1O0O2O001N2O000O3N1N2O1O1O001O0O2N2N2N1O2O1M200O1O1O2N2O0N3M3N2N2`MgjNV2ZU1iMhjNV2XU1iMQkNS2kT1mMYkNY2^T1fMekNX2ZT1gMPlNn1RT1RNQlNf0ROI0IRU1FTlN>oNN1GPU1MRlN5UO0hV1K\\\\iN0T^70fa]5\", \"size\": [1280, 720]}, {\"counts\": \"eXja0c0PW1d0_O=A?F;C<I6J6L3M4M3M3M2N2N2N2N1O101N2O001O0O11O0000O1000000000000001O00001O001O0000100O001O001N101O00001O00001O001O010N1O2N1N3O01O01N10001O0O2N1O1O1010O0000O10001O0N2O2N2N1N3N2K5YNTkN4RU1A^kNKSOGfU14R2HhXd6\", \"size\": [1280, 720]}, {\"counts\": \"RiXa09^W1c0A<D:XOf0A>J6I8J5L4M2M4M1O2N1O2O1N1001O000000001O0001O01O2M2O0O2O00010N2O001O001O0O2O001O00001N101O001O0O2O0O101N100O2N1O1O3M2M4K4K7I7I6D?`N[iS8\", \"size\": [1280, 720]}, {\"counts\": \"g`m`0b0kV1o0]O:E>F8H6K5L3L4M4M2N2O1N101O0O2N1O1O2O00000O1100O1O1O1O1N101O1O1N101N3N1N2N1M3M4M2O2M3N1M4M4L4J7I7H:B]`U9\", \"size\": [1280, 720]}, {\"counts\": \"eoj`0T1cV1o0VO:G7K3M3M4L2O2N001O1O1O1O1O1O1O1O2O0O1O01O00001O001N2O001N2N1N3N2N1M4N2K5K6K7H7H:E[Xc9\", \"size\": [1280, 720]}, {\"counts\": \"SXX`0h1oU1d0B6J6K4M3L4M3M2N2N3O1N1O1O001O001N11O01O01O001O00001O0001O01O0000001O00000O101O000O2O000O2O1O001O1N2O1O1N3N3L3K4L8I9G:G5J9D;^OTiNAhm\\\\9\", \"size\": [1280, 720]}, {\"counts\": \"b`Q?m0jV1`0F7J5K5L4J6H7H9J5L4L3M3N2N1N2N3M2O100O1O10001N10000000000000000000000000000001O000O101O000000001O001O000O2O001O001O0O2O001O1O1M3M2M4N100O2N1O1N3N2L3M4K4M3K6D<\\\\Ob0N3M3M4M3M7A`hN1lof9\", \"size\": [1280, 720]}, {\"counts\": \"Y_Q?R1jV1=[iNcNSV1l1H7I6J7I3M4N2M2N3M3M2N2N2O0O2O1N2N2O0O101N2O1O0O101O0000001O001O001O00001O000001O0000001O001O00001O001N2N1O2O001O1O001O1O1O0O1M4N2N3N1O1N1O2N2N2M3N2M4L3N2N2N2N2N2M3M3N2L3L5WOPjN[OUV1c0e0O2N1O001N4M4K4H_PX9\", \"size\": [1280, 720]}, {\"counts\": \"ihW?8VW11khN`0^V1m0B=H7K5K3M4M3L3N2N3M2N2N2M4M2O1N3M3N001N2O0O101O01OO2O001O00O100001OO10000000001O0O10001O001N2O1N2O1N101N101O1O1N3N2M2O1O1O1O1O1O1N3N2N2M2L5M4L3L3N4K5K5K4J6F9I7Hb0@_Wc9\", \"size\": [1280, 720]}, {\"counts\": \"e_`?>UW1o0YO<E8J6I6L4L6K3L4L3N2M4L2O1O1N2O00001O1O0000000O10000000001O0001O000000001O001O000O2O1O001O1O1O1O1N2O1O2M2O2N1N2O2N1N2N3N2M3M3N2M3M4I6M3G;H6J8E:I8Hgge9\", \"size\": [1280, 720]}, {\"counts\": \"gWd?i0lV1a0C8J6J6L4M3M101O1O1O2N2N1O3N1N1O10000O010000000000O1O100O100O1001O0001O0011N100O010O100O1000001ON2O2N1O10000O1O1O2N2O1N2N2O1M3L4N3K5J6L5I8G7L3L4M:Bgoa9\", \"size\": [1280, 720]}, {\"counts\": \"\\\\Pc??VW1a0E9E9F<K4K3M4K4L5M2M4L3M3M2O2O1O001N1O2O1O0O2O001N101O001O0O2O00001O00000000000001O000000001O001O001O1O1O0O2O1O1O2N1N3M2N3L4M3M2O2N1M3N4K5J6K5K5I7J9G:E>@`__9\", \"size\": [1280, 720]}, {\"counts\": \"jXd?1aW1h0^O<G;E8H7K5K5K5L3M3N2M3M4M2N2N2M3N2N2N2N1O100O10001O000000001O000000000000000O100010O000000001O000O101O1O1O1O001O2N1O2M2M4L3N3N1N2O100N3K5L5L2M4M4F:J<F6I5K8H6J8DXWY9\", \"size\": [1280, 720]}, {\"counts\": \"b`j?=\\\\W1=D9G9G8J6I7K5L4K4M4L3M4L2O3L3M3M2O101N1O2O1O0O2N10000O101OO010001O0O10000000000000000001O00001O0000001O00001N101N2N2N2O1N2O1N3M3M2O2M4L2N3M2N2M3M3M5K5J6J6L5J5mNWiNf0VW1J8FfgQ9\", \"size\": [1280, 720]}, {\"counts\": \"hhZ`0>]W19E:I7F8L5I7K6K3L5L3M4L2N4M2N3L3N1N3N2O1M3N2O1N1O2N1O100O100000000000000000001OO10000000000000001O001O001O00100O1O0O200O1O1N2O1N2N3M2M3N2N2N2O1O2M4J4L5L4K5K5L6J:G9E7J5K8GVg]8\", \"size\": [1280, 720]}, {\"counts\": \"lQPa0`0XW1<G=A9L5H6K6J6K4M3N2M3L4L5M3M2M3N2N2N2N2N1O1O1O10000000000O100001OO10O1001O1O00000001O00000O1001O000001O1O001O1N2O1O2L3N2N2N3N1N2O1N2O2M2N2N101N5J5F9I7K5L6J8H:E7ZOj^m7\", \"size\": [1280, 720]}, {\"counts\": \"d`Ra053:SW1d0B:G6I6I8L3M3L5L3L4L4M3N2M3N1O2N2N2N1O2N101N1O2M20000O10001O0O01001O001O00001OO100000000O200O001O0000001O001N101O1N2O2L201N2O1N2O1N2O0O2M4M2N4M1N3N1N3L4H9H7K7K9G6J4K5I7JYoe7\", \"size\": [1280, 720]}, {\"counts\": \"PhXa07cW1`0C6J8D9K4L5K5J5L5L3M3M3N2M3N2M3N2M2O2M2O1O1O2N1O1O101O0O10001N100000000000000O011O0000000000000000O1000001O00000O101O00001N2O1O10OO3N1N2O1N3M2O3L3N2M2O2K5J6L4J5L5L6J6H:G<B[`^7\", \"size\": [1280, 720]}, {\"counts\": \"ihba04`W1h0@6J7J5J6K5K5L3M3L5L3M3N2M4L2O2N1N2O2M2O1N2O1O1O1O2N100O100O10O02O0O1000000000000O1000000000000000000001O00000000001N10001O0O2O0O2O2N2N2N1N3M3L3N3M3L4M3L4L4L4M4K5K4L7hNZiNi0XW1C;Ad_T7\", \"size\": [1280, 720]}, {\"counts\": \"dhga0c0XW19H7J6F9L4M3L4L3L4M3M4L2N3M3N2N2M3N1O2N1O2N3M1O1O1O1N3O0N1000101N2O00O00100000O2O00001O1O0000O100000000O1O2O000000001O000O2O1O001N2N2N2O2M2O2M3M3M2N3L4M3L5L3L4K6K4L6J9H9G8F8_OfhN0b^o6\", \"size\": [1280, 720]}, {\"counts\": \"gXoa0e0VW1;F8I5L4K5L3L4M3M3M3M3M4L3M3M3N2M3N2M4M1N3N1O2N2N1O100O1O100O100O100O2O000O100000O010000000001OO100000000O10000O2N1O1O2O0O101O001N2O1L5K5M2O2M2N3N2L5L3L4L4L4M3M3M4K;E?_O7JZWi6\", \"size\": [1280, 720]}, {\"counts\": \"QRSb0a0TW1`0E9J5K5K5L3L4L5K4L4M3N2M3M3N2N2M3L3O2M2N3N1O2N2O0N200O100O10O1O1O11N2O0000O1O010000O101O001O0000N2O100O2O001O0O10001O001O001O1O2M2N3N1O1O2M2O1O1M4L3M3N2L4K8G8J5K5M2L6J7H;E9I_nb6\", \"size\": [1280, 720]}, {\"counts\": \"giga0e0UW1;F=D7I5M4M2M3M4L2L5L4M2N2O2M2O1O1N2O1O2N1O101M2O1O1O1O101N1O10000O101O01O01O00000000000000O2O0O100O20O001OO2N1O101N101M3O0N3M3I7M3M3N1O2N101M3J6H8B>E;L4M2M5I6K5M3K__T7\", \"size\": [1280, 720]}, {\"counts\": \"QY`a0e0VW1<F:G5K4M4K4M2O1M2N3M2O2M2N2N2N3M2O1N2O1N2M3N2O1N2O1O1O1O100O1O1O1O101N10000O2O01O01O10O0000100O1O1O000000001O00100O100N101N10100N2N2N2N2M3N2M3M4L3kN_jNFeU17^jNDgU14`jNJdU14]jNEkU1:n0O1M3N2M2O2N102M2L5Kc_T7\", \"size\": [1280, 720]}, {\"counts\": \"XY[a0b0YW1a0A7I7H8K4K4K4L4L4M3M3L3M4M2N2N3M2N3M2O1O1O1O001O100O11O1O000000001O00001O00001N1010O01O001O001O001O0N3L4O1O1N2O0O2O1N2O1O1O2O0M3N2N2N2L3O3nNZjNBkU10jjNC\\\\U19X1L7I_oj7\", \"size\": [1280, 720]}, {\"counts\": \"_i]a0`0UW1a0C=G7I8G6K5K5K5L4K5M2M3N1O2M3M3O0O1O2O00000O100002N1O1O1O000000001O001O000O101O003M2N1O001O01OO1O0O3O1O00002N1N100O1O2M3K4O2N2N2O1O1O1N1O:VNkiNM25Kb0YW1I6FWnj7\", \"size\": [1280, 720]}, {\"counts\": \"WaWa0a0WW1`0D8H9H7J4K5K5L3M3L4N1N2N2O1N3L3N2N2N2O1N200O100O1O1O1O1O100O1O100000010O0000010O00000001O010O000O3N2N2O0N1O2N3M2O1O1N3N1O2M2N2N3K7J3K5oN\\\\jN@jU1;P1GPiN_ORW1>;FchNLo^R8\", \"size\": [1280, 720]}, {\"counts\": \"XYVa0l0oV19I8H6K4K4K5K5K5L5K6J5L2N2N2O1O1N2N2O1N2N2O1O100O1O1O100O10000000000O1001O001O01O01O0O2O00100O1O1O1O1O1O1O2N1L5M2O2N1O1O2N1O1N3M2N3L3N3L3M3N1L5WOliN@YV14_jN[OfU1d0l0O1N4K5D_oj7\", \"size\": [1280, 720]}, {\"counts\": \"hQUa07dW18K9G4H9J7J4M4K6J9G4K4M3L4M3L4M4L3M2O1N3N2O1N2O0O2O0O100O1O100000001O000000000000000000000001O1O2O0O1O2N1O011M3M3L300O1O1N2N3M3N1O2N2M2N2O1N1O110O001N1O2M3N6I5cNliNe0mV1I7J7Dkme7\", \"size\": [1280, 720]}, {\"counts\": \"RQUa08dW1a0B3M2N5L0J6O1O2M2N3M4M4J4L4N2N2L4M3L4L4L3L4N3N1N3N2N1N2O2O0O1O2O0O100O100000000O10000000000001O000O101O0000010O00001O002N010O001O001O1O1O1O1O0O2N2N3N1O3Mi0UO6UNPjN^1]V1K3K7K4K4K7K:CZVb7\", \"size\": [1280, 720]}, {\"counts\": \"^`Ub0<1LUW1h0G6H8K5K5I9H4K5L4L4K5M2M4M2N3M2N2N2O1O1O1N20000O100O100O010O10000000000001O001O1O1N200O0010O0001O001O010O00001N2O001O0O2O1O1O2N1N3N1O2N3L3L4M2N3M4J8G=jN`iNMZW1BTPm6\", \"size\": [1280, 720]}, {\"counts\": \"`Plb0P1cV1a0E:I6J6G9L3N2M2O2N2O001O001O001O01O0000010O001N20O01O001O010O1O001O001O001O00010O001O1O1O1O0O2O010N2O1O0O2O1O1O2M2N2O0O2O1O001O1N2O1O2N2M3M2N2N3L5K4L3L6K>]OQWU6\", \"size\": [1280, 720]}, {\"counts\": \"koPc0l0oV1b0YO`0H5K4N3L4M2N2N1O1N10100O001O1O01O01O0000010O010O01O001O010O001O00001O01O01O001N10001O001O0O2O1O0O2O1O1O2OO01N101O1O1O1O1N2O1O2N0O2N1O2M4M2N2L5M2M4K6K4K5K5I7G]gm5\", \"size\": [1280, 720]}, {\"counts\": \"jheb02kW18YOd0G:H8J5J6J6L3M2N3M3M3M3N1N2N2M3M4L3O1O2N1O2M2O1O1N2O2N10001N101N1O10001N1000000O101OO100000000010O010O010O01N1O2O001N2O1O1O1N2N2N2O1N1O2M3O1L3N3M3M3L5L3M3L5AWjN_NoU1[1`0I7I7J6K5L3K5DhPo5\", \"size\": [1280, 720]}, {\"counts\": \"\\\\`Zb0j0PW1;H6K5J6J5J5M3L4M3N1N3N1N3M2N3L3M4M2M3N2N2O2M2O1O1N2O100O100O100O2O000O10001O000000000001N101O010O10O0001O00100N2N2O1O0O2O1O1O1N101N2N2N2N3M3L4L4L3M3M4I7XO]jNiNkU1T1c0M3E;J6M3J7JYX_6\", \"size\": [1280, 720]}, {\"counts\": \"[X`a01iW1>YhN6fV1i0F<E5N2M3M2O1N2N3M2O4L3M2M4M2M3N1O1O1N2O1N2N2O001N100O100O2O0O1O1O10000O1000001O00000O1000001O01O1O001O001O00001O1N2N20O01N2O0O2O1O1O1N2M3O1O100O1O1N200O100M3O1N2N2M4L3L4M3N2K5M3E;Da0A;K=DPgk6\", \"size\": [1280, 720]}, {\"counts\": \"dX`a04cW1`0A;H7L4M2L5N1N3N1N2N4M6J2N1N3L4N1N2O2L3M3M3N2L4M3M3M2O1O2N1N2O1O1O100O2O0000000001O001O001O001O1N3N001O1O1O001O0010O01O001N101O1O1O0O2O000O2O1O2N3M6I;aM`jNi1UV1F4M2M4M2O2L2N3K6J5K6EkfU7\", \"size\": [1280, 720]}, {\"counts\": \"b`Pb0e0VW1>C6K6J5K6K3M3L4M3L4H7L5L3N2N3M2N2O1N3M2O1O1O1O1N2O1O100O2N100O02O1O3M2N2N3M1O2N2N2N2N2N2O2N1N1O2N1O2N2N1N3M3N4N0O1N4K4K5M4L3M3L3M2O2M4K5K3N2M4L6J6HhfU7\", \"size\": [1280, 720]}, {\"counts\": \"mPXb0=RW1d0G>E;C8E;I6J6L4L4M2N2N110O00010O100O0010O100O00010O1O010O00001O10O01O1O1O1O1O2N1O1O1O1O1O100N102M3N2N1O1O1N2O1N3L3O1O2M2N3M3N0O2N1O2N1N3N2O2M3L5L4M2L4L4M4J:G^ng6\", \"size\": [1280, 720]}, {\"counts\": \"Z`Ub0g0QW1>G;D9F8I9I6I7K5L3M1O1O100O1O002OO01O00001O0001O01O001O10O00000000001O01O01O001O1N101O1O0O20O01O1N2N2O1O2O0O1N2O1O00001N2O1O1N2O0O2O1O1M3N2N101N2M4M3L4L3N2M4M3M9E8I5I7JfVd6\", \"size\": [1280, 720]}, {\"counts\": \"^oca0;dW14K8I3L8I=C5K>A8I8G6J6J4M4K5L2N2M3O1O1001O00O10O00O2O0O1000001O01O00001O00O100000010N00100002N01O000001O0000001N2O1O1N2O1O1N101N2N2O1O1N2O002O0O100N2N3N1M4K4M3N3M2O1N2N1N3L5K3L5J6J7Ca0BQhk6\", \"size\": [1280, 720]}, {\"counts\": \"mhba0g0TW1;G6G:G8H7J6K4L3M4L3M3M4M2M4L3O2L3N2N2N2M3L5F9C=@`0N2N3O000O10000O10000000001O000000000000O1N201N101N1UKQmNd4PS1ZKRmNe4TS11O04L2N2O0O2O0000O01gK`lNR4aS151O3N6J2N0QLUlNg3mS1[LQlNd3oS153TLQlNb3XT1M4I5N3N2N9Ci1VN<FPP\\\\7\", \"size\": [1280, 720]}, {\"counts\": \"YY[a01dW1=chNCoV1Q1F7H8J6I5K4L4L4M4K3M4L4L2N3N2M2N3M2N3N1O1N3M200O10000O10000O10000O100O10001O000001O0000000000001O2M3N001N101O001O1O0O2O1O001N2O001O001N2O1O1N0N3N3N2O2M3N0O2M5K4K4L7E<B<Fb0XOghNOZn[7\", \"size\": [1280, 720]}, {\"counts\": \"Ya\\\\a01cW15_hN0\\\\W1=N2L5L4L3M2N3M3K4K6M3M2M4N1O2N1M4L3O2M2N2N2N2N2N2L5L3N2N2O2N1O1O1N2O1O1O1O1O100O1O10000O100000000000010O01O1O100O010O1N2O1N101O1N3N1O1O1O3M001O2M2O1N2O2N1N3M2M4M4I6K4_OniNmNYV1a0l0L4M4M3M6GT`T7\", \"size\": [1280, 720]}, {\"counts\": \"iXTb0<[W1?E8G8J7K3L4M3L5L3M2N2N2M4M2N2N2M3N2N3L3N2N2O1N2N2O1O1O2M2O100O1O100O100O10000O100000001O01O01O0001O2N001O002O0O002N1O1N2O100O0O101O2M2O2M2N2O2M2M3N3K5J6K5K5VOZjNQOlU1i0i0L4N2M5K^`e6\", \"size\": [1280, 720]}, {\"counts\": \"dXYb0l0PW19H6K6H6K3N3M3L5K4L4M3L3O2N1O2M2N3L3N2O1N2N3N1O1O1N2O1N200O101N1O1O100O10YOQlN]MnS1`2WlN_MiS1m1llNSNRS1l1PmNTNPS1k1PmNUNPS1l1nlNVNRS1j1QmNRNoR1n1SmNPNnR1P2RmNoMoR1P2SmNmMoR1Z1mkNfNb1ObR1V1nmNjNRR1T1PnNmNnQ1R1SnNPOlQ1n0VnNSOjQ1k0WnNTOjQ1k0VnNVOjQ1i0XnNUOjQ1j0WnNTOjQ1k0WnNTOkQ1h0XnNXOhQ1c0]nN]OdQ1:ZUn0KPnQO0O10O2NY_[6\", \"size\": [1280, 720]}, {\"counts\": \"f`Zb08aW1`0B8H6L5J6K4M3L4M3M3M3N1N3M3N1N3M3M2O1N3M2N2O2N1N2N3N1O1O2N1O2O0O2N1O2N1O1O100O100O100O100000000O100001OO100O02O000010O010O01O1O0O2O1N2O00001N2O001N1N3N1O2M2N2O1L4N2O4K:G2M5K4L4L5K6K4K6K5K4L2M4N1N2N2N2M4M5Ji_g5\", \"size\": [1280, 720]}, {\"counts\": \"dXRc03dW1d0B8H<D5K5I8H9G8J4M3M2O1N2N2O1N2O1M3O1N2O2M200O001O1O10001O0O10001N010O1O10O1001N10000000000O010000O11O1O00000001O0O10000O1001O1OO2O0O1O1O1N3N1O2N101O0N3M2O2M2N3N2M4M3M3M4K7I>TOfiNWOaV1a0fiNWOdV1b0c0H6IS`]5\", \"size\": [1280, 720]}, {\"counts\": \"jQjc0e0TW1:I5L4M2N3N1N2N2N2M4M3M3L3N4L2N2N2O2M2N2N1O2N2N3N2N2O000O001O1O100O2N100O100O010O100O1000O02O0000000000O1O1O100O100O1O10001O0O1O100O1O1N3N100O1O2N1O2M2O2N1N3M3M2O3M3L3M5K6H7I;ROPiN9cnk4\", \"size\": [1280, 720]}, {\"counts\": \"Ub^e03gW1`0B5J6K4M4H7N2N2N2M3H7K7L3M4M2N2N2N2N2O2M101N2O3M2M200O10O02O00O1O1000O1000O1O1O1O1O1O1O1O1N2O1N2O2M2O1O2M2N3L4M3M4J6J6C`0Gh0XO\\\\gV4\", \"size\": [1280, 720]}, {\"counts\": \"hgie09cW18I6L4K5K6J;E4M3M3M2N2N2O2M3N3M2M3N2N2M3N1O1O1O1O1O0O2O00001O000000O10000O2N2N1N3M2O2M3N2N3L4M2M4L4M6I;C?@ngV4\", \"size\": [1280, 720]}, {\"counts\": \"hhne0R1jV1:F7K4L6J4L3N2M3N2M3M2O1N2O2N2N100O100O1O10O01000O010O1O10O01O1O1N2O1N2O1N2O1N3M2N3N1N3M4L4K7J:_Oi0[O^gV4\", \"size\": [1280, 720]}, {\"counts\": \"mQ_f02hW1h0ZO9H6K4M3L3M2O2M2M2O2N1000O10O10O2O0O1O2N1N3M3N1M4N2M4M2M3M3L5E;G`_Z4\", \"size\": [1280, 720]}, {\"counts\": \"\\\\PPf0<^W1:G9H<F7J4L3L3O1N2N1O2N1N1010O00000O2M3N2L4N2M4M2M4L6I9G`0YOX`n4\", \"size\": [1280, 720]}, {\"counts\": \"dhUe02[W1n0@=F6I7K3L4L3N10O1OO1O10O10OO2O2N1O2M3M2N4L5J5M3L4L4K6H>\\\\Oehh5\", \"size\": [1280, 720]}, {\"counts\": \"c`jd04eW1h0ZO<F5M4K6K4L2N2M01O1O00001O2N1N3N2L4M4M3L5K4F<E\\\\XZ6\", \"size\": [1280, 720]}, {\"counts\": \"fhmc0?YW1:@`0J;D6L4M3M2N3N3M3M2N2M3N101N1O2N1O1O100O2OO010O010O010O010O1M4L3J;XO\\\\jNbNZV1JliNOjWi6\", \"size\": [1280, 720]}, {\"counts\": \"Tanb0<^W1<A>G6I6J9H5L3L4NO02M3O2O2M1O2O1O0O101O0O1000O100000O20O02O0O10O0O10100N201O000O1N3M2L5K4L6K7YNkiNW1iV1lN_iNNZ_Y7\", \"size\": [1280, 720]}, {\"counts\": \"ZYhb0e0QW1?A=I4M4L3O2L3N2N2O1N101N1O1O1O101O001O1O00100O010O01O001O1N3L3M3K5N3N2N2N5I5I8DXiNXOlV1<TiNF4JkV13RiN32LUWg7\", \"size\": [1280, 720]}, {\"counts\": \"QPna01oV1c1UO<H6L<C6K6J5K4L3N2N3M4L2N1O1O2O1N100O2O000000O00101N100O10000O1O2O0N2O1O101N2N2N2O2M5K3M3L4L4L3M4K=C6Eb0UO`iNXO^gS8\", \"size\": [1280, 720]}, {\"counts\": \"jmo`0g0SW1b0A:G=C7I6K5K3M3M3N1N2N3M2N2N2O1N3N3L3M2N2N3N1O2N2M3N1O1O2N2N1O1O1O001O2N2N1O1O01O01O000001O1O001O1O1O1O00001O1O0O2O1O2N1O1O1O002N1O1N2O1N3M4K3N2M4L3M3L5M3G9K5J5K5L4E<G;F<D`0\\\\O\\\\Qf7\", \"size\": [1280, 720]}, {\"counts\": \"Uf]a0U1gV1`0A:F6J6K4M2N3M3M2N3M2N2N2O1N2O1N2N2N2O0O2O0O2O1N2O1O001N101O1O001O1O0O2O1N101O1O1O001O001O100O00000000000001O00001O0O101O001O0O2O001O001N2O001O001O1O1O0O4M2N1N2N2L5J5K5L4L4M3M4J6K4M5K4K5I6L5J5J8E<BcYi6\", \"size\": [1280, 720]}, {\"counts\": \"WWWc0;VW1JlhNi0dV1`0H;C:H8I5L5L4L5J4M2N2N2M2O2O1N3M1O2N2N1O3N1N2O0O2O0O10000O101O000O100O2N2O0000000000O100001O001OO10000000001O00001O001O0O2O001O1O001O0O20O01N2O1O1O1O1N2O1N2N3L3M3M3M3N3L3L5K6J7J4K5J6J7K5F<H6I8F<Fjho4\", \"size\": [1280, 720]}, {\"counts\": \"in_c01P_<3TaC1^W10bhN0=N]V14SiNOeW15HGghN9XW1JehN6[W18M3O1O012N1XiNYOTV1h0biNXO03^V1d0aiN[O01_V1X1O000O10000O3N1NO110006J2M1O2O2M2N2N2[jNkM\\\\U1Y241N1O2N3N1N10N1101N1000O1OSkN`M\\\\T1a2a02N1N10000O01O100O2OSkN^M\\\\T1b2`03N100O001O00O1010O1QkNYMcT1g2]kNYMcT1h2:3N0O0001O2H7O1O11hjN`MnT1^2QkNcMoT1^2RkN_MPU1a2801N1O0000002N1O3N0O2N1O2O2M2N2M2N3N4L2N3L4L3N4L5J4M1J8CYil3\", \"size\": [1280, 720]}, {\"counts\": \"hnbd02Q`2i0SgN4M1O00100O2N0001O03M1O000O1001O000000000010O000O0011O1O0O1O101O0O4M2N1N3N0O2N1O2O1N3M4L3L3M201N10]^f06]aYO10O002N1O1O1NYh33eWL10N2NZh81fWG01OWYj3\", \"size\": [1280, 720]}, {\"counts\": \"ohmc0i0RW1:H6I8I4J8I5I9L3L3M4L3L4N2M3N1N2N3N1O1O1O1O1O1O1O2O0O001O1O2N100O2O0O100O1000O10O1O101O001O1O001O0000N2O1O1000000000001N101O000O101O1O001N2O1M3N1N4L4K5L2O2M3N2M3N3L5K4L5K5K5K9H6I6J5K4K4M6IZ__4\", \"size\": [1280, 720]}, {\"counts\": \"bPVc0>\\\\W1a0A:F9H6J6I5L5K4M3L4M3M2O2M2N2M3O2M3N2M3N1N3N1O2N100O2N2N2O001N101N101O0O101N101O0O2O0000O11O00O10000001O00O2O00000O2O1N1O2O000O2N2O1O1N101N3M3L3N2N2N3L3N2N3L4L4M3L5K5K6I6J5K7J7H9G5K8IbgY5\", \"size\": [1280, 720]}, {\"counts\": \"PXYb0a0ZW1<E8G:F9G9I5L3M3L4M3L4N2L4M3L4M3M3M3N1O2O1N1O2N1O2N001O100O2O000O1000000O2O000000001OO10000001O01O0O100001OO10001O00001O1N1O2O1O001N2O1O001O2M4K4M2N2O1O1N2O2L4K4L7I7H7I7J7J5K7I6I6K8EUhW6\", \"size\": [1280, 720]}, {\"counts\": \"^hXa0:WW1f0D:G6K5G8M4J7J6J4L5K4M4L3M3N2M3N1N4M3M2N2N101N1O1O1O0010001O2N1N101OO10000O10O100O10O100O11O1O00000000O10O11O0O10010O001O001N2N2N2O1O1O1O1N101O1N2O3K4L4L4L5L3M3L4M3K6I7J5K5L9F8H5G;I9EPPW7\", \"size\": [1280, 720]}, {\"counts\": \"kWX`0Q1dV1b0D7I8I7J6J5K5K4M4L3M3N2M3N2M2O2N1N2O2N1000001N100O2O000000000O1000O100001O00O10000000000O100O2O00001O001O0O101O2M2O1O1N2O1O1N2O2M2O1N3K4M3O2M2N3N2N3M2M4L4M2M4M4E=G7H:E8K=@fo^8\", \"size\": [1280, 720]}, {\"counts\": \"Qg\\\\?3jW14N1O1O1M4L3N2M4L3M4M2N201N1O1O1O1O2N1O101N1O2N2N100O2O00000O100000000O1000001O0001O0000001O000000100O001O001O0010O01O001O1O1O1O00101N100O2O0O3M2N2N2O1N1O10003L4K3N2N5K4Lah[9\", \"size\": [1280, 720]}, {\"counts\": \"bPT?=ZW1=C<D;E;K5L5J4K5M2M3N2N3N1N2N3O0N3N2N1O2N2O0O2N101N2O1N2O1N2O1O001O2M4M2N1O2N0000N23M2N2N3M1O0001O000O1N3N1010OO2O001O1O1O1M3I7M201O1O0O3N1O1O1O1M4L3N3N4L3L3N5H9H7H8I5H6L5M5F?XOog`9\", \"size\": [1280, 720]}, {\"counts\": \"lPT?d0UW1=D9G9E;K4J6K6K3M4K5L3M4L3N2M3N2N3L5K3O1N3M3N0O3M2OO01O2N4M1O1O1NK]lNPLbS1V4001O10000001O00001O1OO2M2O10000O2O001O0O2N1010O10N2O0O2M4K5M200O100N2O1O1N2O1M4L4M3N2N2N1N3M4K4L5K7G9A?B;I7K5L3N2M;DPg[9\", \"size\": [1280, 720]}, {\"counts\": \"ehf?7dW1>^Oc0[O?G7K4K6J6L3M4L3M3M2O2M5K6K3M2N3M1O2O0O1O011O2M2O000O3NO1O0100000001N1000001O00O1O100000001O00001O000O10001O0O2O001N2N2O0O200O001O1N3N2M4M2M2O001N3M2N3N2M3M5H6M5I6_Od0E9I6I6I8Hegl8\", \"size\": [1280, 720]}, {\"counts\": \"jPW`0f0RW1=E:G7G9K5K4L4M3L3L4N5L4K3M2N3N1N2O1O1O1N2N201N2O0O100O101N10000O1000001N100000000000O1000000000000O101O000000001O001O0O2O001O1N2O1O1N2N2N3M2N2O1O1N2O1N3L3M4K8H6I7J5K5L?A8I7G7H[g]8\", \"size\": [1280, 720]}, {\"counts\": \"Zji`0`0XW1?D7I7K:F4L5H9L4J6K7I4L3M2N2L4N2N101O3L3N2N2N2N1O0000100O1O1O1N20001N1000O0100000001O5K00N20\\\\lNkK]S1U472N1O0000O101N2N1O101O01O10O1N2N2L4K5M3O2N001O1N3M2M3M3L5J5K6L4M3M3M3K6J6K7I9G;B:H9@Sfi7\", \"size\": [1280, 720]}, {\"counts\": \"ScRa06^W1>F:H7L4L4L4K4M4L3M4M2N3L4N1N2M4M2O1N3N1N2O2N2M3M2O2N1O1O101N100O100O1O100O2O001N101O000O10000O1000000000000000O2N101N101O00001N1O2O1N2M2N3M3L8J3L4L4L4M4L3M3M1N9G4L6J5Ja0^O8Eled7\", \"size\": [1280, 720]}, {\"counts\": \"fZVa01oW100000O2M2M3N2M3M4L3N3L3M3M3N2N2N2N2N3N1N4M2N1N2O1O2N1O1O2N1O2O0O1O1O1O100O101N100O1O2N1O10000O100000001O00O1000000001O000001O01OO1000001O1O1O2N1O1O1O3M2M2O2M7J5K4L6I;F5J9E_m`7\", \"size\": [1280, 720]}, {\"counts\": \"_kga0<]W1;G;F4L5I6L5M6I5K5L2N3L3N2M3O1N3N1N1O2O1O1N3N1O1O1O2N100O1O101N100O1000001N100000O10000001O000000001OO1O1O2O0O10001O0O100O2N4K4M2M3N1O3L3L4L5M3L4L5J8G9H8H6K;C[UX7\", \"size\": [1280, 720]}, {\"counts\": \"`RXb02bW1S1chNnNZV1j1A:G7I7I7H7J7I3M3M2O2M2O2N4M2M2N100O100O2O000O01002N1O1O000000O1O100O1O110O10OO10001N2O001N101O001O1O2N3L101N2N2N2N2N3M3M2K6N2M3N2K6L3M3L4M2M3K7UOTjNYO`V1ESjN8lV1N1OS\\\\j6\", \"size\": [1280, 720]}, {\"counts\": \"SQXb0g0UW19H6J6L6I9H7H5L3L3O2M3M2O1N2N2N2N2O1N101N2O1O001N2O001O001O1O001O1O1O10O000100O010O10O0100O01O100O001O1O0010OO2O1O001N3N3L3M2N2O1N2O1N2O2M3N2M2M4K7G6M3K6J5G;I7I6G[^e6\", \"size\": [1280, 720]}, {\"counts\": \"kPSb0c0ZW19H7H7J5K5L3M4K5L5J6K3N2M2N2N2N3M1O2O1O1O1N2O1O1O1O001O1N2O001O001O1O010O1O001O010O0010O1O1000O10O010O1O1O1O2N1O1N2O001O1N2O1N2O0O2O1N4M2N2N1O1O1O2M3M2L4L4N2J7G9H:F8Aa0F`ff6\", \"size\": [1280, 720]}, {\"counts\": \"^iba01`W1k0_O>B=F6J5K5L4K4M3M3M3L5L2O1N3N1N2O1N2O001O1O001O1O1O1O1O1O1N2O1O0010O0001OO2O0000001O1O1N3N001O001N2O001O1O1O001O1O1N2N2O100N3N2M3N2M3M3M2O1O2N2N1O2L4L3J8B=B?E;I[fZ7\", \"size\": [1280, 720]}, {\"counts\": \"^PZa0=YW1f0]O;F7J5L5N2N1O2N001O10000O1O101N1O1O2O001ijNcMhT1a2RkNcMmT1[2VkNcMkT1\\\\2WkNcMiT1]2=0O1001O000001O2gjNaMmT1^2TkNbMlT1]2VkNbMkT1]2;101O01O00njNgM]T1Y2akNnM[T1S2bkNnM_T1T2YkNQNgT1o1YkNQNhT1n1XkNRNhT1n1XkNRNiT1b2001O001O1N2O001N2O001O2M2N2O2N1O1O2N0O2O1N2N3N2M2N4K5L3M3N2N1N3M3K5G8H;Bbg_7\", \"size\": [1280, 720]}, {\"counts\": \"_`\\\\a07[W1f0C<D;F9I5K5K5L4L4L4M2M3M2O1N101O0O101O000O2O00001O1O0000001O00001O00010O000001O00010O001000O0001N2O1N2O100O1O10O01O1O2N1N3M2O10O01O2N001O1O1O3J6I6L3N3N2L4L4M3K7K6I`0]Oj^^7\", \"size\": [1280, 720]}, {\"counts\": \"b`\\\\a0h0QW1=D<F8K5K5K4L3M4L3M3M3N2M2N2O2M2O2N1N2O1O2N1O1O1O1O1O10O01O1O2N00100O1O0010O001O10O0010O010O010O1M3M3N2O1O1N2O1O1O1N2O1O2N2N10O0njNXMmT1n20N2K5N2J6O1N4K4M3M3N2M2N3M4M2L7Cc0XOef_7\", \"size\": [1280, 720]}, {\"counts\": \"h`\\\\a0<\\\\W1i0[O;G8H5K6J6K4L3M3M3N2M2O2M3N1O1N3N1O1O2N1O001O1O2N1O1O001O1O100O1O10O01O01N101N10010N2O1O001O1O2M2O1N2O1O1O001N2O001O1N2O1N2O1O1O2N1O1N3N1O1N3M3N2N2N2N2M4M3K4K8Ch0mN\\\\f_7\", \"size\": [1280, 720]}, {\"counts\": \"hh]a0?5ElV1Q1F:H6J7I5K5K5K5M1N3M3N2M2O1N2O1O1O2N1N2O1O1O001O1O1O1O100O1O1O1O1O010O1O01O00001O001O0O101O001O2N1N2O1O1O001O1N101O2M2O001O0O2O1O1O1O001N3N2N1N3N1O2N1O1N3N2N2M2O2M3L4K;D`0lNf^Y7\", \"size\": [1280, 720]}, {\"counts\": \"eiga07_W1e0@:G8I8I5L4K4L4M3M4L3M2N2O1O2M2O1O1N2O2N1O1O2M101O1O1O1O1O1O1O1O001O1O0010O001O001O001O00001O1O001O1O1O2N1N101O0O2O1O001O1N3N1O1O1O001O1N2O1O2M3N2N2M2N3N2M3N2M3N2M3M4L3]OdiNVOTW1On]o6\", \"size\": [1280, 720]}, {\"counts\": \"ZYja0?\\\\W1`0B7J6K4L4K4M4L4L4M3L3M3N2M3N1N3N1O1O2M2O1O1O1O1O1O1O1N2O2N1O100O001O1O0010O001N110O001O00001O001O1O2N1N10001O1O1N101O1N20O01O1N2O1O001O1N2O2N2M2O2N1O1O2M2O2M2O2M3N2M3M1L8D=Dlek6\", \"size\": [1280, 720]}, {\"counts\": \"_XVa03cW1a0E;H8H6I6K4L4L4L4M2M3M2O1N3N1O1N2O2M2O2M2O1O1O1N3N1O1O1O1O1O01O1O01O1O100O1O001O10O0O1N3O0010O01N10001O001N2O0O2O010O0O2O00001O1N1O2O1N2O1O1O001N2O1O1N3N2N1N2O1O1O2N1N3N2M3N2M2O2L5K6I8EjVX7\", \"size\": [1280, 720]}, {\"counts\": \"P`m`0h0TW19I9H6I5L4L4K4M3M3M3N2M2O3L3N1N2O1N2O2N1O1O1O2N001N2O010O1O1O001O00001O00010O1O10O010O0010O0001O001O1N101O001O1O001O0O101N2N2N10100O000LmjN]MRU1b2ojN]MRU1b27N2O001N2O2M2M3O1O1O1O1N3N2M3L4M3N2N1O1N4M3J8H9UOlhN4Un`7\", \"size\": [1280, 720]}, {\"counts\": \"cXQa0;`W1`0B8H9G8J5J5L4L3M3M3N2M3M2O2M2O2N1O1O2M2O1O1O2N1N2O1O1O1O10O01O001O100O1O101N1O1000O0010O2OO0M3N3O0O2O1O010O001O1N2O2M2O1O1O001O1N2O001N2N2N2N2O1O001O1N2O1O2N1N2O2M4K3N3M2O2N1O2J8I7Fcf_7\", \"size\": [1280, 720]}, {\"counts\": \"fhSa0e0VW1=D9I8H4M3M5J5K4M3M3M3N1O2M2O1N3N1O1O1O1N3N001O1O1O1O1O100O001O10O01O1O100O100O2O0O00100OO2N2N20O01O001O001O1O1N2N2N2O1O001O001O1N2O001N2O1O1N102N1O1O1O1O1O2M3N1O2N2M3N2N1N3M4M4K;@W^^7\", \"size\": [1280, 720]}, {\"counts\": \"^XQa08]W1c0F9H6I7J5K4L4L4L4L3M3N2M3N1N2O2M3N1O1N2O1O1O1O1N2O1O1O001O1O1O010O1O01000O10O010O010O00001O001N2O001O0010O01O0O2O1O1O001O1N101O0O2O001O1N3N2M101O1O1O1N2O1O1O0O3M2N3N1N3N2N1O2N1N4L5K9Ei0ROSV]7\", \"size\": [1280, 720]}, {\"counts\": \"XY`a03iW17H?_O<G8J6K5J6K4K4M4L4L3N2M2N3M2O2N0O2O1O1N2O1O2N2N1O1N2O001O001O001O100O1O0000010O1O01N1O21O0O010O2OO0O2N10001N101O1O1N101N2O001N2O0O2O1N3N0O2O1O001O1N2O1N2O2M3M2N2O2N1O2M3N2N3L3M4K6J9F=QOViNNfTn6\", \"size\": [1280, 720]}, {\"counts\": \"PP_a0l0RW17I8H6K5K6J4L5K4L4M3L3N2M2N3M3N1O1O1N2O1O1N2O002N2N1O1O2N100O001O1O1O10O1O00001O0001O01O1N10100O1O0010O01N101O0O2O0O2O1N2N2O1O001N2O001N3M2N3N2M20O02M2O1N3N1O2M2O3L2N3N2M3M4J6J9@o^T7\", \"size\": [1280, 720]}, {\"counts\": \"lWQa0h0QW1?D;F9H6K4L3M4L3M3N2M2N3N2M3M2O1N2O001N2O1O1O0O2O1N3N001O001N10100O100O010O011N1O00010O01O001O001O001N101O001O0O2N2N2N1N30000O1M2N3O1O1N2N3M2O1N2O1O1O1O1N3N1O1N4M2M4M2N2N1M5J5J6He0nN_o`7\", \"size\": [1280, 720]}, {\"counts\": \"WhSa0S1eV1`0D:H6J5K5L4L3L4M3M2N3M2O1O1N2O1N101O1O1N2O1O1N101O1O1O00O101O01O010O000001O001O000O2O001O0O2O1O1O001O0O2O1O001O2M2O1O001N2O001O1O001N2O2N1O2M2O2N1N3N2M2N4L3M4L3L3K7G;F>B8Fj^c7\", \"size\": [1280, 720]}, {\"counts\": \"cPUa0Z1bV1=C:H6J4L5J5M4L3M3N3M2M3N1N2O1N2O1N2O1O0O2O1N2O1N101O01N1O11O0001O01O01O1O01O0001O1N2O001N2N10100O1N2O1O1O1O1N3N1N200O1O1O002N1O1O1N4L2N2O2N1N3N2M2M5K4L4L6H6Fg0WO\\\\fi7\", \"size\": [1280, 720]}, {\"counts\": \"jhn`02bW1Q1ROa0D>D5K5K5K4M4K4M3M3N1N3M3M2N2O3L101N2O0O2O1O1N2O001O0000001O00O2O0000001O100O001O001O0O2O001O1O1N2O10O01O1N2O001N2O1O1O002N6J1O1N3M2N3N2N1N2N2M4E<M3M2K6G9SOgiNGcV1Ocoo7\", \"size\": [1280, 720]}, {\"counts\": \"egSa0S1fV1e0_O4L5L5K5K3M4L4L4K6M1N2N2N2O4K3N2M2O2N3L4M00N1N31O1O1O1O1O1O1O1O00O1O1O110O000000001O0O2O1O001N101O001O001N3M3M2M3M30O0100O100O2N1O1N1J7N3M4L5K3K5L4K5J6G9I6K7[O_Pk7\", \"size\": [1280, 720]}, {\"counts\": \"]P_a0e0RW1c0A;D<F8H6J8I5K4M4L6J3L5L4M5K5K1N2O100O1O1O100O01000O100O010O2O000O1000O10O010000000000O1000000O10001N100O1O2N10001N2N2O1L4O001N2N2N200O3G8J5K6K5M2N3K5J6L5J:D9H9H7H9Hmo[7\", \"size\": [1280, 720]}, {\"counts\": \"\\\\iVb0`0WW1<H7K3L4M4L4K6I6K5K5L3M3N3M2N2N2N3M2N3N1N2O1O1N2O1O1O1O1O1N3O0O1O100O1O1O010000O1O010000000000000O100O1K50000O101M2O1N2N4L4L3K5J8J5K5J6H9Dh0oNh`T7\", \"size\": [1280, 720]}, {\"counts\": \"kg^c07`W1m0ZO5K6I6J4M3L4M3M3M2O1N10100O010O100O100O10O100O10000O1000O01O100O2O1O2M2O2N2M3N2L6J9F8ZOPQm6\", \"size\": [1280, 720]}, {\"counts\": \"nnPc01nW13M4M4TiNO`U1=QjNLiU1X1M2N6J6I6I4M4M3L2N3M3N2M2O2N3N2M2O2M2O1O1O1N2O1N2O000O102N10N1N2O2O1O001O1O0O3M2N2N3M2O2L3N2N3J6J7L7H7I7I9F7mN`iN;WW1[O]hf6\", \"size\": [1280, 720]}, {\"counts\": \"ZV\\\\c01mW17I3jiNFcT1<hjNDG2`U1;SjNDQ10mT1c0X11O0O0miN\\\\O[O3lT1?hkNAUO9PU10kkNg0_U1:L3]OdNYjNd1SU1XNPkN<J4Nh0WU1gNPkN_2UU15J6K5M200001O0001N1010O000O010O3M2N2N2M3N2M3N3L3L4M3M4I7I;G6H7HPhk6\", \"size\": [1280, 720]}, {\"counts\": \"of[b0g0PW1a0E4M3M5M3L5K5K5L4K5L4K3N2M2O2N2N2N1O1O1O1O010OO2N10001O0O2O0O2O0O2N2O1N3N1N2O1N2O1O2N2N1O1O1O1O2N2N2N2N2N2N2N2N1O2M3N3L3N3M3L4K5M6H`0_OYWS7\", \"size\": [1280, 720]}, {\"counts\": \"dfba0S1jV1;F7J6J6J5K6J3M2O3L4M2M3N001O0O01O1O100O0101O1O00O101N1O100O1O1O1N2O1O1O2O001O001N101N2003L;FM2N3FfjNkM\\\\U1R2hjNlMXU1S2jjNkMXU1S2<O001O1O1O1O100O1O0O2O2N1N2O2N1O2N1O1O1O1O2N1O2M2O1N2O2N1O3L3M>@5L3Kn_T7\", \"size\": [1280, 720]}, {\"counts\": \"VW`a0?\\\\W1=C;H6J6K4L4L4L4L3M3M3M3M2M4N1N3N1N2O1N2O1O0O2O1N101O1O1O1O1N2O001O1O0O1001O0001O0000000000000001O00001O000O1O2N5M`0E0J2N2O0O000O3gM^jNP2nU1K5K3M2N1OO2O0O11N2O1O001O0N2O10000O10000O2O000O1O1O3N1N1O2N1WOUiN:oV1DRiN:SW1@QiN:`W1JYgf6\", \"size\": [1280, 720]}, {\"counts\": \"jXYb01mW13^Nh0YjN[OaU1f1J5L3O2N2M3N2N2N2N1O2N101O0O2N2O1N2O1N2O000O102MIVkN\\\\MgT1d2[kN]McT1Q3O]O^kNkM_T1T2bkN`MO2_T1_2akN_M02^T1e2hkNZMXT1d2kkN[MUT1Y2ZlNdMfS1[2]lNcMcS1`2m0;E\\\\OPkNZNnT1e1SkN]NkT1`1XkN`NhT1_1WkN[NLHmT1l1WkNdNjT1]1TkNdNlT1\\\\1SkNdNoT1R1ojN[N;<gT1Y1S101OkiNeNeU1m1OO0^O\\\\jNgNfU1X1[jNgNeU1Y1\\\\jNfNeU1Y1\\\\jNfNdU1Z1\\\\jNfNeU1X1_jNeNaU1[1ajNbN`U1^1b0N2OojNgNiNJZT1]1jlNoNhNG]T1Z1llNmNhNI]T1Y1mlNlNfNK]T1Y1mlNlNfNK^T1X1klNnNgNI`T1Y1hlNnNhNI`T1X1jlNmNhNJ_T1X1mlNjNeNM_T1X1nlNhNeNO]T1Y1mlNjN^S1T1alNPO]S1o0blNTO]S1k0clNTO_S1k0`lNUObS1j0^lNUOdS1j0[lNWOfS1h0YlNYOgS1g0YlNXOiS1g0WlNXOkS1g0VlNXOkS1g0TlNZOnS1d0QlN^OPT1`0PlNAPT1>PlNART1>mkNCTT1<kkNDWT1;ikNEXT1:ikNDZT1:fkNF]T17ckNI_T15`kNKcT13]kNKhT12XkNMmT1OTkN0nT1MSkN2PU1LojN5^V1Menn5\", \"size\": [1280, 720]}, {\"counts\": \"ifQb08^1MeT1?ojNEkT1c0ijNFTU1?bjNH]U1_1M2N3N1N102O3L5L1N2N101N1O2O0O2N2O0O101N101O2M101O1O0O2O1O1O1O1O2N1O1O2N1O1O10O000O101O0O110O01OO2O1O00100O1N101O0010OO2O1O1O1O1O0O2N2O001O1O1N101N2M3O1O1O1O1N101N2O1N2N2N4L3N2M3M2N4L3M3L4L5J6H:oNUiN=VoS6\", \"size\": [1280, 720]}, {\"counts\": \"Vf]a0P1mV1<C8J7I5K5K4M3M3M3L4M2O2M2N2OO10O101NJQkN]MoT1i21N1O2N20O100O10O10O10000000O100O10O01O1N2O2N1O1O2N101N1L4M3O2N101N101O0O2O0O2M3M2M5N0O2N2O1O1I7O1O001O1N2O1O1N2O1O1O1O1O1O1O2M3N2N1O2N1O1O2N1O001N4M1O1OWQm6\", \"size\": [1280, 720]}, {\"counts\": \"Sfba0f0UW1=D=D:G8H8]jNgMQU1f2N3M2N4L4L3N2M2N2O1N2O2M2O1N2N2O1N2N101O1O1O1N2O1O1O2N0O10001O1O0001O0000001O010O001O00001O001O001O1O0O2O1O001O001O001N2O001O002N1N3N1N2O1O1O1N101N2O1N2O1N102N0O2O1N2O3L=C6J4L3QNSjNe1]V1F5L7H8I7G8^OWPc6\", \"size\": [1280, 720]}, {\"counts\": \"aUVa0i0RW1a0A8I7J8G7J5J7J5K4L4L5L2M2N3N1O1N2O1O0O2O1N2O1O1N101O1O001O1O1N2O3M1O1O2N01O0000010O01O010O00010O01O00001O00001O1O1N101O1O1O1N101N2O1N2O1O001O1O1O2M2O001N2O1O2N1O1O2M2O2N1N2O2N1N3N2N2N1O2M3N1O2M3Nl0SO9H9G5XObPm6\", \"size\": [1280, 720]}, {\"counts\": \"Zm[`0`0WW1k0[O7J8H7I6J5L5J5K4L4M3N3L3M2N3M2N2O2N1N2O1O0O2O1O001N1000000O1000001O0000O100000O0100O101O001O001O0O101O1O001O001O001O001O001O1O1O1N2O001N2N2N1O3N1N2O1N2N2N3M2N2O2M2O1N3M2O1N3M2N3M3M3M3M3M2M4M3M3N2M3K6GSbh7\", \"size\": [1280, 720]}, {\"counts\": \"nU]`05XW1_1oN;G7I6K5L5J4M4L4K4M4L2N3M3N1N2N1O3M2N2O1O1N2O1N101N2N3N000000001O1N10O11O1O1O001O1O000001O00010N10001N2O0O20O00O2O0O2O001O1O001O1O1O0O2N101N2O1N2N1O2M3O1O1O2M2N2O2N1O1M3M4M2N2N3N2N1N3K5N2O0M4K5K5M2O1N3I8J5I8A?ElQa7\", \"size\": [1280, 720]}, {\"counts\": \"T^\\\\a02dW1T1lNa0E:G9G7I7J4M3L4M3M3L3N1O1O1O1O1O2N2O1N1O10000O2N2O4K6J3N000O100O100O1O1O1N1O200O1O001N110O001O1N3N100O0010O010O101OO1J7M200O1N20O1O001O0100O1N200001O0O01OO2O100N2O100UKRmNc4SS12IWKYmN4Lk02\\\\2jR1fLXmN2Lj0=T2`R1PMWmNR1<l1_R1TMSmNn0a0k1_R1SMRmNo0c0k1cR1SNbmNj1^R1UNemNh1\\\\R1WNfmNg1TR1^MnlNk0Q1d1QR1eMklN4O7Y1m1SR1]NRnN^1gQ1iMRmN3L=^1e1cQ1mMRmN0N=a1b1aQ1oMQmN5I:i1]1iQ1iNYnNS1cQ1[NdlN`0m1o0iQ1POZnNl0gQ1SO^nNg0eQ1WO_nNb0aQ1]OmnN7WQ1HRoNLTQ14]3EbhN1]W1OchN1]W1ObhN2]W12_hNN_W1;4ColW6\", \"size\": [1280, 720]}, {\"counts\": \"VU^b0=^W17L;E5K4K5L5J4N2L7G6VjNPN`U1Z2L6K4M2N3M1N2O005K1N2O8I3L5K4M2M2N4M0O2]lNjKZS1W474K6J5L1N101O00001O1N01O1001O1O00000000001OO100O11O01O0000001O1O000O2N20O01O1N2O001N2O1O0O2O2M3N1N2N2O1O1N3N1N3N1N3M4N1N2N2L3O2L4M3K5H9H8I7H9G8I:H8G9G<_Ooi^5\", \"size\": [1280, 720]}, {\"counts\": \"`]Xc0a0TW1e0_O=B?C:G7K6J6K4K6K3M4K5L4L3M3M3N3L2O2N2N2N2M4M2N2N2N1O2O0O100O101O0O2O001N101O0000O1000O100000001O000000O1000000001O00001O01N101N10001O0O1O101O0O2N2O0O2O1N1O3N2N2N001N3N1N2N2L4L5L5M3L3N3L3K4M5K4L8H6H8J6J=B7H;C>ZOkhNIeQg4\", \"size\": [1280, 720]}, {\"counts\": \"nfad01oW11fd=5S[B3N1N2N1O1O2O1O0O2O0O101N1O101O0O2O001O1O001O00O11O001O1O1O001O00010O100O1O0010O01O00100O0010O10O100O010000O1000O1000O1O3L3O1N3N1N3L3O3L4L5KX[Y4\", \"size\": [1280, 720]}, {\"counts\": \"SVZd06aW1<I5J6K4L3N2N3N2L5M1N2O0O3N3M4L2O1N2O1N2O1N1O10002M4M1O1O00000O2O00100O2N101N10O01O1O1O1O1O100O2O0O1000O2K7Ca0G3O1O01O1O2N0cjNkMPU1T2PkNlMPU1S2`02^jNkMTU1V2ijNlM_U1m1]jNUNdU1j1^jNSNcU10[jNd10\\\\NQV1b1oiN`NoU1b1niNaNQV1i1N10ITjNXNmU1f18^NhiNY1kV1D3G701[OjhN400VW1JmhN4O10KnV1OUiN4L4^W172M4K2N1O2OdhN0iV1OViN8LE]V12hiN<EI_V1KliN;GH^V1MliN:FI^V1LmiN9GJPW14TiNImV17?M2M3NhYo3\", \"size\": [1280, 720]}, {\"counts\": \"oUPd0h0RW1;G6K6J5J7J5K5K6I6K4K6L3M3M4L4J5M4M2M4L2N3N2N2M3N1O2M2N3N2M3N2M4M1O2M3N1O2N1O1O2N100O100O100O10000O2O000O10000O10000000O11N1000000000O10000O101N1O100O2O0O2O0O2O0O101N1O2O0O2O0O2O001N2N2N2N2N3M3M2N2N3M2N3L3M4M3L5I8G9G:F>Ce0XOojg3\", \"size\": [1280, 720]}, {\"counts\": \"Xfcc0=]W1`0_O;G8I8G9G7J6J7H7J6J6K5K4L5M4K5K3M3N3L3N2M4L3N1N3N3M2N2M3N2N2N2N2N2O1N1O0000100O2O0O1000O010000O1000000O100000000000O101O000000000O1O1O2O00000O2O0O101N100O101N1O2O1O1O1N101O1M3N2N1O2N2N2N3L3N2N2M4M3M3M3L4M4L5J5K6J8F:F;E=Ch0YO;_OoiQ4\", \"size\": [1280, 720]}, {\"counts\": \"ldTc04kW12L4M2N4K4L4N2M2N3M2M4M2O1N3N1O2M2O2N101N1O2M201O0O2N100O1O2O0O101O1O000O101O00001N1000001O00001O010O00001O00001O001O001O0010O01O01O010O0010O01O1O001O100O1O1O002N1O1O1O1O2N2N1O2N2N1O1O2N2N3M2N2N2N4L7I5K6I7I5JZRl4\", \"size\": [1280, 720]}, {\"counts\": \"clca0?1e0RV1R1@=F8H7I8J6J3M5K5K6I6L3L4M2N2N3M3M1O2N2O1N2N2N2N102N1N2O1N101O0O2N2O0O101O0O010000001O000O10000O100O010002Nj0`JmlN_4\\\\S1N0SLdlNC0Y3]S1RMglN^O2]3XS1lLblNM5I1^3YS1TMnlNk2TS1TMklNm2VS1QMklNn2YS1oLhlNo2\\\\S1mLhlNo2ZS1oLglNQ3YS1QM]lNY3`S1f0OO4oKVlNd3UT1YLmkN\\\\3\\\\T10O0003M6J2M3O0gMVkN\\\\OOR1NBiT1KhkN?B@MCiT1<ikN=DDMASU10]kNj0CEO_OSU10\\\\kNk0CE0_OPU12]kNj0CDZU1BTkNg0@JVV15kiNXON<YV1:QjNBRV1>oiN@RV19_iNG>1RV16ciNI:5oU10iiNL66PV1KliN125SV1HjiN523XV1DgiN3JN76iV1N[iN0iV1MYiN0iV1OXiNOiV10YiNNiV10d02M:G5KO0N3O2M2N3N1M3N^W10bhN`0B0OO2O1M4L3M5KiQj5\", \"size\": [1280, 720]}, {\"counts\": \"ZUg`0j0kV1k0XO>E:G7H8J6K5K4M4K5K4L5L3M4L2N3L4M3N2M2N2N3M3M2O1N3N1N2N2N2O1N2O1N2O0O2O001O0O2O1O002N2N2M2O1O1O001O000000O13M1O1O3M2N0001IZnNRJfQ1n5ZnNRJgQ1S60M3K6L3O10001O001O1O001O1O001N2O00001O1O010N101O1N2M3O1O1O1O001OcmNmJnQ1R5a0O1N3M3M2O1O2M2O1O101M3M2O2M3M3N2N1N2O2M3L3M4L4K5K5J5L4L3M4M3I7J6[Og0_Oa0G9K6F9IYjR6\", \"size\": [1280, 720]}, {\"counts\": \"j`Y`02U_2O_hN2bhN1<N]V11WiN1^W1O`hN1d02PV12QjN0lU14SjNKZU1K]jN<<EWU12l0MWiN>1DeV1n0ljNoNXS1X1c15M3_jN`N_T1d1k05K5PjNTNeU1o143M3K8J3N2N0O2O1O2N1O1O001O1YlNXMTR1l2hmNWMUR1n2VmNjLN:kR1j2jmNVMVR1i2UmNmL4<eR1h2VmNoL3;dR1g2ZmNkL5=`R1j2PnNVMoQ1k2RnNTMnQ1k2SnNUMmQ1k2SnNVMkQ1k2UnNTMlQ1l2TnNSMlQ1n2TnNPMnQ1P3RnNPMnQ1o2RnNSMmQ1m2]mNiL2;aR1m2RnNQMoQ1o2QnNQMoQ1o2RnNPMmQ1Q3SnNnLnQ1Q3SnNoLmQ1Q3SnNoLmQ1S3RnNkLoQ1f3`mNYL`R1i3`mNTLbR1o2klNhMc0XOcR1n2mlN`MMFb0MdR1n2klNaMMEe0JdR1n3g0jNQlN\\\\Nh0lN[R1h2nlNZNnS1f1XlNSN>UOcR1h2XmN[MD3c0JaR1h2m1O1000000000001N100001O8Hf0[OM2K6M2O101N1O2AZkN]MlT1^2>J5B?O1O0O10100N101O1O2N1O1O1O001N2O1O1O1O1O0O104L2M2O1O1O1O2N2N1O2M2N3M4M1O1O1N3N2M5LQbe6\", \"size\": [1280, 720]}, {\"counts\": \"[ed`0:VW1m0\\\\O;F=E=D8I7I8H6K5K3L5L4K5L4L3M3L3O2M3M2N2N2O1O0O2O1N2O1O2M2O1O0O2O0O2N2O1O1O1N10O10000001O100O00001O0O10000001O00000000010O0O101O001O01O2N100O010O0001O1N101O1O100O001O001O001O0O2O2N1000O01N2O1O1O1N2N2O2M2O2M3N1O2N1N2O2N1M4M3M2O2M3N2M3M3L3N3K4M4K4K5M4L3L5L4K6F8G;@a0C=DWbV6\", \"size\": [1280, 720]}, {\"counts\": \"glYa0;12mV1m0Bf0[Oc0]O<D9I6L4L4K4M3M3L4M3L4M2N3M2N3M4L2N2N3N0O2N3N1N2O1O2N2N1O1N3N1O1O1O2M2O2N1N3N1O1N2O00O100O10000001O0000000000000000O2O000O10010O0O2O001N100O2O1O0O2O001O001O0O2N2O1N1O2O00100N101N2O002N1O1N2O1O1O1N2O1O2N1N2N2O2O1N1O1N2O1O2M2O1M4M4L3M2N3M3M2M4M3M3L4M3M3L3M3M3M4L4L4F:A`0]Oc0F:J7H[jY5\", \"size\": [1280, 720]}, {\"counts\": \"jlPc0i0nV1m0ROk0YO>C9H7J6K5J7J6J4M3M3L4M2M3N2M3N2N3N2M2N101N101N2O0O3N0O2O1N2O0O2O00O001000O102M3N2N0O10000000000000001N3N00O1001O01N100O1000001O00001N10001O00001O000O2O1O001O1N2O001O1O1O1N2O1O001N2O1N101O1O1O1O001bLVmN]N7;Ho1lR1TOfmNkNBo1iR1WOcmNkNBn1mR1VOamNbNAc2oR1jNkmNP1WR1eNRmNmNo0X2PR1bNenNY1\\\\Q1fNenNX1]Q1gNdnNX1]Q1fNdnN\\\\OVNg1WS1lNdnNYO[Nh1TS1kNPoNo0RQ1nNQoNQ1PQ1nNonNS1RQ1jNPoNU1RQ1fNQoN[1QQ1bNToNZ1mP1bNXoN[1gS10O0100O1O2O0O1O2O0O5K5J4XOmhN5NNoWo3\", \"size\": [1280, 720]}, {\"counts\": \"jTdd0i0nV1;G:H:F8I5K3N3N2M4M2N2N1O2O1O2N1N2N10101N1O1O00010O2N100O010O2O0O10O01O100O1000000O101O00O01000001O00O100001O1ON10100O100001O01O00000O101O0000001O010O001O0O2O001O1N101O10O01O1O1O1O100O1O001O1O1O010O101N2M2O1O100O1O1O1O100O1O1O3L3O1N2N2N2M4M2O1N2N3M2M3N3M3N2L3M3M5M4K2M4K5EgbT2\", \"size\": [1280, 720]}], \"bboxes\": [[341.0, 328.0, 12.0, 57.0], [333.0, 326.0, 13.0, 60.0], [333.0, 313.0, 16.0, 77.0], [333.0, 296.0, 32.0, 88.0], [343.0, 271.0, 54.0, 103.0], [360.0, 247.0, 80.0, 109.0], [399.0, 223.0, 103.0, 112.0], [445.0, 201.0, 104.0, 119.0], [478.0, 187.0, 98.0, 123.0], [500.0, 185.0, 91.0, 125.0], [510.0, 173.0, 90.0, 128.0], [513.0, 163.0, 93.0, 133.0], [508.0, 172.0, 102.0, 141.0], [490.0, 178.0, 103.0, 143.0], [473.0, 187.0, 103.0, 135.0], [456.0, 204.0, 94.0, 131.0], [442.0, 218.0, 70.0, 124.0], [433.0, 219.0, 52.0, 118.0], [431.0, 225.0, 43.0, 110.0], [416.0, 235.0, 64.0, 117.0], [385.0, 222.0, 87.0, 118.0], [385.0, 205.0, 98.0, 123.0], [390.0, 215.0, 84.0, 123.0], [397.0, 210.0, 75.0, 113.0], [400.0, 202.0, 75.0, 96.0], [399.0, 211.0, 78.0, 113.0], [400.0, 215.0, 82.0, 115.0], [405.0, 209.0, 83.0, 115.0], [418.0, 217.0, 86.0, 115.0], [435.0, 254.0, 82.0, 116.0], [437.0, 220.0, 86.0, 117.0], [442.0, 194.0, 87.0, 109.0], [450.0, 212.0, 87.0, 110.0], [454.0, 215.0, 88.0, 114.0], [460.0, 220.0, 86.0, 116.0], [463.0, 252.0, 88.0, 117.0], [454.0, 252.0, 83.0, 111.0], [448.0, 232.0, 89.0, 114.0], [444.0, 240.0, 75.0, 111.0], [446.0, 249.0, 73.0, 109.0], [441.0, 233.0, 73.0, 113.0], [440.0, 235.0, 79.0, 115.0], [439.0, 257.0, 84.0, 115.0], [439.0, 227.0, 87.0, 112.0], [465.0, 208.0, 78.0, 114.0], [483.0, 230.0, 79.0, 97.0], [487.0, 230.0, 81.0, 94.0], [478.0, 195.0, 89.0, 123.0], [469.0, 203.0, 85.0, 120.0], [448.0, 234.0, 96.0, 118.0], [448.0, 216.0, 88.0, 115.0], [461.0, 206.0, 75.0, 119.0], [467.0, 231.0, 80.0, 112.0], [465.0, 236.0, 85.0, 108.0], [451.0, 213.0, 93.0, 123.0], [450.0, 170.0, 81.0, 166.0], [444.0, 237.0, 88.0, 117.0], [445.0, 204.0, 92.0, 115.0], [464.0, 200.0, 85.0, 119.0], [468.0, 207.0, 89.0, 124.0], [469.0, 199.0, 104.0, 122.0], [488.0, 205.0, 93.0, 117.0], [507.0, 245.0, 89.0, 104.0], [549.0, 244.0, 63.0, 101.0], [558.0, 223.0, 54.0, 93.0], [562.0, 244.0, 50.0, 94.0], [575.0, 277.0, 34.0, 74.0], [563.0, 237.0, 30.0, 73.0], [542.0, 230.0, 30.0, 80.0], [533.0, 244.0, 25.0, 73.0], [510.0, 221.0, 37.0, 97.0], [485.0, 217.0, 49.0, 106.0], [480.0, 239.0, 43.0, 90.0], [459.0, 190.0, 54.0, 126.0], [435.0, 156.0, 88.0, 145.0], [446.0, 164.0, 100.0, 133.0], [492.0, 169.0, 100.0, 135.0], [499.0, 157.0, 121.0, 102.0], [527.0, 191.0, 95.0, 68.0], [510.0, 221.0, 95.0, 126.0], [491.0, 207.0, 93.0, 132.0], [468.0, 190.0, 92.0, 127.0], [442.0, 193.0, 93.0, 133.0], [416.0, 193.0, 87.0, 125.0], [394.0, 184.0, 86.0, 63.0], [387.0, 200.0, 89.0, 139.0], [387.0, 217.0, 93.0, 142.0], [402.0, 210.0, 90.0, 128.0], [415.0, 213.0, 89.0, 121.0], [430.0, 257.0, 90.0, 141.0], [437.0, 268.0, 87.0, 115.0], [440.0, 279.0, 87.0, 78.0], [454.0, 295.0, 80.0, 110.0], [467.0, 290.0, 78.0, 136.0], [467.0, 263.0, 82.0, 117.0], [463.0, 255.0, 85.0, 116.0], [450.0, 258.0, 82.0, 119.0], [443.0, 225.0, 85.0, 108.0], [445.0, 229.0, 84.0, 110.0], [445.0, 245.0, 83.0, 124.0], [445.0, 253.0, 83.0, 118.0], [446.0, 252.0, 87.0, 115.0], [454.0, 272.0, 87.0, 112.0], [456.0, 275.0, 88.0, 109.0], [440.0, 242.0, 94.0, 108.0], [433.0, 235.0, 95.0, 110.0], [436.0, 249.0, 92.0, 119.0], [438.0, 253.0, 91.0, 117.0], [436.0, 242.0, 94.0, 111.0], [448.0, 259.0, 95.0, 119.0], [447.0, 238.0, 90.0, 118.0], [436.0, 222.0, 91.0, 122.0], [438.0, 233.0, 87.0, 116.0], [439.0, 248.0, 81.0, 122.0], [434.0, 236.0, 81.0, 125.0], [438.0, 210.0, 81.0, 127.0], [447.0, 205.0, 84.0, 139.0], [466.0, 216.0, 71.0, 116.0], [498.0, 212.0, 45.0, 81.0], [487.0, 191.0, 61.0, 128.0], [496.0, 196.0, 48.0, 119.0], [470.0, 201.0, 68.0, 101.0], [450.0, 197.0, 87.0, 97.0], [448.0, 198.0, 100.0, 116.0], [468.0, 202.0, 99.0, 122.0], [462.0, 189.0, 102.0, 125.0], [446.0, 168.0, 97.0, 101.0], [450.0, 162.0, 101.0, 139.0], [440.0, 150.0, 103.0, 140.0], [419.0, 132.0, 102.0, 126.0], [420.0, 137.0, 107.0, 145.0], [445.0, 96.0, 115.0, 182.0], [472.0, 111.0, 108.0, 158.0], [493.0, 94.0, 107.0, 166.0], [526.0, 107.0, 84.0, 117.0], [520.0, 106.0, 98.0, 121.0], [512.0, 97.0, 112.0, 173.0], [502.0, 101.0, 114.0, 182.0], [490.0, 110.0, 105.0, 83.0], [451.0, 83.0, 120.0, 185.0], [428.0, 102.0, 136.0, 201.0], [417.0, 133.0, 132.0, 161.0], [426.0, 106.0, 135.0, 183.0], [443.0, 92.0, 141.0, 195.0], [487.0, 83.0, 132.0, 196.0], [528.0, 81.0, 137.0, 112.0]], \"areas\": [459.0, 484.0, 898.0, 2233.0, 4414.0, 6633.0, 8701.0, 9456.0, 9100.0, 8734.0, 9023.0, 9546.0, 11175.0, 11905.0, 10461.0, 10317.0, 7045.0, 4955.0, 4010.0, 6399.0, 8291.0, 9120.0, 8141.0, 6807.0, 5300.0, 7048.0, 7374.0, 7572.0, 7741.0, 7644.0, 7869.0, 7638.0, 7677.0, 7800.0, 7808.0, 8001.0, 6949.0, 7121.0, 6438.0, 6295.0, 6410.0, 6902.0, 7242.0, 7315.0, 7172.0, 5881.0, 5732.0, 8099.0, 7704.0, 8846.0, 7281.0, 5833.0, 6129.0, 6947.0, 8857.0, 10183.0, 8115.0, 7337.0, 7635.0, 4612.0, 9062.0, 8816.0, 7209.0, 4696.0, 3785.0, 3726.0, 1578.0, 1558.0, 1552.0, 1195.0, 2796.0, 3594.0, 2765.0, 5529.0, 9741.0, 10468.0, 10788.0, 7259.0, 1531.0, 9224.0, 9440.0, 9256.0, 9609.0, 8733.0, 3449.0, 9088.0, 9807.0, 9085.0, 8669.0, 9368.0, 7512.0, 4730.0, 6881.0, 8352.0, 7115.0, 7259.0, 7496.0, 6423.0, 7279.0, 7665.0, 7462.0, 7707.0, 7497.0, 7313.0, 7497.0, 7817.0, 7869.0, 7903.0, 7799.0, 8327.0, 8045.0, 8359.0, 7960.0, 7861.0, 8026.0, 8137.0, 9241.0, 6346.0, 2937.0, 5711.0, 3233.0, 4815.0, 5960.0, 7592.0, 6385.0, 9886.0, 5822.0, 10968.0, 11125.0, 9891.0, 11896.0, 13909.0, 12687.0, 14313.0, 2070.0, 5561.0, 15445.0, 16311.0, 6043.0, 12424.0, 20280.0, 9504.0, 19291.0, 21276.0, 19534.0, 11911.0], \"iscrowd\": 0, \"video_id\": 0, \"height\": 1280, \"width\": 720, \"category_id\": 3827, \"noun_phrase\": \"the face\"}, {\"id\": 996, \"segmentations\": [{\"counts\": \"[Rj;1j_21ThN200N1O00001O0O2O1O0O10000O1N2O10O0100O1000O10O10000000O10000O100O00100O101M2O1N3N2NPfa>\", \"size\": [1280, 720]}, {\"counts\": \"lYW;1jW16O1O0012OO1O0001ON1O2O0O101N11O0000000000O10000O10000O1000O010000000O101OO0100O10O01O10000O2O1M4LWVn>\", \"size\": [1280, 720]}, {\"counts\": \"eQ[;4kW12O0O2O001O001O00001O0000000000000000000000000O100O10000000O010000000O01000O1000O0100O100O101N1N4M2N_Vi>\", \"size\": [1280, 720]}, {\"counts\": \"aa];5kW12N1O0O20O01O001O1N11O01O00000000010N100000000000000O10000000O01000000O1O1O10O10O10O1O2O000O100O011N2M3N2N3N1N_^`>\", \"size\": [1280, 720]}, {\"counts\": \"bQ`;4lW12OO001O1OO101O0N3O001O000000001O00001N1000000000000O100O1O1000000O1O100O0100O1000O01000O10O10O10O011O1O1N2N2N2O2M3M`VZ>\", \"size\": [1280, 720]}, {\"counts\": \"XQ[;8hW11O001N2OO100001N100000O100000000001O00000000000001OO1000000000000O1000O1000000O1000O10O10O10O01O100O010000O10O01000001N2O0O2O0O2O0N2NonS>\", \"size\": [1280, 720]}, {\"counts\": \"bQ`;1kW14N3O0O100O1O10000O1000000O10001O000O11O001O000001N100O100O1001N1000O10000O100N2N200O001001O1OO1O1O1O100O1000O0100O100000O4L3M3M1O2O0O2N3N1N2O0O2O0O2Ohnd=\", \"size\": [1280, 720]}, {\"counts\": \"fa];6iW12N2N1O100001OO100010N100000O2O0001O00O100000000O1000001O00000O1001O0000O100000000O1O100001O00O10000O10000O10000000O0100000000000O100001O01N2O1N2O1N2O1N3M2N3MZ^b=\", \"size\": [1280, 720]}, {\"counts\": \"fab;4kW14L100O1O10000000O02N100000000O1O11N2O00O1000001N10000O1001O1OO10000000000O100O10000O1000000O100000000O1O1O11N1000O1O100000O100000000O10000000000O2O2N1O1N3N2M2M3N2N_fY=\", \"size\": [1280, 720]}, {\"counts\": \"]ih;1nW12N2M3N100O2O00O1000O10001O00000000001O000000000000000000000000000O100001O00000001O000O10000000000O1000000000000O10O101OO010000O10000000000O100O0100001O001N3M2N2MjfT=\", \"size\": [1280, 720]}, {\"counts\": \"Yim;7gW13N2O1O00000O100001N1000O010010O000000000000000000000001O0000010O000000001O000000001O01O00001O01N100000001O0000O11O00000O100000000O01O100000O0100000O1001N1O1O10000O1000000O1000001N10001N100N3N1Njf[<\", \"size\": [1280, 720]}, {\"counts\": \"]Qh<1kW15N1O1O1O100O100O1000O1000000000000001O000000O101O01O01O00000001O0001O01O0000001O0000001O00000000001O000000000000000000000O0100000O1000O1O10000O100000O1000O10000000000000000O1O2N2M5L^Vc30nYa4\", \"size\": [1280, 720]}, {\"counts\": \"WQW=4jW14L3N20N10000000O11O000001O0O100000000001O0O1001O00000000001O000001O0001O0000001O00001O0000001O0000000000000000000O101OO10O1000000000O10000O1000O10O100000000O10000O0100O01O101O0O10001O0O101N2O0O1L4O11N2N2OO2O0O2NnVg:\", \"size\": [1280, 720]}, {\"counts\": \"XiU=6hW14M2N1O100001OO10O10001O0000O1000000000001OO1000000000010O0001O00010O00000000001O0000000010O000000000000000001N010000000O1000O10O1001O001O0O010000000O0100O010O100000O0100O11N10000010O01N101N2O1O001O2NM1020O100O10001O3Ljfd:\", \"size\": [1280, 720]}, {\"counts\": \"\\\\Qm<3lW1101N1N3N1001O0000O101O00001O0O100001O00000000000010O00000000000010O00O11O01O0001O0O1001O00O10O1000000O10O1000000000O10000000O1O100O0100000000O1000O1000O100O100O10O10002O0O2N10O01O1O001O0O100O10O1O100O03N1O1O1Oc^R;\", \"size\": [1280, 720]}, {\"counts\": \"gQo;1kW15N2N001N2O1O20N4L1000000000O101O0001O0001O00010O001O1O000010O01O1O001O001O0000000000O1000O10000000O10O11O0O2OO1000O10O100O010O100O10O10O10O01000O0100N101000O102M3M2O1N2N3M1000002NkW;0h^k;\", \"size\": [1280, 720]}, {\"counts\": \"ka[<2mW11O2N11O1O00O2N20O10O001O001O00000O1O100001N0100O1O1000O01O001O0O2O1O10000O1O10000O010N2N2O1O1O1000O2O001N3N1O101M3M3M3L7KYnZ=\", \"size\": [1280, 720]}, {\"counts\": \"fQj;2R`22igN4K200O010O01000O10000000001O000O1001O000O10O10000O1O0010O1000O10O10O10000O1000O1000O100O1O1N2O1O001000O2O001O0O101O1N2N2N2O1N4L3N0000O11O^n_=\", \"size\": [1280, 720]}, {\"counts\": \"QbQ<1oW1001N3L3N20O1O1OO100000000O100O20O0O2O000000O100O10O010O1O101N1000O1000000O100N2O0K60000000000O100000O1000001N101O1O0O2O0O2O1O002M3NUf^=\", \"size\": [1280, 720]}, {\"counts\": \"Qj\\\\<3lW12N3N000O101N11O1O3MO100O100O1O1O1O0101O2N000000O2O0O1N2O2N1O100N200O1000000O10000N2000000000O10001O1O1N2O001N2M3N2N2O001MVfY=\", \"size\": [1280, 720]}, {\"counts\": \"kim;2nW1000O101N10000O1000000O101O000000000O1001O1O00O100000001N1O100N2O2N11O00O2OO0101N100000O1000000000O100O00100O10O2O1N101N2N3M2O1N2O00001N2N2OY^]=\", \"size\": [1280, 720]}, {\"counts\": \"QbV<1oW11o_70o_H2O0O2O1N2O000O1O2M2O1O1M3M4N1O10000O10000000O011O000000001N2O1O1N3L201N2N2N0O2O101NYfm=\", \"size\": [1280, 720]}, {\"counts\": \"kic;2mW12N2O000001O10O2OO000000O2O001O0101N3NO1N10000O1N2O101O01O1N10N2O2N1O1O1O001O1O1N200000N2N3M3N101N100O101O0O2N1M\\\\nX>\", \"size\": [1280, 720]}, null, {\"counts\": \"]ag;1oW11N2OO2O00O3N2N001O1O1O1O10O1000O00001O01O00010O0001O000O100001O0O100000O010O10000O001000O100O001O1O1O2K4NenX>\", \"size\": [1280, 720]}, {\"counts\": \"]ih;1nW12O01N02O0N3N110O0111N00O1O1N20O0000001O00100O1N3N2N00O1001O00YVS?\", \"size\": [1280, 720]}, {\"counts\": \"]ih;1oW11O002N2M101O001O001O0001000O000O2O0O10000000000000000000O1000000O010O001O01000O10O0001O1O00O001011O1N2O2N2Mi^V>\", \"size\": [1280, 720]}, {\"counts\": \"gYa;1oW1000O100O1O1N2O100O2O001O00001O00010O01O1OO1O1O20N1000O1001O001N0100O10O1O1000O0100N2O1O01O00N3O1000000O2OcV_>\", \"size\": [1280, 720]}, {\"counts\": \"biP=1nW12O1N4M000O10O100O2N101Nmo4NifW>\", \"size\": [1280, 720]}, {\"counts\": \"cQT<5jW111O00O1O1O101O000001O0001O01OO100000000000O0100O2OO2O00000O01000O100O1O010O10O01N2M3000000000000000cVP>\", \"size\": [1280, 720]}, {\"counts\": \"bi\\\\<1oW12M1O2O1O10O00000000O100001O00000000000000O100O01000000000000O1O10000O0100000O0100cfR>\", \"size\": [1280, 720]}, {\"counts\": \"aif<3lW13M2N1O101O0001O0001O000000000O1O1000000000O100000000O100000O10O010O00100O010O2N1O2N2N3N00cfc=\", \"size\": [1280, 720]}, {\"counts\": \"gia<1oW10O1O1O2N2O0O2N101O01O01O00000000000O10000010OO10O1000000000000O1O10000O010O1O100N200O100O1O3N^Vf=\", \"size\": [1280, 720]}, {\"counts\": \"gQh<6iW11O2O0O2O01O0001O000001O000000O10000000000000O010000000O10000O100O100O00010O1O1000^^g=\", \"size\": [1280, 720]}, {\"counts\": \"lae<1nW11O2O1M2O2N11O001OO2O0001O0010N10000001OO10000000O100000O10O1000O10O0100000O01000O101N1O2N2N1OZ^b=\", \"size\": [1280, 720]}, {\"counts\": \"lQc<1oW10O2O1N101N2O1N11O0010N10000001O001O0000000000O1000000000000000O10000000O01O100O00100O101OW^g=\", \"size\": [1280, 720]}, {\"counts\": \"lYd<1nW12N2N100M4L4M2000000O10001O0001OO1000O100000O100001O0O010000000O10000O100O101N2Lb^l=\", \"size\": [1280, 720]}, {\"counts\": \"kY_<2jW14O101N1001O100O1O00000001O000001O1O0010O12M001OQX1OogN100kW`00Zf^=\", \"size\": [1280, 720]}, {\"counts\": \"lQY<1oW10O1O2O0O2O001O000010O101N1O1O0001O0001O01O012ON00001O000000002No_21Q`MOkg80PhI0Y^l=\", \"size\": [1280, 720]}, {\"counts\": \"eY_<6hW14M10001O01O3M3N0O0100O1O0010012NO2L2O2MV`70j_H11Oko>0om_=\", \"size\": [1280, 720]}, {\"counts\": \"gQm<1nW12N2L400O0101N100O1O0001O010O011O04JPV_>\", \"size\": [1280, 720]}, {\"counts\": \"Vjn=1nW120N10PX10PhN1O1N2O000O1000O101N10O001O1O10000OO2O01O10OO2O001OO11O01O00[Vh<\", \"size\": [1280, 720]}, {\"counts\": \"Qbh=2nW1O42K001O001000O110N000000O1O11O01O000O1001O00N101O10O1O002O0O10O0N12O00N21N10002O0Om_2OU`M0Yf`<\", \"size\": [1280, 720]}, {\"counts\": \"gii=1oW10PX10PhN1O4OM1O0001O001O1O1O1N2O01O1O1O1O000000O100O10000O10O011N1O1O01000OnW1NPX1Odna<\", \"size\": [1280, 720]}, {\"counts\": \"]QP>2SP:OkWG101N1010O01O1O1O001O00001O000010O000000O10O100O01000O1O1N20O0011N2OMN51N11NdVY<\", \"size\": [1280, 720]}, {\"counts\": \"XQf=1oW100000O1O100O1O13M010O01O30M10O001O1O2OO011N000100O000O2O001O01O00000000O10O100O01O01O1O0O10010OO2O010O1O01N3MON5000mVT<\", \"size\": [1280, 720]}, {\"counts\": \"Sin=1nW110000000001O00101N1O1O02O0O1O01O100O001O0000000001O1O01O00OO200O10O0100O010O01O01O00100O01O000O2O1O1N102NSgQ<\", \"size\": [1280, 720]}, {\"counts\": \"XY^?1oW60j_M2S`M1M3N110O1O000100O00010N[_k;\", \"size\": [1280, 720]}, {\"counts\": \"XQ]?1mW13O0000O101O00K221O1M2O2O0O02N10O2OO3N102MZ_k;\", \"size\": [1280, 720]}, {\"counts\": \"SQP>1oW10UP50joJ101O001011N1N00000001O1O2N1N100000000000000O100O1O100O1O000001O001O001O001O0O2NO20O2N2O2O000000RWo;\", \"size\": [1280, 720]}, {\"counts\": \"XQU>1oW10O2O00001O010O20N1O01O0000000000000O1O1O1000000O1O01000O1O10N1010O10O10O1O02N1O1O1OO2O03NP_U<\", \"size\": [1280, 720]}, {\"counts\": \"XQa=1oW1000O10000O1001O0001O0010N50K20O01O001O2N001O000100O000000O100000000O1000O1000O0100000O010O000010O10O01O2M2O1O2O00h^Z<\", \"size\": [1280, 720]}, {\"counts\": \"]YX=1oW100000O2O001O1O2N01O00001000O001O010O100O11OO001O001O0000000000O100O10000O1O0100000O001N2O101N0100O12N^fj<\", \"size\": [1280, 720]}, {\"counts\": \"baa>1oW10P`20P`M0O2OO1000N2N2O11O002N1O1O1O^fj<\", \"size\": [1280, 720]}, {\"counts\": \"]aT=1oo40PXL1O0O2O00010O2OO2N00O2O00001O00001O1O010O0000010N10000001O00000O100000O100O02O000O100O100O100O01O12NRh3NPXL0^^d<\", \"size\": [1280, 720]}, {\"counts\": \"bam=1oW12M2O2N001O00000000000O1001O000000O001000000O010001N2O0O1O1000O1000O101O1O0000_^d<\", \"size\": [1280, 720]}, {\"counts\": \"]i_=1nW1101O00102M000010O0001O0010000O00001O000000000001O0010OO0100O10O1000O11N1O2N1O1000O10O011O1O_^i<\", \"size\": [1280, 720]}, {\"counts\": \"XQ\\\\=1nW11O1N3N1O12N2N1O02N2N000100O1O010O010O01N2O0000000O101O01O0000000000000000O01000000O100O10O10O10O010000O100LlVc<\", \"size\": [1280, 720]}, {\"counts\": \"XY]=1nW1101N1000001O001O0010O01O00000100O2NO10001O00000O2O000100O0000O10000O010O10O10000O010000O0100000001N1O2N1O1O10hf`<\", \"size\": [1280, 720]}, {\"counts\": \"SY]=1nW12O1O100O11N0001O001O001O001N21N2O1N000001O0O2O1O001O01O1O00000000O010O0010O100O1000O10O010O1O100O2N1O2Oh^d<\", \"size\": [1280, 720]}, {\"counts\": \"SQa=1nW11O2N100010O01O001O1O000100O2N01N101O001O1O00000001O000000O0010O10O02O0O10000O10O10M211N1O2O00mnf<\", \"size\": [1280, 720]}, {\"counts\": \"n`c=1oW1001O1N3O0O01O10O001O001O1O1O00001O001O000001O001OO10000O1000O100O101O000O1O1O000100001N2Oonf<\", \"size\": [1280, 720]}, {\"counts\": \"nXX=1oW100000000001O0O200O2N000010O1O0001O1O010O1O001O001O000000001O01O00O10O1000000O1000000O10000O100O1O100O0100001O0000002Mnf`<\", \"size\": [1280, 720]}, {\"counts\": \"nXX=1oW10O2O001N200O10O0001O00001O00001O1O1O000100O0001O00000000000000O10000O1000000O1O100O100O2O0O012N1OmVm<\", \"size\": [1280, 720]}, {\"counts\": \"nPm<1oW1000000000000001O0O2O1O1O1O01O010O0001O00010O1O0O1010O01O0001O0000O100000000O1001N10000O1O001O10O1000O100M3O1OQX10PhN000Rok<\", \"size\": [1280, 720]}, {\"counts\": \"RYX=2mW11O101O001O00001O001O001O001O0000001O0001O01O00000O10O10000000000O10O10O1O1O010O100O1O1N2N2O1000000000Rgj<\", \"size\": [1280, 720]}, {\"counts\": \"n`j<1oW100000000001N10100O2N00001O000100O0001O1O1O00001O00001O000000000000000000000000O10000O100O1O100O10000O10001NNN5000RoP=\", \"size\": [1280, 720]}, {\"counts\": \"n`e<1oW100000000000000000R`23i_M200000001O0001O1O01O00000O110O00001O00001O00000O010000O010O10000O10000O10000O1O100OQP5NT_n<\", \"size\": [1280, 720]}, {\"counts\": \"SYd<1nW11001O000000O10000000001O0O2O00001O1O001O00000001O01O0000000O101O00O1000000O10O100000O1O100000001N100000000O2O000O100000O12NmnP=\", \"size\": [1280, 720]}, {\"counts\": \"nPR=1oW1000O2O1O1O2N0001O011N0010N10001O001O1O00000000000O1000O1O10001N101O001N2Nn^b=\", \"size\": [1280, 720]}, {\"counts\": \"nhP=1oW1000nW62PhI000O110O00001O00001N1001O01O10O000000O1001O01O0001O000000O010001O004L001OmfY=\", \"size\": [1280, 720]}, {\"counts\": \"kXb=4nW1N01O0O2O001O0010O01O00000O10001O01O00000010O0O10O1000001O1O2N1O1NnVW=\", \"size\": [1280, 720]}, {\"counts\": \"ihZ=2nW11O2M2O001O010O2O0O00010O001O0001OO1O1002N1O100O2M2On^g=\", \"size\": [1280, 720]}, {\"counts\": \"iXS=1oW12N00O11N10001O0021M1O01O001O1O00001O0010O001O000000O100000000000000000N200O010000000000O0102MTgT=\", \"size\": [1280, 720]}, {\"counts\": \"iXn<1nW12O0O2N1O2O00000001O1O10O1O0010O00010O0001O00000000000001O0O01000000000000O11O001O00O1O12M2N3NRgY=\", \"size\": [1280, 720]}, {\"counts\": \"n`j<1oW1000O100O1O1N3O02N1O0001O001O001O10O000000000001O0000O10000000000000O100000O10000O100O2N01000000001O0O2OPX10ogN11ORWR=\", \"size\": [1280, 720]}, {\"counts\": \"nXd<1oW11O00O1000O101O00001O001O2N0000001O00000001O01O00O01000000000000000000000O2OO10000000O100O100O1O101O000000000O10R_X=\", \"size\": [1280, 720]}, {\"counts\": \"Sa`<1oW11O1O001O2N00001O001O001N100001O001O0000O101O0000O0100000O10000000O01000000O2O1O000MXh3NmWL0h^g=\", \"size\": [1280, 720]}, {\"counts\": \"gYZ<1oW1000O2O010O3M10O01000O0100O000000000O1000000O100001N10O010000O0001O1000O1N2N2O2O00000000Y^Q>\", \"size\": [1280, 720]}, {\"counts\": \"_YU<4kW12O100O100O00001O0001O000010N100000000000000000O100O10O10O100O10000O10001N10000O1000001O^^V>\", \"size\": [1280, 720]}, {\"counts\": \"bYU<1oW1000O2N3N1O1O000100O001O01O001O0001O0000O10000000O101N10O1O010000000000O1N2O1000O10O100000001O00^^Q>\", \"size\": [1280, 720]}, {\"counts\": \"_a`<4kW12O1O1O1O1O01O010O00000000001O1O1O10OO010000O10O10O100000O1O1O1O100000O100O02O1O1O000000000000^^g=\", \"size\": [1280, 720]}, {\"counts\": \"bQh<1nW12N2N2N200O001O01O1O2OO01O0000O10000O10000001O00O1O10O1000000O01000N2O100O101N101O^^g=\", \"size\": [1280, 720]}, {\"counts\": \"lQR=1oW19G21M1O0001O0000000000000000000O10000000O010000000O2OO10N1000010O101O0L4OU^b=\", \"size\": [1280, 720]}, {\"counts\": \"Xao<1oW11RX11lgN10O01O1O001O1N10000000000000O100O1000O10000O1O010O10O100O010O001N2O3N1Oh^b=\", \"size\": [1280, 720]}, {\"counts\": \"]Qm<1oW10000000O2O010O1O20N0010O0000001O0000000000000000000000000000000000000000O100O101O1O^Va=\", \"size\": [1280, 720]}, {\"counts\": \"]Yn<1oW100001O1O12ON00001O000000000000000001OO1000001O0O100O11O001O00O1000O1000N110ON3OO20O2O2Nc^]=\", \"size\": [1280, 720]}, {\"counts\": \"`Yi<3lW110001O001O001O002OO1O100O1O00O1000000000000000001O0O10000O1001O1O1O00O0010O0100000N2N20O11N`n_=\", \"size\": [1280, 720]}, null, {\"counts\": \"bYd<1nW11010O01ON61J101O00100O002OO2N01O0000000O10000O20O000000O010000000O100001O00O10O010OO12N4K[Vf=\", \"size\": [1280, 720]}, {\"counts\": \"Vj\\\\<1nW13M200O1O0100O1000O2OO000010O01O01O000100O0000000000O10000000000000O100O10002N1NamS>\", \"size\": [1280, 720]}, {\"counts\": \"XaV<1nW12O101N12M1O01O01O01O2N10O2OO0001O000001O00001O00O1000000000O1000000010N100O1000000000O010M30L4O100000jo91nVW=\", \"size\": [1280, 720]}, {\"counts\": \"bYd<1nW13O2NO100O0100O002OO00010O1O01O000O1001O00O10O101O01O00O1000O1O1000O011O00000O0O2O100O1000001OYfc=\", \"size\": [1280, 720]}, {\"counts\": \"\\\\ae<2nW1001O1O01O0021NO01O001O1O1O1O010O000000000000000000000O100000O100000000N2O010O0100O01000OO2O00010OO20N2N200OifY=\", \"size\": [1280, 720]}, {\"counts\": \"bao<1mW12N3O1O11N10O10O100O10O010O010001N01OO1001O00000O1000010O0O10O01001OO0O20N1001N1100O100N101O101NlW10ThN2N00100ffo<\", \"size\": [1280, 720]}, {\"counts\": \"]Yn<1mW12O20001N10O002OO01O0001O10O02N010O0001O10O0000O1001O0000000010O00O1000O10O1O000001N10010O10O100OO2O00O1010M3O1Oi^n<\", \"size\": [1280, 720]}, {\"counts\": \"bQ\\\\=1oW11O120M0001OQ`2Oo_M3N0O2N3N00O10001O000O1O1O01N101O0N12O0010OO2O01O000001N2O2NlW10UhN0mVR=\", \"size\": [1280, 720]}, {\"counts\": \"WQW=3mW1010O13NOO00O2O00100O002OO0001N101O000O11O1O000001O0O10000O10O10O01O001O0000010O1O001O0001O00O101O1OO22NMN50RWh<\", \"size\": [1280, 720]}, {\"counts\": \"bQf=1nW1101N1000001O0010O01O1O0000000O100000000001O0N10010O0001O10O001N11O0010O1M30O02O00h^i<\", \"size\": [1280, 720]}, {\"counts\": \"lah=1oW11O1O1O20N01O0O2N110O01OO2N2O0000O001O10O01O10O01O0001O01O01OO011O1O001N2O1O2O^^i<\", \"size\": [1280, 720]}, {\"counts\": \"baY=1mW13N101O10O1000Shl02iWSO2O0O0100O01000N101O01O10O1M20100O2O2NkW10hnf<\", \"size\": [1280, 720]}, {\"counts\": \"aa^=2nW1001O01NVX10jgN101O00001O1O001O11N2NO1N20000O100O100000001OO100O00000001O10O0N3OO2O00O2O00101N3Nc^i<\", \"size\": [1280, 720]}, {\"counts\": \"bid=1oW11OO42K00100O002N10O2N10O00O1001O0O1O1O100O1000O10000N2O001000O1M20O103LanP=\", \"size\": [1280, 720]}, {\"counts\": \"Sa^=1oW1001O2N1001N1O0001N101O001O10N101O0002N000O10O11O0000O1000001OO0100O1000O1O1N2N1100O10000O1O2O1MoVh<\", \"size\": [1280, 720]}, {\"counts\": \"Wa\\\\>2lW13O010O1O1O001O00000O100O10O1O1O10O10O0100O01000000O101N1O2N1O100000O100O2N12M2OmVT<\", \"size\": [1280, 720]}, {\"counts\": \"Xam=2mW14L2O1O000010O00000001O01O001O101M^Wj0LnWj;\", \"size\": [1280, 720]}, {\"counts\": \"bYl=1nW11O2N21N1NSX14hgN5M1O001O10000O00N201O0002NO101O0000000000000000000O100O1O0100O10O01ON3O_f`<\", \"size\": [1280, 720]}, {\"counts\": \"]QP>6iW1N30O100O2N2N3M3M3N1O2O00O10O000000000000000000000000O1000000O100O1O001M3O1O1O2N3M3MdV^<\", \"size\": [1280, 720]}, {\"counts\": \"fiX>2jW15N5L1O2M2O1O1O1O1O2N00001O0O1001O001OO2O000000000000O101O000O1O1L5M5K]V^<\", \"size\": [1280, 720]}, {\"counts\": \"lYl=1oW10PP50noJ2M4L3N3O1O1N2O1O1O1O1O0010O000001O0O1000000001O0001O0O1000000O010000O101M3L6J4L4K]f[<\", \"size\": [1280, 720]}, {\"counts\": \"XQa=1oW10000000jg81RXG5M3M2O001N110O01O0001O00000000001O01O00O100000O100000000000000O2O0O2O001N1O2M3Nnf`<\", \"size\": [1280, 720]}, {\"counts\": \"Va^=3kW13O1N4NO00000001O0001O00001O0000000001O01O0000000000001O00000000O1000000O100000O10O02N2N1O1O2N1000O1001OhVc<\", \"size\": [1280, 720]}, {\"counts\": \"bac=6iW13N1O01O01000O01O010O10O0100O0002N0O2O0O2N101N1OUVa=\", \"size\": [1280, 720]}, {\"counts\": \"bYb=2nW14K101O001O1O00010O1O1O010O00010O001O01O0000000001O000001O0O10000000000O101N1O5KV^n<\", \"size\": [1280, 720]}, {\"counts\": \"iaf>7gW14M100O001N100101N4L3J4OQP50ooJ1O12NYVc<\", \"size\": [1280, 720]}, {\"counts\": \"nYb=4kW11O1000000002O0O0001O01001N00001O0001O01N1O1000000000000000000O100000O2OO001O10O100O1O2N2NnW10ShN000TVc<\", \"size\": [1280, 720]}, {\"counts\": \"]a^=1nW1101O01O0O1001O4L1O001O1O002OO01O000O2O00000000000001O1OO10000000O10O100001O1N10O0001O10000O1O1KhVh<\", \"size\": [1280, 720]}, {\"counts\": \"WiZ=4lW1000000001O1O10O00010O1O1O01O001O1000O1OO100000000O100O100O1O1000000O001O10O010O01O0001O1O1N3N1K50mfj<\", \"size\": [1280, 720]}, {\"counts\": \"TiZ=5kW11O1O01O01O010O010O10O0000010O1O01O1O001O01O0O01O1O11O00O0O2O100O0011O0O10O10O010O001O1O1O2M3LPWm<\", \"size\": [1280, 720]}, {\"counts\": \"XYS=1nW11000001O001O002N100O0101N0101N0000001N10000O20N1000000000O100O100O10O2O00O1O01O010O010O1O1N3N3LmW`00RgQ<\", \"size\": [1280, 720]}, {\"counts\": \"SaT=1oW15K001O000100O01O010O10O00010O1O0001N10010O00001N11O1N10O1O10000O100O1000O10O10O1O10O10O010O1O1N2L4O100000000000000001O0000Onf`<\", \"size\": [1280, 720]}, {\"counts\": \"UY]=4kW12O000001O0O2O01O1O001O1O0O2O010O01O000000O10000O100000000O100000O1000O0010O100O010O010O2N2M3Mofj<\", \"size\": [1280, 720]}, {\"counts\": \"Xi_=1oW11O1O001O2N010O0001O01O002O00O1O000001O0O100000000002NO100O10000O100O1001N100000O1O000010O100O1O1M300O2N1000O11O000000J6N13LP_U<\", \"size\": [1280, 720]}, {\"counts\": \"Xac=1nW12O001O001O1O001O000102M01O10O01N2O0000001O01O000000000000O10000O11O1O00000O010000O1O0010O100N2OQ`2KU`M0PX60h^P<\", \"size\": [1280, 720]}, {\"counts\": \"Xam=1oW11N2O000O20O01O1O000010O002N100O01O1O1O000O1000000001O00000000O10000O100001O0000O1O000010O1O2O0O1N2O1O1O100000h^P<\", \"size\": [1280, 720]}, {\"counts\": \"Xin=1oW11O0O0100001O00001O1O1O10O2O00O01O01O1O1N1000000001O01O00000O10000000000000000000O0100O1O1000O1O1O1O1JV`20P`M0hVo;\", \"size\": [1280, 720]}, {\"counts\": \"Sii=2nW11O1O1O001O03M010O01O1O002N001000O1000O1O00000O100O101O0001O001O0000O10000O1001N101OO000100O1O4Lmg3OUXL0hfV<\", \"size\": [1280, 720]}, {\"counts\": \"iPa=4lW11O001O01O002OO010O1011M01O001O1O010O1O00001O0002N1O0000O1000000O100O11N10000000OO2O01N2O0010O0M5NT_d<\", \"size\": [1280, 720]}, {\"counts\": \"dXn<1mW12O2M200010O10O1O10O10O010O21N2M1O100O1NQh30PXL0RWU>\", \"size\": [1280, 720]}, {\"counts\": \"dha<1oW12M3N1O03N000O0101N0100O01O1000O0O21N02O0N3NP`20PPi00m^i<\", \"size\": [1280, 720]}, {\"counts\": \"ih\\\\<1nW11O101O1O10O02O000O001O1O010O00001O01O10O01O0001O00000000000000O1000O2OO10000000O0100O2N001O1O1000001O1N10Rgc=\", \"size\": [1280, 720]}, {\"counts\": \"iP^<1oW13M2OO10O00100O1O1O010O0000010O00010O000O2O00000000000O1001N2O1OO1O0010O100000O10N3M2O1O2O000000Rgh=\", \"size\": [1280, 720]}, {\"counts\": \"SQj;1RX12J1O1O2N1O1000001O0000O10001O010O1O10O001O10O100O002N0000O1O11O001O00O11O00000000O1000000O100O10O1000000O1O100O2N100Oinn=\", \"size\": [1280, 720]}, {\"counts\": \"Xag;1mW12O2O000O101N10000O0100000001O1O10O010O2O0O0100O1O1010N0001OO10000O100011N1O0000O1O010O100O10000000O2N1O1O2N10000000cfR>\", \"size\": [1280, 720]}, {\"counts\": \"UYP<5iW130O10N1O0O200O10O10001O010O1O2OO1000O0000101NO100001O01O01O1N10000001N00101O00000000O0100O100O01000O11N1N2O2O0O10000000PP50hV\\\\=\", \"size\": [1280, 720]}, {\"counts\": \"]a[<1nW12N1O100O00101O02O00O01000O000100O00010O001O00O101O00000000000001N10O100000000000O0100000O10NN400O10000000cnd=\", \"size\": [1280, 720]}, {\"counts\": \"giW<1nW110000O2N1O100O10000O2OO100O10010O1O100O01O2N02N2N01O0O1001O0000O1O12N1O000000000000O100N2001O1OO001000L4O1000000000Y^b=\", \"size\": [1280, 720]}, {\"counts\": \"gY_<1oW10PX10PhN0O2O1N2O1N10O010010O01O1N11O1O00001O01O100O000001O01O1O01O1O0O01000O101OO1O2OO100000O1000O100O01000000O1N2N2OZnZ=\", \"size\": [1280, 720]}, {\"counts\": \"fiP=7hW11000001N1000000001O1O01O01O0010O001O1O01O000000O20O1N1010O000O01000O10000000O1000O10O100O01O1000O2O000O100O1O1N2000000O10Yfe<\", \"size\": [1280, 720]}, {\"counts\": \"laY=1mW13N2O00000O1O110O001O0010O001N11O1O01O001O1O1O0000O1001O01O00O1000000000O1O1001O0000O10000O010000O2N1O1O1O1000T^d<\", \"size\": [1280, 720]}, {\"counts\": \"QbY=1nW11l_20S`M2M3O1O001O00000001O00001O01O0001O002N01O000000010O00011NO10O1000000000O10O10000000O10O1000000000O1O3N2NoUc<\", \"size\": [1280, 720]}, {\"counts\": \"QRa=7hW120O1O000000001O001O01O001O0010O1O10O0001O0000000000000010N010O100001O0000000000O0010000000000OO3N1O1O10000000Ph30oWL11Oj]U<\", \"size\": [1280, 720]}, {\"counts\": \"`jS>1nW1100O2N100O1O1O20O2N000000O100000O0100O100O10000O02O1O001O0M3O1001O00O1O10ee`<\", \"size\": [1280, 720]}, {\"counts\": \"YRS?6gW13N3M20O2O4K5Jgmf<\", \"size\": [1280, 720]}, null, null], \"bboxes\": [[302.0, 574.0, 45.0, 18.0], [287.0, 567.0, 50.0, 18.0], [290.0, 558.0, 51.0, 16.0], [292.0, 555.0, 56.0, 20.0], [294.0, 551.0, 59.0, 22.0], [290.0, 543.0, 68.0, 20.0], [294.0, 537.0, 76.0, 29.0], [292.0, 555.0, 80.0, 21.0], [296.0, 550.0, 83.0, 23.0], [301.0, 546.0, 82.0, 18.0], [305.0, 548.0, 98.0, 20.0], [326.0, 547.0, 185.0, 211.0], [338.0, 543.0, 107.0, 22.0], [337.0, 543.0, 110.0, 23.0], [330.0, 546.0, 106.0, 20.0], [306.0, 551.0, 101.0, 27.0], [316.0, 548.0, 62.0, 31.0], [302.0, 549.0, 72.0, 28.0], [308.0, 561.0, 67.0, 23.0], [317.0, 562.0, 62.0, 23.0], [305.0, 555.0, 71.0, 23.0], [312.0, 558.0, 51.0, 27.0], [297.0, 562.0, 57.0, 22.0], null, [300.0, 556.0, 54.0, 17.0], [301.0, 553.0, 32.0, 16.0], [301.0, 545.0, 55.0, 24.0], [295.0, 556.0, 54.0, 17.0], [333.0, 557.0, 18.0, 11.0], [310.0, 557.0, 51.0, 13.0], [317.0, 557.0, 42.0, 11.0], [325.0, 552.0, 46.0, 17.0], [321.0, 560.0, 48.0, 14.0], [326.0, 562.0, 42.0, 14.0], [324.0, 564.0, 48.0, 14.0], [322.0, 568.0, 46.0, 11.0], [323.0, 559.0, 41.0, 19.0], [319.0, 565.0, 43.0, 13.0], [314.0, 569.0, 48.0, 14.0], [319.0, 562.0, 43.0, 26.0], [330.0, 562.0, 19.0, 17.0], [357.0, 566.0, 36.0, 20.0], [352.0, 567.0, 47.0, 22.0], [353.0, 562.0, 43.0, 17.0], [358.0, 556.0, 47.0, 15.0], [350.0, 547.0, 59.0, 21.0], [357.0, 541.0, 54.0, 19.0], [395.0, 535.0, 21.0, 18.0], [394.0, 532.0, 22.0, 22.0], [358.0, 541.0, 55.0, 22.0], [362.0, 542.0, 46.0, 17.0], [346.0, 550.0, 58.0, 15.0], [339.0, 556.0, 52.0, 17.0], [372.0, 556.0, 19.0, 8.0], [336.0, 556.0, 60.0, 13.0], [356.0, 558.0, 40.0, 11.0], [345.0, 556.0, 47.0, 14.0], [342.0, 546.0, 55.0, 18.0], [343.0, 550.0, 56.0, 12.0], [343.0, 546.0, 53.0, 17.0], [346.0, 544.0, 48.0, 14.0], [348.0, 541.0, 46.0, 14.0], [339.0, 541.0, 60.0, 15.0], [339.0, 540.0, 50.0, 15.0], [330.0, 541.0, 60.0, 13.0], [339.0, 542.0, 52.0, 14.0], [328.0, 541.0, 58.0, 14.0], [324.0, 542.0, 60.0, 11.0], [323.0, 544.0, 63.0, 11.0], [334.0, 541.0, 38.0, 12.0], [333.0, 539.0, 46.0, 10.0], [347.0, 539.0, 34.0, 11.0], [341.0, 536.0, 27.0, 15.0], [335.0, 536.0, 48.0, 12.0], [331.0, 533.0, 48.0, 13.0], [328.0, 537.0, 57.0, 12.0], [323.0, 541.0, 57.0, 9.0], [320.0, 545.0, 48.0, 12.0], [315.0, 566.0, 45.0, 12.0], [311.0, 558.0, 45.0, 12.0], [311.0, 560.0, 49.0, 11.0], [320.0, 558.0, 48.0, 12.0], [326.0, 558.0, 42.0, 12.0], [334.0, 571.0, 38.0, 14.0], [332.0, 549.0, 40.0, 14.0], [330.0, 556.0, 43.0, 9.0], [331.0, 554.0, 45.0, 11.0], [327.0, 559.0, 47.0, 11.0], null, [323.0, 561.0, 46.0, 14.0], [317.0, 580.0, 41.0, 16.0], [312.0, 551.0, 61.0, 15.0], [323.0, 561.0, 48.0, 14.0], [324.0, 552.0, 55.0, 17.0], [332.0, 554.0, 55.0, 21.0], [331.0, 552.0, 57.0, 19.0], [342.0, 547.0, 43.0, 26.0], [338.0, 542.0, 55.0, 24.0], [350.0, 551.0, 42.0, 17.0], [352.0, 561.0, 40.0, 22.0], [340.0, 557.0, 53.0, 18.0], [344.0, 554.0, 48.0, 20.0], [349.0, 559.0, 37.0, 15.0], [344.0, 545.0, 49.0, 13.0], [368.0, 544.0, 41.0, 14.0], [356.0, 537.0, 40.0, 25.0], [355.0, 559.0, 44.0, 19.0], [358.0, 550.0, 43.0, 23.0], [365.0, 559.0, 36.0, 26.0], [355.0, 560.0, 48.0, 25.0], [346.0, 540.0, 53.0, 17.0], [344.0, 547.0, 53.0, 13.0], [348.0, 561.0, 25.0, 17.0], [347.0, 561.0, 41.0, 17.0], [376.0, 561.0, 21.0, 17.0], [347.0, 572.0, 50.0, 13.0], [344.0, 556.0, 49.0, 14.0], [341.0, 546.0, 50.0, 17.0], [341.0, 545.0, 48.0, 18.0], [335.0, 547.0, 63.0, 18.0], [336.0, 547.0, 63.0, 15.0], [343.0, 544.0, 48.0, 15.0], [345.0, 544.0, 63.0, 20.0], [348.0, 551.0, 59.0, 13.0], [356.0, 550.0, 56.0, 13.0], [357.0, 551.0, 56.0, 13.0], [353.0, 547.0, 54.0, 17.0], [346.0, 537.0, 50.0, 17.0], [331.0, 527.0, 26.0, 17.0], [321.0, 531.0, 49.0, 18.0], [317.0, 535.0, 54.0, 16.0], [318.0, 537.0, 49.0, 15.0], [302.0, 545.0, 60.0, 14.0], [300.0, 546.0, 59.0, 18.0], [307.0, 544.0, 66.0, 16.0], [316.0, 552.0, 54.0, 14.0], [313.0, 561.0, 59.0, 13.0], [319.0, 563.0, 59.0, 15.0], [333.0, 564.0, 62.0, 15.0], [340.0, 567.0, 56.0, 13.0], [340.0, 569.0, 57.0, 14.0], [346.0, 576.0, 62.0, 14.0], [361.0, 584.0, 38.0, 11.0], [386.0, 578.0, 8.0, 14.0], null, null], \"areas\": [441.0, 600.0, 532.0, 710.0, 685.0, 807.0, 1158.0, 1189.0, 1280.0, 1032.0, 1287.0, 1065.0, 1366.0, 1480.0, 1102.0, 1118.0, 823.0, 867.0, 647.0, 838.0, 797.0, 716.0, 571.0, null, 522.0, 169.0, 524.0, 413.0, 78.0, 341.0, 281.0, 460.0, 427.0, 399.0, 399.0, 346.0, 599.0, 158.0, 144.0, 168.0, 114.0, 169.0, 193.0, 193.0, 247.0, 314.0, 314.0, 100.0, 129.0, 279.0, 263.0, 324.0, 316.0, 52.0, 308.0, 257.0, 298.0, 385.0, 347.0, 346.0, 325.0, 341.0, 389.0, 378.0, 363.0, 398.0, 401.0, 306.0, 345.0, 240.0, 225.0, 219.0, 231.0, 254.0, 319.0, 295.0, 303.0, 333.0, 251.0, 299.0, 257.0, 268.0, 280.0, 314.0, 239.0, 170.0, 186.0, 288.0, null, 254.0, 249.0, 190.0, 247.0, 322.0, 257.0, 313.0, 202.0, 319.0, 219.0, 270.0, 190.0, 254.0, 176.0, 257.0, 236.0, 149.0, 421.0, 648.0, 734.0, 782.0, 505.0, 418.0, 209.0, 428.0, 139.0, 341.0, 433.0, 362.0, 410.0, 376.0, 412.0, 382.0, 386.0, 355.0, 351.0, 349.0, 365.0, 284.0, 131.0, 120.0, 385.0, 343.0, 385.0, 350.0, 372.0, 252.0, 295.0, 356.0, 478.0, 340.0, 355.0, 304.0, 211.0, 74.0, null, null], \"iscrowd\": 0, \"video_id\": 0, \"height\": 1280, \"width\": 720, \"category_id\": 847, \"noun_phrase\": \"a neck\"}, {\"id\": 997, \"segmentations\": [{\"counts\": \"anPg02iW15O2N2N1O2N1O1O100000000O100O1000O1000000O10000O1000000O00O2O03N1N3N2M3N00hac3\", \"size\": [1280, 720]}, {\"counts\": \"`^Xg03jW14M3N2N1O2N1O1O100O100O2M2O01000N2O10O10O1001N10000O10001N10001N2N6J3M2M6MO01OcaY3\", \"size\": [1280, 720]}, {\"counts\": \"]f^g06iW12M2O2O0N3N1O1N2O100O100000000O100N110O1O1000O10000O10000O100O2O1N2O5J2N3N1OhaT3\", \"size\": [1280, 720]}, {\"counts\": \"\\\\fcg07hW12N1O1O1O2N1O1O1O101N10000O1O00100O10000O10O0100000O010O100O2N101N2N4M3L3M2N2N1OiQm2\", \"size\": [1280, 720]}, {\"counts\": \"\\\\Vfg04kW13L3N2O0O1O2N1O1O1O1O100O1O1000000O10O10O100O1O010O100O02N1O1O2O0O2O3M2M3M4L2N2O0OiYi2\", \"size\": [1280, 720]}, {\"counts\": \"]nig01mW15L3M1O2O0O1O2M2O1O1O100O1000000O10000O0O2O1O1O1O010000O1O2O0O2O1N1O2O3L4L3M3M2O1Niae2\", \"size\": [1280, 720]}, {\"counts\": \"[^Qh08gW12N2O000O2O0O1O100O2N100O1000000O10O0100O100O010O001O10O01000O2N1O2N3M3N3L2N3N1M2OiQ^2\", \"size\": [1280, 720]}, {\"counts\": \"XVdh0>`W14L2N3M200O100000O010000000O2ON200O010N20O101N10001N2O1N8I2M2N3N2LfYU2\", \"size\": [1280, 720]}, {\"counts\": \"_nlh08aW18J6N100O1O100O10O010000O10O10O010O1O02O1O0O2O1N2O7H3M3M2O1N101Ndim1\", \"size\": [1280, 720]}, {\"counts\": \"afkh04iW16F8J5O10O10O100O100O1O010O0010O01001N101O0O2O1O6I3M3M4L3M100000cQo1\", \"size\": [1280, 720]}, {\"counts\": \"[^oh0;\\\\W1;L2N2001O00N101O1N110O02O000O2O1O1N3N7H2N3N2M2NfYU2\", \"size\": [1280, 720]}, {\"counts\": \"SnQi0?\\\\W18M2O1N1N0M30010O0101N2O1N2O1N8I2N1N3M2N2O1MfYU2\", \"size\": [1280, 720]}, {\"counts\": \"meZi0e0YW14L3O10N2N2O001N5L4K3N2M3M2N2NdYU2\", \"size\": [1280, 720]}, {\"counts\": \"Q^^i0g0UW14O10O2N3M4L4L3M2O2M2N3MdYU2\", \"size\": [1280, 720]}, {\"counts\": \"ZV]i0>WW1;NO211N5L4K4M1N2O1N2N2N1OdYU2\", \"size\": [1280, 720]}, {\"counts\": \"bn[i01hW1:J3OO111N4L2N2N1O2O0OdQY2\", \"size\": [1280, 720]}, null, {\"counts\": \"WfZi0<cW1000101N2M3Mja`2\", \"size\": [1280, 720]}, {\"counts\": \"angh02gW19J3M4M3N1O20O2O2N3L4M1N3N1N102M3Ndae2\", \"size\": [1280, 720]}, {\"counts\": \"[Vdh08aW18J5K5N2000O1000001N2O2N2M3M3N2M2O1O1O0O2O0M3Njia2\", \"size\": [1280, 720]}, {\"counts\": \"]VUh07_W1;J501O0O100O1000O101O00000O1000000O01000O1O101N2N2O1N3N2M2N2O1N100O2O0Oia`2\", \"size\": [1280, 720]}, {\"counts\": \"]^lg01nW16J1O2O0O2O0O1O1O1O1O10000O2N1O100O1O001O100O1O01000O0010O2N2N1O101N5K2O1O2M2O1N101N1Oja`2\", \"size\": [1280, 720]}, {\"counts\": \"]nng01nW13L3N2N2O0O1O101N1O1O1O1O100O100O2N10O0100O10000O010000O2N0010O02N2O3L2O1N2O1O1N2O1N2N2O00ha[2\", \"size\": [1280, 720]}, {\"counts\": \"ZnSh04jW13N2N2O1N1O1O2O0O1O1O10000O1000000O100O010O10O100O100O011N100O1O2O1N2O1O1O1N101N10ma[2\", \"size\": [1280, 720]}, {\"counts\": \"XnXh06iW12N1O101N1O1O1O2N1N2000000O10000O10000000O10O1O02O000O100O100O2O0O2O1N2O2M101O1M2MPbV2\", \"size\": [1280, 720]}, {\"counts\": \"]^[h01nW11M5M2N1O3M100O1O100O1O100O100O100O1000000O1000O010O100O10000O1O011N3N1N3N1N2O0O3N1N3NhaQ2\", \"size\": [1280, 720]}, {\"counts\": \"\\\\^[h02lW14M2N2N2N100O1O1O100O100O2O0O00100O100O100O1000O100O100O100O100O1O3M3N1N2O1N2O1O1N3NhiR2\", \"size\": [1280, 720]}, {\"counts\": \"\\\\^[h02kW15L3O0N3M3O0O1O1O1000000O100O1O100O10000O0010O10000O100O101O0O1O004M2M2O1N2O1O0O1000miR2\", \"size\": [1280, 720]}, {\"counts\": \"]f\\\\h01lW14M3N1N3N1N2O2O000O100O100O1O1O100O100O100O010O010O10O2O0O2O0O1O2N3M3N2M2O1M3NnYU2\", \"size\": [1280, 720]}, {\"counts\": \"[fah03jW14N2N1O2N1O2N100O1O1O1O100O1O100O10O010000O10O01O010O0100O100O2N4M2M3N1O1N3N1M2MPRo1\", \"size\": [1280, 720]}, {\"counts\": \"\\\\ngh02jW15N1O1O2M2O1O1O100O1O1O2O00000O1O1O10O01O1O1O0O2O10O100O2N102N1N4L2O2M3N1N2N3MnQj1\", \"size\": [1280, 720]}, {\"counts\": \"]^jh01mW13M3N2N1O2N1N2O100O1O100O10000O1O2N1O1O001O001N2O0100O101N2O1N1O2O3L3N2M2O2M101OmYf1\", \"size\": [1280, 720]}, {\"counts\": \"anlh02kW14M3N100O2N1O1O101N100O10O0100N2O100O100O1O001O1O10O1000000O101O0N5L4L3M2O0O2O1M3Mjab1\", \"size\": [1280, 720]}, {\"counts\": \"aVnh04kW14K201N1O1O101N1O100O100O1O1O1O010N2O1N2O10O01000O10000O100O2O0O7J2M2O1N2O2M2N2N1OdYa1\", \"size\": [1280, 720]}, {\"counts\": \"gnlh01kW15M3N2N1N3O0O100O100O2O0O1O001O1O1O1N101O1N110000O10000O100O102M6K2M3M2O1N2O1OcQe1\", \"size\": [1280, 720]}, {\"counts\": \"fnlh03kW13N2M3M3N100O1O1O1O100O100O1O1O1O1O1N20ON3O10O100O100O011O0O107H4M1N2N2N2O1O3L3N^ic1\", \"size\": [1280, 720]}, {\"counts\": \"gnlh01mW14L3N2M3N1O1O1O2N100O100O1O1O1O1O001O100O1L310000O100O011N100O7J2N2M3M2N2O1N3N2M2O^ab1\", \"size\": [1280, 720]}, {\"counts\": \"f^jh02lW14L3N1O2M3N1O1O100O100O100O1O1N2O1L4N1100O01000O100O10000O101N7I3N3L2N2O1N2Ocih1\", \"size\": [1280, 720]}, {\"counts\": \"fVdh03kW14M2M3M2O2N1O100O1O1O1O1O1O1O1O10O01O1O1O1M2010O2O0O100O1O101O6I4L3N1N3M2O0O4M`im1\", \"size\": [1280, 720]}, {\"counts\": \"gVih01mW14L3N3L2O1N3N1N111N1O100O1O100O1O1N1N3O10O10O0100O100O2OO10O2O6I3M3N2M2N2O1N5L_ih1\", \"size\": [1280, 720]}, {\"counts\": \"gVSi01mW13M4L3M3M2O100O1O2N001O1O101N00100N200O1N1O20O10000O100O100O2O3L5L1N4L2O1N2N2Oci^1\", \"size\": [1280, 720]}, {\"counts\": \"eV]i04jW13M3N2N1N3M200O1O100O1O1O100O1O1O1O1O001O10O01O010O0100O101O0O2O6I4L2N3N1N2O1MeiT1\", \"size\": [1280, 720]}, {\"counts\": \"afdi05jW13M101N101O0O1N2O10000O1O2N1O1O1O1O0O2M3000O01000O100O10O0100O2O1N4M4K2O1N2N3M2N3Mdij0\", \"size\": [1280, 720]}, {\"counts\": \"]fii04kW13M2N1O1O2O0O1O100O2O0O1O1O1O001O1O1O100O01000O02O0000O01O1O101N2O2M3M2O0O2O2M2N2N2Ohad0\", \"size\": [1280, 720]}, {\"counts\": \"]nei01nW12M3N2N2N2O0O2N100O1O100O100O100O1O1O1O100O1O010000O010O2N10O01O012M2O1N2N3N0O2O1N101KQRg0\", \"size\": [1280, 720]}, {\"counts\": \"]nVi01nW13N3L2N2O0O2O0N200O2O0O1000000O1000000N2O00100O1O010O01O010O001O1O101N2O1N2N2N1O2O1N2L3NoiT1\", \"size\": [1280, 720]}, {\"counts\": \"nekh06jW11O1O2M2O0O101N1N20001O0O100000000O100O1O100O010O10O010O1O100O10O0100O1O1O2O0O2N2N2N2N2N2N2NXb]1\", \"size\": [1280, 720]}, {\"counts\": \"Seah03mW11O4L1O1N2O0O2O0O2O000O2O000001O01N10000O010001O0O10000O100O100O010O101N100O001O010O10O0100O1N3N3MRkc1\", \"size\": [1280, 720]}, {\"counts\": \"Wl]h01mW14M4L20O2N2NO2N100O2O0O10001N100O101O0O100O10000O0101O0O100O01000O10000O10001N1O010O1O100O2N1O2MQlh1\", \"size\": [1280, 720]}, {\"counts\": \"akbh04jW13N2O1O1N1000000O2O000001OO1O1O100O10000O10O3N1O000O0010O010O0100O10000O1O10O0100O101N1N3Midg1\", \"size\": [1280, 720]}, {\"counts\": \"Tcah06jW10O2M2O2O001O0010O01O0000000000001O00000O1O1001N1000000000O000010O10O010O0100O00100O1O1O2O2M2M3MSUe1\", \"size\": [1280, 720]}, {\"counts\": \"S[jh02eW13\\\\hN1cW17O1O1N2O001N10001O00000O100001O01O0O1O10O2O1OO10O10001N10O010000O100O10O02O1N100O1N3N4M2MSm^1\", \"size\": [1280, 720]}, {\"counts\": \"oZoh02iW1:J101N101O1O0000001N1000001O000O100001OO100O11O1O0O01O100000O10O0101O1N2OO10O100O1O1O1O100O3M1O2LZUV1\", \"size\": [1280, 720]}, {\"counts\": \"kbUi06gW14O2N1N2O001O00001O000O2N1000001O0O11O1O00O1O100001O00O1001N2O000O10O010000O100O10000O1O100O1O2N2M4LYen0\", \"size\": [1280, 720]}, {\"counts\": \"jbZi02lW15L2O1O1O1O0O2N2O001N101O0O110O000O101N100O1000000000000001N10O100O11N1000000O100O100O1O1O2O1N1O3L4M3MX]h0\", \"size\": [1280, 720]}, {\"counts\": \"iZ^i02lW14N0O2N2O0O2O000O101O001O001O0O2O1O00001O0000000000010O0O10000000O10001O00O1000O10O11N1O100O2O0O1O101N2M2O\\\\Ub0\", \"size\": [1280, 720]}, {\"counts\": \"ebdi01oW1001O1N4M001O1O1N2O001O1O001O0O101O0O2O001O01OO10000001O000000000000O100O02O000O101N10000O100O2O0N3N1O2K_U=\", \"size\": [1280, 720]}, {\"counts\": \"ebii06iW12O1O001O1O1O0O2O001O001O1O001O00001N10000000000001O0000O2O00000O01000001O00001N10O0100O1O1O2N2L4M]m;\", \"size\": [1280, 720]}, {\"counts\": \"jZhi01mW13M3O1O2N1O0O2N2N101O1O001O001O01OO101N100000000001O001OO100O100O100000O10001O0O101OO0100O1O1O2O1M4J^m;\", \"size\": [1280, 720]}, {\"counts\": \"iRgi04kW13N0O2O1O1O1O1N2N101O010O0O2O0010O00O2N100001O1OO1O11O1O0000N2O11O001OO10O101O1O0O010O010O010O100O3L4L[m;\", \"size\": [1280, 720]}, {\"counts\": \"RcXj03jW1310O00001N1O101N1O100O2O0O10000O1O10000O1N110001O2N00O1O010O10O010O01000O1O2M3N3L4LYe5\", \"size\": [1280, 720]}, {\"counts\": \"TS`j01oW13L3M100O101O0001O00O101N01000O2O0O1O1N2O1000O1000O1000O10O100O1O0100O101N10O01000Z]O\", \"size\": [1280, 720]}, {\"counts\": \"Ycbj01oW1001M3N2O01O0O02O2N0O1O1O100O1000000000000000000O10000O1N2N2O0010O10000O101N10U]O\", \"size\": [1280, 720]}, {\"counts\": \"TSVj01oW11O4K11O0O1N23NO01M2O10001O000000000002NM30000O100O1O1L40O1000000O10000O2O0O010O010O100O100O1O2N10V]O\", \"size\": [1280, 720]}, {\"counts\": \"ojji01nW120NP`22o_M1N2N1N4N01O00O100O2O01O00O1O100001OO10000O1O1O02O0000O10O010O1O100O10O010O10O1O101M3N2O0K`]9\", \"size\": [1280, 720]}, {\"counts\": \"njTj02lW12O101O000O1O2M2N20000O100O1O10000000O1000O1O10O10O10O2O1N0010O10O00100O1O1O4L`U=\", \"size\": [1280, 720]}, {\"counts\": \"ibdi02mW1101O0N2O1O2O01N10N3O0O101M2000001N100O11O1O1ON2O10000000O10O1001N1000O010000O100O1O000001O02O0O2O0O3Le]>\", \"size\": [1280, 720]}, {\"counts\": \"ebdi03lW13N1N1O100O1O100010O1OO1O1O2O00000O1000O101O2NM30000O01000O0100O0010001N10O1O0100N10001O1O3Kied0\", \"size\": [1280, 720]}, {\"counts\": \"eR]i01oW10PX60PhI000M5M2N2O00001O01OO1O100O1O10000000000O1O11N100O01O100N1O2000O1O3N000O1O1O01O01O1O2M6Jgme0\", \"size\": [1280, 720]}, {\"counts\": \"eZhi01nW12N2O10NW`2Mg_M600O1N2O101N100000000000000O100O1000O10000O00100O10000O01O010O00000010O00101N2N3M3LgU=\", \"size\": [1280, 720]}, {\"counts\": \"eZWj06iW11O2O0N3N11O01O0O1O10000O1000000000000O10O10O1000O2O1N1000O0100O10O01O001O00001O1O3Jim6\", \"size\": [1280, 720]}, {\"counts\": \"jjYj01oW100000O2N101N3M2O0O1O2O01O1O00N2O2O0O100O1O01000O100001N010000O01O101N2O1N01O01O00100O01O010d]O\", \"size\": [1280, 720]}, {\"counts\": \"oZaj01nW11N3N2O1O1O1O0O10000K50001O0O10000001OO0010000O1O0100O100O100O2O1N100O10O01O0010_]O\", \"size\": [1280, 720]}, {\"counts\": \"QSej04jW12O2O0002NO1N200N3L3O1N200O11O2N0O01O100O100O001000O2N2O0O100O100O01O00010_]O\", \"size\": [1280, 720]}, {\"counts\": \"Tk^j01lW13O1O2M2N2000001M20000O1O100O100O1O1001N101OO100O010O10O100O3N1N2N010O1O1O1O2N1OZm1\", \"size\": [1280, 720]}, {\"counts\": \"oR[j01oW10O2O1O2M2N1O1O1O2M2O11O00O1O1O1000000O1000O101O000O00100O1O2O0O100O2N1O1O1O1N]m6\", \"size\": [1280, 720]}, {\"counts\": \"oRVj01mW13N2N2N1O1O100O1O2O0O100O1O1001O000O10O100O1N20O101O0O2O0O100O1O010O100O1O1N1O2Oae:\", \"size\": [1280, 720]}, {\"counts\": \"R[Rj03kW12N2O2N100O1O10000O10000O100O100O100001OO10O1000O10O01O01O2O2M00010O001O001O2N2L_]>\", \"size\": [1280, 720]}, {\"counts\": \"YSli01o_70ogI2N2M3N2N2O000000000O10O10O100O001N2M30O0010O0100O1O1O10000O100O1N2O2M3J[Ub0\", \"size\": [1280, 720]}, {\"counts\": \"^S]i01oW10000001O0O2O2M2O000O2N11O0000O100O1O1O1O1O1O1000000000O1000O10O01O01O100O00100O1O1O1O1O2N2M3Nnlj0\", \"size\": [1280, 720]}, {\"counts\": \"bS]i02mW12N100O110O1N1O2N2O01O00O100O10000O1O100O1O1O1000O2OO10O10O1O0010O100O100O1O001O1O100O1O2N2NlTl0\", \"size\": [1280, 720]}, {\"counts\": \"c[ci01mW12O1O2O001O1O0O1O1O10001O0O100O1O100001O000O0100O1O01000000O01000O1O100O01O01O10O01N102N2NmTg0\", \"size\": [1280, 720]}, {\"counts\": \"^[hi01nW12O001O1O1O10O01N1000001O02NN2O10000O2N10000O1O0100000000O10O100O001000O1O1O00100O1O0O10100O101Om\\\\>\", \"size\": [1280, 720]}, {\"counts\": \"^kji01nW12O01O0001O3M0O10000100ON200O10001N1O10000O1O1000O2OO10000O10O0100O10O0000101N100O010N2O100O1O001O1O2MoT8\", \"size\": [1280, 720]}, {\"counts\": \"a[Rj03lW1101O0010O01N101N101O00000O100000000O1000000O100O010O10O011N100O1O10000O1O001O10O01M3O2Nh\\\\9\", \"size\": [1280, 720]}, {\"counts\": \"ckoi01mW12O3N002O0O1O001O0N2O11O01O000O100001O00O1O1000000000O100000000O1000O010O01O100O1O002N1N201N1O2Ocl6\", \"size\": [1280, 720]}, {\"counts\": \"ekoi04jW1201O1000O01N2O1O00000O1O100001O00O2O0O1O00100001OO1O10O2O0000O000100O010O101N001N2O1O2N1N201NcT8\", \"size\": [1280, 720]}, {\"counts\": \"g[mi02nW13M2N10O01O0O20O10O1O0O100001O1OO1O1O11O00O1O1O10O100O10000O010O01O001O01O2N1O100O1N2O2N2N^T=\", \"size\": [1280, 720]}, {\"counts\": \"hkoi04lW12O0O001O021M001O000O110O000O1O100001OO1O1O11N2O1OO1N11000O10O00001O10O0101M20O01O002O0L4O^d:\", \"size\": [1280, 720]}, {\"counts\": \"ecSj05kW1101N1O00100O10000O01OO1N2000000O100O100O1O10O100O10O10O01N2O02O0O101N1O1O1O2O0O1O1N2O1O10001O^\\\\4\", \"size\": [1280, 720]}, {\"counts\": \"_kTj06iW12O11OO1O001O00001O0O1O100001O00O100O100000000O1000O10001OO010O10O1O1O1O010O1N2O1O010O100L4O10000000g\\\\O\", \"size\": [1280, 720]}, {\"counts\": \"]cXj04lW13M001O1O001O001O1N100000000000000O1000000O001001O0O10O1O010O0100O100O1N2O100O1N100001O1000O10m\\\\O\", \"size\": [1280, 720]}, {\"counts\": \"XS`j04kW13N100O001O0100O01O0O2O000O100O10000002NO100O010O1N110000O01O1O0100O100O1O001O0100Q]O\", \"size\": [1280, 720]}, {\"counts\": \"Tclj01nW14L3N010O01000O100O01O000000O1N200000000O1000O10O01000O010O010P]O\", \"size\": [1280, 720]}, {\"counts\": \"QkWk05jW12O1O1N2O0100OO101O01O0000000000000000O01000P]O\", \"size\": [1280, 720]}, {\"counts\": \"njWk03kW15M1O001O0010O01O000O100O11O0000000000000000T]O\", \"size\": [1280, 720]}, {\"counts\": \"jbVk06jW11O001O010O1O1N11O01O0O10000000000000000001O00U]O\", \"size\": [1280, 720]}, {\"counts\": \"ij\\\\k03lW13N1N2O010O001N100000000000000000000Y]O\", \"size\": [1280, 720]}, {\"counts\": \"SS^k02mW12N4M0O2O01O01O00000O100O1O100O100S]O\", \"size\": [1280, 720]}, {\"counts\": \"ak\\\\k03kW13O1O1O001O1O000O100O1O1O100O1O10000g\\\\O\", \"size\": [1280, 720]}, {\"counts\": \"hcQk01nW11O1O1011M2OO3L3O1N2O0O20O0001N1O1000001N10O1000000000_\\\\O\", \"size\": [1280, 720]}, {\"counts\": \"hSoj01nW11O101N1O1001000O1N2O0O2O000O101O0O1O10000000000O100000000`\\\\O\", \"size\": [1280, 720]}, {\"counts\": \"cSjj01nW11N200O2O0O1000100O1N101O00000O2O0O1000000000000O100O11O00O100O100g\\\\O\", \"size\": [1280, 720]}, {\"counts\": \"YSoj01oW10O2O2M2N2O0000001O000O100O101N1000O1000001O00O1O100000000n\\\\O\", \"size\": [1280, 720]}, {\"counts\": \"Skmj02lW13O1N2O1O001O0001O0000O10000000001O0000000O0010000O100000000S]O\", \"size\": [1280, 720]}, {\"counts\": \"jZkj03nW12M1O1O001O1O001O0O100000000O10001O0O1000O1000O100O11O0000O100O1[]O\", \"size\": [1280, 720]}, {\"counts\": \"jjmj01nW13M2N2O001N1010O000000O2O00000O1O01000000000O10000O100000000^]O\", \"size\": [1280, 720]}, {\"counts\": \"jRoj01nW1101N2N2N2O010O00000O101OO10O1O1O1001OO100O100O1O100000000`]O\", \"size\": [1280, 720]}, {\"counts\": \"eRTk06iW11010O1O00O101N100O10O01000000O1000000O1O10000O100b]O\", \"size\": [1280, 720]}, {\"counts\": \"_ZPk02mW12N101O1O10O0001O01O00000000010OO1O010001O0000O11O001O00`]O\", \"size\": [1280, 720]}, {\"counts\": \"_ZPk02mW1101N1N2O20O10N1N201N101N1001O00000000000O0100O11O000000i]O\", \"size\": [1280, 720]}, {\"counts\": \"hjmj03lW11O2N1O2N101N110O00001O0O100O10000000000000000O10000O1000O10a]O\", \"size\": [1280, 720]}, {\"counts\": \"Tcgj01nW12O1N2O1N2O0O10000O2O000001O0000O11O00000000000O1000O1000000000000O100R]O\", \"size\": [1280, 720]}, {\"counts\": \"YSej02mW15K1O101M21O1O01N1O1O100000000001O0000O1O010O10000O001N2000O1O00100O101O00S]O\", \"size\": [1280, 720]}, {\"counts\": \"][\\\\j02mW11N3M200O2N10000O110O03MO1O100O1N20000O01000O1001O1N01O10O10O01O1O010O100O010O100O1O1O10Q]O\", \"size\": [1280, 720]}, {\"counts\": \"YSVj01nW12N2N2O1O1N11O0000O1N201O0O11O000000001ON1010000O1O10OO1N31N100O1O012M1O2O0O2O1N00100M4MSe5\", \"size\": [1280, 720]}, {\"counts\": \"Tkoi01nW11N3N2N2O0O2O001N100O100O100O10000O10000O1000000O02O00000O100O100O100O010O2N1O1O10O01O1O2N3LX]9\", \"size\": [1280, 720]}, {\"counts\": \"R[mi03lW11O1N3N101O0O2O0000000O1O10000O101O0O10O0100001OO010O11N10000O10O11N1O0010O01O10000N2O1O5KXU=\", \"size\": [1280, 720]}, {\"counts\": \"SSgi02mW11O1N2O2O00000O2N100O2N1000001O000O100000000O0100000000O10O10O011O0O10000O1O00100O010O1O1O002N1O\\\\e?\", \"size\": [1280, 720]}, {\"counts\": \"mZhi03kW1201O1N1O1O101N1O2O0O1O2O0O1001O000O010000O10O01O1O010000O010O1000O01O100O1O2O0O10O02M3N3M`Ub0\", \"size\": [1280, 720]}, {\"counts\": \"jbii01nW12O3L2N1O1O2N10000O1000001O000000O0100O100O100O010O100O10O010O2O0O010O100O1O1O1O10O02N101M5Jce?\", \"size\": [1280, 720]}, {\"counts\": \"hjoi03jW13O2O00001N100O2N1O100010O00O1O100O010O10000O001O11O001N01000O00M31O101N2N100O1O2N1O2NgU=\", \"size\": [1280, 720]}, {\"counts\": \"ejTj06jW1001O0O2O0O1O2O00001N1O1000000O10O0100O1O1000O1000O10OO2M210000O1O1O1O100O2N100O101N1N2O3Kie5\", \"size\": [1280, 720]}, {\"counts\": \"iR[j02mW12O1N1O2N101O0O10000O2O000O1000000O1O100O0100000000O10O10O010O10O0101N1O0000010O10000O1O10f]O\", \"size\": [1280, 720]}, {\"counts\": \"mbbj03kW13O010O1N2N101N1O1O2O00000000001OO100000O100000000O1000O100000O01O10000O10O100]]O\", \"size\": [1280, 720]}, {\"counts\": \"obgj04kW13M1O1O2N2N1O2O001N10000000000O1O1000000000O010000000O01000O010O10O10O`]O\", \"size\": [1280, 720]}, {\"counts\": \"jR`j06iW12O0O2N101N2O0O1O1O101N100O10000O1O1000O0100O10O100O01O0101O1N100O100O100O100O1O10c]O\", \"size\": [1280, 720]}, {\"counts\": \"eZRj03lW14M0O2O0O2O0O1O101N10000O10000O100O1000000O010001O0O10O10O01OO110O1O2N2O1N1O10O02N1Ofm;\", \"size\": [1280, 720]}, {\"counts\": \"dRbi02jW15O0O2O001O0O2O0O101O0O100O10000O01000O1O1N20O10000O10000O2OO10O010O1O1O2O0O2O0O1O1O2N2N3Mf]h0\", \"size\": [1280, 720]}, {\"counts\": \"`RSi01nW13N3L2O001O001N100O1O1N3O00OO2O100O1000000000O1000000O100O010O100O100O1O100O1O1O2N1O2NkmY1\", \"size\": [1280, 720]}, {\"counts\": \"[ZYi03lW15K4M2N102N0O1OO010O100O100O00O2O0010O001O2N2O0O1O011N1N2O6Jfmc1\", \"size\": [1280, 720]}, {\"counts\": \"Ubni09fW13O0O000000O1M4IPVj1\", \"size\": [1280, 720]}, {\"counts\": \"XZci05jW13N3L3M3OO01O00N3N1O1N2N6IhUo1\", \"size\": [1280, 720]}, {\"counts\": \"VR]i0`0_W12O1O0O100N12O1O1N1O1O101N2N2NimR2\", \"size\": [1280, 720]}, {\"counts\": \"VZci0=aW16M0O0010N10000O1O1O1N2O1OkUo1\", \"size\": [1280, 720]}, {\"counts\": \"cZci09dW17K2N2N10O1O1O1O2O0O1O1O2M2O2Mbel1\", \"size\": [1280, 720]}, null, {\"counts\": \"Ucdi07gW13N2N2N001O1N1O2N20O1O1O2M3N1DdhN2ddl1\", \"size\": [1280, 720]}, {\"counts\": \"h[^i02lW12M3N2M2M4001N2OO010O010N1002N1O10O0002N3IlTj1\", \"size\": [1280, 720]}, null, null, {\"counts\": \"bkei0:eW13M20N2N6H\\\\dV2\", \"size\": [1280, 720]}, null, null, null, null], \"bboxes\": [[589.0, 703.0, 38.0, 23.0], [595.0, 700.0, 40.0, 28.0], [600.0, 700.0, 39.0, 26.0], [604.0, 699.0, 41.0, 27.0], [606.0, 697.0, 42.0, 28.0], [609.0, 696.0, 42.0, 29.0], [615.0, 699.0, 42.0, 28.0], [630.0, 698.0, 34.0, 31.0], [637.0, 698.0, 33.0, 31.0], [636.0, 698.0, 33.0, 31.0], [639.0, 696.0, 25.0, 32.0], [641.0, 696.0, 23.0, 34.0], [648.0, 697.0, 16.0, 29.0], [651.0, 700.0, 13.0, 28.0], [650.0, 700.0, 14.0, 28.0], [649.0, 710.0, 12.0, 16.0], null, [648.0, 709.0, 7.0, 14.0], [633.0, 699.0, 18.0, 26.0], [630.0, 696.0, 24.0, 28.0], [618.0, 700.0, 37.0, 26.0], [611.0, 697.0, 44.0, 29.0], [613.0, 698.0, 46.0, 27.0], [617.0, 699.0, 42.0, 24.0], [621.0, 698.0, 42.0, 23.0], [623.0, 699.0, 44.0, 24.0], [623.0, 700.0, 43.0, 24.0], [623.0, 698.0, 43.0, 25.0], [624.0, 695.0, 40.0, 27.0], [628.0, 694.0, 41.0, 28.0], [633.0, 691.0, 40.0, 30.0], [635.0, 692.0, 41.0, 31.0], [637.0, 699.0, 42.0, 28.0], [638.0, 701.0, 42.0, 29.0], [637.0, 702.0, 40.0, 31.0], [637.0, 701.0, 41.0, 32.0], [637.0, 701.0, 42.0, 33.0], [635.0, 701.0, 39.0, 32.0], [630.0, 700.0, 40.0, 34.0], [634.0, 701.0, 40.0, 33.0], [642.0, 701.0, 40.0, 33.0], [650.0, 700.0, 40.0, 33.0], [656.0, 700.0, 42.0, 31.0], [660.0, 699.0, 43.0, 27.0], [657.0, 697.0, 44.0, 27.0], [645.0, 699.0, 45.0, 28.0], [636.0, 689.0, 47.0, 26.0], [628.0, 668.0, 50.0, 22.0], [625.0, 639.0, 49.0, 19.0], [629.0, 615.0, 46.0, 19.0], [628.0, 602.0, 49.0, 22.0], [635.0, 598.0, 47.0, 23.0], [639.0, 596.0, 50.0, 23.0], [644.0, 596.0, 51.0, 22.0], [648.0, 592.0, 52.0, 25.0], [651.0, 593.0, 54.0, 22.0], [656.0, 592.0, 53.0, 21.0], [660.0, 593.0, 50.0, 22.0], [659.0, 593.0, 51.0, 23.0], [658.0, 595.0, 52.0, 23.0], [672.0, 595.0, 43.0, 22.0], [678.0, 598.0, 42.0, 22.0], [680.0, 602.0, 40.0, 21.0], [670.0, 601.0, 50.0, 19.0], [661.0, 594.0, 51.0, 20.0], [669.0, 590.0, 40.0, 20.0], [656.0, 585.0, 52.0, 22.0], [656.0, 584.0, 47.0, 21.0], [650.0, 582.0, 52.0, 21.0], [659.0, 581.0, 50.0, 23.0], [671.0, 585.0, 43.0, 21.0], [673.0, 588.0, 47.0, 22.0], [679.0, 593.0, 41.0, 21.0], [682.0, 593.0, 38.0, 22.0], [677.0, 595.0, 41.0, 20.0], [674.0, 595.0, 40.0, 19.0], [670.0, 592.0, 41.0, 20.0], [667.0, 594.0, 41.0, 20.0], [662.0, 600.0, 43.0, 23.0], [650.0, 608.0, 48.0, 21.0], [650.0, 611.0, 47.0, 22.0], [655.0, 610.0, 46.0, 22.0], [659.0, 610.0, 49.0, 21.0], [661.0, 610.0, 52.0, 20.0], [667.0, 616.0, 45.0, 17.0], [665.0, 618.0, 49.0, 18.0], [665.0, 620.0, 48.0, 20.0], [663.0, 624.0, 46.0, 19.0], [665.0, 626.0, 46.0, 19.0], [668.0, 622.0, 48.0, 21.0], [669.0, 617.0, 51.0, 18.0], [672.0, 611.0, 48.0, 22.0], [678.0, 607.0, 42.0, 21.0], [688.0, 608.0, 32.0, 15.0], [697.0, 607.0, 23.0, 13.0], [697.0, 603.0, 23.0, 13.0], [696.0, 602.0, 24.0, 12.0], [701.0, 599.0, 19.0, 11.0], [702.0, 605.0, 18.0, 14.0], [701.0, 617.0, 19.0, 16.0], [692.0, 625.0, 28.0, 16.0], [690.0, 624.0, 30.0, 16.0], [686.0, 617.0, 34.0, 17.0], [690.0, 610.0, 30.0, 15.0], [689.0, 605.0, 31.0, 14.0], [687.0, 597.0, 33.0, 17.0], [689.0, 594.0, 31.0, 16.0], [690.0, 592.0, 30.0, 17.0], [694.0, 590.0, 26.0, 15.0], [691.0, 589.0, 29.0, 10.0], [691.0, 582.0, 29.0, 16.0], [689.0, 591.0, 31.0, 17.0], [684.0, 606.0, 36.0, 13.0], [682.0, 604.0, 38.0, 21.0], [675.0, 607.0, 45.0, 19.0], [670.0, 602.0, 45.0, 22.0], [665.0, 599.0, 47.0, 19.0], [663.0, 597.0, 46.0, 20.0], [658.0, 596.0, 49.0, 21.0], [659.0, 589.0, 46.0, 24.0], [660.0, 587.0, 47.0, 23.0], [665.0, 582.0, 44.0, 25.0], [669.0, 582.0, 46.0, 25.0], [674.0, 586.0, 46.0, 22.0], [680.0, 595.0, 40.0, 20.0], [684.0, 593.0, 36.0, 24.0], [678.0, 587.0, 42.0, 26.0], [667.0, 584.0, 43.0, 22.0], [654.0, 581.0, 46.0, 22.0], [642.0, 580.0, 44.0, 22.0], [647.0, 580.0, 31.0, 25.0], [664.0, 580.0, 9.0, 13.0], [655.0, 581.0, 14.0, 18.0], [650.0, 578.0, 16.0, 22.0], [655.0, 580.0, 14.0, 21.0], [655.0, 589.0, 16.0, 21.0], null, [656.0, 600.0, 16.0, 23.0], [651.0, 614.0, 22.0, 20.0], null, null, [657.0, 624.0, 6.0, 15.0], null, null, null, null], \"areas\": [555.0, 702.0, 718.0, 706.0, 722.0, 743.0, 710.0, 746.0, 658.0, 615.0, 548.0, 479.0, 284.0, 219.0, 185.0, 89.0, null, 69.0, 246.0, 410.0, 658.0, 709.0, 705.0, 659.0, 645.0, 666.0, 654.0, 716.0, 685.0, 704.0, 706.0, 723.0, 700.0, 725.0, 743.0, 761.0, 780.0, 770.0, 801.0, 800.0, 810.0, 812.0, 758.0, 695.0, 708.0, 713.0, 757.0, 673.0, 608.0, 558.0, 641.0, 789.0, 841.0, 865.0, 942.0, 865.0, 773.0, 831.0, 904.0, 919.0, 585.0, 577.0, 491.0, 539.0, 557.0, 491.0, 642.0, 540.0, 531.0, 510.0, 551.0, 566.0, 564.0, 521.0, 526.0, 499.0, 526.0, 499.0, 463.0, 538.0, 590.0, 529.0, 497.0, 492.0, 413.0, 535.0, 561.0, 448.0, 436.0, 516.0, 511.0, 530.0, 483.0, 300.0, 239.0, 248.0, 248.0, 183.0, 179.0, 209.0, 279.0, 319.0, 382.0, 317.0, 313.0, 377.0, 353.0, 323.0, 255.0, 199.0, 331.0, 401.0, 359.0, 505.0, 494.0, 507.0, 590.0, 568.0, 597.0, 664.0, 632.0, 610.0, 653.0, 575.0, 546.0, 616.0, 735.0, 587.0, 613.0, 633.0, 425.0, 89.0, 164.0, 268.0, 228.0, 241.0, null, 234.0, 222.0, null, null, 65.0, null, null, null, null], \"iscrowd\": 0, \"video_id\": 0, \"height\": 1280, \"width\": 720, \"category_id\": 847, \"noun_phrase\": \"a neck\"}, {\"id\": 998, \"segmentations\": [null, null, null, null, null, null, {\"counts\": \"abUb01nW13J?C4N0N3N2O100N1N7K3M36HAPORjNn0bV1JWOVOejNd0[U1]OljN;YSY9\", \"size\": [1280, 720]}, {\"counts\": \"lYcb02oW1OY`f03a_YO>D3L3O2N1O1O000O10000O1O1O100000000O10O01N200O1O2OO1O101O1N2O0OKXOeiN5ZV1JPjNMlU1NbiN1]W109Oh^<1XaC000N2M3L4YXd6\", \"size\": [1280, 720]}, {\"counts\": \"la[d03lW1`0A4K2O1O0O2N1O1000000000000O1O0010000O2N100O10O100000O10000O10000O10000O12M3N2Ma0@2N3M4LdUP6\", \"size\": [1280, 720]}, {\"counts\": \"lQmd03hW1a0B>C5M0O001O0O100010O00O1000000O100O100000O01000000O1O100000000O0011N1O2NN20001OROPjNKRV1L]iNOl0LjU11g`71^`H3VoU5\", \"size\": [1280, 720]}, {\"counts\": \"gQWe02eW1>K1N5L2M3N2N1N4M3M1O2M3N001N2O0000010O1N100O10O1000O10000O0100O001O001N101O1TOoiNGSV14o0M4JQWW5\", \"size\": [1280, 720]}, {\"counts\": \"^QRe04kW1<C8I2L5L5M1O1O1N2O0010O01O00000O100O10000O10000O10001OO1000O100O10001N10000000O100O1O1N1O1YOf0O3J6M4KYWR5\", \"size\": [1280, 720]}, {\"counts\": \"QZnd03jW15H8M2O1O1N3N1O1O001O0O101O00000000001O000O10000O20N1001N1000000O1O100O100O1O1001O00000O101N1^OXiNJiV13b0O2N3N2O2NdVR5\", \"size\": [1280, 720]}, {\"counts\": \"Rjkd02lW15L3N1N2O2N1O1O1M3O1N2N1N210O000M3O100O1O110O0JTOXiNl0iV1510O0N2O100001OO2O0000000000O100mNZiNk0nV1N1J8I7J6L4Mio4LonP5\", \"size\": [1280, 720]}, {\"counts\": \"RZdd01nW17I2O2L4M3N2N1J6O1O0O2N2O0O1O1O20O0000M3O1O1O1O1001O10N1O10000000O2O0gNciNo0mV1IOO2J\\\\nn5\", \"size\": [1280, 720]}, {\"counts\": \"ajWd01lW14N1O1O2N2N2O1N2O2N1O1O00000000000O1O1O11O1N2N1OO1:Ce]j6\", \"size\": [1280, 720]}, null, null, null, null, {\"counts\": \"fRPa01oW1002NO1O100003L4L5K5L10O001M3DchN1_W1Mee^:\", \"size\": [1280, 720]}, {\"counts\": \"[Rf`04kW16K3M6J4K3N1O1OO010000O10000O0100O100O2N1CV^b:\", \"size\": [1280, 720]}, {\"counts\": \"\\\\bo?1oW15K2N3N3M3L4M8H2M2N001O1O001O00000000000O10O10000000O0100000O10O10O100O10000O100000000O10O101TOaiN4`V1HkiNNZV1LcnU:\", \"size\": [1280, 720]}, {\"counts\": \"WZn?1dW13`hN<UW1EhhNj0jV18N0000nNXiNl0nV10O2O0O101N20O00010O000001O001O0000O10000O010000000O100000000O1000O010O1O1O100O1000O100O1XOfiNIZV16niNCSV19k0N2N4NW^n9\", \"size\": [1280, 720]}, {\"counts\": \"fb\\\\a01oW10jW12RhN6J4N001N1001N2O3M5J\\\\mU:\", \"size\": [1280, 720]}, {\"counts\": \"VRk`04lW14K5L1O2M3N3M2N2N1O0000000000000000000O11N1O1D;N200O1OO3O01O001O1O1O2K0O41nf8NRYG2M300`g[9\", \"size\": [1280, 720]}, {\"counts\": \"^Z]`04hW17L5L5J4M2N2N1O2N1O00010O000000000001N100001O00000000000000001O00000O1000000000E;1O00J5M4L4M4N2Ng]i9\", \"size\": [1280, 720]}, {\"counts\": \"YRa`05hW18J7J5J3N2N001O0000O2O00000000O1001O0O100000000000000001O00000001OO100000O01O01L4H2@lhN=]W1O00002L5M1OnW1MRX1OXn\\\\9\", \"size\": [1280, 720]}, {\"counts\": \"]jXa07gW15L9H9G2N1O001O000O1O0012OO001OO011O1O01OO10N21O001O00O10O10000J5N2N3O2M2O10O01O2N1O1O0000001O0N3K8KE5:L^W;1bhD1O1COihN4bW1N2O1NjnT8\", \"size\": [1280, 720]}, {\"counts\": \"hSia02jW17`hNIPW1j0J5J[OSiN>hV1>O1O1OO01O11O0000O1O11O1N101OO100000O1000O1O20O1O00O1O1001O0000O010O1N2N20O10O1O10O01O0001O2L4\\\\Of0[OX]W8\", \"size\": [1280, 720]}, {\"counts\": \"aRna02mW12ZhN4WW1c0G4J2O10000000O10O1002N1O00O1000000O100000000000000O100000O10000000O1O100O2N10O10O100O10O10O0100O1N2N2_Od0GmUQ8\", \"size\": [1280, 720]}, {\"counts\": \"`QSb07hW14M4L3M3L3O0O1O0O2N2O0000001O01ON2O2O02N001O000O10O01000O1O001O1N2O1000000O10000000000000O00N22J5N2O1M3M3N1L4M2O2I8OUW;MohD1OOPXX7\", \"size\": [1280, 720]}, {\"counts\": \"YYea01nW11010Neh80[WG2O1YhNO^W10`hN4`W1=I3M101M1O0O1100OIWOXiNh0PW100001O000000000000O10000000000000001O000000O2O0001O01O00O0OO04L3N2O00001O1N3M2N1N2N3Ofnh0M^QWO1ZWn6\", \"size\": [1280, 720]}, {\"counts\": \"_Zcb064M^W1<M2O2O000O2O001O000000000000000O11O1O010N101OO101O0001O0001N11K5L3N2N2N101O2O0OoW1MRgl00SQRO0g^j6\", \"size\": [1280, 720]}, {\"counts\": \"fRlb0<`W15N2O0O2O00000000001O000000000O10000000000001O0000O2O1OO1N2O0O2M3O2M4K3N1N300O1O10`]c7\", \"size\": [1280, 720]}, {\"counts\": \"g[Wc04kW13L;E3O1N100O11O1O00O1O1001O0000O1O101O001N100O101O000000000O01N2N2N2O1M011O5L3M1L3M11lW1MXhN1nlQ7\", \"size\": [1280, 720]}, {\"counts\": \"i[Wc0:^W1:L3L3N3O0O2N1N2O1N2O1O1100O2NO1O1N3OO0100O1O1N101L4O10O10O1O1O12M4M1N000M4L8E9G:FWUX7\", \"size\": [1280, 720]}, {\"counts\": \"aZRc01S`21jgN2L5M7[hNITW1f0K2M30O001OO1O1O010L43M2M1N3L4LLYOViNc0UW1M2O004LKFhhN7[W1IehN4fW1N1Oi_2NSfd7\", \"size\": [1280, 720]}, {\"counts\": \"QSVc06hW1;G2N01O1N1L4L4O1O001KPP50UPK1OO01go4Oiei7\", \"size\": [1280, 720]}, {\"counts\": \"U[Wc01oW1;D3M2N2ON2O1O1N1O2O2L3O101N100O1000mlT8\", \"size\": [1280, 720]}, {\"counts\": \"kbnb01nW14M<D00O100O1O2N1O010N1O2M3N4MgW60WhI3beX8\", \"size\": [1280, 720]}, {\"counts\": \"PSQc0`0^W12O1O010O01O1O2N1OO11O00001O0O2O1O1NO110nW1JXhN12M`eS8\", \"size\": [1280, 720]}, {\"counts\": \"bkYc04kW1;E3L3N2N100N2002N1O0OO0012L5MICnhN;TW1CmhN<\\\\W1M7JaTQ8\", \"size\": [1280, 720]}, {\"counts\": \"^jcc05gW1?A7M10O10M2N201M101OO1O10000O0O2O3N1O1O000[OlhN>\\\\W1M2BehN7eW1Lme_7\", \"size\": [1280, 720]}, {\"counts\": \"QRoc03lW19G:E4L2O2M2M301M2OnN[iNl0eV1SOZiNo0hV13GnNdiNR1]V1lNciNS1cV1O4O0OO12SOSiNe0QW114N1ON02^OjhN:YW135M2N2FbhNOmmQ7\", \"size\": [1280, 720]}, {\"counts\": \"[jad07eW17M2N00001O001O0000N2N2O1O0ON34L1O1O2L3O1O10O1mW1NUhN0n]`6\", \"size\": [1280, 720]}, {\"counts\": \"ZZ_d05hW15M2O0O100O01100O0O2O4L0O10O1N101O1O2N1O0O2O0O2O1O2N1K50kW10ThN11OkW11\\\\fW6\", \"size\": [1280, 720]}, {\"counts\": \"baXc03VPd00gW]O1O0O0O20>C1H^OnhNc0UW1200O100N2100O1OO1O1O2O01OO10O0O2O3M1O1O00001N3M2O1N011O1O3L3MN31M3OmW1O1NUhN2\\\\o4NePK0[WU6\", \"size\": [1280, 720]}, {\"counts\": \"maXc01og=5n_CM2NoW1=bgN7I5M001N1000000O1O01N2O1O1O1O0O3N2M2O10N11O2O1N1N2M3N2O1O100O2OL0\\\\Vn6\", \"size\": [1280, 720]}, {\"counts\": \"XSec03hW18L2O0O101O001O001O1OO011N2N4K2O0O1O2O1O010O10nl`7\", \"size\": [1280, 720]}, {\"counts\": \"\\\\Rjc0190ZW1=L4M4M1O1N2O1M3N10O01N1N30010N10OO101001O1O1O2N1N2N3L4L8I6DeVS7\", \"size\": [1280, 720]}, {\"counts\": \"RRjc0<dW13L3M2O001N10O00011M2N2N010O2O2N3ML40000O1O0M33N1O0N3M3OcfU7\", \"size\": [1280, 720]}, {\"counts\": \"fjhc01oW100000O3L4N1HHehN9[W16O2O01N1BdhN7cW10M21O0O10011M3N3M1000`U]7\", \"size\": [1280, 720]}, {\"counts\": \"kRTd06jW10PP5MSPK04OL30LL321O0O11N2Mng30UXL0ko40iUi6\", \"size\": [1280, 720]}, {\"counts\": \"djhc06iW13M2M3N2O0O1N3O0O1010O001N1O1O1O1N1O2N2N2O1O100Oem[7\", \"size\": [1280, 720]}, {\"counts\": \"aR[c0>`W14M2O1N101O0O10ON2002M2O0000001M3N2O1N2O2O4IhUl7\", \"size\": [1280, 720]}, {\"counts\": \"PS[c0?`W13N2M1O110O1O000O1O11O01O0N2O2ON4L2OOO2N3N3NMChhN:XW1FihN7]W130K6N3Mh_20Y`M1ae_7\", \"size\": [1280, 720]}, {\"counts\": \"abgc07WW1c0M2O101N100O010OO2N2N100O2O1N2M2O2N1O2M3N3K^f_7\", \"size\": [1280, 720]}, {\"counts\": \"`blc03dW1<D:O0O100O100O101O0000000O1000N2O1O1N3M2N1O2N2N2M2O1N5LXVS7\", \"size\": [1280, 720]}, {\"counts\": \"bjhc0:bW16M2M2N3O0O2N1O1O100001OO1000000O010OO2O1O2N1O2N1O2N1N3N2N1O0N3OjmQ7\", \"size\": [1280, 720]}, {\"counts\": \"[Zac08_W1=K1O101O0O101O0O1000001O000000000000000O101O000000O102M1O2M101O1O2N1O1N2O2M2N3IT^o6\", \"size\": [1280, 720]}, {\"counts\": \"ab]c01iW18L300O001O0O1O1L5M21O1O2ON10O10O1O20O1O1O001O001O000000O100O100O11O1O00000000O101OO10001O1O000O1O1O100N2O1N2O1O1O5Kee\\\\6\", \"size\": [1280, 720]}, {\"counts\": \"lRjc0<cW12N2N1100O1O01O0O1N2M4O03N0O100O000001O00000000000001O0O1000O20N2O1O001O1OO1O010010O1N2N4L1O010N3Mm\\\\[6\", \"size\": [1280, 720]}, {\"counts\": \"ZSmd02mW11O2K6L2N2N2L5L4L3O1O1N3O0O11O2N1O2N2O2M7H5L1O1O1O0O01O1001O001N2N2M2Nhli5\", \"size\": [1280, 720]}, null, null, null, null, null, null, null, null, null, null, {\"counts\": \"di[b09dW17K4J5N2N010O0O100000001N100000001N101O001N1O2O0O10000O1001O000000O1001O0nN`iNe0_V1ZOgiN?[V1@i01chNOhW1O_U;1hS\\\\7\", \"size\": [1280, 720]}, {\"counts\": \"UQVc06iW13N3OO0001M3N1O2N2N1O1O2M2O1O3M2N1O0O2O001O001N10000O2O001O1O00001O01O2Nc0]O5K4L9FfeZ7\", \"size\": [1280, 720]}, {\"counts\": \"^YPd01mW16L3L6L3L4M2M2N1O1O001O1O00000010O01O00001O00000O2O10O01O1O001O001N100000000000000000000000O100O101N1O2K5K5N1K6L4M2N_ni5\", \"size\": [1280, 720]}, {\"counts\": \"[Phd01nW13M2[hNKZW1<31O1O100M2O01O11O1O0000000O2O00002liN@dT1c0XkN_OgT1b0XkN_OgT1c0XkN\\\\OhT1e0WkN\\\\OhT1d0XkN]OgT1d0XkN]OgT1d0XkN\\\\OhT1c0YkN]OfT1d0[kN[OeT1e0[kN\\\\OeT1c0ZkN^OfT1b0ZkN_OeT1`0\\\\kN@bT1b0_kN]O`T1d0ckNZO]T1d0fkNZO\\\\T1b0hkN\\\\OQT1JkjNi0U1]OVT1c0lkN\\\\OTT1c0nkN[OQT1f0g1M2M3NMEPiNNnV12UiNMlV11UiNO^W10YONSiNO51iV1OaiN1XW1O\\\\O0XiN0hV60dPK1o_<OPPX4\", \"size\": [1280, 720]}, {\"counts\": \"ajXa01oW10P`<0a_[30]PXL7K1O1O001O001O001O0010O01O01O01O01O0001O001O00000010O000010O0001O1N3K3O2N_ic2NlbJ\", \"size\": [1280, 720]}, {\"counts\": \"P[dd01mW14M3M2O1N2O0O2O0O101N1O2O00010O001O01O01O00001O01O00011N001OO2O01O010O1O0000000N2O1M3M4MR]g5\", \"size\": [1280, 720]}, {\"counts\": \"USe>1Zom52aXSJ6J5L3N1O1O001O1O001O00001O001O00001O000O2O000100O01N1O2O000O10N2N3L4I9H8Ic]Q6\", \"size\": [1280, 720]}, {\"counts\": \"hQVc06jW11O1O1O1O1O1O1O100O1O1O1N200O2O0O1O1O1O1O1O1O0000101N1O1O00001O00000000000000000O1000000O1000000O1000O010O1O012LZff6\", \"size\": [1280, 720]}, {\"counts\": \"UbUb04kW12M5M1O2O0O1N2O001O2N1O2M2O1O2M3N1O2N1O1N2O000000O10000O100000O01000O1000O101O000O100000O11O001O00000O010000000O101M3N7H8Iem`7\", \"size\": [1280, 720]}, null, {\"counts\": \"eaT`05hW15O2N0O1O002N00100O1O1O1O1O01O000100O1O1O1O000010O01O00000000O1O1O2M3N2M4K8IPf^:\", \"size\": [1280, 720]}, {\"counts\": \"kZg`07fW13O2N2O0O1O1000O0100O0100000O1O1O01000N2N2N2M300000000O10O100O1O001M3N2M3JRVm9\", \"size\": [1280, 720]}, {\"counts\": \"PkP`01mW16K4M3L3M5K5H5000O01O00000001O00O1O10010O000O1O11O0000O0100000000000O11O0000O10O0100000000000O1000000O001O1N1O3J5J6M5EdUh9\", \"size\": [1280, 720]}, {\"counts\": \"\\\\jZ`01nW12N4M2O2M2N1O2N2N2N2N2N1O1O1O1O1O2N1O1O10O010O01O00000000001N100000000000O01000000O100O0100001OO100000O1000000000000O01O001N2L4I9DRfV9\", \"size\": [1280, 720]}, {\"counts\": \"Yjd`07iW17I5K5J3N3M2N3M1O1O1O100O1O1O0010O01O0000000001O00000000000O10000000O01000000000000O10O1000O10000001OO10O0100O10OO2M3M3M2O2L4L4L6Imel8\", \"size\": [1280, 720]}, {\"counts\": \"SdWa0i0TW16L3O0O2N1N1010O010OO10001O00000O100001O000000000000000O01001O0O100000000O011O00001N3M2O0O2O0O2O0O101N1O1K5L4M4KlSe8\", \"size\": [1280, 720]}, {\"counts\": \"dkXa01nW1101O1O00002[iNLZU15Y1OXiNKeU16ZjNJfU16ZjNJgU15S1O0000001O000000O11O001O0000O1_iNMVU12jjNNVU12jjNNVU13ijNMWU13Z120O0O101N^iNOdV1001O0mNOWjN0`U10giN1dW12O2N1O00001O001O000000001O0O100000000001O1Mo[\\\\8\", \"size\": [1280, 720]}, {\"counts\": \"]T_a01lW13O1O10N221M00O2O00100O4L3L1N21O1O1O0O010O2O001O2N1N101O0000001N1000001N_cV9\", \"size\": [1280, 720]}, {\"counts\": \"Wdfa07fW14O010N1O2O0O2N2N2O010O0O1N3O0011O0O1M4N0000O20O1OI@nhN`0XW12N1011N2N001O01N100O2O000002M2O5K00O2N1N3M3O01O1O1N3O0O000000001O000O002N3L3Jckj7\", \"size\": [1280, 720]}, {\"counts\": \"cTbb0?`W14L3M4M2M2O0O20O01O1O0000000O100O1N2N2M4N100O1O11O000O01O100101N00O1O20N2O0O10O0010O001O10000N3N2N00001O0N10102N101N1O1O1O100O000N2HlhNDWW1<62NO1K5O1M6M00oSd6\", \"size\": [1280, 720]}, {\"counts\": \"ikmc01oW10P`22m_M5L4L4K7J3MO100O010000O1000O100O1001N10O1O1O1N1I9IbdP7\", \"size\": [1280, 720]}, {\"counts\": \"c[Pd0<cW13M4M3L3O1N2N1O001N01000O1O10000O0100000000O100O2O0\\\\OTiN4fW1DjcP7\", \"size\": [1280, 720]}, {\"counts\": \"i[mb06iW13M2N4M2N3L3N2N1N2O1N101N1000000O10000O11O00000000O1000000000000000O101O00000O2M3XOSiN:_W1Jmk`7\", \"size\": [1280, 720]}, {\"counts\": \"abnb07hW13M3N3M2N000000O02N2N100O2O0O0100000O1000O0100O10000O100000O10O1M3002M3M4L>]O`ed7\", \"size\": [1280, 720]}, null, {\"counts\": \"YkTc04kW13L5L4L2M3M3O01O0000O100O01000O10O1000000000000O1000001O1O00000O10O11N2M6Kld_7\", \"size\": [1280, 720]}, {\"counts\": \"aSgb05jW12N3N1O2N100N2O1O1O1O1O0O2O1O000O1000000O01O1000O1000O01000000000000O100000O0100001O0O2O`0_O5LRTb7\", \"size\": [1280, 720]}, {\"counts\": \"bSlb03kW13N2O1N3N1O1N2O1O1O1O1N2O1O1N2O000O010O0100O01000000O11O0000O1000O10O10000000000000000001N1O2M`0@\\\\dZ7\", \"size\": [1280, 720]}, {\"counts\": \"ncXc07kW12L001O1O2N001O1O1O001N2O1N3N1N2O1N1000000O10001O000O10000000O0100O1000O10O101OO10O02L3H9CdlQ7\", \"size\": [1280, 720]}, {\"counts\": \"ST[c08gW14L2O1O2N1O1O0O2O1O1O1N1000O1000O00100O10000000000O100000000O1000O100000O1000000O01O1M4J8D;KUTn6\", \"size\": [1280, 720]}, {\"counts\": \"njjb03iW15L4N20000O1O1O1O001O0O1O2O000001O000O10O1O0010O10O10O10O100000000O10O100000O1000O100000001N2N5@fm[7\", \"size\": [1280, 720]}, {\"counts\": \"ebdb03mW11N101O1O1O1O1O1N1N3N2O000O1O100O1O1O0100O2O000O10O1O0100000O100O1000O1000O1000OO2O11O1O002M101N5L7G^e_7\", \"size\": [1280, 720]}, {\"counts\": \"Zcdb01nW13N2N1O1O2M101O1N1O2N1100O00O10O010000N20O10O10000000O0100N11000O100000O010O011N100O1000000O101O0O4LPe_7\", \"size\": [1280, 720]}, {\"counts\": \"Zkeb02nW13M2N4L2N1O1O1O1O001O1O00001O000O101N100O02O0O10O0100O10O010O1O1001OO10O01001O0O10O1000000000O2O003L3MhT]7\", \"size\": [1280, 720]}, {\"counts\": \"PSbb01nW12O2O1N2M2O2M2O00001O1O1N20O00O2O1O001O1O001N1000001N10O1000O10O100O100O01000O10O1O10O0101N10O10000000000O00109F7In\\\\Y7\", \"size\": [1280, 720]}, {\"counts\": \"hkob02mW1101O001N4M1N3O00010M2N2O1N2O1N2N2O1O1N3N2N1N101OO1000O1O10O1O1O01000O01000O10O10O001O00100O010O1O1O1O1O0N3N102M3@Uek6\", \"size\": [1280, 720]}, {\"counts\": \"Pcib02mW15L1O1O2N1N2O1O2N1O1N2O1O2M2O1N2O1O2N1N101O00000O1000O100O010O1O010000O01O0100000000000O10O1000000O10O101O0O3M[]T7\", \"size\": [1280, 720]}, {\"counts\": \"_bZb03lW12O12LT`21j_M3N101N2O1O1N101N101O001O1O0O10001N1O1O1O100O10O101O00O10O011N10O100O1000O1000O01000001O00001N5Kfed7\", \"size\": [1280, 720]}, {\"counts\": \"kbUb06jW11O1N2O1N2O010O1O001O001O0O2O1O00000O100000O10000000O10O100O01000O10000000O0100000000000O100000O2N1O6HY]m7\", \"size\": [1280, 720]}, {\"counts\": \"`kQb06iW12N3N2N1O1N2O1O1O1O1O1O001O1OO1O100O100000O010001O00O1000O10000O01000000000000000O10O1O10O1000O02M3CR]R8\", \"size\": [1280, 720]}, {\"counts\": \"Y[ja02mW13M2O2N2N3N0N2O1N3N010O1N10000000000O1O10O01000O100O100000000000O10O1000001O001O00O1000000000000002M3NidX8\", \"size\": [1280, 720]}, {\"counts\": \"aZTb01nW11O2N3N2L3N1O4K4L4M110O00000O11N100O101O0000O1000O1000O1001N2O1O0000001O001O00O11O0M5HjeX8\", \"size\": [1280, 720]}, {\"counts\": \"cjla04fW17K6M3H7O02OO0N200O11O1O1ON100101O01OO0100001O000O1O1O10000000O1001O1OO0100O1O1O1O1000000O3NN11O0O20001N01000I^hN0bW162O1M1O2N2N2NP^h7\", \"size\": [1280, 720]}, null, null, {\"counts\": \"YYac01nW110001O01N^X13\\\\gN6M3N1M2N3N101O10OO5L3M10O01O0000O1000000010O01N2O1O003L01N2004DcfP7\", \"size\": [1280, 720]}, {\"counts\": \"fYUd08hW1003M2M3L5OO0O01O000010O1O1O0O101N01001N101G]nQ7\", \"size\": [1280, 720]}, null, null, {\"counts\": \"kaed05jW13L3O1N1010OO2N2O1N1O00010O10O01O100O1O1N2N2I64M4L1O2N5K5J7EXfW6\", \"size\": [1280, 720]}, {\"counts\": \"VZdd04jW14N1O001O00001N101O1N2N1O2O0O100O100O100O100O0100000O100O101N00100O1O1O10000O100000000003K\\\\Vf5\", \"size\": [1280, 720]}, {\"counts\": \"hYfc01mW17I4K5N001O1O1O1O1O1O001O1N2O1O10OO1000000000000O10000000O10000000000O10001O01O0O10O1000O10000O100000000O0100000O010001N1O3M:E[fm5\", \"size\": [1280, 720]}, {\"counts\": \"kabc04iW14O0O0O3M2O2N1O001O100O010O1000000000O010000N21O001O001O0000000000000O1O1N4CmfP7\", \"size\": [1280, 720]}, {\"counts\": \"[ijb04hW15M3N2O100O1O2N1N2O1O11O3M00O1N2O1O001O1O00001O00000O100O0100O10000O1000000000O10O100000O100O00100O100O10O10O10O10O10O10O11O0O2N1O3\\\\O__e6\", \"size\": [1280, 720]}, {\"counts\": \"oPbb01mW18I3N2N3L4M2N2N2O1N1O1O1O1O10O01O001O00O2OO10O1000000O10O100000O1000000000O1000O10O101O00O0100000O1000O10O10000O011N1O1O101N1O0O2I7F<JZol6\", \"size\": [1280, 720]}, null, {\"counts\": \"ehba01nW17J2N2O0O2O1N2M2N21N2N0O2O00000O010000O0101N1O1N200O100O1N2O2Mn_<OU`C0aW`00_h_O0j_W8\", \"size\": [1280, 720]}, {\"counts\": \"ePSb01nW12N3N2M1N2O2O0O11O0000002NM3O100O1O1O11O000000O1000000O100000O10000O1000O2N1O11O0O00000O2O2O1M020O1000O10O02N1O10O10O100O2O00O1EdhN2[W18101O101N100N200O00101N3NR`2IU`M0o_j6\", \"size\": [1280, 720]}, {\"counts\": \"d`gc02jW17L3M2O2N1N3N1O2N001O001O001OO100000000O01001O1N1000O010000000O100001N10O100000000O1000000O101O000O1000O010000O0001O0O2O1K6HQhm5\", \"size\": [1280, 720]}, {\"counts\": \"UP^d02lW14M2O2N1O2O1N1N2O1O1N2O1O001O100O1O1O0O2O00O10000000001O000000O1000000000000O10000O10000O100O01000000N2N3L3L6KSP`5\", \"size\": [1280, 720]}, null, null, {\"counts\": \"^XXe03lW13M3N2N1N3O0O100N2O2N001O1O1O1O1O10O01O1O001O00000O10000001O0000O100000000O1O10000O10O001O0N3L3M4L4M6Hlgj4\", \"size\": [1280, 720]}, {\"counts\": \"eXdd0;bW17K20000OCehN6[W1IehN9ZW1FfhN:ZW1EghN<XW1DhhN=VW1CkhN>UW1700O001N2O1O100O00001O010O1O00O100O100000O1000000O100O100000000O10000O0100000O100O1O1O01O1L5K4L5J5K5LdWR5\", \"size\": [1280, 720]}, null, {\"counts\": \"chjb03jW1O4000000O13L2O1O001N10O1O1O1001O1O0000001O000O10O11O001O010O1N1000002N001O001O1OO1001O1O1N01000O2O00010O000O100O2O001OO2O0O2M2N3N1O1NeWd6\", \"size\": [1280, 720]}, {\"counts\": \"Ra_b04lW11O2O1N001O1O2M2N2O1O1O1N2N2O1O2O0O0O2O000O2O000000O100O100O1000001O000O100O10000O0100000O1000000O100000O10000O0100010O0001N100O100O101N4J:Fjfk6\", \"size\": [1280, 720]}, null, {\"counts\": \"Si[b07gW14M2I8N01O1OO1O1O2OO2O1O003M3ML40DdhN4[W1NdhN2ZW1;0000000001N100010O00O100O101O01O0001O011N1N10001O0O1001O01O1O0000000O1001O01N101O00000001O001O1O1N1B<B?0XWn6\", \"size\": [1280, 720]}, {\"counts\": \"Sanb02jW1402N3M1O1O1O001N5K5J5M102M2N0O3N1O00000O1O100O010O010O010O01000000000O10000000O100O1000O100000O100000O01000N201O00000O101N10O10O10O1O1D=K5I6F:N2OboX6\", \"size\": [1280, 720]}, {\"counts\": \"`PYd01RX12J2N2O2M6L2M5K3N2M4N1L3N2N2N2N3L3N2N2O00101L101N101O00O10O02O00000O01000O1000O100001O000000O01000O010000O10O100001OO010O1000000O100000000O10O10O01O010N2O1F:G8J7N2N2L4N2N2O2O1Mfg[4\", \"size\": [1280, 720]}, {\"counts\": \"RbWf02mW12O3M1N3M3M2N3M2O2N001O1O0O12N1000O00O100000O100O10010O0O100O011O00000O01OO2O10000O1O010O1O1O10O1000O1000000O10000000O2O4K3N0O1N3@fnY3\", \"size\": [1280, 720]}], \"bboxes\": [null, null, null, null, null, null, [465.0, 316.0, 18.0, 57.0], [476.0, 247.0, 74.0, 101.0], [521.0, 301.0, 45.0, 40.0], [535.0, 280.0, 52.0, 70.0], [543.0, 291.0, 43.0, 54.0], [539.0, 282.0, 51.0, 54.0], [536.0, 300.0, 54.0, 39.0], [534.0, 301.0, 53.0, 42.0], [528.0, 301.0, 39.0, 44.0], [518.0, 325.0, 27.0, 23.0], null, null, null, null, [435.0, 339.0, 17.0, 19.0], [427.0, 326.0, 22.0, 31.0], [409.0, 329.0, 50.0, 43.0], [408.0, 313.0, 57.0, 49.0], [445.0, 331.0, 14.0, 15.0], [431.0, 272.0, 49.0, 79.0], [420.0, 328.0, 49.0, 35.0], [423.0, 317.0, 54.0, 38.0], [442.0, 281.0, 69.0, 84.0], [455.0, 357.0, 54.0, 55.0], [459.0, 326.0, 55.0, 42.0], [463.0, 257.0, 71.0, 75.0], [452.0, 277.0, 90.0, 75.0], [476.0, 297.0, 69.0, 57.0], [483.0, 334.0, 42.0, 26.0], [492.0, 354.0, 47.0, 41.0], [492.0, 339.0, 42.0, 55.0], [488.0, 327.0, 34.0, 41.0], [491.0, 337.0, 25.0, 33.0], [492.0, 353.0, 19.0, 20.0], [485.0, 334.0, 23.0, 29.0], [487.0, 335.0, 25.0, 33.0], [494.0, 356.0, 20.0, 32.0], [502.0, 312.0, 26.0, 44.0], [511.0, 301.0, 29.0, 46.0], [526.0, 322.0, 27.0, 23.0], [524.0, 312.0, 35.0, 32.0], [493.0, 277.0, 69.0, 60.0], [493.0, 312.0, 49.0, 32.0], [503.0, 352.0, 24.0, 19.0], [507.0, 295.0, 31.0, 52.0], [507.0, 302.0, 29.0, 39.0], [506.0, 331.0, 24.0, 18.0], [515.0, 332.0, 27.0, 23.0], [506.0, 331.0, 25.0, 22.0], [495.0, 326.0, 23.0, 30.0], [495.0, 335.0, 33.0, 40.0], [505.0, 309.0, 23.0, 37.0], [509.0, 312.0, 29.0, 32.0], [506.0, 323.0, 33.0, 30.0], [500.0, 317.0, 41.0, 30.0], [497.0, 322.0, 59.0, 24.0], [507.0, 342.0, 50.0, 26.0], [535.0, 335.0, 36.0, 36.0], null, null, null, null, null, null, null, null, null, null, [470.0, 216.0, 52.0, 119.0], [491.0, 291.0, 41.0, 43.0], [512.0, 299.0, 59.0, 42.0], [531.0, 252.0, 70.0, 85.0], [442.0, 320.0, 215.0, 58.0], [528.0, 343.0, 45.0, 27.0], [375.0, 329.0, 190.0, 31.0], [491.0, 310.0, 57.0, 37.0], [465.0, 317.0, 62.0, 40.0], null, [413.0, 306.0, 39.0, 30.0], [428.0, 324.0, 38.0, 32.0], [410.0, 338.0, 60.0, 39.0], [418.0, 326.0, 66.0, 42.0], [426.0, 326.0, 66.0, 46.0], [441.0, 383.0, 57.0, 41.0], [442.0, 371.0, 63.0, 53.0], [447.0, 392.0, 37.0, 16.0], [453.0, 382.0, 66.0, 31.0], [475.0, 381.0, 75.0, 53.0], [510.0, 371.0, 30.0, 30.0], [512.0, 365.0, 28.0, 34.0], [484.0, 368.0, 43.0, 34.0], [485.0, 322.0, 39.0, 33.0], null, [490.0, 350.0, 38.0, 26.0], [479.0, 360.0, 47.0, 29.0], [483.0, 358.0, 49.0, 31.0], [493.0, 374.0, 46.0, 35.0], [495.0, 376.0, 47.0, 32.0], [482.0, 336.0, 49.0, 27.0], [477.0, 324.0, 51.0, 29.0], [477.0, 348.0, 51.0, 27.0], [478.0, 355.0, 52.0, 28.0], [475.0, 340.0, 58.0, 34.0], [486.0, 360.0, 58.0, 45.0], [481.0, 339.0, 56.0, 42.0], [469.0, 325.0, 55.0, 28.0], [465.0, 340.0, 52.0, 24.0], [462.0, 360.0, 51.0, 29.0], [456.0, 354.0, 52.0, 26.0], [464.0, 325.0, 44.0, 27.0], [458.0, 321.0, 63.0, 29.0], null, null, [500.0, 296.0, 40.0, 35.0], [516.0, 307.0, 23.0, 24.0], null, null, [529.0, 291.0, 31.0, 37.0], [528.0, 307.0, 46.0, 32.0], [504.0, 299.0, 64.0, 34.0], [501.0, 297.0, 39.0, 24.0], [482.0, 287.0, 67.0, 37.0], [475.0, 279.0, 68.0, 40.0], null, [450.0, 262.0, 59.0, 35.0], [463.0, 252.0, 82.0, 32.0], [505.0, 261.0, 63.0, 34.0], [523.0, 253.0, 56.0, 32.0], null, null, [544.0, 262.0, 52.0, 35.0], [528.0, 270.0, 62.0, 41.0], null, [482.0, 266.0, 68.0, 26.0], [473.0, 284.0, 71.0, 32.0], null, [470.0, 280.0, 72.0, 35.0], [485.0, 269.0, 74.0, 53.0], [519.0, 266.0, 89.0, 67.0], [569.0, 305.0, 66.0, 38.0]], \"areas\": [null, null, null, null, null, null, 467.0, 1059.0, 1344.0, 1745.0, 1625.0, 2007.0, 1421.0, 1360.0, 1242.0, 425.0, null, null, null, null, 131.0, 526.0, 1570.0, 1964.0, 121.0, 673.0, 1341.0, 1225.0, 1396.0, 2156.0, 1776.0, 1631.0, 1217.0, 733.0, 763.0, 994.0, 1569.0, 692.0, 142.0, 184.0, 171.0, 311.0, 375.0, 665.0, 794.0, 280.0, 377.0, 791.0, 617.0, 268.0, 849.0, 451.0, 191.0, 54.0, 359.0, 433.0, 566.0, 520.0, 633.0, 710.0, 961.0, 950.0, 815.0, 628.0, null, null, null, null, null, null, null, null, null, null, 1231.0, 1137.0, 1825.0, 1282.0, 474.0, 776.0, 835.0, 1471.0, 1823.0, null, 700.0, 733.0, 1788.0, 2008.0, 2368.0, 1964.0, 369.0, 271.0, 1130.0, 2495.0, 554.0, 756.0, 1215.0, 857.0, null, 792.0, 952.0, 1091.0, 1107.0, 1079.0, 906.0, 849.0, 830.0, 974.0, 1223.0, 1454.0, 1600.0, 962.0, 863.0, 968.0, 1021.0, 856.0, 1252.0, null, null, 785.0, 368.0, null, null, 568.0, 983.0, 1678.0, 631.0, 1581.0, 1926.0, null, 403.0, 1205.0, 1399.0, 1315.0, null, null, 1221.0, 1756.0, null, 925.0, 1524.0, null, 1791.0, 2563.0, 3927.0, 1657.0], \"iscrowd\": 0, \"video_id\": 0, \"height\": 1280, \"width\": 720, \"category_id\": 847, \"noun_phrase\": \"a neck\"}, {\"id\": 999, \"segmentations\": [{\"counts\": \"n\\\\l67gW15K3N3N2M3M2M4M2N3L4M3L4M2N200O010000O001O002N1N1O1O101N1O1O2N3N2M3L4E:E;N2Oojfc0\", \"size\": [1280, 720]}, {\"counts\": \"ido56gW17J4L4M3M2O2L3N3N1N3N1O1O1O1O1O1O100O1O010O00000001O1O1O1O1O1N2O2N2M4J9HPSfd0\", \"size\": [1280, 720]}, {\"counts\": \"i\\\\k44hW15K6J5N3L3M4L4L3L43N1N2ON001O1O1O10O10O0101N10O00000001O0000001O6J5K1O2N1O1O1O1O0O100O2O1O2N2N2L4MbRZe0\", \"size\": [1280, 720]}, {\"counts\": \"]TQ41jW16M4L3M4L4M2N2O10001N1O1O1O1O2O0O1O1O1001N0010O0001O0000001O1O1O1O1O2N1O001O001N101O1O7I1O2FahNOcZYf0\", \"size\": [1280, 720]}, {\"counts\": \"nSl38hW13M2M4M1N3N1O2N2N1N3N1O1O100O1N2O1O1O00000O1010O00000001O0000000O1001O001OO10000001O0000000O101OO01O1O1N3E:M7K=CdcPf0\", \"size\": [1280, 720]}, {\"counts\": \"W\\\\R43kW15K4M5K2O2M3M2O2N2M2O1O001O000O101N11O0001N10000000000O10000O101O0O100O100000O10O100O100O100001O000O2O1N2O2M2L6GW\\\\je0\", \"size\": [1280, 720]}, {\"counts\": \"ncX41mW16J5K5K4L3N3N2N1O2N2N1O1O1O0O2O01O01O0O100000000101N1O00000001O000001OO1O1O100001OO100O11O001OO0011N101O0O100O4L4J7I6EV\\\\`e0\", \"size\": [1280, 720]}, {\"counts\": \"ecX4<cW14L3M2M4N1O2M3N100O1O001O001O1N10001O000000001O01O1O000001O00000000000001O00000001OO10000000001O0O101N3N000O2N4J7G:ESdae0\", \"size\": [1280, 720]}, {\"counts\": \"n[h3;eW12N2M4M1O2O0O2M2O2N1O100N2O001O1O001O0000001O000000000001O0001O000000000000000000000000000001O1O1O3M1O0O1O1O2M2M5EQTSf0\", \"size\": [1280, 720]}, {\"counts\": \"nSn2;dW12O002N1O1N2O1O1O2N1O1O1O2N1O1O1O001O1O1O1O1O001O0001O01O0000001O00000001O00001O000000O12N1O003M2N1N101O0O1O1O1O2N3KhSlf0\", \"size\": [1280, 720]}, {\"counts\": \"iS_21bW1>M3N2O1O1O1N2O1O100O1O1O0O200O1O1O1O1O1O1O010O1N110O1O10O1O01O0001O1O002N2N2N1O1O000000001O1N2O2L4L3AlhNOWSeg0\", \"size\": [1280, 720]}, {\"counts\": \"\\\\cW2a0^W13M2O010O2O0O1O1O010O2OO01O1N101O2N010O0100000O1N110O1O1O001O02N3N3L00O100001O0000001O1N3M1O1O3M3A[lmg0\", \"size\": [1280, 720]}, {\"counts\": \"Vln15cW19O2M2O001O2O0O1O1N200O1000O10O00101N001O1000O01O1O1O10000O10O10O0O100001O1O2O1N2NO1000O2O1O0O100N3L6J6KRX1KUhN0YSog0\", \"size\": [1280, 720]}, {\"counts\": \"U\\\\Q25dW18N2O2N001N2N3N1O10O01O1O1O100O100O001O1O1O1O001O1O1O1O100O01O01O01O01O02N2N1O1O1O001O001O0O2O2M2M3N2J5J7L4N_kmg0\", \"size\": [1280, 720]}, {\"counts\": \"Pdm14hW16M3N1N2O1N2N2N2O100O001O1N200O00100O1O001O001O10O01O0010O010O00000000000001O01O1O1O1O1O1N2O1O1O001N2M5K4K4MecQh0\", \"size\": [1280, 720]}, {\"counts\": \"[ln18eW14M4M2N2O1O100O0O2O100O1O1O001O100O001O1O010O1O001O000001O00001O0000001O00101N1O1N100000001O000000001O0O101N1O1N3N3M3L7IVkhg0\", \"size\": [1280, 720]}, {\"counts\": \"\\\\TZ22fW1;N2M2N2N1N2O00101N001N3OO02N1O010O1O010O1O010O000O1001O010O2M2O01O01O001N1000001O001O0000001O001O000O1000000N3N2M4M3M3M2NV[\\\\g0\", \"size\": [1280, 720]}, {\"counts\": \"lS_24iW14N2N2M3M201N2O100O1O1O1O1O1O1O10O0100O0O2O1O1O0100O1O2N00O20O001O000000001N2O00O1001O001O0000000O1000O1001O0O2O6Go[\\\\g0\", \"size\": [1280, 720]}, {\"counts\": \"mk]25jW15L2N1N2O1O1N2O1N2O1O1O2M101O100O100N2O0O2O0000000002N0O2O0001O00000000O10001O000000O11O0O100000000O2N100O100O4G>Cmc]g0\", \"size\": [1280, 720]}, {\"counts\": \"bd\\\\2=bW13N1O2M4M1O1O2N1O1O2N1O2N1O001O001O000000000000001O0000O1000000000001O000O0100001N0100001O0O2O000O1000O02N1O1O5\\\\OPiNJ`Zag0\", \"size\": [1280, 720]}, {\"counts\": \"XdW21kW16H7O1O1O1N3N2N2N1O1O2N001O1O1O0000000000001O000000000000000O11O0O100000001O00000O100000000O1000001O0O2O0O101N1N3KRlcg0\", \"size\": [1280, 720]}, {\"counts\": \"lca23iW15L3N3N4L2O2N1O1O1O1O001N10000O1O2O01O1O00O100001N1000N2001O1O0000O2OO2O0000O1001O001O0000001O1O0O2O1O0O001N4L4J\\\\T[g0\", \"size\": [1280, 720]}, {\"counts\": \"bTS33mW19F2O1N2O1O1O2N1O2N1O1O1O0O100001O00000001OO1001O0O11O00000000000001O0000000001O0000O10000000000001O0000000O1O1O1O2M2M5K5L\\\\[cf0\", \"size\": [1280, 720]}, {\"counts\": \"Vle34kW12N4M001N101O001O2N1O2N100O1O001O1O1O1N2N2O0011N0000O2O01O001OO1O10010O000O1001O1O1O000000000O10000O100000001O00O10O100O2N2M5K6Hekle0\", \"size\": [1280, 720]}, {\"counts\": \"ako35jW1101N3OO01O1O00100O1O1O1O1O1O1O100O1O1O002N1O10O1O01N101O0100O010O4MM200O2O001O01O000O3N000O01O11O1O0O01000O2O00000O100O100O1N2O1N2O2L3NWl]e0\", \"size\": [1280, 720]}, {\"counts\": \"Vd]45iW15L2N2N2O2N1O1O1O20O10O1N0O2O001O1O001O00010O000O100000001O0O11O1O000000000000O10000001O1O001O1O00O100001N2O1N3M2N001O1N3M3L6Ja[Ve0\", \"size\": [1280, 720]}, {\"counts\": \"oSQ4:eW13N2M2O1O1O1N3N1O1O1O2M101O1O0O2O0O10010O01OO100O10010M101O11O2O0OO1N200O1O1O0100000O1000001O00O001O1O002N2L4L4K5M3M4J[lge0\", \"size\": [1280, 720]}, {\"counts\": \"lSg38gW13N2M2O0O2K5M4M2N2M30O01O0O1O10O01M30O2O0000O100000000O100000000000000000000000001O00000000O100N2O1N2N3M2O1N2N4L<A_\\\\Tf0\", \"size\": [1280, 720]}, {\"counts\": \"Vle37hW12O1N2O2N1O2I8L2O1O1O1O0000000O010000000000000000000O10000000000O2O00O10O1000000001OO100O100000O101N1O2L3N2O1N2M4N2M7HQTSf0\", \"size\": [1280, 720]}, {\"counts\": \"gTQ4;dW14L7J5K1O1N2O1N3M3M3N2N002N00000000000000O10O1000000000000O100000O11O0O0101O00O010000000N20O010O100O1O1N3N2M3L3N3K6LaSie0\", \"size\": [1280, 720]}, {\"counts\": \"nkY47hW17J1N3N1O001O000000000O1000O10000000000000O1000O101O00O10000000000O10000O1000O1000001O0O1O2O0O2O1N2M3MTTie0\", \"size\": [1280, 720]}, {\"counts\": \"d[R4=aW1:I0O1O1N100O2N1N2O1O10000O1000000O100O1001N100000000000O01000001O0000000O10000O101O00001O0O010O001O2OO01M3O001O1J7K[oW1GafVc0\", \"size\": [1280, 720]}, {\"counts\": \"Zdi35iW14M2N2N3L5M1O1O1O1N2O1O00000O1O1000000000000O1000000O10000000000000O1000001O000010O00O101O00000O100O1O1O1O1O1O1N2N2K5N3LQTne0\", \"size\": [1280, 720]}, {\"counts\": \"T\\\\h35jW12N2N2N2O1N3N2N1O010O000000001O0O100000000000O1000O11O000000O100000000O10010O01O000000000000O010000O2O0M2N3N2N2L4OSTSf0\", \"size\": [1280, 720]}, {\"counts\": \"V\\\\h33kW13K5N2O0O2O1O1O1O1O1O001O0000000O100000000001O00O1001O0000001O00000000O1001O010O0000O10001O0O10O11O0000O100O1O1O100O1O1O2M2O3LQdke0\", \"size\": [1280, 720]}, {\"counts\": \"ilY4;cW15K3M3O1O1O1O100O1O01O00O1N3O0001O01N100001O00O1000000000000000000000000000000000000000O101O2N3M1OO10O100O2OO01O1N6L4K4L2N3NjZ[e0\", \"size\": [1280, 720]}, {\"counts\": \"lTY57hW17J2N1O1O1O1O0010O01O000010O10N1O2O001O1001O10O0N2N2O00000000000001O01N10000001N1000O12N001OO100000000000000001O0000O10000001O00O02N1O2M3L3N3N4L3IjZnc0\", \"size\": [1280, 720]}, {\"counts\": \"SdT61mW14L3N2O1O010O010O001O0O3N100O1O2N1O1O001O1O1N2O101N1O1O0000001O000N20000001OO1000000000000000000000000000000000000000O1000001O001N10OO1O2O1N2N3H:J4JRdQc0\", \"size\": [1280, 720]}, {\"counts\": \"gSf63jW14O10001N001O1O100O1O1O1O2N1O100O001O101O0N2O001O1O1O001O0001O1O01O00000000000O2O01O000001O000000000O1001O001O01N100001N1000O11O0000000O100N2O1N2M4M2N4LSd]b0\", \"size\": [1280, 720]}, {\"counts\": \"ldf71lW14O1N2O1N2N1O2O1O002N2N100O1O10001N1O2N1O1O1O2N1O1O2O1O001O00O01O1N2O1O00000104LM2O10000O1O100002N4L1O0000O1001N100O1N2M4G9K;FfZma0\", \"size\": [1280, 720]}, {\"counts\": \"Wl]74kW13M2N101O001O101N1O10001N2O1N101N0O2O2N1O1O1O3N001N1O1O1O1N2O10O01001O1ON1O1010O01O000001O1O1OO100O11O001O00O11O1O0O100N3M3J8D>D[kTb0\", \"size\": [1280, 720]}, {\"counts\": \"nkZ61mW13O1N2O1O2N102M1N2O2N1O2N3M1O2N20O0100O0O1O1O1O1O001O00101N10O01O010O001O0O1001O1O0000O11O000000O100000000000000000000000O0100001O000O2O1N3CS\\\\Pc0\", \"size\": [1280, 720]}, {\"counts\": \"a\\\\Z53lW13L3N3O1N2N2N1N8J0O3N1N2M2O1O1O2O0N3N101O1O0O1O001O00000000001O000001O00000000000000000000010O0000O1001O001OO010000O2N1N2O1N2M3N2N2N2N2N3M3M3G:M2MXSmc0\", \"size\": [1280, 720]}, {\"counts\": \"`dn34kW15K2O2M4N1N2N5K00100O2O1N1O1O1O1O00001O0000001O0000000010O00O1000000000000000O1000000O1000000000O01O2N1N2O1O001O10O01O000M4O0O2O1N3M3L4LeS_e0\", \"size\": [1280, 720]}, {\"counts\": \"ckg26iW13N2O2N1N2N2N3O000O2L2O2N0O101O000O12N2N0000O2O00O10O1000000O100O10000O1O10000000O01O0100O10O01O100O1O000000001O001O0O1O2N2O1L4N2O1M3L4N2Middf0\", \"size\": [1280, 720]}, {\"counts\": \"Wd\\\\22mW13M3N1O1N2O3K4N4K3N3M2N2N001O0O100O2N11O1O00O1O1001O1ON2O100000000O0100O100000O01000O1O10O100O1O1O0O1O001O2O1O1L3O1O2N2N2N3L4Ja\\\\Wg0\", \"size\": [1280, 720]}, {\"counts\": \"\\\\ll27hW14L4L3M5K4L6K1O2N1O1O1O00001O1O10O01O01OO100O1000O11O000O100O11O0000O010O1O1N2O11N1000O1O11O0O10O00O2O001N2L4N2N2N2N1O3K4L4K5N3J6JWddf0\", \"size\": [1280, 720]}, {\"counts\": \"Tk`32lW13O1O1N201N1O1O0O2O1O1N3N1O2N1O2N2N2N1N3N1O1O1O10O0000O01000O10000O2O000O1000O100000O100O1O100000000000O100000O10O010O100N2K5M3M2O2M4K4L6J[eke0\", \"size\": [1280, 720]}, {\"counts\": \"Zcg47hW15L1O1O1N2O002N3M2N1O2N2N102M3M001O1O00001OO1000000000000000000000000O10000000000O1000000O1000000O1000000O1O1O1O1N1O2M3N2N2N101O2M2N3Mi\\\\gd0\", \"size\": [1280, 720]}, {\"counts\": \"elZ67hW12O01000O1O01OO101N2O1O3M2N2N3N1O2O0O1M2O1O1O1O1O000000010O00000000001O000O11O001O0010O000O100000000O1O2N100000O1O1O1HYiNVOhV1g0:M2M3M2O3N1O3N2N3K\\\\cQc0\", \"size\": [1280, 720]}, {\"counts\": \"^ll76eW16N2N2N2O2N2M2O1O1N30O0000O0O100O2O0O101N12O1N2N000000O1O1001O1O0000O10O2O0O01O1O02O000OO2N2O01O1N2O1L4L5I7JP\\\\Rb0\", \"size\": [1280, 720]}, {\"counts\": \"ncZ81nW13N3M3L3N10O01O1O001O2N2N2N1O1O1O1O001O1O00001N2O000000001O000O02O10O0000000000000O1000000000000O10O1O100O1N2N2L4N2N2M3L4M4LWdZa0\", \"size\": [1280, 720]}, {\"counts\": \"STQ91oW11O2N3M1O00001N3N2O1N2O1N2N2N111N2N0O2N1O2M2O001O0002O1O0OO2O000O101O000002N2N01O0O100001OO10000001O1OO100O1000O10OO2N2N3N2M2O000N3O1M3O1N2N2O1O000N3OJNahN1_W1NchN0[cR`0\", \"size\": [1280, 720]}, {\"counts\": \"gdS91nW15K3M3L3O2N3N0O2M4M2O0O1O200O2M1O1O2N001O1O1O010000O10O01OO100O10000O13M2N5KN200O100O1001O00001O000O01000O10O02N1O1N2L4M3M3L4M3L4N2O2O1OoRZ`0\", \"size\": [1280, 720]}, {\"counts\": \"[l[83jW14M3N5L2O2M1O2N1O2O1N3M3M101N1O2M2O1O00010O1O10O0010N100O11O00001O0010O00000O1O11O00001N3N1OO1O10000000O10001N2N2M2N1N3N2K5N3M2N3KdcUa0\", \"size\": [1280, 720]}, {\"counts\": \"Xd\\\\71nW12M6L6I3N2M3N3N0O2N1O101N3N0O10O0001O0O10000010O01O01O1O00O1O10001O0001O001O0010N00100001O00O100001O00O10O011O000O2N1O2N001H8L5L3N5GicSb0\", \"size\": [1280, 720]}, {\"counts\": \"_Uh53jW15M2N20O01O0010O0101O1N3M3M2N1O1O1O1O1O110N20OO1O1O1O1N2O00000101N10O0O1000000000000O100O01001O0O001O1000O01O1N1010O10O01O100O10O0O10G9K6G9L5L4Job`c0\", \"size\": [1280, 720]}, {\"counts\": \"W]\\\\45jW13N3M010N10001O001O1N2N3N1O1O1N110O00001O1O000000000O11O0O100001O00O100000001O0000000O1000000000O11O001O000O10000O1O1O1O1N2O0N4K4MmRUe0\", \"size\": [1280, 720]}, {\"counts\": \"XlV31oW10000001O2M3O0O2N2N1N2O2N1O2N2N1O100001OO1N101O1O1N2O1O0100O1O0000O100000000001OO100O100O0010000001N1O01N2M3000O100O2O000O100N2N3L1O01005EWdUf0\", \"size\": [1280, 720]}, {\"counts\": \"[dU34kW14L200O1O1O1O1O1O2N2N2N1O2N2M3N2N001O001O00010O0O100O100O1001O0000001OO1O1O1000O10O10000O1O010001N01O0100N2N102M2O1O1M2O2N2N2N2J7M3MocZf0\", \"size\": [1280, 720]}, {\"counts\": \"i[c37gW13N2O4M1N2N2N2N2O0O2N00001O000O1001O00001O01N10000O1000O1000O100000000O1O10O010000O01O010O01O01O2OO01N2O000N2O2N3N1O1O1N2M4L3O1M4LdTne0\", \"size\": [1280, 720]}, {\"counts\": \"Rko34fW17M3N3N2N2N2N1O2O0O2O0O1O000O1000001O00000O1O11O0000O100001O00N2O1O11O000000000O100000O1000O10O11O0O10000O1O1O1O1O0O2O1N2N2O00100O2N1O2M1O2L4N3L^mXe0\", \"size\": [1280, 720]}, {\"counts\": \"ZSo42lW17K2N1O1O1O1O1O1O1O1N2O1O1O1O1O0010O0001O000O02O00000001O00O2O00000000000000000000000000O11O000O10000000001O00O1000O2O00O01N2O101N010O001N1N3O2M3Kk\\\\Xd0\", \"size\": [1280, 720]}, {\"counts\": \"n[X64kW17J100O001O1O1O1O1O1O1O001O10OO2O001O0001O0000O2O00000O2O000001OO1000000000000O1001O1O0O10O1001O0000O10O100O1O01OJnhN@RW1>:M2O2N2N2OQd[c0\", \"size\": [1280, 720]}, {\"counts\": \"R[S64kW13N?@3jhNZOmV1g060O0100O01O1O0201N1OO1O100001OO2O0010O2O00O000O11O001O001O1O00100N101N101O1O2N2M3GhTWd0\", \"size\": [1280, 720]}, {\"counts\": \"dRV45jW15K8I2N02N01O000000001O000O100001O000000000000O1000000O10000000000O1002M101O1O1O00000O105K1O001M20O1O31L3JT]je0\", \"size\": [1280, 720]}, {\"counts\": \"cSg32iW1;I2O1O0O2M3N101O010O001O1O00010O000010O0000O1O20O1O001O001O000O100000000000000O1O10001O0O2O0000000O1O10001N100O101N1O2N2N]\\\\oe0\", \"size\": [1280, 720]}, {\"counts\": \"P[j26jW13L4M1O1N2O0O100O1O20O0O2O00O10001N1O2O000001O00000000O0N3O01000000O100O10000O10O10O100O1O10000O1O100000O10O101O0O1O1O1O2M\\\\Ulf0\", \"size\": [1280, 720]}, {\"counts\": \"YSS32lW14N0O2O1O1O0N3J6M2O2O2N001N2O001O0O2O1O010O01O01N10000O10001O00010O1O000000010O0000000000O10000O2N1O2N1O1O1O2M2O2M2N3M3M3N3MlTbf0\", \"size\": [1280, 720]}, {\"counts\": \"fZe22mW15L2N1N3N001O00100O10O2OO00010O0010O01O2N0100OO2O1N2000O01O000010001N010O0O2O001O010O10O0100O10OO1O2N11O2N01O00000000000O1O1001O1O1O2N1O00O1O11O00000000001O000O000N4N2N1N2O0O2N4K4Mjlle0\", \"size\": [1280, 720]}, {\"counts\": \"_[m31nW16J2O2M2O1O001O100O1O1000O01N2O1O10O01O1O00101O0O001N2O010O1O0O1011N1O2N10O00000N2001O010O1O2NO100O101N1O11O1O00010O00000O001O002N100O1N3N1N2N101N3M3MYdWe0\", \"size\": [1280, 720]}, {\"counts\": \"WZT31nW12O:F4L010O01O1OO1N2O1001O1O0O10002N1OPaU1D\\\\_jN0n_29Y_Mb0L3O001N100000O0100001O00000O100O001000000O1O2M200O2N102M5K5J6Kmlbe0\", \"size\": [1280, 720]}, {\"counts\": \"fjb21mW12M3K5O1O1000O10O010000N100O10100O2NVQd0Nkn[O4N1N2O001N10000O2O1G9L4N2N1O1O2O0O2N1000001O001O00O1000O2OO1O1O1O0O200O00101N100O10001N2N5K2OO01@ghN0hTXf0\", \"size\": [1280, 720]}, {\"counts\": \"ajb27lW1OO4XhNKZW1?N2N1O1O10O1O1O1O1O00101O0O101N1M5M2O1M[Q?Ljn@3M2OO100000O1010OO2C<K6O01O0000O10000000O1O1O01000O1O2L3N2O1O4HhTlf0\", \"size\": [1280, 720]}, {\"counts\": \"oRS32lW14M2M3N2N1N3O001N2O1O1N2O1O001N2O1O001O1O0010OO101O0000000000010O01O0000O11O00001N100O1O1N2O2N101N1O2N101M201N2O2N2M3N2Mmddf0\", \"size\": [1280, 720]}, {\"counts\": \"fRS36iW13N1N3N001O10O01O1O1O1O1O1O1O1O1O10OO2O1O001O1N11O01O001O000000000000001O0000000000000001O00000000000O10000O100O1N110O100O2N1O1N20OO2O1O100O100O100O2O1N4IZ]oe0\", \"size\": [1280, 720]}, {\"counts\": \"]cd38fW14O0O1O1N101N102N1O1O1O001O1O1O010O1N2O1O1O1O00001O1O001O00001O0010O00O1000000001O1O2N000001O0O1001O0O2O1O1O00000O10000O100O001N2N2M2M3O3N2O0O100O103Hb\\\\`e0\", \"size\": [1280, 720]}, {\"counts\": \"^ci32lW1201M3N3M2O0O101O0O2O001O010O001O001O00001O1O00001O010O0O2O001O1O001O00100O010O0O2O01O0001O0O11O010O0000000O1O2O0O002M5K6J4N0M6K[Tie0\", \"size\": [1280, 720]}, {\"counts\": \"fj]23lW19G2O1O0000O1010O1O1O1O0001O000O1N2O1O1O1001N2O002O1VQS1AWolN0O1001OO1_O5jhNORW1a001O000000O100O1N2O2O3M3M5J5KXlef0\", \"size\": [1280, 720]}, {\"counts\": \"_cR21oW14L4L4L2O2M2O0O00O10O100001O1O01O001O000000000000M3N2O1001OO100O11O0011K8HlPS10WolN1N3N2M3N2M2OO010OL4O110O100O01000O1O1000O1O001N2M4N:Gdk[f0\", \"size\": [1280, 720]}, {\"counts\": \"_[V28gW19IO01O000001O001O00001N10000000000O2O0O1O100000000000001O0O100Oohg0LUWXO3N1N2N2F:M3N2O1O0O1000000O020000O0O10OO3N1O001O1O3M3N1O1N4Lekef0\", \"size\": [1280, 720]}, {\"counts\": \"n[^32lW14L4J4N3N1N3N2N2N2O0O2001N10O1N1O2N101N10001O110O000N1O101O01OO100002O1N1O0000000000000O2O1N2N3M01000N3N2N1000000O2O000O1O012M2O1N2N3N2IR\\\\oe0\", \"size\": [1280, 720]}, {\"counts\": \"nkj36hW15N000O001O0O200O1O001O1O10001N1O001O1O1O1O1O010001O00N10000010OO100O11O1O010O3M00M301N1000000001O00001OO1O1000000000O1O10O0010N3N1O1O1O010O0001O0100O1O1O1O2O0O1O0N3NRTPe0\", \"size\": [1280, 720]}, {\"counts\": \"nkc4;dW12O1O1O1O101O0N3N00100O1O1O1O100O101N001O10O010OO2N1101N1O01O01OO2O000O1O11O1O2N002OO000N2O2O01O001O001N101O0O00001O2N1O1N1O2O2N10000O010O1N2M3O2N3Mf[bd0\", \"size\": [1280, 720]}, {\"counts\": \"gjo3a0^W12O1O001N10001O0000001O001N110O001O001O000010O01O001O1O001O0010O5L3MO10O01N2N3N1O0010O2O1N01O0000000000000002N1N1O1O100O1O100000001O1N1000O02O0O2GndWe0\", \"size\": [1280, 720]}, {\"counts\": \"kRS39gW1:F1O1O00000001O0000O2O0001O0000O101N1001O1O001N100O100O1000000O3L4L2O10O2OO3M2O1Nm\\\\\\\\g0\", \"size\": [1280, 720]}, {\"counts\": \"Z[o29fW18K0O2N000O1O000O20O0O101M3M2O100O10000O011O00RY`0HUg_O3N001N010O1000000I8L3O1O10000O01O10O10O0100O010O1000O10OO2O1O1O1000001O0G[lVf0\", \"size\": [1280, 720]}, {\"counts\": \"_ke31oW12XhNN^W1:O3N3L101O1O001N100O2O1O00001O00100O010O1O001O001O1O11O0O10O1O001O100O0O2O010000O100O00O1O2O01O001O1O10O000001O0O2O00000N2N2M4N2M3LVTie0\", \"size\": [1280, 720]}, {\"counts\": \"PSX32nW1>B3chNBRW1f010O02N2N2N0000000000001O0O1O1L4K5O1O100O2N2M2000dhV10\\\\WiN000\\\\O0XiN1gV14RiN1jV1g0L001O00010O0O10001O010OO1O1L4J6J8Fl\\\\ee0\", \"size\": [1280, 720]}, {\"counts\": \"a\\\\^35lW103MO3VhNN`W1;N3M1O4L2N2N00002N2N2N1N3N2N2O1N100O0010O001OO1O11O1O000000O11O1N100000O11O00O100O11N101O00000000000O01000O010O01O100002N0O10O10O0J7J5N3O1AkhN1YW1Jikge0\", \"size\": [1280, 720]}, {\"counts\": \"`UQ41nW11O2O1O0O2O1O1O01O00000001O00001O0O100001O000000O10001O0000001O00000000001O00001O001O0000001O00000O101O1O0O001001N100000O1000000O001O10O010O1N4M7I6J^RUe0\", \"size\": [1280, 720]}, {\"counts\": \"g\\\\\\\\46hW14N1O1O005J5L3L4M4L4L4L100O1O1O1O0IdNjiN]1\\\\V10001O0O10O11N1000HfNjiNZ1VV1fNjiNZ1^V100O101O0O011O00000O2O00000O2O1O1N2O1O2NO10O012M3N2M2N1O2N1O1N2O2O0I`hNNaW1OS[Ve0\", \"size\": [1280, 720]}, {\"counts\": \"lf`52mW120O00N3L5L2000O1O101O000O101O2M5Lhn90]aH0f_M5O1O2O00001N3M4M2N2N1O0O0100O2N1000O2N2N2M3N0010O01O0011N2OO1M3M41M7H7J7H]boc0\", \"size\": [1280, 720]}, {\"counts\": \"Sf`5:dW19H9G4NO0100N2N2O1O010O00000O1O1O10000000000O11O00001O2N1O1O1O00O0100000O10000001O000000O100001OO010O1001N1O100O101N1O1O1N101O1N3M2M3L5M3N2N4MhYic0\", \"size\": [1280, 720]}, {\"counts\": \"Vma51oW14K;F101N3M001O1O1O1O1O001O0000010O000001O00000000O101O00000001O0000000001O0000001O000000010O0001O2OO0O1010O001O00O001000O1O1O2M4Ldboc0\", \"size\": [1280, 720]}, {\"counts\": \"omP61mW14M4N0O1O1O001O1O0O2O0O1O21N100O000001O001O0001O2O2M4L001O0001O01O001O001001N001O01O00000001O02N10O000000001O1ON2O102L_Qhc0\", \"size\": [1280, 720]}, {\"counts\": \"jUm51nW12N2N2N2O1O1O1O1O1N2N100O2O001O0O1O101O1O0O2O001O001O0O1O2O0O2O0O20O01O0O2N100100OO1O2O000000000O2O01O100OO1O12L3J5O3K6ZOPcjc0\", \"size\": [1280, 720]}, {\"counts\": \"Xl\\\\56jW110O10O1N2N2O1N101N2O001O1O001O0O20O1000O001N2O1O010mhNYOjV1h0TiNZOlV1g0RiNZOnV1f0QiN\\\\OoV1k000O0O1O10001O0O1O10O2O1O1OO1O1O10000O10000001N2O000O00100O100O100O3M3L3M5L3M3Idcoc0\", \"size\": [1280, 720]}, {\"counts\": \"PUh52mW13M4M1O1O1O1M3N2O1N2N2N10000O10000000000O1O1O10000001O00O1O1O100001OO100001O0000O10O10000000O1O11O1O00O010001N1O100N102Kbcoc0\", \"size\": [1280, 720]}, {\"counts\": \"`Uh5341`W1:N2M2O1O2M3N00001O2N1O1M3N101O01OO1000000O1N210O000O1O100000000O01001O0O11O0000O100000000O1O1O1100O00000O100O100O001N2N2N2M4G9L8AQ[ic0\", \"size\": [1280, 720]}, {\"counts\": \"U]d58hW12O1N0O2L5L3O1O2M2N100O2M3N10000N3O01O0000O1O10000N20000O100O100001OO1O010001O01N10000000O1000000000O100000O1O1O1O2M2N2O1O1[OQiNL_W1Nfcoc0\", \"size\": [1280, 720]}, {\"counts\": \"bdQ56iW13N1O2N001O00001N2O1O1O2N001N10001O1N1O1O1001O1OO1O1001O10M2O100O1O1O1001O00N2O100O1O1O11O0000O1O11O1O1OO1N2000O01M32L2N3M5K5K3L6KIFad^d0\", \"size\": [1280, 720]}, {\"counts\": \"U]k42kW15M2N3M2N2N3N2M3N3M2M3N1O11N01N1O1O10000O2O0O100N201O02N00O1O1000O3NL41O00O1O1O0101O00O1O100001O00O10O101O000N2N2N2M3L5L2N3N3Kachd0\", \"size\": [1280, 720]}, {\"counts\": \"gdg47hW14L3N4L3L2O2N1O1O1O001N1O101N10000000010O0O10000000000O1O11O1O00000000000000O1O11O1O0O100000000000O0010O2N2N1O1O1N2N3L3N3L4M5I][ld0\", \"size\": [1280, 720]}, {\"counts\": \"nZ\\\\46gW19I8I5K1O1O001O001O0O1O1O11O1O00O10000O100O1000000000000O10000O100000O11N1N21O1O0000O1001O1N10O1O1001O1OO10O101N2O1N1O2M2N1N3J6N5D`UUe0\", \"size\": [1280, 720]}, {\"counts\": \"PkT41oW13L8J2N2M6I8H3M101N1001O00O1000O0100N20000000000O1000000O1N22N001OO1O1O11O1OO1O1O1001O1ON2000001O0000001O000O010000O100O1O2N1N3O0O1N3UOSVZe0\", \"size\": [1280, 720]}, {\"counts\": \"Vl^48gW12N3N2N8H3M0O2O0M3N200O100001O0O1000O2OO100000O1001O0000000000O10000O1O11O1O0000000000000000O1001O1OO10000000O101N1O1N4Ef0AfcWe0\", \"size\": [1280, 720]}, {\"counts\": \"]dg43lW16J3K6L5M2N1N1M3O1O000O10O010O01N2001O1O00N200O1O1N2001O0000O010000000000000000000O100000000001O0000000O10000O101O3Ka0]Oc[Qe0\", \"size\": [1280, 720]}, {\"counts\": \"gdl48hW13M2N4L4L1O0010ON3M2N3O0001O0O1000O10000O10000O100001O00O1000000000000O11O0O2OO10000000O1000O1001O0000001O0O1N2N5L=@Tcmd0\", \"size\": [1280, 720]}, {\"counts\": \"Qmh48jW13K9G3L2M3N2O2M3N2N1N2O2N1O10O0000O1O1O2O000O001O1O1O1O20N100000O1001O2M10O1O11O1N1000O1001O1O0O01O1001O1N100O1O2M3N1N4L4K5I?ChRkd0\", \"size\": [1280, 720]}, {\"counts\": \"o\\\\\\\\49[W1>M3N2M3N2O1N3M2N1O1N2O00000O1010O000000O100001OO1000000000000000O1000O100O11O001OO0100000000O100000001N100O1O1N2N2N2N2N2O1M4N1N3L7J\\\\kSe0\", \"size\": [1280, 720]}, {\"counts\": \"fci3=aW15L3M3M3M3N2M3N1O1O1O00001O01O10O000000000000000O1001O00O100001O0000O100O1O1O10000000O10O10000000000000000O0100O1O1O2M2O1O2M4L8G8G7JVdfe0\", \"size\": [1280, 720]}, {\"counts\": \"n[^32lW1;F4M2M2O1N3N2N1O0000001O000O100O10000000000002N1O00O1O10000O1000000001O00O100O1O1O100001OO10000001O00O1O1O2OO101O000O101N2NO10O2N3N3L5L9ESTne0\", \"size\": [1280, 720]}, {\"counts\": \"ll[3<bW14M3M2M4L3O1O0O20O000000O1O10001O000O11O000000O100000O100001O000000O100000000000O1001O000000O11O0001O0000O101O0000O02N1N2O1O1O1O1O3L4L7I7JQcPf0\", \"size\": [1280, 720]}, {\"counts\": \"Q\\\\T3>`W13N2O2N100O0O2O000O101O0000000000O1000000000O1000000000000000000000O1001OO100000O100001O00O100001O000O10O11O0O2N001O1O1O2M2O2N2N3KRT]f0\", \"size\": [1280, 720]}, {\"counts\": \"mkl25jW14M5K2N2N2M2O1N3N1O001O0000000000000O1O1O100O100O10O2O1OO100O100O1O1000O01000O2O0000O100O10000O11O00000O10000O100O100O2O1N2N1O2M3N2L5LYTbf0\", \"size\": [1280, 720]}, {\"counts\": \"_TZ29eW17K1O1O1O1N201O1N2N1O1M4N1N101N10000101O00M2O1O100O10000O10000001O0000O1O100000O0101O000000O10O100000O101O0000001OO010000O101N3M5K2N1N1K7L6L`SQg0\", \"size\": [1280, 720]}, {\"counts\": \"bTb33lW1101N1O1N2O1O1O1N2N2M3O100O1O1O100O2N1O10O1O1O103M4K5J8Gfc]g0\", \"size\": [1280, 720]}, {\"counts\": \"mbT1;dW17I4M1O1O1O1O001O0010O01O010O100O1O10000N3N1N101O1O2N1O1O01O00O1000O100O100O2N1O2N2N1OO02O1O2N1O0O101O1N2O1N2N2O1N2N2M20OO022L4LYU^h0\", \"size\": [1280, 720]}, {\"counts\": \"dk]21lW13J7N1O2M3J5N3M2N2O1N2N2O100O10001O000O1O100O1000000001O0000O1O10000001N2O2_OWiNEnV1NPiNMi\\\\Uh0\", \"size\": [1280, 720]}, {\"counts\": \"T[d07`W1<M2M2O1N2M3N2N3N001O1O100N101O1O0O2O00O1O0O3N1O1N2N101N2N2N2O0O2M2O2O0O3L3N2N2N2O3M^mii0\", \"size\": [1280, 720]}, {\"counts\": \"Yc6:dW14M2O1O2N100O2N1O1O0O3N1O1O1O1O1000000N2O1O00000000000000000000O100O1O100O002O0O100O1N2O1O100O1O1O1O00100O1N2O001O1N2O1O1O0O110O2MR`2KT`M1llUi0\", \"size\": [1280, 720]}, {\"counts\": \"bcW2=`W13N2O2M2N2N2O2N1002N1OO1O1N2O1O1O1O100001O1O10O0O100O1O0100O2N1M3I7K6J6K6Mml\\\\h0\", \"size\": [1280, 720]}, {\"counts\": \"obY1=aW13N3M3N1O1O100O00001O001O0O2O1O001O1O00001O1O1O2M10001O1O001O001O01O1O1O00000000000000002N1O1O0000O1000000O1001O0O2O0000000O01001O0O2O3M1O1N1O1O010O1O100O2HfhNGTlhg0\", \"size\": [1280, 720]}, {\"counts\": \"Z[Q23lW19H1O1O1O1O1N101O001N2M3O1O0010O01O001N3N1O1O1N2O100O001O00000O100000000010O000000O1000000000O101O1O000000O10000001O1N0101O01N100000001O001O001O000O10000000001N1O3L3L4M2O1O1O4L3M3M3N1OW\\\\^f0\", \"size\": [1280, 720]}, {\"counts\": \"obZ32lW17K1O2L4K6K4N1O1O1O2N2N1O1N2O1O1O0O101O00000000000001O00001O0000O10O100000001O000000000O10000000001N101O0000O1000000001O2N2N1O000O100000000O2O00001N2O0O1O1N2M4M4L3N7J00fT_e0\", \"size\": [1280, 720]}, {\"counts\": \"SZY35jW14L3M5L2M5L1O1O0O2O1O0O2O1O1N2N3M2M3O010O0N200O10000O100O1O100O1001O0000O100N2N2000000000O100000O10000O10O100000000000O100O100O1O1000O100000000O2O1N1O1O002M2N4L7I7Ie0ZOjeae0\", \"size\": [1280, 720]}, {\"counts\": \"kQ`44lW12M4M2M3L4M3N2L4L4L3O1O2N1O1O1N101O2M2O1N3N001N11O000000O001O0N30O100000O1001N101O2M3N0O100O100O10000O10000O100O2O0O100O101N100000000O2N100O001O2N1O1N1O2N2N2N3M3N2N3N2L]fYd0\", \"size\": [1280, 720]}, {\"counts\": \"RbQ51mW15M2M2O2O1O1O4K3L10100O2N1O1N10100O0O101O00O100O10O10O100O100O1O100000O010000O1O01000O10O010O02N1ON3O1N2O01O0100N110000O01O01O1N101O001N2O2N3M3L^VWd0\", \"size\": [1280, 720]}, {\"counts\": \"`Qo46hW16L3N2K4M3N2M2M4M5L1O0O2O0O2O000O2O0O10000O100O100O010O1O100O1O100000O010000O01000O1000O2O00O0010O100O2O00000000O10O01O1000O100N2N2O1O1N101O105Ih0XOa^Xd0\", \"size\": [1280, 720]}, {\"counts\": \"TZZ57fW17K3M1O3L4M4L4K4N1O1O1N1O2N1O100O1O100O11O0000O101O0O100000000000000O10000001OO10000O1000000000001O0000000001O2N00001O0000001N1O2N2O1N100O1N3N2M3M5UOSiN9`W1Gjejc0\", \"size\": [1280, 720]}, {\"counts\": \"cic4:eW16J6K`0_O2O1N2O4L3M1O1N101O001O1O0000001O0000000O10000001O01O000000001O0O100000O101O0001O000O1000000000010O0000001O00000O1000000001OO100000O1O100O1O2N1N3N2N0N2K6M4L4F9H9N7I6IIO:0]nPd0\", \"size\": [1280, 720]}, {\"counts\": \"fQl3=cW15J4M7I1O0O200O2N2O1N1O1O010O10000O0O1010O001O002OO0O1000000O11O10O02N001OO1O1001O001O000000000000O2N11O1O0000O11N2O1N10000000000000000000001O0000000O100O1O1O4K6I7K2M4Jdemd0\", \"size\": [1280, 720]}, {\"counts\": \"VRS32jW1a0C3L2O2O0O3M2N001O1O101N0O110O00000010O01O01O0001O00001O0000O2O00010O00000000000000001O1O00001O0000000000000001O01O001ON200000000000010O0001ON200O10O101N3L3M3M2O1O2N1N3M3O1N2N^eae0\", \"size\": [1280, 720]}, {\"counts\": \"RRZ2d01ATW1h0N2N1O1O2N2N1O100O1O001O1O002OO01O1O0010O0001O01O01O01N100100O1OO100010O01O001N2O1O002O0O000000001O0O2O0100O0O2O000001O0000001O10O0010O000000001O0001O0000000000O1O100O1O1N3M2L4N3M2N2O1O2O0N2O3G\\\\]oe0\", \"size\": [1280, 720]}, {\"counts\": \"Rjd11oW16J5L3M0N2O2M2O1O2N1O1N2O00001O00001O001O1N100001O1O0001O000O100000000000001O000000000000000000000001O000000000000001O001O100O001O000000001O001O1O1O000O2O001O0000001O0O2O1O0O1O101N1O1000001M3N2M3N2N[Ubf0\", \"size\": [1280, 720]}, {\"counts\": \"RRm08gW17K2M2N7I2N1O1O1N2O1O1O1O00100N101O10O01O001O001O00001O0O110O00000O20O000000000000000000010O1O1OO0O200001O1O002N2M0100O11O00000000001O1O001N2OO1001O1O3MO1O1N2001O3L10MTOUiNh0RW1N2M3N1N01N3001O3N1K301Hme]g0\", \"size\": [1280, 720]}, {\"counts\": \"Pcj01mW12M3K61N11L4L2O6K000010O0O1001O0O2O1O001N2O000000001N1O11O0000000000001OO2O0O1001O0000O100O1N2O11O1OO2N1O10O2O1ON2O1O100MlN]iNQ1cV1PO\\\\iNP1eV151OO1O1O11O001ON2O10000M2101O1O1O2N2NO1O0012L3M3M2O2M6Kbegg0\", \"size\": [1280, 720]}, {\"counts\": \"iRf15hW1;G2O3M10O00N3O0O2O000000001O00000000000000000001O0001O0000O1000001OO100001O00000O1000001O001O1O1O001N100000001O1O1O1O1O2N1OO1O1O12N1O1ON1O21O1O1O00O1N23M2JPU[g0\", \"size\": [1280, 720]}, {\"counts\": \"]Z^395H[W1:5000O100O3K6K3N1N3N100N2M202N1O1O100O1O1O1O1M3M30000O1000000000000N2L3M4O100000000000000O1N3N12N2N1O2N3M6J5Jb0^O5L2J;CeeZf0\", \"size\": [1280, 720]}, {\"counts\": \"ZS`41nW12O0O1O2O0O1N2N2O1N2O1N2N2N2N2O1N2N2N2N2N2M3O1N2N2N2O1O1O1O1L4K5M201N20000001O10O0O0017H7J1N2N1N2O3M2N2N1N3N2N3M4L2L5L4JgeWe0\", \"size\": [1280, 720]}, {\"counts\": \"\\\\Zi51lW16L8F4N2N2N3M2O1O1O1O2N1O2M2O1O1O1N3N1O1O3M1O1N3O1N1010O0O2O1N1N3N3L3M3N1M4M3L3M3N2N2L4L7EXUad0\", \"size\": [1280, 720]}, {\"counts\": \"obS42kW14N3M3M2O1N2M4M2O001N101O1N2N2O1N20000O1O0O2O000N3O0O1O1100O0001N1O1000O0100O11O001O00N2000O10O100O1O100O0100000O2O000O1000O100N2O2N100O0000100O1OO1O2O10O1O0L5M2N2N3N2O1M20100N2O1N2O1N2O2M2O1N2M2O3KW^Sd0\", \"size\": [1280, 720]}, {\"counts\": \"USY51kW17F9K5M2O1O1O1O1O00210O0N1O1N20O00000000011O000O00000000000000002N10O01O00O100O10000001O1O10O000O100O10000001O00001O00000O01000000000001O01O1N10000000O1000O10001O1N1000000O1O1001O1O10N1N2O1N2N2O1O2M1O10O12N4K4Mg\\\\fb0\", \"size\": [1280, 720]}, {\"counts\": \"Y[n52eW11^hN6`W15O1O2M3L3O11O00O1M3N2O10O10O2O1N2OO01N1O1O100010O001N2O000001O0O1O110O0001000O0000O10O0100000010O100O001N0010O10O10001N2N2O0O2O000O2N101O1OM3O101OO101N100O100O2N0101N10O100O1O10000O10000M3O1O1O1O1O1N3M2O2Ng\\\\Rb0\", \"size\": [1280, 720]}, {\"counts\": \"Ukm48eW16H8J6J7K4M2M33L6K00ON2N2NO02O1N3I8KSaX2EY_gM3L4M2N2O1N20000O1K4L5K5O10O100O001N2N2L4K6M2BehN5fW1L_dVc0\", \"size\": [1280, 720]}], \"bboxes\": [[176.0, 396.0, 39.0, 52.0], [153.0, 396.0, 37.0, 48.0], [124.0, 385.0, 50.0, 53.0], [103.0, 382.0, 47.0, 42.0], [99.0, 378.0, 57.0, 41.0], [104.0, 373.0, 57.0, 46.0], [109.0, 370.0, 60.0, 42.0], [109.0, 367.0, 59.0, 41.0], [96.0, 381.0, 58.0, 37.0], [75.0, 380.0, 59.0, 39.0], [63.0, 360.0, 52.0, 44.0], [57.0, 362.0, 50.0, 46.0], [50.0, 381.0, 56.0, 44.0], [52.0, 379.0, 55.0, 44.0], [49.0, 375.0, 55.0, 39.0], [50.0, 388.0, 61.0, 37.0], [59.0, 388.0, 62.0, 37.0], [63.0, 370.0, 58.0, 36.0], [62.0, 376.0, 58.0, 34.0], [61.0, 400.0, 57.0, 38.0], [57.0, 380.0, 58.0, 33.0], [65.0, 366.0, 57.0, 31.0], [79.0, 399.0, 62.0, 30.0], [94.0, 386.0, 65.0, 32.0], [102.0, 367.0, 69.0, 35.0], [113.0, 385.0, 64.0, 34.0], [103.0, 369.0, 60.0, 44.0], [95.0, 361.0, 58.0, 40.0], [94.0, 377.0, 60.0, 35.0], [103.0, 395.0, 59.0, 51.0], [110.0, 376.0, 52.0, 24.0], [104.0, 337.0, 92.0, 60.0], [97.0, 382.0, 61.0, 32.0], [96.0, 381.0, 58.0, 24.0], [96.0, 381.0, 64.0, 24.0], [110.0, 402.0, 63.0, 31.0], [135.0, 411.0, 74.0, 35.0], [157.0, 382.0, 75.0, 33.0], [171.0, 372.0, 77.0, 37.0], [197.0, 405.0, 64.0, 50.0], [190.0, 388.0, 65.0, 51.0], [162.0, 379.0, 71.0, 47.0], [136.0, 397.0, 74.0, 52.0], [101.0, 395.0, 69.0, 41.0], [70.0, 362.0, 70.0, 43.0], [61.0, 370.0, 64.0, 50.0], [74.0, 379.0, 66.0, 54.0], [90.0, 342.0, 70.0, 46.0], [121.0, 359.0, 68.0, 40.0], [162.0, 403.0, 70.0, 44.0], [202.0, 387.0, 55.0, 36.0], [213.0, 379.0, 63.0, 33.0], [231.0, 386.0, 78.0, 44.0], [233.0, 402.0, 69.0, 47.0], [214.0, 389.0, 66.0, 47.0], [189.0, 387.0, 67.0, 43.0], [147.0, 420.0, 73.0, 51.0], [112.0, 419.0, 66.0, 27.0], [82.0, 382.0, 70.0, 42.0], [81.0, 385.0, 67.0, 42.0], [92.0, 364.0, 66.0, 41.0], [102.0, 339.0, 73.0, 38.0], [127.0, 359.0, 74.0, 27.0], [160.0, 379.0, 64.0, 28.0], [156.0, 347.0, 46.0, 36.0], [107.0, 337.0, 54.0, 23.0], [95.0, 361.0, 62.0, 29.0], [72.0, 340.0, 62.0, 29.0], [79.0, 346.0, 63.0, 38.0], [68.0, 340.0, 91.0, 43.0], [100.0, 364.0, 76.0, 39.0], [80.0, 325.0, 87.0, 49.0], [66.0, 326.0, 85.0, 56.0], [66.0, 335.0, 68.0, 54.0], [79.0, 339.0, 61.0, 34.0], [79.0, 340.0, 78.0, 32.0], [93.0, 362.0, 76.0, 37.0], [97.0, 358.0, 65.0, 35.0], [62.0, 340.0, 77.0, 45.0], [53.0, 367.0, 94.0, 46.0], [56.0, 366.0, 83.0, 48.0], [88.0, 365.0, 69.0, 42.0], [98.0, 380.0, 84.0, 36.0], [118.0, 381.0, 75.0, 41.0], [102.0, 340.0, 74.0, 46.0], [79.0, 346.0, 42.0, 24.0], [76.0, 361.0, 75.0, 47.0], [94.0, 363.0, 68.0, 46.0], [83.0, 352.0, 82.0, 43.0], [88.0, 401.0, 75.0, 49.0], [103.0, 422.0, 75.0, 26.0], [112.0, 397.0, 65.0, 53.0], [141.0, 418.0, 67.0, 64.0], [141.0, 444.0, 72.0, 41.0], [142.0, 421.0, 66.0, 35.0], [154.0, 442.0, 60.0, 31.0], [151.0, 426.0, 61.0, 43.0], [138.0, 388.0, 70.0, 39.0], [147.0, 400.0, 61.0, 32.0], [147.0, 417.0, 66.0, 45.0], [144.0, 401.0, 64.0, 43.0], [129.0, 382.0, 67.0, 42.0], [124.0, 401.0, 64.0, 47.0], [121.0, 399.0, 64.0, 36.0], [112.0, 337.0, 66.0, 41.0], [106.0, 340.0, 68.0, 46.0], [114.0, 378.0, 62.0, 37.0], [121.0, 381.0, 60.0, 41.0], [125.0, 399.0, 59.0, 33.0], [122.0, 405.0, 64.0, 53.0], [112.0, 398.0, 67.0, 47.0], [97.0, 363.0, 67.0, 44.0], [88.0, 370.0, 70.0, 37.0], [86.0, 400.0, 70.0, 35.0], [80.0, 379.0, 66.0, 28.0], [74.0, 368.0, 68.0, 38.0], [59.0, 389.0, 71.0, 44.0], [91.0, 380.0, 29.0, 27.0], [29.0, 344.0, 65.0, 48.0], [62.0, 337.0, 40.0, 41.0], [16.0, 336.0, 43.0, 46.0], [5.0, 356.0, 70.0, 39.0], [57.0, 351.0, 38.0, 35.0], [33.0, 346.0, 79.0, 42.0], [52.0, 356.0, 93.0, 40.0], [85.0, 340.0, 85.0, 41.0], [84.0, 296.0, 84.0, 60.0], [115.0, 294.0, 85.0, 59.0], [129.0, 303.0, 73.0, 49.0], [127.0, 278.0, 74.0, 59.0], [136.0, 302.0, 76.0, 53.0], [118.0, 301.0, 89.0, 62.0], [99.0, 308.0, 85.0, 49.0], [79.0, 321.0, 89.0, 44.0], [59.0, 321.0, 98.0, 57.0], [42.0, 320.0, 100.0, 36.0], [23.0, 321.0, 97.0, 47.0], [21.0, 322.0, 91.0, 49.0], [43.0, 339.0, 79.0, 28.0], [88.0, 286.0, 60.0, 64.0], [115.0, 302.0, 61.0, 63.0], [148.0, 318.0, 46.0, 61.0], [105.0, 316.0, 100.0, 62.0], [135.0, 342.0, 106.0, 43.0], [152.0, 351.0, 105.0, 42.0], [126.0, 341.0, 102.0, 55.0]], \"areas\": [1292.0, 1341.0, 1370.0, 1256.0, 1867.0, 2021.0, 2044.0, 1946.0, 1786.0, 1747.0, 1575.0, 1439.0, 1341.0, 1492.0, 1438.0, 1518.0, 1488.0, 1403.0, 1509.0, 1830.0, 1557.0, 1427.0, 1561.0, 1528.0, 1322.0, 1524.0, 1918.0, 1848.0, 1627.0, 2392.0, 980.0, 1746.0, 1570.0, 1110.0, 1228.0, 1614.0, 1775.0, 1874.0, 1777.0, 1840.0, 1910.0, 2180.0, 2675.0, 1958.0, 1724.0, 1972.0, 2464.0, 2139.0, 1983.0, 1839.0, 1451.0, 1541.0, 1848.0, 1958.0, 2181.0, 2080.0, 2133.0, 1347.0, 1644.0, 1881.0, 1654.0, 2001.0, 1533.0, 1323.0, 1054.0, 943.0, 1255.0, 1210.0, 1675.0, 1952.0, 1689.0, 1189.0, 1622.0, 977.0, 1434.0, 1797.0, 1962.0, 1436.0, 669.0, 977.0, 1001.0, 1839.0, 1782.0, 1809.0, 1906.0, 740.0, 987.0, 1871.0, 961.0, 2513.0, 1023.0, 2089.0, 932.0, 2267.0, 1687.0, 581.0, 1671.0, 1828.0, 1510.0, 2248.0, 2156.0, 1781.0, 2229.0, 1859.0, 2194.0, 2503.0, 1898.0, 1949.0, 1591.0, 2643.0, 2605.0, 2414.0, 2045.0, 2081.0, 1533.0, 1930.0, 2475.0, 470.0, 1813.0, 1269.0, 1191.0, 1635.0, 1004.0, 2527.0, 2764.0, 2621.0, 3777.0, 3254.0, 1754.0, 3180.0, 3303.0, 4574.0, 2939.0, 2826.0, 3552.0, 2592.0, 3408.0, 2946.0, 1586.0, 2443.0, 2070.0, 1642.0, 3480.0, 2792.0, 2794.0, 1040.0], \"iscrowd\": 0, \"video_id\": 0, \"height\": 1280, \"width\": 720, \"category_id\": 847, \"noun_phrase\": \"a neck\"}, {\"id\": 1020, \"segmentations\": [{\"counts\": \"U^W>8Y1IfT1]1_jNhN[U1S2J6I9Hd0^O8ekN^LkS1m3N21N=B5Kb0^O3M3N1N3N1N2O1N7J2N1O1L4M201M3N2O111N1O2L2O1O4L4K6K4L5J4M5I7JggV<\", \"size\": [1280, 720]}, {\"counts\": \"Q_m=>4LVV1b1_Oa0D9I7I5M7G9G63L7J1N1M2N3N4K4M2N2M3N2M3N4K2O3M2M2N101N2N2N2O1O1N2O2M4M3L2O1O3M5J4M6J3M4K4M5Kj_Z<\", \"size\": [1280, 720]}, {\"counts\": \"doo=6XW1h0oNo0E8Db0A<I6K9E5K4M4K40O3M6K3L3N1O;D6J3M3N1N4M2M3N4L4L2M3N1N3N0O2O1O1N2O1O3L4M2N2M2O1O8G:G4J6K3L7Jo_U<\", \"size\": [1280, 720]}, {\"counts\": \"Xhb>6]V1a1^O`0C;F9L5H8K4M4N2N3L2O2N2O0N3L4N2N1O23M4K3N6J4L3M2N5dL]kNQ3mT1K5L2N3L5K4M4L2N1O1N3N1O1N2N2O4L4L3M1N2O9F7J4Ka0\\\\OVX`;\", \"size\": [1280, 720]}, {\"counts\": \"[^k>6fW19_hNDPW1h0O1O2M3N2N100N101O1O02K5Ag0AkiN9`NIRW1j0@>L3B>J6F9J7N2M3L3O2M3N2M3N2M3M3K5M2O2N2N2N1N3N2N2M3M2O2N2O13M2N1O2N4K6K1N3N2N5J4M`0@6J2N6J5J4M1O2N1N2O1O2N2N2N1O2N3L9G`0_OhXX:\", \"size\": [1280, 720]}, {\"counts\": \"oej?9cW17L3N1N4L6J:F6J3M3N8H7H=C1O2N3M00000O10O100FnjNfMSU1h1k0M4KjjNkNkS1e0]kNoNe0d0mS19`lNK^S12okNe0QT1WOokNn0PT1POjkNW1XT1fNhkN\\\\1YT1cNfkN^1]T1VNSkN05LMT2\\\\U19N3K5K5L4M2O2M3N2L4O10000N2N2N1N3L4M2O2M3N2N3M2O1O1O1O1O1O1N101002N001O1O1O000O2O1O4lKZlNf3gS1XL\\\\lNf3QT1N3nLckN[2_T1bMdkN\\\\2gT1VM`kNe2WU1G4L3M2N2N2M3N3M7H5L3L[Qe8\", \"size\": [1280, 720]}, {\"counts\": \"_]l`0a0XW1=Cc0@f0YO8J9H6J6J7H8I4L4L3M2N3L3M5NO1PM[lNd1eS1\\\\N]lNb1eS1\\\\N]lNd1bS1\\\\N^lNe1aS1[N_lNe1aS1ZNalNd1`S1\\\\NalNb1aS1\\\\N`lNc1aS1\\\\N_lNe1bS1XN`lNh1`S1UNclNj1]S1UNflNi1[S1QNllNl1VS1RNklNh1]S1VNclNi1`S1UN`lNk1gR1eMXmNa00k1hR1eMWmN?1l1hR1fMWmN=1l1kR1eMXmN63R2hT1N3N1I8M2M3M3N2O\\\\jNYORT1e0ikNEST1:nkNHQT13TlNOkS1JYlN:eS1A^lNb0cS1XOWlNR1mS1[NalNf1kT12I7L4M3L3N3K5L4L3M5M2L4N200N2L4M3M3N2N2M3M3O2N1N2O1O0N3N2O1O1N20O100000O100001N10001O002N2N1Oa0_O3nLjkNT2YT1fMVlNm1VU1I2N2N2M6J<WOiYo6\", \"size\": [1280, 720]}, {\"counts\": \"_mla0>YW1?Cd0@c0^O5K9G>B;E4L4K6K1OO13M4L2O2N1N3N000001O101O2O000O0010OO1O2O1O2N1N300O2NN10O101O1O000O2O10O001O01O1O4]LfkNX3bT1O1OO1O1O10O001M2_OZkNbMnT1m1j0N3H7ZOTjNTOQV1g0f0L5CnhNKn`2JifN6K7\\\\hNIRW1j0_iNoN^U1P2H8K4M2D<L4H8N2M3M3N2L3M300O2N2N2O001N2O1N2N1O2O1O000N2O12N100O10000O1O2O0O3M3N3WLSlNf0e0h0lS1ROYlNj0QT1kNTlNo0oU1QOkYb5\", \"size\": [1280, 720]}, {\"counts\": \"\\\\f_b0b0jV1l0_Og0]O9J3M2L4M3M3N1O3M2M3N3M2N2N2O1N2N2M3O1O2L4N1N2N2O1O001N2O1O20O2OO0O100O1000O101O01N2O1O1O00000001N100010N2M3N100O1O1O1O100000O11O0001O0O3N1N2N1N2lNT1\\\\Od0A?I7F;M2L4I7J7LSjNWOiT1f0mjNIk0HWR1<olNO;3gR1ETmN;Ac0[S1nNWmN`0[Oe0]S1jNWmNc0]Od0ZS1iNmlNL_Og0=d0XS1cNQmN3WO6Q1U1oS1hNekNd1]T1ZNakNi1bT1TN]kNl1eT1SNYkNn1XU15I6L5N1O2N1N3K5M3N1O2O001O0O1N2M300O10000O00N02O10K7lNdkN\\\\NhT1Y1fkN\\\\NeT17bjNb0eV1VOgiN`0QW1Lace4\", \"size\": [1280, 720]}, {\"counts\": \"j^Yb04]W1c0Fb0\\\\O`0D?C8G3M4L3N2N101O1N2O1N2M3O1N2N3M1O2N1O2O1N2O1N2N102M2O2N0O001O2N203L101O0100O2M5L2M1001N10000O2N1N2O1N100101O0010O000000000O1N12O001O00O1001O00ImKalNR4_S18O0001N2J7013MOO01O0O10O1001L3QOklNQMWS1j2R1D<L4@a0K4I7J6K5LojNROXS1i0mlNXORS1b0hlNKWS11flN7XS1IhlNH]OEYS1?`lNf06SOVS11jlNk00WOWS1GklNk02EQT1?kkNGST19hkNNVT1MlkN8oS1JQlN8PT1^OYlNb0iS1YOZlNg0iS1VOWlNj0nS1ROikNW1ZT1eN`kNa1eT1[NXkNh1_U11I6K5HnMajNT2_U17O1N1O01M2oNfjNVObU1j0gjNhN_U1X1d0O0O0OO21L400O2[ObiNE`R^4\", \"size\": [1280, 720]}, {\"counts\": \"^fZb0>\\\\W19G9Lb0^O5K2L4L4N0O2N1N4L4L3M3M3O2kjNZMlT1h243K3O2N1O2N2N2O1N3M2N2O2M3M1O2N2O1N2O1O3M3L2N101O101O00O2N2O4M2O1N1M4LmkNZLPT1d37N3L3O1O01O6C]LRlNd3YS1bLjlNNK`3^S1_LblNP4_S1oK`lNR4dS1_L[lNm2fS1f00O1O2]LWlNm2iS1SMXlNl2jS1RMUlNo2mS1b00O2N2M2O1O2M2O000]LkkNZ3VT1gLhkNZ3WT18O2N001O1M201O0O1O2N1N3O1O1001N01000O2N1O1O1N2WOi0QOo0J7H7J6I7JkjNkNiN7aT1h0clNNZS1MhlN6XS1GelN?iNkN\\\\T1d0hlNd0^S1RO]lNZ1[S1mNilNm0_S1mNelNS1EaNgR19`mNP1kNnNj00lR1OYmNX15lNgS1U1WlNoNaS1W1^lNlNaS1S1\\\\lNRObS1`0jlNBSS1?jlNHVS16hlNMZS1]OmkNEd0U1`T1gN^kN[1bU12N1O2AkNRjNT1mU1?010L4M2OK33O2L4H7N4M3M203I7J8EZSo3\", \"size\": [1280, 720]}, {\"counts\": \"cn[b01hW1a0C8D<H8J4L3L5L3M6J1O1N2N2O1N2N2N2M3M3M2N3N1O2K6M3L3N2N3M2N1O2N2N2N2O1N2N20000O11O1N2N2O2N10O1O2N0001O00O01O2O0010O2O1OO000O10000O10001OO2O1O0O100N1L5E:N3O1O0100000O1O1N200O010000O101O1N2O0O1O2O0O2O0O2O0[Od0d0]O1N11O0O100O100O100O1G8aN`1M3F:G9K5J5IfjN]OgS1=\\\\lNHQO]OQT1?RmN;kNXOkU1S1SjNoNjU1S1\\\\jNhNRU1i1YkNWNZT1i1dkN`NTT1V1WlNlNdS1Y1XlNkNbS1Z1\\\\lNiNcS1X1[lNkNbS1h0bkNmNk0?cS11fkNVO231Mg0l0aS1KUmN;mR1BPmNb0SS1nN`kNM]1Z1nT13M3GcNoiN^1nU192OON2M4000OO1H93N1N11L5K4L5B`0F:Eakc3\", \"size\": [1280, 720]}, {\"counts\": \"af_b07^W1b0B>G5K5K6K3M2M4M2N2O1N2M2N3M2N101M3N2N2O2I6M4M2M3O1N2M3O1N2N22O6K0OO01O0000000O1000O2N1O2N1000000O10000O1N2M5L3M3L3N2M2O2O0M4K5N112N2OO00O11O0O100O100O100001M101O1O100000O10001O1O001N1O2O2N1O0O101N11O01O0O100O100N2GWlNXLlS1]3a0K5L4mNS1M3O1H8JhjNeNWT1X1gkNlNBEXT1\\\\1UlNSOACZT1U1VlN\\\\O_OAZT1m0UlNK\\\\O]O7JfS1c0elNf0YOoN5OmS11mlNn0XOCmT1f0XkNVOeT1k0_kNSO]T1P1akNSOZT1g0lkN^OnS1g0PlN[OnS1g0okN]OkS1f0UlN\\\\OkS1b0UlNAkS13jkNcN;_1kS1^OmkNYO?X1gS1ZOPlN[O4`1mS1oNilNS1XS1cNnlN`1VS1YNglNl1aT121K8E:\\\\OgiN]O_V1?a0100N1N2NO4M4M2N3M4N4LXSe3\", \"size\": [1280, 720]}, {\"counts\": \"gneb04`W1b0WOQ1@8H4M2N2N3L2N2N2M3O1M3L4L4O2M3L4N1N2N22N2N3M2N10O0N3M2N200O100O2O0O1N3O01N4MO1J7L3N2O1N2M3M3O2M3K5J6N100000O2O0O100010O010O000001O0000000000O2L3O1O10O2O001O001O001O001O001O00001O0O1O100O101M2J6YOg0F:D<L4G9FckNSN\\\\S1e1WlN]NXO=]T1o0`lNhNoN9bT1j0ZlN]OUO]Oe0LZS1R1nlNJJ^OTS1?XmN4\\\\OG]S13olN=CC_S1MjlNd0GA`S1GhlNj0IA`S1ZOhlNZ1I^OZT1>VkNhN`0l0TT1a0nkNDQT19PlNKnS12okN5QT1\\\\OekNWO;`1oS1WO]lNk0bS1TO]lNn0dS1oN\\\\lNS1eS1lN[lNS1iS1cN]lN_1RU1O000O100O00J51L5nNliN2YV1NPjNEWV17j0M3O4DobQ4\", \"size\": [1280, 720]}, {\"counts\": \"dmjb07h00nU1V1K4L<D=B;C;I4M1N100O1000N20O1O1N200O1O1O1O2N1N2M4M2M4N1N2L5K4M3N2O101O00000000001O000000001O1O1OO1000000001O00000O1J7OO10O100O10O2O1O1O1O1O1O001O001O0O1O1K5WO\\\\lNQMjS1h2clNhLfS1b2X1I6@`0F;I6L5KlkNnN`M8dS1g0dnNK[Q1O\\\\mN^ONh0fR1ESmN6O8mR1@mlN?46nR1ZOilNf073PS1VOglNi074SS1mNhlNl08:QS1_NmlNY12:cS1G^lN:aS1AdlN?\\\\S1_OglNa0YS1\\\\OilNe0WS1XOmlNg0SS1WOolNi0RS1SOQmNm0PS1oNTmNP1nR1`NfkN6`1X1iT13M3O1N1O1N2N3O01I:G?D6H7Chae4\", \"size\": [1280, 720]}, {\"counts\": \"e]cb0429YW1<H8Hc0_O3O1N2O4K4M2M4M4QmNfM^M0TR1e2WPO\\\\MeMOSR1k2h26J4K5K3O1O0bkNdLXT1b3N2N101N2M3L5I7H7O200O1O10O00000010OO100001N3NO0N2O1O1N3L3O2L3M5F9FmkNcLWT1Z39M4KclNQMRR1j2hmNbMXR1R2mmNTNSR1f1nmN^NSR1V1TnNnNlQ1m0nmN^OSR1:PnNJoQ13SnNOmQ1IZnN8eQ1B`nNa0`Q1]O`nNc0aQ1\\\\O[nNi0eQ1TO[nNn0fQ1nN]nNS1cQ1iN`nNX1aQ1dNanN]1`Q1]NYmNG`0k1YR1YNYmNO=h1]R1SNgnNl1gS101OO010O010O100O1N2O0O2N2O1O0O2O01O0PN\\\\jNf1dU1VNejNd1\\\\U1ZNhjNb1UU1_NPkN]1PV1E9E9JUac5\", \"size\": [1280, 720]}, {\"counts\": \"j]^b0>_W16K6K8Gc0]O8H5K4K4M3N1M4M2N2N2M2N2N2M3N2N2okNSMPS1m2ilN]MUS1d2alNfM^S1m1]lNgNaS1W1elNeN[S1Y1dlNkN[S1Q1glNROXS1h0nlNXORS1d0QmN^OnR1?TmNBlR1;VmNFjR15[mNKeR10`mNO[R12imNOTR12nmNMPR13QnNOoQ1KVnN5lQ1GWnN9jQ1CYnN=mQ1YOTnNi0QR1nNSnNR1PR1iNcmN^OROk1YU1UNfjNk1ZU1VNejNk1[U1UNdjNk1gU1100000OO11M4M2M3J7L4M9_Oa0CbQ]7\", \"size\": [1280, 720]}, {\"counts\": \"\\\\_^b01gW1:H6L3O2N1M111M11N20O1N2001N1N20L4N30N2L4O1O1N1O11O2L302NJohN_OoV18^iNFbV16biNJ]V16eiNI^V13b\\\\6MWdI2N3M1O2M3L3O101N2N3N1N3M2O1N2N102N1O10O011O1N5POSiNe0[W1F[W1EbaP7\", \"size\": [1280, 720]}, {\"counts\": \"X^oa02fW1:L2O1M300SiNDUV1<jiNEVV1<hiNEWV1<fiNHYV16giNLXV10kiN2UV1JliN9jV1I9O11M2O2O10ON1M4N3M30M4N10N1O101O10O1O0G809Fd[;N]dD7L2N2O2N1N3M2N3L4M2O1O10001O02M3N9E=Dg_d7\", \"size\": [1280, 720]}, {\"counts\": \"QnXa0=]W1>F7I:F5J?@7L1N1O2O0O000O1M2001N2O1[OgjNdN[U1[1e0LXkNQOiR1H]lNKh02cN=XT1DnmNj0UR1UOhmNl0dT1O01010001O001M300O0001OO10N3L3O2N2O01M2N2O110O01O2L8F8J8Iajh8\", \"size\": [1280, 720]}, {\"counts\": \"nmZ`0>]W18K5K7H6J6J5L:E>_O8J5J8I:G8J5K4M7I4M1N3L100O2O00000000000O01000N3L34K3N1O3L>C:[LekNQ3QU1lLnjNb2[U1M3N3N005I:RN^jNh0am9B_XG2aiN<QV1X1]O:J;G6I4H8K3L5M22L4J7L3hL_kNOOe2WU1M4J8I5K2N2M3N1N3L4gNeiN77EZV10diNLeXf8\", \"size\": [1280, 720]}, {\"counts\": \"nma??6BgV1W1C=G5L4L4J6K7J5L8I3L4L3M3N5J8F9K4L2N1N1O100000000000O11O1O001O00000O101OO1000O2J5O12N10OO101N10000jL`lNl1_S1oMglNQ2XS1oMjlNQ2TS1oMmlNQ2TS1jMPmNV2QS1fMRmNZ2PS1aMSmN_2WT1000000O1O1O2M5B`0Af0PO^i8WOUWG0]iNa0gU1b1VOi0_O:H4L4H8H6L5L4O1O2O0001O0O2O1iKelNg3\\\\S1ULilNh3iS1O1O1fLkkNg2VT1SMRlNj2VU1\\\\O3M2N1N3N1O2N2N3M2N>Ac0hNWiN3_gY8\", \"size\": [1280, 720]}, {\"counts\": \"h]U?R1hV1:G6K6L4I7M3L8G:G6J4L4K6L4K6K5L6K3L3M2N1N10O1O0001O11O0010O0O100O010000O100001N10000O100O1O11O000010O0101N1O1O0O2O2N1O1OO1O2O0001O1O1O3M1O00O11O00012M3M2N0O101O001N2O3M5L2^ObkNYMdT1a2T1UO?Be0TOm_7Ol_H[1hNg0@c0A=E<C8J5K4N0N10110000O8I4L<D2N3M2Md0]Oc0]O5K3M2M4K:ESYR8\", \"size\": [1280, 720]}, {\"counts\": \"]e[?;YW1FnhN`0oV1>G?B5TkN_NSS1d1UlNgNWO2\\\\T1T3M3M3L3N2O2N10001O000000000001O00001OO2O0O100000O010000O2N101N1O100O10000O10O11O0O101N10000O02O000010000O00001O00001O000001O000000000O100000O010O11O0001N2L6E<C<G9QM`kNX2XU1K3M3G9L:D^a7\\\\OTXGAhV1o0M3]Od0H6G8I7K5N1J7I7I6I8M3O2M3O00O2O3M7H6bMdkNm0VU1WNSkNo0\\\\V1TOVbn7\", \"size\": [1280, 720]}, {\"counts\": \"h^_?n04[OPV1f1VORNmjN]2XT1R1M4K5N2O1L4N2N2O2N100N2O1O100000001O0001O00O1O100N2N1103M2N01O00000001N201N00001O011N001O1N2O001O001O01O01O00001O1O001O001O2N1O1O1O2N5K7I4L1O1O1O1O100O1O2M101O1O1N2O1N2G;E:L4Mf0kNj0AZ`7<S_HQ1WO6M1001O0N3O001O5K5K3N1N1O1O2N2M3N4J8I5FbXm7\", \"size\": [1280, 720]}, {\"counts\": \"_g`?550YW1`0M1O000001N3N1O0O2O001O0000000O2O001O001O000O02RL2goNOWP15foNMYP15\\\\lNCR37bP1e0^lNTO`26SQ1U1gnNjN\\\\Q1d1UnNZNmQ1h1QnNUNQR1m1nmNQNTR1P2kmNnMVR1S2k1001N1O2O0O20O1O1OO100jkNhMcR1Y2\\\\mNiM`R1Z2`mNfM]R1\\\\2dmNdM\\\\R1\\\\2dmNdM\\\\R1\\\\2emNbM]R1]2f1M2N1O010O001O001O0000000000001O00001O000O1O0010O11O00001O0O2O3L5I6oNeiN6TW1N2O1N6HmjR9\", \"size\": [1280, 720]}, {\"counts\": \"Y`i?2jW16I8M0O1fM@SmNa0mR1_ORmNb0mR1_OQmNd0`R1L^mN4SR1c0dmN^OXR1g0fmNZOWR1j0gmNWOYR1i0emNYOZR1h0emNYOZR1h0fmNXOYR1i0gmNWOXR1j0hmNVOXR1i0hmNXOXR1h0hmNXOXR1h0hmNXOXR1h0hmNXOXR1i0gmNVOZR1j0emNWO[R1h0emNYO[R1g0dmNZO\\\\R1e0dmN\\\\O[R1e0emN[OYR1f0gmN[OXR1e0imN[OWR1e0imN[OSR1h0mmNYOnQ1l0QnNTOoQ1l0QnNUOoQ1k0PnNVOTR1d0nmN]OVR1<lmNDUR1;jmNFVR19kmNGTR1:kmNGUR19kmNHUR17jmNJVR15kmNKUR15jmNLVR14imNMWR13hmNNXR12gmNOXR12fmN0ZR10emN1\\\\R1MemN3\\\\R1LdmN4\\\\R1KemN5[R1KfmN4ZR1KhmN4XR1LhmN4XR1KimN5XR1JgmN7XR1JgmN7YR1IfmN:WR1FimN>TR1BlmN>TR1BkmN?UR1AkmN?UR1@lmN`0UR1^OkmNc0UR1]OkmNb0VR1]OkmNmNeN`1aS1BjmNmNfNa1`S1BjmNoNdN^1cS1BjmNDmNK[S1`0gmNDQOJYS1b0fmNDTO^O_O2iS1j0emNE]O]OPS1l0dmNG]O\\\\OPS1k0dmNH\\\\S18clNI]S16dlNI]S16dlNI]S16dlNJ\\\\S14flNK\\\\S13elNM\\\\S11elNN]S1OdlN1^S1MclN2`S1L`lN4bS1I_lN8aS1F`lN:cU11O00O2O1O2N2N0O2N2N1N1O201N10J8H9H8J3N2M20O10NNFSOiiNn0VV1[OaiN44M]V18QjN_OZV11P1NQjj7\", \"size\": [1280, 720]}, {\"counts\": \"kgj?8QV1IRkNm0fT1Z1L4G8WOdMfkNd2VT1e0N2N2N2O2L301N10000O10000O1N2N2O1O100O100O10000000000L40000000001O000000000O100O2O01O00000000001O1O0001O000000000001N1000O1O101O0000000001N2O1N3L4M2N2N3I6I8G8J6J6K5L6F:D>[Of0^O[kNAUU1=\\\\Oc0M2M3K5H8K5N2K6J6K4I7O10000001N1000O1O1O1O1N2MHPkNcMUU1d0kjNd0eU1kNmjN7AA\\\\V12WjN:QV1DUjN0^Pb7\", \"size\": [1280, 720]}, {\"counts\": \"ShT`07aW1?A:F9B>D<D;L5J5K5L4F;J5N2N2N1O2O1000000000O10O100O0O2M2O200O100001O01N0O20000O101O000O10000O1N2O1O100O0100000000001O1N2O0000000001O0O2O01O001O1O01O1O1O001M2N3K5M3J6M3E;A?G8\\\\Of0G8L5H9EojNVO]N`0WT11ZmN^O\\\\NQ1YV1`0O0O2D;K5O1O1O2J6L4O101O2O1OO001O1O1O0O101N1nNR1POS1Ioi[7\", \"size\": [1280, 720]}, {\"counts\": \"gag`02dW1`0@;H8G8F:I7K5L4GjMfjNX2YU18N2E;I7M3N2M3M4M2N101O2M2N2O1O1N200N2N2O01000102M000000001O0000O100N3M3N1O1O1O100000000O1O1O2N100O1O1O1001O01O02N2O0O001O000001O00O101O0000001O002M3M6J2O1N1O200O2N1J6G:E=G:I:^N^jNK``7@gfI?C;J3FQO_iNS1_V18O0O0<@;SO[iN0l]P7\", \"size\": [1280, 720]}, {\"counts\": \"_Po`01hW1=E7H7H7VOoN_jNX1\\\\U1nNYjN\\\\1dU1hNVjN\\\\1gU1a0N1M3G9H8E<H7O1O1O2N1N2N2O1O1O1O1O1L4M3J6000000O1000000000000O101O01O000000O01000N3L3N3O0O1000000000O01O2M2O2O00000001O002O0O2N0010O000000O2N101O0O100O2N2M2H8H8M4N1N3J6L4M5G9J5F;^Oc0CkkNfNZR1g0nmNBVR10mmN5dT1;J6M4I6I8K4L5O00000011NO0N2MWNZjN^1iU1_N]jNZ1XV1UOgiN\\\\OlV16UY`6\", \"size\": [1280, 720]}, {\"counts\": \"inn`0d0TW19D<C=aN]NRlNP2iS1WNQlNm1jS1YNhkNT2ST1P1J5M3M3M3N2N2N3N010O002N101O01N01N2O1100O2N0010O0001N1000O010000O2OO2O0000000000000N2M3N200O10000O1000000O1O2O0O10000O1001O00001O1O1N1001O0000O1000001O010O004L1O1O1O0O2J6F:L7J4^OckNSMjT1g2>aNdjNXO3EkU1>kl3DeTL:ZOe0F9F:I7L5F:J6M3N2O100000000HXMZkNg2eT1ZMXkNh2iT1700O1O0002[OXkNjMPU1f1o0L4K7fNmiNDK50N]V15TjNH[V14l0MPkn5\", \"size\": [1280, 720]}, {\"counts\": \"Zon`09YW1b0C>ROoNVjN_1ZT1aNflNo1US1`1M3N3M2O1O100N2O2N1O101N10000O11O000000O1000010N10O10001O000O10002M2O1O01O0000000000000N2O1M4OO10O1O10000001N01O1N110O1O11O1O1O1O2N001N10000001O000001O1O1O3M1O1O2N1O1O1O1O1N2O2N4M3OO2:Bl0SO:E8E:Gk0nN`h8ORVG0UjNb1cU1a0M3M3M3O1O1O0H8O2O0O1O[MQkN[2oT1eMUkNV2lT1hMXkNU2]U1L7\\\\N`jN:fU1SOQlS6\", \"size\": [1280, 720]}, {\"counts\": \"Ygm`0>WW1<E;C=^N_NSlN6QOd1fT1^NSlNm1kS1U1O2N1M3N2O1N2N2M4L300N3N3L110O110O000000O2O3L2OO100O0O2O1O1O10O01O1N20000LhKclNU4dS1OO20lKZlNn3fS162N0000O1001O0001O0000O1000N2N2O10000O100000O2O1O2N10O01O0000001O0000001O2O1N1O1O1O2N1O2M2O1O3L3O1N4K_kNiLeT1j2e1cN9Dj0nN_`7f0i^Hc0@<N2O1N20O100O2O2O1N00O010000O10O1O02M4ZOi0UOXbR6\", \"size\": [1280, 720]}, {\"counts\": \"YWk`0a0TW1<G:E:J5B>O2ROQNhkNo1VT1VNfkNl1XT1o0O1N2N2O1N2N2O1N2O10O01O100O1000O1000001O1000O0O1000O1000O1000010O00000O100N2N2N2M30001O01O00O1001O000000O11O0O100000000000O100O110O01O00001O00000000001N3O2N1L4J5M3L5L5K4H8D=iNZ1]Ohj35YnJ]OgV1T1IO_iNnNZV1Z1hiNdNTV1`1liN`NQV1c1niN^NPV1j1OL31L5O1N2O0010000100O2nMYjNg1UV1Jc0\\\\O4C>CVi]6\", \"size\": [1280, 720]}, {\"counts\": \"ahm`07cW19D:H8J6L3K7H7I6M4I6C=M3N2N2N2O1O1O1O1N2N2O1O1O1O1N2000000O1O1O1O1O1O10000O1M2L500000000001O1O1O1O00000000O1001O00001O0001M3L3O3N1N2O0000O1N101M2N2O101M3L4M3L5L3L4L4I6M4N1M5EmkN_N\\\\R1[1amNPObR1e0`mN\\\\OcR1>^mNEeR14WmN5mR1CRmNb0TS1POSmNQ1lT1101M3N2M2O2L4L4N2N1H9@`0O200O03M2N1N1UMQkNe2VU1O1O3L6K2K4B=dNVjNDiV18f0HQi]6\", \"size\": [1280, 720]}, {\"counts\": \"mon`09eW12NJbhNN]W13dhNLZW16fhNK2N01]V1=_iN;ZV1j0L3M2M4D;M3M3M4M2L5J5N3N0O2O1O1O1000000O100N3N1M3O10000O1O10000000000O2N100O100O1O1O110O2N000N2O1N2N2001O0001N10001O000O1000O1O0O2N1M111O2M3K3N3N2O3I6K5M4I7MTlNfMWR1V2bmNUN]R1d1cmNdN]R1W1dmNlN]R1o0amNWO_R1d0]mNEcR16`mNKaR12_mN1aR1IcmN9^R1BemN?[R1^OgmNc0YR1TOomNl0TR1lNPnNV1ZR1^NemNe1YT12M3N1N2N3H8A?0010O0M3K5N2O1O1001N3N2K:@:I8J7J6_OPjNiNVV1Q1PjNjNSV1S1V1VObhX6\", \"size\": [1280, 720]}, {\"counts\": \"ggRa09ZW1b0ZOa0hNYOTkNk0iT1W1L4N2N2L4M3N2L4N2O1N200O100O1O1000000O101N1N2O2N1001N2OO1O1001000O1N1O1O100O1000000O1O1O1000O1O1N2M3L4L4K4N110L4L4L5M3N2N2M3I8IPlNTNWR1h1emNdNXR1V1amNTNnNm0`S1h0bmNG]R15emNM\\\\R1LhmN6XR1FhmN>ZR1[OimNg0YR1SOjmNm0YR1lNfmNZ1^R1^NcmNe1YT13B>K6F9N2O1O1IjLckNW3\\\\T17O1O1M2M42O0O100O1O001O000O10O04K3J5K7K4M5]OmjNQNXV1h0niNSOVV1f0f0J6I9L4J`ib6\", \"size\": [1280, 720]}, {\"counts\": \"k_l`0=jV1DbiNi0[V1`0L3jN\\\\OmjNg0oT1W1L4N3M2N2N3N1N2M3O1O1O1O1O2N1O1O1O1O101M2O1N1O10O100O2M2M3M4L3M3N2N3L4N3I7M2N1N3M4KakNYNmR1d1QmNcNmR1Z1TmNhNlR1o0olN@PS1<SmNElR17VmNLdR16ZmN1bR1M`mN6_R1FemN;[R1@hmNb0XR1ZOimNi0XR1SOgmNQ1YR1lNfmNX1]R1bNYmNk1TS1cMllNb2VT12N4J5N3N001O10000O0N3O1O10O1000O0100O1N1I9L4fMQkN\\\\1UU1aNojNR1eU1`NRkNf0mWm7\", \"size\": [1280, 720]}, {\"counts\": \"dWPa03:4kV1e0eN\\\\OojNm0lT1]OljNg0oT1CijN`0SU1X1N2N2O1N3M20O01O001N3L4N001O0O100O1O0002O0NL43M3M6KO11N3M3N2N1N2M4N2O0NSlNeM[R1Y2bmNmM]R1P2^mNYNaR1e1ZmNbNfR1[1VmNlNjR1R1TmNROkR1l0VmNVOiR1h0nlNDQS19QmNImR14UmNOgR1O^mN1aR1NbmN2]R1JhmN6XR1FkmN;`R1SOgmNm0\\\\R1mNgmNS1bT11N2M202N1000000O1000O2O000000N2N2O10000O10O100N2N3I:Ba0hNWiNb0[W1DXai7\", \"size\": [1280, 720]}, {\"counts\": \"]fRa02ZW1f0aiNB^OOTU1g0RkN5jT1c1L3N2O1N3M2N3N1N3M101O001N2N2O1N1O1O1O0100M3M3M3N2O2O1O010MikNQMcS1m2\\\\lNWMcS1f2_lN[MaS1c2P1K4N1O2O1OmkNeMcR1X2[mNnMcR1o1]mNUNaR1k1]mNYN`R1g1]mN_NbR1_1]mNeNcR1Y1ZmNmNfR1k0SmNCRS1LXmN6hR1IVmN:jR1DTmN`0mR1[OTmNi0oR1hNZmNZ1oR1XNUmNi1^T13_Oa0N2N3L3O1M3L4O11O1O1O1N101O2YMWkNS2_U1L4L3MnNlM_kN2O05R2fT1nMdkNR2\\\\T1nMckNR2^T1mM`kN2_On1lU1000000000000O101O2ZNPjNW1^V1N3BbiNTO_Wc7\", \"size\": [1280, 720]}, {\"counts\": \"SfRa07bW1;H8Id1]N8G<E5L1N2M3N1O101N110N2O1N101N1O1O2N2N10O1N12O0000O1L4N2M3M4L7H8I5J4O1O1O1L4K5L4KekNROoQ1e0PnNGTR1OhmN9aR1TOhmNm0dR1_NcmNc1ZT13H8M3O1\\\\OPNSkNU2lT1`0000000O1G900N2O03N1O001gMWkNY1mT1aNVkN^1XU1nMnjNR2^U12000O1000001O1O0O010O2O2N1N2M2QOR1Bdie7\", \"size\": [1280, 720]}, {\"counts\": \"]VUa0<_W1:I7G<F<Ee0[O9F:G4K4L4L4M2N1O1O1O1N2O1O1N3N1M3L4O1O1O1O100O002M20001O1N2O0001O1O001N2O0O000O1O2M2J6L5K4I8F9B?K3M2N5LmkNdNjQ1R1ZnNVOeQ1>_nNJ`Q1JinN:ZQ1POWoNQ1kS1100O1J50101O0O00100O100O1O1O001N3N2M2O0010O10001NKPN\\\\jNP2dU1oM]jNQ2jU1N4M2M3M4L4L7H[hV7\", \"size\": [1280, 720]}, {\"counts\": \"cmga0b0[W18H6K6JS1mN;D:F7J4M2N2M2O1N2O1O0O2O1N2L5K4M3O1O1O1O1O100O101N1001O000000000000000001O0O100O10001N0010O0O3M3N2I8H7I8F?ZOSkNdM]U1_1cjNgN`V1>gb78`]H5I6O2N2N2N2M3K5J6O1O1O11O0O2O00001O000000O001O1N101O1O0101O3M1N101O2N2M4M<@ZaW6\", \"size\": [1280, 720]}, {\"counts\": \"hUSb04UW1o0nhNBoU1f1_O>C5L3M2O1N6K3M2N2N1O1O1O1O1O1N2N2O1N2M4K4M3O1O1O2M2O1O011M2M30000000001O03L2OO1O1N3O00O100001O000000O1M3J7K4J6H9I8H8G;H8G;VO_jN\\\\NWV15Zl38STLS1]iN_NPV1i1O2L3N2K6kNoMmkNKHV2VT1lNbkNX1ZT1X12O10O03L3bM^kNZ1dT1dN\\\\kN\\\\1eT1o05K3L3L:G100O1O10O100O0010O5M2M1O2M2O2M2N2N2@VjNfNPV17]ch5\", \"size\": [1280, 720]}, {\"counts\": \"[]Yb0<g0j0iT1@ojNf0lT1Z1M3L7I6K3N001O2N100O2N1O1O2N1N2O1O0O2N2O2M2O0O2N2L5J5O001O1O1O100O1000104L0O00O1L4L4L4M3I7K5J6L3N2J6J6N2N3H7J^lNTN^Q1e1\\\\nNiNcQ1R1]nNTObQ1b0YnNMhQ1NYnN4iQ1A_nN`0fQ1VO^nNl0fQ1lN\\\\nNV1UT12NLTjNXNhU1R2L2M5M5E3njN^MRU1b28N3M2N1O1N10000000001N3N1O000O2N101N2M3@ZjNbNhU1T1h0D<G[jS6\", \"size\": [1280, 720]}, {\"counts\": \"YWSb09QW1i0aN]OPkNo0hT1X1L5L3N2N2O2L5L3M201N1O00001O1O1O1O1N2O1N3N1M4L3M3N2N1O2OO1N12O01000O10O1O1N3L3L4L4L4M2N3M3M3M`lNmLYR1P3fmNVMXR1h2fmN\\\\MYR1c2cmNcM]R1\\\\2`mNhM_R1U2bmNnM\\\\R1P2emNSNZR1i1gmN[NYR1a1gmNcNYR1[1bmNlN^R1S1_mNQOaR1k0bmNVO^R1f0dmN\\\\O\\\\R1`0gmNAYR1:jmNHVR13lmN0UR1JomN8QR1_OWnNf0dQ1WO_nNj0aQ1ROanNo0`Q1eNjnN\\\\1kS12M3D;M3N5JNUkNWMjT1i2700O102[MRkNT2eU1H2N0000O1M300001O1O0O2O0O1O2M3I8dNciNa0l`W6\", \"size\": [1280, 720]}, {\"counts\": \"ndka0f0c0K_U1?QjNe0SU1W1K4K5M4L2O1N200O1O1N3N1O010O001O1O1O1O2M2M2O2M2O1N201O001O00O100O1L302N3M200O1M4L3N3L2N3N1O1N20blNPMkQ1Q3mmN[MQR1b2mmNcMRR1Z2nmNiMSR1S2jmNTNVR1j1imNYNWR1d1imN_NVR1_1dmNjN]R1Q1cmNTO]R1f0emN]O[R1`0emNCZR1:hmNHXR12jmN3TR1IomN9QR1ATnN?mQ1^OVnNb0jQ1[OYnNe0iQ1TO]nNk0lQ1fN[nNZ1RR1PNRnNS2]S1a0L4J6M4L3L3M4N2N2N101O1O19G2YMckNg1^T1UNgkN^OJT2`T1]NlkNd1VT1YNkkNg1_T1oMakNQ2`T1mM`kNT2aT1jM`kNV2TU1100N21O8G3N1N1N3VNUjNY1]V1[OVZ[6\", \"size\": [1280, 720]}, {\"counts\": \"Reaa0g0VW17J8]iNNiT1o1D6L4K2O1N1O1O101O3M100O00003M1O2M101N2N4K4M2N2N2N2O001ON2N2N2N3M3N1O2L4L5L6J2N3M2N3N1L3M401NmlNPMYQ1l2hnNYMXQ1_2fnNjMZQ1P2[nNbNdQ1\\\\1nmNgMTOU1nR1P1imNAVR17QnNKoQ10UnN1kQ1KYnN4hQ1D_nN;cQ1@_nNc0aQ1ZO`nNh0`Q1VOanNk0`Q1oNUnN`1kS1;M2N3L4K5M4M2O1N2003M3L2`M\\\\kNb1eT1YN_kNg1cT1UN_kNk1fT1nM\\\\kNR2YU1001O1OO01000O2O5J2F;ZNTjNd0oV1BjaP7\", \"size\": [1280, 720]}, {\"counts\": \"nUZa0=]W1:H<F=ViNZObU1R2_O;E4M3M3N1N2N2N0O2O1O1N2O1O2N1N2N2O1N3M2N2N2N2N2O1O2M2O0O201N1O2NO2O003K7F7J5L4M3N2I7K5K5M1O1N3G:H8E:NkkNiNgQ1R1VnNXOhQ1b0[nNAdQ1;]nNIcQ14^nNNbQ1O`nN2`Q1IbnN:^Q1BbnNb0^Q1VOgnNn0YQ1^NVoNg1_S12M3CgMRkNZ2[U1O1O1N10001O20OO01N10O01O1O0010O012L9G3M3E;XOk0^OXQX7\", \"size\": [1280, 720]}, {\"counts\": \"_eaa0:_W1;WiN?gT1h1D5L3N1M3N101O1O1O1O1O1O1O1O1O2M2O1O1O0O2M4M3M2N2M4M2N2O002N1O1O101O01O10O001O01O0000000O100O0K6L2N2N3N3L3L4L4N2O3I7K6G:C?F_lNnMgQ1Y1^nNTOeQ1<ZnN0QR1ZOomNQ1\\\\T14K5M2N3L5I6M2M4CPNejNU2ZU1lMfjNT2VU1=O13\\\\MmjNU2aU1N1O1O2N1O2N1O0000O101O01O1N4M2M2N2_Od0_Okaf6\", \"size\": [1280, 720]}, {\"counts\": \"e]`a01[1e0UT1NQkNi0gT1^1I6K3N2N2N2O1O1O1O100O101N1O1N2O100O2M2O1O1N3N1N2N2M3N2M4L3O1O1O10001O000000000001OO1000001O00M3M3M3K5M3L3L3L4M3L5M4L5L4J7E>B<C`0@ikNDaQ1OXnNc0lQ1PORnNZ1YR1XNhmNi1WT1000O2N1M4M2N3D;\\\\Oe0FPMakNR3\\\\T1<M2004L1O1O3M5K2SMWkN_2iT1aMWkN_2jT1_MZkN^2XU1L4M6I3M1O1O001O1N1F;D<J6H9K5AVR_6\", \"size\": [1280, 720]}, {\"counts\": \"a_[a01`W1e0_O=D;oN^O_jNf0UU1Y1M3M3N1M3N2O1O1O2N1O2O1N2N2N2N1N200O001O1N2O1N2O1N2N2O1N2O1N3N1N101O2N1000N2M3M3L5K4L4L5K3M4L4M3L5K4N2M3L^lN[MQR1a2imNnMQR1n1imN]NVR1_1lmNeNQR1W1lmNQOSR1l0imN[OXR1`0jmNBWR19mmNISR13PnNNPR1OQnN3PR1GTnN:lQ1BWnN>lQ1\\\\OWnNe0QR1mNQnNW1XT14M3K6K4M4K4L4FXMZkNj2bT1^MXkNd2^T1ZNbkNR1]T1oNckNQ1\\\\T1\\\\NdkNN0f1\\\\T1\\\\NdkNN0g1jT1iM[kNi2RU1L9G3L4M2M2M2J8F:F;I4J7M3K7E;Ifak6\", \"size\": [1280, 720]}, {\"counts\": \"`_`a09_W1<D;F7E;M3POhNPkN_1lT1n0M2N2N2O2N2M2N3N1N2O010O100O2O0O1M4N1O1O1O1O1O1N2N2O0O2N000001O2O002M2N101M2N3N2N2N2K5N2N2N3L3M2O[lNXMVR1e2fmNcMYR1[2dmNjM\\\\R1R2emNQN[R1l1ZmNeMWOe0^S1b1\\\\mNjNdR1P1_mNSOaR1j0YmN_OfR1?ZmNDeR1:\\\\mNHbR16`mNKaR10cmN1]R1JfmN8ZR1EimN;VR1DlmN<SR1_OSnNb0mQ1VO]nNg0iQ1nN\\\\nNR1gQ1eN`nNZ1ST10O100JZNRjNf1kU1`NRjN`1kU1cNUjN]1iU1eNWjN[1[U1l0N2NRNmjNS1bT1ZNckN>3W1YT1\\\\NdkN5;`1TT1VNdkN6:d1aT1WNckNi1ZU101O001O1OIYNWjNe1hU1]NVjNc1jU1_NTjNa1mU1aNPjN\\\\1TV118I6K6G9G?]Okib6\", \"size\": [1280, 720]}, {\"counts\": \"ZW_a0=oV1j0jNoNljNW1fT1ZOSkNk0hT1\\\\OljNm0QU1Q1O2N2M3N3N1N200O2N2N1O2N2O0M3N10100O2N1O2N2N1M3O1N1O2N10O0O2O2N2N2N1O2N2N3M2M3M2N3M2N2O1N3L4M2N3N`lNXMkQ1e2UnN`MiQ1^2UnNgMkQ1V2cmNdMROd0YS1c1dmNmNZR1P1amNYO_R1c0`mNB`R19cmNH^R11gmN1YR1KhmN8XR1EhmN>XR1@hmNb0XR1[OhmNh0YR1ROimNQ1XR1kNjmNV1YR1aNlmN`1YR1WNjmNj1QT15K5J6K5N2N2L4K5L4M3N2O2M2003M2N3M4L2N2N2N4L:VMUkNj1oT1QN\\\\kNe1hT1WN\\\\kNe1aU1H7I7F<H7I7M2O8HWQd6\", \"size\": [1280, 720]}, {\"counts\": \"]mXa0j0SW17J9[iNJPU1k1G4L3N1O2N1O2N1O1O2M2N2O2M2O2M2N3M3M2N2M3O2M2O100O0010N1N300N3M2L3L5L4K5K5L4I7L300N1I8L5J6H8KnkNlNaQ1m0UnNFjQ12\\\\nN1cQ1J`nN7bQ1E^nN=cQ1@]nNb0fQ1UO_nNm0cQ1iNdnNW1QT12K5L4M3H8M3I7O1N3M2K5D<OhLckNo2\\\\T1RMdkNm2]T1SMckNm2`T182N3M6J:G3L3M3L:G2M4L2N3A>]Oc0H=D_Q]7\", \"size\": [1280, 720]}, {\"counts\": \"RVUa0;`W1;G9G9Hh0YOa0_O=C4L7I3M1O2N2N001O1N2N2O1M3N2N2M4M2N2O1N102N1O2OO0101O1N2O0O10O200O0000O1O10O1O1N011L3J6L4L5L4L3J7J5C>A?K6HRlNiNbQ1P1UnNAiQ18\\\\nNJcQ12anN0_Q1HfnN9`Q1\\\\ObnNh0XT12M4M4L3N2O1O0100000000O110SNTjNe1mU1UNXjNk1nU10O1J600000O01O1002M2O1N2O1N2N4JnhV7\", \"size\": [1280, 720]}, {\"counts\": \"_mba04`W1`0H;E[1fNe0\\\\O5L3L3N1M3N3M4M2N1N1O2O1O1O2N1O1N2O2M2N2N2O101N1O1N2O1O2O0100O0000001O000O10O1O1O2I7O0001N001N2L4K5K4L5K4J8L3K6I:F:@?D?A=@?DSlN@RQ1N`nNl0WT12L5L4N110O001O0I`NoiNa1XU1eNRkNND]1WU1hNVkNICa1nT1oNekNR1YT1oNgkNQ1XT1oNdkNV1\\\\T1iNdkNX1^T1fNbkNZ1aT1[NgkNe1ZU1O01O100O1O1000000O100O1O001O11N4K:E4H8K7TOQiN2mh]6\", \"size\": [1280, 720]}, {\"counts\": \"_Uda03Q18TU1a0YjN=kT1d1G4K4M2O2N2O1O2N2M2O1O1N2O1O1O1N3O0O10100M2M4N1M3N2N2O2N1O101N101N100001O1O0O1000O101O00O100O1M3M2N3K4M3N1N1O4K4O2L4K6L3N2K5I6HdlNoM[Q1k1]nNbNcQ1V1_nNQObQ1f0ZnNEiQ15YnNMmQ1FXnN<QR1VORnNk0VR1iNnmNX1\\\\T1001J6H8K6F9H8O1M3N2O1O1D<00O11OQ1oN1OO100O100001O:F3L3M3M2M4D=B=_Ob0GgY[6\", \"size\": [1280, 720]}, {\"counts\": \"`eWa0c0VW1i0\\\\OR1mN<C9I4K4N2N1O100M3O1O1O1O1M4N1O2N1O1O1O1O1O2M2N2O2N1O1O1O010O1O1O1O1O2N1O1O1O13L4L2O0O1O0N3L4K5M3L3M4L3M4J5O1N2M3L5K5IalNeMfQ1U2TnNYNkQ1`1YnNdNfQ1Y1PnNTOPR1g0RnN\\\\OnQ1=RnNJnQ12TnN0lQ1LWnN5jQ1GWnN;iQ1AYnNa0hQ1ZOZnNh0iQ1QOXnNR1nQ1`NRnNh1iS1:N2O1J6K5N2M3O1L4M3L3N3N21O1O3M4L3M2N1N2O3K9Bb0ROo0_O7J6L5K5Fgak6\", \"size\": [1280, 720]}, {\"counts\": \"XeWa0X1bV1]1fN7I4M3L3N2O1O2O0O2L3M3O100O1N2O1O1O1O1N3N1O1O1N2N2M3O1N2O2N2N2N1O1O1O100000000003N1O0O000010O00000000O01O01O1N2L3N2M2O1O2N2M5J6L4M2N3K5K5L4J_lN]MoQ1\\\\2omNoMPR1i1PnN^NPR1[1SnNiNmQ1R1QnNUOnQ1g0SnN]OmQ1:UnNLlQ12SnN1nQ1GXnN:jQ1^OZnNd0lQ1oNZnNQ1nQ1^N[nNc1PT11M3L3F;N\\\\N]jNP1\\\\U1mNbjND9[1SU1mNZkNR1dT1lN]kNW1`T1kN_kNW1_T1kN`kNV1`T1iN`kNX1`T1hN_kNY1aT1fN_kN[1`T1eN`kN[1aT1cN_kN_1cT1XNakNl1VU1\\\\NTjNZ1jU1gNXjNX1gU1iNYjNW1fU1kNXjNV1gU1lNWjNU1iU1kNWjNT1jU1a00O1K3^NRjNl0VV1TOmiNa0]V1SOfiN21;NDaV1O^[`6\", \"size\": [1280, 720]}, {\"counts\": \"[e\\\\a0c0[W15H8H8^jNkNnS1[1kkNnNmS1Y1mkNmNnS1Y1ikNnNTT1`2N3M2O1O1O1O100O10000O100O10000O1O1O10001O2M1O1O1K5N200N2O1N2N3N2N1N2O1O100O1O11O001O1000O000000000001O0O11N101OO1N200N2M3M3N2M2O2N2L4J5N3K5K5M3N3N2N1O2M3M2L4L4M4K4I7M3L4L5K3N3L1K?D<FXdk6\", \"size\": [1280, 720]}, {\"counts\": \"ff\\\\a05U1LVU1h1]O`0H8I6N2M3N2N2N3L3L4M3M3M3O1N2O001N2L4M3M3N2O1O1O00100000O2OO100O100002N001O1ON3N1O100000000003N5J3NL3N2N2O100N2N2L4O1010O0010O0000010O00O01L4M2101O1O1O1N2O0N2L4L44L3M1OM4M2M3M4L3L4L3M7I5H8M2HWkNXMl05fR1^2bmNgM]R1o1[mNcNeR1X1[mNmNfR1n0\\\\mNTOdR1j0]mNWOdR1d0^mN_ObR1>_mNCaR1<_mNEbR1:YmNKhR14UmNOnR1_O`mNb0hR1QO\\\\mNQ1iT10N2O100HZNUjNg1iU1\\\\NVjNc1iU1`NVjN^1eU1iN\\\\jNi0kU1]OXjN7nU1LSjNEZV1<hiN^O[V1c0?1O2MAQiN2PW1MRiN2nV1MTiN1oV1LTiNNbah5\", \"size\": [1280, 720]}, {\"counts\": \"^fUb0`1]V15J5L4J5M3K5M4M2O1O1O1O1O2M2O1O1O1O1O1O10001N1O0O]MVkNJNQ2kT1PN_kNW2`T1fMgkNW2XT1jMikNV2WT1hMikNY2WT1fMjkNZ2XT1cMhkN^2kT1200O1000000O1O1O2N4L3M1O1O1O1N2O1N3N1N3M2M4M2N2N2N2M3N2N2L4L4N101N200O10001N1001O0O100O10OO2O1O0O2M3N2L4N2M4L6K4M1KVlNhLWS1S3glNVMVS1f2nlN[MQS1c2PmN^MPS1^2PmN[MWO4iS1]2TmNjMmR1P2TmNUNkR1d1\\\\mN[NcR1d1`mNXN^R1m1cmNiMfR1U2e1L3N2M3M3N;A?Bj0WOjiNL\\\\V13L3I63GbhNLTg31^`M7M1101LgW1M\\\\hN5M0O1O1eRR5\", \"size\": [1280, 720]}, {\"counts\": \"`]Rc08gW1d0oNUOfiN[1eU1lNYjN\\\\1^U1iN]jNe1UU1j0I3M3NUMSkN20Y2lT1a03M2N2N1O0000002O003OMO010O11N101N3N2M4M1O1N2N1M4M2N3N1O100O1O2M2O2N0000O1N10O21M2O02M4L4NN3O1N101N2O1N2N2O11N1UlNbLTS1[3mlNhLRS1V3nlNmLQS1Q3mlNSMEAWS1[3SmN]MmR1a2TmN`MkR1_2UmNcMkR1\\\\2UmNeMkR1Y2VmNhMiR1S2ZmNQNcR1m1]mNZN`R1c1bmNbNZR1\\\\1fmNfNZR1X1fmNhN\\\\R1V1emNiN^R1T1amNmNbR1o0_mNSObR1i0_mNXObR1f0^mN[OcR1c0]mN_OcR1>^mNBcR1<_mNDbR1:]mNHeR15[mNKhR13XmNKkR12VmNNmR1NRmN5oR1GSmN9oR1DRmN<PS1AQmN?QS1\\\\ORmNc0QS1VOTmNj0nR1TOTmNi0nR1WOQmNg0SS1SOQmNn0PS1POPmNo0TS1nNllNS1VS1iNjlNX1[S1_NilN`1kT100O2L3010O1O1ON2EfiNROZV1n0jiNlNXV1T19O010M3N4@jk\\\\4\", \"size\": [1280, 720]}, {\"counts\": \"`UQc02UW10RiN057M6oU1BoiN24W1`U1kN]jNO0`1gU1bNWjNe1\\\\U1cNajNR2lT1oMRkNT2gT1PN[kNR2`T1PNakNS2VT1QNlkNo1PT1TNokNm1oS1UNPlNl1oS1UNPlNl1nS1WNQlNj1lS1YNSlNg1kS1X1N2M30bL\\\\lNb2cS1[MalNd2^S1[MelN[OJR3bS1aMllN_2SS1`MnlN`2US1\\\\MllNd2SS1mL]mNS3cR1mL^mNR3hS1000001O1O2N01N100N3M2O1O1O1O1O0000O2M4K4N0OD=N10001200010M02NUlNiLPS1V3nlNoLQS1Q3nlNPMRS1o2mlNSMSS1m2llNTMTS1j2llNXMTS1g2llNZMTS1c2klNbMVS1Y2hlNlMYS1Q2elNTN\\\\S1e1hlN]NZS1\\\\1hlNfN\\\\S1R1flNPO_S1h0blNZOdS1?[lNBhS1;WlNGjS15WlNMiS1OXlN4iS1HVlN=jS1_OWlNe0iS1TO[lNo0dS1oN\\\\lNS1cS1lN\\\\lNV1fR1^NRmN98Z1eR1bNPmN2;]1eR1fNmlNJ>a1fR1iNilNBb0f1eR1kNglN]Od0i1fR1XOYmNl0hR1nN\\\\mNl0lR1oNWmNo0mR1nNTmNR1oR1hNUmNW1mR1aN[mN^1gR1\\\\N]mNc1aT1MM3NN5N1N3L5K5N1O01K50O1100OO1OO13M3M3N3K4ZOg0L5JbSc4\", \"size\": [1280, 720]}, {\"counts\": \"[VXb081<eV1S1^Oc0VO=L4N2L4N2N1O1O1N2O101O0M3L4L5M2N2M4N1O1M3O2N1M40O0100N1O1003M1O002N1O?B0eLikNk2XT1RMnkNk2QT1UMPlNj2QT1TMQlNk2oS1UMRlNj2eT1M1O0000000010O000000000O2O00O1O1O2L4L3N2O0N10O1001O0O2O4L3N2O1N2MejNWNeT1`1_kNcNfT1R1^kNoNfT1=ckNKcT1K_kN7dT1D]kN=fT1_OZkNb0hT1ZOXkNh0kU18K5K3M1O2M3N1N3L4M3M3O2H9L3N1O1H500101N2K6J7B=H9L4K4J7G:G<FTZ]5\", \"size\": [1280, 720]}, {\"counts\": \"l^Tb0`0ZW18AYO]iNl0`V1;BkNoiNX1oU1jNoiNW1PV1kNmiNW1SV1<O000000001N1000000EVN\\\\jNn1cU18L4MZNajNQ1\\\\U1QOfjNn0WU1TOjjNm0nT1YOSkNg0kT1[OTkNg0lT1ZORkNf0nT1[OQkNe0oT1V1N2G:N2M3K5M2N2O1O10000NlLokN]2oS1`M]lNY2hS1XMkkN1b0f2cT1N1O1O3M3M2M101N3N5K3N1O2OOM2N3N2N2M3N1N3L3L7F>^OkhNH]W1LaY1?\\\\fN3L3L5M3M4M3N1M3L4L4D<N2N2O1N2M3N2K6J6L5M2N1O1O1O1N1000001O3M2N00O1O1N1M01N12N3J:J6N1N0J7L3I6M20O13N2K;G7I5K6H>^O^[S5\", \"size\": [1280, 720]}, {\"counts\": \"aWda01eW1:L5L3K5K5M3M3N2O2M2N3M2N2G:K4N1I8N2D<M3G9M4N100O100O2M2N2I8N100000010O06J2fLbkNn2`T1nLkkNj2iT1L1O1O1O4M>AN2N3N1N2O2N1N2N2O100O1N0L5O01M3O102K4L5E<K4L4GhjNiNXT1R1ekNYOYT1a0gkNEXT11mkN3TT1EPlN>ST1WORlNl0\\\\U15M3K5K5H8M3N2L4M30OO2O1O1O1001O001O00001O1O1O002M02\\\\OnMWkNT2dT1[NPkNh1iT1j0M2O1N2I6N3N01O3M4G9J5G8I7K7D>H6TOkiNIXV12miND\\\\V17j0Fo[g5\", \"size\": [1280, 720]}, {\"counts\": \"_gT`0m0fV1a0A<POQ1I6L3N3M2N3N1O101N2O02O5J1O100O1O0000O1_OiLYlNX3eS1iLZlNX3WT1O10O100O1O1O000O2O2M2N2O2N10000O1001O2N7I1O2N10O00001O00O01001O00002N5K0O10O1000000O1N2O1O2N1O100001O1N2N1N2M3K6J5M5D9J7N1OF9OO3OfkNmMkR1o1mlNbNoR1X1RmNnNmR1n0RmNXOoR1c0nlNBVS18glNM^S1N^lN8eS1YOclNj0dS1lN\\\\lNW1TU13K4M3M4N2M2L5L30100O001N2H7I8M3N1010O100O2N4L7I7YMgjNZ2cU1M3L7J7G7H8YO]iN@RXo6\", \"size\": [1280, 720]}, {\"counts\": \"bnP`05hW1?C5Jc0]O8H6K5K3M2M4M2M2N3N3L3M3K4M3M3N2O1N2N2O0G:K5M3N2O100M3M3ORLWlNg3hS1ZLXlNf3hS1[LWlNe3hS1\\\\LXlNd3hS1[LZlNd3fS1[L]lNc3dS1[LalNa3ZT1E2N1O0000001O2N1O1O1N3N4M2ML4L4N2O1O1M3N2N102002MF:H8M3L4N2N1N3M3M211O010O01N2O001O1O1N3N1N3L5K4Ld0ZN\\\\kNWNZU1Z1m0F9M4F;L6H^jNGWN30LdV1[1\\\\O`0J5N2N1O2J5K6O01O1O102N1O1O1O1O1N5K5L2N3L4M4L5J3N2M3N1N101O1N2N2Md0]O``P7\", \"size\": [1280, 720]}, {\"counts\": \"^nk?1mW17J9G9H9F4L4L5L2M4L5L3K4M3M3M3N2M2O1O0L5M2O11O3M6KO0N110001N1oM_jNc1bU1=10O0O1M3O13M10OO0M4N2NTO^N^kN`1\\\\T1iNckNT1]T1V101M2O3K5N12N1M40SlNiLQS1R3\\\\lNkL?7SS1d2llNTMM;WS10`lNn1;hMK>ZS1JclNm18mMH?_S1CdlNm17QNE`0bS1_OelNh1:YN]Oa0iS1ZOflNb18dNWO`0gT1c0fkNPOEd0hT19dkN3`T1IbkN7_T1HbkN7mS1;RlNROIA^S1KllNa10QOgS1LjkNk0LjNf0=2DdR1j1bmNZNM2`R1c1UmNQNo0a0lQ1]1`nNiNaQ1U1XnNSOiQ1j0YnNVOhQ1g0jmNZNQOU1VS1<imNgNhNR1aS12gmN;ZR1@hmNa0ZR1[OhmNf0ZR1VOgmNk0[R1POfmNR1\\\\R1gNamNa1aR1VNamNo1RT16L3M4K4N2M4M3L4L4K4_OjLVlNZ3iS1?00000O2O1O3M2M4M2N2M2O1N2N5M2Mc0\\\\O2N1O1O2N2N100O1N3N4M2N1O1O15MK4M9H7I?^OohNZOngV7\", \"size\": [1280, 720]}, {\"counts\": \"cV^?8fW14M5J5M3K9H8H5K4N00001N4M2N2M3M3N1N2O1M3N2I7L2O1O1O101O00000000O10O001N1O000N1O3ON1001N1\\\\O^MkkNc2YT1`MbkN_2c0YMSS1U3klNPMUS1m2klNUMUS1h2llNZMTS1c2mlN_MUS1KWlNV2b0RNYS1CYlNX2=XNgS1c1[lN^NhS1[1YlNiNjS1P1RlNQOYT1h0ikNUOeT1`0[kN\\\\OlT1`0VkN]OoT1>SkNARU16SkNHQU10SkNEVOMPV18hjN0eU1I\\\\jN94GTU12jiN8m0LWU1LmiN7h02XU1GVjN3=<TU1H\\\\kN:aT1H]kN;`T1HZkN>lR1^OcmN4^Ob0QS1YO`mNGHU1jR1SO_nNQ1cQ1nNYnNU1gQ1kNVnNX1iQ1iNVmNFGe1US1bNjmNf1QT16O2L3L5L4M3L4J5O2L4L45K4K3N001N2O2M3M2OO10OO2O01000000O2O000O01O0O1O10001O001O001O001N2O1O0O1002O0O101N2N4M6I=C`0@;E<E6I9F^XY7\", \"size\": [1280, 720]}, {\"counts\": \"lUW`0>_W17G=F6K5K;E6J3K5L8H4N2O1]O^MekNd2UT1`MikNe2ST1ZMmkNj2nS1RMUlNV3fS1jLXlNY3gS1gLWlN\\\\3cS1iL[lNZ3cS1b0N3L3OQLalN_3[S1dLglNZ3VS1jLilNW3XS1gLglN[3ZS1aLhlN`3kS10O11O000O2ON2ON2N20000001[lNZLPS1f3QmN\\\\LlR1e3SmN]LkR1c3UmN^LiR1c3WmN^LgR1c3YmN\\\\LiR1b3WmN_LjR1_3PmNhLRS1U3QmNbLCN^S1^3RmNbLVS1\\\\3llNcLUS1[3mlNeLTS1X3olNfLSS1X3k0O1NokNlL13]R1n2\\\\nNUMgQ1e2WnNaMnQ1U2omNRNPR1l1omNWNQR1g1omNZNQR1d1omN_NPR1`1mmNeNRR1[1kmNiNTR1T1mmNoNQR1Q1nmNQOQR1m0PnNVOPR1h0omN[OSR1a0lmNAWR18kmNKVR10kmN3UR1KjmN8VR1EjmN>WR1^OkmNc0UR1ZOlmNh0TR1TOomNm0RR1lNSnNT1oQ1iNQnNY1QR1aNRnN`1VT1100000O101N2O0000O0010O010000O001O1O1N1N3L3O2N1O2N101O1O001N101O0O2M3M2K5N2N3N11OO24K4L2N3M2N3K5L4mMmdk6\", \"size\": [1280, 720]}, {\"counts\": \"cgc`0>[W18E;]OTOliNQ1UU1WOojN[1fT1V1I6O1L4F:0001M2K6G9J501O0O1O100O100N110O1L4002M2ON2O01O2M3M2N1O00101N1O2O1N1OblNULkR1k3TmNWLjR1h3XmNXLhR1g3XmNZLhR1f3WmNWLmR1g3llN`LVS1_3klN]LYS1a3jlN]LWS1a3klN^LVS1a3klN]LWS1b3jlN]LWS1b3jlN]LWS1b3d0L4L31flNfLnQ1Y3omNoLmQ1P3PnNUMnQ1g2VnNZMjQ1e2UnN^MiQ1b2WnN_MiQ1`2WnN`MiQ1_2YnNaMgQ1]2SnNkMmQ1T2omNQNQR1l1omNVNRR1i1lmN[NSR1d1mmN^NRR1`1nmNcNRR1Y1omNiNQR1T1PnNnNPR1Q1omNQORR1l0mmNWOTR1f0lmN\\\\OUR1a0kmNAUR1<kmNGUR17kmNKUR13jmNOXR1MimN5WR1FlmN;UR1ClmN>TR1AlmN`0UR1]OlmNe0SR1YOmmNh0TR1VOlmNl0TR1QOmmNP1UR1mNjmNV1VR1fNkmN]1VR1]NmmNd1TR1\\\\NjmNf1VR1YNimNi1^R1oMbmNR2_R1iMdmNW2_R1_MhmNb2iS12O010O1N2GTM]kNl2dT1TM\\\\kNl2dT1TM\\\\kNl2dT1TM[kNm2eT1SMZkNm2gT16000O1O10O1O002N100OO1M3L5_Oa0C=L3N3K3J7O10O0001O1M3L401N2M6EhSU6\", \"size\": [1280, 720]}, {\"counts\": \"i_Qa04cW1;B>D;D<M3N1O1O1O1F;N1M4M2O1O2N1M3M4L3L4M3N2N2D<J6M3O1O1N3N001M3O1O001O001J501N2O2N1N2O1N200OO2O00000N2O2N1O10010N2O01000N2M3L5K4M3O1O1N200O2MolN\\\\LjQ1b3RnNiLhQ1U3XnNoLgQ1o2YnNTMfQ1j2YnNYMgQ1e2WnN_MiQ1_2WnNcMiQ1\\\\2UnNgMkQ1W2TnNlMkQ1S2SnNQNnQ1m1QnNUNoQ1i1SnNWNnQ1f1RnN]NmQ1`1TnNbNmQ1Z1TnNhNlQ1V1TnNlNlQ1R1QnNROQR1k0nmNXOSR1e0lmN^OTR1`0kmNCVR1:jmNHVR16jmNLWR1NlmN3UR1ImmN9SR1EmmN=TR1_OmmNc0SR1ZOnmNh0RR1UOmmNn0TR1nNmmNU1TR1hNlmNY1VR1aNlmNb1UR1[NimNi1WR1VNgmNl1[R1SNbmNo1aR1nM_mNS2bR1cMilNAd0n2cR1_McmNd2aR1WM^mNl2jS11O1O001O1O100O1O1O1O10O00000K5I7G9F;N1L5H7L4O1O1O1N2O1N3N1O002N1M6lNmiN1WW1HPc^5\", \"size\": [1280, 720]}, {\"counts\": \"gW_a09bW17I6H8M2L5M3M2N2N2M4N1O100O2N100O2N10000O1000001N100000001O0O1000000O200O1O001O000QNnMPnNR2mQ1SNQnNm1lQ1VNSnNk1jQ1XNVnNh1hQ1ZNWnNg1gQ1\\\\NXnNd1gQ1bNRnNb1eQ1fNYnN\\\\1fQ1dNYnN]1fQ1eNXnN\\\\1gQ1eNYnN[1gQ1eNXnN\\\\1gQ1fNXnN[1fQ1hNWnNY1hQ1iNVnNX1jQ1iNUnNW1jQ1jNVnNW1hQ1kNWnNW1gQ1jNXnNX1eQ1iN[nNX1cQ1iN]nNW1bQ1kN\\\\nNV1cQ1kN]nNU1bQ1lN^nNT1bQ1lN^nNT1bQ1lN^nNS1bQ1nN^nNQ1dQ1nN\\\\nNR1dQ1nN\\\\nNT1bQ1lN_nNT1`Q1lN`nNR1bQ1mN^nNR1dQ1nN\\\\nNQ1eQ1oN[nNo0gQ1POZnNo0gQ1QOYnNm0iQ1SOWnNk0kQ1UOTnNj0oQ1UOQnNi0QR1WOPnNf0RR1ZOnmNd0TR1\\\\OlmNb0UR1_OkmN?WR1AhmN=[R1CemN;]R1EcmN8`R1GamN7NYNaQ1`1anN5O]N_Q1^1bnN20bN]Q1\\\\1dnN1OeN\\\\Q1Z1enNOOhN]Q1Y1dnNKOoN^Q1U1enNGOTO^Q1U1cnNDN\\\\O^Q1o0enNCIDaQ1j0fnN@ELeQ1c0hnN^OA3hQ1>gnN\\\\OB7hQ1=fnNXOD=hQ18fnNVO_Oh0nQ10cnNUO[OR1SR1FenNSOYOZ1RR1BhnNQOUO^1TR1@inNPOSOa1UR1]OknNoNPOd1VR1[OboNf0^P1XOcoNh0_P1VO`oNl0`P1TO^oNn0dP1hNaoN[1aP1aN_oNa1]S13M3K7K7J21O00O1OO2OO01M4M2M3O2O1M3L4M3L5G9J5M4H;ImYX5\", \"size\": [1280, 720]}, {\"counts\": \"g^Tb0>aW15J3L4M3N1O2N2M3K5M3M2N3M2L4N2N2F:K6M2O1O10001O0M3N2N2N2O100102NL4O2N1O02N1O3M2OO0O5K3L2_OnLRlNT3mS1nLQlNR3oS1PMmkNS3RT1oLikNV3VT1kLhkNV3WT1:N20002O1M1N010O00O2OM40000N2M4G8O2N11O0000000N2O2O01O01N1L\\\\mNmKeQ1m3^nNVLaQ1j3]nNZLbQ1c3^nN`LaQ1_3_nNcLbQ1Y3^nNkLbQ1Q3\\\\nNTMeQ1j2ZnNXMhQ1d2XnN_MiQ1^2UnNfMiQ1Z2VnNhMiQ1W2VnNmMiQ1Q2WnNQNiQ1m1XnNTNhQ1i1ZnNXNfQ1f1[nN[NeQ1d1YnN_NgQ1^1VnNhNjQ1V1TnNnNkQ1Q1TnNROjQ1o0VnNROiQ1m0WnNUOjQ1h0WnNZOhQ1d0XnN^OhQ1?YnNCgQ1;YnNGfQ18ZnNIhQ14XnNNhQ11VnN2jQ1LVnN6kQ1GUnN<lQ1@TnNc0lQ1ZOTnNh0mQ1QOXnNQ1hQ1jN[nNW1fQ1dN\\\\nN^1dQ1`N\\\\nNb1dQ1\\\\N\\\\nNf1dQ1XN\\\\nNi1fQ1UNYnNl1hQ1RNWnNo1jQ1PNWnNP2jQ1mMVnNU2jQ1hMWnNZ2jQ1cMXnN\\\\2kQ1bMTnN_2oQ1^MPnNa2XR1lLTmNNe0T3iS1O001N3N1O3M2OO1O0010M12O10O001M6L3N1N2L4M2O101N1O2M2O2N0O2N2N2N2N3M1O3N2M4L3H9I7L4I7L8FmkY3\", \"size\": [1280, 720]}, {\"counts\": \"WWna0:bW15M3N2K4K5M4L3M3N2O1N3M2N2L5L3M4N1N3N1K5L4M4M2N3M4L4M1O002N1N3N101N100O2O1N100O1O100000000011O00N1O100OO2N100O1O1N1N4O010O1bMWkNd1jT1\\\\NYkNa1gT1^NZkNa1hT1]NYkNa1iT1_NVkN`1mT1_NQkNa1c0oMeS1?hkN_1d0TNhS18dkNc19[NbT1JUkNj19_NaT1HSkNi1<`NaT1f1akNVNaT1i1akNVN`T1i1akNVN^T1j1dkNSN^T1l1ekNQNYT1R2]lNmMgR1Y2`lNjM2MmR1g2SmN]MOMiR1i2SnNZMbQ1m2UnN`MdQ1e2[nN^MdQ1a2[nNbMdQ1]2ZnNfMfQ1Y2YnNjMeQ1U2ZnNnMfQ1P2YnNSNgQ1l1XnNVNhQ1h1WnN\\\\NhQ1b1XnN`NhQ1^1XnNdNhQ1Z1WnNjNhQ1T1XnNoNgQ1o0YnNSOgQ1k0WnNXOjQ1f0TnN^OmQ1?SnNDlQ1:TnNHlQ16TnNLlQ13SnNOmQ1OSnN3mQ1KSnN7lQ1HTnN;jQ1CWnN?hQ1@XnNb0hQ1\\\\OWnNg0jQ1VOVnNl0jQ1SOUnNo0kQ1oNUnNS1kQ1kNTnNY1lQ1dNTnN^1mQ1_NSnNc1nQ1YNRnNj1oQ1TNPnNn1PR1PNomNT2QR1iMomNY2QR1eMnmN]2SR1bMmmN^2TR1aMkmNa2UR1]MkmNe2UR1YMjmNk2UR1SMkmNn2VR1PMimNQ3YR1mLgmNT3YR1jLgmNW3YR1hLgmNY3ZR1fLemN\\\\3\\\\R1aLfmN^3\\\\R1\\\\LhmNd3[R1XLfmNh3YS1N20O01O000O1000101M3N3N1N3M6J4L4UMSkN]2]U1I6K3M2N3M5L3K5L2N3L3M3N3M2N1M4M3M3L3N3N2DehN0^W1O;O202Lkb]3\", \"size\": [1280, 720]}, {\"counts\": \"P``a0:aW1:I5J5G8K5L4K4L4M3N2N2M3G9N2L4I8H7O1N2L4M3N2N11000O1O1O100O1000000001O3PM]kN^2VU1LM3N2M3O1O1O1M3M3N3K4M4N1O1O1O100O100O100101mLkkNX2UT1gMkkNY2WT1eMhkN\\\\2ZT1d03L9G5J2M2O00N1D:N7PlNaLXS1f3`lNcL_S1]3^lNhL`S1X3^lNjLaS1k30WO_lNSMaS1k2alNUM_S1h2elNVM\\\\S1e2ilN[MVS1g2ilNPMIF^S1_3j020@ikNSMZT1k2jkNQMg05ZR1h2QnN[MoQ1c2PnN`MPR1]2omNgMQR1W2gmN[MROa0WS1S2dmNXN\\\\R1c1emNaNXR1^1jmNeNTR1Y1nmNhNQR1W1QnNjNnQ1S1SnNoNmQ1n0nmNZORR1d0mmN_OSR1?kmNEVR18kmNIUR16imNMXR1OhmN4YR1HgmN;YR1BgmNa0[R1ZOemNi0\\\\R1TOdmNn0\\\\R1QObmNR1_R1jNbmNX1_R1eNamN]1^R1bNbmN`1^R1^N_mNg1aR1WN^mNl1bR1RN]mNP2dR1mM\\\\mNV2dR1gM\\\\mN\\\\2dR1bM[mNa2eR1XM\\\\mNn2fR1mL[mNU3hR1cLZmN`3_S14O1N2K5N200O1N2M3M3N1O2OO2N1L6K5K5J8_O`0J8I6J4K5K5K5H=cNkiN;Rao4\", \"size\": [1280, 720]}, {\"counts\": \"R`Tb09bW17K4K4M4J5L4M4K4L5I6L5H7L5K4J6L5L3N2E;H8M2O3L3N2O1DVL`lN4Hb3hS1ZL_lNm3hS12M40O10O0L5M2O10jK^lNn3bS1QLblNk3_S1TLblNl3_S1RLclNn3gS1000O1O1001O1O2N3M00N2N200O2O1N101N11O1N2O2N2N1O2O1OO1M1C>O10O0O2N2K4N3M4M2N10ONOOalNaLhR1]3[mNbLfR1[3Q1N2M[lNPMaR1j2cmNWM]R1d2b1M3M3N3K4G9L4M3NblNQNPQ1k1lnN]NSQ1_1cnN\\\\NRN<[S1S1WnNEiQ17nmN7QR1GmmN=SR1AkmNc0VR1XOjmNl0WR1kNlmNZ1VR1`NkmNc1XR1WNimNk1mS19J6G9M3K5E<M3L3N1N3N10N3E;A`0E<Cd0A9K5J8EfZS5\", \"size\": [1280, 720]}, {\"counts\": \"PhPb01iW1;F6F:K5L3M3L4J6M4G8J6@`0N3N2M2O2_OUMikNm2RT1ZMgkNk2UT1b0N2N201O1O1F9K5O12N10N100O0;G2M2N1O1O2N3M1O10O01O00O2N1N2N2O110O000N2N2O101N2O0O10000N2HmKalNV4_S151O00O2N3N1M2O1N1L5O00O2O0O1O2K5L4N2K6I6M3L4L4M3H8K5N1N4J6I6KolNeMgP1W2XoNnMhP1n1WoNWNiP1d1WoNaNjP1Z1SoNnNmP1m0onN[ORQ1`0lnNEXQ12`nN;gQ1WO]nNl0hQ1mNWnNV1WT13L4N2M3G9L4^N`M[lN044JK5g2XS1f1N3M2010N1O2N2M4J6I7G9G8J7F;J5E;B>I8I7E<G>BhRm4\", \"size\": [1280, 720]}, {\"counts\": \"TX_a07`W1<ZN_ObkNg0ZT1_O`kNe0_T1CTkNc0fT1BQkNg0`T1jNWkN?KA2\\\\1kT1gNTkN]2nT1`MQkNa2PU1_MojNa2QU17L4M3L4L4O1O2N1N20O2O00006J5K3N4K101M2O001O00M3M3N2O2N1N2N2O2N100O2N1O1O11O5KWOfLPmN9WO`2hS1[MYlNN>7@b2hS1cMelNi2[S1\\\\M`lNd2^S1P1M3O1O0O2N3K6L3N100O10O01000000O2O0O2O00O1O2N1M3L4L5J5J6K5H8N2K4I8M3L4M2L5J_lN\\\\MQR1\\\\2RnNkMnQ1Q2[nNhMhQ1R2W2IglN\\\\NcP1^1SoNPOPQ1e0PoNBTQ12fnNUOoMn0^S1CdnNl0ST15K5M3K5BPNdjNW2\\\\U16SNXNilN@7X2kR1SOmlNS1QS1o1N2N101O1000O1010O10O1O1N3N1O3M3J5L4K6B=E<mN\\\\kNnM^U1\\\\1^jNdN\\\\V1Q1c0E=\\\\OQc^5\", \"size\": [1280, 720]}, {\"counts\": \"cPca07dW15N3L3N2M4K4K5N2N2N3M2M4L3M3L4M3O1M2N3L4N2N3M3M2O1M3M3O1N2O1O100O1000000O1O1000O01O1000O2O1N010000O100O100O1001001N000001O1O001O01O02O0O0000010N100000O1000O2O0O1000000O101O001OO10001OO10O100000000001N3N1M2I8K4A`0_Oa0TO^RZ6\", \"size\": [1280, 720]}, {\"counts\": \"UWUa0721O9_V1AeiN_1PV1;M2M3O1N3M2O1K5K5L5K4L4N2O2K4LZM`kNT2_T1lMdkNR2[T1PNdkNP2[T1QNekNo1[T1QN`kNT2aT1kM^kNV2gS1_MmlN9\\\\OX2dS1hMilNk2US1[MelNf2ZS1_MalNc2^S1Q1N1O1O1O011N3M3N00M3J6N2L5L4L4J5L3K5G8J8N2MUlNXMdR1c2^mNaM`R1Z2dmNiM[R1Q2imNQNVR1i1emNcN[R1Z1`mNoN]R1`0klN]N8OK]1\\\\S1nNelNMe0LTOY1hS1iNlmN\\\\1XNaNRT1NamNc1dN[N^U1h184LUO[jNZOWV1`0h0YOkhN;WW1EohNL20b10YS1>QmNLfQ11jlN8Y1OmQ1JflNl035US1mNalN`1HLgS1bNYlN9Og2hS1n01N2O1O1N2O3M2M3N3M3M4L5J3N4L4L4L0O100O101N10O1O00100O1O1O010O100[Oe0N2N1001O100O6K;Df0ZOd0\\\\Oe0mNSiNHR`a6\", \"size\": [1280, 720]}, {\"counts\": \"UfY`07k0JaU13jiN;4Q1QU1Y1J8H5K3N2N2ZOhLWlN0O[3eS1g0M3M4L3M3N3M20O01O1O100001O1OO1O100000O0100O1001O00000000001OO01000001O1O0000000O101N10O100O3N3M0000N3cNolNnLmS1_2T1fNmY;POYhDm0ohNVOXU1[2_Of0YO3O2M4M0O01O0O2M3M3N2M2N3N001N1000000O100000000O1000O1O0O2O0O2N101L3K51N2O2N2O1O0O01OeLgkNQ3ZT1lLjkNo2eT1YOg0hN`jN[OVV1W100jiN]NPV1a1QjN_N[V11eiNd0\\\\W1_OVbk6\", \"size\": [1280, 720]}, {\"counts\": \"U^d?911TW1b0I8G9L3N1O2N2M4L4M4L5J9hjNdM]T1d3ZO4L4L;B8L10001N100O1O2N11O1O1O00O101N010O10000000000000O100000O11O000001O01OO101O01O1O000O2O000O100O100O10000O10001O200O1N2O0O3M2N2N3M4L5K6K5K7H3M3M`0TMbjNT2VV1QNmiNk0Xn9XO`XGH[iNl0SV1Q1giNSN`U1[2E;L5L:E5L3N1N2M3N2M3O001O010O02O1O2N2M5L3M3M2N2N1N3N3L6J5J4K5L5QO`jNQOkU1<hjN\\\\OfU15UjV7\", \"size\": [1280, 720]}, {\"counts\": \"mfj?1mW13M2M4L3TjNFoS1>gkNLUT18fjNGKg0YU1b1I7M3H8I7K5I6L4L5L3L4M3M3M4M2O10000001O001O1OO1000001O000000000000O1000O101O01OO101O00O01000O010002N3M00O1O001N2N2M4N1O2N2N1N2M3K5L4M3N2M3L3MSmNbL`Q1S3\\\\nNRMmQ1l2RnNZMlQ1a2QnNgMPR1Q2RnNTNoQ1_1VnNhNmQ1a0cnNCbQ14^nN0gQ1HZnN;iQ1]OVnNg0QR1lNRnNX1WT14K5K5K5N1L5M3M3M4M2L3O2O1000O01O10OO2N101O1O001O001O001N2N2N101O0N2N20VOckNRN]T1c1alNkM_S1T1ckNnNOOSV1R1QjNjNYV1U1:G8M3M6JlZT7\", \"size\": [1280, 720]}, {\"counts\": \"hXm?2ZW1g0^O`0C=C]NVjNi1gU1:M2M4M3K5F9J6AUMckNR3UT1`0M3L4N2J6M3N2N2O10001O0001O1O3M2N00M3M3M300O100000000000001O01O0000000O2O00O01O20O4M1M01ON10O201N2N2N3M4J5L4L3M4K4K4M4F:M3L`lN^MjQ1[2UnNnMiQ1l1XnNZNgQ1c1UnNdNlQ1Q1YnNUOgQ1h0YnN[OhQ1a0XnNBmQ13SnN3XR1XOomNj0\\\\R1bNkmNa1XT12K5L3O3L3N3M2N2N20000O001O03M4M2M01O0N31O1N1000JgMejNX2[U171O01N100O01O01N1L4O10M3F9J6K5O100ROTjN@VV1a0liNTO\\\\V1k0iiNPOZV1k0`0L4H=D[bP7\", \"size\": [1280, 720]}, {\"counts\": \"SXW`03`W1>L4I6I8L3N2bjNXOcS1j0QlNBlS1`0jkNKPT1:lkNNjS18PlN2kS1T2M2O1N100O1O1N3N100O1N2010O00N2O1O1N200O100M31O000000O100O10000O2O001O1O1O000O100O100O10O020O002N1O001O1N2M3J6L3M3O1N2M4H7K6I7G9L3M3L5B=I9K5KckNQOnQ1d0VnNDiQ17UnN2jQ1CTnNj0oQ1kNUnNY1QT18J6K4G:I7N2M3M3L4M4N1O001N2O1002M2N2N2O1O0O01000O10000001N1O001O1O1O10O00OiNjkNXNLI\\\\T1h1_1A?I6I7BZiNZOnV1d09FihNG[W10\\\\ig6\", \"size\": [1280, 720]}, {\"counts\": \"ZWa`0:bW15Ea0@UOZiNn0dV1233I7Oc0]Of0YjN[MgT1l3RO3M3N1O2O0O101N1N2O1O100O100O1O13M1000NO20000001O00O0000O0020M3M3M3N3L3L4M3M3M1IYlNQMmR1P3RmNUMjR1k2VmNWMiR1i2VmNVMlR1i2SmNXMoR1g2olN[MQS1d2mlN_MTS1]2llNeMVS1V2llNiMYS1R2hlNkM]S1S2dlNiMbS1U2blNeMaS1Y2U1J5M3N2K5L]lNQN^Q1j1anN]N]Q1`1anNfN^Q1V1`nNPO`Q1l0[nN]OfQ1:ZnNOfQ1OTnN8lQ1HRnN;nQ1BPnNb0PR1XOimNU1XR1eNimN`1\\\\R1RNjmNP2RT11N2L4A?FPMbkNR3WT1ZMgkNc2nS1iM[lNm1eS1QNZlNR2fS1nMYlNS2hS1kMYlNU2hS1jMVlNW2lS1hMPlN\\\\2QT1cMnkN^2RT1cMkkN_2VT1`MhkNa2YT1c000N21O1O002N2M6KO100004K4M1N2O1O001N102N3M000000O0O2O1N1100000001N7_MRkNd1WV1]O<DTW`6\", \"size\": [1280, 720]}, {\"counts\": \"l^b`02QW1=UiNNfV1g0O1O1M3hiNiNeU1Z1TjNlNjU1f101N1O2N2M?B5L9G5K8G5K4hkN]LlS1P4K2N3M101N2O1O010clNcKUS1]4700O1O001J7N1O0M2O2L4O2N2N101M2N3M3L[lNlLeR1Q3ZmNUMcR1j2ZmN\\\\MeR1c2[mN_MeR1_2ZmNdMfR1Y2YmNjMhR1S2VmNQNkR1l1VmNUNkR1i1TmNYNmR1d1TmN]NlR1`1WmNZNPS1_1SmN`NRS1[1olNgNTS1X1blNQN^Of0UT1S1`lNoNhS1l0[lNkNRT1m0TlNiNXT1P1mkNkNYT1l0PlNoNVT1i0e1FTlNFeP12_oNOjS1KQiN7iV12QiN2fV1g0@`0]Ob0G;I7C=N2O1O1O1O11O010O0000O100O100O1O001O1IeLhkN\\\\3WT17001N101O1N3N1O4L8H1O0000O001J7L3M3O0011O1N2O2N3M4K5L3M1O2N1YMljN`2`U1J4L3M1O1O2N5K7I2M4M6I5L6I9F7J7J5JSgn5\", \"size\": [1280, 720]}, {\"counts\": \"fnXa08cW17L:G6Hd0]O9G7J4L4K5M6Ia0@6I>B7I2N1N3O0001O0000001O0000000000O1O2O00O1N3N1O2M3K4L5K5K5G9J6L4M2M4JfkNcM[S1[2clNQNVS1l1ilNYNUS1c1ilNcNXS1^OkkNc1e0\\\\OgS1;`lN_OiS18jkNoNMf0bT13dkNA^O7WU15\\\\kN^OE1ZU1>TkNZOJN[U1b0c1J8FV[1NjdN9H7L3TOFgiNg0SV1f0@`0F:J6F:G9N1O2O11O10O0O1001O0O1000001O1O0O2O1O001O1O1O001O0O111OO1N100000N2O100O101N101O0O100O11O0001O1N6K6J3N3L3L5L3M3M5K3L6K6J6I9Gofd5\", \"size\": [1280, 720]}, {\"counts\": \"^gZb04d22bQ18RlNL[2M`Q1<nkNLa2I[Q1o0gnNQOXQ1n0jnNROVQ1m0jnNTOUQ1m0inNUOVQ1k0jnNVOVQ1h0knNYOUQ1g0jnNZOVQ1e0jnN\\\\OUQ1d0hnN@XQ1?gnNCYQ1;fnNG[Q17dnNNZQ10gnN2WQ1NinN4TQ1MlnN3UQ1LjnN6VQ1IjnN8VQ1HinN9WQ1FinN;XQ1ChnN>XQ1AgnNa0ZQ1]OfnNd0\\\\Q1XOenNh0_Q1QOcnNQ1`Q1hNbnNZ1QT11N0O3N3N2M2N1N2O3N2M6KK5L4N2F;L3K400O1L3N202K5I8K6KWR58^fI^ObV1o017J6C=J6M3N2N2M3F:N2O1O1O2OO2O00001O2N1O2N1O4M4K1O1O1N4M3N1N6J4K4M2N2N2M4M1N2O2N1O2N1O001O1N2O1N5L6I3L4ITV]5\", \"size\": [1280, 720]}, {\"counts\": \"Waea02_W1b0G7H8I6L4K5L5L2N3I7O2N1000010O0001O001O1N10010O000000O1000000001N10000000000000O10001N1O01O001N2N1O2M2N2M4N101O0N3L3N2N2ORjNlNSU1S1ojNPOmT1o0UkNVOeT1i0^kNSOdT1k0Y1M3O`iNSOQV1k0b0O1O1O[jNDiS1:UlNLjS11VlN3iS1IXlN:hS1BXlNc0gS1YOZlNl0dS1QO\\\\lNS1TU15K4K5I7L4M3N2N2N2L4G9L4L5L3L4N2O1O100O1O001N2001O010O1N2O010O1ON2O2N1M2O20O2O1O1O0gLakNP3aT1mLckNQ3hT1M2N101N3N3XNPkN3ZU1nN`jN`0f0OVf_5\", \"size\": [1280, 720]}, {\"counts\": \"mPYa07bW1<G5J5L5N1O20O00010O010O0001O0000000000O1001O000O2O0000000O1O1O100O01000000000000000O10O010N2O0N3O1NnMPOWmNn0gR1XOXmNe0fR1AXmN=gR1HXmN8gR1GZmN8eR1J\\\\mN3eR1OZmN0fR11[mNKgR17YmNGhR18YmNGiR19WmNDnR1;QmNDVOEcR1g0YnNBmR1<YmN\\\\OlR1b0[2O001O0O0100SlNEVP1:foNMXP11eoN4\\\\P1IaoN<cP1[O\\\\oNk0hS14M4L3lLlNYoNW1gP1jNVoNX1iP1iNRoN\\\\1nP1eNmnN_1SQ1aNinNc1WQ1]NfnNf1ZQ1ZNdnNh1]Q1UNbnNn1`Q1nM`nNU2cS13M2L4M3L4O001O100O100O1O101N10O10O100O101N2N2N10O01O001O01000O102N7I2NN2N2WObkNhMfT1g1R1D<N2J6M4H8N1O0O2I7GnhNAUW1>8N6D_Q\\\\5\", \"size\": [1280, 720]}, {\"counts\": \"]ifa02mW13N0O1O2O0O1O100O001N2N2O2N1O1O1O0011O1O0O1OZMMbmN1\\\\R16`mNI_R19bmNE]R1>bmNA^R1`0bmN_O^R1b0cmN\\\\O]R1e0cmNYO`R1f0amNWOaR1i0\\\\2O00000O1N1OakNZOhQ1f0WnN\\\\OgQ1d0XnN^OhQ1a0WnNAfQ1a0ZnN@dQ1`0[nNBfQ1=TnNHmQ18TnNGfQ1FmkNd0]2DdQ1e0^nNSOhQ1n0e200O010O1NXlNUO_P1i0ZoNCcP1<[oNHdP17ZoNLfP13XoN1gP1NVoN6kP1IPoN<QQ1CknNb0UQ1\\\\OjnNf0WQ1YOgnNi0YQ1WOdnNl0\\\\Q1SObnNP1^Q1PO]nNU1cQ1jNZnNZ1fQ1eNXnN^1hQ1bNVnN`1jQ1`NUnNa1kQ1^NUnNc1kQ1]NTnNd1lQ1\\\\NSnNe1mQ1[NSnNe1nQ1YNSnNg1mQ1XNTnNh1RT11N10000O100O1O1O1N101O100O1M3O1O1O00100001O1O1O1OO100O100O1O11M2D<G;H8lNRjNCTgS6\", \"size\": [1280, 720]}, {\"counts\": \"cg\\\\a0<RW1d0H7XOh0K5J5J7N2N1O1O2N1N2O10000O100O1N2N2N2N2M3N2M3M2N4K4K4N2N1001O1O100N3N2N3M2O1OPlNaLaS1[3_lNkL_S1S3alNPM^S1o2alNRM`S1l2_lNWMaS1g2^lN\\\\MaS1c2_lN_M`S1_2`lNdM_S1Y2blNiM_S1V2^lNjMeS1S2^lNkMeS1T2ZlNkMiS1S2WlNmMjS1R2WlNmMlS1Q2UlNnMlS1P2UlNPNkS1o1WlNkMoS1S2QlNmMQT1o1QlNlMUT1S2nkNhMVT1W2llNoMmQ1m1klNmMd0;`R1f1nmNcNQR1Z1nmNjNRR1S1mmNQORR1m0mmNWORR1g0nmN\\\\ORR1a0omNAPR1>nmNFRR17mmNLTR11lmN3SR1JhmN>WR1@gmNf0XR1UOimNo0WR1mNjmNV1VR1gNhmN^1XR1_NfmNf1ZR1UNhmNn1XR1QNfmNR2[R1jMcmN[2]R1cM`mNb2aR1ZM`mNg2bR1nLamNZ3]S15L4J6M3N1N3N2N2L4N2O1N2N2O1N2O1000000001N2O1O1O1O1O001O001O1O1O1O0[LZlNP3fS1nL]lNQ3dS1kL`lNT3cS1cLdlN\\\\3PT100O2O0O2L4J7eMakNa0UU1hNYUf5\", \"size\": [1280, 720]}, {\"counts\": \"f_ea01iV1Z1_Oa0F7F9N3M2O1O1O1N101O1O2N1N2N2M3N2N2N2N2N3L3L4N2L4L5M101N11N2O1N2M3N2O1N2N2O1M3M3L4JikNWMcS1g2[lNdM^S1\\\\2^lNgMcS1X2\\\\lNiMdS1U2\\\\lNmMeS1P2]lNQNcS1m1]lNTNdS1i1\\\\lNYNeS1e1\\\\lNXNhS1f1ZlNWNfS1f1mkNgM;a0iS1e1QlN_N]T1_1ekN]N_T1`1dkN^N^T1_1fkNVNdT1g1_kNUNbT1l1mlNSNfQ1k1WnN]NfQ1a1YnNcNgQ1[1VnNjNjQ1R1WnNQOiQ1l0TnNZOmQ1b0lmNHTR16imNOXR1MgmN7XR1EimN?WR1]OjmNf0VR1WOemNQ1[R1lNcmNY1]R1dNcmN_1]R1[NgmNf1ZR1XNfmNj1ZR1TNfmNn1ZR1nMemNW2[R1eM[mN1nNa2gS1\\\\MWmNQ3gS14M4N2N2M3M3N2M3L4M2N3O1O1O1N2O1O1O01000010O0000O11O001O001O001O100O1O001O1O2M2O2N3M2O0O1O2N1O1N2jLPlND4S2mS1RNjlNk1XS1QN\\\\mN\\\\1mT1E`iZ5\", \"size\": [1280, 720]}, {\"counts\": \"\\\\Wia0:34hV1i0K5J5H8K4N2O2O0O1000000000000O1O1O1O2O0O100O1000000O02N1N2O01000000O100N101N1O1O2OO100O1L3OeNXNclNf1`S1\\\\N^lN^1eS1hNWlNV1kS1mNSlNP1oS1ROQlNi0RT1YOmkNb0XT1^OgkNb0ZT1_OekN`0\\\\T1@dkN>]T1DakN:bT1F^kN2jT1OUkNOmT11SkNNoT12PkNLSU14ljNJWU17gjNFW1AYR1j0blNZOR18[R1>elNROo0g0[R17TnN0lQ10omN5QR1JmmN9SR1GimN=WR1BgmN`0ZR1_OcmNf0\\\\R1WO`mNP1`R1lN`mNX1`R1aNemNa1\\\\R1ZNfmNh1ZR1UNfmNn1[R1lMhmNV2iS18K5N2N2_O]MdkNd2YT1_MfkNb2XT1`MgkN`2VT1g0M3MbMTlNn0kS1POYlNo0eS1RO\\\\lNn0cS1RO_lNm0aS1SOalNk0^S1VOblNj0\\\\S1WOflNg0YS1YOilNg0VS1WOnlNh0QS1XOPmNh0oR1YORmNf0nR1YOSmNh0kR1XOVmNh0jR1WOWmNi0hR1WOZmNh0fR1VO[mNk0eR1SO\\\\mNn0dR1QO]mNn0dR1oN_mNQ1aR1jNdmNV1]R1eNgmN[1ZR1aNimN`1VR1^NlmNb1UR1\\\\NmmNc1TR1\\\\NlmNd1TR1\\\\NmmNc1UR1[NkmNe1VR1YNkmNg1XR1VNhmNj1ZR1SNgmNm1WT1N2O10O10O00002N9F3N2N3L8I8G8[OehN6fWX5\", \"size\": [1280, 720]}, {\"counts\": \"^f\\\\a01nW11L4F;N100O1O2N1O10000]jNEbS1:[lNKeS13[lNNeS11[lNOgS1OXlN2kS1KTlN6RT1DnkN<ST1CmkN=TT1ClkN=ST1CmkN=ST1DlkN<ST1EnkN:RT1GmkN9QT1IokN6PT1LQlN3PT1LQlN4RT1InkN7ST1GmkN9ST1GnkN8PT1JPlN5nS1ORlN0lS12TlNLhS1:XlNEfS1?YlN@hS1`0YlN_OgS1a0[lN\\\\OfS1e0[lNWOgS1i0ZlNVOdS1l0\\\\lNTOaS1o0_lNQOaS1o0h1001O0O100O2N1VOSOajNm0YU1A^jNa0_U1Q101O0O101O001O001O001N10001O00001O01O00100O1O000001O01O000000O10[Od0N3N8iNck3BbUL`0H7H5K4J7E;E;@`0I7^O`M]kNd2[T1f0K4N2N101N2N20O1O0000O0101O001OO1010O1O001N2O2N2N2N3dLkkNg2jT1M2O3L;jMcjNS1bV1D5I6K8G9H3LggZ5\", \"size\": [1280, 720]}, {\"counts\": \"m^Tb01nW120N:6^O3J7M2010N2O000000001O00N2N2M3N3M2O1N2O1M3K5L4O1O1O0N3N0101N3MZOTO[jNl0aU1l0I6N3O1O10O100O100O1O1O1O1N2O101M3M3M2M2O2M3N2L4N3K4L4L4M2L^jNSO[T1i0ckN]O]T1?dkNE[T18ekNLZT10gkN3XT1FnkN=PT1^OSlNe0`U15L3L5J5N2M3M4L3L4L5L3M3N3L3M3L5L301O010O001000000O1M3M3O001O1O001O01000001O2M2O1O2NOoMljNW1TU1`NTkNa1mT1WNZkNh1aU1N8fNfjN^O\\\\U1:PcY5\", \"size\": [1280, 720]}, {\"counts\": \"baYb01oW1000oUo03kiPO4L3N6J3L3N1N1H810O1001OjiNPO]U1o0djNSO[U1l0ejNVOaT1>ljN<a0YOWT1i0UkNMc0]OQT1l0\\\\kNEd0AoS1j0]kNCd0EoS1h0^kN@d0JmS1g0]kN\\\\Oi0NoS1S1TlNoNPT1j0QlNWOPT1e0QlN]OiS1[O[kNU1k0EiS1WO[kNS1j0IkS1VOYkNo0l0NkS15UlNMlS10TlN3kS1JVlN8jS1FUlN>jS1@VlNc0fS1]OZlNg0dS1VO_lNk0_S1TOclNn0\\\\S1nNglNS1ZS1hNglN[1YS1aNglNd1YS1TNklNo1aT12M3N2N2N2N2M3L4N3M2M4L3N3M2N3N2M2N2O2O0O20O1O010O2O0YMgkN\\\\ONn1\\\\T1dNPlN^1RT1`NnkN`1UT1\\\\NlkNe1ZT1PNkkNP2SU10N101N1010O0fMajNS2jU1K6K2M3L6J5K5I9ZOaX]5\", \"size\": [1280, 720]}, {\"counts\": \"fiUb03aW1?K3I8L3N3M100O10N2N101N2N101K5N2O1O2N1M4M2N3KU1NlN6L3K5L4M3L3N3N2N2cMUOdmNl0ZR1ZOamNg0]R1\\\\OamNd0WR1FhmN:kQ13UnNMgQ18WnNIhQ19WnNGhQ1;WnNEiQ1<UnNFiQ1;WnNEiQ1;XnNDhQ1<XnNDgQ1=YnNCeQ1?ZnNBfQ1=[nNCeQ1=ZnNDgQ1;YnNEfQ1;[nNEdQ1<]nNBcQ1:bnNF^Q18klNcNb1T1cQ16nlNfN`1S1aQ15RmNhN^1R1_Q14VmNkNZ1Q1`Q12XmNnNW1P1`Q11ZmNQOV1m0_Q11]mNTOR1j0bQ1O_mNYOm0h0dQ1M`mN_Oi0d0gQ1HemNEd0b0gQ1EhmNK`0`0iQ1AdmN6c09hQ1_OcmN=d04jQ1\\\\ObmNb0e01kQ1WOamNl0d0LnQ1SO^mNS1d0JiR19XmNFgR1;ZmNDdR1>\\\\mNBbR1`0_mN_O^R1e0bmNZO[R1i0fmNVOXR1l0hmNTOVR1n0jmNROSR1Q1mmNoNRR1R1nmNnNPR1T1PnNlNoQ1U1QnNkNnQ1V1RnNjNmQ1W1TnNgNkQ1[1VnNdNkQ1[1UnNeNnQ1g0alNoN`1:QR1c0dlNPOZ1>TR1>elNSOW1?ZR10hlN@m0a0bR1^OklN2b0a0jS1]OVlNd0kS1[OUlNe0kS1[OUlNe0lS1[OSlNd0nS1^OQlNa0PT1_OPlN`0QT1@okN?ST1_OnkN`0XS1AfkN4R19VS1c0glN]OXS1e0glN[OXS1g0hlNXOXS1h0hlNWOYS1j0glNUOYS1k0glNUOYS1l0flNTO[S1k0flNTO[S1k0elNTO^S1j0blNTO`S1m0_lNROaS1Q1]lNPOaS1R1^lNoN`S1T1^lNmN`S1U1_lNkN_S1X1_lNiN`S1Y1_lNgN_S1\\\\1^lNfNaS1^1ZlNdNfS1Y1alN]NeS1a1]1O4D<J6J5G;H]ao4\", \"size\": [1280, 720]}, {\"counts\": \"TnQb02nW14M0IKahN5_W18Mb1fhNQN_U1[2M2L32M2O1N1O1N2M3N2N2ON001O10O00ETNbjNn1_U17D>gkN^MnR1j2PmN\\\\MiR1d2YmN_MfR1_2[mNcMdR1[2]mNhMbR1S2_mNQN`R1l1`mNXN_R1e1`mNaNYR1X1PnNlNmQ1T1SnNoNlQ1o0UnNSOjQ1l0VnNVOjQ1i0TnNZOlQ1d0RnN@nQ1>RnNDnQ1:RnNHnQ16QnNMoQ11QnN1nQ1NQnN5oQ1HQnN;nQ1DPnN`0PR1^OomNe0QR1YOomNi0QR1UOomNm0QR1oNQnNS1oQ1jNQnNY1oQ1eNPnN^1QR1_NmmNd1UR1XNkmNk1UR1TNjmNn1WR1PNgmNT2ZR1gMfmN\\\\2[R1aMemNa2[R1\\\\MfmNf2\\\\R1UMdmNn2]R1oLdmNR3^R1gLfmNZ3]S12N3N2M3N2N2O1O1M2O2O1O1N2O1N3L3N2N200O010001O001O1O1O2N7J0O2N2N0O2O1OO1O1001O00000O010000N2N2N2N110O10000O10O10O0101O1O3N1N1O3M3M2N2gLgkN06P2hT1dMbkN0FP2nU1H4Jk0TOjhk4\", \"size\": [1280, 720]}, {\"counts\": \"`USb06c0KbV18UiN0gV14ViNO5GoU1U1hiNVOSV1^1N`0_O2M21N5I4L3M3M3N2M2O2M10O0O1L4nNS101QlNVMgR1k2VmN\\\\MfR1c2[mN_MdR1`2[mNdMcR1Y2_mNiM`R1U2bmNlM^R1Q2bmNRN^R1i1bmN\\\\N^R1a1XmNlNgR1R1[mNoNdR1g0emN\\\\OXR1b0jmNAUR1:nmNHQR17PnNJPR14nmN0QR1OmmN4SR1LkmN7UR1HjmN:WR1CjmN>WR1@imNa0WR1]OjmNc0VR1]OgmNg0YR1WOemNm0[R1QOcmNS1\\\\R1kNemNW1[R1fNfmN\\\\1ZR1bNfmN`1ZR1^NfmNd1ZR1ZNfmNh1[R1TNfmNm1[R1QNdmNR2]R1kMbmNX2^R1fMamN]2aR1^M`mNd2aR1YM_mNi2aR1UM_mNm2aR1RM^mNP3cR1mL]mNU3dR1iL\\\\mNX3eS11N2L3O2M3N101O1O1N2O1N2O1N1O2N2O1O2N1O10000001N2O1O2N2N2O1N1O2N001O0O01O1000000001O10OO1000000000000001OO10O100O1O1O010002O2M1O001O1O1O1\\\\LVlNo2RU1\\\\O3M4K4M7I5K5J;E=C`0]Ooha4\", \"size\": [1280, 720]}, {\"counts\": \"ifUb06oV1NUiN4N9gV1Z1\\\\O:D4L3O03J4N2L3L4N3L3N3M1N3NH7_Oa0_Ob0O100O1okN_MeR1a2WmNfMfR1Y2ZmNkMdR1S2\\\\mNPNcR1n1^mNTNbR1h1_mN[NaR1a1`mNbN_R1[1[mNoNeR1l0\\\\mNWOeR1b0^mN@cR1=^mNG`R16bmNL]R13`mN2aR1J`mN7aR1H^mN:bR1E]mN<cR1C^mN>bR1A]mNa0cR1]O^mNc0cR1\\\\OWmNk0iR1TOSmNP1nR1nNQmNT1oR1jNQmNY1oR1eNQmN]1oR1`NRmNb1oR1ZNRmNh1nR1UNSmNm1lR1SNSmNo1mR1oMSmNS2mR1lMSmNU2mR1iMRmNZ2PS1bMQmN_2PS1^MolNe2RS1XMnlNj2RS1UMmlNm2SS1QMnlNP3SS1mLnlNT3RS1kLnlNV3jS14M201O0O2N2O1N2O1O1O1N2O0O2N2O1O002O000000000001O1O2N3M2N2N3M100O2NO10O11N1001O001O000001O00O1002M2O000O100O00100O0101O1N3N1O1O002N1fLkkNg2\\\\U1[O3M2M4M5K6J4K6J;E<D<C^ha4\", \"size\": [1280, 720]}, {\"counts\": \"Y_cb07_W1`0ehN\\\\OgV1X1H4L0N3N14L3M2O0N110N2O1N\\\\N[OhkNn0UT1_1L310OO3000lkN[MoR1c2PmNaMnR1^2RmNdMnR1Y2SmNiMlR1U2TmNmMmR1Q2SmNQNlR1l1SmNXNnR1e1mlNbNTS1[1mlNgNSS1S1QmNnNPS1n0QmNTOPS1h0RmNZOnR1c0QmNAoR1=PmNFQS17olNKQS13PmNNPS10QmN0PS1NPmN4PS1JPmN8PS1EllNb0US1YOklNl0TS1ROllNP1TS1oNklNS1US1iNllNZ1TS1cNmlN_1SS1_NmlNc1SS1[NmlNh1SS1UNnlNl1SS1QNmlNQ2TS1kMllNX2US1eMjlN^2VS1aMilNa2US1]MnlNe2mR1XMYmNi2mS12L4L3O2N2N2M3N1O2N2M3O1N2N2O1O100O1O1001O1O1O2M2O2N2O1N2M4M3M2N4L2N1O000000O1001O00O10001O0O00100O2O00O10O2N1O1O1O100001O1O001O1O2O5J?TMTkNf1gU1L2N4L4L3L6K8Ga0]O^h\\\\4\", \"size\": [1280, 720]}, {\"counts\": \"Qgnb0`0\\\\W17L5L2M3N2M3N3L4M00O2OOO1O2O2M2M3NPNQOPmNo0lR1FYlN\\\\OSOQ1dT1JXkNEOQ1gT1c10J7O2NmkNWMSS1h2jlN]MUS1_2ilNgMWS1U2ilNoMWS1n1jlNTNTS1i1nlNZNPS1b1SmNaNlR1]1TmNeNlR1Z1QmNkNoR1Q1QmNSOoR1j0RmNWOoR1f0RmN]OmR1a0QmNCnR1;SmNGmR16UmNJlR14nlN_NZO_1hS1OolN9QS1CRmN>nR1^OSmNe0mR1UOSmNQ1lR1nNRmNV1nR1dNVmN_1jR1\\\\NXmNf1iR1UNXmNn1jR1gM\\\\mN\\\\2RT12M3N2O1M3L4J5M4M3M3L4N2M3N2N1O2O1N2O2O000001O1O1N3N101N3M3M1O3M3M3M4K3N1O1O1OO101N1O100O10000O10000000000O100O1N2O1O1001O001O102M3M4K9cMSkNW1QV1J5K8H:F>@\\\\h\\\\4\", \"size\": [1280, 720]}, {\"counts\": \"Pahb065M[W1=N2C@UiNc0jV1:01J40O1000O110O010O10M3M4M3N1OO1M202N1O11O1O^jNTORT1m0jkNYOST1g0jkN^OUT1a0kkNATT1?jkNDUT1:jkNJVT15gkNOnS1;QlNGnS17RlNLmS13SlNOlS10TlN1jS1OWlN3fS1NYlN5fS1KXlN8hS1EYlN>eS1A\\\\lN`0dS1^O[lNe0eS1ZOXlNj0gS1UOWlNo0iS1oNVlNT1iS1kNVlNX1jS1eNWlN]1iS1^NYlNf1gS1VNYlNn1gS1oMYlNS2hS1iMYlNY2cT14M3N2N3L2N3M3N3M2O1I7M4N1N2O10O0100O2O001O0O10000O1000000001O0WLXlN[3UT11O1O001O100O100O100O1N2M4N1O1O10O101O002N2M3NN2O1O001O1O010001N10013K4L4L5A>bNXkNjN`U1h0bb`4\", \"size\": [1280, 720]}, {\"counts\": \"^n`b02mW15\\\\O4ihNOUW1b0L8H3N1O1\\\\iNAeU1d1L001NN1M3L4M3J7K5M:F9G5G8I:_OiiN4kT1MVkN7cT10ZkN3aT10^kN2bT1K`kN6aT1G`kN9_T1H`kN:\\\\T1JakN9]T1I`kN9jR1CblNHGm0k0LfR1\\\\1XmNhNcR1]1YmNgNgR1W1WmNmNiR1Q1WmNQOhR1n0YmNSOgR1k0XmNXOhR1e0YmN\\\\OhR1b0XmN@gR1?YmNCgR1;WmNIiR15VmNNjR10UmN4jR1JTmN;kR1CRmNb0nR1\\\\OQmNg0oR1VOQmNm0oR1PORmNR1mR1mNSmNV1kR1iNUmNY1jR1eNWmN]1iR1`NXmNa1hR1^NWmNe1hR1ZNXmNi1fR1SN\\\\mNQ2bR1nM\\\\mNV2dR1iMZmNZ2fR1cM\\\\mN^2eR1]M^mNd2bR1XMcmNg2]R1VMfmNj2fS12L3G:M3L4M3N2N2N2O1O1O0O2N2N21O00001O001O100O4K4eLVlNZ2mS1`MWlN_2gT1N1O1O0000000O0100O100O1N2M4M2O1001O00O10O01O1O1O1O1O1N2O1O1O11O00000O2O2N2N2PN^kN>dT1VOhkNh0\\\\T1^N\\\\lN0oNS1jV1FW``4\", \"size\": [1280, 720]}, {\"counts\": \"gUSb0e0UW1i0ZO4K4N20O1O1M7K;A6M2N1O1NQjNIUT1:ikNIVT16kkNLTT11mkN3RT1IPlN9PT1GokN;jS1KUlN7iS1JUlN9gS1JVlN:dS1KXlN:XS1POnkNe0i0>nR1ZOVlN7k0b0mR16TmNKiR17WmNLfR14YmNOfR10YmN3eR1M[mN4dR1L]mN5dR1I[mN8hR1ESmNa0nR1]OolNg0QS1VOPmNl0oR1SOQmNo0oR1oNPmNS1PS1kNolNY1QS1fNmlN]1RS1bNnlN`1RS1^NolNc1QS1[NPmNe1PS1ZNQmNh1mR1XNPmNl1oR1TNolNo1QS1nMPmNT2PS1jMolNY2QS1eMnlN^2QS1aMPmN`2PS1^MQmNc2oR1ZMSmNg2mR1XMTmNh2lR1VMUmNj2lR1UMUmNk2jR1TMWmNm2iR1nL[mNR3fR1kL[mNW3fR1dL\\\\mN^3dR1`LXmNf3_S12N2O001O1N2N2O1O1N2O001O1O1O1O1O100N2O11O1O1O1O1O2M3N2N2N2RLRlNf3XT1L001O0000O01000O1001O0000O10000000O10000001N2O0O100O10000001O002N101N2N4dLclNl1kT1N2N<C>Bj0UOfPm4\", \"size\": [1280, 720]}, {\"counts\": \"ZU_a02kW14K4K6M3N7I3N2`iN\\\\OcU1`11N0O1N2M5L<D6L0N^jNZOmS1e0QlN@mS1>okNJoS14PlNOoS1OQlN4nS1HUlN;kS1DRlN`0oS1_OokNc0oS1^OPlNd0mS1^OQlNf0mS1[OPlNh0oS1WOokNn0fS1ZOXlNj0dS1XO\\\\lNj0XS1_OilNb0QS1DolN=oR1DolN>oR1BRmN`0mR1_ORmNd0lR1]OSmNd0nR1ZORmNh0nR1WOPmNm0PS1QOnlNQ1SS1mNmlNU1TS1iNklNY1US1eNklN\\\\1US1cNjlN`1US1`NjlNb1US1^NjlNc1VS1\\\\NilNg1WS1WNilNk1VS1SNklNo1US1oMklNS2TS1nMjlNT2VS1kMjlNV2VS1iMjlNX2US1hMklNY2TS1fMllN\\\\2TS1cMklN_2US1`MjlNb2US1\\\\MmlNd2TS1ZMllNh2SS1WMnlNj2QS1TMRmNl2mR1TMSmNm2nR1QMSmNo2nR1oLRmNQ3PS1mLPmNT3RS1fLQmN[3hS12M3M3O1N1O2O1O1N10100O1O1O1O1O1O1O1N200O10000001O001O3M4L2N0O2O3M2N0O2O0000001O10O01O1OO100001N100001O0O100O100O100O0100001O10O3M4aLdlNR2gT1M4M6J7I7H<DWaY5\", \"size\": [1280, 720]}, {\"counts\": \"V^Va05iW12L4N2O1HDkhN>SW1710O026J30NXiNFfU1;QjNMoU1R1001K5N2M4J7K3N3LXjNTOaT1i0]kN^O_T1?bkNEZT19gkNLUT14lkNOST1LokN6RT1ImkN9ST1FlkN<VT1_OkkNb0XT1[OhkNf0VT1ZOkkNg0iS1EQlNa0lS1BPlNc0_S1dNikNR1g0<XS1OilN3TS1NklN5QS1NolN3kR10WmN0hR11XmN0gR1NZmN4eR1KYmN9fR1GYmN;gR1CWmNa0jR1]OTmNf0lR1YOSmNi0lR1WORmNl0nR1RORmNP1nR1nNPmNV1PS1iNolNY1QS1eNolN]1QS1aNPmN`1PS1^NPmNe1PS1XNQmNi1PS1SNPmNP2PS1oMolNT2PS1kMnlNX2RS1gMnlNZ2RS1eMmlN]2SS1bMmlN_2SS1_MnlNb2RS1\\\\MolNe2QS1YMPmNh2PS1WMolNj2SS1RMolNo2QS1oLPmNR3QS1iLRmNY3nR1dLRmN^3oR1`LPmNb3eS12N2O0O2N2O1O1O1N2N2O2N1O1O001O1O100O1100O1O001O1O2M4M1O1O5J4N5J4MN1O1O011O00O101O0010O000000000000O010000O2N10001N1000O101O2N2lLhkNMGU2gU1J5K6Ic0]Oc0kNdPa5\", \"size\": [1280, 720]}, {\"counts\": \"S^`a09eW14N0O1DMkhN3SW1>N3O3M5\\\\iNkNSV1g1MM3N100O0O3K3M2OajNjNZT1P1ekNUO\\\\T1g0ckN[O_T1a0`kNBbT18`kNJbT10_kN4aT1G`kN<`T1@`kNd0`T1\\\\O]kNg0dT1VO\\\\kNl0dT1RO\\\\kNP1iU11O1aLVOjlN2Z2i0kP1MnnN6QQ15llNeN\\\\1Z1hQ1=SnNEmQ1=jlN[Nj0Z1\\\\R1c0emN_O\\\\R1`0amND^R1<_mNGaR17_mNJbR14_mNMaR11_mN1aR1L_mN7aR1G_mN;bR1C]mN?cR1_O]mNc0cR1\\\\O\\\\mNf0dR1XO\\\\mNj0dR1SOYmNS1fR1lNYmNW1hR1dNZmN^1fR1`NYmNc1gR1[NZmNg1dR1XN\\\\mNj1dR1TN[mNo1eR1nM[mNU2gR1fMYmN]2gR1`MYmNc2hR1YMYmNi2gR1RM\\\\mNP3iR1iLWmNY3gS11O1O1K5M3M3N2N2N2M3M3N2O1N2O1N2O1O1000O102N1O1O2N3M102M2N2N2M202M5L1N5K2M101O0000001O100OO10000000O0100O10001N100O1O1O100O1001O1N4cLPlN8He1kU1AQ1QOb`c5\", \"size\": [1280, 720]}, {\"counts\": \"[][a01mW18ALdhN;PW1b0F8L3N<D8J1N10O1O4L3M\\\\kNWNSS1f1klN^NUS1`1elNhN]S1R1dlNPO_S1j0alNWOcS1e0\\\\lN]OfS19_lNKcS10\\\\lN4eS1IZlN;eS1DZlN>eS1CYlN?gS1@YlN`0iS1]OVlNf0lS1WORlNk0kS1WOTlNl0iS1XOSlNl0kS1VORlNl0jS1VOWlNk0dR1cNdlNY1f08cR16[mNMYR1=hmNCWR1=imNEVR1:imNIVR16imNMVR11imN2WR1MgmN7ZR1GdmN<\\\\R1CcmN?^R1_O`mNd0`R1[O_mNh0_R1XO`mNj0`R1TOamNm0_R1PObmNS1^R1jNcmNW1\\\\R1hNdmNZ1\\\\R1cNemN_1ZR1`NfmNb1[R1\\\\NdmNf1]R1WNamNm1`R1PN^mNT2aR1jM`mNX2`R1fMamN[2_R1cMbmN^2^R1_MdmNb2]R1[McmNg2]R1WMdmNj2\\\\R1SMemNo2ZR1PMhmNP3ZR1kLgmNW3[R1aLgmNc3WS15L2O2N2O1O1N2N2O1N2O1N2N2O1O1O1O2O00000002N2N6J1O2N3M1O4L1O2N3M2ZLkkN]3]T1O4L001O001O001O1O1O1O000001O000O10O011O00N1N2OO2ZNckN]OlT11j1]Oe0L:FR[l5\", \"size\": [1280, 720]}, {\"counts\": \"oVk`0c0RW1j0]O8H5MM4N2O1J7N2N2M4L1N1020OO1100NN3OgjN\\\\OZS1d0elNAVS1>klNFQS1:nlNKmR16QmNOlR12nlN5nR1NQmN4mR1LSmN7jR1JUmN9iR1FWmN=gR1BVmNd0gR1]OWmNg0gR1YOXmNk0gR1TOXmNn0gR1PO[mNQ1cR1POZmNT1fR1iNWmN]1iR1bNVmN_1jR1aNSmNc1mR1[NSmNg1lR1XNRmNl1nR1RNRmNP2mR1oMRmNU2mR1iMRmNZ2mR1fMQmN^2mR1`MUmNa2kR1]MUmNe2jR1YMWmNi2iR1UMVmNm2jR1TMTmNn2kR1PMVmNR3iR1lLXmNV3gR1iLZmNX3iR1cLYmN^3dS10000000N1N3L4K5L4M2O2N2M3N2O1O1N2N2N2O1N200O1O100O0100000000O2O1O3M6J4Le0[O4L1O1O00O1O10O100O10000O2O00O100000000000000O100000O1000O02O3eMSlN<VT1eNmlNn0ZU1G]P_6\", \"size\": [1280, 720]}, {\"counts\": \"jeY`03`W14ehN0UW1a0K5N2hiNUOVU1m0gjNDjT1=QkN6_T1LPkNh0kT1YOUkNj0nT1PORkNR1oT1\\\\NSkNMOe1SU1YN^kNf1eT1VNYkNk1kT1RNUkNl1`U1M2N2O1O1L2O4M2N2010N1O0WkNkNkR1T1UmNPOhR1o0WmNUOgR1j0TmN^OiR1b0SmNEjR1;RmNLlR14TmNNjR12UmN1hR1OUmN7hR1JVmN;dR1G\\\\mN<`R1F_mN=`R1B`mN`0_R1_OamNc0_R1ZO]mNm0bR1SO[mNQ1eR1mNYmNW1gR1gNYmN[1fR1dNZmN^1fR1`NWmNe1hR1[NUmNj1jR1TNUmNn1lR1oMUmNS2jR1lMUmNW2kR1gMUmN[2kR1cMUmN_2lR1_MSmNc2mR1YMUmNi2kR1TMUmNo2kR1oLVmNR3lR1jLVmNW3mR1aLWmN`3cS12M2O0N3L4M3M3N2O0O2N2N1O2M3O1O100O1O1O1O1N101O1000O01001O1N3N1O2N2N0000O12N1N3N1O1O4K=D2M2N8I6I101O1OO10O10N2O00100O1000O2O7I1N3cMnjNh1fU1Oa0_O2M3N=B7J8G:F5Jh_f6\", \"size\": [1280, 720]}, {\"counts\": \"QUa`01l01UV1OjiN3oV13M4I6K401N2N2L;F5L6Hd0]O?WjNcMSU1R3A3N2N2O0O11N3M4L7G:F5L3N2TNfjNP1\\\\U1oNfjNn0\\\\U1SOajNm0aU1SO]jNl0fU1TOYjNk0iU1d02M3M^kNaNiR1^1RmNiNlR1V1SmNmNnR1P1PmNTOQS1h0ilNAWS1<ilNFTS1:llNJRS15olNMoR12RmN0kR10UmN2hR10VmN4fR1NXmN6_R11_mN3^R1NbmN4\\\\R1LdmN5ZR1JgmN9XR1FemN>\\\\R1A`mNd0_R1[OamNg0^R1XO`mNl0_R1SO`mNP1^R1POamNS1^R1lNamNV1`R1hN^mN]1_R1cN`mN`1_R1_NbmNb1]R1\\\\NdmNf1[R1ZNcmNi1[R1XNbmNl1\\\\R1TNdmNn1ZR1PNimNQ2UR1mMmmNU2SR1hMomNY2oQ1fMRnN\\\\2nQ1bMRnN`2mQ1^MUnNc2jQ1\\\\MWnNd2iQ1YMZnNh2fQ1TMXnNS3gQ1iLZnNZ3fQ1aL\\\\nNb3dQ1\\\\L]nNd3dQ1XL^nNj3bQ1nKdnNT4\\\\Q1kKcnNW4aR1101N2N2N2N2O1N2O1O1N2O0O2N2O11O1O0O10001O0O101O0O2O2N2N1O1N2O1O1O1N101O1bL\\\\lN]2hS1_MYlNa2kS1WMZlNg2hS1VM[lNi2fS1RM^lNn2bS1nLclNQ3]S1lLglNS3ST1O0100O1O010O1O001O1O001000000O^MlkNF_OU1iT1POQlN@]O1=4\\\\O8SU1HZmNKVc8Oadc5\", \"size\": [1280, 720]}, {\"counts\": \"`\\\\Wc08dW1<B8K5liNXOKHWT1Z2bkNlM\\\\T1S2dkNnM[T1S2dkNmM]T1R2bkNPN^T1P2`kNSN^T1n1`kNTN`T1l1_kNTNbT1k1^kNVNaT1j1]kNXNcT1h1[kNZNfT1f1XkN[NhT1f1WkNZNkT1c1XkN\\\\NhT1b1ZkN_NeT1`1\\\\kNaNLEWT1i1nkNRORT1n0mkNROVT1k0jkNVOZT1f0ekN[O]T1b0dkN^O^T1?bkNB_T18ekNH^T12dkN0XT12hkNOST16lkNLPT16QlNLlR1U1UmNkNcR1[1^mNeN`R1]1amNcN_R1[1`mNhN_R1X1amNiN^R1V1cmNkN\\\\R1T1emNmN[R1R1amNTO]R1k0dmNVO]R1h0bmNZO^R1a0emNA[R1<fmNE[R19fmNH[R11imN1WR1LgmN9ZR1CdmNa0]R1]OcmNe0^R1XO_mNm0aR1hNdmN_1\\\\R1_NcmNc1bR1WN\\\\mNl1PS1TMjlN14n2WT14L3O2N2EfLkkN_3QT1:H9K5K5N100O10000000O2O010O010O001O001O0O2O1O2N101O0O1O1O1O1O2N2N2N1O1O10002M2N2N1O1M4H7I7I8POQ1_Oa0C>^Olkf4\", \"size\": [1280, 720]}, {\"counts\": \"iSQc0428YW1=I5L5M1O0O112ljNXOkR1k0`lNG`S1:ekNfNO:Kk0fT10^kNPOK^1mT1^OUkNj0QU1o06I4LTkNfMXT1W2hkNjMZT1R2hkNoMZT1l1gkNUN\\\\T1f1kkNTN\\\\T1e1X1F01N02NN4J5N2O1XkN\\\\NWS1c1glNbNYS1[1glNhNZS1T1elNoN]S1m0blNUO`S1g0`lN\\\\OaS1a0^lNCbS1:]lNIdS15ZlNNgS1OXlN4jS1GVlN<jS1BTlNb0mS1ZORlNi0QT1ROPlNP1UT1hNkkN\\\\1mQ1bNZPOa1do0eNUPO]1lo0jNjoNX1WP1hNdoN\\\\1\\\\P1fNaoN[1`P1eN]oN]1cP1eNYoN]1gP1eNVoN\\\\1kP1fNQoNZ1iP1oMZmNj0i1X1SQ1mNenNV1[Q1mN^nNX1bQ1iN[nNY1gQ1dNXnN^1jQ1PNmlNNX1S2lQ1lMenNU2^Q1hM`nNZ2bQ1cMZnNb2iQ1ZMRnNl2WS1:M3N2F:L4L4K5J7G7N3O100001O01O01O1O1O001O1O00O1000001O0000010O01O001N1001O0011N1O1O1O001O1O100O10O11N2N2M2O2N8G5H8I8VNnkNQOeT15Ve[4\", \"size\": [1280, 720]}, {\"counts\": \"j_ea08WW1e0C:E;@?C=N2M3POcMkkNO1b2QT1P1N11O5]LdkNX3bT1mL]kNi2eT1TM^kNk2nT1M00O1O010001ON2L4G9K401O1000O011O1O1O1O2N1O1N101O0O102N1O3N0O1O1O1N2OO1O1M301O001O0000001O001N000O100O10IUOQMTmNk2hR1UM_lN1g0g2nR1WM[lN4N;0V2UT1i06@`0H8K6K4I7G]lNeNnP1_ORmNU1b2CdP1APPOd0UP1SOjoNR1^S14K5L4M3G9M3J6I7I7L4I7K5K5M2L5@`0^Oa0H9M3O101O000000000001N101O1O0O2O1O001O1O1O1O1O1O1O100O1O1O1O2N3M1N3N2L5dMQmNWO^S10dkNAdV18[iNHiV15TiNMnV12RiNG1MPW1:a0OSco4\", \"size\": [1280, 720]}, {\"counts\": \"hfib0j0lV1:J6SOgNljN`1jT1`NijN31h1RU1ZNijNU2SU1>N2K5O1O1O100N2I8N10000100O001O1O1N2O1O1N010000001O1O1O002N1O1O1O1O1O1O1OPNcM^nN[2bQ1fM_nNY2_Q1jManNU2_Q1kMcnNR2^Q1oMcnNP2\\\\Q1TNgnNe1YQ1_NdnN`1\\\\Q1dNYnNTOgNY2PS1gNTnNTOiNV2RS1iNPnN`1PR1bNkmNa1UR1POVmNT1jR1oNPmNT1PS1m11001O0O20O000O1000000O1001O0010N3M2L6E<J6G=]Ol0]NW1\\\\NV\\\\1j0VdN:H8K5A>F:fMiMcnN_2ZQ1ZNfmNP2WR1n1N200O10000001O000O101O001O1O100O1O1O001N2O2N2N1O3M1O2N2N2N2M3N4L4K5L6J9G4K4L6YNXkNZO[PY4\", \"size\": [1280, 720]}, {\"counts\": \"kdjd07c15jR1g0fkN^OS16US1f1gkNjNYT1_20O010O10O001O0001O0000000010O2O2M01N101O00oNaLbmN^3\\\\R1fLcmNY3\\\\R1kLbmNS3^R1PMamNo2^R1TMamNk2]R1XMcmNh2\\\\R1ZMcmNd2^R1]MbmNb2^R1_MbmN`2^R1bMbmN[2`R1eMamNY2_R1iM`mNV2aR1jM`mNT2`R1nM_mNP2cR1PN_mNm1aR1TNcmNj1[R1WNfmNj1XR1VNemNl1[R1VN_mNm1bR1SNXmNR2hR1PNRmNT2PS1]11O2N1N2O2N3M2M3N2M4M1N4J5K6G;F8K=aNQkNUOoc3]O_cM5N3^jNKRS1e0PlN2gNEaT1]3A8RlNRL[S1Z4000002M000001O1O1O1O1N201O00020M1nK\\\\lNe3jS1SLZlNl3mS10101K7J4F;B>WOi0D;C>BdS[3\", \"size\": [1280, 720]}, {\"counts\": \"^Woc09`W18K5L3N2gNVOWkNP1eT1TORkNU1jT1oNnjNY1oT1jNmjNY1QU1l0N3M2M3M3O1N2N2L5D;O2M2N200O1O2O0000000001O00001O0O2O1O001O1000010N001O001O002N2O1N4mL\\\\kNb2iT1VM]kNg2mT11O1O1ZN`MlmN_2QR1iMkmNW2TR1mMimNS2XR1nMdmNT2\\\\R1oM_mNS2aR1PNZmNS2eR1oMVmNU2iR1nMRmNV2lR1_10000O100001O0O10O0G\\\\KXmNa4iR1bKUmN[4QS1cKolNU4ZS1hKhlNQ4gS1C>K4@b0A`0B`0B?A>nNSiN3[37fS1b0B;@a0G8I7H7K6J7H7H9I6H9H8I6]Oc0001O00001O002O0O001N3N1N2L5L3L4G8K6H8H7K8D;G:H7I<B<F?A=AlbS3\", \"size\": [1280, 720]}, {\"counts\": \"agZb0<_W17G8K4N2M3I8H7N2L5hNWNQlNo1kS1WNdkNMHP2bT1WNbkNMGP2dT1VNakNW2_T1e0DiLnkNX3PT1>O0O1O2M2L302O0N21O3M2M11O02N0O101O00001O10O1O01O00001O0O20O01O1O1O00100O1O1O1O001O2N1O2O2M1O00YNoL_nNQ3`Q1SM]nNm2cQ1XMYnNg2gQ1gMimN[2VR1kMdmNV2\\\\R1nM_mNS2`R1QNVmNW2eR1e1J6O0O1O100O10000000100O2NO1O101M2M3M3L5J5J7@`0J7L5BTlN`LPT1]3;M3M2_OZkNeMiT1X2ZmNcMkP1Y2inNaM`N?cR1j1PoNoMUN=hR1`1YnNUN]Ok0VR1m0anN]NPOl0ZR1j0enN0[Q1KcnN<\\\\Q1]OfnNh0[Q1POgnNV1]Q1bN_nNe1cQ1nMcnNW2\\\\S17L4K5I7K6L3O1N1D=C=L4M3N2O011O0010O1O1O1O100O001O2N0M4K4N2N2O2K5L4N2_O^lN_LfS1[3d0L5I6L3H9I7L4M4L6J9F9^Ob0_OUiN\\\\OQW1k0N6ML4CkZ\\\\3\", \"size\": [1280, 720]}, {\"counts\": \"\\\\_Va0>iV1S1C5L2N2N2J6N2N21O00000000O1M3N2L4ZOcMckNc2]T1^M`kNd2`T1\\\\M^kNf2bT1[MYkNi2gT1XMVkNj2jT160001N001N2ROmLklNW3mR1TMnlNn2mR1[MllNh2QS1V1M3M3M3M2O000O10O00001N101M2O2O1N3L3N3N3K4N3N4L2N1J6M2LWmNdLXQ1W3gnNoLYQ1n2bnNZM^Q1c2anNaM_Q1[2anNiM_Q1R2anNSN_Q1i1cnNYN^Q1b1`nNdN`Q1X1anNkN`Q1Q1`nNRO`Q1i0cnNZO\\\\Q1d0dnN^O\\\\Q1?cnND^Q18bnNL_Q10anN3_Q1JbnN8^Q1DcnN>^Q1\\\\OenNg0\\\\Q1TOenNo0[Q1oNenNS1\\\\Q1iNenNZ1ZQ1bNenNc1[Q1ZNdnNj1\\\\Q1TNbnNP2^Q1nManNU2_Q1jM`nNX2aQ1eM_nN]2aQ1`M`nNb2`Q1[M_nNi2bQ1QM`nNR3bQ1fL`nN^3nR13N2M3M3N2M3N2L4N101O1M3M3N2N2N2N2M3L4N1O2O1N2N2N2N2O2N1O1000O01O1O10O2N2N2N1L5E;H7G:L4D<H8M5J6I7J8I6F8G9L4M4nNR1AcTW5\", \"size\": [1280, 720]}, {\"counts\": \"dmQb03c16]T1LSkNe0iT1_O]jNMHY1eU1m0M1O2M2O1O1N1OO11O101O1N100O1O001N2NO02N010O2gkNSM_S1m2clNXMUS1i2llN[MjR1m2UmNUMdR1Q3\\\\mNPM\\\\R1V3dmNlLYR1U3gmNmLVR1T3jmNPMQR1Q3nmNSMPR1m2PnNUMoQ1j2lmN\\\\MTR1c2jmN`MUR1`2imNcMXR1[2gmNgMYR1X2fmNjMZR1T2gmNmMYR1P2hmNRNXR1l1gmNWN[R1e1fmN]NZR1_1hmNcNWR1Z1imNjNVR1U1imNnNVR1P1kmNROSR1l0nmNVORR1g0omN[OQR1c0PnN^OPR1a0PnN@PR1>QnNCoQ1:QnNIoQ15QnNMPR11PnN1oQ1MRnN4nQ1JSnN7mQ1EUnN=kQ1@VnNb0jQ1]OVnNd0kQ1XOWnNi0iQ1TOYnNm0gQ1QOYnNQ1hQ1lNWnNW1jQ1gNUnN[1lQ1aNTnNb1nQ1XNTnNj1lQ1TNTnNo1lQ1iMZnNX2fQ1fMZnN\\\\2fQ1bMZnN`2fQ1[M]nNf2dQ1VM^nNl2bQ1SM]nNo2cQ1oL]nNS3cQ1kL\\\\nNX3dQ1fL]nN[3dQ1aL^nN`3dQ1]L\\\\nNd3eQ1[LZnNf3nR13N1N2O1O1O1O1O100O1000000000000001O00O10000000001N10000000000O101N1O1O1O00100O100O2O000O1O1O100O1O1O1O1N2N3M3N1O1O10O1O2N4F9F;F?]Oe0]Oe0VNfjNLWV1^OmiNJ4OUV10RdV4\", \"size\": [1280, 720]}, {\"counts\": \"\\\\gfa06bW19C=XO@eiNi0XV1a0M3I7O1N2O1O1O010O01N2N1N3N00C;02O2SkNeMTT1]2e02M3]kN^MgS1c2VlNbMhS1_2TlNfMkS1Y2VlNhMeS1[2\\\\lNeM^S1a2blN`MYS1b2hlN_MUS1b2ilNbMlR1i2SmNXMkR1j2VmNSMlR1m2TmNRMnR1m2RmNSMnR1l2SmNQMQS1n2llNUMTS1j2mlNTMTS1m2llNRM`0EbQ1X3PnNQM<KdQ1R3RnNQMTS1o2SmNjLI9UR1j2hnN[MWQ1c2hnN`MWQ1`2gnNdMXQ1[2fnNhMZQ1W2dnNlM[Q1R2fnNQNYQ1n1fnNTN[Q1i1fnNXNZQ1g1PnNYMLR1TR1d1cnN_N]Q1_1anNfN_Q1X1_nNlN`Q1S1^nNPObQ1n0]nNUOcQ1j0\\\\nNXOdQ1f0ZnN^OfQ1`0XnNDhQ1;WnNGiQ17VnNLjQ12UnN1kQ1LUnN8jQ1EWnN=jQ1ASnNd0lQ1XOUnNk0kQ1ROUnNQ1kQ1nNSnNU1mQ1iNRnNZ1nQ1eNPnN^1oQ1^NTnNd1mQ1WNUnNk1kQ1PNWnNS2jQ1iMWnNY2iQ1eMVnN_2iQ1aMSnNc2mQ1\\\\MRnNf2nQ1XMSnNi2nQ1TMRnNo2mQ1oLQnNU3oQ1hLQnN[3PR1cLomN_3QR1]LPnNf3PR1ULSnNm3nQ1oKTnNS4iR13oNeK[nN]4eQ1cKWnN`4aR15L3M3N101N2O1M3M3M3L4O1O1O100O2O0001O000001O03M2N4L1N2O2N3M2N1O7J1NO100001N3N2N3M7I6J2N1N3N1O1O2O0O0000O000JOO26N3L3N3L5lNakN[NiT1a1\\\\kNVNlT1\\\\1mjN_N>1hT1b0TkNCQV1FiiN100:3TR^4\", \"size\": [1280, 720]}, {\"counts\": \"`Va`0a0aU1IgjN1e0e0RT1e0[kNDZT1U2G7L4O1O1IZLTlNf3gS1<M3N20O00000100O10000O10O10000O2OM3O2O0O100000O10001O10O0O2N2O001O02N2N1N4L2NNO0K7I5F>J6E;M2O2M3L3ZOQkNYNSU1c1g0I7I7N1HRkN[OSS1>mlNM\\\\N\\\\O4<2GnR1;enNj1YQ1SNhnNQ2VQ1mMcnN]2^Q1]M`nNj2dQ1oL]nNS3SS15B>M4H7N1N3L4O1O1O1O0O2L4L4J6O1O1O100O10O100000007I3M00001O0O1000001O00010O000O101O001O1N2O2RL]lN7d0g1lV1hMXYo6\", \"size\": [1280, 720]}, {\"counts\": \"^^]`02mW15L9`NBmiN5a0?oT19njN4bT11WkN;aT1ISkNc0gT1b1H9H4M2N3N000000000K55J100O1000O1000000000001N101N10000O001O10O1001FoKblNR4XS1VLflNk3[S1QLglNn3fS100O1O1N2N1N2M2K4H6N6I7J6J7L4M4M2C>]Od0K4E<K4M7[O\\\\2?`fNHWV1g0`iNBRV1V2PO7H7G8L4J6I7K6K3O2N2O1O001O1N101M4M2O1O1O10O010000O10O1000O1000O0100O1O001O1O1O2N1O10O10000O2O1N2oLXmNg0nR1nN^mNhN01iS1>i2K4H8Lidk6\", \"size\": [1280, 720]}, {\"counts\": \"dW_a09\\\\V1IoiNY1kU1c0YOVNnjNn1kT1i0K3M7H4M2K5N3L3M2O3N1M4L3L4N2O1O1N2L4L3L5M4K4M3O1N2O1O001O1O0O21O1O1O00001O1O1O00000O1N1M3O2O1O2H7L4L4N3M2M3L2O201O2N1O1N3L3M4K4L4L5J5O1O1N2NklNWMWQ1e2enNgMWQ1V2gnNPNXQ1l1fnN[NZQ1a1bnNgN]Q1U1anNQO_Q1k0_nN\\\\O_Q1>dnNGZQ14fnN3XQ1F\\\\mN`NQ10oNo1dR1YOdnNV1\\\\Q1hNanN]1_Q1`N_nNe1aQ1TN`nNR2`Q1YMPoNl2kR17J6K4K6M2L5N2M3M201N2O100O1O1O1O001N2N200O1O1O01000O010O1000O01000000O10O01O1N2O1O1O100O0N300O11O1O1O1O0O2O001O2N4L4_LbmNX1aR1[NomN^OfNe1]S1aNelNEg1h1hQ1ZNonN\\\\1hT1PO`hf4\", \"size\": [1280, 720]}, {\"counts\": \"]lVb01nW11GObhN3]W1MbhN3YW12bhN2_W1NdhNOZW128021`kN4jP1NhnNg0PQ1YObnNX1\\\\Q1hN]nN_1aQ1bN[nNc1dQ1UN_lN2g1o1jQ1PN_lNO[1^2VR1eMkmN_2UR1aMjmN`2cS18N2N2N2N3M101O1N2N2M3N1N3N2N2M2N3N2N2M2N3N2G8O2O1L4L4M3K5N2O1N1N3N2M3O1N2O010OO3L3N2M2N3M4L3L4N2N2McmN\\\\KcQ1c4]nNaKaQ1]4_nNeKaQ1X4`nNkK^Q1R4dnNPL\\\\Q1n3enNRL\\\\Q1k3`nN\\\\L`Q1b3bnN^L^Q1`3anNbL`Q1\\\\3WnN^L^O1[R1]3ZnNTMfQ1i2[nNYMdQ1d2^nN^MbQ1_2anNaM_Q1\\\\2dnNcM^Q1Z2dnNUMROKZR1n2lnNQMeQ1l2`nNQMbQ1l2j1McmNoLQP1o2loNXMRP1e2eoNhMZP1T2eoNRNZP1k1coN[N]P1b1_oNeNaP1W1^oNnNaP1P1\\\\oNVOdP1g0YoN_OgP1=XoNIgP13VoN4jP1FRoNd0oP1SOUoNR1jP1gNWoN_1iP1\\\\NVoNj1kP1mMWoNY2iP1^M[oNg2eP1PM`oNT3aP1cLdoN_3[R14M3L4K5N2N2L4L3M4M3L4L3L5N2M3M4L3O1N2N200N2N2O1O2O0001O0000000O1000000O10001oJhmNX4XR1gKimNY4XR1eKlmNX4TR1gKWnNo3oR1M100000O1O1N2N2N2O1O1O010O1O100O1001O014J9H3eK^lNR4RT1]LalNP2jT1oMajN]1aU1[NgjNc1^V1YOiiT3\", \"size\": [1280, 720]}, {\"counts\": \"Scbc0=`W15J9I4M103M=A6[jN[NJc1`R1[3M1N2L5K4O1O1O11O4QK[mN[4gR1bK[mN]4bR1gK\\\\mNZ4cR1gK\\\\mNZ4eR1eK\\\\mNZ4fR1dK[mN[4fR1dK]mNY4cR1gK[mN[4fR1?1O4UKTmNMOT4QS1jK]mNT4cR1jK_mNOBP4PS1PL^mNW4VS1O00001ON2N110N3M2M4N1O1O1M2O100M4N2N2M5J4L4K5N2O1N101O1N1N3N2O100000000000O10000O11N10O2M5J3M3N2M4N2N10O2O7F8K0001O2M3SMhnNlN^Q1j0knNSOZQ1=RoNBdQ1bNboN]1ST1]Oi0SOd00Wd36hSK3ZV1;]iN`0M0^T1NbkN_1hS1Q2^O7B=@`0L3M400O10000O11O00000000010O1O001N3N1O1O00O1N3M21O1O100O1O001O1N2O010ZJkmN_5\\\\R1O1O1O2M2O1O3M2N3M4L2N101N2^LglNY2\\\\S1cMglN[2`S1]MalN\\\\ON04a2eS1hM]lNDh0OSOP2[V1dNiZY2\", \"size\": [1280, 720]}, {\"counts\": \"_WQc0m0fV1>[OgNWjNd1dU1?_ORNijNS2UU1>M2[OWMQlNk2nS1XMfkNR3WT1=M3L4N3L201N2N2O1O1N3M3N1N2O1N2M3M3O001O1O2OcLWmNd1gR1]NVmNf1jR1h1O1000O2OO1001O4M=D;C2N1N4L2O0O0100O011N100N2O001M3M3N2N1N3M4L4J5N2M3M3kNVKnnNP5mP1T1H710000001O000001N100000001N100100O0001N100O3N00O10O302ON5JM4J5M2O2M3K6I6G9B=H8G;F;J5G9J6E;IbmN_Mlo0R2^POQNeo0b1]POfNgo0n0ZPOcNTM<hR1CYmN=Pb<h1a`DQN]nNKMj2]Q1fM[nNI]O[3SR1ZMQnN\\\\3oQ1Y12O1O0O2O0O2N1O11O1O1O01O001N2N2O0O1010O1000O1O3M8H8G8I1N2N2M4aM[lN=hS1ZO[mNJhR1KdmN]O[N2XT12[nNE^e`2\", \"size\": [1280, 720]}, {\"counts\": \"ZPic01cW1>QOn0@?D=B=J6N2M3N2VOPMZlNZ3aS1e0K5N2N2N2N2O1M3O1O1DdKTmN\\\\4kR1=M3I8LlJdmNi4YR1YKjmNf4TR1ZKmmNg4SR1WKnmNj4QR1QKTnNP5mQ1nJTnNR5]R10N2G9O11jJ`mNj4eR1nJ_mNQ5iR1O1O001O005K4L9G;F2N4K1O00000O10N2O1O1O1O1N3M2N2L4L3N3hN`KmnNc4QQ1eKfnN^4YQ1oKomN\\\\4SR1i0O1O0000000000000O1000000100N2O00001O001O0O11N10000001O0000001O1N2O2O1N2N2N2N2M200O5J7J8Hh0XOf0ZLekN[2gU1XOdl4ZNQUK7K4L4M2N2M7mhNTOeV1X1VjNhNdT1Y1SkN[O`T1l0PkNCjT1\\\\2_O2MT1mN2O2MN2N2N2M2O2N2N2N4K6K2M3N3M2N2N1eMTlN`0nS1YOblN<`S1YOmlNb0VS1mN]mNo0mT1M6J4K3NZhV2\", \"size\": [1280, 720]}, {\"counts\": \"hcTe04iW15K6J`0TiNYO_13hQ1i0blN]O1^1WS1_2N1N3M3M2O2M2O1O1O1000000000000001O01OO2O0000011N1O1O1O3M2N1O2N2N8H1O3M5K2N000000O1O1O1N2N2aNQLjnNR4UQ1]LXnNh3hQ1X1O2O001O10O000O2O0O100000N2N3O000001O1O001N2O2N02O0O2OO1O4L2N1O1N3N2N2O3M2M8H>B8Hn0RO7H8I7H:GjLQN`POj1]o0cNdPOS1Zo0QOhPOl0Wo0UOnPOd0Ro0EjPO6Wo0OjPOJWo07RQO\\\\OQo0f0S43N3M7QjNdNB6dT1X1`kNmNF1gT1h2L2O0O2O1O2O3bkN_LTT1`3lkNaLWT1f302Lc0^O[Oe0K4M3bLckNl16ROaT1MblN0`S1WOYmNf0VU17ihNoNeV1V12Md0TOcXj1\", \"size\": [1280, 720]}, {\"counts\": \"Zhlc01dW1?E9H6@a0G8C=M3@`0cNgMXlNE=l2XS1X1N2N2O1N2N2O1N2N2N1N3O1N2N3N1000000O010100O1O00000000002N2M102O4K3N1N6I5L1O2N2N5K2N001O00O1O1N2N2O1N1O2M3M3M4K5lNgKomNj4nQ1e0N2N2O1O010001OO1N3M21O001O0000001O0001N3M3N01O0000O01O2O0000O100O11O0000001O1O1O1O2N101N2N2N3N1N2N2N3M3L6L5J5Lh1VN>A8eMgjN`1VV1mNhT6B]lI\\\\1hNV1jN2N1kjN_MiT1Q3L3M3M8H5J:WlNlKRS1Y483N3O4K9ilNTK2O\\\\R1[53M4L6J5K5_MWmNAnR12fmNA_R14PnNDYR1IUnN3PR1[ObnN`0hT1G4KhWo1\", \"size\": [1280, 720]}, {\"counts\": \"PY]b0c0lV1_OYiNl0VV1g0F:K6@?O1N2^Ob0A?G9N3M2O1N2N2O1O100O100O1I71O3M1OO101N02O000000O12N3M3M7I1O2N1O001O2N3L3N2N0000N2O1O1O001N2M3N2N2M3K5L5K4L4L4M2G:A?M3M3N2M3O1O1O1001OO10001N101N2O000O1001O00000O20O0O2O000001O0000001N1001N101O000001O1O000O10O2M3N2O0001O00001N2N1N3J7K6L5UNjmNoLmR1f2^1lMjkNiN4Kil>1aZBc0@8Ig0ZO2ijNdNdS1e1QlNfN_O3US1\\\\1TmN:YR1\\\\3ZO8IO2O0O1O0O3N2N2N01O001O0001O1O1O1N3N1O0000002N2N2gJ^mNP5jR1O101N3M3L3nLYmNaNJ`1RS1]OgnNUOoMKUT19[3M3Lfb_2\", \"size\": [1280, 720]}, {\"counts\": \"PeWa04hW15L4XjNK^S1:\\\\lN]1QR1eNhlN]O6b2nR1RNalN@N1=c2QS1aNhlNf1SS1^NflNi1WS1b1M3M3O00001O0000000001O3L6K:F1O8H3M1O2N1N4M2N0000N2N2N3N0M4M3M3L3N3N2M3N2M3M3N2N2O1O1O100000001O0O100000000000000O2O1O00O10001O000O100001O2NO0O1M2MN31M40001O0I8L3M5K6I`mNVLUQ1d3mnNdLoP1Y3QoNlLmP1P3QoNWMnP1`2WoNfMgP1X2ToNPNlP1o1SoNTNlP1h1VoNZNiP1d1RoNdNnP1V1nnNTOoP1`0_oNA`P1;coNG\\\\P15goNMWP1OkoN5TP1JcoNQNeNY2gQ1EdoNd0[P1[OeoNf0\\\\P1SOioNm0XP1gNWPOR1mo0dNaPO[OcMS1RR1B`POnNQNl0kQ13WPOnNaQ1n0j2L5G?Bkk31UTL<fhNIQ1G^S1j1^lNXNnQ19QmNb2KbM51[R1m4J6M4L3L4ZOf0K5O11O001O0000000O10O1000O10001O00001O1O2N2N1O3M00H8O1O100O10O2O00001N3N7J1N10OO101_JZnNf4TR1fJSnNY5\\\\R1N2N1N2O1O2O3nJXmNd4\\\\S1\\\\KllNg3iS1gLQlN]2mT1UMnjN_2mU1\\\\O4L2XNZjN5I;nnd3\", \"size\": [1280, 720]}, {\"counts\": \"lTk`01jW1=H7I3K9G4M1O2M_O]iNIbV17_iNJaV16\\\\iNN5@RV1Z1jiNmNf1IaR1g1[lNUNDi1YS19elN7XS1[2O00O01n0QO7I4K00O1001O0O01M3N2N2O100N2N2O1N2N1O2N2M3N1N2N2N3M4L3N2L3M3M4K5J7G9I7H8M1M11101M2O0O01N3N2O1O2M2N3M4L3N3L4M3H8K5K6L5LjmN`KoP1]4mnNiKSQ1V4lnNmKSQ1P4nnNRLRQ1k3nnNXLQQ1f3onN]LQQ1a3lnNeLRQ1W3QoNkLoP1S3QoNoLoP1o2nnNUMTQ1i2gnN[MZQ1d2fnN^MYQ1_2inNaMWQ1^2knNaMUQ1^2nnN`MTQ1\\\\2onN_MVQ1^2nnN\\\\MXQ1a2jnN\\\\M[Q1`2dnN]McQ1^2anNXMlQ1a2Q2H5K3KTnNRNPn0i1mQOaNRn0[1eQOQO[n0j0eQO[O[n0b0cQOC^n08cQOK]n02_QO5bn0E_QO?an0^O\\\\QOh0dn0VO[QOm0en0oNTQO\\\\1ln0`NoPOi1Ro0QNkPOW2Uo0eMiPO`2Zo0WMgPOo2lQ19I7L3I7H9J6H8J6M3K5M3K6L3O1N101000000N2N2O1N2M3N1O2O11O100O0000001O1O00O1000O2M2N20000000O1O1O1O1O2N100O100O11N10000000001O101N2N1O1O1O1TLZnN\\\\1gQ1[NgnN_1ZQ1hMboNS2`P1jMeoN^O`Nk1PR1]NYQO4`o0Ea4JV\\\\k3\", \"size\": [1280, 720]}, {\"counts\": \"XYUa0:<0_V1o0B?I4K4E;M2N2O2N1O1O100M2O200O100O0010O01N2O0O2N1O2O1O001N2O2N1N2N1N3N0N111O1L30N02O000M4M2N2L6I6L5M3J5O0O21N1L5O10O1O11N2N2M2N2N4L5L3O0O1M2O5J3M3M3M4L3M2N2N3MlmNiMfn0R2ZQOTNgn0g1VQO^Nhn0a1VQOdNin0Z1VQOkNhn0S1WQOQOin0k0YQOWOgn0e0XQO@hn0<YQOGjn02UQO3nn0ETQO>ln0^OSQOg0nn0TOSQOo0mn0nNQQOW1on0gNmPO_1To0[NjPOl1Vo0QNjPOR2Wo0iMjPOZ2Wo0bMiPOa2Xo0\\\\MhPOf2Yo0XMfPOj2_o0PMaPOQ3TR13I7DaLQlNf3gS1=L3N3M3M3L3O200O1O1O1O1000000O10001O0000O010000O100O10O0100O2N1N2O1O1O1O10O01O1O1O0O102N1N200O01000O0K6O1N2N2N2N3N1O1N2O1O2O000001O007I2N7TLWnNS1jQ1hN^nNS1dQ1fNdnNV1^Q1kMdoNCWN[1gR1POUoN_OWO;nQ11Z4Jfbl3\", \"size\": [1280, 720]}, {\"counts\": \"WaQa02`W18ahN5PW1c0F;B>H7J4I6N2M3N2M2O2102MCdMXkN[2eT1a0O1N2N1N3N1N3M3M3M3J7J6K5K4N2M3L4M3M2M3O00M5L4L4M3L4L4M3L4I8L3K6K4L4O1N\\\\lNQM[R1l2emNYMYR1b2imNaMWR1]2gmNhMXR1S2kmNlMXR1P2kmNQNVR1l1kmNUNVR1g1mmNVNWR1f1YmNlMVO2ZT1m1alN`N]S1Z1ilN`N\\\\S1]1glN]N_S1_1c1J6M3L4L^lN]OPP1>RPOFlo05SPO3lo0IQPO=oo0^OPPOi0PP1SOooNQ1TP1gNloN^1VS16I5K6J6N2M3L4H8L4L5L2N3M3L4K5J6J6G9M2M4L4L3O2O100O1000000O100O1001O0000001O000O01O1O1O10000O100O1O100O0100000O1O1O010O010O100O2N2N1N2N2N2N2M2O1O2N2N2N2O1N2O2N1000000001O1O1O2O0PLVnNg1lQ1QM\\\\nNCg0j2nP1^MRPO_2QP1^MSPOFQNV2PR1QN]RO0gm0NX5Lc[_4\", \"size\": [1280, 720]}, {\"counts\": \"^`oa0;hV1<\\\\iNGdU15^jNm01iNiT1k2ZkNQMgS1l3A?H5H8L5L4M2N3L5K4L4M2O00O2M3N1K401O1O2M2010O01K4O2O0N4L4L5J9J4H6O1M`mNPLVQ1m3lnNULSQ1g3QoNYLoP1d3cnNaLnN1`R1[3bnNPMaQ1j2cnNWM]Q1f2dnN[M^Q1_2fnN_M^Q1[2X2I5N3MlmNiMdn0S2YQOZNan0a1YQOjNfn0R1WQOVOhn0g0UQO_Oln0=QQOJon02oPO3Qo0JQQO7on0ESQO=nn0]OnPOl0Ro0oNnPOV1Ro0hNkPO]1Uo0`NgPOg1Yo0WN`POR2ao0jM^POZ2bo0bM_POa2ao0\\\\M^POh2co0TM]POo2do0nL[POU3eo0iL[POX3go0aLUPOi3iQ18G8L5L3M4M2M4L3M4K5L5K4M3N2M3O1O1N2O1O00101N100O010000000O1000O02O0O100O1O10O0100O100O10000O100O10000O010O100O1O1O100O10O10000O10000000001O001O001O1N2O1O1N3M4oKfnNU1\\\\Q1ZM`nNJi0i2iP1YMZPOb2io0YMXQOQ1XMeNfQ16`5MUlR4\", \"size\": [1280, 720]}, {\"counts\": \"laac0<SW1HRiNj0_V1a0K5J5K7B=D=K2[OVMPlNl2nS1e0M2F:J6O000010010N01N1N3N2M2O2N1O1M4K4N3M2O1O1O2L4M2M4J5L5K6J5M4L3K6J5N3N3L3M3M4G9K;C:FmR:MYmE9@a0C8G:E9I6L4G9L3I7J7G9J5M4L3M3N2O1N2L4K5J6N2N2M3M2O2N2M3O1N101N1N3M3N2N1O2N110O1N2O1O11O00N2M3M3M3M3L5H6N3M3O1N2N20O0100O00001O00100000000O10OO2O1N2N101N2M3O100O1O100O1O010O1O011O0001N2N4M2N3M2N4L2N2N2O1[JdnNb4]Q1\\\\KknN31o2UQ1lL^POl2co0RMbPOj2ao0RMbPOl2_o0RMdPOl2ao0kLgPOQ3dR1@1O3M8H<D:E9F`0@T^a1\", \"size\": [1280, 720]}, {\"counts\": \"]QXd02;U1mU1i0ZOh0C7J3N1N2O1O1O2N2O1N1O1O1O100O1N2M3O1O1O2M2O1N3N1M3N1O2L4M3N3N2M2M3M3N2L5M2M3N101ONN2O2O1N011L22O101L3FamNSK`R1n4801O1O000O2N2O1O2M3L4K4N3N101N2I7L4M2O2M3N2L3M4L4M3M2L4L4O2M3K4M4L4KWnNVN`LJdP1l1iROSOWm0k0cRO]O]m0`0`ROF_m07_ROOam0M^RO8bm0E[ROa0em0]OXROi0fm0UOYROo0fm0POWROU1hm0iNTRO^1lm0_NoQOi1Tn0QNiQOU2Zn0dMeQOa2]n0ZM`QOl2`n0QM`QOR3bn0fLbQO\\\\3`Q12M2N2I7D<F:M3M4K4M3N2N2N1N3O1N2O0100000001O000O1000001OO100O10000O100O100O1O10O02O0O1O1O1O1O100O1O1O1001O01O0O100O10O01O100O10000000000O10O1O1O100O1O002O0O2O0O1O00100000N3M2O2N1N2N2O2L4OeKdnNUNJ20X3cQ1[NeoN[1[P1lM_nNQOQ25PNW2aQ1cNgPOTOjMX2Td0\", \"size\": [1280, 720]}], \"bboxes\": [[364.0, 416.0, 43.0, 129.0], [356.0, 420.0, 48.0, 124.0], [358.0, 409.0, 50.0, 131.0], [373.0, 393.0, 52.0, 139.0], [380.0, 381.0, 77.0, 147.0], [405.0, 375.0, 93.0, 148.0], [432.0, 356.0, 109.0, 165.0], [458.0, 370.0, 119.0, 149.0], [473.0, 365.0, 127.0, 149.0], [468.0, 373.0, 139.0, 147.0], [469.0, 361.0, 149.0, 157.0], [470.0, 362.0, 157.0, 151.0], [473.0, 363.0, 153.0, 154.0], [478.0, 368.0, 138.0, 151.0], [482.0, 370.0, 118.0, 156.0], [476.0, 378.0, 100.0, 220.0], [472.0, 381.0, 58.0, 155.0], [472.0, 412.0, 67.0, 127.0], [460.0, 404.0, 64.0, 128.0], [442.0, 401.0, 53.0, 117.0], [418.0, 392.0, 80.0, 150.0], [398.0, 377.0, 110.0, 148.0], [388.0, 391.0, 125.0, 148.0], [393.0, 383.0, 123.0, 145.0], [396.0, 374.0, 121.0, 147.0], [397.0, 368.0, 90.0, 146.0], [404.0, 382.0, 115.0, 152.0], [405.0, 380.0, 122.0, 145.0], [413.0, 393.0, 118.0, 144.0], [428.0, 435.0, 113.0, 149.0], [434.0, 387.0, 119.0, 160.0], [434.0, 346.0, 133.0, 153.0], [434.0, 367.0, 129.0, 150.0], [433.0, 367.0, 131.0, 146.0], [431.0, 381.0, 124.0, 138.0], [433.0, 415.0, 122.0, 140.0], [434.0, 411.0, 125.0, 147.0], [437.0, 385.0, 114.0, 143.0], [432.0, 383.0, 86.0, 144.0], [435.0, 386.0, 85.0, 144.0], [437.0, 387.0, 89.0, 144.0], [437.0, 389.0, 86.0, 148.0], [439.0, 401.0, 96.0, 152.0], [454.0, 379.0, 106.0, 147.0], [463.0, 362.0, 109.0, 158.0], [468.0, 362.0, 95.0, 158.0], [463.0, 357.0, 98.0, 161.0], [457.0, 351.0, 100.0, 160.0], [449.0, 361.0, 91.0, 159.0], [443.0, 386.0, 91.0, 160.0], [449.0, 364.0, 99.0, 156.0], [448.0, 360.0, 106.0, 159.0], [444.0, 366.0, 100.0, 154.0], [448.0, 371.0, 103.0, 149.0], [447.0, 369.0, 103.0, 152.0], [442.0, 377.0, 88.0, 153.0], [439.0, 391.0, 96.0, 154.0], [450.0, 360.0, 106.0, 158.0], [451.0, 360.0, 106.0, 158.0], [441.0, 371.0, 103.0, 152.0], [441.0, 363.0, 112.0, 155.0], [445.0, 361.0, 99.0, 153.0], [445.0, 371.0, 128.0, 152.0], [465.0, 381.0, 125.0, 145.0], [488.0, 375.0, 119.0, 161.0], [487.0, 378.0, 115.0, 151.0], [467.0, 387.0, 114.0, 144.0], [464.0, 377.0, 125.0, 142.0], [451.0, 386.0, 122.0, 128.0], [413.0, 413.0, 129.0, 127.0], [410.0, 385.0, 130.0, 144.0], [406.0, 381.0, 130.0, 148.0], [395.0, 402.0, 138.0, 139.0], [415.0, 369.0, 129.0, 162.0], [425.0, 363.0, 137.0, 163.0], [436.0, 358.0, 144.0, 168.0], [447.0, 362.0, 138.0, 178.0], [464.0, 346.0, 171.0, 181.0], [459.0, 359.0, 173.0, 175.0], [448.0, 390.0, 145.0, 155.0], [464.0, 391.0, 125.0, 152.0], [461.0, 378.0, 133.0, 162.0], [447.0, 378.0, 133.0, 161.0], [450.0, 445.0, 108.0, 101.0], [439.0, 377.0, 114.0, 159.0], [417.0, 380.0, 127.0, 158.0], [400.0, 402.0, 135.0, 156.0], [405.0, 396.0, 132.0, 162.0], [407.0, 397.0, 133.0, 158.0], [415.0, 441.0, 132.0, 158.0], [423.0, 430.0, 130.0, 154.0], [424.0, 412.0, 143.0, 161.0], [442.0, 444.0, 133.0, 137.0], [469.0, 450.0, 112.0, 148.0], [452.0, 452.0, 128.0, 117.0], [442.0, 420.0, 140.0, 158.0], [453.0, 426.0, 111.0, 145.0], [445.0, 371.0, 129.0, 149.0], [452.0, 368.0, 131.0, 147.0], [455.0, 384.0, 131.0, 143.0], [445.0, 401.0, 138.0, 135.0], [464.0, 401.0, 120.0, 107.0], [468.0, 422.0, 113.0, 141.0], [465.0, 425.0, 127.0, 154.0], [462.0, 365.0, 133.0, 159.0], [463.0, 353.0, 140.0, 151.0], [465.0, 375.0, 138.0, 146.0], [476.0, 382.0, 131.0, 137.0], [485.0, 384.0, 122.0, 136.0], [480.0, 441.0, 124.0, 124.0], [474.0, 391.0, 130.0, 151.0], [463.0, 361.0, 131.0, 154.0], [447.0, 367.0, 137.0, 149.0], [440.0, 391.0, 138.0, 145.0], [448.0, 385.0, 128.0, 150.0], [444.0, 356.0, 125.0, 164.0], [431.0, 381.0, 123.0, 157.0], [417.0, 387.0, 131.0, 157.0], [423.0, 362.0, 147.0, 180.0], [492.0, 332.0, 107.0, 169.0], [487.0, 337.0, 121.0, 171.0], [452.0, 361.0, 140.0, 161.0], [481.0, 341.0, 130.0, 163.0], [533.0, 334.0, 101.0, 176.0], [511.0, 354.0, 129.0, 177.0], [469.0, 346.0, 164.0, 185.0], [440.0, 338.0, 146.0, 184.0], [462.0, 341.0, 150.0, 182.0], [453.0, 341.0, 154.0, 180.0], [423.0, 338.0, 118.0, 163.0], [420.0, 345.0, 124.0, 173.0], [447.0, 356.0, 152.0, 175.0], [466.0, 318.0, 173.0, 189.0], [501.0, 304.0, 160.0, 196.0], [487.0, 322.0, 169.0, 216.0], [506.0, 339.0, 157.0, 191.0], [541.0, 328.0, 132.0, 191.0], [509.0, 340.0, 160.0, 199.0], [471.0, 364.0, 185.0, 207.0], [441.0, 326.0, 186.0, 225.0], [431.0, 346.0, 190.0, 217.0], [439.0, 389.0, 181.0, 215.0], [436.0, 384.0, 169.0, 201.0], [460.0, 362.0, 155.0, 221.0], [500.0, 375.0, 180.0, 240.0], [518.0, 410.0, 202.0, 231.0]], \"areas\": [2843.0, 3323.0, 3719.0, 4475.0, 6296.0, 8594.0, 11431.0, 12633.0, 13431.0, 13899.0, 14419.0, 15429.0, 15439.0, 14576.0, 12826.0, 9732.0, 4716.0, 1292.0, 1146.0, 2437.0, 6965.0, 11296.0, 13934.0, 13683.0, 11878.0, 4576.0, 6608.0, 13597.0, 12612.0, 11998.0, 13483.0, 15178.0, 14230.0, 13511.0, 12173.0, 11586.0, 12213.0, 11661.0, 8569.0, 6804.0, 8196.0, 7786.0, 8885.0, 9659.0, 11414.0, 9723.0, 10158.0, 10863.0, 9821.0, 9351.0, 10019.0, 11035.0, 9807.0, 9290.0, 10509.0, 8764.0, 9528.0, 10369.0, 11371.0, 11163.0, 11907.0, 11230.0, 14352.0, 10914.0, 11702.0, 10394.0, 9777.0, 8896.0, 9133.0, 12253.0, 11845.0, 11107.0, 10215.0, 12394.0, 15017.0, 15690.0, 11845.0, 17440.0, 16627.0, 15312.0, 13051.0, 14501.0, 14867.0, 8455.0, 11398.0, 14335.0, 15322.0, 14682.0, 13897.0, 14862.0, 13957.0, 13353.0, 11948.0, 5648.0, 9125.0, 7485.0, 5547.0, 14196.0, 14158.0, 9715.0, 8229.0, 6794.0, 6059.0, 6962.0, 14830.0, 14679.0, 14109.0, 12418.0, 11333.0, 10587.0, 12656.0, 13881.0, 13654.0, 13432.0, 12498.0, 13230.0, 12613.0, 13272.0, 15720.0, 12428.0, 12490.0, 14504.0, 15033.0, 11842.0, 13832.0, 20185.0, 18309.0, 19065.0, 19297.0, 15244.0, 14479.0, 20513.0, 23480.0, 24756.0, 24039.0, 20987.0, 17443.0, 22612.0, 27369.0, 28383.0, 27649.0, 23792.0, 21357.0, 25810.0, 23674.0, 31155.0], \"iscrowd\": 0, \"video_id\": 0, \"height\": 1280, \"width\": 720, \"category_id\": 49504, \"noun_phrase\": \"the nike air jordan logo\"}, {\"id\": 1021, \"segmentations\": [{\"counts\": \"ST`;Q1aV1a0H6M3O2N1N1O2O1O1N3N1O0000001N10000000mNajNF^U16hjNIWU15mjNISU14PkNMoT11TkNNmT1OVkN0iT1NZkN2fT1L]kN3cT1K`kN4`T1JbkN6^T1IckN7]T1GfkN8ZT1GgkN9YT1FhkN:XT1ClkN<TT1BokN>PT1AQlN?iU1O00100N2O001O2M1000001O001O001O001O0O2O003M1O001O000000\\\\bR>\", \"size\": [1280, 720]}, {\"counts\": \"adS;2YW15PiNa0YV1h0J6L3K5M3N2N3M3M100O2O00UO]jNYOcU1c0bjN\\\\O]U1b0fjN^OZU1?jjN@VU1>mjNBRU1;RkNDnT1;TkNEkT1:WkNFhT18ZkNIeT15^kNJbT14akNK_T14bkNL]T13fkNLZT12ikNMWT11kkNOUT1OnkN0RT1NPlN2PT1MQlN3oS1KSlN5mS1IVlN7iS1IWlN7hS1H[lN7eS1G]lN9bS1G`lN8`S1FclN9^S1DelN;\\\\S1CelN=`U1O00001O1O001O2M101O01O00O101O002N1O1O00001O000000\\\\j]>\", \"size\": [1280, 720]}, {\"counts\": \"cko:8?<SV1S1D3N2O2L3M4L4N2N101N101O000O101OnNgjN]OXU1?njN@RU1>RkN@nT1=VkNBjT1<YkNCgT1=ZkNCeT1;]kNFbT19`kNG_T17dkNH[T18gkNGYT17ikNIWT15kkNLTT13nkNLRT13okNLST10QlNOoS1OSlN1mS1NUlN1lS1MUlN3kS1LWlN4hS1KYlN5gS1J[lN6dS1I]lN7bS1HalN7_S1GdlN8\\\\S1FflN:YS1FilN9WS1FjlN:VS1EllN9^U101N11O0001O1O001O1O0O101O002N1N2O001O00ajb>\", \"size\": [1280, 720]}, {\"counts\": \"a\\\\m:1UW17\\\\iNK_V1`0RiN9[V1m0I3K5L3N2M3M3M2N3M3N2O0O2O00000O101O00000000001O0000_NnjN5QU1HVkN4jT1IZkN6fT1H]kN7cT1G`kN8`T1FdkN9[T1EgkN;ZT1BikN=WT1BkkN=UT1AnkN>RT1AokN?QT1_ORlNa0mS1^OUlNa0kS1^OWlNa0iS1^OXlNb0hS1\\\\O[lNb0fS1]O[lNc0eS1[O^lNd0aS1\\\\O`lNd0`S1\\\\O`lNd0`S1ZOclNe0]S1ZOelNe0[S1XOhlNh0YS1UOjlNj0VU101O001N100O1O2O001O1O001O1O002M100O1N3KlZ[>\", \"size\": [1280, 720]}, {\"counts\": \"bko::T1JWU1m1B3M3M3M3L3M4M2N3N10001N1O100O2O0O10000O2O01O000001O0O100000000000001O0000000000000000\\\\NUkN3kT1J[kN3eT1J`kN4aT1JakN5_T1HfkN6ZT1HikN7WT1HkkN7UT1GokN7PT1IRlN6nS1ISlN7mS1GVlN8jS1FXlN9jS1EWlN;iS1DYlN;gS1DZlN<fS1C\\\\lN<dS1C^lN<bS1C_lN=aS1AblN>^S1AclN>^S1@elN?[S1@hlN>XS1AjlN=XS1_OklN`0WS1]OklNc0US1XOQmNf0WU1K5C=LmjX>\", \"size\": [1280, 720]}, {\"counts\": \"Yko:5hW18^iN0gT16djNFD=aU1e1M3M2N2N3M2N3O1N1O2O0O1O100O10000O2O000000O1000O1000000000001O1O001O0000001O000000000O1000000000000000O10000000000000O1000O100O10001OO1000000001N10001^NXkNIiT1ES2Ekmn=\", \"size\": [1280, 720]}, {\"counts\": \"XcX;c0WW19SjN@QT1l0PkNPOHa0RU1T2M2L3N3N1O2O00000O2O0O10001O000O1010O0000O1001O00000001O000000000000001O00001O0000000000O11O0001OO1000000000000000000000000000000000000000010O00O10000000000O100O1O10000O1O2M2SNglNiNeS1f0]_X=\", \"size\": [1280, 720]}, {\"counts\": \"m[f;<]W1>QiN[Of0MRT1k2H7L3M2N3O1N10000O1000000O1000000O100000000O1000001O001O000000000000001O0000000000000000000000001O0000000000001O0000000000000000000000000000000000000001O00001O1O2N1O2N1O1N2O1O001N2O1N4L5K4hM[kNm0hT1oNdkNg0`T1lNmkN3UQc<\", \"size\": [1280, 720]}, {\"counts\": \"T[U<U1iV15L4M3N0O2M5Ld0XjNZNnS1m2N101N2O001O001O00000O101O00000O10001O0000000O101O000000000000O11N1000001O00001O0001O0000O11O00001O1O2O0O1O1O0000001O1O000000001O001O1O1O1O1O1O001O000000O10000000O010000001N101O2M3N2N6I7Jd0[O3N1N:E9G4L5Jj0SOTRe;\", \"size\": [1280, 720]}, {\"counts\": \"mZi<?6MgV1i0L4N2M3N6J3L7QjNVN[U1]2K5ljNTNiS1T3N2M2O00000000000O2O00001O00000000001O0O100001O0O100001O0O1001O0000O100001O00100O1O1O1O0010O001O0001O00000000001O0000001O1O1O1O1O001O0O101N10O10000001N101O001O1O2M2O2N001O1N2O2N3M4L:F4L5K6I6K4K4L6K5J5K;D;AZZm:\", \"size\": [1280, 720]}, {\"counts\": \"oZX=>3KoV1i0F9M2Ma0@4oiNXNaU1Y2J4MW1iN2N2M2O00000000001O0000001O0000001O0O1000000000000000001O00000000000001O0001O01O001O001O1O001O0000000010O01O00001O00001O00001O1O001O1O0O2O00000000000000000O1000001O1O1O1O1N2O1O1O1O2N1N2O2N2N3M9F5L2N6I7J4K3M3M6K5J9G;D>UOaRX:\", \"size\": [1280, 720]}, {\"counts\": \"dbm=:`W1;Jb0]O7J3N6Ja0oiNjMVU1f2\\\\kNkMQS1X2blNTN\\\\S1Y3O010O001O001O01O01O00000000001O00001O0001OO10001O000000000000000001O01O000001O001O1O010O00001O001O010O10O01O1O001O0000001O0O1O1010O01O1O1O000000000O1000000000001O0O2O1O001O1O1N2O1O2N2N2M3N1O2N1O=B5L:E7I3M4L9G;De0mNdZe9\", \"size\": [1280, 720]}, {\"counts\": \"RcW>;31iV1P1mjNVO^R1\\\\1nkN[N?a0aS1h1SlN`NjS1m2N1O3M2N2O001O00001O0000001O00001O00001N1000001N110O00001N10000000001N11O010O0000010O0O2O10O01O001O01O01O001O0O101O001O0000001O00001O00001O000000001O1O1O1N1000000O100O1000001N3N1O1O2M2O2N2M2N3N2M4L4L7H8I?@8G6F;F9Id0SOnZ`9\", \"size\": [1280, 720]}, {\"counts\": \"bSU>;X2IcR1j0mlNEhR1U1ckNbN>a0jS1c1nkNaNoS1j2M3N1O1O2O1N101O00001O0000001N101O1O00001O000000001O0000001O000O10001O00000010O000010O0001O001O001O1O1O010O00001O001O00000O1000001O001O1O1O1O001O0O2O00O10O2O00000O011N10001N2O2N1N2N3M2N2O4J3N3L4L7WMmjNU2_V1oNVkl9\", \"size\": [1280, 720]}, {\"counts\": \"ak_=:cW1e0[O;UiNmNUV1j2]N<F9N2N2O001O0O101O001O001O00001O001O0000000000001O00000000001O00001O0000001O000000000001O0010O0001O001O001O001O1O0000000000001O0000000000001O0O2O2N1O001N110O000000000O100O1O101N2N2N2N2M3N2J8I:PNjjNXO:l0UU1[O\\\\fd:\", \"size\": [1280, 720]}, {\"counts\": \"]mR<?\\\\W18\\\\Oe0I5L4B<[Oe0M4M2M4L3L4M4L3M4N1O1O1O1O2O0O1000000O101O0001O000001O00O1001O0001O001O01O0000000000000001O0000001O0000001O002N1O1N10010O0O2O001O00001N10001N10001N102N1N2O1O001N001N2N2M2O2TOl0K5J6L5M3M2K6F9O4DihNFnkR<\", \"size\": [1280, 720]}, {\"counts\": \"Zmc;<bW15G7_Oa0QOn0K6M3M3M3M2M4M2O1N2N2H9N100000O11N10000000001O01O01O1O01O0000000000000000100O1O000000001O1O002N1O1O001O00001O1O1O2N001O0O101O1O001N2YMljN`2TU1]MojNc2XU1O000000O01000O01O100O2@>\\\\Oe0K4G;C>GglP=\", \"size\": [1280, 720]}, {\"counts\": \"^[f;7U15QU16bjN8RU1M[jNBOd0dU1\\\\1M4K4L4N1O2N1O100O1N2N3N1O100000001O00000O100001O00001O001O01O00000000000001O01O00000000O101O001O0000000000000O101O001O00000O0100001O001O1N2F:cNPkN[O^U1`0T1E<KoT\\\\=\", \"size\": [1280, 720]}, {\"counts\": \"fTo;1hV1?liN8^U1MZjNa0\\\\U1S1N1M4L3M4M2O1O100O10000O2O0000001O00001O0O11O000000O11O00010O00000000000001O0001O000O10O10000001O0000001O000O10001O001O001RNijNQ1WU1kNTkNn0lT1POWkNo0jT1mN[kNQ1hT1iN^kNS1iT1eNZkNZ1hU1L3lNmiNO^V1Ok0N`[X=\", \"size\": [1280, 720]}, {\"counts\": \"mkW<1kW1:Fd0^O<[iNiNkU1m1I4L1M4M2N3N101O0O2O0O101O000000001O0000001O000001O001O00000010O001O000O101O00gNkjNGUU15XkNAiT1<ckN[O]T1`0lkN\\\\OTT1`0TlN[OmS1c0ZlNWOgS1g0j1N2O2N1M4K4M5KWb7Mj]H8H5O2M3O0001BmhNKVW12>MmjP=\", \"size\": [1280, 720]}, {\"counts\": \"hST<a0YW1b0@8J7J8G3M3M2N2N3M2O1O2O0O1010O01O1O0O2O001O00001O001O10O000001O00000000001O0000000O2O000000000001N100000000O2O000O100O2O1N101N102M<DP1QO5I`[X=\", \"size\": [1280, 720]}, {\"counts\": \"RTo;1cW1`0I;E3O1K9K;E6J5L1N101N10000O2O001O001O1O1O1O00001O00000000001O01O0000O2O0O101O000000000000O10001O0000000O1N3N1O101N101O1O5KU1jN4K]cc=\", \"size\": [1280, 720]}, {\"counts\": \"dkm;<aW18H`0@5L4N;D2O1N2OO01O10O10O10O2O0O1O010O10000N2N2N3N2N1O100O0O2N10001O1M4L4M2N3N1O1O2M2N3L5K6HhTZ>\", \"size\": [1280, 720]}, {\"counts\": \"Ykh;b0XW1:I5K`0A5K3N2N1N3O0O2N2O0O2O00001N10001O000000010O001O000000000000000000000000000000O10000O101N101N1O1O1O100O2O002M3M5kNliN^O01e[Q>\", \"size\": [1280, 720]}, {\"counts\": \"Pkm;i0SW18J3N3M2O7H7J3L2N2N100O2O0000001O0000001O010N101O00000000000O1000000000001O000000O10001N1O100O101N100O2O0O2N3M4K=Bo0lNV\\\\l=\", \"size\": [1280, 720]}, {\"counts\": \"kjm;e0YW19F;F3M3N4L7I4M2N1O1O1O00001O000O1000000000000000001O0000000O1000000O100001O00000000000O1000000O2O0O101O0O2N2N2N4K6G\\\\]l=\", \"size\": [1280, 720]}, {\"counts\": \"nbl;9cW16_hNOmV1f0J6J5L9H5L2M3N2NO0100000000O10000000000000O02O00000O10001O0O100000000000000000000000O100O1000000O1O2O0O2O2L5I7F`0TOgem=\", \"size\": [1280, 720]}, {\"counts\": \"Skm;46OVW1k0B8I6L9G5L1N3N1O1O000O100O10000O1000O10O100000000O1000000O2O00000O10001O0000O010000000O01000O2O0O101N2N2O2M3L5K7E=ROZiNFTdm=\", \"size\": [1280, 720]}, {\"counts\": \"ibQ<?aW14L2O4ehNO`V1j02M2L2N1O2N1O101N1O000O2O1O001O2N1O1O1O00001O00001O0000010O0000001O001N1010OO2O1O001N100O2O0O00100O2LQdm=\", \"size\": [1280, 720]}, {\"counts\": \"nZU<7dW19J?fhNVOaV1[1H8I3O00001N1000O0100O100O100O010000000001OO10001N01000O10001O00000010O00O2O00000000000O11N100O101O2M6K6J3dNniN3C5gch=\", \"size\": [1280, 720]}, {\"counts\": \"gb`<>^W15M4L4N2M2N1O2N100000O10O10O100O10O01000000000000000O100O1000000O1000001O0O101O1O00001O0001O00O2O01O2N3L4M2H8Ff]]=\", \"size\": [1280, 720]}, {\"counts\": \"oZd<1lW15XiNM\\\\U19]jNM\\\\U18bjNLYU18ejNJYU17fjNJYU18djNJ[U18bjNJ^U17]jNMbU1Z1O000O10000O10000O100000001O000000000O2O00000O100000000O100000000000O100000001N100O2O001N2O3hNYjN0mU1F\\\\jN5iU1B_jN9hV1JWlU=\", \"size\": [1280, 720]}, {\"counts\": \"YSc<1fW1<ohNDTV1j0YiNBaV1S1J6L5K2N2N1O100O10000O2O0000000O10000000000O100000000O100O10000000000000001O000O0100000000O101O00000O3N2M4M5K4Kn0SO7FTdT=\", \"size\": [1280, 720]}, {\"counts\": \"U[d<6fW17I:TiNZOoU1b1I7I3N1O2O000O2O00000O2O000000001O000000000O1O1000001O0O100000O10O2O000000000000000000O100O1O100O2O001O2N2N4K4M7eNiiN?TW1GR\\\\S=\", \"size\": [1280, 720]}, {\"counts\": \"W[i<6i02kU1a0`iNIYV1R1L4M2O2N10001N1O2O1N101O0000000000000O100O20O0000000000O1000000O10000000000001O00000O10000O100O100O2O001O1N6K=BS1iNlkP=\", \"size\": [1280, 720]}, {\"counts\": \"cce<1hW1;I9Fe0\\\\O6J5M2N1O101O001N101N101N1000000O100000000O101O000O10000000000O10000O10000001O000O100O100O10000O101O002N1N4M7HR1mN8FjSR=\", \"size\": [1280, 720]}, {\"counts\": \"T[d<i0RW17L3O3L`0@3N1O1N101N1000001N1O101O0O100000000O100O100O10000O1000000000O10000000000000000O2O0O1O1O1O2O1O1O2N2N8bNdiNh0XW1A>^OjSW=\", \"size\": [1280, 720]}, {\"counts\": \"Xce<2kW15J6J7Id0khNQOXV1^1M4M00001N1O100O2O001N100000000O1000O01O10000O10O100000000000O1O2O00000000O10001O001O1N101O2N4L5K3L9G=B8D]lU=\", \"size\": [1280, 720]}, {\"counts\": \"oZd<;aW16L4M;Eb0^O6J5K3N001O1N1000001O0000000000000O1000O0100O001000000O1000000O10O1000O1000000001O1O1O1O1N2O1O1O2M4M4K5L3M7I6I6J[\\\\S=\", \"size\": [1280, 720]}, {\"counts\": \"Skf<:cW1>hhN^OZV1^1E4L3L4N2N10001N10000O100000000000000000000O100000000000000000000O100000O10001O001O0O2O1O1N2O2N2M4M3M4L6J9F4M6ITdT=\", \"size\": [1280, 720]}, {\"counts\": \"W[S=a0WW1i0\\\\O>C4L3N1O1O1O2O0000000000O1000O10001O0000O1000O2O00000O11O00000000O101N1001N10001O000O2O1O1N3M5L4L3M4K9H9G7HSdj<\", \"size\": [1280, 720]}, {\"counts\": \"Q\\\\]=a0ZV1W1K4M2O2N2M20000O2O00O1000000000O101O00O2O001O0000O1000001O0O10O11O0000O1000O2O0001O0O2O001N2O3L8I5J7J2N3La0@6Imc`<\", \"size\": [1280, 720]}, {\"counts\": \"PcY=>6`0UV1h0M3M2O2N1O1O2OO0100000001O01O00O10000O2N11O0000001O01N100O1O10O100000000O101O00000O101O0O2O1O1O1O4K8In0ROPTh<\", \"size\": [1280, 720]}, {\"counts\": \"kRf=g0VW1k0UO6K2O2N1N2O1O1O001O100000000O10000O11O000000000000O1O100000000000000000000000O101O10O01O1O1O2M7I5L5J?B;D<@TTY<\", \"size\": [1280, 720]}, {\"counts\": \"`kS>c0iV1f0D;L3O1N2N3N001O1O2N1O1000000001N02O000000000001O000000000000O2N1O1O10001O01O000O101O010O001N2O1O2M5L6J6J8G9G7G8G`dg;\", \"size\": [1280, 720]}, {\"counts\": \"cbh=\\\\1_V17L3L4N1N2N101N2O010O1000001O000O11O001O00000000O2O000O1O1O1N20000000O101OO100001N2O1O001O1N2O2M4M6J3M7I`0@8H6G`\\\\U<\", \"size\": [1280, 720]}, {\"counts\": \"VRk=`0g0LdU1c0giNHTV1T1M3M2N2N2O1O1O10000O1000000002N1O0001O0000O10O1O10001N1000000O1000000000000000000000O2O000O101O1N3N2M6K8HU1kNblR<\", \"size\": [1280, 720]}, {\"counts\": \"XZ[>?b0BD9SV1^1L4N2N1N2N2N2O100000O1000001O0000O2O000001OO10000O101O000000000O1000000010O01OO10O11O00001N1O2L5J5L5J7G<Ee0\\\\O7JZeb;\", \"size\": [1280, 720]}, {\"counts\": \"_SP>8eW16]Oa0A?F9K5N3N1N2N2N10100O10000O10000O1O11O000000O11O010O00000000O1000000000000O1001O000000010O10OO2O1O1O1O3M6Jc0]O5Ja0XOnTj;\", \"size\": [1280, 720]}, {\"counts\": \"XZg=8;i0nU1h0L2O1O2M2N2O1N2N200O100000000O10000002N1O000000000000O2O0O101O000000000001OO10O101O0O101O001O1O1O2N2M4GliN`NZV1a0fVY<\", \"size\": [1280, 720]}, {\"counts\": \"cSk=;kV1k0A?K4L4N3N1O2O0O1O1000001O0000000000000000001O0001O00O101O00000000001OO10000000000O101O0O2O0O2O1N2N3M2M5E=@nUT<\", \"size\": [1280, 720]}, {\"counts\": \"a[g=`0VW1=VOUOPjNU1lU1a0M4M2N2N2O1O1O2O000O100000000000001O0000000001O00000001O0O1000000000000000O1001O00000001O0O2O1O1O1O2M4K6J7aN_jN0ScV<\", \"size\": [1280, 720]}, {\"counts\": \"ckU=430hV1NhiNb0oU1G`iNj0[V1`0L3N2N1N3M2O100O1O1O10000000000O20O00000000001O0000O100000000000000O10O100000O10O101O0O101O002M2O1O1M8hNfiNBddj<\", \"size\": [1280, 720]}, {\"counts\": \"gbT=4iW1<bhN0YV1U1E3M3M3N1O1O1O1O2O0O1000000000000000001O0001O000000000001O000O10000000O10O100000000000O2N2O0O2O001N2N2N9\\\\Nhfj<\", \"size\": [1280, 720]}, {\"counts\": \"QT\\\\=3]W1g0oNk0L3N3N1N2M3O1O10000O10O1001O000O101O00000001O000000O10000000001O00000000O10000000000000001N101O1O1N4K9D<dNmiN9VW1FWla<\", \"size\": [1280, 720]}, {\"counts\": \"jS\\\\=:\\\\W1?ROk0K3N2N2N2N3M200O10000O10000000001O00001O0000000001O00000000O1000001O0000O1000000000O11O00000O2O001N2O2M4L8ROniN_O[V1F[jN5hja<\", \"size\": [1280, 720]}, {\"counts\": \"SkU=f0lV1e0F8I5K2O2M2O1O1O100O1000000000000O100001O000010O00000O1000000000000O10000000O10O11O000O2O0O101O001N2N2lNfjN@_U16hfj<\", \"size\": [1280, 720]}, {\"counts\": \"kRW=S1eV1:J5M4M2M3M2O1N2O2O0O2O0O1O10000000001O0000010O00000000000O10000000000O1001O00000O100000000O101O0O2O1N2N8eNh^i<\", \"size\": [1280, 720]}, {\"counts\": \"]kU=b0TW1;H8K4F:M3O1O1O1N3N1O1O2M200O10001O001O0000000000000000000000001N100000000O011O000000000O100001N101O001N2O2N2bNejN^OM0Z[i<\", \"size\": [1280, 720]}, {\"counts\": \"ckk<8bW19I4D<M3L5H7K5N2N2O1O2N1O1O1O002O0O1O101N101O000001O0000001O010O0000O10000000000O1O1O100000O10000000001O1N2N2M3J6WOh0H<BUVm<\", \"size\": [1280, 720]}, {\"counts\": \"V[n<b0SW1=L2M4A>L4O1O1O1N2N2O1O2N1O1O1O100O1000001O000000000001O2O1NO1O100000000O1O100000000000000000O110O1O1O1N2L4XOh0F<GU^n<\", \"size\": [1280, 720]}, {\"counts\": \"[kP=9_W1:G8L3M4G8H8L5M2N2O1N2O1O1O1O1O10000O100000001O001O00001O000000001O0000000O100O1000000000000001O1O00001O00001O1O2K5G:TOjVh<\", \"size\": [1280, 720]}, {\"counts\": \"U[n<;ZW1?J4D;D<M2N2O1N2N2O1O2N1O100O100000001O00001O000000000010O01O000000000O100000000000000000O2O000O101O0O2N2L5@?^Og0AW^n<\", \"size\": [1280, 720]}, {\"counts\": \"nRh<<jV1o0G6L4M3L2O2N1O2N1O2N2N2O0000001O0O1001O01O01O01O01O00001OO10000O10000000000000O1000000000001N1O1N2K6@?K6G:H9FZnU=\", \"size\": [1280, 720]}, {\"counts\": \"mRc<:cW17J:G3]Od0I5M2N2O2M101O1O1O1O2N100O100000001O0000000100O010O0001O0O10000O1000O10000000000O1000000000001N1N2M4J6B>\\\\Of0A`^X=\", \"size\": [1280, 720]}, {\"counts\": \"Sce<3gW1=F:B9^Oa0L3N3O0O1O1N2O1O2N1O1O100O2O0000000000010O01O000001O00000000000O100000000O10O100000O1000O101N1O2L3L5@`0E<Ea0[O\\\\nU=\", \"size\": [1280, 720]}, {\"counts\": \"mja<:ZW1g0YO>K3N3N2M3N1N2O1O1O100O2N100O1O101O001O0000000010O001O00001O0000000000O1000000000000000000O0100O2H7K5M4I6K6E;I9AafY=\", \"size\": [1280, 720]}, {\"counts\": \"lja<h0jV1`0D;M3M3M2M3N2O1O1O100O101O0O1O100O2O000000001O01O00100O1O000000001OO1000000000000O100000O10010O0O101M2K6H9E;^OdV\\\\=\", \"size\": [1280, 720]}, {\"counts\": \"ibe<d0ZW1:WOc0J5L4M3N1O1O1O1O1O100O1O2O0000000O1010O00001O001O01O0001O01O00000O1000O100000O100000000000O2O000O2N2N2K5I>lNi^X=\", \"size\": [1280, 720]}, {\"counts\": \"lRh<>jV1IXiNm0]V1`0K3N2M2O2O0N2O1N2O1N11001O000O1000000010O00100O001O01O000O10000O10000000000O10000000O1000000O101N1O1N3AVjNaNPV1Q1h0CdnU=\", \"size\": [1280, 720]}, {\"counts\": \"gZn<c0SW1?B<I5L4M3N2N1O2N1O1N2O1O2O000O110O0010O000000O110O01O0001O0000000O100000000O100O10000000O10000O101N2H9E;C>^OenP=\", \"size\": [1280, 720]}, {\"counts\": \"djU=358hV1Q1C5M2N3N1O2M3N001O1O2N2N11O000001O0001O00010O01O010O00001O0O010O10O001N2N2N1O2O1O1O2N101M3K6^Oc0Db0DPfo<\", \"size\": [1280, 720]}, {\"counts\": \"hRW=?ZW1?]O<M201N1O2N1O100O2O1O1O0O10001O0000000O1O2N2O00001O001O00O1O1N2O001L6J:I6Ge0YO8H\\\\eY=\", \"size\": [1280, 720]}, {\"counts\": \"TRR=h0UW1<E7I9F6J4N2O0O101O0000000001O0010O001O10O01O001OO1O1N2M3N2N2O1N2M3O1N1O2M2O2O1O1O1F[iNWOgV1c0>E<K4J_VW=\", \"size\": [1280, 720]}, {\"counts\": \"Vjk<1jW17PiNJQV19YiN?aV1d0L4J2O2N8I1O00O2N1N3N2L4M3O1N2N2O2M3L4L4N1O2M2N2O100O1000000O1N2N2O1M2N3L4L5N]X1:SgN8M3O1N1N2N201O0001O002M3L4IYfo<\", \"size\": [1280, 720]}, {\"counts\": \"Ujf<3f00ZV17QiNh0_V1d0G3N3M2O2N100O101N101O1O00001O0000010O01O1O10O001O000001O00O10001O001N2O000000O1N2M3M3N2M3M3K6L3O1L4N2N2N3N3E=IUfT=\", \"size\": [1280, 720]}, {\"counts\": \"`ja<3`W15ahNa0jV1>K5L:E7K2N1O101N100O10001O000001O01O0001O0001O01O010O001O0001OO100000O10000O10000001O000000000O1000000O101YOg0A?L:ES^X=\", \"size\": [1280, 720]}, {\"counts\": \"\\\\ja<Q1kV1f0[O3O2M3N3M3M101N1010O0000010O1O1O1O00000010O000O100O2O000O101N11O000O1000O010O010000O101N1O1O1O2YO[jNPOhU1k0`jNnNfU1j0`jNROjU1b0P1J6J[]]=\", \"size\": [1280, 720]}, {\"counts\": \"Pc`<9dW16Lb0hhNSOYV1d1E4M2M3M4N0O101O000000001O01N100000010O0O100001O000O1O10000000001O00000O010O0100O101O000O2N1O2N2N3N3ZOSjNROQV1e0\\\\jNTOgU1e0[1ZOiT\\\\=\", \"size\": [1280, 720]}, {\"counts\": \"djW<;cW15Ka0@7ZiNPOnU1g1I4M2O0O2N1000000000001O00000000O2O0000000000000000O10000000000O10001O0000O10000O101O00010N10001O1O2M6K4Kb0^O7G=^OaTa=\", \"size\": [1280, 720]}, {\"counts\": \"gZU<;<FiV1Q1Gd0]O7I3M2M2N2O101N100000000000O1010O0000O10001O000000000001O00000O101O000O01000O100001O00000O101O1O1O2M3N5Ji0XO8G=CPTf=\", \"size\": [1280, 720]}, {\"counts\": \"ijW<4jW16J5ehNIfV1l0XiNZOnU1_1L3N2M2O2M2O1O1000001O01O0000000O11O0001O1O000001O01O00O100O1000000O100000O1000000O100001OO2O00001O2N3KW1iN]ld=\", \"size\": [1280, 720]}, {\"counts\": \"nZ_<i0oV1n0XO5K3N1M4L3O101N10000000000001O001O1N110O0000010O00000001O00000000000O1000000N101000O01001O0O101O1O1O1N3L5_NljNKfRa=\", \"size\": [1280, 720]}, {\"counts\": \"fSc<716kV1k0G8K4N6I7J1N100O2O0O2O000O1000001O01O000000000000100O000000000001OO1000000000000O100000000O100O1O100N3M2L4]Oc0H9G;BX]X=\", \"size\": [1280, 720]}, {\"counts\": \"`Zi<1Q1b0WU1V1M3N2N1O2N100O1O1O2M20000000010O01O001N11O01O00001O01O001O001M2O1N200001O000O100000O1000O100O101N100O2N2M3_Od0@g0VOj]S=\", \"size\": [1280, 720]}, {\"counts\": \"ckf<1gW1f0ZhN@_V1]1G5K6L4M200O1O1O2O0O10000O2O000000000001O01O01O010O000001OO1000000001OO100000O1001O00000O100O10000O2N2I9Dk]X=\", \"size\": [1280, 720]}, {\"counts\": \"d[_<5fW19WOj0D<H4L3N3N2N2N100O1O2O0O100O2O0000001O01O0010O01O000000O100O10O1001O000000O1001O00O2O0000002M:G6\\\\NmiNo0mV1_Oedc=\", \"size\": [1280, 720]}, {\"counts\": \"e[_<e0RW1<_O>L4M4M2O1N2M3O1O1O2O0O100O100010O0001O10O0010O00000O10O10O1O1O1000000000000001N10001O1O6I8If0WOd\\\\g=\", \"size\": [1280, 720]}, {\"counts\": \"Qch==\\\\W1d0^O8J4N2N3N2M4M4L2N1O0013L8Ie0YO?\\\\O9GTld=\", \"size\": [1280, 720]}, {\"counts\": \"lka<9\\\\W1a0^O`0F9J4M3O1N2M3N101O1N101N10001O1O01O0001O01OO100O101O000O1000000000000000O0100000O2O2N3K5Ii0gNTUf=\", \"size\": [1280, 720]}, {\"counts\": \"iTc<5XW10khN`0gV1d0F8K6K5M1O2N2O0O2O1N100O2O00010O0000000O100000001O001O0000000O1000000000O100000000O2O003L3M2M4L7Ij0WO9ZOdhN6hj_=\", \"size\": [1280, 720]}, {\"counts\": \"h[_<1dW1?E;Ba0A9L4M2O1N10101N1O100O10000001O001O000001O000O1000001O0O100O10000000O10001O1O1O2M2O2N2M3N2N:F:E5L3J6GcTa=\", \"size\": [1280, 720]}, {\"counts\": \"bSh<4570G\\\\V1`1J4M3M2O1O1O1O1O10001N1000000000000000000000000O2O0O2O0O100O1O100O100000000001N101O1O3L3N4K3N1O2N>B8H6J6GU\\\\X=\", \"size\": [1280, 720]}, {\"counts\": \"jRm<6gW1a0@e0\\\\O:F2N2N2N2M200O100O100O10000001O000000O1000001O0O010000000000000O10O101O0O2O001O1N101O1O2M4M3M5J9H5I5K4L5L:DYdo<\", \"size\": [1280, 720]}, {\"counts\": \"iRW=Y1fV17H2N3M2O1O2N100O10O0100O1000000000000O010O1O1O1O1O10010O0000000001O000000001O001O1O001O2N4L5K7H7J4K4M3L4K<BYTh<\", \"size\": [1280, 720]}, {\"counts\": \"akZ=g0SW17^Ob0L3N2N2O2N010O1O1O100O1000000O100000O01O1O2N1O1O1000000O2O00001N11N101O00001N2O3M3M2M2O2M6K6I8H4M3M3K5K4Ja\\\\d<\", \"size\": [1280, 720]}, {\"counts\": \"^kZ=a0ZW16E<I7H7L4M2O1O1O10000O101O0O100O1O10O0100O2O0O100000O0100O1O10000000O2O001N2O1O1O4L4L3L5K4M4K4L3N4K5K5Jdlf<\", \"size\": [1280, 720]}, {\"counts\": \"^k_=1lW19ZOMPiN8fV1l0B9L3M2N3O0O101OO01000O1000000000000O10N2N2N2O1O1010O0000000O2N2N1M3L5K5L4M2O3L3N3L4M4L5J9H3K4J[]d<\", \"size\": [1280, 720]}, {\"counts\": \"]k_=:\\\\W1`0C9F<H8K2N1N2O1O1O00100O1O100000000001O000000O1O1N2M3N20000000000001O0000O11O0O2O1O2N101M3N3M3M4K6I6I9I4J7E;HhT^<\", \"size\": [1280, 720]}, {\"counts\": \"RTa=1mW18@<G9D:J7M2O1N2O1N1N3N2001O0O100000001N01O1O1N2O100O101N10001O0O10000000O100O101O001O2N2M2O3L5L3L7J6J4K6K6I;CRl\\\\<\", \"size\": [1280, 720]}, {\"counts\": \"Rl_=2bW13bhN5QW1c0K5G7J7L3L4N20000O1O1O1O1O1000000000000001O0000O1O2O0O01001O000000000O10000001O001O1N2O2N1N4M4L4I:F9J6G9E]\\\\_<\", \"size\": [1280, 720]}, {\"counts\": \"Yc^=P1^V1c0L4N100O2N1O1O100O010O1O1O1O100O01001O00O1000O10000O10000O10001O00000001O0000001O001O1O1O2N2N2N4K4M2L5L:F7H5I8I`T^<\", \"size\": [1280, 720]}, {\"counts\": \"ac^=g0TW16]Oc0M2M3O2N1O1O100O1O10O01O100N3N1000000O1000001O00000000000O101O000O10001O0001O00001O00001O1O1N4M3M3M4K4L8I9E;F\\\\T^<\", \"size\": [1280, 720]}, {\"counts\": \"X[]=:VW1e0B:E:M4N100O101N1000O0100O1O100O100O2O0000000000000001O000000O100O10001N1000000O1000000001O0O2O1O3M2N2M4M4K;E9FX]_<\", \"size\": [1280, 720]}, {\"counts\": \"]c^=6eW19G6L3O2N2O0100O001O1O1O0O2O1O10O0000001O000O2O0O001O10O10000000100O0000O100N2N2O1O1oNROojNo0oT1TOojNm0PU1VOmjNl0RU1UOgjNQ1XU1ROcjNP1^U1PO^jNT1cU1lNZjNV1gU1iNVjNZ1kU1=0O2O0O6J;UO`iNAcW1Igl\\\\<\", \"size\": [1280, 720]}, {\"counts\": \"ek_=d0WW16M3M3M3G9M2J7N1N2N2M4N1O1N2O00100O101O000O100001O1O2N0000O101O000O100000O2O000000001O1N1000001O1O001O0O100N3K5TOl0J`e[<\", \"size\": [1280, 720]}, {\"counts\": \"kc^=8_W1`0D8K4M3B=M3N1O2O0000010O0000000000001O00010O0000001O00000001O1O001O001O001O1O1O1O1N2O1N2O1N2O1N2O1N2N4AkTc<\", \"size\": [1280, 720]}, {\"counts\": \"T[]=l0PW16I6K5O1O002N1O01O01001N01O00010O1O0010O001O00100OVOkNjjNT1VU1mNjjNR1XU1mNgjNS1ZU1mNgjNQ1YU1POgjNo0XU1SOhjNl0XU1UOijNi0WU1YOijNe0XU1[OijNc0WU1^OijNa0WU1_OjjN`0WU1@hjN`0XU1BgjN=YU1DgjN;YU1EhjN:YU1EhjN:XU1FkjN7UU1JljN3UU1MljN2TU1OljN0UU1OmjNOTU11mjNLUU14ljNHWU18jjNC\\\\U17Y1K6I`]d<\", \"size\": [1280, 720]}, {\"counts\": \"obc=;cW13NT1jN7K1O2O0O101N1O1OYOTjN]OkU1a0YjN^OfU1a0[jN@dU1`0]jN_ObU1a0`jN^O`U1b0bjN\\\\O_U1c0bjN\\\\O^U1c0cjN]O^U1b0cjN^O\\\\U1c0cjN]O]U1c0djN\\\\O\\\\U1g0bjNYO^U1g0bjNYO]U1h0cjNYO[U1h0djNXO\\\\U1h0ejNXO[U1g0ejNYOZU1h0fjNXOZU1h0fjNXOZU1h0gjNWOYU1i0hjNVOXU1j0ijNUOWU1k0ijNUOXU1j0gjNWOZU1h0fjNXOZU1h0fjNXOZU1h0gjNVO[U1h0gjNWOYU1j0gjNUOYU1k0gjNUOZU1j0fjNVOZU1j0fjNVOZU1j0fjNVOZU1i0ijNSOZU1l0hjNoN\\\\U1Q1i0O2L4N2N2M2H<GRm\\\\<\", \"size\": [1280, 720]}, {\"counts\": \"^ki=1iW1a0C7jhN[OdV1[1D4M3N1N2O2N100O100O2O00010O1O1O1O000000001O0000000O2O1O000O11O0001OO10000O10001N1O1O1O100O100O2O3L2O1M3J6C>JReQ<\", \"size\": [1280, 720]}, {\"counts\": \"ZZQ>9dW1b0^O5PiNCVV1V1O1N100O2N100O11O00000000O10000O1000000N2O010O2O0O10001O00001O000000000000000000O100O2O000O2N101N2O1O4K4K4K=ZOmUj;\", \"size\": [1280, 720]}, {\"counts\": \"bc\\\\><[W1:N2N0O2H9A>G9O1O1O101N1O1O1O1O100O010O1000000000001O0000000001O001O000000000O1N3N2M3M4L3M4M4K7H9H5I<DU]f;\", \"size\": [1280, 720]}, {\"counts\": \"Y[[>1dW1<L5L5K<Da0A:F2N1N2N2N10000O1000O1O001N2O1O101M2O1N2O1N2O1N2O1N2N2O1O2M2N2O2N1N2N2N3M3M6K4K4L5JRel;\", \"size\": [1280, 720]}, {\"counts\": \"[k]>4hW1<chNAgV1W1A6K5H8N1O101N1O100O101O0O2N1O1O2N10000000O100O2M3M2M4M3M4L3M4L3N1N4K<E6DT]U<\", \"size\": [1280, 720]}, {\"counts\": \"gbR>i0UW1:Fb0@3L3M1O2O1O001O1O1O2M01O001O10O01N3M1000001N2O2M2N2N2O1N3M2N2O001N2N3M3N2B>ClU^<\", \"size\": [1280, 720]}, {\"counts\": \"S[g=:4LoV1Y1]O6J3N2M2O2M5LO10O01N2N2O1N2O1O1N1O2O1O1O1O1N101N3M101N2O1N2O0O2N3M2N2O2M2N2O2M2N2N1N3N4J^Uc<\", \"size\": [1280, 720]}, {\"counts\": \"ebc=2jW1g0[Oi0VO5L3N2N100O1000001O2N00000O10001O00O10O100O1000000O10O100001OO10O10001O1O10O01ON101O1O2O0O1N3L3O2M2N3M2L4M8J=C4G\\\\eV<\", \"size\": [1280, 720]}, {\"counts\": \"^bc=9dW15M6HR1nN5M1O100O1000001N101O0000001O0000000O101N1O1O1000000000000O10001O000O1001O0000000O01000000000O2N2O1N3N2N7H<D^mW<\", \"size\": [1280, 720]}, {\"counts\": \"^b^=:j0LhU1d0eiNFUV1V1L3N1O1N3N100O1000000O10001N2O1O000000000000001O0O0010000O2O000000000O10000O100001N1000001O0O100O1000001O2M4M5lNWjN\\\\OoS^<\", \"size\": [1280, 720]}, {\"counts\": \"cRR=4gW18Ih0[Oc0]O3L2N3N100O2N100O101O001N2O00000010O00000000O10O1000000000O10M3L4M3K5N2N2O1N2N2M3N3L3N2M201N2N2N3M2O2L3N3N2N3K5HPnf<\", \"size\": [1280, 720]}, {\"counts\": \"ZbY=?\\\\W1;RiNXOTV1[10001OO10001O1N101N2N2N2N2O001N2N2O1O1O1O1N2O1N2O001N10001O1O1N2N2O1O1O100O1O1O11O00O1L40Yfo<\", \"size\": [1280, 720]}, {\"counts\": \"[b^=2kW1:G5K>B=C=D3L2O2O0O100O11O00O1O1000000000000O10000O10O1000O10000000000000001N101O001OO1O100O1O2N10000M4M200N3L6H=Ef0\\\\O3L4KYUY<\", \"size\": [1280, 720]}, {\"counts\": \"^Zb=9eW14Kb0ghNVO\\\\V1_1F7J3N2O0O100O01001O001N1000000000000000O01000000O10000O100O1000010O0000000001N1000000O100000000O2O001O1O2M7J:bNciN9XTY<\", \"size\": [1280, 720]}, {\"counts\": \"]Zg=<^W1j0YO>D7I2N2N100O101N101O0O10000000000000O1000000000O010O1O1001O01O0001O0000001O0000000O100000001O001N10001O1O2M4TOmiNDYV1HYjN3oV1FelR<\", \"size\": [1280, 720]}, {\"counts\": \"]RU>:^W1o0UOb0A4M1N100O10001O00000O1000001OO1001N100O2O01O000000O100O1000O100000001O00000O10000000001O0O10001O0O2O1N2M6A>Bc0TOU^f;\", \"size\": [1280, 720]}, {\"counts\": \"^jX>e0;A[V1^1D3K4M4O0O1O2O000O1000000000000O100O1001O000000O010O10000O11O0001O0000000001O001O0000000O2O00001N2O1M3@b0K6E>UOTVe;\", \"size\": [1280, 720]}, {\"counts\": \"`Rk=7cW1U1nN9D8L20001O0O10000O100000000000000000000001N10O100O10010O00000000O10000000001O0000000000000O2O000O2D<I7G;B<@hhNOYUT<\", \"size\": [1280, 720]}, {\"counts\": \"QRa=1nW12M3L5Ln0QO<E4M10001N1O100O10000O10000000000O1O101O1O1O0O100000O1O1O1O1000O0100O2N2M3N2M2O1N2O2N1O100O2N1N3L4M4M:E7J2LoUY<\", \"size\": [1280, 720]}, {\"counts\": \"liU=6cW19L4Kd0^O`0@4L2O1O0O100O2O000O1O1000000000O100001O0000000000000000000000000000O2O0000000O1010O1O01N1O1L5K4K5M7J;E;C6ETfe<\", \"size\": [1280, 720]}, {\"counts\": \"gYd<7fW17K3N1O2M8H5K4M2N2N3M4K5L9F3N1O1N101O000000000000000000000000000000O10000O100000000O2O1O000O2N2M3L4L3K6L5J7H<@YfY=\", \"size\": [1280, 720]}, {\"counts\": \"eYS=`0^W1=E4L4K5K4M1N3N1O2M3N1N3O0O100O1O2N1O01O00O1001N1O2N1000001N2O1N3N?\\\\NSjN5iSf=\", \"size\": [1280, 720]}, {\"counts\": \"dQc<5iW16K3N1O1O1O1O2N3M2N4L4L4L4L5K2N1O1O2N2N1O2N2N1O1O1O100O1O1O2M101O01O0O100O2O000O100O2O0O2N5Ln0`NlhN8fW1Ghdc=\", \"size\": [1280, 720]}, {\"counts\": \"`jR<6bW1l0YO?B8H4L3N1N3M2O10000O2O01O00001N2O000000O10000000000O10000O100001O00000O100010OO10000O2O000O10001N3M3N2M3M2M8H=B5L4[OiUf=\", \"size\": [1280, 720]}, {\"counts\": \"^bQ<m0mV1i0ZO4L4N1O2N100O1O100O2O000000000000O2O1O0000001O0000000000O1000000000000000000001N10000O10001O00000O2O1M4M2M3KZ1gNgTk=\", \"size\": [1280, 720]}, {\"counts\": \"_Z_<2eW1c0Ch0YO=D4K3O0O1O101O000O10000O100001O0001O0000000000000O1000O100000000000000O100000001O0O10001O001N2O2M5K6YOciN_OoV1FRn_=\", \"size\": [1280, 720]}, {\"counts\": \"cRc<3eW1:K=D;E?YiN`NSV1l1J2N1O20O01O0O10000010O00O10000001O00O100000000O1000000000O1000000O100000001N10000O2O0O2O1O001N2N4M6Ie0[O9H1M4HilU=\", \"size\": [1280, 720]}, {\"counts\": \"hja<b0[W19Gc0^O:D6L3M4N00000O101O000001O000O1000000000000000000000O100000O100000O01010O000001N100O10001N101N2L4M4N2oNdiN:YW1H1]OTU\\\\=\", \"size\": [1280, 720]}, {\"counts\": \"gjk<i0SW1g0[O6J4K4N2N2N2O001O00000O100000001O0000O101O000000000000O10000000000000000000O100000001O0O101N101N2N3L5K5K6G;mNViN4\\\\\\\\S=\", \"size\": [1280, 720]}, {\"counts\": \"VkU=5_W1a0PiN@QV1\\\\1K5L3M3N1O2N2N10001O000000000O10000000000000000000000000000O1000000001O0000000O1000001O001O0O1O2M3N2M4L5K6Id0SOTmf<\", \"size\": [1280, 720]}, {\"counts\": \"Q\\\\X=3PW1o0I7H7L4L4M3N2N1O2N1O100O10001O0000000001O0000000000000000O110O0000000000000000000001O00001O0O100O2N1O2L4L5E:Ab0D`]d<\", \"size\": [1280, 720]}, {\"counts\": \"W[]=632VW1o0]O4L5L3M3M3N1N3N100O1O1O2O0O1001O0000000000000000O11O1O01O00000O100000000000000001O0O10001O0O101N1O2M2L5D<E<G:G]]_<\", \"size\": [1280, 720]}, {\"counts\": \"i[]=752eV1Q1I4M2N3M2N3M3N1O1O2O0O10000O11O000001O000001O00O10001O100O0O10000001OO010000O100O10010N10001N1O1O1N2O2G:D<^Of0@Ve`<\", \"size\": [1280, 720]}, {\"counts\": \"alZ=1gW1;XOf0D;N4M2M3N3N0O2M2N2O1O2N10000O11O2N1O000001O00000000001O00000O1000000000O10001O01OO100O1O1O2M2N2N2AhiNmNcV1m0=]Oe0Co\\\\d<\", \"size\": [1280, 720]}, {\"counts\": \"Rdh=8gW17J1O1N3N2N3L2O1O0O101N2O0O100O2N100O101N1O1N2O1O1O1O0O2L4M4J5L4O1O10O100O2N1O2N1O1J8@a0QORiN8X\\\\d<\", \"size\": [1280, 720]}, {\"counts\": \"ijn==aW15N2N1O01O1O001O000000010O1O01N100O01000O101N2O2N1O4K5K3M00000001N100O1O100N3^OnTc<\", \"size\": [1280, 720]}, {\"counts\": \"fkd=3lW1110O1O1O2O1N001O100O01OO2O001O1N3N1O1N2O1O3L5L2N3M3L2O1O1N10000O010O01O010O0O2N2I8]Od0FfTh<\", \"size\": [1280, 720]}], \"bboxes\": [[294.0, 618.0, 65.0, 78.0], [284.0, 609.0, 66.0, 85.0], [281.0, 603.0, 65.0, 88.0], [279.0, 605.0, 73.0, 94.0], [281.0, 599.0, 73.0, 100.0], [281.0, 588.0, 81.0, 105.0], [288.0, 592.0, 92.0, 114.0], [299.0, 607.0, 99.0, 119.0], [311.0, 603.0, 110.0, 131.0], [327.0, 594.0, 113.0, 138.0], [339.0, 592.0, 118.0, 139.0], [356.0, 586.0, 116.0, 147.0], [364.0, 588.0, 112.0, 150.0], [362.0, 591.0, 104.0, 144.0], [345.0, 594.0, 102.0, 134.0], [309.0, 594.0, 102.0, 125.0], [297.0, 595.0, 89.0, 114.0], [299.0, 596.0, 78.0, 102.0], [306.0, 603.0, 74.0, 97.0], [313.0, 613.0, 73.0, 90.0], [310.0, 604.0, 70.0, 88.0], [306.0, 611.0, 65.0, 79.0], [305.0, 611.0, 48.0, 65.0], [301.0, 593.0, 60.0, 74.0], [305.0, 594.0, 59.0, 71.0], [305.0, 588.0, 59.0, 72.0], [304.0, 585.0, 59.0, 73.0], [305.0, 585.0, 59.0, 74.0], [308.0, 601.0, 55.0, 57.0], [311.0, 583.0, 57.0, 69.0], [320.0, 581.0, 56.0, 42.0], [323.0, 583.0, 59.0, 74.0], [322.0, 586.0, 61.0, 74.0], [323.0, 590.0, 61.0, 74.0], [327.0, 593.0, 59.0, 72.0], [324.0, 596.0, 61.0, 70.0], [323.0, 594.0, 58.0, 71.0], [324.0, 589.0, 58.0, 72.0], [323.0, 588.0, 61.0, 75.0], [325.0, 593.0, 58.0, 71.0], [335.0, 592.0, 56.0, 73.0], [343.0, 592.0, 56.0, 72.0], [340.0, 593.0, 53.0, 71.0], [350.0, 583.0, 55.0, 71.0], [361.0, 577.0, 58.0, 72.0], [352.0, 570.0, 56.0, 73.0], [354.0, 565.0, 56.0, 74.0], [367.0, 565.0, 56.0, 75.0], [358.0, 562.0, 59.0, 77.0], [351.0, 566.0, 54.0, 75.0], [354.0, 573.0, 55.0, 73.0], [351.0, 576.0, 57.0, 73.0], [337.0, 581.0, 55.0, 71.0], [336.0, 582.0, 55.0, 72.0], [342.0, 584.0, 56.0, 71.0], [342.0, 583.0, 57.0, 72.0], [337.0, 581.0, 54.0, 69.0], [338.0, 576.0, 54.0, 73.0], [337.0, 573.0, 56.0, 73.0], [329.0, 571.0, 60.0, 72.0], [331.0, 566.0, 57.0, 72.0], [333.0, 563.0, 60.0, 74.0], [331.0, 565.0, 57.0, 72.0], [326.0, 563.0, 56.0, 74.0], [322.0, 564.0, 58.0, 74.0], [324.0, 563.0, 58.0, 73.0], [321.0, 563.0, 58.0, 72.0], [321.0, 562.0, 56.0, 75.0], [324.0, 564.0, 56.0, 74.0], [326.0, 561.0, 56.0, 73.0], [331.0, 561.0, 55.0, 75.0], [337.0, 562.0, 50.0, 72.0], [338.0, 570.0, 41.0, 64.0], [334.0, 565.0, 47.0, 71.0], [329.0, 562.0, 58.0, 66.0], [325.0, 563.0, 58.0, 77.0], [321.0, 563.0, 59.0, 73.0], [321.0, 574.0, 55.0, 77.0], [320.0, 592.0, 57.0, 75.0], [313.0, 583.0, 60.0, 74.0], [311.0, 584.0, 58.0, 75.0], [313.0, 584.0, 57.0, 76.0], [319.0, 585.0, 55.0, 76.0], [322.0, 603.0, 58.0, 75.0], [327.0, 578.0, 57.0, 76.0], [325.0, 582.0, 55.0, 79.0], [319.0, 580.0, 52.0, 75.0], [319.0, 585.0, 49.0, 74.0], [352.0, 591.0, 18.0, 65.0], [321.0, 594.0, 48.0, 76.0], [322.0, 616.0, 53.0, 75.0], [319.0, 583.0, 54.0, 69.0], [326.0, 587.0, 54.0, 74.0], [330.0, 585.0, 57.0, 70.0], [338.0, 582.0, 55.0, 68.0], [341.0, 580.0, 55.0, 72.0], [341.0, 578.0, 53.0, 69.0], [345.0, 572.0, 51.0, 71.0], [345.0, 574.0, 56.0, 74.0], [346.0, 587.0, 56.0, 67.0], [345.0, 589.0, 55.0, 66.0], [344.0, 582.0, 57.0, 70.0], [344.0, 583.0, 57.0, 71.0], [343.0, 567.0, 57.0, 68.0], [344.0, 568.0, 58.0, 69.0], [345.0, 586.0, 58.0, 71.0], [344.0, 602.0, 53.0, 62.0], [343.0, 582.0, 53.0, 73.0], [348.0, 594.0, 54.0, 75.0], [353.0, 598.0, 58.0, 72.0], [359.0, 567.0, 58.0, 71.0], [368.0, 572.0, 52.0, 69.0], [367.0, 592.0, 48.0, 72.0], [369.0, 587.0, 39.0, 73.0], [360.0, 587.0, 41.0, 73.0], [351.0, 594.0, 46.0, 71.0], [348.0, 578.0, 59.0, 73.0], [348.0, 570.0, 58.0, 74.0], [344.0, 568.0, 58.0, 75.0], [334.0, 574.0, 60.0, 74.0], [340.0, 567.0, 47.0, 56.0], [344.0, 565.0, 61.0, 73.0], [347.0, 570.0, 59.0, 72.0], [351.0, 570.0, 59.0, 72.0], [362.0, 570.0, 58.0, 73.0], [365.0, 570.0, 56.0, 71.0], [354.0, 572.0, 56.0, 67.0], [346.0, 555.0, 59.0, 70.0], [337.0, 553.0, 58.0, 67.0], [323.0, 557.0, 56.0, 69.0], [335.0, 558.0, 35.0, 67.0], [322.0, 561.0, 49.0, 69.0], [309.0, 568.0, 60.0, 73.0], [308.0, 572.0, 57.0, 70.0], [319.0, 570.0, 55.0, 71.0], [322.0, 575.0, 60.0, 72.0], [321.0, 582.0, 56.0, 72.0], [329.0, 584.0, 56.0, 71.0], [337.0, 584.0, 57.0, 70.0], [339.0, 587.0, 57.0, 69.0], [343.0, 589.0, 57.0, 68.0], [343.0, 598.0, 56.0, 69.0], [341.0, 602.0, 55.0, 70.0], [352.0, 605.0, 45.0, 66.0], [357.0, 599.0, 40.0, 64.0], [349.0, 623.0, 44.0, 47.0]], \"areas\": [2288.0, 2190.0, 2569.0, 3936.0, 5625.0, 7652.0, 9713.0, 10888.0, 12168.0, 12782.0, 13672.0, 14005.0, 14250.0, 13139.0, 12195.0, 10392.0, 8174.0, 6917.0, 6140.0, 3947.0, 5366.0, 4331.0, 2136.0, 3920.0, 3625.0, 3815.0, 3824.0, 3717.0, 1396.0, 3447.0, 1907.0, 3640.0, 3905.0, 3956.0, 3820.0, 3619.0, 3562.0, 3453.0, 3766.0, 3609.0, 3560.0, 3568.0, 3403.0, 3430.0, 3552.0, 3501.0, 3806.0, 3630.0, 3893.0, 3693.0, 3616.0, 3794.0, 3495.0, 3556.0, 3543.0, 3654.0, 3359.0, 3595.0, 3594.0, 3620.0, 3581.0, 3823.0, 3589.0, 3562.0, 3647.0, 3566.0, 3576.0, 3712.0, 3649.0, 3639.0, 3635.0, 2937.0, 2054.0, 2497.0, 1771.0, 3568.0, 3658.0, 3574.0, 3648.0, 3934.0, 3851.0, 3877.0, 3796.0, 3742.0, 3839.0, 3879.0, 3437.0, 3188.0, 803.0, 3171.0, 3441.0, 3101.0, 3370.0, 3285.0, 3099.0, 3105.0, 2889.0, 2584.0, 3300.0, 2878.0, 3000.0, 3382.0, 3446.0, 3378.0, 1788.0, 3454.0, 2723.0, 2373.0, 2642.0, 3487.0, 3620.0, 2888.0, 2313.0, 2188.0, 2164.0, 1985.0, 3581.0, 3756.0, 3872.0, 3177.0, 1103.0, 3633.0, 3774.0, 3732.0, 3769.0, 3541.0, 3360.0, 3100.0, 3366.0, 3074.0, 1868.0, 2339.0, 3784.0, 3611.0, 3512.0, 3692.0, 3535.0, 3610.0, 3579.0, 3484.0, 3387.0, 3314.0, 3170.0, 1823.0, 981.0, 1045.0], \"iscrowd\": 0, \"video_id\": 0, \"height\": 1280, \"width\": 720, \"category_id\": 49504, \"noun_phrase\": \"the nike air jordan logo\"}, {\"id\": 1022, \"segmentations\": [{\"counts\": \"RX^f03V1:oT1NjjN6RU11jjN1RU13mjNMRU14njNMQU14njNLRU14mjNMSU13mjNNQU13ojNMQU14njNMQU13ojNMQU13njNOQU11ojN0oT11RkNNnT13QkNMoT13QkNMoT13PkNNPU12PkNNPU12PkNNPU13njNNRU12njNNRU12njNNRU13ljNMVU12jjNNVU12jjNNUU13kjNMUU14jjNLWU13jjNLVU14jjNKWU15ijNKXU14hjNLXU14hjNLXU14ijNKWU16ijNGYU19hjNEYU1;mjN^OTU1b0V1N3[OkhN04M[WV4\", \"size\": [1280, 720]}, {\"counts\": \"ei`f04jW14F9M2O2hNBkjN?QU1KijN6RU1NmjN3RU1OnjN1PU10PkN0oT11QkNOPU10PkN1oT1OPkN2PU1NPkN3oT1NPkN2PU1NPkN2PU1NPkN2oT1OQkN1oT10PkN0PU10PkN0PU11ojNOQU11ojNOQU11ojNOQU11njN0RU10njN0RU10njN0SU1OmjN1SU1OmjN1SU10ljN0TU10ljNOUU11ljNNTU13kjNMUU14jjNKWU15ijNKWU15ijNKXU14hjNLXU14hjNKYU15hjNJYU15gjNJZU16fjNJZU16fjN^O@OlU1b0^1ZO^`m3\", \"size\": [1280, 720]}, {\"counts\": \"cQgf06iW13N0_N:ejNJVU1:ijNHTU1:kjNGQU1>njNBQU1?ojNAPU1`0PkNAoT1?QkNAPU1>ojNCQU1=ojNCQU1=ojNCQU1>mjNCRU1>njNCQU1=ojNCQU1=ojNDPU1<PkNDPU1=njNDQU1=ojNCRU1=mjNCSU1=mjNCSU1=mjNDRU1<njNDRU1<njNDRU1=mjNCSU1=mjNBTU1?jjNBVU1>jjNBVU1>jjNBVU1=kjNBWU1=ijNCWU1=ijNCWU1=jjNAWU1`0hjN@YU1?gjNAYU1?hjN@XU1?ijN@YU1?hjN_OYU1<PkNYOYU1>Z1LV`h3\", \"size\": [1280, 720]}, {\"counts\": \"faif02nW13M1N1kN3WjNM\\\\U1b0ajN_O[U1f0djNZO[U1g0djN[OXU1h0hjNXOVU1j0jjNVOUU1k0kjNUOTU1m0ljNSOSU1m0mjNSORU1m0ojNSOQU1m0njNUOPU1l0PkNTOPU1l0ojNUOQU1k0ojNUOQU1k0ojNUOPU1l0PkNTOPU1l0PkNUOoT1k0PkNVOQU1i0njNXORU1i0mjNWOSU1i0mjNWOSU1i0mjNWOSU1i0mjNWOSU1i0mjNWOSU1i0mjNWOSU1i0mjNWOSU1i0ljNXOTU1h0ljNXOTU1h0ljNWOVU1h0jjNXOVU1h0jjNXOVU1h0ljNUOUU1k0mjNROUU1l0ojNPOSU1o0o0O101N1N3M6]OaXb3\", \"size\": [1280, 720]}, {\"counts\": \"cXmf01kW1>mhNBnU1c0oiN^OoU1c0QjN_OeU1i0ZjNZOaU1i0_jNWO^U1l0bjNTO[U1o0ejNQOWU1T1hjNmNVU1T1jjNlNVU1S1ljNlNTU1T1mjNkNSU1U1njNjNRU1W1mjNiNSU1W1mjNiNSU1V1mjNlNRU1T1njNlNQU1U1njNlNRU1T1njNlNRU1T1mjNmNSU1S1mjNmNSU1S1ljNoNSU1Q1mjNoNSU1Q1ljNPOTU1P1kjNQOUU1o0kjNQOUU1o0jjNROVU1n0jjNROVU1n0jjNROVU1n0jjNROVU1n0jjNROVU1n0ijNROYU1m0hjNROXU1n0hjNROXU1n0hjNQOZU1m0hjNROXU1n0ijNQOWU1o0jjNoNXU1o0jjNPOVU1P1n0N1N3GoiNWO_U19\\\\i_3\", \"size\": [1280, 720]}, {\"counts\": \"^PQg09fW19F3I7K5L3N3M3N1RjNfNUU1[1kjNeNTU1\\\\1ljNdNSU1^1kjNdNTU1[1mjNeNSU1[1mjNeNSU1Z1njNgNQU1Y1ojNfNRU1Z1ojNeNPU1\\\\1ojNeNQU1[1njNgNQU1Y1mjNjNRU1V1mjNkNSU1U1mjNkNSU1U1ljNlNTU1S1mjNmNSU1S1ljNoNSU1Q1mjNoNSU1Q1mjNoNSU1Q1ljNoNUU1Q1kjNoNVU1P1jjNPOVU1P1jjNPOVU1P1jjNPOVU1P1jjNPOVU1P1jjNPOVU1P1jjNPOVU1P1jjNoNWU1Q1hjNPOYU1o0gjNQOYU1o0gjNQOZU1m0fjNSOZU1n0hjNoNYU1P1kjNiNYU1W1h0O2N2K>VO]XX3\", \"size\": [1280, 720]}, {\"counts\": \"ahcg0`0[W16fhN@fV1T1I5miNhN^U1Z1bjNhN[U1Y1ejNiNYU1V1hjNiNZU1V1fjNjNYU1W1gjNjNXU1V1hjNjNXU1V1ijNiNWU1W1hjNjNWU1W1ijNiNWU1W1ijNiNWU1W1jjNhNVU1W1kjNhNVU1X1kjNgNVU1X1jjNgNWU1Y1ijNgNWU1Y1ijNgNVU1Z1jjNfNVU1Z1ijNgNWU1Y1ijNgNWU1Y1hjNhNXU1X1hjNhNXU1W1ijNjNVU1U1jjNlNVU1T1jjNlNVU1T1jjNlNWU1S1ijNmNWU1S1ijNlNXU1S1jjNkNXU1T1hjNkNZU1S1ljNgNVU1X1i003XOXiNOPW1HdhP3\", \"size\": [1280, 720]}, {\"counts\": \"fhWh0b0VW19I7G8L4M3000000000000SjNdNUU1\\\\1kjNeNUU1[1jjNfNVU1Z1ijNgNWU1X1jjNhNVU1X1jjNhNWU1W1hjNjNXU1V1hjNjNXU1V1hjNjNXU1V1hjNjNXU1V1hjNjNXU1U1hjNlNXU1T1ijNiNYU1W1gjNhN[U1V1gjNgN[U1Y1f000XO]iN3fV1HliNHYV1MniN2QW1NPWd2\", \"size\": [1280, 720]}, {\"counts\": \"UQ^h04^W1a0F6M4J6J6PjNfNVU1^1jjNbNTU1`1kjNbNRU1`1njN`NRU1a1ljN`NUU1^1ljNbNTU1^1ljNbNUU1]1jjNdNVU1\\\\1ijNeNWU1[1hjNfNXU1Z1hjNfNXU1Y1hjNiNWU1W1ijNiNWU1W1hjNjNXU1U1ijNjNXU1V1hjNjNXU1V1hjNjNYU1T1ijNjNXU1V1hjNiNYU1W1h002M5QO`iNJkVd2\", \"size\": [1280, 720]}, {\"counts\": \"mh\\\\h0a0XW17J6K4K5M4L3SjNeNSU1_1ljNbNTU1^1kjNcNTU1^1jjNdNVU1\\\\1ijNeNXU1Z1hjNfNXU1Z1gjNgNZU1X1fjNhNZU1W1gjNiNYU1W1gjNiNYU1W1hjNhNXU1W1ijNiNWU1W1ijNiNWU1W1ijNiNWU1W1ijNhNXU1W1jjNhNVU1X1i000000004QObiNFM3d^e2\", \"size\": [1280, 720]}, {\"counts\": \"mX_h028OVW1b0I7L3ciNTObU1P1]jNQO^U1T1ajNnNWU1Y1ijNgNTU1\\\\1kjNeNSU1]1kjNeNVU1Y1jjNhNVU1X1ijNiNWU1W1ijNiNWU1W1ijNiNWU1W1jjNhNWU1W1ijNhNXU1X1hjNhNXU1X1ijNgNWU1X1jjNhNVU1X1jjNdNZU1[1f000O101TOciNA44`W1Oo^e2\", \"size\": [1280, 720]}, {\"counts\": \"VQch03_W1?J5]iNZOfU1l0ZjNTO`U1Q1`jNPO[U1U1ajNoNZU1V1fjNjNWU1Y1hjNhNVU1Z1jjNfNVU1Z1ijNgNWU1Y1ijNfNYU1Y1hjNfNXU1Y1jjNgNVU1X1ojNcNQU1]1i0O101N102UO]iNE61aW1O1O0MQWd2\", \"size\": [1280, 720]}, {\"counts\": \"eikh03jW15ANlhN2kV18UiNG`V1c0ciN\\\\OUV1j0RjNVOeU1l0\\\\jNUOZU1T1ejNlNYU1W1gjNiNUU1Z1kjNfNWU1Y1jjNdNYU1[1g0N1F^iNVOdV17oiNGWW1MoVd2\", \"size\": [1280, 720]}, {\"counts\": \"eQmh04iW16B=K@QiN5eV14`iNKVV1<PjN^OjU1g0VjNZOeU1k0ZjNTO`U1T1`jNkN^U1W1cjNhN\\\\U1Z1ejNdNYU1_1f0N1@diNZO_V11UjNHR^e2\", \"size\": [1280, 720]}, {\"counts\": \"Rikh07fW14G8K5L3M4K5I7M3M22O1O2RObiN6`V1@VjN2mV1O1OnVd2\", \"size\": [1280, 720]}, {\"counts\": \"WYih01iW17dhNIdV1a0[iN_O`V1f0`iNZOZV1l0fiNTOUV1P195L4M201O1O12TOmiNGUV15oiNHVW1N2N1NP_e2\", \"size\": [1280, 720]}, {\"counts\": \"aafe01jg31[gNOTaM0[W12chN01NV`g20j_XM1\\\\W1373YO1ViN0fV13]iNHaV1=_iNA`V1b0`iN]O]V1g0:33GhoQ3\", \"size\": [1280, 720]}, {\"counts\": \"fQ^h01_W14khNMoV19RiNFiV1?WiNAdV1c0]iN]O^V1h0biNYOXV1k0iiNWOQV1m0oiNROoU1P1RjNnNjU1X1=2M2OYOaiNA52[V19fiN_O45XV15XjNJn]T3\", \"size\": [1280, 720]}, {\"counts\": \"PiRh0;^W19I5L4J6K5K5L3N3N11000000O2N1O10001N101TOaiND42bW1N1O001LQoQ3\", \"size\": [1280, 720]}, {\"counts\": \"ohmg0=^W15J5K6K5K5L4L4M3N2O00100000O10001N2N2O0O101O1N3mNZiN02M80aW1N101OnVS3\", \"size\": [1280, 720]}, {\"counts\": \"lhcg0=]W17J6K4K5J6L4L4K5O0010000001N2O1OO100000O10001O000O2N1O10001N2O1_OgiNXObW1O00n^T3\", \"size\": [1280, 720]}, {\"counts\": \"aPQg0?^W15M8H3L3L3L4I7L4O1O1O1000000O10000000O100000000O1000000O100000001O000O100000000O2N1O101O0O2O1BciNVO_V1`0miN]OYnV3\", \"size\": [1280, 720]}, {\"counts\": \"_hYg0e0XW18F6G8L4L4N2O100O101OO100000000000000O010O2O00000000000O101O00000O10000O2O0O101O1N2O1@g0^OYPR3\", \"size\": [1280, 720]}, {\"counts\": \"\\\\X\\\\g0h0TW18C:I6M3M3O1N2000O100000001O000000000O10000000O02O0000001O000O10000O101O0O10001N1M4F9J9I\\\\`o2\", \"size\": [1280, 720]}, {\"counts\": \"^P`g01mW1g0XO5G8I6K5N1O2N200O2O0000000001OO10000O010000O101O0000001O0O1000000O2O000O100O1O1O1N3F9M5L6_OdXi2\", \"size\": [1280, 720]}, {\"counts\": \"^Xfg0j0RW16I6G8M3N2N2O1O100O100O010O10O2O00000O10O100000O101O0000001N1000000O100O2O000O101M2E<K6G_`e2\", \"size\": [1280, 720]}, {\"counts\": \"^Xfg0j0RW16G9G7M3N101O1O1O10000O10O10001O00O010000O1000000000001N10000O1000000O2O0O100O101M2E<K6G_`e2\", \"size\": [1280, 720]}, {\"counts\": \"oXfg06WW1g0E9G8J5N10100O1O100O10O01O1001N1000O010000O10000000001O0O100O1000001N100O100O2O0N2L5F:Ha`e2\", \"size\": [1280, 720]}, {\"counts\": \"]hhg0g0SW19C=I5M2N2N2O1O100O0100O10001O00000O1000O10O1000001O0O101O000O1000000O2O0O101N1O1O2I6Eh0^OSPc2\", \"size\": [1280, 720]}, {\"counts\": \"^Xfg01nW16K5J8H5K4B<J5O2M1O2N2O1O1O10000000000000000000O10000O10000O10001O000000000O101O0O101O0O1O2M2I8H8L6_OeP^2\", \"size\": [1280, 720]}, {\"counts\": \"^XPh02lW1c0[O:G5E7N301L4N2O1O1O1000001OO2O0000000O100O10000O1000O11O0000000O2O00000O1O101N1O1O2M2H9M3L6BmhNE`oX2\", \"size\": [1280, 720]}, {\"counts\": \"_XUh0g0VW15H7D;N2M3N2N101O101OO01000O2O00000O100O100000000O100000001O000O2O000O100O2O0O101N1N2L5E?@ihNHb_V2\", \"size\": [1280, 720]}, {\"counts\": \"b`Vh0h0UW15G9F9L3M3N2N2O001000O1000000000000O2O000O010O10000O100000001O000O100000000O100O101N2N1ChiNQOZV1j0a0Ha0_OT`Q2\", \"size\": [1280, 720]}, {\"counts\": \"gXUh04jW1f0UO8J6H8L3M2O101O1O2OO1000000000000O10000O1000O10O10001O00000O10001O0O1000001N100O1O2N1N3J6]OViNEUW17b0Gm_Q2\", \"size\": [1280, 720]}, {\"counts\": \"WQTh01^W1i0B8I6I6L4M2N3O001O1O2OO2O0000000000001N01000O10O11O000O101O00000O101O000O100O101N100O2M2L6E=]Of0HhgR2\", \"size\": [1280, 720]}, {\"counts\": \"WiRh01]W1j0A9J5H7M3N2N2N110O100O100000000001O0O101O0O1000O100000O10001O00001N10001N10000O1O101N1O1N3J7H:[O]PT2\", \"size\": [1280, 720]}, {\"counts\": \"jPTh0a0ZW1<A9M3J4M4M3N2N102N1O1001O000000000O2O0O100O10000000O1001O00000O2O000O101O0O100O101N1O1O2I7K6BQiNAWW15[hR2\", \"size\": [1280, 720]}, {\"counts\": \"hhRh0i0TW16F7G9M3M3N1O2O1O10000O1000O2O0000000O2O0O10O10000O100001N101O000O101O0O101O0O100O1O101M3M4Db0VOZXU2\", \"size\": [1280, 720]}, {\"counts\": \"chmg0k0oV1;J2M2L4K6K301O10000O100000O2O00000O2O00O0100000O11N10000000000O10000O2O000O10001N1O101M3N4SOiiNHXf\\\\2\", \"size\": [1280, 720]}, {\"counts\": \"dPTh0i0oV1;E9M3L3N2O1O010O1000000001O000000000O100000O010O02O0000001O0O1000000O2O0O10000O2O0O3M3CXiN^OiW1HR_V2\", \"size\": [1280, 720]}, {\"counts\": \"ah\\\\h09cW17L9E<C5L4K4N20000O100O100000000001O00000000O1000O100O101O0000000O10001O000O10000O101N101N2O4_OXiNBeW1M2Oofh1\", \"size\": [1280, 720]}, {\"counts\": \"h`eh02mW14H9^hNJoV1e0N2N2N2I7L4O1O1O100000000000000001O000000O1000000000000ZjN_NnT1`1RkN`NnT1`1QkNaNoT1_1mjNeNSU1[1mjNeNSU1[1mjNeNSU1Z1njNeNSU1[1mjNeNSU1[1mjNfNRU1Z1njNfNRU1Y1ojNgNRU1X1mjNjNRU1V1njNjNRU1U1n0O0O2N4Mo0PO2LQo_1\", \"size\": [1280, 720]}, {\"counts\": \"ahPi0?]W1=E2K6K4L4N2N3O0O100000O10O1000000001O\\\\jN`NhT1`1XkN`NhT1`1XkN`NgT1a1YkN_NgT1a1XkN`NhT1`1VkNcNiT1\\\\1VkNfNjT1Z1VkNfNjT1Z1VkNfNkT1Y1TkNhNlT1Y1SkNhNlT1X1SkNiNmT1W1SkNiNmT1V1TkNjNlT1V1TkNjNlT1V1TkNhNnT1X1PkNiNQU1W1njNiNSU1V1njNjNSU1U1mjNlNRU1S1ojNnNPU1R1PkNnNPU1R1PkNnNQU1Q1ojNoNQU1Q1ojNfN[U1Y1ejNfN_U1U1k0TO[iNJmfY1\", \"size\": [1280, 720]}, {\"counts\": \"cXXi02kW1=E2D<I7L4M3N100O100000000O1O101O0YjNdNjT1\\\\1TkNgNkT1Y1TkNhNlT1X1TkNiNkT1W1TkNjNlT1U1UkNlNjT1T1VkNlNjT1T1UkNmNkT1S1UkNmNkT1S1UkNmNkT1T1TkNkNmT1T1RkNnNnT1R1QkNoNoT1Q1QkNoNoT1Q1QkNoNoT1P1RkNPOnT1P1QkNQOoT1o0QkNQOoT1o0QkNQOoT1o0QkNQOPU1n0PkNROPU1n0PkNQORU1m0ojNRORU1n0njNQOUU1l0mjNkN]U1Q1P1_OZhT1\", \"size\": [1280, 720]}, {\"counts\": \"^`Yi07gW18F7J4G8M3N200O1TjNiNoT1X1ojNjNPU1V1ojNlNPU1T1njNoNQU1R1mjNPOQU1Q1ojNnNRU1R1njNnNQU1S1ojNmNQU1S1njNnNRU1R1njNoNQU1Q1ojNPOPU1P1ojNQOQU1o0ojNQOQU1o0ojNQOQU1o0ojNQOQU1o0njNRORU1n0njNRORU1n0njNRORU1n0mjNSOSU1m0njNSOQU1m0ojNSOPU1n0PkNROQU1m0ojNSOQU1m0ojNSOQU1m0ojNSOQU1m0njNTORU1k0ojNUOQU1k0ojNUOQU1k0njNVOSU1i0mjNWOTU1h0ljNXOUU1g0ljNWOVU1g0kjNXOYU1d0ijNYOaU13miNGh00[mU1\", \"size\": [1280, 720]}, {\"counts\": \"YhPi0421`W1:H9J7L3N2N001N101O0O101O0O100000010O0O100010OniNhN\\\\U1X1djNkNYU1V1ejNkN[U1T1fjNmNYU1S1gjNmNYU1S1gjNnNXU1R1hjNnNYU1Q1gjNPOXU1P1hjNPOXU1P1hjNPOXU1P1ijNnNXU1R1gjNQOWU1n0ijNTOVU1l0jjNTOWU1k0ijNUOWU1k0ijNTOXU1k0ijNUOYU1i0gjNWOZU1g0fjNZO\\\\U1d0djN\\\\OcU1KkiN8c0LoU10RjN0QW1K^fY1\", \"size\": [1280, 720]}, {\"counts\": \"[_eh09fW16J4L5L1N2O1O2N1000000O1O1O1O2N1O1O1O1O1O001O000VjNiNkT1W1UkNiNlT1V1kjNhND4`U1U1kjNkN_O3fU1Q1kjNVOTU1j0ljNVOTU1j0ljNWOSU1i0mjNWOSU1i0ljNXOUU1g0kjNYOUU1f0ljNZOTU1f0ljNZOTU1f0ljNZOUU1e0kjN[OUU1e0kjN[OUU1e0kjN[OUU1e0kjN[OVU1d0jjN\\\\OWU1c0ijN]OZU1?gjNAgU1O[jN1jU1GYjN9gV1000000DdhN4]W1HihN4Qn_1\", \"size\": [1280, 720]}, {\"counts\": \"Pgah02fW19I7N3N2N2N1O1O1O1O1O2N1O2N2jiNnNAN`U1T1ojNPO\\\\O0dU1Q1ojNXOPU1i0PkNYOmT1h0QkNZOnT1f0RkNZOnT1g0QkNZOnT1f0RkN[OmT1e0SkN\\\\OlT1e0SkN\\\\OlT1e0TkNZOlT1g0SkNYOmT1g0SkNYOnT1f0RkN[OmT1e0SkN[OmT1e0SkN[OmT1e0SkN[OmT1e0SkN[OmT1e0RkN\\\\OnT1c0SkN]OnT1b0RkN^OnT1b0RkN^OnT1a0SkN_OnT1`0RkN@nT1`0RkN@nT1`0RkNAnT1>RkNAoT1?QkNAoT1>RkNBPU1<PkNDUU17kjNIeU1E^jN:dU1CojNJUU12a1L[Pe1\", \"size\": [1280, 720]}, {\"counts\": \"nUZh02lW17K1O1O2N1M4L3L4M300O101M3N1N2O1O1O10OQjNkNSU1V1ljNkNSU1V1ljNlN\\\\O3ZU1R1XkNmNZO4^U1P1WkNXOiT1g0WkN[OgT1f0XkN[OgT1e0YkN\\\\OgT1d0XkN\\\\OhT1d0XkN]OgT1d0XkN\\\\OhT1d0XkN\\\\OgT1e0YkN\\\\OfT1d0ZkN\\\\OfT1d0YkN]OgT1c0YkN^OfT1b0ZkN^OgT1a0YkN^OhT1b0XkN^OhT1b0XkN^OhT1a0YkN@fT1`0YkN@iT1?XkN@hT1`0YkN^OiT1`0XkN@hT1`0XkN_OjT1?WkNAkT1=TkNCPU19QkNG\\\\U1JfjN6^U1BejN>_U1[OejN6ZOMRW13a0K^`g1\", \"size\": [1280, 720]}, {\"counts\": \"`mbh0:XW1`0I6M5L2O1O1O001N2O1O00010N101O1O000000010O000fjNgNmS1Y1PlNkNoS1U1okNmNQT1S1mkNoNST1Q1lkNPOTT1P1kkNPOVT1P1dkNVO\\\\T1j0ckNVO^T1j0bkNVO^T1i0dkNVO\\\\T1j0dkNUO^T1j0ekNPO^T1P1dkNcN[O1SU1Z1Y1O2O1N2O3Li0XO2N1OSjh1\", \"size\": [1280, 720]}, {\"counts\": \"aTPh0a0`1A]T1e0\\\\kNC\\\\T1?bkNC]T1>]jN]Ol0:cT1`0]kNCaT1=[kNGeT19YkNIgT18YkNGgT19YkNHfT19ZkNFfT1:ZkNFgT19YkNHfT19YkNGfT1:YkNGgT19YkNGgT18ZkNIdT18]kNHbT17_kNJ`T17`kNGaT19`kNF`T1:akNE_T1;akNF^T1;`kNG_T19bkNF_T1:`kNF`T1;hkN\\\\OXT1e0jkNWOWT1j0jkNSOVT1n0lkNoNUT1R1kkNlNVT1U1jkNjNVT1W1jkNgNWT1Y1ikNgNWT1Z1ikNdNXT1\\\\1V11O001O2N2N00O2O000O10000O2OO01O101O0O1000001N2N2H9K]cg1\", \"size\": [1280, 720]}, {\"counts\": \"S^lg01nW14VhNN_W1<N2N001O101TO_OUjNa0eU1FZjN:cU1J\\\\jN7]U1OcjN3VU12jjNNSU16ljNKQU17PkNInT19QkNGoT19QkNHmT1:RkNFmT1:ZkNAdT1`0\\\\kNAbT1`0^kN@]O]OlT1S1gkN@]O]OlT1T1fkN_O^O]OlT1T1ekNA^O[OmT1T1ekNA]O\\\\OnT1S1ekNA]O]OmT1S1ekNA\\\\O]OoT1R1fkNAZO]OPU1R1fkNAYO^OQU1R1fkNFZT1:gkNFWT1<hkNDYT1;gkNEYT1<fkNDZT1<ekNE[T1<ekNC\\\\T1=ckNC]T1=ckNC]T1>ckNA]T1`0bkNA^T1?bkNA\\\\T1a0dkN_O[T1a0ekN@ZT1a0fkN^O[T1b0dkN^O\\\\T1b0ekN^OZT1c0ekN\\\\O\\\\T1e0dkNZO\\\\T1g0ckNYO]T1h0bkNWO_T1j0`kNUOaT1l0]kNfNJIkT1e1kkN[NUT1h1gkNZNXT1f1hkNZNXT1f1ikNWNYT1h1ekNUNaT1j1k0O2M2000000O2M2ObjNPOnS1P1SlNoNmS1P1TlNPOmS1o0TlNPOlS1l0\\\\lNPOeS1n0^lNPOcS1l0alNSOcS1g0`lNXOPT1OZlN0QV1J`hY1\", \"size\": [1280, 720]}, {\"counts\": \"\\\\^lg02oW1Of_20Z`M001N=D2N001O1POA]jN?VU1OijN1UU12jjNOSU14ljNLQU18njNIoT1:PkNHlT1;TkNCkT1`0VkN]OkT1d0VkNYOjT1h0VkNXOjT1h0UkNZOjT1f0VkNZOjT1g0UkNYOjT1h0WkNWOiT1i0WkNXOiT1g0WkNYOiT1h0WkNVOjT1j0WkNXOfT1h0[kNZO@^OhT1Y1hkNZO]O^OkT1X1ikNZOZO_OmT1W1ikNZOYOAmT1V1jkNYOWOBoT1U1kkNXOVOCoT1U1kkN_OUT1b0kkN]OUT1d0jkN\\\\OWT1c0ikN^OVT1b0ikN^OXT1c0gkN\\\\O[T1c0ekN[O]T1f0bkNZO^T1f0bkNZO^T1g0akNYO_T1g0akNYO_T1g0akNYO_T1h0`kNXO`T1i0_kNVOcT1j0\\\\kNVOdT1k0ZkNUOfT1n0YkNUOcT1o0ZkNROdT1S1WkNoNgT1T1VkNlNjT1W1RkNjNnT1S210001O0O10000000001O0O1000001O000^OojNTNSU1S1jjNROnU1d0`jNROfU1f0P1EckT1\", \"size\": [1280, 720]}, {\"counts\": \"SnXh01nW1101O001ZhNM]W13bhNN^W12bhNO^W10chNO]W11ahN5aW12N3jNK_jN5SU1:ljNFQU1=ojNDnT1>RkNAmT1a0TkN^OjT1d0VkN\\\\OhT1g0XkNXOfT1k0YkNUOgT1k0YkNWOdT1j0]kNXO`T1h0akNYO]T1g0ckNYO]T1h0ckNWO]T1i0dkNVO\\\\T1j0ekNUO[T1l0dkNTO\\\\T1m0dkNRO\\\\T1n0dkNRO\\\\T1o0dkNPO]T1o0ckNRO\\\\T1o0ckNQO]T1o0ckNRO\\\\T1n0dkNRO\\\\T1o0ckNRO\\\\T1n0ckNSO]T1n0bkNRO^T1o0akNRO_T1m0akNSO_T1n0`kNROaT1m0`kNRO`T1o0_kNRO`T1n0`kNSO_T1m0akNTO^T1m0`kNVO^T1l0`kNSOaT1o0]kNQOcT1P1[kNQOeT1T1ojN]NL?UU1V20O000001O1N2OO2O0O1O1O2N1O100O1O2M2K5M3N2L5F9G9J7G9J<VOmkj0\", \"size\": [1280, 720]}, {\"counts\": \"ZVdh05jW12I7O1O1O000cNIQkN7jT12SkNNjT15UkNKiT19VkNGeT1=\\\\kNCaT1`0^kNB`T1>`kNB_T1`0akN_O_T1b0`kN_O^T1b0bkN^O^T1b0ckN]O\\\\T1e0dkN[O[T1e0fkNZOZT1f0fkNZOZT1g0fkNXO[T1g0ekNZOZT1g0fkNXOZT1h0fkNXOZT1i0ekNWO[T1i0fkNWOYT1j0fkNVOZT1k0ekNUO[T1l0dkNUO[T1k0ekNUO\\\\T1k0bkNWO]T1j0bkNVO^T1j0bkNWO]T1j0bkNWO]T1j0bkNWO^T1i0akNWO_T1j0`kNVO`T1k0^kNVObT1k0]kNUOcT1l0\\\\kNTOdT1n0YkNSOhT1n0RkNVOnT1n100O1001O00000000000O10000O2N100O1O1O2M2N2M3M4K4B?K4M4J5I8L6Bj[c0\", \"size\": [1280, 720]}, {\"counts\": \"Wffh06hW13N2N1N3N2N1M4L4L3N3M2kN[OljNi0PU1AdjNe0XU1B`jNb0^U1Q1O2N1O2O00000000001O0000001O001O2N1O1O0001O001O00001O00000000000000000000000O1000000O2O0O10001N1N2L4L5N1N2K5_Ob0N2N1I8K5L4Em[>\", \"size\": [1280, 720]}, {\"counts\": \"kTih0173;b0fU1S1N2N101O0M3K5M3N3M4M2M2O2O0O2O000O10001O010O0000001O001O2N1O1OO101O000O101O1O01O000000000000000000O101O0O100O101N100O2M2M3M4M2O1L4E;H9M2M3L5J6K5C?FhS=\", \"size\": [1280, 720]}, {\"counts\": \"\\\\ffh02kW15K4M3[O0TiN5\\\\V1EeiNo0UV1b0J6K5M3L4N2M2M4M2N3N101O1O1N101O001O0000001O0001O01O001O0001O00O12N1O00010O02NO1000000O10000O100001OO2O0O10000O2N1O2M2L5J5K5G9J6J7J5K6L3L4O3FmhNAXW13nk;\", \"size\": [1280, 720]}, {\"counts\": \"gmgh04cW1:EFnhN9PW1LPiN1PW10QiNOnV12mhN3SW1MlhN5`0GiU1i0RjN[OkU1`1M2L3M4M3M2N3N2L4N2M202M2O001O001O0000000000001O00000000010O0000010O1O1O0000000000O101O01N101O0000O100O101O0O101O001N1O1O1M3L5J5B>E<L4J6G;J5J`[9\", \"size\": [1280, 720]}, {\"counts\": \"b^oh01bV11PjNO56cU1c0YjNAbU1i0liNCPV1Y1L4M3M3N2M2O2N1N3L4N2N2O0O101O001O01O00000O1001O0000000001O0002O0O1O000000O1000001OO101O000001OO10O2O000O2O000000001N1O101fNPkNAQU1=SkN]OQU19\\\\kN^OkT18`kNAfT18SU=\", \"size\": [1280, 720]}, {\"counts\": \"anQi03aV13djNOXU17fiN4:IlU1_1L4L4L4N2M2N3N2L3N3N2N1N3O0O2O001O0000001O000000000000000001O01O1O01O0000001O000000O101O0010O0O100O101N100100O00gNljNFTU1:mjNESU1:ojNDSU1:PkNCQU1;RkNCoT1MbkNOaT1ObkNNaT1OckNM`T12dkNBeT1=k1CZX1Hjj6\", \"size\": [1280, 720]}, {\"counts\": \"bnVi01nW12N2N2N3nNK]iN13M1:[V1R1K6K4K4M4J6M2N2N3M2O2M2O2N1O1O2O000000000000001O010O000000001O000001O010O01O0000O1O10001O00O2O1O0000O10001O0O1O2N1O1N3M2O1O2L3L4L4N3N1VNPjNc1QV1\\\\NQjNc1^a0\", \"size\": [1280, 720]}, {\"counts\": \"bn`i02lW14M3M2ZOGciN;YV1HfiN8WV1LhiN5UV1N\\\\iNa0_V1c0M3J5E<J6M2N3N1O1O2N100O2O0O2O1O00000000001O0000001O1O000001O0O2O00000001N2O001O0001O000O100O101N1O10000O1O101N1O2N10^[O\", \"size\": [1280, 720]}, {\"counts\": \"bn[i02mW15K3M2SO2ciN3YV1k0N3M3M2M4L3L4J7M2L5J5O1O2N1O101O000000000O101O01O000001O01O00000000000000001O00001N10001O001O0000000aMejNY2[U1cMljNY2gU1G3L2N3M2M2N2O1L5K4L5M4A>H70aZO\", \"size\": [1280, 720]}, {\"counts\": \"UfPi0=]W18J5M3M2L5K4L4L5K4M3L5G9H8L3N2O2N1O2O001O000O2O1O00010O0000000001O01O1O000000000000O10001O01O00000000O101O1N2O1N1O2N:F6I5L4L2M2N3L4K4G:J7K5K3L4M4Ljj6\", \"size\": [1280, 720]}, {\"counts\": \"Wnlh03lW15K3K4L4K5K5K5L3@ROkiNQ1RV1`0H8J7N2M3K4O1N201N1O1O2O0000001N2O1O000001O0001O0000010O1O1O00000000O10001O00000000001N10000O101N1dMhjNo1jU1I2N4L3N1O3GmiN`NUV1X1>H7M3N3L4K=Amj;\", \"size\": [1280, 720]}, {\"counts\": \"Sfkh07_W1JhhN9UW1MehN8UW1<H8D<@?J7K4M4M2N200O2O1N101O001O001O0000O11N1000000000001O00001O0000001O1O2NO100O11O000O10O2O0O2O000O2O0O100O2N1O5^MjjNQ2fU1L3L3N2N1N2N2L5I6M3M3N3K7GXk;\", \"size\": [1280, 720]}, {\"counts\": \"R^eh03hW19J3N2000O01N2H8\\\\O\\\\OfiNi0TV1d0E;I7L4N3L3M3O2O0O1000100O001N2OO2O000001O00001O0O101O0000O1000000001O000001O000O10000000001N10000O3N2^MPkNo1cU1M2N2M3N100M4K4F;L2L5M2N4K6J5NV[>\", \"size\": [1280, 720]}, {\"counts\": \"Qffh06hW14K5M101WOBgiNf0QV1DgiNb0QV1FliN=lU1P1H7M3N2N2O2N10001O00001N101N101O0001O00000001O0O100000000000000000O11O01O000001OO100000001O0O101O0O2O2N>A3N1M3N1O2M2N2M4G8E;L4L4M3N4LZc?\", \"size\": [1280, 720]}, {\"counts\": \"gmgh056NXW17dhN0`V12hiN1GN_V15ciN>[V1e0O1F^NTjNd1fU1cNUjN_1gU1`0L3M3O2N1O100O101O001N10001O001O00000000001O00000000000000001O000000000000000000000001O001O0000001O001O1O`0_O2O1N2M3M3N1N2M3L4H8F;L3K5M6JXS=\", \"size\": [1280, 720]}, {\"counts\": \"SnQi08dW16oNCWjNb0eU1HliNa0oU1EhiNc0UV1e001M2L5L3M4L3N2N3M2O2N1000000O2O1O001O0000000000001O0000001O0O100000000000O2O01O0010O0000000O10001O00010O00002M?B3L3M1N3N1N3J5K5E;K5K5N3L3L5LXS3\", \"size\": [1280, 720]}, {\"counts\": \"PVXi0>^W14L4M4I7M3J7C<F:J6J5L4N2O1O2O1N1O2O001O0000001O0O2O00001O001O0001O000010O00001O2N2N0000O100O100000010O01O000001N101N100O2O1N>A4M2M3M3M3M3L4L3I7D=NU[O\", \"size\": [1280, 720]}, {\"counts\": \"feZi06c0N]V16`iNMZV18diNJSV1IdiN`05KTV1V1K4L5J6M2M3N2N2M3N2N3O2M3N1O0O2O001O0000010N101N101O000001O001O00010O01O0000000O0101O000010O1O00O100000001N101O003VMojN_2[U1M3M3L4L5J4L5J5K6\\\\O_iN^OhV1a0W[O\", \"size\": [1280, 720]}, {\"counts\": \"bnVi01nW12N2oN7hiNKTV18liNHkU1b0SjNAjU1a0SjNAjU1]1L4K4K6L3N3N100N3M2N2O3N1O2N00001O0O11O000000O2O001O0000000010O0000000001O001OO1O10001O000001O01O0O10001O000O2O1O?A2N1O2M3L3L4M3L3I7dNdiN=0EiV1NXiN;1Dda0\", \"size\": [1280, 720]}, {\"counts\": \"bfUi01jW17N1O1YOJaiN8VV12_iNDN>`V11]iN:`V1e0L4L4H7H9L3L5M2N2N2N3O00001O1O00001O00000000000O2O00001O01O000001O000000000010O0O100O101O00O2O1O0001O0O101O000O2`MljNR2cU1N2O0O001N2M3M2M4L4H7nNYjNEmU1;VjNBh`0\", \"size\": [1280, 720]}, {\"counts\": \"bnQi01ZW12TiN3iV15lhN1oV1c0K5K5J6L4J5I8L3L6I5M4M2O2N1O1O101O0O101O1O00001O000000000000001N110O000010O0000000001O001O0000O1O2O0000000001N20N100Ob0_O3M2M2O1O1O1O0O1O1N2M4hNPjN;TV1VO\\\\jNd0iU1YOZjN`0hX4\", \"size\": [1280, 720]}, {\"counts\": \"Sfkh01nW18H6I5E9I7TOk0O2M3N101N2M3H7N3N1O100O101O001O0O10001O001N2O1O00001O00000001O0010O1O00001O0000000000001O1O0000O1000001O0O2O0101O09E4K4N1O0O2O0O2N1O1M3M3L4M4POQjNA[V10S1L8HSS3\", \"size\": [1280, 720]}, {\"counts\": \"bnlh01lW15K4YOK_iN9[V1i0SOkNXjNk1gU17O2O1L3L5M2N3N1O2N101O001O0O2O00000000001N10000000000000010O00001O001O0000000001O0000O1000000001O000O>C1O1O2N10O000000O2O00000O1O1O1RNZjN]1kU1^N`jNV1gU1iNhjNb0^U1[OjT8\", \"size\": [1280, 720]}, {\"counts\": \"dVih04jW16@InhN9mV1NmhN6PW1?M3N2L4F:I7_OYN^jNP2_U1;L3O1O101N1O2O1O0O101O00001O0000001O0O01001O0001O0000001OO100000001O000000O10000000001XMnjN`2ZU100O3M2M2O001O0001O1O00000O10O1O2N2N2M4mM\\\\jNc1]V1B6F;[OPiNLTa:\", \"size\": [1280, 720]}, {\"counts\": \"`nbh04hW1?D3M3L3M3M2XO@niNc0oU1h0M4J6G9M3M3O001N2O0000001O000000001O00000O2O1O0000001O1O1O001O01N100001O001O1O1O0000000001O006J2N1O1O1N2O1O2N0O2O000O2O0O1O1O2N100M4M3Ke0\\\\O6ZO_iN@lV1OgZ>\", \"size\": [1280, 720]}, {\"counts\": \"gf\\\\h03kW15L2M6I4I6N3M3O1N2N2N1O2N1O2N2N2O1N2O1O0O100O2N2O0O1O1O2O0O1N2O1O2N1O1N2N2O2N1I8H7O100000001O0000001N2O0001O0000001N105K2N1O001O1O1O1O0O2O0000000O1O1O2N1O1M4K4hNTjN\\\\O32aV1Lgk`0\", \"size\": [1280, 720]}, {\"counts\": \"fnbh02cW1`0H5L5L2N2N2O1N2O1N1O2M3N2O1N2N1O2N1O1N3M2O101N2N1N2N2O1N2N3N1N2M3M4M3L4N100O100000000001O01O2N000000O1000001O00O10O2O010O7H2O2N2N1O1O001O1N100O1O100O2M2M4`NSjNB5i0VV1XOhS=\", \"size\": [1280, 720]}, {\"counts\": \"]ffh028OZW1`0H8K3O1N2N3M2O1N2N2O1N101N1O2N1O2N2N1N2O1O1O1O2N1O101O0O100O2O1N2N1O1O1O1O100O1N2O2N2M2N2M4L3O1N2O100001O10O1O001O0000O101O010O1O3M5J7J2N1N102M101O0O1N2N2M3L4UOliNB\\\\V1<iiNZO^V1e0?D=M6J`j1\", \"size\": [1280, 720]}, {\"counts\": \"f^jh03gW19K4H7O2M2N3M2N2N2O0N3M2N2O2M3N1N3M2O1N2O2N1O1O002O0O101O1N1O1O1O2O0O1O100N201N1O2L3N2O1N2N3M2O1N2101O00O000010N100O11O00000001O`0@2N2N2M2O1O1N10O0001N2N2M2D=C=L4F;I;I4NYZO\", \"size\": [1280, 720]}, {\"counts\": \"gfkh02lW16K3K6I5K5M4M2N2N1O2L3M4L3M4K5M2O2L3M3O1O2N1O1O1N3N1O3L2O2M2N3M2O1O1O10000100O1O000001OO100001O01O01O001O0001N1000001O0001O000000\\\\MRkNV2`U1K4M2N1N3N1O1N2O0N2O2K4DeiNoN^V1k0?M3[OQiN5SW1Eg0O10XZO\", \"size\": [1280, 720]}, {\"counts\": \"g^oh01lW16E>`hN@RW1i0M3H8VOj000O001M3L3N3M3N3M1N2O2O00000010O0001O1N101O0000000001O0000O10001O000001O01O00000000001O01O0O101O0O2O001O01O01O8H4K7J3M3M2N001N101N1M3L5_OgiNSO_V1f0a0K?\\\\ORR3\", \"size\": [1280, 720]}, {\"counts\": \"V_oh01mW15VOLaiN7ZV11aiN0ZV16ciNLcU1Q1WjNROfU1Q1WjNQOiU1d1O1O0K6M3N1N3N2O1N101O00000000001O000000001O00000000000000001O01O0000000001O00000001O001O0O2O00000O200^MRkNP2`U100O1O001O1N101O0O10000O1O2aNRjNf0U`:\", \"size\": [1280, 720]}, {\"counts\": \"Volh01nW16J2M3YOI\\\\iN`0^V1d0N2O0O2L4L3N3M3M2N3L3M3N3N1O1N3N1O1O101M2O1M3O1O10000000000000010O010O0000000000000001O000010O000000O100Oe0\\\\O1O10O10O0000000O10000O101M3Lb0\\\\O:YOSiNMeW1HoP8\", \"size\": [1280, 720]}, {\"counts\": \"WgPi06PW1LfiN9UV1LgiN6UV10eiN4WV14aiN0XV18eiNJVV1R1J7I6N3M3M3M2N3N2O0O2O0000001O0001N1000O100000000000001O00001O01O000000000000001O00010O00001N1000002N6_MajNV2fU1N2N1O1O1O0O10000O2O0O1O1N3]NoiNR1[W1ROoP8\", \"size\": [1280, 720]}, {\"counts\": \"UoQi03UW1l0G5G:UOdN^jNo1aU1QN]jNR2bU17O1N2N1O2O1N2O1O1O1O0O10O100001O00001O01O000001O00O1001O0000001O000001O000000010O0000000O1O2O00000010001N2N2N3eM^jNR2iU1O2N2M2O0O2O001N1O1K6nNXjNDoU17Pc5\", \"size\": [1280, 720]}, {\"counts\": \"fnQi0<ZW1=I5M3N2M3N2N1N3K5L4K5J6L3L4M4M2N2O2O0O101O1N2O1N1O2O00000000000O1000001O0000000001O000000O20O000000000O2O000001O0010N2O:F5J4M1O1O1O001N2O0O001O2M2K6eNTjNKaV1Nn0M10WZO\", \"size\": [1280, 720]}, {\"counts\": \"XVXi07fW15L5L4K3N2N1N3M4I7H7M4K4M3M3L4K5M4L4M2N3M2N3M2N3N1O2M20M3L3O10000O11O01O000O20O100O0O100000000000000000001O0000000001O1O1WMUkN[2[U1M2M2O0O101O1O000000O100aZO\", \"size\": [1280, 720]}, {\"counts\": \"[Vbi0;aW1:F7BVO^iNn0WV1c0K4L5L3O2J6G9O1N101N101O00001O000000001O000000001O0000000O100001O0O1000000001O1O1O00000000000000O10000O101O00001N10001N4M1OU[O\", \"size\": [1280, 720]}, {\"counts\": \"]Vli01mW14K;^O=K4J5L3L5M3L3M3M4K5G9K5L4M2O101O0001O01O0O1000000O100000O10000001O1O000000000000000000001O00000000O101O0O101O_[O\", \"size\": [1280, 720]}, {\"counts\": \"\\\\fXj04bW1a0F6F9M3L2N2O2M3M2N2N3N2M2O2L4K4L5K4N2O1O1O100N3M2O2N1O1O10000000O100000010OO0100010O0000O1000e[O\", \"size\": [1280, 720]}, {\"counts\": \"iU[j035:SW1a0D9@>E;K5M2O1N3M3N1N3N3M2O001O1N1000000010O000000000000000000001OO10O1001O000000000001O00f[O\", \"size\": [1280, 720]}, {\"counts\": \"gU[j054;nV1a0@?E:H8K4M4N2L3N3N101O2M2O00000O2O00001O00000000001O0000000001O0000000000000000000001O000j[O\", \"size\": [1280, 720]}, {\"counts\": \"ie]j01mW16UhNN^W1=dhNCPW1?mhNEPW1g0N1O2N1O2M3N1M4L3L5K5M2L4M4L3K5N2N3N1O2N1O1O100O2O0000001O0010O0000001O00000000k[O\", \"size\": [1280, 720]}, {\"counts\": \"]n^j06hW15ZO5ohN0aV1P1D<I5N3N1N3K5N2N2N2N1000001O0O100O2O1N2O01O00O10000000000O0100000001O001O1O1N10a[O\", \"size\": [1280, 720]}, {\"counts\": \"\\\\^\\\\j02kV1b0^iN3SV1X1D2N2O001N1O2M3M3L4N2N2N1O10001O00001O0O10000000000000001O0000001N11O000000000010O000V[O\", \"size\": [1280, 720]}, {\"counts\": \"ZW[j03jW16H6M3N1CDUiN>gV1`0K4E<J5K6J6K4L4M4M2O110O001O000O101O0O10001OO101O000000O10O01000000001O00000000nZO\", \"size\": [1280, 720]}, {\"counts\": \"WW[j08eW15L3ZOEbiN>[V1L]iN6gU1EUjNh00EgU1a1O2N2M2O2N1O2O0O1O2O000010O00O10001N101O000000O2O000O2OO1O100000000001O00000O10mZO\", \"size\": [1280, 720]}, {\"counts\": \"lnoi06iW12J7L3M3N1M5J5\\\\OUOjiNS1lU1f0K5M3O1N1M3L5M3N1O2N1O2N2N1O2O001O0O01001O00001N11O000000001O1O00001O000000000000O11N10000OX[O\", \"size\": [1280, 720]}, {\"counts\": \"\\\\fni09eW1;E3N2M3N1nND^jN?`U1LTjN6kU1Q1M2L5M4L3O0O1O2O000000001O000000001O1OO100O100000O1O1000000000000000001O001O000001O00000O101N10\\\\[O\", \"size\": [1280, 720]}, {\"counts\": \"SfSj01nW1e0YO5J5K4K5Bb0C:J5O101N1N2N2O101N100O2N10000O2O01O000000O1O10000001O0000000001O000000000000000001O0O2O00c[O\", \"size\": [1280, 720]}, {\"counts\": \"ldSj0S1hV19K4L4L4L4L3H:I7J4M101N101N10O10O101O000000O11O0O1010O01N2O1O001O0001N101O0001O00001O000000O1000000000l[O\", \"size\": [1280, 720]}, {\"counts\": \"Zeni01[W12jhN4L3UW1e0K2M4L6J3M3M3M3L4F;I6O001M2O1N3N2N2M2O1N100000O1000000001O000O10O101O00000010N1000000000000000001O0000O2O00o[O\", \"size\": [1280, 720]}, {\"counts\": \"j\\\\Wj0=aW1f0[O7D9M3L4L2M4L4K4L4N3M2O1N4L2N3N000000O100O11N1000N2O100000000000000001OO10000001O00010N1000000Q\\\\O\", \"size\": [1280, 720]}, {\"counts\": \"h]\\\\j0365SW1b0J4L4M2L5UOnNajNX1YU1j0M4M2O101N10001O1OO10000000O2N100O2O0010O0O1000O10000O100000000000000000000T\\\\O\", \"size\": [1280, 720]}, {\"counts\": \"be]j0553TW1a0I6K5TOSO^jNS1]U1k0M3N1O2N2N1N3M3N1O10000O10010N1O1O1O10010O0O1001O000O01O10001O000000000000000\\\\\\\\O\", \"size\": [1280, 720]}, {\"counts\": \"Y]\\\\j03cW1R1UO4D<M2O2N2I7K4N3N2M3L4M2O2O00000O10001O0O1O2O0000000000000000000O100000000000000000000\\\\\\\\O\", \"size\": [1280, 720]}, {\"counts\": \"iUVj02jW1b0]O6N2N5I5A?L2E<L3K5K5N3O00000O10001N101O000000001O000O1000000001O0000O101O000000001O00O2O00O10O10Q\\\\O\", \"size\": [1280, 720]}, {\"counts\": \"`VQj04gW19K2O100M3]Oc0C<H9B>M3N2N1N3N010O10000N3N1000001O0000001O00000O10001O000000000000001N1O1O100001O000O2O000000g[O\", \"size\": [1280, 720]}, {\"counts\": \"]fdi02mW111NQX10ngN4YhN3VW10ehN4TW1`0nNS1I6M2O2M2N3N1O2N100O2N101O001O00000000000O100O011O001OO2O00000000O10001N100000000001O0O01000001O0001N10001N1000_[O\", \"size\": [1280, 720]}, {\"counts\": \"ff_i07]W11dhN4XW1<L4gNKajN:[U1X1M2O2O0O2O1N1O2O1N101O0000000O2O1N010O10001O0000O100001O0001OO1000000000001O001O0O100000O11O01O00000000000000000001O000000000`[O\", \"size\": [1280, 720]}, {\"counts\": \"]nVi01XP52RWLOlhN`0oV1BohN`0RW17IXOViNg0gV1]OUiNf0ZV1i0I7@`0N100M4M201N101O001O0O1000000O2O00000000000000000001O0000000001O000O1000O10000001O000010O000000000000000000000000001N10000000`[O\", \"size\": [1280, 720]}, {\"counts\": \"^nVi07gW14M2N2N1J7H8K6E:H7I8F9M4M2O2M2N201N100O2N100O10001O0000000000000000010O0000000O10000000000000000001O0010O1O000000O101_MjjNV2YU1dMRkNS2aU1M1O1O1O1O1N1000000O101_NRjNn0aV1N10UZO\", \"size\": [1280, 720]}, {\"counts\": \"TVSi0:bW18J4M2N1N3M3ETO^iNo0_V1;L4L4L4A>L4N3N1O1O20O0001N2N2O1O001O00O10000001N1O1000001O00000000000O10001O000000O101O001O0010O14K4K3N6I3N1O1N2O1O1N1O1O1O1O1O2jNmiNBO?XV1FejNJ`U1LZT3\", \"size\": [1280, 720]}, {\"counts\": \"mUSi0b0]W14J5D;N2N2N2H8F9M4J7J5M2O1O2O0O101O00001N2O1O001O00000O1001O00O100O1000001O00000000000O101O000001N10001O001O0100007G5K6K1N2O1N101O1N2N1O1O101iNliN?VV1YOYjN=SW1]OgY4\", \"size\": [1280, 720]}, {\"counts\": \"S^oh01kW1`0A6K3M2L5XOg0H9K4M4L4N1N3N1O1O10001N100000001O0O2O1O00000000001O01N10010O01O0O10000000000000010O1O1O01O000O10001O0bMQkNk1dU1O0O001O1O0O1O100O101O0N2M4M2N4hNoiN8SY9\", \"size\": [1280, 720]}, {\"counts\": \"neUi01iW1=H7nN\\\\OUjNm0eU1i0N3M3M5I5J5N3N2O0O2N1O100000000000001O00001O000000000000O1001O000000001O00001O1O00000000000001O0000010O4eMfjNh1bU1mMajNR2hU100O1O1O1O0O10O10O2O000O101_NTjNl0nU1POWjNl0PV1lNVjNo0`V1M2OUZO\", \"size\": [1280, 720]}, {\"counts\": \"eTXi03n04hU15niN6iU10PjN8gU11miN8oU1n0GSN\\\\jNo1_U1;01N2O001O1O1O0O2O1N2OO100001O1O00001O0O2O00O001O1000O2OO100001O01O001N1001O00001O00000000010O00O100010O1OO2O2\\\\MQkNS2_U1O100O000O2O0000001O0O10Y[O\", \"size\": [1280, 720]}, {\"counts\": \"ie_i02lW16kNIWjNa0aU1BZjNc0aU1R1J3O2M2L5L3M4M3N2O1O001O0000000O101O0O10001O0000000000001O00O011O0000000000000001O0000000000000000O101O001O1O1O0fMdjNQ2^U1iMfjNU2eU1N3M0O10000O^[O\", \"size\": [1280, 720]}, {\"counts\": \"hedi0=bW12N3N1N4M2G9G8WOi0J6L3N3M3N1O101O001N2O0O110O00001O000O2O00O100000000000000000000000O10O110O00000000O100001O00000000000O2O0O1000P\\\\O\", \"size\": [1280, 720]}, {\"counts\": \"nmoi07dW17TO2YiN?\\\\V1f0M3L2L5M3M2N3K5M3N2N101N10O10000001N2O1O001O0000000000001O001O0000000000O11O0000001O1OO100000O01010O000k[O\", \"size\": [1280, 720]}, {\"counts\": \"Vnoi05cW1>VOE^iNa0_V1c0E9M4I7M3M3M3K5M3O0O100O10000000001O1N2O00000000000000001O1O00010O00000000001O000O10000000O2O00000001O0h[O\", \"size\": [1280, 720]}, {\"counts\": \"klei0;bW14M4nhNKUV1S1M4K4I6J5L5L3N2N100O2O001O1OO010O11O001O0O1000001O0O101O00000001O000001O001O0000000O10000001O000000O02O001O0000001O0000k[O\", \"size\": [1280, 720]}, {\"counts\": \"deZi0<cW13L5K4N4F9C:ZOg0L5L200O002O0O1O2O5K5K0O011O001O000O10O0L5O10O110OO1000000000000O11O01O000O1000000001O01O010O2N3L>C1O1O1N2O1O1O0O100000000O3[NjiN\\\\1lV1_O3O_ZO\", \"size\": [1280, 720]}, {\"counts\": \"UePi06QW11biN4PV15oiN2hU12PjN8kU1R1M4K3N3L4L3M2O0O100O2N101O0000O11N101O001O1O0000010O0000000O10O100000000000000O2O00010O2N3L3N3M6J3L3N2N001O0O1O1O2O0O1O1N3K5hNeiN6Xj`0\", \"size\": [1280, 720]}, {\"counts\": \"RTXi02kW19I9G7I9He0[O5Lc0[O3M1O2N0100000O10001N10000000000001N100001O2N25I2M2N6J7J1N3N1O1N1O1N2O2O0O1N2G:K6PO^iN8[W1HlZm0\", \"size\": [1280, 720]}, {\"counts\": \"lkoi06jW1OfhNLjV14jhN>j0BZO6VU1JfjNa0On0WU1aNljNV2TU1kMojNR2PU1mMTkNQ2kT1nMXkNP2hT1oMYkNQ2hT1mM[kNn1]U1O2N1O01000O10000O1M3N2O2K5Ld0\\\\O;]OnbS1\", \"size\": [1280, 720]}, null, null, {\"counts\": \"lcdi0=cW12O1N100O1065E31L2N102M1N1O2N3L4M4K3N4L3M100O3OHa0jNQiN`0TR`1\", \"size\": [1280, 720]}, null, {\"counts\": \"klTj01nW13N3N2M8G5L1O2N1O2N3M2N2N2N101N0=BibX1\", \"size\": [1280, 720]}, null, null, {\"counts\": \"Zm[i02]W16jhN1QW1`0O1O1O3M9F6L1N2N2N2ciNbNTV1h1M2N3L2O]N[jNm0cU1iNijNV1VU1iNljNW1SU1hNnjN[1PU1cNQkN`1nT1]NSkNh1_U12O00ImiNeNnU1]1:0O10O2N1dNeiNQ1^V1nNbiNQ1_V1nNbiN<0OcV1@biN931kh^1\", \"size\": [1280, 720]}, null, null, null, null, null, null, null], \"bboxes\": [[574.0, 755.0, 39.0, 75.0], [576.0, 758.0, 43.0, 75.0], [581.0, 756.0, 42.0, 77.0], [583.0, 756.0, 45.0, 76.0], [586.0, 757.0, 44.0, 74.0], [589.0, 757.0, 47.0, 75.0], [604.0, 758.0, 38.0, 75.0], [620.0, 760.0, 32.0, 73.0], [625.0, 760.0, 28.0, 75.0], [624.0, 760.0, 28.0, 74.0], [626.0, 760.0, 25.0, 74.0], [629.0, 760.0, 23.0, 73.0], [636.0, 759.0, 16.0, 75.0], [637.0, 760.0, 15.0, 73.0], [636.0, 760.0, 16.0, 50.0], [634.0, 761.0, 17.0, 62.0], [555.0, 781.0, 86.0, 52.0], [625.0, 760.0, 15.0, 66.0], [616.0, 759.0, 25.0, 54.0], [612.0, 757.0, 28.0, 55.0], [604.0, 755.0, 35.0, 56.0], [589.0, 756.0, 49.0, 56.0], [596.0, 754.0, 45.0, 57.0], [598.0, 752.0, 45.0, 57.0], [601.0, 751.0, 47.0, 56.0], [606.0, 753.0, 45.0, 58.0], [606.0, 754.0, 45.0, 58.0], [606.0, 753.0, 45.0, 59.0], [608.0, 752.0, 45.0, 59.0], [606.0, 752.0, 51.0, 57.0], [614.0, 750.0, 48.0, 59.0], [618.0, 751.0, 46.0, 58.0], [619.0, 756.0, 48.0, 59.0], [618.0, 758.0, 49.0, 60.0], [617.0, 759.0, 49.0, 60.0], [616.0, 760.0, 49.0, 59.0], [617.0, 760.0, 49.0, 60.0], [616.0, 760.0, 48.0, 60.0], [612.0, 762.0, 47.0, 59.0], [617.0, 761.0, 46.0, 57.0], [624.0, 762.0, 50.0, 54.0], [631.0, 763.0, 50.0, 76.0], [640.0, 760.0, 47.0, 80.0], [646.0, 757.0, 44.0, 80.0], [647.0, 756.0, 43.0, 78.0], [640.0, 759.0, 46.0, 74.0], [631.0, 743.0, 51.0, 80.0], [628.0, 721.0, 49.0, 84.0], [622.0, 691.0, 53.0, 90.0], [629.0, 665.0, 45.0, 95.0], [614.0, 655.0, 61.0, 89.0], [611.0, 649.0, 75.0, 104.0], [611.0, 647.0, 79.0, 100.0], [621.0, 646.0, 77.0, 97.0], [630.0, 644.0, 74.0, 93.0], [632.0, 642.0, 76.0, 93.0], [634.0, 640.0, 75.0, 96.0], [632.0, 641.0, 78.0, 101.0], [633.0, 646.0, 79.0, 98.0], [639.0, 648.0, 70.0, 99.0], [641.0, 647.0, 72.0, 97.0], [645.0, 649.0, 75.0, 95.0], [653.0, 653.0, 67.0, 94.0], [649.0, 651.0, 71.0, 91.0], [640.0, 644.0, 74.0, 95.0], [637.0, 641.0, 73.0, 96.0], [636.0, 638.0, 74.0, 96.0], [631.0, 636.0, 77.0, 92.0], [632.0, 636.0, 75.0, 91.0], [633.0, 637.0, 76.0, 90.0], [641.0, 638.0, 76.0, 93.0], [646.0, 639.0, 74.0, 101.0], [648.0, 643.0, 72.0, 99.0], [645.0, 646.0, 75.0, 98.0], [644.0, 646.0, 76.0, 97.0], [641.0, 644.0, 76.0, 100.0], [636.0, 641.0, 81.0, 104.0], [637.0, 645.0, 76.0, 97.0], [634.0, 655.0, 78.0, 94.0], [629.0, 660.0, 79.0, 100.0], [624.0, 665.0, 82.0, 95.0], [629.0, 664.0, 80.0, 96.0], [632.0, 660.0, 86.0, 99.0], [635.0, 659.0, 85.0, 99.0], [636.0, 661.0, 84.0, 98.0], [639.0, 667.0, 78.0, 95.0], [639.0, 671.0, 73.0, 92.0], [637.0, 673.0, 76.0, 92.0], [640.0, 675.0, 73.0, 91.0], [641.0, 672.0, 74.0, 92.0], [641.0, 667.0, 79.0, 94.0], [646.0, 660.0, 74.0, 99.0], [654.0, 659.0, 66.0, 91.0], [662.0, 655.0, 58.0, 90.0], [672.0, 650.0, 48.0, 93.0], [674.0, 648.0, 46.0, 91.0], [674.0, 645.0, 46.0, 93.0], [676.0, 643.0, 44.0, 92.0], [677.0, 651.0, 43.0, 92.0], [675.0, 664.0, 45.0, 94.0], [674.0, 673.0, 46.0, 91.0], [674.0, 674.0, 46.0, 90.0], [665.0, 661.0, 55.0, 98.0], [664.0, 658.0, 56.0, 89.0], [668.0, 650.0, 52.0, 91.0], [668.0, 642.0, 52.0, 97.0], [664.0, 639.0, 56.0, 93.0], [671.0, 639.0, 49.0, 92.0], [675.0, 636.0, 45.0, 90.0], [676.0, 628.0, 44.0, 93.0], [675.0, 628.0, 45.0, 92.0], [670.0, 639.0, 50.0, 91.0], [666.0, 647.0, 54.0, 95.0], [656.0, 655.0, 64.0, 91.0], [652.0, 655.0, 68.0, 95.0], [645.0, 654.0, 75.0, 93.0], [645.0, 650.0, 75.0, 93.0], [642.0, 648.0, 75.0, 94.0], [642.0, 647.0, 74.0, 96.0], [639.0, 643.0, 74.0, 95.0], [644.0, 642.0, 76.0, 91.0], [646.0, 636.0, 74.0, 96.0], [652.0, 638.0, 68.0, 89.0], [656.0, 638.0, 64.0, 93.0], [665.0, 644.0, 55.0, 93.0], [665.0, 647.0, 55.0, 91.0], [657.0, 642.0, 63.0, 90.0], [648.0, 636.0, 72.0, 97.0], [640.0, 633.0, 67.0, 91.0], [646.0, 631.0, 50.0, 94.0], [665.0, 636.0, 26.0, 88.0], null, null, [656.0, 636.0, 26.0, 71.0], null, [669.0, 666.0, 18.0, 40.0], null, null, [649.0, 664.0, 34.0, 89.0], null, null, null, null, null, null, null], \"areas\": [1865.0, 1960.0, 1996.0, 1961.0, 2025.0, 2176.0, 1978.0, 1450.0, 1294.0, 1336.0, 1062.0, 842.0, 533.0, 462.0, 450.0, 438.0, 171.0, 398.0, 897.0, 1074.0, 1503.0, 2354.0, 2186.0, 2230.0, 2201.0, 2221.0, 2231.0, 2211.0, 2250.0, 2326.0, 2248.0, 2218.0, 2357.0, 2371.0, 2362.0, 2402.0, 2404.0, 2407.0, 2371.0, 2249.0, 2310.0, 2208.0, 2306.0, 2141.0, 2120.0, 1927.0, 1962.0, 2271.0, 2348.0, 2052.0, 3080.0, 4232.0, 4789.0, 4272.0, 4404.0, 5369.0, 5814.0, 6029.0, 6120.0, 5911.0, 5632.0, 5804.0, 5168.0, 4900.0, 5250.0, 5161.0, 5528.0, 5274.0, 5336.0, 5620.0, 5603.0, 5935.0, 5772.0, 5834.0, 5895.0, 6048.0, 6466.0, 6062.0, 5857.0, 6220.0, 5330.0, 5547.0, 5604.0, 5314.0, 5661.0, 5897.0, 5576.0, 5239.0, 5514.0, 5763.0, 5695.0, 5517.0, 5317.0, 4408.0, 3530.0, 3770.0, 3857.0, 3000.0, 3414.0, 3714.0, 3369.0, 3589.0, 4344.0, 4316.0, 4104.0, 4462.0, 4404.0, 3952.0, 3482.0, 3566.0, 3629.0, 3883.0, 4207.0, 4896.0, 5773.0, 5910.0, 5531.0, 5425.0, 5645.0, 5629.0, 5638.0, 6122.0, 5285.0, 5153.0, 4449.0, 4405.0, 5065.0, 5382.0, 5063.0, 3446.0, 1384.0, null, null, 673.0, null, 439.0, null, null, 1623.0, null, null, null, null, null, null, null], \"iscrowd\": 0, \"video_id\": 0, \"height\": 1280, \"width\": 720, \"category_id\": 49504, \"noun_phrase\": \"the nike air jordan logo\"}, {\"id\": 1023, \"segmentations\": [{\"counts\": \"na_57eW19I5I6I5L5M2M3N2N3N1N2O2N1O100O10000O2N1O1O2N1O1O1N2O1L4D<N1O3M2M4GYMVkNh2hT1:M2O1O1O101O0N2O101N1O1O1O10000003L01N3N100O11O1O100O2M5I6J4K5M3K4SOkjNgN^U1AbjN\\\\1aV1H=@=DPXic0\", \"size\": [1280, 720]}, {\"counts\": \"QjV58bW18G9H7L4L3N3J5M3O101N1O10000O2N1O1N2N2M3L5E:M3N10002K4K6M2N2M4N1M3O1O1O1O2N100O100O11O2N1O1O1OEeLSlNZ3lS1eLVlNZ3kS1eLWlNY3YT1O2N1O3L4M2N1O3L2O1N3N3M2N3N1N1O3L?A:_Oj0mNj^Td0\", \"size\": [1280, 720]}, {\"counts\": \"Sbk4:`W18G8I7B>K4O1O1O2N1O2K5\\\\Oc0I8_OXM^kNR3WT1b0M2N200O1O10001O0000000010O001O001O1O1O1O1O1O1O2N1O1O2N2M3N2N3M2N2N2N2N1N3N3M1O1O100O1N10001O1O000O2O1N7H7J4L3M4K<Da0nNQiN2\\\\U]d0\", \"size\": [1280, 720]}, {\"counts\": \"QR_4;_W17I7XO[OmiNk0RV1c0N1kMcNikN807h0o0]S1L`lN5]S1O`lN2`S1W2O101N1O1O1VOoKemNR4ZR1oKemNQ4[R1nKemNS4RS12O1013MHnKelNn3ZS1QLglNP4XS1PLglNQ4YS1oKglNQ4YS1PLglNo3YS1QLhlNn3XS1RLhlNn3YS1PLilNo3WS1QLilNo3dS1001O0001O2N0000001YLPlN\\\\3PT1bLTlN\\\\3jS1fLYlNW3eS1lL^lNP3aS1RM`lNm2_S1SMblNk2`S1SMhlNf2YS1XMilNg2[S1UMelNk2XT10001O0O10O1N2O1ROTMglNl2WS1jMUlNW2jS1P1000O10000O101N2O1O1N4M4jMnkN5`T1ZOfkNc0aT1`NUkN;<T1oT1fNRkNX1mU1N00M2L38WOeiNAfUbd0\", \"size\": [1280, 720]}, {\"counts\": \"bWQ4;hV1S1[kNhNSR1_1amNkN\\\\R1W1_mNoN]R1U1clN`NIc0cS1U3O001O000000001O001N10000O100O10000010O10O01O002N2N001O100O1O001O010YL`lNl2aS1RMalNm2`S1QMalNo2`S1oLblNP3WT1O;F1O100O1O001O0000000O2OhN`MPmN`2jR1iM[lNJ7^2YS1`NalNc1\\\\S1b1000O10000000000000000O101N01000O2O1N2O0O1O2N1O1O2M8I4K7Io0QO<F2Ma0@1N100ZOfiNB_V15ngZd0\", \"size\": [1280, 720]}, {\"counts\": \"dgn3<gV1KeiNc0i1YORR1b1fmNdNWR1b1alNWN8=SS1[2clNjM[S1\\\\2]lNgMbS1]3O00001O00001O001O000O1O2O000001O001O010O1O00100O1O1O1000000O000VL[lNY3fS1fLblNR3_S1lLdlNR3]S1lLelNS3]S1jLdlNV3ST100000MmLakNk2kT1O1O1O1O1O1OO10000O1AVMgkNj2WT1ZMfkNh2cS1\\\\M^lN0Md2bS1bM]lNMNc2aS1[1O01000O1001O000000000O10000000000000000O10000O100O1O1O2M4M3M1O3M2M4M4K4QNjkNOkT1[OWkNd0]U1gNfjNV1TV1N2N2N1O1O0^OUiNMRnQd0\", \"size\": [1280, 720]}, {\"counts\": \"_oo38cW18Ia0_O6ZOh0UjN[Nk0LmR1c2hlNcMVS1e2`lN`M]S1c2]lNaMcS1a2UlNfMiS1Y3100OO1O2N1O1O2O0O10001N100O10001O00000100O002N2O00O01O000010O01O1O1O00100WL^lNR3dS1jLalNS3`S1kLhlNn2ZS1PMglNo2^S1kLdlNT3UT1O0000001NKQM]kNm2cT1RMakNk2jT100000oNVMklNk2SS1XMklNi2jR1cMVmN\\\\2jR1dMWmN[2hR1hMjlN_OJi2[S1_1O2O1O1O1001N10001O1OO10000000000000O1000000O1O1O1O1O1O100O1O2O1N2M3N3L4N4K4bMmkNSOHX1lT1QOPlNj0XT1kNnkNS1aU1M4M4K5H4I8FahN0_ngc0\", \"size\": [1280, 720]}, {\"counts\": \"XWl3:cW14M4XOB`iNi1^U1f0E6F<kkNmLPS1W3dlNTMZS1k3M2N101O0010OO101N100O101O0000000O101O000010O001O100O2N100O010O001O00001O1O1O011N2N2N`0@6J6J3M2N1O00O000M45K000000000000kNQMYmNo2eR1bMilNa2US1cMglN_2VS1eMflN^2XS1V1O1O100000000001O00000000O1000O2O0O1O1O100O1O1O1O1O2O1N1O2N2N4L3M3N4K4L8iMZkNe0kT1iNfkNQ1_T1lNfkNn0dT1hNakNS1lU1I7H5IYogc0\", \"size\": [1280, 720]}, {\"counts\": \"^gd3:bW15Hc0\\\\O;C<Kh0[O2M3N2N2N2N3M2M2O1N2O1O001N2M4M101O010O1O02O0000O011O0000000O0100O101N1O10O1O0100hLdkNj2]T1UMkkNc2UT1\\\\MPlNa2PT1^MPlNb2QT1\\\\MQlNd2nS1\\\\MRlNd2fT1O007I01L300O2O001O001M2L5XOXMPlNn2mS1WMnkNm2nS1d0K6M201O000000000000001O0000000000000010O01O0O1000O10000O101O1N3M2N3M2N3L6K5K?B7Hj0cMkiN[1fV1G2Lc0VOlVSd0\", \"size\": [1280, 720]}, {\"counts\": \"]Z]36`W1>I3K6I7L4N101iMmN_mNS1_R1RO_mNm0]R1YOamNg0[R1]OfmNc0VR1AjmN>QR1HomN7gQ14YnNKeQ19ZnNFfQ1;YnNEgQ1=XnNCfQ1?ZnN@fQ1a0YnN_OgQ1c0WnN]OiQ1f0TnNZOlQ1g0TnNXOlQ1i0SnNXOlQ1i0TnNVOmQ1k0QnNUOoQ1l0QnNSOPR1m0omNSORR1m0mmNSOTR1m0lmNROUR1n0jmNROWR1n0hmNROYR1n0gmNQOZR1o0emNQO\\\\R1o0cmNQO^R1P1amNPO`R1o0_mNQObR1o0]mNQOdR1n0\\\\mNROdR1o0\\\\mNPOeR1o0\\\\mNPOeR1o0\\\\mNQOcR1o0]mNQOdR1o0\\\\mNPOdR1P1]mNoNcR1Q1]mNoNiR1l0VmNTOQS1f0nlNZOSS1h0klNWOVS1i0ilNWOWS1i0ilNWOWS1VOmkN0LT1P1EaS1WO_kNT1P1E_S1d0alN[OkR1\\\\O\\\\lN\\\\1i0XOgR1^1YmNaN]R1i1cmNWNZR1m1fmNRNZR1o1emNQN\\\\R1o1dmNPN\\\\R1Q2cmNoM]R1R2cmNmM]R1S2cmNmM]R1T2cmNkM]R1U2cmNkM^R1U2bmNjM^R1V2cmNiM]R1X2bmNgM_R1Z2amNeM_R1[2amNeM`R1Z2`mNgM_R1Z2`mNfM`R1Z2amNeM_R1\\\\2`mNdM`R1\\\\2bmNbM^R1_2amNaM_R1_2bmN`M^R1`2bmN`M_R1_2amN`M`R1`2amN_M_R1b2amN\\\\M`R1d2_mN[MdR1d2\\\\mN[MeR1f2ZmNZMgR1e2ZmNUMkR1l2U100O2O6J<C5L2M4M7I7Hc0]O1O1O1O1O1O3M9^OahN1Rned0\", \"size\": [1280, 720]}, {\"counts\": \"ja^3b0XW17M3L4J5K6L3I8G8N21O00000000lMUNUlN3f1g1QR1fNmmNZ1jQ1nNVnNR1iQ1POWnNo0iQ1QOXnNn0gQ1TOYnNk0iQ1TOWnNk0jQ1UOUnNk0lQ1UOTnNj0nQ1UOQnNk0PR1TOQnNk0PR1UOomNk0RR1UOnmNk0SR1UOlmNj0TR1VOlmNj0UR1VOjmNj0VR1WOjmNh0VR1YOimNg0XR1ZOfmNf0[R1ZOemNe0aR1TO`mNl0eR1lN_mNT1cR1bNemN]1`R1_N_mNa1aR1_N`mN`1`R1bNgkNN`1`1iR1jNVmNV1jR1nNSmNQ1mR1PORmNP1nR1TOolNk0QS1UOPmNj0PS1WOPmNh0PS1gNdkN:]1n0oR1fNhkN:Z1P1lR1hNhkN:[1n0mR1[OTmNd0ZR1dN`lN1Li0Y1b0eR1eNQlNk0Y1`0_R1MbmN1VR19imNGUR1;lmNDSR1>lmNBTR1>mmNASR1?mmNASR1`0mmN_OSR1b0mmN]OTR1c0lmN\\\\OTR1e0lmN[OTR1d0lmN[OUR1f0kmNYOVR1g0imNYOXR1g0hmNXOXR1i0hmNUOZR1j0fmNVO\\\\R1i0cmNVO_R1j0amNUO`R1j0`mNUOnR1>SmN@WS16klNHZS14flNKeS1CdlN<`U1O2NcN]O`kNa0aT1_O`kN=TV1M3N2MVf]e0\", \"size\": [1280, 720]}, {\"counts\": \"mac3c0XW16J6M2N2M4K4M3I7L4N2O1O101N1N2K501O000000001O01O001O1O1O1O1O1O00ZNSNZmNl1fR1TNZmNm1eR1TN[mNk1eR1UN[mNk1eR1UN[mNj1fR1WNZmNh1fR1[NWmNe1hR1\\\\NXmNd1gR1]NZmNb1bR1cN]mN]1VR1POjmNP1UR1ROjmNn0WR1ROimNm0WR1TOimNk0WR1VOhmNj0YR1UOhmNi0YR1WOgmNi0ZR1VOfmNj0[R1UOfmNj0[R1VOdmNj0`R1ROamNm0gR1iN[mNX1gT1O00001O0O2O1O0O3N2M4K7E=_Oe^ke0\", \"size\": [1280, 720]}, {\"counts\": \"[ZX3:^W1;J4M3N1N3N1O2N1O1N3M2M3H8L4O1O1O1O2^OTNnjNl1RU1ZNejNi1[U1>00001O000000001O010O001O_NeM]mN[2bR1fM^mN[2_R1hM`mNX2_R1jMamNU2^R1mMamNS2_R1oM`mNP2^R1RNbmNn1^R1SNbmNl1]R1UNcmNk1]R1VNcmNi1]R1WNcmNh1^R1YNbmNf1_R1ZN`mNf1aR1YN`mNf1`R1[N_mNe1bR1ZN_mNe1bR1[N]mNe1cR1[N^mNd1aR1]N_mNc1`R1^N`mNc1cR1XN_mNf1dR1UN`mNj1\\\\T1O00001O001O2M4M2N4K6J5K7I<@PVTf0\", \"size\": [1280, 720]}, {\"counts\": \"WZS3?]W17J5L2N2O2M2N2N2N3N1N2G9L4N2N3M2O1O1N2N3O0000000000000010O001O001O1O001O[NmM]mNS2cR1nM]mNQ2cR1nM^mNR2eR1PNWmNo1hR1RNXmNn1hR1RNXmNn1gR1TNYmNk1cR1YN^mNf1XR1dNhmN\\\\1VR1gNjmNX1VR1iNimNW1WR1kNhmNS1YR1mNhmNR1XR1oNgmNQ1YR1oNhmNP1XR1QOgmNo0[R1oNfmNP1[R1POemNo0`R1mN`mNR1bR1lN^mNU1aR1lN_mNS1iR1eNWmN[1kR1cNUmN\\\\1nR1`NTmN`1fT100O2O001N101N3N3L4L6Fb0_Oo]Uf0\", \"size\": [1280, 720]}, {\"counts\": \"lao2a0YW19J4L4ROZO[jNl0eU1g000100O0000001O00010O00fMfNnmNZ1RR1hNmmNW1SR1jNlmNV1SR1lNmmNS1SR1nNmmNQ1SR1POlmNP1UR1POjmNP1VR1POkmNo0UR1QOlmNn0TR1ROlmNn0WR1oNjmNP1XR1oNhmNP1XR1ROfmNn0ZR1SOfmNl0ZR1UOemNk0[R1VOemNi0^R1UOamNk0`R1UO`mNj0bR1TO_mNk0QS1eNPmNZ1WS1aNhlN^1YS1aNhlN^1nT10O01O0000^NcNalN]1\\\\S1gNdlNX1[S1jNdlNV1ZS1lNflNT1XR1eN`lNO=;j0Q1YR1TOelNOP1m0ZR1ZO_lNLV1j0[R1\\\\O[lNMZ1g0ZR15emNJ\\\\R16dmNJ\\\\R16dmNJ\\\\R17dmNG]R1:bmNF^R1:cmNE]R1;cmNE^R1:bmNG]R19cmNG^R19amNG_R19amNG`R19_mNGcR17^mNHdR16\\\\mNJfR15YmNJiR15WmNKkR13VmNK\\\\S1DdlN<cS1[O_lNe0^U1O101N101N101N101M2O3N2_OmhN0^mme0\", \"size\": [1280, 720]}, {\"counts\": \"kaT3b0ZW16N2N0gM@RmNa0iR1EVmN:aR11]mN0TR1WOTlNS1f1FTR1e0jmNZOTR1i0lmNVOSR1l0mmNSOSR1o0lmNPOTR1Q1kmNoNYR1n0gmNROZR1l0fmNTOYR1n0gmNROVR1Q1imNoNXR1R1gmNmN`R1m0_mNSOfR1j0YmNUOgR1l0XmNTOfR1o0YmNQOfR1Q1YmNoNjR1o0UmNQOlR1o0SmNROmR1o0RmNPOnR1P1RmNPOnR1Q1PmNPOPS1P1PmNPOQS1P1olNoNQS1Q1olNoNRS1P1mlNQO]S1e0clNZObS1b0^lN^OdS1a0[lN_OfS1`0ZlN@gS1?YlNBfS1?YlNAgS1?YlNAgS1?YlN@hS1`0XlN@hS1a0WlN_OiS1a0WlN^OjS1a0WlN@iS1@RkNh0T1IcR1\\\\OemNh0ILbR1\\\\O_lNOm0j02JbR1_OZlN2P1f03JbR1HZmN>5IaR1I]lND>3Cd0Q1LaR1I[lNG?2Bb0S1LaR1JYlNI>m0h0@aR1JYlNL;j0k0_ObR19alN8m0_ObR1KXlN]1V1hNcR1JWlN^1V1iNcR1HWlN_1V1iNcR1e1]mN[NcR1e1\\\\mN\\\\NdR1d1\\\\mN[NeR1e1[mN[NeR1f1ZmNZNfR1f1ZmNZNfR1f1ZmNYNgR1g1YmNYNhR1f1YmNYNhR1f1XmNZNiR1e1WmN[NjR1d1VmN\\\\NlR1b1TmN]NnR1b1RmN^NRS1]1olNcNUS1Y1klNgNWS1W1ilNhN_S1Q1alNoNiS1g0WlNXOkS1g0UlNYOlS1GPkNe0T1C[S1BPlN2M`0h0L[S1CclN?2MdT1J[jNJQ1:]V1O2M5Jn]fe0\", \"size\": [1280, 720]}, {\"counts\": \"]gZ391L;O]V1f0`iNZORV1Y1giNjNTV1e1M3M5K2O2O0O1O2N3N0001OO1N2O0100O010O010O2N2O0O000gMejNn1gU1O1O1O5K1O0000000000O`NmiNU1SV1eNUjNY1YV101O00000001N100001OhNiNijNNf0Y1_T1TO_kNm0`T1UOkjND0OOZ1UU1Y1M3O100O2N10000000000000O2O0000O20O1O2N2N2N2N4L2N9dM\\\\jNj1cV1YO3N001O001YOQiN:QW1CSiN;QW1_OSiN>YW1N2N3KV_We0\", \"size\": [1280, 720]}, {\"counts\": \"Wo[31dW1c0_O;WjNTOUT1R1[kN^OcT1f0TkNAjT1l1L6H7L3O1N10001O001N1O200000O0001O01O1O10O001O010000O001O00101N1O8H2O005J01O1O0001O001O9G3M01O0000001N1TOhMnkNZ2oS1TNakNQ2RT1[MXlN]3gS1cLYlN]3gS1cLYlN^3eS1iLTlNY3kS1>O101O00000000001O010O01O00O100O100MQLXlNl3PT1N4L3TMPlNb1TT1SNYlNg1aU1^O101O0O2O000IfiNgN[V1V1:HmWQe0\", \"size\": [1280, 720]}, {\"counts\": \"iYS39YU1K_lN6[N6lT11nkNT1bS1\\\\OokNQ1jS1[OikNn0ST1a1O1O1O2N2N2O1N2O000000O1O101O010O001N110O1O1O00001O00001N3N2N1O1O0000007I2N1O0eLckNS3kT1K1N1O1O001O00000000O1O1nNWMVlN36i2cS1WMSlN39f2bS1ZMSlN29e2cS1hMYlN[2gS1n01000N2O1N2O010O1O01000000O10001N3N1O1O1N2O1N2N2O2M2M3Mf0ZO:F4oM_jN\\\\1gU1ZNjjNZ1PV1M2L5K6Gk0WOWWVe0\", \"size\": [1280, 720]}, {\"counts\": \"Xjk2:oT1GemN<PR10lmN3RR11imN1WR19]mNJaR1=WmNEhR1h0hlN\\\\OVS1k0alNZO^S1l0XlNXOhS1k0QlNYOoS1]2O00000O10001O0010O001N2O1O1O1O1O1O1O001O001O002N1O1O1O=C9G1N10010O000000O100001O;E6JO1N2ZNSNnkNMP1S2QS1\\\\NilNg1VS1]NclNh1\\\\S1ZN^lNj1bS1Y110O2N1O1O1O1O1000000O10001O001O00O1O101N2O00001N2O0O4M2M2M4M5J6I4K6K6J7I9D;HQ1QO?@eV[e0\", \"size\": [1280, 720]}, {\"counts\": \"h^`21iW1<H300chNGMNoV1Q1J3L4L4L4M3YkNbNeR1a1WmNcNgR1_1TmNeNjR1`1olNdNPS1`1ilNdNVS1c1alN_N`S1Q3O10O001O100O1O4L2NN3L3O11cLkkNm2VT1QMmkNm2TT1QMnkNn2ST1QMnkNn2RT1RMokNn2RT1PMokNo2ST1nLPlNP3RT1mLPlNR3aT1N2N2N1O1O00000000000000O010O100O1N2O2O0D<O1M22hL`kNo2lT1L@PMQlNo2aS1PMVlN;4g2eS1cMWlN_2hS1eMTlN\\\\2iS1o0M3O1O1N2000O0100O0100000000O10001O000O2O0O101N2O1N2O2M2N3M3M4L5J4M2M4K6J<C:G?Ba0^O<E<D4L5IVoYe0\", \"size\": [1280, 720]}, {\"counts\": \"n][26fW1:dhNL`V18WiN0eV1i0M2M6K3N3M:D?[jN`MmT1R3Gm0SO3N1N3O0O001O010O10O1O10O0001O2N1O1O002XLUlNW3lS1gLWlNW3iS1iLYlNU3hS1iL\\\\lNT3dS1lL^lNR3cS1mL^lNR3cS1mL]lNS3dS1lL\\\\lNT3eS1kL[lNU3fS1jLYlNW3gS1hLZlNX3gS1gLYlNY3gS1fLZlNZ3XT1N3M00O1001N1000M3B>O10000HZLUlNg3dS1>M300O100O1O1000000O1O0N3O1O1003M1O00000000O010000O10001O00000O101O0O101O0O2O2N2M3M2N3M4L4K6K5J6I7I7`MfjNh1gU1SN\\\\jNg1SV1K9lNjiNN[V1IdY[e0\", \"size\": [1280, 720]}, {\"counts\": \"\\\\ol2933nV1e0K5M6K7H9Gb0]O6K4L4M2M4M3L3`kNTMhS1f3L2N100O01O0001N11O1O1O1O1O1O2N3M2N01O0001O00O1N2K5O11O00000000O10001O0O1N2O1O1O1O10000O1N21O001OO1O100O1O10O1000001O1OO1O1000000O10O101O001O1O0O10001N101O0O2O1O2N1O2N2M2N4L8HQ1PO=B5L1N4M5I<A<ROQ_md0\", \"size\": [1280, 720]}, {\"counts\": \"eoe3d04FhV1R1HS1lN4M2M2O1N101O00000000001O000010O0000001O00O100001O002O000O0001O000010O000001O0gN`MRmNa2mR1aMQmN_2QS1`MnlN`2RS1aMnlN^2QS1eMmlN[2SS1eMmlN[2RS1gMmlNY2SS1`MUlNLh0d2RS1`M[lNGe0h2PS1lMllNT2SS1nMllNS2SS1mMnlNR2PS1PNPmNP2oR1QNRmNn1lR1UNSmNk1lR1VNTmNj1kR1XNUmNg1jR1[NUmNe1kR1[NVmNd1iR1]NXmNb1hR1^NXmNb1jR1\\\\NWmNc1jR1\\\\NVmNd1jR1\\\\NWmNc1iR1]NWmNc1hR1_NXmN`1hR1aNXmN^1hR1cNXmN\\\\1hR1dNYmN[1hR1eNXmNZ1hR1gNXmNX1iR1hNVmNX1jR1iNUmNW1kR1jNSmNW1mR1jNRmNV1nR1jNSmNU1mR1lNRmNS1oR1mNRmNR1nR1nNSmNQ1mR1oNTmNP1lR1POTmNP1lR1QOTmNm0mR1SOSmNm0mR1SOUmNk0kR1UOVmNj0jR1WOUmNi0kR1WOVmNh0jR1XOWmNf0jR1ZOWmNe0jR1ZOWmNd0jR1\\\\OWmNc0iR1]OWmNb0kR1]OVmNb0jR1]OXmNa0kR1\\\\OWmN`0nR1YOYmN`0mR1VO`mNa0W\\\\cd0\", \"size\": [1280, 720]}, {\"counts\": \"`gS4b0\\\\W17F7B>M101N2iNeNgkN]1VT1eNhkN]1ST1gNmkNZ1PT1hNPlNX1nS1jNRlNW1lS1kNSlNU1mS1kNSlNV1lS1kNSlNV1kS1kNUlNW1iS1jNVlNX1hS1hNXlNY1gS1gNZlNY1eS1fN\\\\lNZ1eS1eN\\\\lN[1dS1dN\\\\lN]1dS1bN]lN]1cS1dN\\\\lN]1bS1dN^lN]1`S1dN`lN]1`S1bNalN^1_S1aNalN`1^S1`NclN`1_S1]NalNc1bS1ZN_lNf1bS1VN`lNj1cS1SN]lNm1dS1RN]lNn1bS1RN^lNn1bS1SN]lNm1cS1SN]lNn1bS1SN^lNl1bS1TN^lNm1bS1RN^lNo1aS1nMblNS2fT1O10O010O01O01O01O00000001bNoMllNP2SS1SNllNl1SS1UNmlNk1SS1UNnlNk1oR1XNPmNh1kR1]NVmNb1iR1_NWmNb1gR1_NYmNa1eR1aN\\\\mN^1aR1fN^mNZ1^R1jNcmNV1[R1kNemNV1ZR1kNfmNT1ZR1lNfmNU1ZR1iNhmNV1XR1jNhmNW1XR1gNimNZ1VR1fNjmNZ1VR1cNmmN]1TR1^NQnNa1oQ1_NQnNb1oQ1]NQnNc1oQ1\\\\NRnNe1nQ1YNSnNg1nQ1WNRnNj1oQ1TNRnNm1QR1lMRnNS2nS101O01OLYjNQNeU1m18N102N2O1N3N2L4M4K>BkWXd0\", \"size\": [1280, 720]}, {\"counts\": \"mfS442:QW1a0H9K7K3L5K5L1N2N2N2O1N10O1001O0010O001O002N000O101O1O1O1O7I4L1O4L1N100O011O00]jNmNYT1R1gkNoN[T1LfjNk0n0[OiT1?VkNCiT1=WkNDeT1>]kNC`T1>`kNB`T1>akNB^T1>bkNBZT1CgjNi0o0E_T1;akNE[T1?ekNA[T1?ekNB[T1>dkNCST1@QkNm0l0DRT1_OQkNn0m0DXT1<hkNEVT1;kkNFTT1:mkNEQT1>nkNCoS1?QlNBmS1>TlNBlS1>TlNBkS1?UlNBiS1?WlNAgS1a0YlN_OfS1b0[lN^OdS1a0]lN_O]S1^O]kNS1V1_O]S1f0dlN[O\\\\S1b0glN]ORS1[OikNT1X1BiR1g0[mNZOdR1a0amN_O^R1`0dmNA[R1>fmNCZR1;gmNFXR1:hmNGWR17kmNIUR14mmNMSR11omN0PR1NRnN1oQ1NRnN1oQ1GRlN^OP2j0oQ1FZnN;fQ1@]nNb0`T1K401O01O1N200O0010O3M01M3M30O3VOohNa0^W1IbfPd0\", \"size\": [1280, 720]}, {\"counts\": \"l^h32P2OaS1>okNFfN3TU1<QlN7hS1MVlN6gS1LWlN6gS1LWlN6eS1NRlN;mS1FRlN:nS1FRlN:nS1FSlN9lS1HTlN8lS1HTlN9kS1GUlN9jS1ITlN8lS1HTlN8lS1HSlN:lS1FTlN:lS1FSlN;mS1ESlN;mS1FQlN<nS1DRlN<nS1DRlN<nS1DRlN=nS1CRlN<nS1DRlN=nS1BRlN>oS1BQlN=QT1@PlNa0PT1^OPlNc0PT1[ORlNe0nS1ZOQlNg0QT1WOokNi0QT1VOPlNj0PT1VOPlNj0QT1UOokNl0QT1SOokNm0QT1TOnkNl0UT1QOkkNo0WT1nNjkNS1YT1hNikNW1]T1bNdkN_1^T1^NbkNb1_T1]NbkNc1]T1]NdkNb1\\\\T1^NdkNb1\\\\T1_NdkN`1\\\\T1`NdkNa1[T1aNdkN^1]T1bNckN]1]T1cNckN^1^U11O1000010O00010O010O0O101O]NiiN]1WV1cNiiN^1VV1aNkiN_1UV1bNjiN^1VV1bNjiN^1VV16XN_NWmN[1fR1nNTmNS1dR1ZOVlNTOc0c1VS1FhlN<XS1EelN>ZS1BelN?ZS1BglN=YS1DflN=YS1CglN=YS1CglN=YS1ChlN<XS1DhlN<XS1DhlN<XS1EhlN:XS1FilN9XS1EjlN:WS1EjlN:VS1GilN:VS1FklN9US1GllN8US1GklN9VS1EllN:VS1DjlN<YS1AhlN?XS1AglN?[S1^OflNb0lS1lNTlNT1QT1fNPlNZ1RT1dNokN[1RT1dNokN[1QT1dNQlN[1[U1M3N2N2M3K6K6H;GQ_Td0\", \"size\": [1280, 720]}, {\"counts\": \"m^Y31dW1>G7J8H?E5L3M8iiN`N\\\\U1[2F9Hb0^O7I8H3N1O1O001N12O1N2ON100002N2N2NO1O11O1O001O2N2N1O0YLnkN`3RT1^LRlN`3nS1`LUlN]3[T1L2lL]kNg2PU1N2N1O1O2N1O0000M3O100000000001OO1N10100N2C=K5O1O1O100O11O1O0O100O101O0000000O4M1O002N000O10O1001O000O2O001O1O1O00000O1000003M2M>B9H:E7I3N2M`0_O7J>]OjfZd0\", \"size\": [1280, 720]}, {\"counts\": \"SWX3<[W1<H6I7L=D<F5K9G6I:F7I4M2M<E:F3M2N1O010O0101NO1O11O1O1O1O00001O1O6JO1N3M24SLPlNb3XT1O1N1cLnkNl2QT1UMQlNi2PT1UMQlNk2cT1O1O00O100000000O101O000O1000000SOTMelNm2YS1WMUlNI3V3gS1\\\\MRlNh2lS1g0O1O10O10ISL[lNm3dS18M3N20000000O1000O10O1000000O10001O001O000O10001N101O0O200O1O3M5K9G5J3N2M8I7H9G;F6J3M1N2O3L6K2N4K8I9F:BffUd0\", \"size\": [1280, 720]}, {\"counts\": \"[`c3>^W16I6M3L3M4M2diNlNiU1W1PjNXOfU1i1H3L`0A6H7K3N2Mb0^O2O1N2O01O1O000001O0O1M35K4L1TLVlNa3fS1bLalNW3]S1kLglNP3ZS1PMglNo2ZS1oLglNR3XS1jLllNV3oS1000O10000000000001O000000O10000N22N3M8H00O1K5O1O1ZORMXlNP3fS1f0N2O1000ON3G9000000O1000O10000O01000O100O010O100O20O010O0O1000O101O00002N5J6J?A7J2QMVkNc2mT1WMZkNf2aU1_O8H3L4M1M3M4M<YO_VSd0\", \"size\": [1280, 720]}, {\"counts\": \"cne3<ZW1=I<E>Ce0\\\\O9G8I6Jb0]O:F4L3N1N2O1O0001O1O5K2N01O03M2N005K2N002N5K3MO1012M2N0000001O0000O100O1O1N2H8K5L3K65K6ZLhkNX3fT1JL4FkLfkNX3QT1eLRlNd3mS18N2M3O1O1M3M21ON3L4O1O100N20000O02O0O100000000O10000O100001O02N2N1O3M2jKZlNm3WT1F3M3M2N2N9G<C5L5K3M2M4M3M4K6J8H5J9G<D8InfPd0\", \"size\": [1280, 720]}, {\"counts\": \"T^^38eW16K8G=E4biNkNjU1j1K7I:F8H`0A:F4K2N2N2O10O0001O0100O100O106I3M1O7I1O0000001O001O9G2M2O1OO010O001O1O1L4G:K4EgLokNY3QT1iLlkNX3ST1kLikNW3WT19O1O01000O1_OhLZlNW3fS1b0O10000O1O00100001N010O01O1N12O2M101N2O0O3M2N2N8H3M3M4L4L4L5K5L:E:F:F8G:Bj`md0\", \"size\": [1280, 720]}, {\"counts\": \"a^c3:cW16K5L4ijN^OiR1R1blNVO[S1T1WlNSOfS1R1okNVOPT1^2O2L4M2N1O1O100101N4L2N1O1O:G2M3M2N0001O01O1O2O8F4M2jM`jNh1lU10LUNXjNf1gU1[NYjNe1cU1`N[jNa1bU1dN\\\\jN\\\\1dU1`0O10jM]jNP2bU1oM`jNP2`U1oMejNm1hU1O00MTNVjNh1iU1YNXjNf1]T1VNilN5hNe1lS1VN^lN:OF80C7Kc1mS1WN\\\\lN?IJ7JJo1jS1PNZlN\\\\1Ie0mS1SNVlNX1Me0kS1o1M[M[lNU1cS1lN`lNoNL`1dS1E[lNo0cS1h1O1O100O100000O2N1O10001O001N101N2O1O1N2O3L5L4K3N3L3M5J5L7J8E;F9G9H6H<@WXld0\", \"size\": [1280, 720]}, {\"counts\": \"Vfd3<T22kR16klN1RS15elN1WS17YkNWOk0h0jS1Z2N1O2O1N101M200O2O000001O3M5K=D5K3L2N1O10O01O00O20O2N2`MejNW2dU1N2N2N01N1O0O3J5O1O1NdMdjNX2[U1iMgjNU2YU1lMejNU2[U1800N2YOiMakN[2XT1j0K5M3K5M3N2N2N200O01O100O10000000000000001O002M2O0O3N1O2M6K3L6K2M5K8G6J8H9G8I5L4J=B5H;EXPkd0\", \"size\": [1280, 720]}, {\"counts\": \"UVb3>ZW1a0D8hjNVOoR1Q1clNXOZS1V1UlNQOhS1U1lkNROST1_2O100N1O101N1N3N2N0O2004M2M2N101N10O02N2N2N2N5K2O1N;E6I4M2OO0000000000000O1N2OgMfjNn1]U191O1O00hNcMllN^2RS1jMglNW2XS1mMdlNT2ZS1TN_lNl1aS1UN]lNm1bS1UN\\\\lNl1`S1YN]lNi1`S1[N]lNg1`S1^N\\\\lNd1cS1]1000O0100O10000000001O00O1000O10O100O2O1N2N101N2N2O3L3M8H3L:G8SMnjN]2eU1E8H5K3L6I6J7I6J5I^hdd0\", \"size\": [1280, 720]}, {\"counts\": \"Z_m39bW18I<F;_O<I8J4L4LRNXjNi1fU1WNXjNn1eU1;MhM_jNQ2<VN^T1P2WkNWNgT1c2N10O10O00000101N3MO2L30lLZkNo2lT1O1O2N2N100O1O1O000000001O00N2N200O1O12N3M2\\\\MjjNZ2VU180010N1^OXMlkNh2RT1]MikNd2WT1c0L4N201O0N1H\\\\LTlNf3kS1800O1M3O1N2O01000O0O2O1001O2NO100O11O1O1O00O11N2O1N101O1O1N2O1N3N2M3M4M3L6K7H4M8G2N2M7J7I5K;E5K8I2M5J5K8G;Egffc0\", \"size\": [1280, 720]}, {\"counts\": \"kbU57TV15ojN2XT1GgjNP1e0_O\\\\T1Z2I7L3L4M3N2N2N201N1000010O0001OO100O20N3N2N8I4K3M100OO1N2O11O1O1O3M;E10O00000O1M3M4OO012N1O1OO1O1N2L400N2O01000O2O2OO0OJlLbkNT3]T18H8J61O0000O1O1N2M2O2N1N210O10O103L101OO0100O1O100O00101O01O00O1O100O100O2N2N2O5J5K6Jc0]O4J5G:F=fNkiN4YW1CgVab0\", \"size\": [1280, 720]}, {\"counts\": \"fi`5:_V10\\\\jN5`U1X1nN_NakNb1ZT1dNbkN_1[T1U1M3M2K5J7J5M3M3O101N11N100000O2N100O1O100O10010O0O2O000O10000000002M2N2N1O2O000001O3M1O2N2O1O0O1O1O1O1O1O2N101N6[LikNU3VU1\\\\O1N1O2N2N001N101O00O00O1VO`jNmNlU1i0j0K5M2M4M5ZOPiN2dU14g16I<_Od0]iNbNjU1l1N2N2O001O00001O1O10000O0O2O1O1O1O0O2O001OO1K5N3N1O100N2L4O101XNmiNg1VV1N6J0O0L5J6La0SOPYha0\", \"size\": [1280, 720]}, {\"counts\": \"fai52iW18L5fNChjN?UU1HejN9[U1L]jN8aU1MYjN5gU1S1N1]Oc0D=J6N2N2N1N3M2N3L3N3K4M4L3M4L3O101N1O2N1001000O1O0O101O0O10001O00001O010O000O1O1O12N1O1O1O2N1O2N2N01O0002N1O1O004L3N0O2RLRlNf3TT100000001O0O11<C6J00000000000O100O10O1N1100O100O00_O[MikNe2nS1PMRlNd3mS17N3O2N4MO1M3L4N2M2O2O1L5L3L4N2M4L3N2L4L3M4M3M3M4H7J9Ia0]OXY^a0\", \"size\": [1280, 720]}, {\"counts\": \"Wc]65gW17J8I5K3L3N3L3N3M2O2M2L4L4L5N1O1L4O2N1O2N1O1O1N2O1O1N2O1M3N2N3M2O1EWM]kNk2aT1;L4N2N2N3M2N2O1N2M3O1N2N2N20001N1N2O1O1O1000000O2O000O2O000000O10O01000000000000O10000O10001O00001O001O0O1N2J6M3L4L5K7H:UMYkNm1PU1lMYkNj1oT1QNTkNi1eU1J5L4J6L4L5L3J7L4N1H\\\\__a0\", \"size\": [1280, 720]}, {\"counts\": \"oZk66kW1OPo4MlPK:F9L3N2M2N3M3M3L3D=A?L4L4L4N2M2O3L4L2N3N2N2I6H9N1O1N3N1O3N2M2O001O0O3N1O2N1O1O100O001O0010O01O002N2N100O12M2M4L3N2M3M2M3N101O1N101O4L3L2O0O2N2N2M3L5K4N2M3M2O2M3M2M4L4M2N3L3N3L3N3L3L3AciNZOaV1?hiNYOiV10P`Za0\", \"size\": [1280, 720]}, {\"counts\": \"kij5:^W1;I5A`0L3M3L4L3O2N2`N\\\\NblNe1ZS1_NdlNb1[S1`NdlNa1ZS1aNelN_1ZS1dNdlN\\\\1[S1eNdlN]1YS1fNglNY1XS1hNhlNY1VS1iNilNW1US1jNmlNU1PS1nNPmNR1nR1PORmNQ1jR1SOUmNm0hR1VOXmNk0dR1YO[mNg0cR1[O]mNe0aR1^O_mNa0aR1_O_mNa0`R1A_mN`0`R1@amN?_R1B`mN>`R1C_mN<cR1C]mN=cR1D\\\\mN>bR1B^mN?aR1A`mN>`R1C_mN=aR1C_mN>`R1C_mN=aR1C_mN=aR1D^mN<bR1E^mN:bR1F^mN;bR1E^mN;aR1E_mN;aR1F^mN:bR1F^mN:aR1H^mN8bR1H_mN6aR1K_mN5bR1K]mN6bR1J^mN6cR1I]mN7dR1H]mN7dR1I[mN8eR1G\\\\mN8dR1H\\\\mN8eR1H[mN7eR1JZmN6fR1JZmN6gR1IZmN6fR1KYmN5gR1KZmN4fR1MYmN3hR1LYmN3gR1MYmN3hR1LYmN3hR1LXmN4iR1LWmN2jR1NWmN1jR1NXmN0hR11YmNMhR12ZmNKgR15ZmNJgR15ZmNJgR16XmNJhR16YmNIhR16XmNJhR17XmNGjR18WmNGiR19XmNEiR1<VmNClR1<TmNDmR1;TmNDmR1<SmNBoR1>PmNBPS1?olN@TS1?llN@VS1>jlNAXS1UOfkNR1R1HdS14\\\\lNKnS1MRlN2PT1LQlN3RT1JokN4VT1IjkN6ZT1FgkN8\\\\T1HckN7^T1HckN6cT1[OgkNe0kU1M4K7HRfYb0\", \"size\": [1280, 720]}, {\"counts\": \"ZXe49cW1<Df0[O:H4K8H5K4L4M3M2M3N3N2M3M4M1N3N1N6J4M1O2OO01O2O00000001N2^MjkNW1UT1dNUlNW1mS1cNZlN[1fS1_NalN_1aS1]NblNb1`S1XNelNh1jT1O2O0001N10O001O1O00001N20O01O001O1O001O001O0010O01O1O1O1O00001O1O1O2O0O100O0000QNhNWmNV1iR1lNVmNT1iR1nNVmNR1jR1POUmNo0kR1SOSmNm0lR1UOSmNk0hR1[OWmNe0gR1_OWmNb0gR1CUmN=jR1FUmN9kR1ISmN7kR1LTmN3mR1ORmN0mR12RmNNoR11QmNOoR13olNMRS13nlNLQS15olNKQS16nlNJRS16nlNJRS17mlNIRS18olNGRS18nlNGSS1:mlNESS1;nlNDRS1=nlNBSS1>llNAUS1?llN@VS1?ilNAYS1=glNB[S1=elNB^S1=blNBaS1;`lNCcS1:_lNEgS13\\\\lNKPT1\\\\OljNOX1c0mU1K\\\\UPc0\", \"size\": [1280, 720]}, {\"counts\": \"noe3<^W1>A;I7J3N3M3M3M3L4M3M8H3M3N1O1O1N2O1M3O0100O0000010O1O1O1O101N1O1O00001O001O3M1O1lM^jNh1QV1MOO10O100BVNejNk1[U1VNcjNj1^U1VNajNk1_U1TNajNm1_U1TN]jNn1dU17001N2O0000O0100000O0N3N2N101N101O0O2000O00100O100O10000O0100000O010O1000000O01000O1000001N_kNkMZS1T2clNRNZS1m1flNUNZS1i1flNXNZS1h1elNYN[S1f1flN[NZS1d1flN\\\\N[S1b1elN_N]S1_1blNbN`S1\\\\1`lNdNbS1Y1_lNhNdS1T1[lNnNgS1n0ZlNTOTT1<lkNFXT13hkNN[T1NfkN3hT1\\\\OZkNd0QV100000O1O100O11O1O001O2M7IeU_c0\", \"size\": [1280, 720]}, {\"counts\": \"^^`2`0^W14N4N\\\\OhhNb0:[OeV1U1H5C;L4M2M3M2O2O1N3O1O1N10O01N1N101001O002M1O2N2N2O1N10000O2O0N3N1O2ROliNB7CUV1?kiNI2IVV16RjNLGOUW1NjhN4VW1MghN6XW1901O000O1N2N2O1O2M2G:K4BjNmiN[1PV1=dkNWN]R1m1]mNXN`R1k1\\\\mNYNbR1j1YmNZNcR1k1XmNZNcR1m1VmNVNhR1m1UmNUNjR1n1SmNSNlR1o1RmNRNkR1S2RmNoMlR1U2olNmMQS1T2klNPNSS1T2dlNRN[S1Z300000O10O100001O0O100O101N2N2O00002N2N2N5K3L3N6J2M3N1O1O1O1N3N1N2O1O2nLVkNj2UU1K2N1N4M1O1O1N100gM_jNS2aU1lMbjNR2hU1M3N2M3N2N1O1N2O3M3M2M4M3M4L2N2M4M2N3L4M2M3KcVXd0\", \"size\": [1280, 720]}, {\"counts\": \"mob25XX6LgoJ0kW11b`M0_W14H8O2O05KO4H5M4N1O1N3N1N3N1O10001M3M2O2L3H9L4[OaN_jNf1[U1d0J6M2O3L3N2M2M4M3M3N2N3M2O2O0N100O1M2O010O1O1O2N8H3M2N1O10O00100000000000000000000010O0001O001O011N2N2N<D4K4M1O2M2N2L5M2N4L3M3N2M4L2O3L2O1N3N2N1N3N2N1O1O2M2O1O0O101N2O1O1N3N2M2O1O001O1N2O2N1O2N3M2N2M2O2N4K2O2M4M2N2N3Kn]oc0\", \"size\": [1280, 720]}, {\"counts\": \"d`c31nW13K5L3L3O1O2N101000=_O6J4K5N2M3L9H4I8K5L4M3K4L3M3N3M2N3N2M2N2O2M4M2M2O1N2O001O1N2O1O1O1O1N101O001N101O001O1O01OO101N10000000000O01O1O1O1001O0000O10001N101N101N2O001O2M4M4L7I<D4L3TMojNc2XU1O2M3N1O2N2N2M3N2M3N4L2M3N2N1N2O1O1O1N3N1O3M1O2N2M3N2N3M3M2M4M4K5Kcefc0\", \"size\": [1280, 720]}, {\"counts\": \"]n`34YW1m0E3N1N2M2N3N1000001hiNdNkU1\\\\1miNSOlU1h1H4L4L002N1O3N1O3L4K4L3O2N7I5J2N1O10000O1;E1N102N0lLZkNl2gT17O1O0100O1O7J2N]OQMUlN0Hb2SU1O101NN2N101N2N2M4L]MPkN\\\\2nT1eMSkN[2oT1:1O1OO1N2O10000O10000jNPMYmNS3fR1nLhlN8Em2^S1PMjlNb3TS1aLjlN_3TS1\\\\LclN24f3XS1XLflN10i3ZS1WLdlNT4\\\\S1710O1O001000O1000000000O11O010O00000O10000000002N2N2O1N1N2O4iKYlNm3oS1N4L5K2N2N3L4M>B6I;F4K3N1N3N3M3M6I5K5K4L<E=Bkfac0\", \"size\": [1280, 720]}, {\"counts\": \"\\\\^a4g0TW17J6J7K6J5fiNbNkU1o1J4M4K4M4L=C4L2O1N2N2N2N1O5J9I1N1O2N1O100O010O01O002O0O1O10O02O2M2N01N0011O1XLPlN_3ZT1N1O1O5J11O0001O1O1OO100000000O100^OgLTlN2MY3_T1O01O0101OEmLikNS3RT1TMlkNl2QT1WMokNh2oS1f0L4N2M201MPL]lNi3bS1;00O1O1O1O1O10O10001O00000000000O11O0000000001O001O1O1N3N1O1O5K6J5ULmkN^3]T1N3M4La0_O9G9G4K3N3M2N1O2N5K>A?@8Gbfcb0\", \"size\": [1280, 720]}, {\"counts\": \"P`i52kW18^hN1fV1[1YO>B6J5L4K5L6K4L5Jf0[O3L2O1O001N2O0O20000O1N1O2O02N100O1O10M20kKblNh3^S1WLjlNb3WS1[LmlNd3hS1O01O0000000001O1aLekNV3gT1L2N1O10O00O10O1O1002M2O00O100O1O1D<00O1008H6J2NL4YOQMZlNR3^S1YM]lNi2dS1YMTlNl2lS1c002N1O100L4N2O1O100000O01N2O1000O10000O100O101O0O10000O100O100O1O2N1O2O2K5K5eMlkNe0bT1RNhkN4NIGe1VV1N2M3M2O2M5gNdiN<LCTW1O^fea0\", \"size\": [1280, 720]}, {\"counts\": \"bQo67hW15H6K5J4J7_O`0J7lNYNhkNl1UT1Q1M3N2M4K5I6N4M2N100O1O1O101N101O1O1O001O0O1001O2N2OO0>TLjkN<6i1_T1kMjkNT2RU1000O00O10000010O0OnM[jNk1eU1TN]jNk1dU1SN\\\\jNo1fU1400N2O100001OO1O1O10001BgMSkNY2jT1PNnjNR2PU1`0N2N2@XMdkNl2\\\\T1UMbkNl2nT1K2TMUkN_2lT1^MWkN3HX2QU1gMRkNb2nT18O1N1O2ZOf0E;N2O0O2O0100O01000O101N10O0100O100O2N1O3M3M2Ne0]LjkNS2_U1B3M2N2M5J9F7K3J:_O\\\\Wj`0\", \"size\": [1280, 720]}, {\"counts\": \"Xi\\\\71hW19G9H6M4K4M4K4I8F:L3J7K5H8BdMSkN`2iT1>N2M2O2N1N3M2M4M2O1O2M2N2O1O1O2O000O2O01O1O2N3M2N1O3M1O:F5L3L0000O20O4L4MO0001O2N002N3N1N1O100OO1O1O02O00001O2N1O0000000O01O1O0I89G2M3kM[jNk1dU19N101N2iNYNSlNh1lS1]NmkNg1PT1V1M21O100O1O0O2O1O0O2O1O1O2N2O1C?K:lNgjNjNcU1?`jNTOMN73iU17mR_`0\", \"size\": [1280, 720]}, {\"counts\": \"mQh75eW19G8J6L3L5K4K5M2L5H8L4M2N3M3L3K5K6J6K4M4I6N2N2O2M2O1N2N2O1N3N1O1O1N2O2M2O1O1000000O100O100001O00O2M2003M1O2N1O1O4L3M2N1O0nLnkNV2ST1gMRlNV2PT1gMQlNX2QT1gMPlNX2QT1gMokNY2RT1fMkkN]2YT1`MckNc2aT1YM]kNi2dT1UM]kNk2cT1TM^kNk2cT1UMdkNd2\\\\T1\\\\MgkNa2YT1_MikN_2XT1^MlkN_2lT1O1OO10O011O002MLgMijNS2TU1QNmjNl1eU1O1O3L100O0OO1L41NN5UOPNekNP2QT1fN]kN`1dT1m00O2N1M3OO01M4N3K4H9K5L3L5M3J7F9K5L4M7H8HUah?\", \"size\": [1280, 720]}, {\"counts\": \"`bo7;`W17J5M4K4L4K4L5C=K5K4I8]Ob0M4N1N2M3O1O1N3N1O1O1N2HdLkkN^3QT1:00O002O0001O100OO100O2O0O1000000000001O001O000N2O11O1O1OO3N0100O002N2N1O2N1N3N015J4L6J2M2OO01O100O101N2oLYkNg2WU1I1O1N2N001O1O0OO03N5K3N1N2L4M3M[O^NojNc1oT1cNljN^1gT1_NljN>1Z1UU1j01N2O2J6I7K4N3M3M4M2I8K4L6H8L4L]Xg?\", \"size\": [1280, 720]}, {\"counts\": \"[b`76eW17I7K5K4M2M3K6_Oa0PO[N^kNk1^T1[NSkNR2iT1e0I7M2L4M3N2L4O1O2M2O100O1000001O000O100O100O1000010O00000001O1O000010O02N1O10O01O2N3M2NOcLokNn2oS1RMVlNk2kS1RM[lNi2_T1O1OO10001O0000000001WMPkN`2PU181O0O0100O1O001O010O[MUkNW2gT1b0M3K60O00O2O0E;M4O2N5KO2N2L4O001O1O2M2K5L3O2N3^N^kNAeT1<`kN^OdT1<bkN]OeT1?^kNXOlT1g0UkNWOoT1f0RkNWOQU1i0PkNQOVU1m0U1_OSaW`0\", \"size\": [1280, 720]}, {\"counts\": \"Yjm64_U13alN8XS1MclN6YS1OblN6PS1l0VlN[OeS1j0UlNZOiS1`2L4L3O1O2O001N101N2O0000000O100001O00O100001O2NO2O0001O2N2N4LEZL^lN>Gd2kS1mLalN:Kf2ST1XMPlNf2RT1VMQlNi2PT1UMRlNj2nS1WMPlNj2PT1YMmkNg2TT1TMQlNk2bT1000]OTMSlN3_Oc2[T1]MUlNMCf2WT1_M[lNa2fS1]M\\\\lNb2aT11O0000001OOJZMTkNf2SU11O010M2L4I7N2G9M3O1O100O2O0CZL^lNh3bS1XL\\\\lNj3dS190O01N2O10000O1O001000O1000000O100O10000O100O1O1O0O2O2N102L5H7dMVlN9ST1^OTlN>TT1mN\\\\lNP1\\\\U1kN`iNb0RW1KEB`iN8dV1H^iNOk]k`0\", \"size\": [1280, 720]}, {\"counts\": \"]`S6b0^W15J6J:RiNQOPV1o1B;G5K4Le0[O5L1O1013MN2K4M2O2O0O10000010O1O1O10O01O1O1O2N1O2N0000O12N1O001N10001O1O6Jf0ZO7J0O0000000000O100001O1O1O000000O1O2N1[ObMckNb2\\\\T1aM]kNe2QU1O0O1ON2iN_MPlNN8M4i2aS1gMUlNF0j2hS1S1O100O1O0101O1OO100O110O00000O02O1N101N2O1O1N4L6K8G8I7I4UMmjN`2bU1G6J2O7H8I2M2O1N2N1O2K4mNeiN2dlaa0\", \"size\": [1280, 720]}, {\"counts\": \"noh48cW17J8J7J7F8J4J6H;K3M3L2O2O2M4L4Ng0XO1N3N00O6K2N01OO02OO2OO1O01O011N2XMWkNU2jT1iMYkNU2jT1gMbkNm1`T1QNfkNK_Og1PU1XNkkNg1UU12M[NPjN`1oU1bNPjN^1oU1:M3M3KTN\\\\jNf1aU1]N_jNc1dU1;0fM`jNT2fU101O0000O1fNoMPlNM2U2nS1oMckNO20:T2PT1oMakN<0Q2^T1m0I8K`MSlNU1kS1lNWlNT1gS1POSlNS1mS1`100O2N1O11O01N10000O2O0O1O100O101O00O101O1N101O1O1O1O2N8G4M;E3M3M2M7J7I3L7J6J1O1O2M4M1N3N3M5J3M2O2M3M5K8H7I6J5Ieehb0\", \"size\": [1280, 720]}, {\"counts\": \"Y_h39dW14M3N2L4N2O2M2N7WiNnNVV1X1diNkNZV1W1ciNkN]V1\\\\1O2O3M6J4M0O4NeNYjN;hU1BVjNb0jU1\\\\OUjNP1`U1PO^jNS1aU1g0O>C1N17H4L0eMjjNl1WU1RNkjNn1SU1SNljNn1TU1RNkjNP2RU1a0N1N201O1O001O000N2M3N2M3N2O1O101M2M3M3N2M3O10000O100O1O1O2O0O1O101N1O1O100O2O00001O0O10001O01O001O002N1O1O2N4K9G6K3M2M3N1O2N2M;E`0A4L3L3N2L3O1N3N2M2O2N2M4M3M3L3N2M6K4L3M2M4M3LZ^ec0\", \"size\": [1280, 720]}, {\"counts\": \"T_^3;bW16J4M5L2M3M302N3ViNlNbV1Y11O02ON6UOn0DSY1NQgN7H6J4M4M2L5K5L2M3K6J6PjN]NYU1]2H4K4K6L4L30001N2N1N3M2N3N1O2M2N2L5N101N2O000000001O0O2O00100O001O001O00000O101O0O10000O2O00000010O00001O1O1O000000001O1N2O1O3M3M2M:G4L1N3N2N2N1N2O>B=C7I2N2M3N2N2N1O4K6K3M3M3L5L4L4L6I5KkUnc0\", \"size\": [1280, 720]}, {\"counts\": \"e^^39eW13M4K;G5J9I3M2N4L3N2N1N10ZNmiN^1^V1M10O1O1N1O3N1001O04oiN[N`U1R2`0AOO0000N2J5N3N1N2011N2NO2J5N2M3M3M4L3K5K5H8J6M3N2N2M3N2L4M3O10001O1O1O1O0O2O0000001N101N1O2O1O0O101O000000000000001OO10000O101O001N2O2N2M3N1O4L6J7I5J3O1N2N5K?A`0_O4M3M2N3L6K3M3M3L4M5K3M5K4K5L3L^Vnc0\", \"size\": [1280, 720]}, {\"counts\": \"beS4:dW16J4L4L3O2M2M2O100WiNUO]V1l0`iNTOaV1U124K8H1O24=H?WO2N2N11N3M0O2N1O100O10N1O2M40O3MO00000001O00000000001O0000O10O10O01O1O1000@`M`kN`2`T1`M^kNb2mS1`MWlN1Ha2nS1gMRlNLN]2lS1mMPlNNOV2oS1Y1I7M33N:GN1I6N3L3O100O1O1O200O100N10000001OO100O11O001O001O0O2O1O1O2N1O2N2N1N5L5K4K3M4M3L3N9G;D>C8H2N2M3N3L3N4K4M3L5L?@7HeoXc0\", \"size\": [1280, 720]}, {\"counts\": \"mUh56fW17K4L6K3N7H5J5L5L4niNDXT1V2L2M2O1O2N1O001001O00OO2N102N1O00010O000O10O2N1J6000001OO100000000000O10000O10000O1O001001O1O0O1001N0O2O100O1O1L4L5L3O1O1000O1000O1002N1O2N1O1N3N1O1O2N2M6K2N3M1O1O4L2gL^kN3Oc2VU1N2M3N3L6dM]jNQ2mU1K9bNhiNd0[W1^O7J]oZb0\", \"size\": [1280, 720]}, {\"counts\": \"bY_77\\\\W1b0C9G9H8@`0L3POPNbkN^2VT1j0L4M3L4N3L3N2M3O1O1O100O1O11O001O00000000001O0000001O2NO100001O001OO100O11O0O010000001O0000O01000000000001O1O001O1O1O1N2L4I7K4H;ZOSkNiMlfja0\", \"size\": [1280, 720]}, {\"counts\": \"``V7c0XW17I7H7H7H9E:J6mNS1J7D;L5M2O001O2N100O1O10000O20N10000000001N2O1O00001O001O011N2N2O1N2O0O002M2O1N2O101N2N10000O001O1O1O1O1L5I9F\\\\1]MjiNd0ZW1@nW\\\\b0\", \"size\": [1280, 720]}, {\"counts\": \"moZ6<XW1e0UN[OUkNEFa1lT1nNZkNa1YT1kN\\\\kN_1\\\\T1X1J6J6L3O1O00000000000000001O0002N1O00001ULVlN]3PT1_LRlN^3oS1aLRlN^3WT11N20kLckNg2]T1>1eLckNQ3^T1lLekN1Oi2\\\\T1VMnkNj2RT1WMlkNj2TT1VMlkNj2UT1SMnkNl2RT1SMokNm2bT10_OSMmkNo2ST1QMkkNQ3bT101YMRkNZ2nT1eMSkN[2mT1fMRkNZ2nT1<002N1N2O00002N4L4K?Be0ZO8I6J;D3Nb_[c0\", \"size\": [1280, 720]}, {\"counts\": \"QYT5:]W1?G6K6I4ZNSORlNT1iS1d1L4K5M2L4N200O100N3M2O1O100O10000000O101O001O1O1N2O0bLnkNm2TT1QMPlNl2QT1QMRlNm2PT1PMSlNo2_T10N1O2001O6I3N00000O2O2N2N6J1O1O1O0000O11N2O0000BjMQkNV2kT1PNTkNP2mT1oMRkNR2[U12L51N3XN_jNm0jU1^NljNW1SV1L4K6J9F5JP_Td0\", \"size\": [1280, 720]}, {\"counts\": \"c`W43dW1h0[O<D8hNbN^kNk1WT1gNZkNa1aT1R1K5J5M3N2M3N2N3N100N2O1N2O100O1O1O100010N2O1O1O001N101N2N2O001N100\\\\LQlNV3QT1fLRlN[3YT101oL]kNd2dT1:2N2TMYkN]2hT1=1oLXkNh2iT1VMXkNj2fT1:MVM[kN]2dT1eM[kN[2dT1a000000O102M3NHlLdkNT3cT11NTM^kN^2aT1cMakN[2`T1cM`kN^2`T1aM^kNb2bT1^M]kNc2cT1=0kL[kNn2mT1O1O1O01O1O11N4Lb0^O9F3N5J9H5J8F9Bi_cd0\", \"size\": [1280, 720]}, {\"counts\": \"TXg3460RW1m0B7K4D<fNcNikNc1TT1eNckN`1ZT1T1N3H7M3O1O2N1O2N1O1O2N2N1000001N100001O1O103L4LN2O1O100O10000001XLnkN_3_T1K3bLfkNP3gT1N2N00O1CSMekNo2XT1>N2FcLokN_3bT1G>B=DO0O1N2C<M4N2YOg0O0101O2[MkkNZ1XT1dNikN[1VT1X1001O0O2O0O2N102N1O1O0O102N3M5K;D8H8H?A7H9F?Ba0]Og_md0\", \"size\": [1280, 720]}, {\"counts\": \"e`\\\\4:\\\\W1<@`0G9F9G8L4M3oNSNdkNY2WT1j0L5M2N2M3L4N3N1N2O1O1O1O1O1O101N1000000001O00001O0000003M4L1O1O2M02O02N2O00O000010N100O10N2O12N1O000O10O11O00J6O00100001OO0100O2N1O2N2O1O3L8H3J=^MYkNe0GTORWbd0\", \"size\": [1280, 720]}, {\"counts\": \"Zil4<^W18I6H8K4M4L3L4E<M2M3K5F;\\\\O_MbkNg2YT1b0N2N2L4K5M3O1O1O1O1N2O2N1O0O200O1000O2O001O010O1N101O1O1O01O001O000000O2N3N1O2N00OdLokNk2QT1TMQlNk2nS1VMRlNj2nS1UMSlNk2mS1SMVlNl2kS1TMUlNk2mS1TMSlNj2oS1VMPlNi2QT1`MekNa2\\\\T1`0O2IaLlkN_3TT17OO2O101N1O2N1N3N2N3M4J6F:gNQkNgNgU1L]jN0\\\\PRd0\", \"size\": [1280, 720]}, {\"counts\": \"_hQ5;aW16F9I8I7L2L5K5D;M4M2N2L4K5D<[Oe0N2L4H9L3N2N200O1000000001O1O000O10001O00001O000100O14K3M2N1O0000O1O02O2N00O1M3O0010O0010001M3M4J5L4L4K5XOg0B?E?Je0[O:F`QWd0\", \"size\": [1280, 720]}, {\"counts\": \"`X`4?PW1c0I6K5H:E:J5]NRNjkN68X2kS1U1M3N2O2N1N2O1N200O100001O0O3N010O0O10O1000000001N2O1O0000O0102N1O001O3NO0O1O1O100O10000O11N101O000000001N100000002M103L9Hb0]O8H9WNcjN<kU1SOfjN>Zfid0\", \"size\": [1280, 720]}, {\"counts\": \"UhX4P1QU1XO_lNS1XS1YOWlNS1bS1\\\\OokNj0lS1g1O2N1O3L2O001N2O1O1O1000001O000001O00001O001O0O10000000001O0O101O1O1O1aLjkNR3WT1fLmkN]3\\\\T1O2N2NCfLUlNY3UT14N2003MEjLnkNT3RT1mLlkNU3ST1<0O10000O101O001O1O001O001O000O100O2O4L4K4M7G7dNkjN@oU1HSjN1[V1FfiNOTWld0\", \"size\": [1280, 720]}, {\"counts\": \"__m3V1WV1e0ZNgN^lN]1_S1jNSlN`1kS1]1M3L2N2O1O101N1O1O1O1O1O11O001O1O1O1O000000000000001O001O0000O10ZL[lNP3fS1oL^lNn2cS1oLalNo2_S1PMclN6]O\\\\2QT1]MalN8]O\\\\2RT1]M`lNP3`S1oL`lNR3aS1kLalNU3UT102N1mL\\\\kNi2TU1JO0L4F]MUkNe2oS1YMblNT3^S1PM]lNQ3bS1RM[lNo2fS1e0000O1O1^LmkNV3UT1hLmkNW3TT1;0O0000O02O1O00001O0O10000O1_LhkNZ3XT1dLnkNW3cT1^NTkNFRU12UkNH[U1HhjN5aU1BbjN<dU1]OajN`0eU1WOQkN4dV1H^gnd0\", \"size\": [1280, 720]}, {\"counts\": \"n_R4i0oV1;H7F9B?K3L5J5kNV1J5N2N3N1O1O1O10000O2N0010000001O00001O1O000O10000000000000001O0000000YLTlNZ3mS1aLXlN^3VT100O1O1N2O001O00O1OoLakNb2`T1\\\\MckNc2^T1^M_kNc2gT1800O0ORM[kNe2fT1:1OO1M3L4GcLmkN_3ST16100O10O02O2M2O1N2N3K4K5M6lMlkNZORO2gU17PTkd0\", \"size\": [1280, 720]}, {\"counts\": \"aaf42cW1`0C;G7J6K5K4M4G8J6N2L4K5L4nN^MmkN46d2hS1Q1J6N3M200O10O1000O100N2O1O100001O00002N2N1O003M3N0O1O1O1O2N2M2O00N2N2OcLokNl2ST1PMPlNP3TT1hLRlNV3^T1N00002M3N1ON3L3@mLRlN;GY2eT1`03L3N001O2M3YMmjN_2TU1^MPkN_2QU1bMmjN[2VU161N1K5H6I:E9M5Im0mN[aYd0\", \"size\": [1280, 720]}, {\"counts\": \"]Qi41iW19I7I6I6J6K5L3M3N3L3N2M4I6K6L3N2M3M3L4L4K5B>H8G9I6N3M4M20O0100O1O10000001O1O1O001O1O1N2O2NO2N10000000000001O1O2N1O1RLSlNg3UT1eLlkNg2ST1ZMkkNg2UT1b00O101NO2L3001N3L3L4M3M4L3G9H8D<F;H7K>B8I7FiYSd0\", \"size\": [1280, 720]}, {\"counts\": \"jX`4?VW1>H<C6K6@`NVjNi1bU1>L5I6I7XOh0K6K4L4N2N2N2N2O1O1O1O1O1O100O100010O2N1O002N1OO1000000O1000RL`lN\\\\3aS1cL_lN]3bS1VLalN5Me3jS1ZLVlNf3kS1XLWlNg3RT1O00O100IYLTlNh3kS1WLWlNi3iS1WLWlNi3iS1WLXlNg3jS1WLVlNj3PT10001O0O10IXLUlNi3kS1YLSlNg3PT131O000O2N100O4G7M9D<\\\\OkjNjMbV1i0d0G<Eloed0\", \"size\": [1280, 720]}, {\"counts\": \"hQU4:ZW1?E:I6K5J6I7H8K4K6cNYNZlNl1dS1\\\\NQlNi1lS1W1N2O100O2N0O2000000000000001O000O01001O1O2NO10O102N3YLmkN[3UT1cLkkN]3XT1410O02N1O1N101O0O2O00O1O1001O2cLakNU3`T14100O10001O00O11N1O2N3M6J7oMWkN>\\\\V1YObWVe0\", \"size\": [1280, 720]}, {\"counts\": \"hi]4>QW1c0I6K5L4G9I6L5_NTNhkNKj0T2ZS1hNPlNb1oS1Y1N1N3N1N2O1O2N1000O10O101N12N2N1OO100001O1O004L6J1O1O4LO100000000O1O001N2O1O0100000000O100O100O10O11O001N3N4K3M6J7nM_kN5kT1\\\\ObkN=YV1Bnfnd0\", \"size\": [1280, 720]}, {\"counts\": \"bY`41UW1n0`MXOYmNQ1aR1VOXmNP1]R1dNPlNa0o0ERO]1mS1VOllN[1RS1hNjlN[1US1hNdlN]1ZS1f1O0O2N100O2O0O1001O001O3M3M001O1O00000000O\\\\LSlNW3nS1gLSlNX3nS1iLQlNW3oS1iLRlNV3nS1jLPlNX3PT1<001O3M1O1O6J0OKcLikN]3VT16101O4MM2N1011O0001N1O10000001OO10O2O1N101O001O2N1O2M5K4L4mMWkNh0nT1jNbkNo0kU1K7J5K4K5B`0H8Io^^d0\", \"size\": [1280, 720]}, {\"counts\": \"^Ye4a0PW1b0D;H9@>L4ZNWNhlNEXOY2hS1_N[lNW2cS1U1N10001O000O11O001O00000001O000001O000O101O001O001O00001O0000O01000001O0000001O0000000000000000000O2O00000000001O001N101O0O101N2O0N3O1N2N101N2N4K6I;G8HS1mN5J9G8C>@V_oc0\", \"size\": [1280, 720]}, {\"counts\": \"VZj41hW1<F8I6H8G8K4M3N3M2K6J5K5N2N3L3K6J5VOaMokNc2jS1m0M3L4O2O000N2O1O1O100O10000000O10000000001O001O1O1OO10001O01O001O1N2O2N1OO1N3O0000O2O00001O2M3NO0010O1O1O2M200O1O2M3M2L3O2N2O2J6L3O10008`NUkNWOXU10Ubjc0\", \"size\": [1280, 720]}, {\"counts\": \"i`P5>ZW1<G8I9G4K6M2N3K4E;M4N1K5L4N2G9WOj0H7O100O1M4L3N2N2O1O1O1N2N2N3O01O001O2N1O2N1O1O1O00000O2O0010O01O0O2O1O1N3N002N2M100TLRlNe3UT101N1O1N1O101M3M3O2N101L4fNZ1I=E9C<YOUbTd0\", \"size\": [1280, 720]}, {\"counts\": \"UYe4:_W1;F9J5L3L4L4L3L4F;G8N2L4kNcMVlN0Fe2QT1P1N2M4L3N2M4M2M3O1O1O1O100000000000000000000001O000000O100001O001O1O1O00001O1O1O0O10001O0O2O0010O10N1O2N2N3M2N5K9E`0dMljNR1ZV1H4K7I6G:F8ITW]d0\", \"size\": [1280, 720]}, {\"counts\": \"WYe4h0QW1:G7_OkNkiN`1SV18M4K5oNoMikNW2oS1R1J5N2M3N2N2K5N3N1O1N3N1O100001O001O00001O0000001N10000000001N101O1QLTlNh3TT1N1N2O1O0000001O0001OO01000O100O10O^LikN]3[T110010O0100O00101O6I9gMYkNi0lT1mNckNg0VV1Ba0@gVbd0\", \"size\": [1280, 720]}, {\"counts\": \"XYo4:24mV1d0K5J6L3E<M3M2jNZNlkNm1PT1[N^kNS2^T1i0M4J5O1M30bLnkNn2RT1`0O2O0O1OaLSlNm2mS1d00O0000O1001O1O1ON2O10O2O0000O1O100000O0100002N2N00N3L3003M3M1O00001O000O10O0101O1N102M3M00O2O0O2O0101N3N2M8H4L3M2jMgkNa0\\\\T1ZOQlN<^V1ZO5HcVnc0\", \"size\": [1280, 720]}, {\"counts\": \"`Qi43_W1c0UOi0VNgNhkNZ2QT1n0L4L3N2N2N2O2N1O2N1O2M2N3M2O1N2O2M2M3002N1N20O02NN200001O2N2N2N2N1O1O1O00O1O11O000000O1O11O1O00011N2M3N1O01N1O1O1O10000O0102N2N1O1O01O00ON31O1O1N2O1N3N2O0O1O2M2O4K5I5L8Gc0\\\\O<Dh0XO;E8J7HUWdc0\", \"size\": [1280, 720]}, {\"counts\": \"lgd39>JbV1a0QiNLiV1f0O2N1O1O12O1N1OO1N2fjNoNdS1R1WlNZOaS1g0]lNNnR14PmN3kR1MSmN7jR1JPmN=oR1D^lNjNLf1fS1V2O2O1O000O11O2N001O2N00O1N2001O2N1O00001O0000O1O1O21N2M100UMXlN^1hS1bNZlN\\\\1fS1dN]lNY1cS1gN_lNW1aS1iNdlNQ1\\\\S1oNelNQ1ZS1oNglNQ1WS1QOilNo0XS1POilNn0YS1SOelNm0[S1SOelNm0[S1SOflNl0ZS1TOflNl0ZS1TOflNl0ZS1TOflNl0ZS1TOflNl0YS1UOglNk0YS1UOglNk0XS1UOjlNk0US1UOjlNl0WS1TOhlNl0YS1SOglNl0YS1UOglNk0XS1VOhlNj0WS1WOilNi0WS1WOjlNh0US1YOklNg0US1YOklNf0VS1ZOklNe0US1[OklNe0US1[OklNe0US1[OklNe0TS1\\\\OllNd0TS1\\\\OmlNb0TS1^OmlNa0SS1_OnlN`0QS1APmN=QS1CQmN;oR1ERmN:mR1HRmN7nR1KQmN5oR1KQmN5nR1MRmN1oR10PmN0PS10QmNOoR11QmNNQS11olNNRS12nlNNRS13mlNLTS14llNLTS14llNLUS13klNLWS13hlNNYS11glNN[S12elNL]S13clNM_S10blNO`S10alNNbS10^lNOeS1M]lN2hS1H[lN7hS1EYlN9kS1ETlN;QT1@okNa0ST1\\\\OnkNc0UT1XOokNf0ST1XOPlNe0QT1WOVlNc0jU1_OVVnc0\", \"size\": [1280, 720]}, {\"counts\": \"UYb34gW16D;M4N1N2O2O1N2M2K6M2N3M3N2O5fiN`N=6_T1f1XkNgNcT1[1YkNnN`T1c2akN]LmS1l3010OO2O00O:G1M3N1O2N010ON2N3O0N3J5O1M3O1O11O00N2AoKmlNS4oR1SLllNP4TS1QLjlNo3XS1ULflNh3ZS1XLflNh3YS1YLglNg3YS1YLglNg3XS1`02O00O10eKglNo3YS1nKllNP4SS1SLilNo3WS1=00000O101O0000O1N2001O001O0O101O001O00O1001N100O00101N102NO1O101OO100O10001N4M1O3M9G2N3L4L4M;C>C:F3M2N1O2N101N1O2O:E6J2N6K=WOg]jc0\", \"size\": [1280, 720]}, {\"counts\": \"\\\\XV4:bW16K5J5L4K6L8H<diNeN\\\\U1T2L5K7H;G8H2N1O1M2O1002N2O3L3M1O3M2N2NN2DkLlkNW3oS1?1O3M3M1O1O000O10N2O100O10O1000N2O1O001002N5`LekNQ3fT1O0000M3F9\\\\OmLZlNV3eS1c000O1O001O1O1O0010000O0010O102N2NO1O10O101O0O010O2O4M2M3L3N4K6K6I6J5L6I7J2M4L2N101N3N3L5L3L7J1N4L4M6mNTiNe0aW1B7Jjmbc0\", \"size\": [1280, 720]}, {\"counts\": \"XY`49bW17L4L4I8J6niNPOlT1S1PkNUOiT1n0TkNWOhT1l0TkNYODZOnT1`1YkNKYT18dkNMXT16bkN0ZT1n1M2N1O2O0O101O000O1100O000000000000O1O0O2001O1O0O10001O0001L3M3N200000000000ULVlN`3jS1`LWlN_3iS1`LXlN`3iS1:101O1O3M1N1000000000O1N2M300001OO0101O0O101O001O007H10O1O0011O0O3NIaLPlN\\\\3lS1hLWlNU3iS1kLWlNU3jS1iLWlNW3kS1fLTlN[3YT1O101N7J4K:F=D4J4N1N3M3M3N2M6J5L4K`0_OXUZc0\", \"size\": [1280, 720]}, {\"counts\": \"iib44fW19K4L2N2N2M4L5Kl0UiNYNjU1T2I6K7I4K4dkNSMaS1P3WlNVMiS1o2hkN\\\\MYT1U3J6N2N20O2N2N1O006J3M3N1N1O10O000000O2O0O10O1O1100kL]kNk2_T1=J6H8000O1XLQlN^3QT1`LokNa3TT1400L4\\\\OWLPmNj3nR1YLQmNg3mR1ZLTmNf3lR1XLVmNh3hR1ZLWmNg3hR1]LUmNb3kR1_LUmNa3jR1bLRmN_3mR1k0M32N9^KflNm3[S1PLglNo3YS1RLelNo3nR1ULolNV4PS1mKnlNT4SS1<0000O0012N8G2O00N2N21N2O00L4N12O2N1O0O11O01O1N2O2N3M3L4M6J4L:gLakN\\\\2^U1D4L2M4M1N3L3N5K6J3M3N3M3L6J6Ji0WOPTPc0\", \"size\": [1280, 720]}, {\"counts\": \"oWj47bW1d0@=Ec0^O5ojNYN^S1X3L4K3N2M2O100O1O1O1O2N1O1N2O20O2lK]lNg3dS1WL\\\\lNj3kS12O]LXlNP3hS1oL[lNP3cS1PMalNm2_S1QMdlNn2\\\\S1SMblNn2_S1RM^lNP3cS1PM[lNQ3gS1jL]lNU3XT1O01000mL\\\\kNi2eT1UM\\\\kNk2dT1SM^kNl2bT1TM`kNj2`T1VM`kNj2`T1WM_kNi2aT1\\\\MZkNd2fT1_MWkNa2kT1:0O8H00@WMjkNh2fT110000^O_M`kNf2RT1SMUlN[3kS1gLRlNZ3nS1<10M2LZLYlN[3fS1fL\\\\lNX3dS1jLVlNY3kS1>00O1O1O10000N2O1O1O1O1O1N2N1011O00002O1N00000000O10O10001O100O1O1O001O1O4K4M;E5J5XMPlNX1PV1[O5J<D^]Vc0\", \"size\": [1280, 720]}, {\"counts\": \"\\\\aP52fW1>ciNDZT1Q1XkN\\\\OUT1@WkNg10SObT1^2L3L3M4L3O1N3N100O2O01O01N100O2O000O11O00011N2N10O01VLTlN^3YT1O4K3MO1O2O01O3N2MFmLlkNn2QT1XMlkNi2RT1XMPlNf2RT1UMSlNi2eT1M1ON2N200O1O1100O2N@UMhkNn2XT1=00O2N1000000O1O1[OaLilN^3XS1aLilN_3mS1O1OO1O2M21N2OO1M3M3L3N3O1O1O1O1001O001O01O01O000000O010001O1O1N3N0aNlkNQOUT1l0WlNkNiS19YlNlN8d0cS19fmNWO`R12PcQc0\", \"size\": [1280, 720]}, {\"counts\": \"``Z5<aW14M3N4_iN]Ob0N^S1l0ekN_Oa00eS1j0^kNH8HWT1i2M010N1O1N3N100O2O000001O2N4L6J2M4M4MO02N001N2O3N0O2N2N00O1N3N1O1M3002NIUMZkNj2fT1VMYkNk2gT180RMXkNb2kT182N00N2M3O100000mLYkNm2UU1H3L02M2J6O1I7M32NGlLhkNR3XT1mLikNS3cT1O=C10L3I7K5O0O2O100I74L00ClLmkNU3RT1=00O1O10000001O00000_LokNS3QT1>1O00O100001O002N3M;E2M3WNUkNjN>i0eT1IVlNNVT1\\\\ORQeb0\", \"size\": [1280, 720]}, {\"counts\": \"VgV57gW14J8Hd1ZiNWNTT1o2J5L2N2O0O13M4ML3I7010O1O1O1O1O2N3M6J1OI8N2O03M2N01O0002N3M1O5K@nLUlNo2kS1QMVlN6B]2XT1[MUlNS3ZT12K5N2N21O10O05K2N1VMXkNZ2hT1?0001OOC>N20000N2O1O1O1N2O2M2N2M22O1OO1O100O11O00O100O11O00001O001O1O01O00O0100001O1O1O1O3M2N6I8I6J4K\\\\1eN4L9F8I5Jb0ZOQfhb0\", \"size\": [1280, 720]}, {\"counts\": \"U_Z5U13D?KWT1g2K3M1O10000002N2N3N0O00001O00001O1O1O1OO2M20000100O001OO100001O1O00000001N01001N1000000001O1O00N2O1N2O1N20OFgLPlNX3nS1nLokNQ3QT1?00O1O10000000000O1FVL\\\\lNl3cS190000O1O10O11O0001O1O1O002N0000003M2N4L3M4K3N1O2N2M8I6J5K5K6]MjjNo1hU1L2M3M7I5K8I9Ee0XOoecb0\", \"size\": [1280, 720]}, {\"counts\": \"R[T58aW18K6L3M3N3N0QNWOflNj0XS1YOglNg0WS1]OflNd0XS1_OflNb0XS1BelN?US1KglN5WS1OflN2YS10flN0ZS12dlNN[S14dlNL[S16dlNJ[S18dlNHZS1;dlNG\\\\S18dlNG]S1:clNE^S1;alNE`S1:`lNFaS19_lNGcS17]lNJdS14\\\\lNNdS1O^lN1bS1O]lN1dS1N]lN1dS1O[lN1eS1O[lN1eS1O\\\\lN1cS10\\\\lN0dS10[lN1eS1O[lN1fS1NZlN2nS1ERlN<`S1iNSlN9AOm0o0^S1jNdlN4NR1aT1oN^kNR1aT1PO]kNQ1]T1WOakNi0\\\\T1\\\\O`kNf0^T1]O`kNe0iS1cNUlNk0Od0lS1bNSlNl0Nc0PT1aNQlNo0M`0RT1aNnkNS1O<ST1bNkkNU119TT1fNfkNR167WT1fNakNU175ZT14ckNM]T14bkNL\\\\T17ckNI[T19ekNGZT1;ekNE[T1<ckNE\\\\T1P2L4K5L30100000000O100O010O1N2O100000000001O00001O000000000000001N101O1O1O1O0RNRlNIRT11ZlNFiS1JflN3QT1oN]lNj0fU1F:YOhhN1^\\\\Qc0\", \"size\": [1280, 720]}, {\"counts\": \"mWo4;Y14`T12SkN<hT1a1I6N2N100O1O1O1L4N2N2O1O1O2O00001N1O20O10O001O0002O3M20OO10001M10O000O100O101O01PMXkNg2RU1ON1O2H700O2M2N3O1O00iLdkNj2jT10FQM`kNP3`T1QM^kNQ3`T1SM\\\\kNo2cT17O2M2O2M2N3L3O1IYLTlNi3kS1oNWlNYNgS1e1[lN\\\\NdS1d1]lN[NcS1e1]lN[NcS1e1^lNZNaS1g1^lNZNbS1f1^lNZNaS1h1^lNXNbS1l1YlNTNgS1S3O10000O1O01O1000001N101O00000O1O1CUlNeLlS1V3YlNgLjS1V3a0N3K6gNdkNgNbT1h0QkNlNb08bT1d0UlNoNVT12kjNO__Vc0\", \"size\": [1280, 720]}, {\"counts\": \"ZWe47^2OcR17XmNOcR14SlNFROa04McT1ORlNZ1kS1hNSlNZ1jS1mNekNZOLl1]T1_1N1O2M200O2M2N2O100O2O01O100O2N2N1O1O1O0000002N100O0000001O1O2N7J2M2OO02NM3O1O1N2M3O14L1O1O001OO1N2I7N10100O100O001N3K4O0O2I70000000001M200O1O1O10O10001OO101O0000O0100001N101O1O1O1N2N1N5QNVlNAnS1@okN_O=k0iS1ROemN8_U1[ORo]c0\", \"size\": [1280, 720]}, {\"counts\": \"_hb47fW16D:M3hjN_OkR1e0\\\\lN8^S1KWlNa0eS1BUlNc0hS1CRlNa0mS1j1M3N1O2M2O1O2O0O2M2O1001O1O2N1O00102M2N00O10[LmkN]3TT1aLnkN^3ST1_LokNa3XT10I^LokNc3QT16002N2N9G3M10O0000000O100000000O1O10mL_kNg2dT191O00N1O2O1C=O1O1N2M2O2M3L4L40000000O2N3N1N100O100000000O101O001O000000000O011N101O2M4M1N4K5K6Ja0PMijNV2eU1nN^jN]OmU1ORjN]O9>iU12W1O1E;L5NTgWc0\", \"size\": [1280, 720]}, {\"counts\": \"l_R4`0SW1g0C5I7L3N2M3_jN[NeT1f1XkNgN]T1e1UkNfNeT1^2K3L6K:E3N0O2O001N101N100010O00O2O01O00010O4L3N0O1O1^LQlNR3TT1mLlkNn2TT1PMPlNn2TT1mLnkNR3cT1M1O2N9G3M2N2N0000N2O1M3M3O11O1O0O01J6O1N2O1O1N2@PMnkNR3cS1m0L4O2OO1000O1O1000O10O0100000O1000O100O10000O2O000000000O100O2O001N2O3M2N2N2N2M7J;E=SMojNQ2oU1A9F:G5K4L6J5K8G6F[]`c0\", \"size\": [1280, 720]}, {\"counts\": \"km`3a0]W1=[iN]OVU1k0`jN^OYU1i1K3M4K6K3N3L7H5K5K=B5M2N3N00O101O2N3N5J4L001O10O0O1O11O0001N2MYLZlNY3dS1hL^lNW3bS1fL`lNZ3TT1O01N1^OdL_lN]3aS1cL^lN_3PT12M30O2O0O001O1OO101]O_LelNa3lS10H90O9G2OM2N3L4O001N2N200J6J5L401000000O1N20010N1O00010O1000O010001O0001O0100OO10000O1000001O00001O2N4L2M`0A6J5iLakNc2_U1_O<C5L3K;F:F6J;E8CX_oc0\", \"size\": [1280, 720]}, {\"counts\": \"[o[3<WV12ejN7PU10UjNn0eU1k0K5J7J5K9G9H6J1N2O1N2O000000001O1O0010O0010O01O010O2N10O0O1O1001O00O1O1O20O2N00O1O01001O000O0O2M3K5M3000000000000000O100O1000O010O101N1O2M2O1O1O11O1N10000000000001N0100000000O1000001O000000000000000O2O1O4L4L`0_O4jLZlNl1SU1J2M6I:G6I;Ej0ROXoVd0\", \"size\": [1280, 720]}, {\"counts\": \"YX]3`0TV1AVkNk0YT1HYkN=`T10RkN7hT1i1SkNQMZT1`3I4N1N2N2O1N2O1N1000001O0O11O000000000O2O3RLRlNd3WT1N00100O1O001O001OO1O1O1O100003M1O00001ON2O1O100001OO100001O00000000M3G_LPlNd3PT160O10O1O100O1N3N1N2O1N200002NI700O1O1N2000000O10000000000001O00000000000O1000001O2N1N2O0N2dMYlN>kS1WOflN?_S1YNVlN4m0`1bS1ZNdlNa1`S1YNYmNj0QdZd0\", \"size\": [1280, 720]}, {\"counts\": \"gPa3=VW1?M3M5UiN\\\\OQV1[1PO`N]kNb1UT1lNjkNU1RT1oNjkNU1TT1mNikNU1UT1mNbkN\\\\1]T1U1N1O1O2N100O100O1O100O1O1O10O11O0001O1O1O2N2N5K5K5K1O1O00O0010001O03L4M3MO1O100O1000000O100O1M30O0100O1N2O1N2O1K5K5N2O1O2O0O1N1O2000000O2M2O1N2O1O100O1O100001O01O000000000O10000001N2O1O000O100O2N1O2bM\\\\lN=fS1TOnlNd0VS1VO_mNWO^N<YU1AfkUd0\", \"size\": [1280, 720]}, {\"counts\": \"lhn39]W1=G9Ki0ZN`N\\\\kNf1\\\\T1`NWkNl1dT1j0N3N1O2N1N2O2N1O2M2O1O1N200O1O10O10001O1O1O2N2N2N101NO1O1N200005K00O1001O00N2O1O100001O0000001O0000O1N2O11O1O00O10O01O1N2M4K4000O10O100O100O100O1O1O1O1O010O100000O10010O0001O0O10O101N10001O1O100O1N2O0N3N2N3M4M>TM\\\\kN_1iT1VNkkNR1[Uic0\", \"size\": [1280, 720]}, {\"counts\": \"ch]4>[W1c0UO`0I7N2O1N101O6TjNQNZU1]2N5K4L1O001O001N11O1O10N101N11O10O0001O1O2N100O2M3fMjjNf1WU1WNljNh1fU101ON2H8O11O2N7JejNoMgT1Q2d010XNRjN`10ZNeU1W20ON2M3N1000O10K5N2F]MWkNd2hT1^MTkNd2lT18HoL`kNR3fT10CSMfkNP3XT1;2N1N3N11000M2LMnLbkNo2bT1RM[kNn2fT142O001O3M3K3K5M2M4K5L4M3N2N2G:G8O3M2M3L4L4J6J7K4K6G_c2;\\\\\\\\M6K0O1O1N1L4M3O2M36H5]OghN9`TZc0\", \"size\": [1280, 720]}, {\"counts\": \"ngS44bW1<I7K4N2N3`jNWOeS1j0RlNAkS1a0PlNOcS12ZlN3cS1L]lN;\\\\S1FdlN<ZS1CglN>WS1ChlNa0RS1BnlN`0mR1DRmNb0hR1]OYmNl0^R1TObmNo0[R1QOdmNR1ZR1nNfmNS1[R1jNfmNV1[R1hNfmNY1[R1eNemN[1\\\\R1dNemN\\\\1[R1cNemN]1]R1bNamN_1_R1aNamN_1`R1`N_mNa1bR1^N]mNc1dR1\\\\N\\\\mNd1eR1[NZmNg1fR1VN\\\\mNk1dR1lMdmNU2\\\\R1fMgmN[2]R1]MfmNd2jS11O1O1M3N2O1001O2N0000O1N2N110O0kNQMZmNP3eR1RMZmNn2gR1PMZmNo2lS1NhNhMllNW2QS1oMjlNT2SS1PNilNS2VS1\\\\1N1O2N200O1O10O010000001O000000001O1O00000O1000000000O11N10001O0O1000001N10002N2N1O1OO10O2O1N105K5J=D5K5L5J8G4M4K?B2M2N2M`0aNfiNHnd\\\\c0\", \"size\": [1280, 720]}, {\"counts\": \"agZ32iW1>`hNAgV1R1ViN]OhU1g1I;E5L3J7J5L5L4L6K3M4L4M1N2N1O001O1O0O1O1100O000kK\\\\lNo3QT1SLTlNX3\\\\T1NO1O1K5G9EYL\\\\lNh3fS1VL[lNi3PT1O3LI_LYlNW3fS1kL[lNS3eS1lL^lNS3dS1iL]lNW3UT12O0O101N1N2N26J4M7HK5O0O2M3]OgL]lN[3YS1fL[lNo3^S1=M3N2M3O1N2N2N2O001O100O11O3M2N000000000O0100001O1O1O0000O02O000000001O00001O1O00001O1O2N2N2N2Mc0^O7I4lL\\\\kNd2VU1J>B5K4L2N1N3N2M4L8H6J;F9F9En]oc0\", \"size\": [1280, 720]}, {\"counts\": \"iVi2`0]W18H6K4M3Kc0^O5oiN]N_U1d1_jN_N^U1c1`jNaN]U1X2G6G8L3N2K5N`LfkN\\\\3cT10K?A2K4N2002OO0O00000010O1OLPLelNd3XS1_LllN2_OQ3fS1jLnlN_3iS1001O0O10000100O8H3N000O10N2O0O2O001O000000000O1O1BcLXlN]3YS1kL_lNe3`S1>001OO0N3O100L4N2M3N2N2O1000O7J7IK5L4N2O1O10O11N11N2O1N2O00000O10000O100010O01O0000O100001O00003M2M4M;E`0bLgkNU2]T1aMWlNQ2PU1K4M3M4K8I4Kl0ROV^cd0\", \"size\": [1280, 720]}, {\"counts\": \"jga2;bW16K4I;liNQOgT1X1RkNnNdT1_1SkNfNfT1c1RkNdNiT1\\\\2M4K5L3N2M3N2N5Kf0ZO2M3N1O00000002OM2O2N12O2M1O0001O01O1O10O1N11VLclNo2_S1jLglNU3[S1hLilNV3WS1iLolNQ3SS1lLnlNT3US1iLklNW3WS1fLjlN[3XS1aLilN_3WS1aLilN_3SS1fLklN\\\\3SS1fLilN^3US1cLklNNGS3^S1nLllNODU3`S1lLmlNNBW3bS1jLllN0AV3cS1jLllN0AV3cS1kLklN]3VS1aLklN_3XS1]LglNe3gS12DXLclNg3]S1^L^lNb3aS1bLZlN_3eS1?M3N2I7O1O100O1O1N2N2N2O1O10000O1O1N25K3M1OO1O1O10000000O101O1N100O100O2O000O100010O010O01O00000O102N6Jj0lKelNo1jT1J3N3L7WNjiN]1gV1Bm]md0\", \"size\": [1280, 720]}, {\"counts\": \"RoX2<^W1:F9K2M5UjN^OPT1o0TkN^N8k0`T1R1UkNWOiT1S2L3M4K9Fb0_O5L3N1N1N30O2N10N101O1O100N100O14L4L2NN2L5NgKklN21S3TS1jLmlN10U3fS1hL]lNW3dS1dL_lN]3aS1b0ElKglNd0Ik2jS1e0101N1O0PL_lNa3aS1_LblN]3UT1L1O1ON2M3FeLnkN\\\\3RT1fLlkNZ3ST1hLkkNY3XT1aLkkN_3\\\\T1N2O00001OO1O1O0WOcLalNm3^S1WL]lNk3aS1;M3M3N2LbKhlN\\\\4US1fKllNZ4RS1iKmlNW4nR1`0000O100000010O0O10000O0100O10O101O0000000000000O100000002N001O00000O101O1O3M7Ib0^O4K6iLikND5V2]U1K5L3N4K:F6Ab0QOZgSe0\", \"size\": [1280, 720]}, {\"counts\": \"lfR2453XW1b0H4K8Ga0A4M3]jNbN_T1d2J4Ml0TO4M3M1O1O001N1N3M200O1M36J3M2N0O1000100O1OO10O102N2N9G@VLklNi3SS1ZLnlNd3hS1O1O00L4N1M4O1O11O3L2^LSlNo2mS1QMRlNP3oS1oLmkNU3TT1jLkkNW3UT1:1O00O1N21O1O1O00N2M3K5N2O01000O1N201N1N2L300K5O1100O1O0001O10M3O1O0O10101N1O100O2N1N2N2N110O1O101O001O1O1O001O1O1O1O9F>B4M2M3]MVkN10U1lT1bNRlN[1]U1M<C:DSoYe0\", \"size\": [1280, 720]}, {\"counts\": \"RXk1:`W1c0@5L4L3liNoNUU1W1ejNB9VO[S1b1RlN9lS1o1M2O1O1N2N3M2O1N2O1O101N1O101O0001O00O100001O00002^KflN[4dS1K1O2N1O00001O001N02M2NTL]lN_3eS1=5K00000000000O4MIVLalNa3^S1`LdlN^3PT1O1O2NN10100000000O1O11O1O1O0000O1O1M3N2O100O1000000O1N21O1O1ON2O1O12N1O1O000O101O001O1O1O0000001O1N2O1O1O0001O0102L3N0O2N100O2L4J6XMekNb1gU1E=DVf]e0\", \"size\": [1280, 720]}, {\"counts\": \"bYZ2X1fV14M3M3M3M4L2O0N2O1N3M2N2O1N2O1I7F:UNXMYnNm2aQ1_MVmNF>Q3YR1i1I7L4L5O00O2O1O1O1O4L7I2N1O1O00000O101O3M4L6I4M1O1O1OO10O101O0O2O000PLWlNi3QT1O0O10O1I7O01000001O0RLXlNd3TT1ND^LWlNe3iS1[LVlNf3jS1\\\\LRlNf3kS19J6O100O1O100FkKglNW4YS1800O1N2O10O100000000000O10000000O10000010O0001M2O1O2M3M4L3M5Lb0]O;QMYkNQ2lT1hMakNl1]U1K4L3M3\\\\NTjNQ1oU1lNUjNP1`V1B?IWoTe0\", \"size\": [1280, 720]}, {\"counts\": \"W_V21kW1<^jN3_R1:alN>[S1V2M2N2N2N1O2N2O0000001O00000O101O00000010O1O01GUL\\\\lNj3cS1YLZlNh3dS1;O20O1O1OO2L3003N8G1OO1GUL]lNk3cS1WLZlNj3hS163M2N000PLdlN\\\\3QT1O00@dL\\\\lN\\\\3cS1gLSlN`3lS1;O11O1O00O10O2XLUlNY3lS1dLVlN]3jS1aL[lN[3eS1fLSlN`3mS1cLnkN`3QT1800^OTLolNm3QS1SLolN7A]3`S1\\\\LolNm3bS11O]LQlNU3nS1?00K5C=H`KolN`4PS19O1O1N3M2O100000000001N10001O1O1O00001O0O101O1O002M3N2N4K9H3M3K5L3M4M3L3MU1jN4L3M2L4J7K4mNdiN>WW1AX_We0\", \"size\": [1280, 720]}, {\"counts\": \"_fc16aW1:G:L1O2O1N3M3I7O1K5M3bkN^O[Q1d0fmN<VR1GUmNZN3T2hR10olN8SS1Z29GE<L3N3N001O000100O6J2N3M3oKelNW3\\\\S1fLglNY3YS1hLflNY3ZS1fLflNZ3[S1fLelNY3`S1aLalN_3aS1^L`lNb3`S1]LblNb3`S1\\\\L_lNe3cS1;1O5K5LO0HTL\\\\lNl3cS1VL\\\\lNj3dS1VL]lNi3cS1VL_lNi3nS1N2N2N10O0O100O1O1O1N2N101M3[OSLUmNo3jR1ULglNW4YS1800O100O1000000M3O1O2M2O1O100O1O1O100O10O1001O000000O100000001O000000001N10001O1O0O101O2N1N2O1N2O1N3N2M2O3L3M6K9F4M1N2N2N3M4K;DS1nN>Cf0YOPg]e0\", \"size\": [1280, 720]}, {\"counts\": \"mfm12lW14iiN0YT12TjN0Og1[U1Z1ZO9G3K4M3L5M2M3M3M3K4L4N2N3N1O101N1O1O1O1O1000000001O2N1O01O0000000001O1eKolNe3gS1ONkK^lNP4bS1PL_lNo3jS1O4L1OO100O]LmkN[3PT1hLPlNX3lS1kLUlNU3oS1fLSlNY3^T1L3M001O01QOfLUmN[3jR1hLelNOO[3[S1jLalN0MZ3aS1j0O11O1O1O000001N1^OjKYmNW4fR1mKVmNS4kR1b0N1000001O1O0000O2O000000000001O01O0O101N2O1N3M3N1N3M3M7I4M2N4K7Jm0RO6Ic0]Od0UOYWoe0\", \"size\": [1280, 720]}, {\"counts\": \"bfo0h0TW19K3M1N2O1N3M;E;E;F3M4djNfMiT1S3F>B4L:F3M2N1O1O2N3M1O2N1O0000001O01O01O01O1O1O00001O001O1O8H4kK`lNb3bS1YLdlNd3lS10O11O0010N001M30001N10OKXLTlNh3lS1600011M2O1O1O5K@]LclNc3nS1N10001O00000000I6F;BmKjlNX4TS1;00O1O1O1O11O001O00010O0O100000001N101O1O4M2M000O2O2M2O2N2N2M5L2O0O2M2O1N3N1N2O2M2O;D4L3M7I6I7I7G6J7H8POXiNM_nRf0\", \"size\": [1280, 720]}, {\"counts\": \"cVP21kW1<D8J3M<C;E7I6K>C3M3djNnMdT1b3UO;E5J4N1N2M4M2O001N101N110O010N101O010O001O000001O1O00O2O1O0001OO1001O00000001N11O1O1O1O1O1O001O000O011O1O2NO1O1001O1O10N1O100100O00000O101O0010O01O1O001O1O1O1O1O2N1O2O0O2N3L4M2M3N2M4M2N1N100O5K4L=C7In0SOl0SOe0XOomce0\", \"size\": [1280, 720]}, {\"counts\": \"l^e22hW1:H6F`0XOh0F7I:F4M4M2N2N4K7G=E8I5J5Kj0XO3L2O1M3M2N200000O001O001O010O001O01000O1O001O1O001O00O11O1O000000001O00002N7IB\\\\KcmNa4QS1M1O0000O1001O00N2OfKjlNl3US1TLolN2JX3WS1eLRmN0IZ3XT101OoNcL_mN]3`R1eL`mNZ3`R1fLbmNX3^R1gL\\\\mN4UOU3`S1fLYmNc3gR1]LRmNj3nR1VLolNm3QS1TLmlNm3SS1RLmlNo3cS1O00[OWLSmNh3mR1[LRmNd3hR1bLXmN^3gR1bL[mN]3dR1dLTmNAOk3lR1eLUmNCKi3oR1fLTmNb3lR1i0000O1000000000001O3M2N5L0N2O4L1O1O4L>B4Kg0VOb0]O8H9@ognd0\", \"size\": [1280, 720]}, {\"counts\": \"TW_29cW16I7J8Jf0[Oa0]O8Hc0YO9J7I7J5N3alNXL_R1o3SmNZLkR1`4N1M5J5OO01O10O0010O01O01000O1O100O1O002N2N2N00000000100O1O000O2O10O000O110O01O0000O11O1O1OO10O0110O000000O2O001O<E4K>B00O1O11O2N00O1N2O100BVLflNi3[S1VLflNj3hS11O00ODTLhlNj3YR1WLbmNO92Lg3dR1WL`mN2Lg3XS1WLjlNh3US1[LhlNf3US1c0ROiKcmNN3]4ZR1SLbmNn3]R1WL_mNi3aR1XL^mNh3bR1k0O10O1000000O100O10000000000O10001O1O010O2M2O1O1O1N3N1O6I;@>F4M2O000O010N3H8Ef0oLkjNU2\\\\V1SOmgUd0\", \"size\": [1280, 720]}, {\"counts\": \"c^j2=\\\\W1;YOf0L5K9G3gjNXNPT1b2XkNdMcT1S3H?D1N3M2N2N2O1O1O3RmNdKRR1^4gmNlKUR1W4dmNnK[R1n4M2O0O1N100010O101N2O2M1O013LN201O02N1O00N3N1003N1kJZmNl4fR1700001O7I1O5K2[KTmNR4aS1L1O001OO2YOmK^mNT4bR1lK_mNS4XS1O1O2NB[LglN_3PS1jLRmNT3oR1kLQmNU3PS1jLQmNU3oR1kLQmNU3oR1kLPmNV3PS1jLPmNV3PS1bLflNL:c3PS1hLPmNX3PS1gLQmNY3oR1hLPmNX3oR1iLQmNW3mR1lLSmNS3oR1jLRmNV3oR1iLRmNV3nR1jLRmNV3nR1jLQmNW3nR1jLRmNV3nR1kLPmNV3PS1iLPmNX3PS1hLolNY3RS1fLnlNZ3^S1YLelNe3lS1O00]O`LflN`3WS1dLglN]3RS1PMflNR3XS1l0N2000000L4D;O2N2F:L400O10O100O10000000O0101O0O10O10O111N1O1OO10000O0101O1O1O2M3N3L;F3L3K7I5L4L4L4Kd0\\\\O6K5K6G8]MkjN12a1SV1K3J6L6UOViNNPW_c0\", \"size\": [1280, 720]}, {\"counts\": \"Sn]2;nV1P1A;I4M2O3Lf0[O2H]MojNe2oT18N3M5J3M3N3M2N2N4L6J1N3N1N2N201O1O001N101N001O110O1O1006IN2K7M4L5LXKmlNc4QS1[KRmNf4lR1\\\\KQmNf4nR1600104K2N100_KSmNm3oR1oKVmNo3aS1M1NO2O1N2O010O100LlKblNl3\\\\S1VLglNh3hS1001N1BVLflNj3YS1XLelNh3\\\\S1XLdlNh3[S1YLelNg3YS1[LflNg3[S1WLflNh3[S1VLglNi3ZS1VLflNj3[S1TLglNk3gS1001L2EUL`lNn3_S1TL_lNm3oR1nK^mN7Al3oR1PLWmNa4jR1^KUmNc4lR1]KRmNd4oR15100000N2O1TORKSnN28l4dQ1UKPnN2:j4bQ1dKZnN]4fQ1o000O1O01000O010O11O0O10000000000O1000O1001N10001O2XJnmN^5SR1aJlmN`5TR15100000:E9H1nJXmNj4QS1N3M1O1O1N3N3M2M3N=C1N3N4L2N7H8H2O5J7H4M4L4L;E;DP1bNghN7eW1Ka^ec0\", \"size\": [1280, 720]}, {\"counts\": \"^no3<aW17D;J7biNlNH0eU1W1ajNiNhU1[1UjNeNjU1]1niNkNnU1m1E5O2O6O3F4L4K3K5M3N2N4L2N3N>F4JNN2N1O1J6M30001N10001O00000O011O000O10O100001N0N3M3GVKYmNm4bR1;O10000001N2O00M3L301O1N101O100O1000OO200000O10O1000O02O0O1000O01O1N2N1O2O1O101N2N00100O0100O1000000001O0O010000O10000O10100O0000000000001O1O1O3M:F;E2N3M2N1O2M2O1N4M1N5gK_lNj3fS1nKclNl3PT1H6I4cLbkNS3hT1L6I7J4M1O3M2N2N3L>B7I6FZXWb0\", \"size\": [1280, 720]}, {\"counts\": \"cnT41gW1=C:H8I6]OnNjiN_1XV1c0@4I4L3O1O1O2YkN\\\\MoS1e2PlN^MnS1c2PlN`MmS1c2QlNaMKFcS1m2^lNcMGEiS1o3O1O5I6`lNgKPS1l4I3N01OO1NkJ\\\\mNQ5cR1oJ^mNQ5gR1100011N1YKWmNU4jR1dK]mN[4SS12L3003M1O01O01dKdlNR4]S1lKflNR4[S1kKhlNT4dS1NN2N200O1O11O1O2M2O1N2O00O010K5I7O16J2NO1H8^OeK]mN]4cR1`0O1O1O0100000N3M2CmJgmNY5YR17O10O01O10000O1L4I601O1O10O100O10O10O100000O100000000000O101O10O0001O00000O101O1O4L:F8H4L2N1N2O1N3L3M3N4L3iKglNc3mS1N3L7I7H6L3L5L1N3N1N01O09H2M4M:ZOc0VOjPVb0\", \"size\": [1280, 720]}, {\"counts\": \"j]^37hW15K3M5G7I7M6G8K4K5I6J7K3M4L6K3K5L5L4L4K4N2M3M5M4L1O1N3N2M1O1N4N0O2N2O0O3N0O1O0010N3N27LMObKclNX42gKYS1[443N10O1O010O001O10002O1O5K0O0O01O0O2N1N3M2O11O1O00O1O0O2K5N2O0O2O11O1O00000O0100XO[KTnNf4^Q1aKomNO>a4bQ1RLomNW4PR1m0O100O0100000O100O10O100010O0001O0O10000UJQnNd5aR1E7I3L4M2N2N2N2N2ZKQmNV4eS1H2M4M3M6I5L7I001N101N2O1N2O1O1O0O2O1N2O2N3M4L3M3L5L3L4L4L5J6L4K7I5K4K5M3L7IWo_b0\", \"size\": [1280, 720]}, {\"counts\": \"l\\\\a4?`W17F7L3M2M4M5L3M3M4fiNcNhU1`1UjNdN9ESU1X2hjNUNmT1l1QkNWNnT1e1TkN`NjT1]1WkNfNgT1Y1YkNhNjT1U1UkNmNkT1WOUkN3LY11AgT1XOXkNg2_T1g0H6K1O2O`LokN41E9;FQ1oS1_ORlNRO=U2^S1hNllNW1eS1oMflN0ROc1cU1YN`jNh1lU12[OUNdjN13m1VU1\\\\NhjNh1SU1[NijNj1TU1e0L2N10O02N2N0017PMPkNa2WU1011N10O01N1O2L3G:M2O17J7I00N1JSMYkNn2eT17J6HdLjkN]3ST1:D<O1106I3NF9M3HkKdlNV4SS1iKPmN^4oR1iKmlNU4QS1>O1O1O11OAVKimNi4VR1a00000O1O1O100000000O101N100O100001O000000000000000000001N101O3M4L2Nb0_O8G3M2N2N2N3YLVlNR3lS1iL]lNQ3bT1G5K2M2O1O2N1N3N1N3N3L5L1N2N3N3M3M3L3N2M4L5L3L2O3L3N4K4K6K5J6Jgnfa0\", \"size\": [1280, 720]}, {\"counts\": \"glY47WP52]WL7F9L210HXOTiNi0kV19M3M1M3M3N2O30O24JN31NO3L21iiNeNcU1\\\\1[jNgNJ1`U1T1fjN[OXU1WOgjNV1NIeU12ZjN1lU1TOQjN;1d0UV1\\\\OjiNg0TV1SOoiNQ1QV1kNRjNW1nU1eNRjN^1mU1aNSjNa1SV14N2O2N]NoiNZ1nU1b0K3K6I2M5L4L4M1M4L202N1O010O0K5N2M3N3O01O1`LgkN3Nl2ZT1PMjkN1On2cS1PMilNd3VS1]LglNf3XS1]LdlNd3\\\\S1`L\\\\lNd3dS1]LZlNd3eS1<O2O000O10jKdlNh3\\\\S1>O2IdKhlN\\\\4XS16K`KklN^4RS1:N3N10000O\\\\KXmNR4gR1d0H8M201O2OO0100O1000O100000000000O100001N2O000000001O0000001O001O1N3O1N1O3M5K=B7J3M2N3M2N2[LnkNY3VT1^LQlN_3^T1K8G6J3M2N2N1O2M5L4K5L1O2N2M2N3N2N1N3M3N2M3M3M3N3L3N3L5L4K3M3M4Lf^_a0\", \"size\": [1280, 720]}, {\"counts\": \"bdn3:dW15M2M2O1N2N2N2O5J4M3N1N2N1O1O1N2N3M31O0O0002M3N:F1N4L302K311M002M0O2N3N21M00O1N301N24NO3SkNkMfS1W3:GON6K1N01O1O1O00010OfLakNT3iT1M000O01O0O2O0M3L41O5K1O10O01O000000O100N21O1O100N3N1OWNmLdnNT3ZQ1PM`mNN?S3oQ1SM_mNOIO6P3cR1VMWmNb0MZ2mR1XMPmNl3oR1c0O1@PKomNQ5QR1QKfmNV5ZR18000000O1N11000O10001OO1000O100001O0000O10O2O000000O11O00002N2N2N001O001N2O2N3M2N2N6J5K7I3M3M2N3M2N4L4L8H9Go0QO=C5K2N2M4M2N3M3L4M1O2M5L8H5J3L3O0O6JZfea0\", \"size\": [1280, 720]}, {\"counts\": \"glV3`0_W15K5L3L3N2N2N1N3N8H3M2N2N1O00100O2N2014LM8F5M3M3L5M6J5K5J2N1O2O1O02NO1N2O101O000O2O1[lNRMVR1`4OM10O1N2N21N2M2ON1O1O13N1N01O00O2O10O000000XKnlNa0N\\\\3[S1bLhlN\\\\3YS1bLhlN_3XS1_LilNa3]S1ZLclNe3^S1\\\\LalNc3aS1\\\\L^lNd3oS100O11O000O101N10000LTLVlNj3jS1VLVlNj3jS1ULWlNk3QT1NO1O11O3M1O00O1N2001O00O1O1POVLjmNj3VR1ULkmNk3UR1ULlmNj3TR1VLmmNi3SR1XLlmNh3TR1\\\\LgmNe3ZR1ZLTmNL8j3gR1WLcmNi3ZS10N2QOVLfmNl3eQ1VLgmNL15OK<n3mQ1`LcmN4<\\\\3PR1YMmmNg2SR1ZMlmNf2TR1[MmmNc2QR1aMnmN^2PR1iMkmNW2UR1j1O11O001cJQnNg4oQ1YKomNh4RR1c0000000002N1O1N7J;E6J001O0O2O001N3N1O2N3M2M5L9G:eLckN^2RU1N2N2M4L2O1N3M2M7J3M2N1N3M4K7H9G=WOlhNO\\\\n_b0\", \"size\": [1280, 720]}, {\"counts\": \"V][2?`W14L3M4M2M2O4J6L4K3N2N2N2M3N2N2N101M4L8H:G5K3N2N9H;E1N1O2N1O2M7J7JW1hN4L0O20O2N1ON2N2N2000O5LO01O1O101O1O1N2N1O100O1O2N002N3M2N0gKWmN[3iR1SLSmN96c3gR1SLVmN66f3dS11O01O0000O1O0O23M2N101N1O00O1O1O11O0O100000001O10N001N200001O00010O1OlM[LRoNMFg3WQ1^LmoNc3no0eLnoN\\\\3QP1nLQoNZODj3[Q1mLonN[ODi3]Q1nLYnNY4gQ1P11O001OO10000000O2O00002M3N1O001O002N1N101O1N3N3M8G5L1O1O1N3N2N1O2M3N8H8HX1gN2O1N101O0O2N2O1N1O1O2N;E2M3M2M4M3M2M6J6I=_O;K8E^^Vc0\", \"size\": [1280, 720]}, {\"counts\": \"adW2b0]W18H6J5K2N3M2O3N5J2M2N3M3N1O2OO01O10O12O002M6J4K4L5K5L4L8H10000O001O000011ONV1\\\\kNeKWS1o4HM4K2OM2M4M]KYmNP4hR1mK\\\\mNR4dR1kKdmNP4[R1mKimNT4UR1kKjmNU4ZR1hKfmNX4ZR1hKfmNX4QS101O00N2K5N3005JEdKRmN\\\\4nR1eKnlN^4PS1eKnlN\\\\4SS1cKmlN]4TS173M5K4M1hK]lNo3bS19O000O1N2FaKRmN`4mR1cKolN`4PS182N6]KglNT4gR1kKcmNL0R4`R1RLXmNO7R4aR1YL\\\\mNj3dR1WLYmNk3gR1g0000000010O8H3`KmlNm3SS1`0QO\\\\KkmN69JE_4lQ1`KXnNd02^OK]4lQ1bKSnNh01P4mQ1T1JSJYnNm5fQ1700O1O100O1O100000O10000000100O1O001O001O001O1O1N2O2O0O0O2O001O001N2O2N1O6J7I4L3M1O1N2O2M4M6Ih0YO4WLVlNT3^T1L101O7I3LF\\\\M[kNa2gT1_M[kN^2WU1M1N2N2M2O5K`0UOaiNROiV1N^iN<[W1J7J]^`c0\", \"size\": [1280, 720]}, {\"counts\": \"ldh116;QW1g0FQ1nN3N2N1N2O10O02N3N3L5L3M2O1L3M4M4N2M3M21ON000O1O2OO0100O1O10O11O1Ob0^Od0]O:E2M2O0000001O1O3L3N0O010O000001O3M1O2N1O1O1O001O00002N101N1O2RKVmNc4WS1L3M6K1N1O2N6K4K4LN2N2N200011N1O0000001OO1O2N[LPlN\\\\3QT1bLQlN]3[R1]LWoN3_N`3nQ1cLomN8[1BjNc3fQ1\\\\MdoNd2ZP1`MdoN`2\\\\P1aMcoN_2\\\\P1dMboN\\\\2]P1SNUoNm1kP1WNQoNj1oP1WNnnNj1SQ1]L\\\\nNd1=o1VQ1]NfnNd1ZQ1bN_nN_1`Q1eN[nN]1eQ1_200O100000001O000001O0O10001O000O201N2M101O001N2O1O1O1N4M1O2N6J5J4M2N2M3N2N1O7H7I`1`N2O0O1O100O1_MljNU2aU1N3N3L2N1O2N101M7Db0@7@nnlc0\", \"size\": [1280, 720]}, {\"counts\": \"VUm0Q1a0ZOaU1m0UjNZOgU1a1O2N2M3N1O2M3M4L7J5K4L5L1N2M4M4L3M2O1M2O2M30N2OO100O2O01OO2O1O1mlNaLbQ1a3RnNlLkQ1e4O102N0O3N01O0000001N1O1O1O10O01O1O1O1O1O1O1O100O00001O010O1O0001O10O1O2N<E7H2N2N3M3M2O0O2N3M3M1O10O01O000000010O4L1O10O0001O00kMZLQPOe3lo0gLioN[3SP1mL]oN_3`P1fLZoN_3cP1fLZoNVOUOi3]Q1WMhnNgN`03Al3UQ1hMaoNW2^P1kMboNT2^P1mMboNR2]P1PNdoNn1\\\\P1SNfoNj1YP1YNgoNe1ZP1_N_oNc1bP1`NUoNe1lP1g20000000000000O10001O000000001O00000O101O1O1O001O001O001O1N2O1O1O1N3N1O2N1O1O4L6J6J2N3L4L4L2jLSmNYNOh2oR1jN]mNT1eR1fNamNY1_R1bNfmN]1]R1SNRnNk1RR1jMVnNU2lS1N104K9G5K2N2N1M3N3K4K6J<E[VXd0\", \"size\": [1280, 720]}, {\"counts\": \"inf09iV10YiNL3a0VV1Q1G8K7L5L3M4L4L5J4M4K4M6J8I2N2N1N001N2O1N2O2M2O1N1jmNSLXP1o3doNVLZP1j3_oN^L`P1d3ZoNbLeP1^3VoNlLeP1S3VoNUMhP1l2PoN[MPQ1g2fnN`M[Q1b41O002OO01O100O001O1O1O1O1O1O100O002N2OO03MJTJXnNl5oQ1O001O2O1N2N0fJnmNJIf4YR1YK^nNd4fR1I4L3M3M2N2N3M4M1N0000100O1O1O1O1OO1N2N3O000001O1O1O001OO1N3M200O10000N2@SLclNW4ZS19L4G9gNUK\\\\nN8<h4UQ1gKanN_4]Q1S1N2M3M3O1O1O100O1O1N101O10000001O0000O02O000000000000000O101O00000000000000001O0O101O001O0O2O1O1O002N1N3N2M4L8I4K9G5L4K3M3N2M2N3M3M4aKhlN8?e2cT1E3L101N2N3L3N3L4E?D8J7G=]OlVSd0\", \"size\": [1280, 720]}, {\"counts\": \"b^i09dW16K4Lf0ZO4J5I7J5D>H;G9I6K7A<InLnkNa2mS1^MZlN`2dS1^M[lNg2cS1YMZlNk2eS1UMXlNP3R2^L_o0X4[POnKdo0S4YPOQLeo0P4WPOTLho0m3TPO[Lgo0g3UPO]Lio0e3QPO`LZP1W3_oNPM`P1Q3WoNVMfP1Q3SoNQMlP1R3mnNSMPQ1Q3jnNTMUQ1m4N1000001O0001O01O0O100O1O2O0001O010O100O2N1O2N1O1O1O2N2O1N2N1O010O1iJhnNd3ZQ1ULmnNi3UQ1TLSoNe3oP1XLZoN`3hP1^L\\\\oN^3hP1]LXoNd3aR12O01O001O010O00001OO101N1001O00000000001OO1O1N2N2BfKRmN^4nR1dKmlN_4RS18O1O1O1J6K5WOiJ_nNa5[Q1e0L4O1M3L4O1O100O1O1O1O1O1O1N2N2O1O1M3N2O1O10O0100000000000000O1001O2N00000000001O0O1000010O00000000001O1O000O101O000000001O1O3L3N1O3L4PLUoNc0lQ1WN^nNb1iQ1mMhnNn1^Q1kMfnNR2^Q1kMenNQ2aQ1hMcnNU2hS1K7E<ZOY_Yd0\", \"size\": [1280, 720]}, {\"counts\": \"R_]1;eW14K2N2M2N2O2M3L8Ga0@5F9H7djNgMaT13WkNa26aM^T1e2akN_MYT1e2dkN^MYT1e2dkN_MYT1W3hkN^LP2Ljo0R4ooNSLoo0R4ioNVLRP1l3joN[LRP1g3joN]LSP1e3ioN^LUP1e3foN_LYP1b3eoN_L[P1c3boN_L]P1c3`oN^L`P1c3]oN_LcP1b3ZoNaLeP1`3WoNcLiP1^3PoNiLoP1T510O010O00001O00001O0100O3NN1000000O1001O013L5XJbnNd4lQ1fJRoNNUO]4iQ1aKkoNS4WR1J1O0000010O0010O001O1O01O0001O0000000O101O0001O00001ON2M3K5C=K5D<O1O100O1000000O1O1J6J71N2N2N1O1O1O1O1O1O2N1O1O00003M2N2N1N3N1O1O1O1O2N^MXKmQOg4Tn0XKmQOg4gP1O2N1O\\\\M_KkQO^4Un0dKlQOZ4Qn0kKnQOT4Pn0PLoQOo3Qn0QLPROn3om0TLQROk3nm0VLSROi3mm0XLSROg3nm0ZLQROe3om0\\\\LQROc3om0^LRRO`3nm0aLSRO]3lm0dLTRO\\\\3lm0eLTROZ3lm0gLSROY3mm0hLSROW3mm0jLSROU3mm0kLTROT3lm0mLTROR3lm0PMSROo2mm0RMSROm2mm0TMTROj2lm0WMUROg2km0ZMUROe2nm0YMSROe2Qn0WMPROg2Sn0XMmQOg2Wn0UMkQOi2Zn0RMiQOj2an0mL`QOR3hQ1M3L4M4N2M2N2M7I7F6kNZjNWOkTSd0\", \"size\": [1280, 720]}, {\"counts\": \"l_[28fW14L2O2N2N2M3Md0ZO6liNbN]U1T2bjNkMh10mP1[2VmNkMe11PQ1h2lnN\\\\MPQ1g2mnN]MjP1k2ToNWMeP1P3YoNRMdP1Q3YoNRMdP1S3WoNPMfP1S3XoNoLeP1V3WoNkLfP1[3VoNgLeP1`3ToNeLjP1^3QoNfLmP1]3onNeLPQ1]3mnNfLRQ1[3knNhLSQ1S501O2N10001N10O01O001O001O000100O3NN1N3O000100O1O1O2O3L7`JinNlN8Y4SQ1oK[nNM50P1S4_P1PLUPOo3mo0mKUPOS4lQ100000000000001O0000001O1O1O000000O1I7fMeKnPO[4YQ13EdKPmN^4SR1aKomNO10O4J^4lQ1aKXnNN;2Dh4gQ1aK_nNo4_Q1j0AnIinNY6VQ19O1O1O10000IZIUoNe6RQ1100@XIhoNh6XP1XIaoNn6hP11HRI]oNo6bP19O00000001O3M1O3M1O001O001O2N2N2N1O3M1O1O2N1O01O03M2N2N1N2O3M4L5J2O2M3L4M3M3M2N3M2M4N1N3M1O2N2N3N1O2M3N2N2N3L6J5J9F9lLZkN\\\\2YU1B>F9E;VOj0G:K5Jcf\\\\c0\", \"size\": [1280, 720]}, {\"counts\": \"`oQ38eW1<E9G4K9G3NL51N1F<I6O>A6B<H7N4ZkNSMRT1d3J7I3L2TnNiKlo0Y4QPOmKjo0V4RPOnKmo0R4PPOSLmo0R4koNSLTP1o3foNVLXP1l3coNXL[P1k3aoNXL^P1i3^oN[L`P1h3\\\\oNZLdP1o3mnNWLRQ1]5O000010O10O1O10O01O0O2O00001N1O2N11O2OO01O1O3M2O1N4L3N1jJjnN^3[Q1UL`oNX3cP1aLboN^3_P1]LgoNa3ZP1XLmoNh3SP1ULooNk3TP1QLnoNn3WP1lKkoNS4QR1001O0O11O2N2N000000000000O1O1N21O1O1O0000N2N2G8I8mNZKinNi4TQ1PLQnNU4oQ1n01OO10000O1000OF;N2N2O10000O1L4O001O1O1O1N101O1O1O1N2O1O1O1O1O001N2O1N21O001O00000O02O000O100000O10001O0000O1O1000O100000001N101OO01O101N1O3`LXoNCjP16]oNHeP12coNJ`P1FPPO5UP1FQPO6SP1ERPO6ZP1TNanN4g1[1oo0RNinN6h1]1kS1@amZb0\", \"size\": [1280, 720]}, {\"counts\": \"PPP4:eW13K5dhNBoV1m0L0OK50J>F3M4N2M3J9PjNYN]U1X2H9J3N3M5J5K6K8I2N5L3L2O1N3N2N1O1O1O10102lmNhKYP1Y4aoNnK]P1S4^oNRL`P1o3[oNWLcP1i3[oNZLdP1h3WoN[LiP1f3SoN^LkP1d3PoNaLoP1X5O1100000O00001O000010O1O1O101O1mJcnN`3]Q1YLlnNmNG3MT4aQ1PLcnNNX1o3WP1PLYPOo3io0oKXPOP4ho0PLYPOP4ho0lKZPOT4iQ10001O100O00001OO1001O001OO1O1O1010O00000000001O00O1DQLelNQ4YS1QLglNo3XS1QLilNo3VS1QLllNm3TS1ULilNm3SS1XLllNh3QS1]LllNd3QS1g0E;L4M3UOlJcnN[5[Q1f0N2O10O0100000O10O100O11O0000O10O10001O00O01O100000O1000O10O02O0O101O1O1N10O11O2N0O2O2M2N2O1O1O0O101O1O001O1O2N1O2N1O1O2M3N2N1iLYmNR1jR1kNYmNS1jR1hNZmNV1iR1`N_mN_1cR1\\\\NamNc1cR1]MllN;f0V2aR1[MmlN:e0Z2`R1YM[nNd2gQ1[M\\\\nNa2eQ1^M^nN`2cQ1_ManN\\\\2bS102M3L5K4K7K5L4K6I:G6I:D^lh`0\", \"size\": [1280, 720]}, {\"counts\": \"Zhg4;dW13K4F:TOVOWjNW1SV16M7A^NRjNm1dU19N2M2J6G^MSkNc2mT1\\\\MSkNg2PU1b0^O6F7N2O4J4N2M2O1O2M2O1N2O1N10100O10ONaKglN\\\\4XS1dKilN[4WS1fKhlNZ4XS18O001O1O01O1O00UnNcKSP1_4hoNfKVP1[4foNiKXP1Y4eoNjK\\\\P1X4]oNmKbP1U4XoNPLgP1S4mnNVLUQ1Y510O01O1OO2O0O17J1N5nIVnNe5jQ1YJWnNh5iQ1WJVnNj5mQ13M4OeJUnNa4kQ1\\\\K^nN^4cQ1^KdnN^4^Q1_KenN_4eQ1VK^nNi4hR1D5L1N2O1N2N2N0000O10001O0O2N100001O1OO100O12N00000000O1O1L41O1O1OUNlKVnN1T1[4YP1P2E_IRoNf6QQ13O100N200O100O1N2N200O10O10O10O01O10O01O100O010O100O100O01000O100O1000000000O10000O101O0O100O1000000O101N101N101N101N101O2N3L2N5L<D=B2O2M2O2fL`mNP1bR1kNbmNT1_R1eNimNX1YR1fNimNY1YR1bNjmN^1YR1dMXmNNd0]2VR1WMnnNh2WS1N2N2N3M2M3N1O0O2N2O1O10001K5M2N5K4L4L4L6J6I4L7J5K6J:EQdU?\", \"size\": [1280, 720]}, {\"counts\": \"nbS63TW12[iN8VV1:`iNJYV1S1J5M3N2H7WOlMVkN10Z2cT1XNXkNV22[M9b0_R1`4F8N1N2M3G9K5L40O1OFnIenNT6QQ1kInnN6LQ6QQ1QJonNY6VQ1^IPoN6JS6_Q1gIknN6I71h4US1AO00001OO011O3M3_KelNW4bS110O]MhKaQO0aMn3nP1RLhQOn3UQ101OmLVLnQOl3Qn0WLkQOk3Tn0VLkQOk3Vn0SLjQOn3Wn0oKkQOQ4RQ10O3M2O1000OO1O1gLeKmRO[4Sm0eKlRO]4[P1000O100000000000000O1_OgK[mNY4bR1PLWmNQ4eR1TLZmNl3VQ1UL^oN4[Og3SQ1eLQoN[4iP1a1M3M3N2N2N2N2L4O1N2M3N2N2O1O10O100000000000001O000O0100000O2O0000000O1000000O2O00000O2O0O2O001O1N2O1O1O1N2O1O1N2O1O003M3M7H8H6K3L5L3L2O2M3N2M3M3N2M3M6QL`mNT2iR1aM\\\\mN[2nR1[MUmNc2VT1M2O2M2N5L3M2M2O1N01O1G90QMXkNe2UU1M3L2O1O1N2N2O1N4M3L:F3N2M4L5K4K7I6JQ\\\\o>\", \"size\": [1280, 720]}], \"bboxes\": [[140.0, 480.0, 73.0, 121.0], [133.0, 476.0, 71.0, 122.0], [124.0, 475.0, 74.0, 126.0], [114.0, 437.0, 80.0, 163.0], [103.0, 459.0, 96.0, 141.0], [101.0, 460.0, 106.0, 143.0], [102.0, 453.0, 113.0, 149.0], [99.0, 446.0, 115.0, 152.0], [93.0, 455.0, 112.0, 141.0], [87.0, 460.0, 104.0, 148.0], [88.0, 448.0, 83.0, 149.0], [92.0, 459.0, 68.0, 140.0], [83.0, 474.0, 70.0, 134.0], [79.0, 472.0, 73.0, 136.0], [76.0, 461.0, 83.0, 138.0], [80.0, 462.0, 84.0, 136.0], [85.0, 462.0, 91.0, 93.0], [86.0, 442.0, 95.0, 134.0], [79.0, 448.0, 98.0, 140.0], [73.0, 472.0, 100.0, 131.0], [64.0, 448.0, 110.0, 143.0], [60.0, 432.0, 113.0, 148.0], [74.0, 466.0, 110.0, 144.0], [94.0, 451.0, 99.0, 144.0], [105.0, 421.0, 96.0, 147.0], [105.0, 454.0, 102.0, 134.0], [96.0, 449.0, 108.0, 130.0], [84.0, 440.0, 115.0, 139.0], [83.0, 444.0, 120.0, 146.0], [92.0, 479.0, 113.0, 145.0], [94.0, 426.0, 113.0, 152.0], [88.0, 412.0, 96.0, 149.0], [92.0, 429.0, 93.0, 144.0], [93.0, 426.0, 93.0, 137.0], [91.0, 421.0, 100.0, 139.0], [100.0, 449.0, 115.0, 140.0], [132.0, 483.0, 113.0, 139.0], [141.0, 451.0, 124.0, 147.0], [148.0, 446.0, 125.0, 152.0], [164.0, 499.0, 108.0, 144.0], [175.0, 479.0, 101.0, 149.0], [149.0, 454.0, 102.0, 151.0], [119.0, 492.0, 114.0, 134.0], [94.0, 479.0, 127.0, 127.0], [64.0, 441.0, 137.0, 149.0], [66.0, 466.0, 142.0, 147.0], [92.0, 477.0, 123.0, 141.0], [90.0, 421.0, 129.0, 152.0], [116.0, 432.0, 127.0, 150.0], [148.0, 488.0, 119.0, 148.0], [178.0, 456.0, 111.0, 139.0], [189.0, 442.0, 109.0, 136.0], [198.0, 463.0, 118.0, 139.0], [204.0, 488.0, 113.0, 136.0], [192.0, 478.0, 112.0, 136.0], [177.0, 474.0, 112.0, 140.0], [156.0, 502.0, 115.0, 136.0], [122.0, 473.0, 117.0, 140.0], [96.0, 450.0, 120.0, 139.0], [88.0, 459.0, 121.0, 139.0], [88.0, 437.0, 121.0, 145.0], [105.0, 401.0, 121.0, 147.0], [147.0, 409.0, 103.0, 144.0], [191.0, 442.0, 73.0, 137.0], [184.0, 416.0, 65.0, 142.0], [162.0, 417.0, 62.0, 132.0], [131.0, 449.0, 73.0, 126.0], [108.0, 427.0, 84.0, 136.0], [95.0, 419.0, 89.0, 132.0], [112.0, 415.0, 82.0, 142.0], [125.0, 440.0, 82.0, 135.0], [129.0, 412.0, 73.0, 139.0], [115.0, 417.0, 73.0, 137.0], [109.0, 428.0, 77.0, 131.0], [100.0, 408.0, 83.0, 135.0], [104.0, 411.0, 82.0, 136.0], [120.0, 436.0, 80.0, 140.0], [122.0, 429.0, 83.0, 143.0], [115.0, 420.0, 75.0, 148.0], [106.0, 456.0, 71.0, 134.0], [113.0, 457.0, 70.0, 135.0], [115.0, 441.0, 81.0, 138.0], [119.0, 448.0, 89.0, 141.0], [123.0, 463.0, 89.0, 138.0], [128.0, 415.0, 76.0, 154.0], [119.0, 433.0, 78.0, 142.0], [119.0, 450.0, 74.0, 138.0], [127.0, 456.0, 82.0, 132.0], [122.0, 432.0, 95.0, 149.0], [93.0, 490.0, 116.0, 141.0], [91.0, 493.0, 121.0, 155.0], [107.0, 467.0, 111.0, 146.0], [115.0, 512.0, 110.0, 134.0], [117.0, 504.0, 116.0, 163.0], [123.0, 480.0, 105.0, 146.0], [128.0, 494.0, 104.0, 137.0], [136.0, 515.0, 106.0, 124.0], [133.0, 460.0, 106.0, 137.0], [136.0, 459.0, 107.0, 139.0], [131.0, 495.0, 102.0, 136.0], [127.0, 476.0, 102.0, 140.0], [119.0, 456.0, 103.0, 141.0], [117.0, 482.0, 110.0, 143.0], [104.0, 476.0, 116.0, 145.0], [90.0, 413.0, 118.0, 156.0], [86.0, 413.0, 116.0, 151.0], [87.0, 441.0, 113.0, 146.0], [90.0, 444.0, 113.0, 139.0], [101.0, 453.0, 113.0, 134.0], [113.0, 491.0, 113.0, 137.0], [105.0, 479.0, 119.0, 159.0], [85.0, 441.0, 123.0, 162.0], [71.0, 441.0, 121.0, 160.0], [65.0, 466.0, 119.0, 166.0], [58.0, 441.0, 121.0, 165.0], [53.0, 437.0, 121.0, 161.0], [47.0, 479.0, 124.0, 150.0], [59.0, 442.0, 119.0, 174.0], [56.0, 438.0, 120.0, 164.0], [41.0, 422.0, 130.0, 169.0], [49.0, 436.0, 108.0, 166.0], [25.0, 456.0, 130.0, 155.0], [51.0, 430.0, 115.0, 171.0], [68.0, 427.0, 115.0, 173.0], [63.0, 437.0, 140.0, 179.0], [72.0, 422.0, 150.0, 189.0], [62.0, 397.0, 154.0, 199.0], [102.0, 384.0, 151.0, 204.0], [106.0, 382.0, 148.0, 203.0], [88.0, 375.0, 158.0, 199.0], [116.0, 395.0, 150.0, 182.0], [110.0, 402.0, 162.0, 179.0], [101.0, 393.0, 166.0, 187.0], [82.0, 403.0, 165.0, 195.0], [60.0, 412.0, 168.0, 208.0], [57.0, 393.0, 163.0, 212.0], [45.0, 404.0, 165.0, 218.0], [23.0, 415.0, 178.0, 219.0], [18.0, 413.0, 187.0, 225.0], [20.0, 401.0, 180.0, 234.0], [36.0, 412.0, 170.0, 241.0], [60.0, 441.0, 163.0, 235.0], [78.0, 432.0, 172.0, 238.0], [102.0, 455.0, 188.0, 224.0], [121.0, 444.0, 210.0, 239.0], [156.0, 447.0, 180.0, 256.0]], \"areas\": [5841.0, 5640.0, 6640.0, 8576.0, 10814.0, 12196.0, 13436.0, 14170.0, 11675.0, 8402.0, 5766.0, 4281.0, 4513.0, 4493.0, 5363.0, 5630.0, 5874.0, 9286.0, 11025.0, 9942.0, 11283.0, 13275.0, 13142.0, 9897.0, 6979.0, 5223.0, 7650.0, 11223.0, 13118.0, 12467.0, 12654.0, 10212.0, 9215.0, 9202.0, 10308.0, 11192.0, 12054.0, 11431.0, 14108.0, 10542.0, 9246.0, 9385.0, 7908.0, 8635.0, 10556.0, 11197.0, 11116.0, 13039.0, 14275.0, 13635.0, 10425.0, 10140.0, 10656.0, 10551.0, 11377.0, 12249.0, 11611.0, 10554.0, 10432.0, 10627.0, 10890.0, 11393.0, 11166.0, 8726.0, 7282.0, 6190.0, 6433.0, 8564.0, 9186.0, 9666.0, 8757.0, 7669.0, 8500.0, 8781.0, 9209.0, 9217.0, 7980.0, 8445.0, 8822.0, 7578.0, 7614.0, 8793.0, 10483.0, 9683.0, 8609.0, 8692.0, 8256.0, 9027.0, 11346.0, 11710.0, 13727.0, 11295.0, 11914.0, 12737.0, 12174.0, 11473.0, 10547.0, 11846.0, 11469.0, 9296.0, 10929.0, 11930.0, 12216.0, 12814.0, 13937.0, 14526.0, 13796.0, 12700.0, 12923.0, 7959.0, 13366.0, 14657.0, 14558.0, 15650.0, 15703.0, 14872.0, 14513.0, 14738.0, 15075.0, 17231.0, 14841.0, 15739.0, 15905.0, 15458.0, 20206.0, 22135.0, 21499.0, 22514.0, 21303.0, 19288.0, 16569.0, 16686.0, 17741.0, 19458.0, 21147.0, 21937.0, 23203.0, 27145.0, 30346.0, 32517.0, 25247.0, 27381.0, 31199.0, 28442.0, 33319.0, 30494.0], \"iscrowd\": 0, \"video_id\": 0, \"height\": 1280, \"width\": 720, \"category_id\": 49504, \"noun_phrase\": \"the nike air jordan logo\"}, {\"id\": 1040, \"segmentations\": [{\"counts\": \"jafe02iW19J6L2N001O01M3N3M2N2M4JYVm5\", \"size\": [1280, 720]}, {\"counts\": \"kafe01mW14L6K4L101N30N1O1O1O1O0N3M3N2MU^i5\", \"size\": [1280, 720]}, {\"counts\": \"fQde08gW14M3L3N001O0O2O1OO001O01O10O2O1O000O2N1N3N1M4N\\\\na5\", \"size\": [1280, 720]}, {\"counts\": \"kYee01nW13N0L7MJ\\\\hN1bW1701N3NO010O0100O0101O0000001O0O2N2M3N[f`5\", \"size\": [1280, 720]}, {\"counts\": \"aaPf06jW14K3N1N2O00000000001O1001M2O1O2L3N2N3MW^Z5\", \"size\": [1280, 720]}, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, {\"counts\": \"faZf01kW1:J0O10001O11N2N2M3M3MV^Z5\", \"size\": [1280, 720]}, {\"counts\": \"aQbf0;eW11O0101M3M[^Z5\", \"size\": [1280, 720]}, {\"counts\": \"`aif09jW1L4L10Z^U5\", \"size\": [1280, 720]}, {\"counts\": \"fQXf01kW17L4L2O1N2O0O2O0000O00100O10001O001N3M;FUfQ5\", \"size\": [1280, 720]}, {\"counts\": \"faZf06gW15L3O1N2O0O2O00O1O1O100O101N101N`VT5\", \"size\": [1280, 720]}, {\"counts\": \"hi[f04jW13L3N4M2O1O0O1000O10000O100O2O002M_fQ5\", \"size\": [1280, 720]}, {\"counts\": \"jiof04jW14N01O1NWnm4\", \"size\": [1280, 720]}, null, {\"counts\": \"eYWg07hW13O0O1Zfg4\", \"size\": [1280, 720]}, {\"counts\": \"eYhf08eW14N2O001N10000O100000O100000000000001N4K_^a4\", \"size\": [1280, 720]}, {\"counts\": \"faif0:fW12M2O0O101N10O100O100O1000000000001M3K_^a4\", \"size\": [1280, 720]}, {\"counts\": \"oief02jW16L5L2O00000O100O1000O1O2N100O101O2M6JRVe4\", \"size\": [1280, 720]}, {\"counts\": \"kiof0:eW12O00000000O1O2O10Sfg4\", \"size\": [1280, 720]}, null, {\"counts\": \"PbSg04gW18L1001O004M4In]f4\", \"size\": [1280, 720]}, {\"counts\": \"mief06hW15L4M2NO1O10O01O100O100O100O101000Tfg4\", \"size\": [1280, 720]}, {\"counts\": \"UZhf01eW1;M2O100O010O10O1O1O100O101O1O5K2MRVe4\", \"size\": [1280, 720]}, {\"counts\": \"kaXg07hW12N1N3M2N3O00000O01O1M4N2K5M]fX4\", \"size\": [1280, 720]}, {\"counts\": \"kQeg01mW15J5L3L300O1000O2N1K5H8O10_^m3\", \"size\": [1280, 720]}, {\"counts\": \"hYPh04gW15N3M201O1N2O00O1N3L6K`fd3\", \"size\": [1280, 720]}, null, {\"counts\": \"QQ^h0;dW11O0O22M2O1O1Non[3\", \"size\": [1280, 720]}, {\"counts\": \"m`[h01jW16N2N11O01OO10000O100OZ_Y3\", \"size\": [1280, 720]}, {\"counts\": \"m`Vh09aW17O2N00IdhNI]W1Nhoe3\", \"size\": [1280, 720]}, null, null, {\"counts\": \"h^Vh07ZW1a0OK4N3FbhNMVah3\", \"size\": [1280, 720]}, {\"counts\": \"XV_h0b0WW17K51F:H8L7IPR\\\\3\", \"size\": [1280, 720]}, {\"counts\": \"i]Vh07dW1501O0O101O001O1O1N2O1O0O2O1O1000O001O1N2O0M4L4O001O3K3MUZi2\", \"size\": [1280, 720]}, {\"counts\": \"c]jh0<cW14M2N001O1O100O0010O01O01O100O001O0O101O1O1N3N1N3M3LPjW2\", \"size\": [1280, 720]}, {\"counts\": \"nekh06hW12M4L4L3O101O1N2O10O0001O010O01O1O001O001O1O001O1O1O2L7GRRT2\", \"size\": [1280, 720]}, {\"counts\": \"nekh01oW11O00003K3K4K6O1O1N2N2O1O001O000001O01O010O0001O0010OO2O1N3N1O2M3M5Koal1\", \"size\": [1280, 720]}, {\"counts\": \"nmlh02mW11O2O1O0O101N2N1O2O0N3N2N2K5N1O2O0000010O0001O1O001O001O001O0O3N1O1N4KWRj1\", \"size\": [1280, 720]}, {\"counts\": \"dlXh0d0XW16M110O00001O1O1O1O1O00100O001O00001OO3M4K6I6J5JUkk2\", \"size\": [1280, 720]}, null, {\"counts\": \"bYbg01oW1e0ZO6K1N2O1N10O001O0O101O1N2O00001N2O1O1O1N2M4M2N3M3Men_3\", \"size\": [1280, 720]}, {\"counts\": \"PZbg0:fW14K3N5K001OO1O010N101N2O00100O2M2O2NSni3\", \"size\": [1280, 720]}, {\"counts\": \"Rjdg05jW16J2O2N1N3N2M2N2O1ON101O10O0100O101N1F<L4MQfc3\", \"size\": [1280, 720]}, {\"counts\": \"iZbg06hW13M2N3N1O002N1O2N1O1O1O10O000000001O001O00001N2N1N4M2N3M3NlU\\\\3\", \"size\": [1280, 720]}, {\"counts\": \"Ykig02kW1`0@9H2O1OO2O010O10O10O10000O0O1M3N3K4O2M2N202O1N101KZ]X3\", \"size\": [1280, 720]}, {\"counts\": \"nkSh06fW16J6L4N1N3M3L2O2OO001O1O1N4Ma0_OQT\\\\3\", \"size\": [1280, 720]}, {\"counts\": \"PmXh01kW16L3N4L2O2N1O10O01O00010O010O0001O1O1O3M4L1O012M1Kjbj2\", \"size\": [1280, 720]}, {\"counts\": \"Pemg01lW15L4N0O2O1O001O1N100000000001O1OO100000O1GahN0aW1MchN1bjZ3\", \"size\": [1280, 720]}, {\"counts\": \"fT\\\\g01nW11N3L3L4M3O0O2N2O1O1000O2O1L4H9KlcR4\", \"size\": [1280, 720]}, {\"counts\": \"bkUg02jW18K2N1L5M2NOPiN_OfV1a0ZiNAfV1=]iN@eV1>a0M8FeT_4\", \"size\": [1280, 720]}, {\"counts\": \"a[Sg09fW15K3N2N1N2O1O2M2M4M2N2O2N001O1O04L6K5K2M1O3L2H8M6L1Oikn3\", \"size\": [1280, 720]}, {\"counts\": \"oRRg09dW16K4N2M3M3M3L4M2O1N2ON2O1O2N101O2M7HUUZ4\", \"size\": [1280, 720]}, {\"counts\": \"[Rag07fW1:G3M3N2N1O101N101O1O1O1OO1O1O1O1O1M3L5N1NQ^g3\", \"size\": [1280, 720]}, {\"counts\": \"Pk_g08eW16K5L3M3M2M3M4N1O0000N2N2N201N1Oi0XOcdm3\", \"size\": [1280, 720]}, {\"counts\": \"ejig01oW1:Eb0_O5K1O1ON2O100O2N1O3M4Ld0\\\\Od\\\\g3\", \"size\": [1280, 720]}, {\"counts\": \"jbmg01gW1<I8J2N2N7IN21O100O2OO0O101O1O100001N20J5K6ESfY3\", \"size\": [1280, 720]}, {\"counts\": \"mZlg05iW1:H7I3ON1O1O0M5MP]l3\", \"size\": [1280, 720]}, {\"counts\": \"nl]h04jW14L3N2M2O1M3O2N2O10O2OO10O1O00O1001O1O1N2N3M4KTSh2\", \"size\": [1280, 720]}, {\"counts\": \"ckPg05kW14L3M3M3L2O2N1O1O1O1O1O1O1O001O00001O100O0102M3L3L5N1O1GchNK`W119Ll[l3\", \"size\": [1280, 720]}, {\"counts\": \"obTg0`0ZW18L4M3M2O2N2N1O2N1O2O10N2O0O1M4M2N101O1O2M5K5K>@[TP4\", \"size\": [1280, 720]}, {\"counts\": \"c[Sg01kW19J5L5L2M3N1N1O2O1N1O1O1O1O1O01O00010O1FRiN@oV1h01M4K5N2N2J80OM1O2N4Jfch3\", \"size\": [1280, 720]}, {\"counts\": \"dd\\\\h0c0\\\\W15J4N2M201O0O2OO1O000O2N1N3N2M5J6HXkP3\", \"size\": [1280, 720]}, {\"counts\": \"bTkg0`0_W14M1N2O1O0O1001O01O00O2O000000000O1N6K:ETc^3\", \"size\": [1280, 720]}, {\"counts\": \"Umlh01lW14M2N3M3M2O3M2N2N2N2O1O001O001O1O01N1O1O100N3M3M4L5JV[U2\", \"size\": [1280, 720]}, {\"counts\": \"odPi07hW10O2O3L3N3L3M3N1O2N2O001O0000O1O1N2N2N2N2N2N3M3L`ST2\", \"size\": [1280, 720]}, {\"counts\": \"Pm[i06hW13M2O1O1O1O2M2O2N1O2N1O1O002N2N1OO2O0001O1O01O001O1O1N2O2M2M3L4M4N2O1MgS[1\", \"size\": [1280, 720]}, {\"counts\": \"k\\\\ci02jW16L3N2N2M3N101N1O2N1O100O1O1O2N000O1100O1O2M2O2M2M3M3N3L5K5MekY1\", \"size\": [1280, 720]}, {\"counts\": \"fTSi09dW14M2O1O2N3M2N1O2M2O1O1O1O1O010O10O1O100O2N1N3K5K7Ik[P2\", \"size\": [1280, 720]}, {\"counts\": \"W\\\\Vh0<aW16I<E9H5L2O0O100001O1O1hN^iNP1cV1mN_iNS1gV100000O11N3L7\\\\ORiNJfbT3\", \"size\": [1280, 720]}, {\"counts\": \"ZlSh09bW19G7K6M5J6K2N001O01O002N5K1N10O101O0002N5Gh0YOT[S3\", \"size\": [1280, 720]}, {\"counts\": \"ZePi01gW18L5L3N3N3M100O1O3N1O00O1O1O2N2N1O2N1N3M4M4JUcV2\", \"size\": [1280, 720]}, {\"counts\": \"kdah0;`W16K4N2N2N3N100O2O1O010000O1O100O1O1M3M3M4L4L5KW[d2\", \"size\": [1280, 720]}, {\"counts\": \"X\\\\Vh07fW17J4J7J5M2O3L2O1O012N0O12N10O00M2O2N2O1N8[OihN1]W1GokP3\", \"size\": [1280, 720]}, {\"counts\": \"`\\\\Ti06hW1:F3M3N1O3M1O2N10000O010000O1O2O2M2N3M2M5K5HdkR2\", \"size\": [1280, 720]}, {\"counts\": \"YdZi0>_W14N2M3N1O1O1N3N2N1O2O1OO01000N2N3N3K9H7GlSo1\", \"size\": [1280, 720]}, {\"counts\": \"hcPi0;aW1<E9G4L4N1O100000000000O102K5J8H6I7K6H]\\\\Z2\", \"size\": [1280, 720]}, {\"counts\": \"c[mi0510bW1;chNFkV1j0N1O3M3M0010001N10O001O1M3M3N3K5XOohN=^W1K6KY\\\\\\\\1\", \"size\": [1280, 720]}, {\"counts\": \"mS`j01mW1>A4N2M3N2M3N2N2M2M4N1O1O0100O1O1O1O2M3N3L3M5K9E]dd0\", \"size\": [1280, 720]}, {\"counts\": \"]cdi0=_W18I5K5M2N3N1O1O00002OO1000000N2N2AUiNFdSj1\", \"size\": [1280, 720]}, {\"counts\": \"^[ci06_W1b0E7L3N4K3N2O0O001000O2N1O1O2O3J>_O_Tj1\", \"size\": [1280, 720]}, {\"counts\": \"gcbj08dW18J4K4M3M2N2O10002N1O001O01M2O1O2N2N3L5K4L5J_dd0\", \"size\": [1280, 720]}, {\"counts\": \"Xkei0f0WW16K5M3M5K5K1O10O2O1N101N1O1O2M2O3Kg0WOl[f1\", \"size\": [1280, 720]}, {\"counts\": \"^[jh01lW1a0A5L3M3L3N2N2N2N1O20O1O10O001O1N102M2NN3O1O10000N5L3L3L9G7I9G^cQ2\", \"size\": [1280, 720]}, {\"counts\": \"\\\\\\\\hi0`0^W1:G6J4M1O1O0O2O1O0000001O1OfNaiNS1gV1O0O2N12OO012M2O2L5H7FWS[1\", \"size\": [1280, 720]}, {\"counts\": \"YTSi08gW17I6K3M2N1O1O1O2O00001O0000000O0000001O01L4O2N2M5K4L5K6Jmbl1\", \"size\": [1280, 720]}, {\"counts\": \"QlSh07iW12O2M11O00100N101N2O1OO2N1O100O1O1OO2N1O2O1N10O101OO3N1N[kk2\", \"size\": [1280, 720]}, {\"counts\": \"bSZh07aW1:^Ob0L4N1O2M3O00O012N100O1K4N2N3O1N0O2N2N5L4J8HZlk2\", \"size\": [1280, 720]}, {\"counts\": \"ccah01nW16K3M3M102M3M3O2M2N1O21OM1O10O01OAohN0PW1`000O02O000M3N3N2O1N3N3L3M4Kh[Z2\", \"size\": [1280, 720]}, {\"counts\": \"]Sdh03lW13N2O2M3M2N3M1011N7I8FokP3\", \"size\": [1280, 720]}, {\"counts\": \"gZVh06cW1?H5L4L4L3L20O1O00000000O1O10O001O1O2M3N4L4L4M5I6K2M3LUdj2\", \"size\": [1280, 720]}, {\"counts\": \"jbRh02lW15K3L5M5N3L2M5L3L30N0O200O00O100O100OO1O1O2O1O000O2O3M5K5K7I3LV\\\\i2\", \"size\": [1280, 720]}, {\"counts\": \"c[jh0;dW16K3M2M3O0O1O01O00010O100O100O01O001O1O001OO100N3O1N3K4O1O1O1O1N2NoSo1\", \"size\": [1280, 720]}, {\"counts\": \"X[`h08gW13N3M4M1N2N2O0O1O2O0000O010O000010O1O0000IPiN^OPW1b08O1N101O1N2O4K5L2N00S\\\\Z2\", \"size\": [1280, 720]}, {\"counts\": \"[Z[h0i0WW14L3M2N3M20O3L2N1001O0O100O2N2O1N000010O1O1O3J8G9J5L4M2M]\\\\d2\", \"size\": [1280, 720]}, {\"counts\": \"VSih0<aW19I4M2N3M3N1N1O1O2O0O1001O0O001O0N2N2N201N2N2N3M4M_\\\\Z2\", \"size\": [1280, 720]}, {\"counts\": \"QdPi0<_W1;I3N1N2O1O0O2O1O000001O001N2O2M3GihNDZW14ohNIeW1OZ[Z2\", \"size\": [1280, 720]}, {\"counts\": \"ek]h0;dW19H4K3N2N2N2N2N2N1O100001N1O1O1M3O000L5N111N2N9Eikf2\", \"size\": [1280, 720]}, {\"counts\": \"g[[h0=cW1:E4M2N2N1O2N2M3O0O1000O2N1O1N101O0O1O2O2N3M4EPdj2\", \"size\": [1280, 720]}, {\"counts\": \"\\\\lgh06eW19K3M2O1N2M4O2M2O10N11O00N2N2N2N3M3N1N3N]ka2\", \"size\": [1280, 720]}, {\"counts\": \"d\\\\jh03_W1?J6M2M3001O1O01N101O0000001000O2M1O2N4J6FoS^2\", \"size\": [1280, 720]}, {\"counts\": \"ckXh02kW19H7J5K6K3M2N4L2N2N1OO1O1O2O1N2M2M3L8BgTR3\", \"size\": [1280, 720]}, {\"counts\": \"X[Qh09fW17J4L4L3M2O0O3M00N2M4N1N2O5K;C\\\\d^3\", \"size\": [1280, 720]}, {\"counts\": \"UcRh0:eW17J5L2M4L2N2M2O1O3MO1O1O2O1N3N1N4L5@ehN0kW1IV\\\\X3\", \"size\": [1280, 720]}, {\"counts\": \"TSPh02lW19H7J5J3N2N3M2M3N3M2M6LN1N2O3O11OO1M2L3O2L4L5JhlU3\", \"size\": [1280, 720]}, {\"counts\": \"oRZh06kW13N1N3L1O102N01O1J`hNLaW1LmT\\\\3\", \"size\": [1280, 720]}, {\"counts\": \"iZlg02kW16N5J4J5M4L1O2N4L4M0001O2N2N000O00100O002N1O2M2O2N1O2M:B^\\\\S3\", \"size\": [1280, 720]}, {\"counts\": \"[Snh0`0\\\\W1:F:I3M3M0100O100O1O2O1O0O3M2N2M8Ei\\\\_2\", \"size\": [1280, 720]}, {\"counts\": \"Ycah0`0^W15H8J5M3M3N2O004MO1N3M2M2N1O2N1O101N2N2N2N4L3M^\\\\d2\", \"size\": [1280, 720]}, {\"counts\": \"Y[Vh0<_W18E:L5M2M2O2N3M3M2N2N4M10O2N1N1O1O1O001O1O1M3Li0WO6K6K2Ojcj2\", \"size\": [1280, 720]}, {\"counts\": \"nbfh05jW16K4M3M2N2N1N2O1O005JM5F:BehN0VTm2\", \"size\": [1280, 720]}, {\"counts\": \"_kbh06dW19`hNCSW1o0D6L3N2N1O1O1O0100O2K6L2N3M3SOSiN<WTm2\", \"size\": [1280, 720]}, {\"counts\": \"Rkng03jW17L5J6K5K2O1N2N002NO3I6K6^OihN1Yld3\", \"size\": [1280, 720]}, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null], \"bboxes\": [[555.0, 819.0, 13.0, 20.0], [555.0, 820.0, 16.0, 20.0], [553.0, 816.0, 24.0, 25.0], [554.0, 817.0, 24.0, 20.0], [563.0, 815.0, 20.0, 19.0], null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, [571.0, 817.0, 12.0, 14.0], [577.0, 817.0, 6.0, 13.0], [583.0, 816.0, 4.0, 9.0], [569.0, 813.0, 21.0, 20.0], [571.0, 815.0, 17.0, 19.0], [572.0, 815.0, 18.0, 18.0], [588.0, 824.0, 5.0, 8.0], null, [594.0, 820.0, 4.0, 12.0], [582.0, 815.0, 21.0, 17.0], [583.0, 819.0, 20.0, 17.0], [580.0, 822.0, 20.0, 17.0], [588.0, 826.0, 10.0, 12.0], null, [591.0, 826.0, 8.0, 14.0], [580.0, 825.0, 18.0, 18.0], [582.0, 822.0, 18.0, 17.0], [595.0, 818.0, 15.0, 19.0], [605.0, 816.0, 14.0, 18.0], [614.0, 814.0, 12.0, 18.0], null, [625.0, 797.0, 8.0, 15.0], [623.0, 790.0, 12.0, 11.0], [619.0, 791.0, 6.0, 18.0], null, null, [619.0, 713.0, 5.0, 24.0], [626.0, 700.0, 7.0, 30.0], [619.0, 689.0, 29.0, 26.0], [635.0, 690.0, 27.0, 25.0], [636.0, 689.0, 29.0, 27.0], [636.0, 689.0, 35.0, 29.0], [637.0, 686.0, 36.0, 29.0], [621.0, 655.0, 25.0, 37.0], null, [603.0, 554.0, 27.0, 38.0], [603.0, 572.0, 19.0, 26.0], [605.0, 571.0, 22.0, 28.0], [603.0, 578.0, 30.0, 32.0], [609.0, 599.0, 27.0, 39.0], [617.0, 622.0, 16.0, 34.0], [621.0, 664.0, 26.0, 24.0], [612.0, 665.0, 23.0, 16.0], [598.0, 642.0, 17.0, 22.0], [593.0, 611.0, 12.0, 33.0], [591.0, 617.0, 27.0, 41.0], [590.0, 595.0, 19.0, 39.0], [602.0, 576.0, 22.0, 33.0], [601.0, 596.0, 18.0, 35.0], [609.0, 596.0, 15.0, 36.0], [612.0, 581.0, 23.0, 39.0], [611.0, 603.0, 9.0, 25.0], [625.0, 657.0, 24.0, 26.0], [589.0, 626.0, 31.0, 34.0], [592.0, 597.0, 25.0, 46.0], [591.0, 622.0, 32.0, 39.0], [624.0, 656.0, 18.0, 37.0], [610.0, 655.0, 21.0, 26.0], [637.0, 661.0, 27.0, 30.0], [640.0, 657.0, 25.0, 30.0], [649.0, 647.0, 36.0, 36.0], [655.0, 645.0, 31.0, 33.0], [642.0, 643.0, 26.0, 34.0], [619.0, 633.0, 21.0, 47.0], [617.0, 636.0, 23.0, 45.0], [640.0, 661.0, 23.0, 29.0], [628.0, 650.0, 24.0, 35.0], [619.0, 634.0, 23.0, 47.0], [643.0, 644.0, 23.0, 31.0], [648.0, 634.0, 21.0, 36.0], [640.0, 617.0, 20.0, 42.0], [663.0, 615.0, 21.0, 43.0], [678.0, 616.0, 25.0, 42.0], [656.0, 609.0, 18.0, 43.0], [655.0, 605.0, 18.0, 47.0], [680.0, 615.0, 23.0, 36.0], [657.0, 611.0, 19.0, 50.0], [635.0, 617.0, 32.0, 56.0], [659.0, 647.0, 26.0, 45.0], [642.0, 647.0, 29.0, 40.0], [617.0, 641.0, 29.0, 27.0], [622.0, 599.0, 24.0, 51.0], [628.0, 626.0, 32.0, 42.0], [630.0, 620.0, 12.0, 24.0], [619.0, 592.0, 28.0, 50.0], [616.0, 594.0, 32.0, 49.0], [635.0, 625.0, 34.0, 34.0], [627.0, 615.0, 33.0, 33.0], [623.0, 587.0, 29.0, 53.0], [634.0, 610.0, 26.0, 43.0], [640.0, 633.0, 20.0, 33.0], [625.0, 627.0, 25.0, 41.0], [623.0, 629.0, 24.0, 42.0], [633.0, 642.0, 21.0, 37.0], [635.0, 636.0, 22.0, 33.0], [621.0, 620.0, 20.0, 43.0], [615.0, 615.0, 16.0, 33.0], [616.0, 612.0, 20.0, 38.0], [614.0, 606.0, 24.0, 48.0], [622.0, 607.0, 11.0, 28.0], [611.0, 597.0, 29.0, 44.0], [638.0, 608.0, 18.0, 43.0], [628.0, 605.0, 24.0, 49.0], [619.0, 601.0, 28.0, 59.0], [632.0, 605.0, 14.0, 38.0], [629.0, 602.0, 17.0, 48.0], [613.0, 606.0, 14.0, 34.0], null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null], \"areas\": [170.0, 197.0, 412.0, 313.0, 257.0, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, 108.0, 62.0, 16.0, 304.0, 252.0, 258.0, 32.0, null, 38.0, 313.0, 275.0, 251.0, 109.0, null, 77.0, 232.0, 210.0, 198.0, 148.0, 155.0, null, 86.0, 96.0, 78.0, null, null, 78.0, 114.0, 485.0, 469.0, 567.0, 658.0, 675.0, 702.0, null, 627.0, 333.0, 397.0, 522.0, 546.0, 386.0, 390.0, 293.0, 219.0, 166.0, 633.0, 547.0, 560.0, 461.0, 387.0, 616.0, 166.0, 426.0, 692.0, 796.0, 760.0, 499.0, 452.0, 552.0, 518.0, 745.0, 611.0, 636.0, 783.0, 787.0, 457.0, 588.0, 698.0, 527.0, 555.0, 632.0, 662.0, 738.0, 609.0, 639.0, 610.0, 715.0, 1070.0, 864.0, 743.0, 221.0, 778.0, 703.0, 142.0, 803.0, 805.0, 729.0, 609.0, 907.0, 798.0, 524.0, 739.0, 756.0, 486.0, 556.0, 616.0, 366.0, 543.0, 767.0, 135.0, 738.0, 624.0, 774.0, 1075.0, 276.0, 622.0, 309.0, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null], \"iscrowd\": 0, \"video_id\": 0, \"height\": 1280, \"width\": 720, \"category_id\": 1985, \"noun_phrase\": \"the right hand\"}, {\"id\": 1041, \"segmentations\": [{\"counts\": \"kgn:`0[W1:J5J3M201N2N11O3N2M4M2M2O2M3M3M2M5Lb_]`0\", \"size\": [1280, 720]}, {\"counts\": \"iWb:?`W1<D5K4L2O1N2O1O1O1O01O007I3M4L3L4M2M4M6J4LO1O12J\\\\_b`0\", \"size\": [1280, 720]}, {\"counts\": \"c`j91dW1`0H7J5N1N2N1M3N2N3L3NjN_iNo0YV1lNliN6Jo0aV19O01O0010O00O3N`0@>A2O1MQWZa0\", \"size\": [1280, 720]}, {\"counts\": \"Wia9?^W18H:F5M3M1O10N5M7I6I6J5J^nQb0\", \"size\": [1280, 720]}, {\"counts\": \"l`V9l0nV1:J5J6L2O2O01N10L4O1O001O1O1O0010O002N4L9Gb0^O3WOWiN4TePb0\", \"size\": [1280, 720]}, {\"counts\": \"ba`96^W1`0H8I5M2M4L2O2N20O100O1O010O1O1O0O101O001RO^iN;VW1N3L7GVVda0\", \"size\": [1280, 720]}, null, null, {\"counts\": \"]jP:X1fV1<G3M2N4J4M4L201N1N1O2M2M3M3N2O00001O0100O101N101N4L2N3N1N2N2M3N7H6J6J5K3K6E>H5Klk_`0\", \"size\": [1280, 720]}, {\"counts\": \"UlW9l0lV1=]Ob0UOYNfjN[2QU1?M3M2O0NWMUkNa2iT1_MWkNb2hT1^MXkNc2iT1:4K01N1N3O02OIkLakNU3eT11L300101N1O011NO2N100106I5K5K4L3M8Hd0cNniNCZW1MZZ[a0\", \"size\": [1280, 720]}, {\"counts\": \"acT:Q1gV1?YOaNVjNk1fU1:L4N2N1N3L3N2O1N2M2O2N2N1O2O1O1N101O001O0O10001O000010O0001O001O000001O1O1O2N1O2N1O1O1N4M2M3M3M4mNbjN]ObU1>cjN^OaU1=bjN@eU1:]jNDgU18[jNEjU17ajN[OfU1a0Q1IZde?\", \"size\": [1280, 720]}, {\"counts\": \"fad<7hW16J4L8I6I4L4K4M3N1N3M3M2O3M4M1N1N2M3O1O1O0DjMmjNX2RU1hMmjNZ2RU1fMmjN\\\\2RU1dMljN^2TU181N3N0O0010O0001O00001O00001O0000001O1O1N2O1O001O1O1dNWjN>kU1@VjN`0jU1@VjN`0kU1^OWjNa0jU1]OWjNc0kU1ZOVjNe0lU1ZOUjNe0kU1[OUjNe0kU1[OUjNe0lU1YOUjNg0mU1UOVjNi0lU1UOVjNj0lU1SOUjNl0`V101N2O001N2O001N3N5K2L6IRU_<\", \"size\": [1280, 720]}, {\"counts\": \"TZP>2oW1Oog33nWL2L8H4M4M2O2M2N3M2N2O1N2L4L5AcNUjN_1gU1cNXjN`1eU1aNZjNc1cU1^N[jNd1dU1^NWjNg1gU1;O1O1O03NO000O2O00000O100O1O10O0100O01O010O10O01O010O1O1O0010O01O2N001O1O00100O1O1O002N1O2N1O1O1N3O0O1O1O1N2O1kNdiNd0aV1TOdiNj0jV1O1N2N3M3N3L6IS\\\\d:\", \"size\": [1280, 720]}, {\"counts\": \"kjo<4jW15L4L9G7J3M5J4M3M3L3M3N2M4M4L1N3M2M3O1O1O1O001O1O1O1O1N1011N2N101N1O1O100O010O010O2N1O1O100O1O2N101N1O1O2N2N1O2N2N1POfiN8]V1EeiN9\\\\V1EfiN:\\\\V1DeiN;]V1DciN:aV1C`iN7gV1E\\\\iN;eV1A_iN=SW1O2L6IZk]<\", \"size\": [1280, 720]}, {\"counts\": \"S[Q;159QW1e0F8H8L3L4L4N1O1N2O1O1N101N2O0010OO2O0010O01O0000001O0O20O001O2N3L5I=Fd0oNPiNISW11TiNI][d?\", \"size\": [1280, 720]}, {\"counts\": \"QQW:Z1dV15M3M3M3L3N2N2N2O0O1O00001O00010O00001O0O1100O1O4L3N3L3M:F5K1N3PO\\\\iN?_W1@X]b`0\", \"size\": [1280, 720]}, {\"counts\": \"``[9h0lV1c0I5L2M2M3N2N1N2O2N001O01O010O1O100O2O1O1N2N6ZOaiN_OdV1;^iNDfV17]iNGfV16\\\\iNHjV10ZiNN_W1M3Mlmba0\", \"size\": [1280, 720]}, {\"counts\": \"SPo8b0[W18F9H7I6K4N2O2N10O2O0O00O2O000O2N1O1N2O000GSN`jN200>g0RW1ZOToVb0\", \"size\": [1280, 720]}, {\"counts\": \"V_e9c0YW19J2N2N2N2N101M0N3O1OO102N2O1N4K4E;L4N1O1O6JViaa0\", \"size\": [1280, 720]}, null, null, null, {\"counts\": \"mme:<aW17L2N2N001O1O1O001N100O12O00005H6AUbg`0\", \"size\": [1280, 720]}, null, null, null, null, null, {\"counts\": \"RoY;8fW16K3M3K5L4M2DROciNP1\\\\V1POciNR1cV14J56I2O0O00001O7G5IoXR`0\", \"size\": [1280, 720]}, null, null, {\"counts\": \"^k^;1lW14L021O2N5K102N1O1O001O1O001O1N101O0O2O1O1O0O2O10O0001O000001N2N2N2M2O2M3N1O2L7HglQ?\", \"size\": [1280, 720]}, null, null, {\"counts\": \"`o`::`W19J4M4L3L3O1N2O11N101O1O2M10O01O1M4K5K6EUih`0\", \"size\": [1280, 720]}, null, null, null, null, null, null, null, null, null, null, {\"counts\": \"UP`;1nW12M3fhNKfV16a01dhNIoV17PiNKoV17;3M2N1O1O1O1O10O0100O1N2L400O10000001N10003L3K4M4L4JYhZ?\", \"size\": [1280, 720]}, null, {\"counts\": \"l^_::dW15K3J6O2M3M2O00000001O0000O12M3N1O4K6CYak`0\", \"size\": [1280, 720]}, null, null, null, null, null, null, null, null, null, {\"counts\": \"\\\\om91kW14N2O2N2N2N2N100O2O1O00O2N4K3NlPba0\", \"size\": [1280, 720]}, {\"counts\": \"gfg9:bW1<F5L3M2N2O00100N3N3M12L3L4L4L4M5J5E`i3Mcf[a0\", \"size\": [1280, 720]}, {\"counts\": \"eg[:3mW100000N6K4M2M101N10010O0000001O1O6J000001L3N3N2N2N2N4L3NUPd`0\", \"size\": [1280, 720]}, null, {\"counts\": \"S^W96hW16L3M2M6K3M1O2N2O2M2O0010O1O001N1N2CnhNHhW1N2KQYRb0\", \"size\": [1280, 720]}, {\"counts\": \"h^k9=^W1;H5M0000O12M10000000001O01N2N1L4M3O2N1N4L4K\\\\i[a0\", \"size\": [1280, 720]}, null, null, {\"counts\": \"f^P:;bW15M2M2O2O00000000O101O1N3N102MYa_a0\", \"size\": [1280, 720]}, null, {\"counts\": \"UWY:=_W19H3N2N2N2O4L4L5KL4O2O001N2N2M4M2O3L4K9EXaPa0\", \"size\": [1280, 720]}, {\"counts\": \"f_U:181]W1355[OLUiN4iV13RiNNkV17SiNHkV1<SiNEkV1i0O1N3N4L2OO0M5M3M3GUiNYOnV1d0:K6K3N4M1MQiQa0\", \"size\": [1280, 720]}, null, {\"counts\": \"Q__:8cW1:I6J7J1N3N1O0001N101O1N2O1O2N1N3M4L4L5LWYj`0\", \"size\": [1280, 720]}, null, {\"counts\": \"iXk;1oW11N3RiNLlU13UjNMkU13[iN8TW1IjhN9UW1HihN;8A`V1n0WiNVOhV1R1OoNYiNj0fV1VO[iNj0dV1910O1O0010O001O1O001O001O001N10001N101O2M6J8H3N1Ggfk>\", \"size\": [1280, 720]}, null, {\"counts\": \"P_k96bW1=ihN^OaV1i0\\\\iNXOaV1l0^iNUO_V1n0_iNSO_V1X1N200O20O01O10N2O2M2O1O2N4L5J7_OZY^a0\", \"size\": [1280, 720]}, {\"counts\": \"onc9;bW17I6fhN^OoV1m0M3M2NO101N2O1O1O1O10001N1O2N2N3N9Abhea0\", \"size\": [1280, 720]}, null, {\"counts\": \"kgn81mW14L4M2N101N100O002O2N2N2N2O1N2O2M2O2N2O0O1O1O101N2O0O001O001O100O1000O000O2O0O100000000O2O001O1O0O10000010O0000O10O010O1O01O00010O001O0000000O2N1M>@^^W`0\", \"size\": [1280, 720]}, {\"counts\": \"Soj89fW15K4M2N2N2N2N2N101N1O1O3N1N3N1O2N2N2O0O301NO2N1O1O011N02N11N01N1O2O1OO1O1O10O0001O1O1O01O0001O1O1O1O00001O01OO10000000001O00O10O1O10O01O1O1N2N3L:Ej]\\\\`0\", \"size\": [1280, 720]}, {\"counts\": \"dXV91iW1=E8K5J7I5M2M4M1O2M4M2N1O1N2O1O1O1O2O1O001O04L6J4K8I1O1O1N10000O2OO100O1001N2O001OO10O10O01O00001O001M5J9YO`Vj`0\", \"size\": [1280, 720]}, {\"counts\": \"nXV96gW1;F5K4K5M2N3N3L3N1O1O001N2O1O010O100000O2O1N3N3L2O2M2O0O1000O100O010O1O10O10O2OO10O10O001O100O000O2O0M4K5E;E:OZfg`0\", \"size\": [1280, 720]}, {\"counts\": \"Qa\\\\93hW19I9I4L3L5L3M4L3N2N001O001O00100O1O0010O2O1N2O4K4M4L1N1000O10O01O010O00100O10O010O001O1O001O001N2K5Ecfg`0\", \"size\": [1280, 720]}, {\"counts\": \"aaf9a0\\\\W18I8B;E:F:I7I6H8K5N2L4K4O2M2O2O0O10010O02N1O2N3M4jM`kNd0cT1TOjkNc0YT1VOokNg0TT1TOPlNj0cU101O1O1O0010O00O2O0000000O102M5L1O3L9Eb]a`0\", \"size\": [1280, 720]}, {\"counts\": \"XZT:a0[W1;D:C:C=J5N101N2O1N101N2OO10000000001N2O2N3M=C5L3L4L3M1O1O1O1O0O2O001N101N2O1N2O1N3M5L3L4LRUV`0\", \"size\": [1280, 720]}, {\"counts\": \"VYo9<WW1EPiNd0aV1e0K4L5K4N2N2M3O0O2N2M3N2M2O1O101O01O10O01hN[jN4gU1^OfjNa0[U1SOPkNl0TU1mNQkNQ1QV1O000O1000O100O10O001O010O0000001N2O3M3L6JVnY`0\", \"size\": [1280, 720]}, {\"counts\": \"Xmm9:bW1=D;H:E5L3M4L6K3L4M3N2M2O1O2M20000O1O01O001O2N3L3N4LO1010O1O001O4MO01O00010O03OM2N1O1O2O0O10001N2N1N101M4K3N3L4K4M7I5I5M6Im^h?\", \"size\": [1280, 720]}, {\"counts\": \"coU;5iW16L3L4M3M2M3M3N3L7I6K3M5K7I4L9G4L101OO2N1N3M4L3L4M4K5L7H5L5K5J5J;GU_c?\", \"size\": [1280, 720]}, {\"counts\": \"]iT;8hW12N001N2O1O000O100O1O1O2M3N2N3M2N3N1O1O1N0100O001O001OO1001M3O1O0O002VOTiN8[^c?\", \"size\": [1280, 720]}, {\"counts\": \"X`k98eW16K4L4M3L4M2N3M3M2N2O1N100O1O0010M3N2O100O002N1O1O2O00100O1000O0100O1001N101N2O2N6I2O1O1O2N3M2N001O001N10O1N3M4L4K5L3M3L4L3N5K3L5FPoe?\", \"size\": [1280, 720]}, {\"counts\": \"XfS93lW14M1N2O1O2N2N1N3M2O1O1O2N2N1O101O1N101N101O1O100O2N1O00100O1O100O0010O01N2O01000O1O100O100O100O102N1N5K8J1O0O1ON2O0O2O1N2N1O3M2N2M3N1N3L3M4L4M3L5J4L5L6IQoo?\", \"size\": [1280, 720]}, {\"counts\": \"mfb91nW16J:G6I2M4M5J7J5J;G5L5J4L4M1O11N2O0010O02N4K3N1N2O1O2O01OO0O2O1O10OO2O0000002M1000O101O0O0000O2N1N2M4K6I6K5K5J6K4M3MV^W`0\", \"size\": [1280, 720]}, {\"counts\": \"lV[9>`W1;H3L2O2M1000010OO100001O000O01001O00000000001OO11O1O00001O1O1O1O1O00100O1O100O2N0000O101N101O0010O001O000000O2L3N2M3M3N4J4L4M6IQ_W`0\", \"size\": [1280, 720]}, {\"counts\": \"S`k97cW1b0^O9K5K4M2101N0000000010O010O01000O001000O100O001O10O010O0000O2O0O100O10O2O000O2O1OO1000O01O1N1M4L3M4L4I8H6J502NdVQ`0\", \"size\": [1280, 720]}, {\"counts\": \"\\\\`d:5gW16L4M2N3M3M3N2M3N2M3N2N2M4O0O2N0010O10O1O100O000001O10O00010OO1O2N2M2N2M4L3M3M3L4L5K4K9JdVg?\", \"size\": [1280, 720]}, {\"counts\": \"UP[;1jW18I6J5G:L4J7K4M3M4N1O1O3N2N1N20O0O11O1O2O00N011N1O101L2N3N2M4L2N3M3M3L6K5K3K6K4L7Hf^T?\", \"size\": [1280, 720]}, {\"counts\": \"k_];`0PW1e0G6J8D:K6K6J4M3M7J2N2O11O4L1N3N6K1N4K4L2N4L3M3M1N2M3N2L4L4M3M3M3L5K5J6KmVX?\", \"size\": [1280, 720]}, {\"counts\": \"fY^:7eW17L1O0010001O00O01000O10000000000O2OO2O000001N10000000000O11O0001O00001O1O6KO000100N2O00Qh3NPXL3SVg?\", \"size\": [1280, 720]}, {\"counts\": \"dWf;=`W16L5J7H8I7H8I5D=J5K4M3N2O0O3N30O0O3K8H4L=C4K3M3M4L3L4L4L7J5I9@]gU?\", \"size\": [1280, 720]}, {\"counts\": \"kgh;;aW17L7G8I8MOO3M2O1N2N1N2O2M2N4L4L01000001N2N2L4M6J4K5L3L5K4M3L6K4K8GlfP?\", \"size\": [1280, 720]}, {\"counts\": \"k`Q<a0]W15L3M4M2M4N002N20N1OZMmN\\\\lN0_1Q1nT1N2N1N2N2O1N1O2N2M2OPMAbnN=aT1N2M4M2O2N1NeVn>\", \"size\": [1280, 720]}, {\"counts\": \"ZiW<7gW15M4L4K4M2PMWO[lN0e1j0mT1000O10O1O100O100O1N2O2M2N3N3M3L4K31O1o_g2Mf^[9\", \"size\": [1280, 720]}, {\"counts\": \"`gh;613UW1h0[O`0YOeNYjNl1ZU1g0Cf0XOd0C3N3K4N2L5L5K4L5L3OO000O1O1O2TM]mNRNJg1nR1@anN8aQ1DenN8_Q1]OnnN=UQ1XOVoNd0PQ1ROYoNj0UT1H7I6J7In^T?\", \"size\": [1280, 720]}, {\"counts\": \"ShY;9cW18H7J6J6L3L3L6L3J6J7J5J6K5J5L4L4L4H8K6I7K5L4L3L\\\\N_lNPO_S1l0glNTOWS1f0RmNYOjR1f0[mNZOdR1a0bmN^O^R1>gmNAXR1>kmNASR1;UnNBiQ1=]nN@cQ1=anNB_Q1;gnNA[Q1=jnN_OVQ1?onN]OQQ1c0RoNZOoP1g0RoNVOPQ1f0VoNVOkP1h0\\\\oNPOhP1m0X3L5J7HQc[3AWVcL0c^f;\", \"size\": [1280, 720]}, {\"counts\": \"`gT;2[g=OWYB201RV1MoiN004]W1LO4J4N1N3N101N2O001N10001O1O2M3NO001000O2OO0L6L3M3M3L4M3LZgZ?\", \"size\": [1280, 720]}, {\"counts\": \"ih`:7dW18K3L4M2N3I6N3L5M1O2N1O2O0001000001N1O100O1O2N1O002SOViNa0nV1VOXiNh0PW100101N00000001O00001O1O010O001O001O1O1O001N3DW_c?\", \"size\": [1280, 720]}, {\"counts\": \"]Ql:1jW18J5K5L3N3M1O2M3L4M3M3M2N3M2N2N2N3L30001O010O0011N010O001jNjiN`0WV1UOjiNL6l0SV1PO[jNm0]V1O1O1O0001O0EUOaiNk0jV101NJWOWiNh0QW1E;G9Ogf_?\", \"size\": [1280, 720]}, {\"counts\": \"Qj^;1mW15H7_Oa0J5K6L2O2N1M3M4L4M201O1N1000000001O002O0O1O001O3M1O1N3WOoiN_OSV1LaiN:d0DVV16n0L2N`^Y?\", \"size\": [1280, 720]}, {\"counts\": \"YYf;8<2bV1o0E7K3H8J5M4L3N3M3N1O100000001N1O1000mMejNGOb1\\\\U1eNQkNIDW1]U1lNZkNT1mU1K5L2N2N?A3M2N2N2L3NbeU?\", \"size\": [1280, 720]}, {\"counts\": \"_Z\\\\;9aW1f0iNj0L2N3K4N1N3M2O1O1M3O1N2O100O1M3000000O1O100OeNnjNJnT18XkNDiT1KhkN4XT1JkkN4VT1KkkN5UT1KnkN0ST10QlNLPT14RlNJnS16TlNGnS18SlNGmS18UlNGjS15]lNHdS17]lNIdS1MgjNLh16cS1KijNKf18bS1LglN3[S1IglN6cU1N3LnlQ?\", \"size\": [1280, 720]}, {\"counts\": \"bRl:9dW19I5L3L4M1O1O2O2N2M3M3M3M3N0O1101O002OO00O10O01OO100000O100O2N4K100O1000O10O01O01O1O2N1O2N1O2N1O2O0O1O2N1O1O2N2N1M5K5L[ll>\", \"size\": [1280, 720]}, {\"counts\": \"ebi:1mW15L3M3N100O001O001O1O1O100O1O1O010O1O2N10O011O0000O11O1O00O1O100O100O1O1O01000O1O010O1N101N1N3O001O1O001O001O1O001O001O00000000000002M101N2M4K5XORiN6k[`>\", \"size\": [1280, 720]}, {\"counts\": \"]kU=3mW14L1O2N2O1O1O0O2gMGglN:YS1FglN;^U12O1N0O01N2O10O10000O1O0001O01000O010O1M1H:Jmld=\", \"size\": [1280, 720]}, {\"counts\": \"X[i<?`W15K6J:F101O1N2O001N2N100O2N100O100O100O1L3OO1N3M201O1O1O1O0OIgNkiNY1^V1M2O20001M2N3N2N2M3L4N11N100100N2M4N2M2O2O3M4KhmU=\", \"size\": [1280, 720]}, {\"counts\": \"k[f;=_W15L3M4L3L4N3M2M3N3N1N2O1N1N2O2N1O10O10O100O02O0000001O1O1M3NKcNkiN[1^V1O001000JfNgiNZ1XV1fNhiNW1bV1M3N2O100O1N10001O2N2N4M2M2O0O10000O1O2O0000N2M4J500000O1O1O1O1M3N5Ilec=\", \"size\": [1280, 720]}, {\"counts\": \"^ke:`0[W16M2M3M4L3G9N2N2N3N10O01O1O0010O01O00000N3N11000O0100O1O1O1O010O1O1O1O2O0O101O0O3N3N1N2M2O4K6I7I:DSe_?\", \"size\": [1280, 720]}, {\"counts\": \"jYT:i0RW1>D:E<B:I7J5M2M2O1FYMXkNi2fT1[MUkNh2iT18O100O2O0O1001O1O001O1N4jMakNe0dT1QOikNf0YT1UOlkNj0VT1hNWlNV1[U1O1O1N2O1O1N2O1N3N3M1N3N1N2O2M2N3M3M2NdlT`0\", \"size\": [1280, 720]}, {\"counts\": \"Ril9>aW1<D8H=^O<I3L4M2N4M2M4N2M2O1O1O2O01N2NO2O00lMkjN\\\\1VU1`NRkNZ1XU1[NkjNc1nU1K2O1N2N1O1O1N3N001N2O1O1O1O0O2O00001N101N2O1N2O0O2O1N101O2L3N2N2KPmo?\", \"size\": [1280, 720]}, {\"counts\": \"Pad:58<_V1V1]O?D:G8H5L2M3N1N2O0O1O100100O1O1O2N1O2oMRkNl0PU1nN[kNk0gT1nNckNm0aT1fNokNS1aU1N2M3N1N2O0O2O1N3N1O1N2O1N3N2M2N2N2N101O0ORUg?\", \"size\": [1280, 720]}, {\"counts\": \"gQb:`0YW1g0]O:@<H7F:L2J7M2J7M2O1O2O00000O101O01N2O0YN\\\\kN2eT1JbkN2aT1BkkN:ZT1\\\\OQlN?[T1POlkNm0eU1O1N2O1O1N3N1N3N1O1N2O2M3M3NVmo?\", \"size\": [1280, 720]}, {\"counts\": \"WYo95fW18K4L4K<E7J4L5K5K4M2M2O2N1O1O1O10O001O100O1O4L3N9F3M1O3M4L3N2L3N1O1O1N2O1O1O1O1N3N3M2N2M2O1N6K[eX`0\", \"size\": [1280, 720]}, {\"counts\": \"j_k94fW1`0D6K6K6J7H3N4K5M8G:G6J5K3N1O0O1001N1O2N2N1N2O3L4M5J8H3M:F3M3N2M3M2N2N2N1N101O1O001N2O1O2N2M2O2N2M2O2M4L^UV`0\", \"size\": [1280, 720]}, {\"counts\": \"U`U:3lW1<D8H2N4L7I7I=D8G7J8H7J3M1O1O1O01O001N2N3N2M8H;E9G4L2N2N5K3N2M2M3N1O001N101O002N0O2O1O1N2O2M3M2O3K4J_en?\", \"size\": [1280, 720]}, {\"counts\": \"nX^:a0YW1?D9H;E>B6J5L3N2N100O0100O002N2N2O2iM[jNm1[V1A1O2N4L4L4L2N1O1O1O1O1N2O1O001N101O2N1O2N1O1O2M3N1N3N2L4MWei?\", \"size\": [1280, 720]}, {\"counts\": \"UY^:5_W1j0]O9H9H6L4K7K4K4M3L2O100O11O001N3M5K:F9G4L2N1O1O2N2N4L4L1O1O1O001O0O2O1O1O1O2N1N2O2M2O2N2M3M3N\\\\ei?\", \"size\": [1280, 720]}, {\"counts\": \"SX^:;bW17K4L5K5Le0[O9G4M4K5L2N3L2O2O1N0100O0O5L8H9G1N3M4L4M1N1O1N2O1O2N2N2N3M1O1N2O1O00001O00001N100O5L2N1O1O2N2N1N2O1O2M2M]U]?\", \"size\": [1280, 720]}, {\"counts\": \"Xhe:a0[W1j0XO>B7J9H4L3M3M3M101O01O001O1O5J9H8H2M5K6J2N2N002N3M2N1O2N1O1O1O001N1001O01O0O102N4K3N1O1O2M2O1O1N3N2N1N\\\\m[?\", \"size\": [1280, 720]}, {\"counts\": \"aQ]:5eW1:B>H6L5K6G7J7I5L4L4L3N3M2N2N2N3N1O1O10001O0000mMVkNXOO^1lT1TOfkNi0]T1SOgkNk0ZT1QOjkNn0eU1O001O001O002N3M2N2N2N1O3M4K4M2N3L3M2O1Ngmj?\", \"size\": [1280, 720]}, {\"counts\": \"fa];<`W17L1O1O1011N3N0O2M3N2M2O2M3MQfS`0\", \"size\": [1280, 720]}, null, null, null, null, null, {\"counts\": \"gaP:7hW15L1O5J4M2N_OihN;UW1CohNa0kV1_OWiN?jV1_OYiN?VW1O00001M3J7L3MnN0S10cf[a0\", \"size\": [1280, 720]}, {\"counts\": \"jiX98gW12LL^hN2_W1NchN8UW1:N101lhN[OmV1m0NDTiNJhV16XiNLPW1`06LK2L2\\\\OoNXjN?_O3mV1`01OO00O1M114M9YOfhN5UV1GZjN6LJH8VV1JSjN227jU1IkiNON23K0<VV1DTjN4FL0;iV1HciN4WW1N00YOKbiN6^V1JaiN7TW101O0O01N1104K2O1OVmWa0\", \"size\": [1280, 720]}, {\"counts\": \"dR_93iW18F8D=I4M4L3L4O2JoNgiN30NWV1X1L3N3M2N2N2H8J6N2N101O10OO2001OK[MPkNS1=KiT15^kNDaT1:ckNE]T17hkNHXT15lkNJTT14okNKRT13QlNKPT10UlNOmS1LVlN3mS1HWlN7kU1M3N1O1N2N2N2O0O2O0OW]k`0\", \"size\": [1280, 720]}, {\"counts\": \"eR_91iW1;G6CDnhNc0lV1;L3M4K4O1M4L3N3M2O1O1O1N2N2N200O1N200O1000OO`NljN8VU1CSkN7nT1CZkN:fT1C^kN<_T1EdkN:[T1A]jNNY1`0ZT1AnkN>ST1^ORlN`0hU1O2M2O2M2O1N4M2NQ]Pa0\", \"size\": [1280, 720]}, {\"counts\": \"eZj92lW1<D3M5K3L5I7I5M4M2O1M3O1N2M201O1N3OO01O1O101O0O1O1000O2O0O1O002O1ZNniN\\\\1RV1cNQjNZ1QV1eNQjN8NIM>UV1_OTjN6MG1a0oU1_OVjN785WV1DmiN9oV1M3LS]f`0\", \"size\": [1280, 720]}, {\"counts\": \"gR]:9aW1;F7J6K4M3N1M4L3O2L3M4L4J5L4L4L4N2O1K500O010O1N2N2N2N2O1O1OiNSkN\\\\OnT1b0XkNZOfT1g0_kNUO_T1a0nkNcN@?eT1d0`lNZObS1a0blN]O_S1?glN^O\\\\S1=hlNB[S12PmNK`ZW`0\", \"size\": [1280, 720]}, {\"counts\": \"db_:?_W15J5M3L2M4N2M2M3N3N2N2K5L3N2N1O2KbNSjNS1mU1`000O1001O00O010000000001O01OgNSjN>nU1jNRkNn0nT1ROWkNh0TV1M1O2M4L2N3N2L3Nndn?\", \"size\": [1280, 720]}, {\"counts\": \"lRQ;5_W1?I8G8H6M3L4N1N3M2N2N2N2N2N2O1O1O2O0O100N101000eNcjN3^U1KdjN3]U1GjjN8VU1FmjN8TU1FojN9QU1DRkN;RU1_ORkNLRO=nU1A]kN<VV1N3M2N2M4Mgdd?\", \"size\": [1280, 720]}, {\"counts\": \"bcX;3iW17J4M3N1O2N101O2L2N101O0O2O02O001N100O2O2N1O1O3M2M3N2M2O2M2N3M^\\\\c?\", \"size\": [1280, 720]}, null, null, null, null], \"bboxes\": [[280.0, 751.0, 19.0, 43.0], [270.0, 756.0, 25.0, 44.0], [251.0, 757.0, 25.0, 56.0], [244.0, 795.0, 13.0, 43.0], [235.0, 784.0, 24.0, 61.0], [243.0, 792.0, 25.0, 49.0], null, null, [256.0, 842.0, 41.0, 91.0], [236.0, 852.0, 39.0, 113.0], [259.0, 841.0, 59.0, 107.0], [323.0, 797.0, 77.0, 89.0], [358.0, 814.0, 89.0, 99.0], [332.0, 845.0, 69.0, 88.0], [282.0, 839.0, 38.0, 74.0], [261.0, 798.0, 34.0, 72.0], [239.0, 771.0, 30.0, 67.0], [229.0, 737.0, 24.0, 74.0], [247.0, 721.0, 23.0, 50.0], null, null, null, [273.0, 696.0, 18.0, 32.0], null, null, null, null, null, [289.0, 717.0, 19.0, 48.0], null, null, [293.0, 609.0, 41.0, 32.0], null, null, [269.0, 732.0, 21.0, 37.0], null, null, null, null, null, null, null, null, null, null, [294.0, 496.0, 33.0, 42.0], null, [268.0, 465.0, 20.0, 31.0], null, null, null, null, null, null, null, null, null, [254.0, 480.0, 16.0, 19.0], [249.0, 462.0, 23.0, 61.0], [265.0, 496.0, 29.0, 27.0], null, [236.0, 448.0, 21.0, 42.0], [252.0, 464.0, 23.0, 32.0], null, null, [256.0, 463.0, 16.0, 22.0], null, [263.0, 466.0, 21.0, 47.0], [260.0, 467.0, 23.0, 47.0], null, [268.0, 465.0, 21.0, 39.0], null, [303.0, 535.0, 36.0, 45.0], null, [252.0, 457.0, 21.0, 50.0], [246.0, 472.0, 21.0, 41.0], null, [229.0, 498.0, 75.0, 91.0], [226.0, 481.0, 74.0, 128.0], [235.0, 514.0, 54.0, 86.0], [235.0, 528.0, 56.0, 69.0], [240.0, 530.0, 51.0, 64.0], [248.0, 498.0, 48.0, 108.0], [259.0, 548.0, 46.0, 75.0], [255.0, 504.0, 47.0, 87.0], [254.0, 416.0, 62.0, 161.0], [286.0, 492.0, 34.0, 80.0], [285.0, 537.0, 36.0, 44.0], [252.0, 500.0, 66.0, 80.0], [233.0, 452.0, 77.0, 124.0], [245.0, 466.0, 59.0, 140.0], [239.0, 474.0, 65.0, 99.0], [252.0, 498.0, 57.0, 93.0], [272.0, 511.0, 45.0, 71.0], [290.0, 486.0, 42.0, 93.0], [292.0, 466.0, 37.0, 111.0], [267.0, 558.0, 50.0, 18.0], [299.0, 470.0, 32.0, 99.0], [301.0, 497.0, 34.0, 76.0], [308.0, 457.0, 29.0, 121.0], [313.0, 457.0, 97.0, 120.0], [301.0, 410.0, 31.0, 165.0], [289.0, 419.0, 132.0, 155.0], [285.0, 477.0, 42.0, 79.0], [269.0, 508.0, 51.0, 56.0], [278.0, 517.0, 45.0, 64.0], [293.0, 531.0, 35.0, 63.0], [299.0, 517.0, 32.0, 82.0], [291.0, 532.0, 43.0, 92.0], [278.0, 589.0, 60.0, 74.0], [276.0, 593.0, 73.0, 56.0], [337.0, 553.0, 33.0, 94.0], [327.0, 575.0, 55.0, 80.0], [299.0, 577.0, 72.0, 75.0], [273.0, 575.0, 50.0, 66.0], [259.0, 532.0, 47.0, 105.0], [253.0, 531.0, 57.0, 99.0], [272.0, 520.0, 45.0, 104.0], [270.0, 514.0, 40.0, 103.0], [255.0, 532.0, 48.0, 71.0], [252.0, 495.0, 53.0, 109.0], [260.0, 509.0, 51.0, 101.0], [267.0, 524.0, 48.0, 86.0], [267.0, 520.0, 48.0, 84.0], [267.0, 509.0, 58.0, 99.0], [273.0, 511.0, 53.0, 96.0], [266.0, 502.0, 48.0, 92.0], [292.0, 559.0, 15.0, 24.0], null, null, null, null, null, [256.0, 557.0, 19.0, 43.0], [237.0, 552.0, 41.0, 62.0], [242.0, 517.0, 46.0, 95.0], [242.0, 536.0, 42.0, 76.0], [251.0, 552.0, 41.0, 67.0], [266.0, 520.0, 39.0, 103.0], [268.0, 556.0, 43.0, 68.0], [282.0, 551.0, 37.0, 73.0], [288.0, 605.0, 32.0, 33.0], null, null, null, null], \"areas\": [544.0, 700.0, 838.0, 399.0, 1181.0, 894.0, null, null, 2730.0, 3542.0, 5089.0, 4455.0, 4875.0, 3877.0, 2285.0, 1954.0, 1466.0, 1175.0, 682.0, null, null, null, 420.0, null, null, null, null, null, 552.0, null, null, 835.0, null, null, 573.0, null, null, null, null, null, null, null, null, null, null, 580.0, null, 504.0, null, null, null, null, null, null, null, null, null, 203.0, 557.0, 449.0, null, 525.0, 577.0, null, null, 300.0, null, 624.0, 599.0, null, 563.0, null, 1123.0, null, 786.0, 639.0, null, 2437.0, 2900.0, 2239.0, 1961.0, 1793.0, 2869.0, 2179.0, 2218.0, 3607.0, 1569.0, 865.0, 2480.0, 3045.0, 2957.0, 2313.0, 2080.0, 1553.0, 1819.0, 2154.0, 596.0, 1791.0, 1419.0, 669.0, 488.0, 3237.0, 3293.0, 498.0, 1574.0, 1661.0, 1556.0, 1690.0, 2458.0, 2147.0, 1981.0, 544.0, 1938.0, 2465.0, 2245.0, 2757.0, 2526.0, 2547.0, 2369.0, 1865.0, 2532.0, 2376.0, 2094.0, 2036.0, 2475.0, 2416.0, 2372.0, 231.0, null, null, null, null, null, 274.0, 827.0, 2140.0, 1729.0, 1931.0, 2485.0, 1911.0, 1688.0, 623.0, null, null, null, null], \"iscrowd\": 0, \"video_id\": 0, \"height\": 1280, \"width\": 720, \"category_id\": 1985, \"noun_phrase\": \"the right hand\"}, {\"id\": 1042, \"segmentations\": [{\"counts\": \"Yd0h1XV101M4M1N0100E`NXjN_1hU1bNXjN\\\\1jU1eNUjNY1mU1fNUjNV1oU1iNRjNT1_V1L4L4K5Heg3AQYL000SlWk0\", \"size\": [1280, 720]}, {\"counts\": \"kU`1m0oV1:H7J2N3N2L3L3L5O1O0O2O010O00001M2O2O1O00000O1O1O11O000000001O2N002N1O1N4M4K5L2N2M5K4L5I8H7J:D]igh0\", \"size\": [1280, 720]}, {\"counts\": \"ZUk31nW14M1V`20h_M3N2M3L5K4N2N1O2N2N2N2N2M2O2N2N2N1O1N2O1O1N2O1O1O1O0O20O01O010O01O001O01O01O01O01O01OO101O2N1O1N2O1O1O2N1OO1000O3N1N2N2N2N3N0N6K6J5J4M6HaY\\\\e0\", \"size\": [1280, 720]}, {\"counts\": \"Xef44kW12O1N1O2I1dhNG[W17ghNIXW18hhNMRW14nhNMoV17mhNLQW1c0O2N1000001O0N2O1N3L3N2O100O1O100O2N1O001O0010O1O100O10O1O01O001O001O0001O01O00010N2O1O001N2O0O2N2N2O1N2N2O1M4M4L4M4K3M4KQbdd0\", \"size\": [1280, 720]}, {\"counts\": \"Zm_31oW10S`22k_M2N2N2M202M3J5N1N3M3N1N3N2M2O3N0O2N1O2N1O2N1O1N3M2M300O1O1O1O001O01O100O0010O01O000010O000001O100O001N20O0002M2O1N2N2O1O1N3N1O0O2O1N2O1N3M3N1WOohN?`W1Hbice0\", \"size\": [1280, 720]}, {\"counts\": \"nee21lW14M6K6J3M3M5L3M2M4M2M4M1O2N2M2O1N3N1N2O1N3M2O010O1O0100O010O001O0010O01O1O10O01O1O2O0O001O1O2N1O1O001O2N1O1O2N1O1O1N3N4K2^OPiN2PW1LSiN2QW1HTiN5nV1GYiN2jV1MYiN0QPmf0\", \"size\": [1280, 720]}, {\"counts\": \"ZUh21mW16K2N3M3N2N1O2M3M3M4L3M3M3N3M3L3N3M2O10O10M2N2N2N3M3O0O10O01O1O0100O001O10O01O010O2N1O010O00001O1O1O0010O01O001O1O1O001O1O2N1N2O2N100N2N2N4L5SOSiN>ZW1M3M4K\\\\Q^f0\", \"size\": [1280, 720]}, {\"counts\": \"Xll47iW11O2K4I9H7N1001O000O0001N3N2E:N2O3N3L2N1O1O1O1O010N101O1O01O00100O010O001O001O001O000000001O001O1O001O001O1O1O1O1O2N1O2N2N3L4L3N1N6I9G9Fkj`d0\", \"size\": [1280, 720]}, {\"counts\": \"^kY61oW1000000000000000PP51ooJ5K1N2N3M1O1[OBgiN?kV12M4N2M2O2N2O0O2FRO_iNo0aV1TOZiNo0dV1701O001O1O1O001O1O001O001O1O1O1N101O00100N101O010O001O00000001O000001O0001O000000001OO100001O0O101O001O00001O3L2N3L4K4K6J7I5HndTb0\", \"size\": [1280, 720]}, {\"counts\": \"Tcj71oW1000O101N2O0@OPiN3nV10ohN3nV10mhN3SW1LohN4PW1LRiN2mV1ORiN4iV1d0L4N1O2O0O20O01O1O1N200O101M2O001O1O001O1O001N100O101N2O1O001O0O101M101000O1O1O2N1O1O2O0O010O100O1O1000001N2N1O2M5L4K201N106I3@QiNIRW1OWiNMlV1NYiN0SlWa0\", \"size\": [1280, 720]}, {\"counts\": \"YR\\\\89bW16N2L5J5M2M4M3M3N3N001O1N101N2O10O01N2O100O1O001O1O1O001O1O100O001O1O00001O00000010O01O00000000O1O1O1O1O1O2N1O10001O0000001O0O100O100O1O101N1O100O1O2N1O2N1O1O2M4H:J:E6Iie]`0\", \"size\": [1280, 720]}, {\"counts\": \"li_81oW10O101O1O1O2M2M3K5N3M101O001N3N1O010O1O002N1O1N101N2O1O1O0O101N10010N2O1N2O0000001O00001O001O1O10O00001O010O0001O010O001O1O0010O000O10001N1O100O2N100O10000001O000001O001O001O000O2O001O0O1O2O0O2L3N3M4M4H8Jo]c?\", \"size\": [1280, 720]}, {\"counts\": \"kiS92lW12O2K4N3O000O2O1O1O0000001O1N2O1O1N101O001O1O1O1O000O2O00001N1O10001O1O0O2O00010O001O001O001O1O1O00001O00001O1O001O00001O001O00001O01O0O2O000O101O001O00010O000001O1O1OO100O101O0O1O2O1M3M3N2M2N4M2K6J8GUnQ?\", \"size\": [1280, 720]}, {\"counts\": \"kYQ92lW13O001N1J7N3N000O101O001O001O001O1O0O2O000O2O1N2O001O001O0O1010O000001O001O1O00001O0001O01O1O001O0010O01O0000001O00001O001O0O2O001O010O1O000010O000O100000000000000001O1O00000001O00000O1O1O2N1O2N1N3N2N2M2M5K7HX^o>\", \"size\": [1280, 720]}, {\"counts\": \"kYQ92mW11O2O0M4K4O2O0O2O1O000000001O00000O2O001O001O1O0O2O00000O2O000O2O0O10001O001O000000001O001O00001O00001O001O001O1O001O00001O001O00001O001O01O0001O0000000000000001N1O1O1O1O1N2O2N1M4M3M3M3M3L`n[?\", \"size\": [1280, 720]}, {\"counts\": \"SaY81og=0TPF3RXL0_W173M10L8K2O3K5M0003M010O1O2NM4N100O101O00O1000001OO1000001O0O11O1O000010O001N11O01O00001O001O010N2O2N1O001O1OO10010O0001N100001O0O1N2O1O2N1O2N1O2M201O0IkfX`0\", \"size\": [1280, 720]}, {\"counts\": \"]iZ81nW11O2O0O2O00001O0O10004L6J2N1O1N2O1O001O00001O0000O0100001O0000001O0O10000001O00000000001O0010O01O2N1O1O2N001O001O00000000O101O00000O1O1O1O1O1O1O2N1O2M3L5L4Ge^f`0\", \"size\": [1280, 720]}, {\"counts\": \"VRc74;2PW13hhN1WW1<01O2N1O1O2N2O0O1N10100O010O1O1O1000O001000O010O10O010O10O100O001O0010O000000000O1O2O0O2N1O1O2N2N101N2O001N2N4L2N2N10QeTb0\", \"size\": [1280, 720]}, {\"counts\": \"gTg5=`W16M1N4L3N1M3M3O1HnNaiNS1dV13M2O2N1O1O1O00100O1O100O1O1O100O10O01000O1O1O1O2N1O0O101O0O2O001O1O0O2O001N10000O2O000O102M3K5L4M3KijQd0\", \"size\": [1280, 720]}, null, {\"counts\": \"Yd0f1ZV100010O010O10O1O10O10O01O01O0010O0010N2O1O102M1O001O10O01O000O2O1O1O001O1O1O1O1O1O1O1N2O1O001O1O1N2O1O1N2O2M2N4M2M4L4LSZhi0\", \"size\": [1280, 720]}, null, null, {\"counts\": \"h`0m0SW10000YOnhN`0RW1@nhN`0RW1@nhN`0RW17001O0YOmhNa0SW162N1O001O0O3N001O1N101O002N2N1O1O5Fh^Pk0\", \"size\": [1280, 720]}, {\"counts\": \"gQi42mW12N4K3O2M3M2O2N2M2O1N2O1O2N1O001O2N001O1N1N3N2O00001O00010O0000000001O00000000001O010O0O2O1O001O0O2O00001N100O2O0O2O0O2N2O001N2N2N2N1O3L3M3L4LRn`d0\", \"size\": [1280, 720]}, {\"counts\": \"^Sc71oW100001O00OTX12hgN4N3N2M2O1O3L3N1O1N2O1G:K4O10000O1O1O1O100O12M100O1O00001O001O1N2N1O1N3N2N1O3L3M3M3K9E^Tab0\", \"size\": [1280, 720]}, {\"counts\": \"VRd41oW11O000000OUX61ggI5O03M11M3M101O0O2O1N1O2O1N101O1O00001O2M101N100N3O1N1O101O1O001O010O000010O01O00001O01O000001O0000000000O2O001N1O2N1O1O2N001O2N2O1M4J5L4IkeZd0\", \"size\": [1280, 720]}, {\"counts\": \"\\\\`0W1iV100001O0000001O000001O000000000000O1001O0001O0000O2O000O100O101N1N200O101O000O10000O101N1O100O101N101N1O1O2N1O2O1N1000001N2N2N6JSWbi0\", \"size\": [1280, 720]}, {\"counts\": \"R\\\\\\\\11mW12M4M4M4L3M3M2N2O1N2O1N2N2N3N1N2O1O0O2O1O1O1O001O010O0010N10010O01O00001OO2O001M3M3M3O0010OO1O2N2O0O2N2O2M1O100O3N1N1O6Gm[[h0\", \"size\": [1280, 720]}, {\"counts\": \"oa[25<2gV1P1B6K5M3N1O0002N00N3O001O1O1O001O11N2N1N3L3O2M4L3jNZiNl0PW1L5K4K;D^mbh0\", \"size\": [1280, 720]}, {\"counts\": \"\\\\Rc06dW17A?L5I7J8I4O1N2N3N0O2N1O10001N2O2N1N101O01O0O100000000000O1000O2O000O100O101N010O011N100N3O0O1O1O101M101O1N3M2O1N3M2O2M3M2O2L6J:Db^nh0\", \"size\": [1280, 720]}, {\"counts\": \"_4k1UV1O010O1001O1N2O00O11O2N00O100N20001N100O2O00O11O0O1O1O1O1O2N1O2M2O1N2O1N3N1N2M4N2M3N2M4K6J8GTSYj0\", \"size\": [1280, 720]}, null, null, null, {\"counts\": \"ibb12cW11dhN1YV10miN5D=kU1[OejN5C;M7[V1^OgiNU1[V173M00M4N11O1OO1N201O09F4M1O00O12N001N11O01O001N2M4J6KR^Yi0\", \"size\": [1280, 720]}, {\"counts\": \"cf^395HXW1V1RO?C<L10000L3O2O000O101O0001O000001O000O1001O2N1O1O2O1N2N2N1O2O1N1O1O2N1O1O1O2N2N1N3N2N2N3M2N2M3N4L3L3M4L2N3M5HZP^f0\", \"size\": [1280, 720]}, {\"counts\": \"e\\\\l35XW1o0C6K2N1N2O000010OO1000001N100000000000000O100N2O1001O2N0000001N2O1O1O2N3M4K8H6I[[df0\", \"size\": [1280, 720]}, {\"counts\": \"bdm3?ZW1<J5L6J6K9G6K3L2K6K3M4NO012M0000100O000001O0M3L4O1:F2N2N1O1O2N2N2N1O2N1O4L2M4M2N2N2M4L5K6J6H;CjYZf0\", \"size\": [1280, 720]}, {\"counts\": \"Snj57hW16K4N3L2L5L8F;C8J5M2M4M2N1N2O1O00000N2O2O02N1O1O001O1N101O2N1O0O3N2N2M2O2N1O2M3N1O0O101O1N101N2N2O1N2N3M2O1N3N3K5L3MlXoc0\", \"size\": [1280, 720]}, {\"counts\": \"jZk62nW1001O1ONhg8O]`H5S`M0\\\\W11chN0\\\\W11bhN2[W1<O001N10O001O11N2O000000001N101O001N3N1O5K2N1O1OO10000O10O0100O01O01O2N2N101N2M3O0O2N2N3L5L6Gkchb0\", \"size\": [1280, 720]}, {\"counts\": \"mh_82mW13N3M2M2O2M3N0E<N101O1O1O001O010O1000O0010O1O100O10O10O010O101O1O0O2O0000O01O01O001O2N1N101N2N1N3N2M3N3L5Kgf`a0\", \"size\": [1280, 720]}, {\"counts\": \"]\\\\U75hW15L5J3M4N2N2N1O3N1O100O2N1O2N2N4L2N3N0O00100O001O100O00000001O001O00001O01O01N100000001O00001O001O000O1001O0001N1O2O001O0O2N10001N100O2N2M2O2M3M3N2M3L4Ed[na0\", \"size\": [1280, 720]}, {\"counts\": \"TkY61jW16M3M4M1O1O2O001O0O101N100O1O2O000O1O1O100O1O1O10O01000O102N2N1O0N2O2N2N1O3M1O2O0O1O2M3N2MXePd0\", \"size\": [1280, 720]}, {\"counts\": \"bYZ21nW13K;B8L4M2O0O2O01OO1O2N1O1N201M101O1N200O1O1O10000O100O1O10000O1O100O1O1O01000O010000O1000O10000001O000000001N101N101N2O1M4M2N2M4J5M3N2N2N4L4JQgof0\", \"size\": [1280, 720]}, {\"counts\": \"TmS43mW11^hNNTW13ehN9TW1=L2O2L3M3M3L4N2N200O1O1001O1O1O1O00O0100O1O1O1O2N001N3N1O001O1O0O2O1O1N2O1O1N2N2N2O2M101N2N2N2N6IoQoe0\", \"size\": [1280, 720]}, {\"counts\": \"i[Z26dW1N^hN5^W19I6N3N1LYOPiNe0mV19L3N200O2LlN^iNP1bV18N1LgNdiNW1\\\\V162NI`NoiN_1WV12N3O01O3M00M2OdNfiNV1[V1iNeiNW1^V141OHaNoiN_1oU1:CWN_jN>Jm0UV1iNQjNW1QV1eNPjN\\\\1\\\\V1N2M2N2N3M2L4M203L6H5IbTPh0\", \"size\": [1280, 720]}, {\"counts\": \"WQV12_W1e0A=J4L3L4M2O2N101O001O001001N10O001O0010O00010O00O101O01O003M3M5K2N5K4L1O2N002N1N4M2N1O1N3N1O1O3L3M[Vnh0\", \"size\": [1280, 720]}, {\"counts\": \"^k;8fW15I5M3M2N3M3M3N1N201IoN]iNQ1cV1QO[iNo0eV1PO\\\\iNP1jV10M3M201O10000O1N10100O0O2N2M3J501N2O0O2M2101N1O1O1O1O1O1O1O1O1O0100O10J7M2N1O101O1O2M3M2O2N2J6L4^OghN7fW1GQVXi0\", \"size\": [1280, 720]}, {\"counts\": \"ZXX36fW15L4M3M2N3O00N1O04M3L8H2L5O1N2O001OK_iNlN`V1S1ciNlN\\\\V1U1biNmN\\\\V1T1ciNmN]V1[1000O1000000O010JfiNfNYV1Z1600O2O2O1O0O010O3M2N2N101N2N1O1O2H8N2M3M\\\\hjf0\", \"size\": [1280, 720]}, {\"counts\": \"USa61nW15L5I7J4K4M2M4K7I3N2N2N3N1N2N2O1N3L3N2N3M2N3M2N3M3N1N2O1N2O1O1O0O200O0O20O10003M2M100O0010O001O010O0100O2O001N105J4M1N001O001O0O2N1L5J4M4H8O2O0O2O10O01N1O2O1O1O011N1O2M2O2M2O1M3N2N4N1DdhN0Pnoa0\", \"size\": [1280, 720]}, {\"counts\": \"abf71nW12N2O1N2O9H4J7I2M3M5J7J4L2N2N2O1N3M2O1N1O2N2N2M3M3N2L4M3N200O10O00100O2N2O1N2O1O0O001N20000O2F:K5L>ZO_iNWOgV1<`iNDaV17eiNE_V16h0N3M2N2NTnTb0\", \"size\": [1280, 720]}, {\"counts\": \"`cS4<bW13M4L6K3M2N3M1O3N4K1O2O001N10000O101O1O0O100giN_NSV1a1iiNdNVV1g1L3NO1001O002NIRjN[NoU1j11N2O2N100N3N2N001O1O002K4O1M3M203L3L4L3K4L6L6Jk[je0\", \"size\": [1280, 720]}, {\"counts\": \"ddi37hW14M3M102M4M1N2N10000O012N2M01O1N11O1O010O010O0000107I1N1O0001O01O10O0101OO02O00O1O1O1O1O1O1O1O1N1O2N2O1N3M4GabUf0\", \"size\": [1280, 720]}, {\"counts\": \"W^^33eW1;M8G5K=D3L2O1N2O1N2O1O001O0O101N11O00000000001O2N0010O01O1O0100O01O1O100O1O001O0000100O1O0010000O00100O1O4Kb0^O2N3M4Khh[f0\", \"size\": [1280, 720]}, {\"counts\": \"lXf16eW1d0\\\\O<F6L4L3L3N2N3M1N2N2N2M2O2N2N2N2O2O0O10000000001N10000001O000O10000O101N10001O00002N2N2N1001O02M3N4K2N2O3L3M3M3M00014J;F3M1N2N5K3M5JXfhg0\", \"size\": [1280, 720]}, {\"counts\": \"omX21dW1>H8I6K5K4L3N3N102M6K3N0O1O100O100O1oiNVNkU1Q2O4K4M2N2M2OO1O1N3M2N2O3K5J8H9D8G;Db0@ia[h0\", \"size\": [1280, 720]}, {\"counts\": \"i[o2515^W1:L1O2N2N2M3O1O2N2N2M4M3L2N1O2N100000001N11O0000O101O01O0O11O000001O00G^NSjNc1lU1_NSjNa1mU1_NSjN`1nU1`NRjN`1nU1`NSjN_1nU1_NSjN`1nU1`NRjN_1YV1N2O1N3L1M4M3O2N2N4L3\\\\OhhN<_W1N3FVdnf0\", \"size\": [1280, 720]}, {\"counts\": \"d[n02lW15J6K4M7C8N3M2O2M3N7H100O100O10O02O0O10000000O010O10O10O1000000000000O100O3M2N3M3M4M3L5L2L2N3HhhNE[W17<K\\\\TWi0\", \"size\": [1280, 720]}, null, null, {\"counts\": \"\\\\8U1kV1000O2O00010O001O2N0000O11O1N2O100O1O1O001O1O1O1O1O1O010O00000000000001O000O101O00000O20O100O00100O010O010O01O002N3N0O1O2N2M3N2N3M2N3L4M1N2O0O2O2M4F^hNOnW1LneTi0\", \"size\": [1280, 720]}, {\"counts\": \"inY22mW12O0O2N100O1O2O1O6I3M2O0O2N2O1N2N1N3N4L3M1N2O0O2O1N2O0O2N2O1O100O10000000000001O000001O0000O1000001N10001O0O1000000O2O000O2O001N1000001N10O10O100[O\\\\iNNcV12`iNL`V12ciNM]V11eiNN\\\\V11eiNO[V10fiNO[V11eiNO[V10giNOYV11giNOYV11giNOYV11giNNZV11giNOXV11iiNOXV10hiNOYV12fiNNZV12fiNM[V13eiNM[V12fiNNZV11giNOYV11hiNNXV12iiNMWV12kiNMUV12miNMSV12n0N4KSZne0\", \"size\": [1280, 720]}, {\"counts\": \"ghZ15dW19M2L6J3L4M3O1O101N3M2N2N1O101N001O10O00100000O01N200O1N3M2HXiNWOhV1i09N1O3M2J7IYP[i0\", \"size\": [1280, 720]}, {\"counts\": \"V8]1cV1010O00000O010001O00O100001O01OO010O2O000O010000001O01O01O1O1N1O2YOViNGn^jj0\", \"size\": [1280, 720]}, {\"counts\": \"bcS7746PW1a0L3N2N8H5L5J3N3L7J4K4L8I1O001N10000000O1N2000O1000001OO2O00000O1O11O00000010N100011N2O1N01O0010O10O001N1O2N101N101O001N10000O2O1N1O2N100O1O2N1O1O2N2M3M2N3N2N1N2O2L4M2O3L3M4K8H8Iik_a0\", \"size\": [1280, 720]}, {\"counts\": \"PX\\\\92eW1OchN1\\\\W11dhNNZW16dhNJVW1c0M3L4O2L3O1N2N2L5M202L1O2N11000O1O010N2O0O2N2O0001O0001O0001O0O020O0O1001N1010O01O1O00001O01O01O1OhNSjN>nU1@UjN?jU1AXjN=iU1BZjN;fU1E\\\\jN9eU1E^jN9cU1F^jN9cU1FajN5aU1IdjN2^U1MijNG^U16X1L3MbZU`0\", \"size\": [1280, 720]}, {\"counts\": \"g_b96jW1Lmg=`0dWB5N1N2N1O1O010N2N2N2O2M3N1O100O1O010O001O01O010O01O1O010O10O0100O1O100O010O10O1000O1000O0100O1000O01O010O01000O001O0010O1N2ROhiN3XV1MkiNNXV11kiNKWV14liNHVV17niNBVV1=g00O0100O101N1O101M2Olio>\", \"size\": [1280, 720]}, {\"counts\": \"nfl71oW11N2O000O3N6I2N1O1O2O1O001N2O000O2O000O10000000000000001O0000001O001O1O00001O001O00000000000000O1000O100001O00O2O1O1O0000O2O0O2N1O1O12N3N0O00J7L3L4O2N1N2O2O10O2OK6IWao`0\", \"size\": [1280, 720]}, {\"counts\": \"d^i31jW17N1O1O1O0O2O0O101O2ghNBkV1j0000000O101O0O01000000000O101OO100000O2O0000001O0O110O001N101O001O00001N11O01O001N101O001O0000000001N100001O0001O0000010O001O001O002N001O1O1O1O001O001O01O01O1O1O001O1M2N3O0O2O00O0101N2O0O2I7J6KVaSd0\", \"size\": [1280, 720]}, {\"counts\": \"gfi14fW18H6I8L3N3N1O1O1O2N1O2O000O2O001N101N102N4L4K2O000000000000O01000O1O1O1O101N1N200O1O2M2O2M3M3L4M3L5J9IPZYh0\", \"size\": [1280, 720]}, {\"counts\": \"n4o1QV1000010O2O1N0000O11O10O01O001O001O001O010O0CniNlNRV1l0WjNTOiU1i0YjNWOhU1g0YjNYOhU1e0YjN\\\\OgU1c0ZjN[OhU1d0YjNZOiU1c0ZjN[OhU1d0j0@ghN32JXW10ihN41KVW1d00O1O1O2N2M101M4J4LXh33jYUj0\", \"size\": [1280, 720]}, {\"counts\": \"RnT76gW15M3K6K4M2M3N10N2OZiNVOWV1k0iiNUOUV1o0hiNROVV1S1giNmNXV1U1hiNjNWV1X1hiNhNWV1Y162M3O2N2N2N1O00001O0O2N1N2J6GRN`jNP2UU1b0O2M2O12N2O1N2N100O2O001N101N100O2N1O1O1N12O001NO2O1O1O1O1O1O1O1O010O1O2N100O1O002N2M2O1O1O2M5I6H=_O:L4M4N2N2M11O1O14Kcc^a0\", \"size\": [1280, 720]}, {\"counts\": \"V^R77fW13L4L5M7I4L3L3N2O1L5F9POeNUkNb1hT1l0O10O1000000O2N2N2N2N2N2O2M2N3M2N1O1O100O1O1O001O010O010O010O0100O0010O010O01O000010O000mNPjN7QV1EUjN7lU1HWjN3lU1LXjNNkU11XjNLjU13XjNJiU16YjNFjU19o0O00010O00001O001O1O1N2N2N2O2J`dma0\", \"size\": [1280, 720]}, {\"counts\": \"ml`62nW13L9F5K200O3M1O1N3N2O1NO10O20000O100O100O0O2O010O1O1O0000001O00001O0000001O001O001N10001O1N2N2N2O100O11O00001N102N2N1OO100O01000000O010O1O00100O001O0\\\\OdiND^V1:fiNC[V1<hiN\\\\O]V1c0a0O1O1O00100O10O0100O1O100O010O1O010O1O1O1O3L5Lfdca0\", \"size\": [1280, 720]}, {\"counts\": \"mdV31oW10O2O0O1O0O2O101O0O1O1N200O101N010000000O2N1O101O1O0O00101O9F4M0O0010000O10000O101O0O100O100O100O1O100O1O100O10000O1O100O10O0100O10O100O100O1O100O1O1O1O1O101O0O2O0O101N10001O0O101O001O1O0O2O001O0O1000000O1O100O1O1O1O1O001N2O100O1000000000O010000O10000O100O100O1O100O2O0O100O1N3Nn[^c0\", \"size\": [1280, 720]}, {\"counts\": \"dVn01lW18H5M101N2M2M3N2N3N1O1O1O1O2N3M1O2O1N2N3N3L2O0O10O10O1000O1000O100000O1O1N2N2N3M2L4N2N2N3M4L3LYjYi0\", \"size\": [1280, 720]}, {\"counts\": \"X9>bW10000000001N10001N100O100O2O0O101O1N101Nc^Yk0\", \"size\": [1280, 720]}, {\"counts\": \"Slb07eW16J5J7M1N3N4L3M2M3N1O1O1O1O1O1O2O0O100N2O2N1O2O000O2O001N10000O2O0O2O2N1N2O1N2O1O1O1N2O1N2O2N1O1O0O2O1ON1010ojN[MgT1e2XkN]MgT1d2WkN^MhT1c2VkN^MjT1b2VkN_MiT1b2UkNaMiT1`2VkN`MjT1`2VkNaMiT1_2WkNaMjT1\\\\2YkN_MlT1]2=O2O01O10O1O10O0100O1O10O01N201N102M3L4L3N3N0O2N3L2O4L4L4K5K4M9VOhhN9cW1FVcfg0\", \"size\": [1280, 720]}, {\"counts\": \"bVh88cW17K8I3N1N3M5K5J3M1N2K7F:M3N1N3O0101N10O01O102N2N1N100O1N3O0O1O1O001O1N1010O10O00010O00100000O100O010O01O000010OO1O101M3XOeiNI\\\\V15iiNC]V1;d0O2O1N2O1O2O0O2M5KZka`0\", \"size\": [1280, 720]}, {\"counts\": \"b_X92UW10biN1\\\\V11ciNM^V15e01lhNJ]V18aiNJ]V19aiNG^V19ciNF]V1:eiNE[V1a0>6J3M2N1N20O01O1O1O000O2O1O1O1O001O000000001O0000001O1O010O1O100O0010000000O010O1O100O0010O001O01O10O01O010N2O001O010YOdiNF_V19e0O001O1O0100000O3Kljh?\", \"size\": [1280, 720]}, {\"counts\": \"]_i82nW12M3M3N8H100O1N3M3N2M2O1N2O1O1O1O0000000O101O001O0O10000O100O2N1000000O2O000O2O00000000001O000000000000000001N101O00001N101O000O2N1O2O1N1O1O2O0O2N101N2N1O2M3M3N1N3N1O2M3N6JZhm?\", \"size\": [1280, 720]}, {\"counts\": \"]ga51mW14L2O2O1O2M2O1O2N1O1N3N1O2N2N2O0O2M2O001O1N101O1O1N2O1O1O1O1N10001O000000001O000O100000001O0001O000O2O00001O001O001O1N2O1O0O2O1N2O1N2O1N2N1O2N2M3O0O101N10001N100O200O1O00000O10001O00000O200O0O4L3MP`fb0\", \"size\": [1280, 720]}, {\"counts\": \"nfl21oW100001O1N2N1\\\\hNK]W1553bhNHQW1;mhNESW1?52M101N3M101M6LN1100O4L3M2N2N2O1N1O3M2O0O10000O100000000000O101N11O001OO2N101O1O1O0O2O1O2N2N1O1O1O1N2O001O1O1O001O1O001O1M2K6N1O101N100O10000000001N10001O1O10O01O1O3Mehae0\", \"size\": [1280, 720]}, {\"counts\": \"a6b01DVW1c0N4M3N2N1O2M2O0O2N2N2N102N2N1O100O1O1N3N001N2O1O1O10O0100O00100OO1FRjNaNPV1U1RjNfN24mU1V1WjNkNiU1V1SjNlNoU1S1QjNmNQV1Q1RjNmNoU1V1miNiNUV1T1liNlNXV1l0miNROWV1k0jiNSOXV1h0d0L4M5L3L3N1O1O1O1MeXPj0\", \"size\": [1280, 720]}, {\"counts\": \"id]25jW1110O0N3K6_hNGWW1i0G1N2O2N1O101N1O1O1O100O2O0O1O2O0O2N101O0O3N2M2O001N2O1O1N3N1O1O1O00001N2O2N1N4M2N3M3M1O1O1N3N5K1N3N3M3M1O2N1ON2M3M31N2ekNgLmS1Z3QlNhLnS1X3QlNjLnS1V3RlNlLlS1T3TlNlLlS1S3UlNnLjS1R3VlNnLjS1o2ZlNmLiS1Q3[lNjLhS1U3b0O010O101O0000001O00001N1O2N101O2M5I:G9XOTjNnNSV1k0miNVOUV1ARjNj0KMlW1XORike0\", \"size\": [1280, 720]}, {\"counts\": \"loa81oW10kW12RhN2N8G3L;G2M2O1O1N2O100O1OO100001O010O000001O01O1O001N110O0010O01O10O10000O01000O100O010O101N001O1O1O100O100O010N2O1O1N2O1O1O1O1O1O1O10O02O1N2M2O3M2N1N3N101MYa[`0\", \"size\": [1280, 720]}, {\"counts\": \"`Pe41oW15J2N1O100N2N3M2O2N1O100O1O1O1O1O3N0O001O2O0O1O1O1O00100O001O0010O01O00010O1O1N2O001O001O1O001O10O001O00100O001O100O100O10000O100O1O100O1O100O101N1000000O10000O100O1O100O1O1O1O2N1O1000001M3N2O0O1O10001O0N3N2O1N3MeXYc0\", \"size\": [1280, 720]}, {\"counts\": \"mSg34kW13M3M2O1M3N1O2N3L3O1N2M3N2O000O1N1O2000000O2O0000000O1000000000000010O000000000O10000000010O0000001O1N2O1O1N2O3M3L2O3L2N2LW\\\\oe0\", \"size\": [1280, 720]}, {\"counts\": \"Q\\\\i22^W1a0J6L5J5G8M4O1N102N1O1N2O1O1O000001M3N3M2N1O1N2O2M101O1O2M7I6K4J7EiTPh0\", \"size\": [1280, 720]}, {\"counts\": \"o?g0YW1001O1O00O1O2O001O0O001O10001O1N2O1N1GchNN_W1OUXYk0\", \"size\": [1280, 720]}, {\"counts\": \"f;d1\\\\V1001O1O0100O1N2M3L3J7M3L4M4M2N3M2N3Mkk[k0\", \"size\": [1280, 720]}, {\"counts\": \"dU93iW1;G5L2M201N2N3N1O1N3N001O1O1O2O1N100O2N10O010N2O1O1N2M3N2O001O1O1O0O4M1O0100O11M4CehNN_W1MehN0gW1N2NcaRj0\", \"size\": [1280, 720]}, {\"counts\": \"XUi24jW12M3L3L6F:H8H5O011O3N2N3M100O2O0O1O2O0O1O101N101N2O00O00100O10O0100O100O1O010O1O010O10O01O1O1O1O1O1O2OO0100O1O2N1O2N1O2M4A>^OhhN6eW1L201O1KVTgf0\", \"size\": [1280, 720]}, {\"counts\": \"blU62mW11kW10ShN3[hNMWW1d0K2L4N2N3L02O0200O2N1N101O00000O2O2N1N0100001O001N101O001O0O2O0O20O01O0O10O100O1O10O2O0001O000000001O0O101O2N1O1O001O1O1O2N3L2O1O2N1O1O2M3N1O2N7I5J3N4Jmjhb0\", \"size\": [1280, 720]}, {\"counts\": \"T]b63c0NeV14PiNL03mV13RiNJ14jV14]iNMcV17WiNKhV17TiNNiV1h0LCWiNHeV1k0O003NO000[O^iNLbV1j0O001O1O1O10O00000001O0000001O01O000000000001O0O11O0000001O000O010000000O10001O00000O2O001N2N2N2N2M4N3L4K7K4L3NURjb0\", \"size\": [1280, 720]}, {\"counts\": \"]nj32mW11002NB944L3N001O00001O000O2O0O100O110O001O00001O01O0001O001O01O01O001OO2O0O11O01O00000001O01O1O1O1O00010O1O000010O000001O000000000010O01OO101O00010O01O01O01O000000001O001O001O1O001O00001O00001O000000002N1O1O1O1O2N1O1NTaoc0\", \"size\": [1280, 720]}, {\"counts\": \"]dm11ng=2P`CNbW6?ogI4L101O002N2N2N001N110O01O01N3OO010O01O000010O000001O010O01O00001O001O01O0001O1O010O0O1010O00000000010O0001O001O0001O01O00001O001O1O001N1010O01O0001O01O001O1O1O1O001O00000O2N2O1N2O1O002M2N3N00obfe0\", \"size\": [1280, 720]}, {\"counts\": \"Yea2:^W1;L2O1N2O00101N5K1O10O01O1O1O001O1O00100O01O010O01O001O010O1O1O001O10O001N11O0010O01O00000001N1000000010O0000001O00001O000101N2N2O1N1O1O0001O12L2O2N2N2N2M3N2M2N2N1O2G9MiQXf0\", \"size\": [1280, 720]}, {\"counts\": \"iPR3172]W1;K6L4M2N1O1O2N001O2N1N101O001O1O10O01O1O001O0O2O001O1O1O1O001O001O01O01O00100O002O0O001O000001O00001O010O01O000011N4L001O1N2O2M2O1N2O1O1O1O1O0O20O010O01O00001O000010O01O1N111N2N3L3M4L3N1N5JmmYe0\", \"size\": [1280, 720]}, {\"counts\": \"hjn38hW10O2O1N2O100O0O2DCohNa0oV19O2N10001O1O00001O010O1O0000001O1O1O001O001O00010O001O1O1O010O1O1O10O1O1O00010O00001O001O2N1O1N2O1O001O001N11O0001O001N101N101O1N2O01O01M2M3O2O03NIdhNH]W17601N1N3N1Omddd0\", \"size\": [1280, 720]}, {\"counts\": \"ZUU41nW12N3N2M2O2M2M4J5K6J6L4N3L2N3M2O2M2M201O1O0010O0001O01O1N101O1O00100O100000000O010000O01O1O1O1O1O001OO2O01O1O1O0O2N2N101N2N2M4L4K5L3L5L4J^bXe0\", \"size\": [1280, 720]}, {\"counts\": \"\\\\fQ23gW1=G8H>D5J2M5L4L5M1N2O0O2N1O0O10001N10001O1O0100O10O00010O2O0O010O01O2N1O1N2O1O3L3N1O1O2N2N2N2N2N2N1N3N2N2N1O2O1N2N2N2N3N1N2O1N1O1O2M2N7GPP\\\\g0\", \"size\": [1280, 720]}, {\"counts\": \"n`:1nW13L4B=J5O10001O1O0O2O1N2O00000000001O0O10001N2N1N2N3N1000005J2O000010O0101O0O00010O1O1O1O1O01O01O00O1N2N2N2N2O2N1O101N3N1N2O001O5K2N4L5K3M8G6K3LdVnh0\", \"size\": [1280, 720]}, {\"counts\": \"a:o0QW11O0000001O000001O01O0000001O1O1O010O1O001O101N1O1N20O00000001O01N10000001O0001O001O1N3N1O1O3M4@ohNIZW1MihN1ZW1JmhN1bW1ORdRj0\", \"size\": [1280, 720]}, {\"counts\": \"mae01mW14L3M5K4M4L5L1N2O2M2OO0101O0O102M2O2M1O011N100O11O000001O01O1O001O001O00001O1O001N3M2N2M3N2K8C]^bi0\", \"size\": [1280, 720]}, {\"counts\": \"Ykk03kW14L9G2N7J01M21O2O3J30O00TiNTOfV1l0XiNWOfV1S1O0O2O0O10000O10000001O1O001O001O00001O0O2O001O1N2O0O2N2K6K4JQm_i0\", \"size\": [1280, 720]}, {\"counts\": \"i;a1_V100O1O2N1O10000O1O10000O100O10000O100O100O1O100O100O1O1O100O101N101N1O2O0O1O2N1O2N2N2N3Mn[[j0\", \"size\": [1280, 720]}, null, {\"counts\": \"Zkk26cW19M1N3N2M2L4M3O1O10001O00001O001O001O0O10001O0O101O0O10001N10001OO10000O10000000000000000000001O00O100000O2O000O2O0O1O2N1O1O2O0O1O1N200O1O1O2N1000000O1O100O2N2N2L4MY]Pf0\", \"size\": [1280, 720]}, {\"counts\": \"d`[21mW16K6J7I2O0000000O1000001O00000O2O00001O1O1O001O000000001OO100000000O1O1O1O2O0000000000001N10001O01O00001N10001O00001N100000O11O0000000000O10000O101N2N2O1N2O2M6I4M2MUg`f0\", \"size\": [1280, 720]}, {\"counts\": \"j<k0UW1000000000000000000O1O101O0000000010O010O0O2O001O10O01O010O000000O1O1000001O0O1M300000001O01O0010IhhNDXW1<701M2N3N3LWX1O[jii0\", \"size\": [1280, 720]}, {\"counts\": \"o:V1jV1000001O00000000010O0001O10O0000100O1M3N3N2N3O2H<^OaTSk0\", \"size\": [1280, 720]}, {\"counts\": \"\\\\:[1eV100000000O1000001N10000O101N2N2M2O2M5J_eZk0\", \"size\": [1280, 720]}, null, null, {\"counts\": \"]kR22lW13ROOkiN1UV12`iNND0lV17YiN3fV1d0J6NG]iNZOcV1P12O1ON3N2M3NTOciN7[V1[OeiN7<3nU1HeiN6=1lU1b0iiNATV1W1O1001O0000002N2N00@\\\\NdjNe1ZU1]NcjNe1\\\\U1]NajNe1^U1]N`jNc1`U1_N\\\\jNd1cU1]N\\\\jNd1cU1]N]jNc1aU1^N`jNb1TU1SNVkN:De1SU1TNZkNW2eT1jM[kNV2cT1kM^kNT2bT1kM_kNT2aT1mM_kNS2aT1mM_kN1Ai1QU1VN^kNODi1oT1XN^kNNCi1PU1YN^kNKDl1oT1WNekN`1YObNRU1NekN`1YObNRU1O]kNME`1LdNRU1O[kN0I8Hc03VOQU1O\\\\kNNJV1MmNnT1N[kNNLU1LnNoT1M[kNOLS1LPOSV1n0PjNoNQV17biN`0nV1_OUiN>nV1^OXiN`0VW1^OfhN8ZW1HghN8XW17BB[iN<YW1Ibecg0\", \"size\": [1280, 720]}, {\"counts\": \"m[l13bW1d0ZOZO[iNl0bV1ZOXiNl0bV1;L4M2N20O0N22I8M2N2N2N101M2002L3N1O11N2O000O100N2N2N4M5L3L100O01O1O001O01OO00012N1O1O100O1O2N1O1N1O1O2N4M2M2O3M2O1N2N2N1H9I8K4M3M4I7I7Lae]g0\", \"size\": [1280, 720]}, {\"counts\": \"Xim43jW15J4@LohN9lV1a0L3L4O1O9G9G101O1N100000000O100000000O10001O00000O10O10O10000O0100O10000O1O100O2N1O1O1O2N1N3N1N3N1O2N2N3K4L4M4L4L3N3K6GgWfd0\", \"size\": [1280, 720]}, {\"counts\": \"]_Y11mW14L3M3^hNHXW1j0E3N2N2N1O2M5M3L4L3M2N5K1O2N1O2N100O1O1O2N100O10001O0O1000002N2N1N101O0000000000001O000000000000O101O0O10001O001O0O2O0O2N10001N2N2N101O1N2O0O2O1N2L4K6G:H8H?]Okhbg0\", \"size\": [1280, 720]}, {\"counts\": \"co\\\\3`0]W14L5M6J9ViNWOlU1b1M1N101N101O000O2O3M2N1O0O2fjNaMQU1_28003MO010O1N2N110001O0O01O100000000O2O000O100O100O1O2N100O2O0O100O2N1O1O2N2M2N3M3M3L4M2N4L3L4L4L7F`hPf0\", \"size\": [1280, 720]}, {\"counts\": \"YaQ51oW10O2M2M4L3N3J7H6I8J7L101O2N1O2O7I3M7H1000O11O000000O10001O000O100O100O10000O1000000O101N1O1O2M2N2N2O2M2N3N2N1O3M2O1N2M5J<A_odd0\", \"size\": [1280, 720]}, {\"counts\": \"PXX12oW1OP`20m_M4M3O0ehN4cV1LViNLL;hV1MXiN?bV1g0I2N2N2L6L2N2M3O1O0O2N2O1O2N1O0O2O1N2O004K2OO001N200OkjN]MoT1c2QkN]MoT1b2RkN^MmT1c2SkN]MmT1d2RkN\\\\MnT1d2QkN]MnT1e2ojN]MQU1b2TkNZMlT1e28002kjNYMlT1o2O0000O0KSkNYMmT1g2TkNXMlT1h2TkNXMlT1g27N2001N4M2NO2N1O100O1O2N1O1010O2NO1O2N10001N1O2N1O1O1O101N1O2N1O2O1M2O2N1O2N1N3N2M3N2M3M4M2L5L5K3K8EW`hf0\", \"size\": [1280, 720]}, null, null, null, {\"counts\": \"d6e1[V10M4M3N2O0O101N1O2O1N101O1N101O001O1N101O00001N100000001O0O1000001O1O1OO001000000O1O100O01000O001O1O1O1O1O1O1O1O1O1O1O1N2M3N2N2M4L3O2A>J8I9^OSR[i0\", \"size\": [1280, 720]}, null, null, {\"counts\": \"PXo22jW14L5M2N3M2N2N2N3M2N2N2M3N4K6J7J4L3N2N1O2N2O1N2N3M8H7I1O100O1O100O1M3N2O100O100O2OO10000000001O1O0O10000O00100O100O1O010O10000O1O1O100O102[MljNZ2cU1J4M1H7O2M2N3M4L2O1N3N2M3M2O1N6K3K7I6K2O6J2M02N11Oko]e0\", \"size\": [1280, 720]}, {\"counts\": \"fgY42jW17M<C4TiN_ORV1d0liN\\\\OQV1h0liNZORV1i0jiNZOTV1i0kiNWOSV1\\\\1K5M3K5N2O2M2O2M2O001N2N2O0O2L42N1O00O1O1O001O1O010O1O001O1O1O0011O00O010O02O2N3N3LO01O2N3O0OO01O1O00010cMhjNQ2ZU1lMkjNP2dU1TNRjNc1WV1NN1103ZNQjNV1dV1G4L3M3Jd`Qe0\", \"size\": [1280, 720]}, {\"counts\": \"l_X41mW15J5L5M4J<E6J2O8G5K3M1N2O1O1O1N2O2M3N2M2N101N2O1O1O001O1O1N20001N1O10000000000O2N100O010O1O10O010O0100O100O101N01000O1O1N2000000001O0O01001N2O000O100O10000O10O01O1O001O1O1O1M4N1O1N2M3L5L4O0O1O0O3N2N101N2N3N:VOjYhc0\", \"size\": [1280, 720]}, {\"counts\": \"i7a1_V10L5G8M3M3K5M3J6L5K3M4M3I7VOj0L5M10100O101OO100O1001O000001OO100001O001O00010O001O001O1O0O100O3O0O010N2O1O1O1O001O0O1010O01O2M5K4N3L7I4J7J7J5K6J4L4L`0_O7hN`iNd0QW1M5J5JZoPi0\", \"size\": [1280, 720]}, {\"counts\": \"VXg12iW1;I9F3M3N2N9E5M4K5L2M2O2M3N1O1N2O2N2M8H2O1O2N1N2O2N1O1O2N2M2O2N1N3N1O1O1O1O1O2N1O1N3O0O10000O002M201O001O0O100000O2O00O2O01O0O2O00000O1N2O1O2N101N1O2N101N1O3M2N1O2N1O101N2O0O1O2O1L4M3M2N2N2O1N3N2N2N2O0O3M3M2M4L3M3O3N0I6N3XOUiN6QW1F]iNIjV12WYne0\", \"size\": [1280, 720]}, {\"counts\": \"\\\\hQ3:]W1=fhN\\\\OlV1S1H6K3M4M2N2N2N3L3O2N2N2M2N2O0O2O0O2N1O2O0O2O1N1O2N1O101N1O1O101N100O101N100O2O0O101N101N101N100O2O0O2N10000M3M3N2N3O0O1000001N1O1N201N101O000000000000010O1O2N01O0O:G3M2N1O1N1001N1000000001N1000000O01O10O100O1O1UOPlNeMQT1X2UlNdMlS1[2o0M1O2O0O101L4K7SNRjNV1fV1D:H9H9DbhYd0\", \"size\": [1280, 720]}, {\"counts\": \"b5X4hS1000000010O0000000001O00001O001O1O011N1O001O100O1O1O00010O010O1O0000010O00010O01O01O000O2N2O1N1eNjkNkNXT1R1lkNlNUT1S1lkNlNVT1P1okNmNUT1m0RlNmNRT1n0UlNkNPT1h0dlNlNaS1KSkN`0NFe1LhS15Scli0\", \"size\": [1280, 720]}, null, null, null, null, null, null, null, {\"counts\": \"Q=n2RU1000006J01O01O00001O0O2O1O001O2N1O2N2N2M;F4Lc0\\\\O6K4L3L4M3M2M4L3M5K3M\\\\Pij0\", \"size\": [1280, 720]}, {\"counts\": \"dmn16hW13M4L3M3M7J6J5K1N2O101O1O1N2N2O003M4K10O01O1O1O1M2O1O2N2O0N3N1O2N1O2O0M4N2M201N1O101O1O00001O1O01O01000O100O1000O01000O1001O0O10O1000000O1000O10001O000000000O1000000000001O0O1000000O10O100001O001N10002N1N3N1O1O1N6K3L2O3M1O1N3N2M2O5cN_iNP1kV1N4L3L2O000N2O2O0O100O2NO1D;L43K5L3N2O2N1O3L5I]Sfd0\", \"size\": [1280, 720]}, {\"counts\": \"U\\\\_58aW1<D8G9K5F9H7F9M6L5I6I8M2N2L3N3M3M2N2N2O1N3N2O2M2O0O1O3N4K2M2O1O100N2O100000I[lNRLeS1o3[lNRLdS1n3\\\\lNSLbS1n3^lNRLbS1l3clNoK_S1P49O2N2N1O2N10N1N3O01O1O1N2O1N2O1O1O1O1O2N1N2N2M3N2N3M3N2M3M2M3M4L5K3N3M3N2N2M4J6J6J8I4N3L5I6B`0HYnRc0\", \"size\": [1280, 720]}], \"bboxes\": [[0.0, 636.0, 22.0, 69.0], [38.0, 676.0, 48.0, 76.0], [98.0, 678.0, 74.0, 62.0], [120.0, 665.0, 71.0, 60.0], [89.0, 675.0, 77.0, 66.0], [68.0, 686.0, 66.0, 70.0], [70.0, 670.0, 75.0, 74.0], [125.0, 628.0, 69.0, 65.0], [161.0, 595.0, 94.0, 62.0], [200.0, 576.0, 79.0, 68.0], [214.0, 560.0, 85.0, 66.0], [217.0, 557.0, 103.0, 57.0], [233.0, 555.0, 101.0, 53.0], [231.0, 556.0, 105.0, 52.0], [231.0, 555.0, 95.0, 46.0], [212.0, 547.0, 91.0, 42.0], [213.0, 551.0, 79.0, 38.0], [194.0, 582.0, 61.0, 50.0], [146.0, 647.0, 60.0, 64.0], null, [0.0, 649.0, 60.0, 63.0], null, null, [0.0, 536.0, 28.0, 29.0], [122.0, 555.0, 72.0, 45.0], [194.0, 607.0, 51.0, 50.0], [118.0, 574.0, 81.0, 40.0], [0.0, 524.0, 65.0, 42.0], [35.0, 624.0, 61.0, 50.0], [60.0, 562.0, 30.0, 60.0], [15.0, 290.0, 66.0, 70.0], [0.0, 141.0, 47.0, 64.0], null, null, null, [40.0, 537.0, 32.0, 72.0], [88.0, 698.0, 57.0, 77.0], [99.0, 635.0, 41.0, 54.0], [100.0, 649.0, 48.0, 82.0], [149.0, 695.0, 59.0, 75.0], [175.0, 592.0, 64.0, 67.0], [217.0, 526.0, 54.0, 55.0], [183.0, 638.0, 77.0, 56.0], [161.0, 589.0, 46.0, 32.0], [59.0, 524.0, 72.0, 57.0], [105.0, 663.0, 52.0, 58.0], [59.0, 582.0, 46.0, 72.0], [30.0, 516.0, 51.0, 61.0], [9.0, 566.0, 64.0, 73.0], [83.0, 491.0, 52.0, 54.0], [167.0, 296.0, 93.0, 94.0], [197.0, 285.0, 58.0, 89.0], [105.0, 352.0, 56.0, 72.0], [97.0, 403.0, 55.0, 58.0], [88.0, 440.0, 59.0, 64.0], [43.0, 500.0, 68.0, 85.0], [58.0, 414.0, 38.0, 88.0], [76.0, 356.0, 56.0, 61.0], [24.0, 347.0, 50.0, 56.0], null, null, [0.0, 267.0, 76.0, 62.0], [59.0, 185.0, 99.0, 57.0], [34.0, 246.0, 37.0, 50.0], [0.0, 262.0, 34.0, 46.0], [182.0, 99.0, 90.0, 90.0], [240.0, 175.0, 66.0, 87.0], [245.0, 195.0, 91.0, 68.0], [202.0, 212.0, 83.0, 36.0], [97.0, 198.0, 108.0, 48.0], [46.0, 180.0, 52.0, 59.0], [0.0, 158.0, 47.0, 69.0], [183.0, 118.0, 90.0, 105.0], [181.0, 116.0, 80.0, 100.0], [167.0, 102.0, 102.0, 76.0], [82.0, 122.0, 140.0, 52.0], [24.0, 181.0, 48.0, 54.0], [0.0, 296.0, 21.0, 14.0], [15.0, 87.0, 98.0, 102.0], [224.0, 147.0, 72.0, 86.0], [237.0, 165.0, 79.0, 80.0], [225.0, 225.0, 87.0, 49.0], [142.0, 226.0, 99.0, 50.0], [74.0, 201.0, 94.0, 52.0], [0.0, 200.0, 54.0, 67.0], [62.0, 122.0, 98.0, 127.0], [219.0, 209.0, 82.0, 59.0], [119.0, 225.0, 107.0, 58.0], [95.0, 360.0, 62.0, 41.0], [71.0, 600.0, 34.0, 57.0], [0.0, 511.0, 21.0, 25.0], [0.0, 374.0, 19.0, 55.0], [7.0, 424.0, 45.0, 47.0], [71.0, 365.0, 67.0, 68.0], [158.0, 375.0, 81.0, 56.0], [168.0, 412.0, 70.0, 54.0], [98.0, 443.0, 110.0, 36.0], [49.0, 379.0, 115.0, 44.0], [65.0, 415.0, 85.0, 59.0], [78.0, 531.0, 96.0, 59.0], [101.0, 583.0, 90.0, 51.0], [106.0, 656.0, 69.0, 67.0], [52.0, 701.0, 69.0, 77.0], [8.0, 510.0, 73.0, 64.0], [0.0, 337.0, 52.0, 47.0], [17.0, 298.0, 48.0, 46.0], [22.0, 342.0, 45.0, 44.0], [0.0, 377.0, 45.0, 49.0], null, [73.0, 592.0, 83.0, 44.0], [60.0, 525.0, 83.0, 31.0], [0.0, 410.0, 58.0, 32.0], [0.0, 351.0, 26.0, 42.0], [0.0, 332.0, 20.0, 43.0], null, null, [53.0, 533.0, 62.0, 96.0], [48.0, 317.0, 72.0, 93.0], [126.0, 251.0, 64.0, 70.0], [33.0, 205.0, 83.0, 84.0], [87.0, 227.0, 69.0, 91.0], [129.0, 256.0, 62.0, 68.0], [32.0, 226.0, 105.0, 98.0], null, null, null, [0.0, 195.0, 71.0, 87.0], null, null, [76.0, 194.0, 95.0, 100.0], [110.0, 186.0, 71.0, 104.0], [109.0, 196.0, 105.0, 102.0], [0.0, 161.0, 79.0, 139.0], [44.0, 196.0, 114.0, 122.0], [78.0, 201.0, 122.0, 135.0], [0.0, 178.0, 57.0, 144.0], null, null, null, null, null, null, null, [0.0, 417.0, 34.0, 95.0], [50.0, 366.0, 140.0, 107.0], [140.0, 281.0, 91.0, 144.0]], \"areas\": [690.0, 2891.0, 2858.0, 2626.0, 3135.0, 3035.0, 3582.0, 3130.0, 3695.0, 3501.0, 4074.0, 3848.0, 3507.0, 3667.0, 2955.0, 2104.0, 1866.0, 1878.0, 2364.0, null, 2389.0, null, null, 619.0, 2195.0, 1334.0, 1790.0, 2012.0, 1994.0, 1389.0, 3625.0, 2333.0, null, null, null, 1286.0, 3115.0, 1881.0, 2751.0, 2929.0, 1490.0, 1656.0, 2942.0, 924.0, 2932.0, 1690.0, 1812.0, 2084.0, 2770.0, 1777.0, 5332.0, 3084.0, 2490.0, 1629.0, 2607.0, 4146.0, 2009.0, 2313.0, 2077.0, null, null, 3324.0, 3582.0, 1294.0, 1459.0, 6062.0, 2913.0, 2906.0, 2033.0, 3538.0, 2235.0, 1808.0, 4801.0, 3976.0, 3415.0, 3827.0, 1776.0, 197.0, 6266.0, 3287.0, 2583.0, 3036.0, 2952.0, 2699.0, 2274.0, 7250.0, 2377.0, 3112.0, 1983.0, 1394.0, 398.0, 748.0, 1322.0, 3094.0, 3193.0, 2987.0, 2022.0, 2431.0, 3042.0, 3603.0, 2810.0, 2848.0, 3408.0, 2909.0, 1853.0, 1623.0, 1493.0, 1550.0, null, 2733.0, 2008.0, 1318.0, 886.0, 766.0, null, null, 3033.0, 4455.0, 3397.0, 5373.0, 4498.0, 2975.0, 7257.0, null, null, null, 4992.0, null, null, 5985.0, 5205.0, 8104.0, 8637.0, 9764.0, 11769.0, 6917.0, null, null, null, null, null, null, null, 2144.0, 8875.0, 8872.0], \"iscrowd\": 0, \"video_id\": 0, \"height\": 1280, \"width\": 720, \"category_id\": 1985, \"noun_phrase\": \"the right hand\"}, {\"id\": 1043, \"segmentations\": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, {\"counts\": \"_bVa03kW14L3M3N2N101N101000O100O1O1O1O1O100O1O2M3K^mQ:\", \"size\": [1280, 720]}, {\"counts\": \"`ZPa01nW12K8L0O2N2O010O0O2O1O01O010O1O2O0O3M1N2O2N3LXmV:\", \"size\": [1280, 720]}, null, null, {\"counts\": \"Pfa>>^W15K4L5M4I>E5K3L4M3N1N2N2N2N3M2O2K4N2O0O21O000O2O0O100O1O1O101N100O002L4]Ob0K6G9Ee[P<\", \"size\": [1280, 720]}, {\"counts\": \"ef];3cW1a0E7K3M4M2M3N1O1O2M2N2O2M3N1O2N1O2O0O2N1O1O1N2N2L4O010O2O0O10O1000O101OO10000000001O001N10000O2O1N101N100O2N101N3M2O2M3N2M3K5J7E_RU>\", \"size\": [1280, 720]}, {\"counts\": \"lQh71iW1`0ZOb0G7I7J5K4M2N3N2O0O2N1O1N2O2N2N1O2M2O2O0O1O1N101O010O0000000000O1000001N101N101N101O0O2O000O2N3N2N2M2O2M2N2N3M3N3L5K6I8H6K6J5Hbeja0\", \"size\": [1280, 720]}, {\"counts\": \"`Rj>2kW15^hN1lV14mhN2nV1b0J7J5M3N3M2N2O1O1O1J5101N1O02N2N101N2N1O001O0N3K411N2O001O1O000O10O1O10000O100O10000O100O1O1O1O1O001O1O2O0O1O01000O1000000O101N1000000O1O1O1O1O2N1M3N2O1O101N100O1O1O1O1hNeiNb0HATW1:PiNCRW1;QiNBQW1;<N2Mdfe9\", \"size\": [1280, 720]}, {\"counts\": \"Qehe0>`W16fhN^OnV1m0L5K2N3N1O1N2O1O1O2M2O1N101O0000001O00001N10001O001O1N2O0000001O00000001N101N1O2N100O1O2N1N3M3M4L3K6L4K5KmbV4\", \"size\": [1280, 720]}, {\"counts\": \"V^g`04jW13N1N3N2N1O2O1N2N1N2N2O2N101N2O1O2M4M2N3M000000000O1000000O1000O01000O010000O2N1O100000O1O10O100O1O10O01O2N1O0O2M210O2OO100O0100O11N1O1O1000001O00000O100N2L4N2O2M2O2M3M4^ORiNKWW1GTiN2eQQ8\", \"size\": [1280, 720]}, {\"counts\": \"mnm944O\\\\W1?L5K3N2M4K4M2M4L3N3M3M4M2N3M2N2N1O1O1O100O01000O100O0100000O01O100O010N2M3H8L4N2N3M2O1N2K5L5J5M3M3L6K4M3K[bR`0\", \"size\": [1280, 720]}, {\"counts\": \"URo;3lW15K3L5M2M3N2M3M3N4J8K1N2N3L3N2N001N2N1M4M3O101N10O010O01O01O01O0001O012M3M2N10O0100O100O010O1O1O010O1O1N2O1N3L3K6J5H8J6L4L5LXUf=\", \"size\": [1280, 720]}, {\"counts\": \"fda><bW14B>I7K5L4L3N2M3101N0O10O00001O0O2O000000010O00001O0000O100O1N11O1L3?B2N2M6J4M3L5J6HUcl;\", \"size\": [1280, 720]}, {\"counts\": \"Pmo=2ZW12lhN;nV1?K4M3N1N3N1I8N2N1O2N1O100O2O000O10O010O10O10001NO2O1N2N2K5O010N2O10000N2N2O1N3M3N2N5J6K3J5I7L4MTdU<\", \"size\": [1280, 720]}, {\"counts\": \"eg[=8fW15M3J4M3N2N5K4M2M5J4N2N001N10000010O01OO010O1O100O1O100O2O001M200O2M4L3K;DahX=\", \"size\": [1280, 720]}, {\"counts\": \"obZ;3eW1;K:G2M5L1O2N101N1O2O1N111N1O2N1O2N1O1O2N2N1N11O2O0O0O2O0O2N2N3M2N2N3M3L6I6K5GQ]W?\", \"size\": [1280, 720]}, {\"counts\": \"Shd9?^W1>E8H<D:F4L2N001O000000O1000000O10000000000O1O1O1001OO1000000000000O100N2L4M3L4L4N2L4M3L4M3L4M3L4I7JV`_`0\", \"size\": [1280, 720]}, {\"counts\": \"URT8a0nV1j0B8K4L4J7K4M3M2N3M2N2N2N3M2O1M3O0O100O1000000O010000O10OO200O10O10O100O1O100O1O100O101N1O2N1O1O1O1O1O1O010O100O1L4H7L4K7L3N2L9Ae0[OT_]a0\", \"size\": [1280, 720]}, {\"counts\": \"[Za93gW1;I4L4K7J5L4L4L3N2M4M2N2O1O002N2M2N2O0O2O2N0O2N101N2N2N2N1O010O1O1O000M4N1O1O10O0M4I6N3L4O1O10O2O1O000O11N1O2N101N101N2O1N2O001O001O0O2O001M2N3M3O2M3N2M3N2N2N2M7D=GSmZ?\", \"size\": [1280, 720]}, {\"counts\": \"Q]\\\\>1lW14M4M2N2N2M3N2N1O3M2N1O2O1N2O1N100O10O10O01001O001N3N1O0000O010O1000000001O00000O10000000001O00001O000O10001O00000000100O0O101O001O00000002O00000O01O00O10O000003M6I:G1O1O2M3N7I2M4LmjU:\", \"size\": [1280, 720]}, {\"counts\": \"i[R>5cW1;F7L5L3N3M3N1N2O2N1N3N2M3M2O1O2N1O2N1O1O1O001O00O100O100O10000000O2O00O1O2O0N2N2N3M2O2O1M4L4K7H6KWVS<\", \"size\": [1280, 720]}, {\"counts\": \"YZQ<6eW1:G:H7H8I6I6J6L4K3N2I600O1O1O001O10O01001O001O000O2O1O1O1O1O2M4L3M3M4K5J7I=Anma>\", \"size\": [1280, 720]}, {\"counts\": \"TSh:2hW18L5L5K4L5L2M3N1O001N2N2L4F9M4O001O1O1O10O1O1O001O1O1O1O1O100O001O1O1O1O001O1O100O2N2O1M4L3L4L4L3M3N3K5KedY?\", \"size\": [1280, 720]}, {\"counts\": \"nZe<=`W18I5K5J5L3M3N2N1C=O2M2001O001O00001O0O2O001N101O1O001N101N101N2O1O1O1M3M3M4M2M2N4K4N2M3M4L4LXmc=\", \"size\": [1280, 720]}, {\"counts\": \"gaS>c0\\\\W15K3L4M2N1O2N1O2N1O101N100O100O100O100O10O0100O1O100O100O100001O0O2N1O2N2M3L4N2O1N2N3M3M1O2N3M2O2L5L6HV^o;\", \"size\": [1280, 720]}, {\"counts\": \"ei_`09aW1=G<E4L1O1O1O1N3N1N2O2N100O101O010O1N101O1N101O0O00FSjNbNPV1k0jiNZOVW1NlhNM6NdUa:\", \"size\": [1280, 720]}, {\"counts\": \"fQR`03jW14K5K5L4J7D;K5M3N1O2N2N2O001N100O010O0010O1O100O100O100000000O1000000O2UOhiNKYV11niNJVV1NU1Hbo_:\", \"size\": [1280, 720]}, {\"counts\": \"S_k<<_W18G<D<A<H8I6M4K5L3M3M101N1000000000000000000001N101N101O1N2O101N2N2M8I3L5L5I<C?_OgXe=\", \"size\": [1280, 720]}, {\"counts\": \"dPj9;cW14L5L3M2N2N3M101O0O2O00001N2O001O001N1O101N1N2L4I7I7M3N200015J1O00001O2N1O001O010O1O00100O1O1O1O101N2N3L3L5K4L4M3M2M3M3L5K5L2M3O100OWWf?\", \"size\": [1280, 720]}, {\"counts\": \"[RW;9\\\\W1`0D9K4L3O1N2O1L4O1O1O010N101M2O2N101O100O10O10000000000O2O0O2O0O2O1N1O2N1O2N2M3H9J5N2M3N1N3N1N3M4J6KeVm>\", \"size\": [1280, 720]}, {\"counts\": \"oX^=a0\\\\W17I6K4L4M3M2N101N1O1O100O1N2O1O1N2O1L4N2N2O1N2O100O0100000001N2N1N3L5L3M4L6I5L5L4M4K3M3M3M3M4LTgi<\", \"size\": [1280, 720]}, {\"counts\": \"Vh\\\\?1iW1=F6K4L4L3M3M3N101N2O0O2O001N101O1N1000000O10O10000000O1000O100O100OO1O2N2N1O2N2O0010O01O01O001N2H8K5M3N3K5Ei0^OgXa:\", \"size\": [1280, 720]}, {\"counts\": \"lW`a0^1aV11O1O00O2N101O10O01O0O2O1O0N3O000O2O1O1N2O1O1L4I7N5GRVl4Ij\\\\H\", \"size\": [1280, 720]}, {\"counts\": \"bh]a0=\\\\W1:G7K6I6K5N2M3N2O1N2O000010O1O1O1N2N4EciNmN`V1P1biNmNbV1o09M3N2N2CWhe9\", \"size\": [1280, 720]}, {\"counts\": \"\\\\gm>5cW1j0ZO8I8I4L3N100O1N2O100O1O2N1O1O1N1O2N2N2N2O1O1N101O1O01O001N2M3N101O1O1O1O1O1O2N1O1O2N2O1N3N2L7J4K5I5K5L5L5J9GWYP;\", \"size\": [1280, 720]}, {\"counts\": \"VXX;6iW12N3M2O1N2O2M2N2O1N2O1O2N1N4M2N4L5K1O1O2N1N2O001O00000000001O0000000O101N1O1N2N2M3L5L3M3L5N1N20O3M3M2N2M2O2M4M1N4M7I5K9Ef0[O:DZo\\\\>\", \"size\": [1280, 720]}, {\"counts\": \"jiS<e0YW17K6J2M2O0O101N101N101N101N100O1O1O1[Oe0M3N200O01000002M2N2O2M2O3L3N2M3M3L4K6J6I6K7K4J5M4L2N6Jo]U>\", \"size\": [1280, 720]}, {\"counts\": \"Vba<7fW14M2N2N3M2N2N2O1O1O2N1O2N2N1O2M2O100O2N100O2O001N100O2N1O1O1O1O1O1N2M2O2N2O001O02O0O101N1O3L4L3L5L4L4M4K4M4L3M3M3M3M4L4J[VV=\", \"size\": [1280, 720]}, {\"counts\": \"cad=3jW16K3N2N1N3M2O2M3M3N101N1O2O0O3N000O2O001N100O2O000O100000000000O001N1010O100O001O0O2N1O1O1O02O001O1O2O2M5K7J7G7I7GlnV<\", \"size\": [1280, 720]}, {\"counts\": \"Uaj?:bW19H4G;I7J5M3M2O000O2N1O1000000000O10001O00001O001O001O2N1O001O1O001O1O1N2O1N2L:BWWf:\", \"size\": [1280, 720]}, {\"counts\": \"cQc?5eW1<D6I8K4K5N2O1N2O2N1O1O1O1O1O2N1N2O1N2M3N20O00O2N1O2O0O1O1000000O11O001O010O101N1O1O2N1N3K5K4C>N2N3K5L4K8G7KQX\\\\:\", \"size\": [1280, 720]}, {\"counts\": \"b`f<f0SW1:C;K5L4M3M3N3L3N2N3M3N2N2M2O1O0000000001O00001N101N2O2N1N2O1O2N1N3N4L6I3M4L5J8F<CWoh=\", \"size\": [1280, 720]}, {\"counts\": \"RjU;1lW15G8N1N3N101N10001N1000001O0O101N1O1O2N1O2N100N3O0O2N2O1N2O0010O01O1O1O1O0010O000001O00100O1O001O010O10O0100000O2O0O2N2N1O2M4L3M4M2N3L3N2M3N2N2N4J5H_]P>\", \"size\": [1280, 720]}, {\"counts\": \"hcl>3:6lV1a0M4J8K2K6O0O2N1O10O0003N1N101O0O10O1000O100000000O100O110O0000O1N101O1O1O10001O1N2O1N2O1M3K7K3N1M202M3M2O3M2O2L2Mgln:\", \"size\": [1280, 720]}, {\"counts\": \"ag[?1dW1?B`0F6I8K4M3L6I5L2N3M2N2N2N2N2N3M2L3O2O0O10O1N3O11O000000O1000000001N1000O1O2M2N3M3L3L5M300O1O1N4L5L6I5K2K5L5K5J7Hfi_:\", \"size\": [1280, 720]}, {\"counts\": \"ojY`0U1iV1<D6K3N2N1N3N1N2O1O1O001O00001O000O1000001O00001O00000O20O1O2N1O1O00100O1O1O1O1O1O2N001O001O1O1O1O1O1O1O1N2O2O0O1O2N2O2M2N2O2L4M6I<_Oa[V9\", \"size\": [1280, 720]}, {\"counts\": \"bSo`03hW1k0]hNTOdV1a1C4K3M2O2N2O1N101N2O0O101N1O10001O000O2O00010O0001O1O1O1O012M3M1O2N2N1O1O1N102O0O1O1O1O010O1O1O1O001O2N1O001N2O00101M2O1N4L3M3M3M3J7Hac^8\", \"size\": [1280, 720]}, {\"counts\": \"R[Sb0;bW1d0^Ob0]O8I4L3N1N2O1N2O1N2O001N100O1O10001O0O1010O0000010O00001O2O1N2N4L1O1O2N2O0O1O1O1O2N1O1O1O001O1O1O1O1O1N2O1O1O1O1O1N2O1O1O1N2N3L4L4L4M4K4M5K`cV7\", \"size\": [1280, 720]}, {\"counts\": \"ck_b0=bW1n0RO7J3L2N2O2M2O1N2O00001N100O2N1O1O1O1O2O00001O00000010O001O1O3M3M1O1O1O2N1O2N100N2O1O1O1O2N1O1O1O1O1N3N1O1N2O2M2M4L4L3M5L7H7HTST7\", \"size\": [1280, 720]}, {\"counts\": \"edoa04hW1b0@9I5J8I3L3M3M2O0O2N2O1N2O1N1O2O0O2O00001O000100O1O1O101M2O1O1O2N1O1O1O001O001O1O1O1O1O1O1N2O001O001N2N2N2O1N2N2M4L3L5L5K7Fcbe7\", \"size\": [1280, 720]}, {\"counts\": \"ZU^a01mW14M4K4L:F4N7H7I5K5L3L2O2M2N2O1N2N2O1N2O001O001O001O100O1O001001O2M2N2O1N001O1N101O1O1O1O001N101N2N2N2N2O1N2O1O0O2O1O1O1N101N2O1N2L5K4L4N3L4L5Ljik7\", \"size\": [1280, 720]}, {\"counts\": \"ZmUb03lW14L2M2J7M2N4M3N2M3M2M3N3M3M3M3M2N3M3N1N2O1N2N2O0O2O000000001O000000O110O001O00001O00001O001O00001N10000000001N10001N100O2O0O2O1N101N1O2N1O2N3M2N2N2M3M4L4\\\\OjhN0\\\\jm6\", \"size\": [1280, 720]}, {\"counts\": \"UR\\\\b0=^W19J4M3M3M2O001O1O001O001O01O0000010O0001O001O001O001O001O1O001O1O1O1O1N2O1O001N2O001N2O0O2O001O1O1N100O101O00O10O10O100O100O100O1O10000O1O1O1N20000O2O00000O100O1N3N1O2N2N2N2N3M3M5J4L5I7L4LbUQ6\", \"size\": [1280, 720]}, {\"counts\": \"S[bb0?_W15K4L4M2M3N2O1O001N100O2O00001O00000100O10N101O1O001O001O1O1O001N101O001N101O1O00001O001N101O1O001O0O101O0O100000O10O010000O2O0O010O101N100O2O0O1O1O1O1N3M2O1N3N1N2O2M3M4L8G>AhdS6\", \"size\": [1280, 720]}, {\"counts\": \"WTca03lW16I9G4M2N1O2O1N1O3N001N101O2N1O001O1O0O2O1O10O01N10001O001O00010O2N1O10O0001O1O010O0000001N11O01O00000000010O0O100000001OO2O0O1O100O1O1N2O1N2N3N1O1N2O2N1N2N2M3O1M4L3FghNIbW1NSdV7\", \"size\": [1280, 720]}, {\"counts\": \"^Uo`08gW12N3M3N1N3N2N1N3N3M2M5L2M1O2O1N2O1N100001N101O01O01O1O10O10O01O1O1O001O1O0010O000100O001O0001O001O0000010O0000001O00000O2O0O1O100O2O0O100O2N100N3N1N2N3O1M4M2K5K6M3JVRm7\", \"size\": [1280, 720]}, {\"counts\": \"Ze`a06hW16K3M2O2M2O1N2N3M2O1N101N2O1O2N2M2O1O1O1000O11O0O2O0000000O100O1O100O1O00010O10O001O010O001O001O10O01O001N101O1O001N2O1O1N1O2L3N4L2O1N3N001O2K5KWRh7\", \"size\": [1280, 720]}, {\"counts\": \"ddVa07hW13M4L4K5L3L4L5K6K3L4N2M2O2M2O2M2O1O2M2O2M2M22O1OO1000O1O100O1O001O10O01O010O1O001O1O1O001N101O1O1O0O2O001O1O1O1N2O1O1O1O1O1O1O1O2M3M2N2N2N2O1M4M2M4N1MbZn7\", \"size\": [1280, 720]}, {\"counts\": \"ibea0b0\\\\W1d0]O5J9F7J4L5K6J4N2M2N3L5L3N1N3M3N1N2M3M3N3M3M3M3O1O0O102N2O01M1O1O01O100O10O01000O1O1O1O1N2N101O1O1O002N1N4M1N2O001O1N2O1N2O2M2M4M3N1N3M2N2N6I6K7H:G7Eo[_7\", \"size\": [1280, 720]}, {\"counts\": \"UUec0>aW19H2L4M2O000O10000000000000001O0O1O100000000O10000O100N2O1001O1N10O2N12N1O00O1O10000O100O1O1O101O0O1O1O1O100O100O1000O10O10000O11O0O0100001O0O100O1000001O0001O000000001O0001O000001O01N1N3K40001O001O000O2O0O2N=@6H7LmjW4\", \"size\": [1280, 720]}, {\"counts\": \"gQ\\\\e06bW1>G4N2O0O1O2M2N3L3O2N2O1N2O1N2O001N101N101N2N101N100O101O0O10000O1000001N100O10000O100O2N101O00000O10001N100000000O11N2N3N1N101N100000001N101O1O2M1O2O1O1N4M1O0O2O2M3M2O2M2O1O1N2O1N2O1N4M4E:K6KTfn2\", \"size\": [1280, 720]}, {\"counts\": \"nPWe08bW19K8D9J5I6K5O2O1O1M2N1O2N2O1M3O0O2O001O1N100O2N1O1O101N1O11O0O10O2N1O1O100O2O001OO1O1O1000000O100O101O0N3N2O0O2O000O10000O001O3K4M3N1O1O1O2N2O1N2N2N2N3M2N1O2N2N1N5K4L8J2M3L]W`3\", \"size\": [1280, 720]}, {\"counts\": \"hai=1mW17G6N1N1O2N3M2N2O1N101N1O2N2O0O2O0O2O1O0O101N101N101N101O1N4M2N0O101O0O1000000O1000000O10000O100000000O2O00000O10000O100O2N000O10100O002N1O10O1O100O10000O100O01000O100O2N2N2N001O1O001N2O1O1O1O2M4M1O2O0O1O2L9D8M4L5K7J2M3Mcfo9\", \"size\": [1280, 720]}, {\"counts\": \"i\\\\f9;bW14L4L4M2O2M3O3K3N5L3L6K2M2M4M1O2O0O1O101N1M3N2O1O1O1O001O1O1O010N2O002N1O10O010O01O0010OO2O0O2O001O0O20O010OO2N1O1010O100O100O0010O01O01O0100O1O2N1O100O10001N001N2O1O1O1N2O1M3N1N2O2N2N3L3N2M3N3K4M4K6J6J5L7B]e[>\", \"size\": [1280, 720]}, {\"counts\": \"VW\\\\;4hW1b0_O8I5L3M4L2O1N2N2N1N2N4M3L3N2N2N1O2N2N2O2L2G:M2O2O0000O03L3O1N1000N2N2O1O1000000001OO0100O0010O2O1N2N2N1O2N4M2L5J5M5K7G<B^YZ>\", \"size\": [1280, 720]}, {\"counts\": \"lPn>b0WW1:E9K6L4L3J6M3N2N3L3O1O0O1O2O1N2O0O3M3M3M2OO2N100001O2O1N4L4L4M1O01O0O2N1O1O1O2N2N2N2N1O2M4M1O2N2N2M4M2M3M4M2M3M3M4K4K5K5F9L5G9Hdfd:\", \"size\": [1280, 720]}, {\"counts\": \"gdZb02nW12N7K1N<B3K5M2N3N2N1N100000000O100O101N100N3M2N30O01O0O1O100000000O1000001O1O1O1O1OO1O1000001O1O1N2O010O01O0O1001O000010O01O010O0000001O001N102N1O1N2O1N3N1N101O2N1M3I7O2N100O1O2K7GibW6\", \"size\": [1280, 720]}, {\"counts\": \"^Yhb06gW16L3J5H8L4M3N2N2M4M3M2O2M3M1O2N2N2N2N2O1N2N1O1O2M1N3N101000000000000O100O101O0O10000O2O000O0100O01O0100O010O10O0010O10O100O10O10O010O0100O001O0O2O00100O1O001N101N2M3N2O0O2O001O1O100O100M2M4N201N1O101N2O0O100O2O1N1O100O1O2N2O2M5K=]OShj4\", \"size\": [1280, 720]}, {\"counts\": \"Yi\\\\?7^W1<N2N2N1N2M4J501O1J5N3L301O1N2O1N3L3M3O13M11O0N2M3M4M1O2N2N001O001O1O1M1N3O10O10O0100O2O2N00O1O100O01O1O10003K3L4L31O0000N3N10O1001Me0XNfiN?f]S:\", \"size\": [1280, 720]}, {\"counts\": \"nU]82cW1`0G7K?@7J4K4L5L3L4N2M2N3N1O1N2O1O1O1O1N2O1O1O1O2M3O0O2N1O2O1N100O101N1O2O0O10N21N2N10H8O0010O100O01O001N1M4K4O2O0O2L301N1N3O0O2N2O010O1O00100O10O0100O101M2O1O1O002M2N3N1N3M2O3N3L2N2N2N1O2N1_O`iN_OdV1:fdm?\", \"size\": [1280, 720]}, {\"counts\": \"[a^8>^W1:H5K4L4M2K6J5M4L5K4I6E;J6M3M4M2O0N3O1O1N20O01O0O2O0O20O01O00001O001O00010O00000000000001O010O1O1O002O1N1O1O104K8G>C4K5K6J6I5H8E>[OenRa0\", \"size\": [1280, 720]}, {\"counts\": \"SmW<3fW19^Oa0K6K5I6K6G7L5N2O0O2O001O010O001N2O1N2O100O1O1O1O00100O1N101000O10M3N1O15L4K4L2O0O2N2N2N2N3M3M1O001O1O1O2N4L3L4M4L6J6J7H9HTZ]=\", \"size\": [1280, 720]}, {\"counts\": \"Ym]>h0jV1a0I7I6L3N1N3N1O1L5L3M300O101N1N2O1O1O101N1O1O12N1O1O001OO2N10001O0010O1O10OO2O000000000O101O3M2M2O00001O2M3N2M3M3M5K6H9Gb0\\\\OfbX;\", \"size\": [1280, 720]}, {\"counts\": \"eTl=>YW1=H=D9H2O2M2N3J5N2O2M2O1N2N200O1O2M2O1O2N1O1O2N1O101O0000010O01O0010O00010OO2N1O2N1O3M3M3N1N2M3N2N2L5K4M4L3L5K5K8F?BfSn;\", \"size\": [1280, 720]}, {\"counts\": \"`RS=`0[W18K4L4M4L3L3N2O2M2N2O2M3N002M2O1N2O001O000O10O101O00O1000000000000000010O0001O00100O1O2N2N2O1M5L4L6I4I5I;AZ]m<\", \"size\": [1280, 720]}, {\"counts\": \"Xlh>`0\\\\W18J6J4N1HoN^iNT1_V18M3M4M2O2M3M2N3M2O1N2N3M2N1O2L3N2O1000000000O10O10001O00000O11O2N1O00000010O01O1O1O0O5J4jNfjN@aU19bjNCeU19\\\\jNDhU19ZjNDkU18WjNDYV1On0NckS;\", \"size\": [1280, 720]}, {\"counts\": \"`UYd01iW16O2N5K1O1O1O2M2O3M1N3N2O2H7L4L5M3N2M3N1N4G:L01N1OO2O01O00O20N2N3NO0101O0000001O010N1O101O1O001O010O1N101O1O1M2O2M3N101O1O1O10O10O1O100O10000O01000O100O1O1N3N2M3N2M3N2N3M2N2LUl\\\\4\", \"size\": [1280, 720]}, {\"counts\": \"UcPg06hW13O2M3L3N2O1010OM4M2N7I4L3M10001N10001O0O1O1O100O2M2N200001O000001OO10000O100O001O0010000O100O100O100O10000O2O0O101O0000001O001O000O101N10000O100O2N2O1N101N2O2M3LSml1\", \"size\": [1280, 720]}, {\"counts\": \"fTSg0:cW1;G4L3N2N1O1N1O1O2O00O11N1O1O11O1O00O1O1O10000O1O1O100O1O1O1O1O100O10000O12O1N3NN010O10O0100O10O02N1O2N100000O10000000N2000O10001O1N2O001O001N2O2N2M101O1N2O1O2N2N2M2M6I]cf1\", \"size\": [1280, 720]}, {\"counts\": \"m]dd06fW1<G2M4M3M2O1N2N3N1O1N2N2N2N1O2O1OO010O010O010000O100O10000O2O00000O010O1O010O10O10O1O01000O01000O100000O1010O00O2O1O0O10O0100O1000000O1N3O000000O10000O1000001O1N2O001N100O2O2N3N03L5J3N100O1O0N3N101O1N5K4K6J3O1N2N2N2N3N1N1OoaS3\", \"size\": [1280, 720]}, {\"counts\": \"Pkf?5hW15M2M3N2M3N2N2M3M2N3N2N1N3N1O1O2N1O100O1O010O1O1O001O1O2N1O1O1O100O1O1O00001O1O01O001O001O1O001O000000100O001O00100N2O100O1O100O00011N1O1O00100O101N010000001O1O0O2O0O2N100O101O0O2O1M2O2O0O1O101M1O2N2O3M2N4L5L1OTfX8\", \"size\": [1280, 720]}, {\"counts\": \"Ukh>7gW13N2N2N2O0O2N2N2N2O1N2O1N2O1N2O1N101N1O10000O100O010000O0100O010O100O01000O01O10O10O010000O10O01000O010000O100000O0100000O100O10O0100000000O1000000000001O001O001O000001O01O00001O00001O0O100O2O0N2N2O2N10001O000000010O00000O101O0O101N1O1O1O2O0O101O0O1O101N1N\\\\mY8\", \"size\": [1280, 720]}, {\"counts\": \"T]_?;bW16L4K5L2N3M3L3O1O1O1N2O1N101N2O0O1O100010OO1O1O21N2N1O00O1001O1N02N101O0O1001O000000O100O00100000O10001OO10001O00000000000000000000000O100000000000000001N1000O11N10001O001O1N101N1000000O100O100O2M2N2N3N1O1O2N1O2O1N2O1O1N101N1000001N10002N2MlZm7\", \"size\": [1280, 720]}, {\"counts\": \"mni`07hW18G5L3M3M3N1N2N2O0O2O2N1N2O001O0000000000000O101O0000100O001O00001O010O000010O01O0010O0001O010O1O1O00100O0001OO2O1O010O1O01O0010O010O1O010O1O0010O10O10O01O10O01O01O000O100N3M3K4O101O00000100O10O002M4M2N2N2N1O1O3L3N2N2M2O2M4M2L6JiWj6\", \"size\": [1280, 720]}, {\"counts\": \"Wglc0a0]W13M4N4K>A3N2O000000001N2O000000001O010O010O100O001O100O1O1O100O2N100O1O100O1O2O1N2O1N3N0O100O01O1O1O1O1O1O1O001O001O1O001N101O1O2O0O1O1O1O1N102M2O2L6I6@f0DagU5\", \"size\": [1280, 720]}, {\"counts\": \"Xiff03kW17G70>A3K3M001O1N2O001O00001N3N1O1O001O1O0O10O2O00O2O1O00O2O000000001N100000000000001O001N2O1N1O2M2M5M2O1O1N1N4M5K5K6JRVR3\", \"size\": [1280, 720]}, {\"counts\": \"\\\\_hb06cW17M4N2N2N3N1N2O1O1O2N1O1O1O2N1O2N1O2O001N10000O2O00O02N100O1O000O2O001O010O00100O1000O100O2O1N10O010O010O1O1O001O0010O000000001O01O0000000001O1N1000010N1O2O100O2O0000000O010O00010O010O01O01O1O2M2N2L5J7J8@ehNOkn[5\", \"size\": [1280, 720]}, {\"counts\": \"hcb>4jW15K3N2N3M2N2N101N2O1N2O0O2N2O1N101N2N2N101N2N1000000O0100000O10000O2O000O1000000000000O10000O2O00000000001O0O2O00000000000000000000O2O01O01O001O001O1O001O1O1O1N2O001N2O001O2N1O2N2M3L5I7G:FXdj9\", \"size\": [1280, 720]}, {\"counts\": \"nkj=1iW16O1N2N3M2N2N2N2N2N3N1O1N2O1O1O1O1O001O1O1O100O1O1O0000O01O2O010O100O1O101N10001O0O100O1000O1000O1001O0000001N10001N100O2O1N1O2M2N3N1O2N4K;EU]`;\", \"size\": [1280, 720]}, {\"counts\": \"fTQ>`0ZW17J5M4M2M3O2M2O1O2M2O1N200O10000O2O000O10O10O01O00100O1O1O100O10O010000O1O100O1O2N1O2N1O2O1N102M4L4K4M3M4Jm[o;\", \"size\": [1280, 720]}, {\"counts\": \"ZlV=6gW1e0\\\\O4M3M:F5K4L2N1O1000O100O0100001N10O1000O2N1000O010001O00O01O1J6M3O1O1N2O1O2M2N2N3M4L3M4KocS=\", \"size\": [1280, 720]}, {\"counts\": \"Tc`?3630KlV1e1PiN_NnU1U2C7K3M2O1O001O000O2O0000O1000O1000O100000000000000000000O1O1000000000000000000O1O2N11O000001O0000001L5E;E:M3J7J7D<J5ZORiN1n[X:\", \"size\": [1280, 720]}, {\"counts\": \"`enb06fW16M4K4L5L2N1O1O2O0O1O2N101N1O1N2O2N1O101N100O1O101N1O10O010O00001O10O1O10O01O1O10O01000000O101N00100O1N2O1N101N101O1O1O0000010O10O01O0100O010000O010000O2O00000O10001O0O2O000O2N101M3N1O2N1O2N2N2M3L7I8CPTW5\", \"size\": [1280, 720]}, {\"counts\": \"i[f>2mW13M3K4N2N3N2M4L2N2N2N2O0O2N2O0O1O2N1O1O1O1000O010O10O01O10O10O010000O10000O100O1O10001O0O10001O0O100O101O0O101O0O2O000O100O101N100000000O1000000001O000O101N101O0O2O001N101N2O2N1O1N3N1O2N2N1O2N2N2M4L4L?@QT^9\", \"size\": [1280, 720]}, {\"counts\": \"gRi<6iW13L3N2N2N1O2N2N3L2O1O1O2N1O100O101N100O1O100O1O001O000001O1O010O10O100O10000O010O100O10000O100O11O0000000O1000000O10000O2O000O101N10001O00001O1O1O2N2M2O3L5J8HdmV<\", \"size\": [1280, 720]}, {\"counts\": \"][e<=_W15J6M3L3M3N3RlNmNkP1U1m23N3M2O1N2O1O1O1O1O2N100O10000O01O10O01O1N2O1O1O100O1O1O100O100O100O2O0O101N100O100O2O1N1O2O1N1O1O2N201N2N3K4M3M2M3NWUl<\", \"size\": [1280, 720]}, {\"counts\": \"]\\\\V<a0[W1<F:G6I4M1O00100O001O1O0010O10O00100O100000000O100000000O1000O100N2N2N200O1N2O1N2O1O1O1O2N1N3N1O1O3K5L3N3K6IQlh=\", \"size\": [1280, 720]}, {\"counts\": \"h[i:5hW1<E5L4M2M2O1O1O2N2N2N2N2M3N3M2N101N1N3N4L1O2N2N2N2N2N001O01O00001N100O2O0O2N2N3M3M4L6J5K6K3L8H5L1M2O2N3M4MQSW?\", \"size\": [1280, 720]}, {\"counts\": \"UkW?1mW12]OOTiN4_V1<WiNK`V1l0O4N3N2N2N2N2M3L4M5M4M<C6I3N1O001N1000001O001O000O10001O000000001O00000O2O00001O00O2O002L9H3L3O1N2N3M3M2N2L5J6J7K6I5M3M2O2M3M2N3M2N1N3J6K4L5N1O1N2O100O[Uh9\", \"size\": [1280, 720]}, {\"counts\": \"YcTe04UW1g0O2N1O1O2N10000010O01O0O2O0010O000100O1O1O001O1O1O001O00000000010O1O001O10O0001O000010O0001O001O010O001O0O100000000001O001O0O101N1N3L3O1O2M2M4K4M4L4L4N3M3MRel3\", \"size\": [1280, 720]}, {\"counts\": \"P[Tg0;[W1:N3K5J7M2O1N2O2O0O1O1O1O100O2N3NO01O001O100O001O001O001O1O100O001O0010O01O001OO10000001O00O2O0001O10O0O10001O00001O001M3L4K6G:D>XOcU]2\", \"size\": [1280, 720]}, {\"counts\": \"U[]`01[_`30_WnNd0X`bMd0E3L101O001O0O10001O1O2N1N101O001O001O1N2O1O1O1O0O2O001O1O1O001O1O00001O0O101O1O001O0000010O000000001O000001O0O101N2O00010O010O001O1O10000O1O100O1O2O1N1O2M4M2M7I9G=_Oc]^2\", \"size\": [1280, 720]}, {\"counts\": \"f_e?3iW1;H5K5K4K4M2N3K4M3L4N2N2N1N3M3M2O1O2O0O100O10000O11O0000O1000000000000O10000000000000001O000001O00000001O00000000000001O01O00000001O0000001O000000001O000000001O1O1O1N2N2O1N2O2N2N3L4M5J<TOfPi8\", \"size\": [1280, 720]}, {\"counts\": \"jgZ`05iW14M1O4L4L2N2N2N3L3N3L4M2N2M3M3N2M2O2N2N1N2N3N2N2N1O1O2N1O1O2N0001O01O1000O010O10O10O100O010O100000O10000000001O1O000O2O1O0000000O2O001O0O101O00000O1O11O000O11N2O1O001O0O2O2N1O2N101N2O1N1O1O1000O100O2O01O1O01O00010N1O102L3M4M2L4M3N2M5H6M3L5K5L4G9D`0Ac0Bdgk6\", \"size\": [1280, 720]}, {\"counts\": \"^Qcd01kW1:H9H4M3L3M3M3N1N2N2N2N1O2O000O100O1O100O100O100O1O1O2O0O1O100O1O1O2OO0101N1O100O100O101O0O1O10000O10000O2O0000000O2O00000000001O000000010O00000001O01O0001O001N2O1O1O001O2N1O2N3M2N3M2M3N2N1O3M2N2M3N1N4M2M5J6J<Cn]\\\\3\", \"size\": [1280, 720]}, {\"counts\": \"[QSb06hW14N5J2O3L4M3L3M2L6J5L4M3G8N101N2O0O2N11O00O100O1001O01O0N2O002N1O1001O00O10000000000O2O000000000O1010N1000O1O01000000000000000002N1O001O0O1010O01O00001N2O001O1O1O1O001O001O1O0O101O0O10001O0O2O1N4K5G=Ca0[Og^Q6\", \"size\": [1280, 720]}, {\"counts\": \"hRf;<]W19L3L5K4M4L4L3M3N1N2O1O2N1O100O10O02N1O2O1O0O2O001O001O001N1000001N10000000000O11O0000O1001O0000000O101O0O2O1N2N2O1N3M2N2O3L3L5L5K6J3M5J6J5IYUe=\", \"size\": [1280, 720]}, {\"counts\": \"gRR;=^W18L3M3L4M3L3N2N2N2O0O2N2N1O101N1O1O2N2N001O1O100O1O2O0O100O2O1O001N1000001O0000000000001O0000000001O001O1O001N2O001O1O2N3L2N3M2O0O1O4L5K5K2N4L3M2N3M3L5KReQ>\", \"size\": [1280, 720]}, {\"counts\": \"Yhf?>YW1<J8H9F6K5M2M4M2O1N2N1O1O2N101N101N100O100O2O0O1O1O100O2O000O101O000O10O0010O10000O01O1O001O1O1O001000000O100O01000O2O0O2O1N2N2O4K5L2M6K1N2N3L5Ia0C23L^O\\\\X^9\", \"size\": [1280, 720]}, {\"counts\": \"`h`b06iW12M3N2N2M3N2N2M5I5N3M2N2M3N2O1N1O2N1O2N2L3N3L4N2M2O2O1N1N2O1O1O00001O1O1O001O1O1O010000000O100O100O100000000O00100000000001O00000O010O2N1O1O101N100000001O0O2O1O001O00001O0O102N001O000O101O1O1O1O1N3N2M4M3L4M3L5ROZiN9[W1K=CXW\\\\5\", \"size\": [1280, 720]}, {\"counts\": \"hYk>`0ZW19J5H8L4K3N3N2M3O1N101N2N1O2N2O1N2N101O0N2O1O2O0O100O10000O101O0O10000O101N100000O00100O1O0100O010000O1O100O100O1000000O1000000000000O101O001O1O1N2O2N1N3N1O1N2O1N3N2M3M5Lb0]O4L6I5K4KQnf9\", \"size\": [1280, 720]}, {\"counts\": \"laS>2iW16AMghN<RW1?I6L4M2M3L4M3N2N2N2N2O1N101N2N100O1O100O101N100O100O1O1O100O1O100O2O000O10O010O10O010000O100N2O1O1000000O1000000000001O000000000O100000001O1O001O0O3M3M3M3M3N2Md0\\\\O8H3M2N2N9E]Va:\", \"size\": [1280, 720]}, {\"counts\": \"QXcb06jW14L8H3M1N2O1N4M3M1N2O1N3N1O1N1O2N2O01O00O1O1O2N00100O1O100O0K`NliN_1UV17N1O1O100O1O1O1000O2OO1O2M2O1O101N10000O1000001N100000000000000000O01000001O000O1001O0001O00000000000000000001O0000001N2O001O1O0O2O1N2N2N2N2N2N2M3M3M3N3G8L4M5L4K6J9Gdhk3LVPSL0SVU1\", \"size\": [1280, 720]}, {\"counts\": \"`Pcd08fW1;F9H2M3N2M2O2N2N1N2O1N100O10000O1O10000O100O100O00100O100O100O2N1O1O2N100O1O100O1O100O2O0O100O1O1O10000O101N1000000O10000001O0001O0001O000000000000001O000000001O001O0000001O00001O001O00001O1O2M2O2N1O2M3N2M3M2N2N2N2M4L3L5I8J6H__R3\", \"size\": [1280, 720]}, {\"counts\": \"aPke05gW1:G;G7I4M3M1N4M1N3N2N0O10001O0O100O10001O0O100O2O000O1O101N1O2N1O101N1O1O1O100O100O2N1O1O100O100O1O1O100O100O100O100O101N100O100O10000O10000O2O000O1000000O0100000000O10O1000000O010000O100000000O1O100O101N100O100O2N1O100O1O2O1O0O2O1N2O0O1O2M3M3M3M2N3N3M2N101N101O002M2O1O1O1O1N2O1N20O02K3N3O0N3N1O2LR`=\", \"size\": [1280, 720]}, {\"counts\": \"S^l>=`W15L5K4L3M3N2M3J6N2M3L5MO11O0O2O2M200O1O10000O2O2M2O0000001N101O00000O01000O100000O10000000O10000O100O10000000000O10001N100O10010N4M7H8J7H2O1N1O2N2N10O0100O2O00101N0000O2O0O3M2O0O3N8G<E3L8G2O3M2N1N102N5J4L7I4L2O3L2O0O1N3O1O7Ib0XOPPi8\", \"size\": [1280, 720]}, {\"counts\": \"cPP<3kW13N1L5N1N2O1I8N1O1O1N2O1O2N1O100O3DlNgiNW1WV1iNhiNZ1UV1iNgiNZ1WV1:M2M5M3M1O1N1O1O10N2M30O4L2O1O1O1O000O2O4K3M2N1O10000000000O10010O0O1000010O0000000001O1O00O1001O0000001N1O1O2N1O110O000O2O00001O0104L0O0001N1O2N2N1O1O2O001N101N1O3M2N1N3N1O2N3N1N1O2N1O1O1O2N1O2N2M5K6K3M2N1O2N2N2N5JhgR;\", \"size\": [1280, 720]}, {\"counts\": \"PX]e02hW1m0XO:G6J4K3L4M3M3M3K4M3N2O0100O00O2N1O1O2M201N2O0N3N1O2O0N2O1O1O1O10000O2O1N2O00001O001O1O001N10001O0O10000000000000001O0000000000001O0000O101O00O010000O101O0O100O1N2O1O2O0O1O100O101O0O101N1O1O1O1O1N2O2N1O2M2L5M2N3L3M4M2N3M2O1N3N2N2O0O2O0O2O1O1N2O1O1N2O2M2N2O1O1N2O1N2N2O1N2N2N2K6L3M3NPPT1\", \"size\": [1280, 720]}, {\"counts\": \"[Pjc02_W1P1[O9E9K4J6G9K4L4I7N1M3M3O2BTMdkNn2YT1WMbkNm2ZT1>N3M2G[LVlNg3hS1\\\\LUlNe3iS1;L3N3N1N3N1N3L3N3K400L4M4K4O1O1O1O2N100M3O1M3N2O1N3M200N20O0100O001O1N1O2N110O0100O010O011O001O000O100O1O2O0O2O6I2N2N2O0O3N0O006J2N3M6J5J:G4L2N8H010O1O4M3L4L2N3M2N2N2N5K2N1O4L4L3M5K3fNajNMcU1YO\\\\kN>]V1\\\\OdhNOlW1Jloh3\", \"size\": [1280, 720]}, {\"counts\": \"SS`4c0ZW17K2M4L4M1O1O2M2O2M4M2N2N2N2N1O1N2M3DWN\\\\jNo1bU18N2O1O1O1O1O00100O001O100O1O01000O10O10O1O010O00100O010000O10O100O10000O1O1000O1000O100000O01000000O01000000O1001O1N101O0O101O0O101O0O2O2N1O1N101O001O001O00O1N2O10O2OO1O1N2000000O100O11O2N00O1O1O100001O1O2N00001OO100O100O10001OO1000000000O1000000O10001O00000O2M2N2N3M20000O1O2N1O1O1O1O2L3O1N2N2O2N1O2N1O1M4N1O1O2M2O2N2M2O2O001N100O2O0O1O100O1O3M1OO02POViNi0kV1VOXiNg0iV1XOZiNe0gV1[O[iNb0\\\\W1F1O001M2O2N1Oo]b?\", \"size\": [1280, 720]}, {\"counts\": \"YS_73iW18XOF`iN=\\\\V1F`iN?\\\\V1F`iN>^V1C_iN`0`V1@]iNd0aV1^O]iNd0aV1>O2N100O101N1giN_NSV1j1K9I1O6H10O100M2O3M22M2O2O1N2O0O2O2N1O4K10000O100O2N100O011O000O100000001O0O10O11O1O0O2O0O100000000O1O111N4L3L2O1N10N2000000O11O00000O10O100O100O1O1O00100O100O0O2N200N2O100010OO1O010O100O1N2N3M3K5L4N1N3N10O0100O1O100O1O1O100O100O100O1O2N1O0O2O2O1O0O2O1O1O2N2M3N0100001K5L:G4K3M6^ORVc>\", \"size\": [1280, 720]}, {\"counts\": \"o`_b0P1hV1<K5L3M2N2N2M3N001N1O2M3N1O1O2O0O10001O0M3M3O2O00001O00000100OO2N1O2O0000001O001O00001O0000001O0000001O000O1O1O1N2000000O1O1001N10N2M201000O100O100O101O001N10000O100O1O2N1O2N1OO1001O1O2O0O1O1O2N1O2N1O1O100O100O1O1O2N1O1O2N1O1O101N2N101N1N3N1O2O1N2N2N3M3N2M4L4L4L2N2N2N2M2N5JVoW4\", \"size\": [1280, 720]}, {\"counts\": \"^Q_a0c0YW1>C7I6J5M2N3N1N2O1N2O1N2O1N1O2N2N101N100O2O1O1N3N2N1O1O2M2O1O1N3N1O1O1N5L4L2N1O0000000000000O1000O10000001O00000000O101O000000000000000O2O0O2N1N2N2O2L3N2O1M3O2M3M3N3M2N101N2N1O2N2N1O2N5Jj0VO4L3N2N2M3MPng6\", \"size\": [1280, 720]}, {\"counts\": \"fjT>4jW17J3I5K6J6G9M3G8N3H8M6K2N3M3M1O2N1O2N2O0O1N2O1O1O1O1O001O2N1O101N2O001O0O2O001O1O001N2O1O1O2N2N2N101N1O1O10O0000001O0000000O100O1O1O2O0O1O1O1O1O2O0O1N2O100O101N100O101O001N101N2O1N2O001N2O001N2N2O0O2N3L4L4M2M2L6K5J5K6L4L4L5K5K5J8FSV^9\", \"size\": [1280, 720]}, {\"counts\": \"jYac05iW17J4M4K5K4M2M2O3L3N2N2N2M2O1O2M2O1O000O2N1O1O1O100O100O1O1O010O1O100O1O100O2N010O1O2O0O2N2O1N2N2O1N2O1O1N2O00001N101O001O001O001O001O00010O001O01O1O1O010O01O01O010O000001O000001O0001O0001O01O001O0001O0010O01O001O2N1O2N1O1O1N2O001N2N2N3L4]Oc0K4N2O2M2O2N1O1O1O2M3M2N4M3L4M3L4J]]W3\", \"size\": [1280, 720]}, {\"counts\": \"SZ`a0?XW1>I8I2N4L3N1O3M3L2O2N3L2O1O1N2O2M2O1N2O1O1O001O3L9H7I2N2N1O1O1O1N2O1O001O001O0000001O001O001O001O001O001O001O0000001O0000001O0001O000000001N11O00000001O00001O1O3M3M00001O001O001O1N2O1O1O1O001O001O0O2O0O2N2O0O2N1N3M3N2L4N2M3M3K6L5H=ZOc]Q6\", \"size\": [1280, 720]}, {\"counts\": \"[VT::]W1>E7J7J5M4L3N2M3N3M4K5K5J6J5M3M2M2O2M101O1O3L2N3N1O1O1O2O0N2O10O0100O001O001O0010O0001O00000O1O2O00100O001O001O1O0001O010000O01000O1000O10O100000O001O1N2N2O1O1O100O0011O001O0001O0000000O1N2N2M3O1O1O2N101N1O1O1O1O2N1N3N1N3M4L3M4M2N3N1L5L3N3M3M3M4K5L8F;D[SQ=\", \"size\": [1280, 720]}, {\"counts\": \"XTSb02mW16H8J2N2N3M7I5L2M3N8G7giNZNlU1o1N2N1O0O2O1O0O2O3M2N1O0O2O000000001O001N2O3M1O001O0000001N10000000001O00000000000000000000000000001O0O100000000001O000O1000O1O02O1O001O000000000000010O12O1M00101M010000000002N010O00000O01000O101O0O2N101N101N101N1O10003J9AYjNVNlU1f18O1O2N1O1O1O1O101H`0F;E10001O0O2DahN5_W1IehN5[W1KfhN4gW1L]R^4\", \"size\": [1280, 720]}, {\"counts\": \"P[Vf0c0[W19H3M3L6F;J3N2N2N1O10O010O1O10N101N20O0001O1O000000001N2O001O01OO01O1000000O100000000000O01000000001O001O001O001O1O2N1N2O1N3M2O001O10O00010O01O1O1O10O01O00001O001O001O001O000000001O000001O01O0000001O000010O01O0000001O00000000001O000010O0000001N100010O01O000O101O0O2O1O001N2O1N2M3L4AajNVNeU1f1<O2N1N2L4O2O0O0100kD\", \"size\": [1280, 720]}], \"bboxes\": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, [440.0, 582.0, 22.0, 22.0], [435.0, 585.0, 23.0, 20.0], null, null, [372.0, 655.0, 40.0, 84.0], [292.0, 674.0, 65.0, 75.0], [198.0, 541.0, 65.0, 87.0], [379.0, 279.0, 93.0, 83.0], [557.0, 407.0, 55.0, 63.0], [428.0, 414.0, 87.0, 64.0], [254.0, 435.0, 54.0, 73.0], [306.0, 565.0, 63.0, 76.0], [372.0, 633.0, 43.0, 62.0], [358.0, 366.0, 50.0, 64.0], [342.0, 231.0, 38.0, 51.0], [290.0, 76.0, 40.0, 57.0], [247.0, 0.0, 51.0, 66.0], [208.0, 21.0, 66.0, 99.0], [244.0, 311.0, 83.0, 92.0], [368.0, 393.0, 91.0, 53.0], [360.0, 311.0, 50.0, 83.0], [308.0, 291.0, 39.0, 80.0], [275.0, 327.0, 53.0, 64.0], [324.0, 318.0, 47.0, 62.0], [361.0, 290.0, 52.0, 52.0], [422.0, 285.0, 29.0, 61.0], [411.0, 267.0, 40.0, 59.0], [329.0, 185.0, 41.0, 88.0], [251.0, 237.0, 67.0, 72.0], [287.0, 283.0, 51.0, 65.0], [344.0, 250.0, 48.0, 69.0], [394.0, 219.0, 56.0, 67.0], [448.0, 177.0, 153.0, 121.0], [446.0, 243.0, 26.0, 58.0], [382.0, 188.0, 56.0, 84.0], [288.0, 226.0, 63.0, 83.0], [310.0, 276.0, 47.0, 77.0], [321.0, 277.0, 61.0, 66.0], [349.0, 266.0, 58.0, 60.0], [405.0, 266.0, 41.0, 55.0], [399.0, 237.0, 55.0, 84.0], [325.0, 239.0, 42.0, 79.0], [286.0, 294.0, 75.0, 61.0], [381.0, 347.0, 58.0, 59.0], [393.0, 429.0, 58.0, 96.0], [417.0, 598.0, 67.0, 79.0], [434.0, 606.0, 69.0, 79.0], [463.0, 595.0, 72.0, 83.0], [473.0, 614.0, 64.0, 77.0], [460.0, 645.0, 63.0, 71.0], [446.0, 664.0, 72.0, 80.0], [465.0, 652.0, 78.0, 68.0], [470.0, 566.0, 95.0, 66.0], [475.0, 595.0, 88.0, 64.0], [450.0, 635.0, 85.0, 59.0], [434.0, 676.0, 83.0, 58.0], [448.0, 671.0, 73.0, 64.0], [440.0, 638.0, 76.0, 78.0], [452.0, 569.0, 76.0, 139.0], [503.0, 398.0, 108.0, 52.0], [547.0, 270.0, 97.0, 73.0], [543.0, 244.0, 87.0, 84.0], [353.0, 271.0, 111.0, 75.0], [248.0, 337.0, 104.0, 111.0], [291.0, 437.0, 62.0, 101.0], [382.0, 499.0, 65.0, 104.0], [469.0, 392.0, 91.0, 62.0], [480.0, 235.0, 116.0, 101.0], [394.0, 256.0, 68.0, 86.0], [215.0, 369.0, 97.0, 120.0], [216.0, 501.0, 66.0, 113.0], [313.0, 622.0, 63.0, 89.0], [369.0, 632.0, 62.0, 90.0], [355.0, 356.0, 59.0, 92.0], [335.0, 316.0, 54.0, 66.0], [378.0, 354.0, 57.0, 87.0], [519.0, 372.0, 88.0, 91.0], [589.0, 337.0, 82.0, 55.0], [591.0, 381.0, 85.0, 55.0], [528.0, 413.0, 112.0, 63.0], [402.0, 297.0, 106.0, 73.0], [378.0, 327.0, 129.0, 53.0], [396.0, 402.0, 121.0, 54.0], [430.0, 466.0, 115.0, 73.0], [509.0, 478.0, 78.0, 84.0], [581.0, 546.0, 60.0, 54.0], [480.0, 478.0, 103.0, 80.0], [373.0, 350.0, 95.0, 54.0], [354.0, 329.0, 71.0, 56.0], [359.0, 371.0, 54.0, 56.0], [338.0, 381.0, 46.0, 58.0], [397.0, 338.0, 61.0, 91.0], [485.0, 372.0, 101.0, 83.0], [376.0, 340.0, 102.0, 60.0], [327.0, 309.0, 80.0, 51.0], [324.0, 325.0, 66.0, 153.0], [312.0, 375.0, 55.0, 62.0], [276.0, 368.0, 54.0, 72.0], [390.0, 294.0, 80.0, 104.0], [541.0, 333.0, 79.0, 56.0], [592.0, 329.0, 66.0, 69.0], [420.0, 285.0, 237.0, 81.0], [401.0, 212.0, 94.0, 71.0], [418.0, 198.0, 126.0, 118.0], [527.0, 264.0, 106.0, 77.0], [463.0, 264.0, 102.0, 77.0], [299.0, 306.0, 71.0, 70.0], [283.0, 311.0, 77.0, 70.0], [402.0, 218.0, 76.0, 91.0], [474.0, 214.0, 108.0, 90.0], [380.0, 267.0, 91.0, 84.0], [361.0, 256.0, 89.0, 84.0], [476.0, 221.0, 215.0, 77.0], [527.0, 239.0, 114.0, 76.0], [559.0, 225.0, 150.0, 97.0], [381.0, 148.0, 114.0, 131.0], [307.0, 221.0, 129.0, 93.0], [548.0, 218.0, 143.0, 112.0], [507.0, 109.0, 116.0, 206.0], [115.0, 283.0, 206.0, 110.0], [191.0, 295.0, 155.0, 118.0], [473.0, 249.0, 138.0, 104.0], [447.0, 273.0, 100.0, 110.0], [362.0, 272.0, 116.0, 121.0], [500.0, 281.0, 137.0, 104.0], [448.0, 302.0, 117.0, 117.0], [259.0, 364.0, 127.0, 142.0], [463.0, 374.0, 143.0, 103.0], [568.0, 337.0, 152.0, 100.0]], \"areas\": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, 295.0, 285.0, null, null, 2498.0, 3790.0, 4321.0, 5596.0, 2765.0, 3579.0, 2618.0, 2971.0, 1951.0, 2203.0, 1481.0, 1588.0, 2639.0, 5135.0, 4930.0, 3311.0, 2467.0, 2390.0, 2194.0, 2015.0, 1943.0, 1324.0, 1759.0, 2946.0, 2694.0, 2246.0, 2252.0, 2403.0, 1005.0, 1116.0, 3213.0, 3000.0, 2218.0, 2335.0, 2014.0, 1872.0, 2881.0, 2616.0, 2663.0, 2582.0, 3924.0, 4060.0, 3895.0, 4135.0, 3564.0, 3140.0, 3670.0, 3972.0, 4419.0, 4147.0, 3568.0, 3148.0, 2738.0, 3735.0, 7484.0, 4312.0, 4948.0, 5307.0, 5400.0, 7192.0, 4583.0, 4430.0, 4108.0, 7457.0, 4254.0, 7201.0, 5617.0, 3900.0, 4461.0, 4064.0, 2844.0, 3706.0, 3975.0, 3216.0, 3554.0, 4882.0, 4203.0, 4064.0, 5159.0, 5029.0, 4266.0, 2421.0, 4478.0, 3920.0, 2614.0, 2233.0, 2030.0, 4815.0, 5163.0, 4361.0, 2678.0, 2659.0, 2519.0, 2517.0, 5285.0, 3148.0, 3385.0, 5287.0, 5625.0, 9025.0, 6004.0, 6208.0, 3887.0, 4073.0, 5251.0, 6918.0, 5852.0, 5755.0, 6656.0, 6683.0, 10369.0, 8155.0, 8172.0, 11311.0, 16734.0, 16424.0, 12590.0, 10406.0, 8310.0, 10003.0, 9505.0, 10830.0, 12278.0, 10729.0, 11284.0], \"iscrowd\": 0, \"video_id\": 0, \"height\": 1280, \"width\": 720, \"category_id\": 1985, \"noun_phrase\": \"the right hand\"}, {\"id\": 1080, \"segmentations\": [{\"counts\": \"QXn>5`W1`0D:H6L4L3O01000000O1O010O100O4L5J6J5K;DZ`[<\", \"size\": [1280, 720]}, {\"counts\": \"j_j>>VW1b0E7L5K6J4N1M30O1N2O0N2O2N4M3L4L5L5J4L6I:Gjo]<\", \"size\": [1280, 720]}, {\"counts\": \"QPh>;YW1`0G7I8J5L1L5O1O1001N101N3N1N4M5J6J4M7H6JQha<\", \"size\": [1280, 720]}, {\"counts\": \"i`e>6aW1c0@:G7K3M3N2N1N2N2N3L300100O01O1O03N1O1O1N2N4K6K5D>FSh\\\\<\", \"size\": [1280, 720]}, {\"counts\": \"QYS?b0WW19I8I6K5K5K4O1M2O10000O100001N2O0O1O1N3N4L3L5GggR<\", \"size\": [1280, 720]}, {\"counts\": \"fQ\\\\?2[W1i0C8I7J5N3I6K5L4L5N11O3M2N0O10001O00001O1N2O0O1O6J6J5K:F6IQ_b;\", \"size\": [1280, 720]}, {\"counts\": \"oQf?:`W18E:I6K6J6L3L5I6M3K500O100001N10000O2N1O100O2N3M2OO00O;D\\\\gY;\", \"size\": [1280, 720]}, {\"counts\": \"`Ye`0l0RW18G`0B4K5K4N1M3M30O1O00N3M2001O0O2O4L3M5K<C5K8E:Cl]_:\", \"size\": [1280, 720]}, {\"counts\": \"ci`a0P2QV12OO000O1O001N2O1O1N2N01NO01?_O?_Ofmm9\", \"size\": [1280, 720]}, {\"counts\": \"[Ykb0`0;FjV12ohN;XW14M5PiNXO58_U1h1O1N2N1O2O0O1O2N101N11O001O00O11O1O2N00O01O4L2O1N4M2M2O3L3L5L4K5K7H9FWmg7\", \"size\": [1280, 720]}, {\"counts\": \"facd0;cW13N4M2L4L4M3N1N3N1O2N20O1O00N2N2O2N1O2M3N2N1O3Ma0_O20N1O1O1O1O1O001O1N3N1O002N001O1O2N1O1O1N2O1O2N1N2O2M3M3M3M3L4M4K5L4JT]U5\", \"size\": [1280, 720]}, {\"counts\": \"PjSe01lW18K3L5K4H7K5M3M3M2010O0O02M2O2M2O2M2N3M3M3O1N3UjNVN[U1X21N2O1N2N1O1O1O1O2N1O1O1O1O1O1O1O1N3N1O1O1O002M2N3N1N3M3M3M4L2M5L4J5KP]f4\", \"size\": [1280, 720]}, {\"counts\": \"Pjid01lW1=E3N3K5K4I7N2O1N1N3N2M3N3L3M4M2miNbN`U1Q2O0000O0001O01O3L3N2N2N2N2N1N2O2M3N1N2O1N3M3N1N3M3L6J5J;DRe`5\", \"size\": [1280, 720]}, {\"counts\": \"nQ\\\\d0;cW16L2N2L5K4K4O2N2N1N3N2N1O2niNhNXU1[1cjNiN[U1Z1`jNhN_U1]1[jNeNeU1k1000001O0001O001O2N3M2N1O1O2N1N3M2O2M2N3N1N3N2M3L4L4L6J<CQ]n5\", \"size\": [1280, 720]}, {\"counts\": \"jiRc03jW1;G6K2M3M3N001O1O2M2M4M2N3N3L5K<F3N00ON101N10001O000001O001O3N1N1O2N1O1O1O2N1O1O2N1O1N3N1N3N1N2N3M2N3M4L5J7HV]l6\", \"size\": [1280, 720]}, {\"counts\": \"RQXa0h0WW14L3O2N1N?A4L2L8I2O1N2O001O0001O00001O0010O01O0O5L3M2N2N2N1N2O0O3N3L3N2M3L3L6L4M3L3L3K5LieS9\", \"size\": [1280, 720]}, {\"counts\": \"ga^?6iW13M3N1nNNSjN4fU1:QjNIkU1>oiNDoU1W101N100M300001O000000001O001O001O0O2OO0100O4L5L2M4K7I6J7Hn^X;\", \"size\": [1280, 720]}, {\"counts\": \"eXn>7gW14M2N2M3I8G8K4M4L3M4N1O1N2N2M3M31O001O0O2O0O3N1N1O1O1N2O2M3M2N3L6J6]OhPj;\", \"size\": [1280, 720]}, {\"counts\": \"noo?7PW16XiN0cV1i0N3OO01O1L4K401O0001N2O1N2O001N10O11O0O2O1N2N2N2K5K6G:AQan:\", \"size\": [1280, 720]}, {\"counts\": \"omh`0=4I0MmV1o0M3L2O001OL5O0O1N2I7M6D;IRbi:\", \"size\": [1280, 720]}, null, null, null, null, null, null, null, null, null, {\"counts\": \"olW<7eW6MWPK2M1000001O001O0O10001O1N2O00001O00000O010010OO101O000001OO2L3003L10000000001O1O00003M0O012M3M1NSch=\", \"size\": [1280, 720]}, {\"counts\": \"VTm<3lW11N3N2N3M1O2O00000O1O2O00000O2O0001O001OO10000O100O10O10O100000O10000O010O1O10001O1O1N2O0O6JoS\\\\=\", \"size\": [1280, 720]}, {\"counts\": \"\\\\Si>5hW17I5L3O010N2O0O2O0001OO2O0O2O1O001O000001O0O1K6J5GU]Z<\", \"size\": [1280, 720]}, null, null, null, null, null, null, {\"counts\": \"lV^?3lW15K2N3M1O3N000O2O0O2N102O1M4M3L4M10L3N2O0K5L5O2M3M3L5K4L[aa;\", \"size\": [1280, 720]}, {\"counts\": \"ago?16b0nV1<K5L3M2N2O1O1O1O2O2M2O1N1O11M2O1O1N3L3N3I7K5]OgXV;\", \"size\": [1280, 720]}, null, null, null, {\"counts\": \"]`b`06fW18J4M4L6J1O1N2O2N1O2N2N100O2N1000O10000O100O1O1O11O0O11N1O2Ac0_O\\\\`Y:\", \"size\": [1280, 720]}, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, {\"counts\": \"UXf`036O]W1g0C2N1N2M3O2N1O001N11N2O4L4L3M8Fn_h:\", \"size\": [1280, 720]}, null, null, null, null, null, {\"counts\": \"Uh^`08dW16K4M3M3N1OOK5110UOSiNd0mV1ZOUiNe0SW10O0014J7AahN2`_R;\", \"size\": [1280, 720]}, null, null, null, null, null, null, null, null, null, null, null, {\"counts\": \"ZhY`03mW10O10O010000N2J5O2OJ42N3O00N12N3N2NM30O2OO02M2O1O11N4L4KmPf:\", \"size\": [1280, 720]}, {\"counts\": \"QPV`0:^W18L301O001O001O2N1O1M3M3O1O3NO1N3L4N1O2M3N1K5L5M6Kc`m:\", \"size\": [1280, 720]}, {\"counts\": \"QWm?2iW18L4L3N1bhNEUW1e0N2N1N10OOKSiN[OkV1f0WiNXOiV1j052N4L4M02O=Bc0\\\\O]h];\", \"size\": [1280, 720]}, {\"counts\": \"f__?2mW16J3M3L3O00001M2O1O00101O00001O0000O2N2N2M2O2O1N1100001N1O7JO1O10O2LRhX;\", \"size\": [1280, 720]}, {\"counts\": \"d`Z?1oW11M3G:^hNGTW1e0O2N1M2O2O0100O03N1O1O1N10000000O1O1O1O100J6J8K4M3M3M6Io_a;\", \"size\": [1280, 720]}, null, null, null, null, null, null, null, null, null, null, null, null, {\"counts\": \"dmf?3lW14M1N1O1O2K6H7N5K5L1O01O2N2M2M`0XObjg;\", \"size\": [1280, 720]}, null, null, null, null, null, null, {\"counts\": \"gTj>8gW16I7J5K5L1O002N001O001O1O1O1O00101O00O0N3I7K4J7L3M4L6IXkV<\", \"size\": [1280, 720]}, {\"counts\": \"U]a>?_W14L3O2N2M3N1O2N00100O1O1O10O10O01O2O0O2O0O0O2O3M2M3L4N2M3N2O3MQbZ<\", \"size\": [1280, 720]}, {\"counts\": \"SVV>2jW1401O0O2O1O1O1O1N110O1O2N2N2N2O0O2N1O0O1000000001O0001O01OO2O0010000O0100O2N1O2N1O0O1O3L4LeYT<\", \"size\": [1280, 720]}, {\"counts\": \"Z`h=3lW14M2N3M1N2L41N2N00O2N2O001N100O10O10O2N100O00100O100O010O01O1O10O10000O1O12N1N101M3N2N1O2O2JPh`<\", \"size\": [1280, 720]}, {\"counts\": \"VZl=1_W11mhN1nV1<hhNGVW1c0O0O100000001O01O0000000O2O0O2O00001O1O1O10O01O1N2O1O1N2N4M1O1NPnk<\", \"size\": [1280, 720]}, {\"counts\": \"[jU=2jW16M2N2O0O10000O2O1N102N1O1O001O1O1O1O000000000001O001O100O1O00100O000O2O0O2N1L6N3HYP51loJO[UR=\", \"size\": [1280, 720]}, {\"counts\": \"WjU=6gW15L3N1O2M2O2N101N101O1O100O001O010O20N1O1O100O2O1N1O011N1O2N2N3I6K_ec=\", \"size\": [1280, 720]}, {\"counts\": \"m[Q>1iW1;J2O1N2O1O1O001O0011O1O1N01O010OO2N100001O001O10O1O10O000001EahN2jW1Lf[i<\", \"size\": [1280, 720]}, null, null, {\"counts\": \"agRa01iP55jVL8L3K6N100012NNO2K5O2M4L3N2No_Y:\", \"size\": [1280, 720]}, {\"counts\": \"bhc`0<_W16G9N101O0000O01000FTiN_OnV1;>L5MkX1K^nj:\", \"size\": [1280, 720]}, null, null, null, null, null, null, {\"counts\": \"RPg?:cW15J5N3M4L3M2N20N2N3N2M2M4M4K4L3N3Mj_f;\", \"size\": [1280, 720]}, {\"counts\": \"R_i?;dW14L7J7I2O1N10000O2O00000000O1001OO1O2O0O3N00010O00O02O0O7I2N4J3N2M3Jkgn:\", \"size\": [1280, 720]}, {\"counts\": \"Qok?9fW14L2N3M2O1O1O01O05K2N2N2O0O3N00O2O2N4L6J0N3N5J6HThX;\", \"size\": [1280, 720]}, {\"counts\": \"YPl??bW100001M1O2O00O0101N2N2O0000001N101O001O00N2O100O100000002N3M001O0100O01O0010000O1O0O2O1O3M2N4JUo[:\", \"size\": [1280, 720]}, {\"counts\": \"XQ[`01nW12M5M3M2bhNHoV1f0O0O2N10O0100O1N2001O00N200O100O000100N2O100O100N2N2001O02M2M3J5D<MUWX:\", \"size\": [1280, 720]}, {\"counts\": \"hoi`06hW13]hNIZW19dhNIZW1`0N4L1O00100001O01O101O1O0O1OO102O1N1O0001O0O1O2N10O0101O1O2ON3M2ZOmhN;]W105K2LjWi9\", \"size\": [1280, 720]}, null, null, {\"counts\": \"dP``05jW12M2N200O00100O1O2O1O1N2OO0010O1O10O1O2N100O2N102Mf_c:\", \"size\": [1280, 720]}, {\"counts\": \"lo_`0=]W18L3N2O1O0000000000O100O2K5L5L9HShn:\", \"size\": [1280, 720]}, {\"counts\": \"aoP`01nW15K4M9G3N0O000100000O0100O1N3K5M25L00O:ChgX;\", \"size\": [1280, 720]}, {\"counts\": \"agj?2cW1a0H4L5L4I6M2N2O1O0O2O2L40N3N1O4J4M;D5L6DchNI`W12Yh];\", \"size\": [1280, 720]}, {\"counts\": \"[Wc?3iW16N1N3M2N2O2M3K5L8H2N2ON3O100001O2N1O0000O2O001N4K4L3M3N8H8DePZ;\", \"size\": [1280, 720]}, {\"counts\": \"Pho?2nW1:E2N3M2M4M2K6H6O1O000101O0010000IXiNTOkV1g09N4K5J6JU`W;\", \"size\": [1280, 720]}, {\"counts\": \"aWo>`0_W13M2N1O202M1O1O1O101O1N20OO0100O1O2M2O1O1N1N3M4N1O1O1O3N2MjWo;\", \"size\": [1280, 720]}, {\"counts\": \"dWo>`0^W13O1O001O000010O00O2O2N1O1O2N00O2M5K5M2M2O1O1N2O1O2N1O100NlWo;\", \"size\": [1280, 720]}, {\"counts\": \"eoW?6iW17I2O1O2M7I3M2M3M2O3L3M3M4L2OL4O3O1O1N2N3M3N10100M3L8F^Pi;\", \"size\": [1280, 720]}, {\"counts\": \"i`f>431`W1;L7I1O2N1O010O1O1N2002M10001N101N2O001N100O2N1O2N1O2L200001O01O1N5KTh3NWoh;\", \"size\": [1280, 720]}, {\"counts\": \"gPS?4iW14M3L5L3L3N3M2N20001N1000010O01O010N2O1O100O1O1O1O00001O001M4N1N2O1N2N2O2NX_a;\", \"size\": [1280, 720]}, {\"counts\": \"Uhj?1nW12M3N3N2N1O2N1O1O2L4G:E9O001O100100O5J4M2M4L7J5I7J4KT_W;\", \"size\": [1280, 720]}, {\"counts\": \"^Xc?5gW18K3M5L4L3N1O2M01O1O1O1O001O1O1O1O1O000000000010O01O01OOO21O2N1O2N1O1O1O1NTWQ;\", \"size\": [1280, 720]}, {\"counts\": \"c`X`03jW15M3N1O1M3G:M3N2M3O0O0002N2N102M3N4M5H4KTWQ;\", \"size\": [1280, 720]}, {\"counts\": \"l`i?4aW1>K3N2M3L5M2M3O00000010001O01O1N1O2N4K8I=_Oln^;\", \"size\": [1280, 720]}, {\"counts\": \"WQg?2eW1:N4H6M3O0O201O0O0100O010O10O10O00000100O01O01001O1O1N2N1O3L3N2N3Ja^R;\", \"size\": [1280, 720]}, {\"counts\": \"bQ[`01nW12M2N3N3L3N2M4L3N2N3M100001L3N2N4O1N5K5J`fn:\", \"size\": [1280, 720]}, {\"counts\": \"]Ya`03kW15L2N1O2N2N3L4M3M2O0O2O00O100O1O1O2N3N2N3M4L2N3Hc^c:\", \"size\": [1280, 720]}, {\"counts\": \"biT`01nW15K4L2N2O1N1O10000O101O0O2O1O1O3MO2N1N3N1O10001O003N3L3M3NUfi:\", \"size\": [1280, 720]}], \"bboxes\": [[382.0, 738.0, 21.0, 46.0], [379.0, 737.0, 22.0, 58.0], [377.0, 735.0, 21.0, 56.0], [375.0, 746.0, 27.0, 69.0], [386.0, 772.0, 24.0, 60.0], [393.0, 763.0, 30.0, 71.0], [401.0, 778.0, 29.0, 69.0], [426.0, 800.0, 25.0, 75.0], [448.0, 819.0, 17.0, 69.0], [482.0, 811.0, 39.0, 90.0], [527.0, 813.0, 60.0, 77.0], [540.0, 814.0, 59.0, 82.0], [532.0, 816.0, 46.0, 82.0], [521.0, 819.0, 46.0, 75.0], [488.0, 813.0, 55.0, 73.0], [441.0, 798.0, 45.0, 70.0], [395.0, 775.0, 36.0, 64.0], [382.0, 745.0, 35.0, 61.0], [409.0, 731.0, 30.0, 51.0], [429.0, 701.0, 14.0, 40.0], null, null, null, null, null, null, null, null, null, [313.0, 660.0, 54.0, 21.0], [330.0, 633.0, 47.0, 23.0], [378.0, 610.0, 26.0, 29.0], null, null, null, null, null, null, [395.0, 466.0, 29.0, 38.0], [409.0, 490.0, 24.0, 57.0], null, null, null, [424.0, 505.0, 32.0, 44.0], null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, [427.0, 502.0, 17.0, 40.0], null, null, null, null, null, [421.0, 496.0, 16.0, 34.0], null, null, null, null, null, null, null, null, null, null, null, [417.0, 482.0, 29.0, 43.0], [414.0, 485.0, 26.0, 38.0], [407.0, 465.0, 20.0, 40.0], [396.0, 492.0, 35.0, 24.0], [392.0, 507.0, 32.0, 40.0], null, null, null, null, null, null, null, null, null, null, null, null, [402.0, 420.0, 17.0, 36.0], null, null, null, null, null, null, [379.0, 402.0, 28.0, 43.0], [372.0, 416.0, 32.0, 40.0], [363.0, 445.0, 46.0, 34.0], [352.0, 507.0, 47.0, 32.0], [355.0, 559.0, 35.0, 27.0], [337.0, 578.0, 48.0, 27.0], [337.0, 571.0, 34.0, 35.0], [359.0, 629.0, 33.0, 27.0], null, null, [437.0, 497.0, 19.0, 36.0], [425.0, 515.0, 16.0, 30.0], null, null, null, null, null, null, [402.0, 502.0, 18.0, 34.0], [404.0, 480.0, 35.0, 53.0], [406.0, 477.0, 25.0, 42.0], [406.0, 518.0, 48.0, 28.0], [418.0, 538.0, 39.0, 38.0], [430.0, 496.0, 39.0, 39.0], null, null, [422.0, 519.0, 26.0, 22.0], [422.0, 499.0, 17.0, 27.0], [410.0, 495.0, 21.0, 31.0], [405.0, 472.0, 22.0, 53.0], [399.0, 473.0, 31.0, 40.0], [409.0, 493.0, 23.0, 40.0], [383.0, 493.0, 30.0, 39.0], [383.0, 498.0, 30.0, 29.0], [390.0, 481.0, 28.0, 52.0], [376.0, 527.0, 39.0, 32.0], [386.0, 515.0, 38.0, 33.0], [405.0, 496.0, 27.0, 49.0], [399.0, 520.0, 38.0, 35.0], [416.0, 515.0, 21.0, 39.0], [404.0, 518.0, 22.0, 42.0], [402.0, 533.0, 34.0, 40.0], [418.0, 546.0, 21.0, 29.0], [423.0, 545.0, 25.0, 29.0], [413.0, 554.0, 30.0, 26.0]], \"areas\": [729.0, 901.0, 858.0, 1330.0, 1198.0, 1594.0, 1591.0, 1489.0, 970.0, 2458.0, 2777.0, 2833.0, 2355.0, 2317.0, 2592.0, 2195.0, 1776.0, 1476.0, 1156.0, 408.0, null, null, null, null, null, null, null, null, null, 655.0, 783.0, 582.0, null, null, null, null, null, null, 570.0, 1020.0, null, null, null, 1077.0, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, 497.0, null, null, null, null, null, 332.0, null, null, null, null, null, null, null, null, null, null, null, 329.0, 639.0, 520.0, 530.0, 893.0, null, null, null, null, null, null, null, null, null, null, null, null, 357.0, null, null, null, null, null, null, 808.0, 844.0, 961.0, 944.0, 677.0, 768.0, 721.0, 527.0, null, null, 246.0, 308.0, null, null, null, null, null, null, 384.0, 945.0, 575.0, 950.0, 1013.0, 980.0, null, null, 371.0, 375.0, 348.0, 733.0, 858.0, 596.0, 730.0, 536.0, 911.0, 704.0, 838.0, 621.0, 836.0, 474.0, 638.0, 798.0, 368.0, 474.0, 489.0], \"iscrowd\": 0, \"video_id\": 0, \"height\": 1280, \"width\": 720, \"category_id\": 3802, \"noun_phrase\": \"the left hand\"}, {\"id\": 1081, \"segmentations\": [{\"counts\": \"jje:1iW16L40N110O1000O010O001M2010O1O100OJHehN9aW1ON21O1O003N1N4M2NeU[`0\", \"size\": [1280, 720]}, {\"counts\": \"`j[:1oW11N2N1O01OO2OO11O1M2N30O01000O1N2O1O100O100M3NgTc32\\\\R^L0mhN00O1OXbo<\", \"size\": [1280, 720]}, {\"counts\": \"YZT:3jW13O100OO2O0100O001N1O0O3O010O0010O010O1O1O0010O0100N200O2N3MZnh`0\", \"size\": [1280, 720]}, {\"counts\": \"[jV:1oW10O1N2N3N001O10M20N2OO2001O1O1O010O100O1000O1O1O100O100O2OYfg`0\", \"size\": [1280, 720]}, {\"counts\": \"Zj[:6iW1O11HO`hN1bW153N00N100O11O01O10O100N2N111O000O1000T^k`0\", \"size\": [1280, 720]}, null, null, null, null, null, {\"counts\": \"`do73iW1>D8J4L3M1O2M2O100001OO4\\\\iNiNWV1b1K5M3M2N2N101O000003M2M2O3L2N2N5K3L3N3M2M4I8HU[gb0\", \"size\": [1280, 720]}, {\"counts\": \"^e[77gW14L5L3M4N0O3N2OO12PiNVOcV1j0ZiN]OcV1S1M2OO3K3N2M3N3M3L8H9H2M7JYkU>0gTjA0001Ok]_5\", \"size\": [1280, 720]}, null, null, null, {\"counts\": \"_Th78gW15K4N2M2O1N2N1O1N2O2O0O1O1O1O01O000001O0000000001O000001O01O2N3M2M2O1N2M4M3O2M7Fibhb0\", \"size\": [1280, 720]}, {\"counts\": \"iXd71jW11400000>0@4G8N101O001N100000000010N10000001O00000001O0000000000000000001N2O001O00002M3N3I^Xc12dg\\\\NO;OF20NZfl`0\", \"size\": [1280, 720]}, {\"counts\": \"XYe97fW14L5K5M2O2N3M3N1O1N11O00O1O100O2O2N3M0O000001O1N11O0001O001O000001O00000100O00010O1O1O1O002M2O1O2M2O1O1N2O2M4KRfX`0\", \"size\": [1280, 720]}, {\"counts\": \"bQS:9eW15L4M1N3M3M3M3N2M3M4M2O1N2N1O2O1N1O101N000100O1O1N101O1O1O1O001O0O10001O1N3M2O1O2N1N3N1O0O2N2M4L3L5LjUQ`0\", \"size\": [1280, 720]}, {\"counts\": \"URX:3jW17K3MH`hN6\\\\W1JdhN8YW1JfhN8VW1=2M0N02N3@UOdiNn0ZV1SOeiNn0]V1;M200O1O100O01O01O1O10O11OO10O0001O1O001O0O2O001O000O1O2K5M3N1O2N101N101M4K?BYmj?\", \"size\": [1280, 720]}, {\"counts\": \"gaf97hW14L3M3M4L4L3N1N3M3M3M2N3N1O10001O001O001OO100O10O01O01O1O1O100O1O1O1O000010N101N3M3I7H`0VOVVe`0\", \"size\": [1280, 720]}, {\"counts\": \"jY[9d0ZW1e0]O7G6H6K4L4O01N4M2N1O1N101O0O3O1N2N3L2O1M4M4L3M3M3L8H5K6I4MYmaa0\", \"size\": [1280, 720]}, {\"counts\": \"mjP8>ZW1e0C5K3M1O001O1N2N1010N3N0O101O0000000000000O100O3N0O2O0O2O1N4M4J4H7M:EeTfb0\", \"size\": [1280, 720]}, {\"counts\": \"Y^`21lW1=C7J5K4N1N101O1O001O1O001O01O000001O001O00100O1O0010O01O00010O01O1O10O010O10O001O10O01O2N1O1O1O1O0000001O00002N2N1O0O10001O1O1N2K5K5M3OP`2MS`M0SYhf0\", \"size\": [1280, 720]}, {\"counts\": \"_<]1cV1001O0O2O001O001N1001O1N10O2O0001O0001O01O1O1O001O100O101N10O02O1N10O0001O000010O001O2OO0D^iNZOdV1a0`iN^OcV1=diN^O`V1=aiNC`V1;aiNE_V1:biNF`V17aiNI`V16`iNJeV13XiNNiV11WiNOhV12YiNMdV16b002M2Ml00c^70b`H21OQP:OPPF0000000Ph80PXG00000[R^h0\", \"size\": [1280, 720]}, {\"counts\": \"_h;1eW1?J4M1N2O001N2O1N2O1O01O01O1O0000000000000000000000O1001O0000000000000O100O1000001N101O001O0O0010O10001O00001O100OO0GoNdiNo0iV1L7XOmhN:gW1EZ_Yi0\", \"size\": [1280, 720]}, {\"counts\": \"PWU2=_W15M2O101O1N1O2O1O001O1O1O3M1O1O1O001O1O01O000N3N1O2O00001N10001O00001O1O001O001O1O10O02O0N2O1N102O0O1O1F_hN2iW1O1O1N2OO1001OZhcg0\", \"size\": [1280, 720]}, {\"counts\": \"gmk59dW15M4L2N2N2O101OO01N2O2M101O0010O01010OO01O0001O1O02O1N002N1O1O1O010O00100O100O001O1O001O1O0O2I6J7NhaYd0\", \"size\": [1280, 720]}, {\"counts\": \"\\\\lm69dW16K7J:F2N2N1O3M2N2M2O2N10000O100O1O10O01O1N2O001O010O00001N1O001O100O101N1O01N1K7M5Li0WO2NF[O[iNe0fV1\\\\OZiNb0SW1L5K6M[dWc0\", \"size\": [1280, 720]}, {\"counts\": \"YPe6455XW1<N1O1OO10000000O7I4E=L1JbNjiNZ1VV1fNliNY1]V1000001O00N2M6C=0N1O3M1N1O002N10O0N10O2N2O3MhiNdNnU1[1SjNeNnU1W1?I8M3L4M3M6J9H\\\\_ec0\", \"size\": [1280, 720]}, {\"counts\": \"XgW5<`W19J4L3N1N2O2N1O1O1O3M3M2N2N2N2O2M2N3M2O0O2O01O02ON4M200O01O00O2M3M2J7_Oa0I6LRiNAaV1=UiNC13>OfU17UjNH71eU16mjNHUU19\\\\1G`_Ve0\", \"size\": [1280, 720]}, {\"counts\": \"cSf4:bW1=F7I4M2M4L2O2N1O2N1O1O2M200O1O2N1O1O1O100O100101N1O003M2N2N1002NO3N1O0O1L3M4L5J5L5JbiN\\\\OgU1`0\\\\jN@eU1?[jNBiU15^i3NiWLO`R^e0\", \"size\": [1280, 720]}, {\"counts\": \"ebY49eW16J6K7I4L3N2M2O2N1O2M4M2N2N1O2N1O2O1N1O1O2N2O1N1O2O1N2O1N1O2O0O1O2O3L2O002NO2N4L7H;E7_OgiNnN_V1X12M3]O\\\\iNDhV1=XiNZOnV1c0UiNZOoV1b0`0^Olcje0\", \"size\": [1280, 720]}, {\"counts\": \"WZg47eW17L4M3M2N2N4L3M3N3L2N2N2N2N4L2N2N3M2N101N2O1O1N101O1N2O1N2O1N2N3N0O2O1N0001O010GdjNlM\\\\U1T2ejNjM]U1U2djNjM\\\\U1V28N2O1N1N2M5L6L4L8Hj0UO:FjkWe0\", \"size\": [1280, 720]}, {\"counts\": \"aTZ5?`W19G5L3M2N2N1O2N2N2N2N3M2N1O2N2O1N2N3M3N1N1N3O2M1O2O1N2O1N10000O3N1O2N0O10000O2O000010N2O000O2N1N3L4M4FZjNSNkU1h1=I5K7H:E:G5K6J[i^d0\", \"size\": [1280, 720]}, {\"counts\": \"]ff73lW16J6J>C7K0O0O2O4L6J6I6K3M4L2M30M3N00O10O0000O20O0O2O3M5J2N1001O0000000000O1O1H8M4M2H8^OTiNHUW15UQeb0\", \"size\": [1280, 720]}, {\"counts\": \"lkk73jW1b0_O6J4L3N3L2M4N1O1O2N100O10001N10000O10000O10O1O1O10O10O1O010O010O102N1N1O1O1O010O1O4L6J3M5K>C0OO010U\\\\Xb0\", \"size\": [1280, 720]}, {\"counts\": \"eYb8a0WW1=I4L4K5L3M2GaNPjNa1nU1cNmiN_1RV18N3O000010O00001O00001O1O002O0O1O1O0O3N001O00001O000O2O001O1O100O1O003L4M2M100O102N3M9G0N004MYh8BVXG000jUo`0\", \"size\": [1280, 720]}, {\"counts\": \"VmZ8c0VW19J5L4M2O1O101N100O1O100O10O010000O1O100O100000O01N2N110N2O1I\\\\NRjNc1mU1aNPjN`1PV181WNoiN`1SV1^NniN`1TV1]NPjN6No0\\\\V1oNeiNo0]V1POdiNo0^V1PObiNn0iV1N3N1N2M3N4L3M3K4J7N\\\\cja0\", \"size\": [1280, 720]}, {\"counts\": \"ZWi93hW1:I4L4J4L5H7M4N0O2L400I7K5O1IWNWjN5M[1lU1`02NoLPNdmN20Ne00ZO5Ba1mU1`NQjNa1SV132[NmiN[1SV1fNkiN[1RV1aNQjNc1[U1[NSkN3Ca1nU181NKWNUjNi1nU14]ORNRkNn1`U104J4[NliNY1_V1O2M3MM4FhNjiNZ1mU1gNQjN21V1aV1M6K2O2POUiNc0_W1EUic`0\", \"size\": [1280, 720]}, {\"counts\": \"Qjh;1nW11fS:a0gjECVjNm0fS1nN\\\\lN000MQ2`U1O2N00N1N3N10000N3O0001O3L=D1N2N2NN3H`NniNa1RV1bNliN^1YV11109F8G5K5L4L4M3L4JWZe>\", \"size\": [1280, 720]}, {\"counts\": \"Y[i71mW1:F7K4K4M1N102M3N2M3N1O1O1O00001O0O101O1O0O10001O0001OO100000001O000O11O000001N100O100000001O0001O00O1000O0100001N100O1O1O1O1O001O1O1O1O2N2N:G5H3O002O0O2M200O1O2O00WOFliN;RV1GmiN8TV1GmiN0\\\\V10j00000000000001O00O101O[U``0\", \"size\": [1280, 720]}, {\"counts\": \"lXm53eW1<H8J4J5L4L4M2O2M2O2O1N2O1N2O1O010O10O01O1O001N2O001O0100O0100O0010O1O10O1000O6L0OO1O1O10O010O2N1O1O1O3M2N5K1O1O100FYiNYOhV1e0YiN[OiV1b0XiN^OiV1?YiNAhV1:^iNCcV17f0O2O00002N1O1N2Mef\\\\c0\", \"size\": [1280, 720]}, {\"counts\": \"jjU31mW13N3M3H7O2M3N2I7D<H7N2O2N1O1O2N1O1O1000O1OO101O000O10000O11O0O101O000000000O10000O2O0O100O1O100O100O100O101N2N10O01O1O10O01O2N101N2O1N2N1O2N100O2N2N2N8H;F2M2N2OVmhe0\", \"size\": [1280, 720]}, {\"counts\": \"okQ5;`W18J3N2N3N1O1O2N2M2O2N3M2N1N2O101O0O1O3N1N2N2O1N10001OO100O0100O2O0O101N10O1000O100O100O0100O0001O01O1O00010O001O010O001O002M3H8K6MJkNdiNR1fV1O2M2O2M2N5L;D5L2N`_<Nd`CN1OWmbc0\", \"size\": [1280, 720]}, {\"counts\": \"hmS9=aW16J3M4K5K3I8K4N3M3M3N1O2N2N2J60O0100O00010O00001O0000000O10O2O000000001N2O5KN1011N6J5J8H`0]O^bVa0\", \"size\": [1280, 720]}, {\"counts\": \"f\\\\U<1kW17G8J6L4L3M2N2M4F9L4D<01N1O1M3M3N2M3N2O001O010O0101N100O10003L6K2N3L6K5K2M3N5K<C5KoSZ>\", \"size\": [1280, 720]}, {\"counts\": \"nXU<2kW1?gNIliN?oU1l0L3N2M3M2N2O000O1010O00O2O11O001N01O100000O100O100O1O100O1O1O1O1O1N11O01O5K3M3M2M3N3M3L7H=WOZ`Q>\", \"size\": [1280, 720]}, {\"counts\": \"UPg:b0WW19K5M2N1O2N1O1O1O2N3N1O4K2O2N010O1N2O001O001O0001O000O1000000O10000O1000001O000N2N2N2O001O2O001N100O3N3M7H5J6H:HcgU?\", \"size\": [1280, 720]}, {\"counts\": \"feZ8`0^W13M3N2N2O1N100N2N3M2N3L5L3N1O1O2O00O11O1O1O1004L2M20O00O100O01001O0O100O011N100O10O001003L1O002O0O00100O1O100000O0122J6K3L1M4DdhN1]W1OdhNNdg30R`M4L40M2M3Mfhg00kW]?\", \"size\": [1280, 720]}, {\"counts\": \"RZW91nW1n0SO4K2ViNPObV1W101N3N1O2M3G8F:M4M2O001O1O1N10001N1O2N2N1001O001O00100N3N1O3M1O2N3M2N2N1O1O2N2N1IoiN]NSV1\\\\1TjNbNmU1Z1WjNeNkU1X1WjNgNmU1T1b0L6K9C7L8FTUi`0\", \"size\": [1280, 720]}, {\"counts\": \"kZb;253UW1c0L9G4M4K3M3N2O0O2N2N1O1O2K5N2N101N2M2N3OO0000O3O0O10O1O100O1L5N1K6N2M3N2nN^jNDgU1NhjNJUo42jSh>\", \"size\": [1280, 720]}, {\"counts\": \"jmY>6hW17I4M9G2N2M4M8H3M3M1N3O0O2O0O100O2O1NO1101O1O1O00000010O0IoiN_NPV1b1oiN_NQV1a1oiN_NQV1h10IoiN^NSV1a1niN^NRV1b1niN^NRV1h100OK6O100O1N2L4L4M3O2N101N2I8F;G8Hfbf;\", \"size\": [1280, 720]}, {\"counts\": \"Vom>4YW1NmhNa0kV1=J7ViNoN^V1]1M1N2O1O001O10HjiNeNVV1\\\\1miN`NTV1_16101N10O010O0100O00O101O1O1N2L4I7O1O1O1O10000O2O0O1O1O1O2L3L4M3O1O2N2NUjX;\", \"size\": [1280, 720]}, {\"counts\": \"^fX>:eW17Ha0_O4L3N1O1O1O101N2O4K2O1N100O100O10000O2O1O0O101N2O0O0100O11O0O2OO10000O100\\\\O_jNfNcU1X1d0N2O1N2N2N2N2N2O1N3N1N2O2M2M5L4LTRd;\", \"size\": [1280, 720]}, {\"counts\": \"e_`<`0^W1a0@:G5J8I5K0O2O001N1O2N2N101N1O1O1O2N10000O101N11O0001OO101O001O1O001O1O001O1O0O2O1O2N2N1O1O2N1O1O1N2O001O1O1N2O0O2N1O3L3O1O2N1L4K5K5M2O9Aeok<\", \"size\": [1280, 720]}, {\"counts\": \"Qei81jW16N101O2N1O2M3N2N2M4L5K5QiNVO`V1X1M2N2N1O001O1O1O1O001O0100O10O01O1O100O2OO01O2O000O2O0O100O2OO0100O0100O0101N100000O15KO10O01000O100O5LO010O003NO2\\\\OYiNKhV12\\\\iNGjV15b0N3N3MU`2Nn_M1O0^QU`0\", \"size\": [1280, 720]}, {\"counts\": \"gk\\\\53jW17L00OO3N1O2Ib0@7L200O1O2N0O10O1100O2L4N1N102N2N1O002N10O01O001O001O1O1O001OO100O1O2N101O2M2N3N1N2N2O2M2O2M2O2N1N2N2N3E;L7ETdYd0\", \"size\": [1280, 720]}, {\"counts\": \"aRR61iW1:J?@7H6N2M6K3M2N1O1O1N2O001N101O1O1O1O1O1O00100O001O10O01O100000O01000O102M2O0O2N1O1O2N2N1O1O1O1O2N1O2M3N1O2M3N2GUiNYOlV1c0<H:C:Nb\\\\dc0\", \"size\": [1280, 720]}, {\"counts\": \"miZ;1[S?5\\\\TB8H4M3N3M2N4M3L3M3M3N3L3N1O1O1N3M2O2O1N1O1O001O001O0000100O0010O00000O2M2001O001O01OO1000O2O1N1O10O10000O1001O00O1O1O2O0MUNUjNg1kU1XNWjNg1PV11N201O00000000XNniNa1XV1100001O1O01O0001O2M5J5L3mNYiNh0iV1TO\\\\iN=LIhW1Jdo90]PF4YhNM^W12chNM^W61ngN0RXL0ahN1^W11ahN0gW1OnW1NeRn;\", \"size\": [1280, 720]}, {\"counts\": \"f^P:4eW1;K3M4M2L3M4K4L5K4L4L4J5O2N1N2N2L5L3M4M3N1O10000O2O000000001O0O2O00O1000001N10000O1O2O0N2O3M4M3L6J4L4L3M3M2N4K6Eb0D1001O02LeQf?\", \"size\": [1280, 720]}, {\"counts\": \"`l`8?YW1:L3N4K3L4M3N2M3M6K5K3L4M2N2O0N11000O001N2N2OO2O010O001O1O1O010O01O10O10O10000OM4G9M2010O01M2O2O10OO2O1O100O100000001O000O1J6H9L4N101N1O2O1L6J5Lhlg`0\", \"size\": [1280, 720]}, {\"counts\": \"SaQ:5gW1;G6J5G6O2N1N1O2N101O001000O11O1N2O1O2N1O2N1O000001OO2O0010O01O0000O2N1M3O20O3ML4O2N2N2N2O8E[gW`0\", \"size\": [1280, 720]}, {\"counts\": \"ZXY85`W1b0E6L4M2O2M2O1O1N2O001OO100000O100000000001O0000000000010O00100O00100O2N1O1O010O1O002N2N2L4G9K5M4M1O2M2O1O[Wga0\", \"size\": [1280, 720]}, {\"counts\": \"`XR9;eW15J3N2M2N2N2N101N1O101N1O10000O1O10000O10000001O00001O01O00001O010O00100O10O010O1O001O1O1N2M4M4L3O0O1N101N1L5L3N7JRWi`0\", \"size\": [1280, 720]}, {\"counts\": \"c`n=3_W1`0E:I6M3N2O2N1O1N3N1O1O2N102M2N101N100O1O10O010000O10N101O1N101O1O2N1O10001N2N2N3N1N4L3L4M2N2N1O7H8EZhU<\", \"size\": [1280, 720]}, {\"counts\": \"QhV=a0]W16K5K8I5K4L2O1N4L2OO1O10O1O001jiN\\\\NnU1k10O11O001O0O10001N100O2O1N2OO10O100000O1O2OO01O1O2N1N3N2N2N4K5K7I5K4L3M4J6J6L4J[Wg<\", \"size\": [1280, 720]}, {\"counts\": \"eoh<g0XW18H5L6K3M5J10O001O1O2M3O00000O01O2N0O001O7H7J4L1N2O1O0O2N10O02O000010N10001O010N2N2N2O1^NYjNj0iU1QO_jNj0dU1RO`jNj0^V1M3L4M5J8G_oY=\", \"size\": [1280, 720]}, {\"counts\": \"VPP<1nW14J5fhNHbV1b0YiN_OfV1c0YiN]OgV1b0ZiN]OfV1d0[iN[OeV1j054M0O00100O010O1O1N200O10000O2O0O1O101N2O0O2O1O000O1000000O10O1O1O1O2O0O1O1O2N1oN`iNa0bV1[OeiN`0\\\\V1_OjiN9lfV>\", \"size\": [1280, 720]}, {\"counts\": \"Y`i89eW14L3N1O1O1O2N2M2O1O1000O04K5J4O1N101N1O2NF;O10O01O100O010O100O010001N1010O0001O0000O1O10010OXiNSO`V1j0biNSOdV1h0<M4K7GR`Ya0\", \"size\": [1280, 720]}, {\"counts\": \"doQ6:dW13O1N3M2N4L6K3L4M1N2O2N1O1O1O2N1O2M2O001O1O000010O1O001O00001O10O01O1O2N2O0O2O0O100O100O1O1O1N2D<O1N2O2M3N2M4MPiNHZV16fiNJ]V13ciNM]V14h00O1ZP5KSX11ggIOgfVc0\", \"size\": [1280, 720]}, {\"counts\": \"[P`91fW1<J6K4L4L2N3O2N2M2N2N2O1N2O1N101O1N2O1O0000O100000000O101N2O2L4M3M4M3L6K4L4L4L5J\\\\gPa0\", \"size\": [1280, 720]}, {\"counts\": \"__i=d0ZW18I<D5L1O0010f0ZO0ON3F:L4L4K6E>Ab`f=\", \"size\": [1280, 720]}, {\"counts\": \"h^f<<bW17J8I4L4L4L2O2M4M5K9GO1L8I4N1O011N1000N101M3O0O3L4L2K6M2N5K4M1N2O002O0O01O001O0L5YObjN]NnU1o0l0J6H8K8J_ib=\", \"size\": [1280, 720]}, {\"counts\": \"Yfl<a0^W1:F8H4M4K202N2N01O1O0O101O1ObiNfNTV1[1kiNeNUV1]1hiNdNXV1b1001OO1O1N10000O2N1M301O0O2O1O1O01O01O00O1O0N4K4O101M2O2O1M3N3M2K5M3L5H9I\\\\jS=\", \"size\": [1280, 720]}, {\"counts\": \"QhW:1lW13N2N2H8N2N2O2N2N5L1N2N2O0O10O001O010O010O1N10100O10O010000O10000O1O100O10000O1000O01000000O10000O01000001O0000000O01001O00001O011N000000100O01O2M8G7J5J]Pl>\", \"size\": [1280, 720]}, {\"counts\": \"^^`5<:MeV17ViNMgV1g0M4M2N3M4N2N2N2M101O100O1O10O2N0001O001O001O001O010O000000001O01O4M0O10N2O0N2100O010000O10000OL4L4O2N3N1N2O1N3N1O2N5K10003J:F5KbPQd0\", \"size\": [1280, 720]}, {\"counts\": \"bah6;dW14L5L1M4M200O1O1OO2N1000001O00001O02N2N01O010O0]OPiNOLOUW11PiNLO2TW1OmhN9TW1HihN9WW1JjhN1WW1N>02N1N2O1O0000SVmc0\", \"size\": [1280, 720]}, {\"counts\": \"P`V:2gW18K6I7L8I5J5L4M1O002M3M2M3N000100O10O100000O1O1O101O0O2N2N1111N2NO2N1N3GbiNlN`V1Q19Mc0ZOR`[`0\", \"size\": [1280, 720]}, {\"counts\": \"fPS=<`W16K4M3M3M4L5L4K3M4L2N2N3N1N2O2O1N100001O00000N\\\\jNVNVU1i1kjNWNUU1h1ljNXNTU1f1njNZNQU1g1ojNYNQU1g1ojNYNQU1g1ojNZNPU1f1PkNZNPU1f1PkNZNPU1f1ojN[NQU1f1njN[NPU1_1ajNbN?OPU1_1XkN_NiT1a1k01M101O10O001O01O1O001L3O2O1M21UNQjN42U1TV1hNSjNR1aV1M00VOoNejNP1[U1ROejNm0[U1WOajNh0_U1YOajNf0`U1ZOajNe0`U1WOcjNi0^U1SOfjNk0YV1O001N2O001O0O1O2L5N1N2N2O106H__o;\", \"size\": [1280, 720]}, {\"counts\": \"iXj<6hW13L3N2L4G9I7K5M3OO00O3N2O2O1O000011O001N1000O01O000010O0O010O2N1J600N1O200O0O2M200O2O1N2O1M3000N2N2L4_NXjNf0lU1SO]jNi0eU1TObjNg0_U1WOejNd0^U1[OejN>gnT=\", \"size\": [1280, 720]}, {\"counts\": \"eX`<1oW1000kW12ShN3L6K3L2O1M3N101O0O1000000O100O100O10O01O1O1O0O200O1O1O1O100O1000O2N11O01O0O10001N100O10000O2O00000000000001O001N10001O000000O1O2N1N2N2O2_OfhN:bW1M4Kfgd<\", \"size\": [1280, 720]}, {\"counts\": \"dPT:612^W1<J2N203L3N1O1M3O2N10O0100O2N01O001O1N2O100O010O100O1O100O100O1000000O100O100O100O100O101O0O11O01N10001O00001O1N2O2N002L5Kh_b?\", \"size\": [1280, 720]}, {\"counts\": \"[Pk61mW16J4M3N1N2O1N1O2N2N3N4L2N3M2N2N100000001O00000000O010O2N1O1O010O2O00O0100O1K5I7O1O1N2O100O1N2O1O2M2O1NmnWc0\", \"size\": [1280, 720]}, {\"counts\": \"RXj76UW1LXiN<fV1a0M2O1N1O2O1O000O2N100000001N1000000O2O00001O00001O00001O010O2N2O0O01O1N3N2N1O2J6J5M4N2M3O2O0O0O:DhoYb0\", \"size\": [1280, 720]}, {\"counts\": \"`Pd<6hW121LHO86hhNH`V1<`iND]V1`0=5M1L5N1O2O1N3M5aiNlNjU1[1oiNhNnU1[1QjNfNmU1Z1TjNeNlU1[1VjNcNhU1`1YjN^NfU1e1ZjNYNfU1h19O0O2002N1O00N1N3M3M3M3M3N2N2K6J5N3M2^OhhN9[W1DihN7bhl=\", \"size\": [1280, 720]}, null, {\"counts\": \"aaa<;gV1HoiN>jU1JPjN:mU1JhiN?XV1CdiN>]V1a0KdNiiNY1ZV18N2N101O001N1O2N210RjNTNdU1_2E00O1O1M3N2N2GZjNWNgU1h1\\\\jNUNfU1i1:000O01M2O2N2M6I3O2M2F`iNROcV1n071O10L4N3M2K7J8H]gg=\", \"size\": [1280, 720]}, {\"counts\": \"Yca7:cW17J6J4L4L3M3M3N2M3N2N1O2N10000O2O0000001O0000000000000000O1000000000O1000O100010O000000001O1O01O010O00010O100O00010O010O10O0100O1000000O100O2O000O3N0O1E]iNVOeV1>hiN_OYV1>kiN@YV1;jiNC[V17giNInV;6lkg`0\", \"size\": [1280, 720]}, {\"counts\": \"SYY52lW1;D3O1O101O000O1O1O10001O0000010O000000000O10001O000000O101O01O000O2O10O0001O010O1O1O1000O2ON2O0001O001N10000000000010O1O00O2O001O0O00O10110O01O1O3L4M4L2NO0O21N5K3L`nbc0\", \"size\": [1280, 720]}, {\"counts\": \"eU41nW13L3N2N3M3L5L5K4L5L1N2N2OO0100O1N110O2O000O2O00O100O2O00000100O000001O001O001N1001O1O01O1O00001O00001N10O2OYO\\\\iNB45`V18fiNJ[V16ciNK]V15ciNK]V14giNIYV16iiNIWV16kiNIWV16hiNI[V15fiNJZV16fiNJ[V15fiNJUV10ZiN3fW1000O13M\\\\ZXi0\", \"size\": [1280, 720]}, {\"counts\": \"XTh08fW1:G7I:G2M100O10000O010O100O100O10001O0000000O010000O010O1O001O100O1O1N201N1]OUiN1lV1IYiN5jV1IWiN6kV1IUiN6\\\\W1M200O2N2O0O1O2N_[]i0\", \"size\": [1280, 720]}, {\"counts\": \"geR24iW16K5K4L:G1O1O102N1O1O0O10011N1O1O1O0000O1O10000O10000000000O100000000O10000O1000000O10001N101N101O000O101N100O1O1O2N3M;EUReg0\", \"size\": [1280, 720]}, {\"counts\": \"kQb37gW16J4L3L3O2N2M3N1O2N1O1O3M2N2N5K4L5J6K4L4L2N101N1O1O2N1O1O1N101O2N4M3L2N2O1O1O1O001N10000O10001O01O001O001O2M1M4L4O2N2N4M5hM]jNg1QV1M5K4M2K2N3M5L5M2M2M4M1O1O7I4K2M5LRU_e0\", \"size\": [1280, 720]}, {\"counts\": \"hbe:c0PW1j0_O5M4L2O2M2M20O1O11O3M2N2N2N2M4N0O2N3N2M2NO110N2O1I7M3M4M3K5J6L4K5J6L4M4K4M3M4L5KSni?\", \"size\": [1280, 720]}, {\"counts\": \"gVc:;aW13J7K5L4M3N3L2N3N5J8H6RjNWN9OaT1k1VkNVN9O_T1l1YkNUN8N^T1n1\\\\kNRNWU1n1jjNRNTU1n1njNRNQU1n1PkNRNnT1o1a000001OJXjNVNhU1h19O0N3M3O1O010O1O2O0N3L3N2O2N2N1O2L5L6J7I<FWbh?\", \"size\": [1280, 720]}, {\"counts\": \"]Tn71mW15J5M2N2L3O2M4L4M2N2O1O001O01O1O1O0100O010O1O10O0010O10O1000O100000000O100O100O10O100O10000O01O10O1O10O01O10O010O2OO10O1000001O001O@UiNGmV18ViNAPW1;=N4K3L3O]`71f_HL\\\\bPa0\", \"size\": [1280, 720]}, {\"counts\": \"]]j7?_W13K5M4M1O1O1O1O1L4O1O1O101O000000000O01000O100O01O0010O100O10O10O100000O10O100O10O100O010O01O0010O001O000O101O00001O1O00100O1O0O2O010O100O1000000N2O1O1O1O1N3N2N3LkSSa0\", \"size\": [1280, 720]}, {\"counts\": \"lVQ92iW1?D8H7J5K3L4L3J5L4M3N2N2N2M3N2O2N100O10000O10O1000000N2O10000O010O001N2N101L3O0O2O2M3M3O1O1O1N2N1N3M3N2O1N3L3O3K5FihNEZW14`0I4O01N5XRd`0\", \"size\": [1280, 720]}, {\"counts\": \"QRP91ZW12mhN=lV1e0F6H8I3I8N1O1O1O1O1O1O1O100O010O10O10000O1O1O010O1O100O1O1O100N2O1N2O1O1O1N1O1O2L5M2O1M4M3M2N2O100O1N6H7J5L5JXgl`0\", \"size\": [1280, 720]}, {\"counts\": \"dbh84hW1a0A7I5J6I5K5O1N2N2N1N3N2M2O1N3N1O2M2N2N2N200O100O1N200O01O010N2M3OO0100N3K4L5K5J6K5K6K5K4O101O1H;B;M2O2N3NcnWa0\", \"size\": [1280, 720]}, {\"counts\": \"Veh8<XW1>K4M2L5K4N2L5L3K5K5O100O1O1M3L41O0000001ON2H7O2N2O1N11000000O101N1O1O2N2N1O2L6E;[OWmfa0\", \"size\": [1280, 720]}, {\"counts\": \"g^Q9h0PW19K5L4M2N2N2N2M3M2O2N2M2N2O1N200O20O100000001OO10O10O10O01O1N2O11O0O10N2O16J2M2O000O100O100O10001N2N3N`0]O=DkQo`0\", \"size\": [1280, 720]}, {\"counts\": \"Ueg;d0WW1=C8K7C9I7F9J6O1001N010O014J3M3O0O01O1001O00O100O1M3N2001O00O10000001O1O1O1O1N4L3N2M4M2M4M2N2M2O0O3M3M2M3N2N2N3M3L4L3M7BTSP>\", \"size\": [1280, 720]}, {\"counts\": \"]iZ=1nW110000O2N2\\\\hN0VW11ghN8SW1GkhNi0hV1=K2M2O0O2N1N3M2N2L43L2N1OO101N1000O1000011M3L3N2O001O010O010O00001O001N3M1O2N2O2M1O2N1N3H8M3L6H6Knma<\", \"size\": [1280, 720]}, {\"counts\": \"QQ_>4iW17K3N101N1N3N1O0O2O1O1M3H8N201N101O0O10O02O000O10O001O001O001O0O100000010O000001O0001O0001O1O10O1O010O000O2O0O2M2O3M3K7BofX;\", \"size\": [1280, 720]}, {\"counts\": \"dXY?5kW11OMYO0f05L5nhNGXV1<diNHZV19^iNOaV1h001O001O010O1O000000002N1O1O0000000O01000000000000O0100O010O001N22M2O001O000O101O0O5L2M5K5K3M7F7En_c:\", \"size\": [1280, 720]}, {\"counts\": \"RWg=8gW19G3M5L3L4L2N3N000O2O0O2O0O2N1O1O1O1O1O101N10001O00000000O10O10000000000001O0O101N0010O02N1O101O2N1O2M3M3M3M2O1N2N5^OjhN0SXT<\", \"size\": [1280, 720]}, {\"counts\": \"iPZ91kW1:D=OMN3M1O1O2M2K6J6L1O1O20O10O0O1O1O100O1O100O101OO0100001OO1O1O02O001N01O02N3N1N2N3N3L2N2N2O1N3M3L4L4M3MP_f`0\", \"size\": [1280, 720]}, {\"counts\": \"kSS5;bW15M2M3N2N1O2N2N100O1O1O2O0O1010O0000O10001O000O2O0O2O1O001O0O110O0000001N10O1000000O10000O0010O02N102M010mNYiNl0hV1SOZiNl0fV1SO\\\\iNk0nV1M3N100O2O1N2O2M4L2N1N3JXl[d0\", \"size\": [1280, 720]}, {\"counts\": \"a\\\\g85eW1?C9I4L3M2M3N2O2N1O1N2O2OO01O2L4N1010O10O000O1N2O1O100O1000000001O1O0O101O00000O100O2O1N10O0O1G9<D3M2N4L4M3L3M6J5J7GikRa0\", \"size\": [1280, 720]}, {\"counts\": \"mYU<6gW17J<G4K4M1M3M3N1O1O1O001O1O10O01O0N2O2N100H^NQjNc1nU1^NRjNc1mU1]NTjNc1kU1]NTjNf1kU1ZNTjNh1jU1XNUjNk1iU1TNXjNm1nU10O2N0100O01O01O001N101O0O2O0O10001O0O100O1O2O0O2N2N2N1N3N2lNYiNk0iV1RO]iNi0eV1VO^iNd0fV1\\\\O_iN5j\\\\b=\", \"size\": [1280, 720]}, {\"counts\": \"jaf>3kW13N2N2N4L2N3L3M3J6H8O001O1N2O1O001O1O000000000000000O101O000000000O1000O10000000O10O10001O0000000O2O0O2M2O2N1N4L5K9FdfS;\", \"size\": [1280, 720]}, {\"counts\": \"bYe>1oW10O2WONbiN2^V1NbiN2^V1NbiN2^V1OaiN1^V10aiN1_V10_iN1aV10UiN:jV1FWiN9iV1HQiNN07oV1KQiN>nV1BQiN`0nV1@QiNb0nV180000000000O10001O1O00100O1N100O100O2O001O000O2O0O1O1O1000001O0O10000000O010000001O0000O11O0000000000000000O1000O2O0O1O2N100O2O0O1O2N2N2N4L=[OUWS:\", \"size\": [1280, 720]}, {\"counts\": \"XQo;6iW11O2N2N2N1O1O2O1N6K3M5J9H2M4L3N1O0O2N1N1010001O01O1O1O10O00000O1000000000O10000000000000000000000O100O100000O11N100O100O1O2N1O2M3K5N3M5L3Kj^]=\", \"size\": [1280, 720]}, {\"counts\": \"hZT57eW16K4M4M2N1N2O1O1N2O1O100O1O1O1N2O1N2O1O2O0O1O100O1000O10010O00O10000O10000000000O100O1O1O01000O1O010O100O001O010O10O10O01O01O0010O0010O001OKjiNbNWV1]1giNeN[V1_12H6N2oN\\\\iNe0hV1WO^iNb0eV1\\\\O_iN>ZW1F5LSh3NQXL0j]ec0\", \"size\": [1280, 720]}, {\"counts\": \"XfZ32mW111O00O4K6J5M1N2N2O2M100O2O000000001O001O2N0010O01O0100000O001O1N2O001N3M20O001O01O1O10O01000O11O0O010000000001N101N2O1N2O2N00O10O11O05K0O3N0O3N4L1O0000O02N1O0001O01O1O00010O002N1N2M3JUPUe0\", \"size\": [1280, 720]}, {\"counts\": \"ji]77eW1:I9F5L2N3N2M4L4M2M3M3N2N2N1N2N3N1O2N2M2N3N2N1O2N1N2N2O1O2M3N3M2M4M2N2M3N1O1N2O1O001OO10001N1O1000000O2O0O101O001M3M2O2K5N2N2N1O2O1O1L5M3K5C=H9F:D=CX^ha0\", \"size\": [1280, 720]}, {\"counts\": \"hPR;b0ZW1<G5K4L4L<kiNZNZU1l1^jNXN_U1Y2L1O00O02O1O1O0O01L46I105KL4O10000OgjN`MTU1`2mjN_MSU1`27O11O7I10H7N3K41OK_jNlMaU1S2ajNkM_U1W25O_jNjMXU1U2jjNjMWU1Q2>O2K5K5L3M4K5M4I6J8F[WR?\", \"size\": [1280, 720]}, {\"counts\": \"jPb81lW14L5I7J7L3M3M4K2O2N3M3M2N3N2N1N4M1O3L2O1N5L2N1O3M5J3N2N1O3L3N1N10000O010O10O1N2N2J62N1OO1IgjNgMYU1Y2jjNcMWU1]263M1O4M2N00N3N1N2O1N4J8F8K3N3L5L5F8G8_OgWn`0\", \"size\": [1280, 720]}, {\"counts\": \"ohj81hW18L7G6N3L4L3L5K6K4L6K5K4L4L8I7I2M3M101N01I6K6O010O0101O0O10001N2O000O100O01O010000001:F5K4KO2N2L3I8J6K5I7K5M4L3M4K5J5J8H:Ghgk`0\", \"size\": [1280, 720]}, {\"counts\": \"ZS\\\\;2^W1f0I8H4L1N2O1O1O1O1O1O2O4K9H2M01N1O2M300O010O02N1000O0O2N1M4O1O001O100O2OO01O1O1O1O100O1O2O0O2O2M3L3K6L3M3M6FaUc>\", \"size\": [1280, 720]}, {\"counts\": \"PYh8d0TW1a0E5L3N2M5J6K4L3M2N3M2O2M4^jNhMSU1e2N2NBPkNoMnT1R2QkNoMoT1S2VkNdMkT1]2UkNcMkT1]2UkNeMiT1Y2SkNmMmT1S2TkNdM1OjT1^2`00O01O10O101O0O10O1001O1O1O00O10001O00O10O02O3L200O01O2M5K2O1O2N1M4M3N1N2N1O3M2M3K5K6C<M4N2N5F9BSga`0\", \"size\": [1280, 720]}, {\"counts\": \"lak22jW14A`0N1O2OO1_OnhN0UW10XiN3XV1n0L6J2O000O1N3N101O1O2M2O1N1O2N2O001N101N2N3N001O0O1O1O1001O00O101O01N2O000O10000000N2M3N22N1O1O001O002M2O2N001O1O1O1O1O1O1O1O1O1O0M5I6M3O1O2M3L3N3J5CnhNFWW1a008H01O4K2M4J5K5J6M2NUnbe0\", \"size\": [1280, 720]}, {\"counts\": \"cYi0<_W19J7J4K4L4L5K3N2N2O1M201O110O0O1LWNRjNf1TV11N10001N101L4I7N11O000001N101O000001O0000001O00O11O001O0000O10010O000000O2O00001O00001O00002N1O0O2O000000001O1N2O1O001O001ZNliNi0MHWV1@jiN^1VV16O11N1[NkiN_1\\\\V1N2N2\\\\OdiNB]V1;giNBZV1=miN\\\\OTV13[iN4`W1M_hN2bW16O11O0O104L2N2M101O00O1O2O0001O1N101O0000000000Sfif0\", \"size\": [1280, 720]}, {\"counts\": \"]_^1n0oV16H8J5K5M3K5L3L5K4O1O001N001O2N7J4K10O10ojNXMiT1j2UkNWMkT1P3ONTkNUMgT1o23O10O2O6J1O01_O]kN\\\\M4J_T1j2]kN]M4H_T1Y3201O010J]kNnLbT1X301O00N3I6001O2OO0O2N1005K100O0001O1O1O01O10O0010O0010O00O1010O1O1O00004LO1030M1N1VMQkNb2QU1ZMSkNe2SU12N22L2N10O0M22eMmjN_1J^N\\\\U1OPkNf1TU1XNfjNMNf1aU1[NjjNc1jU1K5_OciNUO30[V1k0miNROTV1P1>OO01K31WiNTObV14XiN=TW1_OPiN?OASW15jhN04K[W15802M2OFOehN0\\\\W10chN2dW13J70J5L]hNNSW10nhN1N0^W1OegUf0\", \"size\": [1280, 720]}, {\"counts\": \"Vlb5`0\\\\W18J5J5L3M4L4L4L4M4K4M3L5K2O2N3M4L3M4L3M3M4L3N1N2O1O3L4M2N1N3N1O2M2O1N2O0O2O4L001O001O2N1O1O1O001O100O001O00O2N10001M2L4EXlN\\\\LkS1b3XlN[LiS1e3901N2O2M2N4L2N3L3M4L4N2K5K6L4L5K5I9E:QOaZ1@ehVc0\", \"size\": [1280, 720]}, {\"counts\": \"WWn0;`W1;F7K4K5K4N2M4M3M2N2N2N3M2O2M5K3M3M2O0O2O0O101N1000000O1000000000000O01000000000000001O00000O101O00000000100O100O001O001N2O010O01N2N2N11O0000O2O001O000O2L3N20O1001N2N2N1O2O0O2N1O1O2O10OO2N3M2O010O1K5N1O2N3F_iNQObV1m0`iNROaV1k0biNSObV1g0ciNWO_V1d0diN\\\\OcV1;_iNEiV11YiNO\\\\W10UO2h01f`hf0\", \"size\": [1280, 720]}, {\"counts\": \"nTe2b0ZW17J5L4K5K4M4L3M2N3M4L5J6L2N2M3N3M3L3M4L3M3N3M3L4M2M3N1O1O1O1O1O1O100O100O2OO0100O100O10000O10000O1000000000000000000000000001N10001N1O2O00001O0O2O1O1O001O010O1O1O1O2N1O1O2N1O2N2M3N2M201N2N2N1O2N2O1N2N3N1N1O2M3N1N3M2N2N2N3K5M2N3M3M2M4I7J5N4L2O1O2O001M`Zad0\", \"size\": [1280, 720]}, {\"counts\": \"Q\\\\e:6YW1l0mhNoNPV14oiN`1hU1n0WjNlMWT1EjkN_3mS1<M;E11J51O001O101O1O001O10N2M5L4M2N3M6J4M3L1O1O1O1N3M2O00O010O100O1O010O1O100N2N1000000O2O0001O0001N0002N1O1O2N100O10000O100O010O1000N2N101O1O1N2M3K5O11N10000O2O1O1N2O001O1N2O1N3N2M3N2N2M3N2N101N2N2N2M3L3N4L3M3J7A?Ab0D<B[nn<\", \"size\": [1280, 720]}, {\"counts\": \"\\\\e[8i0VW1>B6K6J5L4K4M3M2N2N3O13KO1N2O1O1O3M5J1OhMejNm1\\\\U1PNdjNP2]U1oMcjNP2_U17O2H8fN\\\\1H7M2O2N1O10000002N100O1O3M6K2L3N3MN3M2ATLflNP4XS1=N2O11O00000000001O001N2O001O001N100O1O1N3M2O1O1O1M4M3M3L4M4K5K5J7K4K5L3M4J5L6H7J6K8CnjW`0\", \"size\": [1280, 720]}, {\"counts\": \"Xoa81nW15ShN3^W1h0_O<eNdNljN7E^1hU1`0L5ROfMmkN`2PT1`MnkNh2mS1]MekNm2YT1SMgkNP3WT1nLkkNS3eT1NYOlLXlNY3gS1mLPlNX3nS1?O06J02M1N3L:H1OF\\\\lNZLbS1c3blNaLYS1a3elNaLXS1b3elNaLYS1T40^O5oKjlNo3=oKRR13_mNo3?oKQR12_mNP4`0oKPR11_mNQ4a0RLlQ1MbmNS4a0oKnQ10]mNT4d0hKSR1`4e0100O1100O1O01O010O0001O1O1O001N101O1N1O2N2O1N3M2M3N1N2L5K7J5F;H7J5L5F9K5L4K5I7G;A`0XOka``0\", \"size\": [1280, 720]}, {\"counts\": \"chW:3lW12M4M3M6I7I7J4K6J6J6J4L5L3L5L3M3L4M3M4K4M2N3L3N2M2O1OO10000001OO00100O1O1O1O1O1M4O0000OO201N1O1O2O0O1010O1001N2N2M3L5M4gN^jNUOK?gW1@Xg^?\", \"size\": [1280, 720]}, {\"counts\": \"k`W77fW15M3K4aNGajNIJi0`U1[1L4L4N101M3L4L4N2O1N3N1O0O2O001N1010O01O0O101O0000000O1010O000000001O1O001O001O1O101N1O1O2N1O002N4L3M5L2N1N3N1N2O1N2N4M3L4L2N3L4M4J8H6J3O1N2M3N1O2N2L4N3L4L3Mi^ca0\", \"size\": [1280, 720]}, {\"counts\": \"^iX79dW15L2O2O1O0fMBQmN?nR1IilN:VS1H[lNf0dS1BbkNQ1]T1RO\\\\kNR1dT1U1O101M3M201N101O000001O001O1O2N2N2N3M5K3M1O2N2N2N3M2N3K6K4L4M3M2M3L4J5L5M3M2M4L4M3N3L3N1N3M2N110O1O0M3N20SX12jgN1N020UX1MngN0gVQb0\", \"size\": [1280, 720]}, {\"counts\": \"bPT:`0ZW1:@?K7J8F7I8I4M4L2N3N1N2N2M4M2N2M3M2O2L[OgkNjMWT1R2UlNfMlS1V2P1O1N2K5O2N1M4O001O01000000000O10001N101N1_OliNTOWV1g0e0I6J6I9HohR`0\", \"size\": [1280, 720]}, {\"counts\": \"Raa<5hW14N4L2N1N4MLfhNJSW1b0O1O1O2N2N3M1O1O1O2M2O0000001OO10O2O1O00L4O2O0001O000001O10O10O010O10O1000O10O100O01000O10000O100O010001O000L4J7N2O2M5K5K3L3N3Mkgi<\", \"size\": [1280, 720]}, {\"counts\": \"gaT67eW17L3L5L4K5D=G9I4M4L4L5L3M3M2O2M3M2N2O0O101N1N2O1O1O1O1O1O1O1O01VO`kNRNaT1m1j0O1O1O001O001O001O1O0002L4N100O00O1010OO2N1011N1O10O2N0O2O00100O100O1O10001N2O1N1O3ZOhiN_OZV1>niN[OTV1b0SjNWOQV1d0k0H:C[^`0NeSha0\", \"size\": [1280, 720]}, {\"counts\": \"iPh03cW13^hN;VW18OD;1O2N3N11001O0F@QiNe0iV1^OUiNi05POWV1\\\\1fiNgN\\\\V1^12NO0O3L5M1O01OO2M4L30005K0N1O2N21O0O01M5J5N10O2NM5L4L211O00ON2O101O010N100O101O1O010O1O2M0O20O21O0O0O1N30O100O100O2NN3O000011N0001DeLQlN[3nS1hLnkNZ3oS1kLlkNX3RT1;20O10\\\\LmkNY3UT19O01O102M1O1N1N4M200O2N2O0O2O0O2O1M3N2N2M3N2N4M1N3N1N2O2C=J5M3N3L3M4L3N1N3M3M3N1M3F:K5L4N2O1N2N101O0OT^oe0\", \"size\": [1280, 720]}, {\"counts\": \"kYd55fW1;E9D=I5K6K8H3N4K5K2O2M2NO2001N2O1O1N1O2N2N2M3M3L4M2O3M1O2M3N2N2O1N2O1N2N101N2O1O0O2O1O1N2O1O1O1O0O2O1O1O0O2O1O1O1O003M2N1O1O1O0O11O01OFflNmK[S1R4:O1O2YNalNiNaS1T1flNhNZS1V1llNeNVS1T1RmNjNnR1Q1YmNmNgR1Q1_mNjNbR1T1bmNiN_R1T1gmNiNYR1R1]2L4K6N1O2N1N2O2M3M3N2M3K6I8Jcohb0\", \"size\": [1280, 720]}, {\"counts\": \"a9j2VU100000000000O1000000O2N10000001O1O1OO2O0O100O10000002O0O1O0000O1000000001O1O101N0000O1000000000O102N2O0OO00`MgjN[2_U10O100001N2OO1O2N12M3N0000001O001O6J103LN2O1O12N3N0O1O22LJRjNZNmU1f1UjNZNiU1f1XjNZNiU1d1XjN]NiU1a1XjN_NgU1`1ZjNaNeU1^1\\\\jNaNeU1]1_jN`NaU1`1b0L2O2N2E_iNUOcV1g0`iNXObV1d0aiNZObV1c0_iN^ObV1`0^iN@cV1>_iN@eV1;b0N3N2N1O0O100O20O01O00000Oglcg0\", \"size\": [1280, 720]}, {\"counts\": \"]9U1kV1000O1O2O1M4N1O010M3L3M4N2O1O0011N2ON2O0012N0O011OO1O1O1N12OO01O01O00001O0101O1OO001O0O2N2M3N100010O2O0O01N1100N2O01O0O20O10O1O100N1N3N1O1O1N2101NJeMhjNZ2^U1100O1O2N1000001O0012NO0N20000O101O001N200O100O000001N3N0001O01O1O002N1O01O1M3O01N2O1O1O2N00101N2N002N10O01O1O2N1O2N1O2N3N1XOmiN_OTV1`0oiN]OSV1a0miN_OSV1<RjNCoU1=RjN@QV1?PjN^ORV1b0oiN\\\\OSV1d0liN\\\\OTV1d0miNZOUV1c0h0K4M3M2N2M3O2N2NQP50c`7N]_C2Wl]e0\", \"size\": [1280, 720]}, {\"counts\": \"h[Q72dW13_hN0_W1:N1N4OOghN_ObV12`iN0M?0A`V14_iN0O;1C]V1^1L2N1N10002N2M3O2N1N1N3M2O2N1O1O1N2O2N101N12N0001O0N2O2O10O00O2O01000O001N1O2O1O1O1O001O2N1O1O1O001O1O2N1O1O1O1O10000O1O2OO10O1O00O2O0O10000M3M3O10000O2N2N2O101O0M3N2ZNRkNnNLQ1UU1I`kN4cT1GakN7aT1EckN8UV1N3L4M3L2N8I3L5OmjQa0\", \"size\": [1280, 720]}, {\"counts\": \"_UY5;`W17L4L3M3O0O2N1O2N2N10000O2N2O0O2N1O1O10010O001O1O0O100001OO100001O0000O100000001O00001O01O0000O2O0000000000010O001O1N100001O00000000O10001O000O100000001O0000000000000001O00001O00000O2O001O1O1O0000001O001O01O0000001O00100O00100O10O010O1O001M4K5F8F;G:L5GfRla0\", \"size\": [1280, 720]}, {\"counts\": \"d]V72lW13M3O0O10OO3M2O2N1O2L4M2M202O3J5M1L5M3O1N2M20O00O2O10000N200O001O1O02N1O10000O101O00O2N1O2N010O100O10O010O02O0O2O0000001N1000O100000000O101O0O100O2O1N1O2O001O1O1O1O1O001O0000001O000001O01N10001N100001O010O10O00001O010O01O0001O01O010O0010O10O10O01O100O010O01O1O2M2M3O1M3M3N2N2N3M2O1N4FmjZ?\", \"size\": [1280, 720]}, {\"counts\": \"[aV:7dW1=F<[iN[O]OO`U1Z2H5mN[MZlNOBm2PT1P1K5H:I7I4M5J4M2O101M2O2N3NO001O0N2000N1O1jmNeJhQ1[5YnNfJfQ1[5YnNfJeQ1\\\\5ZnNiJaQ1W5_nNgJcQ1W5d0L5M3O1OWmNoJeR1Q55O2N1O2O008F:F5L4M3O`0\\\\O;C;Gb0aM]jN_1PW1SO<G1Dmki?\", \"size\": [1280, 720]}], \"bboxes\": [[273.0, 577.0, 28.0, 26.0], [265.0, 577.0, 122.0, 152.0], [259.0, 564.0, 31.0, 24.0], [261.0, 566.0, 30.0, 23.0], [265.0, 571.0, 23.0, 21.0], null, null, null, null, null, [204.0, 646.0, 36.0, 66.0], [188.0, 680.0, 391.0, 158.0], null, null, null, [198.0, 653.0, 41.0, 37.0], [195.0, 532.0, 92.0, 37.0], [247.0, 542.0, 56.0, 54.0], [258.0, 553.0, 51.0, 56.0], [262.0, 567.0, 52.0, 57.0], [248.0, 556.0, 45.0, 65.0], [239.0, 556.0, 31.0, 68.0], [205.0, 595.0, 36.0, 50.0], [64.0, 446.0, 73.0, 49.0], [0.0, 397.0, 94.0, 61.0], [9.0, 502.0, 63.0, 46.0], [55.0, 471.0, 60.0, 38.0], [150.0, 432.0, 50.0, 43.0], [177.0, 604.0, 50.0, 81.0], [170.0, 503.0, 46.0, 70.0], [134.0, 225.0, 43.0, 99.0], [120.0, 106.0, 51.0, 98.0], [110.0, 76.0, 51.0, 94.0], [121.0, 66.0, 55.0, 93.0], [136.0, 143.0, 60.0, 105.0], [197.0, 458.0, 45.0, 85.0], [201.0, 610.0, 51.0, 56.0], [219.0, 533.0, 66.0, 65.0], [213.0, 635.0, 50.0, 67.0], [250.0, 597.0, 44.0, 163.0], [301.0, 576.0, 43.0, 149.0], [199.0, 596.0, 98.0, 61.0], [151.0, 508.0, 72.0, 77.0], [81.0, 569.0, 81.0, 63.0], [129.0, 591.0, 89.0, 77.0], [233.0, 658.0, 46.0, 72.0], [311.0, 593.0, 42.0, 82.0], [311.0, 488.0, 49.0, 76.0], [274.0, 498.0, 57.0, 59.0], [213.0, 415.0, 93.0, 83.0], [236.0, 294.0, 54.0, 90.0], [296.0, 306.0, 43.0, 78.0], [366.0, 422.0, 54.0, 62.0], [382.0, 440.0, 49.0, 66.0], [365.0, 440.0, 57.0, 68.0], [320.0, 485.0, 70.0, 81.0], [225.0, 407.0, 81.0, 86.0], [138.0, 355.0, 62.0, 68.0], [155.0, 319.0, 62.0, 77.0], [290.0, 317.0, 123.0, 158.0], [256.0, 415.0, 62.0, 86.0], [218.0, 351.0, 73.0, 89.0], [257.0, 265.0, 47.0, 55.0], [212.0, 245.0, 54.0, 48.0], [232.0, 260.0, 58.0, 44.0], [357.0, 223.0, 51.0, 65.0], [338.0, 252.0, 56.0, 71.0], [327.0, 231.0, 52.0, 83.0], [307.0, 234.0, 50.0, 50.0], [225.0, 238.0, 52.0, 50.0], [155.0, 235.0, 73.0, 68.0], [243.0, 241.0, 41.0, 52.0], [353.0, 235.0, 16.0, 71.0], [325.0, 203.0, 47.0, 84.0], [330.0, 181.0, 54.0, 82.0], [262.0, 224.0, 77.0, 43.0], [141.0, 192.0, 66.0, 77.0], [173.0, 301.0, 37.0, 33.0], [261.0, 227.0, 40.0, 59.0], [335.0, 225.0, 78.0, 100.0], [328.0, 217.0, 56.0, 75.0], [320.0, 252.0, 76.0, 38.0], [259.0, 253.0, 62.0, 43.0], [175.0, 257.0, 52.0, 54.0], [200.0, 230.0, 51.0, 54.0], [323.0, 215.0, 42.0, 72.0], null, [321.0, 251.0, 47.0, 84.0], [193.0, 337.0, 90.0, 70.0], [135.0, 537.0, 83.0, 36.0], [3.0, 418.0, 70.0, 46.0], [19.0, 379.0, 50.0, 46.0], [53.0, 426.0, 61.0, 42.0], [91.0, 271.0, 79.0, 103.0], [273.0, 302.0, 42.0, 87.0], [271.0, 415.0, 45.0, 109.0], [203.0, 380.0, 81.0, 72.0], [200.0, 387.0, 82.0, 62.0], [231.0, 426.0, 63.0, 83.0], [230.0, 524.0, 57.0, 75.0], [224.0, 545.0, 54.0, 88.0], [224.0, 609.0, 42.0, 86.0], [231.0, 672.0, 54.0, 82.0], [300.0, 632.0, 61.0, 83.0], [341.0, 552.0, 57.0, 63.0], [370.0, 530.0, 61.0, 55.0], [391.0, 492.0, 57.0, 54.0], [351.0, 465.0, 59.0, 53.0], [238.0, 521.0, 54.0, 55.0], [130.0, 615.0, 68.0, 47.0], [223.0, 612.0, 59.0, 72.0], [311.0, 556.0, 62.0, 74.0], [376.0, 543.0, 59.0, 50.0], [375.0, 527.0, 86.0, 50.0], [306.0, 536.0, 70.0, 58.0], [131.0, 548.0, 85.0, 66.0], [85.0, 447.0, 93.0, 74.0], [190.0, 291.0, 75.0, 112.0], [283.0, 268.0, 51.0, 91.0], [219.0, 249.0, 67.0, 95.0], [226.0, 242.0, 62.0, 104.0], [291.0, 326.0, 55.0, 69.0], [224.0, 255.0, 72.0, 103.0], [73.0, 272.0, 94.0, 83.0], [20.0, 281.0, 116.0, 75.0], [37.0, 204.0, 115.0, 118.0], [143.0, 92.0, 84.0, 142.0], [24.0, 195.0, 113.0, 94.0], [68.0, 93.0, 126.0, 126.0], [273.0, 32.0, 115.0, 178.0], [214.0, 130.0, 90.0, 150.0], [219.0, 174.0, 78.0, 175.0], [262.0, 211.0, 62.0, 130.0], [185.0, 207.0, 84.0, 114.0], [186.0, 215.0, 72.0, 107.0], [259.0, 220.0, 49.0, 116.0], [321.0, 244.0, 71.0, 69.0], [157.0, 242.0, 95.0, 216.0], [19.0, 231.0, 138.0, 129.0], [144.0, 257.0, 95.0, 150.0], [0.0, 302.0, 115.0, 105.0], [0.0, 289.0, 171.0, 105.0], [180.0, 317.0, 103.0, 118.0], [135.0, 408.0, 127.0, 64.0], [184.0, 384.0, 143.0, 70.0], [261.0, 235.0, 54.0, 195.0]], \"areas\": [255.0, 171.0, 245.0, 234.0, 199.0, null, null, null, null, null, 1615.0, 644.0, null, null, null, 1064.0, 643.0, 1849.0, 1861.0, 1827.0, 1806.0, 1402.0, 1392.0, 2212.0, 2238.0, 1901.0, 1422.0, 1282.0, 2247.0, 1764.0, 1747.0, 2083.0, 2811.0, 3069.0, 3693.0, 2286.0, 2080.0, 2660.0, 2021.0, 2029.0, 1673.0, 3336.0, 3184.0, 3609.0, 3707.0, 2597.0, 2332.0, 2739.0, 2658.0, 2433.0, 3413.0, 2218.0, 2516.0, 1899.0, 2648.0, 4241.0, 3093.0, 2876.0, 3224.0, 4576.0, 3701.0, 3876.0, 2030.0, 1869.0, 1733.0, 2362.0, 2916.0, 2750.0, 1782.0, 1558.0, 2177.0, 1536.0, 731.0, 2430.0, 2710.0, 1969.0, 3434.0, 714.0, 1768.0, 3479.0, 2612.0, 1923.0, 1737.0, 1411.0, 1968.0, 1546.0, null, 2367.0, 3894.0, 1917.0, 2145.0, 1593.0, 1953.0, 5074.0, 2449.0, 1984.0, 2184.0, 2962.0, 3242.0, 2964.0, 2887.0, 2704.0, 3190.0, 3615.0, 2287.0, 2105.0, 2161.0, 2465.0, 2139.0, 2204.0, 2987.0, 3002.0, 2323.0, 3170.0, 3122.0, 3373.0, 2582.0, 5895.0, 3480.0, 4364.0, 4142.0, 2348.0, 5198.0, 5158.0, 5580.0, 8780.0, 8204.0, 7778.0, 11406.0, 11883.0, 9965.0, 8838.0, 4827.0, 6346.0, 3742.0, 3121.0, 2568.0, 5325.0, 10581.0, 8904.0, 8013.0, 9886.0, 8113.0, 5978.0, 6401.0, 7395.0], \"iscrowd\": 0, \"video_id\": 0, \"height\": 1280, \"width\": 720, \"category_id\": 3802, \"noun_phrase\": \"the left hand\"}, {\"id\": 1082, \"segmentations\": [{\"counts\": \"Reea06aV13`iNN5K7;hU1g0PjNAjU1]1J6L3L5K4N1L30012O3M10100O0O100O001N101O000000O100O10001N2O1O001O00O0011N2N2ON001O1100O2O3K3N4K4M3L5K5K8H5L4L4K7J5I8I:Cgco7\", \"size\": [1280, 720]}, {\"counts\": \"[[Pa0m0jV1>G6K4L?A7J4K5L4M1M4N1M3K5O1N2M4N1M3O14N0O0O00O2OO00O0101O013L3M1000O0103L3M2M3M2O2N1O2M2O2N3M6J6J3M00002N3L3N4K7I9G<SOlhNMSli8\", \"size\": [1280, 720]}, {\"counts\": \"[\\\\a`02fW1:@`0F;Ge0]O8J3M3L5L2N2N2N1O2N2N2N2N3M3M3M2O2M2O1N2M21O2N2N01O01O2O1O0O010O1O2N1O1O00103K3N1O2N2N1O2N1O1O2M2O1O2gM_jNP2cU1lMbjNQ2_U1PN^jNQ2cU142O?^O<Ai0mNSmS9\", \"size\": [1280, 720]}, {\"counts\": \"mTj`07cW19H8jhN^O`V1[1D9G7L3M4M2N2N1O2N2O1N2N2N2N3M2O2N2M2O1N2N1O2N1O2O1N1O2O1O001O0101N2N2N1O1O102M3M2N1N2O101N1O2O0O1ZMljN_2UU1_MmjN`2ZU1O1O1O1O2M4L4M1N3L3K>^Oodh8\", \"size\": [1280, 720]}, {\"counts\": \"_]da08eW15K7G9G;F9E8K4K5M2O1N2N2O1M3O2M2N2N3N2M2O2N2N1N2O1N101N101M2O100O2N101N2O04L2O0O2NBjLUlNU3ZT10001O2N2N2O0O1O2N101N1O1O2N100\\\\MkjN\\\\2VU1cMljN\\\\2^U1L5K3M2M3K8_OdTm7\", \"size\": [1280, 720]}, {\"counts\": \"eTV`0:`W19^O`0M3N2N2O1N1O2N2M2O2N2N1O1O1N2O1O1N2O1O2N101N00100O1O100O1O010O1O1O01000O1O100O010O100O10O01O1O100O001O0001O010O1N101N100O2N2N2O0O1SN\\\\jN_1bU1aNajN]1RV1O1N2N2N2M4M3M5I4O0N204J9G5M2M3Nedc8\", \"size\": [1280, 720]}, {\"counts\": \"Xd_?=\\\\W18H9G8K5K4M4L4M2O0O2O100O1O1O1O1O1O1O0O10O10010O0100O100O0001O000100O001O0010O001O0O2N1O101N101O001O0O10001O1O00001O001N1000001O1O001O001O001O001O1O1O1N6J4M2N3M4L2M2N2O1O010L4O2H70100L4O0O10j]l8\", \"size\": [1280, 720]}, {\"counts\": \"jbk>1oW13L3M3K6K3N2N2N2N2N2M4M2N2N2N2M3N2N2K5J5O2OO10O10000O10000O10O100000O100O010O1O10O01O100O1O10000000O0O2O1O10O01O002N2O0O1O0010O0100O001O1O1O100O0010O01O000000100O001O10O02N1O1O10O01O2N3M8H1O1O1O1O2N1O1L4O2N2M2FdhNN_W10gfR9\", \"size\": [1280, 720]}, {\"counts\": \"jR_>1nW14_O1jhN2RW13jhNNUW1`0N1O2O0O2N100O2O0O101O1N2O001O1N2O1N101O000O1N101N2O10O01O10O10O10002N1O1O1O1N101O000O1000O010000O010O01O1O1O010O00100O1O100O100O100O1O10O010000O01000O01000O1O1O1O010O1O1O010O01O0001O00001O1O001O100O001O1O2N4L2M3M7J2OO001OFhhNN\\\\W167J_^Q9\", \"size\": [1280, 720]}, {\"counts\": \"`Ze>1lW15@NmhN6nV1`0M3N2N2N2O0O1N201O001O001O0O10001O000O101O010N1JdNhiN\\\\1XV16010O1OO100000O100000001O01O00001O001O1O1O00000000000O10O01000O00100O001O1O010O1O010O100O0010O0100O010O10O10O010000O1O10O1O00100O100O1O1O01000O1O010O00100O2N2NO1100O1N2O10;D6J1O1O2N2N2O_^g8\", \"size\": [1280, 720]}, {\"counts\": \"`jV?1`W10ihN6TW1MjhN5RW1OlhN3QW10mhN2PW11nhN0QW1a0N100O100O010O101O1O1O0O2N1O101N2O0O2N2O010O1O0O10000000O100000O10001N101O0O2O001N101O1O1N10O10O010O0010O001O001O1O010O1O1O1O001O001O100O10000O01000O10O0010O010O1O10O0100O10O0100000O01O100O1O1O1O1O100O2N1O2N1O2O0O1O2M2O2AnhNNbW1O2O0O2M2O2O1N10^nP8\", \"size\": [1280, 720]}, {\"counts\": \"Zbn?3lW13M2COhhN3RW1`0N3N1O2N1O2N100O1O100O1O2N2O1O1N100O2O0O3N1N2O000000000O100O10O10O100O101N101N101N101N101O0O1O01000O1O01O001O10O01N2O1N101N2O10O01N101O1O10O0100000O01000O01O1N20O010O10O010O1O10O0100O1O100O10O0100O001O1O1O1O1O010O001O010M;F:H1M2O2N3MSW^7\", \"size\": [1280, 720]}, {\"counts\": \"ZbX`03YW17ohNMoV16mhNLQW1c0N101N2N1O2N1O11O0001N1O100O1O1O2O1N3N000O2O0O101N2O1N1O1O100000O100000O2O1O0O2N2O001O2M10001OO01000O001O000010O01O1O001O1N101O1O1N2O100N2O00100O100O10O010O0100O010O10O0100O1000O10O0010O0010O1O002O00O01O1O1O1O1O1O010O2QOYiNb0UW1N2Ma0_O3MaVY7\", \"size\": [1280, 720]}, {\"counts\": \"lQ[`069LTW17jhNOPW14lhNOQW14mhNNRW1`0O2N1O101N101N1O100O2N1O3M2O001O001N10001N10000O100O1000O10O2N101O1N101O1N101O1N2OO10O01O100O10O010OO1O2O001O1O1O1O1O1O1O001O010O1O1O1O100O100O1000O02O000O101N7J3K7J3N0010N0O1HYOXiNg0TW1L3N1M2O1O101N4M2I41001OYfe7\", \"size\": [1280, 720]}, {\"counts\": \"QRl?1=2QW10lhN5oV1NmhN6QW1LmhN5RW1MkhN5TW1LlhN5SW1<O1O1O100O1O101N2O0O3N0O2O0O2O0O2O1O0O10000O010O10O1000O101O001N2O1O1O0O100O1O1000O100O001O100O1O1O001O1O10O01O1O100O001O01O0100O00100O1O1000O10O0100000O1O2N2N2N2N01O1OVOXiN?hV1[OZiNg0gV1XOZiNg0hV1WOZiNg0RW1M3M100N2O2N2O1N3N0M4N[fT8\", \"size\": [1280, 720]}, {\"counts\": \"jba>1mW13N2M3O1N101N1O1O101N1O2N1O2N1O1O2M2O2I7O001N10000O1O001O1O001000000O2N2N101N2N101N1001O000O001O00100O1O001O10O010O01O010O1O1O010O1O1000O010O01O100O010O1000O0100O001O1O1N2N2N4M2FohNCUW1OViN0_W1Ml]o9\", \"size\": [1280, 720]}, {\"counts\": \"njS>3kW13M201N2N101N2M3O0O2N1O100O1O2N1O1O2N1O100O100O1O101O1N2N1O2N2N1O1O001O0000001O01O1O100O10O01O1O001N2O1O0O20O01O1O100O1O1O100O100O100TOYiN?gV1@[iN?eV1A[iN?SW1O2O7I1N1O2O1N01001O`Ul:\", \"size\": [1280, 720]}, {\"counts\": \"Zbk>b0XW18J6J5M2N3N3N13L101N100mNYiNl0lV1100WOoNcjNQ1]U1oNbjNP1`U1PO_jNn0dU1RO]jNl0\\\\V1O2N1O00YOXOXjNf0cV1M3N1O2N101N01N02N3N2Niel;\", \"size\": [1280, 720]}, {\"counts\": \"dbk>3iW1a0B3K4H7L3100nNYiNj0hV1UO\\\\iNf0RW1N1N2N1O2M3M2O1N101O1O1N2NRh3OQXL1_eV<\", \"size\": [1280, 720]}, {\"counts\": \"idm=3eW1b0C6I8H9H:G3M3N2M2N3L3M3M2O1M20O3O001O1M2K6M3M2O2K4H8O1N2N2O2N5L3M2M3M3YOlhN?[W100O100N2O2M3N1O2M2N3N^\\\\Z<\", \"size\": [1280, 720]}, {\"counts\": \"l^`>6`W1?I4L3M3L3N3M2N2O2M2N3M2N3M2O1O2M3M2N2O1O1M3N3M2N2O1O1N2N2O1O1O2N0O2N2O1N2N20000O010O1O010O010O1O03N3M2M101O2M3N2M2N2N1O2M2O2N1O1O1O2L5L2M3O0O3K6L3M3L4L5H6L103]O^[^:\", \"size\": [1280, 720]}, {\"counts\": \"kmgc06eW18G;E8K5E;F9I7K5M2M3N2L4M3M3M3M4M2O0O2N2J5O1O1N3N1N3N2N11000O001O001O1N101O010000O100O101N3N3L100O100O1cLckNV3eT1O0O2O0O10000O2O1N2M3N1O2N2N2N3M4L3M3L3M`0_O6Hb0]OjS]5\", \"size\": [1280, 720]}, {\"counts\": \"jbRh02kW15L3N1O1M4L3M4M2M4M2N2O1O1N2O1O1O1O1N2O1O1N2N2L4N2N3N1N2O1O1O1O1O1N2O1O1N2O100O100O1O10000000000000000000O10001O1N10000O5L2M2N4M2M3EkiNeNcV1j0`0[OjV[1\", \"size\": [1280, 720]}, {\"counts\": \"_mai02iW1:D8L4M3L4I7H9H9J5I8H7I6L5M2N2N2N100000100O01O0000001O1O00100O1O1O001O001O0O2O001O001O1O1O00001N101N1O2N1O2N2N2N3N1N3M3K5L5I6C`0SOViN3QW1LQiN1Zj0\", \"size\": [1280, 720]}, {\"counts\": \"mcPb09eW16K2N101N2O1O2N5K4K4M1N2N2N2N2M4M3L3O0O2O1N10002M5L3M1O100O1O000001O0000ZjNmM^U1T2bjNnM\\\\U1Q2ejNPNZU1n1ijNRNWU1j1mjNUNSU1j1njNWNQU1i1ojNXNPU1g1QkN]NkT1b1WkNTNRU1k1b000O2FoiNbNSV1[1QjNbNPV1\\\\1<G:J6M3M3M200O3M4I9GlSg7\", \"size\": [1280, 720]}, {\"counts\": \"iXZ<1mW14M2N4L:F3M7I3M1O2O1N2O1O0O100O2O00000O10000O1O100O02O000000O1O1O1O2O00O101O1O1O0000O100001OO1000O1000000O1000O1O10O10O010001N1000000O011O0O1O4M1O1N3N2M3M2N5L3L00N36H2O2M3O1M3M13M3N00O1O2N2MTgQ<\", \"size\": [1280, 720]}, {\"counts\": \"kgZb07eW18K3M0O2O2N5L2N2N1N2O1N3N1O1O1O1N2O1O2N1O1O0000O11O0000000000000000O1O101N2O0O00101N1000000O1000000O100000000O1O100O1ROUiNf0lV1ZOWiNb0jV1]OZiN?hV1_OZiN`0fV1_O\\\\iN?eV1AaiN9_V1FdiN6^n4J[Qn6\", \"size\": [1280, 720]}, {\"counts\": \"^]Ui06hW14M2N3N008H4K2OM2O20O3M4M4L001O001O001O1O1O3M2M101O00001O0O1000001O001O0000010O00001O01O00001O1O001O001O1O1N2O1O1O1N3M6J6J4L4L:EgY=\", \"size\": [1280, 720]}, {\"counts\": \"Widg02mW12M7I7J4L3N3L3M2O2M2N2O1N2K6L3O0001O100O001O000001O01O001N101N100O100O10O01N3M2O1N3N2M3M2N3L5JRWc2\", \"size\": [1280, 720]}, {\"counts\": \"W[Uf03jW15N1N2\\\\hNJ\\\\W1b0J4M0O2O1M5L3M2N2O1N2M2L51N1N1001N100O101O0000O10001N101O1N2N2N2O2L3M4L7ZOjhN1Wd\\\\4\", \"size\": [1280, 720]}, {\"counts\": \"lWmg08eW17J6K2N3M3N2M4L2O1O1N3N3M3M3M1O2N2N2N3L3N2N2N2N2N2N1O2N3N3L2N2N3M3M10001O0O10O1F:I8L4M3M4M1N4K5L3M4L4M3L4L7I4L5I8J5Jgnl1\", \"size\": [1280, 720]}, {\"counts\": \"RVbe09bW1:H7K4K5L3M4L2N2N2N3N3L3N1O1O001O1O0O100001O000O10001N1000000O2O0O2O0O2N1O2N1N3N1N3N1L4M4J6I7K5LXbd4\", \"size\": [1280, 720]}, {\"counts\": \"cSXe0V1gV16K4L3N2N3M2N1O1O100O1O10O1O10O01O1O1O1O1O10001O001O00100O1O1O2N101N1O101M]iNjN`V1S1biNlN`V1Q1aiNPO_V1j0iiNROYV1e0g0M2O1O1N2O1N3N3M2N1O00100MiSg4\", \"size\": [1280, 720]}, {\"counts\": \"^Yme0:dW14L4K4M4L4K4N3L3M3N2N2N3M2N2N101N2O000O2O000O2O0O100O10001O1N2O1N3NO1O1M3O10000O100O1000000D<L4000001O1N3N2N5J8I4L1ZOkhN`0]W1Lcfk3\", \"size\": [1280, 720]}, {\"counts\": \"PPWh0?aW15K4L5K4L3M2N1O3M1O2N3M1O3M1O2N1O2N2N2N1O1O000000000000001O000000O1000000000000001O00000000000000000000001ON2O100O1O1N20000O1N2O1O5I4L9C>D8J^WT1\", \"size\": [1280, 720]}, {\"counts\": \"_oZh0P1hV1>G8G9H8H5K5K4L4M2O2M2O2N1O2O1N1O3N2N2M3N1N2O00000O1000001O1O000O10O1N2N2N2M3M4J5L4N2N2O1N2N2N2O2N1O1N3M2N2M4M2M3N3N101N1O2O0O2N3K5J6I8D>E]aQ1\", \"size\": [1280, 720]}, {\"counts\": \"cajd07^W1n0ZO6K5J3N2G\\\\NTjNg1kU18N3M1O1N2O3M2N2N2N2O0O1O0000000000N2O1O100O2O0000000O2O000O100N2M4O0O2N1N2O2O0O1000O101O01O001O0O2O1O1N101000O0100O001O001N2M3O1O1O1O2M3M3QOgiN3[V1LgiN1[V1OgiNL]V12k0MdXT4\", \"size\": [1280, 720]}, {\"counts\": \"Tmbd05hW16K4L4M2N4L;F3L2N10O010000O010O010O01O0O20O10M3N2N20O1000O010O10O10000O01001O001O1`iNQOmU1P1miNVORV1]1O1O002N1O001O00001O0000001OO1N2O1000010N1000010O0O1J7L3000001N10000O2N101N2K4O20O3\\\\iNjNYV1R1<N3O0O2N2N2O1N101N4M1N2F9JT[e3\", \"size\": [1280, 720]}, {\"counts\": \"QcSa01nW11M4N2N2O1O1O1N1O2N2N1O2O1N2N2N2O1O1N5RiNTO`V1Z1K5giN]NmU1l1N101N101N2O1O0O2O00000O0100O11O00000000000000000000000001O0000000001O00000000001O00010O1O1O0010000000N2L4M3N2N2L4M3I7O1O10OM5K4N2N2N2N2N1M5K41N02N2H7HllU7\", \"size\": [1280, 720]}, {\"counts\": \"]_n=1nW13M3M7I4L100O2N2M2O2N1O2N2N2N2M3N2M3O2M3M3M3M3M3M3M2N2N2O1N10O0100O100OI7K5J6G:L3O1O2O01O0000O2O00010O010O001O010O0100O010O01O10O01000O01O10O2OO1O1O1O100O2O000O1O1B>O2N1O2L3KY`74i_HKTb]:\", \"size\": [1280, 720]}, {\"counts\": \"a^S>5hW13O2N3J4M211O4L1O00000000000O2O00001O000O1O2N1O2O1N2N1O2N2N3M2N3M3M3M2N1O2O3K4M2O1O1N2O1N3N00001N10000O010O01O10OoNajN@bU17ijNEXU1;jjNBXU1<ojN[OTU1e0S10001O01O2O1N10N1010O010O0100O01N2O01O1O010O100N1O200O2H7O10000O2E:K5M3NRX1OQhN2N3MLWko9\", \"size\": [1280, 720]}, {\"counts\": \"Rm]?1nW12N3L4L2O2N1_OF[iN;dV1HZiN8eV1b0O1O100O1000000000O10000O10000O10001O00000O2O000O2O00000O101O00000001O003M6J6J3M5K4L3M4L2N1O1N2O000O010O01O010O010N101N101O1O1N1N3N2J6E;M3IYiNTOgV1l0700000O010O100O10000O1000000O100000000O100000000O100000000O1001O000000000O10000O101M2H8N3N10001N2O1M3J6M2O1O52J1lS\\\\7\", \"size\": [1280, 720]}, {\"counts\": \"Pfbf02ZW16RiNIiV1?mhNIoV1g0K6L3OO1O01O2N2O0O2O00000O101O000000001O00000000001O9G<D00K5N2O1O1M3N2O1N3N100O100O2N1O2O0O2O00000001OO2O00001O1O1O1O1O1O3M7I2M2O1O1O001O1O000O101O000O2O0000001O01O000001N10001O001O0O2O1O0O4LSbe1\", \"size\": [1280, 720]}, {\"counts\": \"UjVe0n0jV1;H5L5L3N2O1O1O00001N3N2N00100O1O2N102M2N1O1O1O001O00100O1N3N2M200O00100O100O1O101N3YkNUMTT1P3gkNWMRT1j2nkNUMRT1l2nkNSMRT1n2nkNQMRT1l2RlNTMmS1l2TlNSMmS1m2SlNRMlS1P3UlNnLlS1R3UlNlLkS1Z3PlNfLPT1]3lkNdLST1_3hkNdLXT1\\\\350N21O100O100O1O11O1OO100O100O1O100O1000000O10000O100O100O1O6L1O1N1O0O1O2O1WMojN`2ZU1N00105J?B2N2M2N2O2M2O1O1N2O2N1N2O1O1N2O1N2N2N2O1N2O3L2N2N4M1N4Lfeo1\", \"size\": [1280, 720]}, {\"counts\": \"j`T>9dW16J4L4N2N2O1N3M4M3M3L5K2O1O4L8I4K2N1000003M1N0010O002OO01O1O1O000000000010O0001O01O001O0010O100O01000O0100O3M2OO0001O1N101N1O2O0O2N2N2O0O1O2N1O1O1O1O1O2N1O2O00000102M4M6I8I3K4I6I;J3M3M2M4L2O2N1O1G9N1F<M2N2N3M2Memj9\", \"size\": [1280, 720]}, {\"counts\": \"aU`<34<SW1>J3N3M2O0O0O200O1O1000O1000O100000O10O2O00O100O1000O10001N1O100O2O00O11N1O101O1O0O2N10001N100O1O1O101N100O2N10000O1000000000O100O101N1O2M2N3N1N2O2N1O101O0O1O2O0O2O000O101O000000000000000O10000000000000000000O11O001O000010N10000001O0O110GXiNXOhV1e0\\\\iNZOeV1e0ZiN\\\\OfV1c0ZiN^OgV1a0ZiN^OfV1c0[iN[OfV14RiN9ZW1EghN9^W121O2O[h3EPXL0_R[:\", \"size\": [1280, 720]}, {\"counts\": \"[Pj>6fW18I5J5L5J5L3M3K5I7J6L3K5N3O00000001O0O1O2N101O1O00100O01000000O100O1O1O1O2N1O100O2O1O001N101N2O000O1O2M2N3N1O1O1O010OmN`kNeNbT1Z1dkN\\\\NaT1c1n001O00001O010O000010O01O1O001O1O100O1O100O1O1O2O0O1O1O2N20O01O0J6[OTiNNUW1N`0NdhN2[W12ma_9\", \"size\": [1280, 720]}, {\"counts\": \"_mj`0?YW1;H6M3K5K6K3N1M3O1N3mNbNakN`1_T1eNZkN]1fT1gNTkN[1lT1l01O00000O2N101M3N2N2N3M2O2N2O1O1O1O0100O10O010O000002N2N2N1O1O1N2O1N3N0O1O100O100O01O000lNSkNUOoT1g0XkNSOkT1l0T110O10O2O00O1O0010O100O01O100000000O1000000O10001B]iNYOiV1a0?ZOVa22ikd7\", \"size\": [1280, 720]}, {\"counts\": \"XTmb04kW13L3N2N2N3N3L3M1O101M2O2M101O1O1O1O001O001O0O100O10001N1O1N3M3O0O2N2N2O1O10JiiNcNVV1^1jiNbNVV1^1jiNcNTV1^1liNbNTV1e1O100O01001N3N1O1O1O2N1O001N2O1O1O0O2O000000hN`jN0_U1OijNKWU14njNFTU19Y110000O100000000000000O2O00000000000000001O000000000000000001O0O1M3M3L5O1N1O2O0O2N2O0O2O1O1O1N2L\\\\me4\", \"size\": [1280, 720]}, {\"counts\": \"Wene01oW15J101N2N101N2N1O2N100O2O0O101O001O001O1O01O0001N101O001N101N2O0O2O2M3M2N2N2N2N2N1O1O2O0O100O1O1O1000O10O01O100O2M200000001M3N2O1N2O1M3M4L201O0JRiNZOPW1d0RiNZOnV1h05O1N2N24LO011N001O2N1L5K6OO01000O1OO_Ra2\", \"size\": [1280, 720]}, {\"counts\": \"YlTf01iW15^OLmhN7M0nV1i0J6L3N2N3N3M001O1O101OO01000O010001O0O100000O10000001O3ZjNWNoT1i1QkNXNnT1f1f01YjNXNJNTU1i1j00^jNWNmT1i1SkNWNmT1h1f00000:G4K0000O2N101O0O1N3N101O1O001O1N2O1N2O1O1O1O1O1O1O1O1O2N1O001O000O100O100000000O100000000O1000000000000O2O001O1O2N2N1O2N1N4M1N4M4K3Mn[i1\", \"size\": [1280, 720]}, {\"counts\": \"bQQa0:>3ZV12]iN8_V1h0L2N3L3O1N2N2N2O1N1O2O1O0O2O1O0O2O001O001O001O010O1O1O1O010O001O01ObjNeMYU1W2jjNhMWU1R2njNoMQU1n1RkNRNnT1n1RkNRNmT1o1SkNQNmT1o1SkNQNnT1n1SkNPNnT1Q2`0000ajNoMoT1Q2`0N3M2N12O0010O0000000JPjN\\\\NPV1e151O00O100000000O1O1O03N1N2O000O10O01OO2M3M3M3O1O0O2M3N2O1N2N2M3N2M3K5M3N2M3M3N2L5L3O1O10kVR7\", \"size\": [1280, 720]}, {\"counts\": \"m^P=371TW16khNNPW1c0L4K5J<F7LO01O00N201O0O1O2O0N2O1N3M2N3N1010OO2N12N1N3N00O01100O0100O2N1O3N4K1O2O0O10000N2O100O010OM4L4M3M2O2N2N1N3N110000O1O1O1O0010O010O010000O100O10O10O1000O10000000O010000O100O10000010O000000O1BXiNCiV13g0L5O001O14]hNISW1b01DhhNMYW10>M2NUcg:\", \"size\": [1280, 720]}, {\"counts\": \"[Pj>7eW18D9J6J7K4M3M4L2O1O1O1O2M2OO1O0100O1O0M31O0101O00001N2O001O1N2O2N1O2N1N3O1N3M2N2N2O0O2O1N2O1N3N1OcNVkNFgT19`kNB`T1<fkNAYT1=nkN^ORT1a0TlN[OkS1c0[lNYOeS1e0_lNYOaS1f0alNYO_S1g0blNXO^S1g0elNWO[S1f0ilNXOXS11WkN6c1FYS12WkN5jV1I[iN2fV1M]iNNgV10_iNJcPj:\", \"size\": [1280, 720]}, {\"counts\": \"fgk?4gW1:F6mNIWjN<gU1JPjN:oU1l0O101N101O1O001N2O003L3N4K1O1O100O1O1O001O1O1O001O0O2ON101J6N2M3M3N2O11N2N2O000000000000000001O000O1O2M201N1O2N1M4N101O1M2N3N2O1O1O1O1O1O1O1O100O1N2K6N1N2Ac0FZ[o8\", \"size\": [1280, 720]}, {\"counts\": \"a]_b01nW14bhNLhV1k0J6K3M1O2N3M4M10N1M3002N3M3M1O1N1000LjiN`NTV1d12000O2N2O0O001O001N2O001O10O01N2O100O1O0000002N4M2M1O2NO2O000N3N101L4H7N2N2N3N2N101O1O00100O100O1N200O1O100O1O2O0O1O2CViNBjV1j00^OYiNKgV11fiNF\\\\V13kiNJXV11iP5Nmkm5\", \"size\": [1280, 720]}, {\"counts\": \"f]]c01mW14M2M3N1O2N1N2O100O0010O0100O10O0100O1O010O1O0100O10O010O0100O0100O1O100O2N101ZiN_OjU1b0TjNBjU1?RjNElU1?niNDQV1V1O1O1O1O1O1O100O1O0001O0O2N1O2N1N3M3N1O2L3N3N2N1010000O010O1000000O1000O100O10O10O100000000O1000O10000O10O10O100O1000O1000O10000001OO2I60000O100O2O0O101N1010O10O1N1N3M2M5K5M2Obkb3\", \"size\": [1280, 720]}, {\"counts\": \"^jQe05iW17J5K3M2N2O001O0O10000000000O100000001N101ciNZO^U1g0[jNAcU1?[jNEdU1;VjNJjU1V1O2O0000000O01000000O10O0100O0O2N2M2I8N2O1O10000O1000000000O11O0001O00000000001O00000001O0L42N1OL4O2N10000N2O2O001O0000001O000O100O1N2N20O10O1I600011N102M8H6K1N01O1C=O1O2M2O2M4M4Lk]b2\", \"size\": [1280, 720]}, {\"counts\": \"aZUb09dW13O2N2N0O2O1O2M2N2O1O100O1O100O10O0100O1O100O100O100000000000O01000000001O00VjNUO_T1k0^kNYOaT1g0\\\\kN\\\\OdT1d0ZkN^OfT1b0XkN_OhT1b0UkN@lT1`0RkNAoT1?ojNCQU1=mjNDTU1=fjNG[U1:`jNIaU1Z11O1O1O1N1010O1000000O2O002M4M1O0O0100O0O2O1N1O2N2L4M3M2O2L4N2O1O10000000000000000O1001N10O1001O0000000O10000O100O01000O1O100010O1O1O1N3N1O1N3M2L4M2N3O0O2N1O2N1L5OO2O0003K3M3MW`7ESVS4\", \"size\": [1280, 720]}, {\"counts\": \"lWPd04jW19A<ghN]O4LaV1[1K2OI7N11O1O1J7N2N2M2L3L5O2N1O1N2N2O1O1N3M2O2M4M3N1N1O2N2N101N1O2O2M3N1O000O100O100O2O001N1010000O1N]NhkNAWT1=lkNCST1;PlNEPT17YlNDgS19]lNGaS16dlNH\\\\S16jlNHUS1SOikNN2<W1a0mR1TOYlN0Y1d0_R1[OanNOVM9ZT1GinN5ZQ1J^300O2Oki^5\", \"size\": [1280, 720]}, {\"counts\": \"lkfd0:]W1=I5K5J6I6I7J8J4L4M3M4M2M3M3M3N2M3N2M3N3M3L4M3N1N2O2M2O1N2O2N001O000000O0TOmkNlMTT1R2QlNjMoS1V2TlNhMlS1W2WlNfMjS1Y2YlNcMiS1\\\\2ZlN`MiS1_2m0M3nNPkNQORU1a0]kN[OfT1>bkN^O`T1`0ckN]O_T1a0`1O3N1O3N2KR^X5\", \"size\": [1280, 720]}, {\"counts\": \"fWYd01eW1=F9I6J6L4M2N2N2N2N3L3N2N3N1N2N102M2O1N2L3N3O1O2N100O001N2O1O1O001O100O10O010O1N3N0O2N002O1N2O1N2O1L5K4K6K3O1O2N2N2N2O1O1O101J6_OUiNGmV1;RiNFnV1i0LGViNDdV1j03\\\\O[iNA0OgV1`0[iN\\\\O40bV1d0`0@hhN8a0DSV11^iN:`0EYV1:e014L3N002N1O00]Zd4\", \"size\": [1280, 720]}, {\"counts\": \"l[Td02kW15M8H9H3M1O2N1N2O100O001O2N2N3M3M3M4K2O2N3M3M2N2N1N10000O110O1O00000001O01O0O10000O100000O100000O10000000000000O10O1000O0010O01O1O1O00O13M2M3N1O1N2O7H4M1O4K4L3N3K4L5L3N2K7Kikb4\", \"size\": [1280, 720]}, {\"counts\": \"bVee01eW1;M5J4J6M3L3N2N2N101O1O2O0O1000000O1000O01O2N1O1O1O001O1N2N2N2O0O2O1N100O1M3O2O00O1M3N2M10N34J6K3N001O2N1O2N1O1O2O1O1N2O1O1N2N2M2O2N3K7Kmch3\", \"size\": [1280, 720]}, {\"counts\": \"idVf08eW17K2N1O2N1N3M3N2N2N100O1O100O11O0O1000O010O100O1O010O1O1O1O1N2O1N2O001O1M3N101O00O01O2O00OO21O00N2000O2O1O1O2N1N2N1N2O1O3K6I7K3N3M3L3N3KlTW3\", \"size\": [1280, 720]}, {\"counts\": \"]UVe03hW18K5J4L4N101N1O20O00O1O1O1000000O100O100O1O1O1N2O1N2N2O100O1O1O1M3O1N2O00N01001O101N2N2O0O2N2N100O2N101N8J0N3N1N1O2N2N3K4L6JST_4\", \"size\": [1280, 720]}, {\"counts\": \"WU]d05gW16J7I5M3L4N1O2N1N2O1O2N1O1O1O1O10000O1O1O100O10O01O10O0010O010O01O01O01O000O100001O0O1M2200O2M2O010N3O3L4K3M2O2M2N3N2N1O1O1O1N2O2L4M4JRlQ5\", \"size\": [1280, 720]}, {\"counts\": \"Yjed09cW18J3M5L3L4L4L4M2M5L2N1O2N1N3N1O1O2N1N2M3O001M210O0O200O1O1O1O1O10O10O1000O10O1000O010O2O0O10O01O011N1O1O1N4L3O001N1O1N2O1O2M2O1M3L8Gb0_OfVi4\", \"size\": [1280, 720]}, {\"counts\": \"na\\\\c0`0\\\\W19H6L4K4L4M3L3N2M3N2O1N2N2O1N1O2O0O2O001O010O0O10000O100000000O1000O01000O10O0100O10O10O1O0001N101M3O001O11N3K5L2N2N102M2N2O0O2N2N4K5fN^iNo0oV1H7IL^ORiN?YW1M3L5Jinj5\", \"size\": [1280, 720]}, {\"counts\": \"nUVe0=_W1;G5J5L4L3M4M3L3N1M202M2O2M2O1N3M2N3M2N2J6M3M3M201M3M30000001O1O2NO1N20O10000000004L3M2M1N2O1N2O2M3L5I9Fcck4\", \"size\": [1280, 720]}, {\"counts\": \"U]cd0a0\\\\W17H5L4L3N2N3N2O1N2N3N2M1O2N1O2O0O2N1O1O1O001O10O01O1O1N2O0O2N101N2O001O1N2N1N3001O0O10O1002M2O1OO01N101N2M1N3N2C[jN]NjU1P1^UX5\", \"size\": [1280, 720]}, {\"counts\": \"odge0454UW1?K4N3M2N2M3N1O2N2N3M2O2M2N2N2N2O2M2N3N0O2O1O1O1M2O2O1N2N1O200O10O10000O0100001O001N1O2N1O1mMYjNl1hU1SNYjNk1oU1O1N2N2J6I9El0TOZdW4\", \"size\": [1280, 720]}, {\"counts\": \"Xned07gW13N2N3L3N2N2N1N3N2O000O1O1O1O1O1O1N2O1O1O1O1O1O1O1N2O1O1O1N2O1N2O1N2N2O001N2N2M20100O01O2O0O01OO1N4N1O1N2N2N2M1O2N3M4M2N1N2N4M:E6H[ck4\", \"size\": [1280, 720]}, {\"counts\": \"d]We02kW1<D5M4K3M2O2O0O1N3N1O1O1O1O1O1O001O100O001N2O1O1O001O1O1O1O001O1O1N2O1O0O2O1O1O1O1L4I7O1O1O10O2O0O01O1O00OO22M2N2L4N1N2N5K3N1N0N3O3L5L4L5Jg0XOTlS4\", \"size\": [1280, 720]}, {\"counts\": \"iRhf07gW16I5L4M2N2N2O1N2M3N2N1O101N2O1M201N1O101N1O1O2O001N100O1O1O1O100N2O1O1O1O100O10000O010000000O001O1O010O10O2N100O1O1O1N3N0O2N2O1O3K4N0N2N2OO2N23Gb0YOe^_2\", \"size\": [1280, 720]}, {\"counts\": \"m`Yg0e02O_V1m0M2M3M2N3N1O1N2O1O1I8M2N3N1N3O0000000O2O0O1O1O10O01O1O100O1O1N2O1O1N200O00100O001O1O10O01O100O010O1O100O0O2NJ6M5L3N4N1N2O1N2O2N1O2M2N3M3M3M2O2N2M3M4L5L3M3J8H7H8J9GUhb1\", \"size\": [1280, 720]}, {\"counts\": \"QRZf05iW15K5L3L3M4M2N2M3N2N2O1N2N3M2N2N1O1000000O100O101N1O2O1N10001N2O000O101O001O000O101O0001O001N2O000O2O00001O010O10O01O2O00O2O3L100O3N2N1O2NZOVjNTOkU1g0[jNnNoU1l0f0N20000K5N1O2O4G6O3OO00N4M1O1N4M1O`eZ2\", \"size\": [1280, 720]}, {\"counts\": \"gV^b0g0VW15M2M3N3M1N3N2N2N2M2N3N1O2M2N3N2M3N2N101N1O2O0O2O1N1O2O0O2N1O1O2N101O001N10001O000000010OO101O01O00001O0001O01O010O00000000O1O10000O2O2N2ON1O1O12M2N101O2N3M9G4K20O0O14L1N3M2O1O3K8I5K2I9N0HjhNEVW1:9KahNI_W14bhNM]W12;MQX1OPhN2O0dhc5\", \"size\": [1280, 720]}, {\"counts\": \"ke\\\\d0:bW17G9K4M3L5L4K7I3N2M3M2N2N2N3M2O1M5K7I7I4K4N2N2N2O1O1N3O0O1O010O2O0O10000O101N101N2O000O11O010O0000001O10O01O1O1O1O2N2N4L3M2N2O1N5K5J=D5K3L6K4J5L3L4F9L4N3M4K9H5J6JPh37hWL5OO2M2O1N4MRX6NPhI0Wak3\", \"size\": [1280, 720]}, {\"counts\": \"ifZj07fW17H7L3L3O1N3N1N2O0O2N2N3N1M2O2N2N10001O1O1OO1M201O1000000000000O0011O0000O001001O0000O1000nI\", \"size\": [1280, 720]}, null, null, {\"counts\": \"jmai01T`20`gN?H8K3M4K5BPOaiNY1WV1`0E9H6M101N2N2O1N2N2N2O1O0O2O001O001O000000001O001O1O1O00001O001O00001O001O001O010O1O00010O2N2N100O001O00100O010O0^C\", \"size\": [1280, 720]}, {\"counts\": \"RZl`01e03aV12WiN3fV16QiNNlV1c001O001N3N002N1O1O10OO2O1O001N2O2N1O001O00000O10000O100O100000O1O1O010O1M3J6O10O010000O1O010000000O01O100O1000O0100O100O1O10O0100O01O1001O0O100000O10000O1000000O1010O0000000001O000001O01O0C]iN^ObV1`0aiN@_V1>biNC^V1;ciNG\\\\V18diNH\\\\V16giNH[V16fiNH\\\\V18diNG^V18ciNG^V18biNH_V11hiNI^V16f0O2O1N2M20SVi6\", \"size\": [1280, 720]}, {\"counts\": \"X`n=?ZW18N3L4]iNXOhU1R1miNTOmU1b1M2FRNajNP2[U1TNcjNn1ZU1VNcjNk1[U1WNdjNj1\\\\U1=O1000O2N10000000000O1000000O100O1000000O10000O100O2O1O000O10001O0000000O2O000001N11N100O100O2001N1O00001O000001O000000000001O0000000010O001O1O0001O0001O00001O01OO1O1O2O00001O0010O01N1000O11O0O2O000O101O1O01O000O1000010O1O001O0000L5I6O1N2O2M2M3N2M3O2O0I8N1K5M3L4N3O0O10001N10001O000O101O0O1000ooo7\", \"size\": [1280, 720]}, {\"counts\": \"Tfga02gW17M6F9K4M3M5K4L3M3M3M2O2N1O2N1O2N2N1N3O2L3VOPN_kNT2[T1ZNXkNk1fT1h0N3N100O10000O1000000O2O00001O1N2OEdkNRM\\\\T1k2hkNTMXT1k2ikNVMVT1i2kkNXMTT1g2mkNYMST1f2okNYMQT1f2PlNZMPT1e2RlNZMnS1f2RlNZMnS1e2SlN[MmS1d2UlNXMnS1f2TlNVMPT1i2d00000N2N2N200O2O00PlN_MaR1c2]mN]McR1e2[mN[MfR1d2ZmN\\\\MfR1c2\\\\101O1O1O1N3L5aNcjN1eU1EfjN5^U1EgjN8cV1N2M3M2N2M4M3MPh32nWL3ON2M\\\\jk6\", \"size\": [1280, 720]}, {\"counts\": \"loWg09bW18J7J3N3N2M2N2O1M3O2M2N3N2M2O1N3M2O0O3N1O1O00O0K6O02O0O101O0O10001OO100O10O2O00001O0O100000O1O10O101O000O100000000O101O1O001O0O10001O012M2N1O001O001O1O0000001N3N1O0000001O00001O01O000001O100O001O010O00O2L3N3G`jNoMaU1P28O2O3L2O4K3M3N2M5L4K3AXiNAlV1:WiNEPW1MYiN0c^8\", \"size\": [1280, 720]}, {\"counts\": \"Xnch0o0gV1d0\\\\O>G9I5J5J6L3N3N1O10O10O1O100O0O2O0100000O001O1O10000O0O2N200006J0O2O00000000001O1O1O1O001O1N101O0000001O1N10O1N30L4M3N2M3M3M3jM[jNj1iV1nNf0WOoYl0\", \"size\": [1280, 720]}, {\"counts\": \"_c`f0:bW19I4L3M3M2O2N1O2N2N1O1O2N100O1O1O1O1O101N1O1O2N1O1O001M3O1O1N2O1N1O2O0010O010O010O2O100O1N1O2N1O2N2O2L3N1N2N2N4L5L3L2L4L7HbmU3\", \"size\": [1280, 720]}, {\"counts\": \"[Tdc0;\\\\W1;M3N2M2N2O1N2O1N3N1O2M2N3N1N2O2N1N2O1N2N2O1N2O1O1O00001N101N1O2O1O0O2O1N1O2N1O2N1O1O1N3O0O0010000000O010000O0101O0O2O1O2M7I2N4M1N2N2O2M2N2M2O1N3iNiiNa0_V1SOXUb5\", \"size\": [1280, 720]}, {\"counts\": \"U[U?1nW11O1O2N1O2M3N4M1N2O2M2O1N2O1O1O001O002OO01O8H3M2O0O101O010OO0012M10O1O010O0101O0000O00100O1O00100O1O2N1O10O0010O010O00100O001O001O00000[OZiN0fV1O[iN1hV1KYiN5hV1H[iN7eV1H\\\\iN7fV1F]iN5ln>IdXB0Y`<1g_CO00ccQ9\", \"size\": [1280, 720]}, {\"counts\": \"d[P?1kW1:^OHlhN>QW1FkhN<TW19N2N0010O<E2O1N1O1N3M2M^d_<\", \"size\": [1280, 720]}, {\"counts\": \"c\\\\X`07gW15M00O10O3N1O1N3M1O1O101N2O2N1N2O1O001O000001O000000001O0001O0001O00001O2O0^OnhN4RW1HWiN1lV1KYiN1]W1NkRW:\", \"size\": [1280, 720]}, {\"counts\": \"`l`b09fW13N2M1O3I=HO0010O2L3N3N10O11O100O1O001M2O1O100000O101O00O1000001O00000000O2O000O0101O001O00O101N10000O1I]iNPOdV1o070O0ZiNoN_V1Q18O2N100N2O100O10O100O10O2N1O1GjhNGXW14lhNKUW12RiNIoV16>O1000000M3NTl]6\", \"size\": [1280, 720]}, {\"counts\": \"gbff06`W1`0G6K1N3M2O3M2O1O1N1O1O2O001O0001000O2OO1OjiNcNkU1]1UjNcNkU1]1UjNcNjU1]1=0000000000O2O0O101N2N3N1O2N2N1O2N2M2O2M2O2M2O2N2N103Kb]\\\\3\", \"size\": [1280, 720]}, {\"counts\": \"TT]g06fW17K3L5L2M3I6D=O1N1O2N20O01O00O20O1O1OM4M3O0100O10O10000O10O2O2N6J1O0000O1O2O1N2O1O2N002L4L4M2N3N3L3K6I5N13M23Icl`2\", \"size\": [1280, 720]}, {\"counts\": \"^R]g0h0SW1j0YO4N3L3N2N2N001O1O1O001O1O1O2N2N4M4K1O2N100O1O3NO10O01O100O1O1O02O00O0O3M2N3M3L4N4OO11F:J5Ec0jN_iNOSSl2\", \"size\": [1280, 720]}, {\"counts\": \"ehZe01kW16M3N2N2N1O1O1O1O010O1O100O1O10000O10O10O001O100O100O100O1O100O001O1000O010O10O100O1O010O100O1O100O101N1O1O01001N100O10000O2O0O01O1O010O10O0100000O010O01O10O0100O10O010O100O100O10000O2O1O1OGbiNoN_V1<UjNDkU17YjNJgU15YjNLfU13[jNNgU1NW1N2OUP5O`U]2\", \"size\": [1280, 720]}, {\"counts\": \"ZZ_d03hW19I5L3N1O1O2N101O1O1O1O0O2O0000000000000O1000001O000000O02O00001O001O001O00O10000000000001O00O100000O100000000000000O1001O0O1000O101O000000000000O2OO2O00000000000000O101O00000001O000000O100O100000000O100000000000000000000000001O0010O0001O010O001O012M100O0O1J6^OjhN6XUb2\", \"size\": [1280, 720]}, {\"counts\": \"aeTe06jW11N101O001O002M2O4L2N2L2O1N3M1N3M3N2M3M3N2N2N3N1O1O1000001N1O1O1N1O2000000O10O1000001O0O100001O00O10000O10100O2OO00O1O1O2N1N12O0000N2O001O1O100O2OO10O0100O1O1000O1O1K5H^iNRObV1m09N3OO1O4L10O100N20O0100O1000O1N1101O002ASiNGnV15ViNJaW1N3NoRg2\", \"size\": [1280, 720]}, {\"counts\": \"k_Ui08YW1h0B6I8K4L3L5M2K6I7K5M3M3N2M4M2N3M2O1N2O1O1O2M3N1O1O1O1N101O00O1O11O000000001O0000000000000001O001O1O002O2N3L2O1N3M3L6K2N1O1O3L3N7I2N4K5K5K2K5K5M3N7I2OS@\", \"size\": [1280, 720]}, {\"counts\": \"Qadi07gW12O001O1O2M2M2VOj0I8M3L4K5M3L3N3L4N2M4L3N2N3N2N3N5J4L4L4M3L4M2N2M100O0100000O0100O101N0100O1000010OO01000M2M3O3N1O2N3N2M2O4K5L00`A\", \"size\": [1280, 720]}, {\"counts\": \"S[gg03hW19H6N2N1N2O2M8I1O1O3M1O1O3M2N2N2M5L2N0O1O2O0O2N2M2O1000001O00000000000O1000010O0101N1O0003L3OO3M2N1M4M2I7N2M3L4L4M3N2M3M4L4L4L4Le\\\\k1\", \"size\": [1280, 720]}, {\"counts\": \"QXhi0<TW1HohNe0lV1:M4K4L4M2O1N101O1O1O0O2O1O000O101O000001O00O2O0O2O0O1O2O0O1000O10O100O10O1O10O1000O100O1000O01O001O01O1001O1N10000O10RA\", \"size\": [1280, 720]}, null, null, null, null, null, {\"counts\": \"emmc01og=000P`C0\\\\_<0XYB0Tf=0lYB000hf=0Sej37YkhK5L01OHEkhNa0lV1<G;DhNiiNY1_V1O10K5@f0G4N2O001N2O1O10O0O2O100O01O00001O1O1O000O1000001OO10O1O1O100010O00m_O\", \"size\": [1280, 720]}, null, null, null, null, null, null, null, null, null, {\"counts\": \"S_dg0?^W17J6J6I5M2N2N2N3M3M1O2O1N3M2N2O1O1N2O1O001N101N10000O2O1O001N101O1O2N1O2N1O1O1O1O001O1O1O1O0000N200000001O00001O000M4M2N22O0O01N1O2O1O100L4M4M101N2O1O0O3N1O2N2002OYOoiN]OUV12WjNKoU1HZjNOXONk^V1\", \"size\": [1280, 720]}, {\"counts\": \"QV^g09cW1`0@8I5L4M3L3N3M3M3M2N2O1N2N2O1N2O1N10001N101O1O001O00001O001O1O001O10O01O1O0010M2M3N2000001O0000001O000N3O000O101O00001O1N101O001N1O2N101O1O1N3N2M3I:]O^jf1\", \"size\": [1280, 720]}, {\"counts\": \"`lTk0g0VW1:E9H7J4L4L8H2N010O3N1N2O1O2N2OOO2O100O101N2N2O0cL\", \"size\": [1280, 720]}, {\"counts\": \"h\\\\ff06gW18H7J6J6K5L2M3M4M2M3N2M2O1N2O2M2O1O1N2O1O1O1N2O1O1O001O001O1O1O001O001O001O010O00001O00001O0000010O001O00001O0001O00001N1O1O2O0O101O001O1O0011N010O0001N1O2K5N1O2O1N2N2M4N2IjiNaNVV1]19N2N3L7J<C]Zi1\", \"size\": [1280, 720]}, {\"counts\": \"hfYc02>5gV11UiN2hV11SiN3lV1OQiN4nV1`0N2N2L4M5M4L3L2M5L3N1N2O1O000O101O1O0O2N2O1O001O00O1001O1O0001N10000000001N110O0001O01O1O1N2O0O2N2O101N1O2N1O1_OoiNPOSV1o0miNQOTV1h0jiNQO35VV1g0QjNYOPV1f0QjNZOnU1e0SjN[OnU1b0TjN^OnU1?TjN@mU1>TjNAlU1`0UjN\\\\OmU1e0j0M2M3L3O1N1O6HXh30kWL0chc5\", \"size\": [1280, 720]}, {\"counts\": \"j`Zb01nW12L7K2N2N2O1N2M3O1N2O0O3@VOfiNk0fV1100O2O1O1O1K5M30000HgNhiNY1UV1;M3M3O001O1N101O1O0O2O1N2N101O2N2N0O100000000O10N20O2O1OO2M20000O100O1O1O010O101O000O101O01O1O10N1O2O1N2N101O1O1N3M2N2O1O0O2O0O2N2D`iNVOaV12eiN1MM^V10YjNOfU16UjNJjU1>PjNAQV1>PjNBQV18UjNGlU1O`iNNg02jU1M_jN2bU1M`jN2`U1N`jN2gV100000000O10001O0000bf^5\", \"size\": [1280, 720]}, {\"counts\": \"ffie0:cW15M4K6K3M4M1N2N1O1O2N2O0O100O6J3M3M1O2N1O2O0O2N101N1O100O2N100O101N100O100O1O100O1O100O100O1O10001N1000000000000000000O100000000000001O00O10000000000O1001O000001O000N3I6N201N2N2M3M4O00OO2M2O2O1N2N3N10O03M2M3M3L5I7K8I8H2N2MUYn1\", \"size\": [1280, 720]}, {\"counts\": \"kdZj0h0nV1?G7K4M3M3M2N2O1N2N2N2N2O2M2O1N2O1O1N2O1N2O1O3M3L3N1O1O2N1N2O1O2N1O1O2N2M2O2N1O00010O100O0RL\", \"size\": [1280, 720]}, {\"counts\": \"n^[d01oW15J4K3^hNHYW1b0N2L3K21O00200OohNXOlV1o0000ON3M3M4M3L3N2N2N1O1O1O100O2N2N2N4L2N100O1O2O0O2N100O100O100O1O101O0000000000000O101O000000000000000O1000001O0001O00000001O001O00001O01O00001O0010O100O01O1O001N3N1O2N1N2O1O2N3M1M4L4L3N4M1M4G9E:K6N2N1N2N6C_hN0_hX3\", \"size\": [1280, 720]}, {\"counts\": \"n^od0;cW16J5J5I;G3O3L3M4M2N3L3N2N2N2M3N1O2N1O1O2O0O1O100O100O100O1O100O2OO010001N1000000O101O000O010000000000000O100000O1000000O100001O00000001O00000001O000001N101N1O1O2LcjNfM[U1U2ijNkMXU1R2jjNoMUU1P2mjNoMSU1Q2mjNoMTU1n1njNRNTU1i1d0M3O1N3N1N3M2N4L3L4L4I7I7L5M2M3M4KYYQ3\", \"size\": [1280, 720]}, null, null, {\"counts\": \"Y\\\\nb01mW12L5M3M3N2N1O2M4N0O000O4M1O2N00100O2M3N4L2N2N1O2N2O0O2N4L1O100O101N1O1O101O00001N2O1N2N2O1N1O2O001O001O001N2O1O1N2O2N1N3N3M1O1O2M3N2N2N1O1N101O000001O01N2O1O00000001O00001O0000001O0000001O001O001O1O00001O00001O001O001O00001O0000001O001O001O002N1O001N101O1O1O001O1O1O1O1O1O001O1O1N2O1O00100O1O1O001O1O1O1O1O1O100O100O1O001O1N2N2O1O1N1]OhiN\\\\OZV1b0iiN[OZV1a0iiN]OZV1a0hiN]OZV1a0hiN]OZV1a0giN]O\\\\V1`0giN^O[V1`0fiN^O]V1`0d0M2M2O101N5JVSR2\", \"size\": [1280, 720]}, {\"counts\": \"RZaf0i0RW1:G7H9J4L4L4M2L4N3N1N2N2O0O2N2N5K2N2N1O1O2N2N2N1O1O2O1N1O2N2M3N101N1O2O0O2O0O2N101N1O2N1O101N1O1O1O1O100O010O101O000O1000000100O4M0O00002N3M5K?@:G5K3N2N3L5J5K3M3L4M3M5L4K00011O3L4K4M3L4M2N2O1N1HmhNCRW1=ohNDPW1;RiND;LRV1<giNFmV15XiNIjV15b0N100O2NQ`20P`M1chNOcSY1\", \"size\": [1280, 720]}, {\"counts\": \"Wblf0`0[W1;G4N3L3N2M4M1O2N2N3M3M2M3N1O2N2N2O0O2N2M4M3M2N2O1N2N3M101N2N2N2N2N1O2N2O1N2N2O1N101N1O010O2O1O001N101O1O1O0O3N1O000001O01O0001O010O1O001O00010O1O001O100O1O1O1O2O0O1O2O3L2N2OO000002N1O100N102M200O1O002N5H5N2DajNRNaU1k1bjNSN`U1k1bjNSN`U1k1ajNUN`U1g1djNWN_U1f1?ON2TjN]NZU1f1cjN\\\\N\\\\U1b1fjN^N[U1_1hjN`NZU1]1gjNcN[U1Z1gjNcNcU1T1^jNkNfU1Q1[jNoNfU1o0[jNPOhU1l0i0N02N3^iNROlU1o0iiNPO22WV1j0R1YOld8\", \"size\": [1280, 720]}, {\"counts\": \"lTea04jW17I;F3L4M3N1N2N2N1O2E:N3M2O2O0O2O001O00001O00001O00001O00000010OO10000O2N1M3O2N1O1O101O0O101O001O001O00001O00001O000010O01O1O100O1O2O0O00000001O01O1O1O10O10O010O00O101O0001O01O000010O00000001O00001O00001O00100O00001O1O1O00001O1O0000100N2O1O1O1O1O001O00001O001O1O0000001O001O1O1O1O001O1O1O001O1O001O001O1N101O10O01O00001O010O1kNiiN?YV1_OiiNJH=`V1HjiNFKa0[V1JoiN6RV1JmiN7SV1LkiN3VV1IPjN3QV1LPjN4QV1JQjN4RV1IXjNMkU10c[e3\", \"size\": [1280, 720]}, {\"counts\": \"S[ff07`W1:L4L6F9XO\\\\OliNj0gU1o0K4N2L4M3M2O2O0O5K7J4K9H2M2O3L2O2N3L3N2N4L2M3N1O1O1O001O1O1O1O1O4L3L101O0000000000O20O0001N2I8M6H9[L`lN[2hS1_MZlN^2jS1^MZlN`2fT1M6I6I8AZjNVNlU1d1?I4M6D=G8HRdh2\", \"size\": [1280, 720]}, null, null, {\"counts\": \"\\\\_`?<cW17H6J4@TOfiNo0VV1WOdiNm0XV1`0N1N2O1O1O100O100O100O1000000O1000000O10000O10O1O010000O10000O1000O10\\\\ORNVkNn1eT1WNZkNj1cT1ZN\\\\kNe1dT1\\\\N\\\\kNd1cT1^N\\\\kNb1dT1^N[kNc1dT1^N\\\\kNa1dT1aN[kN_1eT1aN[kN_1dT1bN\\\\kN^1dT1bN\\\\kN^1dT1bN[kN_1dT1bN\\\\kN]1eT1cN[kN]1eT1cN[kN]1eT1cNZkN^1fT1bNZkN_1eT1aN[kN_1eT1bNZkN_1eT1aN[kN`1dT1`N\\\\kNa1cT1_N]kNc1aT1^N^kNb1bT1_N]kNb1bT1_N]kNa1cT1`NZkNc1eT1^NXkNd1hT1k0O1O10O0010O01O100O0001O0010O010O00001M2002N0001N1N21O1O1OO1M3O2M200O11O2OO001N101O010M2O10000O1O20O11N2O13K1OKjjNaMUU1^2njN`MSU1_2ojN_MQU1c260ON3N1O1O1N2O11O10O0N201N101XN[jNP1hU1e0O1O0001M201O010O0XNTjNY1PV1fNWjNKL00f0mU1_OVjN6K<PV1]OniN^1PV1:O01WNUjNZ1lU1fNWjNLL5Ld0RV1ZObjNLAh0mU1\\\\ORkN;oT1DUkN5PU1IQkN7oT1IQkN2kNMRV11SkN2kNMRV11TkN9kT1HTkN:jT1FVkN:jT1EVkN3gN2TV1JUkN3iN1SV1LUkN1iN3RV1Lg11ciNNmT10b100000000000g10bT10giN0PX60PhI000000000\\\\Y68\\\\fI00O100O2M2M30PX10PhN1O2N00000^hV4\", \"size\": [1280, 720]}, {\"counts\": \"oRkc03WW16nhN2kV1f0L3J7N1N2N3N1miN`NeU1b1SjNjNgU1i1N2N2O1N3M7J7H2O2M4M3L8I4K1O101O1O1O00001OO1O100001O01O10O0100001O2N1O1N001000O100O101N100O1O1O010O1O2OOO101O0100O10O1O1O1O1N2O0O2N2O1N101O1N101N102M2O1N102N1O1O0O2O1M3N2N2N2N2N2FZjNYNfU1e1[jN\\\\NeU1a1^jN^NcU1_1`jN_NbU1^1ajN`N`U1]1cjNbN`U1Z1cjNcN`U1Z1e0E;L4M300OUiNYO53cU1c0WjN\\\\O52eU14liNLS1OhV1N1OQ`70c^H0ijN3bV1N1O10nRZ3\", \"size\": [1280, 720]}, {\"counts\": \"e[ff01hW1:F;E9L4K5M3L2N3N2N2M2O3M2M3N2M4M1O2M3N2M3N2N1N3N2aN\\\\M]mNh2aR1eMPmN_2nR1iMhlN[2VS1hMglNY2YS1hMclN\\\\2[S1V1N1O2O002M2N1O2O1N2O0O2O0O2O00001N1O1000000000000001N2O1O1O2N2N1O1N3N1bKdlNV4eS1N2N2M3M11000N2N1O2N3M3M3N3L2O0O2O002M3M2O0N3L3N5K3L40O2N12EhjNfM\\\\U1U2;K5L3J8J5L5L2N11O5LFb0YOh]U1\", \"size\": [1280, 720]}, {\"counts\": \"d`j?=fV10ciN:WV1k0L3N2M2O1N3O0O2N001O010O1O1O1O001O100O1OO1010O10O10O100O0O2O100000O01O1O1O100O2O0O1N2O2O0O2O0O2N2M3N101O1O001O0001O10M20000000000O2K6J50O01O0O1O20O001O1N3N1O0O1001N1O1000001N1M3N3O000000O100O2O000000000O10000O1O2O0O2N10O1O10000001N100O2N1O2N10000O100O2N1O1O1O2O000N2O1O1O101N1O1O100O100O2O001hM`jNl1`U1mMPkNk1QU1UNRkNh1dU1M3M3M4M:dNYiNk0QW1NH:K32N=B?B1N2N2N2O0O10O2N2O0000O2O1N10j_]5\", \"size\": [1280, 720]}, {\"counts\": \"ae[g01jW1`0@:I4L4I7J6K4L5M3L3M3N2N2N1O2N1O2N2N2N1N3N1O3M2N2M3N2UOYM[lNg2bS1_MRlNB2P3jS1jMQlNX2mS1n0O1O1N2O2N2O000O101O1N10O0101O0O2O000O11O000000O01O10000O010O100O100O010O1O001O1O100O01O0100O010O10000O1O1O101N100O100N2O2N3M2O2M100O2OO1000O101N101N101N2O1O00001O1O1O1O2M2O1O1O3M00lL\", \"size\": [1280, 720]}, null, {\"counts\": \"SfSf04`00hV10WiN1iV1OViN2XV1KkiN4G5_V1HfiN7H5_V1EeiN;H4`V1DfiNm0\\\\V1nNhiNm0jV1TO[iN3]W1LoN0aiN:GIgV14ZiNb0L]O_V13biNj0YV1a0F\\\\NWjN5IY1PV1dNTjNf1jU1[NSjNR2aU19L2OIcjNlM^U1j1bjNXN2MZU1^2OMhjNfMQU1[2ojNeMPU1^2mjNcMRU1d21003M2N2N3L4M2N2N2N0001O10O1N2N3M4N2M100O100O10O0010O2N5L0O10O1000O10001N10000000000O20O0001N101O100O001O0O101O001O00000000O1001O000000000000000010O00O1000O100000000O2O00000000O010001N10000O10000O100O1O100O101O0O100O10001N1O100O100O110O01OO2O001N1O2O0O2N10RL\", \"size\": [1280, 720]}, {\"counts\": \"YQda09eW15K5K6J4^OYOfiNi0VV1]ObiNi0ZV1b0L3N2M3N2N2N2N100O100O2N100O100O100O1O10O01O1O100O1O1DeMQkN\\\\2nT1<O1N2O1O001O1O100O001O100O1O010O1O010O10O10O10O0100O01O0100O010O010O10O010O10O01O01O001O0010O0100O010O100O2O000O10000000000000001N2O00001N11O000001O000O2O1O001O1O00O1000000O100O1000000O100O1000000001O000O1000O2O00000O1000000000000O10000O100O10000O10000O1O101N100O10001N10000O10000O2O0O11O10OO100001O01OO2N1O100O1O1O1O1O1O2N1O100O1O100O100O100O2N1O1O2N2O0N2O2N2N3M2N2O2M3N2M4L2O1O1]NniNV1bV1L1N2N1N3CmhNIVW11ohNNdW1N1O1M2O10jgW1\", \"size\": [1280, 720]}], \"bboxes\": [[452.0, 596.0, 63.0, 103.0], [435.0, 566.0, 60.0, 118.0], [423.0, 571.0, 63.0, 115.0], [430.0, 587.0, 65.0, 119.0], [451.0, 597.0, 66.0, 128.0], [414.0, 587.0, 85.0, 98.0], [396.0, 566.0, 96.0, 102.0], [380.0, 548.0, 107.0, 82.0], [370.0, 546.0, 118.0, 78.0], [375.0, 542.0, 121.0, 72.0], [389.0, 537.0, 125.0, 76.0], [408.0, 522.0, 121.0, 87.0], [416.0, 526.0, 117.0, 86.0], [418.0, 540.0, 105.0, 73.0], [406.0, 544.0, 105.0, 66.0], [372.0, 553.0, 92.0, 65.0], [361.0, 567.0, 80.0, 56.0], [380.0, 557.0, 35.0, 59.0], [380.0, 577.0, 27.0, 38.0], [356.0, 609.0, 48.0, 92.0], [371.0, 652.0, 81.0, 103.0], [505.0, 591.0, 76.0, 132.0], [616.0, 535.0, 69.0, 76.0], [654.0, 367.0, 66.0, 97.0], [461.0, 363.0, 61.0, 91.0], [315.0, 516.0, 96.0, 52.0], [469.0, 494.0, 70.0, 49.0], [644.0, 420.0, 65.0, 54.0], [605.0, 533.0, 48.0, 53.0], [567.0, 597.0, 41.0, 53.0], [612.0, 240.0, 59.0, 100.0], [552.0, 175.0, 49.0, 62.0], [544.0, 99.0, 55.0, 65.0], [561.0, 14.0, 60.0, 72.0], [620.0, 0.0, 71.0, 66.0], [623.0, 195.0, 70.0, 119.0], [533.0, 231.0, 81.0, 118.0], [527.0, 142.0, 99.0, 65.0], [438.0, 76.0, 98.0, 81.0], [357.0, 184.0, 96.0, 95.0], [361.0, 155.0, 103.0, 99.0], [395.0, 119.0, 136.0, 84.0], [578.0, 151.0, 99.0, 71.0], [543.0, 0.0, 126.0, 130.0], [362.0, 11.0, 106.0, 106.0], [320.0, 147.0, 135.0, 64.0], [379.0, 172.0, 98.0, 111.0], [431.0, 92.0, 91.0, 106.0], [484.0, 76.0, 116.0, 81.0], [562.0, 141.0, 93.0, 58.0], [567.0, 83.0, 107.0, 85.0], [436.0, 35.0, 103.0, 89.0], [333.0, 151.0, 112.0, 101.0], [379.0, 188.0, 65.0, 102.0], [406.0, 151.0, 84.0, 120.0], [473.0, 135.0, 91.0, 88.0], [497.0, 130.0, 131.0, 80.0], [539.0, 63.0, 115.0, 71.0], [465.0, 42.0, 144.0, 92.0], [512.0, 191.0, 68.0, 122.0], [530.0, 312.0, 55.0, 124.0], [519.0, 420.0, 82.0, 93.0], [515.0, 621.0, 87.0, 79.0], [554.0, 641.0, 69.0, 89.0], [568.0, 608.0, 69.0, 75.0], [542.0, 632.0, 63.0, 66.0], [522.0, 627.0, 68.0, 67.0], [529.0, 535.0, 68.0, 83.0], [496.0, 534.0, 74.0, 85.0], [542.0, 641.0, 53.0, 97.0], [527.0, 621.0, 58.0, 90.0], [556.0, 609.0, 55.0, 84.0], [529.0, 654.0, 66.0, 74.0], [543.0, 618.0, 71.0, 93.0], [582.0, 554.0, 74.0, 76.0], [596.0, 476.0, 83.0, 117.0], [571.0, 292.0, 89.0, 85.0], [472.0, 176.0, 104.0, 92.0], [522.0, 119.0, 99.0, 121.0], [674.0, 194.0, 46.0, 55.0], null, null, [654.0, 397.0, 66.0, 116.0], [432.0, 303.0, 114.0, 67.0], [357.0, 229.0, 158.0, 84.0], [454.0, 113.0, 90.0, 130.0], [595.0, 221.0, 119.0, 90.0], [630.0, 397.0, 67.0, 113.0], [576.0, 575.0, 62.0, 73.0], [502.0, 613.0, 75.0, 90.0], [388.0, 346.0, 100.0, 67.0], [384.0, 352.0, 16.0, 32.0], [416.0, 394.0, 42.0, 34.0], [474.0, 380.0, 81.0, 47.0], [581.0, 304.0, 52.0, 59.0], [599.0, 338.0, 56.0, 62.0], [599.0, 326.0, 48.0, 97.0], [546.0, 272.0, 108.0, 96.0], [524.0, 311.0, 131.0, 39.0], [541.0, 388.0, 109.0, 67.0], [644.0, 441.0, 76.0, 116.0], [656.0, 449.0, 64.0, 129.0], [607.0, 591.0, 65.0, 75.0], [659.0, 476.0, 61.0, 73.0], null, null, null, null, null, [510.0, 397.0, 210.0, 201.0], null, null, null, null, null, null, null, null, null, [605.0, 202.0, 85.0, 91.0], [600.0, 167.0, 76.0, 84.0], [695.0, 109.0, 25.0, 86.0], [581.0, 133.0, 93.0, 87.0], [494.0, 194.0, 82.0, 77.0], [469.0, 244.0, 111.0, 79.0], [558.0, 175.0, 112.0, 84.0], [674.0, 124.0, 46.0, 117.0], [521.0, 180.0, 116.0, 92.0], [537.0, 176.0, 105.0, 89.0], null, null, [485.0, 87.0, 182.0, 112.0], [577.0, 3.0, 111.0, 140.0], [586.0, 20.0, 127.0, 131.0], [452.0, 118.0, 174.0, 100.0], [581.0, 24.0, 68.0, 160.0], null, null, [397.0, 162.0, 215.0, 111.0], [508.0, 40.0, 127.0, 142.0], [581.0, 3.0, 109.0, 162.0], [405.0, 190.0, 176.0, 116.0], [598.0, 76.0, 122.0, 152.0], null, [566.0, 118.0, 154.0, 144.0], [451.0, 194.0, 237.0, 135.0]], \"areas\": [4970.0, 5151.0, 5397.0, 5619.0, 5476.0, 5040.0, 4794.0, 5061.0, 5326.0, 5423.0, 5416.0, 5877.0, 5769.0, 4483.0, 4163.0, 3437.0, 2284.0, 970.0, 389.0, 2069.0, 5406.0, 6997.0, 3700.0, 4715.0, 2988.0, 3456.0, 2487.0, 2567.0, 1788.0, 1606.0, 3346.0, 2315.0, 2344.0, 3038.0, 3913.0, 6041.0, 4879.0, 4167.0, 5130.0, 3416.0, 4074.0, 4815.0, 3527.0, 9863.0, 6685.0, 5553.0, 5378.0, 5479.0, 4766.0, 2899.0, 5014.0, 5684.0, 4404.0, 3954.0, 5085.0, 4468.0, 4410.0, 4408.0, 6197.0, 4874.0, 4351.0, 4350.0, 4789.0, 3006.0, 2567.0, 2169.0, 2515.0, 4040.0, 4283.0, 3867.0, 3601.0, 3257.0, 2810.0, 3591.0, 3718.0, 6527.0, 3985.0, 6626.0, 6778.0, 1924.0, null, null, 5695.0, 4561.0, 9157.0, 6177.0, 7535.0, 6088.0, 3020.0, 4708.0, 2486.0, 305.0, 1001.0, 2704.0, 1857.0, 2275.0, 3471.0, 3135.0, 4368.0, 4411.0, 6695.0, 5920.0, 3220.0, 3562.0, null, null, null, null, null, 2976.0, null, null, null, null, null, null, null, null, null, 5853.0, 4976.0, 1700.0, 6253.0, 4235.0, 5278.0, 6989.0, 3896.0, 7573.0, 7213.0, null, null, 13225.0, 9275.0, 11008.0, 11777.0, 7517.0, null, null, 12776.0, 10681.0, 11522.0, 14022.0, 13971.0, null, 18063.0, 22460.0], \"iscrowd\": 0, \"video_id\": 0, \"height\": 1280, \"width\": 720, \"category_id\": 3802, \"noun_phrase\": \"the left hand\"}, {\"id\": 1083, \"segmentations\": [{\"counts\": \"_`Pf0:eW14L3N100O010N101O1N2O1O00000000O1000001O000001O0001O0O100O2O00001O00001N10000O101O00001K`W[4\", \"size\": [1280, 720]}, {\"counts\": \"c`Uf07gW15K5M2OO01O10O02M2O001N100000000000O1000000000001O001O000000000O2O00000O20O000000O2O1N2O1FbhNO`W1NbgS4\", \"size\": [1280, 720]}, {\"counts\": \"m`Zf02iW1;J3L2N2N101N1O2O001N101O0001N0100000000001O000O100O2O001O001O1O000000000O2O0O100000002N2N2M5GS_m3\", \"size\": [1280, 720]}, {\"counts\": \"UYYf07fW17K3N1N2O0O2N1O2N3L3OO01001O00001O000O2O0O1000000O100O10000O1000000O1O2N1000000O2O00O01000000O1O3FYWl3\", \"size\": [1280, 720]}, {\"counts\": \"Wa_f0=aW13N2N2N2N1O2O000010O000000O101O0001O000000O02O000000000O100O1N2O1N200O2O0O1O1O9FhVQ4\", \"size\": [1280, 720]}, {\"counts\": \"YYhf0`0[W1;I0100O10000000000000000000000000000O100000000O001N2O1O100O1O1O2O1N2N2N6IgVl3\", \"size\": [1280, 720]}, {\"counts\": \"\\\\abg09cW1=F3N1O0000O1000000000000000O1000O10001N100O1O1O101M3M3J:K2N_fZ3\", \"size\": [1280, 720]}, {\"counts\": \"\\\\aVh0d0XW15O000000O100O100O2O0O2N2M3N8H4L10ZnV3\", \"size\": [1280, 720]}, {\"counts\": \"^a[h0d0YW14N2O00O100O100O100O101N1O101N1O5K6I]fP3\", \"size\": [1280, 720]}, {\"counts\": \"aQYh0>]W18K3OO10000O10000O101N1O2N1AfhN7dW1N1M\\\\fU3\", \"size\": [1280, 720]}, {\"counts\": \"aYZh0=_W16J4O1O11O0O1O2O1N1O2N2CbhN4iW1M1N[nV3\", \"size\": [1280, 720]}, {\"counts\": \"aQ^h0>`W12M3N110000O2O0O2N8I1N2O0N3N[VS3\", \"size\": [1280, 720]}, null, null, null, null, null, {\"counts\": \"_i\\\\h0`0\\\\W15N1O1N11N2O1O5L5J4L3M2N[nV3\", \"size\": [1280, 720]}, {\"counts\": \"Pblg01eW1=H6K400O10O101N10O10O100O01000O1O2N2N8F3N4M[fZ3\", \"size\": [1280, 720]}, {\"counts\": \"PZfg02gW1<C8O1N2M3O010O11N1O1O100O100O100O100O1O1O2O1N8G5J]V]3\", \"size\": [1280, 720]}, {\"counts\": \"jQ`g09]W1=J4M2O100000O100O10O0100O1O100O100O100O1O100O1O101N1O3Lff_3\", \"size\": [1280, 720]}, {\"counts\": \"fYRg02lW19G4L3M3N1O2O0O100O1000001O00O11O001O00O100O01000O00100O1O1O100O00100O100O1O1N4Kj^^3\", \"size\": [1280, 720]}, {\"counts\": \"fanf01nW14L3M2O1N2N2N1O2M3N2N1O100O100O11O0001O0000000000O10000O010O10O01O100O1O1O10000N2L5O3Kg^^3\", \"size\": [1280, 720]}, {\"counts\": \"faSg02lW16K2M3M3N2N2M3N1N2000000O101O0001O00000000O0100000000O010O100O1O100O10O0100O1O2N100O2N1M7GjnV3\", \"size\": [1280, 720]}, {\"counts\": \"aQ[g0<cW12M2O2M3N2N1O1O10000O2O0001O00000000000000O0100000O01O100O100O1O10O01O1O2O0O100O1N2O3E`hN0[VS3\", \"size\": [1280, 720]}, {\"counts\": \"fY\\\\g06iW13M2N2N2M3N1O2N1O2M200O1000000O11O00000000000000O10O1000O0100O100O1O1O100O1O100O1O1O4LgfP3\", \"size\": [1280, 720]}, {\"counts\": \"ga]g06iW12M3N3M2N1N3N2M2O2N100O100000000000000000000000O0100000O100O100O00100O1O1O100O100O2N3M6I3K5N`fk2\", \"size\": [1280, 720]}, {\"counts\": \"fQ`g07gW13N2N2M3M3N2M3N100O10000000000000000000000O1000O01000O100O100O1O010O1O100O100O1O2N1O1DbhN5_W1IehN2Wnl2\", \"size\": [1280, 720]}, {\"counts\": \"fYag01nW15K3M2M3M4L3N2N1O2N100000000000000000000O100000O10O10O100O100O00100O1O100O100O1O1O1O2BdhN5]W1HhhN2Ufk2\", \"size\": [1280, 720]}, {\"counts\": \"^Ykg0<aW17K3M2O1N10O1001O0000001O0000000O10O10000O010O100O001O2O0O100O00101N1O1Ok^j2\", \"size\": [1280, 720]}, {\"counts\": \"dYkg03iW16M3M3L3N1O2M2O2N100O10001O01O000000000000O01000O0100O10O0100O100O100O1O101N1O2O1MlVd2\", \"size\": [1280, 720]}, {\"counts\": \"eQog03jW15M3M1O2M3N2M3O0O2O0O10000000000O100000000O1000O010O10O01O100O10000O100O1O10O10O101M3L5Mjf\\\\2\", \"size\": [1280, 720]}, {\"counts\": \"iaQh03jW15M3L3N2M2O2O1N101O0O1000000O2O00000000000O100000O10O1000O1O100O100O100O2N1O1O2CbhN5fW1L6K\\\\f\\\\2\", \"size\": [1280, 720]}, {\"counts\": \"jiRh04iW16K3M4M2M201N100O2O0000010O1O1O0000000O10000O0100O10000O10O0100O100O1O100O100O2O3J8F`^[2\", \"size\": [1280, 720]}, {\"counts\": \"iiRh08eW15L4L3N2O1O1N2O00001O000000001O000000O10O100000O001O10000O100O10O10O100O101N1O2M3M5K4K4M\\\\nX2\", \"size\": [1280, 720]}, {\"counts\": \"kaQh06hW13M4L3N2N2O1N2O1O1O0000000O101O00000000O10O100000O1O10O0100O100O100O100O1O2N101M2Mbn]2\", \"size\": [1280, 720]}, {\"counts\": \"kiRh06hW13M4L3M3N2O1N2O1O1O000000001O00000000000O100000O10O10O100O1O101N010O100O1O100O2N1N4L3M3O00\\\\fW2\", \"size\": [1280, 720]}, {\"counts\": \"jaQh07gW14L4M2M3O1N2N1010O01O0O10001O000000000O100000O01000O1O100O100O100O010O1O2O0O2N1N3L6L3J^^[2\", \"size\": [1280, 720]}, {\"counts\": \"nQjg09cW15M3O0O2N100O100O101N1000001O00000O10O1000O1000O100O1O10O10O100O2OO0100O2N1O2N2M4L]^e2\", \"size\": [1280, 720]}, {\"counts\": \"ka]g01lW15K5L3N2N2O1O1O010O0000000000O10O2O00001O0000000000O10000000000000O1000O1O100O100O100O1O1N3N5K\\\\^j2\", \"size\": [1280, 720]}, {\"counts\": \"kQjg01lW15J5L5N1O2M101O0000000000000000000000001O1O0000O1000000000000O1000000O100O100O1O2N1O4M\\\\nb2\", \"size\": [1280, 720]}, {\"counts\": \"aQYh02mW12O1N:G3L100O110O0000000O2O0000000O2O00001O000000O100O1000000000000O100O2O0O1O2O0O6J^VU2\", \"size\": [1280, 720]}, {\"counts\": \"cYih05iW13M4M2N2N2N2O1O0O2O00001O00001O001OO01000O100O1O100O100000000O100O1O101O0000001Lh^g1\", \"size\": [1280, 720]}, {\"counts\": \"WYih07gW15K5L3N1N110O1O1O1O0O2O00100O001O01O0000000O100N2O10000O101O0000O0101O000O100O101O001N2N4LgVa1\", \"size\": [1280, 720]}, {\"counts\": \"VYih0:dW14L3O001O0O101O1N101O0O2O001OO0100010O0000001N100O10000O101N100000000O10000O100O1O2N11O00O1E;O1Oo^]1\", \"size\": [1280, 720]}, {\"counts\": \"mXZh01lW16K;D7K3N1O1N101O001O01O1O001O1O0O3N1O1O1O1O1O1O000O2O00010O02N00000001O0O1O2N2N0000102K5KmVP2\", \"size\": [1280, 720]}, {\"counts\": \"[`Vh0?\\\\W18L2M3O001N1000O010000O11O001O1O0010OO2O100001OO100O1O2N1O1O1O1O00001O001O1N1J7M4LZoX2\", \"size\": [1280, 720]}, {\"counts\": \"n_Vh07]W1=G9J5N3O00000001O1O1O1O2N1O1O1O1O010O1O2N2N2O0O1O010O1N2O002M2N1O101L3N\\\\hNN_W10bhN1]W1N\\\\h\\\\2\", \"size\": [1280, 720]}, {\"counts\": \"WWUh08^W1<A>J5N3O00010O01O1O1O1O1N2O1O2N1N2O1O1O2M101O1O1O1O1O1N2N1N3N10o`e2\", \"size\": [1280, 720]}, {\"counts\": \"Y^`h0i0lV1<OO101N20O01O010O00001O001N2O1O1O0O2N100O2O1O1N102N1N2O2M7F]i\\\\2\", \"size\": [1280, 720]}, {\"counts\": \"XfZi02lW16K6I4N001N1O2O2M2O000O100O010O0010O1O101O0O100O1O2L3N8I4Kkic1\", \"size\": [1280, 720]}, {\"counts\": \"SV[j0:eW13M3M3M2N2N1O101N10000O1O1000O100O010O1N2O1O1O2N101N4L5K7I2OhQb0\", \"size\": [1280, 720]}, {\"counts\": \"RV^k03eW1>G5M2N2N2N2O0O2O000O101N2OO10O010aZO\", \"size\": [1280, 720]}, null, null, null, null, null, null, null, null, null, null, {\"counts\": \"fdek03lW1:E3N2N3M2O0O100000000a[O\", \"size\": [1280, 720]}, {\"counts\": \"fl\\\\k01hW1:J6M1N101N1O101N1O101O00000000O1000i[O\", \"size\": [1280, 720]}, {\"counts\": \"[\\\\Zk07gW17I4N1N2O1O0O101OO010001O000000000000000l[O\", \"size\": [1280, 720]}, {\"counts\": \"[dVk08eW14N3M2N2N2N100O100000000O101O000001O0000000000n[O\", \"size\": [1280, 720]}, {\"counts\": \"WTTk07fW17J3O0O101N101N100O100000000O101O00O10001O2N2N3L10k[O\", \"size\": [1280, 720]}, {\"counts\": \"SdQk08gW14L3M3M1O2N1000000O100000001O000O10O2O001O1O2N2M:Bjk1\", \"size\": [1280, 720]}, {\"counts\": \"W\\\\Uk07fW16K3O0O2N1O2N1O10000000001O001O00000001O1O1N2N10o[O\", \"size\": [1280, 720]}, {\"counts\": \"V\\\\_k0=aW13N2N2O0O2O001N100000000O1000000Q\\\\O\", \"size\": [1280, 720]}, {\"counts\": \"^dek05fW19J4M3N1N10000O100O100m[O\", \"size\": [1280, 720]}, {\"counts\": \"aThk01mW16I7K4L2N1O2N10000i[O\", \"size\": [1280, 720]}, {\"counts\": \"aThk05iW18I3L3N3N0O100O100g[O\", \"size\": [1280, 720]}, {\"counts\": \"_lfk0:dW15L2N1O2O0O1O2O0O100i[O\", \"size\": [1280, 720]}, {\"counts\": \"_d`k09eW14M3M2N1O2N100O100O100000001O0j[O\", \"size\": [1280, 720]}, {\"counts\": \"\\\\d[k06eW18L2N2N2O0O1O1O2O00000000001O000O10000P\\\\O\", \"size\": [1280, 720]}, {\"counts\": \"\\\\dVk07eW16M3M1O2N101N100O100O10000000001O00010O0O10000o[O\", \"size\": [1280, 720]}, {\"counts\": \"`lRk09fW14L2N2N1O101O0O100O10000000001O001O001O1O1O001O001O0g[O\", \"size\": [1280, 720]}, {\"counts\": \"edlj07gW14L3O2N1O1N10001O0O100000001O1O001O001O0O2_O`0`0A1N4K7HXc5\", \"size\": [1280, 720]}, {\"counts\": \"j\\\\kj07fW14M3O1N2O001N10000O100000001O001O00100N2O1L4_O`0>C2M7HZk6\", \"size\": [1280, 720]}, {\"counts\": \"k\\\\Pk01nW16I3N3M3M2O1O1N2O0000001N1000000O2O0N3O1O1O100O12L6IYk1\", \"size\": [1280, 720]}, {\"counts\": \"kl\\\\k07hW13M3M3M2N2O1O1O000000000001O0001O000Y[O\", \"size\": [1280, 720]}, {\"counts\": \"md[k0=aW14M3M3N1N2O0O10000O0100O101N1000000000Y[O\", \"size\": [1280, 720]}, {\"counts\": \"UU^k03iW1<F3N2N2N2N3N000O100001O0O10000000U[O\", \"size\": [1280, 720]}, {\"counts\": \"U]_k09fW16I4M2N4M0O101OO10000O10000O1000Q[O\", \"size\": [1280, 720]}, {\"counts\": \"_]Zk01jW19H6M2N2N1O1O1O101O0O1000O1001O0000001O0Q[O\", \"size\": [1280, 720]}, {\"counts\": \"_eVk01mW18G4M3M3N2N1O100O100O100000000000001O00001O1O0oZO\", \"size\": [1280, 720]}, {\"counts\": \"Ze[k0;cW15L2M3O0O100O100000000000001O00001O001lZO\", \"size\": [1280, 720]}, {\"counts\": \"Z]_k06fW19J2N2O1N2N2O000O10O1000001O0000nZO\", \"size\": [1280, 720]}, {\"counts\": \"U]_k02kW1:G4M2N2N1O2O0O10000000000000000U[O\", \"size\": [1280, 720]}, {\"counts\": \"R]dk0<`W17K3N2N1O10O10O1000O1000X[O\", \"size\": [1280, 720]}, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, {\"counts\": \"Pmfk02iW1:G5N2N101N6K0000000\\\\[O\", \"size\": [1280, 720]}, {\"counts\": \"flfk0<cW12M5L2O1N10001O00000_[O\", \"size\": [1280, 720]}, {\"counts\": \"alak05iW1;F3M2O1N101O00000O1001O0000d[O\", \"size\": [1280, 720]}, {\"counts\": \"`T^k07fW18J2N1O2O0O2O1O1N100000001O0000000h[O\", \"size\": [1280, 720]}, {\"counts\": \"`T^k09dW16L2N2N1O2O001N2O0000001O000000000h[O\", \"size\": [1280, 720]}, {\"counts\": \"aTYk06gW16J5L3N2N101N100O10000000000O10001O0000000l[O\", \"size\": [1280, 720]}, {\"counts\": \"\\\\T^k09cW16L3O1N2N1O2N100000000000000O10000o[O\", \"size\": [1280, 720]}, {\"counts\": \"[Tck07dW17L3L4N2O0O1000O0100000000R\\\\O\", \"size\": [1280, 720]}, {\"counts\": \"VThk08fW16J4M2N101N2O0O2O0R\\\\O\", \"size\": [1280, 720]}, null, null, null, null, {\"counts\": \"Vd`k0=`W14N1O2N2N3M2O0000O010O100001O0S\\\\O\", \"size\": [1280, 720]}, {\"counts\": \"mSoj01oW12XhNN]W1?L2M3N1O1O00000O100000000000001N1O1M32M4M4K3L4LP\\\\4\", \"size\": [1280, 720]}, {\"counts\": \"mS`j02lW1;G3L4M1N2O2N000O10O1000000000000001N1N2N25J7I4KQdd0\", \"size\": [1280, 720]}, {\"counts\": \"m[Wj0b0^W13M1N100O1000O010000000000O100000O2O1N2O2M2N6GPdn0\", \"size\": [1280, 720]}, null, {\"counts\": \"lkYj09gW15K5L4K0001OO10O2N2O1N4L8FicX1\", \"size\": [1280, 720]}, {\"counts\": \"RTgi01lW18K4K3N3M2N2M2O0000000O010000O10O1000O101O1N2O2M2N5KlS[1\", \"size\": [1280, 720]}, {\"counts\": \"PdXj04jW19H4M3M2N2O0O2N010O01N1N3M3M8Gk[W1\", \"size\": [1280, 720]}, {\"counts\": \"Wdni03lW16K4L4L2M3N1O1O1O1OO10O010001O0000000O100O2N1O;CcSV1\", \"size\": [1280, 720]}, null, null, {\"counts\": \"Ymji0:`W18N001N1N1O2O1O0010O10O010000O01000000001O0O100O1O1O;DnRV1\", \"size\": [1280, 720]}, null, null, null, null, null, null, null], \"bboxes\": [[563.0, 781.0, 45.0, 24.0], [567.0, 783.0, 47.0, 25.0], [571.0, 786.0, 48.0, 28.0], [570.0, 793.0, 50.0, 32.0], [575.0, 800.0, 41.0, 27.0], [582.0, 802.0, 38.0, 29.0], [603.0, 806.0, 31.0, 27.0], [619.0, 808.0, 18.0, 25.0], [623.0, 810.0, 19.0, 26.0], [621.0, 810.0, 17.0, 25.0], [622.0, 807.0, 15.0, 25.0], [625.0, 810.0, 15.0, 21.0], null, null, null, null, null, [624.0, 807.0, 13.0, 25.0], [611.0, 811.0, 23.0, 26.0], [606.0, 809.0, 26.0, 30.0], [601.0, 809.0, 29.0, 30.0], [590.0, 807.0, 41.0, 31.0], [587.0, 807.0, 44.0, 29.0], [591.0, 805.0, 46.0, 30.0], [597.0, 804.0, 44.0, 30.0], [598.0, 807.0, 44.0, 29.0], [599.0, 807.0, 47.0, 30.0], [601.0, 806.0, 45.0, 30.0], [602.0, 806.0, 45.0, 30.0], [610.0, 805.0, 37.0, 30.0], [610.0, 803.0, 42.0, 30.0], [613.0, 803.0, 45.0, 30.0], [615.0, 810.0, 43.0, 28.0], [616.0, 812.0, 43.0, 30.0], [616.0, 813.0, 45.0, 31.0], [615.0, 815.0, 42.0, 29.0], [616.0, 815.0, 46.0, 29.0], [615.0, 814.0, 44.0, 30.0], [609.0, 816.0, 42.0, 28.0], [599.0, 816.0, 48.0, 23.0], [609.0, 816.0, 44.0, 23.0], [621.0, 812.0, 43.0, 25.0], [634.0, 809.0, 41.0, 27.0], [634.0, 801.0, 46.0, 30.0], [634.0, 798.0, 49.0, 27.0], [622.0, 787.0, 46.0, 34.0], [619.0, 768.0, 42.0, 36.0], [619.0, 739.0, 39.0, 40.0], [618.0, 713.0, 33.0, 43.0], [627.0, 702.0, 31.0, 37.0], [648.0, 700.0, 30.0, 30.0], [674.0, 696.0, 31.0, 30.0], [702.0, 687.0, 18.0, 37.0], null, null, null, null, null, null, null, null, null, null, [708.0, 655.0, 12.0, 24.0], [701.0, 647.0, 19.0, 26.0], [699.0, 644.0, 21.0, 25.0], [696.0, 641.0, 24.0, 25.0], [694.0, 637.0, 26.0, 25.0], [692.0, 635.0, 26.0, 25.0], [695.0, 637.0, 25.0, 25.0], [703.0, 639.0, 17.0, 25.0], [708.0, 643.0, 12.0, 25.0], [710.0, 647.0, 10.0, 24.0], [710.0, 649.0, 10.0, 24.0], [709.0, 647.0, 11.0, 24.0], [704.0, 646.0, 16.0, 25.0], [700.0, 640.0, 20.0, 26.0], [696.0, 641.0, 24.0, 26.0], [693.0, 649.0, 27.0, 29.0], [688.0, 638.0, 27.0, 44.0], [687.0, 639.0, 27.0, 47.0], [691.0, 656.0, 27.0, 34.0], [701.0, 662.0, 19.0, 23.0], [700.0, 662.0, 20.0, 28.0], [702.0, 666.0, 18.0, 28.0], [703.0, 671.0, 17.0, 27.0], [699.0, 671.0, 21.0, 27.0], [696.0, 673.0, 24.0, 28.0], [700.0, 675.0, 20.0, 26.0], [703.0, 673.0, 17.0, 25.0], [703.0, 667.0, 17.0, 24.0], [707.0, 664.0, 13.0, 27.0], null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, [709.0, 660.0, 11.0, 27.0], [709.0, 657.0, 11.0, 24.0], [705.0, 651.0, 15.0, 25.0], [702.0, 648.0, 18.0, 26.0], [702.0, 648.0, 18.0, 26.0], [698.0, 644.0, 22.0, 28.0], [702.0, 641.0, 18.0, 26.0], [706.0, 638.0, 14.0, 25.0], [710.0, 638.0, 10.0, 26.0], null, null, null, null, [704.0, 637.0, 16.0, 28.0], [690.0, 629.0, 26.0, 30.0], [678.0, 627.0, 25.0, 31.0], [671.0, 633.0, 24.0, 26.0], null, [673.0, 636.0, 14.0, 24.0], [658.0, 635.0, 27.0, 27.0], [672.0, 637.0, 16.0, 28.0], [664.0, 644.0, 25.0, 27.0], null, null, [661.0, 665.0, 28.0, 29.0], null, null, null, null, null, null, null], \"areas\": [867.0, 944.0, 1008.0, 1113.0, 901.0, 852.0, 658.0, 330.0, 390.0, 324.0, 263.0, 201.0, null, null, null, null, null, 205.0, 418.0, 542.0, 673.0, 938.0, 925.0, 1021.0, 1019.0, 1007.0, 1044.0, 1035.0, 1006.0, 889.0, 972.0, 980.0, 915.0, 986.0, 1025.0, 937.0, 1018.0, 984.0, 893.0, 891.0, 845.0, 858.0, 845.0, 1029.0, 957.0, 985.0, 978.0, 895.0, 940.0, 816.0, 630.0, 660.0, 519.0, null, null, null, null, null, null, null, null, null, null, 237.0, 406.0, 466.0, 519.0, 531.0, 520.0, 526.0, 374.0, 247.0, 176.0, 191.0, 215.0, 332.0, 434.0, 526.0, 596.0, 569.0, 580.0, 601.0, 367.0, 480.0, 424.0, 392.0, 465.0, 531.0, 440.0, 361.0, 351.0, 305.0, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, 226.0, 230.0, 324.0, 396.0, 403.0, 524.0, 406.0, 296.0, 203.0, null, null, null, null, 381.0, 546.0, 565.0, 523.0, null, 250.0, 554.0, 316.0, 515.0, null, null, 599.0, null, null, null, null, null, null, null], \"iscrowd\": 0, \"video_id\": 0, \"height\": 1280, \"width\": 720, \"category_id\": 3802, \"noun_phrase\": \"the left hand\"}, {\"id\": 1438, \"segmentations\": [{\"counts\": \"SfX>R14^OmU1c1I;B=D=F9G4K5OO8H4L2N9F5L4L1N4M2M2O2N1N6K1O1N2O1O1N2O1N3M3N1N3N2N2M4M3M5J4M8G3N2N4J6KiWT<\", \"size\": [1280, 720]}, {\"counts\": \"kfn=S1WV1l0_O=I5L5L9E<G8I2N2O17H2N1O6J4L3M3M2M2O2M2O3M2N2M3M3M2O2M101N2O1N2O1N3N3L5L1O2M4M3M2N4K6K3M4L3L9FjoW<\", \"size\": [1280, 720]}, {\"counts\": \"Y^R>7`0?oU1P1^Ob0B?F8K7G<D4L4L3N2M3M2N3O10O100lK]lNk3VT1E5K6K8G2O2M2N5L4L1N3N2N2M3N1O2M2O0O2O1O1N3N2N2N3L5L9F;F2M2O1O3L7J5GP`P<\", \"size\": [1280, 720]}, {\"counts\": \"lnc>??DHOWV1d1]Of0@:J5K5K5K5J6M3N2N2M3O1N1N3N2M201O100001N2O1O1O2N<D2M4M4eL_kNo2RU1F3N1O2N4K3N3M1N2O002N2N2N0O2O2N6I5L9G7H:G2M6JW`\\\\;\", \"size\": [1280, 720]}, {\"counts\": \"X^k>7fW16L8H4L3M2M3N100N2O00O011M0H901OQjN[OlN?VU1EVlNP1eS1mNVlN[1mT1:J5M4F:L4I7L4M3N1N3N2N2N2L4L4M2O2M3M3O0O2L4O1N2N2O1O0010000O100O10O10001O0oK]lNd3RT1N7I2iLhkNb2[T1ZMkkNa2mT1L4M2N6J2N1O1N2O2N1O2N1O1O1O7H9H=C<C8H5ISXS:\", \"size\": [1280, 720]}, {\"counts\": \"Rfj?4fW1:J3O2M3M7I<E5J3M4L:F?B3L2N2N1O1O0O10N2N2I7N2O1B?MmjN]NnS1`1RlNeNmS1W1okNROPT1;^lNK`S12VlN<kS1@okNi0ST1QOgkNY1[T1cNfkN^1\\\\T1]NckNg1PU1<L3I7L4N2L4M3N2N1N3N2O1O1N2N2N2L4M2O2N2M3O1M3M3O1N2O1O1O1O100O1O100O1000O10000000000O101O2N2N1O1O1O?A7I5K7H8I>B5K6J3L5K5K;PObiNI_W1CcP`8\", \"size\": [1280, 720]}, {\"counts\": \"[em`0f0VW1:Dc0_Oc0^O9I6H7H7K9G6J5K5K3MQMRlNQ2lS1PNTlNP2iS1SNWlNn1gS1SNYlNl1gS1UNYlNj1fS1XNZlNf1hS1YNZlNg1fS1XNZlNh1fS1XNZlNi1dS1WN^lNh1bS1XN^lNg1bS1YN_lNg1bS1XN_lNf1bS1YN_lNg1aS1WNalNj1^S1TNelNk1ZS1TNilNl1WS1PNklNo1WS1nMllNl1ZS1QNhlNk1]S1TNclNj1`S1UN`lNj1bS1TN_lNk1eS1RN\\\\lNk1jS1iM^lNV2fT1N3G8O1O2I6N2O^jNXOoS1f0okN@nS1?okNGQT16okNNnS1MXlN5gS1E\\\\lN>dS1\\\\OZlNk0iS1bN_kNKm0h1gS1WNalNk1eT14L5L4L4N2N2K5L4J6M3N2N2N2N2M3M3M3L4N2M3M3O1N2O1O1N2N1O2O100O100N2N2O1N21O0O2O0000001O1O001O1O1O1O1N3nK`lN`3ST1M1O9bLPlNY2YU1C7G5J=bN\\\\iN8ahl6\", \"size\": [1280, 720]}, {\"counts\": \"^]oa0:WW1P1[Oh0ZO9G`0@:G4K4L3N0N3N3L8H3N3N1N3N0010N21N1001O000001O0000O00100001N101N101O0000O100O11O000O2I6O1O100O1000ZLmlN`2RS1_MPmN_2QS1`MQmN_2oR1aMQmN_2QS1^MPmNb2RS1[MolNd2TS1YMmlNe2VS1YMklNa2[S1^MelNk1HcMUT1?UlNg1YT1XNhkNd1]T1YNdkNl0BVOTU1M[kNk0YU1TOgjNh0^U1oNkjNn0VV1JPZ1ZOgfN=C8^iN@TU1l1F8N2H8F:M3L4M3M3M2N3N1N3N1O2N2N101N2N2N2O1O0O2O001O0O1O101O00100O0100O2N100O1O2N3M6I6dMSlN<`T1jNjkNo0^T1jNnkNg0QV1E4F:K6IgiZ5\", \"size\": [1280, 720]}, {\"counts\": \"X_cb05`W1LfhN=VV1f0eiN\\\\OfU1DgjNd1WU1^NejNf1YU1\\\\N`jNj1_U1WN_jNl1[U1?J7L4N1N2O2M2M3N2O1N2N2N3M2L4M3O1O1O10000000010O001O0O100000O10O2N1O2N3M10000001O000000O101N2O1OO10O1N2O2N00100010O1O001N2N1O2M2]Oc0XOh0XOh0J6I7H8L4J7J5K5M4L3LmiNF89`R1IWmN905jR1^OXmN>Ba0US1oNamN8XOn0QT1kNZkN5W1Q1_S1VOalNk0XU1110M2C=N3H7L5L3O2N1N3M3K4N3N2O0010O00O1M3L5O0O01000OO1O00OO6kNXkNfNYU1j0`kNaNXU1c0X1K6Kace4\", \"size\": [1280, 720]}, {\"counts\": \"`Wbb02gW1;G:H4M2_Oa0N3nN`N_kNc1aT1jNdjNI2a1XU1l0O2N1M2QkNXMfT1j263N1O1N2O1M3N2N2O1N2N21O01M2J5J7O2N2M31O1N1LSlNVLoS1e3701N0O0101O2M2N2O1N1O00001O1O01001O001O0000000000000000000000000O1000O001N2I7007H2N1O1000O01000QOWlNdMlS1Z2n0M3@`0M4@?I7K5J6K5LmjNVOXS1e0elNVO_N8kT1=clN2\\\\S1JdlN:[S1DflN0[OA]S1;blNg04SOXS13flNj02WOWS1IilNk03BRT1b0kkNCST1=lkNFST17kkNOPT12QlN1oS1GTlN=nS1YOZlNg0hS1WOVlNk0mS1ROlkNU1ZT1eNekN\\\\1]U13O1O1@?N3I610O0M30M2lNfjN^OaU1a0hjNPO_U1o0h0O1N3O1I8N2M3\\\\OmhN:UW1BSiN7]R^4\", \"size\": [1280, 720]}, {\"counts\": \"T_hb0>nV1HViNe0QV1\\\\OXjNR1^U1\\\\OYjNh0_U1Q1J501N1O1N2J6O1L4N2O1N2M3N3N1O1O1O2M2N2N2O1O2L3N2O1N20000001O001O4M2M2O1M3N2N2M3N001O0014oNeLSmN]3iR1hLilNI0`3US1UMklNi2VS1YMglNg2ZS1YMelNf2_S1XM`lNh2bS1WM]lNh2gS1TM[lNk2fS1TMZlNk2gS1UMYlNk2iS1TMVlNm2lS1PMSlNQ3oS1`01M2O1O2N2\\\\LikNZ3aT1NHcLokN[3QT1eLPlNZ3PT1fLnkN\\\\3YT12L4L4N110N2N101O0O1N3N2L40O1000O10O100O100N3N1VOj0WOi0E;I7I7I7JhjNPOhN:`T1`0elN7WS1DilNc0kNdNTT1e0QmNQ1nR1lNPmNi0YOdNdS1;olN]1YS1jNilNT1\\\\S1hNelNT1I^NiR1:YmN_1J^NmR1OXmN[1SObNj07kR1FZmNd1NjNfS1W1[lNmNcS1T1ZlNPOaS1R1\\\\lNSOcS1?flNHYS16dlNO^S1[OkkNFd0S1bS1hNckN5?I=[1bU11O2O0CiNRjNW1kU1?011M2M3N10I34N3FoiNcNTV1Y1<L4N2E[iNYOhV1>c0J;ESSo3\", \"size\": [1280, 720]}, {\"counts\": \"Tgnb0?^W13bNFnjNc0QU1_OfjNh0`U1UOSjNU1]U1iNgjN5EU1bU1hNhjNd1WU1]NgjNe1TU1_NljNb1SU1bNejNc1YU1c0G9O2M2O11O0000N2O1O1N2CPMgkNS3YT1:3M1OO100002N1O0001O2N001O01O0O2O1O1O01O01O01O010O0O01O100100N4L3N1O00O1000@cL]lN\\\\3_S1mL]lNS3aS1oL_lNP3aS1QM_lNo2`S1SM_lNm2aS1SM_lNm2aS1i000O1O1O1N1011O0O2O1O0O2O0O100O2N1IZlNTLfS1g3alNWL`S1^3klN`LVS1_3klNaLUS1b3elNaL[S1R40000000O1O010O2N1_OTlNmLlS1n1glNiMUT1R2o0E;H8K5J6H8KgjN\\\\O`S1<alNL`S1GhlN=PU1=K4LDaiN_OQV1P1VkNnNdS1S1[lNmNbS1W1WlNhNlN0hT1^1YlNmNgS13`kN0m0DjS17SmNCQN5jT10XkNLi17SNLmT1L\\\\mN:gMKlT1JUmNd0oMDkV1l0I5L4N10MJiNiiN762ZV15kiNEXV1;b05O000L3NDTiNGnV1:<10O1JbhNJ_W14:K\\\\[f3\", \"size\": [1280, 720]}, {\"counts\": \"a_mb01iW19J5C;H9J4O2N2N3M2M4D;\\\\ORNQkNS2[U13IjMcjNX2ZU1mMcjNS2ZU1;K5N2O1N2N201N3N1O0001O0O1000O101O0010O00000001N1N2N2O1N2M3M3N2M3M3M3M20100O103M100M2N2O001O010O10000000000O1O1O1O1N11000001O001N101N2N1O2N10000ZO`lNlLaS1j3O010OO1O010O1WOXlNXMjS1f2jlNfLXS1X3i0]Od0C<I8H8G9K4M3K5HbjN\\\\OPT1`0PlNEoS15QlN1DAVS14VmNd0^O@kT1e0SkN]OgT1<cjN]Od0;_T1a0kkNAPT1c0nkN@nS1c0okNAPT1?nkNCST1]OUkN`0h06ST1XOZkN1LOe0k0VT1QO`kNOf0S1jS1lNglNW1TS1mNglNW1PU11N2N3N1ONHhNoiN235_V14giNF[V1:e001O1O1M6BL5911O2M2O1OLOYSe3\", \"size\": [1280, 720]}, {\"counts\": \"[_mb08_W1<J5J6J6D;oNeNYkN_1cT1cN\\\\kN_1bT1fNXkN\\\\1hT1^NhjNO6LNj1RU1o0N1N2L4M2N2O1O10O3O0O1O001OO1M3O100000000O100000O01M4M2M3M3M2O3M2M3L5J5N3N1O100000O10O100O1000O11O000O100O1001OO100O1O1O1O0101O00000O2O10O0001O00000O10000O101N100SO]lN\\\\MeS1_2blNTMhS1]2PlN[MoT1Z2a0J6J6H8J6J6K5IijNSOgS1h0UlNEjS16TlNOE^OTS1`0WmN3AFUS13XmNOoNFb0<XS1E^mN9F5YT11fkN3WT1NikN3QT1WOTkN2K6P1e0eS1@fkNGc0n0dS1^OolNc0PS1_OllNd0nR1DjlNb0WS1XOmlNh0WS1mNjlNZ1]S1]NblNd1mT11O0N30N1N3`NliNn0[V1nNliNf0PW1ZOihN6g`[4\", \"size\": [1280, 720]}, {\"counts\": \"a]mb09eW1P1QO:Fe0[O8G4L3N1N2O1O2O4L1N100O100O1O1O1N3M2M3M3N3M2N2N2N2M3M3M3M3O3N1O1O00O11O000000001O1O1O00000000O1000O101O000O1N2M3O001O1001N3N2M3O0O00000000001N1H8YOXlNPMnS1j2ZlNlLoS1c2o0F:E;C>H8I7K5KRlNZOjP1a0onNKRQ1OinN;WQ1@amNFJm0cR1]O[mN2Le0fR1[OnlNb095iR1TOnlNk083kR1kNPmNQ167`S1M_lN5_S1JalN9^S1_OhlNd0WS1[OjlNe0WS1YOklNg0US1VOnlNj0SS1SOolNm0RS1mNTmNR1oT1N1OO1M3N1O1O10000OM=UOk0XOfhN8fW1KoPh4\", \"size\": [1280, 720]}, {\"counts\": \"medb04cW1=H>D7H7K6K2M2O1O:F3M9F5K6H7J4M2M3N1O2N1N2N3M2N3M3K6J6H7M3N2O0000001O0001O000001O2NO0N3N1M3N2N2M3N3F:M4G8M4M3H8EclNeMjQ1U2TnNTNkQ1`1[nNeNfQ1U1]nNmNcQ1l0^nNZOcQ1?WnNMiQ1OXnN4hQ1D^nN>aQ1@`nNb0aQ1\\\\O_nNe0aQ1WOanNj0`Q1SO^nNR1cQ1iN_nNX1bQ1dNanN]1`Q1]NdnNd1^Q1TNhnNk1gS1000001OO010O0100000N2O1O001N2O100M27J4K2N3M2L5_Oe0C9@\\\\id5\", \"size\": [1280, 720]}, {\"counts\": \"ie_b0`0_W14M5Jd0ZO:F7J3L5L3N2M3L3N3M1O1OON5M2N200okNTMoR1k2QmNYMlR1h2llNaMSS1T2\\\\lNdMLl0eS1_1dlNiN[S1U1clNoN]S1m0flNUOXS1f0mlN[OSS1a0PmNAoR1;TmNFlR16WmNKhR12\\\\mNNdR1N_mN3aR1HdmN7UR1[NjlN\\\\1T18nQ11UnNOkQ1LYnN4hQ1I[nN7gQ1E[nN;nQ1WOTnNk0nQ1oNUnNR1mQ1jNTnNW1YT12O10OO2N1000O1O2M2M3M4H8K6L5@nY^7\", \"size\": [1280, 720]}, {\"counts\": \"Ug_b0:`W16L3O2M2N1O1O00O21OOO1011N2N1000L5OO3M2N2N3M1O11O11O10GEQiN5PW1Lb0O004L^c<6Y\\\\C6L2O2M2O2M2N2N2O1N2N2O1N2000000004K:Fn_U7\", \"size\": [1280, 720]}, {\"counts\": \"Ynla02=OmV19ihN4nV11PiNOPW188N2N1100ZiN^OoU1b0miN_OVV1a0iiNCSV1=kiNGTV16niNJRV14niNOTV1KPjN3mV1H65N1O0L5000NO1M5N2L40N1O20O0N201N20O10NK5M9Hmc7K\\\\\\\\H1O1K5M3O0O102N2M2N2N3L4M2O1O1O100000O3N;D6Jn_d7\", \"size\": [1280, 720]}, {\"counts\": \"UnXa07aW1:dhN@QW1c0lhNCoV1?PiNMdV1j0K6ciNeNkU1m1]jNlMRU1V2:2N2N0001O1ON1O13L4VO^jNSOcU1n0ajNjNaU1X1?1WkNiNQS1OUlNLT17fR1LimNc0XR1\\\\OdmNg0hT1000O201O1O1N2N2O1O010OO01O02K4O2N2O10N1N2N2010O1O2K8G7L6Jcjh8\", \"size\": [1280, 720]}, {\"counts\": \"SnZ`06bW1<J5K7J5G7K6J5J6I:QjNPN]U1e2D7J8I:F7J5K6K4M2L2O0M3CbKUmNa4US11N1O1H[KVmNd4jR1]KUmNc4kR1\\\\KUmNd4PS14O1M3M3IQK\\\\mNo4lR1O002Ma0@5K<Dk0UO`0@200O2N5I;YNoiNi0Un9UO_XGNgiN;nU1S1iiNYNbU1]2G6L6I7G7G8J5L41M2L4N2M3N3M9F6K5K2N5J:F;D4K4VOl0@lic8\", \"size\": [1280, 720]}, {\"counts\": \"`]d?f0TW1:H6J6I8H`0@:J5K7H3N3L3N3M4K5J:I8AQL\\\\lNR4bS16010OO10000001O1O010O01O0O10000O1000000N2N3OjLblNj1_S1UNalNk1_S1UNalNl1^S1TNblNk1_S1UNalNk1^S1UNclNk1]S1UNdlNi1]S1WNclNi1^S1UNclNk1\\\\S1TNglNk1YS1RNjlNo1TS1QNmlNo1US1mMmlNS2TS1jMnlNW2TS1dMnlN\\\\2^S1SMglNm2VT10000O2N1M3N4D`0@h0mNYQ:EbnE[1\\\\Oj0^Oc0@5K4K5H7K4M4M4M3N2OO2O001N2O=C3L2O3Mb0_Oa0^O3M1N103M3M1O4L2N4K6K`0_O=kNPiNa0eW1_OTPV8\", \"size\": [1280, 720]}, {\"counts\": \"emW?b0YW1>C:I4J8H8H4L9I>A4K5M3M6I8J5K4M2L3N2N0O001O0100000O2O000O10O10O10000O1000001O00000000000001O4L1O101ON1001N2O1O0000001O00100O2N2N1O1O0000O11O0000001O000000001N2O2M4M3@akNYMaT1c2c0\\\\MljNo1RV1^Od0TOh0]Oag8?PQFITV1b1VOi0D>@?E9F9J5LO2O00101O1O:F4L1N3N3Mc0]O=C;E3M3L2O7H6HQYR8\", \"size\": [1280, 720]}, {\"counts\": \"^e[?;`W16F>J=A4N2njNiNWS1e1bkNXOYT1Z2N2L3N2N3M2O2O0O100001O0000001O2N10O0O10000000O01O1O1M4M201N1O100O100O10O1001N100O100O1000000001O01000O010O001O00000O1001O000O10000000000000O0101N20O1M3L6H8Aa0D?lL]kN[2WU1L3N1H9K8Hga7mNTXGKbV1P1L4^Oc0H6L3G9H8N2J5K6K5F:K5N2M30000003M5J5L4_Oa0ZNojNI`U1nNmkN3f_n7\", \"size\": [1280, 720]}, {\"counts\": \"Yna?68U1fT1gNjkNX2hS1V1I7N3M2M3M3O1O2N1O1O1O1O100000000000010O000O01000N2001O1O0001O0000O10001O100O001O1O10OO3N001O001O1O10O01O001O1O1O1O001O1O1N10100O6J6J4L2N2N1O1O1O1O1O1O1O001O1O1N2N2O2E<F8Nb0PO^iN]OkV18Ri8LXWGk0]O9F6M10O2O0O101O6J5K2O1N2N1O1O3L3N3L4K9CbXm7\", \"size\": [1280, 720]}, {\"counts\": \"ig`?4bW1=K3O0001O001O1N10001O001O000O101O0O11O000001N02VLNcoN3[P12boNO\\\\P1:\\\\oNEdP1`0XoN@hP1Q1gnNnN[Q1^1YnN`NiQ1f1PnNYNQR1j1mmNUNSR1l1mmNSNTR1m1kmNSNUR1m1kmNRNUR1P2imNPNXR1P2hmNPNWR1R2imNkMYR1U2hmNiMXR1X2i100O1OO1O1ikNjMbR1X2^mNhM_R1[2`mNfM_R1Z2bmNgM\\\\R1Z2dmNeM]R1[2dmNdM[R1]2fmNaM_R1[2f1M100O00001O0000000000O100001O001O00000O10O010O11N2O001O1N2O1N5K4K8iN_iNb0UW1J3M5J4LlbQ9\", \"size\": [1280, 720]}, {\"counts\": \"Thj?8dW16N1N2N1N3N1`M[OdmNg0SR1N`mN2TR1:okNQOe1f0ZR1d0cmN]OZR1g0cmN[O\\\\R1g0bmNZO]R1g0cmNYO\\\\R1g0emNYOYR1i0fmNXOZR1h0fmNXOZR1h0fmNXOZR1h0fmNXOZR1h0fmNXOZR1h0fmNWO[R1i0dmNXO]R1g0bmN[O]R1e0bmN\\\\O^R1d0amN]O_R1b0bmN^O]R1b0cmN_O\\\\R1a0emN^O[R1b0emN_O[R1`0fmN@YR1a0fmN@nQ1VOdlNZ1_1_OnQ1l0RnNTOPR1h0PnN[OQR1b0omN_ORR1?omNAQR1>omNCQR1=nmNDQR1<PnNDPR1<PnNEoQ1;PnNFPR19PnNHoQ19PnNHPR18omNIPR18omNIoQ19PnNHoQ19PnNIPR16PnNJPR15RnNJnQ15SnNKQR11PnNNRR10nmN0SR1NnmN1SR1OmmN1TR1MmmN3SR1MlmN4TR1LkmN7SR1ImmN9QR1GnmN:RR1FnmN:RR1EnmN<RR1DomN;QR1DPnN<QR1CRnN:nQ1ESnN;mQ1ETnN:lQ1ETnN;lQ1FnmN@lNLWS1d0lmN@nNJWS1g0jmN_OSOFUS1i0imN@^O[OjR1T1imN@_O[OiR1S1imNAXS1>glNCYS1<glNE[S18flNG\\\\S17elNH]S16elNH]S16dlNJ]S13elNM\\\\S10flN0[S1NflN2[S1LflN2]S1MclN4_S1HalN9`S1F`lN:fS1^O\\\\lNb0bU1O001O2N3L3M3M2O1Nm_22Q`M3H7I7K5L4L3M4NM3JDYOjiNj0UV1YOfiNi0ZV1?0XOdiNZO55^V18P1FXjj7\", \"size\": [1280, 720]}, {\"counts\": \"^ok?h0_V1j0]Ob0I8G8G9_O]MbkNf2ZT1`MckNa2[T1c00001N110O000O1N101O1O1O1J6O1O100O1O1M3N200O10000O2O0001O000000O10000O101O000O1000000O1O1000001O03M2N1O000000001N10N3N1001O000001O01O1N2N2M4M2N2N2I7I8H8I7H8I6L6I7E<_Oc0XO]3GiLLViN=dV1b0N2L4I7I7N2M4I7M2M3H82N1O00O1O01000000OO10HhjNhM[U1=ijNMNOTV1DQdd7\", \"size\": [1280, 720]}, {\"counts\": \"RPV`0<YW1?C:D=G8K4@WNcjNP2WU1?L4J6K6I6L4L4L4O1O1O1001O000O10O100O1O2M1M4O10000000O10O1O2M2000000O100O10000N2N3L4O0O2N10O00010OO201O0010O2N1O0O101O1O1N11N2O0O3OO1O1O1O1O1M3L3L5M3I8M2K4\\\\Oe0[OmjNTN[U1c1f0H8J7G;D^kNDhMDTT1?kmNb0[T1=N4O0G:I6N2N3N2I6N3O102N2O00N101O1N2O001O1N1SOhjN`NmU14^S]7\", \"size\": [1280, 720]}, {\"counts\": \"bih`0;ZW1=F9G8G9H8K5K5M3H8N3B=K5M3N2M3M3N2N2O1O2N1M3N2O1O1N2N2N200O10O2O3M01M2M3N2000000000001N101O000O10O0100O1O1O2M2N2O1000001O01O00001O4M2NO0001O0000000000O100000O10001N1O1M4L4C=8I5KO1M4I7\\\\OhkNRMcT1h2`0I:F:`NajNFc`7^OdfIa0C>G5F:M2G8N31N=AaWo6\", \"size\": [1280, 720]}, {\"counts\": \"YXPa0=^W16H8I7UOoN^jNY1\\\\U1oNWjN\\\\1fU1`0M4M2M3J6G9H8I8L3N3N1O1O1O100N3M2N1OZLPlN`3lS1<K5K5000000O10000000000O101O0000000000000O100M3N2M4N10000O010O10O10O2O1N2O0000000001O1O002O0O10O000O11N2N2O0O2O0ON2M32M3I7G:K5L4L3M4J5L6I7H8I6]Oe0BQlN^NXR1P1mmN[OXR15dmN9lT15J7L3J6G:L3M4OO01O011O1M2M1002O2C>D<B`0QOXiN9PP_6\", \"size\": [1280, 720]}, {\"counts\": \"Pon`0<XW1>A>E;_N^NTlNP2iS1VNokNo1lS1XNckNW2XT1k0M4K4O1N2L4M3N2N2O1N200O101O00O10N2O1001O010O00001O0O10O10O10000O11O00000000000O100N2M4M2000000O010O100O101N1N20001O0O010001O1O00O1O11O1O1O001O1O00O0200000O01O1O2N2M2O1K6G8I9K5L3^OfkNVMaT1a2h0VNVe2ZOo\\\\M8C:_Ob0A=J6I7L5F:J6M3N1010000O2HWMWkNi2fT1<NO1O01O01O1H[kNSMgT1h2;E<F<O1N0N2M4L5EiiNeN_V1P1f0mNRiN000lYl5\", \"size\": [1280, 720]}, {\"counts\": \"TWPa0c0PW1a0oNROYjN^1WT1cNflNn1VS1a1L3N2N3N100O1O1O1O100O2N1000000001O0O10O1000010OO0100000O10000O2O1O1O01O01O00O10O11O000N3M2L5OO100000O1001O0O01N2N200O1000000001N101O001O00O1002N1O1O1O1O2N2N2O0O1O2N1O1N3N1O1N3N3M4N048Am0SO;B;F:FTW;oN[bCU1IROlU11TjN^1gU1`0M2N2O2O1N3M1L3O`MjjNY2WU1eM[kNk1fT1SN^kNj1cT1TN`kNj1eU1Ee0[OWYV6\", \"size\": [1280, 720]}, {\"counts\": \"[gm`0<ZW1:G;F8ZOiNYjNb1cU1?POYNakNn1XT1P10001N1O010O101M2M3N3M4M010O101O01O000O1O1O1O1000O100O100O001O10000000000O100000000O1N1O2O100O10000O1000O10N3M3N1O1O100O100000O2O00010O001O001O1O1O1O002N101N2N1O1O2N1N3N2N1N4M3N2N2M?hLVkN2Ll1XV1@7A[i8ROUWG7jhN8oV1a0CPObiNT1^V1oN^iNR1aV17O2N1011O4M5LL0N3N20O1O1O001jN^iNl0W`W6\", \"size\": [1280, 720]}, {\"counts\": \"]oi`0<XW1?F8F:J6BbNQjNf1PU1VNgkNU2WT1nMgkNS2UT1SNfkNP2XT1l001O1N2O1N3N1N2N2O1O101L3000O10000O10000000100O00O0100000O10O1000101N1OO100O1N3L3K5N10101O000001O00O10000000O1000O100000O11O0O100O100O10000001O1O1O1O1O001O2002NL4G9J5L5L5J7F8mNfjNnNkU1`0V1Akj38mTL<I6H8K5M3N3M1M3M301N2O1O100001L4N2N=TNoiNn0kgb6\", \"size\": [1280, 720]}, {\"counts\": \"_hm`0;\\\\W1:K6G8J5M4I7D<M3L3L4H8J6M3N2N2N2O1O1O1N2O1N2O1O1O001O10001O0O1O1N10100O100O1M2L50O1000001O002N001O00100OO10000001N101O01O1O2M;A5O0001N2O001N2N2L4N2N1O2M2N3L4L4M4L3L4I5M5N2I7IkkN_NZR1[1cmNPO_R1e0cmN^O`R1;bmNFcR12YmN7lR1^OVmNf0QS1lNSmNW1iT12O1M2O2M3N1O2K5N2N2N1L5012N3LN200O010001N2O1N2J6E;aN]Z`6\", \"size\": [1280, 720]}, {\"counts\": \"gon`0c0[W14M6J2N;F3J4M3I6N2G9J6M3N2N2L5J5N3M2O1N2000O10001OO1O1O2N100O1N2O1O1M3O1000O101O00010N100OM4O1O2101O0OOO01N2N2N2L^LlkNa3RT1^LPlNb3PT1^LPlNb3oS19O3M3L100O010O01N2N2M2N2M300N3L3M2N3N2N3L4K6K5J7LQlNgM\\\\R1T2amNTN^R1f1`mNcN`R1W1amNnN^R1m0dmNUO\\\\R1h0]mNCcR18_mNKaR11bmNO_R1KdmN8\\\\R1EemN=[R1_OhmNb0XR1VOomNk0TR1mNomNU1[R1^NfmNc1YT13O1N2N101O1GnMcjNS2gU1N6K1N1O0000M3@TNfjNR2ZU1nMejNS2[U1mMejNR2eU1N2K4I5aNniNo0\\\\V1oNciNk0dV1UO[iN30NTW1OlhN2Xh]6\", \"size\": [1280, 720]}, {\"counts\": \"j_Qa0<oV1f0H7iNnN[kNX1^T1YOhjN_O2]1SU1V1L4N2N2M3N2M3L4N2N101O10000O1O10000O10001N1N2O1O1O1O1O10000001001M1O1O100000001O01OM3O2HkKblNV4^S1jKblNU4bS121N5KLoK]lNo3kS1L4K4L5M2O0N4K4M4L4M3N2L5H\\\\lN`MVR1[2dmNUNWR1g1fmNcNWR1W1kmNnNTR1l0cmNC^R17emNK[R1OimN3WR1GlmN<VR1^OkmNe0WR1UOkmNm0WR1mNjmNU1]R1_NdmNf1WT14B>K6G8N2N2O1O1J6O0O2O1J61O1O01O00000001N1O1O1M4I8I7J5I8D<D`0eNVjN1oV1I7L8K1O]Qd6\", \"size\": [1280, 720]}, {\"counts\": \"P`l`06aW1<WOh0L2jNXOQkNk0lT1AhjNb0RU1Z1M2N3M2O2M2O1N2M210O1O2N2N7IK51O1O2N2M2O0O1O01N201N1N3K5L4L4L3O0N4M3M4J4O2N1N3L5JgkNTNhR1j1QmNbNlR1Y1VmNkNiR1o0PmN^OPS1=RmNGlR14XmNNgR10VmN7fR1G]mN;`R1DbmN>\\\\R1_OhmNb0XR1ZOimNi0XR1ROgmNS1ZR1jNfmNW1_R1`NYmNm1WT16M3M3K6L3O1O1O10O10O1N2N1010O01O01O0O1J7F9M5F9I8bN[jNA1:lU1Nf1@kjj7\", \"size\": [1280, 720]}, {\"counts\": \"eWPa02>OiV1j0E:mNXOjjNk0PU1_OijNe0RU1BhjN`0UU1W1N2N2N2O2N001O003N2L3L11000OO1100O0002N2M9G3M00101O2M3bMdjNV2aU103N0NK7O12O1MPlNhM]R1V2dmNmM[R1P2cmNVN\\\\R1g1amN_N_R1^1]mNiNbR1V1WmNSOiR1k0VmNXOjR1e0llNHSS14QmNMoR1NTmN3mR1HVmN:jR1CYmN=gR1^O]mNc0dR1XO`mNh0aR1oNgmNP1]R1jNemNW1bT10O1L4N200O100000000000O101OO100M3O1O1000O100O1N3F;F=Db0_OVij7\", \"size\": [1280, 720]}, {\"counts\": \"oeRa02<0e06[OJXU1e0SkN;eT1NmjN>mT1^1K3N2N2O1O1N2O1N2N2O1O1N2O1N2N2O2M1O0O20I7M3M4N1M202O1O001NekNUMdS1i2[lN\\\\MdS1b2]lN_MdS1^2elNUMaS1h2m0M3N2N2MPlNiM]R1T2`mNRN`R1j1_mN[NaR1b1]mNcNbR1[1YmNnNfR1P1ZmNROeR1k0^mNVO_R1c0`mNH`R13`mN2eR1D`mN<bR1\\\\OcmNe0aR1POfmNP1dR1aN`mN`1`T11N1000O110O2N0000002N1OO1M3O010O100O00100000000O1O1O11O00O001N21N2N3M3ZNkiN8l0HcVc7\", \"size\": [1280, 720]}, {\"counts\": \"QnSa0:_W1>Gc1\\\\N9H;D6M0O2N000O1O2M3N3M101N2N1O2O0O3M2N1000O1M4O0O1N3K5L4K4N3N3J6J5N1N3L3L5G9L4J6C>H\\\\kN3fQ1^OVnNGUNj0WV1a0O100O1O100O1N2K5N200000O1000001O001O1O001O1O1O0O010000O1O10O100O2O1O0N2L4SOm0B?M4LVYc7\", \"size\": [1280, 720]}, {\"counts\": \"^VUa04hW1?C6J:E>Cg0YO;E9H4L3L5L4K3M2O001O1N2O1O1N2N2M3M4N1O00100O1O1O1O1O2O1N3N1O0001O0O2O1N2O0O001N001O1H9J5M4C<C>I6L4K3O3KRlNcNeQ1U1`nNPO_Q1c0fnNFZQ1MinN;ZQ1eNflN4a2X1kS11O1O1O1N2O100O1N101O100O1O1O0O2O2M2N2O1O0101O00JnM^jNQ2bU1SN[jNm1eU1SN[jNm1mU1O2M3N2M4L6I9FZhV7\", \"size\": [1280, 720]}, {\"counts\": \"cUia0d0XW19I7Ho0SO=B;D7J4L3N2N1N2N2O1O1N101N2M3L4L3O2O2N1O1O1O1001O100O0O01000000O1000001O1O1N100O101N2O0O00O2N3M2O3G8I7H<Bb0WOg0iM`jNm0RW1\\\\Oma7?e]H6E:L4M2O1N2N2N2N2O1O1O1O010N2O001001O0O01O1O1N1O2O1O1O1002N2M2O1O002N3L7I?dN]iNMThX6\", \"size\": [1280, 720]}, {\"counts\": \"]^Tb08\\\\V1=RjN8aU1e1]O4K4K4O1N3N5L2M2N1O1M3O1O1O1O1N2N2O2M2M3L4M3O2N1N2O1O1001000O00O1001O0000O2OO1N3N1000O0101O001N100OO2N2L4I7H9I7G:F9H<I7D=WNSjN?kY6WOPmJ1^iNh0VV1e0L8G2N3NJRjN\\\\NnU1j10N30O00O11SjNSNfU1n152N1O00010001O000O10N2N2N20O010000001O001N3N2M3L6TOeji5\", \"size\": [1280, 720]}, {\"counts\": \"T_Yb0>gU11ljNi0kT1FdjNe0YU1T1L4K5K4N1O2N001O100O1O1O3M3M1O1N1O2N3M2O1N1O2N3L3M4M101N200O1O2N0010105K0O00N2L4K5L4L4K5J6I7N1O1I7K5N2L5G8J7JZlNTN\\\\NHmR1l1cnNTO\\\\Q1d0gnNBXQ19]nN6dQ1AcnN`0cQ1VO`nNi0gQ1fN`lNFn1c1UNZNmU1f1TjNZNjU1h1VjNXNgU1k144J5L3O2O10O101O1O1O1O001O000000000O10000O2O1M3N1@a0E;B>IXjS6\", \"size\": [1280, 720]}, {\"counts\": \"d^Tb0P1fV1;kNWOijNR1QU1Q1K5N2M3O1N2L6M1O1O1O1O100N3N1O1O1N2O1N2O2M2M3M4L3N1O2OO1O1001O0100O1O1O1N2L4L4N2L4L4M3M3N2L4N]lNTMVR1j2imNYMWR1e2dmNbM\\\\R1\\\\2bmNhM]R1V2dmNkM\\\\R1R2fmNPNYR1n1gmNUNXR1g1hmN^NWR1a1hmNcNWR1Z1`mNcM]OX1SS1P1cmNYO]R1c0emN_O[R1=imNCWR16nmNLRR10QnN1PR1HWnN7iQ1C^nN>aQ1_O`nNc0`Q1QOknNm0[Q1^NUoNc1cS11O001N2N2N23L5L5K5K1O1O0000HfNiiN[1UV1iNiiNW1WV1iNiiNW1VV1kNiiNU1WV1:000000O001OO2O9^O>^OoYV6\", \"size\": [1280, 720]}, {\"counts\": \"[fka0;WV1>XjNc0jT1^1K3L4K6M1O0O2O2O0O1O1O2N0O2O010N2O1O1N3N1N1N3M2O1N3N110O0000N201L4L5L2010000O1L4M2N3N2M3N2M3O1N1O]lNWMRR1f2omN_MmQ1`2nmNhMPR1W2mmNoMSR1m1hmN\\\\NXR1a1hmNcNWR1[1hmNhNXR1T1dmNTO\\\\R1g0emN]O\\\\R1=fmNF[R13kmNNUR1LomN5RR1DSnN=mQ1_OVnNb0kQ1[OUnNg0lQ1SOZnNl0hQ1kNanNS1lQ1VN^nNk1jS10O2O1M3J6L41O0O2O1O8H4L2N2N1O1O3MO2M2O1000O1000O11O3L3M1O1N3F<\\\\ORZ[6\", \"size\": [1280, 720]}, {\"counts\": \"Smba0e0XW18diNEgT1V2B;E5L2O2N1O001N2N4M2N1O00OkkN]LPT1h311O0O2N2O1N4K4N2M2M20001O00N2M4M3M4M3M2M2L5M3L4M4L2N3M3M2L4O001N2K6GllNeMUQ1X2YnNaNcQ1[1[nNkNeQ1m0SnNCmQ16TnN1kQ1JYnN6gQ1F^nN9aQ1DcnN<^Q1@enNa0^Q1YOdnNh0]Q1QOhnNP1PT14H7J7N2N21O3M2O1N3M1O2N1O2M2O1O1OO101L3O1O10O2O001N;\\\\O<_Oc0AhaP7\", \"size\": [1280, 720]}, {\"counts\": \"n][a0?ZW1>F=YiNgN00gU1a2\\\\O>B7J3M3M2O1N1O1OO0200O2M2N3N1N2N2N3M2N2O1N2N2N2N3N1O1O1N201O000O0101N3K4J6K4K5M3K5J6J6K5N1O0L4H9E<K5LglNnMlP1l1WnN]NcN;US1R1ZnNBfQ1:[nNJdQ13\\\\nN0dQ1K_nN7aQ1EanN=`Q1YOgnNi0YQ1lNnnNX1WQ1XNRoNj1bS10O1O1001O00000010O011M1O1N2O1O1O1O1O0013L7H3G8]Od0Ac0ASQX7\", \"size\": [1280, 720]}, {\"counts\": \"]mba0=m0EYO`0^U18ijNi0hT1Z1K4M3N1M3N2O1O1O001O1O1O2N1O1O1N2O1N2O1M3N3L3N3M2O1N2O1O1O1O1101N10N1O10001O001O000O0010O0O2K5L1N4N2M4L3M4K4NO15I7J6C`0D<D?VOXlNXOgQ10\\\\nN;WT1=M3L4L4N2N2N2IZNSjNg1lU1]NQjNc1nU18O010006J2N1O00000000O10001N12N0000N2O0001O1I7D=@`0BPRd6\", \"size\": [1280, 720]}, {\"counts\": \"Z_`a09kV1o0fNYOkjNBG\\\\1TU1]1J4M2N2O1N2N3N1O1O100O01001N1O1O2N1N1O2N2O3L4M1N2M202M2M4L3O00110O10OO10000000000001ON2O1O10M3M3L4K5M2M3K6I5O1L5M4M4M4E<A`0B=_Oe0_OikNOYQ1BinNf0dS1e0L20001O100N2M2N2O2N2N12O001O01O01O1N2O1O3M1O1O1O2N00000000001N1O1D=K5B=[OWZ`6\", \"size\": [1280, 720]}, {\"counts\": \"Vg\\\\a0>ZW1:A>ROPOdjNY1nT1U1F;J6K2LM[kNRMbT1P3_kNoL_T1Y301O002N1O2N1O100O101N010N2N2N2N1O2O0O2N2O1N2O1O2N001N20100N1M03M2M4K5L5K5K5K3M4M2N3L5K4M3M3L4LWlNlMnQ1o1kmN^NTR1[1PnNhNlQ1X1omNQOoQ1n0jmN[OVR1>omNCSR17QnNKoQ11TnN0lQ1KWnN7iQ1EZnN<gQ1^O]nNb0fQ1YO\\\\nNh0oQ1dNWnN_1RT13L4O1L5M21OHmMcjNS2]U1kMfjNT2[U1iMhjNV2bU1O00010O001N10001N1O1O2J6G9I8I6J6K6L3F:Khak6\", \"size\": [1280, 720]}, {\"counts\": \"]gaa08`W1?A:G7I7H8VOj0K6L3N2N2O2N1N3N0O2N102O0O2O0O2N101M3N1O1N2O1N2N2O1N1O20N000O3O1O1N002N2O1M3M2O2N2N2M2M4N2M3M3N2M\\\\lNZMTR1d2lmN^MTR1_2jmNgMTR1W2kmNmMUR1P2kmNSNTR1h1mmN]NRR1_1cmNQO]R1l0dmNVO[R1h0^mNB_R1=cmNE\\\\R18gmNIYR10mmN1SR1LnmN6RR1GPnN:PR1BVnN<iQ1@]nN?dQ1XOdnNh0dQ1mN_nNS1WT1000001O00001O1O1O00O2N10000000000O11O001NO10O1L5I6L3M4I7K9Ckib6\", \"size\": [1280, 720]}, {\"counts\": \"aW_a01]W1k0]O>aNXOYkNP1`T1WOXkNP1eT1W1N2M2M2N3N2O1O1N2O1O2N1O2O0N2O1O1O100O1O2N3L3M1O2N000010O1O1N3N2O1N2M3N2N2N2M2N3L3M300O0O3L4M2N2N3MdlNYMdQ1e2WnNdMgQ1Y2lmNYNRR1b1imNiNVR1S1fmNWOXR1g0gmN]OWR1a0imNCXR14omNMQR1OQnN2PR1KPnN8QR1EPnN<QR1_OQnNc0PR1WORnNl0oQ1mNVnNT1QR1aNRnN`1VT1100M4A>M3M3N2M3N2H8M3M3N201O01O3M3M3M003L3_M[kNc1QU1oMRkNH8g1hU1O2N2N2I7H8B?I7H8L4MUYe6\", \"size\": [1280, 720]}, {\"counts\": \"_UZa0i0TW18aiN@RU1X2^O6J3L3N3O1N2O0O1O1O2L3M3O2M2N3M3M3N1O1N2O2M2O100O0O20O0O2O1M3M3K4L5L4J6L4K5I7N101L2I8L4J6H9IQlNQO[Q1h0YnNeN`Nl0US18`nN6]N_NmR1W1gnN;bQ1A_nN`0cQ1[O]nNh0jQ1hN_nNY1ST10O2M3K5M3L4L4002N4L00000000000O100000010O2N1O2N1N3M3N1M4B>C?_ObY^7\", \"size\": [1280, 720]}, {\"counts\": \"R^Va09cW17K>B:En0SOb0_O5K4L8H2M3N101M2O1O1N2N2O1N2N3M2L4O1N2N200L400002N00O3M2O00000000000O100O10O0O2O0O2L3J5M4K6M3K4J6L4E;A`0I8HXlNcN]Q1U1VnNbN^Nb0YS1f0^nNIaQ12cnNOaQ1FenN;`Q1VOgnNm0RT13O3K4M4N1M3O10O1000000O110nMXjNk1oU1O01N1O1J51000O1O1001N2O1N100O2N3L3SOQ1UO_YT7\", \"size\": [1280, 720]}, {\"counts\": \"UUda0a0]W14K:[iNZO^U1_2TO7J6J3N2N1O2K5M4M1N011O1O2M3N1O1O1M4M2N2N2O100O1O1O1N3N11O2O2MN2O1000000001N1001M2G_KRmNc4TS11O1O0N2O02L4K5K5L3L4K7K5J6I:E:A`0B?A>@?FSlN^OSQ1LcnNk0ST15M4L4N2N20O01N2O100O10001N1O1O010O1O1O1O001O1O100O100N2O100N2000O2N4UNniNa1YV1N2N2@b0D;D<HiiX6\", \"size\": [1280, 720]}, {\"counts\": \"b^ea0d0oU16^jN?oT1\\\\1N6H4J5O1N3N1O2N2N100N2N2O100N2O1N3O0O2N1M4M2N2M3N2O2N1O12O1N10O0000000O100O001O2M2O2OO1O1N2M3L4M4K4L3N002M2M4N3K5K5N2M4I8D:J\\\\lNWN\\\\Q1c1dnNcNXQ1X1enNUO^Q1b0^nNHgQ1NYnN7mQ1]OWnNe0QR1mNSnNT1[T12O010N2N2O1O1O11O01N0O200001N3N2O1N1O1O1O1O1O1O00O1N2O02N2L3K6Aa0WOUiN1_h]6\", \"size\": [1280, 720]}, {\"counts\": \"`mXa0a0ZW1f1]N6H<E6J5K6L1N3O0O1O1O2M2N200O1O2M2O1N2N3N1O1N2N2O1O1O1N3N1O100O10000O2N1O1O2M25J2M200O1N2M3L3L5M3L3M3M3M2M4O1M3N2K6K5IelNaMcQ1X2XnNVNeQ1d1]nNaNcQ1]1RnNQOkQ1l0WnNXOdQ1d0^nNBcQ18`nNJ`Q12cnNO^Q1McnN5[Q1IgnN9XQ1DinN>XQ1^OinNe0XQ1YN[mN6^1h1gS1:N2N2K6L3O1N101N2L4K5L4M32N7H3N001O1O001N4L?_O;^Ob0C7E;H7L5D>HbYj6\", \"size\": [1280, 720]}, {\"counts\": \"[mXa0]12YOcU1n1D5L4M2N1O1K6O0O2M2O10000O100IiLekNW3YT19O1O1N3O0O10OL6L3M3O1N2N2O2N2N1O1O100O1O100001O4M0000O00O20O000000000O000O11N3K3N1N012N2M3N4K5K5M3L4K5L4K4KclNYMkQ1`2RnNmMlQ1g1XnN`NhQ1\\\\1XnNiNgQ1S1VnNSOjQ1h0XnN\\\\OhQ1<ZnNIgQ13[nNOfQ1I_nN9cQ1^OcnNb0lQ1eN^nN\\\\1ST10001O006I200O1OO1M3O100O100O100001O2NN2N2N200O001OO0G<Ce0WOiaa6\", \"size\": [1280, 720]}, {\"counts\": \"h]`a0:TW1f0UjNTOQT1W1ekNTORT1W1ckNROYT1[201N1M3O100O100M3N3M2O100O11O1O1N10O101M3M3N2M2O1O0O2O1L4N2N2N3M2N3N1O1O1O00100001O0100O1OO2O00000000000O1100O1ON2O1O1N2M3N2M3N2N1O2L3L5L4I7M3M3N3N2N1O1N3L3L4L4M3L5I6M3L4L5J5M3J7I=Dd0[Onck6\", \"size\": [1280, 720]}, {\"counts\": \"^o]a069NgV1o0ZOWOgiNS1VV1<00O1O2N1N2J6J6N2N2O2M1]OcM_kNc2_T1`0O1I7N20000O1N101O14LHbLPlN[3PT1fLPlNZ3PT1eLQlN[3mS1gLSlNY3lS1hLTlNX3kS1iLTlNW3lS1jLTlNV3nS1fLTlN[3YT1O00001O1O0001N1O1N2M3N200O1O2O0N2N2N3N1N2O1N2N2N200O20O0010O0O1010O0O10O10000O10O1000O10N1J7J66J4M0OM3M3L5L3N3K6H7M2L4J6AUkNhMlT1T2c0JQlNSNUR1j1`mNdN`R1X1\\\\mNPOdR1m0\\\\mNVOeR1f0\\\\mN]OdR1>_mNCbR19`mNHaR16_mNKbR13^mNNdR1IamN9fR1XO^mNl0kT11N1O1O2L3O1HXNWjNh1iU1[NUjNd1kU1^NUjN\\\\1nU1fNUjNl0SV1WOoiN7]V1Mi020O101N1O3JUbh5\", \"size\": [1280, 720]}, {\"counts\": \"RgUb01824OYV1\\\\1K3M3K4M4K4J6L5O0001OO100O100M3NhMdjNQ2VU1SNTkNd1jT1]NZkN`1dT1bN\\\\kN^1fT1_NYkNc1aU12N1O1O1O1O1N2O1O1O1O1O1O2N001O1O1O1O2N3L5L100O1N2O1N2N3N1O1N3M3L3O1N2N2N2L400O1N2N3K4N3O0O101N100O10O100N101O0O1O2O0O2N1M5L3NjlNXLYR1c3lmN\\\\LWR1_3Y1L1O1I7LmkNYMUS1d2nlN]MQS1_2TmNaMkR1]2XmNdMhR1V2WmNQNgR1i1]mNZNcR1Y1dmNlN[R1U1dmNjNSR1XOglNP2V1gNUR1]O_lNk1^1_N]R1DWlNj1^1`NdR1^1WmNgNkR1W1VmN`NUS1]1i1KRkNdNhS1n0alNnNXT15b3N[N1M3J5_ac5\", \"size\": [1280, 720]}, {\"counts\": \"VnTc0470iV1MXiNn0[V1h0E9H3MXNUjN^1hU1aN]jN^1`U1bN\\\\jNb1dU1_NZjNa1fU1=01N1O1N101O102M2O001N1O101N102N3L5M0O1O0O2N2M2K5N3N1O10000O2K5OO001N1N3N101M2OL5N1O200000000010O1N2ZMlkN]1WT1gNdkNY1^T1U10OM1[lNaLkR1_3SmNhLiR1W3RmNRMkR1m2SmNWMkR1i2VmNXMhR1g2YmN[MiR1b2WmN`MhR1^2YmNdMfR1V2^mNnM`R1o1_mNWN^R1g1cmN]N[R1a1emNaN\\\\R1\\\\1emNdN\\\\R1Z1dmNhN\\\\R1U1emNlN\\\\R1R1dmNoN]R1o0cmNRO_R1k0amNWO`R1e0amN[OaR1b0_mN_OcR1>^mNBcR1>\\\\mNCfR1:ZmNGhR16XmNJkR12WmNMkR11VmNOlR1LTmN6nR1FTmN;mR1BSmN?oR1\\\\OTmNc0PS1VOTmNj0nR1SOTmNj0PS1POUmNl0oR1SOQmNn0QS1oNolNQ1TS1lNmlNS1VS1iNjlNY1ZS1_NhlNb1jT1O101K42OG90O1N20H701OO01O1N3N4]Ohk\\\\4\", \"size\": [1280, 720]}, {\"counts\": \"`]Rc02`05CMlV11[iNc0^V1A]iNj0ZV1c0Ja0_O5K3N4L5J1O002O0O101O1N2N1O10000000dMSkNf1lT1g00000001O003M4MO0M3O2N1N2N3N1O1N20O00OO6J4K4N3L4L3MGjMnjNT2iT1fM[kN;Mg1W1lMXR11glNc2VU1L[O_MlkN^2RT1iMikNU2VT1PNhkNm1=gMPS1b0]lNe1b0nMoR1P3nlNRMRS1n2llNUMSS1j2mlNWMRS1i2nlNXMRS1g2mlN[MTS1a2klNcMWS1X2hlNlMZS1o1dlNVN]S1e1flN]N]S1Z1flNiN]S1P1elNQOdS1d0[lN^OiS1=WlNEjS19UlNHmS14TlNNlS1NWlN3jS1IUlN;kS1AVlNa0lS1[OUlNg0lS1WOUlNh0lS1VOUlNk0nR1bNnlN`04P1lR1kNhlN2:V1nR1lNflNJ=\\\\1lR1nNelNC?_1mR1oNelN^O?c1nR1QOamNQ1aR1POZmNR1hR1nNVmNQ1mR1oNRmNo0QS1nNQmNR1QS1cNYmN[1kR1`NXmN^1eT1OMO0L8N10101N2O0O2OI70N2100OO1ON22N2O2O2L4@a0EhhNH\\\\W15hhNG]W12eka4\", \"size\": [1280, 720]}, {\"counts\": \"]VXb047;iV1o0ZOf0_O4BPNijNR2TU1SNijNn1oT1f0N2M2M4N1O1M3K5M3O1003NN1M3L4O1O2M2O1N3L32O2N2NN100O11O0000001O6QLPlNc3ZT1bLekNP3jT1L0O1O1N4M2O0O00000001O2N1OO2OO101O00N2O0O3M3L3N3M2N1O001O1N3mM]jNg1l0RNiS18\\\\kNb1j0\\\\NhS12]kN`1R1XNdS17YkNa1nU18fjNRNkT1d1UkN]NmT1^1RkNeNQU1R1SkNQOQU1>\\\\jNZOf0;QU1G_kNa0cT1\\\\O\\\\kNf0nU1001N3J9J5K2N1O2L3O2N1M4L4N2N2J6O1N4M1I5N0201O2I6F<E:J7L4L4I6G<H9HSZ]5\", \"size\": [1280, 720]}, {\"counts\": \"n^Tb0=^W18J4K6J4L4O1N2O1O1N2N3N1001O00O2N1O11O000001N100O2O0O101N101O1O00000O1L5M200O1O2N1O101N100O11O1O2N3N1N4L2O10OL4K4N3N1N2O1N2N3J8H;EdY1_OgfN?I3J5N3L4N3L300O2L3M3C=L4N2O1O1M3N2K6I7K6M2O0O1O1N2O1O000O112M1O00N201M1M0M34M02I:L4N2N1H7L4L2O002O3N1L;H6I5K6H8Gb[S5\", \"size\": [1280, 720]}, {\"counts\": \"Y_ea09cW15K4K5M3M3L4N2O1M4L3N3M2H8J6I7@PNmjNS2PU1UNjjNl1TU1a0N2I7O101O0001O2N2OO01O011N2TNPkNc0XU1UOijNl0WU1TOgjNm0[U1k01OOSNijNS1WU1mNijNT1VU1kNjjNV1WU1iNgjNY1YU1fNgjN[1^U1UNfjNONl1mU11O0O1N2N2N3M3M2N2O002N2M6KM3N010M400N2L5H9K5M3M3G9I^jNZOWT1>lkNHTT1OnkN6TT1AokNc0dU13K5M3L4K5H8M3N2L3O2001OK401O11O01O01O001O1N2O1O1O2N0002WOoM^kNU2ZT1SNWkNG6U2_T1`NakN^1^T1iN]kNV1cT1kN^kNR1_T1TOakNi0]T1[OekNa0XT1DikN8WT1KikN2YT1OgkNN]T12akNFiT1`18J6E;G:G8M4XOeiNH\\\\V16hiNE[V14m0Eo[g5\", \"size\": [1280, 720]}, {\"counts\": \"YhT`02SW1n0@`0C<lNS1K4M4M2O1O2N101O10O:G2M1O01O0O1O1O1O1O1O1000O01O1O10O01O1N2O1N2O2NRMdkNZ2[T1gMekNZ2ZT1f000002M8I3MO1000010O001O00000O0101O001O3M4L00O10000O100O1N2O1O2M20000010N2O1M2N2L4J7K4N4G7E;M4M1NC]NYjNi1iU14akNXNnR1`1PmNjNPS1R1nlNTOTS1[OgkNT1U1DaS13ZlN4lS1EnkNc0TT1YOjkNk0ZT1bNRlNa1TU13L3L4M4L3O2M2L5M30O10O0101O00ChMmjN\\\\2kT1aMWkNi2gT1\\\\MUkNe2jT170iNakNPO`T1HUkN`0c0@[T1o0mkNjNZT1Q1jkNgN`T1:ajN9nV1^OViN=YW1L7JPZT7\", \"size\": [1280, 720]}, {\"counts\": \"Q_S`02iW19Ig0TO<I8I6J3M4L3M2N3L4N2N2M2I7M3N3M2N2O001O1CQMhkNP3TT1VMjkNi2UT1YMkkNg2TT1c0000000001O0O101O3dLjkNh2jT1L11N2O02N1O1O2N2WMnjN`2YU10000O10O101M2M3K5O1O1O1O001O111N1EkLhkNV3nS1c0M3M2N3N2M2N2N301N2O10N2O1O001N2O1O2M2O2M3M4Ke0YNXkN\\\\N\\\\U1S1Q1E:F;N3K8E^jNMSN25IaV1]1\\\\O?K5M3O1M2L4L4O2O0101N2O1O001O2M4M6I3M3N3M5J7J2M2O2L4H9D;CYiV7\", \"size\": [1280, 720]}, {\"counts\": \"bnP`02=5iV12QiN7iV1b0N2N5L1HiNciN[1XV1fNhiN_1SV1aNmiNa1PV1`NPjNb1mU1`NRjNb1mU1\\\\NTjNf1kU1YNUjNi1iU1VNXjNk1hU1SNZjNm1lU11N1I7M31O2O0O4L3M1N2O1O00O1N2O1N200N2M4NoMZjNj1fU1VNZjNi1fU154O1M4MQOTNUkN2`0d1_T1gN_kNW1_T1]OPkNa0hT1aNYkNf2fT1]MXkNb2eT1=RlNjLUS1U3klNnLTS1P3klNRMnR1S3SmNoLjR1R3UmNQMkR12alNQ2m0nMcR10blNP2k0QNhR1I`lNR2i0VNgR1DflNl1;SNE>lS1[1`lNYNA?RT1Q1alN^NYOd0aT1d0^lN]OdS1a0gkNmNOa0]T1?dkNXOF9lT1:`kNB^O4SU19_kND^O1oT1?fkN]O`T1P1ekNiNQT1K_kNT1iU1ZOliNg0\\\\2POSQ1c1gnNcNXQ1]1`nNlN_Q1S1\\\\nNROfQ1k0ZnNVOiQ1f0TnNAlQ1:SnNKnQ12mmN5TR1FmmN=TR1_OmmNc0VR1XOgmNl0\\\\R1oNemNS1\\\\R1eNimN]1YR1[NfmNl1QT16H8L3M4L3N2N3K5L4L4@eLYlN]3cS1a01000001N2O3M3L3N2N2M2N2N2N5K5TMYkNW2ZU1M2O000O2N7J2M6UNniN\\\\1UV1bNniN[1\\\\V1O3jNbiNe0_W1]OjV1K[iN0ki[7\", \"size\": [1280, 720]}, {\"counts\": \"hVc?155ZW1?Ha0B5K3O06J2jNYiNo0eV1:L3N3M2M4K3J5L5O2N1O0O0DjMljNX2SU1lMgjNW2YU18O100O2O0O1000O01N10dMkjNn1TU1>O1N1O11OO110M3N0ikNVMRS10_lNi2>[MRS1o2mlNUMSS1i2mlNYMTS1c2mlN`MSS1]2mlNfMUS1V2ilNlMaS1g1alNYNbS1`1blNbN`S1Y1_lNiNhS1P1QlNYOST1`0okNVOeT1?]kN]OjT1>XkN^OPU1;SkNBRU14XkNGnT11i1FViN7fV12^iNGZU1:ajNM^U12]jN3gU1GVjN>iR1_O_nN1VN9P1:\\\\T1^ObjN7P1?gR1\\\\OgmNMeN5i0g0iR1_OonNe0SQ1ZOhnNj0YQ1WOanNm0aQ1QO[nNS1dQ1nNZnNS1gQ1lNWnNW1jQ1gNomNa1PT17M3M4L3L5M3L4J6O1O0L54L1O002M2O3L6[MhjNX2cU1L10000OE<M2010O1000001N100O01N0O1100001O1O10O10O1O10O4M3cMbjN87R1`V1E9H`0@3MXhe7\", \"size\": [1280, 720]}, {\"counts\": \"\\\\nZ`09_W1>G?[O>I5K4L5K6J4M3M5K6J4L5K2N2O1L4L4M3NO10N2O3M2O00O10000000O00N3N00ZlN]LSS1c3olN]LnR1e3QmN]LnR1b3QmNaLmR1_3SmNcLkR1]3SmNfLmR1Y3SmNgLmR1Y3RmNgLQS1W3PmNhLRS1V3olNjLRS1T3hlNRMYS1l2jlNjLCOfS1S3klNlL^S1R3elNlL\\\\S1Q3glNnL\\\\S1o2n0LckNVMfS1i2ZlNWM7ObR1h2YmNYM04hR1`2RnNbMnQ1\\\\2QnNhMnQ1U2PnNQNoQ1l1PnNXNPR1e1QnN]NoQ1`1QnNcNoQ1[1omNhNRR1V1kmNoNTR1P1jmNTOUR1j0kmNYORR1g0nmN\\\\OQR1b0PnN_OTR1=jmNFWR14lmNNTR1OlmN4TR1IkmN;WR1AhmNb0ZR1ZOfmNh0[R1ROimNo0^R1bNhmN`1YR1]NhmNc1ZR1YNhmNh1YR1SNjmNn1TT10000O10001N2O00000O01O01000N101O1O00100K5L3O2N1O1O101O010O001N2O0O2O0N2O02N2N3dMfjN46k0bU1nNYjN5;6FIXV1LUjN7HEPW13UZT7\", \"size\": [1280, 720]}, {\"counts\": \"fod`0<\\\\W19H7I8BnNfiNX1UV1=M2O1N2I8M2N3L3M4SOPNfkNR2WT1UN`kNQ2]T1j0N2N2O1O1O1N2N2N1N3L4O010O000001O0O1O1O0000101N1O10\\\\lNXLQS1g3PmN[LnR1e3RmN\\\\LmR1d3SmN^LlR1`3TmNaLmR1]3TmNcLmR1\\\\3olNdLWS1Y3klNcLYS1[3ilNcLYS1\\\\3hlNcLYS1\\\\3ilNaLXS1^3jlNaLWS1^3f0N3L3M21blNlLoQ1T3nmNQMPR1n2nmNWMPR1g2QnN[MnQ1e2RnN\\\\MnQ1c2QnN_MnQ1a2SnN_MlQ1_2WnNaMiQ1^2PnNjMPR1T2mmNQNSR1m1lmNVNTR1i1kmN[NTR1c1kmN`NTR1]1nmNeNQR1Y1nmNjNSR1R1nmNQOQR1n0nmNTORR1j0nmNXOSR1e0mmN]OTR1`0kmNBVR1<imNGVR17kmNKVR12jmN0VR1MkmN5UR1GmmN:TR1DmmN=SR1AnmN`0RR1_OnmNb0SR1[OnmNf0RR1WOomNj0SR1SOmmNo0SR1nNlmNV1TR1fNmmN\\\\1TR1`NnmNb1SR1\\\\NmmNe1SR1ZNkmNh1]R1PNcmNQ2^R1jMemNW2]R1`MimN`2kS11O1O1O001O100O010O1O1O01O00N2N3OO10000O2O0F:]OkjNRN`U1i1>M3G9M3O0K4O20O1O1001L3N3O2K=@][V6\", \"size\": [1280, 720]}, {\"counts\": \"aWPa0182XW16ahNOVW1c0J4L5H8J5M4M2N2N2L5H7N3N101O1I6M4M2M3L4G9L4N2L4K5L4O1O1N3N10OO2N2O0O2O1N1M4N3M2O1O1O1O0O2N20O1O000O100O2N1O101O001O010O100M3L4N2J6N2O1L4004LIPlN`LnS1^3VlN_Le07PR1X3^mN^L`0?RR1P3XnNRMgQ1m2XnNVMhQ1h2XnNZMhQ1d2XnN_MgQ1_2YnNbMgQ1]2XnNfMhQ1X2WnNkMiQ1S2UnNRNkQ1l1UnNUNkQ1i1UnNYNiQ1f1XnN\\\\NgQ1b1YnNaNhQ1\\\\1XnNfNjQ1V1TnNnNlQ1P1SnNSOmQ1k0RnNXOoQ1e0PnN]OQR1a0omNARR1<nmNFRR16PnNLPR10RnN2nQ1LRnN6oQ1CTnN`0lQ1^OTnNc0mQ1[ORnNh0nQ1UOQnNn0PR1oNQnNS1oQ1jNRnNW1oQ1fNQnN]1oQ1bNomNa1PR1`NmmNb1TR1]NjmNf1WR1oMUmNUOd0m2YR1kMnmNX2SR1eMlmN^2[R1XMfmNj2gS1001O1O1O1O1O2N10O010O0O2OOF:N4^Oa0N2J7H7O2N1O2O0O0N3O1N3N00101L4oNfiNFM:cV1KniNJRRa5\", \"size\": [1280, 720]}, {\"counts\": \"e_`a0;aW15L4L3K6I7L3N3N1N2N3N1O2N100O100O2O000000000O1O1O10001O0000000O10001O100O1O001N10000PNPNPnNo1nQ1UNPnNl1iQ1\\\\NVnNd1iQ1`NSnNb1jQ1dNQnN]1lQ1gNRnN[1lQ1fNSnN\\\\1kQ1fNSnN[1lQ1fNSnN[1lQ1gNRnNZ1mQ1iNPnNX1PR1iNomNX1oQ1jNPnNV1oQ1lNomNU1oQ1mNQnNT1mQ1mNSnNU1jQ1lNVnNW1fQ1jNZnNV1eQ1lNZnNS1fQ1nNZnNR1eQ1oN[nNQ1dQ1PO\\\\nNQ1bQ1PO]nNQ1cQ1oN]nNP1dQ1QO[nNn0fQ1ROZnNn0eQ1RO\\\\nNP1bQ1PO_nNQ1_Q1oNanNP1`Q1PO_nNo0dQ1PO\\\\nNn0fQ1QO[nNm0fQ1TOZnNj0hQ1VOXnNi0jQ1VOVnNh0lQ1XOSnNg0PR1XOPnNf0RR1ZOnmNd0UR1[OkmNd0VR1\\\\OjmNa0YR1_OgmN?[R1AemN<^R1DbmN:_R1FbmN7aR1I_mN5cR1K]mN31]N_Q1_1anN12bN\\\\Q1\\\\1cnN02fNZQ1Z1dnNN4fNZQ1\\\\1bnNK4gN_Q1^1^nNF4PO]Q1Z1_nNC3XO]Q1T1anNBMB`Q1l0dnN@JHbQ1h0enN\\\\OG0eQ1d0dnNYOF7gQ1>enNVOE>hQ1;dnNRODg0iQ15fnNoN]OR1oQ1MfnNlN[O[1PR1FdoN<]P1BboN`0_P1]OboNc0`P1ZO`oNh0`P1VOaoNj0aP1TO_oNm0cP1nN]oNU1dP1fN]oN]1fP1]N[oNe1^S11O2M4L8I4NO1N010001NK6M3N2L5H8J5J6E<M4M3D<L4N2MnaY5\", \"size\": [1280, 720]}, {\"counts\": \"Q_Tb03iW18I4M3M3M2N3M3L4L3N3M2N3M2N2N2M4N1O1N3H7M4N1O1N2M301N1O010O2N1H9O0O1010O010O2O5J10O001L3\\\\OXMnkNj2PT1YMnkNh2QT1ZMikNk2VT1XMckNl2]T1<O1N201N8I4K1OO01O0O0002O101O0101L3E:O101O010OO010N3O2O0N2K5MWmNoKhQ1l3[nNXLdQ1g3YnN^LfQ1`3YnNcLhQ1Y3YnNiLgQ1T3YnNoLhQ1m2XnNWMiQ1f2VnN\\\\MmQ1_2RnNeMnQ1Y2PnNkMoQ1T2omNoMQR1n1PnNTNQR1i1nmN[NQR1d1omN]NQR1`1QnNaNnQ1^1SnNcNnQ1Z1RnNhNnQ1V1omNoNnQ1R1omNSOmQ1o0QnNUOkQ1n0UnNSOhQ1o0WnNSOjQ1i0XnNYOgQ1e0ZnN\\\\OfQ1a0[nNAeQ1<[nNGeQ17[nNJfQ15YnNMfQ12ZnN0fQ1NYnN5gQ1JXnN9gQ1EYnN>gQ1_OYnNc0iQ1UO\\\\nNl0fQ1mN^nNS1eQ1hN\\\\nNY1eQ1eN[nN]1eQ1`N\\\\nNb1dQ1]N\\\\nNc1fQ1[NYnNf1hQ1XNWnNi1kQ1UNUnNl1lQ1RNSnNn1PR1oMomNQ2UR1lMmmNS2VR1kMimNV2ZR1fMgmNY2]R1dMcmNZ2cR1SMmmNk2cS10101O1J8I6I41M20010OJajNmM`U1Q270002O1O1M5I6L4J5OO1001O01OE;O110O1N3SORiNc0YW1Jdcg3\", \"size\": [1280, 720]}, {\"counts\": \"ZWna05gW17J3N3L3K6L3N2L4N2O2M2O1N2N3K4N2O1N2O2M2M3L301O2M3M3O1N1K5N2O1O1O2M201O10000O000O100O2O0O1O3M3M4L1O0010N2O1N2N1O2K7L>UMjjNS2bU100O10N1000O1O1NljNRN[T1k1gkNWNXT1i1\\\\kNdNcT1]1\\\\kNaNfT1_1ZkNaNdT1a1]kN\\\\NeT1c1^kNZNcT1f1^kNYNcT1e1^kNYNbT1h1YkN]NgT1c1[kNZN_T1m1bkNSN[T1n1hkNRNoS1N]kNo1f1lMkQ1d2PnNcMmQ1^2kmNkMUR1T2kmNnMSR1R2jmNSNSR1n1lmNTNRR1l1lmNYNPR1h1omN[NPR1e1nmN^NQR1a1omNbNnQ1^1QnNeNoQ1Y1RnNhNnQ1V1SnNkNmQ1FRmN9Q1YOoN5lR1f1SnNSOlQ1l0RnNXOmQ1f0QnN_OoQ1`0omNCPR1;QnNGoQ17QnNKoQ14PnNNPR10PnN2PR1MomN5QR1IomN:PR1CQnN?kQ1CUnN`0iQ1_OVnNc0kQ1ZOVnNh0kQ1VOTnNl0lQ1ROTnNQ1lQ1mNRnNV1PR1fNPnN\\\\1QR1`NPnNb1QR1[NnmNh1RR1WNlmNl1UR1RNjmNP2VR1nMimNU2WR1hMjmNX2XR1fMimNZ2XR1eMhmNW2]R1gMcmNZ2^R1dMamN^2`R1`M_mNb2cR1[M\\\\mNh2dR1VM\\\\mNk2dR1TMZmNm2fR1RM\\\\mNh2kR1WMUmNe2QS1XMQmNe2SS1VMPmNh2US1SMmlNj2XS1QMllNj2WT1O10O1O1M3OK5L6N2O1O2N1O3N4SN\\\\jNU1hU1bNbjNX1UV1K4M3K5M4E;H;K2N2NPkR4\", \"size\": [1280, 720]}, {\"counts\": \"T``a03gW1>D7J4J5L5K5K4M3M3N2N3M2L3H9O1N2L5N1N2L4N2N2O10O1000002NN2O1O2OO2O3M9H1NO1M3M4L3L4N2N2M3M3O1O2N1N2O2N1N2N110O1O1020nLhkNZ2VT1eMnkNY2RT1fMmkN[2TT1dMlkN\\\\2WT1`MikNa2jT100O2M1O2N2O0N11O3G8^kNVMnS1l2nkNYMQT1f2okN\\\\MPT1a2PlNaMPT1]2QlNeMoS1Y2RlNhMnS1S2WlNlMiS1S2YlNmMdS1V2]lN^MaS1m2h0L5M3O00000O3N_lN]MhQ1b2UnNdMiQ1Y2omNaMhN<WS1Q2TnNbMgN>RS1n1cnNQN\\\\Q1m1gnNTNXQ1j1[nNnMdN:QS1e1ZnNmNdQ1R1[nNQOdQ1n0SnN]OmQ1a0RnNBnQ1=QnNEoQ18QnNJPR13omN1QR1LQnN5QR1FomN<VR1^OhmNf0ZR1VOfmNl0ZR1QOfmNR1ZR1lNfmNV1[R1fNfmN\\\\1[R1aNcmNc1]R1\\\\NamNg1_R1VNamNl1`R1QN`mNR2`R1lM_mNW2aR1gM^mN]2bR1]M]mNj2bR1SM^mNP3bR1lL_mNV3cR1cL`mN_3_S12N2L5N00000NK4YOXlNWMlS1g2f0O2K6K6N2O101N3N2N2N2N5CQjN^NRV1W1a0N2M4\\\\ObiNAYiP5\", \"size\": [1280, 720]}, {\"counts\": \"YXSb02fW1:J5K4L5M2M3M4L3L4K5J7L3N2N2I8M2N201N1N1M4M3M3N2N2O1N2O1O2O0HQM^kNP3bT18N2OO1lL[kNm2eT1RM]kNm2cT1TM[kNm2fT166J3M7IN3M2O2N1M4M2O1M3N20000003PM[kN_2hT1[M`kNa2dT1YM]kNg2bT1ZM]kNg2cT1ZM[kNh2dT1XM\\\\kNg2fT1WM[kNi2eT1WM[kNh2mT12O1N3L5M3M1N2M3M2N2[OkLZlNZ3gS1=3I7M11DXL`lNi3`S1YMXlNl1iS1UNWlNi1kS1R1flNTLgR1h3ZmN^LdR1^3^mNeL`R1Y3`mNjL`R1T3amNmL_R1Q3cmNmL_R1m2gmNQM[R1k2_1N2LgkNbMYS1[2klN[M]S1a2T1E:L5N2M2J6MclNZNfP1d1WoNbNhP1Y1[nN\\\\NbN:M>SS1m0ZnNNfQ10YnN3iQ1FXnN>iQ1ZOYnNk0kQ1lNWnNW1mQ1aNSnNc1QT13H9F:K5K6B>L3N2K5J6O0002J7^Oa0_Oc0^Om0^O6M4J:nNhiNDn`T5\", \"size\": [1280, 720]}, {\"counts\": \"kgPb0;`W17I5J7L3M3M3M3O1N3J5G9M4M2O2N1O100O100JnM^jNR2aU17O2M3M3N101N1O1O001N2N2O100NZMYkNW2jT1>2OO0L301O101O03N2M4M0O1O1O010WMnjNb2YU1OD_MYkNa2fT1`MYkN`2hT1`MXkNa2RU12O1M3N0M4L4O1O1N2O1N2N4K5_Oa0]OWLflNn3XS1ULflNl3RS1e0N101OO1O101M3L3M4M3K6J5L5K4L4L4G9M2O2M3L3K6K5K5LjlNlMjP1m1WoNXNjP1a1ToNfNnP1U1nnNROTQ1g0knN_OXQ16bnN8eQ1\\\\O\\\\nNh0kQ1jNYnNX1UT13M3M3N2M3D;YNlMglNJ4^2kR1n1O2N3N10N2O1L4M3K6F;H6I8ZOf0H9L3K6D:J9H7H;CP[n4\", \"size\": [1280, 720]}, {\"counts\": \"SX_a0;[W1LfhN7VW1=J4K6H7L4N2N2D<N2N3K4N2O1ROhMbkNM8\\\\2UT1iMakNL9\\\\2VT1hMakNL6_2ZT1dM`kNM5`2gT1_MVkNc2kT1]MSkNe2mT1\\\\MQkNe2PU15001O3M4M2M3M7H02N1N2L4N2N2N2O1O1O1O100O2N1N2N2O1:F5_MejNS2dU10M3O001K500VO^MRlNf2nS1ZMnkNj2ST1]M^kNj2`T1=lNT1L4O1N010O10001N100O200O2N0O01O2N1L4L5J5M3K5J6I7M3K5I6N3L4M3L3G9L6K5K5KjlNRNgP1d1VoNgNkP1n0RoN]OSQ16nnN0VQ1AfnNn0PT16K5N1M4H7M3nMlM_lN9;D=X2cR1hNblN^O`0o1lR1ZOdlNR1ZS1j1O2N20O00101N1O1M4K5L5F:WOi0H9K5L3N2D?C=I6H8F<DPSa5\", \"size\": [1280, 720]}, {\"counts\": \"g``a03jW13O1L4N3M2M3M4K4K5N2N2N2N2N3M2M3K5N2N2O0M4M3M3O2N2N1O1O1N2O001O1N200001OK6N1O1O1O010O1N2O02O0O1000O2O00O100000O11O101N0001O0001O0000001000000O001OO1000000O0101N2O2M2O1O1N101OO0O2O01001O02M2O2M100O1N3L4]NWjN=0XOYV1Nbjb6\", \"size\": [1280, 720]}, {\"counts\": \"VWUa039h0[V1e0CbNmiNa1PV1:L3N2O1O1O1O1O1O1I7100O1O000000^OeM^kN\\\\2aT1fM]kN[2cT1eMZkN^2PU14]NdMamN]2]R1jM[mNY2dR1kMTmNZ2jR1jMSmNW2lR1nMklNW2TS1nMalNY2^S1U1O1O1O1O2O2N00L4M3M3L4M5J5J5J6M0I:H8L4M3H[lNaMXR1]2mmNaMSR1W2lmNjMdNM`S1g1ZlNdNe1K]NOaS1`0dlNLm17ZQ1OVmNYOb1l0dQ1]OfnNe0`MYOPT1MmmNO`Ni0JVOkU1;oiNb0_V1XOjiNa0ZV1\\\\OjiN2HMdV1OgiNN^P54aVL<G:eLYOenN[1WQ1hNbmNg0fNh0jS1`NmlNQ3US1mL\\\\lNb3fS1:101O0O4M1O3M4L2M7J3M3M5K5K4L1N4M2M1O00K6O001O1000OO20O002O00000O2NAmMSkNR2PU1kMQkNS2RU1lMojNQ2UU1jMPkNOMk0KM[V1HiiN5cV1\\\\OgiN=kWe6\", \"size\": [1280, 720]}, {\"counts\": \"PoZ`0Y1oU1iNdjN`1cT1_NRkN=3`1bT1mNZkNW1cT1U1M2O2M2XOTM[lNm2aS1YMZlNk2aS1l0M3N3M2O1O001O10001N01000O10000O1000O01000001N10000000O101O000O2O1O1O0000000O101O000O11N2N4M01O0N3UOXmNULPS1T2blNXNWU1h0Xb<PO`XB220h0NRU1W1TjNB`U1Q2]O`0C5J4L2OO003L3L2O2M2N3N0O10000O10O1000O10000000001N01O1O1O0010O010O101M3M>C3M3N3L2_NejN9^U1nN]jN4OM=n0PW1POmXT7\", \"size\": [1280, 720]}, {\"counts\": \"T^d?g0VW14K6D;O0O@ciN^O^V1a0giNXOZV1j0diN\\\\OXV1d0diNBYV1`0eiNBZV1>eiNCZV1R1O100miNUOM_OeT1]1\\\\kN0VT1V2E7F7O1N1O2N1O1O1N2O1O10000O100O2OO01O10000000O0100001O000000001O001O0001O0001O00000000000O2O0O1O1O1O101N2O0000102N2N0O2N2N1000004K6J9H1M6K2N3M7H=Cf0mMQjN6NJ^U;KajDb0`V1e0eiNhNhU1]1TjNgN]U1f1ajN\\\\N8EhT1R2njN[N7FgT1Q3J3N1M3K3O3M2OjLgkNe2\\\\T1?O100101O10002N2O1M7J1M2O2N1N4M2oMnjNo0bV1_O\\\\X^7\", \"size\": [1280, 720]}, {\"counts\": \"^ho?>QV1CTkNi0`T1DhjNS1TU1ROdjNU1RU1R1K5M2E<K5J5M4M2J6N2M3M3N2N3M2N2O10000001O1O00O1O1000001O00O1000000O1000O2O0O2O0000000O010000O11O3M3M00O1O1N2N2M3L4O2O0O2N1N2M3K5M3M3M3M3M3L4EilNSMQR1j2mmN]MQR1_2mmNhMRR1U1ilNROW1MRR1l0nlNaN]1h0hQ1>fnNH`Q1O_nN5gQ1BZnN`0kQ1XOTnNl0\\\\T14N2J6J5K6N1O2L4M3N3L3N1N3O100O001O010L3O2N101O10O02N1O001O1O0O1010O1O2N1dM^kNZObV1d0>O0O2^OihN5haZ7\", \"size\": [1280, 720]}, {\"counts\": \"iXm?2]W1e0B;D<D<DXN[jNl1bU1XN[jNi1bU1<N3L4F9L5]OSMkkNS3oS1VMjkNn2ST1a0K5N2N2J6M3N2O1O2O0001O003M1O1O00N2N2M4N00100000000001O01O00000000O100000000O12N2N3MO001N2N1N3M3M3O2M2N2L4N3K4L4J6L4F:L4M`lNYMmQ1a2RnNkMhQ1n1YnNYNfQ1d1TnNfNkQ1P1\\\\nNTOeQ1h0[nNZOgQ1b0YnNAnQ10SnN7\\\\R1oNlmNT1]T13N2L3L5N2M4M200N2O11O1OO1N2N12N3M3M01O0O21O1N10O10nMZjNi1gU1WNYjNi1gU19O0O1O1O1M2001O00O1O02RNgjNKbV1FfR]7\", \"size\": [1280, 720]}, {\"counts\": \"h_X`0?]W16H7G8L4]iNiNS19gS1W1_kNeNGc0hT1m0WkNDgT1?VkNFbT1R2J4J5L4N101N1O1O1O1O1O1O2O00000O100N2O11O0000O100O1M3N200O100O10001O0O100O2O1O0O1000000O10000O10000002N1O1N2N2M3K5M2M3O2N1J6J7J5J8E:M3K4L4E;H7NO0RlNcNbQ1S1bnNTO^Q1b0cnNE_Q1OanN<fQ1kN]nNc1kS14H9J6K4I8J6O1M4K4L4O100N2M3O0002O2M2N2O1N10O100O01O1010O1O0O1O001N200O01O1PNQ2I6^O[iNEjV18c0Lkhl6\", \"size\": [1280, 720]}, {\"counts\": \"`Wa`02iW17G9F>G1I8K8`iNiNQV1h1Kd0XjNbM:1dS1e3N2O0M4M2O2N1O1O1N2O100O100O100O12OO012M0O3N1OO100N2L3000O0N30O2M4L5K4M2L4M4M1M1J[lNmLmR1T3RmNRMjR1m2WmNSMiR1m2VmNSMkR1l2TmNUMnR1j2PmNXMQS1f2mlN\\\\MTS1c2klN^MXS1[2jlNfMYS1V2glNjM]S1S2dlNhMbS1V2blNeMbS1Y2S1O2G8N2J6M4LWlN[N]Q1b1anNdN^Q1X1`nNnN`Q1n0ZnN\\\\OgQ1<XnNMhQ10TnN6lQ1IRnN;nQ1AQnNc0PR1VOlmNT1TR1fNlmN`1ZR1TNjmNn1RT12N2L4A?M3D\\\\M`kN`2ST1mMnkNS2QT1mMokNR2ST1hMokN[2QT1dMPlNALd2UT1kMnkN\\\\2RT1dMmkN]2ST1g0100O2O0mLhkN^2XT1bMjkN]2UT1cMjkN^2WT1d02O1O2N2N1O001N5L3L3N001O000O101O002N002N1NO2O001O100000O105_MbkNU1TV1_OV_a6\", \"size\": [1280, 720]}, {\"counts\": \"Ufc`06?OaV1l0N2N1N3`iNoNmU1d1M2O2N1O2M=D6K5J9G9G5K5gkN\\\\LmS1R4I4L5K2O1N1O1O109F2OO1M3N2J501HblNlKaS1R49MN0000N3O3N2N2N2N2M3LblNdLaR1X3]mNPM`R1k2^mN^M]R1c2cmN`MYR1a2gmNaMYR1]2fmNfMZR1W2emNkM]R1Q2bmNQNaR1m1`mNQNcR1l1]mNVNdR1h1\\\\mNYNfR1b1]mN]NfR1\\\\1^mN`NiR1Y1YmNdNoR1Z1flNoM_Of0QT1V1alNnNgS1m0TlNVOVT1a0QlN`N]O>nT1i0SlNlNXT1j0RlNQOTT1g0g1GSlNHdP10^oN2UT13@OnhN5hV1e0C=\\\\Od0F<I6B>N1O101O1O1001O001O0000O100O1O1O001N2I7O1001N102N1O2M3N:F1O001OO010O1L4K6L2O2O11O1N10001N2O6J4L3M4K3YMkjN_2aU1J4L3N0O1N3N4L6J4K5L6H6K8H8G7H_WQ6\", \"size\": [1280, 720]}, {\"counts\": \"hnXa02hW1;I8I7Hc0\\\\O:H8G4L6J5M6Ia0@6I?A6J2M3N100O1O1001O1O00001O000000O1O2OO1O2N1O2N1N3K4L5K5K6G8J5M5K3M3KgkNbM[S1Z2dlNPNVS1n1ilNXNUS1d1ilNbNVS1Z1alNYN[O?VT1YOmkNb1f0\\\\OjS19kkNlNNi0`T12fkN:cT1B^kND]O3\\\\U15ZkN]OG1[U1=SkN_OeU1;V1EPc26i\\\\M9L2UOBliNh0mU1EhiN`0iU1S1G9H8E;G9M2O200001O00O100001O00001O0O20O01O1O1O1O0O2O1O001O01O001O000O11N1O1O100O2N10001N100OSkNUMgT1l260001O1N3N7I6J5K3M4L2N2M5L5K6J6J7GVoe5\", \"size\": [1280, 720]}, {\"counts\": \"ZZXb02jW14O2aLOQlN2W20aQ18PlNM^2L[Q1m0gnNSOVQ1o0knNQOSQ1P1mnNQOSQ1o0lnNROSQ1n0mnNSOSQ1l0mnNUOjP1CklNW1Z2XOkP1CilNT1\\\\2ZOTQ1e0inN_OYQ1>fnNDZQ1;dnNH[Q17enNLZQ12fnN1XQ1NinN3VQ1MjnN4UQ1LknN4VQ1KjnN6VQ1JinN7WQ1HinN9WQ1FhnN<XQ1ChnN>XQ1AgnNa0YQ1]OhnNd0XQ1YOinNh0YQ1SOhnNo0YQ1oNhnNR1XQ1kNinNW1XQ1gNgnN[1ZQ1^NknNc1gS12O1M3M3O2M2O3MJ5O2O1K5H8L3O2M1N1O112J8I6K5L4Dej3;RnJCaV1n009J5B>K5L4N2N2N2F:N2N2O1O1000O1000010O002N2N4L5K3M1O1N3O2M2N6J5K3L3N2N2N3L2O2M2O1O2N1O1O1O1N2N1O4K6K4K5KSV]5\", \"size\": [1280, 720]}, {\"counts\": \"Uaea05]W1a0H6H7J7K4L4L4M3M3I6O3N101O01O001O001O001O1O0000000000O10000001O00001O00O100000000O100O2ON101N101N1O2M2N3L301O\\\\jN]NlT1b1j0M2M3N2N2OSjNiNUU1U1mjNmNPU1R1QkNSOjT1l0U1O1LaiNROUV1m0miNUOPV1j0d0N1O2MZjNIgS17VlNOiS1NXlN4hS1GZlN=eS1_O\\\\lNe0dS1VO]lNn0VU14M3L5J5H8K5M3N1O2N2L5H7K5L4M3M4M2N2O1O100O001N20O2O001O2N001O1O1OO2N1O1M3L3101O1fLbkNP3^T1nLhkNn2YT1QMikNl2XT1SMlkNj2eT1O001O2M4^NPkNIYU1UO_jNd0b0GdU10Sb^5\", \"size\": [1280, 720]}, {\"counts\": \"kPYa0<^W19I4K6L4N1O11O000010O01O0001O00000000000000000O2O000000000O1O2N001000000000000000000O10O001O1N1O2N2NnMQOVmNn0iR1VOVmNg0hR1_OVmN?fR1HZmN7fR1IZmN7cR1L^mNOcR15\\\\mNJeR16\\\\mNFgR1;YmNDhR1<WmNCkR1=UmN@nR1`0RmN_OXOF`R1i0\\\\nN_OkR1>]2N3O001O0O1000SlNEVP1:foNLXP14doN1]P1L`oN8eP1_O[oNg0iS15N3L3lLnNWoNU1hP1mNToNV1lP1jNPoNZ1PQ1fNknN_1UQ1bNfnNb1YQ1_NdnNd1\\\\Q1\\\\NbnNf1_Q1WNanNk1aQ1PN_nNT2eS11N2L4M3L4N2O1O100O100O100O100O10000O1O100O101N001O100O1O10O010O10O10001O00ZOf0B>F:N3I6M4H8N1OOO3I7H9N2L_Y]5\", \"size\": [1280, 720]}, {\"counts\": \"aiaa02jW1400O2O0O100O100O1O100O1O1O0O2O1O1O2M3N1O10O1000kMZOQmNc0PS1_OolN`0cR1H[kNJR2<cR13]mNLaR17_mNI_R19amNG^R1:bmNC`R1>`mN_ObR1c0]mNZOgR1e0ZmNXOhR1h0XmNTOmR1j0V2O1O0O1N3M20`kN^OaQ1b0m22^kN@dQ1=_nN_OeQ1?]nNAbQ1>QnNDbM4aT16nmND_R1;amNE`R19Q2LZiN4dV12WiNOiV11WiNNiV14XiNGkV19ViNEW3NRP1=glNEP36WP19elNAm2>^P14alN_Om2a0bP10aoN4^P1L_oN7bP1IZoN:gP1EVoN?jP1APoNd0QQ1[OlnNh0TQ1XOknNi0UQ1VOinNm0WQ1SOenNQ1[Q1nNanNW1_Q1iN\\\\nN\\\\1cQ1dN\\\\nN^1dQ1bNYnNa1gQ1_NXnNb1hQ1]NXnNd1hQ1\\\\NWnNe1iQ1[NVnNf1jQ1ZNVnNf1kQ1YNUnNg1kQ1XNVnNh1kQ1oM]nNQ2hS11O2OO10N2O2N1O001N21O1N3NK5M3O1O1O1O11O1O1O1O1O0000O010O2N01O1H9D<eNRjN@03N6[V10laR6\", \"size\": [1280, 720]}, {\"counts\": \"i_[a08UW1e0H7B>_Oa0F9L5M2O1O2O0O1O1O1O1O1000001M2O1O1M3N2M3N2N1N2N3K5L3N1N20O2O002N2M201N3N1M3M3NSlNkLSS1S3klNRMTS1k2nlNVMRS1h2olNZMPS1d2PmN]MQS1a2PmN`MeR1FilNg2b0eMdR1EilNe2>XMB:[S1GclNf2`0]M[S1MTlNi2=`M_S1d2clN[M^S1e2alNZMaS1e2`lNYMcS1e2]lN[MdS1d2^lNZMdS1c2_lNYMdS1f2k0N3N3LclNgMZQ1T2dnNSN[Q1l1`nNZNaQ1b1]nNcN`Q1ZOllNm1c1mNgQ1Q1WnNSOiQ1j0VnNZOjQ1d0UnN_OlQ1=UnNEkQ18SnNMlQ12SnN1mQ1LRnN9kQ1CTnNd0lQ1YORnNl0mQ1QOSnNS1lQ1kNUnNW1lQ1eNTnN^1mQ1_NSnNc1nQ1ZNmmNm1VR1mMgmNY2ZR1dMbmNb2_R1YMamNk2fS14J6M3L4L4M3L4N1O2M3M3N3N1O1O1N2N2O1O010O1N21O2N6J00O100O010001O1[L^lNj2bS1TMalNl2bS1nL`lNR3aS1jLclNU3]S1eLjlNZ3VS1`LPmN`3hS1O101O0O1N2L5H>WMcUf5\", \"size\": [1280, 720]}, {\"counts\": \"\\\\^ea0a1UV1=@>M3M2N2O1O2O0O010O001O2O0O1N2L4N2N2N2M4N1M4L3N3N1H8O0O101ON2N2N3M3N2N2N2N3K4L4N2L4N2N1N3MI^MVkN`2gT1fMXkNW2iT1lMVkNQ2lT1PNSkNo1R1cMgR1`0VlNj1T1iMdR1>XlNg1R1oMfR1b2amNXM`R1e2_1MUkN^MI5WT1Z2SlNaM[T1\\\\2hkNcMYT1Z2h0N1NalNPNSQ1o1gnN^NTQ1_1inNfNZQ1U1gnNlN[Q1o0bnNVOcQ1e0XnNAkQ1;QnNKoQ12PnN2QR1HPnN<RR1_OnmNc0mQ1_ORnNf0iQ1]OQnNk0nQ1TOomNQ1RR1iNQnNY1SR1`NnmNb1SR1[NmmNg1SR1TNQnNm1oQ1oMdmNFSOb2\\\\S1cM^mNl2hS13L4M3M3L4N2N2L3M4N2N2O1N101N2N2N200O020O010O0O1O1001O00000O10101N3M1O2N1O1O1O2M4M2N2O0O100N4M2N2fLlkNN2Q2]U1Me0[Oc0SOnhNEVP\\\\5\", \"size\": [1280, 720]}, {\"counts\": \"[Wia0n0iV1;L3M3K4F;M2O1O1000000000000O100O1O100O1O100O1000O10O11N1O1O010000O100O100N2O0O1O11N10OO1NdNnMjkN0n0Q2YS1[NdlNc1]S1aNalNY1dS1kNYlNR1jS1oNUlNn0nS1ROQlNm0QT1TOnkNg0VT1[OikNc0YT1]OgkNa0[T1_OdkN`0]T1BbkN;aT1F^kN2jT1NVkN1kT10TkNNnT13QkNKRU15mjNIUU19ijNEV1AWR1l0^lNnNJ:^18ZR1`0dlNSOP1b0\\\\R1;SnNMmQ13nmN2RR1MlmN6UR1GimN<XR1CemNa0[R1^OcmNe0]R1XO_mNo0bR1kN_mNZ1_R1aNemNa1ZR1[NimNg1WR1WNjmNj1VR1RNlmNP2SR1jMomN[2`S1:01N2@^M`kNd2]T1_MbkNb2\\\\T1`MbkNb2ZT1d0L4N^MVlNR1hS1nN[lNQ1cS1PO_lNo0`S1QOblNn0^S1SOclNk0^S1TOblNk0[S1XOglNg0VS1ZOllNf0SS1XOQmNg0oR1XORmNh0nR1XORmNh0mR1YOSmNg0mR1XOTmNh0kR1XOWmNg0iR1XOXmNh0hR1VOYmNk0gR1SOZmNn0fR1QO[mNo0eR1mN_mNS1aR1iNcmNW1^R1dNfmN\\\\1ZR1aNimN_1WR1^NlmNb1UR1]NkmNd1UR1[NlmNd1UR1[NlmNd1UR1ZNlmNf1WR1WNimNh1ZR1VNfmNj1XT101O001O10O0001O005K7H3N2N8H:Ea0[OUPW5\", \"size\": [1280, 720]}, {\"counts\": \"Y`ja0=_W15O0O2O0O0100000010O0000001O002O0O[N@jkN`0ST1CmkN<RT1GmkN8RT1JokN4PT1NPlN1nS12SlNLlS16UlNIjS19WlNDhS1>YlN@iS1`0WlN^OjS1b0VlN]OkS1c0j1O01O00001N1L401hN]OQkNc0lT1HmjN7RU1LljN5RU10ijN1WU13djNO[U14bjNL]U17_jNL`U1Z101O001O00001O0O10010O010O0001O010O000000000001O01O00O[Oc003N8iNbS5K[mJ=H6L3K5I7D=F:C=L5WOg0L5L3N102O02M0000O10O01O1O10000001O001O1O1O1O3L3N2gLmkNa2jT1O2O2M5PN`jNV1dV1A<E3K8I7I7Hgo[5\", \"size\": [1280, 720]}, {\"counts\": \"lfUb02oW1O^X11XgN9J6010N2O1OO1O12N1O00O1N2L4M4M2N3M2O1N2L4K5O001O1O000O11N1OZOSO]jNl0`U1\\\\O[jNe0]U1G_jN8^U1W1O1O1000O101N2N1O1O001N2O101M2N3M2M3N2M3M3M3N2K6K4L4M2M3MWjN]O]T1?ekNDYT1:fkNKXT11jkN2VT1FPlN=nS1@SlNc0bU14K4L5K4M3N2N2L5K4M3L5L3N3L3N2M3K6O10O01O010O010010OO010N2L4N2O0O1010000002M4M4L0O1PNfjNY1\\\\U1^NkjNb1jU10002L7eNY1GjaY5\", \"size\": [1280, 720]}, {\"counts\": \"`g]c05gW15M4K5K3N1N2H701O1O11OkiNoN]U1o0djNSOeT15PkNh0:UO^T1d0mjN7e0WOoS1R1ZkNFf0[OnS1P1\\\\kNCf0@lS1n0^kN@g0CkS1m0^kN^Og0HjS1j0_kNZOj0MiS1Z1YlNgNhS1V1YlNlNfS1P1\\\\lNROeS1j0ZlN[OeS1d0YlN_OgS1?YlNCgS1;YlNHeS17[lNKfS11[lN2eS1KZlN9eS1EZlN>eS1A[lNb0cS1ZOalNh0]S1VOelNk0[S1QOhlNP1WS1mNjlNW1VS1fNilN]1WS1^NjlNg1US1QNQmNQ2]T12M3O1N2N2M3L4N2N3L3L4N3N1N3M3L3N3O0O2N11O10O01O101ZMckNe1]T1ZNmkN^1RT1aNPlN^1RT1`NokN`1ZT1TNikNl1VU10O001N110O02N7H6K3L3M5J4L7I6WOfX]5\", \"size\": [1280, 720]}, {\"counts\": \"faTb02dW11bhN2ZW1<HBkhN`0SW19L2O2N4M0O01N2N2O0N201N2K5O1O2N1N2M4M2N3ISY16kfN3K4K5L3L4O1O2M4bMUOdmNl0ZR1YObmNg0]R1]O`mNd0ZR1CemN=kQ1WOYlNa0j19iQ18VnNHiQ1:VnNFkQ19TnNHmQ18RnNIlQ18TnNHjQ1:WnNFgQ1;YnNEfQ1<ZnNDfQ1<YnNDhQ1<XnNDhQ1;YnNEgQ1;XnNFgQ1:ZnNFcQ1<_nNC`Q1;cnND\\\\Q1;hnNDXQ19lnNFTQ17PoNHoP17WmNhN[1Q1]Q15[mNkNX1o0\\\\Q15^mNmNU1n0^Q12^mNROS1l0_Q10`mNWOn0i0bQ1McmN^Oh0d0fQ1IemNEd0b0gQ1EimNJ?a0hQ1BhmN2`0;hQ1AdmN:d06hQ1\\\\OemNa0c02jQ1XOcmNi0d0NlQ1UO_mNo0f0KlQ1TO\\\\mNU1g0GgR1;ZmNDdR1>]mNAaR1a0_mN_O_R1c0bmN\\\\O[R1h0emNWOYR1k0hmNTOVR1n0imNSOUR1o0kmNPOTR1R1lmNnNRR1T1nmNlNPR1V1omNlNoQ1U1RnNiNnQ1X1SnNgNlQ1Y1XnNdNiQ1k0`lNoNh14kQ1h0clNQOa17mQ1f0dlNRO`17nQ1>jlNZOX18TR11llNFP19[R1DklN4h09eS1E[lN;fS1C[lN=fS1BTlNd0mS1[OVlNb0mS1^OQlNa0oS1@PlN`0QT1_OokNa0QT1_OnkNa0QT1CmkN=YS1D^kN<W1OYS1d0flN[OZS1g0flNXOZS1h0flNXOZS1i0flNWOYS1i0glNVOZS1j0glNUOZS1k0flNTO[S1k0elNTO^S1j0blNTO`S1m0_lNRO`S1R1]lNPOaS1R1^lNnNaS1T1^lNlNaS1W1]lNjN`S1Y1_lNgN`S1[1_lNeN_S1_1^lNaNbS1b1[lN^NfS1`1]1M4N4C=K5I6F;J]ao4\", \"size\": [1280, 720]}, {\"counts\": \"`ePb06=7bV1MYiN7LE_V14diN7OF[V12eiN:5_OXV15biN<7_OVV18_iNm07ROeU1m1TjNQN5LTU1f20L2O5J4M2M2M1O2N3M3L3OJ401O011M0GSNejNk1\\\\U1:D>hkN^MlR1j2QmN]MhR1c2ZmNaMcR1_2\\\\mNeMbR1W2amNkM_R1P2cmNSN\\\\R1k1cmNYN]R1c1amNcN^R1P1lmNTOmQ1P1SnNSOlQ1l0TnNVOkQ1j0TnNXOlQ1f0TnN\\\\OlQ1c0QnNAoQ1=PnNFPR18PnNJPR14omNOQR1NPnN4nQ1LQnN7nQ1HQnN;mQ1EQnN?oQ1_OPnNd0QR1XOPnNj0PR1TOQnNm0oQ1oNSnNS1mQ1jNSnNY1nQ1dNPnN`1PR1^NnmNe1TR1XNkmNk1UR1TNjmNn1WR1oMhmNU2XR1gMhmN\\\\2YR1aMgmNa2ZR1[MgmNg2ZR1VMfmNl2_R1lLcmNU3bS12N1N3M3N2M3O1N200M3N2O0O2O1N3M2M3N2O100000000001O0O2O2N5L7H2N2N3MO010O0O2O1O110O0000O2O000000O0K6M300N200O10O1000O02O1O2N3M3N2M1O1O3eLhkN15P2gT1dMlkNR2YU1H3Kc0]OSQm4\", \"size\": [1280, 720]}, {\"counts\": \"_ePb0>oV1CYiN3K?NG[1IPT1T1nkNkNST1V1Z12aiNnNmU1e1M3PjNYNcU1U2K3O01N6H5K3M3N2N2L5M5K0O1OM4I6lNU1O1O1SlNZMbR1e2]mN`MaR1_2_mNcM`R1Z2bmNhM]R1V2emNkM[R1Q2fmNRNZR1i1gmN[NXR1c1bmNfN]R1W1`mNPO_R1g0imN\\\\OUR1b0lmN@SR1<QnNDPR1:PnNHoQ17RnNJmQ16nmN0RR1NlmN6TR1IlmN8UR1FjmN<WR1AkmN?UR1@jmNa0WR1]OhmNf0XR1YOemNk0ZR1SOemNQ1[R1lNemNW1[R1gNemN[1[R1bNfmN`1ZR1^NgmNc1YR1[NgmNg1YR1VNhmNk1ZR1RNemNQ2[R1mMdmNV2\\\\R1hMbmN]2\\\\R1bMdmN`2\\\\R1^MdmNd2]R1YMcmNi2^R1TMcmNm2^R1QMamNQ3`R1mL_mNU3eS11O1K4O2M3N10100O1N2N2N2O1N1O2O1N2N201O0O1001O0O3N1O2N2O1N1O3M2N1O2N1OM2O200O101O01O1O0000000O100000010OO100O1O010O1O0011O4L2N010O1O0O2cLikNo2kT1Ic0\\\\O4M4L4L9G5K4K<De0ZOQQc4\", \"size\": [1280, 720]}, {\"counts\": \"UVna06iW12O005]hNKQW15nhNLSW10PiN1iV1GViN615fV1i0[iNnNUV1i1fiNWNjU1S2L2N3NO3L3M2M4L4L3N3M3M3L4L3NQOn0^Oc0O100O1PlN_McR1a2XmNgMdR1X2]mNlMaR1S2_mNoMaR1n1_mNUNaR1f1bmN\\\\N]R1a1dmNbN\\\\R1Z1]mNQOcR1j0_mNXOaR1c0`mNA`R1;cmNG\\\\R17emNK[R12dmN1]R1MamN7_R1H`mN9aR1F^mN<bR1B_mN?aR1_O_mNc0aR1\\\\O_mNe0`R1[O\\\\mNi0eR1VOUmNP1lR1mNTmNV1lR1hNTmNZ1lR1dNTmN^1lR1_NUmNc1kR1ZNVmNh1jR1UNVmNn1jR1QNUmNP2lR1nMUmNS2jR1mMUmNU2lR1hMTmNZ2lR1dMTmN^2mR1_MRmNd2PS1XMPmNj2PS1UMPmNl2QS1RMolNo2QS1oLPmNR3PS1lLQmNU3QS1hLllN\\\\3iS13O0O2N10001N2O1O1N3N0O2O1N2N10100000001O0001O002N2N3M1O3N2M2O1N2NN101O101N1100O02ON0010000O1001O1N2O0O100O00010O1001N3M3N001O1O1Oc0]Oe0ZO4M3M3M6J6I5L5I<Eb0^O5I]ha4\", \"size\": [1280, 720]}, {\"counts\": \"Z`^b07gW13NOZO1_iNLVV1T11L32I7NO20M31M3N2N2N1O2N1O2M3NYN]OQkNO;FBj0PU1>VkNmNIb0SU1m13M2O2NakNdM]S1[2_lNoM\\\\S1P2clNSN\\\\S1k1flNTNZS1k1glNVNYS1i1flNYN\\\\S1b1dlNbN\\\\S1Z1_lNoN`S1o0alNSO^S1g0glN[OWS1c0klN_OTS1?mlNCRS1<nlNFQS19llNLUS11klN1US1NjlN4VS1JklN6VS1GmlN9TS1DllN>QS1CklNc0US1XOjlNo0TS1POllNR1TS1lNllNV1SS1gNnlN\\\\1RS1bNnlN`1RS1^NnlNd1SS1YNmlNj1SS1SNnlNn1SS1oMmlNS2TS1hMmlN[2US1`MklNc2US1[MjlNh2WS1VMilNl2lR1TM^mNl2iS12N3I5O2N1O2N2M3N2N1O2N2N3N1O1O1O0010000001O1O2N1O3M2N2N2N3M2N5K2N1O1O000000O1001O00O10000O1O10000O100O10000O1N2O100O1001O2N1O1O106I8_MUkN^1iU1M3M3M4L4K4M4K7I`0@^h\\\\4\", \"size\": [1280, 720]}, {\"counts\": \"Qgnb0<cW18H4L4L3M2O2M3N3L101OO0O2O1O1M4L3N00kMFclN<_S1I]kN_O0U1cT1b13K6O0OikN[MVS1e2glN`MWS1\\\\2hlNjMXS1S2flNRNZS1k1glNWNXS1e1jlN^NVS1[1nlNhNQS1W1nlNlNkR1X1SmNlNmR1R1SmNPOnR1m0SmNUOmR1h0SmN[OmR1c0RmN@nR1<TmNFlR18TmNJkR15VmNLkR1OQmN9oR1DRmN>nR1]OUmNe0jR1WOTmNP1lR1nNSmNU1mR1eNVmN_1iR1]NYmNe1gR1XNZmNj1gR1kM`mNX2aR1`McmNc2jS13O1O1N2K4K6K5N2M3L4M3M3O1N1O2N2N2O10000010N2O1O2N2N2N3N1N2N3M2N5K3L3N1O2N1OO1O2N1O1O100O1000000O100001OO1O1O1O1N2O1001O1O1O2O2M5K5J6`M\\\\kNZ1jU1K5K9F<DcP^4\", \"size\": [1280, 720]}, {\"counts\": \"n`hb07hW12M4_hNEmV11TiNa0lV1;O0L30O010001O01O0100ON3M4M3N2NN2M30O01N21O11N1[jNWOST1i0jkN\\\\OUT1c0jkNATT1?kkNCTT1;jkNJVT15hkNNmS1<QlNGoS16RlNKnS14QlNOmS10TlN2iS1OWlN3eS1OZlN4eS1KZlN8fS1FZlN<eS1B]lN`0bS1^O^lNd0bS1[O\\\\lNh0dS1WOWlNo0hS1POWlNS1iS1jNWlNX1iS1gNWlN[1iS1`NYlNe1eS1XN\\\\lNk1cS1TN\\\\lNn1dS1QNZlNR2gS1jMYlNY2cT14N2N2N2L4N2N2M4M2I8M2O1N2O1O010O101O001N1000000O100000000001WLVlN[3WT10O001O1O100O100O100O1O1L5N1O1O0100000002N2M4MM3O010O1O1O0100O2O00006J3L3ZN[kNJmT1KfkN[OkT1=h1ZOda`4\", \"size\": [1280, 720]}, {\"counts\": \"Rneb0:cW18biNCVO4UU1;`kNITOEeU1e0hjN8XU1IZjN_O0l0hU1MVjN:eU1S1NO0N5K6I4K5Ja0B6L2M2L5H;I_iNLUU1LUkN8hT1JVkN8iT1IVkN8lT1DUkN=VV11\\\\NF`kN;^T1H^kN<^T1H_kN;YT1LfkN6RS1XOYlNW1b0FoR1P1olNSOPS1l0nlNXOQS1g0olN[OQS1d0olN]OmR1e0RmN^OlR1a0UmNAjR1=XmNChR1<XmNFhR18UmNMkR11TmN3kR1KTmN8lR1FRmN?mR1_OQmNf0mR1XORmNl0nR1RORmNP1nR1mNSmNU1lR1jNTmNX1lR1fNSmN^1jR1aNWmN`1jR1^NVmNd1iR1[NWmNg1hR1XNWmNl1gR1oM]mNS2aR1nM]mNU2dR1hM]mNY2cR1eM\\\\mN^2eR1]M]mNe2cR1VMcmNi2_R1RMdmNn2eS12L4E:N3L5M2M3N2O1O001N2O1N2O10O101O010O001O1O5cLWlNY2lS1bMXlN[2mS1]MXlNb2dT1O1O00000000000O100001N1O1M3M3O1O11O00O1O1O1O1O100O1N2N2O001O100O11O001O5J2mMckNa0_T1WOjkNf0ZT1nNokNQ1QV1_O9FU``4\", \"size\": [1280, 720]}, {\"counts\": \"V]ja06iW1100L4M4fhNBhV1i0QiN^O3K_V1e1niNbNnT1b1fjNeNZU1m14N5J3N3M3L3M9G`0]O6M2O1N2LSjNJWT14fkNOTT17kkNLST1NRlN5mS1KQlN9nS1HPlN:mS1IPlN:mS1HRlN:iS1IVlN;cS1JZlN:WS1mNPlNg0g0a0lR1XOYlN6j0d0lR1YOXlN2l0g0hR16XmNLfR14YmNOeR11ZmN2dR1N\\\\mN3cR1M^mN4bR1J^mN8bR1GYmN>hR1AVmNb0jR1[OWmNg0iR1WOWmNk0iR1SOVmNP1jR1nNUmNT1kR1lNRmNX1nR1fNRmN\\\\1nR1aNTmN`1lR1^NUmNb1jR1^NVmNd1jR1[NSmNi1mR1VNQmNm1oR1QNRmNP2mR1oMSmNS2mR1kMRmNY2mR1eMSmN\\\\2nR1bMSmN_2mR1_MTmNb2kR1]MVmNd2iR1[MYmNe2gR1ZMYmNg2gR1WM[mNi2eR1SM^mNm2cR1PM_mNQ3bR1lL^mNV3cR1fLZmN`3hR1\\\\LWmNg3_S12O0O2O1N2N2O1O1N2O001O1O1O1O1O1O1N200002N1O1O2N1N3N3M2N9G3M1O0000O1000O0100000000000000O11O000000O10001N2N10O010000001O1O1O2O1N2N5aLdlNo1iT1N1Na0@:Ej0TOfPm4\", \"size\": [1280, 720]}, {\"counts\": \"TUda06]16oT1LcjN0bU11TjN5nU1MkiN7TV1M_iN?`V1`01O1M4L>D1O1O[jNYOST1e0jkNCST19mkNMQT11lkN4ST1IlkN<ST1]OQlNf0nS1YOQlNj0mS1YOPlNj0nS1XOokNk0oS1WOokNk0lS1YOSlNi0lS1XOQlNk0mS1UORlNn0iS1UOTlNP1fS1TOYlNo0dS1RO]lNo0WS1[OilNf0QS1_OolNc0PS1\\\\OPmNe0nR1\\\\OQmNg0mR1ZORmNh0oR1UOQmNm0PS1ROmlNQ1SS1nNklNU1US1hNllNY1US1fNjlN\\\\1US1cNklN^1US1aNjlNb1US1^NilNe1WS1YNilNi1VS1VNjlNk1VS1UNilNm1WS1PNjlNR2WS1lMhlNV2XS1iMglNY2YS1fMglN[2XS1eMhlN\\\\2XS1bMhlN`2XS1_MhlNb2YS1\\\\MflNf2ZS1XMflNj2ZS1SMhlNn2WS1QMilNP3VS1oLllNR3PS1PMRmNP3jR1TMUmNm2lR1QMUmNo2lR1oLUmNQ3lR1mLTmNS3oR1fLTmN\\\\3fS12M3M3N2O1N101N2O1O001O1O100O1O1O100N2O1O100001O1O1O1O3M2N1O1O2M5L1O1OO100O11O001O001O1O000000000000000001O0O1000O0100000000012Ob0[Ok0UO3N6J5J8I6I;EUaY5\", \"size\": [1280, 720]}, {\"counts\": \"XWda0:gV1GoiNe0mU1\\\\OoiNh0oU1YOniNj0RV1VOliNg0YV1YOdiNi0^V1UObiNi0lV1M102OOOniNAgT1?XkNFmT1KXkN9TV14L3N2M2O2N2bNUOekNm0YT1VOdkNl0WT1XOgkNk0nS1_OnkNf0oS1\\\\OPlNf0^S1eNikNn0h0`0XS1LhlN7TS1JllN7oR1MQmN5iR1OXmN2fR1N[mN3dR1L[mN7dR1H[mN;hR1AUmNc0mR1ZORmNh0oR1RNXlNn0h0R1oR1SOPmNP1oR1POolNS1QS1kNnlNX1QS1hNmlN[1SS1cNmlN_1SS1_NnlNb1RS1[NolNh1PS1VNPmNl1PS1RNPmNP2QS1nMnlNU2QS1jMnlNX2RS1hMllNZ2TS1dMmlN]2SS1bMllN`2SS1_MnlNb2RS1\\\\MPmNd2PS1ZMQmNg2PS1VMQmNk2oR1SMQmNo2oR1oLRmNR3oR1iLUmNW3kR1gLUmN[3dS14N2N2O1N2N2N101O1N2O1O1O1O1O1O2N1O1O1O11O2N001O1O1O2N3M2N2M6K4M4K3NM101O100000000001O001O00O1000000000000O101N1O101O0000O101O3jL_kNg2eU1XO7H5La0^O=[OcXb5\", \"size\": [1280, 720]}, {\"counts\": \"dWda07^V1JkjN7SU1KkjN7cT1GfjN3g04TU1O^jNOQV12jiN3RV12^iNE7;XV1:diNH\\\\V1:_iNKaV1i02M101L4N1O[jNoN_T15]jN>T1BhT1:WkNIjT15TkNNmT1LUkN7mT1CTkN`0TV13K4M3N2O1N2O1O2RMVObnNl0[Q1\\\\O\\\\nNh0cQ1CPnN?PR1KemN7[R12ZmN0gR11TmN3lR1KSmN7lR1ISmN8lR1HUmN9jR1FVmN<jR1AWmNa0hR1^OWmNe0iR1ZOVmNh0jR1VOVmNl0jR1SOTmNP1jR1oNWmNS1gR1lNXmNX1fR1iNWmN[1iR1aNYmNa1gR1]NYmNe1gR1YNYmNi1gR1UNYmNn1fR1PNZmNR2fR1lMXmNX2iR1cMXmN`2hR1^MWmNe2jR1UMZmNl2fR1PM\\\\mNR3gS12O1O1O1L4K5N2N2L4N1O2N2N2O1N2O1O1O2N00100001O2N2N2N1O3N1N2N2M3N2O2M3M2N6J2N00000000010O001O00O10000000O010000O2O1O0O100O1O001001O2Mk0fLUkNj1TV1TOm0QO[`c5\", \"size\": [1280, 720]}, {\"counts\": \"Z][a05bW1MbhN<UW1=D;I6L3N=C6TjNQN^U1P2`jNoMbU1W21M3M6K3N]kNVNRS1g1llN]NTS1a1flNgNZS1U1glNlN`S1k0`lNWOcS1d0]lN^OfS16alNM`S10^lN4cS1I[lN;eS1EYlN=fS1CYlN?gS1@XlNb0jS1ZOVlNh0lS1SOTlNo0kS1QOTlNR1gS1UOTlNn0jS1UOTlNl0jS1TOVlNn0cR1bNglN7Hd0k0g0dR15ZmNNdR12]mNOXR1:hmNGWR19hmNJXR13hmN0XR1NfmN6YR1IemN;[R1DdmN>\\\\R1AbmNa0`R1]O^mNf0aR1YO_mNi0aR1VO^mNm0aR1QO`mNP1`R1nN`mNT1`R1jNamNX1^R1eNcmN]1]R1aNcmNa1\\\\R1^NdmNd1]R1ZNbmNh1`R1TN_mNo1bR1nM\\\\mNV2dR1fM_mN[2aR1cM_mN_2bR1_M^mNb2aR1\\\\MamNe2^R1ZMbmNh2^R1VMcmNk2]R1RMdmNP3^R1lLcmNU3]R1hLdmNZ3]S14L4N2M3O1N2O1N2N101O1N3N1N2O1O1O1O1O100001O2N4M3L1O3M2N1O4K3N3M9G2N3N1N1O001O000O2O1O1O01O01OO1000000O1000O1O1O1O0jMlkNB\\\\ONVU1>m1YOg0MQkn5\", \"size\": [1280, 720]}, {\"counts\": \"RWk`04c00ZV1X1B5K5ON2M4N1L4N3N2M4L2ML43O0O1002NM20fjN]O[S1c0elNAWS1=jlNGSS18mlNLnR15olN1mR11nlN6mR1MQmN6lR1KTmN7kR1HUmN;hR1EXmN>fR1AVmNf0hR1[OVmNh0hR1YOWmNi0hR1VOXmNl0gR1RO[mNP1cR1PO[mNS1dR1kNYmNZ1gR1eNXmN^1hR1aNUmNc1jR1\\\\NUmNg1kR1WNTmNl1kR1SNTmNP2lR1oMRmNU2lR1jMSmNY2lR1gMSmN[2mR1bMTmNa2jR1^MWmNc2iR1ZMXmNh2gR1WMWmNl2iR1TMVmNn2iR1PMYmNQ3fR1mL[mNU3eR1iL[mNY3eR1fL]mNY3fS1O00O100N2M3K5K5L3N3N2M3N2O1N2O1M3O1N2N2O1O1O1O10O010000000O101O1O2N6J5K<fKXlNd3UT1M1O1O1OO1O10O10O0101N10000000000000000000O100000O1000O001O01hM^lN2bS1XO_mN;cR1UOQnN4`MM23^W`6\", \"size\": [1280, 720]}, {\"counts\": \"^nZ`01\\\\`2b0dfN@XiNh0bV1n0]O1O3L7KO0N4K4L2N3L1OO12O1L4M3O2L3001N0SkNlNQS1T1llNUOoR1k0PmNXOnR1f0RmN_OkR1`0PmNHkR1:RmNLkR14RmN2kR1OTmN4jR1KWmN7eR1JXmN<dR1GXmN>fR1AZmNb0`R1B`mN`0^R1@bmNb0^R1[OcmNg0\\\\R1WOamNo0_R1PO^mNT1aR1kN]mNY1bR1fN]mN]1cR1aN\\\\mNb1cR1]N[mNg1dR1YNYmNl1fR1QNZmNR2fR1lMZmNV2eR1jMZmNX2fR1fMZmN\\\\2gR1aMXmNa2iR1]MWmNe2iR1XMXmNj2hR1SMYmNo2gR1oLZmNR3gR1jL\\\\mNW3gS10N1N2O1M3K5L4L301N2O1N1O2O1M201N2O1O1O1O1O1O010O1000O10001O1O2M2O4L7I5K8H6J1O2N00O1O11O3K3N2N5L2M2O1O001O0O01O1N1O2N2O1O010O1O1N1011nLWkN90I3o1YV1\\\\O4M1Na0@4K:F9G4Lg_f6\", \"size\": [1280, 720]}, {\"counts\": \"QWPa0b0[W1i0POa0E210KTNXjNk1iU142N1O2N2N1N3K4M4N001O1O0O101O0N20kjNTOXS1l0dlNZOZS1f0dlN_OXS1b0flNBWS1>elNIWS18hlNLUS13klN0SS10nlN2nR1OSmN2lR1NSmN5jR1LUmN6iR1KVmN8fR1HYmN=bR1F]mN=^R1EbmN=ZR1EgmN=WR1DhmN=XR1CbmNd0]R1[ObmNi0\\\\R1VOdmNl0[R1SOcmNQ1[R1nNemNT1[R1kNdmNX1\\\\R1fNbmN^1]R1aNbmNc1\\\\R1\\\\NdmNf1ZR1ZNfmNh1YR1XNfmNj1XR1WNemNm1ZR1PNhmNR2VR1lMmmNU2QR1iMRnNX2lQ1gMVnNZ2hQ1fMXnN\\\\2gQ1cMYnN^2gQ1bMWnNa2hQ1]MZnNd2fQ1XM]nNi2bQ1RMbnNP3^Q1lL`nNZ3`Q1eL^nN^3bQ1`L^nNb3cQ1[L]nNg3cQ1TL`nNm3aQ1nKbnNT4cR12O0O2N2N2N2O1N2O1N2O1O001N2N2001O001N10001N10001N101O2N1O2N1N2O1O0O2O1O2aL[lN^2iS1^MXlNb2kS1XMYlNg2iS1SM\\\\lNl2dS1oLblNo2_S1lLglNS3ST100000O001O100O00100O00100O10O0_MakN^OKm1cT1cNRlNM\\\\Oe0iT1mNZkNNf0O\\\\O0h0;dVV6\", \"size\": [1280, 720]}, {\"counts\": \"RoYc01^U12nkN8ZOJcT1`0UkN64]OBJnT1:PkNN5d0<ZOYT1I_kN;HN2d0>[OYT1I^kN:H10a0a0[OZT1J[kNi19_N\\\\T1k1bkNXN^T1g1bkNYN_T1e1ckNZN^T1e1bkNZN_T1f1bkNXN]T1j1fkNRN[T1o1k0O1O2N4L8GmjNiNcS1R1[lNTOfS1i0YlNYOhS1d0YlN]OgS1b0XlN@hS1>YlN@iS1`0VlNAiS1`0TlNDgS1`0WlNBfS1`0YlNBeS1?[lNC`R1a1ZmNfN_R1`1amNbN]R1]1cmNdN]R1[1dmNfN[R1Z1emNgNYR1Y1hmNiNVR1W1jmNkNUR1T1kmNmNUR1n0lmNVOUR1e0kmN_OUR1>jmNEXR16kmNKUR12lmN0TR1MlmN6UR1GgmN>ZR1@emNc0\\\\R1ROlmNQ1TR1lNlmNW1UR1fNdmNb1fR1cMflNNa0c2ZT12L4L4M3N2M2C>M4J6K5L3N200O10000001N110O100O00001O001O1O2N1O101O0O001O2N1O1O3M1O1O1O101O1N2N2N1N3M2I7G9H:hNX1^Og0AbSh4\", \"size\": [1280, 720]}, {\"counts\": \"dcXc0a0gW1GbhNIWW11lhN0f1=mR1B\\\\kN7Q1k0lS1UOTlNj0nS1VOPlNk0RT1TOlkNm0WT1QOdkNR1bT1mNWkNV1lT1hNRkNX1hU13L32N3M2O1N2M4M01O0J6H8RkN`N]S1a1alNeN[S1Z1elNiN]S1S1blNPO_S1l0alNVOaS1f0_lN]ObS1`0]lNDbS1<\\\\lNFfS16XlNNiS1OVlN4lS1HSlN:oS1BQlNa0QT1ZOmkNk0UT1oNlkNT1^U13M2N2hLcNloN_1RP1jNboNZ1^P1hN]oN[1cP1fN[oNZ1gP1gNUoN[1kP1hNPoNZ1QQ1hNknNX1lP1SN[mNg0e1W1[Q1lN_nNW1`Q1mNXnNX1gQ1jNVnNY1kQ1dNTnN^1lQ1aNTnN`1oQ1jMcnNW2cQ1bM\\\\nN`2^S13I8E;N2L5G8L4M3K5K5G9M3O100O11O0010O01O1O1O1O1O1O001ON201N11O1O10O000001O1N11O01O1O010O1O001O100O1O1O101N1N3N2L>C4L3L6E<WNhkNmNWP^4\", \"size\": [1280, 720]}, {\"counts\": \"g_ea0>RW1b0G8G9K4D<N2J6L4L4M3O1O2O0000000O010O1O1O001N2L4M3O001O1N1011O2N0000O1000O2OO100O13M4L2N1O1O1O000O01N2N2O2N2N2O0000010O0000O0001O01ONN2L6L4IWO`MmkN;Li1JQNcT1MbkN^2PU1dMljNW2XU144L7H:A[lNjNZQ1K]lN9j22ZT17J5K5L4J6M3H8L4L4I7J5L6I6K5K4M4K4J7C<]Od0J7M200000001O0000O2O1O0O101O1O001N2O1O1O1O002N100O101N1N3N2N1O2M4M2dMQlN`Ne0T1_S17SmNRO_S19e2H3M2M3N3MRco4\", \"size\": [1280, 720]}, {\"counts\": \"Sgib0;ZW1>A=G8L5I6N2M3M3O1N2N200O1K5O2N100O11O2NO1M300L4C<01001O2N000O2O1O00001O1O0010O01N2O1O4K8I1O1O1OjMQN[nNo1dQ1TNYnNn1fQ1UNVnNl1iQ1WNTnNj1lQ1YNjmNBgNU2^S1_NbmNl1^R1`NglNkN1l2WS1e1N2M3N3M21O001O0O10001N1001O1O10N2M3L8D;I7@m0`NRkNdN]U1CbkNLch3`0[]M>H8J7^O`0F:G9QNcMklNOl0d2TR1`NolNm1mR1e1O100O10000000O101O00001O1O002O0O1O1N2O1O2N1O3M2M2O2N2N3M4K4M3L7J3M3jLYkNm2QU1J5_NijNN`U1nNijN4a06RXZ4\", \"size\": [1280, 720]}, {\"counts\": \"gfjd08dW15VNMXkNa0fS1VOjkN2C[2\\\\T1mMdkNd2[T1b0O001O000000000001O0000010O010O001O1O1O1OmNgL_mNY3aR1jL^mNT3aR1QM\\\\mNn2cR1UM\\\\mNj2cR1YM[mNg2dR1\\\\M[mNc2eR1^M[mNb2cR1`M]mN_2bR1dM^mNZ2bR1gM_mNV2bR1lM^mNR2cR1nM^mNP2bR1QNbmNSOZO^2US1_NjmN`1WR1`NfmNb1[R1^NcmNd1]R1^N_mNe1`R1\\\\N]mNe1dR1]NVmNf1kR1ZNolNk1SS1UNflNP2[S1Z12N101N2N2O1N2M3M3L4M4K5J9F7J:G;UN`jNXOk08_S6J^SK4WjN3VS12hjNKY1W1XS1a2_O6UlNlK]S1\\\\40O02O0O01O1O1O100O2M3N01010O1PLWlNe3ST1O1O1N2O3L4I7E;C<VOk0D<B`0A^S[3\", \"size\": [1280, 720]}, {\"counts\": \"`Woc07bW18K4M4M2N2G:I7L3M4ZO`NfjNd1VU1e0M2O2N2N2L4M4K5L3L5G8O1O1M3000000O10000001O002N1O1O1O000010000O001O00001O2N3N2QM_kNY2fT1_M]kNa2RU10TN^MWnNc2eQ1cMYnN\\\\2gQ1iMSnNY2lQ1lMPnNT2oQ1PNnmNP2QR1TNlmNl1TR1VNgmNm1YR1UNdmNk1]R1XN^mNj1\\\\R1YMmlNo0b0m1]R1^N]mNh1^R1o10O00000000001N2M0101O03E;E:D?E8_Ob0@a0B?D`0_O`0UOklNE^S1h0A>E:B>L4H8G8K7H7H9I7E;I7WOQLZmNT4aR1ULWmNo3iR1d0001O00001O2N2M3N1O1O1N2K6K4K6I6K4G:F:G;H8H9H:C;E>D=A?DdbS3\", \"size\": [1280, 720]}, {\"counts\": \"jWXb02hW19J4L4H7J6N3N1M3J7L3N2O1M3N3K4H8nNoMPlNV2oS1mMjkNX2VT1hMhkNZ2YT1fMckN^2\\\\T1bMbkN`2]T1aMakNa2^T1aM_kNa2`T1aM]kNa2bT1?EeLPlN\\\\3ZT100000O1N2M3L4O1000O1001O1O0001O010N2O0O20O01O000010O01O1O1O1O1O2N2N3M4M0O1O00001O00O0kNQMYmNQ3dR1WMUmNk2gR1_MQmNd2hR1gMnlN_2nR1^1J201O101N1O100O101M3L4N01O1O1O1L4M3L5H9D<A?M2CokNgLTT1V3<00N3M2_O[kN^MnT1^2<LllNjMfP1o1ZoNZNdP1c1VnNPNGj0oQ1Q1\\\\nN^NTOn0^R1b0`nNdNkNo0dR16dnNc0\\\\Q1WOcnNo0]Q1kNenNY1\\\\Q1_NgnNe1mQ1\\\\MTnNn2ZS15J6K5J7H7L4N2N2M2B?F:M2O2N2O101O10O1O100O1O1O100O1O1M3K4L4M3J6M4_OblNYLdS1]3e0K5N3M4A>J6L4K5G9L9F;[O`iNXOiV18hkc3\", \"size\": [1280, 720]}, {\"counts\": \"fgRa01kW17Ca0XOf0E7L3O2N1N2L4L4N201N1J61O1O00N2O1N2N2O0N4L3F:00001OM3O1N2N1nNPMRmNT3iR1XMnlNj2nR1_MjlNe2RS1W1M5L2K4N01N3N001O001N3M2M201O1N1O3L4M3L5N2N2M2M3L4L4LVmNeLWQ1U3jnNQMUQ1m2dnN]MZQ1a2fnNbMZQ1Z2enNkM[Q1o1gnNUNYQ1h1hnNZNXQ1b1gnNcNYQ1Y1gnNjNZQ1Q1inNQOXQ1j0jnNXOVQ1f0jnN\\\\OVQ1a0jnNBVQ1;inNIWQ13hnN1YQ1LhnN6YQ1DjnN>VQ1]OlnNf0VQ1UOjnNn0WQ1mNknNU1UQ1fNmnN^1RQ1_NmnNe1SQ1[NinNi1WQ1UNhnNn1YQ1PNenNS2[Q1kMdnNX2\\\\Q1fMenN[2[Q1cMenN_2\\\\Q1]McnNg2]Q1SMenNP3\\\\Q1lLdnNX3]Q1eLanN_3_Q1^LbnNe3\\\\Q1[LcnNg3gR14N2M2O2M3N2N2N2M3N2O1N2M2O2M3N2N2N2M3N2N2O1N2N2O1O100O100O1O0101N2N2N1L4C[mN[KjR1_4>J7J6B>C>C<J7EojNbMUU1S2hjNoMcU1P2;L3J5J4GgiNhN]V1k0e0DaTW5\", \"size\": [1280, 720]}, {\"counts\": \"\\\\_oa06eW18G=aNY1M3N1K6M2N2N3N1N3M1O2N01001N10O10O2MKSkN[MlT1g241M2O200akNZMfS1d2]lNcMZS1Y2llNhMlR1]2UmNeMhR1]2WmNdMdR1`2]mNaM\\\\R1e2cmN]M[R1c2dmN`MXR1b2imN_MRR1d2mmN_MQR1a2PnN`MnQ1a2omNcMQR1\\\\2lmNhMSR1W2mmNkMSR1T2kmNoMUR1P2jmNRNVR1m1jmNSNWR1j1kmNXNTR1f1kmN]NUR1`1lmNbNSR1]1mmNfNRR1X1nmNjNRR1T1omNnNPR1o0QnNSOoQ1k0PnNXOPR1e0RnN\\\\OmQ1c0TnN^OlQ1`0UnNAkQ1=VnNDjQ1:VnNHjQ16VnNLkQ11UnN1kQ1NUnN4jQ1IWnN9iQ1CZnN>fQ1@[nNa0eQ1]O\\\\nNd0dQ1YO]nNi0cQ1UO^nNl0bQ1RO_nNo0bQ1nN^nNT1bQ1jN]nNY1eQ1bN[nNa1fQ1[NZnNh1fQ1TN\\\\nNn1eQ1lM_nNU2aQ1iM_nNY2aQ1eM_nN]2aQ1aM_nNa2`Q1[McnNg2]Q1XMbnNi2_Q1VM`nNl2`Q1SM_nNo2bQ1lL`nNV3`Q1hL`nNZ3aQ1dL_nN]3aQ1aLanN_3_Q1`LanNa3`Q1\\\\L`nNf3kR13N1O1O1O1O100O10000O10000001O000000O10000O10010O0O1000000O100O1O100N2O100O10000O101O0O1O100O1N2O1O1N2N3M2O1O1O01O1O1M7D:F:E`0]Oa0\\\\NakNnNRU1e0]1oNXiN0L2oRY4\", \"size\": [1280, 720]}, {\"counts\": \"X_ea0<VW1`0I5_OQOgiN\\\\1QV1>L2O2M2NOO3N2001O010O1N3McjNVNhT1h1XkNZNhT1c1YkN`NcT1a1[kNcNTT1k1lkNWNQT1j1QlNTNoS1n1QlNRNnS1n1SlNSNiS1P2VlNRNfS1Q2YlNRNcS1o1\\\\lNTNbS1l1_lNVN_S1i1blNVN_S1j1`lNWNVS1R2jlNoMPS1U2olNkMRS1V2nlNgMQS1]2PmNbMQS1^2olNaMQS1`2olN_MSS1_2mlNaMoR1d2PmN\\\\MQS1c2olN\\\\M8@oQ1T3jmNYM8FkQ1Q3mmNXM9IhQ1o2omNWM:LfQ1m2PnNVM53lQ1d2nmN[M53mQ1a2nmNYM74PR1a2lmNTM8>jQ1^2omNQM6f0jQ1X2lnNkMSQ1S2lnNPNTQ1n1lnNTNTQ1k1knNWNUQ1h1jnN[NUQ1c1WnNXMJW1oQ1`1inNcNWQ1\\\\1gnNhNXQ1V1gnNmNYQ1Q1gnNQOYQ1n0enNUO[Q1j0cnNYO]Q1e0anN_O_Q1?_nNEaQ1:^nNHbQ16]nNMbQ12]nN1cQ1L]nN7bQ1H]nN;bQ1D]nN?cQ1^O]nNf0bQ1XO]nNk0cQ1SO]nNo0cQ1nN]nNU1dQ1hNZnN\\\\1fQ1`N\\\\nNa1eQ1ZN]nNi1cQ1SN_nNo1aQ1oM_nNS2bQ1jM]nNY2cQ1fMZnN^2fQ1aMYnNb2fQ1\\\\MZnNf2fQ1YMZnNh2fQ1UMYnNP3fQ1nLYnNU3gQ1hLZnNZ3fQ1cLZnN`3fQ1]L[nNe3fQ1WL\\\\nNj3eQ1QL]nNQ4eQ1hK]nN[4`R14L4M3N2M3N2N2O1M3M3M3N2N2N2O1O100O1010O00001O003M2N3M2N1O4L5K=CM4M200N2M31N3N3M4L8H5K4L001O2N1O1O2N1O000000OOLOO05N4L5^OfkNSMeT1g2=WOWkNVNnT1c1k0M3YOniN[OXV19ol\\\\4\", \"size\": [1280, 720]}, {\"counts\": \"^^]`01[W11ViN2gV11WiN<\\\\V1K\\\\iNb0nT1^OUkNG5?Bc0fT1X2H6I6M2N2K5J6L4O1O100O00000000001N2O1000000O1O011N1J\\\\KPmNe4US1O01O11O1O0000O1O2J6L4N10000M3OXKXmNY4iR1fK^mNT4bR1mK_mNo3cR1SL\\\\mNj3eR1XLXmNd3lR1b06I7E<H7F;L5L3N1ZObkNgMcT1T2f0N4H7L4^NRjNo0QV188HbkNQO[R1g0fmNG\\\\N6ZR1OYoN^1fP1^NSoNn1lP1nMQoNY2oP1_MVoNd2jP1YMSoNl2nP1RMinNY3kR15I6N1O2M3M3N2O1N1O2L4M3J501O1O1O1N2L400001O3M1O5J4M1O1O001O0000000000001O000O2O001N1ZMdlNg0^S1kNUmNTOB7HBST1e0Q3\\\\O]]o6\", \"size\": [1280, 720]}, {\"counts\": \"_eY`04iW18F7chNC9L\\\\V1^1H5K^OiiNCPV1\\\\1@h0_O:F8I6I8K3M2N2O1O010J5L3011O0O1jK]lNo3cS1QL\\\\lNo3hS13N2N2O1000000N3H7O2N0O200000O10O010O2M3N2OO3N3La0_O3L3K4L4J2NK25N4K5I8I8L5K4N4VOi0I7M6C<K3L5D\\\\20kfND^V1d0YiNHoU1Z2oN6G8G8M3K5G9J6M3M2O2O1O1O0O2O1N2N2N2O1O1O100O010O100000O0100000O01O1O1O1N10100O2N10O010000O0101lL_mNf0cR1aN`nNnNb0OSQ1m0R4H4J4K5M5Kf\\\\j6\", \"size\": [1280, 720]}, {\"counts\": \"dg\\\\a07eW16F:nNU1@[NXjNk1^U1b0F<J3M3L4I6L3M3M3M4M1O2M2N3M3N101O100M3H8L4K5L5M2N101N2O1N2O1M201O1N2M3N2O1O1O100O10O1O1O1M3M3O2M2H7M4M3N2N2M3N2M3M3N3O0O1N2M3M3L4L4L5K5M2N2N2N2MWmNTMdP1h2[oN^MdP1_2YoNiMdP1T2XoNTNhP1h1VoN_NiP1\\\\1WoNjNhP1S1WoNQOiP1g0ZoN^OgP1>UoNJjP1OPoN=QQ1^OonNg0QQ1TOonNQ1QQ1hNPoN^1QQ1WNToNn1mP1fMVoNb2oP1QMVoNR3gR14I8L4K5N1N3N1O2N2O1O100O1O1O1O1N2O0O2O1O10O10O100O01000O10O1000O10000O0O2N2O1N2O1O001O1O1O010N200003M3M1O1O2M6XLmmNY1XR1_NPnN^OeNa1^S1fNenNV1`Q1`N`oNe0dP1VOVXh4\", \"size\": [1280, 720]}, {\"counts\": \"Xg_b09_W1?]O>D<F8L3M4G9L4M3G9M3M3O1N2O1N2O1N101M3M3M3L4N2N2N1N3N2N2N1N3N2L3J7M3L4M4L2N3O1O0O2K5L4J6O01N2O0O2M3M3M3M3L4M3N2N1O2M3N2N2L3MfmNhKTQ1V4mnNkKSQ1R4mnNQLRQ1n3PoNRLnP1l3VoNSLhP1m3[oNoKiP1o3f1M2M101N3N2N2M6K4K6J3M^mN]Mmo0`2loNlMRP1Q2loNUNSP1g1joN_NWP1]1goNiNYP1U1coNQO]P1l0_oN[O`P1a0_oNF`P15aoNO`P1K[oN?eP1TO`oNU1_P1gN`oN^1aP1\\\\N^oNj1bP1mMcoNW2^P1bMcoNc2]P1YM`oNn2bP1hLcoN[3^R12N3L4K5L4N2L4M2M4L4M2M4M3M3K6K4N2N2N2O1N2N2N2N3M2O100001O001OO10O10001N101O1O1PKimNU4XR1hKomNS4RR1bK`nNV4fR1O1N100000O1M3N2N2O1O1O010O1O1O001100O1O3M8H;^LhlNe1bS1kMflNT2eT1dMljNg1nV1eNUQV3\", \"size\": [1280, 720]}, {\"counts\": \"XWjc05jT14UlNOXOY1`T1i1N2M2O2N100O1O1000O11O000O101N1O100N2M3O011M2O1N2O1O1O1O1N2O1O1O1N2O1O1N2O0N3M2O2M3M4K4L4M3M3M3M201O1N2N2M3M4O000O10O10O010000001O1N12L3M3L3L4L4O2N1N23L=D2N0000SNRnNhMQR1d1RoNjMRQ1`1aoN^NdP1Q1ioNoNZP13imNkN[2R1WT1N5Le]7\\\\Oa[Gi0HOfT1M\\\\kN`1oS1i1RlNlKTS1f4E:B=K6L3N3N2O2O0O1001N1001000O001O001O1N2O1O00O1O2N11O001O1O00002N001O1O6J1O1O1O2N1O2N3M2M4M5L1N2N2N2N7PM`lNQ1jS1jMgmN`1]R1[NkmNJXNi0MZO3N]h[2\", \"size\": [1280, 720]}, {\"counts\": \"ooob0;XW1?@`0C<B>L3J6F:N2L4A?N2N3K4N2N2K40100O2N1O2O0000000000O104aLokNg2kS1gL]lN0b0m2ZT1M01M2K5K5M3D;C[L`lNf3jS13O1001WLokNb3XT10O0000O010O10O001O011O0O2N0O2O1N2N2N2M2O2M4M2H9N1N2M3M3K5VOPKTnN`5lQ19L5O1O2L3O11O1OM3N110O1O1001O010O000O101O1N100O4M10O3N6IJ7L3M3M3M3K5F:H8B>K5A`0K5K4H9F:H8Ba0D<C?@[c<IXVB5XU1VOfjNU2^R1oM`nNIIP3bQ1gMSnNDGT3VR1P2000O2O0O1O100O2O0100O0001O1N2N100O101O20O01N1O3L:G:E3N1N3M2M4TMXnNPOjMNWT13VWb2\", \"size\": [1280, 720]}, {\"counts\": \"XPic03^W1a0VOi0C=F9G9G9K5O1O1O1L4F:L4O100O1O1I72N1O00I7N2001O00001O00:F=C3ML4O1N2N2O100O100O100O100O2O0O1N2O2N001O1O1O00101M2O1O1L5N0O2K5N2M3G8M5\\\\Oc0I8N100O10000001O1O001O0000001O000000001OO1O1O11O00001O00001O0O3O2M1O1O2N2N2M2O1O8H7Ig0YO:TLPlNT3WU1XOl0QO^d8iNl\\\\G5J2O6J<D2RjN[O\\\\T1g0]kNC_T1`2_O6K3_kN[M_S1f3N4MM2N2N1O2N2M3N4L3M5K2M4M2N101gMfkNh0[T1QOTlNf0nS1SOblNb0`S1ROmlNj0YU1L3L6J5L4J\\\\hV2\", \"size\": [1280, 720]}, {\"counts\": \"hkUe06hW12M<]hNC=MS2NXQ1P1^lN\\\\O>MHe0YS1V1ZlNMcS1Z2M3N2N1O1O2N1N2O1O1N20O100001N10000010O00001O2N1O002N3M001O2N3M9G1O4L7I0000O100O1O1O1QN`L]oNa3bP1cLYoN_3gP1dLSoN_3mP1hLhnN\\\\3XQ1RMomNY3QR1\\\\1O101N01N1O101O00000000O0011O1O001O001O1O1O1O1110O0OO2N1O4K3M2O2N2O1N6J5L8G>BS1mN4K7J9F6J9H8IYLhNoPOo0on0SOVQOg0in0[OXQOb0hn0@\\\\QO9en03]QO_Oen0b0[43N1O1N3N5Lh0WO006I6K:jjNWM`T1U31O0O5L1011RkNTMcT1U31N2N1jNhkNcN`T19^lNH`S1JolN:]U17ghNSOgV1S11N7J3L=AYhg1\", \"size\": [1280, 720]}, {\"counts\": \"WPnc08]W1=H7G8A`0F9F:A?eN]MUlN87i2bS1o0M4M2N2N2O1O1N2N2N2N1O200M4M2N20O01000001O000001O0000003M2N004L6J7J3L1N2O1O001O7I2N0O20O0O1O1O1N1O2N2N2N2M3M4\\\\NTLmnNR4RQ1WLRnN\\\\4mQ1l0O1N2L4N2O10O101O010O1O1O1O1O1O01O0000002O1N00000000O1O10000000O1000000001O001O2N1O3M2O1N2N3N1N2N2N3M3M4L7I4L7bLhlNf1nT1Ia0_Om0`NhiNZO^b8OQeH\\\\1iNW1iN2N2O<D5K3M4L7I8G7J4L2O4K:H9nlNmJcR1V54L5K6K4bMSmN_OSS1OcmNGaR1NPnNHWR1GWnN5nQ1YOdnNb0cT1J6J<A`om1\", \"size\": [1280, 720]}, {\"counts\": \"PY]b0c0gV1g0D<F9M3I8EjMfjNY2ZU17N2_Oa0A`0M2M3N2O11O01O000O10O01O11O01O000OPLWlNk3hS1VLZlNh3fS1YLYlNg3fS1[LZlNd3fS1[L[lN2J[3mS1aLZlN4I[3VT1fLkkNY3UT1gLjkNZ3ST1;O020O01O4L5J2OO100O10O10O1O2N1O0O2O1N2O1M2N4L3L4M3J7K4L3N3fN[KRoNl4jP1aK\\\\nNP5dQ1f0O1O1000000O10000O1000001O001O1O0O1O1O00100O10001O10O1O1O1O001OO010000100O1O01O00O100000O10O2N2M4OO0000001O2M2M3M5I8H9I7nMRnNPMiR1i2VQ?`L]SA?YhNDUW1k0I;F`0cjN\\\\N^S1j1WlNdNA0mR1d1VmNJ4QNgQ1l5E6I01O011N1O1N2O0010O1O10N2O1O00001O1O1O101O1O0O2N2M4N5K1N3M3M4J8fLilNY1aS1UNYnN4TR1ZO_nN9mQ1C\\\\3KbZ^2\", \"size\": [1280, 720]}, {\"counts\": \"RWZa087I>b0_U1\\\\1PNRN\\\\mN4cNP2nS1mM\\\\mN`2bR1`M\\\\mNc2bR1^M\\\\mNf2fS19M3G87I10O0000000_OkLWlNU3dS1SMYlNm2fS1WMWlNi2hS1YMWlNf2iS1\\\\MWlNc2iS1^MPlNh2PT1d0001N2O2N5K1O000000N2M3N2N2O1N1L5M3N2N2M3K5O0O2N2M3N2O1O1O1001O01N100O1001OO1O100O2O0O2O00O1000001O00000001O1N01N1K5LO21N30O11N1M3L4M6G8J^mN[LTQ1a3lnNgLPQ1T3RoNQMlP1k2VoNXMjP1_2ZoNgMeP1V2XoNPNhP1m1UoNZNiP1e1WoN]NiP1`1UoNdNlP1f0TnNPNg0g1RQ1:boNF\\\\P18hoNHWP11ooN1PP1NQPO3no0MboNQNoNT2^Q1JcoNWNeNV2gQ1_OioNj0VP1hNXPOW1ko0^NaPOZ1eo0cN`PO[OjMT1iQ1_OcPOYOjMi0PR1JZPOWO^Q1d0S3EQd7LT\\\\H=^jN^OaR1S2jkNSN07kR1LWmNHGl3_R1QM^mN_3]R1T1K4O2I7^Oc0J500O02O00001O1O1N3N1O2M3N3M3N1N1O3M2N2N2M2O1O2N2N1O1O1O1O1N2O001O2M2O100O010O0000000O2O1O1O3M;SLflN^2kT1C4L2YMojNZ2_U1Md0[Od0\\\\Ol0QOdgc3\", \"size\": [1280, 720]}, {\"counts\": \"SQYa01WV1:PjNQ1aU1o0K4M2N2I8J5M3N10000000O3N2N0000DTMdkNl2WT1^MdkNb2[T1bMbkN^2]T1c0O0O2N2N2O1N2N1N3M3M3L4N2K5O1N2M3J6M3L4G;_Oa0I7L0O2I151N100OO2N3O000O3M2N2O2M2N3N1N3H9L4LToNjJUo0R5mPOPKPo0Q5^POmJXO2WP1Q5dPOmJPP1R5moNSKSP1j4moN[KQP1c4noNaKQP1]4noNgKQP1U4noNPLRP1m3ooNVLPP1h3PPOYLSP1c3moN\\\\LZP1_3^oNhLdP1V3\\\\oNkLeP1R3]oNmLfP1n2]oNQMeP1l2]oNQMgP1k2\\\\oNoLlP1l2XoNQMkP1l2YoNfLUQ1V3m1K5J6L3LnnNWMUm0d2fROfMZm0V2dROPN\\\\m0l1]RO_Nbm0]1]ROiNcm0T1ZROQOfm0l0[ROWOem0e0ZROAfm0;WROKhm03TRO4lm0ITRO:lm0ARROf0nm0SOTROQ1nm0jNgQOe1Yn0iMlnNJk2a2Xn0eMPROb2Pn0[MjQOn2^Q15K6K5F:I6H9K5M3M3J6M3O1K5N2O100000O2O00L4M3N2N2N2000001O00000001O1O00O1000O100O1000000000O1N2O001N101O100O1000000000000O1O13M4L2N2N1O1QLbnNZ1_Q1iMjmNAW1`2QQ1fMkoNDQNT2UR1TNnPOX1Vo0dNW5JYdl3\", \"size\": [1280, 720]}, {\"counts\": \"[QTa0h0oV1`0C;H6J4K5G9N1O1O2N1N2N2O1N2O100O100000O0100O2M2M3O1N2N2O1N10001N2N1L5N010ON2000O00O11O2N0J7K410L4L4J7N1G9N200N1N2O0210O1O1O1O2M3M3M2O2N2O1O1M3N1N3M2N3M3N1N0O111MenNUMhm0i2XROZMhm0b2VROdMim0[2TROjMkm0T2TROPNkm0n1UROUNjm0g1YRO[Ngm0a1XROdNgm0Z1YROiNjm0P1SROWOWn0:kQOIVn02kQO2Un0IkQO;Un0BkQOa0Un0\\\\OgQOk0Qn0_M^oNc1^2U1Tn0XM^oN`1]2\\\\1\\\\n0`NfQOb1Zn0\\\\NeQOf1\\\\n0YNaQOk1^n0UNbQOl1^n0SNbQOn1^n0oMcQOS2^n0hM_QO`2an0]M[QOi2fn0RMQQO[3on0^LTQOf3kn0WLUQOl3mn0lKWQOW4WQ12M3M3N101O100O1000000O2O0O10000O1000O10O100O100O100O1O1O1O1O1O1O100O100N1O2O1O1N2O100O001000OK6N2N2N2M3N2O1O1N2O1O1O2O0O1001O1O000[LinN`0XQ1YOSoNc0oP1aNYnNoNR1[2TQ1PNkoN\\\\OWN`1`R1POlPOG_o07X4L6J4HfRj3\", \"size\": [1280, 720]}, {\"counts\": \"nPo`0;cW14\\\\OHViNb0cV1e0H6I5L5J5H8L3L3N2O1M3O2O02MF]M^kN^2aT1eMZkN^2eT1gMUkN[2jT1>O1M3N2N1L4N4K5H7M3N3L3I7N2M3M2N3N0K42K6K4M4L4N2K6L3G9N2L4K6K4N2O1M4L2N3L5K4M3N3L5K4KXkNQNhS1j1Y1L4L4K5M3K6L3L]lNEjo06XPONfo0MYPO;fo0@VPOh0ko0TOSPOR1YS14J6J6K4J7N2N2L4I7I8K4M3M3M3L4L3K6L4J6H8K4N3L4M3O10O1000O100O1N3N1001O001N2O1O0000000O01N200O2M101O10000O1000O0100O1O1O100O010O1O2N1O2M2N2O0O2M2O101N1N4M2N2O2M2O100O1001O001O1O2N0lKmnN[1TQ1iMYnNWOQ1k2hP1fMRPODoMS2RR1UNVQOT1mn0jNaRODfm09R5M5JdS^4\", \"size\": [1280, 720]}, {\"counts\": \"WYna02VW14[iNO`V16_iNHjT1<WkNLH48B25hS1a0SlN?1QOM2lS1b0QlN?NUO0OdS1U3VlNnL\\\\S1R4E:L5M2N3K5K4M4K4O0O10L5K5M2O0000O010M42M3M2N2M3M3K5M3N3L4M3N2M3N2M3L4N2L4K5M3K5K4N3M3N2M`nNaMem0\\\\2XROlMfm0Q2UROXNjm0e1SROaNmm0\\\\1TROgNkm0V1SROoNlm0o0RROVOnm0e0PROCom0_NVoNY1o2;km0\\\\NVoNV1k2e0om0FPRO>Qn0jMXoN\\\\1d2n0Tn0gMZoNV1Y2gNcMa2jP1cM[oNo0T2l1an0VMZoNk0T2R2dn0QMYoNj0R2Y2en0kL[oNj0o1^2fn0gL\\\\oNj0l1b2Xo0ZMhPOh2_o0jLePOX3dQ1>K5E;K4N3L3M4M3L3K6K5M3O1M4L3N2N2O1N2O1O001O100000000O10000O01001O0O2N1O1O1O010O1O1O1000O0100O100O001O1O1O1O001O1O1O1O1O001O1O1N2O001N10100O1O1O2N1000O100Oc0]O4cK_oNT1dP1`NioN\\\\1ZP1iMcPOWO\\\\Nn1VQ1fN]5K5M3M3M5HWTo3\", \"size\": [1280, 720]}, {\"counts\": \"Yaac079OK3bV1W1F7^OZNbjNj1ZU1[N`jNi1^U1?K9VO\\\\MkkNj2PT1WMnkNk2PT1ZMikNj2ST1[MikNh2VT1a0AbLXlNb3gS1<O1O002N2O1NO2M3N2M3K5N1O2N2K2O4L3O2M2N3N2L4M3EUkN]MnT1`2:M4J6N2O1L4KZMSNcoNj1_P1WNaoNg1aP1YN`oNd1aP1^N^oN`1cP1aN]oN]1dP1dN\\\\oN[1eP1eN]oNR1kP1nN[oNg0iP1YO[oN=kP1Ch31000010O0O1L4003M2N^nN3Qk0MhTOe0lj0\\\\OoTOP1ij0ROQUOU1nj0jNnTO[1Qk0eNgTOc1Yk0]NdTOf1\\\\k0ZN]TOn1ak0TNZTOP2ek0RNWTOQ2kk0mMnSO[2Rl0dMdSOf2\\\\l0ZM\\\\SOo2cl0RMYSOQ3fl0PMWSOS3il0mLUSOU3ll0jLRSOX3nl0hLPSO[3ol0eLoRO]3Rm0bLlRO`3Tm0aLfROd3[m0VL`ROS4bm0iK_ROY4cP10N3M3M3M3M3O001O1N1M4N2O0N3O0O200O1O100O1O100O1M3M3M4L3N2I7L4M3N1N3N11000O0000010O1O01001O0O01000O1N2N1O2N2M3N2O1O1O1O1O010O1O1N10100O10000001O3M2M6K2N3M7I3[J_nNf4bQ1WKhnN:Mk2\\\\Q1jLQoNH6T3jP1RMaPOi2`o0UMcPOi2_o0TMdPOj2^o0RMgPOk2]o0nLhPONbMf2SU1K2N6I=D<D9E9Ha0ZOo]a1\", \"size\": [1280, 720]}, {\"counts\": \"RRXd0c0eV1[1QOSN`jN\\\\2TU1>G5M2O2N1N2O2O0O2O0O1O1O1O1O1O1N2M3O100O2M2N2O2M1O2M3N2L4O3N11NN2N2N2K`KklN_4QS1fKnlN[4iR1nKVmNR4hR1c0O1O10LM500O1O3N0O2O01AbmN[KaR1e4emNRK^R1n4:N101O1OO02N200O2M3J6L3N3O010L4I7M3M3N1O2N2L3L5L3O1M4K4M4M2M4L4L3LSoNiM[l0S2mROWN_LKcP1k1kROROUm0m0dRO\\\\O\\\\m0`0cROE\\\\m08bROO]m0MdRO6[m0IaRO=_m0@_ROe0am0YO[ROm0dm0RO[ROQ1em0lNWRO[1im0aNQROi1Rn0QNlQOT2Vn0fMhQO`2Zn0[McQOk2^n0PMcQOS3_n0dLgQO^3\\\\Q11O1N2N2_Oa0C=N2N3K4M3N2M3N1O2N2O1N20O10001O0000000O101O0000O100O100O10000O1O1O10O02O0O1O1O100O010O1O1O100001N02O0O01O000100O010O2O0000N20O010000O100001N1001N100O1O1O100000L5N2N2M2NWLXnN^1eQ1aNcnNfML`3_Q1gNPoNX1oP1dNYoNY1gP1bNdoNX1\\\\P1aNQPOX1oo0TNZQOW1gn0iNhQOh0o`0\", \"size\": [1280, 720]}], \"bboxes\": [[365.0, 421.0, 44.0, 122.0], [357.0, 421.0, 49.0, 120.0], [360.0, 402.0, 52.0, 137.0], [374.0, 395.0, 54.0, 135.0], [380.0, 386.0, 81.0, 142.0], [405.0, 375.0, 97.0, 147.0], [433.0, 371.0, 111.0, 150.0], [460.0, 368.0, 123.0, 152.0], [476.0, 367.0, 124.0, 147.0], [475.0, 379.0, 132.0, 139.0], [480.0, 361.0, 138.0, 157.0], [485.0, 362.0, 140.0, 151.0], [484.0, 367.0, 142.0, 149.0], [484.0, 371.0, 125.0, 149.0], [484.0, 371.0, 114.0, 155.0], [477.0, 380.0, 98.0, 155.0], [473.0, 382.0, 56.0, 154.0], [473.0, 414.0, 63.0, 123.0], [458.0, 403.0, 66.0, 129.0], [442.0, 402.0, 53.0, 111.0], [418.0, 372.0, 81.0, 169.0], [400.0, 378.0, 110.0, 147.0], [390.0, 398.0, 123.0, 140.0], [393.0, 383.0, 124.0, 146.0], [398.0, 378.0, 119.0, 142.0], [397.0, 367.0, 91.0, 146.0], [405.0, 381.0, 114.0, 153.0], [406.0, 376.0, 118.0, 149.0], [414.0, 387.0, 116.0, 152.0], [429.0, 431.0, 112.0, 152.0], [435.0, 385.0, 120.0, 162.0], [434.0, 343.0, 136.0, 157.0], [435.0, 366.0, 126.0, 149.0], [433.0, 366.0, 128.0, 145.0], [430.0, 377.0, 122.0, 144.0], [433.0, 417.0, 120.0, 140.0], [434.0, 413.0, 122.0, 145.0], [436.0, 382.0, 114.0, 145.0], [432.0, 385.0, 87.0, 148.0], [435.0, 398.0, 84.0, 132.0], [437.0, 390.0, 89.0, 141.0], [438.0, 398.0, 87.0, 140.0], [439.0, 401.0, 96.0, 152.0], [455.0, 381.0, 105.0, 145.0], [464.0, 366.0, 107.0, 153.0], [468.0, 362.0, 95.0, 158.0], [464.0, 359.0, 97.0, 154.0], [457.0, 356.0, 100.0, 155.0], [450.0, 362.0, 90.0, 157.0], [444.0, 390.0, 90.0, 153.0], [450.0, 362.0, 100.0, 159.0], [448.0, 361.0, 105.0, 159.0], [445.0, 366.0, 99.0, 154.0], [449.0, 371.0, 102.0, 149.0], [447.0, 364.0, 102.0, 159.0], [443.0, 379.0, 86.0, 159.0], [440.0, 390.0, 97.0, 156.0], [451.0, 360.0, 108.0, 158.0], [452.0, 362.0, 104.0, 156.0], [442.0, 361.0, 103.0, 164.0], [442.0, 361.0, 110.0, 158.0], [448.0, 362.0, 96.0, 152.0], [446.0, 378.0, 126.0, 145.0], [465.0, 382.0, 111.0, 156.0], [490.0, 372.0, 117.0, 160.0], [488.0, 381.0, 115.0, 157.0], [467.0, 387.0, 114.0, 153.0], [464.0, 386.0, 125.0, 133.0], [452.0, 390.0, 121.0, 125.0], [413.0, 420.0, 124.0, 120.0], [412.0, 386.0, 123.0, 143.0], [410.0, 382.0, 121.0, 147.0], [399.0, 397.0, 124.0, 146.0], [418.0, 378.0, 119.0, 152.0], [426.0, 364.0, 135.0, 162.0], [435.0, 355.0, 144.0, 170.0], [448.0, 363.0, 136.0, 177.0], [464.0, 346.0, 160.0, 180.0], [459.0, 358.0, 156.0, 173.0], [448.0, 385.0, 144.0, 160.0], [463.0, 383.0, 126.0, 158.0], [461.0, 375.0, 132.0, 164.0], [447.0, 378.0, 131.0, 161.0], [448.0, 448.0, 103.0, 96.0], [439.0, 377.0, 111.0, 157.0], [418.0, 379.0, 119.0, 159.0], [400.0, 403.0, 129.0, 155.0], [409.0, 397.0, 124.0, 158.0], [407.0, 400.0, 123.0, 157.0], [416.0, 442.0, 127.0, 157.0], [423.0, 429.0, 129.0, 160.0], [425.0, 407.0, 140.0, 171.0], [442.0, 442.0, 132.0, 139.0], [467.0, 442.0, 114.0, 156.0], [452.0, 453.0, 128.0, 115.0], [442.0, 419.0, 139.0, 148.0], [449.0, 426.0, 115.0, 146.0], [444.0, 367.0, 130.0, 154.0], [452.0, 361.0, 131.0, 156.0], [455.0, 384.0, 131.0, 142.0], [456.0, 401.0, 126.0, 136.0], [465.0, 401.0, 119.0, 107.0], [497.0, 419.0, 84.0, 133.0], [464.0, 423.0, 128.0, 156.0], [461.0, 364.0, 133.0, 160.0], [461.0, 352.0, 141.0, 153.0], [459.0, 374.0, 144.0, 148.0], [472.0, 382.0, 135.0, 148.0], [485.0, 384.0, 121.0, 136.0], [480.0, 441.0, 124.0, 124.0], [478.0, 392.0, 126.0, 150.0], [456.0, 358.0, 138.0, 158.0], [451.0, 367.0, 133.0, 151.0], [451.0, 391.0, 126.0, 145.0], [451.0, 390.0, 125.0, 146.0], [444.0, 358.0, 123.0, 163.0], [431.0, 380.0, 123.0, 158.0], [418.0, 390.0, 130.0, 154.0], [435.0, 361.0, 127.0, 182.0], [494.0, 333.0, 104.0, 168.0], [493.0, 336.0, 114.0, 170.0], [452.0, 362.0, 140.0, 160.0], [481.0, 343.0, 129.0, 161.0], [533.0, 336.0, 101.0, 170.0], [511.0, 352.0, 129.0, 178.0], [467.0, 352.0, 160.0, 179.0], [437.0, 338.0, 149.0, 188.0], [460.0, 343.0, 151.0, 180.0], [452.0, 340.0, 155.0, 182.0], [420.0, 326.0, 121.0, 176.0], [417.0, 343.0, 128.0, 178.0], [445.0, 342.0, 153.0, 193.0], [473.0, 310.0, 165.0, 198.0], [507.0, 304.0, 153.0, 195.0], [486.0, 326.0, 168.0, 211.0], [506.0, 342.0, 157.0, 187.0], [542.0, 320.0, 133.0, 201.0], [510.0, 331.0, 160.0, 208.0], [471.0, 353.0, 186.0, 221.0], [443.0, 329.0, 184.0, 222.0], [442.0, 332.0, 178.0, 235.0], [438.0, 365.0, 184.0, 239.0], [434.0, 383.0, 172.0, 202.0], [459.0, 342.0, 159.0, 241.0], [500.0, 369.0, 180.0, 249.0], [518.0, 409.0, 202.0, 232.0]], \"areas\": [3040.0, 3480.0, 4117.0, 4802.0, 6440.0, 9357.0, 11884.0, 13901.0, 12269.0, 12188.0, 13622.0, 12828.0, 12585.0, 12685.0, 11879.0, 8894.0, 4391.0, 1136.0, 1209.0, 2284.0, 7340.0, 11065.0, 12877.0, 13739.0, 11530.0, 4671.0, 6977.0, 13217.0, 12733.0, 12267.0, 13844.0, 15676.0, 13747.0, 12648.0, 12457.0, 11211.0, 11148.0, 11854.0, 8287.0, 6376.0, 7229.0, 6712.0, 8736.0, 9184.0, 10117.0, 9283.0, 9127.0, 9354.0, 8666.0, 8677.0, 9622.0, 9977.0, 9507.0, 8556.0, 10256.0, 7984.0, 9356.0, 10133.0, 10086.0, 11147.0, 11133.0, 10855.0, 12648.0, 10084.0, 10964.0, 9241.0, 9567.0, 7535.0, 8346.0, 11088.0, 10926.0, 9198.0, 8800.0, 10652.0, 13520.0, 15872.0, 10925.0, 15806.0, 13963.0, 13463.0, 11640.0, 12346.0, 12705.0, 7297.0, 9678.0, 13411.0, 14209.0, 14140.0, 13121.0, 14476.0, 14008.0, 13659.0, 11990.0, 5908.0, 8861.0, 7303.0, 5444.0, 14064.0, 14109.0, 9690.0, 7374.0, 6504.0, 6222.0, 6659.0, 14653.0, 14667.0, 14203.0, 11941.0, 11091.0, 10480.0, 12209.0, 14183.0, 13098.0, 12310.0, 11687.0, 12888.0, 12541.0, 12584.0, 14348.0, 11212.0, 11197.0, 13568.0, 13507.0, 10748.0, 13512.0, 18304.0, 18390.0, 19363.0, 19830.0, 15935.0, 15153.0, 21647.0, 22681.0, 22946.0, 22142.0, 18901.0, 17207.0, 22687.0, 27554.0, 25576.0, 27316.0, 24883.0, 20922.0, 26249.0, 24082.0, 30684.0], \"iscrowd\": 0, \"video_id\": 0, \"height\": 1280, \"width\": 720, \"category_id\": 49272, \"noun_phrase\": \"air jordan\"}, {\"id\": 1439, \"segmentations\": [{\"counts\": \"Semd03kW16K2N33J5Kbof1:TPYN5L2N000O01O1N3K5MY[k4\", \"size\": [1280, 720]}, {\"counts\": \"mTQg0;dW14M2M2O0O0100N2N2N3M3M4LTcb4\", \"size\": [1280, 720]}, {\"counts\": \"o\\\\mf0;eW14L1O1O1O0O2O000O100O01O1O1O1M3M4N3IVk^4\", \"size\": [1280, 720]}, {\"counts\": \"o\\\\cf0>bW16J2N000001N1000000O10000O1000000O10O10000000O010O1M4M2M8InR[4\", \"size\": [1280, 720]}, {\"counts\": \"Qmjf0b0^W11N2O01O00000O10000000000000O10O10O10O1O1O1N2M3NSkY4\", \"size\": [1280, 720]}, {\"counts\": \"TeXg0?`W13M2O00O1000O1000O010O1O1O1M4N2MPSV4\", \"size\": [1280, 720]}, {\"counts\": \"Telg0<cW1010ONQ[R4\", \"size\": [1280, 720]}, {\"counts\": \"cmQf06gW15M3N1O0013K^Zi5\", \"size\": [1280, 720]}, {\"counts\": \"bmVf0:eW13M101N21N2M^Zd5\", \"size\": [1280, 720]}, {\"counts\": \"hmQf01jW18J5N1O01O3M8GURh5\", \"size\": [1280, 720]}, {\"counts\": \"gePf03iW18J3N2O02N7IWbj5\", \"size\": [1280, 720]}, {\"counts\": \"cePf0;dW12N2N2O001O13K8HVRh5\", \"size\": [1280, 720]}, {\"counts\": \"fmQf08dW15M3O1N2N2N1N2O13N1N4L9FUja5\", \"size\": [1280, 720]}, {\"counts\": \"gUSf06eW17M2N2N101N2N1O1011N3M5K;DPZ_5\", \"size\": [1280, 720]}, {\"counts\": \"cmQf0<cW12M3N2N1N3O0O1011N3M8HXRc5\", \"size\": [1280, 720]}, {\"counts\": \"cUne0<cW11O2N2N1N3N2N100001O1O7I\\\\be5\", \"size\": [1280, 720]}, {\"counts\": \"hUie01jW19J3N2N1N3N1O1O2N100000000003Mdjf5\", \"size\": [1280, 720]}, {\"counts\": \"b]ee08fW14M2M2O2M2O2N1000000010O5IdRm5\", \"size\": [1280, 720]}, {\"counts\": \"aUde08eW15K4N2O1O02N2NbjU6\", \"size\": [1280, 720]}, null, {\"counts\": \"odgg02lW1;G0ScX4\", \"size\": [1280, 720]}, {\"counts\": \"R]\\\\g0=aW13N2O000000O010N2N2O2LTcX4\", \"size\": [1280, 720]}, {\"counts\": \"T]Rg03hW1>F3OO000000O100000O100000O1000O1N2O1N3L4MSSV4\", \"size\": [1280, 720]}, {\"counts\": \"Yenf06_W1=L3OO100000000O10000000000O100O01000O1O001O2M3N3LTkT4\", \"size\": [1280, 720]}, {\"counts\": \"Zm[f07bW18K5J5M3N2O11O001O001O1N2O00001O000O10001O0O10001O0O10000O100O010O100O1000O0100N3N2M5HVSQ4\", \"size\": [1280, 720]}, {\"counts\": \"\\\\eUf03eW1;H6N2M2O1O2N100O101O00000O1O1001O1O1O0O2O001O000O2O001O00001N100O10000O10000O1000000O10O1000O10O0101M2O2LUkj3\", \"size\": [1280, 720]}, {\"counts\": \"WeUf0;aW16L2O2O0N3N1O2N1000000O10000O1O11O001O001O001O001N10001O0O101O0O1000000O100O100O1000000O10000O10O10O1O1O1O1N3MS[h3\", \"size\": [1280, 720]}, {\"counts\": \"XmVf09bW17L3N101M2O2N1O2O000O100000000O101O00O2O001O001O001N10001N1000001N10000O100O1000000O100O10000O1000OO20O02N1N3MSSg3\", \"size\": [1280, 720]}, {\"counts\": \"aUgf03fW19I6K4M3M3O1O1O11O001O1O001N101O0O2O00000O2O00000O10000O100O10000O10000O010O1O010O1O1N3M3KU[c3\", \"size\": [1280, 720]}, {\"counts\": \"^mcg02kW15K4K4M3N20O01000O1000000000000O010000O10O1N2N1O4KVc_3\", \"size\": [1280, 720]}, {\"counts\": \"[mof07dW17J4M3M3M4M2001O001O00001N101O001O001O00000O2O0O10000O1000000O100000000O1000O10N2N3M3LTS]3\", \"size\": [1280, 720]}, {\"counts\": \"WUbf07eW17K3N2O1M2O2N1O1O2O00000O101O0O1O1001O001O00001O1N101O1O001O000O2O00000O10001O0O10000O10000O10000O010O1O1N2O1O1NScZ3\", \"size\": [1280, 720]}, {\"counts\": \"X]cf08fW16I5L3N2N1O2O1N10000000001O000000000000000001O1O001O0O2O001O001N1000001N10000O10000O10000O1000000O1000OO102M2O3KnZY3\", \"size\": [1280, 720]}, {\"counts\": \"Zedf0=_W16L3O1N2O001N10001O0000000000000O1000000001O1O001O001O0O2O00001N10000O2O000O100000000O100000000O10O010O01N2O2M4LiRX3\", \"size\": [1280, 720]}, {\"counts\": \"a]hf0:bW16K4M3N101O000O100000001N1O0O21O001O001O0O2O1O001O00000O2O00000O100O101O0O1000000000O010000O100O1N2N4L4LeZY3\", \"size\": [1280, 720]}, {\"counts\": \"lmTg03hW17J4L4L4M4M2O010001O001O001O00001N10000O101N10000000000O1000000O100O10O0100O1O1M3N5JgZY3\", \"size\": [1280, 720]}, {\"counts\": \"dmof0;`W16K4K5N2O2O0O1O1O11O2N001O1O001O1O0O2O001O00000O101N100000000O1000000O10000O100O010O1O1N2O1M8HeRX3\", \"size\": [1280, 720]}, {\"counts\": \"hedf01fW1;J5L4M3O001O000O2O0O10000001OO1O1O11O1O00001N2O001O001O00001N10000O100O101O000O10000000000O10000O101M3M3Kgj[3\", \"size\": [1280, 720]}, {\"counts\": \"_Ubf0<^W18K4L3010O00O2N101O00000O1000000001O100O001N101O1O0O2O00001O0O1000000O101N1000000O1000000O10000O100O100N2O2L5Kfj[3\", \"size\": [1280, 720]}, {\"counts\": \"bmjf08cW18I5L4N101N1O101O00000O2N10000O12N1O001N2O1O1O001O00001N10001O0O10000O101O000O1000000O10000O100O100O2N1N4LdbU3\", \"size\": [1280, 720]}, {\"counts\": \"bU[g0=\\\\W18L4L4O0O100001OO2M2N200001O2N1O1O1N101O001O1O0000001N100O2O000O1000001N10000O10000O10000O1O1O1O2N5IbRi2\", \"size\": [1280, 720]}, {\"counts\": \"cegg0<\\\\W1:K4M2O1O101O0001O00O1N2001O1O1O1O1N101O1O00000O2O000O2O0O10001N1000000O10000O10000O100O100O1O2N2Mdj]2\", \"size\": [1280, 720]}, {\"counts\": \"le[h02fW19H8J5N2N20000001O001N101O00001O0O10000O2O000O10000O10000000000O10000O1O1O2N2N1N4KfjS2\", \"size\": [1280, 720]}, {\"counts\": \"bUYh03gW1:G6L3N2O1O1O1O1O1O11O00001O1O001O001N101O001O0O10001N100O10000O1000000O2O00000O101N1N3M4KjZQ2\", \"size\": [1280, 720]}, {\"counts\": \"WmWh0<\\\\W19L3M301O000000000O2LWORiNf0nV160O2O2N1O1O00001O1N2O001O0000001N10000O101O0O10001O0O10001O000O1N3N2N4LjRP2\", \"size\": [1280, 720]}, {\"counts\": \"S]Uh0=\\\\W18L4N1O101O000000000O1O1O11O1O1O1O1O0O2O001O1O1N101O000O101O00000O2O00001O001N1O2O0O2N1O3N1O2NgbR2\", \"size\": [1280, 720]}, {\"counts\": \"Wmmg05cW1;G7L3M3N2O2N11O2N1O0000001O1O001O1O0O2O1O1O000O2O001O001O00001N10001N100000000O2O0O2O001N2M3LoRZ2\", \"size\": [1280, 720]}, {\"counts\": \"Ymhg01gW1<G6J5N3O0O100000000O2O00000002M101O1O1O001O1N10001O001N10001O0O2O00000O2O00001O1N2O1N1O2KPca2\", \"size\": [1280, 720]}, {\"counts\": \"VUeg09bW16J5J7L3N2O1O10001O01O0O10010N3N1O1O1O1O1O1O1N101O001N101O000O2O00000O2O000O10000O1O1O2N3LP[e2\", \"size\": [1280, 720]}, {\"counts\": \"\\\\]fg04fW17H7L5K4M3O1000000O100001O001O1O1N2O1O1O1N101O001O000O2O00001O00000O100O2O0O1O1O1N4KRkg2\", \"size\": [1280, 720]}, {\"counts\": \"Xebg08bW17J5M4K4N3N1O10000O100000O101O001O1N2O1O1O0O2O001O0O2O00001N10000O101N10000O100O2O0N3MT[j2\", \"size\": [1280, 720]}, {\"counts\": \"Y]ag07`W1:D<J5K6M2O10000001O1N2O1O1O1N2O1O001N100O2O1N1O1O2N1O1O1O1O1O100O1O100O1O01O00010O0002M2NbSi2\", \"size\": [1280, 720]}, {\"counts\": \"WU`g05\\\\W1b0@>L3O11O1O001O0O2O1N101N1O2N101N2N1O1O2N1N3M200O1O100O00001O10O01N10001O002M4LccP3\", \"size\": [1280, 720]}, {\"counts\": \"Y]ag01]W1e0D:M2000O2N1N3N1N3N1N3N1N2O2M2O100O2N1N3N3L]ke3\", \"size\": [1280, 720]}, null, {\"counts\": \"ddfe04mW12M3OOX[X6\", \"size\": [1280, 720]}, {\"counts\": \"ndfe05iW16J3M3M3N2N1O2N21O5IWco5\", \"size\": [1280, 720]}, {\"counts\": \"gebg02dW1=F8I7K4M2011O000O2O0O1O2O1N2N2N1O2O0O2O0O101O000O10O10O100O010O01000O1N3LTkQ3\", \"size\": [1280, 720]}, {\"counts\": \"^U[g0=[W18J7J5L4O010000O101O1N101N2O0O2N2N101N2N101O00000O2O00000O1000O010O010O01O1O1O1O2JW[T3\", \"size\": [1280, 720]}, {\"counts\": \"aeSg08`W19H7L4K5N2O100000O2O0O2O0O2N2N2O1O0O2O1N10001N1000000O2OO010000O10O01O00100N3L4HXS]3\", \"size\": [1280, 720]}, {\"counts\": \"`Ugf06dW18E:J5K5M3M3M3N13N002N1O1N2O1N2N102M2O1N101N2O0O2O1O001O0O2O0000000O1000O01000O0100O001O1N2N2M6JTc_3\", \"size\": [1280, 720]}, {\"counts\": \"cedf01iW19G7I6L4L4M3L4O1M2011O001O2N1N2O1N2O0O2O1N2N2O1O1N101O1O1O1O001O0O1000000O1000O10O100O010N2N2N3K4MTk`3\", \"size\": [1280, 720]}, {\"counts\": \"`eif08eW14K5L4L3M3M3M3M4L3M3N2O1001O001N2O2M2O1O1N2N1O2O1N2N2N2N2O0O2N2O0O2O001N10001N2M3JQke3\", \"size\": [1280, 720]}, {\"counts\": \"^Ubf06dW1;H4L4M2M4M2L4N2O2O0O1O2N1O1000000001O001O1O001O1N2O1N101N2N2N1O2M3N2N1O2N1N3N2M3N2M4IQkj3\", \"size\": [1280, 720]}, {\"counts\": \"X]^f0:bW18G6H8L4N1O1O1O2N1O1O1O100O1010O1O001O0O2O1O001O1N101N2N101N2M3N2M2O2N101M2O2N1N3L3M4MTcn3\", \"size\": [1280, 720]}, {\"counts\": \"W]^f0:bW16K5J4M4K5M2N2O1O2N1N2O1O10001O01O001N101O1O1N101O0O2N2O1N2O0O2M3N1O2M2O2N1O2N1N2M4MXko3\", \"size\": [1280, 720]}, {\"counts\": \"U]^f0:aW17K5L3I6L4N3N1O1N201N1N2000000001O001O001O001N2O1O0O2O1N2O0O2N2N2M2O2N1N3N1O2N1N2N3M3L4LY[m3\", \"size\": [1280, 720]}, {\"counts\": \"Wm[f03gW19J6K4L2L5I6N2N2O2N1O1O1O1O2O000000O2O1O001O001N2O001N2O1N2N101N2M3N1O2M201M2O2N1N2N3L6JZko3\", \"size\": [1280, 720]}, {\"counts\": \"Sm[f07eW17K4K5K3L4L5L3O1O1O1O2N1O1O1000000001O00001N2O1O1N101N2O0O2O1N2M2O2M3N2N1O2O0N3N1N2M4L^SQ4\", \"size\": [1280, 720]}, {\"counts\": \"kl`f0a0[W16L4K4L3M4M2O2N1O1O2M200O110O01O001O0O2O1O1O1N2O1N2O0O2M3N2M3M2O2M3M3N2N2M3N3KZSQ4\", \"size\": [1280, 720]}, {\"counts\": \"QU]f0:_W19I6M3M3L3N3M2O1O1O2N1O1O11O00002N1O0O2O1N2O1N2O1N2M3N2O1O1N2O1O1N2O1O1N2N2N3M3JUcS4\", \"size\": [1280, 720]}, {\"counts\": \"Tm`f09eW14L3N2N2N1O2N2N1N3L3O2N1O2O0O2O0O2O001O00010O0001O1O001N2O1O1N2O0O2O1N2O1O1O1N2O1N2O1O1O1N1O2L5Inje3\", \"size\": [1280, 720]}, {\"counts\": \"Vmhg0n0nV16L3N2N2O1O1O001O1O010O000010O1O0010O0001O100O1O001O1O10O01O1O1O1N2O001O1N1O3[ORiN3SW1Gf0MYbf2\", \"size\": [1280, 720]}, {\"counts\": \"o]dh0d0UW1;G7J5M2O2N10001O00001O01O00001O001O00100N101O001O0O2O001O0O2O1O1N5K;ElaR2\", \"size\": [1280, 720]}, {\"counts\": \"a^ih01eW1<H9L2M3M3J6G9K40001O01O01O0001O001O010O001O001O10OO2O00001N2O1N101M3M6JVZl1\", \"size\": [1280, 720]}, {\"counts\": \"W^dh0>_W15J5L4L4K4M4L4M2O1O2N101O0001O0010O01O001O001O001N101O0O2O0O2O0O2M3M2I8I7MZRP2\", \"size\": [1280, 720]}, {\"counts\": \"Znah05eW1:F9J4K5I7J5N3N101O000O2O00010O0001O1O001O001O001O1O0O2O001N101N1O2N1N3K4I8L6I\\\\RP2\", \"size\": [1280, 720]}, {\"counts\": \"YVch0<`W17I6L3K4N3H8L3N201O0O101O0010O00001O001O001O001O001N101O0O2O0O2N1O2L3L5H8K4L\\\\RP2\", \"size\": [1280, 720]}, {\"counts\": \"anah0;bW16H7J5L4L3M4K4N3N1O101N10001O01O001O1O001O1O00001O001N2O001N101N1N3N1L5F:M5IQZQ2\", \"size\": [1280, 720]}, {\"counts\": \"ef`h0>^W17H6K4L5L4K4N3N1O2N1000010O0001O001O001O001O001O001O0O2O001N2N1N2N3K5F:L5LkiS2\", \"size\": [1280, 720]}, {\"counts\": \"kf`h06eW1:H4J6I7J6L4L3O2N10001N11O0000001O0010O0001O1O1O000O2O001O0O2O0O2N2M2N3D<L7J8FcYQ2\", \"size\": [1280, 720]}, {\"counts\": \"gnfh0;aW18H5K5L3I8J6N2N10001O00000000010O0001O00001O001N2O001O001N101M2O2N2K4I9J7Ikam1\", \"size\": [1280, 720]}, {\"counts\": \"dVmh0>\\\\W18H7L4K5K4N3N1N3O001O001O000001O001O001O001O001O000O2O0O2O1N1O2N2M2L6C=L8EiYg1\", \"size\": [1280, 720]}, {\"counts\": \"b^ih0=`W16G7K5L4I7M2N3M3O0000001O000001O0010O0001O1O0O2O001O1N100O101N2M3M3K5F:L8GlQk1\", \"size\": [1280, 720]}, {\"counts\": \"dfeh0?\\\\W16K6J4M4G8M4N101N101O00010O000001O001O001O00001O1O001N101N2O0O2N1N3L4I7J7Jnin1\", \"size\": [1280, 720]}, {\"counts\": \"iVch0:bW17I6J4L5K5J6L3N2O2O0000001O000001O001O001O001O001O1O001N101N101N2M3M2K6I7L9GfQP2\", \"size\": [1280, 720]}, {\"counts\": \"knah0;bW14K6J6K3L5K4K6L301O0O10001O0001O0000002N1O001O001O00001N2O0O2O1N1N3L3I8K5M7HeYQ2\", \"size\": [1280, 720]}, {\"counts\": \"mf`h0:bW16K5J5L4L3J7L3L5O0O101O0O10000001O1O10O01O001O001O001N2O0O2O1N1O2N1M4J6I7M3LgaR2\", \"size\": [1280, 720]}, {\"counts\": \"n^dh09aW1:I4L3L5K5J5M4N1O2O0O101O000001O0010OO2O001O010N2O001N101O0O2N2N2M2H9J6LgYQ2\", \"size\": [1280, 720]}, {\"counts\": \"jfjh0<`W17I5K5M3L3L5L4L3O101O0O10010O01O001O001O001O001O001O1N101O0O2N1O2L4G9K6JhQk1\", \"size\": [1280, 720]}, {\"counts\": \"ifoh08_W1<I5L4L4K5L3O2N1N3O0O2O00000O11O0000001O001O001O00001N2O0O2N1O2M3L4J7G:JkQf1\", \"size\": [1280, 720]}, {\"counts\": \"oVai05iW13M3L4M3N1O2N1O2N1O1O1O1N2N2O1O1O1O1N1O2O1O0011M2O1O2M2J7EXZ]1\", \"size\": [1280, 720]}, {\"counts\": \"afhi0:]W1;I6G:I5M4N2O0O20O01O00000010O0000001O1O1O00001O001N101O1N1O2O1M2N3G:G>FmQm0\", \"size\": [1280, 720]}, {\"counts\": \"`^Vj08cW17J6J5J5L5J6L3O2N1O2N10000010O001O00001O001O001O1O0O2O1O0O2O0O2M2O2L4E>HVZ?\", \"size\": [1280, 720]}, {\"counts\": \"ZV_j0>^W16J5L4K5L4M2N3N1N3M2O2O0O10000001O001O010O0O2O001O1O001N101N1O2N2N1N3D;K<DVZ5\", \"size\": [1280, 720]}, {\"counts\": \"Ynbj09aW19H7G8K5L4L4M201O001O00001O0001O001O001O0000001O1O0O2O001N101N1O2N2N1M5Ca0CZb1\", \"size\": [1280, 720]}, {\"counts\": \"UVdj0;aW17G7G9L5H6O2O1N101O00001O000000010O00O2O1O00001O001N101O1O0O2O0O2N2M2M6_Oib1\", \"size\": [1280, 720]}, {\"counts\": \"Rffj0<_W18D:I8K4M3M3N101O0O101O01O0000001O00001O001O001O001O001N101N101N1O3L4I5O_RO\", \"size\": [1280, 720]}, {\"counts\": \"`nbj07`W1<G8G8N2I7M3N1O2O0O2O0001O01O0001O001O00001O001O0O2O1O001O0O2N1O2N1N3K5E=HXb1\", \"size\": [1280, 720]}, {\"counts\": \"hf\\\\j0?[W17J6I8K3K6N2N101O0O2O001O000001O00001O1O1O001O001O0O101N101N2O1N1O2L3H:H;Gfi7\", \"size\": [1280, 720]}, {\"counts\": \"mfWj0`0ZW19H6J6H8N2N1O101O00001O000000010O0001O001O001O0O2O1O001N101N1O2N2M2L5H9J6Kai<\", \"size\": [1280, 720]}, {\"counts\": \"PgRj0:aW18G8J5J6L3M4N2N2N1O2O0000001O01O001O00001O001O001O001N2O001N1O2N1O2M3L4E;Ldia0\", \"size\": [1280, 720]}, {\"counts\": \"mmdi01hh8?c^H6I7I7K5L4N2O0O2O000O2O00001O01O00001O001O001O001O0O2O0O2O1N1O2N2N1N3I7H<Ghif0\", \"size\": [1280, 720]}, {\"counts\": \"[nii0`0[W17K5I6L3L5N2L3O2O001O00001O0O100001O001O001O1O001O1O001N1O2O0O2N2M3M3I6I<H8FnYi0\", \"size\": [1280, 720]}, {\"counts\": \"Znii08cW18I6J5E;M3N2M2O2N1O101O000O2O0O11O1O0010O01O001N101O001N101N2N1O2O0N3M3K5G9M7D[Rh0\", \"size\": [1280, 720]}, {\"counts\": \"PVPj0>]W18B=J4M4N2N2N101N1O2O000000010O0001O001O00001O1O001N101O1N101N2N1O2M3J7F<G_Zd0\", \"size\": [1280, 720]}, {\"counts\": \"oeWj0?]W16J5M3L4M2N3M2N3M2N2O1O2N10000010O00001O001N101O0O2O001N101N2N1O2M3K6Bc0E^R>\", \"size\": [1280, 720]}, {\"counts\": \"ne\\\\j0>_W15L3M3M3L4M2N3M2O1O2N1O2N1O1O1O1O1001O000000001O0O2O0O2O1N101N1O2N2M2F<J7Hej7\", \"size\": [1280, 720]}, {\"counts\": \"je\\\\j09_W1=H4G9L3N3L4N1O2O0O101O000000001O01O001O001O001O1O001N101N101N2N1O2M2J7H9Jkj7\", \"size\": [1280, 720]}, {\"counts\": \"`e\\\\j0a0[W16I6K5J5N3L4O0O101N101N100001O01O01O001O001O001O0O2O001N101N2O0O1O2M3K5E<Koj7\", \"size\": [1280, 720]}, {\"counts\": \"aUZj0?_W14K4L4L4L3M4M2M4M2N2N3N10001O000001O001O001O1O001O1O0O2O0O2N101N2M2O2L4F;J7IoR9\", \"size\": [1280, 720]}, {\"counts\": \"jmSj0`0\\\\W16H7K5M2M4G8O2O001O1O000000010O000001O001O1O1O000O2O001N2O0O2N2N1N3L3G;J:Gbb`0\", \"size\": [1280, 720]}, {\"counts\": \"\\\\Vki06aW1<K4J5J6J5M4L3N3O001N100O2O001O0001O1O001O001O001O001N101N1O2O1N1N3N1N3F:L5J]Rh0\", \"size\": [1280, 720]}, {\"counts\": \"`fci05dW1=F5K5EZOWiNj0gV19N1M4M3N101O00001O000001O00001O001O001O001O1N101O0O2N1O2N2M3L4F;K:EPbo0\", \"size\": [1280, 720]}, {\"counts\": \"mUWi01]h3>o^M7K4I7I7L3N3M3O00001O0000001O000001O001O001O001O1O001N101N2N1O2N2M3L4G:J<CoaY1\", \"size\": [1280, 720]}, {\"counts\": \"VfTi0a0ZW16K5M3I7L4N1M3O2O001O001O0000001O01O00001O001N101O0O2O1O0O2N1O2N2N2J6G:K:CUj_1\", \"size\": [1280, 720]}, {\"counts\": \"U^nh09bW18J5H7H8L3O2L4N1O2O000O2O0001O00001O0010O0001N2O001O001N101N1O2N2M3M3G9J:H7IWjd1\", \"size\": [1280, 720]}, {\"counts\": \"QVmh0=^W17H7J6J6M3M2N3N101N101O000001O001O001O001O00001O001O001N2N1O2N2N1N4I6F<J_bh1\", \"size\": [1280, 720]}, {\"counts\": \"R^nh0<_W17J6H7J6M2M4M2O2N2O00001O000001O00001O001O001O001O001N101N2N1O2N2M3L4E=I:HXRf1\", \"size\": [1280, 720]}, {\"counts\": \"neoh0=_W17H6K5L4J6M2N3M2O2O00010O00000010O00001N101O001O00001N2O0O2N1O2N2M3M3D?I8G]jd1\", \"size\": [1280, 720]}, {\"counts\": \"kUWi0?]W16I6F:L4N2M2N3N101N10000010O001O001O001O00001O001N101O1O0O2N101M2N3L5E=Hdb^1\", \"size\": [1280, 720]}, {\"counts\": \"ke^i08dW19E8J4K6K5M3M2O2N100O2O00001O000001O001O00001O1O001N101O0O2O0O2N2N2M2M4E;]OihN7RRW1\", \"size\": [1280, 720]}, {\"counts\": \"lmdi03gW1<D9@>N1O2L4M2O2O1O0O110O0000000001O001O001O001O00001O0O2O1N101N2M2O2L4G:Jlbo0\", \"size\": [1280, 720]}, {\"counts\": \"jUki07dW1:G6H7L3M4I6O2N2N101O0000001O00000001O001O001O000O2O001O001M3N2N1O2M4K4F=HhZi0\", \"size\": [1280, 720]}, {\"counts\": \"mUPj0;bW16H7J5L4I6L5N1M4N101O001O0000000001O001O001O001O001O0O2O1N101N2O0O2M2L6D>HeZd0\", \"size\": [1280, 720]}, null, null, null, null, {\"counts\": \"[Tmh03mW15K6J5K8I2NO1O1N2K5M5L4J\\\\cf2\", \"size\": [1280, 720]}, null, null, null, null, null, null, null, null, {\"counts\": \"Ym^g01]h89X_H6K2O0O11O0O2O0O2N2N2O1O1Niij3\", \"size\": [1280, 720]}, null, null, null, null, {\"counts\": \"iebg0d0WW16K5G8L4N3N10001O0010O1O6J:F4L4[OihN9YW1CjhN<^W1M4M5JgQg3\", \"size\": [1280, 720]}, null, null], \"bboxes\": [[535.0, 919.0, 60.0, 22.0], [589.0, 922.0, 13.0, 20.0], [586.0, 924.0, 19.0, 22.0], [578.0, 926.0, 30.0, 23.0], [584.0, 927.0, 25.0, 22.0], [595.0, 928.0, 17.0, 22.0], [611.0, 930.0, 4.0, 14.0], [564.0, 943.0, 7.0, 16.0], [568.0, 943.0, 7.0, 17.0], [564.0, 944.0, 8.0, 15.0], [563.0, 944.0, 7.0, 16.0], [563.0, 944.0, 9.0, 19.0], [564.0, 938.0, 13.0, 26.0], [565.0, 940.0, 14.0, 24.0], [564.0, 939.0, 12.0, 25.0], [561.0, 939.0, 13.0, 24.0], [557.0, 937.0, 16.0, 24.0], [554.0, 936.0, 14.0, 24.0], [553.0, 938.0, 8.0, 20.0], null, [607.0, 925.0, 3.0, 13.0], [598.0, 926.0, 12.0, 19.0], [590.0, 925.0, 22.0, 21.0], [587.0, 922.0, 26.0, 24.0], [572.0, 916.0, 44.0, 31.0], [567.0, 918.0, 54.0, 32.0], [567.0, 920.0, 56.0, 31.0], [568.0, 921.0, 56.0, 31.0], [581.0, 920.0, 46.0, 31.0], [604.0, 923.0, 26.0, 24.0], [588.0, 919.0, 44.0, 30.0], [577.0, 919.0, 57.0, 32.0], [578.0, 925.0, 57.0, 30.0], [579.0, 929.0, 57.0, 29.0], [582.0, 930.0, 53.0, 31.0], [592.0, 933.0, 43.0, 29.0], [588.0, 928.0, 48.0, 33.0], [579.0, 930.0, 54.0, 30.0], [577.0, 929.0, 56.0, 33.0], [584.0, 928.0, 54.0, 34.0], [597.0, 928.0, 51.0, 35.0], [607.0, 930.0, 50.0, 33.0], [623.0, 932.0, 42.0, 28.0], [621.0, 924.0, 46.0, 30.0], [620.0, 917.0, 48.0, 33.0], [618.0, 916.0, 48.0, 31.0], [612.0, 911.0, 48.0, 34.0], [608.0, 915.0, 46.0, 30.0], [605.0, 911.0, 46.0, 35.0], [606.0, 914.0, 43.0, 32.0], [603.0, 913.0, 44.0, 34.0], [602.0, 904.0, 46.0, 43.0], [601.0, 903.0, 41.0, 41.0], [602.0, 908.0, 23.0, 34.0], null, [555.0, 916.0, 4.0, 11.0], [555.0, 914.0, 11.0, 28.0], [603.0, 920.0, 38.0, 38.0], [597.0, 918.0, 42.0, 38.0], [591.0, 918.0, 41.0, 36.0], [581.0, 908.0, 49.0, 45.0], [579.0, 909.0, 50.0, 43.0], [583.0, 910.0, 42.0, 46.0], [577.0, 912.0, 44.0, 46.0], [574.0, 908.0, 44.0, 47.0], [574.0, 906.0, 43.0, 47.0], [574.0, 904.0, 45.0, 46.0], [572.0, 902.0, 45.0, 47.0], [572.0, 901.0, 44.0, 46.0], [576.0, 899.0, 40.0, 50.0], [573.0, 900.0, 41.0, 46.0], [576.0, 912.0, 49.0, 43.0], [608.0, 926.0, 42.0, 52.0], [630.0, 939.0, 36.0, 51.0], [634.0, 939.0, 37.0, 53.0], [630.0, 939.0, 38.0, 52.0], [628.0, 936.0, 40.0, 53.0], [629.0, 940.0, 39.0, 52.0], [628.0, 947.0, 39.0, 52.0], [627.0, 952.0, 38.0, 52.0], [627.0, 954.0, 40.0, 52.0], [632.0, 954.0, 38.0, 51.0], [637.0, 952.0, 38.0, 51.0], [634.0, 950.0, 38.0, 52.0], [631.0, 951.0, 38.0, 52.0], [629.0, 954.0, 39.0, 51.0], [628.0, 956.0, 39.0, 51.0], [627.0, 957.0, 39.0, 52.0], [630.0, 959.0, 37.0, 51.0], [635.0, 957.0, 37.0, 51.0], [639.0, 953.0, 37.0, 50.0], [653.0, 959.0, 30.0, 43.0], [659.0, 945.0, 37.0, 52.0], [670.0, 942.0, 37.0, 52.0], [677.0, 941.0, 38.0, 53.0], [680.0, 939.0, 38.0, 51.0], [681.0, 936.0, 37.0, 52.0], [683.0, 933.0, 37.0, 52.0], [680.0, 942.0, 38.0, 53.0], [675.0, 956.0, 38.0, 52.0], [671.0, 963.0, 38.0, 52.0], [667.0, 962.0, 38.0, 52.0], [656.0, 955.0, 45.0, 52.0], [660.0, 945.0, 39.0, 52.0], [660.0, 937.0, 40.0, 54.0], [665.0, 932.0, 38.0, 53.0], [671.0, 931.0, 37.0, 52.0], [675.0, 931.0, 38.0, 50.0], [675.0, 924.0, 38.0, 51.0], [675.0, 918.0, 38.0, 52.0], [673.0, 916.0, 39.0, 53.0], [668.0, 926.0, 38.0, 53.0], [661.0, 937.0, 39.0, 53.0], [655.0, 943.0, 39.0, 52.0], [645.0, 944.0, 41.0, 51.0], [643.0, 941.0, 38.0, 51.0], [638.0, 934.0, 39.0, 52.0], [637.0, 932.0, 37.0, 51.0], [638.0, 933.0, 38.0, 51.0], [639.0, 930.0, 38.0, 52.0], [645.0, 927.0, 37.0, 52.0], [651.0, 925.0, 38.0, 51.0], [656.0, 922.0, 38.0, 52.0], [661.0, 924.0, 38.0, 50.0], [665.0, 927.0, 38.0, 52.0], null, null, null, null, [637.0, 907.0, 13.0, 30.0], null, null, null, null, null, null, null, null, [600.0, 937.0, 21.0, 37.0], null, null, null, null, [603.0, 929.0, 21.0, 49.0], null, null], \"areas\": [216.0, 184.0, 315.0, 523.0, 460.0, 298.0, 46.0, 88.0, 96.0, 81.0, 81.0, 130.0, 233.0, 235.0, 232.0, 251.0, 303.0, 265.0, 131.0, null, 28.0, 188.0, 375.0, 465.0, 903.0, 1206.0, 1231.0, 1236.0, 935.0, 434.0, 919.0, 1272.0, 1282.0, 1247.0, 1109.0, 856.0, 1038.0, 1142.0, 1286.0, 1229.0, 1178.0, 1118.0, 814.0, 926.0, 1031.0, 945.0, 976.0, 910.0, 1011.0, 916.0, 1028.0, 1202.0, 909.0, 434.0, null, 27.0, 209.0, 885.0, 982.0, 913.0, 1151.0, 1142.0, 1148.0, 1407.0, 1412.0, 1403.0, 1426.0, 1427.0, 1412.0, 1375.0, 1235.0, 1430.0, 1662.0, 1534.0, 1506.0, 1539.0, 1639.0, 1584.0, 1569.0, 1549.0, 1563.0, 1545.0, 1537.0, 1542.0, 1578.0, 1561.0, 1534.0, 1540.0, 1467.0, 1471.0, 1475.0, 833.0, 1527.0, 1507.0, 1559.0, 1568.0, 1580.0, 1614.0, 1613.0, 1591.0, 1608.0, 1592.0, 1605.0, 1598.0, 1632.0, 1639.0, 1497.0, 1471.0, 1575.0, 1594.0, 1592.0, 1603.0, 1614.0, 1599.0, 1558.0, 1543.0, 1536.0, 1520.0, 1533.0, 1526.0, 1553.0, 1558.0, 1597.0, 1530.0, 1595.0, null, null, null, null, 237.0, null, null, null, null, null, null, null, null, 198.0, null, null, null, null, 696.0, null, null], \"iscrowd\": 0, \"video_id\": 0, \"height\": 1280, \"width\": 720, \"category_id\": 49272, \"noun_phrase\": \"air jordan\"}, {\"id\": 1440, \"segmentations\": [{\"counts\": \"WVX<?_W16I5L2O2N1O2O00000000000000000000O100000001O000000001N101O001O1N2N2M3M3HQb]>\", \"size\": [1280, 720]}, {\"counts\": \"in[<=aW15L3N2N1N2O001O0000001N100000O100000001O00000000001O0O1000000O2O2M4K;BPQ[>\", \"size\": [1280, 720]}, {\"counts\": \"if_<`0_W14M2N1O1N101O0000000000000000001OO10000000000000001N100000001N2N4KUiY>\", \"size\": [1280, 720]}, {\"counts\": \"kf_<=bW18H3N1O1O1O01O10;BkhW?\", \"size\": [1280, 720]}, {\"counts\": \"bVn;`0[W1=F5L3M2O1O100O1000O00100O001O1O0O2O0N2I8J6GeQY?\", \"size\": [1280, 720]}, {\"counts\": \"TQ`:8eW15K<B>C6J5_Oa0K5K4O2O0O110O01O010O001O1O001O001O000O2O001O0O2O0O101M2O1O1O1N3M2O1M3N2O1M3M3L5J5L4M3K5L4M5Jnga?\", \"size\": [1280, 720]}, {\"counts\": \"fc[;6hW1g0YO9H8I5K2N2N2N1O1O2M101O1N2O1O0000000000000O100O10O1000O10O100000000O100O100000001N101O1N2O1O002N2M2O2N1O2M2O3M2M4M4L6I6J6I6JV[X>\", \"size\": [1280, 720]}, {\"counts\": \"PVP=8dW1?B9H9I5J3N2N1O1O2N1O1O1N2O1O1O001O1O001O00001O000000000001O0000000000000000000O1000001N1000001O000O101O1O1N2O1O2M3N3L6K5J5K6J7G:DQYa<\", \"size\": [1280, 720]}, {\"counts\": \"eVZ=9eW1a0A4K4M5K101N4L0000000O1000000000000000000000000O100000000000000000000000000000000001O000000000000001O0000001O1O6J4L6J5Gj`]<\", \"size\": [1280, 720]}, {\"counts\": \"f^V=d0[W19H4L4L3M3NO000000000000O1000000000000000000000000000000000000000000000000000000001O000000001O0000000000001O1O4L8H5K6Gc`b<\", \"size\": [1280, 720]}, {\"counts\": \"h^Q=5hW1e0^O6J5K3M3NO0000000000000000000000000000000000000000O1001O00000000000000000000000000001O000000001O00001O00001O1O4L6J6J9AbXf<\", \"size\": [1280, 720]}, {\"counts\": \"ffR=i0VW18I4L2N4L01O00001O0000000000000000000000000000000000000000000000001O000000000000001O00000000001O00001O0000001O1O3M4L7I7I^Pe<\", \"size\": [1280, 720]}, {\"counts\": \"efW=g0XW16K4K5M1N5K0001O00000000001O000000000000001O00000000000000001O00000000001O000000000000001O00000000001O0000001O1O3M5K6J7I^P`<\", \"size\": [1280, 720]}, {\"counts\": \"mTk<?YW1`0C9M2M2O100O10O10O0000000001O00O010O010O0010O010O00001O001O000000001O0000000000000O100O0010O01O1O2N2M5K5J6JbRT=\", \"size\": [1280, 720]}, {\"counts\": \"`ZQ=6gW1=F4L3M2N2N2O1N1O2O000O1O100O10O01O010O001O01O00000000000O10000O100O1O1O001OO11OO1O02N3M3L6HcmW=\", \"size\": [1280, 720]}, {\"counts\": \"nYg<3jW19I4N5K2N1O1N2O0O2O0O1O1O10O01O0010O01O00001O00000000001O00000000O2O00000000000O10000O2O0O100O2N2N2O6G9Cd]Z=\", \"size\": [1280, 720]}, {\"counts\": \"nQf<7hW15L5K3M2N2N2N2N1O1O001O001O001O1O00001O000000000000000000000000000000O100O101O000O10000O2O0O2O2M4L5K6Hem\\\\=\", \"size\": [1280, 720]}, {\"counts\": \"hQa<6gW1;H2N3M2M3N001N2O001O001O00001N100000000000000000O1000000000000O10001N100000000O2O00001O1N4M3L3M3M4KoUc=\", \"size\": [1280, 720]}, {\"counts\": \"khU<=aW19G7J3M3M2O001N10001N10O10O100O10O100000000O010000001N1000000O101O0O2O1O1N2O1N2O2M3N2M3N3L4L4KjVR>\", \"size\": [1280, 720]}, {\"counts\": \"_ol;a0[W15M3N1O1O2O0O101N100000O02O0000000001O00000000001O0000001O00001O1O001O001O000O2O0000001O1O0O101N1N3L4LcPV>\", \"size\": [1280, 720]}, {\"counts\": \"_]Y<a0[W17K3O1O100O10O001O010O00001O001O000000001O0O1000000O1O1O1N2M4J7IbZa>\", \"size\": [1280, 720]}, {\"counts\": \"Zdd<<cW16K2N2N10O0010O00001O00000000000001O0O100000000O010O1O2M5IekY>\", \"size\": [1280, 720]}, {\"counts\": \"ZTg<6gW19I3N2N1O1O001O00001O00000000000000000000O1000001N1O1O2N2N3LgSV>\", \"size\": [1280, 720]}, {\"counts\": \"Ulj<<cW14M2N1O1N11O01O00010O00000000O100000001O00000O101N1O2O1N4JgcS>\", \"size\": [1280, 720]}, {\"counts\": \"Tlo<=bW13M3N1O1O00001O0000000001O0000000O1000001O0O1O101N101MlSQ>\", \"size\": [1280, 720]}, {\"counts\": \"Z\\\\R=2iW1<H3M2O1N2O00000000O1000000000000000000O100O10001O0O100O2N1O2Koci=\", \"size\": [1280, 720]}, {\"counts\": \"Zlo<5hW1:H3N1N2O1O000000000000000000O100000001O0O10000O10000O101N1N3L5Ikkj=\", \"size\": [1280, 720]}, {\"counts\": \"^\\\\R=8eW18J4M1N2O1O0000000000000000001O0000O1000000O1000000O101N2N1Ngkj=\", \"size\": [1280, 720]}, {\"counts\": \"[TV=`0^W15M2M101O00001O0000000000000000000000O10000000000O1O2N1O2M3BRTg=\", \"size\": [1280, 720]}, {\"counts\": \"ZdX=<aW18J3N1N2O00001O00000000010OO10000000000000000O100O100O1O2M2L6Fo[c=\", \"size\": [1280, 720]}, {\"counts\": \"Y\\\\\\\\==_W17L3M2O1O1O00001O000000O100000001O00000000000O100O1O1O2N1N2L4K6IP\\\\^=\", \"size\": [1280, 720]}, {\"counts\": \"[l^=;`W19K3M2O1O001O0000000000000000001O00O101O00000O100O100N2O1N3M2GST]=\", \"size\": [1280, 720]}, {\"counts\": \"^T`=<`W17K5M0O2O1O00000000001O0000000000000001O000O10000O1O1O1O1N3I8Dok[=\", \"size\": [1280, 720]}, {\"counts\": \"^T`=a0]W15K3O1O001O0O101O0000000000000001O000000000O2O000O1O002N1M3HmS]=\", \"size\": [1280, 720]}, {\"counts\": \"`l^=>`W16K3M2O1N101O000000000000001O000000O101O0000000O2O0O001O2M3Jg[^=\", \"size\": [1280, 720]}, {\"counts\": \"al^=`0^W15L3N1N101O0O2O000000000000000000000001O0O1000001N10O01O2M3Jf[^=\", \"size\": [1280, 720]}, {\"counts\": \"`T`=`0_W15K3M101O1O000O100000000000000000000001O000O1000000O1O2N1M4GiS]=\", \"size\": [1280, 720]}, {\"counts\": \"cl^=>_W17J3N2O0O2O000000000000001O00000000O10001O00000O100O1O1O1N3Ih[^=\", \"size\": [1280, 720]}, {\"counts\": \"f\\\\\\\\=:cW19I4L2O1O0O2O000000000000000000000000000000O10001O0O1O1O2M3J7Cfc_=\", \"size\": [1280, 720]}, {\"counts\": \"odb=1hW1`0D5N1N2O0O2O0000000O2O01O000000000000000O1000001N1O1O1O1N3L^cZ=\", \"size\": [1280, 720]}, {\"counts\": \"mlm=9bW1:J3M2N2O000O2O000000000000000000000001O00000O10000O1O1O1O1M4EdSn<\", \"size\": [1280, 720]}, {\"counts\": \"ilW>`0^W15L2O1N2O1O00001O00000000000O100000O2O0000000O100O101N1O2L6Ab[e<\", \"size\": [1280, 720]}, {\"counts\": \"iT^>;aW18K3M2O1N101N1000000000001O0000000001N10000000000O1O1O2N1N3K5Cgk]<\", \"size\": [1280, 720]}, {\"counts\": \"a\\\\d>?]W16L3O1N101O0O20O01O000000000000000000000000O2O0O100O1O2M3K4H;IdcW<\", \"size\": [1280, 720]}, {\"counts\": \"Wlf>c0[W14N0O2N2O001O00001O00O1000O100001O000000000O10000O2N1N2O1M3H9KoSU<\", \"size\": [1280, 720]}, {\"counts\": \"R\\\\d>`0]W15M2N2O1O001N110O00000000000000000000000000O10001N1N3N2M3DZTZ<\", \"size\": [1280, 720]}, {\"counts\": \"VT^>1hW1?D6L3O0O2O0000001O000O10000001O000000000000001O000O2M2N3L4F]T_<\", \"size\": [1280, 720]}, {\"counts\": \"STY>5cW1=I3N2N2O001N10001O000000000000000000000001O000O100O2N2M3H]\\\\e<\", \"size\": [1280, 720]}, {\"counts\": \"oST>>^W16M3M100O2O000O2O000000000000000000000000000000O2O0O2M2O3IZ\\\\j<\", \"size\": [1280, 720]}, {\"counts\": \"S\\\\P>=`W15K4N2N101N2O000000000000000000000000O1000001O00000O2N1N2M6C[ll<\", \"size\": [1280, 720]}, {\"counts\": \"VTo=9bW1:J3M2N2O001N100000001O00000000000O11N10000000001N1O1O2N1N3LSTn<\", \"size\": [1280, 720]}, {\"counts\": \"[dQ>3hW1>E4M2N2O001N101O0000001O000000000000O10000000000O1O2N1O2M3KPdk<\", \"size\": [1280, 720]}, {\"counts\": \"YTT>b0]W13M2N2O0O101O000O101O0000000000O1000000000001O0O101N1O1N4Kk[j<\", \"size\": [1280, 720]}, {\"counts\": \"]\\\\U>>^W17L3N001N101O0O2O00000000000000000000000000O100O2O0O2N1O3KiSi<\", \"size\": [1280, 720]}, {\"counts\": \"^dV>;bW19J1N3N0O2N101O00001O000000000O1000000000O2O0O101N1O1O2M3K9Bicf<\", \"size\": [1280, 720]}, {\"counts\": \"^dV><bW17J3M2O0O2O0O2O001O0000000000000O10000001N100O101N100N3M3L6Clcf<\", \"size\": [1280, 720]}, {\"counts\": \"[\\\\U>;cW17J4L2O1N101N10001O000000000000000000000000O10000O2N1O1O2N3Hmkg<\", \"size\": [1280, 720]}, {\"counts\": \"WlR>?^W16L2N2O1N101O00001O00000000000000000000000000O101N1O1O2N2M3GS\\\\j<\", \"size\": [1280, 720]}, {\"counts\": \"Ydl=8cW19I4N2N2O1N10001O000O10000000000000000000O101O000O2N1O1N3L4EYdP=\", \"size\": [1280, 720]}, {\"counts\": \"RTe==`W16K3N2N100O2O00000000001O0000000000000000000001N1O101M2N3JX\\\\Y=\", \"size\": [1280, 720]}, {\"counts\": \"Ql^=9`W1:K3N2O1N10001O00000000000O101O000001O0O1000001O0O1O1O1N3I8I[\\\\^=\", \"size\": [1280, 720]}, {\"counts\": \"lS[=:bW19J3M2O0O101N100000000000001O000000000000O10001N100O2M3M2Gb\\\\c=\", \"size\": [1280, 720]}, {\"counts\": \"kcX=;aW19J2O1N101N1000001O0000000O1000000000000001O000O2N100N3M3K7F]dd=\", \"size\": [1280, 720]}, {\"counts\": \"mcS=:`W1;J2N101O0O10000O101O0000000000000000000001N1000001N1O2M3L4G`di=\", \"size\": [1280, 720]}, {\"counts\": \"kko<?^W15L3O1N101O000O100000001O00000O10000001N1000001N101N1N2N2K6J^\\\\m=\", \"size\": [1280, 720]}, {\"counts\": \"lko<<aW17J3O1N101N10001O00000O100000000000000000O101O0O101N1O1M3L5I_\\\\m=\", \"size\": [1280, 720]}, {\"counts\": \"oko<8cW1:I3N2N2O001N1000000000000O100000000001O00000O101O0O1O1M3M4I_\\\\m=\", \"size\": [1280, 720]}, {\"counts\": \"lcn<=`W16K3O1N2O0O2O0000000000001O0O010000000001O000O101N101N1M3K5I`dn=\", \"size\": [1280, 720]}, {\"counts\": \"n[m<6eW1<G3N2O1N101N100000000000001O0O0100000001O000O101O0O1O2N1N3Galo=\", \"size\": [1280, 720]}, {\"counts\": \"kko<<`W17L3M2N101O0O10000000001O000O1000000000O2O00001O0O1O2N1M4K5Ga\\\\m=\", \"size\": [1280, 720]}, {\"counts\": \"kcS=:aW19K2N2N2O0O101O00000000000000000000000000000001O0O101N1N3M4Dcdi=\", \"size\": [1280, 720]}, {\"counts\": \"g[W=`0]W16M1N2O0O10001N100000001O00000000O1000001O00000O2N1O2N2M3HaTg=\", \"size\": [1280, 720]}, {\"counts\": \"i[W=`0]W15M2O1N101N1000000000001O00000000O1000001O0000001N101N2M2M\\\\Tg=\", \"size\": [1280, 720]}, {\"counts\": \"jkT=`0]W15M1O2N101O0O10000000001O0000000000000001O0O1000001N1N3N2K6G^\\\\h=\", \"size\": [1280, 720]}, {\"counts\": \"jSQ==_W16M3M2N101O000O1000001O000000000000000001O000O2O000N3N1N3K^\\\\m=\", \"size\": [1280, 720]}, {\"counts\": \"k[m<:bW17L3M2N2N2O000000000O101O0000000000000001O000O2O000O2N2N1M4H`lo=\", \"size\": [1280, 720]}, {\"counts\": \"oSl<5cW1=J3M2O1N1O101O000000000000000000000000000001O0O2O0O1O2N2L4G`TQ>\", \"size\": [1280, 720]}, {\"counts\": \"QTl<<^W18M3N1N101N1000001O00000000000000000000000001N10001N1O2N2M3E]TQ>\", \"size\": [1280, 720]}, {\"counts\": \"[lj<;`W17N2M2N2O0O2O00001N1001O00000000000000000000000O100O1O3M4L4K:G^SQ>\", \"size\": [1280, 720]}, {\"counts\": \"_di<>aW14L2N2O1N101O00000O101O0001O00000000000000000O2O0O2O0N3M4KckT>\", \"size\": [1280, 720]}, {\"counts\": \"hdi<8cW17L5M1N101N10001O000O1000010N1000O110O001N2O2N4K5K4LScX>\", \"size\": [1280, 720]}, {\"counts\": \"edn<;bW16K3N2O1O1N101O000000000000000000000000000000O2O000O2O0O2L6Cfcn=\", \"size\": [1280, 720]}, {\"counts\": \"`lT=>`W14M2N2O1N101O000O1000001O0000000001O0000000O10000O2N2N2N2Keci=\", \"size\": [1280, 720]}, {\"counts\": \"^\\\\R=9cW18K2O1N2O1O00001N100000000000001O0000O1000001O000O2N101M4KgSl=\", \"size\": [1280, 720]}, {\"counts\": \"YTQ=>`W14M2O1O1O1O00000O1000001O0000000000000001O0O10001N1O2M4Lhcn=\", \"size\": [1280, 720]}, {\"counts\": \"\\\\TQ=:aW17K5M2N2O1N100000001O0O1000000000000000000000O10001N1O1N2O1L6EVlj=\", \"size\": [1280, 720]}, {\"counts\": \"ZTQ=`0]W15M3N0O2O0O2O000000000O10001O00000000000000O1000001N1N2N3M3HQTl=\", \"size\": [1280, 720]}, {\"counts\": \"_TQ=>^W16L4M101O1O0O100000000O2O000000000000000000O10001N1N2N2N3I8IkSl=\", \"size\": [1280, 720]}, {\"counts\": \"_dS=a0\\\\W15M2N101N1000001O000O1000001O000000000000O10000O100O2N1N3I7DPdi=\", \"size\": [1280, 720]}, {\"counts\": \"`dX=`0\\\\W16M2N2O001N10001O00000O100000000000000000000000O2O0O1O2M3Fnke=\", \"size\": [1280, 720]}, {\"counts\": \"bd]==^W17M2N1O101O1O0O100000001N100000000001O0O1000001N2N2N3Ld[c=\", \"size\": [1280, 720]}, {\"counts\": \"dlc=9eW16K3N1O001N101O0000001N1000000O1000O100000000O1O100O1O2M=@[cZ=\", \"size\": [1280, 720]}, {\"counts\": \"dTo=9eW16K2N2O1O0O101O001O000O101O0O100000O10000000000O100O1O2M5L`[o<\", \"size\": [1280, 720]}, {\"counts\": \"jdY?1nW13L2N2O11Hbkb<\", \"size\": [1280, 720]}, {\"counts\": \"\\\\Tc>`0_W14M1O1N101O00001N10001O000000000O1000O10000O100O2N1O1O2N2Lh[[<\", \"size\": [1280, 720]}, {\"counts\": \"^lf>:cW17K3N1O0O2O1O001OO1000O1000001O00000000O1000000O100O1O3MgkX<\", \"size\": [1280, 720]}, {\"counts\": \"hdY?4`W1?K3N1O1000000000O101N1O2M8FdcW<\", \"size\": [1280, 720]}, {\"counts\": \"VTh>=bW14L2O1O1N101O1O000O10000000001OO0100000000000000001N1O1O0O4LoSU<\", \"size\": [1280, 720]}, {\"counts\": \"_lf>;`W18L2N2N10001O1O000000000O1000000000000000O1000000O1O1O2N4HkcW<\", \"size\": [1280, 720]}, {\"counts\": \"ed`>>aW14M1O1O1O000000001O0001O00000000000000000000001O0O101N2N=Bnj]<\", \"size\": [1280, 720]}, {\"counts\": \"j\\\\Z>1lW1a0A2O1O1O001O0O1000001O000000000O100000000000O1000000O100N2N2B?L^[`<\", \"size\": [1280, 720]}, {\"counts\": \"idV>5gW1<G2O1O1N2O001O00001N1000000000000000000O10O10000O101N1O1O3J`cf<\", \"size\": [1280, 720]}, {\"counts\": \"blR>:dW16K2O0O2O001O00000O2O001O00000000000000O010000000O101N1O2M3Ld[j<\", \"size\": [1280, 720]}, {\"counts\": \"[\\\\P>2jW1=F2O1O1N101O00001N10001O0000000O100000O100000000O2N100O2N2Mlkl<\", \"size\": [1280, 720]}, {\"counts\": \"VdQ>7eW18K2N2O1O00001N10001OO100000O100000000000000000O100O1O2N1N4KRdk<\", \"size\": [1280, 720]}, {\"counts\": \"QdV>=cW13L101O001N1000001O000O100000000000000000O10O11N100O1O2M2N3JUdf<\", \"size\": [1280, 720]}, {\"counts\": \"U\\\\Z>3jW1:I2N1N101N2O00001O0O1000000000000O100000O1000000O100O1O2N1N3L4HV\\\\`<\", \"size\": [1280, 720]}, {\"counts\": \"VT^>3iW1;H3N001O0O2O00000O100000000000001N010000000000O100O1O100N2N2M5HVd\\\\<\", \"size\": [1280, 720]}, {\"counts\": \"oS^>5jW16K3L2O1O0O2O001O000O1000001O00O10O100000000000O100O1O1O1O1N2M3L6IY\\\\[<\", \"size\": [1280, 720]}, {\"counts\": \"h[_>8gW16K2N1O1N2O0000001N1000001O00O1000000000000O10000O001O1O1N2K7H`d\\\\<\", \"size\": [1280, 720]}, {\"counts\": \"gk\\\\>2lW1:G3N2N1O1N10001O000O1000001O000000O1000000O10000O1O1O1O001N2M3K6Jcd\\\\<\", \"size\": [1280, 720]}, {\"counts\": \"lSY>;dW14M1N2O1O1O0O2O00000000000000000O10000000O100O2N100O1O1O0O2L4M5KYda<\", \"size\": [1280, 720]}, {\"counts\": \"UdQ><bW14N2N0O2O1O00001O000O101O000000000000O1000000O2N1O100O1O1O1N2L4M2O2KTdf<\", \"size\": [1280, 720]}, {\"counts\": \"[Tj=3jW1:H3N1O1O1O1O0O2O0000000000000O1000000000O10001O0O1O1O1O1O1N2M3L6Mi[o<\", \"size\": [1280, 720]}, {\"counts\": \"Zdb=8fW17J3M2O001O0000001O000000001OO010000001O0O1000000O1O1O1O2N1N3K6KiSX=\", \"size\": [1280, 720]}, {\"counts\": \"Tl^=>aW13N1O1N2O001O0000000000000000001O00O10000O100O1O2O0O1N3N1L4M4LnS]=\", \"size\": [1280, 720]}, {\"counts\": \"okY==bW13M2O1O1O001O0000000000000000000000000000000001N1O1O2N1N3M2M4JTTb=\", \"size\": [1280, 720]}, {\"counts\": \"P\\\\W=9eW16K2O1O1O001O000O10001O0000000000O1000000000000O1O1O2N1O2M3K6JSdd=\", \"size\": [1280, 720]}, {\"counts\": \"Q\\\\W=;dW15K2O1O1O001O0O1000000000001O00O1000000000000O100O1O1O2N1N3M2J7LR\\\\c=\", \"size\": [1280, 720]}, {\"counts\": \"S\\\\W=8fW18I2O1O001N10001O00000000000000000001O0O1000000O1O2O0O1O2M4Lnke=\", \"size\": [1280, 720]}, {\"counts\": \"Rd]=<cW15K2O1O1O000010O000000O100000000000O101O00000O1O101N1O2M3M3JPd_=\", \"size\": [1280, 720]}, {\"counts\": \"Vlc=1jW1<H4M1O1O001O00001O000000000000000O1000O101O0O100O101N1O1N2O4GRTX=\", \"size\": [1280, 720]}, {\"counts\": \"Qdl=;dW14L3N1O1O001O00001O000000000000O100000000O10001N1O100O1O2L3GXdP=\", \"size\": [1280, 720]}, {\"counts\": \"QlR>:dW16L1N2O1O00001O000000000000000000000000000000O101N101M2O2M2HW\\\\j<\", \"size\": [1280, 720]}, {\"counts\": \"VlW>1jW1;I4L3N001O1O01O0001N10000000000000000000O10001N100O1O2N1O1NQ\\\\e<\", \"size\": [1280, 720]}, {\"counts\": \"QdV>1lW1;G3N1O1O1O1O0O2O0000001O000000000O100000O10000O2N1O1O1O1O1M3K5KYTd<\", \"size\": [1280, 720]}, {\"counts\": \"e[P>=bW13M3N1O001O001O00000O1000001OO1000000O10000O10001N1O1N3M3K_Tn<\", \"size\": [1280, 720]}, {\"counts\": \"]Se=6gW18K2M2O1O1O001O00000000000000000O1000O1000000O2N100O2M2N4Hj\\\\Y=\", \"size\": [1280, 720]}, {\"counts\": \"Skc=;eW14K3N1O2N000000000000000000O2N1O1N2MP]h=\", \"size\": [1280, 720]}, {\"counts\": \"S[\\\\=4jW1:G4M1N2O1O0000O101N100N3N1M4HUeS>\", \"size\": [1280, 720]}, null, null, null, {\"counts\": \"dcX=:bW16L4N0O2O1O0000010O1OO2K401M3Gg\\\\W>\", \"size\": [1280, 720]}, {\"counts\": \"fSV=8dW18K2O1N2O1O1O1O000O11O00000O1O1O1O1O2M3LadS>\", \"size\": [1280, 720]}, {\"counts\": \"fSl<=bW14M100N101O001O1O001N1000O10000000O10O1000000O100O2O0N3J`dS>\", \"size\": [1280, 720]}, {\"counts\": \"Qdi<1kW1<G3M2O001O00001O000O2O000000000000O1000O100000O2N101M2O2LWlT>\", \"size\": [1280, 720]}, {\"counts\": \"Qlj<5jW17I4M1O1O1O0O2O000000000000000O100000000000O100O2N100O2M4ITdS>\", \"size\": [1280, 720]}, {\"counts\": \"oci<>bW12M2O001O001O000000001N10000000O1000000000000O1O101N1N3MSTV>\", \"size\": [1280, 720]}, {\"counts\": \"oke<:dW16L1O1O1O001O001O0000000O10000000000000O10000O10000O2L5KTlY>\", \"size\": [1280, 720]}, {\"counts\": \"Qle<6iW18H4M1O001O001O0000001O00000000000000O100000001N100O2M2MRlY>\", \"size\": [1280, 720]}, {\"counts\": \"Ydd<7fW19I3N1O1O001O01O0001O00000000000000000000O1000001M2O2M4HlS[>\", \"size\": [1280, 720]}, {\"counts\": \"[d]=9eW16L1O1O00000001M2MikY>\", \"size\": [1280, 720]}, null, null, null], \"bboxes\": [[313.0, 957.0, 37.0, 33.0], [316.0, 980.0, 36.0, 29.0], [319.0, 983.0, 34.0, 27.0], [319.0, 985.0, 10.0, 28.0], [305.0, 969.0, 23.0, 45.0], [268.0, 1015.0, 53.0, 81.0], [290.0, 1132.0, 64.0, 70.0], [332.0, 1205.0, 66.0, 66.0], [340.0, 1233.0, 61.0, 47.0], [337.0, 1237.0, 60.0, 43.0], [333.0, 1237.0, 61.0, 43.0], [334.0, 1237.0, 61.0, 43.0], [338.0, 1235.0, 61.0, 45.0], [328.0, 1168.0, 55.0, 65.0], [333.0, 1101.0, 47.0, 46.0], [325.0, 1082.0, 53.0, 43.0], [324.0, 1085.0, 52.0, 35.0], [320.0, 1074.0, 51.0, 34.0], [311.0, 1039.0, 48.0, 44.0], [304.0, 995.0, 52.0, 33.0], [314.0, 937.0, 33.0, 32.0], [323.0, 905.0, 30.0, 25.0], [325.0, 902.0, 31.0, 24.0], [328.0, 899.0, 30.0, 23.0], [332.0, 898.0, 28.0, 22.0], [334.0, 898.0, 32.0, 22.0], [332.0, 901.0, 33.0, 22.0], [334.0, 905.0, 31.0, 25.0], [337.0, 904.0, 31.0, 26.0], [339.0, 901.0, 32.0, 28.0], [342.0, 899.0, 33.0, 28.0], [344.0, 900.0, 32.0, 28.0], [345.0, 903.0, 32.0, 28.0], [345.0, 905.0, 31.0, 29.0], [344.0, 907.0, 31.0, 29.0], [344.0, 908.0, 31.0, 29.0], [345.0, 908.0, 31.0, 28.0], [344.0, 908.0, 31.0, 29.0], [342.0, 912.0, 32.0, 28.0], [347.0, 914.0, 31.0, 29.0], [356.0, 916.0, 32.0, 28.0], [364.0, 916.0, 31.0, 29.0], [369.0, 913.0, 32.0, 29.0], [374.0, 905.0, 32.0, 30.0], [376.0, 898.0, 32.0, 30.0], [374.0, 892.0, 30.0, 29.0], [369.0, 887.0, 31.0, 29.0], [365.0, 886.0, 30.0, 28.0], [361.0, 887.0, 30.0, 28.0], [358.0, 890.0, 31.0, 28.0], [357.0, 892.0, 31.0, 30.0], [359.0, 897.0, 31.0, 29.0], [361.0, 900.0, 30.0, 29.0], [362.0, 902.0, 30.0, 29.0], [363.0, 903.0, 31.0, 30.0], [363.0, 903.0, 31.0, 30.0], [362.0, 901.0, 31.0, 29.0], [360.0, 897.0, 31.0, 29.0], [355.0, 893.0, 31.0, 29.0], [349.0, 890.0, 30.0, 28.0], [344.0, 885.0, 31.0, 29.0], [341.0, 884.0, 30.0, 28.0], [339.0, 883.0, 31.0, 28.0], [335.0, 883.0, 31.0, 28.0], [332.0, 883.0, 31.0, 29.0], [332.0, 884.0, 31.0, 28.0], [332.0, 884.0, 31.0, 28.0], [331.0, 884.0, 31.0, 29.0], [330.0, 883.0, 31.0, 29.0], [332.0, 882.0, 31.0, 29.0], [335.0, 882.0, 31.0, 27.0], [338.0, 881.0, 30.0, 29.0], [338.0, 883.0, 30.0, 28.0], [336.0, 883.0, 31.0, 28.0], [333.0, 882.0, 30.0, 28.0], [330.0, 882.0, 31.0, 28.0], [329.0, 883.0, 31.0, 27.0], [329.0, 888.0, 31.0, 28.0], [328.0, 898.0, 32.0, 28.0], [327.0, 906.0, 30.0, 27.0], [327.0, 910.0, 27.0, 27.0], [331.0, 910.0, 31.0, 26.0], [336.0, 906.0, 30.0, 27.0], [334.0, 903.0, 30.0, 26.0], [333.0, 901.0, 29.0, 25.0], [333.0, 896.0, 32.0, 30.0], [333.0, 899.0, 31.0, 30.0], [333.0, 902.0, 31.0, 30.0], [335.0, 904.0, 31.0, 30.0], [339.0, 904.0, 30.0, 30.0], [343.0, 904.0, 28.0, 29.0], [348.0, 910.0, 30.0, 25.0], [357.0, 909.0, 30.0, 26.0], [391.0, 916.0, 6.0, 9.0], [373.0, 904.0, 30.0, 28.0], [376.0, 904.0, 29.0, 26.0], [391.0, 904.0, 15.0, 24.0], [377.0, 897.0, 31.0, 27.0], [376.0, 902.0, 30.0, 27.0], [371.0, 916.0, 30.0, 22.0], [366.0, 916.0, 33.0, 26.0], [363.0, 913.0, 31.0, 27.0], [360.0, 908.0, 31.0, 25.0], [358.0, 899.0, 31.0, 25.0], [359.0, 894.0, 31.0, 24.0], [363.0, 893.0, 31.0, 23.0], [366.0, 894.0, 33.0, 23.0], [369.0, 894.0, 33.0, 23.0], [369.0, 890.0, 34.0, 23.0], [370.0, 884.0, 32.0, 24.0], [368.0, 881.0, 34.0, 24.0], [365.0, 888.0, 33.0, 23.0], [359.0, 897.0, 35.0, 24.0], [353.0, 901.0, 34.0, 23.0], [347.0, 901.0, 33.0, 24.0], [344.0, 898.0, 32.0, 23.0], [340.0, 893.0, 32.0, 21.0], [338.0, 892.0, 32.0, 22.0], [338.0, 894.0, 33.0, 23.0], [338.0, 895.0, 31.0, 22.0], [343.0, 896.0, 31.0, 22.0], [348.0, 895.0, 32.0, 22.0], [355.0, 895.0, 31.0, 22.0], [360.0, 894.0, 31.0, 21.0], [364.0, 895.0, 31.0, 22.0], [363.0, 891.0, 33.0, 23.0], [358.0, 882.0, 30.0, 24.0], [349.0, 872.0, 30.0, 22.0], [348.0, 866.0, 19.0, 21.0], [342.0, 863.0, 16.0, 22.0], null, null, null, [339.0, 877.0, 16.0, 25.0], [337.0, 879.0, 21.0, 25.0], [329.0, 883.0, 29.0, 25.0], [327.0, 889.0, 30.0, 23.0], [328.0, 893.0, 30.0, 22.0], [327.0, 893.0, 29.0, 22.0], [324.0, 892.0, 29.0, 22.0], [324.0, 895.0, 29.0, 22.0], [323.0, 901.0, 29.0, 23.0], [343.0, 905.0, 10.0, 17.0], null, null, null], \"areas\": [1064.0, 885.0, 828.0, 227.0, 808.0, 3058.0, 3593.0, 3639.0, 2638.0, 2416.0, 2385.0, 2353.0, 2350.0, 2221.0, 1597.0, 1530.0, 1537.0, 1443.0, 1624.0, 1424.0, 802.0, 621.0, 652.0, 550.0, 540.0, 592.0, 607.0, 689.0, 729.0, 762.0, 773.0, 772.0, 779.0, 803.0, 795.0, 814.0, 787.0, 804.0, 791.0, 768.0, 796.0, 778.0, 792.0, 801.0, 804.0, 758.0, 769.0, 747.0, 766.0, 770.0, 799.0, 785.0, 780.0, 790.0, 780.0, 783.0, 803.0, 809.0, 775.0, 758.0, 760.0, 738.0, 749.0, 763.0, 764.0, 754.0, 755.0, 756.0, 756.0, 762.0, 752.0, 762.0, 754.0, 765.0, 749.0, 750.0, 737.0, 774.0, 758.0, 705.0, 597.0, 718.0, 712.0, 677.0, 648.0, 840.0, 826.0, 807.0, 814.0, 819.0, 711.0, 624.0, 663.0, 29.0, 727.0, 674.0, 299.0, 724.0, 729.0, 585.0, 726.0, 721.0, 660.0, 655.0, 631.0, 598.0, 622.0, 616.0, 611.0, 610.0, 621.0, 622.0, 665.0, 630.0, 631.0, 603.0, 584.0, 586.0, 624.0, 590.0, 581.0, 575.0, 581.0, 577.0, 587.0, 595.0, 606.0, 554.0, 355.0, 270.0, null, null, null, 307.0, 429.0, 607.0, 567.0, 563.0, 562.0, 565.0, 564.0, 571.0, 150.0, null, null, null], \"iscrowd\": 0, \"video_id\": 0, \"height\": 1280, \"width\": 720, \"category_id\": 49272, \"noun_phrase\": \"air jordan\"}, {\"id\": 1441, \"segmentations\": [{\"counts\": \"WY^f0`0XV1KdjN6XU1OgjN2UU12ijN0TU12ljNNTU13kjNMUU13jjNNVU12jjNNVU12jjNOTU13kjNNTU12ljNOSU11mjN0QU11ojNOQU11ojN0PU10PkN0PU11ojNOPU12PkNNPU12PkNNPU12ojNOQU12njNNRU12mjNOSU11mjNOTU10ljNOUU12jjNNVU12jjNNVU12jjNNVU13ijNMWU13ijNMWU13jjNKWU15ijNKXU14hjNLXU14hjNLXU14hjNLXU15hjNHZU18gjNFZU1:hjNCYU1=ojNZORU1f0W1YOlhN021iW1JdgS4\", \"size\": [1280, 720]}, {\"counts\": \"^`_f01oW1000O1O1jhN;mU1JQjN9fU1NZjN1cU13\\\\jNN`U16_jNJD@eU1g0ijNOVU11kjN0TU10mjN0RU10mjN1RU10njN1QU1OojN1RU1OmjN1SU1OnjN0RU10mjN1RU10njN0RU11mjN0RU10njN0RU11njNOQU11ojNOQU11ojNOQU11ojNOQU11njN0RU10njN0SU1OmjN1SU1OmjN1SU10ljNOTU12ljNNTU13ljNLTU14ljNKVU15ijNJXU16hjNJXU16hjNIYU17gjNIZU16fjNJZU16gjNH[U17ejNG]U19mjNZOVU1f0R106ZOa`m3\", \"size\": [1280, 720]}, {\"counts\": \"b`if02]W1a0L4biN^O[U1e0djN^OWU1f0gjN[OXU1f0hjNZOWU1g0ijNYOWU1g0ijN[OUU1e0jjN\\\\OVU1e0ijN\\\\OVU1d0ijN]OWU1d0hjN]OWU1c0ijN_OUU1a0jjNAUU1?kjNBTU1>ljNBTU1>ljNBSU1`0kjNAUU1?kjNBUU1>jjNBVU1>jjNBVU1>jjNBVU1>jjNBVU1>jjNBVU1?ijNAWU1?ijNAWU1?ijNAWU1?ijNAWU1>jjNBVU1>jjNAWU1?ijNAWU1`0hjN@YU1?hjN@XU1`0hjN_OZU1`0fjN@ZU1?hjNYO_U1g0cjNUO`U1?W1N2M8FTXg3\", \"size\": [1280, 720]}, {\"counts\": \"eijf03UW1N`iN6^V1JbiN8nU10^iNNb02eU1c0ZjN^ObU1g0]jNYOaU1h0`jNXO]U1k0cjNUO[U1m0ejNSOZU1o0ejNQOZU1P1fjNPOZU1P1fjNQOXU1o0ijNQOVU1o0kjNROSU1o0mjNQOSU1o0mjNQOSU1o0mjNQOSU1o0mjNQOSU1n0njNRORU1n0mjNSOSU1n0ljNSOSU1n0kjNSOUU1m0jjNTOVU1l0jjNTOVU1l0jjNTOVU1l0jjNTOVU1k0kjNUOUU1k0ljNTOTU1l0ljNTOTU1l0kjNTOVU1l0jjNTOWU1k0ijNUOWU1k0jjNTOVU1l0jjNTOVU1l0kjNQOWU1n0o0N101O0O1N3L4L6C^Xb3\", \"size\": [1280, 720]}, {\"counts\": \"^Xmf09gW18F4J4L4L4N3L3M3N2O1QjNfNWU1[1hjNgNWU1Y1ijNgNWU1Y1jjNgNUU1Y1njNeNQU1[1ojNeNQU1Z1ojNgNQU1Y1mjNiNSU1X1ljNhNTU1X1kjNiNUU1W1kjNiNUU1W1jjNlNTU1T1kjNnNTU1R1kjNPOTU1P1kjNPOVU1o0jjNROVU1o0ijNQOWU1o0ijNQOWU1o0ijNQOWU1o0ijNQOWU1o0hjNROXU1n0hjNROYU1m0gjNROZU1n0gjNPOZU1P1fjNPOZU1P1fjNPO[U1n0gjNQOYU1o0gjNjNA1hU1U1gjNPOZU1P1fjNoN[U1P1fjNQOZU1n0fjNkNCOgU1U1fjNlNDOfU1m0XjNUOfV1a0fiN[O[V1b0kX]3\", \"size\": [1280, 720]}, {\"counts\": \"^hof0a0^W13M1N3J5J6N3M2N3N1O1O101OOVjNcNSU1]1mjNcNSU1\\\\1mjNfNQU1[1PkNcNQU1]1ojNcNQU1]1ojNdNPU1\\\\1njNgNPU1Z1njNhNRU1X1mjNiNSU1W1ljNjNTU1V1ljNjNTU1U1mjNlNRU1T1mjNmNTU1R1ljNnNTU1R1kjNoNUU1Q1kjNoNUU1Q1kjNoNUU1Q1kjNoNUU1Q1kjNoNUU1Q1kjNnNVU1R1jjNnNVU1R1ijNoNWU1Q1ijNoNWU1Q1ijNoNWU1Q1ijNoNXU1P1hjNPOXU1P1gjNPO[U1n0fjNROYU1o0hjNoNYU1P1ljNhNWU1Y1i0N1O2M4_O`0BcPW3\", \"size\": [1280, 720]}, {\"counts\": \"ahcg0?[W18Bb0E5N2O10O10000000O1O10000000O1000000001OO1000VjN`NTU1`1jjNdNTU1\\\\1ijNhNVU1W1jjNkNUU1U1kjNjNVU1V1jjNjNVU1V1jjNjNVU1V1jjNiNXU1V1ijNiNXU1V1hjNiNYU1V1hjNiNZU1V1i0N2YOi0K7JoWn2\", \"size\": [1280, 720]}, {\"counts\": \"fhWh0b0VW19J6F9L4M3O11O0000000OUjNdNSU1]1kjNeNUU1[1jjNfNUU1Z1ljNfNUU1Y1jjNhNVU1X1jjNhNVU1X1jjNhNWU1W1ijNiNWU1W1ijNiNWU1W1hjNjNXU1V1hjNjNXU1U1hjNlNXU1T1hjNkNYU1U1gjNjNZU1U1gjNjN[U1U1h00002TOmiNGWV10oiNORW1NPWd2\", \"size\": [1280, 720]}, {\"counts\": \"TQ^h04`W1>G8I6J6J5N3M3M3O21N1O1OO10O101NXjNaNQU1_1jjNgNUU1Y1kjNgNUU1Y1kjNgNUU1X1kjNiNUU1W1kjNiNUU1W1kjNiNUU1W1kjNhNVU1X1jjNhNVU1X1kjNgNUU1Y1j0N2VOeiNI`V14h0N3MRP^2\", \"size\": [1280, 720]}, {\"counts\": \"mh\\\\h0=\\\\W18I7K4I6M4L4L302M2O12N1OWjN_NSU1a1ljN`NTU1_1ljNbNSU1_1kjNcNVU1[1kjNeNUU1[1kjNeNUU1[1kjNeNVU1Z1jjNfNVU1Y1kjNgNUU1Y1jjNhNVU1X1jjNhNVU1X1jjNhNVU1X1jjNhNVU1X1kjNfNVU1Z1jjNbN[U1]1e0UOniNDWV17k0N4L7Jho]2\", \"size\": [1280, 720]}, {\"counts\": \"j``h081MXW1c0L4J5fiNQO_U1S1_jNPOXU1X1gjNiNUU1[1jjNfNTU1\\\\1kjNfNUU1Y1jjNhNVU1X1jjNhNVU1X1jjNgNWU1Y1ijNgNWU1Y1ijNgNXU1W1ijNhNXU1X1ijNgNWU1Y1ijNeNYU1Z1g000O1O1N2J7[Ojhf2\", \"size\": [1280, 720]}, {\"counts\": \"UQch04`W1=I6J6J5K6jiNlN\\\\U1X1cjNiNZU1Z1ejNhNXU1Z1hjNeNYU1[1gjNeNYU1[1gjNeNZU1Z1gjNcN[U1\\\\1f0O000O1O101D^iNWOeV18_iNB63`W1O0Oo^e2\", \"size\": [1280, 720]}, {\"counts\": \"Taoh08<0WV1`0jiNAoU1d0SjN\\\\OfU1j0ZjNVO]U1R1djNnNXU1V1ijNhNVU1Y1kjNgNVU1X1i0M3L5H7@f`e2\", \"size\": [1280, 720]}, {\"counts\": \"WiPi01c00eV14YiN2RV1?miNAmU1d0TjN\\\\OgU1i0ZjNUO^U1S1cjNiN^U1Z1b01OL7L3K8XOhXd2\", \"size\": [1280, 720]}, {\"counts\": \"kYih03lW13N1\\\\O0UiN1\\\\V1O_iN160VV16aiNJ92PV1c0RjN\\\\OkU1g0VjNXOdU1m0^jNROZU1U1f03N12L3K6J5Ba0D\\\\`e2\", \"size\": [1280, 720]}, {\"counts\": \"jQhh07`W1JfhN8PW1H\\\\iN6YV1K]iN194RV16`iNI>NnU1`0biNBa0MiU1m0WjNROfU1R1>5M4NL6N1N4^Oc0E^Xi2\", \"size\": [1280, 720]}, {\"counts\": \"Pbeh01mW12I7N23MRO3kiNKPV1<oiN_OQV1e0b0MnhN^OQW1`0=ChgP3\", \"size\": [1280, 720]}, {\"counts\": \"Wiah01eW10bhN4:OSV1c0liN@lU1f0TjNZOgU1k0YjNUOdU1m0^jNRO\\\\U1T1f01M30BaiNZOcV1d0=NYXS3\", \"size\": [1280, 720]}, {\"counts\": \"WYUh06UW1e0J7J4L5K5M2O20000000N2O10SjNaNYU1^1e0O1O2AciNXO_V11hiNN0K`VX3\", \"size\": [1280, 720]}, {\"counts\": \"mPog06eW1:E7I7I6L4L4L4N2N200O010000O101N2O0O1O2O0G_iNROdV1e0fiNWOa^Y3\", \"size\": [1280, 720]}, {\"counts\": \"Picg08aW18I7J5J6K5K5L4L4N101O1001O1O1O00000O0100000O101O0O1O100O2O0O101B>G:Bb`T3\", \"size\": [1280, 720]}, {\"counts\": \"^hTg0:eW1:F;C4A`0K3N2O1N200O100000O101N10000O10000O10000O100000000000000O2O000O100O10000O1O2O0O2L4D<G:AcXS3\", \"size\": [1280, 720]}, {\"counts\": \"^XWg01nW18Ge0YO6D:L4M3N2O001O2N1001O0O1000000000000O100O101O000000000O10001O0O1000000O100O2N101N1N3I8C=I[hP3\", \"size\": [1280, 720]}, {\"counts\": \"\\\\X\\\\g0k0oV18C<J5M3N2O1O0100O10000000000000000O101O000000O02O000000000O2O001OO0101N100O100O2M2N3E:M4K8G\\\\Pm2\", \"size\": [1280, 720]}, {\"counts\": \"^P`g01b01dV1k0I5F:K4M3M2O2O002O000O1000000001O001OO010000O1000001O001O1O0O01001N100O100O100O100O1M3G:L5J7EaPh2\", \"size\": [1280, 720]}, {\"counts\": \"]Peg0j0TW15I6@?N1O1M3N1O2O10O0100000O101O0O10000O10000O100001OO1000001N10001N100O10000O100O1O1O2N2L3E<K5Lb0@m_`2\", \"size\": [1280, 720]}, {\"counts\": \"^Peg0882kV1f0G7C=M2N2N2N2O00100O10O10001N1000000O101O0O10O100000000001N100O101O0O10000O1O100O1O1O2N1O2C=J6M5La0@1Ogo]2\", \"size\": [1280, 720]}, {\"counts\": \"^Xfg03>4iV1f0I4L4C;N3O1O1O1O1O10O0100O1O01000000O100O100000000000000O2O0O10000O100O2O000O1O1O1O1O2C<M5I`Pc2\", \"size\": [1280, 720]}, {\"counts\": \"jhhg0;UW1c0F9E9K6L2O2O10000O01000001O00000O10000O10000000000O2O00000O2O0000001N100O1O101N1O1N3C<L5[OjhN;dob2\", \"size\": [1280, 720]}, {\"counts\": \"^`gg07hW19G5K5I6B<K4O2L2O2O1O001O100O2O000000O10001O0O10000O01000O2O00001O001O000O100O101N100O100O1N3J6F:I:Bah\\\\2\", \"size\": [1280, 720]}, {\"counts\": \"^`Qh06<MlV1h0J4M2D;O2M3K410O2N10000O11O1O000O010O1000000O100000000000000O2O00000O100O1O2O0O1O1O100G^iNROdV1l0aiNPOaV1l0<M5XOPYU2\", \"size\": [1280, 720]}, {\"counts\": \"^PTh01lW1k0UO5@?N1N2M2N3N2O100O10O101O0O01001O0O10000O100000000O1000001O001N10000O100O2O000O2O0O1O1L5F;J7K4I[`Q2\", \"size\": [1280, 720]}, {\"counts\": \"b`Vh0i0RW17H7G9K4N2N1O2O100O0100000O11O000O2O00000O100O1000O010001O000O10000000000O100O1O101N1O2J6H7L5I8JZXP2\", \"size\": [1280, 720]}, {\"counts\": \"f`Vh0h0PW1:J5I7J4N2O2O1N2000000000000O1001N10O01000000O100000001N100000000O2O00000O100O101N1O1N2M4I6K6F=ITXP2\", \"size\": [1280, 720]}, {\"counts\": \"gXUh0h0UW15G8J5K5M3M3M201O1O20N1000000000001O0O01000O1000000O10001O00000O10001O0O100O100O100O1O1M4K6E:D<N4L7Iion1\", \"size\": [1280, 720]}, {\"counts\": \"jPTh0e0UW18F9G9M2M3N2N2O00100O100001O000000001N10000O10O100000O101O0000001N100O2O000O100O100O1O1O2K4L5H9B>L4MmWP2\", \"size\": [1280, 720]}, {\"counts\": \"mPTh01iW1i0TO;L2L4K5M3N2N1O3N1O11O00000000001N100O101OO010000000000000001N10000O100O10001N100O1O1O1M4J6K4I9E<JPXP2\", \"size\": [1280, 720]}, {\"counts\": \"lhRh0b0YW19E8E:L5M1N3O1N200O100O10O2O000000001N10000O010000O1001N10001O0O10001N1000000O100O2N1O1O2M3J7\\\\Of0HPPT2\", \"size\": [1280, 720]}, {\"counts\": \"ghmg0k0lV1;M2M2M2O2H8N2O1000000000000000O101O000O1000O10O10000000001O0O0100001N10TjN^NZU1b1cjNbN\\\\U1^1`jNfN`U1Z1`jNgN_U1X1cjNgN]U1Y1ejNeN[U1Z1fjNfNZU1Z1ejNfN\\\\U1X1ejNgN^U1V1ejNfN^U1S1l0H8Ah0CjgW2\", \"size\": [1280, 720]}, {\"counts\": \"ZQTh03YW1g0A>G7M3N2O1O1O010O2O0001O000O101OO1000O100000O10O10VjN]NWU1c1hjNaNUU1_1jjNcNVU1\\\\1jjNdNVU1\\\\1ijNcNYU1\\\\1gjNdNZU1\\\\1ejNeN[U1[1ejNeN[U1[1ejNeN[U1[1fjNeNZU1Y1gjNgNYU1Y1gjNgNYU1X1gjNiNXU1X1hjNhNYU1V1hjNcN_U1\\\\1c0N3J7L6HQ`V2\", \"size\": [1280, 720]}, {\"counts\": \"gh\\\\h03dW1b0E=D2L5J4K5N2O10000O10000000000001O000O10O1000000O02O000000XjN_NQU1`1mjNcNSU1]1ljNdNUU1[1kjNeNUU1[1kjNcNWU1\\\\1jjNcNWU1]1hjNeNWU1[1hjNhNVU1X1jjNhNVU1W1kjNiNUU1W1ljNhNTU1X1mjNgNSU1X1k000O2N1O2O3M=jN[iN7Wni1\", \"size\": [1280, 720]}, {\"counts\": \"RYdh02cW1d0E3I6O1N2N2I6L5O1O1O1000001O00O10000000001O0000O10000000000001O00YjN`NnT1`1ljNfNTU1Z1ljNgNSU1Y1ljNhNTU1X1ljNgNUU1X1ljNhNTU1X1ljNhNTU1X1ljNhNTU1X1ljNiNSU1W1mjNiNSU1W1njNiNRU1U1RkNfNPU1Y1l0O1O2N3C\\\\iNVOlV19`P`1\", \"size\": [1280, 720]}, {\"counts\": \"_Xnh07hW1c0\\\\O4L3G9M2L4O001O101N1000O100O100000000000000000000000XjN_NQU1a1njNbNPU1^1ojNcNRU1\\\\1njNdNRU1\\\\1mjNfNRU1Z1mjNgNSU1Y1mjNgNSU1Y1njNfNRU1Y1ojNhNPU1X1ojNhNRU1X1mjNiNSU1W1mjNiNSU1V1njNjNRU1V1njNkNQU1U1ojNkNRU1S1ojNlNRU1T1m00001N3K5F<]O\\\\XW1\", \"size\": [1280, 720]}, {\"counts\": \"b`Yi0=bW12GCkhNa0oV1;H7M4M2O100O1000000O1O1000000O100000000RjNcNYU1]1gjNdNXU1\\\\1hjNdNXU1\\\\1hjNeNXU1Z1hjNgNVU1Z1jjNiNSU1W1ljNmNQU1S1ojNnNPU1Q1QkNoNoT1Q1RkNnNnT1R1RkNmNoT1S1PkNoNoT1Q1QkNoNoT1Q1QkNoNoT1Q1QkNnNQU1P1PkNPOPU1P1PkNoNRU1P1ojNhNYU1V1hjNgN]U1V1k0C>GR`S1\", \"size\": [1280, 720]}, {\"counts\": \"^hZi0<aW17H5F9M3M300O1O2O01O01N1O10000O1RjNdNXU1\\\\1hjNdNXU1\\\\1hjNdNXU1\\\\1f00RjNdNXU1\\\\1hjNeNWU1[1ijNeNWU1[1hjNfNXU1Z1hjNgNWU1Y1ijNhNVU1X1jjNiNUU1W1kjNlNRU1T1mjNPOPU1P1QkNoNoT1Q1QkNnNPU1R1PkNnNPU1R1PkNoNoT1P1QkNQOoT1o0QkNQOoT1o0QkNQOoT1o0QkNQOPU1n0PkNQORU1n0njNnNWU1Q1ijNmN[U1o0hjNnN_U1j0S1_OW`S1\", \"size\": [1280, 720]}, {\"counts\": \"^Xih01oW1001O0O2O2M3B>G9N2N2N1O1O00001N10001N1000001O0000000oiNfN\\\\U1[1cjNeN]U1[1cjNfN]U1Y1cjNgN]U1Y1cjNgN]U1Y1cjNgN]U1Y1bjNiN]U1W1cjNjN\\\\U1V1djNjN\\\\U1V1djNlN[U1S1ejNmN[U1S1djNoN[U1Q1ejNoN[U1Q1ejNPOZU1P1fjNQOYU1o0gjNROXU1n0hjNSOWU1l0jjNSOXU1l0hjNTOXU1l0gjNUOYU1j0hjNVOZU1g0gjNYO[U1e0ejN[OaU1MmiN7b0LnU11SjNOQW1J_fY1\", \"size\": [1280, 720]}, {\"counts\": \"cgah04dW1:L6J4N101N1N2O001O100O010O1QjNROkT1n0UkNQOkT1P1ojNQOUU1P1ijNRO]OMaU1R1QkNQOWU1Q1gjNQOWU1P1hjNSOUU1n0jjNTOTU1l0ljNUOSU1l0ljNTOTU1m0ljNSOSU1n0ljNSOSU1m0mjNSOTU1l0ljNTOTU1l0ljNTOTU1l0ljNTOTU1l0ljNTOUU1k0jjNVOVU1k0ijNVOVU1j0kjNUOUU1k0kjNUOUU1k0jjNVOVU1i0kjNXOUU1g0kjNYOUU1g0kjNZOTU1e0mjN[OSU1e0mjN[OSU1f0ljNZOTU1f0ljNZOUU1e0kjN[OUU1e0kjN[OUU1e0kjN[OWU1b0jjN^OYU1=ijNCfU1K]jN5fU1G[jN9hU1BZjN>dV1O1FahN3`W1IfhN3[W1LV_]1\", \"size\": [1280, 720]}, {\"counts\": \"bn]h04lW15K7I1N2N2N3N1O2N1O1O1O1O1O1O2O0O3M001O1O001QjNiNSU1W1ljNjNTU1V1mjNiNSU1X1j00QjNiNUU1X1ijNmNTU1S1kjNTOnT1l0RkNTOnT1l0RkNTOnT1l0RkNTOnT1l0QkNVOnT1j0RkNVOoT1i0QkNWOoT1i0PkNXOoT1i0QkNWOoT1h0QkNYOoT1g0QkNZOoT1d0SkN\\\\OlT1d0TkN\\\\OlT1d0SkN]OmT1c0SkN]OmT1c0TkN\\\\OmT1c0SkN]OmT1c0SkN\\\\OnT1c0SkN]OoT1a0QkN^OTU1=mjNAXU1DUjNa0ZW1M2N10O1001M2DbhN5fW1Nd`]1\", \"size\": [1280, 720]}, {\"counts\": \"lmXh04_W1d0H2001OO1O1N3N1O1O001O2N1O2SjNkNkT1V1TkNjNmT1V1QkNlNnT1U1QkNkNoT1V1PkNjNPU1V1PkNkNoT1V1PkNlNZO3XU1R1]kNXOcT1h0[kNYOeT1h0ZkNYOeT1h0ZkNYOeT1g0[kNYOeT1g0[kNZOdT1g0[kNYOeT1g0[kNYOfT1f0ZkNZOfT1g0YkNYOhT1f0YkNZOfT1f0ZkNZOfT1f0ZkNZOfT1f0ZkNZOfT1f0ZkN[OeT1e0ZkN\\\\OfT1d0ZkN[OgT1e0YkN[OgT1e0YkN[OgT1d0ZkN\\\\OfT1d0ZkN\\\\OgT1c0YkN]OgT1c0[kNZOgT1d0[kN[OeT1e0ZkN\\\\OgT1c0YkN\\\\OhT1c0XkN^OiT1a0VkN@lT1=UkNCnT1:QkNFZU10fjNBBNQV18\\\\jNIXV1NoiNM_gh1\", \"size\": [1280, 720]}, {\"counts\": \"klbh0P1kV17L301N2OO100O0001N20000O001O0O2O00000010O0000000000000000000O10001O0O100O2N101O1N2O2TOdiN0XW1N2MSbg1\", \"size\": [1280, 720]}, {\"counts\": \"f\\\\Qh09dW1;giNB^T1b0]jNYOj09gT1?ZjN\\\\Om0:eT1a0XkNDfT1<XkNGgT1:XkNGgT1:XkNFhT1;WkNFhT1:XkNFhT1;WkNEiT1;WkNEhT1<WkNEiT1<UkNEjT1<WkNDhT1<YkNCfT1>[kNAeT1?\\\\kN@dT1`0]kN_OcT1a0]kN@bT1a0]kN_OcT1a0]kN@bT1a0]kN_OdT1`0]kN_OcT1b0\\\\kNjN]O>XU1h0fkNQO[T1o0hkNmNYT1T1jkNgNVT1[1W1001O1O00001O001O2N10O0O100O101O0O100O100O1000000O10001O0O2M4K5I_[f1\", \"size\": [1280, 720]}, {\"counts\": \"Sfmg01nW14ThN1aW18N2N1ZO@liNa0RV1AniN`0nU1CQjN>mU1DSjN;lU1FTjN;cU1M]jN4YU15gjNLTU19kjNGSU1;mjNFQU1;ojNEPU1=ojNDoT1=QkNCmT1?TkNAiT1a0XkN_OgT1a0ZkN_OeT1a0[kN_OeT1b0ZkN]OgT1c0YkN^OfT1b0ZkN^OfT1b0[kN^OdT1b0\\\\kN^OdT1c0[kN^OeT1a0\\\\kN^OdT1c0\\\\kN]ObT1e0^kN[OaT1f0_kN[O_T1f0`kNZOaT1e0_kN[OaT1f0^kNZObT1g0]kNZOcT1f0\\\\kNZOdT1f0\\\\kNZOdT1g0\\\\kNXOeT1h0ZkNXOfT1h0ZkNYOeT1h0[kNYOcT1h0\\\\kNZObT1f0_kNZO`T1g0_kNXObT1h0_kNWOaT1i0_kNVObT1l0\\\\kNSOeT1n0TkN[NMf0PU1P1PkNUOSU1b1:K5L4L4O1N2O1O2N1N200O1O1N2N2N3O000O2N2O1L4N4FWiNVOTW1:_kY1\", \"size\": [1280, 720]}, {\"counts\": \"SVUh01nW1;F1O2N2N1O0dNESkN<jT11kjNOQU19kjNHPU1=ojNDnT1?RkN_OnT1b0SkN]OlT1d0VkNZOjT1g0TkNZOlT1f0TkNZOkT1g0UkNZOjT1g0UkNYOkT1g0VkNYOiT1h0VkNXOjT1h0WkNWOiT1i0WkNWOiT1i0XkNXOeT1j0\\\\kNWOaT1i0`kNXO^T1i0akNWO_T1i0akNXO_T1h0akNWO_T1i0akNWO_T1j0akNVO^T1j0ckNUO]T1l0akNUO_T1k0akNUO`T1k0_kNUOaT1k0_kNUOaT1l0^kNUOaT1k0_kNUOaT1l0^kNUObT1k0\\\\kNVOdT1j0\\\\kNVOdT1k0[kNUOeT1k0[kNUOeT1l0ZkNSOgT1m0XkNTOhT1m0VkNTOjT1R2N2O1O01N1001O000001O01O1O0001O0O10000O100O2M2@a0M3UOajNQOcU1f0S1EccS1\", \"size\": [1280, 720]}, {\"counts\": \"SVZh01oW10O3N1YhNM^W1<O1O2O00OoNG^jN7]U1NbjN2TU19kjNGRU1<njNEoT1=QkNCmT1?TkN@jT1b0VkN_OgT1d0YkN[OfT1g0YkNYOfT1h0[kNYObT1h0^kNZO`T1g0`kN[O]T1e0dkNZO\\\\T1f0dkNZO\\\\T1f0ekNYO[T1h0ekNWO[T1j0dkNWO[T1i0fkNVOZT1k0ekNUO[T1k0ekNUO[T1l0ekNSO[T1m0ekNSO[T1n0ekNQO\\\\T1n0dkNRO\\\\T1o0bkNSO]T1m0ckNSO]T1n0bkNSO]T1n0bkNRO^T1n0bkNRO_T1n0`kNRO`T1n0_kNSOaT1n0^kNROcT1m0]kNTObT1m0]kNSOcT1n0[kNTOdT1m0ZkNTOfT1m0WkNUOiT1m0RkNVOnT1n10001O0000001O0000O100O2O0O10001N1O1O1O1J6N3L3M3K6J6D<D?Dekj0\", \"size\": [1280, 720]}, {\"counts\": \"\\\\Vdh02lW13N2L4M3N2O001]N4PkNLnT16QkNKlT19TkNGhT1<ZkNDcT1=^kND_T1>`kNB_T1?bkN@^T1a0akN@]T1a0ckN_O\\\\T1b0dkN_O[T1a0fkN_OYT1b0gkN]OYT1c0hkN\\\\OXT1d0hkN\\\\OXT1e0hkN[OWT1f0hkN[OWT1e0jkNZOVT1g0ikNYOWT1g0jkNXOVT1i0hkNYOWT1i0gkNWOYT1j0fkNVOZT1k0dkNVO]T1j0akNWO_T1i0akNXO^T1i0akNWO_T1j0`kNWO_T1j0_kNXO`T1i0_kNWObT1i0]kNWOcT1j0\\\\kNVOdT1l0XkNVOhT1k0UkNWOlT1j0ljN\\\\OTU1h100O100000000000000001N100O10O11N100O100O2O0O1N2O1L5I6H9M2O1L6A`0F>VOlhNOiRb0\", \"size\": [1280, 720]}, {\"counts\": \"Xffh01mW16J3N2M2O2M2M4L4L4M2M4oN[OajNj0[U1o0O2M2M301O000O1O2N100000001O00010O1O1O2N001O01O000O1000000001O00000000000000000000O10000O1000001N1O1O1O1N2M3N2N3N1O2N1M4L4M3G<Co0WOmj;\", \"size\": [1280, 720]}, {\"counts\": \"Xnlh03lW14L1D>B=VOj0L3J8L3L4M3N2N1O10001N101O01OO101O00100O001O1O1O01N1000001O00000010O000O1000000001O0O10000O100O101O0O101N1N2N2M4M2N2O1O1N2N2N3M3M2I8EgiNjNbV10liN3RR=\", \"size\": [1280, 720]}, {\"counts\": \"\\\\^eh02lW13M4L2@KRiN9eV1f0B>I6K6L4M4L2N3M2N3M2N3M3O1O0O2O1N101O00001O0001O000001O1O0001O0000001O1O001O01O1OO10000000000O1000000000000O2O00000O2N1O2J5J6L4N3C<J7H7L5K4L5N2N4^Onk;\", \"size\": [1280, 720]}, {\"counts\": \"bfPi01QW15_iN4kU1]1J5L4L3N3M3M2N3M4L2N3O1O1N101O0000001O0000000000001O000001O00010O01O0000000000000001O00O1000000000000001N10001O001N100O1N2L5K4I7L5F9K6M3N2N5mNaiN<lY9\", \"size\": [1280, 720]}, {\"counts\": \"bfPi02aV10ijN6oT1m0kiN[OPV1]1K5L4M2N3N2N1N3M2M4N2N2O0O2O001O000001OO100000001O000001O000010O100O0O1001O00000O10000000000000000O100O2N1002OO0000000O2iNljN_OUU1>QkN^OPU1`0TkN\\\\OoT1a0UkNYOoT1>\\\\kN\\\\OiT1;ckN[OdT1a0b1Bgc:\", \"size\": [1280, 720]}, {\"counts\": \"anQi03ZW1NViN6nU12oiNd0mU1j0L4L4L4M3M3M2N3M2N3N2N1O2N1O2O0000001O0000001O00000000000000010O1O000001O0000001OO100O11O00000O2O000O101O01O10O0hNkjNEUU1;kjNDVU1;mjNCTU1;ojNBRU1<QkNCoT1NakNOaT1ObkNN`T11ckNK`T14bkNFcT19dkNYOdT1f0\\\\1N8I6ISk6\", \"size\": [1280, 720]}, {\"counts\": \"hmVi03lW11khNM[V15diNMTV1;jiNGTV1>giNDUV1b0giN@VV1W1N3L3L4L4K4M3M4M2N2N3N1N3N1O2O001N11O00000000000001O00000010O000000010O000000001N10001N2O1OO10hNljNDTU1J`kN4aT1IckN5^T1IfkN4ZT1KhkN4ZT1GjkN8WT1DnkN:kU11O01O2L]W1C\\\\[9\", \"size\": [1280, 720]}, {\"counts\": \"an`i03lW13N1N2YOHdiN9ZV1JWiNN59aV1MUiN074^V1?\\\\iNC_V1R1L4L3D<K6N1N3N1O2N2O0O100O101O0000000001O1O1O0001N10iNljNCSU1<ojNCRU1<ojNCQU1<QkNCoT1<VkN@kT1>XkN@hT1>[kNAfT17akNI`T1MikN2XT1NhkN2VT10gkN4WT1NekN5[T1JfkN6]T1FekN8]T1GekN7\\\\T1FhkN8XT1GjkN8VT1GlkN8VT1DlkN;WT1@mkN?jU1000000O2O001O;ERa5\", \"size\": [1280, 720]}, {\"counts\": \"b^^i02mW16nN2fiN2UV17ciNLZV1o0M3M2M3M3D=K4N3N1M4L4N100O11O01O0O0011O1O0nNljNXOUU1e0ljN\\\\OTU1b0ljNATU1>ljNBTU1=mjNfNLf0VU1>hjNnN5NOe0TU1>mjNjN22Mf0TU1>mjNjN23Kg0UU1WOmjNe0M@3K4o0oT1ROljNc01]O`0k0cT1WOijNa0o06XT1OokNKQT14QlNKoS15QlNKoS15QlNKoS13SlNMlS12WlNMiS12XlNNhS11WlN1iS1OUlN2lS1MUlN3kS1KVlN6kS1ElkNg0UT1WOlkNj0TT1VOmkNj0TT1ROokNm0RT1QOPlNn0PT1ROQlNm0oS1ROTlNk0mS1UOUlNi0kS1WOVlNg0lS1XOUlNe0nS1YO]lN2VN1nX>\", \"size\": [1280, 720]}, {\"counts\": \"PnQi0c0WW17M3M3J5M3L4K5M3K5H8M6G8K4N2M200O100O2O001N100O20O1O0000OhNPkNARU1=ojNDPU1:RkNFnT19SkNGnT13XkNLiT11ZkNNfT1ROkjNh0`06fT1N]kN1cT1N^kN2bT1N^kN2aT1NakN1_T1NbkN2^T1NbkN2^T1KekN5[T1HhkN8XT1EkkN;UT1BmkN?ST1WOmjNKQ1P1RT1TOVlNl0jS1TOVlNl0mS1POTlNP1^U1O2O0000ZNoN]lNP1ZU100O1O1M3N200M3N2O1M3GFH^iN4\\\\a?\", \"size\": [1280, 720]}, {\"counts\": \"Wnlh02jW17J6K4L2O2H8L4K5B<M4C=M4N2M3M2O2N1O2N100O2O000O2O00001O1O0001O00001O0dNRkNGmT18TkNHlT16VkNJkT14VkNMiT13XkNMgT1O]kN1cT1HdkN7]T1FfkN:ZT1FgkN9ZT1EgkN:ZT1EgkN;YT1DikN;XT1BjkN>VT1_OnkNa0QT1]OPlNd0QT1YOQlNg0oS1XORlNh0nS1XORlNh0nS1oNZlNR1[U1N100O1O1O1O2L3M4N100HlhNDYW10\\\\bd0\", \"size\": [1280, 720]}, {\"counts\": \"Qnlh09^W1;I6K5A?_Oa0J5N2N2O1M3O1O2N2O1O001O00001OO100O1001O0000hNmjNDRU19TkNDlT1<UkNDjT1;XkNDhT1<XkNDhT1<XkNEgT1:[kNEeT1;[kNFdT19^kNEcT1DjjN5c07cT1EhjN1h0<_T1LikN6VT1EnkN=QT1ARlN>nS1ATlN>lS1AUlN?kS1@WlN?iS1@XlN`0iS1^OXlNa0iS1]OZlNb0fS1]O[lNc0aU1O01N20O2O0O2O0O2O0N2N4L:Bnij0\", \"size\": [1280, 720]}, {\"counts\": \"Qffh08^W1MfhN5XW1NehN4YW1LhhN5VW1LjhN5QW10nhN0gV1C^iNQ1ZV1?B`NVjNd1cU1gNVjN]1eU1a0M3O1N200O100O1O101O010O00001O01O0O11OPOgjNYOYU1>fjNkN3f0WU1>UkN@jT1?XkN@hT1`0XkNAgT1>[kNAeT1?[kNAeT1>]kNAcT1=_kNCaT1<`kNCaT1KfjNMl06`T1KfjNIQ18[T1KVlN3kS1LUlN6iS1LSlN8lS1HRlN;nS1DRlN=mS1CTlN=kS1CUlN=kS1AXlN=jS1@XlN`0hS1^O[lN`0gS1]O\\\\lNb0bU1N1OO10010002ON001003L4DbhN0[an0\", \"size\": [1280, 720]}, {\"counts\": \"R^eh03jW15F:K4M3K4A`0_Oa0J6L4L3M3N2O1N201N100O2O0010O1O0000010O001N11OQOfjNXOZU1`0ojN_OQU1=VkN@jT1?XkN@hT1?ZkN@gT1>ZkNCeT1;^kNDbT1:`kNF`T1JfjNGP1?ZT1HjjNEn0c0YT1DUlN:lS1DVlN<kS1BWlN=jS1AVlN`0jS1]OXlNd0hS1[OTlNk0kS1TOWlNk0aU1N1O1O001N3N1O0O000010O100000O102MRbn0\", \"size\": [1280, 720]}, {\"counts\": \"i]eh01mW16H<oNJdiN=WV1HbiN=\\\\V1e0G9M3M3L4L3M3O2N1N20000O101N101O01N11O0O2O0001O000001O01eNRkNBnT1=UkNAkT1?VkN@iT1`0YkN@fT1`0ZkN@fT1?[kNAeT1>\\\\kNBeT1<\\\\kNDdT1<\\\\kNEcT1:^kNFcT1NhkN2XT1HnkN7ST1FokN;QT1CQlN=oS1BRlN>nS1ATlN>mS1@UlN?kS1@WlN?jS1^OXlNb0iS1[OYlNe0bU1OO10001N10O101O00001N1N3L<Aoij0\", \"size\": [1280, 720]}, {\"counts\": \"R^oh02eW12]hN6\\\\W1:WO]OoiNf0mU1CgiNd0SV1h0F:N1O2N1O2N2N1N2N2N2N3M200O100O2O0010O010OO101O01O000000000cNVkNEiT19ZkNFgT18[kNGdT19^kNFbT1:_kNEaT19bkNF^T18dkNH\\\\T17ekNI\\\\T12hkNNYT1LmkN3TT1DTlN;mS1CUlN>jS1^OYlNc0gS1[OWlNj0hS1QO]lNo0[U1O002N2M2O1O1O00N3N1O1000O1O2N1N3M5CXb?\", \"size\": [1280, 720]}, {\"counts\": \"mUXi0=_W16J6H6N2O2B?K4D<K5K4M3O1O002N1O2O1O1O0000001O001O0000001OgNQkNAoT17[kNGeT19[kNGeT19\\\\kNGcT19]kNGdT17]kNIcT17]kNIdT16\\\\kNKcT14^kNLcT12^kNNbT11_kN0`T1NbkN2^T1IgkN7YT1EkkN;UT1CmkN=ST1BokN=RT1AokN>RT1@PlN`0PT1]OTlNb0mS1\\\\OTlNd0lS1ZOWlNf0iS1VOZlNj0^U11N2N1O1000000001O0O1O2M3K7EVb:\", \"size\": [1280, 720]}, {\"counts\": \"i]Yi03602NmV1n0\\\\OVOfiNo0UV1a0L4N1M4L3M4M3M2N101N2N3N102M2O1O1N101O010O00001O000000001OcNSkNGmT17VkNHjT16XkNJiT12ZkNNfT10\\\\kN0dT1O]kN1dT1M^kN2bT1N_kN2`T1O_kN1aT1JdkN6\\\\T1HfkN9YT1DjkN<VT1BmkN=TT1AmkN>TT1BmkN=TT1@nkN`0ST1]OPlNb0PT1\\\\ORlNd0nS1YOTlNh0lS1WOSlNk0mS1TOTlNm0kS1SOTlNn0mS1QOSlNo0_U101O0000001O1N2M3M5K4^OhhN4]W1HihN0TQ8\", \"size\": [1280, 720]}, {\"counts\": \"`VXi03oV19TiNG74lU1V1miNmNQV1c1N2M3N2N2N1M3M4N1O1O2N1N2O102N1O1O001O0000001O000000O010001O01O0gNmjNESU1:njNFRU1:njNGRU14PkNNPU10UkNNjT10YkNOgT1O\\\\kN0dT1GPkNVO>S1cT1DfkN<ZT1AikN`0WT1^OjkNa0WT1]OlkNb0UT1\\\\OlkNd0UT1XOnkNh0RT1UOPlNl0QT1oNSlNQ1^U1000001O0000001O1O001O1O1N1O3XOohN;TW1CohN:TW1BohN;]W1Fga5\", \"size\": [1280, 720]}, {\"counts\": \"jeUi0;<GhV1?nhNJmV1g0K5L4M3H8M3N2M3J5J7G8O2M2O1O1O2O0000001O001O0000000000000000eNojNGQU16SkNImT15UkNLjT12XkNNhT1N]kN1dT1L_kN3aT1KakN6^T1IckN7^T1GckN9]T1EekN;ZT1EfkN<ZT1CfkN>ZT1AhkN?XT1\\\\OlkNd0UT1WOPlNh0PT1WORlNh0oS1WOQlNi0oS1VORlNi0PT1POVlNP1]U100001N1000000001O00002M101O2N2M2WOQiN=QW1BQiN<QW1CPiN:kP8\", \"size\": [1280, 720]}, {\"counts\": \"nmQi06=LiV1=PiNGlV1i0J6K5K5M2N3I7I6M4H8K5L3O2O0O2O0O10001O00001O001O0000000000000000bNnjNNRU1OVkNKkT13WkNNhT1O\\\\kN0eT1K_kN6`T1GckN9]T1FekN9\\\\T1EekN<ZT1CgkN=YT1BhkN>XT1BhkN>XT1\\\\OmkNe0ST1UOSlNk0mS1TOTlNm0kS1ROWlNm0QT1jNQlNU1^U1O000O2O1O1OO101M2O10O101O001O1O1N101N2N3XOPiN:TW1CnhN:_W1Egi6\", \"size\": [1280, 720]}, {\"counts\": \"S^jh01mW18H5H8H8I5C<_Oa0N3L4L301O0O2K5M2O10000000001N101O00001O0O2O1O1O000000000010cNnjNJRU15PkNJQU13PkNNQU1LTkN3mT1EnjNTO5X1mT1CQkNSO2[1lT1A^kN>bT1@`kNa0_T1^ObkNb0_T1[ObkNf0^T1YOdkNf0\\\\T1UOkkNi0UT1UOnkNj0ST1SOokNm0QT1ROPlNn0QT1oNRlNP1_U1001O02N2N1O0000O1O1O1000000001O1O2M101N1N1J7K9DUZ9\", \"size\": [1280, 720]}, {\"counts\": \"P^jh0:bW16K:G3L4M3\\\\OPOTjNT1_U1m0N2H8N1O2N1N201O001N101O001O00000O11O01O0000\\\\NojN9QU1CWkN9iT1FXkN:hT1FXkN;gT1D[kN;eT1E\\\\kN:dT1E]kN;cT1C`kN<aT1@bkN`0^T1POmjNNk0Q1YT1SOnkNo0QT1QOmkNQ1TT1nNhkNV1YT1iNhkNV1XT1jNikNV1WT1iNPlNP1QT1nNQlNQ1PT1jNTlNV1\\\\U1O0000O1O1I8N100O1002N5JKlNbiNR1[V1QOeiNo0[V1;O100000000010O00O01O1O>@4K5M5DYZ9\", \"size\": [1280, 720]}, {\"counts\": \"`ngh04hW18C;M4K3M3M2O2N2E;K5D;I8L3N2O1O2N1O2O0O2O000O2O01O0001O0000QNjjNT1VU1lNVkNh0jT1WOXkNh0hT1XOXkNh0hT1XOXkNi0gT1WOZkNh0gT1VOZkNj0fT1UO\\\\kNj0dT1UO\\\\kNl0dT1QO_kNo0bT1nN_kNS1bT1dNfkN\\\\1`U1O1O1O010O1O0O1000O1000001O00000001O000000001O01O00O100000007H4K4FTiN[OTW15oQ=\", \"size\": [1280, 720]}, {\"counts\": \"eV_h04iW14H9M2O1N3L4K4N2N1YOAjiNc0SV1g0M3D<M2M4N2O001O0000001O00001OO1nMnjNV1QU1jNRkN@G^1WU1ROYkNm0gT1ROZkNn0fT1ROZkNo0eT1QO[kNP1dT1PO]kNo0cT1PO^kNP1bT1PO]kNR1cT1mN^kNR1bT1oN]kNQ1cT1PO]kNo0cT1QO]kNP1bT1oN`kNP1`T1oNakNQ1_T1nNbkNR1^T1nNbkNS1]T1kNekNU1\\\\T1fNhkNZ1YT1cNikN^1XT1^NjkNb1ZU1000O2O01O00000O1001O0000O100O10O11O01O00O011O0O6_NciNT1iV1K01002L7I^Yc0\", \"size\": [1280, 720]}, {\"counts\": \"fn]h04jW15E:L4L3N1N3N2N2N3N0O2M2O2N2O1N2O1N100O1O2O0O1O1O2N11O0000O2N1O101N100O2M26J;EPOdN^kN[1aT1eNakNY1^T1gNdkNX1]T1dNfkN\\\\1`U1O1O101N1ON2H8L41O000000100O0O2O00O1O1O100000000000O101O3L;F3L3M2N3Kd0[OoXc0\", \"size\": [1280, 720]}, {\"counts\": \"[Vdh0?_W15J5M3M3M2N2O1N2N2M2O2O1N2N1O2N1N2O2M2O1O101N1O1O2M2O1N3L3N3L3N2O1N3L_NojN9QU1FQkN9oT1FRkN:nT1ETkN:lT1FVkN8jT1HWkN7iT1HXkN9gT1@`kN`0`T1[OekNe0\\\\T1WOfkNj0ZT1SOekNQ1[T1nN]kN\\\\1fT1\\\\N_kNc1`U1O6J2N2N6J001N01K5N2O1001O0000002N1O1O1O1O1O0O2O1N101N2L;C[a?\", \"size\": [1280, 720]}, {\"counts\": \"]ffh0270ZW1?J6L4M2N2N3M2N2O1N2O1N101N1O2N1N3N2N1O1O1O1O1O100O1O2O000O101O1N1O1O1O1O2N1O1O1O1O1O2M2N2MaNnjN6PU1KXkNNhT10ZkN1dT1O^kN0bT1O_kN1aT1KbkN6_T1FdkN:]T1@hkN`0YT1oNUlNS1ZU130O2N1O1O1N101O0000O2O00000001O000O101O003J6H]i;\", \"size\": [1280, 720]}, {\"counts\": \"c^jh06eW18G7N3M2N3M3M2O1N101M2O2M3M2N3N1N2O2M2O1O1O1O1O1O101O0O2O0O1O2O0O1O1O2O0O1O1O1O1O1N2N3M2O1O2N11\\\\NljN:TU1AXkN8kT1@YkNb0QU1lNTkNV1jU111O100O1N2O3M00000001O000O1O11O1O0O101O0O2O1M3M4J`Y9\", \"size\": [1280, 720]}, {\"counts\": \"gfkh02lW16G6J8K3M4M2N2N2N2M2N3L3M4K5M2N3M2M3N3M200O1O2M2O1O1O2N2M3M2N2O101O0O100O12N1O00000001O01O0]MijN]2WU1cMijNR103bU1[OPkN`0QU1ZOUkNe0TV100O00000000000000000000O10000O11O2N001N10001O1N5K4D]Y9\", \"size\": [1280, 720]}, {\"counts\": \"fVnh08bW19H7M4K3K6TOk0K42OGoMajNQ2^U1PNbjNQ2^U1oM`jNS2_U1701O1O1N2M2O100001O00010OO2O1N101O010O000001O000O10000mNhjN^OXU1b0hjN^OXU1`0kjN_OVU11YkN0fT1O\\\\kN0dT1UOhjNb0h07aT1SOjjNe0f08fT1F\\\\kN:dT1E^kN:eT1UOikNk0gU1O000001OO10001O0000000000O1O11O1O0000010O0O100MXa:\", \"size\": [1280, 720]}, {\"counts\": \"c^oh0<6FoV1c0lhNBPW1k0K3L3UOl0M20100K5M2O2N1O2O1O0O2O1OO1000000001O0000001O000000000001O0eNijNMWU10njNNRU1LUkN3kT1KWkN6hT1IXkN8hT1F[kN:eT1D\\\\kN<dT1C]kN=cT1B^kN>bT1A_kN?bT1\\\\ObkNd0^T1YOdkNh0\\\\T1oNmkNQ1ST1mNnkNT1ST1jNnkNV1RT1jNnkNV1ST1hNmkNY1TT1fNnkNX1^U1O1O0O10001O0001N100001O001N2O1O1O3M9F4^OehN4Z`:\", \"size\": [1280, 720]}, {\"counts\": \"ffkh05;LoV1<lhNHoV1=mhNHnV1g0N2M3N2N2N2N2M2M4N1N3K5M2N2M3N3N1O2N1O1O1O1N3L3O1O1N200O1010O0000000ZNQkN;PU1^OWkNa0jT1^OXkNa0gT1_O]kN=dT1A]kN?cT1@^kN`0bT1_O^kNc0aT1[OakNe0_T1ZObkNf0^T1XOckNh0^T1VOdkNj0\\\\T1TOekNm0\\\\T1POfkNP1ZT1hNmkNZ1\\\\U1000O1010N3N1O00O01010O1O0001N1000O11N2O1O9D8G6DUa:\", \"size\": [1280, 720]}, {\"counts\": \"VWnh01mW15VOKViN5N2iV1;TiNHiV1k0J3M4H8K4J7J5N3M2O2M2M4M2O101O000000000001O1N2OO2O0O10_NljN6TU1InjN6RU1HPkN8QU1FQkN9oT1BVkN?iT1BWkN=iT1CWkN>hT1BYkN>fT1A[kN?eT1A[kN?eT1@\\\\kN`0eT1^O\\\\kNb0dT1^O[kNc0eT1\\\\O\\\\kNd0dT1ZO^kNf0bT1WOakNi0_T1ROfkNn0eU12N1O11O001O001N100000100O00O1O100000O100000;D6J3K6HoX9\", \"size\": [1280, 720]}, {\"counts\": \"inQi048OmV1l0E7G:VOj0O1O001N2N2O001O1N2O0O2O00O10000O1001O1O001O0000000001O00000[NjjNa0UU1]OXkN8hT1GYkN9gT1FZkN:fT1E[kN;fT1DZkN=eT1C\\\\kN<eT1B\\\\kN>dT1A]kN?cT1@^kN`0bT1_O^kNb0cT1ROhkNn0XT1mNkkNV1TT1iNmkNW1ST1hNokNW1QT1iNokNW1RT1gNokNX1]U101N1000001O0001N10000001O00000O6K8H4L1K6DTQ8\", \"size\": [1280, 720]}, {\"counts\": \"enQi06^W1d0F3M3N3M2M3N1O1I8M4J5K5K4M3M4N1O1O2N10000O2N2O1N1O2O000000001O000O10O1hNkjNEUU15RkNJnT13UkNMkT11WkNOiT1OYkN1hT1L[kN3eT1K]kN5cT1J^kN6bT1I_kN8`T1G`kN:`T1E`kN<`T1CakN>^T1@dkN`0]T1]OekNc0[T1]OekNc0[T1]OekNc0[T1\\\\OgkNc0ZT1YOikNg0XT1SOmkNl0UT1ROlkNn0XT1lNkkNT1`U1000O10000001O0O1000001N101N2O1O2L>AQY4\", \"size\": [1280, 720]}, {\"counts\": \"XnVi07gW13L6L4L3L3N1O2M2N4I7I7K3N4I6M3L3N4M3M2M3O1N2N3M2N2O1O0O20K4M4N100lNkjN]OUU1`0ojN@QU1>PkNBPU1=QkNDnT1;TkNEkT1:UkNGkT17WkNIjT15WkNLiT12YkNNfT10\\\\kN1cT1N^kN2bT1LakN3_T1LbkN4^T1HfkN8ZT1GgkN9YT1FhkN:XT1DjkN<VT1CkkN>TT1AmkN?TT1_OlkNb0VT1[OikNg0YT1VObkNP1^T1oNckNR1\\\\T1nNdkNR1aT1hNckNU1hU1K2O000000O01O002O01O010O000010O00_YO\", \"size\": [1280, 720]}, {\"counts\": \"]Vbi08]W1?I7_O>H8_Oa0M3L4N101O1O0O2O1O000O101O0001O000001O1O0000O1001O0000O2gNmjNCSU1;PkNDPU1;RkNEmT1:UkNEkT1;UkNEkT1:WkNDkT1;UkNEkT1:VkNGiT15\\\\kNKcT13_kNM`T13`kNN`T11_kN2`T1M_kN5aT1HbkN9^T1EdkN:\\\\T1EhkN8XT1GikN9WT1ElkN:TT1CPlN<PT1UOojN0V1j0fU10^OVOliNk0SV1b0N21O0O10001N100O2N10SZO\", \"size\": [1280, 720]}, {\"counts\": \"]Vli01nW12K;_O=J5K5J4M4M2M4H7J6O2K4N3H8N1O2OO100000000010O000O2O00O10000000001O010ZNdjNZO7l0UU1HhjNXO6o0QU1G[kN9eT1F_kN7bT1G`kN9_T1FdkN8\\\\T1HdkN8[T1HfkN8ZT1HfkN8[T1ChkN?YT1XOmkNi0VT1QOlkNP1\\\\T1SOZkNl0dT1XO[kNg0bT1\\\\O^kNd0]T1@ekN?YT1BjkN;WT1BlkN>UT1@mkN>UT1]OPlNa0QT1_OokN0aN9l`0\", \"size\": [1280, 720]}, {\"counts\": \"X^Wj06dW18EFihN?TW1=I4L2M3N1N2O2M3M2N3M2O2M2N2M4J5L5N1N101O2N1O1O1O1O1O1O2O01N2OO100O13bNgjNO]U1HijN7YU1EjjN:VU1@PkN`0PU1^OSkNb0lT1]OUkNc0kT1\\\\OVkNd0kT1ZOWkNf0QV1O100000RZO\", \"size\": [1280, 720]}, {\"counts\": \"WfXj02_W12hhN5TW1?K6E;_O>J6E:01O0N2M4N2N2O1O1N101O0O10000O101O01O1O00000fNkjNITU1OVkN0jT1MZkN2fT1M[kN3eT1L\\\\kN4dT1K]kN5cT1K^kN4cT1L\\\\kN4dT1NZkN1hT1OWkN1iT1MZkN2fT1K^kN4bT1K_kN6`T1I`kN8`T1FbkN:^T1CekN=[T1@hkN`0XT1ROkjN0S1m0ST1QOkjN2R1m0g?\", \"size\": [1280, 720]}, {\"counts\": \"heXj029OWW1c0J8F9^O>H9G7M4M3M3M2N3N101O1O1N10001N10001O00001OO100001O00000001O00000cNPkNIRU12TkNLlT14TkNLlT13UkNLlT12VkNNjT1K^kN4bT1J`kN7_T1FdkN:\\\\T1EekN;]T1@fkN`0fT1PO^kNP1bT1oN_kNQ1l?\", \"size\": [1280, 720]}, {\"counts\": \"S^\\\\j01bW1a0J3N6K2M2N3M1O1O2N2M3L3L5L3M4M2M3N3I6K5O1N3N1O2N1O1O101N1001O00010aNQkNLnT13UkNKkT15UkNKlT14SkNMmT13TkNLlT1AijNL<b0mT1PO^kN7Gh0kT1POfkN0@P1^V100000UZO\", \"size\": [1280, 720]}, {\"counts\": \"Zf]j09[W1>J6I7\\\\Od0H7K5M3O0N3N1O2M2O2N10001O00000O1001OO10000001O2N0bMdjNW2lU1nMVjN\\\\1nU1^NZjN\\\\1^U1nNcjNo0VU1YOjjNBDm0`U1BmjN^OGn0]U1BVkN>jT1BVkN>kT1_OXkNa0TV1O003M2N1N10gYO\", \"size\": [1280, 720]}, {\"counts\": \"XnYj07hW13]hNH]V13`iN68=PV1Q1M7H2O1N10001J6N1O2N3N1N1O1O2O0O10001O000O10000000000O10001OeNmjNISU15QkNIoT15VkNHkT15XkNIjT16WkNIjT15WkNKiT14XkNLhT14XkNLgT14ZkNMeT12]kNMcT11_kNOaT1OakN1^T1OckN1]T1NdkN2c?\", \"size\": [1280, 720]}, {\"counts\": \"X_\\\\j05iW14K4^OE\\\\iN=`V1KYiN7eV1d0G8G:G9K4N3M2N3J5N2O10000000000O1100O00O1N3O0000dNljNLTU14ljNLTU14ljNLTU14ojNIRU1LZkN1iT1KZkN4fT1K\\\\kN4cT1L_kN3aT1MakN1^T1OckN2]T1MckN3]T1MckN3]T1LdkN4\\\\T1IhkN5YT1FlkN:TT1FlkN:R?\", \"size\": [1280, 720]}, {\"counts\": \"ZW[j02jW17J4YON]iN4_V11\\\\iN2gU1m0UjNVOgU1d1O0O2M2O2N100O2N1O101OO1000000000O101O0000000001O001O0000OfNhjNNXU10kjNOUU1NojN1QU1LSkN3nT1IUkN8jT1EYkN:hT1C[kN=eT1B\\\\kN>dT1]OckNa0^T1ZOhkNd0XT1UOdjNOU1l0S?\", \"size\": [1280, 720]}, {\"counts\": \"fnTj03kW16C;ihN]OjV1o0]OVOjiNo0mU1g0K5N2O1N2N1M4L3N3O0O1O2N1O2O001N10000000001O000000000001cNkjNMUU13ojNHRU16QkNIoT15SkNKnT13SkNMmT11UkNOkT1OWkN1iT1OWkN1iT1MZkN2fT1J]kN7cT1CckN=]T1@gkN`0XT1ZOnkNf0ST1WOPlNh0PT1WOQlNh0RT1VOnkNi0VT1TOjkNc0mNYOZU14ikNb0^?\", \"size\": [1280, 720]}, {\"counts\": \"\\\\noi0=aW16I4L4oN_O_jNc0^U1JVjN9hU1P1O2L4N1N3M3N1O101N10O1001O000000000001O0O1000000000O101O1O00001O01O0aNgjN7YU1FPkN4PU1H[kN1eT1J^kN8bT1FVkN^O@5Mb0\\\\U1JXkN@^O20d0[U1HdkN<_T1@[kNC[Ol0\\\\U1TOcjN3P1h0fT1VO\\\\kNh0oU1N1O1N2M30aYO\", \"size\": [1280, 720]}, {\"counts\": \"QnTj0>\\\\W1`0B5M2_OoNjiNZ1oU1a0J6K4N1O101M1O2O100O101N1O10001N11O000O11O0O10O100001OgNhjNKWU13ljNLUU12mjNMSU1OQkN1oT1LTkN4lT1HXkN8hT1FZkN:fT1FZkN:fT1B^kN>cT1ROojNJa0T1_T1SOnkNl0RT1SOokNm0ST1POokNo0aU101N3L4L2WOPiN`0YW10fYO\", \"size\": [1280, 720]}, {\"counts\": \"odSj0533N7jV1j0J5K5L4M2M3M3L5H7M2N2O0O1O100O100O2O0O10001O00O10001O00O2O1O10O0O1hNljNESU19PkNFQU15TkNJlT14VkNLjT12XkNNhT12XkNNhT12WkNOiT13TkNOkT11ojN5QU1BYkN=gT1\\\\ObkNb0^T1ZOikNc0WT1ZOnkNd0RT1[OokNe0ST1XOokNg0UT1TOlkNk0XT1ROhkNf0`T1ZO`kNf0P`0\", \"size\": [1280, 720]}, {\"counts\": \"Zeni01[W11khNb0TW15K4L5K7J3M3M3N2N2M2O3J4K5M4L3M3O2N2M3M1O01O0010000O10000000000O100000010O0mNhjN^OXU1`0ljN^OTU1?PkN@PU1=TkNBlT1>TkNAnT1>QkNEmT1;RkNGnT17SkNImT1O]kNOdT1EhkN:YT1BjkN?lU1O001N1J8L20PZO\", \"size\": [1280, 720]}, {\"counts\": \"g\\\\Wj0b04DPW1l0I8I3L4M3I5N2N2N3K4L4M2O2N1O2N102N1O0O10O1000000O010O101O00000ROgjNUOYU1h0jjNWOWU1g0kjNYOUU1f0mjNYOSU1f0njNZORU1e0njN]OQU1c0njN^ORU1a0njNAQU1<TkNBlT19[kNFdT17_kNIbT12ckNM]T10fkN0ZT1LjkN4VT1[OhjN3X1a0lU1N20SZO\", \"size\": [1280, 720]}, {\"counts\": \"ee]j0e0XW13K6K5J5[Of0I7J5N3N10000O1000000000000000000O10000000000000000O10lNmjN[OSU1d0PkNZOQU1d0QkN[OoT1d0TkNZOlT19ajN]Og0:hT16gjNXOe0b0dT16dkNJ]T14dkNL\\\\T1GRlN8oS1_OZlN?eU1N10PZO\", \"size\": [1280, 720]}, {\"counts\": \"_m^j02;7iV11SiN2gV1g0WOnN\\\\jNY1^U1h0J6O1N1O2N1N2N2O2N1O100O1000000O1O10000000000001O0000OnNjjN\\\\OVU1a0PkN\\\\OPU1c0UkNYOkT1>`kNA_T18ikNGWT18lkNFTT1:lkNFTT19nkNFST18nkNHST16nkNJYT1GPlN8QT1GokN9R`0\", \"size\": [1280, 720]}, {\"counts\": \"f\\\\\\\\j025d0jV1l0^O2N2N1N3L4K5K4N3O0N2M4L300O2O00O0101O000O10001O0000000O11O001bNPkNJoT16TkNHlT16WkNIiT13\\\\kNLeT1O`kN0`T1ObkN1]T1NhkNNYT10kkNMUT1]OijN>T14\\\\T1DkkN;mU101_OkhN5UW1KkhN5\\\\a0\", \"size\": [1280, 720]}, {\"counts\": \"gmTj06dW1b0A5L3M3K5F:F:N1M4L3M3J6N20000O101O000000000000O1000O1000001O0010nNfjN\\\\OZU1`0ljN^OTU1a0njN^ORU1?RkN@nT1?TkNAkT1?UkNAkT1?UkNAkT1>TkNCmT1OejNA?`0lT1KhkN2YT1KjkN4VT1JlkN6TT1^OYlNc0aU110K7IRj1\", \"size\": [1280, 720]}, {\"counts\": \"a^Rj04hW17\\\\O0nhN6mV1a0F:B>I6K6L3O2N1N2O2N001O10000O100O2O0000000000000000001O01O00000aNPkNOUU1BVkN<lT1_OZkN>gT1[OgkN=ZT1_OjkN`0lU1OO1jNZOPkNh0PU1XO[jN7Ke0nV1L2N2N1O3CiY4\", \"size\": [1280, 720]}, {\"counts\": \"nmji01k02UV12`iN7gU14oiNI74eU1U1oiNjNnU1f1N1O2M3M2N2O100O1O2O0000000000000O100000O101O0001O000000000O1000lNjjN_OUU1=SkN@lT1<ZkNBgT13ekNK[T11jkNNWT1MmkN3TT1HQlN6QT1_OZlN?dU10001N3NY`2Go_M2N11NJMbhN2^W1NchN1]W1OchN1]W1OchN1]W1OchN1f`0\", \"size\": [1280, 720]}, {\"counts\": \"fn`i07fW15L3^NNQkN5nT1=SjNYOO`0lU1[101M2O1O1M4N2N1O10000000000O10000O2O0000000O010000000010O1gNPkNAoT1>SkNBkT1>XkN@hT1>[kNBdT18ckNG]T15gkNKYT12kkNMTT12nkNNRT10PlN0PT1NRlN2nS1NRlN2nS1NRlN2nS1NRlN2nS1LUlN3kS1LWlN4hS1J[lN5eS1J]lN5cS1J^lN6bS1I`lN6aS1HalN7_S1GdlN8\\\\S1JalN6_S1CijN3f1:bS1AjjN6b14\\\\NHYU1:[lN3eS1N[lN1eS1N\\\\lN2dS1M\\\\lN4dS1L\\\\lN3eS1M[lN2fS1M]lN0dS10`lNFfS1:ZlNEU?\", \"size\": [1280, 720]}, {\"counts\": \"d^^i06iW13M2[OF`iN=RV12kiN0lU1W1I6H9N2L3L4O1O1000001O000O10000000O11O001N100000000001O0010OjNljN@TU1NjjNXOa0d0dT13hkNLWT13kkNMUT11nkNNRT12nkNNRT11okNOQT10QlNNPT11QlNOPT1ORlNOoS11QlNOoS12PlNOoS12QlNNnS13PlNNPT13VkNZO6d0dT14hjNZO5O?b0eT10fjNDQ1;YT1MljNEl0=YT1LnjNEm0<UT1NojNFn0:ST10ojNFT13nS17njNFT13nS19ljNBR1;RT16UlNImS1;lkNGVT18lkNFTT1:mkNEST1;PlNAPT1`0h1OajN\\\\OeS1c0l1N2O10^[O\", \"size\": [1280, 720]}, {\"counts\": \"`VXi04hW16M2E:L4N4K5@`0J4H9G8O2M2O1N2O2N101N2M200O1000001OO01001O001kNijN_OWU1a0jjN^OVU1a0mjN]OSU1c0njN\\\\OSU1VOhjNW15CZU1;hjNDXU1;ljNBSU1?njN@lT1e0UkN[OjT1:ckNE]T16gkNLWT13kkNMUT12lkNNTT10okNOQT10PlN0QT1MRlN2nS1LWlN1iS1OYlNOgS10ZlN0fS1O[lN1eS1M\\\\lN4dS1NPlN<PT1HekN`0[T1oNjjN9k0i0ZT1jNRkN:d0k0eT1SO]kNm0cT1POakNo0iU1O0000O1002M6RORiN?QW1^OQiN`0nV1CQiN=nV1EQiN<gV1c0O0O1000O1O1OTZO\", \"size\": [1280, 720]}, {\"counts\": \"RfUi03iW1b0A1N3L4@`0L5L2N4J5F9L5M2O1O1O101O001N2O001O1O001OO10000000O100O10001OaNljNmNOl0UU12ZkNNfT1N_kN0cT1N^kN2bT1M_kN3aT1LakN3^T1NbkN3]T1MbkN4]T10`kN0`T1NbkN3]T1LekN3\\\\T1KekN5[T1JfkN6ZT1JgkN4ZT1KikN3XT1HnkN6ST1CVlN:gU11J6N2O100OAUiNKkV1e000001N1O2^OUiNLbW1M5Ki_2OX`M20N_a5\", \"size\": [1280, 720]}, {\"counts\": \"nUSi01kW1?_O<B;K3M3FfNjiN]1oU1?L2O2L5K4M2N3O0O101O0O101O001O001O1O00000O1001O00O10O10000lNjjN_OUU17UkNIkT12[kNLfT10^kN0bT1O`kN0`T1OakN1_T1NbkN2^T1MckN4[T1MdkN4[T1NckN3]T1LekN4[T1KekN5[T1KekN5[T1JgkN5ZT1JgkN4ZT1KikN3XT1FPlN8RT1AUlN=gU10J6O1O1O10@TiNLlV14TiNLlV1d0000O2@RiNKPW12TiNKbW1N2N10K5\\\\OG_iN;aV1JXiN6jV1F[iN6ZW1MbY4\", \"size\": [1280, 720]}, {\"counts\": \"mmQi0b0\\\\W15K3I6ZOXOgiNU1RV1a0K4K6M2O2M3M2O101N10000O10000000001O2N00000000001O01O0O20O00mNhjN^OXU1?ljN@TU16WkNIhT12`kNL`T13bkNM]T11fkNNZT10hkN0YT1MjkN2VT1LlkN4TT1LlkN4TT10gkN2XT11dkN0]T1NdkN2\\\\T1LgkN2ZT1KikN5WT1GmkN9ST1EPlN;PT1BSlN=mS1_OWlN`0fU1O00N3L4O00BSiNImV16UiNHlV10QiNM42lV18TiNHmV17TiNHmV1M^iN1[W1NWa?\", \"size\": [1280, 720]}, {\"counts\": \"lmVi04hW19^O?YOg0L3M3L5J7J5L3N101N1O100O0100001O001O00000000O100000001oNbjN^O^U1:ljNDTU1:ojNEPU1;RkNEmT1:UkNElT1:VkNDjT1;WkNFhT19ZkNFfT19[kNHdT16_kNIaT16`kNK_T10gkN0XT1HPlN8PT1HQlN7oS1IQlN7PT1GQlN9oS1GRlN8nS1IRlN7mS1ITlN6mS1GVlN7lS1GUlN9lS1BWlN?cU13L4N100001O1O1O001O00M2O011000001O01O3M0O2M20PZO\", \"size\": [1280, 720]}, {\"counts\": \"fTXi01mW15M7ihN2mU18aiN3\\\\V1j0O2K7J4K3N2M3N3O0O100O2O0O2O00000O1001O0O10O10000O1000nNfjN^OYU1a0ljN\\\\OTU1c0ojN[OQU1d0RkNZOoT1e0RkNZOnT1f0SkNYOmT1f0TkNZOlT1e0UkN\\\\OjT1c0WkN]OjT1`0WkNBhT16^kNMaT10ckNO]T1OekN2ZT1LikN3WT1MikN3XT1KjkN4VT1KlkN3UT1MkkN4TT1KmkN5ST1KlkN6UT1IkkN7VT1HgkN;YT1DekN?[T1]OhkNd0XT1ROhjN1Q1n0kU1M2M4M1O2N00N1100000O100000000000000TZO\", \"size\": [1280, 720]}, {\"counts\": \"a\\\\^i01V12fV16hNLYjN;_U1J\\\\jN;^U11VjN3hU1U1M2M2O1L5L3O2O1N101N100000000000000O2O00000000000000O11O000000kNhjNCWU15fjNoN5k0UU14UkNKkT13[kNIeT15^kNKaT14`kNL`T13akNM_T12ckNM]T12dkNN\\\\T10fkN1YT1NfkN4ZT1KakN;_T1DakN=_T1AckN?]T1^OgkNa0YT1_OhkN`0YT1^OikNa0WT1\\\\OlkNc0UT1[ORlN`0oS1]OYlN=eU10N2000000001O1O001N100000PZO\", \"size\": [1280, 720]}, {\"counts\": \"hmei08gW15K3M4H7I7C<^Ob0J5N3L3O2M201O0O2O00000O020O0000000O1000000000lNejNB\\\\U1=fjNCYU1=gjNCYU1=gjNDXU1;ljNBTU1=njNATU1<ojNCPU1=SkNAmT1>UkNAkT1>VkNCiT1;YkNEgT16\\\\kNMcT11^kN0bT1O_kN1bT1LakN4^T1KdkN4\\\\T1MdkN2\\\\T1NekN1[T1LikN3WT1JmkN5ST1JnkN5ST1InkN8ST1_OTlNb0cU121O001O1O1O1N2O0000QZO\", \"size\": [1280, 720]}, {\"counts\": \"meni06cV1;oiNMmU19giNNXV1P1L5L001M4L3M2O2L4M3N2O0O2N0100000O10001O1O1O00000000001O01O01O1ObNSkNImT14WkNJjT14YkNKgT14ZkNLfT12]kNMcT11`kNO`T1NbkN2^T1MbkN4^T1KckN5]T1LbkN5]T1DhjNEg0h0aT1^OnjNJ>j0dT1^OljNFd0j0`T1BijNCk0i0\\\\T1DlkN;UT1DlkN<TT1CnkN<RT1_OSlN`0gU100O1OQZO\", \"size\": [1280, 720]}, {\"counts\": \"n]mi07jV15hiN2PV17giNOUV1P1O0N2L4K5N1O2L4N2L4M3N1O10000O101OO1001O1O0O1000O11O0010O00010O0fNPkNEoT15XkNJhT14ZkNLfT11^kNNbT11_kNOaT10`kN0`T10`kN1`T1MbkN2^T1MckN3]T1JfkN6ZT1LdkN4\\\\T1NakN3_T1N`kN1aT1N`kN2`T1LbkN4^T1JdkN6\\\\T1^OgjNHS1j0VT1^OjjNEQ1l0UT1]OTlNb0nS1ZOUlNe0dU1O00kYO\", \"size\": [1280, 720]}, {\"counts\": \"fddi0`0_W14jhNBcV1T1J5K3L5K3K5I6L4N100O2O000O1000001N2O00000O2OO10001O00O1000iNjjNEUU19QkNCoT1<TkNCkT1<WkNCiT1<XkNDiT1;XkNEhT19YkNGgT19ZkNGeT16^kNJcT14^kNM`T12bkNN^T10bkN3]T1JekN7\\\\T1CkkN;UT1CmkN=ST1BokN=QT1CQlN;oS1DSlN;oS1[OYlNe0gS1YO\\\\lNe0aU1M11N1010000001O1O1O2N0O2OkYO\", \"size\": [1280, 720]}, {\"counts\": \"n]Yi01lW15A3fhNOWW14hhNNSW16lhNLQW1d0FUOYiNo0YV1c0E;L4M2O100O1O00101O000O10001N100000000O100000lNijN@VU1?ljN@TU1?PkN^OPU1a0RkN^OnT1a0TkN_OkT1`0WkN_OiT1?YkNAgT1<\\\\kNDdT1:^kNFcT12ckNO]T10^kN7aT1H`kN8aT1EakN;`T1CbkN<^T1DdkN:\\\\T1FfkN7\\\\T1HfkN6ZT1FikN;nU10O1N2N2N3O0N21N2O1OO100008G3K4N12OCbhN8\\\\W17M2O1003EZj1\", \"size\": [1280, 720]}, {\"counts\": \"ellh0<cW14J:C8H:I7L5J5L3L4L3M4L2O2M100O010O1000000001O01O1M2O_NfjNTO8k0QU1N^kNNbT10bkNN^T11dkNN\\\\T12gkNK]T11dkNN^T10ckNO^T1OdkN0]T1NdkN3[T1MekN3[T1LekN5ZT1LdkN6[T1KakN9`T1EakN;`T1CakN=_T1BbkN>^T1BckN=^T1AdkN>\\\\T1AgkN=ZT1@ikNNeN9eU1CikN2eN:SW1GmhN9QW1JnhN6oV1NPiN2oV1OQiN1nV10QiN1nV1a01O1O001O1]ORiN2SW1FRiN8]W1L4J40O1N22Niad0\", \"size\": [1280, 720]}, {\"counts\": \"QTXi02eW1>K6I4L;F3OK9I]iNUOSV1k0miNVOF9MJdU1h0jjN0PU14PkNNnT11SkN0lT1OUkN1kT1OVkN0jT1MYkN4eT1J_kN6`T1IakN8^T1GbkN9_T1FakN:`T1EmjN[O:P1jT1TOdjN9m0c0_T1ROhjN7k0h0\\\\T1QOijN7k0h0\\\\T1QOhjN7m0h0[T1QOhjN7m0h0cT1VOdkNd0lU1001O0N2N3O0000000O11O1O001M5L4YOjhN=_W1N1N2H`hNNbiT1\", \"size\": [1280, 720]}, {\"counts\": \"`eSj0o0lV16M200001N001N20000000000O2O0N:D=D6JUbX1\", \"size\": [1280, 720]}, null, null, {\"counts\": \"h[ci0342`W1<K2O1N1O1O0011M1HRiNFgV17[iNIeV16]iNIcV15e0O2N1O1OT\\\\k1\", \"size\": [1280, 720]}, null, null, null, null, {\"counts\": \"ddZi0=cW12dhNBRW1`0ihNFTW1d00M2O2M2M4O1N2J6N2KiY12YfN2N3M5K1N3O0O2O1N1O01^OhhN:fW1FSag1\", \"size\": [1280, 720]}, null, null, null, null, null, null, null], \"bboxes\": [[574.0, 755.0, 40.0, 75.0], [575.0, 758.0, 44.0, 75.0], [583.0, 756.0, 41.0, 75.0], [584.0, 756.0, 44.0, 75.0], [586.0, 756.0, 46.0, 76.0], [588.0, 756.0, 49.0, 75.0], [604.0, 757.0, 40.0, 75.0], [620.0, 759.0, 32.0, 74.0], [625.0, 757.0, 32.0, 77.0], [624.0, 757.0, 33.0, 76.0], [627.0, 759.0, 23.0, 76.0], [629.0, 760.0, 22.0, 72.0], [639.0, 760.0, 12.0, 74.0], [640.0, 761.0, 12.0, 72.0], [634.0, 760.0, 17.0, 74.0], [633.0, 760.0, 15.0, 73.0], [631.0, 781.0, 11.0, 52.0], [628.0, 760.0, 12.0, 73.0], [618.0, 760.0, 19.0, 69.0], [613.0, 757.0, 23.0, 53.0], [604.0, 754.0, 35.0, 56.0], [592.0, 754.0, 48.0, 60.0], [594.0, 752.0, 48.0, 59.0], [598.0, 751.0, 47.0, 59.0], [601.0, 749.0, 48.0, 59.0], [605.0, 750.0, 50.0, 63.0], [605.0, 750.0, 52.0, 63.0], [606.0, 752.0, 47.0, 60.0], [608.0, 750.0, 46.0, 61.0], [607.0, 750.0, 51.0, 60.0], [615.0, 749.0, 49.0, 58.0], [617.0, 749.0, 50.0, 61.0], [619.0, 754.0, 49.0, 61.0], [619.0, 758.0, 49.0, 60.0], [618.0, 759.0, 51.0, 59.0], [617.0, 760.0, 51.0, 59.0], [617.0, 760.0, 51.0, 59.0], [616.0, 760.0, 49.0, 60.0], [612.0, 763.0, 50.0, 72.0], [617.0, 762.0, 46.0, 76.0], [624.0, 762.0, 50.0, 76.0], [630.0, 763.0, 51.0, 77.0], [638.0, 760.0, 50.0, 77.0], [647.0, 756.0, 44.0, 79.0], [648.0, 756.0, 43.0, 77.0], [634.0, 759.0, 52.0, 74.0], [628.0, 743.0, 55.0, 80.0], [625.0, 719.0, 58.0, 83.0], [621.0, 686.0, 54.0, 94.0], [629.0, 660.0, 46.0, 54.0], [615.0, 653.0, 61.0, 88.0], [612.0, 647.0, 74.0, 99.0], [618.0, 646.0, 73.0, 97.0], [622.0, 646.0, 76.0, 94.0], [630.0, 644.0, 76.0, 94.0], [632.0, 644.0, 78.0, 91.0], [637.0, 641.0, 73.0, 95.0], [631.0, 641.0, 79.0, 100.0], [640.0, 646.0, 73.0, 95.0], [640.0, 648.0, 71.0, 98.0], [641.0, 647.0, 73.0, 95.0], [645.0, 649.0, 66.0, 92.0], [653.0, 654.0, 62.0, 87.0], [651.0, 649.0, 58.0, 91.0], [641.0, 642.0, 67.0, 95.0], [637.0, 641.0, 66.0, 95.0], [637.0, 641.0, 61.0, 89.0], [632.0, 637.0, 64.0, 88.0], [631.0, 634.0, 64.0, 90.0], [631.0, 636.0, 67.0, 90.0], [639.0, 637.0, 68.0, 92.0], [646.0, 641.0, 65.0, 94.0], [647.0, 644.0, 67.0, 95.0], [646.0, 645.0, 69.0, 96.0], [644.0, 646.0, 70.0, 95.0], [641.0, 645.0, 73.0, 96.0], [635.0, 642.0, 77.0, 96.0], [635.0, 645.0, 77.0, 95.0], [633.0, 655.0, 76.0, 93.0], [626.0, 661.0, 78.0, 94.0], [625.0, 671.0, 79.0, 86.0], [630.0, 666.0, 77.0, 90.0], [632.0, 664.0, 78.0, 90.0], [635.0, 665.0, 77.0, 90.0], [636.0, 663.0, 76.0, 93.0], [638.0, 668.0, 73.0, 91.0], [639.0, 671.0, 73.0, 90.0], [636.0, 672.0, 75.0, 93.0], [638.0, 674.0, 74.0, 90.0], [641.0, 672.0, 72.0, 90.0], [641.0, 667.0, 75.0, 91.0], [645.0, 661.0, 75.0, 93.0], [654.0, 659.0, 66.0, 89.0], [662.0, 655.0, 58.0, 87.0], [671.0, 651.0, 49.0, 90.0], [672.0, 648.0, 48.0, 89.0], [672.0, 645.0, 48.0, 91.0], [675.0, 643.0, 45.0, 91.0], [676.0, 654.0, 44.0, 85.0], [673.0, 664.0, 47.0, 91.0], [675.0, 672.0, 45.0, 88.0], [674.0, 673.0, 46.0, 87.0], [669.0, 666.0, 51.0, 90.0], [665.0, 660.0, 55.0, 84.0], [669.0, 651.0, 51.0, 87.0], [668.0, 645.0, 52.0, 89.0], [664.0, 641.0, 56.0, 88.0], [671.0, 640.0, 49.0, 85.0], [676.0, 638.0, 44.0, 82.0], [677.0, 629.0, 43.0, 89.0], [675.0, 628.0, 45.0, 90.0], [669.0, 640.0, 49.0, 86.0], [667.0, 651.0, 49.0, 87.0], [661.0, 658.0, 59.0, 83.0], [653.0, 657.0, 67.0, 88.0], [651.0, 655.0, 69.0, 87.0], [646.0, 649.0, 74.0, 92.0], [644.0, 648.0, 71.0, 91.0], [642.0, 647.0, 74.0, 92.0], [641.0, 643.0, 66.0, 92.0], [645.0, 642.0, 75.0, 89.0], [646.0, 637.0, 74.0, 88.0], [651.0, 639.0, 69.0, 86.0], [657.0, 640.0, 63.0, 86.0], [664.0, 645.0, 56.0, 89.0], [663.0, 647.0, 57.0, 89.0], [656.0, 642.0, 64.0, 87.0], [647.0, 638.0, 71.0, 83.0], [637.0, 632.0, 66.0, 87.0], [646.0, 630.0, 45.0, 88.0], [668.0, 681.0, 19.0, 39.0], null, null, [655.0, 632.0, 17.0, 31.0], null, null, null, null, [648.0, 660.0, 27.0, 82.0], null, null, null, null, null, null, null], \"areas\": [1874.0, 1884.0, 1886.0, 2029.0, 2191.0, 2249.0, 1842.0, 1459.0, 1370.0, 1393.0, 1051.0, 806.0, 471.0, 356.0, 489.0, 389.0, 143.0, 391.0, 798.0, 961.0, 1520.0, 2364.0, 2337.0, 2350.0, 2337.0, 2535.0, 2569.0, 2305.0, 2324.0, 2435.0, 2299.0, 2431.0, 2441.0, 2420.0, 2413.0, 2437.0, 2431.0, 2413.0, 2518.0, 2353.0, 2441.0, 2347.0, 2377.0, 2046.0, 1904.0, 1967.0, 2196.0, 2247.0, 2727.0, 2005.0, 3073.0, 4191.0, 4797.0, 4521.0, 4858.0, 5529.0, 5671.0, 6061.0, 5953.0, 5888.0, 5694.0, 4389.0, 3266.0, 3353.0, 3892.0, 3844.0, 3318.0, 3190.0, 3221.0, 3769.0, 3785.0, 3739.0, 4181.0, 4466.0, 4326.0, 4406.0, 4651.0, 4639.0, 4282.0, 4427.0, 3696.0, 3880.0, 3799.0, 3877.0, 4078.0, 4181.0, 4459.0, 4194.0, 4240.0, 4369.0, 4400.0, 3973.0, 4162.0, 3468.0, 3031.0, 3241.0, 3539.0, 2661.0, 2740.0, 3506.0, 2924.0, 3253.0, 3514.0, 3479.0, 3344.0, 3619.0, 3458.0, 3184.0, 2775.0, 3038.0, 3096.0, 3043.0, 2998.0, 3084.0, 3711.0, 3710.0, 4095.0, 3934.0, 4242.0, 4050.0, 3902.0, 3917.0, 3947.0, 3643.0, 3693.0, 3709.0, 3444.0, 3391.0, 3098.0, 1714.0, 594.0, null, null, 223.0, null, null, null, null, 386.0, null, null, null, null, null, null, null], \"iscrowd\": 0, \"video_id\": 0, \"height\": 1280, \"width\": 720, \"category_id\": 49272, \"noun_phrase\": \"air jordan\"}, {\"id\": 1442, \"segmentations\": [{\"counts\": \"eQ[?1hW1;H6K4M3M2N3M2O2M2N2N2O2M2N2O2M2N2O1M4M2F:N2N200O1N2HfMhjN\\\\2RU1<N2O1N101O1O1N101000O101O2N1N101N1O2O0O2N1O1O2N1O1O2L3N2N2O1N2N2O2O0O1O10O10O00O10001O01N2O1O101N2N2N2N3N2N3L9G8I;Bm_]9\", \"size\": [1280, 720]}, {\"counts\": \"ji^?2iW1;G5K4L4M3N1N3M2O2L3O1N2N3M2O1N2N2O2N1K5L4H8O1O1O1N2N2F:M3M3O1O1O001N200O10O101O1N2O1N2O1N1O2N1O1O2N1O1O1O2N1O1N2N3N1N2N2O1O1O1O1O1O0001N100010O001O100O2M3N2N2N2N3M4L7I8F:G=AdWW9\", \"size\": [1280, 720]}, {\"counts\": \"kic?;`W18J4L4M3M2N3N1N3M2O1N3M2O1N2N2N3M2O1N2L4F:N2O2N1N2H8L4M2O2N2N2O1O1O10O0100001N2O2M101N101N1O1O2O0N2O1O2N1O1N2N3N1N2N2N2N2O2M2O010O01O00000010O1O010O1O1O2O0O2N2N3M3M3M8H5K7H=CcWR9\", \"size\": [1280, 720]}, {\"counts\": \"PRe?:bW16K4L4M3L4M3N1N2N2N3M2O1N3M2O1N3M2N2N2M3N2E;O1O1O1N2M3F:M3N2N2O1O1O1O001O1001N101N2O1N2O1N2N1O2O0O1O2N1N3N1N2O1N3M2N2M3N2O2M2O100O1O000001N100O200O01O101N1O1O2N3M2O2M4K7J7H:F:Dcgo8\", \"size\": [1280, 720]}, {\"counts\": \"XRe?2gW1<G6J5M3M2N3N1N3M2N2N3N1N2N3N1N2N3N1N2N2N3K4H8O1N2O1N2GbMmjNa2mT1<N2M3N101O1O1O001000O101N101N3N1O1N1O2N1O2N101M2O1O2N1N2O1M3N2N2N3M2O1O1O1O1O1O0100N1O1O20O1O00100O1O2N2N2O1N3M3M7I6J8H8De_n8\", \"size\": [1280, 720]}, {\"counts\": \"^Zf?1gW1=F7K4M2N3M3M2N3M2O1N3M2N2N3N1N2N3N1O1N2M3M3I8L3N2O1O1M2K7I6N1N3N2O1O1N20O01O010000O2O1N2O1N2N1O2N2O0O1N3N1O1O1O2M2O1M4M2M3N2N2O2N1O100O1O0001N10000010O00100O1O1O2O1N2N3M3M5K8H7I:E^ok8\", \"size\": [1280, 720]}, {\"counts\": \"^Zk?8cW19I4L4M3M3M3N1N3M2N3N1N3M2O2M2N2O1N2O1N3L3N2N2G9M3O1O100N2M3F:M3N1O2N2O100O001O10O100O2N2O2M2N2N1O2O0O2M2O1O1O2M2O2L3N2M4M2N2O1N3N1O100O100O1O01O0O1O2O0001O10O02N1O1O1O2N2N3M5K9G5K:F`0\\\\OP_d8\", \"size\": [1280, 720]}, {\"counts\": \"eZZ`06eW19H7J4L4M3L3N3M3M2N3M2N2N3M2O1N3M2N2O2M2N2N3L3FjMgjNY2WU1:M2O1O1O1M3J6L3O2M3N2N101O1O0100O010O2O0O3M2N2O1N3M3M1N3N1O2M2O1M4M2N2M4L3M4M2N2N2N3M2O1O1O2O0O001O01O0O2O000001O001O001O1N3N2M6K6J;BcgV8\", \"size\": [1280, 720]}, {\"counts\": \"ZRka0k0mV1<F7I8I7I6_OPNkjNV2RU1=N3I7H7F;K4O1N3O00001O000000010O01O00001O1O1O1N2O00001O0O10000O100O100O1O100O1O1O1O1O2M2O1L4M2I8_O?L4N3M3N201M2O3K6I6@c0E<Eooc7\", \"size\": [1280, 720]}, {\"counts\": \"nRka0:[W1>I6H7M3L4L4L4K4M4M2M4L3L4E<N1O1O2L3K5I7L5L3N2N2O100000000000000001O0O101O1N3N1N1O101N100O1O1O2N1O1O2M2O1M3N2M3K6J5J6M3N2N101N2O1O000O01O10O1O2O1M4M4J7^Od0Cj_W7\", \"size\": [1280, 720]}, {\"counts\": \"^SUb04hW15M3M3M2N3M2N2J7J5L4J6I7K5J6I8[Od0K5L4L3M4M3M3N2O1000O101N102M2N101N1O1O2N1O2N1O1N2N3L3N2L4I8I6K5M3N2N2N2N2O1O1O1N2O0O1O0000002K5L4G:J9GmoY7\", \"size\": [1280, 720]}, {\"counts\": \"oZQb0:WW1c0E8K5K5K6J4L5K4L5L3M4G8M4N1N3M2J6E<L3N2O1O2O000000000100O00O1001N101O001O1O1N2O000O2O0O2N100O2N1O1O2N1N2N2M3L4L4N2I7G9G9K5N2N2N1N2O0N1O3M3G:K6L4K9HkoT7\", \"size\": [1280, 720]}, {\"counts\": \"ijbb0<\\\\W1f0\\\\O8I7F9I7@UNbjNS2XU1>M3G9]Ob0N2N3N101N11O10O0100O1O00000000O11O001O1O1O1O0000000O2O0O100O101N1O101N1O1O1O1O1O1N2N2M3J6]Ob0H8M3N2O001M3N3D=F:J7K7GgWl6\", \"size\": [1280, 720]}, {\"counts\": \"mbfb0`0[W1;E7J5J7K4L4K4M4K4K6B=N2N3L3J7A>K6L301O0001O10O01O0000000O10001O001O1O1N2O0O101N100O2O0O1O1O2N1O1O1O1N2N2M4L3K5G9K5K5L4M3O001OO001O01O1O2M5F9G;D>Bgo`6\", \"size\": [1280, 720]}, {\"counts\": \"Wcab02hW1<E8I5M4K4M3L3N3M2N3M3M2M3O1N3M2N2M4J5C=O1N3M2N2H8L4J6L4K5O1O1001O1N2O001O1O0O2O1N3N1O1N1O2O0O1O2M200O1O2N1N2O2N1N2N2M3M3L5L3N3M1O2O1O001O000001O000000010O02N2O0N4L3N4L5J7J:F9G6J:EZfP6\", \"size\": [1280, 720]}, {\"counts\": \"mZeb07fW17G8I6L3N2L4M3N1N3M2N3M2N2N3N1M3N3M2N3N1L4D<N2O2M2N2L4K5L4N2M3N1O200O1O10O010O101N2O1N2O1N101N1O2O0O2N2M2O1O2N1O1N3N1N2M3N2M3M4M2N2O1O1O2N1O1O000001N101O0000O200O1N3M2SOPjNIRV1DojNMTU1NPkNKkTS6\", \"size\": [1280, 720]}, {\"counts\": \"Qckb05eW1:F9I5L4L4M3L3M4M2M4L3M4L3M4L3M3B?L3O1N2M3H8M4L3M3N2O1O1O100000O10000O2O1N2O1N2O1N10000O2O1N1O2N100O1N3N1O1N2O2M2O1L4M3K5L4L4M3M3N2O000OO2N2001O0O2N1M4[OTjNROQV1<S1L5GT`T6\", \"size\": [1280, 720]}, {\"counts\": \"Uk]b0?YW1<I5K5I5M4J6L3K6WOZNnjNn1PU1b0O2M2H8D<L5N1O1O1O1O1O10000O1000000001O1O001O1N10001O0O10001N10000O100O100O1O1O1O101M2O001O1O0O1O0O1N3M3H8L5N1N3N2O1O1O1O2N3M3M3L6K4K6J;D<ElVb6\", \"size\": [1280, 720]}, {\"counts\": \"VSc`0e0VW18J4L4L4K5L3M4K4M3M4[OQNSkNS2lT1TNjjNP2TU1`0M2N2K5F:J7L3O1O1O100O100O10000O1000000001O00001O1O1N2O1O000O2O000O2O0O101N1O1O1O100O1O1N2O1N2N2N1O2L3I6M203N101N1010O02N1O2N2N2O2M3L5K5J9H=D`0]O9HV^U8\", \"size\": [1280, 720]}, {\"counts\": \"kZP`0a0XW1>D9F7J6J6ZO^NdjNk1ZU1>YOaMikNd2RT1g0M3M300O2N100O11O3NO00000000000001O1O1O001O001O000000001N1000001O0O100O1O100O1O1O1O1O0O2N1M3J6@?O1O2N3N3L4M4]OliNQO[V1?n0DgWa9\", \"size\": [1280, 720]}, {\"counts\": \"mRo?6`W1c0C8F9H6J6K5_OVNdjNQ2VU1?M4I6A?L4O2M2L400O11O101N1O0000O10001O01O001O1O1O0O2O00001O00000O10001N100O100O2N1O1O1O1O1N2M2L5]Ob0M3M200O2O2M3CmiNiNXV1o0a0F;I7Jho_9\", \"size\": [1280, 720]}, {\"counts\": \"gRo?`0YW1=D8H8I6K5I6A`0L4K4B>H9J5O1N2N3N100002N4L0000O11O01O0001O001N2O1O1O0000000000001O0O1O101N1O100O100O1N2O1N2N3K3E<D;M20O10O3M3L5A`0D;J8J8Ego_9\", \"size\": [1280, 720]}, {\"counts\": \"mZP`01cW1f0@;E8I6K5K5K4AVNcjNo1ZU1>L3M3I8C<M301O000000010O00000O100000000100O1O1O000O10000000001N100O2O0O10O01O1O1O1O1O2M2N1N3^Ob0F:N1O1N010N3H9K5E<I9I8Ihg^9\", \"size\": [1280, 720]}, {\"counts\": \"ejR`0:\\\\W1c0A8J6I6J6K4M4H7F;M2M4C<E;L5N100001O0010O01O00000001O01O001O001N2O0000001O00001N100O100O100O001O2N1O1O1N3L3N2XOg0M4N1N2OO0100N4K6C>F9K7I9HioZ9\", \"size\": [1280, 720]}, {\"counts\": \"[RY`0i0PW1;G6J6K6J4L5K5C<M3K6^Oa0J6N3O0O1100O00001O01O00000000000001O1O1O001O1O00000O10000O10000O1O2O0O1O1O2M101N2O1H8A>K2O122N2M3M4K5I7B?H:G<CjWW9\", \"size\": [1280, 720]}, {\"counts\": \"hR^`06aW1c0@9G8I7J5J6L3DRNbjNR2YU1>L3K6_O`0L4N3N100010O1O1O01O0O100000000001O100O1O0O2O000000000O10001N100O100O100O1O1O1O1N2N2M3L4\\\\Od0J5N01O02M3M5G9E;I9G;GhoP9\", \"size\": [1280, 720]}, {\"counts\": \"gZd`0=ZW1c0@9H6J6K5J6K4E<L4WOh0K5M4O01O1O100O1O000000000001O0001O001O1O1O00000000000O10000O10000O100O1O2N1O2N001N2N2L4L3ZOf0N2OO01O2N5K4]Od0F;H8JdWm8\", \"size\": [1280, 720]}, {\"counts\": \"jZi`08`W1b0^O<F8I6J7G8C<J7ZOe0M4M2001O10O000001O00O1O2O000O11O001O001O1O001O0000000O1000001O0O10001O0O1O1O1O1O1O1O0O2N0N1YOg0O4M3N2M4L4K6YOi0E:Jc_i8\", \"size\": [1280, 720]}, {\"counts\": \"dRm`0`0ZW1a0^O:G7J6H8B>K5I6]Od0L3N201O01O2N00010O0000O10000001O10O01O1O1O0O100000000000000O101O000O100O1O1O1O001O1N2N2M1M4YOe002N2O2M4AmiNlNWV1j0VjNmNQV1i0j0H9Hcge8\", \"size\": [1280, 720]}, {\"counts\": \"iZn`07`W1c0@<D8I5L5J6J6C<O2L4K4^Ob0N201O01O2N010O1O001O000000O11O00010N2O1O001O0000000000000O100O10O01O1O1O1O1O0O2N2M2M4YOg0M2N2O1N1O001J8E<H9J7J7Hdg`8\", \"size\": [1280, 720]}, {\"counts\": \"mjPa01aW1c0Cb0_O8K6I6K5B=M4L3M4]Ob0M3L50O1O100O000001O000O10000O1001O001O1O1O000000001O00000O100O1O101N100O1O1O1O1N2O1M3M3XOg0M4M3N0NO21O3D=F:J7J7J7HgW^8\", \"size\": [1280, 720]}, {\"counts\": \"cZSa0=YW1?E>kNiNjjN`1QU1jN]jNc1fT1Z1J6N1O2O1O0100O01O000000000010O00001O1O1O001O0000000000001O00001N1000000O100O1O100O10O01N10M3O01O2L5POP1K5L5J7POniNGnmf8\", \"size\": [1280, 720]}, {\"counts\": \"_jUa0f0VV1^O]jNT1_T1VO[kNj1\\\\T1S1J300O1000O01O001N100O2O00000001O1O00100O00001O0O10000000001O10OO1000000O10000O10O1O000O2N10O101O2L4oNbkN[NcT1^1kkNQN`T1i1o0mNoiNNZV1Bfhe8\", \"size\": [1280, 720]}, {\"counts\": \"abTa0h0SU1LXkNF5Q1ZT1o1K4L2O100O10O01O001O0000001O01O001O1O1O00001O000000000000000000001O0O1000000O01000O01N1M4O01O2N1O3L3M4jNekNaN`T1X1ikN[NaT1c0ijN@N9bW1E]Vh8\", \"size\": [1280, 720]}, {\"counts\": \"hZSa0`0VW1a0kMYOckNOJV1YT1n1L1N3N001000O010O01N101O01O0000010O1O1O001O000000000000O100000O2O1O1O0000O0100O1000O0M4N1001O1O2N2M2M5kNhkNZN^T1_1U1L7hNXjNIZ]i8\", \"size\": [1280, 720]}, {\"counts\": \"lZSa0<YW1b0@?ROlNYjNc1iT1^N_kNU2\\\\T1j0O100O10O01O001O0O100O101O0001O1O1O000000000000000000000000000000000O1000000O100O0010O000K42O1O1N3nNgkNWN^T1b1T1K6H9ROm0G`ge8\", \"size\": [1280, 720]}, {\"counts\": \"hjUa0h0oV1?B;F9]Ob0L4XOh0L3O2O10O100O000001O000O100O100010O1O1O1O00000000001O00000O100000000O1000000O1O100O1O1N101N1O0ROekNVN^T1g1o0L5K5BmiNhN[V1m0c0H9I9D\\\\g`8\", \"size\": [1280, 720]}, {\"counts\": \"QSRa0g0VW15K4L4K5L4M2L5L3M4L4L3AoMkjNU2RU1?M2N2J6C=J7N1O1O1O10000100O00000O101O001O1O1O1N2O0O101O0O100O1O2N100O1O1O1N2O1N3L3N2M3L4J6M3N2N1O1O01O0011O001O1O2N1N3N3M2M5K6J8FT_P8\", \"size\": [1280, 720]}, {\"counts\": \"`[n`07bW1:F8M3M3M3M2M4M2N3L4M2N2N3M2N2N2M4L3M3G9M4N1N2E[MVkNj2eT1;J6N2O1O1O1N2O11O0O10001O1O2M2O1N2O001N1O2N100O2N1O1O2N1O1N3M2O0O3M2M3L4M3M3N2O10O00000O101O00001M21O1O2N2N3N1M3N3M5K9F7J8Fg0XOQfg7\", \"size\": [1280, 720]}, {\"counts\": \"`cYa0;`W18H6K5N2M3M2N3L3N3L3N3M2N2N3L3N2N2M3CnMhjNV2UU1=N1N2H8K5N2L4L4M3O11O1OO100O11O0O2O1O1O1O1N2O0O2O0O101N1O100O2M2O1O1O1N3M2O1M3M3M4J5M3N1O2O010OO1O2N10000010O00011M3N3M3M3M3M7H6K7I?^OZf]7\", \"size\": [1280, 720]}, {\"counts\": \"acha0<_W17J6J5M2N3M3M3M2M4L3N2N2M4M2N2N2M4@mMnjNV2RU1<O1O1O1M4B=L4M3M3N2000000O10O10001O1N2O1O1O1O0O2O0O101N1O2O0O1O1O1O2N1N2N2N2O2L3M3L4K5M3O1O001O01N1O1N210O0001O100O2N3M3M3M4K7J6I8I<B=AQfn6\", \"size\": [1280, 720]}, {\"counts\": \"`cRb0<`W16I7J5M2O2M3M2M4M2N3L3N2N3M2N2N2M3M4D;M3O1N2J6E;K5M3N2O1O1O1O11O1O000O101O1O1O1O0O2O1O0O2O0O1O2O0O2N1O1O1O1N3N1N2N2N3M2L4L4M3N2O1O01O000O1O2O00001O01O1O2O0O2N4L3M4L4L6I8I8G;DW^c6\", \"size\": [1280, 720]}, {\"counts\": \"a[Qb01hW1>E6K4L4N2M2N3M3M2N3N1N2N3M2O1N2N2N3M2N2M3M3C>N1O1N2K5I7M3M3N2M3O00100O10O1000001N3N1O0O2O0O2N101N1O100O1O1O2M2O1O2N1N2O1N3L3M3N2N3M10100O0001O0O101O001O10OO1N23N0O2N3M102M2N3M4L5K6J6I7I:F;CUfZ6\", \"size\": [1280, 720]}, {\"counts\": \"UkXb0>_W14L5J5K5N1N3N1N2N3M2N3M2N3N1N2N3M2N2O1N2M3M4E:M3N2L4J6J6M3M3M3O1O010O100000O101O1N2O2M101O1N1O101N1O2N1O2N1O1O1O2M2O1N2N2M3L4N2O2M2O1O10O00001N100O1010O0000010O2N1O2O2M2O2M4L4L7H6K6I8H=B\\\\fU6\", \"size\": [1280, 720]}, {\"counts\": \"mRZb0<aW16I5L4M3M2N3M2N3N1N3M2N2N3M2O1N2N3N1N2N2N3BoMhjNT2UU1SNdjNP2[U1;O1O1M3D<N2L4M3O1N2N2O1O010001N2O1O002M2O1O1N2O0O2N1O2N1O2N1O1O1O2M2O1O1N3L3M3M3N2N2O100O1O01O0O101O001O01O0000100O2O1O1N3M2N3M5K5K6I8I6I:Ff^T6\", \"size\": [1280, 720]}, {\"counts\": \"iRUb0<aW16I5K5M3M2N3M3N1N3N1N2N3M2N2O1N3M2N3N1N2M3N2FkMfjNY2VU1:N2O1O1L4G9N2N2L3O2N2O1O10000O010001N2O2M2O1O1N2N1O1O2N101N1O2M2O2N1N2N2O1M4L3M3O1N2O1O1O100O1O00O2O0O2O0010O001O10O02N2O1N2N2N3M5K7I5J7J:D<AiVX6\", \"size\": [1280, 720]}, {\"counts\": \"lZla02fW1;J5K4K5M3M3M3M2O1N3M2O1N3M2N3N1N3N1N3M2O1N2N2N2L4G:M2O1O1O1G[MUkNg2fT1<N2L3O2O1O1O1N20O100O2O0O3N2M2O1N2N101N1O2N1O1O2N1N2O2M2N2N2M4L3N3N1N2O1O2O00O00000O2O001O0010O001O10O0003N0O2N3M3N2M6I8I7H=C;@lf_6\", \"size\": [1280, 720]}, {\"counts\": \"dRfa09dW15J6K4L4M3M2N3M3N1N3N1N2N3M2O1N2O2M2N2N3N1N2N2M3I8J5O1O1O1J6I7M3L4N2O0O2O1O10O01O11N2O002M2N2O2M2N1O2O0O1O2N1O2N1N3N1N2N2N2M3M4M2O2N1O1O100O1O100OO2O0O2O00001O0001O011N2N2O2M2N3M6J8G7J7G?@nVg6\", \"size\": [1280, 720]}, {\"counts\": \"dRaa0;cW14K5I6M3L3O2M2N3N2M2N2N3N1N3N1N2N2O2M2O1N3M2N2L4F:N2O100O1O1G9K5M3L4O1O0O2O1O010001N101O1N3N1N3N0O2N1O2N101M2O1O2N1N3M2M3N3L3N2O1N3N1O100O1O1O10O0O2O0O2O001O01ON2O21N2O1N2N2N3M3M4L7I8H5J`0^OoVl6\", \"size\": [1280, 720]}, {\"counts\": \"hb^a07eW18I4L4K5M3N2M2N3M2O2M2N3M2N3N1N3N1N2N2N2N3N1M3N2G9M3N2L4L4L4N2N2M3N2N101O100000O100O2O1N2O1N2O1N101N1O1O2N1O1O1O1N3N1N2N3L3M3N3M2O1O1O1O100O1O01N1O101N101M2L5O0101N1O2N4L3N1N3M3M4L6J6I:Ea0_Olfn6\", \"size\": [1280, 720]}, {\"counts\": \"hRaa09dW16J5J5L4M3M3M2O2M3M2O2M2N2N3N1N2O1N2N3M2N2M3M3G9M4N1N2N2K5K5N2M3M2O2O1O100O00100001N2O1O1N3M101N1O2N1O1O2N1O1O2N1O2M2O1M3M3M4M2N2O1O1O1O1O10O01O01N1O2O001O0O100O20O3M2N3N1N3M4L5K6I8I7H<Cm^m6\", \"size\": [1280, 720]}, {\"counts\": \"kZga09cW17I6K4M3M3M2N3M3M2O1N3M2N2N3N1N2N3M2N2O1M3L5F9N2O1N2N2D<M3O1N2M3N2O1N2O01000000O2O1N3N1N2O1N101N1O1O2N1O2N1O1O2N1O1N2N3L3N2M3N2N2N3N100O010O00O101N101N101O01O1O2O0O3M2N3M4L4L6K5I7I:F9Fi^h6\", \"size\": [1280, 720]}, {\"counts\": \"oZla0:cW16I6J5M3M3M3M2N2N3M2N3M2N2O2M2O1N3M2N2N2N3BnMijNU2UU1<N2O1N2E;M3N2L4N2O1N2O1O1O0101N101N2O1N2O1O1N2O0O2N1O2N1O2N1O1O1O1O2M2N2N3M2N2M3M3N2O1O1O00010OO101O001O0000000011N102M2N2N3M4L5K7I5J9H9F8Gc^c6\", \"size\": [1280, 720]}, {\"counts\": \"PSPb0<`W16J6K4M3L4N1N3M2M3N3M2O2M2O2M2N2N2N2O1M4L3D<O1N2O1F:K5M3N2M3O1N2O1O10O1000001O1N2O1N2O0O2O1N2N1O1O2N100O1O1N3N1O1O1N3M2M3M3N2M4M2O001OO2O001O00001O00001O0002N101N3N2M3M3L5L7I6I7J9F9Gbn`6\", \"size\": [1280, 720]}, {\"counts\": \"U[Qb04gW1:G6K5K4M2N3M3M2N3M2N3M2O1N2N3M2N2O1N3M2M3M3C=O100O1N2D^MVkNf2fT1<N2N2N2O1O1O1O01000001O1N3N1O0O2O1N1O2N101N1O1O2N1O1O2M2O1O1M4M2M3L4N2N3M2O0010O001N1O1O2O0010O0001O0102L3N2N3N2M4K8I5K7H8I8H;A]^^6\", \"size\": [1280, 720]}, {\"counts\": \"W[Qb01hW1=G5J6K4M2N3M2N3M3M2N3M2O1N2N3M2N2O1N2N2M4L3C=O100O1N2D^MWkNe2eT1=M3O1N2O1O1O10O1000001O1N2O1O1O0O2N101N1O2N1O1O2N1O1O2N1O1N2O2L3N2L4M3N2N2N20O00001N1O101O0010OO110O0101M4M102M2N3M4K7J6J6I:G8Gc^^6\", \"size\": [1280, 720]}, {\"counts\": \"QSPb09cW17H7K4M3L4M2N3M2O1N3M2N2N3N1N2O2M2N2N3L3N2L4I7M3N2O1M3D<M3M3O1O1O1O1O1000O101O1N2O1O1N100O2N2O0O1O2N1O1O2N1O1O1O1N2N2N3L3N2M3M4M2O100O0000O2N100O2O001O01O001O101O1N2N2N3M3M4L6I7J7H=C=B]f_6\", \"size\": [1280, 720]}, {\"counts\": \"lbha09dW16J4L4L4M3M3L4M2O1N3M2N3M2O1N3M2N2N3N1N2N2M3L4K5M4M2N2N2B>N2N2M3N2O001O100O1001O1N2O2M2O0O2N101N1O2N1O2N1O2N1O1O1O2M2N2M3N2L5M2N2O1O1O1O100O00O2N101N110O01O1O1O100O1O2N3M3N2M6J6I7J9F:FhVg6\", \"size\": [1280, 720]}, {\"counts\": \"hb^a09dW16J5J5M3L3O2M2M4N1N3M2N3N1N2N3M2O1N2M4M2N2N2K5K5M3N2L4L4J6L4M3N2N2N2O1O10O0100001N2O1O2M2O0O2N2O0O2N1O2N1O2N1O1O1O2M2N3L3M3N2N3M2O100O1O100OO1N3O00001O01O010O100O1O2N2O2M3M4L6I6K6J8G9FnVQ7\", \"size\": [1280, 720]}, {\"counts\": \"eZSa0=`W15K4K5M3M2N3M2N3M2N2N3M2N3N1N3N1N2N2N3N1M3N2K5I7M3N2N2N2K5L4M3N2N2N2O1O1O001000O2O0O3N1N2O1N1O2N1O2N101N1O1O1O2N1N3M2M3M3N3M2N2N3N100O10N10000O2O001O0010O00010O002O2M2N2N3M3M5K7H6K9F=@o^\\\\7\", \"size\": [1280, 720]}, {\"counts\": \"aZi`0:cW16I5L4M3M2N3M3M2N3N1N2N3M2O1N3N1N2N3M2O1N2N2N2K5H9N1O1N2M3H8L3N3N2N2O1O1O001O1000O101O0O2O2M2O1N2N2N1O2N1O1O2N1O1N3M2N2N2N2N3M2O1O1O2N001O10O001O00000O2O001O0010O1O2N2N3M2N3M4L6J8H6H;FW_f7\", \"size\": [1280, 720]}, {\"counts\": \"_Rc`0:cW15K5K3M4M3M2O2M2N3M2O2M2N2N3N1N2N2O1N3M2N2N2N2L4H8M3O1O1O1J6I7M3N2N2N2O1O001O100O02O0O2O1O1N2O1N1O2N2N1O2N1O3M1O1N2O1M4M2N2N2N2O2O0O1O100O1O001O01N101O001O010O0010O102M2N3M4L4K6K9G5K:D=_OR_k7\", \"size\": [1280, 720]}, {\"counts\": \"cZ_`08dW17J4K6K4M3M2N3N1N3M2O2M2N3N1N2O2M2N2N2O2M2O1N2N2M3M3I700O1O1N2L4J6M3L4M201O1O10O011N101N2N3N1N2N101N1O1O2N1N3N1O1O1N2M3N3L3N2N2O1N2O1O100O2N1O10O0000001O0000001O0001O1N3O1N2N3M4K:F8G>@X_P8\", \"size\": [1280, 720]}, {\"counts\": \"bb[`09cW17I6K4L4M3L3N3M2O2M2N3M2N3M2N3N1N2O2M2N2N2O2L3N2H8L4O1O1N2O1N2L4J5O2L4M30O01O100O11N1O2O0O2N101N2N2N2N2N1O2N1N3M2N3L3N2M4L3N2M3O1N2N2O1O100O1O2N1O010O1O1O01OO10O2OO2O001O1N2N5L4L>XOSiN_OdnW8\", \"size\": [1280, 720]}, {\"counts\": \"aUi`0:cW1b1_N=F9F8I3M2N1O1O1O1O1N2O1N1O2N1O1O2N1O1O1O100O1O1O001O1O001O0O2O001O0000000O100O10001O0000000000O101O1O1N2O1N2O3M3LQ1oNe0[O9G9G7H6J7I`Wh8\", \"size\": [1280, 720]}, {\"counts\": \"^\\\\d>a0VW1a0C<E8J5J6J6F9L4J5I8I6K6J4M4L5L2N3L4M2O2N2N101N1000WL]lNU3eS1\\\\LnlN_3mS1L1O1O1O1N10001O001O1N1O2O0O1O1O1O2M2N2M3M3L4H8G8K6K5L3L5L4M3M3H8J6M3M3N3Jn\\\\n:\", \"size\": [1280, 720]}, {\"counts\": \"fb[>4TW1m0F8J5K6L3L2N3M3N2M2O2N1O2M2N2N2N3M2N2M3M3M3N2M2N3N2N2N1O1O2O0O1O1O1O10O100O1O2jMbkNi0^T1TOhkNh0ZT1TOkkNj0VT1PORlNm0nS1QOWlNl0jS1ROYlNl0hS1RO[lNk0gS1SO\\\\lNi0gS1UO\\\\lNh0fS1VO]lNd0hS1[OZlN?kS1@XlN<jS1BZlN:iS1C[lN:gS1D^lN7dS1G`lN5bS1IblNOo[b;\", \"size\": [1280, 720]}, {\"counts\": \"gki?19;bV1V1[O<A?C>A=F<F7N2M3N2M3M2O1O001O1001O`0_O3M100O1O2N1O1O1O001O001O00001O0O2O000O2N1O1O1O1O1M3L4L4I7F:E:E<K6L3K6I7WOoUT:\", \"size\": [1280, 720]}, {\"counts\": \"gng`0k0nV1j0[O?B`0B>C4N1M0O2M2N1O2N1O1O1O1O101M2O2N1O1O2M2O1O00100O001O0000001O0000000000000O10000001O0000O1001O002M5Lc0\\\\O?Bd0[O6K8G6K8G4M4K7H8HSfj8\", \"size\": [1280, 720]}, {\"counts\": \"bPm`08^W1e0Bj0XO5K4M4L3M2O1O011N1O3L6K2N2M4M0O2N1O1O1O1O1O2N001N3N1N2O001O100O010O1O1O001O001O0000001O0O10000O100O1000000001O1O2N<D;E2M5L<D5J4M3M3M3L3N3M3M2M4M3L4L5K5K4K6IjdQ8\", \"size\": [1280, 720]}, {\"counts\": \"XPm`0e0YW16K5L7I<D8I3M4L1O1N3N1N3M3M7I4L5K2M2O2N1O1O1O1O1O1O1O1O10000O001O1O001O1O001O001O001O00000O100000000O1N2001O1O2N2M;F9G4L1O4L9F5L5K3M3L4M3M3M2N2M4M3L3N3L6J4L5J7HidQ8\", \"size\": [1280, 720]}, {\"counts\": \"fhPa07^W1a0Fl0XO7H6K4M1N3N101N2N6I5L4L1O1N1O1N2O2N1O1O1O1N2N2O0O2O10O01000000O001O0O2O001O000000000000001N001O10000001O001O2N3Me0[O1O2N5K:F5J4M3M4L3L4M3L4M3L4M3L6J6I6JgdQ8\", \"size\": [1280, 720]}, {\"counts\": \"kPRa0;1L83XV1]1C8J7K4L3N2N3M5L6J3M2N3L10O1001O1OOO2O1O1O100O1N2O1O1O1O001O10O010O010O000001O00000O1000001N010O10000O100000000002N1O5Ke0[O3_MjjNR2fU1L6I5L3M3L4M4K4M3L5K5J;EeTT8\", \"size\": [1280, 720]}, {\"counts\": \"ePm`0]1]V1=F7J5L3M5M1O1O2N7I4M1N101N0O1O2N1O1O001O1N2O1N2O1N2O1N2O1O10O011N0001O00001O00000000001N100000000O10000O10O2O002N3Md0\\\\O3M3M>B6J4K5L3M4L3L5L3L4L3M5K9F9DalW8\", \"size\": [1280, 720]}, {\"counts\": \"dPh`0h0<APV1b1F7J5M3M2M4N3M4M4K3N1O1O0O0O1O1O2N1O1O1N2N2N2N101N2O1O1O010O0100O01O001O00000000001N1O1O01001O0000O100001O01N2O2N>B9G3M4\\\\MkjNU2dU1K7J4L3M3M4K4M4K4M2M4L7H8HdT^8\", \"size\": [1280, 720]}, {\"counts\": \"f`e`0>k0CeU1f1D:I5L3M4N2M4M5L3M3N1M010N1O100O1O1N200O1O1N2O1M3N2O1O1O001O0100O010O01O000O2O0000000000000O1000000O100000000001O1O:bL`kNg2PU1M3M`0@6J4K5L2N4L4K5L3L4L4L7H9Gbd`8\", \"size\": [1280, 720]}, {\"counts\": \"gXi`01dW1^1hN`0C7J4M4M5K:G3M1O2M2M1O101O000O1O2N1N2N2N2O100O1O1O1O001O10O0010O001O00000O2O0000000O10000000000O100000O10O1001O2N;\\\\LfkNl2jT1L5XMRkNV2bU1J6J4L4K6K4K5K4K4M6J8Fe\\\\_8\", \"size\": [1280, 720]}, {\"counts\": \"UmS`0l0RW1P1QO<F7J8H3L3N3M2N1N2N1N2O1N101N2N2N1O2N2N2N2N1O2aLVLeROj3hP1001O1O1O001O0O2O00001O0O101O0O10000O10000O10000O11O1O0O2O1O1N4M5J9HS1bLajNU2lU1H>B6J5J6J7I:FYoZ9\", \"size\": [1280, 720]}, {\"counts\": \"[So=e0VW18_O`0Db0YOd0@>F;F8L4K5L4L5K4N2M3N1O1N2O2N1O1N2O1O001N2O00SLbmNX2_R1cMimNZ2VR1dMnmNZ2RR1eMPnN[2PR1bMSnN]2mQ1aMWnN]2jQ1`MXnNa2gQ1]M\\\\nNb2eQ1XManNg2]S1O001O0O2O000O2O0O2N1N2O2N2M3M3L4K6I8G<A=VOTem;\", \"size\": [1280, 720]}, {\"counts\": \"oY^<7cW1;F;E8J5I6M3M2N2O1N2O2M3N1O2M2O1O1N200O1O1O1N200O001N2O0O2N1O1N2O1N3N001O1O100O10001N100000000000000000000001O00001O1O00100O01000ROQkNkNoT1S1WkNiNiT1T1\\\\kNjNdT1T1_kNkNbT1o0ekNnN\\\\T1n0ikNQOWT1m0lkNPOWT1m0mkNPOTT1l0SlNnNQT1o0a1N3M3M3N1N3N3M3M2O2N2M3MbPY<\", \"size\": [1280, 720]}, {\"counts\": \"X[a=g0TW1:F:I7H7K4J8I4M3L4L2O2N2N2N3M1N3N1N2N3M2N2N2N2N2M3M2N3N1O1O2N3M2N2N10I7O02N1000001O1O00100O1O00N3N2N2N2N2O2M2N2N3mMkkN8VT1BTlN7PT1EUlN8mS1DXlN8kS1EYlN6lS1GWlN4mS1HXlN3mS1IWlNOPT1MV2KRon;\", \"size\": [1280, 720]}, {\"counts\": \"kP`?<VW1g0A:C<D>G6K5K6H7J6H8J6F:G9K5K4N3N1O12N4L2N1N100O1O100O10O01O01O0000000002N1O001O00000000001O000O10000O1O010O1O1N2O1N2M3F:YOh0E:F;H9H7J8C?CQim9\", \"size\": [1280, 720]}, {\"counts\": \"SYga0c0[W15M5K6J8H9H<E7H6K9E8I3L2O1N2N1O1O2M2N3N1O1O001O100O010O1O001O001O000000000000000O2O0000000O10O100000O1N2O1O12N3L3N;E>A7J?A7H5L5J6K6I4L4L6I7J5Imcb7\", \"size\": [1280, 720]}, {\"counts\": \"Pida0>`W17J6K7J>Bc0]O7I8H7J4K3M001N2N1O1O2M2O1N2O2M3M2O1O10O010O10O01O000001O000000000000000000000O1000000O100O1O1N2O11O0O5Lc0]O9G3L?B8H6I4M7H6J5K5K8F9Go[f7\", \"size\": [1280, 720]}, {\"counts\": \"mPaa0c0\\\\W16K4L>TiNfNVV1R2B6I8H6I7J3L3L3N1O2N1O1N2O1O2N1O1O001O1O0010O001O10O0000000001O10O00000000000000000O001N2O1O1O1000O3N4Lh0XO3Mb0]O6K6J5J8H6J5L3L4K7I;BQdl7\", \"size\": [1280, 720]}, {\"counts\": \"Xaca0?XW1j0^Ok0TO<F>C4L3M2N100O0001OO2N1O2N1O1O1N2O1N2O0O2O00010O01O1O1O01O00000000000001O0000O1000000000000O100O100O1O03N3M3bLokNg2iT1`MhjNo1RV1C8G9G6J5K7H:Fokm7\", \"size\": [1280, 720]}, {\"counts\": \"Saca0f0WW1>Eo0QO>B;F5L2OOO1O0O2N1O2N1O1O2N1O1O1O1N2O1O001O01O01O01O1O01O0000001O00000000000000001OO10O10000O100O1N110003M8Gd0]O6YMmjNT2hU1H7H8H8H5K6I:EPlm7\", \"size\": [1280, 720]}, {\"counts\": \"YYba0:`W1=G9J?Ac0]O:H9F5J3M1N2N2N2N1O1N3N1O1O1O1N2N2O1O001O01O010O0010O0000000010O000000000000O100000000O10000O001002N2Nd0[O7J2_MkjNS2lU1D5L5K9F5K4K7J8Fokm7\", \"size\": [1280, 720]}, {\"counts\": \"Uaca0a0\\\\W18K5L5K6Jd0^O7G9F9H6I3M2M2O2N1O2N1O1O1N2O1N2O1O1O10O0010O0001O0001O000000000000001O000000O010000O100O1O1O11O003L6Kd0\\\\O6ZMijNW2hU1H6I5L4K7J5J4L4K6K7H:Amkh7\", \"size\": [1280, 720]}, {\"counts\": \"SYla0d0[W16K8Ia0XiN^NPV1S2H4K7H7I4L2M2N101N2N1O1O001O1N2O1N2O1O1O10O1O01O0000001O0000000O1000001OO1000O10000O100O1O1N2O11O0O3N3Md0\\\\O5K4\\\\MgjNY2jU1E6K4L5J8H5K4L5K6I9Fm[a7\", \"size\": [1280, 720]}, {\"counts\": \"Xiia07hW15L4M2M6K6I6J4M4J;F7I6J6J8G6J5K4M010O1O100O2N1O2N001O2N1O1O0O2O001O0O10001N10000O100O1O1O1M3N2O1O1001N2O2N2N2N4K;F>B6J7H5L5K4L4K5L4L4K5K4M2L4M3L7I7HT\\\\\\\\7\", \"size\": [1280, 720]}, {\"counts\": \"[Yla06jW12M5L4L3M4L8G6K2M2N3N3L8I6J6K5K3L4L4L4L3M2N2N2M3N2N2M3N2N1N2O1N2O001N100O1O2O0O1O1O1M2L5O1O1001O0O101O2M2O2N1N3N2N2M3N5J5L3M6I8H4M1N2O2M5K7nM`jNW1YV1K4L4K5L3L6I^\\\\W7\", \"size\": [1280, 720]}, {\"counts\": \"YQXc03cW1g0diNZOdT1_2C9F60MN1O2N1O001O1O0O2O0O2N001O1O00001O000010O00001O01O000O1000000O10000O10000O1O0100000O1O1O100O010001N3oL`kN^2gU1YO<E:E:E:G8G9FnS]6\", \"size\": [1280, 720]}, {\"counts\": \"oh`c0=bW16K6J7K<Cc0_O8H7G8H3L2O2M2O1N2O1N1N3N2N1N2O100O0010O01O001O000O1000001N10000O10000O10000O100O100O1O1M3L4O0011O3M2M4M6fLgkNa2TU1Ga0_O:G5J9G6J7H7I7I:Dlkl5\", \"size\": [1280, 720]}, {\"counts\": \"Vigb02nW15K4L101O2M3M3M4K5L6I4L2N3M2N3N3L3O2M3M3M2N3M3M4L3L3N2M4M2M4M2M3N2M4M0O2O1N2M4M3L3FZLVlNj3hS18O1O10O010O1O001O1O1O2N1O1O3L4M4L2M5L3M5J4M2N2M2O2N3L4M4K6K5J6K4K3N3L3L4M2N3N1N3L4L5K4L6IcTn5\", \"size\": [1280, 720]}, {\"counts\": \"lXYc0`0_W14L5K3N4L4K4N3L7I5L3M2O1O1N1O1O1O1N2N2N4M3L3M3L2N3N1O2N2N1N2O2O0O1O1N2O1O1O2N001N2O1O0O2O0O2O0O101O0O100000000001O1O1O1O3M6J8H3M3M2M5L8H4L3M3M3M3M2M4M2N3M2M3N2N1N3N4K5L3L3L5K8EW\\\\[5\", \"size\": [1280, 720]}, {\"counts\": \"ih`c0a0^W16J5L5K<E:F3N101M100N2O0O2O1N3N3L4L2N2N3N1N2M2O1N2O2M2N2O1O1O2O0O1O1O1O1O001N101O1N101N10000O101N100O1N2N21O1O1O3M6J8H6J2N2N3M8H5J4M3M3M2N2N3L4M3M2M4M2N2N2M3N4K5K4K4M5JXlX5\", \"size\": [1280, 720]}, {\"counts\": \"eX^c0>aW16J5M2M4M4L4M7H5K3M3L2O2M2M3N2N2M4M3L4M3L4L3N2M3M2O2N2N1O1O100O100O1N2O1O1O1O001O1O10O01O1N100000000O2N1O1O1O12N2N1O4L6J8H5J3N2N3M7I5K4L2N3L4M2N2M3N3M2N3L4M2M3N1N3N3L5K5K3M5J]dW5\", \"size\": [1280, 720]}, {\"counts\": \"QiVc01jW1?D7L3L3N103L4M5J6J4L3L3M2N3L3N2N3L4M3M3M3M3L3M3N2M3M2O2N10000O1O1O2N001O1O1O1O1O001O001O001N101N100O100O100O12N2N2N5K5K6J4L3M2N3L8I4L4L2M4M3M2N2M4M2N3L3N2N2N2N2M3M4M3L5J5L4JWT_5\", \"size\": [1280, 720]}, {\"counts\": \"XYob0b0]W16J5L3N3N2M6K7H2N2N3M2M3M2N2M3N4L3L6K5J3M2O2M3N1N3N1O1O2O000O1O1O1O1O1O001O1O1O1O001O0010OO1000000O2O0O10000O1003M5K:F6J3M2M3N4L7I4L3L5L2N2N3M2M4M3M3L3N2M3N2M4M3L5K4L3L5JiSi5\", \"size\": [1280, 720]}, {\"counts\": \"_Qib08dW1>E5L4L6J7J<E3L2N2N2M2O1N3M2M5L5K3M2M3N3M2N2N3L2O1N2O1O2N100O1O1O100O11N01O1O1O001O000000001O00001G800O20O1O1O4L2M3N6J6J6J5K2N3L:G4L4L3M3L3N3M3M3M3L3N3M2M3N3L4L4L5K4K6IbcP6\", \"size\": [1280, 720]}, {\"counts\": \"\\\\afb0`0^W18I5L6Jb0_O6K2O1M101N0O2N3N2M4L7H3N2M3N2N2M3N1N3N1N2N3N1O1O1000000N2O001O1O0010O01O0000001O00000O100M3M4N11O2N2N2M5L7I9G5K2N1O6J8H4K5L3M2N3M3M4K3N2N3M1N4M3L4L5K6I4L9CbcU6\", \"size\": [1280, 720]}, {\"counts\": \"Uibb0e0WW19K6J?@8J3O1O0O0O2O0N2O2N2M5L3L3M3N2M2N1O1O2N1N2N2N2O1O1O2OO10O100O1O1N10001O0O2O001O001N1O100O1O100O1O100O1003M7I4L9G3M2N7H7J5K4L3M3L3N3M3L4M3M2M3N2N2M5L4J6K5K6GjcZ6\", \"size\": [1280, 720]}, {\"counts\": \"lPdb0m08@SV1]1I6K4M2M4M30O1O0004L2M1N2O2M2N2O0O1O2O0O1O1O1O1O1O1O2M2O1O0O20000O01O01O00000O2O001O0O100O100O1O100O1O1O1O11O2N9G=B3N2N=C5K4L3M2N3L4M4L2M3N2N3L4L4L6J6I5KW\\\\^6\", \"size\": [1280, 720]}, {\"counts\": \"dX`b0d0[W16K6I7L=D2M2M2N2N2M2N101N4L5K5K3M2N3M2N2M2O1N3M2N2O1O00100O10O01O1O1O1N101O001O0O2O00001O000O10000O1O1O100003M4L5K8H5J4M2N2N9G4L4K5L2N2N2M4M4L3L3N2N2M2O2M5K5L4J6K9CZl[6\", \"size\": [1280, 720]}, {\"counts\": \"f`Wb03jW18J5L2N3N2O1O0O1O2N1N3M3M3M2N3L5K5K4M3M3L4M2N2N3M2N3M3M3N2M3L3N2N1O1N3N1O1O2N1N2O1O1N2O001M3N1O2N2L4M3N1101N2N2N3N2M5K5K4L3M2M3N3M4L4L2M4M2N2N3M3M2M3N3M2N2M3N2M3N2M3N2M2O1N4L3M3M3M4L4K;D^lV6\", \"size\": [1280, 720]}, {\"counts\": \"ghXb03lW19H2N2O200O0O1O1N1O2N2N3M2N2N3L3N2N2M3N3L3M4L4M2N3M2N2O1N2M3N1O2N3L3N2M4M1N3N1N2O1N2O1N2N3M2N2M3L4N2N1101O001N3N1O3L4M2M2N3M2O1N2N2N4L2M4M2N2N1O2N2N2N2M3N2N2M3N2N2M2O2M2O1N3N2N1N3M2O3L3M2N3M3L4M4Ji\\\\o5\", \"size\": [1280, 720]}, {\"counts\": \"gh]b02nW15K4M2N1O1O1O1O1N2O1N3L3N2M3N2N2N2N3M2M3M3N3L3M4M2N3N1N2N2N1O2M3N3M2N2M4M2M2O2M2O1N3N1N2N2M3K5K5O1O1000O10001N2O1O2N2N3L3N2M2N2N2N1O3N2M2M3N3M1O2N2N2N2N1O2M3N3M1O3L3N1N2O2M2O2N1N2O2M2N3N2M3M3M3M3M3M4Khlg5\", \"size\": [1280, 720]}, {\"counts\": \"\\\\`kb0`0_W14L4L3O1N4M2N3M4M3N0N2N1N3M3M2N3M2N1O2N2M4M3L3N2M5L2N2N3M1O2O0O1O1O1N2N2O1N2O1O1O1N2O0O2N2N2N2O1N1M4N110O01O2N2O1N3M3M7I5K3M1O2N2N4K7J3M2N2N3M2M3N2N2M3N3M2N2M3N2N1N3N2M3N2M3M3N3L4L3K8IhTd5\", \"size\": [1280, 720]}, {\"counts\": \"XPXc0a0YW1f0_O?A6J5M2M2N200O12N01O2M2M3M3N1N3N1N1O100O1O1O1N2O002N001O1O1O1O100O010O1O1O1O0O101O0O100O1O2O0O1O100O1O100002N4L7I8H2N2N3M;D4M3M3M3M2N3M2N2N3L4M2N2M4M2M4L6J5K4L:CiTd5\", \"size\": [1280, 720]}, {\"counts\": \"S`Pc08eW1<F6K5K7J>C2N2N2N0O2M2O1N2N2O3L4M4K3M1O2N3M1O1N2O1N2O001O2N1O010O1O1O001O1N2O001N2O001N101N1O100O1O2N1O1O100002N7I6J7I3L3N2N2N8H4L3M3L4M2N2N3M2M4M3M2N3L2O2M3N3L4L5K4L5KRUi5\", \"size\": [1280, 720]}, {\"counts\": \"\\\\Xeb04jW19H6L3M3M4M3L5L7G5L3L3M2N3M2N2M4M4L4K4M3L4L3N2M3N1O2N1O10O01O2N1O2N001O001O1N101O1N2O001O0O101N100O1L5M21O3M2N3M7I5J8I4L2N1O3L6K7I3M3L3N2N2N3L4M2N3M2N2M3N1N3N3L3N4K3M3M4K6JilQ6\", \"size\": [1280, 720]}, {\"counts\": \"gP_b06hW1?C4L4L4M5L;E3L3N2L2N2N2N2N2N2N3M4K4M4K4M2M2N3M3M2O1O1O1O101N010O1O1O1N101O1O001O1N1O2O10O1O00O1O2N1M3O11O1N3N4L4L7I6J5K3M2N5K7H4M3M3M2M3N3M2M4M3L3N2N2M3N1O2M4M3L5J5L4K9FY\\\\Y6\", \"size\": [1280, 720]}, {\"counts\": \"lXVb08gW19G5K5N2M7J9G4K4L2N2N1N3M2N2N2O3K5L3M2N2M4M2N1N3M3N1O1O1O2N100O1O1O1O1O0O2N2O1O0O2O001O0O101O0O10000J6O1010O2M5L3M8H8H5K2N1O2M6K7I4L4L3M2M3N3M3M3L3N1O2N2M3N3L4M4K4K4M5JX\\\\c6\", \"size\": [1280, 720]}, {\"counts\": \"k`ma0=aW19I3N3M6K;E4K4L2N2N2M2N2N3M2N3L4M4L3M2N2N2M3N2M3N1N2O1O010O100O1O1O1O2M101O1O001N101O00000O2O0O1F:1O1O001O2N2N5K6J8H6J3M1O2N5J9H4L2N3M3M2M4M2N3M3L3N2N2M3N2M4M4K4K5L4LX\\\\m6\", \"size\": [1280, 720]}, {\"counts\": \"gXga08fW19H5L4L6K7J<D1N4M1M3N1N2N2N3M3M4K6K4K2N2N3N2M2O1N2O2N1O100O2OO10O1O1N101O1O0O2O1O010O1O0O101O0O1O1O1O1O100002N4L7I:F5J3N3M3M6I6K3M3M3L4M2N2N3L4M3M2N2M3N2M3N4K5K5K4K8FZdS7\", \"size\": [1280, 720]}, {\"counts\": \"]hda0`0aW15J4L6L6J:E5L3L2M3M3M3M3M3M4L4L4K4M3M2M2O1N2O1O1O1O100O100O1O1O1N2O1O001N101O001O001O0O100O2N1O100O1O11O1O3M5K7I9F4M2N4L7I5J6K2N3M3M2M4M3M3L4M2N2M3N3L3M5K4K6K9D_lY7\", \"size\": [1280, 720]}, {\"counts\": \"[`Rb0=dW14M4Kc0\\\\O2O5K<C8I7H8H7H4L3N3L3N2N3M4M3M001N00100N101O001O00001O0000000O2O000000O0100O100O1O10O01N2I7N1101O1N2O2N2M5Kk0UO9Gm0SO7I6J6J4K5K8Gmd]7\", \"size\": [1280, 720]}, {\"counts\": \"]PUb0<aW19J5L5J8J4L2N3N0O3L4K4M4K3M4L4L5K5J7J2N3M3L3N2M2O1O1O2O0O101N2N1O1N2O1O1N2O1O001N2O001O1O0O101N1O100O1O1N3J5004L3M5K8H:F4L3M2N6J8H3L4M5K3M3M3L5L5J4M2M3N2M4L3M5K5K4K5J6I[dd6\", \"size\": [1280, 720]}, {\"counts\": \"RZVb0c1[V15L4L3N2N1O1N2O001N2O1O1O001O100O1000O2O000O2OO1O1O00100N2L4M201N2O1O1O1O2N1N3N1O1N3N1O1O1O1O1N2O1O1O0O2L3O200O010O00001O0010O010O3N3L4M3L2N2N101N1O01000O2N1O5K3M2O1N2O1N2N100O2N1O1O1O1O2N1O1O1O1N2O1O1O1O1O1O2M2O1O1N3N1O1N3N1N2O1N2O2N1N3M2N3M3N2M2N4K4LTkn4\", \"size\": [1280, 720]}, {\"counts\": \"lZ`b0h0^V1m0K5L3M3N1O1O1O1N200O1O1O001O010O11O001O0O10000O01O1O001O1O0O2O1N2O1N2O2N1N2N2N4L2N2O1N3N1O001O1O1O1O1N101O1N2N2O0O10001N100100O103M2M2O1N3M2O1N1O1O2O0O2N1O105J2O1N2N2O0O2N1O101N1O1O1O1O2N1O1O1O1O1O1O1O1N2O2N1O1O1O1N3N1O0O2O2M2O2M2O1N2O2N2M3M3M2N3M2M5LTkd4\", \"size\": [1280, 720]}, {\"counts\": \"`QVd0:aW1>F8H6K6I6K2N3M2O1O2N200N2O10OO2O0O1O2M2O2M4L2N2N3M3M5K4L2M4M1N2O1N2O2M2O1O100O1O010O1O1O1O1N2O1O2M3N1N2O1O0O2O0O2N1N3O0003N2M1O3N2M2N4L1O012L3N2N1O1O4L5K5K4L3M3L4M3M3M4K3N3M3L4M3M3L3M3M3M4L4L5K3M5I8FWco3\", \"size\": [1280, 720]}, {\"counts\": \"Ucgd0?\\\\W1;G7@?A?F:G9K4L5M2N2O2N1O2N;G3NOO10OO2N000O2N101N1O1O1O1O100O1O0O2O1O1O1O1N3M3M2O1N200O1O10O01O001O00001O00001O001O0O101O000O100O1N2N22N2N3M4M3L4L6J5K3L5L7Ia0_O3M4L2N3M2N3L5L4L3L4M2M4L5K4K8G7F:H_Rc3\", \"size\": [1280, 720]}, {\"counts\": \"nZPe0f0WW1`0B:F:G7I3M3N2N1O2O0000000O011N1N2N3M4L3M3M3M4K4M2M3N2N1N2N2N2N3M2O1O1O1O1O100O101N001O1O1O1O1O001O1O000O2O001O0O2O0O1N200O1O21N001O1O3M;E;E4L2N2N1O1O2N<D4L4L2N3L3N2N3M2N3M3M3L3N2N3M2M3N1O4K4M2M4L5K4L5J6I\\\\iR3\", \"size\": [1280, 720]}, {\"counts\": \"oZUe0j0TW1;F;E=D3M3N3L3N2O0001OO100O1O1N2N3N4K3M2M3N3M4L3M2M3N1O1N3N1N3N002N1O1O1O00101N1O001O1O1O1O1N101O1O1O0O2O1O00001N100O100O2N100O11O1O2N9G6J9G4L2N1O2N2N2N:F4L4L3M3M2N2N2N3L4M2N2N3M2N3M3L3N3M2M3M4L5K5K4L4K\\\\Qo2\", \"size\": [1280, 720]}, {\"counts\": \"jjmd0f0XW1:H<C>D4L4M1N2N2O0O100O1O1N2N2O2M4L3M4L3M3L4M3M2N3M2M2M3M3O1O1O1O1O2N10000O1O1O1O1O1O1O1O001O1N101O1N101O0O100N3O0O1O1O2N1002N3M2N3M7I7I6J3M3M2N1O3M9G6J5K4K3N2N3M2N3M2N3L4M2N3M2M3N2M4M4K3N2N3L4L5J5J;C\\\\iW3\", \"size\": [1280, 720]}, {\"counts\": \"dZad0:_W1<I=Eh0XO3N3M3N1O0000000O2M3N2M3M3M3M2N4L3M3M3M4L2N2L3M3N2O1N2O100O1O101O0O001O1O1O1O1O0O2O1O001O1O001N100O1O2N1000000O2N1N22N2N3M7I;E5K4L1O2N2N3M;E4L4L3L3N3M3M3M2N2N3L4M3M2N3L3N2M4L3N4K4L4K6JQbj3\", \"size\": [1280, 720]}, {\"counts\": \"WRQd04dW1e0A=Db0_O5L4L3M2O000O10001N1O1N3N1N3M4M2M3M3M3M2M5L1O1N2N3N1O1N2O2N1O1O01000O100O1O0O2O1O1O1N101O0O2O001N100O2O0O101N100O1O1O1003M3M:F<D3M1O1O1O2N2N=C4L4L3L4M3M2N3M2N3L3N3M3L3N3M2M4M3L7I4L4K9F\\\\jZ4\", \"size\": [1280, 720]}, {\"counts\": \"fa_c0g0WW1:H9Ga0@4L2N3N1O1O1O0O001N3N1N4L3N4K2N1O3M3M1O3L3N1O1N3M2O2M2O1O1O1O100O100O1O1O1O1O1O1O00001N101N1O101N100O101O0O10000O11O1O3M7I9G4L4L2N2N1O3M>B4L3M2N2N3L4M2N3L4M3M2M4M3L3N3L3N6I5K5J7HhRP5\", \"size\": [1280, 720]}, {\"counts\": \"biQc0c0\\\\W17J:Fc0^O5L3N0O1O1N2N1N2N2N3N3L3M3M4K4M2N2N2N2N2M2N2N3N2N1O1O001O2OO10O1N2O1O1O001O1O001N101O0O100O2O0O1O1O1O2OO10001O1O6J7I<D2N1O1O4QMWkN_2WU1N3M3M3L4M2N3M2M4M2N3M3L3N2M3N2N3L5K5K4L4L4JTkb5\", \"size\": [1280, 720]}, {\"counts\": \"iilb0<[W1`0H<QiNSOUV1a1K5L2O1O1O1O1O0O1O2N2M4L4L3M3N2M3M2N2N1O1N2O1N2O2M2O1O1O1O2O0O010O1O1O1N101O001O000O2O001N100O100O1O101N1O1O1O12N2N9G;E5K2N1O2N>B3M3M4K4M2N3L4M2N3M3L4M2N2M4M3L4M5J5J5K6GUSi5\", \"size\": [1280, 720]}, {\"counts\": \"`akb09hW14K3N101N2O1O1O4J5K9G5M1N4L2M4L3N3L3M3M3N2M3M4K4L4M2N2N2N2N2N2O1O1N2N1N3N1O2N2M3N2M2O1N2N2O1N2O1N1O2N1O2M2O2N1O1M4H7N23M3M6J:F5K6J5K3M2O2M6J4K5L5K2N3L4M4L3L3N3M2M4M2M4L4L3M4L4K7H9G8DW[e5\", \"size\": [1280, 720]}, {\"counts\": \"_kgb0b1oU1m0_O:I4L3M4L401N100O2N4K3L5K3M2M3N3L3N2M3M0000000001O00000000O1000000000000001O000000000000000000O1000000O100O1O1O10000O1O100O10000O100O11O2N5K8H`0@9G8H:F?A5K4L5K5K5K4L3M3M4L5K5K5K5K8HXPi5\", \"size\": [1280, 720]}, {\"counts\": \"jUSc0c1[V1<E7I4M0001O1O1O001O1O1O1O1O1O2N1O3M3M1O001O000000001OO1O1O1000000000000000000N2N2O10000000000001O0000001O00O1000000O10000O1O100O1O1O100O10000O10000O12N9G4L<D3M3M1O2N2N1O001O6J6J3M3M2N4LUX[5\", \"size\": [1280, 720]}, {\"counts\": \"UV]c04eW1>@>H6K4K7G7M4I8J4M3L5M2O0O3N01000O002O1NO2N1O1O002N2N1O4L2N5K2N1O1O1O1O00001O00001OO100001O0000000000000000O1N2O100O1001O0000001O000000O1000000O100O100O100O1N2N2O1O1O10000002N;E6J8H7I2N1O2N1O2N:F6J4L3M3MVPf4\", \"size\": [1280, 720]}, {\"counts\": \"nfnc0:^W1a0D;E:D5K5L4N2N2N2M3M3N2M3N2N2N2O11O1O1O2N2N1O2N1O1O2N3M2N3M7I3M2N1O1O1O001O001O00001O001O001O001O000000000000O1O100O1000000001O1O1O001O0000000000O1000000O10000O100O100O1O1O1O10000000000001O4WObiNH^W1M2N3M5KSPW4\", \"size\": [1280, 720]}, null, null, null, null, null, null, null, null, null, null], \"bboxes\": [[392.0, 990.0, 86.0, 101.0], [395.0, 996.0, 88.0, 103.0], [399.0, 999.0, 88.0, 106.0], [400.0, 1001.0, 89.0, 107.0], [400.0, 1004.0, 90.0, 109.0], [401.0, 1008.0, 91.0, 110.0], [405.0, 1013.0, 93.0, 111.0], [417.0, 1013.0, 92.0, 120.0], [456.0, 1018.0, 68.0, 124.0], [456.0, 1026.0, 78.0, 117.0], [464.0, 1026.0, 68.0, 118.0], [461.0, 1023.0, 75.0, 123.0], [475.0, 1026.0, 68.0, 127.0], [478.0, 1028.0, 74.0, 125.0], [474.0, 1025.0, 91.0, 124.0], [477.0, 1025.0, 87.0, 118.0], [482.0, 1023.0, 80.0, 120.0], [471.0, 1026.0, 80.0, 127.0], [424.0, 1034.0, 86.0, 124.0], [409.0, 1029.0, 66.0, 121.0], [408.0, 1026.0, 68.0, 121.0], [408.0, 1022.0, 68.0, 122.0], [409.0, 1027.0, 68.0, 116.0], [411.0, 1022.0, 69.0, 118.0], [416.0, 1020.0, 67.0, 118.0], [420.0, 1025.0, 68.0, 118.0], [425.0, 1027.0, 66.0, 119.0], [429.0, 1029.0, 65.0, 117.0], [432.0, 1030.0, 65.0, 117.0], [433.0, 1030.0, 68.0, 116.0], [435.0, 1026.0, 68.0, 117.0], [437.0, 1025.0, 60.0, 119.0], [439.0, 1029.0, 58.0, 118.0], [438.0, 1031.0, 57.0, 119.0], [437.0, 1034.0, 58.0, 119.0], [437.0, 1037.0, 60.0, 117.0], [439.0, 1040.0, 62.0, 115.0], [436.0, 1036.0, 78.0, 120.0], [433.0, 1038.0, 88.0, 118.0], [442.0, 1042.0, 87.0, 117.0], [454.0, 1045.0, 87.0, 116.0], [462.0, 1042.0, 88.0, 117.0], [461.0, 1038.0, 96.0, 118.0], [467.0, 1034.0, 94.0, 116.0], [468.0, 1024.0, 94.0, 117.0], [464.0, 1022.0, 95.0, 116.0], [457.0, 1018.0, 96.0, 116.0], [452.0, 1015.0, 95.0, 116.0], [448.0, 1017.0, 95.0, 115.0], [446.0, 1016.0, 95.0, 118.0], [448.0, 1020.0, 94.0, 116.0], [453.0, 1023.0, 93.0, 115.0], [457.0, 1026.0, 93.0, 117.0], [460.0, 1028.0, 92.0, 116.0], [461.0, 1030.0, 93.0, 115.0], [461.0, 1030.0, 93.0, 115.0], [460.0, 1029.0, 93.0, 114.0], [454.0, 1024.0, 93.0, 115.0], [446.0, 1020.0, 93.0, 114.0], [437.0, 1020.0, 93.0, 113.0], [429.0, 1015.0, 93.0, 113.0], [424.0, 1012.0, 94.0, 112.0], [421.0, 1018.0, 93.0, 111.0], [418.0, 1013.0, 91.0, 117.0], [429.0, 940.0, 66.0, 132.0], [374.0, 833.0, 65.0, 141.0], [367.0, 752.0, 57.0, 127.0], [404.0, 819.0, 56.0, 141.0], [428.0, 973.0, 65.0, 140.0], [432.0, 1027.0, 81.0, 125.0], [432.0, 1029.0, 81.0, 122.0], [435.0, 1030.0, 78.0, 124.0], [436.0, 1032.0, 75.0, 127.0], [432.0, 1032.0, 76.0, 127.0], [428.0, 1031.0, 75.0, 127.0], [426.0, 1031.0, 75.0, 127.0], [429.0, 1028.0, 73.0, 133.0], [412.0, 837.0, 68.0, 240.0], [357.0, 792.0, 57.0, 160.0], [318.0, 733.0, 87.0, 126.0], [346.0, 786.0, 67.0, 149.0], [396.0, 962.0, 69.0, 155.0], [453.0, 1057.0, 72.0, 127.0], [451.0, 1053.0, 71.0, 126.0], [448.0, 1052.0, 69.0, 123.0], [450.0, 1052.0, 66.0, 127.0], [450.0, 1056.0, 66.0, 127.0], [449.0, 1057.0, 67.0, 125.0], [450.0, 1058.0, 70.0, 123.0], [457.0, 1058.0, 69.0, 123.0], [455.0, 1050.0, 75.0, 126.0], [457.0, 1044.0, 77.0, 133.0], [492.0, 1055.0, 63.0, 125.0], [499.0, 1051.0, 69.0, 129.0], [479.0, 1037.0, 88.0, 135.0], [493.0, 1048.0, 89.0, 121.0], [499.0, 1047.0, 85.0, 121.0], [497.0, 1041.0, 88.0, 123.0], [491.0, 1050.0, 88.0, 122.0], [485.0, 1062.0, 86.0, 123.0], [480.0, 1064.0, 85.0, 128.0], [478.0, 1065.0, 83.0, 125.0], [475.0, 1056.0, 82.0, 126.0], [476.0, 1045.0, 78.0, 125.0], [473.0, 1042.0, 83.0, 121.0], [466.0, 1032.0, 94.0, 129.0], [467.0, 1033.0, 99.0, 126.0], [471.0, 1032.0, 101.0, 127.0], [482.0, 1029.0, 93.0, 124.0], [492.0, 1023.0, 83.0, 122.0], [486.0, 1023.0, 85.0, 120.0], [477.0, 1027.0, 87.0, 125.0], [472.0, 1041.0, 86.0, 123.0], [465.0, 1046.0, 85.0, 124.0], [458.0, 1043.0, 84.0, 125.0], [453.0, 1043.0, 84.0, 120.0], [451.0, 1037.0, 81.0, 121.0], [462.0, 1025.0, 67.0, 138.0], [464.0, 1032.0, 85.0, 142.0], [465.0, 1079.0, 127.0, 130.0], [473.0, 1082.0, 127.0, 127.0], [516.0, 1064.0, 101.0, 150.0], [530.0, 1076.0, 97.0, 168.0], [537.0, 1113.0, 103.0, 151.0], [541.0, 1115.0, 102.0, 150.0], [535.0, 1111.0, 101.0, 150.0], [525.0, 1099.0, 96.0, 146.0], [512.0, 1084.0, 96.0, 144.0], [498.0, 1076.0, 93.0, 142.0], [487.0, 1073.0, 89.0, 136.0], [483.0, 1071.0, 88.0, 137.0], [482.0, 1062.0, 92.0, 155.0], [479.0, 1117.0, 92.0, 163.0], [488.0, 1206.0, 94.0, 74.0], [496.0, 1182.0, 103.0, 98.0], [510.0, 1194.0, 101.0, 86.0], null, null, null, null, null, null, null, null, null, null], \"areas\": [5559.0, 5775.0, 6042.0, 6051.0, 6224.0, 6333.0, 6402.0, 6636.0, 6398.0, 6335.0, 4718.0, 6426.0, 6435.0, 6488.0, 6999.0, 6619.0, 6393.0, 7421.0, 7707.0, 6272.0, 6161.0, 6134.0, 5837.0, 6001.0, 6013.0, 5992.0, 5964.0, 5965.0, 5941.0, 5672.0, 5893.0, 6048.0, 5922.0, 5983.0, 6032.0, 5807.0, 5750.0, 6625.0, 6730.0, 6736.0, 6681.0, 6828.0, 7082.0, 7049.0, 6995.0, 6974.0, 6874.0, 6825.0, 6856.0, 6851.0, 6843.0, 6840.0, 6895.0, 6898.0, 6756.0, 6819.0, 6760.0, 6738.0, 6771.0, 6757.0, 6711.0, 6648.0, 6403.0, 6565.0, 6800.0, 5633.0, 4273.0, 5494.0, 6935.0, 7040.0, 6890.0, 6882.0, 7140.0, 7041.0, 7012.0, 6949.0, 7184.0, 7343.0, 5977.0, 5944.0, 6519.0, 7805.0, 6752.0, 6688.0, 6485.0, 6632.0, 6589.0, 6341.0, 6375.0, 6259.0, 6264.0, 6658.0, 6087.0, 6416.0, 6762.0, 6842.0, 6675.0, 7016.0, 6861.0, 6864.0, 7122.0, 6917.0, 6699.0, 6633.0, 6684.0, 6656.0, 6496.0, 6590.0, 6782.0, 6594.0, 6599.0, 6606.0, 6717.0, 6618.0, 6615.0, 6555.0, 6481.0, 6605.0, 7546.0, 9997.0, 9768.0, 9090.0, 10925.0, 10094.0, 9939.0, 9817.0, 9169.0, 8915.0, 8669.0, 7956.0, 7950.0, 7940.0, 10653.0, 5252.0, 6300.0, 5181.0, null, null, null, null, null, null, null, null, null, null], \"iscrowd\": 0, \"video_id\": 0, \"height\": 1280, \"width\": 720, \"category_id\": 49272, \"noun_phrase\": \"air jordan\"}, {\"id\": 1443, \"segmentations\": [{\"counts\": \"Wno72iW1<G5M4M5J6K8H2M1O1O1O1O1N2O1O1O001N101O1N2O1O3L4L4L2N3N1O1O1O1O010N100O2O0O1O11O1O1O2N3M3M3M2N3M2N2N2N3L4M2N1O2M4M2N1N3N2M2O2N2M3N2M3M3M8GW`\\\\a0\", \"size\": [1280, 720]}, {\"counts\": \"X^R8=bW14L4M4M4K5L7H1O2N1O2M2O1O1O001O1N2O1O1O0O3N2N3L4L3M2O2N1O1O1O1O001O0O2N10000010O2N1O3M3L3N2N2N2N2N2N3M3L3N2N2N2N1N3N2M4M1O2M2O2M2N4M2M4L7HQPZa0\", \"size\": [1280, 720]}, {\"counts\": \"[VQ8;cW1;G6I<E2N2O1N100O1O1O001O1N101O1O1N101O0O2O1O2M3N2N3L3M3M2O1O1O1O1O1O1O0O2O00001O02N1O2N3N2M1O2N2N3M2N3M3L3N2N1O2M3N2N1O2M3N1O2M2O1N3N2M3N1N5K4L4K7FmoTa0\", \"size\": [1280, 720]}, {\"counts\": \"afi73jW1o0SO3M3M2O1O2M2000O10O1O1O1O001O001N2O001N2O001O2M2N3N2M4L3N2M2N3N1O2O0O1O1O1N2O001O0100O2N101N2N2N2N3N1N3M3M2N2N2N1O2M3N2N1O2M2O1O2M3N1O2N1N2O1N4L2O2M4K4M5J6JfWVa0\", \"size\": [1280, 720]}, {\"counts\": \"Uoj75iW1:G5K3O1N2O2N6I=C3M2N2N2N2N1N2O1O1N2O1O3M4K5L4L3L3N1N2N2N101O001O0000000O2O0O10000O100001N2O3M2N4L6I3N3M3M2M5L4L2N2M3N3L3N2M3N3L5K3M3N3L4K9H8Djn^a0\", \"size\": [1280, 720]}, {\"counts\": \"dgd71kW1;G5M3M3N2N2N3M5J8H3M3M1O1O2M2O1O1O1N2O1O2N2N3L4M3M3L4M2M3L3O1O1O1O1O10O00O2O000O2O000O100001O1O2N3M4L3M3M3M3L3N2N3M2M4M2N2N3L3M3N3L3N2M3N2M3N2M7I4L9F[V`a0\", \"size\": [1280, 720]}, {\"counts\": \"f_h79eW16K4M3N1O1O3M4L7H4L2N3L3N2N1O1O1N2O1O1O1O2M3N3M3M5J4L4L2N3N1N200O1O1O001O0O101N10001N100001O1O2N3M3L5L4L1O2M4M2N4L3M2M4M2N1O3L3N2M3M3M5J6[OSiNLfUea0\", \"size\": [1280, 720]}, {\"counts\": \"h_m7=aW15M3M2O2N1O4L7H5K4L3M2N1O1N3N1O002M2O1O1O2M3N4L4K4M4K3M2N3N1O001O1O1O1O000O2O0O101N1O10000001O2N2N4L6J2M4M3M2N3M2M5L3M1N3N2N2N2M3N2M3N2M3N2M4M2M4L5J:G8DQVVa0\", \"size\": [1280, 720]}, {\"counts\": \"ngn71kW1<G4L4N1N2O1O4K6K5J5K3M2N1O1O1N2O1O2N1O1O2N2M3N2N4L4K4M3L3M2N2O1O1O1O1O001O0O2O0O2O0O100O1001O002N2N2N4L3M3M3L4M2N3L4M3M2M3N2N2N2M3N2N2M3N2M3M3N3L3M5K8G9GoeSa0\", \"size\": [1280, 720]}, {\"counts\": \"mgi76gW18J4M2N2O2N2N3L4L4L5K3M3M2M2O2N1O2N1N2O1O2N1N3N3M4K5K4L4L3N1O1O1O1O1O1O001O1N100O2O0O1O10001O01N3N2N3M5K3M3L3N3M2N2M4M3M2M3N2N2M3N2N2M4M2N2M3N2M4L4L3N5I:FQfXa0\", \"size\": [1280, 720]}, {\"counts\": \"nWg71jW1<H5K3N3N2N4K5L7I4K3L2O2N2N1O1N2N2N2O2N1N3N2N3L5L5K2M3L4M2O1O1O1O001O1O001O00001N100O100O1010O0O3N3M2N3M7I3L3N2N3L4M4L2N3M2M3N3M2M3N2M4M2M3N2M3N2M5K5J:FR^\\\\a0\", \"size\": [1280, 720]}, {\"counts\": \"lWg7:dW16K4M2M4N2N4L6I7J3K3N1O2N1O1N3N1N2O1N2O2M2O3M2M5L4K4L3M2N3N1N2O10O01O1O001O001N100O100O100001O1O2N2N2N4L5K3L4M3M2N3L4M2N3L3N3M2M3N2N3L3N2M4M2M3M4M3L5J9GR^\\\\a0\", \"size\": [1280, 720]}, {\"counts\": \"nWl7;cW17J3M4M5J5M5K6J2M2N3M1O1O2N1O1N2O1O2M2O2N2N3L4M4K4M2M3N2M2N2N2O001O100O000O2O000O1O100N3O00O102N2N3M3M4L4L4K4M3M3M4L2M4M3M3L2O2N2M3N3L3N2N2M4L3M4L4L7I9Dm]Wa0\", \"size\": [1280, 720]}, {\"counts\": \"ehn78]W1>B=H8L4M2O1N3M2N2O0O2000000O10O01O001O2N2N2N3L6K4L4L2M3N2N1O1N2O1O1N2N1O2O1N2O010O00010O00000000001O01O2N2N2N3M2N3M2N4K6K:F3M3M2N3M2M3N3M2M3M3N2M4L3N3L5J9G8FlUQa0\", \"size\": [1280, 720]}, {\"counts\": \"mgd72iW1;I4L5K7Ia0@4M2N2N1O1N2O0N4M3M8H:F4K4M3M2N1N2O1O1O001N2O1N1O2N10001O0000000001OO10000O100O1O001O12N003M4L4K4M3M3L4M3L3N3La0@3L5K5K4L5K5K4K6JWfga0\", \"size\": [1280, 720]}, {\"counts\": \"SXS7`0_W13N2M4N1O5K3M8G4M0N3N1O2N2N1N102N1N2O1O1O1N2O2N2N4K4M3L3L4N2N1O1O1O1O1O1O000O2O0O101N1O1O12N2N1O3M2N3M3M2N2N3L3N2N3M2N4K3N2M4M2N1O2M2N3N2M3N2N2M2N4L3N2M5J8Hlmma0\", \"size\": [1280, 720]}, {\"counts\": \"SPR79eW18I4L4M3N5K4L9F3N1N1O1O001O2M2O1O1O1O1N2O1O1O2M4M3M5K3L3M3M2O1N2O1O1O1O001O00001N100O1O100O11O2N2N4L3M2N3M2N2N4K3N3M3L4M3M2M3N2N1O2M3N1N3N2M3N2M3M4L3N4K6I8GjUoa0\", \"size\": [1280, 720]}, {\"counts\": \"S`o61kW1=E5L4M3M4M5K5K7I2N1N2N1N2O001N2O2N0O2O1O1N2O1O2M4M4K5L3L2N3M3N1O1O1O001O001O000O101O0O1O100001O2N2N3M4L3M2N4L2M4M4L3L4M2N2N2N2M3N2M3N2M2O2M3N2M3N2M3M4L6J7ImeQb0\", \"size\": [1280, 720]}, {\"counts\": \"nWn67gW19H4L3M4N3M5K9G3M1O1M3N1O1O1N2O1O1N2O1O1N2O1O1O3M2M5L4K3M3M2N3M200O1O010O1O0O101N100O2O00000001O2N2N6J2N2N3L5L2N3M2M5L2N2M3N3L3N1O2N2M3N2N1N3N2M3M3M3N3L7H8GPnRb0\", \"size\": [1280, 720]}, {\"counts\": \"mgP73hW1<G5M4L6J>C001O1N2O1N1N2O1O1O2N001O1N2O1O2N2M4M4L4K5L3M1N3M2O1N2O001O1O001O000O101N100O10000002N2N3M4L3M2N3M2N3L4M2N5K3M2M3N2M3N2N2M4M1N3N2M3N3M2L5L5J:ETnRb0\", \"size\": [1280, 720]}, {\"counts\": \"^WX76fW19J4Lh0YO3M2O1O2N001N1O1O1O1O1O1O2N3M3M5J6K6J3L4M1O2N1O2M1O2M3N101O00010O001O00000O100O1O10000002N2N5K3M3M4L3M4L4K5L5J5L2N3L3N2M5K4M3L3M4L4L5J<D[fQb0\", \"size\": [1280, 720]}, {\"counts\": \"T_h7=bW15K8H8J:E1O2O1O000O1O2N001O2M101N2O1O1N101O1O1N2O2M2N4L5K4L2N3N1O2O0O1O1O1N2O001N1O110O0101N2N2N2N1O2N3M2N2N2N2N3M3M2N3L3N2N1O2M3N2N2M3N1O2N1N3M2N3M3N1N4L3M5J6K6GknTa0\", \"size\": [1280, 720]}, {\"counts\": \"Sgn7<bW15L7I8J5J5K3N0O1O2OO02N0O2O1O1O1O1O001O1O1N2O1O2N3M2M3M3N2M2N2O1O1O1O1O1N101O0O101O000100O2N3M1O2N1O2N2N2N2N3L5L2N2N1N3N2N2N2N2M2O2N1N3N2M2O1N3M4M2M4K7JUoTa0\", \"size\": [1280, 720]}, {\"counts\": \"PWQ86fW18K4L6J8I;E4M1O000O100O1O1N101O2N1O001N10002N1O2N2M3N3M2M3N2L3N2O1O001O100O0O2O001O000010O1O1O2N2N2N3M1O2N3L4M3M3M2M3N2N2N1O2M3N1O2M2O2N2M2O2M3N1N3M4L6J5JXWQa0\", \"size\": [1280, 720]}, {\"counts\": \"jVV8<cW13M4L7J7I8I3M1O1N1O2N1O1O1N2O1O002M2O001N101O1N3N2M3M4L4L2N3N1O100O1O1O1N2O000O2O000010O2N3M2N3N1N2N2N2M3N3M3M3M2N2M3N2N2N1N3N2N1O2M3N1N3N2M2O2M2O2M5K4L6IYWl`0\", \"size\": [1280, 720]}, {\"counts\": \"RW[8=aW15L6K5K5K7J2N2M1O1O1O1O1N2O001O1O1O1N2O001N2O2M2N3N2M3N2M3N1O2N1O1O1O1O1N2O0O2O0O2O01O011N2N3M3M2N2O1M3N2N2N2N2N3M2N1N3N1O2N2M2O2N2N1N3N1N3N2M2O2M3M3N2L6K6IToe`0\", \"size\": [1280, 720]}, {\"counts\": \"X_\\\\88eW17L3L5L3M5L8G4M2M2N1O1O1O1O1O1O0O2O1O1N2O001N2O1N2O1O2M3M2N4M1O2N2N1O1O1N2O1N101N101N11O101N2O1N3M2N2N2N2N2N1N3N2N3M2N2N2M3N1O2N2N1N3N1O2M3N1O2M3M2O2M3N2M3M5K5JoVb`0\", \"size\": [1280, 720]}, {\"counts\": \"Ygb88fW17I6K6K;E8H3N2M10000O1O1N2O001O1O1N2O001N101O1N2O1O2M2N3M4L2O2M2O100O1O1O1O0O2O1N10010O02N1O3M2N2N2N2N1O3M3M2N3M2N3M2N2M3N2N1N3N1O2N2M2O2M3N1O2N1N2N3M3M5K5K6HkV]`0\", \"size\": [1280, 720]}, {\"counts\": \"ZWe8;cW15L5L4K7J8I4L1O2M001O2N1N2O1O001O1N2O1O0O2O1O0O3N1O1N2N3M4M2M3N1O2N10O01N2O1N2O001O01O1O101N3M2N2N1O2N2N2N2N2N2M5L2N2N1O2N2N1N3N1O2M2O2M2O2N1N3N1N3M3N2M4L4L6IkfZ`0\", \"size\": [1280, 720]}, {\"counts\": \"__f81kW1:I4L5K7J6J6J2O1N2N1O2O0O1O001O1O1O1N101O1O1O1O1O1N2O1O2M4M2M2O2M2O2N1O1O1N101N2O001O0010O1O2O1N2N1O2N1O1O2N2N2N2N2N2M3N1O2N1O2M3N1N3N2N1N2O2M3M2O2M3N1N3M4L5K5JnVX`0\", \"size\": [1280, 720]}, {\"counts\": \"XWj83hW1:J4L4M4L4M6J7H2N2N2N1O1O1O1O1N101O0O2O1O1N2O1N102M2O1N3L5M2N2N1O1O2N1O1O0O2O1N101O0010O101N3M3M1O3M2N2N2N1O2N2N2M4M2N2N2N2M3N1O2N1N3N1N2O2N1N2O2N2M3M3M3M5K4K6IQ_T`0\", \"size\": [1280, 720]}, {\"counts\": \"Tgl89fW15K4M3L4N3M;E5J101N1O1O1N2O1O0O2O1N2O1O0O2O1O1N1O2O2M2N3M3M4N1N1O2N1O1O1N2O1N101N2O01O101N2N3N1N2N2N2N2N2N2N2M3N3M2N2N2N2M2O2N1O2M2O2M2O2N1N2O2M3N1N3N2M3M5K5J6IonQ`0\", \"size\": [1280, 720]}, {\"counts\": \"Zom8;dW15K3N3M7J9G3L2N2O1N0O2O1O1O1O1O001N2O1O1N101O1N2O2M2O2M3N2M3N2M201N1O1N1O2O1N2N101O0011N2N103L2N1O2N2N1O2N2N2N2N3L3N2N2N2N1O2M2O2M2O2N1N3N2M2O2M2O2N2M3M3M5K4K8EjfP`0\", \"size\": [1280, 720]}, {\"counts\": \"]_f8d0YW19I4L3M3N1O1O10000O100O1O1O001O001O1N101O1N2O1O1N2O1O1N3N2M3M3M3N2M4M2N1O2N1N2O1N2O1N2O010000O2O0O3N1N2N1O101N2N2N2N2N2N3M3M1O1O2N1O2N2N1O2M2O2N2M2O2N1N3N1N3M3N2M3L6K5K4JhnQ`0\", \"size\": [1280, 720]}, {\"counts\": \"goc85aW1c0D7K3M2N2N2O1N2O0010000000000O001O1O001O001O0O2O1O1N2O1N2N2O1N2O1N2N3M3L4N2N2N3M1O2M2O1N2O1O1O100000O10001N101N1O2O1N2O2M2O2M2N2N2N2N1O2N1O2N1O2N1O1O2N1N2O1N3N2N1O1N2O2M3M2N3M3M3M5K4K5Ii^j?\", \"size\": [1280, 720]}, {\"counts\": \"QPd89dW1i0YO5K4M2M4L2O1O1O11O0O10O1O100O1O1O001N3N2N2N2N3M3M4K4M2N2M3N1N2N2N2O1N2O00100O1O0O2O0O1O2O01O002N2N2N2N2N1O1O3M1O3M2N2N4K5L8G4M2N4K3N2N2M3N2M3N1N3M4K7J8G5KjU]`0\", \"size\": [1280, 720]}, {\"counts\": \"UPd88eW1=Ea0_O3N2M3N1O2N10O1000O1O1O1O010O001O1O1O1O2N1O2N3M2M5L3M3M1O2M3M2O1N101N2O100O0O2O001O0O110O0002N2N2N3M2O1N2M4M3M3M2N3M2M3N1O2M3N3M2M2O2N1N3N1N3N1N3M4L5K7H5Kmm[`0\", \"size\": [1280, 720]}, {\"counts\": \"oWe8>`W17Jj0WO2M3M3O000000000O001O1O1O1O1O001O1N2O1N3N2N3M3L4L4M1N3N1N2N2N110O1O1O1O001O000O101O01O1O1O2N2N2N2N2N3M3M3L6K6J3M3M1N3N2N2M2O2N2M2O2N1N3N2M3N1N4K6K7I5JlU]`0\", \"size\": [1280, 720]}, {\"counts\": \"]`W8183NNoV1X1_O5L2N2N2O1N2O1000O0100O10O0001N101O00001O0O2O0O2O001O1N2O1N3M3N2M3M3N1O2M3N100O1O0O2O1N2O0011O0O101N1O2O1N2N2N3M3M4M2M3M2N2N1O2M3N1O2N1O1N2O2N1N3N1O1N3N1N2O1O2M2O1N3M4L3M4K6K4Jim[`0\", \"size\": [1280, 720]}, {\"counts\": \"bPU83jW1U1mN7J3M1O1O1O1O1O001O001O001O10O1O0001O0000001N1000001N100O101O0000001O001N1O2N2N3N2N1O2N1O1N2O1O10O01000O100O1O100O2O0O3N1N3M2N2N1O1O2O0O1O2N1O1O1O1O1O2M2O001O1O1O1N2O1N101N2O1N2O2M2N2O1N3M3M4L3M2M6JfeP`0\", \"size\": [1280, 720]}, {\"counts\": \"ghb839<gV1k0E6K1O2N1N2O1O0O2N20O01O0100O1O010OO101O0O100O2O001N100O100O10001N100O2N1O2N2O2N1O2N1O1N2O2N10O0100000O100O011N1O2O1N2N3N2M2N2N2N2N1O2N100O1O1O2M2O1O1O1O1N2O1O001N2O1O1O1N2O1N2N2O1N2N2O2M2N3M3M3M4JgUd?\", \"size\": [1280, 720]}, {\"counts\": \"cPn8V1iV18H3N2M2O1O0O2N2N2O010O0100O0001O001O00001N1O2N100000000O100O2O0O1O1O2N1O2O1O1O2N1O2N1O1O1O1O100000O010O10000O2N3M2O1N2N4M1N2N2N2N1O1O2N1O1O1O2N1O1O1O1O1O1N101O1O1N2O1O1O1N2O1O1N2O1N2N2N2N3M3M3M3M4KemX?\", \"size\": [1280, 720]}, {\"counts\": \"ahV9W1hV16J3N1O1O1N2O1N2O0O2O0010O010O01O0O101O00001O00000O101O000O100O2O0O100N3O1N2O1O1O2M3N1O1O1O1O1000000O01O100O101O1N2O1N2N3N2M2N2N1O1O2N1O2N1O1O1O1O1O1O1O1O1O1N101O1N2O1O0O3N1N2O1N2O1N2N2O2M3M3M3M3L7IeUP?\", \"size\": [1280, 720]}, {\"counts\": \"S`n92lW18G5L4L3M4L3M3L4M3M3M4M1N4L3M3M3M3M2N3M3N1N3N101N200O010O2O1N1O2N3M2N1O2N2N1O2N2N1O1O1O100N101O1O1O1O001O001O001O001O0O2O0O10001O000O10001O01O001O2N2N2N2N2N2N4L9G9F8I3L3M4M2M3M3M3M3L4M3M3L5K4L5K5K6J6HiVW>\", \"size\": [1280, 720]}, {\"counts\": \"ij`<2iW1c0@5K4M3M4K5L6J5L5K:E6K4L4L2N3M1O2N3M1O2N1O1O1O1O100O0O101O0O101O000O1000000O1O1O11N2O2N2N3M2M5L3K8I7H6K8G9F_\\\\^=\", \"size\": [1280, 720]}, {\"counts\": \"ld^=<bW13M2M4N1O1O101O1N2O001O1O1O1O1O3M1O2ljNPOTS1Q1glNVOWS1k0blN\\\\O\\\\S1e0`lN@_S1a0]lNCbS1?YlNDhS1<TlNGlS1;okNHRT19ikNJWT1P2001O0O101N2O0O100O101N3M5L2M2N2M3N3M2N2N2M3M2N2N3M3L3N4K6I9G:ETa[<\", \"size\": [1280, 720]}, {\"counts\": \"XUm<4iW15L4M101N2N2N2N3N2N2N2M3N2N2N1N3N2N1N4M2N2N3]jN[NeT1k1PkN^NlT1^2M103L2O001N2N10000O2O100O01O001O1O0O2O2M3M3M2N2N2N2N2N1O2N1O1N2O1N2N2O1N2N3L4L4K6K5J8I6HSa`<\", \"size\": [1280, 720]}, {\"counts\": \"bb\\\\;8fW14M4M3M4K3N3M3M3M4K5L4K4M2N2M4M2M3N2N2M2O2M2O1O1N2O1N2N1N3M3M2L4M4M2M3L30100001O0O2O1N3N1N4L3M2M4M4L4L4K4L5J5L5K5I9G7J8FVU[>\", \"size\": [1280, 720]}, {\"counts\": \"SnU:e0WW18H7I6K5M3M3M2O1O2N1O2N2M4N1N3M3M3M5L5J4L4L3M2N2N1O2N1O1O1O1O1O1O1N101O001N101O00001O0001OO2O0001O002N3M3M3L5L5K6I5L3L5L4K3M4M3L4L5J6J8H<AX`V?\", \"size\": [1280, 720]}, {\"counts\": \"Y__99dW1c0_O9H3L2O1O1N2O1O1O100O1O1O010N2O001O010O001O1O001N2O1O3M2N3M2M3N1O1N2O2N001N2O1O1O1O00001O01O011N1O3M3N1N1O2N3M1O2N2N2N2N3L2O1O1O2M3N1O1N3N2N001N3N1N3N1N3M3N3K6K4KQg\\\\?\", \"size\": [1280, 720]}, {\"counts\": \"[g`98bW1m0XO7I4M1N2O1N2O1O10O1000O10O01O001O001O001N101O001O1N2O1N4M3M1O2M3N1N2N2O1O1O1O001O1O001O01O100O1O100O3M3M2N3M2N4L3M2N3M2N1N3N1O2N1N2O2N1N3N1O1O1N3N1N2O2M2N3M4L5K4Ko^[?\", \"size\": [1280, 720]}, {\"counts\": \"QPg9b0SW1=@?K6M2O1N3N001O1N101N20O00011O0O0001O001O0O101O1O2M2O3M2N2N2M2O2N1N101N1O2N2O001O1O01O010O000000100O1O1O1O1O2N3M3M4L5K7I4L4K3N2N2N1O2N1O2M2O2N1N3N1N3M2O2M3M3M5K4KjVU?\", \"size\": [1280, 720]}, {\"counts\": \"_Pl96`W1>E8I8F9J6M3M3N2N2O1O001O0O2O001O1O01O0010O1O002N1O2N3M3L3N2N1O1O1O1O001O1O1N1O2N1000001O01O000001O000001O1O2N2N3M2N7I:F6J5J3N2N2N2N2M3N1O2N1N3N1N2O2M2O2M4L4L4J7Ff^Q?\", \"size\": [1280, 720]}, {\"counts\": \"fXm92]W1g0_O<D=J5N2N2O1O1O1O001N101O0O20O1O10O0100OO101O1O1O1O2N3M3M1O2N1N2O1O1N2O0O2N2N1O2O001O00010O0001O0000002N1O2N1O2N4L4L4L6J7I5K3L3N2N2M2O2N1N2O1O2M2O1N2O2M3M4L4L4L6Fbnn>\", \"size\": [1280, 720]}, {\"counts\": \"hok9?[W1l0XO7J3N1N3N1O001N2O00100O10O01O001O001O001N101O001N2O1O1N3N2N1N3N2M2N2O1N2O1O1O1O001O00001O000100O1O2N1O2N3M2N2N4L4L3M5K3M3M2N3M1N3N1O1N3N1O1N3N1O1N2O2M2O1N3M3N3L4L4K8G\\\\fm>\", \"size\": [1280, 720]}, {\"counts\": \"cWm9o0oV1?C3L3N2N1N2O1O0O200O10O100O01O1O0O101O001N101O0O2O1O1N3N2N2M2O2M3M2N2O001O1O1O1O001O001O0001O100O2N1O2N2N2N3M6J2N3M4L3M2N2N2N2M3N1O1N2O2N1N3N1O1N2O1O2M2O2M3M3M5K3M4K_fm>\", \"size\": [1280, 720]}, {\"counts\": \"aWm9T1gV1<H3M3N2N10O01O1O1O1O001O10O010O0100O000O2O001O1O1O1O2M4M2N1O2M2O1O2N0O2O1N2O1N2O00001O00001O0001O2N2N1O2N3M2N3M3M5K3M4L3M2N2M3N1O2N1O2M3N1O1N2O2M2O1N3N1N3M3M5K4L3Kdnn>\", \"size\": [1280, 720]}, {\"counts\": \"XWk:;aW17K5J5I7K5K4K5J6M3M4L5M6I<E5J3N2N1O1O1O2N001O1O001O10O01O000O2O001O01O00O2O000000000O100000000001O000O101O0O2O1N3N2M3M3M4L8H:F=C6I6K3M5J6J6J;[Oog^>\", \"size\": [1280, 720]}, {\"counts\": \"XlV<h0oU1\\\\1I7J5K5M2N2O1O1O1O0001O01O000000000O10O10O1000O1O001O010N10000O0000O00OL4N26J5J8I;Fh0RObUV>\", \"size\": [1280, 720]}, {\"counts\": \"VYm<3lW17J6I4L5K4QNXO_lNk0]S1WOblNl0ZS1VOelNm0XS1UOflNm0XS1UOflNm0XS1VOdlNm0ZS1UOblNn0^S1SO^lNQ1`S1RO\\\\lNP1cS1TOXlNn0hS1SOUlNP1iS1VOlkNP1TT1^1010O0000000O101N101N101N2N2N3M2N3L5L4K9Fl0TO8I6I5J:DYg_=\", \"size\": [1280, 720]}, {\"counts\": \"`Z`;`0]W19G=D8I7I5K3N3L3N1O1O1O1O001O001N100O1O100fNfMmlN[2jR1PNSmNQ2kR1TNRmNl1nR1UNPmNl1oR1WNmlNj1TS1XNilNi1WS1YNelNi1[S1ZN`lNg1`S1^NSlNj1oS1R1100O1O1N3N1O2M5L7H6J5J9H6I9H9G;E>\\\\Oo]f>\", \"size\": [1280, 720]}, {\"counts\": \"YUc9e0XW18G7K4M3M2N4L2O3L3N3M4L4L5K:G3L3M2N2O2M2N1O2N1O1O1O2N1O1O1O001O00001N101O00000O100000000O100O1O11O3L7J5J6J7I6J6J6J7I8H6I8Fe0mNXQR`0\", \"size\": [1280, 720]}, {\"counts\": \"jo^88bW1:H6J5K5K5L4K6L2O2O1N2O1O1O010N101O00001O010001N0001O001O001O1O2N2N2N2M4M2N1O2M2O2N1N2O1N2N1O2O1O001O010N101O01O010O1O100O1O2O1N3M7I;E3M3M3M2N1O2N2N2M3N1O1N2O1O2M2O1N3N1N2O1N4L3L5L3L5JofP`0\", \"size\": [1280, 720]}, {\"counts\": \"koY8e0nV1?E;I6O1N2O10O01O1O010N101O000010O1O10O001O0000001O001O001O0O101O2N2N1N3N2N1O1N2N3N0O2N2O1O010O1O0O1100O00100O010O1O1O1O2O1N3M6J8H3M3M1O2M2O1O2N1O1N2O1O1O1N3N1O1N2O1N2O1N2N3N3L3M3M3LQoV`0\", \"size\": [1280, 720]}, {\"counts\": \"bWV8R1bV1=L5M2O1N2O001O1O001O001N1001O010O1O010O000001N101O0O100O2O001O1N102N2N2M2N3M2N2N2O1O1O001O1O001O010O0100O1O10O02N1O1O1O3N1N4L4L5K4L4L2M2O2N1O2N1N2O1O1O1N2O1O1N2O1O1N2N2O2M2N3N3L3M3M2MQ_Y`0\", \"size\": [1280, 720]}, {\"counts\": \"`WV8R1eV1=K3M2O1N2O1O1O0O2O0O2O001O001O01O0001O0000001O000O2N10001N101O1N101N3N2M2N3M2O1O1O1O1O001O1N1010O100O1O100O100O1O2N1O2N3M3M3M4M2M3L4M2N2N2N1O1O2M2O1O1O1N2O1O1O1N2O1N2N2O2M2N3N2M3M3M2N4KoVX`0\", \"size\": [1280, 720]}, {\"counts\": \"[_W8Y1eV17J2O1O1O1N2O001O0O2O1O0O20O1O1O01O00001O0O101O0O101O0O101O0O101O0O3N1N3M3M2N3N1O1O1O1O1O001N1100O100O010O1O1O100O2N1O2N2N5K4L3M4L2N2N2N1O2N1N2O1O1O1O1O1O1N3N1N2O1N2O1N2O0O3M3N1N4L3M2N5ImnV`0\", \"size\": [1280, 720]}, {\"counts\": \"\\\\WV8W1gV17K2N2M2O1N101N1O2O1N110O010O01O00O2O001O000O1O2O001N100O100O2N101N1O3M2O2N2N2N1O100N2O0O20O100O10O0100O100O1O2N2N2N2O2M3M2N3M3M2N2N2M2O2N1O1O2N1O1O1O1N2O1O1N2O1O1N2O1N2N2O1N3M3N1N4L2N3L9FinV`0\", \"size\": [1280, 720]}, {\"counts\": \"]oT8<aW1n0UO5K1N3N1N2N1O2N2O001O010O01O01O00000O2O000O101O0O2O000O101N1O1O2N1O2N3N1O2N2O0O2N1N2O001O10O1O100O10O0100O1O2O1N2N2N2N4L3M2N2N3M2N2N1O1O2N1O1O2N1N2O1O1O1N2O1N2O1O1N2O1N2N2O1N3M3N2M3M2N3LoVX`0\", \"size\": [1280, 720]}, {\"counts\": \"YWV8V1jV17H4M1N2M3N2O1O1O000010O01N2O0000001O00001O00001N101O0O2O0O2O0O2N2N2O1N2O2N2O0O1O1N101O1O10O100O00100O100O101N1O2N2N2N3M4L2N2N3M2N2N1O1O2N1O1O1O1O2M2O1O1N2O1N3N1O002M2N2N2N2O2M3N2M2N3L5KnVX`0\", \"size\": [1280, 720]}, {\"counts\": \"ZgX8P1nV1;F3M3M2O1N2O0O2N2O0100O01O010O000O101O001N101O0O100O101O0O101N101N3M2N3M3N1O2O0O1O1N2O001O10O10O0100O100O1O1O2O0O2N2N3M3M4M1N3M2M3N2N1O2N1O1N3N1O1O1O1N2O1O1O1N2O2M2O1N2N2O1N3N3L3M3M2MRoV`0\", \"size\": [1280, 720]}, {\"counts\": \"YoY8W1fV16K4N1N2O1N2N1O2O10O0100O01O0001O0O101O0O2O000O1O2O0O101N100O2O1N2N3M2O2N2N1O1O1O1O1O1O1O0010O10000O1O1O101N1O1O2N2O2M3M4L3M2N2N2N2N2N1O2N1N2O2N1O1O1N2O1O1O1N2N2O2M2O1N2O2M2O3L3M2N4KRoV`0\", \"size\": [1280, 720]}, {\"counts\": \"V`W84oV1V1F3M2M4N1N1O2O1N10100O010O010O00001N101O0O101N10000O101O0O2O0O2O2M2N3M3M2O2N100O1O1N2O1O001O02O0O10O0100O1O1O101N2N2N3M2N5K2N3M2N3M2N1O1O2N1N2O2N1O1N2O1O1N2O2N1O1N2N2N2O1N3N2M3N2M3L4LPWX`0\", \"size\": [1280, 720]}, {\"counts\": \"ZgS8V1hV16K2N3N1N2N1O2N20O01000O0010N101O000O2O000O2O000O101O00001N101N2N3M2N3M3N1O2N1O100N2O1N20O010O100O100O001O100O1O3M2O2M3M3M2N3M2N4L2N1O1O1N2O2N1O1O1N3N1O1N102N1N2O1N3N1O1N2N2N4M3L3M2M6Hon[`0\", \"size\": [1280, 720]}, {\"counts\": \"Zgn7m0nV1?E3N1N3N1N2N101N11000O010O001O00001N101N100O2O0O10001N100O2O1N2N2N3M2O2N2N1O1O1O1O1O1O1O1O010000O1O1O100O1O1O2N1O2O2M4L3M3M3M2N2N2N2N1O1N2O1O2N1O1N2O2N1O1N2O1N2O1N2O2M2O1N3N2M4L2N4K7Gon``0\", \"size\": [1280, 720]}, {\"counts\": \"SPf71YW1S1^O8L2N2O1N2N2O0O2N1010O01000O0001O00000O2O000O101N10001N10001N101N1O2O1M4M2O2N2N1O1O1O1O1N2O10O0100000O1O10O01O2O0O1O1O2O2M3M3N2M3M2N3M2N1O1O1O1O2N1O1N2O1O1N3N1O0O2O2N1N2O1N2O1N2N3N1N4L3M3M3L5IP_c`0\", \"size\": [1280, 720]}, {\"counts\": \"_Wb7V1fV18K2O1O1O1O1O001O0O2N101N100010O0001O0001OO101N10000O101O001O000O101O0O101N2N2N2N3N1O1O1O1O1O1O1O1O10O010O010O10O0100O100O2N1O2N3M4M2M4L3M2N1O1O1O2N1O1O001N2O1O1O1O1O1N2O1N2O1N2O1N2O1N2O2M2N3M3M3M2N4Jo^c`0\", \"size\": [1280, 720]}, {\"counts\": \"_Xb79lV1P1G7M2N1O1O1O1O001O001N100O20O01O01O01O001O00000O2O000O100O2O000O10001N100O2N2N2N3M2O1N2O1O1O1O1O1O010000O010O10O01O100O100O2N1O3N1N4L4L2O2M2N1O2N1O1O1N2O1O1O1O1N2O1O1N3N001O1N102M2O0O2N2O2M2O2M3M3M2N3L8Efn``0\", \"size\": [1280, 720]}, {\"counts\": \"_`c7e0jV1f0K3L2O1N2O1O1N101N1O2O001O000100O01O000O10001N100O100O2O0O101O0O100O2N100N3N1O2O1O2N1O1O1O2N1O001000000O10O100O001O2O0O1O2O1N3M2O2M3M2N2N3M2N1O1O1O1O1O1O1O1O1N2O1O1N2O1O1O0O2O1N2O1O1N2N2O2M2N3N2M2N3L4L7G[f_`0\", \"size\": [1280, 720]}, {\"counts\": \"_h_7S1fV1;L1O1O2N001O001O001N10001N101O00010O00001O000O2O000O100O2O0O100O1000000O100O2N1O2N101N2O1O2M2O1M31N10O10O010O010O01000O00101N1O2O1N2N2O4K3M2N2N2O0O1O1O1O1O1O001N2O1O1O1O1N2O1O1O0O2O1N2O0O2O1N2O1N3N1N2O1N3M3L4M6GS^^`0\", \"size\": [1280, 720]}, {\"counts\": \"]Xb7^1aV12N2O1O1O1O001O00001O0O2O000O2O00010O000000001O00000O2O000O100O1O10000O2O0O100O1O2N1O2N102N1O1O1O1N110O10O01000O010O010O100O101N2O0O2N5K2N2O2M2N2N2N1O1O001O2N001O1N2O1O001O1O0O2O1N2O1O0O2O1N2O1N2O1N2O1N3M3M2N3M2MSn[`0\", \"size\": [1280, 720]}, {\"counts\": \"^hi7Y1dV16M1N2O1O001O1N101O000O2O0O1O2O01O010O0001O00001N10000O1O1O2O0O10000O100O100O1O2N1O101N2O1O2N1O001O100O010O0100O010O100O101N1O2O1N2N2O2M2N3N1N2N2N1O1O1O1O002N1O001N2O1O1O0O2O1O1O0O2O1O1N2O001N2N2O1N2N2O2M3M2N3L4M7EQVS`0\", \"size\": [1280, 720]}, {\"counts\": \"nPP8>RW1g0C8M2O1O1O1O1O1O00001O0O2O0O101O0000010O0000001N10000O101N10000O10000O101O0O1O1O2N1O2N2O1O1O1O1N2O10O10O10O0100O000100O100O1O101N1O2O2M3M3M3N1N2N1O1O1O1O1O1O1O001O1O1O1O1O001N2O1N2O0O2O1O0O2O1O1N2O1N2N3M2O1N3L4L6JUnl?\", \"size\": [1280, 720]}, {\"counts\": \"aXl7a0SW1f0B7L2O1O1O0010OO2O001O0O2O0O10001O01O01O00001O00001N100O101N10000O10000O101O0O1O2N1O2N2O1O1O1O1O001O1O010O1O010O10O010O100O1O101N1O2N2O1N3M3M2O2M1O2N1O1O1O1O001O1O1O1O1O1O1N101O1O1N101O1N2O001N2N2O1N3M2N3M2N3M2M5K]fP`0\", \"size\": [1280, 720]}, {\"counts\": \"^hi7h0lV1?I5M3N2O001N2O001N101N101N1O1O11O10O01O0001O0O101O0O100O100O101N1O100O101N1O1O2N1O2O1O1O1O1O1O1O1O01000O010000O10000O100O1O2O0O3M3M2O1N3M2O1N1O2N2O0O1N2O1O1O1O1O0O2O1O1O1N2O1O001N2O1O001N102M2N2O1N2N3N1N3M3L4LbfU`0\", \"size\": [1280, 720]}, {\"counts\": \"^`m7e0mV1`0G9M2N2O1O1N2O001N101N1O101O0010O01O00001O00001N100O100O101N100O101O0O2O1N2N2N2O1O1O1O1O001O1O1O10O01000O10O02O0O1O101N1O2N2N3N2M3M3M2N2N2N2N2N1O1O1O1O1O1O1N2O1N2O1O1O1O1N101O1N2O1N2N2O2M2O2M3M2M5Kb^Y`0\", \"size\": [1280, 720]}, {\"counts\": \"Vhn7Q1fV1;M2N3N1O0O2O1O001N1O2N10000010O1O01O001O00000O2O000O100O101N10000O101N101N2N2M3O1O1O1O1O1O1O00100O100O01O010O100O100O1O1O3N1N3M3M2N3M2N2N3N0O2M2O1O1O1O1O1O1O1N2O1O1O1N101O1O0O2O1N2O1N2O2M2N2N3N2M2N4JanV`0\", \"size\": [1280, 720]}, {\"counts\": \"]PP8m0hV1>J4N2O1O1O001N101N100O2N1O2O00010O01O01O01OO2O0O10000O101N10000O101N2O0O1O2O1N2N2O1O1O1O1O001O1O10O010O10O10O010O1O101N1O2N2N2N3N1N3M2N3M3M2N1O1O1O1O1O1O1O1O1N2O1O1O1N101O1O1N2O1N101N3M2O1N2O2M3M3L4M]fU`0\", \"size\": [1280, 720]}, {\"counts\": \"d`R8f0PW1<G8M3N2O0O2O1O001O001O0O2O0O101N1001O01O01O001O01O0O101N1O100O101N101N101N101N1O3M2N3N1O1O1O1O1N2O0010000O10O10O0100O1O2O0O101N1O2N3M3M2N2O3L3M2N1O1O1O1O1O1O2N1O1O1N2O1O1O001N2O1O1N2O1N101N3N1N3M2N3M3M2N5HZ^o?\", \"size\": [1280, 720]}, {\"counts\": \"\\\\hV99`W1;G9J3O2N2N1O100O2N1O1N2N2O2N1O1O001O10O01O1O001O1O001O001O1O1N101O1N2O002M2O1N3M3N1N3M3N1N3N1N2O1O1O1O1O1O100O1O10000O0100O100O100O101N1O101N2N2O1N2N5K4L3N1N3M1O1O1O2N1O1O2N1O1O1O1O2N1O2M2O1N2O1N2O2M3N2M3M3M3M3M3L5Jkmd>\", \"size\": [1280, 720]}, {\"counts\": \"Qie9>TW1b0F8F:K4O1O1O010001N100O010O00010O01N10001O001O0000001O00001O00001N101O001O0O2O2N1N2O1O2M2N2O1N2O1N2O1O1O001O1O001O010O0010O1O100O1O1O100O2N3M4L6K2M3M2N2N1O2N1O1O1O1O1N2O1O1O1O1N2O1N2O1N102M2O1N2O2M2N3M4L3L5JlmZ>\", \"size\": [1280, 720]}, {\"counts\": \"fXR:`0_W19G4L3O1N1O1N2O1N3N0O2O1O001O10O01N100O2O001O00001O000O2O0O101O0O101O0O2N2O2M1O2O2M2O2N1O2M2O1O1O1O2OO01O100O010O10O1O100O2O1N3M2O1N2O1N1O1O100O2N1O1O1O1O1O1O1O1O1O2M2O1O1N2O1O1N2O1N2N2N2O2M2N3M2O2M3M3L4L6Hieo=\", \"size\": [1280, 720]}, {\"counts\": \"PQo:2kW14N2O2N1O1N2O2N2N1O1O1N2O2M2O1O1O1N2O1N2O2M2O1O1N2O1N2O1N2N2O1N3N1N2N3L4L4L3K5N3M3N2O1O0010O02N01O000O100O100O2N1O1O100O2N1O1O1O1N2O1O2N1N3N2M2O1N3N1O1N3M2O1N3M2N4L3M2N2N2O2M5J6K3LjUc=\", \"size\": [1280, 720]}, {\"counts\": \"iPR<?RW1c0J5L3N1O2N1O2N1O0O3N1O001O1O1O1O002N1O101N2N2N5K3M2M3N2N2N2N1O1O1N2O1O001O1N101O001O000000001O0001O1O1O1O001O1O1O1O1O4L=B6K4L2N2M3N2N2N2M2O2M3N2N2M2N4L4K9H5Jdmm<\", \"size\": [1280, 720]}, {\"counts\": \"a`o;7hW12N4M6I5K5L9G3M1O1O1O2N1O001O1O1O1O1N101O001O1O1N101O002M2O2M3M3N2N2N3L2O2M2O1N2O1O1O1O010O0100O10O01O100O1O1O2N2N4L3M2N2N2N1O1O2N2N2N1N2O2N1O2M2O2N1N2O1N3N1N3N1N3M2N3N3L4L4K4LhUe<\", \"size\": [1280, 720]}, {\"counts\": \"_`T<6hW18I9G8I8G3N2M2O1O2N100O1O1N101O1O001O001O1N101O1O001O1N2O2N2M3N2M3N2M3N1O2M2O1O001N2O1O00001O0011N100O1O010O1O1O1O2N1O4L6J3L4M2N1O2N2N2M2O2N2N1N2O2N1N3N1N2O1N3N2M2O2M4L6J3M3Kj]a<\", \"size\": [1280, 720]}, {\"counts\": \"XPR<T1jV18I5M1O1N1000O0100O001O1O00001O1O00001O001O1O00000O2O00000O2O001O1O0O2O2N1N3N2M2O2M2N2O1O1O1O1O1O0010O010O10O00010O01O00100O2N1O3M5L4K4L2N2N2N2N1O1N2O1O1O1O1N2O1O1N3N1O1N101N3N1N2O2M3N1N4L3M3M4K6HhUV<\", \"size\": [1280, 720]}, {\"counts\": \"[XS<4hW1l0XO7H5L2N2N1N2O001N2O1N1010O01O001O00001O0O101O000O2O001N10001N101N2O2M2N3N1M3N3N1O1O1N2O1O1O010O10O1000O10O1O100O1O1O101N1O2O2M6J2N3M2N2N2N1O1O2M2O2N1O1O1N3N1O001N3N1O1N2O1N2O1N2O1N3N2M3N3K4M3L8EmmT<\", \"size\": [1280, 720]}, {\"counts\": \"\\\\hP<[1bV15N2N1O1O100O01O10O00001O00001O000O2O0O10001O00001O0O101O0O2O0O10000O100O100O2O0O100O1O1O2N101N101O1O1O1O1O001O1O010O010O0010O100O1O102M3N3L4L2O0O1O2N2N2OO01O1O1O001O1O1O1O002N0O2O1O001O1O1N101O0O2O1O1N2O0O2N2O1N2O1N3M3M2N2N3L6IR^c;\", \"size\": [1280, 720]}, {\"counts\": \"ohf;;4KRW1P1C3M3O000O1O010O00001N101O0O2N1O101O001O0O10001O0O10001O000O10000O101O0O1O1O101N1O1O1O2O001O1O1N101O100O1O00100O1O001000O01000O2N2O2M3M3N1N100O2N1O1O1O1O1O001O10O01O1O1N2O1O1O001O001O1O001O1N101O1N2O0O2O1N2O1N2N2N2N3N1N2N3M3K9Dh]m;\", \"size\": [1280, 720]}, {\"counts\": \"kh\\\\;a0]W1?C8G4M2N1N2O1O0O2N2O000O20O0001O000O2O000O2O0O101O0O10000O100O100O1O1O1O1O2N100O1000001O00010O1O1O010O1O100O10O010O100O100O1O2O1N1O2O1N2N2N1O100O1O1O1O1O001O1O1O1O1O001O1N2O001O1O001O0O2O1O001N2O0O2O1O1N2N3N1N2N2N2N2N2N3M4JmmY<\", \"size\": [1280, 720]}, {\"counts\": \"bP`:2lW17I6K4M3M2N2O0O100O2N2N2M3N2N2M5L3M2M3N3M1N3N1N2O1N2N101M2O2N101N2O001O1O1O2M3N1N2N2O1O1O10O100O1O2N1O2N1O1O1O1O2N2M3N2N2M3N1O1N2O2N1O1N2O2M2O1N2O1N2O1N2O1N2O1N3N1N2N101N3N0O2O2M3M4L3M3L6HPVm=\", \"size\": [1280, 720]}, {\"counts\": \"bhV9<bW1l0TO;G1O1O2N001O1O001N101N101O000010O000001O000000001N10001N10001O0O101O0O2O000O2M3O1N3N1N2O1O001O1O001O10O010O10O0100O00100O1O1O1O101N3M2N2N3M2N3M2N3M2N2N1O1N2O1N3N1O002M101O1O1O1N2O1N2O1N2O1N2O1N3N1N3M2M4M4Jjmi>\", \"size\": [1280, 720]}, {\"counts\": \"^hV9X1`V19N3N1O1O1O010O1O001O0010OO10001O0000010O001O001O00001N100O101O000O101N10001O0O2O0O2N3N1M3O1N101O1O1O1O10O010O01000O100O1O2N101N2N3M4L3M100O2N2N2N2N1O001O1O1O1O1O1N2O1O1O1O1O1N2O1O1N101N2O1N2O1N2O2M3M2O2M2N2N3KSfh>\", \"size\": [1280, 720]}, {\"counts\": \"n`Z95_W1`0F7J6L5K4L4L3O2N2O1O010O1O001O001O1O001N101O001O00001O010O00001N101O001O001N2O2N1N3N2M2N2O2M2N2N2N2O1N2O1O001O001O010O000100O100O1O100O2N2N3N3L6J6J3M3M3M2N2N1O2N1O2N1O1O1O1N3N1N2O1O1N2O1O1N2O1N3N2M3M3M2N3M4KSVf>\", \"size\": [1280, 720]}, {\"counts\": \"ZPQ:<_W17K5L3L4L5K4L4M3M3N2N2N20O10O10O01O10O01O1O1O001O1N3O0O1O2N2N3M3M2M3N2N2N2N1O2M2O1O1O001N2O1O1N101O1N1O2N101O00010O0O2O01O00100O2N001O1O2N1O0O3eMXkNZ1iT1cN\\\\kNZ1eT1cNakNY1`T1gNakNW1bT1fN`kNW1bT1hN`kNV1bT1gNbkNV1aT1gN`kNX1fU1M2O1O1N4L2O2M2N3M6J4K5KSn_>\", \"size\": [1280, 720]}, {\"counts\": \"YX\\\\:9dW16J5K4M3M4K4M3M4K4M3N2M3M2O2N2N2O1001O0O2O00O100O100O2N2N2N3M4L3M2N2N2N2N2N1O1O1O1O1O1O1O001O1O1N101O001N100O2O0O2O0O1010O0000001O001O1O1O2N2M3N2iMTkNV1oT1cN[kNW1kU1K4M2M3N2N1N2O2N1N3M3M2N6J7HR^X>\", \"size\": [1280, 720]}, {\"counts\": \"Y`b:7eW17I6K5K4L5L3M4K4M3N2N2N2O10000000000O010O1O100O1N2O1O2N2N3M2N4L3M3M3M2N101M2O1O1O1O1N2O001N2O1N2O1O0O2O0O1O2O010O001O01O00O1001O1O1O1O1O1O1O1^MUkNm1lT1QNbkNb1^T1\\\\NikN^1YT1_NmkN]1TT1dNmkNY1TT1fNokNW1QT1jNPlNS1ST1lNnkNQ1TT1oNlkNP1UT1POkkNn0WT1QOkkNm0VT1ROlkNk0WT1SOkkNk0VT1TOkkNj0ZT1ROhkNk0hU1N6J5J:EmmP>\", \"size\": [1280, 720]}, {\"counts\": \"nWa:6hW17H;H;D3N2N1O1O100O1O1O1O1O1O001O1O1O000O2O1O1N2O1O1N2O1N3N1N2N4M2M4L4M2M2O2M2O1N2O0O2O1O1O1001N2N101N100O100O2N1O2O1N3M2N3M3M2N2N2N2N2N2M3N2N1O2M2O1N2O2N1N3N1N3N1N3M3M4L3M4L4J9EVVR>\", \"size\": [1280, 720]}, {\"counts\": \"noZ:1mW15L3M3N2N2N2M3N100O2N2M4M3M3M2N1O2N1O001O1O1O1O1O1N101N2N101N2O1N2O2N1N3N2M3N3L3N3L2O1N2O1O1000O2O000O100O2O0O1O1O1O1O2N1O2N1O2N1O2N1N2O1O2N1N2O2M3M2O1N2O2N1N2N3N1N3M2O2M3M3M4L4L3L5JanU>\", \"size\": [1280, 720]}, {\"counts\": \"`o_:;dW14L3N2N3M4L4M7I3L2N1O2N100O1O1N2O1O001O1O1O1O0O2O1O1O1N2O2M3M3N2M3N2N2N2M2O1N2O1O1O001O1O10O1O2O0O2N1O1O2N1O1O2N1O2N2N2N2M3N2N1O2N1O2M3N1N3N2M2O1N3N1O1N3N2M2O2M3M3M4L4L4K5KcVW>\", \"size\": [1280, 720]}, {\"counts\": \"hoU:3iW19J4M2N2N2N3N3L7J5J5K2N1O1O1O1O1O1N2O1O1O1N2O001O1N2N2O2M2N3M3N2M3N3M101M2O1O1N1O2O1N101O0100O1O2N3M1O2N2N2N1O2N2N2N2N2N3L3N2N2N2N2N2M2O2N1N3N2M2O2M3N1O2M3N1N3M3M4L4L4L5I\\\\^b>\", \"size\": [1280, 720]}, {\"counts\": \"QaX:1SW1R1C9L4N1O1O1O1O1N101N1O2O0010O0100O00001O0O2O0O101O1O1N2O1N2O2M3N2N2M3N1N3N1N2N2O001O001O001O001O000100O1O100O1O2N1O2N1O2N2N2N3M2N3M:F3M2M3N2N1O1N3N1O2N2M2O1N2O1O1N3N2M3M4L4L4L4JTnZ>\", \"size\": [1280, 720]}, {\"counts\": \"h`n9=\\\\W1:H6J7J5L4M3N2O1O10O0100O001O1O10OO2O0O20O01O002N001O001O1O1O1O2N3L3N3M2N2M2O1N3N1O1N2O1N1O2O1N2O001O001O001O0001O100O1O2N1O2O0O3M4L4L8H5K4L2N3L3N2N2M2O1O2M2O1O2M2O1N2O1N3N2M3M3M4L4K6JkUa>\", \"size\": [1280, 720]}, {\"counts\": \"bhe9<^W1:F7L5M3N1O1O1N2O1N2O1O1N2O0010O0100O0O2O1O001O001O1N2O1O2N1N3N2N2N3L4L3N2M3L3O1N2O1O1O1O1N2O1O1N1010O010O2N100O100O2N1O101N2N3M3M4L5K4L2N3M1O2N2N2M3N2N1O2M2O1N3N2M2O1N2O3L3M4L3M4L4KmUk>\", \"size\": [1280, 720]}, {\"counts\": \"Rh[97gW14O1N100O101N2N3M3M3M1O2N2N1N2O1O1N2O1O1N2O1O1N101N2N2N2N2N2N3N2N1O1N3N1M4M3M3M3M4L3L5M10001O01N2O1O001O1N2O0O101N101N101N2O0O1O1O1O1O2N1O2N1O1O2N1O2N1N3N1O1N3N2M2N3N1N2O2M3M3M3M3M4L5J5K6HR^Q?\", \"size\": [1280, 720]}, {\"counts\": \"`hS83iW19J6J2O1N3N1O001O1N101O1N101N101N1O2N101O001O0001O01O00000000001O000O2O001N101N2O0O2O1N1O2N2N2N2N1O2O1N2O1N2O1N2O2N1O1N2O1O1O1O11OO10O10O001000O100O100O100O1O2N2O1N1O1O1O100O1O1O1O1O100O1O2N1O1O1O1N3N1O1O1O1N101O2M2O1N3M2N3M3M3M2N3L4M3K^^[?\", \"size\": [1280, 720]}, {\"counts\": \"eRV5`0VW1<E:J6L4M2N2N3M3N1N3N1O2O0O1O2O00000001O001O00010O000000000000000000000O01O11O00001O0000000000O10000O10000O10000O100O2N1O101N1O2N1N3O1O0O2O1O1O000010O100O1O2O1N5K4L1O2O0O1O1O100O00101N1O1O001O1O1O1O00100O1O1O1O001O001O001O1N10001O001O0O2O1O001N2O0O2O0O2O0O2O1N2O1N2M3N2N2N2M4M3JSm^a0\", \"size\": [1280, 720]}, {\"counts\": \"hjT5e0mV1a0J5J5K5N1O2N2O0O2O0O101O000000001O000000000000000000000O100O100O11O001O000O1001O000O100O1O100O010O1O10O0100O1O1O1O1O1O2M200000001O01O01000O100O2N2O1N101N2O0O10O02OO01O001O001O1O010O1O1O1O001O0000100O001O1O001O001O001O001N101O001O001O0O101O00001N101N2O0O2N101N2O0O2N2N2N2O1N2N3L3N4Iod]a0\", \"size\": [1280, 720]}, {\"counts\": \"USV56TW1n0E5K5J5N2N1O2O001N101O00001O0000000O1000001O0O100O001O1000010O0001O000O100000O100O1O100O100O1O1O100O1O001O1N2N2O1O100O1001O001O010O100O100O10001N2N101N2N010O1O100O1O001O010O001O1O10O01O00001O010O1N110O001O001O1O001O001N10001O00001O0O2O001N101O0O101O0O2O0O2N101N2N2O1N2N101M3N2M4L5Kk\\\\\\\\a0\", \"size\": [1280, 720]}, {\"counts\": \"cj^5m0PW19H4J6L3O1N1O2N1010O01O0010O0001O0O010000000O2O000O1O1O100001O000000000000O10O0100O100O100O1O00100O1O1O0O2O1N2O1O100O1000000010O00100O1O101N2O0O2O0O1O100O100O1O00100O001O0000100O001O1O00010O001O001O0010O01O1O001O000O2O00001O1O000O2O001O000O2O001N101N101N101N101N1O2N2N2N2N2O1M3N3L4LjdSa0\", \"size\": [1280, 720]}, {\"counts\": \"hRj5;^W1g0^O6G7L3N3N0O2O001O0010O01O00001O00000000000O10000O1O1O1O100001O00000000O10O0100O100O100O10O01O100O1O1O1O0O2N2O1O1O10000O11O000010O0100O1O2O1N2N101N011N10O01O100O0000100O00001O1O001O10O0001O001O00100O001O001O001O1O000O2O00001O001N10001O001O0O1O2O0O2O0O2O0O2O0O2N101N2N2N2N2N2N2N3L4Kl\\\\h`0\", \"size\": [1280, 720]}, {\"counts\": \"cZU6<`W1i0XO6H6L4O1O1O001O0O20O0001O01O0001O000000000O1000000O100O1O1O1O100001OO01000O100O100O1O100O1O1O1O010O1O1O1O0O2O1N2O1000000000001O01O100O100O101N2O0O2N100O1O100O1O1O010O1O001O00100O1O001O00001O001O1O010N101O001O001O001O001O0O101O0O101O0O2O000O2O001N101N2O0O2O0O2O0O2N2O1N2N2M3N2N4J6ImT]`0\", \"size\": [1280, 720]}, {\"counts\": \"eZZ6h0QW1:J5L3M3N2N2O1O00001O0010O1O10O01O00001O000000000000O101OO1000O1O1O100O1O10000O100O100O1O1O010O1O100O1O1O001O1O1O1N2N2O1O1O100O1O11O010O00100O101N101N2O0O1O100O010O10O010O01O001O001O1O10O01O0000001O001O001O001O001O1O00001O1O00001O0O101O001O00001N101O0O101N2O0O2O0O2O1N1O2N2O1N2N1O2M4M3L4KP]T`0\", \"size\": [1280, 720]}, {\"counts\": \"giZ76eW1:G6L4M2N3L3N2N2O1O1O1N101O1O1O001O001001N101O2M2O1N100O010O010O1O01O001O0O2O00000O101O010O010O001O1O1O1O1N2O1O1N2O1N2O1N3M2N2O2M2N2N3M3M2N3M2N2N2O1N2O1O2M2O1O1O100O100001N102N1O001N2O1N2O1O0O10001N1O101N1O100O101N100O2N1O1O1O1O1O1O1O1O1O1O1N2O1O2N2M2O1N2O1O1N3M3M4L3L4N2L4L4K8E[TZ?\", \"size\": [1280, 720]}, {\"counts\": \"gSY;b0ZW1:H7J5K4M2M3N1O1N2O1O1N2N2N2O2N1O100O100O10O01O10OO2O0O101N1O2O0O2O1N101N101N100O2N100O2N1O1O2M2O1O2N1O10000O2O0000001O001O010O1000O100001N100000000000O10000000000O10001O0O101N2O0O2O1N2O1N2O0O10001N1O101N1O100O100O001O1O1O1O1O1O001O1O1O1O1O1O1O001O1O1N2O1O1N2O001N2O1O1N2O1N2O2M2N2O2M2N2M3N3N1N2M3N3L5HYjl:\", \"size\": [1280, 720]}, {\"counts\": \"QdY<?_W1h0XO;F3M3M2O2N1O100O1001O01N01000O0O2O1O0O2O00001N101N2O0O2O000O101N101N1O2O1N2N2N1O3M2M4N101N2N1N4M2M3M3M2O2M3O000010O2N1N3N2N001O1N2O1N101OO0101N2N2O1N100O2N101N101N1O2N1O1O1O1O2N2N1O1O2N1O1N2O1O2N1O1N2O1N2O1N3N1O2N1N2O2M2O1N3M2O2M3N2L5L2N3L5K3N8DYQn:\", \"size\": [1280, 720]}, {\"counts\": \"eSm;k0SW1`0A4L3M2N3M3M2O1O1O11O00O01000O00100N2O1O000O2O00001N101O001N101N100O2O1O0O2O1N2N2N3L3N3M2O2N2N2N2M4M1N3N2M3N1N2O1001O001O1O001N101O0O102N1N2O1N2O1N2O1N2O1N2N2O0O1O2N1O1O2O0O1O1O1O2N1O1O2N1O1N3N1O1O1O2M2O1N3N1O1N2O1O2M2O1N3M2N3M3N2M3M4L2N4L3M4K9Dfi[;\", \"size\": [1280, 720]}, {\"counts\": \"`k\\\\;b0\\\\W1`0@8I4L2N3N1N3M200O10000O100O001O1O001O1N101O001O000O2O00000O2O001N101O0O2O1N2O1N2N3M2N3N1N4L4M2N2N2N1N3M2O2M2O1O1O1001O002N000O100O1000O10O2O0O2O0O2O1N2N2O2M2N2N2O1N2N2N2N1O2N1O1O2N1O2N1N2O1O2N1O1N2O1O1N3N1N2O2N1N2O2M2N3N1N3N3L3L4M4L3M3L4L8GPbn;\", \"size\": [1280, 720]}, {\"counts\": \"WcQ;?^W1k0WO5K3M2O2M2N3N1O1O0100000000O10O1O001O1O001O001N101O000O2O001N1O2N101N101O1N2O1N3M2N2M4M4M2N2N3M2N1O2M2O2M2O1N2O2O0001N101O000O100000001N101N2N2O1N3M2O2M3M2N2O1N2N2N1O1O2N1O1O1O2N1O2N1N2O1O2N1O1O2M2O1O1N2O2N1N2N3N1N2N3N2M3M3M4L3M4L3L3M7HXZ\\\\<\", \"size\": [1280, 720]}, {\"counts\": \"dkm:g0gV1g0J3N2M2N2O2N1N2O1N2O1O1O001000O1000O001O010O001O1O0O101N101N101N101O001O1N3N1O1O1N3N1N3M4K4M3M3N2N1O1O1O2N1O1N3N1O100O10O1000001N10000O10001N2O1N2N2O2M3M3M3M3N1N2N2N2N1O1O1O2N1O2N1O2N1N2O2N1O1O1O1N2O2N1O1N3M2O2M2N2O2M3N2M3M4L4L3M3M3L4L:CVR`<\", \"size\": [1280, 720]}, {\"counts\": \"XkR;X1fV16K4M1N3N1N3M2O1N2N110O10O1000000O01O1O1O010N101O00001N101O0O2O0O101O0O2O1O1N2O1N2N2N3M3M3M3N2N3L2O2N1O1N3N1N2O1O2N100O10O2O00000O101O0O2N101N2O1N2N3N3L3N1N2N3N1N1O2N1O2N1O1O2N1O1O1O1O2M2O1O2N1O1N2O1N3N1O1N3N1O1N3N1N3M2O2M3M4L4L3M3M2N4K7GVR[<\", \"size\": [1280, 720]}, {\"counts\": \"akW;4iW1U1mN5L2N1N2O1N2O2M2N2O1N110O100O0100O01O001O0O2O1O00001N10000O101O0O2O001N2O0O101N2N2N2N3M3N1N3N3L3N2N2M2O1O2M2O1O1O1000000001O0O01000O101N100O101N2O1O2M3M3M2O1N3M2N2N1O2N2N1O1O1O1O1O1O2N1O1O1O2M2O1O1O1O1O2M2O1N2O1N2O2M2N3N2M3M3M3M3M3M3M3L5J:FPbS<\", \"size\": [1280, 720]}, {\"counts\": \"fS^;;dW1b0^O:G3L2N3M2N2N2N2O1O10000O10O01O1O001O001N101N10001O000O2O001O0O2O000O101N1O2N1O2M4M2N2O1N3N2N2N1O2M3M3N2N2N100O10001OO10000000000O10000O101N102M2N2O1N2N2O1N2N2N2O0O2N1O1O1O1O1O2N1O1O2N1O1N2O1O1O1O1O1N3N1O1N2O1O1O2M2N2O2M2N2O2L5L3M3N2M3L4L6J5JQRl;\", \"size\": [1280, 720]}, {\"counts\": \"jce;a0]W1<E7I4M2M3M2M4M1O2O2N001O100O1O010O001N101O000O2O001O0O101N2O00000O2O000O2O0O2N1O2M4M2N2O2M2O2N1O3L2N3M3N2M201N10000000000O1000O2O0O10000O2O000O3M2O1N2O1N2N2N2O1N2N2N1O2N100N2O1O100O1O2M2O1O1O1O1O1N3N1O1O1N2O1O1O2M2N2O1N3M2O2M3M3M2N4L3M3L4L6J6HQbd;\", \"size\": [1280, 720]}, {\"counts\": \"TdV;g0WW1:H4L2N1O1O1N2O1O1O001N1O2N2N1N3O001N110O001O00001N2O0O100O2O0O100O1O100O2O0O100O1O1O2N1O1N3N1O2N1O2O001N2O1N2N2O2M2O2O001O001N1000000000O100000O1000O2O0O2O1N101N2O1N1O2O000O3N0O2N1O100O1O1O1O1O1O2N10O01O1N200O1N2O1O2N1O1O001N2O1O2N1N101N2O1N2O2N1N3M2N2N3M2N3M3M3L3N4J8GPZc;\", \"size\": [1280, 720]}, {\"counts\": \"]Ub9b0RW1`0H6L4K5N1N2O100O100O010O0100O1O1O100O1O1O1O1O10O01O001O0001O0000000000000O1000000O100O1O1O1N2N101000O100000O001O1O1O1N1O2O1O001N2O1N2O1N2L4L4N2O100O101O0O100010O0001O01O0010O010O010000O100O2N100O100O2O0O010O00100O1O1O1O000010O00010O01O001O01O01O00001O00001O001O001O001O00001O000O2O0O2O001O00001N101O001N2O1O0O2O0O2N2O0O2N2N2N2N2N2N2N2N3L4K7IYjj;\", \"size\": [1280, 720]}, {\"counts\": \"lU]9;_W19J6J5K5J6M3O0O100010O0O2O1O1O0O2O000O100O010O010O0100O001O001O001O0O101N1O1O2O0O1O110O01O010O0001O001N1O101N1000001N101N100O2N2N2N1O2N1N3N2N1O2N2L4K4O2N2N2N103L1O100O100O1000000001O002N2N2N3M2N2N2N1O2N1O1O001O1O1O1O1O1O001O1O1O1O001O1O1O001O1O001O1O001O1O1O1O1O001O1O001O001O1O001O1O1O1O1O1O001O1O1O1O1O1O2N1O1O1O1O2N1O1O1O1O1O2N1O1O2N3M4L9GVho;\", \"size\": [1280, 720]}, {\"counts\": \"m_V=3kW12N2O1O100O100O1N2N20000002N1O1O001O001O001O1O1O1O1O1OS`j=\", \"size\": [1280, 720]}, null, null, null, null, null, null, null], \"bboxes\": [[204.0, 953.0, 70.0, 85.0], [206.0, 960.0, 70.0, 85.0], [205.0, 962.0, 75.0, 87.0], [199.0, 965.0, 80.0, 92.0], [200.0, 986.0, 72.0, 100.0], [195.0, 1001.0, 76.0, 99.0], [198.0, 1006.0, 70.0, 100.0], [202.0, 1007.0, 77.0, 103.0], [203.0, 1010.0, 78.0, 102.0], [199.0, 1009.0, 78.0, 104.0], [197.0, 1006.0, 77.0, 106.0], [197.0, 1007.0, 77.0, 105.0], [201.0, 1008.0, 77.0, 107.0], [203.0, 1007.0, 80.0, 108.0], [195.0, 1005.0, 70.0, 115.0], [181.0, 1017.0, 79.0, 103.0], [180.0, 1016.0, 79.0, 102.0], [178.0, 1013.0, 79.0, 102.0], [177.0, 1012.0, 79.0, 101.0], [179.0, 1008.0, 77.0, 101.0], [185.0, 995.0, 72.0, 108.0], [198.0, 985.0, 82.0, 101.0], [203.0, 986.0, 77.0, 90.0], [205.0, 982.0, 78.0, 90.0], [209.0, 976.0, 78.0, 95.0], [213.0, 983.0, 79.0, 94.0], [214.0, 987.0, 81.0, 95.0], [219.0, 989.0, 80.0, 94.0], [221.0, 992.0, 80.0, 92.0], [222.0, 995.0, 81.0, 88.0], [225.0, 985.0, 81.0, 93.0], [227.0, 985.0, 81.0, 95.0], [228.0, 992.0, 81.0, 93.0], [222.0, 991.0, 86.0, 98.0], [220.0, 990.0, 94.0, 101.0], [220.0, 1014.0, 79.0, 104.0], [220.0, 1021.0, 80.0, 97.0], [221.0, 1015.0, 78.0, 100.0], [210.0, 1019.0, 90.0, 102.0], [208.0, 1032.0, 101.0, 92.0], [219.0, 1031.0, 100.0, 95.0], [228.0, 1030.0, 100.0, 95.0], [235.0, 1029.0, 100.0, 94.0], [254.0, 981.0, 101.0, 119.0], [320.0, 836.0, 55.0, 109.0], [344.0, 657.0, 59.0, 122.0], [330.0, 667.0, 69.0, 116.0], [291.0, 814.0, 61.0, 109.0], [260.0, 944.0, 70.0, 119.0], [242.0, 993.0, 83.0, 88.0], [243.0, 991.0, 83.0, 92.0], [248.0, 989.0, 83.0, 96.0], [252.0, 994.0, 82.0, 96.0], [253.0, 997.0, 83.0, 96.0], [252.0, 1000.0, 85.0, 97.0], [253.0, 1002.0, 84.0, 95.0], [253.0, 1000.0, 83.0, 94.0], [277.0, 962.0, 72.0, 109.0], [312.0, 821.0, 44.0, 123.0], [330.0, 731.0, 44.0, 112.0], [294.0, 778.0, 49.0, 131.0], [245.0, 920.0, 63.0, 116.0], [216.0, 983.0, 93.0, 100.0], [212.0, 989.0, 92.0, 93.0], [209.0, 987.0, 93.0, 95.0], [209.0, 986.0, 94.0, 97.0], [210.0, 990.0, 94.0, 94.0], [209.0, 989.0, 95.0, 95.0], [208.0, 989.0, 95.0, 94.0], [209.0, 989.0, 94.0, 94.0], [211.0, 987.0, 93.0, 95.0], [212.0, 985.0, 92.0, 95.0], [210.0, 987.0, 93.0, 95.0], [207.0, 987.0, 93.0, 95.0], [203.0, 985.0, 93.0, 95.0], [196.0, 986.0, 98.0, 96.0], [193.0, 992.0, 101.0, 92.0], [193.0, 997.0, 103.0, 95.0], [194.0, 1007.0, 103.0, 95.0], [191.0, 1017.0, 107.0, 94.0], [193.0, 1022.0, 107.0, 92.0], [199.0, 1020.0, 108.0, 92.0], [204.0, 1018.0, 108.0, 90.0], [201.0, 1013.0, 108.0, 89.0], [199.0, 1004.0, 106.0, 94.0], [202.0, 1003.0, 100.0, 93.0], [203.0, 1007.0, 101.0, 91.0], [204.0, 1010.0, 101.0, 90.0], [206.0, 1011.0, 104.0, 93.0], [235.0, 1013.0, 109.0, 107.0], [247.0, 1026.0, 105.0, 93.0], [257.0, 1031.0, 104.0, 92.0], [280.0, 1027.0, 91.0, 100.0], [308.0, 1028.0, 80.0, 97.0], [306.0, 1029.0, 89.0, 94.0], [310.0, 1026.0, 88.0, 96.0], [308.0, 1027.0, 99.0, 94.0], [309.0, 1017.0, 99.0, 99.0], [307.0, 1022.0, 115.0, 91.0], [299.0, 1035.0, 115.0, 89.0], [291.0, 1032.0, 113.0, 88.0], [268.0, 1024.0, 95.0, 93.0], [235.0, 1028.0, 105.0, 94.0], [235.0, 1021.0, 106.0, 93.0], [238.0, 1008.0, 105.0, 104.0], [256.0, 1002.0, 92.0, 109.0], [265.0, 1000.0, 89.0, 112.0], [270.0, 1004.0, 90.0, 109.0], [269.0, 1005.0, 90.0, 103.0], [264.0, 1007.0, 92.0, 91.0], [268.0, 1001.0, 87.0, 93.0], [260.0, 1002.0, 86.0, 97.0], [262.0, 1010.0, 90.0, 98.0], [254.0, 1016.0, 93.0, 102.0], [247.0, 1014.0, 92.0, 104.0], [239.0, 1007.0, 95.0, 106.0], [207.0, 1013.0, 119.0, 93.0], [132.0, 1057.0, 140.0, 93.0], [131.0, 1066.0, 142.0, 89.0], [132.0, 1068.0, 142.0, 91.0], [139.0, 1073.0, 142.0, 88.0], [148.0, 1072.0, 142.0, 88.0], [157.0, 1069.0, 142.0, 89.0], [161.0, 1069.0, 145.0, 85.0], [187.0, 1061.0, 140.0, 116.0], [288.0, 1113.0, 152.0, 133.0], [314.0, 1133.0, 125.0, 139.0], [304.0, 1121.0, 124.0, 137.0], [291.0, 1116.0, 122.0, 131.0], [282.0, 1109.0, 120.0, 132.0], [279.0, 1109.0, 120.0, 131.0], [283.0, 1112.0, 120.0, 131.0], [287.0, 1117.0, 122.0, 127.0], [292.0, 1121.0, 123.0, 125.0], [298.0, 1120.0, 123.0, 127.0], [286.0, 1124.0, 136.0, 125.0], [244.0, 1148.0, 172.0, 99.0], [240.0, 1179.0, 172.0, 101.0], [337.0, 1265.0, 28.0, 15.0], null, null, null, null, null, null, null], \"areas\": [3834.0, 3851.0, 4166.0, 4596.0, 4709.0, 4842.0, 4657.0, 5057.0, 5037.0, 5087.0, 5227.0, 5275.0, 5290.0, 5753.0, 5704.0, 5163.0, 5144.0, 5162.0, 5119.0, 5069.0, 5299.0, 5195.0, 4540.0, 4587.0, 4676.0, 4682.0, 4763.0, 4827.0, 4680.0, 4526.0, 4650.0, 4684.0, 4639.0, 5077.0, 5511.0, 5407.0, 5019.0, 5179.0, 5716.0, 5907.0, 5957.0, 6056.0, 5952.0, 7992.0, 4574.0, 3654.0, 4403.0, 4208.0, 6075.0, 4775.0, 5029.0, 5371.0, 5303.0, 5374.0, 5490.0, 5334.0, 5316.0, 6027.0, 3756.0, 3167.0, 4040.0, 5410.0, 5809.0, 5620.0, 5787.0, 5878.0, 5714.0, 5737.0, 5725.0, 5774.0, 5671.0, 5676.0, 5667.0, 5600.0, 5655.0, 5825.0, 5999.0, 6130.0, 6153.0, 6208.0, 6288.0, 6294.0, 6128.0, 6192.0, 6162.0, 5979.0, 5866.0, 5918.0, 5981.0, 6784.0, 6265.0, 5698.0, 4723.0, 5382.0, 5167.0, 5400.0, 5991.0, 6051.0, 6679.0, 6323.0, 6406.0, 5163.0, 6480.0, 6354.0, 6733.0, 6483.0, 6319.0, 6412.0, 5478.0, 4708.0, 5007.0, 5036.0, 5783.0, 5961.0, 5778.0, 5219.0, 6214.0, 8389.0, 8315.0, 8322.0, 8173.0, 8140.0, 8246.0, 7800.0, 8211.0, 11407.0, 9720.0, 9657.0, 9255.0, 9194.0, 9343.0, 9309.0, 9102.0, 8876.0, 8909.0, 9478.0, 10358.0, 9907.0, 263.0, null, null, null, null, null, null, null], \"iscrowd\": 0, \"video_id\": 0, \"height\": 1280, \"width\": 720, \"category_id\": 49272, \"noun_phrase\": \"air jordan\"}, {\"id\": 1444, \"segmentations\": [{\"counts\": \"f`P4b0ZW15M3L5K3N2M4L3M3M3L4L4M4K4F:J6L4M300001OO1000O10O10000O10000O10O100O010O100O1O2O0O2M2O1N2N2M4J5L4M2O2N2N10O0O11O2N2N3XOQjNXOWV1`0l0DaQfe0\", \"size\": [1280, 720]}, {\"counts\": \"l`P4d0XW15L4M3L3M3M4L3M3M4L3K5L4H8F:M3M3O100000000000O01000000O1000O10O100O10O100O2O0O1O1O1O2N1N2N2N2M3K5L4N2N1N2O00010O2O1N3L5YOkiNYO_V1:nQfe0\", \"size\": [1280, 720]}, {\"counts\": \"PaP4b0[W15K4K5M3L3L5M2M3M4K4M3L4K5G9I7M3O1O1000000000000O10O010000O1000O01O0100O100O2O0O1O1O1O2N1N2O1M3M3K5L4N1N2O0000010O3L3N4J8TOQ1_OTide0\", \"size\": [1280, 720]}, {\"counts\": \"ZQn34dW1`0D6K4M3L4L3L4N2M3M3L5K4L4J6E;K5N200O100000000O01000O10000O10O1000O10O011N1O2O0O100O1O1O1N3N1N2N2L4J5M4N2M2O1O01O1O02N4L4K8lNoYge0\", \"size\": [1280, 720]}, {\"counts\": \"Vak3b0[W15K5K4L4L3M3M3M3M4L3M3L4K5L4G9I7M3O100000000O10000O1000O01000O10O10O1000000O1O101N1O1O1O1N2N2O1N2M3K5L3N3N1O1O1N101O101O2M6UOjiN@aV1Lkahe0\", \"size\": [1280, 720]}, {\"counts\": \"ZYj3b0ZW16K4L4L4M2L5M2M3L4M3M3M3L4K5J6H8L4N2O1000000000000O1000O0100000O0100O1000O10O02N100O1O1O1N2O1O1M4L3M3L3M4M2O2N1O0O20O01O102L6VOjiNB^V1Nkahe0\", \"size\": [1280, 720]}, {\"counts\": \"^Yj3<_W1?B7H6J6I7J5I8I6F:D=J5O100O10000000000000000O01000O10O10O1000O100O101N100O1O100O1N101O1O1O0O2N1O00ON3B?L4L5N2N3K7K6J:Fa0^O6LWXle0\", \"size\": [1280, 720]}, {\"counts\": \"kYV3;^W1?A;F8G8J6E;VOj0K4N2O2N1000000001O00001O000000O1000000O10000O01000O0100O100O2O001N10000O1O1O1O001M3N1M3K6ZOd0N2O1O4H7DeiNmNbV1g0d0Elhbf0\", \"size\": [1280, 720]}, {\"counts\": \"]XQ31jW1i0ghN_OfU1S2\\\\O7I4M2N2O1N2O0O1O1O01O000000O2O0O10O10O1000000O10000O1O10O1000000O10001O0O1000O01000O10O01O01O002N4oMfkN5^T1VO[lNa0mU1Clmmf0\", \"size\": [1280, 720]}, {\"counts\": \"]Xl25dW1P2TN<F6K2N2N2O0O1O100O00001O00000000000O10000O1000000O100O1O010O10000001N2O00000O10O010O10O010O00100O2N3lMnkN4XT1UOalNd0dU1GlmRg0\", \"size\": [1280, 720]}, {\"counts\": \"[Xg2;^W1l0chNYOUV1n1[O6K4L2O1N2O0O1O010N101O0000000O100O1000000O1000000O010O100O1000O2O00001N100000O010000O010O1O01O03M4aMVlN`0nS1VO`lNb0hU1EkmWg0\", \"size\": [1280, 720]}, {\"counts\": \"]Xg2<]W1j1ZN:H5L3M3M2O0O100O1O1O0000001O000O10000O1000000O10000O010O1O100O11O0O101O0000000O100O10000O0001O0011M4M3eMVlN;nS1YOclN=\\\\SYg0\", \"size\": [1280, 720]}, {\"counts\": \"\\\\Xl2m1PV1b0_O6L3L3O0O100O100O001O01O01O000O1000000O100O10000O100O1O10O0100000O10001O000O10000O10O10O010O010O2N2N3hMSlN7RT1YOblN>X[Ug0\", \"size\": [1280, 720]}, {\"counts\": \"^hn27eW1i0YOZ1hN8H3M3M2O0O1O100O1O1O00001O000O100O100O10000O100O100O010O100000O101O001O00O010000O10O10O01000O1O2N3hMdkNg0`T1iN\\\\lNf0gU1G7IdUof0\", \"size\": [1280, 720]}, {\"counts\": \"Zhn2h0VW1\\\\1eN>D2M3M2N2O0O1O001O1O001O0000000O100O1000000O100O1O100O10O1000000O2O001O000O10O10O01000O10O10O2N2N4_MSlNh0kU1F9GgeQg0\", \"size\": [1280, 720]}, {\"counts\": \"\\\\Xl2k0RW1\\\\1fN;E5L2N101N100O10O01O001O00000O2O000O10O010000O10000O1O1O1000O1000000O2O0000000O10O10O10O10O001O2O1RNUkNc0PU1jNVlN?lU1H9FemRg0\", \"size\": [1280, 720]}, {\"counts\": \"\\\\hi2b0[W1_1bN:G8I2N3M2N101N100O00001O0000001N10000O100O10000O10O0100O10000000O10001O0O1000O1000O01000O00010O2N1O4fMSlN9ST1ZO\\\\lN>jU1Gh]Ug0\", \"size\": [1280, 720]}, {\"counts\": \"]bY2:`W19J4M3L4N2M2O2M2N2N3N1N2N3M2N2N2M3M3L5N0O2N2M3M3N2K5K5I7N2N2O1000000000O01000O10000O010O10O01O1O010O2M3N2N2M2N3M2M3L4L3N3O1O2N001O0010O0O10O10010N101O1O2N1N6J;`NZjNOh]ff0\", \"size\": [1280, 720]}, {\"counts\": \"lah2?[W18L3L4M3M2N2M4M2N2M3M4M2M3L4M3M3M3L4M3M3M3H8K5N20O0100000O10O1000O1000O01O10O01O100O1O1O2M200N2O1O1N2N2N2M3M3L4M3M3N1001O000O10000010N200N3N4L7H<C`0A8Gh_\\\\f0\", \"size\": [1280, 720]}, {\"counts\": \"jii2:_W19J5L4M3M3M2N2N3N1N2M3N2N2M3M3N2M4M1N3M3M3N2L4K5L4N2O1O10O1000000O01000O10O0100O001O1O1O1O1O2N1O1O1N2O1N2N2M3L4M201O1O001O1N11N1N2001O0O2O1O2M4L7JP1nNi_\\\\f0\", \"size\": [1280, 720]}, {\"counts\": \"aQk2<^W19K3L4N2L3N3M2O1N3L3N2N2N2M3M3M3N2M3M3M3M3M3K5I7N2O100O01000O10O1000O0100O010O10O01O1O1O101M2O1O1O2N1N2N2N2M3M3N2N2N101O000000O2OO101O1O1O1N5L6Ij0VO;DlW[f0\", \"size\": [1280, 720]}, {\"counts\": \"_Yl29`W1:J4L4M3M2N3N1N2M3N2N2N3L3M3M3N2M3N2M3M3M3M3L4K5K5N200O1000O1000O010000O1000O0010O1O1O1O1O1O2N1O2M2O1N2N2M3M3M3N2O001O1O0001N10O011O001N2O2N3L9H>Bc0\\\\OPPZf0\", \"size\": [1280, 720]}, {\"counts\": \"_in23eW1<G7L3M3L4M2N2N2N3M2N2N2N2N2N2L5M2N1O2L4N2M3N2M3L4N2M3O1O01000001O0O10O0100O100O1O10O01O1O1O1O2N1N2O1N1O2M3M2O2N2O0O2O1O001O001OO0010000001O2M2O5J8iNeiN9\\\\gXf0\", \"size\": [1280, 720]}, {\"counts\": \"WaR3:_W19K5L3M3L3N2O2M2N2N3M2N2N3M2L4M3N2M3N2M3L4M3N2J5M4M3O10O010000000O10O0100O100O00100O1O1O1O2N2N1N2O1N2O1M3N2M2N3N2O1O1O001O00000O1000O10O101O2N2M6K4K<jNUiN`0cW1BYXQf0\", \"size\": [1280, 720]}, {\"counts\": \"XYg2>_W14L5L3M2N3M2N2N3N1N2N2N2O1N2M3M3M3M3M3N2N2N2M3N1O2M3N2K5I6M4O1O010O10O010O010O10O01O2N1O1O2N1O1N3O0O1O001N3N1O1N2N2N2N1O2N2N101OO1O1000000001O1O00100O1O1O2O1N2N4K4M3M3M4L3M4L4L3L4L5K9Fa`he0\", \"size\": [1280, 720]}, {\"counts\": \"dam2=^W17K5K4M3M2N3M2N2N3M2N2M3N2N2M3M3N2M4L3M3N2M3M3M3K5M2O2N2000O1000000O10000O01000O001O1O001O1O1N2O2N1O2M2N2N2N2L3N3O1O1N2O100O0O10000O1000001O1O1O2M5K7SOeiNHTgXf0\", \"size\": [1280, 720]}, {\"counts\": \"mYQ3=^W16K6L3M3L3N2N3M2N2N2M3N3M2M3M3M3N2M3N2M3N2L4M3J6K4O2N20000000O0100O10O100O100O00100O10O01O2M2O2N1N2O1N3M2N2M3M2N4M2O1O001O000O11O0O02O001O002N2M5K6kNdiNEO`0dW1B^gSf0\", \"size\": [1280, 720]}, {\"counts\": \"VRU37aW1;I5M3M3M3M2M3N2N3L3N2M3M3L4N2M3N2M3K5N2L4M3K5I7M300O10O10O100O10000O0100O10O0100O002O0O1O2M2O2N1O1O1N2N2N3L3M4L201N2O1N100O10O10000010N2O2N2M3M7Jn0QO9F\\\\ooe0\", \"size\": [1280, 720]}, {\"counts\": \"SjX3;_W19J4M3L4M2N3M2N2M3N2M3N2M3M4L3M3N2N2L4M3N2L4L4K5L4N110O100000O10O1000O1000O0100O010O1O100N2O1O2N1O2N1N2N2N2L4M3M3N2N2O1O1O00O100O100001N2O1O2M4L6KP1PO7GZWle0\", \"size\": [1280, 720]}, {\"counts\": \"oY[3<^W18K5L3M2M4M2N3M2N2N2N2N2N3L3M3M3N2N2M3M3N2N2M3N2M3M2O2M300O0101O0O101N10000O010O1O1O0010OO2O1O2N1N2O2M1O2M3L4N1O2N2N101O001O001O00000O010O11OO2O1O3L8jNQjNL_nje0\", \"size\": [1280, 720]}, {\"counts\": \"iQ_3>]W17K4L4M3M2N2N3L3N2N2N3L3N2M3M3M3N2N2M3N2M3N2M3J6K5M201O1000O1000O10O010O100O10O01O010O1O1O1N2O2N1N3N1N2N2N2N2L4M3O1O2N0O2O1O0O010O02O01O001O1O2M6Kg0XOa0^Oeoee0\", \"size\": [1280, 720]}, {\"counts\": \"kY`3;_W18K5L2N3M2M4M2N3M2N2N3M2M3M3N2M3N2M3N2M3M3N2M3K5I7L4N110O1000O0100000O010O1000O01O1O010O1O2M2O1O2N2M2O1N2N2M3N2M3N3N1N2O1O001O00O0100000001O1O2N2N4K8Hm0SOg_ce0\", \"size\": [1280, 720]}, {\"counts\": \"Pba3<^W18K4M3M2N3M2N3M2M3N2N3M2N2M3M3M3M3N2M3M3N2M3M3K5J6N2O0010000000O0100O010000O01O01O1O100O1O2M2O2N1O1N2N2N2M3M3N2N2O1N101N1000O10O011O00011N1O2M3N4L7H`0@`0_ObWbe0\", \"size\": [1280, 720]}, {\"counts\": \"UZ`39_W1:K5K4M2N3N1N2N2N2M4M2N2M3M3L4M3N2M3M3M3N2N2K5F:N2O1O10O10O10000O10O100O010O10O0010O1O2N1O2N2N1N3N1N2O1M3N2M3N2N2O1O1N1000O1O100001O001N201M3N3M6I8I8H?^Od_ce0\", \"size\": [1280, 720]}, {\"counts\": \"TR_3<^W17L5L3M2N3M2O1N2N2M4M2N2M3M3L4M3M3N2L4N2N2M3K5J6I7O1O1000O010000O10O10000O010O1O010O1O2N1O2N1O2N1O1N2N3N1N2N2M3M3O1O1O000O10O1O1001N2O002N1O2N3M6J8G=C;F;BY_ce0\", \"size\": [1280, 720]}, {\"counts\": \"YR_39`W1:I5L4M2N3M2N3M2N2N2N2M3N2M3M4M2M3N2N2M3N2M3N2M2N3L4L4N2O1O1000O10000O1O100O100O00100O001O1O1O2M2O1O1N2N3M2N2M3M3N101O1O1O001N2O0000O10O01010O1N2O2N4K9GP1nNZ_ce0\", \"size\": [1280, 720]}, {\"counts\": \"VZ`3?ZW19K4K5L4M2M4K4L4M4K4L4L5J5K5L4L4N2K5K5N2O1O1000O2O001N1000001N100O010O1O1O100O1O1O1O1O1O1O001O1N2O0O2N1N3M2M4K4N10010O10O11O0O111M2O1N3N2N2M5L7H:E<D:Eegde0\", \"size\": [1280, 720]}, {\"counts\": \"]`g09`W1;H6K4M3K5L4M3L4M2M4L4L3M4K6L2M4L4K4M4L3N3L3M4L3M3N3N1O2O000O1000000000000000000000O1000001N10000O100O100O00100O001O1O1O001O00O01O001O2N1N3M3N2L4N3K4M4M2M4L3N3M3M4M3M3N2M3M3L5L4K5I<F6I8FZQgg0\", \"size\": [1280, 720]}, {\"counts\": \"bk0P1PW101N1M4M2N2O2N2M3BehN3fkgk0\", \"size\": [1280, 720]}, null, {\"counts\": \"`m0l0TW10H:J5J5M4G9L3M4K5M3L4M3M3L3L5L3N3M3N1O2O1N100O200O01O2O0O1O2N100N3N1O1O1O1O1O1N3N2N1N2N2O1N2M2O2N1N3M2O2M2M3M4M2N3L3M4M2L4M3M3M4L3M3N2O2M4K5K]SYi0\", \"size\": [1280, 720]}, {\"counts\": \"k`f3:^W1=G8G8J5J6L4J5K6L3L4L4K5G9I7L3M4L4L3N3N1000001O0000000000000000O1000001N100O10000O100O001O1O00100N10000O001O001O0O2N2M4L3M4L3L5L4N3L4L4M4M2M4M3L4L4M2N3L4L5K5J8GVYXe0\", \"size\": [1280, 720]}, {\"counts\": \"\\\\cb5j0RW16J6K4L4K5J5M4K4L4L4J7J5F:E;L4O1O0100000000000000000O01000O10O10O1O0100O100O100O1O1O1O2N1O1O1N3N1O1O1M2O2K4I6J50200O2O001O1O1N2N2O2M3M3M6H>dNmiN3Rmic0\", \"size\": [1280, 720]}, {\"counts\": \"Tcg5h0TW16L3M3L4M3L3N2M4M2M4M2M3M3M3N2M3L4M3M4L3I7J6M3N1100000000O100000O0100O10O10O1O0000100O1O2N3M1N2O1O1N2O0O2M3K5I6N3N2N2O0O2N10O001O100O0101O2M3M4L6YOeiN^ObV1KUjNLXm_c0\", \"size\": [1280, 720]}, {\"counts\": \"mjh5b0YW18J4L4M3M3M3M2N2N3L3N2M3N2L4M3M3N2N2L4M3N2M3L4J6J6M3N200O1000O0100000O010O100O1O01O0100O1O1N2O101L4N1O1N3M2N3L3K5K5M3N2O1N2O1O000000O100O10O101O1O1N4L5L8Fc0^Ob0\\\\OfVWc0\", \"size\": [1280, 720]}, {\"counts\": \"lbb54gW1:F8I5M3M3M2O2L4N1N2N2O1N3M2N2N2O1N2M3N2N2N2N2N2N2N2N2N2M3N2N2M2N3L4M3N11O010O100O0010O01O1O1O001O1O001O1O1N2O1N2M4M3M3L4M2N2N2O1O1O1O100O001O10O00000O100O100001O1O1O1O1N3M4M:ClWRc0\", \"size\": [1280, 720]}, {\"counts\": \"cZ\\\\5;aW16H7L4L4N2M2N3M2N2O2M2O1N2N2N2O2M2N2N2N2M3N2O1O1N2N2N2N2N2N1O2M3N1L5L4O001O0100O1O100O1O1O100O1N101O001O1O1N2N2N3L3N2M3N2N2N2O1O2O0O1O1O010O1O010O00O1O100000O101O1O1O2M3N2M;Ei0UO[_Xc0\", \"size\": [1280, 720]}, {\"counts\": \"^RV5;`W18J4K5L4N1N3M2N3N1N3M2N2N2O1M3O2M2N2N2M3M3N2O0N3N3M2M3N2M3M2M4J6N20O01O0O2000O1O10O01O1O10O01O001O1N2O1O2N2M2O2L3N2N3L3M3N2N2O1O100O001O01O0O1000000001O001O1O1O2N2N3M9E<F9E:F_o_c0\", \"size\": [1280, 720]}, {\"counts\": \"PPg4;aW1:H9H6J:G6J5L4K4L6K5K2N3L3M2N2N1O2N1O1O1O001N2O1N101N1000001N1O100O100O10O2O001O1O0O0100O10000O010000O2O1O1ZMWkNR2kT1cMjkNR2\\\\U1E6K5K5J5K4M4K5J7J7Gk]jd0\", \"size\": [1280, 720]}, {\"counts\": \"SPg4c0ZW1d0^O:G5L5J4L5K4M2N3L3N2M2N2N1O100O1O1O010O1O1O0O2O001N1000000000000O100O100O0100000O100O10O0100O010O12M4M1ZM_kNj1cT1RNhkNf1YT1VNnkNe1\\\\U1K4L4K4L5K4L7I6Iimld0\", \"size\": [1280, 720]}, {\"counts\": \"ioa4W1cV1=E7WOi0@`0I7K5N1N3N1N1O1000O010O00010O01O0000000000000O10O10000000O01000000000000O0100O010O01O1O1O1N101VNPlNZNJn0YT16QmN^OSS1:`2HjY]e0\", \"size\": [1280, 720]}, {\"counts\": \"QlV21dW1k0[O9C;F;G8I6K6H8H8M2N3L3M3M3M3N2O1N3N00100000O3N1N2O1N2N1O1O1O010O2N1O001O00001N2O00001N2O1O0O2N1O2M2N2N3M2M3L4G9L4L4I7H8L4J6N3J5K6M3N2O1N3LT]Tg0\", \"size\": [1280, 720]}, {\"counts\": \"jf0k3UT1000O1O1O100O001O1O010O0100O1O1O100N2N3L3N2N3M2O1O2N1N3N1O2M2N2N3M2O2N2M3YNZkNOjT1NYkNNkT1L\\\\kNFSOIgU1=[kNFPU1O]kNKfT10fb]j0\", \"size\": [1280, 720]}, {\"counts\": \"Yh0]1cV10L5M3M2N2N3M2O101N1O1O1N2O1N2N2M3N2M3N2M3N2N2N2N2N1O2O1N1O2N1N3O00001O001O010O1O010O01O001O1N2N2N2N2O1N2N2N2O1M3O2M2N2O1M4M2O1ZO]jNkNhU1k0k0M4K4N3M2N3N1N3N3M2M5J5M_iQi0\", \"size\": [1280, 720]}, {\"counts\": \"hSe37YW1e0E9G9H7H8K5J5L5M2M4L3L5L4L4M3M2O1O1001O002N0O3M2O2M2O0O1O2N1O1O1O1O00001O00001O1O001O0O1O100O1O1N2O1N2N2L4M4L3M3K5J6I7K6I6L4M2N3M3N3J5L[ehe0\", \"size\": [1280, 720]}, {\"counts\": \"eih56aW1c0A:D<H6E<G7I7H8D;^Oa0K6O00001O00000000001O0O10000000000O1000O100000000O100O00100O1O00100O001N101O0O0010OO2L5ZOf0H8L6K6J6K9F;F8G;CXXkc0\", \"size\": [1280, 720]}, {\"counts\": \"\\\\jR6c0YW18H5K5L4L4K4M4L3L4M3L5K4L4K5J6K5K5N200000000000000000000000O10O1000O10O1O10O1O100O1O2N1O1O2N001N2N2N2L3H9I6O2N2M200O00O20O1N3L6[Od0F>HR`]c0\", \"size\": [1280, 720]}, {\"counts\": \"Ybl51gW1g0]O6K4L4L4K5L3L4N2L5L3L4M3K5L4H9G8M3N2O11O00000000000O1000000O0100000O01000O01000O100N3N1O2N1N2O1N2N2O1M3G9K5M3N1N2O100O01000O1N3\\\\OliNXOZV1<n0G^`bc0\", \"size\": [1280, 720]}, {\"counts\": \"Sjc5<\\\\W1;J5J5K5M3L3L5M2M3M4L3M3M3L4L4K5H8I7L4N2N200001OO1000000000000O010000O10O10O1O100O1O001O2N1O1O2M3N1N3L3K5I7K401N2N2N2O1N1O001O0O2N3B?I7I9Idhhc0\", \"size\": [1280, 720]}, {\"counts\": \"hiY5d0XW17J4L4L4L3M4M2M3M4L3N2M3M3M3M3L4L4K5I7M4L3O1O0101O000000000000000O010O100O100O00100O001O1O1O2M2N3M2N2L4I7K5M3N2N2O1N101N1O100N2O0N3K6E:K8J8Hg`Qd0\", \"size\": [1280, 720]}, {\"counts\": \"oQQ51`W1e0F6K4L4L4M2M4L3N2M4L3N2M3N2M3L4M4K4J6K5M3L4O1O1000000001O000O10000O010O10000O001O1O001O1O1N2O1O2M2N3M3K4J6L4M3N1N3O1N2O001N1O001O1N2E<I6M5J9H<DehWd0\", \"size\": [1280, 720]}, {\"counts\": \"`Ql4`0ZW18K5K4L3N3L3N2M4L3M3N3M2M3N2M3L4M4K4L4J6L4N2L4N200O11O0O2OO100001O000O10O0100O1O1O10O01O001N2N2O1N3M2L5J5L5K4N2N2O1N2N2O1N1O2N1O1N1M4F:L5L5K6JSQ^d0\", \"size\": [1280, 720]}, {\"counts\": \"bYh4=\\\\W1:J4L4M3L4M2M3M4M2M3N2M3N2M4L3M3L4L4L5J5L4M3N2N2O1O1001O0O10001O00000O10000O1O10O01O1O1O1O001N2N2O2L3N2I8I6M3N2O1N2O001O1N1O2N1O001N1M4G:J6K6K9ERa`d0\", \"size\": [1280, 720]}, {\"counts\": \"dYc4=]W19I6K3N3L4M2M4L3M4L3M3M3N2M3M4K4L4K5K5L4M3N200O100001O00000O2O001O0O100000O0101N100O001N2O1N2O1N2O2M1N3L4I7L3O2N2N1O2O1O0O2N1O100N1FbiNPOcV1k0;L3M6K8Inhfd0\", \"size\": [1280, 720]}, {\"counts\": \"gWT4=^W1?B>D<E7I7J7J6I4L2O1N2O001O0O10001N1O001O0000001N100O10000O100000000O100O010O100001O0000000O0100O1000O01000O2dM\\\\kNY1gT1]NTlNo0nS1lN\\\\lNm0fS1oNblNj0`U1J9F;BVVbe0\", \"size\": [1280, 720]}, {\"counts\": \"moh36dW1R1PO>C9I4M5K9G7J2M100O2O00000O1O1O1O001O000O10000O2O0O101N100000O1000O100O100000O101O000O2OO1000000O010001N2[MbkNe1`T1WNmkN_1ST1_NUlNZ1mS1dNYlNV1iS1gN]lNU1\\\\U1I6K7I6IWVle0\", \"size\": [1280, 720]}, {\"counts\": \"PPi3a0[W1c0_O?C;D:G7I4M2M2O1N100O2N002OO0100O000O101O0O10000O10000O100O01000O10000000O101OO1000O010000O10O010O102M3_MckNZ1bT1[NQlNY1bU1H6J6J6I7GPnoe0\", \"size\": [1280, 720]}, {\"counts\": \"n_f3d0ZW1;F;Ea0@5L:E7I3N1O1N2O0O1O1O1N2O1O1O0O2O0O1000000O101O000O00100O10000000O101O0000000O10O10O10O1000O101O1N2eMVkN^1SV1B;F5J7I5J7J6HnUQf0\", \"size\": [1280, 720]}, {\"counts\": \"VXe35bW1b0E<E<E>B9G8H4L3N1N3N0O1O1O1O1O1O1O1O0O2O000O100O1000001N10O01O100O100001N10001O000O100000O010000O101O1N2O5RMhkNj1aU1E8H7I6J5K6I8FlUQf0\", \"size\": [1280, 720]}, {\"counts\": \"S`f32gW1b0C>C>C>B7I;E3M3N0O2O1N1O1O1O001O1O1O0O10000O2O0O1000000O00100O1O100000O101O001O0000O10O10O10O100O101O1N3aMojNE4k1nT1QNQlNd1`U1F6I6J6I7J6GQVQf0\", \"size\": [1280, 720]}, {\"counts\": \"oWj36dW1`0D>Dc0\\\\O;F;F3L3N1N101N1O2N1O1O001O001O0000000O101O000O1000O0100O1O1000O10001O000O010000O010O1000O01O12M3N4VMhkNc1fU1E6I7I7H8HUnoe0\", \"size\": [1280, 720]}, {\"counts\": \"hWj3<`W1`0Bb0_Oa0@<D6K2M3N1N2O0O2N1O001O1O1O00001N100O10000O100O10000O01000O100001O001N1000000O0100000O010000O02O2M4YMkkN]1fU1E7I6J7H8HWnoe0\", \"size\": [1280, 720]}, {\"counts\": \"loh35eW1k0XOh0ZO`0A9G3N2M2O1N100O1O1O1O1O1O01OO10001O00O010000O101N10O10O100O10000000O2O0000000O1000O10O010O10O10O3N3]MhkNW1nU1B8H7H9GV^Rf0\", \"size\": [1280, 720]}, {\"counts\": \"gWe3g0UW1n0UO?A9H7I4M1N10001N1O010O1O001O001O0000000O10000O1000000O100O1O001000000001N100000000O10O100O010O10O2O1N4]MmkNR1kU1E8G9G:ETVVf0\", \"size\": [1280, 720]}, {\"counts\": \"go^3;`W1d0^Ok0XO`0@7I3M3N1N2O1N1O1O1O1O0010O0000O2O000O100O100000000O100O010O1000000001O1N100000O10O10O100O0100O1O2O2RNQkNf0SU1]NTlNU1gU1D7H:FY^\\\\f0\", \"size\": [1280, 720]}, {\"counts\": \"f_\\\\3e0XW1n0SOe0]O7I4L3N2M101N1O1O1O1O001O010N10000O10000O10000O100O1000000O10O1000001O00000O1000O01000O100O010O02O2M4[MPlNS1TT1dN\\\\lNQ1aU1H8G9FUn^f0\", \"size\": [1280, 720]}, {\"counts\": \"mW[3:cW1n0TOe0[O;F7I3M3N1N1O2O0O1O1O001O1O0010O00000000O1000000O10000000O0100O100000O10000O0100O10O01O100O10O01O12M4jMUkNP1oT1`NokNn0ST1mNYlNj0dU1J9EVV`f0\", \"size\": [1280, 720]}, {\"counts\": \"WX[38cW1e0^Oi0XO<E9H5J3N2M2O0O2N1O1O1O1O10O01O000000000O2O000O010000000O10O01O100000O101O00O010O100O010O1O1O01O101N5aMTkNb1oT1RNmkNZ1VT1bNVlNS1bU1H8I8Emm^f0\", \"size\": [1280, 720]}, {\"counts\": \"_`\\\\3>^W1>Eg0YO>C9G6J3N3L3N001N1O1O100O1O1O0O101O0O10000O10000O1000000O10O010000000O1000000O10O10O0100O100O0101O2M3ZMkkN\\\\1XT1^NWlNV1aU1H6J7I7Gae]f0\", \"size\": [1280, 720]}, {\"counts\": \"ih]3`0]W1d0^Oe0\\\\O9G9G5L2M3N1N2O1N1O1O1O1O10O01O0000000O100O10000000000O100O100O10O100000000O10O1O010O100O001O11N2O2^M\\\\kNc1hT1UNkkN\\\\1XT1^NVlNW1aU1H6J6I9GT]\\\\f0\", \"size\": [1280, 720]}, {\"counts\": \"mX`3c0ZW1`0Bd0\\\\O=D7I4M3L3N2N1N101N1O1O1O001O0010OO10001N100O1000O10O10000O100O1O10O101O0O100000O1O010O1000O01001N2N3\\\\MakNc1bT1VNnkN]1fU1D6J6J7H6JQeXf0\", \"size\": [1280, 720]}, {\"counts\": \"o`f35eW1?D>D?A>C;E6K3M2M2O1O1N1O2N100O1O1N101O000O10000O10000O100O100O1O010000000001N101O00O100O01000O010O11N2O1O3UMmkNb1`U1H:F6J4L7H8H5EVmoe0\", \"size\": [1280, 720]}, {\"counts\": \"g`P4g0WW1>B=E=C9G8H3N1O1N2O0O100O1O1O1O1O001O001O0O101OO1000O100000000O1O10000O010001OO01000O1O1O10O0100O0100O5L3YMgkN`1gU1E8I5J5K7H8GV]he0\", \"size\": [1280, 720]}, {\"counts\": \"bhl38eW1a0@c0^Oa0@9H5J3M3N1N3N0O1O1O2N1O001O10O0001O000O1000000O100000000O1000000O01000000O01O10O01O100O1O10O100O3N3^MbkN[1aT1_NokNT1eU1I6J6J7I6H^Ule0\", \"size\": [1280, 720]}, {\"counts\": \"\\\\`f34fW1>E;G<E=C;E9H5K3L3N1O1N1O2N1O1O1O1N101O001N10000O1000000O100O1O100O1000O2O001O000O10O100000O01000O101O2M2TMekNQ2_U1E9G8H6J6J7I8FfUQf0\", \"size\": [1280, 720]}, {\"counts\": \"Zhg37dW1;H;F=D?A<D8I3L2O2N1M2O2N1O101N1N101O000O2O000O1000000O100O1O100O1000O101O001OO1000O10O100O10O10002M3N2RM`kNW2`U1E8G8H7I6J7I8F8Hemoe0\", \"size\": [1280, 720]}, {\"counts\": \"\\\\Xj3;aW1=E;G=C<D<E4K3N2M2N2O1N2N1O1O1O001O001N100O101O0O100O100O100O10O1000000000001OO010O100O01000O11O1N2O2N3TMbkNo1`U1G8I6I7I8G8I7Hcene0\", \"size\": [1280, 720]}, {\"counts\": \"[Xj3`0\\\\W1<G7J8I>A=C6K4L2M3N1O2M1O2N1O1O1O1O1N101O0O2O0O10000O101N10O0100O1001O00001N10O10000O010O10O100O2O1O3SM\\\\kNW2dU1C8G7I7J5J6J6I7J7H]]me0\", \"size\": [1280, 720]}, {\"counts\": \"oRf2:cW15L3M3M3L3O2N1N2O2N1O1N3N1O1N3N1N2O1N2N2N2O1M3M3N2N2O1N2N2N2N2O0O2N2N2M3K4M4M3O10O01000O0100O0100O1O1O010O1O001O1O1O1N3N1N2M4M2N2O1O2N1O1O100O1O100O010O01O001O0O11O1O001O2N2N2N4K<EP1nN]nje0\", \"size\": [1280, 720]}, {\"counts\": \"aki21iW1<G5K5K3N3N2M2N3M2O2M2N3N1N2O1N2N2O1N2N3M2N2M3N1O2O1N2N2N2N2N2N101N2N2N1M4N2O001O0O20O100O1O1O1O100O1O001O1O0O2O2N1N3M2N2N2M3N2N2O1N2O1O001O1O1O001OO2O1O000000000010O001O1O2N1N4M7H9H;D=C\\\\fde0\", \"size\": [1280, 720]}, {\"counts\": \"\\\\SP3`0\\\\W16K4L4M3M3N1N2N3N1N2N2N2N3M2M3M3J6L4N2L4M3M3L4C^MUkNi2jT1601O1O10O010000O0010O100O100O1O2O0O100O1O1O001O1O1O001O1O001N1O2N01O0N11M5M2O2N101N2O001O100O1O2N100O2N2N2N3M2N4M2M3M5K4K4L5L4L3K7J5K7G_f_e0\", \"size\": [1280, 720]}, {\"counts\": \"acW31gW1`0D9G6nMQOjlNV1SS1SO]kNV2aT1f0N2N2N101N1O1O1O1O1000000000O2O0000000O0100O100O0100O010O0001K4N3O0O100010O1O001O001O1O1N2O2M3M3N2L4J6L5_O`0H8L4M4L3M3N2N3L3N2O2L3M3M5J4N3K4K7JnVle0\", \"size\": [1280, 720]}, {\"counts\": \"ekl39`W1;G6L4L4M3M2N3M2M3N2N3L3M3M3M4M2M3N2N2K5N2M3N2N2M3M3O0O2O10000O10O101O1N1O2O0O100O1O100O1O1O1O1O1N2O0O2O1N2N2N2M2L5L3M4O0N3O0O101O00001O0000O100O02N1O2N2L4G:C>G[oQe0\", \"size\": [1280, 720]}, {\"counts\": \"^SX4c0YW16L4K4L4M2N3M2M3N2M3M4K4K5L4L4K5L4D<O1O1O1O1O10000O10O0100O01000O10O10001N1O100O0010O01O1O001O001N100O100O1OO0101M3L4N200O2O1N2O1O1N2O1O2N1O2N3N2M5K5J5L4K5L5K7H6J5J[Vdd0\", \"size\": [1280, 720]}, {\"counts\": \"dk[48dW16J7J4L3N3M3M2N2O2M2N2N2N3M2N2N2N2M3M3M3N2N3M1O2L4M3N2N2K5M3N101O1O100O0100O100O100O10O01O1O010O1N2O1O2N2N1N2O1N1O2N1O2M3L4M2O2O0010O00000O1O100001O001O010N2O100O2N2M4L;F9F8I:D[nXd0\", \"size\": [1280, 720]}, {\"counts\": \"\\\\Sb4e0XW14L5K4M3L4L3N3L3M4M2M3K5M3M3M3L4J6M3M3K5M3N2O100O10O2O001O001O000O1000O0100O010O100O1O1O100N2O1O1N101N101M2N2M4J5N3M2N20001ON2O1O11O0O2O1N2O2N3K=Dl0QOYVdd0\", \"size\": [1280, 720]}, {\"counts\": \"`k`4`0[W18J5K4M3M3M2M4M2M4K4N2L4K5M3M3L4M3J6L5K4L4O1000O010000000O100000O10O10O010O10O10001N1O1O1O001O1N101O0O101N1O1O1N2M3L3M300100001O0O2O1O1O1N3N2N3M8G:G6H=E8G5DZ^`d0\", \"size\": [1280, 720]}, {\"counts\": \"aSb42fW1?D7L4K4M3L4M2M3N3L3M3M3M3L4M4L3M3K5M3L4M3L4I7N2N2O10O10000000000O10O100O10O00100O100O1O2N1O2N1O1O1O001N2O1N1O2M2M4K4M3O100O000100001O1O1N2O1O1N3N2N3L6Kc0[O`0A7G[n]d0\", \"size\": [1280, 720]}, {\"counts\": \"^kS3=_W16L4L3M2O2N1N3N1O1O2N1O2N1N2O1O1O2N1N2O1O1N2O1N2O1N2N2N2M2O2N2O1O001N2N101N2O0O2O1O000O2O0H]MSkNe2lT170000010O1O1O010O1O1O1O1O1O1O2N1O2M2O1N2O2N001O1O1O100O1O1O0010O1O001O001O000010O010O100O100O2O0O1O1O101N100O2N4L4L5L4K3M4L6J5K5K8BYnbd0\", \"size\": [1280, 720]}, {\"counts\": \"kSk26eW17J6L3M3N2M2O2M3N1O2N1O1O1O2N1O1O1O2N1O1O1N2O1N2O1N101N2N2O1M3N2O001O1O1N1O2O0O2N101O0O2O001N1O2M2N2010O001O1O1O1O001O1O1O1O1O1O2M2N2N3N1N2O001O1O101N10O01O100O010O01O0O2O1O1OO110O00100O10000N2O100O2N1O2O1N5K8H4L6K5I5L7HoUnd0\", \"size\": [1280, 720]}, {\"counts\": \"`[b27eW17J4L4N1N3N1O2N1N3N1O2N1O1O2N1O100O2M2O1O1O1O1O1O1O1N2O1O1N2N2N2N2N2O1O010O1O1N2O0O2O001O001O001O1N1M3O2N10001O01O010O1O001O1O1O1O1N2O1O2N2N1O1O2O0O1O10000O100O01000O1000O0001O01O1N110O1000O010O2N1O1O100O2O0O2N3M9H7H8G8H;_OTfUe0\", \"size\": [1280, 720]}, {\"counts\": \"TkQ47bW1:J4M2M4M2O1N2O2M2N2N2N2N2N2N2M3M4L3M3O1M2N3M4K4J6M3M3O1O1O100O100000000O010000000O100O010O010000O011N1O2M2O2N1M3N3L3O0O2O1O1O1O001O01OO1000O02O1N2]Od0C>G;G`gPe0\", \"size\": [1280, 720]}, {\"counts\": \"_[j3;_W1:H6K3N3L3N3M2N2M3M4M2M3N2M3L4L4N2M3K5N2N2L4I7L4N2O1O1O11O000O100000O1000O10O0100O0010O0101N1O1O1O2N1O1N2N2N1O2N2K5K4O2N1O1O10O100O01000001O1O1N3N4K6JQ1oN\\\\^Ye0\", \"size\": [1280, 720]}, {\"counts\": \"\\\\Si31bW1e0E5K4M3L3N2M3N3L3M3M3N2M3L4M3M4K4K5N2M3J6J6M3N2O1O11O00000O100O01000O10O10O01000O100O1O101N1O1O1O1N2O1O1N1N3N2J6J6L3O1O10O2N1O01000001N2O2N2M5K5L7Il0RO8EbfZe0\", \"size\": [1280, 720]}, {\"counts\": \"nZj3c0ZW16J6J4K4N3L3L5K4L4K6J5K5H8J6\\\\Od0M300O10000000000O1000O1000O01000O010000O101O1N2O0O1O1O1O100O1O001O1N2O1N1O10O0OO2M4I7M3N3N2O1N2O1N3N2N3M4L5J6K4L4K5K5K6J8F7InVXe0\", \"size\": [1280, 720]}, {\"counts\": \"bhl37Y24kR14ilN6QS11TkNCT1e0eS1Z2L2O2M2O1N101N100O2O0O100000O101O1O0O1000O10O10O10O10O010O10O01M2L5N10001O01O001O1O001O1O1N3N2M3M4L3M3K6I6XOi0K4N2N3L4M2L4M3N2M3N2M4K5K5J6K[W]e0\", \"size\": [1280, 720]}, {\"counts\": \"lXj31nW16J3M5K7J7H7J5J6J6]jN`N\\\\T1e1XkNhNaT1_2L4L3N3M1O1O1O010O00001O0O100O10O10O1O100O010N2J5M4N110O0010O100O010O2O0O2N2O1N3N1N2M3N2N2N3L3N2N1N3M3K5mNSkNmNRU1o0TkNiNRU1T1o0M2N4L3M4L4J6J6IXW]e0\", \"size\": [1280, 720]}, {\"counts\": \"lhV41nW17I4M6I:F8H9H7RjNjNdT1]2K4M3L3M3M2N2O1O1O001O0001O000000O100O10000O010O1O001N1M4L4N110O10O10O02N1O2O1N3N1N3M2N3M2M4M2M3N2N2M3L5jNnjNXOUU1d0SkNSORU1j0T1M4K6K4L4I7JRoVe0\", \"size\": [1280, 720]}, {\"counts\": \"mRg41gW1=B?C<]NiNikNd1oS1SOPkN^1mT1k001N2O001O001O00000000000O10000O100O010O100000O10000O01000000000O1000O00010N1O1N3O00O2O0O3N1M3N2VOk0K8I8Hm0SO9FjnQe0\", \"size\": [1280, 720]}, {\"counts\": \"^bi4>]W1;E9G7I6I6L5L3I8I6J6H9K4N2O1000000001O00001O0000001O00000O1000000O010000000O100O010O1O100O001O0O101M2N2G9@`0N3M2O0O1N2J8F9J8K7IS`jd0\", \"size\": [1280, 720]}, {\"counts\": \"[Zm44bW1a0C;C;H7H7G8E<B=L5N1O1000001O01O00001O001O001O000000O010000000O100O10000000O01000O100O2O0O001O001O0O1N2UOSkN`NRU1[1j0M3I6L7I7J7J:GW`jd0\", \"size\": [1280, 720]}, {\"counts\": \"\\\\ho4`0\\\\W1e0\\\\Oi0YO`0@5L3M4M1N2O0000O010O01O0000000000000O10000O10000O100N1010000O1000O101O000000000O0100O001O0100O2O2M2_MTlNl0mS1nNalNg0dU1G:Eemld0\", \"size\": [1280, 720]}, {\"counts\": \"ZcW3>]W17J6L3M3M3L4M2N2N2M3M4L3M3L4K5M3M3L4M3L4L4D<N2O1O1O1000O01000O100O010O0100O010O10O10O02N1O100O1O1N2O1O2N2M3N1N1N3N2M2N3L3O0O011000001O001O1O1O1N2O2O2M3M7I9F9G;D:F9GZfde0\", \"size\": [1280, 720]}, {\"counts\": \"]cm2`0[W19I4L5M2M2M4M2M3N2L5L3H8J6F:J6A?O100O10000O10000O100O010O1O10O10O010000O101N101N010O1O1O001O10O01N10000O10O01OO02N3M2L5M2M4M3N2N2O1O2N2N3M3L3N5K3N3K4M4K6J5L3L5J7Icnoe0\", \"size\": [1280, 720]}, {\"counts\": \"bcc2:^W1=G6J5M3M3M2N2N3L3L4M4L3J6I7I7L4L5AWMakNm2\\\\T1=O001O10000O1000O10O100O01000O10O010O100O2N101N100O1O2N1O1O1O0O2O1O1N1O2O0N2N2M2M3J6O2N2O101O1O0O2O1O101M4M4L3L5L3M3L6J6J5K5J6JifXf0\", \"size\": [1280, 720]}, {\"counts\": \"YSa2:_W1:K4K4L4L4M2N3M2N3M2O1N3L3N2N3M2N3M2N2N2N2N2M3N2N2M3N2M3M3N1N3M3M2N3M201O0010O1O1O2O0O2N1O2N1O1O2N1O1N2O0O2N2O1M3M3M3N2K5N2M4M3M3N1N2O1O2N1O1O1O1O1O001O1N10000O1O01O11N1O1N3K5H9J8HfWQf0\", \"size\": [1280, 720]}, {\"counts\": \"RcY29dW17J4K4M3L3N3N1N2O2M2O2M2O1N2O2M2O1N2N2O1N2O1N2M3N2O1N2O1O1M3O0O2O0N3N1O2O001O1N101N2O0001O1O100O1O1O1O1O1O001O001O1O1N2N3N1N2N2M3O1N2O001O1O0010O01O1O01O0000000O2O000O20O0000100O1O1O1O2N3M4L6I:F9H`0^Od^me0\", \"size\": [1280, 720]}, {\"counts\": \"TkU2;`W18I6K4L4M3L3N3M2N2N3M2N2M3N3M2M3N2N2N2M3N2N2N2M3M3M2N3M3N2N1O2L4N101O0010O2O0O2N101N1O101N1O2N1O1O1O1O1O001N2O1N3M2N3L3M3L4L4M2O2N2N2N101O1O001N1O2N1O1000O1O000010N3M3J6K5L5L:^OPP_f0\", \"size\": [1280, 720]}, {\"counts\": \"iRY1V1[V1b0F<B>[Oc0_O`0I6L4M3O1N2O0010O01O01O00100O1O00001O001O0000001O000000O1000O100O100O10000O100O1O10O01O1N2O1N2N2N2N2M2N3L3M4ZOXkNdMXU1i1h0B?F9J7L4M5L4L4L4KX_Sh0\", \"size\": [1280, 720]}, {\"counts\": \"`n4m0PW16K4K5L3L4M3L4M2M3N3L3M4M2M4L3M4L3L4M3K5K5L4K5L4M3M2O2L4M30O2O00001O0000001O0000O100O100O10000O1000000O100O1000000O100O1000O101O001O1O000O1O1O1O1O1N2N2K6B=H8K5K4K6H8J6L3N2M3M3M6G;I8GYT\\\\h0\", \"size\": [1280, 720]}, {\"counts\": \"d]Z15YW1i0G6J7J9G7J5J8I7I4K6K2N2N2O0O2O002NO001O0000O10000O100O100O100O100O1O100O100O1000000O10000N2N2O100O10000000000001O1O000000000000000000O10000001O00000iLVlNX2kS1dM^lNV2dS1gMdlNR2jT1K4L5K4L4L3M2N2N3M3M3M2N3M3M5K2N3M3M4LTXPg0\", \"size\": [1280, 720]}, {\"counts\": \"e]i1:ZW1b0F7L5K8H7J5J6K7I4L4L4L3M2N2O2NO1O1O1O1O0000000000O10000O100O1O1O100O100O100O100O100O1O1O1O1N200O100O10000001O1O1O00O1000000O1000000O1000000001O0nL\\\\kNh2dT1VMnkNZ2ST1dMSlNW2mS1hMYlNS2hS1lM]lNo1nT1L4L3M5K3M3M2N3M2N3M3M3M2N2N3M3M3M3M3M2NUP`f0\", \"size\": [1280, 720]}, {\"counts\": \"b]b28fW1h0XO>C8I7I5J3N3M2N2N1O1O2M101O3M0001O001O001O1O1O1O1O0000O10000O10000O1O1O1O1O100O1O100O1O1O100O100O10000O1O1N2O1O100O100001O000000O100O10000001O2N2N0_MSkNo1nT1hM\\\\kNV2eT1gM_kNW2`T1jMbkNT2^T1lMdkNR2\\\\T1nMfkNP2YT1QNikNm1VT1TNkkNk1UT1UNnkNh1RT1XNPlNf1PT1ZNSlNc1oS1ZNUlNc1VU1N2N3M2N2N1O2N2N2N2N1O2N2N2N2N1O2N2N2N1O1O2N2N2N1O3M2NSPSe0\", \"size\": [1280, 720]}, {\"counts\": \"]em29eW1i0XO:G6J9F5L2N2N2N2N1O2N1O1O2N1O1O1O100O012NO1O001O00O10000O1000000O100O1O10000O1O1O1O100O1O100O100O1O100N2O1O1O100O100001O00O10000O1O1000000001O1O001O0UMckNS2]T1lMekNS2[T1lMhkNR2XT1mMjkNR2VT1mMmkNQ2ST1oMokNo1RT1oMQlNo1PT1oMRlNP2oS1oMSlNo1RU1M4L2N2N3M2N1O2N2N2N2N2N1O2N1O2N2N1O2N2N1O2N2N1O2N2N1O2N1O2N2N2NTPdd0\", \"size\": [1280, 720]}, {\"counts\": \"`]V37aW1m0ZO8I4K9H6J2N3M1O1O2N1O2N1O1O1O1O1O101N01000O1O00001O003L1000000O1O100O100O100O1O1O100O100O100O1O1O1N2O1O1O10000O100001O00O100O1O1O1001O002N1O2N000jLgkNe2YT1[MjkNb2UT1aMkkN]2ST1fMnkNX2RT1hMQlNU2oS1jMTlNT2lS1kMWlNS2jS1kMYlNS2iS1kMYlNS2lT1N3M2N2N3M2N2N2N2N2N2N1O2N2N1O2N2N1O2N2N2N1O2N1O2N1O2N2N1O2N2N1O2N2N2NTPZd0\", \"size\": [1280, 720]}, {\"counts\": \"nTU3h0VW1>C6J9H5K4K4M2N2N2N1O2N1O1O1O2N100O100O01000O1O00001N2O1N2O2M3M4M2M4M0O100O1O100O100O100O1O100O1O1O100O1O10000O11O000000O10000O10000001O2N1O1O0eLlkNj2TT1QMTlNl2bT1N2N1OWOYM[lNe2eS1[M]lNc2cS1]M_lNa2aS1_MalN_2bT1N4L2N2N4L2N1O2N1O2N2N2N2N1O2N2N2N2N1O1O2N1O2N1O2N1O2N2N1O2N1O2N2N2N1O3M6JXX[d0\", \"size\": [1280, 720]}, {\"counts\": \"dmX37WW1g0D:_O`0H8H8K4N3N2M2O2N1O1O1N200O2N1O1O2N3N2M4M2N1O1N2O1O0O101N1O1O001O1O1N2O1O0O2O3M0O1O100O100O100O10000O1O100O1O100O10000001O0000000000O1O100001O1O1YLXlNT3hS1hLblNR3^S1kLjlNP3VS1nLQmNm2oR1RMUmNk2kR1TM\\\\mNf2dR1YMamNc2_R1]MgmN]2YR1bMjmN\\\\2WR1cMjmN\\\\2WR1bMomNY2oS1M3M2N2N2K5N2N2N2M3N2M3N2M3N3L4L4L3L4L:Ej`fd0\", \"size\": [1280, 720]}, {\"counts\": \"fkb38cW1?@c0@=E;F9G7I7I7I:F5K4M1O2N1O1O001N1O1N3N1O1O001O1N2O1O0O2O000O101N100000000O1O1O1O100O10O1000001O001N101O00000O100000O01000001N2O1O2TLPmNc2SS1UM[mNc2ZT1F8H9G6K3L6J5J5L6I5K7GcQ]e0\", \"size\": [1280, 720]}, {\"counts\": \"XkS3d0UW1f0_O;G9F;F8H4L6K7H5L5J4M2N2M2O1N2N1O2M2O1O100O1M3N2O001N101O000O100O1O100O1O1000000O100001O0O101O000O101OO10O1000000O100O100O103L3[LilN]2YS1[MUmN\\\\2^T1J:E7I7J4K5K4L4K6K4L5I9DmYme0\", \"size\": [1280, 720]}, {\"counts\": \"lUU3h0QW1:I5L5J5J6K4J7J6K4L4M4K4J6K5K5J6J7H7M3M3M3K5L4N2O1O100000000001N101O00001N101O2N1N2O001N100O10O01O1O100O1O100N2O1N2O1N2M3N2N2M4K4K5K6K4L4M3L4L3N3N2N1O1O00010O1O101N101O0N3M3M3K4M4J6K5K6K5L4LemVe0\", \"size\": [1280, 720]}, {\"counts\": \"cn[4;YW1b0E8J5K5J6K4M3L4L5K4L4L4L5L3L4L3N3L4M2M4L3N2L5K4L4M3M3H8L4K8G6M3N20000000M4O00001O00001O0O2O0O1O2O000O1O2OO01O1O101N1O2N2M3N1O1O1N2N2N2N2N2N2N2M3K5J5J7E;I7J6L3M4K2N210000001000OO2O1N2O1O3L5K8H8Gb0]Ok\\\\lc0\", \"size\": [1280, 720]}, {\"counts\": \"moi23kW12M3M3N2M3N2M3L4K5K5L4N2L4L4L4M3L4N2M3M3L4N2N2N2O1O1O1O1001O1O1O001O00001O000000000000001O00000000001O0000000000000000001O001O1O2N1O0000000000000000000M3N2M3N2M3M3L4L4M2N2M1N2N3N3M3N2O1M5J:FbRbe0\", \"size\": [1280, 720]}, {\"counts\": \"c_V3=aW1200O1000000001O0000001O001O001O00001O1O1O1O1O00000000N2N2N2N2M300001O0000001O0000O1O1O1O1O1000000O10000000000001O0000001O001O1O001OO100O10000O10000O100O11O1O1O1O0J8Ie`de0\", \"size\": [1280, 720]}, {\"counts\": \"ko]35hW13000000001O0000001O1O1O1O00P`k00o_TO10000O100O1O1O1O1O1O100O100000000000000002N1O1OO10000O100O100O1O1O100O1O11O001O2N3M1O1O1O001O002NSXYe0\", \"size\": [1280, 720]}, {\"counts\": \"mog33mW1000000000PXc10Ph\\\\N0O100O1O1000000001O0000000000O100O1O1O1O1O100O1O100O1002N3M2N1O1O000000001O1OSXod0\", \"size\": [1280, 720]}, {\"counts\": \"mg]53mW10O100O10000000000001O0000001OO100O100O1O100O100O1O1O100O11O2N2N2N1O001O00000000SPSe0\", \"size\": [1280, 720]}, {\"counts\": \"m_P63mW1000O1O1O1O1O1001O1O2NT`de0\", \"size\": [1280, 720]}, {\"counts\": \"mg]53mW10P`<0P`C00000O1O1N2K5K5003M1O3M5KUhee0\", \"size\": [1280, 720]}, {\"counts\": \"k_m45iW12O1O1O100O100O11O001O001O000000000000O1000000O1O1O1M3J6O1001O3M1O2N4L4L2N1O1O001OSPbe0\", \"size\": [1280, 720]}, {\"counts\": \"l_V34iW13N2N200000000001O1O001O2N1O1O0000001O000000O1000000O100O100O100O10000O100O100O100O1N2O1O1O100O100000000001O2N00000000O100O100O100O1O1O1M3M3N2O11O2N2N3M4L2N2N1O4F_Pge0\", \"size\": [1280, 720]}, {\"counts\": \"l_[34hW14N2N20000001O001O1O1O1O1O1O0000001O000000000000O100O1O10000O100O100O100O10000O100N2O1N200O10000000000001O1O000000O100O10000O1O100O1O1O1N2N2M3001O3M6J3M2N2J7J]`de0\", \"size\": [1280, 720]}, {\"counts\": \"lo]34kW110000001O001O00PXj00PhUO000000000O1N2M3O100O1O10000O1001O1O1O001O0000O100O100O10000O1O1N2I7K5N200001O2N2N2N4L3M5K3M3MU`de0\", \"size\": [1280, 720]}, {\"counts\": \"mo^53mW10O10000O10000O1000000001O00000000O10000O100N2N2J6M3N2O1O1O1O10000001O1O4L6J6JXPSe0\", \"size\": [1280, 720]}, {\"counts\": \"mWh63mW1000000000P`n10o_QN1O1O1N200O1O1O1O1O1O1O100N2O100O1O1O1O1N2O1N2J6O100O1000000001O001O1O2N001O1O2N1O2N3M4L3M7IVP[a0\", \"size\": [1280, 720]}, null, null], \"bboxes\": [[102.0, 973.0, 62.0, 97.0], [102.0, 979.0, 62.0, 98.0], [102.0, 980.0, 63.0, 101.0], [100.0, 983.0, 63.0, 101.0], [98.0, 985.0, 64.0, 102.0], [97.0, 986.0, 65.0, 104.0], [97.0, 984.0, 62.0, 116.0], [81.0, 999.0, 60.0, 114.0], [77.0, 1018.0, 55.0, 106.0], [73.0, 1020.0, 55.0, 104.0], [69.0, 1019.0, 55.0, 104.0], [69.0, 1019.0, 55.0, 106.0], [73.0, 1025.0, 54.0, 103.0], [75.0, 1026.0, 56.0, 103.0], [75.0, 1026.0, 54.0, 102.0], [73.0, 1025.0, 55.0, 104.0], [71.0, 1023.0, 55.0, 104.0], [58.0, 1013.0, 81.0, 109.0], [70.0, 996.0, 76.0, 110.0], [71.0, 995.0, 75.0, 105.0], [72.0, 990.0, 75.0, 103.0], [73.0, 985.0, 75.0, 103.0], [75.0, 984.0, 75.0, 101.0], [78.0, 977.0, 77.0, 105.0], [69.0, 973.0, 93.0, 111.0], [74.0, 993.0, 76.0, 105.0], [77.0, 999.0, 76.0, 106.0], [80.0, 1003.0, 76.0, 106.0], [83.0, 1005.0, 76.0, 105.0], [85.0, 1003.0, 76.0, 104.0], [88.0, 996.0, 76.0, 106.0], [89.0, 997.0, 77.0, 105.0], [90.0, 1001.0, 77.0, 106.0], [89.0, 1004.0, 77.0, 105.0], [88.0, 1005.0, 78.0, 105.0], [88.0, 1009.0, 78.0, 105.0], [89.0, 1002.0, 76.0, 115.0], [18.0, 944.0, 94.0, 126.0], [0.0, 882.0, 10.0, 32.0], null, [0.0, 879.0, 72.0, 113.0], [94.0, 960.0, 81.0, 132.0], [142.0, 1044.0, 71.0, 123.0], [146.0, 1036.0, 75.0, 121.0], [147.0, 1026.0, 80.0, 118.0], [142.0, 1020.0, 89.0, 114.0], [137.0, 1014.0, 89.0, 113.0], [132.0, 1008.0, 88.0, 115.0], [120.0, 1013.0, 66.0, 106.0], [120.0, 1019.0, 64.0, 104.0], [116.0, 966.0, 55.0, 125.0], [56.0, 825.0, 71.0, 124.0], [0.0, 722.0, 43.0, 131.0], [0.0, 707.0, 78.0, 119.0], [93.0, 825.0, 69.0, 111.0], [147.0, 984.0, 64.0, 136.0], [155.0, 1017.0, 67.0, 112.0], [150.0, 1009.0, 68.0, 112.0], [143.0, 1003.0, 70.0, 110.0], [135.0, 999.0, 71.0, 110.0], [128.0, 994.0, 73.0, 108.0], [124.0, 990.0, 72.0, 107.0], [121.0, 990.0, 73.0, 107.0], [117.0, 992.0, 72.0, 107.0], [105.0, 1001.0, 62.0, 104.0], [96.0, 1006.0, 63.0, 105.0], [96.0, 1013.0, 60.0, 103.0], [94.0, 1014.0, 61.0, 104.0], [93.0, 1013.0, 62.0, 105.0], [94.0, 1012.0, 61.0, 103.0], [97.0, 1008.0, 59.0, 103.0], [97.0, 1005.0, 59.0, 103.0], [96.0, 1006.0, 58.0, 103.0], [93.0, 1005.0, 58.0, 103.0], [88.0, 1003.0, 58.0, 103.0], [86.0, 1003.0, 58.0, 104.0], [85.0, 1008.0, 58.0, 104.0], [85.0, 1017.0, 59.0, 105.0], [86.0, 1027.0, 59.0, 106.0], [87.0, 1038.0, 59.0, 105.0], [89.0, 1043.0, 60.0, 105.0], [94.0, 1040.0, 62.0, 105.0], [102.0, 1037.0, 60.0, 105.0], [99.0, 1030.0, 60.0, 102.0], [94.0, 1023.0, 61.0, 103.0], [95.0, 1021.0, 61.0, 103.0], [97.0, 1024.0, 60.0, 103.0], [97.0, 1027.0, 61.0, 107.0], [68.0, 1031.0, 92.0, 107.0], [71.0, 1041.0, 94.0, 113.0], [76.0, 1034.0, 93.0, 122.0], [82.0, 1027.0, 77.0, 133.0], [99.0, 1049.0, 81.0, 112.0], [108.0, 1040.0, 83.0, 121.0], [111.0, 1044.0, 89.0, 115.0], [116.0, 1045.0, 75.0, 116.0], [115.0, 1043.0, 79.0, 120.0], [116.0, 1037.0, 80.0, 118.0], [79.0, 1044.0, 113.0, 113.0], [72.0, 1055.0, 111.0, 110.0], [65.0, 1050.0, 112.0, 104.0], [103.0, 1038.0, 78.0, 100.0], [97.0, 1039.0, 77.0, 116.0], [96.0, 1030.0, 77.0, 117.0], [97.0, 1022.0, 78.0, 125.0], [99.0, 1016.0, 72.0, 135.0], [97.0, 1021.0, 74.0, 133.0], [107.0, 1023.0, 69.0, 130.0], [120.0, 1023.0, 60.0, 120.0], [122.0, 1021.0, 64.0, 111.0], [125.0, 1015.0, 61.0, 109.0], [127.0, 1022.0, 57.0, 103.0], [82.0, 1033.0, 83.0, 120.0], [74.0, 1035.0, 82.0, 124.0], [66.0, 1034.0, 83.0, 127.0], [64.0, 1035.0, 91.0, 117.0], [58.0, 1030.0, 100.0, 113.0], [55.0, 1027.0, 89.0, 119.0], [32.0, 1041.0, 70.0, 136.0], [3.0, 1136.0, 92.0, 140.0], [33.0, 1167.0, 97.0, 113.0], [45.0, 1174.0, 98.0, 106.0], [65.0, 1180.0, 114.0, 100.0], [74.0, 1176.0, 117.0, 104.0], [81.0, 1171.0, 118.0, 109.0], [80.0, 1162.0, 118.0, 118.0], [83.0, 1142.0, 106.0, 138.0], [91.0, 1123.0, 80.0, 143.0], [79.0, 1113.0, 79.0, 143.0], [80.0, 1099.0, 96.0, 149.0], [111.0, 1103.0, 99.0, 177.0], [71.0, 1197.0, 96.0, 83.0], [81.0, 1258.0, 84.0, 22.0], [87.0, 1264.0, 87.0, 16.0], [95.0, 1266.0, 87.0, 14.0], [138.0, 1268.0, 41.0, 12.0], [153.0, 1272.0, 12.0, 8.0], [138.0, 1263.0, 26.0, 17.0], [125.0, 1257.0, 42.0, 23.0], [81.0, 1250.0, 82.0, 30.0], [85.0, 1250.0, 80.0, 30.0], [87.0, 1250.0, 78.0, 30.0], [139.0, 1254.0, 40.0, 26.0], [172.0, 1245.0, 103.0, 35.0], null, null], \"areas\": [4148.0, 4313.0, 4375.0, 4399.0, 4551.0, 4632.0, 5043.0, 5226.0, 4955.0, 4990.0, 4948.0, 5114.0, 4988.0, 4956.0, 4858.0, 4955.0, 4943.0, 5673.0, 5315.0, 4957.0, 4999.0, 4972.0, 4669.0, 4980.0, 5920.0, 5178.0, 5155.0, 5259.0, 5158.0, 4833.0, 5145.0, 5228.0, 5117.0, 5210.0, 5241.0, 5111.0, 5475.0, 7957.0, 230.0, null, 5308.0, 7038.0, 6133.0, 5761.0, 6054.0, 6086.0, 5925.0, 6122.0, 5276.0, 5087.0, 5957.0, 5786.0, 4416.0, 5942.0, 4864.0, 6466.0, 5111.0, 5250.0, 5281.0, 5101.0, 5055.0, 5074.0, 4987.0, 4849.0, 5246.0, 5429.0, 5025.0, 5137.0, 5158.0, 5054.0, 4858.0, 5028.0, 5001.0, 5055.0, 5107.0, 5093.0, 4964.0, 4998.0, 5099.0, 4988.0, 5098.0, 5142.0, 5009.0, 4869.0, 5039.0, 4972.0, 4902.0, 5084.0, 6035.0, 6034.0, 6547.0, 6792.0, 5400.0, 6163.0, 5948.0, 5585.0, 5854.0, 5968.0, 7187.0, 6640.0, 6762.0, 5227.0, 5769.0, 5704.0, 6390.0, 6792.0, 6605.0, 6267.0, 5526.0, 4874.0, 4868.0, 4955.0, 6296.0, 6440.0, 6505.0, 6089.0, 6148.0, 5898.0, 7437.0, 9679.0, 8210.0, 7667.0, 7870.0, 8225.0, 8416.0, 9239.0, 10425.0, 8897.0, 8751.0, 9476.0, 10749.0, 5649.0, 1384.0, 574.0, 297.0, 230.0, 64.0, 122.0, 465.0, 1027.0, 1042.0, 663.0, 466.0, 1017.0, null, null], \"iscrowd\": 0, \"video_id\": 0, \"height\": 1280, \"width\": 720, \"category_id\": 49272, \"noun_phrase\": \"air jordan\"}, {\"id\": 1445, \"segmentations\": [{\"counts\": \"PU`;1gV1=aiNH30SV1>fiNG20SV1\\\\1N4N2M2O1N2O2N2N1O00000O2OPO\\\\jNDdU13fjNMYU11jjNOUU10ljN0TU10mjNOSU10PkNNPU12PkNNPU10SkN0lT1NWkN1iT1LZkN5eT1H_kN7aT1FckN9]T1EekN;[T1DgkN;YT1DhkN<XT1CjkN<VT1CkkN=UT1CkkN<VT1AnkN>ST1_OokNb0hU1O0010O01O1O1N2O2M2OO1001O00001O10O00O2O0O3N2N0000001O00\\\\jS>\", \"size\": [1280, 720]}, {\"counts\": \"cdS;>oV1E]iNb0WV1K^iN`0ZV1h0J4L4M3M2O2N2O0O2O00POajN@^U1>ejNAZU1?hjNAWU1<mjNCSU1:QkNEnT1;TkNEkT19XkNGgT18ZkNHfT17\\\\kNIcT15_kNKaT14akNK_T13dkNM[T11gkNOYT10ikNOWT1OkkN1UT1LokN4PT1JRlN6mS1IVlN6jS1HYlN7fS1I[lN7fS1G[lN9eS1F\\\\lN:dS1E^lN:aS1EalN;_S1CdlN<\\\\S1BglN=[S1@glN?^U1O0000001O1O002M2O1O0000000O10002N1N2O01O01N10\\\\ba>\", \"size\": [1280, 720]}, {\"counts\": \"\\\\\\\\m:1nW1;nNKdiN=RV1IaiNi0UV1c0M2N2N3L4L5L2O2O001N10001O1NmNijN@VU1<PkNBPU1;UkNDiT1:[kNEeT19^kNFbT19_kNGaT19`kNG_T18bkNI]T16ekNJZT15gkNKYT14ikNKWT13kkNNTT10okNOPT11RlNNnS10TlN0mS1LVlN5iS1IZlN6fS1H\\\\lN8eS1F]lN8dS1G]lN9cS1F_lN9aS1G_lN9aS1FalN9_S1EclN;]S1DelN;[S1BhlN?WS1@klN?US1@llN`0[U1O1N5LO1O1O02O001O1O001O0O1O2O1O1O1N101O0O10aba>\", \"size\": [1280, 720]}, {\"counts\": \"i\\\\m:3eW1>nNLfiN>TV1m0I3M4L3N2M3M3M3M3M2O2N2N10001N1000000O2O000000001O00[NVkN4jT1IZkN7eT1G^kN8bT1G`kN8`T1FckN9]T1EgkN9YT1EjkN:WT1CkkN=UT1BmkN=ST1APlN>PT1@SlN`0lS1_OUlNa0kS1]OXlNb0hS1]OZlNb0fS1]O[lNc0eS1[O^lNd0bS1ZO`lNe0bS1YO_lNg0aS1YO_lNg0cS1VO^lNj0]U1O1O1O001O001O0O2O0O100O2O001O1O1O002M2OO10O1O2M4GmRZ>\", \"size\": [1280, 720]}, {\"counts\": \"gko:2T17VU1g1D3M3N2M3L3N3L3N3N100O1O2N100O2O0O10001O00000000001N1000000000000001O000001O000000O0\\\\NWkN3jT1H^kN4bT1K`kN4`T1KakN5_T1JckN5]T1IgkN5YT1IkkN5UT1JnkN4RT1JQlN5oS1JSlN5mS1JTlN6lS1HWlN7iS1GYlN9gS1E[lN;eS1D[lN<fS1C\\\\lN<dS1C]lN=dS1A^lN>aS1B`lN=aS1AblN>^S1@dlN`0\\\\S1_OglN>[S1_OhlN`0XS1_OjlN`0YS1[OilNe0ZU1N103LnZ[>\", \"size\": [1280, 720]}, {\"counts\": \"idn:5`V1LhjNc0cT1M^jNl0^U1m0M3M2N3M2N2N3N1O1O2N1O1O2O0O1O10001O000O101O000O101O000000000000000000000000001O000000000000000O100000O1000001O000001OO10O10000001N1000000O100O2O00001N2N4\\\\MalN?mXQ>\", \"size\": [1280, 720]}, {\"counts\": \"`cX;;Y18XT14PkN\\\\OFl0TU1OgjNh0VU1R1N3N1M4M2O2N1O1O2O0O101N101N2O00000O10000000000000001O0000000000001O00000000000000000001O01N10000000000000O100000001O00O1000000001O0000000001OO1000001O00000O100O2N1000001N3UNakN0bT1SOSmNNXhZ=\", \"size\": [1280, 720]}, {\"counts\": \"m[f;=]W1=iiNLmS1X2K5K6K4N2N2O0O1000001O000O101N101O00000O100000000000000000001O000000000000001N100001O0000000000000000000000000000000000000000000000001O000000000O2O000010O01N2O1O1O2N1O1O001N2O1N2O2M3N2M4L3PNRkNh0RU1bNijN8>n0kT1hNmkN<oNF^U1LfkN1Wia<\", \"size\": [1280, 720]}, {\"counts\": \"Y[U<g0TW1?D5M3M3M2N1Nh0ZjNUNiS1R3N101N2O001O001O0000000O2O00000O101O001O00000O100000001O00O100001O000000001O0001O10O00010O00O2O1O3M2N1O000001O01O001O00001O0O2O0010O01O1O001N101O000001O0O1000O1000001N101O2N2M2O1O3L5L3L8I;D4M6I5J8H5K5Kh0SOURe;\", \"size\": [1280, 720]}, {\"counts\": \"oRh<6fW1<^hNJiV1j0L2N3N3L5L5Jf0[O4ckN_MRS1m3K1O1N101O000000000O2O00001O000000001O00000000000000000000001O01O000000001O1O001O1O1O1O1O10O00100O01O001O001O1O1O0000001O00001O00000O100O2O0O101O0000000O2O001O001N3N1O001N2O002N2M3N:G4K3M7H5L4K5L3L6J6K4K4K<C\\\\Zm:\", \"size\": [1280, 720]}, {\"counts\": \"dZX=44=ODjV1T1H4N3L8I5jiNXN5N[U1`2J4MZ1fN3N0O1O1O0001O000001O00001O00001O00000000000000000000001O00000001O000010O01O001O00001O1O001O001O10O01O00001O1O1N11O01O001O001O1O001O1O0O01000000001O1O001O0O1000O11O1O001N2O001O2N1O1O1O1N3N3M7I4K4M5K5J5L3L4L4L7I8H9G<A[ZY:\", \"size\": [1280, 720]}, {\"counts\": \"jjn=1eW1b0Ge0[O4K4M;Fk0hjN\\\\MWO8iS1_2blNUN[S1Y3O0010O01O001O001O01OO100010O000001N101O000000000000001O000O11O000000010O00001O1O1O10O0001O010O0O2O100O2N1O1O1O00001O1O001OO010001O001O1O000000O1000O101O00001O001O1N101O1O001O1O1N2O2N2N2M3N2N5K4L5J7J9F4L4L6J9G:Eb0POdZe9\", \"size\": [1280, 720]}, {\"counts\": \"_ZV>b0a0@YV1[1H2ciNdNg1;QR1_1clNZN9`0SS1Z1UlNeN<:^S1W3N1O1O100O000010O0010O0000001O001O00000000001N10001O1O0000001O00000000010O01O00010O001O1O1O001O0010O01O001O1O001O2N1O0O101O001O1O1O001O00000000000000001O000O01001O0O2O1O1N1000001N2O1O1N4L102M3N3L3M4K9H:E>B7I5D=Gb0]Oiba9\", \"size\": [1280, 720]}, {\"counts\": \"bSU>:UW1e0ojNXOTR1]1WmNSOeR1k1[lNZNcS1l1SlNZNkS1n2O2N20O0100O001O00010O00001O001O1O000000001O000000001O0000001O00000000010O010O01O1O001O10O0001O1O002N1O1O1O1O1O1O001O0000001O0O2O1O1O00000000000000O0100O2O1O1N101N1000001O1N2N2O1N2O2M3M2N1N7I;Bj0VO_Sn9\", \"size\": [1280, 720]}, {\"counts\": \"ak_=9cW17K`0SOV1jiNTNbT1[3\\\\O4L2O2O1O1N100O2O0100O10O00001O001O0000001O0000001O00000000001O01000O00000000000001O00100O1O1O1O001O1O001O1O001O2N00001O1O1O00001O1OO1001O001O1N2O1OO10O10100O2N4L0O100O011N2N2N1O1O3L3N3M3J9`MgjNj1bV1TOSke:\", \"size\": [1280, 720]}, {\"counts\": \"_UT<<YW1`0\\\\Oc0J4L2ROn0M4L3N3L4L3M3L4M4M2O2N1N2O1O1O2O000O10000000010O00000000000000001O01O000001O00000001O00001O00001O00001O4L1O001O1N101O001O1O1O001O001N101N101O0O2O001N2O001N2O1O0O2N1N2N2M3K5UOk0J7H8M2N3L4G;I:HXlR<\", \"size\": [1280, 720]}, {\"counts\": \"ZUe;;_W19^Oa0oNoNYjNh1^U1b0M3N3L3M2N3M2N2DQMekNQ3ZT1RMakNQ3^T1:N100001OO1000010O0000001O000010O0000000000001O01O01O1O1O000000001O1O1O1O1O1O001O001O1O1O1N3N0000001O0O2O1O001XMmjNa2SU1]MojNb2YU1O00O10000000O00100O2N2nNQ1F:N3_Oo\\\\S=\", \"size\": [1280, 720]}, {\"counts\": \"Teb;2eV11WjN;XU15cjN1YU13bjN7VU1[1M3N2L5L2N3M2O2O0O2O0O1O2N1O100O10000000000000001O0001O00001O00000000000000000000010O000O10000000001N1000000000001O00001O000000000O100O2O001O1N3M1kNfjNZOeU15njN\\\\O_U1?W1ATU\\\\=\", \"size\": [1280, 720]}, {\"counts\": \"Pmm;1gV10UjNo0nT1AfjNd0XU1@[jN@0T1cU1R1N2N1M4M3K3O2O100O100O2O000000001O0000001O0000000000000001O000001O0001O0000000000000000000O100000001O001O0000000O101O1O1O1O0nMRkNR1nT1kNWkNS1jT1jNYkNT1mT1dNXkNZ1jT1cNXkN\\\\1gU1N2N1G]iNROhV13P1N2N2O0O`[S=\", \"size\": [1280, 720]}, {\"counts\": \"kSY<7eW18I:hhN\\\\OdV1W1aiNgNiU1Q2G2M2N3M2O2N2O0O101O0O2O000000001O0000001O00001O01O1O0000001O01O001O00000000fNmjNGRU17XkNBhT1;dkN\\\\O\\\\T1?lkN^OTT1>TlN]OmS1`0\\\\lNWOgS1g0k1N1N2N3L3M4K7JTb73f]H8K3O2M200001O1A?NljP=\", \"size\": [1280, 720]}, {\"counts\": \"PlR<5cW1>E?B8I7K8H3L3M2O2L3N2O2N10000O2O1O1O1O001O000O20O01O001O1O000001O01O00000000000O2O000000000000000001O00000O1000000O2O0O101O001O0O2N5Kl0TO=D5I`0^OTSW=\", \"size\": [1280, 720]}, {\"counts\": \"Rlm;3jW18B?G6J4N3WiNmN^V1`1I6M3L2N1O2O000O2O0O1O2O001O100O1O001O00001O0000000010O000000O2O000O100000000000000001O00000O101N100N201N101N102N`0_Oj0WO2L4K][b=\", \"size\": [1280, 720]}, {\"counts\": \"fkm;:cW1<ehN[OfV1S1K4M3]iNjNXV1c1M1N2O1O0O010O010000O100O1O100O010O1O1M3N3N2N100O1O1N1O2O001M3L5M2N3N1O2N2M2L6K5Led\\\\>\", \"size\": [1280, 720]}, {\"counts\": \"Vkh;:=GgV1e0mhN@oV1l0K?B7J1N3O0O2N2O0O101N2N2O1O000000000000001O001O001OO1000000000000001O0000O100000000O101O0O1O2N1O2N100O100O2O2N1N5@jiNiN^[Q>\", \"size\": [1280, 720]}, {\"counts\": \"Tkm;h0TW16H7O1N2O8G6K3L2N2O0O2O0O10001N1000000010O01O001N100000O11O000O10001O0000000000000001N1O1O2O0O100O101N101N2N3M4K?Ak0QOT\\\\l=\", \"size\": [1280, 720]}, {\"counts\": \"Rkm;;]W1i0^O3L3L4M7J4M3L3N1O1O1N101O00000000000O100O101O0001O0000000000O10000000000000000001OO10O1000001N1O2O001N101M3O1N5I;^NiiN=EGicm=\", \"size\": [1280, 720]}, {\"counts\": \"Rcl;4gW1k0VO7K3L5K;F5M2M3N1O0O01000O1000000O1000000001N02O000O1000001N100000000000000000000000O100O1000000O101N101N2O2L5I8Ea0ROeem=\", \"size\": [1280, 720]}, {\"counts\": \"Tkm;42b0nV1<H`0E7H2N2O1O2O000O10000O100O10000O1000O01000000O10000000000O10001N10000000000000O01000O10000O101N101N2N3N2M4K5H:FYem=\", \"size\": [1280, 720]}, {\"counts\": \"nbQ<3lW13WhNO^W1<10chN9aV1c0OO0O1N103M100O1O1O001O001N101O2N2N1O0000001O00001O00001O000000001O001O001O001O1N10001N100O100O1O1O1LSdm=\", \"size\": [1280, 720]}, {\"counts\": \"lbV<:cW16J<C<UiNnNUV1c1M000O100O10000O101N1M30O101OO10001OO2O000001N10O1001O00001N1001O01O00001O00000O100001N100O2O1O2M8I4KT1lNW\\\\g=\", \"size\": [1280, 720]}, {\"counts\": \"jZ_<=^W1=E3N2N2O1O2N1O100000O10O101OO1000O01O100O100001O0O100O1000O01000000O2O001O00001O001O00000001O00O2O01N2O2M5L2N2H<@dU\\\\=\", \"size\": [1280, 720]}, {\"counts\": \"mbe<5hW15XiNL\\\\U19`jNMYU18djNKYU17fjNJYU18djNJ[U18bjNJ^U17^jNLaU19TjNNlU1T1N10000O100O1000001O0000000000000O10001N100000000O10000000000000O100000001N101N10000O2O3lNSjNNRV1HXjN4lU1C\\\\jN8jU1_O^jN=eV1LWlU=\", \"size\": [1280, 720]}, {\"counts\": \"XSc<3eW1:dhN1`V1n0H5K5M4K3N2N100O100O101O0O1O1000000000000O1000000O100O10001O0O100000000000000000000000O1000000O101O0O102N2M6K4L2Ml0UO9E6CW\\\\S=\", \"size\": [1280, 720]}, {\"counts\": \"U[d<7dW1d0fhN[OWV1]1J5K3N2N100O2N10001N1000001O0O101O00000000000O1O1O10010N1000000O01000O2O0000000000000001N1O1O1O100O2O001O2N3M3M2M7Jn0QOS\\\\S=\", \"size\": [1280, 720]}, {\"counts\": \"\\\\\\\\d<1mW16I4L4oNP1L5L4L3O1O2O0O101O1N101O0O101O00000O1000000O2O00001O0000O1001O00O010000O10000001O001O0O01000000000O101O1N1O2N3M4L8HR1kNRlP=\", \"size\": [1280, 720]}, {\"counts\": \"^ce<9cW1=lhN]OUV1Z1J6M3N2N1O2O0O2O001O1N1O2O000O100000001N100000001O000O10000000000O10000000000000O100O100O100O100010OO2O001N2O3L8G:dN_iNg0_W1\\\\OlSR=\", \"size\": [1280, 720]}, {\"counts\": \"aka<?<D[V1h0aiN^OVV1i0eiN[OYV1X1M3O1N2O1N2O1N10001N10001N100O2O000000O10O100O10000O10000O10000001O0O01000O1000000000000O2O0N2O1O2O1O1O2N2N3L9Hb0]OZTW=\", \"size\": [1280, 720]}, {\"counts\": \"Wce<6fW1:PiNIfU1`0iiNYO1<TV1Y1M2O2N101N10001N1O100O2O0000000O10000O10O01O00100000000000O2O00O2N1O1000000000O2O001O0O2O2N1O1N2O4L3L5L7H>A6F^lU=\", \"size\": [1280, 720]}, {\"counts\": \"nbe<<aW15M4L;Eb0]O9H3N1N2O001O0O10001O00000000000O1000O010O010000O10000000000O0010O010001O0001O001O1N2O2N1O001O3L3M4M3L5L6J7H6J8HUTR=\", \"size\": [1280, 720]}, {\"counts\": \"QSh<;cW1V1jN5J5L3M2O2O0O101O0O100O2O0000O1000000000000000000000O10000000O100000000000001O001N101O1N2O1N3N2N3M3L3N8H9F5L6HTdT=\", \"size\": [1280, 720]}, {\"counts\": \"[SR=5_W1l0]Oe0\\\\O5M2L2O2N1O1O10001O0O10O10000000O1000000001N02O001O0O10000000000000000O2OO2O00000O2O001N2O1N3N3L5L4L3M6J8H7HWdj<\", \"size\": [1280, 720]}, {\"counts\": \"ejZ=1oW10]iN?RU1T1F9M3O1M2O2O1N1000001O0O10O2O00O100O20O001O001O000000O101N10O100000000000000000O1001O0O2O001O1N3N4K7I4M4K6K9G9FSd`<\", \"size\": [1280, 720]}, {\"counts\": \"[\\\\X=3bV1_1H5M3M3N1O2N1O100O1000000000000000000O2N1O2O01O001O0000O100O1O100000O1000000O10001O0O2O001N101O1O1O2N4K9cNiiNa0TW1FQlf<\", \"size\": [1280, 720]}, {\"counts\": \"ojd=1jW1^1dN7J3M3M2O1N2O2M2O10O010000O10000O101O0001O00000000O10000O100000O100000O1001O0O10001O1O1O1O001O2N3L6K2M3M<Dc0\\\\O\\\\TY<\", \"size\": [1280, 720]}, {\"counts\": \"ccR>=TW1d0\\\\O`0J6M2N3N1O1N3N1O1O1O1O2O0O11O0O1000O101O000000000001O1O00O100O101N1000000001O0O1000001O010N101O1O1O3L5L3M4K;E;C8H;E]\\\\f;\", \"size\": [1280, 720]}, {\"counts\": \"`bh=`1]V15L3M3M2N1O2O1O001O10000000000O10001O000010O01OO100000O101M2O1O100001O0O10000O10001N101O001O1N3N1O2N4K7J8H<D;Ce\\\\U<\", \"size\": [1280, 720]}, {\"counts\": \"Zji=6m05aU1]1H3M2M3O1M3N2O1O100O10000O100001O1O2N000000000000O1O10000000001O0O100O100000O1000000000000O2O0000002M2N3N2N7I6]NZjN?TW1^O[dQ<\", \"size\": [1280, 720]}, {\"counts\": \"fSZ>3\\\\V1c1K5L2N3M2N2O1N2O1000000000000001O00O10000000000O10001O0000000O10000000000001O01OO100000001N1O2N2M3K6K4K9C=Gf0ZO\\\\mc;\", \"size\": [1280, 720]}, {\"counts\": \"^SP>;aW16\\\\Oc0C=G8L4M4N1N2O1O1O1O02O00O10000001O00O100001O1O01N100O10000O10000O1000000O1001O000O1000001O001O1O1O1N3N3M7Hj0UOb0VOl\\\\k;\", \"size\": [1280, 720]}, {\"counts\": \"ZRf=6X1KUU1f1J3M3N1N2N3M2O1N2O01000O101O0O10000001O1O2N01O000001OO100O1O10100N1000000001N0100000O101N10001O1O1O1N3N2N3_Od0APnW<\", \"size\": [1280, 720]}, {\"counts\": \"eki=9UW1c0ZOf0J5L4M4N102M2N1O100O100000001O00001O0000000000000000001O000000000000000000O11O00000000O2O0O2O1N1O2N3M2M4M6A?nN]nR<\", \"size\": [1280, 720]}, {\"counts\": \"a[g=c0RW1=WOUOSjNR1gU1f0M3N2O1N200O10000O101O0O1000000000001O00000001O0000000000O10001O0000O10000000000001OO101O001O0O3N1O1O1N4L5`NUjNHK<YV1DcjNNoZU<\", \"size\": [1280, 720]}, {\"counts\": \"hkP=1jg30_XL0ZW1?SOFhiNd0QV1i0L4L3N2M2N3N1O1O1O1O001O2O0000001N100001O1O000000000000000000O1000000O1001O0O1000000O10001N10001N2O1O1N3K9POYVh<\", \"size\": [1280, 720]}, {\"counts\": \"gkP=3kf35kYLJ@2^V1[1L8H2L4M3N1O1O100O1O100O10001O00000000000001O01O0000000000000000000O10000O1000000000O10001N2N2O001N101N2N3fNfjNAVkk<\", \"size\": [1280, 720]}, {\"counts\": \"fS\\\\=d0`V1]OoiNR1jU1e0M2M4M2M3O1O1O100O1000000O100000001O0000000000000000000000000000000O100000O2O000000O11O0O2O001O1N2O3M5K5jNiiN9VW1G[la<\", \"size\": [1280, 720]}, {\"counts\": \"mkZ=1gW1c0jNn0I5L4M2M4M2N2O1O1000000O1000000000001O000000000000000001O0O10O101O000000O1000000O1001O0000O101O001N2O1O2M3M7I7aN^jN2ija<\", \"size\": [1280, 720]}, {\"counts\": \"\\\\cT=;PW1o0B;G3M3M3M2O2N1O101N100O0100000O100000O1000001O01O000000000000000O010O1000000001O0O10000O2O001N10001N2N2WOmiNBUV12ijNYO^U17k^i<\", \"size\": [1280, 720]}, {\"counts\": \"ojU=191L51JdV1V1I8K4M3M3M3M3N1N2O2N101O0O100O100000001O0001O01O00000000000O100000000O10000001O00000O100000000O100O2O1N2N4SOUjN^OYV1@kVh<\", \"size\": [1280, 720]}, {\"counts\": \"k[S=3`W1?H7D<K5F9L5M2O2N1O1N2N2O2O0O2N10000O100000001O000000000000O100001O000O101N0100000000000000000000000000O2O001N2O2M9]Nf1A_ee<\", \"size\": [1280, 720]}, {\"counts\": \"ekk<5cW1<I4C<N2K6I6H8N2O1O1O1O2N1O1O100O1O2O0O2N10001O01O00000O1010O01OO10000000000O10000N2O100000000001O000O101N3M1N3L3C>\\\\Od0JTVm<\", \"size\": [1280, 720]}, {\"counts\": \"U[n<d0RW1;M3M3@?L5N001O1N2O2M2O1O1O1O1O2N100O101O0001O0O1001O01O1O2NO10001O00O0100O10000O10000001O0O1000001O1O1O1M3I7WOj0G;CVVm<\", \"size\": [1280, 720]}, {\"counts\": \"[kP=:^W19H8K5L3H8H9L3N2N3N1N101O1O1O2N100O1000000O101O001O0000010O000000000000000O10000O100000000001O001O001O00001N101N3L4Ba0UOeVh<\", \"size\": [1280, 720]}, {\"counts\": \"obo<e0VW17J5\\\\Od0L4N1N2O1N2N2O1O1O101N100O10001O001O0O100000001O01O00010OO100O100000000000000001O0O01001O0O2O1O1N1O2L4K5]Oc0B?IYnk<\", \"size\": [1280, 720]}, {\"counts\": \"XSh<2kV1V1I7K5K4L2O2N1O2N2N2O1N10001O0O2O00001O0001O001O01O00000000000000000000000O10O1000001O000O100O1O1M4I6A`0F;I7HZVW=\", \"size\": [1280, 720]}, {\"counts\": \"jRc<a0^W1:E5\\\\OXOfiNn0VV1`0N1N3M2N2O1N2O1N3N100O1O101O001O0O1001O0000001O0001O000000000O1000O1000000000000O100000001O001N1N2N2K6A?]Of0]Oc^X=\", \"size\": [1280, 720]}, {\"counts\": \"oZd<9cW19I9F5^OSOdiNW1YV1:L3N3N1O1N3N1O2M2O1O1O101O0O10000001O0001O01O000000000001O0O10O01O10001O00O1000000O1000001N1O1O2M2L5L4YOg0J9_ObnU=\", \"size\": [1280, 720]}, {\"counts\": \"kja<b0RW1d0]O<L4N2M2N3N1O1N2O2N1O100O2N100O2O00001O0000000001O0000001O01O00O100000000000000000000O1000000O1O1J7I6N2L5H7K6E;CdfY=\", \"size\": [1280, 720]}, {\"counts\": \"Y[_<1`W1j0A6^Ob0K4M3M2N2M3O1N2O100O101N100O100O2O00001O00000000010O01O001O000000000O11O00O10O1000000000O100010OO2N2N1M4H9C=]OeV\\\\=\", \"size\": [1280, 720]}, {\"counts\": \"jZd<c0ZW1;YOXOaiNo0ZV1?L2M4M3N1N2O1O2N1O1O1O100O10001O000000001O0001O1O000001O00000O100000O100000000000000000001N101N101N1O2L4J<QOg^X=\", \"size\": [1280, 720]}, {\"counts\": \"iRh<d0gV1j0H5K4N2M3N2N1O2M2N2O001O100000001O0000000010O010O001O0000O100000000O10000000000000O1000000O10000O2N1O2M2K7XOh0@SiNB\\\\UW=\", \"size\": [1280, 720]}, {\"counts\": \"hZn<d0QW1?C<I5L4L4N2N1O2N1N2O1O2N1000001O001O00010O000O020O0001O0001O0000000O1000000O100O1000O10000O101O0O102L4F:G8YOo0VO`fo<\", \"size\": [1280, 720]}, {\"counts\": \"ebT=272ONfV1W1H5L3L4M3M2O2M2O1O1O1O2N2N20O000001O1O0001O01O0001O010O0000O10O10O01O010M3N2N2N2N2O1O1O2O1M3K6_Og0_O=Joeo<\", \"size\": [1280, 720]}, {\"counts\": \"cRW=l0mV18H8N2N101N1O1O100O2O2N1O0O1000001O00001N1O2N2O001O000000O1O1O1O1N102K7I9I6Hd0[O7KZeY=\", \"size\": [1280, 720]}, {\"counts\": \"YjP=4gW1l0UO<F9G5I6M2N010O101N2O0001O02O0O1O100O00001ON2O101N1N2N2M3N2N2N1O2M3O1N2N1N3N2O1O1N2HXiNVOjV1b0=H:K5I_VW=\", \"size\": [1280, 720]}, {\"counts\": \"Vjk<1m00oU17diNLD2eV15ZiNa0aV1d0K3K5L5K2N101ON3N2N2M3M3L4O1N2O1N4L3L4L4N1O2M2N2O100O1000000O2M3M2N2M4J\\\\P59ZoJ8M2N100O1N2O2O0010N102M3L6FWfo<\", \"size\": [1280, 720]}, {\"counts\": \"Vjf<2e05XV13ViNg0\\\\V1e0H3N3M3N1O2N100O1O2O001N110O0000001O01O001O10O010O0001O0000O10001O00001O001N01O1O2L3M3N2M3M3K5N2N2L4N3M2N3N2H<I:Em]S=\", \"size\": [1280, 720]}, {\"counts\": \"ab`<676eV1IUiNj0fV1<K6J8F6N2O1N101N100O101N10001O001O01O01O00000001O00001O00010O0000O100000000000000000000000O100O2O00000O100O1I8B=C`0K:F>AemU=\", \"size\": [1280, 720]}, {\"counts\": \"`b`<1jW1b1_N6L3N1O3M3M2O1N1O2O001O01O00011N1O00000001O01OO10000O2O00000O2O00000000O10O010O10O10001O0O1O1O2N1XO[jNSOiU1g0bjNQOaU1i0o0K;F7I7G[U\\\\=\", \"size\": [1280, 720]}, {\"counts\": \"Tc`<1hW1>H3ahNK9HUV1d1G2N3L3M3O1O0O2O00000001O00001O0000000000000001O0O101N10000000000000000O001O10O101O0O100O2O1N1O2M4M3M4UOZjNVOjU1c0]jNYOPV13fU\\\\=\", \"size\": [1280, 720]}, {\"counts\": \"cjW<=aW16Jb0^Of0ZO7J2N2O1N101N10001O000O100001O0000000000001OO1000000000000000000000000O1001O00000O1000001O0000001O1N2O4K5K4L?@:E5Igl_=\", \"size\": [1280, 720]}, {\"counts\": \"jRT<6gW1j0dhN[OkU1a1L5K2O1O1O1N201N10001O000O10O1000000000001O000000001O00O10000000001O000000O10O1000000000000001O001N2O1N4M3L<Eb0]O9GZTf=\", \"size\": [1280, 720]}, {\"counts\": \"ijW<4jW16J6hhNCdV1T1SiNXORV1^1L3N2M3M2O2N1O100000000000000000001O000001O000000010O00O10000000O10000000O10000000O101O0000000O101O2N2M7HV1`Nddc=\", \"size\": [1280, 720]}, {\"counts\": \"mZ_<i0QW1n0UO5M3L2L5M2O1O1000001O00O1001O01O001O1O0001O0001O0001O000000000O10000000000O1O010O0100000000O2O001O1N101M4L4cNjf^=\", \"size\": [1280, 720]}, {\"counts\": \"Y[d<P1jV1?E4M6J7J2M100O2O001N10000O101O0001O00000001O0001O001O000001O0000000000O1000000O100000000O100O1O100N3M2M3^Oc0C=D>FS]X=\", \"size\": [1280, 720]}, {\"counts\": \"fSh<`0YV1HQjNo0kU1e0L3O2N2N101N1O2N1O1O101O000001O0000010O00000000001O001O00O2O0O1O10000000O100000000O010000000O1O2O0O2N1O2N2I=TOl0E`]S=\", \"size\": [1280, 720]}, {\"counts\": \"akf<7?JXV10diNQ1RV1b0K5J6J6N1O1O100O2O0O100O10001N100001O00010O00001O01O0000000000000001OO100000000000O10001N100O10001N1N3K:_Oj]X=\", \"size\": [1280, 720]}, {\"counts\": \"^k\\\\<:fW18G4[Oi0D6K5K4L4O1O2O0O1O2O0O100O1O2O00001O0000001O01O0000000000000000O02O000000000001O0000O10001N101N7J7Ii0SOhdc=\", \"size\": [1280, 720]}, {\"counts\": \"gc[<7gW1a0\\\\O6L4A?K4N2M4M3N0N2O2O1O1O100O2O0000001O0001OO2O010O01O00O1000O10O1O10000000O100000001N1O101O2N9F5Kh0UOc\\\\g=\", \"size\": [1280, 720]}, {\"counts\": \"Pch=d0VW1>C7K4N3M2O3L4M3M2N1O0104K6J4]NQjNl0PW1\\\\OWTf=\", \"size\": [1280, 720]}, {\"counts\": \"mka<8]W1`0A=D<K4L4M2N2N1O2O1N101N1O101O010O1O00000000001N100000001OO0100000000000000O01O10001O3L4L6Gl0aNQUf=\", \"size\": [1280, 720]}, {\"counts\": \"fT^<9gW19E2L5B=L4H8K6M3M4N0O1O2O0O2O0O2O00001O0000000000001N10O11O0010OO10001O0O10O10000000000O10001O0O2O2M3M2M4K6K>Cf0nNehN3hW1IT[]=\", \"size\": [1280, 720]}, {\"counts\": \"bS^<=^W18D>A<I6L4K5M2O1O00101N100O10000O10000010O000000000000O10010O000O100O1001O0O01002N1N2O1O1O2N2M3N3M4K<E7H5J4J9F`l_=\", \"size\": [1280, 720]}, {\"counts\": \"lSh<b0kV1f0@>M2N3N1O1N2O100O101N10000000000O11O0000000001N100O2O0O100O1O100O10000000O10001O1O2N2M4L4M2N1O3L>C8H5J7HU\\\\X=\", \"size\": [1280, 720]}, {\"counts\": \"jRm<3kW1b0]On0SO4L3N2M3N2O0O1O100O100O100000000000O10O2O0O1O1001O0O2O10O000000O10O11O0O2O001N2O001O1O1O2M4M3M3L9H7G5K4M4K<CXdo<\", \"size\": [1280, 720]}, {\"counts\": \"jjU=86?cV1i0F2N2L3O2M3N1O2N1O2OO1O10O1O100O100000001N010O1O1O100001O000000001O00000O101O001O001O1O2N2N3M4L4K8I5J4L5K4K6H`Th<\", \"size\": [1280, 720]}, {\"counts\": \"ccY=e0TW19@?I6N3N1N3M2N1010O01O2O0O100O100O100N2O10000O1000000000000O2O000000000001O000O2O1O4K4M1N3N1N4M6J5J6K3L5K4J7I`\\\\d<\", \"size\": [1280, 720]}, {\"counts\": \"ccY=163YW1>H9F9K7H7J5K4O01O10O1O100O2N1000000O1O001O101O00000000O0100O1O1000000001O1N101O1N2O2O4J4M4L4J6L3L3M4L5L4K5Gede<\", \"size\": [1280, 720]}, {\"counts\": \"\\\\k_=8[W1InhN=gV1f0A>J5K4N1O2N1000000O1O10O10N200O100O10OO2N2N21O1O1O001O01O0000O1O2M3M3K4L5M3L4N3L3M4M3M3M5J:G3L3L4LYUc<\", \"size\": [1280, 720]}, {\"counts\": \"b[]=6dW1=]O=H8J6K:I2M2O1O2M200O1O1O1O100O10O10O10001O000O1N2O1O0011O000000000000001O00O11O0O2N2O2N100N3N3K6K6J5K6H9H4K9D:GgT^<\", \"size\": [1280, 720]}, {\"counts\": \"Ql_=8WW1d0I6L2B?K4N2N3M2O1O1N2O1N22M2O0O101O00O1O1O1N2O2O0000000O100000000O1001O0O10001O0O101O1O1O2N2N3M3M3M3M7I5K4J6I9G]l\\\\<\", \"size\": [1280, 720]}, {\"counts\": \"dk_=e0TW19_OTOeiNS1WV1=M2M3M3O2N1O1O1N2O1N20O01O1000000O10001N100001O000000O11O000000001O010O000O2O001O1O1O2M3N3M3M3L7G;G6H:E_\\\\_<\", \"size\": [1280, 720]}, {\"counts\": \"a[]=h0aV1j0J4L3O2N1N101O2O0O1O100O1O1O10O01O011O000O1000000O1000001O00000001O000O1000001O00001N101O1O2N2M3N3L5J4K5L=D8H6Ha\\\\_<\", \"size\": [1280, 720]}, {\"counts\": \"b[]=>6DoV1m0YOd0M3L3O1O1O1O2N1N200O1O100N2O1O1O10000O2O000O10000001O00O100001O00000000000000001O00001N101O1O002N3M3M2M3M4M4Kb0]O8Dbl\\\\<\", \"size\": [1280, 720]}, {\"counts\": \"\\\\S\\\\=5\\\\W1d0D9D<F9L4OO10O100O100O100O100O002N100O2O00000000000O11O1O1O000000O1O100O100O10000O1000000001N101O1O2N2M3N3L4M5J>BZ]_<\", \"size\": [1280, 720]}, {\"counts\": \"Yk_=?[W17I7O0000010O01O1O1O1O1O1O1O000001O00001O0000000N2O00100001O1O00100O000O10000000bNZObkNf0`T1]O[kNc0eT1_OYkNa0gT1@VkNc0iT1^OgjNKD2Kb0jU1CejNLB1O`0jU1DbjNNCO1?kU1C_jN1Ef0mU1WO]jN5Gc0mU1VO[jN9Gb0nU1YOSjN[1mU1;2O3L3M8Gb0UO<AhhNNed[<\", \"size\": [1280, 720]}, {\"counts\": \"gk_=b0XW17L4M3K5H7I8M2O2M2M3N2N3N1O00100000001O0O10000001O1O1O1O0000000001N100001N100000001N2O000000001O1O000O1O2N1N3K6TOP1EYe[<\", \"size\": [1280, 720]}, {\"counts\": \"mc^=7`W1`0D8I6BmNhiNW1UV1<N2M2O1010O1O01O00000001O0001O000010O000001O0000000001O1O001O001O1O1O1O1N2O1N2O0O2O1N2O1O1N3M4ChTc<\", \"size\": [1280, 720]}, {\"counts\": \"kkZ=3QW1n0DPOaiNU1]V19N1O1O100O001O00010O11N10O01O010O01O01O00100OWOaNRkN^1nT1eNojN[1QU1hNmjNW1SU1jNmjNU1TU1nNijNQ1YU1oNgjNo0XU1TOgjNk0XU1VOijNi0WU1XOijNg0XU1YOhjNf0XU1[OijNc0WU1]OjjNb0WU1]OjjNb0VU1_OijNa0WU1_OjjN`0WU1BhjN<XU1EhjN:XU1FijN8XU1IijN5XU1JjjN4VU1MkjN1UU1OljN0TU11mjNMTU13ljNKVU14ljNIVU17jjNF[U16hjNG[U13]1Ha]d<\", \"size\": [1280, 720]}, {\"counts\": \"Pcc=6gW15K4N5jhN_OdV1Q1_iNSOkU1m0QjNYOlU1i0SjNXOlU1h0SjNYOmU1g0TjNXOkU1h0ZjNTOeU1j0cjNQO\\\\U1o0djNTOZU1j0djN[OZU1e0djN_O[U1b0ejN]OZU1e0fjNZOZU1f0fjNZO[U1e0fjNZOZU1e0gjN[OZU1e0fjNZOZU1g0fjNXOZU1h0gjNWOYU1k0ejNVOZU1l0cjNWO\\\\U1i0cjNYO[U1g0ejNYO[U1g0fjNXOZU1i0ejNXOZU1h0fjNXOZU1h0fjNXOZU1h0fjNXOZU1h0gjNWOYU1i0hjNVOXU1j0ijNTOXU1l0gjNUOYU1k0gjNUOZU1j0fjNVOZU1j0gjNTO[U1k0fjNTOZU1l0gjNROZU1n0fjNRO[U1n0djNQO]U1n0djNRO\\\\U1n0ejNPO\\\\U1P1fjNmN\\\\U1R1k0N001L4N2N1M4M5EVm\\\\<\", \"size\": [1280, 720]}, {\"counts\": \"d[g=;^W18J6M=D7F5M3N2M2O1O1O1O10001N101O001O1O1O1O001O00000000001N10001O00000000001OO10000O10001N100O1O1O10000O3M4L2O1N2L4J7Cc0\\\\OR]P<\", \"size\": [1280, 720]}, {\"counts\": \"ajn=>WW1`0khNXOgV1Y1G6J2M3N101N1O2N1000000001O00O10000O101OO0100O1O1O1O2O000O1000010O00000000001OO1000000O10001O0O2N1O2O1N2O5J4M1M4Jm]k;\", \"size\": [1280, 720]}, {\"counts\": \"cc\\\\>;[W1;N1O1O1I7A`0E:0000O1N2N200O1O1O100O1000000O100000001O00000001O010O0O2O0000000O2M3N2M4L3M4L8I:D4M3L[mh;\", \"size\": [1280, 720]}, {\"counts\": \"W[[>3eW1:K5K:H:F<D;E3N1N2N2O1N01000O1O10O1N2O1O1O1O1N2O2M2O1N101N2O2M2O1N2O1N2O2M2O2M2N1O3N1N3N2M2YOnhN>\\\\W1N2L3J21Ymh;\", \"size\": [1280, 720]}, {\"counts\": \"Zk]>6fW1;Ch0ZO8E8N101N100O101N100O1O001O100O1O2O001O001OO100O2L4M2N3M3N1N3M3N1N1O1O3L3H9F<H;C]]P<\", \"size\": [1280, 720]}, {\"counts\": \"jRP>2lW19G?B9Ga0_O4L3M1O2O1O001N2O002N0O01O1O001O1O1N2OO11O0O2O1O1N2N2N2N2O1M3O001O1N2N2N2K5E=Fmm\\\\<\", \"size\": [1280, 720]}, {\"counts\": \"P[g=l0nV1f0_O3M3M3N2M3N3MO00100M3O1O0O2O1O1N2N2O1O001N2O1O1M3O1N2O0O2O1N2O1N2N2N3N1N3M2N3M2N2N2N3M4K[Uc<\", \"size\": [1280, 720]}, {\"counts\": \"dbc=5gW1g0ZOi0XO4L3N100O10001N101O1O1O00000O100000000O1O1O1O100001O0O10000000O2O2M4N0O00O1O2N1N2N2O1O100O2M2N3M3M2N3M2L5L7K<D4LXeV<\", \"size\": [1280, 720]}, {\"counts\": \"bRa=4hW16K5L9G<PiNVOSV1`1L2N1O100O2O000O101O001O00000O100000001N1O2O000O1000000000000O10001O0000000000O2O00O1O10000001N2N1O3N2N2N3L8H9G8PO\\\\iNKUdV<\", \"size\": [1280, 720]}, {\"counts\": \"dZ]=2m04fU18fiNEL`0UV1T1L3M4N10000O1O2O00000O100O101O1O1O001O00000000000O1O2O0O1000000O10000000000O0100000001N100000000O2O0O10001O2M3M4L4L7dNXjNE_W6OkoJ0]lR<\", \"size\": [1280, 720]}, {\"counts\": \"cRR=5eW1b0\\\\hNBjV1]1^O3L4M1O1O2N1O2O0O101O0O2O001O00001O00000001OO1000000000001O0O01M3K5N2K5M3M3O1O1M3M4M2M3N2M3O002L3N2O1N3L3O2M4M4JmUh<\", \"size\": [1280, 720]}, {\"counts\": \"^SW=1hV1m0_iNWO\\\\V1Y1L5MN2000UjN`NXU1^1jjNbNUU1]1g0O2O001O1N2N2N2N2O0O2N2N2O1O1O1O1N2O1N2O1O1N1O2O010O1N2O1N2O2N1O2Nng33oWL10M202N[^n<\", \"size\": [1280, 720]}, {\"counts\": \"Yj_==`W16Jf0\\\\O9G7H3N2N1O2N1O1000001O0O1000000O100000000O10000O011O000000000O101O001O1O000000001O00M3O2N1N2O1O1N3M2O2L5I<E8Kc0\\\\O8GVUY<\", \"size\": [1280, 720]}, {\"counts\": \"^Zb=:cW15Kn0SO9G6J3N1O100O2O0O10001O0000001O000000000O01O0100000000O101N10000001O0000000000001O000O100O1000000O101O002N1N4M6I7Hc0VOY]U<\", \"size\": [1280, 720]}, {\"counts\": \"]Zg=>\\\\W1o0TO=E3M2N1O1O101O1O1N1000000O1001O00000O001000000000O10000O11O0000000000001O0000000O100000000O2O00001O001N2O2N1O3L8bNTjN9mV1EjdQ<\", \"size\": [1280, 720]}, {\"counts\": \"[RU>>[W1Q1TO<E3M2O0O100O2O000O1000001O000000000O1O2O000010O0000O1O1000O1O1000001O0000000000000O1000001O0O101O0O2O1N3N3K4K8lNeiNGN2Wdg;\", \"size\": [1280, 720]}, {\"counts\": \"^bW>P1lV1a0A6I4K401N1O2O0O2O0000001O0000000O1O01001O000001N11O000N110O100010O0000001N10001O000000000O101O1N101N3M3K4G=B?WOSVe;\", \"size\": [1280, 720]}, {\"counts\": \"[Rk=^1^V19F5M4M3O01O0000O10000O2O00000000O1000000000000001O000O10O1000000000001O1O0000O0101O00000O10001O000O2N2EQjN`NSV1Z1>B>DUnR<\", \"size\": [1280, 720]}, {\"counts\": \"PZb=2lW14K5La0`hN\\\\OcV1\\\\1D4M2O0O100O100O1O100O1000001O0O1000000O3N1O001O00O100O1O1O1O10000O2N2N1O2M3N1N2N2O1O100O1O2N1O2L4L6L9F7J2MnUY<\", \"size\": [1280, 720]}, {\"counts\": \"giU=:cW15K6Kc0^Oa0_O3N1N2O0O101O0O011N1O10000000000000O2O000001O00000000000000O10001O000000000O100010O0001O0O1K5K6K4M7J=D8F5DUfe<\", \"size\": [1280, 720]}, {\"counts\": \"cYd<<cW14M3M1O3L7I5K4M2N2N3L4M6J7H3N2M101N11O00000000001O00000000000000000O10000O100000O11O1O0O100O2N3L3M3L3K6L5I7J<@XfY=\", \"size\": [1280, 720]}, {\"counts\": \"^Qm<7hW14M2N3L5L7I9G4L4L3M3L3N1O1O3L2O2N1N3M2O1O1O2N10O1O1N10001N2N3N0O101O1N3N1N3Ma0\\\\NVjN1eSf=\", \"size\": [1280, 720]}, {\"counts\": \"`Qc<:fW13L3N1O1O1O2M3N2N3M4L3K6L6J4L100O2M3M2O2N2N2N1O1O1O100O1O1N3O00O1O000O002O1N3N0O101N2N101N4M>[NeiNi0PW1TOSiN3Wld=\", \"size\": [1280, 720]}, {\"counts\": \"]jR<6dW1o0UO?@6K4L3N1O1M301N1000000010O0O2O1N1000O2O00000000000O10O10O101OO2O000000001O000O100O101O00000O2O1N3N2M2N3M3Ma0^O4K4\\\\OkhN11N]dh=\", \"size\": [1280, 720]}, {\"counts\": \"jbQ<5[W1^1oN5K4M2O2N100O1O100O2O0O1000000001O1N1000001O000000000000O10000000000000000001N10000O1000001N1000001N3L4M2Kl0dNmhN7Ydm=\", \"size\": [1280, 720]}, {\"counts\": \"`Z_<1gW1f0_Oh0YO:F4L3O0O10001N10000O10000000001O00000000000O10000000O100000O100000O1000001O000O10001O000O2O1N2O2M7I>eNhiNOi[b=\", \"size\": [1280, 720]}, {\"counts\": \"dRc<2fW1:J6ahNCUW1i0mhNUOhV1^1C>B2N2N101O00000O2O0001OO100000000000000000000000000O11O0000O10O101N100000000O10001N100O2O1O1N2O1N6J:Fg0ZO1L5IhTW=\", \"size\": [1280, 720]}, {\"counts\": \"ija<a0\\\\W1=C`0A:B8L4M1O2O00001O000000000000000001O00O010000000000000000000O1000000000O2O000000001N100O2O0O2O1M4L3N2O1O5]NmiNm0kV1E6I6Kf\\\\X=\", \"size\": [1280, 720]}, {\"counts\": \"jjk<c0XW1k0XO6I5K5L3N2N101O00001N1000000000000000000001O00000000O10000000000000000000O10000000001N10001N1O101N3L5K5K6H7@cUR=\", \"size\": [1280, 720]}, {\"counts\": \"oRW=>b0HRV1X1K5I6M3N2M3N101N1001O00O101O000O1000000000000000000000000O10000001O000000000000000O101O001O0O1O2N2N2M3M5K5I>D:WOZee<\", \"size\": [1280, 720]}, {\"counts\": \"P\\\\X=4PW1o0G8I6M3K5M3N2M200O101N1O1000001O00000001O000000O11O0001N100001O000001O0000O100000001O00000O2O0O101N1O1N3L5K5G:]Oi]d<\", \"size\": [1280, 720]}, {\"counts\": \"b[]=:WW1l0_O5K6L3M3N1O2M2O1O2O0O1O2N1000000000000000001O00000001O0001O000O1000000000O1000001O00000O2O000O1O2N2N1N3J5A`0G=Aa]_<\", \"size\": [1280, 720]}, {\"counts\": \"P\\\\]=b0gV1CZiNo0bV1:L3M4M2N3M2O2N1O2O0O1000000000001O00001O01N11N10001O001O0M2103M001OO1000O101O0O11O0001N100O1O1O2N1N3J6C?]Od0DVe`<\", \"size\": [1280, 720]}, {\"counts\": \"ZlZ=:QW1f0A?L4N3N1N3M101N1O1N3N1O1O1O100O1001O1O000001O000001O1O01O000000001OO10000000001O000O100O2N1O1O1N3M2J9@`0^OUiNFikf<\", \"size\": [1280, 720]}, {\"counts\": \"Qdh=<cW15L1O2N3M5J4MO1O10O100O2O0O100O2N100O100O1N2O1O1O1O1O1N1K7L3L5L3O001000O101N1O2M2O1M3E?YOm0@ila<\", \"size\": [1280, 720]}, {\"counts\": \"iZl=4kW1;E4L5M0000O101O1O1O1O0000001O01O2NO0100O101O000O3N1N103M5J2N0000O1N2O2O0O100I8B=FgTc<\", \"size\": [1280, 720]}, {\"counts\": \"kkn=3kW1300O1O1O1N101N3N1N3N1O2M2O3M3M2M3N4L1N2O1N10001N100O01O001O0O003G9^Od0DgTh<\", \"size\": [1280, 720]}], \"bboxes\": [[294.0, 619.0, 64.0, 78.0], [284.0, 610.0, 63.0, 86.0], [279.0, 603.0, 68.0, 91.0], [279.0, 605.0, 74.0, 94.0], [281.0, 600.0, 71.0, 99.0], [280.0, 589.0, 81.0, 106.0], [288.0, 594.0, 91.0, 114.0], [299.0, 606.0, 100.0, 123.0], [311.0, 604.0, 110.0, 131.0], [326.0, 595.0, 114.0, 138.0], [339.0, 590.0, 117.0, 141.0], [357.0, 587.0, 115.0, 146.0], [363.0, 587.0, 112.0, 150.0], [362.0, 589.0, 103.0, 145.0], [345.0, 589.0, 101.0, 140.0], [310.0, 593.0, 100.0, 126.0], [298.0, 594.0, 86.0, 116.0], [296.0, 598.0, 81.0, 102.0], [305.0, 604.0, 79.0, 97.0], [314.0, 613.0, 72.0, 90.0], [309.0, 604.0, 72.0, 88.0], [305.0, 611.0, 67.0, 79.0], [305.0, 613.0, 46.0, 63.0], [301.0, 594.0, 60.0, 74.0], [305.0, 594.0, 59.0, 71.0], [305.0, 589.0, 59.0, 71.0], [304.0, 585.0, 59.0, 73.0], [305.0, 586.0, 58.0, 73.0], [308.0, 604.0, 55.0, 55.0], [312.0, 583.0, 56.0, 69.0], [319.0, 580.0, 58.0, 45.0], [324.0, 583.0, 58.0, 74.0], [322.0, 586.0, 62.0, 74.0], [323.0, 589.0, 61.0, 76.0], [323.0, 593.0, 63.0, 75.0], [324.0, 597.0, 61.0, 72.0], [321.0, 594.0, 60.0, 74.0], [324.0, 589.0, 58.0, 74.0], [324.0, 588.0, 61.0, 75.0], [326.0, 593.0, 57.0, 72.0], [334.0, 592.0, 57.0, 74.0], [341.0, 592.0, 58.0, 75.0], [339.0, 591.0, 55.0, 73.0], [349.0, 581.0, 56.0, 75.0], [360.0, 577.0, 60.0, 74.0], [352.0, 570.0, 56.0, 75.0], [353.0, 565.0, 58.0, 75.0], [366.0, 564.0, 56.0, 76.0], [358.0, 563.0, 58.0, 75.0], [350.0, 565.0, 56.0, 78.0], [353.0, 573.0, 57.0, 74.0], [351.0, 575.0, 58.0, 75.0], [333.0, 579.0, 60.0, 74.0], [333.0, 581.0, 58.0, 73.0], [342.0, 582.0, 56.0, 74.0], [341.0, 583.0, 58.0, 73.0], [336.0, 580.0, 56.0, 73.0], [337.0, 575.0, 56.0, 75.0], [335.0, 573.0, 60.0, 76.0], [329.0, 570.0, 60.0, 74.0], [331.0, 565.0, 58.0, 75.0], [333.0, 563.0, 60.0, 74.0], [332.0, 562.0, 58.0, 76.0], [326.0, 564.0, 55.0, 74.0], [322.0, 563.0, 58.0, 76.0], [323.0, 562.0, 59.0, 76.0], [321.0, 562.0, 58.0, 76.0], [319.0, 562.0, 58.0, 77.0], [323.0, 564.0, 57.0, 75.0], [326.0, 561.0, 56.0, 74.0], [331.0, 560.0, 56.0, 77.0], [336.0, 561.0, 51.0, 75.0], [338.0, 570.0, 41.0, 66.0], [333.0, 564.0, 48.0, 77.0], [329.0, 562.0, 58.0, 67.0], [325.0, 561.0, 59.0, 79.0], [320.0, 562.0, 62.0, 76.0], [320.0, 575.0, 57.0, 78.0], [320.0, 592.0, 57.0, 76.0], [313.0, 582.0, 61.0, 76.0], [310.0, 584.0, 59.0, 76.0], [313.0, 584.0, 58.0, 77.0], [319.0, 585.0, 56.0, 77.0], [323.0, 603.0, 57.0, 75.0], [326.0, 578.0, 58.0, 78.0], [325.0, 581.0, 55.0, 80.0], [317.0, 579.0, 54.0, 78.0], [316.0, 585.0, 52.0, 75.0], [352.0, 591.0, 17.0, 64.0], [321.0, 594.0, 48.0, 76.0], [318.0, 616.0, 58.0, 76.0], [318.0, 581.0, 56.0, 73.0], [326.0, 587.0, 54.0, 75.0], [330.0, 582.0, 57.0, 72.0], [337.0, 581.0, 56.0, 73.0], [340.0, 580.0, 56.0, 73.0], [340.0, 577.0, 55.0, 71.0], [345.0, 567.0, 52.0, 75.0], [343.0, 573.0, 58.0, 77.0], [345.0, 583.0, 57.0, 73.0], [345.0, 583.0, 55.0, 73.0], [343.0, 581.0, 57.0, 73.0], [343.0, 581.0, 59.0, 75.0], [342.0, 566.0, 58.0, 70.0], [345.0, 567.0, 59.0, 70.0], [345.0, 586.0, 58.0, 71.0], [344.0, 601.0, 53.0, 63.0], [341.0, 582.0, 55.0, 73.0], [348.0, 594.0, 54.0, 75.0], [351.0, 598.0, 61.0, 73.0], [357.0, 566.0, 59.0, 74.0], [368.0, 572.0, 50.0, 70.0], [367.0, 592.0, 51.0, 73.0], [369.0, 587.0, 43.0, 71.0], [358.0, 585.0, 44.0, 74.0], [351.0, 594.0, 46.0, 71.0], [348.0, 576.0, 59.0, 79.0], [346.0, 569.0, 62.0, 76.0], [343.0, 568.0, 67.0, 78.0], [334.0, 574.0, 59.0, 76.0], [338.0, 565.0, 50.0, 78.0], [345.0, 565.0, 60.0, 77.0], [347.0, 570.0, 61.0, 74.0], [351.0, 571.0, 60.0, 73.0], [362.0, 570.0, 58.0, 74.0], [364.0, 570.0, 57.0, 74.0], [354.0, 571.0, 56.0, 70.0], [347.0, 556.0, 58.0, 70.0], [337.0, 552.0, 58.0, 68.0], [323.0, 555.0, 56.0, 71.0], [330.0, 552.0, 40.0, 74.0], [322.0, 555.0, 49.0, 76.0], [309.0, 568.0, 59.0, 73.0], [308.0, 572.0, 56.0, 70.0], [319.0, 570.0, 54.0, 71.0], [322.0, 575.0, 59.0, 72.0], [321.0, 582.0, 59.0, 73.0], [329.0, 584.0, 56.0, 71.0], [338.0, 584.0, 57.0, 71.0], [339.0, 587.0, 57.0, 71.0], [343.0, 590.0, 57.0, 69.0], [343.0, 595.0, 56.0, 73.0], [341.0, 600.0, 54.0, 73.0], [352.0, 605.0, 46.0, 67.0], [355.0, 598.0, 42.0, 65.0], [357.0, 621.0, 36.0, 50.0]], \"areas\": [2127.0, 2158.0, 2607.0, 3834.0, 5369.0, 7856.0, 9745.0, 11240.0, 11900.0, 12591.0, 13427.0, 13718.0, 13963.0, 12636.0, 11801.0, 10033.0, 8155.0, 7225.0, 6367.0, 3886.0, 5350.0, 4434.0, 2013.0, 3940.0, 3602.0, 3762.0, 3821.0, 3764.0, 1389.0, 3353.0, 2176.0, 3606.0, 3960.0, 4065.0, 4004.0, 3847.0, 3873.0, 3594.0, 3704.0, 3577.0, 3662.0, 3797.0, 3562.0, 3682.0, 3834.0, 3646.0, 3927.0, 3745.0, 3790.0, 3865.0, 3795.0, 3938.0, 3753.0, 3677.0, 3784.0, 3860.0, 3613.0, 3756.0, 3900.0, 3728.0, 3705.0, 3837.0, 3852.0, 3526.0, 3810.0, 3790.0, 3783.0, 3898.0, 3817.0, 3732.0, 3762.0, 3102.0, 2142.0, 2649.0, 1746.0, 3628.0, 4045.0, 3708.0, 3669.0, 4165.0, 4095.0, 3999.0, 3943.0, 3710.0, 4074.0, 3946.0, 3727.0, 3350.0, 808.0, 3108.0, 3611.0, 3406.0, 3399.0, 3359.0, 3356.0, 3344.0, 2996.0, 2764.0, 3519.0, 3388.0, 3376.0, 3576.0, 3845.0, 3541.0, 1806.0, 3431.0, 2700.0, 2516.0, 2395.0, 3707.0, 3776.0, 2790.0, 2383.0, 2300.0, 2268.0, 2004.0, 3705.0, 3973.0, 4174.0, 3274.0, 1189.0, 3814.0, 3968.0, 3870.0, 3856.0, 3757.0, 3599.0, 3094.0, 3375.0, 3136.0, 2125.0, 2558.0, 3742.0, 3516.0, 3424.0, 3625.0, 3739.0, 3665.0, 3632.0, 3579.0, 3463.0, 3429.0, 3332.0, 1902.0, 1008.0, 981.0], \"iscrowd\": 0, \"video_id\": 0, \"height\": 1280, \"width\": 720, \"category_id\": 49272, \"noun_phrase\": \"air jordan\"}, {\"id\": 1446, \"segmentations\": [{\"counts\": \"\\\\Tj=`0_W13N001O00000000000000000O1000000000O1000000O100O011N1N3JjcU=\", \"size\": [1280, 720]}, {\"counts\": \"_To==bW13O0O100O1O1O1O10O01O1N1000000O2O000O010O10O01O01O000O100M4LmSn<\", \"size\": [1280, 720]}, {\"counts\": \"Pom=7^W1a0G5J6K3N3O1O000O2O0000000O1000000O1000O0100O10O010O010O001O1N101M3N2M4L4LQbf<\", \"size\": [1280, 720]}, {\"counts\": \"gah<4cW1<G8I6K5L3N3M2O2O00001O0000000000000O1000000000000000000000000O10O10000O100O100O101N1O1O1O2N2M4K9VOTiNNRV^=\", \"size\": [1280, 720]}, {\"counts\": \"dYg<8cW19J5K3O1N2O001O00001O001O00001O0000000O1000000O1O1O100O100O100O010O1O10O1000O0100O010O001N3N1O3L?@eV^=\", \"size\": [1280, 720]}, {\"counts\": \"eii<=aW16I5M2O1N101O0010O010O01O001O000001O00000001O0000000001N100000000000O0100O010O10O01O0O100O3N2L6L`n\\\\=\", \"size\": [1280, 720]}, {\"counts\": \"mQi=f0VW15O0O10001O0000O010000000000O01O1O0010O01N10002M2N3M=@VnW=\", \"size\": [1280, 720]}, null, null, {\"counts\": \"]\\\\P`0h0hV1f0F6L3O1N2N101N2O000O100O1O10000001O001O1O1N3N1N2O1N3M4L3M3L4M7Fi[j:\", \"size\": [1280, 720]}, {\"counts\": \"`]U`07gW15M3M2O1O2M2O0O100O2O0O1O1O1O1O010O1O00001O0O2O001N100O1O101N1O2O0O2N2N00010O001N2O001O1N2O0100O2N2N2O2M2N3L5L4K6IaRf9\", \"size\": [1280, 720]}, {\"counts\": \"geV`01kW1:I4L2O2N2O1N1O1O1O1O1O1O1O001O1O0O2O0O2O0O101O0O1O100O1O101N1O100O1O1O1O00100O1O1O1O100O11O1O1O1O101O1N2O2M5L6J6I9G1N2N3Lei_9\", \"size\": [1280, 720]}, {\"counts\": \"ee[`08fW15L3N1O2N2N1O2N1O1O001O1O1O1O1O0O2O1O1N1O100O2O0O4M2M3N2M4M4K1O2N1O001ON3H7O1O11O010O1O02N1O2N2O1N3M3M3M5J7I8H;BTRa9\", \"size\": [1280, 720]}, {\"counts\": \"_\\\\W?1lW15M4L31N00001O00O1100O000O2O002N10OO01O01000001OO101O100N3N3L4M0O2O1N2O1N2OO01O1O1O1O1O1O1O1O1O001O001N101N10001N2O1N2OO01O1O3N6I4L2N2O0O00N2J600O1000101N2N2N2O1N3M3M3M4L5J8G;DVZ]9\", \"size\": [1280, 720]}, {\"counts\": \"[mW`06iW13M3O2M3N1N2O1N2N1O2N1O1O001O1O1O1O1O1O001O1N101O0O2O000O2O001N101N1O2O0O5L6I3N0O01IoiN^NQV1`180001O00001O1O3N1O1N2N3N2M4K5L6I8F\\\\j_9\", \"size\": [1280, 720]}, {\"counts\": \"Qel?3lW12N2O1O2O0O2N1O4L2N3M3M2N2N2N2N2N1O2N1O1O1O1O001O1O001O0O2O1O001N10001N101O000O2O2N7I3M2M2OO10OG\\\\jNXNdU1d1>O010O1O2O1N2O1N3M2O2M2N4L4L4K7I:EZbc9\", \"size\": [1280, 720]}, {\"counts\": \"`bX?`0WW1d0A=H3L3N1N3M3N2N3M2M2O1O0O010O01O1N1O2O1N2O0O1000000O100000O01000000000O0O2O1O0001O1O011N1O3M4K5L5I7I7HWUi:\", \"size\": [1280, 720]}, {\"counts\": \"fV^>1nW16J6I4M4M5K2N3M2N3M2N1O2M3M4M2N001OO001O100O10O0010O10O01O01O010O00O1O1N2N5K5Je0UOaaR<\", \"size\": [1280, 720]}, {\"counts\": \"_^b?8gW14L4M2N1N3N1O1N3N0O2O1N1O2N10001N100O100O1O1O1O1000000001N102M2O2M4L4K5LaYS;\", \"size\": [1280, 720]}, {\"counts\": \"ZVm>5jW11M4M3O1O0010O10O01O01O00001O2N1O1N2O2N1N3N1N101N100O2O0O10000O2O000O1O100O100000O101O1O1O3K7HkiZ;\", \"size\": [1280, 720]}, {\"counts\": \"\\\\^i>1kW15M3O010O010O001O0010O0001O0O101N101O1N2N2O1N2N3M2N101N100O1O101O0O10000O10000O100O10000001O1O2M3M4J>BeaY;\", \"size\": [1280, 720]}, {\"counts\": \"Vnk>3lW12N2O2N001O00001O000010OO1N200O3N1N2N2N3N1N2O1N101O0O2O0O1000001N10000000000O10O101O1N4L4M8FjY];\", \"size\": [1280, 720]}, {\"counts\": \"ZUm>6iW15K3N1O1O>BOO2N10O0N2O2N200010O1O100O010O10000000O101O2N3M5K4L5Ifbm;\", \"size\": [1280, 720]}, {\"counts\": \"[de>7gW16L3L3N1N2O0O2N1O1O1O2N1O100O100O10000000000001O1N2O2M2N4L5IhcW<\", \"size\": [1280, 720]}, {\"counts\": \"ZlR>2eW1>H4M3N101N101N1000000000001O00000000000000O10000O100O2N2LXdk<\", \"size\": [1280, 720]}, {\"counts\": \"Plf>610`W1=J4M100O1O100O10000O100000000O1000O10O100000000001O0O2O2M2O4J5KQlS<\", \"size\": [1280, 720]}, {\"counts\": \"[de>1jW1>F4M2M2O1N2O001O0O101O0O10O100000O100000000O10O100000001N1O3N4J;DdkS<\", \"size\": [1280, 720]}, {\"counts\": \"`dj>:eW14L3N1N2O1N2O1O001O0O2O0000000O0100000O1000000O10O1000000O101N101N3M6Ia[l;\", \"size\": [1280, 720]}, {\"counts\": \"edj><aW16L2O1N2O001O0000000O2O0000000000000000O01000000000O10000O100O2N1O2Lccm;\", \"size\": [1280, 720]}, {\"counts\": \"oT^>1nW11N3O0O1O1O100O01O0000O2N4K4N2N2N2N2O001O0O101O0000000000000000000O10O2O000O100O100O2O0N4Le[l;\", \"size\": [1280, 720]}, {\"counts\": \"ol\\\\>1lW14N2N010O100O10O10O10O01O001O0N4M2N3N2O0O2O001N101O001O000O10000000000000O1000000O10000O101N1O2N9Each;\", \"size\": [1280, 720]}, {\"counts\": \"oT^>2mW13M100O1O1O10O10O10O0100O100O0O2N2N2N2O2N101N2O001N101O00000O10000000000000000000O10000O100O2N101M4KfSf;\", \"size\": [1280, 720]}, {\"counts\": \"T]_>1nW12N2O0O1N200O010O1000O10O10O00O1N3N3M2O3M101N2N10001N10000000001N11O0O10O10000001O00000O100O2N1O2N2Mbkd;\", \"size\": [1280, 720]}, {\"counts\": \"YU^>1nW11O1N3N1O1O10O10O10O100O0100N101N2M3M3O1O2N2O1N2N101O000O10000000001OO100000000O100000000O100O1O2N1O4Iakd;\", \"size\": [1280, 720]}, {\"counts\": \"Y]_>1nW11O1O1N2O100O100000O1O0010N2O0O2M3N3M202M2O1N1O2N10001O00000000001N01000000000001N10000O100O1O2N3K_Sf;\", \"size\": [1280, 720]}, {\"counts\": \"YU^>1nW13N1N2O0O001O0010O0100O001O1O1N1O2N2N3N1O2O1O1N101N1000001N100001O00001N2O4K8H7HmbR<\", \"size\": [1280, 720]}, {\"counts\": \"Wma>4kW11N2O10000O100O001000N02N2N2O2N1O2N1O2O1N2N100O2O0000000O101O0000O1000000000000O2O1O0O3N5J9EQ[g;\", \"size\": [1280, 720]}, {\"counts\": \"Wma>4kW11O1O1O100O010O1O1O000O2N3M2O1O2N2O1N101N101N100000001O000O10000000O100000000O2O000O1O2N1O2N2M8FYkd;\", \"size\": [1280, 720]}, {\"counts\": \"^e[>1mW12M300O2O000O1000O0010O010O1O1O0N3M3N3N1O2N2O001N101N1000000O1000000000000O1001N1000000O100O1O2N1N4M7HU[g;\", \"size\": [1280, 720]}, {\"counts\": \"^]d>1oW11N1O2O0O1O001O1N200000O01N2O0O2N2O2L3O2N101N101O001N1000000000000O1000000000000001N100O1O101N2M4L;BRk_;\", \"size\": [1280, 720]}, {\"counts\": \"^mP?2nW13M1N1O01O0100O10O0001O1O1M3M3O1N3N2N1O2N10001N10000000001O000O100000O1000000000001N100O2N1O2N4K>AjbT;\", \"size\": [1280, 720]}, {\"counts\": \"^mZ?1oW11N3M2N1O1O010O010O10O01N1L5N2O2M3O1O0O2O00001N10000000000O100000000000000000O2O00000O100O3M3M7GQkk:\", \"size\": [1280, 720]}, {\"counts\": \"^e^?1oW10O101N010O1O1O1O100O1O01O01N2N2N2N3M2N3N2M20001N1000001O00000O1000000000000000O101O000O10000O2N2M4M:BS[d:\", \"size\": [1280, 720]}, {\"counts\": \"Umd?5jW11O100O100O2OO10O010O0010O01O0N3L3N4N2N101N2O0O2O001O00000000000000000O100001O000O10000O101N101N2N3M3J_S^:\", \"size\": [1280, 720]}, {\"counts\": \"odc?1oW11N101N2N10000O10000O001O1O10O01O00ON4K6N2O0O2N101N101O1O001N100000000000000000O1000000O100O100O2O1M3N4K:Cak\\\\:\", \"size\": [1280, 720]}, {\"counts\": \"jl_?1oW10O2O0O1O1O1O110O000O100O010O010O1O001N2J5N4N2O0O2N2O000O2O0000001O00000000000000O100000000O101N100O2N1O4K9Hd[_:\", \"size\": [1280, 720]}, {\"counts\": \"ed^?1nW12O0O1O10000O100O1O1O1O001O1O1O1N2O1N3M3N2N1O2O00000O2O000000000000000000000000000001N1O2N100O2N1O4K7Hj[d:\", \"size\": [1280, 720]}, {\"counts\": \"dTW?2lW12N2O20O0000O10000O10O10O100O0O001O3N1O2M3N2O0O2O0O2O000O1000000O101O0000000000O100000000O101N100O2M3N5Jncj:\", \"size\": [1280, 720]}, {\"counts\": \"edT?1nW11O2N1O1O101O0O100O10O01O010O1O0N4L3N3N101N10001N1000000O2O00000000000O10000000O101N100O101N101M3N;Dgco:\", \"size\": [1280, 720]}, {\"counts\": \"edo>1nW13N1N1O10000O10000N2O1O001O100O001N2O2N1O2N2N2O000O101O00000O100000000000000000000000O100O2N1O101N3LncT;\", \"size\": [1280, 720]}, {\"counts\": \"j\\\\n>1mW13N2O0O1O10O1000O100O001N10010N2O1O1N2O2N2N2N2O0O2O00000O10000O10000000000O1001O00000O10001N100O2N2M7JgcT;\", \"size\": [1280, 720]}, {\"counts\": \"n\\\\S?2lW12N200O1O1O10000O1O10O000010N2O1N2M4N1O2N1O2O001O0O100000001N10000000O1000000000000O100O2O0O2N3M6JcSR;\", \"size\": [1280, 720]}, {\"counts\": \"olU?5jW12N100O1001OO100O010N20O01N2O1M2M3O2O2O1N101N101O001O00000000000O100000000001N10000O101N1O1O2N4K<C[co:\", \"size\": [1280, 720]}, {\"counts\": \"SmU?2mW12M2O1000001O0O100O10O0100O1N2M2O1N3O1O2N1O2N10001N10001O00000O100000000000000000O2O000O2O0O2N101N3M5K]Sm:\", \"size\": [1280, 720]}, {\"counts\": \"TUW?1nW12O0O2N1O101N100O010O10O01O1N1N2O2M4N1O2N101N101N1000001O0O1000000000000000000000O100O2O000O2N2N4K=CVSm:\", \"size\": [1280, 720]}, {\"counts\": \"TUW?1nW12N1O2N100O2O0O10O010O01O100M2N2O2M4N1O2N1O2O0O2O0000001O000O100000000000000000O10000O2O0O101N2N4K;EYSm:\", \"size\": [1280, 720]}, {\"counts\": \"Q]X?4jW12O10000O2OO0100O10O01O001M3N1M3O3N1O2O1N101N101O0000001O0O100000O11O00O1000000O101N100O2O0O2N5J;D[Sm:\", \"size\": [1280, 720]}, {\"counts\": \"n\\\\S?2mW11O2N1O1O1O10000O001O1O01O0001N2N2M3N2O2N2N2O000O2O00001O0000000000000000000000O1000000O2O0O2N1O2N3LhkP;\", \"size\": [1280, 720]}, {\"counts\": \"j\\\\n>1nW11N3M201N010O100O1O1O001N1O2O1O1N3N2N101N2O0O1O2O00000O10000000000000000000O100000001N100O2N2N3L:FdSW;\", \"size\": [1280, 720]}, {\"counts\": \"e\\\\d>4kW13M00100O100O0100O0100O1O010O001O1N2M4M3N2N101N10000O2O0000000000000000000000000000O100O2O0O2N101N4Jnk_;\", \"size\": [1280, 720]}, {\"counts\": \"`l\\\\>6iW110001N10O010000O10O0100O01O00O2O0O2M3O2N2N2O1N2O0O101O000000000O2O0000000000O2O0000000O10001N1O2N2N2LSTf;\", \"size\": [1280, 720]}, {\"counts\": \"_T^>2nW11N2N2O0O1O10O0100O001O1O0O2M3N3M2O2O1N101N101O000000000O100000001O0000O1000001N101O0O1O2N2N6HQli;\", \"size\": [1280, 720]}, {\"counts\": \"`\\\\Z>1nW11O1O2N10000O1O1O100O010O1N101M4M2N3N1O2O001N101O0O10000000001O00O100000000000001O000O2O0O2N2N5IR\\\\l;\", \"size\": [1280, 720]}, {\"counts\": \"`TT>1nW12M3N1O10000O100O10O1000O1O1N2N2N2N3N1N3N101N10001N1000001O0000000000000000O100000001N101N1O2N2N3KT\\\\Q<\", \"size\": [1280, 720]}, {\"counts\": \"`dQ>1nW12N2N1O1O100O010O10O10O1N003L3N2N3M3O1N101N100O2O000000000000000000001O00O1000000O2O0O2O0O2M4LU\\\\V<\", \"size\": [1280, 720]}, {\"counts\": \"_dQ>2mW12M3N1O1O1000O10O100O1O1L4OO1N2MUT]=\", \"size\": [1280, 720]}, {\"counts\": \"`lR>1nW12N1O2N1O01000O100O001OOO3N2N2M4M3N2O1N1O101O0O1000001O0000000000000000O1000000O2O000O2N3M2N3KUTU<\", \"size\": [1280, 720]}, {\"counts\": \"`dQ>1mW13M3N1O10000O010O100O001O0M4M3N2O2N2N101N1000001N10000000000000000000000000O10001O1N3M`0\\\\OkSZ<\", \"size\": [1280, 720]}, {\"counts\": \"_dQ>2lW13M2O100O10000O010O1O1O0M3N3M4N1N3O0O2O0O101O000000001O000000000000O100000000O101N101N1O2N4KVdW<\", \"size\": [1280, 720]}, {\"counts\": \"`lR>1iW16O101N10O1000O010O0O2N1N3N2O1N3N2N2N101N10001O00000000000O1000000000000001O0O100O2O001N2M3N3KWTU<\", \"size\": [1280, 720]}, {\"counts\": \"[TT>1oW11N3M2N10O1000O1O001O001O001O1N2O1M4N1N3N2O0O101O000O101O0000000000000000000000O2O001O0O1O2O0O2N3M9DRTP<\", \"size\": [1280, 720]}, {\"counts\": \"[TY>4kW12N101N1000O100O01O1O001O0O2O1N2L4M3O2N101N1000001O00000000000000000000000001O000O2O0O2O0O2N2N6HUdm;\", \"size\": [1280, 720]}, {\"counts\": \"`lW>1mW12N2O2N10O011O00O100O00001O0O2O1N2M3N3N3M1O2O000O101O0000000000001O0000O100000001O00001N101O1N2N3LTdm;\", \"size\": [1280, 720]}, {\"counts\": \"_TT>2mW12N1N3O0O010O100000O10O1O010M2O2O002M2N3M3N1O2O0O101O00000000000000001O0000000000O101O001O0O2O1N101M5KSln;\", \"size\": [1280, 720]}, {\"counts\": \"[lR>6iW11O101N10O100000O1O00001N2N2O1N2N3N2N1O2O0O101O0O1000000000000000000000000001O000O10001N2N1O3M4KTlS<\", \"size\": [1280, 720]}, {\"counts\": \"[\\\\P>5jW12N1O1000000O10O1O01O001O000N3N3M3M3N2O0O2O0O2O000000001O0000000000000000000000O2O000O2O0O2N2M4L7BXTU<\", \"size\": [1280, 720]}, {\"counts\": \"_lm=2jW14O2O00000O1000O010O01O001O000O0O4L4N3M2N1O2O000O101O00000000000000000000000001O00000O2O0O101N2M6JTdW<\", \"size\": [1280, 720]}, {\"counts\": \"dlm=2jW14O100O1O100O1000O0010O001O001M2N4M3N2N2O1N1000001O0000001O0O1000000000000001O0O1000000O2O0O2N2N2N<_Ok[V<\", \"size\": [1280, 720]}, {\"counts\": \"olh=1nW12N1O2O0O101N1O0011O001N3N3LR[h=\", \"size\": [1280, 720]}, {\"counts\": \"Teg=1nW12N2O0O1O1O1000000000O01O10O0001O001N2M3N3N2N101N101O0O101O00000000001O0000000000O101O000O101O0O2O1N2M3M7E]kX<\", \"size\": [1280, 720]}, {\"counts\": \"YUj=1nW11N3O0O10000O10O10O1000000O10000O1O2N10000O1O1N2O101O0O100N2N2O10O010001N10000O010000O101N101N2N2M][[<\", \"size\": [1280, 720]}, {\"counts\": \"TmR>1nW16J100O1000O00010O01O0O1N3N2O1O2N2N2O1O0O101N1000001O0000000000000O11O0000000O10000O2O0O2N2N2M^SU<\", \"size\": [1280, 720]}, {\"counts\": \"TeV>1nW11O1O2M200O1000O0100O1O1O0O101N2O1O1N3M3O0O2O000O2O000000000O2O0000000001O0000000O2O000O2O0O2N2N6I\\\\kn;\", \"size\": [1280, 720]}, {\"counts\": \"nTT>3mW10O2N10000O100M3000O10O001O1O1N2O1O1O2N2N1O2N1000001O000O2O0000000000000001O0O10000O2O001N1O2N2N2N5H`SP<\", \"size\": [1280, 720]}, {\"counts\": \"lTT>4kW11O1O100O100O00010O01O01N2N1O3N2N1O2N2O1N101O000O1000001O0000000000000000O1000000O2O001N1O2N3M7EbcR<\", \"size\": [1280, 720]}, {\"counts\": \"olm=1nW12N2N10000O101M110O00100O10OO2N1N2M4O1O1O101N2N101O001N101O0000000000001OO10000000000O10001O0O2O0O2N2N6IdkS<\", \"size\": [1280, 720]}, {\"counts\": \"TeQ>1jW15O1O1000000O01000O010N1O1O2M3M2M5O0O2O1N2O001N10001O00000000000000000000000000O2O00001N1O2O1N3L4LbkS<\", \"size\": [1280, 720]}, {\"counts\": \"SeQ>2mW13M2N1O1O010000O0100O001N1O2L4N2N3N1O2N2O001N10000000001O0000000000000000000000O2O00000O2O2M2M4L`SU<\", \"size\": [1280, 720]}, {\"counts\": \"TUT>1oW15J1O10000O10O010O01O1O0O2O1M3N1O2N3N1O2O0O2O0O10001O000000000O100000000001O00000O101O0O2N2N2N5J]cR<\", \"size\": [1280, 720]}, {\"counts\": \"TUY>3lW12N2OO010000O100O1O001O001N2L3O2N3M200O2O1N10001O0O10001O00000000000000000000O101O0O101N101N2M4L`cm;\", \"size\": [1280, 720]}, {\"counts\": \"TmW>1oW10O2N1N2O1O2O01OO10000O01000O001O1N101M3N2N2O2N1O1O2O0O101O000000000002M8H;BV[[<\", \"size\": [1280, 720]}, {\"counts\": \"Ye`>1jW15O100O100O1000O0100O010O10O100O1O1M4MS[j<\", \"size\": [1280, 720]}, {\"counts\": \"Teo>2lW13O0O1O10000O10000O01O010OO2O0O2N1O2N3N1N3N101N101N1000001O0000000000000O11O0000000O2O001O001N2N5K7GYcT;\", \"size\": [1280, 720]}, {\"counts\": \"Te^?1jW1500O10000O100O10N1001N1O2N2N2N3N1O2O1N2N1O101O0O100000000000001O00000000O2O00000O2O1N2N2N101N2KcSh:\", \"size\": [1280, 720]}, {\"counts\": \"Tm_?1nW12N1N2O100O100O1O10O01O1O0001O0001OO2N2Jaki;\", \"size\": [1280, 720]}, {\"counts\": \"odc?1oW13L2N2O000000O01000O01O001O1O010O0O2N2M3N2O1O2N100O2O000O2O00001O0O10O11O0000O100O1000000O2O000O2O2M3M4K7Eak\\\\:\", \"size\": [1280, 720]}, {\"counts\": \"oTf?1nW11O2O0O100O1O100O1O010O00001O001M3M3O2N1O2N2O0O101O0O101O000O1000000000000000O1000001O0O101O0O2O2L3N4Kck\\\\:\", \"size\": [1280, 720]}, {\"counts\": \"ili?3lW12N2O0O001000000O010O1O0N2O1O2O1N2O1O101N2O0O2N1000000O1000000O10000000000000000001O0O101O1N2N2N3M6Jd[Z:\", \"size\": [1280, 720]}, {\"counts\": \"o\\\\g?6iW11O100O01000O10O0001O1O10O01M2M4M3O1O1O100O1O101O001O0O1000000000000000O10000000000O101O001O1N3M5JcS^:\", \"size\": [1280, 720]}, {\"counts\": \"XUa?3lW12O0O2OO010000O1O010O1O001O01O01O0O3M3M3N2O0O2O0O100000001N100000000O1000000000O101N10001N2O0O3N2M6HUSc:\", \"size\": [1280, 720]}, {\"counts\": \"]]]?2lW13M2O1O10000000O1000O10O0010N1O2M2M4L4O2N2N2O0O2O0000000O2O0000000000000000000001O0O10001N2N2N2O2M8FTkf:\", \"size\": [1280, 720]}, {\"counts\": \"YU\\\\?3lW12N100O100O010O10O01O000N102N2O1N3N2N2N2O0O2N101O0O2O00000000001O00O1000000000001N10001O0N3O2M7HVcj:\", \"size\": [1280, 720]}, {\"counts\": \"TmU?1mW12N3N1O11O000000O00100O1O001O1N1O2N1O3M2O2O1N1O2N10001O0O1000000000000000000000000001O0O2O001N102M7G^[n:\", \"size\": [1280, 720]}, {\"counts\": \"jTW?1nW11O1O1O1O1O1O1O001O001N10001N1N3N2O1O2N100O2N10000O101O00000000001O00000001N100O2O001N2O0O2N2N2Mmco:\", \"size\": [1280, 720]}, {\"counts\": \"`Tf?1oW12M4L00001L4I7000O010O10O100N10100O1O100O1O2N1O10000O10000000000O10000000O10O10O101O001N2O1O1N4L3M3M5IPl\\\\:\", \"size\": [1280, 720]}, {\"counts\": \"QdR`01>0PW13nhNNQW14ohNKQW15ohNKRW14mhNNSW11khN1VW1NjhN3UW1NihN3WW1MdhN8\\\\W1HdhN8\\\\W1510000000O10000O100O1O100O2N100O1O2O0O2OO1000001O1N5JTTc:\", \"size\": [1280, 720]}, {\"counts\": \"P\\\\V`0b0\\\\W1201O1O000001O1O000O2O000000000O1O1O1O1O100O3Mnco:\", \"size\": [1280, 720]}, {\"counts\": \"PTZ`04jW1?B2N110O0001O010O1O000O100000000O1000O100O10000O11N3N8Fjce:\", \"size\": [1280, 720]}, {\"counts\": \"ZTZ`03^W12jhN1SW1?01O000001O001O001O1N10000O1O10O0100O02N2O0O1O2O0O101O0O101N10000000000O1000000000O10O02O0O101N2O1N3N2M4LSTe9\", \"size\": [1280, 720]}, {\"counts\": \"fc\\\\`03jW15N1_hNKUW1`010O0001O1O001O000O100000O0100O11N100O2O0O2O0O100O2O000O2O00O010000O1000000000O10001N101O1N2N3M3M3MYTe9\", \"size\": [1280, 720]}, {\"counts\": \"o[[`08[W1>N11O00000O101O1O1O0O1O1O1O10O010O100O2N2O0O2O0O100O101N10000O2O0000000000O1000O100000O101N2O1N2O2M2N4L7GXTe9\", \"size\": [1280, 720]}, {\"counts\": \"jSU`0b0]W13N00000001O001N10001O0O10O10O10000O0100O101O0O2N100O101O0O10001O0O1000000000O01001N2O1N5K5JTTT:\", \"size\": [1280, 720]}, {\"counts\": \"Rln?`0_W15L010O000010N2O00001O000O01000O100O0100O100O2O000O2O0O101N100O10001O0O100000000000O01001N101N2N3M3L6JlST:\", \"size\": [1280, 720]}, {\"counts\": \"V\\\\g?=bW19IO01O00000001O0O2O0000000O100O100O10O100O101N10000O2N101N1000000O101OO10000000000O1O3M5IlSc:\", \"size\": [1280, 720]}, {\"counts\": \"Xl_?`0^W15M1O0001O00001O001N1000000O10O0100000O100O10001N101N100O101O0O10000O100000O100000000O2N3L5Kk[i:\", \"size\": [1280, 720]}, {\"counts\": \"UlZ?5iW1=D2O1O01O001O001O0000000O10000O10O1000000O2O0O1O101O0O2O000O10001O0O100O100000O100000001N1O2N3L5Jocj:\", \"size\": [1280, 720]}, {\"counts\": \"PlU?7eW1>F001O01O1O001O1O0O1000001O0O010O010000O100O2O001N100O2N100000000O10001O000000O1000000O2N2N4K8FPlP;\", \"size\": [1280, 720]}, {\"counts\": \"lcT?`0`W13M2N0001O1O001O1O000O10000O100O10O1O100O100O2O1O0O2O0O100O2O00000O1000000000000O02O00000O2O1N2O1N4M2KR\\\\n:\", \"size\": [1280, 720]}, {\"counts\": \"Q\\\\S?1mW1;G6I200O0001O001O001O000O100O1000O10000O100O100O2N10001N1O10001O0O10000000000O1000O1000000O101O1N2O1N3M3L5Lmkk:\", \"size\": [1280, 720]}, {\"counts\": \"PdT?4kW18WhNH[W1`0O1O01O001O001O001N010O10000O10O100O100O101N101N100O2O0O10000000000O10001O00O01000O10001O000O2O1N4L3M3L6Hncj:\", \"size\": [1280, 720]}, {\"counts\": \"PlZ?a0^W12O001O01O001O00001O0O10O10O100000O100O10001N1O100O101N1000000O101O0O010000000O1000O1000001N2N2O2M2O2M2N4Lmce:\", \"size\": [1280, 720]}, {\"counts\": \"n[b?`0`W14L0010O001O0O101O00000O100O1O10O100O1O100O101N101N100O2O0O100O1000000O1000000000O01000O2O0O2O1O1N2O3L3M5HPT^:\", \"size\": [1280, 720]}, {\"counts\": \"Pdh?=aW15L2O1O01O00001O0O101O0O01O0100O0010O10001O0O1O2O0O101O0O101O000O1000000O10000000000000O100O2O0O2O2M2O2M4K6JncV:\", \"size\": [1280, 720]}, {\"counts\": \"lkn?3mW1>B4L001O01O00001O0O10000O1O010O10O010O100O100O2O0O2O000O2O000000000O101O00000000O100O10000O101O1N3N1N3N3KRdQ:\", \"size\": [1280, 720]}, {\"counts\": \"TTU`05hW19I3N1N1000010OO100000000O01O010O10O010000O2O0O101N100O101O000O101O00000O10O10000000001O0O2O1N3N1N3N1N3M5Ho[k9\", \"size\": [1280, 720]}, {\"counts\": \"lcR`0<dW14L2N2N1O0001O00000O1000O01000O010O10O0100O101O0O101N100O2O000O10001O00000O100000O2O001O0O2O1O1N3N2M3N2L5JPlm9\", \"size\": [1280, 720]}, {\"counts\": \"fki?2kW17K6K4L2N01O00000001O00000O0100000O10O0100O1O2O0O1000000O101O000O1000000O10000001O00001O001N2O2N1N3M3N2L7FYdV:\", \"size\": [1280, 720]}, {\"counts\": \"Xc^?7iW12N5K3M1O2OO00001O00000000O01000O1000O02N1O10000O2O0O2O000O101O0O10000000000000000001N101O1N2O1N3N1O2M3L7Fcla:\", \"size\": [1280, 720]}, {\"counts\": \"SkP?8hW11O8H1O1O01O0001O00000000O010O1000O100O100O1O100O2O000O101O0O100O2O000O100001O0000001N2O001N2O1O2M3N2M4JjlP;\", \"size\": [1280, 720]}, {\"counts\": \"R[d>5iW16L3M5K10O0000001O0O10000000O01000O011N100O2O0O10000O2O0O101O0O10000000000000000000O10001O0O2O2N1O2L5L5Hl\\\\];\", \"size\": [1280, 720]}, {\"counts\": \"QS^>>aW16LO01O000001O000O100000000O10O100O100O100O101N10000O2O000O100000001N10O1000000000001O001N2O2N1N3M3L6Hlld;\", \"size\": [1280, 720]}, {\"counts\": \"WS^>9gW13M4L1O1O001O01O0O010000O1000O10O100O10O02N100O100O101O000O1000001N11O00000O1000001O0O2O001N3M2O2M3LiTf;\", \"size\": [1280, 720]}, {\"counts\": \"]S^>2lW17J4L6K1O001O0000000O1000O10O10O10O1O100O100O100O2O0O1000000O101O0000000000O10001O001O001N2O1N2N3M3L4Lfld;\", \"size\": [1280, 720]}, {\"counts\": \"\\\\ka>:fW14L4L2N1O1O01O00O10O010O10O10000O100O100O100O2N10000O1000001N1000O1000O1010N10001O001N2O1N3M2O2M3L5Ic\\\\b;\", \"size\": [1280, 720]}, {\"counts\": \"ace>5jW14M3L7K0O1O1OO1000O10O10O100O10000O00100O100O100O2O0O10000O100O1000000O1001O00001O0O2O1O2N1O1N2O2L4Kbd^;\", \"size\": [1280, 720]}, {\"counts\": \"bce>8hW12O3L4L3M001OO100000O1000O10O100O10O01O1O101O0O10000O101O0O1O10000O100000000001O1O0O2O1O1O1N2O2M2N3K6J\\\\\\\\];\", \"size\": [1280, 720]}, {\"counts\": \"h[d>8gW16K4L3N0O00000000000O10O10000O0100O100O100O100O10001N10000O100O100O100000000001O001O1N3N1O1N2O1O2N2L5LSd^;\", \"size\": [1280, 720]}, {\"counts\": \"l[d>4kW16K6J3M2N1O0001O0000000O0100000O0100O1O10O02O0O10001N10001N100O1000000000000000O101O001O1N2O2N1N3M3L5JRd^;\", \"size\": [1280, 720]}, {\"counts\": \"kSc>7iW13L8J1N2N1O01O000000000O10O10O10O100O1O100O10000O101O0O101O0O100O10000000000001N101O0O2O1O2M2O2M3M3LRTa;\", \"size\": [1280, 720]}, {\"counts\": \"k[_>8gW1:G3M1O001O01O00000O10O1000O1000O10O0100O2N10000O10001N10001O0O1000000O10000001O001N2O001N3N1N3M3M3M8Encc;\", \"size\": [1280, 720]}, {\"counts\": \"P\\\\_>3iW1<H5K1O1O001O01O0000000O10O1000O10O100O100O100O2O0O100O1000001O0O10000O0100000001O001O1O1N3N1N3M3M3M6EPdc;\", \"size\": [1280, 720]}, {\"counts\": \"Vl\\\\>2mW1:F5L3M2N10O0000000000O10O10O01000O100O10000O100O100O1000001N100000000O1000O10001O001O1N2O1N2O2M4L4Ji[g;\", \"size\": [1280, 720]}, {\"counts\": \"Xl\\\\>9fW14`hNDVW1d0O1O0000001OO1000000O0100000O10O100O2O0O100O100O2O000O100000000O100000O1000001O1O1O1N2O2N2M3L5Ldch;\", \"size\": [1280, 720]}, {\"counts\": \"WT\\\\??]W15M3N101N10000O10001O0O10000O1001O0O2O1O001O2N1N3M3M4L7Fdcc;\", \"size\": [1280, 720]}, null, null], \"bboxes\": [[353.0, 905.0, 29.0, 22.0], [357.0, 903.0, 31.0, 33.0], [356.0, 960.0, 38.0, 52.0], [326.0, 1045.0, 50.0, 48.0], [325.0, 1054.0, 50.0, 44.0], [327.0, 1068.0, 49.0, 36.0], [352.0, 1074.0, 28.0, 35.0], null, null, [409.0, 1137.0, 33.0, 65.0], [413.0, 1184.0, 58.0, 57.0], [414.0, 1183.0, 62.0, 58.0], [418.0, 1183.0, 57.0, 71.0], [389.0, 1162.0, 89.0, 90.0], [415.0, 1182.0, 61.0, 68.0], [406.0, 1177.0, 67.0, 73.0], [390.0, 1091.0, 53.0, 80.0], [369.0, 964.0, 41.0, 62.0], [398.0, 961.0, 37.0, 41.0], [381.0, 957.0, 48.0, 39.0], [378.0, 954.0, 52.0, 38.0], [380.0, 952.0, 47.0, 37.0], [381.0, 920.0, 33.0, 46.0], [375.0, 894.0, 31.0, 33.0], [360.0, 889.0, 30.0, 29.0], [376.0, 885.0, 33.0, 30.0], [375.0, 895.0, 34.0, 32.0], [379.0, 904.0, 36.0, 30.0], [379.0, 910.0, 35.0, 27.0], [369.0, 905.0, 46.0, 29.0], [368.0, 904.0, 50.0, 28.0], [369.0, 905.0, 51.0, 28.0], [370.0, 908.0, 51.0, 29.0], [369.0, 911.0, 52.0, 30.0], [370.0, 912.0, 50.0, 30.0], [369.0, 913.0, 41.0, 30.0], [372.0, 913.0, 47.0, 30.0], [372.0, 913.0, 49.0, 30.0], [367.0, 915.0, 52.0, 30.0], [374.0, 917.0, 51.0, 30.0], [384.0, 919.0, 50.0, 30.0], [392.0, 919.0, 49.0, 29.0], [395.0, 916.0, 52.0, 30.0], [400.0, 909.0, 52.0, 30.0], [399.0, 902.0, 54.0, 31.0], [396.0, 897.0, 55.0, 30.0], [395.0, 894.0, 52.0, 29.0], [389.0, 893.0, 53.0, 28.0], [387.0, 893.0, 51.0, 29.0], [383.0, 896.0, 51.0, 28.0], [382.0, 897.0, 52.0, 29.0], [386.0, 901.0, 50.0, 29.0], [388.0, 904.0, 50.0, 30.0], [388.0, 906.0, 52.0, 30.0], [389.0, 907.0, 51.0, 30.0], [389.0, 906.0, 51.0, 31.0], [390.0, 904.0, 50.0, 31.0], [386.0, 901.0, 51.0, 29.0], [382.0, 897.0, 50.0, 30.0], [374.0, 895.0, 51.0, 28.0], [368.0, 891.0, 52.0, 29.0], [369.0, 889.0, 48.0, 30.0], [366.0, 889.0, 49.0, 29.0], [361.0, 889.0, 50.0, 30.0], [359.0, 888.0, 48.0, 30.0], [359.0, 895.0, 17.0, 20.0], [360.0, 887.0, 48.0, 30.0], [359.0, 887.0, 45.0, 29.0], [359.0, 886.0, 47.0, 30.0], [360.0, 885.0, 48.0, 30.0], [361.0, 884.0, 51.0, 29.0], [365.0, 884.0, 49.0, 29.0], [364.0, 887.0, 50.0, 29.0], [361.0, 887.0, 52.0, 29.0], [360.0, 886.0, 49.0, 29.0], [358.0, 886.0, 50.0, 29.0], [356.0, 886.0, 50.0, 28.0], [356.0, 891.0, 51.0, 29.0], [352.0, 920.0, 15.0, 11.0], [351.0, 910.0, 54.0, 27.0], [353.0, 913.0, 50.0, 27.0], [360.0, 911.0, 48.0, 28.0], [363.0, 908.0, 50.0, 28.0], [361.0, 906.0, 51.0, 27.0], [361.0, 904.0, 49.0, 26.0], [356.0, 901.0, 53.0, 31.0], [359.0, 904.0, 50.0, 30.0], [359.0, 907.0, 49.0, 30.0], [361.0, 908.0, 49.0, 30.0], [365.0, 908.0, 49.0, 30.0], [364.0, 907.0, 39.0, 28.0], [371.0, 926.0, 20.0, 12.0], [383.0, 907.0, 51.0, 28.0], [395.0, 906.0, 49.0, 29.0], [396.0, 917.0, 21.0, 17.0], [399.0, 903.0, 54.0, 30.0], [401.0, 902.0, 52.0, 28.0], [404.0, 897.0, 51.0, 29.0], [402.0, 902.0, 50.0, 31.0], [397.0, 914.0, 51.0, 27.0], [394.0, 913.0, 51.0, 31.0], [393.0, 913.0, 49.0, 30.0], [388.0, 906.0, 51.0, 29.0], [389.0, 894.0, 49.0, 29.0], [401.0, 883.0, 52.0, 35.0], [411.0, 888.0, 37.0, 29.0], [414.0, 894.0, 24.0, 22.0], [417.0, 892.0, 29.0, 24.0], [417.0, 883.0, 55.0, 28.0], [419.0, 878.0, 53.0, 28.0], [418.0, 875.0, 54.0, 29.0], [413.0, 883.0, 47.0, 27.0], [408.0, 891.0, 52.0, 29.0], [402.0, 897.0, 46.0, 27.0], [396.0, 897.0, 47.0, 27.0], [392.0, 893.0, 50.0, 28.0], [388.0, 889.0, 49.0, 26.0], [387.0, 887.0, 52.0, 27.0], [386.0, 889.0, 55.0, 27.0], [387.0, 888.0, 55.0, 28.0], [392.0, 888.0, 54.0, 28.0], [398.0, 886.0, 54.0, 29.0], [403.0, 886.0, 55.0, 28.0], [408.0, 886.0, 54.0, 28.0], [413.0, 886.0, 54.0, 29.0], [411.0, 883.0, 54.0, 30.0], [404.0, 874.0, 54.0, 30.0], [395.0, 865.0, 54.0, 29.0], [384.0, 858.0, 53.0, 30.0], [374.0, 858.0, 53.0, 27.0], [369.0, 858.0, 52.0, 28.0], [369.0, 862.0, 51.0, 28.0], [369.0, 864.0, 52.0, 30.0], [372.0, 866.0, 51.0, 32.0], [375.0, 868.0, 51.0, 33.0], [375.0, 872.0, 52.0, 32.0], [374.0, 878.0, 52.0, 31.0], [374.0, 883.0, 52.0, 30.0], [373.0, 883.0, 51.0, 30.0], [370.0, 882.0, 52.0, 31.0], [370.0, 884.0, 52.0, 31.0], [368.0, 892.0, 51.0, 31.0], [368.0, 895.0, 50.0, 31.0], [393.0, 892.0, 29.0, 30.0], null, null], \"areas\": [555.0, 676.0, 1556.0, 2070.0, 1625.0, 1366.0, 721.0, null, null, 1821.0, 2012.0, 2190.0, 2194.0, 2538.0, 2290.0, 2732.0, 3191.0, 1599.0, 1141.0, 1229.0, 1203.0, 1103.0, 797.0, 811.0, 771.0, 841.0, 878.0, 877.0, 829.0, 908.0, 948.0, 973.0, 953.0, 1013.0, 942.0, 717.0, 939.0, 1008.0, 1007.0, 1008.0, 996.0, 1004.0, 998.0, 1061.0, 1066.0, 1034.0, 997.0, 968.0, 966.0, 988.0, 983.0, 978.0, 1019.0, 1052.0, 1022.0, 1066.0, 1046.0, 1023.0, 1043.0, 989.0, 1001.0, 975.0, 949.0, 1022.0, 976.0, 165.0, 980.0, 913.0, 1005.0, 1025.0, 989.0, 963.0, 965.0, 1016.0, 1028.0, 1003.0, 972.0, 1002.0, 94.0, 923.0, 764.0, 938.0, 922.0, 906.0, 874.0, 1089.0, 1044.0, 1045.0, 1013.0, 1019.0, 600.0, 147.0, 915.0, 950.0, 166.0, 981.0, 945.0, 947.0, 989.0, 887.0, 1047.0, 965.0, 993.0, 899.0, 1128.0, 692.0, 426.0, 547.0, 1140.0, 1068.0, 1121.0, 960.0, 1128.0, 978.0, 1007.0, 1057.0, 986.0, 1058.0, 1105.0, 1118.0, 1110.0, 1107.0, 1143.0, 1143.0, 1148.0, 1202.0, 1232.0, 1160.0, 1157.0, 1098.0, 1115.0, 1071.0, 1206.0, 1231.0, 1193.0, 1205.0, 1202.0, 1205.0, 1175.0, 1232.0, 1219.0, 1197.0, 1192.0, 692.0, null, null], \"iscrowd\": 0, \"video_id\": 0, \"height\": 1280, \"width\": 720, \"category_id\": 49272, \"noun_phrase\": \"air jordan\"}, {\"id\": 1447, \"segmentations\": [{\"counts\": \"na_58dW19I4J6G7M4M2M3N3N1O1N3M200O1O1000000O1O2N1O1O100N2O1N2M3D<N2N3N1N2N3M2L4O100N3M3M2O1O1O1010O003M3M2N1O1N2O010ON2L4M31O1O001O1L>B4K4N4J6mN_jNZOlU1`0n0K7B`0DgWic0\", \"size\": [1280, 720]}, {\"counts\": \"niV5;_W18H8I7L3K5K4N2M4O0O1O1O2O0O100O2M2N2N2M3L5G8M3N101N2N3M2N2N2N2N3N1O1O100O11O1O001O001O001ON2N2O12N2O0O6I2O2N1O1O2N2M3N3M1O1O2M4M3M3M8G7Gb0[O8Eh^Td0\", \"size\": [1280, 720]}, {\"counts\": \"UZj47bW19I7I6F:G8M4M200O2M2O2I6@a0J5L5H7K5K5M3O2M2001O001O00001O2OO02N1O00O1N1101O001O00002N2N1O1O3M1O1O1O1O0O2O0000004L2N002N1O1O002N1O0O012N3L>B4L2N2N9F`0mNUiN2\\\\U]d0\", \"size\": [1280, 720]}, {\"counts\": \"mY`4?\\\\W16J6YOg0M3nN_NbkNb1QT1TOckNo0RT1_OakNl0\\\\T1]1N2N2N3N1O1O1O1010O2N0001O0010O1O00O1100O001O3M001O0001O000001O0000O001O1001O0000O100000O1000H_LPlNb3nS19O1O11N1000O10O2O1O2M2O3L4M2M4L;E8H9G5K;E9F;F;jNXiNMVn`d0\", \"size\": [1280, 720]}, {\"counts\": \"eWQ47[W1LhhN`0S2CZR1U1XmNTOeR1n0UmNYOgR1k0YlNjNKb0gS1o2M3O2O01O0001O0O101O0O1001O0000000001O01O002N0000011N1O1O01O0002N1O1O0001OO3N1O1O100OO1O1000000O1O001N2N2O1N20O01O1N2O1O1N200001O000O00101O000O2N101N1O1O1O2N3M2M5L5J8Ie0[O5K2lM^jNh1nU1N100O1gNliNKQW1I_`Yd0\", \"size\": [1280, 720]}, {\"counts\": \"bgn3`0fV1AliNi0b1YOXR11lkNW1^1oNdR1R2blNTNVS1DPlN_2>RNaS1Z2WlNjMhS1W3O1O2O00001O001O000O2O0O1000010O01N110O10O01O001O100O100O10O01O1O001O1O00001O0000001O000000000000O1L4M3O0O2N2N1O200000000O100000O2O0000O11N10000000O11O00000O010O2N1O1O3M2M3N1O2M3N4K4M4L6J5K<E5J5L2M4M8ROniN\\\\OUV19n0FSPmc0\", \"size\": [1280, 720]}, {\"counts\": \"^oo3;aW1>A:I4G`0aiNdNT1IWS1Z2[lNnMXS1d2_lNaM_S1a2[lNdMcS1_2VlNfMjS1Y3O000O2O0O101N100O101O000001O1O10O0O1O110O01O1O01O10O1O0000011N1O0000101N0000O12N00001O00O10000O001N2O1M3O1000000O1N1011O0000000000001OO1O10000000O1000000O100O100O1O1O100O1O100O2N2N3L4L5L4M2M5iM^kNg0lT1fNVkN@O11\\\\1PU1kNTkNFL23LMW1WU1mNmkNP1cU1N3LO2I6011O00`obc0\", \"size\": [1280, 720]}, {\"counts\": \"YWl39cW16L`0XOR1UO9E:C^MPkNi2c0\\\\MZS1n2\\\\lNZMaS1k2TlN^MhS1_3M3O0O2O00000O101N1000001O000001O10O00001O0001O1O2O0O1O100O1O0000O11O001O10N1001O1O2NO1O1O12N1O0O01N1102N01OOO2O11O1O1OK5L4M300O10000O11O002NM3O101O0000O10000O101N1O00100O2N1O100N3N100O2N4L3M4L3N2M4L5J5J8J4K7fMejNc1bU1XN^jNf1mU1N<D7bNaiNT1gV1H8M3B>CRX_c0\", \"size\": [1280, 720]}, {\"counts\": \"^gd3:bW16Ic0YO<diNcNeU1Z2F3L4M3N2N2N2N2N2N1N2O1O1O1N2O1O100O010O0O2O1000O10O1O10000O1000O100O101N1O01000O2OO00001N101O0O10000O2O0O101O0O2O0O2L3M4K5N110O000O2O0001O0000O100010O00000000000001O001O000000O10000O101O1N3M2O2M2M4M5J5L9H;D5Ka0_O5QN\\\\jNY1WV1I7F:D;N2I7OSolc0\", \"size\": [1280, 720]}, {\"counts\": \"]Z]36aW1=H5G9L4L3O2O0jMlN`mNT1^R1RO_mNm0^R1XO`mNh0\\\\R1]OdmNc0WR1BimN=TR1FmmN9mQ1ORnN0gQ18YnNGgQ1:YnNFfQ1<XnNDhQ1=XnNBhQ1?WnNAiQ1a0UnN_OkQ1c0SnN^OlQ1d0SnN[OnQ1e0QnN[OPR1e0omN[ORR1f0mmNYOTR1g0lmNXOUR1h0jmNXOWR1h0hmNXOXR1i0hmNVOYR1j0fmNVO[R1j0dmNVO\\\\R1k0dmNTO]R1l0bmNUO^R1k0bmNTO_R1m0_mNSObR1m0]mNSOdR1m0[mNSOeR1n0[mNQOfR1n0ZmNROgR1m0ZmNROgR1n0YmNQOgR1o0ZmNQOeR1o0[mNQOeR1o0[mNQOeR1P1ZmNPOfR1Q1ZmNnNeR1T1ZmNlNeR1W1YmNiNdR1[1\\\\mNdNdR1]1[mNcNeR1]1\\\\mNbNdR1^1\\\\mNbNcR1`1]mN_NbR1b1^mN^NaR1d1_mN[N_R1h1`mNXN^R1j1bmNVN]R1l1cmNSN\\\\R1P2cmNoM\\\\R1S2cmNmM]R1S2dmNlM]R1T2bmNlM^R1T2cmNkM]R1V2bmNjM^R1V2cmNiM^R1W2amNhM`R1X2amNgM_R1Z2`mNfM`R1Z2`mNfM`R1[2`mNdM`R1\\\\2`mNeM_R1\\\\2`mNdM`R1\\\\2amNcM_R1^2`mNaMaR1_2amN_M_R1a2bmN^M_R1a2amN_M_R1a2amN_M_R1b2amN\\\\M`R1d2amNZMaR1f2^mNYMcR1g2\\\\mNYMfR1g2ZmNXMfR1h2Z10O02O005J:G5J4M2M3N6I;Eb0^O000010O01O4Kofdd0\", \"size\": [1280, 720]}, {\"counts\": \"la^3`0XW19M3K5J6K4M4I6G9O10001O00OVNSNcmNm1TR1^NkmNa1RR1dNmmN\\\\1QR1fNnmNZ1RR1gNomNW1QR1jNomNU1kQ1WNblNd0c1U1PR1mNPnNR1oQ1POPnNP1QR1POomNo0QR1ROnmNn0SR1ROmmNm0SR1TOlmNl0UR1TOkmNl0UR1TOkmNk0UR1VOjmNj0UR1XOjmNh0VR1YOjmNf0VR1\\\\OhmNd0YR1\\\\OfmNd0ZR1]OemNc0ZR1^OgmNa0XR1AhmN?WR1AjmN>VR1CimN=XR1ChmN<XR1EgmN;XR1GgmN9YR1HgmN7YR1JfmN6ZR1KfmN4ZR1MemN3WR11jmNNRR17nmNHQR19PnNFPR1;PnNDPR1<PnNEoQ1<QnNCPR1<QnNBPR1?omNARR1>omNARR1?mmNATR1>mmNARR1a0mmN_OSR1b0lmN^OSR1d0mmN[OTR1e0lmNZOTR1f0mmNYOTR1f0mmNZOSR1f0lmNYOVR1g0jmNXOVR1i0imNWOXR1h0imNWOXR1i0gmNVO[R1j0emNUO\\\\R1k0cmNTO_R1l0amNSOaR1k0_mNTOlR1a0VmN]OWS16jlNIZS14glNKcS1I`lN5fS1YOPkN2]1d0dU1O2M3M3M3M3NUf]e0\", \"size\": [1280, 720]}, {\"counts\": \"ZZb33^W1c0H5J5N3M2M3L4M4H7L3O2O2O0O1N3K4O100O100QNjMTnNV2kQ1lMUnNT2jQ1lMVnNT2kQ1mMYlN0d1S2SR1TNmmNk1SR1WNkmNi1UR1YNjmNf1VR1[NjmNd1VR1\\\\NkmNc1UR1^NkmNa1UR1aNjmN^1SR1fNmmNZ1oQ1jNPnNV1lQ1oNTnNP1mQ1oNSnNR1mQ1nNSnNQ1mQ1PORnNP1nQ1QORnNm0nQ1UOQnNk0PR1TOPnNl0QR1TOnmNl0RR1TOomNk0RR1UOmmNk0SR1UOmmNk0SR1VOlmNj0UR1VOkmNi0UR1WOlmNg0VR1XOjmNh0WR1XOimNg0XR1XOhmNh0YR1WOgmNi0ZR1VOgmNi0[R1VOdmNi0_R1UObmNk0_R1SOamNm0cR1nN_mNR1gR1gNYmNY1iR1dNXmN[1jR1dNWmN[1iR1bNZmN^1fR1]N`mNa1aT1O1O1N3N4J7F:@g^ke0\", \"size\": [1280, 720]}, {\"counts\": \"VZX3a0[W16L3M2O2M2O2M2O2N1N2N2M3I7M3N3N1O1O1O1M3F:O100O1001O01O01O001O000010O000001O001O1O[NiMamNW2TT10_NiMZmNV2fR1jMZmNV2YT1O0O2O1O1O0000001O1O001O001O1O1O2M5L2N3L5L3L9G:AQVTf0\", \"size\": [1280, 720]}, {\"counts\": \"^RR34cW1?F6L3M2O2M2O1O1N3M2N2N2F:M4N1N2O1N2O1O1M4N10000000000001O01O01O001O1O1O[NmM]mNS2bR1oM^mNP2bR1QN]mNo1cR1QN^mNn1bR1UN[mNk1fR1TN[mNk1fR1TNZmNl1fR1UNYmNk1cR1YN^mNf1VR1fNkmNY1TR1iNkmNW1VR1iNjmNV1VR1lNhmNS1YR1mNhmNR1XR1oNgmNQ1YR1oNhmNo0ZR1POgmNo0ZR1QOfmNo0[R1POemNo0^R1nNbmNR1`R1mN_mNS1bR1lN_mNS1fR1hNZmNX1mR1aNSmN_1oR1]NTmNb1eT100O2O0O2O1O1N2O3L3N4Hb0]OP^Uf0\", \"size\": [1280, 720]}, {\"counts\": \"lao2`0ZW1:I4K5XOROXjNS1eU1e00001O00000001O001O00fMeNomN\\\\1oQ1iNnmNV1RR1kNmmNU1SR1lNmmNS1RR1oNmmNQ1SR1POmmNo0SR1QOmmNo0SR1ROlmNn0UR1ROkmNm0UR1SOlmNl0TR1TOlmNk0VR1UOjmNk0VR1UOjmNj0WR1VOhmNj0XR1WOhmNh0YR1XOfmNh0ZR1YOfmNf0[R1ZOdmNf0\\\\R1[OdmNd0\\\\R1]OdmNb0]R1]OdmNb0]R1^OcmNa0]R1@cmN?\\\\R1BemN>YR1CgmN=WR1FimN9TR1LjmN4UR1NjmN2VR1NjmN2VR1OjmN0VR11imNOVR13imNMWR14hmNLWR17gmNIZR16fmNJZR17fmNH[R17emNI[R18dmNH\\\\R19cmNG]R19dmNE^R1:bmNF^R1:cmNE]R1<bmND^R1<bmND]R1=cmND]R1;cmNE^R1:cmNE^R1;amNE`R1:`mNFbR18^mNHdR17[mNHgR17YmNIkR1QOmkNh0Y15oR1kNRlNj0o0;`S1_OalNa0dS1YO]lNf0fS1SO`lNl0ZU1O101O0O2O1N1O2M4M\\\\nme0\", \"size\": [1280, 720]}, {\"counts\": \"laT3a0[W17L2mMXOllNi0nR1^OPmNb0mR1CRmN<kR1HTmN9fR1OXmN0cR17[mNIdR19\\\\mNFcR1<]mNCbR1`0]mN_OdR1a0[mN@dR1`0]mN_OcR1b0\\\\mN_OcR1b0]mN]OdR1d0ZmN\\\\OfR1e0ZmNZOgR1f0XmNZOhR1h0WmNWOiR1j0VmNVOjR1k0UmNUOkR1l0TmNTOlR1l0UmNTOkR1l0TmNTOmR1m0QmNSOoR1m0QmNSOoR1n0PmNROQS1n0nlNROQS1o0olNQOPS1Q1olNoNPS1R1PmNnNPS1S1nlNnNRS1R1nlNmNSS1S1mlNmNSS1S1mlNnNSS1R1llNnNTS1R1llNnNSS1S1mlNmNSS1S1mlNlNSS1U1mlNkNSS1V1llNjNSS1W1mlNiNQS1Y1olNgNmR1\\\\1TmNdNiR1_1WmNaNiR1`1VmN`NjR1`1VmNaNiR1_1XmN`NhR1`1WmNaNiR1_1WmN`NjR1`1WmN_NiR1b1VmN^NkR1a1UmN_NhR1d1XmN\\\\NgR1e1YmN\\\\NfR1c1[mN]NeR1d1YmN]NgR1c1YmN]NgR1c1YmN\\\\NhR1d1XmN\\\\NiR1c1WmN]NiR1c1WmN\\\\NjR1d1VmN\\\\NkR1d1TmN\\\\NnR1b1RmN^NoR1a1QmN_NPS1_1QmNaNPS1^1QmNaNPS1^1PmNbNRS1[1olNeNTS1X1llNgNZS1T1flNlN_S1i0glNWO^S1FVkNe0\\\\1DnS15SlNKPT1GgjNMZ1;QV100O1O1O1O1N4JQ^fe0\", \"size\": [1280, 720]}, {\"counts\": \"c_Y3;`W1:chNAhV1P1I7K7H6M5K3O1O2O1O1N2O00000O1M3O0O2O1O0010O010O2N100O1O001O0000000000O100O1L4M30000O1O2N1O10000O100O11O00000000000001O000000000001O1N101O01O02N1O1O2M5L2N2N:F;E9G7I2N1N2O4L1O7H5J4LV_We0\", \"size\": [1280, 720]}, {\"counts\": \"ZhU31oW12N1O00OXhN2\\\\V1O`jN;SU17ejNLoT1>PkNEiT1a0fjNkN4i0SU1k1M3L4K4M3N2O0010O10O0O2O100O02NO2N1001O10O001O010O10O000001O01O0001O0000100O001N1000000N3M2M300O1O1O2O0O1O2O0O101O0O2O001O0001O0000000100O01O1O1O001O1O2N2N2N3M4L8H:nLYkNW2YU1OO10O101PN[jNc1hU1XN_jNc1oU101O2]NhiN30m0mV1Kb0^O4IbVld0\", \"size\": [1280, 720]}, {\"counts\": \"jQR36eW19`MH\\\\lNo0ZS1]OmkNW1iS1VOhkNW1QT1`1K4O1O2M3N2O1O0000000001N1O100001O01N101O001OO10000001O000000001O1OO100O02O100ON2M3O11O0000000000001OM3J6N20000000O100000O1000O10O1000O10001O0O101O1O1O1O1N3N1N2N2N4L4K;E=C8H:F4QNVjNO1Z1^V1M3L4L4J?]OeWVe0\", \"size\": [1280, 720]}, {\"counts\": \"Xjk2:dW13L4M3M2kM_OilNc0nR1;XlNKaS1`0^kNfN`0o0PT1g0ikN^OUT1h0`kN^O_T1R2O2O000001O0010O00000001O001O001O00O10O101O00001O001O1O00N2N2O1O1N200O1M3002N00N2N3O0O100O100N2000000O1O1N30O000000O10000000000O2OO2O000O10001N2O1O1N2N3N2L3N3M5J5K4K6I6K;F9En0oNk0ZOc^\\\\e0\", \"size\": [1280, 720]}, {\"counts\": \"V_e2`0YW1;D;I7L4\\\\kN`NaR1e1UmNeNfR1^1VmNeNhR1^1TmNeNkR1`1nlNcNQS1a1flNeNYS1b1[lNdNcS1m201O01O01N1001O1O1OO2M200002N00O1O1003M1OO1M3N2001O1ON2O101OO2O00N2000000000000000000O1M3M3O10000O10001O00O01000O1000O10O2O0000000O101O00000O2O0O2O001N3N1N3M3M3M2M8I2N3L4K`0@:E9Hd0]O9SOViN3_W1K4L4LUW[e0\", \"size\": [1280, 720]}, {\"counts\": \"hf\\\\28aW1:E;^Ob0M4L4L4La0_Oa0^O8Il0SO4M1N2O1O1O0010O10000O0000O11O2N00001O2N00O1001O1O00O1O1002N000000N22N0000O1N2O1O1N1100000O100O10000O1N2N2M30000O1000O2O00001O000O1010O00O1000000O1000001O000O10001N10001N4M1N3M3M3N2L5L5J6J4L6J8\\\\MkjNg1RV1D=F4UO^iN2gV1I`iN1jV1FnPZe0\", \"size\": [1280, 720]}, {\"counts\": \"`ol2438PW1e0H6M6J9H8Ga0_O5K4L4K4M4M2N6YkNTMmS1c3M2N1O1O0O101O00000001O1O1O2N00100O0000JoK]lNQ4gS1200O1M3N2000001O000001O1O1OM3O10000O100O1O1O2OO10O100N2001O0000O1001O001N2O1O00000000O1001O000O101O00000O2O1O001N2O001O2N1O2M3N2M3N7Hf0ZOe0[O9H1N3N6H<B8ZOSiNKaend0\", \"size\": [1280, 720]}, {\"counts\": \"fWg3X1gV1b0^iNdN]U1S2L3N1M3O0O2O00000000001O0001O01O0001O00000000001O002O0O01O000010O000001O00001O0lN\\\\MllNe2SS1\\\\MmlNc2RS1_MmlNa2SS1`MllN`2SS1cMklN]2US1cMklN]2TS1]MWlNKe0h2RS1^MWmNa2iR1aMVlNLg0c2RS1lMmlNS2SS1nMmlNQ2RS1PNnlNQ2oR1QNQmNo1nR1SNRmNl1mR1VNRmNj1mR1WNSmNi1mR1XNSmNg1lR1ZNUmNe1kR1[NUmNe1kR1[NVmNd1kR1[NUmNe1mR1YNSmNg1mR1YNTmNf1lR1[NTmNd1kR1]NUmNc1kR1^NUmNa1kR1`NUmN_1jR1bNWmN]1jR1cNVmN\\\\1jR1eNVmNZ1jR1gNUmNY1kR1hNTmNX1lR1iNRmNW1oR1jNPmNV1PS1jNQmNU1oR1lNPmNT1PS1lNQmNS1oR1mNRmNR1nR1nNSmNP1nR1POSmNo0mR1RORmNn0nR1ROSmNl0nR1TOSmNk0nR1TOSmNk0mR1VOSmNi0mR1WOTmNh0lR1XOUmNf0lR1ZOUmNe0kR1[OVmNd0jR1\\\\OVmNc0kR1]OVmNb0kR1\\\\OWmNb0kR1\\\\OWmNc0kR1XOYmNe0jR1VO\\\\mNb0VS1iNWmNn0T\\\\cd0\", \"size\": [1280, 720]}, {\"counts\": \"f_R41hW1c0A8\\\\Ob0L2O001hN`NmkNb1PT1eNkkN]1QT1gNlkN[1QT1gNokNY1PT1hNPlNY1lS1jNUlNU1kS1kNUlNV1jS1kNTlNW1kS1iNUlNX1jS1iNUlNY1iS1gNWlNZ1hS1fNXlN[1jS1bNWlN^1nS1\\\\NSlNd1oS1XNRlNh1nS1YNRlNg1mS1YNSlNh1lS1XNUlNg1jS1[NUlNf1iS1[NWlNf1hS1ZNYlNf1gS1YNYlNh1fS1XNZlNi1iS1QNZlNn1lT110O01O00001O010O1O010000O0010O001O0001O01O000bNoMnlNP2QS1RNnlNn1QS1UNnlNj1QS1WNPmNi1lR1ZNTmNf1iR1^NWmNa1hR1`NXmNa1eR1bNZmN^1eR1cN[mN]1bR1fN^mN[1_R1gNbmNX1\\\\R1kNdmNU1ZR1lNfmNU1ZR1jNgmNV1XR1jNhmNV1XR1iNjmNW1WR1gNimNY1WR1fNjmNZ1VR1eNkmN\\\\1TR1cNnmN\\\\1RR1cNomN]1RR1`NPnNa1oQ1^NRnNb1oQ1[NRnNf1nQ1YNSnNg1nQ1WNSnNj1mQ1TNTnNl1PR1lMTnNT2lS110O010O0J[jNQNdU1m18O3N2O1N3N2M2M6K<Ch0WOWoVd0\", \"size\": [1280, 720]}, {\"counts\": \"lfS4d0RW1=H9J7I6J5K5M1N3N2N0O2O00O2OO101N10000O100O11O001O1N2O1O2N1O2N1O1O1O000O10002NnjN[NhS1e1WlN]NhS1d1WlN^NgS1c1XlN^NiS1a1WlNaNgS1_1YlNcNfS1\\\\1ZlNdNeS1]1\\\\lNcNcS1]1^lNbNaS1_1_lNbNaS1]1_lNcNaS1]1_lNdN`S1\\\\1_lNeN`S1\\\\1alNdN]S1]1clNdN\\\\S1\\\\1dlNeN[S1[1elNfN[S1Y1elNhNYS1Y1glNgNYS1Y1glNhNXS1W1jlNiNUS1W1klNiNUS1W1klNiNTS1X1llNiNSS1V1nlNjNRS1V1nlNkNQS1U1PmNjNoR1V1RmNkNkR1V1VmNjNjR1U1WmNkNiR1T1YmNlNgR1Q1[mNPOdR1k0amNUO`R1f0dmN[O[R1c0gmN]OZR1a0gmN@XR1>jmNCVR1;kmNFTR18mmNISR15omNLPR12RnNNnQ11SnNOnQ1ORnN2nQ1MSnN4lQ1LTnN4mQ1^OTlNIP2j0iT101O0N22N3MKViNXOgV1j061N20O2L2O02M3000O2N4SO^iN2dePd0\", \"size\": [1280, 720]}, {\"counts\": \"cVg37T2KZS1>_lNH\\\\S1:SlN9hS1LUlN7hS1KWlN7eS1LYlN5eS1NXlN5gS1LRlN;mS1FRlN:nS1FRlN:nS1FRlN;mS1ESlN;lS1FTlN:lS1GSlN9mS1GSlN9lS1HUlN8jS1HUlN9kS1GTlN:lS1FSlN;mS1FQlN;oS1EQlN<nS1DQlN=oS1CQlN=oS1CRlN=mS1DRlN<oS1CQlN>oS1BPlN>QT1APlN?PT1@PlN`0RT1^OokNb0QT1]OokNd0QT1[OokNe0RT1ZOmkNg0TT1XOlkNi0ST1WOmkNi0ST1WOmkNi0ST1WOmkNi0TT1VOlkNj0UT1UOkkNl0TT1TOlkNl0TT1TOmkNk0ST1UOmkNl0ST1SOmkNm0ST1SOmkNn0ST1ROlkNn0ST1SOnkNl0RT1TOnkNl0RT1TOnkNm0QT1TOnkNl0RT1TOnkNl0PT1WOokNj0nS1YOPlNh0oS1ZOPlNf0PT1ZOPlNf0PT1ZOPlNf0PT1[OokNf0PT1ZOPlNf0PT1ZOQlNe0nS1\\\\ORlNe0mS1[OTlNd0lS1\\\\OTlNe0jS1\\\\OWlNc0gS1_OYlNa0dS1B\\\\lN?aS1C`lN<^S1FblN:[S1IelN8XS1JhlN6XS1JhlN6XS1JhlN6XS1JhlN6XS1KhlN4YS1KglN6YS1IglN7[S1GdlN;[S1EelN;[S1EelN;[S1EflN:ZS1FglN9YS1GhlN8XS1HhlN8XS1HhlN8XS1HilN8VS1HjlN8VS1HklN7US1ImlN5SS1KmlN5SS1KnlN5QS1KPmN4PS1LPmN4PS1LQmN3PS1LPmN4QS1JQmN6oR1IQmN7SS1EmlN;WS1AjlN>XS1@hlN`0YS1_OhlN`0YS1_OhlN`0XS1@hlN`0XS1_OjlN`0VS1@klN>VS1BklN<US1DllN;VS1CmlN9VS1DmlN7\\\\S1\\\\OZkND^1l0aU1K7HX_Td0\", \"size\": [1280, 720]}, {\"counts\": \"bfZ3?VW1>E<I;G8Hc0^O:F6J7I`0_O7J6I3N1N2O00100O0100O00O1001O2N1O1O1O1O00O21N1N101O2N1O1O1O1O1O1O00000000O100O1N2O100O100N2O1001O1N2O0000001O002N1O1O2N1N10000O101O2N001O0O101O002N1O1O00oNhLZmNX3eR1iL[mNV3gR1iLZmNV3fR1jLZmNV3iS1O1O0O110O000O101O1O001O0O4M3L8H:G2M;oMniN`1[V1M:E=C>CifZd0\", \"size\": [1280, 720]}, {\"counts\": \"lVX3c0TW1<H7I7M?A6K:F6K6H8H6K3L3M?B7I3M1O1O1O1O00001O000001O1O002N2N001O1O0001O0001O1OO010001O00000000001O00O1O1O2N1O1N2N2N200O1O1O001J6000000000000000O100000000O1000001O0O10000000001O0O101O0O3N001O1O001O2N4L5K7H6K3M4K6K6I9H2N7H3N5K3L4M5J4M7H7H>AifUd0\", \"size\": [1280, 720]}, {\"counts\": \"[`c3`0ZW18J5L4M=B9C=H7J4L4cjNaMRU1m2H8I5K3N?@3N1N2N2O1O0O1O10010N1O1O11O2N001O1O1OO1010O001OO1O1O1002N1O00O1000000JkKalNU4bS1300O1N20O1001O0O010O100O1N2001O00001N1000001O0000O100000O10000O10O10O101O00001N10000000001O1O2M4M4K6J>C3L4M4L2TMWkN\\\\2[U1La0^O4M2M3L;B?TO]nQd0\", \"size\": [1280, 720]}, {\"counts\": \"cfd3;WW1b0I8Ha0Ac0\\\\O8I6J8Ha0@<C5L2N2M2N2N2O0O100001O1O10O00000O1000000N2M3O011O0001O001O0000N2O1N200O1O100O1O100O1000OO[KmlNa4YS100001O000J63L10000O10001O000O10000000000O010O1000001O000O2O000000002N1O1O3M3M4L8H9G1N3N3M>B5K6I5L3M3M2M5L5J9G6I8I<C7HRgPd0\", \"size\": [1280, 720]}, {\"counts\": \"T^^38dW1:G9H`0Aa0_O6J8G8I;E=C9G4M2N1O1O0O3N2M2O1O01O1O001O1O2N001O0000000000O1M3M3O100000O010O01O1O10O01L4000000O10O010000M3O0N300O1O12M101O00000O01O001000O2O1O1N2O1N1O2O1N3M3M6K3L4L5K5L4J6K8H7H8I<C:F;]OjXld0\", \"size\": [1280, 720]}, {\"counts\": \"c^c38dW18J5K7gjNZOlR1U1alNTO[S1U1XlNROfS1R1okNWOnS1_2N2N3N1N1O2N1O1O10000003N0O2N4L01O0001O000001O0L4K5000000O10000O100O1O1N2L4O1N2O1O1N2001N10O1L4O10000000000001O00O01O00101N2O1O1O1O1N2O1O0O2O2M5L4L4K4L3M5J5L8H6I8H9H6I;D;D>[OTPkd0\", \"size\": [1280, 720]}, {\"counts\": \"Y^c37dW1:]jNNlR18llNNPS19flNNWS1e0PlNCnS1Y2N1O2N2M2O1O2O1O1O00O10O2O0010O10O01O1OO100O1010O000000O11O0000O100O1O1O2L3M3M3N2N2O1O1O100001O00000000000O10000000000O010O101O001O1N2O1O0O2O2M3N1O4K5K3N4K5K6J7H7I9H8H8H5J:F7D;FZPkd0\", \"size\": [1280, 720]}, {\"counts\": \"WVb3:_W1d0@6gjNYOQS1o0ZkNjNQ1`0bS1S1SlNUOiS1c2N2O001N2O000O2N101N10O0110O0101N2O1N1O002O0O1O2N100O00O100001O00000O2O10O0I7M3M4OO01O1O1O1O1O001N2M3N2O100O10O100000O10O1000000000000O1000O10O2O1N101N101N2N3N3L8H4K;F`0@5K7I9G5J4M4J7I6I7J=^OWhdd0\", \"size\": [1280, 720]}, {\"counts\": \"P_m3e0YW17J9B=E9J4M3K5L3N5L4K4njNiMRT1^2dkNiMYT1P300O1O0O1O1010O3M1O5L3L2OO000000001O00000DfLnkN^3RT180000O1000000000000O1N2O1O100M300N2M31O1O00O1O1O100O1O10000O1N11000001N1000001O1O001O001O0000001O0O2O1O0O2O1O1N3N2M3N2M3N3L3M8I5J7J5J3M5J8I6J7I5K6K6I5K4K6J9Flngc0\", \"size\": [1280, 720]}, {\"counts\": \"mbU53^U10PkNa0e0ITT1U1]kNSO\\\\T1]2J4M4M2M3O1O1J6O1N3O01O1ON2N2O2O000001O1O1O3N1N100O000000O11O1O0001O00001O000O10O1001O000O1000O1000000000O1O100N3M3N100O0O200O1O10000001O0O01O000100O10000O101N10O01O100O1O10000O100010OO1O100O2O1N2O1M3N7I4SNZkN9YU1SOSkNb0RU1XOQkNf0UU1SOmjN4^O4jU1EjjN4@MTV1H]jN8PW1Id^bb0\", \"size\": [1280, 720]}, {\"counts\": \"fi`5;bV1G]jN>[U1W1PO_N^kNc1YT1POPkN@0c1mT1X1L3L3J7G8N2N2O100O101OO2O00O2O000O100O1O1O1100O0001O0O1O10000O10010O11OO1O001O0000000002N10O01O1O1OO2NoKXlNl3hS1RL[lNm3lS10O22M1O0001O000O10O1000O1M3001OJRLZlNn3hS13100OOQOolNRMRS1g2XmNTMjR1i2X1M2N2GhjNiM[U1S2:O101O101O1O01O0O2O1000O01N2O1O1O00001N1001N1M3K6O000N2L4O2N2O6J6J4M0O0N3M5[OXiNBngja0\", \"size\": [1280, 720]}, {\"counts\": \"fai52iW1;I4dNGjjN;dT1@`jN5M7f07lT1@^jNf0a0LQU1e13NF9G:L4M4M1N3M2N2O1K6K4N3M3L3K501N1O2N101N1O1001000O1O000O101O00001O00001O0000001O0001O00O12N2N2M4N0O1O000000001O1O01O00010O000O10O100000O1O1O20O1O1OO010O0100O0100N20O01O010O0102M101O01000N1M3N1N3N2M3N2M3M4L4L3M4L3M3M3L3M5L3M4J6J5I:I=A>oN\\\\iNLlW^a0\", \"size\": [1280, 720]}, {\"counts\": \"Vc]67fW1:G6I5K3M4M2N3L3O2M2N1M5L3L4O1M3M3O1O2M2O1O1N200O1O1N2J7M2N2M3N2N2L4M3N2M4M2L4N2O2N1O1M3N2O1N2N200O1O2N1O1O100O1000000O1O2O01O00000O01000000000O011O000O1000000O10000000000O2O0N1M4L4L4L4L5K4I8I7J5L5K4K4L6L4L3N2K4N108G5L4K5J8I7HjoWa0\", \"size\": [1280, 720]}, {\"counts\": \"R[k63lW11N2005K00iN5m09F7M3M2M4M3M3L4K4C>E:L5L4M3N1N3N2L5L4L3M3M3J6J5O1N3N1N3O1N3N1N1000002O0N2O2N1O1O1O1O01O0100O1O1O1O1O2O0000O3K4M2O1N4L0000GPLdlNP4fS1O2OJTLXlNl3kS130O100O2N1O1O1N2M3N2N1O2O1N2N2N2M4M3K5M2N2M4M3M2M4K5L4L5E;EhiNhN]V1S1`0lNTiN1?L_nWa0\", \"size\": [1280, 720]}, {\"counts\": \"kij5;\\\\W1;K5@?M3N2L4L4M2O2`N\\\\NblNe1ZS1_NdlNb1[S1`NdlNa1ZS1aNelN_1ZS1cNdlN^1ZS1eNelN\\\\1YS1fNflNZ1YS1gNhlNY1US1iNklNW1SS1kNnlNT1PS1nNPmNR1nR1PORmNP1kR1TOTmNm0iR1UOWmNl0dR1YO[mNg0cR1[O]mNe0aR1^O_mNa0aR1_O_mNb0_R1@`mN`0`R1@amN?_R1B`mN>aR1B^mN>bR1B^mN=cR1D\\\\mN?aR1A`mN?_R1AamN?_R1B`mN>`R1B`mN?_R1B`mN>`R1C_mN=aR1C`mN<`R1E_mN;bR1D_mN<aR1D^mN<bR1D_mN;bR1E]mN;cR1E]mN;cR1E]mN;bR1G]mN9cR1G]mN9cR1H\\\\mN8dR1H\\\\mN8eR1G\\\\mN9dR1F\\\\mN:dR1F]mN9dR1G[mN9fR1FZmN:fR1GZmN8fR1IYmN7gR1IZmN6fR1KYmN5hR1JXmN6iR1JWmN5jR1JVmN6jR1JWmN5kR1IUmN7lR1HUmN7kR1JTmN6mR1ITmN6lR1JUmN4lR1MUmN1kR1OVmN0jR10XmNNhR12ZmNLfR15ZmNJgR15YmNJiR15XmNJhR17WmNIiR17XmNHhR19XmNEiR1;WmNDkR1;UmNEkR1<UmNBmR1=SmNCnR1=RmNBoR1>PmNAQS1`0olN_OPS1c0PmN[OoR1g0QmNXOQS1h0olNWOQS1i0olNWOSS1h0mlNVOTS1j0mlNUOSS1l0mlNROTS1o0llNPOTS1P1llNPOTS1P1mlNnNTS1S1klNmNTS1T1mlNjNQS1Z1olNdNRS1\\\\1olNaNTS1_1nlN\\\\NUS1c1PmNUNTS1k1llNSNVS1l1`1O1O1O1N2K4O1N3M3N2N4K6J7H8H6I9HXPba0\", \"size\": [1280, 720]}, {\"counts\": \"YXe4:aW1=Db0_O>D5J7I5K4M3M3M2N2N3M4L3N2M2O2M2N5L4L2N1O1O011N11O00001_MikNU1XT1gNnkNW1RT1eNVlNV1kS1fNZlNX1gS1cN_lN\\\\1aS1^NelNa1\\\\S1ZNilNf1iT1OZNVNXmNi1fR1YNZmNh1dR1WN^mNi1aR1VN`mNk1`R1TNamNk1_R1TNbmNl1^R1TNcmNk1]R1TNemNk1[R1UNemNk1[R1UNemNk1[R1UNfmNi1ZR1YNfmNg1YR1ZNfmNf1YR1[NhmNd1YR1[NhmNd1XR1[NimNe1WR1[NjmNd1VR1]NimNc1WR1]NjmNb1VR1^NjmNb1VR1^NkmNa1UR1_NkmNa1UR1aNjmN_1TR1dNkmN[1UR1fNkmNY1UR1gNkmNY1UR1hNkmNX1SR1jNmmNU1SR1lNlmNT1TR1mNkmNS1UR1mNlmNR1TR1oNlmNP1TR1QOmmNm0RR1TOomNk0PR1XOomNg0PR1]OnmNc0QR1^OomNa0QR1@nmN`0RR1AmmN?SR1BmmN=SR1DlmN<TR1DlmN;UR1GjmN8VR1IimN7WR1JhmN6XR1KgmN6XR1MemN3\\\\R10`mN0`R12_mNMaR14^mNLdR14ZmNLgR14YmNJiR16VmNJjR17UmNIlR17SmNImR18SmNGmR19SmNGnR19QmNGoR1:PmNFPS1:PmNFPS1:QmNEoR1;QmNDQS1<olNCQS1=PmNBQS1>olNAQS1`0nlN@RS1a0nlN]OTS1b0llN^OVS1`0jlN_OXS1`0hlN_OZS1`0glN_O[S1?flN_O_S1=alNCdS15`lNInS1^OmjNLY1d0kU1L7E]mnb0\", \"size\": [1280, 720]}, {\"counts\": \"ooe3=\\\\W1=B;K5J6K5L3M2N3L3N3N6I4L4L2N2O1O1O1O1O010O0000000010O001O100O1O1O001O1O001O001O1O10OO10001O00000O010O01000O0100000O010000O1N101O00100O0100O10000O00100O10O010000O100O100000O010000O10000O10001N10\\\\kNnM[S1P2dlNTN[S1k1elNUN[S1j1elNXNZS1h1elNYN]S1d1dlN]N\\\\S1b1clN_N^S1_1clNaN_S1]1`lNeNbS1X1^lNhNdS1U1\\\\lNmNfS1P1ZlNROeS1l0\\\\lNUOPT1>PlNCXT13hkNN[T1NfkN2kT1\\\\OVkNe0RV100000O1N20000001O100N2O5J=B\\\\m]c0\", \"size\": [1280, 720]}, {\"counts\": \"^^`24jW1b0@5UiNVO_V1h0WiN^OjV1n0J5J5F;L2M3M3O1N3N2N101N0O1O100O10000000O2O1O0O1O1O100O2O0O3N1N1N2O2O0O2N1N30010N2N5aNaiNT1^V1kNciNU1[V1:L3N2N3N2O000O1O2gkNTN\\\\R1n1_mNVN_R1k1^mNYN^R1k1^mNYN^R1k1^mNZN]R1k1^mNXNaR1k1[mNWNeR1l1WmNUNgR1n1WmNTNgR1o1VmNRNiR1S2QmNoMoR1S2llNPNTS1S2dlNSNZS1Z3O10O1000O1001O0O10000O2N2N2O001O002N3M3M5J3N1O6I3N1O2N1O1N2O1N3N1O1O9G4L2N2M3N1O001N100dMcjNU2fU1N2M3N2M3N1O1O1N2O3M3L3N3M4L3L3N2N3M2M5L3M2L4KbVXd0\", \"size\": [1280, 720]}, {\"counts\": \"RW_293L[W1l0A7H4K4M3N100O2N1O2N1O1N3N1N2N2M4N1O2N1O2N1O1O1O2N1O2N1O1N3N1O2M2O2N2M2O2N2N2N2N1O2M2O3M2M3N2N2N3M2N2N1O2N1O00001O2M2O1O0O2OO0100000O10000001O00000000001O0001O001O001O2N1O2M5L;E4K3N2N1N3L3N2N3N3M2M3M3M3N2M3N1N3N2M2O2N1O1O1N3N2N1O0O101N2O1N2O1N2O2N001O1N3N1O2N2N1N4M3M2N1O2M5L2M3M3N3M2N3Jn]oc0\", \"size\": [1280, 720]}, {\"counts\": \"lWS3<]W19J6L5L3N2M3N2N5K2N2N101M4M2M3N3L4M10O10O00001N3M3L3O1O2M2O2L4N2M6J3M2O1O2N1O1O2N1O1O2M4L3N2N1O001O0O2O1O1O1O1O1O1O1N10001O00001O001O000O2O00000O1000000000N101000001O00000O10001N1O2O1N101O1O2N3L5L6J>B4L8H1O2M3N1O2N2M3N2N2M3N4L2M3N1O1N3N1O1O2M2O2N2N2N1O2N2M3N3M3M3M3L4L5Kcefc0\", \"size\": [1280, 720]}, {\"counts\": \"^^T31Th30f_M0]hN3\\\\W1ObhN3]W1NbhN3\\\\W1;WO_OaiN2O=JHhV1P1N2N3M0O0biNgN:M`U1]1UjNhN70aU1`1`jN`N_U1^1djNdNYU1e1_jN^N^U1S2N3N5K010O1O1O1N3N1O2O3M2M3N2O2M4L4L3L1O1N102O0O1O001O1000O01N101O0100OO2O0O2O10O000000O11O00O1O1O101O000O1O1O1O1O1O1L4M3O1O2O0O0100O100000O01O0100000000000O100001O0001O0000001N101O001O1O2N2N1O1O5K7I5K4L2M3N3M3L?B5K7I6I5L2M2O2N3M5J8H3M5L6I?APgac0\", \"size\": [1280, 720]}, {\"counts\": \"YV`4j0PW19J5L4L4Lc0_O5J4L4L4M4L7I9G2N2N2N2N2N1O3N5J6K001N1N2O1O1O010O00001O0100OO2N1004M0O01O01O00O2N11O1OJkKblNS4fS10N3N1O1M3O11O0000O100O10000N2N101O10O01O1000O2O00O010O010000000000000000000000000000O2O0000001O00000000001O2N1O2N1O1O4K7J<D3M3M3M`0_O9H6J6J4L4K4M2N1O5J>B3M:E;Dc^bb0\", \"size\": [1280, 720]}, {\"counts\": \"oWh58fW15bhNGOOcV1i1XO8H5L4K6K3M4L6I6Jh0ZO3M3M1O001N2O001O1O1O1O00001O001O0000001O00001O00010O01O1O001O1O2M2O001O01OO10000000000O01001O1OO1O100O1O1O1O1N2N200O1001O1N10100ON2O1O11O0000O01000O100O02O00000O10000O10000O100O101O0O100O2N1O2N3M3M7GY1_LgjNNLi1SV1K4N1N3N1Oa0UOohNEVW1LZiNMdlfa0\", \"size\": [1280, 720]}, {\"counts\": \"bQo67gW17G5L5K3I8@?H9ROZNVkNP2eT1h0M4M2L5L4K5M3N1N3N1O1O101N100O2O00001O0010O002N2O2M1O2OO01O001O10O01O1O001O3NO01O000001O0000000O1L4J6O10000O2O0000O10000001N10O01000000O010O1L3N3O10O100O100O10001O0O100O1O101N2N3M2N9GY1gN;E4L4L4J7H:F5J7@`_k`0\", \"size\": [1280, 720]}, {\"counts\": \"Xi\\\\71hW1:E9J6L4L3L5J5I8G9K4K6K5H8E^MSkNf2gT1=M2O2N1O2M3M2M3O2N1O1N3N1O1O1O2N101O0002N3M3M1O2N2N1O2N2N4M2M1O0010O01O10N100001O1O1O100O00N2O1O100O1000O1000O1O100O1N200O010O100GdLlkN]3RT1:M201O1O00100000O100O001O1O1O1O1N2O1N2O2N1O2J6G:L8fNTkNkN_U1=fZ``0\", \"size\": [1280, 720]}, {\"counts\": \"kQh78cW18H7J5L5K5K4L4L4I7J5N3L3N3L3L5J5M4I6M4L3K6M2N2N3M2O1O1N2N3M2O1O1O2N1N2O2N100O1000000O1000000001O1O1O00001O1O1O003M1O1O2N1O1O1O1O001N2O001O001N4M2NO01000000O01M2O200M3N21O0O2OO1O01O0O0010O1N20O2O0O2L5K4M3M3L4L4G9J6L3M4K5M3K5F;K5K4L5L6I6K7GTQf?\", \"size\": [1280, 720]}, {\"counts\": \"`bo7<_W16L5K5J5L4M2K6C=K4K6H7_Ob0L3N2N2N2N2O2M2O1O100N3HbLkkN_3ST18O1O1000001O00001O000O1O1000001O000010O01O01OO1O1O11O010O01O000010O2N1O2M101O001O00001O0O100O100O101N10N2N20O1O2O0O1O1O0O1N3N1O2N1O1N3M3N2M3N1O2M3M3M3H8H7M4J6N2L4O2K4M4HaiNmNaV1m0<J9I^Xg?\", \"size\": [1280, 720]}, {\"counts\": \"\\\\b`75eW19H7J6L3M2M4I6@a0ROXNZkNQ2aT1j0L4I7K4L4M3N2N3N1N2O1O100O101O00000O101N10000001O00001O00O101O01O00001O010O1O1O1O1O00010O2O0O001O001O010O001O00O1N2O1O1O001O100N10100N2O0100O100O0010O01O01O102M3NN2O1N2M3N2O1O1O2K4L4M3M4_N]kN_OgT1>^kNZOiT1>^kNQOBJVU1Q1\\\\kNRORU1l0PkNmN_OLdU1T1ojNlN[U1P1n0I:Dn`W`0\", \"size\": [1280, 720]}, {\"counts\": \"Yjm63^U17alN4PS1FTkN<g13mR1<llNKnR1o0XlNVOcS1U1QlNPOlS1d2N2M2N2O2O1N101O001O00000O2O000O100001O1O001O01N100O1010O00001O2N1O001OO100001O0001O0010O00000000000000O2N1O10001O000O10000N110N3N1O100O0100000001O0O100000000O1000001N1000O010000O100O100O2N1O2N2M4L4K5K4aMnkNQOGb1bT1QOXlNl0mS1eNYkNKo0]1^U1B=_OTiNFUW10eoh`0\", \"size\": [1280, 720]}, {\"counts\": \"a`S6<bW19G8H:YiNjNQV1Q2@;F5L5Kd0\\\\O4M2N110N2N2N1N2O0O1N21O2O0O0000001O101N001O1O001O00000O10010O0O100000000000003NN10000O10000O1000000O1001O00000000O1L4O1N3L3N2O1N20000000000O1O100O100000000000000001N2N2O1O1N3N2M9H6I8I6J<C:F3mMWjNj1SV1M7H2O0O2O1N2O6I>POXiNLQTca0\", \"size\": [1280, 720]}, {\"counts\": \"koh4<aW16I:H9G9G4L3M5I6K6M2N102M2O4Jh0YO5L1N2O000O1002N100O1O0O10O000010O0000M3N1011O1O1O1O1O001O1ON2O1M3O1O1M3N200N2N3N1N2N2O2O0000001O00000O101N1N20001N10000000000001N10001O1O1O2N1O3M7H5L9G2N2M4M3M8H4K6K6J3M1O2M3N2N2N2M3N6I2O2M2N5K6I9H5J7Jfehb0\", \"size\": [1280, 720]}, {\"counts\": \"\\\\Wg31hW1<F7I7K5O2M5L3ZiNiNZV1`1L5M3L3N3M3M2O3M2N41NN1M;F101O7H5L10OHjLekNU3_T15M20100O10O01O0011NJcLkkN\\\\3\\\\T10O10000O100M3O2N1N2O1O1N2N200O1O110O00000O1O1O1N200O101N100O101N1000001N100001O01O01O1O001O2N1O2M5L5K6I6K2M3N1O2M3N9F=D6J4K4M2M2N2O2M3N1O1N4M3M3M2M4L4L4M4L4L1N4L5KZ^ec0\", \"size\": [1280, 720]}, {\"counts\": \"P_^3a0]W14L4L5L2L5L4N8H3N1O1O20000O0O2M101M2O2M3N2L5M3O0O001N3cjNgMnT1h2L2N2O0O2O0O2N2M3L4N1O2M200O2L3O1O2O1O1N2O0001O01N10001O01O01O00001O001N10000O10000O101N10000010O0000001O1O10OO101O001O1O2M2O2N3L3N9G4L1N3N2N2N1N5L<D=C7I2M2O2N2N2N1O6J3L4M3M3L5L4L4L5J7IjUnc0\", \"size\": [1280, 720]}, {\"counts\": \"b^^3=aW14K7J8I9F7I5L4K4M3M3M32M2O1NO3M101N2N2N2N2M4Md0]O1N3N00000N101N1010O1O01N1010O0000O100O100O1N2N2N2N2O2M2M3M20101O00002N1O001O0000001N100O2N1O2N2O001O000O11O00000001O0000O101O0O101O0O3N2M3N1O2N4L6J5J7J2N3M2N5K`0@?A3M3M2N3L7J2N3M4K4M3M4K5L4L5J5J^Vnc0\", \"size\": [1280, 720]}, {\"counts\": \"deS47gW16K3L4L4M4L1O1O1O1O2N4M2N6J:F43j0RO_MdjNc2WU13M4L1O1O1O01O010O01O0O2N2L5OO01O01O1O000000O1K5N2000000O1O1N1100O01N110N2O1O100O1O10000O1L5N3M1O2O0O1O1O101N100O2O1O0O2O000000000O1001O1O1O00001N2O002N1O2N2N2N3M5J4M2N2M4L3N9F7J8H<D9F2O2N3L3N3L3M5L4K5L?_O7IeoXc0\", \"size\": [1280, 720]}, {\"counts\": \"mUh57eW16L4L5M2M3M9G5J6L9iiNA\\\\T1U2ON1OO2O0O1O1O1O001O00000000000001O00000000O1O1I8O01O00N200001O00O1O00100O2O000O10O100000O01000O1000000O1001N01M4K4O100000000001O1O1O0O10O14L2N1O1O2M3N2N2N1O1N2O4L9G;E1N3N4K5L;D6Kf0YOd0\\\\OaW\\\\b0\", \"size\": [1280, 720]}, {\"counts\": \"`Y_7:YW1b0C9G:I5@`0N2POoM_kNd2YT1e0L4M3K5M3M3N2N2O2N100O100O100000O11O0000O2O0000000000000000000000001O00O100O11O00001N10001O0000O1O1001O1O001O1O1O1O1O1N1N4I7I6F=_NVkNlN1Ggfja0\", \"size\": [1280, 720]}, {\"counts\": \"iXU72eW1a0B9G7J6I7C=I6C=QOo0G:H7N3M2O1O1O100O1O1O1000001OO10O100001O001N101O010O0O10001O2O1N2O1N1O2N1O0O102N2N00101O1N100O1O1O1N3N1L5I8Gj1YMYiN>[g^b0\", \"size\": [1280, 720]}, {\"counts\": \"ZhY61_W1e0^Oc0[NUOXkN`1XT1nN\\\\kN_1ZT1Z1J7H5L4N1000000O1000000000001O0000000001O0000001O1O1O1N2O1O1O1O1O1O1O1O1O1O1O1O003M1O2N1O2M2O2N3M3M6J4K7Jf0YO`0A6J4L6I9FcWZc0\", \"size\": [1280, 720]}, {\"counts\": \"PYT5>ZW1<I5L6VNROWlNS1dS1h1L4J5L3L4N2N2O1O1O1O2M2O1O100O100000O01001O0O1000001N100O10000O010000001N1000001O1N101O0000001O001O001N101O001N3N1O1N102N4K=gMSkNg0[V1H4L5K6I6I8FmVSd0\", \"size\": [1280, 720]}, {\"counts\": \"c`W46aW1g0\\\\O:E9dNhN^kNg1[T1U1H6J6L3M3N2N2L4O1O2N001N2O1O1O100O1O100O11O001O000000001O00O100001O1O0000001O00001O1O2N1L5N1N101O000O2O00001O1O001O001O001N2O0O2O00101O1O5J4L<D?@7J5K7I4K9E8G<WOmWbd0\", \"size\": [1280, 720]}, {\"counts\": \"QXg3g0QW1<H5I7cNdNkkNf1mS1hNdkNa1YT1U1M3K4J6M4N1O1O2M2N2O2N102N0O1O2O00000000001O000001O00000000000000001N10000000001O0000001O10O01O001N10001O00001O1N101O0O2O001N2O001N2O1O3M2N001O1N3N6I:G7H8H=C:F9F:G<Bl_md0\", \"size\": [1280, 720]}, {\"counts\": \"c`\\\\4;ZW1?@>H7E<I5L4K5QOQNakN\\\\2XT1i0L5L3L4M3M4N1N2N2O1O1O100O2N10001N100001N1000010O0O1000000000000000000001O1N10001OO100O2O001O001O1N10000000001O0O101O1O1N2O0O2O1N2O1N2O0O4M5J9D>ZMXkNDH4LP1VWbd0\", \"size\": [1280, 720]}, {\"counts\": \"Yil4>]W16J7F9K4M3N3K4F;L3L5H8G8I7A`0M2N1M4K6L3N2O10OO3N1O2M101O1O1O1000O1000001O0000000000001O0001O00000O001O001O11O1O10O01O000O1O101N2N2OO0101N2M2O2O1O1O1M3M3N3M4J7F:Fm0`MSjN2L1>LnnQd0\", \"size\": [1280, 720]}, {\"counts\": \"_hQ5;aW16F:G9K4K6J5J6F9M4L3L4M3K5D<\\\\Od0J6I7N2N2N3M2O1O10000O11O001O0000000000000000O101O001O000001O00001O1N101O1O001N2O1O1N101N2O1N2M3J6M3M3L4L4I7ZOg0Ld0]O=QOXiNMVW1CPiN0OOWPWd0\", \"size\": [1280, 720]}, {\"counts\": \"_X`4`0oV1c0J6I8E:H7L4]NQNjkN;2W2PT1S1M3N2O1N3N1O1O001O2O00O101O1O001O0000O100O1001O00000O011O0000001O001O01O000001O0O2O0000001O0O2O001O001O1O0O2O1O1O2N3M3L5K`0@9G9G<B>A;nNUiNMhWgd0\", \"size\": [1280, 720]}, {\"counts\": \"_`W4;ZW1g0fMUO`lNV1WS1XOVlNT1cS1[OmkNl0nS1e1N4L3M3N001O1O1O100O1000000000000000000001O1O0000000O1000000000001N100000010N101O1O0001O01O0000000O10001N101O1O2N1O001O00001N2O1O0O5L5J6K4J7Hk0SNkiN;eV1XO`iN<ZW1_OShid0\", \"size\": [1280, 720]}, {\"counts\": \"PXl38TW1R1cMPO]lNc1\\\\S1gNXlNb1cS1a1L4M2M2N200O1O1O1O2O0O100O100001O001O001O1O0000000O1000000000000000000O1001O0001O00001O10O0001O00000000001N101O1N101O001O1O1O1O1N2O1O1O000O2O3M2TNbkN1fT1GbkN2gT1D\\\\kN9QU1[OQkNb0^U1nNfjNo0[V1Jf0[O_ood0\", \"size\": [1280, 720]}, {\"counts\": \"`XQ45YW1e0E;F9G9B<L5K4I8jNV1K4N3M2O1O100O100N3N100O10000000O101O00001O001N100000000000000000O2M2O2N1N200001O1O001O001N10001O001N10001O001O001O00001N101N1O2O0O2N2O1N1N4K6I:lMfkN3cT1\\\\OaTkd0\", \"size\": [1280, 720]}, {\"counts\": \"aaf42eW1=D;F9I7J4L5L3G:J4O2N2J6M3J7POo0K5M3N2N2O1O1O100O1O001O1O101N11O0O101O1O001O10O01O001O000000000O10O1000000000O10001O0001N1O2OO0O3N1O2N1N201O1N2J7K4H7J8]Oc0B?E<D>@iaYd0\", \"size\": [1280, 720]}, {\"counts\": \"WYj4;`W18I6I7J4L5L4M2M3M3N3K4J6K6L3M3N2L4L4L4J6E;G9F:H8N2N2N2O100O100O1001O001O001O0O10001O000000000000O100O100001O001O1001NO3M1O2O0O2O0O3M3M2M2M4M2M4J5J6C>B=G:G9K9F=@kaTd0\", \"size\": [1280, 720]}, {\"counts\": \"hX`4b0UW1<H;C8J6@_NXjNh1aU1`0L3G9I8YOf0K5K5M3N3M2N2O1O1O1O1N2O1O10000O1001O010N10001O0001O0O10000001O00000O3O0O2N1O1O1O0O2O1O00001O1O1O1O001N3N1O2N1N2N2N2O1G<J=AX1hN>D9]OnhNF`W1Mngdd0\", \"size\": [1280, 720]}, {\"counts\": \"iQU48ZW1c0D8K6J5J5I8I7K5K4gNnM_lNW2]S1WNVlNl1hS1W1N3N1O1N2O1N2O1O1O10O1000000000000O100000O10000000O1000010O1O1O1O001N2O1O001O1O1O0000000O2O1N1000001O3L3N5J3M4L6J9jMYkNa0XV1EYWVe0\", \"size\": [1280, 720]}, {\"counts\": \"ai]4f0PW1<I6K5J6H8J6L3`NSNjkNJh0X2XS1fNPlNf1nS1W1O1N3N1N2N3O0O1O10O0100000001O0001O00000000001N2O01O0001O001O00001O0O2O1O0O101O001O00001N100O2O00001N3N1N4M4K4L7QN[kN6oT1XO`kN>YV1G<@hfnd0\", \"size\": [1280, 720]}, {\"counts\": \"aY`42TW13PiNc0_T1AZmNP1aR1UOYmNQ1cR1SOilNHnN]1RT1TOjlN\\\\1SS1gNilN[1VS1kNTlNQO1]2iS1b1O1N1O2O0O100O10000O11O2N2N0001O01O0O10O10000O01000O100000000001O000O2O0O20O10O1N101O000001O01O0000001N10000O2O1O1O102L4M2N4K5K4jMZkNk0kT1iNckNP1iU1M5K7I6H6J5E<HU_^d0\", \"size\": [1280, 720]}, {\"counts\": \"^Ye4b0QW1?D=G8@?K6eNiM`lNMYO]2nS1UNVlN_2eS1P1O110O0O1O100N2N2O101O00000000000001O00001O001O001O0O11O01N100O10000000000000001O0000001O000000O11O00000000001N10001O001N10001N2N3M2N2O3L3N1N4L5L4I6J<^MkjNf1VV1@>A4L5A_gPd0\", \"size\": [1280, 720]}, {\"counts\": \"UZj44dW1=G7J5I7F8L4N2N3M2K6I6K5N3M2M4J5K5YO[MokNi2lS1h0J6M4M2O1O2N1O001O1O10000O1000O01O100O1001O000000001O010O00001O001O0000000000000000000O1000001N100O2N001O1O2N2N2N2N1M4L4L3N3M3J6L4N2M8H9]Nb1_Ok`jc0\", \"size\": [1280, 720]}, {\"counts\": \"i`P5`0YW1:H9H7H5L5L4L3J7G8M3N2L4L4M3G:YOf0I7N2O1N3L3N2N2O1O1O1N2N2N3N1O11O1O1O1O001O1O1O1O00001O00001O00001O001N2O1N10001O001N101N3M2N2M2N3L4L4M201N1M4gNY1I7L6Ef0XOnaTd0\", \"size\": [1280, 720]}, {\"counts\": \"UYe4;^W1;F8K5K4L4M3L3L5E:H8N2L5I6oNQ1N2M4K4N2N3M2N2N2O1O1O10000O10000001O0000000000001N0100000000001O1O1O001O0O2O1O000000001N2O0010O10M3N101N3M3M5K:C`0aMRkNS1VV1J5J6G;B9E[_^d0\", \"size\": [1280, 720]}, {\"counts\": \"WYe4i0PW19H8I6C=M5J4ROkMikN[2oS1o0K5M4L3N2N2M3M3M4L3N2O1O100000000001O001O00000000000000000O101O0000000O2O001O1O1O00O11O001O001O0O2O1O1N2O2M2N1O100O2O1N2O4L5mM]kN>nT1nNgkNg0QV1Eb0\\\\OhVbd0\", \"size\": [1280, 720]}, {\"counts\": \"YYo4i0PW18I7K6H6G:M3kNVNnkNo1kS1V1L5N1M3M4M2N2O1O1O2N100K5O11O00O1O1001O2OOOO2O100000O101O0000000000O10000O1000000001O1O001O001O001O0O10001N101N100O1O2O1N101N101N2O2M4M3L6jM]kNc0iT1ROokN<VT1[OSlN?[V1\\\\O4IbVnc0\", \"size\": [1280, 720]}, {\"counts\": \"`Qi44\\\\W1e0iNU1dNYN[kNg2^T1a0L4K5M2N2O1N2O2N1O2M2N2N3N1N2L4M3N2N3O02M101O00010O1N102N1O1O1O2N001O01O00O1000000001O0O11O1O000000001O1OO1O101O01O1N101O001O001O1O1O1O001O1O000O3N2N1O2N2N2M3N2N5J4K5K9F`0_O=Cf0]O;A:H6HWWdc0\", \"size\": [1280, 720]}, {\"counts\": \"PXb31kW1n0PO9K2O2M2O2N1O3liN^NgU1e1RjN^NPV1h13BkiNlNSV1a1O1_jNeN\\\\T1\\\\1ckNkNVT1W1gkN_OeS1c0QlNJiS1Y2N1N10001O0000000100O1O000000O100O11N101O0010OO010XMWlNY1iS1_1010O000O2WMTlN\\\\1lS1cNYlNY1gS1dN^lNZ1bS1fN`lNX1_S1iNelNS1ZS1oNhlNn0VS1TOjlNk0PS1^OnlNb0QS1BllN>SS1CmlN=SS1AolN?QS1@PmN`0PS1_OQmN`0PS1@QmN?oR1AQmN?oR1AQmN?oR1AQmN?nR1BSmN=mR1CSmN>jR1DVmN<iR1EWmN;jR1DVmN<jR1DVmN;kR1EUmN;kR1EUmN;kR1ETmN<mR1CTmN<lR1DUmN;lR1DTmN<kR1FUmN9kR1GUmN9jR1HWmN6kR1IUmN7kR1IUmN7kR1IVmN6jR1JVmN6kR1IWmN4jR1LXmN2hR1NYmN1gR1OZmN0gR10XmNOiR12UmNOlR10TmN0lR10UmNNlR13SmNMmR13TmNLmR13SmNLnR14QmNMPS12QmNLPS14PmNLPS14PmNLPS14PmNKRS15llNKUS15klNKVS14jlNKXS14hlNKZS14glNJ\\\\S14dlNK_S13blNL`S12`lNMcS10_lNNdS1N^lN1fS1J\\\\lN5gS1HYlN8kS1EUlN:nS1CSlN<QT1]OTlNa0QT1ZOQlNe0PT1UOWlNg0fU1AohNDP]oc0\", \"size\": [1280, 720]}, {\"counts\": \"PQa3;XW1>M3K6M1IPO]iNR1`V18O1N3N1N3N1O5K8I5J3hjNPN\\\\T1n2L7H8I>A3N1M5J4_OaKVmNi4kR151UKRmNb4nR1^KRmNb4kR1YKZmNj4fR1VK[mNi4PS1O0O00O101N100001O0001OO2O0000000000000O100000O100O1N2N200000O10O100000000001O001O1O0O2O1O1N2O00001O000000001N1O10000O1001O00O2O000O2O0O3N1O1O1O6I9H3L3N4Ja0@<D7I1O2O1N1O2N1O103K=D4M5J;Ad]jc0\", \"size\": [1280, 720]}, {\"counts\": \"\\\\PU4:_W19K5F9K5J7L7I=miNXNWU1^2J4L5K9G9G6K2M2O1N1M3N3O000001O00000000N201N100000000O10O1000000000O10O01O1000000O10ON3L4L400O10000O02O001O000O101O00000O10000O2O000000000O10001N101O1O1O1O2N5J5L3L6J5L5J4L4M6I:F3M2O1N2O1N4M2M5L5J5L4K4M4Kc0]O9G4Kleac0\", \"size\": [1280, 720]}, {\"counts\": \"XY`49bW17K5M4I7K5miNPOnT1S1njNUOjT1P1QkNWOhT1m0UkN_O_T1e0[kNKXT18ckNMZT1o1L4M1O2O0O101N10001O00001O0O1000000O10O01O10O10O100O1001O0000N2O2N10000O100O010O100O100O001O1N2O100O10000001O2N001O0O1000000O10000000O10O10000O101O1O0O10001N3N3M2M6K5J6K5J;F`0_O5J4N1N3M2O2M3M6K4K7I=B;CTmXc0\", \"size\": [1280, 720]}, {\"counts\": \"bib4>aW13K4L3L5M2N4TiNSO_V1j1\\\\O9H5I8H8J3M3hkNnL]S1V3XlNSMfS1f3M3M2O1N2O0O1O2O0O1O1O1O1O2O01O000000001O000O1000M4N2N10O0100O10O0100O01000O100O100O001O100O1O010O100O1001O1N2O000O3N1O1O000O01000O1000001N1000001O001N2O1O3M3L6K2N4L:F7hL_kNb2ZU1F4K3N2M3M2N3M5K5K4M2M4L4L5K7I`0@YTPc0\", \"size\": [1280, 720]}, {\"counts\": \"mWj4:bW17`hNF1MiV1T1Kc0\\\\O5RkNZN[S1Y3L3L4M1N2O2N1000OO3O0O2N1O100O2O0001O1O01O0001O1O001O000100O1N2N1N20000O10001O00000000000000000000001N10O10000000O1O1001OJcKjlN\\\\4VS1dKklN[4]S1000O100O10O10001O00001O10O00000000O2O0000001O01O01O1O1O3L4M;E6I5_M]kN]1iT1QNUlN\\\\1fU1A6J8G`0]OUUUc0\", \"size\": [1280, 720]}, {\"counts\": \"]aP51fW1?aiNH[T1m0WkN^OVT1@VkNh2cT1>L4M3L4M2N3M2O2N1O101O0O101O00001O0O10000O101O01O1O2N2OO01O00010O0001O0001O01O01O0000000000N2O101N100000000O2O00O1000O100O100O1O1O1O1O100005J4M00O1O11O1O1O1O1O001O01O00000001N101O00001N2O0O2O1\\\\NTlNQOoS12UmNDoR1EfkNIh18cjSc0\", \"size\": [1280, 720]}, {\"counts\": \"fXY55dW1<H5L6J;ViNVOn0J_S1T2mkNWNnS1R2ckNXNYT1i2N2N2O001N1O101N100O2N100O1O1L3N3O11O10OO0100001O00O101O01O1O00O10000100O0000010O00000000000000O1O1O100000000O100O100O1O1O1O1001O1O1O1OO1000000O12N4L2N2N1O1O1O00001O001O0000000000001O002N2M3N1N2fNfkNlN_T1;bkNfNa0e0aT1G]lNMfRfb0\", \"size\": [1280, 720]}, {\"counts\": \"V_U53hW1;F7Ka0_OU1iiNVNQT1P3K4L4M100O2N1O2O0O1N2O2O0L42N000000001O01O0010O0000000001O00000O11O0010O000O1001O00O10001O000O10000O1O1O1O1001OO1O1O1O100001O1O1O1O1O1O0000001O000O101O00010O001O0000O11O001O1O1O1O1N4M2N9G8G6KY1fN7J7H:G4Kc0ZOQfhb0\", \"size\": [1280, 720]}, {\"counts\": \"VWY5?^W1f1RiN_NVT1j2L2M100O1000001O0O10000O2N1000O10O100O2N1N2O2O0N2N20000O1O1001O000001O0000000O10000O1N200000000O1O1O100O11O1O001O0000O1O1000O10O100O1001O000000000000001O1O1O001O000000003M1O6J3L5L2N1O3M9F6K5K6J7]MgjNQ2iU1J4L4M4K5K7I9Ff0XOoecb0\", \"size\": [1280, 720]}, {\"counts\": \"R[T58`W1:J5M3N3M2QNSOhlNo0TS1WOilNi0TS1\\\\OjlNd0TS1AhlN`0US1DilN=RS1JllN6RS1OjlN3RS12klNOUS12jlNNVS13jlNKWS16hlNJVS19hlNHWS1:hlNFVS1=hlNEUS1=llNBRS1a0mlN_OQS1c0olN]OPS1e0olN[OQS1e0olN[ORS1d0nlN]ORS1b0nlN_OQS1a0PmN^OQS1a0olN@PS1a0PmN^OQS1b0nlN^ORS1b0nlN^OQS1c0PmN\\\\OPS1e0olN\\\\ORS1b0nlN^OSS1a0llN@US1`0jlN@YS1=flND[S1;elNE[S1;dlNF\\\\S1:dlNF\\\\S1;blNF^S1<`lND`S1=]lNEcS1<ZlNFfS1:YlNGhS1=QlNEPT1;nkNFoS1=QlNCoS1=RlNAnS1`0RlN@kS1d0TlN\\\\OdS1`N[lNV20ZOeS1bNYlNU20ZOjS1j0WlNUOiS1k0WlNUOiS1l0VlNTOkS1k0TlNVOlS1n0SlNoNmS1b2O0100O01OcNTlNhNmS1f2O0000O0100M3M3O1O1O1001O1O1O1O001O1O00001O00001O0O2O001O1O1N2oMVlNJlS12]lNFgS1KglN3oS1oN_lNj0fU1E5IZUPc0\", \"size\": [1280, 720]}, {\"counts\": \"nWo4:Y15bT10^jND5l0WU1^1J6K3O1O100O1O1N2N2N3M2O1O1O2N101O01O00010OO2O0001001N2O1O1O0O001N10000010O00100O01O0O2N101O010O1O0O1O2L301N101O001O1O0O2O001O1N2O1N3OO001fNZlNXNfS1Q3O10O00010O11O0O1000O1000000O101O00001N1O1O2N1CTlNbLQT1X3?J8dNdkNkNaT1d0TkNkNb0<aT1>ZlNPOcdWc0\", \"size\": [1280, 720]}, {\"counts\": \"[We44`23aR15`lNKPO5\\\\T13QlNa0TOFgT1MokN[1mS1jNckN^OMk1\\\\T1_1N1O2M2O2N1O1O1N2O1O2O0000001O02N2N1O1O1O1O002N1O10O0000000000000O2L4N11O2NO100001O000000O100000000O1000O2O00N2K5N101N3N10O1000O100O1O1N3O000000000001N1000O1000000000000000O2O1O1O1O1N3L4SNSlNBQT1]OmkND9l0oS1YOPlNB;n0VV1[OUW_c0\", \"size\": [1280, 720]}, {\"counts\": \"`hb47eW15G:L3fjNCkR1b0[lN9aS1JTlNb0gS1BRlNe0jS1k1N2N2N1O2M2O101N100O1O1O20O1O2N1O001O10O01O001O001O00000000000000O1O2O000000000000O2O00O10000O1O1001O00000O01N2L4M3O1O1O1O10O101O000O2N10000O10001O0O101O0000001O00000O2O0O2O2N1N3N1O1N5J7SMhkNf1]U1K6XNajNd0dV1G9Hjm]c0\", \"size\": [1280, 720]}, {\"counts\": \"m_R4a0YW1>B8K6L3J6L7UjN\\\\NPU1h1ijNiNiT1]2K4J7H9H5L1N2O001N101O001O00001N1000001O0010O2N4MN100010O1O1O2N2NO1M3O2N1O1O1001O1O2M4M00L4M3N200000O0100O10000O1O2O0O01000000000O010O010O01001OO100O101N1000000O1001O01N100O2O001N3N2N1O3M2M3N:F<Dl0TO`0_O5L8H6J4L4L6I9G[eac0\", \"size\": [1280, 720]}, {\"counts\": \"ie_3b0\\\\W18I<\\\\iNXOXU1P2F7J4L4K7K3M5J8J3L:G3L3N2M2O0O01001O001N100001O10O000000100O1O001O1O01O0001N11O011OK4M3L5O01O1ON20VKPmNd4VS10001O00O01O100000001N10O1O1O1N1N20100O1001O00002N1O00O01000O100001O00001O00010O000O1000001O00001O001O2N2N3L9H>B5Kk0UO:E;F5I:G:E6K8H;BW_oc0\", \"size\": [1280, 720]}, {\"counts\": \"agZ31]V1:`jN7RU1;[jN3[U1_1K6J6J5J8I9G9H3L2N2M3N1O2N1O2O2N001N101O000000000001O1O1OO10000011N00O1O1O11O1O00O1O11O1O1ON2N2O1000000000O1000000000000000000000O100N102N1M3N200O1002M3N1O001OO1000000000000O11O0O10000000000000000O2O001O1O2M5L<D:eLdkNK1T2_T1eM_lNR2lT1K5K:F6I9Hj0SOXoVd0\", \"size\": [1280, 720]}, {\"counts\": \"XX]3b0nU10njN<dT12ojN5kT1e1Jd0]O6J3M2N1O3M2N2N2N1O1O1O2O0O1O100O1O1O1004K2O1O2N3NO0J6O11O3M001O1O1O001O000000000000O1O100000000000O10000000000000O1L400O100O1O1N2O1N2N200000000O2OO2O00O100O10000001O0000O100001O0000000000001O000O2O1O1N101N2N2^M`lN`0fS1WNTlN6l0[1gS1ZNclN`1aS1YNQmNT1WdZd0\", \"size\": [1280, 720]}, {\"counts\": \"ZPa334`0QW1:M4ROUO_jNe1dT1aNojNG1l1cT1jNXkN_OOk1dT1Y1M2N2O2N2N1O1O2N100O1O1O1O1O1O100O010O100O100O11O001O1O002N1O1O00O10000001O0000001O001N01O100001O00O1000000000000O01000O1O2M2O1O1O1O1O10000O1N200O2N1O10000O001001O0010O0000000000000000000001N1000001N1O2N2N2cMSlNd0oS1nNllNb0XS1UOnmN[O[S1JckUd0\", \"size\": [1280, 720]}, {\"counts\": \"n`m35eW1:^Oa0K4Lj0[N^NQkNP2hT1g0L3N3N1O2L3N3M2O2N1N2O1O1O100O1O100O1O02O10O0O0011O100O00O1001O1O0000O11O1O1O00002N000000001O0000O1O11N10J62N01M1010000001OO1O1O10000000000O1O1O0O2N2N2O100O11O1O0O101O010O00000000000O101O001O001O1O1O1N2N2N3M4L7I=UM\\\\kNa1gT1WNjkNY1hU1gNgiNJZngc0\", \"size\": [1280, 720]}, {\"counts\": \"e`\\\\49`W1e0TOa0G7L3O2N2O2M9RjNlM`U1]2L2O6I3N001O1N1O2N1000010O02O0O00100O0000001O01O0O1O01010O000O100001OO3ckNnLfS1V3`0?B10O10O1O000001O000000O11O00O100O000N11N1100000O2N1K8L3N1O2M3N2N1M3L4L4K5K5M3M3N3L3G9I7O1O1N4J6K5J6J6J6Icc21\\\\\\\\M8L2N100O1M2L5M2O0023K6L2ZOjeWc0\", \"size\": [1280, 720]}, {\"counts\": \"fWQ43kW1>@7L2M3N2N3[jNZOkS1i0mkNClS1?RlNIhS16WlNOeS11ZlN6`S1I`lN;\\\\S1FclN<\\\\S1CelN`0WS1AhlNb0VS1^OjlNf0RS1ZOnlNj0mR1WOSmNR1cR1oN\\\\mNU1`R1lN_mNV1`R1jN`mNW1`R1hN`mNX1bR1fN^mNZ1bR1fN^mN[1aR1eN_mN[1bR1dN^mN\\\\1cR1cN\\\\mN^1eR1aNZmN`1gR1_NXmNb1iR1]NWmNc1jR1\\\\NUmNf1kR1ZNTmNf1nR1YNPmNh1QS1YNmlNh1SS1XNklNj1US1VNflNn1ZS1[N\\\\lNf1dS1[1O10O10000000O100000000IkKclNT4US1gKRmN`4VS110O011N1N20N2M201O1O100O010O1O1O11O001O1O000000001N2O00000000000000000O101O1O0O101O001O1N2O3M2N1O3M0000000O2O0O3N6J9aL]kNo2mT1M<D1O2N2Ma0@1N2N2XNmiN`1bT_c0\", \"size\": [1280, 720]}, {\"counts\": \"agZ33fW1j0WOo0lNf0C6K3M4J6K5L3M5K4L4M4K4M3M1O1O1O1O0O1O2N10000010O001O0010O0001O0001O01O01O1O1O00001O0000001OO01O1O1O1N21O0000N2O1N2N2O1O001O1O1O1O100001O2N1O1O0O1000000O100000001O000000001O0O10001O0000001O001O1O1O2M4M2N5K=C9G3Ma0_O4VMmjN_2_U1K5J5L2N3L3N2M8H5K:F9H9DP^oc0\", \"size\": [1280, 720]}, {\"counts\": \"iVi2a0[W19I6J4EPO`iN]1SV1b0niNTN\\\\U1\\\\2L4M4K8H4K4N2M4M2N2M7J<E2N1O0O1O2M2O001O001O01O1O10O00010O01O003M10O0O2N1010O10000O01N101N100O100O100O1N110O10000N2O0O2O1O1O10000O100000O100000000000000O1000001N2O1O00000O100O101O0001O000001O000O101O001O2N2N3M7Ia0^O7kLQlNm1TU1M5K3L5L8G6J>Ab^cd0\", \"size\": [1280, 720]}, {\"counts\": \"kga2:bW17H9H:niNmNgT1]1PkNkNgT1_1RkNeNhT1h1ijNaNRU1Z2K5L3M3M3M3N2N8Hb0^O3M2M101N10001O00100O02N1O01O00010O0001O0010O01O01O000003N0O0001N1O10000000000001OO1N2O010O10000O100O1N2O1O1O1O00100O2O000001N3N00O1O10000000000001O001N100O101N100000001O000000010O001N2O00004L:eKblN`3`U1_N1M4L3N7I=@P^md0\", \"size\": [1280, 720]}, {\"counts\": \"WgW21fW1a0C9G7L4WjNWOUT1R1`kNXOVT1]2K5L5K5L5K?A7I3N2M2O1N2O1O0O200O2OO01O0O1000000010O1O1O1O000010O0001O3M010O0O100000001O0O1000O100O1O1O1O1O1O1O010O1O1O1N2O1O1O1000O01O10000O11O00001O0O1000000O10000000000O100000000O1000001O1O001O00001N2O1O2N3M4L9G;D7Jm0^LQkNX2`U1K3M5K<C7HnnTe0\", \"size\": [1280, 720]}, {\"counts\": \"l^Q2539TW1<K6K>C9F6K9\\\\jNlMjT1T3@6L3ikNmL[S1Q4J5L3M2O1O001O00001N1O001000000O100001O000000001O1O1O000O100001OO10M[KnlNb4RS1\\\\KPmNd4TS12000O3N1O00O1O11O1N10O1N200011NJ61O1ON20O01000000O101O0000O1O001O10O0001O1O100N2O1O0O2O1O1O100O1O1O1O1N2O1O1O1O101O001N2O001O1O1O2N3M5Ka0^O3N1N3M4\\\\MYkNLN[1VV1L<E7G<CmfXe0\", \"size\": [1280, 720]}, {\"counts\": \"QXk1=^W1a0_O7L4K7kiNUOkT1Q1ojN]O<UO[S1i3N2M3N1O1O1M3N3N001O1O2N101N101O1O0000000000O10000010O01O001O0000000O100010O0001O0000O0102N1OO1O001001O1O1O2N1N010000001O100O2N1O0O2O1O001OO100000010O00O2O1O2N1O0000001O2N1O00001O001O1O1N2O001O001O2N1N2O1O0001O1O04L2M101N1O2N2M3JU1kN:E>De0WOk]\\\\e0\", \"size\": [1280, 720]}, {\"counts\": \"aYZ2[1dV13M3M3M3M2N2N2M2N2O1N2N2N2N2N2H8H8L4VN`MmlNN<l2]R1j1M3M3O10000N24L00004L2N2N00000OO200002N00O1M2011O00000O1000O10O10000000O0010000KQKZmNn4hR132O002N2N1O00O1001O0000000000000000001O1O1O1N101OO100O1001N10000000001O000001OO2N2N2M4M2M6J6WLTlNT3`T1K6J4WMZkNR2jT1iM`kNm1^U1Jc0\\\\O5K5M3F:HYWVe0\", \"size\": [1280, 720]}, {\"counts\": \"mfR26iW14L7J9G?UjNCTS1f0okN1oS1R2N2N000O2O1N101O0000001N1O100O1N2O2M2N2O2M2O1O2N1N2M3O1O2L300L5N11O010ON110O11O1O00N2O1003M2NM3N11000000000O11O001O00O10O01000001O001O0000O0100000000000001O0000O1000000O2O001O1O1O001O000O2O2N1O1O1N2O3M7H5L3L5K4L4L4L6Jl0SO;E3M2M4H7L4`NgiN6Ld0\\\\W1@T_We0\", \"size\": [1280, 720]}, {\"counts\": \"]^b17hW13J5H8K5K5N2N1O5K3O0O3M?fiN]N2T1\\\\S1n2M3M2H9J5O2O0O1N2N101O00000O10000100O00O1O11O1O1O1O2N2NO2N10000LoJ[mNo4kR10O1O11O3MHQK`mNn4iR1000O101O000N2N1101O2NO100O11O001O004L00K5O1O1000001O01OO1000O10O10000001O000000000O101O000000001O1O001N2O001O0O2O1N2O1N2O2N3L4M3L3M3N5J7J2N1N3M3M4L5iL[kNh2dU1YOe0ZOo0nNQo^e0\", \"size\": [1280, 720]}, {\"counts\": \"mfm14kW1d0dhN4[U1i1@e0[O8H3L4J5L4N2M4L3M2L4N3M2N2N3N1N200O2N1O1O100O10000O1N200000000000000000000000001O000000O10002N00O1000001O0001O1O1O1O1O0010O0001O00000000001OO2O1O0001O00000O2O00001O01O01O1N101N2O1N3M3N2M4L2O5J4L4L3N5Kg0XO5J6fMijN`1_V1VOYWoe0\", \"size\": [1280, 720]}, {\"counts\": \"cfo033>SW1a0G2N2N3N0O8I8G<D7J4Kf0[O6J>B5K8H3L3N2N101N3L2O1N2O001O0O101O0O1O100001OO1O2O01O1N10N3N10000000000O11O00MPKYmNm4mR10O100000000O100001OJVKWmNi4QS1N100000O10000000O2O1OO1N101000000000010O0001O00001O001N101O001O1N2O101N1O1O1N2O1O3L4M3M3M4L1O1O2M2O2N1N3N2M2O8G6J5K6J5K5J8H6I6I7Hc0kN[gQf0\", \"size\": [1280, 720]}, {\"counts\": \"`^Q2;bW17H6L3Kb0@7H5M3M>A5djNnMbT1^3ZO?A6I4M2M3N3M2N2N101N101O001N110O010N1O20O1O0000001O0001O010O0000000000000000001O000001O001O1O1O1N1000001O000000001O2N10O0000010O01O00001O001O001O1O001O002N1O1O2N2N1O101N2N4K3N2N2M4M2M3N2N2M2N4L5K<Db0_Ob0kMiiN\\\\1QW1\\\\O_Vee0\", \"size\": [1280, 720]}, {\"counts\": \"k^e23hW1:G8I>^O;C`0D:K4L1O2M4K7I<D8G:I4L5Kf0[O4K2O1M2N3M2O1O100OO101O010O1O100O100O00000000000000000000001O00000O1000O1O2M2O10000000000001O0000O100001O1oJVmNj4lR13101O2N1OJ6O1N21O00001O000O2O1O00O1O1001O1O00000O2O1O2N2O001N2N3L7J3M7I4K:G6Jc0eLPkN\\\\2bU1F:Ec0TObgnd0\", \"size\": [1280, 720]}, {\"counts\": \"TW_29cW16J7I7JQ1QO9E:G>D3G8L5L4J7YlN]LeR1n3llN\\\\LRS1Z4M4L5L3N1O1O001N10010O01O2N3M010O101N1O1O1O1O1O1O001O1O2NN2O1O11O000001O0001O01OO0100001O00O1O100001O000000O1000000M3K5001O001O1O001OIoJ_mNQ5_R19L4N200001OO1O1O10O2O00000O10000000000000000O100O10001O0000000O10001O1O001O2N1O2N2M2O2N3M2M:C:F:I4WMkkNa1XT1mMhkNJ3W2TT1oMgkNO4Q2UT1eMjkN2N02N0[2VT1eMkkN0N22MOZ2_U1G8F=Ec0SOlgUd0\", \"size\": [1280, 720]}, {\"counts\": \"lVi23aW1?D<^OYO^iNR1]V1b0E4M3SjNTN1N25dT1R3I:H6J2K6M1O1O0O2O1O2N4glNPL[R1l4O3K2O1N2O00001O0010O3M2OO01O01O01O010O1O0000O2N1O1ObJkmNS5UR1kJmmNV5YR15N21O7I00N2N2001O1O1ON200002N0000O2O000000000000000000000O11O00000000001O00O1L4O1N2N2N2O1O1O1O1O1O1M2O2O11N1000001O00001N01O10O1000001N101OO10O1000001O000000001O1O1O1N2O1N4L5L7I3K6I7J4L4M4J:D?C5L3M3J8`MkjNl1eU1N2UNYjN[1]V1Af0kNZh\\\\c0\", \"size\": [1280, 720]}, {\"counts\": \"Xf\\\\23`W1f0ZOd0B;F7J6Od0\\\\O[MkjNd2PU1703M0O6J1O1N4M2N2L5L6K3L3M3M20O02N1O2O1N2O0O2N1O101N2O001O001O2O1N1N4M1N2N0000001O1O001O1O10000O001O1O010O0O2N2N1010OO10000O100N2O1N2N2O1O1O1O100N2O1O1N2N101001O0000O10000000O01O010O1000O010000001N0100000O10000000000O101O001N2O1O1O001N2O6J:F3M1nJWmNk4iR1RK[mNm4nR1M2O1O1N2O1O:E6K8H3M4K7J8G2O2M7I6J4L5K9F=Da0]Oj0WOc^ec0\", \"size\": [1280, 720]}, {\"counts\": \"e^m34XW1k0C<XiNjN31NNbU1X2K6H8K1O2L4O1N3M7J=C6J4M2M2N2N3N1N5L2N:F=C2N1N101O0000010O0001O0001N100001O1O1O000O01N2O1000000001O00001ON1N3J600001O0O1000000000N1010O100000O1O010N2O1O1M3O1000000O01000O1O011N1O2O000O0100O1000O1000001N10O101O0O10000O101O0010O000001N101O1O1N4M6K3L9F4M4L1O2N2M2O2M3N3L6Jb0^O7I4K2N5K3PMVkNf2lT1YMWkNc2kT1]MYkN^2XU1L3M3M3L>A7J6GYXWb0\", \"size\": [1280, 720]}, {\"counts\": \"hno31oW11O6B0ahN03NgV1f0ViN_OcV1j0]iNRO_V1P1biNQOZV1^1]O^NYjN]1OhN0NdU1U2L7J4fjN^MQU1j2M3NDUkNiMgT1W2XkNlMeT1k2M3L5K7hkNZLjS1^4_O3M2O2N6J3L4M1N102N0O20O01O001O0O1001O0001N10000JbJjmN_5UR1`JlmN`5ZR10O12NIaJomN]5QR1cJPnN\\\\5[R1O1OIeJimN[5]R10O2O001O11O00IbJlmN]5[R11N200O1M3000000000O010000O1O1O2N1O1M3N20O10O1M30RJTnNh5kQ1610O100O1000O100O010O1000O10000O1001N100000001O0010O000000O2O1O1O1N4M6J;E4L1O2N1N2N3M3M2N4L3lKclNa3QT1M3L6J7J1N3M5K2N5K3M3M6K2M3M3M;^O?A_PVb0\", \"size\": [1280, 720]}, {\"counts\": \"k]^37fW17K4J8F7J8I5L5K6J4K5L3J7L5K6J4N2M5K2N3M3N2M4L6K5L1O0O3M3M2M2N3N2N2N3OOO2O1N101N2N2M3N2N2M2O2N1O1O100O1OO1001N2N4M1O1O0O2N1O101N101O0J[JQnNe5nQ17O0N3O1000O1000O10O10O100O1O11N1000O1O1000O10O100O10O10O10000000O100001O000000O10001O2M9H>B7I3M2M2O1O4\\\\KglNX4kS1F6J3M3L5L5K3L7J001N2O0O2O1O1N2O1O1O1N2O2N3M3L5L3M3L5K4M3L5J6K4M7H5K4L4L5K7H5KTg^b0\", \"size\": [1280, 720]}, {\"counts\": \"Web48cW19I6K4K4N4L6K6J;oiNXNTU1m1ejN\\\\NSU1^2J6J3N2N2O2N3L5F9L4L3N1O1L3O23L3OO1O2N1N3L3N0O1O1O1O1N2N1O2O1N2O1000ON3N1O2O00001O1M3O0O1O2O0001OO0N3O101M2O2O01O00O1O1000000O1O1O1000000O1N20000O100O10001O0000000001O001OO1000000001O00001N2O005K3M2O3Le0[O4L3M2N4L2ZLUlNS3eT1G7I3M2M3N1O2M2O2N2M3N4K3N2M2N4M2N2M5L2M3N3L5L3L3N2M4M4K3M5K4K8Gfnfa0\", \"size\": [1280, 720]}, {\"counts\": \"YU`45fW19K5J3N3N1N3M3O0O2O1O29HN2N0HSOYiNS1gV13miNiNVU1[1ejNhNZU1NWjNP2jU12L5L60NO0N111N1N3N010F;L5K6K7I5K3M2N3M3M2N2M2N2M3N200O001N1O1O1O101N1O2O001O0O2N1O10000O2N1O1O2O0O1M3O10O0100O1N2N3M2O1O01000O100000000O010O10000000O101O000000001O0000000O2O001O2N0000002N1O3M2oJUmNi4XS1I5K8H2N2N2N1O2YLQlNZ3^T1L:F7I4L2N2N2N2N3M4K5L2N1N2O2M3N2M2O2M3N2M3M3M3N1N4M2M6K4K5K3M4L6Ib^_a0\", \"size\": [1280, 720]}, {\"counts\": \"a\\\\m38fW17J3M3N1N2O1N2O4L3L4N1N3N0O1O1O1N2O1N3O1O1O012M2giN\\\\NPV1m1N2N2M5L4K202M1O2O0O1O2M3N0O11O01O1O3L4M6ckN\\\\MQS1l3M2O0O010O1N101000O01M3N2N20O01O000000000O2N1N2O100001O1O1O000000O2M2N200O1O1O100O1N2O0O2M3N3O000000O10O11O00O100O1000000O100000O101OO1000000001O0O1000001O1O2N2N1O001O1O2N2M3N2N3M3M6K5J4L4L3M2N3M3L5L<D:Fi0XO9E:G2N2N2N3L4M3M4L1N3N4K6K7H3M2N2N1O_fea0\", \"size\": [1280, 720]}, {\"counts\": \"olV38`W1>I4L3N3M2N2N1O6J4L2N3M2N1O001O1O102N401J4K3N4L3M5K5L4L4L2M2N2N20O10N001O1O1O2O1O2N8dkNZMmR1P4OOO002N1O0O1O0013L001O100O2N00O1001O00O2M2J600N2OlJ\\\\mNn4eR161OJhJemNW5aR11O14L1O01O0O100O1O100O1N200M3M4N11O00O100O1O1O10OL6J5O10000000000000000001O000000001O00001N101O1O001O1O1O1N2O1O1O2N1O2N3M4K4M6J2N3L2O2N1O2N1N2O1O3M6J9F:gLckN]2`T1RMfkN3Nj2nT1N2N4M0O000^MijN\\\\2aU1L8H2N2M3M5K6K6F?AVg^b0\", \"size\": [1280, 720]}, {\"counts\": \"Y][2=`W15M4K4M3M3N4K6K3M2N2N2M3N2M3O1N2N2N3M;E6J4L4M4L9RkNjL_T1]3N3N1O1O3M2N5L7H4ZlNdL_R1c4K2O001O01O00O10O101O1N01O000100O2O0O4M0O0001O01O001O2N1OM3N2IhJfmNY5_R11N2O1M3OcJemNW5[R171N3NO2L3O11O3M00N2O1000O2OO1O10001O01OO1O0O2O1O1M3K5L5N10000001O000O1000100O0000000O2O001O001N101N2O1O001O1O2M2O1O1O2M3N6J4L2M2O2N2N3M2M3N2N7I8Gb0TLokNg2gT1N001N101N3N1N2N1O2N2N4hM[jNn1mU1N2M3M2M4M4K5K7H=A7L5Ka^Vc0\", \"size\": [1280, 720]}, {\"counts\": \"bdW2d0[W16J5K4L4M1N3M4M5K2N3M3N0O100N2N101O1O2O105K5J3L4L4M5J4M6J002N011O0O1N01012MS1lNd0]O1N10100O001O100O1N01O1O001O1O100O1O0001O0O1O1O1J6O1N3L3O20O001O00000001O000001O0001N1O1N2O100O1M3O2N1O1L4O100O1001O1OO1O1000000000000000000001O001O001O1O1O1O001O1O001O1O1O1O0O2O1O1O2N1O2N5J7J3M4L2N1O1O3L3N5]KhlNT4kS1F:RLmkN3NQ3iT1M0O8I2N2M2NE]M_kN^2TU1N2M2N4K7I:F4K8Fc0^O6M;CWV_c0\", \"size\": [1280, 720]}, {\"counts\": \"kdh144>PW1FmhN`0f0_OXU1m1L4M1N2O00100O1O4M5N0L3N2M2N4L2N3M5L2N2O1OOO101N1O1O00100O101O1O01?@f0ZO:F2N1N100010O02N2N2OO1N10N1O1002N2N0000O12N1O0000N2O13N0O001O3NO01O010O00003M0SKWmNc4iR1]KWmNd4gR1;N2O10001O000001O1OJlJamNS5TR1nJnmNZ5lQ1a0G8000001N1O0100000O2O000001O01N01000000000O2O0001O000O2O001O001N2O1O1O001O1O1N2O1O2N1N3N1O2M6K5K2N4K3N2N2N2N3L8Hb1^N2O0O2O0O0ZMnjN`2RU1_MQkN^2\\\\U1L3M3N1N2N2N101N6Db0A8\\\\Onnlc0\", \"size\": [1280, 720]}, {\"counts\": \"\\\\mk0;T1IRU1P1WjN[ObU1j0UjN]OiU1_1N1O1N2O2N2M3M4M7I6J4M2O1M2M4L3N3M3M2N2O000O100O100O101N10001O2nlN_LbQ1`3TnNkLiQ1X3dmNZM[R1V4O3N002N000001O000O1O1O1O01O0002N1O1O1O1O1O010O00000000101N0001O1O01O2N2OO01O1O1OO1O2O0001O1OO2N100O1O1O1M3E_JTnNe5kQ1\\\\JTnNd5lQ1[JTnNf5oQ14M3N2O1O2N1N2K5L4L401N100O100000000001O000000001N10000000001O0O1000001O000000001O001O001O1O001N2O1O1O1O1O1N2O2N2N1O3M5K6J6J2N2M2N3N1lLXmNm0jR1nN[mNQ1fR1fNcmNX1_R1cNemN\\\\1^R1PNTnNo1PR1hMWnNV2kS1O1N2N3M8H5K3M1N3M2M4J8G=DZVXd0\", \"size\": [1280, 720]}, {\"counts\": \"kfe05dW1:kNU1H7H8L6L5K2N3MhMdjNQ2WU1?L6K3L3N6J8I3M1O0O1O1N2O1N2O2M2N2O0jmNSLYP1n3doNVL[P1j3^oN^LaP1b3YoNdLgP1\\\\3ToNmLhP1S3SoNTMjP1n2nnNYMRQ1h2gnN^MXQ1e41O00100O2N10O01O1O001O2N1O1O10O01O1O2OO0O1001O1O1O2N3N0O0001O01O001O1O2N0000O20O1O1OO1000001O000001O0001O0N2M3N2M3N200N2O100100ON2M3M3O1N2N1O3N1N2N2O1O1O1O1O01000N2N2N20000O11O1N100000000000000000001N100000000000000000000O2O001O001N101O1O1O1O1O1N3N2M2O3L7J5J8H5L5J2N4M1N3M3N1N4L7ZKTmN03U3UT1N2O4K8H3M2M4L6C?D6L5J>A:^OlnQd0\", \"size\": [1280, 720]}, {\"counts\": \"bVh08fW16J4Le0[O6H6K5H9CUN]jNP2ZU1`0G9L4M3L7C<G8KhLTlNb2kS1j0O^LVlNP3hS1PMXlNR3n1`Lco0U4[POoKco0R4YPORLfo0o3VPOULio0m3SPO[Lho0e3UPO_Llo0a3moNdLZP1V3`oNPM_P1Q3[oNSMdP1T3SoNPMjP1T3PoNPMnP1S3mnNQMRQ1Q3hnNTMWQ1k401O0O110O01O1O01O0000001O0001O2O0O002O1N0000001O1O2N2O2M2N0010O02N1O1O2N1O2N100O1O2N00O2N1002N000001M22N001O00O1O11O1OO1L4O1O1O1M3N200O1O1M3O1O1M300000000O1O1O1O1O1O100O1N2N2N200O1M3O1O1000O0100001O000000O100001O001OO1001O001N10000001O00000001O00001N2O000000001O00001O2M2O2N2M4]LgnN8^Q1VManNe1;o0SR1YN^nNa1gQ1mMknNn1]Q1kMfnNR2^Q1jMfnNS2aQ1eMbnNW2hS1K7F:\\\\OY_Yd0\", \"size\": [1280, 720]}, {\"counts\": \"QW\\\\1>bW13L3M2M3RiNXO`V1l0\\\\iNYO_V1j0_iNZO\\\\V1V1KNfiNhNXV1c1Lc0TO=K4J6E<K2M3N;G5mmNPLno0S4loNRLRP1Q4ioNULSP1m3ioN[LPP1j3joN[LTP1e3joN\\\\LVP1f3eoN^L[P1b3coN`L[P1b3coN_L]P1c3`oN^L`P1c3]oN_LcP1c3YoN`LfP1a3VoNbLjP1`3onNeLPQ1W5O1O0100O0001O000010OO2M200010O0000O1000001O0O1010O1O00000TISoNf6lP1YIUoNg6kP173UIPoN`6QQ181O0O100O001O011N2N001O1O00101N1O000000002N1O4L2N1O003M3M00001O1O1O1O1O2N2N2O0O2N1O2N1O002N1N3N0010O03L4M1O2N1O1O2N1O1O1O1O\\\\MZKoQOe4Qn0\\\\KoQOc4Qn0_KoQO_4Qn0cKnQO[4Sn0eKoQOY4Pn0jKPROT4Pn0nKoQOQ4Pn0QLPROn3Pn0SLPROl3Pn0ULPROj3om0XLQROg3om0ZLRROd3om0\\\\LQROc3om0]LRROb3nm0_LSRO_3mm0bLSRO]3mm0dLRRO\\\\3nm0eLRROZ3mm0hLRROX3nm0iLRROV3nm0jLSROU3mm0lLSROS3mm0nLSROQ3nm0oLSROo2mm0RMTROl2mm0TMSROk2mm0VMTROh2lm0YMTROf2nm0YMSROd2Pn0YMRROf2Qn0VMQROi2Tn0QMPROk2Wn0fLSROY3[Q1N2M4L3M3O2M2N3L7H8F<QNTjN4[USd0\", \"size\": [1280, 720]}, {\"counts\": \"ngW248MWW1a0N8Ha0_O5K6J5K3L8H4I7K6I6glNZMQQ1j2knN[MoP1j2nnNZMlP1k2RoNXMhP1m2VoNUMdP1Q3ZoNQMcP1S3ZoNoLbP1V3ZoNmLcP1X3YoNjLaP1^3ZoNdLcP1a3XoNcLfP1`3UoNdLiP1_3SoNdLlP1^3onNfLPQ1[3mnNhLRQ1Y3knNjLTQ1Q501N2O1000000O001O0010O01N10010O10O0001O10O1O1O1O1O101N2N101N2N2N2NHcIonN[6_Q1L2N1OO1O1N2O100M3CiIonNY6PQ1kIlnNV6TQ1;O100N2M3N110O1O1001ON2O100O1N2OmH[oNm6eP172N1ON2O1O11O2N00M3O11O1O1O1O1O3M1O1O1O1O1O1O2N2N1O2N1O1O1O2N2N1O2O0O2N1N3N2N2N2N4K4M2M3M3M3M3M3M2O1M4M2O1N2N3L3N2O2N2M3N2M3N2N3L5J9E7mLakNX2XU1E;E:H8YOPjNRO\\\\V1`0j0I7Jcf\\\\c0\", \"size\": [1280, 720]}, {\"counts\": \"agP33jW1:F>D4L3Kc0_O5J4L@QjNSOlU1b1L1IF_jNhN4@YU1Y2;L]OlM[kNS2eT1nMXkNU2LmMVT1ObkN15V2JYNXT1BhkN03T3lS1j0J3M2SnNjKmo0X4PPOmKlo0U4PPOQLno0P4noNULRP1l3hoNXLXP1i3boN\\\\LZP1h3boN[L[P1i3_oN[L`P1h3\\\\oN[LaP1h3[oN\\\\LcP1h3WoN[LiP1^501N1001O10O1000O01O000O2O00001O0010O100O001O2N1O106I3N1N2N1O1O00O21N2N1O5K2N2O1N3M2NN2N2O11O001OO1001O1O1N02N1000O10O1G9N2J6001N0101N1000O10O1O100O10000N2N110O1O1N2O001N2N200N200O100O1O1N101N2O1O11O1O000O1000O2O0M3O100000O2O00M3004K2O0000O100000O10001O0O2N101N2N2O1N2bLRoNGoP12]oNGgP1DooN7TP1FQPO6SP1\\\\N`nN6d1Y1TP1YNcnN1g1Y1PP1[NbnN1l1Y1go0\\\\NgnN3Q2o0RQ\\\\b0\", \"size\": [1280, 720]}, {\"counts\": \"RXl35hW15M7H<D5K2jNnN\\\\kNU1ZT1WOjjNC<\\\\1eT1_OTkNf0kT1]OkjNl0SU1P10101PkNUMjT1Z3F3M1O101M3N1O1N5L4L3O1N1N2N1N2O2N1O1O1O2N102N1RnNdKUP1]4foNjKVP1X4doNnKZP1S4boNQL]P1P4`oNTL_P1l3]oNWLcP1j3YoNZLgP1g3ToN]LkP1\\\\510O100O001O01O0001O000001O0100O010O0000000001O00O2O000001O01N10000001O00O1001O000000000000001O1O1O000000000000000O10001O0000O11O000O1000000000000000001N10O100000O10O01000000O0100000O101N101O001N101O001O1N102M2O1O2M2O001N101O0O2O1O3N0O1N2O1O2N2M3N1O2hLXmNT1kR1fNZmNX1iR1_N_mN_1dR1[N`mNd1dR1\\\\MklN=d0U2eR1YMmlN;b0[2[T1O4K4M4K2O1N4L2M4J7L7J4K6I:G6I8F`lh`0\", \"size\": [1280, 720]}, {\"counts\": \"Zhg4;cW14H8nN\\\\OmiN08l0gU1XOliN`1nU1<CnMejN6J20a14XNPU1ORkN5LU2YU1kMfjNW2lT1fMVkN43V2gT1f00O1O08K5J1I5M3O3K6K3M3N1N2O2M2O1N2O1O001O001O1O000O2O00001O1O00000XnN`KPP1b4loNbKQP1`4koNdKTP1^4hoNfKWP1]4boNhK\\\\P1[4\\\\oNkKdP1W4SoNQLkP1b510O01O01O01O0010O0010O100OO1O1001O1O10003L01N1OjIgnNg5ZQ1?1O00N2O10001K[IPoNc6oP1]IPoNd6SQ1300O1N2OYIonNa6RQ161O1O00NWISoNc6mP1]IToNb6SQ12M3O1M3O1001O00N2O1O1000000O100000000O100O10O10O100O00100O001O10O010000O100O10O10O100O100000000000O10000O10000O101N1000000O101N101N101N101N2O1O2N2M3N5J9H>B2M3N1O2M2bLemNU1]R1eNhmNZ1YR1cNjmN[1ZR1`NimN_1ZR1ZMUmNJ5<c0^2VR1VMmnNi2VS1O1O2N4K3N1O2M101N2O1O100O2L3N3M5K4L4M3L6I6J5K7J5K6J:EQdU?\", \"size\": [1280, 720]}, {\"counts\": \"jbS6;jV18ZiN0UV1U1N3M2N1O2F:AjMmjNh2XT1V2]N7H7J5G9G9M2K6L4L4K4M4J5N2N3M2N201O0O1O1000000000001O01O1O1ON3N100000000O2O1O1O00^HioN[7WP18O000O100002N3M1bHdoNV7_P142N003MIhHhoNV7XP1iHhoNX7^P12N22N2N00O1M3N2O1N2N2N2N2N2M3N2O1O1O10O10001O0000001O00000O1000000O100000000O10001O000O10001O0O101N101O001O1O1N2O1O2M2O1O1N2O2N4L3M6I8I4K4M3L3N2M3N3L2O2M3M4M2M6oK\\\\mN[2PS1YMXmN`2nR1ZMTmNc2^S1kLflNQ3WT1N1N2O4K4M2N2M2OO010001N2O3L2O1O2M103L4L2O3L7I4M1N4L5K5J7I5JR\\\\o>\", \"size\": [1280, 720]}], \"bboxes\": [[140.0, 489.0, 73.0, 111.0], [133.0, 492.0, 71.0, 107.0], [123.0, 484.0, 75.0, 117.0], [115.0, 467.0, 80.0, 132.0], [103.0, 462.0, 97.0, 138.0], [101.0, 461.0, 109.0, 142.0], [102.0, 457.0, 116.0, 145.0], [99.0, 449.0, 122.0, 149.0], [93.0, 460.0, 117.0, 136.0], [87.0, 462.0, 104.0, 146.0], [88.0, 449.0, 83.0, 148.0], [91.0, 452.0, 69.0, 148.0], [83.0, 482.0, 70.0, 126.0], [78.0, 471.0, 74.0, 137.0], [76.0, 458.0, 82.0, 140.0], [80.0, 466.0, 84.0, 132.0], [84.0, 463.0, 92.0, 92.0], [81.0, 445.0, 104.0, 131.0], [78.0, 447.0, 99.0, 142.0], [73.0, 471.0, 99.0, 132.0], [68.0, 447.0, 105.0, 144.0], [61.0, 431.0, 113.0, 149.0], [74.0, 462.0, 110.0, 149.0], [95.0, 454.0, 98.0, 141.0], [104.0, 420.0, 98.0, 148.0], [105.0, 448.0, 103.0, 142.0], [95.0, 442.0, 109.0, 137.0], [85.0, 436.0, 114.0, 144.0], [83.0, 442.0, 120.0, 149.0], [92.0, 477.0, 114.0, 148.0], [93.0, 422.0, 114.0, 155.0], [88.0, 411.0, 97.0, 152.0], [92.0, 427.0, 94.0, 146.0], [92.0, 421.0, 94.0, 143.0], [91.0, 422.0, 100.0, 141.0], [100.0, 446.0, 114.0, 142.0], [132.0, 486.0, 112.0, 137.0], [141.0, 455.0, 123.0, 144.0], [148.0, 447.0, 126.0, 152.0], [164.0, 499.0, 114.0, 144.0], [175.0, 479.0, 104.0, 150.0], [149.0, 454.0, 121.0, 151.0], [119.0, 492.0, 115.0, 134.0], [94.0, 479.0, 128.0, 128.0], [64.0, 441.0, 137.0, 149.0], [63.0, 465.0, 145.0, 146.0], [79.0, 476.0, 136.0, 142.0], [80.0, 421.0, 139.0, 154.0], [115.0, 431.0, 129.0, 152.0], [147.0, 488.0, 120.0, 148.0], [178.0, 456.0, 110.0, 139.0], [189.0, 445.0, 108.0, 133.0], [198.0, 466.0, 120.0, 137.0], [204.0, 490.0, 113.0, 135.0], [192.0, 476.0, 112.0, 139.0], [177.0, 471.0, 113.0, 144.0], [156.0, 503.0, 114.0, 134.0], [122.0, 473.0, 117.0, 140.0], [95.0, 449.0, 121.0, 141.0], [88.0, 458.0, 121.0, 141.0], [88.0, 435.0, 121.0, 147.0], [105.0, 402.0, 121.0, 146.0], [147.0, 415.0, 102.0, 138.0], [191.0, 443.0, 73.0, 137.0], [183.0, 416.0, 65.0, 143.0], [161.0, 415.0, 64.0, 136.0], [131.0, 447.0, 74.0, 129.0], [108.0, 425.0, 85.0, 139.0], [95.0, 416.0, 89.0, 137.0], [112.0, 413.0, 82.0, 145.0], [125.0, 436.0, 82.0, 141.0], [129.0, 409.0, 74.0, 144.0], [115.0, 417.0, 75.0, 138.0], [108.0, 428.0, 79.0, 133.0], [99.0, 408.0, 83.0, 136.0], [103.0, 405.0, 83.0, 143.0], [120.0, 436.0, 80.0, 140.0], [123.0, 428.0, 81.0, 147.0], [115.0, 419.0, 76.0, 150.0], [106.0, 454.0, 71.0, 136.0], [113.0, 456.0, 70.0, 137.0], [115.0, 440.0, 81.0, 140.0], [119.0, 450.0, 88.0, 139.0], [123.0, 460.0, 89.0, 143.0], [128.0, 415.0, 76.0, 154.0], [119.0, 432.0, 77.0, 143.0], [119.0, 448.0, 74.0, 140.0], [127.0, 450.0, 82.0, 139.0], [122.0, 427.0, 95.0, 155.0], [91.0, 482.0, 118.0, 149.0], [90.0, 482.0, 122.0, 167.0], [106.0, 460.0, 113.0, 153.0], [115.0, 499.0, 111.0, 148.0], [117.0, 506.0, 116.0, 163.0], [123.0, 477.0, 106.0, 150.0], [128.0, 490.0, 103.0, 142.0], [135.0, 495.0, 107.0, 145.0], [132.0, 452.0, 107.0, 145.0], [135.0, 457.0, 108.0, 141.0], [131.0, 489.0, 102.0, 142.0], [127.0, 477.0, 101.0, 141.0], [119.0, 457.0, 102.0, 140.0], [117.0, 484.0, 105.0, 142.0], [104.0, 476.0, 115.0, 147.0], [89.0, 409.0, 119.0, 160.0], [85.0, 408.0, 117.0, 156.0], [87.0, 440.0, 113.0, 148.0], [90.0, 442.0, 113.0, 142.0], [100.0, 448.0, 115.0, 140.0], [112.0, 487.0, 115.0, 142.0], [103.0, 478.0, 119.0, 160.0], [85.0, 442.0, 123.0, 163.0], [71.0, 441.0, 121.0, 161.0], [65.0, 470.0, 119.0, 162.0], [57.0, 439.0, 121.0, 169.0], [52.0, 436.0, 123.0, 163.0], [47.0, 478.0, 125.0, 152.0], [59.0, 442.0, 118.0, 173.0], [53.0, 437.0, 123.0, 166.0], [40.0, 420.0, 130.0, 171.0], [49.0, 431.0, 108.0, 171.0], [25.0, 446.0, 130.0, 165.0], [52.0, 427.0, 113.0, 174.0], [68.0, 431.0, 115.0, 170.0], [63.0, 437.0, 140.0, 180.0], [71.0, 420.0, 152.0, 192.0], [61.0, 395.0, 155.0, 201.0], [100.0, 382.0, 153.0, 207.0], [102.0, 381.0, 152.0, 205.0], [88.0, 373.0, 159.0, 202.0], [117.0, 393.0, 149.0, 184.0], [115.0, 403.0, 157.0, 179.0], [100.0, 394.0, 167.0, 186.0], [82.0, 405.0, 165.0, 194.0], [60.0, 414.0, 168.0, 207.0], [57.0, 396.0, 164.0, 209.0], [45.0, 405.0, 165.0, 217.0], [22.0, 417.0, 179.0, 217.0], [17.0, 411.0, 189.0, 227.0], [19.0, 402.0, 181.0, 233.0], [35.0, 412.0, 171.0, 241.0], [57.0, 439.0, 166.0, 237.0], [77.0, 427.0, 173.0, 243.0], [99.0, 449.0, 191.0, 230.0], [121.0, 444.0, 210.0, 239.0], [156.0, 445.0, 180.0, 258.0]], \"areas\": [5382.0, 5446.0, 6451.0, 8657.0, 11343.0, 13310.0, 14291.0, 15267.0, 12299.0, 8643.0, 6306.0, 4913.0, 4210.0, 4486.0, 5961.0, 5690.0, 6652.0, 10155.0, 11700.0, 10590.0, 12264.0, 13915.0, 13651.0, 9645.0, 6908.0, 6593.0, 9501.0, 12530.0, 14442.0, 14040.0, 14366.0, 11582.0, 10801.0, 10985.0, 11136.0, 12522.0, 12998.0, 12809.0, 15366.0, 11348.0, 10283.0, 10793.0, 8600.0, 9049.0, 11087.0, 13134.0, 12568.0, 15137.0, 15584.0, 15109.0, 11947.0, 11034.0, 12038.0, 11563.0, 12446.0, 14140.0, 12265.0, 11745.0, 11389.0, 11589.0, 12224.0, 12175.0, 11001.0, 8867.0, 7420.0, 7032.0, 7723.0, 9455.0, 10244.0, 10258.0, 9300.0, 8307.0, 8763.0, 9093.0, 9755.0, 10189.0, 8917.0, 8806.0, 9241.0, 7966.0, 7979.0, 9266.0, 10321.0, 9978.0, 8833.0, 8624.0, 8684.0, 9567.0, 11870.0, 12727.0, 15219.0, 12866.0, 12803.0, 14123.0, 13451.0, 12840.0, 13581.0, 13271.0, 12943.0, 10407.0, 11596.0, 12404.0, 12489.0, 13902.0, 15756.0, 15482.0, 14878.0, 13784.0, 13650.0, 9166.0, 14390.0, 15876.0, 16106.0, 16364.0, 17218.0, 16539.0, 15776.0, 15949.0, 17009.0, 18495.0, 16190.0, 17315.0, 16010.0, 16506.0, 21050.0, 24112.0, 23894.0, 24747.0, 23607.0, 21321.0, 19112.0, 17951.0, 20341.0, 22438.0, 23849.0, 23616.0, 24672.0, 28761.0, 32889.0, 34642.0, 28725.0, 30302.0, 33367.0, 32740.0, 36791.0, 35286.0], \"iscrowd\": 0, \"video_id\": 0, \"height\": 1280, \"width\": 720, \"category_id\": 49272, \"noun_phrase\": \"air jordan\"}, {\"id\": 1448, \"segmentations\": [{\"counts\": \"Wmof0`0WW1>J3L4M2N2O000010O00000000000000000O100000000O01000O100O10O01O1O1O001N101N100N3N2L6J7G]S]3\", \"size\": [1280, 720]}, {\"counts\": \"YUVg0b0\\\\W15EZOTiNj0jV18N2M2N10001O00000001O00000000000000000O0100000O10000O10OO2O100O001O0O2O0O1N3N2M4H9G\\\\cU3\", \"size\": [1280, 720]}, {\"counts\": \"gU[g06dW1:I5K4L4M3M2L4O1O2O0001O000000000000000O100000O1000O10000O10O0100O1O1O1O001O0O2O0O1N3N3M2N5E[Sn2\", \"size\": [1280, 720]}, {\"counts\": \"gU`g08dW18H6I6L3N3L3O2N1O10000000000000000000000000000000O10O0100O100O100O001O1O1O001N1O2O0N3N2L6J7FXSi2\", \"size\": [1280, 720]}, {\"counts\": \"dmcg0?]W16H8K4K4M4N1O10000010O0000000000000000000O1000000O10000O1O010O1O1O1O001O1N2O0O2O0O1N2O2L4K6H][e2\", \"size\": [1280, 720]}, {\"counts\": \"begg0c0YW16K4L4M2L5N1O2N1000001O0001O000000000000000O10000O10O0100O100O1O1O1O1O1O001N2O0O2N1N3N1M4K4M6HZ[`2\", \"size\": [1280, 720]}, {\"counts\": \"Qflg03VW1m0D7M3O1N1O2O00001O01O001O00001O000000000000000O100000O0100O1O1O100O1O0O2O1O1N2O0O2O1L3N3M3H;GT[[2\", \"size\": [1280, 720]}, {\"counts\": \"bUSf07gW15M1O10O6GbPl13ToSN=J4G9G9L3N200001O00001O001O00000000000000000O10O1000O1O1O100O1O1O1O0O2O1O1N101O1L3N2N3L4K5IUcR2\", \"size\": [1280, 720]}, {\"counts\": \"bUXf07iW13L3N02NcXm1NXgRN:F8H8G9M200000001O01O00001O0000000000000O10000O10O10O100O1O1O1O1O100O1O0O2O1O1M3O0N2M3N3L5J6JoZl1\", \"size\": [1280, 720]}, {\"counts\": \"hUSf01iW1:L1O0018FaXm1OWgRN>H6E:D<N1O101O0001O000010O0001O000O100000O100000O100O100O100O1O1O1O001O1O1O1O1N1O2N2M2L4O2M3L5Knjn1\", \"size\": [1280, 720]}, {\"counts\": \"cUSf0;cW12107G4MVXm1=VgRN9I6H8H7N3O00001O01O001O0000001O000O0100000O100O100O100O100O1O001O1O1O1N2O1O1N1O2N2M200L4M4L8FnZQ2\", \"size\": [1280, 720]}, {\"counts\": \"cePf0;dW12N2N101O0013L7G]Pl17UoSN;I6I7F9N3O000001O001O00000000000000000O100O100O100O1O100O1O1O1O1O0O2O1O1N2N2O0N3M2O2K5L6JlbR2\", \"size\": [1280, 720]}, {\"counts\": \"cUSf0;cW14M1O2O1M3N1O13M3M4LkXm1EZgRN8G7K6K5I6J6N2O1O10000000000000O100O10000O00100O1O1O1O1O1O1N2O1N2O0O2N2M2O2K5L4LmbR2\", \"size\": [1280, 720]}, {\"counts\": \"b]Tf0=bW12N1O2N2N2N100010O4L4L7GdPl11ToSN:K5J5I7K5L4N20000O10000000O10000O100O100O100O002N1O1O0O2O1N2O1O1M2O2N1N2M4M4I9GmRP2\", \"size\": [1280, 720]}, {\"counts\": \"emQf09dW14N3M1N3M3O000002N4M9EdPl1MXoSN:G8J5K6J5L4M31O00000000000O0100000O100O100O1O010O1O1O1O1O1N2O1O1N1O2O1L3O2K5L5JmjS2\", \"size\": [1280, 720]}, {\"counts\": \"dmle0:dW13M3N1O2M3N2N100O11O100O4KPQl1BXoSN8H8J6K4J6K5N200O1000000000O10O10O100O100O1O100O1O1O1O001N2O1O1N2N2N1N3N1M4L5I8HmZV2\", \"size\": [1280, 720]}, {\"counts\": \"fUie04hW17L2M3N2M2O1O2N10001N100001O1O7FnPl1IUoSN8J5L4K5K5K5N2N2O10000000O100O10O10O1O2N010O1O1O1N2O1O1O1N1O2N1N3L3N3K5KQ[[2\", \"size\": [1280, 720]}, {\"counts\": \"b]ee07fW17K2M2O1O2M201N10000010O3L8AQQl11PoSN6K5I7K5L5I6N10100O10000000000000O1O100O100O1O1O1O1O1O001O1N2O1N1O2N1L4N3L6IQS_2\", \"size\": [1280, 720]}, {\"counts\": \"bUde05hW16J6L2N1002N2N5HdXm13SgRN:G8K5K6K4N2001O0001O01N10000000000O1000000O100O010O1O1O1O1O1O1O1N2O001M3N2L3N3M3L4L6GS[`2\", \"size\": [1280, 720]}, {\"counts\": \"Tfgg05`W1=E:F:I7N100001O00010O00001O0O100000000000000O10000O1O100O001O1O1O1O1O001N2N2N2N2N1M4N2I9ISkb2\", \"size\": [1280, 720]}, {\"counts\": \"i]fg0>YW1;G8F9O2N10001O01O0000001O0000001O000000000O10000O1O10O0100O1O1O001O1N2O1O1O1N2M3M3N1L5L4KWSd2\", \"size\": [1280, 720]}, {\"counts\": \"kmcg06dW19I6J5M3M3L3N3L3O2N1000000000000001O00000000000O10000O100O100O001O1O1O100N2O1O1N2N1O2M3N2L4L4KWSd2\", \"size\": [1280, 720]}, {\"counts\": \"c]fg0>^W17I5L4L4I7M200O101O000001O00001O000000000000001N1000O0100O100O1O1O001O1O1O1O1O1N1O2M3N1N3M3M2K;FV[`2\", \"size\": [1280, 720]}, {\"counts\": \"gegg07bW19J6I6L4I7M2O2N1000001O0001O0000001O0O1000000000000O10000O10O01O1O1O1O1O1N2O1O1N2N1O2N2M2N3L4L7IXS_2\", \"size\": [1280, 720]}, {\"counts\": \"belg0<_W17H8J5K5M2O2O0O10000O2O00000001O00001O000O10O100000O10000O1O1O1O10O01O1N2O1N2O1N101O1N2M2L6I7KZ[[2\", \"size\": [1280, 720]}, {\"counts\": \"heQh06dW18K6F8M3M2K6N1O2O0O1000001O01O00001O000000000O1000O010000O1O100O1O1O001O1O1O1N2N2N2N1O2M3M3J8IX[V2\", \"size\": [1280, 720]}, {\"counts\": \"f]Uh0:bW17J4K5L4M3K4N3N1O100000000001O00000000000000000O100O10O0100O1O100N2O1O1O001N2O1N2M2N3M3M4K5IXkS2\", \"size\": [1280, 720]}, {\"counts\": \"imWh05dW1:J5I6K5L4K4O2N10001O0000000001O0000000000000O100O1000O01O100O1O1O1O1O001O1N2O0N3N2M3N1O2K8GY[Q2\", \"size\": [1280, 720]}, {\"counts\": \"h]Zh06dW19I6H7L4I7M201O0O2O00000001O000000001O0000000O10O10O10000O100O1O001O1O1O1N2N2O1N1O2N1O2M3K6I[kn1\", \"size\": [1280, 720]}, {\"counts\": \"gm\\\\h07dW17J6I7L3G8O2N1O101N10000001O00001O000000000000000O10000O100O00100N2O1O1O1O001N2O1N2N1N3M3L4J\\\\[l1\", \"size\": [1280, 720]}, {\"counts\": \"fe`h07dW18E9M4I6K5N1O2N100O101O01O001O0000000000000O2O00000O01000O100O1O1O001O1O1O1O1N2O0N3N2N2M3L4K7IY[g1\", \"size\": [1280, 720]}, {\"counts\": \"g]dh09aW19I5L4M3L3K6N1O1O2O000001O000000000000000O101OO1000O100O100O00100N2O1O1O1O1O0O2O1K5L4M3M3L5JXkd1\", \"size\": [1280, 720]}, {\"counts\": \"jeeh0;_W18K4L4L4K5L3O2N1O101O00000001O000000001N1000000O010O100O100O1O00100O1O1O1N2N101M3M3M3M2N3M7ETcc1\", \"size\": [1280, 720]}, {\"counts\": \"meeh0<aW15I7K4L3M4L3O1N3N100000000001O00000000000O10000O100000O0100O1O1O1O1O1O1O1N2O1N1O2L4M3M3M2MQkd1\", \"size\": [1280, 720]}, {\"counts\": \"Q^dh09aW19H6K5M3L3M4N101N100O100001O0000001OO100000O10000O100O100O010O1O1O1O1O1O1O1N2O1M3L3N3N1M4L3LQkd1\", \"size\": [1280, 720]}, {\"counts\": \"UVch04dW1=E7K5M3L4L3N200O2O000000000001O0000000000000O1000000O10O01O100O1O1O1O1O1O1N2O1N1O2M3M2N3L5JnRf1\", \"size\": [1280, 720]}, {\"counts\": \"R^dh08cW18I6I6M3M2M3N3N100O100O20O0000000O10001O00000000000O010O1O1O100O1O1O1O1N200N2N2N1N3M3M3L4K6Imjd1\", \"size\": [1280, 720]}, {\"counts\": \"Vnah02hW1;E7K5L4L4M2M3O2N1000000000000000000000000000O2O000O010O100O1O100N2O1O100O1N2O1N2M2O3J5Inji1\", \"size\": [1280, 720]}, {\"counts\": \"Vf`h03fW1;G6K5L4M3L3N3N1O10000001O0000001O000000O100000O100O100O100O10O01N2O1O1O100N2O0O2J6M3KlZl1\", \"size\": [1280, 720]}, {\"counts\": \"T^ih05eW1:F7K5L4M3M3N1O1000001O000001O00001O000O10000000O10O100O100O100O1O001O1O1N2O1N1O2L4K6K7FiZb1\", \"size\": [1280, 720]}, {\"counts\": \"QVWi0<_W16J7K4L4M2O2N1000000000000000000001O0000000000000O0100000O1O1O1O1O100N101O1N2N2N1K6M4KhjU1\", \"size\": [1280, 720]}, {\"counts\": \"Q^bi0<_W17J5L4L4M2N2O2N11O00000001O000000O101O000000000O1000O10O1O1O100O1O1O1O1O001O1N2L4K5M5Fjbj0\", \"size\": [1280, 720]}, {\"counts\": \"lmii0?\\\\W17J5M3M3M2N3N100O100001O01O01O000000000O100000000O100O100O100O100O1O1O001O1O1N2N2L4L4M5Hjja0\", \"size\": [1280, 720]}, {\"counts\": \"jUPj0;_W18J5K5M2O2N1O2N1000001O000001O00000000000O100000000O100O100O100O100O1O1O1O1O1N2N1N3M4K6Fob;\", \"size\": [1280, 720]}, {\"counts\": \"l]Qj02bW1?F9J5M3N10001O00001O0001O0000000000000000001O00000O10000O100O100O100O1O1O1O0O2N2N2N1M4M6KmR9\", \"size\": [1280, 720]}, {\"counts\": \"hemi01hW1=F5L4M3N2N1O101O0O10000010O00O100000000000000000O1000000O10000O1O1O1O1O1O1O1O1N1O4J8IjZ?\", \"size\": [1280, 720]}, {\"counts\": \"`]gi0=[W1:K4N1O2O1O001N1000000000001O0000000000000000O100000000O100O100O1O1O100N101O1O0O3L8Gnjf0\", \"size\": [1280, 720]}, {\"counts\": \"am_i09_W1;J5L3M2O2N2O0O2O000000000000000001O0000000000000000000O100O100O101N1O10O01N2O1O1N3K7IPSm0\", \"size\": [1280, 720]}, {\"counts\": \"cmZi09dW15I6K5K5N2N2N1O101O00000000000000000000000000000000O10000O100O2N100O1O1O1O1O1N2M2O2M6IRSR1\", \"size\": [1280, 720]}, {\"counts\": \"gmUi02dW1?G5L4I7N2N2O00001O000O1000001O0001O000000000O10000000000O100O100O1O100O1O1O1N2O1N2M3N2M3N3DbhN0cbT1\", \"size\": [1280, 720]}, {\"counts\": \"cmUi07eW18E8G9N2M3O0O2O00001N100000000001O00001O00000000000O1000000O100O1O100O1O1O2N1N2O1N2M3M3N3LTcT1\", \"size\": [1280, 720]}, {\"counts\": \"amZi0;_W18B=M3O1O1N101N100O1000001O01O001O000000001O0O10000000O10O2N100O1O1O1O1O1O2N1O1M3M3N3K=DkjP1\", \"size\": [1280, 720]}, {\"counts\": \"cU\\\\i08\\\\W1?G7M4M2N2N1O101O000O101O0001O0000001O00000000000O100000000O2O0O100O1O1O1O1O1N2O1N2M4M2M7HR[n0\", \"size\": [1280, 720]}, {\"counts\": \"ce^i09cW17H7F9L4M2O2O0O2O0000001O000000000001O000000000O101O000O10000O2O000O1O1O1O1O1O1N2N2N2M4L9Fojk0\", \"size\": [1280, 720]}, {\"counts\": \"gm_i03eW1=F7F9L4N2N2N1000001O000000000001O0000000000001O000O10000O10000O100O2N1O001O2M2O1N2L4N2M;Dmbj0\", \"size\": [1280, 720]}, {\"counts\": \"gm_i03eW1>F5G9K5N2N1O2O000000001O0000000001O0000000000000O10000O101O0O100O1O1O1O1O1O1O1N2N2M3N3L7IPcj0\", \"size\": [1280, 720]}, {\"counts\": \"ee^i07cW19H7H7L4L4N1O1O2O000000001O00000001O0000000O100000001O0O10000O100O1O2N1O1O1O1O1O1N2O1M3N3L8GPcj0\", \"size\": [1280, 720]}, {\"counts\": \"bU\\\\i0;`W16A`0N0O2N2O0O2O0O10001O0001O00001O000000001O00000O1000000O1O1O101N1O1O1O1O1O1O1N2N2M3N5IRco0\", \"size\": [1280, 720]}, {\"counts\": \"c]Si06eW19E8I7L4N2N2N1O2N11O01O00000000000001O000000001O00000O101O0O100O2N1O100O1O1N2O1N2N2N2N2N3M5JQkU1\", \"size\": [1280, 720]}, {\"counts\": \"aejh0;`W17C<L4M3N1O2O0O2O000000001O000001O00001O0000001O0O10000O100O2O0O1O2N100N2N2O1N2N2M3N2N2N4LRk_1\", \"size\": [1280, 720]}, {\"counts\": \"`Uch0;_W18I6H7N3M2O2N101O001O00000000010OO101O0000001O00000O100O2N101N100O1O1O2N1O1O1N2O1N2N2O1M3N6HRSf1\", \"size\": [1280, 720]}, {\"counts\": \"`U^h0;aW15I7I7L4L3O2O0O2O0000001O00000001O00001O00001O000O10000O2O0O2O0O1O2N1O1O1N2O1N2N2N2N2N3M5HS[l1\", \"size\": [1280, 720]}, {\"counts\": \"a]Zh08dW16J5K5M2N3M3M2N3N101N100O101O00000001O001O0000000O2O00001N1O101N1O1O2O0N2O1N3N1N2N1O2N2N2O2MSkn1\", \"size\": [1280, 720]}, {\"counts\": \"b]Uh02hW1:J4J4N3M2M4N1N3N1N2O2N1O10001O000001O00001O1O000O10000O2N100O2N100O2O0N2N2O2L3N2M4M2M3N3KUSU2\", \"size\": [1280, 720]}, {\"counts\": \"YeQh09bW17L3M3M3M2N2N3M2O1O2O0O1O10001N11O00001O001O0000000O101N100O1O2N2O0O2N1N2O1O1M3N3L3N3L8ISSZ2\", \"size\": [1280, 720]}, {\"counts\": \"XeQh07dW17K5L2N3M2O2M2N2O1N3N100O101N100001O000010O00O10000O101N101N101N1O1O101N1O1O1M3N2N2N2M5L[SZ2\", \"size\": [1280, 720]}, {\"counts\": \"SmRh0<aW14L4M2O2M2N3M2N2N201N100O1O1O2O000001O001O000O2O00000O2O0O100O2O0O2N100N2O2M2N1N4N1N2N4J_kX2\", \"size\": [1280, 720]}, {\"counts\": \"S]Ph08dW16K4M3M2N3N1N2M4N1N2O2N10000O1000000001O01O0001N1000000O2O0O2O0O2N1O1O1O1O1O1N2N2M3N3M2N5J`SZ2\", \"size\": [1280, 720]}, {\"counts\": \"P]Ph0:cW14L5K4M2O2M2M3N3M200O1O101O0O1001O00001O0000001O000O100O2O0O1O2N1O2O0O1N2N2O1M3N2O2M2N4Kb[[2\", \"size\": [1280, 720]}, {\"counts\": \"ReQh09cW16L3L4M2O2M2N2N3M2O1O2N1O100O1000000001O01O00O2O00000O2O0O101N101M200O1O1O1N2O1N2N2N2O2L4M7F`cW2\", \"size\": [1280, 720]}, {\"counts\": \"Y]Uh01jW19I4M3M3M2N2N3L3N2N3M2O1N200000000010O001O001O001O0O10000O2O0O100O2O0O1O1N2O2M2N2N2N1O2O1N2O3L_cR2\", \"size\": [1280, 720]}, {\"counts\": \"Ym\\\\h0;cW13N1O1O1O2N1N2O2M2N2M3M3O2N1O10000000000000O101O0O100O2N100O1O2N100O1O1M3N2M3O1O1O1N2N2Fekn1\", \"size\": [1280, 720]}, {\"counts\": \"cUWi01lW19G4M2N2N2N1N201N1N2O11O1O1O1O1N2O0N3N2O001N3MYkn1\", \"size\": [1280, 720]}, {\"counts\": \"aeVh04fW1:I4K5N20O001O010O1O0O2M3L3E;Nf`<0PXB0khl2\", \"size\": [1280, 720]}, {\"counts\": \"^mRh08bW18K5K4M3L3M4N1O1N2N2O1010O4L4M3L2N2N2N2M1O2L4L5HjjQ3\", \"size\": [1280, 720]}, {\"counts\": \"^]Ph07cW19I5L5M1N2N3N1O2O01O000000003M2N3M2M3M2N2ImRX3\", \"size\": [1280, 720]}, {\"counts\": \"]Uog07cW18J5L4M3N2N1O1O2O0001O0000001O3M2N2N2M3L3M3CSSX3\", \"size\": [1280, 720]}, {\"counts\": \"^]Ph0;`W16K5L4N1N3M201N10000001O00003M3M1O3K4M3L5DkRX3\", \"size\": [1280, 720]}, {\"counts\": \"jmmg04fW18I7J5M2N3M2N3N100O10000001O2N2N2O2M2N2L3N3I6HdRX3\", \"size\": [1280, 720]}, {\"counts\": \"ielg0;`W17K4M3M3L3N3O0O2N100001O1O1O3M3M2N1O1N2N2L5K\\\\bZ3\", \"size\": [1280, 720]}, {\"counts\": \"jmmg0=_W15L5L2M4L3O2N2N10000001O102M2N2N3L2N3M2L5G]bZ3\", \"size\": [1280, 720]}, {\"counts\": \"imRh0<`W17J3M4L4M2O2N1O2O0O100001O102N3K3N2M2N3L3M3L[ZT3\", \"size\": [1280, 720]}, {\"counts\": \"mmWh05eW18I7L3L4L3O2O0O100000000001O1O2N3M2M3N1M3L4I8J\\\\Rn2\", \"size\": [1280, 720]}, {\"counts\": \"g]Uh0<_W16K5M3M2N3N1O1000001N100002N2N3M2N2M3M2M3H9K]jQ3\", \"size\": [1280, 720]}, {\"counts\": \"leQh05eW19J5K4L4K5N1O101N10000001O00101N3M3L3M2M3L5FbbU3\", \"size\": [1280, 720]}, {\"counts\": \"j]Ph0;aW16L3L4M3L4M2O2O0O10001O000002N3N1N3K5K3N2K5G`jV3\", \"size\": [1280, 720]}, {\"counts\": \"Rnmg01jW1;F6K3N3M2M4M3N10001O00000001O003M2N2N3K3M4L5C]RX3\", \"size\": [1280, 720]}, {\"counts\": \"Qnmg09bW17I6M3M2M4N1N3N101O000001O1O1O1O4L2M3M3L4K4JXZY3\", \"size\": [1280, 720]}, {\"counts\": \"U^Ph04dW1<I4L3N3K5M3O0O2O0000O1000O22M3M2O2K4M3L3L6FWjV3\", \"size\": [1280, 720]}, {\"counts\": \"V^Uh02dW1=J5K4L3N3L3O1O10001O000100O1O2N2N3M2M2N3K4M2L5G[Zo2\", \"size\": [1280, 720]}, null, null, null, null, null, {\"counts\": \"_emi07`W1<J4L4M2O2N1000000000000100O1O2N2N1N3M3L4H9JfbY1\", \"size\": [1280, 720]}, {\"counts\": \"]emi04fW1:H5M3L4M3N1O1O101O01O1O001O002N1O2N2N1N3L3M4GnZX1\", \"size\": [1280, 720]}, {\"counts\": \"^mni01jW18I6I6M3L3N2O10001O000O11O001O3M1O2N1O2N1N2MnZX1\", \"size\": [1280, 720]}, {\"counts\": \"aemi0:aW17J6K3L5N1O1O1O100000001O03M1O2N3M2M2N3L4I6KfbY1\", \"size\": [1280, 720]}, {\"counts\": \"m]gi0:bW18H5L3M4N1O2O0O101O0001O001O1O2N2N101L4L3L5G\\\\j_1\", \"size\": [1280, 720]}, {\"counts\": \"R^bi0=`W15J6K3M3N201N1000001O0001O1O101N3L3M4L2M3I7GVjd1\", \"size\": [1280, 720]}, {\"counts\": \"S^]i0:`W18L4J6K3O1O2O0O10000000000010O2N2N3L4L2N3J5K6HTbh1\", \"size\": [1280, 720]}, {\"counts\": \"nU\\\\i0:cW14L4I6M4L3L4J600O2O01O2N6J5K2N2M4K3K5J]jn1\", \"size\": [1280, 720]}, {\"counts\": \"j]bi09bW15K6M2N2O100000O2M3L4J5N6IaRP2\", \"size\": [1280, 720]}, {\"counts\": \"feci05fW16M2O1O1N3M2O10O1O2M2I7N2Lkjn1\", \"size\": [1280, 720]}, {\"counts\": \"_ehi09dW14M2M4M3M3N1000O1N2N2M4L4M2Nnbh1\", \"size\": [1280, 720]}, null, null, {\"counts\": \"WeWj09cW17K4MO0OO13O100O1J6MYc^1\", \"size\": [1280, 720]}, {\"counts\": \"mdhi04cW1;H7K5B]O[iNe0cV1>O0100O1O1O1O2N6J3M3N1N2O0M4M2L5H^k_1\", \"size\": [1280, 720]}, null, {\"counts\": \"X]gi03iW17K3M3L3M4M2O10000003M1O1N1K6K6IV[g1\", \"size\": [1280, 720]}, {\"counts\": \"[UWi0:bW16J5L4K4K5K6K4O1010O1O001O2N2N7J4K2N2N2M2M4I9Ghjn1\", \"size\": [1280, 720]}, {\"counts\": \"e]Si08eW14L3N3M2M4L3N2M3M3N3M21O5K4L4K3M3M2K5KgbW2\", \"size\": [1280, 720]}, {\"counts\": \"^mfh0>_W15K4M2M3N3M2N2G:O0010O002O5J6J2M4L3M3M1M4I7LeZ`2\", \"size\": [1280, 720]}, {\"counts\": \"_e`h08aW1:K3L3L4N3N1O2M2L4001O011N4L2O3K4L3N1M3M3I8Kgbf2\", \"size\": [1280, 720]}, {\"counts\": \"Ve[h0<`W16K5L3L3L4N2K5K60O1O1O2N1O4M6I3M2M2O1M3L4JQkl2\", \"size\": [1280, 720]}, {\"counts\": \"VUYh0:bW16K5K3K5N3N1O1N2L4N21O1O1O3M5K5K2N3M0N3M2L6I6Lojl2\", \"size\": [1280, 720]}, {\"counts\": \"W]Zh09bW17K4L4M2K6N1N2K6L31O1O1O1O2N6J5K3M1O1N1N3L5GTkl2\", \"size\": [1280, 720]}, {\"counts\": \"X]Zh02gW1<H5K4L3M3N3M2L4L41O010O2N3M4L3M2N2M3N0O2M3K7GTck2\", \"size\": [1280, 720]}, {\"counts\": \"Rmah09bW18J4K5M2N2O2O0O1000000001O011N2N3L3N2M2HdhNI^W14<JP[e2\", \"size\": [1280, 720]}, {\"counts\": \"UUmh05gW16I6F9L5L3L4N2N30O1O4L5K2M3N3M1O2L3M2J:IUc\\\\2\", \"size\": [1280, 720]}, {\"counts\": \"ilPi0?]W16K5L2N2N3N1O1N2000001O0003M3M2N3M2M2N2M3K5K5J[SU2\", \"size\": [1280, 720]}, {\"counts\": \"iTWi0`0]W15K4M2M4M2N2O2M201O01O1O2N2N4L2N2M2N2N1N3J7JZSP2\", \"size\": [1280, 720]}, null, null, null, null, null, {\"counts\": \"[Tmh03mW15J8J3L8I2NO001O1J6N4K5K6HZ[e2\", \"size\": [1280, 720]}, null, null, null, null, null, null, null, null, {\"counts\": \"fmhg0?aW12O1N10O2O1N101N2N2N2NkQl3\", \"size\": [1280, 720]}, null, null, null, null, {\"counts\": \"oebg0>YW1:J6E:L5N1O10001O1OO102N6K;D3L5L8H0O2MmYh3\", \"size\": [1280, 720]}, null, null], \"bboxes\": [[588.0, 916.0, 44.0, 47.0], [593.0, 920.0, 45.0, 46.0], [597.0, 921.0, 47.0, 46.0], [601.0, 922.0, 47.0, 46.0], [604.0, 921.0, 47.0, 49.0], [607.0, 922.0, 48.0, 50.0], [611.0, 927.0, 48.0, 48.0], [565.0, 930.0, 101.0, 49.0], [569.0, 932.0, 102.0, 50.0], [565.0, 933.0, 104.0, 50.0], [565.0, 932.0, 102.0, 51.0], [563.0, 934.0, 103.0, 49.0], [565.0, 935.0, 101.0, 48.0], [566.0, 933.0, 102.0, 50.0], [564.0, 935.0, 101.0, 48.0], [560.0, 934.0, 103.0, 49.0], [557.0, 932.0, 102.0, 48.0], [554.0, 930.0, 102.0, 49.0], [553.0, 930.0, 102.0, 48.0], [607.0, 928.0, 46.0, 47.0], [606.0, 926.0, 46.0, 46.0], [604.0, 926.0, 48.0, 45.0], [606.0, 922.0, 49.0, 47.0], [607.0, 921.0, 49.0, 46.0], [611.0, 920.0, 48.0, 46.0], [615.0, 923.0, 48.0, 45.0], [618.0, 924.0, 47.0, 44.0], [620.0, 922.0, 47.0, 46.0], [622.0, 922.0, 47.0, 46.0], [624.0, 922.0, 47.0, 45.0], [627.0, 922.0, 48.0, 45.0], [630.0, 924.0, 47.0, 44.0], [631.0, 927.0, 47.0, 46.0], [631.0, 931.0, 46.0, 45.0], [630.0, 932.0, 47.0, 46.0], [629.0, 934.0, 47.0, 45.0], [630.0, 936.0, 47.0, 43.0], [628.0, 936.0, 45.0, 42.0], [627.0, 937.0, 44.0, 41.0], [634.0, 938.0, 45.0, 41.0], [645.0, 939.0, 44.0, 40.0], [654.0, 940.0, 44.0, 40.0], [660.0, 937.0, 45.0, 41.0], [665.0, 934.0, 45.0, 38.0], [666.0, 930.0, 46.0, 37.0], [663.0, 931.0, 44.0, 34.0], [658.0, 928.0, 43.0, 36.0], [652.0, 927.0, 44.0, 36.0], [648.0, 927.0, 44.0, 37.0], [644.0, 925.0, 47.0, 40.0], [644.0, 923.0, 46.0, 41.0], [648.0, 922.0, 45.0, 42.0], [649.0, 922.0, 46.0, 43.0], [651.0, 923.0, 46.0, 43.0], [652.0, 924.0, 46.0, 42.0], [652.0, 924.0, 46.0, 42.0], [651.0, 923.0, 47.0, 43.0], [649.0, 923.0, 45.0, 41.0], [642.0, 921.0, 47.0, 43.0], [635.0, 921.0, 46.0, 43.0], [629.0, 920.0, 47.0, 43.0], [625.0, 920.0, 46.0, 42.0], [622.0, 920.0, 47.0, 42.0], [618.0, 918.0, 46.0, 41.0], [615.0, 913.0, 45.0, 41.0], [615.0, 911.0, 45.0, 41.0], [616.0, 908.0, 45.0, 41.0], [614.0, 906.0, 46.0, 41.0], [614.0, 904.0, 45.0, 41.0], [615.0, 905.0, 47.0, 42.0], [618.0, 907.0, 48.0, 41.0], [624.0, 914.0, 45.0, 38.0], [645.0, 920.0, 24.0, 35.0], [619.0, 931.0, 27.0, 47.0], [616.0, 915.0, 25.0, 44.0], [614.0, 922.0, 22.0, 35.0], [613.0, 922.0, 23.0, 33.0], [614.0, 924.0, 22.0, 34.0], [612.0, 929.0, 24.0, 37.0], [611.0, 934.0, 23.0, 37.0], [612.0, 936.0, 22.0, 38.0], [616.0, 934.0, 23.0, 40.0], [620.0, 936.0, 24.0, 33.0], [618.0, 933.0, 23.0, 35.0], [615.0, 934.0, 23.0, 37.0], [614.0, 936.0, 23.0, 38.0], [612.0, 940.0, 24.0, 35.0], [612.0, 940.0, 23.0, 37.0], [614.0, 941.0, 23.0, 38.0], [618.0, 940.0, 25.0, 37.0], null, null, null, null, null, [663.0, 924.0, 23.0, 33.0], [663.0, 921.0, 24.0, 33.0], [664.0, 919.0, 23.0, 32.0], [663.0, 925.0, 23.0, 36.0], [658.0, 940.0, 23.0, 36.0], [654.0, 946.0, 23.0, 36.0], [650.0, 945.0, 24.0, 36.0], [649.0, 929.0, 20.0, 43.0], [654.0, 939.0, 14.0, 25.0], [655.0, 936.0, 14.0, 21.0], [659.0, 930.0, 15.0, 26.0], null, null, [671.0, 922.0, 11.0, 27.0], [659.0, 891.0, 22.0, 46.0], null, [658.0, 919.0, 17.0, 26.0], [645.0, 909.0, 24.0, 47.0], [642.0, 921.0, 20.0, 40.0], [632.0, 916.0, 23.0, 47.0], [627.0, 917.0, 23.0, 42.0], [623.0, 906.0, 22.0, 47.0], [621.0, 907.0, 24.0, 42.0], [622.0, 906.0, 23.0, 44.0], [622.0, 905.0, 24.0, 43.0], [628.0, 912.0, 23.0, 34.0], [637.0, 900.0, 21.0, 43.0], [640.0, 904.0, 24.0, 38.0], [645.0, 903.0, 23.0, 40.0], null, null, null, null, null, [637.0, 906.0, 14.0, 31.0], null, null, null, null, null, null, null, null, [608.0, 950.0, 12.0, 22.0], null, null, null, null, [603.0, 929.0, 20.0, 49.0], null, null], \"areas\": [1575.0, 1589.0, 1598.0, 1629.0, 1708.0, 1759.0, 1834.0, 1809.0, 1848.0, 1875.0, 1828.0, 1847.0, 1611.0, 1652.0, 1632.0, 1684.0, 1565.0, 1616.0, 1696.0, 1667.0, 1692.0, 1680.0, 1736.0, 1708.0, 1695.0, 1656.0, 1572.0, 1615.0, 1663.0, 1659.0, 1688.0, 1593.0, 1617.0, 1592.0, 1596.0, 1637.0, 1542.0, 1504.0, 1433.0, 1438.0, 1422.0, 1410.0, 1482.0, 1398.0, 1404.0, 1217.0, 1278.0, 1367.0, 1382.0, 1534.0, 1554.0, 1524.0, 1636.0, 1634.0, 1592.0, 1596.0, 1642.0, 1495.0, 1586.0, 1570.0, 1534.0, 1494.0, 1472.0, 1344.0, 1342.0, 1355.0, 1367.0, 1395.0, 1370.0, 1439.0, 1381.0, 1199.0, 465.0, 283.0, 707.0, 589.0, 572.0, 571.0, 630.0, 638.0, 614.0, 660.0, 582.0, 599.0, 625.0, 637.0, 621.0, 638.0, 616.0, 645.0, null, null, null, null, null, 581.0, 587.0, 559.0, 621.0, 629.0, 614.0, 652.0, 565.0, 248.0, 191.0, 276.0, null, null, 173.0, 620.0, null, 306.0, 729.0, 490.0, 673.0, 618.0, 662.0, 628.0, 651.0, 648.0, 582.0, 548.0, 662.0, 657.0, null, null, null, null, null, 249.0, null, null, null, null, null, null, null, null, 174.0, null, null, null, null, 682.0, null, null], \"iscrowd\": 0, \"video_id\": 0, \"height\": 1280, \"width\": 720, \"category_id\": 49272, \"noun_phrase\": \"air jordan\"}, {\"id\": 1449, \"segmentations\": [{\"counts\": \"njRe0:`W19G8I6K6I5M4K5L3K5H9K4L5hNW1M4J5K5N3O0O101N100001O00000000001O00000O2O000O1000001N100O100O1O100O1O1O100O1O1O010O1O1N2O2N1O2M3M3M3N2L5K4K5I7K6L5L3L5K6J5K6I8H=CT_n3\", \"size\": [1280, 720]}, {\"counts\": \"QSYe0a0]W14I8I5K5L4L3M4K4K5K6I6M3mNjMXlN[2]S1VNUlNQ2dS1Y1L4M4N1N201N100000000001O001O1O1O0000001N1000000O100O100O100O1O2O0O100O10OO200O1O1O1N2O1O1O1O1N2O1O1N3L4L5K4J6L6J6J5L5K6J4M4J7J7H9G8I<\\\\OjVc3\", \"size\": [1280, 720]}, {\"counts\": \"QS^e0?[W1:F8J6H7H8TNiNblNb1PS1oNflNY1US1oN`lNX1]S1h1M2O101O00010O00000001O01O01O00001O000O10000000000000000O1000O10O10000O100O1O2O0O1O100O1N102O0O2N2N3M3M2M4L3M4L5J5J7F;F8I9H<F9F:D>BcVh3\", \"size\": [1280, 720]}, {\"counts\": \"[XZe0<QW1g0H7L4L5L8I:EW1iN?A3M3M101N1O1O010O001O001O0000010O00000O10001OO10000001OO100000000000001N101O1O1O2M3N1O1N2O1N2N4M2M4K6K4Lg0YOa0^Oc0]O<C;EYUW4\", \"size\": [1280, 720]}, {\"counts\": \"`XZe03`W1g0_O;I6L5L6J9G>B>Bd0\\\\O9G4L3N0O2N100N101O0100O0001O000010OO100000001O0000000001O00000000O0101O001O2N1N3N1O2N1N3N1N4M3L6Ka0^O<D9G9G9F8I:E:ETmU4\", \"size\": [1280, 720]}, {\"counts\": \"\\\\X_e0e0TW1<H7I9I9F>Cb0@c0\\\\O=D2M2N2N1O1O1O1O1O0010O0001O001O000001O01O0001O00000000001O00O1001O000O11O00O2O1O2N2M2O2N1N2O3M2M6K?@`0@9G8G8I;D;E8G:BnlP4\", \"size\": [1280, 720]}, {\"counts\": \"``ee0d0YW19E:F<G<E<D?Ai0YO9F4L2N2N1O100O1O100O001O001O0001O000000010O0000000000001O0000001O1O1O1O001O000O10001O1O1O1N2O2M3N2M4L6Jj0VO?A8H8G=C9G:Ehdj3\", \"size\": [1280, 720]}, {\"counts\": \"b`oe0h0VW18G8G;F`0Ac0]O9Ho0RO4K4M2O0O1O100O0010O01O01O01O01O0001O0001O00000000001O01O0000000000000000O101O0000001O1O1O3L2O2M5L4K5L6IS1mN:E8H:G;C`0\\\\Odla3\", \"size\": [1280, 720]}, {\"counts\": \"k`Tf0:aW1<E9I7I8G;Dc0@?PkN\\\\MfS1g3H5L2M3N100O1O1O10O0010O01O01O00001O0001O01O01O000001O01O00000000000000000000000O1000002N3M2M4M3L3N3M3L5K5dLYlNV2PU1H8H7I7H<D<B_\\\\Z3\", \"size\": [1280, 720]}, {\"counts\": \"PiPf0:`W1<G8H8I8H;Ec0]O=ljNdMkS1a3G5M2N2N1N2O1O10O01O010O0010O01O0001O000000010O000000000000000000000000001O0000001O001N2O1O2N4K4M3M4K5K5SM]kNT2fU1A8H6J8G8I>A8G=@Vl\\\\3\", \"size\": [1280, 720]}, {\"counts\": \"fXne0h0WW16J6I9G:F>Bb0]OS1nN8I3N2M3O000O1O001O1O010O000010O001O01O000001O000000000001O00O101O0000000000001O00001N2O2N2N2N3L4M5J5Kj0VO<D8H7I8G9G>B>\\\\O]la3\", \"size\": [1280, 720]}, {\"counts\": \"gPme0c0[W18I5J7J7H:G=Aa0@S1mN8I4L3M2O100O1O001O10O01O001O01O1O010O000000001O000001O0000000000000001O000000001O0O2O1O1O2N2M4M3M4K5K6QM_kNT2eU1A7I7H8I8G<C?^O^la3\", \"size\": [1280, 720]}, {\"counts\": \"fXne0c0\\\\W15K5J6K5K6I6K8Fc0^O<ER1mN7K2N2N1O1O1O1O1O010O1O0100O1O01O01O000010O0000000001O00000000000000000000000000000O101O1O1O4K5L4L4K5KY1gN7I5K7I6I6J9G<D]T^3\", \"size\": [1280, 720]}, {\"counts\": \"ghPf0d0YW17K5J6K4K8H9G:E?Ba0QkNRMnS1g3I5J4M1O1O100O1O1O01000O010O01O01O01O0000010O000000001O01O0000000000000000O10000000001O1N3N4L3M4K5L4K4fLVlNW2PU1I6J6J7I6I7J7H=A^d[3\", \"size\": [1280, 720]}, {\"counts\": \"e`oe0g0WW16K5J9G5J6K8H=C>C=RkNUMnS1g3H3N2M1O1O100O100O001O0100O10O0000010O0000000000010O00000000000O100IeKilN[4^S1000000O100000N21O3M3L6K4L4K6J4PMdkNV2`U1B8H5K6J7H6K8F<E9E[l\\\\3\", \"size\": [1280, 720]}, {\"counts\": \"bPme01b02gV1n0E6J:B9I9H?C9F:XkNoLoS1k3F4K3N2O000O100O1O010O000010O010O010O0000001O01O00000000000000000000000O1001O1O0O10001O2M3N4L4K5L5JR1oN8G5K6J5J9H4I?B<\\\\Oela3\", \"size\": [1280, 720]}, {\"counts\": \"Raje0<TW1j0D8H6G=Cb0B=Cf0ZO9H3M3M2O1N100000O0100O000010O0000010O1O01O000001O000000O10000000000O11N1000O1O1003M1O2M2O2N2M5L4L5J7fLokNY2TU1G6J5K6I7I8H;D:Cede3\", \"size\": [1280, 720]}, {\"counts\": \"b`ee0c0[W19H5J7I6G?C>D=D>Aa0@5K5K2N2O0O100O1000000O000010O000010O0001O000001O01O00000000000000000O100000000000000O4M2N2M4M4K6KQ1nN8I5J5J7J6I8H8H;Cg\\\\i3\", \"size\": [1280, 720]}, {\"counts\": \"_Pce0h0UW17I8G9I:G;E=D9Fe0\\\\O8H6K2M2N2N100O100O10O010O010O00000010O00000001O0001O0O1001O0000O1O02O001O0000000O2O1O2N2N1O3L5L4K7Jl0SO8H7I5K7H8H8H;D;Celk3\", \"size\": [1280, 720]}, {\"counts\": \"```e0`0]W19G8G8H:I:F:G:F;Fc0\\\\O7I5L2M2O0O1O1O100O10O01O01O010O00010O000000000001N1O1O1O100O10000O10O102N1O00001N3N2N1O3L2O3M4K6Kk0TO9G7I6J6J6I9G8H9F;BgTm3\", \"size\": [1280, 720]}, {\"counts\": \"\\\\``e0b0\\\\W19B<H9H8H?C=C9Hf0YO7I4L2N100O1O1O01000O010O001O01O01O0000000000001O000001OO1O100000O0100O10000000000002M3N5K3L4M2N5Jn0RO:F7I6J6I9G8H;E=[Ondo3\", \"size\": [1280, 720]}, {\"counts\": \"WX_e0e0XW19F9G:H7J:F>D<Ch0XO8I2M2N2O0O1O010O1O10O01O00001O01O0001O0000000001O00O100O1000000O100O100O11O0O2O1O2N2M5L3M2N3L4M4Kl0TO;E7I6J7H9G:F=APUR4\", \"size\": [1280, 720]}, {\"counts\": \"WP^e0`0\\\\W1:E9K5K8H6K5K<E:E?Ab0_O5J3M2N1O1O100O001O10O01O001O00000001O01O00000O10000O1O100000000O100O1000O2O002N2N2N3L3N2N3L4M3Lj0VO<D6J7I5K8G;E=A;@RmP4\", \"size\": [1280, 720]}, {\"counts\": \"_hRe08\\\\W1T1SO8K5L3M3N3K4M3M2M3O2N2N2M3N3M3M4L:F8H6J3N2M101O1N1O1O1O1O1O001N2O010O1N10001O01OO2O0O1001O011M2O1O1O1O1O2N2N2N3M2N2N2N1N5dLSlN]2mT1K4L4L3M4K4M5J5L3L4L4L4L5J4L9EPeo3\", \"size\": [1280, 720]}, {\"counts\": \"`XRd08dW17D<G7J7J6I6J6M3N3M2N2O1O1O001O1N1O2M3N101N101N101O1N2O1O1O2N2N2O3L4L2N3M2N2N1O2N1O1O1O1O1O1O1O1O100O1O0100O10O0100O1O2O1N1O3M100O1O1O2N1RMdkNV2WU1L4M3L2N2N2N3M2N1O2N2N2N2M3N1O2N2M3N2N1N3N1N3N2M3M5K5J5K5EjmU4\", \"size\": [1280, 720]}, {\"counts\": \"f`Sd0:XW1b0D;G8I6L5L3J6N2N3N1O1O1O1N101O1O1N2O010O1O001O1O001O1O1O2N2N2N3M3M2N3M2N2N1O2N1O101N1O1O1O1O1O1O10O1O100O2O0O2O0O2N100O1O1O3M3oL]kN`2XU1I3M3M3M2N2N2N2N1O2N2N2M2O2N2N2N1N3N1N3N1N2O2N1N3M3M3N3L4K6J9F]]X4\", \"size\": [1280, 720]}, {\"counts\": \"UXWd0R1eV1>H5I8J4O1N2O1O1N2N2O1N102N0O2O100O1O001O001O1O1O001O1O1O1O1O2N1O2N2O1N2N2N2N1O2N1O1O1O2N100001O0O100O101O0O1O100O1O102RMXkN^2[U1J3M2N3M2N2N2N2O0O2N1N3N1O2N1O2N1O2N1O2N1N3N0O3N1O2M2O2M2O2M3M4L4L3M5J8F\\\\]S4\", \"size\": [1280, 720]}, {\"counts\": \"nW\\\\d0V1gV1;F5J6K3N2N2N2M3N2N2N2O1O1O1O001O1O1O001O001O001O001O1O1O002N1O2N1O2N2N2O1M3N2N2N1N3O0000O10000O10000O3N1O0O1O100O1O2N`0A3L2N3M3M2O1N2M2O1O2N1O2N2N1O2N1O2N1N3N1O1O2M2O1O1N3N1N3N1N3N1N4L4L3M3L7I_]n3\", \"size\": [1280, 720]}, {\"counts\": \"^h^d0d0kV1k0D4K6J4M3M3N3M1O2O10001M101N2O1N101N2O0010O01O001O1O002N1O1O1O100O2N2N2M4M2N1N3N2O0O1O1O1O10O01O11O2N1N2O0O1O10O01O2N105J>B3M3M3M2N2N2N2N2N1O2N1O1O2N2N1O2N1N2O2N1O2N1N2O2N1N2O2M2O2M2N4L4L3M3M5J`ej3\", \"size\": [1280, 720]}, {\"counts\": \"no_d0T1iV1:F6K5L3M2M3N2M4N0011O1OO1O100O001N2N101O001N101O00100O1O1O1O1O101N2N1O3M2M3N2M2O1O2O0O100O1N2O1001O000O100O1O010O1O2O4K<D4M2M2N3M2N2N2N2N1O2N1O1O2N1N2O2N1O1O2N1O2M2O1O2M2O1N3N1O1N3M2O2M4L4L4L4Kb]i3\", \"size\": [1280, 720]}, {\"counts\": \"kgcd0Y1dV18I5L3K4L5N1O13M1OO1O1N2L3N3N2O1N2O1O001O1O001O001O001O1O1O1O1O1O2N2N2N2N2N1O2N1O2N1O101N1003M2NO0O2O010O10O1O100O100O3M<D6J3N2M2N2N2N2N2N1O1O2N1O1O2N1O2M2O1O2N1O2M3N1O1N3N1O1N3M2O2M3N2M5K4L3L7Gbee3\", \"size\": [1280, 720]}, {\"counts\": \"\\\\`gd0l0hV1`0F8K4L5J5M3N2M3O2M101N2O1N2O001O1O1O001O1O001O001O00100O1O1O1O2N3M1O3M1O2N2N1O2N1O2N1O1O2O01O0O10000O101N1O2O1N1O1O1O100O3M`0@5K4M2M3M2N2N1O2N1O1O2N2M2O2N2N1O2N1N3N1O1O1N3N1O2M3N1N3M4L4M2M3M6GaUc3\", \"size\": [1280, 720]}, {\"counts\": \"Sihd04]W1e0D:G6I8I6L5K4M3M3M3N2N2N2O0O2O1N2O1N101O1O1O1O001O001O001O101N1O2N2N3M2N1O2N2N1O2O1N100O1O010O10O10O0101N2O1N1O2N10O01O1O2N104mLVkNf2[U1G5K4L2N2N2N2N2N2N2N1O2N2M2O2N1O1O2N2M2O1O2N1N3N1N3N2M3M4L4L4L8FXma3\", \"size\": [1280, 720]}, {\"counts\": \"Rihd0<ZW1?D9J6G8K6K4L4M3M3M3N2N2O1N101N2M3N2O001N2O1O001O010O001O1O1O2N2N2N3M2N2N101N2N1O2O00O01O1O001000O10O0101O1N2O0O1O101N1O1O1O1O3Mc0^O4K4L2N3M2N2N2N1O2N2N1N3N2N2N1O2N1O2M2O2N1N3N1N3N2M2N4M4K4L4KXUc3\", \"size\": [1280, 720]}, {\"counts\": \"c`gd0Q1hV1:F9J6L3L4M3N3M2O1N1O2N2N2N101O1O1O001O1O001O1O001O1O001O1O1O2N2N2O1N3M1O2N2N1O1O2N1001O1N01O100O001000O100O2O1N1O1O101N1O4Lb0^O4L3M3N2M2N2M2O2N2N1O2N1O2N1O2N1O2M2O2N1O1N3N1O1N3N2M2N4M2M4K5L5JU]d3\", \"size\": [1280, 720]}, {\"counts\": \"\\\\Ped0T1fV1=F7I4M3N3M2O1O1O100O1O1O1O1O001O1O001O001O00001O001O100O1O002N1O2M3N2M4M2N2N1O2N1O1O2N1O1O1O1O1O010001N3N1O0O101N2O5nLSkNe2YU1K3M2N2O1N2N2N2N2N1O2N2N1O1O2N1O1O1O2N2N1N2O2N1O1N3N1N3N1O1N3N1N3M4L3N3K5K5KT]d3\", \"size\": [1280, 720]}, {\"counts\": \"YPed0m0oV1`0A9H5K3N2O2O0O1O1O1O1O1O10O01O1O001O1O010O0010O01O001O1O1O1O1O1O1O2M3N3M2O1M3N1N3N101N1N2O1N21O000O10O10O10000O2O1N6J;F2M2N2N3M2O1N1O2N1O2N2N1O1O2N1O1O1O2N2N1N2O2N1O1O2M2O2M2O1O2M2N3M3N2M4L4L4K6HTUc3\", \"size\": [1280, 720]}, {\"counts\": \"WPed0W1eV1<E6K4L3N200O100O1OQNWjNi1gU18O2N2O1O10O0100O010O001O0O2O010O1O1N2O1O1O2N2N2M3N2N2N2N2N1N2O2N1O100O1O100O11N2O0O100O100O100O5L=B3M3M2N2N2N1O2N2O1N1O2N1N3N1O1O2N1O1O2N1O2M2O1O2M2O2N1N3N1N2O2M3M4L4L3M4K5JV]d3\", \"size\": [1280, 720]}, {\"counts\": \"cPed0?TW1P1YO:G4L4N2O2M2O1O1O1O1N2O1O001O001O1O001O001O00001O1O1O001O101N1O2M3M4M2N2N2N2N1O1O1O2O0O10000000O102M2O000O100O2O1N5PMSkNc2[U1J3N1N2N2N2N2N1O2N2O0O2M2O1O2N1O1O1O2N2N1N3N1O1O1N3N2M2O1O1N3N1N3M4L4L3M4K5KT]d3\", \"size\": [1280, 720]}, {\"counts\": \"[Pod0k0QW1c0]O;G3N3L3N2N2O1O1O1O1O001O1O1O1O001O010O1O001O1O001O001O1N101O2N1O2N2N3M2N2N1O2N1O1O1O2N100O11O1O1O0O10000O100O2N104K>C2M2N2N2N2N2O0O2N1O2N2N2N1O1O1O1O1N3N1O2N1O1N3N1O2N1N3N1N2O1N2O1N3N2M3M4L4L4J7HRmW3\", \"size\": [1280, 720]}, {\"counts\": \"ZP^e0U1fV1`0C5K5K3N2N2N200O1O1O001N2O1O1O001O010O1O1O001O001O001O001O001O1O2N1O2N3M2N2N2N1N2O1O2N100O10001O01N2O1O0O10O02O0O2O2M=C5L1N2N2N2N2O1N1O2N2N1O2N1O1O1O2N1O1N3N2N1O1O1O2M2O1O2M2O1O1N2O2M2O2M3M3M4L3M4KSmh2\", \"size\": [1280, 720]}, {\"counts\": \"[Phe0d0WW1a0A>D7H5K4N1O1N3M101O1O1O1O001O1O001O100O1O001O001O001O001O001O1O1O2N2N2N2N2M3N1O2N1O1O2O0O1O100O11O1O002N0O100O1O101N5mLUkNg2XU1J5L2M2O1N1O2N1O2N1O2N1O2N1O1O2N1O1O2M2O2N1N2O2N1O1N3N1O1N2O1N2O1N3N2M4L3N3K4L6GSe]2\", \"size\": [1280, 720]}, {\"counts\": \"bhPf0;ZW1o0YO8H8I4M3M2N2O1N2O1O1N2O001O1O00100O00100O010O1O001O001O1O1O001O1O1O2N2N2N2N3M2N1O2N1O1O1O1O100O1O1001N101O1O000O101N5K=C4L2O1N2N2N2N2N2N1O2N1O2N1O1O1O1O1O2N1N3N1O2N1N2O2N1O1O2M2O1N2N2O2M3N2M4L3L5L4JTmT2\", \"size\": [1280, 720]}, {\"counts\": \"QXXf0[1bV19G7J4M2M3M3N2N2N20O01O1O1N101O1O1O1O00010O001O00001O100O1O1N200O2N1N3N3M2N2N2N1O2N1O1O1O1O100002N1N2O1OO0101OO02O0O3N9F8H3M2N3M2N1O1O2O0N3N2N2N101M2O1O2N1O1O2N1O2N1N2O2M2O1O2M2O1O2M2O1N3N3L3M4K4M6HYen1\", \"size\": [1280, 720]}, {\"counts\": \"c`Yf0?oV1j0@<J4L4K4M3N2N2N1O2N2O1N3N001O001O001O10O01O0010O0100O1O1O001O1O1O2N2N3L3N2N1O2M2O2N1O100O2O000001O0O100O3N0O100O1O100O1Ob0_O3L2N2N3M2N2N2N1O2O0O2N2N1N2O2N1O2N1O2N1O2M2O2M102N1O2M2O1O2M2O2M3M4L4L3M5I`]m1\", \"size\": [1280, 720]}, {\"counts\": \"ogUf0U1dV1<G7J4L5L3O1M3N2O1N2O1O001O1O1O1O1O001O0010O01O001O1O001O001O2N1N3O1N3M2N2N2M3N101N1O1O1O1O1000O11O01N101O1N2O0O1O100O2Nc0]O3M3N1N2N2N2N2N1O2N1O2N1O2N1N3N1O2N1O2N1O2M2O1O2N1O2M2O1N3N2M2N3N2M4L3M4K7Ec]R2\", \"size\": [1280, 720]}, {\"counts\": \"kWne0X1cV1:G6K5K4M3N2N2O1N2O1O001N2O1O001O10O01O1O1O0010O0001O001O1O1O2N1O3M2N3M2M3N1O1O2N1O1O1O101N1O1000O2O0O2O001N100O100O2N2O?@5K4L2N3M1O2N2N2N1O2N2N1O1O1O2N1O2M3N1O2N1N3N1O2M2O1O1N2O2N2M3M3M4L4L4Kf]\\\\2\", \"size\": [1280, 720]}, {\"counts\": \"^hfe0>mV1l0C:I5L4K4M3N2N2O1N2O1N101O1O001O1O1O1O001O001O10O010O01O0O2O2N1O2N2N3M2N2N2N2O0O1O1O2N1O1O010O1O1000O2O1O1O0O1O2OO01O2N2N5kLZkNg2TU1K4L3M2N2N2O1N2N2N1O2N1N2O2N1O2N1O2M3N1O2N1N3N1O1O2M2O1N3M3N2M4L4K5Lgmc2\", \"size\": [1280, 720]}, {\"counts\": \"Yhae0j0eV1e0G7K5J5M3N2N2O1O1O1N2O1O001O1O001O1O1O00100O00001O001O1O1O1O1O2N2N3M3L3N2N1O1O2N1O1O1O101N1O10O101N100O2O000O100O101N2Nb0^O4L3M2O2M2N2N2N2N2N1O2N1O2N1O2M2O2N1O1O2M2O2N1N2O2N1N2O2N2M2O2M3M5K4K5Jfmh2\", \"size\": [1280, 720]}, {\"counts\": \"iW_e0\\\\1aV17I5K4N2M3M4N1N101O1O1O1O10O01N2O1O10O01O001O1O010O001O1O1O100O2N2N3M2M4M1O2N100O2N1O1O1O1O1001O1O0O011N010O100O1O2O2oL^kN^2UU1N1N2N2N2N2N2N1O2N2O0O2N1N2O2N1O1O2N2N1O2N1O1N3N1O1N3N1N2O2M2N3N3L4L4L4K8Eb]k2\", \"size\": [1280, 720]}, {\"counts\": \"m_`e0W1cV1;G6L3M3M3O1N3M101N2O1O1N2O1O001O100O001O1O0010O10O001O001O1O1O2N2N2N3L3N2N1O1O2M2O100O2N10000001O0O100O100O010O100O2N3oLVkNg2[U1F4L2N2N2N2O1N2N1O2N1O2N1O1O2M2O2N1O1O2M2O2N1O2M2O1O1N3N1N3N1O2M4L4L3M5IfUj2\", \"size\": [1280, 720]}, {\"counts\": \"^Xde0b0kV1i0E9I5L3M4L3N3L201O1O1O1N2O010O1O1O1O001O001O0010O01O001O1O001O2N2N1O2M3N2N2N2N1O2N1N2O2O0O11O0O10001O000O2O000O1O100O2N3M`0A3L2N2N2N2N2N2N2N1O2N1O1O2N1O1O1O2N2N1O2N1N2O2N1N2O2N1N2O2M2O3L3M3N3K5L4Jdmc2\", \"size\": [1280, 720]}, {\"counts\": \"lWie0V1fV1;G6J4K5M2N2N2N2O1O1O0O2O1O1O1O001O1O1O001O010O001O1O001O1O1O1N2O1O2N2N2N2N2N2N1O1O101N100O10000001O001O1N100O00101N2N?A5L1N2N2O1N2N2N2N2N1O2N1O1O2N1O2N1O1N3N1O1O2N1N2O1O2M2O1O1N3N1N3N1N3M4M3L3L6Jam^2\", \"size\": [1280, 720]}, {\"counts\": \"ogke0S1hV1<G6I5K5M2N3M2N2O001O1O1O1O1N101O1O001O10O01O1O001O1O001O1N101O1O1O1O2N2N2O1N1N3N2N100O2O0O10010O00O2O00000O1O100O102M:F7J3L3M2N2O1N1O2N2N1O2N100N3N1O2N1O1O2N1O2M2O1O2N1N102N1N2O2N1N3N1N3N2M4L3M3M5IaU[2\", \"size\": [1280, 720]}, {\"counts\": \"VPme09]W1P1XO8G7J4M3L3O1O100N1O3L3O1O001N2O001O1O00100O001O001O1O001O1O001O1O1O2N2N2N1O2M3N1O2N2O0O1010O0000O11O000O100O1O100O102M:G8G2N3M2N2N2O1N1O2N101M2O1O2N1O2N1O1O2N1O2N1N2O1O1N2O2N1O1N3N1N3N1N3M3N3K4M4L4J`eX2\", \"size\": [1280, 720]}, {\"counts\": \"oWne0P1lV1?C6I6L2M3O1O2OO01N2M3M3O1O001O1O001O1O1O001O0100OO2O1O001O1O1O001O2N2O0N3N2N2N2N1O2N1O2N101O0000O101O000O100O011N101N4L`0A1N2N2N2N2N2O1N1O1O1O2N1O1O2N1O2N1O2N1O1O2M2O1O1N2O1O2M2O2N1N2O1N3N2M3M4L3M3L7H_]W2\", \"size\": [1280, 720]}, {\"counts\": \"mole0Y1dV18I4L5K3M3M300001OO100N2M2O2N2O1O001O1O1O001O010O10OO2O001O1O1N2O1O2N2O1N2N1O2M3N1O2N1O1N3O0001O00000O101OO0100O101N3M;F7H2N3M2O1N1O2N2N1O100O2N1O2M2O1O2N1O1O2N1O2M2O1O1N2O1O1O2M2O2M2O2M2O2M4L3M4L4JbmY2\", \"size\": [1280, 720]}, {\"counts\": \"lgfe0Y1cV17J7J3M4L3O1N2O1N2O1N2O001O1O1O1O0O2O1O010O001O001O1O001O1O1O1O1O2N1O2N2N2M3N2N2O04LO2M2N2N2N1011O00001O001N100O1O101N2N;E6J5L1N2N2N2N2N2N1O2N1O2N1O2N1O1O2N1O2N1N3N1O1O1O2M2O1O2M2O2M2N3N2M3M4L4K4Ld]a2\", \"size\": [1280, 720]}, {\"counts\": \"\\\\h\\\\e0d0hV1k0F6K4K4M4M2N2M30O01N2O1O1O1N2O001O1O1O010O001O001O001O1O001O1O2N2N1O3M2N2N2N2N1N3N1O1O1O1O100002N1O1N10000O1O010O2O0O3M?A6K1N2N2N2N2N2N1O2N1O2N1O2N1O1O2N2N1O1N3N2N1O1O2M2O1N2O2M2O1N3N2M5K3M4L4Jeel2\", \"size\": [1280, 720]}, {\"counts\": \"_`Qe0c0jV1h0E8J6L3L4N2N2N2O1O1N2O1O0O2O1O100O1O00100O1O00001O00100N2O1O1O2N3M2N2N2N2N1O2N1O1O2N1O1O1O1O1002N1N2O0O100O010O1O2O0O4La0@2M3M2N2N2N2N2N1O2N2N1O1O1O2N2N1N3N1O2N1O2M2O2N1N2O1N3N1O2M3M4M2M4K5Kf]Z3\", \"size\": [1280, 720]}, {\"counts\": \"Rhhd0n0gV1?F8K5K4M3O2M2N101N2N2O1N2O010O1O001O1O1O1O001O1O001O1O100O1N2O2N3M2O1N2N1O2N1O2N100O1O1O1O100O1000O2O0O2O1N100O1O102M3PMWkNc2YU1J4L3N1N2N2N2N2N1O2N2N1O2N1O2M2O2N1O2M2O2N1N3N1O2M2O2M2O2M3M3N3K5L4L6FgUc3\", \"size\": [1280, 720]}, {\"counts\": \"f`bd03]W1i0]O<E;J6K4L4M3N2N101O1O1N2N2N2O1N1O2O001O1O001O10O0100O1O1O1O1O2N3M2N2N2N2N2N1O100O2N1O1O1O02O10O0O0100O2N2O0O1O100O1O1O3Nb0]O4L3M3M2N2N2N2N2N2N1O2N1O2N1N3N1O2N2N1N3N1O2M2O2M2O1O2M3M3M5K4L6Hhej3\", \"size\": [1280, 720]}, {\"counts\": \"h`Sd0:ZW1b0C;G6I7K5L4L3O2M3N1O2N2N101O1O000010O0001O0O2O1O1O0O101O001O000010O01O001O001O001O010O1O1O1O2N1O2M2O1O1O1O010O11O002N2N001O1N3N2N5J4M2M2O0O2O1O1N2N2N1O2O0O1O2O0O1O2O0O1O1O002M2O1O1O1O1O001O1O1N2O1O1N2O1O1N2O1O1N3M2O2M2O2M2N3L4M6Gi]_3\", \"size\": [1280, 720]}, {\"counts\": \"k`nc03`W1e0^O=A>K4K5L4N2M3N2N101O001O1O010O0O2O001N100O2O001N2O001O0O101O01O010O0001O001O001O1O010O1O1O2M2O1O2N100O100O1001O1N2O0000O02O2N1O5J6K2M3N1N101N1O2O1N2O0O2N1O2N1O100O1O1O1O1O1O1O1O1O1O1N2O1O1O1O1N2O1O1N2O1O1N2O1O1N3M2N3N1N3M3L4M6Fl]d3\", \"size\": [1280, 720]}, {\"counts\": \"Zhjc0h0mV1?C;I6L4K5N2O1N101O10O01O001N1O2N1O2O1O0O2N101O00001O001O00001O000O1010O01O00001O001O00100O1O1O101N10000O2O01O1O0000000O101N102N3L5L3L2O1N2O1N101N1O2O0O2N100O2N1O1O1O100O1O1O1O1N2O1O1O1O1O1O1O1N2O1O0O2O1O1N2O1N2O2N1N3M3N1N3M2N3L6IPVh3\", \"size\": [1280, 720]}, {\"counts\": \"ehjc05[W1h0@:F9J7I6M3N2N2N2O1O010O001O0O2N2O0O2N1O2O0O101O001O001O00001O00001O00001O00001O1O001O001O100O1O1O101O00000100O00000O10O10O2O1O1N6K4K2O2M2O0O3N1N2N100O101N1O1O2N100O1O1O1O1O1O1O1O1O1O1O1O001O1N3N1O0O2O1O1N2O1O1N2O1N3N2M2N3M2N3M3M5JPnf3\", \"size\": [1280, 720]}, {\"counts\": \"^Xmc0a0SW1`0G6K6G8K5K5L4N2N2N1O1O2O010O1O0O2O1N1O1O2O000O2O001O01O01N101N11O01O00001O001O010O001O1O001O1O100O2N11O1O0000000000000O10000O2O2N3M5J4M2M2O2M2N2O1N1O101N1O1O2N1O1O1O1O1O1O100O1O1N2O1O1O1O1O1O001O1N2O1N101N3N002M2O1N3N1N3M3M2N4KUnf3\", \"size\": [1280, 720]}, {\"counts\": \"WPlc0f0QW1>G5H8I8I6N2N2M201N2N101O001N101N1O2N1O2N1O101N101O001O00001O00001O00001O00001O00001O100O001O1O1O1000000001O1O0000000O01000O10002M2O4L3L3M4M2M2O1N2N2O1N2N100O1O1O2N1O1O1O1O1O1O1O1O1O1O1N2O1O1O1O1O001O1N2O1O0O2O1N2O1N3N1N3N1N3M2N4KYnf3\", \"size\": [1280, 720]}, {\"counts\": \"PPlc0n0lV1:C:H8L4M3N2N2N2O001O1O001N1O1O2O0O2M3N100O2O001N110O00001N11O01O00001O00001N101O0010O01O1N2O1O1O10000O1101N1O1N1000O010000O10002M5L3L3N2M3N2M2O1N3M2N1O101N1O1O1O1O2N1O1O1O1O1O1O1O1O1O1O1O002M2O001O1O1N2O1O1N1O2O1N3N1N3N1N3M2N3M4JYnf3\", \"size\": [1280, 720]}, {\"counts\": \"aXmc07]W1b0D7K7J4K5H9G8N2M2O2N2N101O010O001O0N3N1O2M2O2O001O1O000O20O000001O00001O001O001O00100O1O1O1O1O1O2M2O1003M2N00O10000000O010O101O1N102N7H4M3L3M3N2M2N2N1O101N1O1O1O2N1O1O1O1O1O1O1O1O1O1O1O1O1O1N2O001N2O1O1N2O1O1N2N2O2M2O2M2N3M3L8FUnf3\", \"size\": [1280, 720]}, {\"counts\": \"moPd0;aW17F:G7K6K4L5L3L4L4M3L4L4N2N2N2O1N2O001O10O010O100O10N2M2N3M3N2N101O2M2O2N1O2O1O1N2M2O100O1O1N2O1O1O100N2O1O001N2O001000O3N2M3N2M3M3N1N1O1O2N2Ob0]O;E4L2N2O1N2N2N2N2N1O2N1O2N2N1O2N1O1O1O2M2O2N2N1N3N2M4L3M4K5IZVR4\", \"size\": [1280, 720]}, {\"counts\": \"aclb0b0[W16J5J6J6M4L3M3M3L4J6L4L4K6J4O3L3O0O2O0O2O1N2N2N2M3N1O2N101N2N1N3N1O1N2O1O001O1O001O]LRlN0Nh2oT1L1000004K4M2M3N0O2N2N2O1N3M2N3M3L3N2N1O3M2M3N2M2O3L3M4L5I8H^R\\\\6\", \"size\": [1280, 720]}, {\"counts\": \"`Wo`0<8EUW1g0K4M1101O1O2K3O101L4M3K9HSWc:\", \"size\": [1280, 720]}, null, {\"counts\": \"jcbb0b0VW1<J4L4J6K6K5K4N3N1N3N2O0N2M4L5K4L4L3M3M4K4M4L3N3M2N1O2N3M2N2O001M3N2N1O1O1O1N2O1O100O100O001O0010O01O01nKnlNT3TT1K2O1N<D5K4L5K3M3M3M2N2N2N2N3L4M3M3K5L3N3K5L5J7I9E\\\\i_6\", \"size\": [1280, 720]}, {\"counts\": \"QQ`d06`W1?C:I7I7J5L4J6K5M3N2N2N3N1N20O0100O10O10O11N01O1O0E<O1N2O001O0000100N2O1O1O2N1O1O2N2N2O1N1O1O2N1O1O100O1O1O100000000O02O001N10000O010O1O2O1N3M2O2M5K;F5J5K2N3M2N2N2N3M2N2N1O2N1O2N1O1O2N2N1O1O2N1O2N1O1N2O2N2M2O2N2M2N4L3M4KXUY3\", \"size\": [1280, 720]}, {\"counts\": \"ghcd0P1iV19G:E9K6L3M2O2N2O1N3N1O001O1O0O2O001N101O001O1O001O001O001O001O001O1O00100O2N1O2N3M2N1O2N2N1O100O1O100O1O1O11O001O2N1O1N100O1O101N4M;D5K3N1O2M3M2N2N2N1O101N1O1O1O2N1O1O2N1O1O1O2M2O2N1O1O1N2O2N1N2O1N2O2M2O2N1N4L4L3L5K5GTeV3\", \"size\": [1280, 720]}, {\"counts\": \"VQed0e0QW1<F:I6I7J6K5M4M2M2O2O1O1N2O1O1N101N101N2N1O2O001O001O001O0010O01O1O001O002N1O1O2N2N2O1N1N3N1O2N001O1O2N10O100000O11O2M3N1N10000O10O02N2O0O5K:F8H4M2M3M3M2N2N101N1O1O1O2N1O2N1O2N1O1O2N1N2O1O2N1N2O1O1O2N1N2O1N3M3N2M4L3M2L5KQUT3\", \"size\": [1280, 720]}, {\"counts\": \"QQed0Q1aV1b0G7K5K4M4M2N2N2O1N2O00100N2O0O2O001N101O1O001O001O001O001O00001O100O001O1O1O1O2N2N2N2N2N1O2O0O1O1O101N100O1002M3N1O0O10000O01001N2O4jLXkNj2TU1L3L3M3N2M2N2N2N2O0O2N1O1O1O2N1O2N1O1O2N1O1O1O1O1O1N2O1O2M2O1O1O2M2O1N2O2M3M2N3M3M4L4KfTT3\", \"size\": [1280, 720]}, {\"counts\": \"`Qed0h0lV1`0E:F8J7L3M3N2N2N2N2O100O1N1O2N101N10001O1O001O1O001O001O001O001O000010O01O1O2N1O2N2N3M2N1O2N1O100O10000O1001O1O2N1O0O010O10001N2O8G;E3N1N2N2O1N2N2N101N1O2N1O1O2N1O1O1O2N1O1O2N1O1N2O1O1O1O2M2O1O1N2O2N1N2O1N3M3N2M3M3L5L5I_TT3\", \"size\": [1280, 720]}, {\"counts\": \"Wagd0U1aV1>E8K6L3M3N2O1N2O100O1N2O1O0O2N101N2O001O001O001O1O001O001O00001O00001O1O002O0O2N1N3N2N2N1O2N100O100O100001O1O1O1O0O1000O0101O0O3N;D4M2M3M2O1N3M2O1N2N2N1O1O2N1O1O1O1O2O0N2O1O1O1O1O2N1O1O1O2M2O1O1N2O1N2O1N3N2M3M2N3M3M4L3KadQ3\", \"size\": [1280, 720]}, {\"counts\": \"nhmd0\\\\1[V1<I7K4L3N2N2O1O001O1O1O1N101N2O00001O001O1O001O1O001O00001O001O00001O001O1O1N101O2N1O2N2M4M1O2O00000O11O1O1O001O000O010000O02O1N3N>A4M1N1O2O1N3M100O2N2N1O1O100O2N1O1O1O2N1O1O1O1O1O2M2O1O1O1O002M2O2M2O1N2O1N2O2M3M3N2M3L5L6GaTj2\", \"size\": [1280, 720]}, {\"counts\": \"TQTe0l0gV1e0B9J5L4M2N2O1O1O1O10O01O1N2O001N101O001O001O1O1O001O001O001O001O0000001O1O001O2M2O2N101O100M2O1N3N1O10000001O001N0100O010001O1N5L7H6K2M2O1N2N2N101N2N1O2O0O1O1O2N1O1O2N1O1O1O1O1O1O1O1N3N1O1O1N2O1O2M2O1O1N3N1N2O2M3M3M3M3M3Lflc2\", \"size\": [1280, 720]}, {\"counts\": \"khcd0e0VW1;G9H4J6H7N2N3M2N2N2M3O1N2N2N2O1N2O1O001N2O001O0O2O1O001O001N200O1O1O0O2O1O2N1O1O2N2N101N1O100O1O1O10O01O1001O0O200N101O000O10O10O100O010O101N2N5L8G4L4L4M2M3M2O1N2N2N1O1O101N1O1O2N2N1O1O1O2N1O1N2O1O1O2N1O1O1O1N2O2N2M2O2M3N1N2N4K5K6Iglm2\", \"size\": [1280, 720]}, {\"counts\": \"S]Pc0l08K`U1k1@9CZMVkNm2cT1=L3O2AdLTlN^3iS1>O1M3N2O1O011O0O1O1O1O01000N1O2N20O1000OlKilNB4b3SS1hLZmNV3gR1gL\\\\mNY3fR1cL\\\\mN\\\\3fR1aLbmNX3lS1G`0@6J3M3M3M4L3L4M2N3L4L4L3M4L4K6J=A_hS7\", \"size\": [1280, 720]}, {\"counts\": \"e_`c01XR:?XUG2N1O11N2O0O2O3M1Neml7\", \"size\": [1280, 720]}, null, {\"counts\": \"_QTc0b0`19bS1KWlN:hS1GPlNa0oS1AlkNd0RT1@ikNb0VT1\\\\OlkNf0ST1XOnkNk0PT1TOQlNo0jS1ROVlNP1hS1POXlNS1eS1mN[lNV1bS1jN^lNX1`S1iN^lNZ1aS1eN_lN^1]S1cNclN`1ZS1\\\\NQlN\\\\Ob0\\\\2[S1WNklNj1TS1XNjlNj1TS1WNklNj1[S1mMglNT2ZS1gMilN[2ZS1^MglNe2ZT10O100O2O0O10O010O010O0100O01O1O100O1O2N1O2O0O7I3M3M3M2N4K5L4L3L5K6H?@?@bZg6\", \"size\": [1280, 720]}, {\"counts\": \"P`Sd0a0WW1>G7I6K4L4L4L3O2M2O1N2O2M2O1O1O2O0O1O001O1O001O1O1O0O2O1O1O001O1O1O1O1O2N1N3N2N101N1O2N1O2N1O2N1O2N2O0O1O1O1O1O2O0O2N1001N4M1O001O0O2O1N2O1O3M5K6I4L3N2M4L3N1N2N102M101N1O2N1O2N1O2N2N1O1O1O2N1N3N1O1N3N1O2M3N1N3M3M3M3M3M3L5K[]d3\", \"size\": [1280, 720]}, {\"counts\": \"UiRe02`W1h0B<F:G6J4M1O2N2N1O1O1O1O001O1O1O001N2O1O1N2M3N2M3N1O2O1O10OO3N1O1O10001M3N2N2N2N2N2N1O2O0O100O1000O101O1O0O1000O10O101N4L8I7H3M3M3M2O0O1O1O2N2N2N1O1O2N2N2M2O1O2N1O2M2O2N1N2O2N1O1N3N1N3N2M3N1N3M4L5J6JdlR3\", \"size\": [1280, 720]}, {\"counts\": \"Wa[e0e0lV1c0E9J6M2N2N3M2O1N10100O1O1O1O1O010O100O001O001O1O1N1O2N3M2M3M201O2N1N3N2N2N2N1O2N2N101N1N2O1O100N2O1O1N2O001O001O1O000011N6J3N1N2N4L8H6J4M5J4L4L2N2N2N1O2N2N1O2N2N1O2M2O1N3N1O2M2O1O2N2M3N1N3N2M4L4L3L6J7Hglh2\", \"size\": [1280, 720]}, {\"counts\": \"Taoe04iW17J3L5K4M3L5L3M3L4M4M1N3M3M3N2M3M3M3M3N1O2N2N2N1O2O2M101N2O1O1O2N2N2O1O0O2O1O010O001O0O2N1O1N2N2O0O2M3N101M3O001O001N1010O010O010O01O001O000O1010O1O0000102M2O0O4_LdkNV3dT1O2N3XMdkNf1bU1J2M2N3M3M2N3M2N3M2N2N2M3N2M4L4L4L5HSeS2\", \"size\": [1280, 720]}, {\"counts\": \"f`oe0o0lV19I:G4J6N1O101N1001OO100O100O1O00001O1O001O0O1O2O001N101O001O0O2M3O0O2O0O2O001O1O002M2O2O1O0O2N1N2O101N1O1N2002N00O11O0O2O3M5K3L4M0O10000O2O3L2N1O2O1N1O2N10O0100O1O002N2N1O1O2N1O001O2N2N1N2O1O1O1O1N2O001O1N3N2M3M2O2M3M3M3M2N2M4Kodd1\", \"size\": [1280, 720]}, {\"counts\": \"``^f0W1dV1;G6K4M2O2M2O1O1O12O1N000O1000O010O010O000010O00O2O0O2O0O2O001N1N3N1N3N2N1010O10N2N2N201N1O2N1O1O2N1O1N2O1O2N100O11N3N1N2O5K3L4M3L1000000O2N2O1N2N101N2N100O1O100O1O1O1O1O1O1O2N001O1O1O2N1O1N2O1O2N1O1N101O1O2M2O2M2O2M2N3M3M3M2N3LReU1\", \"size\": [1280, 720]}, {\"counts\": \"RYgf0i0nV1<E:K5K4L4M3M3M3N2N101N2O001O0010O10O12M3N0O10O010O001N101O0M4K4M3O2N1O2N2O1O1O1O001O100O1O2N1O2O000O1O1O1O1O1O1O001003M2NO1O0O20O010O3N1N2O1N2O4K5K6J102M2N2N2N2N1O1O2O2L2O1O1O1O1O1O1O1O1O1O2M2O2N1O1N3N1O1N2O1O1O1O2M2O2M2N3M2N4L3L5JT]j0\", \"size\": [1280, 720]}, {\"counts\": \"_hif0S1jV1:H5K3N1O2M200O100O100O1O010N101N101O1N2O0N3O001N101O0O110O000O2O001O01O01N101O1O0O2O1O1N3N1O2M3O0O1O1O1O100O1O10000O20N2O1N4M6I3N1O1N2O1N101N101N1O2N101N1O1O1O100O2N1O1O1O1O1O1O1O1O001O2N1N2O1O1O1N2O1O1N2O1O1O1N2O2M2O1N3M3N1N3M4K5JTmg0\", \"size\": [1280, 720]}, {\"counts\": \"\\\\Xlf0X1cV1<F4M4M2O110O000000O1000O1N101O0O2O0O2N101N1O2O1O1N10001O00001O00001O01O01O000O110O1O1O2M3N2N1O2N1O1O100O1O1O1O1O100000O3N2N4L4K3N1O1N2O001N2O0O1O2N1O2O0O1O100O2N1O1O1O1O1O1O1O1O1O1O1O1O1N2O1O1O1O1N2O1O1O1O2M2O1N2O1N2O1N3N2M3L4M3L6IV]e0\", \"size\": [1280, 720]}, {\"counts\": \"h`mf0733nV1o0^O;I6K4M3O1O1O1O1O1O1O1O1N1O2N1O2O001N101O0O2O001O001O00001O001O001O0O2O00010N101O1O1O1O2M2N3N1O1O110O01O0001O00O2O000O10000O3N2N4L2N2M4L2O1N3M2O1N1O2N100O2O0O1O1O1O2N1O1O1O2N1O1O1O1O001O2M2O1O1O1O1N102N1N2O1O1O1N3N1N3M102M3M3M3L5KZUd0\", \"size\": [1280, 720]}, {\"counts\": \"_ahf08WW1k0YO?K4L5L3M3N11000O1O1N2N2N1N3N1O2N101N101N101O1O001O001O001O001O001O001O1O1O001N101O1N2O1O2M2O1O110O0001O00O2O0001O0000000O101N2O1O7H4L3N2M2O1N2O2M1O2N2O1N1O2N001O100O1O2M2O1O1O1O2N001O2N1O1O2N0O3N1O1O1N2O1N2O1N3N1N3N1N3M3M3M2M5JQ]j0\", \"size\": [1280, 720]}, {\"counts\": \"ji_f07XW1i0[Oa0I6K3L5M2O1N2O1O1O1O1N101O1N1O2O0O2O1O001O001N2O1O001O00001N10001O00001O001O1O1O1O2O1O1N1N2O1O1N3O000O100O1001O1O1O000O10001N3N1O5K3L4L3N1N2N2O1N2N2O1N101N2N1O1O1O1O2N1O1O1O001O1O2N1O1N2O2N1O1O1N2O1O1N2O1O1N3N1N2O2M3M3M3M3M4Ka\\\\T1\", \"size\": [1280, 720]}, {\"counts\": \"PbYf09TW1l0[O?K4L3K6M2N2O11O0000N2O1M2O2N2N1O101O001N101O001O1O0O2O0000010O001O001O1O1N2O1O1O1O2N1O1N3N1O1O2N10000001OO10000001O0000000O2O0O3N7H5L3L2O2M2N3N1N1O2N2O1N1O2N10O01O2N1O1O2N1O1O1O1O2N1O1O1N2O1O1O1N2O1O1O1N2O2M2O1N3N3L3M3M3L3M:DVdZ1\", \"size\": [1280, 720]}, {\"counts\": \"faTf0c0lV1g0E8J6G7M4L3N2N2O1O1O1O1O100O0O2O1O001O0O101O001O00010O0O2O1O001O00001O001O1O1O1O1O2N1O2N1O1O1O2N1O1O100100O001O000O1000O10O2O001O3M7H4L3N2M2O2M2N101N2N101N2N1O1O1O1O1O1O2O0O1O1O1N2O2N1O1O2N1O1O1N2O0O2O1O1N2O2N1N3N2M2N4L3M3M3L8GXd_1\", \"size\": [1280, 720]}, {\"counts\": \"Vaoe0l0jV1a0D7I7L4M2N2N2O1O100O1O1O010O1O0100O100O01O001N2O0O101O0O2O1O001O0O101O00100O0O2O1O2O0O2N1O2N1O101M2O1O100O101OO1000O1000O2O1O5K4L3L4M1O1O1N2N1O100O2N1O2O0O1O2N1O1O1O2N1O1O1O1O1O1O1O1O1O2N1N2O2N1O1O1N2O1O1N3N1N2O2M2O2M4L3M3L6Jble1\", \"size\": [1280, 720]}, {\"counts\": \"`Pme0\\\\1^V1;I3M4L3M3N2O1O1O1002M2ON2N2O1O0O2O0010O01O1O0102M01O0O1O2O1N101O0O2O1O1O1N101O1O1O1O1O2M2O101N1O1O1O101N1000O01001O00001O2N4L5J3N2M2N100O101O2M2N2N1O2N101N1O1O001O1O002N1O2N1O1O2N1O002N1N3N1O1O1N2O1O1N2O2N1N2O2M3M3M3M3M3M3KQei1\", \"size\": [1280, 720]}, {\"counts\": \"lXne0j0mV1;J6H7K5L4L4M3M4M2N2N2O1O1O001O0100O100O01O1O001O0O2N101N2N1O2O0O2O0O2O001N10101N100N3N1O2N1O2N1O1O100O100N2O1O04M10N001O1000O2O0O101O1N3N2M7I6J4M2M3M1O2N2N3M1O101N2N2N2M2O1O1O1O1O001O2N1N3N1O1O2N1N2O2N1O1N2O1N2O2M3N2M3M3M3L6JUUg1\", \"size\": [1280, 720]}, {\"counts\": \"PQWf09`W19I7I7J5L5K4M3L4L4L3N3M3N2M3L4N1O2N1O2O0O20O010O100O100000O010O1O010N2L4N2M3M4M2N2O2N1O1O2N2N1O2N1O2O0O1O1O1O10000O0O2O10O1O10N101O1O000010O011N6K3L2N100O2N?A:G5J3M2N2N3M3M2N2N3M1O2N1O1O1O1O2N1O1O2M2O1O1O2M2O2N1N3N1N3N3L3M4K6JX]^1\", \"size\": [1280, 720]}, {\"counts\": \"l`^f0:_W19J6J6J5L4M4K4M3M3L4M3N2M2N3M2N3N2N2N2O0O2O0O101O001O00_MgjN\\\\2XU1bMkjN^2TU1bMkjN_2UU1bMjjN_2[U110O01O101N001O1O1K6L3N2N3M2O2N1O2N2N1O1O1O1O1O1010OO0O2O10O10O1O001O10O10O00001O0011N2N2N5L0O011N?B4K>B3M2O0O2M5L2O1N3M2N2N1O1O1O1N2O2N1O1N2O1O2M2O2N1N3N2M3N1N3M3L5L\\\\eU1\", \"size\": [1280, 720]}, {\"counts\": \"f`cf0b0YW17J6J5K6K4L4M3L4M3M3N2M3M2N3N2N2O1O001O001O01O2O0O101N10M2O2O0O2O00100O1M2N3O1N2N3N1N2N2O2N2N1O2N2N2N10000O1O100O1O001O1O10O01O1O1O1O001O01O01O02O6I2O0O2N3M:G3L4L;F4K2N1O2O1N2N2N2N2N1O1O2N1O1O0O2O2N1O1O1N2O1O2M2O1O1N3N1N4L3N1N4L3L4L:EWml0\", \"size\": [1280, 720]}, {\"counts\": \"b`cf0a0YW19G8J5L5H7L4M4K4M3M2O2N2N2O001000O100O100O100O2O0O01O001M2N3N2O0O2O0O2O001O1N200O2O0O2N1O1O2N2N1O2N1O1O2N1O1N2O1O1O001O1O1O1O1002N1N101N101N6J7J2M3N2M4L3N2M2N2N2N2O0O1O1O1O2N1O2N1O1O1O1O1O2N1O1O1O2N1O1N2O1O1N2O1O2M2O2M2N4M2M2N3M4L6G`Un0\", \"size\": [1280, 720]}, {\"counts\": \"Shdf0h0mV1`0G:G5K5N2M2N2001O1O2M2O0O0100O0O2N101N10001O1O0N3N2N1100O01N1O2O000O2O0O2O1O100O1O1N2O2N1O2M2O1O2N1O2N100O10O1000001O1O2N1O1O4L2M3M2O1N1O2O1N1O100O2N1O2N100O1O1O1O2N001O1O2N1O002N1O1O1O2M101O1O2N1N2O1N2O1N3N2M2O2M2N3M3N2L5J9EheP1\", \"size\": [1280, 720]}, {\"counts\": \"\\\\Paf0`0nV1g0E8J6K5L3N3L3O1M3N2O1O0101O00O0010O010O2O0O01N101O1O0O2O1N110O0O2O0O2O001O001O1O100O1O2N2N1O2N1O1O1N2N2O1O2N1O010O1O100000O2O003M3M3L4L4M2M3M2N2O1N1O100O2N2N2N1O1O1O2N1O2OO01O1O1O1N2O2N1O1N2O2N0O2O1N3N1N3N1O2M2O2M2N3M3M3M3L8Fk]T1\", \"size\": [1280, 720]}, {\"counts\": \"oP\\\\f04aW1?H6H8I6K5L4L4L4L4L4M3M3N2M3N1O2O1O001O1O1O010O02N10N101M201O0O2O0O2O2M2O1N3N1O101N2N1O2N1O2N100O1O1O1O1O1O1O1O1O100O1O00001O010O02O0O4M3MO02O2M3M<D4M3L5K3M3M4M1N2N1O1O2N2N1O2N2N1O1O1O2N2M2O1O1O1N2O1O2N1N3N1N3N2M4L3M3M3L6I`m[1\", \"size\": [1280, 720]}, {\"counts\": \"UQRf0<]W1;F8H7L5K4K5K5L4M3M3M3M3N101O1O100000OO2O001O001O001N2O0O2N101O0O2N2O0O2O1O001O1O2N2N2N1O2N1O1O2N100N3N1O100000OO2O1N2O1O1001N10000O102M101N1O2N3N4K5K5L:E2N2O4K2N2N3M2N1O2N1O1O002N1N3N2N1O1N2O2N1O1O1N2O2N1N2O1N3N2M3M3M3N3L3K8DU]c1\", \"size\": [1280, 720]}, {\"counts\": \"Vaje0e0RW1<F9I7I6M3N2L4L4M2O2M3O1O1N110O1O001O02O2M2ON1O2N2N1O101M201O001O001O100O1O1O2N1O2O001N2O0O2N1O1N3N1O1O1O1O1O00100O1O1002N1O0O00100O2O1N3M;F2M4L3N3L3M3M3M101N1O1O1O2N1O2N1O1O2N1O1O1O2N1O1N102N1O1N2O2M2O2N1N2O2M3N2M3M3L3N6Hm\\\\m1\", \"size\": [1280, 720]}, {\"counts\": \"ZY_e0=ZW1?_O=I6J7K4J6M3L4N1O2O1O1O1O1O1O0010O011OO00001O0N20001O001O1O001O010N2O1O1O1O100O1O3M2N2N1O2O01ON2O1O1O1N2N200O1001O1O00000O10000O1O2O1N;F3L3M5L2M3M3N1N2N2N100O1O1O1O2N1O1O2N1O1O1O2N1O1N2O1O2M2O1O1O1N3N1O1N3N1N3M3N2L4M3M3KPeX2\", \"size\": [1280, 720]}, {\"counts\": \"]Ykd07_W1>D;H6K6J6L3L4L4L4K5L3N3N2O001O010O1O00100O001O001O1O001O0O100O2O001O001O001O1O0O2O1O2N1O100O2N1O1O1O10000N2O1O1O2N1O100O11N2O1O1O00O10O10000O2O2M5L7I5J3M2O2M3M2N2O1N1O1O2O0O1O1O1O2N1O1O1O1O1O1O1O2N1N2O1O1O1O2N1N2O1O1N2O2N1N3M3M2O2M3M2M4L5JQ]a2\", \"size\": [1280, 720]}, {\"counts\": \"iihd0<TW1d0F9H8I6J5L5K4M3N2N101N2O0100O10O1O001O001N1O1O2O000O2O001N101O001O00001O00001O0000010O0O2O00010O1O1O1N30O2N01N1O1O1O1O1000000O100O0101O1N3N5K5K2M3N1O1N2O1O1N100O2N100O2N1O100O1O1O1O1O1O1O1O100O1O001O1O1O1N2O1O1O1O2M2O001O1O1N2O1N2O1N3N1N2O2M2N2N3L5L5Id\\\\\\\\2\", \"size\": [1280, 720]}, {\"counts\": \"nagd04UW1o0_O;C=L3L4N2N2M4N0O2O1N101N200O0100O01O001N1O2N10000O2O00001O001O001O0O1010O01N100010O0001O0O2O1O100N2O1O1010O2NO1O1000000O100000O010O101O2N:E3N1N3N1O1N101O1N2N2O0O1O100O1O2O0O1O100O1O1O1O1O1O1O1O1O001O1O1O0O3N1O1O1N2O1O1O1N2O1O1N2O1N2O2M2N2O1N3M3L4L6Igd]2\", \"size\": [1280, 720]}, {\"counts\": \"^Qjd0d0nV1c0A<I8K5L3L3O2M2O1N101O001O100O010O001O0O2O0O2O00001N1O2O001N101O001O01O01O000O110O001O0000100O001O1N201O0O100O1O1O101O0000000000O10000O3N2N6I4M1O2N1N2O1O1N2N101N1O100O2O0O1O1O1O2N100O1O1O1O1O001O1O1O001O1O2M2O1O1O1O1O001N2O1N2O1N2O1O2M2O1N2N3M2N3M3M3L6JdlY2\", \"size\": [1280, 720]}, {\"counts\": \"iYkd06[W1f0^O=D<J6L4L3N2N2O1O1O1000000O1O001M3N2N1N3O0O2O0O2O0O1O1O2O00001O001O001O001O0000001O0000001O001O1O0010000O1O1O100O10000001O001OO10O010O10001O1N4M7I1N2N3N2M101N2O1N101N100O2N1O100O1O2N1O1O1O001O100N2O1O1O1O1O1O1O001O1N2O1O001O1O1N2O1O1N2N2O1N3N2M2O1N2N3M2N3M6HhlT2\", \"size\": [1280, 720]}, {\"counts\": \"[QTe0475lV1g0A`0G6M4L4M3N1N3M3N100O0100000O1O001O001O1N1O2N100O2O001N10001O00001O00001O001O001O000O101O001O00001O1O1N2O1002O0OO1O1O10000O10O010000O11O1N4M8G2O1N2O1N2O1N101O1N101O0O1O1O100O10O0101N1O1O1O1O1O1O1O010O1O1N2O1O1O001O1O1N2O001N2O1O002M2O1O1N2O2M2N2N3N1N2N2N3M2N5Iilj1\", \"size\": [1280, 720]}, {\"counts\": \"bXbf0h0VW15K3N4L7J5K3L3M2N102N2N1O2N3L5L5K4L4L2N1O1O1N3N001N101N3O02N1O01N100O100O01000001O2N8H3M1N2O1O1N2O0O1O101O0O101N100O1O1O100O1O10O02N1O1O1O001O1O1O100N101O1O1O1O1O1O1O0O2O1O1N102N1N2O1N2O1N3N1N2N3M2N2N3M3L7Ihd_1\", \"size\": [1280, 720]}, {\"counts\": \"SYQg04hW19_OKkhN9oV1?L5I7J6N6J]NiiN_1UV18N3M4L4L5K6J4L4L2N2N1O2N1O1O100000O2O01O000O10O10001N103M6I3N2N001N2O1N100O2O0O2O0O2O0O1O100O1O1O1O1O100O1O1O1O1O1O001O1O1O1O1O1O1O001N2O1O1O001N2O2N1O0O2N2O2M2O1O1N3M2N2N3M3M6GnTX1\", \"size\": [1280, 720]}, {\"counts\": \"g`hf07eW1a0XOh0_O8K5L2M2O2N0O2O1O1O2N1O3M2N1O1O2N2N2N1O1O1O1N2O1O1O001O001O00100O10000001O000000O10000001O1N2O6J3L2O1N2O1N2O1N10001N2N100O1O1O100O1O1O1O100O1O1O1O1O1O1O1O1O001O1O1O1O1O1O1N101O1O1O0O2O1O1N2O1O1N2N3N1N2N3M2N2N3L5LQUS1\", \"size\": [1280, 720]}, {\"counts\": \"aXlf0=_W1>@>G6J5L4M2M2N2O1N1O1O1O1O2N20OO1O100O001O100N1M4N1O2N2N101N2O1O1O2N1N2O1O1O2N1O1N2O1O1O1O1O1O1O1O100O010O10001O0O010O0101N2O1N4M8G6K3M2M3M3N2M2O0O1O2O1N1O100O1O2N100O1O1O2N2N1O1O1O1O1O1O1O1O1O1O2N1O1O1N2O2N1N2N2O2M2O2M3M3L4M3L5Jhdf0\", \"size\": [1280, 720]}, {\"counts\": \"eijh0:]W1a0B:I7I4M4L3M3O1N2O1O1O1O1O1O2N1O1O1O1O100001OO1O1M3J5M4N2O1O1O001O1O001O1O1O1N2O2N1N2O2N1O2N2N2N2N2N1O2N2M3N1O1O2N1O1O2N1O2M2O100O001O1O20O1O2N2N2N00000O10O100O02O1N00knN\", \"size\": [1280, 720]}, {\"counts\": \"]bjj0>]W1:C<G8L;_iN_NoU1R3WjN\\\\MdS1i3G3M3M2N101N1O1O1O1O00100O001O1O001N101O0001O010O000000SnN\", \"size\": [1280, 720]}, {\"counts\": \"eihi01mW1f0ZO<E8H5L6J5K6J7I8I8H>Ad0[O6J6I6J5M3M1O2N1O2N1O2N1O1O10000O001O1O1N1O200O001N101O0O2O000O1000001O02N2N1O0000O100N12O1O1O00bnN\", \"size\": [1280, 720]}, {\"counts\": \"PcPh06[W1f0]Oa0D>F8H<DV2kM7J4M2N2O1O010O0O2O03MN2N2N200O1O1N2O1N2N1N3O00010O0O1010O0001O000001O0001O00000000000001O00000000000O10001N100O2N2O3L2N3M3L4L3M3M2M4L4bL^mNY1fR1aNQnNh0UR1UOWnN9RR1CbnNEfQ16U3Le]o0\", \"size\": [1280, 720]}, {\"counts\": \"Yd\\\\g0?]W1=C:G6K5I8K5K7J6J8H5K7I7I7J5K5J5K6K5J0001O00000000001O00001O001O001O000000000000000000000000000000O10000003M6J000000O100O11O001O1O1O1O1O1O1O2N3M4L4L9G>B<D7I6J5K5K4L4L5K5K5K4L5K5KXhV1\", \"size\": [1280, 720]}, {\"counts\": \"V\\\\Vg0e0XW19I6I5K5K6J8H9I4L7I8H7I9G8H9G8H7JO000000001O00001O1O1O0000001O00000000001O0000000000000000000000O11O000000000000001O2N1O1O1O1O00001O001O1O1O4L3M3M3M5mLdkNX2\\\\U1F7I6J5K3M4L5K5K5K7I4L7IZ`_1\", \"size\": [1280, 720]}, {\"counts\": \"XlXg0a0^W18H6I5L4K6K7I7H7J8H7J7I8H7I6J7I7I9HO001O00001O1O001O00001O1O0000001O00001O000000001O00001O0000001O1O2N1O00000000O100O100O100O100001O001O2N2N4L3M3M4Lk0UO8H6J4L4L3M4L4L4L5K5K6J4L8HYh[1\", \"size\": [1280, 720]}, {\"counts\": \"Z\\\\`g04iW1`0@8J5J5K5L7H9G7K4K9H5K6J:F6J6K4K8G;F01O00000000001O001O1O00001O00001O000000001O001O00000000000000001O00001O00000000001O002N00001O001O1O1O3M3M3M3M2Nl0TO:F6J4L4L3M3M4L5K4L3M4L6J4L7IYPS1\", \"size\": [1280, 720]}, {\"counts\": \"a]Sf0<aW1>^O`0B:H8I3L4L6K2N2O2N2N3O11O01N1N2OO100O0001O1O1O1O1O1O001O1N2O0O2N1O2O0O1O2O0O2N1O4L1O10000000000000000000000000000O1000000001O000000000000001O00004L3M2N00O1001O00001O001O1O1O1O3M3M3M5K4L3M2N2N6J2N3M2N1O2N2N1O2N2N1O2N1O1O1O1O1O1O1O1O1O1O1O1O002N1O1O1O1O1O1O1O1O001O1O1O1O1O1O1O1O001O1O1O001O1O1O1O1O001O2N00\", \"size\": [1280, 720]}, {\"counts\": \"W]lf0b0\\\\W19H<E6H7H7K2O1O2N1O:E:G3M2N3L3N010O3L10000M3O100O100O1O1O1000000000000000000001O000000000000000000000000000000O100001O001O001O0000001O1O1O2N1O1O2N2N4L6J3M3M4L2N2N3M3M1O2N2N2N2N2N1O1O1O2N1O2N1O1O1O1O1O1O1O1O1O1O1O1O1O001O1O001O1O1O1O1O1O1O1O1O1O1O1O1O001O1O1O001O2N1O00\", \"size\": [1280, 720]}, {\"counts\": \"[]eg0m0V1TObT1X1ajN@XU1h1M2N3M4L3M5K1O1000000M3O1O100001O0000000000000000000000000000001O000000001O2N00000000001O1O002N2N1O2N3M4L4L3M6J2N2N3M3M2N1O2N2N2N2N2N1O1O2N1O2N1O1O1O1O1O1O1O1O001O2N1O001O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O001O001O1O001O1O1O2N1O00\", \"size\": [1280, 720]}, {\"counts\": \"`^`g0X1]V1?B;K7G6I7J6N2O10000O1000000000000O100000000O1O100001OO1O1001O001O00001O00001O001O001O1O1O1O1O1O1O2N2N2N3M5K7I4L1O2N2N2N3M2N2N2N2N2N1O2N1O1O2N1O1O1O1O1O2N001O1O1O1O1O1O1O1O1O1O1O001O1O1O1O1O1O1O1O1O001O1O1O1O1O1OSh=\", \"size\": [1280, 720]}, {\"counts\": \"nf\\\\g08YW1i0]O?G5K6L6J1O1L4M300O1O1N2O100O1O1O1O1O100O1000000000000O100001O00000000000000O1O100O1000000000000O100000000O11O2N1O2N1O2N4L7I;E2N1O1O5K4L2N2N2N2N2N2N1O1O1O2N1O2N1O1O1O2N1O1O1O1O1O1O2N001O1O1O1O1O1O1O1O1O1OSXe0\", \"size\": [1280, 720]}, {\"counts\": \"beWg02f01QV1[1E8L4L3K6J6L3M4N1N2N2N2O1M4L3O1O1N2N2OZMVkNW2iT1a00YM\\\\kNP2eT1nM]kNR2bU1G3M1O000O02N1O2O0O1O2N10000O3M000002N00O10000000000O10000000000O100001O00000000000000O10000000000000000000000000000O1O10000O1N20000001O1O002N1O1O3M5TN_jNo0]V1L4L4L3M2N2N2N2N2N1O2N1O2N3M2N1O3M2NTP:\", \"size\": [1280, 720]}, null, null, null, null, null, null, null], \"bboxes\": [[539.0, 1005.0, 79.0, 138.0], [544.0, 1011.0, 83.0, 140.0], [548.0, 1009.0, 75.0, 142.0], [545.0, 1010.0, 66.0, 140.0], [545.0, 1016.0, 67.0, 137.0], [549.0, 1019.0, 67.0, 139.0], [554.0, 1022.0, 67.0, 142.0], [562.0, 1024.0, 66.0, 145.0], [566.0, 1026.0, 68.0, 146.0], [563.0, 1030.0, 69.0, 145.0], [561.0, 1029.0, 67.0, 147.0], [560.0, 1028.0, 68.0, 147.0], [561.0, 1029.0, 70.0, 146.0], [563.0, 1028.0, 70.0, 147.0], [562.0, 1027.0, 70.0, 147.0], [560.0, 1027.0, 68.0, 146.0], [558.0, 1025.0, 67.0, 145.0], [554.0, 1025.0, 68.0, 143.0], [552.0, 1024.0, 68.0, 142.0], [550.0, 1024.0, 69.0, 139.0], [550.0, 1020.0, 67.0, 139.0], [549.0, 1016.0, 66.0, 140.0], [548.0, 1019.0, 68.0, 135.0], [539.0, 1011.0, 78.0, 144.0], [513.0, 989.0, 99.0, 133.0], [514.0, 999.0, 96.0, 129.0], [517.0, 1004.0, 97.0, 125.0], [521.0, 1003.0, 97.0, 125.0], [523.0, 1004.0, 98.0, 123.0], [524.0, 1007.0, 98.0, 120.0], [527.0, 1005.0, 98.0, 120.0], [530.0, 1000.0, 97.0, 127.0], [531.0, 1004.0, 97.0, 126.0], [531.0, 1006.0, 96.0, 128.0], [530.0, 1011.0, 96.0, 126.0], [528.0, 1020.0, 98.0, 120.0], [528.0, 1021.0, 99.0, 119.0], [528.0, 1019.0, 98.0, 120.0], [528.0, 1016.0, 98.0, 124.0], [536.0, 1020.0, 100.0, 121.0], [548.0, 1020.0, 100.0, 122.0], [556.0, 1020.0, 101.0, 122.0], [563.0, 1018.0, 101.0, 122.0], [569.0, 1009.0, 100.0, 125.0], [570.0, 1002.0, 100.0, 126.0], [567.0, 1000.0, 99.0, 125.0], [561.0, 998.0, 97.0, 124.0], [555.0, 997.0, 97.0, 124.0], [551.0, 999.0, 97.0, 123.0], [549.0, 1002.0, 97.0, 122.0], [550.0, 1001.0, 97.0, 122.0], [553.0, 1001.0, 99.0, 122.0], [557.0, 1004.0, 99.0, 122.0], [559.0, 1005.0, 100.0, 123.0], [560.0, 1006.0, 101.0, 123.0], [561.0, 1007.0, 101.0, 121.0], [560.0, 1007.0, 100.0, 121.0], [555.0, 1003.0, 99.0, 121.0], [547.0, 1001.0, 98.0, 122.0], [538.0, 1000.0, 96.0, 122.0], [531.0, 996.0, 96.0, 123.0], [526.0, 992.0, 95.0, 125.0], [514.0, 1000.0, 116.0, 120.0], [510.0, 997.0, 116.0, 120.0], [507.0, 994.0, 116.0, 119.0], [507.0, 993.0, 117.0, 118.0], [509.0, 989.0, 115.0, 120.0], [508.0, 986.0, 116.0, 119.0], [508.0, 985.0, 116.0, 119.0], [509.0, 985.0, 115.0, 121.0], [512.0, 969.0, 103.0, 131.0], [483.0, 846.0, 73.0, 129.0], [434.0, 752.0, 14.0, 47.0], null, [475.0, 860.0, 78.0, 144.0], [524.0, 1001.0, 111.0, 135.0], [527.0, 1012.0, 110.0, 132.0], [528.0, 1012.0, 111.0, 134.0], [528.0, 1024.0, 111.0, 133.0], [528.0, 1030.0, 111.0, 132.0], [530.0, 1033.0, 111.0, 131.0], [535.0, 1031.0, 112.0, 130.0], [540.0, 1032.0, 112.0, 126.0], [527.0, 1017.0, 117.0, 134.0], [486.0, 882.0, 51.0, 152.0], [499.0, 757.0, 18.0, 96.0], null, [489.0, 814.0, 58.0, 151.0], [514.0, 998.0, 112.0, 139.0], [539.0, 1030.0, 101.0, 127.0], [546.0, 1025.0, 102.0, 126.0], [562.0, 1011.0, 103.0, 128.0], [562.0, 1029.0, 115.0, 121.0], [574.0, 1028.0, 115.0, 119.0], [581.0, 1019.0, 117.0, 124.0], [583.0, 1027.0, 117.0, 115.0], [585.0, 1025.0, 117.0, 115.0], [586.0, 1011.0, 117.0, 126.0], [582.0, 1019.0, 116.0, 129.0], [575.0, 1036.0, 115.0, 126.0], [570.0, 1041.0, 115.0, 128.0], [566.0, 1040.0, 115.0, 128.0], [562.0, 1037.0, 114.0, 122.0], [560.0, 1025.0, 113.0, 123.0], [561.0, 1012.0, 114.0, 128.0], [568.0, 1001.0, 114.0, 132.0], [574.0, 1001.0, 115.0, 132.0], [578.0, 1002.0, 118.0, 131.0], [578.0, 1002.0, 117.0, 126.0], [579.0, 1004.0, 114.0, 117.0], [576.0, 997.0, 114.0, 123.0], [572.0, 998.0, 112.0, 130.0], [564.0, 1012.0, 114.0, 130.0], [558.0, 1021.0, 112.0, 127.0], [549.0, 1021.0, 112.0, 127.0], [533.0, 1016.0, 121.0, 130.0], [531.0, 1033.0, 127.0, 127.0], [530.0, 1029.0, 127.0, 128.0], [532.0, 1031.0, 128.0, 128.0], [533.0, 1027.0, 131.0, 128.0], [540.0, 1031.0, 132.0, 126.0], [577.0, 1029.0, 104.0, 125.0], [589.0, 1027.0, 98.0, 122.0], [582.0, 1024.0, 109.0, 122.0], [585.0, 1024.0, 116.0, 129.0], [635.0, 1038.0, 85.0, 154.0], [686.0, 1082.0, 34.0, 163.0], [659.0, 1065.0, 61.0, 173.0], [614.0, 1074.0, 80.0, 188.0], [598.0, 1142.0, 90.0, 138.0], [593.0, 1144.0, 88.0, 136.0], [595.0, 1147.0, 89.0, 133.0], [601.0, 1145.0, 90.0, 135.0], [565.0, 1166.0, 155.0, 114.0], [585.0, 1172.0, 135.0, 108.0], [605.0, 1175.0, 115.0, 105.0], [601.0, 1180.0, 107.0, 100.0], [598.0, 1190.0, 104.0, 90.0], [594.0, 1157.0, 117.0, 123.0], null, null, null, null, null, null, null], \"areas\": [8200.0, 8490.0, 8501.0, 7520.0, 7270.0, 7412.0, 7668.0, 7795.0, 7893.0, 7919.0, 7860.0, 7963.0, 7982.0, 7940.0, 7930.0, 7729.0, 7670.0, 7535.0, 7524.0, 7375.0, 7365.0, 7376.0, 7210.0, 7944.0, 8004.0, 7590.0, 7359.0, 7373.0, 7444.0, 7181.0, 7261.0, 7604.0, 7482.0, 7541.0, 7474.0, 7263.0, 7102.0, 7208.0, 7334.0, 7288.0, 7407.0, 7385.0, 7348.0, 7554.0, 7568.0, 7518.0, 7413.0, 7388.0, 7347.0, 7128.0, 7215.0, 7361.0, 7342.0, 7364.0, 7374.0, 7346.0, 7248.0, 7337.0, 7315.0, 7146.0, 7171.0, 7188.0, 7987.0, 8000.0, 7982.0, 7995.0, 7970.0, 8162.0, 8171.0, 8021.0, 8050.0, 5514.0, 283.0, null, 6889.0, 8903.0, 8623.0, 8886.0, 8817.0, 8651.0, 8675.0, 8605.0, 8418.0, 9072.0, 5160.0, 162.0, null, 4597.0, 8755.0, 7556.0, 7932.0, 8285.0, 7983.0, 7930.0, 8644.0, 7807.0, 7962.0, 8557.0, 8612.0, 8503.0, 8560.0, 8641.0, 8157.0, 8217.0, 8707.0, 8983.0, 9037.0, 9083.0, 8455.0, 7693.0, 8197.0, 8647.0, 8815.0, 8413.0, 8372.0, 9096.0, 9295.0, 9383.0, 9504.0, 9688.0, 9445.0, 6790.0, 6322.0, 7461.0, 8475.0, 8498.0, 4658.0, 8581.0, 12414.0, 9290.0, 9233.0, 8841.0, 9026.0, 11234.0, 9567.0, 7335.0, 6528.0, 6135.0, 8089.0, null, null, null, null, null, null, null], \"iscrowd\": 0, \"video_id\": 0, \"height\": 1280, \"width\": 720, \"category_id\": 49272, \"noun_phrase\": \"air jordan\"}], \"categories\": [{\"id\": 1, \"name\": \"a monkey\"}, {\"id\": 2, \"name\": \"a horse\"}, {\"id\": 3, \"name\": \"a fan\"}, {\"id\": 4, \"name\": \"a pile of fish\"}, {\"id\": 5, \"name\": \"a shoe\"}, {\"id\": 6, \"name\": \"a nipple\"}, {\"id\": 7, \"name\": \"a tennis ball\"}, {\"id\": 8, \"name\": \"a green shirt\"}, {\"id\": 9, \"name\": \"a table\"}, {\"id\": 10, \"name\": \"the lower body of the person\"}, {\"id\": 11, \"name\": \"a toy train\"}, {\"id\": 12, \"name\": \"a person's lower body\"}, {\"id\": 13, \"name\": \"a can\"}, {\"id\": 14, \"name\": \"a dog\"}, {\"id\": 15, \"name\": \"a thumb\"}, {\"id\": 16, \"name\": \"a blanket\"}, {\"id\": 17, \"name\": \"girl\"}, {\"id\": 18, \"name\": \"a shirt\"}, {\"id\": 19, \"name\": \"a hand\"}, {\"id\": 20, \"name\": \"a dog vest\"}, {\"id\": 21, \"name\": \"a life jacket\"}, {\"id\": 22, \"name\": \"chicken\"}, {\"id\": 23, \"name\": \"a person\"}, {\"id\": 24, \"name\": \"a cymbal\"}, {\"id\": 25, \"name\": \"a eye\"}, {\"id\": 26, \"name\": \"rooster\"}, {\"id\": 27, \"name\": \"a goat\"}, {\"id\": 28, \"name\": \"a bridge\"}, {\"id\": 29, \"name\": \"a tent\"}, {\"id\": 30, \"name\": \"a basket\"}, {\"id\": 31, \"name\": \"a helmet\"}, {\"id\": 32, \"name\": \"a road divider\"}, {\"id\": 33, \"name\": \"a chicken\"}, {\"id\": 34, \"name\": \"a wooden post\"}, {\"id\": 35, \"name\": \"a picture frame\"}, {\"id\": 36, \"name\": \"a white trim\"}, {\"id\": 37, \"name\": \"child\"}, {\"id\": 38, \"name\": \"boy\"}, {\"id\": 39, \"name\": \"a boat\"}, {\"id\": 40, \"name\": \"a person's neck\"}, {\"id\": 41, \"name\": \"a train\"}, {\"id\": 42, \"name\": \"a goose\"}, {\"id\": 43, \"name\": \"a cake\"}, {\"id\": 44, \"name\": \"a tree trunk\"}, {\"id\": 45, \"name\": \"a hair\"}, {\"id\": 46, \"name\": \"train\"}, {\"id\": 47, \"name\": \"a person's hair\"}, {\"id\": 48, \"name\": \"a cow\"}, {\"id\": 49, \"name\": \"truck\"}, {\"id\": 50, \"name\": \"a toy\"}, {\"id\": 51, \"name\": \"a woman\"}, {\"id\": 52, \"name\": \"a car\"}, {\"id\": 53, \"name\": \"a person's arm\"}, {\"id\": 54, \"name\": \"a side view mirror\"}, {\"id\": 55, \"name\": \"bird\"}, {\"id\": 56, \"name\": \"mannequin\"}, {\"id\": 57, \"name\": \"a windshield wiper\"}, {\"id\": 58, \"name\": \"a truck\"}, {\"id\": 59, \"name\": \"hand\"}, {\"id\": 60, \"name\": \"an arm\"}, {\"id\": 61, \"name\": \"shoe\"}, {\"id\": 62, \"name\": \"dog\"}, {\"id\": 63, \"name\": \"a calf\"}, {\"id\": 64, \"name\": \"sign\"}, {\"id\": 65, \"name\": \"a pair of pants\"}, {\"id\": 66, \"name\": \"motorcycle\"}, {\"id\": 67, \"name\": \"cat\"}, {\"id\": 68, \"name\": \"window\"}, {\"id\": 69, \"name\": \"a windshield\"}, {\"id\": 70, \"name\": \"a cat\"}, {\"id\": 71, \"name\": \"cake\"}, {\"id\": 72, \"name\": \"woman\"}, {\"id\": 73, \"name\": \"a lizard\"}, {\"id\": 74, \"name\": \"a pole\"}, {\"id\": 75, \"name\": \"a turtle\"}, {\"id\": 76, \"name\": \"person\"}, {\"id\": 77, \"name\": \"a duck\"}, {\"id\": 78, \"name\": \"bottle\"}, {\"id\": 79, \"name\": \"a foot\"}, {\"id\": 80, \"name\": \"fish\"}, {\"id\": 81, \"name\": \"a kitten\"}, {\"id\": 82, \"name\": \"a pig\"}, {\"id\": 83, \"name\": \"a black chicken\"}, {\"id\": 84, \"name\": \"a drumhead\"}, {\"id\": 85, \"name\": \"duck\"}, {\"id\": 86, \"name\": \"a tail\"}, {\"id\": 87, \"name\": \"a bird\"}, {\"id\": 88, \"name\": \"car\"}, {\"id\": 89, \"name\": \"a hat\"}, {\"id\": 90, \"name\": \"mountain\"}, {\"id\": 91, \"name\": \"man\"}, {\"id\": 92, \"name\": \"a scooter\"}, {\"id\": 93, \"name\": \"a tinsel garland\"}, {\"id\": 94, \"name\": \"robot\"}, {\"id\": 95, \"name\": \"a fish\"}, {\"id\": 96, \"name\": \"a mirror\"}, {\"id\": 97, \"name\": \"a carrot\"}, {\"id\": 98, \"name\": \"a pigeon\"}, {\"id\": 99, \"name\": \"flag\"}, {\"id\": 100, \"name\": \"logo\"}, {\"id\": 101, \"name\": \"a curtain\"}, {\"id\": 102, \"name\": \"cup\"}, {\"id\": 103, \"name\": \"a sign\"}, {\"id\": 104, \"name\": \"a wall\"}, {\"id\": 105, \"name\": \"traffic cone\"}, {\"id\": 106, \"name\": \"a bag\"}, {\"id\": 107, \"name\": \"a white shirt\"}, {\"id\": 108, \"name\": \"a concrete post\"}, {\"id\": 109, \"name\": \"a dog toy\"}, {\"id\": 110, \"name\": \"a piece of wood\"}, {\"id\": 111, \"name\": \"pig\"}, {\"id\": 112, \"name\": \"yellow flower\"}, {\"id\": 113, \"name\": \"strawberry\"}, {\"id\": 114, \"name\": \"rock\"}, {\"id\": 115, \"name\": \"a magazine\"}, {\"id\": 116, \"name\": \"a bench\"}, {\"id\": 117, \"name\": \"a stove\"}, {\"id\": 118, \"name\": \"a satellite dish\"}, {\"id\": 119, \"name\": \"a person's head\"}, {\"id\": 120, \"name\": \"a purple umbrella\"}, {\"id\": 121, \"name\": \"a guitar\"}, {\"id\": 122, \"name\": \"a smoke plume\"}, {\"id\": 123, \"name\": \"a person's foot\"}, {\"id\": 124, \"name\": \"a feather\"}, {\"id\": 125, \"name\": \"bear\"}, {\"id\": 126, \"name\": \"a person's mouth\"}, {\"id\": 127, \"name\": \"a wooden frame\"}, {\"id\": 128, \"name\": \"a bandage\"}, {\"id\": 129, \"name\": \"a black rubber seal\"}, {\"id\": 130, \"name\": \"a human ear\"}, {\"id\": 131, \"name\": \"a cup\"}, {\"id\": 132, \"name\": \"a horse's head\"}, {\"id\": 133, \"name\": \"chair\"}, {\"id\": 134, \"name\": \"a skateboard\"}, {\"id\": 135, \"name\": \"a bra\"}, {\"id\": 136, \"name\": \"a white backpack\"}, {\"id\": 137, \"name\": \"a dog's ear\"}, {\"id\": 138, \"name\": \"a skirt\"}, {\"id\": 139, \"name\": \"a hoof\"}, {\"id\": 140, \"name\": \"a cellphone\"}, {\"id\": 141, \"name\": \"a rabbit\"}, {\"id\": 142, \"name\": \"a person's lower leg\"}, {\"id\": 143, \"name\": \"a plastic cup\"}, {\"id\": 144, \"name\": \"a bus\"}, {\"id\": 145, \"name\": \"a bear\"}, {\"id\": 146, \"name\": \"a snowbank\"}, {\"id\": 147, \"name\": \"a door\"}, {\"id\": 148, \"name\": \"a pants\"}, {\"id\": 149, \"name\": \"a pen\"}, {\"id\": 150, \"name\": \"a clock\"}, {\"id\": 151, \"name\": \"a necklace\"}, {\"id\": 152, \"name\": \"a christmas tree\"}, {\"id\": 153, \"name\": \"a white cat\"}, {\"id\": 154, \"name\": \"a brown skirt\"}, {\"id\": 155, \"name\": \"a wave\"}, {\"id\": 156, \"name\": \"a dog's head\"}, {\"id\": 157, \"name\": \"a bottle\"}, {\"id\": 158, \"name\": \"a backpack\"}, {\"id\": 159, \"name\": \"bench\"}, {\"id\": 160, \"name\": \"door\"}, {\"id\": 161, \"name\": \"a pant leg\"}, {\"id\": 162, \"name\": \"a bathtub\"}, {\"id\": 163, \"name\": \"a fork\"}, {\"id\": 164, \"name\": \"a pool\"}, {\"id\": 165, \"name\": \"horse\"}, {\"id\": 166, \"name\": \"a person's jacket\"}, {\"id\": 167, \"name\": \"bus\"}, {\"id\": 168, \"name\": \"a deer\"}, {\"id\": 169, \"name\": \"a laptop\"}, {\"id\": 170, \"name\": \"a trash can\"}, {\"id\": 171, \"name\": \"a finger\"}, {\"id\": 172, \"name\": \"a tooth\"}, {\"id\": 173, \"name\": \"a knee\"}, {\"id\": 174, \"name\": \"a pillow\"}, {\"id\": 175, \"name\": \"a toothbrush\"}, {\"id\": 176, \"name\": \"elephant\"}, {\"id\": 177, \"name\": \"a shadow\"}, {\"id\": 178, \"name\": \"a smoke trail\"}, {\"id\": 179, \"name\": \"a pink towel\"}, {\"id\": 180, \"name\": \"a van\"}, {\"id\": 181, \"name\": \"a rug\"}, {\"id\": 182, \"name\": \"a wristwatch\"}, {\"id\": 183, \"name\": \"a tuk-tuk\"}, {\"id\": 184, \"name\": \"doorway\"}, {\"id\": 185, \"name\": \"a green plastic bucket\"}, {\"id\": 186, \"name\": \"a case of water bottles\"}, {\"id\": 187, \"name\": \"baby\"}, {\"id\": 188, \"name\": \"a plastic bag\"}, {\"id\": 189, \"name\": \"a television remote control\"}, {\"id\": 190, \"name\": \"a person's left hand\"}, {\"id\": 191, \"name\": \"a barrel\"}, {\"id\": 192, \"name\": \"fly\"}, {\"id\": 193, \"name\": \"a turtle's head\"}, {\"id\": 194, \"name\": \"a dashboard\"}, {\"id\": 195, \"name\": \"stop sign\"}, {\"id\": 196, \"name\": \"baby girl\"}, {\"id\": 197, \"name\": \"a red bag\"}, {\"id\": 198, \"name\": \"plastic container\"}, {\"id\": 199, \"name\": \"a wheelbarrow\"}, {\"id\": 200, \"name\": \"a vase\"}, {\"id\": 201, \"name\": \"person's hand\"}, {\"id\": 202, \"name\": \"a pink shirt\"}, {\"id\": 203, \"name\": \"a palm tree\"}, {\"id\": 204, \"name\": \"mouse\"}, {\"id\": 205, \"name\": \"vending machine\"}, {\"id\": 206, \"name\": \"a bull\"}, {\"id\": 207, \"name\": \"a thong\"}, {\"id\": 208, \"name\": \"cow\"}, {\"id\": 209, \"name\": \"a smartphone\"}, {\"id\": 210, \"name\": \"a yellow curved surface\"}, {\"id\": 211, \"name\": \"a panda\"}, {\"id\": 212, \"name\": \"a watch\"}, {\"id\": 213, \"name\": \"plate\"}, {\"id\": 214, \"name\": \"ball\"}, {\"id\": 215, \"name\": \"building\"}, {\"id\": 216, \"name\": \"a dresser\"}, {\"id\": 217, \"name\": \"turtle\"}, {\"id\": 218, \"name\": \"a person's forearm\"}, {\"id\": 219, \"name\": \"snake\"}, {\"id\": 220, \"name\": \"face\"}, {\"id\": 221, \"name\": \"a green plastic container\"}, {\"id\": 222, \"name\": \"a elephant\"}, {\"id\": 223, \"name\": \"a pair of shorts\"}, {\"id\": 224, \"name\": \"a book\"}, {\"id\": 225, \"name\": \"trash can\"}, {\"id\": 226, \"name\": \"a man\"}, {\"id\": 227, \"name\": \"a person's hand\"}, {\"id\": 228, \"name\": \"a pizza\"}, {\"id\": 229, \"name\": \"a statue\"}, {\"id\": 230, \"name\": \"a cabinet\"}, {\"id\": 231, \"name\": \"a dog's nose\"}, {\"id\": 232, \"name\": \"faucet\"}, {\"id\": 233, \"name\": \"man's hand\"}, {\"id\": 234, \"name\": \"boat\"}, {\"id\": 235, \"name\": \"a car air vent\"}, {\"id\": 236, \"name\": \"a green rectangular object with white text on it\"}, {\"id\": 237, \"name\": \"a plank\"}, {\"id\": 238, \"name\": \"pole\"}, {\"id\": 239, \"name\": \"a pile of wood\"}, {\"id\": 240, \"name\": \"a nose\"}, {\"id\": 241, \"name\": \"a marker\"}, {\"id\": 242, \"name\": \"a train car\"}, {\"id\": 243, \"name\": \"a white plastic or metal piece of an object, possibly part of a cage or enclosure\"}, {\"id\": 244, \"name\": \"a pair of legs\"}, {\"id\": 245, \"name\": \"a flag\"}, {\"id\": 246, \"name\": \"a person's arms\"}, {\"id\": 247, \"name\": \"keyboard\"}, {\"id\": 248, \"name\": \"a street light\"}, {\"id\": 249, \"name\": \"a bicycle handlebar\"}, {\"id\": 250, \"name\": \"a pair of khaki pants\"}, {\"id\": 251, \"name\": \"a bumper\"}, {\"id\": 252, \"name\": \"a jacket\"}, {\"id\": 253, \"name\": \"a tractor\"}, {\"id\": 254, \"name\": \"a gray metal shelf\"}, {\"id\": 255, \"name\": \"a hog\"}, {\"id\": 256, \"name\": \"a door handle\"}, {\"id\": 257, \"name\": \"a blue plastic bucket\"}, {\"id\": 258, \"name\": \"a leg\"}, {\"id\": 259, \"name\": \"mirror\"}, {\"id\": 260, \"name\": \"a scarf\"}, {\"id\": 261, \"name\": \"a chair\"}, {\"id\": 262, \"name\": \"a curb\"}, {\"id\": 263, \"name\": \"a rock\"}, {\"id\": 264, \"name\": \"a tree stump\"}, {\"id\": 265, \"name\": \"a hula hoop\"}, {\"id\": 266, \"name\": \"van\"}, {\"id\": 267, \"name\": \"puppy\"}, {\"id\": 268, \"name\": \"a lower body\"}, {\"id\": 269, \"name\": \"scooter\"}, {\"id\": 270, \"name\": \"a child\"}, {\"id\": 271, \"name\": \"a turkey\"}, {\"id\": 272, \"name\": \"fan\"}, {\"id\": 273, \"name\": \"gift\"}, {\"id\": 274, \"name\": \"an elephant\"}, {\"id\": 275, \"name\": \"a handle\"}, {\"id\": 276, \"name\": \"a squirrel\"}, {\"id\": 277, \"name\": \"a traffic cone\"}, {\"id\": 278, \"name\": \"a white bird\"}, {\"id\": 279, \"name\": \"a metal container\"}, {\"id\": 280, \"name\": \"a wristband\"}, {\"id\": 281, \"name\": \"a rooster\"}, {\"id\": 282, \"name\": \"a concrete wall\"}, {\"id\": 283, \"name\": \"a stack of large brown boxes\"}, {\"id\": 284, \"name\": \"lower body\"}, {\"id\": 285, \"name\": \"a painting\"}, {\"id\": 286, \"name\": \"a flagpole\"}, {\"id\": 287, \"name\": \"a trailer\"}, {\"id\": 288, \"name\": \"glass beaker\"}, {\"id\": 289, \"name\": \"a brick\"}, {\"id\": 290, \"name\": \"a tortoise\"}, {\"id\": 291, \"name\": \"a motorcycle\"}, {\"id\": 292, \"name\": \"a cardboard box\"}, {\"id\": 293, \"name\": \"pillow\"}, {\"id\": 294, \"name\": \"a pair of white pants\"}, {\"id\": 295, \"name\": \"a tree\"}, {\"id\": 296, \"name\": \"a leash\"}, {\"id\": 297, \"name\": \"a window\"}, {\"id\": 298, \"name\": \"tire\"}, {\"id\": 299, \"name\": \"a cat's eye\"}, {\"id\": 300, \"name\": \"backpack\"}, {\"id\": 301, \"name\": \"a leaf\"}, {\"id\": 302, \"name\": \"cat's paw\"}, {\"id\": 303, \"name\": \"a suitcase\"}, {\"id\": 304, \"name\": \"a sheet of music\"}, {\"id\": 305, \"name\": \"a motorbike\"}, {\"id\": 306, \"name\": \"a cell phone\"}, {\"id\": 307, \"name\": \"a white pigeon\"}, {\"id\": 308, \"name\": \"a cowrie shell\"}, {\"id\": 309, \"name\": \"a knob\"}, {\"id\": 310, \"name\": \"a tire\"}, {\"id\": 311, \"name\": \"a plastic container\"}, {\"id\": 312, \"name\": \"sandwich\"}, {\"id\": 313, \"name\": \"a shell\"}, {\"id\": 314, \"name\": \"a mouse\"}, {\"id\": 315, \"name\": \"a lamp\"}, {\"id\": 316, \"name\": \"a chair leg\"}, {\"id\": 317, \"name\": \"a poster\"}, {\"id\": 318, \"name\": \"a sock\"}, {\"id\": 319, \"name\": \"a banana\"}, {\"id\": 320, \"name\": \"a taxi\"}, {\"id\": 321, \"name\": \"a wrench\"}, {\"id\": 322, \"name\": \"a wooden pole\"}, {\"id\": 323, \"name\": \"a long, dark-colored, possibly black or gray, sleeve the sleeve is likely from a shirt, possibly a t-shirt or a long-sleeved shirt\"}, {\"id\": 324, \"name\": \"a sweater\"}, {\"id\": 325, \"name\": \"a bracelet\"}, {\"id\": 326, \"name\": \"a drawer\"}, {\"id\": 327, \"name\": \"a wrist\"}, {\"id\": 328, \"name\": \"a metal pole\"}, {\"id\": 329, \"name\": \"a blue ball\"}, {\"id\": 330, \"name\": \"a bush\"}, {\"id\": 331, \"name\": \"a black shoulder bag\"}, {\"id\": 332, \"name\": \"a green fruit\"}, {\"id\": 333, \"name\": \"a building\"}, {\"id\": 334, \"name\": \"person's head\"}, {\"id\": 335, \"name\": \"a donkey\"}, {\"id\": 336, \"name\": \"a peach\"}, {\"id\": 337, \"name\": \"a water heater\"}, {\"id\": 338, \"name\": \"a street sign\"}, {\"id\": 339, \"name\": \"a stone bridge\"}, {\"id\": 340, \"name\": \"a hamster\"}, {\"id\": 341, \"name\": \"a couch\"}, {\"id\": 342, \"name\": \"a red and white chicken comb\"}, {\"id\": 343, \"name\": \"a white picket fence\"}, {\"id\": 344, \"name\": \"a blackboard\"}, {\"id\": 345, \"name\": \"a shelf\"}, {\"id\": 346, \"name\": \"a polar bear\"}, {\"id\": 347, \"name\": \"a dolphin\"}, {\"id\": 348, \"name\": \"a zipper\"}, {\"id\": 349, \"name\": \"a boy\"}, {\"id\": 350, \"name\": \"a license plate\"}, {\"id\": 351, \"name\": \"a basketball\"}, {\"id\": 352, \"name\": \"a plane\"}, {\"id\": 353, \"name\": \"a cat ear\"}, {\"id\": 354, \"name\": \"a red wall\"}, {\"id\": 355, \"name\": \"a paw\"}, {\"id\": 356, \"name\": \"a tire cover\"}, {\"id\": 357, \"name\": \"a white mouse\"}, {\"id\": 358, \"name\": \"a tower\"}, {\"id\": 359, \"name\": \"a white, curved, and possibly plastic or glass object it appears to be part of a piece of furniture or a decorative element\"}, {\"id\": 360, \"name\": \"a ring\"}, {\"id\": 361, \"name\": \"a bundle of electrical wires\"}, {\"id\": 362, \"name\": \"a person, specifically, a person's lower body\"}, {\"id\": 363, \"name\": \"a vent\"}, {\"id\": 364, \"name\": \"a green cup\"}, {\"id\": 365, \"name\": \"a horn\"}, {\"id\": 366, \"name\": \"a brown bag\"}, {\"id\": 367, \"name\": \"a white dress\"}, {\"id\": 368, \"name\": \"a power strip\"}, {\"id\": 369, \"name\": \"a soap bar\"}, {\"id\": 370, \"name\": \"a tablecloth\"}, {\"id\": 371, \"name\": \"a red plastic bench\"}, {\"id\": 372, \"name\": \"a refrigerator\"}, {\"id\": 373, \"name\": \"a soap\"}, {\"id\": 374, \"name\": \"a cylindrical piece of metal\"}, {\"id\": 375, \"name\": \"a mammal\"}, {\"id\": 376, \"name\": \"a lid\"}, {\"id\": 377, \"name\": \"a doctor\"}, {\"id\": 378, \"name\": \"a beanie\"}, {\"id\": 379, \"name\": \"a red strap\"}, {\"id\": 380, \"name\": \"a ceiling\"}, {\"id\": 381, \"name\": \"a white cylindrical object\"}, {\"id\": 382, \"name\": \"a food\"}, {\"id\": 383, \"name\": \"a apron\"}, {\"id\": 384, \"name\": \"a person's right hand\"}, {\"id\": 385, \"name\": \"a bucket\"}, {\"id\": 386, \"name\": \"a sleeve\"}, {\"id\": 387, \"name\": \"a cart\"}, {\"id\": 388, \"name\": \"a rocky shore\"}, {\"id\": 389, \"name\": \"a pair of white shorts\"}, {\"id\": 390, \"name\": \"a yellow vehicle\"}, {\"id\": 391, \"name\": \"a bamboo stick\"}, {\"id\": 392, \"name\": \"a bed\"}, {\"id\": 393, \"name\": \"a toy car\"}, {\"id\": 394, \"name\": \"a stuffed animal\"}, {\"id\": 395, \"name\": \"a santa claus figurine\"}, {\"id\": 396, \"name\": \"a umbrella\"}, {\"id\": 397, \"name\": \"a plastic shopping bag\"}, {\"id\": 398, \"name\": \"the legs\"}, {\"id\": 399, \"name\": \"a towel\"}, {\"id\": 400, \"name\": \"a pot\"}, {\"id\": 401, \"name\": \"a computer mouse\"}, {\"id\": 402, \"name\": \"a basketball hoop\"}, {\"id\": 403, \"name\": \"a bunch of bananas\"}, {\"id\": 404, \"name\": \"a mask\"}, {\"id\": 405, \"name\": \"a hoodie\"}, {\"id\": 406, \"name\": \"a ear\"}, {\"id\": 407, \"name\": \"a rope\"}, {\"id\": 408, \"name\": \"a water tank\"}, {\"id\": 409, \"name\": \"the lower body and legs\"}, {\"id\": 410, \"name\": \"a pink long-sleeved shirt\"}, {\"id\": 411, \"name\": \"a sphere\"}, {\"id\": 412, \"name\": \"a dog's eye\"}, {\"id\": 413, \"name\": \"a speaker\"}, {\"id\": 414, \"name\": \"a dog ear\"}, {\"id\": 415, \"name\": \"a tool\"}, {\"id\": 416, \"name\": \"a yellow shirt\"}, {\"id\": 417, \"name\": \"a metal tube\"}, {\"id\": 418, \"name\": \"their back\"}, {\"id\": 419, \"name\": \"a spoon\"}, {\"id\": 420, \"name\": \"a pair of pants and shoes\"}, {\"id\": 421, \"name\": \"a white plastic window\"}, {\"id\": 422, \"name\": \"pair of shoes\"}, {\"id\": 423, \"name\": \"a fingernail\"}, {\"id\": 424, \"name\": \"a floor\"}, {\"id\": 425, \"name\": \"a mountain\"}, {\"id\": 426, \"name\": \"a lower body of a child\"}, {\"id\": 427, \"name\": \"a picture\"}, {\"id\": 428, \"name\": \"a sidewalk\"}, {\"id\": 429, \"name\": \"a rat\"}, {\"id\": 430, \"name\": \"a dog harness\"}, {\"id\": 431, \"name\": \"a stairway\"}, {\"id\": 432, \"name\": \"a black and gold purse\"}, {\"id\": 433, \"name\": \"a plastic container with a blue lid\"}, {\"id\": 434, \"name\": \"a logo\"}, {\"id\": 435, \"name\": \"a chain\"}, {\"id\": 436, \"name\": \"a flower arrangement\"}, {\"id\": 437, \"name\": \"a ball\"}, {\"id\": 438, \"name\": \"a red rug\"}, {\"id\": 439, \"name\": \"a person, specifically, the person's back\"}, {\"id\": 440, \"name\": \"a television\"}, {\"id\": 441, \"name\": \"bowl\"}, {\"id\": 442, \"name\": \"a vending machine\"}, {\"id\": 443, \"name\": \"a bee\"}, {\"id\": 444, \"name\": \"a metal plate\"}, {\"id\": 445, \"name\": \"a mattress\"}, {\"id\": 446, \"name\": \"a pan\"}, {\"id\": 447, \"name\": \"a yellow bucket\"}, {\"id\": 448, \"name\": \"a black plastic electronic device with a speaker\"}, {\"id\": 449, \"name\": \"a gate\"}, {\"id\": 450, \"name\": \"a packaging of a product\"}, {\"id\": 451, \"name\": \"a swing\"}, {\"id\": 452, \"name\": \"a turban\"}, {\"id\": 453, \"name\": \"a pink cloth\"}, {\"id\": 454, \"name\": \"a computer monitor\"}, {\"id\": 455, \"name\": \"a container of pencils\"}, {\"id\": 456, \"name\": \"a white container\"}, {\"id\": 457, \"name\": \"an umbrella\"}, {\"id\": 458, \"name\": \"a box\"}, {\"id\": 459, \"name\": \"a button\"}, {\"id\": 460, \"name\": \"a projector screen\"}, {\"id\": 461, \"name\": \"a notebook\"}, {\"id\": 462, \"name\": \"a coat\"}, {\"id\": 463, \"name\": \"a green bucket\"}, {\"id\": 464, \"name\": \"a whiteboard\"}, {\"id\": 465, \"name\": \"a dress\"}, {\"id\": 466, \"name\": \"a freezer\"}, {\"id\": 467, \"name\": \"a white, fluffy animal\"}, {\"id\": 468, \"name\": \"a shrubbery\"}, {\"id\": 469, \"name\": \"a small white animal, possibly a kitten\"}, {\"id\": 470, \"name\": \"a white plastic bag\"}, {\"id\": 471, \"name\": \"a shoulder bag\"}, {\"id\": 472, \"name\": \"a vest\"}, {\"id\": 473, \"name\": \"a white umbrella\"}, {\"id\": 474, \"name\": \"a flower\"}, {\"id\": 475, \"name\": \"a wicker sculpture\"}, {\"id\": 476, \"name\": \"a flower bouquet\"}, {\"id\": 477, \"name\": \"a radio\"}, {\"id\": 478, \"name\": \"a debit card\"}, {\"id\": 479, \"name\": \"a toy tricycle\"}, {\"id\": 480, \"name\": \"a plant\"}, {\"id\": 481, \"name\": \"a lamp shade\"}, {\"id\": 482, \"name\": \"a traffic light\"}, {\"id\": 483, \"name\": \"a green plastic bag\"}, {\"id\": 484, \"name\": \"a headband\"}, {\"id\": 485, \"name\": \"a garage door\"}, {\"id\": 486, \"name\": \"a banknote\"}, {\"id\": 487, \"name\": \"a earbud\"}, {\"id\": 488, \"name\": \"a road\"}, {\"id\": 489, \"name\": \"a white pants\"}, {\"id\": 490, \"name\": \"a person, specifically, a person\"}, {\"id\": 491, \"name\": \"a black plastic bag\"}, {\"id\": 492, \"name\": \"a baby's shirt\"}, {\"id\": 493, \"name\": \"a chest of drawers\"}, {\"id\": 494, \"name\": \"a person's torso\"}, {\"id\": 495, \"name\": \"the lower body\"}, {\"id\": 496, \"name\": \"a cylinder\"}, {\"id\": 497, \"name\": \"a pink umbrella\"}, {\"id\": 498, \"name\": \"a broom\"}, {\"id\": 499, \"name\": \"a person's hands\"}, {\"id\": 500, \"name\": \"a cape\"}, {\"id\": 501, \"name\": \"a walker\"}, {\"id\": 502, \"name\": \"a piece of furniture\"}, {\"id\": 503, \"name\": \"a cage\"}, {\"id\": 504, \"name\": \"a sheep\"}, {\"id\": 505, \"name\": \"a fluffy, white animal\"}, {\"id\": 506, \"name\": \"a mailbox\"}, {\"id\": 507, \"name\": \"a bus shelter\"}, {\"id\": 508, \"name\": \"a wall outlet\"}, {\"id\": 509, \"name\": \"a person's shirt\"}, {\"id\": 510, \"name\": \"a black sign\"}, {\"id\": 511, \"name\": \"a sink\"}, {\"id\": 512, \"name\": \"a person's elbow\"}, {\"id\": 513, \"name\": \"a tv\"}, {\"id\": 514, \"name\": \"a gray wall\"}, {\"id\": 515, \"name\": \"a concrete slab\"}, {\"id\": 516, \"name\": \"a stack of plates\"}, {\"id\": 517, \"name\": \"a dog's arm\"}, {\"id\": 518, \"name\": \"a head of hair\"}, {\"id\": 519, \"name\": \"a trash bag\"}, {\"id\": 520, \"name\": \"a paper\"}, {\"id\": 521, \"name\": \"a person's wrist\"}, {\"id\": 522, \"name\": \"a parrot\"}, {\"id\": 523, \"name\": \"a person's thumb\"}, {\"id\": 524, \"name\": \"a yellow plastic bag\"}, {\"id\": 525, \"name\": \"a metal table leg\"}, {\"id\": 526, \"name\": \"a wood\"}, {\"id\": 527, \"name\": \"a stool\"}, {\"id\": 528, \"name\": \"a tongue\"}, {\"id\": 529, \"name\": \"a xxx trash can\"}, {\"id\": 530, \"name\": \"a pair of silver tongs\"}, {\"id\": 531, \"name\": \"a blue object\"}, {\"id\": 532, \"name\": \"a motorcycle wheel\"}, {\"id\": 533, \"name\": \"a frisbee\"}, {\"id\": 534, \"name\": \"a balcony\"}, {\"id\": 535, \"name\": \"a red handbag with a white trim\"}, {\"id\": 536, \"name\": \"a ceiling tile\"}, {\"id\": 537, \"name\": \"a washing machine drum\"}, {\"id\": 538, \"name\": \"pink ball\"}, {\"id\": 539, \"name\": \"a stack of boxes\"}, {\"id\": 540, \"name\": \"a woman with dark hair\"}, {\"id\": 541, \"name\": \"a water bottle\"}, {\"id\": 542, \"name\": \"a fruit\"}, {\"id\": 543, \"name\": \"a person's leg\"}, {\"id\": 544, \"name\": \"a seal\"}, {\"id\": 545, \"name\": \"a paddle\"}, {\"id\": 546, \"name\": \"a cat's tail\"}, {\"id\": 547, \"name\": \"a shutter\"}, {\"id\": 548, \"name\": \"a ceiling fan\"}, {\"id\": 549, \"name\": \"birdcage\"}, {\"id\": 550, \"name\": \"a container\"}, {\"id\": 551, \"name\": \"a hose\"}, {\"id\": 552, \"name\": \"a pink bucket\"}, {\"id\": 553, \"name\": \"a kettle\"}, {\"id\": 554, \"name\": \"a person's head and shoulders\"}, {\"id\": 555, \"name\": \"a wire\"}, {\"id\": 556, \"name\": \"a person's right arm\"}, {\"id\": 557, \"name\": \"a concrete block\"}, {\"id\": 558, \"name\": \"a bow\"}, {\"id\": 559, \"name\": \"a bowl\"}, {\"id\": 560, \"name\": \"a baby\"}, {\"id\": 561, \"name\": \"a waterfall\"}, {\"id\": 562, \"name\": \"a jar\"}, {\"id\": 563, \"name\": \"a ladle\"}, {\"id\": 564, \"name\": \"a toe\"}, {\"id\": 565, \"name\": \"a person, lower body\"}, {\"id\": 566, \"name\": \"a bolt\"}, {\"id\": 567, \"name\": \"a blue screen\"}, {\"id\": 568, \"name\": \"a pink baby stroller\"}, {\"id\": 569, \"name\": \"a shallow, round, silver metal pan\"}, {\"id\": 570, \"name\": \"a minivan\"}, {\"id\": 571, \"name\": \"a wall-mounted air conditioning unit\"}, {\"id\": 572, \"name\": \"a funnel\"}, {\"id\": 573, \"name\": \"a red heart\"}, {\"id\": 574, \"name\": \"a cross\"}, {\"id\": 575, \"name\": \"a cd\"}, {\"id\": 576, \"name\": \"a tablet\"}, {\"id\": 577, \"name\": \"a pair of hands\"}, {\"id\": 578, \"name\": \"a person's left arm\"}, {\"id\": 579, \"name\": \"a tuk tuk\"}, {\"id\": 580, \"name\": \"a pink mat\"}, {\"id\": 581, \"name\": \"a hedge\"}, {\"id\": 582, \"name\": \"a pair of blue shorts\"}, {\"id\": 583, \"name\": \"a camera\"}, {\"id\": 584, \"name\": \"a piece of paper\"}, {\"id\": 585, \"name\": \"a car windshield\"}, {\"id\": 586, \"name\": \"a pair of sunglasses\"}, {\"id\": 587, \"name\": \"a green object\"}, {\"id\": 588, \"name\": \"a pipe\"}, {\"id\": 589, \"name\": \"a black umbrella\"}, {\"id\": 590, \"name\": \"a person's id card\"}, {\"id\": 591, \"name\": \"plastic bag\"}, {\"id\": 592, \"name\": \"a pickup truck\"}, {\"id\": 593, \"name\": \"a bison\"}, {\"id\": 594, \"name\": \"a laundry basket\"}, {\"id\": 595, \"name\": \"a balloon\"}, {\"id\": 596, \"name\": \"a shoe box\"}, {\"id\": 597, \"name\": \"a yellow plastic basket\"}, {\"id\": 598, \"name\": \"a telephone pole\"}, {\"id\": 599, \"name\": \"a sari\"}, {\"id\": 600, \"name\": \"a shoulder strap\"}, {\"id\": 601, \"name\": \"a glass jar\"}, {\"id\": 602, \"name\": \"a bicycle fender\"}, {\"id\": 603, \"name\": \"a tube\"}, {\"id\": 604, \"name\": \"a comb\"}, {\"id\": 605, \"name\": \"toilet\"}, {\"id\": 606, \"name\": \"a pair of black stockings\"}, {\"id\": 607, \"name\": \"a green curtain\"}, {\"id\": 608, \"name\": \"a knife\"}, {\"id\": 609, \"name\": \"a key\"}, {\"id\": 610, \"name\": \"a volleyball\"}, {\"id\": 611, \"name\": \"a reflective arm band\"}, {\"id\": 612, \"name\": \"a white, rectangular table top\"}, {\"id\": 613, \"name\": \"a table leg\"}, {\"id\": 614, \"name\": \"a barrier\"}, {\"id\": 615, \"name\": \"a chicken's foot\"}, {\"id\": 616, \"name\": \"a yellow bag\"}, {\"id\": 617, \"name\": \"a planter\"}, {\"id\": 618, \"name\": \"a red plastic water bottle\"}, {\"id\": 619, \"name\": \"a stone\"}, {\"id\": 620, \"name\": \"a pink baby bottle\"}, {\"id\": 621, \"name\": \"a green jellyfish\"}, {\"id\": 622, \"name\": \"a bottle cap\"}, {\"id\": 623, \"name\": \"a cricket bat\"}, {\"id\": 624, \"name\": \"a yellow taxi\"}, {\"id\": 625, \"name\": \"a pile of sand\"}, {\"id\": 626, \"name\": \"a purse\"}, {\"id\": 627, \"name\": \"a roll of tape\"}, {\"id\": 628, \"name\": \"stuffed animal\"}, {\"id\": 629, \"name\": \"a red flag\"}, {\"id\": 630, \"name\": \"a billboard\"}, {\"id\": 631, \"name\": \"light fixture\"}, {\"id\": 632, \"name\": \"a window blind\"}, {\"id\": 633, \"name\": \"a mug\"}, {\"id\": 634, \"name\": \"a volkswagen suv\"}, {\"id\": 635, \"name\": \"a butter dish\"}, {\"id\": 636, \"name\": \"a giraffe\"}, {\"id\": 637, \"name\": \"a sheet\"}, {\"id\": 638, \"name\": \"a pink bag\"}, {\"id\": 639, \"name\": \"a fruit stand\"}, {\"id\": 640, \"name\": \"the lower body of a person\"}, {\"id\": 641, \"name\": \"a cloud\"}, {\"id\": 642, \"name\": \"a tentacle\"}, {\"id\": 643, \"name\": \"a tie\"}, {\"id\": 644, \"name\": \"a car dashboard\"}, {\"id\": 645, \"name\": \"a shark\"}, {\"id\": 646, \"name\": \"a ground\"}, {\"id\": 647, \"name\": \"a gray beam\"}, {\"id\": 648, \"name\": \"a coffin\"}, {\"id\": 649, \"name\": \"a blue shirt\"}, {\"id\": 650, \"name\": \"a metal grate\"}, {\"id\": 651, \"name\": \"a fire extinguisher\"}, {\"id\": 652, \"name\": \"a fence\"}, {\"id\": 653, \"name\": \"a red shirt\"}, {\"id\": 654, \"name\": \"a bone\"}, {\"id\": 655, \"name\": \"a grassy patch\"}, {\"id\": 656, \"name\": \"a monitor\"}, {\"id\": 657, \"name\": \"a string of four plastic balls\"}, {\"id\": 658, \"name\": \"a rail\"}, {\"id\": 659, \"name\": \"a pink bird perch\"}, {\"id\": 660, \"name\": \"a briefcase\"}, {\"id\": 661, \"name\": \"a faucet\"}, {\"id\": 662, \"name\": \"a white plastic tube\"}, {\"id\": 663, \"name\": \"pants\"}, {\"id\": 664, \"name\": \"a person's torso and lower body\"}, {\"id\": 665, \"name\": \"spoon\"}, {\"id\": 666, \"name\": \"a tile floor\"}, {\"id\": 667, \"name\": \"a yellow bowl\"}, {\"id\": 668, \"name\": \"a glass\"}, {\"id\": 669, \"name\": \"a baseball cap\"}, {\"id\": 670, \"name\": \"a red and gold chinese lantern\"}, {\"id\": 671, \"name\": \"a chicken toy\"}, {\"id\": 672, \"name\": \"a blue metal bar\"}, {\"id\": 673, \"name\": \"a drain\"}, {\"id\": 674, \"name\": \"a surfboard\"}, {\"id\": 675, \"name\": \"a sky\"}, {\"id\": 676, \"name\": \"a part of a vehicle\"}, {\"id\": 677, \"name\": \"a plastic bottle\"}, {\"id\": 678, \"name\": \"a grassy area\"}, {\"id\": 679, \"name\": \"a kiosk\"}, {\"id\": 680, \"name\": \"a phone case\"}, {\"id\": 681, \"name\": \"a young girl\"}, {\"id\": 682, \"name\": \"a cat's ear\"}, {\"id\": 683, \"name\": \"a car bumper\"}, {\"id\": 684, \"name\": \"a box of instant noodles\"}, {\"id\": 685, \"name\": \"a ladder\"}, {\"id\": 686, \"name\": \"a lower arm\"}, {\"id\": 687, \"name\": \"the upper body\"}, {\"id\": 688, \"name\": \"a xxx umbrella\"}, {\"id\": 689, \"name\": \"a pencil\"}, {\"id\": 690, \"name\": \"a package\"}, {\"id\": 691, \"name\": \"a nail polish brush\"}, {\"id\": 692, \"name\": \"the silver frame\"}, {\"id\": 693, \"name\": \"a pedestrian\"}, {\"id\": 694, \"name\": \"the construction zone\"}, {\"id\": 695, \"name\": \"taxi cab\"}, {\"id\": 696, \"name\": \"highway\"}, {\"id\": 697, \"name\": \"a garden\"}, {\"id\": 698, \"name\": \"steel beam\"}, {\"id\": 699, \"name\": \"a roadblock\"}, {\"id\": 700, \"name\": \"the boat wheel\"}, {\"id\": 701, \"name\": \"the pedestrian bridge\"}, {\"id\": 702, \"name\": \"a green motorcycle\"}, {\"id\": 703, \"name\": \"a mosque\"}, {\"id\": 704, \"name\": \"a cloudy sky\"}, {\"id\": 705, \"name\": \"the train station\"}, {\"id\": 706, \"name\": \"garden hose\"}, {\"id\": 707, \"name\": \"the stone vase\"}, {\"id\": 708, \"name\": \"the fabric curtain\"}, {\"id\": 709, \"name\": \"stone statue\"}, {\"id\": 710, \"name\": \"the ceramic plate\"}, {\"id\": 711, \"name\": \"the wooden mug\"}, {\"id\": 712, \"name\": \"the wooden pier\"}, {\"id\": 713, \"name\": \"a hockey rink\"}, {\"id\": 714, \"name\": \"a big tree\"}, {\"id\": 715, \"name\": \"a golden crown\"}, {\"id\": 716, \"name\": \"hotel\"}, {\"id\": 717, \"name\": \"a glass window\"}, {\"id\": 718, \"name\": \"a cap\"}, {\"id\": 719, \"name\": \"a door frame\"}, {\"id\": 720, \"name\": \"a metal bowl\"}, {\"id\": 721, \"name\": \"a crayon\"}, {\"id\": 722, \"name\": \"woman's face\"}, {\"id\": 723, \"name\": \"a security camera\"}, {\"id\": 724, \"name\": \"violin\"}, {\"id\": 725, \"name\": \"a snake\"}, {\"id\": 726, \"name\": \"a face\"}, {\"id\": 727, \"name\": \"a white and gray, plastic, long, rectangular, outdoor, electrical, outlet or junction box\"}, {\"id\": 728, \"name\": \"a xxx truck\"}, {\"id\": 729, \"name\": \"a black sheep\"}, {\"id\": 730, \"name\": \"a stainless steel drawer\"}, {\"id\": 731, \"name\": \"a dark circle\"}, {\"id\": 732, \"name\": \"a tarp\"}, {\"id\": 733, \"name\": \"a cave\"}, {\"id\": 734, \"name\": \"a green book\"}, {\"id\": 735, \"name\": \"a carpet\"}, {\"id\": 736, \"name\": \"a brown circle\"}, {\"id\": 737, \"name\": \"a light fixture\"}, {\"id\": 738, \"name\": \"a concrete curb\"}, {\"id\": 739, \"name\": \"a drum\"}, {\"id\": 740, \"name\": \"a head\"}, {\"id\": 741, \"name\": \"a video game machine\"}, {\"id\": 742, \"name\": \"a red awning\"}, {\"id\": 743, \"name\": \"a stuffed toy\"}, {\"id\": 744, \"name\": \"a part of a bridge\"}, {\"id\": 745, \"name\": \"a piano\"}, {\"id\": 746, \"name\": \"a heart\"}, {\"id\": 747, \"name\": \"a headboard\"}, {\"id\": 748, \"name\": \"a pair of jeans\"}, {\"id\": 749, \"name\": \"a tail light\"}, {\"id\": 750, \"name\": \"a sofa\"}, {\"id\": 751, \"name\": \"a lower body of a cat\"}, {\"id\": 752, \"name\": \"a black animal\"}, {\"id\": 753, \"name\": \"a lantern\"}, {\"id\": 754, \"name\": \"a backboard\"}, {\"id\": 755, \"name\": \"a truck bed\"}, {\"id\": 756, \"name\": \"a pair of blue jeans\"}, {\"id\": 757, \"name\": \"a cityscape\"}, {\"id\": 758, \"name\": \"a pink fabric, possibly a blanket or a curtain, attached to the cage in the image\"}, {\"id\": 759, \"name\": \"a gong\"}, {\"id\": 760, \"name\": \"a hut\"}, {\"id\": 761, \"name\": \"a light bulb\"}, {\"id\": 762, \"name\": \"piece of paper\"}, {\"id\": 763, \"name\": \"a black awning\"}, {\"id\": 764, \"name\": \"a metal manhole cover\"}, {\"id\": 765, \"name\": \"a lampshade\"}, {\"id\": 766, \"name\": \"a green fingernail\"}, {\"id\": 767, \"name\": \"a log\"}, {\"id\": 768, \"name\": \"a xxx green and white checkered fabric\"}, {\"id\": 769, \"name\": \"a control panel\"}, {\"id\": 770, \"name\": \"ceiling fan\"}, {\"id\": 771, \"name\": \"a dark-colored, long, horizontal, rectangular-shaped object it is likely a truck bumper or a section of a vehicle's body\"}, {\"id\": 772, \"name\": \"a concrete bench\"}, {\"id\": 773, \"name\": \"a newspaper\"}, {\"id\": 774, \"name\": \"a person, specifically, a lower body\"}, {\"id\": 775, \"name\": \"a wooden board\"}, {\"id\": 776, \"name\": \"a person, specifically, a woman\"}, {\"id\": 777, \"name\": \"a pink hoodie\"}, {\"id\": 778, \"name\": \"a cutting board\"}, {\"id\": 779, \"name\": \"a blue sign with a yellow and white moon on it\"}, {\"id\": 780, \"name\": \"a dog crate\"}, {\"id\": 781, \"name\": \"a car door\"}, {\"id\": 782, \"name\": \"a fish tail\"}, {\"id\": 783, \"name\": \"a luggage\"}, {\"id\": 784, \"name\": \"a pair of black pants\"}, {\"id\": 785, \"name\": \"a earring\"}, {\"id\": 786, \"name\": \"a butterfly\"}, {\"id\": 787, \"name\": \"a wallet\"}, {\"id\": 788, \"name\": \"a plate\"}, {\"id\": 789, \"name\": \"a pink jacket\"}, {\"id\": 790, \"name\": \"a metal bracket\"}, {\"id\": 791, \"name\": \"a roller coaster track\"}, {\"id\": 792, \"name\": \"a white plastic bucket\"}, {\"id\": 793, \"name\": \"a cylindrical metal object\"}, {\"id\": 794, \"name\": \"a gutter\"}, {\"id\": 795, \"name\": \"a cushion\"}, {\"id\": 796, \"name\": \"a fin\"}, {\"id\": 797, \"name\": \"a purple shirt\"}, {\"id\": 798, \"name\": \"a radiator\"}, {\"id\": 799, \"name\": \"a toilet\"}, {\"id\": 800, \"name\": \"a crown\"}, {\"id\": 801, \"name\": \"a motorcycle seat\"}, {\"id\": 802, \"name\": \"a dog coat\"}, {\"id\": 803, \"name\": \"a hockey stick\"}, {\"id\": 804, \"name\": \"a roof\"}, {\"id\": 805, \"name\": \"a board game\"}, {\"id\": 806, \"name\": \"a chandelier\"}, {\"id\": 807, \"name\": \"a beam\"}, {\"id\": 808, \"name\": \"a phone\"}, {\"id\": 809, \"name\": \"a person's left forearm\"}, {\"id\": 810, \"name\": \"a lion\"}, {\"id\": 811, \"name\": \"a cliff\"}, {\"id\": 812, \"name\": \"a grape\"}, {\"id\": 813, \"name\": \"a bullet\"}, {\"id\": 814, \"name\": \"a dirt-covered floor\"}, {\"id\": 815, \"name\": \"a mushroom\"}, {\"id\": 816, \"name\": \"a remote control\"}, {\"id\": 817, \"name\": \"a man's back\"}, {\"id\": 818, \"name\": \"a white line\"}, {\"id\": 819, \"name\": \"a piece of fabric\"}, {\"id\": 820, \"name\": \"a microphone\"}, {\"id\": 821, \"name\": \"a label\"}, {\"id\": 822, \"name\": \"a car antenna\"}, {\"id\": 823, \"name\": \"a wheel\"}, {\"id\": 824, \"name\": \"a pair of furry slippers\"}, {\"id\": 825, \"name\": \"a human arm\"}, {\"id\": 826, \"name\": \"a person's yellow hat\"}, {\"id\": 827, \"name\": \"a ponytail\"}, {\"id\": 828, \"name\": \"a power button\"}, {\"id\": 829, \"name\": \"shirt\"}, {\"id\": 830, \"name\": \"a person's right eye\"}, {\"id\": 831, \"name\": \"a person's pants\"}, {\"id\": 832, \"name\": \"a cooler\"}, {\"id\": 833, \"name\": \"a mossy stone wall\"}, {\"id\": 834, \"name\": \"a boat sail\"}, {\"id\": 835, \"name\": \"a blue wall\"}, {\"id\": 836, \"name\": \"a pink ball\"}, {\"id\": 837, \"name\": \"a parachute\"}, {\"id\": 838, \"name\": \"a hood\"}, {\"id\": 839, \"name\": \"a large white heart sculpture\"}, {\"id\": 840, \"name\": \"fire hydrant\"}, {\"id\": 841, \"name\": \"a block of butter\"}, {\"id\": 842, \"name\": \"a handbag\"}, {\"id\": 843, \"name\": \"a white cloth with a pattern on it\"}, {\"id\": 844, \"name\": \"a diaper\"}, {\"id\": 845, \"name\": \"their hand holding a glass of what appears to be wine or champagne\"}, {\"id\": 846, \"name\": \"a horse's leg\"}, {\"id\": 847, \"name\": \"a neck\"}, {\"id\": 848, \"name\": \"a black hose\"}, {\"id\": 849, \"name\": \"a pink, fuzzy tuft of hair\"}, {\"id\": 850, \"name\": \"a crab\"}, {\"id\": 851, \"name\": \"a staircase\"}, {\"id\": 852, \"name\": \"a refrigerator door\"}, {\"id\": 853, \"name\": \"a wooden pallet\"}, {\"id\": 854, \"name\": \"a person wrapped in a blanket\"}, {\"id\": 855, \"name\": \"a red metal box with wheels\"}, {\"id\": 856, \"name\": \"a pink gingham cloth bag\"}, {\"id\": 857, \"name\": \"a blue skirt\"}, {\"id\": 858, \"name\": \"a green oval\"}, {\"id\": 859, \"name\": \"a pink mohawk\"}, {\"id\": 860, \"name\": \"a cat's leg\"}, {\"id\": 861, \"name\": \"a gravel\"}, {\"id\": 862, \"name\": \"a mouth\"}, {\"id\": 863, \"name\": \"a doorknob\"}, {\"id\": 864, \"name\": \"a black dog\"}, {\"id\": 865, \"name\": \"a apple\"}, {\"id\": 866, \"name\": \"the object within the red bounding box is a fish\"}, {\"id\": 867, \"name\": \"a microphone stand\"}, {\"id\": 868, \"name\": \"a arm\"}, {\"id\": 869, \"name\": \"a red fabric seat\"}, {\"id\": 870, \"name\": \"a gun\"}, {\"id\": 871, \"name\": \"a motorcycle fender\"}, {\"id\": 872, \"name\": \"stick\"}, {\"id\": 873, \"name\": \"a chipmunk\"}, {\"id\": 874, \"name\": \"a white dog\"}, {\"id\": 875, \"name\": \"a paper roll\"}, {\"id\": 876, \"name\": \"a screen\"}, {\"id\": 877, \"name\": \"a floor map\"}, {\"id\": 878, \"name\": \"a spatula\"}, {\"id\": 879, \"name\": \"pot\"}, {\"id\": 880, \"name\": \"a brown leather couch or chair\"}, {\"id\": 881, \"name\": \"a dog's tail\"}, {\"id\": 882, \"name\": \"a zebra\"}, {\"id\": 883, \"name\": \"a bicycle wheel\"}, {\"id\": 884, \"name\": \"a belt\"}, {\"id\": 885, \"name\": \"a dragon\"}, {\"id\": 886, \"name\": \"a back of a chair\"}, {\"id\": 887, \"name\": \"a person's left arm and torso\"}, {\"id\": 888, \"name\": \"a person's red shirt\"}, {\"id\": 889, \"name\": \"street sign\"}, {\"id\": 890, \"name\": \"a metal conveyor belt\"}, {\"id\": 891, \"name\": \"a metal tray\"}, {\"id\": 892, \"name\": \"a cabinet door\"}, {\"id\": 893, \"name\": \"traffic light\"}, {\"id\": 894, \"name\": \"a bicycle seat\"}, {\"id\": 895, \"name\": \"a planet\"}, {\"id\": 896, \"name\": \"a red and gold balloon\"}, {\"id\": 897, \"name\": \"a hard hat\"}, {\"id\": 898, \"name\": \"a wooden object\"}, {\"id\": 899, \"name\": \"a worm\"}, {\"id\": 900, \"name\": \"a pile of grass\"}, {\"id\": 901, \"name\": \"a white awning\"}, {\"id\": 902, \"name\": \"a line\"}, {\"id\": 903, \"name\": \"the left arm\"}, {\"id\": 904, \"name\": \"a white towel\"}, {\"id\": 905, \"name\": \"a chopstick\"}, {\"id\": 906, \"name\": \"a bun\"}, {\"id\": 907, \"name\": \"a part of a person's clothing\"}, {\"id\": 908, \"name\": \"a bicycle tire\"}, {\"id\": 909, \"name\": \"a concrete sidewalk\"}, {\"id\": 910, \"name\": \"a black animal leg\"}, {\"id\": 911, \"name\": \"a white pole\"}, {\"id\": 912, \"name\": \"a red pole\"}, {\"id\": 913, \"name\": \"a tray\"}, {\"id\": 914, \"name\": \"a jet ski\"}, {\"id\": 915, \"name\": \"a toy pig\"}, {\"id\": 916, \"name\": \"a green bowl\"}, {\"id\": 917, \"name\": \"a bicycle\"}, {\"id\": 918, \"name\": \"a blue flower\"}, {\"id\": 919, \"name\": \"a white box with a blue label\"}, {\"id\": 920, \"name\": \"a curved metal railing\"}, {\"id\": 921, \"name\": \"a bed frame\"}, {\"id\": 922, \"name\": \"a metal ladle\"}, {\"id\": 923, \"name\": \"a pressure cooker\"}, {\"id\": 924, \"name\": \"a pink skirt\"}, {\"id\": 925, \"name\": \"a bouquet of flowers\"}, {\"id\": 926, \"name\": \"a pizza box\"}, {\"id\": 927, \"name\": \"a side-view mirror\"}, {\"id\": 928, \"name\": \"a dog chew treat\"}, {\"id\": 929, \"name\": \"a copper-colored bowl\"}, {\"id\": 930, \"name\": \"a blue and white plastic container\"}, {\"id\": 931, \"name\": \"a candle\"}, {\"id\": 932, \"name\": \"a counter\"}, {\"id\": 933, \"name\": \"a pink rubber duck toy\"}, {\"id\": 934, \"name\": \"a white canopy\"}, {\"id\": 935, \"name\": \"a person's right leg\"}, {\"id\": 936, \"name\": \"a product package\"}, {\"id\": 937, \"name\": \"a air conditioner\"}, {\"id\": 938, \"name\": \"a green button\"}, {\"id\": 939, \"name\": \"a yellow plastic bag of chips\"}, {\"id\": 940, \"name\": \"bull\"}, {\"id\": 941, \"name\": \"a pair of blue jean capri pants\"}, {\"id\": 942, \"name\": \"a person, a costume\"}, {\"id\": 943, \"name\": \"the upper body of a baby\"}, {\"id\": 944, \"name\": \"a brown wooden sign\"}, {\"id\": 945, \"name\": \"a person, a shirt\"}, {\"id\": 946, \"name\": \"a circle\"}, {\"id\": 947, \"name\": \"a beak\"}, {\"id\": 948, \"name\": \"a yellow parakeet\"}, {\"id\": 949, \"name\": \"a collar\"}, {\"id\": 950, \"name\": \"the lower body of a woman\"}, {\"id\": 951, \"name\": \"a pair of skis\"}, {\"id\": 952, \"name\": \"a person, specifically, the lower body\"}, {\"id\": 953, \"name\": \"a horse's bridle\"}, {\"id\": 954, \"name\": \"a blue rubber glove\"}, {\"id\": 955, \"name\": \"a green slug\"}, {\"id\": 956, \"name\": \"a wooden beam\"}, {\"id\": 957, \"name\": \"their lower body\"}, {\"id\": 958, \"name\": \"a tile\"}, {\"id\": 959, \"name\": \"a stack of wooden planks\"}, {\"id\": 960, \"name\": \"a shorts\"}, {\"id\": 961, \"name\": \"a yellow duck toy\"}, {\"id\": 962, \"name\": \"a shopping cart\"}, {\"id\": 963, \"name\": \"a hot dog\"}, {\"id\": 964, \"name\": \"a tv stand\"}, {\"id\": 965, \"name\": \"a railing\"}, {\"id\": 966, \"name\": \"a transparent umbrella\"}, {\"id\": 967, \"name\": \"a painting of a deity\"}, {\"id\": 968, \"name\": \"a glove\"}, {\"id\": 969, \"name\": \"cardboard box\"}, {\"id\": 970, \"name\": \"a man's upper body\"}, {\"id\": 971, \"name\": \"a bike rack\"}, {\"id\": 972, \"name\": \"a metal cage\"}, {\"id\": 973, \"name\": \"a grill\"}, {\"id\": 974, \"name\": \"a grass\"}, {\"id\": 975, \"name\": \"a pair of shoes\"}, {\"id\": 976, \"name\": \"a white refrigerator door\"}, {\"id\": 977, \"name\": \"a side mirror\"}, {\"id\": 978, \"name\": \"a white smoke detector\"}, {\"id\": 979, \"name\": \"a piece of cardboard\"}, {\"id\": 980, \"name\": \"a paintbrush\"}, {\"id\": 981, \"name\": \"a fist\"}, {\"id\": 982, \"name\": \"a red curtain\"}, {\"id\": 983, \"name\": \"a person, a lower body\"}, {\"id\": 984, \"name\": \"a panda bear's arm\"}, {\"id\": 985, \"name\": \"a roll of paper\"}, {\"id\": 986, \"name\": \"a tissue\"}, {\"id\": 987, \"name\": \"a burrito\"}, {\"id\": 988, \"name\": \"a guardrail\"}, {\"id\": 989, \"name\": \"a fireplace\"}, {\"id\": 990, \"name\": \"a white metal fence\"}, {\"id\": 991, \"name\": \"a piece of metal\"}, {\"id\": 992, \"name\": \"a net\"}, {\"id\": 993, \"name\": \"a storefront\"}, {\"id\": 994, \"name\": \"a white cloth\"}, {\"id\": 995, \"name\": \"a red pepper\"}, {\"id\": 996, \"name\": \"a pink pole\"}, {\"id\": 997, \"name\": \"a person, a torso\"}, {\"id\": 998, \"name\": \"a shirtless man\"}, {\"id\": 999, \"name\": \"a red light\"}, {\"id\": 1000, \"name\": \"a sale sign\"}, {\"id\": 1001, \"name\": \"a motor scooter\"}, {\"id\": 1002, \"name\": \"a calendar\"}, {\"id\": 1003, \"name\": \"a shirt cuff\"}, {\"id\": 1004, \"name\": \"a wooden wall\"}, {\"id\": 1005, \"name\": \"a rocket\"}, {\"id\": 1006, \"name\": \"a whale tail\"}, {\"id\": 1007, \"name\": \"a black bear\"}, {\"id\": 1008, \"name\": \"a shrimp\"}, {\"id\": 1009, \"name\": \"a handrail\"}, {\"id\": 1010, \"name\": \"a guinea pig\"}, {\"id\": 1011, \"name\": \"a sack\"}, {\"id\": 1012, \"name\": \"a clothing rack\"}, {\"id\": 1013, \"name\": \"a potato\"}, {\"id\": 1014, \"name\": \"a hippopotamus\"}, {\"id\": 1015, \"name\": \"a blue stage\"}, {\"id\": 1016, \"name\": \"a shovel\"}, {\"id\": 1017, \"name\": \"a bat\"}, {\"id\": 1018, \"name\": \"a gray electrical box\"}, {\"id\": 1019, \"name\": \"a plastic bowl\"}, {\"id\": 1020, \"name\": \"a white curtain\"}, {\"id\": 1021, \"name\": \"the back of a person\"}, {\"id\": 1022, \"name\": \"a blue chair\"}, {\"id\": 1023, \"name\": \"a wooden plank\"}, {\"id\": 1024, \"name\": \"a blue plastic measuring cup\"}, {\"id\": 1025, \"name\": \"a pair of black leather pants\"}, {\"id\": 1026, \"name\": \"a sword\"}, {\"id\": 1027, \"name\": \"a trough\"}, {\"id\": 1028, \"name\": \"a tissue box\"}, {\"id\": 1029, \"name\": \"a green leaf\"}, {\"id\": 1030, \"name\": \"a pink, plastic, ribbed tube\"}, {\"id\": 1031, \"name\": \"a headlight\"}, {\"id\": 1032, \"name\": \"a piece of bread\"}, {\"id\": 1033, \"name\": \"a credit card\"}, {\"id\": 1034, \"name\": \"a plank of wood\"}, {\"id\": 1035, \"name\": \"a cat's head\"}, {\"id\": 1036, \"name\": \"a tire rim\"}, {\"id\": 1037, \"name\": \"a xxx vehicle\"}, {\"id\": 1038, \"name\": \"a crosswalk\"}, {\"id\": 1039, \"name\": \"a red, shiny fabric\"}, {\"id\": 1040, \"name\": \"a plastic sheet\"}, {\"id\": 1041, \"name\": \"a blue sign\"}, {\"id\": 1042, \"name\": \"a person, specifically, a shirt\"}, {\"id\": 1043, \"name\": \"a yellow square\"}, {\"id\": 1044, \"name\": \"a sculpture\"}, {\"id\": 1045, \"name\": \"a yellow plastic handrail\"}, {\"id\": 1046, \"name\": \"a ceiling light fixture\"}, {\"id\": 1047, \"name\": \"a person's left thumb\"}, {\"id\": 1048, \"name\": \"a yellow and white striped barrier\"}, {\"id\": 1049, \"name\": \"a person's lower arm\"}, {\"id\": 1050, \"name\": \"a flower pot\"}, {\"id\": 1051, \"name\": \"a xxx notebook\"}, {\"id\": 1052, \"name\": \"a metal part of a drum\"}, {\"id\": 1053, \"name\": \"a yellow cloth\"}, {\"id\": 1054, \"name\": \"their lower body, including their pants\"}, {\"id\": 1055, \"name\": \"a keychain\"}, {\"id\": 1056, \"name\": \"a long-sleeved shirt\"}, {\"id\": 1057, \"name\": \"a wooden box\"}, {\"id\": 1058, \"name\": \"a human hand\"}, {\"id\": 1059, \"name\": \"a part of a sign\"}, {\"id\": 1060, \"name\": \"a double white line\"}, {\"id\": 1061, \"name\": \"a white purse\"}, {\"id\": 1062, \"name\": \"a tote bag\"}, {\"id\": 1063, \"name\": \"a green rope\"}, {\"id\": 1064, \"name\": \"a xxx bowl\"}, {\"id\": 1065, \"name\": \"a white object, possibly a car roof or hood\"}, {\"id\": 1066, \"name\": \"a concrete pipe\"}, {\"id\": 1067, \"name\": \"a boy's left arm and torso\"}, {\"id\": 1068, \"name\": \"a doorway\"}, {\"id\": 1069, \"name\": \"a person's face\"}, {\"id\": 1070, \"name\": \"a baby stroller\"}, {\"id\": 1071, \"name\": \"a wheelchair\"}, {\"id\": 1072, \"name\": \"a pile of dirt\"}, {\"id\": 1073, \"name\": \"a canopy\"}, {\"id\": 1074, \"name\": \"a keyboard\"}, {\"id\": 1075, \"name\": \"a person's right forearm\"}, {\"id\": 1076, \"name\": \"a red ribbon\"}, {\"id\": 1077, \"name\": \"a wooden cutting board\"}, {\"id\": 1078, \"name\": \"a window sill\"}, {\"id\": 1079, \"name\": \"a heater\"}, {\"id\": 1080, \"name\": \"a ship\"}, {\"id\": 1081, \"name\": \"a red box\"}, {\"id\": 1082, \"name\": \"a green sleeve\"}, {\"id\": 1083, \"name\": \"a buffalo\"}, {\"id\": 1084, \"name\": \"a red jacket\"}, {\"id\": 1085, \"name\": \"a yellow wall\"}, {\"id\": 1086, \"name\": \"a metal panel\"}, {\"id\": 1087, \"name\": \"a jet\"}, {\"id\": 1088, \"name\": \"a boot\"}, {\"id\": 1089, \"name\": \"a green cloth\"}, {\"id\": 1090, \"name\": \"a cylindrical object\"}, {\"id\": 1091, \"name\": \"a step\"}, {\"id\": 1092, \"name\": \"a blue and white stripe\"}, {\"id\": 1093, \"name\": \"a brick wall\"}, {\"id\": 1094, \"name\": \"a black, horizontal, metal bar\"}, {\"id\": 1095, \"name\": \"a piece of cake\"}, {\"id\": 1096, \"name\": \"a headscarf\"}, {\"id\": 1097, \"name\": \"a pair of sandals\"}, {\"id\": 1098, \"name\": \"a marble wall\"}, {\"id\": 1099, \"name\": \"a part of a person's arm\"}, {\"id\": 1100, \"name\": \"a black jacket\"}, {\"id\": 1101, \"name\": \"a sweatshirt\"}, {\"id\": 1102, \"name\": \"a wooden bench\"}, {\"id\": 1103, \"name\": \"a hockey zood table\"}, {\"id\": 1104, \"name\": \"a dark-colored eye\"}, {\"id\": 1105, \"name\": \"a ping pong paddle\"}, {\"id\": 1106, \"name\": \"the lower body of a man\"}, {\"id\": 1107, \"name\": \"a frying pan\"}, {\"id\": 1108, \"name\": \"a menu\"}, {\"id\": 1109, \"name\": \"a white cloth napkin\"}, {\"id\": 1110, \"name\": \"a bike\"}, {\"id\": 1111, \"name\": \"a dirt path\"}, {\"id\": 1112, \"name\": \"a escalator step\"}, {\"id\": 1113, \"name\": \"a cherry\"}, {\"id\": 1114, \"name\": \"a yellow post-it note\"}, {\"id\": 1115, \"name\": \"a person's long dark hair\"}, {\"id\": 1116, \"name\": \"an inflatable dolphin\"}, {\"id\": 1117, \"name\": \"a iron\"}, {\"id\": 1118, \"name\": \"a green rectangular object with a white text on it\"}, {\"id\": 1119, \"name\": \"a ledge\"}, {\"id\": 1120, \"name\": \"a metal bar\"}, {\"id\": 1121, \"name\": \"a red metal bar\"}, {\"id\": 1122, \"name\": \"a wind turbine\"}, {\"id\": 1123, \"name\": \"a suv\"}, {\"id\": 1124, \"name\": \"a shoe rack\"}, {\"id\": 1125, \"name\": \"a dog food bowl\"}, {\"id\": 1126, \"name\": \"a smartwatch\"}, {\"id\": 1127, \"name\": \"a penguin\"}, {\"id\": 1128, \"name\": \"a metal cup\"}, {\"id\": 1129, \"name\": \"a light\"}, {\"id\": 1130, \"name\": \"a white van\"}, {\"id\": 1131, \"name\": \"a gold bracelet\"}, {\"id\": 1132, \"name\": \"a wireless charging pad\"}, {\"id\": 1133, \"name\": \"a ring light\"}, {\"id\": 1134, \"name\": \"a roll of plastic wrap\"}, {\"id\": 1135, \"name\": \"a party hat\"}, {\"id\": 1136, \"name\": \"a washing machine\"}, {\"id\": 1137, \"name\": \"a yellow and gray safety vest\"}, {\"id\": 1138, \"name\": \"a christmas tree decoration\"}, {\"id\": 1139, \"name\": \"sewing machine\"}, {\"id\": 1140, \"name\": \"a water fountain\"}, {\"id\": 1141, \"name\": \"a peacock\"}, {\"id\": 1142, \"name\": \"a hair bun\"}, {\"id\": 1143, \"name\": \"a fuel container\"}, {\"id\": 1144, \"name\": \"a pair of arms\"}, {\"id\": 1145, \"name\": \"a rice\"}, {\"id\": 1146, \"name\": \"a pink, spiky dog toy\"}, {\"id\": 1147, \"name\": \"a toddler\"}, {\"id\": 1148, \"name\": \"a white rectangular object\"}, {\"id\": 1149, \"name\": \"a pink scrunchie\"}, {\"id\": 1150, \"name\": \"a hairbrush\"}, {\"id\": 1151, \"name\": \"a stupa\"}, {\"id\": 1152, \"name\": \"a metal ring\"}, {\"id\": 1153, \"name\": \"a pink object\"}, {\"id\": 1154, \"name\": \"a frog\"}, {\"id\": 1155, \"name\": \"a bread roll\"}, {\"id\": 1156, \"name\": \"a white sign\"}, {\"id\": 1157, \"name\": \"a stove burner\"}, {\"id\": 1158, \"name\": \"a red garment\"}, {\"id\": 1159, \"name\": \"a cat food pouch\"}, {\"id\": 1160, \"name\": \"their upper body\"}, {\"id\": 1161, \"name\": \"a white, rounded corner, rectangular object\"}, {\"id\": 1162, \"name\": \"a toddler, riding in a stroller\"}, {\"id\": 1163, \"name\": \"a robot vacuum cleaner\"}, {\"id\": 1164, \"name\": \"a blue plastic object\"}, {\"id\": 1165, \"name\": \"bottle of mountain dew\"}, {\"id\": 1166, \"name\": \"a head of a man\"}, {\"id\": 1167, \"name\": \"a car side-view mirror\"}, {\"id\": 1168, \"name\": \"a light source\"}, {\"id\": 1169, \"name\": \"a white headscarf\"}, {\"id\": 1170, \"name\": \"a baby's arm\"}, {\"id\": 1171, \"name\": \"an air conditioner\"}, {\"id\": 1172, \"name\": \"a person, specifically, a lower body of a person\"}, {\"id\": 1173, \"name\": \"a mound\"}, {\"id\": 1174, \"name\": \"a gazebo\"}, {\"id\": 1175, \"name\": \"a plastic food storage container\"}, {\"id\": 1176, \"name\": \"a person, specifically, a head\"}, {\"id\": 1177, \"name\": \"a white wall\"}, {\"id\": 1178, \"name\": \"a white switch\"}, {\"id\": 1179, \"name\": \"a yellow plastic object\"}, {\"id\": 1180, \"name\": \"a person riding a bicycle\"}, {\"id\": 1181, \"name\": \"a ceramic bowl\"}, {\"id\": 1182, \"name\": \"a yellow sign\"}, {\"id\": 1183, \"name\": \"a red pipe\"}, {\"id\": 1184, \"name\": \"a xxx water tank\"}, {\"id\": 1185, \"name\": \"a white animal, possibly a rabbit\"}, {\"id\": 1186, \"name\": \"a floor sign\"}, {\"id\": 1187, \"name\": \"a blue plastic container\"}, {\"id\": 1188, \"name\": \"a person, specifically, the lower body of a person\"}, {\"id\": 1189, \"name\": \"a dog collar\"}, {\"id\": 1190, \"name\": \"a crate\"}, {\"id\": 1191, \"name\": \"a yellow line\"}, {\"id\": 1192, \"name\": \"plant\"}, {\"id\": 1193, \"name\": \"a soccer goal\"}, {\"id\": 1194, \"name\": \"a power pole\"}, {\"id\": 1195, \"name\": \"a concrete barrier\"}, {\"id\": 1196, \"name\": \"a yellow rubber toy\"}, {\"id\": 1197, \"name\": \"a gorilla\"}, {\"id\": 1198, \"name\": \"a bouquet\"}, {\"id\": 1199, \"name\": \"a forearm\"}, {\"id\": 1200, \"name\": \"a forest\"}, {\"id\": 1201, \"name\": \"guitar\"}, {\"id\": 1202, \"name\": \"a white rectangle\"}, {\"id\": 1203, \"name\": \"a digital scale\"}, {\"id\": 1204, \"name\": \"a nail gun\"}, {\"id\": 1205, \"name\": \"a structure\"}, {\"id\": 1206, \"name\": \"a tube of lotion\"}, {\"id\": 1207, \"name\": \"a head covering\"}, {\"id\": 1208, \"name\": \"hands\"}, {\"id\": 1209, \"name\": \"piece of clothing\"}, {\"id\": 1210, \"name\": \"a metal spatula\"}, {\"id\": 1211, \"name\": \"ladder\"}, {\"id\": 1212, \"name\": \"a black bag\"}, {\"id\": 1213, \"name\": \"a branch\"}, {\"id\": 1214, \"name\": \"a stroller\"}, {\"id\": 1215, \"name\": \"a person, specifically, a person's head\"}, {\"id\": 1216, \"name\": \"a meerkat\"}, {\"id\": 1217, \"name\": \"hat\"}, {\"id\": 1218, \"name\": \"a wooden railing\"}, {\"id\": 1219, \"name\": \"a ferret\"}, {\"id\": 1220, \"name\": \"a white bag\"}, {\"id\": 1221, \"name\": \"a column\"}, {\"id\": 1222, \"name\": \"a rod\"}, {\"id\": 1223, \"name\": \"a pink flamingo statue\"}, {\"id\": 1224, \"name\": \"a boxing glove\"}, {\"id\": 1225, \"name\": \"a teapot\"}, {\"id\": 1226, \"name\": \"a seat\"}, {\"id\": 1227, \"name\": \"a fur jacket\"}, {\"id\": 1228, \"name\": \"a blue mat\"}, {\"id\": 1229, \"name\": \"a black air conditioning duct\"}, {\"id\": 1230, \"name\": \"a dog shirt\"}, {\"id\": 1231, \"name\": \"a suit jacket\"}, {\"id\": 1232, \"name\": \"a toy carrot\"}, {\"id\": 1233, \"name\": \"a teddy bear\"}, {\"id\": 1234, \"name\": \"a chicken leg\"}, {\"id\": 1235, \"name\": \"a part of a walking stick\"}, {\"id\": 1236, \"name\": \"a pile of clothes\"}, {\"id\": 1237, \"name\": \"a black metal rod\"}, {\"id\": 1238, \"name\": \"a stack of papers\"}, {\"id\": 1239, \"name\": \"a shoulder\"}, {\"id\": 1240, \"name\": \"a black electrical cord\"}, {\"id\": 1241, \"name\": \"a soil\"}, {\"id\": 1242, \"name\": \"a wedding dress\"}, {\"id\": 1243, \"name\": \"a child's right arm\"}, {\"id\": 1244, \"name\": \"a card\"}, {\"id\": 1245, \"name\": \"a statue of a smiling buddha\"}, {\"id\": 1246, \"name\": \"a child's lower body\"}, {\"id\": 1247, \"name\": \"a motorcycle taillight\"}, {\"id\": 1248, \"name\": \"a headrest\"}, {\"id\": 1249, \"name\": \"a switch\"}, {\"id\": 1250, \"name\": \"a scoop\"}, {\"id\": 1251, \"name\": \"a watermelon slice\"}, {\"id\": 1252, \"name\": \"a lettuce\"}, {\"id\": 1253, \"name\": \"a pizza crust\"}, {\"id\": 1254, \"name\": \"a chalkboard\"}, {\"id\": 1255, \"name\": \"a street lamp\"}, {\"id\": 1256, \"name\": \"a car radio control knob\"}, {\"id\": 1257, \"name\": \"a yellow plastic stool\"}, {\"id\": 1258, \"name\": \"a security guard\"}, {\"id\": 1259, \"name\": \"a gas pump\"}, {\"id\": 1260, \"name\": \"a parking lot\"}, {\"id\": 1261, \"name\": \"a bouquet of white flowers\"}, {\"id\": 1262, \"name\": \"a cable\"}, {\"id\": 1263, \"name\": \"a blurry, rectangular, black object\"}, {\"id\": 1264, \"name\": \"a tree branch\"}, {\"id\": 1265, \"name\": \"a pedal\"}, {\"id\": 1266, \"name\": \"a rectangular object\"}, {\"id\": 1267, \"name\": \"a brush\"}, {\"id\": 1268, \"name\": \"a person's back and head\"}, {\"id\": 1269, \"name\": \"a body of water\"}, {\"id\": 1270, \"name\": \"a blue and white plastic cup\"}, {\"id\": 1271, \"name\": \"a pile of wooden chairs\"}, {\"id\": 1272, \"name\": \"a piece of slate\"}, {\"id\": 1273, \"name\": \"a dough\"}, {\"id\": 1274, \"name\": \"a yellow hula hoop\"}, {\"id\": 1275, \"name\": \"a trackpad\"}, {\"id\": 1276, \"name\": \"a glass table top\"}, {\"id\": 1277, \"name\": \"piece of wood\"}, {\"id\": 1278, \"name\": \"a stack of logs\"}, {\"id\": 1279, \"name\": \"a tusk\"}, {\"id\": 1280, \"name\": \"a wiper blade\"}, {\"id\": 1281, \"name\": \"a girl\"}, {\"id\": 1282, \"name\": \"a mermaid tail\"}, {\"id\": 1283, \"name\": \"a dial\"}, {\"id\": 1284, \"name\": \"a dessert box\"}, {\"id\": 1285, \"name\": \"a lower body of a person\"}, {\"id\": 1286, \"name\": \"a pile of reeds\"}, {\"id\": 1287, \"name\": \"a tail feather\"}, {\"id\": 1288, \"name\": \"a vehicle\"}, {\"id\": 1289, \"name\": \"a person's left arm and hand\"}, {\"id\": 1290, \"name\": \"a flowerbed\"}, {\"id\": 1291, \"name\": \"a red sign\"}, {\"id\": 1292, \"name\": \"a white rectangular label with black text\"}, {\"id\": 1293, \"name\": \"a ditch\"}, {\"id\": 1294, \"name\": \"a person's gray hoodie\"}, {\"id\": 1295, \"name\": \"a water dispenser\"}, {\"id\": 1296, \"name\": \"a desert\"}, {\"id\": 1297, \"name\": \"a pink cloud\"}, {\"id\": 1298, \"name\": \"a pool floor\"}, {\"id\": 1299, \"name\": \"a bird's beak\"}, {\"id\": 1300, \"name\": \"a gray, concrete block\"}, {\"id\": 1301, \"name\": \"a rodent\"}, {\"id\": 1302, \"name\": \"a three-wheeled vehicle\"}, {\"id\": 1303, \"name\": \"a snow-covered roof\"}, {\"id\": 1304, \"name\": \"a wooden bar\"}, {\"id\": 1305, \"name\": \"a water buffalo\"}, {\"id\": 1306, \"name\": \"an auto rickshaw\"}, {\"id\": 1307, \"name\": \"a long piece of wood\"}, {\"id\": 1308, \"name\": \"the head\"}, {\"id\": 1309, \"name\": \"a windshield wiper blade\"}, {\"id\": 1310, \"name\": \"a mound of dirt\"}, {\"id\": 1311, \"name\": \"a cucumber\"}, {\"id\": 1312, \"name\": \"a xxx motorcycle helmet\"}, {\"id\": 1313, \"name\": \"a pile of wet brown dirt\"}, {\"id\": 1314, \"name\": \"a pair of chicken legs\"}, {\"id\": 1315, \"name\": \"a red floatation device\"}, {\"id\": 1316, \"name\": \"a dirt road\"}, {\"id\": 1317, \"name\": \"a washing machine control panel\"}, {\"id\": 1318, \"name\": \"a folded sheet\"}, {\"id\": 1319, \"name\": \"a white, rectangular, plastic tube\"}, {\"id\": 1320, \"name\": \"a cat tail\"}, {\"id\": 1321, \"name\": \"a truck bumper\"}, {\"id\": 1322, \"name\": \"a shawl\"}, {\"id\": 1323, \"name\": \"a pink blanket\"}, {\"id\": 1324, \"name\": \"a treadmill\"}, {\"id\": 1325, \"name\": \"a pink purse\"}, {\"id\": 1326, \"name\": \"a white turkey\"}, {\"id\": 1327, \"name\": \"a blue wire\"}, {\"id\": 1328, \"name\": \"a person's right foot\"}, {\"id\": 1329, \"name\": \"a cargo container\"}, {\"id\": 1330, \"name\": \"a baby's foot\"}, {\"id\": 1331, \"name\": \"a whisk\"}, {\"id\": 1332, \"name\": \"a goalpost\"}, {\"id\": 1333, \"name\": \"a rubber chicken\"}, {\"id\": 1334, \"name\": \"a statue of a person\"}, {\"id\": 1335, \"name\": \"a pink sleeve\"}, {\"id\": 1336, \"name\": \"a hairnet\"}, {\"id\": 1337, \"name\": \"a reindeer\"}, {\"id\": 1338, \"name\": \"a blue and white sippy cup\"}, {\"id\": 1339, \"name\": \"a trolley\"}, {\"id\": 1340, \"name\": \"a ventilation unit\"}, {\"id\": 1341, \"name\": \"a leather jacket\"}, {\"id\": 1342, \"name\": \"a road sign\"}, {\"id\": 1343, \"name\": \"a leopard\"}, {\"id\": 1344, \"name\": \"a pair of denim shorts\"}, {\"id\": 1345, \"name\": \"a black metal baseboard\"}, {\"id\": 1346, \"name\": \"a piece of flatbread\"}, {\"id\": 1347, \"name\": \"a digital sign\"}, {\"id\": 1348, \"name\": \"doll\"}, {\"id\": 1349, \"name\": \"a right arm\"}, {\"id\": 1350, \"name\": \"a metal pipe\"}, {\"id\": 1351, \"name\": \"tractor\"}, {\"id\": 1352, \"name\": \"a tomato\"}, {\"id\": 1353, \"name\": \"a jeep\"}, {\"id\": 1354, \"name\": \"a lower body part\"}, {\"id\": 1355, \"name\": \"a saxophone\"}, {\"id\": 1356, \"name\": \"a costume\"}, {\"id\": 1357, \"name\": \"a red fabric\"}, {\"id\": 1358, \"name\": \"a pedestrian crosswalk\"}, {\"id\": 1359, \"name\": \"shopping cart\"}, {\"id\": 1360, \"name\": \"a megaphone\"}, {\"id\": 1361, \"name\": \"a person's shoulder\"}, {\"id\": 1362, \"name\": \"a tarp or canvas covering the top of a truck\"}, {\"id\": 1363, \"name\": \"white cloth\"}, {\"id\": 1364, \"name\": \"a bright blue wall\"}, {\"id\": 1365, \"name\": \"the back of a person's head\"}, {\"id\": 1366, \"name\": \"a steering wheel\"}, {\"id\": 1367, \"name\": \"a cabbage\"}, {\"id\": 1368, \"name\": \"a man's lower body\"}, {\"id\": 1369, \"name\": \"a bride\"}, {\"id\": 1370, \"name\": \"a turquoise wall\"}, {\"id\": 1371, \"name\": \"a star\"}, {\"id\": 1372, \"name\": \"a go-kart\"}, {\"id\": 1373, \"name\": \"a cat bed\"}, {\"id\": 1374, \"name\": \"a pair of boots\"}, {\"id\": 1375, \"name\": \"a black rectangle\"}, {\"id\": 1376, \"name\": \"a blue tarp\"}, {\"id\": 1377, \"name\": \"a woman's lower body\"}, {\"id\": 1378, \"name\": \"a yellow tube\"}, {\"id\": 1379, \"name\": \"a parking space\"}, {\"id\": 1380, \"name\": \"a row of concrete barriers\"}, {\"id\": 1381, \"name\": \"a dinosaur\"}, {\"id\": 1382, \"name\": \"a jar lid\"}, {\"id\": 1383, \"name\": \"a hole\"}, {\"id\": 1384, \"name\": \"an otter\"}, {\"id\": 1385, \"name\": \"a pink storage bin\"}, {\"id\": 1386, \"name\": \"the lower body of a child\"}, {\"id\": 1387, \"name\": \"a red metal can\"}, {\"id\": 1388, \"name\": \"a tripod\"}, {\"id\": 1389, \"name\": \"a digital display\"}, {\"id\": 1390, \"name\": \"a braid\"}, {\"id\": 1391, \"name\": \"a fish eye\"}, {\"id\": 1392, \"name\": \"a green and white sign\"}, {\"id\": 1393, \"name\": \"gate\"}, {\"id\": 1394, \"name\": \"a sunglasses lens\"}, {\"id\": 1395, \"name\": \"a water jet\"}, {\"id\": 1396, \"name\": \"a robe\"}, {\"id\": 1397, \"name\": \"a blue pole\"}, {\"id\": 1398, \"name\": \"a statue of an elephant\"}, {\"id\": 1399, \"name\": \"a pair of black tights\"}, {\"id\": 1400, \"name\": \"a countertop\"}, {\"id\": 1401, \"name\": \"a baseboard\"}, {\"id\": 1402, \"name\": \"a bamboo\"}, {\"id\": 1403, \"name\": \"a bird's foot\"}, {\"id\": 1404, \"name\": \"a piece of corrugated metal\"}, {\"id\": 1405, \"name\": \"a manhole\"}, {\"id\": 1406, \"name\": \"a golf club\"}, {\"id\": 1407, \"name\": \"a paper bag\"}, {\"id\": 1408, \"name\": \"a headwrap\"}, {\"id\": 1409, \"name\": \"a pair of blue and white underwear\"}, {\"id\": 1410, \"name\": \"a xxx luggage\"}, {\"id\": 1411, \"name\": \"a hooded sweatshirt\"}, {\"id\": 1412, \"name\": \"a marble floor\"}, {\"id\": 1413, \"name\": \"a marble countertop\"}, {\"id\": 1414, \"name\": \"a wing\"}, {\"id\": 1415, \"name\": \"a white and blue plastic jug\"}, {\"id\": 1416, \"name\": \"a white refrigerator\"}, {\"id\": 1417, \"name\": \"a white piece of paper\"}, {\"id\": 1418, \"name\": \"a power line\"}, {\"id\": 1419, \"name\": \"a blue bow\"}, {\"id\": 1420, \"name\": \"a bike lane\"}, {\"id\": 1421, \"name\": \"a blue box\"}, {\"id\": 1422, \"name\": \"a car hood\"}, {\"id\": 1423, \"name\": \"a thigh\"}, {\"id\": 1424, \"name\": \"a white cloth, possibly a shirt or a piece of clothing\"}, {\"id\": 1425, \"name\": \"a wrapped pallet\"}, {\"id\": 1426, \"name\": \"a black plastic trim\"}, {\"id\": 1427, \"name\": \"a pair of chopsticks\"}, {\"id\": 1428, \"name\": \"a mannequin\"}, {\"id\": 1429, \"name\": \"a blue plastic spinning chair\"}, {\"id\": 1430, \"name\": \"a scale\"}, {\"id\": 1431, \"name\": \"a yellow trash can\"}, {\"id\": 1432, \"name\": \"a dog's paw\"}, {\"id\": 1433, \"name\": \"a white food item\"}, {\"id\": 1434, \"name\": \"a cake display case\"}, {\"id\": 1435, \"name\": \"a part of a boat\"}, {\"id\": 1436, \"name\": \"a costume of a green monster with one eye and a big smile\"}, {\"id\": 1437, \"name\": \"a book cover\"}, {\"id\": 1438, \"name\": \"a black, shiny metal railing\"}, {\"id\": 1439, \"name\": \"a black cabinet\"}, {\"id\": 1440, \"name\": \"a stage\"}, {\"id\": 1441, \"name\": \"a glass door\"}, {\"id\": 1442, \"name\": \"a display rack\"}, {\"id\": 1443, \"name\": \"a sausage\"}, {\"id\": 1444, \"name\": \"a police car\"}, {\"id\": 1445, \"name\": \"a bollard\"}, {\"id\": 1446, \"name\": \"a soccer ball\"}, {\"id\": 1447, \"name\": \"a desk\"}, {\"id\": 1448, \"name\": \"a tattoo\"}, {\"id\": 1449, \"name\": \"house\"}, {\"id\": 1450, \"name\": \"a person's right knee\"}, {\"id\": 1451, \"name\": \"a lower body part of a person\"}, {\"id\": 1452, \"name\": \"their left arm\"}, {\"id\": 1453, \"name\": \"a pink circular object\"}, {\"id\": 1454, \"name\": \"a monster costume\"}, {\"id\": 1455, \"name\": \"a blue collar\"}, {\"id\": 1456, \"name\": \"a black display screen\"}, {\"id\": 1457, \"name\": \"a gift box\"}, {\"id\": 1458, \"name\": \"a sheet of stickers\"}, {\"id\": 1459, \"name\": \"a red plastic bottle cap\"}, {\"id\": 1460, \"name\": \"a part of a vehicle door handle\"}, {\"id\": 1461, \"name\": \"a shopping bag\"}, {\"id\": 1462, \"name\": \"a white tile\"}, {\"id\": 1463, \"name\": \"a two-person scooter\"}, {\"id\": 1464, \"name\": \"a nail\"}, {\"id\": 1465, \"name\": \"a green plastic object\"}, {\"id\": 1466, \"name\": \"a chain-link fence\"}, {\"id\": 1467, \"name\": \"a black squirrel\"}, {\"id\": 1468, \"name\": \"a streetlight\"}, {\"id\": 1469, \"name\": \"a tricycle\"}, {\"id\": 1470, \"name\": \"a pant\"}, {\"id\": 1471, \"name\": \"yellow plastic basket\"}, {\"id\": 1472, \"name\": \"a pink fuzzy blanket\"}, {\"id\": 1473, \"name\": \"a black shirt\"}, {\"id\": 1474, \"name\": \"painting\"}, {\"id\": 1475, \"name\": \"a lunch box\"}, {\"id\": 1476, \"name\": \"a pancake\"}, {\"id\": 1477, \"name\": \"a red cart\"}, {\"id\": 1478, \"name\": \"a white plate\"}, {\"id\": 1479, \"name\": \"a white plastic cup\"}, {\"id\": 1480, \"name\": \"a utensil holder\"}, {\"id\": 1481, \"name\": \"a patch of grass\"}, {\"id\": 1482, \"name\": \"a metal baseboard\"}, {\"id\": 1483, \"name\": \"couch\"}, {\"id\": 1484, \"name\": \"a dog's leg\"}, {\"id\": 1485, \"name\": \"a wooden fence\"}, {\"id\": 1486, \"name\": \"a white, plastic, rectangular device\"}, {\"id\": 1487, \"name\": \"a car window\"}, {\"id\": 1488, \"name\": \"corn on the cob\"}, {\"id\": 1489, \"name\": \"a plastic or metal box with wheels\"}, {\"id\": 1490, \"name\": \"a boat cover\"}, {\"id\": 1491, \"name\": \"a green plastic bottle\"}, {\"id\": 1492, \"name\": \"a person's ear\"}, {\"id\": 1493, \"name\": \"a water gun\"}, {\"id\": 1494, \"name\": \"a rebar\"}, {\"id\": 1495, \"name\": \"the lower body of the person on the left\"}, {\"id\": 1496, \"name\": \"a motorized scooter\"}, {\"id\": 1497, \"name\": \"a person, a child\"}, {\"id\": 1498, \"name\": \"a wooden handle\"}, {\"id\": 1499, \"name\": \"square\"}, {\"id\": 1500, \"name\": \"a pair of glasses\"}, {\"id\": 1501, \"name\": \"a man playing a guitar\"}, {\"id\": 1502, \"name\": \"a white duck\"}, {\"id\": 1503, \"name\": \"a shopping cart handle\"}, {\"id\": 1504, \"name\": \"a person riding a motorcycle\"}, {\"id\": 1505, \"name\": \"a yellow sign with white text\"}, {\"id\": 1506, \"name\": \"a trunk\"}, {\"id\": 1507, \"name\": \"a white railing\"}, {\"id\": 1508, \"name\": \"a zebra crossing\"}, {\"id\": 1509, \"name\": \"a escalator\"}, {\"id\": 1510, \"name\": \"a truck mirror\"}, {\"id\": 1511, \"name\": \"a mahjong tile\"}, {\"id\": 1512, \"name\": \"a plastic bandage\"}, {\"id\": 1513, \"name\": \"a girl's lower body\"}, {\"id\": 1514, \"name\": \"a blue jacket\"}, {\"id\": 1515, \"name\": \"palm tree\"}, {\"id\": 1516, \"name\": \"table\"}, {\"id\": 1517, \"name\": \"a white object\"}, {\"id\": 1518, \"name\": \"a blue bag\"}, {\"id\": 1519, \"name\": \"a coffee pot\"}, {\"id\": 1520, \"name\": \"man's head\"}, {\"id\": 1521, \"name\": \"a black desk\"}, {\"id\": 1522, \"name\": \"a brown paper bag\"}, {\"id\": 1523, \"name\": \"camera\"}, {\"id\": 1524, \"name\": \"a swimmer\"}, {\"id\": 1525, \"name\": \"a tanker\"}, {\"id\": 1526, \"name\": \"a water filter\"}, {\"id\": 1527, \"name\": \"flagpole\"}, {\"id\": 1528, \"name\": \"a black shelving unit\"}, {\"id\": 1529, \"name\": \"a person's thigh\"}, {\"id\": 1530, \"name\": \"a yellow shutter\"}, {\"id\": 1531, \"name\": \"a wood grain surface\"}, {\"id\": 1532, \"name\": \"a fluorescent light fixture\"}, {\"id\": 1533, \"name\": \"water cooler\"}, {\"id\": 1534, \"name\": \"a soccer player\"}, {\"id\": 1535, \"name\": \"a banner\"}, {\"id\": 1536, \"name\": \"a potted plant\"}, {\"id\": 1537, \"name\": \"a video game controller\"}, {\"id\": 1538, \"name\": \"a chick\"}, {\"id\": 1539, \"name\": \"a cable car\"}, {\"id\": 1540, \"name\": \"a plastic shopping basket\"}, {\"id\": 1541, \"name\": \"finger\"}, {\"id\": 1542, \"name\": \"a person's chest\"}, {\"id\": 1543, \"name\": \"the upper body of a man\"}, {\"id\": 1544, \"name\": \"a wagon\"}, {\"id\": 1545, \"name\": \"tattoo\"}, {\"id\": 1546, \"name\": \"a house\"}, {\"id\": 1547, \"name\": \"a metal railing\"}, {\"id\": 1548, \"name\": \"a packaging\"}, {\"id\": 1549, \"name\": \"a decorative light-up sign\"}, {\"id\": 1550, \"name\": \"a pillar\"}, {\"id\": 1551, \"name\": \"a cow's leg\"}, {\"id\": 1552, \"name\": \"a row of chairs\"}, {\"id\": 1553, \"name\": \"a taillight\"}, {\"id\": 1554, \"name\": \"a number\"}, {\"id\": 1555, \"name\": \"a large white bag\"}, {\"id\": 1556, \"name\": \"a fluorescent light\"}, {\"id\": 1557, \"name\": \"a goblet\"}, {\"id\": 1558, \"name\": \"a candy cane\"}, {\"id\": 1559, \"name\": \"a decorative stone disc\"}, {\"id\": 1560, \"name\": \"a yellow plastic container\"}, {\"id\": 1561, \"name\": \"watermelon\"}, {\"id\": 1562, \"name\": \"a display case\"}, {\"id\": 1563, \"name\": \"a red plastic cap\"}, {\"id\": 1564, \"name\": \"a metal pot\"}, {\"id\": 1565, \"name\": \"a brown leather saddle\"}, {\"id\": 1566, \"name\": \"a bird leg\"}, {\"id\": 1567, \"name\": \"flower\"}, {\"id\": 1568, \"name\": \"a pink and white object\"}, {\"id\": 1569, \"name\": \"a mortar and pestle\"}, {\"id\": 1570, \"name\": \"a chicken's leg\"}, {\"id\": 1571, \"name\": \"a stand\"}, {\"id\": 1572, \"name\": \"a cord\"}, {\"id\": 1573, \"name\": \"a grassy field\"}, {\"id\": 1574, \"name\": \"a wok\"}, {\"id\": 1575, \"name\": \"a paper cup\"}, {\"id\": 1576, \"name\": \"a stone wall\"}, {\"id\": 1577, \"name\": \"a plate of food\"}, {\"id\": 1578, \"name\": \"a head of a white cat\"}, {\"id\": 1579, \"name\": \"a mermaid\"}, {\"id\": 1580, \"name\": \"a woman's torso\"}, {\"id\": 1581, \"name\": \"a yellow motor vehicle\"}, {\"id\": 1582, \"name\": \"road\"}, {\"id\": 1583, \"name\": \"a metal ladder\"}, {\"id\": 1584, \"name\": \"a lemur\"}, {\"id\": 1585, \"name\": \"a turnstile\"}, {\"id\": 1586, \"name\": \"a racket\"}, {\"id\": 1587, \"name\": \"a motorized vehicle\"}, {\"id\": 1588, \"name\": \"a yellow tarp\"}, {\"id\": 1589, \"name\": \"a glass container\"}, {\"id\": 1590, \"name\": \"a hairline\"}, {\"id\": 1591, \"name\": \"a hot dog bun\"}, {\"id\": 1592, \"name\": \"a person, a hand\"}, {\"id\": 1593, \"name\": \"a gas station canopy\"}, {\"id\": 1594, \"name\": \"a blue suitcase\"}, {\"id\": 1595, \"name\": \"a toy quad bike\"}, {\"id\": 1596, \"name\": \"a brown box\"}, {\"id\": 1597, \"name\": \"a child's back\"}, {\"id\": 1598, \"name\": \"a grassy hill\"}, {\"id\": 1599, \"name\": \"a gold-colored metal object\"}, {\"id\": 1600, \"name\": \"a white surface\"}, {\"id\": 1601, \"name\": \"handbag\"}, {\"id\": 1602, \"name\": \"bouquet of flowers\"}, {\"id\": 1603, \"name\": \"a xxx tanker\"}, {\"id\": 1604, \"name\": \"a nostril\"}, {\"id\": 1605, \"name\": \"a lamp post\"}, {\"id\": 1606, \"name\": \"a fishing rod\"}, {\"id\": 1607, \"name\": \"a blue bowl\"}, {\"id\": 1608, \"name\": \"a wall-mounted air conditioner\"}, {\"id\": 1609, \"name\": \"a white paper\"}, {\"id\": 1610, \"name\": \"a white wedding dress\"}, {\"id\": 1611, \"name\": \"newspaper\"}, {\"id\": 1612, \"name\": \"fence\"}, {\"id\": 1613, \"name\": \"a pair of metal tongs\"}, {\"id\": 1614, \"name\": \"a window frame\"}, {\"id\": 1615, \"name\": \"a slotted spoon\"}, {\"id\": 1616, \"name\": \"a wooden log\"}, {\"id\": 1617, \"name\": \"a car tire\"}, {\"id\": 1618, \"name\": \"a shed\"}, {\"id\": 1619, \"name\": \"a part of an escalator\"}, {\"id\": 1620, \"name\": \"a person jumping in the air\"}, {\"id\": 1621, \"name\": \"a page of a book\"}, {\"id\": 1622, \"name\": \"a stick\"}, {\"id\": 1623, \"name\": \"a lower leg\"}, {\"id\": 1624, \"name\": \"a woman sitting in a chair\"}, {\"id\": 1625, \"name\": \"a field of grass\"}, {\"id\": 1626, \"name\": \"a face mask\"}, {\"id\": 1627, \"name\": \"a dome\"}, {\"id\": 1628, \"name\": \"a molding\"}, {\"id\": 1629, \"name\": \"an escalator\"}, {\"id\": 1630, \"name\": \"a car side mirror\"}, {\"id\": 1631, \"name\": \"a panel\"}, {\"id\": 1632, \"name\": \"a plant stem\"}, {\"id\": 1633, \"name\": \"a page\"}, {\"id\": 1634, \"name\": \"a green surface\"}, {\"id\": 1635, \"name\": \"a balloon animal\"}, {\"id\": 1636, \"name\": \"a red car\"}, {\"id\": 1637, \"name\": \"a person's upper body\"}, {\"id\": 1638, \"name\": \"a bleacher\"}, {\"id\": 1639, \"name\": \"a wooden floor\"}, {\"id\": 1640, \"name\": \"a bird feeder\"}, {\"id\": 1641, \"name\": \"a skillet\"}, {\"id\": 1642, \"name\": \"feather\"}, {\"id\": 1643, \"name\": \"monkey\"}, {\"id\": 1644, \"name\": \"a motorcycle mirror\"}, {\"id\": 1645, \"name\": \"a metal grid\"}, {\"id\": 1646, \"name\": \"a goal\"}, {\"id\": 1647, \"name\": \"a pair of black slippers\"}, {\"id\": 1648, \"name\": \"a metal rod\"}, {\"id\": 1649, \"name\": \"a four-wheeled vehicle\"}, {\"id\": 1650, \"name\": \"a muffler\"}, {\"id\": 1651, \"name\": \"laptop\"}, {\"id\": 1652, \"name\": \"a pair of tongs\"}, {\"id\": 1653, \"name\": \"a man's head and shoulders\"}, {\"id\": 1654, \"name\": \"a pork rib\"}, {\"id\": 1655, \"name\": \"a person's right arm and hand\"}, {\"id\": 1656, \"name\": \"a blue curtain\"}, {\"id\": 1657, \"name\": \"a hockey zooo air hockey table\"}, {\"id\": 1658, \"name\": \"a white animal\"}, {\"id\": 1659, \"name\": \"a flatbread\"}, {\"id\": 1660, \"name\": \"a spiderman balloon\"}, {\"id\": 1661, \"name\": \"picture frame\"}, {\"id\": 1662, \"name\": \"a yellow bag with chinese characters\"}, {\"id\": 1663, \"name\": \"a black cord\"}, {\"id\": 1664, \"name\": \"a pair of black rubber gloves\"}, {\"id\": 1665, \"name\": \"a white pvc pipe\"}, {\"id\": 1666, \"name\": \"a yellow sack with chinese characters\"}, {\"id\": 1667, \"name\": \"a dog's neck\"}, {\"id\": 1668, \"name\": \"a panda costume\"}, {\"id\": 1669, \"name\": \"a watermelon\"}, {\"id\": 1670, \"name\": \"a window cleaning tool\"}, {\"id\": 1671, \"name\": \"a yellow plastic bag with red writing on it\"}, {\"id\": 1672, \"name\": \"a christmas ornament\"}, {\"id\": 1673, \"name\": \"drum\"}, {\"id\": 1674, \"name\": \"a hermit crab\"}, {\"id\": 1675, \"name\": \"a pile of debris\"}, {\"id\": 1676, \"name\": \"a pack of chewing gum\"}, {\"id\": 1677, \"name\": \"chinese character\"}, {\"id\": 1678, \"name\": \"a red object\"}, {\"id\": 1679, \"name\": \"a soccer field\"}, {\"id\": 1680, \"name\": \"a group of people\"}, {\"id\": 1681, \"name\": \"a xxx shelf\"}, {\"id\": 1682, \"name\": \"a yellow, blue, and red spotted animal, possibly a dog, on a chain\"}, {\"id\": 1683, \"name\": \"a sea\"}, {\"id\": 1684, \"name\": \"a white puppy\"}, {\"id\": 1685, \"name\": \"green shoe\"}, {\"id\": 1686, \"name\": \"a bus stop\"}, {\"id\": 1687, \"name\": \"the bicycle lane\"}, {\"id\": 1688, \"name\": \"a black and white duck\"}, {\"id\": 1689, \"name\": \"a piece of blue material\"}, {\"id\": 1690, \"name\": \"an island\"}, {\"id\": 1691, \"name\": \"their torso and left arm\"}, {\"id\": 1692, \"name\": \"a person, a dress\"}, {\"id\": 1693, \"name\": \"a young girl with long dark hair\"}, {\"id\": 1694, \"name\": \"a rectangular, white object wrapped in string it is located in the center of the image\"}, {\"id\": 1695, \"name\": \"a moving walkway\"}, {\"id\": 1696, \"name\": \"their pants\"}, {\"id\": 1697, \"name\": \"a metal object\"}, {\"id\": 1698, \"name\": \"a stingray\"}, {\"id\": 1699, \"name\": \"a white tube\"}, {\"id\": 1700, \"name\": \"a green, long, cylindrical, possibly vegetable object\"}, {\"id\": 1701, \"name\": \"a woman with long hair\"}, {\"id\": 1702, \"name\": \"a parasol\"}, {\"id\": 1703, \"name\": \"refrigerator\"}, {\"id\": 1704, \"name\": \"a person, specifically, a man\"}, {\"id\": 1705, \"name\": \"a white counter\"}, {\"id\": 1706, \"name\": \"a stack of paper plates\"}, {\"id\": 1707, \"name\": \"a brown cup\"}, {\"id\": 1708, \"name\": \"a folder\"}, {\"id\": 1709, \"name\": \"a tongs\"}, {\"id\": 1710, \"name\": \"a safety vest\"}, {\"id\": 1711, \"name\": \"a doll\"}, {\"id\": 1712, \"name\": \"a painted line\"}, {\"id\": 1713, \"name\": \"a cooking pot\"}, {\"id\": 1714, \"name\": \"a metal handrail\"}, {\"id\": 1715, \"name\": \"a bank note\"}, {\"id\": 1716, \"name\": \"a pair of blue pants\"}, {\"id\": 1717, \"name\": \"a candle flame\"}, {\"id\": 1718, \"name\": \"a cow's ear\"}, {\"id\": 1719, \"name\": \"a part of a metal counter\"}, {\"id\": 1720, \"name\": \"a ceiling vent\"}, {\"id\": 1721, \"name\": \"foot\"}, {\"id\": 1722, \"name\": \"a yellow paper bag\"}, {\"id\": 1723, \"name\": \"a blue plastic bag\"}, {\"id\": 1724, \"name\": \"cannon\"}, {\"id\": 1725, \"name\": \"a turtle head\"}, {\"id\": 1726, \"name\": \"person's face\"}, {\"id\": 1727, \"name\": \"a background\"}, {\"id\": 1728, \"name\": \"a person, a woman\"}, {\"id\": 1729, \"name\": \"a tea kettle\"}, {\"id\": 1730, \"name\": \"a vertical line\"}, {\"id\": 1731, \"name\": \"a greenhouse\"}, {\"id\": 1732, \"name\": \"a feeding trough\"}, {\"id\": 1733, \"name\": \"a white sheet of fabric\"}, {\"id\": 1734, \"name\": \"a white, horizontal, cylindrical, smooth surface\"}, {\"id\": 1735, \"name\": \"a router\"}, {\"id\": 1736, \"name\": \"necklace\"}, {\"id\": 1737, \"name\": \"a red bag with chinese writing\"}, {\"id\": 1738, \"name\": \"a toy gun\"}, {\"id\": 1739, \"name\": \"a basketball backboard\"}, {\"id\": 1740, \"name\": \"a pet brush\"}, {\"id\": 1741, \"name\": \"a pink garment\"}, {\"id\": 1742, \"name\": \"lantern\"}, {\"id\": 1743, \"name\": \"a raccoon\"}, {\"id\": 1744, \"name\": \"a black metal gate\"}, {\"id\": 1745, \"name\": \"a pathway\"}, {\"id\": 1746, \"name\": \"a gray, textured wall\"}, {\"id\": 1747, \"name\": \"a fire\"}, {\"id\": 1748, \"name\": \"a garland\"}, {\"id\": 1749, \"name\": \"a piece of drywall\"}, {\"id\": 1750, \"name\": \"a suspension bridge\"}, {\"id\": 1751, \"name\": \"the long, horizontal board\"}, {\"id\": 1752, \"name\": \"a red lantern\"}, {\"id\": 1753, \"name\": \"their legs\"}, {\"id\": 1754, \"name\": \"black line\"}, {\"id\": 1755, \"name\": \"a letter\"}, {\"id\": 1756, \"name\": \"street light\"}, {\"id\": 1757, \"name\": \"a pair of legs and feet\"}, {\"id\": 1758, \"name\": \"the white T-shirt\"}, {\"id\": 1759, \"name\": \"a tambourine\"}, {\"id\": 1760, \"name\": \"a red canopy\"}, {\"id\": 1761, \"name\": \"a pom-pom\"}, {\"id\": 1762, \"name\": \"a yellow box\"}, {\"id\": 1763, \"name\": \"a gray metal edge\"}, {\"id\": 1764, \"name\": \"pointed roof\"}, {\"id\": 1765, \"name\": \"a brown leather bag\"}, {\"id\": 1766, \"name\": \"a triangular roof\"}, {\"id\": 1767, \"name\": \"bed\"}, {\"id\": 1768, \"name\": \"a ceiling light\"}, {\"id\": 1769, \"name\": \"vehicle\"}, {\"id\": 1770, \"name\": \"a clear plastic lid\"}, {\"id\": 1771, \"name\": \"a shipping package\"}, {\"id\": 1772, \"name\": \"a baby seat\"}, {\"id\": 1773, \"name\": \"a red umbrella\"}, {\"id\": 1774, \"name\": \"a part of a moving staircase\"}, {\"id\": 1775, \"name\": \"a metal piece of machinery\"}, {\"id\": 1776, \"name\": \"a orange\"}, {\"id\": 1777, \"name\": \"a panda bear\"}, {\"id\": 1778, \"name\": \"a food item\"}, {\"id\": 1779, \"name\": \"a person, specifically, a man's lower body\"}, {\"id\": 1780, \"name\": \"a hummingbird\"}, {\"id\": 1781, \"name\": \"their hooded sweatshirt\"}, {\"id\": 1782, \"name\": \"bag\"}, {\"id\": 1783, \"name\": \"a spider\"}, {\"id\": 1784, \"name\": \"cartoon character\"}, {\"id\": 1785, \"name\": \"a metal ball on top of a metal pole\"}, {\"id\": 1786, \"name\": \"the hands\"}, {\"id\": 1787, \"name\": \"a block of soap\"}, {\"id\": 1788, \"name\": \"a white rectangular sign\"}, {\"id\": 1789, \"name\": \"a stone column\"}, {\"id\": 1790, \"name\": \"curtain\"}, {\"id\": 1791, \"name\": \"statue\"}, {\"id\": 1792, \"name\": \"a gray plastic container\"}, {\"id\": 1793, \"name\": \"toy car\"}, {\"id\": 1794, \"name\": \"bee\"}, {\"id\": 1795, \"name\": \"a ramp\"}, {\"id\": 1796, \"name\": \"a xxx laundry basket\"}, {\"id\": 1797, \"name\": \"a bird's leg\"}, {\"id\": 1798, \"name\": \"a dirt mound\"}, {\"id\": 1799, \"name\": \"a toy duck\"}, {\"id\": 1800, \"name\": \"panda\"}, {\"id\": 1801, \"name\": \"a tortilla\"}, {\"id\": 1802, \"name\": \"a swan\"}, {\"id\": 1803, \"name\": \"a wireless earbud\"}, {\"id\": 1804, \"name\": \"a large clay pot\"}, {\"id\": 1805, \"name\": \"metronome\"}, {\"id\": 1806, \"name\": \"a chicken's eye\"}, {\"id\": 1807, \"name\": \"a weasel\"}, {\"id\": 1808, \"name\": \"a glass and metal railing\"}, {\"id\": 1809, \"name\": \"a long, broken bamboo stalk\"}, {\"id\": 1810, \"name\": \"a teeth\"}, {\"id\": 1811, \"name\": \"a table tennis racket\"}, {\"id\": 1812, \"name\": \"a green cylindrical display stand\"}, {\"id\": 1813, \"name\": \"a dirt pile\"}, {\"id\": 1814, \"name\": \"a leather ottoman\"}, {\"id\": 1815, \"name\": \"a red and gold book\"}, {\"id\": 1816, \"name\": \"a mat\"}, {\"id\": 1817, \"name\": \"a fabric awning\"}, {\"id\": 1818, \"name\": \"a part of a table\"}, {\"id\": 1819, \"name\": \"dress\"}, {\"id\": 1820, \"name\": \"a black metal sheet\"}, {\"id\": 1821, \"name\": \"a blue plastic basin\"}, {\"id\": 1822, \"name\": \"a xxx escalator\"}, {\"id\": 1823, \"name\": \"the upper body and left arm of a woman\"}, {\"id\": 1824, \"name\": \"toilet bowl\"}, {\"id\": 1825, \"name\": \"clock\"}, {\"id\": 1826, \"name\": \"a scanner\"}, {\"id\": 1827, \"name\": \"a person's left foot\"}, {\"id\": 1828, \"name\": \"a belly\"}, {\"id\": 1829, \"name\": \"a woman with long black hair\"}, {\"id\": 1830, \"name\": \"a black leather jacket\"}, {\"id\": 1831, \"name\": \"a forklift\"}, {\"id\": 1832, \"name\": \"a black power adapter with multiple cords\"}, {\"id\": 1833, \"name\": \"a blue container\"}, {\"id\": 1834, \"name\": \"a dog bed\"}, {\"id\": 1835, \"name\": \"a white marble or stone floor with brown spots\"}, {\"id\": 1836, \"name\": \"a brownish, grainy material, possibly a type of food or bedding\"}, {\"id\": 1837, \"name\": \"a plastic tube\"}, {\"id\": 1838, \"name\": \"a spray bottle\"}, {\"id\": 1839, \"name\": \"a person's shorts\"}, {\"id\": 1840, \"name\": \"a black object\"}, {\"id\": 1841, \"name\": \"a pool fence\"}, {\"id\": 1842, \"name\": \"a drone\"}, {\"id\": 1843, \"name\": \"a white hamster\"}, {\"id\": 1844, \"name\": \"a white metal container\"}, {\"id\": 1845, \"name\": \"a long, outdoor, wooden table\"}, {\"id\": 1846, \"name\": \"a yellow metal post\"}, {\"id\": 1847, \"name\": \"a legging\"}, {\"id\": 1848, \"name\": \"a pedicab\"}, {\"id\": 1849, \"name\": \"barcode\"}, {\"id\": 1850, \"name\": \"a bag of banana chips\"}, {\"id\": 1851, \"name\": \"a person, a baby\"}, {\"id\": 1852, \"name\": \"a metal beam\"}, {\"id\": 1853, \"name\": \"a metal structure\"}, {\"id\": 1854, \"name\": \"a hurdle\"}, {\"id\": 1855, \"name\": \"a utility pole\"}, {\"id\": 1856, \"name\": \"book\"}, {\"id\": 1857, \"name\": \"a blue flag\"}, {\"id\": 1858, \"name\": \"a bird wing\"}, {\"id\": 1859, \"name\": \"giraffe\"}, {\"id\": 1860, \"name\": \"a gray sign\"}, {\"id\": 1861, \"name\": \"a tv screen\"}, {\"id\": 1862, \"name\": \"orange ball\"}, {\"id\": 1863, \"name\": \"colorful mural\"}, {\"id\": 1864, \"name\": \"a piece of black fabric\"}, {\"id\": 1865, \"name\": \"conical hat\"}, {\"id\": 1866, \"name\": \"an orange traffic cone\"}, {\"id\": 1867, \"name\": \"khaki pant\"}, {\"id\": 1868, \"name\": \"the red brick wall\"}, {\"id\": 1869, \"name\": \"the grey pant\"}, {\"id\": 1870, \"name\": \"the workbook\"}, {\"id\": 1871, \"name\": \"image\"}, {\"id\": 1872, \"name\": \"a yellow label\"}, {\"id\": 1873, \"name\": \"a concrete patio\"}, {\"id\": 1874, \"name\": \"geese\"}, {\"id\": 1875, \"name\": \"red dump truck\"}, {\"id\": 1876, \"name\": \"the gray floral pattern\"}, {\"id\": 1877, \"name\": \"the plumage\"}, {\"id\": 1878, \"name\": \"the bangs\"}, {\"id\": 1879, \"name\": \"a wooden base\"}, {\"id\": 1880, \"name\": \"brown and white chicken\"}, {\"id\": 1881, \"name\": \"long, fluffy tail\"}, {\"id\": 1882, \"name\": \"a white shelving unit\"}, {\"id\": 1883, \"name\": \"red frame\"}, {\"id\": 1884, \"name\": \"a soccer jersey\"}, {\"id\": 1885, \"name\": \"casual attire\"}, {\"id\": 1886, \"name\": \"courtyard\"}, {\"id\": 1887, \"name\": \"the group of five kittens\"}, {\"id\": 1888, \"name\": \"dark pant\"}, {\"id\": 1889, \"name\": \"the orange shirt\"}, {\"id\": 1890, \"name\": \"a silver car\"}, {\"id\": 1891, \"name\": \"an image\"}, {\"id\": 1892, \"name\": \"small chick\"}, {\"id\": 1893, \"name\": \"the blue bench\"}, {\"id\": 1894, \"name\": \"yellow roof\"}, {\"id\": 1895, \"name\": \"the group of cats\"}, {\"id\": 1896, \"name\": \"the white baseball cap\"}, {\"id\": 1897, \"name\": \"the geese\"}, {\"id\": 1898, \"name\": \"the light blue wall\"}, {\"id\": 1899, \"name\": \"a water's surface\"}, {\"id\": 1900, \"name\": \"large yellow container\"}, {\"id\": 1901, \"name\": \"the ceramic vase\"}, {\"id\": 1902, \"name\": \"the chocolate chip\"}, {\"id\": 1903, \"name\": \"a red stool\"}, {\"id\": 1904, \"name\": \"silver car\"}, {\"id\": 1905, \"name\": \"group of ducks\"}, {\"id\": 1906, \"name\": \"a small dog\"}, {\"id\": 1907, \"name\": \"maroon shirt\"}, {\"id\": 1908, \"name\": \"a small patch of grass\"}, {\"id\": 1909, \"name\": \"the large metal bridge\"}, {\"id\": 1910, \"name\": \"air vent\"}, {\"id\": 1911, \"name\": \"dirt enclosure\"}, {\"id\": 1912, \"name\": \"a dark blue jacket\"}, {\"id\": 1913, \"name\": \"a wattle\"}, {\"id\": 1914, \"name\": \"dark hair\"}, {\"id\": 1915, \"name\": \"metal trough\"}, {\"id\": 1916, \"name\": \"a black saddle\"}, {\"id\": 1917, \"name\": \"the metal utensil\"}, {\"id\": 1918, \"name\": \"a plumage\"}, {\"id\": 1919, \"name\": \"the prominent escalator\"}, {\"id\": 1920, \"name\": \"blue background\"}, {\"id\": 1921, \"name\": \"a black and white striped curb\"}, {\"id\": 1922, \"name\": \"silver watch\"}, {\"id\": 1923, \"name\": \"a blue top with horizontal stripes\"}, {\"id\": 1924, \"name\": \"the patterned rug\"}, {\"id\": 1925, \"name\": \"an outdoor court\"}, {\"id\": 1926, \"name\": \"the clear blue sky\"}, {\"id\": 1927, \"name\": \"the train station platform\"}, {\"id\": 1928, \"name\": \"a close-up view of a fish tank\"}, {\"id\": 1929, \"name\": \"a gold crown\"}, {\"id\": 1930, \"name\": \"pink surface\"}, {\"id\": 1931, \"name\": \"a wicker cage\"}, {\"id\": 1932, \"name\": \"the hardwood floor\"}, {\"id\": 1933, \"name\": \"a large blue mushroom\"}, {\"id\": 1934, \"name\": \"a brown jar\"}, {\"id\": 1935, \"name\": \"gold jacket\"}, {\"id\": 1936, \"name\": \"the large piece of driftwood\"}, {\"id\": 1937, \"name\": \"white plastic chair\"}, {\"id\": 1938, \"name\": \"the room\"}, {\"id\": 1939, \"name\": \"colorful woven basket\"}, {\"id\": 1940, \"name\": \"an aluminum roof\"}, {\"id\": 1941, \"name\": \"a green facade\"}, {\"id\": 1942, \"name\": \"the red and black leopard-print dress\"}, {\"id\": 1943, \"name\": \"the book's cover\"}, {\"id\": 1944, \"name\": \"white railing\"}, {\"id\": 1945, \"name\": \"the small black and yellow insect\"}, {\"id\": 1946, \"name\": \"a red arrow\"}, {\"id\": 1947, \"name\": \"the orange beak\"}, {\"id\": 1948, \"name\": \"the dark hair\"}, {\"id\": 1949, \"name\": \"muddy, grassy area\"}, {\"id\": 1950, \"name\": \"the black tail\"}, {\"id\": 1951, \"name\": \"yellow beak\"}, {\"id\": 1952, \"name\": \"red door frame\"}, {\"id\": 1953, \"name\": \"the porcelain doll\"}, {\"id\": 1954, \"name\": \"the green head covering\"}, {\"id\": 1955, \"name\": \"the brown dog\"}, {\"id\": 1956, \"name\": \"the group of pigeons\"}, {\"id\": 1957, \"name\": \"gray car\"}, {\"id\": 1958, \"name\": \"a brown wooden table\"}, {\"id\": 1959, \"name\": \"residential building\"}, {\"id\": 1960, \"name\": \"the black and white striped shirt\"}, {\"id\": 1961, \"name\": \"the large black rock\"}, {\"id\": 1962, \"name\": \"wire mesh fence\"}, {\"id\": 1963, \"name\": \"white sneaker\"}, {\"id\": 1964, \"name\": \"man in a hard hat\"}, {\"id\": 1965, \"name\": \"short, dark hair\"}, {\"id\": 1966, \"name\": \"concrete\"}, {\"id\": 1967, \"name\": \"a fluffy tail\"}, {\"id\": 1968, \"name\": \"the open door\"}, {\"id\": 1969, \"name\": \"the rack of clothes\"}, {\"id\": 1970, \"name\": \"a yellow truck\"}, {\"id\": 1971, \"name\": \"a silver bowl\"}, {\"id\": 1972, \"name\": \"the blue leash\"}, {\"id\": 1973, \"name\": \"a black t-shirt\"}, {\"id\": 1974, \"name\": \"the river's water\"}, {\"id\": 1975, \"name\": \"navy blue shirt\"}, {\"id\": 1976, \"name\": \"the porcelain plate\"}, {\"id\": 1977, \"name\": \"orange cone\"}, {\"id\": 1978, \"name\": \"the right wrist\"}, {\"id\": 1979, \"name\": \"the elephant's mouth\"}, {\"id\": 1980, \"name\": \"silver hubcap\"}, {\"id\": 1981, \"name\": \"wire fence\"}, {\"id\": 1982, \"name\": \"gray t-shirt\"}, {\"id\": 1983, \"name\": \"the grassy verge\"}, {\"id\": 1984, \"name\": \"a small plant\"}, {\"id\": 1985, \"name\": \"the right hand\"}, {\"id\": 1986, \"name\": \"the iron rod\"}, {\"id\": 1987, \"name\": \"black star\"}, {\"id\": 1988, \"name\": \"rectangular vent\"}, {\"id\": 1989, \"name\": \"clear blue sky\"}, {\"id\": 1990, \"name\": \"rocky shoreline\"}, {\"id\": 1991, \"name\": \"a photo\"}, {\"id\": 1992, \"name\": \"a black burka\"}, {\"id\": 1993, \"name\": \"the wire mesh door\"}, {\"id\": 1994, \"name\": \"red train\"}, {\"id\": 1995, \"name\": \"the cat's body\"}, {\"id\": 1996, \"name\": \"wooden trough\"}, {\"id\": 1997, \"name\": \"round metal dish\"}, {\"id\": 1998, \"name\": \"cylindrical base\"}, {\"id\": 1999, \"name\": \"the storefront window\"}, {\"id\": 2000, \"name\": \"a white truck\"}, {\"id\": 2001, \"name\": \"the people\"}, {\"id\": 2002, \"name\": \"an orange trim\"}, {\"id\": 2003, \"name\": \"left hand\"}, {\"id\": 2004, \"name\": \"a hardwood floor\"}, {\"id\": 2005, \"name\": \"wooden perch\"}, {\"id\": 2006, \"name\": \"white geese\"}, {\"id\": 2007, \"name\": \"a group of wild pigs\"}, {\"id\": 2008, \"name\": \"room's wall\"}, {\"id\": 2009, \"name\": \"the flatbed\"}, {\"id\": 2010, \"name\": \"the basket of food\"}, {\"id\": 2011, \"name\": \"the traditional Vietnamese conical hat\"}, {\"id\": 2012, \"name\": \"the dirt enclosure\"}, {\"id\": 2013, \"name\": \"concrete walkway\"}, {\"id\": 2014, \"name\": \"a black scale\"}, {\"id\": 2015, \"name\": \"parked car\"}, {\"id\": 2016, \"name\": \"scaffolding\"}, {\"id\": 2017, \"name\": \"a white LED light\"}, {\"id\": 2018, \"name\": \"a yellow beak\"}, {\"id\": 2019, \"name\": \"right hand\"}, {\"id\": 2020, \"name\": \"small tree\"}, {\"id\": 2021, \"name\": \"index finger\"}, {\"id\": 2022, \"name\": \"a black border\"}, {\"id\": 2023, \"name\": \"a green head\"}, {\"id\": 2024, \"name\": \"the white frame\"}, {\"id\": 2025, \"name\": \"red metal post\"}, {\"id\": 2026, \"name\": \"the gray concrete floor\"}, {\"id\": 2027, \"name\": \"red plate\"}, {\"id\": 2028, \"name\": \"serrated edge\"}, {\"id\": 2029, \"name\": \"the distinctive red comb\"}, {\"id\": 2030, \"name\": \"a black feather\"}, {\"id\": 2031, \"name\": \"the rocking chair\"}, {\"id\": 2032, \"name\": \"a brown top\"}, {\"id\": 2033, \"name\": \"navy blue tracksuit\"}, {\"id\": 2034, \"name\": \"white polo shirt\"}, {\"id\": 2035, \"name\": \"the colorful woven basket\"}, {\"id\": 2036, \"name\": \"silver bowl\"}, {\"id\": 2037, \"name\": \"a corrugated metal roof\"}, {\"id\": 2038, \"name\": \"a paved road\"}, {\"id\": 2039, \"name\": \"the white entertainment center\"}, {\"id\": 2040, \"name\": \"the Dell logo\"}, {\"id\": 2041, \"name\": \"compact parking lot\"}, {\"id\": 2042, \"name\": \"the yellow bone toy\"}, {\"id\": 2043, \"name\": \"a room\"}, {\"id\": 2044, \"name\": \"the small white pellet\"}, {\"id\": 2045, \"name\": \"coop\"}, {\"id\": 2046, \"name\": \"balcony's railing\"}, {\"id\": 2047, \"name\": \"the black cursive\"}, {\"id\": 2048, \"name\": \"the largest cow\"}, {\"id\": 2049, \"name\": \"a black-and-white striped shirt\"}, {\"id\": 2050, \"name\": \"the blue sneaker\"}, {\"id\": 2051, \"name\": \"an orange toy\"}, {\"id\": 2052, \"name\": \"the elephant's skin\"}, {\"id\": 2053, \"name\": \"the yellow tablecloth\"}, {\"id\": 2054, \"name\": \"the green arrow\"}, {\"id\": 2055, \"name\": \"a hen\"}, {\"id\": 2056, \"name\": \"the group of five baby chickens\"}, {\"id\": 2057, \"name\": \"a water trough\"}, {\"id\": 2058, \"name\": \"a t-shirt\"}, {\"id\": 2059, \"name\": \"white logo\"}, {\"id\": 2060, \"name\": \"white tablecloth\"}, {\"id\": 2061, \"name\": \"fur-lined hood\"}, {\"id\": 2062, \"name\": \"a rural road\"}, {\"id\": 2063, \"name\": \"a wooden cage\"}, {\"id\": 2064, \"name\": \"red bicycle\"}, {\"id\": 2065, \"name\": \"a red sweater\"}, {\"id\": 2066, \"name\": \"storefront window display\"}, {\"id\": 2067, \"name\": \"a large truck\"}, {\"id\": 2068, \"name\": \"an orange dress\"}, {\"id\": 2069, \"name\": \"the group of men\"}, {\"id\": 2070, \"name\": \"the brown leather booth\"}, {\"id\": 2071, \"name\": \"a crystal-clear water\"}, {\"id\": 2072, \"name\": \"a green grate floor\"}, {\"id\": 2073, \"name\": \"a golden retriever\"}, {\"id\": 2074, \"name\": \"a bright shirt\"}, {\"id\": 2075, \"name\": \"gecko\"}, {\"id\": 2076, \"name\": \"room\"}, {\"id\": 2077, \"name\": \"the spout\"}, {\"id\": 2078, \"name\": \"a group of ducks\"}, {\"id\": 2079, \"name\": \"raised platform\"}, {\"id\": 2080, \"name\": \"the blue boat\"}, {\"id\": 2081, \"name\": \"the dark wood table\"}, {\"id\": 2082, \"name\": \"the purple bridle\"}, {\"id\": 2083, \"name\": \"a parking area\"}, {\"id\": 2084, \"name\": \"the group of ducks\"}, {\"id\": 2085, \"name\": \"a red storefront\"}, {\"id\": 2086, \"name\": \"beige wall\"}, {\"id\": 2087, \"name\": \"a black pot\"}, {\"id\": 2088, \"name\": \"adult\"}, {\"id\": 2089, \"name\": \"red and black striped shirt\"}, {\"id\": 2090, \"name\": \"the row of buildings\"}, {\"id\": 2091, \"name\": \"a blue border\"}, {\"id\": 2092, \"name\": \"the black bicycle\"}, {\"id\": 2093, \"name\": \"the light blue shirt\"}, {\"id\": 2094, \"name\": \"green truck\"}, {\"id\": 2095, \"name\": \"an orange wall\"}, {\"id\": 2096, \"name\": \"the puddle of water\"}, {\"id\": 2097, \"name\": \"red helmet\"}, {\"id\": 2098, \"name\": \"tan shoe\"}, {\"id\": 2099, \"name\": \"the out of focus background\"}, {\"id\": 2100, \"name\": \"the large escalator\"}, {\"id\": 2101, \"name\": \"long, dark hair\"}, {\"id\": 2102, \"name\": \"a flower shop\"}, {\"id\": 2103, \"name\": \"the green watering can\"}, {\"id\": 2104, \"name\": \"the blue roof\"}, {\"id\": 2105, \"name\": \"stucco\"}, {\"id\": 2106, \"name\": \"the pony\"}, {\"id\": 2107, \"name\": \"a small black dog\"}, {\"id\": 2108, \"name\": \"a group of black and white ducks\"}, {\"id\": 2109, \"name\": \"the glass top\"}, {\"id\": 2110, \"name\": \"a smaller screen\"}, {\"id\": 2111, \"name\": \"pink and black jacket\"}, {\"id\": 2112, \"name\": \"the white rooster\"}, {\"id\": 2113, \"name\": \"the black hatchback\"}, {\"id\": 2114, \"name\": \"a wooden canoe\"}, {\"id\": 2115, \"name\": \"shoreline\"}, {\"id\": 2116, \"name\": \"an orange tabby cat\"}, {\"id\": 2117, \"name\": \"small aquarium\"}, {\"id\": 2118, \"name\": \"a hind leg\"}, {\"id\": 2119, \"name\": \"the red vending machine\"}, {\"id\": 2120, \"name\": \"dark brown cat\"}, {\"id\": 2121, \"name\": \"the white t-shirt\"}, {\"id\": 2122, \"name\": \"a tiled floor\"}, {\"id\": 2123, \"name\": \"green border\"}, {\"id\": 2124, \"name\": \"the concrete road\"}, {\"id\": 2125, \"name\": \"a large building\"}, {\"id\": 2126, \"name\": \"a third goose\"}, {\"id\": 2127, \"name\": \"the red bottle\"}, {\"id\": 2128, \"name\": \"a beige exterior\"}, {\"id\": 2129, \"name\": \"a group of pigeons\"}, {\"id\": 2130, \"name\": \"the boat's railing\"}, {\"id\": 2131, \"name\": \"smaller calf\"}, {\"id\": 2132, \"name\": \"the black head\"}, {\"id\": 2133, \"name\": \"a metal bowl of water\"}, {\"id\": 2134, \"name\": \"a large fish\"}, {\"id\": 2135, \"name\": \"brown dog\"}, {\"id\": 2136, \"name\": \"a green cat\"}, {\"id\": 2137, \"name\": \"the asphalt road\"}, {\"id\": 2138, \"name\": \"a wood shavings\"}, {\"id\": 2139, \"name\": \"ridge\"}, {\"id\": 2140, \"name\": \"the dirt area\"}, {\"id\": 2141, \"name\": \"an assortment of toys\"}, {\"id\": 2142, \"name\": \"the woman's attire\"}, {\"id\": 2143, \"name\": \"a yellow cab\"}, {\"id\": 2144, \"name\": \"the white paw\"}, {\"id\": 2145, \"name\": \"red and yellow tinsel garland\"}, {\"id\": 2146, \"name\": \"the floral design\"}, {\"id\": 2147, \"name\": \"aquarium\"}, {\"id\": 2148, \"name\": \"the phone number\"}, {\"id\": 2149, \"name\": \"a metal chair\"}, {\"id\": 2150, \"name\": \"a grey stone floor\"}, {\"id\": 2151, \"name\": \"group of men\"}, {\"id\": 2152, \"name\": \"a six kittens\"}, {\"id\": 2153, \"name\": \"a skink\"}, {\"id\": 2154, \"name\": \"saddle\"}, {\"id\": 2155, \"name\": \"reflection\"}, {\"id\": 2156, \"name\": \"brown duck\"}, {\"id\": 2157, \"name\": \"the Venezuelan flag\"}, {\"id\": 2158, \"name\": \"a yellow brick wall\"}, {\"id\": 2159, \"name\": \"a player\"}, {\"id\": 2160, \"name\": \"the coop\"}, {\"id\": 2161, \"name\": \"the sequin\"}, {\"id\": 2162, \"name\": \"the blue harness\"}, {\"id\": 2163, \"name\": \"a white paper cup\"}, {\"id\": 2164, \"name\": \"the grey ear\"}, {\"id\": 2165, \"name\": \"black leggings\"}, {\"id\": 2166, \"name\": \"large cardboard box\"}, {\"id\": 2167, \"name\": \"a dark hair\"}, {\"id\": 2168, \"name\": \"the staggered pattern\"}, {\"id\": 2169, \"name\": \"cartoon panda\"}, {\"id\": 2170, \"name\": \"a vehicle's interior\"}, {\"id\": 2171, \"name\": \"large, dark-colored fish\"}, {\"id\": 2172, \"name\": \"a black tarp\"}, {\"id\": 2173, \"name\": \"black shorts\"}, {\"id\": 2174, \"name\": \"a brown pattern\"}, {\"id\": 2175, \"name\": \"a wire mesh top\"}, {\"id\": 2176, \"name\": \"people\"}, {\"id\": 2177, \"name\": \"the small brown dog\"}, {\"id\": 2178, \"name\": \"the clean bird\"}, {\"id\": 2179, \"name\": \"the yellow building\"}, {\"id\": 2180, \"name\": \"a red trim\"}, {\"id\": 2181, \"name\": \"the gray skin\"}, {\"id\": 2182, \"name\": \"the large truck\"}, {\"id\": 2183, \"name\": \"right shoulder\"}, {\"id\": 2184, \"name\": \"the white SUV\"}, {\"id\": 2185, \"name\": \"a concrete building\"}, {\"id\": 2186, \"name\": \"a wooden desk\"}, {\"id\": 2187, \"name\": \"a small white cup\"}, {\"id\": 2188, \"name\": \"weed\"}, {\"id\": 2189, \"name\": \"the old book\"}, {\"id\": 2190, \"name\": \"the small drawer\"}, {\"id\": 2191, \"name\": \"a diverse array of plants and flowers\"}, {\"id\": 2192, \"name\": \"the enclosure\"}, {\"id\": 2193, \"name\": \"the dark background\"}, {\"id\": 2194, \"name\": \"a dark wooden table\"}, {\"id\": 2195, \"name\": \"a gymnasium\"}, {\"id\": 2196, \"name\": \"a coop's interior\"}, {\"id\": 2197, \"name\": \"the green flip-flop\"}, {\"id\": 2198, \"name\": \"the water's surface\"}, {\"id\": 2199, \"name\": \"a tennis court\"}, {\"id\": 2200, \"name\": \"a white, textured surface\"}, {\"id\": 2201, \"name\": \"the dirt lot\"}, {\"id\": 2202, \"name\": \"the large window\"}, {\"id\": 2203, \"name\": \"sow\"}, {\"id\": 2204, \"name\": \"the driftwood\"}, {\"id\": 2205, \"name\": \"the school classroom\"}, {\"id\": 2206, \"name\": \"a slender trunk\"}, {\"id\": 2207, \"name\": \"the football field\"}, {\"id\": 2208, \"name\": \"the play area\"}, {\"id\": 2209, \"name\": \"the small glass bowl\"}, {\"id\": 2210, \"name\": \"the white sneaker\"}, {\"id\": 2211, \"name\": \"the orange bowl\"}, {\"id\": 2212, \"name\": \"the insect\"}, {\"id\": 2213, \"name\": \"the large pile of wooden planks\"}, {\"id\": 2214, \"name\": \"a white neck\"}, {\"id\": 2215, \"name\": \"concrete road\"}, {\"id\": 2216, \"name\": \"the student\"}, {\"id\": 2217, \"name\": \"sandcastle\"}, {\"id\": 2218, \"name\": \"limousine\"}, {\"id\": 2219, \"name\": \"the old microphone\"}, {\"id\": 2220, \"name\": \"the snow-capped peak\"}, {\"id\": 2221, \"name\": \"new year hat\"}, {\"id\": 2222, \"name\": \"the blue curb\"}, {\"id\": 2223, \"name\": \"the bird bath\"}, {\"id\": 2224, \"name\": \"an ancient ruin\"}, {\"id\": 2225, \"name\": \"copper pipe\"}, {\"id\": 2226, \"name\": \"desert landscape\"}, {\"id\": 2227, \"name\": \"a school zone\"}, {\"id\": 2228, \"name\": \"silver bike\"}, {\"id\": 2229, \"name\": \"a stone path\"}, {\"id\": 2230, \"name\": \"the airport\"}, {\"id\": 2231, \"name\": \"ocean\"}, {\"id\": 2232, \"name\": \"a small elevator\"}, {\"id\": 2233, \"name\": \"a paper poster\"}, {\"id\": 2234, \"name\": \"a pig pen\"}, {\"id\": 2235, \"name\": \"brick building\"}, {\"id\": 2236, \"name\": \"city street\"}, {\"id\": 2237, \"name\": \"a stone statue\"}, {\"id\": 2238, \"name\": \"an iron fence\"}, {\"id\": 2239, \"name\": \"valentine heart\"}, {\"id\": 2240, \"name\": \"the copper pot\"}, {\"id\": 2241, \"name\": \"a lake\"}, {\"id\": 2242, \"name\": \"a high-rise building\"}, {\"id\": 2243, \"name\": \"green leash\"}, {\"id\": 2244, \"name\": \"coffee shop\"}, {\"id\": 2245, \"name\": \"a desert landscape\"}, {\"id\": 2246, \"name\": \"dry soil\"}, {\"id\": 2247, \"name\": \"centerpiece\"}, {\"id\": 2248, \"name\": \"cat litter box\"}, {\"id\": 2249, \"name\": \"the office building\"}, {\"id\": 2250, \"name\": \"a hotel\"}, {\"id\": 2251, \"name\": \"a European-inspired clothing\"}, {\"id\": 2252, \"name\": \"iron gate\"}, {\"id\": 2253, \"name\": \"the glass castle\"}, {\"id\": 2254, \"name\": \"the iron gate\"}, {\"id\": 2255, \"name\": \"steel fence\"}, {\"id\": 2256, \"name\": \"a steel beam\"}, {\"id\": 2257, \"name\": \"a yellow rubber chicken\"}, {\"id\": 2258, \"name\": \"a tile ceiling\"}, {\"id\": 2259, \"name\": \"the metal table\"}, {\"id\": 2260, \"name\": \"the red helmet\"}, {\"id\": 2261, \"name\": \"the image\"}, {\"id\": 2262, \"name\": \"a city\"}, {\"id\": 2263, \"name\": \"the black body\"}, {\"id\": 2264, \"name\": \"bird's-eye view\"}, {\"id\": 2265, \"name\": \"the subway entrance\"}, {\"id\": 2266, \"name\": \"the green head\"}, {\"id\": 2267, \"name\": \"a light brown coat\"}, {\"id\": 2268, \"name\": \"shopping mall\"}, {\"id\": 2269, \"name\": \"a bright hallway\"}, {\"id\": 2270, \"name\": \"a brick house\"}, {\"id\": 2271, \"name\": \"the two-story apartment building\"}, {\"id\": 2272, \"name\": \"the park\"}, {\"id\": 2273, \"name\": \"stone facade\"}, {\"id\": 2274, \"name\": \"the brown turkey\"}, {\"id\": 2275, \"name\": \"iron door\"}, {\"id\": 2276, \"name\": \"purple mat\"}, {\"id\": 2277, \"name\": \"a library\"}, {\"id\": 2278, \"name\": \"a fabric curtain\"}, {\"id\": 2279, \"name\": \"the old man\"}, {\"id\": 2280, \"name\": \"a striped socks\"}, {\"id\": 2281, \"name\": \"a yellow motorcycle\"}, {\"id\": 2282, \"name\": \"the iron lamp\"}, {\"id\": 2283, \"name\": \"the lake\"}, {\"id\": 2284, \"name\": \"a paper sign\"}, {\"id\": 2285, \"name\": \"fabric curtain\"}, {\"id\": 2286, \"name\": \"a white boat\"}, {\"id\": 2287, \"name\": \"the dirty floor\"}, {\"id\": 2288, \"name\": \"the orange flyer\"}, {\"id\": 2289, \"name\": \"mortar\"}, {\"id\": 2290, \"name\": \"the silver bike\"}, {\"id\": 2291, \"name\": \"a green window\"}, {\"id\": 2292, \"name\": \"pedestrian bridge\"}, {\"id\": 2293, \"name\": \"the light pant\"}, {\"id\": 2294, \"name\": \"vibrant orange\"}, {\"id\": 2295, \"name\": \"a tropical setting\"}, {\"id\": 2296, \"name\": \"a football player\"}, {\"id\": 2297, \"name\": \"the question mark\"}, {\"id\": 2298, \"name\": \"the orange cone\"}, {\"id\": 2299, \"name\": \"the blonde hair\"}, {\"id\": 2300, \"name\": \"the plastic spoon\"}, {\"id\": 2301, \"name\": \"stone path\"}, {\"id\": 2302, \"name\": \"the large tire\"}, {\"id\": 2303, \"name\": \"black robot\"}, {\"id\": 2304, \"name\": \"the brick column\"}, {\"id\": 2305, \"name\": \"sandy beach\"}, {\"id\": 2306, \"name\": \"the blue tablecloth\"}, {\"id\": 2307, \"name\": \"a colorful flowers\"}, {\"id\": 2308, \"name\": \"a glass elevator\"}, {\"id\": 2309, \"name\": \"the kitchen sink\"}, {\"id\": 2310, \"name\": \"iron rod\"}, {\"id\": 2311, \"name\": \"a ceramic vase\"}, {\"id\": 2312, \"name\": \"food court\"}, {\"id\": 2313, \"name\": \"the fluffy carpet\"}, {\"id\": 2314, \"name\": \"orange bicycle\"}, {\"id\": 2315, \"name\": \"a purple scarf\"}, {\"id\": 2316, \"name\": \"a football helmet\"}, {\"id\": 2317, \"name\": \"blue facade\"}, {\"id\": 2318, \"name\": \"a rusty pipe\"}, {\"id\": 2319, \"name\": \"wooden dock\"}, {\"id\": 2320, \"name\": \"steel column\"}, {\"id\": 2321, \"name\": \"a feed\"}, {\"id\": 2322, \"name\": \"a roadside diner\"}, {\"id\": 2323, \"name\": \"the green truck\"}, {\"id\": 2324, \"name\": \"green lawn\"}, {\"id\": 2325, \"name\": \"ceramic plate\"}, {\"id\": 2326, \"name\": \"the leather saddle\"}, {\"id\": 2327, \"name\": \"a wooden bridge\"}, {\"id\": 2328, \"name\": \"bird bath\"}, {\"id\": 2329, \"name\": \"the white boat\"}, {\"id\": 2330, \"name\": \"park bench\"}, {\"id\": 2331, \"name\": \"a steel door\"}, {\"id\": 2332, \"name\": \"the coffee shop\"}, {\"id\": 2333, \"name\": \"the iron fence\"}, {\"id\": 2334, \"name\": \"a wooden shutter\"}, {\"id\": 2335, \"name\": \"the rolling hill\"}, {\"id\": 2336, \"name\": \"the metal fork\"}, {\"id\": 2337, \"name\": \"empty room\"}, {\"id\": 2338, \"name\": \"the yellow flower\"}, {\"id\": 2339, \"name\": \"a black rug\"}, {\"id\": 2340, \"name\": \"a wooden cabin\"}, {\"id\": 2341, \"name\": \"a stone vase\"}, {\"id\": 2342, \"name\": \"the empty trash can\"}, {\"id\": 2343, \"name\": \"the brown duck\"}, {\"id\": 2344, \"name\": \"New Year's hat\"}, {\"id\": 2345, \"name\": \"a bare tree\"}, {\"id\": 2346, \"name\": \"a dense jungle\"}, {\"id\": 2347, \"name\": \"a steel bridge\"}, {\"id\": 2348, \"name\": \"the thanksgiving turkey\"}, {\"id\": 2349, \"name\": \"the gray hat\"}, {\"id\": 2350, \"name\": \"sunny weather\"}, {\"id\": 2351, \"name\": \"a soft cushion\"}, {\"id\": 2352, \"name\": \"rabbit's whisker\"}, {\"id\": 2353, \"name\": \"the goat beard\"}, {\"id\": 2354, \"name\": \"the library\"}, {\"id\": 2355, \"name\": \"a streetlamp\"}, {\"id\": 2356, \"name\": \"a paper box\"}, {\"id\": 2357, \"name\": \"green motorcycle\"}, {\"id\": 2358, \"name\": \"a sheep's wool\"}, {\"id\": 2359, \"name\": \"metal lamp\"}, {\"id\": 2360, \"name\": \"paved road\"}, {\"id\": 2361, \"name\": \"a swimming pool\"}, {\"id\": 2362, \"name\": \"a toll booth\"}, {\"id\": 2363, \"name\": \"spring flower\"}, {\"id\": 2364, \"name\": \"the riverbank\"}, {\"id\": 2365, \"name\": \"a quiet library\"}, {\"id\": 2366, \"name\": \"the white bicycle\"}, {\"id\": 2367, \"name\": \"the copper wire\"}, {\"id\": 2368, \"name\": \"autumn leaf\"}, {\"id\": 2369, \"name\": \"the pig's snout\"}, {\"id\": 2370, \"name\": \"velvet curtain\"}, {\"id\": 2371, \"name\": \"a pedestrian path\"}, {\"id\": 2372, \"name\": \"a train station\"}, {\"id\": 2373, \"name\": \"old book\"}, {\"id\": 2374, \"name\": \"a brown door\"}, {\"id\": 2375, \"name\": \"kitchen sink\"}, {\"id\": 2376, \"name\": \"the chinese language\"}, {\"id\": 2377, \"name\": \"purple line\"}, {\"id\": 2378, \"name\": \"an old computer\"}, {\"id\": 2379, \"name\": \"the halloween costume\"}, {\"id\": 2380, \"name\": \"a metal shutter\"}, {\"id\": 2381, \"name\": \"the soft carpet\"}, {\"id\": 2382, \"name\": \"the pedestrian\"}, {\"id\": 2383, \"name\": \"a concrete bridge\"}, {\"id\": 2384, \"name\": \"swimming pool\"}, {\"id\": 2385, \"name\": \"the grey carpet\"}, {\"id\": 2386, \"name\": \"the iron railing\"}, {\"id\": 2387, \"name\": \"the goat pen\"}, {\"id\": 2388, \"name\": \"a stone tunnel\"}, {\"id\": 2389, \"name\": \"gray mouse\"}, {\"id\": 2390, \"name\": \"the peaceful garden\"}, {\"id\": 2391, \"name\": \"steel tower\"}, {\"id\": 2392, \"name\": \"metal chair\"}, {\"id\": 2393, \"name\": \"a city square\"}, {\"id\": 2394, \"name\": \"a city street\"}, {\"id\": 2395, \"name\": \"the winter snowflake\"}, {\"id\": 2396, \"name\": \"the swim goggles\"}, {\"id\": 2397, \"name\": \"white helmet\"}, {\"id\": 2398, \"name\": \"a purple tree\"}, {\"id\": 2399, \"name\": \"the exercise bike\"}, {\"id\": 2400, \"name\": \"white piano\"}, {\"id\": 2401, \"name\": \"the white line on the floor\"}, {\"id\": 2402, \"name\": \"a playground\"}, {\"id\": 2403, \"name\": \"paper poster\"}, {\"id\": 2404, \"name\": \"an email address\"}, {\"id\": 2405, \"name\": \"the sandcastle\"}, {\"id\": 2406, \"name\": \"the sandy beach\"}, {\"id\": 2407, \"name\": \"an ocean\"}, {\"id\": 2408, \"name\": \"the wooden bowl\"}, {\"id\": 2409, \"name\": \"an old camera\"}, {\"id\": 2410, \"name\": \"a coffee\"}, {\"id\": 2411, \"name\": \"a dock\"}, {\"id\": 2412, \"name\": \"the swimming pool\"}, {\"id\": 2413, \"name\": \"a green bicycle\"}, {\"id\": 2414, \"name\": \"a camera flash\"}, {\"id\": 2415, \"name\": \"a dimly lit room\"}, {\"id\": 2416, \"name\": \"a hospital\"}, {\"id\": 2417, \"name\": \"the steel column\"}, {\"id\": 2418, \"name\": \"a pigpen\"}, {\"id\": 2419, \"name\": \"an orange car\"}, {\"id\": 2420, \"name\": \"a silver van\"}, {\"id\": 2421, \"name\": \"a wooden pan\"}, {\"id\": 2422, \"name\": \"wooden barn\"}, {\"id\": 2423, \"name\": \"the iceberg\"}, {\"id\": 2424, \"name\": \"a small table\"}, {\"id\": 2425, \"name\": \"a bright lamp\"}, {\"id\": 2426, \"name\": \"orange pillow\"}, {\"id\": 2427, \"name\": \"the orange truck\"}, {\"id\": 2428, \"name\": \"the blue building\"}, {\"id\": 2429, \"name\": \"a dark room\"}, {\"id\": 2430, \"name\": \"the brown shoe\"}, {\"id\": 2431, \"name\": \"bright clothing\"}, {\"id\": 2432, \"name\": \"white sheep\"}, {\"id\": 2433, \"name\": \"a paper book\"}, {\"id\": 2434, \"name\": \"the rusty pipe\"}, {\"id\": 2435, \"name\": \"water bucket\"}, {\"id\": 2436, \"name\": \"the European city\"}, {\"id\": 2437, \"name\": \"the shiny metal\"}, {\"id\": 2438, \"name\": \"bathroom mirror\"}, {\"id\": 2439, \"name\": \"the hotel\"}, {\"id\": 2440, \"name\": \"orange shoes\"}, {\"id\": 2441, \"name\": \"an abstract painting\"}, {\"id\": 2442, \"name\": \"red barn\"}, {\"id\": 2443, \"name\": \"a small window\"}, {\"id\": 2444, \"name\": \"a highway\"}, {\"id\": 2445, \"name\": \"the black jean\"}, {\"id\": 2446, \"name\": \"urban city\"}, {\"id\": 2447, \"name\": \"the book store\"}, {\"id\": 2448, \"name\": \"apple print design\"}, {\"id\": 2449, \"name\": \"the football helmet\"}, {\"id\": 2450, \"name\": \"a cowshed\"}, {\"id\": 2451, \"name\": \"Easter egg\"}, {\"id\": 2452, \"name\": \"blue rug\"}, {\"id\": 2453, \"name\": \"copper pot\"}, {\"id\": 2454, \"name\": \"a metal table\"}, {\"id\": 2455, \"name\": \"clean sidewalk\"}, {\"id\": 2456, \"name\": \"rusty gate\"}, {\"id\": 2457, \"name\": \"the kitchen\"}, {\"id\": 2458, \"name\": \"a ceramic mug\"}, {\"id\": 2459, \"name\": \"the steel knife\"}, {\"id\": 2460, \"name\": \"a brown table\"}, {\"id\": 2461, \"name\": \"a ceramic plate\"}, {\"id\": 2462, \"name\": \"dim lighting\"}, {\"id\": 2463, \"name\": \"a soft blanket\"}, {\"id\": 2464, \"name\": \"plastic toy\"}, {\"id\": 2465, \"name\": \"the calm ocean\"}, {\"id\": 2466, \"name\": \"small TV\"}, {\"id\": 2467, \"name\": \"black horse\"}, {\"id\": 2468, \"name\": \"brown rabbit\"}, {\"id\": 2469, \"name\": \"the hockey rink\"}, {\"id\": 2470, \"name\": \"a newspaper stand\"}, {\"id\": 2471, \"name\": \"a copper pot\"}, {\"id\": 2472, \"name\": \"a green door\"}, {\"id\": 2473, \"name\": \"coffee machine\"}, {\"id\": 2474, \"name\": \"a pigeon loft\"}, {\"id\": 2475, \"name\": \"the row of old computers\"}, {\"id\": 2476, \"name\": \"green taxi\"}, {\"id\": 2477, \"name\": \"the plastic tub\"}, {\"id\": 2478, \"name\": \"urban skyscraper\"}, {\"id\": 2479, \"name\": \"bright lamp\"}, {\"id\": 2480, \"name\": \"a hay bale\"}, {\"id\": 2481, \"name\": \"deep pool\"}, {\"id\": 2482, \"name\": \"silver necklace\"}, {\"id\": 2483, \"name\": \"the yellow pillow\"}, {\"id\": 2484, \"name\": \"horse stable\"}, {\"id\": 2485, \"name\": \"the frozen lake\"}, {\"id\": 2486, \"name\": \"a broken glass\"}, {\"id\": 2487, \"name\": \"the high angle\"}, {\"id\": 2488, \"name\": \"a park\"}, {\"id\": 2489, \"name\": \"the trumpeter\"}, {\"id\": 2490, \"name\": \"a glass plate\"}, {\"id\": 2491, \"name\": \"the black sneaker\"}, {\"id\": 2492, \"name\": \"a large shark\"}, {\"id\": 2493, \"name\": \"leather sofa\"}, {\"id\": 2494, \"name\": \"lake\"}, {\"id\": 2495, \"name\": \"red cardinal\"}, {\"id\": 2496, \"name\": \"the broken vase\"}, {\"id\": 2497, \"name\": \"construction sign\"}, {\"id\": 2498, \"name\": \"the bedroom\"}, {\"id\": 2499, \"name\": \"taxi stand\"}, {\"id\": 2500, \"name\": \"Mexican sombrero\"}, {\"id\": 2501, \"name\": \"tennis court\"}, {\"id\": 2502, \"name\": \"the snorkeling gear\"}, {\"id\": 2503, \"name\": \"the stone statue\"}, {\"id\": 2504, \"name\": \"the empty chair\"}, {\"id\": 2505, \"name\": \"brown horse\"}, {\"id\": 2506, \"name\": \"an iron railing\"}, {\"id\": 2507, \"name\": \"the silk fabric\"}, {\"id\": 2508, \"name\": \"the garden\"}, {\"id\": 2509, \"name\": \"the haystack\"}, {\"id\": 2510, \"name\": \"a broken bench\"}, {\"id\": 2511, \"name\": \"the tennis court\"}, {\"id\": 2512, \"name\": \"a broken swing\"}, {\"id\": 2513, \"name\": \"purple curtain\"}, {\"id\": 2514, \"name\": \"the glass plate\"}, {\"id\": 2515, \"name\": \"the green cake\"}, {\"id\": 2516, \"name\": \"hospital\"}, {\"id\": 2517, \"name\": \"flower shop\"}, {\"id\": 2518, \"name\": \"an electric mixer\"}, {\"id\": 2519, \"name\": \"the white candle\"}, {\"id\": 2520, \"name\": \"the blue water\"}, {\"id\": 2521, \"name\": \"a concrete foundation\"}, {\"id\": 2522, \"name\": \"porcelain vase\"}, {\"id\": 2523, \"name\": \"iron chain\"}, {\"id\": 2524, \"name\": \"a grey shirt\"}, {\"id\": 2525, \"name\": \"a bicycle lane\"}, {\"id\": 2526, \"name\": \"a stormy weather\"}, {\"id\": 2527, \"name\": \"the porcelain vase\"}, {\"id\": 2528, \"name\": \"the wooden shutter\"}, {\"id\": 2529, \"name\": \"black sunglasses\"}, {\"id\": 2530, \"name\": \"rabbit's ear\"}, {\"id\": 2531, \"name\": \"small club\"}, {\"id\": 2532, \"name\": \"yellow bike\"}, {\"id\": 2533, \"name\": \"a silver frame\"}, {\"id\": 2534, \"name\": \"a brown floor\"}, {\"id\": 2535, \"name\": \"paper sheet\"}, {\"id\": 2536, \"name\": \"anchor\"}, {\"id\": 2537, \"name\": \"the steel bookshelf\"}, {\"id\": 2538, \"name\": \"tea\"}, {\"id\": 2539, \"name\": \"pink feather\"}, {\"id\": 2540, \"name\": \"the blue jay\"}, {\"id\": 2541, \"name\": \"torn paper\"}, {\"id\": 2542, \"name\": \"velvet cloth\"}, {\"id\": 2543, \"name\": \"the wooden bridge\"}, {\"id\": 2544, \"name\": \"the bicycle path\"}, {\"id\": 2545, \"name\": \"park\"}, {\"id\": 2546, \"name\": \"the metal lamp\"}, {\"id\": 2547, \"name\": \"the dim light\"}, {\"id\": 2548, \"name\": \"the steel pipe\"}, {\"id\": 2549, \"name\": \"the anchor\"}, {\"id\": 2550, \"name\": \"snorkeling gear\"}, {\"id\": 2551, \"name\": \"the purple helmet\"}, {\"id\": 2552, \"name\": \"a night club\"}, {\"id\": 2553, \"name\": \"a seaweed\"}, {\"id\": 2554, \"name\": \"a turkey enclosure\"}, {\"id\": 2555, \"name\": \"police station\"}, {\"id\": 2556, \"name\": \"the metal whisk\"}, {\"id\": 2557, \"name\": \"a grey cat\"}, {\"id\": 2558, \"name\": \"wooden desk\"}, {\"id\": 2559, \"name\": \"a fish pond\"}, {\"id\": 2560, \"name\": \"a sheep pen\"}, {\"id\": 2561, \"name\": \"a black sock\"}, {\"id\": 2562, \"name\": \"red turkey\"}, {\"id\": 2563, \"name\": \"the ocean\"}, {\"id\": 2564, \"name\": \"grey mouse\"}, {\"id\": 2565, \"name\": \"a brick foundation\"}, {\"id\": 2566, \"name\": \"clean water\"}, {\"id\": 2567, \"name\": \"the wooden dock\"}, {\"id\": 2568, \"name\": \"wooden frog\"}, {\"id\": 2569, \"name\": \"stone pedestal\"}, {\"id\": 2570, \"name\": \"a wooden tablecloth\"}, {\"id\": 2571, \"name\": \"a denim jeans\"}, {\"id\": 2572, \"name\": \"the green wing\"}, {\"id\": 2573, \"name\": \"the soft fabric\"}, {\"id\": 2574, \"name\": \"an old shoe\"}, {\"id\": 2575, \"name\": \"the bottom door\"}, {\"id\": 2576, \"name\": \"a steel slab\"}, {\"id\": 2577, \"name\": \"an airport\"}, {\"id\": 2578, \"name\": \"the grey mat\"}, {\"id\": 2579, \"name\": \"a scuba diver\"}, {\"id\": 2580, \"name\": \"a green sofa\"}, {\"id\": 2581, \"name\": \"iron railing\"}, {\"id\": 2582, \"name\": \"the picnic blanket\"}, {\"id\": 2583, \"name\": \"wooden countertop\"}, {\"id\": 2584, \"name\": \"the black motorcycle\"}, {\"id\": 2585, \"name\": \"the steel frame\"}, {\"id\": 2586, \"name\": \"ceramic vase\"}, {\"id\": 2587, \"name\": \"a basketball jersey\"}, {\"id\": 2588, \"name\": \"a dirty puddle\"}, {\"id\": 2589, \"name\": \"a silver lamp\"}, {\"id\": 2590, \"name\": \"a restaurant menu\"}, {\"id\": 2591, \"name\": \"an ocean wave\"}, {\"id\": 2592, \"name\": \"wooden bridge\"}, {\"id\": 2593, \"name\": \"the red collar\"}, {\"id\": 2594, \"name\": \"the brown rabbit\"}, {\"id\": 2595, \"name\": \"the grey fur\"}, {\"id\": 2596, \"name\": \"ocean wave\"}, {\"id\": 2597, \"name\": \"iron chair\"}, {\"id\": 2598, \"name\": \"the laboratory table\"}, {\"id\": 2599, \"name\": \"a black boots\"}, {\"id\": 2600, \"name\": \"green beak\"}, {\"id\": 2601, \"name\": \"the paper book\"}, {\"id\": 2602, \"name\": \"the rusty gate\"}, {\"id\": 2603, \"name\": \"cow stall\"}, {\"id\": 2604, \"name\": \"an empty stall\"}, {\"id\": 2605, \"name\": \"empty street\"}, {\"id\": 2606, \"name\": \"teacher's desk\"}, {\"id\": 2607, \"name\": \"an iron pipe\"}, {\"id\": 2608, \"name\": \"the rugby pitch\"}, {\"id\": 2609, \"name\": \"the baseball stadium\"}, {\"id\": 2610, \"name\": \"the shower\"}, {\"id\": 2611, \"name\": \"a black bean\"}, {\"id\": 2612, \"name\": \"a large dog\"}, {\"id\": 2613, \"name\": \"the paper towel roll\"}, {\"id\": 2614, \"name\": \"copper bowl\"}, {\"id\": 2615, \"name\": \"the ocean scene\"}, {\"id\": 2616, \"name\": \"the dim lamp\"}, {\"id\": 2617, \"name\": \"a round opening\"}, {\"id\": 2618, \"name\": \"the gymnastics mat\"}, {\"id\": 2619, \"name\": \"the metal grill\"}, {\"id\": 2620, \"name\": \"brown shoes\"}, {\"id\": 2621, \"name\": \"a wooden gate\"}, {\"id\": 2622, \"name\": \"a plain white shirt\"}, {\"id\": 2623, \"name\": \"black toy\"}, {\"id\": 2624, \"name\": \"a water fall\"}, {\"id\": 2625, \"name\": \"fabric wall\"}, {\"id\": 2626, \"name\": \"a silver vase\"}, {\"id\": 2627, \"name\": \"the metal watch\"}, {\"id\": 2628, \"name\": \"a wooden bowl\"}, {\"id\": 2629, \"name\": \"silver spoon\"}, {\"id\": 2630, \"name\": \"airport\"}, {\"id\": 2631, \"name\": \"a green forest\"}, {\"id\": 2632, \"name\": \"library\"}, {\"id\": 2633, \"name\": \"a copper wire\"}, {\"id\": 2634, \"name\": \"the plain shirt\"}, {\"id\": 2635, \"name\": \"a small vase\"}, {\"id\": 2636, \"name\": \"the small tree\"}, {\"id\": 2637, \"name\": \"a green flower\"}, {\"id\": 2638, \"name\": \"golden retriever\"}, {\"id\": 2639, \"name\": \"steel bridge\"}, {\"id\": 2640, \"name\": \"blue fish\"}, {\"id\": 2641, \"name\": \"an old book\"}, {\"id\": 2642, \"name\": \"stone paver\"}, {\"id\": 2643, \"name\": \"a large snake\"}, {\"id\": 2644, \"name\": \"turned-off lamp\"}, {\"id\": 2645, \"name\": \"steel railing\"}, {\"id\": 2646, \"name\": \"the treasure chest\"}, {\"id\": 2647, \"name\": \"a sandbox\"}, {\"id\": 2648, \"name\": \"coral reef\"}, {\"id\": 2649, \"name\": \"succulent\"}, {\"id\": 2650, \"name\": \"the velvet curtain\"}, {\"id\": 2651, \"name\": \"soft carpet\"}, {\"id\": 2652, \"name\": \"stone rock\"}, {\"id\": 2653, \"name\": \"the red flower\"}, {\"id\": 2654, \"name\": \"a red canary\"}, {\"id\": 2655, \"name\": \"a beach scene\"}, {\"id\": 2656, \"name\": \"an orange flower\"}, {\"id\": 2657, \"name\": \"the blue ocean\"}, {\"id\": 2658, \"name\": \"a steel pipe\"}, {\"id\": 2659, \"name\": \"rocky terrain\"}, {\"id\": 2660, \"name\": \"a football pads\"}, {\"id\": 2661, \"name\": \"a smooth floor\"}, {\"id\": 2662, \"name\": \"single book\"}, {\"id\": 2663, \"name\": \"a wooden deck\"}, {\"id\": 2664, \"name\": \"steel pipe\"}, {\"id\": 2665, \"name\": \"the red barn\"}, {\"id\": 2666, \"name\": \"the yellow canary\"}, {\"id\": 2667, \"name\": \"red toy\"}, {\"id\": 2668, \"name\": \"the foreground\"}, {\"id\": 2669, \"name\": \"a silver phone\"}, {\"id\": 2670, \"name\": \"a running track\"}, {\"id\": 2671, \"name\": \"baseball field\"}, {\"id\": 2672, \"name\": \"green grasshopper\"}, {\"id\": 2673, \"name\": \"green alien\"}, {\"id\": 2674, \"name\": \"a stainless steel fork\"}, {\"id\": 2675, \"name\": \"the stone foundation\"}, {\"id\": 2676, \"name\": \"sink faucet\"}, {\"id\": 2677, \"name\": \"a pebble\"}, {\"id\": 2678, \"name\": \"black building\"}, {\"id\": 2679, \"name\": \"a restaurant table\"}, {\"id\": 2680, \"name\": \"grey pigeon\"}, {\"id\": 2681, \"name\": \"gold necklace\"}, {\"id\": 2682, \"name\": \"the construction crane\"}, {\"id\": 2683, \"name\": \"the old building\"}, {\"id\": 2684, \"name\": \"a torn fabric\"}, {\"id\": 2685, \"name\": \"the farm truck\"}, {\"id\": 2686, \"name\": \"the soft pillow\"}, {\"id\": 2687, \"name\": \"the nurse\"}, {\"id\": 2688, \"name\": \"an old building\"}, {\"id\": 2689, \"name\": \"blue decoration\"}, {\"id\": 2690, \"name\": \"a pedestrian bridge\"}, {\"id\": 2691, \"name\": \"the yellow bike\"}, {\"id\": 2692, \"name\": \"iron fence\"}, {\"id\": 2693, \"name\": \"an old tire\"}, {\"id\": 2694, \"name\": \"fabric box\"}, {\"id\": 2695, \"name\": \"a ping pong table\"}, {\"id\": 2696, \"name\": \"a computer screen\"}, {\"id\": 2697, \"name\": \"metal floor\"}, {\"id\": 2698, \"name\": \"the brown belt\"}, {\"id\": 2699, \"name\": \"a farmer\"}, {\"id\": 2700, \"name\": \"a blue jay\"}, {\"id\": 2701, \"name\": \"striped shirt\"}, {\"id\": 2702, \"name\": \"a brown dog\"}, {\"id\": 2703, \"name\": \"the copper pipe\"}, {\"id\": 2704, \"name\": \"the gray squirrel\"}, {\"id\": 2705, \"name\": \"the tea cup\"}, {\"id\": 2706, \"name\": \"a limousine\"}, {\"id\": 2707, \"name\": \"the paper box\"}, {\"id\": 2708, \"name\": \"the ukulele\"}, {\"id\": 2709, \"name\": \"the yellow fish\"}, {\"id\": 2710, \"name\": \"a fabric cloth\"}, {\"id\": 2711, \"name\": \"a fabric mesh\"}, {\"id\": 2712, \"name\": \"stone container\"}, {\"id\": 2713, \"name\": \"human singer\"}, {\"id\": 2714, \"name\": \"the stadium\"}, {\"id\": 2715, \"name\": \"the blue bike\"}, {\"id\": 2716, \"name\": \"doghouse\"}, {\"id\": 2717, \"name\": \"the beach scene\"}, {\"id\": 2718, \"name\": \"the Asian-inspired jewelry\"}, {\"id\": 2719, \"name\": \"sun hat\"}, {\"id\": 2720, \"name\": \"a yellow flower\"}, {\"id\": 2721, \"name\": \"an orange scarf\"}, {\"id\": 2722, \"name\": \"the empty room\"}, {\"id\": 2723, \"name\": \"a blue vase\"}, {\"id\": 2724, \"name\": \"a torn paper\"}, {\"id\": 2725, \"name\": \"scuba diver\"}, {\"id\": 2726, \"name\": \"medical chart\"}, {\"id\": 2727, \"name\": \"blue jay\"}, {\"id\": 2728, \"name\": \"iron curtain rod\"}, {\"id\": 2729, \"name\": \"an empty shelf\"}, {\"id\": 2730, \"name\": \"the rusty metal railing\"}, {\"id\": 2731, \"name\": \"golden frame\"}, {\"id\": 2732, \"name\": \"the bus station\"}, {\"id\": 2733, \"name\": \"an iron gate\"}, {\"id\": 2734, \"name\": \"woven basket\"}, {\"id\": 2735, \"name\": \"silver lamp\"}, {\"id\": 2736, \"name\": \"a toad\"}, {\"id\": 2737, \"name\": \"broken chair\"}, {\"id\": 2738, \"name\": \"an anchor\"}, {\"id\": 2739, \"name\": \"an old boat\"}, {\"id\": 2740, \"name\": \"the red toy\"}, {\"id\": 2741, \"name\": \"the red couch\"}, {\"id\": 2742, \"name\": \"green dog\"}, {\"id\": 2743, \"name\": \"the highway\"}, {\"id\": 2744, \"name\": \"asphalt road\"}, {\"id\": 2745, \"name\": \"the ocean floor\"}, {\"id\": 2746, \"name\": \"concrete foundation\"}, {\"id\": 2747, \"name\": \"big brown dog\"}, {\"id\": 2748, \"name\": \"green bicycle\"}, {\"id\": 2749, \"name\": \"large tree\"}, {\"id\": 2750, \"name\": \"football field\"}, {\"id\": 2751, \"name\": \"a green snake\"}, {\"id\": 2752, \"name\": \"a grey rock\"}, {\"id\": 2753, \"name\": \"office building\"}, {\"id\": 2754, \"name\": \"a red flower\"}, {\"id\": 2755, \"name\": \"the blue berry\"}, {\"id\": 2756, \"name\": \"a yellow snail\"}, {\"id\": 2757, \"name\": \"the picture on wall\"}, {\"id\": 2758, \"name\": \"a coral reef\"}, {\"id\": 2759, \"name\": \"highway overpass\"}, {\"id\": 2760, \"name\": \"metal fork\"}, {\"id\": 2761, \"name\": \"a first aid kit\"}, {\"id\": 2762, \"name\": \"the velvet fabric\"}, {\"id\": 2763, \"name\": \"a space helmet\"}, {\"id\": 2764, \"name\": \"the metal basket\"}, {\"id\": 2765, \"name\": \"the steel chain\"}, {\"id\": 2766, \"name\": \"the plastic decoration\"}, {\"id\": 2767, \"name\": \"black motorcycle\"}, {\"id\": 2768, \"name\": \"the bright room\"}, {\"id\": 2769, \"name\": \"a sandy beach\"}, {\"id\": 2770, \"name\": \"a porcelain vase\"}, {\"id\": 2771, \"name\": \"a swimwear\"}, {\"id\": 2772, \"name\": \"the rabbit hutch\"}, {\"id\": 2773, \"name\": \"metal desk\"}, {\"id\": 2774, \"name\": \"ice cream cone\"}, {\"id\": 2775, \"name\": \"the silver necklace\"}, {\"id\": 2776, \"name\": \"an animal\"}, {\"id\": 2777, \"name\": \"broken glass\"}, {\"id\": 2778, \"name\": \"a kitchen sink\"}, {\"id\": 2779, \"name\": \"farm\"}, {\"id\": 2780, \"name\": \"subway tunnel\"}, {\"id\": 2781, \"name\": \"a red cardinal\"}, {\"id\": 2782, \"name\": \"an empty street\"}, {\"id\": 2783, \"name\": \"a fluffy carpet\"}, {\"id\": 2784, \"name\": \"paperclip\"}, {\"id\": 2785, \"name\": \"a horse stable\"}, {\"id\": 2786, \"name\": \"a grey jacket\"}, {\"id\": 2787, \"name\": \"the red pen\"}, {\"id\": 2788, \"name\": \"drummer\"}, {\"id\": 2789, \"name\": \"black boots\"}, {\"id\": 2790, \"name\": \"hockey rink\"}, {\"id\": 2791, \"name\": \"grey pants\"}, {\"id\": 2792, \"name\": \"animal costume\"}, {\"id\": 2793, \"name\": \"large dog\"}, {\"id\": 2794, \"name\": \"a parked car\"}, {\"id\": 2795, \"name\": \"the brown shoes\"}, {\"id\": 2796, \"name\": \"a baby boy\"}, {\"id\": 2797, \"name\": \"the wooden ruler\"}, {\"id\": 2798, \"name\": \"the beer glass\"}, {\"id\": 2799, \"name\": \"the indoor party\"}, {\"id\": 2800, \"name\": \"a sandcastle\"}, {\"id\": 2801, \"name\": \"the orchestra\"}, {\"id\": 2802, \"name\": \"a silk scarf\"}, {\"id\": 2803, \"name\": \"the silver ring\"}, {\"id\": 2804, \"name\": \"a velvet pillow\"}, {\"id\": 2805, \"name\": \"the taxi stand\"}, {\"id\": 2806, \"name\": \"green frog\"}, {\"id\": 2807, \"name\": \"the fabric sofa\"}, {\"id\": 2808, \"name\": \"a long blonde hair\"}, {\"id\": 2809, \"name\": \"old clock\"}, {\"id\": 2810, \"name\": \"a ripe fruit\"}, {\"id\": 2811, \"name\": \"movie theater\"}, {\"id\": 2812, \"name\": \"the green sofa\"}, {\"id\": 2813, \"name\": \"glass cup\"}, {\"id\": 2814, \"name\": \"red flower\"}, {\"id\": 2815, \"name\": \"the green laser\"}, {\"id\": 2816, \"name\": \"a white butterfly\"}, {\"id\": 2817, \"name\": \"the red cardinal\"}, {\"id\": 2818, \"name\": \"golfer\"}, {\"id\": 2819, \"name\": \"metal table\"}, {\"id\": 2820, \"name\": \"old bookshelf\"}, {\"id\": 2821, \"name\": \"a porcelain plate\"}, {\"id\": 2822, \"name\": \"a football field\"}, {\"id\": 2823, \"name\": \"the sparkling pool\"}, {\"id\": 2824, \"name\": \"the diving helmet\"}, {\"id\": 2825, \"name\": \"large computer\"}, {\"id\": 2826, \"name\": \"the purple berry\"}, {\"id\": 2827, \"name\": \"bicycle lane\"}, {\"id\": 2828, \"name\": \"a golden bowl\"}, {\"id\": 2829, \"name\": \"the soft toy\"}, {\"id\": 2830, \"name\": \"the urban building\"}, {\"id\": 2831, \"name\": \"the green door\"}, {\"id\": 2832, \"name\": \"blue motorcycle\"}, {\"id\": 2833, \"name\": \"bare tree\"}, {\"id\": 2834, \"name\": \"a red graphic\"}, {\"id\": 2835, \"name\": \"office chair\"}, {\"id\": 2836, \"name\": \"fabric tablecloth\"}, {\"id\": 2837, \"name\": \"a copper pipe\"}, {\"id\": 2838, \"name\": \"pond\"}, {\"id\": 2839, \"name\": \"a white tent\"}, {\"id\": 2840, \"name\": \"goat pen\"}, {\"id\": 2841, \"name\": \"a black swan\"}, {\"id\": 2842, \"name\": \"a cotton socks\"}, {\"id\": 2843, \"name\": \"the red bird\"}, {\"id\": 2844, \"name\": \"a hawk\"}, {\"id\": 2845, \"name\": \"brown boots\"}, {\"id\": 2846, \"name\": \"the rural road\"}, {\"id\": 2847, \"name\": \"a velvet curtain\"}, {\"id\": 2848, \"name\": \"artificial decoration\"}, {\"id\": 2849, \"name\": \"red coral\"}, {\"id\": 2850, \"name\": \"a coffee shop\"}, {\"id\": 2851, \"name\": \"a garden hose\"}, {\"id\": 2852, \"name\": \"dry riverbed\"}, {\"id\": 2853, \"name\": \"a metal lamp\"}, {\"id\": 2854, \"name\": \"the water trough\"}, {\"id\": 2855, \"name\": \"the stream\"}, {\"id\": 2856, \"name\": \"the aquarium\"}, {\"id\": 2857, \"name\": \"blue tag\"}, {\"id\": 2858, \"name\": \"the cat litter\"}, {\"id\": 2859, \"name\": \"wooden shutter\"}, {\"id\": 2860, \"name\": \"velvet carpet\"}, {\"id\": 2861, \"name\": \"an orange umbrella\"}, {\"id\": 2862, \"name\": \"the construction cone\"}, {\"id\": 2863, \"name\": \"blonde hair\"}, {\"id\": 2864, \"name\": \"a tile roof\"}, {\"id\": 2865, \"name\": \"the red scarf\"}, {\"id\": 2866, \"name\": \"the boat dock\"}, {\"id\": 2867, \"name\": \"abandoned factory\"}, {\"id\": 2868, \"name\": \"the golden retriever\"}, {\"id\": 2869, \"name\": \"a tile counter\"}, {\"id\": 2870, \"name\": \"the pond\"}, {\"id\": 2871, \"name\": \"the wooden building\"}, {\"id\": 2872, \"name\": \"a rubber tire\"}, {\"id\": 2873, \"name\": \"the movie theater\"}, {\"id\": 2874, \"name\": \"the dark glasses\"}, {\"id\": 2875, \"name\": \"moon\"}, {\"id\": 2876, \"name\": \"the grey sweater\"}, {\"id\": 2877, \"name\": \"a rusty bike\"}, {\"id\": 2878, \"name\": \"the steel door\"}, {\"id\": 2879, \"name\": \"subway entrance\"}, {\"id\": 2880, \"name\": \"the orange scarf\"}, {\"id\": 2881, \"name\": \"a brown carpet\"}, {\"id\": 2882, \"name\": \"the grey mouse\"}, {\"id\": 2883, \"name\": \"a taxi cab\"}, {\"id\": 2884, \"name\": \"the elevator\"}, {\"id\": 2885, \"name\": \"a concrete path\"}, {\"id\": 2886, \"name\": \"a purple car\"}, {\"id\": 2887, \"name\": \"paper book\"}, {\"id\": 2888, \"name\": \"the candle lighting\"}, {\"id\": 2889, \"name\": \"goat enclosure\"}, {\"id\": 2890, \"name\": \"office\"}, {\"id\": 2891, \"name\": \"the turkey pen\"}, {\"id\": 2892, \"name\": \"the horse stable\"}, {\"id\": 2893, \"name\": \"the computer screen\"}, {\"id\": 2894, \"name\": \"old person\"}, {\"id\": 2895, \"name\": \"laptop bag\"}, {\"id\": 2896, \"name\": \"a fire engine\"}, {\"id\": 2897, \"name\": \"the ice cream cone\"}, {\"id\": 2898, \"name\": \"restaurant table\"}, {\"id\": 2899, \"name\": \"an office building\"}, {\"id\": 2900, \"name\": \"a Brazilian flag\"}, {\"id\": 2901, \"name\": \"meeting room\"}, {\"id\": 2902, \"name\": \"the steel beam\"}, {\"id\": 2903, \"name\": \"the iron door\"}, {\"id\": 2904, \"name\": \"a metal sign\"}, {\"id\": 2905, \"name\": \"restaurant kitchen\"}, {\"id\": 2906, \"name\": \"a blue bike\"}, {\"id\": 2907, \"name\": \"an outdoor cafe\"}, {\"id\": 2908, \"name\": \"the stone sculpture\"}, {\"id\": 2909, \"name\": \"the limousine\"}, {\"id\": 2910, \"name\": \"the harmonica\"}, {\"id\": 2911, \"name\": \"a black crow\"}, {\"id\": 2912, \"name\": \"a fabric banner\"}, {\"id\": 2913, \"name\": \"the metal staircase\"}, {\"id\": 2914, \"name\": \"the metal chair\"}, {\"id\": 2915, \"name\": \"steel frame\"}, {\"id\": 2916, \"name\": \"the grey scarf\"}, {\"id\": 2917, \"name\": \"the grey cat\"}, {\"id\": 2918, \"name\": \"the telescope\"}, {\"id\": 2919, \"name\": \"plastic tub\"}, {\"id\": 2920, \"name\": \"a polluted air\"}, {\"id\": 2921, \"name\": \"the dark alley\"}, {\"id\": 2922, \"name\": \"a green tree\"}, {\"id\": 2923, \"name\": \"a American flag\"}, {\"id\": 2924, \"name\": \"a street performer\"}, {\"id\": 2925, \"name\": \"the wooden desk\"}, {\"id\": 2926, \"name\": \"swing set\"}, {\"id\": 2927, \"name\": \"football goal\"}, {\"id\": 2928, \"name\": \"shiny car\"}, {\"id\": 2929, \"name\": \"a horse trailer\"}, {\"id\": 2930, \"name\": \"the wooden steering wheel\"}, {\"id\": 2931, \"name\": \"a cat litter\"}, {\"id\": 2932, \"name\": \"the blue couch\"}, {\"id\": 2933, \"name\": \"an orange monkey\"}, {\"id\": 2934, \"name\": \"the wool hat\"}, {\"id\": 2935, \"name\": \"garden\"}, {\"id\": 2936, \"name\": \"the yellow sun\"}, {\"id\": 2937, \"name\": \"garden bench\"}, {\"id\": 2938, \"name\": \"the leather sofa\"}, {\"id\": 2939, \"name\": \"the purple scarf\"}, {\"id\": 2940, \"name\": \"the grey rock\"}, {\"id\": 2941, \"name\": \"water pool\"}, {\"id\": 2942, \"name\": \"the living room\"}, {\"id\": 2943, \"name\": \"stone trough\"}, {\"id\": 2944, \"name\": \"fluffy carpet\"}, {\"id\": 2945, \"name\": \"the golden earring\"}, {\"id\": 2946, \"name\": \"an iron rod\"}, {\"id\": 2947, \"name\": \"the silver lamp\"}, {\"id\": 2948, \"name\": \"the orange hat\"}, {\"id\": 2949, \"name\": \"a yellow building\"}, {\"id\": 2950, \"name\": \"restaurant booth\"}, {\"id\": 2951, \"name\": \"the cowshed\"}, {\"id\": 2952, \"name\": \"the office desk\"}, {\"id\": 2953, \"name\": \"a yellow leaf\"}, {\"id\": 2954, \"name\": \"rusty pipe\"}, {\"id\": 2955, \"name\": \"the dirt shoulder\"}, {\"id\": 2956, \"name\": \"pink background\"}, {\"id\": 2957, \"name\": \"the first floor\"}, {\"id\": 2958, \"name\": \"small white kitten\"}, {\"id\": 2959, \"name\": \"brown vest\"}, {\"id\": 2960, \"name\": \"an orange beak\"}, {\"id\": 2961, \"name\": \"a small aquarium\"}, {\"id\": 2962, \"name\": \"a large billboard\"}, {\"id\": 2963, \"name\": \"floor tom\"}, {\"id\": 2964, \"name\": \"the yellow dog\"}, {\"id\": 2965, \"name\": \"first floor\"}, {\"id\": 2966, \"name\": \"a brown body\"}, {\"id\": 2967, \"name\": \"a raised platform\"}, {\"id\": 2968, \"name\": \"an enclosure\"}, {\"id\": 2969, \"name\": \"nighttime scene\"}, {\"id\": 2970, \"name\": \"framed picture\"}, {\"id\": 2971, \"name\": \"vanity\"}, {\"id\": 2972, \"name\": \"a building's facade\"}, {\"id\": 2973, \"name\": \"a light blue t-shirt\"}, {\"id\": 2974, \"name\": \"the narrow street\"}, {\"id\": 2975, \"name\": \"black and white patterned cuff\"}, {\"id\": 2976, \"name\": \"the concrete\"}, {\"id\": 2977, \"name\": \"the large black screen\"}, {\"id\": 2978, \"name\": \"an orange plate\"}, {\"id\": 2979, \"name\": \"a large blue water tank\"}, {\"id\": 2980, \"name\": \"the brown tree trunk\"}, {\"id\": 2981, \"name\": \"a maroon car\"}, {\"id\": 2982, \"name\": \"the yellow Crocs\"}, {\"id\": 2983, \"name\": \"small dog\"}, {\"id\": 2984, \"name\": \"the forest floor\"}, {\"id\": 2985, \"name\": \"maroon jacket\"}, {\"id\": 2986, \"name\": \"dark background\"}, {\"id\": 2987, \"name\": \"a rustic outdoor scene\"}, {\"id\": 2988, \"name\": \"red and yellow tinsel\"}, {\"id\": 2989, \"name\": \"light brown cat\"}, {\"id\": 2990, \"name\": \"large white building\"}, {\"id\": 2991, \"name\": \"dark sky\"}, {\"id\": 2992, \"name\": \"orange beak\"}, {\"id\": 2993, \"name\": \"the red bracelet\"}, {\"id\": 2994, \"name\": \"a blue bottle\"}, {\"id\": 2995, \"name\": \"the yellow and green tuk-tuk\"}, {\"id\": 2996, \"name\": \"a thick coat\"}, {\"id\": 2997, \"name\": \"the yellow beak\"}, {\"id\": 2998, \"name\": \"large mirror\"}, {\"id\": 2999, \"name\": \"a white body\"}, {\"id\": 3000, \"name\": \"black and white sneaker\"}, {\"id\": 3001, \"name\": \"a cloud of dust\"}, {\"id\": 3002, \"name\": \"black comb\"}, {\"id\": 3003, \"name\": \"a red long-sleeved shirt\"}, {\"id\": 3004, \"name\": \"a kitchen scene\"}, {\"id\": 3005, \"name\": \"dirt parking lot\"}, {\"id\": 3006, \"name\": \"a handful of birdseed\"}, {\"id\": 3007, \"name\": \"dark brown chick\"}, {\"id\": 3008, \"name\": \"hay\"}, {\"id\": 3009, \"name\": \"dark wood door\"}, {\"id\": 3010, \"name\": \"small, dark-colored lizard\"}, {\"id\": 3011, \"name\": \"the fluffy tail\"}, {\"id\": 3012, \"name\": \"a blue bone-shaped toy\"}, {\"id\": 3013, \"name\": \"the gray pant\"}, {\"id\": 3014, \"name\": \"the dark surface\"}, {\"id\": 3015, \"name\": \"a small breed\"}, {\"id\": 3016, \"name\": \"the cartoon cow design\"}, {\"id\": 3017, \"name\": \"a tan-colored sedan\"}, {\"id\": 3018, \"name\": \"a concrete road\"}, {\"id\": 3019, \"name\": \"the white marble floor\"}, {\"id\": 3020, \"name\": \"cloudy sky\"}, {\"id\": 3021, \"name\": \"large body of water\"}, {\"id\": 3022, \"name\": \"the small goat\"}, {\"id\": 3023, \"name\": \"the gray sky\"}, {\"id\": 3024, \"name\": \"bottle of Yakult\"}, {\"id\": 3025, \"name\": \"a silver hatchback\"}, {\"id\": 3026, \"name\": \"oval-shaped mirror\"}, {\"id\": 3027, \"name\": \"truck's bed\"}, {\"id\": 3028, \"name\": \"a slab\"}, {\"id\": 3029, \"name\": \"an orange motorcycle\"}, {\"id\": 3030, \"name\": \"a striped shirt\"}, {\"id\": 3031, \"name\": \"black label\"}, {\"id\": 3032, \"name\": \"purple dress\"}, {\"id\": 3033, \"name\": \"tram\"}, {\"id\": 3034, \"name\": \"the green bottle\"}, {\"id\": 3035, \"name\": \"white mannequin\"}, {\"id\": 3036, \"name\": \"black fence\"}, {\"id\": 3037, \"name\": \"a green chair\"}, {\"id\": 3038, \"name\": \"the cobblestone street\"}, {\"id\": 3039, \"name\": \"a tan vest\"}, {\"id\": 3040, \"name\": \"a pink t-shirt\"}, {\"id\": 3041, \"name\": \"a dirt lot\"}, {\"id\": 3042, \"name\": \"the orange wall\"}, {\"id\": 3043, \"name\": \"a lush green bank\"}, {\"id\": 3044, \"name\": \"the yellow donut-shaped toy\"}, {\"id\": 3045, \"name\": \"green star-shaped toy\"}, {\"id\": 3046, \"name\": \"the spacious enclosure\"}, {\"id\": 3047, \"name\": \"a bin\"}, {\"id\": 3048, \"name\": \"a gray car\"}, {\"id\": 3049, \"name\": \"a white logo\"}, {\"id\": 3050, \"name\": \"a pink countertop\"}, {\"id\": 3051, \"name\": \"the green, red, and white plaid tablecloth\"}, {\"id\": 3052, \"name\": \"a dirt yard\"}, {\"id\": 3053, \"name\": \"the small lizard\"}, {\"id\": 3054, \"name\": \"a hazy sky\"}, {\"id\": 3055, \"name\": \"metal bridge\"}, {\"id\": 3056, \"name\": \"the white and grey marble floor\"}, {\"id\": 3057, \"name\": \"the white body\"}, {\"id\": 3058, \"name\": \"a coupe\"}, {\"id\": 3059, \"name\": \"yellow dump truck\"}, {\"id\": 3060, \"name\": \"a tiled roof\"}, {\"id\": 3061, \"name\": \"group of white geese\"}, {\"id\": 3062, \"name\": \"the grey wooden floor\"}, {\"id\": 3063, \"name\": \"a stuffed green Christmas tree\"}, {\"id\": 3064, \"name\": \"the matte surface\"}, {\"id\": 3065, \"name\": \"small Christmas tree\"}, {\"id\": 3066, \"name\": \"a brown base\"}, {\"id\": 3067, \"name\": \"muddy enclosure\"}, {\"id\": 3068, \"name\": \"framed photo\"}, {\"id\": 3069, \"name\": \"a dusty road\"}, {\"id\": 3070, \"name\": \"the silver bracelet\"}, {\"id\": 3071, \"name\": \"spiral-bound book\"}, {\"id\": 3072, \"name\": \"distinctive green head\"}, {\"id\": 3073, \"name\": \"blue plate\"}, {\"id\": 3074, \"name\": \"a dense thicket of trees and bushes\"}, {\"id\": 3075, \"name\": \"light-colored wood wall\"}, {\"id\": 3076, \"name\": \"a kitchen setting\"}, {\"id\": 3077, \"name\": \"silver sedan\"}, {\"id\": 3078, \"name\": \"the collection of strawberries\"}, {\"id\": 3079, \"name\": \"the gray suit\"}, {\"id\": 3080, \"name\": \"bright blue sky\"}, {\"id\": 3081, \"name\": \"wicker\"}, {\"id\": 3082, \"name\": \"the black leggings\"}, {\"id\": 3083, \"name\": \"the large logo\"}, {\"id\": 3084, \"name\": \"the blue and white checkered scarf\"}, {\"id\": 3085, \"name\": \"the mixing bowl\"}, {\"id\": 3086, \"name\": \"the orange tabby cat\"}, {\"id\": 3087, \"name\": \"the turkey feeder\"}, {\"id\": 3088, \"name\": \"the light-colored wooden surface\"}, {\"id\": 3089, \"name\": \"stainless steel mixing bowl\"}, {\"id\": 3090, \"name\": \"a woven mat\"}, {\"id\": 3091, \"name\": \"a red metal scaffolding structure\"}, {\"id\": 3092, \"name\": \"a short, dark hair\"}, {\"id\": 3093, \"name\": \"the silver car\"}, {\"id\": 3094, \"name\": \"spiral pattern\"}, {\"id\": 3095, \"name\": \"dark green pant\"}, {\"id\": 3096, \"name\": \"the polished concrete floor\"}, {\"id\": 3097, \"name\": \"the gray wood floor\"}, {\"id\": 3098, \"name\": \"a gray wooden floor\"}, {\"id\": 3099, \"name\": \"the light blue jeans\"}, {\"id\": 3100, \"name\": \"the black long-sleeved shirt\"}, {\"id\": 3101, \"name\": \"red and white cup\"}, {\"id\": 3102, \"name\": \"white marking\"}, {\"id\": 3103, \"name\": \"green head\"}, {\"id\": 3104, \"name\": \"a dark pant\"}, {\"id\": 3105, \"name\": \"the white plastic bowl\"}, {\"id\": 3106, \"name\": \"a wooden ladder\"}, {\"id\": 3107, \"name\": \"a brown fur collar\"}, {\"id\": 3108, \"name\": \"larger dog\"}, {\"id\": 3109, \"name\": \"a large window\"}, {\"id\": 3110, \"name\": \"shelving unit\"}, {\"id\": 3111, \"name\": \"a red brick building\"}, {\"id\": 3112, \"name\": \"the interior of the store\"}, {\"id\": 3113, \"name\": \"yellow house\"}, {\"id\": 3114, \"name\": \"a light blue building\"}, {\"id\": 3115, \"name\": \"a grey wood floor\"}, {\"id\": 3116, \"name\": \"a carrot-shaped toy\"}, {\"id\": 3117, \"name\": \"white plastic slipper\"}, {\"id\": 3118, \"name\": \"a small black and white puppy\"}, {\"id\": 3119, \"name\": \"black boot\"}, {\"id\": 3120, \"name\": \"a hay\"}, {\"id\": 3121, \"name\": \"the vibrant mural\"}, {\"id\": 3122, \"name\": \"white SUV\"}, {\"id\": 3123, \"name\": \"a barn\"}, {\"id\": 3124, \"name\": \"the city street corner\"}, {\"id\": 3125, \"name\": \"a blue license plate\"}, {\"id\": 3126, \"name\": \"large bag of feed\"}, {\"id\": 3127, \"name\": \"a cartoon cow\"}, {\"id\": 3128, \"name\": \"wood shavings\"}, {\"id\": 3129, \"name\": \"a two-week-old\"}, {\"id\": 3130, \"name\": \"barn\"}, {\"id\": 3131, \"name\": \"a red boat\"}, {\"id\": 3132, \"name\": \"orchestra\"}, {\"id\": 3133, \"name\": \"the ocean liner\"}, {\"id\": 3134, \"name\": \"stone vase\"}, {\"id\": 3135, \"name\": \"the office\"}, {\"id\": 3136, \"name\": \"a gray dog\"}, {\"id\": 3137, \"name\": \"iron lamp\"}, {\"id\": 3138, \"name\": \"the plastic dashboard\"}, {\"id\": 3139, \"name\": \"classroom\"}, {\"id\": 3140, \"name\": \"a stone pavement\"}, {\"id\": 3141, \"name\": \"the metal counter\"}, {\"id\": 3142, \"name\": \"the stone ground\"}, {\"id\": 3143, \"name\": \"the harmonica player\"}, {\"id\": 3144, \"name\": \"wooden guitar\"}, {\"id\": 3145, \"name\": \"silver microphone\"}, {\"id\": 3146, \"name\": \"stone bench\"}, {\"id\": 3147, \"name\": \"the old bike\"}, {\"id\": 3148, \"name\": \"the yellow leaf\"}, {\"id\": 3149, \"name\": \"iron beam\"}, {\"id\": 3150, \"name\": \"silver drum\"}, {\"id\": 3151, \"name\": \"the silver tiara\"}, {\"id\": 3152, \"name\": \"an arcade\"}, {\"id\": 3153, \"name\": \"the bakery\"}, {\"id\": 3154, \"name\": \"silver drumstick\"}, {\"id\": 3155, \"name\": \"the Asian-inspired clothing\"}, {\"id\": 3156, \"name\": \"paper napkin\"}, {\"id\": 3157, \"name\": \"the stainless steel knife\"}, {\"id\": 3158, \"name\": \"a small white light\"}, {\"id\": 3159, \"name\": \"an actor\"}, {\"id\": 3160, \"name\": \"a volleyball court\"}, {\"id\": 3161, \"name\": \"velvet dress\"}, {\"id\": 3162, \"name\": \"a closed door\"}, {\"id\": 3163, \"name\": \"the summer beach ball\"}, {\"id\": 3164, \"name\": \"the busy street\"}, {\"id\": 3165, \"name\": \"an old man\"}, {\"id\": 3166, \"name\": \"the aquarium filter\"}, {\"id\": 3167, \"name\": \"the dark blue dress\"}, {\"id\": 3168, \"name\": \"badminton racket\"}, {\"id\": 3169, \"name\": \"the wedding ring\"}, {\"id\": 3170, \"name\": \"the Valentine's Day heart\"}, {\"id\": 3171, \"name\": \"the street performer\"}, {\"id\": 3172, \"name\": \"a foggy morning\"}, {\"id\": 3173, \"name\": \"frozen lake\"}, {\"id\": 3174, \"name\": \"old shoe\"}, {\"id\": 3175, \"name\": \"a white substance\"}, {\"id\": 3176, \"name\": \"worn shoes\"}, {\"id\": 3177, \"name\": \"electric fence\"}, {\"id\": 3178, \"name\": \"ocean liner\"}, {\"id\": 3179, \"name\": \"misty valley\"}, {\"id\": 3180, \"name\": \"a brown rug\"}, {\"id\": 3181, \"name\": \"outdoor garden\"}, {\"id\": 3182, \"name\": \"sparkling river\"}, {\"id\": 3183, \"name\": \"a clean feather\"}, {\"id\": 3184, \"name\": \"a red toy\"}, {\"id\": 3185, \"name\": \"newspaper stand\"}, {\"id\": 3186, \"name\": \"a black uniform\"}, {\"id\": 3187, \"name\": \"mess\"}, {\"id\": 3188, \"name\": \"the city square\"}, {\"id\": 3189, \"name\": \"fire station\"}, {\"id\": 3190, \"name\": \"fabric cloth\"}, {\"id\": 3191, \"name\": \"the turkey coop\"}, {\"id\": 3192, \"name\": \"sunny day\"}, {\"id\": 3193, \"name\": \"seaweed\"}, {\"id\": 3194, \"name\": \"the city street\"}, {\"id\": 3195, \"name\": \"large nose\"}, {\"id\": 3196, \"name\": \"a rubber hose\"}, {\"id\": 3197, \"name\": \"the fabric texture\"}, {\"id\": 3198, \"name\": \"gray duck\"}, {\"id\": 3199, \"name\": \"the paper label\"}, {\"id\": 3200, \"name\": \"the subway station\"}, {\"id\": 3201, \"name\": \"a black design\"}, {\"id\": 3202, \"name\": \"the modern skyscraper\"}, {\"id\": 3203, \"name\": \"a duck's head\"}, {\"id\": 3204, \"name\": \"a chicken foot\"}, {\"id\": 3205, \"name\": \"woman's hair\"}, {\"id\": 3206, \"name\": \"a clay pot\"}, {\"id\": 3207, \"name\": \"their coat\"}, {\"id\": 3208, \"name\": \"a yellow and black striped barrier\"}, {\"id\": 3209, \"name\": \"a pink life jacket\"}, {\"id\": 3210, \"name\": \"a bird's wing\"}, {\"id\": 3211, \"name\": \"a white sticker\"}, {\"id\": 3212, \"name\": \"a green toy cat with pink paws and a green and white checkered shirt\"}, {\"id\": 3213, \"name\": \"a car spoiler\"}, {\"id\": 3214, \"name\": \"a person, a head\"}, {\"id\": 3215, \"name\": \"a concrete column\"}, {\"id\": 3216, \"name\": \"a turkey's head\"}, {\"id\": 3217, \"name\": \"a person's back\"}, {\"id\": 3218, \"name\": \"a patch of dry grass\"}, {\"id\": 3219, \"name\": \"a sandal\"}, {\"id\": 3220, \"name\": \"sidewalk\"}, {\"id\": 3221, \"name\": \"a car roof\"}, {\"id\": 3222, \"name\": \"a sandbag\"}, {\"id\": 3223, \"name\": \"a part of a subway escalator\"}, {\"id\": 3224, \"name\": \"a white, circular, concave planter\"}, {\"id\": 3225, \"name\": \"a ceiling panel\"}, {\"id\": 3226, \"name\": \"a lawn\"}, {\"id\": 3227, \"name\": \"a colander\"}, {\"id\": 3228, \"name\": \"a kayak\"}, {\"id\": 3229, \"name\": \"a woman's upper body\"}, {\"id\": 3230, \"name\": \"the right side of a person's head\"}, {\"id\": 3231, \"name\": \"a pink nubbin\"}, {\"id\": 3232, \"name\": \"a flame\"}, {\"id\": 3233, \"name\": \"man's face\"}, {\"id\": 3234, \"name\": \"a concrete structure\"}, {\"id\": 3235, \"name\": \"a toy truck\"}, {\"id\": 3236, \"name\": \"a playing card\"}, {\"id\": 3237, \"name\": \"a mop\"}, {\"id\": 3238, \"name\": \"a stanchion\"}, {\"id\": 3239, \"name\": \"the lower body of a female dancer\"}, {\"id\": 3240, \"name\": \"a stack of gold containers\"}, {\"id\": 3241, \"name\": \"a wooden rail\"}, {\"id\": 3242, \"name\": \"a metal escalator handrail\"}, {\"id\": 3243, \"name\": \"a piece of a green playground structure\"}, {\"id\": 3244, \"name\": \"a food cart\"}, {\"id\": 3245, \"name\": \"a painting of a white building\"}, {\"id\": 3246, \"name\": \"goat\"}, {\"id\": 3247, \"name\": \"cell phone\"}, {\"id\": 3248, \"name\": \"a brown cow\"}, {\"id\": 3249, \"name\": \"the back of a person's upper body\"}, {\"id\": 3250, \"name\": \"a woman's back\"}, {\"id\": 3251, \"name\": \"a round sign\"}, {\"id\": 3252, \"name\": \"a tattoo machine\"}, {\"id\": 3253, \"name\": \"a yellow curb\"}, {\"id\": 3254, \"name\": \"a yellow object\"}, {\"id\": 3255, \"name\": \"a person, specifically, lower body\"}, {\"id\": 3256, \"name\": \"a ceiling beam\"}, {\"id\": 3257, \"name\": \"a seat back\"}, {\"id\": 3258, \"name\": \"a pair of black socks\"}, {\"id\": 3259, \"name\": \"a brown blanket\"}, {\"id\": 3260, \"name\": \"a ride vehicle\"}, {\"id\": 3261, \"name\": \"a windsurfing sail\"}, {\"id\": 3262, \"name\": \"a pair of black shoes\"}, {\"id\": 3263, \"name\": \"white bowl\"}, {\"id\": 3264, \"name\": \"a pair of bare legs\"}, {\"id\": 3265, \"name\": \"train car\"}, {\"id\": 3266, \"name\": \"a dog bowl\"}, {\"id\": 3267, \"name\": \"a raft\"}, {\"id\": 3268, \"name\": \"a fire pit\"}, {\"id\": 3269, \"name\": \"a black animal with a long tail\"}, {\"id\": 3270, \"name\": \"a person's red coat\"}, {\"id\": 3271, \"name\": \"a word\"}, {\"id\": 3272, \"name\": \"a vehicle canopy\"}, {\"id\": 3273, \"name\": \"a dog bandana\"}, {\"id\": 3274, \"name\": \"a sea lion\"}, {\"id\": 3275, \"name\": \"a rearview mirror\"}, {\"id\": 3276, \"name\": \"wall\"}, {\"id\": 3277, \"name\": \"knife set\"}, {\"id\": 3278, \"name\": \"steering wheel\"}, {\"id\": 3279, \"name\": \"a clock tower\"}, {\"id\": 3280, \"name\": \"a decorative archway\"}, {\"id\": 3281, \"name\": \"a green bag\"}, {\"id\": 3282, \"name\": \"a yellow, textured, vertical bar\"}, {\"id\": 3283, \"name\": \"a white tissue\"}, {\"id\": 3284, \"name\": \"a leg of a table\"}, {\"id\": 3285, \"name\": \"a stainless steel pot\"}, {\"id\": 3286, \"name\": \"a blue eye\"}, {\"id\": 3287, \"name\": \"a horse tail\"}, {\"id\": 3288, \"name\": \"a large metal bowl\"}, {\"id\": 3289, \"name\": \"satellite dish\"}, {\"id\": 3290, \"name\": \"a black, metal object it appears to be a part of a kitchen utensil, such as a spatula or a knife\"}, {\"id\": 3291, \"name\": \"a white machine or device\"}, {\"id\": 3292, \"name\": \"a head of a person\"}, {\"id\": 3293, \"name\": \"a white metal railing\"}, {\"id\": 3294, \"name\": \"a metal part\"}, {\"id\": 3295, \"name\": \"a person's fingers\"}, {\"id\": 3296, \"name\": \"deer\"}, {\"id\": 3297, \"name\": \"yellow bottle cap\"}, {\"id\": 3298, \"name\": \"a person's armor\"}, {\"id\": 3299, \"name\": \"a brown and white awning\"}, {\"id\": 3300, \"name\": \"a pallet\"}, {\"id\": 3301, \"name\": \"a white molding\"}, {\"id\": 3302, \"name\": \"a soft top\"}, {\"id\": 3303, \"name\": \"a person's lower body, including their pants and shoes\"}, {\"id\": 3304, \"name\": \"a wooden structure\"}, {\"id\": 3305, \"name\": \"a person's\"}, {\"id\": 3306, \"name\": \"knife\"}, {\"id\": 3307, \"name\": \"a parking meter\"}, {\"id\": 3308, \"name\": \"a green building screen\"}, {\"id\": 3309, \"name\": \"a bumper car\"}, {\"id\": 3310, \"name\": \"a white display stand\"}, {\"id\": 3311, \"name\": \"wedding dress\"}, {\"id\": 3312, \"name\": \"a brown leather purse\"}, {\"id\": 3313, \"name\": \"a fur\"}, {\"id\": 3314, \"name\": \"bottle of water\"}, {\"id\": 3315, \"name\": \"a woman with long, dark hair and a white shirt\"}, {\"id\": 3316, \"name\": \"suitcase\"}, {\"id\": 3317, \"name\": \"their lower body and shirt\"}, {\"id\": 3318, \"name\": \"a fortification\"}, {\"id\": 3319, \"name\": \"a white coat\"}, {\"id\": 3320, \"name\": \"a path\"}, {\"id\": 3321, \"name\": \"a brown jacket\"}, {\"id\": 3322, \"name\": \"can\"}, {\"id\": 3323, \"name\": \"a person, specifically, their lower body\"}, {\"id\": 3324, \"name\": \"a wall vent\"}, {\"id\": 3325, \"name\": \"a black pole\"}, {\"id\": 3326, \"name\": \"a person, specifically, the head and upper body of a person\"}, {\"id\": 3327, \"name\": \"a white rope\"}, {\"id\": 3328, \"name\": \"a stack of tasty tom crackers\"}, {\"id\": 3329, \"name\": \"a person, specifically, the back of a man\"}, {\"id\": 3330, \"name\": \"a black strap\"}, {\"id\": 3331, \"name\": \"a pavement\"}, {\"id\": 3332, \"name\": \"staircase\"}, {\"id\": 3333, \"name\": \"a camera strap\"}, {\"id\": 3334, \"name\": \"a pallet jack\"}, {\"id\": 3335, \"name\": \"a green plastic sheet\"}, {\"id\": 3336, \"name\": \"a microwave\"}, {\"id\": 3337, \"name\": \"a person, riding a scooter\"}, {\"id\": 3338, \"name\": \"a part of a fence\"}, {\"id\": 3339, \"name\": \"a xxx metal pole\"}, {\"id\": 3340, \"name\": \"a person, a right arm\"}, {\"id\": 3341, \"name\": \"a black plastic or rubber part of a car\"}, {\"id\": 3342, \"name\": \"crab\"}, {\"id\": 3343, \"name\": \"a blue plastic structure\"}, {\"id\": 3344, \"name\": \"a inflatable, blue and white whale\"}, {\"id\": 3345, \"name\": \"bicycle\"}, {\"id\": 3346, \"name\": \"a blue awning\"}, {\"id\": 3347, \"name\": \"a measuring cup\"}, {\"id\": 3348, \"name\": \"a dinosaur's foot\"}, {\"id\": 3349, \"name\": \"a pile of stone slabs\"}, {\"id\": 3350, \"name\": \"a white, fluffy, dog-like head\"}, {\"id\": 3351, \"name\": \"a dirt patch\"}, {\"id\": 3352, \"name\": \"yellow cylinder\"}, {\"id\": 3353, \"name\": \"a white sleeve\"}, {\"id\": 3354, \"name\": \"a pair of overalls\"}, {\"id\": 3355, \"name\": \"a stack of wooden pallets\"}, {\"id\": 3356, \"name\": \"a red plastic handle\"}, {\"id\": 3357, \"name\": \"a yellow and white fabric\"}, {\"id\": 3358, \"name\": \"a baby's face\"}, {\"id\": 3359, \"name\": \"a utility box\"}, {\"id\": 3360, \"name\": \"a white window frame\"}, {\"id\": 3361, \"name\": \"a pink triangle\"}, {\"id\": 3362, \"name\": \"a skin\"}, {\"id\": 3363, \"name\": \"a vertical metal bar\"}, {\"id\": 3364, \"name\": \"a bell pepper\"}, {\"id\": 3365, \"name\": \"a green piece of fabric\"}, {\"id\": 3366, \"name\": \"a metal container with a handle\"}, {\"id\": 3367, \"name\": \"a wind chime\"}, {\"id\": 3368, \"name\": \"a head wrap\"}, {\"id\": 3369, \"name\": \"a black purse\"}, {\"id\": 3370, \"name\": \"a cartoon bear\"}, {\"id\": 3371, \"name\": \"a person's head and upper body\"}, {\"id\": 3372, \"name\": \"chick\"}, {\"id\": 3373, \"name\": \"a person, specifically, a person's upper body\"}, {\"id\": 3374, \"name\": \"a strap\"}, {\"id\": 3375, \"name\": \"a cake pan\"}, {\"id\": 3376, \"name\": \"black circle\"}, {\"id\": 3377, \"name\": \"metal hook\"}, {\"id\": 3378, \"name\": \"a cardboard\"}, {\"id\": 3379, \"name\": \"a rectangular tag\"}, {\"id\": 3380, \"name\": \"a white rectangular object with a blurred image on it\"}, {\"id\": 3381, \"name\": \"a square\"}, {\"id\": 3382, \"name\": \"a yellow awning\"}, {\"id\": 3383, \"name\": \"a cylindrical structure\"}, {\"id\": 3384, \"name\": \"a quad bike\"}, {\"id\": 3385, \"name\": \"a white lego piece\"}, {\"id\": 3386, \"name\": \"a person's sleeve\"}, {\"id\": 3387, \"name\": \"piece of meat\"}, {\"id\": 3388, \"name\": \"a bundle of wires\"}, {\"id\": 3389, \"name\": \"a toilet paper roll\"}, {\"id\": 3390, \"name\": \"a plug\"}, {\"id\": 3391, \"name\": \"a sippy cup\"}, {\"id\": 3392, \"name\": \"a fish balloon\"}, {\"id\": 3393, \"name\": \"a shirt sleeve\"}, {\"id\": 3394, \"name\": \"a fox\"}, {\"id\": 3395, \"name\": \"letter w\"}, {\"id\": 3396, \"name\": \"rectangle\"}, {\"id\": 3397, \"name\": \"a yellow plastic bottle\"}, {\"id\": 3398, \"name\": \"a painting of a building\"}, {\"id\": 3399, \"name\": \"marker\"}, {\"id\": 3400, \"name\": \"a xxx electrical outlet\"}, {\"id\": 3401, \"name\": \"laptop computer\"}, {\"id\": 3402, \"name\": \"punching bag\"}, {\"id\": 3403, \"name\": \"airplane\"}, {\"id\": 3404, \"name\": \"machine\"}, {\"id\": 3405, \"name\": \"a jar of peanut butter\"}, {\"id\": 3406, \"name\": \"a pocket\"}, {\"id\": 3407, \"name\": \"keychain\"}, {\"id\": 3408, \"name\": \"crane\"}, {\"id\": 3409, \"name\": \"white cat\"}, {\"id\": 3410, \"name\": \"tennis racket\"}, {\"id\": 3411, \"name\": \"a turtle shell\"}, {\"id\": 3412, \"name\": \"cart\"}, {\"id\": 3413, \"name\": \"skateboarder\"}, {\"id\": 3414, \"name\": \"a red, horizontal bar\"}, {\"id\": 3415, \"name\": \"a water cooler\"}, {\"id\": 3416, \"name\": \"a white plastic or metal object with a rounded shape and a hole on one side\"}, {\"id\": 3417, \"name\": \"whiteboard\"}, {\"id\": 3418, \"name\": \"tent\"}, {\"id\": 3419, \"name\": \"bridge\"}, {\"id\": 3420, \"name\": \"computer monitor\"}, {\"id\": 3421, \"name\": \"butterfly\"}, {\"id\": 3422, \"name\": \"a monument\"}, {\"id\": 3423, \"name\": \"blue ball\"}, {\"id\": 3424, \"name\": \"a pink fuzzy onesie\"}, {\"id\": 3425, \"name\": \"phone\"}, {\"id\": 3426, \"name\": \"a pink foil wrapper\"}, {\"id\": 3427, \"name\": \"a padlock\"}, {\"id\": 3428, \"name\": \"the torso\"}, {\"id\": 3429, \"name\": \"vase\"}, {\"id\": 3430, \"name\": \"a polar bear's hind leg\"}, {\"id\": 3431, \"name\": \"a polar bear's leg\"}, {\"id\": 3432, \"name\": \"soccer ball\"}, {\"id\": 3433, \"name\": \"a shower curtain rod\"}, {\"id\": 3434, \"name\": \"pen\"}, {\"id\": 3435, \"name\": \"a tape\"}, {\"id\": 3436, \"name\": \"faucet handle\"}, {\"id\": 3437, \"name\": \"blanket\"}, {\"id\": 3438, \"name\": \"a shower head\"}, {\"id\": 3439, \"name\": \"microwave oven\"}, {\"id\": 3440, \"name\": \"dream catcher\"}, {\"id\": 3441, \"name\": \"lion dance\"}, {\"id\": 3442, \"name\": \"a blue and red bag\"}, {\"id\": 3443, \"name\": \"a gas cap\"}, {\"id\": 3444, \"name\": \"yellow skateboard\"}, {\"id\": 3445, \"name\": \"black handle\"}, {\"id\": 3446, \"name\": \"a green chalkboard\"}, {\"id\": 3447, \"name\": \"metal ring\"}, {\"id\": 3448, \"name\": \"a blue green shirt\"}, {\"id\": 3449, \"name\": \"bone\"}, {\"id\": 3450, \"name\": \"a tortoise shell\"}, {\"id\": 3451, \"name\": \"a plastic water bottle\"}, {\"id\": 3452, \"name\": \"woman's hands\"}, {\"id\": 3453, \"name\": \"security camera\"}, {\"id\": 3454, \"name\": \"a red chair\"}, {\"id\": 3455, \"name\": \"a white floor\"}, {\"id\": 3456, \"name\": \"tree\"}, {\"id\": 3457, \"name\": \"a black cow's head\"}, {\"id\": 3458, \"name\": \"green bowl\"}, {\"id\": 3459, \"name\": \"a soundbar\"}, {\"id\": 3460, \"name\": \"a green umbrella\"}, {\"id\": 3461, \"name\": \"a cylindrical container with a lid, likely a metal canister\"}, {\"id\": 3462, \"name\": \"a fish tank\"}, {\"id\": 3463, \"name\": \"black cat\"}, {\"id\": 3464, \"name\": \"broom\"}, {\"id\": 3465, \"name\": \"a hedgehog\"}, {\"id\": 3466, \"name\": \"an airplane\"}, {\"id\": 3467, \"name\": \"a horse's neck\"}, {\"id\": 3468, \"name\": \"a goat's leg\"}, {\"id\": 3469, \"name\": \"a green tractor\"}, {\"id\": 3470, \"name\": \"a stone archway\"}, {\"id\": 3471, \"name\": \"rope\"}, {\"id\": 3472, \"name\": \"green and white striped shirt\"}, {\"id\": 3473, \"name\": \"virtual reality headset\"}, {\"id\": 3474, \"name\": \"water dispenser\"}, {\"id\": 3475, \"name\": \"purple pillow\"}, {\"id\": 3476, \"name\": \"a green mattress\"}, {\"id\": 3477, \"name\": \"a toilet seat\"}, {\"id\": 3478, \"name\": \"fire extinguisher\"}, {\"id\": 3479, \"name\": \"yellow safety vest\"}, {\"id\": 3480, \"name\": \"remote control\"}, {\"id\": 3481, \"name\": \"a pink toothbrush\"}, {\"id\": 3482, \"name\": \"mop\"}, {\"id\": 3483, \"name\": \"a yellow perch\"}, {\"id\": 3484, \"name\": \"a fort\"}, {\"id\": 3485, \"name\": \"a sail\"}, {\"id\": 3486, \"name\": \"banner\"}, {\"id\": 3487, \"name\": \"rabbit\"}, {\"id\": 3488, \"name\": \"woman's hand\"}, {\"id\": 3489, \"name\": \"a person's neck and chin\"}, {\"id\": 3490, \"name\": \"frog\"}, {\"id\": 3491, \"name\": \"a red and yellow striped object\"}, {\"id\": 3492, \"name\": \"barrel\"}, {\"id\": 3493, \"name\": \"a plastic bucket\"}, {\"id\": 3494, \"name\": \"a black shoe\"}, {\"id\": 3495, \"name\": \"a red shoe\"}, {\"id\": 3496, \"name\": \"a glass tabletop\"}, {\"id\": 3497, \"name\": \"a neck pillow\"}, {\"id\": 3498, \"name\": \"a necktie\"}, {\"id\": 3499, \"name\": \"a green elevator door\"}, {\"id\": 3500, \"name\": \"a pair of white underwear\"}, {\"id\": 3501, \"name\": \"person's hands\"}, {\"id\": 3502, \"name\": \"chess piece\"}, {\"id\": 3503, \"name\": \"a black cow\"}, {\"id\": 3504, \"name\": \"a person's legs\"}, {\"id\": 3505, \"name\": \"green ball\"}, {\"id\": 3506, \"name\": \"a water barrier\"}, {\"id\": 3507, \"name\": \"ruler\"}, {\"id\": 3508, \"name\": \"a xxx wireless router\"}, {\"id\": 3509, \"name\": \"a battery\"}, {\"id\": 3510, \"name\": \"a kangaroo\"}, {\"id\": 3511, \"name\": \"a rocky cliff\"}, {\"id\": 3512, \"name\": \"a doormat\"}, {\"id\": 3513, \"name\": \"a white earbud\"}, {\"id\": 3514, \"name\": \"a power outlet\"}, {\"id\": 3515, \"name\": \"a red bench\"}, {\"id\": 3516, \"name\": \"a projector\"}, {\"id\": 3517, \"name\": \"a white ceramic shell\"}, {\"id\": 3518, \"name\": \"a leather purse with diamond stitching\"}, {\"id\": 3519, \"name\": \"a green cap\"}, {\"id\": 3520, \"name\": \"a mango\"}, {\"id\": 3521, \"name\": \"a green hook\"}, {\"id\": 3522, \"name\": \"a water slide\"}, {\"id\": 3523, \"name\": \"a bandana\"}, {\"id\": 3524, \"name\": \"a blue earpiece\"}, {\"id\": 3525, \"name\": \"a white guinea pig\"}, {\"id\": 3526, \"name\": \"a white furry pillow\"}, {\"id\": 3527, \"name\": \"a plastic food container\"}, {\"id\": 3528, \"name\": \"a gravel surface\"}, {\"id\": 3529, \"name\": \"a concrete wedge\"}, {\"id\": 3530, \"name\": \"a long piece of clothing\"}, {\"id\": 3531, \"name\": \"pencil\"}, {\"id\": 3532, \"name\": \"cloud\"}, {\"id\": 3533, \"name\": \"a white animal, possibly a sheep\"}, {\"id\": 3534, \"name\": \"a white, curved, plastic or fiberglass surface\"}, {\"id\": 3535, \"name\": \"a long-handled cat toy\"}, {\"id\": 3536, \"name\": \"a paper towel holder\"}, {\"id\": 3537, \"name\": \"a horizontal brown bar\"}, {\"id\": 3538, \"name\": \"a handlebar\"}, {\"id\": 3539, \"name\": \"a lower body of a white cat\"}, {\"id\": 3540, \"name\": \"the black shorts\"}, {\"id\": 3541, \"name\": \"the plant\"}, {\"id\": 3542, \"name\": \"woman in a blue shirt\"}, {\"id\": 3543, \"name\": \"the jacket\"}, {\"id\": 3544, \"name\": \"the person\"}, {\"id\": 3545, \"name\": \"a red oval\"}, {\"id\": 3546, \"name\": \"black bed frame\"}, {\"id\": 3547, \"name\": \"the cat\"}, {\"id\": 3548, \"name\": \"high ceiling\"}, {\"id\": 3549, \"name\": \"teal-framed window\"}, {\"id\": 3550, \"name\": \"white counter\"}, {\"id\": 3551, \"name\": \"distinctive curved tile pattern\"}, {\"id\": 3552, \"name\": \"the front of the temple\"}, {\"id\": 3553, \"name\": \"the blue shirt\"}, {\"id\": 3554, \"name\": \"the purple outfit\"}, {\"id\": 3555, \"name\": \"the teal-colored wall\"}, {\"id\": 3556, \"name\": \"white dog\"}, {\"id\": 3557, \"name\": \"brown dress\"}, {\"id\": 3558, \"name\": \"the long dark hair\"}, {\"id\": 3559, \"name\": \"open doorway\"}, {\"id\": 3560, \"name\": \"a red, black, and blue motorbike\"}, {\"id\": 3561, \"name\": \"license plate\"}, {\"id\": 3562, \"name\": \"the decals\"}, {\"id\": 3563, \"name\": \"a dirt ground\"}, {\"id\": 3564, \"name\": \"a yellow border\"}, {\"id\": 3565, \"name\": \"the blue or white shirt\"}, {\"id\": 3566, \"name\": \"the man in a suit and tie\"}, {\"id\": 3567, \"name\": \"the shirt\"}, {\"id\": 3568, \"name\": \"the speaker\"}, {\"id\": 3569, \"name\": \"a silver metal\"}, {\"id\": 3570, \"name\": \"prairie dog\"}, {\"id\": 3571, \"name\": \"the bed of straw\"}, {\"id\": 3572, \"name\": \"the enclosure's wall\"}, {\"id\": 3573, \"name\": \"a grey, plastic object\"}, {\"id\": 3574, \"name\": \"small, grey and white plastic stool\"}, {\"id\": 3575, \"name\": \"the pair of pants\"}, {\"id\": 3576, \"name\": \"the person's arm\"}, {\"id\": 3577, \"name\": \"the stool\"}, {\"id\": 3578, \"name\": \"a large package of water bottles\"}, {\"id\": 3579, \"name\": \"shelf\"}, {\"id\": 3580, \"name\": \"the shoe\"}, {\"id\": 3581, \"name\": \"a large white umbrella\"}, {\"id\": 3582, \"name\": \"green concrete wall\"}, {\"id\": 3583, \"name\": \"the bee\"}, {\"id\": 3584, \"name\": \"large shopping center\"}, {\"id\": 3585, \"name\": \"the white, geometric-patterned wall\"}, {\"id\": 3586, \"name\": \"a small, open-sided cart\"}, {\"id\": 3587, \"name\": \"image of a person\"}, {\"id\": 3588, \"name\": \"large billboard\"}, {\"id\": 3589, \"name\": \"the tree\"}, {\"id\": 3590, \"name\": \"the tiled floor\"}, {\"id\": 3591, \"name\": \"price tag\"}, {\"id\": 3592, \"name\": \"a small hook\"}, {\"id\": 3593, \"name\": \"a khaki pant\"}, {\"id\": 3594, \"name\": \"the mixed breed\"}, {\"id\": 3595, \"name\": \"a black leggings\"}, {\"id\": 3596, \"name\": \"a large black and white bag\"}, {\"id\": 3597, \"name\": \"the fur-trimmed backpack\"}, {\"id\": 3598, \"name\": \"the person's head\"}, {\"id\": 3599, \"name\": \"prominent tree\"}, {\"id\": 3600, \"name\": \"the large orange bag\"}, {\"id\": 3601, \"name\": \"the blue jeans\"}, {\"id\": 3602, \"name\": \"the building\"}, {\"id\": 3603, \"name\": \"the driver\"}, {\"id\": 3604, \"name\": \"the yellow puffy jacket\"}, {\"id\": 3605, \"name\": \"digital sign\"}, {\"id\": 3606, \"name\": \"the hand\"}, {\"id\": 3607, \"name\": \"a lettuce leaf\"}, {\"id\": 3608, \"name\": \"a distinctive red and yellow mark\"}, {\"id\": 3609, \"name\": \"young kitten\"}, {\"id\": 3610, \"name\": \"billboard\"}, {\"id\": 3611, \"name\": \"dirt path\"}, {\"id\": 3612, \"name\": \"photograph\"}, {\"id\": 3613, \"name\": \"the hind leg\"}, {\"id\": 3614, \"name\": \"a bright sun\"}, {\"id\": 3615, \"name\": \"a pile of hay bale\"}, {\"id\": 3616, \"name\": \"a sun\"}, {\"id\": 3617, \"name\": \"pile of hay\"}, {\"id\": 3618, \"name\": \"the large bale of hay\"}, {\"id\": 3619, \"name\": \"pair of pants\"}, {\"id\": 3620, \"name\": \"the beige hat\"}, {\"id\": 3621, \"name\": \"the short white hair\"}, {\"id\": 3622, \"name\": \"a blue character\"}, {\"id\": 3623, \"name\": \"black box of toys\"}, {\"id\": 3624, \"name\": \"orange and gray shoe\"}, {\"id\": 3625, \"name\": \"the couch\"}, {\"id\": 3626, \"name\": \"a distinctive red taillight\"}, {\"id\": 3627, \"name\": \"the low stone wall\"}, {\"id\": 3628, \"name\": \"the overcast and gray sky\"}, {\"id\": 3629, \"name\": \"the truck\"}, {\"id\": 3630, \"name\": \"adult man\"}, {\"id\": 3631, \"name\": \"man in the white and black shirt\"}, {\"id\": 3632, \"name\": \"streetlight\"}, {\"id\": 3633, \"name\": \"white satellite dish\"}, {\"id\": 3634, \"name\": \"educational poster\"}, {\"id\": 3635, \"name\": \"group of children\"}, {\"id\": 3636, \"name\": \"red sweater\"}, {\"id\": 3637, \"name\": \"the glass\"}, {\"id\": 3638, \"name\": \"the palm tree\"}, {\"id\": 3639, \"name\": \"a white sneaker\"}, {\"id\": 3640, \"name\": \"large advertisement\"}, {\"id\": 3641, \"name\": \"the wall\"}, {\"id\": 3642, \"name\": \"white shoe\"}, {\"id\": 3643, \"name\": \"large, brown, ceramic pot\"}, {\"id\": 3644, \"name\": \"small tan-colored dog\"}, {\"id\": 3645, \"name\": \"the black mat\"}, {\"id\": 3646, \"name\": \"the tire\"}, {\"id\": 3647, \"name\": \"a small circular sign\"}, {\"id\": 3648, \"name\": \"an attire\"}, {\"id\": 3649, \"name\": \"ceiling\"}, {\"id\": 3650, \"name\": \"the advertisement\"}, {\"id\": 3651, \"name\": \"the red brick exterior\"}, {\"id\": 3652, \"name\": \"the restaurant\"}, {\"id\": 3653, \"name\": \"an individual\"}, {\"id\": 3654, \"name\": \"a man in black\"}, {\"id\": 3655, \"name\": \"dirt road\"}, {\"id\": 3656, \"name\": \"the black bag\"}, {\"id\": 3657, \"name\": \"the man\"}, {\"id\": 3658, \"name\": \"the paved walkway\"}, {\"id\": 3659, \"name\": \"the sidewalk\"}, {\"id\": 3660, \"name\": \"a wooden platform or perch\"}, {\"id\": 3661, \"name\": \"small platform\"}, {\"id\": 3662, \"name\": \"the small, dark-colored platform\"}, {\"id\": 3663, \"name\": \"dirt ground\"}, {\"id\": 3664, \"name\": \"the helmet\"}, {\"id\": 3665, \"name\": \"the lone green tennis ball\"}, {\"id\": 3666, \"name\": \"a pointed tip\"}, {\"id\": 3667, \"name\": \"the red symbol\"}, {\"id\": 3668, \"name\": \"a wooden cabinet\"}, {\"id\": 3669, \"name\": \"large window\"}, {\"id\": 3670, \"name\": \"the high, ornate ceiling\"}, {\"id\": 3671, \"name\": \"the woman\"}, {\"id\": 3672, \"name\": \"wood-paneled ceiling\"}, {\"id\": 3673, \"name\": \"the jeans\"}, {\"id\": 3674, \"name\": \"the task\"}, {\"id\": 3675, \"name\": \"the sky\"}, {\"id\": 3676, \"name\": \"the white light\"}, {\"id\": 3677, \"name\": \"a rolled-up sleeve\"}, {\"id\": 3678, \"name\": \"the metal plate\"}, {\"id\": 3679, \"name\": \"a casual clothing\"}, {\"id\": 3680, \"name\": \"a customer\"}, {\"id\": 3681, \"name\": \"camouflage shirt\"}, {\"id\": 3682, \"name\": \"the crumbling roof\"}, {\"id\": 3683, \"name\": \"the sloping roof\"}, {\"id\": 3684, \"name\": \"basketball hoop\"}, {\"id\": 3685, \"name\": \"glass backboard\"}, {\"id\": 3686, \"name\": \"the dark night sky\"}, {\"id\": 3687, \"name\": \"bright light\"}, {\"id\": 3688, \"name\": \"enclosure\"}, {\"id\": 3689, \"name\": \"the blue wall\"}, {\"id\": 3690, \"name\": \"the phone\"}, {\"id\": 3691, \"name\": \"the photographer\"}, {\"id\": 3692, \"name\": \"the screen\"}, {\"id\": 3693, \"name\": \"a colorful rug\"}, {\"id\": 3694, \"name\": \"the brown hose\"}, {\"id\": 3695, \"name\": \"the light-colored tile\"}, {\"id\": 3696, \"name\": \"black feather\"}, {\"id\": 3697, \"name\": \"shadow\"}, {\"id\": 3698, \"name\": \"English and Korean sign\"}, {\"id\": 3699, \"name\": \"glass door\"}, {\"id\": 3700, \"name\": \"the space\"}, {\"id\": 3701, \"name\": \"gray and white marble surface\"}, {\"id\": 3702, \"name\": \"nasal spray's nozzle\"}, {\"id\": 3703, \"name\": \"the ceiling\"}, {\"id\": 3704, \"name\": \"the escalator\"}, {\"id\": 3705, \"name\": \"the green shopping cart\"}, {\"id\": 3706, \"name\": \"brown wall\"}, {\"id\": 3707, \"name\": \"record\"}, {\"id\": 3708, \"name\": \"a red curved wall\"}, {\"id\": 3709, \"name\": \"colorful graphic\"}, {\"id\": 3710, \"name\": \"the black pant\"}, {\"id\": 3711, \"name\": \"black metal security grate\"}, {\"id\": 3712, \"name\": \"the dark blue pant\"}, {\"id\": 3713, \"name\": \"the window\"}, {\"id\": 3714, \"name\": \"a white structure\"}, {\"id\": 3715, \"name\": \"a white column\"}, {\"id\": 3716, \"name\": \"a medium-brown wood\"}, {\"id\": 3717, \"name\": \"headband\"}, {\"id\": 3718, \"name\": \"brown wooden object\"}, {\"id\": 3719, \"name\": \"decorative arch-shaped design\"}, {\"id\": 3720, \"name\": \"handle\"}, {\"id\": 3721, \"name\": \"the towel\"}, {\"id\": 3722, \"name\": \"a red shopping bag\"}, {\"id\": 3723, \"name\": \"a short blonde hair\"}, {\"id\": 3724, \"name\": \"black pant\"}, {\"id\": 3725, \"name\": \"mask\"}, {\"id\": 3726, \"name\": \"the hair\"}, {\"id\": 3727, \"name\": \"a lit-up sign\"}, {\"id\": 3728, \"name\": \"the black hat\"}, {\"id\": 3729, \"name\": \"a blue shoe\"}, {\"id\": 3730, \"name\": \"the television\"}, {\"id\": 3731, \"name\": \"the toothbrush\"}, {\"id\": 3732, \"name\": \"a curved floor\"}, {\"id\": 3733, \"name\": \"red and white design\"}, {\"id\": 3734, \"name\": \"white shirt\"}, {\"id\": 3735, \"name\": \"tan hat\"}, {\"id\": 3736, \"name\": \"black coat\"}, {\"id\": 3737, \"name\": \"bush\"}, {\"id\": 3738, \"name\": \"the hedge\"}, {\"id\": 3739, \"name\": \"the metal archway\"}, {\"id\": 3740, \"name\": \"tan sweater\"}, {\"id\": 3741, \"name\": \"black metal railing\"}, {\"id\": 3742, \"name\": \"the parasol\"}, {\"id\": 3743, \"name\": \"a large, red and white striped teddy bear\"}, {\"id\": 3744, \"name\": \"a vibrant red teddy bear\"}, {\"id\": 3745, \"name\": \"fuzzy red trim\"}, {\"id\": 3746, \"name\": \"the white sheet\"}, {\"id\": 3747, \"name\": \"the white surface\"}, {\"id\": 3748, \"name\": \"the sign\"}, {\"id\": 3749, \"name\": \"a bakery counter\"}, {\"id\": 3750, \"name\": \"the leafy green\"}, {\"id\": 3751, \"name\": \"balcony\"}, {\"id\": 3752, \"name\": \"the red sign\"}, {\"id\": 3753, \"name\": \"the white lettering\"}, {\"id\": 3754, \"name\": \"white trim\"}, {\"id\": 3755, \"name\": \"wooden pallet\"}, {\"id\": 3756, \"name\": \"ground\"}, {\"id\": 3757, \"name\": \"the patchwork of dirt\"}, {\"id\": 3758, \"name\": \"a cookie\"}, {\"id\": 3759, \"name\": \"silver pan\"}, {\"id\": 3760, \"name\": \"the small plastic bag\"}, {\"id\": 3761, \"name\": \"a light gray wall\"}, {\"id\": 3762, \"name\": \"green and white sweater\"}, {\"id\": 3763, \"name\": \"green pattern\"}, {\"id\": 3764, \"name\": \"a thin, white cord\"}, {\"id\": 3765, \"name\": \"balloon\"}, {\"id\": 3766, \"name\": \"the faint cloud\"}, {\"id\": 3767, \"name\": \"the pole\"}, {\"id\": 3768, \"name\": \"pair of feet\"}, {\"id\": 3769, \"name\": \"the long, fluffy coat\"}, {\"id\": 3770, \"name\": \"the long, fluffy ear\"}, {\"id\": 3771, \"name\": \"costume\"}, {\"id\": 3772, \"name\": \"the bowl\"}, {\"id\": 3773, \"name\": \"the busy road\"}, {\"id\": 3774, \"name\": \"a shiny white tile\"}, {\"id\": 3775, \"name\": \"the display case\"}, {\"id\": 3776, \"name\": \"a black rain boot\"}, {\"id\": 3777, \"name\": \"black and blue rain boot\"}, {\"id\": 3778, \"name\": \"the door\"}, {\"id\": 3779, \"name\": \"individual\"}, {\"id\": 3780, \"name\": \"recessed lighting\"}, {\"id\": 3781, \"name\": \"speaker\"}, {\"id\": 3782, \"name\": \"the glass panel\"}, {\"id\": 3783, \"name\": \"dark road\"}, {\"id\": 3784, \"name\": \"green and yellow sign\"}, {\"id\": 3785, \"name\": \"the car's headlights\"}, {\"id\": 3786, \"name\": \"a yellow cup\"}, {\"id\": 3787, \"name\": \"metal pan\"}, {\"id\": 3788, \"name\": \"a spotlight\"}, {\"id\": 3789, \"name\": \"the floor\"}, {\"id\": 3790, \"name\": \"a black lamppost\"}, {\"id\": 3791, \"name\": \"short black hair\"}, {\"id\": 3792, \"name\": \"red fabric canopy\"}, {\"id\": 3793, \"name\": \"the black jacket\"}, {\"id\": 3794, \"name\": \"the rider\"}, {\"id\": 3795, \"name\": \"lane\"}, {\"id\": 3796, \"name\": \"the curb\"}, {\"id\": 3797, \"name\": \"the tall antenna\"}, {\"id\": 3798, \"name\": \"the parked motorbike\"}, {\"id\": 3799, \"name\": \"metal shelving\"}, {\"id\": 3800, \"name\": \"a clear plastic bowl\"}, {\"id\": 3801, \"name\": \"a turquoise cushioned bench\"}, {\"id\": 3802, \"name\": \"the left hand\"}, {\"id\": 3803, \"name\": \"black grate\"}, {\"id\": 3804, \"name\": \"curb\"}, {\"id\": 3805, \"name\": \"a black and white plaid shirt\"}, {\"id\": 3806, \"name\": \"a decorative element\"}, {\"id\": 3807, \"name\": \"a covered waiting area\"}, {\"id\": 3808, \"name\": \"the digital sign\"}, {\"id\": 3809, \"name\": \"white pillar\"}, {\"id\": 3810, \"name\": \"the car\"}, {\"id\": 3811, \"name\": \"the motorcycle\"}, {\"id\": 3812, \"name\": \"a condominium\"}, {\"id\": 3813, \"name\": \"the grass\"}, {\"id\": 3814, \"name\": \"the vehicle\"}, {\"id\": 3815, \"name\": \"gray sweatpants\"}, {\"id\": 3816, \"name\": \"the serene landscape\"}, {\"id\": 3817, \"name\": \"unique bamboo structure\"}, {\"id\": 3818, \"name\": \"blurred face\"}, {\"id\": 3819, \"name\": \"orange umbrella\"}, {\"id\": 3820, \"name\": \"the mirror\"}, {\"id\": 3821, \"name\": \"a barred window\"}, {\"id\": 3822, \"name\": \"large screen\"}, {\"id\": 3823, \"name\": \"the green cap\"}, {\"id\": 3824, \"name\": \"a large white wall\"}, {\"id\": 3825, \"name\": \"belt\"}, {\"id\": 3826, \"name\": \"rough, unfinished surface\"}, {\"id\": 3827, \"name\": \"the face\"}, {\"id\": 3828, \"name\": \"black patch\"}, {\"id\": 3829, \"name\": \"dog's head\"}, {\"id\": 3830, \"name\": \"the concrete surface\"}, {\"id\": 3831, \"name\": \"the individual\"}, {\"id\": 3832, \"name\": \"a boy's legs\"}, {\"id\": 3833, \"name\": \"the black t-shirt\"}, {\"id\": 3834, \"name\": \"a large, pink claw machine\"}, {\"id\": 3835, \"name\": \"arcade game\"}, {\"id\": 3836, \"name\": \"road's surface\"}, {\"id\": 3837, \"name\": \"the grey car\"}, {\"id\": 3838, \"name\": \"a short black hair\"}, {\"id\": 3839, \"name\": \"a white metal cabinet\"}, {\"id\": 3840, \"name\": \"locker\"}, {\"id\": 3841, \"name\": \"locker's surface\"}, {\"id\": 3842, \"name\": \"refrigerator door\"}, {\"id\": 3843, \"name\": \"the narrow white cabinet\"}, {\"id\": 3844, \"name\": \"a blurred face\"}, {\"id\": 3845, \"name\": \"a gray tile\"}, {\"id\": 3846, \"name\": \"a large, cream-colored building\"}, {\"id\": 3847, \"name\": \"cracked and faded road\"}, {\"id\": 3848, \"name\": \"the thin, light-colored line\"}, {\"id\": 3849, \"name\": \"the solid white line\"}, {\"id\": 3850, \"name\": \"white pillow\"}, {\"id\": 3851, \"name\": \"a rear window\"}, {\"id\": 3852, \"name\": \"bumper\"}, {\"id\": 3853, \"name\": \"small brown dog\"}, {\"id\": 3854, \"name\": \"the green uniform\"}, {\"id\": 3855, \"name\": \"the small logo\"}, {\"id\": 3856, \"name\": \"the brown marking\"}, {\"id\": 3857, \"name\": \"the dog\"}, {\"id\": 3858, \"name\": \"the stuffed animal\"}, {\"id\": 3859, \"name\": \"stuffed toy chicken\"}, {\"id\": 3860, \"name\": \"gray pant\"}, {\"id\": 3861, \"name\": \"the dark pant\"}, {\"id\": 3862, \"name\": \"the striped shirt\"}, {\"id\": 3863, \"name\": \"a glassware item\"}, {\"id\": 3864, \"name\": \"red canister\"}, {\"id\": 3865, \"name\": \"red section\"}, {\"id\": 3866, \"name\": \"a black mat\"}, {\"id\": 3867, \"name\": \"a young boy\"}, {\"id\": 3868, \"name\": \"moving stairs\"}, {\"id\": 3869, \"name\": \"the camouflage jacket\"}, {\"id\": 3870, \"name\": \"the navy blue uniform\"}, {\"id\": 3871, \"name\": \"a freezer drawer\"}, {\"id\": 3872, \"name\": \"a grey shoe\"}, {\"id\": 3873, \"name\": \"the blue, white, and red striped sweater\"}, {\"id\": 3874, \"name\": \"a black band\"}, {\"id\": 3875, \"name\": \"a grey curtain\"}, {\"id\": 3876, \"name\": \"bottle of wine\"}, {\"id\": 3877, \"name\": \"curved railing\"}, {\"id\": 3878, \"name\": \"the brand name\"}, {\"id\": 3879, \"name\": \"a red KFC bag\"}, {\"id\": 3880, \"name\": \"the individual's head\"}, {\"id\": 3881, \"name\": \"the long-sleeved, vertically striped shirt\"}, {\"id\": 3882, \"name\": \"the ruffled collar\"}, {\"id\": 3883, \"name\": \"the dirt strip\"}, {\"id\": 3884, \"name\": \"a clothesline\"}, {\"id\": 3885, \"name\": \"the plain white wall\"}, {\"id\": 3886, \"name\": \"tray\"}, {\"id\": 3887, \"name\": \"white power outlet\"}, {\"id\": 3888, \"name\": \"large metal container\"}, {\"id\": 3889, \"name\": \"the hat\"}, {\"id\": 3890, \"name\": \"the yellow three-wheeled vehicle\"}, {\"id\": 3891, \"name\": \"the black dog\"}, {\"id\": 3892, \"name\": \"the stage\"}, {\"id\": 3893, \"name\": \"a CHOREOGRAPHY\"}, {\"id\": 3894, \"name\": \"a blue door frame\"}, {\"id\": 3895, \"name\": \"a vibrant red dress\"}, {\"id\": 3896, \"name\": \"cat or dog\"}, {\"id\": 3897, \"name\": \"the tan coat\"}, {\"id\": 3898, \"name\": \"white coat\"}, {\"id\": 3899, \"name\": \"the illegible red writing\"}, {\"id\": 3900, \"name\": \"the panda design\"}, {\"id\": 3901, \"name\": \"individual's face\"}, {\"id\": 3902, \"name\": \"the right arm\"}, {\"id\": 3903, \"name\": \"a thin brown stripe\"}, {\"id\": 3904, \"name\": \"person's arm\"}, {\"id\": 3905, \"name\": \"a large, black, Christmas tree-like decoration\"}, {\"id\": 3906, \"name\": \"an orange sign\"}, {\"id\": 3907, \"name\": \"the furniture\"}, {\"id\": 3908, \"name\": \"the red banner\"}, {\"id\": 3909, \"name\": \"audience\"}, {\"id\": 3910, \"name\": \"the bottle\"}, {\"id\": 3911, \"name\": \"black glove\"}, {\"id\": 3912, \"name\": \"ski track\"}, {\"id\": 3913, \"name\": \"the green and black object\"}, {\"id\": 3914, \"name\": \"the red background\"}, {\"id\": 3915, \"name\": \"a left hand\"}, {\"id\": 3916, \"name\": \"a flat roof\"}, {\"id\": 3917, \"name\": \"telephone pole\"}, {\"id\": 3918, \"name\": \"the two-story brown brick house\"}, {\"id\": 3919, \"name\": \"yellow blanket\"}, {\"id\": 3920, \"name\": \"a multi-story building\"}, {\"id\": 3921, \"name\": \"the green canopy\"}, {\"id\": 3922, \"name\": \"gray building\"}, {\"id\": 3923, \"name\": \"yellow and blue bus\"}, {\"id\": 3924, \"name\": \"yellow vehicle\"}, {\"id\": 3925, \"name\": \"black baseball cap\"}, {\"id\": 3926, \"name\": \"the chrome fender\"}, {\"id\": 3927, \"name\": \"the green roof\"}, {\"id\": 3928, \"name\": \"yellow car\"}, {\"id\": 3929, \"name\": \"a fluffy white coat\"}, {\"id\": 3930, \"name\": \"a storage box\"}, {\"id\": 3931, \"name\": \"white fur\"}, {\"id\": 3932, \"name\": \"the passenger-side rear window\"}, {\"id\": 3933, \"name\": \"the single light source\"}, {\"id\": 3934, \"name\": \"a black top hat\"}, {\"id\": 3935, \"name\": \"black hat\"}, {\"id\": 3936, \"name\": \"large white tent\"}, {\"id\": 3937, \"name\": \"red and white sign\"}, {\"id\": 3938, \"name\": \"the adjacent road\"}, {\"id\": 3939, \"name\": \"traffic sign\"}, {\"id\": 3940, \"name\": \"the cloud\"}, {\"id\": 3941, \"name\": \"black long-sleeved shirt\"}, {\"id\": 3942, \"name\": \"the herringbone pattern\"}, {\"id\": 3943, \"name\": \"stone step\"}, {\"id\": 3944, \"name\": \"the telephone pole\"}, {\"id\": 3945, \"name\": \"a small speedboat\"}, {\"id\": 3946, \"name\": \"a water trampoline\"}, {\"id\": 3947, \"name\": \"ship\"}, {\"id\": 3948, \"name\": \"the body of water\"}, {\"id\": 3949, \"name\": \"a gray roof\"}, {\"id\": 3950, \"name\": \"the street sign\"}, {\"id\": 3951, \"name\": \"large black backpack\"}, {\"id\": 3952, \"name\": \"the dark jacket\"}, {\"id\": 3953, \"name\": \"white metal roof\"}, {\"id\": 3954, \"name\": \"headboard\"}, {\"id\": 3955, \"name\": \"red pillow\"}, {\"id\": 3956, \"name\": \"dirt and rock surface\"}, {\"id\": 3957, \"name\": \"the chicken\"}, {\"id\": 3958, \"name\": \"the wooden chicken coop\"}, {\"id\": 3959, \"name\": \"a bustling shopping mall corridor\"}, {\"id\": 3960, \"name\": \"the group of four men\"}, {\"id\": 3961, \"name\": \"the store\"}, {\"id\": 3962, \"name\": \"an elevator\"}, {\"id\": 3963, \"name\": \"the cell phone\"}, {\"id\": 3964, \"name\": \"a black marble wall\"}, {\"id\": 3965, \"name\": \"the white wall\"}, {\"id\": 3966, \"name\": \"a right hand\"}, {\"id\": 3967, \"name\": \"hair\"}, {\"id\": 3968, \"name\": \"pink curtain\"}, {\"id\": 3969, \"name\": \"the sock\"}, {\"id\": 3970, \"name\": \"the black skirt\"}, {\"id\": 3971, \"name\": \"khaki shorts\"}, {\"id\": 3972, \"name\": \"pair of shorts\"}, {\"id\": 3973, \"name\": \"red bow\"}, {\"id\": 3974, \"name\": \"the central air vent\"}, {\"id\": 3975, \"name\": \"the streetlight\"}, {\"id\": 3976, \"name\": \"the large red chair\"}, {\"id\": 3977, \"name\": \"the large, red, and black gaming chair\"}, {\"id\": 3978, \"name\": \"the chair\"}, {\"id\": 3979, \"name\": \"the mask\"}, {\"id\": 3980, \"name\": \"animal\"}, {\"id\": 3981, \"name\": \"small, dark-colored animal\"}, {\"id\": 3982, \"name\": \"the box\"}, {\"id\": 3983, \"name\": \"the material\"}, {\"id\": 3984, \"name\": \"the small dog\"}, {\"id\": 3985, \"name\": \"a purple object\"}, {\"id\": 3986, \"name\": \"a white lettering\"}, {\"id\": 3987, \"name\": \"recessed light\"}, {\"id\": 3988, \"name\": \"green scrubs\"}, {\"id\": 3989, \"name\": \"the curly brown hair\"}, {\"id\": 3990, \"name\": \"the white tiled table\"}, {\"id\": 3991, \"name\": \"a brown fabric\"}, {\"id\": 3992, \"name\": \"a floral pattern\"}, {\"id\": 3993, \"name\": \"the toy\"}, {\"id\": 3994, \"name\": \"a large black pole\"}, {\"id\": 3995, \"name\": \"muddy road\"}, {\"id\": 3996, \"name\": \"smaller green plant\"}, {\"id\": 3997, \"name\": \"the planter\"}, {\"id\": 3998, \"name\": \"a shirtless\"}, {\"id\": 3999, \"name\": \"the white license plate\"}, {\"id\": 4000, \"name\": \"the atmosphere\"}, {\"id\": 4001, \"name\": \"a basketball net\"}, {\"id\": 4002, \"name\": \"a pitched roof\"}, {\"id\": 4003, \"name\": \"the fence\"}, {\"id\": 4004, \"name\": \"a wide, empty street\"}, {\"id\": 4005, \"name\": \"black van\"}, {\"id\": 4006, \"name\": \"prominent sign\"}, {\"id\": 4007, \"name\": \"the desk\"}, {\"id\": 4008, \"name\": \"the table\"}, {\"id\": 4009, \"name\": \"white table\"}, {\"id\": 4010, \"name\": \"curved wall\"}, {\"id\": 4011, \"name\": \"the windshield\"}, {\"id\": 4012, \"name\": \"dirt yard\"}, {\"id\": 4013, \"name\": \"white liquid\"}, {\"id\": 4014, \"name\": \"wet floor\"}, {\"id\": 4015, \"name\": \"large, dry and barren dirt field\"}, {\"id\": 4016, \"name\": \"an atm\"}, {\"id\": 4017, \"name\": \"black jacket\"}, {\"id\": 4018, \"name\": \"floor\"}, {\"id\": 4019, \"name\": \"large display\"}, {\"id\": 4020, \"name\": \"the pants\"}, {\"id\": 4021, \"name\": \"a yellow emblem\"}, {\"id\": 4022, \"name\": \"green and white scarf\"}, {\"id\": 4023, \"name\": \"jacket\"}, {\"id\": 4024, \"name\": \"the low brick wall\"}, {\"id\": 4025, \"name\": \"row of traffic cones\"}, {\"id\": 4026, \"name\": \"the railing\"}, {\"id\": 4027, \"name\": \"red-roofed building\"}, {\"id\": 4028, \"name\": \"the dark trousers\"}, {\"id\": 4029, \"name\": \"a browsing\"}, {\"id\": 4030, \"name\": \"a yellow and black painted section\"}, {\"id\": 4031, \"name\": \"drainage\"}, {\"id\": 4032, \"name\": \"grassy area\"}, {\"id\": 4033, \"name\": \"gray rectangle\"}, {\"id\": 4034, \"name\": \"the dog's ear\"}, {\"id\": 4035, \"name\": \"a brown coat\"}, {\"id\": 4036, \"name\": \"a gray concrete sidewalk\"}, {\"id\": 4037, \"name\": \"the blue shorts\"}, {\"id\": 4038, \"name\": \"umbrella\"}, {\"id\": 4039, \"name\": \"yellow shirt\"}, {\"id\": 4040, \"name\": \"the wooden-framed mirror\"}, {\"id\": 4041, \"name\": \"dark blue shirt\"}, {\"id\": 4042, \"name\": \"the cartoon yellow rabbit\"}, {\"id\": 4043, \"name\": \"the pile of playing cards\"}, {\"id\": 4044, \"name\": \"display\"}, {\"id\": 4045, \"name\": \"tan jacket\"}, {\"id\": 4046, \"name\": \"brown jacket\"}, {\"id\": 4047, \"name\": \"christmas tree\"}, {\"id\": 4048, \"name\": \"the shopping cart\"}, {\"id\": 4049, \"name\": \"a grey fur-trimmed hood\"}, {\"id\": 4050, \"name\": \"a row of shopping carts\"}, {\"id\": 4051, \"name\": \"the colorful carousel\"}, {\"id\": 4052, \"name\": \"a brown shirt with white stripes\"}, {\"id\": 4053, \"name\": \"rodent\"}, {\"id\": 4054, \"name\": \"the red wooden structure\"}, {\"id\": 4055, \"name\": \"purple purse\"}, {\"id\": 4056, \"name\": \"a red concrete barrier\"}, {\"id\": 4057, \"name\": \"the silver motorbike\"}, {\"id\": 4058, \"name\": \"yellow bus\"}, {\"id\": 4059, \"name\": \"pale green wall\"}, {\"id\": 4060, \"name\": \"the curtain\"}, {\"id\": 4061, \"name\": \"colorful striped pattern\"}, {\"id\": 4062, \"name\": \"the ground\"}, {\"id\": 4063, \"name\": \"the lamp post\"}, {\"id\": 4064, \"name\": \"the cardboard box\"}, {\"id\": 4065, \"name\": \"the white mannequin head\"}, {\"id\": 4066, \"name\": \"a grey stone wall\"}, {\"id\": 4067, \"name\": \"brown border\"}, {\"id\": 4068, \"name\": \"hallway\"}, {\"id\": 4069, \"name\": \"white stuffed animal\"}, {\"id\": 4070, \"name\": \"short, choppy cut\"}, {\"id\": 4071, \"name\": \"the large pink chair\"}, {\"id\": 4072, \"name\": \"guardrail\"}, {\"id\": 4073, \"name\": \"the paved parking lot\"}, {\"id\": 4074, \"name\": \"a person's white shirt\"}, {\"id\": 4075, \"name\": \"large X symbol\"}, {\"id\": 4076, \"name\": \"the long black dress\"}, {\"id\": 4077, \"name\": \"white hoodie\"}, {\"id\": 4078, \"name\": \"a dark gray marble platform\"}, {\"id\": 4079, \"name\": \"a large trailer\"}, {\"id\": 4080, \"name\": \"yellow object\"}, {\"id\": 4081, \"name\": \"body of water\"}, {\"id\": 4082, \"name\": \"a black van\"}, {\"id\": 4083, \"name\": \"wet pavement\"}, {\"id\": 4084, \"name\": \"the banner\"}, {\"id\": 4085, \"name\": \"the dirt path\"}, {\"id\": 4086, \"name\": \"the black hoodie\"}, {\"id\": 4087, \"name\": \"a colorful light\"}, {\"id\": 4088, \"name\": \"a high-rise apartment building\"}, {\"id\": 4089, \"name\": \"rear light\"}, {\"id\": 4090, \"name\": \"a red swing\"}, {\"id\": 4091, \"name\": \"a tall, yellow building\"}, {\"id\": 4092, \"name\": \"concrete curb\"}, {\"id\": 4093, \"name\": \"street\"}, {\"id\": 4094, \"name\": \"tan facade\"}, {\"id\": 4095, \"name\": \"tail\"}, {\"id\": 4096, \"name\": \"the body\"}, {\"id\": 4097, \"name\": \"the nocturnal marsupial\"}, {\"id\": 4098, \"name\": \"car's front bumper\"}, {\"id\": 4099, \"name\": \"the reflection\"}, {\"id\": 4100, \"name\": \"the light fixture\"}, {\"id\": 4101, \"name\": \"the long coat\"}, {\"id\": 4102, \"name\": \"a pattern\"}, {\"id\": 4103, \"name\": \"floral pattern\"}, {\"id\": 4104, \"name\": \"the leash\"}, {\"id\": 4105, \"name\": \"patterned shirt\"}, {\"id\": 4106, \"name\": \"yellow box\"}, {\"id\": 4107, \"name\": \"back of a black chair\"}, {\"id\": 4108, \"name\": \"monitor\"}, {\"id\": 4109, \"name\": \"a Chihuahua\"}, {\"id\": 4110, \"name\": \"a tan patch\"}, {\"id\": 4111, \"name\": \"a tan-colored paw\"}, {\"id\": 4112, \"name\": \"a dryer\"}, {\"id\": 4113, \"name\": \"red banner\"}, {\"id\": 4114, \"name\": \"the yellow ceiling\"}, {\"id\": 4115, \"name\": \"grey long-sleeved shirt\"}, {\"id\": 4116, \"name\": \"light beige color\"}, {\"id\": 4117, \"name\": \"a pink curtain\"}, {\"id\": 4118, \"name\": \"a long black hair\"}, {\"id\": 4119, \"name\": \"the makeup palette\"}, {\"id\": 4120, \"name\": \"the white shirt\"}, {\"id\": 4121, \"name\": \"brown exterior\"}, {\"id\": 4122, \"name\": \"gingerbread house\"}, {\"id\": 4123, \"name\": \"light pole\"}, {\"id\": 4124, \"name\": \"tall, slender, metal structure\"}, {\"id\": 4125, \"name\": \"the small, rustic wooden building\"}, {\"id\": 4126, \"name\": \"magazine\"}, {\"id\": 4127, \"name\": \"chicken's shadow\"}, {\"id\": 4128, \"name\": \"the dark brown speckle\"}, {\"id\": 4129, \"name\": \"the rooster's shadow\"}, {\"id\": 4130, \"name\": \"an instructional purposes\"}, {\"id\": 4131, \"name\": \"label\"}, {\"id\": 4132, \"name\": \"red plastic bottle\"}, {\"id\": 4133, \"name\": \"poster\"}, {\"id\": 4134, \"name\": \"the handlebar\"}, {\"id\": 4135, \"name\": \"a cage's white bar\"}, {\"id\": 4136, \"name\": \"a curved white wall\"}, {\"id\": 4137, \"name\": \"light orange and white\"}, {\"id\": 4138, \"name\": \"a festive red and white decoration\"}, {\"id\": 4139, \"name\": \"the floral pattern\"}, {\"id\": 4140, \"name\": \"the festive green garland\"}, {\"id\": 4141, \"name\": \"the green background of the cage\"}, {\"id\": 4142, \"name\": \"lawn\"}, {\"id\": 4143, \"name\": \"peacock\"}, {\"id\": 4144, \"name\": \"a two-story structure\"}, {\"id\": 4145, \"name\": \"gray asphalt surface\"}, {\"id\": 4146, \"name\": \"the stone pathway\"}, {\"id\": 4147, \"name\": \"white umbrella\"}, {\"id\": 4148, \"name\": \"a vibrant purple, blue and green pattern\"}, {\"id\": 4149, \"name\": \"the stack of clothing\"}, {\"id\": 4150, \"name\": \"cuff\"}, {\"id\": 4151, \"name\": \"the skirt\"}, {\"id\": 4152, \"name\": \"blue jeans\"}, {\"id\": 4153, \"name\": \"captivating image of a cave\"}, {\"id\": 4154, \"name\": \"the blue and black chair\"}, {\"id\": 4155, \"name\": \"the tv\"}, {\"id\": 4156, \"name\": \"a long, thin, white ribbon\"}, {\"id\": 4157, \"name\": \"gray brick wall\"}, {\"id\": 4158, \"name\": \"sandy bank\"}, {\"id\": 4159, \"name\": \"the white table\"}, {\"id\": 4160, \"name\": \"stair\"}, {\"id\": 4161, \"name\": \"the red headscarf\"}, {\"id\": 4162, \"name\": \"a blue and green garment\"}, {\"id\": 4163, \"name\": \"a medical mask\"}, {\"id\": 4164, \"name\": \"hospital sign\"}, {\"id\": 4165, \"name\": \"the green metal\"}, {\"id\": 4166, \"name\": \"the tan boot\"}, {\"id\": 4167, \"name\": \"a blue coat\"}, {\"id\": 4168, \"name\": \"a blue dress\"}, {\"id\": 4169, \"name\": \"a fabric\"}, {\"id\": 4170, \"name\": \"white crosswalk marking\"}, {\"id\": 4171, \"name\": \"shadow of the dog\"}, {\"id\": 4172, \"name\": \"forest floor\"}, {\"id\": 4173, \"name\": \"the bird\"}, {\"id\": 4174, \"name\": \"the person's torso\"}, {\"id\": 4175, \"name\": \"watch\"}, {\"id\": 4176, \"name\": \"white band\"}, {\"id\": 4177, \"name\": \"a photo of jeans\"}, {\"id\": 4178, \"name\": \"the sign with Chinese characters\"}, {\"id\": 4179, \"name\": \"the white sweatshirt\"}, {\"id\": 4180, \"name\": \"screen\"}, {\"id\": 4181, \"name\": \"small bag\"}, {\"id\": 4182, \"name\": \"green flat cap\"}, {\"id\": 4183, \"name\": \"the pile of leaves\"}, {\"id\": 4184, \"name\": \"the small sign\"}, {\"id\": 4185, \"name\": \"a black spot\"}, {\"id\": 4186, \"name\": \"green body\"}, {\"id\": 4187, \"name\": \"the paw\"}, {\"id\": 4188, \"name\": \"the woven rug\"}, {\"id\": 4189, \"name\": \"a grey and white cat\"}, {\"id\": 4190, \"name\": \"a tabby\"}, {\"id\": 4191, \"name\": \"the silver metal\"}, {\"id\": 4192, \"name\": \"a grassy verge\"}, {\"id\": 4193, \"name\": \"head\"}, {\"id\": 4194, \"name\": \"natural light\"}, {\"id\": 4195, \"name\": \"a two-door coupe\"}, {\"id\": 4196, \"name\": \"red brick section\"}, {\"id\": 4197, \"name\": \"light gray diamond\"}, {\"id\": 4198, \"name\": \"the wall of reflective silver panels\"}, {\"id\": 4199, \"name\": \"the person's back\"}, {\"id\": 4200, \"name\": \"gold hooves\"}, {\"id\": 4201, \"name\": \"large pink sign with white lettering\"}, {\"id\": 4202, \"name\": \"the gold trim\"}, {\"id\": 4203, \"name\": \"unicorn\"}, {\"id\": 4204, \"name\": \"clear plastic bag\"}, {\"id\": 4205, \"name\": \"coat\"}, {\"id\": 4206, \"name\": \"left wall\"}, {\"id\": 4207, \"name\": \"brown frame\"}, {\"id\": 4208, \"name\": \"a concrete surface\"}, {\"id\": 4209, \"name\": \"a large white plastic bag\"}, {\"id\": 4210, \"name\": \"black edge\"}, {\"id\": 4211, \"name\": \"black strap\"}, {\"id\": 4212, \"name\": \"spool of thread\"}, {\"id\": 4213, \"name\": \"wooden stool\"}, {\"id\": 4214, \"name\": \"the bag\"}, {\"id\": 4215, \"name\": \"the mix of green and brown vegetation\"}, {\"id\": 4216, \"name\": \"the paved road\"}, {\"id\": 4217, \"name\": \"a marking\"}, {\"id\": 4218, \"name\": \"a dirt\"}, {\"id\": 4219, \"name\": \"lid\"}, {\"id\": 4220, \"name\": \"low brick wall\"}, {\"id\": 4221, \"name\": \"power pole\"}, {\"id\": 4222, \"name\": \"the bucket\"}, {\"id\": 4223, \"name\": \"the roof\"}, {\"id\": 4224, \"name\": \"sideline\"}, {\"id\": 4225, \"name\": \"green and grey pillow\"}, {\"id\": 4226, \"name\": \"a British flag\"}, {\"id\": 4227, \"name\": \"the basket of red flowers\"}, {\"id\": 4228, \"name\": \"the shiny floor\"}, {\"id\": 4229, \"name\": \"black and gray stripe\"}, {\"id\": 4230, \"name\": \"box\"}, {\"id\": 4231, \"name\": \"a black and white helmet\"}, {\"id\": 4232, \"name\": \"a motorcycle helmet\"}, {\"id\": 4233, \"name\": \"dark brown couch or chair\"}, {\"id\": 4234, \"name\": \"a speckled tile\"}, {\"id\": 4235, \"name\": \"cellphone\"}, {\"id\": 4236, \"name\": \"a red building\"}, {\"id\": 4237, \"name\": \"green and white sign\"}, {\"id\": 4238, \"name\": \"a cobblestone sidewalk\"}, {\"id\": 4239, \"name\": \"a warning sign\"}, {\"id\": 4240, \"name\": \"the black cross-body bag strap\"}, {\"id\": 4241, \"name\": \"a red and purple column\"}, {\"id\": 4242, \"name\": \"a white marble\"}, {\"id\": 4243, \"name\": \"the cylindrical protrusion\"}, {\"id\": 4244, \"name\": \"a brown and tan rug\"}, {\"id\": 4245, \"name\": \"the brown and white rug\"}, {\"id\": 4246, \"name\": \"a long blue bench\"}, {\"id\": 4247, \"name\": \"a metal leg\"}, {\"id\": 4248, \"name\": \"arcade-style claw machine\"}, {\"id\": 4249, \"name\": \"large, black, triangular machine\"}, {\"id\": 4250, \"name\": \"a camouflage shorts\"}, {\"id\": 4251, \"name\": \"the red jacket\"}, {\"id\": 4252, \"name\": \"white cloud\"}, {\"id\": 4253, \"name\": \"dark roof\"}, {\"id\": 4254, \"name\": \"orange tank top\"}, {\"id\": 4255, \"name\": \"a large grey building\"}, {\"id\": 4256, \"name\": \"the road\"}, {\"id\": 4257, \"name\": \"the tall monument\"}, {\"id\": 4258, \"name\": \"a vibrant, low-quality photograph\"}, {\"id\": 4259, \"name\": \"grey asphalt\"}, {\"id\": 4260, \"name\": \"dark-colored bottle\"}, {\"id\": 4261, \"name\": \"the close-up, low-resolution photograph\"}, {\"id\": 4262, \"name\": \"food\"}, {\"id\": 4263, \"name\": \"a facade\"}, {\"id\": 4264, \"name\": \"the pile of construction materials\"}, {\"id\": 4265, \"name\": \"contrasting dark gray border\"}, {\"id\": 4266, \"name\": \"paved with smaller, rectangular stones\"}, {\"id\": 4267, \"name\": \"serene stone pathway\"}, {\"id\": 4268, \"name\": \"colorful sign\"}, {\"id\": 4269, \"name\": \"short brown hair\"}, {\"id\": 4270, \"name\": \"the large apartment building\"}, {\"id\": 4271, \"name\": \"prominent white arrow\"}, {\"id\": 4272, \"name\": \"large, dome-shaped metal enclosure\"}, {\"id\": 4273, \"name\": \"the large, rusty metal cage\"}, {\"id\": 4274, \"name\": \"the tree trunk\"}, {\"id\": 4275, \"name\": \"a blue jeans\"}, {\"id\": 4276, \"name\": \"a left index finger\"}, {\"id\": 4277, \"name\": \"colorful, partially visible design\"}, {\"id\": 4278, \"name\": \"teal trim\"}, {\"id\": 4279, \"name\": \"the light blue pencil\"}, {\"id\": 4280, \"name\": \"a rider's safety vest\"}, {\"id\": 4281, \"name\": \"a truck's tire\"}, {\"id\": 4282, \"name\": \"the canopy\"}, {\"id\": 4283, \"name\": \"the shorts\"}, {\"id\": 4284, \"name\": \"a tabby counterpart\"}, {\"id\": 4285, \"name\": \"large and white cat\"}, {\"id\": 4286, \"name\": \"the cream-colored floor\"}, {\"id\": 4287, \"name\": \"white wall\"}, {\"id\": 4288, \"name\": \"beige gate\"}, {\"id\": 4289, \"name\": \"the black coat\"}, {\"id\": 4290, \"name\": \"a red section\"}, {\"id\": 4291, \"name\": \"circle\"}, {\"id\": 4292, \"name\": \"man in a gray jacket\"}, {\"id\": 4293, \"name\": \"a ISA symbol\"}, {\"id\": 4294, \"name\": \"a large blue triangle\"}, {\"id\": 4295, \"name\": \"a large, blue building\"}, {\"id\": 4296, \"name\": \"the light blue building\"}, {\"id\": 4297, \"name\": \"a brown patch\"}, {\"id\": 4298, \"name\": \"gray gate\"}, {\"id\": 4299, \"name\": \"small green stuffed animal\"}, {\"id\": 4300, \"name\": \"kite\"}, {\"id\": 4301, \"name\": \"a black motorcycle\"}, {\"id\": 4302, \"name\": \"large stone sphere\"}, {\"id\": 4303, \"name\": \"the green sign\"}, {\"id\": 4304, \"name\": \"the flowing white outfit\"}, {\"id\": 4305, \"name\": \"a blue line\"}, {\"id\": 4306, \"name\": \"player in red\"}, {\"id\": 4307, \"name\": \"black door\"}, {\"id\": 4308, \"name\": \"stairwell\"}, {\"id\": 4309, \"name\": \"the light grey wall\"}, {\"id\": 4310, \"name\": \"the double solid white line\"}, {\"id\": 4311, \"name\": \"picture\"}, {\"id\": 4312, \"name\": \"the ballet position\"}, {\"id\": 4313, \"name\": \"brick wall\"}, {\"id\": 4314, \"name\": \"gold bracelet\"}, {\"id\": 4315, \"name\": \"the foliage\"}, {\"id\": 4316, \"name\": \"person's legs\"}, {\"id\": 4317, \"name\": \"the blue and green block\"}, {\"id\": 4318, \"name\": \"the cup\"}, {\"id\": 4319, \"name\": \"the dark jeans\"}, {\"id\": 4320, \"name\": \"the large white cup\"}, {\"id\": 4321, \"name\": \"the turquoise shirt\"}, {\"id\": 4322, \"name\": \"the white floor\"}, {\"id\": 4323, \"name\": \"a man in a black jacket\"}, {\"id\": 4324, \"name\": \"a large glass wall\"}, {\"id\": 4325, \"name\": \"the white jacket\"}, {\"id\": 4326, \"name\": \"a paved and gray road\"}, {\"id\": 4327, \"name\": \"the white taxi cab\"}, {\"id\": 4328, \"name\": \"a black helmet\"}, {\"id\": 4329, \"name\": \"a yellow and black striped curb\"}, {\"id\": 4330, \"name\": \"TV's screen\"}, {\"id\": 4331, \"name\": \"white wallpaper\"}, {\"id\": 4332, \"name\": \"black car\"}, {\"id\": 4333, \"name\": \"the blue toy\"}, {\"id\": 4334, \"name\": \"railing\"}, {\"id\": 4335, \"name\": \"pair of legs\"}, {\"id\": 4336, \"name\": \"the leg\"}, {\"id\": 4337, \"name\": \"the room's wall\"}, {\"id\": 4338, \"name\": \"a short dark hair\"}, {\"id\": 4339, \"name\": \"the fluorescent light\"}, {\"id\": 4340, \"name\": \"the gray refrigerator\"}, {\"id\": 4341, \"name\": \"decorative item\"}, {\"id\": 4342, \"name\": \"the coat\"}, {\"id\": 4343, \"name\": \"the cream-colored wall\"}, {\"id\": 4344, \"name\": \"an asphalt\"}, {\"id\": 4345, \"name\": \"the black helmet\"}, {\"id\": 4346, \"name\": \"the poster\"}, {\"id\": 4347, \"name\": \"prominent green arrow\"}, {\"id\": 4348, \"name\": \"the blurry photograph\"}, {\"id\": 4349, \"name\": \"a playground equipment\"}, {\"id\": 4350, \"name\": \"red wheel\"}, {\"id\": 4351, \"name\": \"a left side of the road\"}, {\"id\": 4352, \"name\": \"brown roof\"}, {\"id\": 4353, \"name\": \"black curtain\"}, {\"id\": 4354, \"name\": \"small silver lock\"}, {\"id\": 4355, \"name\": \"the long and black tail\"}, {\"id\": 4356, \"name\": \"a blue metal grate\"}, {\"id\": 4357, \"name\": \"blue grid pattern\"}, {\"id\": 4358, \"name\": \"dirty and littered area\"}, {\"id\": 4359, \"name\": \"the man in a gray jacket and black pants\"}, {\"id\": 4360, \"name\": \"the red metal railing\"}, {\"id\": 4361, \"name\": \"the red shirt\"}, {\"id\": 4362, \"name\": \"the pointy ear\"}, {\"id\": 4363, \"name\": \"hedge\"}, {\"id\": 4364, \"name\": \"a patterned wallpaper\"}, {\"id\": 4365, \"name\": \"a plaid shirt\"}, {\"id\": 4366, \"name\": \"green hue\"}, {\"id\": 4367, \"name\": \"the bed's interior\"}, {\"id\": 4368, \"name\": \"white molding\"}, {\"id\": 4369, \"name\": \"a light gray concrete\"}, {\"id\": 4370, \"name\": \"the closed storefront\"}, {\"id\": 4371, \"name\": \"the curved ceiling\"}, {\"id\": 4372, \"name\": \"black beanie\"}, {\"id\": 4373, \"name\": \"display screen\"}, {\"id\": 4374, \"name\": \"blue t-shirt\"}, {\"id\": 4375, \"name\": \"a light-colored bucket hat\"}, {\"id\": 4376, \"name\": \"long-sleeved shirt\"}, {\"id\": 4377, \"name\": \"bright light source\"}, {\"id\": 4378, \"name\": \"small, red, cushioned seat\"}, {\"id\": 4379, \"name\": \"a light blue shirt\"}, {\"id\": 4380, \"name\": \"a yellow tunic\"}, {\"id\": 4381, \"name\": \"a bridge's metal structure\"}, {\"id\": 4382, \"name\": \"a concrete pillar\"}, {\"id\": 4383, \"name\": \"a flat, gray surface\"}, {\"id\": 4384, \"name\": \"paved walkway\"}, {\"id\": 4385, \"name\": \"the arched design\"}, {\"id\": 4386, \"name\": \"the metal structure\"}, {\"id\": 4387, \"name\": \"the white metal framework\"}, {\"id\": 4388, \"name\": \"a dark red vehicle\"}, {\"id\": 4389, \"name\": \"a small flag icon\"}, {\"id\": 4390, \"name\": \"a time remaining in the game\"}, {\"id\": 4391, \"name\": \"bank employee\"}, {\"id\": 4392, \"name\": \"black top\"}, {\"id\": 4393, \"name\": \"the red and white ice cream freezer\"}, {\"id\": 4394, \"name\": \"the red bull logo\"}, {\"id\": 4395, \"name\": \"a green metal structure\"}, {\"id\": 4396, \"name\": \"a jungle gym\"}, {\"id\": 4397, \"name\": \"bark\"}, {\"id\": 4398, \"name\": \"child's hat\"}, {\"id\": 4399, \"name\": \"child's red snowsuit\"}, {\"id\": 4400, \"name\": \"white plastic shopping basket\"}, {\"id\": 4401, \"name\": \"maroon tracksuit\"}, {\"id\": 4402, \"name\": \"a red pillar\"}, {\"id\": 4403, \"name\": \"green facade\"}, {\"id\": 4404, \"name\": \"a torso\"}, {\"id\": 4405, \"name\": \"a shower curtain\"}, {\"id\": 4406, \"name\": \"a single yellow line\"}, {\"id\": 4407, \"name\": \"blurry image\"}, {\"id\": 4408, \"name\": \"the group of five young women\"}, {\"id\": 4409, \"name\": \"container\"}, {\"id\": 4410, \"name\": \"the bag of food\"}, {\"id\": 4411, \"name\": \"the colorful rug\"}, {\"id\": 4412, \"name\": \"the wooden paneling\"}, {\"id\": 4413, \"name\": \"a backdrop of trees\"}, {\"id\": 4414, \"name\": \"the stack of wood\"}, {\"id\": 4415, \"name\": \"the front bumper\"}, {\"id\": 4416, \"name\": \"the dark brown pillar\"}, {\"id\": 4417, \"name\": \"the white cover\"}, {\"id\": 4418, \"name\": \"a rural, two-lane road\"}, {\"id\": 4419, \"name\": \"a painted concrete block\"}, {\"id\": 4420, \"name\": \"green patterned dress\"}, {\"id\": 4421, \"name\": \"the red, yellow, and blue brick\"}, {\"id\": 4422, \"name\": \"a man in green and white checkered pant\"}, {\"id\": 4423, \"name\": \"the prominent red billboard\"}, {\"id\": 4424, \"name\": \"a red t-shirt\"}, {\"id\": 4425, \"name\": \"a small child\"}, {\"id\": 4426, \"name\": \"advertisements\"}, {\"id\": 4427, \"name\": \"a dark brown stripe\"}, {\"id\": 4428, \"name\": \"the short, dark hair\"}, {\"id\": 4429, \"name\": \"the white ceiling\"}, {\"id\": 4430, \"name\": \"a large glass door\"}, {\"id\": 4431, \"name\": \"a white blouse\"}, {\"id\": 4432, \"name\": \"a black fan\"}, {\"id\": 4433, \"name\": \"the enticing donut graphic\"}, {\"id\": 4434, \"name\": \"the dresser\"}, {\"id\": 4435, \"name\": \"the frying pan\"}, {\"id\": 4436, \"name\": \"the plate\"}, {\"id\": 4437, \"name\": \"gray sweatshirt\"}, {\"id\": 4438, \"name\": \"young man's face\"}, {\"id\": 4439, \"name\": \"a small food stall\"}, {\"id\": 4440, \"name\": \"orange hijab\"}, {\"id\": 4441, \"name\": \"the lattice design\"}, {\"id\": 4442, \"name\": \"white lattice design\"}, {\"id\": 4443, \"name\": \"the brown and tan patterned fabric\"}, {\"id\": 4444, \"name\": \"the brown sofa\"}, {\"id\": 4445, \"name\": \"tile\"}, {\"id\": 4446, \"name\": \"the casual attire\"}, {\"id\": 4447, \"name\": \"the man's attire\"}, {\"id\": 4448, \"name\": \"blury computer screen\"}, {\"id\": 4449, \"name\": \"the Tecno Taivos Camon smartphone\"}, {\"id\": 4450, \"name\": \"the dark blue color\"}, {\"id\": 4451, \"name\": \"grey tile\"}, {\"id\": 4452, \"name\": \"a khaki shorts\"}, {\"id\": 4453, \"name\": \"the long black and white patterned tunic\"}, {\"id\": 4454, \"name\": \"a tall, ornate pole\"}, {\"id\": 4455, \"name\": \"glass lantern\"}, {\"id\": 4456, \"name\": \"the column\"}, {\"id\": 4457, \"name\": \"red and blue zip-up jacket\"}, {\"id\": 4458, \"name\": \"sleeve\"}, {\"id\": 4459, \"name\": \"the air conditioner unit\"}, {\"id\": 4460, \"name\": \"drawer\"}, {\"id\": 4461, \"name\": \"the mouse\"}, {\"id\": 4462, \"name\": \"a white brick wall\"}, {\"id\": 4463, \"name\": \"the light-colored tank top\"}, {\"id\": 4464, \"name\": \"a bread\"}, {\"id\": 4465, \"name\": \"pastry\"}, {\"id\": 4466, \"name\": \"smaller loaf\"}, {\"id\": 4467, \"name\": \"the cabinet\"}, {\"id\": 4468, \"name\": \"the t-shirt\"}, {\"id\": 4469, \"name\": \"tiled floor\"}, {\"id\": 4470, \"name\": \"the billboard\"}, {\"id\": 4471, \"name\": \"a bank of the water\"}, {\"id\": 4472, \"name\": \"concrete overpass\"}, {\"id\": 4473, \"name\": \"large building\"}, {\"id\": 4474, \"name\": \"the person in a yellow safety vest\"}, {\"id\": 4475, \"name\": \"the black leash\"}, {\"id\": 4476, \"name\": \"the greenery\"}, {\"id\": 4477, \"name\": \"white tail\"}, {\"id\": 4478, \"name\": \"dark-colored jacket\"}, {\"id\": 4479, \"name\": \"the black and white houndstooth jacket\"}, {\"id\": 4480, \"name\": \"cabinet\"}, {\"id\": 4481, \"name\": \"floor of a building\"}, {\"id\": 4482, \"name\": \"the menu\"}, {\"id\": 4483, \"name\": \"a mesh back\"}, {\"id\": 4484, \"name\": \"armrest\"}, {\"id\": 4485, \"name\": \"a textured grey wallpaper\"}, {\"id\": 4486, \"name\": \"grey pant\"}, {\"id\": 4487, \"name\": \"the frisbee\"}, {\"id\": 4488, \"name\": \"the khaki pant\"}, {\"id\": 4489, \"name\": \"the red frisbee\"}, {\"id\": 4490, \"name\": \"the right index finger\"}, {\"id\": 4491, \"name\": \"the top right shelf\"}, {\"id\": 4492, \"name\": \"the abstract logo\"}, {\"id\": 4493, \"name\": \"group of six individuals\"}, {\"id\": 4494, \"name\": \"the black shirt\"}, {\"id\": 4495, \"name\": \"a blue vehicle\"}, {\"id\": 4496, \"name\": \"the low, yellow metal railing\"}, {\"id\": 4497, \"name\": \"the yellow and green auto rickshaw\"}, {\"id\": 4498, \"name\": \"yellow metal fence\"}, {\"id\": 4499, \"name\": \"a foreground\"}, {\"id\": 4500, \"name\": \"faint outline of the barn's walls and ceiling\"}, {\"id\": 4501, \"name\": \"the boat\"}, {\"id\": 4502, \"name\": \"the layer of dirt and debris\"}, {\"id\": 4503, \"name\": \"light-colored tile flooring\"}, {\"id\": 4504, \"name\": \"the large black wok\"}, {\"id\": 4505, \"name\": \"the wall-mounted rack\"}, {\"id\": 4506, \"name\": \"the white container\"}, {\"id\": 4507, \"name\": \"a seated passenger\"}, {\"id\": 4508, \"name\": \"a red cushion\"}, {\"id\": 4509, \"name\": \"wide road\"}, {\"id\": 4510, \"name\": \"a black motorbike\"}, {\"id\": 4511, \"name\": \"a pink helmet\"}, {\"id\": 4512, \"name\": \"the red barrier\"}, {\"id\": 4513, \"name\": \"the green curtain\"}, {\"id\": 4514, \"name\": \"the striking red and yellow hat\"}, {\"id\": 4515, \"name\": \"a young adult\"}, {\"id\": 4516, \"name\": \"a flat surface\"}, {\"id\": 4517, \"name\": \"the parked car\"}, {\"id\": 4518, \"name\": \"a tall apartment building\"}, {\"id\": 4519, \"name\": \"colorful dress\"}, {\"id\": 4520, \"name\": \"the package\"}, {\"id\": 4521, \"name\": \"the hazy sky\"}, {\"id\": 4522, \"name\": \"the large black bucket\"}, {\"id\": 4523, \"name\": \"a green light\"}, {\"id\": 4524, \"name\": \"woman in a white sari\"}, {\"id\": 4525, \"name\": \"a man in a black shirt\"}, {\"id\": 4526, \"name\": \"light yellow wall\"}, {\"id\": 4527, \"name\": \"the rolled-up sleeve\"}, {\"id\": 4528, \"name\": \"the teddy bear\"}, {\"id\": 4529, \"name\": \"the silhouette\"}, {\"id\": 4530, \"name\": \"the silhouetted figure\"}, {\"id\": 4531, \"name\": \"a closet door\"}, {\"id\": 4532, \"name\": \"a soap dispenser\"}, {\"id\": 4533, \"name\": \"the tail light\"}, {\"id\": 4534, \"name\": \"green leaf\"}, {\"id\": 4535, \"name\": \"porch light\"}, {\"id\": 4536, \"name\": \"black tank top\"}, {\"id\": 4537, \"name\": \"the barbell\"}, {\"id\": 4538, \"name\": \"the man in a black shirt\"}, {\"id\": 4539, \"name\": \"the checkered head wrap\"}, {\"id\": 4540, \"name\": \"the large water jug\"}, {\"id\": 4541, \"name\": \"customer\"}, {\"id\": 4542, \"name\": \"the fur-lined hood\"}, {\"id\": 4543, \"name\": \"the large pile of tomatoes\"}, {\"id\": 4544, \"name\": \"the green jeans\"}, {\"id\": 4545, \"name\": \"a man in a military uniform\"}, {\"id\": 4546, \"name\": \"large storefront\"}, {\"id\": 4547, \"name\": \"the beige cabinet\"}, {\"id\": 4548, \"name\": \"black and yellow patterned floor\"}, {\"id\": 4549, \"name\": \"the column's base\"}, {\"id\": 4550, \"name\": \"the decorative pattern\"}, {\"id\": 4551, \"name\": \"the dress\"}, {\"id\": 4552, \"name\": \"the black sunglasses\"}, {\"id\": 4553, \"name\": \"the green dress\"}, {\"id\": 4554, \"name\": \"log\"}, {\"id\": 4555, \"name\": \"white facade\"}, {\"id\": 4556, \"name\": \"a grass patch\"}, {\"id\": 4557, \"name\": \"a red roof\"}, {\"id\": 4558, \"name\": \"the cloudy sky\"}, {\"id\": 4559, \"name\": \"a mural\"}, {\"id\": 4560, \"name\": \"a school soccer field\"}, {\"id\": 4561, \"name\": \"the white circle\"}, {\"id\": 4562, \"name\": \"the tablecloth\"}, {\"id\": 4563, \"name\": \"black Ugg boot\"}, {\"id\": 4564, \"name\": \"long dark hair\"}, {\"id\": 4565, \"name\": \"pink light strip\"}, {\"id\": 4566, \"name\": \"bicycle wheel\"}, {\"id\": 4567, \"name\": \"the gray car\"}, {\"id\": 4568, \"name\": \"basketball court\"}, {\"id\": 4569, \"name\": \"ceiling light\"}, {\"id\": 4570, \"name\": \"red shirt\"}, {\"id\": 4571, \"name\": \"white lab coat\"}, {\"id\": 4572, \"name\": \"a man in the lab coat\"}, {\"id\": 4573, \"name\": \"the cow\"}, {\"id\": 4574, \"name\": \"the long tail\"}, {\"id\": 4575, \"name\": \"a white lace cloth\"}, {\"id\": 4576, \"name\": \"black dress\"}, {\"id\": 4577, \"name\": \"black handbag\"}, {\"id\": 4578, \"name\": \"crack\"}, {\"id\": 4579, \"name\": \"blue short\"}, {\"id\": 4580, \"name\": \"the interlocking paver\"}, {\"id\": 4581, \"name\": \"a long, flat instrument\"}, {\"id\": 4582, \"name\": \"large xylophone-like instrument\"}, {\"id\": 4583, \"name\": \"pair of small drums\"}, {\"id\": 4584, \"name\": \"set of drums\"}, {\"id\": 4585, \"name\": \"deer statue\"}, {\"id\": 4586, \"name\": \"the statue of a deer\"}, {\"id\": 4587, \"name\": \"glass railing\"}, {\"id\": 4588, \"name\": \"the performer\"}, {\"id\": 4589, \"name\": \"black and white shirt\"}, {\"id\": 4590, \"name\": \"the brown umbrella\"}, {\"id\": 4591, \"name\": \"front bumper\"}, {\"id\": 4592, \"name\": \"the truck's tire\"}, {\"id\": 4593, \"name\": \"lab coat\"}, {\"id\": 4594, \"name\": \"small dish\"}, {\"id\": 4595, \"name\": \"the faucet\"}, {\"id\": 4596, \"name\": \"the glass bottle\"}, {\"id\": 4597, \"name\": \"a motorcyclist\"}, {\"id\": 4598, \"name\": \"the van's license plate\"}, {\"id\": 4599, \"name\": \"an orange cat\"}, {\"id\": 4600, \"name\": \"the dark attire\"}, {\"id\": 4601, \"name\": \"a large yellow ball pit\"}, {\"id\": 4602, \"name\": \"a prominent yellow paw print pattern\"}, {\"id\": 4603, \"name\": \"ball pit\"}, {\"id\": 4604, \"name\": \"the dark shirt\"}, {\"id\": 4605, \"name\": \"a parakeet\"}, {\"id\": 4606, \"name\": \"a vibrant yellow crest\"}, {\"id\": 4607, \"name\": \"the bird's plumage\"}, {\"id\": 4608, \"name\": \"the red and black logo\"}, {\"id\": 4609, \"name\": \"tv\"}, {\"id\": 4610, \"name\": \"a white car\"}, {\"id\": 4611, \"name\": \"a dark wood floor\"}, {\"id\": 4612, \"name\": \"a shelving unit\"}, {\"id\": 4613, \"name\": \"the plastic bag\"}, {\"id\": 4614, \"name\": \"a storefront's entrance\"}, {\"id\": 4615, \"name\": \"silver Chevrolet\"}, {\"id\": 4616, \"name\": \"the black and grey striped shirt\"}, {\"id\": 4617, \"name\": \"metal object\"}, {\"id\": 4618, \"name\": \"the small metal bowl\"}, {\"id\": 4619, \"name\": \"a large mirror\"}, {\"id\": 4620, \"name\": \"the brown puppy\"}, {\"id\": 4621, \"name\": \"a black grill\"}, {\"id\": 4622, \"name\": \"a car emblem\"}, {\"id\": 4623, \"name\": \"gray sedan\"}, {\"id\": 4624, \"name\": \"silver Hyundai Elantra\"}, {\"id\": 4625, \"name\": \"a wine\"}, {\"id\": 4626, \"name\": \"the crisp white shirt\"}, {\"id\": 4627, \"name\": \"the gold charger plate\"}, {\"id\": 4628, \"name\": \"a large, round, metal manhole cover\"}, {\"id\": 4629, \"name\": \"round manhole cover\"}, {\"id\": 4630, \"name\": \"the bag of chips\"}, {\"id\": 4631, \"name\": \"man in a blue shirt\"}, {\"id\": 4632, \"name\": \"the curly tail\"}, {\"id\": 4633, \"name\": \"a large, rectangular aquarium\"}, {\"id\": 4634, \"name\": \"pipe\"}, {\"id\": 4635, \"name\": \"the slender, elongated shape\"}, {\"id\": 4636, \"name\": \"white chin\"}, {\"id\": 4637, \"name\": \"a floppy ear\"}, {\"id\": 4638, \"name\": \"the green and orange spiral toy\"}, {\"id\": 4639, \"name\": \"a man in a black shirt and dark pants\"}, {\"id\": 4640, \"name\": \"the blue and white patterned top\"}, {\"id\": 4641, \"name\": \"a small sign\"}, {\"id\": 4642, \"name\": \"white and red duck-shaped seat\"}, {\"id\": 4643, \"name\": \"suv\"}, {\"id\": 4644, \"name\": \"a plaque\"}, {\"id\": 4645, \"name\": \"the black wristband\"}, {\"id\": 4646, \"name\": \"the green shirt\"}, {\"id\": 4647, \"name\": \"red and white shirt\"}, {\"id\": 4648, \"name\": \"gray coat\"}, {\"id\": 4649, \"name\": \"the brown quilted vest\"}, {\"id\": 4650, \"name\": \"the red and white striped ball\"}, {\"id\": 4651, \"name\": \"a sneaker\"}, {\"id\": 4652, \"name\": \"a pink microfiber cloth\"}, {\"id\": 4653, \"name\": \"small, raised ball\"}, {\"id\": 4654, \"name\": \"the marble floor\"}, {\"id\": 4655, \"name\": \"a ballet flat\"}, {\"id\": 4656, \"name\": \"the red hair\"}, {\"id\": 4657, \"name\": \"the woman's shoe\"}, {\"id\": 4658, \"name\": \"wooden barre\"}, {\"id\": 4659, \"name\": \"a white, brown, and beige shelving unit\"}, {\"id\": 4660, \"name\": \"the concrete floor\"}, {\"id\": 4661, \"name\": \"a freezer or refrigerator\"}, {\"id\": 4662, \"name\": \"the light-colored tile floor\"}, {\"id\": 4663, \"name\": \"the white basket\"}, {\"id\": 4664, \"name\": \"red column\"}, {\"id\": 4665, \"name\": \"gray wall\"}, {\"id\": 4666, \"name\": \"black tail\"}, {\"id\": 4667, \"name\": \"a gray van\"}, {\"id\": 4668, \"name\": \"painted wall\"}, {\"id\": 4669, \"name\": \"an unpaved road\"}, {\"id\": 4670, \"name\": \"pickup truck\"}, {\"id\": 4671, \"name\": \"a rustic wooden facade\"}, {\"id\": 4672, \"name\": \"restaurant or coffee shop\"}, {\"id\": 4673, \"name\": \"the metal awning\"}, {\"id\": 4674, \"name\": \"the clothes\"}, {\"id\": 4675, \"name\": \"a large red and white present\"}, {\"id\": 4676, \"name\": \"paved path\"}, {\"id\": 4677, \"name\": \"left sleeve\"}, {\"id\": 4678, \"name\": \"the bench\"}, {\"id\": 4679, \"name\": \"the wooden structure\"}, {\"id\": 4680, \"name\": \"a sleek silver metal frame\"}, {\"id\": 4681, \"name\": \"a curved concrete ledge\"}, {\"id\": 4682, \"name\": \"the gray tile\"}, {\"id\": 4683, \"name\": \"eyeglass frame\"}, {\"id\": 4684, \"name\": \"glasses\"}, {\"id\": 4685, \"name\": \"left arm of the glasses\"}, {\"id\": 4686, \"name\": \"pair of dark sunglasses\"}, {\"id\": 4687, \"name\": \"the gray surface\"}, {\"id\": 4688, \"name\": \"the smaller sign\"}, {\"id\": 4689, \"name\": \"a large pothole\"}, {\"id\": 4690, \"name\": \"a yellow canopy\"}, {\"id\": 4691, \"name\": \"canopy\"}, {\"id\": 4692, \"name\": \"the food stand\"}, {\"id\": 4693, \"name\": \"a white display case\"}, {\"id\": 4694, \"name\": \"leggings\"}, {\"id\": 4695, \"name\": \"the photo\"}, {\"id\": 4696, \"name\": \"yellow coat\"}, {\"id\": 4697, \"name\": \"a white building\"}, {\"id\": 4698, \"name\": \"an individuals in yellow suits\"}, {\"id\": 4699, \"name\": \"a busy street\"}, {\"id\": 4700, \"name\": \"black wall\"}, {\"id\": 4701, \"name\": \"the vibrant sketch\"}, {\"id\": 4702, \"name\": \"the multi-story apartment\"}, {\"id\": 4703, \"name\": \"a thatched roof\"}, {\"id\": 4704, \"name\": \"fish tank\"}, {\"id\": 4705, \"name\": \"the grey headband\"}, {\"id\": 4706, \"name\": \"the zoo or aquarium\"}, {\"id\": 4707, \"name\": \"car's rearview mirror\"}, {\"id\": 4708, \"name\": \"interior of the car\"}, {\"id\": 4709, \"name\": \"smartphone\"}, {\"id\": 4710, \"name\": \"a SALE\"}, {\"id\": 4711, \"name\": \"a man in a light blue and white plaid shirt\"}, {\"id\": 4712, \"name\": \"a white marble floor\"}, {\"id\": 4713, \"name\": \"the silver trim\"}, {\"id\": 4714, \"name\": \"a green plaid shirt\"}, {\"id\": 4715, \"name\": \"the string of beads or charms\"}, {\"id\": 4716, \"name\": \"black sign\"}, {\"id\": 4717, \"name\": \"the menu board\"}, {\"id\": 4718, \"name\": \"a pink beanie\"}, {\"id\": 4719, \"name\": \"a pink puffy jacket\"}, {\"id\": 4720, \"name\": \"the yellow toy\"}, {\"id\": 4721, \"name\": \"the red basket\"}, {\"id\": 4722, \"name\": \"the short dark hair\"}, {\"id\": 4723, \"name\": \"a vibrant red and yellow pom-pom garland\"}, {\"id\": 4724, \"name\": \"the garland\"}, {\"id\": 4725, \"name\": \"the green top\"}, {\"id\": 4726, \"name\": \"the metal grate\"}, {\"id\": 4727, \"name\": \"a white t-shirt\"}, {\"id\": 4728, \"name\": \"black t-shirt\"}, {\"id\": 4729, \"name\": \"colorful fabric\"}, {\"id\": 4730, \"name\": \"purple wall\"}, {\"id\": 4731, \"name\": \"the black strap\"}, {\"id\": 4732, \"name\": \"large television screen\"}, {\"id\": 4733, \"name\": \"pair of jeans\"}, {\"id\": 4734, \"name\": \"television\"}, {\"id\": 4735, \"name\": \"blue top\"}, {\"id\": 4736, \"name\": \"the yellow liquid\"}, {\"id\": 4737, \"name\": \"a glass case\"}, {\"id\": 4738, \"name\": \"carpet\"}, {\"id\": 4739, \"name\": \"red and gold balloon\"}, {\"id\": 4740, \"name\": \"the purple fabric\"}, {\"id\": 4741, \"name\": \"the predominantly black and white coat\"}, {\"id\": 4742, \"name\": \"soccer goal\"}, {\"id\": 4743, \"name\": \"the tall apartment building\"}, {\"id\": 4744, \"name\": \"a blonde highlight\"}, {\"id\": 4745, \"name\": \"a dark green jacket\"}, {\"id\": 4746, \"name\": \"a people\"}, {\"id\": 4747, \"name\": \"the packaged meat\"}, {\"id\": 4748, \"name\": \"the blanket\"}, {\"id\": 4749, \"name\": \"the covered parking space\"}, {\"id\": 4750, \"name\": \"the gray hoodie\"}, {\"id\": 4751, \"name\": \"the spray can\"}, {\"id\": 4752, \"name\": \"grey and white cat\"}, {\"id\": 4753, \"name\": \"the large, curved escalator\"}, {\"id\": 4754, \"name\": \"a glossy white tile floor\"}, {\"id\": 4755, \"name\": \"kitten's head\"}, {\"id\": 4756, \"name\": \"a plain, off-white wall\"}, {\"id\": 4757, \"name\": \"a white door\"}, {\"id\": 4758, \"name\": \"small red spot\"}, {\"id\": 4759, \"name\": \"tongue\"}, {\"id\": 4760, \"name\": \"a hanging paper lantern\"}, {\"id\": 4761, \"name\": \"a row of illuminated menu boards\"}, {\"id\": 4762, \"name\": \"image of various food item\"}, {\"id\": 4763, \"name\": \"altar\"}, {\"id\": 4764, \"name\": \"tripod\"}, {\"id\": 4765, \"name\": \"man's attire\"}, {\"id\": 4766, \"name\": \"the chest freezer\"}, {\"id\": 4767, \"name\": \"a tan unitard\"}, {\"id\": 4768, \"name\": \"exposed beam\"}, {\"id\": 4769, \"name\": \"the trampoline\"}, {\"id\": 4770, \"name\": \"a concrete railing\"}, {\"id\": 4771, \"name\": \"flat roof\"}, {\"id\": 4772, \"name\": \"a hydrangea\"}, {\"id\": 4773, \"name\": \"the flower\"}, {\"id\": 4774, \"name\": \"pedestrian\"}, {\"id\": 4775, \"name\": \"black suitcase\"}, {\"id\": 4776, \"name\": \"the display\"}, {\"id\": 4777, \"name\": \"white building\"}, {\"id\": 4778, \"name\": \"bowl of rice\"}, {\"id\": 4779, \"name\": \"contents of the bowl\"}, {\"id\": 4780, \"name\": \"table's surface\"}, {\"id\": 4781, \"name\": \"the cd\"}, {\"id\": 4782, \"name\": \"the row of white tiles\"}, {\"id\": 4783, \"name\": \"white circle\"}, {\"id\": 4784, \"name\": \"glossy floor\"}, {\"id\": 4785, \"name\": \"the green and white checkered dress\"}, {\"id\": 4786, \"name\": \"the marble-like pattern\"}, {\"id\": 4787, \"name\": \"green stripe\"}, {\"id\": 4788, \"name\": \"a black circular sign\"}, {\"id\": 4789, \"name\": \"the backpack\"}, {\"id\": 4790, \"name\": \"a white and red toy\"}, {\"id\": 4791, \"name\": \"a blue bucket\"}, {\"id\": 4792, \"name\": \"a blue slipper\"}, {\"id\": 4793, \"name\": \"the shoulder\"}, {\"id\": 4794, \"name\": \"the wooden plank\"}, {\"id\": 4795, \"name\": \"a vibrant African print fabric\"}, {\"id\": 4796, \"name\": \"a white outlet\"}, {\"id\": 4797, \"name\": \"the white electrical outlet\"}, {\"id\": 4798, \"name\": \"shiny, polished floor\"}, {\"id\": 4799, \"name\": \"the refrigerator\"}, {\"id\": 4800, \"name\": \"the large advertisement\"}, {\"id\": 4801, \"name\": \"brake light\"}, {\"id\": 4802, \"name\": \"taillight\"}, {\"id\": 4803, \"name\": \"blue trash can\"}, {\"id\": 4804, \"name\": \"large, white bag of flour\"}, {\"id\": 4805, \"name\": \"the equipment\"}, {\"id\": 4806, \"name\": \"a hazy blue sky\"}, {\"id\": 4807, \"name\": \"a bald\"}, {\"id\": 4808, \"name\": \"person's leg\"}, {\"id\": 4809, \"name\": \"a gray, striped mat\"}, {\"id\": 4810, \"name\": \"a light-colored tile floor\"}, {\"id\": 4811, \"name\": \"small round mirror\"}, {\"id\": 4812, \"name\": \"the dark gray door\"}, {\"id\": 4813, \"name\": \"a grey vest\"}, {\"id\": 4814, \"name\": \"concrete pillar\"}, {\"id\": 4815, \"name\": \"gray hoodie\"}, {\"id\": 4816, \"name\": \"the large yellow sign\"}, {\"id\": 4817, \"name\": \"yellow tractor\"}, {\"id\": 4818, \"name\": \"a yellow tuk-tuk\"}, {\"id\": 4819, \"name\": \"green bush\"}, {\"id\": 4820, \"name\": \"the yellow motorbike\"}, {\"id\": 4821, \"name\": \"tuk-tuk\"}, {\"id\": 4822, \"name\": \"the light and dark color\"}, {\"id\": 4823, \"name\": \"a product\"}, {\"id\": 4824, \"name\": \"a puffy coat\"}, {\"id\": 4825, \"name\": \"fur trim\"}, {\"id\": 4826, \"name\": \"nylon\"}, {\"id\": 4827, \"name\": \"the lush tree\"}, {\"id\": 4828, \"name\": \"the motorcycle's rearview mirror\"}, {\"id\": 4829, \"name\": \"the drum\"}, {\"id\": 4830, \"name\": \"the person in a black coat\"}, {\"id\": 4831, \"name\": \"a white floral design\"}, {\"id\": 4832, \"name\": \"the green hedge\"}, {\"id\": 4833, \"name\": \"the mid-action\"}, {\"id\": 4834, \"name\": \"a brown cabinet\"}, {\"id\": 4835, \"name\": \"gray tile flooring\"}, {\"id\": 4836, \"name\": \"yellow lettering\"}, {\"id\": 4837, \"name\": \"the light-brown liquid\"}, {\"id\": 4838, \"name\": \"the succulent\"}, {\"id\": 4839, \"name\": \"a darkened room\"}, {\"id\": 4840, \"name\": \"a white top\"}, {\"id\": 4841, \"name\": \"a dark tea\"}, {\"id\": 4842, \"name\": \"skirt\"}, {\"id\": 4843, \"name\": \"a yellow roll of tape\"}, {\"id\": 4844, \"name\": \"red hoodie\"}, {\"id\": 4845, \"name\": \"Egyptian license plate\"}, {\"id\": 4846, \"name\": \"wet-looking concrete\"}, {\"id\": 4847, \"name\": \"a nut\"}, {\"id\": 4848, \"name\": \"dark puffy vest\"}, {\"id\": 4849, \"name\": \"ground meat\"}, {\"id\": 4850, \"name\": \"a stair\"}, {\"id\": 4851, \"name\": \"railroad track\"}, {\"id\": 4852, \"name\": \"the rusty metal\"}, {\"id\": 4853, \"name\": \"the solitary figure\"}, {\"id\": 4854, \"name\": \"the prominent purple sign\"}, {\"id\": 4855, \"name\": \"brown hose\"}, {\"id\": 4856, \"name\": \"the row of parked cars\"}, {\"id\": 4857, \"name\": \"white plastic bag\"}, {\"id\": 4858, \"name\": \"a gray slipper\"}, {\"id\": 4859, \"name\": \"a household item\"}, {\"id\": 4860, \"name\": \"lap\"}, {\"id\": 4861, \"name\": \"set of earbuds\"}, {\"id\": 4862, \"name\": \"the gray and white Puma sweatshirt\"}, {\"id\": 4863, \"name\": \"the white and black Nike sneaker\"}, {\"id\": 4864, \"name\": \"the white shoe\"}, {\"id\": 4865, \"name\": \"the yellow strap\"}, {\"id\": 4866, \"name\": \"the red knee-high sock\"}, {\"id\": 4867, \"name\": \"a brown object\"}, {\"id\": 4868, \"name\": \"the green and yellow brush head\"}, {\"id\": 4869, \"name\": \"the drink\"}, {\"id\": 4870, \"name\": \"the garment\"}, {\"id\": 4871, \"name\": \"thumb\"}, {\"id\": 4872, \"name\": \"blue and red uniform\"}, {\"id\": 4873, \"name\": \"sticker\"}, {\"id\": 4874, \"name\": \"a yellow light fixture\"}, {\"id\": 4875, \"name\": \"the lush arrangement of green plants\"}, {\"id\": 4876, \"name\": \"shop's front\"}, {\"id\": 4877, \"name\": \"a black pant\"}, {\"id\": 4878, \"name\": \"gold design\"}, {\"id\": 4879, \"name\": \"a skylight\"}, {\"id\": 4880, \"name\": \"a person in a white robe\"}, {\"id\": 4881, \"name\": \"the blue and white robe\"}, {\"id\": 4882, \"name\": \"woman in a long dress\"}, {\"id\": 4883, \"name\": \"fire alarm\"}, {\"id\": 4884, \"name\": \"the suitcase\"}, {\"id\": 4885, \"name\": \"the black outfit\"}, {\"id\": 4886, \"name\": \"the green section\"}, {\"id\": 4887, \"name\": \"a student\"}, {\"id\": 4888, \"name\": \"the decoration\"}, {\"id\": 4889, \"name\": \"a brown wooden cabinet\"}, {\"id\": 4890, \"name\": \"the bracelet\"}, {\"id\": 4891, \"name\": \"the meal\"}, {\"id\": 4892, \"name\": \"the sandwich\"}, {\"id\": 4893, \"name\": \"light-colored jeans\"}, {\"id\": 4894, \"name\": \"shopping bag\"}, {\"id\": 4895, \"name\": \"green digital display\"}, {\"id\": 4896, \"name\": \"the night sky\"}, {\"id\": 4897, \"name\": \"the rocky outcrop\"}, {\"id\": 4898, \"name\": \"car window\"}, {\"id\": 4899, \"name\": \"white bag\"}, {\"id\": 4900, \"name\": \"a manhole cover\"}, {\"id\": 4901, \"name\": \"a small metal grate\"}, {\"id\": 4902, \"name\": \"the black sweater\"}, {\"id\": 4903, \"name\": \"a German Shepherd puppy\"}, {\"id\": 4904, \"name\": \"a black back\"}, {\"id\": 4905, \"name\": \"a distinctive red collar\"}, {\"id\": 4906, \"name\": \"a TABLE CREATININE\"}, {\"id\": 4907, \"name\": \"a textured pattern\"}, {\"id\": 4908, \"name\": \"a white table\"}, {\"id\": 4909, \"name\": \"red liquid\"}, {\"id\": 4910, \"name\": \"textured pattern\"}, {\"id\": 4911, \"name\": \"the woman's hand\"}, {\"id\": 4912, \"name\": \"the yellow stripe\"}, {\"id\": 4913, \"name\": \"calligraphy piece\"}, {\"id\": 4914, \"name\": \"red cloth\"}, {\"id\": 4915, \"name\": \"small black stone\"}, {\"id\": 4916, \"name\": \"the red fabric roll\"}, {\"id\": 4917, \"name\": \"ornate altar\"}, {\"id\": 4918, \"name\": \"the chandelier\"}, {\"id\": 4919, \"name\": \"the high altar\"}, {\"id\": 4920, \"name\": \"a blue jean\"}, {\"id\": 4921, \"name\": \"the red dumpster\"}, {\"id\": 4922, \"name\": \"the yellow and black tuk-tuk\"}, {\"id\": 4923, \"name\": \"blurry photograph\"}, {\"id\": 4924, \"name\": \"the dark-colored shirt\"}, {\"id\": 4925, \"name\": \"the white polo shirt\"}, {\"id\": 4926, \"name\": \"bike\"}, {\"id\": 4927, \"name\": \"red car\"}, {\"id\": 4928, \"name\": \"the white coat\"}, {\"id\": 4929, \"name\": \"man in a yellow safety jacket\"}, {\"id\": 4930, \"name\": \"the school or office building\"}, {\"id\": 4931, \"name\": \"brown hair\"}, {\"id\": 4932, \"name\": \"the cat's head\"}, {\"id\": 4933, \"name\": \"the white or cream background\"}, {\"id\": 4934, \"name\": \"a light-colored vehicle\"}, {\"id\": 4935, \"name\": \"the concrete car hood\"}, {\"id\": 4936, \"name\": \"a high-rise structure\"}, {\"id\": 4937, \"name\": \"a lone cow\"}, {\"id\": 4938, \"name\": \"a dog's front paws\"}, {\"id\": 4939, \"name\": \"the black object\"}, {\"id\": 4940, \"name\": \"a store's floor\"}, {\"id\": 4941, \"name\": \"box or container\"}, {\"id\": 4942, \"name\": \"cushion\"}, {\"id\": 4943, \"name\": \"the pile of dirt\"}, {\"id\": 4944, \"name\": \"a Pekingese\"}, {\"id\": 4945, \"name\": \"the circular image\"}, {\"id\": 4946, \"name\": \"the plate of brownie\"}, {\"id\": 4947, \"name\": \"the vending machine\"}, {\"id\": 4948, \"name\": \"bathtub\"}, {\"id\": 4949, \"name\": \"the blue plastic tray\"}, {\"id\": 4950, \"name\": \"the fur\"}, {\"id\": 4951, \"name\": \"the green sign with white Chinese character\"}, {\"id\": 4952, \"name\": \"window air conditioner\"}, {\"id\": 4953, \"name\": \"a large white dog\"}, {\"id\": 4954, \"name\": \"the black suit\"}, {\"id\": 4955, \"name\": \"the bread\"}, {\"id\": 4956, \"name\": \"a machine's screen\"}, {\"id\": 4957, \"name\": \"large yellow crane\"}, {\"id\": 4958, \"name\": \"wispy cloud\"}, {\"id\": 4959, \"name\": \"a white sky\"}, {\"id\": 4960, \"name\": \"a red bottle\"}, {\"id\": 4961, \"name\": \"metal pot\"}, {\"id\": 4962, \"name\": \"the wooden table\"}, {\"id\": 4963, \"name\": \"a small white stone\"}, {\"id\": 4964, \"name\": \"the asphalt street\"}, {\"id\": 4965, \"name\": \"a white bin\"}, {\"id\": 4966, \"name\": \"white metal ceiling\"}, {\"id\": 4967, \"name\": \"a white base\"}, {\"id\": 4968, \"name\": \"the red top\"}, {\"id\": 4969, \"name\": \"the red and white clothing\"}, {\"id\": 4970, \"name\": \"the white sweater\"}, {\"id\": 4971, \"name\": \"a black display case\"}, {\"id\": 4972, \"name\": \"a black support column\"}, {\"id\": 4973, \"name\": \"a glass-enclosed storefront\"}, {\"id\": 4974, \"name\": \"a store's name\"}, {\"id\": 4975, \"name\": \"metal handrail\"}, {\"id\": 4976, \"name\": \"a vibrant purple and pink robe\"}, {\"id\": 4977, \"name\": \"an earthy backdrop\"}, {\"id\": 4978, \"name\": \"reddish-brown color\"}, {\"id\": 4979, \"name\": \"the house\"}, {\"id\": 4980, \"name\": \"large mound\"}, {\"id\": 4981, \"name\": \"tool\"}, {\"id\": 4982, \"name\": \"a transparent cover\"}, {\"id\": 4983, \"name\": \"a patron\"}, {\"id\": 4984, \"name\": \"advertisement\"}, {\"id\": 4985, \"name\": \"a high ceiling\"}, {\"id\": 4986, \"name\": \"grey stone floor\"}, {\"id\": 4987, \"name\": \"the toy or miniature breed of dog\"}, {\"id\": 4988, \"name\": \"a couch or chair\"}, {\"id\": 4989, \"name\": \"individual's attire\"}, {\"id\": 4990, \"name\": \"the red bowl\"}, {\"id\": 4991, \"name\": \"Chihuahua\"}, {\"id\": 4992, \"name\": \"short, tan coat\"}, {\"id\": 4993, \"name\": \"the brown and tan blanket\"}, {\"id\": 4994, \"name\": \"a wall of white tiles\"}, {\"id\": 4995, \"name\": \"a pale green sweater\"}, {\"id\": 4996, \"name\": \"the ribbed texture\"}, {\"id\": 4997, \"name\": \"dark wood or tile\"}, {\"id\": 4998, \"name\": \"the small purple plastic bowl\"}, {\"id\": 4999, \"name\": \"facade\"}, {\"id\": 5000, \"name\": \"a leg of the tripod\"}, {\"id\": 5001, \"name\": \"black shoe\"}, {\"id\": 5002, \"name\": \"circular LED panel\"}, {\"id\": 5003, \"name\": \"red background\"}, {\"id\": 5004, \"name\": \"the black rectangle\"}, {\"id\": 5005, \"name\": \"the piece of artwork\"}, {\"id\": 5006, \"name\": \"the red and black poster\"}, {\"id\": 5007, \"name\": \"circular hole\"}, {\"id\": 5008, \"name\": \"a kitchen counter\"}, {\"id\": 5009, \"name\": \"the black frame\"}, {\"id\": 5010, \"name\": \"the brown cabinet\"}, {\"id\": 5011, \"name\": \"the dark grey t-shirt\"}, {\"id\": 5012, \"name\": \"the stove burner\"}, {\"id\": 5013, \"name\": \"bowling alley\"}, {\"id\": 5014, \"name\": \"a gray and white tile floor\"}, {\"id\": 5015, \"name\": \"a prominent blue sign\"}, {\"id\": 5016, \"name\": \"row of vendors\"}, {\"id\": 5017, \"name\": \"long coat\"}, {\"id\": 5018, \"name\": \"the intersection\"}, {\"id\": 5019, \"name\": \"a blue dustpan\"}, {\"id\": 5020, \"name\": \"the child\"}, {\"id\": 5021, \"name\": \"the miniature replica of a real vacuum cleaner\"}, {\"id\": 5022, \"name\": \"car's windshield\"}, {\"id\": 5023, \"name\": \"asphalt\"}, {\"id\": 5024, \"name\": \"black metal fence\"}, {\"id\": 5025, \"name\": \"grey sedan\"}, {\"id\": 5026, \"name\": \"a gray stone path\"}, {\"id\": 5027, \"name\": \"low gray stone wall\"}, {\"id\": 5028, \"name\": \"the black-and-white dog\"}, {\"id\": 5029, \"name\": \"the blurry orange pillar\"}, {\"id\": 5030, \"name\": \"a dark-colored clothing\"}, {\"id\": 5031, \"name\": \"a long, patterned coat\"}, {\"id\": 5032, \"name\": \"cream-colored column\"}, {\"id\": 5033, \"name\": \"glass wall\"}, {\"id\": 5034, \"name\": \"a short hair\"}, {\"id\": 5035, \"name\": \"hoodie\"}, {\"id\": 5036, \"name\": \"the range hood\"}, {\"id\": 5037, \"name\": \"a black dress\"}, {\"id\": 5038, \"name\": \"single-story building\"}, {\"id\": 5039, \"name\": \"the rock\"}, {\"id\": 5040, \"name\": \"yellow T-shirt\"}, {\"id\": 5041, \"name\": \"the small, one-story house\"}, {\"id\": 5042, \"name\": \"yard\"}, {\"id\": 5043, \"name\": \"the wooden spoon\"}, {\"id\": 5044, \"name\": \"the forceps\"}, {\"id\": 5045, \"name\": \"the red line\"}, {\"id\": 5046, \"name\": \"low concrete wall\"}, {\"id\": 5047, \"name\": \"brown railing\"}, {\"id\": 5048, \"name\": \"white door\"}, {\"id\": 5049, \"name\": \"white folding chair\"}, {\"id\": 5050, \"name\": \"a black vest\"}, {\"id\": 5051, \"name\": \"cauliflower\"}, {\"id\": 5052, \"name\": \"vendor\"}, {\"id\": 5053, \"name\": \"a yellow pepper\"}, {\"id\": 5054, \"name\": \"the black bowl\"}, {\"id\": 5055, \"name\": \"the whole black peppercorn\"}, {\"id\": 5056, \"name\": \"fruit\"}, {\"id\": 5057, \"name\": \"the gray shirt\"}, {\"id\": 5058, \"name\": \"the outfit\"}, {\"id\": 5059, \"name\": \"a white box\"}, {\"id\": 5060, \"name\": \"the dark blue shirt\"}, {\"id\": 5061, \"name\": \"the dark suit\"}, {\"id\": 5062, \"name\": \"beige tiled roof\"}, {\"id\": 5063, \"name\": \"white canopy\"}, {\"id\": 5064, \"name\": \"a water container\"}, {\"id\": 5065, \"name\": \"a rack of clothing\"}, {\"id\": 5066, \"name\": \"brown geometric pattern\"}, {\"id\": 5067, \"name\": \"shop\"}, {\"id\": 5068, \"name\": \"a waiter's right arm\"}, {\"id\": 5069, \"name\": \"the store's floor\"}, {\"id\": 5070, \"name\": \"wreath\"}, {\"id\": 5071, \"name\": \"the purple box\"}, {\"id\": 5072, \"name\": \"a janitorial cart\"}, {\"id\": 5073, \"name\": \"a prominent cleaning cart\"}, {\"id\": 5074, \"name\": \"sign with Chinese characters\"}, {\"id\": 5075, \"name\": \"a gray curtain\"}, {\"id\": 5076, \"name\": \"large poster\"}, {\"id\": 5077, \"name\": \"river's bank\"}, {\"id\": 5078, \"name\": \"the dark, rippled surface of the water\"}, {\"id\": 5079, \"name\": \"patterned border\"}, {\"id\": 5080, \"name\": \"tall building\"}, {\"id\": 5081, \"name\": \"a paved brick sidewalk\"}, {\"id\": 5082, \"name\": \"red, blue, and white hue\"}, {\"id\": 5083, \"name\": \"a grid-like pattern\"}, {\"id\": 5084, \"name\": \"a round, pouf-like seating option\"}, {\"id\": 5085, \"name\": \"the large poster\"}, {\"id\": 5086, \"name\": \"the potted plant\"}, {\"id\": 5087, \"name\": \"a black trash bag\"}, {\"id\": 5088, \"name\": \"a black trousers\"}, {\"id\": 5089, \"name\": \"a navy-blue puffy coat\"}, {\"id\": 5090, \"name\": \"roof\"}, {\"id\": 5091, \"name\": \"white frame\"}, {\"id\": 5092, \"name\": \"the christmas tree\"}, {\"id\": 5093, \"name\": \"black metal structure\"}, {\"id\": 5094, \"name\": \"the red awning\"}, {\"id\": 5095, \"name\": \"dinosaur toy\"}, {\"id\": 5096, \"name\": \"lamp\"}, {\"id\": 5097, \"name\": \"red stuffed animal\"}, {\"id\": 5098, \"name\": \"toy\"}, {\"id\": 5099, \"name\": \"a glossy, beige tile floor\"}, {\"id\": 5100, \"name\": \"shiny tile\"}, {\"id\": 5101, \"name\": \"prominent silver car\"}, {\"id\": 5102, \"name\": \"a grey circular dog bed\"}, {\"id\": 5103, \"name\": \"black and white plate\"}, {\"id\": 5104, \"name\": \"small toy\"}, {\"id\": 5105, \"name\": \"the black shelf\"}, {\"id\": 5106, \"name\": \"large pillar\"}, {\"id\": 5107, \"name\": \"the shelf\"}, {\"id\": 5108, \"name\": \"wood and tile pattern\"}, {\"id\": 5109, \"name\": \"the long dress\"}, {\"id\": 5110, \"name\": \"a white banner\"}, {\"id\": 5111, \"name\": \"an arcade game\"}, {\"id\": 5112, \"name\": \"family\"}, {\"id\": 5113, \"name\": \"the claw machine\"}, {\"id\": 5114, \"name\": \"the tile\"}, {\"id\": 5115, \"name\": \"a green fabric\"}, {\"id\": 5116, \"name\": \"the rope\"}, {\"id\": 5117, \"name\": \"black SUV\"}, {\"id\": 5118, \"name\": \"grill\"}, {\"id\": 5119, \"name\": \"the yellow truck\"}, {\"id\": 5120, \"name\": \"a pink label\"}, {\"id\": 5121, \"name\": \"the gold ring\"}, {\"id\": 5122, \"name\": \"a bright overhead lighting\"}, {\"id\": 5123, \"name\": \"the white tiled floor\"}, {\"id\": 5124, \"name\": \"outdoor ice skating rink\"}, {\"id\": 5125, \"name\": \"man's image\"}, {\"id\": 5126, \"name\": \"wooden object\"}, {\"id\": 5127, \"name\": \"a winter clothing\"}, {\"id\": 5128, \"name\": \"blue object\"}, {\"id\": 5129, \"name\": \"blue table\"}, {\"id\": 5130, \"name\": \"student\"}, {\"id\": 5131, \"name\": \"the silver bowl\"}, {\"id\": 5132, \"name\": \"a white ornament\"}, {\"id\": 5133, \"name\": \"red and black object\"}, {\"id\": 5134, \"name\": \"the black and white breed\"}, {\"id\": 5135, \"name\": \"the black and white coat\"}, {\"id\": 5136, \"name\": \"tool used for painting\"}, {\"id\": 5137, \"name\": \"red tassel\"}, {\"id\": 5138, \"name\": \"the cartoon pig's head\"}, {\"id\": 5139, \"name\": \"the red Chinese character\"}, {\"id\": 5140, \"name\": \"the square fluorescent light fixture\"}, {\"id\": 5141, \"name\": \"the mushroom\"}, {\"id\": 5142, \"name\": \"gray concrete road\"}, {\"id\": 5143, \"name\": \"a man in the yellow shirt\"}, {\"id\": 5144, \"name\": \"the metal object\"}, {\"id\": 5145, \"name\": \"the saucepan\"}, {\"id\": 5146, \"name\": \"a cluster of potted plants\"}, {\"id\": 5147, \"name\": \"blue recycling symbol\"}, {\"id\": 5148, \"name\": \"the digital screen\"}, {\"id\": 5149, \"name\": \"the recycling bin\"}, {\"id\": 5150, \"name\": \"the trash can\"}, {\"id\": 5151, \"name\": \"a Samoyed\"}, {\"id\": 5152, \"name\": \"animal's head\"}, {\"id\": 5153, \"name\": \"a small, four-door sedan\"}, {\"id\": 5154, \"name\": \"the damaged roof\"}, {\"id\": 5155, \"name\": \"a puddle of water\"}, {\"id\": 5156, \"name\": \"the Pepsi truck\"}, {\"id\": 5157, \"name\": \"a gravel or sand\"}, {\"id\": 5158, \"name\": \"large blue container\"}, {\"id\": 5159, \"name\": \"distinctive white dome\"}, {\"id\": 5160, \"name\": \"the vibrant red and orange tablecloth\"}, {\"id\": 5161, \"name\": \"a prominent red wall\"}, {\"id\": 5162, \"name\": \"the market's floor\"}, {\"id\": 5163, \"name\": \"the car bumper\"}, {\"id\": 5164, \"name\": \"yellow bib\"}, {\"id\": 5165, \"name\": \"structure\"}, {\"id\": 5166, \"name\": \"a large, square tile\"}, {\"id\": 5167, \"name\": \"white, rectangular metal box\"}, {\"id\": 5168, \"name\": \"a colorful flower\"}, {\"id\": 5169, \"name\": \"silver handle\"}, {\"id\": 5170, \"name\": \"the brown wardrobe\"}, {\"id\": 5171, \"name\": \"the fan\"}, {\"id\": 5172, \"name\": \"white minivan\"}, {\"id\": 5173, \"name\": \"the red and white signage\"}, {\"id\": 5174, \"name\": \"a red plant pot\"}, {\"id\": 5175, \"name\": \"a small yellow toy\"}, {\"id\": 5176, \"name\": \"a yellow tennis ball\"}, {\"id\": 5177, \"name\": \"a brown wall\"}, {\"id\": 5178, \"name\": \"teal wall\"}, {\"id\": 5179, \"name\": \"a dark green color\"}, {\"id\": 5180, \"name\": \"the Christmas decoration\"}, {\"id\": 5181, \"name\": \"black blazer\"}, {\"id\": 5182, \"name\": \"the brown curtain\"}, {\"id\": 5183, \"name\": \"tan and white striped hoodie\"}, {\"id\": 5184, \"name\": \"the stroller\"}, {\"id\": 5185, \"name\": \"a brown fur\"}, {\"id\": 5186, \"name\": \"a wooden pillar\"}, {\"id\": 5187, \"name\": \"the tail\"}, {\"id\": 5188, \"name\": \"yellow butterfly\"}, {\"id\": 5189, \"name\": \"a blurry dog\"}, {\"id\": 5190, \"name\": \"a soft, blue material\"}, {\"id\": 5191, \"name\": \"blue towel or blanket\"}, {\"id\": 5192, \"name\": \"the tan dog\"}, {\"id\": 5193, \"name\": \"the terrier\"}, {\"id\": 5194, \"name\": \"dashboard\"}, {\"id\": 5195, \"name\": \"white car\"}, {\"id\": 5196, \"name\": \"a dark clothing\"}, {\"id\": 5197, \"name\": \"the red trim\"}, {\"id\": 5198, \"name\": \"a framed picture\"}, {\"id\": 5199, \"name\": \"large orange bowl\"}, {\"id\": 5200, \"name\": \"puppy's front leg\"}, {\"id\": 5201, \"name\": \"red bowl\"}, {\"id\": 5202, \"name\": \"the green ball\"}, {\"id\": 5203, \"name\": \"the plush toy\"}, {\"id\": 5204, \"name\": \"towel\"}, {\"id\": 5205, \"name\": \"yellow duck\"}, {\"id\": 5206, \"name\": \"the yellow sign\"}, {\"id\": 5207, \"name\": \"black roof\"}, {\"id\": 5208, \"name\": \"red and blue sticker\"}, {\"id\": 5209, \"name\": \"a TV screen\"}, {\"id\": 5210, \"name\": \"the large poster of Santa Claus\"}, {\"id\": 5211, \"name\": \"the metal railing\"}, {\"id\": 5212, \"name\": \"a celebration\"}, {\"id\": 5213, \"name\": \"a Olympic ring\"}, {\"id\": 5214, \"name\": \"the blue banner\"}, {\"id\": 5215, \"name\": \"a small closet\"}, {\"id\": 5216, \"name\": \"closet door\"}, {\"id\": 5217, \"name\": \"the grey sweatshirt\"}, {\"id\": 5218, \"name\": \"the light-colored wood floor\"}, {\"id\": 5219, \"name\": \"the subtle sheen\"}, {\"id\": 5220, \"name\": \"a fog\"}, {\"id\": 5221, \"name\": \"a blurry photograph\"}, {\"id\": 5222, \"name\": \"young boy\"}, {\"id\": 5223, \"name\": \"a black robe\"}, {\"id\": 5224, \"name\": \"the white bag\"}, {\"id\": 5225, \"name\": \"the white sign\"}, {\"id\": 5226, \"name\": \"a Prada store\"}, {\"id\": 5227, \"name\": \"tan pant\"}, {\"id\": 5228, \"name\": \"blue SUV\"}, {\"id\": 5229, \"name\": \"parking lot\"}, {\"id\": 5230, \"name\": \"the backdrop of power lines\"}, {\"id\": 5231, \"name\": \"wooden beam\"}, {\"id\": 5232, \"name\": \"the utility pole\"}, {\"id\": 5233, \"name\": \"a large pink floral design\"}, {\"id\": 5234, \"name\": \"headscarf\"}, {\"id\": 5235, \"name\": \"light blue jacket\"}, {\"id\": 5236, \"name\": \"the adult\"}, {\"id\": 5237, \"name\": \"the dark blue jeans\"}, {\"id\": 5238, \"name\": \"a rocky area\"}, {\"id\": 5239, \"name\": \"a curved escalator\"}, {\"id\": 5240, \"name\": \"a left leg\"}, {\"id\": 5241, \"name\": \"black hair\"}, {\"id\": 5242, \"name\": \"a yellow railing\"}, {\"id\": 5243, \"name\": \"long orange dress\"}, {\"id\": 5244, \"name\": \"a long, glass-topped counter\"}, {\"id\": 5245, \"name\": \"counter\"}, {\"id\": 5246, \"name\": \"the shopper\"}, {\"id\": 5247, \"name\": \"the large, white, metal cart\"}, {\"id\": 5248, \"name\": \"the smaller gray building\"}, {\"id\": 5249, \"name\": \"person's bare feet\"}, {\"id\": 5250, \"name\": \"the black counter\"}, {\"id\": 5251, \"name\": \"the kitchen item\"}, {\"id\": 5252, \"name\": \"the large wooden cabinet\"}, {\"id\": 5253, \"name\": \"orange shirt\"}, {\"id\": 5254, \"name\": \"a gray pant\"}, {\"id\": 5255, \"name\": \"the grate\"}, {\"id\": 5256, \"name\": \"the single-story shop\"}, {\"id\": 5257, \"name\": \"large tiger\"}, {\"id\": 5258, \"name\": \"lush green tree\"}, {\"id\": 5259, \"name\": \"the tiger\"}, {\"id\": 5260, \"name\": \"parked vehicle\"}, {\"id\": 5261, \"name\": \"the black top\"}, {\"id\": 5262, \"name\": \"the large, white C logo\"}, {\"id\": 5263, \"name\": \"seated man\"}, {\"id\": 5264, \"name\": \"the drawer\"}, {\"id\": 5265, \"name\": \"a Maltese\"}, {\"id\": 5266, \"name\": \"black wrought iron gate\"}, {\"id\": 5267, \"name\": \"the small, fluffy white dog\"}, {\"id\": 5268, \"name\": \"the shoe store\"}, {\"id\": 5269, \"name\": \"short dark hair\"}, {\"id\": 5270, \"name\": \"metal bar\"}, {\"id\": 5271, \"name\": \"the wire fence\"}, {\"id\": 5272, \"name\": \"the chicken's outdoor enclosure\"}, {\"id\": 5273, \"name\": \"the dirt floor\"}, {\"id\": 5274, \"name\": \"floral-patterned stand\"}, {\"id\": 5275, \"name\": \"small floral pattern\"}, {\"id\": 5276, \"name\": \"the beige tile\"}, {\"id\": 5277, \"name\": \"beam\"}, {\"id\": 5278, \"name\": \"the tan cushion\"}, {\"id\": 5279, \"name\": \"a light-colored concrete\"}, {\"id\": 5280, \"name\": \"grey jacket\"}, {\"id\": 5281, \"name\": \"dark gray floor\"}, {\"id\": 5282, \"name\": \"a corrugated metal wall\"}, {\"id\": 5283, \"name\": \"forearm\"}, {\"id\": 5284, \"name\": \"the black uniform\"}, {\"id\": 5285, \"name\": \"the clown costume\"}, {\"id\": 5286, \"name\": \"seated person\"}, {\"id\": 5287, \"name\": \"the dark blue chair\"}, {\"id\": 5288, \"name\": \"large head\"}, {\"id\": 5289, \"name\": \"large white lion head\"}, {\"id\": 5290, \"name\": \"the navy t-shirt\"}, {\"id\": 5291, \"name\": \"the brown jacket\"}, {\"id\": 5292, \"name\": \"a dark-colored shirt\"}, {\"id\": 5293, \"name\": \"bare feet\"}, {\"id\": 5294, \"name\": \"a red line\"}, {\"id\": 5295, \"name\": \"right wall\"}, {\"id\": 5296, \"name\": \"silver circular object\"}, {\"id\": 5297, \"name\": \"white ceiling\"}, {\"id\": 5298, \"name\": \"stack of cardboard boxes\"}, {\"id\": 5299, \"name\": \"the back wheel\"}, {\"id\": 5300, \"name\": \"the woman in a black tank top and white shorts\"}, {\"id\": 5301, \"name\": \"a beige plastic chair\"}, {\"id\": 5302, \"name\": \"black watch\"}, {\"id\": 5303, \"name\": \"document or paper\"}, {\"id\": 5304, \"name\": \"the sleeve\"}, {\"id\": 5305, \"name\": \"a blue and white logo\"}, {\"id\": 5306, \"name\": \"the green chair\"}, {\"id\": 5307, \"name\": \"the white pearl trim\"}, {\"id\": 5308, \"name\": \"the dirt road\"}, {\"id\": 5309, \"name\": \"the tree-lined road\"}, {\"id\": 5310, \"name\": \"the red and gold decoration\"}, {\"id\": 5311, \"name\": \"the shop\"}, {\"id\": 5312, \"name\": \"pink puffer jacket\"}, {\"id\": 5313, \"name\": \"the cart\"}, {\"id\": 5314, \"name\": \"the dark-colored mat\"}, {\"id\": 5315, \"name\": \"a model\"}, {\"id\": 5316, \"name\": \"a pink leather handbag\"}, {\"id\": 5317, \"name\": \"the black hair\"}, {\"id\": 5318, \"name\": \"the high ceiling\"}, {\"id\": 5319, \"name\": \"a tall, dark building\"}, {\"id\": 5320, \"name\": \"the white vehicle\"}, {\"id\": 5321, \"name\": \"the left foot\"}, {\"id\": 5322, \"name\": \"grey puffer coat\"}, {\"id\": 5323, \"name\": \"a sow's head\"}, {\"id\": 5324, \"name\": \"discoloration\"}, {\"id\": 5325, \"name\": \"penguin\"}, {\"id\": 5326, \"name\": \"pink patch\"}, {\"id\": 5327, \"name\": \"the mother\"}, {\"id\": 5328, \"name\": \"the mother's underbelly\"}, {\"id\": 5329, \"name\": \"the right ear\"}, {\"id\": 5330, \"name\": \"a long, narrow corridor\"}, {\"id\": 5331, \"name\": \"maintenance worker\"}, {\"id\": 5332, \"name\": \"foreign language\"}, {\"id\": 5333, \"name\": \"the bus\"}, {\"id\": 5334, \"name\": \"yellow three-wheeled vehicle\"}, {\"id\": 5335, \"name\": \"bright, sunny day\"}, {\"id\": 5336, \"name\": \"dark blue vest\"}, {\"id\": 5337, \"name\": \"a silver handrail\"}, {\"id\": 5338, \"name\": \"horizontal stripe\"}, {\"id\": 5339, \"name\": \"pattern of brown, tan, and red tile\"}, {\"id\": 5340, \"name\": \"the black and silver escalator\"}, {\"id\": 5341, \"name\": \"the brown door\"}, {\"id\": 5342, \"name\": \"the khaki cargo shorts\"}, {\"id\": 5343, \"name\": \"wooden structure\"}, {\"id\": 5344, \"name\": \"a watermark\"}, {\"id\": 5345, \"name\": \"the crack\"}, {\"id\": 5346, \"name\": \"tan shirt\"}, {\"id\": 5347, \"name\": \"the large piece of artwork\"}, {\"id\": 5348, \"name\": \"feed or bedding material\"}, {\"id\": 5349, \"name\": \"the pillow\"}, {\"id\": 5350, \"name\": \"wall of the barn\"}, {\"id\": 5351, \"name\": \"the blue car\"}, {\"id\": 5352, \"name\": \"track\"}, {\"id\": 5353, \"name\": \"train track\"}, {\"id\": 5354, \"name\": \"olive green jacket\"}, {\"id\": 5355, \"name\": \"a row of green bushes\"}, {\"id\": 5356, \"name\": \"horizontal bar\"}, {\"id\": 5357, \"name\": \"red shopping cart\"}, {\"id\": 5358, \"name\": \"small slot\"}, {\"id\": 5359, \"name\": \"the gray wall\"}, {\"id\": 5360, \"name\": \"selection of items\"}, {\"id\": 5361, \"name\": \"the short brown hair\"}, {\"id\": 5362, \"name\": \"light blue sign\"}, {\"id\": 5363, \"name\": \"extended tailgate\"}, {\"id\": 5364, \"name\": \"the set of wheels\"}, {\"id\": 5365, \"name\": \"a blue denim shirt\"}, {\"id\": 5366, \"name\": \"a light blue denim jacket\"}, {\"id\": 5367, \"name\": \"the row of cart\"}, {\"id\": 5368, \"name\": \"the motorbike\"}, {\"id\": 5369, \"name\": \"a large red heart\"}, {\"id\": 5370, \"name\": \"the price tag\"}, {\"id\": 5371, \"name\": \"the black and yellow sign\"}, {\"id\": 5372, \"name\": \"young woman\"}, {\"id\": 5373, \"name\": \"white sticker\"}, {\"id\": 5374, \"name\": \"worn yellow building\"}, {\"id\": 5375, \"name\": \"the rear of the van\"}, {\"id\": 5376, \"name\": \"black chair\"}, {\"id\": 5377, \"name\": \"gold cup\"}, {\"id\": 5378, \"name\": \"pile of wood pieces\"}, {\"id\": 5379, \"name\": \"the large metal container\"}, {\"id\": 5380, \"name\": \"the white cloth mask\"}, {\"id\": 5381, \"name\": \"a striped sweater\"}, {\"id\": 5382, \"name\": \"the dark door\"}, {\"id\": 5383, \"name\": \"trim\"}, {\"id\": 5384, \"name\": \"black interior\"}, {\"id\": 5385, \"name\": \"the white bus\"}, {\"id\": 5386, \"name\": \"a hazy and gray sky\"}, {\"id\": 5387, \"name\": \"the gray sneaker\"}, {\"id\": 5388, \"name\": \"the hoodie\"}, {\"id\": 5389, \"name\": \"a black office chair\"}, {\"id\": 5390, \"name\": \"dirty, white surface\"}, {\"id\": 5391, \"name\": \"an air conditioning unit\"}, {\"id\": 5392, \"name\": \"t-shirt\"}, {\"id\": 5393, \"name\": \"the teller station\"}, {\"id\": 5394, \"name\": \"car side mirror\"}, {\"id\": 5395, \"name\": \"cement truck\"}, {\"id\": 5396, \"name\": \"a woman in a tan jacket and black pants\"}, {\"id\": 5397, \"name\": \"the dark roof\"}, {\"id\": 5398, \"name\": \"the large red lantern\"}, {\"id\": 5399, \"name\": \"a green bush\"}, {\"id\": 5400, \"name\": \"children\"}, {\"id\": 5401, \"name\": \"a lake's surface\"}, {\"id\": 5402, \"name\": \"the flat roof\"}, {\"id\": 5403, \"name\": \"the brown building\"}, {\"id\": 5404, \"name\": \"the red tracksuit\"}, {\"id\": 5405, \"name\": \"a long, dark grey puffer coat\"}, {\"id\": 5406, \"name\": \"the gray puffer coat\"}, {\"id\": 5407, \"name\": \"red jacket\"}, {\"id\": 5408, \"name\": \"black bumper\"}, {\"id\": 5409, \"name\": \"the back of the truck\"}, {\"id\": 5410, \"name\": \"the wheel\"}, {\"id\": 5411, \"name\": \"windshield\"}, {\"id\": 5412, \"name\": \"a bunch of dragon fruit\"}, {\"id\": 5413, \"name\": \"an aisle\"}, {\"id\": 5414, \"name\": \"display of oranges\"}, {\"id\": 5415, \"name\": \"the head wrap\"}, {\"id\": 5416, \"name\": \"a light-colored tile\"}, {\"id\": 5417, \"name\": \"the basket\"}, {\"id\": 5418, \"name\": \"the lantern\"}, {\"id\": 5419, \"name\": \"a colorful tablecloth\"}, {\"id\": 5420, \"name\": \"small silver bowl\"}, {\"id\": 5421, \"name\": \"the banana\"}, {\"id\": 5422, \"name\": \"the potato\"}, {\"id\": 5423, \"name\": \"the brown sandal\"}, {\"id\": 5424, \"name\": \"the decorative gold and white accent\"}, {\"id\": 5425, \"name\": \"wooden leg\"}, {\"id\": 5426, \"name\": \"woman in a yellow and green patterned dress\"}, {\"id\": 5427, \"name\": \"pink jacket\"}, {\"id\": 5428, \"name\": \"a large tree\"}, {\"id\": 5429, \"name\": \"the large building\"}, {\"id\": 5430, \"name\": \"the cell tower\"}, {\"id\": 5431, \"name\": \"the power line\"}, {\"id\": 5432, \"name\": \"a narrow, unpaved street\"}, {\"id\": 5433, \"name\": \"the stall\"}, {\"id\": 5434, \"name\": \"large white wall\"}, {\"id\": 5435, \"name\": \"the large stone\"}, {\"id\": 5436, \"name\": \"an individual's hand\"}, {\"id\": 5437, \"name\": \"unique signage\"}, {\"id\": 5438, \"name\": \"the candle\"}, {\"id\": 5439, \"name\": \"the dollop of whipped cream\"}, {\"id\": 5440, \"name\": \"the strawberry\"}, {\"id\": 5441, \"name\": \"a paved walkway\"}, {\"id\": 5442, \"name\": \"the raised sidewalk\"}, {\"id\": 5443, \"name\": \"a beige brick building\"}, {\"id\": 5444, \"name\": \"the red and black stripe\"}, {\"id\": 5445, \"name\": \"kiwi\"}, {\"id\": 5446, \"name\": \"a driver's side window\"}, {\"id\": 5447, \"name\": \"a front left wheel\"}, {\"id\": 5448, \"name\": \"a rear passenger door\"}, {\"id\": 5449, \"name\": \"the third man\"}, {\"id\": 5450, \"name\": \"the purple food stand\"}, {\"id\": 5451, \"name\": \"the vendor's stall\"}, {\"id\": 5452, \"name\": \"white t-shirt\"}, {\"id\": 5453, \"name\": \"the advertising\"}, {\"id\": 5454, \"name\": \"the dark grout\"}, {\"id\": 5455, \"name\": \"white and gray tile floor\"}, {\"id\": 5456, \"name\": \"a black suitcase\"}, {\"id\": 5457, \"name\": \"a red motorcycle\"}, {\"id\": 5458, \"name\": \"red telephone booth\"}, {\"id\": 5459, \"name\": \"the calm body of water\"}, {\"id\": 5460, \"name\": \"the dog's coat\"}, {\"id\": 5461, \"name\": \"turban\"}, {\"id\": 5462, \"name\": \"the vent\"}, {\"id\": 5463, \"name\": \"the black banner\"}, {\"id\": 5464, \"name\": \"stool\"}, {\"id\": 5465, \"name\": \"a large cardboard box\"}, {\"id\": 5466, \"name\": \"pink and white striped shirt\"}, {\"id\": 5467, \"name\": \"pink knit hat\"}, {\"id\": 5468, \"name\": \"the black screen\"}, {\"id\": 5469, \"name\": \"the orange object\"}, {\"id\": 5470, \"name\": \"the pink scarf\"}, {\"id\": 5471, \"name\": \"blue exercise mat\"}, {\"id\": 5472, \"name\": \"the instructional video\"}, {\"id\": 5473, \"name\": \"air conditioner\"}, {\"id\": 5474, \"name\": \"large green grassy area\"}, {\"id\": 5475, \"name\": \"banana\"}, {\"id\": 5476, \"name\": \"the stucco\"}, {\"id\": 5477, \"name\": \"the blue and white betta fish\"}, {\"id\": 5478, \"name\": \"the grayish-brown rock\"}, {\"id\": 5479, \"name\": \"a shelter's tarp\"}, {\"id\": 5480, \"name\": \"the yellow strip\"}, {\"id\": 5481, \"name\": \"a glass elevator door\"}, {\"id\": 5482, \"name\": \"blue light\"}, {\"id\": 5483, \"name\": \"a winter attire\"}, {\"id\": 5484, \"name\": \"pink coat\"}, {\"id\": 5485, \"name\": \"yellow excavator\"}, {\"id\": 5486, \"name\": \"face mask\"}, {\"id\": 5487, \"name\": \"musical instrument\"}, {\"id\": 5488, \"name\": \"a white streetlight\"}, {\"id\": 5489, \"name\": \"domestic shorthair kitten\"}, {\"id\": 5490, \"name\": \"the couch or chair\"}, {\"id\": 5491, \"name\": \"the person's left hand\"}, {\"id\": 5492, \"name\": \"a fluffy white fur\"}, {\"id\": 5493, \"name\": \"cat's left paw\"}, {\"id\": 5494, \"name\": \"a colorful poster\"}, {\"id\": 5495, \"name\": \"a small, white and brown cat\"}, {\"id\": 5496, \"name\": \"pink tongue\"}, {\"id\": 5497, \"name\": \"a dark wooden dresser\"}, {\"id\": 5498, \"name\": \"small, white and tan kitten\"}, {\"id\": 5499, \"name\": \"dog's fur\"}, {\"id\": 5500, \"name\": \"the ear\"}, {\"id\": 5501, \"name\": \"the arcade's ceiling\"}, {\"id\": 5502, \"name\": \"a blue counter\"}, {\"id\": 5503, \"name\": \"the white hat\"}, {\"id\": 5504, \"name\": \"a white and blue monitor\"}, {\"id\": 5505, \"name\": \"a baked good\"}, {\"id\": 5506, \"name\": \"yellow jacket\"}, {\"id\": 5507, \"name\": \"a prominent red star flag\"}, {\"id\": 5508, \"name\": \"red star flag\"}, {\"id\": 5509, \"name\": \"a bright light\"}, {\"id\": 5510, \"name\": \"a large dumpster\"}, {\"id\": 5511, \"name\": \"sedan\"}, {\"id\": 5512, \"name\": \"the van\"}, {\"id\": 5513, \"name\": \"a sauce or oil\"}, {\"id\": 5514, \"name\": \"a small white container\"}, {\"id\": 5515, \"name\": \"piece of chicken\"}, {\"id\": 5516, \"name\": \"red and white pole\"}, {\"id\": 5517, \"name\": \"the teal and white vending machine\"}, {\"id\": 5518, \"name\": \"a brown trunk\"}, {\"id\": 5519, \"name\": \"motorbike\"}, {\"id\": 5520, \"name\": \"restaurant or food establishment\"}, {\"id\": 5521, \"name\": \"a silver pickup truck\"}, {\"id\": 5522, \"name\": \"tailgate\"}, {\"id\": 5523, \"name\": \"the white sedan\"}, {\"id\": 5524, \"name\": \"the street light\"}, {\"id\": 5525, \"name\": \"basket\"}, {\"id\": 5526, \"name\": \"the circular sign\"}, {\"id\": 5527, \"name\": \"the crate of fruit\"}, {\"id\": 5528, \"name\": \"a subway station\"}, {\"id\": 5529, \"name\": \"a yellow coat\"}, {\"id\": 5530, \"name\": \"dark blue jacket\"}, {\"id\": 5531, \"name\": \"the yellow with black and white stripe\"}, {\"id\": 5532, \"name\": \"black metal frame\"}, {\"id\": 5533, \"name\": \"soft texture\"}, {\"id\": 5534, \"name\": \"a game\"}, {\"id\": 5535, \"name\": \"a green display stand\"}, {\"id\": 5536, \"name\": \"the pink strip\"}, {\"id\": 5537, \"name\": \"a dog's fur\"}, {\"id\": 5538, \"name\": \"a white headboard\"}, {\"id\": 5539, \"name\": \"the blue door\"}, {\"id\": 5540, \"name\": \"the corrugated metal roof\"}, {\"id\": 5541, \"name\": \"a dark jacket\"}, {\"id\": 5542, \"name\": \"a grid pattern\"}, {\"id\": 5543, \"name\": \"a person in the red coat\"}, {\"id\": 5544, \"name\": \"person wearing a red coat\"}, {\"id\": 5545, \"name\": \"a large black vehicle\"}, {\"id\": 5546, \"name\": \"the large white and blue airplane\"}, {\"id\": 5547, \"name\": \"a black leather bag\"}, {\"id\": 5548, \"name\": \"a glass cup\"}, {\"id\": 5549, \"name\": \"a green and gold\"}, {\"id\": 5550, \"name\": \"a hijab\"}, {\"id\": 5551, \"name\": \"glass\"}, {\"id\": 5552, \"name\": \"jewelry\"}, {\"id\": 5553, \"name\": \"white-tiled floor\"}, {\"id\": 5554, \"name\": \"the dark blue carpeting\"}, {\"id\": 5555, \"name\": \"a relaxed position\"}, {\"id\": 5556, \"name\": \"plaid fabric\"}, {\"id\": 5557, \"name\": \"the table's leg\"}, {\"id\": 5558, \"name\": \"the lavender pant\"}, {\"id\": 5559, \"name\": \"the short black hair\"}, {\"id\": 5560, \"name\": \"a white roof\"}, {\"id\": 5561, \"name\": \"metal structure\"}, {\"id\": 5562, \"name\": \"right logo\"}, {\"id\": 5563, \"name\": \"white T-shirt\"}, {\"id\": 5564, \"name\": \"a blue lettering\"}, {\"id\": 5565, \"name\": \"a leftmost shelf\"}, {\"id\": 5566, \"name\": \"a black coat\"}, {\"id\": 5567, \"name\": \"a yellow and black warning sign\"}, {\"id\": 5568, \"name\": \"the gray stone wall\"}, {\"id\": 5569, \"name\": \"a prominent white column\"}, {\"id\": 5570, \"name\": \"pillar\"}, {\"id\": 5571, \"name\": \"the counter\"}, {\"id\": 5572, \"name\": \"clothing\"}, {\"id\": 5573, \"name\": \"shorts\"}, {\"id\": 5574, \"name\": \"a white boot\"}, {\"id\": 5575, \"name\": \"a white pant\"}, {\"id\": 5576, \"name\": \"bunny ear\"}, {\"id\": 5577, \"name\": \"a concrete\"}, {\"id\": 5578, \"name\": \"a black hat\"}, {\"id\": 5579, \"name\": \"a white and black net\"}, {\"id\": 5580, \"name\": \"barefoot\"}, {\"id\": 5581, \"name\": \"black banner\"}, {\"id\": 5582, \"name\": \"the pair of shorts\"}, {\"id\": 5583, \"name\": \"the red sign reading \\\"liquor\\\"\"}, {\"id\": 5584, \"name\": \"the dark blue outfit\"}, {\"id\": 5585, \"name\": \"the plaid object\"}, {\"id\": 5586, \"name\": \"the arm\"}, {\"id\": 5587, \"name\": \"a spout\"}, {\"id\": 5588, \"name\": \"the lush green tree\"}, {\"id\": 5589, \"name\": \"white tile floor\"}, {\"id\": 5590, \"name\": \"the lap\"}, {\"id\": 5591, \"name\": \"the pink shirt\"}, {\"id\": 5592, \"name\": \"white and brown square\"}, {\"id\": 5593, \"name\": \"a curved roof\"}, {\"id\": 5594, \"name\": \"a dark blue bus\"}, {\"id\": 5595, \"name\": \"green bus\"}, {\"id\": 5596, \"name\": \"the archway\"}, {\"id\": 5597, \"name\": \"the balcony\"}, {\"id\": 5598, \"name\": \"black cap\"}, {\"id\": 5599, \"name\": \"crisp white uniform\"}, {\"id\": 5600, \"name\": \"the beige wall\"}, {\"id\": 5601, \"name\": \"vibrant floor pattern\"}, {\"id\": 5602, \"name\": \"vibrant tiled floor\"}, {\"id\": 5603, \"name\": \"brown paneling\"}, {\"id\": 5604, \"name\": \"gazebo\"}, {\"id\": 5605, \"name\": \"the pointed roof\"}, {\"id\": 5606, \"name\": \"the statue\"}, {\"id\": 5607, \"name\": \"concrete surface\"}, {\"id\": 5608, \"name\": \"a stone floor\"}, {\"id\": 5609, \"name\": \"patio\"}, {\"id\": 5610, \"name\": \"the pot\"}, {\"id\": 5611, \"name\": \"the stone porch\"}, {\"id\": 5612, \"name\": \"the stone stair\"}, {\"id\": 5613, \"name\": \"a black slide\"}, {\"id\": 5614, \"name\": \"the red horn\"}, {\"id\": 5615, \"name\": \"a stainless steel gas burner\"}, {\"id\": 5616, \"name\": \"bracelet\"}, {\"id\": 5617, \"name\": \"the food\"}, {\"id\": 5618, \"name\": \"the small, round metal bowl\"}, {\"id\": 5619, \"name\": \"white floor\"}, {\"id\": 5620, \"name\": \"red and white background\"}, {\"id\": 5621, \"name\": \"the red cross\"}, {\"id\": 5622, \"name\": \"an orange bow\"}, {\"id\": 5623, \"name\": \"the red object\"}, {\"id\": 5624, \"name\": \"an overhead lighting\"}, {\"id\": 5625, \"name\": \"green pillar\"}, {\"id\": 5626, \"name\": \"the large, rectangular screen\"}, {\"id\": 5627, \"name\": \"black backpack\"}, {\"id\": 5628, \"name\": \"the assortment of fruits and vegetables\"}, {\"id\": 5629, \"name\": \"dirt area\"}, {\"id\": 5630, \"name\": \"a large yellow sign\"}, {\"id\": 5631, \"name\": \"a large whiteboard\"}, {\"id\": 5632, \"name\": \"the ceiling fan\"}, {\"id\": 5633, \"name\": \"woman in a black dress\"}, {\"id\": 5634, \"name\": \"a colorful tile backsplash\"}, {\"id\": 5635, \"name\": \"a yellow oval\"}, {\"id\": 5636, \"name\": \"peas\"}, {\"id\": 5637, \"name\": \"stainless steel pot\"}, {\"id\": 5638, \"name\": \"a reddish-brown color\"}, {\"id\": 5639, \"name\": \"vendor's table\"}, {\"id\": 5640, \"name\": \"patch of grass\"}, {\"id\": 5641, \"name\": \"the headlight\"}, {\"id\": 5642, \"name\": \"the left side of the road\"}, {\"id\": 5643, \"name\": \"glass storefront\"}, {\"id\": 5644, \"name\": \"the television screen\"}, {\"id\": 5645, \"name\": \"the wooden trim\"}, {\"id\": 5646, \"name\": \"the red shelf\"}, {\"id\": 5647, \"name\": \"yellow rectangle\"}, {\"id\": 5648, \"name\": \"a long, red carpeted walkway\"}, {\"id\": 5649, \"name\": \"a white tiled floor\"}, {\"id\": 5650, \"name\": \"a red doorway\"}, {\"id\": 5651, \"name\": \"the long brown braid\"}, {\"id\": 5652, \"name\": \"white jean\"}, {\"id\": 5653, \"name\": \"white sweatshirt\"}, {\"id\": 5654, \"name\": \"pine cone\"}, {\"id\": 5655, \"name\": \"a small blue motorbike\"}, {\"id\": 5656, \"name\": \"a litter\"}, {\"id\": 5657, \"name\": \"the light blue plastic bag\"}, {\"id\": 5658, \"name\": \"a black auto rickshaw\"}, {\"id\": 5659, \"name\": \"a front of the motorcycle\"}, {\"id\": 5660, \"name\": \"small vehicle\"}, {\"id\": 5661, \"name\": \"a blurry black and white dog\"}, {\"id\": 5662, \"name\": \"a tile's surface\"}, {\"id\": 5663, \"name\": \"a bottle of beer\"}, {\"id\": 5664, \"name\": \"horizontal striped pattern\"}, {\"id\": 5665, \"name\": \"diaper\"}, {\"id\": 5666, \"name\": \"the white display case\"}, {\"id\": 5667, \"name\": \"a light pink tile floor\"}, {\"id\": 5668, \"name\": \"grey coat\"}, {\"id\": 5669, \"name\": \"the green jacket\"}, {\"id\": 5670, \"name\": \"white jacket\"}, {\"id\": 5671, \"name\": \"lemon wedge\"}, {\"id\": 5672, \"name\": \"yellow long-sleeve shirt\"}, {\"id\": 5673, \"name\": \"curved road\"}, {\"id\": 5674, \"name\": \"low curb\"}, {\"id\": 5675, \"name\": \"the white building\"}, {\"id\": 5676, \"name\": \"the dog's collar\"}, {\"id\": 5677, \"name\": \"the red and white painted line\"}, {\"id\": 5678, \"name\": \"a large, two-story shopping center\"}, {\"id\": 5679, \"name\": \"the exterior of a shopping center\"}, {\"id\": 5680, \"name\": \"the section of the street\"}, {\"id\": 5681, \"name\": \"a curved top\"}, {\"id\": 5682, \"name\": \"white pole\"}, {\"id\": 5683, \"name\": \"green awning\"}, {\"id\": 5684, \"name\": \"the multi-story structure\"}, {\"id\": 5685, \"name\": \"large, anthropomorphic orange cat\"}, {\"id\": 5686, \"name\": \"the street lamp\"}, {\"id\": 5687, \"name\": \"a white long-sleeved shirt\"}, {\"id\": 5688, \"name\": \"the assortment of glassware\"}, {\"id\": 5689, \"name\": \"the customer\"}, {\"id\": 5690, \"name\": \"raised bed\"}, {\"id\": 5691, \"name\": \"brown and green patterned shirt\"}, {\"id\": 5692, \"name\": \"orange vest\"}, {\"id\": 5693, \"name\": \"a blue and black vest\"}, {\"id\": 5694, \"name\": \"a picture of Elsa from Frozen\"}, {\"id\": 5695, \"name\": \"the Frozen-themed design\"}, {\"id\": 5696, \"name\": \"the gray shoe\"}, {\"id\": 5697, \"name\": \"a man's hair\"}, {\"id\": 5698, \"name\": \"black and white stripe\"}, {\"id\": 5699, \"name\": \"low wall\"}, {\"id\": 5700, \"name\": \"a yellow cross\"}, {\"id\": 5701, \"name\": \"a cash register area\"}, {\"id\": 5702, \"name\": \"a white suit jacket\"}, {\"id\": 5703, \"name\": \"the can\"}, {\"id\": 5704, \"name\": \"the keyboard\"}, {\"id\": 5705, \"name\": \"a large white building\"}, {\"id\": 5706, \"name\": \"a white SUV\"}, {\"id\": 5707, \"name\": \"woman in a purple dress\"}, {\"id\": 5708, \"name\": \"a red kiosk\"}, {\"id\": 5709, \"name\": \"the car emblem\"}, {\"id\": 5710, \"name\": \"the green license plate\"}, {\"id\": 5711, \"name\": \"large white truck\"}, {\"id\": 5712, \"name\": \"a red helmet\"}, {\"id\": 5713, \"name\": \"the yellow and blue box\"}, {\"id\": 5714, \"name\": \"a light-colored shirt\"}, {\"id\": 5715, \"name\": \"a purple shoulder strap\"}, {\"id\": 5716, \"name\": \"shopper\"}, {\"id\": 5717, \"name\": \"the license plate\"}, {\"id\": 5718, \"name\": \"a right side of the street\"}, {\"id\": 5719, \"name\": \"the shadow\"}, {\"id\": 5720, \"name\": \"red and cream-colored wall\"}, {\"id\": 5721, \"name\": \"the green refrigerated display case\"}, {\"id\": 5722, \"name\": \"the price of the drink\"}, {\"id\": 5723, \"name\": \"a white Chinese character\"}, {\"id\": 5724, \"name\": \"a cue stick\"}, {\"id\": 5725, \"name\": \"a white and black patterned border\"}, {\"id\": 5726, \"name\": \"black patterned section\"}, {\"id\": 5727, \"name\": \"a CASIO sign\"}, {\"id\": 5728, \"name\": \"a Casio store\"}, {\"id\": 5729, \"name\": \"a store\"}, {\"id\": 5730, \"name\": \"a grey cap\"}, {\"id\": 5731, \"name\": \"the gray baseball cap\"}, {\"id\": 5732, \"name\": \"a small shop\"}, {\"id\": 5733, \"name\": \"a black security camera\"}, {\"id\": 5734, \"name\": \"the green and white patterned mattress\"}, {\"id\": 5735, \"name\": \"gray concrete wall\"}, {\"id\": 5736, \"name\": \"a sticker\"}, {\"id\": 5737, \"name\": \"a white oval sticker\"}, {\"id\": 5738, \"name\": \"the blue jacket\"}, {\"id\": 5739, \"name\": \"boxing glove\"}, {\"id\": 5740, \"name\": \"red and black t-shirt\"}, {\"id\": 5741, \"name\": \"the clock on the wall\"}, {\"id\": 5742, \"name\": \"a black barrier\"}, {\"id\": 5743, \"name\": \"the baseball cap\"}, {\"id\": 5744, \"name\": \"a pink bow\"}, {\"id\": 5745, \"name\": \"the baby\"}, {\"id\": 5746, \"name\": \"the light green seat\"}, {\"id\": 5747, \"name\": \"the person's face\"}, {\"id\": 5748, \"name\": \"a light-blue shorts\"}, {\"id\": 5749, \"name\": \"joystick\"}, {\"id\": 5750, \"name\": \"the red wall\"}, {\"id\": 5751, \"name\": \"a large, two-story building\"}, {\"id\": 5752, \"name\": \"the residence\"}, {\"id\": 5753, \"name\": \"the additional product\"}, {\"id\": 5754, \"name\": \"the canned good\"}, {\"id\": 5755, \"name\": \"black shirt\"}, {\"id\": 5756, \"name\": \"large, open door\"}, {\"id\": 5757, \"name\": \"the left leg\"}, {\"id\": 5758, \"name\": \"clear bag\"}, {\"id\": 5759, \"name\": \"the blue sign\"}, {\"id\": 5760, \"name\": \"pom-pom\"}, {\"id\": 5761, \"name\": \"vendor's stall\"}, {\"id\": 5762, \"name\": \"a camouflage shirt\"}, {\"id\": 5763, \"name\": \"a shaved head\"}, {\"id\": 5764, \"name\": \"the black and white headboard\"}, {\"id\": 5765, \"name\": \"the black sweatpants\"}, {\"id\": 5766, \"name\": \"the white logo\"}, {\"id\": 5767, \"name\": \"the blue and purple wall\"}, {\"id\": 5768, \"name\": \"a claw machine\"}, {\"id\": 5769, \"name\": \"the carpeted floor\"}, {\"id\": 5770, \"name\": \"the large cotton candy\"}, {\"id\": 5771, \"name\": \"a black SUV\"}, {\"id\": 5772, \"name\": \"white sedan\"}, {\"id\": 5773, \"name\": \"a top level of the stadium\"}, {\"id\": 5774, \"name\": \"the blue and white shoe\"}, {\"id\": 5775, \"name\": \"the silver and gray prosthetic leg\"}, {\"id\": 5776, \"name\": \"the silver metal frame\"}, {\"id\": 5777, \"name\": \"the thigh\"}, {\"id\": 5778, \"name\": \"a large, multi-story structure\"}, {\"id\": 5779, \"name\": \"the silver joystick\"}, {\"id\": 5780, \"name\": \"a large plastic container\"}, {\"id\": 5781, \"name\": \"a silver headband\"}, {\"id\": 5782, \"name\": \"the pink zip-up hoodie\"}, {\"id\": 5783, \"name\": \"a blue lid\"}, {\"id\": 5784, \"name\": \"the gray rectangle\"}, {\"id\": 5785, \"name\": \"left lane\"}, {\"id\": 5786, \"name\": \"side mirror\"}, {\"id\": 5787, \"name\": \"the stair\"}, {\"id\": 5788, \"name\": \"the tablet\"}, {\"id\": 5789, \"name\": \"third person\"}, {\"id\": 5790, \"name\": \"the white box\"}, {\"id\": 5791, \"name\": \"white electrical cord\"}, {\"id\": 5792, \"name\": \"a white button\"}, {\"id\": 5793, \"name\": \"rectangular button\"}, {\"id\": 5794, \"name\": \"the light blue sleeve\"}, {\"id\": 5795, \"name\": \"white switch plate\"}, {\"id\": 5796, \"name\": \"a red-handled shopping cart\"}, {\"id\": 5797, \"name\": \"a black car\"}, {\"id\": 5798, \"name\": \"the pile of fabric\"}, {\"id\": 5799, \"name\": \"the yellow line\"}, {\"id\": 5800, \"name\": \"yellow-painted crosswalk\"}, {\"id\": 5801, \"name\": \"a young child\"}, {\"id\": 5802, \"name\": \"grey long-sleeved undershirt\"}, {\"id\": 5803, \"name\": \"the small green and white object\"}, {\"id\": 5804, \"name\": \"the glass jar\"}, {\"id\": 5805, \"name\": \"the vibrant fruit mural\"}, {\"id\": 5806, \"name\": \"a casual attire\"}, {\"id\": 5807, \"name\": \"a purple dress\"}, {\"id\": 5808, \"name\": \"red curtain\"}, {\"id\": 5809, \"name\": \"a large cage\"}, {\"id\": 5810, \"name\": \"upper level of a wooden staircase\"}, {\"id\": 5811, \"name\": \"winter attire\"}, {\"id\": 5812, \"name\": \"light-colored striped button-down shirt\"}, {\"id\": 5813, \"name\": \"the dark-colored bottle\"}, {\"id\": 5814, \"name\": \"the large appliance\"}, {\"id\": 5815, \"name\": \"a striking mural\"}, {\"id\": 5816, \"name\": \"blue wall\"}, {\"id\": 5817, \"name\": \"patterned pant\"}, {\"id\": 5818, \"name\": \"room's ceiling\"}, {\"id\": 5819, \"name\": \"the colorful drawing\"}, {\"id\": 5820, \"name\": \"an adult\"}, {\"id\": 5821, \"name\": \"building's wall\"}, {\"id\": 5822, \"name\": \"silver Toyota SUV\"}, {\"id\": 5823, \"name\": \"the green metal roof\"}, {\"id\": 5824, \"name\": \"a brown container\"}, {\"id\": 5825, \"name\": \"jar\"}, {\"id\": 5826, \"name\": \"person's right arm\"}, {\"id\": 5827, \"name\": \"stainless-steel stove\"}, {\"id\": 5828, \"name\": \"the metal container\"}, {\"id\": 5829, \"name\": \"the red label\"}, {\"id\": 5830, \"name\": \"a staggered pattern\"}, {\"id\": 5831, \"name\": \"the white chair\"}, {\"id\": 5832, \"name\": \"a person in a camouflage jacket\"}, {\"id\": 5833, \"name\": \"an open rear door\"}, {\"id\": 5834, \"name\": \"the black collar\"}, {\"id\": 5835, \"name\": \"the blue pant\"}, {\"id\": 5836, \"name\": \"a silver railing\"}, {\"id\": 5837, \"name\": \"a bakery or cake shop\"}, {\"id\": 5838, \"name\": \"white fuzzy jacket\"}, {\"id\": 5839, \"name\": \"white lettering\"}, {\"id\": 5840, \"name\": \"the store owner\"}, {\"id\": 5841, \"name\": \"attire\"}, {\"id\": 5842, \"name\": \"cucumber\"}, {\"id\": 5843, \"name\": \"person's lower body\"}, {\"id\": 5844, \"name\": \"a two-lane road\"}, {\"id\": 5845, \"name\": \"the dark-colored sedan\"}, {\"id\": 5846, \"name\": \"the man on a motorcycle\"}, {\"id\": 5847, \"name\": \"a brown brick floor\"}, {\"id\": 5848, \"name\": \"the white counter\"}, {\"id\": 5849, \"name\": \"handrail\"}, {\"id\": 5850, \"name\": \"the man in a red shirt\"}, {\"id\": 5851, \"name\": \"a short brown hair\"}, {\"id\": 5852, \"name\": \"the blue sky\"}, {\"id\": 5853, \"name\": \"the store's entrance\"}, {\"id\": 5854, \"name\": \"the cracked screen\"}, {\"id\": 5855, \"name\": \"coffee table\"}, {\"id\": 5856, \"name\": \"light blue sheet\"}, {\"id\": 5857, \"name\": \"white ceiling fan\"}, {\"id\": 5858, \"name\": \"a red warning sign\"}, {\"id\": 5859, \"name\": \"a vibrant design\"}, {\"id\": 5860, \"name\": \"black design\"}, {\"id\": 5861, \"name\": \"the colorful kite\"}, {\"id\": 5862, \"name\": \"the kite\"}, {\"id\": 5863, \"name\": \"a white ceiling\"}, {\"id\": 5864, \"name\": \"the black design\"}, {\"id\": 5865, \"name\": \"the building's exterior\"}, {\"id\": 5866, \"name\": \"a tall building\"}, {\"id\": 5867, \"name\": \"a large wooden beam\"}, {\"id\": 5868, \"name\": \"a woven design\"}, {\"id\": 5869, \"name\": \"front paws\"}, {\"id\": 5870, \"name\": \"the white curtain\"}, {\"id\": 5871, \"name\": \"the TV\"}, {\"id\": 5872, \"name\": \"the floral-patterned off-the-shoulder crop top\"}, {\"id\": 5873, \"name\": \"the gold necklace\"}, {\"id\": 5874, \"name\": \"the loose wave\"}, {\"id\": 5875, \"name\": \"the pair of feet\"}, {\"id\": 5876, \"name\": \"snow-capped mountain\"}, {\"id\": 5877, \"name\": \"dark blue and purple striped shirt\"}, {\"id\": 5878, \"name\": \"stack of boxes\"}, {\"id\": 5879, \"name\": \"the black and white striped sweater\"}, {\"id\": 5880, \"name\": \"black motorcycle tire\"}, {\"id\": 5881, \"name\": \"a pink wire frame\"}, {\"id\": 5882, \"name\": \"bird carrier\"}, {\"id\": 5883, \"name\": \"the layer of wood shavings\"}, {\"id\": 5884, \"name\": \"a top of the canopy\"}, {\"id\": 5885, \"name\": \"the umbrella\"}, {\"id\": 5886, \"name\": \"the red and yellow Chinese lantern\"}, {\"id\": 5887, \"name\": \"an illegible logo\"}, {\"id\": 5888, \"name\": \"storefront\"}, {\"id\": 5889, \"name\": \"the rabbit\"}, {\"id\": 5890, \"name\": \"glass side\"}, {\"id\": 5891, \"name\": \"a canopy's roof\"}, {\"id\": 5892, \"name\": \"lot\"}, {\"id\": 5893, \"name\": \"driver\"}, {\"id\": 5894, \"name\": \"the gas pump\"}, {\"id\": 5895, \"name\": \"the rearview mirror\"}, {\"id\": 5896, \"name\": \"the silver pickup truck\"}, {\"id\": 5897, \"name\": \"a striking blue corrugated metal exterior\"}, {\"id\": 5898, \"name\": \"the blue metal roof\"}, {\"id\": 5899, \"name\": \"chandelier\"}, {\"id\": 5900, \"name\": \"a green arrow icon\"}, {\"id\": 5901, \"name\": \"a black laptop\"}, {\"id\": 5902, \"name\": \"a laptop screen\"}, {\"id\": 5903, \"name\": \"shade of white\"}, {\"id\": 5904, \"name\": \"the seated person\"}, {\"id\": 5905, \"name\": \"the tapestry\"}, {\"id\": 5906, \"name\": \"a brown rock\"}, {\"id\": 5907, \"name\": \"large, dark-brown object\"}, {\"id\": 5908, \"name\": \"the fish\"}, {\"id\": 5909, \"name\": \"a dark green, blurry, and grainy surface\"}, {\"id\": 5910, \"name\": \"a shark's large, round head\"}, {\"id\": 5911, \"name\": \"the small shopping cart\"}, {\"id\": 5912, \"name\": \"white linoleum\"}, {\"id\": 5913, \"name\": \"green pillow\"}, {\"id\": 5914, \"name\": \"the white pillow\"}, {\"id\": 5915, \"name\": \"the auto-rickshaw\"}, {\"id\": 5916, \"name\": \"green blouse\"}, {\"id\": 5917, \"name\": \"the yellow shelving unit\"}, {\"id\": 5918, \"name\": \"the left wrist\"}, {\"id\": 5919, \"name\": \"the pair of Crocs\"}, {\"id\": 5920, \"name\": \"silver metal surface\"}, {\"id\": 5921, \"name\": \"the center burner\"}, {\"id\": 5922, \"name\": \"the foamy texture\"}, {\"id\": 5923, \"name\": \"the frothy liquid\"}, {\"id\": 5924, \"name\": \"a Tuition Classes\"}, {\"id\": 5925, \"name\": \"a black scooter\"}, {\"id\": 5926, \"name\": \"a yellow banana logo\"}, {\"id\": 5927, \"name\": \"the mattress\"}, {\"id\": 5928, \"name\": \"a red metal box\"}, {\"id\": 5929, \"name\": \"gray floor\"}, {\"id\": 5930, \"name\": \"large, pink, arrow-shaped sign\"}, {\"id\": 5931, \"name\": \"a light-colored jacket\"}, {\"id\": 5932, \"name\": \"a small cup\"}, {\"id\": 5933, \"name\": \"computer mouse\"}, {\"id\": 5934, \"name\": \"a white bus\"}, {\"id\": 5935, \"name\": \"left side of the road\"}, {\"id\": 5936, \"name\": \"white shirt sleeve\"}, {\"id\": 5937, \"name\": \"white van\"}, {\"id\": 5938, \"name\": \"a fluffy white cloud\"}, {\"id\": 5939, \"name\": \"green chair\"}, {\"id\": 5940, \"name\": \"green plastic bench\"}, {\"id\": 5941, \"name\": \"the cartoon child\"}, {\"id\": 5942, \"name\": \"the light blue jacket\"}, {\"id\": 5943, \"name\": \"the scooter\"}, {\"id\": 5944, \"name\": \"black bus\"}, {\"id\": 5945, \"name\": \"the white van\"}, {\"id\": 5946, \"name\": \"a small, yellow cargo van\"}, {\"id\": 5947, \"name\": \"a white license plate\"}, {\"id\": 5948, \"name\": \"the long black puffer coat\"}, {\"id\": 5949, \"name\": \"an utility pole\"}, {\"id\": 5950, \"name\": \"the grassy area\"}, {\"id\": 5951, \"name\": \"white chest\"}, {\"id\": 5952, \"name\": \"the yellow mouth\"}, {\"id\": 5953, \"name\": \"large yellow truck\"}, {\"id\": 5954, \"name\": \"a black cap\"}, {\"id\": 5955, \"name\": \"silver exterior\"}, {\"id\": 5956, \"name\": \"a dark brown tail\"}, {\"id\": 5957, \"name\": \"short, blunt snout\"}, {\"id\": 5958, \"name\": \"the dark eye\"}, {\"id\": 5959, \"name\": \"a long black hijab\"}, {\"id\": 5960, \"name\": \"a right side of the window\"}, {\"id\": 5961, \"name\": \"the dark-colored hijab or headscarf\"}, {\"id\": 5962, \"name\": \"a metal rail\"}, {\"id\": 5963, \"name\": \"column\"}, {\"id\": 5964, \"name\": \"a toy cactus\"}, {\"id\": 5965, \"name\": \"the cactus\"}, {\"id\": 5966, \"name\": \"the white and tan Jack Russell Terrier mix\"}, {\"id\": 5967, \"name\": \"black purse\"}, {\"id\": 5968, \"name\": \"red and gray tile\"}, {\"id\": 5969, \"name\": \"the blue sweatpants\"}, {\"id\": 5970, \"name\": \"a smaller sign\"}, {\"id\": 5971, \"name\": \"the handrail\"}, {\"id\": 5972, \"name\": \"the sleek black escalator\"}, {\"id\": 5973, \"name\": \"a beige wall\"}, {\"id\": 5974, \"name\": \"a wooden panel\"}, {\"id\": 5975, \"name\": \"blue sign\"}, {\"id\": 5976, \"name\": \"tote bag\"}, {\"id\": 5977, \"name\": \"greenhouse\"}, {\"id\": 5978, \"name\": \"large white tile floor\"}, {\"id\": 5979, \"name\": \"pair of white sneakers\"}, {\"id\": 5980, \"name\": \"the chew toy\"}, {\"id\": 5981, \"name\": \"the pink banner\"}, {\"id\": 5982, \"name\": \"the yellow dustpan\"}, {\"id\": 5983, \"name\": \"a sloping roof\"}, {\"id\": 5984, \"name\": \"the bicycle\"}, {\"id\": 5985, \"name\": \"the black car\"}, {\"id\": 5986, \"name\": \"the multi-story apartment complex\"}, {\"id\": 5987, \"name\": \"a red and white patterned floor\"}, {\"id\": 5988, \"name\": \"video or presentation\"}, {\"id\": 5989, \"name\": \"black metal gate\"}, {\"id\": 5990, \"name\": \"large black gate\"}, {\"id\": 5991, \"name\": \"the bush\"}, {\"id\": 5992, \"name\": \"the patterned shirt\"}, {\"id\": 5993, \"name\": \"the dirt ground\"}, {\"id\": 5994, \"name\": \"an archway\"}, {\"id\": 5995, \"name\": \"metal bucket\"}, {\"id\": 5996, \"name\": \"the dark green t-shirt\"}, {\"id\": 5997, \"name\": \"the cushion\"}, {\"id\": 5998, \"name\": \"a small tree or shrub\"}, {\"id\": 5999, \"name\": \"microphone\"}, {\"id\": 6000, \"name\": \"a store's shelves\"}, {\"id\": 6001, \"name\": \"large, light-colored tile\"}, {\"id\": 6002, \"name\": \"a prominent road\"}, {\"id\": 6003, \"name\": \"a secure and controlled area\"}, {\"id\": 6004, \"name\": \"the puffy, white cumulus cloud\"}, {\"id\": 6005, \"name\": \"the long, paved road\"}, {\"id\": 6006, \"name\": \"white and orange facade\"}, {\"id\": 6007, \"name\": \"brush\"}, {\"id\": 6008, \"name\": \"large square tile\"}, {\"id\": 6009, \"name\": \"the green hedge wall\"}, {\"id\": 6010, \"name\": \"the platform\"}, {\"id\": 6011, \"name\": \"metal grate\"}, {\"id\": 6012, \"name\": \"rug\"}, {\"id\": 6013, \"name\": \"a clear plastic\"}, {\"id\": 6014, \"name\": \"foil covering\"}, {\"id\": 6015, \"name\": \"the decorative design\"}, {\"id\": 6016, \"name\": \"dirt alleyway\"}, {\"id\": 6017, \"name\": \"the tan exterior\"}, {\"id\": 6018, \"name\": \"the embankment\"}, {\"id\": 6019, \"name\": \"a surrounding tree\"}, {\"id\": 6020, \"name\": \"dark brown building\"}, {\"id\": 6021, \"name\": \"the lush, well-manicured lawn\"}, {\"id\": 6022, \"name\": \"the surveillance camera\"}, {\"id\": 6023, \"name\": \"a grip\"}, {\"id\": 6024, \"name\": \"helmet\"}, {\"id\": 6025, \"name\": \"the piece of metal\"}, {\"id\": 6026, \"name\": \"stall\"}, {\"id\": 6027, \"name\": \"the gray border\"}, {\"id\": 6028, \"name\": \"the wooden pole\"}, {\"id\": 6029, \"name\": \"bus window\"}, {\"id\": 6030, \"name\": \"the plaid shirt\"}, {\"id\": 6031, \"name\": \"the roof of the bus\"}, {\"id\": 6032, \"name\": \"corrugated metal wall\"}, {\"id\": 6033, \"name\": \"the dirt surface\"}, {\"id\": 6034, \"name\": \"a blue shopping bag\"}, {\"id\": 6035, \"name\": \"the reflection of the mirror\"}, {\"id\": 6036, \"name\": \"a pile of straw\"}, {\"id\": 6037, \"name\": \"dirt mound\"}, {\"id\": 6038, \"name\": \"a building's roof\"}, {\"id\": 6039, \"name\": \"a fan blade\"}, {\"id\": 6040, \"name\": \"roof of another building\"}, {\"id\": 6041, \"name\": \"a red plaid shirt\"}, {\"id\": 6042, \"name\": \"a pair of brown slippers\"}, {\"id\": 6043, \"name\": \"the rug\"}, {\"id\": 6044, \"name\": \"the small breed\"}, {\"id\": 6045, \"name\": \"the white fur\"}, {\"id\": 6046, \"name\": \"pink cloth\"}, {\"id\": 6047, \"name\": \"the beige stone floor\"}, {\"id\": 6048, \"name\": \"a dog's shadow\"}, {\"id\": 6049, \"name\": \"a lemon\"}, {\"id\": 6050, \"name\": \"a shiny white tile floor\"}, {\"id\": 6051, \"name\": \"a drink\"}, {\"id\": 6052, \"name\": \"sheet of paper\"}, {\"id\": 6053, \"name\": \"the guitar\"}, {\"id\": 6054, \"name\": \"small cat\"}, {\"id\": 6055, \"name\": \"the front paw\"}, {\"id\": 6056, \"name\": \"the small, fluffy, white rabbit\"}, {\"id\": 6057, \"name\": \"the light-colored wood finish\"}, {\"id\": 6058, \"name\": \"front leg\"}, {\"id\": 6059, \"name\": \"partially visible cart\"}, {\"id\": 6060, \"name\": \"the green and yellow tuk-tuk\"}, {\"id\": 6061, \"name\": \"the room's floor\"}, {\"id\": 6062, \"name\": \"teeter-totter\"}, {\"id\": 6063, \"name\": \"a blue and white box\"}, {\"id\": 6064, \"name\": \"a partially open white door\"}, {\"id\": 6065, \"name\": \"the Bichon Frise puppy\"}, {\"id\": 6066, \"name\": \"the Maltese puppy\"}, {\"id\": 6067, \"name\": \"leafless tree\"}, {\"id\": 6068, \"name\": \"sled\"}, {\"id\": 6069, \"name\": \"a calm water\"}, {\"id\": 6070, \"name\": \"sparkly blue dress\"}, {\"id\": 6071, \"name\": \"a blue headscarf\"}, {\"id\": 6072, \"name\": \"framed certificate\"}, {\"id\": 6073, \"name\": \"the video camera\"}, {\"id\": 6074, \"name\": \"a white metal grid structure\"}, {\"id\": 6075, \"name\": \"the tall, brown building\"}, {\"id\": 6076, \"name\": \"the camera\"}, {\"id\": 6077, \"name\": \"white object\"}, {\"id\": 6078, \"name\": \"the gazebo\"}, {\"id\": 6079, \"name\": \"the tan shirt\"}, {\"id\": 6080, \"name\": \"a white tag\"}, {\"id\": 6081, \"name\": \"the curved white beam\"}, {\"id\": 6082, \"name\": \"silver hardware\"}, {\"id\": 6083, \"name\": \"the dome\"}, {\"id\": 6084, \"name\": \"the prominent dome\"}, {\"id\": 6085, \"name\": \"blue and yellow mural\"}, {\"id\": 6086, \"name\": \"the black floor\"}, {\"id\": 6087, \"name\": \"circular light fixture\"}, {\"id\": 6088, \"name\": \"a large, white, illuminated sign\"}, {\"id\": 6089, \"name\": \"a passenger\"}, {\"id\": 6090, \"name\": \"small dirt patch\"}, {\"id\": 6091, \"name\": \"black-and-white outfit\"}, {\"id\": 6092, \"name\": \"a beige coat\"}, {\"id\": 6093, \"name\": \"the matching white outfit with black spots\"}, {\"id\": 6094, \"name\": \"dark jacket\"}, {\"id\": 6095, \"name\": \"the beige dress\"}, {\"id\": 6096, \"name\": \"the black or white attire\"}, {\"id\": 6097, \"name\": \"the subtle floral arrangement\"}, {\"id\": 6098, \"name\": \"rainbow backdrop\"}, {\"id\": 6099, \"name\": \"the cartoon character\"}, {\"id\": 6100, \"name\": \"the cartoon owl\"}, {\"id\": 6101, \"name\": \"the colorful cartoon character\"}, {\"id\": 6102, \"name\": \"bag of pasta sauce\"}, {\"id\": 6103, \"name\": \"blue and yellow advertisement\"}, {\"id\": 6104, \"name\": \"the bag of mayonnaise\"}, {\"id\": 6105, \"name\": \"a dark eye\"}, {\"id\": 6106, \"name\": \"a front right paw\"}, {\"id\": 6107, \"name\": \"the cat ear\"}, {\"id\": 6108, \"name\": \"the left paw\"}, {\"id\": 6109, \"name\": \"a green conveyor belt\"}, {\"id\": 6110, \"name\": \"hard hat\"}, {\"id\": 6111, \"name\": \"the pipe\"}, {\"id\": 6112, \"name\": \"the telephone booth\"}, {\"id\": 6113, \"name\": \"pink label\"}, {\"id\": 6114, \"name\": \"dark blue suit\"}, {\"id\": 6115, \"name\": \"a yellow head wrap\"}, {\"id\": 6116, \"name\": \"black head covering\"}, {\"id\": 6117, \"name\": \"a coach\"}, {\"id\": 6118, \"name\": \"a decoration\"}, {\"id\": 6119, \"name\": \"the bouquet of flowers\"}, {\"id\": 6120, \"name\": \"a red fire alarm\"}, {\"id\": 6121, \"name\": \"a gray t-shirt\"}, {\"id\": 6122, \"name\": \"the prominent yellow cart\"}, {\"id\": 6123, \"name\": \"parent or guardian\"}, {\"id\": 6124, \"name\": \"the blue plastic object\"}, {\"id\": 6125, \"name\": \"the laptop\"}, {\"id\": 6126, \"name\": \"a man's body\"}, {\"id\": 6127, \"name\": \"a room's wall\"}, {\"id\": 6128, \"name\": \"black mask\"}, {\"id\": 6129, \"name\": \"plastic curtain\"}, {\"id\": 6130, \"name\": \"a bright sky\"}, {\"id\": 6131, \"name\": \"a sleek, silver escalator\"}, {\"id\": 6132, \"name\": \"the escalator handrail\"}, {\"id\": 6133, \"name\": \"fabric\"}, {\"id\": 6134, \"name\": \"a white air conditioning vent\"}, {\"id\": 6135, \"name\": \"an air vent\"}, {\"id\": 6136, \"name\": \"ceiling vent\"}, {\"id\": 6137, \"name\": \"the black square\"}, {\"id\": 6138, \"name\": \"a large, dark-colored structure\"}, {\"id\": 6139, \"name\": \"the manhole cover\"}, {\"id\": 6140, \"name\": \"the prominent, black sign\"}, {\"id\": 6141, \"name\": \"white painted line\"}, {\"id\": 6142, \"name\": \"woman's head\"}, {\"id\": 6143, \"name\": \"the small, pink toy car\"}, {\"id\": 6144, \"name\": \"a dirt surface\"}, {\"id\": 6145, \"name\": \"fox's head\"}, {\"id\": 6146, \"name\": \"nose\"}, {\"id\": 6147, \"name\": \"a yellow facade\"}, {\"id\": 6148, \"name\": \"tall hedge\"}, {\"id\": 6149, \"name\": \"the row of green hedges\"}, {\"id\": 6150, \"name\": \"the large blue sign\"}, {\"id\": 6151, \"name\": \"the black pillar\"}, {\"id\": 6152, \"name\": \"the woven material\"}, {\"id\": 6153, \"name\": \"tan coat\"}, {\"id\": 6154, \"name\": \"the knife\"}, {\"id\": 6155, \"name\": \"the spoon\"}, {\"id\": 6156, \"name\": \"the wooden wall\"}, {\"id\": 6157, \"name\": \"man in a red jacket\"}, {\"id\": 6158, \"name\": \"the red and black plaid jacket\"}, {\"id\": 6159, \"name\": \"front right tire\"}, {\"id\": 6160, \"name\": \"man in a plaid shirt and black pants\"}, {\"id\": 6161, \"name\": \"red sign\"}, {\"id\": 6162, \"name\": \"red tuk-tuk\"}, {\"id\": 6163, \"name\": \"jeans\"}, {\"id\": 6164, \"name\": \"the dark wall\"}, {\"id\": 6165, \"name\": \"a rough texture\"}, {\"id\": 6166, \"name\": \"concrete structure\"}, {\"id\": 6167, \"name\": \"the stage's structure\"}, {\"id\": 6168, \"name\": \"a red plastic crate\"}, {\"id\": 6169, \"name\": \"scale's bowl\"}, {\"id\": 6170, \"name\": \"the chain\"}, {\"id\": 6171, \"name\": \"the food item\"}, {\"id\": 6172, \"name\": \"the neck\"}, {\"id\": 6173, \"name\": \"the storefront\"}, {\"id\": 6174, \"name\": \"dark strip of tile\"}, {\"id\": 6175, \"name\": \"drain\"}, {\"id\": 6176, \"name\": \"the container\"}, {\"id\": 6177, \"name\": \"the tray\"}, {\"id\": 6178, \"name\": \"red and green detail\"}, {\"id\": 6179, \"name\": \"the small, multicolored ball\"}, {\"id\": 6180, \"name\": \"a large bucket\"}, {\"id\": 6181, \"name\": \"bucket\"}, {\"id\": 6182, \"name\": \"a pink stuffed animal\"}, {\"id\": 6183, \"name\": \"grey hood\"}, {\"id\": 6184, \"name\": \"pigtail\"}, {\"id\": 6185, \"name\": \"white headband or hair tie\"}, {\"id\": 6186, \"name\": \"large concrete box\"}, {\"id\": 6187, \"name\": \"a chimney\"}, {\"id\": 6188, \"name\": \"the white arrow\"}, {\"id\": 6189, \"name\": \"an industrial accident\"}, {\"id\": 6190, \"name\": \"the large chimney or stack\"}, {\"id\": 6191, \"name\": \"the muted blue and yellow sky\"}, {\"id\": 6192, \"name\": \"the prominent chimney\"}, {\"id\": 6193, \"name\": \"thick smoke\"}, {\"id\": 6194, \"name\": \"a long brown hair\"}, {\"id\": 6195, \"name\": \"gray and white speckled floor\"}, {\"id\": 6196, \"name\": \"left sign\"}, {\"id\": 6197, \"name\": \"the large, rectangular object\"}, {\"id\": 6198, \"name\": \"the young person\"}, {\"id\": 6199, \"name\": \"the back of the vehicle\"}, {\"id\": 6200, \"name\": \"the man in a white shirt and black niqab\"}, {\"id\": 6201, \"name\": \"yellow food cart\"}, {\"id\": 6202, \"name\": \"sloping roof\"}, {\"id\": 6203, \"name\": \"road or sidewalk\"}, {\"id\": 6204, \"name\": \"shrub\"}, {\"id\": 6205, \"name\": \"the arched window\"}, {\"id\": 6206, \"name\": \"white truck\"}, {\"id\": 6207, \"name\": \"a large dark stain\"}, {\"id\": 6208, \"name\": \"red sash\"}, {\"id\": 6209, \"name\": \"the dog's head\"}, {\"id\": 6210, \"name\": \"a pink box\"}, {\"id\": 6211, \"name\": \"large green leaf\"}, {\"id\": 6212, \"name\": \"potted plant\"}, {\"id\": 6213, \"name\": \"the brown food\"}, {\"id\": 6214, \"name\": \"black crossbar\"}, {\"id\": 6215, \"name\": \"a gray trim\"}, {\"id\": 6216, \"name\": \"the blue and white plaid scarf\"}, {\"id\": 6217, \"name\": \"the rolled-up white paper\"}, {\"id\": 6218, \"name\": \"a red and black interlocking tile\"}, {\"id\": 6219, \"name\": \"striking black stripe\"}, {\"id\": 6220, \"name\": \"the balloon\"}, {\"id\": 6221, \"name\": \"the large white cabinet\"}, {\"id\": 6222, \"name\": \"a navy blue shirt\"}, {\"id\": 6223, \"name\": \"the gentle slope\"}, {\"id\": 6224, \"name\": \"a pink sneaker\"}, {\"id\": 6225, \"name\": \"giant brown teddy bear\"}, {\"id\": 6226, \"name\": \"the brown scarf\"}, {\"id\": 6227, \"name\": \"a black sweater\"}, {\"id\": 6228, \"name\": \"a white frame\"}, {\"id\": 6229, \"name\": \"a large structure\"}, {\"id\": 6230, \"name\": \"the long wooden pole\"}, {\"id\": 6231, \"name\": \"the white, red, and green line\"}, {\"id\": 6232, \"name\": \"person's shadow\"}, {\"id\": 6233, \"name\": \"the gray tile floor\"}, {\"id\": 6234, \"name\": \"the textured surface\"}, {\"id\": 6235, \"name\": \"the sandy shore\"}, {\"id\": 6236, \"name\": \"women\"}, {\"id\": 6237, \"name\": \"model\"}, {\"id\": 6238, \"name\": \"smaller, one-story building\"}, {\"id\": 6239, \"name\": \"the backrest\"}, {\"id\": 6240, \"name\": \"brown cabinet\"}, {\"id\": 6241, \"name\": \"the black spotlight\"}, {\"id\": 6242, \"name\": \"metal food preparation surface\"}, {\"id\": 6243, \"name\": \"the layer of food residue\"}, {\"id\": 6244, \"name\": \"the metal bar\"}, {\"id\": 6245, \"name\": \"the metal tube\"}, {\"id\": 6246, \"name\": \"a tall white building\"}, {\"id\": 6247, \"name\": \"crosswalk\"}, {\"id\": 6248, \"name\": \"the train\"}, {\"id\": 6249, \"name\": \"a lap\"}, {\"id\": 6250, \"name\": \"the striped top\"}, {\"id\": 6251, \"name\": \"a dark green Renault\"}, {\"id\": 6252, \"name\": \"the gray shorts\"}, {\"id\": 6253, \"name\": \"a black and yellow helmet\"}, {\"id\": 6254, \"name\": \"the black and yellow hockey stick\"}, {\"id\": 6255, \"name\": \"light-colored wood top\"}, {\"id\": 6256, \"name\": \"the pothole\"}, {\"id\": 6257, \"name\": \"the long table\"}, {\"id\": 6258, \"name\": \"large cartoonish design\"}, {\"id\": 6259, \"name\": \"a long black coat\"}, {\"id\": 6260, \"name\": \"a gold star\"}, {\"id\": 6261, \"name\": \"the silver minivan\"}, {\"id\": 6262, \"name\": \"a light blue wall\"}, {\"id\": 6263, \"name\": \"the number 10\"}, {\"id\": 6264, \"name\": \"a chest\"}, {\"id\": 6265, \"name\": \"sneaker\"}, {\"id\": 6266, \"name\": \"the small wooden structure\"}, {\"id\": 6267, \"name\": \"young girl\"}, {\"id\": 6268, \"name\": \"an exposed pipe\"}, {\"id\": 6269, \"name\": \"the low wall\"}, {\"id\": 6270, \"name\": \"red shelving unit\"}, {\"id\": 6271, \"name\": \"an orange, green, and red striped section\"}, {\"id\": 6272, \"name\": \"faded orange and green sign\"}, {\"id\": 6273, \"name\": \"the piece of paper\"}, {\"id\": 6274, \"name\": \"white paper\"}, {\"id\": 6275, \"name\": \"bass drum\"}, {\"id\": 6276, \"name\": \"a yellow stripe\"}, {\"id\": 6277, \"name\": \"the ceiling of the station\"}, {\"id\": 6278, \"name\": \"a large, illuminated Christmas tree\"}, {\"id\": 6279, \"name\": \"the wooden beam\"}, {\"id\": 6280, \"name\": \"the prominent white logo\"}, {\"id\": 6281, \"name\": \"the tall pole\"}, {\"id\": 6282, \"name\": \"a yellow train car\"}, {\"id\": 6283, \"name\": \"a prominent light fixture\"}, {\"id\": 6284, \"name\": \"man in a white shirt\"}, {\"id\": 6285, \"name\": \"a black and white striped barrier\"}, {\"id\": 6286, \"name\": \"busy street corner\"}, {\"id\": 6287, \"name\": \"a tan building\"}, {\"id\": 6288, \"name\": \"gray minivan\"}, {\"id\": 6289, \"name\": \"blue shirt\"}, {\"id\": 6290, \"name\": \"the right side of the road\"}, {\"id\": 6291, \"name\": \"covered upper deck\"}, {\"id\": 6292, \"name\": \"lone individual\"}, {\"id\": 6293, \"name\": \"green plant\"}, {\"id\": 6294, \"name\": \"central air vent\"}, {\"id\": 6295, \"name\": \"the long, dark red coat\"}, {\"id\": 6296, \"name\": \"gray stone lion statue\"}, {\"id\": 6297, \"name\": \"large beige building\"}, {\"id\": 6298, \"name\": \"the prominent tower\"}, {\"id\": 6299, \"name\": \"the tall building\"}, {\"id\": 6300, \"name\": \"a red and yellow advertisement\"}, {\"id\": 6301, \"name\": \"tiled counter\"}, {\"id\": 6302, \"name\": \"poorly lit room\"}, {\"id\": 6303, \"name\": \"the bridge\"}, {\"id\": 6304, \"name\": \"the predominantly white floor\"}, {\"id\": 6305, \"name\": \"a large concrete ditch\"}, {\"id\": 6306, \"name\": \"a single-story building\"}, {\"id\": 6307, \"name\": \"small, one-story building\"}, {\"id\": 6308, \"name\": \"a gold frame\"}, {\"id\": 6309, \"name\": \"black bag\"}, {\"id\": 6310, \"name\": \"the radiator\"}, {\"id\": 6311, \"name\": \"the gray asphalt road\"}, {\"id\": 6312, \"name\": \"a green grassy roof\"}, {\"id\": 6313, \"name\": \"plastic dome\"}, {\"id\": 6314, \"name\": \"the small screen\"}, {\"id\": 6315, \"name\": \"left arm\"}, {\"id\": 6316, \"name\": \"the man's right hand\"}, {\"id\": 6317, \"name\": \"a multi-story beige structure\"}, {\"id\": 6318, \"name\": \"small storefront\"}, {\"id\": 6319, \"name\": \"the assortment of green peppers\"}, {\"id\": 6320, \"name\": \"a green collar\"}, {\"id\": 6321, \"name\": \"a green trim\"}, {\"id\": 6322, \"name\": \"the white patterned wallpaper\"}, {\"id\": 6323, \"name\": \"a high neckline\"}, {\"id\": 6324, \"name\": \"the red plastic bag\"}, {\"id\": 6325, \"name\": \"the musician\"}, {\"id\": 6326, \"name\": \"a black grout\"}, {\"id\": 6327, \"name\": \"a white notebook\"}, {\"id\": 6328, \"name\": \"the index finger\"}, {\"id\": 6329, \"name\": \"the notebook's page\"}, {\"id\": 6330, \"name\": \"the orange tip\"}, {\"id\": 6331, \"name\": \"a thin bracelet\"}, {\"id\": 6332, \"name\": \"gray stone\"}, {\"id\": 6333, \"name\": \"white sign\"}, {\"id\": 6334, \"name\": \"a white marble tile floor\"}, {\"id\": 6335, \"name\": \"clear bottle\"}, {\"id\": 6336, \"name\": \"the running water\"}, {\"id\": 6337, \"name\": \"the spray bottle\"}, {\"id\": 6338, \"name\": \"white cap\"}, {\"id\": 6339, \"name\": \"dome\"}, {\"id\": 6340, \"name\": \"the darkened road\"}, {\"id\": 6341, \"name\": \"a black clothing\"}, {\"id\": 6342, \"name\": \"the woman in black\"}, {\"id\": 6343, \"name\": \"night sky\"}, {\"id\": 6344, \"name\": \"the red border\"}, {\"id\": 6345, \"name\": \"the young girl\"}, {\"id\": 6346, \"name\": \"the large yellow plant\"}, {\"id\": 6347, \"name\": \"small slide\"}, {\"id\": 6348, \"name\": \"the blue and yellow patterned wall\"}, {\"id\": 6349, \"name\": \"a hammer\"}, {\"id\": 6350, \"name\": \"right wrist\"}, {\"id\": 6351, \"name\": \"the colorful toy\"}, {\"id\": 6352, \"name\": \"a green lid\"}, {\"id\": 6353, \"name\": \"a tan jacket\"}, {\"id\": 6354, \"name\": \"a tan shirt\"}, {\"id\": 6355, \"name\": \"white long-sleeved shirt\"}, {\"id\": 6356, \"name\": \"a building's entrance\"}, {\"id\": 6357, \"name\": \"the red-framed sign\"}, {\"id\": 6358, \"name\": \"a large, shiny tile\"}, {\"id\": 6359, \"name\": \"a white ceiling fan\"}, {\"id\": 6360, \"name\": \"protective gear\"}, {\"id\": 6361, \"name\": \"the early morning\"}, {\"id\": 6362, \"name\": \"red bucket\"}, {\"id\": 6363, \"name\": \"the small black chicken\"}, {\"id\": 6364, \"name\": \"a blue and white sign\"}, {\"id\": 6365, \"name\": \"a white protective suit\"}, {\"id\": 6366, \"name\": \"a white drawer\"}, {\"id\": 6367, \"name\": \"ear\"}, {\"id\": 6368, \"name\": \"the pink object\"}, {\"id\": 6369, \"name\": \"the white door\"}, {\"id\": 6370, \"name\": \"a dog's body\"}, {\"id\": 6371, \"name\": \"a black and white chicken\"}, {\"id\": 6372, \"name\": \"prominent male duck\"}, {\"id\": 6373, \"name\": \"a brown substance\"}, {\"id\": 6374, \"name\": \"the kitchen counter\"}, {\"id\": 6375, \"name\": \"a harsh glare\"}, {\"id\": 6376, \"name\": \"the black shoe\"}, {\"id\": 6377, \"name\": \"snack\"}, {\"id\": 6378, \"name\": \"the bright light\"}, {\"id\": 6379, \"name\": \"green shirt\"}, {\"id\": 6380, \"name\": \"the blue rope\"}, {\"id\": 6381, \"name\": \"the blue rope barrier\"}, {\"id\": 6382, \"name\": \"a dark-colored, collared shirt\"}, {\"id\": 6383, \"name\": \"green sign\"}, {\"id\": 6384, \"name\": \"the small green arrow\"}, {\"id\": 6385, \"name\": \"the boat's floor\"}, {\"id\": 6386, \"name\": \"the orange jacket\"}, {\"id\": 6387, \"name\": \"the door's reflection\"}, {\"id\": 6388, \"name\": \"the logo\"}, {\"id\": 6389, \"name\": \"green eye\"}, {\"id\": 6390, \"name\": \"the chain-link fence\"}, {\"id\": 6391, \"name\": \"a white top with blue flowers\"}, {\"id\": 6392, \"name\": \"flower arrangement\"}, {\"id\": 6393, \"name\": \"a 7-Eleven convenience store\"}, {\"id\": 6394, \"name\": \"a blonde hair\"}, {\"id\": 6395, \"name\": \"plate of food\"}, {\"id\": 6396, \"name\": \"a copier machine\"}, {\"id\": 6397, \"name\": \"door handle\"}, {\"id\": 6398, \"name\": \"the dark clothing\"}, {\"id\": 6399, \"name\": \"an overhanging roof\"}, {\"id\": 6400, \"name\": \"the black scooter\"}, {\"id\": 6401, \"name\": \"cracked concrete surface\"}, {\"id\": 6402, \"name\": \"tabby cat\"}, {\"id\": 6403, \"name\": \"the orange tabby\"}, {\"id\": 6404, \"name\": \"the yellow bag\"}, {\"id\": 6405, \"name\": \"a green sign\"}, {\"id\": 6406, \"name\": \"the blue display case\"}, {\"id\": 6407, \"name\": \"yellow line\"}, {\"id\": 6408, \"name\": \"a white and brown plaid pattern\"}, {\"id\": 6409, \"name\": \"blue plaid pattern\"}, {\"id\": 6410, \"name\": \"the black metal panel\"}, {\"id\": 6411, \"name\": \"the kiosk\"}, {\"id\": 6412, \"name\": \"yellow stripe\"}, {\"id\": 6413, \"name\": \"a white tarp\"}, {\"id\": 6414, \"name\": \"a metal cabinet\"}, {\"id\": 6415, \"name\": \"white finish\"}, {\"id\": 6416, \"name\": \"the torch\"}, {\"id\": 6417, \"name\": \"the yellow taxi\"}, {\"id\": 6418, \"name\": \"white and tan tile floor\"}, {\"id\": 6419, \"name\": \"a left arm\"}, {\"id\": 6420, \"name\": \"a pink veil\"}, {\"id\": 6421, \"name\": \"a blue spotlight\"}, {\"id\": 6422, \"name\": \"projector\"}, {\"id\": 6423, \"name\": \"the audience\"}, {\"id\": 6424, \"name\": \"the room's ceiling\"}, {\"id\": 6425, \"name\": \"a steam\"}, {\"id\": 6426, \"name\": \"the cigar\"}, {\"id\": 6427, \"name\": \"the white mug\"}, {\"id\": 6428, \"name\": \"gray skirt\"}, {\"id\": 6429, \"name\": \"white license plate\"}, {\"id\": 6430, \"name\": \"a pair of white sandals\"}, {\"id\": 6431, \"name\": \"a blue and red uniform\"}, {\"id\": 6432, \"name\": \"the boy\"}, {\"id\": 6433, \"name\": \"the jersey\"}, {\"id\": 6434, \"name\": \"the trunk of the tree\"}, {\"id\": 6435, \"name\": \"tree's trunk\"}, {\"id\": 6436, \"name\": \"brown wooden cabinet\"}, {\"id\": 6437, \"name\": \"the red piping\"}, {\"id\": 6438, \"name\": \"Santa hat\"}, {\"id\": 6439, \"name\": \"blue sneaker\"}, {\"id\": 6440, \"name\": \"the network of white beam\"}, {\"id\": 6441, \"name\": \"brown pant\"}, {\"id\": 6442, \"name\": \"the green plastic bag\"}, {\"id\": 6443, \"name\": \"a brown balcony\"}, {\"id\": 6444, \"name\": \"a row of blue and grey train car\"}, {\"id\": 6445, \"name\": \"a tracks\"}, {\"id\": 6446, \"name\": \"gravel bed\"}, {\"id\": 6447, \"name\": \"the blue dumpster\"}, {\"id\": 6448, \"name\": \"the railroad track\"}, {\"id\": 6449, \"name\": \"the train car\"}, {\"id\": 6450, \"name\": \"a white Arabic writing\"}, {\"id\": 6451, \"name\": \"the tall light pole\"}, {\"id\": 6452, \"name\": \"gas pump\"}, {\"id\": 6453, \"name\": \"gray line\"}, {\"id\": 6454, \"name\": \"registration number\"}, {\"id\": 6455, \"name\": \"the gas station's canopy\"}, {\"id\": 6456, \"name\": \"short hair\"}, {\"id\": 6457, \"name\": \"the phrase\"}, {\"id\": 6458, \"name\": \"the sweatshirt\"}, {\"id\": 6459, \"name\": \"the shower curtain\"}, {\"id\": 6460, \"name\": \"orange romper\"}, {\"id\": 6461, \"name\": \"the purple plastic object\"}, {\"id\": 6462, \"name\": \"the pink flower\"}, {\"id\": 6463, \"name\": \"a front left paw\"}, {\"id\": 6464, \"name\": \"a large cartoon drawing of a fish\"}, {\"id\": 6465, \"name\": \"a blue crate\"}, {\"id\": 6466, \"name\": \"a large fan\"}, {\"id\": 6467, \"name\": \"green trash can\"}, {\"id\": 6468, \"name\": \"the white-tiled corridor\"}, {\"id\": 6469, \"name\": \"a horizontal band\"}, {\"id\": 6470, \"name\": \"a middle garment\"}, {\"id\": 6471, \"name\": \"the white sheet or cloth\"}, {\"id\": 6472, \"name\": \"white top\"}, {\"id\": 6473, \"name\": \"plastic bottle\"}, {\"id\": 6474, \"name\": \"a blue towel or rag\"}, {\"id\": 6475, \"name\": \"a stack of clear jar\"}, {\"id\": 6476, \"name\": \"piece of fabric\"}, {\"id\": 6477, \"name\": \"the woman's hands\"}, {\"id\": 6478, \"name\": \"the yellow tray\"}, {\"id\": 6479, \"name\": \"a furniture\"}, {\"id\": 6480, \"name\": \"the snake\"}, {\"id\": 6481, \"name\": \"the blue outfit\"}, {\"id\": 6482, \"name\": \"a number 25\"}, {\"id\": 6483, \"name\": \"a woman in a black hijab\"}, {\"id\": 6484, \"name\": \"long, light blue dress\"}, {\"id\": 6485, \"name\": \"the yellow shorts\"}, {\"id\": 6486, \"name\": \"a red tail light\"}, {\"id\": 6487, \"name\": \"a wooden wheelbarrow\"}, {\"id\": 6488, \"name\": \"central pillar\"}, {\"id\": 6489, \"name\": \"sink\"}, {\"id\": 6490, \"name\": \"black device\"}, {\"id\": 6491, \"name\": \"the white tag\"}, {\"id\": 6492, \"name\": \"the tall, weathered corrugated metal fence\"}, {\"id\": 6493, \"name\": \"a cage's wall\"}, {\"id\": 6494, \"name\": \"the pink hood\"}, {\"id\": 6495, \"name\": \"the stone wall\"}, {\"id\": 6496, \"name\": \"the price\"}, {\"id\": 6497, \"name\": \"blue helmet\"}, {\"id\": 6498, \"name\": \"go-kart\"}, {\"id\": 6499, \"name\": \"glove\"}, {\"id\": 6500, \"name\": \"the door's glass surface\"}, {\"id\": 6501, \"name\": \"the yellow label\"}, {\"id\": 6502, \"name\": \"treadmill's console\"}, {\"id\": 6503, \"name\": \"figurine\"}, {\"id\": 6504, \"name\": \"a stack of green plastic crates\"}, {\"id\": 6505, \"name\": \"large, yellow, multi-story building\"}, {\"id\": 6506, \"name\": \"bag of chips\"}, {\"id\": 6507, \"name\": \"large wooden beam\"}, {\"id\": 6508, \"name\": \"the stack of plastic chairs\"}, {\"id\": 6509, \"name\": \"chain\"}, {\"id\": 6510, \"name\": \"the large canopy\"}, {\"id\": 6511, \"name\": \"a floor of the store\"}, {\"id\": 6512, \"name\": \"puddle\"}, {\"id\": 6513, \"name\": \"the dark-colored cow\"}, {\"id\": 6514, \"name\": \"a large screen\"}, {\"id\": 6515, \"name\": \"glowing red and white sign\"}, {\"id\": 6516, \"name\": \"a BUTLER\"}, {\"id\": 6517, \"name\": \"a tan button-down shirt\"}, {\"id\": 6518, \"name\": \"brown body\"}, {\"id\": 6519, \"name\": \"the floppy ear\"}, {\"id\": 6520, \"name\": \"grocery store shelf\"}, {\"id\": 6521, \"name\": \"small purse\"}, {\"id\": 6522, \"name\": \"the bottle of juice\"}, {\"id\": 6523, \"name\": \"the black roof rack\"}, {\"id\": 6524, \"name\": \"the white and red sign\"}, {\"id\": 6525, \"name\": \"the thin, yellow and black cord\"}, {\"id\": 6526, \"name\": \"the yellow wire\"}, {\"id\": 6527, \"name\": \"wire\"}, {\"id\": 6528, \"name\": \"a Trends sign\"}, {\"id\": 6529, \"name\": \"a curved counter\"}, {\"id\": 6530, \"name\": \"the handle\"}, {\"id\": 6531, \"name\": \"the red and white sign\"}, {\"id\": 6532, \"name\": \"the blue jean\"}, {\"id\": 6533, \"name\": \"a price tag\"}, {\"id\": 6534, \"name\": \"black phone\"}, {\"id\": 6535, \"name\": \"selection of Samsung mobile phones\"}, {\"id\": 6536, \"name\": \"the prominent display of cell phones\"}, {\"id\": 6537, \"name\": \"the Bisc\"}, {\"id\": 6538, \"name\": \"a floor tile\"}, {\"id\": 6539, \"name\": \"the face mask\"}, {\"id\": 6540, \"name\": \"the metal strip\"}, {\"id\": 6541, \"name\": \"a puddle\"}, {\"id\": 6542, \"name\": \"stone or concrete\"}, {\"id\": 6543, \"name\": \"a ribbed texture\"}, {\"id\": 6544, \"name\": \"the drawer handle\"}, {\"id\": 6545, \"name\": \"a black box\"}, {\"id\": 6546, \"name\": \"white Chinese character\"}, {\"id\": 6547, \"name\": \"a black and white plumage\"}, {\"id\": 6548, \"name\": \"the small, fluffy white cat\"}, {\"id\": 6549, \"name\": \"the small, white kitten\"}, {\"id\": 6550, \"name\": \"a gray minivan\"}, {\"id\": 6551, \"name\": \"the edge of the curb\"}, {\"id\": 6552, \"name\": \"product image\"}, {\"id\": 6553, \"name\": \"the grocery store\"}, {\"id\": 6554, \"name\": \"the holiday decoration\"}, {\"id\": 6555, \"name\": \"metal fence\"}, {\"id\": 6556, \"name\": \"white corrugated metal wall\"}, {\"id\": 6557, \"name\": \"curved shape\"}, {\"id\": 6558, \"name\": \"the tall, dark skyscraper\"}, {\"id\": 6559, \"name\": \"air conditioning unit\"}, {\"id\": 6560, \"name\": \"the scarf\"}, {\"id\": 6561, \"name\": \"a red table\"}, {\"id\": 6562, \"name\": \"matching blue top\"}, {\"id\": 6563, \"name\": \"the blue t-shirt\"}, {\"id\": 6564, \"name\": \"the female dancer\"}, {\"id\": 6565, \"name\": \"larger green plant\"}, {\"id\": 6566, \"name\": \"the dense foliage\"}, {\"id\": 6567, \"name\": \"the large concrete container\"}, {\"id\": 6568, \"name\": \"a light-colored floor\"}, {\"id\": 6569, \"name\": \"kitchen area\"}, {\"id\": 6570, \"name\": \"the black and white tile\"}, {\"id\": 6571, \"name\": \"red brick pathway\"}, {\"id\": 6572, \"name\": \"the taillight\"}, {\"id\": 6573, \"name\": \"the large cylindrical wooden structure\"}, {\"id\": 6574, \"name\": \"the podium\"}, {\"id\": 6575, \"name\": \"a glass wall\"}, {\"id\": 6576, \"name\": \"neon green sign\"}, {\"id\": 6577, \"name\": \"person in a green jacket\"}, {\"id\": 6578, \"name\": \"a black interior\"}, {\"id\": 6579, \"name\": \"the windshield wiper\"}, {\"id\": 6580, \"name\": \"niqab\"}, {\"id\": 6581, \"name\": \"the white towel\"}, {\"id\": 6582, \"name\": \"a forested mountain\"}, {\"id\": 6583, \"name\": \"grass\"}, {\"id\": 6584, \"name\": \"hazy blue sky\"}, {\"id\": 6585, \"name\": \"large truck\"}, {\"id\": 6586, \"name\": \"a white shoe\"}, {\"id\": 6587, \"name\": \"small gray footstool\"}, {\"id\": 6588, \"name\": \"the pickup truck\"}, {\"id\": 6589, \"name\": \"a women's hairstyle\"}, {\"id\": 6590, \"name\": \"the aisle\"}, {\"id\": 6591, \"name\": \"a black oval\"}, {\"id\": 6592, \"name\": \"a circular pattern\"}, {\"id\": 6593, \"name\": \"glare on the windshield\"}, {\"id\": 6594, \"name\": \"the beam of light\"}, {\"id\": 6595, \"name\": \"a traffic police\"}, {\"id\": 6596, \"name\": \"the young boy\"}, {\"id\": 6597, \"name\": \"large white screen\"}, {\"id\": 6598, \"name\": \"the white fabric\"}, {\"id\": 6599, \"name\": \"black, textured surface\"}, {\"id\": 6600, \"name\": \"overcast and gray sky\"}, {\"id\": 6601, \"name\": \"a large concrete pillar\"}, {\"id\": 6602, \"name\": \"an orange tank top\"}, {\"id\": 6603, \"name\": \"large wall\"}, {\"id\": 6604, \"name\": \"the shrub\"}, {\"id\": 6605, \"name\": \"wooden table\"}, {\"id\": 6606, \"name\": \"low green hedge\"}, {\"id\": 6607, \"name\": \"the gray sweater\"}, {\"id\": 6608, \"name\": \"a median\"}, {\"id\": 6609, \"name\": \"a trench\"}, {\"id\": 6610, \"name\": \"the white and gray design\"}, {\"id\": 6611, \"name\": \"the desk or countertop\"}, {\"id\": 6612, \"name\": \"a crocodile\"}, {\"id\": 6613, \"name\": \"a murky green body of water\"}, {\"id\": 6614, \"name\": \"grid pattern\"}, {\"id\": 6615, \"name\": \"center of the road\"}, {\"id\": 6616, \"name\": \"the white object\"}, {\"id\": 6617, \"name\": \"gray, black, white, and pink plaid cover\"}, {\"id\": 6618, \"name\": \"small portion of a white chair\"}, {\"id\": 6619, \"name\": \"the white drawer\"}, {\"id\": 6620, \"name\": \"the rectangular object\"}, {\"id\": 6621, \"name\": \"a restaurant's decor\"}, {\"id\": 6622, \"name\": \"a television screen\"}, {\"id\": 6623, \"name\": \"a white lattice-patterned ceiling\"}, {\"id\": 6624, \"name\": \"the mall\"}, {\"id\": 6625, \"name\": \"a stuffed dog\"}, {\"id\": 6626, \"name\": \"driver's seat\"}, {\"id\": 6627, \"name\": \"a KFC restaurant\"}, {\"id\": 6628, \"name\": \"a dark brown hood\"}, {\"id\": 6629, \"name\": \"the prominent green arrow sign\"}, {\"id\": 6630, \"name\": \"the red lantern\"}, {\"id\": 6631, \"name\": \"a red onion\"}, {\"id\": 6632, \"name\": \"a sliced red onion\"}, {\"id\": 6633, \"name\": \"bottle of red liquid\"}, {\"id\": 6634, \"name\": \"chicken wing\"}, {\"id\": 6635, \"name\": \"the celebration\"}, {\"id\": 6636, \"name\": \"a patterned sleeve\"}, {\"id\": 6637, \"name\": \"maroon skirt\"}, {\"id\": 6638, \"name\": \"the red skirt\"}, {\"id\": 6639, \"name\": \"the plastic water bottle\"}, {\"id\": 6640, \"name\": \"trash\"}, {\"id\": 6641, \"name\": \"yellow machine\"}, {\"id\": 6642, \"name\": \"large, dirty tire\"}, {\"id\": 6643, \"name\": \"the brownish-gray feather\"}, {\"id\": 6644, \"name\": \"Coca-Cola refrigerator\"}, {\"id\": 6645, \"name\": \"generous serving of nachos\"}, {\"id\": 6646, \"name\": \"the row of arcade claw machines\"}, {\"id\": 6647, \"name\": \"blue tight\"}, {\"id\": 6648, \"name\": \"the long desk\"}, {\"id\": 6649, \"name\": \"a purple vegetable\"}, {\"id\": 6650, \"name\": \"a light-colored hoodie\"}, {\"id\": 6651, \"name\": \"plastic covering\"}, {\"id\": 6652, \"name\": \"dusty street\"}, {\"id\": 6653, \"name\": \"the prominent altar\"}, {\"id\": 6654, \"name\": \"the metal bed frame\"}, {\"id\": 6655, \"name\": \"a white grid\"}, {\"id\": 6656, \"name\": \"the dark object\"}, {\"id\": 6657, \"name\": \"white countertop\"}, {\"id\": 6658, \"name\": \"a cracked concrete floor\"}, {\"id\": 6659, \"name\": \"texture of the concrete\"}, {\"id\": 6660, \"name\": \"small terrier\"}, {\"id\": 6661, \"name\": \"a beige door\"}, {\"id\": 6662, \"name\": \"concrete sidewalk\"}, {\"id\": 6663, \"name\": \"the pile of garbage\"}, {\"id\": 6664, \"name\": \"the large window wall\"}, {\"id\": 6665, \"name\": \"a small brown rock\"}, {\"id\": 6666, \"name\": \"lying down\"}, {\"id\": 6667, \"name\": \"deserted street\"}, {\"id\": 6668, \"name\": \"the red car\"}, {\"id\": 6669, \"name\": \"the white and black striped curb\"}, {\"id\": 6670, \"name\": \"a blue truck\"}, {\"id\": 6671, \"name\": \"a high-visibility vest\"}, {\"id\": 6672, \"name\": \"a large white tent\"}, {\"id\": 6673, \"name\": \"a colorful backdrop\"}, {\"id\": 6674, \"name\": \"the SpongeBob SquarePants theme\"}, {\"id\": 6675, \"name\": \"the cartoon-style design\"}, {\"id\": 6676, \"name\": \"aisle floor\"}, {\"id\": 6677, \"name\": \"the event\"}, {\"id\": 6678, \"name\": \"the black chair\"}, {\"id\": 6679, \"name\": \"the picture frame\"}, {\"id\": 6680, \"name\": \"the black puffy coat\"}, {\"id\": 6681, \"name\": \"a cell tower\"}, {\"id\": 6682, \"name\": \"the large column\"}, {\"id\": 6683, \"name\": \"white-tiled hallway\"}, {\"id\": 6684, \"name\": \"dark attire\"}, {\"id\": 6685, \"name\": \"red object\"}, {\"id\": 6686, \"name\": \"the beam\"}, {\"id\": 6687, \"name\": \"the broom\"}, {\"id\": 6688, \"name\": \"dark grey pant\"}, {\"id\": 6689, \"name\": \"woman's left hand\"}, {\"id\": 6690, \"name\": \"pink canopy\"}, {\"id\": 6691, \"name\": \"the tent\"}, {\"id\": 6692, \"name\": \"a blue and white truck\"}, {\"id\": 6693, \"name\": \"a rider\"}, {\"id\": 6694, \"name\": \"yellow and white tuk-tuk\"}, {\"id\": 6695, \"name\": \"the beige cushion\"}, {\"id\": 6696, \"name\": \"a white wallpaper\"}, {\"id\": 6697, \"name\": \"front paw\"}, {\"id\": 6698, \"name\": \"the eye\"}, {\"id\": 6699, \"name\": \"a black shorts\"}, {\"id\": 6700, \"name\": \"the artificial surface\"}, {\"id\": 6701, \"name\": \"the dog's harness\"}, {\"id\": 6702, \"name\": \"large display case\"}, {\"id\": 6703, \"name\": \"the paved stone pathway\"}, {\"id\": 6704, \"name\": \"the predominantly black coat\"}, {\"id\": 6705, \"name\": \"a large stack of white boxes\"}, {\"id\": 6706, \"name\": \"a pig's eye\"}, {\"id\": 6707, \"name\": \"snout\"}, {\"id\": 6708, \"name\": \"the pig\"}, {\"id\": 6709, \"name\": \"a blue cushion\"}, {\"id\": 6710, \"name\": \"the dark red shirt\"}, {\"id\": 6711, \"name\": \"the white mask\"}, {\"id\": 6712, \"name\": \"torso\"}, {\"id\": 6713, \"name\": \"the navy blue and red tracksuit\"}, {\"id\": 6714, \"name\": \"a wooden stump\"}, {\"id\": 6715, \"name\": \"the mulch\"}, {\"id\": 6716, \"name\": \"tree stump\"}, {\"id\": 6717, \"name\": \"young child\"}, {\"id\": 6718, \"name\": \"the yellow hat\"}, {\"id\": 6719, \"name\": \"a Chinese writing\"}, {\"id\": 6720, \"name\": \"a tall cup\"}, {\"id\": 6721, \"name\": \"yellow container\"}, {\"id\": 6722, \"name\": \"brown wooden door frame\"}, {\"id\": 6723, \"name\": \"dark-colored shirt\"}, {\"id\": 6724, \"name\": \"a large banner\"}, {\"id\": 6725, \"name\": \"the book\"}, {\"id\": 6726, \"name\": \"yellow light\"}, {\"id\": 6727, \"name\": \"white shorts\"}, {\"id\": 6728, \"name\": \"a red pillow\"}, {\"id\": 6729, \"name\": \"a track lighting\"}, {\"id\": 6730, \"name\": \"a white wire mesh bin\"}, {\"id\": 6731, \"name\": \"pallet\"}, {\"id\": 6732, \"name\": \"tall shelving unit\"}, {\"id\": 6733, \"name\": \"a glass panel\"}, {\"id\": 6734, \"name\": \"a tall brown curtain\"}, {\"id\": 6735, \"name\": \"a white chair\"}, {\"id\": 6736, \"name\": \"lobby's white ceiling\"}, {\"id\": 6737, \"name\": \"the brown handrail\"}, {\"id\": 6738, \"name\": \"the monitor\"}, {\"id\": 6739, \"name\": \"the white vase\"}, {\"id\": 6740, \"name\": \"a bulldozer\"}, {\"id\": 6741, \"name\": \"metal pole\"}, {\"id\": 6742, \"name\": \"clear sky\"}, {\"id\": 6743, \"name\": \"a black cuff\"}, {\"id\": 6744, \"name\": \"the curved roof\"}, {\"id\": 6745, \"name\": \"the large hole\"}, {\"id\": 6746, \"name\": \"the white and blue van\"}, {\"id\": 6747, \"name\": \"the tuk-tuk\"}, {\"id\": 6748, \"name\": \"yellow tuk-tuk\"}, {\"id\": 6749, \"name\": \"clean line\"}, {\"id\": 6750, \"name\": \"the silver clock face\"}, {\"id\": 6751, \"name\": \"red and orange design\"}, {\"id\": 6752, \"name\": \"a teal lehenga\"}, {\"id\": 6753, \"name\": \"red and yellow tunic\"}, {\"id\": 6754, \"name\": \"the pink sari\"}, {\"id\": 6755, \"name\": \"black bowl\"}, {\"id\": 6756, \"name\": \"the black plate\"}, {\"id\": 6757, \"name\": \"the pair of black chopsticks\"}, {\"id\": 6758, \"name\": \"a pink outfit\"}, {\"id\": 6759, \"name\": \"the dog's tongue\"}, {\"id\": 6760, \"name\": \"the surrounding building\"}, {\"id\": 6761, \"name\": \"a strip of grass\"}, {\"id\": 6762, \"name\": \"the concrete slab\"}, {\"id\": 6763, \"name\": \"the pink nose\"}, {\"id\": 6764, \"name\": \"knee-length hem\"}, {\"id\": 6765, \"name\": \"performer\"}, {\"id\": 6766, \"name\": \"ponytail\"}, {\"id\": 6767, \"name\": \"bell pepper\"}, {\"id\": 6768, \"name\": \"pile of green vegetables\"}, {\"id\": 6769, \"name\": \"the fruit\"}, {\"id\": 6770, \"name\": \"a large cross\"}, {\"id\": 6771, \"name\": \"the striking red wall\"}, {\"id\": 6772, \"name\": \"red Chinese flag\"}, {\"id\": 6773, \"name\": \"the long-sleeved leotard\"}, {\"id\": 6774, \"name\": \"the track light fixture\"}, {\"id\": 6775, \"name\": \"long, flowing skirt\"}, {\"id\": 6776, \"name\": \"a long, dark hair\"}, {\"id\": 6777, \"name\": \"the white long-sleeve shirt\"}, {\"id\": 6778, \"name\": \"Poodle\"}, {\"id\": 6779, \"name\": \"tray of food\"}, {\"id\": 6780, \"name\": \"peanut\"}, {\"id\": 6781, \"name\": \"a boat's exterior\"}, {\"id\": 6782, \"name\": \"a large inflatable dolphin\"}, {\"id\": 6783, \"name\": \"bright pink ball\"}, {\"id\": 6784, \"name\": \"a colorful sari\"}, {\"id\": 6785, \"name\": \"basket of bread\"}, {\"id\": 6786, \"name\": \"blue and white truck\"}, {\"id\": 6787, \"name\": \"the walkway\"}, {\"id\": 6788, \"name\": \"a black and white dress\"}, {\"id\": 6789, \"name\": \"the blue umbrella\"}, {\"id\": 6790, \"name\": \"a black hoodie\"}, {\"id\": 6791, \"name\": \"a grassy bank\"}, {\"id\": 6792, \"name\": \"a green vegetation\"}, {\"id\": 6793, \"name\": \"a red stripe\"}, {\"id\": 6794, \"name\": \"the blue trim\"}, {\"id\": 6795, \"name\": \"the lane\"}, {\"id\": 6796, \"name\": \"a large gray rock\"}, {\"id\": 6797, \"name\": \"the lush greenery\"}, {\"id\": 6798, \"name\": \"an informational message\"}, {\"id\": 6799, \"name\": \"the blue dress\"}, {\"id\": 6800, \"name\": \"the car window\"}, {\"id\": 6801, \"name\": \"blue front\"}, {\"id\": 6802, \"name\": \"a dark coat\"}, {\"id\": 6803, \"name\": \"a gray pole\"}, {\"id\": 6804, \"name\": \"black sneaker\"}, {\"id\": 6805, \"name\": \"ceiling of the store\"}, {\"id\": 6806, \"name\": \"green section\"}, {\"id\": 6807, \"name\": \"the circular hole\"}, {\"id\": 6808, \"name\": \"a brown sari\"}, {\"id\": 6809, \"name\": \"a circular, raised platform\"}, {\"id\": 6810, \"name\": \"beige floor\"}, {\"id\": 6811, \"name\": \"the wooden stool\"}, {\"id\": 6812, \"name\": \"a patterned collar\"}, {\"id\": 6813, \"name\": \"white outfit\"}, {\"id\": 6814, \"name\": \"a red hat\"}, {\"id\": 6815, \"name\": \"green box\"}, {\"id\": 6816, \"name\": \"white line\"}, {\"id\": 6817, \"name\": \"the glass-fronted refrigerator\"}, {\"id\": 6818, \"name\": \"partial view of the car's dashboard\"}, {\"id\": 6819, \"name\": \"a dark jeans\"}, {\"id\": 6820, \"name\": \"a dustpan\"}, {\"id\": 6821, \"name\": \"a gray concrete floor\"}, {\"id\": 6822, \"name\": \"a person's legs and feet\"}, {\"id\": 6823, \"name\": \"a red handle\"}, {\"id\": 6824, \"name\": \"the concrete area\"}, {\"id\": 6825, \"name\": \"the red puffer jacket\"}, {\"id\": 6826, \"name\": \"a long-sleeved light blue shirt\"}, {\"id\": 6827, \"name\": \"a white lid\"}, {\"id\": 6828, \"name\": \"the interior of a small refrigerator\"}, {\"id\": 6829, \"name\": \"the black wheel\"}, {\"id\": 6830, \"name\": \"orange and grey design\"}, {\"id\": 6831, \"name\": \"the black puffer vest\"}, {\"id\": 6832, \"name\": \"bookshelf\"}, {\"id\": 6833, \"name\": \"couch or chair\"}, {\"id\": 6834, \"name\": \"pink hair clip\"}, {\"id\": 6835, \"name\": \"the bottle of green liquid\"}, {\"id\": 6836, \"name\": \"a short, dark beard\"}, {\"id\": 6837, \"name\": \"a white jacket\"}, {\"id\": 6838, \"name\": \"large hoop earring\"}, {\"id\": 6839, \"name\": \"light-colored wood\"}, {\"id\": 6840, \"name\": \"merchandise\"}, {\"id\": 6841, \"name\": \"a prominent logo\"}, {\"id\": 6842, \"name\": \"gray polo shirt\"}, {\"id\": 6843, \"name\": \"parked motorcycle\"}, {\"id\": 6844, \"name\": \"the large white advertisement\"}, {\"id\": 6845, \"name\": \"a hospital's sign\"}, {\"id\": 6846, \"name\": \"stone wall\"}, {\"id\": 6847, \"name\": \"the light\"}, {\"id\": 6848, \"name\": \"assortment of plates\"}, {\"id\": 6849, \"name\": \"a top\"}, {\"id\": 6850, \"name\": \"cargo pant\"}, {\"id\": 6851, \"name\": \"the white pant\"}, {\"id\": 6852, \"name\": \"napkin\"}, {\"id\": 6853, \"name\": \"large red crate\"}, {\"id\": 6854, \"name\": \"sweatshirt\"}, {\"id\": 6855, \"name\": \"the collection of plastic bags\"}, {\"id\": 6856, \"name\": \"the serving utensil\"}, {\"id\": 6857, \"name\": \"red and white ball\"}, {\"id\": 6858, \"name\": \"a neat bun\"}, {\"id\": 6859, \"name\": \"the black purse\"}, {\"id\": 6860, \"name\": \"the ticket machine\"}, {\"id\": 6861, \"name\": \"a concrete step\"}, {\"id\": 6862, \"name\": \"door to the room\"}, {\"id\": 6863, \"name\": \"the remaining steps\"}, {\"id\": 6864, \"name\": \"a gym's wall\"}, {\"id\": 6865, \"name\": \"the blue ball\"}, {\"id\": 6866, \"name\": \"the plank position\"}, {\"id\": 6867, \"name\": \"the large green banner\"}, {\"id\": 6868, \"name\": \"a silver leg\"}, {\"id\": 6869, \"name\": \"the colorful rope\"}, {\"id\": 6870, \"name\": \"the short beard\"}, {\"id\": 6871, \"name\": \"the gray, fuzzy rug\"}, {\"id\": 6872, \"name\": \"an illuminated signage\"}, {\"id\": 6873, \"name\": \"menu\"}, {\"id\": 6874, \"name\": \"a thermos\"}, {\"id\": 6875, \"name\": \"room's floor\"}, {\"id\": 6876, \"name\": \"white seat\"}, {\"id\": 6877, \"name\": \"gleaming, metallic ceiling\"}, {\"id\": 6878, \"name\": \"the shiny, silver material\"}, {\"id\": 6879, \"name\": \"pink sign for Sweet Shop\"}, {\"id\": 6880, \"name\": \"the man in a green jacket\"}, {\"id\": 6881, \"name\": \"black and white vehicle\"}, {\"id\": 6882, \"name\": \"the wooden table or desk\"}, {\"id\": 6883, \"name\": \"a black short\"}, {\"id\": 6884, \"name\": \"a cyclist\"}, {\"id\": 6885, \"name\": \"black and yellow line\"}, {\"id\": 6886, \"name\": \"the red coat\"}, {\"id\": 6887, \"name\": \"the fire extinguisher\"}, {\"id\": 6888, \"name\": \"blue quilted cover\"}, {\"id\": 6889, \"name\": \"pink nose\"}, {\"id\": 6890, \"name\": \"a low barrier\"}, {\"id\": 6891, \"name\": \"a second level of the mall\"}, {\"id\": 6892, \"name\": \"gold stripe\"}, {\"id\": 6893, \"name\": \"the white dress\"}, {\"id\": 6894, \"name\": \"the yellow box\"}, {\"id\": 6895, \"name\": \"the large fan\"}, {\"id\": 6896, \"name\": \"a long, straight escalator\"}, {\"id\": 6897, \"name\": \"gray metal surface\"}, {\"id\": 6898, \"name\": \"walkway\"}, {\"id\": 6899, \"name\": \"a lime green shirt\"}, {\"id\": 6900, \"name\": \"a red tank top\"}, {\"id\": 6901, \"name\": \"the logo for TOTTUS\"}, {\"id\": 6902, \"name\": \"the mural\"}, {\"id\": 6903, \"name\": \"a red banner\"}, {\"id\": 6904, \"name\": \"a red mat\"}, {\"id\": 6905, \"name\": \"pan\"}, {\"id\": 6906, \"name\": \"the large container\"}, {\"id\": 6907, \"name\": \"the green bucket\"}, {\"id\": 6908, \"name\": \"a glass railing\"}, {\"id\": 6909, \"name\": \"a wooden stool\"}, {\"id\": 6910, \"name\": \"vibrant floral dress\"}, {\"id\": 6911, \"name\": \"white chair\"}, {\"id\": 6912, \"name\": \"white plastic container\"}, {\"id\": 6913, \"name\": \"the ice rink\"}, {\"id\": 6914, \"name\": \"a rainbow-colored stripe\"}, {\"id\": 6915, \"name\": \"a husky\"}, {\"id\": 6916, \"name\": \"husky or similar breed\"}, {\"id\": 6917, \"name\": \"the grey rug\"}, {\"id\": 6918, \"name\": \"the dark blue coat\"}, {\"id\": 6919, \"name\": \"the shiny beige marble\"}, {\"id\": 6920, \"name\": \"the toilet tank\"}, {\"id\": 6921, \"name\": \"type of seafood\"}, {\"id\": 6922, \"name\": \"white-tiled wall\"}, {\"id\": 6923, \"name\": \"a left knee\"}, {\"id\": 6924, \"name\": \"the mountain range\"}, {\"id\": 6925, \"name\": \"feline\"}, {\"id\": 6926, \"name\": \"a crop\"}, {\"id\": 6927, \"name\": \"a red boot\"}, {\"id\": 6928, \"name\": \"a vast field of green crops\"}, {\"id\": 6929, \"name\": \"the greenhouse\"}, {\"id\": 6930, \"name\": \"red coat\"}, {\"id\": 6931, \"name\": \"the navy blue jacket\"}, {\"id\": 6932, \"name\": \"a Korean writing\"}, {\"id\": 6933, \"name\": \"a recessed light\"}, {\"id\": 6934, \"name\": \"a red booth-style seating area\"}, {\"id\": 6935, \"name\": \"a children\"}, {\"id\": 6936, \"name\": \"a white hatchback\"}, {\"id\": 6937, \"name\": \"canal\"}, {\"id\": 6938, \"name\": \"a large blue sign\"}, {\"id\": 6939, \"name\": \"illegible logo\"}, {\"id\": 6940, \"name\": \"multicolored circle\"}, {\"id\": 6941, \"name\": \"a white blind\"}, {\"id\": 6942, \"name\": \"the man in a white shirt\"}, {\"id\": 6943, \"name\": \"the water bottle\"}, {\"id\": 6944, \"name\": \"long table\"}, {\"id\": 6945, \"name\": \"the vibrant sign\"}, {\"id\": 6946, \"name\": \"vibrant red sign\"}, {\"id\": 6947, \"name\": \"the beige exterior\"}, {\"id\": 6948, \"name\": \"a metal fork\"}, {\"id\": 6949, \"name\": \"the stainless steel strainer\"}, {\"id\": 6950, \"name\": \"the gold star-shaped decoration\"}, {\"id\": 6951, \"name\": \"a low brick wall\"}, {\"id\": 6952, \"name\": \"a beige building\"}, {\"id\": 6953, \"name\": \"man in a light blue outfit\"}, {\"id\": 6954, \"name\": \"partially visible piece of paper\"}, {\"id\": 6955, \"name\": \"Chinese character\"}, {\"id\": 6956, \"name\": \"a high, white ceiling\"}, {\"id\": 6957, \"name\": \"dark night sky\"}, {\"id\": 6958, \"name\": \"a corrug\"}, {\"id\": 6959, \"name\": \"a rusty roof\"}, {\"id\": 6960, \"name\": \"black skirt\"}, {\"id\": 6961, \"name\": \"green shed\"}, {\"id\": 6962, \"name\": \"the puddle\"}, {\"id\": 6963, \"name\": \"wheel\"}, {\"id\": 6964, \"name\": \"a pink candy\"}, {\"id\": 6965, \"name\": \"candy\"}, {\"id\": 6966, \"name\": \"red and black patterned garment\"}, {\"id\": 6967, \"name\": \"red, black, and white patterned cloth\"}, {\"id\": 6968, \"name\": \"the chest\"}, {\"id\": 6969, \"name\": \"cartoon character design\"}, {\"id\": 6970, \"name\": \"a black top\"}, {\"id\": 6971, \"name\": \"an auto-rickshaw\"}, {\"id\": 6972, \"name\": \"thick material\"}, {\"id\": 6973, \"name\": \"blurred receipt\"}, {\"id\": 6974, \"name\": \"long, flowing yellow skirt\"}, {\"id\": 6975, \"name\": \"the graceful gesture\"}, {\"id\": 6976, \"name\": \"vibrant yellow and purple curtain\"}, {\"id\": 6977, \"name\": \"a black and white kitten\"}, {\"id\": 6978, \"name\": \"the brown roof\"}, {\"id\": 6979, \"name\": \"the pink wand toy\"}, {\"id\": 6980, \"name\": \"a dusty\"}, {\"id\": 6981, \"name\": \"narrow, dirt road\"}, {\"id\": 6982, \"name\": \"the neon sign\"}, {\"id\": 6983, \"name\": \"a statue of an animal\"}, {\"id\": 6984, \"name\": \"a table's base\"}, {\"id\": 6985, \"name\": \"a make-shift shelter\"}, {\"id\": 6986, \"name\": \"closet\"}, {\"id\": 6987, \"name\": \"yellow price tag\"}, {\"id\": 6988, \"name\": \"man in a black coat and pants\"}, {\"id\": 6989, \"name\": \"the white puffer coat\"}, {\"id\": 6990, \"name\": \"a predominantly white and black color scheme\"}, {\"id\": 6991, \"name\": \"dark-colored sweater\"}, {\"id\": 6992, \"name\": \"lush grassy area\"}, {\"id\": 6993, \"name\": \"the blue patch\"}, {\"id\": 6994, \"name\": \"the light blue umbrella\"}, {\"id\": 6995, \"name\": \"the wall of hexagonal tile\"}, {\"id\": 6996, \"name\": \"a black and red checkered floor\"}, {\"id\": 6997, \"name\": \"the narrow, residential street\"}, {\"id\": 6998, \"name\": \"restaurant\"}, {\"id\": 6999, \"name\": \"black puffy coat\"}, {\"id\": 7000, \"name\": \"passenger\"}, {\"id\": 7001, \"name\": \"a dark suit jacket\"}, {\"id\": 7002, \"name\": \"a green card\"}, {\"id\": 7003, \"name\": \"a large, intricate design\"}, {\"id\": 7004, \"name\": \"the bill\"}, {\"id\": 7005, \"name\": \"the blue and white paisley pattern\"}, {\"id\": 7006, \"name\": \"the gold bracelet\"}, {\"id\": 7007, \"name\": \"the chocolate bar\"}, {\"id\": 7008, \"name\": \"bottle cap\"}, {\"id\": 7009, \"name\": \"egg\"}, {\"id\": 7010, \"name\": \"the stack of pink circular soap bar\"}, {\"id\": 7011, \"name\": \"a black metal grate\"}, {\"id\": 7012, \"name\": \"a yellow cylindrical object\"}, {\"id\": 7013, \"name\": \"blue-framed window\"}, {\"id\": 7014, \"name\": \"the large black metal gate\"}, {\"id\": 7015, \"name\": \"the red and green decoration\"}, {\"id\": 7016, \"name\": \"a large plastic bag\"}, {\"id\": 7017, \"name\": \"the dark grey trousers\"}, {\"id\": 7018, \"name\": \"the yellow and black striped caution line\"}, {\"id\": 7019, \"name\": \"a right leg\"}, {\"id\": 7020, \"name\": \"long brown hair\"}, {\"id\": 7021, \"name\": \"pants with green and white stripes\"}, {\"id\": 7022, \"name\": \"a single seat\"}, {\"id\": 7023, \"name\": \"dirt floor\"}, {\"id\": 7024, \"name\": \"a rear door\"}, {\"id\": 7025, \"name\": \"the blue glove\"}, {\"id\": 7026, \"name\": \"the displays\"}, {\"id\": 7027, \"name\": \"small, gray tabby kitten\"}, {\"id\": 7028, \"name\": \"brick\"}, {\"id\": 7029, \"name\": \"pile of hay or straw\"}, {\"id\": 7030, \"name\": \"small building\"}, {\"id\": 7031, \"name\": \"a large opening\"}, {\"id\": 7032, \"name\": \"a large tree trunk\"}, {\"id\": 7033, \"name\": \"a jeans\"}, {\"id\": 7034, \"name\": \"a long, thin object\"}, {\"id\": 7035, \"name\": \"the hexagonal pattern of dark gray stones\"}, {\"id\": 7036, \"name\": \"a grassy median\"}, {\"id\": 7037, \"name\": \"the hood\"}, {\"id\": 7038, \"name\": \"the white car\"}, {\"id\": 7039, \"name\": \"blue shorts\"}, {\"id\": 7040, \"name\": \"the blue jersey with red and white trim\"}, {\"id\": 7041, \"name\": \"dumbbell\"}, {\"id\": 7042, \"name\": \"mat\"}, {\"id\": 7043, \"name\": \"red visor\"}, {\"id\": 7044, \"name\": \"the black yoga mat\"}, {\"id\": 7045, \"name\": \"red tie\"}, {\"id\": 7046, \"name\": \"the brick wall\"}, {\"id\": 7047, \"name\": \"the green railing\"}, {\"id\": 7048, \"name\": \"the white and brown horse\"}, {\"id\": 7049, \"name\": \"the gray and white tiled floor\"}, {\"id\": 7050, \"name\": \"the white rectangle\"}, {\"id\": 7051, \"name\": \"dark brown wicker\"}, {\"id\": 7052, \"name\": \"package\"}, {\"id\": 7053, \"name\": \"a crocheted tablecloth\"}, {\"id\": 7054, \"name\": \"a doily\"}, {\"id\": 7055, \"name\": \"the lace\"}, {\"id\": 7056, \"name\": \"the white lace doily\"}, {\"id\": 7057, \"name\": \"toothbrush\"}, {\"id\": 7058, \"name\": \"a light green shirt\"}, {\"id\": 7059, \"name\": \"a vibrant green foliage\"}, {\"id\": 7060, \"name\": \"the teal shirt\"}, {\"id\": 7061, \"name\": \"a back window\"}, {\"id\": 7062, \"name\": \"blue sky\"}, {\"id\": 7063, \"name\": \"number 35 sticker\"}, {\"id\": 7064, \"name\": \"rear of the van\"}, {\"id\": 7065, \"name\": \"the recessed lighting\"}, {\"id\": 7066, \"name\": \"the yellow jacket\"}, {\"id\": 7067, \"name\": \"a white sedan\"}, {\"id\": 7068, \"name\": \"the green roofed building\"}, {\"id\": 7069, \"name\": \"a two-lane street\"}, {\"id\": 7070, \"name\": \"a blue car\"}, {\"id\": 7071, \"name\": \"a tall, multi-story structure\"}, {\"id\": 7072, \"name\": \"the black hijab\"}, {\"id\": 7073, \"name\": \"the metal pole\"}, {\"id\": 7074, \"name\": \"a colorful Christmas light\"}, {\"id\": 7075, \"name\": \"a small patch of white fur\"}, {\"id\": 7076, \"name\": \"dark, murky water surface\"}, {\"id\": 7077, \"name\": \"the pigeon\"}, {\"id\": 7078, \"name\": \"worn, cracked concrete platform\"}, {\"id\": 7079, \"name\": \"a purple elastic band\"}, {\"id\": 7080, \"name\": \"the pink tassel toy\"}, {\"id\": 7081, \"name\": \"the white comforter\"}, {\"id\": 7082, \"name\": \"the white pillowcase\"}, {\"id\": 7083, \"name\": \"white duvet\"}, {\"id\": 7084, \"name\": \"small palm tree\"}, {\"id\": 7085, \"name\": \"large pile of small, red shrimp\"}, {\"id\": 7086, \"name\": \"the red, rectangular platform\"}, {\"id\": 7087, \"name\": \"a colorful, multi-level playground structure\"}, {\"id\": 7088, \"name\": \"a wooden seat\"}, {\"id\": 7089, \"name\": \"the pink curtain\"}, {\"id\": 7090, \"name\": \"the red jacket with a hood\"}, {\"id\": 7091, \"name\": \"blue pant\"}, {\"id\": 7092, \"name\": \"orange padding\"}, {\"id\": 7093, \"name\": \"purple and white sneaker\"}, {\"id\": 7094, \"name\": \"a red taillight\"}, {\"id\": 7095, \"name\": \"a black boot\"}, {\"id\": 7096, \"name\": \"the bun\"}, {\"id\": 7097, \"name\": \"the clear plastic bag\"}, {\"id\": 7098, \"name\": \"green t-shirt\"}, {\"id\": 7099, \"name\": \"the grey wall\"}, {\"id\": 7100, \"name\": \"the blue camouflage shirt\"}, {\"id\": 7101, \"name\": \"wooden bench\"}, {\"id\": 7102, \"name\": \"a creamy white substance\"}, {\"id\": 7103, \"name\": \"a delicate floral design\"}, {\"id\": 7104, \"name\": \"the dark fabric surface\"}, {\"id\": 7105, \"name\": \"the small amount of white yogurt\"}, {\"id\": 7106, \"name\": \"a blue pallet\"}, {\"id\": 7107, \"name\": \"a thick, gray fur-lined hood\"}, {\"id\": 7108, \"name\": \"dark brown frame\"}, {\"id\": 7109, \"name\": \"dark wood doorframe\"}, {\"id\": 7110, \"name\": \"striped green and white pajama set\"}, {\"id\": 7111, \"name\": \"exercise equipment\"}, {\"id\": 7112, \"name\": \"the floral patterned curtain\"}, {\"id\": 7113, \"name\": \"a black metal\"}, {\"id\": 7114, \"name\": \"a curved bar\"}, {\"id\": 7115, \"name\": \"a decorative swirl\"}, {\"id\": 7116, \"name\": \"a metal\"}, {\"id\": 7117, \"name\": \"a blue mat or platform\"}, {\"id\": 7118, \"name\": \"a bushy tail\"}, {\"id\": 7119, \"name\": \"a small bowl\"}, {\"id\": 7120, \"name\": \"a rice-based dish\"}, {\"id\": 7121, \"name\": \"a frame\"}, {\"id\": 7122, \"name\": \"brown boot\"}, {\"id\": 7123, \"name\": \"striped, woven mat\"}, {\"id\": 7124, \"name\": \"a tall screen\"}, {\"id\": 7125, \"name\": \"a woman in the foreground\"}, {\"id\": 7126, \"name\": \"red pant\"}, {\"id\": 7127, \"name\": \"a white charging cord\"}, {\"id\": 7128, \"name\": \"the small rearview mirror\"}, {\"id\": 7129, \"name\": \"a green traffic light\"}, {\"id\": 7130, \"name\": \"the man in a black hat and tan jacket\"}, {\"id\": 7131, \"name\": \"yellow and black scooter\"}, {\"id\": 7132, \"name\": \"a small rock\"}, {\"id\": 7133, \"name\": \"a small black logo\"}, {\"id\": 7134, \"name\": \"forefinger\"}, {\"id\": 7135, \"name\": \"the earpiece\"}, {\"id\": 7136, \"name\": \"the table or countertop\"}, {\"id\": 7137, \"name\": \"prominent window\"}, {\"id\": 7138, \"name\": \"vibrant tapestry\"}, {\"id\": 7139, \"name\": \"yellow curtain\"}, {\"id\": 7140, \"name\": \"a dumpster\"}, {\"id\": 7141, \"name\": \"countertop\"}, {\"id\": 7142, \"name\": \"pool's turquoise water\"}, {\"id\": 7143, \"name\": \"the red, yellow, and white line\"}, {\"id\": 7144, \"name\": \"clear, light blue sky\"}, {\"id\": 7145, \"name\": \"single spotlight\"}, {\"id\": 7146, \"name\": \"the blue water tank\"}, {\"id\": 7147, \"name\": \"black metal chair\"}, {\"id\": 7148, \"name\": \"subtle floral pattern\"}, {\"id\": 7149, \"name\": \"the green and white patterned top\"}, {\"id\": 7150, \"name\": \"a polished floor\"}, {\"id\": 7151, \"name\": \"a short, shorn coat\"}, {\"id\": 7152, \"name\": \"patch of greenery\"}, {\"id\": 7153, \"name\": \"a curved sign\"}, {\"id\": 7154, \"name\": \"a bare tree branch\"}, {\"id\": 7155, \"name\": \"a prominent food stand\"}, {\"id\": 7156, \"name\": \"awning\"}, {\"id\": 7157, \"name\": \"a blue coverall\"}, {\"id\": 7158, \"name\": \"a person in an orange vest\"}, {\"id\": 7159, \"name\": \"vertical striped wallpaper\"}, {\"id\": 7160, \"name\": \"the small poster or picture\"}, {\"id\": 7161, \"name\": \"the young woman\"}, {\"id\": 7162, \"name\": \"a plain white wall\"}, {\"id\": 7163, \"name\": \"black flat\"}, {\"id\": 7164, \"name\": \"the light switch\"}, {\"id\": 7165, \"name\": \"the long, dark gray coat\"}, {\"id\": 7166, \"name\": \"white long-sleeved button-down shirt\"}, {\"id\": 7167, \"name\": \"blurred background\"}, {\"id\": 7168, \"name\": \"the vehicle's windshield\"}, {\"id\": 7169, \"name\": \"the foreign language\"}, {\"id\": 7170, \"name\": \"the large sign in an Asian language\"}, {\"id\": 7171, \"name\": \"a polished concrete or tile\"}, {\"id\": 7172, \"name\": \"a red circle\"}, {\"id\": 7173, \"name\": \"a shiny floor\"}, {\"id\": 7174, \"name\": \"geometric pattern\"}, {\"id\": 7175, \"name\": \"a Middle Eastern or North African city\"}, {\"id\": 7176, \"name\": \"silver stanchion\"}, {\"id\": 7177, \"name\": \"the grey cloud\"}, {\"id\": 7178, \"name\": \"a horizontal vent\"}, {\"id\": 7179, \"name\": \"wall outlet\"}, {\"id\": 7180, \"name\": \"a gray jeans\"}, {\"id\": 7181, \"name\": \"the cluttered workbench\"}, {\"id\": 7182, \"name\": \"a black cross-body bag\"}, {\"id\": 7183, \"name\": \"a black hair\"}, {\"id\": 7184, \"name\": \"the short brick wall\"}, {\"id\": 7185, \"name\": \"the yellow light fixture\"}, {\"id\": 7186, \"name\": \"a photograph\"}, {\"id\": 7187, \"name\": \"the bank of the body of water\"}, {\"id\": 7188, \"name\": \"Western-style signage\"}, {\"id\": 7189, \"name\": \"the clothing store\"}, {\"id\": 7190, \"name\": \"speedometer\"}, {\"id\": 7191, \"name\": \"central dome\"}, {\"id\": 7192, \"name\": \"the smaller dome\"}, {\"id\": 7193, \"name\": \"the blue plastic chair\"}, {\"id\": 7194, \"name\": \"the red writing\"}, {\"id\": 7195, \"name\": \"the purple lamp\"}, {\"id\": 7196, \"name\": \"orange ear\"}, {\"id\": 7197, \"name\": \"a baby's lap\"}, {\"id\": 7198, \"name\": \"a framed artwork\"}, {\"id\": 7199, \"name\": \"a lit candle\"}, {\"id\": 7200, \"name\": \"an older woman\"}, {\"id\": 7201, \"name\": \"the cake\"}, {\"id\": 7202, \"name\": \"a hole in the front\"}, {\"id\": 7203, \"name\": \"a gray and white rug\"}, {\"id\": 7204, \"name\": \"gray and white tent\"}, {\"id\": 7205, \"name\": \"the girl with long hair and purple shirt\"}, {\"id\": 7206, \"name\": \"a red and white object\"}, {\"id\": 7207, \"name\": \"the black cap\"}, {\"id\": 7208, \"name\": \"a gray doormat\"}, {\"id\": 7209, \"name\": \"stainless steel base\"}, {\"id\": 7210, \"name\": \"the mat\"}, {\"id\": 7211, \"name\": \"the red curtain\"}, {\"id\": 7212, \"name\": \"the wooden frame\"}, {\"id\": 7213, \"name\": \"blue and white sign\"}, {\"id\": 7214, \"name\": \"dark blue car\"}, {\"id\": 7215, \"name\": \"the shop or market\"}, {\"id\": 7216, \"name\": \"a large digital screen\"}, {\"id\": 7217, \"name\": \"large shopping bag\"}, {\"id\": 7218, \"name\": \"the yellow auto-rickshaw\"}, {\"id\": 7219, \"name\": \"light-colored wooden table\"}, {\"id\": 7220, \"name\": \"the pale green shirt\"}, {\"id\": 7221, \"name\": \"brightly lit sign\"}, {\"id\": 7222, \"name\": \"single white line\"}, {\"id\": 7223, \"name\": \"the large screen\"}, {\"id\": 7224, \"name\": \"the large, multi-story building\"}, {\"id\": 7225, \"name\": \"the prominent white car\"}, {\"id\": 7226, \"name\": \"the rear window\"}, {\"id\": 7227, \"name\": \"the dark coat\"}, {\"id\": 7228, \"name\": \"the white cabinet\"}, {\"id\": 7229, \"name\": \"white floral-patterned sheet\"}, {\"id\": 7230, \"name\": \"a left paw\"}, {\"id\": 7231, \"name\": \"a rubble\"}, {\"id\": 7232, \"name\": \"construction waste\"}, {\"id\": 7233, \"name\": \"the yellow and brown striped barrier\"}, {\"id\": 7234, \"name\": \"the wooden door\"}, {\"id\": 7235, \"name\": \"the white crosswalk line\"}, {\"id\": 7236, \"name\": \"eye\"}, {\"id\": 7237, \"name\": \"fur\"}, {\"id\": 7238, \"name\": \"rough, textured surface\"}, {\"id\": 7239, \"name\": \"tree's surface\"}, {\"id\": 7240, \"name\": \"small breed\"}, {\"id\": 7241, \"name\": \"the piece of food\"}, {\"id\": 7242, \"name\": \"arched window\"}, {\"id\": 7243, \"name\": \"a white, powdery substance\"}, {\"id\": 7244, \"name\": \"the teal-colored bowl\"}, {\"id\": 7245, \"name\": \"a structure's framework\"}, {\"id\": 7246, \"name\": \"the dynamic rope climbing structure\"}, {\"id\": 7247, \"name\": \"a stacks of boxes\"}, {\"id\": 7248, \"name\": \"pink ear\"}, {\"id\": 7249, \"name\": \"yellow dog toy\"}, {\"id\": 7250, \"name\": \"a small, circular, multi-colored decoration\"}, {\"id\": 7251, \"name\": \"the black power cord\"}, {\"id\": 7252, \"name\": \"a vibrant red and yellow archway\"}, {\"id\": 7253, \"name\": \"the log\"}, {\"id\": 7254, \"name\": \"tube\"}, {\"id\": 7255, \"name\": \"a red plastic chair\"}, {\"id\": 7256, \"name\": \"a tile flooring\"}, {\"id\": 7257, \"name\": \"the blue short-sleeved shirt\"}, {\"id\": 7258, \"name\": \"tablecloth\"}, {\"id\": 7259, \"name\": \"white tiled pattern\"}, {\"id\": 7260, \"name\": \"the darker brown tail\"}, {\"id\": 7261, \"name\": \"the rooster\"}, {\"id\": 7262, \"name\": \"man in a red jacket and blue jeans\"}, {\"id\": 7263, \"name\": \"a camera equipment\"}, {\"id\": 7264, \"name\": \"ornate floral garland\"}, {\"id\": 7265, \"name\": \"the mobile device\"}, {\"id\": 7266, \"name\": \"a blue fabric\"}, {\"id\": 7267, \"name\": \"the brown paper bag\"}, {\"id\": 7268, \"name\": \"blue metal sheet\"}, {\"id\": 7269, \"name\": \"the small bird\"}, {\"id\": 7270, \"name\": \"a commercial building\"}, {\"id\": 7271, \"name\": \"a makeshift platform or stage\"}, {\"id\": 7272, \"name\": \"a tan coat\"}, {\"id\": 7273, \"name\": \"the large, green and white sign\"}, {\"id\": 7274, \"name\": \"the tiled corridor\"}, {\"id\": 7275, \"name\": \"graphic design\"}, {\"id\": 7276, \"name\": \"the black-and-white graffiti design\"}, {\"id\": 7277, \"name\": \"white, circular structure\"}, {\"id\": 7278, \"name\": \"a windshield of a car\"}, {\"id\": 7279, \"name\": \"a yellow license plate\"}, {\"id\": 7280, \"name\": \"the tall skyscraper\"}, {\"id\": 7281, \"name\": \"a green roof\"}, {\"id\": 7282, \"name\": \"corrugated metal structure\"}, {\"id\": 7283, \"name\": \"gray roof\"}, {\"id\": 7284, \"name\": \"small patch of grass\"}, {\"id\": 7285, \"name\": \"a white cover\"}, {\"id\": 7286, \"name\": \"blinds\"}, {\"id\": 7287, \"name\": \"the large, white, circular table\"}, {\"id\": 7288, \"name\": \"a colorful package\"}, {\"id\": 7289, \"name\": \"shopping cart handle\"}, {\"id\": 7290, \"name\": \"the red pendant light\"}, {\"id\": 7291, \"name\": \"a beige fedora\"}, {\"id\": 7292, \"name\": \"the grey shirt\"}, {\"id\": 7293, \"name\": \"the gate\"}, {\"id\": 7294, \"name\": \"a large white sack\"}, {\"id\": 7295, \"name\": \"street vendor\"}, {\"id\": 7296, \"name\": \"a purple bowl\"}, {\"id\": 7297, \"name\": \"paw\"}, {\"id\": 7298, \"name\": \"small, light-blue container\"}, {\"id\": 7299, \"name\": \"the nose\"}, {\"id\": 7300, \"name\": \"a black writing\"}, {\"id\": 7301, \"name\": \"a bowl of soup\"}, {\"id\": 7302, \"name\": \"clear plastic container\"}, {\"id\": 7303, \"name\": \"the non-English language\"}, {\"id\": 7304, \"name\": \"a small black motorcycle\"}, {\"id\": 7305, \"name\": \"a winding road\"}, {\"id\": 7306, \"name\": \"bike's speedometer\"}, {\"id\": 7307, \"name\": \"a high-low hem\"}, {\"id\": 7308, \"name\": \"closed door\"}, {\"id\": 7309, \"name\": \"the forearm\"}, {\"id\": 7310, \"name\": \"the light-colored tile or linoleum\"}, {\"id\": 7311, \"name\": \"the ponytail\"}, {\"id\": 7312, \"name\": \"a silver handle\"}, {\"id\": 7313, \"name\": \"the brown, black, and tan spot\"}, {\"id\": 7314, \"name\": \"a colorful painting\"}, {\"id\": 7315, \"name\": \"artwork\"}, {\"id\": 7316, \"name\": \"high ponytail\"}, {\"id\": 7317, \"name\": \"the bold black Chinese character\"}, {\"id\": 7318, \"name\": \"dense thicket of trees and bushes\"}, {\"id\": 7319, \"name\": \"the clear sky\"}, {\"id\": 7320, \"name\": \"a peeling\"}, {\"id\": 7321, \"name\": \"patch of blue\"}, {\"id\": 7322, \"name\": \"the pile of discarded items\"}, {\"id\": 7323, \"name\": \"the red comb\"}, {\"id\": 7324, \"name\": \"white concrete\"}, {\"id\": 7325, \"name\": \"the white exterior\"}, {\"id\": 7326, \"name\": \"a building's wall\"}, {\"id\": 7327, \"name\": \"the beige car\"}, {\"id\": 7328, \"name\": \"a small, delicate necklace\"}, {\"id\": 7329, \"name\": \"brick and concrete structure\"}, {\"id\": 7330, \"name\": \"man in a red shirt\"}, {\"id\": 7331, \"name\": \"hood\"}, {\"id\": 7332, \"name\": \"plush toy box\"}, {\"id\": 7333, \"name\": \"darker brown ear\"}, {\"id\": 7334, \"name\": \"a pot or pan\"}, {\"id\": 7335, \"name\": \"cream and blue patterned rug\"}, {\"id\": 7336, \"name\": \"molding\"}, {\"id\": 7337, \"name\": \"the blue robe\"}, {\"id\": 7338, \"name\": \"a fluffy white tail\"}, {\"id\": 7339, \"name\": \"small red and white crocheted toy\"}, {\"id\": 7340, \"name\": \"the red and white rope toy\"}, {\"id\": 7341, \"name\": \"a white sheet or blanket\"}, {\"id\": 7342, \"name\": \"the brown wall\"}, {\"id\": 7343, \"name\": \"white sandal\"}, {\"id\": 7344, \"name\": \"white slipper\"}, {\"id\": 7345, \"name\": \"the staircase\"}, {\"id\": 7346, \"name\": \"he\"}, {\"id\": 7347, \"name\": \"a cooking wok\"}, {\"id\": 7348, \"name\": \"cooking station\"}, {\"id\": 7349, \"name\": \"dark suit\"}, {\"id\": 7350, \"name\": \"a light-colored stone floor\"}, {\"id\": 7351, \"name\": \"brand name\"}, {\"id\": 7352, \"name\": \"a yellow blouse\"}, {\"id\": 7353, \"name\": \"large display of bananas\"}, {\"id\": 7354, \"name\": \"pink cage\"}, {\"id\": 7355, \"name\": \"a lime\"}, {\"id\": 7356, \"name\": \"vibrant green tree\"}, {\"id\": 7357, \"name\": \"the Vizzano display\"}, {\"id\": 7358, \"name\": \"the table leg\"}, {\"id\": 7359, \"name\": \"the large fish tank\"}, {\"id\": 7360, \"name\": \"the patterned design\"}, {\"id\": 7361, \"name\": \"a distant mountain\"}, {\"id\": 7362, \"name\": \"the bison\"}, {\"id\": 7363, \"name\": \"the pair of tongs\"}, {\"id\": 7364, \"name\": \"pink fabric\"}, {\"id\": 7365, \"name\": \"a white cap\"}, {\"id\": 7366, \"name\": \"the large white building\"}, {\"id\": 7367, \"name\": \"a textured, shiny leather surface\"}, {\"id\": 7368, \"name\": \"the dog's resting place\"}, {\"id\": 7369, \"name\": \"the tan marking\"}, {\"id\": 7370, \"name\": \"a white arched ceiling\"}, {\"id\": 7371, \"name\": \"a gray jacket\"}, {\"id\": 7372, \"name\": \"a red and white jersey\"}, {\"id\": 7373, \"name\": \"blue awning\"}, {\"id\": 7374, \"name\": \"the green bus\"}, {\"id\": 7375, \"name\": \"the gingerbread man\"}, {\"id\": 7376, \"name\": \"the yellow caution tape\"}, {\"id\": 7377, \"name\": \"red blouse\"}, {\"id\": 7378, \"name\": \"the orange spot\"}, {\"id\": 7379, \"name\": \"a central lane\"}, {\"id\": 7380, \"name\": \"the large, dark shadow of a bicycle wheel\"}, {\"id\": 7381, \"name\": \"parasol\"}, {\"id\": 7382, \"name\": \"a striped tie\"}, {\"id\": 7383, \"name\": \"a traffic sign\"}, {\"id\": 7384, \"name\": \"the dark, wet sidewalk\"}, {\"id\": 7385, \"name\": \"the metal sign\"}, {\"id\": 7386, \"name\": \"gray puffer jacket\"}, {\"id\": 7387, \"name\": \"the black railing\"}, {\"id\": 7388, \"name\": \"a blue and white accent\"}, {\"id\": 7389, \"name\": \"a grey floor\"}, {\"id\": 7390, \"name\": \"the light-colored Labrador retriever\"}, {\"id\": 7391, \"name\": \"short-sleeved, turquoise and white patterned dress\"}, {\"id\": 7392, \"name\": \"the green ornament\"}, {\"id\": 7393, \"name\": \"white and blue swimsuit\"}, {\"id\": 7394, \"name\": \"white sunglasses\"}, {\"id\": 7395, \"name\": \"the blue sandal\"}, {\"id\": 7396, \"name\": \"a black and bronze drill bit\"}, {\"id\": 7397, \"name\": \"a workbench\"}, {\"id\": 7398, \"name\": \"small drill bit\"}, {\"id\": 7399, \"name\": \"the wooden drawer\"}, {\"id\": 7400, \"name\": \"workbench or table\"}, {\"id\": 7401, \"name\": \"a large, green metal shed\"}, {\"id\": 7402, \"name\": \"bottle of mouthwash\"}, {\"id\": 7403, \"name\": \"a silver ring\"}, {\"id\": 7404, \"name\": \"person in a red dress\"}, {\"id\": 7405, \"name\": \"the green hat\"}, {\"id\": 7406, \"name\": \"the dustpan\"}, {\"id\": 7407, \"name\": \"a coca-cola logo\"}, {\"id\": 7408, \"name\": \"a tan boot\"}, {\"id\": 7409, \"name\": \"the trolley\"}, {\"id\": 7410, \"name\": \"the large white garage door\"}, {\"id\": 7411, \"name\": \"a large red bag\"}, {\"id\": 7412, \"name\": \"black canopy\"}, {\"id\": 7413, \"name\": \"the black SUV\"}, {\"id\": 7414, \"name\": \"a yellow door\"}, {\"id\": 7415, \"name\": \"a light gray puffer coat\"}, {\"id\": 7416, \"name\": \"manhole cover\"}, {\"id\": 7417, \"name\": \"the red bag\"}, {\"id\": 7418, \"name\": \"the white tote bag\"}, {\"id\": 7419, \"name\": \"bed frame\"}, {\"id\": 7420, \"name\": \"the brown ear\"}, {\"id\": 7421, \"name\": \"the brown wooden table\"}, {\"id\": 7422, \"name\": \"a patch of grass and dirt\"}, {\"id\": 7423, \"name\": \"the sheet\"}, {\"id\": 7424, \"name\": \"auditorium\"}, {\"id\": 7425, \"name\": \"black rectangle\"}, {\"id\": 7426, \"name\": \"red and gold hairpiece\"}, {\"id\": 7427, \"name\": \"red cushion\"}, {\"id\": 7428, \"name\": \"the red heart\"}, {\"id\": 7429, \"name\": \"the tower\"}, {\"id\": 7430, \"name\": \"the geometric pattern\"}, {\"id\": 7431, \"name\": \"vibrant, red, green, and purple rug\"}, {\"id\": 7432, \"name\": \"a grid pattern of black lines\"}, {\"id\": 7433, \"name\": \"the stack of yellow bags\"}, {\"id\": 7434, \"name\": \"a yellow accent\"}, {\"id\": 7435, \"name\": \"a large, colorful graphic\"}, {\"id\": 7436, \"name\": \"a glass of water\"}, {\"id\": 7437, \"name\": \"a room's high ceiling\"}, {\"id\": 7438, \"name\": \"a metal gate\"}, {\"id\": 7439, \"name\": \"a black shorts with white trim\"}, {\"id\": 7440, \"name\": \"yellow auto rickshaw\"}, {\"id\": 7441, \"name\": \"dark coat\"}, {\"id\": 7442, \"name\": \"a light-colored wood floor\"}, {\"id\": 7443, \"name\": \"small wooden stool\"}, {\"id\": 7444, \"name\": \"glass facade\"}, {\"id\": 7445, \"name\": \"prominent pillar\"}, {\"id\": 7446, \"name\": \"wide brick walkway\"}, {\"id\": 7447, \"name\": \"central escalator\"}, {\"id\": 7448, \"name\": \"cat's fur\"}, {\"id\": 7449, \"name\": \"a black canopy\"}, {\"id\": 7450, \"name\": \"can of soda\"}, {\"id\": 7451, \"name\": \"the brown awning\"}, {\"id\": 7452, \"name\": \"a backrest\"}, {\"id\": 7453, \"name\": \"a rectangular vent\"}, {\"id\": 7454, \"name\": \"a silver ceiling\"}, {\"id\": 7455, \"name\": \"man in a brown jacket\"}, {\"id\": 7456, \"name\": \"the bus's exterior\"}, {\"id\": 7457, \"name\": \"a woman's shadow\"}, {\"id\": 7458, \"name\": \"an artificial grass\"}, {\"id\": 7459, \"name\": \"the blue lighting\"}, {\"id\": 7460, \"name\": \"the large banner\"}, {\"id\": 7461, \"name\": \"a colorful mural\"}, {\"id\": 7462, \"name\": \"a large grey bag\"}, {\"id\": 7463, \"name\": \"the dynamic performance\"}, {\"id\": 7464, \"name\": \"the flower design\"}, {\"id\": 7465, \"name\": \"advertising\"}, {\"id\": 7466, \"name\": \"a recessed lighting\"}, {\"id\": 7467, \"name\": \"white Nike logo\"}, {\"id\": 7468, \"name\": \"a white floral shirt\"}, {\"id\": 7469, \"name\": \"a white shirt with pink design\"}, {\"id\": 7470, \"name\": \"the large, inflatable slide\"}, {\"id\": 7471, \"name\": \"a metal piece\"}, {\"id\": 7472, \"name\": \"brown chicken\"}, {\"id\": 7473, \"name\": \"a carriage\"}, {\"id\": 7474, \"name\": \"the merry-go-round\"}, {\"id\": 7475, \"name\": \"the narrow track\"}, {\"id\": 7476, \"name\": \"the prominent green exit sign\"}, {\"id\": 7477, \"name\": \"bed's mattress\"}, {\"id\": 7478, \"name\": \"the blue carpet\"}, {\"id\": 7479, \"name\": \"the soft, textured fabric\"}, {\"id\": 7480, \"name\": \"a horizon\"}, {\"id\": 7481, \"name\": \"the brown dirt\"}, {\"id\": 7482, \"name\": \"the black and grey sweater\"}, {\"id\": 7483, \"name\": \"the cyclist\"}, {\"id\": 7484, \"name\": \"the yellow tactile paving strip\"}, {\"id\": 7485, \"name\": \"yellow tactile strip\"}, {\"id\": 7486, \"name\": \"commuter\"}, {\"id\": 7487, \"name\": \"white vehicle\"}, {\"id\": 7488, \"name\": \"a pink furry rug\"}, {\"id\": 7489, \"name\": \"brown door\"}, {\"id\": 7490, \"name\": \"the peach-colored blanket\"}, {\"id\": 7491, \"name\": \"the picture\"}, {\"id\": 7492, \"name\": \"a silver metal container\"}, {\"id\": 7493, \"name\": \"surface of the table\"}, {\"id\": 7494, \"name\": \"a signage\"}, {\"id\": 7495, \"name\": \"corridor\"}, {\"id\": 7496, \"name\": \"the shoppers\"}, {\"id\": 7497, \"name\": \"the orange accent wall\"}, {\"id\": 7498, \"name\": \"a light-colored wood paneling\"}, {\"id\": 7499, \"name\": \"the white chest\"}, {\"id\": 7500, \"name\": \"yellow garment\"}, {\"id\": 7501, \"name\": \"kitten's tail\"}, {\"id\": 7502, \"name\": \"the right forearm\"}, {\"id\": 7503, \"name\": \"the fire pit\"}, {\"id\": 7504, \"name\": \"the small, circular area\"}, {\"id\": 7505, \"name\": \"the wrapper\"}, {\"id\": 7506, \"name\": \"the grey long-sleeved shirt\"}, {\"id\": 7507, \"name\": \"a cartoon character\"}, {\"id\": 7508, \"name\": \"the mall's floor\"}, {\"id\": 7509, \"name\": \"blue bird\"}, {\"id\": 7510, \"name\": \"chimney\"}, {\"id\": 7511, \"name\": \"a worn, green metal object\"}, {\"id\": 7512, \"name\": \"white collar\"}, {\"id\": 7513, \"name\": \"a black and white patterned dress\"}, {\"id\": 7514, \"name\": \"a colorful geometric pattern\"}, {\"id\": 7515, \"name\": \"the colorful outfit\"}, {\"id\": 7516, \"name\": \"black-and-white scarf\"}, {\"id\": 7517, \"name\": \"a Christmas tree\"}, {\"id\": 7518, \"name\": \"a large silver thermos\"}, {\"id\": 7519, \"name\": \"a wooden workbench\"}, {\"id\": 7520, \"name\": \"metal cup\"}, {\"id\": 7521, \"name\": \"a wall behind the bed\"}, {\"id\": 7522, \"name\": \"cat's eye\"}, {\"id\": 7523, \"name\": \"fluffy, light orange coat\"}, {\"id\": 7524, \"name\": \"greenery\"}, {\"id\": 7525, \"name\": \"the entrance of the building\"}, {\"id\": 7526, \"name\": \"a pink trash can\"}, {\"id\": 7527, \"name\": \"pink towel\"}, {\"id\": 7528, \"name\": \"the black-and-white striped cap\"}, {\"id\": 7529, \"name\": \"the green vegetable\"}, {\"id\": 7530, \"name\": \"a blue and white directional sign\"}, {\"id\": 7531, \"name\": \"rear of the car\"}, {\"id\": 7532, \"name\": \"a small, gray brick building\"}, {\"id\": 7533, \"name\": \"a small, weathered wooden structure\"}, {\"id\": 7534, \"name\": \"the red bucket\"}, {\"id\": 7535, \"name\": \"a display of various product\"}, {\"id\": 7536, \"name\": \"the exposed ductwork\"}, {\"id\": 7537, \"name\": \"open cardboard box\"}, {\"id\": 7538, \"name\": \"the comb\"}, {\"id\": 7539, \"name\": \"the person in a yellow shirt\"}, {\"id\": 7540, \"name\": \"the baseboard\"}, {\"id\": 7541, \"name\": \"a white and green tiled floor\"}, {\"id\": 7542, \"name\": \"the black carpeted floor\"}, {\"id\": 7543, \"name\": \"the gray carpeting\"}, {\"id\": 7544, \"name\": \"a man on a motorbike\"}, {\"id\": 7545, \"name\": \"a small patch of dirt\"}, {\"id\": 7546, \"name\": \"man in a black jacket\"}, {\"id\": 7547, \"name\": \"a red cup\"}, {\"id\": 7548, \"name\": \"overhang's white wall\"}, {\"id\": 7549, \"name\": \"tree branch\"}, {\"id\": 7550, \"name\": \"white and red building\"}, {\"id\": 7551, \"name\": \"the black and tan bag\"}, {\"id\": 7552, \"name\": \"the large white sign\"}, {\"id\": 7553, \"name\": \"the picnic table\"}, {\"id\": 7554, \"name\": \"a gray metal building\"}, {\"id\": 7555, \"name\": \"a wet concrete area\"}, {\"id\": 7556, \"name\": \"the mountain\"}, {\"id\": 7557, \"name\": \"a SUV\"}, {\"id\": 7558, \"name\": \"wet, reflective surface\"}, {\"id\": 7559, \"name\": \"black chicken\"}, {\"id\": 7560, \"name\": \"the smaller black chicken\"}, {\"id\": 7561, \"name\": \"the red design\"}, {\"id\": 7562, \"name\": \"a round black platform\"}, {\"id\": 7563, \"name\": \"cream-colored cylinder\"}, {\"id\": 7564, \"name\": \"large curved bar\"}, {\"id\": 7565, \"name\": \"the black footrest\"}, {\"id\": 7566, \"name\": \"a silver metal base plate\"}, {\"id\": 7567, \"name\": \"surrounding area\"}, {\"id\": 7568, \"name\": \"a cat's coat\"}, {\"id\": 7569, \"name\": \"a cat's distinctive coat pattern\"}, {\"id\": 7570, \"name\": \"a cat's fur\"}, {\"id\": 7571, \"name\": \"the pink jacket\"}, {\"id\": 7572, \"name\": \"passerby\"}, {\"id\": 7573, \"name\": \"the well-maintained brick walkway\"}, {\"id\": 7574, \"name\": \"a red top\"}, {\"id\": 7575, \"name\": \"a serving of rice\"}, {\"id\": 7576, \"name\": \"the salt shaker\"}, {\"id\": 7577, \"name\": \"green bucket\"}, {\"id\": 7578, \"name\": \"small patch of greenery\"}, {\"id\": 7579, \"name\": \"the duck\"}, {\"id\": 7580, \"name\": \"the lighting\"}, {\"id\": 7581, \"name\": \"a gray sweatpants\"}, {\"id\": 7582, \"name\": \"a red, blue, and black design\"}, {\"id\": 7583, \"name\": \"greyish tint\"}, {\"id\": 7584, \"name\": \"the person's attire\"}, {\"id\": 7585, \"name\": \"a driver\"}, {\"id\": 7586, \"name\": \"the line\"}, {\"id\": 7587, \"name\": \"large metal bucket\"}, {\"id\": 7588, \"name\": \"small black nose\"}, {\"id\": 7589, \"name\": \"the small pink nose\"}, {\"id\": 7590, \"name\": \"the front windshield\"}, {\"id\": 7591, \"name\": \"the white helmet\"}, {\"id\": 7592, \"name\": \"metal grid structure\"}, {\"id\": 7593, \"name\": \"cartoon monkey character\"}, {\"id\": 7594, \"name\": \"white and yellow vehicle\"}, {\"id\": 7595, \"name\": \"a red-speckled granite step\"}, {\"id\": 7596, \"name\": \"red-speckled rug\"}, {\"id\": 7597, \"name\": \"a large red and yellow lantern\"}, {\"id\": 7598, \"name\": \"the large mirror\"}, {\"id\": 7599, \"name\": \"the white drawstring\"}, {\"id\": 7600, \"name\": \"a large white truck\"}, {\"id\": 7601, \"name\": \"a white trailer\"}, {\"id\": 7602, \"name\": \"concrete floor\"}, {\"id\": 7603, \"name\": \"photo\"}, {\"id\": 7604, \"name\": \"the white truck\"}, {\"id\": 7605, \"name\": \"the yellow wall\"}, {\"id\": 7606, \"name\": \"black sweater\"}, {\"id\": 7607, \"name\": \"black wheel\"}, {\"id\": 7608, \"name\": \"the light blue screen\"}, {\"id\": 7609, \"name\": \"a purple garland\"}, {\"id\": 7610, \"name\": \"the tiled walkway\"}, {\"id\": 7611, \"name\": \"battery\"}, {\"id\": 7612, \"name\": \"a jersey\"}, {\"id\": 7613, \"name\": \"a hair in a bun\"}, {\"id\": 7614, \"name\": \"a red and black plaid shirt\"}, {\"id\": 7615, \"name\": \"vent\"}, {\"id\": 7616, \"name\": \"a black traffic light\"}, {\"id\": 7617, \"name\": \"chrome handlebar\"}, {\"id\": 7618, \"name\": \"the yellow sleeping bag\"}, {\"id\": 7619, \"name\": \"aisle\"}, {\"id\": 7620, \"name\": \"the grey trousers\"}, {\"id\": 7621, \"name\": \"large white structure\"}, {\"id\": 7622, \"name\": \"tall white building\"}, {\"id\": 7623, \"name\": \"the yellow bus\"}, {\"id\": 7624, \"name\": \"a deck\"}, {\"id\": 7625, \"name\": \"an illuminated building\"}, {\"id\": 7626, \"name\": \"the distinctive cable-stayed bridge\"}, {\"id\": 7627, \"name\": \"a red accent\"}, {\"id\": 7628, \"name\": \"a white fan\"}, {\"id\": 7629, \"name\": \"large, multi-story building\"}, {\"id\": 7630, \"name\": \"galaxy design\"}, {\"id\": 7631, \"name\": \"the large, open parking\"}, {\"id\": 7632, \"name\": \"the striking ceiling\"}, {\"id\": 7633, \"name\": \"the rear seat\"}, {\"id\": 7634, \"name\": \"the green and white packaging\"}, {\"id\": 7635, \"name\": \"an orange top\"}, {\"id\": 7636, \"name\": \"the light-colored dress\"}, {\"id\": 7637, \"name\": \"the timestamp\"}, {\"id\": 7638, \"name\": \"a dirt area\"}, {\"id\": 7639, \"name\": \"the light-brown marking\"}, {\"id\": 7640, \"name\": \"a row of wooden chair\"}, {\"id\": 7641, \"name\": \"the grey coat\"}, {\"id\": 7642, \"name\": \"a flowing white ribbon\"}, {\"id\": 7643, \"name\": \"long-sleeved white shirt\"}, {\"id\": 7644, \"name\": \"the gray tile table\"}, {\"id\": 7645, \"name\": \"a stylized blue and white flower\"}, {\"id\": 7646, \"name\": \"black stack of chairs\"}, {\"id\": 7647, \"name\": \"circular design\"}, {\"id\": 7648, \"name\": \"large glass storefront\"}, {\"id\": 7649, \"name\": \"a weathered wood\"}, {\"id\": 7650, \"name\": \"garland\"}, {\"id\": 7651, \"name\": \"a fried food\"}, {\"id\": 7652, \"name\": \"a light-brown sauce or batter\"}, {\"id\": 7653, \"name\": \"a stainless steel gas stove\"}, {\"id\": 7654, \"name\": \"a stainless steel stovetop\"}, {\"id\": 7655, \"name\": \"piece of food\"}, {\"id\": 7656, \"name\": \"yellow-brown sauce\"}, {\"id\": 7657, \"name\": \"Pomeranian\"}, {\"id\": 7658, \"name\": \"the white pole\"}, {\"id\": 7659, \"name\": \"a gray stone floor\"}, {\"id\": 7660, \"name\": \"a large yellow poster\"}, {\"id\": 7661, \"name\": \"a large, dirty bucket\"}, {\"id\": 7662, \"name\": \"a red bandana\"}, {\"id\": 7663, \"name\": \"colorful cartoon design\"}, {\"id\": 7664, \"name\": \"the scrunchie\"}, {\"id\": 7665, \"name\": \"the silver exterior\"}, {\"id\": 7666, \"name\": \"right side of the road\"}, {\"id\": 7667, \"name\": \"the compact sedan\"}, {\"id\": 7668, \"name\": \"a boy's shirt\"}, {\"id\": 7669, \"name\": \"the french fry\"}, {\"id\": 7670, \"name\": \"the predominantly white and shiny floor\"}, {\"id\": 7671, \"name\": \"a Burger King bag\"}, {\"id\": 7672, \"name\": \"the red rope\"}, {\"id\": 7673, \"name\": \"fluorescent light\"}, {\"id\": 7674, \"name\": \"a black beanie\"}, {\"id\": 7675, \"name\": \"a dark-colored beanie\"}, {\"id\": 7676, \"name\": \"a small fire\"}, {\"id\": 7677, \"name\": \"wire mesh cover\"}, {\"id\": 7678, \"name\": \"the black shopping bag\"}, {\"id\": 7679, \"name\": \"light-colored sweater\"}, {\"id\": 7680, \"name\": \"the own fur\"}, {\"id\": 7681, \"name\": \"snack bag\"}, {\"id\": 7682, \"name\": \"the black cell phone\"}, {\"id\": 7683, \"name\": \"the ring\"}, {\"id\": 7684, \"name\": \"dark shoe\"}, {\"id\": 7685, \"name\": \"the white sandal\"}, {\"id\": 7686, \"name\": \"green pant\"}, {\"id\": 7687, \"name\": \"the watch\"}, {\"id\": 7688, \"name\": \"gray shorts\"}, {\"id\": 7689, \"name\": \"the colorful umbrella\"}, {\"id\": 7690, \"name\": \"the concrete wall\"}, {\"id\": 7691, \"name\": \"the subtle wood grain pattern\"}, {\"id\": 7692, \"name\": \"a display\"}, {\"id\": 7693, \"name\": \"black storefront\"}, {\"id\": 7694, \"name\": \"the bustling shopping mall corridor\"}, {\"id\": 7695, \"name\": \"a fuzzy cuff\"}, {\"id\": 7696, \"name\": \"a yellow fuzzy sweater\"}, {\"id\": 7697, \"name\": \"pink container\"}, {\"id\": 7698, \"name\": \"the spiral-bound, lined notebook\"}, {\"id\": 7699, \"name\": \"moving vehicle\"}, {\"id\": 7700, \"name\": \"windshield wiper\"}, {\"id\": 7701, \"name\": \"white metal railing\"}, {\"id\": 7702, \"name\": \"concrete barrier\"}, {\"id\": 7703, \"name\": \"metal lamppost\"}, {\"id\": 7704, \"name\": \"the low concrete barrier\"}, {\"id\": 7705, \"name\": \"the woman in a red dress\"}, {\"id\": 7706, \"name\": \"a blue and yellow toy truck\"}, {\"id\": 7707, \"name\": \"a red and white striped pillow\"}, {\"id\": 7708, \"name\": \"an intricate gold pattern\"}, {\"id\": 7709, \"name\": \"coloring book\"}, {\"id\": 7710, \"name\": \"the toy car\"}, {\"id\": 7711, \"name\": \"a passenger door\"}, {\"id\": 7712, \"name\": \"the tan-colored building\"}, {\"id\": 7713, \"name\": \"a black device\"}, {\"id\": 7714, \"name\": \"a white plastic base\"}, {\"id\": 7715, \"name\": \"an orange accent wall\"}, {\"id\": 7716, \"name\": \"the adjacent wall\"}, {\"id\": 7717, \"name\": \"the large stage\"}, {\"id\": 7718, \"name\": \"a gray brick sidewalk\"}, {\"id\": 7719, \"name\": \"a large round sign\"}, {\"id\": 7720, \"name\": \"the traffic sign\"}, {\"id\": 7721, \"name\": \"the yellow and black bollard\"}, {\"id\": 7722, \"name\": \"the black countertop\"}, {\"id\": 7723, \"name\": \"row of smaller windows\"}, {\"id\": 7724, \"name\": \"a black metal railing\"}, {\"id\": 7725, \"name\": \"a drumstick\"}, {\"id\": 7726, \"name\": \"apron\"}, {\"id\": 7727, \"name\": \"the dark gray couch or sofa\"}, {\"id\": 7728, \"name\": \"covered walkway\"}, {\"id\": 7729, \"name\": \"the basketball hoop\"}, {\"id\": 7730, \"name\": \"the person in a blue shirt and black pants\"}, {\"id\": 7731, \"name\": \"black uniform\"}, {\"id\": 7732, \"name\": \"green tarp\"}, {\"id\": 7733, \"name\": \"photographer\"}, {\"id\": 7734, \"name\": \"the green plastic sheeting\"}, {\"id\": 7735, \"name\": \"the large bag\"}, {\"id\": 7736, \"name\": \"black truck\"}, {\"id\": 7737, \"name\": \"the car windshield\"}, {\"id\": 7738, \"name\": \"the wiper blade\"}, {\"id\": 7739, \"name\": \"a Wow China\"}, {\"id\": 7740, \"name\": \"a horizontal top bar\"}, {\"id\": 7741, \"name\": \"a life preserver\"}, {\"id\": 7742, \"name\": \"dark blue\"}, {\"id\": 7743, \"name\": \"a Honda Civic\"}, {\"id\": 7744, \"name\": \"rough texture\"}, {\"id\": 7745, \"name\": \"the dog's fur\"}, {\"id\": 7746, \"name\": \"concert\"}, {\"id\": 7747, \"name\": \"a blue and white striped jacket\"}, {\"id\": 7748, \"name\": \"sofa\"}, {\"id\": 7749, \"name\": \"the gray couch\"}, {\"id\": 7750, \"name\": \"the gray marble\"}, {\"id\": 7751, \"name\": \"a black shopping cart\"}, {\"id\": 7752, \"name\": \"the black handle\"}, {\"id\": 7753, \"name\": \"the large, dark grey hood\"}, {\"id\": 7754, \"name\": \"low concrete barrier\"}, {\"id\": 7755, \"name\": \"motorcyclist\"}, {\"id\": 7756, \"name\": \"the brown sweater\"}, {\"id\": 7757, \"name\": \"the motorcycle's license plate\"}, {\"id\": 7758, \"name\": \"intersection\"}, {\"id\": 7759, \"name\": \"the prominent glass door\"}, {\"id\": 7760, \"name\": \"a young man\"}, {\"id\": 7761, \"name\": \"chest\"}, {\"id\": 7762, \"name\": \"silver chain necklace\"}, {\"id\": 7763, \"name\": \"a red carpet\"}, {\"id\": 7764, \"name\": \"the red table\"}, {\"id\": 7765, \"name\": \"light blue surgical mask\"}, {\"id\": 7766, \"name\": \"the black head covering\"}, {\"id\": 7767, \"name\": \"the small white light\"}, {\"id\": 7768, \"name\": \"a silver shopping cart\"}, {\"id\": 7769, \"name\": \"the sticker\"}, {\"id\": 7770, \"name\": \"a red net\"}, {\"id\": 7771, \"name\": \"row of tall trees\"}, {\"id\": 7772, \"name\": \"a light wood panel\"}, {\"id\": 7773, \"name\": \"the brown, granular substance\"}, {\"id\": 7774, \"name\": \"the light-colored wood\"}, {\"id\": 7775, \"name\": \"the white planter\"}, {\"id\": 7776, \"name\": \"the black backpack\"}, {\"id\": 7777, \"name\": \"a Polo Club store\"}, {\"id\": 7778, \"name\": \"wave\"}, {\"id\": 7779, \"name\": \"yellow panel\"}, {\"id\": 7780, \"name\": \"the shelf of accessory\"}, {\"id\": 7781, \"name\": \"a large painting\"}, {\"id\": 7782, \"name\": \"right arm\"}, {\"id\": 7783, \"name\": \"the black leather jacket\"}, {\"id\": 7784, \"name\": \"a maroon-colored hatchback vehicle\"}, {\"id\": 7785, \"name\": \"damaged car\"}, {\"id\": 7786, \"name\": \"the car's tail light\"}, {\"id\": 7787, \"name\": \"a grey building\"}, {\"id\": 7788, \"name\": \"a IN MIRROR ARE THEY APPEAR\"}, {\"id\": 7789, \"name\": \"the black metal rack\"}, {\"id\": 7790, \"name\": \"mud puddle\"}, {\"id\": 7791, \"name\": \"green tablecloth\"}, {\"id\": 7792, \"name\": \"tall lamp post\"}, {\"id\": 7793, \"name\": \"the cobblestone\"}, {\"id\": 7794, \"name\": \"a bright blue sky\"}, {\"id\": 7795, \"name\": \"the black and teal Adidas tracksuit\"}, {\"id\": 7796, \"name\": \"a blue barrel\"}, {\"id\": 7797, \"name\": \"exposed brick wall\"}, {\"id\": 7798, \"name\": \"a tennis racket\"}, {\"id\": 7799, \"name\": \"a man's head\"}, {\"id\": 7800, \"name\": \"left leg\"}, {\"id\": 7801, \"name\": \"arm muscle\"}, {\"id\": 7802, \"name\": \"clothes\"}, {\"id\": 7803, \"name\": \"the black metal frame\"}, {\"id\": 7804, \"name\": \"white leg\"}, {\"id\": 7805, \"name\": \"seafood meal\"}, {\"id\": 7806, \"name\": \"a blue backpack\"}, {\"id\": 7807, \"name\": \"red wall of the building\"}, {\"id\": 7808, \"name\": \"lush greenery\"}, {\"id\": 7809, \"name\": \"the blue plastic bag\"}, {\"id\": 7810, \"name\": \"round metal object\"}, {\"id\": 7811, \"name\": \"the chicken's foot\"}, {\"id\": 7812, \"name\": \"overcast sky\"}, {\"id\": 7813, \"name\": \"gray headscarf\"}, {\"id\": 7814, \"name\": \"the gray and cloudy sky\"}, {\"id\": 7815, \"name\": \"a green wall\"}, {\"id\": 7816, \"name\": \"a small metal cup\"}, {\"id\": 7817, \"name\": \"the close-up view of a floor\"}, {\"id\": 7818, \"name\": \"the red sign with white lettering\"}, {\"id\": 7819, \"name\": \"a back door\"}, {\"id\": 7820, \"name\": \"the white roof\"}, {\"id\": 7821, \"name\": \"a parked van\"}, {\"id\": 7822, \"name\": \"larger vehicle\"}, {\"id\": 7823, \"name\": \"a pale yellow hue\"}, {\"id\": 7824, \"name\": \"a black street lamp\"}, {\"id\": 7825, \"name\": \"a purple package\"}, {\"id\": 7826, \"name\": \"the black mark\"}, {\"id\": 7827, \"name\": \"the verge\"}, {\"id\": 7828, \"name\": \"the bag of cattle feed\"}, {\"id\": 7829, \"name\": \"a studio's floor\"}, {\"id\": 7830, \"name\": \"wide-legged pant\"}, {\"id\": 7831, \"name\": \"a courtyard\"}, {\"id\": 7832, \"name\": \"a minaret\"}, {\"id\": 7833, \"name\": \"a folding chair\"}, {\"id\": 7834, \"name\": \"a red and black basketball hoop\"}, {\"id\": 7835, \"name\": \"rack\"}, {\"id\": 7836, \"name\": \"the dog's tail\"}, {\"id\": 7837, \"name\": \"a curly fur\"}, {\"id\": 7838, \"name\": \"a yellow toy ring\"}, {\"id\": 7839, \"name\": \"tan tank top\"}, {\"id\": 7840, \"name\": \"the exposed brick\"}, {\"id\": 7841, \"name\": \"the tarp\"}, {\"id\": 7842, \"name\": \"lush green Christmas tree\"}, {\"id\": 7843, \"name\": \"beige tile floor\"}, {\"id\": 7844, \"name\": \"large floral arrangement\"}, {\"id\": 7845, \"name\": \"the red blanket\"}, {\"id\": 7846, \"name\": \"a dark puffer jacket\"}, {\"id\": 7847, \"name\": \"the black and white sneaker\"}, {\"id\": 7848, \"name\": \"the display of food\"}, {\"id\": 7849, \"name\": \"the freezer\"}, {\"id\": 7850, \"name\": \"a name tag\"}, {\"id\": 7851, \"name\": \"a bathroom\"}, {\"id\": 7852, \"name\": \"an ear\"}, {\"id\": 7853, \"name\": \"the strap\"}, {\"id\": 7854, \"name\": \"hotel or office complex\"}, {\"id\": 7855, \"name\": \"a snout\"}, {\"id\": 7856, \"name\": \"the blue shoe\"}, {\"id\": 7857, \"name\": \"the drill\"}, {\"id\": 7858, \"name\": \"white and pink material\"}, {\"id\": 7859, \"name\": \"white tile\"}, {\"id\": 7860, \"name\": \"black and white patterned scarf\"}, {\"id\": 7861, \"name\": \"brown surface\"}, {\"id\": 7862, \"name\": \"shaggy texture\"}, {\"id\": 7863, \"name\": \"a white sheet\"}, {\"id\": 7864, \"name\": \"the gray sweatpants\"}, {\"id\": 7865, \"name\": \"a black and yellow plaid pant\"}, {\"id\": 7866, \"name\": \"patch of green grass\"}, {\"id\": 7867, \"name\": \"strip of grass\"}, {\"id\": 7868, \"name\": \"the short tail\"}, {\"id\": 7869, \"name\": \"a black windshield\"}, {\"id\": 7870, \"name\": \"a dirt road or street\"}, {\"id\": 7871, \"name\": \"power line\"}, {\"id\": 7872, \"name\": \"a bowls of food\"}, {\"id\": 7873, \"name\": \"a juice box\"}, {\"id\": 7874, \"name\": \"a red drink carton\"}, {\"id\": 7875, \"name\": \"the black leopard print design\"}, {\"id\": 7876, \"name\": \"the person's hair\"}, {\"id\": 7877, \"name\": \"the red carton\"}, {\"id\": 7878, \"name\": \"long, dark-colored coat\"}, {\"id\": 7879, \"name\": \"a black patch\"}, {\"id\": 7880, \"name\": \"a large, dark stone bowl\"}, {\"id\": 7881, \"name\": \"a green and black striped surface\"}, {\"id\": 7882, \"name\": \"brown shoe\"}, {\"id\": 7883, \"name\": \"colorful bag\"}, {\"id\": 7884, \"name\": \"floor of the store\"}, {\"id\": 7885, \"name\": \"the blue, white, orange, and black pattern\"}, {\"id\": 7886, \"name\": \"a two-story building\"}, {\"id\": 7887, \"name\": \"black metal rack\"}, {\"id\": 7888, \"name\": \"red Coca-Cola vending machine\"}, {\"id\": 7889, \"name\": \"the side of the road\"}, {\"id\": 7890, \"name\": \"the red and yellow bag\"}, {\"id\": 7891, \"name\": \"two-story house\"}, {\"id\": 7892, \"name\": \"a blue roof\"}, {\"id\": 7893, \"name\": \"the green train\"}, {\"id\": 7894, \"name\": \"the red exit sign\"}, {\"id\": 7895, \"name\": \"the whiteboard\"}, {\"id\": 7896, \"name\": \"a small puddle of water\"}, {\"id\": 7897, \"name\": \"a tall palm tree\"}, {\"id\": 7898, \"name\": \"shed\"}, {\"id\": 7899, \"name\": \"a purple wall\"}, {\"id\": 7900, \"name\": \"gray concrete floor\"}, {\"id\": 7901, \"name\": \"a fuzzy black-and-white pattern\"}, {\"id\": 7902, \"name\": \"large container of margarine or butter\"}, {\"id\": 7903, \"name\": \"the jar\"}, {\"id\": 7904, \"name\": \"the knob\"}, {\"id\": 7905, \"name\": \"step\"}, {\"id\": 7906, \"name\": \"the large beige house\"}, {\"id\": 7907, \"name\": \"the large TV screen\"}, {\"id\": 7908, \"name\": \"the red flag\"}, {\"id\": 7909, \"name\": \"a blue and white sticker\"}, {\"id\": 7910, \"name\": \"light-colored brick\"}, {\"id\": 7911, \"name\": \"the blue lanyard\"}, {\"id\": 7912, \"name\": \"black attire\"}, {\"id\": 7913, \"name\": \"the yellow and white van\"}, {\"id\": 7914, \"name\": \"a child's hand\"}, {\"id\": 7915, \"name\": \"a long, orange rubber bone toy\"}, {\"id\": 7916, \"name\": \"fluffy coat\"}, {\"id\": 7917, \"name\": \"the orange toy\"}, {\"id\": 7918, \"name\": \"the tennis ball\"}, {\"id\": 7919, \"name\": \"building's exterior\"}, {\"id\": 7920, \"name\": \"blue car\"}, {\"id\": 7921, \"name\": \"the large, multi-story structure\"}, {\"id\": 7922, \"name\": \"blue pole\"}, {\"id\": 7923, \"name\": \"wide, grey street\"}, {\"id\": 7924, \"name\": \"a person's blue pants\"}, {\"id\": 7925, \"name\": \"the dirty, gray surface\"}, {\"id\": 7926, \"name\": \"the white sheep\"}, {\"id\": 7927, \"name\": \"a blue fire hydrant\"}, {\"id\": 7928, \"name\": \"the vehicle's tire\"}, {\"id\": 7929, \"name\": \"work boot\"}, {\"id\": 7930, \"name\": \"tall tree\"}, {\"id\": 7931, \"name\": \"the thick trunk\"}, {\"id\": 7932, \"name\": \"the reflective surface\"}, {\"id\": 7933, \"name\": \"a vehicle's headlights\"}, {\"id\": 7934, \"name\": \"a white, blue, and green patterned comforter\"}, {\"id\": 7935, \"name\": \"long black-painted nail\"}, {\"id\": 7936, \"name\": \"the dog's nose\"}, {\"id\": 7937, \"name\": \"the soccer\"}, {\"id\": 7938, \"name\": \"brown counter\"}, {\"id\": 7939, \"name\": \"wall of the store\"}, {\"id\": 7940, \"name\": \"sauce\"}, {\"id\": 7941, \"name\": \"sliced piece of fruit\"}, {\"id\": 7942, \"name\": \"the large metal bowl of food\"}, {\"id\": 7943, \"name\": \"a man in a blue jacket\"}, {\"id\": 7944, \"name\": \"Arabic sign\"}, {\"id\": 7945, \"name\": \"decorative design\"}, {\"id\": 7946, \"name\": \"patterned design\"}, {\"id\": 7947, \"name\": \"black and silver motorbike\"}, {\"id\": 7948, \"name\": \"black and yellow auto rickshaw\"}, {\"id\": 7949, \"name\": \"black and yellow tuk-tuk\"}, {\"id\": 7950, \"name\": \"the backdrop of buildings\"}, {\"id\": 7951, \"name\": \"a salad\"}, {\"id\": 7952, \"name\": \"a yellow brick line\"}, {\"id\": 7953, \"name\": \"the shoulder-length blonde hair\"}, {\"id\": 7954, \"name\": \"a black metal pole\"}, {\"id\": 7955, \"name\": \"a distant tree\"}, {\"id\": 7956, \"name\": \"black awning\"}, {\"id\": 7957, \"name\": \"the rooftop\"}, {\"id\": 7958, \"name\": \"tall, imposing wall\"}, {\"id\": 7959, \"name\": \"line\"}, {\"id\": 7960, \"name\": \"small, round white object\"}, {\"id\": 7961, \"name\": \"the artificial turf\"}, {\"id\": 7962, \"name\": \"the soccer goal\"}, {\"id\": 7963, \"name\": \"grey and white top\"}, {\"id\": 7964, \"name\": \"the white Arabic lettering\"}, {\"id\": 7965, \"name\": \"a strawberry\"}, {\"id\": 7966, \"name\": \"pink and orange pattern\"}, {\"id\": 7967, \"name\": \"the woman in a blue shirt and white pants\"}, {\"id\": 7968, \"name\": \"the curly dark hair\"}, {\"id\": 7969, \"name\": \"red wooden chair\"}, {\"id\": 7970, \"name\": \"all-black attire\"}, {\"id\": 7971, \"name\": \"the glass partition\"}, {\"id\": 7972, \"name\": \"the sneaker\"}, {\"id\": 7973, \"name\": \"black metal bar\"}, {\"id\": 7974, \"name\": \"the floor space\"}, {\"id\": 7975, \"name\": \"the cabbage\"}, {\"id\": 7976, \"name\": \"the colorful mural\"}, {\"id\": 7977, \"name\": \"a blue frame\"}, {\"id\": 7978, \"name\": \"the black slacks\"}, {\"id\": 7979, \"name\": \"a red blanket\"}, {\"id\": 7980, \"name\": \"the pink tongue\"}, {\"id\": 7981, \"name\": \"worn, light-colored wooden floor\"}, {\"id\": 7982, \"name\": \"a multicolored paint\"}, {\"id\": 7983, \"name\": \"hose\"}, {\"id\": 7984, \"name\": \"the painted concrete curb\"}, {\"id\": 7985, \"name\": \"a cartoon dolphin\"}, {\"id\": 7986, \"name\": \"white sheet or ghost costume\"}, {\"id\": 7987, \"name\": \"the red lipstick\"}, {\"id\": 7988, \"name\": \"a clear, curved structure\"}, {\"id\": 7989, \"name\": \"a brown ear\"}, {\"id\": 7990, \"name\": \"a white and tan coat\"}, {\"id\": 7991, \"name\": \"the couch cushion\"}, {\"id\": 7992, \"name\": \"the matching brown pillow\"}, {\"id\": 7993, \"name\": \"drink\"}, {\"id\": 7994, \"name\": \"bottle of drink\"}, {\"id\": 7995, \"name\": \"tan long-sleeved shirt\"}, {\"id\": 7996, \"name\": \"tongs\"}, {\"id\": 7997, \"name\": \"a row of wooden chairs and tables\"}, {\"id\": 7998, \"name\": \"a reflection\"}, {\"id\": 7999, \"name\": \"a scissors\"}, {\"id\": 8000, \"name\": \"table or countertop\"}, {\"id\": 8001, \"name\": \"a shopping center\"}, {\"id\": 8002, \"name\": \"reflective stripe\"}, {\"id\": 8003, \"name\": \"silver shopping cart\"}, {\"id\": 8004, \"name\": \"child's hand\"}, {\"id\": 8005, \"name\": \"red fabric\"}, {\"id\": 8006, \"name\": \"water bottle\"}, {\"id\": 8007, \"name\": \"multi-story building\"}, {\"id\": 8008, \"name\": \"a large, cartoonish ride\"}, {\"id\": 8009, \"name\": \"the Chinese writing\"}, {\"id\": 8010, \"name\": \"a magenta sweater\"}, {\"id\": 8011, \"name\": \"the light brown fur\"}, {\"id\": 8012, \"name\": \"a green label\"}, {\"id\": 8013, \"name\": \"a red-handled knife\"}, {\"id\": 8014, \"name\": \"a silver stove\"}, {\"id\": 8015, \"name\": \"a yellow item\"}, {\"id\": 8016, \"name\": \"the blue package\"}, {\"id\": 8017, \"name\": \"the gas stove\"}, {\"id\": 8018, \"name\": \"the yellow-handled knife\"}, {\"id\": 8019, \"name\": \"a front row\"}, {\"id\": 8020, \"name\": \"corrugated metal roof\"}, {\"id\": 8021, \"name\": \"a sound equipment\"}, {\"id\": 8022, \"name\": \"rink's wall\"}, {\"id\": 8023, \"name\": \"the hooded jacket\"}, {\"id\": 8024, \"name\": \"black puffer coat\"}, {\"id\": 8025, \"name\": \"display case\"}, {\"id\": 8026, \"name\": \"short bob\"}, {\"id\": 8027, \"name\": \"the young child\"}, {\"id\": 8028, \"name\": \"large sign\"}, {\"id\": 8029, \"name\": \"a central median\"}, {\"id\": 8030, \"name\": \"a concrete median\"}, {\"id\": 8031, \"name\": \"a grey SUV\"}, {\"id\": 8032, \"name\": \"right lane\"}, {\"id\": 8033, \"name\": \"a floral face mask\"}, {\"id\": 8034, \"name\": \"a light blue surgical mask\"}, {\"id\": 8035, \"name\": \"the green mat\"}, {\"id\": 8036, \"name\": \"the white patch\"}, {\"id\": 8037, \"name\": \"the car roof\"}, {\"id\": 8038, \"name\": \"the green trash can\"}, {\"id\": 8039, \"name\": \"a yellow and black striped caution tape\"}, {\"id\": 8040, \"name\": \"the yellow and black striped pole\"}, {\"id\": 8041, \"name\": \"the yellow tape\"}, {\"id\": 8042, \"name\": \"the distinctive orange wall\"}, {\"id\": 8043, \"name\": \"the dark grey tile\"}, {\"id\": 8044, \"name\": \"the man in a blue kurta\"}, {\"id\": 8045, \"name\": \"the dark, murky water surface\"}, {\"id\": 8046, \"name\": \"the pink cushion\"}, {\"id\": 8047, \"name\": \"a green sweater\"}, {\"id\": 8048, \"name\": \"the brown and white wall\"}, {\"id\": 8049, \"name\": \"the cuff\"}, {\"id\": 8050, \"name\": \"the left side of the walkway\"}, {\"id\": 8051, \"name\": \"a purple pillow\"}, {\"id\": 8052, \"name\": \"a white baseboard\"}, {\"id\": 8053, \"name\": \"an upper body\"}, {\"id\": 8054, \"name\": \"floral-patterned pant\"}, {\"id\": 8055, \"name\": \"shiny beige tile floor\"}, {\"id\": 8056, \"name\": \"black FINISH logo\"}, {\"id\": 8057, \"name\": \"glass table\"}, {\"id\": 8058, \"name\": \"the vase\"}, {\"id\": 8059, \"name\": \"the red food\"}, {\"id\": 8060, \"name\": \"the whisker\"}, {\"id\": 8061, \"name\": \"pool\"}, {\"id\": 8062, \"name\": \"a large crystal chandelier\"}, {\"id\": 8063, \"name\": \"a Castrol\"}, {\"id\": 8064, \"name\": \"a blue and white body\"}, {\"id\": 8065, \"name\": \"seat\"}, {\"id\": 8066, \"name\": \"the man's face\"}, {\"id\": 8067, \"name\": \"the yellow-green spout\"}, {\"id\": 8068, \"name\": \"gray stone floor\"}, {\"id\": 8069, \"name\": \"boot\"}, {\"id\": 8070, \"name\": \"the vertical wooden slat\"}, {\"id\": 8071, \"name\": \"a circular sign\"}, {\"id\": 8072, \"name\": \"the exposed pipe\"}, {\"id\": 8073, \"name\": \"the gold-colored band\"}, {\"id\": 8074, \"name\": \"the soccer field\"}, {\"id\": 8075, \"name\": \"white and dark-colored uniform\"}, {\"id\": 8076, \"name\": \"a pile of long, thin green branch\"}, {\"id\": 8077, \"name\": \"the pile of sticks\"}, {\"id\": 8078, \"name\": \"support column\"}, {\"id\": 8079, \"name\": \"a large chandelier\"}, {\"id\": 8080, \"name\": \"the light-colored shirt\"}, {\"id\": 8081, \"name\": \"the server\"}, {\"id\": 8082, \"name\": \"black marking\"}, {\"id\": 8083, \"name\": \"the small square opening\"}, {\"id\": 8084, \"name\": \"a gray SUV\"}, {\"id\": 8085, \"name\": \"nearby plant\"}, {\"id\": 8086, \"name\": \"the person's shadow\"}, {\"id\": 8087, \"name\": \"side-view mirror\"}, {\"id\": 8088, \"name\": \"the vehicle's interior\"}, {\"id\": 8089, \"name\": \"a fair-skinned left hand\"}, {\"id\": 8090, \"name\": \"a light-colored wall\"}, {\"id\": 8091, \"name\": \"a mural or painting\"}, {\"id\": 8092, \"name\": \"light brown fur\"}, {\"id\": 8093, \"name\": \"the sandy surface\"}, {\"id\": 8094, \"name\": \"the vast expanse of sand\"}, {\"id\": 8095, \"name\": \"colorful mural or graffiti\"}, {\"id\": 8096, \"name\": \"stone\"}, {\"id\": 8097, \"name\": \"a large advertisement\"}, {\"id\": 8098, \"name\": \"the white cloth\"}, {\"id\": 8099, \"name\": \"the brown woodchip floor\"}, {\"id\": 8100, \"name\": \"a wide, grey concrete road\"}, {\"id\": 8101, \"name\": \"a row of metal security gate\"}, {\"id\": 8102, \"name\": \"commercial building\"}, {\"id\": 8103, \"name\": \"pink hoodie\"}, {\"id\": 8104, \"name\": \"white tiled wall\"}, {\"id\": 8105, \"name\": \"yellow wall\"}, {\"id\": 8106, \"name\": \"a multi-tiered design\"}, {\"id\": 8107, \"name\": \"the fountain\"}, {\"id\": 8108, \"name\": \"tiered stone step\"}, {\"id\": 8109, \"name\": \"a dark-colored floor\"}, {\"id\": 8110, \"name\": \"the large, white wall\"}, {\"id\": 8111, \"name\": \"large, multi-colored neon light display\"}, {\"id\": 8112, \"name\": \"car's side mirror\"}, {\"id\": 8113, \"name\": \"man's figure\"}, {\"id\": 8114, \"name\": \"a drum set\"}, {\"id\": 8115, \"name\": \"a pyramid-shaped structure\"}, {\"id\": 8116, \"name\": \"the light blue plastic bin\"}, {\"id\": 8117, \"name\": \"metal frame\"}, {\"id\": 8118, \"name\": \"rural street\"}, {\"id\": 8119, \"name\": \"a palm frond\"}, {\"id\": 8120, \"name\": \"small structure\"}, {\"id\": 8121, \"name\": \"the ski\"}, {\"id\": 8122, \"name\": \"the snow-capped mountain\"}, {\"id\": 8123, \"name\": \"a volleyball net\"}, {\"id\": 8124, \"name\": \"a black sedan\"}, {\"id\": 8125, \"name\": \"a white and grey marble countertop\"}, {\"id\": 8126, \"name\": \"rugged scene\"}, {\"id\": 8127, \"name\": \"a game machine\"}, {\"id\": 8128, \"name\": \"the white panel\"}, {\"id\": 8129, \"name\": \"a woman in a black dress\"}, {\"id\": 8130, \"name\": \"the large brown pillar\"}, {\"id\": 8131, \"name\": \"the railing of the balcony or porch\"}, {\"id\": 8132, \"name\": \"a river or stream\"}, {\"id\": 8133, \"name\": \"a yellow pole\"}, {\"id\": 8134, \"name\": \"rugged mountain range\"}, {\"id\": 8135, \"name\": \"a row of windows\"}, {\"id\": 8136, \"name\": \"the denim skirt\"}, {\"id\": 8137, \"name\": \"the narrow, dirt road\"}, {\"id\": 8138, \"name\": \"the round, bright light fixture\"}, {\"id\": 8139, \"name\": \"the blue and white shopping bag\"}, {\"id\": 8140, \"name\": \"the glass-enclosed display case\"}, {\"id\": 8141, \"name\": \"the pink wall\"}, {\"id\": 8142, \"name\": \"a black skirt\"}, {\"id\": 8143, \"name\": \"a worker\"}, {\"id\": 8144, \"name\": \"a kitchen floor\"}, {\"id\": 8145, \"name\": \"a large, red, cartoon bear costume\"}, {\"id\": 8146, \"name\": \"the costume\"}, {\"id\": 8147, \"name\": \"the sweater\"}, {\"id\": 8148, \"name\": \"white ball\"}, {\"id\": 8149, \"name\": \"a yellow and green jacket\"}, {\"id\": 8150, \"name\": \"a stylized logo\"}, {\"id\": 8151, \"name\": \"blue menu bar\"}, {\"id\": 8152, \"name\": \"the clear plastic cover\"}, {\"id\": 8153, \"name\": \"the door handle\"}, {\"id\": 8154, \"name\": \"the hinge\"}, {\"id\": 8155, \"name\": \"white numeric pad\"}, {\"id\": 8156, \"name\": \"newspaper clipping\"}, {\"id\": 8157, \"name\": \"the small TV screen\"}, {\"id\": 8158, \"name\": \"a small silver machine\"}, {\"id\": 8159, \"name\": \"a gray circle\"}, {\"id\": 8160, \"name\": \"inner circle\"}, {\"id\": 8161, \"name\": \"a vibrant stall\"}, {\"id\": 8162, \"name\": \"a black backpack\"}, {\"id\": 8163, \"name\": \"spoiler\"}, {\"id\": 8164, \"name\": \"rusty metal\"}, {\"id\": 8165, \"name\": \"the single piece of sheet metal\"}, {\"id\": 8166, \"name\": \"a long dark hair\"}, {\"id\": 8167, \"name\": \"a stack of cardboard boxes\"}, {\"id\": 8168, \"name\": \"the sink\"}, {\"id\": 8169, \"name\": \"the blue backpack\"}, {\"id\": 8170, \"name\": \"the yellow and blue bag\"}, {\"id\": 8171, \"name\": \"decorative pattern\"}, {\"id\": 8172, \"name\": \"red sauce\"}, {\"id\": 8173, \"name\": \"the emblem\"}, {\"id\": 8174, \"name\": \"a rough and uneven surface\"}, {\"id\": 8175, \"name\": \"the tan wall\"}, {\"id\": 8176, \"name\": \"artificial tree\"}, {\"id\": 8177, \"name\": \"the pink and black patterned dress\"}, {\"id\": 8178, \"name\": \"tile or stone\"}, {\"id\": 8179, \"name\": \"a black vehicle\"}, {\"id\": 8180, \"name\": \"a brick sidewalk\"}, {\"id\": 8181, \"name\": \"a signboard\"}, {\"id\": 8182, \"name\": \"the vibrant atmosphere\"}, {\"id\": 8183, \"name\": \"wooden board\"}, {\"id\": 8184, \"name\": \"small, round, pink basket\"}, {\"id\": 8185, \"name\": \"the carpet\"}, {\"id\": 8186, \"name\": \"sign board\"}, {\"id\": 8187, \"name\": \"a footpath\"}, {\"id\": 8188, \"name\": \"a blue leggings\"}, {\"id\": 8189, \"name\": \"a red plate\"}, {\"id\": 8190, \"name\": \"crate\"}, {\"id\": 8191, \"name\": \"the man's head\"}, {\"id\": 8192, \"name\": \"the mic\"}, {\"id\": 8193, \"name\": \"foil-covered container\"}, {\"id\": 8194, \"name\": \"knob\"}, {\"id\": 8195, \"name\": \"marble countertop\"}, {\"id\": 8196, \"name\": \"plastic water bottle\"}, {\"id\": 8197, \"name\": \"the left side of his chest\"}, {\"id\": 8198, \"name\": \"an energetic atmosphere\"}, {\"id\": 8199, \"name\": \"stage\"}, {\"id\": 8200, \"name\": \"the yellow attire\"}, {\"id\": 8201, \"name\": \"a blue-and-white tiled front\"}, {\"id\": 8202, \"name\": \"black keyboard\"}, {\"id\": 8203, \"name\": \"a foothold\"}, {\"id\": 8204, \"name\": \"a green section\"}, {\"id\": 8205, \"name\": \"the concrete pillar\"}, {\"id\": 8206, \"name\": \"the bags\"}, {\"id\": 8207, \"name\": \"the microwave\"}, {\"id\": 8208, \"name\": \"a dance outfit\"}, {\"id\": 8209, \"name\": \"a large gray wall\"}, {\"id\": 8210, \"name\": \"the ceiling decoration\"}, {\"id\": 8211, \"name\": \"the ceiling detail\"}, {\"id\": 8212, \"name\": \"the dancer\"}, {\"id\": 8213, \"name\": \"a corn husk\"}, {\"id\": 8214, \"name\": \"type of squash\"}, {\"id\": 8215, \"name\": \"the small, makeshift sign\"}, {\"id\": 8216, \"name\": \"small, dark-colored car\"}, {\"id\": 8217, \"name\": \"a gas cylinder\"}, {\"id\": 8218, \"name\": \"road divider\"}, {\"id\": 8219, \"name\": \"the awning\"}, {\"id\": 8220, \"name\": \"the street\"}, {\"id\": 8221, \"name\": \"tree trunk\"}, {\"id\": 8222, \"name\": \"atmosphere\"}, {\"id\": 8223, \"name\": \"hanging item\"}, {\"id\": 8224, \"name\": \"the large plastic bag\"}, {\"id\": 8225, \"name\": \"the red floral pattern\"}, {\"id\": 8226, \"name\": \"the taxi\"}, {\"id\": 8227, \"name\": \"a linen\"}, {\"id\": 8228, \"name\": \"the light source\"}, {\"id\": 8229, \"name\": \"the microphone\"}, {\"id\": 8230, \"name\": \"the purple object\"}, {\"id\": 8231, \"name\": \"a pond's surface\"}, {\"id\": 8232, \"name\": \"a blue storefront\"}, {\"id\": 8233, \"name\": \"blue banner\"}, {\"id\": 8234, \"name\": \"woman in a red dress\"}, {\"id\": 8235, \"name\": \"a black metal fence\"}, {\"id\": 8236, \"name\": \"brightly colored clothing\"}, {\"id\": 8237, \"name\": \"the woman in a yellow coat\"}, {\"id\": 8238, \"name\": \"a light orange cat\"}, {\"id\": 8239, \"name\": \"a purple door\"}, {\"id\": 8240, \"name\": \"brown floor\"}, {\"id\": 8241, \"name\": \"orange object\"}, {\"id\": 8242, \"name\": \"a white paw\"}, {\"id\": 8243, \"name\": \"the person's leg\"}, {\"id\": 8244, \"name\": \"textured surface\"}, {\"id\": 8245, \"name\": \"white baseball cap\"}, {\"id\": 8246, \"name\": \"black sandal\"}, {\"id\": 8247, \"name\": \"a cupboard\"}, {\"id\": 8248, \"name\": \"diligent student\"}, {\"id\": 8249, \"name\": \"a circular stain\"}, {\"id\": 8250, \"name\": \"a TikTok Lite logo\"}, {\"id\": 8251, \"name\": \"a TikTok logo\"}, {\"id\": 8252, \"name\": \"a light switch\"}, {\"id\": 8253, \"name\": \"black and white floral accent\"}, {\"id\": 8254, \"name\": \"the pattern\"}, {\"id\": 8255, \"name\": \"the stylish look\"}, {\"id\": 8256, \"name\": \"rider\"}, {\"id\": 8257, \"name\": \"the water\"}, {\"id\": 8258, \"name\": \"Marvel character\"}, {\"id\": 8259, \"name\": \"a glass of red liquid\"}, {\"id\": 8260, \"name\": \"a remote\"}, {\"id\": 8261, \"name\": \"pearl earring\"}, {\"id\": 8262, \"name\": \"the wooden accent\"}, {\"id\": 8263, \"name\": \"a clothes\"}, {\"id\": 8264, \"name\": \"a colorful fabric\"}, {\"id\": 8265, \"name\": \"the bottom floor\"}, {\"id\": 8266, \"name\": \"the red taillight\"}, {\"id\": 8267, \"name\": \"a mayonnaise or another sauce\"}, {\"id\": 8268, \"name\": \"bread\"}, {\"id\": 8269, \"name\": \"lettuce\"}, {\"id\": 8270, \"name\": \"metal bowl\"}, {\"id\": 8271, \"name\": \"shredded cheese or chicken\"}, {\"id\": 8272, \"name\": \"the countertop\"}, {\"id\": 8273, \"name\": \"the slice of bread\"}, {\"id\": 8274, \"name\": \"a brown, stone-like surface\"}, {\"id\": 8275, \"name\": \"a long, rustic picnic table\"}, {\"id\": 8276, \"name\": \"large, concrete table\"}, {\"id\": 8277, \"name\": \"matching yellow and black tracksuit\"}, {\"id\": 8278, \"name\": \"busy road\"}, {\"id\": 8279, \"name\": \"silo\"}, {\"id\": 8280, \"name\": \"the yellow taxi cab\"}, {\"id\": 8281, \"name\": \"a fuel tank cap relay\"}, {\"id\": 8282, \"name\": \"a fuel tank cap switch\"}, {\"id\": 8283, \"name\": \"a wiper\"}, {\"id\": 8284, \"name\": \"the fuel tank cap\"}, {\"id\": 8285, \"name\": \"the asphalt\"}, {\"id\": 8286, \"name\": \"the dark and moody atmosphere\"}, {\"id\": 8287, \"name\": \"the small, dark-brown metal plate\"}, {\"id\": 8288, \"name\": \"the white and brown patterned rug\"}, {\"id\": 8289, \"name\": \"a rectangular metal container\"}, {\"id\": 8290, \"name\": \"a white long-sleeve shirt\"}, {\"id\": 8291, \"name\": \"edge of a pool\"}, {\"id\": 8292, \"name\": \"the firework\"}, {\"id\": 8293, \"name\": \"the hot spring\"}, {\"id\": 8294, \"name\": \"the slippers\"}, {\"id\": 8295, \"name\": \"the crane\"}, {\"id\": 8296, \"name\": \"the crane's metal frame\"}, {\"id\": 8297, \"name\": \"the cloudy, gray sky\"}, {\"id\": 8298, \"name\": \"wires\"}, {\"id\": 8299, \"name\": \"black robe\"}, {\"id\": 8300, \"name\": \"a black tuk-tuk\"}, {\"id\": 8301, \"name\": \"yellow front panel\"}, {\"id\": 8302, \"name\": \"a walkway\"}, {\"id\": 8303, \"name\": \"a white sock\"}, {\"id\": 8304, \"name\": \"stack of white spools of wire\"}, {\"id\": 8305, \"name\": \"a large blue truck\"}, {\"id\": 8306, \"name\": \"a row of trees\"}, {\"id\": 8307, \"name\": \"matching shirt\"}, {\"id\": 8308, \"name\": \"serene body of water\"}, {\"id\": 8309, \"name\": \"the subtle reflection of the sky\"}, {\"id\": 8310, \"name\": \"a gray sky\"}, {\"id\": 8311, \"name\": \"arm\"}, {\"id\": 8312, \"name\": \"a subtle floral pattern\"}, {\"id\": 8313, \"name\": \"grey floor\"}, {\"id\": 8314, \"name\": \"lighter gray color\"}, {\"id\": 8315, \"name\": \"a scattered debris\"}, {\"id\": 8316, \"name\": \"slippers\"}, {\"id\": 8317, \"name\": \"the large white trash can\"}, {\"id\": 8318, \"name\": \"a blue planter\"}, {\"id\": 8319, \"name\": \"the website address\"}, {\"id\": 8320, \"name\": \"a child's head\"}, {\"id\": 8321, \"name\": \"the monkey\"}, {\"id\": 8322, \"name\": \"the flat surface\"}, {\"id\": 8323, \"name\": \"right foot\"}, {\"id\": 8324, \"name\": \"a beige pant\"}, {\"id\": 8325, \"name\": \"the long, patterned dress\"}, {\"id\": 8326, \"name\": \"the black and red clothing\"}, {\"id\": 8327, \"name\": \"a large green trailer\"}, {\"id\": 8328, \"name\": \"wrist\"}, {\"id\": 8329, \"name\": \"a dirt courtyard\"}, {\"id\": 8330, \"name\": \"smaller planter\"}, {\"id\": 8331, \"name\": \"the parking meter\"}, {\"id\": 8332, \"name\": \"a gray sweatshirt\"}, {\"id\": 8333, \"name\": \"a white and blue tile pattern\"}, {\"id\": 8334, \"name\": \"the close-up photograph\"}, {\"id\": 8335, \"name\": \"the dark blue T-shirt\"}, {\"id\": 8336, \"name\": \"the doorframe\"}, {\"id\": 8337, \"name\": \"the pile of rocks\"}, {\"id\": 8338, \"name\": \"a black marble surface\"}, {\"id\": 8339, \"name\": \"gas stove\"}, {\"id\": 8340, \"name\": \"person's right hand\"}, {\"id\": 8341, \"name\": \"the gas cylinder\"}, {\"id\": 8342, \"name\": \"the red garment\"}, {\"id\": 8343, \"name\": \"a distinctive orange collar\"}, {\"id\": 8344, \"name\": \"maroon car\"}, {\"id\": 8345, \"name\": \"white bucket hat\"}, {\"id\": 8346, \"name\": \"a gray upper surface\"}, {\"id\": 8347, \"name\": \"the smaller white building\"}, {\"id\": 8348, \"name\": \"a low stone wall\"}, {\"id\": 8349, \"name\": \"roof of one of the structures\"}, {\"id\": 8350, \"name\": \"a road surface\"}, {\"id\": 8351, \"name\": \"the cap\"}, {\"id\": 8352, \"name\": \"the signboard\"}, {\"id\": 8353, \"name\": \"traffic signal\"}, {\"id\": 8354, \"name\": \"yellow pole\"}, {\"id\": 8355, \"name\": \"a black railing\"}, {\"id\": 8356, \"name\": \"cage\"}, {\"id\": 8357, \"name\": \"the vibrant orange fur\"}, {\"id\": 8358, \"name\": \"a small red logo\"}, {\"id\": 8359, \"name\": \"the open window\"}, {\"id\": 8360, \"name\": \"the practicality\"}, {\"id\": 8361, \"name\": \"a gray floor\"}, {\"id\": 8362, \"name\": \"a shoes\"}, {\"id\": 8363, \"name\": \"the arrow\"}, {\"id\": 8364, \"name\": \"red glow\"}, {\"id\": 8365, \"name\": \"a black and white shirt\"}, {\"id\": 8366, \"name\": \"the Lexus\"}, {\"id\": 8367, \"name\": \"light\"}, {\"id\": 8368, \"name\": \"a photo frame\"}, {\"id\": 8369, \"name\": \"a tan sling\"}, {\"id\": 8370, \"name\": \"gray beanie\"}, {\"id\": 8371, \"name\": \"the gray sling\"}, {\"id\": 8372, \"name\": \"a shiny and beige\"}, {\"id\": 8373, \"name\": \"store\"}, {\"id\": 8374, \"name\": \"a brown roof\"}, {\"id\": 8375, \"name\": \"the train track\"}, {\"id\": 8376, \"name\": \"store window\"}, {\"id\": 8377, \"name\": \"store's sign\"}, {\"id\": 8378, \"name\": \"a decorative trim\"}, {\"id\": 8379, \"name\": \"stack of two light blue plastic storage bins\"}, {\"id\": 8380, \"name\": \"a nighttime scene\"}, {\"id\": 8381, \"name\": \"the auto rickshaw\"}, {\"id\": 8382, \"name\": \"the building light\"}, {\"id\": 8383, \"name\": \"red-and-white triangular sign\"}, {\"id\": 8384, \"name\": \"the road marking\"}, {\"id\": 8385, \"name\": \"the stack of green bags\"}, {\"id\": 8386, \"name\": \"the QR code\"}, {\"id\": 8387, \"name\": \"a sidewalk's edge\"}, {\"id\": 8388, \"name\": \"lane of traffic\"}, {\"id\": 8389, \"name\": \"the tan-colored bus\"}, {\"id\": 8390, \"name\": \"a mountainous landscape\"}, {\"id\": 8391, \"name\": \"the lights\"}, {\"id\": 8392, \"name\": \"the sign board\"}, {\"id\": 8393, \"name\": \"a brown design\"}, {\"id\": 8394, \"name\": \"a yellow sponge cake\"}, {\"id\": 8395, \"name\": \"the ball\"}, {\"id\": 8396, \"name\": \"vibrant attire\"}, {\"id\": 8397, \"name\": \"red and blue stripe\"}, {\"id\": 8398, \"name\": \"the white plastic container\"}, {\"id\": 8399, \"name\": \"a TV\"}, {\"id\": 8400, \"name\": \"large purple and gold object\"}, {\"id\": 8401, \"name\": \"walkway's metal structure\"}, {\"id\": 8402, \"name\": \"the rough terrain\"}, {\"id\": 8403, \"name\": \"hair salon style\"}, {\"id\": 8404, \"name\": \"the large, square tile\"}, {\"id\": 8405, \"name\": \"a red motorbike\"}, {\"id\": 8406, \"name\": \"a road side footpath drainage\"}, {\"id\": 8407, \"name\": \"road side footpath drainage cover\"}, {\"id\": 8408, \"name\": \"signboard\"}, {\"id\": 8409, \"name\": \"a matching head covering\"}, {\"id\": 8410, \"name\": \"boutonniere\"}, {\"id\": 8411, \"name\": \"pink flower\"}, {\"id\": 8412, \"name\": \"the hair accessory\"}, {\"id\": 8413, \"name\": \"a brick pattern\"}, {\"id\": 8414, \"name\": \"a hallway\"}, {\"id\": 8415, \"name\": \"an object\"}, {\"id\": 8416, \"name\": \"the feet\"}, {\"id\": 8417, \"name\": \"blue container\"}, {\"id\": 8418, \"name\": \"a stoop\"}, {\"id\": 8419, \"name\": \"sky\"}, {\"id\": 8420, \"name\": \"the overcast sky\"}, {\"id\": 8421, \"name\": \"the bed\"}, {\"id\": 8422, \"name\": \"the hazard symbol\"}, {\"id\": 8423, \"name\": \"a poignant moment\"}, {\"id\": 8424, \"name\": \"the black sedan\"}, {\"id\": 8425, \"name\": \"fabric's surface\"}, {\"id\": 8426, \"name\": \"the folded blanket\"}, {\"id\": 8427, \"name\": \"the rod\"}, {\"id\": 8428, \"name\": \"dark area\"}, {\"id\": 8429, \"name\": \"tan spot\"}, {\"id\": 8430, \"name\": \"the bone\"}, {\"id\": 8431, \"name\": \"the yellow and white blanket\"}, {\"id\": 8432, \"name\": \"truck's tailgate\"}, {\"id\": 8433, \"name\": \"the pallet\"}, {\"id\": 8434, \"name\": \"the large floral arrangement\"}, {\"id\": 8435, \"name\": \"well-lit\"}, {\"id\": 8436, \"name\": \"a green trailer\"}, {\"id\": 8437, \"name\": \"gray hexagonal stone road\"}, {\"id\": 8438, \"name\": \"the green frame\"}, {\"id\": 8439, \"name\": \"the trailer\"}, {\"id\": 8440, \"name\": \"a black wire rack\"}, {\"id\": 8441, \"name\": \"the conditioner\"}, {\"id\": 8442, \"name\": \"woman with long hair\"}, {\"id\": 8443, \"name\": \"black curtain rod\"}, {\"id\": 8444, \"name\": \"small wooden table\"}, {\"id\": 8445, \"name\": \"a bag of dog food\"}, {\"id\": 8446, \"name\": \"a brown countertop\"}, {\"id\": 8447, \"name\": \"a decor\"}, {\"id\": 8448, \"name\": \"bags\"}, {\"id\": 8449, \"name\": \"a brown stone exterior\"}, {\"id\": 8450, \"name\": \"the THE LORD'S DESPOT\"}, {\"id\": 8451, \"name\": \"three-story white building\"}, {\"id\": 8452, \"name\": \"a yellow t-shirt\"}, {\"id\": 8453, \"name\": \"blue cloth\"}, {\"id\": 8454, \"name\": \"surrounding landscape\"}, {\"id\": 8455, \"name\": \"the electric poles\"}, {\"id\": 8456, \"name\": \"the path\"}, {\"id\": 8457, \"name\": \"the shed\"}, {\"id\": 8458, \"name\": \"a weathered brick wall\"}, {\"id\": 8459, \"name\": \"the row of storefronts\"}, {\"id\": 8460, \"name\": \"the woman in a white jacket\"}, {\"id\": 8461, \"name\": \"the red and white shirt\"}, {\"id\": 8462, \"name\": \"green shelf\"}, {\"id\": 8463, \"name\": \"a small, dark-colored bag\"}, {\"id\": 8464, \"name\": \"large brown building\"}, {\"id\": 8465, \"name\": \"silver SUV\"}, {\"id\": 8466, \"name\": \"a bag of flour or sugar\"}, {\"id\": 8467, \"name\": \"the sugar\"}, {\"id\": 8468, \"name\": \"white blender\"}, {\"id\": 8469, \"name\": \"a dancer\"}, {\"id\": 8470, \"name\": \"early morning\"}, {\"id\": 8471, \"name\": \"hazy, pastel-colored sky\"}, {\"id\": 8472, \"name\": \"lone child\"}, {\"id\": 8473, \"name\": \"dark red backdrop\"}, {\"id\": 8474, \"name\": \"a tall, thin pole\"}, {\"id\": 8475, \"name\": \"car back\"}, {\"id\": 8476, \"name\": \"car side\"}, {\"id\": 8477, \"name\": \"the black interior\"}, {\"id\": 8478, \"name\": \"the car front\"}, {\"id\": 8479, \"name\": \"the white crosswalk marking\"}, {\"id\": 8480, \"name\": \"a small, white, furry creature\"}, {\"id\": 8481, \"name\": \"the animal's fur\"}, {\"id\": 8482, \"name\": \"the white animal\"}, {\"id\": 8483, \"name\": \"the zoo\"}, {\"id\": 8484, \"name\": \"a tall, brown building\"}, {\"id\": 8485, \"name\": \"beige facade\"}, {\"id\": 8486, \"name\": \"blue sock\"}, {\"id\": 8487, \"name\": \"the brown couch\"}, {\"id\": 8488, \"name\": \"younger child\"}, {\"id\": 8489, \"name\": \"the gold chain strap\"}, {\"id\": 8490, \"name\": \"the number 1\"}, {\"id\": 8491, \"name\": \"a yellow container\"}, {\"id\": 8492, \"name\": \"scissors\"}, {\"id\": 8493, \"name\": \"the ceiling light\"}, {\"id\": 8494, \"name\": \"a red tablecloth\"}, {\"id\": 8495, \"name\": \"room's high ceiling\"}, {\"id\": 8496, \"name\": \"white chair cover\"}, {\"id\": 8497, \"name\": \"the parked vehicle\"}, {\"id\": 8498, \"name\": \"a noodle\"}, {\"id\": 8499, \"name\": \"gas cylinder\"}, {\"id\": 8500, \"name\": \"plastic crate\"}, {\"id\": 8501, \"name\": \"stove\"}, {\"id\": 8502, \"name\": \"the young man\"}, {\"id\": 8503, \"name\": \"circular object\"}, {\"id\": 8504, \"name\": \"the bike path\"}, {\"id\": 8505, \"name\": \"a low wall\"}, {\"id\": 8506, \"name\": \"a yellow and green car\"}, {\"id\": 8507, \"name\": \"sign reading \\\"vivo\\\"\"}, {\"id\": 8508, \"name\": \"the dashboard\"}, {\"id\": 8509, \"name\": \"a clear and bright sky\"}, {\"id\": 8510, \"name\": \"a driver's-side rearview mirror\"}, {\"id\": 8511, \"name\": \"the side-view mirror\"}, {\"id\": 8512, \"name\": \"a barefoot\"}, {\"id\": 8513, \"name\": \"purple arch\"}, {\"id\": 8514, \"name\": \"the butterfly\"}, {\"id\": 8515, \"name\": \"the purple and yellow paper cutout\"}, {\"id\": 8516, \"name\": \"blue-handled dolly\"}, {\"id\": 8517, \"name\": \"plank of wood\"}, {\"id\": 8518, \"name\": \"single-story house\"}, {\"id\": 8519, \"name\": \"the sleek black marble counter\"}, {\"id\": 8520, \"name\": \"the colorful pattern\"}, {\"id\": 8521, \"name\": \"the pitcher\"}, {\"id\": 8522, \"name\": \"the light-colored fur\"}, {\"id\": 8523, \"name\": \"the white circular object\"}, {\"id\": 8524, \"name\": \"a wooden chair\"}, {\"id\": 8525, \"name\": \"golden fur\"}, {\"id\": 8526, \"name\": \"the middle shelf\"}, {\"id\": 8527, \"name\": \"chicken's feet\"}, {\"id\": 8528, \"name\": \"the concrete block border\"}, {\"id\": 8529, \"name\": \"a small plastic toy\"}, {\"id\": 8530, \"name\": \"the brown and white patterned blanket\"}, {\"id\": 8531, \"name\": \"a concrete walkway\"}, {\"id\": 8532, \"name\": \"a red lettering\"}, {\"id\": 8533, \"name\": \"an array of colorful fruits and vegetables\"}, {\"id\": 8534, \"name\": \"the leaf\"}, {\"id\": 8535, \"name\": \"the red grape\"}, {\"id\": 8536, \"name\": \"the wood paneling\"}, {\"id\": 8537, \"name\": \"a long, empty road\"}, {\"id\": 8538, \"name\": \"central white line\"}, {\"id\": 8539, \"name\": \"a gray tiled floor\"}, {\"id\": 8540, \"name\": \"a large bag\"}, {\"id\": 8541, \"name\": \"red handle\"}, {\"id\": 8542, \"name\": \"layer of overgrown grass and weeds\"}, {\"id\": 8543, \"name\": \"a bamboo pole\"}, {\"id\": 8544, \"name\": \"the gathering\"}, {\"id\": 8545, \"name\": \"the sandy area\"}, {\"id\": 8546, \"name\": \"the tall bamboo pole\"}, {\"id\": 8547, \"name\": \"purple rectangle\"}, {\"id\": 8548, \"name\": \"a black and white striped concrete barrier\"}, {\"id\": 8549, \"name\": \"a vibrant scene\"}, {\"id\": 8550, \"name\": \"the hard hat\"}, {\"id\": 8551, \"name\": \"the urban area\"}, {\"id\": 8552, \"name\": \"the man with the gold crown\"}, {\"id\": 8553, \"name\": \"a clear plastic bag\"}, {\"id\": 8554, \"name\": \"dog toy\"}, {\"id\": 8555, \"name\": \"a top of their head\"}, {\"id\": 8556, \"name\": \"the high neckline\"}, {\"id\": 8557, \"name\": \"the large, white Mickey Mouse head logo\"}, {\"id\": 8558, \"name\": \"a large pile of potatoes\"}, {\"id\": 8559, \"name\": \"a water\"}, {\"id\": 8560, \"name\": \"black bucket\"}, {\"id\": 8561, \"name\": \"the large stone well\"}, {\"id\": 8562, \"name\": \"the barrier\"}, {\"id\": 8563, \"name\": \"the black trampoline mat\"}, {\"id\": 8564, \"name\": \"the cracked concrete\"}, {\"id\": 8565, \"name\": \"a yellow and green patterned stripe\"}, {\"id\": 8566, \"name\": \"the low-quality photograph\"}, {\"id\": 8567, \"name\": \"the stuffed toy\"}, {\"id\": 8568, \"name\": \"red motorcycle\"}, {\"id\": 8569, \"name\": \"white headlight\"}, {\"id\": 8570, \"name\": \"a white plastic chair\"}, {\"id\": 8571, \"name\": \"large archway or gate\"}, {\"id\": 8572, \"name\": \"sail\"}, {\"id\": 8573, \"name\": \"a food delivery person\"}, {\"id\": 8574, \"name\": \"a road surface stain\"}, {\"id\": 8575, \"name\": \"a white tiled table\"}, {\"id\": 8576, \"name\": \"dark brown square mat\"}, {\"id\": 8577, \"name\": \"the pink heart-shaped decoration\"}, {\"id\": 8578, \"name\": \"the white icing\"}, {\"id\": 8579, \"name\": \"the green and yellow bus\"}, {\"id\": 8580, \"name\": \"a man on a motorcycle\"}, {\"id\": 8581, \"name\": \"a rear license plate\"}, {\"id\": 8582, \"name\": \"rear passenger door\"}, {\"id\": 8583, \"name\": \"the silver SUV\"}, {\"id\": 8584, \"name\": \"the pillar\"}, {\"id\": 8585, \"name\": \"gray tracksuit\"}, {\"id\": 8586, \"name\": \"the concrete curb\"}, {\"id\": 8587, \"name\": \"the large blue truck\"}, {\"id\": 8588, \"name\": \"dark gray plastic\"}, {\"id\": 8589, \"name\": \"a vibrant yellow sign\"}, {\"id\": 8590, \"name\": \"the light grey floor\"}, {\"id\": 8591, \"name\": \"peach sari\"}, {\"id\": 8592, \"name\": \"red and pink garland\"}, {\"id\": 8593, \"name\": \"the stunning gold necklace\"}, {\"id\": 8594, \"name\": \"a hook\"}, {\"id\": 8595, \"name\": \"the gift\"}, {\"id\": 8596, \"name\": \"the sofa\"}, {\"id\": 8597, \"name\": \"gas station sign\"}, {\"id\": 8598, \"name\": \"the road barrier\"}, {\"id\": 8599, \"name\": \"blue and red advertisement\"}, {\"id\": 8600, \"name\": \"the large tan building\"}, {\"id\": 8601, \"name\": \"a light-colored wooden cabinet\"}, {\"id\": 8602, \"name\": \"black granite countertop\"}, {\"id\": 8603, \"name\": \"washer door\"}, {\"id\": 8604, \"name\": \"a large silver column\"}, {\"id\": 8605, \"name\": \"the light-colored t-shirt\"}, {\"id\": 8606, \"name\": \"the photo frame\"}, {\"id\": 8607, \"name\": \"white marble flooring\"}, {\"id\": 8608, \"name\": \"a Jotun storefront\"}, {\"id\": 8609, \"name\": \"the blue and yellow awning\"}, {\"id\": 8610, \"name\": \"the small gift\"}, {\"id\": 8611, \"name\": \"a busy street scene\"}, {\"id\": 8612, \"name\": \"white tracksuit\"}, {\"id\": 8613, \"name\": \"an action-packed game\"}, {\"id\": 8614, \"name\": \"colorful control panel\"}, {\"id\": 8615, \"name\": \"a row of red seat\"}, {\"id\": 8616, \"name\": \"black suit\"}, {\"id\": 8617, \"name\": \"the circular, black platform\"}, {\"id\": 8618, \"name\": \"the small concrete curb\"}, {\"id\": 8619, \"name\": \"a denim shorts\"}, {\"id\": 8620, \"name\": \"the yellow writing\"}, {\"id\": 8621, \"name\": \"a green jacket\"}, {\"id\": 8622, \"name\": \"the white tablecloth\"}, {\"id\": 8623, \"name\": \"the red pole\"}, {\"id\": 8624, \"name\": \"thick plastic material\"}, {\"id\": 8625, \"name\": \"a litter mat\"}, {\"id\": 8626, \"name\": \"pile of dirt\"}, {\"id\": 8627, \"name\": \"a dark window\"}, {\"id\": 8628, \"name\": \"smaller vent\"}, {\"id\": 8629, \"name\": \"white curtain\"}, {\"id\": 8630, \"name\": \"large quantity of yellow produce\"}, {\"id\": 8631, \"name\": \"small body of water\"}, {\"id\": 8632, \"name\": \"the yellow corn\"}, {\"id\": 8633, \"name\": \"a large bundle\"}, {\"id\": 8634, \"name\": \"the large, red, fabric bag\"}, {\"id\": 8635, \"name\": \"the long black and white skirt\"}, {\"id\": 8636, \"name\": \"blue chest section\"}, {\"id\": 8637, \"name\": \"a light pole\"}, {\"id\": 8638, \"name\": \"a Indian city\"}, {\"id\": 8639, \"name\": \"a white tank\"}, {\"id\": 8640, \"name\": \"lush grass\"}, {\"id\": 8641, \"name\": \"tanker truck\"}, {\"id\": 8642, \"name\": \"steps\"}, {\"id\": 8643, \"name\": \"the light-colored jacket\"}, {\"id\": 8644, \"name\": \"a rectangle\"}, {\"id\": 8645, \"name\": \"large black bag\"}, {\"id\": 8646, \"name\": \"green and yellow pole\"}, {\"id\": 8647, \"name\": \"a peaked roof\"}, {\"id\": 8648, \"name\": \"a red clothes\"}, {\"id\": 8649, \"name\": \"tan roof\"}, {\"id\": 8650, \"name\": \"a stack of plastic crates\"}, {\"id\": 8651, \"name\": \"the clothesline\"}, {\"id\": 8652, \"name\": \"the dark gray dashboard\"}, {\"id\": 8653, \"name\": \"the long robe\"}, {\"id\": 8654, \"name\": \"a brown fence\"}, {\"id\": 8655, \"name\": \"black item\"}, {\"id\": 8656, \"name\": \"grey cabinet\"}, {\"id\": 8657, \"name\": \"the neon-lit sign\"}, {\"id\": 8658, \"name\": \"the pencil\"}, {\"id\": 8659, \"name\": \"the woman's dance move\"}, {\"id\": 8660, \"name\": \"the black sign\"}, {\"id\": 8661, \"name\": \"the pathway\"}, {\"id\": 8662, \"name\": \"a brown shirt\"}, {\"id\": 8663, \"name\": \"footpath\"}, {\"id\": 8664, \"name\": \"the stone curb\"}, {\"id\": 8665, \"name\": \"a paved sidewalk\"}, {\"id\": 8666, \"name\": \"concrete path\"}, {\"id\": 8667, \"name\": \"the white line\"}, {\"id\": 8668, \"name\": \"back of their head\"}, {\"id\": 8669, \"name\": \"a makeshift building or shelter\"}, {\"id\": 8670, \"name\": \"the chicken display\"}, {\"id\": 8671, \"name\": \"a dish\"}, {\"id\": 8672, \"name\": \"the oven\"}, {\"id\": 8673, \"name\": \"a beige pillar\"}, {\"id\": 8674, \"name\": \"a brown cardboard box\"}, {\"id\": 8675, \"name\": \"a lip\"}, {\"id\": 8676, \"name\": \"dog's white coat\"}, {\"id\": 8677, \"name\": \"a dark interior of the car\"}, {\"id\": 8678, \"name\": \"black vehicle\"}, {\"id\": 8679, \"name\": \"dark fur\"}, {\"id\": 8680, \"name\": \"floral-patterned rug\"}, {\"id\": 8681, \"name\": \"the view\"}, {\"id\": 8682, \"name\": \"the partially drawn blind\"}, {\"id\": 8683, \"name\": \"the small orange tabby cat\"}, {\"id\": 8684, \"name\": \"the worn brick floor\"}, {\"id\": 8685, \"name\": \"bottle of yellow liquid\"}, {\"id\": 8686, \"name\": \"pink brush\"}, {\"id\": 8687, \"name\": \"small pink container\"}, {\"id\": 8688, \"name\": \"spray bottle\"}, {\"id\": 8689, \"name\": \"a woman in the black coat\"}, {\"id\": 8690, \"name\": \"small, clear plastic container\"}, {\"id\": 8691, \"name\": \"the woman in the chair\"}, {\"id\": 8692, \"name\": \"guitarist\"}, {\"id\": 8693, \"name\": \"a large ceramic pot\"}, {\"id\": 8694, \"name\": \"a low concrete barrier\"}, {\"id\": 8695, \"name\": \"the tree line\"}, {\"id\": 8696, \"name\": \"a wooden roof\"}, {\"id\": 8697, \"name\": \"grassy square\"}, {\"id\": 8698, \"name\": \"lush\"}, {\"id\": 8699, \"name\": \"the wooden pillar\"}, {\"id\": 8700, \"name\": \"a deep blue hue\"}, {\"id\": 8701, \"name\": \"a school or government building\"}, {\"id\": 8702, \"name\": \"dimly lit, outdoor setting\"}, {\"id\": 8703, \"name\": \"the tall vegetation\"}, {\"id\": 8704, \"name\": \"suit\"}, {\"id\": 8705, \"name\": \"the long black beard\"}, {\"id\": 8706, \"name\": \"dark-colored car\"}, {\"id\": 8707, \"name\": \"the crate\"}, {\"id\": 8708, \"name\": \"the cream-colored puffer coat\"}, {\"id\": 8709, \"name\": \"dirt\"}, {\"id\": 8710, \"name\": \"park area\"}, {\"id\": 8711, \"name\": \"a dark wooden beam\"}, {\"id\": 8712, \"name\": \"the large display of shopping carts\"}, {\"id\": 8713, \"name\": \"the large, black digital screen\"}, {\"id\": 8714, \"name\": \"wood shaving\"}, {\"id\": 8715, \"name\": \"gravel pile\"}, {\"id\": 8716, \"name\": \"red short\"}, {\"id\": 8717, \"name\": \"the brick building\"}, {\"id\": 8718, \"name\": \"a large red building\"}, {\"id\": 8719, \"name\": \"a light-brown concrete\"}, {\"id\": 8720, \"name\": \"a white fence\"}, {\"id\": 8721, \"name\": \"the black pants\"}, {\"id\": 8722, \"name\": \"the blue water barrel\"}, {\"id\": 8723, \"name\": \"the small yellow leaf\"}, {\"id\": 8724, \"name\": \"a man in a plaid shirt\"}, {\"id\": 8725, \"name\": \"the beanie\"}, {\"id\": 8726, \"name\": \"the blue and green patterned shirt\"}, {\"id\": 8727, \"name\": \"brown blanket or comforter\"}, {\"id\": 8728, \"name\": \"tan rug\"}, {\"id\": 8729, \"name\": \"white, long-sleeved shirt\"}, {\"id\": 8730, \"name\": \"the metal step\"}, {\"id\": 8731, \"name\": \"a decorative border\"}, {\"id\": 8732, \"name\": \"a glass entrance\"}, {\"id\": 8733, \"name\": \"an overcast and grey sky\"}, {\"id\": 8734, \"name\": \"the commercial building\"}, {\"id\": 8735, \"name\": \"the mannequin\"}, {\"id\": 8736, \"name\": \"white design\"}, {\"id\": 8737, \"name\": \"a distinctive grey tail\"}, {\"id\": 8738, \"name\": \"black frame\"}, {\"id\": 8739, \"name\": \"dustbin\"}, {\"id\": 8740, \"name\": \"black long-sleeved shirt with white polka dots\"}, {\"id\": 8741, \"name\": \"green garment\"}, {\"id\": 8742, \"name\": \"photo frame\"}, {\"id\": 8743, \"name\": \"beige seat\"}, {\"id\": 8744, \"name\": \"car's gearshift\"}, {\"id\": 8745, \"name\": \"a white and fluffy dog\"}, {\"id\": 8746, \"name\": \"a bar counter\"}, {\"id\": 8747, \"name\": \"a moment of urban congestion\"}, {\"id\": 8748, \"name\": \"industrial or commercial structure\"}, {\"id\": 8749, \"name\": \"a woman in a white dress\"}, {\"id\": 8750, \"name\": \"black abaya\"}, {\"id\": 8751, \"name\": \"the neon blue light\"}, {\"id\": 8752, \"name\": \"toy truck\"}, {\"id\": 8753, \"name\": \"a flower pattern\"}, {\"id\": 8754, \"name\": \"an embroidered flower\"}, {\"id\": 8755, \"name\": \"left wrist\"}, {\"id\": 8756, \"name\": \"the newspaper\"}, {\"id\": 8757, \"name\": \"a fallen leaf\"}, {\"id\": 8758, \"name\": \"the large pothole\"}, {\"id\": 8759, \"name\": \"architectural style\"}, {\"id\": 8760, \"name\": \"playground structure\"}, {\"id\": 8761, \"name\": \"a gray shoe\"}, {\"id\": 8762, \"name\": \"a red floor\"}, {\"id\": 8763, \"name\": \"blue bag\"}, {\"id\": 8764, \"name\": \"blue toy\"}, {\"id\": 8765, \"name\": \"the indoor setting\"}, {\"id\": 8766, \"name\": \"the paper\"}, {\"id\": 8767, \"name\": \"lively atmosphere\"}, {\"id\": 8768, \"name\": \"red and white bag\"}, {\"id\": 8769, \"name\": \"the rural street scene\"}, {\"id\": 8770, \"name\": \"the single light fixture\"}, {\"id\": 8771, \"name\": \"the toy's neck\"}, {\"id\": 8772, \"name\": \"the wall mirror\"}, {\"id\": 8773, \"name\": \"the flag\"}, {\"id\": 8774, \"name\": \"the car door\"}, {\"id\": 8775, \"name\": \"the digital display\"}, {\"id\": 8776, \"name\": \"store's window display\"}, {\"id\": 8777, \"name\": \"the white label\"}, {\"id\": 8778, \"name\": \"a black plastic bowl\"}, {\"id\": 8779, \"name\": \"the dirt yard\"}, {\"id\": 8780, \"name\": \"a rainy day scene\"}, {\"id\": 8781, \"name\": \"black sedan\"}, {\"id\": 8782, \"name\": \"the brown animal\"}, {\"id\": 8783, \"name\": \"stack of cardboard box\"}, {\"id\": 8784, \"name\": \"long, fluffy white fur\"}, {\"id\": 8785, \"name\": \"the fluffy white fur\"}, {\"id\": 8786, \"name\": \"the long, fluffy fur\"}, {\"id\": 8787, \"name\": \"the pair of eyes\"}, {\"id\": 8788, \"name\": \"the wall or other object\"}, {\"id\": 8789, \"name\": \"a maroon pencil\"}, {\"id\": 8790, \"name\": \"the brush\"}, {\"id\": 8791, \"name\": \"the green tag\"}, {\"id\": 8792, \"name\": \"the long white string\"}, {\"id\": 8793, \"name\": \"the recessed light\"}, {\"id\": 8794, \"name\": \"a lights\"}, {\"id\": 8795, \"name\": \"the long, dark hair\"}, {\"id\": 8796, \"name\": \"the pair of silver shoes\"}, {\"id\": 8797, \"name\": \"man in a white jacket and black pants\"}, {\"id\": 8798, \"name\": \"the gray dress\"}, {\"id\": 8799, \"name\": \"a room's floor\"}, {\"id\": 8800, \"name\": \"casual clothing\"}, {\"id\": 8801, \"name\": \"trash bag\"}, {\"id\": 8802, \"name\": \"the mall's entrance\"}, {\"id\": 8803, \"name\": \"a black baseball cap\"}, {\"id\": 8804, \"name\": \"a white soccer net\"}, {\"id\": 8805, \"name\": \"black short\"}, {\"id\": 8806, \"name\": \"a busy and potentially chaotic environment\"}, {\"id\": 8807, \"name\": \"ambulance\"}, {\"id\": 8808, \"name\": \"metal container\"}, {\"id\": 8809, \"name\": \"red plastic bag\"}, {\"id\": 8810, \"name\": \"white rim\"}, {\"id\": 8811, \"name\": \"a light-colored wooden chair\"}, {\"id\": 8812, \"name\": \"the gray stone curb\"}, {\"id\": 8813, \"name\": \"the gold-colored storefront\"}, {\"id\": 8814, \"name\": \"the SUV\"}, {\"id\": 8815, \"name\": \"pink package\"}, {\"id\": 8816, \"name\": \"the blue cart\"}, {\"id\": 8817, \"name\": \"the blue sticker\"}, {\"id\": 8818, \"name\": \"white package\"}, {\"id\": 8819, \"name\": \"a green grassy top\"}, {\"id\": 8820, \"name\": \"a surrounding table\"}, {\"id\": 8821, \"name\": \"the prominent sign\"}, {\"id\": 8822, \"name\": \"a marble-like finish\"}, {\"id\": 8823, \"name\": \"green soccer field\"}, {\"id\": 8824, \"name\": \"large poster of a goalkeeper\"}, {\"id\": 8825, \"name\": \"a green garland\"}, {\"id\": 8826, \"name\": \"a white and black dog\"}, {\"id\": 8827, \"name\": \"lush green grass\"}, {\"id\": 8828, \"name\": \"small white dog with black spots\"}, {\"id\": 8829, \"name\": \"brown couch cushion\"}, {\"id\": 8830, \"name\": \"the red and black wireless mouse\"}, {\"id\": 8831, \"name\": \"a colorful attire\"}, {\"id\": 8832, \"name\": \"a chopsticks\"}, {\"id\": 8833, \"name\": \"a left wrist\"}, {\"id\": 8834, \"name\": \"a small blue bowl\"}, {\"id\": 8835, \"name\": \"a spoons\"}, {\"id\": 8836, \"name\": \"a wooden drawer\"}, {\"id\": 8837, \"name\": \"bamboo steamer\"}, {\"id\": 8838, \"name\": \"black table\"}, {\"id\": 8839, \"name\": \"teapot\"}, {\"id\": 8840, \"name\": \"vibrant and inviting scene\"}, {\"id\": 8841, \"name\": \"large speaker\"}, {\"id\": 8842, \"name\": \"the handbag\"}, {\"id\": 8843, \"name\": \"the purple hijab\"}, {\"id\": 8844, \"name\": \"tunic\"}, {\"id\": 8845, \"name\": \"a distinctive outfit\"}, {\"id\": 8846, \"name\": \"a tan rectangle\"}, {\"id\": 8847, \"name\": \"large, wrapped object\"}, {\"id\": 8848, \"name\": \"a stove's burners\"}, {\"id\": 8849, \"name\": \"green plastic spoon\"}, {\"id\": 8850, \"name\": \"spatula\"}, {\"id\": 8851, \"name\": \"the gas burner\"}, {\"id\": 8852, \"name\": \"white sauce\"}, {\"id\": 8853, \"name\": \"light-colored wood grain finish\"}, {\"id\": 8854, \"name\": \"the yellow shirt\"}, {\"id\": 8855, \"name\": \"the yellow substance\"}, {\"id\": 8856, \"name\": \"a circular patch of greenery\"}, {\"id\": 8857, \"name\": \"a darker gray border\"}, {\"id\": 8858, \"name\": \"a feathery texture\"}, {\"id\": 8859, \"name\": \"the photograph\"}, {\"id\": 8860, \"name\": \"a white and brown striped curb\"}, {\"id\": 8861, \"name\": \"a pink sweater\"}, {\"id\": 8862, \"name\": \"a black flip-flop\"}, {\"id\": 8863, \"name\": \"a light-colored blanket or sheet\"}, {\"id\": 8864, \"name\": \"brown patch\"}, {\"id\": 8865, \"name\": \"the small cat\"}, {\"id\": 8866, \"name\": \"a stack of red books\"}, {\"id\": 8867, \"name\": \"a black logo\"}, {\"id\": 8868, \"name\": \"the round light\"}, {\"id\": 8869, \"name\": \"cooking equipment\"}, {\"id\": 8870, \"name\": \"the man in a red plaid shirt\"}, {\"id\": 8871, \"name\": \"the purple pants\"}, {\"id\": 8872, \"name\": \"person's hair\"}, {\"id\": 8873, \"name\": \"the woman in a multicolored top\"}, {\"id\": 8874, \"name\": \"small white dog\"}, {\"id\": 8875, \"name\": \"the floor of the cage\"}, {\"id\": 8876, \"name\": \"a road pavement\"}, {\"id\": 8877, \"name\": \"an overcast and cloudy sky\"}, {\"id\": 8878, \"name\": \"broken white line\"}, {\"id\": 8879, \"name\": \"the road divider\"}, {\"id\": 8880, \"name\": \"the road sidewalk\"}, {\"id\": 8881, \"name\": \"a vegetation\"}, {\"id\": 8882, \"name\": \"a clear plastic container\"}, {\"id\": 8883, \"name\": \"the Christmas-themed balloon bouquet\"}, {\"id\": 8884, \"name\": \"vibrant plaid tablecloth\"}, {\"id\": 8885, \"name\": \"the small rock\"}, {\"id\": 8886, \"name\": \"a moving truck\"}, {\"id\": 8887, \"name\": \"designated bike lane\"}, {\"id\": 8888, \"name\": \"a yellow sash\"}, {\"id\": 8889, \"name\": \"the air conditioner\"}, {\"id\": 8890, \"name\": \"white dish\"}, {\"id\": 8891, \"name\": \"yellow ribbon\"}, {\"id\": 8892, \"name\": \"a bicycle basket\"}, {\"id\": 8893, \"name\": \"a black basket\"}, {\"id\": 8894, \"name\": \"a souvenir shop\"}, {\"id\": 8895, \"name\": \"an overcast sky\"}, {\"id\": 8896, \"name\": \"the electric pole\"}, {\"id\": 8897, \"name\": \"the white kitten\"}, {\"id\": 8898, \"name\": \"a pointed ear\"}, {\"id\": 8899, \"name\": \"short, pointed ear\"}, {\"id\": 8900, \"name\": \"the light brown color\"}, {\"id\": 8901, \"name\": \"black puffer jacket\"}, {\"id\": 8902, \"name\": \"a person in a gray outfit\"}, {\"id\": 8903, \"name\": \"the green and black patterned outfit\"}, {\"id\": 8904, \"name\": \"traffic\"}, {\"id\": 8905, \"name\": \"a black pan\"}, {\"id\": 8906, \"name\": \"blurry nighttime scene\"}, {\"id\": 8907, \"name\": \"adult's hand\"}, {\"id\": 8908, \"name\": \"a dark blue shirt\"}, {\"id\": 8909, \"name\": \"a cloth\"}, {\"id\": 8910, \"name\": \"a light blue tarp\"}, {\"id\": 8911, \"name\": \"driver's side\"}, {\"id\": 8912, \"name\": \"headlight\"}, {\"id\": 8913, \"name\": \"small orange light\"}, {\"id\": 8914, \"name\": \"blue and black motorcycle\"}, {\"id\": 8915, \"name\": \"blue lettering\"}, {\"id\": 8916, \"name\": \"the brick-paved sidewalk\"}, {\"id\": 8917, \"name\": \"a patch of dirt\"}, {\"id\": 8918, \"name\": \"a stone pathway\"}, {\"id\": 8919, \"name\": \"light brown wall\"}, {\"id\": 8920, \"name\": \"the hillside\"}, {\"id\": 8921, \"name\": \"the road sign\"}, {\"id\": 8922, \"name\": \"the warmth and vibrancy\"}, {\"id\": 8923, \"name\": \"a rectangular, dark brown table\"}, {\"id\": 8924, \"name\": \"cashier\"}, {\"id\": 8925, \"name\": \"a parked bench\"}, {\"id\": 8926, \"name\": \"the two-story structure\"}, {\"id\": 8927, \"name\": \"gutter\"}, {\"id\": 8928, \"name\": \"the brown leather suitcase\"}, {\"id\": 8929, \"name\": \"the curved roofline\"}, {\"id\": 8930, \"name\": \"the pointed arch of the window\"}, {\"id\": 8931, \"name\": \"black and white tile strip\"}, {\"id\": 8932, \"name\": \"the shoe rack\"}, {\"id\": 8933, \"name\": \"red skirt\"}, {\"id\": 8934, \"name\": \"the store's name\"}, {\"id\": 8935, \"name\": \"black and white hoodie\"}, {\"id\": 8936, \"name\": \"the yellow and black jacket\"}, {\"id\": 8937, \"name\": \"hairnet\"}, {\"id\": 8938, \"name\": \"a coffee machine\"}, {\"id\": 8939, \"name\": \"a phone number\"}, {\"id\": 8940, \"name\": \"green and black bus\"}, {\"id\": 8941, \"name\": \"the warm winter attire\"}, {\"id\": 8942, \"name\": \"notebook\"}, {\"id\": 8943, \"name\": \"a circular sticker\"}, {\"id\": 8944, \"name\": \"area\"}, {\"id\": 8945, \"name\": \"a cluttered and makeshift outdoor clothing market\"}, {\"id\": 8946, \"name\": \"a hillside\"}, {\"id\": 8947, \"name\": \"the black and white design\"}, {\"id\": 8948, \"name\": \"the skeleton design\"}, {\"id\": 8949, \"name\": \"the white and black patterned rug\"}, {\"id\": 8950, \"name\": \"a small white sign\"}, {\"id\": 8951, \"name\": \"small section of a green forest\"}, {\"id\": 8952, \"name\": \"small, green vehicle\"}, {\"id\": 8953, \"name\": \"temple\"}, {\"id\": 8954, \"name\": \"the large temple\"}, {\"id\": 8955, \"name\": \"tie\"}, {\"id\": 8956, \"name\": \"electric pole\"}, {\"id\": 8957, \"name\": \"marble sphere\"}, {\"id\": 8958, \"name\": \"the beige bollard\"}, {\"id\": 8959, \"name\": \"the designated parking spot\"}, {\"id\": 8960, \"name\": \"a pink bowl\"}, {\"id\": 8961, \"name\": \"a food truck\"}, {\"id\": 8962, \"name\": \"the red vehicle\"}, {\"id\": 8963, \"name\": \"small child\"}, {\"id\": 8964, \"name\": \"the scene\"}, {\"id\": 8965, \"name\": \"the traffic light\"}, {\"id\": 8966, \"name\": \"large slab of concrete\"}, {\"id\": 8967, \"name\": \"metal box\"}, {\"id\": 8968, \"name\": \"a blender\"}, {\"id\": 8969, \"name\": \"a pink basin\"}, {\"id\": 8970, \"name\": \"a red apple\"}, {\"id\": 8971, \"name\": \"person's left hand\"}, {\"id\": 8972, \"name\": \"pink bucket\"}, {\"id\": 8973, \"name\": \"the straw\"}, {\"id\": 8974, \"name\": \"white lid\"}, {\"id\": 8975, \"name\": \"small black square\"}, {\"id\": 8976, \"name\": \"the small, black square object\"}, {\"id\": 8977, \"name\": \"the small, white Persian cat\"}, {\"id\": 8978, \"name\": \"the white Persian cat\"}, {\"id\": 8979, \"name\": \"thick, fluffy coat\"}, {\"id\": 8980, \"name\": \"a colorful design\"}, {\"id\": 8981, \"name\": \"an orange safety vest\"}, {\"id\": 8982, \"name\": \"low-quality photograph\"}, {\"id\": 8983, \"name\": \"the floral arrangement\"}, {\"id\": 8984, \"name\": \"the large piece of fabric\"}, {\"id\": 8985, \"name\": \"a diverse assortment of vehicles\"}, {\"id\": 8986, \"name\": \"game machine\"}, {\"id\": 8987, \"name\": \"the blue hijab\"}, {\"id\": 8988, \"name\": \"a gray shirt\"}, {\"id\": 8989, \"name\": \"person's back\"}, {\"id\": 8990, \"name\": \"cupboard\"}, {\"id\": 8991, \"name\": \"a green bus\"}, {\"id\": 8992, \"name\": \"the pattern of interlocking concrete pavers\"}, {\"id\": 8993, \"name\": \"the person on a motorcycle\"}, {\"id\": 8994, \"name\": \"a pebbles\"}, {\"id\": 8995, \"name\": \"a plants\"}, {\"id\": 8996, \"name\": \"the wall of the warehouse\"}, {\"id\": 8997, \"name\": \"a dark blue sweater\"}, {\"id\": 8998, \"name\": \"a small bowl of salad\"}, {\"id\": 8999, \"name\": \"beer\"}, {\"id\": 9000, \"name\": \"car's rear seat\"}, {\"id\": 9001, \"name\": \"chopsticks\"}, {\"id\": 9002, \"name\": \"rearview mirror\"}, {\"id\": 9003, \"name\": \"red light\"}, {\"id\": 9004, \"name\": \"the smartphone\"}, {\"id\": 9005, \"name\": \"salad\"}, {\"id\": 9006, \"name\": \"the glasses\"}, {\"id\": 9007, \"name\": \"a grey sneaker\"}, {\"id\": 9008, \"name\": \"windshield of the vehicle\"}, {\"id\": 9009, \"name\": \"central area\"}, {\"id\": 9010, \"name\": \"a colorful candy\"}, {\"id\": 9011, \"name\": \"back wall\"}, {\"id\": 9012, \"name\": \"reddish-brown dirt\"}, {\"id\": 9013, \"name\": \"the concrete or brick\"}, {\"id\": 9014, \"name\": \"a QR code\"}, {\"id\": 9015, \"name\": \"a train car's ceiling\"}, {\"id\": 9016, \"name\": \"a tiger statue\"}, {\"id\": 9017, \"name\": \"the statue of a lion and a dog\"}, {\"id\": 9018, \"name\": \"the white column\"}, {\"id\": 9019, \"name\": \"green sweatshirt\"}, {\"id\": 9020, \"name\": \"the blue tarp\"}, {\"id\": 9021, \"name\": \"the steamroller\"}, {\"id\": 9022, \"name\": \"the smaller beige building\"}, {\"id\": 9023, \"name\": \"a blurry, low-quality photograph\"}, {\"id\": 9024, \"name\": \"the narrow sidewalk\"}, {\"id\": 9025, \"name\": \"the trees\"}, {\"id\": 9026, \"name\": \"a red shawl\"}, {\"id\": 9027, \"name\": \"the shuttered store\"}, {\"id\": 9028, \"name\": \"a black seat\"}, {\"id\": 9029, \"name\": \"city sidewalk\"}, {\"id\": 9030, \"name\": \"a grey carpet\"}, {\"id\": 9031, \"name\": \"a large, cream-colored seat\"}, {\"id\": 9032, \"name\": \"a screens\"}, {\"id\": 9033, \"name\": \"a blue and silver ticket machine\"}, {\"id\": 9034, \"name\": \"a large, intricately decorated lion puppet\"}, {\"id\": 9035, \"name\": \"backdrop of trees\"}, {\"id\": 9036, \"name\": \"a calm\"}, {\"id\": 9037, \"name\": \"body of the plane\"}, {\"id\": 9038, \"name\": \"diverse array of vehicles\"}, {\"id\": 9039, \"name\": \"the black minivan\"}, {\"id\": 9040, \"name\": \"a pale blue sky\"}, {\"id\": 9041, \"name\": \"car door\"}, {\"id\": 9042, \"name\": \"a visible portion of the calendar\"}, {\"id\": 9043, \"name\": \"the lottery ticket\"}, {\"id\": 9044, \"name\": \"yellow outfit\"}, {\"id\": 9045, \"name\": \"a road side path tile\"}, {\"id\": 9046, \"name\": \"blue bus\"}, {\"id\": 9047, \"name\": \"tall, narrow pole\"}, {\"id\": 9048, \"name\": \"blue structure\"}, {\"id\": 9049, \"name\": \"lemon\"}, {\"id\": 9050, \"name\": \"the man in a yellow safety vest\"}, {\"id\": 9051, \"name\": \"a decorative top\"}, {\"id\": 9052, \"name\": \"the rust\"}, {\"id\": 9053, \"name\": \"the rust stain\"}, {\"id\": 9054, \"name\": \"the kids\"}, {\"id\": 9055, \"name\": \"a light orange feline\"}, {\"id\": 9056, \"name\": \"mouth\"}, {\"id\": 9057, \"name\": \"the plug\"}, {\"id\": 9058, \"name\": \"white bar\"}, {\"id\": 9059, \"name\": \"the man's arm\"}, {\"id\": 9060, \"name\": \"the pair of scissors\"}, {\"id\": 9061, \"name\": \"plank\"}, {\"id\": 9062, \"name\": \"the track\"}, {\"id\": 9063, \"name\": \"a yellow, red, and black playground structure\"}, {\"id\": 9064, \"name\": \"light-colored paver\"}, {\"id\": 9065, \"name\": \"the yellow and black vehicle\"}, {\"id\": 9066, \"name\": \"a formal attire\"}, {\"id\": 9067, \"name\": \"the green coat\"}, {\"id\": 9068, \"name\": \"an inflatable structure\"}, {\"id\": 9069, \"name\": \"small blue animal figure\"}, {\"id\": 9070, \"name\": \"a green, brown, and white accent\"}, {\"id\": 9071, \"name\": \"a thin strap\"}, {\"id\": 9072, \"name\": \"a vibrant yellow, orange, white, and brown patterned dress\"}, {\"id\": 9073, \"name\": \"a black marble section\"}, {\"id\": 9074, \"name\": \"the large, white pot\"}, {\"id\": 9075, \"name\": \"the silver watch\"}, {\"id\": 9076, \"name\": \"game\"}, {\"id\": 9077, \"name\": \"predominantly white background\"}, {\"id\": 9078, \"name\": \"the console\"}, {\"id\": 9079, \"name\": \"dark grey suit\"}, {\"id\": 9080, \"name\": \"a cover\"}, {\"id\": 9081, \"name\": \"blue and white cooler\"}, {\"id\": 9082, \"name\": \"plastic cover\"}, {\"id\": 9083, \"name\": \"the large grey stone counter\"}, {\"id\": 9084, \"name\": \"tall, thin streetlight\"}, {\"id\": 9085, \"name\": \"a white lattice wall\"}, {\"id\": 9086, \"name\": \"the red, white, and black plaid shirt\"}, {\"id\": 9087, \"name\": \"chain link fence\"}, {\"id\": 9088, \"name\": \"the green plastic container\"}, {\"id\": 9089, \"name\": \"the yellow item\"}, {\"id\": 9090, \"name\": \"silver bin\"}, {\"id\": 9091, \"name\": \"white puffer jacket\"}, {\"id\": 9092, \"name\": \"loose dirt\"}, {\"id\": 9093, \"name\": \"lively scene\"}, {\"id\": 9094, \"name\": \"gray concrete step\"}, {\"id\": 9095, \"name\": \"a cooking or food preparation activity\"}, {\"id\": 9096, \"name\": \"a steel pan\"}, {\"id\": 9097, \"name\": \"steel plate\"}, {\"id\": 9098, \"name\": \"steel tongs\"}, {\"id\": 9099, \"name\": \"the red packet\"}, {\"id\": 9100, \"name\": \"the steel bowl\"}, {\"id\": 9101, \"name\": \"the steel handle\"}, {\"id\": 9102, \"name\": \"the steel lid\"}, {\"id\": 9103, \"name\": \"the steel spatula\"}, {\"id\": 9104, \"name\": \"the yellow spice packet\"}, {\"id\": 9105, \"name\": \"a black dashboard\"}, {\"id\": 9106, \"name\": \"the water tank\"}, {\"id\": 9107, \"name\": \"a neon sign\"}, {\"id\": 9108, \"name\": \"a lush green tree\"}, {\"id\": 9109, \"name\": \"military tank\"}, {\"id\": 9110, \"name\": \"the man in suit\"}, {\"id\": 9111, \"name\": \"a mid-dance\"}, {\"id\": 9112, \"name\": \"a sign board\"}, {\"id\": 9113, \"name\": \"row of buildings\"}, {\"id\": 9114, \"name\": \"the dark-colored car\"}, {\"id\": 9115, \"name\": \"a shop\"}, {\"id\": 9116, \"name\": \"black gate\"}, {\"id\": 9117, \"name\": \"a grey knit hat\"}, {\"id\": 9118, \"name\": \"the loose curl\"}, {\"id\": 9119, \"name\": \"a white sign with green Chinese character\"}, {\"id\": 9120, \"name\": \"red and blue design\"}, {\"id\": 9121, \"name\": \"yellow and red sign\"}, {\"id\": 9122, \"name\": \"the upper half of a person\"}, {\"id\": 9123, \"name\": \"the white stripes\"}, {\"id\": 9124, \"name\": \"green vegetation\"}, {\"id\": 9125, \"name\": \"a bright yellow delivery van\"}, {\"id\": 9126, \"name\": \"cloth\"}, {\"id\": 9127, \"name\": \"orange and white striped pattern\"}, {\"id\": 9128, \"name\": \"the cat scratcher\"}, {\"id\": 9129, \"name\": \"the tractor\"}, {\"id\": 9130, \"name\": \"the white banner\"}, {\"id\": 9131, \"name\": \"a faint white line\"}, {\"id\": 9132, \"name\": \"sparkle\"}, {\"id\": 9133, \"name\": \"the plain white background\"}, {\"id\": 9134, \"name\": \"pile of small, colorful stones\"}, {\"id\": 9135, \"name\": \"narrow, paved road\"}, {\"id\": 9136, \"name\": \"the single-story home\"}, {\"id\": 9137, \"name\": \"a dark blue sky\"}, {\"id\": 9138, \"name\": \"a jack\"}, {\"id\": 9139, \"name\": \"electrical outlet\"}, {\"id\": 9140, \"name\": \"large, fluffy golden retriever\"}, {\"id\": 9141, \"name\": \"the bike\"}, {\"id\": 9142, \"name\": \"a laundry\"}, {\"id\": 9143, \"name\": \"a taxi's open rear window\"}, {\"id\": 9144, \"name\": \"a living room setting\"}, {\"id\": 9145, \"name\": \"the cupboard\"}, {\"id\": 9146, \"name\": \"the toy bike\"}, {\"id\": 9147, \"name\": \"a large, ornate carving of a deity\"}, {\"id\": 9148, \"name\": \"array of colorful flowers\"}, {\"id\": 9149, \"name\": \"the gold lettering\"}, {\"id\": 9150, \"name\": \"a stack of colorful bottles or cans\"}, {\"id\": 9151, \"name\": \"field of greenery\"}, {\"id\": 9152, \"name\": \"wedding\"}, {\"id\": 9153, \"name\": \"the group of four goats\"}, {\"id\": 9154, \"name\": \"the exposed metal beam\"}, {\"id\": 9155, \"name\": \"the glass display case\"}, {\"id\": 9156, \"name\": \"a leopard print\"}, {\"id\": 9157, \"name\": \"rack's metal structure\"}, {\"id\": 9158, \"name\": \"red and yellow sari\"}, {\"id\": 9159, \"name\": \"terrier\"}, {\"id\": 9160, \"name\": \"a street\"}, {\"id\": 9161, \"name\": \"an atmosphere\"}, {\"id\": 9162, \"name\": \"pinkish-orange sky\"}, {\"id\": 9163, \"name\": \"red top\"}, {\"id\": 9164, \"name\": \"street lamp\"}, {\"id\": 9165, \"name\": \"slipper\"}, {\"id\": 9166, \"name\": \"a bathroom sink\"}, {\"id\": 9167, \"name\": \"black cuff\"}, {\"id\": 9168, \"name\": \"the woven pattern\"}, {\"id\": 9169, \"name\": \"a beige tile flooring\"}, {\"id\": 9170, \"name\": \"long, spiral-shaped metal hose\"}, {\"id\": 9171, \"name\": \"shower head\"}, {\"id\": 9172, \"name\": \"the shower hose\"}, {\"id\": 9173, \"name\": \"wide eye\"}, {\"id\": 9174, \"name\": \"a large transparent plastic bag\"}, {\"id\": 9175, \"name\": \"a transparent plastic bag\"}, {\"id\": 9176, \"name\": \"vibrant red fabric\"}, {\"id\": 9177, \"name\": \"a brown pant\"}, {\"id\": 9178, \"name\": \"path\"}, {\"id\": 9179, \"name\": \"the food truck\"}, {\"id\": 9180, \"name\": \"the vibrant sari\"}, {\"id\": 9181, \"name\": \"gray mat\"}, {\"id\": 9182, \"name\": \"brick-paved road\"}, {\"id\": 9183, \"name\": \"a beige tile\"}, {\"id\": 9184, \"name\": \"a light-colored wood\"}, {\"id\": 9185, \"name\": \"black collar\"}, {\"id\": 9186, \"name\": \"wooden hanger\"}, {\"id\": 9187, \"name\": \"a cartoon design\"}, {\"id\": 9188, \"name\": \"the baby's peaceful slumber\"}, {\"id\": 9189, \"name\": \"the piece of clothing\"}, {\"id\": 9190, \"name\": \"the metal shopping cart\"}, {\"id\": 9191, \"name\": \"the drumhead\"}, {\"id\": 9192, \"name\": \"wooden plank\"}, {\"id\": 9193, \"name\": \"pile of bricks\"}, {\"id\": 9194, \"name\": \"a white brick facade\"}, {\"id\": 9195, \"name\": \"a blue tank top\"}, {\"id\": 9196, \"name\": \"black pole\"}, {\"id\": 9197, \"name\": \"middle ground\"}, {\"id\": 9198, \"name\": \"large chandelier\"}, {\"id\": 9199, \"name\": \"pink object\"}, {\"id\": 9200, \"name\": \"a black manhole cover\"}, {\"id\": 9201, \"name\": \"the manhole\"}, {\"id\": 9202, \"name\": \"a colorful streamer\"}, {\"id\": 9203, \"name\": \"a wall of windows\"}, {\"id\": 9204, \"name\": \"tan parka\"}, {\"id\": 9205, \"name\": \"a black plastic object\"}, {\"id\": 9206, \"name\": \"a trim\"}, {\"id\": 9207, \"name\": \"the red cloth\"}, {\"id\": 9208, \"name\": \"a skateboarder\"}, {\"id\": 9209, \"name\": \"a tan t-shirt\"}, {\"id\": 9210, \"name\": \"the blue polo shirt\"}, {\"id\": 9211, \"name\": \"the dustbin\"}, {\"id\": 9212, \"name\": \"worker\"}, {\"id\": 9213, \"name\": \"a black handle\"}, {\"id\": 9214, \"name\": \"the black logo\"}, {\"id\": 9215, \"name\": \"the gray comforter\"}, {\"id\": 9216, \"name\": \"a bag of chips\"}, {\"id\": 9217, \"name\": \"the apron\"}, {\"id\": 9218, \"name\": \"the dark apron\"}, {\"id\": 9219, \"name\": \"the tall shelf\"}, {\"id\": 9220, \"name\": \"the red billboard\"}, {\"id\": 9221, \"name\": \"the vibrant array of buildings\"}, {\"id\": 9222, \"name\": \"yellow sidewalk\"}, {\"id\": 9223, \"name\": \"the car cover\"}, {\"id\": 9224, \"name\": \"the ladder\"}, {\"id\": 9225, \"name\": \"a stainless steel refrigerator\"}, {\"id\": 9226, \"name\": \"an oven\"}, {\"id\": 9227, \"name\": \"the appliance\"}, {\"id\": 9228, \"name\": \"pavement\"}, {\"id\": 9229, \"name\": \"the clock\"}, {\"id\": 9230, \"name\": \"warm clothing\"}, {\"id\": 9231, \"name\": \"a black trash can\"}, {\"id\": 9232, \"name\": \"road cone\"}, {\"id\": 9233, \"name\": \"the gray sidewalk\"}, {\"id\": 9234, \"name\": \"greyish-brown fur\"}, {\"id\": 9235, \"name\": \"tan\"}, {\"id\": 9236, \"name\": \"Egyptian city\"}, {\"id\": 9237, \"name\": \"dark blue vehicle\"}, {\"id\": 9238, \"name\": \"an audience member's head\"}, {\"id\": 9239, \"name\": \"cymbals\"}, {\"id\": 9240, \"name\": \"instrument\"}, {\"id\": 9241, \"name\": \"microphone stand\"}, {\"id\": 9242, \"name\": \"the small, brownish-colored animal\"}, {\"id\": 9243, \"name\": \"the road turn\"}, {\"id\": 9244, \"name\": \"white motorcycle\"}, {\"id\": 9245, \"name\": \"a beach chair\"}, {\"id\": 9246, \"name\": \"a couch or sofa\"}, {\"id\": 9247, \"name\": \"black turtleneck sweater\"}, {\"id\": 9248, \"name\": \"a back\"}, {\"id\": 9249, \"name\": \"a pink mattress\"}, {\"id\": 9250, \"name\": \"a wardrobe\"}, {\"id\": 9251, \"name\": \"an orange long-sleeved shirt\"}, {\"id\": 9252, \"name\": \"a green helmet\"}, {\"id\": 9253, \"name\": \"a boxes\"}, {\"id\": 9254, \"name\": \"a broom handle\"}, {\"id\": 9255, \"name\": \"board\"}, {\"id\": 9256, \"name\": \"the black and brown carrom board\"}, {\"id\": 9257, \"name\": \"a poodle\"}, {\"id\": 9258, \"name\": \"the chalkboard\"}, {\"id\": 9259, \"name\": \"a field\"}, {\"id\": 9260, \"name\": \"the black tracksuit\"}, {\"id\": 9261, \"name\": \"wooden surface\"}, {\"id\": 9262, \"name\": \"hieroglyphic-style design\"}, {\"id\": 9263, \"name\": \"a red item\"}, {\"id\": 9264, \"name\": \"a tap\"}, {\"id\": 9265, \"name\": \"a purple t-shirt\"}, {\"id\": 9266, \"name\": \"a red pant\"}, {\"id\": 9267, \"name\": \"the bright pink outfit\"}, {\"id\": 9268, \"name\": \"the light brown, grid-like material\"}, {\"id\": 9269, \"name\": \"a gray hair\"}, {\"id\": 9270, \"name\": \"a single black pole\"}, {\"id\": 9271, \"name\": \"a large brown couch\"}, {\"id\": 9272, \"name\": \"garment\"}, {\"id\": 9273, \"name\": \"a grey pant\"}, {\"id\": 9274, \"name\": \"a night\"}, {\"id\": 9275, \"name\": \"prominent spotlight\"}, {\"id\": 9276, \"name\": \"a slippers\"}, {\"id\": 9277, \"name\": \"water tap\"}, {\"id\": 9278, \"name\": \"blue section\"}, {\"id\": 9279, \"name\": \"urban setting\"}, {\"id\": 9280, \"name\": \"Christmas tree\"}, {\"id\": 9281, \"name\": \"a festive Christmas garland\"}, {\"id\": 9282, \"name\": \"a wooden table\"}, {\"id\": 9283, \"name\": \"red hat\"}, {\"id\": 9284, \"name\": \"a girl's hair\"}, {\"id\": 9285, \"name\": \"relaxed and casual vibe\"}, {\"id\": 9286, \"name\": \"the long blonde hair\"}, {\"id\": 9287, \"name\": \"slide's structure\"}, {\"id\": 9288, \"name\": \"the denim shorts\"}, {\"id\": 9289, \"name\": \"the escalator's railing\"}, {\"id\": 9290, \"name\": \"moment of everyday life\"}, {\"id\": 9291, \"name\": \"a motorcycle driver\"}, {\"id\": 9292, \"name\": \"the N WEY ABI\"}, {\"id\": 9293, \"name\": \"yellow rubber band\"}, {\"id\": 9294, \"name\": \"dirt patch\"}, {\"id\": 9295, \"name\": \"a white and black attire\"}, {\"id\": 9296, \"name\": \"glass-enclosed storefront\"}, {\"id\": 9297, \"name\": \"dark wooden chair\"}, {\"id\": 9298, \"name\": \"a rack of pants\"}, {\"id\": 9299, \"name\": \"leafy green\"}, {\"id\": 9300, \"name\": \"the vegetable\"}, {\"id\": 9301, \"name\": \"red graffiti\"}, {\"id\": 9302, \"name\": \"the dark brown hue\"}, {\"id\": 9303, \"name\": \"the metal gate\"}, {\"id\": 9304, \"name\": \"the black and white directional arrow\"}, {\"id\": 9305, \"name\": \"the large orange sign\"}, {\"id\": 9306, \"name\": \"a trash\"}, {\"id\": 9307, \"name\": \"the passerby\"}, {\"id\": 9308, \"name\": \"a low curb\"}, {\"id\": 9309, \"name\": \"a yellow backpack\"}, {\"id\": 9310, \"name\": \"large bag\"}, {\"id\": 9311, \"name\": \"a large metal container\"}, {\"id\": 9312, \"name\": \"a light green polo shirt\"}, {\"id\": 9313, \"name\": \"an outdoor setting\"}, {\"id\": 9314, \"name\": \"small white cup\"}, {\"id\": 9315, \"name\": \"a road marking\"}, {\"id\": 9316, \"name\": \"a black tie\"}, {\"id\": 9317, \"name\": \"the gold and white floral arrangement\"}, {\"id\": 9318, \"name\": \"blue umbrella\"}, {\"id\": 9319, \"name\": \"the central median\"}, {\"id\": 9320, \"name\": \"yellow logo\"}, {\"id\": 9321, \"name\": \"the background of the image\"}, {\"id\": 9322, \"name\": \"brick-paved street\"}, {\"id\": 9323, \"name\": \"colorful bracelet\"}, {\"id\": 9324, \"name\": \"skateboard\"}, {\"id\": 9325, \"name\": \"street scene\"}, {\"id\": 9326, \"name\": \"the black cane\"}, {\"id\": 9327, \"name\": \"white-framed glass display case\"}, {\"id\": 9328, \"name\": \"fist\"}, {\"id\": 9329, \"name\": \"the salt\"}, {\"id\": 9330, \"name\": \"yellow substance\"}, {\"id\": 9331, \"name\": \"the silver cell phone\"}, {\"id\": 9332, \"name\": \"a dusty, unpaved road\"}, {\"id\": 9333, \"name\": \"a tan wall\"}, {\"id\": 9334, \"name\": \"a wooden counter\"}, {\"id\": 9335, \"name\": \"the dark blue apron\"}, {\"id\": 9336, \"name\": \"pink item\"}, {\"id\": 9337, \"name\": \"grey and overcast sky\"}, {\"id\": 9338, \"name\": \"tan, brown, and black coat\"}, {\"id\": 9339, \"name\": \"the dark gray shirt\"}, {\"id\": 9340, \"name\": \"a baking tray\"}, {\"id\": 9341, \"name\": \"a red, white, and blue plaid design\"}, {\"id\": 9342, \"name\": \"man on the sofa\"}, {\"id\": 9343, \"name\": \"person's dress\"}, {\"id\": 9344, \"name\": \"a tall metal pole\"}, {\"id\": 9345, \"name\": \"an electric pole\"}, {\"id\": 9346, \"name\": \"the silver van\"}, {\"id\": 9347, \"name\": \"a backdrop of other arcade games\"}, {\"id\": 9348, \"name\": \"a rainbow\"}, {\"id\": 9349, \"name\": \"the arcade game\"}, {\"id\": 9350, \"name\": \"the digital display screen\"}, {\"id\": 9351, \"name\": \"a large white sign\"}, {\"id\": 9352, \"name\": \"the shoes\"}, {\"id\": 9353, \"name\": \"a benchtop saw\"}, {\"id\": 9354, \"name\": \"leg\"}, {\"id\": 9355, \"name\": \"the sandals\"}, {\"id\": 9356, \"name\": \"the wood\"}, {\"id\": 9357, \"name\": \"the red license plate\"}, {\"id\": 9358, \"name\": \"red mat\"}, {\"id\": 9359, \"name\": \"a lighter-colored stone\"}, {\"id\": 9360, \"name\": \"a bright and overexposed sky\"}, {\"id\": 9361, \"name\": \"a rear end\"}, {\"id\": 9362, \"name\": \"the yellow van\"}, {\"id\": 9363, \"name\": \"garland of orange flowers\"}, {\"id\": 9364, \"name\": \"the yellow object\"}, {\"id\": 9365, \"name\": \"brown collar\"}, {\"id\": 9366, \"name\": \"a corner\"}, {\"id\": 9367, \"name\": \"a vibrant indoor concert or performance\"}, {\"id\": 9368, \"name\": \"dimly lit auditorium\"}, {\"id\": 9369, \"name\": \"santa hat\"}, {\"id\": 9370, \"name\": \"the left side of the image\"}, {\"id\": 9371, \"name\": \"a white vehicle\"}, {\"id\": 9372, \"name\": \"gray shirt\"}, {\"id\": 9373, \"name\": \"orange fur\"}, {\"id\": 9374, \"name\": \"single streetlight\"}, {\"id\": 9375, \"name\": \"a left thigh\"}, {\"id\": 9376, \"name\": \"a solid green color\"}, {\"id\": 9377, \"name\": \"the red heart-shaped decoration\"}, {\"id\": 9378, \"name\": \"a scrunchie\"}, {\"id\": 9379, \"name\": \"the dark-colored clothing\"}, {\"id\": 9380, \"name\": \"backdrop of buildings\"}, {\"id\": 9381, \"name\": \"large gray road\"}, {\"id\": 9382, \"name\": \"the patch of mud\"}, {\"id\": 9383, \"name\": \"a blue and white striped curb\"}, {\"id\": 9384, \"name\": \"a blue and white striped pole\"}, {\"id\": 9385, \"name\": \"the traffic signal\"}, {\"id\": 9386, \"name\": \"a long, green, curved climbing feature\"}, {\"id\": 9387, \"name\": \"the green tunnel\"}, {\"id\": 9388, \"name\": \"a mermaid accessories\"}, {\"id\": 9389, \"name\": \"a mermaid tail textures\"}, {\"id\": 9390, \"name\": \"the mermaid jewelry\"}, {\"id\": 9391, \"name\": \"a cat's body\"}, {\"id\": 9392, \"name\": \"brown and black long-haired coat\"}, {\"id\": 9393, \"name\": \"a child in a white shirt\"}, {\"id\": 9394, \"name\": \"prominent green and white sign\"}, {\"id\": 9395, \"name\": \"the black motor scooter\"}, {\"id\": 9396, \"name\": \"a glass of ice cream\"}, {\"id\": 9397, \"name\": \"glass floor\"}, {\"id\": 9398, \"name\": \"the everyday life\"}, {\"id\": 9399, \"name\": \"the long strap\"}, {\"id\": 9400, \"name\": \"the blue and white sneaker\"}, {\"id\": 9401, \"name\": \"a transmission tower\"}, {\"id\": 9402, \"name\": \"the ditch\"}, {\"id\": 9403, \"name\": \"a long-sleeved blue and white checkered shirt\"}, {\"id\": 9404, \"name\": \"a wall of corrugated metal sheets\"}, {\"id\": 9405, \"name\": \"flip-flop\"}, {\"id\": 9406, \"name\": \"pile of white plastic bags\"}, {\"id\": 9407, \"name\": \"plaid sarong\"}, {\"id\": 9408, \"name\": \"tail light\"}, {\"id\": 9409, \"name\": \"the white bucket\"}, {\"id\": 9410, \"name\": \"a white tile floor\"}, {\"id\": 9411, \"name\": \"short fur\"}, {\"id\": 9412, \"name\": \"the red and pink floral patterned fabric\"}, {\"id\": 9413, \"name\": \"the short, light-brown coat\"}, {\"id\": 9414, \"name\": \"sandy area\"}, {\"id\": 9415, \"name\": \"blue pants\"}, {\"id\": 9416, \"name\": \"small hut\"}, {\"id\": 9417, \"name\": \"the navy blue sweatpants\"}, {\"id\": 9418, \"name\": \"the thatched roof\"}, {\"id\": 9419, \"name\": \"dark shirt\"}, {\"id\": 9420, \"name\": \"leopard-print purse\"}, {\"id\": 9421, \"name\": \"a black and yellow directional sign\"}, {\"id\": 9422, \"name\": \"blue hatchback car\"}, {\"id\": 9423, \"name\": \"green plaid shirt\"}, {\"id\": 9424, \"name\": \"a cloud-shaped balloon display\"}, {\"id\": 9425, \"name\": \"blue ceiling\"}, {\"id\": 9426, \"name\": \"purple sticker or decal\"}, {\"id\": 9427, \"name\": \"the colorful decoration\"}, {\"id\": 9428, \"name\": \"a photographer\"}, {\"id\": 9429, \"name\": \"the blue Chevrolet sedan\"}, {\"id\": 9430, \"name\": \"the large sign\"}, {\"id\": 9431, \"name\": \"the white bowl\"}, {\"id\": 9432, \"name\": \"plaid\"}, {\"id\": 9433, \"name\": \"a tall cell tower\"}, {\"id\": 9434, \"name\": \"blue shoe\"}, {\"id\": 9435, \"name\": \"grey plaid hat\"}, {\"id\": 9436, \"name\": \"the green helmet\"}, {\"id\": 9437, \"name\": \"the matching yellow dupatta\"}, {\"id\": 9438, \"name\": \"a large, tinted window\"}, {\"id\": 9439, \"name\": \"small, blue-roofed structure\"}, {\"id\": 9440, \"name\": \"the barrel\"}, {\"id\": 9441, \"name\": \"the green pole\"}, {\"id\": 9442, \"name\": \"water tower\"}, {\"id\": 9443, \"name\": \"the man in a purple shirt and light-colored pants\"}, {\"id\": 9444, \"name\": \"the piece of furniture\"}, {\"id\": 9445, \"name\": \"the rack\"}, {\"id\": 9446, \"name\": \"white folding bed\"}, {\"id\": 9447, \"name\": \"a dry and dusty ground\"}, {\"id\": 9448, \"name\": \"green jacket\"}, {\"id\": 9449, \"name\": \"the black baseball cap\"}, {\"id\": 9450, \"name\": \"the tape\"}, {\"id\": 9451, \"name\": \"a larger piece of trash\"}, {\"id\": 9452, \"name\": \"a red sale banner\"}, {\"id\": 9453, \"name\": \"lively nighttime scene\"}, {\"id\": 9454, \"name\": \"a dark shadow\"}, {\"id\": 9455, \"name\": \"back door\"}, {\"id\": 9456, \"name\": \"the long, rectangular cardboard box\"}, {\"id\": 9457, \"name\": \"the white spherical light\"}, {\"id\": 9458, \"name\": \"gray frame\"}, {\"id\": 9459, \"name\": \"green exit sign\"}, {\"id\": 9460, \"name\": \"a row of scooters and motorcycles\"}, {\"id\": 9461, \"name\": \"an ice cream cart\"}, {\"id\": 9462, \"name\": \"roof of building\"}, {\"id\": 9463, \"name\": \"a black and white polka-dot jacket\"}, {\"id\": 9464, \"name\": \"the vest\"}, {\"id\": 9465, \"name\": \"the dusty and possibly polluted environment\"}, {\"id\": 9466, \"name\": \"man's left arm\"}, {\"id\": 9467, \"name\": \"padded armrest\"}, {\"id\": 9468, \"name\": \"the tongue\"}, {\"id\": 9469, \"name\": \"rural atmosphere\"}, {\"id\": 9470, \"name\": \"the black car mirror\"}, {\"id\": 9471, \"name\": \"vibrant green\"}, {\"id\": 9472, \"name\": \"the dark suit jacket\"}, {\"id\": 9473, \"name\": \"a distinctive curly coat\"}, {\"id\": 9474, \"name\": \"brown mat\"}, {\"id\": 9475, \"name\": \"the dog bowl\"}, {\"id\": 9476, \"name\": \"the food bowl\"}, {\"id\": 9477, \"name\": \"a glass of juice\"}, {\"id\": 9478, \"name\": \"wallpaper\"}, {\"id\": 9479, \"name\": \"the pen\"}, {\"id\": 9480, \"name\": \"a store's exterior\"}, {\"id\": 9481, \"name\": \"gray tweed jacket\"}, {\"id\": 9482, \"name\": \"purse\"}, {\"id\": 9483, \"name\": \"a vibrant pink garment\"}, {\"id\": 9484, \"name\": \"the white long-sleeved shirt\"}, {\"id\": 9485, \"name\": \"black brush\"}, {\"id\": 9486, \"name\": \"the grey top\"}, {\"id\": 9487, \"name\": \"the patch of grass\"}, {\"id\": 9488, \"name\": \"the raised, grassy strip\"}, {\"id\": 9489, \"name\": \"a median strip\"}, {\"id\": 9490, \"name\": \"city or urban area\"}, {\"id\": 9491, \"name\": \"scarf\"}, {\"id\": 9492, \"name\": \"a TOYOTA\"}, {\"id\": 9493, \"name\": \"the driver's seat\"}, {\"id\": 9494, \"name\": \"blue jacket\"}, {\"id\": 9495, \"name\": \"the black metal structure\"}, {\"id\": 9496, \"name\": \"the metal\"}, {\"id\": 9497, \"name\": \"white and red poster or sign\"}, {\"id\": 9498, \"name\": \"a dune buggy\"}, {\"id\": 9499, \"name\": \"a sand\"}, {\"id\": 9500, \"name\": \"the vast desert landscape\"}, {\"id\": 9501, \"name\": \"a musician\"}, {\"id\": 9502, \"name\": \"a tuk-tuk's driver\"}, {\"id\": 9503, \"name\": \"musician\"}, {\"id\": 9504, \"name\": \"the person's hand\"}, {\"id\": 9505, \"name\": \"blue, pink, and black plastic stool\"}, {\"id\": 9506, \"name\": \"the colorful plastic stool\"}, {\"id\": 9507, \"name\": \"the stack of plastic stools\"}, {\"id\": 9508, \"name\": \"green and black motorcycle\"}, {\"id\": 9509, \"name\": \"card reader\"}, {\"id\": 9510, \"name\": \"person's shoulder\"}, {\"id\": 9511, \"name\": \"a black iron fence\"}, {\"id\": 9512, \"name\": \"gray and blue bus seat\"}, {\"id\": 9513, \"name\": \"metal armrest\"}, {\"id\": 9514, \"name\": \"group of pedestrians\"}, {\"id\": 9515, \"name\": \"a black strip\"}, {\"id\": 9516, \"name\": \"flower vase\"}, {\"id\": 9517, \"name\": \"the yellow and orange wall\"}, {\"id\": 9518, \"name\": \"lightweight material\"}, {\"id\": 9519, \"name\": \"the black and white shoe\"}, {\"id\": 9520, \"name\": \"a round mirror\"}, {\"id\": 9521, \"name\": \"row of white boxes\"}, {\"id\": 9522, \"name\": \"the lace sleeve\"}, {\"id\": 9523, \"name\": \"manhole\"}, {\"id\": 9524, \"name\": \"the paver\"}, {\"id\": 9525, \"name\": \"light-colored clothing\"}, {\"id\": 9526, \"name\": \"paved sidewalk\"}, {\"id\": 9527, \"name\": \"the tan body\"}, {\"id\": 9528, \"name\": \"blue design\"}, {\"id\": 9529, \"name\": \"casual, at-home setting\"}, {\"id\": 9530, \"name\": \"the red life jacket\"}, {\"id\": 9531, \"name\": \"a black flag\"}, {\"id\": 9532, \"name\": \"a road divider brick wall\"}, {\"id\": 9533, \"name\": \"the dark green lamppost\"}, {\"id\": 9534, \"name\": \"the lit-up sign\"}, {\"id\": 9535, \"name\": \"long, curved beak\"}, {\"id\": 9536, \"name\": \"photo of a man in a suit and tie\"}, {\"id\": 9537, \"name\": \"the dark gray concrete\"}, {\"id\": 9538, \"name\": \"a large mirror frame\"}, {\"id\": 9539, \"name\": \"the man in a white chef's coat\"}, {\"id\": 9540, \"name\": \"small grate\"}, {\"id\": 9541, \"name\": \"the arch\"}, {\"id\": 9542, \"name\": \"a green building\"}, {\"id\": 9543, \"name\": \"the green car\"}, {\"id\": 9544, \"name\": \"the man in black shirt\"}, {\"id\": 9545, \"name\": \"the man in white shirt\"}, {\"id\": 9546, \"name\": \"pile of food\"}, {\"id\": 9547, \"name\": \"the pile of grains\"}, {\"id\": 9548, \"name\": \"the tripod\"}, {\"id\": 9549, \"name\": \"vendor's attire\"}, {\"id\": 9550, \"name\": \"yellow patch\"}, {\"id\": 9551, \"name\": \"large beige-colored building\"}, {\"id\": 9552, \"name\": \"the multiple stories\"}, {\"id\": 9553, \"name\": \"the brown horse statue\"}, {\"id\": 9554, \"name\": \"the brown plastic horse toy\"}, {\"id\": 9555, \"name\": \"a red container\"}, {\"id\": 9556, \"name\": \"a green and white bus\"}, {\"id\": 9557, \"name\": \"street corner\"}, {\"id\": 9558, \"name\": \"road surface\"}, {\"id\": 9559, \"name\": \"the red and white plaid jacket\"}, {\"id\": 9560, \"name\": \"she\"}, {\"id\": 9561, \"name\": \"the yellow plastic jug\"}, {\"id\": 9562, \"name\": \"a stack of buckets\"}, {\"id\": 9563, \"name\": \"the large silver pot\"}, {\"id\": 9564, \"name\": \"the top shelf\"}, {\"id\": 9565, \"name\": \"white refrigerator\"}, {\"id\": 9566, \"name\": \"a concrete ground\"}, {\"id\": 9567, \"name\": \"a blue plastic crate\"}, {\"id\": 9568, \"name\": \"blue and yellow bin\"}, {\"id\": 9569, \"name\": \"blue metal plate\"}, {\"id\": 9570, \"name\": \"striped pants\"}, {\"id\": 9571, \"name\": \"the black\"}, {\"id\": 9572, \"name\": \"a covered walkway or awning\"}, {\"id\": 9573, \"name\": \"palm frond\"}, {\"id\": 9574, \"name\": \"the bright sky\"}, {\"id\": 9575, \"name\": \"a beige and gray pattern\"}, {\"id\": 9576, \"name\": \"a light beige color\"}, {\"id\": 9577, \"name\": \"a napkin\"}, {\"id\": 9578, \"name\": \"blue and purple lighting\"}, {\"id\": 9579, \"name\": \"a tissue paper box\"}, {\"id\": 9580, \"name\": \"green placemat\"}, {\"id\": 9581, \"name\": \"brown fur\"}, {\"id\": 9582, \"name\": \"green sticker\"}, {\"id\": 9583, \"name\": \"the brilliant blue sky\"}, {\"id\": 9584, \"name\": \"a commercial property\"}, {\"id\": 9585, \"name\": \"a brown bun hairstyle\"}, {\"id\": 9586, \"name\": \"a plastic bottle of water\"}, {\"id\": 9587, \"name\": \"red face paint design\"}, {\"id\": 9588, \"name\": \"the name board\"}, {\"id\": 9589, \"name\": \"a middle of the road\"}, {\"id\": 9590, \"name\": \"dark SUV\"}, {\"id\": 9591, \"name\": \"a neon green sneaker\"}, {\"id\": 9592, \"name\": \"arch\"}, {\"id\": 9593, \"name\": \"the intricate stone carving\"}, {\"id\": 9594, \"name\": \"the red sneaker\"}, {\"id\": 9595, \"name\": \"the yellow and white design\"}, {\"id\": 9596, \"name\": \"a brown road\"}, {\"id\": 9597, \"name\": \"the large yellow grader\"}, {\"id\": 9598, \"name\": \"yellow bulldozer\"}, {\"id\": 9599, \"name\": \"child's hair\"}, {\"id\": 9600, \"name\": \"blue bucket\"}, {\"id\": 9601, \"name\": \"the dirt mound\"}, {\"id\": 9602, \"name\": \"the large, dirty, dark blue bucket or barrel\"}, {\"id\": 9603, \"name\": \"the vibrant pink shirt\"}, {\"id\": 9604, \"name\": \"a green scooter\"}, {\"id\": 9605, \"name\": \"the brown skirt\"}, {\"id\": 9606, \"name\": \"the large tree\"}, {\"id\": 9607, \"name\": \"white support beam\"}, {\"id\": 9608, \"name\": \"the white edge\"}, {\"id\": 9609, \"name\": \"the marble or ceramic\"}, {\"id\": 9610, \"name\": \"the pink floral pattern\"}, {\"id\": 9611, \"name\": \"a white scooter\"}, {\"id\": 9612, \"name\": \"the purple, pink, and cream-colored dress\"}, {\"id\": 9613, \"name\": \"substantial load of white sacks\"}, {\"id\": 9614, \"name\": \"the sand dune\"}, {\"id\": 9615, \"name\": \"grey, textured, fabric ramp\"}, {\"id\": 9616, \"name\": \"the white and brown coat\"}, {\"id\": 9617, \"name\": \"a dark green t-shirt\"}, {\"id\": 9618, \"name\": \"arched top\"}, {\"id\": 9619, \"name\": \"decorative mosaic pattern\"}, {\"id\": 9620, \"name\": \"the clear plastic bottle\"}, {\"id\": 9621, \"name\": \"pink comb\"}, {\"id\": 9622, \"name\": \"the short sleeve\"}, {\"id\": 9623, \"name\": \"smaller, black metal pot\"}, {\"id\": 9624, \"name\": \"the white headscarf\"}, {\"id\": 9625, \"name\": \"a gray tile floor\"}, {\"id\": 9626, \"name\": \"the colorful page\"}, {\"id\": 9627, \"name\": \"a distinctive sign\"}, {\"id\": 9628, \"name\": \"blue and white license plate\"}, {\"id\": 9629, \"name\": \"design\"}, {\"id\": 9630, \"name\": \"red superhero costume\"}, {\"id\": 9631, \"name\": \"neck\"}, {\"id\": 9632, \"name\": \"the green piece of paper or plastic\"}, {\"id\": 9633, \"name\": \"the white badge\"}, {\"id\": 9634, \"name\": \"a motorcycle rider\"}, {\"id\": 9635, \"name\": \"pink phone\"}, {\"id\": 9636, \"name\": \"rectangular window\"}, {\"id\": 9637, \"name\": \"the long blue dress\"}, {\"id\": 9638, \"name\": \"a boat oar\"}, {\"id\": 9639, \"name\": \"a turnip\"}, {\"id\": 9640, \"name\": \"a fish fin\"}, {\"id\": 9641, \"name\": \"a person's upper arm\"}, {\"id\": 9642, \"name\": \"a yellow fabric\"}, {\"id\": 9643, \"name\": \"a patio umbrella\"}, {\"id\": 9644, \"name\": \"a mobile phone\"}, {\"id\": 9645, \"name\": \"his lower body\"}, {\"id\": 9646, \"name\": \"a design\"}, {\"id\": 9647, \"name\": \"bottle of bathroom cleaner\"}, {\"id\": 9648, \"name\": \"a machine\"}, {\"id\": 9649, \"name\": \"candle\"}, {\"id\": 9650, \"name\": \"a person from the waist up\"}, {\"id\": 9651, \"name\": \"a money\"}, {\"id\": 9652, \"name\": \"a red and white sign\"}, {\"id\": 9653, \"name\": \"a text\"}, {\"id\": 9654, \"name\": \"a skyscraper\"}, {\"id\": 9655, \"name\": \"a white fluorescent light\"}, {\"id\": 9656, \"name\": \"a part of a wall\"}, {\"id\": 9657, \"name\": \"a packaged food product\"}, {\"id\": 9658, \"name\": \"a packaged noodle product\"}, {\"id\": 9659, \"name\": \"a car's rearview mirror\"}, {\"id\": 9660, \"name\": \"a green tiled wall\"}, {\"id\": 9661, \"name\": \"a ring toy\"}, {\"id\": 9662, \"name\": \"a green vegetable\"}, {\"id\": 9663, \"name\": \"a blue, plastic, wheeled container\"}, {\"id\": 9664, \"name\": \"a store sign\"}, {\"id\": 9665, \"name\": \"a stainless steel container\"}, {\"id\": 9666, \"name\": \"a piece of food\"}, {\"id\": 9667, \"name\": \"a strainer\"}, {\"id\": 9668, \"name\": \"ticket machine\"}, {\"id\": 9669, \"name\": \"carrot\"}, {\"id\": 9670, \"name\": \"button\"}, {\"id\": 9671, \"name\": \"a rectangular air vent\"}, {\"id\": 9672, \"name\": \"a person, a boy\"}, {\"id\": 9673, \"name\": \"a wooden shelf\"}, {\"id\": 9674, \"name\": \"a military vehicle\"}, {\"id\": 9675, \"name\": \"a woman's upper body and head\"}, {\"id\": 9676, \"name\": \"a display box\"}, {\"id\": 9677, \"name\": \"a rose\"}, {\"id\": 9678, \"name\": \"a ceramic figurine of a white rabbit\"}, {\"id\": 9679, \"name\": \"the lower body and the upper body of a girl\"}, {\"id\": 9680, \"name\": \"a yellow and black tuk-tuk\"}, {\"id\": 9681, \"name\": \"a letter \\\"a\\\"\"}, {\"id\": 9682, \"name\": \"a rear light\"}, {\"id\": 9683, \"name\": \"a metal frame\"}, {\"id\": 9684, \"name\": \"a white goose\"}, {\"id\": 9685, \"name\": \"a red plastic stool\"}, {\"id\": 9686, \"name\": \"a fried food item\"}, {\"id\": 9687, \"name\": \"a person's hair bun\"}, {\"id\": 9688, \"name\": \"a food product label\"}, {\"id\": 9689, \"name\": \"a person, specifically, the lower body of the person\"}, {\"id\": 9690, \"name\": \"a plastic crate\"}, {\"id\": 9691, \"name\": \"a person, specifically, the person's lower body\"}, {\"id\": 9692, \"name\": \"a moped\"}, {\"id\": 9693, \"name\": \"a blue cloth\"}, {\"id\": 9694, \"name\": \"the back of a woman's head\"}, {\"id\": 9695, \"name\": \"a rectangular object, possibly a phone\"}, {\"id\": 9696, \"name\": \"a horse's hindquarters\"}, {\"id\": 9697, \"name\": \"a decorative railing\"}, {\"id\": 9698, \"name\": \"a car's rear windshield\"}, {\"id\": 9699, \"name\": \"a person, specifically, their lower body, including their pants\"}, {\"id\": 9700, \"name\": \"a red hose\"}, {\"id\": 9701, \"name\": \"a green railing\"}, {\"id\": 9702, \"name\": \"a whole roasted animal\"}, {\"id\": 9703, \"name\": \"a green fence\"}, {\"id\": 9704, \"name\": \"a person's yellow vest\"}, {\"id\": 9705, \"name\": \"a headpiece\"}, {\"id\": 9706, \"name\": \"a sailboat\"}, {\"id\": 9707, \"name\": \"a yellow and black striped sign\"}, {\"id\": 9708, \"name\": \"a person, specifically, a man in a black suit\"}, {\"id\": 9709, \"name\": \"a person's right shoulder\"}, {\"id\": 9710, \"name\": \"a person, specifically, the person's torso\"}, {\"id\": 9711, \"name\": \"a claw\"}, {\"id\": 9712, \"name\": \"a glow stick\"}, {\"id\": 9713, \"name\": \"a row of seats\"}, {\"id\": 9714, \"name\": \"coffee cup\"}, {\"id\": 9715, \"name\": \"a topiary\"}, {\"id\": 9716, \"name\": \"the upper body of a person\"}, {\"id\": 9717, \"name\": \"a pair of camouflage shorts\"}, {\"id\": 9718, \"name\": \"a pair of legs and a lower body\"}, {\"id\": 9719, \"name\": \"a green pepper stem\"}, {\"id\": 9720, \"name\": \"menu board\"}, {\"id\": 9721, \"name\": \"a wallaby\"}, {\"id\": 9722, \"name\": \"a black pants\"}, {\"id\": 9723, \"name\": \"a cow's tail\"}, {\"id\": 9724, \"name\": \"a christmas tree ornament\"}, {\"id\": 9725, \"name\": \"a red scooter\"}, {\"id\": 9726, \"name\": \"a fountain\"}, {\"id\": 9727, \"name\": \"a person's black pants\"}, {\"id\": 9728, \"name\": \"red and gold lantern\"}, {\"id\": 9729, \"name\": \"a metal floor\"}, {\"id\": 9730, \"name\": \"the beige building\"}, {\"id\": 9731, \"name\": \"a dark blue paint job\"}, {\"id\": 9732, \"name\": \"a thin, horizontal line\"}, {\"id\": 9733, \"name\": \"the large red sign\"}, {\"id\": 9734, \"name\": \"a page of a document\"}, {\"id\": 9735, \"name\": \"a medium-length brown hair\"}, {\"id\": 9736, \"name\": \"banana tree\"}, {\"id\": 9737, \"name\": \"the light gray marble tile\"}, {\"id\": 9738, \"name\": \"the cityscape\"}, {\"id\": 9739, \"name\": \"gray marbled tile\"}, {\"id\": 9740, \"name\": \"the floor of the walkway\"}, {\"id\": 9741, \"name\": \"the brick sidewalk\"}, {\"id\": 9742, \"name\": \"a vendor\"}, {\"id\": 9743, \"name\": \"an escalator area\"}, {\"id\": 9744, \"name\": \"the small, dirty pot\"}, {\"id\": 9745, \"name\": \"a pirate costume\"}, {\"id\": 9746, \"name\": \"white sweater\"}, {\"id\": 9747, \"name\": \"store's ceiling\"}, {\"id\": 9748, \"name\": \"cashier station\"}, {\"id\": 9749, \"name\": \"a music note icon\"}, {\"id\": 9750, \"name\": \"the vast expanse of dirt or sand\"}, {\"id\": 9751, \"name\": \"a red runner\"}, {\"id\": 9752, \"name\": \"large fan\"}, {\"id\": 9753, \"name\": \"bright and sunny sky\"}, {\"id\": 9754, \"name\": \"the chrome accent\"}, {\"id\": 9755, \"name\": \"the colorful poster\"}, {\"id\": 9756, \"name\": \"a short brown coat\"}, {\"id\": 9757, \"name\": \"a trampoline's frame\"}, {\"id\": 9758, \"name\": \"the floral patterned sheet\"}, {\"id\": 9759, \"name\": \"the prominent archway entrance\"}, {\"id\": 9760, \"name\": \"pile of green pellets\"}, {\"id\": 9761, \"name\": \"black mat\"}, {\"id\": 9762, \"name\": \"hoarding\"}, {\"id\": 9763, \"name\": \"a leggings\"}, {\"id\": 9764, \"name\": \"a slanted roof\"}, {\"id\": 9765, \"name\": \"a blue denim jacket\"}, {\"id\": 9766, \"name\": \"a sheet of corrugated metal\"}, {\"id\": 9767, \"name\": \"a music stand\"}, {\"id\": 9768, \"name\": \"the intricate design\"}, {\"id\": 9769, \"name\": \"the black net enclosure\"}, {\"id\": 9770, \"name\": \"driver's arm\"}, {\"id\": 9771, \"name\": \"yellow toy keyboard\"}, {\"id\": 9772, \"name\": \"brown accent wall\"}, {\"id\": 9773, \"name\": \"the patch of dirt\"}, {\"id\": 9774, \"name\": \"the white sock\"}, {\"id\": 9775, \"name\": \"the cage\"}, {\"id\": 9776, \"name\": \"black leather\"}, {\"id\": 9777, \"name\": \"the orange and blue striped cushion\"}, {\"id\": 9778, \"name\": \"gaming arcade\"}, {\"id\": 9779, \"name\": \"the shiny white tile flooring\"}, {\"id\": 9780, \"name\": \"the orange polo shirt\"}, {\"id\": 9781, \"name\": \"the rectangular opening\"}, {\"id\": 9782, \"name\": \"fountain\"}, {\"id\": 9783, \"name\": \"the pink dress\"}, {\"id\": 9784, \"name\": \"dead leaf\"}, {\"id\": 9785, \"name\": \"the white diamond pattern\"}, {\"id\": 9786, \"name\": \"the black seat\"}, {\"id\": 9787, \"name\": \"the lone jogger\"}, {\"id\": 9788, \"name\": \"cap\"}, {\"id\": 9789, \"name\": \"green metal fence\"}, {\"id\": 9790, \"name\": \"foliage\"}, {\"id\": 9791, \"name\": \"a person's blue winter coat\"}, {\"id\": 9792, \"name\": \"a blue face mask\"}, {\"id\": 9793, \"name\": \"a child's attire\"}, {\"id\": 9794, \"name\": \"the small flag\"}, {\"id\": 9795, \"name\": \"bowl of food\"}, {\"id\": 9796, \"name\": \"a small blue car\"}, {\"id\": 9797, \"name\": \"large yard\"}, {\"id\": 9798, \"name\": \"the orange curtain\"}, {\"id\": 9799, \"name\": \"the red motorcycle\"}, {\"id\": 9800, \"name\": \"a bell\"}, {\"id\": 9801, \"name\": \"the yellow tag\"}, {\"id\": 9802, \"name\": \"the white Mitsubishi bus\"}, {\"id\": 9803, \"name\": \"the long pole\"}, {\"id\": 9804, \"name\": \"the blue stripe\"}, {\"id\": 9805, \"name\": \"the spiral notebook\"}, {\"id\": 9806, \"name\": \"a stunning red, green, and white patterned dress\"}, {\"id\": 9807, \"name\": \"a yellow railing structure\"}, {\"id\": 9808, \"name\": \"white pant\"}, {\"id\": 9809, \"name\": \"black hijab\"}, {\"id\": 9810, \"name\": \"Western Union\"}, {\"id\": 9811, \"name\": \"black countertop\"}, {\"id\": 9812, \"name\": \"a dirt road surface\"}, {\"id\": 9813, \"name\": \"white power strip\"}, {\"id\": 9814, \"name\": \"the shirt or dress\"}, {\"id\": 9815, \"name\": \"the large, grey, sloping structure\"}, {\"id\": 9816, \"name\": \"the black metal fence\"}, {\"id\": 9817, \"name\": \"mound\"}, {\"id\": 9818, \"name\": \"store's entrance\"}, {\"id\": 9819, \"name\": \"a small, orange cat\"}, {\"id\": 9820, \"name\": \"the brown road\"}, {\"id\": 9821, \"name\": \"the dark sky\"}, {\"id\": 9822, \"name\": \"high collar\"}, {\"id\": 9823, \"name\": \"white substance\"}, {\"id\": 9824, \"name\": \"the red and black leather or vinyl seat\"}, {\"id\": 9825, \"name\": \"the small, plastic device\"}, {\"id\": 9826, \"name\": \"worn-out yellow line\"}, {\"id\": 9827, \"name\": \"the gray puffer jacket\"}, {\"id\": 9828, \"name\": \"the goal's netting\"}, {\"id\": 9829, \"name\": \"pale, slender finger\"}, {\"id\": 9830, \"name\": \"a red arch\"}, {\"id\": 9831, \"name\": \"red Chinese lantern\"}, {\"id\": 9832, \"name\": \"a restaurant or bar\"}, {\"id\": 9833, \"name\": \"store entrance\"}, {\"id\": 9834, \"name\": \"a large gray asphalt parking lot\"}, {\"id\": 9835, \"name\": \"the brown paw print mat\"}, {\"id\": 9836, \"name\": \"frisbee\"}, {\"id\": 9837, \"name\": \"blue skirt\"}, {\"id\": 9838, \"name\": \"red trash can\"}, {\"id\": 9839, \"name\": \"the fabric\"}, {\"id\": 9840, \"name\": \"a colorful floral pattern\"}, {\"id\": 9841, \"name\": \"white baseboard\"}, {\"id\": 9842, \"name\": \"the brown bag\"}, {\"id\": 9843, \"name\": \"the black border\"}, {\"id\": 9844, \"name\": \"a stone bench\"}, {\"id\": 9845, \"name\": \"painting of a tree with yellow leaves\"}, {\"id\": 9846, \"name\": \"the hand of the person operating it\"}, {\"id\": 9847, \"name\": \"water\"}, {\"id\": 9848, \"name\": \"bright yellow pant\"}, {\"id\": 9849, \"name\": \"dark, wet street\"}, {\"id\": 9850, \"name\": \"plowed area\"}, {\"id\": 9851, \"name\": \"a large bowl\"}, {\"id\": 9852, \"name\": \"a lush green field\"}, {\"id\": 9853, \"name\": \"large, light blue plastic container\"}, {\"id\": 9854, \"name\": \"long, thin piece of wood or paper\"}, {\"id\": 9855, \"name\": \"collection of plush toys\"}, {\"id\": 9856, \"name\": \"a pink ribbon pattern\"}, {\"id\": 9857, \"name\": \"the black entertainment center\"}, {\"id\": 9858, \"name\": \"the pigeon coop\"}, {\"id\": 9859, \"name\": \"the vibrant orange shirt\"}, {\"id\": 9860, \"name\": \"compact, four-door SUV\"}, {\"id\": 9861, \"name\": \"the red-brown dirt\"}, {\"id\": 9862, \"name\": \"shiny, reflective floor\"}, {\"id\": 9863, \"name\": \"school name\"}, {\"id\": 9864, \"name\": \"a small white car\"}, {\"id\": 9865, \"name\": \"a dark purple object\"}, {\"id\": 9866, \"name\": \"a pink floral dress\"}, {\"id\": 9867, \"name\": \"a black-and-white striped curb\"}, {\"id\": 9868, \"name\": \"the gray, overcast sky\"}, {\"id\": 9869, \"name\": \"a green sticker\"}, {\"id\": 9870, \"name\": \"the glass globe\"}, {\"id\": 9871, \"name\": \"large bowl of banana peel\"}, {\"id\": 9872, \"name\": \"long aisle\"}, {\"id\": 9873, \"name\": \"the horse\"}, {\"id\": 9874, \"name\": \"a purple bag\"}, {\"id\": 9875, \"name\": \"the black mesh side\"}, {\"id\": 9876, \"name\": \"a white polo shirt\"}, {\"id\": 9877, \"name\": \"mobile phone\"}, {\"id\": 9878, \"name\": \"the floral dress\"}, {\"id\": 9879, \"name\": \"the dirt\"}, {\"id\": 9880, \"name\": \"black and white tile\"}, {\"id\": 9881, \"name\": \"metal plate\"}, {\"id\": 9882, \"name\": \"brown and tan patterned floor\"}, {\"id\": 9883, \"name\": \"the graphic\"}, {\"id\": 9884, \"name\": \"light-colored shirt\"}, {\"id\": 9885, \"name\": \"blue Chinese character\"}, {\"id\": 9886, \"name\": \"hazy sky\"}, {\"id\": 9887, \"name\": \"a MILK PRODUCTS\"}, {\"id\": 9888, \"name\": \"larger rickshaw\"}, {\"id\": 9889, \"name\": \"the gray minivan\"}, {\"id\": 9890, \"name\": \"the dark vest\"}, {\"id\": 9891, \"name\": \"the rough texture\"}, {\"id\": 9892, \"name\": \"a purple blanket\"}, {\"id\": 9893, \"name\": \"the vibrant pink sari\"}, {\"id\": 9894, \"name\": \"white fence\"}, {\"id\": 9895, \"name\": \"a dark gray sidewalk\"}, {\"id\": 9896, \"name\": \"brick stack\"}, {\"id\": 9897, \"name\": \"black bag or purse\"}, {\"id\": 9898, \"name\": \"the green facade\"}, {\"id\": 9899, \"name\": \"the light gray color\"}, {\"id\": 9900, \"name\": \"the paved street\"}, {\"id\": 9901, \"name\": \"the front of a white car\"}, {\"id\": 9902, \"name\": \"the girl\"}, {\"id\": 9903, \"name\": \"the ceiling shape\"}, {\"id\": 9904, \"name\": \"the white tile floor\"}, {\"id\": 9905, \"name\": \"the large stone archway\"}, {\"id\": 9906, \"name\": \"a man's face\"}, {\"id\": 9907, \"name\": \"the large knife\"}, {\"id\": 9908, \"name\": \"a black pen\"}, {\"id\": 9909, \"name\": \"a dish antenna\"}, {\"id\": 9910, \"name\": \"the turban\"}, {\"id\": 9911, \"name\": \"a striking red sari\"}, {\"id\": 9912, \"name\": \"the slipper or shoe\"}, {\"id\": 9913, \"name\": \"the thick, chunky material\"}, {\"id\": 9914, \"name\": \"a small pothole\"}, {\"id\": 9915, \"name\": \"silver railing\"}, {\"id\": 9916, \"name\": \"a dark gray fur\"}, {\"id\": 9917, \"name\": \"a person's head and ponytail\"}, {\"id\": 9918, \"name\": \"a black marble table\"}, {\"id\": 9919, \"name\": \"a holiday decoration\"}, {\"id\": 9920, \"name\": \"a gray color\"}, {\"id\": 9921, \"name\": \"a goddess Kali\"}, {\"id\": 9922, \"name\": \"dimly lit, deserted street\"}, {\"id\": 9923, \"name\": \"the black and white canopy\"}, {\"id\": 9924, \"name\": \"the wreath\"}, {\"id\": 9925, \"name\": \"chessboard\"}, {\"id\": 9926, \"name\": \"a saw\"}, {\"id\": 9927, \"name\": \"building's facade\"}, {\"id\": 9928, \"name\": \"the large pile of green chili peppers\"}, {\"id\": 9929, \"name\": \"a bright green shirt\"}, {\"id\": 9930, \"name\": \"a white furniture\"}, {\"id\": 9931, \"name\": \"a dark SUV\"}, {\"id\": 9932, \"name\": \"a photo of the woman\"}, {\"id\": 9933, \"name\": \"line of trees\"}, {\"id\": 9934, \"name\": \"black mug\"}, {\"id\": 9935, \"name\": \"a gray suit\"}, {\"id\": 9936, \"name\": \"the neon green shorts\"}, {\"id\": 9937, \"name\": \"red vest\"}, {\"id\": 9938, \"name\": \"an awning\"}, {\"id\": 9939, \"name\": \"the red archway\"}, {\"id\": 9940, \"name\": \"tyre\"}, {\"id\": 9941, \"name\": \"the beige baseball cap\"}, {\"id\": 9942, \"name\": \"the truck's cab\"}, {\"id\": 9943, \"name\": \"dark brown\"}, {\"id\": 9944, \"name\": \"an iron\"}, {\"id\": 9945, \"name\": \"the slide\"}, {\"id\": 9946, \"name\": \"the blue toy motorbike\"}, {\"id\": 9947, \"name\": \"the blue and white design\"}, {\"id\": 9948, \"name\": \"the pair of black sneakers\"}, {\"id\": 9949, \"name\": \"small blue and white bowl of vegetable\"}, {\"id\": 9950, \"name\": \"the small duck\"}, {\"id\": 9951, \"name\": \"large, stainless steel elevator door\"}, {\"id\": 9952, \"name\": \"a shiny white floor\"}, {\"id\": 9953, \"name\": \"red plastic container\"}, {\"id\": 9954, \"name\": \"the gray scarf\"}, {\"id\": 9955, \"name\": \"the yellow dress\"}, {\"id\": 9956, \"name\": \"smoke\"}, {\"id\": 9957, \"name\": \"a long, fluffy coat\"}, {\"id\": 9958, \"name\": \"concrete median\"}, {\"id\": 9959, \"name\": \"an opposite side of the street\"}, {\"id\": 9960, \"name\": \"a white scale\"}, {\"id\": 9961, \"name\": \"a red comb\"}, {\"id\": 9962, \"name\": \"the smile logo\"}, {\"id\": 9963, \"name\": \"a teal cloth\"}, {\"id\": 9964, \"name\": \"the one-story yellow building\"}, {\"id\": 9965, \"name\": \"chocolate cake\"}, {\"id\": 9966, \"name\": \"the schoolyard\"}, {\"id\": 9967, \"name\": \"the black keyboard\"}, {\"id\": 9968, \"name\": \"a fluffy green rug\"}, {\"id\": 9969, \"name\": \"the patch of tall grass\"}, {\"id\": 9970, \"name\": \"the green and white toy\"}, {\"id\": 9971, \"name\": \"the interior of the black car\"}, {\"id\": 9972, \"name\": \"the red tile pathway\"}, {\"id\": 9973, \"name\": \"diverse array of women's clothing items\"}, {\"id\": 9974, \"name\": \"the snowman balloon\"}, {\"id\": 9975, \"name\": \"a India\"}, {\"id\": 9976, \"name\": \"orange sweatsuit\"}, {\"id\": 9977, \"name\": \"the beige plaid shirt\"}, {\"id\": 9978, \"name\": \"a large red banner\"}, {\"id\": 9979, \"name\": \"the yellow bucket\"}, {\"id\": 9980, \"name\": \"dark blue wall\"}, {\"id\": 9981, \"name\": \"the blue headscarf\"}, {\"id\": 9982, \"name\": \"vibrant red and pink hue\"}, {\"id\": 9983, \"name\": \"the turquoise-colored water\"}, {\"id\": 9984, \"name\": \"a large metal pot\"}, {\"id\": 9985, \"name\": \"white tile flooring\"}, {\"id\": 9986, \"name\": \"the clear chair\"}, {\"id\": 9987, \"name\": \"a lively atmosphere\"}, {\"id\": 9988, \"name\": \"the large, open field\"}, {\"id\": 9989, \"name\": \"a blue and white patterned pillow\"}, {\"id\": 9990, \"name\": \"a blue plastic step stool\"}, {\"id\": 9991, \"name\": \"red bag\"}, {\"id\": 9992, \"name\": \"row of cars\"}, {\"id\": 9993, \"name\": \"the dried grasses\"}, {\"id\": 9994, \"name\": \"a tree's bark\"}, {\"id\": 9995, \"name\": \"a hindquarters\"}, {\"id\": 9996, \"name\": \"the horns\"}, {\"id\": 9997, \"name\": \"a tiered bleacher\"}, {\"id\": 9998, \"name\": \"package of food or household item\"}, {\"id\": 9999, \"name\": \"the wall's surface\"}, {\"id\": 10000, \"name\": \"the black vest\"}, {\"id\": 10001, \"name\": \"the back wall\"}, {\"id\": 10002, \"name\": \"brick-paved sidewalk\"}, {\"id\": 10003, \"name\": \"curtain with a leaf pattern\"}, {\"id\": 10004, \"name\": \"driver's side window\"}, {\"id\": 10005, \"name\": \"piece of furniture\"}, {\"id\": 10006, \"name\": \"a light-brown tabby\"}, {\"id\": 10007, \"name\": \"a lush green bush\"}, {\"id\": 10008, \"name\": \"the rack of long-sleeved shirt\"}, {\"id\": 10009, \"name\": \"beige loafer\"}, {\"id\": 10010, \"name\": \"the blue lantern\"}, {\"id\": 10011, \"name\": \"large arcade game\"}, {\"id\": 10012, \"name\": \"the young dog\"}, {\"id\": 10013, \"name\": \"striking mural\"}, {\"id\": 10014, \"name\": \"a dry grass\"}, {\"id\": 10015, \"name\": \"a yellow car\"}, {\"id\": 10016, \"name\": \"carton of orange juice\"}, {\"id\": 10017, \"name\": \"the silver plate\"}, {\"id\": 10018, \"name\": \"turquoise wall\"}, {\"id\": 10019, \"name\": \"the man's lap\"}, {\"id\": 10020, \"name\": \"bright red shirt\"}, {\"id\": 10021, \"name\": \"black plastic clip\"}, {\"id\": 10022, \"name\": \"tall, brown stone wall\"}, {\"id\": 10023, \"name\": \"the stove\"}, {\"id\": 10024, \"name\": \"the natural light\"}, {\"id\": 10025, \"name\": \"a noticeable gap\"}, {\"id\": 10026, \"name\": \"herb\"}, {\"id\": 10027, \"name\": \"a white one with a red seat\"}, {\"id\": 10028, \"name\": \"the serene and expansive body of water\"}, {\"id\": 10029, \"name\": \"a black vest with yellow and black stripes\"}, {\"id\": 10030, \"name\": \"a blue stool\"}, {\"id\": 10031, \"name\": \"light blue coat\"}, {\"id\": 10032, \"name\": \"a vibrant, multicolored shirt\"}, {\"id\": 10033, \"name\": \"dark gray carpeted floor\"}, {\"id\": 10034, \"name\": \"a prominent sign\"}, {\"id\": 10035, \"name\": \"the black and white patterned dress\"}, {\"id\": 10036, \"name\": \"black puppy\"}, {\"id\": 10037, \"name\": \"grey hoodie\"}, {\"id\": 10038, \"name\": \"self-checkout machine\"}, {\"id\": 10039, \"name\": \"the multicolored fence\"}, {\"id\": 10040, \"name\": \"a blurred background\"}, {\"id\": 10041, \"name\": \"a blue Superman costume\"}, {\"id\": 10042, \"name\": \"the female performer\"}, {\"id\": 10043, \"name\": \"gray sky\"}, {\"id\": 10044, \"name\": \"the player's leg\"}, {\"id\": 10045, \"name\": \"grayish-white ceiling\"}, {\"id\": 10046, \"name\": \"polished cement\"}, {\"id\": 10047, \"name\": \"abandoned concrete structure\"}, {\"id\": 10048, \"name\": \"brown top\"}, {\"id\": 10049, \"name\": \"the colorful bag\"}, {\"id\": 10050, \"name\": \"a sleek white floor\"}, {\"id\": 10051, \"name\": \"person riding a bicycle\"}, {\"id\": 10052, \"name\": \"a brown door frame\"}, {\"id\": 10053, \"name\": \"a white fabric\"}, {\"id\": 10054, \"name\": \"a Officer Dibble\"}, {\"id\": 10055, \"name\": \"the four-lane highway\"}, {\"id\": 10056, \"name\": \"a black and yellow sign\"}, {\"id\": 10057, \"name\": \"a row of green plants\"}, {\"id\": 10058, \"name\": \"the gray skirt\"}, {\"id\": 10059, \"name\": \"the white and red sneaker\"}, {\"id\": 10060, \"name\": \"a disposable surgical mask\"}, {\"id\": 10061, \"name\": \"the concrete bridge\"}, {\"id\": 10062, \"name\": \"a gray, white, and brown vein\"}, {\"id\": 10063, \"name\": \"the light blue polo shirt\"}, {\"id\": 10064, \"name\": \"the red t-shirt\"}, {\"id\": 10065, \"name\": \"a white air conditioner\"}, {\"id\": 10066, \"name\": \"a blue canopy\"}, {\"id\": 10067, \"name\": \"a small red tag\"}, {\"id\": 10068, \"name\": \"the shiny beige tile floor\"}, {\"id\": 10069, \"name\": \"the gas stove burner\"}, {\"id\": 10070, \"name\": \"a rough, uneven texture\"}, {\"id\": 10071, \"name\": \"a brilliant blue sky\"}, {\"id\": 10072, \"name\": \"the pink and red container\"}, {\"id\": 10073, \"name\": \"a brown hoodie\"}, {\"id\": 10074, \"name\": \"a white and red KFC cup\"}, {\"id\": 10075, \"name\": \"the beagle or Jack Russell terrier\"}, {\"id\": 10076, \"name\": \"a brown pillow\"}, {\"id\": 10077, \"name\": \"a stacked black crate\"}, {\"id\": 10078, \"name\": \"woman in a white shirt\"}, {\"id\": 10079, \"name\": \"the white ice rink\"}, {\"id\": 10080, \"name\": \"red and beige sign\"}, {\"id\": 10081, \"name\": \"dark gray upholstery\"}, {\"id\": 10082, \"name\": \"the gray floor\"}, {\"id\": 10083, \"name\": \"the light blue, striped button-down shirt\"}, {\"id\": 10084, \"name\": \"the large yellow building\"}, {\"id\": 10085, \"name\": \"white bowl of sauce\"}, {\"id\": 10086, \"name\": \"the car hood\"}, {\"id\": 10087, \"name\": \"rocky outcrop\"}, {\"id\": 10088, \"name\": \"a large black logo\"}, {\"id\": 10089, \"name\": \"white air conditioning unit\"}, {\"id\": 10090, \"name\": \"a gray tabby cat\"}, {\"id\": 10091, \"name\": \"blue bar\"}, {\"id\": 10092, \"name\": \"the open red frame\"}, {\"id\": 10093, \"name\": \"the blurred white pickup truck\"}, {\"id\": 10094, \"name\": \"khaki short\"}, {\"id\": 10095, \"name\": \"the dramatic backdrop\"}, {\"id\": 10096, \"name\": \"man in a grey robe\"}, {\"id\": 10097, \"name\": \"liquid\"}, {\"id\": 10098, \"name\": \"the yellow pant\"}, {\"id\": 10099, \"name\": \"sparse, dry, and patchy lawn\"}, {\"id\": 10100, \"name\": \"the black ceramic bowl\"}, {\"id\": 10101, \"name\": \"the gravel bed\"}, {\"id\": 10102, \"name\": \"brown plastic stool\"}, {\"id\": 10103, \"name\": \"a narrow, murky canal\"}, {\"id\": 10104, \"name\": \"the white hair\"}, {\"id\": 10105, \"name\": \"an adult's arm\"}, {\"id\": 10106, \"name\": \"the green tuk-tuk\"}, {\"id\": 10107, \"name\": \"red and white sweater\"}, {\"id\": 10108, \"name\": \"a ball pit\"}, {\"id\": 10109, \"name\": \"the blue logo\"}, {\"id\": 10110, \"name\": \"blue fabric\"}, {\"id\": 10111, \"name\": \"rustic fence\"}, {\"id\": 10112, \"name\": \"the elegant updo\"}, {\"id\": 10113, \"name\": \"the water cooler\"}, {\"id\": 10114, \"name\": \"the steering wheel\"}, {\"id\": 10115, \"name\": \"a large, circular, stained-glass ceiling fixture\"}, {\"id\": 10116, \"name\": \"a light wood floor\"}, {\"id\": 10117, \"name\": \"barbed wire\"}, {\"id\": 10118, \"name\": \"the teal ceiling\"}, {\"id\": 10119, \"name\": \"the outlet on the wall\"}, {\"id\": 10120, \"name\": \"the small table\"}, {\"id\": 10121, \"name\": \"the light-colored wood grain\"}, {\"id\": 10122, \"name\": \"the presenter\"}, {\"id\": 10123, \"name\": \"dark grey tile floor\"}, {\"id\": 10124, \"name\": \"a building's exterior\"}, {\"id\": 10125, \"name\": \"black pepper grinder\"}, {\"id\": 10126, \"name\": \"black and white checkered shirt\"}, {\"id\": 10127, \"name\": \"red collar\"}, {\"id\": 10128, \"name\": \"a Bauducco Choco Biscuit\"}, {\"id\": 10129, \"name\": \"large, rectangular concrete slab\"}, {\"id\": 10130, \"name\": \"teddy bear\"}, {\"id\": 10131, \"name\": \"cane\"}, {\"id\": 10132, \"name\": \"the small light source\"}, {\"id\": 10133, \"name\": \"the tan shoe\"}, {\"id\": 10134, \"name\": \"the dark-colored couch or sofa\"}, {\"id\": 10135, \"name\": \"distinctive black nose\"}, {\"id\": 10136, \"name\": \"frying pan\"}, {\"id\": 10137, \"name\": \"a hoop\"}, {\"id\": 10138, \"name\": \"slanted roof\"}, {\"id\": 10139, \"name\": \"a green shorts\"}, {\"id\": 10140, \"name\": \"the red and white striped pattern\"}, {\"id\": 10141, \"name\": \"a gold garland\"}, {\"id\": 10142, \"name\": \"the short, fluffy coat\"}, {\"id\": 10143, \"name\": \"a line of bushes and trees\"}, {\"id\": 10144, \"name\": \"red and white rope\"}, {\"id\": 10145, \"name\": \"the white, rectangular box\"}, {\"id\": 10146, \"name\": \"the cluster of white bottles\"}, {\"id\": 10147, \"name\": \"the dog's body\"}, {\"id\": 10148, \"name\": \"a white sheet of paper or plastic\"}, {\"id\": 10149, \"name\": \"the metal hanger\"}, {\"id\": 10150, \"name\": \"the white and red striped sleeve\"}, {\"id\": 10151, \"name\": \"the grey, paved road\"}, {\"id\": 10152, \"name\": \"the corridor\"}, {\"id\": 10153, \"name\": \"long black coat\"}, {\"id\": 10154, \"name\": \"yellow and white strap\"}, {\"id\": 10155, \"name\": \"the playful cat\"}, {\"id\": 10156, \"name\": \"blue and white patterned box\"}, {\"id\": 10157, \"name\": \"the printer\"}, {\"id\": 10158, \"name\": \"a section\"}, {\"id\": 10159, \"name\": \"a metallic gold jacket\"}, {\"id\": 10160, \"name\": \"the white ladder\"}, {\"id\": 10161, \"name\": \"a soft blue sky\"}, {\"id\": 10162, \"name\": \"bottom section\"}, {\"id\": 10163, \"name\": \"a small white dog\"}, {\"id\": 10164, \"name\": \"the center console\"}, {\"id\": 10165, \"name\": \"the blurry, low-quality photograph\"}, {\"id\": 10166, \"name\": \"a pointy ear\"}, {\"id\": 10167, \"name\": \"the hijab\"}, {\"id\": 10168, \"name\": \"a plastic bag filled with ice\"}, {\"id\": 10169, \"name\": \"a large Christmas tree\"}, {\"id\": 10170, \"name\": \"a central radio display\"}, {\"id\": 10171, \"name\": \"black headscarf\"}, {\"id\": 10172, \"name\": \"cash register\"}, {\"id\": 10173, \"name\": \"the pair of jeans\"}, {\"id\": 10174, \"name\": \"an orange coat\"}, {\"id\": 10175, \"name\": \"stands of a stadium\"}, {\"id\": 10176, \"name\": \"a blue and white Lomi advertisement\"}, {\"id\": 10177, \"name\": \"a train's curved metal frame\"}, {\"id\": 10178, \"name\": \"the large pole\"}, {\"id\": 10179, \"name\": \"the wall backdrop\"}, {\"id\": 10180, \"name\": \"a stadium\"}, {\"id\": 10181, \"name\": \"green bedspread\"}, {\"id\": 10182, \"name\": \"a black and white patterned shirt\"}, {\"id\": 10183, \"name\": \"vibrant yellow hue\"}, {\"id\": 10184, \"name\": \"a silver metal structure\"}, {\"id\": 10185, \"name\": \"white tiled floor\"}, {\"id\": 10186, \"name\": \"the orange top\"}, {\"id\": 10187, \"name\": \"a meat\"}, {\"id\": 10188, \"name\": \"vibrant blue wall\"}, {\"id\": 10189, \"name\": \"large, abstract design\"}, {\"id\": 10190, \"name\": \"the colorful geometric pattern\"}, {\"id\": 10191, \"name\": \"the pearl necklace\"}, {\"id\": 10192, \"name\": \"the make-shift stand\"}, {\"id\": 10193, \"name\": \"a red lid\"}, {\"id\": 10194, \"name\": \"the white lion costume\"}, {\"id\": 10195, \"name\": \"the white puffy coat\"}, {\"id\": 10196, \"name\": \"train station platform\"}, {\"id\": 10197, \"name\": \"a gray hoodie\"}, {\"id\": 10198, \"name\": \"grey marble floor\"}, {\"id\": 10199, \"name\": \"a silver laptop\"}, {\"id\": 10200, \"name\": \"white stucco wall\"}, {\"id\": 10201, \"name\": \"a top calendar\"}, {\"id\": 10202, \"name\": \"the white, glossy surface\"}, {\"id\": 10203, \"name\": \"the tube of cat food or a treat\"}, {\"id\": 10204, \"name\": \"dark shorts\"}, {\"id\": 10205, \"name\": \"a group of young girls\"}, {\"id\": 10206, \"name\": \"the white stuffed animal\"}, {\"id\": 10207, \"name\": \"light blue slipper\"}, {\"id\": 10208, \"name\": \"the light blue hue\"}, {\"id\": 10209, \"name\": \"white, enclosed cargo trailer\"}, {\"id\": 10210, \"name\": \"a small, dark-colored rabbit\"}, {\"id\": 10211, \"name\": \"brown plastic object\"}, {\"id\": 10212, \"name\": \"vibrant green and yellow fabric\"}, {\"id\": 10213, \"name\": \"mailbox\"}, {\"id\": 10214, \"name\": \"the large blue water tank\"}, {\"id\": 10215, \"name\": \"white column\"}, {\"id\": 10216, \"name\": \"plaid shirt\"}, {\"id\": 10217, \"name\": \"a ceiling of the subway station\"}, {\"id\": 10218, \"name\": \"dirty workbench\"}, {\"id\": 10219, \"name\": \"red and white image\"}, {\"id\": 10220, \"name\": \"wide, multi-lane road\"}, {\"id\": 10221, \"name\": \"a beige long-sleeve shirt\"}, {\"id\": 10222, \"name\": \"pool of water\"}, {\"id\": 10223, \"name\": \"the ceiling beam\"}, {\"id\": 10224, \"name\": \"the green and white package\"}, {\"id\": 10225, \"name\": \"darkened photograph\"}, {\"id\": 10226, \"name\": \"the poster or calendar\"}, {\"id\": 10227, \"name\": \"the cream-colored tile floor\"}, {\"id\": 10228, \"name\": \"a black plastic mesh body\"}, {\"id\": 10229, \"name\": \"pump\"}, {\"id\": 10230, \"name\": \"a prominent red life ring\"}, {\"id\": 10231, \"name\": \"the dusty road\"}, {\"id\": 10232, \"name\": \"a distinctive white tail tip\"}, {\"id\": 10233, \"name\": \"an entrance\"}, {\"id\": 10234, \"name\": \"a black eye\"}, {\"id\": 10235, \"name\": \"a thick, white substance\"}, {\"id\": 10236, \"name\": \"a serving of yellow noodles\"}, {\"id\": 10237, \"name\": \"a large photo of a woman holding a phone\"}, {\"id\": 10238, \"name\": \"the blue jersey\"}, {\"id\": 10239, \"name\": \"a ground floor\"}, {\"id\": 10240, \"name\": \"a vibrant graphic\"}, {\"id\": 10241, \"name\": \"downward-facing dog yoga pose\"}, {\"id\": 10242, \"name\": \"a cream-colored bed\"}, {\"id\": 10243, \"name\": \"the blue van\"}, {\"id\": 10244, \"name\": \"the white shirt with black writing\"}, {\"id\": 10245, \"name\": \"the brown purse\"}, {\"id\": 10246, \"name\": \"the sleek black steering wheel\"}, {\"id\": 10247, \"name\": \"the two-story building\"}, {\"id\": 10248, \"name\": \"gray Chevrolet\"}, {\"id\": 10249, \"name\": \"the bundle of yellow plastic straw\"}, {\"id\": 10250, \"name\": \"the white linoleum flooring\"}, {\"id\": 10251, \"name\": \"female\"}, {\"id\": 10252, \"name\": \"a Beagle\"}, {\"id\": 10253, \"name\": \"colorful patterned cloth\"}, {\"id\": 10254, \"name\": \"the white shorts\"}, {\"id\": 10255, \"name\": \"a polished tile floor\"}, {\"id\": 10256, \"name\": \"brown rectangle\"}, {\"id\": 10257, \"name\": \"a bottle of drink\"}, {\"id\": 10258, \"name\": \"a brown bowl\"}, {\"id\": 10259, \"name\": \"a blue and white floral plate\"}, {\"id\": 10260, \"name\": \"a paved with bricks\"}, {\"id\": 10261, \"name\": \"the purple dress\"}, {\"id\": 10262, \"name\": \"a blue sports bra\"}, {\"id\": 10263, \"name\": \"two-lane road\"}, {\"id\": 10264, \"name\": \"the temporary structure\"}, {\"id\": 10265, \"name\": \"the Arabic writing\"}, {\"id\": 10266, \"name\": \"the fluffy white coat\"}, {\"id\": 10267, \"name\": \"a large white SUV\"}, {\"id\": 10268, \"name\": \"the long red carpet\"}, {\"id\": 10269, \"name\": \"the glass jug\"}, {\"id\": 10270, \"name\": \"a red button\"}, {\"id\": 10271, \"name\": \"the tall, red pole\"}, {\"id\": 10272, \"name\": \"tan and white coat\"}, {\"id\": 10273, \"name\": \"the gray towel\"}, {\"id\": 10274, \"name\": \"brown background\"}, {\"id\": 10275, \"name\": \"sand\"}, {\"id\": 10276, \"name\": \"a round base\"}, {\"id\": 10277, \"name\": \"concrete staircase\"}, {\"id\": 10278, \"name\": \"store aisle\"}, {\"id\": 10279, \"name\": \"the red dress\"}, {\"id\": 10280, \"name\": \"green plastic grate floor\"}, {\"id\": 10281, \"name\": \"a black table\"}, {\"id\": 10282, \"name\": \"blue support beam\"}, {\"id\": 10283, \"name\": \"the soft, overcast white sky\"}, {\"id\": 10284, \"name\": \"shiny floor\"}, {\"id\": 10285, \"name\": \"daytime setting\"}, {\"id\": 10286, \"name\": \"large plastic water bottle\"}, {\"id\": 10287, \"name\": \"a silver Hyundai\"}, {\"id\": 10288, \"name\": \"janitor or maintenance worker\"}, {\"id\": 10289, \"name\": \"large green leafy plant\"}, {\"id\": 10290, \"name\": \"green military uniform\"}, {\"id\": 10291, \"name\": \"the dark red car\"}, {\"id\": 10292, \"name\": \"long black hair\"}, {\"id\": 10293, \"name\": \"a barren landscape\"}, {\"id\": 10294, \"name\": \"a white sheet or duvet\"}, {\"id\": 10295, \"name\": \"matching pants\"}, {\"id\": 10296, \"name\": \"the small brown and gold bag\"}, {\"id\": 10297, \"name\": \"shiny white floor\"}, {\"id\": 10298, \"name\": \"the large box\"}, {\"id\": 10299, \"name\": \"the black patch\"}, {\"id\": 10300, \"name\": \"large clock\"}, {\"id\": 10301, \"name\": \"a shoe store\"}, {\"id\": 10302, \"name\": \"a colorful play structure\"}, {\"id\": 10303, \"name\": \"the red and blue character\"}, {\"id\": 10304, \"name\": \"vast expanse of green grass\"}, {\"id\": 10305, \"name\": \"the large, silver pot\"}, {\"id\": 10306, \"name\": \"ledge\"}, {\"id\": 10307, \"name\": \"the glass dome\"}, {\"id\": 10308, \"name\": \"building constructed from corrugated metal\"}, {\"id\": 10309, \"name\": \"the chair or cushion\"}, {\"id\": 10310, \"name\": \"a large white screen\"}, {\"id\": 10311, \"name\": \"the cracked concrete surface\"}, {\"id\": 10312, \"name\": \"a blue and white striped polo shirt\"}, {\"id\": 10313, \"name\": \"large window or door\"}, {\"id\": 10314, \"name\": \"long black abaya\"}, {\"id\": 10315, \"name\": \"the pink hoodie\"}, {\"id\": 10316, \"name\": \"sock\"}, {\"id\": 10317, \"name\": \"a solid surface\"}, {\"id\": 10318, \"name\": \"the bright and overexposed sky\"}, {\"id\": 10319, \"name\": \"the white dress or tunic\"}, {\"id\": 10320, \"name\": \"red and green foliage\"}, {\"id\": 10321, \"name\": \"the red Arabic writing\"}, {\"id\": 10322, \"name\": \"yellow accent\"}, {\"id\": 10323, \"name\": \"muddy dirt road\"}, {\"id\": 10324, \"name\": \"a red rim\"}, {\"id\": 10325, \"name\": \"gray and overcast sky\"}, {\"id\": 10326, \"name\": \"a blue t-shirt\"}, {\"id\": 10327, \"name\": \"a red scarf\"}, {\"id\": 10328, \"name\": \"a pinkish sky\"}, {\"id\": 10329, \"name\": \"the instrument\"}, {\"id\": 10330, \"name\": \"wooden floor\"}, {\"id\": 10331, \"name\": \"large, barren dirt field\"}, {\"id\": 10332, \"name\": \"the wallpaper\"}, {\"id\": 10333, \"name\": \"a man in a red jacket\"}, {\"id\": 10334, \"name\": \"light-colored tile floor\"}, {\"id\": 10335, \"name\": \"the green lantern\"}, {\"id\": 10336, \"name\": \"a yellow hard hat\"}, {\"id\": 10337, \"name\": \"backdrop\"}, {\"id\": 10338, \"name\": \"an ornate black metal grate or fence\"}, {\"id\": 10339, \"name\": \"the black fabric\"}, {\"id\": 10340, \"name\": \"the row of houses\"}, {\"id\": 10341, \"name\": \"the polished stone floor\"}, {\"id\": 10342, \"name\": \"small grey dog\"}, {\"id\": 10343, \"name\": \"camouflage pant\"}, {\"id\": 10344, \"name\": \"a man in traditional African attire\"}, {\"id\": 10345, \"name\": \"the short, curly hair\"}, {\"id\": 10346, \"name\": \"a yellow exterior\"}, {\"id\": 10347, \"name\": \"a pair of black sunglasses\"}, {\"id\": 10348, \"name\": \"a shopping basket\"}, {\"id\": 10349, \"name\": \"the grassy field\"}, {\"id\": 10350, \"name\": \"blue plaid shirt\"}, {\"id\": 10351, \"name\": \"the wooden chopstick\"}, {\"id\": 10352, \"name\": \"a forefinger\"}, {\"id\": 10353, \"name\": \"a plain white surface\"}, {\"id\": 10354, \"name\": \"a cartoonish style\"}, {\"id\": 10355, \"name\": \"the minimalist backdrop\"}, {\"id\": 10356, \"name\": \"man in black vest\"}, {\"id\": 10357, \"name\": \"small bird\"}, {\"id\": 10358, \"name\": \"a grand building with a domed roof\"}, {\"id\": 10359, \"name\": \"plastic bottle cap\"}, {\"id\": 10360, \"name\": \"ceiling panel\"}, {\"id\": 10361, \"name\": \"the blue metal door\"}, {\"id\": 10362, \"name\": \"the red baseball cap\"}, {\"id\": 10363, \"name\": \"the brown cat\"}, {\"id\": 10364, \"name\": \"a yellow rubber ball\"}, {\"id\": 10365, \"name\": \"large umbrella\"}, {\"id\": 10366, \"name\": \"the wooden tissue box\"}, {\"id\": 10367, \"name\": \"the residential building\"}, {\"id\": 10368, \"name\": \"a wrinkled white sheet\"}, {\"id\": 10369, \"name\": \"white garment bag\"}, {\"id\": 10370, \"name\": \"the cello\"}, {\"id\": 10371, \"name\": \"the mobile phone\"}, {\"id\": 10372, \"name\": \"the trash\"}, {\"id\": 10373, \"name\": \"left paw\"}, {\"id\": 10374, \"name\": \"a black glass cooktop\"}, {\"id\": 10375, \"name\": \"a stall\"}, {\"id\": 10376, \"name\": \"a plywood\"}, {\"id\": 10377, \"name\": \"red, white, and blue striped couch\"}, {\"id\": 10378, \"name\": \"the swing\"}, {\"id\": 10379, \"name\": \"bathroom floor\"}, {\"id\": 10380, \"name\": \"the red and blue light\"}, {\"id\": 10381, \"name\": \"a green-framed window\"}, {\"id\": 10382, \"name\": \"the black hat or hood\"}, {\"id\": 10383, \"name\": \"dirty, yellowed floor\"}, {\"id\": 10384, \"name\": \"the person wearing red pants\"}, {\"id\": 10385, \"name\": \"the white and blue bus\"}, {\"id\": 10386, \"name\": \"silver metal bar\"}, {\"id\": 10387, \"name\": \"a bright yellow sari\"}, {\"id\": 10388, \"name\": \"white box\"}, {\"id\": 10389, \"name\": \"dark brown wooden door\"}, {\"id\": 10390, \"name\": \"the red hat\"}, {\"id\": 10391, \"name\": \"a pile of trash\"}, {\"id\": 10392, \"name\": \"red border\"}, {\"id\": 10393, \"name\": \"the circular base\"}, {\"id\": 10394, \"name\": \"the he\"}, {\"id\": 10395, \"name\": \"gold jewelry\"}, {\"id\": 10396, \"name\": \"the light-colored floor\"}, {\"id\": 10397, \"name\": \"the mesh wall\"}, {\"id\": 10398, \"name\": \"the concrete lot\"}, {\"id\": 10399, \"name\": \"central focus\"}, {\"id\": 10400, \"name\": \"a tan bag\"}, {\"id\": 10401, \"name\": \"someone who is seated\"}, {\"id\": 10402, \"name\": \"red do not walk signal\"}, {\"id\": 10403, \"name\": \"the gold option\"}, {\"id\": 10404, \"name\": \"white Maltese\"}, {\"id\": 10405, \"name\": \"the parquet flooring\"}, {\"id\": 10406, \"name\": \"the boot\"}, {\"id\": 10407, \"name\": \"a video\"}, {\"id\": 10408, \"name\": \"a dirt pitch\"}, {\"id\": 10409, \"name\": \"brick sidewalk\"}, {\"id\": 10410, \"name\": \"man in a blue shirt and shorts\"}, {\"id\": 10411, \"name\": \"board's wheel\"}, {\"id\": 10412, \"name\": \"a platform's ceiling\"}, {\"id\": 10413, \"name\": \"sign or flyer\"}, {\"id\": 10414, \"name\": \"long, shaggy, light-colored coat\"}, {\"id\": 10415, \"name\": \"a microscope\"}, {\"id\": 10416, \"name\": \"a board\"}, {\"id\": 10417, \"name\": \"the human hand\"}, {\"id\": 10418, \"name\": \"a black mesh pen holder\"}, {\"id\": 10419, \"name\": \"the power pole\"}, {\"id\": 10420, \"name\": \"the gray car dashboard\"}, {\"id\": 10421, \"name\": \"stack of white napkins\"}, {\"id\": 10422, \"name\": \"a pair of slippers\"}, {\"id\": 10423, \"name\": \"light blue, long-sleeved button-up shirt\"}, {\"id\": 10424, \"name\": \"small table\"}, {\"id\": 10425, \"name\": \"a green and white logo\"}, {\"id\": 10426, \"name\": \"the white plastic handle\"}, {\"id\": 10427, \"name\": \"the burger\"}, {\"id\": 10428, \"name\": \"a backdrop\"}, {\"id\": 10429, \"name\": \"the car's hood\"}, {\"id\": 10430, \"name\": \"cobblestone driveway\"}, {\"id\": 10431, \"name\": \"the striking white geometric ceiling\"}, {\"id\": 10432, \"name\": \"the white and gray speckled floor\"}, {\"id\": 10433, \"name\": \"the large white pot\"}, {\"id\": 10434, \"name\": \"large apartment building\"}, {\"id\": 10435, \"name\": \"the beige puffer jacket\"}, {\"id\": 10436, \"name\": \"a game table\"}, {\"id\": 10437, \"name\": \"a small opening\"}, {\"id\": 10438, \"name\": \"a large, white and blue logo\"}, {\"id\": 10439, \"name\": \"the small trailer\"}, {\"id\": 10440, \"name\": \"pallet jack\"}, {\"id\": 10441, \"name\": \"a brown and black cat\"}, {\"id\": 10442, \"name\": \"large, curved screen\"}, {\"id\": 10443, \"name\": \"a long-handled tool\"}, {\"id\": 10444, \"name\": \"pink ruffled shirt\"}, {\"id\": 10445, \"name\": \"a clear glass\"}, {\"id\": 10446, \"name\": \"gray and white tile flooring\"}, {\"id\": 10447, \"name\": \"the pigeon's white plumage\"}, {\"id\": 10448, \"name\": \"a long pole\"}, {\"id\": 10449, \"name\": \"large white bowl\"}, {\"id\": 10450, \"name\": \"the long black robe\"}, {\"id\": 10451, \"name\": \"a colorful abstract design\"}, {\"id\": 10452, \"name\": \"gray brick sidewalk\"}, {\"id\": 10453, \"name\": \"a man in a blue shirt\"}, {\"id\": 10454, \"name\": \"the cluttered store counter\"}, {\"id\": 10455, \"name\": \"a red and yellow balloon\"}, {\"id\": 10456, \"name\": \"a rugged mountain range\"}, {\"id\": 10457, \"name\": \"the doorway\"}, {\"id\": 10458, \"name\": \"gray cap\"}, {\"id\": 10459, \"name\": \"overpass\"}, {\"id\": 10460, \"name\": \"canine\"}, {\"id\": 10461, \"name\": \"a body\"}, {\"id\": 10462, \"name\": \"the dog's fluffy black tail\"}, {\"id\": 10463, \"name\": \"a ATM\"}, {\"id\": 10464, \"name\": \"the sign in Japanese characters\"}, {\"id\": 10465, \"name\": \"a yellow and blue sawhorse\"}, {\"id\": 10466, \"name\": \"soothing light green\"}, {\"id\": 10467, \"name\": \"tiled sidewalk\"}, {\"id\": 10468, \"name\": \"a blue and white striped shirt\"}, {\"id\": 10469, \"name\": \"woman in a red coat\"}, {\"id\": 10470, \"name\": \"red scarf\"}, {\"id\": 10471, \"name\": \"the lush, green rice field\"}, {\"id\": 10472, \"name\": \"a small, round, blue cookie cutter\"}, {\"id\": 10473, \"name\": \"a red and white box\"}, {\"id\": 10474, \"name\": \"the label\"}, {\"id\": 10475, \"name\": \"black and white patterned border\"}, {\"id\": 10476, \"name\": \"the light blue color\"}, {\"id\": 10477, \"name\": \"unidentifiable object\"}, {\"id\": 10478, \"name\": \"the dark brown and black striped fur\"}, {\"id\": 10479, \"name\": \"circular\"}, {\"id\": 10480, \"name\": \"a man in a green coat\"}, {\"id\": 10481, \"name\": \"the cart or trolley\"}, {\"id\": 10482, \"name\": \"the cross-body bag strap\"}, {\"id\": 10483, \"name\": \"the high, open ceiling\"}, {\"id\": 10484, \"name\": \"yellow and black leash\"}, {\"id\": 10485, \"name\": \"yellow building\"}, {\"id\": 10486, \"name\": \"the row of parked motorcycles\"}, {\"id\": 10487, \"name\": \"the red, yellow, and black Mickey Mouse toy\"}, {\"id\": 10488, \"name\": \"the large, curved wall\"}, {\"id\": 10489, \"name\": \"interior\"}, {\"id\": 10490, \"name\": \"a turquoise shorts\"}, {\"id\": 10491, \"name\": \"the large, circular table\"}, {\"id\": 10492, \"name\": \"pink headscarf\"}, {\"id\": 10493, \"name\": \"a small counter\"}, {\"id\": 10494, \"name\": \"large piece of artwork\"}, {\"id\": 10495, \"name\": \"pink pillow\"}, {\"id\": 10496, \"name\": \"thatched roof structure\"}, {\"id\": 10497, \"name\": \"a central figure\"}, {\"id\": 10498, \"name\": \"digital screen\"}, {\"id\": 10499, \"name\": \"the wall of clothing racks\"}, {\"id\": 10500, \"name\": \"the white scarf\"}, {\"id\": 10501, \"name\": \"an outlet\"}, {\"id\": 10502, \"name\": \"the tissue roll\"}, {\"id\": 10503, \"name\": \"orange sweatshirt\"}, {\"id\": 10504, \"name\": \"a large herd of sheep and goats\"}, {\"id\": 10505, \"name\": \"the armrest\"}, {\"id\": 10506, \"name\": \"stain\"}, {\"id\": 10507, \"name\": \"the gold border\"}, {\"id\": 10508, \"name\": \"the green and white logo\"}, {\"id\": 10509, \"name\": \"blue glove\"}, {\"id\": 10510, \"name\": \"an orange polo shirt\"}, {\"id\": 10511, \"name\": \"a granite countertop\"}, {\"id\": 10512, \"name\": \"a dark blue pant\"}, {\"id\": 10513, \"name\": \"the navy blue coat with white polka dots\"}, {\"id\": 10514, \"name\": \"lush tree\"}, {\"id\": 10515, \"name\": \"the light brown body\"}, {\"id\": 10516, \"name\": \"a predominantly white floor\"}, {\"id\": 10517, \"name\": \"large white goal\"}, {\"id\": 10518, \"name\": \"a convenience store\"}, {\"id\": 10519, \"name\": \"the small, black handbag\"}, {\"id\": 10520, \"name\": \"a large metal hopper or funnel\"}, {\"id\": 10521, \"name\": \"rusty metal wheel\"}, {\"id\": 10522, \"name\": \"blue\"}, {\"id\": 10523, \"name\": \"a woman in a red dress\"}, {\"id\": 10524, \"name\": \"the brown and white cabinet\"}, {\"id\": 10525, \"name\": \"wide overhang\"}, {\"id\": 10526, \"name\": \"black fanny pack\"}, {\"id\": 10527, \"name\": \"the black, flat-screen model\"}, {\"id\": 10528, \"name\": \"red marking\"}, {\"id\": 10529, \"name\": \"torn black headrest\"}, {\"id\": 10530, \"name\": \"the gray paisley shirt\"}, {\"id\": 10531, \"name\": \"the blue character\"}, {\"id\": 10532, \"name\": \"the concrete planter\"}, {\"id\": 10533, \"name\": \"circular table\"}, {\"id\": 10534, \"name\": \"a light-gray puffer coat\"}, {\"id\": 10535, \"name\": \"gray marble pattern\"}, {\"id\": 10536, \"name\": \"the Krispy Kreme donut shop\"}, {\"id\": 10537, \"name\": \"small light switch\"}, {\"id\": 10538, \"name\": \"small dark wood table\"}, {\"id\": 10539, \"name\": \"a warehouse's wall\"}, {\"id\": 10540, \"name\": \"a subtle grid pattern\"}, {\"id\": 10541, \"name\": \"the sari\"}, {\"id\": 10542, \"name\": \"the gold pillar\"}, {\"id\": 10543, \"name\": \"a small yellow sticker\"}, {\"id\": 10544, \"name\": \"a cart's handle\"}, {\"id\": 10545, \"name\": \"lone man\"}, {\"id\": 10546, \"name\": \"glass panel\"}, {\"id\": 10547, \"name\": \"the light green jacket\"}, {\"id\": 10548, \"name\": \"the bright orange jacket\"}, {\"id\": 10549, \"name\": \"red, yellow, and pink blanket\"}, {\"id\": 10550, \"name\": \"an airport floor\"}, {\"id\": 10551, \"name\": \"right leg\"}, {\"id\": 10552, \"name\": \"the tan building\"}, {\"id\": 10553, \"name\": \"blue water tank\"}, {\"id\": 10554, \"name\": \"a stack of old, worn playing cards\"}, {\"id\": 10555, \"name\": \"a turbulent water\"}, {\"id\": 10556, \"name\": \"small, fluffy brown dog\"}, {\"id\": 10557, \"name\": \"a sidewalk or path\"}, {\"id\": 10558, \"name\": \"red cup\"}, {\"id\": 10559, \"name\": \"dirt field\"}, {\"id\": 10560, \"name\": \"pale blue sky\"}, {\"id\": 10561, \"name\": \"the green hue\"}, {\"id\": 10562, \"name\": \"purple sweatshirt\"}, {\"id\": 10563, \"name\": \"a flowers\"}, {\"id\": 10564, \"name\": \"the green grass\"}, {\"id\": 10565, \"name\": \"white and pink floral patterned sheet\"}, {\"id\": 10566, \"name\": \"the grass field\"}, {\"id\": 10567, \"name\": \"large blue and white semi-trailer truck\"}, {\"id\": 10568, \"name\": \"the Subaru\"}, {\"id\": 10569, \"name\": \"white plate of food\"}, {\"id\": 10570, \"name\": \"the shopping mall\"}, {\"id\": 10571, \"name\": \"the hazy grey sky\"}, {\"id\": 10572, \"name\": \"white rope\"}, {\"id\": 10573, \"name\": \"a yellow and red object\"}, {\"id\": 10574, \"name\": \"a light blue sky\"}, {\"id\": 10575, \"name\": \"the courtyard area\"}, {\"id\": 10576, \"name\": \"red plastic basin\"}, {\"id\": 10577, \"name\": \"a white bowl\"}, {\"id\": 10578, \"name\": \"blue roof\"}, {\"id\": 10579, \"name\": \"the brown patterned item\"}, {\"id\": 10580, \"name\": \"traditional lion dance\"}, {\"id\": 10581, \"name\": \"cooler\"}, {\"id\": 10582, \"name\": \"the light pink floral blouse\"}, {\"id\": 10583, \"name\": \"a deck of cards\"}, {\"id\": 10584, \"name\": \"a shades of white, gray, and brown\"}, {\"id\": 10585, \"name\": \"the blue rug\"}, {\"id\": 10586, \"name\": \"distinctive curved horn\"}, {\"id\": 10587, \"name\": \"a yellow knit hat\"}, {\"id\": 10588, \"name\": \"a white hat\"}, {\"id\": 10589, \"name\": \"a red long-sleeve top\"}, {\"id\": 10590, \"name\": \"the dark-colored hoodie\"}, {\"id\": 10591, \"name\": \"a black tire\"}, {\"id\": 10592, \"name\": \"a green exit sign\"}, {\"id\": 10593, \"name\": \"the green and white patterned cloth\"}, {\"id\": 10594, \"name\": \"a dark grey puffer jacket\"}, {\"id\": 10595, \"name\": \"microwave\"}, {\"id\": 10596, \"name\": \"a black pickup truck\"}, {\"id\": 10597, \"name\": \"the tissue paper roll\"}, {\"id\": 10598, \"name\": \"the drain\"}, {\"id\": 10599, \"name\": \"a red vehicle\"}, {\"id\": 10600, \"name\": \"the green wall\"}, {\"id\": 10601, \"name\": \"a blue-and-white striped shirt\"}, {\"id\": 10602, \"name\": \"the red door\"}, {\"id\": 10603, \"name\": \"cluster of balloons\"}, {\"id\": 10604, \"name\": \"the large, flat canopy\"}, {\"id\": 10605, \"name\": \"a large black paw print\"}, {\"id\": 10606, \"name\": \"brown and tan color scheme\"}, {\"id\": 10607, \"name\": \"a small white fish\"}, {\"id\": 10608, \"name\": \"corrugated iron roof\"}, {\"id\": 10609, \"name\": \"the small cymbal\"}, {\"id\": 10610, \"name\": \"beige pillow\"}, {\"id\": 10611, \"name\": \"the left front paw\"}, {\"id\": 10612, \"name\": \"British Shorthair breed\"}, {\"id\": 10613, \"name\": \"an orange border\"}, {\"id\": 10614, \"name\": \"light blue floor\"}, {\"id\": 10615, \"name\": \"the red and white striped scarf\"}, {\"id\": 10616, \"name\": \"the left wall\"}, {\"id\": 10617, \"name\": \"red shirt with a white pattern\"}, {\"id\": 10618, \"name\": \"a red and blue object\"}, {\"id\": 10619, \"name\": \"cashier's station\"}, {\"id\": 10620, \"name\": \"a round, white birthday cake\"}, {\"id\": 10621, \"name\": \"the small wooden table\"}, {\"id\": 10622, \"name\": \"the prominent arched bridge\"}, {\"id\": 10623, \"name\": \"person's elbow\"}, {\"id\": 10624, \"name\": \"kiosk\"}, {\"id\": 10625, \"name\": \"the unique attire\"}, {\"id\": 10626, \"name\": \"the medium-sized dog\"}, {\"id\": 10627, \"name\": \"the road's surface\"}, {\"id\": 10628, \"name\": \"a light green dress\"}, {\"id\": 10629, \"name\": \"a rusted metal frame\"}, {\"id\": 10630, \"name\": \"set of keys\"}, {\"id\": 10631, \"name\": \"a Water\"}, {\"id\": 10632, \"name\": \"the low concrete wall\"}, {\"id\": 10633, \"name\": \"dark gray carpet\"}, {\"id\": 10634, \"name\": \"blue hairnet\"}, {\"id\": 10635, \"name\": \"green fabric\"}, {\"id\": 10636, \"name\": \"a barricade\"}, {\"id\": 10637, \"name\": \"a pile\"}, {\"id\": 10638, \"name\": \"a linoleum\"}, {\"id\": 10639, \"name\": \"the dark screen\"}, {\"id\": 10640, \"name\": \"blue cart or stand\"}, {\"id\": 10641, \"name\": \"the bed frame\"}, {\"id\": 10642, \"name\": \"wall of the mall\"}, {\"id\": 10643, \"name\": \"vibrant orange sari\"}, {\"id\": 10644, \"name\": \"a motorcycle engine\"}, {\"id\": 10645, \"name\": \"a blue and white plaid shirt\"}, {\"id\": 10646, \"name\": \"a mascot's left arm\"}, {\"id\": 10647, \"name\": \"the large Christmas tree\"}, {\"id\": 10648, \"name\": \"wall or other surface\"}, {\"id\": 10649, \"name\": \"a passageway\"}, {\"id\": 10650, \"name\": \"green dress\"}, {\"id\": 10651, \"name\": \"the white band\"}, {\"id\": 10652, \"name\": \"black backrest\"}, {\"id\": 10653, \"name\": \"pink garment\"}, {\"id\": 10654, \"name\": \"the small white hair clip\"}, {\"id\": 10655, \"name\": \"the prominent red Coca-Cola sign\"}, {\"id\": 10656, \"name\": \"a green metalworking machine\"}, {\"id\": 10657, \"name\": \"a textured wallpaper\"}, {\"id\": 10658, \"name\": \"red hatchback\"}, {\"id\": 10659, \"name\": \"the black hoodie or hat\"}, {\"id\": 10660, \"name\": \"a dark floor\"}, {\"id\": 10661, \"name\": \"wireless earbud\"}, {\"id\": 10662, \"name\": \"a yellow crop top\"}, {\"id\": 10663, \"name\": \"a large brown bag\"}, {\"id\": 10664, \"name\": \"festive Christmas tree\"}, {\"id\": 10665, \"name\": \"wheelchair\"}, {\"id\": 10666, \"name\": \"the black and yellow shape\"}, {\"id\": 10667, \"name\": \"a light blue shorts\"}, {\"id\": 10668, \"name\": \"the white plastic canopy\"}, {\"id\": 10669, \"name\": \"bald head\"}, {\"id\": 10670, \"name\": \"blue, yellow, and white jacket\"}, {\"id\": 10671, \"name\": \"a yellow shopping cart\"}, {\"id\": 10672, \"name\": \"the large stack of cardboard boxes\"}, {\"id\": 10673, \"name\": \"red, green, and yellow umbrella\"}, {\"id\": 10674, \"name\": \"the prominent stack of colorful shipping containers\"}, {\"id\": 10675, \"name\": \"the hazy and gray sky\"}, {\"id\": 10676, \"name\": \"baguette\"}, {\"id\": 10677, \"name\": \"left kitten\"}, {\"id\": 10678, \"name\": \"the board\"}, {\"id\": 10679, \"name\": \"cream-colored sweater\"}, {\"id\": 10680, \"name\": \"a mermaid top\"}, {\"id\": 10681, \"name\": \"red banner with Chinese writing\"}, {\"id\": 10682, \"name\": \"the blue basket\"}, {\"id\": 10683, \"name\": \"a long escalator-like walkway\"}, {\"id\": 10684, \"name\": \"a large blue container\"}, {\"id\": 10685, \"name\": \"the yellow t-shirt\"}, {\"id\": 10686, \"name\": \"orange dragon's head\"}, {\"id\": 10687, \"name\": \"the shiny, tile floor\"}, {\"id\": 10688, \"name\": \"drill's handle\"}, {\"id\": 10689, \"name\": \"white marble floor\"}, {\"id\": 10690, \"name\": \"a grate\"}, {\"id\": 10691, \"name\": \"the colorful Alvin and the Chipmunks image\"}, {\"id\": 10692, \"name\": \"the grid pattern\"}, {\"id\": 10693, \"name\": \"the white floral top\"}, {\"id\": 10694, \"name\": \"a liquid\"}, {\"id\": 10695, \"name\": \"a child's dark hair\"}, {\"id\": 10696, \"name\": \"a white air conditioner unit\"}, {\"id\": 10697, \"name\": \"dark blue dog bed\"}, {\"id\": 10698, \"name\": \"floral-patterned canopy\"}, {\"id\": 10699, \"name\": \"medium-sized breed\"}, {\"id\": 10700, \"name\": \"a man in a blue shirt and shorts\"}, {\"id\": 10701, \"name\": \"a large umbrella\"}, {\"id\": 10702, \"name\": \"tall, weathered brick wall\"}, {\"id\": 10703, \"name\": \"the low-quality camera\"}, {\"id\": 10704, \"name\": \"white Arabic writing\"}, {\"id\": 10705, \"name\": \"a blue pant\"}, {\"id\": 10706, \"name\": \"safety net\"}, {\"id\": 10707, \"name\": \"cracked grey sidewalk\"}, {\"id\": 10708, \"name\": \"person wearing a yellow helmet\"}, {\"id\": 10709, \"name\": \"the predominantly white coat\"}, {\"id\": 10710, \"name\": \"the purse\"}, {\"id\": 10711, \"name\": \"a rickshaw\"}, {\"id\": 10712, \"name\": \"barrier tape\"}, {\"id\": 10713, \"name\": \"the brown hair\"}, {\"id\": 10714, \"name\": \"the brown patterned rug\"}, {\"id\": 10715, \"name\": \"the black wallet\"}, {\"id\": 10716, \"name\": \"the yellow labrador retriever\"}, {\"id\": 10717, \"name\": \"small shrine\"}, {\"id\": 10718, \"name\": \"a wall of plush toys\"}, {\"id\": 10719, \"name\": \"red stripe\"}, {\"id\": 10720, \"name\": \"the tan purse\"}, {\"id\": 10721, \"name\": \"the black head and neck\"}, {\"id\": 10722, \"name\": \"long, black coat\"}, {\"id\": 10723, \"name\": \"the dog's uniform\"}, {\"id\": 10724, \"name\": \"vibrant green shrub\"}, {\"id\": 10725, \"name\": \"a light-colored t-shirt\"}, {\"id\": 10726, \"name\": \"packets\"}, {\"id\": 10727, \"name\": \"a small green object\"}, {\"id\": 10728, \"name\": \"a small pink teddy bear\"}, {\"id\": 10729, \"name\": \"shoulder\"}, {\"id\": 10730, \"name\": \"the pink tank top\"}, {\"id\": 10731, \"name\": \"the row of cars\"}, {\"id\": 10732, \"name\": \"the glass and metal railing\"}, {\"id\": 10733, \"name\": \"the vibrant patterned rug\"}, {\"id\": 10734, \"name\": \"blue, white, and purple lanyard\"}, {\"id\": 10735, \"name\": \"yellow and blue ring\"}, {\"id\": 10736, \"name\": \"the large green orange\"}, {\"id\": 10737, \"name\": \"the man on a bicycle\"}, {\"id\": 10738, \"name\": \"a desolate desert landscape\"}, {\"id\": 10739, \"name\": \"the tank\"}, {\"id\": 10740, \"name\": \"vibrant, multicolored top\"}, {\"id\": 10741, \"name\": \"water's surface\"}, {\"id\": 10742, \"name\": \"the beige floor\"}, {\"id\": 10743, \"name\": \"a red and blue sign\"}, {\"id\": 10744, \"name\": \"a brown facade\"}, {\"id\": 10745, \"name\": \"a black and blue hijab\"}, {\"id\": 10746, \"name\": \"a laptop computer\"}, {\"id\": 10747, \"name\": \"silver water bottle\"}, {\"id\": 10748, \"name\": \"the large, cylindrical brick wall\"}, {\"id\": 10749, \"name\": \"grey brick sidewalk\"}, {\"id\": 10750, \"name\": \"a white patterned bag\"}, {\"id\": 10751, \"name\": \"a blue and white shirt\"}, {\"id\": 10752, \"name\": \"the fire truck\"}, {\"id\": 10753, \"name\": \"a baby's outfit\"}, {\"id\": 10754, \"name\": \"Santa Claus\"}, {\"id\": 10755, \"name\": \"the headrest\"}, {\"id\": 10756, \"name\": \"the grey tiled floor\"}, {\"id\": 10757, \"name\": \"a large, light-colored tile\"}, {\"id\": 10758, \"name\": \"a cactus\"}, {\"id\": 10759, \"name\": \"a large, curved piece of wood\"}, {\"id\": 10760, \"name\": \"a metal scaffolding structure\"}, {\"id\": 10761, \"name\": \"the yellow logo\"}, {\"id\": 10762, \"name\": \"large hospital building\"}, {\"id\": 10763, \"name\": \"Golden Retriever mix\"}, {\"id\": 10764, \"name\": \"the long black hair\"}, {\"id\": 10765, \"name\": \"the small child\"}, {\"id\": 10766, \"name\": \"nail\"}, {\"id\": 10767, \"name\": \"a black-handled knife\"}, {\"id\": 10768, \"name\": \"cat's pink nose\"}, {\"id\": 10769, \"name\": \"colorful structure\"}, {\"id\": 10770, \"name\": \"the blue undershirt\"}, {\"id\": 10771, \"name\": \"the wet pavement\"}, {\"id\": 10772, \"name\": \"a road shoulder\"}, {\"id\": 10773, \"name\": \"white hatchback\"}, {\"id\": 10774, \"name\": \"small storage box\"}, {\"id\": 10775, \"name\": \"the barbed wire\"}, {\"id\": 10776, \"name\": \"a floral-patterned shirt\"}, {\"id\": 10777, \"name\": \"a black sandal\"}, {\"id\": 10778, \"name\": \"green triangle\"}, {\"id\": 10779, \"name\": \"white scooter\"}, {\"id\": 10780, \"name\": \"a man in a red shirt\"}, {\"id\": 10781, \"name\": \"a beige tiled floor\"}, {\"id\": 10782, \"name\": \"the green tarp\"}, {\"id\": 10783, \"name\": \"a road drain\"}, {\"id\": 10784, \"name\": \"golf cart\"}, {\"id\": 10785, \"name\": \"a blue motorcycle\"}, {\"id\": 10786, \"name\": \"the blonde highlight\"}, {\"id\": 10787, \"name\": \"a multicolored bracelet\"}, {\"id\": 10788, \"name\": \"a dimly lit, makeshift room\"}, {\"id\": 10789, \"name\": \"a long tail\"}, {\"id\": 10790, \"name\": \"the railings\"}, {\"id\": 10791, \"name\": \"single yellow dumpster\"}, {\"id\": 10792, \"name\": \"the silver bowl or dish\"}, {\"id\": 10793, \"name\": \"the woman on a motorbike\"}, {\"id\": 10794, \"name\": \"wooden railing\"}, {\"id\": 10795, \"name\": \"the green polo shirt\"}, {\"id\": 10796, \"name\": \"the red toolbox\"}, {\"id\": 10797, \"name\": \"the light-colored hair\"}, {\"id\": 10798, \"name\": \"orange and white paper\"}, {\"id\": 10799, \"name\": \"red and yellow display\"}, {\"id\": 10800, \"name\": \"the yellow car\"}, {\"id\": 10801, \"name\": \"a stack of napkins\"}, {\"id\": 10802, \"name\": \"a concrete floor\"}, {\"id\": 10803, \"name\": \"tower\"}, {\"id\": 10804, \"name\": \"atm\"}, {\"id\": 10805, \"name\": \"the rear of a red car\"}, {\"id\": 10806, \"name\": \"blue surgical mask\"}, {\"id\": 10807, \"name\": \"the small building\"}, {\"id\": 10808, \"name\": \"long, narrow walkway\"}, {\"id\": 10809, \"name\": \"the tan floor\"}, {\"id\": 10810, \"name\": \"the green surface\"}, {\"id\": 10811, \"name\": \"the Chinese advertisement\"}, {\"id\": 10812, \"name\": \"the striking red dress\"}, {\"id\": 10813, \"name\": \"dirt path or road\"}, {\"id\": 10814, \"name\": \"a pink floor sign\"}, {\"id\": 10815, \"name\": \"the steel bucket\"}, {\"id\": 10816, \"name\": \"a white strap\"}, {\"id\": 10817, \"name\": \"green roof\"}, {\"id\": 10818, \"name\": \"the passenger\"}, {\"id\": 10819, \"name\": \"the tie\"}, {\"id\": 10820, \"name\": \"the stainless steel counter\"}, {\"id\": 10821, \"name\": \"the passenger seat\"}, {\"id\": 10822, \"name\": \"the laundry detergent bottle\"}, {\"id\": 10823, \"name\": \"the pink pant\"}, {\"id\": 10824, \"name\": \"a large cat\"}, {\"id\": 10825, \"name\": \"striped crosswalk pattern\"}, {\"id\": 10826, \"name\": \"the stack of cloth item\"}, {\"id\": 10827, \"name\": \"a long, covered walkway\"}, {\"id\": 10828, \"name\": \"white skirt\"}, {\"id\": 10829, \"name\": \"a small cat\"}, {\"id\": 10830, \"name\": \"the pink exterior\"}, {\"id\": 10831, \"name\": \"a sheet of lined paper\"}, {\"id\": 10832, \"name\": \"large green building\"}, {\"id\": 10833, \"name\": \"a grey fur\"}, {\"id\": 10834, \"name\": \"yellow shorts\"}, {\"id\": 10835, \"name\": \"the woven roof\"}, {\"id\": 10836, \"name\": \"the open field\"}, {\"id\": 10837, \"name\": \"the white speckled floor\"}, {\"id\": 10838, \"name\": \"a busy road\"}, {\"id\": 10839, \"name\": \"a professional drum set\"}, {\"id\": 10840, \"name\": \"a brown tank top\"}, {\"id\": 10841, \"name\": \"gray brick patterned walkway\"}, {\"id\": 10842, \"name\": \"the baking tray\"}, {\"id\": 10843, \"name\": \"a front end\"}, {\"id\": 10844, \"name\": \"the knee\"}, {\"id\": 10845, \"name\": \"the black and white dog\"}, {\"id\": 10846, \"name\": \"a four shelves\"}, {\"id\": 10847, \"name\": \"a darker shade\"}, {\"id\": 10848, \"name\": \"strap\"}, {\"id\": 10849, \"name\": \"the blue and grey striped blanket\"}, {\"id\": 10850, \"name\": \"a yellow tactile paving strip\"}, {\"id\": 10851, \"name\": \"edge of the basket\"}, {\"id\": 10852, \"name\": \"vest\"}, {\"id\": 10853, \"name\": \"neutral setting\"}, {\"id\": 10854, \"name\": \"small, round, white object\"}, {\"id\": 10855, \"name\": \"navy-blue suit\"}, {\"id\": 10856, \"name\": \"a circular light\"}, {\"id\": 10857, \"name\": \"a sign with arrows and symbols\"}, {\"id\": 10858, \"name\": \"a Poodle\"}, {\"id\": 10859, \"name\": \"the yellow cardigan\"}, {\"id\": 10860, \"name\": \"an iconic three-stripe design of Adidas\"}, {\"id\": 10861, \"name\": \"a brick median\"}, {\"id\": 10862, \"name\": \"the black and white checkered shirt\"}, {\"id\": 10863, \"name\": \"a grayish-white feather\"}, {\"id\": 10864, \"name\": \"the dirt patch\"}, {\"id\": 10865, \"name\": \"basketball\"}, {\"id\": 10866, \"name\": \"Jack Russell Terrier\"}, {\"id\": 10867, \"name\": \"the corridor's floor\"}, {\"id\": 10868, \"name\": \"a large box\"}, {\"id\": 10869, \"name\": \"the long handle\"}, {\"id\": 10870, \"name\": \"a bookshelf\"}, {\"id\": 10871, \"name\": \"a red wicker design\"}, {\"id\": 10872, \"name\": \"light-colored wooden counter or ledge\"}, {\"id\": 10873, \"name\": \"a long skirt\"}, {\"id\": 10874, \"name\": \"shadow on the ground\"}, {\"id\": 10875, \"name\": \"a center stage\"}, {\"id\": 10876, \"name\": \"concrete step\"}, {\"id\": 10877, \"name\": \"back\"}, {\"id\": 10878, \"name\": \"the wooden bench\"}, {\"id\": 10879, \"name\": \"minivan\"}, {\"id\": 10880, \"name\": \"plush toy\"}, {\"id\": 10881, \"name\": \"a small, two-door hatchback\"}, {\"id\": 10882, \"name\": \"a green and white checkerboard pattern\"}, {\"id\": 10883, \"name\": \"tag\"}, {\"id\": 10884, \"name\": \"the small pink soap dispenser\"}, {\"id\": 10885, \"name\": \"the peeling paint\"}, {\"id\": 10886, \"name\": \"a guitarist\"}, {\"id\": 10887, \"name\": \"a round concrete planter\"}, {\"id\": 10888, \"name\": \"orange construction equipment\"}, {\"id\": 10889, \"name\": \"the footpath\"}, {\"id\": 10890, \"name\": \"Chinese writing\"}, {\"id\": 10891, \"name\": \"the lampshade\"}, {\"id\": 10892, \"name\": \"a mesh\"}, {\"id\": 10893, \"name\": \"the red roof\"}, {\"id\": 10894, \"name\": \"woman's attire\"}, {\"id\": 10895, \"name\": \"white cart\"}, {\"id\": 10896, \"name\": \"a lush greenery\"}, {\"id\": 10897, \"name\": \"the striking red tower\"}, {\"id\": 10898, \"name\": \"the price of 44 yuan\"}, {\"id\": 10899, \"name\": \"large banner\"}, {\"id\": 10900, \"name\": \"rugged peak\"}, {\"id\": 10901, \"name\": \"light-colored wood floor\"}, {\"id\": 10902, \"name\": \"the blue patterned shirt\"}, {\"id\": 10903, \"name\": \"the mop\"}, {\"id\": 10904, \"name\": \"a green carpet\"}, {\"id\": 10905, \"name\": \"light-colored lid\"}, {\"id\": 10906, \"name\": \"sliding glass door\"}, {\"id\": 10907, \"name\": \"street food stall\"}, {\"id\": 10908, \"name\": \"edge of the road\"}, {\"id\": 10909, \"name\": \"a large, dark-colored statue or sculpture\"}, {\"id\": 10910, \"name\": \"a small, white car\"}, {\"id\": 10911, \"name\": \"a white tight\"}, {\"id\": 10912, \"name\": \"a bare foot\"}, {\"id\": 10913, \"name\": \"the dark grey accent\"}, {\"id\": 10914, \"name\": \"the black-and-white striped jacket\"}, {\"id\": 10915, \"name\": \"the tall, metal transmission tower\"}, {\"id\": 10916, \"name\": \"the dark blue shorts\"}, {\"id\": 10917, \"name\": \"the single-story structure\"}, {\"id\": 10918, \"name\": \"black tube\"}, {\"id\": 10919, \"name\": \"red and blue shirt\"}, {\"id\": 10920, \"name\": \"the long, thin light fixture\"}, {\"id\": 10921, \"name\": \"the inflatable toy\"}, {\"id\": 10922, \"name\": \"red trim\"}, {\"id\": 10923, \"name\": \"dark brown background\"}, {\"id\": 10924, \"name\": \"music stand\"}, {\"id\": 10925, \"name\": \"small yellow bowl\"}, {\"id\": 10926, \"name\": \"basket of fruit\"}, {\"id\": 10927, \"name\": \"black bollard\"}, {\"id\": 10928, \"name\": \"the polished tile floor\"}, {\"id\": 10929, \"name\": \"a large brick building\"}, {\"id\": 10930, \"name\": \"a roof rack\"}, {\"id\": 10931, \"name\": \"a wall of the mall\"}, {\"id\": 10932, \"name\": \"brown shirt\"}, {\"id\": 10933, \"name\": \"the headscarf\"}, {\"id\": 10934, \"name\": \"a brown hook\"}, {\"id\": 10935, \"name\": \"a green metal plate\"}, {\"id\": 10936, \"name\": \"the church steeple\"}, {\"id\": 10937, \"name\": \"the small black dog\"}, {\"id\": 10938, \"name\": \"brown wood\"}, {\"id\": 10939, \"name\": \"brown square\"}, {\"id\": 10940, \"name\": \"red bus\"}, {\"id\": 10941, \"name\": \"the mug\"}, {\"id\": 10942, \"name\": \"a red liquid\"}, {\"id\": 10943, \"name\": \"clear backboard\"}, {\"id\": 10944, \"name\": \"gray hatchback\"}, {\"id\": 10945, \"name\": \"purple banner\"}, {\"id\": 10946, \"name\": \"a santa claus costume\"}, {\"id\": 10947, \"name\": \"grey tiled floor\"}, {\"id\": 10948, \"name\": \"dark-colored facade\"}, {\"id\": 10949, \"name\": \"the partial view of a person's arm\"}, {\"id\": 10950, \"name\": \"a pothole\"}, {\"id\": 10951, \"name\": \"a green and white bag\"}, {\"id\": 10952, \"name\": \"the discounted price\"}, {\"id\": 10953, \"name\": \"a white air conditioning unit\"}, {\"id\": 10954, \"name\": \"a large, blue, illuminated structure\"}, {\"id\": 10955, \"name\": \"performer's attire\"}, {\"id\": 10956, \"name\": \"large, dark green Christmas tree\"}, {\"id\": 10957, \"name\": \"the beige concrete\"}, {\"id\": 10958, \"name\": \"the red storefront\"}, {\"id\": 10959, \"name\": \"narrow, dirty street\"}, {\"id\": 10960, \"name\": \"the vehicle's rear window\"}, {\"id\": 10961, \"name\": \"the blue plastic bucket\"}, {\"id\": 10962, \"name\": \"a grey shorts\"}, {\"id\": 10963, \"name\": \"the men's robe\"}, {\"id\": 10964, \"name\": \"a large, empty expanse of road\"}, {\"id\": 10965, \"name\": \"the rectangular metal tray\"}, {\"id\": 10966, \"name\": \"single vehicle\"}, {\"id\": 10967, \"name\": \"the front paws\"}, {\"id\": 10968, \"name\": \"a large, yellow tarp\"}, {\"id\": 10969, \"name\": \"the white plastic bag\"}, {\"id\": 10970, \"name\": \"the black and yellow stripe\"}, {\"id\": 10971, \"name\": \"the directional sign\"}, {\"id\": 10972, \"name\": \"brown cat\"}, {\"id\": 10973, \"name\": \"the brake light\"}, {\"id\": 10974, \"name\": \"the man in a black shirt and yellow shorts\"}, {\"id\": 10975, \"name\": \"the black, textured fabric\"}, {\"id\": 10976, \"name\": \"a blue lower section\"}, {\"id\": 10977, \"name\": \"a black Nissan SUV\"}, {\"id\": 10978, \"name\": \"a colorful drum\"}, {\"id\": 10979, \"name\": \"muzzle\"}, {\"id\": 10980, \"name\": \"the red chair\"}, {\"id\": 10981, \"name\": \"small white chair\"}, {\"id\": 10982, \"name\": \"the dark-colored towel or blanket\"}, {\"id\": 10983, \"name\": \"the checkered hat\"}, {\"id\": 10984, \"name\": \"the attire\"}, {\"id\": 10985, \"name\": \"video\"}, {\"id\": 10986, \"name\": \"the smooth surface\"}, {\"id\": 10987, \"name\": \"the man in the yellow shirt\"}, {\"id\": 10988, \"name\": \"a supermarket worker\"}, {\"id\": 10989, \"name\": \"dark skirt\"}, {\"id\": 10990, \"name\": \"a navy blue cardigan\"}, {\"id\": 10991, \"name\": \"calm water\"}, {\"id\": 10992, \"name\": \"the warehouse or shop\"}, {\"id\": 10993, \"name\": \"a tropical-style off-the-shoulder blouse\"}, {\"id\": 10994, \"name\": \"the floral-patterned comforter\"}, {\"id\": 10995, \"name\": \"stainless steel appliance\"}, {\"id\": 10996, \"name\": \"pedestrian crossing\"}, {\"id\": 10997, \"name\": \"red and yellow light\"}, {\"id\": 10998, \"name\": \"a gray, fuzzy area rug\"}, {\"id\": 10999, \"name\": \"a driveway\"}, {\"id\": 11000, \"name\": \"a dark grey step\"}, {\"id\": 11001, \"name\": \"a foreground table\"}, {\"id\": 11002, \"name\": \"a black cell phone\"}, {\"id\": 11003, \"name\": \"a dirt pit\"}, {\"id\": 11004, \"name\": \"a shallow water\"}, {\"id\": 11005, \"name\": \"dark red sauce\"}, {\"id\": 11006, \"name\": \"man wearing a tan hat and black leather jacket\"}, {\"id\": 11007, \"name\": \"the cutlery\"}, {\"id\": 11008, \"name\": \"dark blue SUV\"}, {\"id\": 11009, \"name\": \"the decorative element\"}, {\"id\": 11010, \"name\": \"a German Shepherd-type dog\"}, {\"id\": 11011, \"name\": \"red path\"}, {\"id\": 11012, \"name\": \"the orange and white backdrop\"}, {\"id\": 11013, \"name\": \"roof of the tank\"}, {\"id\": 11014, \"name\": \"a long tie\"}, {\"id\": 11015, \"name\": \"the polished grey floor\"}, {\"id\": 11016, \"name\": \"the brick pattern\"}, {\"id\": 11017, \"name\": \"a grey t-shirt\"}, {\"id\": 11018, \"name\": \"a pink dress\"}, {\"id\": 11019, \"name\": \"parked cars\"}, {\"id\": 11020, \"name\": \"blue scooter\"}, {\"id\": 11021, \"name\": \"front windshield\"}, {\"id\": 11022, \"name\": \"the blue lightsaber\"}, {\"id\": 11023, \"name\": \"the white trim\"}, {\"id\": 11024, \"name\": \"the long, wavy brown hair\"}, {\"id\": 11025, \"name\": \"dark-brown wooden platform\"}, {\"id\": 11026, \"name\": \"a distinctive black and white coat\"}, {\"id\": 11027, \"name\": \"the group of five boys\"}, {\"id\": 11028, \"name\": \"curved paved walkway\"}, {\"id\": 11029, \"name\": \"a napkins\"}, {\"id\": 11030, \"name\": \"large bone\"}, {\"id\": 11031, \"name\": \"blurred, low-quality photograph\"}, {\"id\": 11032, \"name\": \"an orange back\"}, {\"id\": 11033, \"name\": \"silver tripod\"}, {\"id\": 11034, \"name\": \"the foam\"}, {\"id\": 11035, \"name\": \"a green skirt\"}, {\"id\": 11036, \"name\": \"white tent\"}, {\"id\": 11037, \"name\": \"a small, dark brown bowl\"}, {\"id\": 11038, \"name\": \"a thick trunk\"}, {\"id\": 11039, \"name\": \"a red container with a lid\"}, {\"id\": 11040, \"name\": \"a worn tire\"}, {\"id\": 11041, \"name\": \"single green ball\"}, {\"id\": 11042, \"name\": \"a conditioner\"}, {\"id\": 11043, \"name\": \"the dirt hill\"}, {\"id\": 11044, \"name\": \"the light-colored pattern\"}, {\"id\": 11045, \"name\": \"the wall-mounted air conditioner\"}, {\"id\": 11046, \"name\": \"spacious, marble-floored room\"}, {\"id\": 11047, \"name\": \"small green bowl\"}, {\"id\": 11048, \"name\": \"the black and white striped top\"}, {\"id\": 11049, \"name\": \"the vehicle's shadow\"}, {\"id\": 11050, \"name\": \"a black center console\"}, {\"id\": 11051, \"name\": \"a white collared shirt\"}, {\"id\": 11052, \"name\": \"the signpost\"}, {\"id\": 11053, \"name\": \"the Maltese\"}, {\"id\": 11054, \"name\": \"the dashboard of the car\"}, {\"id\": 11055, \"name\": \"weathered facade\"}, {\"id\": 11056, \"name\": \"the baby's head\"}, {\"id\": 11057, \"name\": \"an orange comb\"}, {\"id\": 11058, \"name\": \"pink mug\"}, {\"id\": 11059, \"name\": \"the central corridor\"}, {\"id\": 11060, \"name\": \"the bakery storefront\"}, {\"id\": 11061, \"name\": \"body\"}, {\"id\": 11062, \"name\": \"a red headscarf\"}, {\"id\": 11063, \"name\": \"the repeating pattern of tan animals\"}, {\"id\": 11064, \"name\": \"the yellow bracelet\"}, {\"id\": 11065, \"name\": \"the small puppy\"}, {\"id\": 11066, \"name\": \"the blue pillow\"}, {\"id\": 11067, \"name\": \"a black door\"}, {\"id\": 11068, \"name\": \"a tall, metal structure\"}, {\"id\": 11069, \"name\": \"the brown frame\"}, {\"id\": 11070, \"name\": \"white screen\"}, {\"id\": 11071, \"name\": \"the colorful design\"}, {\"id\": 11072, \"name\": \"black dog\"}, {\"id\": 11073, \"name\": \"grey shirt\"}, {\"id\": 11074, \"name\": \"the large body of water\"}, {\"id\": 11075, \"name\": \"the red light\"}, {\"id\": 11076, \"name\": \"the wall poster\"}, {\"id\": 11077, \"name\": \"a makeshift drum\"}, {\"id\": 11078, \"name\": \"prominent storefront\"}, {\"id\": 11079, \"name\": \"a gray concrete surface\"}, {\"id\": 11080, \"name\": \"the colorful packaging\"}, {\"id\": 11081, \"name\": \"an advertisement\"}, {\"id\": 11082, \"name\": \"the white and dark gray marbling\"}, {\"id\": 11083, \"name\": \"a narrow, unpaved alleyway\"}, {\"id\": 11084, \"name\": \"the flat-screen TV\"}, {\"id\": 11085, \"name\": \"large yellow plastic bag of rice\"}, {\"id\": 11086, \"name\": \"the pink coat\"}, {\"id\": 11087, \"name\": \"pink and black plaid collar\"}, {\"id\": 11088, \"name\": \"the pair of sunglasses\"}, {\"id\": 11089, \"name\": \"the frond\"}, {\"id\": 11090, \"name\": \"the wooden counter\"}, {\"id\": 11091, \"name\": \"the skin\"}, {\"id\": 11092, \"name\": \"yellow sticker\"}, {\"id\": 11093, \"name\": \"gloves\"}, {\"id\": 11094, \"name\": \"the someone\"}, {\"id\": 11095, \"name\": \"sturdy wooden beam\"}, {\"id\": 11096, \"name\": \"a sign on the cart\"}, {\"id\": 11097, \"name\": \"long black table\"}, {\"id\": 11098, \"name\": \"grey concrete sidewalk\"}, {\"id\": 11099, \"name\": \"red wall\"}, {\"id\": 11100, \"name\": \"the dark gray and white marble floor\"}, {\"id\": 11101, \"name\": \"the blue and white patterned fabric\"}, {\"id\": 11102, \"name\": \"a plain gray wall\"}, {\"id\": 11103, \"name\": \"a black baseboard\"}, {\"id\": 11104, \"name\": \"white rack\"}, {\"id\": 11105, \"name\": \"the blue handle\"}, {\"id\": 11106, \"name\": \"the royal blue headscarf\"}, {\"id\": 11107, \"name\": \"the air conditioning unit\"}, {\"id\": 11108, \"name\": \"a silver buckle\"}, {\"id\": 11109, \"name\": \"leopard-print blouse\"}, {\"id\": 11110, \"name\": \"the blue and white patterned outfit\"}, {\"id\": 11111, \"name\": \"tan body\"}, {\"id\": 11112, \"name\": \"the yellow and black shirt\"}, {\"id\": 11113, \"name\": \"glass bowl\"}, {\"id\": 11114, \"name\": \"white button-down shirt\"}, {\"id\": 11115, \"name\": \"wide, paved road\"}, {\"id\": 11116, \"name\": \"white long-sleeved onesie\"}, {\"id\": 11117, \"name\": \"red produce\"}, {\"id\": 11118, \"name\": \"the green, plastic toy or playset\"}, {\"id\": 11119, \"name\": \"the pink rectangle\"}, {\"id\": 11120, \"name\": \"a small blue and white package\"}, {\"id\": 11121, \"name\": \"refrigerated shelf\"}, {\"id\": 11122, \"name\": \"smaller Christmas tree\"}, {\"id\": 11123, \"name\": \"a green flip-flop\"}, {\"id\": 11124, \"name\": \"silver border\"}, {\"id\": 11125, \"name\": \"the medley of diced fruit\"}, {\"id\": 11126, \"name\": \"a red and yellow patterned fabric\"}, {\"id\": 11127, \"name\": \"a stack of white papers\"}, {\"id\": 11128, \"name\": \"black and gold top\"}, {\"id\": 11129, \"name\": \"the gravel parking lot\"}, {\"id\": 11130, \"name\": \"brick-paved walkway\"}, {\"id\": 11131, \"name\": \"a white tuk-tuk\"}, {\"id\": 11132, \"name\": \"a young animal\"}, {\"id\": 11133, \"name\": \"brown, black, and white kitten\"}, {\"id\": 11134, \"name\": \"green carpet\"}, {\"id\": 11135, \"name\": \"the grey brick or stone pathway\"}, {\"id\": 11136, \"name\": \"the Tantalizing\"}, {\"id\": 11137, \"name\": \"a serene grassy area\"}, {\"id\": 11138, \"name\": \"the blue eye\"}, {\"id\": 11139, \"name\": \"slide\"}, {\"id\": 11140, \"name\": \"a plastic\"}, {\"id\": 11141, \"name\": \"dark blue tracksuit\"}, {\"id\": 11142, \"name\": \"the large green and white pillow\"}, {\"id\": 11143, \"name\": \"a metal drain\"}, {\"id\": 11144, \"name\": \"the maroon sweatshirt\"}, {\"id\": 11145, \"name\": \"vibrant orange jacket\"}, {\"id\": 11146, \"name\": \"debris\"}, {\"id\": 11147, \"name\": \"an outdoor basketball court\"}, {\"id\": 11148, \"name\": \"the two-lane highway\"}, {\"id\": 11149, \"name\": \"the large air hockey table\"}, {\"id\": 11150, \"name\": \"lone figure\"}, {\"id\": 11151, \"name\": \"narrow dirt path\"}, {\"id\": 11152, \"name\": \"a vast expanse of sandy terrain\"}, {\"id\": 11153, \"name\": \"the table tennis table\"}, {\"id\": 11154, \"name\": \"blue package\"}, {\"id\": 11155, \"name\": \"the large shadow\"}, {\"id\": 11156, \"name\": \"a gray and white checkerboard pattern\"}, {\"id\": 11157, \"name\": \"the pair of shoes\"}, {\"id\": 11158, \"name\": \"a large tool\"}, {\"id\": 11159, \"name\": \"the vibrant, colorful air hockey table\"}, {\"id\": 11160, \"name\": \"a tan cargo pant\"}, {\"id\": 11161, \"name\": \"the red building\"}, {\"id\": 11162, \"name\": \"yellow sign\"}, {\"id\": 11163, \"name\": \"socket\"}, {\"id\": 11164, \"name\": \"an image of fruit\"}, {\"id\": 11165, \"name\": \"man in a dark suit jacket and light blue shirt\"}, {\"id\": 11166, \"name\": \"the large gate\"}, {\"id\": 11167, \"name\": \"the yellow and white pattern\"}, {\"id\": 11168, \"name\": \"white drop ceiling\"}, {\"id\": 11169, \"name\": \"the aisle of a store\"}, {\"id\": 11170, \"name\": \"the linoleum\"}, {\"id\": 11171, \"name\": \"the framed picture\"}, {\"id\": 11172, \"name\": \"white patch\"}, {\"id\": 11173, \"name\": \"tree bark\"}, {\"id\": 11174, \"name\": \"a grey metal frame\"}, {\"id\": 11175, \"name\": \"distinctive blue and tan patterned floor\"}, {\"id\": 11176, \"name\": \"black plastic chair\"}, {\"id\": 11177, \"name\": \"a red hijab\"}, {\"id\": 11178, \"name\": \"the fluffy, curly coat\"}, {\"id\": 11179, \"name\": \"the pile of fish\"}, {\"id\": 11180, \"name\": \"a clear blue sky\"}, {\"id\": 11181, \"name\": \"a red and white pole\"}, {\"id\": 11182, \"name\": \"clothesline\"}, {\"id\": 11183, \"name\": \"a blue structure\"}, {\"id\": 11184, \"name\": \"the educational video game\"}, {\"id\": 11185, \"name\": \"a hairy leg\"}, {\"id\": 11186, \"name\": \"dancers' attire\"}, {\"id\": 11187, \"name\": \"yellow and black speed bump\"}, {\"id\": 11188, \"name\": \"a row of buildings\"}, {\"id\": 11189, \"name\": \"a shiny, light-colored tile\"}, {\"id\": 11190, \"name\": \"a black wallet\"}, {\"id\": 11191, \"name\": \"the wooden stick\"}, {\"id\": 11192, \"name\": \"a large curtain\"}, {\"id\": 11193, \"name\": \"the hole in the wall\"}, {\"id\": 11194, \"name\": \"a fishing lure\"}, {\"id\": 11195, \"name\": \"a white and yellow van\"}, {\"id\": 11196, \"name\": \"the SALMON\"}, {\"id\": 11197, \"name\": \"a yellow circular object\"}, {\"id\": 11198, \"name\": \"the man in black pants\"}, {\"id\": 11199, \"name\": \"the purple pant\"}, {\"id\": 11200, \"name\": \"car's rear end\"}, {\"id\": 11201, \"name\": \"black-and-white patterned top\"}, {\"id\": 11202, \"name\": \"red and white accent\"}, {\"id\": 11203, \"name\": \"the doormat\"}, {\"id\": 11204, \"name\": \"the pink ensemble\"}, {\"id\": 11205, \"name\": \"the prominent pink stripe\"}, {\"id\": 11206, \"name\": \"large, fuzzy, red bear\"}, {\"id\": 11207, \"name\": \"mortar and pestle\"}, {\"id\": 11208, \"name\": \"an index finger\"}, {\"id\": 11209, \"name\": \"a flat top\"}, {\"id\": 11210, \"name\": \"hatchback\"}, {\"id\": 11211, \"name\": \"black helmet\"}, {\"id\": 11212, \"name\": \"the purple bell-bottom pant\"}, {\"id\": 11213, \"name\": \"green canopy\"}, {\"id\": 11214, \"name\": \"a pink container\"}, {\"id\": 11215, \"name\": \"the dark wood\"}, {\"id\": 11216, \"name\": \"red and green curtain\"}, {\"id\": 11217, \"name\": \"the blue background\"}, {\"id\": 11218, \"name\": \"red dish\"}, {\"id\": 11219, \"name\": \"the metal bowl\"}, {\"id\": 11220, \"name\": \"the white rabbit toy\"}, {\"id\": 11221, \"name\": \"long black robe\"}, {\"id\": 11222, \"name\": \"black and white checkered scarf\"}, {\"id\": 11223, \"name\": \"the blue and red shirt\"}, {\"id\": 11224, \"name\": \"a small orange reflector\"}, {\"id\": 11225, \"name\": \"the large white tiled floor\"}, {\"id\": 11226, \"name\": \"a light pink coat\"}, {\"id\": 11227, \"name\": \"brick walkway\"}, {\"id\": 11228, \"name\": \"the folded brown rug or blanket\"}, {\"id\": 11229, \"name\": \"the building's facade\"}, {\"id\": 11230, \"name\": \"the dark gray cabinet\"}, {\"id\": 11231, \"name\": \"the grey shorts\"}, {\"id\": 11232, \"name\": \"the vibrant yellow plant\"}, {\"id\": 11233, \"name\": \"grey cloud\"}, {\"id\": 11234, \"name\": \"a blue blanket\"}, {\"id\": 11235, \"name\": \"an equipment\"}, {\"id\": 11236, \"name\": \"gray concrete street\"}, {\"id\": 11237, \"name\": \"large white van\"}, {\"id\": 11238, \"name\": \"a woman in a headscarf\"}, {\"id\": 11239, \"name\": \"a blue and white metal frame\"}, {\"id\": 11240, \"name\": \"festive banner\"}, {\"id\": 11241, \"name\": \"a white center line\"}, {\"id\": 11242, \"name\": \"a pink umbrella graphic\"}, {\"id\": 11243, \"name\": \"the cloudy blue sky\"}, {\"id\": 11244, \"name\": \"the low stool\"}, {\"id\": 11245, \"name\": \"an ice rink's surface\"}, {\"id\": 11246, \"name\": \"the lower stage\"}, {\"id\": 11247, \"name\": \"blue and white jacket\"}, {\"id\": 11248, \"name\": \"a large camera\"}, {\"id\": 11249, \"name\": \"white background\"}, {\"id\": 11250, \"name\": \"a beige-colored building\"}, {\"id\": 11251, \"name\": \"court's yellow floor\"}, {\"id\": 11252, \"name\": \"back of another laptop\"}, {\"id\": 11253, \"name\": \"the left sign\"}, {\"id\": 11254, \"name\": \"dark asphalt road\"}, {\"id\": 11255, \"name\": \"the seated man\"}, {\"id\": 11256, \"name\": \"pair of grey slipper\"}, {\"id\": 11257, \"name\": \"a gourd\"}, {\"id\": 11258, \"name\": \"a light-colored wooden table\"}, {\"id\": 11259, \"name\": \"a large statue of the Virgin Mary\"}, {\"id\": 11260, \"name\": \"yellow smiley face\"}, {\"id\": 11261, \"name\": \"an electrical outlet\"}, {\"id\": 11262, \"name\": \"small wooden frame\"}, {\"id\": 11263, \"name\": \"door of the refrigerator\"}, {\"id\": 11264, \"name\": \"the muddy, wet field\"}, {\"id\": 11265, \"name\": \"the green background\"}, {\"id\": 11266, \"name\": \"a black tile accent\"}, {\"id\": 11267, \"name\": \"a gray fur-lined hood\"}, {\"id\": 11268, \"name\": \"a vibrant pattern of multicolored polka dots\"}, {\"id\": 11269, \"name\": \"large red archway\"}, {\"id\": 11270, \"name\": \"toddler\"}, {\"id\": 11271, \"name\": \"the white blouse\"}, {\"id\": 11272, \"name\": \"traditional Indian attire\"}, {\"id\": 11273, \"name\": \"red woven basket\"}, {\"id\": 11274, \"name\": \"the large, dark building\"}, {\"id\": 11275, \"name\": \"the set of stairs\"}, {\"id\": 11276, \"name\": \"a crack\"}, {\"id\": 11277, \"name\": \"a garland of greenery\"}, {\"id\": 11278, \"name\": \"the yellow keyboard\"}, {\"id\": 11279, \"name\": \"a large, cartoon turtle\"}, {\"id\": 11280, \"name\": \"a blue sky\"}, {\"id\": 11281, \"name\": \"the barcode\"}, {\"id\": 11282, \"name\": \"beige platform\"}, {\"id\": 11283, \"name\": \"fruit bowl\"}, {\"id\": 11284, \"name\": \"a white screen\"}, {\"id\": 11285, \"name\": \"brown coat\"}, {\"id\": 11286, \"name\": \"the massive ball pit\"}, {\"id\": 11287, \"name\": \"a large stone\"}, {\"id\": 11288, \"name\": \"the wooden box\"}, {\"id\": 11289, \"name\": \"the dinosaur\"}, {\"id\": 11290, \"name\": \"the black bag or backpack\"}, {\"id\": 11291, \"name\": \"the yellow tarp\"}, {\"id\": 11292, \"name\": \"the upper section of a brown building\"}, {\"id\": 11293, \"name\": \"a small, long-haired dog\"}, {\"id\": 11294, \"name\": \"the piece of bread\"}, {\"id\": 11295, \"name\": \"white wristband\"}, {\"id\": 11296, \"name\": \"the green exit sign\"}, {\"id\": 11297, \"name\": \"spinach\"}, {\"id\": 11298, \"name\": \"a marble-topped table\"}, {\"id\": 11299, \"name\": \"a polished concrete\"}, {\"id\": 11300, \"name\": \"an edge of the sidewalk\"}, {\"id\": 11301, \"name\": \"the cotton or a similar material\"}, {\"id\": 11302, \"name\": \"white oscillating fan\"}, {\"id\": 11303, \"name\": \"the blue and black object\"}, {\"id\": 11304, \"name\": \"a long-sleeved, black and white striped shirt\"}, {\"id\": 11305, \"name\": \"the black dress\"}, {\"id\": 11306, \"name\": \"the yellow baseball cap\"}, {\"id\": 11307, \"name\": \"the yellow and orange garland\"}, {\"id\": 11308, \"name\": \"a dark collar\"}, {\"id\": 11309, \"name\": \"the brown armchair\"}, {\"id\": 11310, \"name\": \"a car's interior\"}, {\"id\": 11311, \"name\": \"dark-colored turtleneck sweater\"}, {\"id\": 11312, \"name\": \"toy chicken leg\"}, {\"id\": 11313, \"name\": \"a metal shopping cart\"}, {\"id\": 11314, \"name\": \"the black and white patterned shirt\"}, {\"id\": 11315, \"name\": \"a pan of food\"}, {\"id\": 11316, \"name\": \"the large, open box truck\"}, {\"id\": 11317, \"name\": \"the metal measuring cup\"}, {\"id\": 11318, \"name\": \"an asphalt surface\"}, {\"id\": 11319, \"name\": \"an uneven surface\"}, {\"id\": 11320, \"name\": \"the black belly dance costume\"}, {\"id\": 11321, \"name\": \"ground outside\"}, {\"id\": 11322, \"name\": \"the black pole\"}, {\"id\": 11323, \"name\": \"the central aisle\"}, {\"id\": 11324, \"name\": \"glossy white floor\"}, {\"id\": 11325, \"name\": \"the vegetation\"}, {\"id\": 11326, \"name\": \"a white and gray floor\"}, {\"id\": 11327, \"name\": \"a green strap\"}, {\"id\": 11328, \"name\": \"an orange bag\"}, {\"id\": 11329, \"name\": \"a store's ceiling\"}, {\"id\": 11330, \"name\": \"the pink camisole top\"}, {\"id\": 11331, \"name\": \"a sloping bank\"}, {\"id\": 11332, \"name\": \"a white analog clock\"}, {\"id\": 11333, \"name\": \"the small wooden workbench\"}, {\"id\": 11334, \"name\": \"the painting of a face\"}, {\"id\": 11335, \"name\": \"the prominent red sign\"}, {\"id\": 11336, \"name\": \"the sand\"}, {\"id\": 11337, \"name\": \"the pink floral patterned cover\"}, {\"id\": 11338, \"name\": \"the small, blue building\"}, {\"id\": 11339, \"name\": \"a gray paved path\"}, {\"id\": 11340, \"name\": \"the white, fan-shaped object\"}, {\"id\": 11341, \"name\": \"a white sweatshirt\"}, {\"id\": 11342, \"name\": \"station\"}, {\"id\": 11343, \"name\": \"a woman in a pink shirt\"}, {\"id\": 11344, \"name\": \"the rural dirt road\"}, {\"id\": 11345, \"name\": \"large, multi-colored mat\"}, {\"id\": 11346, \"name\": \"the large, shiny, black wall\"}, {\"id\": 11347, \"name\": \"a firework\"}, {\"id\": 11348, \"name\": \"a wall frame\"}, {\"id\": 11349, \"name\": \"a yellow and black floral pattern\"}, {\"id\": 11350, \"name\": \"black dashboard\"}, {\"id\": 11351, \"name\": \"a sign with a green background\"}, {\"id\": 11352, \"name\": \"the ledge\"}, {\"id\": 11353, \"name\": \"brown and white striped button-up shirt\"}, {\"id\": 11354, \"name\": \"a blue plastic ride-on toy\"}, {\"id\": 11355, \"name\": \"a pink plastic bag\"}, {\"id\": 11356, \"name\": \"a collection of papers\"}, {\"id\": 11357, \"name\": \"a large stone wall\"}, {\"id\": 11358, \"name\": \"the blue and white checkerboard pattern\"}, {\"id\": 11359, \"name\": \"sink's ledge\"}, {\"id\": 11360, \"name\": \"the man in a striped shirt\"}, {\"id\": 11361, \"name\": \"a large white cabinet\"}, {\"id\": 11362, \"name\": \"person walking\"}, {\"id\": 11363, \"name\": \"the gray box\"}, {\"id\": 11364, \"name\": \"stand\"}, {\"id\": 11365, \"name\": \"gray-tiled floor\"}, {\"id\": 11366, \"name\": \"the speckled pattern\"}, {\"id\": 11367, \"name\": \"purple shorts\"}, {\"id\": 11368, \"name\": \"handwritten note\"}, {\"id\": 11369, \"name\": \"a larger bundle of herbs\"}, {\"id\": 11370, \"name\": \"a surrounding vegetation\"}, {\"id\": 11371, \"name\": \"a white-tiled floor\"}, {\"id\": 11372, \"name\": \"the rust-colored outfit\"}, {\"id\": 11373, \"name\": \"the reflection of the room\"}, {\"id\": 11374, \"name\": \"a yellow van\"}, {\"id\": 11375, \"name\": \"the red gate\"}, {\"id\": 11376, \"name\": \"the man sitting on a bench\"}, {\"id\": 11377, \"name\": \"a horse-drawn carriage\"}, {\"id\": 11378, \"name\": \"a black and white striped shirt\"}, {\"id\": 11379, \"name\": \"cracked and dry ground\"}, {\"id\": 11380, \"name\": \"a red SUV\"}, {\"id\": 11381, \"name\": \"the stainless steel pan\"}, {\"id\": 11382, \"name\": \"the barefoot woman\"}, {\"id\": 11383, \"name\": \"short gray hair\"}, {\"id\": 11384, \"name\": \"a book or pamphlet\"}, {\"id\": 11385, \"name\": \"a light blue jeans\"}, {\"id\": 11386, \"name\": \"the guardrail\"}, {\"id\": 11387, \"name\": \"red dirt road\"}, {\"id\": 11388, \"name\": \"a blue hoodie\"}, {\"id\": 11389, \"name\": \"white base\"}, {\"id\": 11390, \"name\": \"colorful illustration\"}, {\"id\": 11391, \"name\": \"blue metal garage door\"}, {\"id\": 11392, \"name\": \"red box\"}, {\"id\": 11393, \"name\": \"the large industrial machine\"}, {\"id\": 11394, \"name\": \"side of the road\"}, {\"id\": 11395, \"name\": \"raised brick border\"}, {\"id\": 11396, \"name\": \"a low concrete curb\"}, {\"id\": 11397, \"name\": \"the large black tarp\"}, {\"id\": 11398, \"name\": \"cream-colored turtleneck\"}, {\"id\": 11399, \"name\": \"stone bar\"}, {\"id\": 11400, \"name\": \"the white minivan\"}, {\"id\": 11401, \"name\": \"the shore\"}, {\"id\": 11402, \"name\": \"a yellow cardigan\"}, {\"id\": 11403, \"name\": \"gray shoe\"}, {\"id\": 11404, \"name\": \"a thick, curly coat\"}, {\"id\": 11405, \"name\": \"a large pot\"}, {\"id\": 11406, \"name\": \"blue blanket\"}, {\"id\": 11407, \"name\": \"digital display\"}, {\"id\": 11408, \"name\": \"a large platform\"}, {\"id\": 11409, \"name\": \"the bar of soap\"}, {\"id\": 11410, \"name\": \"the red and white logo\"}, {\"id\": 11411, \"name\": \"a triangle\"}, {\"id\": 11412, \"name\": \"a cracked concrete surface\"}, {\"id\": 11413, \"name\": \"a dark fur\"}, {\"id\": 11414, \"name\": \"the light-colored concrete floor\"}, {\"id\": 11415, \"name\": \"a street scene\"}, {\"id\": 11416, \"name\": \"red bowler hat\"}, {\"id\": 11417, \"name\": \"a silk\"}, {\"id\": 11418, \"name\": \"the smaller dog\"}, {\"id\": 11419, \"name\": \"a silver kettle\"}, {\"id\": 11420, \"name\": \"yellow and black striped speed bump\"}, {\"id\": 11421, \"name\": \"the white design\"}, {\"id\": 11422, \"name\": \"olive green cargo shorts\"}, {\"id\": 11423, \"name\": \"the small, light-colored wooden table\"}, {\"id\": 11424, \"name\": \"small, rustic wooden building\"}, {\"id\": 11425, \"name\": \"tap\"}, {\"id\": 11426, \"name\": \"small green bike\"}, {\"id\": 11427, \"name\": \"dark green engine\"}, {\"id\": 11428, \"name\": \"blue and black checkered shirt\"}, {\"id\": 11429, \"name\": \"a round bolt\"}, {\"id\": 11430, \"name\": \"a Virginia Coach bus\"}, {\"id\": 11431, \"name\": \"a glass of soda\"}, {\"id\": 11432, \"name\": \"a large stack of green cucumbers\"}, {\"id\": 11433, \"name\": \"light brown wood\"}, {\"id\": 11434, \"name\": \"a metal trash can\"}, {\"id\": 11435, \"name\": \"the man in a white jacket and brown strap\"}, {\"id\": 11436, \"name\": \"a fluffy tuft\"}, {\"id\": 11437, \"name\": \"black sleeveless shirt\"}, {\"id\": 11438, \"name\": \"a dusty area\"}, {\"id\": 11439, \"name\": \"the asphalt area\"}, {\"id\": 11440, \"name\": \"the green bumper\"}, {\"id\": 11441, \"name\": \"the front leg\"}, {\"id\": 11442, \"name\": \"a plate of noodles\"}, {\"id\": 11443, \"name\": \"the light-colored wooden floor\"}, {\"id\": 11444, \"name\": \"game of golf\"}, {\"id\": 11445, \"name\": \"the silver railing\"}, {\"id\": 11446, \"name\": \"the liquid\"}, {\"id\": 11447, \"name\": \"large, two-story building\"}, {\"id\": 11448, \"name\": \"a wispy cloud\"}, {\"id\": 11449, \"name\": \"blue edge\"}, {\"id\": 11450, \"name\": \"a black and yellow auto rickshaw\"}, {\"id\": 11451, \"name\": \"the central black stripe\"}, {\"id\": 11452, \"name\": \"light blue shirt\"}, {\"id\": 11453, \"name\": \"yellow polo shirt\"}, {\"id\": 11454, \"name\": \"the frame\"}, {\"id\": 11455, \"name\": \"a white and brown coat\"}, {\"id\": 11456, \"name\": \"the transparent plastic bag\"}, {\"id\": 11457, \"name\": \"the white shelving unit\"}, {\"id\": 11458, \"name\": \"a row of dark wooden tables\"}, {\"id\": 11459, \"name\": \"the blue and gold floral patterned sheet\"}, {\"id\": 11460, \"name\": \"a low railing\"}, {\"id\": 11461, \"name\": \"the gray van\"}, {\"id\": 11462, \"name\": \"neon yellow and green brush\"}, {\"id\": 11463, \"name\": \"a qr code\"}, {\"id\": 11464, \"name\": \"the clear liquid\"}, {\"id\": 11465, \"name\": \"a someone\"}, {\"id\": 11466, \"name\": \"green car\"}, {\"id\": 11467, \"name\": \"room's background\"}, {\"id\": 11468, \"name\": \"the right front paw\"}, {\"id\": 11469, \"name\": \"a road barrier\"}, {\"id\": 11470, \"name\": \"a silver minivan\"}, {\"id\": 11471, \"name\": \"large digital display screen\"}, {\"id\": 11472, \"name\": \"brown wooden hutch or cabinet\"}, {\"id\": 11473, \"name\": \"man's right hand\"}, {\"id\": 11474, \"name\": \"the kettle\"}, {\"id\": 11475, \"name\": \"the row of red, white, and green fabric\"}, {\"id\": 11476, \"name\": \"the black sandal\"}, {\"id\": 11477, \"name\": \"black cushion\"}, {\"id\": 11478, \"name\": \"the brown wooden handrail\"}, {\"id\": 11479, \"name\": \"a yellow three-wheeled vehicle\"}, {\"id\": 11480, \"name\": \"the pink plastic bag\"}, {\"id\": 11481, \"name\": \"thick, durable material\"}, {\"id\": 11482, \"name\": \"the vibrant multicolored shirt\"}, {\"id\": 11483, \"name\": \"a large yellow box\"}, {\"id\": 11484, \"name\": \"dark brown door\"}, {\"id\": 11485, \"name\": \"the light green sedan\"}, {\"id\": 11486, \"name\": \"blue wristband\"}, {\"id\": 11487, \"name\": \"an orange and white striped wall\"}, {\"id\": 11488, \"name\": \"the blue awning\"}, {\"id\": 11489, \"name\": \"the black cross-body bag\"}, {\"id\": 11490, \"name\": \"a small gray toy poodle\"}, {\"id\": 11491, \"name\": \"the plastic barrier\"}, {\"id\": 11492, \"name\": \"a blue Mickey Mouse hoodie\"}, {\"id\": 11493, \"name\": \"a yellow plastic crate\"}, {\"id\": 11494, \"name\": \"green felt-covered table\"}, {\"id\": 11495, \"name\": \"brown leather bag\"}, {\"id\": 11496, \"name\": \"blue dress\"}, {\"id\": 11497, \"name\": \"black metal leg of a chair\"}, {\"id\": 11498, \"name\": \"a dark roof\"}, {\"id\": 11499, \"name\": \"the medium-length fur\"}, {\"id\": 11500, \"name\": \"the tree-lined sidewalk\"}, {\"id\": 11501, \"name\": \"the black cat\"}, {\"id\": 11502, \"name\": \"black cast iron pan\"}, {\"id\": 11503, \"name\": \"worn, grey-tiled floor\"}, {\"id\": 11504, \"name\": \"a yellow jumpsuit\"}, {\"id\": 11505, \"name\": \"a black coat with a hood\"}, {\"id\": 11506, \"name\": \"large wooden structure\"}, {\"id\": 11507, \"name\": \"orange and cream-colored pattern\"}, {\"id\": 11508, \"name\": \"large framed picture\"}, {\"id\": 11509, \"name\": \"a fuzzy pink sweater\"}, {\"id\": 11510, \"name\": \"the prominent red metal scaffolding structure\"}, {\"id\": 11511, \"name\": \"the white ceramic bowl\"}, {\"id\": 11512, \"name\": \"white metal bar\"}, {\"id\": 11513, \"name\": \"a car's front windshield\"}, {\"id\": 11514, \"name\": \"a tall, dark brown pillar\"}, {\"id\": 11515, \"name\": \"wiper blade\"}, {\"id\": 11516, \"name\": \"brick curb\"}, {\"id\": 11517, \"name\": \"strand\"}, {\"id\": 11518, \"name\": \"black logo\"}, {\"id\": 11519, \"name\": \"green door\"}, {\"id\": 11520, \"name\": \"white machine\"}, {\"id\": 11521, \"name\": \"black satchel\"}, {\"id\": 11522, \"name\": \"residential street\"}, {\"id\": 11523, \"name\": \"large brown backpack\"}, {\"id\": 11524, \"name\": \"the rumpled white comforter\"}, {\"id\": 11525, \"name\": \"the gray graphic design\"}, {\"id\": 11526, \"name\": \"rear door\"}, {\"id\": 11527, \"name\": \"a small, white shelf\"}, {\"id\": 11528, \"name\": \"a brown couch or bed\"}, {\"id\": 11529, \"name\": \"brick pathway\"}, {\"id\": 11530, \"name\": \"the stack of red plastic stools\"}, {\"id\": 11531, \"name\": \"a dusty, unpaved street\"}, {\"id\": 11532, \"name\": \"large pile of wooden boards\"}, {\"id\": 11533, \"name\": \"a chef hat\"}, {\"id\": 11534, \"name\": \"the large, light-colored box\"}, {\"id\": 11535, \"name\": \"a decorative band\"}, {\"id\": 11536, \"name\": \"brown trunk\"}, {\"id\": 11537, \"name\": \"car cover\"}, {\"id\": 11538, \"name\": \"brown building\"}, {\"id\": 11539, \"name\": \"snail\"}, {\"id\": 11540, \"name\": \"a price\"}, {\"id\": 11541, \"name\": \"a purse strap\"}, {\"id\": 11542, \"name\": \"black scooter\"}, {\"id\": 11543, \"name\": \"grey and white floral rug\"}, {\"id\": 11544, \"name\": \"the pink, stuffed animal head\"}, {\"id\": 11545, \"name\": \"the white collared shirt\"}, {\"id\": 11546, \"name\": \"a mashed potato\"}, {\"id\": 11547, \"name\": \"the white cup\"}, {\"id\": 11548, \"name\": \"the white cloth or sponge\"}, {\"id\": 11549, \"name\": \"the bedspread\"}, {\"id\": 11550, \"name\": \"red ball\"}, {\"id\": 11551, \"name\": \"prominent escalator\"}, {\"id\": 11552, \"name\": \"green cargo shorts\"}, {\"id\": 11553, \"name\": \"dark glasses\"}, {\"id\": 11554, \"name\": \"colorful decoration\"}, {\"id\": 11555, \"name\": \"the gray fabric\"}, {\"id\": 11556, \"name\": \"a bear's head\"}, {\"id\": 11557, \"name\": \"tray of green apples\"}, {\"id\": 11558, \"name\": \"road side path\"}, {\"id\": 11559, \"name\": \"the bed or couch\"}, {\"id\": 11560, \"name\": \"black metal link\"}, {\"id\": 11561, \"name\": \"block\"}, {\"id\": 11562, \"name\": \"a graffiti\"}, {\"id\": 11563, \"name\": \"a diamond pattern\"}, {\"id\": 11564, \"name\": \"red and orange shoe rack\"}, {\"id\": 11565, \"name\": \"the grassy hill\"}, {\"id\": 11566, \"name\": \"a rim of the pot\"}, {\"id\": 11567, \"name\": \"a tall, slender stem\"}, {\"id\": 11568, \"name\": \"a small, thatched-roof hut\"}, {\"id\": 11569, \"name\": \"blue face mask\"}, {\"id\": 11570, \"name\": \"the colorful headscarf\"}, {\"id\": 11571, \"name\": \"a striped orange and blue top\"}, {\"id\": 11572, \"name\": \"the sleek, shiny floor\"}, {\"id\": 11573, \"name\": \"a dark silhouette of shrub\"}, {\"id\": 11574, \"name\": \"series of buildings\"}, {\"id\": 11575, \"name\": \"a tall white structure\"}, {\"id\": 11576, \"name\": \"large vehicle\"}, {\"id\": 11577, \"name\": \"the black garment\"}, {\"id\": 11578, \"name\": \"a man in a white coat\"}, {\"id\": 11579, \"name\": \"a prominent Christmas tree\"}, {\"id\": 11580, \"name\": \"a metal and plastic\"}, {\"id\": 11581, \"name\": \"the white sweatpants\"}, {\"id\": 11582, \"name\": \"a tapestry of dried leaves, twigs, and other debris\"}, {\"id\": 11583, \"name\": \"a stack of green cucumber\"}, {\"id\": 11584, \"name\": \"a glass top\"}, {\"id\": 11585, \"name\": \"a brake light\"}, {\"id\": 11586, \"name\": \"a large ear\"}, {\"id\": 11587, \"name\": \"the pan\"}, {\"id\": 11588, \"name\": \"a white underside\"}, {\"id\": 11589, \"name\": \"a smaller rectangular hole\"}, {\"id\": 11590, \"name\": \"screenshot of a video\"}, {\"id\": 11591, \"name\": \"the driver's seat of a car\"}, {\"id\": 11592, \"name\": \"a vibrant blue wall\"}, {\"id\": 11593, \"name\": \"gear shift\"}, {\"id\": 11594, \"name\": \"roller coaster\"}, {\"id\": 11595, \"name\": \"a salmon-colored wall\"}, {\"id\": 11596, \"name\": \"the large, black nose\"}, {\"id\": 11597, \"name\": \"man in a green robe\"}, {\"id\": 11598, \"name\": \"a white and green color scheme\"}, {\"id\": 11599, \"name\": \"maroon-colored yoga mat\"}, {\"id\": 11600, \"name\": \"the monkey's fur\"}, {\"id\": 11601, \"name\": \"a vibrant yellow and black helmet\"}, {\"id\": 11602, \"name\": \"a large air vent\"}, {\"id\": 11603, \"name\": \"a large wooden cabinet\"}, {\"id\": 11604, \"name\": \"the woman in a blue dress\"}, {\"id\": 11605, \"name\": \"the large, blue plastic container\"}, {\"id\": 11606, \"name\": \"the forklift\"}, {\"id\": 11607, \"name\": \"the large, curved bookshelf\"}, {\"id\": 11608, \"name\": \"CLEARANCE SALE sign\"}, {\"id\": 11609, \"name\": \"utility pole\"}, {\"id\": 11610, \"name\": \"warm, brown fur\"}, {\"id\": 11611, \"name\": \"a gray rectangle\"}, {\"id\": 11612, \"name\": \"the large television screen\"}, {\"id\": 11613, \"name\": \"the pink package\"}, {\"id\": 11614, \"name\": \"a silver grill\"}, {\"id\": 11615, \"name\": \"the saw's handle\"}, {\"id\": 11616, \"name\": \"the traditional Indian clothing\"}, {\"id\": 11617, \"name\": \"blue and red object\"}, {\"id\": 11618, \"name\": \"posture\"}, {\"id\": 11619, \"name\": \"a monkey design\"}, {\"id\": 11620, \"name\": \"small black animal\"}, {\"id\": 11621, \"name\": \"the white pillar\"}, {\"id\": 11622, \"name\": \"plastic shopping bag\"}, {\"id\": 11623, \"name\": \"the dark blue and white pajama set\"}, {\"id\": 11624, \"name\": \"the round cake\"}, {\"id\": 11625, \"name\": \"white decorative screen\"}, {\"id\": 11626, \"name\": \"vibrant pink long-sleeve shirt\"}, {\"id\": 11627, \"name\": \"shelves of merchandise\"}, {\"id\": 11628, \"name\": \"the small, dark-colored couch\"}, {\"id\": 11629, \"name\": \"the black, blue, and white patterned outfit\"}, {\"id\": 11630, \"name\": \"long white rope\"}, {\"id\": 11631, \"name\": \"the woman in a brown coat\"}, {\"id\": 11632, \"name\": \"a set of stairs\"}, {\"id\": 11633, \"name\": \"the dough\"}, {\"id\": 11634, \"name\": \"watermelon slice\"}, {\"id\": 11635, \"name\": \"left pocket\"}, {\"id\": 11636, \"name\": \"a weight plate\"}, {\"id\": 11637, \"name\": \"the small pane\"}, {\"id\": 11638, \"name\": \"a concrete stairs\"}, {\"id\": 11639, \"name\": \"blue stuffed animal\"}, {\"id\": 11640, \"name\": \"a blue column\"}, {\"id\": 11641, \"name\": \"the teal and white striped t-shirt\"}, {\"id\": 11642, \"name\": \"maroon pant\"}, {\"id\": 11643, \"name\": \"red motorbike\"}, {\"id\": 11644, \"name\": \"the light grey stone tile\"}, {\"id\": 11645, \"name\": \"a green purse\"}, {\"id\": 11646, \"name\": \"a dark-colored bus\"}, {\"id\": 11647, \"name\": \"a mural's surface\"}, {\"id\": 11648, \"name\": \"a posture\"}, {\"id\": 11649, \"name\": \"a low orange wall\"}, {\"id\": 11650, \"name\": \"the Rottweiler mix\"}, {\"id\": 11651, \"name\": \"dirt surface\"}, {\"id\": 11652, \"name\": \"the fluffy pink rug\"}, {\"id\": 11653, \"name\": \"a cracked and wet street\"}, {\"id\": 11654, \"name\": \"a blue floral skirt\"}, {\"id\": 11655, \"name\": \"a multi-lane highway\"}, {\"id\": 11656, \"name\": \"the drainage channel\"}, {\"id\": 11657, \"name\": \"the large table\"}, {\"id\": 11658, \"name\": \"the charming cat\"}, {\"id\": 11659, \"name\": \"black and white leaf design\"}, {\"id\": 11660, \"name\": \"glass tower\"}, {\"id\": 11661, \"name\": \"dark, curly hair\"}, {\"id\": 11662, \"name\": \"pink machine\"}, {\"id\": 11663, \"name\": \"a pair of white sneakers\"}, {\"id\": 11664, \"name\": \"the gray concrete sidewalk\"}, {\"id\": 11665, \"name\": \"a blue and tan tuk-tuk\"}, {\"id\": 11666, \"name\": \"black pen\"}, {\"id\": 11667, \"name\": \"the pizza\"}, {\"id\": 11668, \"name\": \"the layer of hay\"}, {\"id\": 11669, \"name\": \"vehicle's interior\"}, {\"id\": 11670, \"name\": \"single light\"}, {\"id\": 11671, \"name\": \"field\"}, {\"id\": 11672, \"name\": \"the white hand sanitizer dispenser\"}, {\"id\": 11673, \"name\": \"the wooden cart\"}, {\"id\": 11674, \"name\": \"large black poster\"}, {\"id\": 11675, \"name\": \"the muddy, wet street\"}, {\"id\": 11676, \"name\": \"an orange dumpster\"}, {\"id\": 11677, \"name\": \"pink shirt\"}, {\"id\": 11678, \"name\": \"a calico cat\"}, {\"id\": 11679, \"name\": \"green wall\"}, {\"id\": 11680, \"name\": \"open notebook\"}, {\"id\": 11681, \"name\": \"the meat counter\"}, {\"id\": 11682, \"name\": \"a white kurta\"}, {\"id\": 11683, \"name\": \"pink sweater\"}, {\"id\": 11684, \"name\": \"black and silver heel\"}, {\"id\": 11685, \"name\": \"a building with a blue roof\"}, {\"id\": 11686, \"name\": \"orange handle\"}, {\"id\": 11687, \"name\": \"a small, dark brown rabbit\"}, {\"id\": 11688, \"name\": \"a gray floral pattern\"}, {\"id\": 11689, \"name\": \"yellow patterned shirt\"}, {\"id\": 11690, \"name\": \"a paved area\"}, {\"id\": 11691, \"name\": \"a curtain rod\"}, {\"id\": 11692, \"name\": \"beige bedding\"}, {\"id\": 11693, \"name\": \"the lamp\"}, {\"id\": 11694, \"name\": \"a yellow and pink striped scarf\"}, {\"id\": 11695, \"name\": \"group of young people\"}, {\"id\": 11696, \"name\": \"a white patch\"}, {\"id\": 11697, \"name\": \"knee\"}, {\"id\": 11698, \"name\": \"metal lip\"}, {\"id\": 11699, \"name\": \"crosswalk pattern\"}, {\"id\": 11700, \"name\": \"a breast\"}, {\"id\": 11701, \"name\": \"the red and black patterned top\"}, {\"id\": 11702, \"name\": \"rooftop patio\"}, {\"id\": 11703, \"name\": \"a row of step\"}, {\"id\": 11704, \"name\": \"a pink knit hat\"}, {\"id\": 11705, \"name\": \"black ceiling\"}, {\"id\": 11706, \"name\": \"the round window\"}, {\"id\": 11707, \"name\": \"the small turtle\"}, {\"id\": 11708, \"name\": \"large glass jug\"}, {\"id\": 11709, \"name\": \"racing game\"}, {\"id\": 11710, \"name\": \"the distinctive red and yellow brick pattern\"}, {\"id\": 11711, \"name\": \"blue denim jacket\"}, {\"id\": 11712, \"name\": \"a metal backrest\"}, {\"id\": 11713, \"name\": \"a metalwork\"}, {\"id\": 11714, \"name\": \"a carved stone head\"}, {\"id\": 11715, \"name\": \"pothole or sinkhole\"}, {\"id\": 11716, \"name\": \"bright pink rattle toy\"}, {\"id\": 11717, \"name\": \"festive attire\"}, {\"id\": 11718, \"name\": \"the purple and white striped towel\"}, {\"id\": 11719, \"name\": \"the small, muddy drainage channel\"}, {\"id\": 11720, \"name\": \"a wooden A-frame leg\"}, {\"id\": 11721, \"name\": \"raised concrete median\"}, {\"id\": 11722, \"name\": \"a red station wagon\"}, {\"id\": 11723, \"name\": \"the feed\"}, {\"id\": 11724, \"name\": \"the boy's hands\"}, {\"id\": 11725, \"name\": \"a prominent storefront\"}, {\"id\": 11726, \"name\": \"large metal spatula\"}, {\"id\": 11727, \"name\": \"accessory\"}, {\"id\": 11728, \"name\": \"a Mickey Mouse design\"}, {\"id\": 11729, \"name\": \"long-sleeved, light blue and white plaid shirt\"}, {\"id\": 11730, \"name\": \"the prominent sign for Renault\"}, {\"id\": 11731, \"name\": \"the woman in a red and blue outfit\"}, {\"id\": 11732, \"name\": \"a large, green and beige structure\"}, {\"id\": 11733, \"name\": \"the red and white sneaker\"}, {\"id\": 11734, \"name\": \"an awards show\"}, {\"id\": 11735, \"name\": \"a gray marble\"}, {\"id\": 11736, \"name\": \"black briefcase\"}, {\"id\": 11737, \"name\": \"wooden ladder\"}, {\"id\": 11738, \"name\": \"a floral dress\"}, {\"id\": 11739, \"name\": \"fingers\"}, {\"id\": 11740, \"name\": \"the row of trees\"}, {\"id\": 11741, \"name\": \"the grey t-shirt\"}, {\"id\": 11742, \"name\": \"a yellow and brown railing\"}, {\"id\": 11743, \"name\": \"yellow rope toy\"}, {\"id\": 11744, \"name\": \"green object\"}, {\"id\": 11745, \"name\": \"a white concrete barrier\"}, {\"id\": 11746, \"name\": \"the large green prayer mat\"}, {\"id\": 11747, \"name\": \"the yellow background\"}, {\"id\": 11748, \"name\": \"stack of wooden blocks\"}, {\"id\": 11749, \"name\": \"a brown pillar\"}, {\"id\": 11750, \"name\": \"the romantic wedding moment\"}, {\"id\": 11751, \"name\": \"a brown and fluffy dog\"}, {\"id\": 11752, \"name\": \"large, round eye\"}, {\"id\": 11753, \"name\": \"a colorful graphic\"}, {\"id\": 11754, \"name\": \"traditional clothing\"}, {\"id\": 11755, \"name\": \"a fluffy coat\"}, {\"id\": 11756, \"name\": \"the pink sheet or blanket\"}, {\"id\": 11757, \"name\": \"road sidewalk\"}, {\"id\": 11758, \"name\": \"small bush\"}, {\"id\": 11759, \"name\": \"a blue pot\"}, {\"id\": 11760, \"name\": \"the dark grey color\"}, {\"id\": 11761, \"name\": \"the white dress shirt\"}, {\"id\": 11762, \"name\": \"a woman in purple dress\"}, {\"id\": 11763, \"name\": \"a white Honda SUV\"}, {\"id\": 11764, \"name\": \"formal clothing\"}, {\"id\": 11765, \"name\": \"the silhouette of a person\"}, {\"id\": 11766, \"name\": \"a white mug\"}, {\"id\": 11767, \"name\": \"the ferris wheel\"}, {\"id\": 11768, \"name\": \"the photo or video\"}, {\"id\": 11769, \"name\": \"a sales associate\"}, {\"id\": 11770, \"name\": \"a gearshift\"}, {\"id\": 11771, \"name\": \"a capsule\"}, {\"id\": 11772, \"name\": \"the man's shadow\"}, {\"id\": 11773, \"name\": \"the red stripe\"}, {\"id\": 11774, \"name\": \"beige stripe\"}, {\"id\": 11775, \"name\": \"the yellow pole\"}, {\"id\": 11776, \"name\": \"player in a black jersey\"}, {\"id\": 11777, \"name\": \"a sign on the wall\"}, {\"id\": 11778, \"name\": \"blue accent\"}, {\"id\": 11779, \"name\": \"red and white poster\"}, {\"id\": 11780, \"name\": \"a large sign\"}, {\"id\": 11781, \"name\": \"the sheep\"}, {\"id\": 11782, \"name\": \"a light-colored wooden leg\"}, {\"id\": 11783, \"name\": \"brown and white design\"}, {\"id\": 11784, \"name\": \"the Hindi sign\"}, {\"id\": 11785, \"name\": \"the closed garage door\"}, {\"id\": 11786, \"name\": \"the red marking\"}, {\"id\": 11787, \"name\": \"a narrow strip of brown dirt\"}, {\"id\": 11788, \"name\": \"the boxes\"}, {\"id\": 11789, \"name\": \"a circular shape\"}, {\"id\": 11790, \"name\": \"the bright yellow sari\"}, {\"id\": 11791, \"name\": \"a prominent yellow sign\"}, {\"id\": 11792, \"name\": \"brown sign\"}, {\"id\": 11793, \"name\": \"an assortment of items\"}, {\"id\": 11794, \"name\": \"the pink rope collar\"}, {\"id\": 11795, \"name\": \"the gray, corrugated metal building\"}, {\"id\": 11796, \"name\": \"row of function keys\"}, {\"id\": 11797, \"name\": \"a stage light\"}, {\"id\": 11798, \"name\": \"the white marble tile floor\"}, {\"id\": 11799, \"name\": \"stone base\"}, {\"id\": 11800, \"name\": \"sleek, white, curved roof\"}, {\"id\": 11801, \"name\": \"light gray tile floor\"}, {\"id\": 11802, \"name\": \"the collection of framed pictures\"}, {\"id\": 11803, \"name\": \"the large red bowl\"}, {\"id\": 11804, \"name\": \"platform's floor\"}, {\"id\": 11805, \"name\": \"the black and white sign\"}, {\"id\": 11806, \"name\": \"residential structure\"}, {\"id\": 11807, \"name\": \"a muddy, green river\"}, {\"id\": 11808, \"name\": \"a pink metal lattice\"}, {\"id\": 11809, \"name\": \"red and yellow bus\"}, {\"id\": 11810, \"name\": \"gray and black patterned rug\"}, {\"id\": 11811, \"name\": \"a dark wall\"}, {\"id\": 11812, \"name\": \"intricate white crochet pattern\"}, {\"id\": 11813, \"name\": \"the large blue curtain\"}, {\"id\": 11814, \"name\": \"pair of scissors\"}, {\"id\": 11815, \"name\": \"the rusty metal frame\"}, {\"id\": 11816, \"name\": \"a car's rear bumper\"}, {\"id\": 11817, \"name\": \"the textured grey pattern\"}, {\"id\": 11818, \"name\": \"fuzzy material\"}, {\"id\": 11819, \"name\": \"the white tiled counter\"}, {\"id\": 11820, \"name\": \"a bustling intersection\"}, {\"id\": 11821, \"name\": \"the right-hand side of the street\"}, {\"id\": 11822, \"name\": \"top left square\"}, {\"id\": 11823, \"name\": \"the small, short-haired Jack Russell Terrier\"}, {\"id\": 11824, \"name\": \"the faded asphalt surface\"}, {\"id\": 11825, \"name\": \"plants\"}, {\"id\": 11826, \"name\": \"red ceiling\"}, {\"id\": 11827, \"name\": \"hammer\"}, {\"id\": 11828, \"name\": \"the vibrant blue sky\"}, {\"id\": 11829, \"name\": \"the road surface\"}, {\"id\": 11830, \"name\": \"the small, dilapidated wooden building\"}, {\"id\": 11831, \"name\": \"the costumed performer\"}, {\"id\": 11832, \"name\": \"TV stand\"}, {\"id\": 11833, \"name\": \"the white tile accent\"}, {\"id\": 11834, \"name\": \"a pink bicycle\"}, {\"id\": 11835, \"name\": \"the vibrant red dress\"}, {\"id\": 11836, \"name\": \"collection of books\"}, {\"id\": 11837, \"name\": \"the large white sticker\"}, {\"id\": 11838, \"name\": \"a multiple shelf\"}, {\"id\": 11839, \"name\": \"a dark wood facade\"}, {\"id\": 11840, \"name\": \"the blue sweater\"}, {\"id\": 11841, \"name\": \"the streetlamp\"}, {\"id\": 11842, \"name\": \"the corrug\"}, {\"id\": 11843, \"name\": \"the spacious aisle\"}, {\"id\": 11844, \"name\": \"the concrete walkway\"}, {\"id\": 11845, \"name\": \"the stainless-steel refrigerator\"}, {\"id\": 11846, \"name\": \"a black long-sleeve shirt\"}, {\"id\": 11847, \"name\": \"the raised dot\"}, {\"id\": 11848, \"name\": \"yellow vest\"}, {\"id\": 11849, \"name\": \"prominent black and white wall\"}, {\"id\": 11850, \"name\": \"vehicle's dashboard\"}, {\"id\": 11851, \"name\": \"an ornate black streetlamp\"}, {\"id\": 11852, \"name\": \"the dry and barren field\"}, {\"id\": 11853, \"name\": \"large orange container\"}, {\"id\": 11854, \"name\": \"handlebar\"}, {\"id\": 11855, \"name\": \"polished finish\"}, {\"id\": 11856, \"name\": \"pink\"}, {\"id\": 11857, \"name\": \"the brown shirt\"}, {\"id\": 11858, \"name\": \"a gray granite border\"}, {\"id\": 11859, \"name\": \"a mountain or hill\"}, {\"id\": 11860, \"name\": \"a decorative pattern\"}, {\"id\": 11861, \"name\": \"the wall outlet\"}, {\"id\": 11862, \"name\": \"a gray and red brick sidewalk\"}, {\"id\": 11863, \"name\": \"red and black bike\"}, {\"id\": 11864, \"name\": \"a hazy and overcast sky\"}, {\"id\": 11865, \"name\": \"a white metal frame\"}, {\"id\": 11866, \"name\": \"a rustic element\"}, {\"id\": 11867, \"name\": \"a large, dark eye\"}, {\"id\": 11868, \"name\": \"the silver handrail\"}, {\"id\": 11869, \"name\": \"the concrete dock\"}, {\"id\": 11870, \"name\": \"the glass railing\"}, {\"id\": 11871, \"name\": \"a white tiled wall\"}, {\"id\": 11872, \"name\": \"standard car tire\"}, {\"id\": 11873, \"name\": \"diverse array of buildings\"}, {\"id\": 11874, \"name\": \"a silver bangle bracelet\"}, {\"id\": 11875, \"name\": \"the tall metal structure\"}, {\"id\": 11876, \"name\": \"small drawer\"}, {\"id\": 11877, \"name\": \"a shack\"}, {\"id\": 11878, \"name\": \"a back of the seat\"}, {\"id\": 11879, \"name\": \"the red panel\"}, {\"id\": 11880, \"name\": \"the desolate, wide street\"}, {\"id\": 11881, \"name\": \"a fluorescent lighting\"}, {\"id\": 11882, \"name\": \"a chair's back\"}, {\"id\": 11883, \"name\": \"the purple shirt\"}, {\"id\": 11884, \"name\": \"the bottle of water\"}, {\"id\": 11885, \"name\": \"a long, white whisker\"}, {\"id\": 11886, \"name\": \"the white refrigerator\"}, {\"id\": 11887, \"name\": \"a red tip\"}, {\"id\": 11888, \"name\": \"sunflower plant\"}, {\"id\": 11889, \"name\": \"the scattered object\"}, {\"id\": 11890, \"name\": \"the marble-like material\"}, {\"id\": 11891, \"name\": \"a man in a purple shirt\"}, {\"id\": 11892, \"name\": \"orange jacket\"}, {\"id\": 11893, \"name\": \"large red flower\"}, {\"id\": 11894, \"name\": \"the rail\"}, {\"id\": 11895, \"name\": \"silver metal device\"}, {\"id\": 11896, \"name\": \"shadow of a person\"}, {\"id\": 11897, \"name\": \"the narrow, paved road\"}, {\"id\": 11898, \"name\": \"the two-lane asphalt road\"}, {\"id\": 11899, \"name\": \"a blue sandal\"}, {\"id\": 11900, \"name\": \"the white circular background\"}, {\"id\": 11901, \"name\": \"a pink house\"}, {\"id\": 11902, \"name\": \"a vibrant clothing\"}, {\"id\": 11903, \"name\": \"dark wood table\"}, {\"id\": 11904, \"name\": \"a modern design\"}, {\"id\": 11905, \"name\": \"the vendor\"}, {\"id\": 11906, \"name\": \"the gold-framed mirror\"}, {\"id\": 11907, \"name\": \"the concrete parking lot\"}, {\"id\": 11908, \"name\": \"young musician\"}, {\"id\": 11909, \"name\": \"silver truck\"}, {\"id\": 11910, \"name\": \"large blue banner\"}, {\"id\": 11911, \"name\": \"the small tan dog\"}, {\"id\": 11912, \"name\": \"road curb\"}, {\"id\": 11913, \"name\": \"the straight leg\"}, {\"id\": 11914, \"name\": \"the blue truck\"}, {\"id\": 11915, \"name\": \"blue and cloudy sky\"}, {\"id\": 11916, \"name\": \"the glimpse of the counter\"}, {\"id\": 11917, \"name\": \"a gray finish\"}, {\"id\": 11918, \"name\": \"a gray concrete road\"}, {\"id\": 11919, \"name\": \"dirty, wet pavement\"}, {\"id\": 11920, \"name\": \"large illuminated sign\"}, {\"id\": 11921, \"name\": \"a black fence\"}, {\"id\": 11922, \"name\": \"the drums\"}, {\"id\": 11923, \"name\": \"the woman in a black puffy coat\"}, {\"id\": 11924, \"name\": \"left side of the sink\"}, {\"id\": 11925, \"name\": \"a terracotta pot\"}, {\"id\": 11926, \"name\": \"the relaxed pose\"}, {\"id\": 11927, \"name\": \"a red logo\"}, {\"id\": 11928, \"name\": \"the nighttime setting\"}, {\"id\": 11929, \"name\": \"the yellow glow\"}, {\"id\": 11930, \"name\": \"net\"}, {\"id\": 11931, \"name\": \"a dog's back\"}, {\"id\": 11932, \"name\": \"a majestic temple\"}, {\"id\": 11933, \"name\": \"metal gate\"}, {\"id\": 11934, \"name\": \"a gray concrete\"}, {\"id\": 11935, \"name\": \"wooden top\"}, {\"id\": 11936, \"name\": \"a large, empty shopping mall\"}, {\"id\": 11937, \"name\": \"a rear wheel\"}, {\"id\": 11938, \"name\": \"a ceiling texture\"}, {\"id\": 11939, \"name\": \"a San Martin Express\"}, {\"id\": 11940, \"name\": \"an armrest\"}, {\"id\": 11941, \"name\": \"a dark background\"}, {\"id\": 11942, \"name\": \"two-person motor scooter\"}, {\"id\": 11943, \"name\": \"the bunch of grapes\"}, {\"id\": 11944, \"name\": \"a traditional attire\"}, {\"id\": 11945, \"name\": \"a shiny tile\"}, {\"id\": 11946, \"name\": \"city\"}, {\"id\": 11947, \"name\": \"a substantial load of hay\"}, {\"id\": 11948, \"name\": \"yellow and red container\"}, {\"id\": 11949, \"name\": \"the large digital display screen\"}, {\"id\": 11950, \"name\": \"dog bed\"}, {\"id\": 11951, \"name\": \"a white countertop\"}, {\"id\": 11952, \"name\": \"a yellow vest\"}, {\"id\": 11953, \"name\": \"navy blue sweater\"}, {\"id\": 11954, \"name\": \"pink pattern\"}, {\"id\": 11955, \"name\": \"gray, plush dog bed\"}, {\"id\": 11956, \"name\": \"front car\"}, {\"id\": 11957, \"name\": \"the debris\"}, {\"id\": 11958, \"name\": \"the river or lake\"}, {\"id\": 11959, \"name\": \"the green bean\"}, {\"id\": 11960, \"name\": \"a pile of concrete blocks\"}, {\"id\": 11961, \"name\": \"light-colored metal\"}, {\"id\": 11962, \"name\": \"a busy road intersection\"}, {\"id\": 11963, \"name\": \"a black handlebar\"}, {\"id\": 11964, \"name\": \"the tan-colored house\"}, {\"id\": 11965, \"name\": \"a red, white, and blue circle\"}, {\"id\": 11966, \"name\": \"blue flip flops\"}, {\"id\": 11967, \"name\": \"drum set\"}, {\"id\": 11968, \"name\": \"the long brown hair\"}, {\"id\": 11969, \"name\": \"gray jacket\"}, {\"id\": 11970, \"name\": \"the impending storm\"}, {\"id\": 11971, \"name\": \"a purple and white shirt\"}, {\"id\": 11972, \"name\": \"a darker gray line\"}, {\"id\": 11973, \"name\": \"brown vent\"}, {\"id\": 11974, \"name\": \"smaller, round top\"}, {\"id\": 11975, \"name\": \"red and black collared shirt\"}, {\"id\": 11976, \"name\": \"gold arch\"}, {\"id\": 11977, \"name\": \"the wooden deck\"}, {\"id\": 11978, \"name\": \"black-and-white cat\"}, {\"id\": 11979, \"name\": \"white sack\"}, {\"id\": 11980, \"name\": \"the person in a tan coat\"}, {\"id\": 11981, \"name\": \"a brown marking\"}, {\"id\": 11982, \"name\": \"the blue and white checkered shirt\"}, {\"id\": 11983, \"name\": \"the long, orange coat\"}, {\"id\": 11984, \"name\": \"cartoon drawing of a yellow fish\"}, {\"id\": 11985, \"name\": \"the large, dirty tile\"}, {\"id\": 11986, \"name\": \"woman's legs\"}, {\"id\": 11987, \"name\": \"light-brown dirt\"}, {\"id\": 11988, \"name\": \"the bottle of the drink\"}, {\"id\": 11989, \"name\": \"glass vase\"}, {\"id\": 11990, \"name\": \"the small, white, rectangular sign\"}, {\"id\": 11991, \"name\": \"an underwater in a pool\"}, {\"id\": 11992, \"name\": \"an outdoor seating area\"}, {\"id\": 11993, \"name\": \"red truck\"}, {\"id\": 11994, \"name\": \"a blue head covering\"}, {\"id\": 11995, \"name\": \"the swimwear\"}, {\"id\": 11996, \"name\": \"round wooden table\"}, {\"id\": 11997, \"name\": \"the net\"}, {\"id\": 11998, \"name\": \"escalator\"}, {\"id\": 11999, \"name\": \"green and yellow warning sign\"}, {\"id\": 12000, \"name\": \"narrow, dimly lit alleyway\"}, {\"id\": 12001, \"name\": \"a woman in a white shirt\"}, {\"id\": 12002, \"name\": \"larger building\"}, {\"id\": 12003, \"name\": \"the hotel room\"}, {\"id\": 12004, \"name\": \"cultural event\"}, {\"id\": 12005, \"name\": \"the small digital display\"}, {\"id\": 12006, \"name\": \"cluster of houses\"}, {\"id\": 12007, \"name\": \"the white and yellow striped curb\"}, {\"id\": 12008, \"name\": \"dark surface\"}, {\"id\": 12009, \"name\": \"the gray line\"}, {\"id\": 12010, \"name\": \"assortment of shoes\"}, {\"id\": 12011, \"name\": \"soap suds\"}, {\"id\": 12012, \"name\": \"silver handrail\"}, {\"id\": 12013, \"name\": \"a row of palm trees\"}, {\"id\": 12014, \"name\": \"pool table\"}, {\"id\": 12015, \"name\": \"a blue stretcher\"}, {\"id\": 12016, \"name\": \"the purple seat\"}, {\"id\": 12017, \"name\": \"brown substance\"}, {\"id\": 12018, \"name\": \"a gray plastic chair\"}, {\"id\": 12019, \"name\": \"the brown and tan patterned tablecloth or rug\"}, {\"id\": 12020, \"name\": \"blue refrigerator\"}, {\"id\": 12021, \"name\": \"the circular toy\"}, {\"id\": 12022, \"name\": \"beige interior\"}, {\"id\": 12023, \"name\": \"the hoarding\"}, {\"id\": 12024, \"name\": \"the red and yellow striped mat\"}, {\"id\": 12025, \"name\": \"a light brown hair\"}, {\"id\": 12026, \"name\": \"the red accent\"}, {\"id\": 12027, \"name\": \"a garland of yellow flower\"}, {\"id\": 12028, \"name\": \"white picket fence\"}, {\"id\": 12029, \"name\": \"the cane\"}, {\"id\": 12030, \"name\": \"dog's front paws\"}, {\"id\": 12031, \"name\": \"the grey and black pattern\"}, {\"id\": 12032, \"name\": \"the stack of green Sprite cans\"}, {\"id\": 12033, \"name\": \"the balcony railing\"}, {\"id\": 12034, \"name\": \"a stainless steel basin\"}, {\"id\": 12035, \"name\": \"the navy blue shirt\"}, {\"id\": 12036, \"name\": \"person in a yellow sari\"}, {\"id\": 12037, \"name\": \"gray dashboard\"}, {\"id\": 12038, \"name\": \"brilliant blue sky\"}, {\"id\": 12039, \"name\": \"the predominantly white tiled floor\"}, {\"id\": 12040, \"name\": \"light source\"}, {\"id\": 12041, \"name\": \"the large planter\"}, {\"id\": 12042, \"name\": \"small blue tray\"}, {\"id\": 12043, \"name\": \"a wide, paved road\"}, {\"id\": 12044, \"name\": \"pitcher\"}, {\"id\": 12045, \"name\": \"the small market or store\"}, {\"id\": 12046, \"name\": \"the worn appearance of the staircase\"}, {\"id\": 12047, \"name\": \"a clear sky\"}, {\"id\": 12048, \"name\": \"the dark gray tarp\"}, {\"id\": 12049, \"name\": \"a large green plant\"}, {\"id\": 12050, \"name\": \"the blue and yellow bench\"}, {\"id\": 12051, \"name\": \"train station\"}, {\"id\": 12052, \"name\": \"a bright green vest\"}, {\"id\": 12053, \"name\": \"a yellow house\"}, {\"id\": 12054, \"name\": \"blue rash guard\"}, {\"id\": 12055, \"name\": \"the collar\"}, {\"id\": 12056, \"name\": \"the bumper car\"}, {\"id\": 12057, \"name\": \"the bar\"}, {\"id\": 12058, \"name\": \"a cursive script\"}, {\"id\": 12059, \"name\": \"a designated area\"}, {\"id\": 12060, \"name\": \"the darker green design\"}, {\"id\": 12061, \"name\": \"the blue helmet\"}, {\"id\": 12062, \"name\": \"the man in the blue hat\"}, {\"id\": 12063, \"name\": \"wiper\"}, {\"id\": 12064, \"name\": \"yellow and pink garment\"}, {\"id\": 12065, \"name\": \"a small structure\"}, {\"id\": 12066, \"name\": \"baby's bottom\"}, {\"id\": 12067, \"name\": \"the paved area\"}, {\"id\": 12068, \"name\": \"light brown color\"}, {\"id\": 12069, \"name\": \"a vibrant green and red play structure\"}, {\"id\": 12070, \"name\": \"the red and white swirl\"}, {\"id\": 12071, \"name\": \"the minivan\"}, {\"id\": 12072, \"name\": \"the pink belt\"}, {\"id\": 12073, \"name\": \"a red package\"}, {\"id\": 12074, \"name\": \"a vibrant red and gold patterned rug\"}, {\"id\": 12075, \"name\": \"the colorful front panel\"}, {\"id\": 12076, \"name\": \"a dog's fluffy\"}, {\"id\": 12077, \"name\": \"purple outfit\"}, {\"id\": 12078, \"name\": \"the long red banner\"}, {\"id\": 12079, \"name\": \"wall of the shop\"}, {\"id\": 12080, \"name\": \"large white tank\"}, {\"id\": 12081, \"name\": \"the pile of trash and debris\"}, {\"id\": 12082, \"name\": \"small truck\"}, {\"id\": 12083, \"name\": \"a white and light-brown tile floor\"}, {\"id\": 12084, \"name\": \"the taller wall\"}, {\"id\": 12085, \"name\": \"the orange fur\"}, {\"id\": 12086, \"name\": \"prominent set of marble steps\"}, {\"id\": 12087, \"name\": \"the glass door entrance\"}, {\"id\": 12088, \"name\": \"black animal\"}, {\"id\": 12089, \"name\": \"a green tablecloth\"}, {\"id\": 12090, \"name\": \"the 1st Floor\"}, {\"id\": 12091, \"name\": \"the rainbow-colored sign\"}, {\"id\": 12092, \"name\": \"person's feet\"}, {\"id\": 12093, \"name\": \"a chessboard\"}, {\"id\": 12094, \"name\": \"group of five young women\"}, {\"id\": 12095, \"name\": \"the light khaki pant\"}, {\"id\": 12096, \"name\": \"the bright and sunny sky\"}, {\"id\": 12097, \"name\": \"a white metal beam\"}, {\"id\": 12098, \"name\": \"outdoor court\"}, {\"id\": 12099, \"name\": \"an overcast and gray sky\"}, {\"id\": 12100, \"name\": \"the lightweight material\"}, {\"id\": 12101, \"name\": \"black spot\"}, {\"id\": 12102, \"name\": \"a small potted plant\"}, {\"id\": 12103, \"name\": \"the interior of the car\"}, {\"id\": 12104, \"name\": \"the section of a stone or brick walkway\"}, {\"id\": 12105, \"name\": \"light blue jeans\"}, {\"id\": 12106, \"name\": \"long, beige sari\"}, {\"id\": 12107, \"name\": \"the blue and white wall\"}, {\"id\": 12108, \"name\": \"a striking red and yellow costume\"}, {\"id\": 12109, \"name\": \"worn, off-white wall\"}, {\"id\": 12110, \"name\": \"a blue patterned skirt\"}, {\"id\": 12111, \"name\": \"a dark shirt\"}, {\"id\": 12112, \"name\": \"the broccoli\"}, {\"id\": 12113, \"name\": \"the khaki shorts\"}, {\"id\": 12114, \"name\": \"greenish-brown object\"}, {\"id\": 12115, \"name\": \"a red, pink, or yellow clothing\"}, {\"id\": 12116, \"name\": \"a low wall or fence\"}, {\"id\": 12117, \"name\": \"grayish-brown sky\"}, {\"id\": 12118, \"name\": \"the dark material\"}, {\"id\": 12119, \"name\": \"the Shiba Inu\"}, {\"id\": 12120, \"name\": \"a drawstring hood\"}, {\"id\": 12121, \"name\": \"large, open space\"}, {\"id\": 12122, \"name\": \"a narrow alleyway\"}, {\"id\": 12123, \"name\": \"the black attire\"}, {\"id\": 12124, \"name\": \"the concrete or stone\"}, {\"id\": 12125, \"name\": \"bottle of beer\"}, {\"id\": 12126, \"name\": \"large, gold circle\"}, {\"id\": 12127, \"name\": \"a mallard\"}, {\"id\": 12128, \"name\": \"the wet, paved surface\"}, {\"id\": 12129, \"name\": \"the gray building\"}, {\"id\": 12130, \"name\": \"tiny tooth\"}, {\"id\": 12131, \"name\": \"a partially visible sign on the dashboard\"}, {\"id\": 12132, \"name\": \"a passenger in a vehicle\"}, {\"id\": 12133, \"name\": \"the vibrant costume\"}, {\"id\": 12134, \"name\": \"left storefront\"}, {\"id\": 12135, \"name\": \"slot machine\"}, {\"id\": 12136, \"name\": \"a row of cars\"}, {\"id\": 12137, \"name\": \"the string of flags\"}, {\"id\": 12138, \"name\": \"a dark blue and red uniform\"}, {\"id\": 12139, \"name\": \"the red glow\"}, {\"id\": 12140, \"name\": \"a spectator\"}, {\"id\": 12141, \"name\": \"large, multicolored geometric pattern\"}, {\"id\": 12142, \"name\": \"a warehouse aisle\"}, {\"id\": 12143, \"name\": \"a piece of concrete\"}, {\"id\": 12144, \"name\": \"a bill\"}, {\"id\": 12145, \"name\": \"small playground area\"}, {\"id\": 12146, \"name\": \"the patch of black\"}, {\"id\": 12147, \"name\": \"large white object\"}, {\"id\": 12148, \"name\": \"the store's exterior\"}, {\"id\": 12149, \"name\": \"the large red screen\"}, {\"id\": 12150, \"name\": \"a brick building\"}, {\"id\": 12151, \"name\": \"a bite of food\"}, {\"id\": 12152, \"name\": \"a small bird\"}, {\"id\": 12153, \"name\": \"the flowering tree\"}, {\"id\": 12154, \"name\": \"a plastic cover\"}, {\"id\": 12155, \"name\": \"golden horn\"}, {\"id\": 12156, \"name\": \"the black metal object\"}, {\"id\": 12157, \"name\": \"the overcast and hazy sky\"}, {\"id\": 12158, \"name\": \"black and white checkered flag logo\"}, {\"id\": 12159, \"name\": \"white and blue striped headband\"}, {\"id\": 12160, \"name\": \"tree-lined road\"}, {\"id\": 12161, \"name\": \"the pile of dark-colored object\"}, {\"id\": 12162, \"name\": \"mix of buildings\"}, {\"id\": 12163, \"name\": \"the small plant\"}, {\"id\": 12164, \"name\": \"white and orange Persian cat\"}, {\"id\": 12165, \"name\": \"the open area\"}, {\"id\": 12166, \"name\": \"the piece of drywall\"}, {\"id\": 12167, \"name\": \"vibrant yellow dress\"}, {\"id\": 12168, \"name\": \"vibrant pink apron\"}, {\"id\": 12169, \"name\": \"a dark blue jeans\"}, {\"id\": 12170, \"name\": \"the intricate detail\"}, {\"id\": 12171, \"name\": \"the large, ornate building\"}, {\"id\": 12172, \"name\": \"white blouse\"}, {\"id\": 12173, \"name\": \"an entrance to a room\"}, {\"id\": 12174, \"name\": \"the DVD player\"}, {\"id\": 12175, \"name\": \"a purple button-up shirt\"}, {\"id\": 12176, \"name\": \"a bus terminal\"}, {\"id\": 12177, \"name\": \"a light-colored roof\"}, {\"id\": 12178, \"name\": \"a tea plantation\"}, {\"id\": 12179, \"name\": \"the lichen\"}, {\"id\": 12180, \"name\": \"the thumb and index finger\"}, {\"id\": 12181, \"name\": \"the light gray concrete\"}, {\"id\": 12182, \"name\": \"the mortar\"}, {\"id\": 12183, \"name\": \"large, shiny, white tile floor\"}, {\"id\": 12184, \"name\": \"the beige jacket\"}, {\"id\": 12185, \"name\": \"the orange accent\"}, {\"id\": 12186, \"name\": \"the silver metal bar\"}, {\"id\": 12187, \"name\": \"a hair clip\"}, {\"id\": 12188, \"name\": \"cozy and intimate setting\"}, {\"id\": 12189, \"name\": \"the elevated walkway\"}, {\"id\": 12190, \"name\": \"the tan carpeted floor\"}, {\"id\": 12191, \"name\": \"a black and red plaid pattern\"}, {\"id\": 12192, \"name\": \"light-colored floor\"}, {\"id\": 12193, \"name\": \"the yellow and red tiled floor\"}, {\"id\": 12194, \"name\": \"the red and white hat\"}, {\"id\": 12195, \"name\": \"a large gold dome\"}, {\"id\": 12196, \"name\": \"red brick sidewalk\"}, {\"id\": 12197, \"name\": \"window's black metal frame\"}, {\"id\": 12198, \"name\": \"the black suitcase on wheels\"}, {\"id\": 12199, \"name\": \"the vertical slat\"}, {\"id\": 12200, \"name\": \"a hanging light fixture\"}, {\"id\": 12201, \"name\": \"the brown dirt and grassy area\"}, {\"id\": 12202, \"name\": \"a bar\"}, {\"id\": 12203, \"name\": \"the green cloth\"}, {\"id\": 12204, \"name\": \"the name tag\"}, {\"id\": 12205, \"name\": \"white-painted wall\"}, {\"id\": 12206, \"name\": \"black cord\"}, {\"id\": 12207, \"name\": \"the traffic cone\"}, {\"id\": 12208, \"name\": \"a decorative gold-colored top\"}, {\"id\": 12209, \"name\": \"the close-up of a woman's face\"}, {\"id\": 12210, \"name\": \"multi-lane highway\"}, {\"id\": 12211, \"name\": \"an assortment of plates\"}, {\"id\": 12212, \"name\": \"plaid shorts\"}, {\"id\": 12213, \"name\": \"a white handbag\"}, {\"id\": 12214, \"name\": \"a bride's head\"}, {\"id\": 12215, \"name\": \"the red car graphic\"}, {\"id\": 12216, \"name\": \"swing's chain\"}, {\"id\": 12217, \"name\": \"single light source\"}, {\"id\": 12218, \"name\": \"the baseball field\"}, {\"id\": 12219, \"name\": \"row of parked bicycles\"}, {\"id\": 12220, \"name\": \"the duffel bag\"}, {\"id\": 12221, \"name\": \"long pink tongue\"}, {\"id\": 12222, \"name\": \"the narrow, makeshift alleyway\"}, {\"id\": 12223, \"name\": \"a shopper\"}, {\"id\": 12224, \"name\": \"the green and yellow packaging\"}, {\"id\": 12225, \"name\": \"a kitchen area\"}, {\"id\": 12226, \"name\": \"large, black SUV\"}, {\"id\": 12227, \"name\": \"QR code\"}, {\"id\": 12228, \"name\": \"a blue sneaker\"}, {\"id\": 12229, \"name\": \"the back of a black chair\"}, {\"id\": 12230, \"name\": \"swing\"}, {\"id\": 12231, \"name\": \"the video\"}, {\"id\": 12232, \"name\": \"a gray road\"}, {\"id\": 12233, \"name\": \"decorative element\"}, {\"id\": 12234, \"name\": \"a pink and orange design\"}, {\"id\": 12235, \"name\": \"a yellow strip of tape\"}, {\"id\": 12236, \"name\": \"the metal pot\"}, {\"id\": 12237, \"name\": \"the front of the flip-flop\"}, {\"id\": 12238, \"name\": \"closed white garage door\"}, {\"id\": 12239, \"name\": \"light pink shirt\"}, {\"id\": 12240, \"name\": \"attention\"}, {\"id\": 12241, \"name\": \"the asphalt or concrete\"}, {\"id\": 12242, \"name\": \"the large, unfinished concrete building\"}, {\"id\": 12243, \"name\": \"a lighting\"}, {\"id\": 12244, \"name\": \"a small sticker\"}, {\"id\": 12245, \"name\": \"bright and hazy sky\"}, {\"id\": 12246, \"name\": \"the small, silver object\"}, {\"id\": 12247, \"name\": \"a background of the image\"}, {\"id\": 12248, \"name\": \"a screenshot\"}, {\"id\": 12249, \"name\": \"a blurry red light\"}, {\"id\": 12250, \"name\": \"a room's white tile floor\"}, {\"id\": 12251, \"name\": \"video game machine\"}, {\"id\": 12252, \"name\": \"viewer\"}, {\"id\": 12253, \"name\": \"the window or mirror\"}, {\"id\": 12254, \"name\": \"the blue pants\"}, {\"id\": 12255, \"name\": \"large grey parking lot\"}, {\"id\": 12256, \"name\": \"roll of tape\"}, {\"id\": 12257, \"name\": \"the orange flower pattern\"}, {\"id\": 12258, \"name\": \"the cream-colored jacket\"}, {\"id\": 12259, \"name\": \"blue cart\"}, {\"id\": 12260, \"name\": \"the accident\"}, {\"id\": 12261, \"name\": \"the wedding or formal event\"}, {\"id\": 12262, \"name\": \"the dark gray pant\"}, {\"id\": 12263, \"name\": \"mural\"}, {\"id\": 12264, \"name\": \"vast expanse of rocky terrain\"}, {\"id\": 12265, \"name\": \"a white hoodie\"}, {\"id\": 12266, \"name\": \"the large yellow advertisement\"}, {\"id\": 12267, \"name\": \"light gray color\"}, {\"id\": 12268, \"name\": \"a floor's lower edge\"}, {\"id\": 12269, \"name\": \"the black windshield wiper\"}, {\"id\": 12270, \"name\": \"metal door\"}, {\"id\": 12271, \"name\": \"brown box\"}, {\"id\": 12272, \"name\": \"the red pant\"}, {\"id\": 12273, \"name\": \"an arcade machine\"}, {\"id\": 12274, \"name\": \"rug's design\"}, {\"id\": 12275, \"name\": \"the mouth\"}, {\"id\": 12276, \"name\": \"a red and white striped pattern\"}, {\"id\": 12277, \"name\": \"a nighttime image\"}, {\"id\": 12278, \"name\": \"the PESA\"}, {\"id\": 12279, \"name\": \"a white Chevrolet van\"}, {\"id\": 12280, \"name\": \"the fairground\"}, {\"id\": 12281, \"name\": \"a pedestrian walkway\"}, {\"id\": 12282, \"name\": \"the blue medical mask\"}, {\"id\": 12283, \"name\": \"floor strip\"}, {\"id\": 12284, \"name\": \"purple bag\"}, {\"id\": 12285, \"name\": \"the storefront of a clothing store\"}, {\"id\": 12286, \"name\": \"the head covering\"}, {\"id\": 12287, \"name\": \"an unusual perspective\"}, {\"id\": 12288, \"name\": \"the large white object\"}, {\"id\": 12289, \"name\": \"the employee\"}, {\"id\": 12290, \"name\": \"the narrow, dimly lit alleyway\"}, {\"id\": 12291, \"name\": \"a large apartment building\"}, {\"id\": 12292, \"name\": \"nature-inspired pattern\"}, {\"id\": 12293, \"name\": \"the bright white sky\"}, {\"id\": 12294, \"name\": \"winding road\"}, {\"id\": 12295, \"name\": \"a golfer\"}, {\"id\": 12296, \"name\": \"the modern dance recital\"}, {\"id\": 12297, \"name\": \"green bench\"}, {\"id\": 12298, \"name\": \"a wicker chair\"}, {\"id\": 12299, \"name\": \"a short white fur\"}, {\"id\": 12300, \"name\": \"traditional Indian clothing\"}, {\"id\": 12301, \"name\": \"large rectangular window\"}, {\"id\": 12302, \"name\": \"a tan exterior\"}, {\"id\": 12303, \"name\": \"the wispy cloud\"}, {\"id\": 12304, \"name\": \"the green plate\"}, {\"id\": 12305, \"name\": \"tan toy\"}, {\"id\": 12306, \"name\": \"a light flare\"}, {\"id\": 12307, \"name\": \"red and white striped backdrop\"}, {\"id\": 12308, \"name\": \"red pole\"}, {\"id\": 12309, \"name\": \"a red, rectangular object\"}, {\"id\": 12310, \"name\": \"a red and white patterned pillow\"}, {\"id\": 12311, \"name\": \"the short hair\"}, {\"id\": 12312, \"name\": \"red billboard\"}, {\"id\": 12313, \"name\": \"the red lining\"}, {\"id\": 12314, \"name\": \"waste\"}, {\"id\": 12315, \"name\": \"tall, narrow display case\"}, {\"id\": 12316, \"name\": \"a bucket of plaster\"}, {\"id\": 12317, \"name\": \"stack of cream-colored rug\"}, {\"id\": 12318, \"name\": \"a paved lot\"}, {\"id\": 12319, \"name\": \"the wattle\"}, {\"id\": 12320, \"name\": \"the piece of wood\"}, {\"id\": 12321, \"name\": \"a border\"}, {\"id\": 12322, \"name\": \"the black hood\"}, {\"id\": 12323, \"name\": \"brown couch\"}, {\"id\": 12324, \"name\": \"yellow-lined sidewalk\"}, {\"id\": 12325, \"name\": \"the small, dark blue stuffed animal\"}, {\"id\": 12326, \"name\": \"the red sticker\"}, {\"id\": 12327, \"name\": \"a thin silver border\"}, {\"id\": 12328, \"name\": \"the front passenger seat of the car\"}, {\"id\": 12329, \"name\": \"the grey animal\"}, {\"id\": 12330, \"name\": \"the pink tablecloth\"}, {\"id\": 12331, \"name\": \"a single dark shadow\"}, {\"id\": 12332, \"name\": \"green garbage bin\"}, {\"id\": 12333, \"name\": \"a red dress\"}, {\"id\": 12334, \"name\": \"a set of keys\"}, {\"id\": 12335, \"name\": \"a black slipper\"}, {\"id\": 12336, \"name\": \"the temple piece\"}, {\"id\": 12337, \"name\": \"the serene indoor garden\"}, {\"id\": 12338, \"name\": \"person's left arm\"}, {\"id\": 12339, \"name\": \"the stand\"}, {\"id\": 12340, \"name\": \"large black billboard\"}, {\"id\": 12341, \"name\": \"a white cloud\"}, {\"id\": 12342, \"name\": \"a black countertop\"}, {\"id\": 12343, \"name\": \"large green sign\"}, {\"id\": 12344, \"name\": \"the shade of orange\"}, {\"id\": 12345, \"name\": \"a greenery\"}, {\"id\": 12346, \"name\": \"the large dirt field\"}, {\"id\": 12347, \"name\": \"the median\"}, {\"id\": 12348, \"name\": \"vibrant green foliage\"}, {\"id\": 12349, \"name\": \"the hen\"}, {\"id\": 12350, \"name\": \"the large advertisement banner\"}, {\"id\": 12351, \"name\": \"the clear glass\"}, {\"id\": 12352, \"name\": \"a concrete border\"}, {\"id\": 12353, \"name\": \"the wooden cabinet or bookshelf\"}, {\"id\": 12354, \"name\": \"a teal t-shirt\"}, {\"id\": 12355, \"name\": \"a row of colorful arcade games\"}, {\"id\": 12356, \"name\": \"the shelves of clothing\"}, {\"id\": 12357, \"name\": \"a ventilation duct\"}, {\"id\": 12358, \"name\": \"the dark green color\"}, {\"id\": 12359, \"name\": \"the small wooden building\"}, {\"id\": 12360, \"name\": \"the black and white vest\"}, {\"id\": 12361, \"name\": \"a row of wooden barrier\"}, {\"id\": 12362, \"name\": \"the large white and red umbrella\"}, {\"id\": 12363, \"name\": \"the painting\"}, {\"id\": 12364, \"name\": \"a metal fire pit\"}, {\"id\": 12365, \"name\": \"a dilapidated wooden bridge\"}, {\"id\": 12366, \"name\": \"the vibrant and eclectic mix of bedding and blankets\"}, {\"id\": 12367, \"name\": \"white textured ceiling\"}, {\"id\": 12368, \"name\": \"the multi-story building\"}, {\"id\": 12369, \"name\": \"a road edge\"}, {\"id\": 12370, \"name\": \"black staircase\"}, {\"id\": 12371, \"name\": \"volleyball\"}, {\"id\": 12372, \"name\": \"cross-body bag\"}, {\"id\": 12373, \"name\": \"yellow and blue sign\"}, {\"id\": 12374, \"name\": \"a barren field\"}, {\"id\": 12375, \"name\": \"the children's play area\"}, {\"id\": 12376, \"name\": \"a dark brown door frame\"}, {\"id\": 12377, \"name\": \"the food stall\"}, {\"id\": 12378, \"name\": \"a yellow robe\"}, {\"id\": 12379, \"name\": \"a vibrant sari\"}, {\"id\": 12380, \"name\": \"the large pink box\"}, {\"id\": 12381, \"name\": \"pile of lumber\"}, {\"id\": 12382, \"name\": \"a single lane\"}, {\"id\": 12383, \"name\": \"clouds\"}, {\"id\": 12384, \"name\": \"the small, four-door car\"}, {\"id\": 12385, \"name\": \"sweater\"}, {\"id\": 12386, \"name\": \"a metal cart\"}, {\"id\": 12387, \"name\": \"a beige floor\"}, {\"id\": 12388, \"name\": \"the blurry, out-of-focus photograph\"}, {\"id\": 12389, \"name\": \"yellow sleeve\"}, {\"id\": 12390, \"name\": \"blury image\"}, {\"id\": 12391, \"name\": \"the white and black cat\"}, {\"id\": 12392, \"name\": \"black floral pattern\"}, {\"id\": 12393, \"name\": \"the blue floral design\"}, {\"id\": 12394, \"name\": \"the metal handrail\"}, {\"id\": 12395, \"name\": \"the poodle's head\"}, {\"id\": 12396, \"name\": \"grey shoe\"}, {\"id\": 12397, \"name\": \"a green crate\"}, {\"id\": 12398, \"name\": \"red cabinet\"}, {\"id\": 12399, \"name\": \"a light blue hoodie\"}, {\"id\": 12400, \"name\": \"a terrier\"}, {\"id\": 12401, \"name\": \"the red, brown, and beige floral pattern\"}, {\"id\": 12402, \"name\": \"white plastic body\"}, {\"id\": 12403, \"name\": \"a meal\"}, {\"id\": 12404, \"name\": \"the large slice of watermelon\"}, {\"id\": 12405, \"name\": \"a large black bag\"}, {\"id\": 12406, \"name\": \"a brown wooden shelf\"}, {\"id\": 12407, \"name\": \"light-colored tile\"}, {\"id\": 12408, \"name\": \"cartoon character print\"}, {\"id\": 12409, \"name\": \"grey stone\"}, {\"id\": 12410, \"name\": \"bag of puffs\"}, {\"id\": 12411, \"name\": \"chain-link fence\"}, {\"id\": 12412, \"name\": \"light-colored, long-sleeved shirt\"}, {\"id\": 12413, \"name\": \"a blue advertisement\"}, {\"id\": 12414, \"name\": \"red and white striped pole\"}, {\"id\": 12415, \"name\": \"a PULL\"}, {\"id\": 12416, \"name\": \"the ball pit\"}, {\"id\": 12417, \"name\": \"yellow and black onesie\"}, {\"id\": 12418, \"name\": \"a purple, red, and white patterned handle\"}, {\"id\": 12419, \"name\": \"a small body of water\"}, {\"id\": 12420, \"name\": \"spreading expanse of dark green ivy leaves\"}, {\"id\": 12421, \"name\": \"the red polo shirt\"}, {\"id\": 12422, \"name\": \"the air vent\"}, {\"id\": 12423, \"name\": \"legging\"}, {\"id\": 12424, \"name\": \"the white and yellow minibus\"}, {\"id\": 12425, \"name\": \"mother's belly\"}, {\"id\": 12426, \"name\": \"the gray jacket\"}, {\"id\": 12427, \"name\": \"green strap\"}, {\"id\": 12428, \"name\": \"small platform or ledge\"}, {\"id\": 12429, \"name\": \"the metal rod\"}, {\"id\": 12430, \"name\": \"the large serving spoon\"}, {\"id\": 12431, \"name\": \"weathered and worn texture\"}, {\"id\": 12432, \"name\": \"hallway or corridor\"}, {\"id\": 12433, \"name\": \"the front of the building\"}, {\"id\": 12434, \"name\": \"a part of a shirt\"}, {\"id\": 12435, \"name\": \"a decorative design\"}, {\"id\": 12436, \"name\": \"tall black sign\"}, {\"id\": 12437, \"name\": \"box of cereal\"}, {\"id\": 12438, \"name\": \"the people in traditional Chinese attire\"}, {\"id\": 12439, \"name\": \"a blue and white design\"}, {\"id\": 12440, \"name\": \"the gray and white marbled surface\"}, {\"id\": 12441, \"name\": \"a small silver handle\"}, {\"id\": 12442, \"name\": \"a large, round, blue-and-white firework-like display\"}, {\"id\": 12443, \"name\": \"the soccer ball\"}, {\"id\": 12444, \"name\": \"a light pink sari\"}, {\"id\": 12445, \"name\": \"metal grate floor\"}, {\"id\": 12446, \"name\": \"white bus\"}, {\"id\": 12447, \"name\": \"a small mirror\"}, {\"id\": 12448, \"name\": \"red cooler box\"}, {\"id\": 12449, \"name\": \"bag or backpack\"}, {\"id\": 12450, \"name\": \"steam or smoke\"}, {\"id\": 12451, \"name\": \"blue long-sleeve shirt\"}, {\"id\": 12452, \"name\": \"a series of metal chains\"}, {\"id\": 12453, \"name\": \"black metal stand\"}, {\"id\": 12454, \"name\": \"the white stool\"}, {\"id\": 12455, \"name\": \"store's floor\"}, {\"id\": 12456, \"name\": \"the light brown brick surface\"}, {\"id\": 12457, \"name\": \"the yellow and brown cartoon character\"}, {\"id\": 12458, \"name\": \"a red stop sign\"}, {\"id\": 12459, \"name\": \"a timestamp\"}, {\"id\": 12460, \"name\": \"a row of building\"}, {\"id\": 12461, \"name\": \"bottle of oil\"}, {\"id\": 12462, \"name\": \"an unidentifiable item\"}, {\"id\": 12463, \"name\": \"long horizontal frame\"}, {\"id\": 12464, \"name\": \"the lone motorcycle\"}, {\"id\": 12465, \"name\": \"shelves\"}, {\"id\": 12466, \"name\": \"the baguette\"}, {\"id\": 12467, \"name\": \"black graphic t-shirt\"}, {\"id\": 12468, \"name\": \"fire truck\"}, {\"id\": 12469, \"name\": \"an empty red seat\"}, {\"id\": 12470, \"name\": \"a red carpet or rug\"}, {\"id\": 12471, \"name\": \"pink and white sleeveless dress\"}, {\"id\": 12472, \"name\": \"a large white billboard\"}, {\"id\": 12473, \"name\": \"a back wall\"}, {\"id\": 12474, \"name\": \"a white pickup truck\"}, {\"id\": 12475, \"name\": \"the large billboard\"}, {\"id\": 12476, \"name\": \"object\"}, {\"id\": 12477, \"name\": \"the teal pickup truck\"}, {\"id\": 12478, \"name\": \"a large digital display\"}, {\"id\": 12479, \"name\": \"covered area\"}, {\"id\": 12480, \"name\": \"a red minivan\"}, {\"id\": 12481, \"name\": \"the blue and red display\"}, {\"id\": 12482, \"name\": \"a woman's hands\"}, {\"id\": 12483, \"name\": \"a small wooden shelf\"}, {\"id\": 12484, \"name\": \"the diagonal pattern of dark brown and light brown bricks\"}, {\"id\": 12485, \"name\": \"large overhang\"}, {\"id\": 12486, \"name\": \"a white power outlet\"}, {\"id\": 12487, \"name\": \"large Christmas tree\"}, {\"id\": 12488, \"name\": \"the green object\"}, {\"id\": 12489, \"name\": \"the green trim\"}, {\"id\": 12490, \"name\": \"a green lamppost\"}, {\"id\": 12491, \"name\": \"the hill\"}, {\"id\": 12492, \"name\": \"a grey concrete surface\"}, {\"id\": 12493, \"name\": \"a dark substance\"}, {\"id\": 12494, \"name\": \"airport environment\"}, {\"id\": 12495, \"name\": \"a green bra\"}, {\"id\": 12496, \"name\": \"a gray and overcast sky\"}, {\"id\": 12497, \"name\": \"a road's surface\"}, {\"id\": 12498, \"name\": \"right shower curtain\"}, {\"id\": 12499, \"name\": \"the green container\"}, {\"id\": 12500, \"name\": \"the pedestrian crossing area\"}, {\"id\": 12501, \"name\": \"a metal awning\"}, {\"id\": 12502, \"name\": \"the design element\"}, {\"id\": 12503, \"name\": \"tall, slender tower\"}, {\"id\": 12504, \"name\": \"a colorful sign\"}, {\"id\": 12505, \"name\": \"hair salon advertising\"}, {\"id\": 12506, \"name\": \"barren landscape\"}, {\"id\": 12507, \"name\": \"a windshield of a vehicle\"}, {\"id\": 12508, \"name\": \"the white Croc\"}, {\"id\": 12509, \"name\": \"the side of the cage\"}, {\"id\": 12510, \"name\": \"a cage's interior\"}, {\"id\": 12511, \"name\": \"the three-story building\"}, {\"id\": 12512, \"name\": \"multi-story apartment building\"}, {\"id\": 12513, \"name\": \"a tinted window\"}, {\"id\": 12514, \"name\": \"a sandy road\"}, {\"id\": 12515, \"name\": \"a blue and white t-shirt\"}, {\"id\": 12516, \"name\": \"a tall glass building\"}, {\"id\": 12517, \"name\": \"the close-up of a dog's face\"}, {\"id\": 12518, \"name\": \"the black and orange sign\"}, {\"id\": 12519, \"name\": \"costumed character\"}, {\"id\": 12520, \"name\": \"a patterned tile design\"}, {\"id\": 12521, \"name\": \"Coca-Cola brand\"}, {\"id\": 12522, \"name\": \"the row of tan buildings\"}, {\"id\": 12523, \"name\": \"nighttime sky\"}, {\"id\": 12524, \"name\": \"a shiny surface\"}, {\"id\": 12525, \"name\": \"the group of five ducks\"}, {\"id\": 12526, \"name\": \"the language on the sign\"}, {\"id\": 12527, \"name\": \"the concrete median\"}, {\"id\": 12528, \"name\": \"the white, yellow, red, blue, and gray pattern\"}, {\"id\": 12529, \"name\": \"the subtle, muted color palette\"}, {\"id\": 12530, \"name\": \"the white writing\"}, {\"id\": 12531, \"name\": \"a wood plank\"}, {\"id\": 12532, \"name\": \"an orange building\"}, {\"id\": 12533, \"name\": \"a red and white striped roof\"}, {\"id\": 12534, \"name\": \"a floor of a mosque\"}, {\"id\": 12535, \"name\": \"a yellow mattress pad\"}, {\"id\": 12536, \"name\": \"an ornate decoration\"}, {\"id\": 12537, \"name\": \"the long, narrow, paved walkway\"}, {\"id\": 12538, \"name\": \"public square\"}, {\"id\": 12539, \"name\": \"a white flower clip\"}, {\"id\": 12540, \"name\": \"dense wall of trees and bushes\"}, {\"id\": 12541, \"name\": \"cluster of trees\"}, {\"id\": 12542, \"name\": \"black border\"}, {\"id\": 12543, \"name\": \"the metal garment stand\"}, {\"id\": 12544, \"name\": \"a darker grey tail\"}, {\"id\": 12545, \"name\": \"a floor of the court\"}, {\"id\": 12546, \"name\": \"a small, light orange kitten\"}, {\"id\": 12547, \"name\": \"a tree-filled area\"}, {\"id\": 12548, \"name\": \"an orange and black machine\"}, {\"id\": 12549, \"name\": \"a white and red wall\"}, {\"id\": 12550, \"name\": \"brown door frame\"}, {\"id\": 12551, \"name\": \"black vest\"}, {\"id\": 12552, \"name\": \"dark blue baseball cap\"}, {\"id\": 12553, \"name\": \"a black nose\"}, {\"id\": 12554, \"name\": \"a large poster of a woman wearing a hijab\"}, {\"id\": 12555, \"name\": \"denim shorts\"}, {\"id\": 12556, \"name\": \"the concrete channel\"}, {\"id\": 12557, \"name\": \"blue trim\"}, {\"id\": 12558, \"name\": \"the ductwork\"}, {\"id\": 12559, \"name\": \"the pink notepad\"}, {\"id\": 12560, \"name\": \"a red vest\"}, {\"id\": 12561, \"name\": \"a right-hand side of the road\"}, {\"id\": 12562, \"name\": \"horizontal silver stripe\"}, {\"id\": 12563, \"name\": \"white body\"}, {\"id\": 12564, \"name\": \"blue triangle\"}, {\"id\": 12565, \"name\": \"the blue lid\"}, {\"id\": 12566, \"name\": \"white sheet or curtain\"}, {\"id\": 12567, \"name\": \"a blurry, low-resolution photograph\"}, {\"id\": 12568, \"name\": \"the brown and tan pattern\"}, {\"id\": 12569, \"name\": \"the row of bar stools\"}, {\"id\": 12570, \"name\": \"blue body\"}, {\"id\": 12571, \"name\": \"a human presence\"}, {\"id\": 12572, \"name\": \"grey roof\"}, {\"id\": 12573, \"name\": \"a commercial-grade model\"}, {\"id\": 12574, \"name\": \"hip\"}, {\"id\": 12575, \"name\": \"a lone pedestrian\"}, {\"id\": 12576, \"name\": \"a building's ground floor\"}, {\"id\": 12577, \"name\": \"the blue shorts with red trim\"}, {\"id\": 12578, \"name\": \"the light orange tabby\"}, {\"id\": 12579, \"name\": \"a green and tan facade\"}, {\"id\": 12580, \"name\": \"the white fluorescent light\"}, {\"id\": 12581, \"name\": \"left shoulder\"}, {\"id\": 12582, \"name\": \"smaller red hoop\"}, {\"id\": 12583, \"name\": \"the central walkway\"}, {\"id\": 12584, \"name\": \"the black and white patterned hat\"}, {\"id\": 12585, \"name\": \"a recycling symbol\"}, {\"id\": 12586, \"name\": \"large white RV\"}, {\"id\": 12587, \"name\": \"a vibrant Christmas tree\"}, {\"id\": 12588, \"name\": \"store's white wall\"}, {\"id\": 12589, \"name\": \"black square\"}, {\"id\": 12590, \"name\": \"a predominantly white ceiling\"}, {\"id\": 12591, \"name\": \"large green wall\"}, {\"id\": 12592, \"name\": \"the distinctive white and green checkered sweater\"}, {\"id\": 12593, \"name\": \"the white Audi sedan\"}, {\"id\": 12594, \"name\": \"the large metal drill bit\"}, {\"id\": 12595, \"name\": \"bag of salad mix\"}, {\"id\": 12596, \"name\": \"the dark gray comforter\"}, {\"id\": 12597, \"name\": \"a green border\"}, {\"id\": 12598, \"name\": \"the large, dark brown wooden structure\"}, {\"id\": 12599, \"name\": \"patch of orange fur\"}, {\"id\": 12600, \"name\": \"the white necklace\"}, {\"id\": 12601, \"name\": \"white-framed window\"}, {\"id\": 12602, \"name\": \"a rack\"}, {\"id\": 12603, \"name\": \"large pile of dirt\"}, {\"id\": 12604, \"name\": \"blue tricycle\"}, {\"id\": 12605, \"name\": \"food vendor\"}, {\"id\": 12606, \"name\": \"rustic landscape\"}, {\"id\": 12607, \"name\": \"tan SUV\"}, {\"id\": 12608, \"name\": \"the light blue background\"}, {\"id\": 12609, \"name\": \"white and red sign\"}, {\"id\": 12610, \"name\": \"a short, metal railing\"}, {\"id\": 12611, \"name\": \"a blue and clear sky\"}, {\"id\": 12612, \"name\": \"the traditional Indian sari\"}, {\"id\": 12613, \"name\": \"cardboard piece\"}, {\"id\": 12614, \"name\": \"purple blanket\"}, {\"id\": 12615, \"name\": \"an indistinct background\"}, {\"id\": 12616, \"name\": \"the large yucca plant\"}, {\"id\": 12617, \"name\": \"floral shirt\"}, {\"id\": 12618, \"name\": \"curved glass railing\"}, {\"id\": 12619, \"name\": \"a black and yellow apron\"}, {\"id\": 12620, \"name\": \"the lid\"}, {\"id\": 12621, \"name\": \"patch of dirt\"}, {\"id\": 12622, \"name\": \"small, blue-painted wooden door\"}, {\"id\": 12623, \"name\": \"the slipper\"}, {\"id\": 12624, \"name\": \"the nighttime photograph\"}, {\"id\": 12625, \"name\": \"the temporary barrier\"}, {\"id\": 12626, \"name\": \"the dark green mug\"}, {\"id\": 12627, \"name\": \"the scattered leaf\"}, {\"id\": 12628, \"name\": \"the plaid shorts\"}, {\"id\": 12629, \"name\": \"a light-colored sandal\"}, {\"id\": 12630, \"name\": \"glass cabinet\"}, {\"id\": 12631, \"name\": \"a bright blue and white stripe\"}, {\"id\": 12632, \"name\": \"exposed wiring\"}, {\"id\": 12633, \"name\": \"solar panel\"}, {\"id\": 12634, \"name\": \"the metal tray\"}, {\"id\": 12635, \"name\": \"window frame\"}, {\"id\": 12636, \"name\": \"row of shops\"}, {\"id\": 12637, \"name\": \"table or floor\"}, {\"id\": 12638, \"name\": \"a black metal slat\"}, {\"id\": 12639, \"name\": \"toy pig\"}, {\"id\": 12640, \"name\": \"a twig\"}, {\"id\": 12641, \"name\": \"a delicate veil\"}, {\"id\": 12642, \"name\": \"a pile of wooden sticks\"}, {\"id\": 12643, \"name\": \"the striped sweater\"}, {\"id\": 12644, \"name\": \"black object\"}, {\"id\": 12645, \"name\": \"the yellow fabric\"}, {\"id\": 12646, \"name\": \"the blurry, dimly lit photograph\"}, {\"id\": 12647, \"name\": \"white face mask\"}, {\"id\": 12648, \"name\": \"pink basket\"}, {\"id\": 12649, \"name\": \"the molding\"}, {\"id\": 12650, \"name\": \"a row of gray metal chairs\"}, {\"id\": 12651, \"name\": \"the blury image\"}, {\"id\": 12652, \"name\": \"a medium-sized breed\"}, {\"id\": 12653, \"name\": \"white storefront\"}, {\"id\": 12654, \"name\": \"the red-tiled roof\"}, {\"id\": 12655, \"name\": \"a small patch of green vegetation\"}, {\"id\": 12656, \"name\": \"scratch\"}, {\"id\": 12657, \"name\": \"yellow and black striped line\"}, {\"id\": 12658, \"name\": \"a partial view of a person's black puffer jacket and pants\"}, {\"id\": 12659, \"name\": \"concrete stair\"}, {\"id\": 12660, \"name\": \"the leather\"}, {\"id\": 12661, \"name\": \"a side of the road\"}, {\"id\": 12662, \"name\": \"a hanging laundry\"}, {\"id\": 12663, \"name\": \"a vigil or memorial service\"}, {\"id\": 12664, \"name\": \"an illuminated road\"}, {\"id\": 12665, \"name\": \"the food bag\"}, {\"id\": 12666, \"name\": \"a Nike logo\"}, {\"id\": 12667, \"name\": \"the wet, paved road\"}, {\"id\": 12668, \"name\": \"a yellow sari\"}, {\"id\": 12669, \"name\": \"a row of columns\"}, {\"id\": 12670, \"name\": \"the black and white marble floor\"}, {\"id\": 12671, \"name\": \"recessed ceiling light\"}, {\"id\": 12672, \"name\": \"the lush green vine\"}, {\"id\": 12673, \"name\": \"bouquet of white flowers\"}, {\"id\": 12674, \"name\": \"a larger building\"}, {\"id\": 12675, \"name\": \"garland of greenery\"}, {\"id\": 12676, \"name\": \"the white plastic clip\"}, {\"id\": 12677, \"name\": \"the black and white patterned blanket\"}, {\"id\": 12678, \"name\": \"a GOSHEN CITY EVENT\"}, {\"id\": 12679, \"name\": \"long, rectangular table\"}, {\"id\": 12680, \"name\": \"black facade\"}, {\"id\": 12681, \"name\": \"bun\"}, {\"id\": 12682, \"name\": \"brown, fuzzy fabric\"}, {\"id\": 12683, \"name\": \"an audience\"}, {\"id\": 12684, \"name\": \"sports enthusiast\"}, {\"id\": 12685, \"name\": \"the narrow canal\"}, {\"id\": 12686, \"name\": \"a group of five young women\"}, {\"id\": 12687, \"name\": \"small, open-air canopy\"}, {\"id\": 12688, \"name\": \"a large vehicle\"}, {\"id\": 12689, \"name\": \"festive garland\"}, {\"id\": 12690, \"name\": \"a light brown fur\"}, {\"id\": 12691, \"name\": \"the home\"}, {\"id\": 12692, \"name\": \"the black cloth\"}, {\"id\": 12693, \"name\": \"a concrete or cement\"}, {\"id\": 12694, \"name\": \"cord\"}, {\"id\": 12695, \"name\": \"a gray and overcast day\"}, {\"id\": 12696, \"name\": \"white bucket\"}, {\"id\": 12697, \"name\": \"a mixture of ground meat\"}, {\"id\": 12698, \"name\": \"the series of vertical bars\"}, {\"id\": 12699, \"name\": \"TV screen\"}, {\"id\": 12700, \"name\": \"the dark sneaker\"}, {\"id\": 12701, \"name\": \"camouflage coat\"}, {\"id\": 12702, \"name\": \"the curved blue light\"}, {\"id\": 12703, \"name\": \"the pavement\"}, {\"id\": 12704, \"name\": \"distant building\"}, {\"id\": 12705, \"name\": \"wide, asphalt road\"}, {\"id\": 12706, \"name\": \"mixture of food and water\"}, {\"id\": 12707, \"name\": \"blue tarp\"}, {\"id\": 12708, \"name\": \"the brown chicken\"}, {\"id\": 12709, \"name\": \"the red and white facade\"}, {\"id\": 12710, \"name\": \"a cartoon owl mascot\"}, {\"id\": 12711, \"name\": \"the woman in a striped shirt\"}, {\"id\": 12712, \"name\": \"long fur\"}, {\"id\": 12713, \"name\": \"a dipping sauce\"}, {\"id\": 12714, \"name\": \"closed blind\"}, {\"id\": 12715, \"name\": \"a multicolored curtain\"}, {\"id\": 12716, \"name\": \"dark-colored material\"}, {\"id\": 12717, \"name\": \"a beige fuzzy jacket\"}, {\"id\": 12718, \"name\": \"a white ceiling tile\"}, {\"id\": 12719, \"name\": \"the white paper bag\"}, {\"id\": 12720, \"name\": \"a vast grassy field\"}, {\"id\": 12721, \"name\": \"older woman\"}, {\"id\": 12722, \"name\": \"nighttime photograph\"}, {\"id\": 12723, \"name\": \"a black hijab\"}, {\"id\": 12724, \"name\": \"the bright, white glow\"}, {\"id\": 12725, \"name\": \"the green fence\"}, {\"id\": 12726, \"name\": \"a white and black patterned shirt\"}, {\"id\": 12727, \"name\": \"the interior of the restaurant\"}, {\"id\": 12728, \"name\": \"silver motorcycle\"}, {\"id\": 12729, \"name\": \"a gray coat\"}, {\"id\": 12730, \"name\": \"a yellow model\"}, {\"id\": 12731, \"name\": \"wheeled cart\"}, {\"id\": 12732, \"name\": \"long, fuzzy coat\"}, {\"id\": 12733, \"name\": \"light-brown fur\"}, {\"id\": 12734, \"name\": \"the black arrow\"}, {\"id\": 12735, \"name\": \"a black ear\"}, {\"id\": 12736, \"name\": \"small tube of paint or marker\"}, {\"id\": 12737, \"name\": \"black and white jacket\"}, {\"id\": 12738, \"name\": \"the grey jacket\"}, {\"id\": 12739, \"name\": \"the beige tiled floor\"}, {\"id\": 12740, \"name\": \"the blury cityscape\"}, {\"id\": 12741, \"name\": \"the wood plank\"}, {\"id\": 12742, \"name\": \"the back seat\"}, {\"id\": 12743, \"name\": \"a white vent\"}, {\"id\": 12744, \"name\": \"the pattern of light tan spots\"}, {\"id\": 12745, \"name\": \"dilapidated building\"}, {\"id\": 12746, \"name\": \"the clear and blue sky\"}, {\"id\": 12747, \"name\": \"a pool cue\"}, {\"id\": 12748, \"name\": \"a red collar\"}, {\"id\": 12749, \"name\": \"the gray t-shirt\"}, {\"id\": 12750, \"name\": \"the red truck\"}, {\"id\": 12751, \"name\": \"the grey metal gate\"}, {\"id\": 12752, \"name\": \"the postal service worker\"}, {\"id\": 12753, \"name\": \"the black roof\"}, {\"id\": 12754, \"name\": \"the clear plastic water bottle\"}, {\"id\": 12755, \"name\": \"the dark brown object\"}, {\"id\": 12756, \"name\": \"passenger in a vehicle\"}, {\"id\": 12757, \"name\": \"blue tent\"}, {\"id\": 12758, \"name\": \"a black and silver object\"}, {\"id\": 12759, \"name\": \"the large glass window\"}, {\"id\": 12760, \"name\": \"the rural highway\"}, {\"id\": 12761, \"name\": \"a white metal structure\"}, {\"id\": 12762, \"name\": \"front of the cart\"}, {\"id\": 12763, \"name\": \"the overexposed sky\"}, {\"id\": 12764, \"name\": \"the gray sweatshirt\"}, {\"id\": 12765, \"name\": \"the display of rice\"}, {\"id\": 12766, \"name\": \"the image of a person holding a tablet\"}, {\"id\": 12767, \"name\": \"the surrounding\"}, {\"id\": 12768, \"name\": \"the grey jean\"}, {\"id\": 12769, \"name\": \"the blue cloth\"}, {\"id\": 12770, \"name\": \"green cushion\"}, {\"id\": 12771, \"name\": \"two men\"}, {\"id\": 12772, \"name\": \"the white robe\"}, {\"id\": 12773, \"name\": \"short-sleeved, pastel-colored striped shirt\"}, {\"id\": 12774, \"name\": \"a square white tile\"}, {\"id\": 12775, \"name\": \"the pink fabric\"}, {\"id\": 12776, \"name\": \"rectangular shape\"}, {\"id\": 12777, \"name\": \"shiny black stripe\"}, {\"id\": 12778, \"name\": \"fuzzy beige jacket\"}, {\"id\": 12779, \"name\": \"the black-and-white shirt\"}, {\"id\": 12780, \"name\": \"a serene blue sky\"}, {\"id\": 12781, \"name\": \"man in a blue uniform\"}, {\"id\": 12782, \"name\": \"the green line\"}, {\"id\": 12783, \"name\": \"the white wall outlet\"}, {\"id\": 12784, \"name\": \"the light blue water\"}, {\"id\": 12785, \"name\": \"the large gray tail\"}, {\"id\": 12786, \"name\": \"a blury and dark image\"}, {\"id\": 12787, \"name\": \"white writing\"}, {\"id\": 12788, \"name\": \"the grey hoodie\"}, {\"id\": 12789, \"name\": \"shower floor\"}, {\"id\": 12790, \"name\": \"the large group of colorful koi fish\"}, {\"id\": 12791, \"name\": \"the white painted line\"}, {\"id\": 12792, \"name\": \"the large, open space\"}, {\"id\": 12793, \"name\": \"the woman in a light-colored jacket\"}, {\"id\": 12794, \"name\": \"the darker fur\"}, {\"id\": 12795, \"name\": \"a toy mouse\"}, {\"id\": 12796, \"name\": \"the green sandal\"}, {\"id\": 12797, \"name\": \"short concrete barrier\"}, {\"id\": 12798, \"name\": \"the vibrant blue and green plumage\"}, {\"id\": 12799, \"name\": \"the gray cover\"}, {\"id\": 12800, \"name\": \"a dark blue t-shirt\"}, {\"id\": 12801, \"name\": \"the black beanie\"}, {\"id\": 12802, \"name\": \"a footprint\"}, {\"id\": 12803, \"name\": \"a pink kurta\"}, {\"id\": 12804, \"name\": \"white sheet\"}, {\"id\": 12805, \"name\": \"the neat bob\"}, {\"id\": 12806, \"name\": \"black duffel bag\"}, {\"id\": 12807, \"name\": \"the large metal tray of dumplings\"}, {\"id\": 12808, \"name\": \"the gray long-sleeve shirt\"}, {\"id\": 12809, \"name\": \"the prominent woman\"}, {\"id\": 12810, \"name\": \"a blue paint\"}, {\"id\": 12811, \"name\": \"small coffee table\"}, {\"id\": 12812, \"name\": \"a prominent spire-shaped structure\"}, {\"id\": 12813, \"name\": \"group of five people\"}, {\"id\": 12814, \"name\": \"the blue label\"}, {\"id\": 12815, \"name\": \"cracked, dry, and barren surface\"}, {\"id\": 12816, \"name\": \"the assortment of fresh fruits\"}, {\"id\": 12817, \"name\": \"polished brown floor\"}, {\"id\": 12818, \"name\": \"an intricate white design\"}, {\"id\": 12819, \"name\": \"a white step or ledge\"}, {\"id\": 12820, \"name\": \"long-sleeved, black shirt\"}, {\"id\": 12821, \"name\": \"the colorful embellishment\"}, {\"id\": 12822, \"name\": \"the flat front\"}, {\"id\": 12823, \"name\": \"the light blue pant\"}, {\"id\": 12824, \"name\": \"a black speaker\"}, {\"id\": 12825, \"name\": \"the black knee pad\"}, {\"id\": 12826, \"name\": \"a store's tile floor\"}, {\"id\": 12827, \"name\": \"local paper\"}, {\"id\": 12828, \"name\": \"a black tracksuit\"}, {\"id\": 12829, \"name\": \"gray sidewalk\"}, {\"id\": 12830, \"name\": \"the kitchen setting\"}, {\"id\": 12831, \"name\": \"the tan-colored structure\"}, {\"id\": 12832, \"name\": \"the SEOUL\"}, {\"id\": 12833, \"name\": \"a row of shops and businesses\"}, {\"id\": 12834, \"name\": \"a street ahead\"}, {\"id\": 12835, \"name\": \"a white exterior\"}, {\"id\": 12836, \"name\": \"the weeds\"}, {\"id\": 12837, \"name\": \"the ice cream cone design\"}, {\"id\": 12838, \"name\": \"a brick planter\"}, {\"id\": 12839, \"name\": \"the white barcode\"}, {\"id\": 12840, \"name\": \"a long, thin, red object\"}, {\"id\": 12841, \"name\": \"adult coach\"}, {\"id\": 12842, \"name\": \"a white cupboard\"}, {\"id\": 12843, \"name\": \"a dark green bed\"}, {\"id\": 12844, \"name\": \"the small cap\"}, {\"id\": 12845, \"name\": \"a long, black table\"}, {\"id\": 12846, \"name\": \"a brightly lit foreground\"}, {\"id\": 12847, \"name\": \"a predominantly white tile floor\"}, {\"id\": 12848, \"name\": \"a pile of cucumbers\"}, {\"id\": 12849, \"name\": \"a large, gray metal wall\"}, {\"id\": 12850, \"name\": \"a green and cream-colored carpet\"}, {\"id\": 12851, \"name\": \"the enigma\"}, {\"id\": 12852, \"name\": \"group of people\"}, {\"id\": 12853, \"name\": \"the white sheet of paper\"}, {\"id\": 12854, \"name\": \"the right leg\"}, {\"id\": 12855, \"name\": \"wooden cabinet\"}, {\"id\": 12856, \"name\": \"the thatched roof hut\"}, {\"id\": 12857, \"name\": \"the black tank top\"}, {\"id\": 12858, \"name\": \"concrete slab\"}, {\"id\": 12859, \"name\": \"light-colored substance\"}, {\"id\": 12860, \"name\": \"a red fire alarm box\"}, {\"id\": 12861, \"name\": \"a dark, rectangular shape\"}, {\"id\": 12862, \"name\": \"the hallway\"}, {\"id\": 12863, \"name\": \"the blue and white patterned bowl\"}, {\"id\": 12864, \"name\": \"the plastic bottle\"}, {\"id\": 12865, \"name\": \"assortment of colorful food items\"}, {\"id\": 12866, \"name\": \"a dimly lit, narrow street\"}, {\"id\": 12867, \"name\": \"car door handle\"}, {\"id\": 12868, \"name\": \"an escalator's moving walkway\"}, {\"id\": 12869, \"name\": \"a curved exterior\"}, {\"id\": 12870, \"name\": \"white shelf\"}, {\"id\": 12871, \"name\": \"oven\"}, {\"id\": 12872, \"name\": \"wooden ship's hull\"}, {\"id\": 12873, \"name\": \"the dilapidated and in disrepair building\"}, {\"id\": 12874, \"name\": \"an interactive game\"}, {\"id\": 12875, \"name\": \"a blue jumpsuit\"}, {\"id\": 12876, \"name\": \"the cracked concrete sidewalk\"}, {\"id\": 12877, \"name\": \"the hole\"}, {\"id\": 12878, \"name\": \"a diverse array of scenes and activities\"}, {\"id\": 12879, \"name\": \"small blue vehicle\"}, {\"id\": 12880, \"name\": \"the back view of three people\"}, {\"id\": 12881, \"name\": \"a blue shorts\"}, {\"id\": 12882, \"name\": \"prominent counter\"}, {\"id\": 12883, \"name\": \"white sleeveless dress\"}, {\"id\": 12884, \"name\": \"large white piece of material\"}, {\"id\": 12885, \"name\": \"a black fur\"}, {\"id\": 12886, \"name\": \"a prominent advertisement\"}, {\"id\": 12887, \"name\": \"a vibrant advertisement\"}, {\"id\": 12888, \"name\": \"the small white box\"}, {\"id\": 12889, \"name\": \"metal rod\"}, {\"id\": 12890, \"name\": \"the rubber toy chicken\"}, {\"id\": 12891, \"name\": \"a traditional Indian dance\"}, {\"id\": 12892, \"name\": \"white and yellow checkered tablecloth\"}, {\"id\": 12893, \"name\": \"the blue pickup truck\"}, {\"id\": 12894, \"name\": \"a gathering of three individuals\"}, {\"id\": 12895, \"name\": \"a school\"}, {\"id\": 12896, \"name\": \"small orange building\"}, {\"id\": 12897, \"name\": \"a station's wall\"}, {\"id\": 12898, \"name\": \"a distinctive dreadlocks\"}, {\"id\": 12899, \"name\": \"the white and brown skull pattern\"}, {\"id\": 12900, \"name\": \"the large bush or tree\"}, {\"id\": 12901, \"name\": \"a white shorts\"}, {\"id\": 12902, \"name\": \"the pink, black, and white skirt or dress\"}, {\"id\": 12903, \"name\": \"a colorful truck\"}, {\"id\": 12904, \"name\": \"the glass side\"}, {\"id\": 12905, \"name\": \"the ornate building\"}, {\"id\": 12906, \"name\": \"a large brown paper bag\"}, {\"id\": 12907, \"name\": \"large white bag\"}, {\"id\": 12908, \"name\": \"a green shrub\"}, {\"id\": 12909, \"name\": \"yellow liquid\"}, {\"id\": 12910, \"name\": \"the month of February\"}, {\"id\": 12911, \"name\": \"the row of tall buildings\"}, {\"id\": 12912, \"name\": \"the dark blue screen\"}, {\"id\": 12913, \"name\": \"the black swing\"}, {\"id\": 12914, \"name\": \"a sandstone or clay structure\"}, {\"id\": 12915, \"name\": \"the blue and white lift\"}, {\"id\": 12916, \"name\": \"the concrete or marble finish\"}, {\"id\": 12917, \"name\": \"large, littered area\"}, {\"id\": 12918, \"name\": \"a black attire\"}, {\"id\": 12919, \"name\": \"a Springsworth School\"}, {\"id\": 12920, \"name\": \"the disco ball\"}, {\"id\": 12921, \"name\": \"a small microwave\"}, {\"id\": 12922, \"name\": \"the pop of black\"}, {\"id\": 12923, \"name\": \"the residential street\"}, {\"id\": 12924, \"name\": \"hazy and overcast sky\"}, {\"id\": 12925, \"name\": \"tan belt\"}, {\"id\": 12926, \"name\": \"a beagle\"}, {\"id\": 12927, \"name\": \"the brown wooden armrest\"}, {\"id\": 12928, \"name\": \"a purple booklet\"}, {\"id\": 12929, \"name\": \"a soccer\"}, {\"id\": 12930, \"name\": \"green swing\"}, {\"id\": 12931, \"name\": \"a brown purse\"}, {\"id\": 12932, \"name\": \"a red brick pattern\"}, {\"id\": 12933, \"name\": \"the small whiteboard\"}, {\"id\": 12934, \"name\": \"a yellow chair\"}, {\"id\": 12935, \"name\": \"the larger enclosure\"}, {\"id\": 12936, \"name\": \"a wet, dark asphalt road\"}, {\"id\": 12937, \"name\": \"a PULL sign\"}, {\"id\": 12938, \"name\": \"the white cap\"}, {\"id\": 12939, \"name\": \"dark T-shirt\"}, {\"id\": 12940, \"name\": \"a traditional Indian attire\"}, {\"id\": 12941, \"name\": \"the metal pipe\"}, {\"id\": 12942, \"name\": \"the red detailing\"}, {\"id\": 12943, \"name\": \"a person wearing a blue hoodie and black pants\"}, {\"id\": 12944, \"name\": \"polished marble floor\"}, {\"id\": 12945, \"name\": \"small logo\"}, {\"id\": 12946, \"name\": \"green bottle\"}, {\"id\": 12947, \"name\": \"a floor of the enclosure\"}, {\"id\": 12948, \"name\": \"the purple curtain\"}, {\"id\": 12949, \"name\": \"a coop\"}, {\"id\": 12950, \"name\": \"an escalator's handrail\"}, {\"id\": 12951, \"name\": \"the blue sweatshirt\"}, {\"id\": 12952, \"name\": \"a low wooden fence\"}, {\"id\": 12953, \"name\": \"a short orange wall\"}, {\"id\": 12954, \"name\": \"a game station\"}, {\"id\": 12955, \"name\": \"the skyline\"}, {\"id\": 12956, \"name\": \"a blurred motorcycle\"}, {\"id\": 12957, \"name\": \"a white and black scooter\"}, {\"id\": 12958, \"name\": \"the large, shiny tile\"}, {\"id\": 12959, \"name\": \"a street corner\"}, {\"id\": 12960, \"name\": \"a checkerboard\"}, {\"id\": 12961, \"name\": \"patch of mud\"}, {\"id\": 12962, \"name\": \"the rear driver's side\"}, {\"id\": 12963, \"name\": \"the porcupine\"}, {\"id\": 12964, \"name\": \"collar\"}, {\"id\": 12965, \"name\": \"a flight of stairs\"}, {\"id\": 12966, \"name\": \"the cat's shadow\"}, {\"id\": 12967, \"name\": \"light purple bucket\"}, {\"id\": 12968, \"name\": \"large glass window\"}, {\"id\": 12969, \"name\": \"snow-capped peak\"}, {\"id\": 12970, \"name\": \"second floor\"}, {\"id\": 12971, \"name\": \"a gray mat\"}, {\"id\": 12972, \"name\": \"the glass door\"}, {\"id\": 12973, \"name\": \"the thick gray cloud\"}, {\"id\": 12974, \"name\": \"the muddy patch\"}, {\"id\": 12975, \"name\": \"a green and white stripe\"}, {\"id\": 12976, \"name\": \"scattered yellow leaf\"}, {\"id\": 12977, \"name\": \"the highway's asphalt surface\"}, {\"id\": 12978, \"name\": \"store counter\"}, {\"id\": 12979, \"name\": \"polished beige floor\"}, {\"id\": 12980, \"name\": \"a dark gray carpet\"}, {\"id\": 12981, \"name\": \"the long, curly tail\"}, {\"id\": 12982, \"name\": \"the Please do not touch sign\"}, {\"id\": 12983, \"name\": \"a small, silver device\"}, {\"id\": 12984, \"name\": \"a black handbag\"}, {\"id\": 12985, \"name\": \"the yellow column\"}, {\"id\": 12986, \"name\": \"a visible tattoo\"}, {\"id\": 12987, \"name\": \"white roof\"}, {\"id\": 12988, \"name\": \"the black trailer\"}, {\"id\": 12989, \"name\": \"the stage lighting\"}, {\"id\": 12990, \"name\": \"the yellow leg\"}, {\"id\": 12991, \"name\": \"the large, dark brown frog\"}, {\"id\": 12992, \"name\": \"the dog's open mouth\"}, {\"id\": 12993, \"name\": \"metal guardrail\"}, {\"id\": 12994, \"name\": \"the street vendor's stall\"}, {\"id\": 12995, \"name\": \"a plush toy\"}, {\"id\": 12996, \"name\": \"the multi-lane road\"}, {\"id\": 12997, \"name\": \"the black marble countertop\"}, {\"id\": 12998, \"name\": \"the dark-colored sidewalk\"}, {\"id\": 12999, \"name\": \"a red pickup truck\"}, {\"id\": 13000, \"name\": \"the pile of grass\"}, {\"id\": 13001, \"name\": \"the long-sleeved shirt\"}, {\"id\": 13002, \"name\": \"bright sky\"}, {\"id\": 13003, \"name\": \"a Jack Russell Terrier\"}, {\"id\": 13004, \"name\": \"a subtle pattern\"}, {\"id\": 13005, \"name\": \"large, gray stone-paved area\"}, {\"id\": 13006, \"name\": \"the white stripe\"}, {\"id\": 13007, \"name\": \"plastic or rubber\"}, {\"id\": 13008, \"name\": \"assortment of multicolored balls\"}, {\"id\": 13009, \"name\": \"a long, fluffy fur\"}, {\"id\": 13010, \"name\": \"the black display\"}, {\"id\": 13011, \"name\": \"a shaggy, light-pink fur\"}, {\"id\": 13012, \"name\": \"a single-story structure\"}, {\"id\": 13013, \"name\": \"hanger\"}, {\"id\": 13014, \"name\": \"the back of the television\"}, {\"id\": 13015, \"name\": \"the old and weathered concrete fountain\"}, {\"id\": 13016, \"name\": \"the long braid\"}, {\"id\": 13017, \"name\": \"a lone individual\"}, {\"id\": 13018, \"name\": \"the vibrant array of red, green, yellow, orange, purple, and blue balls\"}, {\"id\": 13019, \"name\": \"an interior of the washing machine\"}, {\"id\": 13020, \"name\": \"a mosaic tile floor\"}, {\"id\": 13021, \"name\": \"a pink accent\"}, {\"id\": 13022, \"name\": \"road intersection\"}, {\"id\": 13023, \"name\": \"a light-colored asphalt\"}, {\"id\": 13024, \"name\": \"a light-washed denim shirt\"}, {\"id\": 13025, \"name\": \"the plastic jug\"}, {\"id\": 13026, \"name\": \"beige pant\"}, {\"id\": 13027, \"name\": \"shiny white tile\"}, {\"id\": 13028, \"name\": \"a black-and-white gate\"}, {\"id\": 13029, \"name\": \"a KCL 226\"}, {\"id\": 13030, \"name\": \"red, corded vacuum cleaner\"}, {\"id\": 13031, \"name\": \"sauce or gravy\"}, {\"id\": 13032, \"name\": \"an oil\"}, {\"id\": 13033, \"name\": \"the dome-shaped top\"}, {\"id\": 13034, \"name\": \"the row of small shops or stalls\"}, {\"id\": 13035, \"name\": \"a green and white circular object\"}, {\"id\": 13036, \"name\": \"busy street\"}, {\"id\": 13037, \"name\": \"the small, yellow plant\"}, {\"id\": 13038, \"name\": \"tan bag strap\"}, {\"id\": 13039, \"name\": \"the store's ceiling\"}, {\"id\": 13040, \"name\": \"a dark-colored coat\"}, {\"id\": 13041, \"name\": \"a light brown body\"}, {\"id\": 13042, \"name\": \"the small, dark-colored dog\"}, {\"id\": 13043, \"name\": \"teal-colored jacket\"}, {\"id\": 13044, \"name\": \"pattern of small black square\"}, {\"id\": 13045, \"name\": \"the tan fabric canopy\"}, {\"id\": 13046, \"name\": \"a white arrow\"}, {\"id\": 13047, \"name\": \"vibrant yellow\"}, {\"id\": 13048, \"name\": \"the bird feeder\"}, {\"id\": 13049, \"name\": \"a granite\"}, {\"id\": 13050, \"name\": \"large dirt area\"}, {\"id\": 13051, \"name\": \"the scoreboard\"}, {\"id\": 13052, \"name\": \"a wall clock\"}, {\"id\": 13053, \"name\": \"ceiling light fixture\"}, {\"id\": 13054, \"name\": \"the email address\"}, {\"id\": 13055, \"name\": \"the orderliness\"}, {\"id\": 13056, \"name\": \"a purple eggplant\"}, {\"id\": 13057, \"name\": \"the more pronounced grid pattern\"}, {\"id\": 13058, \"name\": \"vibrant array of buildings\"}, {\"id\": 13059, \"name\": \"a rural street\"}, {\"id\": 13060, \"name\": \"white and blue jetway\"}, {\"id\": 13061, \"name\": \"the textured pattern\"}, {\"id\": 13062, \"name\": \"the wooden crate\"}, {\"id\": 13063, \"name\": \"the red X symbol\"}, {\"id\": 13064, \"name\": \"metal rail\"}, {\"id\": 13065, \"name\": \"the small white device\"}, {\"id\": 13066, \"name\": \"orange and black pattern\"}, {\"id\": 13067, \"name\": \"the white border\"}, {\"id\": 13068, \"name\": \"Labrador Retriever\"}, {\"id\": 13069, \"name\": \"the black belt\"}, {\"id\": 13070, \"name\": \"pink cap\"}, {\"id\": 13071, \"name\": \"red interior\"}, {\"id\": 13072, \"name\": \"the brown countertop\"}, {\"id\": 13073, \"name\": \"vibrant red and yellow design\"}, {\"id\": 13074, \"name\": \"the white button-up shirt\"}, {\"id\": 13075, \"name\": \"the dealership\"}, {\"id\": 13076, \"name\": \"laundry room\"}, {\"id\": 13077, \"name\": \"white tile or linoleum\"}, {\"id\": 13078, \"name\": \"the motorcyclist\"}, {\"id\": 13079, \"name\": \"the blue line\"}, {\"id\": 13080, \"name\": \"the Maltese or similar breed\"}, {\"id\": 13081, \"name\": \"a large wall\"}, {\"id\": 13082, \"name\": \"a red and white t-shirt\"}, {\"id\": 13083, \"name\": \"bright white sky\"}, {\"id\": 13084, \"name\": \"the man in a dark uniform\"}, {\"id\": 13085, \"name\": \"a dimly lit, gray-tiled walkway\"}, {\"id\": 13086, \"name\": \"the wooden cabinet\"}, {\"id\": 13087, \"name\": \"blue metal object\"}, {\"id\": 13088, \"name\": \"store's glass door\"}, {\"id\": 13089, \"name\": \"the orange tabby coat\"}, {\"id\": 13090, \"name\": \"the grey plastic grate\"}, {\"id\": 13091, \"name\": \"the steep hill\"}, {\"id\": 13092, \"name\": \"a busy highway\"}, {\"id\": 13093, \"name\": \"the right side of the image\"}, {\"id\": 13094, \"name\": \"prominent stack of tires\"}, {\"id\": 13095, \"name\": \"grey wooden flooring\"}, {\"id\": 13096, \"name\": \"blackboard\"}, {\"id\": 13097, \"name\": \"shrubbery\"}, {\"id\": 13098, \"name\": \"a large picture of a car\"}, {\"id\": 13099, \"name\": \"the shopping center\"}, {\"id\": 13100, \"name\": \"the black sock\"}, {\"id\": 13101, \"name\": \"a construction or repair project\"}, {\"id\": 13102, \"name\": \"dog's coat\"}, {\"id\": 13103, \"name\": \"the bamboo or wood slat\"}, {\"id\": 13104, \"name\": \"a FamilyMart convenience store\"}, {\"id\": 13105, \"name\": \"a chocolate birthday cake\"}, {\"id\": 13106, \"name\": \"a Chinese temple\"}, {\"id\": 13107, \"name\": \"a black puffer jacket\"}, {\"id\": 13108, \"name\": \"a piece of gym equipment\"}, {\"id\": 13109, \"name\": \"warm, golden light\"}, {\"id\": 13110, \"name\": \"a grey chair\"}, {\"id\": 13111, \"name\": \"white sky\"}, {\"id\": 13112, \"name\": \"cat's body\"}, {\"id\": 13113, \"name\": \"the AV. PANAMERICANA SUR\"}, {\"id\": 13114, \"name\": \"the tan\"}, {\"id\": 13115, \"name\": \"the fluorescent light fixture\"}, {\"id\": 13116, \"name\": \"a red purse\"}, {\"id\": 13117, \"name\": \"a glass surface\"}, {\"id\": 13118, \"name\": \"the hand saw\"}, {\"id\": 13119, \"name\": \"windshield of a car\"}, {\"id\": 13120, \"name\": \"the group\"}, {\"id\": 13121, \"name\": \"the brown playing surface\"}, {\"id\": 13122, \"name\": \"shiny, reflective flooring\"}, {\"id\": 13123, \"name\": \"a prominent display of colorful products\"}, {\"id\": 13124, \"name\": \"the blue and white net\"}, {\"id\": 13125, \"name\": \"a silver metal railing\"}, {\"id\": 13126, \"name\": \"the white and red bike\"}, {\"id\": 13127, \"name\": \"silver two-door sedan\"}, {\"id\": 13128, \"name\": \"store's name\"}, {\"id\": 13129, \"name\": \"light green curtain\"}, {\"id\": 13130, \"name\": \"the flatbed trailer\"}, {\"id\": 13131, \"name\": \"a large room\"}, {\"id\": 13132, \"name\": \"pink sole\"}, {\"id\": 13133, \"name\": \"a rectangular pool of water\"}, {\"id\": 13134, \"name\": \"black shoes\"}, {\"id\": 13135, \"name\": \"a tan facade\"}, {\"id\": 13136, \"name\": \"the green plastic cup\"}, {\"id\": 13137, \"name\": \"the grayish-brown building\"}, {\"id\": 13138, \"name\": \"mermaid tail shapes\"}, {\"id\": 13139, \"name\": \"a moss-covered rock\"}, {\"id\": 13140, \"name\": \"white appliance\"}, {\"id\": 13141, \"name\": \"prominent brown wooden pole\"}, {\"id\": 13142, \"name\": \"the large, purple building\"}, {\"id\": 13143, \"name\": \"a thin concrete curb\"}, {\"id\": 13144, \"name\": \"dark ceiling\"}, {\"id\": 13145, \"name\": \"a train track\"}, {\"id\": 13146, \"name\": \"patterned skirt\"}, {\"id\": 13147, \"name\": \"low concrete curb\"}, {\"id\": 13148, \"name\": \"blue floral dress\"}, {\"id\": 13149, \"name\": \"hair salon package\"}, {\"id\": 13150, \"name\": \"a clear and sky\"}, {\"id\": 13151, \"name\": \"a green floral design\"}, {\"id\": 13152, \"name\": \"the pile of trash\"}, {\"id\": 13153, \"name\": \"a blue jean short\"}, {\"id\": 13154, \"name\": \"a seating\"}, {\"id\": 13155, \"name\": \"a small food stand or stall\"}, {\"id\": 13156, \"name\": \"predominantly black body\"}, {\"id\": 13157, \"name\": \"holiday season\"}, {\"id\": 13158, \"name\": \"large, weathered green wagon\"}, {\"id\": 13159, \"name\": \"small black bag\"}, {\"id\": 13160, \"name\": \"the window or door\"}, {\"id\": 13161, \"name\": \"speckled floor\"}, {\"id\": 13162, \"name\": \"a bustling outdoor cafe\"}, {\"id\": 13163, \"name\": \"the high, curved ceiling\"}, {\"id\": 13164, \"name\": \"slightly open mouth\"}, {\"id\": 13165, \"name\": \"the three-wheeled motorized vehicle\"}, {\"id\": 13166, \"name\": \"the brown floor\"}, {\"id\": 13167, \"name\": \"the brown and white paint\"}, {\"id\": 13168, \"name\": \"the beach\"}, {\"id\": 13169, \"name\": \"blue seat\"}, {\"id\": 13170, \"name\": \"the white and green bus\"}, {\"id\": 13171, \"name\": \"trampoline-like surface\"}, {\"id\": 13172, \"name\": \"the orange plastic container\"}, {\"id\": 13173, \"name\": \"the prominent tiled floor\"}, {\"id\": 13174, \"name\": \"the muddy, unpaved street\"}, {\"id\": 13175, \"name\": \"the circular pattern\"}, {\"id\": 13176, \"name\": \"a paint can\"}, {\"id\": 13177, \"name\": \"a resident of the village\"}, {\"id\": 13178, \"name\": \"the gravel area\"}, {\"id\": 13179, \"name\": \"a photo of a cup of coffee or soup\"}, {\"id\": 13180, \"name\": \"green rectangle\"}, {\"id\": 13181, \"name\": \"the dark hoodie\"}, {\"id\": 13182, \"name\": \"the tall minaret\"}, {\"id\": 13183, \"name\": \"navy blue wall\"}, {\"id\": 13184, \"name\": \"a mall's floor\"}, {\"id\": 13185, \"name\": \"row of parked car\"}, {\"id\": 13186, \"name\": \"the majority of its body\"}, {\"id\": 13187, \"name\": \"the salad\"}, {\"id\": 13188, \"name\": \"person's pink slipper\"}, {\"id\": 13189, \"name\": \"a light blue cage\"}, {\"id\": 13190, \"name\": \"the metal rail\"}, {\"id\": 13191, \"name\": \"a cylindrical shape\"}, {\"id\": 13192, \"name\": \"the She\"}, {\"id\": 13193, \"name\": \"red barricade\"}, {\"id\": 13194, \"name\": \"pink children's bicycle\"}, {\"id\": 13195, \"name\": \"a thin, yellowish material\"}, {\"id\": 13196, \"name\": \"the red and white floral patterned object\"}, {\"id\": 13197, \"name\": \"colorful tile floor\"}, {\"id\": 13198, \"name\": \"a dirty beige tile floor\"}, {\"id\": 13199, \"name\": \"the yellow and gray body\"}, {\"id\": 13200, \"name\": \"the tall tree\"}, {\"id\": 13201, \"name\": \"a Asian cuisine\"}, {\"id\": 13202, \"name\": \"a stack of orange plastic containers\"}, {\"id\": 13203, \"name\": \"a xxx arcade game machine\"}, {\"id\": 13204, \"name\": \"a snack bar\"}, {\"id\": 13205, \"name\": \"a green plastic or metal object\"}, {\"id\": 13206, \"name\": \"the torso and upper body\"}, {\"id\": 13207, \"name\": \"a individual\"}, {\"id\": 13208, \"name\": \"a display of painting\"}, {\"id\": 13209, \"name\": \"the navy blue pant\"}, {\"id\": 13210, \"name\": \"the control panel\"}, {\"id\": 13211, \"name\": \"concrete ledge\"}, {\"id\": 13212, \"name\": \"black legging\"}, {\"id\": 13213, \"name\": \"the concrete sidewalk\"}, {\"id\": 13214, \"name\": \"purple padding\"}, {\"id\": 13215, \"name\": \"a pliers\"}, {\"id\": 13216, \"name\": \"the white saddle blanket\"}, {\"id\": 13217, \"name\": \"the black and white writing\"}, {\"id\": 13218, \"name\": \"floor-to-ceiling window\"}, {\"id\": 13219, \"name\": \"a small jar\"}, {\"id\": 13220, \"name\": \"the dragon's head\"}, {\"id\": 13221, \"name\": \"the small, round object\"}, {\"id\": 13222, \"name\": \"the dark green triangle\"}, {\"id\": 13223, \"name\": \"the white price tag\"}, {\"id\": 13224, \"name\": \"red and white stripe\"}, {\"id\": 13225, \"name\": \"a gla wall\"}, {\"id\": 13226, \"name\": \"waist\"}, {\"id\": 13227, \"name\": \"a tool or device\"}, {\"id\": 13228, \"name\": \"large white pillar\"}, {\"id\": 13229, \"name\": \"a brown ball of food\"}, {\"id\": 13230, \"name\": \"dark wood cabinet\"}, {\"id\": 13231, \"name\": \"the brown baseboard\"}, {\"id\": 13232, \"name\": \"a lanyard\"}, {\"id\": 13233, \"name\": \"the taxi only sign\"}, {\"id\": 13234, \"name\": \"workbench\"}, {\"id\": 13235, \"name\": \"large yellow sign\"}, {\"id\": 13236, \"name\": \"the black band\"}, {\"id\": 13237, \"name\": \"a white dres shirt\"}, {\"id\": 13238, \"name\": \"vibrant graffiti\"}, {\"id\": 13239, \"name\": \"a metal handle\"}, {\"id\": 13240, \"name\": \"red railing\"}, {\"id\": 13241, \"name\": \"a person's hands and arm\"}, {\"id\": 13242, \"name\": \"a pink leopard-print box\"}, {\"id\": 13243, \"name\": \"the white piece of trash\"}, {\"id\": 13244, \"name\": \"clear storage bin\"}, {\"id\": 13245, \"name\": \"the store shelf\"}, {\"id\": 13246, \"name\": \"the signs and logo\"}, {\"id\": 13247, \"name\": \"white center line\"}, {\"id\": 13248, \"name\": \"the white boot\"}, {\"id\": 13249, \"name\": \"a silver trim\"}, {\"id\": 13250, \"name\": \"the white bed\"}, {\"id\": 13251, \"name\": \"the red jersey\"}, {\"id\": 13252, \"name\": \"the black trim\"}, {\"id\": 13253, \"name\": \"lanyard\"}, {\"id\": 13254, \"name\": \"console\"}, {\"id\": 13255, \"name\": \"purple top\"}, {\"id\": 13256, \"name\": \"the vertical image\"}, {\"id\": 13257, \"name\": \"the vanity\"}, {\"id\": 13258, \"name\": \"blue car lift\"}, {\"id\": 13259, \"name\": \"a black stand\"}, {\"id\": 13260, \"name\": \"an instrument\"}, {\"id\": 13261, \"name\": \"lane of the road\"}, {\"id\": 13262, \"name\": \"wooden stock\"}, {\"id\": 13263, \"name\": \"a lower step\"}, {\"id\": 13264, \"name\": \"the cat breed\"}, {\"id\": 13265, \"name\": \"white plastic bag or container\"}, {\"id\": 13266, \"name\": \"the black glove\"}, {\"id\": 13267, \"name\": \"the left photograph\"}, {\"id\": 13268, \"name\": \"wood trim\"}, {\"id\": 13269, \"name\": \"a gray asphalt surface\"}, {\"id\": 13270, \"name\": \"dark green beret\"}, {\"id\": 13271, \"name\": \"the green hydrangea\"}, {\"id\": 13272, \"name\": \"a slogan\"}, {\"id\": 13273, \"name\": \"a company's logo\"}, {\"id\": 13274, \"name\": \"the large black and red sign\"}, {\"id\": 13275, \"name\": \"a weight\"}, {\"id\": 13276, \"name\": \"large blue stick figure\"}, {\"id\": 13277, \"name\": \"green tile\"}, {\"id\": 13278, \"name\": \"the belt\"}, {\"id\": 13279, \"name\": \"blurred green field\"}, {\"id\": 13280, \"name\": \"a black salon chair\"}, {\"id\": 13281, \"name\": \"prominent staircase\"}, {\"id\": 13282, \"name\": \"white and blue pant\"}, {\"id\": 13283, \"name\": \"the black toyota pickup truck\"}, {\"id\": 13284, \"name\": \"the black bike frame\"}, {\"id\": 13285, \"name\": \"a blue forklift\"}, {\"id\": 13286, \"name\": \"thick black sidewall\"}, {\"id\": 13287, \"name\": \"a blue glove\"}, {\"id\": 13288, \"name\": \"the yellow structure\"}, {\"id\": 13289, \"name\": \"the plastic container\"}, {\"id\": 13290, \"name\": \"the yellow cleat\"}, {\"id\": 13291, \"name\": \"bat\"}, {\"id\": 13292, \"name\": \"a beard\"}, {\"id\": 13293, \"name\": \"the black shelving unit\"}, {\"id\": 13294, \"name\": \"black bull\"}, {\"id\": 13295, \"name\": \"the purple banner\"}, {\"id\": 13296, \"name\": \"a metal grating\"}, {\"id\": 13297, \"name\": \"a symbol\"}, {\"id\": 13298, \"name\": \"the yellow rubber dinosaur\"}, {\"id\": 13299, \"name\": \"garage door\"}, {\"id\": 13300, \"name\": \"the pool area\"}, {\"id\": 13301, \"name\": \"vintage baby doll\"}, {\"id\": 13302, \"name\": \"the green plant\"}, {\"id\": 13303, \"name\": \"black and white glove\"}, {\"id\": 13304, \"name\": \"the casual clothing\"}, {\"id\": 13305, \"name\": \"a red and yellow striped pole\"}, {\"id\": 13306, \"name\": \"the black bumper\"}, {\"id\": 13307, \"name\": \"a red goal\"}, {\"id\": 13308, \"name\": \"the black sledgehammer\"}, {\"id\": 13309, \"name\": \"a garage\"}, {\"id\": 13310, \"name\": \"white cup\"}, {\"id\": 13311, \"name\": \"a red, three-wheeled vehicle\"}, {\"id\": 13312, \"name\": \"a grey fabric\"}, {\"id\": 13313, \"name\": \"the mountain bike\"}, {\"id\": 13314, \"name\": \"a pajamas\"}, {\"id\": 13315, \"name\": \"face paint\"}, {\"id\": 13316, \"name\": \"black leash\"}, {\"id\": 13317, \"name\": \"the knee pad\"}, {\"id\": 13318, \"name\": \"a list of news headline\"}, {\"id\": 13319, \"name\": \"steep roof\"}, {\"id\": 13320, \"name\": \"metal railing\"}, {\"id\": 13321, \"name\": \"the green camouflage outfit\"}, {\"id\": 13322, \"name\": \"the reporter's microphone\"}, {\"id\": 13323, \"name\": \"the blue can of whipped cream\"}, {\"id\": 13324, \"name\": \"blue jean\"}, {\"id\": 13325, \"name\": \"the tool cart\"}, {\"id\": 13326, \"name\": \"a bright white sky\"}, {\"id\": 13327, \"name\": \"yellow billboard\"}, {\"id\": 13328, \"name\": \"the line of palm tree\"}, {\"id\": 13329, \"name\": \"white baseball\"}, {\"id\": 13330, \"name\": \"radiator or heating unit\"}, {\"id\": 13331, \"name\": \"a table or counter\"}, {\"id\": 13332, \"name\": \"red building\"}, {\"id\": 13333, \"name\": \"a large red punching bag\"}, {\"id\": 13334, \"name\": \"a light-colored stone or concrete\"}, {\"id\": 13335, \"name\": \"a gras field\"}, {\"id\": 13336, \"name\": \"a long, thin, white rope\"}, {\"id\": 13337, \"name\": \"the purple wall\"}, {\"id\": 13338, \"name\": \"the white measuring tape\"}, {\"id\": 13339, \"name\": \"short, gray hair\"}, {\"id\": 13340, \"name\": \"the braille keyboard\"}, {\"id\": 13341, \"name\": \"the time\"}, {\"id\": 13342, \"name\": \"the digital scoreboard\"}, {\"id\": 13343, \"name\": \"sunglass\"}, {\"id\": 13344, \"name\": \"horizontal handle\"}, {\"id\": 13345, \"name\": \"the villarreal cf logo\"}, {\"id\": 13346, \"name\": \"black trash can\"}, {\"id\": 13347, \"name\": \"dark sunglass\"}, {\"id\": 13348, \"name\": \"narrow aisle\"}, {\"id\": 13349, \"name\": \"the laptop computer\"}, {\"id\": 13350, \"name\": \"a dark wood cabinet\"}, {\"id\": 13351, \"name\": \"a flat tire\"}, {\"id\": 13352, \"name\": \"the red brake light\"}, {\"id\": 13353, \"name\": \"a purple stripe\"}, {\"id\": 13354, \"name\": \"the backdrop of building\"}, {\"id\": 13355, \"name\": \"silver door handle\"}, {\"id\": 13356, \"name\": \"the white headboard\"}, {\"id\": 13357, \"name\": \"the large black weight\"}, {\"id\": 13358, \"name\": \"small, rectangular opening\"}, {\"id\": 13359, \"name\": \"a sunglass\"}, {\"id\": 13360, \"name\": \"the white cloud\"}, {\"id\": 13361, \"name\": \"black and green sneaker\"}, {\"id\": 13362, \"name\": \"the spectator\"}, {\"id\": 13363, \"name\": \"of colorful candy\"}, {\"id\": 13364, \"name\": \"a folded white napkin\"}, {\"id\": 13365, \"name\": \"a long-handled shovel\"}, {\"id\": 13366, \"name\": \"the pocket\"}, {\"id\": 13367, \"name\": \"blue curtain\"}, {\"id\": 13368, \"name\": \"ring\"}, {\"id\": 13369, \"name\": \"the sky sport logo\"}, {\"id\": 13370, \"name\": \"the blue short\"}, {\"id\": 13371, \"name\": \"brown stripe\"}, {\"id\": 13372, \"name\": \"dark suv\"}, {\"id\": 13373, \"name\": \"a badge\"}, {\"id\": 13374, \"name\": \"the photo of a young girl\"}, {\"id\": 13375, \"name\": \"the black spatula\"}, {\"id\": 13376, \"name\": \"vehicle's track\"}, {\"id\": 13377, \"name\": \"the high collar\"}, {\"id\": 13378, \"name\": \"a red label\"}, {\"id\": 13379, \"name\": \"a blue and yellow jersey\"}, {\"id\": 13380, \"name\": \"a small bowl of melted butter\"}, {\"id\": 13381, \"name\": \"a gray and brown coat\"}, {\"id\": 13382, \"name\": \"the charred piece\"}, {\"id\": 13383, \"name\": \"a heathrow logo\"}, {\"id\": 13384, \"name\": \"the black handlebar\"}, {\"id\": 13385, \"name\": \"a blue and white square\"}, {\"id\": 13386, \"name\": \"red hot performance\"}, {\"id\": 13387, \"name\": \"a black bottom\"}, {\"id\": 13388, \"name\": \"a yellow metal gate\"}, {\"id\": 13389, \"name\": \"red poster\"}, {\"id\": 13390, \"name\": \"a large wooden fence\"}, {\"id\": 13391, \"name\": \"glas door\"}, {\"id\": 13392, \"name\": \"the pink and blue balloon\"}, {\"id\": 13393, \"name\": \"vibrant blue, red, and green dress\"}, {\"id\": 13394, \"name\": \"pink trim\"}, {\"id\": 13395, \"name\": \"the stainles steel instant pot\"}, {\"id\": 13396, \"name\": \"brake lever\"}, {\"id\": 13397, \"name\": \"the shirtles wrestler\"}, {\"id\": 13398, \"name\": \"the metal fence\"}, {\"id\": 13399, \"name\": \"black microphone\"}, {\"id\": 13400, \"name\": \"distinctive white stripe\"}, {\"id\": 13401, \"name\": \"the white banister\"}, {\"id\": 13402, \"name\": \"the khaki short\"}, {\"id\": 13403, \"name\": \"the orange bottle\"}, {\"id\": 13404, \"name\": \"a p button\"}, {\"id\": 13405, \"name\": \"a glas bowl\"}, {\"id\": 13406, \"name\": \"a dark-colored car\"}, {\"id\": 13407, \"name\": \"a glasses\"}, {\"id\": 13408, \"name\": \"a blue item of clothing\"}, {\"id\": 13409, \"name\": \"the white kitchen cabinet\"}, {\"id\": 13410, \"name\": \"a short, red-painted fingernail\"}, {\"id\": 13411, \"name\": \"black tire\"}, {\"id\": 13412, \"name\": \"a glas door\"}, {\"id\": 13413, \"name\": \"shirtles man\"}, {\"id\": 13414, \"name\": \"back of the couch\"}, {\"id\": 13415, \"name\": \"the toy gun\"}, {\"id\": 13416, \"name\": \"the woman in a pink sweater\"}, {\"id\": 13417, \"name\": \"pant\"}, {\"id\": 13418, \"name\": \"a back button\"}, {\"id\": 13419, \"name\": \"a dark brown hair\"}, {\"id\": 13420, \"name\": \"inflatable obstacle course tunnel\"}, {\"id\": 13421, \"name\": \"a front grill\"}, {\"id\": 13422, \"name\": \"a dark brown wall\"}, {\"id\": 13423, \"name\": \"sheer, beaded sleeve\"}, {\"id\": 13424, \"name\": \"brown wooden floor\"}, {\"id\": 13425, \"name\": \"a blue helmet\"}, {\"id\": 13426, \"name\": \"the red bicycle\"}, {\"id\": 13427, \"name\": \"the red mat\"}, {\"id\": 13428, \"name\": \"a purple bloom\"}, {\"id\": 13429, \"name\": \"concrete shelf\"}, {\"id\": 13430, \"name\": \"the black metal leg\"}, {\"id\": 13431, \"name\": \"a silver wheel\"}, {\"id\": 13432, \"name\": \"amplifier\"}, {\"id\": 13433, \"name\": \"a bright light source\"}, {\"id\": 13434, \"name\": \"the red golf bag\"}, {\"id\": 13435, \"name\": \"the red pitching mound\"}, {\"id\": 13436, \"name\": \"the matching blue jacket\"}, {\"id\": 13437, \"name\": \"white stovetop\"}, {\"id\": 13438, \"name\": \"the glove\"}, {\"id\": 13439, \"name\": \"red countertop\"}, {\"id\": 13440, \"name\": \"the white sleeveles top\"}, {\"id\": 13441, \"name\": \"the red carpeting\"}, {\"id\": 13442, \"name\": \"the soccer ball graphic\"}, {\"id\": 13443, \"name\": \"vibrant, intricately designed leather bag\"}, {\"id\": 13444, \"name\": \"gray bucket\"}, {\"id\": 13445, \"name\": \"a concrete dock\"}, {\"id\": 13446, \"name\": \"a scoreboard\"}, {\"id\": 13447, \"name\": \"a white skate ramp\"}, {\"id\": 13448, \"name\": \"loincloth\"}, {\"id\": 13449, \"name\": \"garland of flower\"}, {\"id\": 13450, \"name\": \"the yellow cape\"}, {\"id\": 13451, \"name\": \"left forearm\"}, {\"id\": 13452, \"name\": \"clear glas container\"}, {\"id\": 13453, \"name\": \"a brown trim\"}, {\"id\": 13454, \"name\": \"the foot\"}, {\"id\": 13455, \"name\": \"the blurred face\"}, {\"id\": 13456, \"name\": \"the dark blue suit\"}, {\"id\": 13457, \"name\": \"the yacht\"}, {\"id\": 13458, \"name\": \"the braid\"}, {\"id\": 13459, \"name\": \"a patch of greenery\"}, {\"id\": 13460, \"name\": \"the small container of pin\"}, {\"id\": 13461, \"name\": \"a large metal machine\"}, {\"id\": 13462, \"name\": \"a crowd\"}, {\"id\": 13463, \"name\": \"pink heart\"}, {\"id\": 13464, \"name\": \"a white mane\"}, {\"id\": 13465, \"name\": \"the orange traffic cone\"}, {\"id\": 13466, \"name\": \"a long hair\"}, {\"id\": 13467, \"name\": \"the green sprinkle\"}, {\"id\": 13468, \"name\": \"bright yellow and pink hue\"}, {\"id\": 13469, \"name\": \"the tray of sliced tomato\"}, {\"id\": 13470, \"name\": \"a yellow rim\"}, {\"id\": 13471, \"name\": \"waffle cone piece\"}, {\"id\": 13472, \"name\": \"a gras track\"}, {\"id\": 13473, \"name\": \"a case of bottled water\"}, {\"id\": 13474, \"name\": \"a can of white paint\"}, {\"id\": 13475, \"name\": \"onlooker\"}, {\"id\": 13476, \"name\": \"the white wig\"}, {\"id\": 13477, \"name\": \"white jersey\"}, {\"id\": 13478, \"name\": \"a pink pant\"}, {\"id\": 13479, \"name\": \"black front grill\"}, {\"id\": 13480, \"name\": \"red glove\"}, {\"id\": 13481, \"name\": \"the large plant\"}, {\"id\": 13482, \"name\": \"large television\"}, {\"id\": 13483, \"name\": \"the dial\"}, {\"id\": 13484, \"name\": \"the round blue tag\"}, {\"id\": 13485, \"name\": \"gym's floor\"}, {\"id\": 13486, \"name\": \"the green lego landscape\"}, {\"id\": 13487, \"name\": \"silver base\"}, {\"id\": 13488, \"name\": \"man wearing headphone\"}, {\"id\": 13489, \"name\": \"the yellow vest\"}, {\"id\": 13490, \"name\": \"the small yellow building\"}, {\"id\": 13491, \"name\": \"the smooth gray surface\"}, {\"id\": 13492, \"name\": \"the wooden ladder\"}, {\"id\": 13493, \"name\": \"the large paper shopping bag\"}, {\"id\": 13494, \"name\": \"the white shell earring\"}, {\"id\": 13495, \"name\": \"black cleat\"}, {\"id\": 13496, \"name\": \"the coffee pot\"}, {\"id\": 13497, \"name\": \"a large dragon sculpture\"}, {\"id\": 13498, \"name\": \"a black handlebar grip\"}, {\"id\": 13499, \"name\": \"a dark gray puffer vest\"}, {\"id\": 13500, \"name\": \"the yellow extension cord\"}, {\"id\": 13501, \"name\": \"device\"}, {\"id\": 13502, \"name\": \"puck\"}, {\"id\": 13503, \"name\": \"black sock\"}, {\"id\": 13504, \"name\": \"the woman's face\"}, {\"id\": 13505, \"name\": \"the side window\"}, {\"id\": 13506, \"name\": \"a yellow caution tape\"}, {\"id\": 13507, \"name\": \"the car's license plate\"}, {\"id\": 13508, \"name\": \"lego character\"}, {\"id\": 13509, \"name\": \"the document\"}, {\"id\": 13510, \"name\": \"vibrant red neon sign\"}, {\"id\": 13511, \"name\": \"tray of raw meatball\"}, {\"id\": 13512, \"name\": \"red barrier\"}, {\"id\": 13513, \"name\": \"a black bag strap\"}, {\"id\": 13514, \"name\": \"a white label\"}, {\"id\": 13515, \"name\": \"a red and white striped shirt\"}, {\"id\": 13516, \"name\": \"ferry's ramp\"}, {\"id\": 13517, \"name\": \"the white porsche 911\"}, {\"id\": 13518, \"name\": \"cast or sling\"}, {\"id\": 13519, \"name\": \"the roll of material\"}, {\"id\": 13520, \"name\": \"video camera\"}, {\"id\": 13521, \"name\": \"a white napkin\"}, {\"id\": 13522, \"name\": \"a long sock\"}, {\"id\": 13523, \"name\": \"the blue and red striped uniform\"}, {\"id\": 13524, \"name\": \"the spotlight\"}, {\"id\": 13525, \"name\": \"a black microphone\"}, {\"id\": 13526, \"name\": \"the orange and black goggle\"}, {\"id\": 13527, \"name\": \"the blue mat\"}, {\"id\": 13528, \"name\": \"a light gray curtain\"}, {\"id\": 13529, \"name\": \"shiny black and silver body\"}, {\"id\": 13530, \"name\": \"neon yellow shirt\"}, {\"id\": 13531, \"name\": \"center console\"}, {\"id\": 13532, \"name\": \"backrest\"}, {\"id\": 13533, \"name\": \"red goalpost\"}, {\"id\": 13534, \"name\": \"a bright and white sky\"}, {\"id\": 13535, \"name\": \"the large black building\"}, {\"id\": 13536, \"name\": \"the gaming controller\"}, {\"id\": 13537, \"name\": \"the lower torso\"}, {\"id\": 13538, \"name\": \"car's hood\"}, {\"id\": 13539, \"name\": \"large potted plant\"}, {\"id\": 13540, \"name\": \"a coat or jacket\"}, {\"id\": 13541, \"name\": \"individual's arm\"}, {\"id\": 13542, \"name\": \"a digital display board\"}, {\"id\": 13543, \"name\": \"blue stripe\"}, {\"id\": 13544, \"name\": \"a translucent watermark\"}, {\"id\": 13545, \"name\": \"the orange outfit\"}, {\"id\": 13546, \"name\": \"dark blue jean\"}, {\"id\": 13547, \"name\": \"a man's attire\"}, {\"id\": 13548, \"name\": \"vibrant orange tablecloth\"}, {\"id\": 13549, \"name\": \"a car's headlights\"}, {\"id\": 13550, \"name\": \"a black plastic armrest\"}, {\"id\": 13551, \"name\": \"the dark-colored engine\"}, {\"id\": 13552, \"name\": \"the blue canopy\"}, {\"id\": 13553, \"name\": \"the green tenni ball\"}, {\"id\": 13554, \"name\": \"orange foot\"}, {\"id\": 13555, \"name\": \"the white string\"}, {\"id\": 13556, \"name\": \"a red suit\"}, {\"id\": 13557, \"name\": \"the blue and white jersey\"}, {\"id\": 13558, \"name\": \"the white bench\"}, {\"id\": 13559, \"name\": \"colorful banner\"}, {\"id\": 13560, \"name\": \"the bag or box\"}, {\"id\": 13561, \"name\": \"a red cleat\"}, {\"id\": 13562, \"name\": \"purple uniform\"}, {\"id\": 13563, \"name\": \"yellow-orange reflector\"}, {\"id\": 13564, \"name\": \"the buffet-style table\"}, {\"id\": 13565, \"name\": \"the overcast and grey sky\"}, {\"id\": 13566, \"name\": \"the blurred background\"}, {\"id\": 13567, \"name\": \"car's wheel\"}, {\"id\": 13568, \"name\": \"large piece of plywood\"}, {\"id\": 13569, \"name\": \"the tall pillar\"}, {\"id\": 13570, \"name\": \"round table\"}, {\"id\": 13571, \"name\": \"the shelf or table\"}, {\"id\": 13572, \"name\": \"a large refrigerator\"}, {\"id\": 13573, \"name\": \"coffee maker\"}, {\"id\": 13574, \"name\": \"a dog's mouth\"}, {\"id\": 13575, \"name\": \"the doll\"}, {\"id\": 13576, \"name\": \"an engine\"}, {\"id\": 13577, \"name\": \"a gold band\"}, {\"id\": 13578, \"name\": \"braid\"}, {\"id\": 13579, \"name\": \"blender's lid\"}, {\"id\": 13580, \"name\": \"a central wheel\"}, {\"id\": 13581, \"name\": \"a black watch\"}, {\"id\": 13582, \"name\": \"the blue and black motorcycle helmet\"}, {\"id\": 13583, \"name\": \"a wooden armrest\"}, {\"id\": 13584, \"name\": \"the red frame\"}, {\"id\": 13585, \"name\": \"the metal box\"}, {\"id\": 13586, \"name\": \"a small orange object\"}, {\"id\": 13587, \"name\": \"white hat\"}, {\"id\": 13588, \"name\": \"white diving block\"}, {\"id\": 13589, \"name\": \"the white short-sleeved button-down shirt\"}, {\"id\": 13590, \"name\": \"black yoga mat\"}, {\"id\": 13591, \"name\": \"budweiser logo\"}, {\"id\": 13592, \"name\": \"silver cylindrical body\"}, {\"id\": 13593, \"name\": \"a blue canoe\"}, {\"id\": 13594, \"name\": \"a hashtag\"}, {\"id\": 13595, \"name\": \"clear glas trumpet\"}, {\"id\": 13596, \"name\": \"a colorful pattern of car\"}, {\"id\": 13597, \"name\": \"bow's grip\"}, {\"id\": 13598, \"name\": \"bmw logo\"}, {\"id\": 13599, \"name\": \"car's body\"}, {\"id\": 13600, \"name\": \"an electronic device\"}, {\"id\": 13601, \"name\": \"the colored circle\"}, {\"id\": 13602, \"name\": \"black steering wheel\"}, {\"id\": 13603, \"name\": \"the bright light pole\"}, {\"id\": 13604, \"name\": \"glassware\"}, {\"id\": 13605, \"name\": \"red and blue union jack pattern\"}, {\"id\": 13606, \"name\": \"the wooden fence\"}, {\"id\": 13607, \"name\": \"the decorative green hedge\"}, {\"id\": 13608, \"name\": \"the van's side window\"}, {\"id\": 13609, \"name\": \"a dark attire\"}, {\"id\": 13610, \"name\": \"the colorful balloon\"}, {\"id\": 13611, \"name\": \"the sleek sports car\"}, {\"id\": 13612, \"name\": \"the metal barrier\"}, {\"id\": 13613, \"name\": \"yellow towel\"}, {\"id\": 13614, \"name\": \"the white countertop\"}, {\"id\": 13615, \"name\": \"the woman in a white shirt\"}, {\"id\": 13616, \"name\": \"a bang\"}, {\"id\": 13617, \"name\": \"the black and white horizontal stripe\"}, {\"id\": 13618, \"name\": \"the black, gray, and blue item\"}, {\"id\": 13619, \"name\": \"surfer's shadow\"}, {\"id\": 13620, \"name\": \"the prize\"}, {\"id\": 13621, \"name\": \"a dark wooden floor\"}, {\"id\": 13622, \"name\": \"green grass\"}, {\"id\": 13623, \"name\": \"large spaceship\"}, {\"id\": 13624, \"name\": \"a black and white sneaker\"}, {\"id\": 13625, \"name\": \"a white pillar\"}, {\"id\": 13626, \"name\": \"the silver rim\"}, {\"id\": 13627, \"name\": \"trunk\"}, {\"id\": 13628, \"name\": \"an orange and black jersey\"}, {\"id\": 13629, \"name\": \"gray ford f-150 pickup truck\"}, {\"id\": 13630, \"name\": \"the yellow helmet\"}, {\"id\": 13631, \"name\": \"large, black plastic cover\"}, {\"id\": 13632, \"name\": \"a white wig\"}, {\"id\": 13633, \"name\": \"the dark brown hair\"}, {\"id\": 13634, \"name\": \"left foot\"}, {\"id\": 13635, \"name\": \"the green bag\"}, {\"id\": 13636, \"name\": \"a stationary bike\"}, {\"id\": 13637, \"name\": \"a wall of the warehouse\"}, {\"id\": 13638, \"name\": \"a river\"}, {\"id\": 13639, \"name\": \"the small black object\"}, {\"id\": 13640, \"name\": \"wooden baby gate\"}, {\"id\": 13641, \"name\": \"the blue and white truck\"}, {\"id\": 13642, \"name\": \"a right wrist\"}, {\"id\": 13643, \"name\": \"a fishnet stocking\"}, {\"id\": 13644, \"name\": \"a black rearview mirror\"}, {\"id\": 13645, \"name\": \"white square\"}, {\"id\": 13646, \"name\": \"a blue, white, and red helmet\"}, {\"id\": 13647, \"name\": \"large yellow 1\"}, {\"id\": 13648, \"name\": \"the horses' head\"}, {\"id\": 13649, \"name\": \"the siding\"}, {\"id\": 13650, \"name\": \"the truck's window\"}, {\"id\": 13651, \"name\": \"the shelving unit\"}, {\"id\": 13652, \"name\": \"the repeating pattern of cartoon santa clause\"}, {\"id\": 13653, \"name\": \"cycist\"}, {\"id\": 13654, \"name\": \"the purple and blue galaxy\"}, {\"id\": 13655, \"name\": \"an orange pillar\"}, {\"id\": 13656, \"name\": \"the large front grille\"}, {\"id\": 13657, \"name\": \"a white nissan gt-r\"}, {\"id\": 13658, \"name\": \"the dark-colored wall\"}, {\"id\": 13659, \"name\": \"a red and white stripe\"}, {\"id\": 13660, \"name\": \"the silver suv\"}, {\"id\": 13661, \"name\": \"large grill\"}, {\"id\": 13662, \"name\": \"kilt\"}, {\"id\": 13663, \"name\": \"a gray surface\"}, {\"id\": 13664, \"name\": \"a pink sock\"}, {\"id\": 13665, \"name\": \"a book or magazine\"}, {\"id\": 13666, \"name\": \"the bleacher\"}, {\"id\": 13667, \"name\": \"blue rail car\"}, {\"id\": 13668, \"name\": \"the wrist\"}, {\"id\": 13669, \"name\": \"beige short\"}, {\"id\": 13670, \"name\": \"a small card\"}, {\"id\": 13671, \"name\": \"large christma tree\"}, {\"id\": 13672, \"name\": \"the lane of traffic\"}, {\"id\": 13673, \"name\": \"goalkeeper\"}, {\"id\": 13674, \"name\": \"a blue bin\"}, {\"id\": 13675, \"name\": \"a black belt\"}, {\"id\": 13676, \"name\": \"glas of beer\"}, {\"id\": 13677, \"name\": \"the white star\"}, {\"id\": 13678, \"name\": \"the white bonnet\"}, {\"id\": 13679, \"name\": \"a yahoo! watermark\"}, {\"id\": 13680, \"name\": \"yellow saw\"}, {\"id\": 13681, \"name\": \"a speedometer\"}, {\"id\": 13682, \"name\": \"the black knob\"}, {\"id\": 13683, \"name\": \"white label\"}, {\"id\": 13684, \"name\": \"white balloon\"}, {\"id\": 13685, \"name\": \"large black tire\"}, {\"id\": 13686, \"name\": \"gray carpeting\"}, {\"id\": 13687, \"name\": \"swimming cap\"}, {\"id\": 13688, \"name\": \"the green pant\"}, {\"id\": 13689, \"name\": \"the large white pillar\"}, {\"id\": 13690, \"name\": \"the audi sport logo\"}, {\"id\": 13691, \"name\": \"the man playing a guitar\"}, {\"id\": 13692, \"name\": \"the large glas jar\"}, {\"id\": 13693, \"name\": \"a coca-cola vending machine\"}, {\"id\": 13694, \"name\": \"a pepper grinder\"}, {\"id\": 13695, \"name\": \"a digital scoreboard\"}, {\"id\": 13696, \"name\": \"maroon long-sleeved shirt\"}, {\"id\": 13697, \"name\": \"the gray jersey\"}, {\"id\": 13698, \"name\": \"a starbuck logo\"}, {\"id\": 13699, \"name\": \"an exposed beam\"}, {\"id\": 13700, \"name\": \"a head of abraham lincoln\"}, {\"id\": 13701, \"name\": \"a blue plate\"}, {\"id\": 13702, \"name\": \"an elaborate mask\"}, {\"id\": 13703, \"name\": \"a bridge's cable-stayed design\"}, {\"id\": 13704, \"name\": \"the spider-man\"}, {\"id\": 13705, \"name\": \"the orange t-shirt\"}, {\"id\": 13706, \"name\": \"the brass-colored bullet\"}, {\"id\": 13707, \"name\": \"the network of wooden beam\"}, {\"id\": 13708, \"name\": \"fluorescent lighting\"}, {\"id\": 13709, \"name\": \"spectator\"}, {\"id\": 13710, \"name\": \"colorful menu\"}, {\"id\": 13711, \"name\": \"brown bull\"}, {\"id\": 13712, \"name\": \"woman with red\"}, {\"id\": 13713, \"name\": \"the man on the bicycle\"}, {\"id\": 13714, \"name\": \"a neckline\"}, {\"id\": 13715, \"name\": \"the kitchen tool\"}, {\"id\": 13716, \"name\": \"the white rim\"}, {\"id\": 13717, \"name\": \"a yellow belt\"}, {\"id\": 13718, \"name\": \"a bright pink sign\"}, {\"id\": 13719, \"name\": \"a long wooden shelf\"}, {\"id\": 13720, \"name\": \"a coffee cup\"}, {\"id\": 13721, \"name\": \"sleek white car\"}, {\"id\": 13722, \"name\": \"a red sport car\"}, {\"id\": 13723, \"name\": \"a dark-colored wall\"}, {\"id\": 13724, \"name\": \"the white icing border\"}, {\"id\": 13725, \"name\": \"the rectangular banner\"}, {\"id\": 13726, \"name\": \"the poster or advertisement\"}, {\"id\": 13727, \"name\": \"temperature\"}, {\"id\": 13728, \"name\": \"multicolored panel\"}, {\"id\": 13729, \"name\": \"the white and green sticker\"}, {\"id\": 13730, \"name\": \"a white pocket\"}, {\"id\": 13731, \"name\": \"the wire rack\"}, {\"id\": 13732, \"name\": \"the elastic waistband\"}, {\"id\": 13733, \"name\": \"median\"}, {\"id\": 13734, \"name\": \"the glowing circular object\"}, {\"id\": 13735, \"name\": \"the large black refrigerator\"}, {\"id\": 13736, \"name\": \"a yellow tank top\"}, {\"id\": 13737, \"name\": \"a black silhouette of a dog\"}, {\"id\": 13738, \"name\": \"a silver refrigerator\"}, {\"id\": 13739, \"name\": \"roll of paper towel\"}, {\"id\": 13740, \"name\": \"the white cooler\"}, {\"id\": 13741, \"name\": \"a white fur-lined hood\"}, {\"id\": 13742, \"name\": \"large red sail\"}, {\"id\": 13743, \"name\": \"field hockey stick\"}, {\"id\": 13744, \"name\": \"paved area\"}, {\"id\": 13745, \"name\": \"a red tractor\"}, {\"id\": 13746, \"name\": \"sand trap\"}, {\"id\": 13747, \"name\": \"a ski lift\"}, {\"id\": 13748, \"name\": \"a bouquet of flower\"}, {\"id\": 13749, \"name\": \"the black jersey\"}, {\"id\": 13750, \"name\": \"the red undershirt\"}, {\"id\": 13751, \"name\": \"a white electrical outlet\"}, {\"id\": 13752, \"name\": \"the white light fixture\"}, {\"id\": 13753, \"name\": \"the black license plate\"}, {\"id\": 13754, \"name\": \"the red fire extinguisher\"}, {\"id\": 13755, \"name\": \"a blurry bar counter\"}, {\"id\": 13756, \"name\": \"navy blue leotard\"}, {\"id\": 13757, \"name\": \"a white unlock icon\"}, {\"id\": 13758, \"name\": \"the blue container\"}, {\"id\": 13759, \"name\": \"long gray hair\"}, {\"id\": 13760, \"name\": \"bald man\"}, {\"id\": 13761, \"name\": \"frame\"}, {\"id\": 13762, \"name\": \"the large, ornate chandelier\"}, {\"id\": 13763, \"name\": \"black rim\"}, {\"id\": 13764, \"name\": \"a red bleacher\"}, {\"id\": 13765, \"name\": \"the red and white beard\"}, {\"id\": 13766, \"name\": \"the clear handle\"}, {\"id\": 13767, \"name\": \"chrome grill\"}, {\"id\": 13768, \"name\": \"a white beekeeping suit\"}, {\"id\": 13769, \"name\": \"green glas bottle of beer\"}, {\"id\": 13770, \"name\": \"a tall grass\"}, {\"id\": 13771, \"name\": \"the slat wall\"}, {\"id\": 13772, \"name\": \"the cyclist's helmet\"}, {\"id\": 13773, \"name\": \"white wheel\"}, {\"id\": 13774, \"name\": \"black and red leather\"}, {\"id\": 13775, \"name\": \"glas of water\"}, {\"id\": 13776, \"name\": \"a prominent red, white, and blue logo\"}, {\"id\": 13777, \"name\": \"a white and purple uniform\"}, {\"id\": 13778, \"name\": \"black grill\"}, {\"id\": 13779, \"name\": \"the bicycle tire\"}, {\"id\": 13780, \"name\": \"the tray of food\"}, {\"id\": 13781, \"name\": \"a windmill\"}, {\"id\": 13782, \"name\": \"dark vest\"}, {\"id\": 13783, \"name\": \"a dark gray floor\"}, {\"id\": 13784, \"name\": \"a gold belt\"}, {\"id\": 13785, \"name\": \"grassy slope\"}, {\"id\": 13786, \"name\": \"white chef's coat\"}, {\"id\": 13787, \"name\": \"red body\"}, {\"id\": 13788, \"name\": \"the passenger door\"}, {\"id\": 13789, \"name\": \"a white glove\"}, {\"id\": 13790, \"name\": \"kitchen backdrop\"}, {\"id\": 13791, \"name\": \"the gable roof\"}, {\"id\": 13792, \"name\": \"a white and red helmet\"}, {\"id\": 13793, \"name\": \"the water tower\"}, {\"id\": 13794, \"name\": \"chalkboard\"}, {\"id\": 13795, \"name\": \"low gray wall\"}, {\"id\": 13796, \"name\": \"the concrete platform\"}, {\"id\": 13797, \"name\": \"the wall of the court\"}, {\"id\": 13798, \"name\": \"scooter's license plate\"}, {\"id\": 13799, \"name\": \"the partially visible sign or poster\"}, {\"id\": 13800, \"name\": \"a neighboring car\"}, {\"id\": 13801, \"name\": \"a woman's head\"}, {\"id\": 13802, \"name\": \"white undershirt\"}, {\"id\": 13803, \"name\": \"yellow rope\"}, {\"id\": 13804, \"name\": \"the soccer player\"}, {\"id\": 13805, \"name\": \"red bridge\"}, {\"id\": 13806, \"name\": \"brown wig\"}, {\"id\": 13807, \"name\": \"the bright yellow vest\"}, {\"id\": 13808, \"name\": \"clear glas cylinder\"}, {\"id\": 13809, \"name\": \"the rider's leg\"}, {\"id\": 13810, \"name\": \"the treadmill\"}, {\"id\": 13811, \"name\": \"a large wooden bowl\"}, {\"id\": 13812, \"name\": \"percentage\"}, {\"id\": 13813, \"name\": \"the red floor\"}, {\"id\": 13814, \"name\": \"green field\"}, {\"id\": 13815, \"name\": \"a purple barrier\"}, {\"id\": 13816, \"name\": \"white rectangle\"}, {\"id\": 13817, \"name\": \"the black bench\"}, {\"id\": 13818, \"name\": \"a wooden ceiling\"}, {\"id\": 13819, \"name\": \"light blue wheel\"}, {\"id\": 13820, \"name\": \"the red and white tape\"}, {\"id\": 13821, \"name\": \"scope\"}, {\"id\": 13822, \"name\": \"a laundry room\"}, {\"id\": 13823, \"name\": \"a colorful floor\"}, {\"id\": 13824, \"name\": \"black bowling uniform\"}, {\"id\": 13825, \"name\": \"the small, cylindrical object\"}, {\"id\": 13826, \"name\": \"a red brake light\"}, {\"id\": 13827, \"name\": \"the glowing blue light\"}, {\"id\": 13828, \"name\": \"a car's hood\"}, {\"id\": 13829, \"name\": \"light-colored electric guitar\"}, {\"id\": 13830, \"name\": \"a golf cart\"}, {\"id\": 13831, \"name\": \"the small radio\"}, {\"id\": 13832, \"name\": \"large turret\"}, {\"id\": 13833, \"name\": \"the building with blue siding\"}, {\"id\": 13834, \"name\": \"spinning ride\"}, {\"id\": 13835, \"name\": \"the red cup\"}, {\"id\": 13836, \"name\": \"the white leaf blower\"}, {\"id\": 13837, \"name\": \"a covered walkway\"}, {\"id\": 13838, \"name\": \"yellow poster or banner\"}, {\"id\": 13839, \"name\": \"the black hiking pole\"}, {\"id\": 13840, \"name\": \"a olympic ring\"}, {\"id\": 13841, \"name\": \"the shirtles man\"}, {\"id\": 13842, \"name\": \"a textile\"}, {\"id\": 13843, \"name\": \"electric guitar\"}, {\"id\": 13844, \"name\": \"the pink seat\"}, {\"id\": 13845, \"name\": \"a dark suv\"}, {\"id\": 13846, \"name\": \"purple shirt\"}, {\"id\": 13847, \"name\": \"a gray barrier\"}, {\"id\": 13848, \"name\": \"the orange jersey\"}, {\"id\": 13849, \"name\": \"a shoulder-length brown hair\"}, {\"id\": 13850, \"name\": \"silver body\"}, {\"id\": 13851, \"name\": \"the white storage bin\"}, {\"id\": 13852, \"name\": \"dumpster\"}, {\"id\": 13853, \"name\": \"a shock absorber\"}, {\"id\": 13854, \"name\": \"a large stone pillar\"}, {\"id\": 13855, \"name\": \"a silhouette of a baseball player's head and shoulders\"}, {\"id\": 13856, \"name\": \"a bald head\"}, {\"id\": 13857, \"name\": \"the green and red hat\"}, {\"id\": 13858, \"name\": \"the large metal pole\"}, {\"id\": 13859, \"name\": \"black zipper\"}, {\"id\": 13860, \"name\": \"long, light brown hair\"}, {\"id\": 13861, \"name\": \"a blue puffer coat\"}, {\"id\": 13862, \"name\": \"a small patch of pavement\"}, {\"id\": 13863, \"name\": \"white plate\"}, {\"id\": 13864, \"name\": \"the wooden chair\"}, {\"id\": 13865, \"name\": \"the suit\"}, {\"id\": 13866, \"name\": \"a cuff\"}, {\"id\": 13867, \"name\": \"a reflective vest\"}, {\"id\": 13868, \"name\": \"the colorful backpack\"}, {\"id\": 13869, \"name\": \"small screen\"}, {\"id\": 13870, \"name\": \"cylindrical object\"}, {\"id\": 13871, \"name\": \"the man in a pink shirt\"}, {\"id\": 13872, \"name\": \"the rung\"}, {\"id\": 13873, \"name\": \"the red and white toy truck\"}, {\"id\": 13874, \"name\": \"black trash bag\"}, {\"id\": 13875, \"name\": \"muscular man\"}, {\"id\": 13876, \"name\": \"the smaller inset image\"}, {\"id\": 13877, \"name\": \"the sleek black counter\"}, {\"id\": 13878, \"name\": \"the wicker basket\"}, {\"id\": 13879, \"name\": \"round cutting board\"}, {\"id\": 13880, \"name\": \"blue bin\"}, {\"id\": 13881, \"name\": \"the white air vent\"}, {\"id\": 13882, \"name\": \"a yellow block\"}, {\"id\": 13883, \"name\": \"wooden post\"}, {\"id\": 13884, \"name\": \"a side braid\"}, {\"id\": 13885, \"name\": \"the blurry metal fence\"}, {\"id\": 13886, \"name\": \"the turkish flag\"}, {\"id\": 13887, \"name\": \"yellow border\"}, {\"id\": 13888, \"name\": \"a large waffle cone graphic\"}, {\"id\": 13889, \"name\": \"the small puddle of melted ice cream\"}, {\"id\": 13890, \"name\": \"bas drum\"}, {\"id\": 13891, \"name\": \"the red and green italian flag\"}, {\"id\": 13892, \"name\": \"the drum set\"}, {\"id\": 13893, \"name\": \"the pink bracelet\"}, {\"id\": 13894, \"name\": \"white hair\"}, {\"id\": 13895, \"name\": \"the green and blue logo\"}, {\"id\": 13896, \"name\": \"large red fire engine\"}, {\"id\": 13897, \"name\": \"blue recycling bin\"}, {\"id\": 13898, \"name\": \"a neutral backdrop\"}, {\"id\": 13899, \"name\": \"the blue ladder\"}, {\"id\": 13900, \"name\": \"rugby ball\"}, {\"id\": 13901, \"name\": \"the black storefront\"}, {\"id\": 13902, \"name\": \"gray soccer jersey\"}, {\"id\": 13903, \"name\": \"a bright yellow body\"}, {\"id\": 13904, \"name\": \"a digital screen\"}, {\"id\": 13905, \"name\": \"large transparent balloon\"}, {\"id\": 13906, \"name\": \"the blue and yellow polka-dot hat\"}, {\"id\": 13907, \"name\": \"black visor\"}, {\"id\": 13908, \"name\": \"a worn road\"}, {\"id\": 13909, \"name\": \"blue swing\"}, {\"id\": 13910, \"name\": \"the white metal rack\"}, {\"id\": 13911, \"name\": \"stormtrooper costume\"}, {\"id\": 13912, \"name\": \"the hedge-lined border\"}, {\"id\": 13913, \"name\": \"a large, green and black striped \\\"0\\\" decoration\"}, {\"id\": 13914, \"name\": \"green frosting\"}, {\"id\": 13915, \"name\": \"desk or dresser\"}, {\"id\": 13916, \"name\": \"large red sign\"}, {\"id\": 13917, \"name\": \"the fretboard\"}, {\"id\": 13918, \"name\": \"a partially visible dashboard\"}, {\"id\": 13919, \"name\": \"a long-sleeved jacket\"}, {\"id\": 13920, \"name\": \"a blue short\"}, {\"id\": 13921, \"name\": \"the person's thumb\"}, {\"id\": 13922, \"name\": \"blue panel\"}, {\"id\": 13923, \"name\": \"the purple towel\"}, {\"id\": 13924, \"name\": \"white and black bicycle\"}, {\"id\": 13925, \"name\": \"red and white car\"}, {\"id\": 13926, \"name\": \"a pink ski jacket\"}, {\"id\": 13927, \"name\": \"a white front bumper\"}, {\"id\": 13928, \"name\": \"the step-and-repeat backdrop\"}, {\"id\": 13929, \"name\": \"a hand tool\"}, {\"id\": 13930, \"name\": \"a large windowed facade\"}, {\"id\": 13931, \"name\": \"red decal\"}, {\"id\": 13932, \"name\": \"the goggle\"}, {\"id\": 13933, \"name\": \"a light-colored tank top\"}, {\"id\": 13934, \"name\": \"reflective strip\"}, {\"id\": 13935, \"name\": \"the grey metal frame\"}, {\"id\": 13936, \"name\": \"yellow canoe\"}, {\"id\": 13937, \"name\": \"large windshield\"}, {\"id\": 13938, \"name\": \"a left sleeve\"}, {\"id\": 13939, \"name\": \"bbc logo\"}, {\"id\": 13940, \"name\": \"the topmost block\"}, {\"id\": 13941, \"name\": \"a black exhaust pipe\"}, {\"id\": 13942, \"name\": \"a bright and overcast sky\"}, {\"id\": 13943, \"name\": \"neon yellow rollerblade\"}, {\"id\": 13944, \"name\": \"the white glove\"}, {\"id\": 13945, \"name\": \"the fan's base\"}, {\"id\": 13946, \"name\": \"the wheelchair\"}, {\"id\": 13947, \"name\": \"blue drum\"}, {\"id\": 13948, \"name\": \"a blue and white tram\"}, {\"id\": 13949, \"name\": \"an elliptical machine\"}, {\"id\": 13950, \"name\": \"a white foam\"}, {\"id\": 13951, \"name\": \"television screen\"}, {\"id\": 13952, \"name\": \"the red short\"}, {\"id\": 13953, \"name\": \"the paved path\"}, {\"id\": 13954, \"name\": \"the bookshelf\"}, {\"id\": 13955, \"name\": \"a large, cartoonish helmet\"}, {\"id\": 13956, \"name\": \"orange strap\"}, {\"id\": 13957, \"name\": \"a red and black device\"}, {\"id\": 13958, \"name\": \"a black sneaker\"}, {\"id\": 13959, \"name\": \"go-kart's frame\"}, {\"id\": 13960, \"name\": \"the goal post\"}, {\"id\": 13961, \"name\": \"a dog poster\"}, {\"id\": 13962, \"name\": \"the bike's frame\"}, {\"id\": 13963, \"name\": \"the green garland\"}, {\"id\": 13964, \"name\": \"a salad or other food item\"}, {\"id\": 13965, \"name\": \"large stone statue of a person\"}, {\"id\": 13966, \"name\": \"swimmer\"}, {\"id\": 13967, \"name\": \"a red baseball cap\"}, {\"id\": 13968, \"name\": \"the sign or banner\"}, {\"id\": 13969, \"name\": \"the white ice cream cone\"}, {\"id\": 13970, \"name\": \"the yellow propeller\"}, {\"id\": 13971, \"name\": \"a woman's face\"}, {\"id\": 13972, \"name\": \"a bartender's hands\"}, {\"id\": 13973, \"name\": \"the red laser beam\"}, {\"id\": 13974, \"name\": \"tall, slender woman\"}, {\"id\": 13975, \"name\": \"a time\"}, {\"id\": 13976, \"name\": \"a truck's exterior\"}, {\"id\": 13977, \"name\": \"a white and black sunglass\"}, {\"id\": 13978, \"name\": \"the left shoulder\"}, {\"id\": 13979, \"name\": \"a dark shoe\"}, {\"id\": 13980, \"name\": \"front door\"}, {\"id\": 13981, \"name\": \"a white sink\"}, {\"id\": 13982, \"name\": \"black tool box\"}, {\"id\": 13983, \"name\": \"the pink collar\"}, {\"id\": 13984, \"name\": \"the man in blue scrub\"}, {\"id\": 13985, \"name\": \"red roof\"}, {\"id\": 13986, \"name\": \"white cloud-shaped label\"}, {\"id\": 13987, \"name\": \"the yellow warning label\"}, {\"id\": 13988, \"name\": \"green wristband\"}, {\"id\": 13989, \"name\": \"shot of a liqueur\"}, {\"id\": 13990, \"name\": \"a white post\"}, {\"id\": 13991, \"name\": \"a date and time of the image\"}, {\"id\": 13992, \"name\": \"a light blue shirt with horizontal stripe\"}, {\"id\": 13993, \"name\": \"woman in a black-and-white striped scarf\"}, {\"id\": 13994, \"name\": \"the light-colored wall\"}, {\"id\": 13995, \"name\": \"paper\"}, {\"id\": 13996, \"name\": \"maroon suit\"}, {\"id\": 13997, \"name\": \"large hedge\"}, {\"id\": 13998, \"name\": \"the pendant\"}, {\"id\": 13999, \"name\": \"the large, round headlight\"}, {\"id\": 14000, \"name\": \"a volume control knob\"}, {\"id\": 14001, \"name\": \"a black tv\"}, {\"id\": 14002, \"name\": \"a white attire\"}, {\"id\": 14003, \"name\": \"large, round air vent\"}, {\"id\": 14004, \"name\": \"a maroon shirt\"}, {\"id\": 14005, \"name\": \"the red and gold sc emblem\"}, {\"id\": 14006, \"name\": \"grassy median\"}, {\"id\": 14007, \"name\": \"the blind\"}, {\"id\": 14008, \"name\": \"canadian flag\"}, {\"id\": 14009, \"name\": \"large, chrome grill\"}, {\"id\": 14010, \"name\": \"room's off-white wall\"}, {\"id\": 14011, \"name\": \"the blue interior\"}, {\"id\": 14012, \"name\": \"black face mask\"}, {\"id\": 14013, \"name\": \"colorful clothing\"}, {\"id\": 14014, \"name\": \"a red sleeveles top\"}, {\"id\": 14015, \"name\": \"the black wall\"}, {\"id\": 14016, \"name\": \"the bright green helmet\"}, {\"id\": 14017, \"name\": \"a white bandage\"}, {\"id\": 14018, \"name\": \"hoop\"}, {\"id\": 14019, \"name\": \"a man's shirt\"}, {\"id\": 14020, \"name\": \"the large red bow\"}, {\"id\": 14021, \"name\": \"vibrant blue canopy\"}, {\"id\": 14022, \"name\": \"the wire frame\"}, {\"id\": 14023, \"name\": \"the black curtain\"}, {\"id\": 14024, \"name\": \"red knob\"}, {\"id\": 14025, \"name\": \"a television set\"}, {\"id\": 14026, \"name\": \"the plastic-wrapped item\"}, {\"id\": 14027, \"name\": \"grey hat\"}, {\"id\": 14028, \"name\": \"a long table\"}, {\"id\": 14029, \"name\": \"light-colored surface\"}, {\"id\": 14030, \"name\": \"rear bumper\"}, {\"id\": 14031, \"name\": \"rear deck of a ferry\"}, {\"id\": 14032, \"name\": \"the purple tie\"}, {\"id\": 14033, \"name\": \"the green awning\"}, {\"id\": 14034, \"name\": \"the white, blue, and maroon jersey\"}, {\"id\": 14035, \"name\": \"a black steering wheel\"}, {\"id\": 14036, \"name\": \"white arch\"}, {\"id\": 14037, \"name\": \"the piece of cardboard\"}, {\"id\": 14038, \"name\": \"black vase\"}, {\"id\": 14039, \"name\": \"a silver suv\"}, {\"id\": 14040, \"name\": \"a blue cup\"}, {\"id\": 14041, \"name\": \"the light-colored wooden frame\"}, {\"id\": 14042, \"name\": \"the concrete patio\"}, {\"id\": 14043, \"name\": \"the snow-covered slope\"}, {\"id\": 14044, \"name\": \"the blue pedal\"}, {\"id\": 14045, \"name\": \"dog's tail\"}, {\"id\": 14046, \"name\": \"large gift box\"}, {\"id\": 14047, \"name\": \"the gray pillow\"}, {\"id\": 14048, \"name\": \"the wooden pallet\"}, {\"id\": 14049, \"name\": \"the white soccer ball\"}, {\"id\": 14050, \"name\": \"the white napkin\"}, {\"id\": 14051, \"name\": \"the clamp\"}, {\"id\": 14052, \"name\": \"the purple accent nail\"}, {\"id\": 14053, \"name\": \"balcony railing\"}, {\"id\": 14054, \"name\": \"the small black beak\"}, {\"id\": 14055, \"name\": \"the bright yellow snow pant\"}, {\"id\": 14056, \"name\": \"the orange headscarve\"}, {\"id\": 14057, \"name\": \"slatted top\"}, {\"id\": 14058, \"name\": \"the rear of the truck\"}, {\"id\": 14059, \"name\": \"number\"}, {\"id\": 14060, \"name\": \"a vibrant tapestry\"}, {\"id\": 14061, \"name\": \"the black purse strap\"}, {\"id\": 14062, \"name\": \"long stick or pole\"}, {\"id\": 14063, \"name\": \"the front end of the car\"}, {\"id\": 14064, \"name\": \"paved surface\"}, {\"id\": 14065, \"name\": \"beige background\"}, {\"id\": 14066, \"name\": \"the stainles steel countertop\"}, {\"id\": 14067, \"name\": \"dark, short, wavy hair\"}, {\"id\": 14068, \"name\": \"lighting\"}, {\"id\": 14069, \"name\": \"the brown wooden deck\"}, {\"id\": 14070, \"name\": \"the pink tumbler\"}, {\"id\": 14071, \"name\": \"the curved line\"}, {\"id\": 14072, \"name\": \"the large bale of hay or straw\"}, {\"id\": 14073, \"name\": \"a bottle of wine\"}, {\"id\": 14074, \"name\": \"the playground\"}, {\"id\": 14075, \"name\": \"the waist\"}, {\"id\": 14076, \"name\": \"the blue bath mat\"}, {\"id\": 14077, \"name\": \"the blue metal frame\"}, {\"id\": 14078, \"name\": \"the mantle\"}, {\"id\": 14079, \"name\": \"red and white logo\"}, {\"id\": 14080, \"name\": \"woman's arm\"}, {\"id\": 14081, \"name\": \"the blue and white container\"}, {\"id\": 14082, \"name\": \"the large white and red plastic bin\"}, {\"id\": 14083, \"name\": \"black monkey\"}, {\"id\": 14084, \"name\": \"stainles steel shaker\"}, {\"id\": 14085, \"name\": \"the lawn mower\"}, {\"id\": 14086, \"name\": \"silver helmet\"}, {\"id\": 14087, \"name\": \"white hull\"}, {\"id\": 14088, \"name\": \"the red, white, and blue design\"}, {\"id\": 14089, \"name\": \"a hill\"}, {\"id\": 14090, \"name\": \"yellow collar\"}, {\"id\": 14091, \"name\": \"open blind\"}, {\"id\": 14092, \"name\": \"a gray cloud\"}, {\"id\": 14093, \"name\": \"white closet door\"}, {\"id\": 14094, \"name\": \"messy bun\"}, {\"id\": 14095, \"name\": \"a small lcd screen\"}, {\"id\": 14096, \"name\": \"a rugby player\"}, {\"id\": 14097, \"name\": \"a yellow writing\"}, {\"id\": 14098, \"name\": \"white banner\"}, {\"id\": 14099, \"name\": \"a covered porch\"}, {\"id\": 14100, \"name\": \"the bald\"}, {\"id\": 14101, \"name\": \"small card\"}, {\"id\": 14102, \"name\": \"a news banner\"}, {\"id\": 14103, \"name\": \"white and gray umbrella\"}, {\"id\": 14104, \"name\": \"a large white desk\"}, {\"id\": 14105, \"name\": \"the glas front\"}, {\"id\": 14106, \"name\": \"the power outlet\"}, {\"id\": 14107, \"name\": \"the smaller photograph\"}, {\"id\": 14108, \"name\": \"a polished wood\"}, {\"id\": 14109, \"name\": \"blue harness\"}, {\"id\": 14110, \"name\": \"formal attire\"}, {\"id\": 14111, \"name\": \"the subtle soccer ball pattern\"}, {\"id\": 14112, \"name\": \"minnie mouse headband\"}, {\"id\": 14113, \"name\": \"a of heart\"}, {\"id\": 14114, \"name\": \"plate of salad\"}, {\"id\": 14115, \"name\": \"the military uniform\"}, {\"id\": 14116, \"name\": \"the sunglass\"}, {\"id\": 14117, \"name\": \"a green and white bollard\"}, {\"id\": 14118, \"name\": \"a brown t-shirt\"}, {\"id\": 14119, \"name\": \"stadium's seating area\"}, {\"id\": 14120, \"name\": \"yellowed headlight\"}, {\"id\": 14121, \"name\": \"a black and red tank top\"}, {\"id\": 14122, \"name\": \"the machine\"}, {\"id\": 14123, \"name\": \"rattan\"}, {\"id\": 14124, \"name\": \"a pink and white floral skirt\"}, {\"id\": 14125, \"name\": \"the goal\"}, {\"id\": 14126, \"name\": \"the player's head\"}, {\"id\": 14127, \"name\": \"gray short\"}, {\"id\": 14128, \"name\": \"image of horse\"}, {\"id\": 14129, \"name\": \"the blue toolbox\"}, {\"id\": 14130, \"name\": \"the wooden beam structure\"}, {\"id\": 14131, \"name\": \"the silver metal base\"}, {\"id\": 14132, \"name\": \"blue lego structure\"}, {\"id\": 14133, \"name\": \"a string\"}, {\"id\": 14134, \"name\": \"the prominent white banner\"}, {\"id\": 14135, \"name\": \"black window\"}, {\"id\": 14136, \"name\": \"a white chalk line\"}, {\"id\": 14137, \"name\": \"the red goal post\"}, {\"id\": 14138, \"name\": \"white sock\"}, {\"id\": 14139, \"name\": \"a white stripe\"}, {\"id\": 14140, \"name\": \"the patterned short\"}, {\"id\": 14141, \"name\": \"the window's frame\"}, {\"id\": 14142, \"name\": \"a rickshaw's canopy\"}, {\"id\": 14143, \"name\": \"the black cleat\"}, {\"id\": 14144, \"name\": \"the car tire\"}, {\"id\": 14145, \"name\": \"plastic cup\"}, {\"id\": 14146, \"name\": \"front wheel\"}, {\"id\": 14147, \"name\": \"metal pitcher\"}, {\"id\": 14148, \"name\": \"a tall, slender, and translucent white pillar\"}, {\"id\": 14149, \"name\": \"snow-covered ground\"}, {\"id\": 14150, \"name\": \"blue batting glove\"}, {\"id\": 14151, \"name\": \"a white mannequin torso\"}, {\"id\": 14152, \"name\": \"the large container of liquid\"}, {\"id\": 14153, \"name\": \"a brown building\"}, {\"id\": 14154, \"name\": \"large rock\"}, {\"id\": 14155, \"name\": \"the black metal railing\"}, {\"id\": 14156, \"name\": \"smaller white logo\"}, {\"id\": 14157, \"name\": \"prominent logo\"}, {\"id\": 14158, \"name\": \"a compound bow\"}, {\"id\": 14159, \"name\": \"a navy baseball cap\"}, {\"id\": 14160, \"name\": \"a silver oven door\"}, {\"id\": 14161, \"name\": \"silver lid\"}, {\"id\": 14162, \"name\": \"the pink and white balloon\"}, {\"id\": 14163, \"name\": \"white sleeve\"}, {\"id\": 14164, \"name\": \"a dark green trash can\"}, {\"id\": 14165, \"name\": \"pink chair\"}, {\"id\": 14166, \"name\": \"black box\"}, {\"id\": 14167, \"name\": \"a black tablecloth\"}, {\"id\": 14168, \"name\": \"black railing\"}, {\"id\": 14169, \"name\": \"a blue and yellow object\"}, {\"id\": 14170, \"name\": \"the checkered pattern\"}, {\"id\": 14171, \"name\": \"the pedestal\"}, {\"id\": 14172, \"name\": \"red and black controller\"}, {\"id\": 14173, \"name\": \"the red surface\"}, {\"id\": 14174, \"name\": \"the prominent exit sign\"}, {\"id\": 14175, \"name\": \"large tiger's head\"}, {\"id\": 14176, \"name\": \"grassy field\"}, {\"id\": 14177, \"name\": \"the wiper arm\"}, {\"id\": 14178, \"name\": \"the screwdriver\"}, {\"id\": 14179, \"name\": \"a green metal object\"}, {\"id\": 14180, \"name\": \"red stadium seat\"}, {\"id\": 14181, \"name\": \"yellow banner\"}, {\"id\": 14182, \"name\": \"black baseball glove\"}, {\"id\": 14183, \"name\": \"well-manicured grassy area\"}, {\"id\": 14184, \"name\": \"car's tire\"}, {\"id\": 14185, \"name\": \"the large, green pipe\"}, {\"id\": 14186, \"name\": \"a kitchen's light-colored wood cabinet\"}, {\"id\": 14187, \"name\": \"plain beige wall\"}, {\"id\": 14188, \"name\": \"the boy in a black t-shirt and short\"}, {\"id\": 14189, \"name\": \"a circular pendant\"}, {\"id\": 14190, \"name\": \"hockey stick\"}, {\"id\": 14191, \"name\": \"the white and red fence\"}, {\"id\": 14192, \"name\": \"graffiti\"}, {\"id\": 14193, \"name\": \"plain white wall\"}, {\"id\": 14194, \"name\": \"a cycling jersey\"}, {\"id\": 14195, \"name\": \"white grout\"}, {\"id\": 14196, \"name\": \"a yellow frame\"}, {\"id\": 14197, \"name\": \"the black toolbox\"}, {\"id\": 14198, \"name\": \"the tattoo\"}, {\"id\": 14199, \"name\": \"white short\"}, {\"id\": 14200, \"name\": \"a top hat\"}, {\"id\": 14201, \"name\": \"a metal shelving unit\"}, {\"id\": 14202, \"name\": \"the red tablecloth-covered table\"}, {\"id\": 14203, \"name\": \"red fire alarm\"}, {\"id\": 14204, \"name\": \"the darker green floor\"}, {\"id\": 14205, \"name\": \"the volleyball court\"}, {\"id\": 14206, \"name\": \"glas of soda\"}, {\"id\": 14207, \"name\": \"cup holder\"}, {\"id\": 14208, \"name\": \"a chicken wing\"}, {\"id\": 14209, \"name\": \"the dark-colored tool\"}, {\"id\": 14210, \"name\": \"a black plastic basket\"}, {\"id\": 14211, \"name\": \"the dark blue fabric\"}, {\"id\": 14212, \"name\": \"the blue and gray jacket\"}, {\"id\": 14213, \"name\": \"the yellow crosswalk\"}, {\"id\": 14214, \"name\": \"an open door\"}, {\"id\": 14215, \"name\": \"a red traffic cone\"}, {\"id\": 14216, \"name\": \"the field of grass\"}, {\"id\": 14217, \"name\": \"the vibrant lantern\"}, {\"id\": 14218, \"name\": \"a lane\"}, {\"id\": 14219, \"name\": \"pane\"}, {\"id\": 14220, \"name\": \"dark green and white uniform\"}, {\"id\": 14221, \"name\": \"a goalie's equipment\"}, {\"id\": 14222, \"name\": \"truck's window\"}, {\"id\": 14223, \"name\": \"smaller banner\"}, {\"id\": 14224, \"name\": \"team name\"}, {\"id\": 14225, \"name\": \"white handbag\"}, {\"id\": 14226, \"name\": \"the dog's leash\"}, {\"id\": 14227, \"name\": \"the men's attire\"}, {\"id\": 14228, \"name\": \"the yellow safety vest\"}, {\"id\": 14229, \"name\": \"tall window\"}, {\"id\": 14230, \"name\": \"a breast pocket\"}, {\"id\": 14231, \"name\": \"stylist's right arm\"}, {\"id\": 14232, \"name\": \"white icing trim\"}, {\"id\": 14233, \"name\": \"a white button-down shirt\"}, {\"id\": 14234, \"name\": \"the blue and black car hood\"}, {\"id\": 14235, \"name\": \"the red and blue uniform\"}, {\"id\": 14236, \"name\": \"a police uniform\"}, {\"id\": 14237, \"name\": \"the toolbox\"}, {\"id\": 14238, \"name\": \"the attendee\"}, {\"id\": 14239, \"name\": \"the white sink\"}, {\"id\": 14240, \"name\": \"knee brace\"}, {\"id\": 14241, \"name\": \"the cup of ice cream\"}, {\"id\": 14242, \"name\": \"the red workbench\"}, {\"id\": 14243, \"name\": \"a line of person\"}, {\"id\": 14244, \"name\": \"colored light\"}, {\"id\": 14245, \"name\": \"black drawstring\"}, {\"id\": 14246, \"name\": \"yellow arrow\"}, {\"id\": 14247, \"name\": \"metal chafing dish\"}, {\"id\": 14248, \"name\": \"a lush green hill\"}, {\"id\": 14249, \"name\": \"a horizontal line\"}, {\"id\": 14250, \"name\": \"the watermark\"}, {\"id\": 14251, \"name\": \"boat dock\"}, {\"id\": 14252, \"name\": \"a numerical rating\"}, {\"id\": 14253, \"name\": \"the sun\"}, {\"id\": 14254, \"name\": \"a black legging\"}, {\"id\": 14255, \"name\": \"mixture of cooked food\"}, {\"id\": 14256, \"name\": \"bottle of soda\"}, {\"id\": 14257, \"name\": \"the black swimsuit\"}, {\"id\": 14258, \"name\": \"a stovetop\"}, {\"id\": 14259, \"name\": \"a red car with a blue stripe\"}, {\"id\": 14260, \"name\": \"a short\"}, {\"id\": 14261, \"name\": \"a predominantly pink background\"}, {\"id\": 14262, \"name\": \"a white ball\"}, {\"id\": 14263, \"name\": \"the black speedometer\"}, {\"id\": 14264, \"name\": \"the tan carpet\"}, {\"id\": 14265, \"name\": \"red side panel\"}, {\"id\": 14266, \"name\": \"a bag of flour\"}, {\"id\": 14267, \"name\": \"player in the white jersey and red helmet\"}, {\"id\": 14268, \"name\": \"a yellow barrier\"}, {\"id\": 14269, \"name\": \"school sign\"}, {\"id\": 14270, \"name\": \"teenage girl\"}, {\"id\": 14271, \"name\": \"jersey\"}, {\"id\": 14272, \"name\": \"white icing\"}, {\"id\": 14273, \"name\": \"radiator or heater\"}, {\"id\": 14274, \"name\": \"a clear square\"}, {\"id\": 14275, \"name\": \"the black table\"}, {\"id\": 14276, \"name\": \"light-colored collared shirt\"}, {\"id\": 14277, \"name\": \"red scooter\"}, {\"id\": 14278, \"name\": \"the pink balloon\"}, {\"id\": 14279, \"name\": \"a pedestal\"}, {\"id\": 14280, \"name\": \"a black guitar case\"}, {\"id\": 14281, \"name\": \"the yellow and green gumdrop\"}, {\"id\": 14282, \"name\": \"a red and gold insignia\"}, {\"id\": 14283, \"name\": \"a white toyota\"}, {\"id\": 14284, \"name\": \"jumpman logo\"}, {\"id\": 14285, \"name\": \"a bright pink cap\"}, {\"id\": 14286, \"name\": \"the ride's base\"}, {\"id\": 14287, \"name\": \"the glas door\"}, {\"id\": 14288, \"name\": \"a light blue collared shirt\"}, {\"id\": 14289, \"name\": \"blue jersey\"}, {\"id\": 14290, \"name\": \"the jeepney\"}, {\"id\": 14291, \"name\": \"the gravel road\"}, {\"id\": 14292, \"name\": \"female athlete\"}, {\"id\": 14293, \"name\": \"monkey's bow tie\"}, {\"id\": 14294, \"name\": \"desk or counter\"}, {\"id\": 14295, \"name\": \"the star decoration\"}, {\"id\": 14296, \"name\": \"diagram of car part\"}, {\"id\": 14297, \"name\": \"wooden shelf\"}, {\"id\": 14298, \"name\": \"the yellow handle\"}, {\"id\": 14299, \"name\": \"a reflection of the shoe\"}, {\"id\": 14300, \"name\": \"orange hubcap\"}, {\"id\": 14301, \"name\": \"the vehicle's license plate\"}, {\"id\": 14302, \"name\": \"dark green marking\"}, {\"id\": 14303, \"name\": \"lower shelf\"}, {\"id\": 14304, \"name\": \"large red bowl\"}, {\"id\": 14305, \"name\": \"a white hairnet\"}, {\"id\": 14306, \"name\": \"black rear tire\"}, {\"id\": 14307, \"name\": \"the black gun\"}, {\"id\": 14308, \"name\": \"a mountain range\"}, {\"id\": 14309, \"name\": \"the matching headscarf\"}, {\"id\": 14310, \"name\": \"a yellow decal\"}, {\"id\": 14311, \"name\": \"a red head\"}, {\"id\": 14312, \"name\": \"the red shoe\"}, {\"id\": 14313, \"name\": \"large circular logo\"}, {\"id\": 14314, \"name\": \"the lemon wedge\"}, {\"id\": 14315, \"name\": \"the blurred wall\"}, {\"id\": 14316, \"name\": \"the black mustache\"}, {\"id\": 14317, \"name\": \"the gold shield shape\"}, {\"id\": 14318, \"name\": \"the black heart design\"}, {\"id\": 14319, \"name\": \"the brown boot\"}, {\"id\": 14320, \"name\": \"a packaged food\"}, {\"id\": 14321, \"name\": \"pink blind\"}, {\"id\": 14322, \"name\": \"the necklace\"}, {\"id\": 14323, \"name\": \"a white ceramic spoon\"}, {\"id\": 14324, \"name\": \"white and black uniform\"}, {\"id\": 14325, \"name\": \"the black wiper blade\"}, {\"id\": 14326, \"name\": \"the blue top\"}, {\"id\": 14327, \"name\": \"a black and blue logo\"}, {\"id\": 14328, \"name\": \"a track\"}, {\"id\": 14329, \"name\": \"a adult\"}, {\"id\": 14330, \"name\": \"the white chalk line\"}, {\"id\": 14331, \"name\": \"a residential building\"}, {\"id\": 14332, \"name\": \"white logo of a bull's head\"}, {\"id\": 14333, \"name\": \"the wooden shelf\"}, {\"id\": 14334, \"name\": \"the garage floor\"}, {\"id\": 14335, \"name\": \"a black sport bra\"}, {\"id\": 14336, \"name\": \"long pole\"}, {\"id\": 14337, \"name\": \"woman's leg\"}, {\"id\": 14338, \"name\": \"a large metal gate\"}, {\"id\": 14339, \"name\": \"a knee-high boot\"}, {\"id\": 14340, \"name\": \"a workout attire\"}, {\"id\": 14341, \"name\": \"the concrete overhang\"}, {\"id\": 14342, \"name\": \"the rain gear\"}, {\"id\": 14343, \"name\": \"long green dress\"}, {\"id\": 14344, \"name\": \"the green t-shirt\"}, {\"id\": 14345, \"name\": \"khaki shirt\"}, {\"id\": 14346, \"name\": \"a black visor\"}, {\"id\": 14347, \"name\": \"a silver faucet\"}, {\"id\": 14348, \"name\": \"the white canopy tent\"}, {\"id\": 14349, \"name\": \"large white column\"}, {\"id\": 14350, \"name\": \"a handle of the pitcher\"}, {\"id\": 14351, \"name\": \"the metal leg\"}, {\"id\": 14352, \"name\": \"a large white star\"}, {\"id\": 14353, \"name\": \"a yellow and red striped curb\"}, {\"id\": 14354, \"name\": \"white office chair\"}, {\"id\": 14355, \"name\": \"the black object with a red string\"}, {\"id\": 14356, \"name\": \"red chair\"}, {\"id\": 14357, \"name\": \"a sparkly dot\"}, {\"id\": 14358, \"name\": \"the white tank top\"}, {\"id\": 14359, \"name\": \"a left foot\"}, {\"id\": 14360, \"name\": \"white glove\"}, {\"id\": 14361, \"name\": \"white guardrail\"}, {\"id\": 14362, \"name\": \"a green pant\"}, {\"id\": 14363, \"name\": \"the man in an orange shirt\"}, {\"id\": 14364, \"name\": \"clock tower\"}, {\"id\": 14365, \"name\": \"the man in a black suit\"}, {\"id\": 14366, \"name\": \"name card\"}, {\"id\": 14367, \"name\": \"the red clothing\"}, {\"id\": 14368, \"name\": \"the rear windshield\"}, {\"id\": 14369, \"name\": \"fishing gear\"}, {\"id\": 14370, \"name\": \"the long, straight black hair\"}, {\"id\": 14371, \"name\": \"a red jersey\"}, {\"id\": 14372, \"name\": \"a american flag\"}, {\"id\": 14373, \"name\": \"the white plastic fairing\"}, {\"id\": 14374, \"name\": \"driver's side door\"}, {\"id\": 14375, \"name\": \"subtle yellow leaf pattern\"}, {\"id\": 14376, \"name\": \"light pink button-down shirt\"}, {\"id\": 14377, \"name\": \"a person's index finger\"}, {\"id\": 14378, \"name\": \"electrical device\"}, {\"id\": 14379, \"name\": \"the workbench\"}, {\"id\": 14380, \"name\": \"blue and white sculpture\"}, {\"id\": 14381, \"name\": \"a walking stick\"}, {\"id\": 14382, \"name\": \"horizontal beam\"}, {\"id\": 14383, \"name\": \"red necktie\"}, {\"id\": 14384, \"name\": \"the medicine ball\"}, {\"id\": 14385, \"name\": \"an indoor raceway\"}, {\"id\": 14386, \"name\": \"a man with short gray hair\"}, {\"id\": 14387, \"name\": \"the green pop-up canopy\"}, {\"id\": 14388, \"name\": \"the black and yellow box\"}, {\"id\": 14389, \"name\": \"a grey concrete curb\"}, {\"id\": 14390, \"name\": \"large exhaust hood\"}, {\"id\": 14391, \"name\": \"a yellow tape\"}, {\"id\": 14392, \"name\": \"digital clock display\"}, {\"id\": 14393, \"name\": \"the large, tan-colored pillar\"}, {\"id\": 14394, \"name\": \"a triangular sign\"}, {\"id\": 14395, \"name\": \"rear of the bike\"}, {\"id\": 14396, \"name\": \"brown tube\"}, {\"id\": 14397, \"name\": \"a rear bumper\"}, {\"id\": 14398, \"name\": \"the dressier button-down shirt\"}, {\"id\": 14399, \"name\": \"box of tissue\"}, {\"id\": 14400, \"name\": \"a bag of instant noodle\"}, {\"id\": 14401, \"name\": \"red and black backpack\"}, {\"id\": 14402, \"name\": \"the blue step\"}, {\"id\": 14403, \"name\": \"the large hoop earring\"}, {\"id\": 14404, \"name\": \"a yellow ball\"}, {\"id\": 14405, \"name\": \"orange safety cone\"}, {\"id\": 14406, \"name\": \"the red and white jersey\"}, {\"id\": 14407, \"name\": \"a curly hair\"}, {\"id\": 14408, \"name\": \"paintbrush\"}, {\"id\": 14409, \"name\": \"a white circle\"}, {\"id\": 14410, \"name\": \"the zipper\"}, {\"id\": 14411, \"name\": \"a gray sweater\"}, {\"id\": 14412, \"name\": \"the spire\"}, {\"id\": 14413, \"name\": \"a pickup truck's bed\"}, {\"id\": 14414, \"name\": \"an electric guitar\"}, {\"id\": 14415, \"name\": \"a dark blue line\"}, {\"id\": 14416, \"name\": \"the red and white object\"}, {\"id\": 14417, \"name\": \"stage lighting\"}, {\"id\": 14418, \"name\": \"the high ponytail\"}, {\"id\": 14419, \"name\": \"black granite or marble\"}, {\"id\": 14420, \"name\": \"a couch or bed\"}, {\"id\": 14421, \"name\": \"a red and white uniform\"}, {\"id\": 14422, \"name\": \"the left thigh\"}, {\"id\": 14423, \"name\": \"the green bag of diaper\"}, {\"id\": 14424, \"name\": \"a paint can lid\"}, {\"id\": 14425, \"name\": \"close-up view of a man's face in profile\"}, {\"id\": 14426, \"name\": \"car's logo\"}, {\"id\": 14427, \"name\": \"gray furry object\"}, {\"id\": 14428, \"name\": \"red button\"}, {\"id\": 14429, \"name\": \"a prominent yellow railing\"}, {\"id\": 14430, \"name\": \"the neon yellow and black uniform\"}, {\"id\": 14431, \"name\": \"a spaghetti\"}, {\"id\": 14432, \"name\": \"a metal roof\"}, {\"id\": 14433, \"name\": \"a car's rear window\"}, {\"id\": 14434, \"name\": \"a paper towel\"}, {\"id\": 14435, \"name\": \"bottle of hot sauce\"}, {\"id\": 14436, \"name\": \"the child's hand\"}, {\"id\": 14437, \"name\": \"the green shutter\"}, {\"id\": 14438, \"name\": \"the black bucket\"}, {\"id\": 14439, \"name\": \"a motorcycle's engine\"}, {\"id\": 14440, \"name\": \"the room's background\"}, {\"id\": 14441, \"name\": \"white sailboat\"}, {\"id\": 14442, \"name\": \"a backward baseball cap\"}, {\"id\": 14443, \"name\": \"a front of a red toolbox\"}, {\"id\": 14444, \"name\": \"the green bush\"}, {\"id\": 14445, \"name\": \"rusty metal arm\"}, {\"id\": 14446, \"name\": \"the red canoe\"}, {\"id\": 14447, \"name\": \"paint sprayer\"}, {\"id\": 14448, \"name\": \"front grille\"}, {\"id\": 14449, \"name\": \"a blue design\"}, {\"id\": 14450, \"name\": \"storefront's sign\"}, {\"id\": 14451, \"name\": \"white tank top\"}, {\"id\": 14452, \"name\": \"yellow helmet\"}, {\"id\": 14453, \"name\": \"the rider's gloved hand\"}, {\"id\": 14454, \"name\": \"the gray striped shirt\"}, {\"id\": 14455, \"name\": \"a navy-blue shirt\"}, {\"id\": 14456, \"name\": \"wooden skewer\"}, {\"id\": 14457, \"name\": \"blonde woman\"}, {\"id\": 14458, \"name\": \"red and black plaid short-sleeve button-down shirt\"}, {\"id\": 14459, \"name\": \"the photographer's head\"}, {\"id\": 14460, \"name\": \"a black porsche 356\"}, {\"id\": 14461, \"name\": \"yellow ball\"}, {\"id\": 14462, \"name\": \"man in a black shirt\"}, {\"id\": 14463, \"name\": \"skate\"}, {\"id\": 14464, \"name\": \"a rink's white surface\"}, {\"id\": 14465, \"name\": \"the referee\"}, {\"id\": 14466, \"name\": \"the red keychain\"}, {\"id\": 14467, \"name\": \"silver chafing dish\"}, {\"id\": 14468, \"name\": \"crowd\"}, {\"id\": 14469, \"name\": \"the black and white checkered top\"}, {\"id\": 14470, \"name\": \"a field's numbers\"}, {\"id\": 14471, \"name\": \"a yellow and blue object\"}, {\"id\": 14472, \"name\": \"grey sweatshirt\"}, {\"id\": 14473, \"name\": \"the rear door\"}, {\"id\": 14474, \"name\": \"a vehicle's wheel\"}, {\"id\": 14475, \"name\": \"drum corps\"}, {\"id\": 14476, \"name\": \"a blue hat\"}, {\"id\": 14477, \"name\": \"the palm frond\"}, {\"id\": 14478, \"name\": \"small container\"}, {\"id\": 14479, \"name\": \"hair care item\"}, {\"id\": 14480, \"name\": \"the prominent american flag\"}, {\"id\": 14481, \"name\": \"a yellow scarf\"}, {\"id\": 14482, \"name\": \"yellow railing\"}, {\"id\": 14483, \"name\": \"the flickering red light\"}, {\"id\": 14484, \"name\": \"the man's raised hand\"}, {\"id\": 14485, \"name\": \"a black chair\"}, {\"id\": 14486, \"name\": \"the black singlet\"}, {\"id\": 14487, \"name\": \"person wearing a green hat and white shirt\"}, {\"id\": 14488, \"name\": \"baseball\"}, {\"id\": 14489, \"name\": \"a workshop's floor\"}, {\"id\": 14490, \"name\": \"a sports-themed item\"}, {\"id\": 14491, \"name\": \"the black cuff\"}, {\"id\": 14492, \"name\": \"green plant arrangement\"}, {\"id\": 14493, \"name\": \"green vegetable\"}, {\"id\": 14494, \"name\": \"a white helmet\"}, {\"id\": 14495, \"name\": \"a pane\"}, {\"id\": 14496, \"name\": \"the large red life ring\"}, {\"id\": 14497, \"name\": \"the man in a brown suit and white shirt\"}, {\"id\": 14498, \"name\": \"the paper or plastic\"}, {\"id\": 14499, \"name\": \"child's arm\"}, {\"id\": 14500, \"name\": \"image of person\"}, {\"id\": 14501, \"name\": \"navy blue sweatshirt\"}, {\"id\": 14502, \"name\": \"a yellow banner\"}, {\"id\": 14503, \"name\": \"a darkened sidewalk\"}, {\"id\": 14504, \"name\": \"sandy enclosure\"}, {\"id\": 14505, \"name\": \"a short, light-brown hair\"}, {\"id\": 14506, \"name\": \"the horizontal bar\"}, {\"id\": 14507, \"name\": \"the plain white floor\"}, {\"id\": 14508, \"name\": \"black gauge\"}, {\"id\": 14509, \"name\": \"white apron\"}, {\"id\": 14510, \"name\": \"black and white label\"}, {\"id\": 14511, \"name\": \"the bubble wrap sheet\"}, {\"id\": 14512, \"name\": \"current score\"}, {\"id\": 14513, \"name\": \"the nail polish design\"}, {\"id\": 14514, \"name\": \"large blue logo\"}, {\"id\": 14515, \"name\": \"dark blue suit jacket\"}, {\"id\": 14516, \"name\": \"the single car\"}, {\"id\": 14517, \"name\": \"the tan sole\"}, {\"id\": 14518, \"name\": \"a gym's ceiling\"}, {\"id\": 14519, \"name\": \"yellow triangle\"}, {\"id\": 14520, \"name\": \"a diamond plate top\"}, {\"id\": 14521, \"name\": \"a raised concrete ledge\"}, {\"id\": 14522, \"name\": \"the brown wooden fence\"}, {\"id\": 14523, \"name\": \"the tan roof\"}, {\"id\": 14524, \"name\": \"purple zipper\"}, {\"id\": 14525, \"name\": \"a rack of shoe\"}, {\"id\": 14526, \"name\": \"green helmet\"}, {\"id\": 14527, \"name\": \"the colorful flag\"}, {\"id\": 14528, \"name\": \"a white door frame\"}, {\"id\": 14529, \"name\": \"shaved head\"}, {\"id\": 14530, \"name\": \"a red tattoo\"}, {\"id\": 14531, \"name\": \"smaller window\"}, {\"id\": 14532, \"name\": \"sweater's neckline\"}, {\"id\": 14533, \"name\": \"the vase of flower\"}, {\"id\": 14534, \"name\": \"the computer monitor\"}, {\"id\": 14535, \"name\": \"wine glass\"}, {\"id\": 14536, \"name\": \"trailer\"}, {\"id\": 14537, \"name\": \"the weathered wooden fence\"}, {\"id\": 14538, \"name\": \"treadmill\"}, {\"id\": 14539, \"name\": \"long black braid\"}, {\"id\": 14540, \"name\": \"a red, white, and blue bag\"}, {\"id\": 14541, \"name\": \"the dog's right front leg\"}, {\"id\": 14542, \"name\": \"red and green icing\"}, {\"id\": 14543, \"name\": \"cricket player\"}, {\"id\": 14544, \"name\": \"a climate control system\"}, {\"id\": 14545, \"name\": \"the child's face\"}, {\"id\": 14546, \"name\": \"black denim jacket\"}, {\"id\": 14547, \"name\": \"chicken coop\"}, {\"id\": 14548, \"name\": \"blue folding chair\"}, {\"id\": 14549, \"name\": \"white sole\"}, {\"id\": 14550, \"name\": \"the red carpet\"}, {\"id\": 14551, \"name\": \"plastic metro grocery bag\"}, {\"id\": 14552, \"name\": \"a man in a white jacket and black hat\"}, {\"id\": 14553, \"name\": \"yellow rope or bar\"}, {\"id\": 14554, \"name\": \"a yellow tag\"}, {\"id\": 14555, \"name\": \"the red and yellow light\"}, {\"id\": 14556, \"name\": \"a blue wheelchair\"}, {\"id\": 14557, \"name\": \"a striped beanie\"}, {\"id\": 14558, \"name\": \"the wooden shelf or table\"}, {\"id\": 14559, \"name\": \"horse's ear\"}, {\"id\": 14560, \"name\": \"a rolled-up grey sleeping pad\"}, {\"id\": 14561, \"name\": \"a wooden door\"}, {\"id\": 14562, \"name\": \"news banner overlay\"}, {\"id\": 14563, \"name\": \"the speaker's table\"}, {\"id\": 14564, \"name\": \"the dish\"}, {\"id\": 14565, \"name\": \"light jean\"}, {\"id\": 14566, \"name\": \"matching hat\"}, {\"id\": 14567, \"name\": \"the long-handled shovel\"}, {\"id\": 14568, \"name\": \"wide tire\"}, {\"id\": 14569, \"name\": \"a picnic table\"}, {\"id\": 14570, \"name\": \"green feather\"}, {\"id\": 14571, \"name\": \"the blue car lift\"}, {\"id\": 14572, \"name\": \"blue collared shirt\"}, {\"id\": 14573, \"name\": \"the winding dirt path\"}, {\"id\": 14574, \"name\": \"a circular pin\"}, {\"id\": 14575, \"name\": \"telephoto len\"}, {\"id\": 14576, \"name\": \"the black backpack strap\"}, {\"id\": 14577, \"name\": \"red hair\"}, {\"id\": 14578, \"name\": \"a green mat\"}, {\"id\": 14579, \"name\": \"the small black device\"}, {\"id\": 14580, \"name\": \"red dirt path\"}, {\"id\": 14581, \"name\": \"large umbrella or awning\"}, {\"id\": 14582, \"name\": \"a small white dish\"}, {\"id\": 14583, \"name\": \"a red outline\"}, {\"id\": 14584, \"name\": \"a matching blue and green uniform\"}, {\"id\": 14585, \"name\": \"a bouquet of pink rose\"}, {\"id\": 14586, \"name\": \"yellow ferrari logo\"}, {\"id\": 14587, \"name\": \"the dark blue wristband\"}, {\"id\": 14588, \"name\": \"a gray chair\"}, {\"id\": 14589, \"name\": \"a white cuff\"}, {\"id\": 14590, \"name\": \"a bold red and white banner\"}, {\"id\": 14591, \"name\": \"knee pad\"}, {\"id\": 14592, \"name\": \"a digital banner\"}, {\"id\": 14593, \"name\": \"black rearview mirror\"}, {\"id\": 14594, \"name\": \"large bengal logo\"}, {\"id\": 14595, \"name\": \"light green jacket\"}, {\"id\": 14596, \"name\": \"the wooden track\"}, {\"id\": 14597, \"name\": \"the blackboard\"}, {\"id\": 14598, \"name\": \"the water container\"}, {\"id\": 14599, \"name\": \"a man in a black suit and white shirt\"}, {\"id\": 14600, \"name\": \"a black stripe\"}, {\"id\": 14601, \"name\": \"the ceiling of the arena\"}, {\"id\": 14602, \"name\": \"shelf's countertop\"}, {\"id\": 14603, \"name\": \"the building's wall\"}, {\"id\": 14604, \"name\": \"a small shelf\"}, {\"id\": 14605, \"name\": \"red and white santa hat\"}, {\"id\": 14606, \"name\": \"rugby player\"}, {\"id\": 14607, \"name\": \"the large red banner\"}, {\"id\": 14608, \"name\": \"the yellow roll of tape\"}, {\"id\": 14609, \"name\": \"single-story structure\"}, {\"id\": 14610, \"name\": \"the yellow box of battery\"}, {\"id\": 14611, \"name\": \"the bedding\"}, {\"id\": 14612, \"name\": \"red and white tag\"}, {\"id\": 14613, \"name\": \"the player\"}, {\"id\": 14614, \"name\": \"a medium-sized brown pit bull\"}, {\"id\": 14615, \"name\": \"a longboard\"}, {\"id\": 14616, \"name\": \"large dish\"}, {\"id\": 14617, \"name\": \"a performer\"}, {\"id\": 14618, \"name\": \"a white vintage car\"}, {\"id\": 14619, \"name\": \"red dirt infield\"}, {\"id\": 14620, \"name\": \"the dark green uniform\"}, {\"id\": 14621, \"name\": \"a toolbox\"}, {\"id\": 14622, \"name\": \"the black tray\"}, {\"id\": 14623, \"name\": \"the cyclist's front wheel\"}, {\"id\": 14624, \"name\": \"young girl with blonde hair\"}, {\"id\": 14625, \"name\": \"a blue metal support\"}, {\"id\": 14626, \"name\": \"a white leg\"}, {\"id\": 14627, \"name\": \"name tag\"}, {\"id\": 14628, \"name\": \"a grey flat cap\"}, {\"id\": 14629, \"name\": \"a large black polka dot\"}, {\"id\": 14630, \"name\": \"red and white uniform\"}, {\"id\": 14631, \"name\": \"colorful sprinkle\"}, {\"id\": 14632, \"name\": \"a short, curly blonde hairstyle\"}, {\"id\": 14633, \"name\": \"the sponsor's emblem\"}, {\"id\": 14634, \"name\": \"yellow jersey\"}, {\"id\": 14635, \"name\": \"canopy of white rope\"}, {\"id\": 14636, \"name\": \"a black tank top\"}, {\"id\": 14637, \"name\": \"the rear of the tractor\"}, {\"id\": 14638, \"name\": \"gray trash can\"}, {\"id\": 14639, \"name\": \"the logo of cctv news\"}, {\"id\": 14640, \"name\": \"the boxing or kickboxing attire\"}, {\"id\": 14641, \"name\": \"gray trim\"}, {\"id\": 14642, \"name\": \"a black and white leotard\"}, {\"id\": 14643, \"name\": \"facebook page\"}, {\"id\": 14644, \"name\": \"the small, open-top vehicle\"}, {\"id\": 14645, \"name\": \"uniform\"}, {\"id\": 14646, \"name\": \"the vibrant green roof\"}, {\"id\": 14647, \"name\": \"an orange container\"}, {\"id\": 14648, \"name\": \"the horse's rein\"}, {\"id\": 14649, \"name\": \"the red and black sign\"}, {\"id\": 14650, \"name\": \"the black facade\"}, {\"id\": 14651, \"name\": \"black belt\"}, {\"id\": 14652, \"name\": \"ford logo\"}, {\"id\": 14653, \"name\": \"a silver rim\"}, {\"id\": 14654, \"name\": \"tan leather\"}, {\"id\": 14655, \"name\": \"a driver's seat\"}, {\"id\": 14656, \"name\": \"the woman's head\"}, {\"id\": 14657, \"name\": \"the large white screen\"}, {\"id\": 14658, \"name\": \"light-colored stone\"}, {\"id\": 14659, \"name\": \"a 12501\"}, {\"id\": 14660, \"name\": \"horizontal slat\"}, {\"id\": 14661, \"name\": \"the magazine\"}, {\"id\": 14662, \"name\": \"can of paint\"}, {\"id\": 14663, \"name\": \"a large, round silver brake rotor\"}, {\"id\": 14664, \"name\": \"a vendor's stall\"}, {\"id\": 14665, \"name\": \"a smaller white bowl\"}, {\"id\": 14666, \"name\": \"black electrical cord\"}, {\"id\": 14667, \"name\": \"the car's dashboard\"}, {\"id\": 14668, \"name\": \"a small purse\"}, {\"id\": 14669, \"name\": \"a blue uniform\"}, {\"id\": 14670, \"name\": \"the camera len\"}, {\"id\": 14671, \"name\": \"white stripe\"}, {\"id\": 14672, \"name\": \"the driver's arm\"}, {\"id\": 14673, \"name\": \"beige-colored wall\"}, {\"id\": 14674, \"name\": \"an exhaust pipe\"}, {\"id\": 14675, \"name\": \"white uniform\"}, {\"id\": 14676, \"name\": \"the white lid\"}, {\"id\": 14677, \"name\": \"a wood floor\"}, {\"id\": 14678, \"name\": \"a large silver pot\"}, {\"id\": 14679, \"name\": \"a shelving\"}, {\"id\": 14680, \"name\": \"green lawn chair\"}, {\"id\": 14681, \"name\": \"the partially open door\"}, {\"id\": 14682, \"name\": \"a wooden baseboard\"}, {\"id\": 14683, \"name\": \"the red and green knob\"}, {\"id\": 14684, \"name\": \"the arcade machine\"}, {\"id\": 14685, \"name\": \"a red flower design\"}, {\"id\": 14686, \"name\": \"video feed\"}, {\"id\": 14687, \"name\": \"the white facade\"}, {\"id\": 14688, \"name\": \"a white uniform\"}, {\"id\": 14689, \"name\": \"rectangular cake\"}, {\"id\": 14690, \"name\": \"the blue scrub\"}, {\"id\": 14691, \"name\": \"the formal attire\"}, {\"id\": 14692, \"name\": \"the guitar image\"}, {\"id\": 14693, \"name\": \"the arm of the chair\"}, {\"id\": 14694, \"name\": \"pink flower design\"}, {\"id\": 14695, \"name\": \"traditional red coat\"}, {\"id\": 14696, \"name\": \"wood cabinet\"}, {\"id\": 14697, \"name\": \"a gray couch\"}, {\"id\": 14698, \"name\": \"a black bag or backpack\"}, {\"id\": 14699, \"name\": \"a dog's snout\"}, {\"id\": 14700, \"name\": \"the grey sock\"}, {\"id\": 14701, \"name\": \"the black and yellow jersey\"}, {\"id\": 14702, \"name\": \"blue and white workbench\"}, {\"id\": 14703, \"name\": \"snowy backdrop\"}, {\"id\": 14704, \"name\": \"light blue sky\"}, {\"id\": 14705, \"name\": \"shirtle man\"}, {\"id\": 14706, \"name\": \"a blurry green area\"}, {\"id\": 14707, \"name\": \"red way sign\"}, {\"id\": 14708, \"name\": \"a jcb logo\"}, {\"id\": 14709, \"name\": \"the tall power line tower\"}, {\"id\": 14710, \"name\": \"a bottle of spice\"}, {\"id\": 14711, \"name\": \"a red hoodie\"}, {\"id\": 14712, \"name\": \"the small white tag\"}, {\"id\": 14713, \"name\": \"a white bow\"}, {\"id\": 14714, \"name\": \"a 7-eleven sign\"}, {\"id\": 14715, \"name\": \"the light-colored jean\"}, {\"id\": 14716, \"name\": \"a small patch of green grass\"}, {\"id\": 14717, \"name\": \"brown fedora hat\"}, {\"id\": 14718, \"name\": \"partially unbuttoned collar\"}, {\"id\": 14719, \"name\": \"diagram of a window regulator\"}, {\"id\": 14720, \"name\": \"the soldier's shoulder\"}, {\"id\": 14721, \"name\": \"the garage door\"}, {\"id\": 14722, \"name\": \"a green piece of paper\"}, {\"id\": 14723, \"name\": \"white name tag\"}, {\"id\": 14724, \"name\": \"a wooden building\"}, {\"id\": 14725, \"name\": \"the black and red uniform\"}, {\"id\": 14726, \"name\": \"green brim\"}, {\"id\": 14727, \"name\": \"a black-framed window\"}, {\"id\": 14728, \"name\": \"a clear drawer\"}, {\"id\": 14729, \"name\": \"the rack of clothing\"}, {\"id\": 14730, \"name\": \"the logo for the nfl afc divisional\"}, {\"id\": 14731, \"name\": \"a glas lid\"}, {\"id\": 14732, \"name\": \"building with a metal scaffolding structure\"}, {\"id\": 14733, \"name\": \"person in the chair\"}, {\"id\": 14734, \"name\": \"a white cord\"}, {\"id\": 14735, \"name\": \"the red plastic chair\"}, {\"id\": 14736, \"name\": \"a dark-colored building\"}, {\"id\": 14737, \"name\": \"a jacket's reflective strip\"}, {\"id\": 14738, \"name\": \"the prominent orange and white striped traffic cone\"}, {\"id\": 14739, \"name\": \"silver logo\"}, {\"id\": 14740, \"name\": \"a green top\"}, {\"id\": 14741, \"name\": \"white stitching\"}, {\"id\": 14742, \"name\": \"the black short\"}, {\"id\": 14743, \"name\": \"the black ceramic pot\"}, {\"id\": 14744, \"name\": \"police patch\"}, {\"id\": 14745, \"name\": \"red headband\"}, {\"id\": 14746, \"name\": \"a gold ribbon\"}, {\"id\": 14747, \"name\": \"the black dumbbell\"}, {\"id\": 14748, \"name\": \"a muscular man\"}, {\"id\": 14749, \"name\": \"a purple sport bra\"}, {\"id\": 14750, \"name\": \"person exercising\"}, {\"id\": 14751, \"name\": \"the navy blue hat\"}, {\"id\": 14752, \"name\": \"picnic table\"}, {\"id\": 14753, \"name\": \"dark-colored cowboy hat\"}, {\"id\": 14754, \"name\": \"an orange and black uniform\"}, {\"id\": 14755, \"name\": \"a screw\"}, {\"id\": 14756, \"name\": \"a grey wing\"}, {\"id\": 14757, \"name\": \"a colored liquid\"}, {\"id\": 14758, \"name\": \"a large rearview mirror\"}, {\"id\": 14759, \"name\": \"the short or athletic pant\"}, {\"id\": 14760, \"name\": \"white cross\"}, {\"id\": 14761, \"name\": \"the silver door handle\"}, {\"id\": 14762, \"name\": \"red uniform\"}, {\"id\": 14763, \"name\": \"the orange and white uniform\"}, {\"id\": 14764, \"name\": \"guitar's headstock\"}, {\"id\": 14765, \"name\": \"a red bin\"}, {\"id\": 14766, \"name\": \"a left wall\"}, {\"id\": 14767, \"name\": \"the yellow trim\"}, {\"id\": 14768, \"name\": \"the stylized outline of south africa\"}, {\"id\": 14769, \"name\": \"white shirt collar\"}, {\"id\": 14770, \"name\": \"blue balloon\"}, {\"id\": 14771, \"name\": \"yellow license plate\"}, {\"id\": 14772, \"name\": \"a lifter\"}, {\"id\": 14773, \"name\": \"coca-cola logo\"}, {\"id\": 14774, \"name\": \"the blue table\"}, {\"id\": 14775, \"name\": \"the beige cinderblock wall\"}, {\"id\": 14776, \"name\": \"a black display booth\"}, {\"id\": 14777, \"name\": \"the large scoreboard\"}, {\"id\": 14778, \"name\": \"quad bike\"}, {\"id\": 14779, \"name\": \"the gold-colored microphone\"}, {\"id\": 14780, \"name\": \"four-wheel drive truck or suv\"}, {\"id\": 14781, \"name\": \"the red umbrella\"}, {\"id\": 14782, \"name\": \"grey brick\"}, {\"id\": 14783, \"name\": \"seated position\"}, {\"id\": 14784, \"name\": \"a short gray hair\"}, {\"id\": 14785, \"name\": \"a concrete ramp\"}, {\"id\": 14786, \"name\": \"warning\"}, {\"id\": 14787, \"name\": \"wall of the warehouse\"}, {\"id\": 14788, \"name\": \"morning shoot review\"}, {\"id\": 14789, \"name\": \"car's license plate\"}, {\"id\": 14790, \"name\": \"long, wavy blonde hair\"}, {\"id\": 14791, \"name\": \"brown tiled roof\"}, {\"id\": 14792, \"name\": \"the red player\"}, {\"id\": 14793, \"name\": \"the white dotted line\"}, {\"id\": 14794, \"name\": \"the blue and white banner\"}, {\"id\": 14795, \"name\": \"the gray short\"}, {\"id\": 14796, \"name\": \"matching skirt\"}, {\"id\": 14797, \"name\": \"a gopro camera\"}, {\"id\": 14798, \"name\": \"the black polo shirt\"}, {\"id\": 14799, \"name\": \"the dark-colored wall or fence\"}, {\"id\": 14800, \"name\": \"the shiny metal teapot\"}, {\"id\": 14801, \"name\": \"a audi's front bumper\"}, {\"id\": 14802, \"name\": \"a blue block\"}, {\"id\": 14803, \"name\": \"the grey shed\"}, {\"id\": 14804, \"name\": \"sand yacht\"}, {\"id\": 14805, \"name\": \"the white short\"}, {\"id\": 14806, \"name\": \"left leg of the short\"}, {\"id\": 14807, \"name\": \"orange wall\"}, {\"id\": 14808, \"name\": \"white advertisement\"}, {\"id\": 14809, \"name\": \"a front bumper\"}, {\"id\": 14810, \"name\": \"a trophy base\"}, {\"id\": 14811, \"name\": \"the dark short\"}, {\"id\": 14812, \"name\": \"the white crosswalk\"}, {\"id\": 14813, \"name\": \"the black cord\"}, {\"id\": 14814, \"name\": \"sand or dirt\"}, {\"id\": 14815, \"name\": \"the blue geometric pattern\"}, {\"id\": 14816, \"name\": \"empty handball court\"}, {\"id\": 14817, \"name\": \"person on a bike\"}, {\"id\": 14818, \"name\": \"the blue uniform\"}, {\"id\": 14819, \"name\": \"white sash\"}, {\"id\": 14820, \"name\": \"the football\"}, {\"id\": 14821, \"name\": \"the chrome light\"}, {\"id\": 14822, \"name\": \"yellow warning label\"}, {\"id\": 14823, \"name\": \"green polo shirt\"}, {\"id\": 14824, \"name\": \"a tall tree\"}, {\"id\": 14825, \"name\": \"the brown wood panel\"}, {\"id\": 14826, \"name\": \"the wooden post\"}, {\"id\": 14827, \"name\": \"white basketball hoop\"}, {\"id\": 14828, \"name\": \"the high-visibility clothing\"}, {\"id\": 14829, \"name\": \"a woman with brown hair and a dark jacket\"}, {\"id\": 14830, \"name\": \"a parking\"}, {\"id\": 14831, \"name\": \"the uniformed man\"}, {\"id\": 14832, \"name\": \"a large bowl of colorful ball\"}, {\"id\": 14833, \"name\": \"the sandal\"}, {\"id\": 14834, \"name\": \"ruffle\"}, {\"id\": 14835, \"name\": \"the arched frame\"}, {\"id\": 14836, \"name\": \"baseball player\"}, {\"id\": 14837, \"name\": \"the red and white design\"}, {\"id\": 14838, \"name\": \"green mushroom statue\"}, {\"id\": 14839, \"name\": \"red pumpkin\"}, {\"id\": 14840, \"name\": \"the slice of watermelon\"}, {\"id\": 14841, \"name\": \"a wicker basket\"}, {\"id\": 14842, \"name\": \"blue baseball cap\"}, {\"id\": 14843, \"name\": \"the white headband\"}, {\"id\": 14844, \"name\": \"the woman with long dark hair\"}, {\"id\": 14845, \"name\": \"the boy's white shirt\"}, {\"id\": 14846, \"name\": \"a rope or chain\"}, {\"id\": 14847, \"name\": \"a person with the cane\"}, {\"id\": 14848, \"name\": \"a bottle of alcohol\"}, {\"id\": 14849, \"name\": \"long sleeve\"}, {\"id\": 14850, \"name\": \"the copper-colored light fixture\"}, {\"id\": 14851, \"name\": \"the new orlean saint logo\"}, {\"id\": 14852, \"name\": \"colorful pez dispenser\"}, {\"id\": 14853, \"name\": \"segway\"}, {\"id\": 14854, \"name\": \"the large handle\"}, {\"id\": 14855, \"name\": \"a vibrant red and yellow spiral design\"}, {\"id\": 14856, \"name\": \"the blue and yellow shirt\"}, {\"id\": 14857, \"name\": \"military personnel\"}, {\"id\": 14858, \"name\": \"soda can\"}, {\"id\": 14859, \"name\": \"the light-colored dre shirt\"}, {\"id\": 14860, \"name\": \"the short-sleeved shirt\"}, {\"id\": 14861, \"name\": \"orange race bib\"}, {\"id\": 14862, \"name\": \"brown cord\"}, {\"id\": 14863, \"name\": \"piece of brownie\"}, {\"id\": 14864, \"name\": \"an overhead light\"}, {\"id\": 14865, \"name\": \"the black iron fence\"}, {\"id\": 14866, \"name\": \"the orange player\"}, {\"id\": 14867, \"name\": \"tiered seating\"}, {\"id\": 14868, \"name\": \"green and pink striped jersey\"}, {\"id\": 14869, \"name\": \"yellow sock\"}, {\"id\": 14870, \"name\": \"a pink chair cover\"}, {\"id\": 14871, \"name\": \"the silver handle\"}, {\"id\": 14872, \"name\": \"basketball-themed wall decoration\"}, {\"id\": 14873, \"name\": \"the black and yellow lego piece\"}, {\"id\": 14874, \"name\": \"the white benny logo\"}, {\"id\": 14875, \"name\": \"a round dial\"}, {\"id\": 14876, \"name\": \"a cartoon\"}, {\"id\": 14877, \"name\": \"the tan strap\"}, {\"id\": 14878, \"name\": \"a reu logo\"}, {\"id\": 14879, \"name\": \"the button\"}, {\"id\": 14880, \"name\": \"hijab\"}, {\"id\": 14881, \"name\": \"the player's name\"}, {\"id\": 14882, \"name\": \"brown baseboard\"}, {\"id\": 14883, \"name\": \"cream-colored wall\"}, {\"id\": 14884, \"name\": \"a lush green ivy\"}, {\"id\": 14885, \"name\": \"a pouch\"}, {\"id\": 14886, \"name\": \"a collared shirt\"}, {\"id\": 14887, \"name\": \"the short\"}, {\"id\": 14888, \"name\": \"vase of flower\"}, {\"id\": 14889, \"name\": \"prominent banner\"}, {\"id\": 14890, \"name\": \"speech bubble\"}, {\"id\": 14891, \"name\": \"a basketball court\"}, {\"id\": 14892, \"name\": \"individual wearing a white and blue shirt\"}, {\"id\": 14893, \"name\": \"gold-colored metal\"}, {\"id\": 14894, \"name\": \"red-and-white barrier\"}, {\"id\": 14895, \"name\": \"a wall of soccer team banner\"}, {\"id\": 14896, \"name\": \"the person's feet\"}, {\"id\": 14897, \"name\": \"a long, rectangular wooden table\"}, {\"id\": 14898, \"name\": \"dark grey attire\"}, {\"id\": 14899, \"name\": \"large tower or antenna\"}, {\"id\": 14900, \"name\": \"red bottle\"}, {\"id\": 14901, \"name\": \"the large open door\"}, {\"id\": 14902, \"name\": \"a red uniform\"}, {\"id\": 14903, \"name\": \"distinctive chrome grille\"}, {\"id\": 14904, \"name\": \"wall adorned with framed picture\"}, {\"id\": 14905, \"name\": \"a combination of open and closed shelf\"}, {\"id\": 14906, \"name\": \"a reflective gla front\"}, {\"id\": 14907, \"name\": \"the long sleeve\"}, {\"id\": 14908, \"name\": \"small stone path\"}, {\"id\": 14909, \"name\": \"a man in black uniform\"}, {\"id\": 14910, \"name\": \"a large orange coral\"}, {\"id\": 14911, \"name\": \"the yellow umbrella\"}, {\"id\": 14912, \"name\": \"a building with a brown roof\"}, {\"id\": 14913, \"name\": \"the horse's face\"}, {\"id\": 14914, \"name\": \"the yellow and white jersey\"}, {\"id\": 14915, \"name\": \"the lush green lawn\"}, {\"id\": 14916, \"name\": \"spotlight\"}, {\"id\": 14917, \"name\": \"the gold floral design\"}, {\"id\": 14918, \"name\": \"a pink handle\"}, {\"id\": 14919, \"name\": \"an athlete\"}, {\"id\": 14920, \"name\": \"black barrier\"}, {\"id\": 14921, \"name\": \"green foliage\"}, {\"id\": 14922, \"name\": \"gold rope trim\"}, {\"id\": 14923, \"name\": \"black and red hurdle\"}, {\"id\": 14924, \"name\": \"the exposed beam\"}, {\"id\": 14925, \"name\": \"the cabinet or shelf\"}, {\"id\": 14926, \"name\": \"clarinet\"}, {\"id\": 14927, \"name\": \"the wooden hull\"}, {\"id\": 14928, \"name\": \"barrier\"}, {\"id\": 14929, \"name\": \"the guitar or similar instrument\"}, {\"id\": 14930, \"name\": \"black and yellow uniform\"}, {\"id\": 14931, \"name\": \"crisp white shirt\"}, {\"id\": 14932, \"name\": \"low stone wall\"}, {\"id\": 14933, \"name\": \"a blue rim\"}, {\"id\": 14934, \"name\": \"the horizontal stripe\"}, {\"id\": 14935, \"name\": \"the grey baseball cap\"}, {\"id\": 14936, \"name\": \"a large, chrome wheel\"}, {\"id\": 14937, \"name\": \"black jersey\"}, {\"id\": 14938, \"name\": \"a knee-high black and white sock\"}, {\"id\": 14939, \"name\": \"the train's roof\"}, {\"id\": 14940, \"name\": \"a blue stripe\"}, {\"id\": 14941, \"name\": \"a character\"}, {\"id\": 14942, \"name\": \"wooden wall\"}, {\"id\": 14943, \"name\": \"a black long-sleeved top\"}, {\"id\": 14944, \"name\": \"a sheet music\"}, {\"id\": 14945, \"name\": \"a white yard line\"}, {\"id\": 14946, \"name\": \"a yellow reflective strip\"}, {\"id\": 14947, \"name\": \"a pink wall\"}, {\"id\": 14948, \"name\": \"back seat of a car\"}, {\"id\": 14949, \"name\": \"a black polo shirt\"}, {\"id\": 14950, \"name\": \"a silhouette of person\"}, {\"id\": 14951, \"name\": \"the chrome wheel\"}, {\"id\": 14952, \"name\": \"the player's left hand\"}, {\"id\": 14953, \"name\": \"tinted window\"}, {\"id\": 14954, \"name\": \"a young woman\"}, {\"id\": 14955, \"name\": \"the cricket ball\"}, {\"id\": 14956, \"name\": \"a black knee pad\"}, {\"id\": 14957, \"name\": \"an orange cord\"}, {\"id\": 14958, \"name\": \"grey sweatpant\"}, {\"id\": 14959, \"name\": \"black off-the-shoulder shirt\"}, {\"id\": 14960, \"name\": \"a blue and black striped band\"}, {\"id\": 14961, \"name\": \"the yellow jersey\"}, {\"id\": 14962, \"name\": \"the stainle steel pizza counter\"}, {\"id\": 14963, \"name\": \"the legging\"}, {\"id\": 14964, \"name\": \"a ballet costume\"}, {\"id\": 14965, \"name\": \"a large staircase\"}, {\"id\": 14966, \"name\": \"small light\"}, {\"id\": 14967, \"name\": \"the short blonde hair\"}, {\"id\": 14968, \"name\": \"a yellow jersey\"}, {\"id\": 14969, \"name\": \"bowling ball return\"}, {\"id\": 14970, \"name\": \"the wall of window\"}, {\"id\": 14971, \"name\": \"the light green cabbage\"}, {\"id\": 14972, \"name\": \"the red balloon\"}, {\"id\": 14973, \"name\": \"suburban sidewalk\"}, {\"id\": 14974, \"name\": \"a white long-sleeve blazer\"}, {\"id\": 14975, \"name\": \"long hair\"}, {\"id\": 14976, \"name\": \"a display of car key\"}, {\"id\": 14977, \"name\": \"the pant\"}, {\"id\": 14978, \"name\": \"the large, multi-colored block\"}, {\"id\": 14979, \"name\": \"gauge\"}, {\"id\": 14980, \"name\": \"the white jersey\"}, {\"id\": 14981, \"name\": \"the golden-brown fried food\"}, {\"id\": 14982, \"name\": \"a large, round, silver object\"}, {\"id\": 14983, \"name\": \"a stadium seating\"}, {\"id\": 14984, \"name\": \"nba player lebron jame\"}, {\"id\": 14985, \"name\": \"wooden armrest\"}, {\"id\": 14986, \"name\": \"the colorful shirt\"}, {\"id\": 14987, \"name\": \"the black leotard\"}, {\"id\": 14988, \"name\": \"a rear windshield\"}, {\"id\": 14989, \"name\": \"pink and white flower\"}, {\"id\": 14990, \"name\": \"the metal wire\"}, {\"id\": 14991, \"name\": \"the overhead light\"}, {\"id\": 14992, \"name\": \"a colorful umbrella\"}, {\"id\": 14993, \"name\": \"the blue sleeve\"}, {\"id\": 14994, \"name\": \"black grip\"}, {\"id\": 14995, \"name\": \"a small logo\"}, {\"id\": 14996, \"name\": \"the suv\"}, {\"id\": 14997, \"name\": \"the badminton and volleyball set\"}, {\"id\": 14998, \"name\": \"the football player\"}, {\"id\": 14999, \"name\": \"high-rise building\"}, {\"id\": 15000, \"name\": \"black gym bag\"}, {\"id\": 15001, \"name\": \"the barber chair\"}, {\"id\": 15002, \"name\": \"athletic attire\"}, {\"id\": 15003, \"name\": \"a sponsor logo\"}, {\"id\": 15004, \"name\": \"a small round light\"}, {\"id\": 15005, \"name\": \"football player\"}, {\"id\": 15006, \"name\": \"the sport car\"}, {\"id\": 15007, \"name\": \"the grassy median\"}, {\"id\": 15008, \"name\": \"netted fence\"}, {\"id\": 15009, \"name\": \"windowsill\"}, {\"id\": 15010, \"name\": \"a small piece of food\"}, {\"id\": 15011, \"name\": \"the packaging material\"}, {\"id\": 15012, \"name\": \"the wooden display rack\"}, {\"id\": 15013, \"name\": \"the tent's canopy\"}, {\"id\": 15014, \"name\": \"the digital score counter\"}, {\"id\": 15015, \"name\": \"large orange ball\"}, {\"id\": 15016, \"name\": \"decoration\"}, {\"id\": 15017, \"name\": \"white rugby ball\"}, {\"id\": 15018, \"name\": \"the fork\"}, {\"id\": 15019, \"name\": \"a yellow bikini\"}, {\"id\": 15020, \"name\": \"string of pink lantern\"}, {\"id\": 15021, \"name\": \"the blonde woman\"}, {\"id\": 15022, \"name\": \"a silver label\"}, {\"id\": 15023, \"name\": \"an open window of the car\"}, {\"id\": 15024, \"name\": \"a blue lighting\"}, {\"id\": 15025, \"name\": \"a white paneling\"}, {\"id\": 15026, \"name\": \"face guard\"}, {\"id\": 15027, \"name\": \"white clothing\"}, {\"id\": 15028, \"name\": \"black hose\"}, {\"id\": 15029, \"name\": \"orange basketball hoop\"}, {\"id\": 15030, \"name\": \"the man wearing a suit and tie\"}, {\"id\": 15031, \"name\": \"walkway pillar\"}, {\"id\": 15032, \"name\": \"the large blue and purple pole\"}, {\"id\": 15033, \"name\": \"a cheerleading uniform\"}, {\"id\": 15034, \"name\": \"the player's face\"}, {\"id\": 15035, \"name\": \"the football pitch\"}, {\"id\": 15036, \"name\": \"a fox nfl logo\"}, {\"id\": 15037, \"name\": \"red and white christmas-themed costume\"}, {\"id\": 15038, \"name\": \"rear window\"}, {\"id\": 15039, \"name\": \"the stadium's seating area\"}, {\"id\": 15040, \"name\": \"sailboat\"}, {\"id\": 15041, \"name\": \"the white and black uniform\"}, {\"id\": 15042, \"name\": \"the frozen meal\"}, {\"id\": 15043, \"name\": \"a white and black uniform\"}, {\"id\": 15044, \"name\": \"the patch\"}, {\"id\": 15045, \"name\": \"a protective gear\"}, {\"id\": 15046, \"name\": \"a beach volleyball court\"}, {\"id\": 15047, \"name\": \"a red-clad team\"}, {\"id\": 15048, \"name\": \"a spice king logo\"}, {\"id\": 15049, \"name\": \"the light blue smock\"}, {\"id\": 15050, \"name\": \"navy t-shirt\"}, {\"id\": 15051, \"name\": \"a tank top\"}, {\"id\": 15052, \"name\": \"front seat\"}, {\"id\": 15053, \"name\": \"the pink and yellow stripe\"}, {\"id\": 15054, \"name\": \"gym's flooring\"}, {\"id\": 15055, \"name\": \"short fingernail\"}, {\"id\": 15056, \"name\": \"a gloved hand\"}, {\"id\": 15057, \"name\": \"the 16 jersey\"}, {\"id\": 15058, \"name\": \"the white stone\"}, {\"id\": 15059, \"name\": \"a blue and green ball\"}, {\"id\": 15060, \"name\": \"a player's right shoulder sleeve\"}, {\"id\": 15061, \"name\": \"the person with red hair\"}, {\"id\": 15062, \"name\": \"the white barrier\"}, {\"id\": 15063, \"name\": \"the blue and green attire\"}, {\"id\": 15064, \"name\": \"the dark-colored long-sleeved shirt\"}, {\"id\": 15065, \"name\": \"gray baseball cap\"}, {\"id\": 15066, \"name\": \"a brown chair\"}, {\"id\": 15067, \"name\": \"the bottle of wine\"}, {\"id\": 15068, \"name\": \"the dark wood cabinet\"}, {\"id\": 15069, \"name\": \"a black boxing glove\"}, {\"id\": 15070, \"name\": \"the pink rose\"}, {\"id\": 15071, \"name\": \"a baseball mitt\"}, {\"id\": 15072, \"name\": \"croissant-style pastry\"}, {\"id\": 15073, \"name\": \"player's head\"}, {\"id\": 15074, \"name\": \"woman with short blonde hair\"}, {\"id\": 15075, \"name\": \"matcha\"}, {\"id\": 15076, \"name\": \"small image\"}, {\"id\": 15077, \"name\": \"a person's apron\"}, {\"id\": 15078, \"name\": \"the light blue jean\"}, {\"id\": 15079, \"name\": \"a player's body\"}, {\"id\": 15080, \"name\": \"the rear light\"}, {\"id\": 15081, \"name\": \"the white paper cup\"}, {\"id\": 15082, \"name\": \"a beige brick wall\"}, {\"id\": 15083, \"name\": \"the kurta\"}, {\"id\": 15084, \"name\": \"the female volleyball player\"}, {\"id\": 15085, \"name\": \"grassy embankment\"}, {\"id\": 15086, \"name\": \"a neatly trimmed bush\"}, {\"id\": 15087, \"name\": \"the brown vegetation\"}, {\"id\": 15088, \"name\": \"the smaller pole\"}, {\"id\": 15089, \"name\": \"colorful logo\"}, {\"id\": 15090, \"name\": \"a tray of vegetable\"}, {\"id\": 15091, \"name\": \"yellow ear cushion\"}, {\"id\": 15092, \"name\": \"the lush green baseball field\"}, {\"id\": 15093, \"name\": \"a black and white logo\"}, {\"id\": 15094, \"name\": \"rectangular frame\"}, {\"id\": 15095, \"name\": \"a large glass\"}, {\"id\": 15096, \"name\": \"a piece of artwork\"}, {\"id\": 15097, \"name\": \"a blue plastic basket\"}, {\"id\": 15098, \"name\": \"men's face\"}, {\"id\": 15099, \"name\": \"large black trash bag\"}, {\"id\": 15100, \"name\": \"the man's shirt\"}, {\"id\": 15101, \"name\": \"the security personnel\"}, {\"id\": 15102, \"name\": \"the bright green and yellow design\"}, {\"id\": 15103, \"name\": \"white stripe on the shoulder\"}, {\"id\": 15104, \"name\": \"rolled-up yoga mat\"}, {\"id\": 15105, \"name\": \"the large teacup ride\"}, {\"id\": 15106, \"name\": \"the upside-down wine glass\"}, {\"id\": 15107, \"name\": \"a racing attire\"}, {\"id\": 15108, \"name\": \"putter\"}, {\"id\": 15109, \"name\": \"the scenic mountainou backdrop\"}, {\"id\": 15110, \"name\": \"white furniture\"}, {\"id\": 15111, \"name\": \"brown trim\"}, {\"id\": 15112, \"name\": \"a gold pole\"}, {\"id\": 15113, \"name\": \"medal\"}, {\"id\": 15114, \"name\": \"a visible tape\"}, {\"id\": 15115, \"name\": \"the purple light\"}, {\"id\": 15116, \"name\": \"black nike short\"}, {\"id\": 15117, \"name\": \"red needle\"}, {\"id\": 15118, \"name\": \"red jumpsuit\"}, {\"id\": 15119, \"name\": \"a polo shirt\"}, {\"id\": 15120, \"name\": \"the dragon ball z\"}, {\"id\": 15121, \"name\": \"the dark shoe\"}, {\"id\": 15122, \"name\": \"the lanyard\"}, {\"id\": 15123, \"name\": \"a green monster energy logo\"}, {\"id\": 15124, \"name\": \"bright green light\"}, {\"id\": 15125, \"name\": \"a colorful seat\"}, {\"id\": 15126, \"name\": \"toaster oven\"}, {\"id\": 15127, \"name\": \"yellow curb\"}, {\"id\": 15128, \"name\": \"planter\"}, {\"id\": 15129, \"name\": \"a bold red and yellow design\"}, {\"id\": 15130, \"name\": \"a decal\"}, {\"id\": 15131, \"name\": \"a red rectangle\"}, {\"id\": 15132, \"name\": \"a granola\"}, {\"id\": 15133, \"name\": \"snowshoe\"}, {\"id\": 15134, \"name\": \"a kitchen cabinet\"}, {\"id\": 15135, \"name\": \"volleyball player\"}, {\"id\": 15136, \"name\": \"a yellow jacket\"}, {\"id\": 15137, \"name\": \"the corset-style top\"}, {\"id\": 15138, \"name\": \"a large, round, golden-brown pastry\"}, {\"id\": 15139, \"name\": \"two-story building\"}, {\"id\": 15140, \"name\": \"a cream-colored facade\"}, {\"id\": 15141, \"name\": \"state farm logo\"}, {\"id\": 15142, \"name\": \"a colorful poster or picture\"}, {\"id\": 15143, \"name\": \"the red and yellow trim\"}, {\"id\": 15144, \"name\": \"the carton of eggnog\"}, {\"id\": 15145, \"name\": \"a camouflage baseball cap\"}, {\"id\": 15146, \"name\": \"black and white uniform\"}, {\"id\": 15147, \"name\": \"red knee pad\"}, {\"id\": 15148, \"name\": \"the buzz cut hairstyle\"}, {\"id\": 15149, \"name\": \"a chrome trim\"}, {\"id\": 15150, \"name\": \"score\"}, {\"id\": 15151, \"name\": \"man's body\"}, {\"id\": 15152, \"name\": \"red and black diamond-shaped decoration\"}, {\"id\": 15153, \"name\": \"an exposed metal beam\"}, {\"id\": 15154, \"name\": \"a colorful bulletin board\"}, {\"id\": 15155, \"name\": \"the basketball court\"}, {\"id\": 15156, \"name\": \"a tram's door\"}, {\"id\": 15157, \"name\": \"green and yellow uniform\"}, {\"id\": 15158, \"name\": \"a thatched umbrella\"}, {\"id\": 15159, \"name\": \"a gray grout\"}, {\"id\": 15160, \"name\": \"the green logo with leaf\"}, {\"id\": 15161, \"name\": \"black-framed door\"}, {\"id\": 15162, \"name\": \"a window of the car\"}, {\"id\": 15163, \"name\": \"inset image\"}, {\"id\": 15164, \"name\": \"the red ribbon\"}, {\"id\": 15165, \"name\": \"sign on the booth\"}, {\"id\": 15166, \"name\": \"black knee-high sock\"}, {\"id\": 15167, \"name\": \"the gray hooded sweatshirt\"}, {\"id\": 15168, \"name\": \"the black camera\"}, {\"id\": 15169, \"name\": \"the pavilion's roof\"}, {\"id\": 15170, \"name\": \"black and red clothing\"}, {\"id\": 15171, \"name\": \"the chef\"}, {\"id\": 15172, \"name\": \"the thin white line\"}, {\"id\": 15173, \"name\": \"the black-painted fingernail\"}, {\"id\": 15174, \"name\": \"a person in the gym\"}, {\"id\": 15175, \"name\": \"white fluorescent light\"}, {\"id\": 15176, \"name\": \"the yellow reflective stripe\"}, {\"id\": 15177, \"name\": \"a green tie\"}, {\"id\": 15178, \"name\": \"a bare-chested person\"}, {\"id\": 15179, \"name\": \"the tall mast\"}, {\"id\": 15180, \"name\": \"an exposed ductwork\"}, {\"id\": 15181, \"name\": \"a white panel\"}, {\"id\": 15182, \"name\": \"a light brown and white english bulldog\"}, {\"id\": 15183, \"name\": \"the fingernail\"}, {\"id\": 15184, \"name\": \"the field\"}, {\"id\": 15185, \"name\": \"the picture of chicken nugget\"}, {\"id\": 15186, \"name\": \"the right rear tire\"}, {\"id\": 15187, \"name\": \"the black grill\"}, {\"id\": 15188, \"name\": \"the white shelf\"}, {\"id\": 15189, \"name\": \"a black cowboy hat\"}, {\"id\": 15190, \"name\": \"partially visible hand\"}, {\"id\": 15191, \"name\": \"sandal\"}, {\"id\": 15192, \"name\": \"tortilla\"}, {\"id\": 15193, \"name\": \"a harness\"}, {\"id\": 15194, \"name\": \"roman numeral\"}, {\"id\": 15195, \"name\": \"a cup of frozen yogurt\"}, {\"id\": 15196, \"name\": \"a social media icon\"}, {\"id\": 15197, \"name\": \"life jacket\"}, {\"id\": 15198, \"name\": \"monster's eye\"}, {\"id\": 15199, \"name\": \"an icon\"}, {\"id\": 15200, \"name\": \"a black and white baseball cap\"}, {\"id\": 15201, \"name\": \"the silver spoke\"}, {\"id\": 15202, \"name\": \"the leggings\"}, {\"id\": 15203, \"name\": \"kitchen appliance\"}, {\"id\": 15204, \"name\": \"jar of jam\"}, {\"id\": 15205, \"name\": \"soldier\"}, {\"id\": 15206, \"name\": \"silver metal leg\"}, {\"id\": 15207, \"name\": \"dark-colored wood\"}, {\"id\": 15208, \"name\": \"white boot\"}, {\"id\": 15209, \"name\": \"the black and white soccer ball\"}, {\"id\": 15210, \"name\": \"a nbc logo\"}, {\"id\": 15211, \"name\": \"the dessert\"}, {\"id\": 15212, \"name\": \"the foreground man\"}, {\"id\": 15213, \"name\": \"the white marking\"}, {\"id\": 15214, \"name\": \"meal\"}, {\"id\": 15215, \"name\": \"the darth vader patch\"}, {\"id\": 15216, \"name\": \"yellow caution tape\"}, {\"id\": 15217, \"name\": \"the white siding\"}, {\"id\": 15218, \"name\": \"bright, illuminated sign\"}, {\"id\": 15219, \"name\": \"the large outdoor basketball court\"}, {\"id\": 15220, \"name\": \"prominent metal structure\"}, {\"id\": 15221, \"name\": \"the right sleeve\"}, {\"id\": 15222, \"name\": \"the player's hair\"}, {\"id\": 15223, \"name\": \"a pink light\"}, {\"id\": 15224, \"name\": \"black high-heeled shoe\"}, {\"id\": 15225, \"name\": \"athletic clothing\"}, {\"id\": 15226, \"name\": \"brown baseball cap\"}, {\"id\": 15227, \"name\": \"medical equipment\"}, {\"id\": 15228, \"name\": \"a white aig logo\"}, {\"id\": 15229, \"name\": \"the light blue t-shirt\"}, {\"id\": 15230, \"name\": \"left hind leg\"}, {\"id\": 15231, \"name\": \"a passenger seating area\"}, {\"id\": 15232, \"name\": \"a grey uniform\"}, {\"id\": 15233, \"name\": \"a red rose\"}, {\"id\": 15234, \"name\": \"the denim capri pant\"}, {\"id\": 15235, \"name\": \"a large, snow-covered hill\"}, {\"id\": 15236, \"name\": \"an orange ball\"}, {\"id\": 15237, \"name\": \"the food product\"}, {\"id\": 15238, \"name\": \"the single spotlight\"}, {\"id\": 15239, \"name\": \"a green and white uniform\"}, {\"id\": 15240, \"name\": \"a green turf field\"}, {\"id\": 15241, \"name\": \"black knee pad\"}, {\"id\": 15242, \"name\": \"black plaid pant\"}, {\"id\": 15243, \"name\": \"gray pole\"}, {\"id\": 15244, \"name\": \"a swim gear\"}, {\"id\": 15245, \"name\": \"deck\"}, {\"id\": 15246, \"name\": \"a red barrier or padding\"}, {\"id\": 15247, \"name\": \"red bike\"}, {\"id\": 15248, \"name\": \"an oven rack\"}, {\"id\": 15249, \"name\": \"stadium's signage\"}, {\"id\": 15250, \"name\": \"an orange kettlebell\"}, {\"id\": 15251, \"name\": \"a brown leaf\"}, {\"id\": 15252, \"name\": \"a taxidermied animal\"}, {\"id\": 15253, \"name\": \"a foreground man\"}, {\"id\": 15254, \"name\": \"a large white banner\"}, {\"id\": 15255, \"name\": \"the crosswalk\"}, {\"id\": 15256, \"name\": \"the black shoe box\"}, {\"id\": 15257, \"name\": \"black tong\"}, {\"id\": 15258, \"name\": \"black garment\"}, {\"id\": 15259, \"name\": \"short\"}, {\"id\": 15260, \"name\": \"large tire\"}, {\"id\": 15261, \"name\": \"green and yellow attire\"}, {\"id\": 15262, \"name\": \"a brown hair\"}, {\"id\": 15263, \"name\": \"a light grey wall\"}, {\"id\": 15264, \"name\": \"a shirtless, muscular man\"}, {\"id\": 15265, \"name\": \"a yellow boot\"}, {\"id\": 15266, \"name\": \"volleyball court\"}, {\"id\": 15267, \"name\": \"the person in a white shirt\"}, {\"id\": 15268, \"name\": \"a white short\"}, {\"id\": 15269, \"name\": \"red sock\"}, {\"id\": 15270, \"name\": \"a yellow shelf\"}, {\"id\": 15271, \"name\": \"the basketball player\"}, {\"id\": 15272, \"name\": \"dark green wall\"}, {\"id\": 15273, \"name\": \"red strap\"}, {\"id\": 15274, \"name\": \"a rv\"}, {\"id\": 15275, \"name\": \"person's torso\"}, {\"id\": 15276, \"name\": \"bride's bouquet\"}, {\"id\": 15277, \"name\": \"a bald person\"}, {\"id\": 15278, \"name\": \"an individual wearing backpack\"}, {\"id\": 15279, \"name\": \"a dark wood step\"}, {\"id\": 15280, \"name\": \"yellow balloon\"}, {\"id\": 15281, \"name\": \"the dark sweater\"}, {\"id\": 15282, \"name\": \"red 1\"}, {\"id\": 15283, \"name\": \"a room's white wall\"}, {\"id\": 15284, \"name\": \"the motion capture suit\"}, {\"id\": 15285, \"name\": \"a pinwheel sticker\"}, {\"id\": 15286, \"name\": \"a white armrest\"}, {\"id\": 15287, \"name\": \"red heart\"}, {\"id\": 15288, \"name\": \"a black suit\"}, {\"id\": 15289, \"name\": \"an orange basketball\"}, {\"id\": 15290, \"name\": \"quarterback\"}, {\"id\": 15291, \"name\": \"the white ball\"}, {\"id\": 15292, \"name\": \"a red and blue striped jersey\"}, {\"id\": 15293, \"name\": \"baseball cap\"}, {\"id\": 15294, \"name\": \"the range of exercise equipment\"}, {\"id\": 15295, \"name\": \"the white and blue uniform\"}, {\"id\": 15296, \"name\": \"speaker on a stand\"}, {\"id\": 15297, \"name\": \"vertical metal bar\"}, {\"id\": 15298, \"name\": \"the black, white, or red helmet\"}, {\"id\": 15299, \"name\": \"gym's wall\"}, {\"id\": 15300, \"name\": \"a jar of jam or preserve\"}, {\"id\": 15301, \"name\": \"blue label\"}, {\"id\": 15302, \"name\": \"a small cloud\"}, {\"id\": 15303, \"name\": \"the white tent\"}, {\"id\": 15304, \"name\": \"a white knee-high sock\"}, {\"id\": 15305, \"name\": \"the black suv\"}, {\"id\": 15306, \"name\": \"black, long-sleeved, cycling-style suit\"}, {\"id\": 15307, \"name\": \"navy blue baseball cap\"}, {\"id\": 15308, \"name\": \"post\"}, {\"id\": 15309, \"name\": \"yellow m logo\"}, {\"id\": 15310, \"name\": \"an officer's\"}, {\"id\": 15311, \"name\": \"baseball mitt\"}, {\"id\": 15312, \"name\": \"cheerleader's body\"}, {\"id\": 15313, \"name\": \"the small, circular metal snap\"}, {\"id\": 15314, \"name\": \"the circular cutout\"}, {\"id\": 15315, \"name\": \"a white and red jersey\"}, {\"id\": 15316, \"name\": \"a white outfit\"}, {\"id\": 15317, \"name\": \"the watcher chocolate spread\"}, {\"id\": 15318, \"name\": \"a red and white advertisement banner\"}, {\"id\": 15319, \"name\": \"a blue legging\"}, {\"id\": 15320, \"name\": \"a gymnast's body\"}, {\"id\": 15321, \"name\": \"a black wheel\"}, {\"id\": 15322, \"name\": \"large green fish\"}, {\"id\": 15323, \"name\": \"white armchair\"}, {\"id\": 15324, \"name\": \"gaming accessory\"}, {\"id\": 15325, \"name\": \"rooftop\"}, {\"id\": 15326, \"name\": \"the large white soccer ball\"}, {\"id\": 15327, \"name\": \"a celtic player\"}, {\"id\": 15328, \"name\": \"the front window\"}, {\"id\": 15329, \"name\": \"stage light\"}, {\"id\": 15330, \"name\": \"a beet\"}, {\"id\": 15331, \"name\": \"the black balloon\"}, {\"id\": 15332, \"name\": \"salvation army logo\"}, {\"id\": 15333, \"name\": \"a for lease sign\"}, {\"id\": 15334, \"name\": \"tan-colored bag\"}, {\"id\": 15335, \"name\": \"man in a black and white jersey\"}, {\"id\": 15336, \"name\": \"small, cylindrical bag\"}, {\"id\": 15337, \"name\": \"large tent\"}, {\"id\": 15338, \"name\": \"a basketball uniform\"}, {\"id\": 15339, \"name\": \"silver metal\"}, {\"id\": 15340, \"name\": \"the small window\"}, {\"id\": 15341, \"name\": \"an upper arm\"}, {\"id\": 15342, \"name\": \"a blue banner\"}, {\"id\": 15343, \"name\": \"basketball machine\"}, {\"id\": 15344, \"name\": \"the athletic attire\"}, {\"id\": 15345, \"name\": \"a colorful decoration\"}, {\"id\": 15346, \"name\": \"white visor\"}, {\"id\": 15347, \"name\": \"the front wheel\"}, {\"id\": 15348, \"name\": \"the bandana\"}, {\"id\": 15349, \"name\": \"the dark-colored flooring\"}, {\"id\": 15350, \"name\": \"employee\"}, {\"id\": 15351, \"name\": \"a blue top\"}, {\"id\": 15352, \"name\": \"the round table\"}, {\"id\": 15353, \"name\": \"the navy blue sleeve\"}, {\"id\": 15354, \"name\": \"revealing outfit\"}, {\"id\": 15355, \"name\": \"the tray of corn dog\"}, {\"id\": 15356, \"name\": \"one-shoulder maroon dress\"}, {\"id\": 15357, \"name\": \"grey wall\"}, {\"id\": 15358, \"name\": \"the small bottle\"}, {\"id\": 15359, \"name\": \"a player's helmet\"}, {\"id\": 15360, \"name\": \"the white fence\"}, {\"id\": 15361, \"name\": \"cup of coffee\"}, {\"id\": 15362, \"name\": \"vibrant peacock design\"}, {\"id\": 15363, \"name\": \"the white triangular flag\"}, {\"id\": 15364, \"name\": \"catcher's crouched position\"}, {\"id\": 15365, \"name\": \"a long sleeve\"}, {\"id\": 15366, \"name\": \"a wall of window\"}, {\"id\": 15367, \"name\": \"woman in white\"}, {\"id\": 15368, \"name\": \"a referee\"}, {\"id\": 15369, \"name\": \"blue and white checkered band\"}, {\"id\": 15370, \"name\": \"a white bumper\"}, {\"id\": 15371, \"name\": \"a pedal-powered vehicle\"}, {\"id\": 15372, \"name\": \"the fluorescent lighting\"}, {\"id\": 15373, \"name\": \"an athletic clothing\"}, {\"id\": 15374, \"name\": \"the green turf\"}, {\"id\": 15375, \"name\": \"trumpet\"}, {\"id\": 15376, \"name\": \"wooden jewelry display\"}, {\"id\": 15377, \"name\": \"the yellow ball\"}, {\"id\": 15378, \"name\": \"a stationary bicycle\"}, {\"id\": 15379, \"name\": \"the left forearm\"}, {\"id\": 15380, \"name\": \"the number\"}, {\"id\": 15381, \"name\": \"the display of mannequin\"}, {\"id\": 15382, \"name\": \"the blue and white patch\"}, {\"id\": 15383, \"name\": \"a colorful chair\"}, {\"id\": 15384, \"name\": \"a teal squiggly line\"}, {\"id\": 15385, \"name\": \"a gray short\"}, {\"id\": 15386, \"name\": \"light-colored wooden floor\"}, {\"id\": 15387, \"name\": \"a dark blue sleeve\"}, {\"id\": 15388, \"name\": \"the red and white wall\"}, {\"id\": 15389, \"name\": \"the low barrier\"}, {\"id\": 15390, \"name\": \"paver\"}, {\"id\": 15391, \"name\": \"the small container\"}, {\"id\": 15392, \"name\": \"long, light-blonde hair\"}, {\"id\": 15393, \"name\": \"the marching band member\"}, {\"id\": 15394, \"name\": \"a football\"}, {\"id\": 15395, \"name\": \"yellow tag\"}, {\"id\": 15396, \"name\": \"a blue hold\"}, {\"id\": 15397, \"name\": \"a military personnel member\"}, {\"id\": 15398, \"name\": \"silver frame\"}, {\"id\": 15399, \"name\": \"the blurred wheel\"}, {\"id\": 15400, \"name\": \"the card\"}, {\"id\": 15401, \"name\": \"a black heel\"}, {\"id\": 15402, \"name\": \"the gla roof\"}, {\"id\": 15403, \"name\": \"a falcons' logo\"}, {\"id\": 15404, \"name\": \"black surface\"}, {\"id\": 15405, \"name\": \"the white blind\"}, {\"id\": 15406, \"name\": \"small archway\"}, {\"id\": 15407, \"name\": \"a stormtrooper\"}, {\"id\": 15408, \"name\": \"women's head\"}, {\"id\": 15409, \"name\": \"the referee's attire\"}, {\"id\": 15410, \"name\": \"the white bar\"}, {\"id\": 15411, \"name\": \"a large stone building\"}, {\"id\": 15412, \"name\": \"a large boat\"}, {\"id\": 15413, \"name\": \"a rainbow-colored sock\"}, {\"id\": 15414, \"name\": \"the american football\"}, {\"id\": 15415, \"name\": \"apartment\"}, {\"id\": 15416, \"name\": \"photo of a player\"}, {\"id\": 15417, \"name\": \"red block\"}, {\"id\": 15418, \"name\": \"a large raft\"}, {\"id\": 15419, \"name\": \"decorative figurine\"}, {\"id\": 15420, \"name\": \"a yellow badge\"}, {\"id\": 15421, \"name\": \"the rugby ball\"}, {\"id\": 15422, \"name\": \"surveillance camera\"}, {\"id\": 15423, \"name\": \"the small, white square\"}, {\"id\": 15424, \"name\": \"a shorter white wig\"}, {\"id\": 15425, \"name\": \"a gray sneaker\"}, {\"id\": 15426, \"name\": \"bright yellow light\"}, {\"id\": 15427, \"name\": \"the person's hands\"}, {\"id\": 15428, \"name\": \"the blue and white sock\"}, {\"id\": 15429, \"name\": \"painted nail\"}, {\"id\": 15430, \"name\": \"the white long-sleeved jersey\"}, {\"id\": 15431, \"name\": \"the kneeling firefighter\"}, {\"id\": 15432, \"name\": \"a dark dress\"}, {\"id\": 15433, \"name\": \"red jersey\"}, {\"id\": 15434, \"name\": \"black and white striped knee-high sock\"}, {\"id\": 15435, \"name\": \"a white poster\"}, {\"id\": 15436, \"name\": \"a yellow and purple uniform\"}, {\"id\": 15437, \"name\": \"a person in a wetsuit\"}, {\"id\": 15438, \"name\": \"dark clothing\"}, {\"id\": 15439, \"name\": \"the vertical bar\"}, {\"id\": 15440, \"name\": \"rowing team\"}, {\"id\": 15441, \"name\": \"a white belt\"}, {\"id\": 15442, \"name\": \"a large silver dish\"}, {\"id\": 15443, \"name\": \"the soccer net\"}, {\"id\": 15444, \"name\": \"a trombone player\"}, {\"id\": 15445, \"name\": \"the front grille\"}, {\"id\": 15446, \"name\": \"photographer's shadow\"}, {\"id\": 15447, \"name\": \"white snowflake decoration\"}, {\"id\": 15448, \"name\": \"gold object\"}, {\"id\": 15449, \"name\": \"the safety barrier\"}, {\"id\": 15450, \"name\": \"a black and white uniform\"}, {\"id\": 15451, \"name\": \"a blue attire\"}, {\"id\": 15452, \"name\": \"large, green umbrella\"}, {\"id\": 15453, \"name\": \"the black sunglass\"}, {\"id\": 15454, \"name\": \"the black snowboard\"}, {\"id\": 15455, \"name\": \"the saddle\"}, {\"id\": 15456, \"name\": \"circular logo\"}, {\"id\": 15457, \"name\": \"a long metal arm\"}, {\"id\": 15458, \"name\": \"an orange shirt\"}, {\"id\": 15459, \"name\": \"a dumbbell\"}, {\"id\": 15460, \"name\": \"lemon slice\"}, {\"id\": 15461, \"name\": \"the prominent mannequin\"}, {\"id\": 15462, \"name\": \"the brochure\"}, {\"id\": 15463, \"name\": \"the white and purple jersey\"}, {\"id\": 15464, \"name\": \"black and red life jacket\"}, {\"id\": 15465, \"name\": \"water splash\"}, {\"id\": 15466, \"name\": \"straw\"}, {\"id\": 15467, \"name\": \"crop top\"}, {\"id\": 15468, \"name\": \"a blue seat\"}, {\"id\": 15469, \"name\": \"stadium seating area\"}, {\"id\": 15470, \"name\": \"a black sleeve\"}, {\"id\": 15471, \"name\": \"the rack of children's clothing\"}, {\"id\": 15472, \"name\": \"a blue baseball cap\"}, {\"id\": 15473, \"name\": \"the partially peeled and discarded cabbage leaf\"}, {\"id\": 15474, \"name\": \"a beautiful painted design\"}, {\"id\": 15475, \"name\": \"a prominent display of toy and merchandise\"}, {\"id\": 15476, \"name\": \"corrugated metal sheet\"}, {\"id\": 15477, \"name\": \"sandal or flip-flop\"}, {\"id\": 15478, \"name\": \"framed painting\"}, {\"id\": 15479, \"name\": \"the yellow hard hat\"}, {\"id\": 15480, \"name\": \"the light-brown leather strap\"}, {\"id\": 15481, \"name\": \"the trash bin\"}, {\"id\": 15482, \"name\": \"the red uniform\"}, {\"id\": 15483, \"name\": \"the white gown\"}, {\"id\": 15484, \"name\": \"a white and red striped sock\"}, {\"id\": 15485, \"name\": \"a blurred football field\"}, {\"id\": 15486, \"name\": \"lush green grassy surface\"}, {\"id\": 15487, \"name\": \"a catcher's black and white cleat\"}, {\"id\": 15488, \"name\": \"a messy ponytail\"}, {\"id\": 15489, \"name\": \"light-colored wall\"}, {\"id\": 15490, \"name\": \"the vessel\"}, {\"id\": 15491, \"name\": \"the winter attire\"}, {\"id\": 15492, \"name\": \"player\"}, {\"id\": 15493, \"name\": \"large stone pillar\"}, {\"id\": 15494, \"name\": \"the green patch\"}, {\"id\": 15495, \"name\": \"a yellow handrail\"}, {\"id\": 15496, \"name\": \"long, brown hair\"}, {\"id\": 15497, \"name\": \"a rounded shape\"}, {\"id\": 15498, \"name\": \"elegant attire\"}, {\"id\": 15499, \"name\": \"the tomato\"}, {\"id\": 15500, \"name\": \"ski pole\"}, {\"id\": 15501, \"name\": \"the white cleat\"}, {\"id\": 15502, \"name\": \"weathered shingle\"}, {\"id\": 15503, \"name\": \"window of the train\"}, {\"id\": 15504, \"name\": \"rack of clothing\"}, {\"id\": 15505, \"name\": \"cleat\"}, {\"id\": 15506, \"name\": \"orange barrier\"}, {\"id\": 15507, \"name\": \"the shoe box\"}, {\"id\": 15508, \"name\": \"sleek, high-tech bike\"}, {\"id\": 15509, \"name\": \"carrot bit\"}, {\"id\": 15510, \"name\": \"large cabbage head\"}, {\"id\": 15511, \"name\": \"light-colored building\"}, {\"id\": 15512, \"name\": \"cowboy hat\"}, {\"id\": 15513, \"name\": \"colorful paraglider\"}, {\"id\": 15514, \"name\": \"a green menu bar\"}, {\"id\": 15515, \"name\": \"a green and yellow uniform\"}, {\"id\": 15516, \"name\": \"person's fingernail\"}, {\"id\": 15517, \"name\": \"a brown dirt path\"}, {\"id\": 15518, \"name\": \"yellow cone\"}, {\"id\": 15519, \"name\": \"a red and white sock\"}, {\"id\": 15520, \"name\": \"the black and red outfit\"}, {\"id\": 15521, \"name\": \"wood paneling\"}, {\"id\": 15522, \"name\": \"the silver pole\"}, {\"id\": 15523, \"name\": \"a blue and white uniform\"}, {\"id\": 15524, \"name\": \"the large windshield\"}, {\"id\": 15525, \"name\": \"an outfit\"}, {\"id\": 15526, \"name\": \"buzz cut\"}, {\"id\": 15527, \"name\": \"person in the image\"}, {\"id\": 15528, \"name\": \"the black plastic bag\"}, {\"id\": 15529, \"name\": \"red brick wall\"}, {\"id\": 15530, \"name\": \"a blue padding\"}, {\"id\": 15531, \"name\": \"a gray cap\"}, {\"id\": 15532, \"name\": \"dark blue coat\"}, {\"id\": 15533, \"name\": \"classic burger\"}, {\"id\": 15534, \"name\": \"a large yellow tire\"}, {\"id\": 15535, \"name\": \"the large stone wall\"}, {\"id\": 15536, \"name\": \"the white horse\"}, {\"id\": 15537, \"name\": \"smaller building\"}, {\"id\": 15538, \"name\": \"a short fingernail\"}, {\"id\": 15539, \"name\": \"the watercraft\"}, {\"id\": 15540, \"name\": \"football\"}, {\"id\": 15541, \"name\": \"rim of the mug\"}, {\"id\": 15542, \"name\": \"the sport jersey\"}, {\"id\": 15543, \"name\": \"cardinals' logo\"}, {\"id\": 15544, \"name\": \"the jockey\"}, {\"id\": 15545, \"name\": \"the white uniform\"}, {\"id\": 15546, \"name\": \"a black light fixture\"}, {\"id\": 15547, \"name\": \"stylish clothing\"}, {\"id\": 15548, \"name\": \"spear\"}, {\"id\": 15549, \"name\": \"small window\"}, {\"id\": 15550, \"name\": \"a long pant\"}, {\"id\": 15551, \"name\": \"a black scoreboard\"}, {\"id\": 15552, \"name\": \"a dog's coat\"}, {\"id\": 15553, \"name\": \"reflection of person\"}, {\"id\": 15554, \"name\": \"gadget\"}, {\"id\": 15555, \"name\": \"mannequin head\"}, {\"id\": 15556, \"name\": \"a black jersey\"}, {\"id\": 15557, \"name\": \"the small, dark-colored boat\"}, {\"id\": 15558, \"name\": \"black and silver motorcycle\"}, {\"id\": 15559, \"name\": \"the colorful graffiti\"}, {\"id\": 15560, \"name\": \"a sport car\"}, {\"id\": 15561, \"name\": \"a white marking\"}, {\"id\": 15562, \"name\": \"a white face\"}, {\"id\": 15563, \"name\": \"the framed photo\"}, {\"id\": 15564, \"name\": \"the green label\"}, {\"id\": 15565, \"name\": \"brown barrier\"}, {\"id\": 15566, \"name\": \"the blue baseball cap\"}, {\"id\": 15567, \"name\": \"the maroon stripe\"}, {\"id\": 15568, \"name\": \"gray cabinet\"}, {\"id\": 15569, \"name\": \"the ox\"}, {\"id\": 15570, \"name\": \"basketball player\"}, {\"id\": 15571, \"name\": \"sport team uniform\"}, {\"id\": 15572, \"name\": \"the wall decoration\"}, {\"id\": 15573, \"name\": \"cartoon eye\"}, {\"id\": 15574, \"name\": \"the baseball player\"}, {\"id\": 15575, \"name\": \"the patch of green grass\"}, {\"id\": 15576, \"name\": \"the bar stool\"}, {\"id\": 15577, \"name\": \"the beige frame\"}, {\"id\": 15578, \"name\": \"the step\"}, {\"id\": 15579, \"name\": \"the purple jersey\"}, {\"id\": 15580, \"name\": \"black and silver uniform\"}, {\"id\": 15581, \"name\": \"yellow peel\"}, {\"id\": 15582, \"name\": \"the paintball marker\"}, {\"id\": 15583, \"name\": \"penn state football player\"}, {\"id\": 15584, \"name\": \"a cracked windshield\"}, {\"id\": 15585, \"name\": \"meat or fish\"}, {\"id\": 15586, \"name\": \"lush landscape\"}, {\"id\": 15587, \"name\": \"a football gear\"}, {\"id\": 15588, \"name\": \"the image of a person\"}, {\"id\": 15589, \"name\": \"the water droplet\"}, {\"id\": 15590, \"name\": \"white and gold uniform\"}, {\"id\": 15591, \"name\": \"a tag\"}, {\"id\": 15592, \"name\": \"the garbage\"}, {\"id\": 15593, \"name\": \"scooter's handlebar\"}, {\"id\": 15594, \"name\": \"the blue area\"}, {\"id\": 15595, \"name\": \"a scene of horse racing on track\"}, {\"id\": 15596, \"name\": \"kitchen counter\"}, {\"id\": 15597, \"name\": \"the chocolate ice cream\"}, {\"id\": 15598, \"name\": \"unique cage-like structure\"}, {\"id\": 15599, \"name\": \"window or gla wall\"}, {\"id\": 15600, \"name\": \"the long, curved light fixture\"}, {\"id\": 15601, \"name\": \"the black grip\"}, {\"id\": 15602, \"name\": \"slender, light-colored tree trunk\"}, {\"id\": 15603, \"name\": \"grey dog carrier\"}, {\"id\": 15604, \"name\": \"man's leg\"}, {\"id\": 15605, \"name\": \"a floral design\"}, {\"id\": 15606, \"name\": \"a police officer\"}, {\"id\": 15607, \"name\": \"person in a black dress\"}, {\"id\": 15608, \"name\": \"grey pickup truck\"}, {\"id\": 15609, \"name\": \"stroller\"}, {\"id\": 15610, \"name\": \"the skate\"}, {\"id\": 15611, \"name\": \"a military uniform\"}, {\"id\": 15612, \"name\": \"a person with a pink umbrella\"}, {\"id\": 15613, \"name\": \"the red, purple, and green ball\"}, {\"id\": 15614, \"name\": \"the metal shelving unit\"}, {\"id\": 15615, \"name\": \"black background\"}, {\"id\": 15616, \"name\": \"the score\"}, {\"id\": 15617, \"name\": \"a fur hat\"}, {\"id\": 15618, \"name\": \"a large eye\"}, {\"id\": 15619, \"name\": \"yellow volleyball\"}, {\"id\": 15620, \"name\": \"a mirrored wall\"}, {\"id\": 15621, \"name\": \"the gold button\"}, {\"id\": 15622, \"name\": \"short, light-colored hair\"}, {\"id\": 15623, \"name\": \"a solid white line\"}, {\"id\": 15624, \"name\": \"exposed wooden beam\"}, {\"id\": 15625, \"name\": \"blue and white uniform\"}, {\"id\": 15626, \"name\": \"a subtle reflection of light\"}, {\"id\": 15627, \"name\": \"the blackberry\"}, {\"id\": 15628, \"name\": \"a metal clasp\"}, {\"id\": 15629, \"name\": \"the single oar\"}, {\"id\": 15630, \"name\": \"the white sticker\"}, {\"id\": 15631, \"name\": \"a predominantly black floor\"}, {\"id\": 15632, \"name\": \"a reflection of the man\"}, {\"id\": 15633, \"name\": \"traditional thai clothing\"}, {\"id\": 15634, \"name\": \"a man in a blue hoodie\"}, {\"id\": 15635, \"name\": \"a colored head\"}, {\"id\": 15636, \"name\": \"the horse's leg\"}, {\"id\": 15637, \"name\": \"a clog in kids..\"}, {\"id\": 15638, \"name\": \"small tool\"}, {\"id\": 15639, \"name\": \"bicycle helmet\"}, {\"id\": 15640, \"name\": \"a reflective surface\"}, {\"id\": 15641, \"name\": \"the white racing stripe\"}, {\"id\": 15642, \"name\": \"brown brick building\"}, {\"id\": 15643, \"name\": \"white collared shirt\"}, {\"id\": 15644, \"name\": \"the wooden framing\"}, {\"id\": 15645, \"name\": \"a black leotard\"}, {\"id\": 15646, \"name\": \"yellow facade\"}, {\"id\": 15647, \"name\": \"a garment\"}, {\"id\": 15648, \"name\": \"a soldier\"}, {\"id\": 15649, \"name\": \"a spoke\"}, {\"id\": 15650, \"name\": \"young man\"}, {\"id\": 15651, \"name\": \"the green-and-black pant\"}, {\"id\": 15652, \"name\": \"a numbered circle\"}, {\"id\": 15653, \"name\": \"a roll of colored tape\"}, {\"id\": 15654, \"name\": \"minnie mouse ear design\"}, {\"id\": 15655, \"name\": \"the snow\"}, {\"id\": 15656, \"name\": \"dashboard of a vehicle\"}, {\"id\": 15657, \"name\": \"large stuffed animal\"}, {\"id\": 15658, \"name\": \"the blue tape strip\"}, {\"id\": 15659, \"name\": \"a person in blue shirt\"}, {\"id\": 15660, \"name\": \"the small stuffed animal\"}, {\"id\": 15661, \"name\": \"large ornate building\"}, {\"id\": 15662, \"name\": \"white ladder\"}, {\"id\": 15663, \"name\": \"the green robe\"}, {\"id\": 15664, \"name\": \"black bowling ball\"}, {\"id\": 15665, \"name\": \"a tan-colored wall\"}, {\"id\": 15666, \"name\": \"green baseball cap\"}, {\"id\": 15667, \"name\": \"the firefighter\"}, {\"id\": 15668, \"name\": \"white outline\"}, {\"id\": 15669, \"name\": \"wall-mounted device\"}, {\"id\": 15670, \"name\": \"stadium's roof\"}, {\"id\": 15671, \"name\": \"the white tooth\"}, {\"id\": 15672, \"name\": \"a large mickey mouse balloon\"}, {\"id\": 15673, \"name\": \"the green hold\"}, {\"id\": 15674, \"name\": \"news channel's logo\"}, {\"id\": 15675, \"name\": \"a goggle\"}, {\"id\": 15676, \"name\": \"lower half of a person\"}, {\"id\": 15677, \"name\": \"the nesn.com logo\"}, {\"id\": 15678, \"name\": \"the red ledge\"}, {\"id\": 15679, \"name\": \"black padding\"}, {\"id\": 15680, \"name\": \"cream-colored top\"}, {\"id\": 15681, \"name\": \"the blue baseball cap without a visor\"}, {\"id\": 15682, \"name\": \"the light gray pant\"}, {\"id\": 15683, \"name\": \"hill\"}, {\"id\": 15684, \"name\": \"hairstyle\"}, {\"id\": 15685, \"name\": \"a confederate flag\"}, {\"id\": 15686, \"name\": \"the bright green paint\"}, {\"id\": 15687, \"name\": \"an orange stripe\"}, {\"id\": 15688, \"name\": \"vehicle's wheel\"}, {\"id\": 15689, \"name\": \"the long, black, oblong plate\"}, {\"id\": 15690, \"name\": \"purple and black geometric shape\"}, {\"id\": 15691, \"name\": \"the computer\"}, {\"id\": 15692, \"name\": \"rear tire\"}, {\"id\": 15693, \"name\": \"a player with the 3 jersey\"}, {\"id\": 15694, \"name\": \"the yellow high-visibility vest\"}, {\"id\": 15695, \"name\": \"red latch\"}, {\"id\": 15696, \"name\": \"a small, tan-colored dog\"}, {\"id\": 15697, \"name\": \"vibrant red sauce\"}, {\"id\": 15698, \"name\": \"blue chair\"}, {\"id\": 15699, \"name\": \"the metal can\"}, {\"id\": 15700, \"name\": \"the gold stripe\"}, {\"id\": 15701, \"name\": \"grey car\"}, {\"id\": 15702, \"name\": \"tan uniform\"}, {\"id\": 15703, \"name\": \"the brown baseball glove\"}, {\"id\": 15704, \"name\": \"the bare leg\"}, {\"id\": 15705, \"name\": \"the rollerblading\"}, {\"id\": 15706, \"name\": \"the foil-wrapped object\"}, {\"id\": 15707, \"name\": \"the diamond pattern\"}, {\"id\": 15708, \"name\": \"maroon and white jersey\"}, {\"id\": 15709, \"name\": \"a blue barrier\"}, {\"id\": 15710, \"name\": \"the qanta logo\"}, {\"id\": 15711, \"name\": \"the blue post\"}, {\"id\": 15712, \"name\": \"navy blue short\"}, {\"id\": 15713, \"name\": \"person in a blue uniform\"}, {\"id\": 15714, \"name\": \"green grassy area\"}, {\"id\": 15715, \"name\": \"skater's attire\"}, {\"id\": 15716, \"name\": \"a baseball diamond\"}, {\"id\": 15717, \"name\": \"a vibrant green football field\"}, {\"id\": 15718, \"name\": \"member\"}, {\"id\": 15719, \"name\": \"a small amount of dessert\"}, {\"id\": 15720, \"name\": \"knee-high sock\"}, {\"id\": 15721, \"name\": \"mcdonald's logo\"}, {\"id\": 15722, \"name\": \"a blue bar\"}, {\"id\": 15723, \"name\": \"the small pouch\"}, {\"id\": 15724, \"name\": \"the white square\"}, {\"id\": 15725, \"name\": \"older man\"}, {\"id\": 15726, \"name\": \"the ride operator\"}, {\"id\": 15727, \"name\": \"metal\"}, {\"id\": 15728, \"name\": \"dark green curtain\"}, {\"id\": 15729, \"name\": \"a coffee mug\"}, {\"id\": 15730, \"name\": \"a diagonal white stripe\"}, {\"id\": 15731, \"name\": \"sheer curtain\"}, {\"id\": 15732, \"name\": \"a bottle's label\"}, {\"id\": 15733, \"name\": \"the man with the balloon\"}, {\"id\": 15734, \"name\": \"the sticker sheet\"}, {\"id\": 15735, \"name\": \"the white folding chair\"}, {\"id\": 15736, \"name\": \"a person's feet\"}, {\"id\": 15737, \"name\": \"a paintball field\"}, {\"id\": 15738, \"name\": \"the altar step\"}, {\"id\": 15739, \"name\": \"lamp post\"}, {\"id\": 15740, \"name\": \"patch\"}, {\"id\": 15741, \"name\": \"a yellow strap\"}, {\"id\": 15742, \"name\": \"the white metal fence\"}, {\"id\": 15743, \"name\": \"a mascot\"}, {\"id\": 15744, \"name\": \"the blue and red vest\"}, {\"id\": 15745, \"name\": \"yellow tinsel\"}, {\"id\": 15746, \"name\": \"a red panel\"}, {\"id\": 15747, \"name\": \"a light brown wood\"}, {\"id\": 15748, \"name\": \"blue collar\"}, {\"id\": 15749, \"name\": \"the silver metal armrest\"}, {\"id\": 15750, \"name\": \"silver drawer pull\"}, {\"id\": 15751, \"name\": \"beige cushion\"}, {\"id\": 15752, \"name\": \"a red border\"}, {\"id\": 15753, \"name\": \"a halloween decoration\"}, {\"id\": 15754, \"name\": \"the beige house\"}, {\"id\": 15755, \"name\": \"a green uniform\"}, {\"id\": 15756, \"name\": \"vibrant red jersey\"}, {\"id\": 15757, \"name\": \"small piece of tape\"}, {\"id\": 15758, \"name\": \"a 67 jersey\"}, {\"id\": 15759, \"name\": \"blue and green banner\"}, {\"id\": 15760, \"name\": \"the circuit board\"}, {\"id\": 15761, \"name\": \"the white card\"}, {\"id\": 15762, \"name\": \"a long stick\"}, {\"id\": 15763, \"name\": \"wooden frame\"}, {\"id\": 15764, \"name\": \"a white cowboy hat\"}, {\"id\": 15765, \"name\": \"large metal tray\"}, {\"id\": 15766, \"name\": \"fermented beverage\"}, {\"id\": 15767, \"name\": \"the nfl logo\"}, {\"id\": 15768, \"name\": \"the small white sign\"}, {\"id\": 15769, \"name\": \"the cockpit's control panel\"}, {\"id\": 15770, \"name\": \"the reflective strip\"}, {\"id\": 15771, \"name\": \"the subtle pinstripe\"}, {\"id\": 15772, \"name\": \"the white baseball\"}, {\"id\": 15773, \"name\": \"a poster or sign\"}, {\"id\": 15774, \"name\": \"the team's logo\"}, {\"id\": 15775, \"name\": \"frozen yogurt\"}, {\"id\": 15776, \"name\": \"the red and black shirt\"}, {\"id\": 15777, \"name\": \"the bare tree\"}, {\"id\": 15778, \"name\": \"a white painted drywall\"}, {\"id\": 15779, \"name\": \"a taylormade logo\"}, {\"id\": 15780, \"name\": \"a basketball logo\"}, {\"id\": 15781, \"name\": \"pink storage bin\"}, {\"id\": 15782, \"name\": \"a black hole\"}, {\"id\": 15783, \"name\": \"the matching leather chair\"}, {\"id\": 15784, \"name\": \"a cat or dog\"}, {\"id\": 15785, \"name\": \"the green section of the track\"}, {\"id\": 15786, \"name\": \"an orange shoe\"}, {\"id\": 15787, \"name\": \"the third person\"}, {\"id\": 15788, \"name\": \"black baseboard\"}, {\"id\": 15789, \"name\": \"white cleat\"}, {\"id\": 15790, \"name\": \"the yellow banner\"}, {\"id\": 15791, \"name\": \"the small nbc logo\"}, {\"id\": 15792, \"name\": \"a blue logo\"}, {\"id\": 15793, \"name\": \"grey baseball cap\"}, {\"id\": 15794, \"name\": \"the red metal component\"}, {\"id\": 15795, \"name\": \"a tan brick wall\"}, {\"id\": 15796, \"name\": \"the basketball\"}, {\"id\": 15797, \"name\": \"red shoe\"}, {\"id\": 15798, \"name\": \"black or gray jersey\"}, {\"id\": 15799, \"name\": \"a sparse foliage\"}, {\"id\": 15800, \"name\": \"the bowling ball return system\"}, {\"id\": 15801, \"name\": \"the person's legs\"}, {\"id\": 15802, \"name\": \"black trousers\"}, {\"id\": 15803, \"name\": \"safety gear\"}, {\"id\": 15804, \"name\": \"a clear plastic barrier\"}, {\"id\": 15805, \"name\": \"a small, orange building\"}, {\"id\": 15806, \"name\": \"dreadlock\"}, {\"id\": 15807, \"name\": \"the robe\"}, {\"id\": 15808, \"name\": \"the branch\"}, {\"id\": 15809, \"name\": \"the man in a hat\"}, {\"id\": 15810, \"name\": \"black clothing\"}, {\"id\": 15811, \"name\": \"a gold braided epaulet\"}, {\"id\": 15812, \"name\": \"white cylinder\"}, {\"id\": 15813, \"name\": \"small white rectangle\"}, {\"id\": 15814, \"name\": \"chair leg\"}, {\"id\": 15815, \"name\": \"the yellow frame\"}, {\"id\": 15816, \"name\": \"a dense forest\"}, {\"id\": 15817, \"name\": \"a small doll\"}, {\"id\": 15818, \"name\": \"stringed instrument\"}, {\"id\": 15819, \"name\": \"a kart's body\"}, {\"id\": 15820, \"name\": \"car's occupants\"}, {\"id\": 15821, \"name\": \"a neck of one of the bottle\"}, {\"id\": 15822, \"name\": \"a dried fruit\"}, {\"id\": 15823, \"name\": \"the green herb\"}, {\"id\": 15824, \"name\": \"a green foliage\"}, {\"id\": 15825, \"name\": \"the construction material\"}, {\"id\": 15826, \"name\": \"the transparent plastic\"}, {\"id\": 15827, \"name\": \"the grey tarp\"}, {\"id\": 15828, \"name\": \"black stand\"}, {\"id\": 15829, \"name\": \"the sport team logo\"}, {\"id\": 15830, \"name\": \"building material\"}, {\"id\": 15831, \"name\": \"bale\"}, {\"id\": 15832, \"name\": \"a black metal object\"}, {\"id\": 15833, \"name\": \"the yellow square\"}, {\"id\": 15834, \"name\": \"stationary tank\"}, {\"id\": 15835, \"name\": \"the yellow section\"}, {\"id\": 15836, \"name\": \"a state farm insurance logo\"}, {\"id\": 15837, \"name\": \"a single fluorescent light fixture\"}, {\"id\": 15838, \"name\": \"stormtrooper\"}, {\"id\": 15839, \"name\": \"a blue tarp or sheet\"}, {\"id\": 15840, \"name\": \"a brown shoe\"}, {\"id\": 15841, \"name\": \"bamboo grove\"}, {\"id\": 15842, \"name\": \"brown and white baseball cap\"}, {\"id\": 15843, \"name\": \"bleacher\"}, {\"id\": 15844, \"name\": \"a small white tooth\"}, {\"id\": 15845, \"name\": \"cobblestone street\"}, {\"id\": 15846, \"name\": \"a prominent chimney\"}, {\"id\": 15847, \"name\": \"the vibrant colored background\"}, {\"id\": 15848, \"name\": \"a real estate sign\"}, {\"id\": 15849, \"name\": \"the wonder woman costume\"}, {\"id\": 15850, \"name\": \"the purple neon light\"}, {\"id\": 15851, \"name\": \"blue surfboard\"}, {\"id\": 15852, \"name\": \"neutral backdrop\"}, {\"id\": 15853, \"name\": \"the red and yellow flower\"}, {\"id\": 15854, \"name\": \"matching crown\"}, {\"id\": 15855, \"name\": \"green cleat\"}, {\"id\": 15856, \"name\": \"a red and blue design\"}, {\"id\": 15857, \"name\": \"the small hole\"}, {\"id\": 15858, \"name\": \"a red punching bag\"}, {\"id\": 15859, \"name\": \"white and red uniform\"}, {\"id\": 15860, \"name\": \"yellow spongebob character\"}, {\"id\": 15861, \"name\": \"the flat tire\"}, {\"id\": 15862, \"name\": \"man in a gray suit\"}, {\"id\": 15863, \"name\": \"the large screen or scoreboard\"}, {\"id\": 15864, \"name\": \"backward baseball cap\"}, {\"id\": 15865, \"name\": \"the blue paint\"}, {\"id\": 15866, \"name\": \"the car's light\"}, {\"id\": 15867, \"name\": \"goalie\"}, {\"id\": 15868, \"name\": \"a basketball-themed bed\"}, {\"id\": 15869, \"name\": \"a brown frame\"}, {\"id\": 15870, \"name\": \"the white sign or board\"}, {\"id\": 15871, \"name\": \"the red ball\"}, {\"id\": 15872, \"name\": \"a person in a white shirt and khaki pant\"}, {\"id\": 15873, \"name\": \"french fry\"}, {\"id\": 15874, \"name\": \"blue slide\"}, {\"id\": 15875, \"name\": \"the doll leg\"}, {\"id\": 15876, \"name\": \"the green corrugated roof\"}, {\"id\": 15877, \"name\": \"an athletic equipment\"}, {\"id\": 15878, \"name\": \"the elegant light pink strapless dress\"}, {\"id\": 15879, \"name\": \"the protective gear\"}, {\"id\": 15880, \"name\": \"a person in the dark blue shirt\"}, {\"id\": 15881, \"name\": \"a shelf of sunglass\"}, {\"id\": 15882, \"name\": \"black beret\"}, {\"id\": 15883, \"name\": \"green label\"}, {\"id\": 15884, \"name\": \"a onlooker\"}, {\"id\": 15885, \"name\": \"floral top\"}, {\"id\": 15886, \"name\": \"a colorful balloon\"}, {\"id\": 15887, \"name\": \"black bar mat\"}, {\"id\": 15888, \"name\": \"the inflatable kite\"}, {\"id\": 15889, \"name\": \"a dollop of frosting\"}, {\"id\": 15890, \"name\": \"small white bowl\"}, {\"id\": 15891, \"name\": \"busine attire\"}, {\"id\": 15892, \"name\": \"the blue seat\"}, {\"id\": 15893, \"name\": \"black and white skate\"}, {\"id\": 15894, \"name\": \"the white basketball jersey\"}, {\"id\": 15895, \"name\": \"person wearing a pink shirt\"}, {\"id\": 15896, \"name\": \"red substance\"}, {\"id\": 15897, \"name\": \"the long ribbon\"}, {\"id\": 15898, \"name\": \"a container of spray cheese\"}, {\"id\": 15899, \"name\": \"a figurine or statue\"}, {\"id\": 15900, \"name\": \"the person wearing a black shirt and blue pant\"}, {\"id\": 15901, \"name\": \"the neon green ring\"}, {\"id\": 15902, \"name\": \"a women's formal attire\"}, {\"id\": 15903, \"name\": \"tall light pole\"}, {\"id\": 15904, \"name\": \"a black and white mask or head covering\"}, {\"id\": 15905, \"name\": \"yellow and black striped rope\"}, {\"id\": 15906, \"name\": \"metal spoon\"}, {\"id\": 15907, \"name\": \"white and yellow ball\"}, {\"id\": 15908, \"name\": \"clothing display\"}, {\"id\": 15909, \"name\": \"vibrant, large-scale teacup ride\"}, {\"id\": 15910, \"name\": \"the person's right hand\"}, {\"id\": 15911, \"name\": \"the lacrosse stick\"}, {\"id\": 15912, \"name\": \"ramp\"}, {\"id\": 15913, \"name\": \"silver turnstile\"}, {\"id\": 15914, \"name\": \"a band\"}, {\"id\": 15915, \"name\": \"a vertical pole\"}, {\"id\": 15916, \"name\": \"person's thumb\"}, {\"id\": 15917, \"name\": \"rockstar energy drink logo\"}, {\"id\": 15918, \"name\": \"a ball of dark gray yarn\"}, {\"id\": 15919, \"name\": \"the checkered-patterned pouch\"}, {\"id\": 15920, \"name\": \"orange juice carton\"}, {\"id\": 15921, \"name\": \"the damaged roofing material\"}, {\"id\": 15922, \"name\": \"a red-framed doorway\"}, {\"id\": 15923, \"name\": \"purple lighting\"}, {\"id\": 15924, \"name\": \"dark green leafy vegetable\"}, {\"id\": 15925, \"name\": \"a yellow and black uniform\"}, {\"id\": 15926, \"name\": \"a round metal piece\"}, {\"id\": 15927, \"name\": \"the man's hand\"}, {\"id\": 15928, \"name\": \"the small, homemade chocolate ice cream cone cupcake\"}, {\"id\": 15929, \"name\": \"horizontal metal bar\"}, {\"id\": 15930, \"name\": \"silver bicycle handlebar\"}, {\"id\": 15931, \"name\": \"white cord\"}, {\"id\": 15932, \"name\": \"the green football field\"}, {\"id\": 15933, \"name\": \"bowl or plate\"}, {\"id\": 15934, \"name\": \"the blue latex glove\"}, {\"id\": 15935, \"name\": \"matching light green jacket\"}, {\"id\": 15936, \"name\": \"a green corrugated metal wall\"}, {\"id\": 15937, \"name\": \"a hand-painted sign\"}, {\"id\": 15938, \"name\": \"lamppost\"}, {\"id\": 15939, \"name\": \"a blackberry\"}, {\"id\": 15940, \"name\": \"escalator's step\"}, {\"id\": 15941, \"name\": \"small white flower\"}, {\"id\": 15942, \"name\": \"a tachometer\"}, {\"id\": 15943, \"name\": \"an entrance to the store\"}, {\"id\": 15944, \"name\": \"the drone's propellers\"}, {\"id\": 15945, \"name\": \"the green turf field\"}, {\"id\": 15946, \"name\": \"trash bin\"}, {\"id\": 15947, \"name\": \"the white pinstripe jersey\"}, {\"id\": 15948, \"name\": \"the waffle-patterned exterior\"}, {\"id\": 15949, \"name\": \"blue and white awning\"}, {\"id\": 15950, \"name\": \"a football graphic\"}, {\"id\": 15951, \"name\": \"a wood or flammable material\"}, {\"id\": 15952, \"name\": \"a white star\"}, {\"id\": 15953, \"name\": \"a photo of a person walking down a path\"}, {\"id\": 15954, \"name\": \"the white railing\"}, {\"id\": 15955, \"name\": \"the starting block\"}, {\"id\": 15956, \"name\": \"the large wheel\"}, {\"id\": 15957, \"name\": \"a large white rabbit\"}, {\"id\": 15958, \"name\": \"condiment\"}, {\"id\": 15959, \"name\": \"the dark wood paneling\"}, {\"id\": 15960, \"name\": \"a sleek, silver finish\"}, {\"id\": 15961, \"name\": \"a minion figurine\"}, {\"id\": 15962, \"name\": \"a colorful headscarf\"}, {\"id\": 15963, \"name\": \"an inflatable floatie\"}, {\"id\": 15964, \"name\": \"the umpire\"}, {\"id\": 15965, \"name\": \"ornament\"}, {\"id\": 15966, \"name\": \"large stone\"}, {\"id\": 15967, \"name\": \"casual shoe\"}, {\"id\": 15968, \"name\": \"the blue light\"}, {\"id\": 15969, \"name\": \"a spongebob squarepant figurine\"}, {\"id\": 15970, \"name\": \"a frond\"}, {\"id\": 15971, \"name\": \"gray box\"}, {\"id\": 15972, \"name\": \"pouch\"}, {\"id\": 15973, \"name\": \"close-up view of a person's hand\"}, {\"id\": 15974, \"name\": \"large plant\"}, {\"id\": 15975, \"name\": \"a chinese character\"}, {\"id\": 15976, \"name\": \"purple and blue line\"}, {\"id\": 15977, \"name\": \"a single white line\"}, {\"id\": 15978, \"name\": \"colorful parachute\"}, {\"id\": 15979, \"name\": \"the black boxing glove\"}, {\"id\": 15980, \"name\": \"a disney frozen bubble wand\"}, {\"id\": 15981, \"name\": \"brown bowl\"}, {\"id\": 15982, \"name\": \"a white planter\"}, {\"id\": 15983, \"name\": \"multiple pane of glass\"}, {\"id\": 15984, \"name\": \"a yellow and white raft\"}, {\"id\": 15985, \"name\": \"rich, dark brown chocolate frosting\"}, {\"id\": 15986, \"name\": \"a small image of person\"}, {\"id\": 15987, \"name\": \"a white and green uniform\"}, {\"id\": 15988, \"name\": \"the plaid short\"}, {\"id\": 15989, \"name\": \"player's face\"}, {\"id\": 15990, \"name\": \"dark outfit\"}, {\"id\": 15991, \"name\": \"groomsman\"}, {\"id\": 15992, \"name\": \"a person's right wrist\"}, {\"id\": 15993, \"name\": \"a yellow metal stand\"}, {\"id\": 15994, \"name\": \"a prominent white building\"}, {\"id\": 15995, \"name\": \"a blue and purple tape pouch\"}, {\"id\": 15996, \"name\": \"a blue cleat\"}, {\"id\": 15997, \"name\": \"a white guardrail\"}, {\"id\": 15998, \"name\": \"a green, anthropomorphic dog costume\"}, {\"id\": 15999, \"name\": \"a male host\"}, {\"id\": 16000, \"name\": \"black glass\"}, {\"id\": 16001, \"name\": \"long wooden pole\"}, {\"id\": 16002, \"name\": \"white skate\"}, {\"id\": 16003, \"name\": \"the green baseball cap\"}, {\"id\": 16004, \"name\": \"the blue shopping bag\"}, {\"id\": 16005, \"name\": \"prominent caption\"}, {\"id\": 16006, \"name\": \"yellow hair bun\"}, {\"id\": 16007, \"name\": \"the inflatable yellow raft\"}, {\"id\": 16008, \"name\": \"the yellow area\"}, {\"id\": 16009, \"name\": \"colorful toy\"}, {\"id\": 16010, \"name\": \"festive decoration\"}, {\"id\": 16011, \"name\": \"rectangular box\"}, {\"id\": 16012, \"name\": \"a large black banner\"}, {\"id\": 16013, \"name\": \"a black leg\"}, {\"id\": 16014, \"name\": \"the piece of paper or cardboard\"}, {\"id\": 16015, \"name\": \"scoop of dark brown chocolate ice cream\"}, {\"id\": 16016, \"name\": \"a white and gray checkerboard pattern\"}, {\"id\": 16017, \"name\": \"the cartoon dog\"}, {\"id\": 16018, \"name\": \"white towel\"}, {\"id\": 16019, \"name\": \"toy's design\"}, {\"id\": 16020, \"name\": \"the video game\"}, {\"id\": 16021, \"name\": \"the fishnet stocking\"}, {\"id\": 16022, \"name\": \"a large red roller coaster\"}, {\"id\": 16023, \"name\": \"a navy blue tracksuit\"}, {\"id\": 16024, \"name\": \"wig\"}, {\"id\": 16025, \"name\": \"an orange jersey\"}, {\"id\": 16026, \"name\": \"camouflage cap\"}, {\"id\": 16027, \"name\": \"baseboard\"}, {\"id\": 16028, \"name\": \"back window\"}, {\"id\": 16029, \"name\": \"high-visibility jacket\"}, {\"id\": 16030, \"name\": \"the white and yellow patterned fabric\"}, {\"id\": 16031, \"name\": \"pole of the carousel\"}, {\"id\": 16032, \"name\": \"the blurred pug dog\"}, {\"id\": 16033, \"name\": \"the gray chair\"}, {\"id\": 16034, \"name\": \"a dim lighting\"}, {\"id\": 16035, \"name\": \"a white, cartoon-style rabbit plush toy\"}, {\"id\": 16036, \"name\": \"a line of tree\"}, {\"id\": 16037, \"name\": \"blue item\"}, {\"id\": 16038, \"name\": \"wooden door\"}, {\"id\": 16039, \"name\": \"the rider's head\"}, {\"id\": 16040, \"name\": \"the large black dog\"}, {\"id\": 16041, \"name\": \"the stormtrooper\"}, {\"id\": 16042, \"name\": \"the man wearing a black baseball cap\"}, {\"id\": 16043, \"name\": \"the blue and yellow dispenser\"}, {\"id\": 16044, \"name\": \"black vintage car\"}, {\"id\": 16045, \"name\": \"the blue and black vest\"}, {\"id\": 16046, \"name\": \"large metal pan\"}, {\"id\": 16047, \"name\": \"a light-colored surface\"}, {\"id\": 16048, \"name\": \"black-and-white sneaker\"}, {\"id\": 16049, \"name\": \"the room's decor\"}, {\"id\": 16050, \"name\": \"a grid of square cutout\"}, {\"id\": 16051, \"name\": \"a red football uniform\"}, {\"id\": 16052, \"name\": \"the blue paw print\"}, {\"id\": 16053, \"name\": \"the beer\"}, {\"id\": 16054, \"name\": \"silver high-heeled shoe\"}, {\"id\": 16055, \"name\": \"musical note\"}, {\"id\": 16056, \"name\": \"the bike's wheel\"}, {\"id\": 16057, \"name\": \"a yellow helmet\"}, {\"id\": 16058, \"name\": \"black and white grid pattern\"}, {\"id\": 16059, \"name\": \"the yellow mat\"}, {\"id\": 16060, \"name\": \"the large video camera\"}, {\"id\": 16061, \"name\": \"the white piece of paper\"}, {\"id\": 16062, \"name\": \"a spongebob squarepant pez dispenser\"}, {\"id\": 16063, \"name\": \"piece of brown tape\"}, {\"id\": 16064, \"name\": \"a basketball graphic\"}, {\"id\": 16065, \"name\": \"the law enforcement vehicle\"}, {\"id\": 16066, \"name\": \"the channel's logo\"}, {\"id\": 16067, \"name\": \"a piece of clothing\"}, {\"id\": 16068, \"name\": \"a minivan's window\"}, {\"id\": 16069, \"name\": \"green-and-white striped awning\"}, {\"id\": 16070, \"name\": \"red-painted metal object\"}, {\"id\": 16071, \"name\": \"cruise ship\"}, {\"id\": 16072, \"name\": \"the photo of white ball on a table\"}, {\"id\": 16073, \"name\": \"the sponsor logo\"}, {\"id\": 16074, \"name\": \"a blue 22\"}, {\"id\": 16075, \"name\": \"the yellow siding\"}, {\"id\": 16076, \"name\": \"an indoor basketball court\"}, {\"id\": 16077, \"name\": \"a sport uniform\"}, {\"id\": 16078, \"name\": \"yellow light fixture\"}, {\"id\": 16079, \"name\": \"a ice hockey player\"}, {\"id\": 16080, \"name\": \"the toy stroller\"}, {\"id\": 16081, \"name\": \"the blurry color photograph\"}, {\"id\": 16082, \"name\": \"the colorful dress\"}, {\"id\": 16083, \"name\": \"the pink and white accent\"}, {\"id\": 16084, \"name\": \"long, dark red dress\"}, {\"id\": 16085, \"name\": \"the large pillar\"}, {\"id\": 16086, \"name\": \"the yellow and orange plume\"}, {\"id\": 16087, \"name\": \"a black sport car\"}, {\"id\": 16088, \"name\": \"the enchilada casserole\"}, {\"id\": 16089, \"name\": \"a firefighters' equipment\"}, {\"id\": 16090, \"name\": \"gla of clear liquid\"}, {\"id\": 16091, \"name\": \"the large pane of glass\"}, {\"id\": 16092, \"name\": \"the horse's head\"}, {\"id\": 16093, \"name\": \"a green herb\"}, {\"id\": 16094, \"name\": \"a red pom-pom\"}, {\"id\": 16095, \"name\": \"the photo of a person operating a tank or armored vehicle\"}, {\"id\": 16096, \"name\": \"protest sign\"}, {\"id\": 16097, \"name\": \"the multicolored stripe\"}, {\"id\": 16098, \"name\": \"a blue bottle cap\"}, {\"id\": 16099, \"name\": \"the rectangular shape\"}, {\"id\": 16100, \"name\": \"a decorative light fixture\"}, {\"id\": 16101, \"name\": \"the orange car\"}, {\"id\": 16102, \"name\": \"the black tie\"}, {\"id\": 16103, \"name\": \"the mountainou terrain\"}, {\"id\": 16104, \"name\": \"black backpack strap\"}, {\"id\": 16105, \"name\": \"the black fence\"}, {\"id\": 16106, \"name\": \"the prominent headdress\"}, {\"id\": 16107, \"name\": \"a red barrier\"}, {\"id\": 16108, \"name\": \"the logo of gaxa handball\"}, {\"id\": 16109, \"name\": \"brown step\"}, {\"id\": 16110, \"name\": \"the jar of nutella\"}, {\"id\": 16111, \"name\": \"a store shelf\"}, {\"id\": 16112, \"name\": \"the white embroidery\"}, {\"id\": 16113, \"name\": \"tan-colored dog\"}, {\"id\": 16114, \"name\": \"long blonde hair\"}, {\"id\": 16115, \"name\": \"a person's finger\"}, {\"id\": 16116, \"name\": \"buzzed haircut\"}, {\"id\": 16117, \"name\": \"white tag\"}, {\"id\": 16118, \"name\": \"a pink tutu\"}, {\"id\": 16119, \"name\": \"timestamp\"}, {\"id\": 16120, \"name\": \"a white tusk\"}, {\"id\": 16121, \"name\": \"the gym's wall\"}, {\"id\": 16122, \"name\": \"turnstile\"}, {\"id\": 16123, \"name\": \"the ethernet port\"}, {\"id\": 16124, \"name\": \"the yellow and red bumper car\"}, {\"id\": 16125, \"name\": \"the white outline of a baseball diamond\"}, {\"id\": 16126, \"name\": \"the red shopping basket\"}, {\"id\": 16127, \"name\": \"the lush green field\"}, {\"id\": 16128, \"name\": \"center of the flower\"}, {\"id\": 16129, \"name\": \"a protester\"}, {\"id\": 16130, \"name\": \"a smaller carton\"}, {\"id\": 16131, \"name\": \"a red and black uniform\"}, {\"id\": 16132, \"name\": \"a circular white light\"}, {\"id\": 16133, \"name\": \"red and green wheel\"}, {\"id\": 16134, \"name\": \"a white drape\"}, {\"id\": 16135, \"name\": \"red and black box\"}, {\"id\": 16136, \"name\": \"a black bottle\"}, {\"id\": 16137, \"name\": \"a suit\"}, {\"id\": 16138, \"name\": \"receipt or label\"}, {\"id\": 16139, \"name\": \"a navy blue cap\"}, {\"id\": 16140, \"name\": \"red baseball cap\"}, {\"id\": 16141, \"name\": \"the silver charm\"}, {\"id\": 16142, \"name\": \"the colorful swimsuit\"}, {\"id\": 16143, \"name\": \"photograph of police vehicle\"}, {\"id\": 16144, \"name\": \"a small clump of dirt or debris\"}, {\"id\": 16145, \"name\": \"the green camouflage uniform\"}, {\"id\": 16146, \"name\": \"the matching belt\"}, {\"id\": 16147, \"name\": \"blue padding\"}, {\"id\": 16148, \"name\": \"vintage microphone\"}, {\"id\": 16149, \"name\": \"a room's decor\"}, {\"id\": 16150, \"name\": \"the personal care product\"}, {\"id\": 16151, \"name\": \"large, black music stand\"}, {\"id\": 16152, \"name\": \"colorful carnival ride\"}, {\"id\": 16153, \"name\": \"the wooden handrail\"}, {\"id\": 16154, \"name\": \"a pink baseball cap\"}, {\"id\": 16155, \"name\": \"ice skate\"}, {\"id\": 16156, \"name\": \"blurry, pixelated photograph\"}, {\"id\": 16157, \"name\": \"small black device\"}, {\"id\": 16158, \"name\": \"the wig\"}, {\"id\": 16159, \"name\": \"the entrance of the dealership\"}, {\"id\": 16160, \"name\": \"the off-the-shoulder sleeve\"}, {\"id\": 16161, \"name\": \"snow-covered rock\"}, {\"id\": 16162, \"name\": \"a yellow and red rope\"}, {\"id\": 16163, \"name\": \"white, wheeled, and covered vehicle\"}, {\"id\": 16164, \"name\": \"a floral arrangement\"}, {\"id\": 16165, \"name\": \"large green door\"}, {\"id\": 16166, \"name\": \"a pin\"}, {\"id\": 16167, \"name\": \"the wooden surface\"}, {\"id\": 16168, \"name\": \"large canopy\"}, {\"id\": 16169, \"name\": \"a ride's base\"}, {\"id\": 16170, \"name\": \"street or sidewalk\"}, {\"id\": 16171, \"name\": \"elderly man\"}, {\"id\": 16172, \"name\": \"the white tuft\"}, {\"id\": 16173, \"name\": \"black speaker\"}, {\"id\": 16174, \"name\": \"a front wheel\"}, {\"id\": 16175, \"name\": \"the white face\"}, {\"id\": 16176, \"name\": \"ribbon\"}, {\"id\": 16177, \"name\": \"a white flower\"}, {\"id\": 16178, \"name\": \"pink outfit\"}, {\"id\": 16179, \"name\": \"bare foot\"}, {\"id\": 16180, \"name\": \"the packet of sugar\"}, {\"id\": 16181, \"name\": \"tan building\"}, {\"id\": 16182, \"name\": \"a blue light\"}, {\"id\": 16183, \"name\": \"a red beam\"}, {\"id\": 16184, \"name\": \"the compartment\"}, {\"id\": 16185, \"name\": \"grocery bag\"}, {\"id\": 16186, \"name\": \"chocolate-frosted ice cream cone\"}, {\"id\": 16187, \"name\": \"the child's attire\"}, {\"id\": 16188, \"name\": \"gray circular mat\"}, {\"id\": 16189, \"name\": \"the textile\"}, {\"id\": 16190, \"name\": \"a green balloon\"}, {\"id\": 16191, \"name\": \"exercise machine\"}, {\"id\": 16192, \"name\": \"the muffin\"}, {\"id\": 16193, \"name\": \"the dark wooden chair\"}, {\"id\": 16194, \"name\": \"high-visibility vest\"}, {\"id\": 16195, \"name\": \"an ornate chandelier\"}, {\"id\": 16196, \"name\": \"the red price tag\"}, {\"id\": 16197, \"name\": \"the vibrant pink sign\"}, {\"id\": 16198, \"name\": \"a blue plastic piece\"}, {\"id\": 16199, \"name\": \"a white baseball cap\"}, {\"id\": 16200, \"name\": \"a large advertisement board\"}, {\"id\": 16201, \"name\": \"the orange bar\"}, {\"id\": 16202, \"name\": \"a white flag\"}, {\"id\": 16203, \"name\": \"a small, illegible black logo\"}, {\"id\": 16204, \"name\": \"black and white bowling ball\"}, {\"id\": 16205, \"name\": \"luggage cart\"}, {\"id\": 16206, \"name\": \"a bag or purse\"}, {\"id\": 16207, \"name\": \"the backdrop\"}, {\"id\": 16208, \"name\": \"the black traffic light\"}, {\"id\": 16209, \"name\": \"a young skateboarder\"}, {\"id\": 16210, \"name\": \"the nike logo\"}, {\"id\": 16211, \"name\": \"display of merchandise\"}, {\"id\": 16212, \"name\": \"a teammate in a white shirt\"}, {\"id\": 16213, \"name\": \"the pepsi logo\"}, {\"id\": 16214, \"name\": \"an individual's outfit\"}, {\"id\": 16215, \"name\": \"the white substance\"}, {\"id\": 16216, \"name\": \"marble top\"}, {\"id\": 16217, \"name\": \"the gray helmet\"}, {\"id\": 16218, \"name\": \"gold lettering\"}, {\"id\": 16219, \"name\": \"the finger\"}, {\"id\": 16220, \"name\": \"bubble\"}, {\"id\": 16221, \"name\": \"the brown shutter\"}, {\"id\": 16222, \"name\": \"a curved, white railing\"}, {\"id\": 16223, \"name\": \"volleyball net\"}, {\"id\": 16224, \"name\": \"a small box\"}, {\"id\": 16225, \"name\": \"a metal barrier\"}, {\"id\": 16226, \"name\": \"the porsche model\"}, {\"id\": 16227, \"name\": \"rusty, brown facade\"}, {\"id\": 16228, \"name\": \"the grassy surface\"}, {\"id\": 16229, \"name\": \"red pillar\"}, {\"id\": 16230, \"name\": \"brown sandal\"}, {\"id\": 16231, \"name\": \"the pink t-shirt\"}, {\"id\": 16232, \"name\": \"a flower or decorative item\"}, {\"id\": 16233, \"name\": \"side window\"}, {\"id\": 16234, \"name\": \"the fluffy white cloud\"}, {\"id\": 16235, \"name\": \"the tiled roof\"}, {\"id\": 16236, \"name\": \"a baseball bat\"}, {\"id\": 16237, \"name\": \"the small boat\"}, {\"id\": 16238, \"name\": \"the condiment\"}, {\"id\": 16239, \"name\": \"the gla railing\"}, {\"id\": 16240, \"name\": \"person in lab coat\"}, {\"id\": 16241, \"name\": \"the circular sticker\"}, {\"id\": 16242, \"name\": \"a single spotlight\"}, {\"id\": 16243, \"name\": \"the police light bar\"}, {\"id\": 16244, \"name\": \"stocking\"}, {\"id\": 16245, \"name\": \"a medium-sized dog\"}, {\"id\": 16246, \"name\": \"red, chunky substance\"}, {\"id\": 16247, \"name\": \"the crown-like headpiece\"}, {\"id\": 16248, \"name\": \"the pink leather handbag\"}, {\"id\": 16249, \"name\": \"white knee brace\"}, {\"id\": 16250, \"name\": \"silver metal pole\"}, {\"id\": 16251, \"name\": \"a pink fingernail\"}, {\"id\": 16252, \"name\": \"clear plastic cup\"}, {\"id\": 16253, \"name\": \"the mix of building\"}, {\"id\": 16254, \"name\": \"chocolate-filled ice cream cone\"}, {\"id\": 16255, \"name\": \"a prominent white car\"}, {\"id\": 16256, \"name\": \"the wall or fence\"}, {\"id\": 16257, \"name\": \"a black and red uniform\"}, {\"id\": 16258, \"name\": \"a packed stadium of spectator\"}, {\"id\": 16259, \"name\": \"the red sleeve\"}, {\"id\": 16260, \"name\": \"a building's sign\"}, {\"id\": 16261, \"name\": \"a low metal fence\"}, {\"id\": 16262, \"name\": \"red tracksuit\"}, {\"id\": 16263, \"name\": \"a black and red cheerleading uniform\"}, {\"id\": 16264, \"name\": \"a large wheel\"}, {\"id\": 16265, \"name\": \"the small silver ring\"}, {\"id\": 16266, \"name\": \"the dinosaur's feet\"}, {\"id\": 16267, \"name\": \"dark gray leather\"}, {\"id\": 16268, \"name\": \"crew of worker\"}, {\"id\": 16269, \"name\": \"ballerina's legs\"}, {\"id\": 16270, \"name\": \"the navy blue and neon green uniform\"}, {\"id\": 16271, \"name\": \"a kimono\"}, {\"id\": 16272, \"name\": \"the storage bin\"}, {\"id\": 16273, \"name\": \"the car's exterior\"}, {\"id\": 16274, \"name\": \"the yellow sticker\"}, {\"id\": 16275, \"name\": \"framed photograph\"}, {\"id\": 16276, \"name\": \"a white bowling pin\"}, {\"id\": 16277, \"name\": \"a food product\"}, {\"id\": 16278, \"name\": \"a black metal component\"}, {\"id\": 16279, \"name\": \"the action figure\"}, {\"id\": 16280, \"name\": \"folding chair\"}, {\"id\": 16281, \"name\": \"black and gold jersey\"}, {\"id\": 16282, \"name\": \"single large slice\"}, {\"id\": 16283, \"name\": \"the black-and-white striped uniform\"}, {\"id\": 16284, \"name\": \"a purple straw\"}, {\"id\": 16285, \"name\": \"the dish of food\"}, {\"id\": 16286, \"name\": \"the box of agricultural chemical\"}, {\"id\": 16287, \"name\": \"a rectangular pouch\"}, {\"id\": 16288, \"name\": \"a white and black baseball-style shirt\"}, {\"id\": 16289, \"name\": \"a white seat\"}, {\"id\": 16290, \"name\": \"a black and red roller skate\"}, {\"id\": 16291, \"name\": \"blue sport car\"}, {\"id\": 16292, \"name\": \"the uniform\"}, {\"id\": 16293, \"name\": \"white tarp\"}, {\"id\": 16294, \"name\": \"food and drink item\"}, {\"id\": 16295, \"name\": \"santander logo\"}, {\"id\": 16296, \"name\": \"vodka\"}, {\"id\": 16297, \"name\": \"the green and white uniform\"}, {\"id\": 16298, \"name\": \"a colorful striped shirt\"}, {\"id\": 16299, \"name\": \"a flip-flop\"}, {\"id\": 16300, \"name\": \"the silver button\"}, {\"id\": 16301, \"name\": \"a biker\"}, {\"id\": 16302, \"name\": \"round, chrome headlight\"}, {\"id\": 16303, \"name\": \"black plaque\"}, {\"id\": 16304, \"name\": \"the matching costume\"}, {\"id\": 16305, \"name\": \"a small white object\"}, {\"id\": 16306, \"name\": \"grey stair\"}, {\"id\": 16307, \"name\": \"the white wheel\"}, {\"id\": 16308, \"name\": \"the rocky surface\"}, {\"id\": 16309, \"name\": \"the espn wild card weekend logo\"}, {\"id\": 16310, \"name\": \"the flip-flop\"}, {\"id\": 16311, \"name\": \"the dark sweatshirt\"}, {\"id\": 16312, \"name\": \"the wine glass\"}, {\"id\": 16313, \"name\": \"a dark grey suv\"}, {\"id\": 16314, \"name\": \"the red and white water bottle\"}, {\"id\": 16315, \"name\": \"a carpeted floor\"}, {\"id\": 16316, \"name\": \"a gold and cream-colored ornate decoration\"}, {\"id\": 16317, \"name\": \"the man's body\"}, {\"id\": 16318, \"name\": \"vibrant green wall\"}, {\"id\": 16319, \"name\": \"overhead light\"}, {\"id\": 16320, \"name\": \"a large poster\"}, {\"id\": 16321, \"name\": \"the person on a stretcher\"}, {\"id\": 16322, \"name\": \"large headlight\"}, {\"id\": 16323, \"name\": \"the ornate lamppost\"}, {\"id\": 16324, \"name\": \"a white cleat\"}, {\"id\": 16325, \"name\": \"a dark blue jersey\"}, {\"id\": 16326, \"name\": \"a blue \\\"news\\\" label\"}, {\"id\": 16327, \"name\": \"weapon\"}, {\"id\": 16328, \"name\": \"a black or red shirt\"}, {\"id\": 16329, \"name\": \"the brown piece of furniture\"}, {\"id\": 16330, \"name\": \"the boat's wheel\"}, {\"id\": 16331, \"name\": \"the stripe\"}, {\"id\": 16332, \"name\": \"the white marble statue\"}, {\"id\": 16333, \"name\": \"red ribbon\"}, {\"id\": 16334, \"name\": \"the overhead lighting\"}, {\"id\": 16335, \"name\": \"dark gray t-shirt\"}, {\"id\": 16336, \"name\": \"the dark t-shirt\"}, {\"id\": 16337, \"name\": \"the red saddle\"}, {\"id\": 16338, \"name\": \"a shoulder-length blonde hair\"}, {\"id\": 16339, \"name\": \"black honda civic\"}, {\"id\": 16340, \"name\": \"a navy blue jersey\"}, {\"id\": 16341, \"name\": \"white headset\"}, {\"id\": 16342, \"name\": \"the black suitcase\"}, {\"id\": 16343, \"name\": \"the black light\"}, {\"id\": 16344, \"name\": \"the white awning\"}, {\"id\": 16345, \"name\": \"the prominent ramp\"}, {\"id\": 16346, \"name\": \"the light wood paneling\"}, {\"id\": 16347, \"name\": \"a large speaker\"}, {\"id\": 16348, \"name\": \"the gray and white sneaker\"}, {\"id\": 16349, \"name\": \"an appliance\"}, {\"id\": 16350, \"name\": \"black floor\"}, {\"id\": 16351, \"name\": \"a rollerblading\"}, {\"id\": 16352, \"name\": \"a yellow triangle\"}, {\"id\": 16353, \"name\": \"jaguar player\"}, {\"id\": 16354, \"name\": \"the grassy hillside\"}, {\"id\": 16355, \"name\": \"the ornate carving\"}, {\"id\": 16356, \"name\": \"white and black dog-like face\"}, {\"id\": 16357, \"name\": \"vibrant inflatable play structure\"}, {\"id\": 16358, \"name\": \"the blue and white bottle\"}, {\"id\": 16359, \"name\": \"black and yellow jersey\"}, {\"id\": 16360, \"name\": \"the black exercise mat\"}, {\"id\": 16361, \"name\": \"the smaller building\"}, {\"id\": 16362, \"name\": \"fan display\"}, {\"id\": 16363, \"name\": \"a marching band\"}, {\"id\": 16364, \"name\": \"red lettering\"}, {\"id\": 16365, \"name\": \"men's casual attire\"}, {\"id\": 16366, \"name\": \"yellow-framed sunglass\"}, {\"id\": 16367, \"name\": \"a black rim\"}, {\"id\": 16368, \"name\": \"the high heel\"}, {\"id\": 16369, \"name\": \"the boat's propeller\"}, {\"id\": 16370, \"name\": \"the shelf of clothing\"}, {\"id\": 16371, \"name\": \"wooden stand\"}, {\"id\": 16372, \"name\": \"an orange space suit\"}, {\"id\": 16373, \"name\": \"a gray and white sneaker\"}, {\"id\": 16374, \"name\": \"a bucket of paint\"}, {\"id\": 16375, \"name\": \"the exercise ball\"}, {\"id\": 16376, \"name\": \"small wooden building\"}, {\"id\": 16377, \"name\": \"packaging material\"}, {\"id\": 16378, \"name\": \"a green trash can\"}, {\"id\": 16379, \"name\": \"a white trek bike\"}, {\"id\": 16380, \"name\": \"the person's foot\"}, {\"id\": 16381, \"name\": \"round emblem\"}, {\"id\": 16382, \"name\": \"a damaged vehicle\"}, {\"id\": 16383, \"name\": \"a person in the robe\"}, {\"id\": 16384, \"name\": \"a white house\"}, {\"id\": 16385, \"name\": \"the clear plastic bowl\"}, {\"id\": 16386, \"name\": \"a large, white column\"}, {\"id\": 16387, \"name\": \"price\"}, {\"id\": 16388, \"name\": \"a purple jersey\"}, {\"id\": 16389, \"name\": \"the tan-colored leather seat\"}, {\"id\": 16390, \"name\": \"the square fluorescent light\"}, {\"id\": 16391, \"name\": \"the black sleeve\"}, {\"id\": 16392, \"name\": \"the blue emblem\"}, {\"id\": 16393, \"name\": \"green design\"}, {\"id\": 16394, \"name\": \"a skier\"}, {\"id\": 16395, \"name\": \"the string of light\"}, {\"id\": 16396, \"name\": \"a corrugated metal\"}, {\"id\": 16397, \"name\": \"a curly dark hair\"}, {\"id\": 16398, \"name\": \"delicate flower piece\"}, {\"id\": 16399, \"name\": \"line of tree\"}, {\"id\": 16400, \"name\": \"black and blue stripe\"}, {\"id\": 16401, \"name\": \"the caption\"}, {\"id\": 16402, \"name\": \"the white plastic object\"}, {\"id\": 16403, \"name\": \"a white light\"}, {\"id\": 16404, \"name\": \"a blurred image of a football field\"}, {\"id\": 16405, \"name\": \"a white collar\"}, {\"id\": 16406, \"name\": \"the mosler car\"}, {\"id\": 16407, \"name\": \"the black and red jacket\"}, {\"id\": 16408, \"name\": \"gla display case\"}, {\"id\": 16409, \"name\": \"red rail\"}, {\"id\": 16410, \"name\": \"a colored swim cap\"}, {\"id\": 16411, \"name\": \"the goalie\"}, {\"id\": 16412, \"name\": \"a large bush\"}, {\"id\": 16413, \"name\": \"the dark-colored top\"}, {\"id\": 16414, \"name\": \"the bright yellow wall\"}, {\"id\": 16415, \"name\": \"large black ear\"}, {\"id\": 16416, \"name\": \"a long black table\"}, {\"id\": 16417, \"name\": \"yellow firefighting gear\"}, {\"id\": 16418, \"name\": \"player's body\"}, {\"id\": 16419, \"name\": \"a blue and grey uniform\"}, {\"id\": 16420, \"name\": \"a tall pole\"}, {\"id\": 16421, \"name\": \"the gray uniform\"}, {\"id\": 16422, \"name\": \"the light jean\"}, {\"id\": 16423, \"name\": \"a hairstyle\"}, {\"id\": 16424, \"name\": \"the paved sidewalk\"}, {\"id\": 16425, \"name\": \"a large tire\"}, {\"id\": 16426, \"name\": \"a piece of rope\"}, {\"id\": 16427, \"name\": \"the pink surface\"}, {\"id\": 16428, \"name\": \"a black-and-white photo\"}, {\"id\": 16429, \"name\": \"lush green baseball field\"}, {\"id\": 16430, \"name\": \"a dark green shirt\"}, {\"id\": 16431, \"name\": \"large purple and white pom-pom\"}, {\"id\": 16432, \"name\": \"staff or spear\"}, {\"id\": 16433, \"name\": \"a green baseball cap\"}, {\"id\": 16434, \"name\": \"a darker blue barrel\"}, {\"id\": 16435, \"name\": \"an adult male\"}, {\"id\": 16436, \"name\": \"the man in a suit\"}, {\"id\": 16437, \"name\": \"the white and red pin\"}, {\"id\": 16438, \"name\": \"the backward baseball cap\"}, {\"id\": 16439, \"name\": \"the checkered pouch\"}, {\"id\": 16440, \"name\": \"a white and black jersey\"}, {\"id\": 16441, \"name\": \"white cabinet\"}, {\"id\": 16442, \"name\": \"a young kid\"}, {\"id\": 16443, \"name\": \"a bass\"}, {\"id\": 16444, \"name\": \"the white and grey checkered bag\"}, {\"id\": 16445, \"name\": \"the white and gray pillow\"}, {\"id\": 16446, \"name\": \"blue and white floral cushion\"}, {\"id\": 16447, \"name\": \"the green traffic light\"}, {\"id\": 16448, \"name\": \"the sidecar\"}, {\"id\": 16449, \"name\": \"symbol\"}, {\"id\": 16450, \"name\": \"the machinery\"}, {\"id\": 16451, \"name\": \"the white hurdle\"}, {\"id\": 16452, \"name\": \"black jean\"}, {\"id\": 16453, \"name\": \"the purple balloon\"}, {\"id\": 16454, \"name\": \"the high-visibility yellow shirt\"}, {\"id\": 16455, \"name\": \"the black box\"}, {\"id\": 16456, \"name\": \"photo of a person holding a sandwich\"}, {\"id\": 16457, \"name\": \"fish tail\"}, {\"id\": 16458, \"name\": \"the leather bag\"}, {\"id\": 16459, \"name\": \"red tail light\"}, {\"id\": 16460, \"name\": \"large circular opening\"}, {\"id\": 16461, \"name\": \"a snack or condiment packet\"}, {\"id\": 16462, \"name\": \"the event's name\"}, {\"id\": 16463, \"name\": \"large, rounded post\"}, {\"id\": 16464, \"name\": \"a yellowish-green vegetable\"}, {\"id\": 16465, \"name\": \"camera logo\"}, {\"id\": 16466, \"name\": \"the go-kart\"}, {\"id\": 16467, \"name\": \"the white top\"}, {\"id\": 16468, \"name\": \"a white rugby ball\"}, {\"id\": 16469, \"name\": \"bmw\"}, {\"id\": 16470, \"name\": \"the red and white patterned fabric\"}, {\"id\": 16471, \"name\": \"the person's shirt sleeve\"}, {\"id\": 16472, \"name\": \"a surrounding chair\"}, {\"id\": 16473, \"name\": \"the plate of food\"}, {\"id\": 16474, \"name\": \"the obstacle\"}, {\"id\": 16475, \"name\": \"a distinctive white stripe\"}, {\"id\": 16476, \"name\": \"a neon green bow\"}, {\"id\": 16477, \"name\": \"sport car\"}, {\"id\": 16478, \"name\": \"black cart\"}, {\"id\": 16479, \"name\": \"a black collared shirt\"}, {\"id\": 16480, \"name\": \"a designated yellow-lined spot\"}, {\"id\": 16481, \"name\": \"a large round clock\"}, {\"id\": 16482, \"name\": \"a cartoon drawing\"}, {\"id\": 16483, \"name\": \"pink top\"}, {\"id\": 16484, \"name\": \"blackberry\"}, {\"id\": 16485, \"name\": \"the large wooden stick\"}, {\"id\": 16486, \"name\": \"the front right tire\"}, {\"id\": 16487, \"name\": \"black lacrosse stick\"}, {\"id\": 16488, \"name\": \"fluffy white cloud\"}, {\"id\": 16489, \"name\": \"the large door\"}, {\"id\": 16490, \"name\": \"a round headlight\"}, {\"id\": 16491, \"name\": \"the muscular physique\"}, {\"id\": 16492, \"name\": \"the orange short\"}, {\"id\": 16493, \"name\": \"wall of the subway car\"}, {\"id\": 16494, \"name\": \"a front leg\"}, {\"id\": 16495, \"name\": \"a blue and white umbrella\"}, {\"id\": 16496, \"name\": \"white and red ball\"}, {\"id\": 16497, \"name\": \"tray of clay ball\"}, {\"id\": 16498, \"name\": \"a long-sleeved top\"}, {\"id\": 16499, \"name\": \"black shelf\"}, {\"id\": 16500, \"name\": \"the red football pad\"}, {\"id\": 16501, \"name\": \"white belly\"}, {\"id\": 16502, \"name\": \"the ornate red and gold detail\"}, {\"id\": 16503, \"name\": \"photo of player on a field\"}, {\"id\": 16504, \"name\": \"a blue rope barrier\"}, {\"id\": 16505, \"name\": \"the ball of clay\"}, {\"id\": 16506, \"name\": \"stone building\"}, {\"id\": 16507, \"name\": \"green icon\"}, {\"id\": 16508, \"name\": \"rack of weight\"}, {\"id\": 16509, \"name\": \"an antler\"}, {\"id\": 16510, \"name\": \"wooden pole\"}, {\"id\": 16511, \"name\": \"the matching helmet\"}, {\"id\": 16512, \"name\": \"the dark brown horse\"}, {\"id\": 16513, \"name\": \"white wig\"}, {\"id\": 16514, \"name\": \"a device\"}, {\"id\": 16515, \"name\": \"a yellow balloon\"}, {\"id\": 16516, \"name\": \"the dilapidated building\"}, {\"id\": 16517, \"name\": \"beige frame\"}, {\"id\": 16518, \"name\": \"a white scarf\"}, {\"id\": 16519, \"name\": \"a silver tray\"}, {\"id\": 16520, \"name\": \"a green flooring\"}, {\"id\": 16521, \"name\": \"the snowy hill\"}, {\"id\": 16522, \"name\": \"shoulder-length brown hair\"}, {\"id\": 16523, \"name\": \"a van's rear window\"}, {\"id\": 16524, \"name\": \"a black bar\"}, {\"id\": 16525, \"name\": \"the yellow waistband\"}, {\"id\": 16526, \"name\": \"kitchen cabinet\"}, {\"id\": 16527, \"name\": \"tight-fitting short\"}, {\"id\": 16528, \"name\": \"the orange marking\"}, {\"id\": 16529, \"name\": \"a reflective strip\"}, {\"id\": 16530, \"name\": \"a figurine\"}, {\"id\": 16531, \"name\": \"wooden fence\"}, {\"id\": 16532, \"name\": \"the white collar\"}, {\"id\": 16533, \"name\": \"stadium light\"}, {\"id\": 16534, \"name\": \"a larger bowl\"}, {\"id\": 16535, \"name\": \"man in a blue jersey\"}, {\"id\": 16536, \"name\": \"black plastic bin\"}, {\"id\": 16537, \"name\": \"a monorail track\"}, {\"id\": 16538, \"name\": \"the chrome rim\"}, {\"id\": 16539, \"name\": \"a skater\"}, {\"id\": 16540, \"name\": \"a gym equipment\"}, {\"id\": 16541, \"name\": \"the dark cap\"}, {\"id\": 16542, \"name\": \"blue kettlebell\"}, {\"id\": 16543, \"name\": \"a red and black diagonal stripe\"}, {\"id\": 16544, \"name\": \"a shipping container\"}, {\"id\": 16545, \"name\": \"a rear tire\"}, {\"id\": 16546, \"name\": \"the ballet shoe\"}, {\"id\": 16547, \"name\": \"a stick or spear\"}, {\"id\": 16548, \"name\": \"a blue canopy tent\"}, {\"id\": 16549, \"name\": \"a framed photo\"}, {\"id\": 16550, \"name\": \"the cylindrical metal object\"}, {\"id\": 16551, \"name\": \"the girl's hand\"}, {\"id\": 16552, \"name\": \"red seat\"}, {\"id\": 16553, \"name\": \"camera's shadow\"}, {\"id\": 16554, \"name\": \"the horizontal slat\"}, {\"id\": 16555, \"name\": \"bow\"}, {\"id\": 16556, \"name\": \"the orange pant\"}, {\"id\": 16557, \"name\": \"decorative fence\"}, {\"id\": 16558, \"name\": \"a grey baseball cap\"}, {\"id\": 16559, \"name\": \"the red box\"}, {\"id\": 16560, \"name\": \"an image of the man\"}, {\"id\": 16561, \"name\": \"a nike logo\"}, {\"id\": 16562, \"name\": \"the black vehicle\"}, {\"id\": 16563, \"name\": \"the red and gold sandal\"}, {\"id\": 16564, \"name\": \"drone's propellers\"}, {\"id\": 16565, \"name\": \"a right sleeve\"}, {\"id\": 16566, \"name\": \"a tram track\"}, {\"id\": 16567, \"name\": \"the mclaren\"}, {\"id\": 16568, \"name\": \"the fish's head\"}, {\"id\": 16569, \"name\": \"plastic-wrapped item\"}, {\"id\": 16570, \"name\": \"a girl's attire\"}, {\"id\": 16571, \"name\": \"a dark short\"}, {\"id\": 16572, \"name\": \"the prominent tram stop\"}, {\"id\": 16573, \"name\": \"the coin\"}, {\"id\": 16574, \"name\": \"the right foot\"}, {\"id\": 16575, \"name\": \"a silver metal base\"}, {\"id\": 16576, \"name\": \"a medical equipment\"}, {\"id\": 16577, \"name\": \"bolt\"}, {\"id\": 16578, \"name\": \"a black surface\"}, {\"id\": 16579, \"name\": \"long, wavy tail\"}, {\"id\": 16580, \"name\": \"the black and white uniform\"}, {\"id\": 16581, \"name\": \"a green net\"}, {\"id\": 16582, \"name\": \"a concrete pier\"}, {\"id\": 16583, \"name\": \"the blurry football field\"}, {\"id\": 16584, \"name\": \"yellow cable\"}, {\"id\": 16585, \"name\": \"a chocolate cupcake\"}, {\"id\": 16586, \"name\": \"a patterned short\"}, {\"id\": 16587, \"name\": \"the titan logo\"}, {\"id\": 16588, \"name\": \"polka dot pattern\"}, {\"id\": 16589, \"name\": \"orange vertical stripe\"}, {\"id\": 16590, \"name\": \"blurry football game\"}, {\"id\": 16591, \"name\": \"an apron\"}, {\"id\": 16592, \"name\": \"piece of metal\"}, {\"id\": 16593, \"name\": \"the striped sock\"}, {\"id\": 16594, \"name\": \"the bright green shirt\"}, {\"id\": 16595, \"name\": \"gold tassel\"}, {\"id\": 16596, \"name\": \"a debris\"}, {\"id\": 16597, \"name\": \"the pink cup\"}, {\"id\": 16598, \"name\": \"red furniture\"}, {\"id\": 16599, \"name\": \"spire\"}, {\"id\": 16600, \"name\": \"rack of dumbbell\"}, {\"id\": 16601, \"name\": \"dirt portion of a baseball field\"}, {\"id\": 16602, \"name\": \"usa pro cycling challenge logo\"}, {\"id\": 16603, \"name\": \"a silver medal\"}, {\"id\": 16604, \"name\": \"the brown liquid\"}, {\"id\": 16605, \"name\": \"white chalk line\"}, {\"id\": 16606, \"name\": \"black pad\"}, {\"id\": 16607, \"name\": \"the kneeling paramedic\"}, {\"id\": 16608, \"name\": \"a metal post\"}, {\"id\": 16609, \"name\": \"the brown door frame\"}, {\"id\": 16610, \"name\": \"black fuzzy sleeve\"}, {\"id\": 16611, \"name\": \"a white embroidery\"}, {\"id\": 16612, \"name\": \"a blue balloon\"}, {\"id\": 16613, \"name\": \"red cooler\"}, {\"id\": 16614, \"name\": \"cyclist\"}, {\"id\": 16615, \"name\": \"white mattress\"}, {\"id\": 16616, \"name\": \"a woman's right hand\"}, {\"id\": 16617, \"name\": \"the green jersey\"}, {\"id\": 16618, \"name\": \"the silver metal tube\"}, {\"id\": 16619, \"name\": \"black bucket hat\"}, {\"id\": 16620, \"name\": \"a side window\"}, {\"id\": 16621, \"name\": \"a red design\"}, {\"id\": 16622, \"name\": \"tall spire\"}, {\"id\": 16623, \"name\": \"large, inflatable, multi-colored obstacle course\"}, {\"id\": 16624, \"name\": \"cone\"}, {\"id\": 16625, \"name\": \"a prominent stone wall\"}, {\"id\": 16626, \"name\": \"black bicycle\"}, {\"id\": 16627, \"name\": \"an outdoor furniture\"}, {\"id\": 16628, \"name\": \"the man with a beard\"}, {\"id\": 16629, \"name\": \"a round table\"}, {\"id\": 16630, \"name\": \"the piper's instrument\"}, {\"id\": 16631, \"name\": \"ring finger\"}, {\"id\": 16632, \"name\": \"the model\"}, {\"id\": 16633, \"name\": \"an dish\"}, {\"id\": 16634, \"name\": \"the red epaulet\"}, {\"id\": 16635, \"name\": \"a large advertisement banner\"}, {\"id\": 16636, \"name\": \"the blue tank top\"}, {\"id\": 16637, \"name\": \"a dark skirt or pant\"}, {\"id\": 16638, \"name\": \"the luggage\"}, {\"id\": 16639, \"name\": \"a tram\"}, {\"id\": 16640, \"name\": \"the maroon helmet\"}, {\"id\": 16641, \"name\": \"large white plastic bag\"}, {\"id\": 16642, \"name\": \"prominent red sign\"}, {\"id\": 16643, \"name\": \"the green tank\"}, {\"id\": 16644, \"name\": \"the kitchen's wall\"}, {\"id\": 16645, \"name\": \"a basket of food item\"}, {\"id\": 16646, \"name\": \"stuffed animal and toy\"}, {\"id\": 16647, \"name\": \"a red and white ball\"}, {\"id\": 16648, \"name\": \"a bucket hat\"}, {\"id\": 16649, \"name\": \"seatbelt\"}, {\"id\": 16650, \"name\": \"the blue lego piece\"}, {\"id\": 16651, \"name\": \"purple and green foam block\"}, {\"id\": 16652, \"name\": \"a podium\"}, {\"id\": 16653, \"name\": \"the green or pink cupcake liner\"}, {\"id\": 16654, \"name\": \"taxi\"}, {\"id\": 16655, \"name\": \"the large white tent\"}, {\"id\": 16656, \"name\": \"the bride's hair\"}, {\"id\": 16657, \"name\": \"a red star\"}, {\"id\": 16658, \"name\": \"pink and black dog-themed blanket\"}, {\"id\": 16659, \"name\": \"a black and white jersey\"}, {\"id\": 16660, \"name\": \"the yellow bar\"}, {\"id\": 16661, \"name\": \"a yellow and black soccer ball\"}, {\"id\": 16662, \"name\": \"cardigan\"}, {\"id\": 16663, \"name\": \"the purple outline\"}, {\"id\": 16664, \"name\": \"a pink armchair\"}, {\"id\": 16665, \"name\": \"a musical instrument\"}, {\"id\": 16666, \"name\": \"seating\"}, {\"id\": 16667, \"name\": \"the vendor's cart\"}, {\"id\": 16668, \"name\": \"the brown fur\"}, {\"id\": 16669, \"name\": \"the stegosaurus\"}, {\"id\": 16670, \"name\": \"a decorative white pattern\"}, {\"id\": 16671, \"name\": \"the bull's horn\"}, {\"id\": 16672, \"name\": \"the light grey trousers\"}, {\"id\": 16673, \"name\": \"the vibrant red costume\"}, {\"id\": 16674, \"name\": \"the rear bumper\"}, {\"id\": 16675, \"name\": \"a large aluminum pan\"}, {\"id\": 16676, \"name\": \"pink bracelet\"}, {\"id\": 16677, \"name\": \"football stadium\"}, {\"id\": 16678, \"name\": \"orange circle\"}, {\"id\": 16679, \"name\": \"a blue curtain backdrop\"}, {\"id\": 16680, \"name\": \"the hockey stick\"}, {\"id\": 16681, \"name\": \"the small illustration of a car\"}, {\"id\": 16682, \"name\": \"white water bottle\"}, {\"id\": 16683, \"name\": \"the woman in a floral dress\"}, {\"id\": 16684, \"name\": \"a large gray exercise ball\"}, {\"id\": 16685, \"name\": \"a green hold\"}, {\"id\": 16686, \"name\": \"the white and brown tent\"}, {\"id\": 16687, \"name\": \"buckle\"}, {\"id\": 16688, \"name\": \"white police vehicle\"}, {\"id\": 16689, \"name\": \"yellow uniform\"}, {\"id\": 16690, \"name\": \"the blue surgical mask\"}, {\"id\": 16691, \"name\": \"a black camera\"}, {\"id\": 16692, \"name\": \"a gray and white checkered pattern\"}, {\"id\": 16693, \"name\": \"a wooden baseball bat\"}, {\"id\": 16694, \"name\": \"the yellow food item\"}, {\"id\": 16695, \"name\": \"yard marker\"}, {\"id\": 16696, \"name\": \"green trim\"}, {\"id\": 16697, \"name\": \"a short sleeve\"}, {\"id\": 16698, \"name\": \"packed stadium of spectator\"}, {\"id\": 16699, \"name\": \"the blue bag\"}, {\"id\": 16700, \"name\": \"green shrub\"}, {\"id\": 16701, \"name\": \"the display of decorative wall art\"}, {\"id\": 16702, \"name\": \"blurry face\"}, {\"id\": 16703, \"name\": \"bikini\"}, {\"id\": 16704, \"name\": \"a yellow cleat\"}, {\"id\": 16705, \"name\": \"the maroon chair\"}, {\"id\": 16706, \"name\": \"ball's surface\"}, {\"id\": 16707, \"name\": \"the left sleeve\"}, {\"id\": 16708, \"name\": \"box or packaging material\"}, {\"id\": 16709, \"name\": \"the woman's feet\"}, {\"id\": 16710, \"name\": \"a black phone keypad\"}, {\"id\": 16711, \"name\": \"a scoreboard of a basketball game\"}, {\"id\": 16712, \"name\": \"a black drawer\"}, {\"id\": 16713, \"name\": \"recording equipment\"}, {\"id\": 16714, \"name\": \"the person wearing a black t-shirt\"}, {\"id\": 16715, \"name\": \"woman's feet\"}, {\"id\": 16716, \"name\": \"the colorful vehicle\"}, {\"id\": 16717, \"name\": \"a small green plant\"}, {\"id\": 16718, \"name\": \"the large, white, plastic bag\"}, {\"id\": 16719, \"name\": \"the entrance of a shopping mall\"}, {\"id\": 16720, \"name\": \"horizontal blind\"}, {\"id\": 16721, \"name\": \"a backdrop of building\"}, {\"id\": 16722, \"name\": \"blue and black ball\"}, {\"id\": 16723, \"name\": \"a tan tent\"}, {\"id\": 16724, \"name\": \"lakers' player\"}, {\"id\": 16725, \"name\": \"the sign on the wall\"}, {\"id\": 16726, \"name\": \"wall of window\"}, {\"id\": 16727, \"name\": \"bag of sugar\"}, {\"id\": 16728, \"name\": \"wooden paneling\"}, {\"id\": 16729, \"name\": \"a red-clad person\"}, {\"id\": 16730, \"name\": \"the cord\"}, {\"id\": 16731, \"name\": \"the cycling kit\"}, {\"id\": 16732, \"name\": \"plain gray wall\"}, {\"id\": 16733, \"name\": \"yellow x logo\"}, {\"id\": 16734, \"name\": \"large wing\"}, {\"id\": 16735, \"name\": \"a cylindrical block\"}, {\"id\": 16736, \"name\": \"the black device\"}, {\"id\": 16737, \"name\": \"the gray metal roof\"}, {\"id\": 16738, \"name\": \"double yellow line\"}, {\"id\": 16739, \"name\": \"song sheet\"}, {\"id\": 16740, \"name\": \"the clear container\"}, {\"id\": 16741, \"name\": \"red cord\"}, {\"id\": 16742, \"name\": \"a seated man\"}, {\"id\": 16743, \"name\": \"pasta\"}, {\"id\": 16744, \"name\": \"black, red, and white uniform\"}, {\"id\": 16745, \"name\": \"the white and blue baseball cap\"}, {\"id\": 16746, \"name\": \"black leather jacket\"}, {\"id\": 16747, \"name\": \"a swirl of white frosting\"}, {\"id\": 16748, \"name\": \"black track\"}, {\"id\": 16749, \"name\": \"the red tail light\"}, {\"id\": 16750, \"name\": \"bar\"}, {\"id\": 16751, \"name\": \"a red roll of tape\"}, {\"id\": 16752, \"name\": \"brown leather duffel bag\"}, {\"id\": 16753, \"name\": \"large badminton shuttlecock\"}, {\"id\": 16754, \"name\": \"the beaded style\"}, {\"id\": 16755, \"name\": \"the dark-painted fingernail\"}, {\"id\": 16756, \"name\": \"the man in a baseball cap\"}, {\"id\": 16757, \"name\": \"yellow and red balloon\"}, {\"id\": 16758, \"name\": \"the lush green grass\"}, {\"id\": 16759, \"name\": \"couch's armrest\"}, {\"id\": 16760, \"name\": \"the pink bow\"}, {\"id\": 16761, \"name\": \"the tall steeple\"}, {\"id\": 16762, \"name\": \"the navy suit\"}, {\"id\": 16763, \"name\": \"black or white sock\"}, {\"id\": 16764, \"name\": \"the large brown dog\"}, {\"id\": 16765, \"name\": \"a small white banner\"}, {\"id\": 16766, \"name\": \"circular door\"}, {\"id\": 16767, \"name\": \"slice of pizza\"}, {\"id\": 16768, \"name\": \"the dark blue jean\"}, {\"id\": 16769, \"name\": \"a hawaiian shirt\"}, {\"id\": 16770, \"name\": \"the small gla jar\"}, {\"id\": 16771, \"name\": \"a drummer\"}, {\"id\": 16772, \"name\": \"the pink blossom\"}, {\"id\": 16773, \"name\": \"a tactical vest\"}, {\"id\": 16774, \"name\": \"a block\"}, {\"id\": 16775, \"name\": \"the circus's name\"}, {\"id\": 16776, \"name\": \"the barber's counter\"}, {\"id\": 16777, \"name\": \"a tan shoe\"}, {\"id\": 16778, \"name\": \"the blurred photograph\"}, {\"id\": 16779, \"name\": \"the score banner\"}, {\"id\": 16780, \"name\": \"blue logo\"}, {\"id\": 16781, \"name\": \"sport field\"}, {\"id\": 16782, \"name\": \"brown pillar\"}, {\"id\": 16783, \"name\": \"the black leather seat\"}, {\"id\": 16784, \"name\": \"the large crowd\"}, {\"id\": 16785, \"name\": \"the camouflage uniform\"}, {\"id\": 16786, \"name\": \"large, translucent, leaf-shaped wing\"}, {\"id\": 16787, \"name\": \"a green grass\"}, {\"id\": 16788, \"name\": \"the man's hair\"}, {\"id\": 16789, \"name\": \"navy blue uniform\"}, {\"id\": 16790, \"name\": \"a dark blue jean\"}, {\"id\": 16791, \"name\": \"a man with prosthetic leg\"}, {\"id\": 16792, \"name\": \"the red wheel\"}, {\"id\": 16793, \"name\": \"the baseball\"}, {\"id\": 16794, \"name\": \"the bright orange life jacket\"}, {\"id\": 16795, \"name\": \"the red hold\"}, {\"id\": 16796, \"name\": \"straple gown\"}, {\"id\": 16797, \"name\": \"women's clothing\"}, {\"id\": 16798, \"name\": \"a woman's body\"}, {\"id\": 16799, \"name\": \"green pole\"}, {\"id\": 16800, \"name\": \"gray bow tie\"}, {\"id\": 16801, \"name\": \"purple bow\"}, {\"id\": 16802, \"name\": \"the small black bowl\"}, {\"id\": 16803, \"name\": \"dark blue helmet\"}, {\"id\": 16804, \"name\": \"white baseball glove\"}, {\"id\": 16805, \"name\": \"the connector\"}, {\"id\": 16806, \"name\": \"the roasted meat\"}, {\"id\": 16807, \"name\": \"white graffiti\"}, {\"id\": 16808, \"name\": \"a prominent window\"}, {\"id\": 16809, \"name\": \"post-it note\"}, {\"id\": 16810, \"name\": \"blue ball with red and yellow design\"}, {\"id\": 16811, \"name\": \"a yellow tablecloth\"}, {\"id\": 16812, \"name\": \"rear wheel\"}, {\"id\": 16813, \"name\": \"a front left headlight\"}, {\"id\": 16814, \"name\": \"dog's neck\"}, {\"id\": 16815, \"name\": \"a male dancer\"}, {\"id\": 16816, \"name\": \"a small black ball\"}, {\"id\": 16817, \"name\": \"red flag\"}, {\"id\": 16818, \"name\": \"a purple leaf\"}, {\"id\": 16819, \"name\": \"a horizontal stripe\"}, {\"id\": 16820, \"name\": \"a formal clothing\"}, {\"id\": 16821, \"name\": \"the stadium's bleachers\"}, {\"id\": 16822, \"name\": \"a large display\"}, {\"id\": 16823, \"name\": \"a brown plastic horse\"}, {\"id\": 16824, \"name\": \"a rugby ball\"}, {\"id\": 16825, \"name\": \"hard-boiled egg\"}, {\"id\": 16826, \"name\": \"the green armchair\"}, {\"id\": 16827, \"name\": \"the large, tinted window\"}, {\"id\": 16828, \"name\": \"an orange chair\"}, {\"id\": 16829, \"name\": \"formula or breastmilk\"}, {\"id\": 16830, \"name\": \"a vibrant green grassy area\"}, {\"id\": 16831, \"name\": \"the wall of the store\"}, {\"id\": 16832, \"name\": \"terracotta roof\"}, {\"id\": 16833, \"name\": \"a silver object\"}, {\"id\": 16834, \"name\": \"white arm sleeve\"}, {\"id\": 16835, \"name\": \"a blue metal frame\"}, {\"id\": 16836, \"name\": \"a right foot\"}, {\"id\": 16837, \"name\": \"the man wearing a hat\"}, {\"id\": 16838, \"name\": \"a silver balloon\"}, {\"id\": 16839, \"name\": \"a red cloth\"}, {\"id\": 16840, \"name\": \"the field's white line\"}, {\"id\": 16841, \"name\": \"the dog's snout\"}, {\"id\": 16842, \"name\": \"a white suv\"}, {\"id\": 16843, \"name\": \"the player's uniform\"}, {\"id\": 16844, \"name\": \"gold medal\"}, {\"id\": 16845, \"name\": \"person's pink shirt\"}, {\"id\": 16846, \"name\": \"a digital clock\"}, {\"id\": 16847, \"name\": \"the wall of a basketball court\"}, {\"id\": 16848, \"name\": \"the red polka dot\"}, {\"id\": 16849, \"name\": \"the vibrant red track\"}, {\"id\": 16850, \"name\": \"the person's right arm\"}, {\"id\": 16851, \"name\": \"the propeller\"}, {\"id\": 16852, \"name\": \"a red and white football\"}, {\"id\": 16853, \"name\": \"the dark gray t-shirt\"}, {\"id\": 16854, \"name\": \"wooden chair\"}, {\"id\": 16855, \"name\": \"the highway image\"}, {\"id\": 16856, \"name\": \"the blue and cloudy sky\"}, {\"id\": 16857, \"name\": \"white soccer ball\"}, {\"id\": 16858, \"name\": \"faint white logo\"}, {\"id\": 16859, \"name\": \"lounge chair\"}, {\"id\": 16860, \"name\": \"a gray metal bar\"}, {\"id\": 16861, \"name\": \"red decoration\"}, {\"id\": 16862, \"name\": \"the rainbow-colored animal\"}, {\"id\": 16863, \"name\": \"outdoor seating\"}, {\"id\": 16864, \"name\": \"a yellow trim\"}, {\"id\": 16865, \"name\": \"full police uniform\"}, {\"id\": 16866, \"name\": \"the black clip\"}, {\"id\": 16867, \"name\": \"a goalie\"}, {\"id\": 16868, \"name\": \"a white wake\"}, {\"id\": 16869, \"name\": \"vertical green bar\"}, {\"id\": 16870, \"name\": \"a finish line\"}, {\"id\": 16871, \"name\": \"the ea sport logo\"}, {\"id\": 16872, \"name\": \"the large, ancient-looking catapult\"}, {\"id\": 16873, \"name\": \"red and yellow equipment\"}, {\"id\": 16874, \"name\": \"the red-roofed building\"}, {\"id\": 16875, \"name\": \"the blue chair\"}, {\"id\": 16876, \"name\": \"the armchair\"}, {\"id\": 16877, \"name\": \"a raised fist logo\"}, {\"id\": 16878, \"name\": \"the crusader logo\"}, {\"id\": 16879, \"name\": \"a red basketball\"}, {\"id\": 16880, \"name\": \"white megaphone\"}, {\"id\": 16881, \"name\": \"an fish\"}, {\"id\": 16882, \"name\": \"black-and-white bowling lane\"}, {\"id\": 16883, \"name\": \"blue bodice\"}, {\"id\": 16884, \"name\": \"the stadium seating\"}, {\"id\": 16885, \"name\": \"the rink's wall\"}, {\"id\": 16886, \"name\": \"a white confetti\"}, {\"id\": 16887, \"name\": \"light blue wall\"}, {\"id\": 16888, \"name\": \"white dress\"}, {\"id\": 16889, \"name\": \"green hill\"}, {\"id\": 16890, \"name\": \"a spectator area\"}, {\"id\": 16891, \"name\": \"a station's logo\"}, {\"id\": 16892, \"name\": \"rod\"}, {\"id\": 16893, \"name\": \"the shaved head\"}, {\"id\": 16894, \"name\": \"globaldance.tv logo\"}, {\"id\": 16895, \"name\": \"a small motorboat\"}, {\"id\": 16896, \"name\": \"the brown chair\"}, {\"id\": 16897, \"name\": \"dark gray material\"}, {\"id\": 16898, \"name\": \"tv logo\"}, {\"id\": 16899, \"name\": \"the blue flooring\"}, {\"id\": 16900, \"name\": \"the large, white stuffed animal\"}, {\"id\": 16901, \"name\": \"goal post\"}, {\"id\": 16902, \"name\": \"black protective gear\"}, {\"id\": 16903, \"name\": \"a puffy sleeve\"}, {\"id\": 16904, \"name\": \"a black high heel\"}, {\"id\": 16905, \"name\": \"a rack of dumbbell\"}, {\"id\": 16906, \"name\": \"a baseball glove\"}, {\"id\": 16907, \"name\": \"the concrete pillar or column\"}, {\"id\": 16908, \"name\": \"brown hat\"}, {\"id\": 16909, \"name\": \"white and navy blue uniform\"}, {\"id\": 16910, \"name\": \"white and yellow uniform\"}, {\"id\": 16911, \"name\": \"wooden shelving\"}, {\"id\": 16912, \"name\": \"an individual in mascot costume\"}, {\"id\": 16913, \"name\": \"the brown tie\"}, {\"id\": 16914, \"name\": \"puma logo\"}, {\"id\": 16915, \"name\": \"yellow spotlight\"}, {\"id\": 16916, \"name\": \"silver and black circular item\"}, {\"id\": 16917, \"name\": \"the circular shape\"}, {\"id\": 16918, \"name\": \"colorful, textured paper item\"}, {\"id\": 16919, \"name\": \"the wooden block\"}, {\"id\": 16920, \"name\": \"rear bicycle frame\"}, {\"id\": 16921, \"name\": \"thai temple\"}, {\"id\": 16922, \"name\": \"a white tablecloth\"}, {\"id\": 16923, \"name\": \"a wall of the gym\"}, {\"id\": 16924, \"name\": \"the sail\"}, {\"id\": 16925, \"name\": \"a long, rectangular bread loaf\"}, {\"id\": 16926, \"name\": \"the white and yellow marking\"}, {\"id\": 16927, \"name\": \"the tank top\"}, {\"id\": 16928, \"name\": \"the visible wheel\"}, {\"id\": 16929, \"name\": \"the orange circle\"}, {\"id\": 16930, \"name\": \"green short\"}, {\"id\": 16931, \"name\": \"a football attire\"}, {\"id\": 16932, \"name\": \"colorful backdrop\"}, {\"id\": 16933, \"name\": \"soccer player\"}, {\"id\": 16934, \"name\": \"a team's name\"}, {\"id\": 16935, \"name\": \"the red and white balloon\"}, {\"id\": 16936, \"name\": \"house or other structure\"}, {\"id\": 16937, \"name\": \"the menu option\"}, {\"id\": 16938, \"name\": \"a red skirt\"}, {\"id\": 16939, \"name\": \"black and green helmet\"}, {\"id\": 16940, \"name\": \"the large backpack\"}, {\"id\": 16941, \"name\": \"the blue and white baseball cap\"}, {\"id\": 16942, \"name\": \"a large archway entrance\"}, {\"id\": 16943, \"name\": \"straw hat\"}, {\"id\": 16944, \"name\": \"the ceramic\"}, {\"id\": 16945, \"name\": \"the red and blue robe\"}, {\"id\": 16946, \"name\": \"a colored bar\"}, {\"id\": 16947, \"name\": \"the cylindrical object\"}, {\"id\": 16948, \"name\": \"snow-covered roof\"}, {\"id\": 16949, \"name\": \"an ice\"}, {\"id\": 16950, \"name\": \"vibrant and colorful illustration\"}, {\"id\": 16951, \"name\": \"a red circular dirt area\"}, {\"id\": 16952, \"name\": \"the outlet\"}, {\"id\": 16953, \"name\": \"shredded carrot\"}, {\"id\": 16954, \"name\": \"the blue and white pompom\"}, {\"id\": 16955, \"name\": \"folding pocket knife\"}, {\"id\": 16956, \"name\": \"the skier\"}, {\"id\": 16957, \"name\": \"the field's number\"}, {\"id\": 16958, \"name\": \"the cattle\"}, {\"id\": 16959, \"name\": \"dark-colored pant\"}, {\"id\": 16960, \"name\": \"the smaller box\"}, {\"id\": 16961, \"name\": \"the black cow\"}, {\"id\": 16962, \"name\": \"the gondola\"}, {\"id\": 16963, \"name\": \"the twig\"}, {\"id\": 16964, \"name\": \"a people's attire\"}, {\"id\": 16965, \"name\": \"the small, rectangular piece of metal\"}, {\"id\": 16966, \"name\": \"a helmet's face mask\"}, {\"id\": 16967, \"name\": \"a black graffiti\"}, {\"id\": 16968, \"name\": \"the white bubble\"}, {\"id\": 16969, \"name\": \"an arch\"}, {\"id\": 16970, \"name\": \"a yellow polka dot\"}, {\"id\": 16971, \"name\": \"purple ball\"}, {\"id\": 16972, \"name\": \"black windshield\"}, {\"id\": 16973, \"name\": \"a sleek white sailboat\"}, {\"id\": 16974, \"name\": \"leaf\"}, {\"id\": 16975, \"name\": \"the crab leg\"}, {\"id\": 16976, \"name\": \"the rusty metal piece\"}, {\"id\": 16977, \"name\": \"the leather good\"}, {\"id\": 16978, \"name\": \"blue quarter-zip pullover\"}, {\"id\": 16979, \"name\": \"barbell\"}, {\"id\": 16980, \"name\": \"the second-story window\"}, {\"id\": 16981, \"name\": \"a sideline\"}, {\"id\": 16982, \"name\": \"a silver paint job\"}, {\"id\": 16983, \"name\": \"cream-colored pole\"}, {\"id\": 16984, \"name\": \"straple top\"}, {\"id\": 16985, \"name\": \"the person's shoe\"}, {\"id\": 16986, \"name\": \"the skylight\"}, {\"id\": 16987, \"name\": \"the colorful bucket\"}, {\"id\": 16988, \"name\": \"the well-manicured sand trap\"}, {\"id\": 16989, \"name\": \"the swimsuit\"}, {\"id\": 16990, \"name\": \"billowy sleeve\"}, {\"id\": 16991, \"name\": \"the red and gold upholstery\"}, {\"id\": 16992, \"name\": \"the red decoration\"}, {\"id\": 16993, \"name\": \"large plastic sheet\"}, {\"id\": 16994, \"name\": \"a target\"}, {\"id\": 16995, \"name\": \"a bowling ball\"}, {\"id\": 16996, \"name\": \"gold insignia\"}, {\"id\": 16997, \"name\": \"the navy blue sock\"}, {\"id\": 16998, \"name\": \"the go-kart racer\"}, {\"id\": 16999, \"name\": \"the wooden pew\"}, {\"id\": 17000, \"name\": \"vibrant firework design\"}, {\"id\": 17001, \"name\": \"a dark green barrier\"}, {\"id\": 17002, \"name\": \"large camera\"}, {\"id\": 17003, \"name\": \"the brown mammoth\"}, {\"id\": 17004, \"name\": \"the metallic outfit\"}, {\"id\": 17005, \"name\": \"a recording equipment\"}, {\"id\": 17006, \"name\": \"the woman in a headscarf\"}, {\"id\": 17007, \"name\": \"the high-visibility vest\"}, {\"id\": 17008, \"name\": \"a white cup\"}, {\"id\": 17009, \"name\": \"lone passenger\"}, {\"id\": 17010, \"name\": \"the monster energy logo\"}, {\"id\": 17011, \"name\": \"shelf of shoe\"}, {\"id\": 17012, \"name\": \"red t-shirt\"}, {\"id\": 17013, \"name\": \"the metal frame\"}, {\"id\": 17014, \"name\": \"the blurred rotor blade\"}, {\"id\": 17015, \"name\": \"the cartoonish car\"}, {\"id\": 17016, \"name\": \"the spring roll\"}, {\"id\": 17017, \"name\": \"an athletic attire\"}, {\"id\": 17018, \"name\": \"green lettuce\"}, {\"id\": 17019, \"name\": \"watermark\"}, {\"id\": 17020, \"name\": \"black wristband\"}, {\"id\": 17021, \"name\": \"a gray baseball cap\"}, {\"id\": 17022, \"name\": \"red life jacket\"}, {\"id\": 17023, \"name\": \"black nike t-shirt\"}, {\"id\": 17024, \"name\": \"a young football player\"}, {\"id\": 17025, \"name\": \"a dark-colored suv\"}, {\"id\": 17026, \"name\": \"red, white, and black jersey\"}, {\"id\": 17027, \"name\": \"white bikini\"}, {\"id\": 17028, \"name\": \"person in a white shirt and yellow short\"}, {\"id\": 17029, \"name\": \"a triangle head\"}, {\"id\": 17030, \"name\": \"a light green curtain\"}, {\"id\": 17031, \"name\": \"brownie or cake batter\"}, {\"id\": 17032, \"name\": \"the green tree\"}, {\"id\": 17033, \"name\": \"the philadelphia eagle football team\"}, {\"id\": 17034, \"name\": \"the red long-sleeved shirt\"}, {\"id\": 17035, \"name\": \"a white and blue tent\"}, {\"id\": 17036, \"name\": \"red and blue life jacket\"}, {\"id\": 17037, \"name\": \"a snow pant\"}, {\"id\": 17038, \"name\": \"a small white plate\"}, {\"id\": 17039, \"name\": \"the matching blue jersey\"}, {\"id\": 17040, \"name\": \"cartoon-style flower\"}, {\"id\": 17041, \"name\": \"a colorful garment\"}, {\"id\": 17042, \"name\": \"the person's left leg\"}, {\"id\": 17043, \"name\": \"a large head\"}, {\"id\": 17044, \"name\": \"red booth\"}, {\"id\": 17045, \"name\": \"logo of liberty university\"}, {\"id\": 17046, \"name\": \"the women's face\"}, {\"id\": 17047, \"name\": \"a horizontal bar\"}, {\"id\": 17048, \"name\": \"a gray and orange wall\"}, {\"id\": 17049, \"name\": \"a black ball with red and white design\"}, {\"id\": 17050, \"name\": \"the yellow frill\"}, {\"id\": 17051, \"name\": \"the box of sparkler\"}, {\"id\": 17052, \"name\": \"a large white box\"}, {\"id\": 17053, \"name\": \"the english sign\"}, {\"id\": 17054, \"name\": \"the person's boot\"}, {\"id\": 17055, \"name\": \"the beige pillar\"}, {\"id\": 17056, \"name\": \"blurred area of white flower\"}, {\"id\": 17057, \"name\": \"goggle\"}, {\"id\": 17058, \"name\": \"the black plumage\"}, {\"id\": 17059, \"name\": \"an athletic shoe\"}, {\"id\": 17060, \"name\": \"the long, wavy blonde hair\"}, {\"id\": 17061, \"name\": \"lush green grassy area\"}, {\"id\": 17062, \"name\": \"the keypad\"}, {\"id\": 17063, \"name\": \"small box\"}, {\"id\": 17064, \"name\": \"a patch\"}, {\"id\": 17065, \"name\": \"a red and black helmet\"}, {\"id\": 17066, \"name\": \"a person in a suit\"}, {\"id\": 17067, \"name\": \"the white sauce\"}, {\"id\": 17068, \"name\": \"white yard line\"}, {\"id\": 17069, \"name\": \"a black blade\"}, {\"id\": 17070, \"name\": \"green circle\"}, {\"id\": 17071, \"name\": \"the red brick facade\"}, {\"id\": 17072, \"name\": \"white canopy tent\"}, {\"id\": 17073, \"name\": \"a yellow protective suit\"}, {\"id\": 17074, \"name\": \"a player's shadow\"}, {\"id\": 17075, \"name\": \"the yellow lettering\"}, {\"id\": 17076, \"name\": \"a rough-hewn wooden plank\"}, {\"id\": 17077, \"name\": \"green brick\"}, {\"id\": 17078, \"name\": \"a small astronaut figure\"}, {\"id\": 17079, \"name\": \"the pinball machine\"}, {\"id\": 17080, \"name\": \"gold border\"}, {\"id\": 17081, \"name\": \"a red detailing\"}, {\"id\": 17082, \"name\": \"white lace\"}, {\"id\": 17083, \"name\": \"player in black\"}, {\"id\": 17084, \"name\": \"an arched window\"}, {\"id\": 17085, \"name\": \"a cream-colored wall\"}, {\"id\": 17086, \"name\": \"the rooftop pool\"}, {\"id\": 17087, \"name\": \"yellow beam\"}, {\"id\": 17088, \"name\": \"a can of spray\"}, {\"id\": 17089, \"name\": \"a black suit jacket\"}, {\"id\": 17090, \"name\": \"a black leather\"}, {\"id\": 17091, \"name\": \"a striped fabric pattern\"}, {\"id\": 17092, \"name\": \"vibrant wall\"}, {\"id\": 17093, \"name\": \"a large white pole\"}, {\"id\": 17094, \"name\": \"blue and white shoe\"}, {\"id\": 17095, \"name\": \"gold star\"}, {\"id\": 17096, \"name\": \"the large pink balloon\"}, {\"id\": 17097, \"name\": \"the wall of the gym\"}, {\"id\": 17098, \"name\": \"the pink respirator\"}, {\"id\": 17099, \"name\": \"the brown leather chair\"}, {\"id\": 17100, \"name\": \"colorful can of soda\"}, {\"id\": 17101, \"name\": \"a khaki short\"}, {\"id\": 17102, \"name\": \"woman in a blue and white cheerleading uniform\"}, {\"id\": 17103, \"name\": \"single white ball\"}, {\"id\": 17104, \"name\": \"the cream-colored cushion\"}, {\"id\": 17105, \"name\": \"white and blue cheerleader's outfit\"}, {\"id\": 17106, \"name\": \"black-painted wood beam\"}, {\"id\": 17107, \"name\": \"folded food\"}, {\"id\": 17108, \"name\": \"a game's status\"}, {\"id\": 17109, \"name\": \"blue canopy\"}, {\"id\": 17110, \"name\": \"an amplifier\"}, {\"id\": 17111, \"name\": \"the baseball bat\"}, {\"id\": 17112, \"name\": \"the small plastic container\"}, {\"id\": 17113, \"name\": \"red and white cone\"}, {\"id\": 17114, \"name\": \"a red serving spoon\"}, {\"id\": 17115, \"name\": \"the red and white uniform\"}, {\"id\": 17116, \"name\": \"a yellow glove\"}, {\"id\": 17117, \"name\": \"a gift\"}, {\"id\": 17118, \"name\": \"a white and black cycling jersey\"}, {\"id\": 17119, \"name\": \"the individuals dressed as stormtrooper from the star war franchise\"}, {\"id\": 17120, \"name\": \"the large storefront\"}, {\"id\": 17121, \"name\": \"distinctive black patch\"}, {\"id\": 17122, \"name\": \"black footrest\"}, {\"id\": 17123, \"name\": \"red awning\"}, {\"id\": 17124, \"name\": \"black metal object\"}, {\"id\": 17125, \"name\": \"the grey chair\"}, {\"id\": 17126, \"name\": \"shovel\"}, {\"id\": 17127, \"name\": \"heart-shaped badge\"}, {\"id\": 17128, \"name\": \"the small, light blue sticker\"}, {\"id\": 17129, \"name\": \"the mallet\"}, {\"id\": 17130, \"name\": \"white star\"}, {\"id\": 17131, \"name\": \"the mannequin head\"}, {\"id\": 17132, \"name\": \"the square design\"}, {\"id\": 17133, \"name\": \"the red and blue box\"}, {\"id\": 17134, \"name\": \"the stacked box\"}, {\"id\": 17135, \"name\": \"a red and purple rectangle\"}, {\"id\": 17136, \"name\": \"the vehicle's roof\"}, {\"id\": 17137, \"name\": \"the photo of a person\"}, {\"id\": 17138, \"name\": \"the red attire\"}, {\"id\": 17139, \"name\": \"yellow backrest\"}, {\"id\": 17140, \"name\": \"the camouflage fatigue\"}, {\"id\": 17141, \"name\": \"large wheel\"}, {\"id\": 17142, \"name\": \"personal genome machine\"}, {\"id\": 17143, \"name\": \"the purple and white banner\"}, {\"id\": 17144, \"name\": \"the long hair\"}, {\"id\": 17145, \"name\": \"running attire\"}, {\"id\": 17146, \"name\": \"red and white baseball cap\"}, {\"id\": 17147, \"name\": \"the yellow and black buoy\"}, {\"id\": 17148, \"name\": \"the clear plastic container\"}, {\"id\": 17149, \"name\": \"a vibrant costume\"}, {\"id\": 17150, \"name\": \"baking tray\"}, {\"id\": 17151, \"name\": \"a vertical metal frame\"}, {\"id\": 17152, \"name\": \"the small bag\"}, {\"id\": 17153, \"name\": \"the red glass\"}, {\"id\": 17154, \"name\": \"the red and blue laser light\"}, {\"id\": 17155, \"name\": \"the symbol\"}, {\"id\": 17156, \"name\": \"a large container\"}, {\"id\": 17157, \"name\": \"prominent white column\"}, {\"id\": 17158, \"name\": \"atm sign\"}, {\"id\": 17159, \"name\": \"a clear container\"}, {\"id\": 17160, \"name\": \"a round bale of hay\"}, {\"id\": 17161, \"name\": \"the clear plastic\"}, {\"id\": 17162, \"name\": \"brown meat\"}, {\"id\": 17163, \"name\": \"a large rear window\"}, {\"id\": 17164, \"name\": \"the silver screw\"}, {\"id\": 17165, \"name\": \"a white dre shirt\"}, {\"id\": 17166, \"name\": \"the small white wave\"}, {\"id\": 17167, \"name\": \"a short haircut\"}, {\"id\": 17168, \"name\": \"a vertical post\"}, {\"id\": 17169, \"name\": \"a red touchdown banner\"}, {\"id\": 17170, \"name\": \"blade\"}, {\"id\": 17171, \"name\": \"dark nail polish\"}, {\"id\": 17172, \"name\": \"camel's leg\"}, {\"id\": 17173, \"name\": \"camera operator\"}, {\"id\": 17174, \"name\": \"bottle of quaker oat\"}, {\"id\": 17175, \"name\": \"the small television\"}, {\"id\": 17176, \"name\": \"white robe\"}, {\"id\": 17177, \"name\": \"a rectangular panel\"}, {\"id\": 17178, \"name\": \"yellow cushion\"}, {\"id\": 17179, \"name\": \"a tattooed arm\"}, {\"id\": 17180, \"name\": \"the red life preserver\"}, {\"id\": 17181, \"name\": \"wing\"}, {\"id\": 17182, \"name\": \"the luxury vehicle\"}, {\"id\": 17183, \"name\": \"the paddle\"}, {\"id\": 17184, \"name\": \"a rectangular piece of wood\"}, {\"id\": 17185, \"name\": \"the kitchen equipment\"}, {\"id\": 17186, \"name\": \"acoustic guitar\"}, {\"id\": 17187, \"name\": \"the prominent banner\"}, {\"id\": 17188, \"name\": \"a blue foam roller\"}, {\"id\": 17189, \"name\": \"a black heart design\"}, {\"id\": 17190, \"name\": \"patriotic display\"}, {\"id\": 17191, \"name\": \"the blue border\"}, {\"id\": 17192, \"name\": \"white diagonal stripe\"}, {\"id\": 17193, \"name\": \"a game's controls\"}, {\"id\": 17194, \"name\": \"a dark-colored pant\"}, {\"id\": 17195, \"name\": \"a purple curtain\"}, {\"id\": 17196, \"name\": \"the gray tent\"}, {\"id\": 17197, \"name\": \"the prosthetic\"}, {\"id\": 17198, \"name\": \"a light purple wall\"}, {\"id\": 17199, \"name\": \"yellow lane divider\"}, {\"id\": 17200, \"name\": \"the yellow jacket with black stripe\"}, {\"id\": 17201, \"name\": \"remote-controlled device\"}, {\"id\": 17202, \"name\": \"a brown suitcase\"}, {\"id\": 17203, \"name\": \"rooftop railing\"}, {\"id\": 17204, \"name\": \"a snowboarder\"}, {\"id\": 17205, \"name\": \"stainle steel bowl\"}, {\"id\": 17206, \"name\": \"the green wheel\"}, {\"id\": 17207, \"name\": \"a traditional tibetan attire\"}, {\"id\": 17208, \"name\": \"the inflatable tube\"}, {\"id\": 17209, \"name\": \"a yellow pom-pom\"}, {\"id\": 17210, \"name\": \"storefront's window\"}, {\"id\": 17211, \"name\": \"vertical green stripe\"}, {\"id\": 17212, \"name\": \"a yellow and green uniform\"}, {\"id\": 17213, \"name\": \"wrench\"}, {\"id\": 17214, \"name\": \"the equal sign (=)\"}, {\"id\": 17215, \"name\": \"red bleacher\"}, {\"id\": 17216, \"name\": \"a white star emblem\"}, {\"id\": 17217, \"name\": \"the light pole\"}, {\"id\": 17218, \"name\": \"a thin pole\"}, {\"id\": 17219, \"name\": \"a blue and yellow flag\"}, {\"id\": 17220, \"name\": \"the wooden rack\"}, {\"id\": 17221, \"name\": \"the red cord\"}, {\"id\": 17222, \"name\": \"a small, clear container\"}, {\"id\": 17223, \"name\": \"the colorful display of block\"}, {\"id\": 17224, \"name\": \"a crop top\"}, {\"id\": 17225, \"name\": \"a purple glove\"}, {\"id\": 17226, \"name\": \"scoreboard\"}, {\"id\": 17227, \"name\": \"a brown boot\"}, {\"id\": 17228, \"name\": \"navy blue and white uniform\"}, {\"id\": 17229, \"name\": \"the white basketball net\"}, {\"id\": 17230, \"name\": \"a brown animal\"}, {\"id\": 17231, \"name\": \"the blue and yellow uniform\"}, {\"id\": 17232, \"name\": \"the yellow band\"}, {\"id\": 17233, \"name\": \"a lush green baseball field\"}, {\"id\": 17234, \"name\": \"swimsuit\"}, {\"id\": 17235, \"name\": \"the dog's mouth\"}, {\"id\": 17236, \"name\": \"the brown support beam\"}, {\"id\": 17237, \"name\": \"black baseball jersey\"}, {\"id\": 17238, \"name\": \"a wooden wheel\"}, {\"id\": 17239, \"name\": \"the dark blue jacket\"}, {\"id\": 17240, \"name\": \"the facade\"}, {\"id\": 17241, \"name\": \"a smaller cliff\"}, {\"id\": 17242, \"name\": \"the tan pant\"}, {\"id\": 17243, \"name\": \"firework box\"}, {\"id\": 17244, \"name\": \"a white wristband\"}, {\"id\": 17245, \"name\": \"the professional attire\"}, {\"id\": 17246, \"name\": \"a team logo\"}, {\"id\": 17247, \"name\": \"blue mat\"}, {\"id\": 17248, \"name\": \"a beige panel\"}, {\"id\": 17249, \"name\": \"a black wall\"}, {\"id\": 17250, \"name\": \"a photo of a person swinging on a swing\"}, {\"id\": 17251, \"name\": \"the white suv\"}, {\"id\": 17252, \"name\": \"a gray insole\"}, {\"id\": 17253, \"name\": \"an aluminum serving tray\"}, {\"id\": 17254, \"name\": \"thin power line\"}, {\"id\": 17255, \"name\": \"a bottle of water\"}, {\"id\": 17256, \"name\": \"a green table\"}, {\"id\": 17257, \"name\": \"the raised hand\"}, {\"id\": 17258, \"name\": \"brown bulldog\"}, {\"id\": 17259, \"name\": \"yellow rope barrier\"}, {\"id\": 17260, \"name\": \"a bmx biking\"}, {\"id\": 17261, \"name\": \"the snow-covered mountain\"}, {\"id\": 17262, \"name\": \"small, red and black package\"}, {\"id\": 17263, \"name\": \"a sleek sport car\"}, {\"id\": 17264, \"name\": \"upright post\"}, {\"id\": 17265, \"name\": \"white and black striped uniform\"}, {\"id\": 17266, \"name\": \"a wind instrument\"}, {\"id\": 17267, \"name\": \"a blue jersey\"}, {\"id\": 17268, \"name\": \"a white blade\"}, {\"id\": 17269, \"name\": \"a red seat\"}, {\"id\": 17270, \"name\": \"the sardine\"}, {\"id\": 17271, \"name\": \"the kayak\"}, {\"id\": 17272, \"name\": \"colorful background\"}, {\"id\": 17273, \"name\": \"hair bun\"}, {\"id\": 17274, \"name\": \"blue box\"}, {\"id\": 17275, \"name\": \"backdrop of advertisement\"}, {\"id\": 17276, \"name\": \"green flag\"}, {\"id\": 17277, \"name\": \"dog's collar\"}, {\"id\": 17278, \"name\": \"a pink-painted nail\"}, {\"id\": 17279, \"name\": \"an orange cone\"}, {\"id\": 17280, \"name\": \"a wicket\"}, {\"id\": 17281, \"name\": \"white fur-trimmed hat\"}, {\"id\": 17282, \"name\": \"a pea\"}, {\"id\": 17283, \"name\": \"large beige tile\"}, {\"id\": 17284, \"name\": \"green siding\"}, {\"id\": 17285, \"name\": \"a pink seat\"}, {\"id\": 17286, \"name\": \"the gold-colored item\"}, {\"id\": 17287, \"name\": \"the squat stand\"}, {\"id\": 17288, \"name\": \"a yellow flag\"}, {\"id\": 17289, \"name\": \"the skateboard\"}, {\"id\": 17290, \"name\": \"a white cricket ball\"}, {\"id\": 17291, \"name\": \"the black blazer\"}, {\"id\": 17292, \"name\": \"a textured blue finish\"}, {\"id\": 17293, \"name\": \"blue rubber flooring\"}, {\"id\": 17294, \"name\": \"blue exercise ball\"}, {\"id\": 17295, \"name\": \"the navy blue and white uniform\"}, {\"id\": 17296, \"name\": \"a short, black hair\"}, {\"id\": 17297, \"name\": \"a blue ice skate\"}, {\"id\": 17298, \"name\": \"small tan and brown dog\"}, {\"id\": 17299, \"name\": \"a conical hat\"}, {\"id\": 17300, \"name\": \"the roll of fabric\"}, {\"id\": 17301, \"name\": \"dark baseball cap\"}, {\"id\": 17302, \"name\": \"a colorful obstacle\"}, {\"id\": 17303, \"name\": \"an armor\"}, {\"id\": 17304, \"name\": \"green stem\"}, {\"id\": 17305, \"name\": \"a can of food\"}, {\"id\": 17306, \"name\": \"a navy-blue jersey\"}, {\"id\": 17307, \"name\": \"long, white table\"}, {\"id\": 17308, \"name\": \"black leather storage ottoman\"}, {\"id\": 17309, \"name\": \"the circular red cheek\"}, {\"id\": 17310, \"name\": \"packaging\"}, {\"id\": 17311, \"name\": \"the white and blue short\"}, {\"id\": 17312, \"name\": \"the tombstone\"}, {\"id\": 17313, \"name\": \"the young individual\"}, {\"id\": 17314, \"name\": \"the prominent arched window\"}, {\"id\": 17315, \"name\": \"the fox sport logo\"}, {\"id\": 17316, \"name\": \"large, curved ramp\"}, {\"id\": 17317, \"name\": \"square skylight\"}, {\"id\": 17318, \"name\": \"red logo\"}, {\"id\": 17319, \"name\": \"a pharmacy sign\"}, {\"id\": 17320, \"name\": \"a long, red hose\"}, {\"id\": 17321, \"name\": \"the palm tree's silhouette\"}, {\"id\": 17322, \"name\": \"large white sheep\"}, {\"id\": 17323, \"name\": \"a small ramp\"}, {\"id\": 17324, \"name\": \"a gathering of person\"}, {\"id\": 17325, \"name\": \"a blue sticker\"}, {\"id\": 17326, \"name\": \"person's black-clad leg\"}, {\"id\": 17327, \"name\": \"a craft supply\"}, {\"id\": 17328, \"name\": \"blue beam\"}, {\"id\": 17329, \"name\": \"black and white attire\"}, {\"id\": 17330, \"name\": \"the dark-colored uniform\"}, {\"id\": 17331, \"name\": \"the rich, dark brown frosting\"}, {\"id\": 17332, \"name\": \"cameraman\"}, {\"id\": 17333, \"name\": \"a black mask\"}, {\"id\": 17334, \"name\": \"the ski boot\"}, {\"id\": 17335, \"name\": \"a pink ballet shoe\"}, {\"id\": 17336, \"name\": \"bow tie\"}, {\"id\": 17337, \"name\": \"flat wall\"}, {\"id\": 17338, \"name\": \"the clear plastic tag\"}, {\"id\": 17339, \"name\": \"shiny silver finish\"}, {\"id\": 17340, \"name\": \"large arched window\"}, {\"id\": 17341, \"name\": \"light-colored long-sleeved shirt\"}, {\"id\": 17342, \"name\": \"metal basket\"}, {\"id\": 17343, \"name\": \"the box of merchandise\"}, {\"id\": 17344, \"name\": \"playing area\"}, {\"id\": 17345, \"name\": \"a test tube\"}, {\"id\": 17346, \"name\": \"the large construction vehicle\"}, {\"id\": 17347, \"name\": \"storage box\"}, {\"id\": 17348, \"name\": \"cle 6\"}, {\"id\": 17349, \"name\": \"school uniform\"}, {\"id\": 17350, \"name\": \"the person on rollerblade\"}, {\"id\": 17351, \"name\": \"the red decal\"}, {\"id\": 17352, \"name\": \"the dark green jacket\"}, {\"id\": 17353, \"name\": \"the black, tinted windshield\"}, {\"id\": 17354, \"name\": \"brightly colored decoration\"}, {\"id\": 17355, \"name\": \"gray capri pant\"}, {\"id\": 17356, \"name\": \"white barrier\"}, {\"id\": 17357, \"name\": \"the smaller banner\"}, {\"id\": 17358, \"name\": \"gray blind\"}, {\"id\": 17359, \"name\": \"a green leash\"}, {\"id\": 17360, \"name\": \"a neon green seat\"}, {\"id\": 17361, \"name\": \"the blue dog paw print\"}, {\"id\": 17362, \"name\": \"a cheerleader\"}, {\"id\": 17363, \"name\": \"the supporting pillar\"}, {\"id\": 17364, \"name\": \"the long stick\"}, {\"id\": 17365, \"name\": \"blue and green soccer ball\"}, {\"id\": 17366, \"name\": \"the roll\"}, {\"id\": 17367, \"name\": \"the large, multicolored umbrella\"}, {\"id\": 17368, \"name\": \"a white square\"}, {\"id\": 17369, \"name\": \"a yellow dog head\"}, {\"id\": 17370, \"name\": \"a black folding chair\"}, {\"id\": 17371, \"name\": \"the white document\"}, {\"id\": 17372, \"name\": \"silver top\"}, {\"id\": 17373, \"name\": \"the large, dark, reflective object\"}, {\"id\": 17374, \"name\": \"a football game\"}, {\"id\": 17375, \"name\": \"the baltimore raven football player\"}, {\"id\": 17376, \"name\": \"large white circle\"}, {\"id\": 17377, \"name\": \"the notebook\"}, {\"id\": 17378, \"name\": \"a green and white jersey\"}, {\"id\": 17379, \"name\": \"shield icon\"}, {\"id\": 17380, \"name\": \"a blue and yellow striped border\"}, {\"id\": 17381, \"name\": \"red balloon\"}, {\"id\": 17382, \"name\": \"front left headlight\"}, {\"id\": 17383, \"name\": \"light brown tile\"}, {\"id\": 17384, \"name\": \"the flag's pole\"}, {\"id\": 17385, \"name\": \"covered seating area\"}, {\"id\": 17386, \"name\": \"the sleeveless, sparkly top\"}, {\"id\": 17387, \"name\": \"blue tank top\"}, {\"id\": 17388, \"name\": \"the large, light-colored building\"}, {\"id\": 17389, \"name\": \"a yellow outline\"}, {\"id\": 17390, \"name\": \"a rescuer\"}, {\"id\": 17391, \"name\": \"rollercoaster's support beam\"}, {\"id\": 17392, \"name\": \"blurred audi logo\"}, {\"id\": 17393, \"name\": \"distinct colored helmet\"}, {\"id\": 17394, \"name\": \"metal leg\"}, {\"id\": 17395, \"name\": \"a cylindrical light\"}, {\"id\": 17396, \"name\": \"backseat of a car\"}, {\"id\": 17397, \"name\": \"a black wig\"}, {\"id\": 17398, \"name\": \"the translucent white banner\"}, {\"id\": 17399, \"name\": \"light-colored pant\"}, {\"id\": 17400, \"name\": \"a brunette cheerleader\"}, {\"id\": 17401, \"name\": \"grape\"}, {\"id\": 17402, \"name\": \"a long, thin stick\"}, {\"id\": 17403, \"name\": \"blue and white obstacle\"}, {\"id\": 17404, \"name\": \"the reflective stripe\"}, {\"id\": 17405, \"name\": \"the large cabinet\"}, {\"id\": 17406, \"name\": \"a plate's surface\"}, {\"id\": 17407, \"name\": \"a beverage\"}, {\"id\": 17408, \"name\": \"a long wooden plank\"}, {\"id\": 17409, \"name\": \"the vibrant pattern of blue and green flower\"}, {\"id\": 17410, \"name\": \"a garbage can\"}, {\"id\": 17411, \"name\": \"light-colored concrete\"}, {\"id\": 17412, \"name\": \"the tree branch\"}, {\"id\": 17413, \"name\": \"an obstacle\"}, {\"id\": 17414, \"name\": \"black-and-white photo\"}, {\"id\": 17415, \"name\": \"the goalkeeper's shadow\"}, {\"id\": 17416, \"name\": \"a pinstripe design\"}, {\"id\": 17417, \"name\": \"the large pot\"}, {\"id\": 17418, \"name\": \"the black cape\"}, {\"id\": 17419, \"name\": \"georgia bulldog logo\"}, {\"id\": 17420, \"name\": \"a white crosswalk\"}, {\"id\": 17421, \"name\": \"the matching black uniform\"}, {\"id\": 17422, \"name\": \"the woman's left arm\"}, {\"id\": 17423, \"name\": \"the small green shrub\"}, {\"id\": 17424, \"name\": \"shrimp\"}, {\"id\": 17425, \"name\": \"a football goalpost\"}, {\"id\": 17426, \"name\": \"a yellow hat\"}, {\"id\": 17427, \"name\": \"the white tiled wall\"}, {\"id\": 17428, \"name\": \"a long, light brown hair\"}, {\"id\": 17429, \"name\": \"red blanket\"}, {\"id\": 17430, \"name\": \"singer\"}, {\"id\": 17431, \"name\": \"a green turf\"}, {\"id\": 17432, \"name\": \"dark jean\"}, {\"id\": 17433, \"name\": \"the prominent white tent\"}, {\"id\": 17434, \"name\": \"the matching pink feathered costume\"}, {\"id\": 17435, \"name\": \"the white-painted nail\"}, {\"id\": 17436, \"name\": \"blue hat\"}, {\"id\": 17437, \"name\": \"metal side\"}, {\"id\": 17438, \"name\": \"the small section of a white wall\"}, {\"id\": 17439, \"name\": \"a black and white shoe\"}, {\"id\": 17440, \"name\": \"brown paper\"}, {\"id\": 17441, \"name\": \"black and green uniform\"}, {\"id\": 17442, \"name\": \"a team with the red uniform\"}, {\"id\": 17443, \"name\": \"the ice rink's glass barrier\"}, {\"id\": 17444, \"name\": \"instagram logo\"}, {\"id\": 17445, \"name\": \"the pink polka dot\"}, {\"id\": 17446, \"name\": \"a small ball\"}, {\"id\": 17447, \"name\": \"the towering tree\"}, {\"id\": 17448, \"name\": \"a large white drum\"}, {\"id\": 17449, \"name\": \"the dog's paw\"}, {\"id\": 17450, \"name\": \"paintball arena\"}, {\"id\": 17451, \"name\": \"a blue face\"}, {\"id\": 17452, \"name\": \"baseball player's foot\"}, {\"id\": 17453, \"name\": \"colorful outfit\"}, {\"id\": 17454, \"name\": \"a rink\"}, {\"id\": 17455, \"name\": \"a colorful gem\"}, {\"id\": 17456, \"name\": \"red and grey court\"}, {\"id\": 17457, \"name\": \"car's right front wheel\"}, {\"id\": 17458, \"name\": \"white handle\"}, {\"id\": 17459, \"name\": \"a football jersey\"}, {\"id\": 17460, \"name\": \"a dark grey suit\"}, {\"id\": 17461, \"name\": \"a ball of dough\"}, {\"id\": 17462, \"name\": \"a duffel bag\"}, {\"id\": 17463, \"name\": \"bright green, diamond-patterned metal grille\"}, {\"id\": 17464, \"name\": \"a dark blue banner\"}, {\"id\": 17465, \"name\": \"the brown football\"}, {\"id\": 17466, \"name\": \"aluminum or steel\"}, {\"id\": 17467, \"name\": \"green bean\"}, {\"id\": 17468, \"name\": \"a small round table\"}, {\"id\": 17469, \"name\": \"a man on the right\"}, {\"id\": 17470, \"name\": \"colorful flag\"}, {\"id\": 17471, \"name\": \"a person in a grey jacket\"}, {\"id\": 17472, \"name\": \"a cheerleading pom-pom skirt\"}, {\"id\": 17473, \"name\": \"the green boston celtic basketball jersey\"}, {\"id\": 17474, \"name\": \"corn dog\"}, {\"id\": 17475, \"name\": \"button-down shirt\"}, {\"id\": 17476, \"name\": \"a dish of food\"}, {\"id\": 17477, \"name\": \"a bowling ball return\"}, {\"id\": 17478, \"name\": \"a flower or butterfly\"}, {\"id\": 17479, \"name\": \"a meat-based barbecue\"}, {\"id\": 17480, \"name\": \"the small blue and white cone\"}, {\"id\": 17481, \"name\": \"the wall behind the court\"}, {\"id\": 17482, \"name\": \"white container\"}, {\"id\": 17483, \"name\": \"an unicorn's body\"}, {\"id\": 17484, \"name\": \"the generou scoop of chocolate ice cream\"}, {\"id\": 17485, \"name\": \"grey short\"}, {\"id\": 17486, \"name\": \"a rugby\"}, {\"id\": 17487, \"name\": \"a maroon baseball cap\"}, {\"id\": 17488, \"name\": \"a gauge\"}, {\"id\": 17489, \"name\": \"the softball glove\"}, {\"id\": 17490, \"name\": \"a black sail\"}, {\"id\": 17491, \"name\": \"the white mesh grate\"}, {\"id\": 17492, \"name\": \"a gold embroidery\"}, {\"id\": 17493, \"name\": \"the black spray-painted graffiti\"}, {\"id\": 17494, \"name\": \"the green light\"}, {\"id\": 17495, \"name\": \"a safety goggle\"}, {\"id\": 17496, \"name\": \"wooden sign\"}, {\"id\": 17497, \"name\": \"the large football-shaped object\"}, {\"id\": 17498, \"name\": \"solid beige background\"}, {\"id\": 17499, \"name\": \"a foliage\"}, {\"id\": 17500, \"name\": \"a large gray ramp\"}, {\"id\": 17501, \"name\": \"large serving dish\"}, {\"id\": 17502, \"name\": \"dark blue lane marker\"}, {\"id\": 17503, \"name\": \"the yellow border\"}, {\"id\": 17504, \"name\": \"the long, thin tube\"}, {\"id\": 17505, \"name\": \"green bar\"}, {\"id\": 17506, \"name\": \"the blue balance ball\"}, {\"id\": 17507, \"name\": \"red metal object\"}, {\"id\": 17508, \"name\": \"the baseball diamond\"}, {\"id\": 17509, \"name\": \"the cbs8 news logo\"}, {\"id\": 17510, \"name\": \"lush, vibrant green forest\"}, {\"id\": 17511, \"name\": \"a destroyed house\"}, {\"id\": 17512, \"name\": \"the football uniform\"}, {\"id\": 17513, \"name\": \"arch-shaped cutout\"}, {\"id\": 17514, \"name\": \"the twitter logo\"}, {\"id\": 17515, \"name\": \"a smaller trampoline\"}, {\"id\": 17516, \"name\": \"the wooden guardrail\"}, {\"id\": 17517, \"name\": \"a grey wall\"}, {\"id\": 17518, \"name\": \"prominent message\"}, {\"id\": 17519, \"name\": \"a make-shift costume\"}, {\"id\": 17520, \"name\": \"the wood piece\"}, {\"id\": 17521, \"name\": \"store's wall\"}, {\"id\": 17522, \"name\": \"advertising signage\"}, {\"id\": 17523, \"name\": \"small bowl\"}, {\"id\": 17524, \"name\": \"large black tree trunk\"}, {\"id\": 17525, \"name\": \"a close-up of a person's arm\"}, {\"id\": 17526, \"name\": \"the clear plastic case\"}, {\"id\": 17527, \"name\": \"a cream-colored exterior\"}, {\"id\": 17528, \"name\": \"a camouflage uniform\"}, {\"id\": 17529, \"name\": \"the black window\"}, {\"id\": 17530, \"name\": \"soldier's attire\"}, {\"id\": 17531, \"name\": \"a sushi roll\"}, {\"id\": 17532, \"name\": \"the large, light blue cylindrical object\"}, {\"id\": 17533, \"name\": \"a white and brown dog\"}, {\"id\": 17534, \"name\": \"a person in a green vest\"}, {\"id\": 17535, \"name\": \"white desk\"}, {\"id\": 17536, \"name\": \"a can of fry's spray\"}, {\"id\": 17537, \"name\": \"a full armor\"}, {\"id\": 17538, \"name\": \"a pink bottle\"}, {\"id\": 17539, \"name\": \"the white and yellow uniform\"}, {\"id\": 17540, \"name\": \"a sport equipment\"}, {\"id\": 17541, \"name\": \"dish\"}, {\"id\": 17542, \"name\": \"a dark wood paneling\"}, {\"id\": 17543, \"name\": \"the pink ruffle\"}, {\"id\": 17544, \"name\": \"individual dressed a clown\"}, {\"id\": 17545, \"name\": \"poster or advertisement\"}, {\"id\": 17546, \"name\": \"a metal or plastic piece\"}, {\"id\": 17547, \"name\": \"a yellowish-white tag\"}, {\"id\": 17548, \"name\": \"hockey player\"}, {\"id\": 17549, \"name\": \"a roll of colorful duct tape\"}, {\"id\": 17550, \"name\": \"the trouser\"}, {\"id\": 17551, \"name\": \"a faux leather\"}, {\"id\": 17552, \"name\": \"the tire rim\"}, {\"id\": 17553, \"name\": \"the yellow body\"}, {\"id\": 17554, \"name\": \"background wall\"}, {\"id\": 17555, \"name\": \"light-colored jersey\"}, {\"id\": 17556, \"name\": \"large serving spoon\"}, {\"id\": 17557, \"name\": \"door or window\"}, {\"id\": 17558, \"name\": \"a colorful cartoon\"}, {\"id\": 17559, \"name\": \"watering can\"}, {\"id\": 17560, \"name\": \"the toasted bread roll\"}, {\"id\": 17561, \"name\": \"the pom pom\"}, {\"id\": 17562, \"name\": \"a long white table\"}, {\"id\": 17563, \"name\": \"a loafer\"}, {\"id\": 17564, \"name\": \"the lamppost\"}, {\"id\": 17565, \"name\": \"ride's structure\"}, {\"id\": 17566, \"name\": \"yard line\"}, {\"id\": 17567, \"name\": \"the scoop of chocolate ice cream\"}, {\"id\": 17568, \"name\": \"cow's head\"}, {\"id\": 17569, \"name\": \"a surrounding advertisement\"}, {\"id\": 17570, \"name\": \"a red and black object\"}, {\"id\": 17571, \"name\": \"large silver aluminum pan\"}, {\"id\": 17572, \"name\": \"the stick\"}, {\"id\": 17573, \"name\": \"blue circuit board\"}, {\"id\": 17574, \"name\": \"the silver armor\"}, {\"id\": 17575, \"name\": \"a large rock\"}, {\"id\": 17576, \"name\": \"the purple swimsuit\"}, {\"id\": 17577, \"name\": \"the dark grey car\"}, {\"id\": 17578, \"name\": \"a dark-colored helmet\"}, {\"id\": 17579, \"name\": \"a motorboat\"}, {\"id\": 17580, \"name\": \"gray helmet\"}, {\"id\": 17581, \"name\": \"a bottle of clear and light blue nail polish\"}, {\"id\": 17582, \"name\": \"a close-up of the person's left arm\"}, {\"id\": 17583, \"name\": \"spongebob's smiling face\"}, {\"id\": 17584, \"name\": \"the grey support\"}, {\"id\": 17585, \"name\": \"a bubble solution bottle\"}, {\"id\": 17586, \"name\": \"a white bag of dog treat\"}, {\"id\": 17587, \"name\": \"amusement park ride\"}, {\"id\": 17588, \"name\": \"a cement\"}, {\"id\": 17589, \"name\": \"a plastic action figure\"}, {\"id\": 17590, \"name\": \"beige-colored concrete wall\"}, {\"id\": 17591, \"name\": \"a duct tape\"}, {\"id\": 17592, \"name\": \"a backpack strap\"}, {\"id\": 17593, \"name\": \"black plate\"}, {\"id\": 17594, \"name\": \"black step\"}, {\"id\": 17595, \"name\": \"the clear plastic packaging\"}, {\"id\": 17596, \"name\": \"the black metal bar\"}, {\"id\": 17597, \"name\": \"the person's body\"}, {\"id\": 17598, \"name\": \"minions-themed label\"}, {\"id\": 17599, \"name\": \"the old, rusted metal object\"}, {\"id\": 17600, \"name\": \"trail of white foam\"}, {\"id\": 17601, \"name\": \"document\"}, {\"id\": 17602, \"name\": \"a circuitry\"}, {\"id\": 17603, \"name\": \"brightly colored duct tape\"}, {\"id\": 17604, \"name\": \"french bulldog puppy\"}, {\"id\": 17605, \"name\": \"tan-colored wall\"}, {\"id\": 17606, \"name\": \"a fire alarm pull station\"}, {\"id\": 17607, \"name\": \"character\"}, {\"id\": 17608, \"name\": \"chopped vegetable\"}, {\"id\": 17609, \"name\": \"the silver-colored link\"}, {\"id\": 17610, \"name\": \"person in the air\"}, {\"id\": 17611, \"name\": \"the small black figurine of a person\"}, {\"id\": 17612, \"name\": \"the bottle of sauce\"}, {\"id\": 17613, \"name\": \"the large, silver, pom-pom-adorned headband\"}, {\"id\": 17614, \"name\": \"luxury sport car\"}, {\"id\": 17615, \"name\": \"a barcode sticker\"}, {\"id\": 17616, \"name\": \"cartoon design\"}, {\"id\": 17617, \"name\": \"the stormtrooper costume-wearing individual\"}, {\"id\": 17618, \"name\": \"a jack-o-lantern kite\"}, {\"id\": 17619, \"name\": \"a staff member\"}, {\"id\": 17620, \"name\": \"man in a suit\"}, {\"id\": 17621, \"name\": \"small orange and white kite\"}, {\"id\": 17622, \"name\": \"the checkered patterned bag and pouch\"}, {\"id\": 17623, \"name\": \"a yellow and green tuk-tuk\"}, {\"id\": 17624, \"name\": \"red soccer uniform\"}, {\"id\": 17625, \"name\": \"cartoon man\"}, {\"id\": 17626, \"name\": \"the shopping bag\"}, {\"id\": 17627, \"name\": \"pink frosted meringue\"}, {\"id\": 17628, \"name\": \"the long metal rod\"}, {\"id\": 17629, \"name\": \"male k-pop performer\"}, {\"id\": 17630, \"name\": \"man in a white uniform\"}, {\"id\": 17631, \"name\": \"large m logo\"}, {\"id\": 17632, \"name\": \"an colorful graffiti\"}, {\"id\": 17633, \"name\": \"the large metal serving bowl\"}, {\"id\": 17634, \"name\": \"a rear leg\"}, {\"id\": 17635, \"name\": \"a kitchenware\"}, {\"id\": 17636, \"name\": \"the long, thin carrot\"}, {\"id\": 17637, \"name\": \"a dance shoe\"}, {\"id\": 17638, \"name\": \"a wooden bat\"}, {\"id\": 17639, \"name\": \"the basketball scoreboard\"}, {\"id\": 17640, \"name\": \"a snow-covered bank\"}, {\"id\": 17641, \"name\": \"brown and black checkered material\"}, {\"id\": 17642, \"name\": \"the blue and white striped wall\"}, {\"id\": 17643, \"name\": \"a toy horse\"}, {\"id\": 17644, \"name\": \"the black-and-white striped sock\"}, {\"id\": 17645, \"name\": \"a high-end sport car\"}, {\"id\": 17646, \"name\": \"an orange climbing helmet\"}, {\"id\": 17647, \"name\": \"a stuffed monkey\"}, {\"id\": 17648, \"name\": \"a sheer curtain\"}, {\"id\": 17649, \"name\": \"box of firework\"}, {\"id\": 17650, \"name\": \"rifle\"}, {\"id\": 17651, \"name\": \"a volleyball player\"}, {\"id\": 17652, \"name\": \"the game's box\"}, {\"id\": 17653, \"name\": \"the multicolored dress\"}, {\"id\": 17654, \"name\": \"obstacle\"}, {\"id\": 17655, \"name\": \"a large brown chicken\"}, {\"id\": 17656, \"name\": \"a doibe\"}, {\"id\": 17657, \"name\": \"a large ramp\"}, {\"id\": 17658, \"name\": \"a single white ball\"}, {\"id\": 17659, \"name\": \"the large sword\"}, {\"id\": 17660, \"name\": \"ice cream\"}, {\"id\": 17661, \"name\": \"teal-colored paper\"}, {\"id\": 17662, \"name\": \"a checkered bag\"}, {\"id\": 17663, \"name\": \"tin of lurpak spreadable butter\"}, {\"id\": 17664, \"name\": \"a pompadour\"}, {\"id\": 17665, \"name\": \"the checkered design\"}, {\"id\": 17666, \"name\": \"large stuffed bear\"}, {\"id\": 17667, \"name\": \"the blue and red patterned short\"}, {\"id\": 17668, \"name\": \"the black and white object\"}, {\"id\": 17669, \"name\": \"crepe\"}, {\"id\": 17670, \"name\": \"a red military-style uniform\"}, {\"id\": 17671, \"name\": \"nail polish bottle\"}, {\"id\": 17672, \"name\": \"spongebob's bright yellow square head\"}, {\"id\": 17673, \"name\": \"shredded meat\"}, {\"id\": 17674, \"name\": \"helmet design\"}, {\"id\": 17675, \"name\": \"a large black and white dog\"}, {\"id\": 17676, \"name\": \"the thumbs-up\"}, {\"id\": 17677, \"name\": \"a feathered regalia\"}, {\"id\": 17678, \"name\": \"the silver football helmet\"}, {\"id\": 17679, \"name\": \"a silver helmet\"}, {\"id\": 17680, \"name\": \"the black and white chicken\"}, {\"id\": 17681, \"name\": \"the photo of person playing football\"}, {\"id\": 17682, \"name\": \"red floral accent\"}, {\"id\": 17683, \"name\": \"a small, black, rectangular object\"}, {\"id\": 17684, \"name\": \"the camera equipment\"}, {\"id\": 17685, \"name\": \"the yellow banana\"}, {\"id\": 17686, \"name\": \"long, cylindrical object\"}, {\"id\": 17687, \"name\": \"fire alarm system\"}, {\"id\": 17688, \"name\": \"a purple fabric\"}, {\"id\": 17689, \"name\": \"thigh\"}, {\"id\": 17690, \"name\": \"the yellow vehicle\"}, {\"id\": 17691, \"name\": \"shiny finish\"}, {\"id\": 17692, \"name\": \"a box of nerd candy\"}, {\"id\": 17693, \"name\": \"pink bag\"}, {\"id\": 17694, \"name\": \"a top bun\"}, {\"id\": 17695, \"name\": \"the pink and white basketball uniform\"}, {\"id\": 17696, \"name\": \"red outfit\"}, {\"id\": 17697, \"name\": \"coniferou tree\"}, {\"id\": 17698, \"name\": \"yellow cheese\"}, {\"id\": 17699, \"name\": \"a sleek black sport car\"}, {\"id\": 17700, \"name\": \"the mountain biker\"}, {\"id\": 17701, \"name\": \"a snare drum\"}, {\"id\": 17702, \"name\": \"a metal stand\"}, {\"id\": 17703, \"name\": \"a dog's harness\"}, {\"id\": 17704, \"name\": \"a black leather chair\"}, {\"id\": 17705, \"name\": \"the small doll\"}, {\"id\": 17706, \"name\": \"a square body\"}, {\"id\": 17707, \"name\": \"the white sack of flour\"}, {\"id\": 17708, \"name\": \"slice of ham\"}, {\"id\": 17709, \"name\": \"a toy dog\"}, {\"id\": 17710, \"name\": \"the gray block\"}, {\"id\": 17711, \"name\": \"orange stripe\"}, {\"id\": 17712, \"name\": \"orange logo\"}, {\"id\": 17713, \"name\": \"large blue punching bag\"}, {\"id\": 17714, \"name\": \"ballet shoe\"}, {\"id\": 17715, \"name\": \"the large, gray concrete wall\"}, {\"id\": 17716, \"name\": \"a dark brown cabinet\"}, {\"id\": 17717, \"name\": \"the small mammal\"}, {\"id\": 17718, \"name\": \"red and white striped jump\"}, {\"id\": 17719, \"name\": \"the small, colorful animal-shaped toy\"}, {\"id\": 17720, \"name\": \"gray top\"}, {\"id\": 17721, \"name\": \"the large eye\"}, {\"id\": 17722, \"name\": \"the football equipment\"}, {\"id\": 17723, \"name\": \"a partially constructed bicycle\"}, {\"id\": 17724, \"name\": \"a network port\"}, {\"id\": 17725, \"name\": \"the small accessory\"}, {\"id\": 17726, \"name\": \"chihuahua\"}, {\"id\": 17727, \"name\": \"a dirt bike\"}, {\"id\": 17728, \"name\": \"a civilian vehicle\"}, {\"id\": 17729, \"name\": \"salmon\"}, {\"id\": 17730, \"name\": \"a lush grassy area\"}, {\"id\": 17731, \"name\": \"a plastic-wrapped item\"}, {\"id\": 17732, \"name\": \"red and white face\"}, {\"id\": 17733, \"name\": \"the mango\"}, {\"id\": 17734, \"name\": \"large bubble illustration\"}, {\"id\": 17735, \"name\": \"the bottle of topping\"}, {\"id\": 17736, \"name\": \"the bright green handle\"}, {\"id\": 17737, \"name\": \"an exercising person\"}, {\"id\": 17738, \"name\": \"chestnut horse\"}, {\"id\": 17739, \"name\": \"a stuffed animal resembling a bear\"}, {\"id\": 17740, \"name\": \"small white soccer ball\"}, {\"id\": 17741, \"name\": \"the soldier\"}, {\"id\": 17742, \"name\": \"small, black, circular dish\"}, {\"id\": 17743, \"name\": \"a red-framed door\"}, {\"id\": 17744, \"name\": \"a packet of garlic and ginger stir-fry sauce\"}, {\"id\": 17745, \"name\": \"lapel\"}, {\"id\": 17746, \"name\": \"vegetable\"}, {\"id\": 17747, \"name\": \"a black vent\"}, {\"id\": 17748, \"name\": \"a minions-themed bubble wand\"}, {\"id\": 17749, \"name\": \"vibrant party blower\"}, {\"id\": 17750, \"name\": \"stuffed monkey\"}, {\"id\": 17751, \"name\": \"the yellow rope\"}, {\"id\": 17752, \"name\": \"a small, stuffed animal\"}, {\"id\": 17753, \"name\": \"a yellow safety vest\"}, {\"id\": 17754, \"name\": \"a water glass\"}, {\"id\": 17755, \"name\": \"a large silver tray\"}, {\"id\": 17756, \"name\": \"yellow window\"}, {\"id\": 17757, \"name\": \"a round link\"}, {\"id\": 17758, \"name\": \"a roll of bright green duct tape\"}, {\"id\": 17759, \"name\": \"wall with graffiti\"}, {\"id\": 17760, \"name\": \"the gondolier\"}, {\"id\": 17761, \"name\": \"plush pikachu toy\"}, {\"id\": 17762, \"name\": \"receipt\"}, {\"id\": 17763, \"name\": \"a light yellow wall\"}, {\"id\": 17764, \"name\": \"the bank of video monitor\"}, {\"id\": 17765, \"name\": \"the white stormtrooper\"}, {\"id\": 17766, \"name\": \"the ice crystal\"}, {\"id\": 17767, \"name\": \"a white spur uniform\"}, {\"id\": 17768, \"name\": \"the snowman design\"}, {\"id\": 17769, \"name\": \"man in a red plaid shirt\"}, {\"id\": 17770, \"name\": \"blue roll of tape\"}, {\"id\": 17771, \"name\": \"the military official\"}, {\"id\": 17772, \"name\": \"note\"}, {\"id\": 17773, \"name\": \"traditional japanese kimono\"}, {\"id\": 17774, \"name\": \"watermelon design\"}, {\"id\": 17775, \"name\": \"small pink kite\"}, {\"id\": 17776, \"name\": \"the laptop's internal components\"}, {\"id\": 17777, \"name\": \"a brown leather couch\"}, {\"id\": 17778, \"name\": \"the lighter\"}, {\"id\": 17779, \"name\": \"the festive item\"}, {\"id\": 17780, \"name\": \"green and blue marking\"}, {\"id\": 17781, \"name\": \"a dog's collar\"}, {\"id\": 17782, \"name\": \"a party supply\"}, {\"id\": 17783, \"name\": \"the passing vehicle\"}, {\"id\": 17784, \"name\": \"a large black sail\"}, {\"id\": 17785, \"name\": \"a dimly lit restaurant dining table\"}, {\"id\": 17786, \"name\": \"brown pillow\"}, {\"id\": 17787, \"name\": \"blue captain america costume\"}, {\"id\": 17788, \"name\": \"a playground slide\"}, {\"id\": 17789, \"name\": \"can of lipton tea\"}, {\"id\": 17790, \"name\": \"the black ink\"}, {\"id\": 17791, \"name\": \"individual's left hand\"}, {\"id\": 17792, \"name\": \"wood\"}, {\"id\": 17793, \"name\": \"wall-mounted fire alarm system\"}, {\"id\": 17794, \"name\": \"a bright red paintwork\"}, {\"id\": 17795, \"name\": \"flute\"}, {\"id\": 17796, \"name\": \"a sarong\"}, {\"id\": 17797, \"name\": \"a white fabric or paper\"}, {\"id\": 17798, \"name\": \"the 11 man\"}, {\"id\": 17799, \"name\": \"the brown wooden paneling\"}, {\"id\": 17800, \"name\": \"kitchen staff member\"}, {\"id\": 17801, \"name\": \"the face paint design\"}, {\"id\": 17802, \"name\": \"tray of white food\"}, {\"id\": 17803, \"name\": \"yellow floor\"}, {\"id\": 17804, \"name\": \"dark-colored uniform\"}, {\"id\": 17805, \"name\": \"a checkered pattern\"}, {\"id\": 17806, \"name\": \"the person with a shaved head\"}, {\"id\": 17807, \"name\": \"the black metal beam\"}, {\"id\": 17808, \"name\": \"a red, pink, purple, and yellow flower\"}, {\"id\": 17809, \"name\": \"multicolored ball\"}, {\"id\": 17810, \"name\": \"a crane\"}, {\"id\": 17811, \"name\": \"woman's right hand\"}, {\"id\": 17812, \"name\": \"an exercise ball\"}, {\"id\": 17813, \"name\": \"the white beam\"}, {\"id\": 17814, \"name\": \"the red vertical stripe\"}, {\"id\": 17815, \"name\": \"can of cranberry\"}, {\"id\": 17816, \"name\": \"black and orange object\"}, {\"id\": 17817, \"name\": \"tomato\"}, {\"id\": 17818, \"name\": \"a white sailboat\"}, {\"id\": 17819, \"name\": \"a cupcake with pink icing\"}, {\"id\": 17820, \"name\": \"the player in a white jersey and green short\"}, {\"id\": 17821, \"name\": \"the dark blue and white uniform\"}, {\"id\": 17822, \"name\": \"the checkered pouches and bag\"}, {\"id\": 17823, \"name\": \"a checkered leather pouch\"}, {\"id\": 17824, \"name\": \"a lush green grass\"}, {\"id\": 17825, \"name\": \"the trout or salmon\"}, {\"id\": 17826, \"name\": \"a army team\"}, {\"id\": 17827, \"name\": \"the white paper\"}, {\"id\": 17828, \"name\": \"a sign or a piece of art\"}, {\"id\": 17829, \"name\": \"an artwork\"}, {\"id\": 17830, \"name\": \"long, white wig\"}, {\"id\": 17831, \"name\": \"a can of spray fryer oil\"}, {\"id\": 17832, \"name\": \"dark-haired girl\"}, {\"id\": 17833, \"name\": \"the silver helmet\"}, {\"id\": 17834, \"name\": \"a red basketball jersey\"}, {\"id\": 17835, \"name\": \"a mural or graffiti\"}, {\"id\": 17836, \"name\": \"a tire's tread\"}, {\"id\": 17837, \"name\": \"black elastic band\"}, {\"id\": 17838, \"name\": \"a bottle of liquid\"}, {\"id\": 17839, \"name\": \"star\"}, {\"id\": 17840, \"name\": \"the child's head\"}, {\"id\": 17841, \"name\": \"the piece of fabric\"}, {\"id\": 17842, \"name\": \"beige and tan stone column\"}, {\"id\": 17843, \"name\": \"the horse and mule\"}, {\"id\": 17844, \"name\": \"red-clad player\"}, {\"id\": 17845, \"name\": \"the red logo\"}, {\"id\": 17846, \"name\": \"the black, white, and brown animal\"}, {\"id\": 17847, \"name\": \"a black one-piece swimsuit\"}, {\"id\": 17848, \"name\": \"bottle of red nail polish\"}, {\"id\": 17849, \"name\": \"the short, wavy hair\"}, {\"id\": 17850, \"name\": \"the small piece of metal\"}, {\"id\": 17851, \"name\": \"the sheet of paper\"}, {\"id\": 17852, \"name\": \"a gray exercise mat\"}, {\"id\": 17853, \"name\": \"a large digital billboard\"}, {\"id\": 17854, \"name\": \"a fox sport cfb logo\"}, {\"id\": 17855, \"name\": \"the primate\"}, {\"id\": 17856, \"name\": \"a brown, black, and white animal\"}, {\"id\": 17857, \"name\": \"a yellow-clad person\"}, {\"id\": 17858, \"name\": \"light blue collared shirt\"}, {\"id\": 17859, \"name\": \"the bag of rice\"}, {\"id\": 17860, \"name\": \"fabric and clothing item\"}, {\"id\": 17861, \"name\": \"the black cabinet\"}, {\"id\": 17862, \"name\": \"the large orange ramp\"}, {\"id\": 17863, \"name\": \"close-up view of a dog's nose\"}, {\"id\": 17864, \"name\": \"the light beige wall\"}, {\"id\": 17865, \"name\": \"close-up of the person's hand\"}, {\"id\": 17866, \"name\": \"small black container\"}, {\"id\": 17867, \"name\": \"orange climbing helmet\"}, {\"id\": 17868, \"name\": \"a bowl in the background\"}, {\"id\": 17869, \"name\": \"a alpro almond milk\"}, {\"id\": 17870, \"name\": \"the black footwear\"}, {\"id\": 17871, \"name\": \"a character from the star war franchise\"}, {\"id\": 17872, \"name\": \"a clip\"}, {\"id\": 17873, \"name\": \"a motorcyclist's vehicle\"}, {\"id\": 17874, \"name\": \"a warriors' player\"}, {\"id\": 17875, \"name\": \"blue and white panel\"}, {\"id\": 17876, \"name\": \"a blue tape roll\"}, {\"id\": 17877, \"name\": \"the blue and black armor suit\"}, {\"id\": 17878, \"name\": \"left front leg\"}, {\"id\": 17879, \"name\": \"white concrete barrier\"}, {\"id\": 17880, \"name\": \"a reflection of palm tree\"}, {\"id\": 17881, \"name\": \"a port and connector\"}, {\"id\": 17882, \"name\": \"light gray t-shirt\"}, {\"id\": 17883, \"name\": \"the serving spoon\"}, {\"id\": 17884, \"name\": \"green nail polish\"}, {\"id\": 17885, \"name\": \"a snack\"}, {\"id\": 17886, \"name\": \"a floor-to-ceiling window\"}, {\"id\": 17887, \"name\": \"a man in a white shirt and black short\"}, {\"id\": 17888, \"name\": \"the white water\"}, {\"id\": 17889, \"name\": \"a dimly lit table\"}, {\"id\": 17890, \"name\": \"wood post\"}, {\"id\": 17891, \"name\": \"a blue metal tool\"}, {\"id\": 17892, \"name\": \"the black bin\"}, {\"id\": 17893, \"name\": \"a usb port\"}, {\"id\": 17894, \"name\": \"frozen-themed bubble wand\"}, {\"id\": 17895, \"name\": \"the gray and black striped cushion\"}, {\"id\": 17896, \"name\": \"the goalkeeper\"}, {\"id\": 17897, \"name\": \"a canned good\"}, {\"id\": 17898, \"name\": \"large boat\"}, {\"id\": 17899, \"name\": \"the black and white baseball cap\"}, {\"id\": 17900, \"name\": \"the colorful box\"}, {\"id\": 17901, \"name\": \"pink-painted fingernail\"}, {\"id\": 17902, \"name\": \"the metal container of utensil\"}, {\"id\": 17903, \"name\": \"the colorful paragliding parachute\"}, {\"id\": 17904, \"name\": \"a logo for fox sport\"}, {\"id\": 17905, \"name\": \"black and red tracksuit\"}, {\"id\": 17906, \"name\": \"a navy blue jacket\"}, {\"id\": 17907, \"name\": \"the pinkish insulation material\"}, {\"id\": 17908, \"name\": \"white and gray checkered pouch\"}, {\"id\": 17909, \"name\": \"white and silver costume\"}, {\"id\": 17910, \"name\": \"hole\"}, {\"id\": 17911, \"name\": \"the white electrical box\"}, {\"id\": 17912, \"name\": \"the piece of white paper\"}, {\"id\": 17913, \"name\": \"the light yellow wall\"}, {\"id\": 17914, \"name\": \"the large rock\"}, {\"id\": 17915, \"name\": \"a piece of meat\"}, {\"id\": 17916, \"name\": \"roll of neon green duct tape\"}, {\"id\": 17917, \"name\": \"a lush green football field\"}, {\"id\": 17918, \"name\": \"a dark gray yoga mat\"}, {\"id\": 17919, \"name\": \"military truck\"}, {\"id\": 17920, \"name\": \"player's attire\"}, {\"id\": 17921, \"name\": \"blurred football field\"}, {\"id\": 17922, \"name\": \"a light pink meringue\"}, {\"id\": 17923, \"name\": \"football helmet\"}, {\"id\": 17924, \"name\": \"the regatta\"}, {\"id\": 17925, \"name\": \"a black and red package\"}, {\"id\": 17926, \"name\": \"a makeup brush set\"}, {\"id\": 17927, \"name\": \"a blurry hand\"}, {\"id\": 17928, \"name\": \"a textured, checkered pattern\"}, {\"id\": 17929, \"name\": \"a parallel tire track\"}, {\"id\": 17930, \"name\": \"heel\"}, {\"id\": 17931, \"name\": \"a man in a yellow shirt\"}, {\"id\": 17932, \"name\": \"a team of player\"}, {\"id\": 17933, \"name\": \"a stripe\"}, {\"id\": 17934, \"name\": \"the excavator\"}, {\"id\": 17935, \"name\": \"the spongebob\"}, {\"id\": 17936, \"name\": \"small black hole pattern\"}, {\"id\": 17937, \"name\": \"the large brown couch\"}, {\"id\": 17938, \"name\": \"the smaller, black, plastic container\"}, {\"id\": 17939, \"name\": \"a brown and black checkered pattern\"}, {\"id\": 17940, \"name\": \"the colorful plastic hold\"}, {\"id\": 17941, \"name\": \"the blue or red life jacket\"}, {\"id\": 17942, \"name\": \"a light blue jean\"}, {\"id\": 17943, \"name\": \"silver light fixture\"}, {\"id\": 17944, \"name\": \"a skateboard ramp\"}, {\"id\": 17945, \"name\": \"the short, light-brown hair\"}, {\"id\": 17946, \"name\": \"a shelf of purse\"}, {\"id\": 17947, \"name\": \"the silver metal link\"}, {\"id\": 17948, \"name\": \"certificate\"}, {\"id\": 17949, \"name\": \"the purple and green equipment\"}, {\"id\": 17950, \"name\": \"the long, rectangular shape\"}, {\"id\": 17951, \"name\": \"a chopped carrot piece\"}, {\"id\": 17952, \"name\": \"the red and black jersey\"}, {\"id\": 17953, \"name\": \"the green or red base\"}, {\"id\": 17954, \"name\": \"a fabulou flamingo bake\"}, {\"id\": 17955, \"name\": \"brightly colored, cylindrical object\"}, {\"id\": 17956, \"name\": \"the cloth\"}, {\"id\": 17957, \"name\": \"the dark wood trim\"}, {\"id\": 17958, \"name\": \"red door\"}, {\"id\": 17959, \"name\": \"banana bunch\"}, {\"id\": 17960, \"name\": \"black exercise mat\"}, {\"id\": 17961, \"name\": \"the partially filled cone\"}, {\"id\": 17962, \"name\": \"larger, dark-colored dog\"}, {\"id\": 17963, \"name\": \"doodle\"}, {\"id\": 17964, \"name\": \"a dark wood\"}, {\"id\": 17965, \"name\": \"a prominent white sign\"}, {\"id\": 17966, \"name\": \"a minion pez dispenser\"}, {\"id\": 17967, \"name\": \"dial\"}, {\"id\": 17968, \"name\": \"a thumbs-up gesture\"}, {\"id\": 17969, \"name\": \"a left front leg\"}, {\"id\": 17970, \"name\": \"the yellow tape barrier\"}, {\"id\": 17971, \"name\": \"the light-colored suit\"}, {\"id\": 17972, \"name\": \"plush reindeer toy\"}, {\"id\": 17973, \"name\": \"the tomato-based dish\"}, {\"id\": 17974, \"name\": \"the black and white jersey\"}, {\"id\": 17975, \"name\": \"the brown teddy bear\"}, {\"id\": 17976, \"name\": \"the neon light\"}, {\"id\": 17977, \"name\": \"the unicorn figurine\"}, {\"id\": 17978, \"name\": \"an alarm\"}, {\"id\": 17979, \"name\": \"a cbs 8 news logo\"}, {\"id\": 17980, \"name\": \"the light-colored pant\"}, {\"id\": 17981, \"name\": \"person in a pluto costume\"}, {\"id\": 17982, \"name\": \"blue and yellow uniform\"}, {\"id\": 17983, \"name\": \"a car's window\"}, {\"id\": 17984, \"name\": \"a player with the 2\"}, {\"id\": 17985, \"name\": \"large gla window\"}, {\"id\": 17986, \"name\": \"orange flower\"}, {\"id\": 17987, \"name\": \"picture of a white dog wearing a cowboy hat\"}, {\"id\": 17988, \"name\": \"the piece of fish\"}, {\"id\": 17989, \"name\": \"margarita\"}, {\"id\": 17990, \"name\": \"a labrador retriever\"}, {\"id\": 17991, \"name\": \"the overgrown grass\"}, {\"id\": 17992, \"name\": \"an colorful box and container\"}, {\"id\": 17993, \"name\": \"the patterned skirt\"}, {\"id\": 17994, \"name\": \"the sideline\"}, {\"id\": 17995, \"name\": \"white snowball\"}, {\"id\": 17996, \"name\": \"a player's right arm\"}, {\"id\": 17997, \"name\": \"a small stuffed animal\"}, {\"id\": 17998, \"name\": \"a rodeo attire\"}, {\"id\": 17999, \"name\": \"the stone building\"}, {\"id\": 18000, \"name\": \"a punching bag\"}, {\"id\": 18001, \"name\": \"the dark cloth\"}, {\"id\": 18002, \"name\": \"a large sail\"}, {\"id\": 18003, \"name\": \"fin\"}, {\"id\": 18004, \"name\": \"a white chef's coat\"}, {\"id\": 18005, \"name\": \"the rough, textured surface\"}, {\"id\": 18006, \"name\": \"the silver coat rack\"}, {\"id\": 18007, \"name\": \"the piece of exercise equipment\"}, {\"id\": 18008, \"name\": \"floral design\"}, {\"id\": 18009, \"name\": \"grassy hill\"}, {\"id\": 18010, \"name\": \"small piece of ice\"}, {\"id\": 18011, \"name\": \"a person in the room\"}, {\"id\": 18012, \"name\": \"a bacon\"}, {\"id\": 18013, \"name\": \"prominent yellow star\"}, {\"id\": 18014, \"name\": \"a small container\"}, {\"id\": 18015, \"name\": \"the neon yellow safety vest\"}, {\"id\": 18016, \"name\": \"referee or coach\"}, {\"id\": 18017, \"name\": \"white and purple uniform\"}, {\"id\": 18018, \"name\": \"the sack of material\"}, {\"id\": 18019, \"name\": \"red-handled knife\"}, {\"id\": 18020, \"name\": \"the small toy or figurine\"}, {\"id\": 18021, \"name\": \"the crisp white military-style uniform\"}, {\"id\": 18022, \"name\": \"foreground tray\"}, {\"id\": 18023, \"name\": \"a fox sport logo\"}, {\"id\": 18024, \"name\": \"the reflective white stripe\"}, {\"id\": 18025, \"name\": \"the roll of tape\"}, {\"id\": 18026, \"name\": \"a blaster\"}, {\"id\": 18027, \"name\": \"the large wooden wall\"}, {\"id\": 18028, \"name\": \"the porsche\"}, {\"id\": 18029, \"name\": \"the green and white attire\"}, {\"id\": 18030, \"name\": \"a white decal\"}, {\"id\": 18031, \"name\": \"a colorful stuffed animal\"}, {\"id\": 18032, \"name\": \"small, round, animal-shaped toy\"}, {\"id\": 18033, \"name\": \"the colorful figurine\"}, {\"id\": 18034, \"name\": \"a foreground scooter\"}, {\"id\": 18035, \"name\": \"a rich chocolate frosting\"}, {\"id\": 18036, \"name\": \"blue oval label\"}, {\"id\": 18037, \"name\": \"partially visible white object\"}, {\"id\": 18038, \"name\": \"chiquita banana\"}, {\"id\": 18039, \"name\": \"the melted cheese\"}, {\"id\": 18040, \"name\": \"a black shin guard\"}, {\"id\": 18041, \"name\": \"the alarm system\"}, {\"id\": 18042, \"name\": \"a standing person\"}, {\"id\": 18043, \"name\": \"white surface\"}, {\"id\": 18044, \"name\": \"a store's sign\"}, {\"id\": 18045, \"name\": \"red and black uniform\"}, {\"id\": 18046, \"name\": \"the child's left hand\"}, {\"id\": 18047, \"name\": \"the golden-brown pastry\"}, {\"id\": 18048, \"name\": \"the black-and-white photograph\"}, {\"id\": 18049, \"name\": \"old dominion logo\"}, {\"id\": 18050, \"name\": \"the multi-story, light-colored structure\"}, {\"id\": 18051, \"name\": \"the piece of fruit\"}, {\"id\": 18052, \"name\": \"black rectangular object\"}, {\"id\": 18053, \"name\": \"the gold shoulder epaulet\"}, {\"id\": 18054, \"name\": \"the prominent black and gray ramp\"}, {\"id\": 18055, \"name\": \"the green toy\"}, {\"id\": 18056, \"name\": \"the white military uniform\"}, {\"id\": 18057, \"name\": \"a military tank\"}, {\"id\": 18058, \"name\": \"the entrance\"}, {\"id\": 18059, \"name\": \"a captain phasma character\"}, {\"id\": 18060, \"name\": \"small, waffle-cone-shaped cupcake\"}, {\"id\": 18061, \"name\": \"rectangular screen\"}, {\"id\": 18062, \"name\": \"cupcake\"}, {\"id\": 18063, \"name\": \"team logo\"}, {\"id\": 18064, \"name\": \"a tall, cylindrical column\"}, {\"id\": 18065, \"name\": \"white support column\"}, {\"id\": 18066, \"name\": \"the yellow cupcake\"}, {\"id\": 18067, \"name\": \"the large black wheel\"}, {\"id\": 18068, \"name\": \"the white magnet\"}, {\"id\": 18069, \"name\": \"the carrot\"}, {\"id\": 18070, \"name\": \"piece of rusted metal\"}, {\"id\": 18071, \"name\": \"a silver component\"}, {\"id\": 18072, \"name\": \"a standing man\"}, {\"id\": 18073, \"name\": \"small, round toy\"}, {\"id\": 18074, \"name\": \"a 104-pound dumbbell\"}, {\"id\": 18075, \"name\": \"the framed poster\"}, {\"id\": 18076, \"name\": \"the children's clothing\"}, {\"id\": 18077, \"name\": \"a watchers peanut butter\"}, {\"id\": 18078, \"name\": \"a colored bowl\"}, {\"id\": 18079, \"name\": \"a toy animal\"}, {\"id\": 18080, \"name\": \"minion character\"}, {\"id\": 18081, \"name\": \"a glossy black finish\"}, {\"id\": 18082, \"name\": \"the can of sardine\"}, {\"id\": 18083, \"name\": \"the seat\"}, {\"id\": 18084, \"name\": \"foreground speedboat\"}, {\"id\": 18085, \"name\": \"card\"}, {\"id\": 18086, \"name\": \"the waffle cone\"}, {\"id\": 18087, \"name\": \"a console\"}, {\"id\": 18088, \"name\": \"a yellow head\"}, {\"id\": 18089, \"name\": \"the chocolate ice cream cone\"}, {\"id\": 18090, \"name\": \"a black mark\"}, {\"id\": 18091, \"name\": \"the black and white striped insert\"}, {\"id\": 18092, \"name\": \"the similar figurine\"}, {\"id\": 18093, \"name\": \"the black air vent\"}, {\"id\": 18094, \"name\": \"the black leg\"}, {\"id\": 18095, \"name\": \"a bubble blower\"}, {\"id\": 18096, \"name\": \"a meringue\"}, {\"id\": 18097, \"name\": \"a full-body costume\"}, {\"id\": 18098, \"name\": \"a dark gray vehicle\"}, {\"id\": 18099, \"name\": \"package of firework\"}, {\"id\": 18100, \"name\": \"the skater\"}, {\"id\": 18101, \"name\": \"an elephant's body\"}, {\"id\": 18102, \"name\": \"the hook\"}, {\"id\": 18103, \"name\": \"the large duffel bag\"}, {\"id\": 18104, \"name\": \"a squishy animal\"}, {\"id\": 18105, \"name\": \"a filleting fish\"}, {\"id\": 18106, \"name\": \"a seated individual\"}, {\"id\": 18107, \"name\": \"the light-colored wood surface\"}, {\"id\": 18108, \"name\": \"the police officer\"}, {\"id\": 18109, \"name\": \"the white snare drum\"}, {\"id\": 18110, \"name\": \"person's black shirt\"}, {\"id\": 18111, \"name\": \"picture of a dog\"}, {\"id\": 18112, \"name\": \"meat\"}, {\"id\": 18113, \"name\": \"a colorful banner\"}, {\"id\": 18114, \"name\": \"dark brown tight\"}, {\"id\": 18115, \"name\": \"soldier in uniform\"}, {\"id\": 18116, \"name\": \"a smaller wheel\"}, {\"id\": 18117, \"name\": \"orange carrot\"}, {\"id\": 18118, \"name\": \"liba sign\"}, {\"id\": 18119, \"name\": \"the red belt\"}, {\"id\": 18120, \"name\": \"dark gray wheel\"}, {\"id\": 18121, \"name\": \"a stain\"}, {\"id\": 18122, \"name\": \"small white container\"}, {\"id\": 18123, \"name\": \"the lush green grassy area\"}, {\"id\": 18124, \"name\": \"a dark outfit\"}, {\"id\": 18125, \"name\": \"a feathered hat\"}, {\"id\": 18126, \"name\": \"halved tomato\"}, {\"id\": 18127, \"name\": \"bright yellow and grey safety vest\"}, {\"id\": 18128, \"name\": \"the beach mat\"}, {\"id\": 18129, \"name\": \"a small, two-story building\"}, {\"id\": 18130, \"name\": \"the patchy grassy area\"}, {\"id\": 18131, \"name\": \"a light-colored wood or laminate\"}, {\"id\": 18132, \"name\": \"a white and yellow checkered pattern\"}, {\"id\": 18133, \"name\": \"a small brush\"}, {\"id\": 18134, \"name\": \"a green cord\"}, {\"id\": 18135, \"name\": \"a main dish\"}, {\"id\": 18136, \"name\": \"the person in the baseball uniform\"}, {\"id\": 18137, \"name\": \"smaller statue\"}, {\"id\": 18138, \"name\": \"the bright yellow hull\"}, {\"id\": 18139, \"name\": \"white and grey checkered patterned purse\"}, {\"id\": 18140, \"name\": \"red sf logo\"}, {\"id\": 18141, \"name\": \"the richard harri law firm logo\"}, {\"id\": 18142, \"name\": \"the case\"}, {\"id\": 18143, \"name\": \"the player's left leg\"}, {\"id\": 18144, \"name\": \"aluminum pan\"}, {\"id\": 18145, \"name\": \"the larger unicorn\"}, {\"id\": 18146, \"name\": \"a bag's contents\"}, {\"id\": 18147, \"name\": \"the white motorboat\"}, {\"id\": 18148, \"name\": \"the character\"}, {\"id\": 18149, \"name\": \"the minion-themed display\"}, {\"id\": 18150, \"name\": \"gold zipper pull\"}, {\"id\": 18151, \"name\": \"the metal bollard\"}, {\"id\": 18152, \"name\": \"zebra-print tape roll\"}, {\"id\": 18153, \"name\": \"the children's toys\"}, {\"id\": 18154, \"name\": \"the percentage\"}, {\"id\": 18155, \"name\": \"a hi-hat\"}, {\"id\": 18156, \"name\": \"the bolt\"}, {\"id\": 18157, \"name\": \"eel's elongated body\"}, {\"id\": 18158, \"name\": \"person's fingers\"}, {\"id\": 18159, \"name\": \"a cocktail glass\"}, {\"id\": 18160, \"name\": \"the statue's head\"}, {\"id\": 18161, \"name\": \"a matching black t-shirt\"}, {\"id\": 18162, \"name\": \"knife and tool\"}, {\"id\": 18163, \"name\": \"small boy\"}, {\"id\": 18164, \"name\": \"music sheet\"}, {\"id\": 18165, \"name\": \"the white foam\"}, {\"id\": 18166, \"name\": \"the black and red boxing glove\"}, {\"id\": 18167, \"name\": \"the colored sticky note\"}, {\"id\": 18168, \"name\": \"a 50-yard line\"}, {\"id\": 18169, \"name\": \"large, muscular figure\"}, {\"id\": 18170, \"name\": \"a tan pug dog\"}, {\"id\": 18171, \"name\": \"pink banner\"}, {\"id\": 18172, \"name\": \"the jar of baby food\"}, {\"id\": 18173, \"name\": \"the large, round eye\"}, {\"id\": 18174, \"name\": \"a woman's attire\"}, {\"id\": 18175, \"name\": \"an ocean's wave\"}, {\"id\": 18176, \"name\": \"burger\"}, {\"id\": 18177, \"name\": \"a chocolate dessert\"}, {\"id\": 18178, \"name\": \"yellow banana\"}, {\"id\": 18179, \"name\": \"spoonful of chocolate frosting\"}, {\"id\": 18180, \"name\": \"baseball bat\"}, {\"id\": 18181, \"name\": \"hdmi port\"}, {\"id\": 18182, \"name\": \"the white circular casing\"}, {\"id\": 18183, \"name\": \"a gray reflective stripe\"}, {\"id\": 18184, \"name\": \"a pink roll of tape\"}, {\"id\": 18185, \"name\": \"the piece of blue cardboard\"}, {\"id\": 18186, \"name\": \"a captain phasma\"}, {\"id\": 18187, \"name\": \"a creative display of canned good\"}, {\"id\": 18188, \"name\": \"a vertical blind\"}, {\"id\": 18189, \"name\": \"a frosting\"}, {\"id\": 18190, \"name\": \"the distinctive \\\"ball\\\" logo\"}, {\"id\": 18191, \"name\": \"the image of the pin and ball\"}, {\"id\": 18192, \"name\": \"the fence post\"}, {\"id\": 18193, \"name\": \"the teammate\"}, {\"id\": 18194, \"name\": \"can of fry's\"}, {\"id\": 18195, \"name\": \"a sox logo\"}, {\"id\": 18196, \"name\": \"a green sea creature\"}, {\"id\": 18197, \"name\": \"the playful dog\"}, {\"id\": 18198, \"name\": \"long wall\"}, {\"id\": 18199, \"name\": \"person in medical attire\"}, {\"id\": 18200, \"name\": \"a red cable machine\"}, {\"id\": 18201, \"name\": \"the white dog\"}, {\"id\": 18202, \"name\": \"car's front tire\"}, {\"id\": 18203, \"name\": \"white house\"}, {\"id\": 18204, \"name\": \"the personnel carrier\"}, {\"id\": 18205, \"name\": \"the eel\"}, {\"id\": 18206, \"name\": \"tape roll\"}, {\"id\": 18207, \"name\": \"plastic bag of dog treat\"}, {\"id\": 18208, \"name\": \"blurred tan wall\"}, {\"id\": 18209, \"name\": \"red attire\"}, {\"id\": 18210, \"name\": \"the dart\"}, {\"id\": 18211, \"name\": \"lens\"}, {\"id\": 18212, \"name\": \"small, red, plastic toy\"}, {\"id\": 18213, \"name\": \"framed artwork\"}, {\"id\": 18214, \"name\": \"the spongebob with a wide, toothy grin\"}, {\"id\": 18215, \"name\": \"the pink cupcake\"}, {\"id\": 18216, \"name\": \"the hanging light\"}, {\"id\": 18217, \"name\": \"the slightly darker pink meringue\"}, {\"id\": 18218, \"name\": \"a formal military uniform\"}, {\"id\": 18219, \"name\": \"the fish piece\"}, {\"id\": 18220, \"name\": \"titans' logo\"}, {\"id\": 18221, \"name\": \"the colorful sticky note\"}, {\"id\": 18222, \"name\": \"the folded clothing\"}, {\"id\": 18223, \"name\": \"a handbag and pouch\"}, {\"id\": 18224, \"name\": \"the tool\"}, {\"id\": 18225, \"name\": \"a yoga mat\"}, {\"id\": 18226, \"name\": \"piece of construction paper\"}, {\"id\": 18227, \"name\": \"the wooden-handled knife\"}, {\"id\": 18228, \"name\": \"an occupant\"}, {\"id\": 18229, \"name\": \"a pink meringue\"}, {\"id\": 18230, \"name\": \"black metal vise\"}, {\"id\": 18231, \"name\": \"white, snow-like ball\"}, {\"id\": 18232, \"name\": \"the red berry\"}, {\"id\": 18233, \"name\": \"the halfpipe\"}, {\"id\": 18234, \"name\": \"raft\"}, {\"id\": 18235, \"name\": \"prominent blue railing\"}, {\"id\": 18236, \"name\": \"container of lurpak spreadable butter\"}, {\"id\": 18237, \"name\": \"a distinctive red line\"}, {\"id\": 18238, \"name\": \"green sarong\"}, {\"id\": 18239, \"name\": \"a large white tent or pavilion\"}, {\"id\": 18240, \"name\": \"a red hair\"}, {\"id\": 18241, \"name\": \"orange vase\"}, {\"id\": 18242, \"name\": \"a tin can\"}, {\"id\": 18243, \"name\": \"a blue tarp or mat\"}, {\"id\": 18244, \"name\": \"young individual\"}, {\"id\": 18245, \"name\": \"a bright orange helmet\"}, {\"id\": 18246, \"name\": \"distinctive bc logo\"}, {\"id\": 18247, \"name\": \"black shoe image\"}, {\"id\": 18248, \"name\": \"the matching white hat\"}, {\"id\": 18249, \"name\": \"chiquita\"}, {\"id\": 18250, \"name\": \"a minion-themed straw\"}, {\"id\": 18251, \"name\": \"red potato\"}, {\"id\": 18252, \"name\": \"small, colorful toy\"}, {\"id\": 18253, \"name\": \"large, white, curved ramp\"}, {\"id\": 18254, \"name\": \"black cow\"}, {\"id\": 18255, \"name\": \"a can of black bean\"}, {\"id\": 18256, \"name\": \"a craft project\"}, {\"id\": 18257, \"name\": \"black toolbox\"}, {\"id\": 18258, \"name\": \"girl's attire\"}, {\"id\": 18259, \"name\": \"a black and green armor suit\"}, {\"id\": 18260, \"name\": \"a rocky cliffside\"}, {\"id\": 18261, \"name\": \"a snowball\"}, {\"id\": 18262, \"name\": \"a stadium's seating area\"}, {\"id\": 18263, \"name\": \"metal step\"}, {\"id\": 18264, \"name\": \"large new era logo\"}, {\"id\": 18265, \"name\": \"a bag of dog treat\"}, {\"id\": 18266, \"name\": \"a minion\"}, {\"id\": 18267, \"name\": \"tube of nail polish\"}, {\"id\": 18268, \"name\": \"the dog or cat\"}, {\"id\": 18269, \"name\": \"the dark tight\"}, {\"id\": 18270, \"name\": \"the white wall or surface\"}, {\"id\": 18271, \"name\": \"the can of spray cheese\"}, {\"id\": 18272, \"name\": \"the rug or mat\"}, {\"id\": 18273, \"name\": \"the marching band\"}, {\"id\": 18274, \"name\": \"rainbow-colored object\"}, {\"id\": 18275, \"name\": \"clothing item\"}, {\"id\": 18276, \"name\": \"a pez dispenser\"}, {\"id\": 18277, \"name\": \"the barbie doll\"}, {\"id\": 18278, \"name\": \"striking silver armor suit\"}, {\"id\": 18279, \"name\": \"small, round metal link\"}, {\"id\": 18280, \"name\": \"the photo of a man\"}, {\"id\": 18281, \"name\": \"viking\"}, {\"id\": 18282, \"name\": \"can of soup\"}, {\"id\": 18283, \"name\": \"the jar of olive oil\"}, {\"id\": 18284, \"name\": \"brown tight\"}, {\"id\": 18285, \"name\": \"the cardboard\"}, {\"id\": 18286, \"name\": \"the grid-like design\"}, {\"id\": 18287, \"name\": \"rectangular piece of metal\"}, {\"id\": 18288, \"name\": \"a coat rack\"}, {\"id\": 18289, \"name\": \"the pink line\"}, {\"id\": 18290, \"name\": \"one-wheeled vehicle\"}, {\"id\": 18291, \"name\": \"black sport car\"}, {\"id\": 18292, \"name\": \"blue hoodie\"}, {\"id\": 18293, \"name\": \"colorful cylindrical package of firework\"}, {\"id\": 18294, \"name\": \"the white plate\"}, {\"id\": 18295, \"name\": \"a chocolate\"}, {\"id\": 18296, \"name\": \"cookie\"}, {\"id\": 18297, \"name\": \"sleek black surface\"}, {\"id\": 18298, \"name\": \"a korean-style food\"}, {\"id\": 18299, \"name\": \"the black and silver one-piece swimsuit\"}, {\"id\": 18300, \"name\": \"a piece of art\"}, {\"id\": 18301, \"name\": \"cylindrical shape\"}, {\"id\": 18302, \"name\": \"a vibrant, cheesy casserole\"}, {\"id\": 18303, \"name\": \"a pink and orange blanket\"}, {\"id\": 18304, \"name\": \"the purple, blue, green, and polka-dotted material\"}, {\"id\": 18305, \"name\": \"a white speedboat\"}, {\"id\": 18306, \"name\": \"can of quaker oat\"}, {\"id\": 18307, \"name\": \"colorful garland of flag\"}, {\"id\": 18308, \"name\": \"a sailor-themed outfit\"}, {\"id\": 18309, \"name\": \"the stuffed animal with light-brown fur\"}, {\"id\": 18310, \"name\": \"a guitar strap\"}, {\"id\": 18311, \"name\": \"large hole\"}, {\"id\": 18312, \"name\": \"a pink icing\"}, {\"id\": 18313, \"name\": \"the smaller image of a person with a camera\"}, {\"id\": 18314, \"name\": \"the fire alarm box\"}, {\"id\": 18315, \"name\": \"a cartoon-style fruit\"}, {\"id\": 18316, \"name\": \"the blue exercise ball\"}, {\"id\": 18317, \"name\": \"funko product\"}, {\"id\": 18318, \"name\": \"the flotrack's tasty race of the week\"}, {\"id\": 18319, \"name\": \"the blue and green panel\"}, {\"id\": 18320, \"name\": \"light brown concrete wall\"}, {\"id\": 18321, \"name\": \"a ride's canopy\"}, {\"id\": 18322, \"name\": \"portion of a person's arm\"}, {\"id\": 18323, \"name\": \"green tissue paper\"}, {\"id\": 18324, \"name\": \"a dark blue helmet\"}, {\"id\": 18325, \"name\": \"the power tool\"}, {\"id\": 18326, \"name\": \"a small, white tag\"}, {\"id\": 18327, \"name\": \"a small animal\"}, {\"id\": 18328, \"name\": \"a blue squiggle\"}, {\"id\": 18329, \"name\": \"a model's hair\"}, {\"id\": 18330, \"name\": \"maroon stripe\"}, {\"id\": 18331, \"name\": \"the small, spherical object\"}, {\"id\": 18332, \"name\": \"wall of the bathroom\"}, {\"id\": 18333, \"name\": \"the pickled vegetable\"}, {\"id\": 18334, \"name\": \"a large, multicolored tissue paper garland\"}, {\"id\": 18335, \"name\": \"a location or type of alarm system\"}, {\"id\": 18336, \"name\": \"the bottle's label\"}, {\"id\": 18337, \"name\": \"the small toy\"}, {\"id\": 18338, \"name\": \"a multicolored plastic ball\"}, {\"id\": 18339, \"name\": \"a brown leather sofa\"}, {\"id\": 18340, \"name\": \"the grille\"}, {\"id\": 18341, \"name\": \"a wall of wooden cabinet\"}, {\"id\": 18342, \"name\": \"interviewee\"}, {\"id\": 18343, \"name\": \"bmx rider\"}, {\"id\": 18344, \"name\": \"distinctive helmet\"}, {\"id\": 18345, \"name\": \"a silver zipper\"}, {\"id\": 18346, \"name\": \"prominent blue banner\"}, {\"id\": 18347, \"name\": \"chopped green herb\"}, {\"id\": 18348, \"name\": \"a tan baseball cap\"}, {\"id\": 18349, \"name\": \"a dark-colored seat\"}, {\"id\": 18350, \"name\": \"a large gray stone\"}, {\"id\": 18351, \"name\": \"the carabiner\"}, {\"id\": 18352, \"name\": \"a renault limoge\"}, {\"id\": 18353, \"name\": \"the small, light-brown dog\"}, {\"id\": 18354, \"name\": \"an orange vegetable scrap\"}, {\"id\": 18355, \"name\": \"a wooden skewer\"}, {\"id\": 18356, \"name\": \"character from the popular video game halo\"}, {\"id\": 18357, \"name\": \"a tan bandage\"}, {\"id\": 18358, \"name\": \"white sailor-style outfit\"}, {\"id\": 18359, \"name\": \"a colored bristle\"}, {\"id\": 18360, \"name\": \"a green pickled vegetable\"}, {\"id\": 18361, \"name\": \"a white cabinet\"}, {\"id\": 18362, \"name\": \"a trout\"}, {\"id\": 18363, \"name\": \"a yellow star\"}, {\"id\": 18364, \"name\": \"the figurine\"}, {\"id\": 18365, \"name\": \"the man in a red baseball cap\"}, {\"id\": 18366, \"name\": \"masseuse\"}, {\"id\": 18367, \"name\": \"the character from the mario franchise\"}, {\"id\": 18368, \"name\": \"a fresh herb\"}, {\"id\": 18369, \"name\": \"a black nail polish\"}, {\"id\": 18370, \"name\": \"screwdriver\"}, {\"id\": 18371, \"name\": \"the small ice cream cone\"}, {\"id\": 18372, \"name\": \"a person on the ground\"}, {\"id\": 18373, \"name\": \"the armored\"}, {\"id\": 18374, \"name\": \"the brown meat patty\"}, {\"id\": 18375, \"name\": \"a yellow raft\"}, {\"id\": 18376, \"name\": \"pineapple\"}, {\"id\": 18377, \"name\": \"the large rock or planter\"}, {\"id\": 18378, \"name\": \"skater\"}, {\"id\": 18379, \"name\": \"the candy package\"}, {\"id\": 18380, \"name\": \"neon green vest\"}, {\"id\": 18381, \"name\": \"the child's dark brown hair\"}, {\"id\": 18382, \"name\": \"plastic toy horse\"}, {\"id\": 18383, \"name\": \"a pink-haired person\"}, {\"id\": 18384, \"name\": \"tall, narrow window\"}, {\"id\": 18385, \"name\": \"a alpro almond milk carton\"}, {\"id\": 18386, \"name\": \"the three-wheeled vehicle\"}, {\"id\": 18387, \"name\": \"the multicolored and fluffy object\"}, {\"id\": 18388, \"name\": \"a red life jacket\"}, {\"id\": 18389, \"name\": \"yellow fishing rod\"}, {\"id\": 18390, \"name\": \"the lipstick\"}, {\"id\": 18391, \"name\": \"breakfast\"}, {\"id\": 18392, \"name\": \"the shiny silver high-heeled boot\"}, {\"id\": 18393, \"name\": \"a wooden stick\"}, {\"id\": 18394, \"name\": \"yellow bubble blower\"}, {\"id\": 18395, \"name\": \"dog's body\"}, {\"id\": 18396, \"name\": \"elephants' back\"}, {\"id\": 18397, \"name\": \"a white outline of a baseball diamond\"}, {\"id\": 18398, \"name\": \"a juice\"}, {\"id\": 18399, \"name\": \"an egg\"}, {\"id\": 18400, \"name\": \"long piece of paper\"}, {\"id\": 18401, \"name\": \"the puzzle\"}, {\"id\": 18402, \"name\": \"the white ceramic mushroom-shaped container\"}, {\"id\": 18403, \"name\": \"the brown and white horse\"}, {\"id\": 18404, \"name\": \"the pouch\"}, {\"id\": 18405, \"name\": \"a pedestrian crossing\"}, {\"id\": 18406, \"name\": \"a wire fence\"}, {\"id\": 18407, \"name\": \"the vibrant and colorful claw machine\"}, {\"id\": 18408, \"name\": \"four-door car\"}, {\"id\": 18409, \"name\": \"the green square\"}, {\"id\": 18410, \"name\": \"the sedan\"}, {\"id\": 18411, \"name\": \"black saddlebag\"}, {\"id\": 18412, \"name\": \"a high heel\"}, {\"id\": 18413, \"name\": \"the massage sign\"}, {\"id\": 18414, \"name\": \"driver's helmet\"}, {\"id\": 18415, \"name\": \"a yellow and blue barrier\"}, {\"id\": 18416, \"name\": \"large eye\"}, {\"id\": 18417, \"name\": \"red and green wavy line design\"}, {\"id\": 18418, \"name\": \"the light blue stripe\"}, {\"id\": 18419, \"name\": \"an overpass\"}, {\"id\": 18420, \"name\": \"the silver grill\"}, {\"id\": 18421, \"name\": \"grid\"}, {\"id\": 18422, \"name\": \"wet asphalt\"}, {\"id\": 18423, \"name\": \"the message in korean\"}, {\"id\": 18424, \"name\": \"the blue heart\"}, {\"id\": 18425, \"name\": \"a crisp white line\"}, {\"id\": 18426, \"name\": \"packed grandstand\"}, {\"id\": 18427, \"name\": \"the vibrant, multicolored ride\"}, {\"id\": 18428, \"name\": \"red bow tie\"}, {\"id\": 18429, \"name\": \"a blue mane\"}, {\"id\": 18430, \"name\": \"a black cable\"}, {\"id\": 18431, \"name\": \"vertical white stripe\"}, {\"id\": 18432, \"name\": \"red bloom\"}, {\"id\": 18433, \"name\": \"the hip\"}, {\"id\": 18434, \"name\": \"the front of the bike\"}, {\"id\": 18435, \"name\": \"the pink and white striped mountain\"}, {\"id\": 18436, \"name\": \"a website url\"}, {\"id\": 18437, \"name\": \"a headline\"}, {\"id\": 18438, \"name\": \"the white skull icon\"}, {\"id\": 18439, \"name\": \"the black circle\"}, {\"id\": 18440, \"name\": \"a double yellow line\"}, {\"id\": 18441, \"name\": \"a green hat\"}, {\"id\": 18442, \"name\": \"the rein of the horse\"}, {\"id\": 18443, \"name\": \"the large basket\"}, {\"id\": 18444, \"name\": \"a couple\"}, {\"id\": 18445, \"name\": \"donation amount\"}, {\"id\": 18446, \"name\": \"the white cane\"}, {\"id\": 18447, \"name\": \"a black-and-white striped pattern\"}, {\"id\": 18448, \"name\": \"the green lettering\"}, {\"id\": 18449, \"name\": \"small white logo\"}, {\"id\": 18450, \"name\": \"the white apron\"}, {\"id\": 18451, \"name\": \"large, muddy area\"}, {\"id\": 18452, \"name\": \"the black folding chair\"}, {\"id\": 18453, \"name\": \"dark blue short\"}, {\"id\": 18454, \"name\": \"a light blue blanket\"}, {\"id\": 18455, \"name\": \"a graphic of a leaf\"}, {\"id\": 18456, \"name\": \"the gray railing\"}, {\"id\": 18457, \"name\": \"the black vr headset\"}, {\"id\": 18458, \"name\": \"alliance killa\"}, {\"id\": 18459, \"name\": \"red and blue figurine\"}, {\"id\": 18460, \"name\": \"the red fire alarm\"}, {\"id\": 18461, \"name\": \"a black and white jacket\"}, {\"id\": 18462, \"name\": \"bright green vest\"}, {\"id\": 18463, \"name\": \"the maroon t-shirt\"}, {\"id\": 18464, \"name\": \"silver chocolate fountain\"}, {\"id\": 18465, \"name\": \"the mountainou backdrop\"}, {\"id\": 18466, \"name\": \"a black body\"}, {\"id\": 18467, \"name\": \"bell\"}, {\"id\": 18468, \"name\": \"a concrete bowl\"}, {\"id\": 18469, \"name\": \"the dark grey jacket\"}, {\"id\": 18470, \"name\": \"a snow-covered road\"}, {\"id\": 18471, \"name\": \"the dark tie\"}, {\"id\": 18472, \"name\": \"one championship\"}, {\"id\": 18473, \"name\": \"a blue and red circular logo\"}, {\"id\": 18474, \"name\": \"a prominent banner\"}, {\"id\": 18475, \"name\": \"the black and white plaid shirt\"}, {\"id\": 18476, \"name\": \"the banner with the headline human trafficking delta state task force creates awarenes on menace\"}, {\"id\": 18477, \"name\": \"a yellow excavator\"}, {\"id\": 18478, \"name\": \"the brown border\"}, {\"id\": 18479, \"name\": \"a small black box\"}, {\"id\": 18480, \"name\": \"large, green vehicle\"}, {\"id\": 18481, \"name\": \"multicolored stripe\"}, {\"id\": 18482, \"name\": \"a blue and purple circular design\"}, {\"id\": 18483, \"name\": \"a surrounding wood chip\"}, {\"id\": 18484, \"name\": \"loading sign\"}, {\"id\": 18485, \"name\": \"the squidward tentacle\"}, {\"id\": 18486, \"name\": \"a black and white checkered skirt\"}, {\"id\": 18487, \"name\": \"the red suitcase\"}, {\"id\": 18488, \"name\": \"the yellow and white striped canopy\"}, {\"id\": 18489, \"name\": \"a clearwater\"}, {\"id\": 18490, \"name\": \"the distinctive red shirt\"}, {\"id\": 18491, \"name\": \"the parking\"}, {\"id\": 18492, \"name\": \"sandal's strap\"}, {\"id\": 18493, \"name\": \"the prominent digital scoreboard\"}, {\"id\": 18494, \"name\": \"the man on the stretcher\"}, {\"id\": 18495, \"name\": \"yellow and black striped barrier\"}, {\"id\": 18496, \"name\": \"gray scooter\"}, {\"id\": 18497, \"name\": \"a purple wing\"}, {\"id\": 18498, \"name\": \"81 degree\"}, {\"id\": 18499, \"name\": \"the entrance of the subway station\"}, {\"id\": 18500, \"name\": \"blue privacy screen\"}, {\"id\": 18501, \"name\": \"the premier league logo\"}, {\"id\": 18502, \"name\": \"a large necklace\"}, {\"id\": 18503, \"name\": \"pink helmet\"}, {\"id\": 18504, \"name\": \"orange banner\"}, {\"id\": 18505, \"name\": \"a rifle\"}, {\"id\": 18506, \"name\": \"silver drawer\"}, {\"id\": 18507, \"name\": \"the crowd\"}, {\"id\": 18508, \"name\": \"dragon head\"}, {\"id\": 18509, \"name\": \"bow and arrow\"}, {\"id\": 18510, \"name\": \"grey banner\"}, {\"id\": 18511, \"name\": \"the large, ornate mirror\"}, {\"id\": 18512, \"name\": \"the snowmobile rider\"}, {\"id\": 18513, \"name\": \"an operator\"}, {\"id\": 18514, \"name\": \"the blue cleat\"}, {\"id\": 18515, \"name\": \"large blue eye\"}, {\"id\": 18516, \"name\": \"rainbow\"}, {\"id\": 18517, \"name\": \"a painted white arrow\"}, {\"id\": 18518, \"name\": \"the large wooden spoon\"}, {\"id\": 18519, \"name\": \"the exterior of a store\"}, {\"id\": 18520, \"name\": \"a black ramp\"}, {\"id\": 18521, \"name\": \"a semi-transparent image\"}, {\"id\": 18522, \"name\": \"metal fencing\"}, {\"id\": 18523, \"name\": \"the shooting platform\"}, {\"id\": 18524, \"name\": \"a statue of a bear\"}, {\"id\": 18525, \"name\": \"a red tram\"}, {\"id\": 18526, \"name\": \"the headline\"}, {\"id\": 18527, \"name\": \"a red ball\"}, {\"id\": 18528, \"name\": \"concrete ground\"}, {\"id\": 18529, \"name\": \"the black bee\"}, {\"id\": 18530, \"name\": \"neon yellow bracelet\"}, {\"id\": 18531, \"name\": \"basket of egg\"}, {\"id\": 18532, \"name\": \"the wblast logo\"}, {\"id\": 18533, \"name\": \"a yellow hoodie\"}, {\"id\": 18534, \"name\": \"blue interior\"}, {\"id\": 18535, \"name\": \"colorful, spinning carousel\"}, {\"id\": 18536, \"name\": \"the electric scooter\"}, {\"id\": 18537, \"name\": \"red sneaker\"}, {\"id\": 18538, \"name\": \"the concrete barrier\"}, {\"id\": 18539, \"name\": \"a blue apron\"}, {\"id\": 18540, \"name\": \"a striped awning\"}, {\"id\": 18541, \"name\": \"the logo rbt\"}, {\"id\": 18542, \"name\": \"the gray rocky structure\"}, {\"id\": 18543, \"name\": \"military trucks and vehicle\"}, {\"id\": 18544, \"name\": \"the gun\"}, {\"id\": 18545, \"name\": \"woman in black dress\"}, {\"id\": 18546, \"name\": \"a character's health bar\"}, {\"id\": 18547, \"name\": \"a dark brown, cracked concrete pathway\"}, {\"id\": 18548, \"name\": \"the bottle of dawn dish soap\"}, {\"id\": 18549, \"name\": \"the muddy water\"}, {\"id\": 18550, \"name\": \"a hoop earring\"}, {\"id\": 18551, \"name\": \"the ornate facade\"}, {\"id\": 18552, \"name\": \"yellow and orange striped traffic cone\"}, {\"id\": 18553, \"name\": \"a carousel\"}, {\"id\": 18554, \"name\": \"a dark-red staircase\"}, {\"id\": 18555, \"name\": \"intricate stonework\"}, {\"id\": 18556, \"name\": \"the shin guard\"}, {\"id\": 18557, \"name\": \"the smiggle brand name\"}, {\"id\": 18558, \"name\": \"the bicycle handle\"}, {\"id\": 18559, \"name\": \"a black base\"}, {\"id\": 18560, \"name\": \"the red saddle blanket\"}, {\"id\": 18561, \"name\": \"the blue handlebar\"}, {\"id\": 18562, \"name\": \"the vertical line\"}, {\"id\": 18563, \"name\": \"dark short\"}, {\"id\": 18564, \"name\": \"a pink tail\"}, {\"id\": 18565, \"name\": \"a yellow uniform\"}, {\"id\": 18566, \"name\": \"white numbered marker\"}, {\"id\": 18567, \"name\": \"lapel of her coat\"}, {\"id\": 18568, \"name\": \"orange dress\"}, {\"id\": 18569, \"name\": \"the neon yellow vest\"}, {\"id\": 18570, \"name\": \"solar-powered light\"}, {\"id\": 18571, \"name\": \"small white sign\"}, {\"id\": 18572, \"name\": \"a lemongrass\"}, {\"id\": 18573, \"name\": \"the yellow and red light\"}, {\"id\": 18574, \"name\": \"person in a red shirt and blue pant\"}, {\"id\": 18575, \"name\": \"the leaderboard\"}, {\"id\": 18576, \"name\": \"a thick black border\"}, {\"id\": 18577, \"name\": \"a stoplight\"}, {\"id\": 18578, \"name\": \"green circular area\"}, {\"id\": 18579, \"name\": \"yellow bicycle\"}, {\"id\": 18580, \"name\": \"an orange arm\"}, {\"id\": 18581, \"name\": \"light-colored gummy\"}, {\"id\": 18582, \"name\": \"red and silver, enclosed vehicle\"}, {\"id\": 18583, \"name\": \"small patch of snow\"}, {\"id\": 18584, \"name\": \"the temperature reading\"}, {\"id\": 18585, \"name\": \"a red, pointed hat\"}, {\"id\": 18586, \"name\": \"a curved line\"}, {\"id\": 18587, \"name\": \"a red brick\"}, {\"id\": 18588, \"name\": \"large green truck\"}, {\"id\": 18589, \"name\": \"the baton\"}, {\"id\": 18590, \"name\": \"a dirt floor\"}, {\"id\": 18591, \"name\": \"white and green marking\"}, {\"id\": 18592, \"name\": \"a black and orange shirt\"}, {\"id\": 18593, \"name\": \"a red taxi sign\"}, {\"id\": 18594, \"name\": \"a white knit hat\"}, {\"id\": 18595, \"name\": \"dark-colored floor\"}, {\"id\": 18596, \"name\": \"red, white, and green striped awning\"}, {\"id\": 18597, \"name\": \"red exterior\"}, {\"id\": 18598, \"name\": \"the hello kitty design\"}, {\"id\": 18599, \"name\": \"blue food delivery box\"}, {\"id\": 18600, \"name\": \"the pedestrian crossing\"}, {\"id\": 18601, \"name\": \"the health bar\"}, {\"id\": 18602, \"name\": \"a black beam\"}, {\"id\": 18603, \"name\": \"a white buoy\"}, {\"id\": 18604, \"name\": \"the small american flag\"}, {\"id\": 18605, \"name\": \"the narrow path\"}, {\"id\": 18606, \"name\": \"a black outline\"}, {\"id\": 18607, \"name\": \"red outline\"}, {\"id\": 18608, \"name\": \"large collar\"}, {\"id\": 18609, \"name\": \"a enterprise\"}, {\"id\": 18610, \"name\": \"a roll of white paper\"}, {\"id\": 18611, \"name\": \"the large head\"}, {\"id\": 18612, \"name\": \"utensil\"}, {\"id\": 18613, \"name\": \"policia municipal\"}, {\"id\": 18614, \"name\": \"a brand name\"}, {\"id\": 18615, \"name\": \"the small white car\"}, {\"id\": 18616, \"name\": \"firearm\"}, {\"id\": 18617, \"name\": \"black motorcycle helmet\"}, {\"id\": 18618, \"name\": \"long metal table\"}, {\"id\": 18619, \"name\": \"the animal\"}, {\"id\": 18620, \"name\": \"chrome wheel\"}, {\"id\": 18621, \"name\": \"the babygizmo.com\"}, {\"id\": 18622, \"name\": \"the large boulder\"}, {\"id\": 18623, \"name\": \"the race car\"}, {\"id\": 18624, \"name\": \"the vans-branded sign\"}, {\"id\": 18625, \"name\": \"the white and blue hull\"}, {\"id\": 18626, \"name\": \"gun\"}, {\"id\": 18627, \"name\": \"yellow crosswalk\"}, {\"id\": 18628, \"name\": \"crystal\"}, {\"id\": 18629, \"name\": \"van's back window\"}, {\"id\": 18630, \"name\": \"an orange logo\"}, {\"id\": 18631, \"name\": \"red tartan kilt\"}, {\"id\": 18632, \"name\": \"white balcony\"}, {\"id\": 18633, \"name\": \"a string of yellow light\"}, {\"id\": 18634, \"name\": \"tuba\"}, {\"id\": 18635, \"name\": \"a light gray concrete floor\"}, {\"id\": 18636, \"name\": \"a brick accent\"}, {\"id\": 18637, \"name\": \"a small, white, space-themed vehicle\"}, {\"id\": 18638, \"name\": \"a colorful swimsuit\"}, {\"id\": 18639, \"name\": \"shield emblem\"}, {\"id\": 18640, \"name\": \"the shadow box\"}, {\"id\": 18641, \"name\": \"a passenger window\"}, {\"id\": 18642, \"name\": \"red vehicle\"}, {\"id\": 18643, \"name\": \"the raised platform\"}, {\"id\": 18644, \"name\": \"the black and red interior\"}, {\"id\": 18645, \"name\": \"a large windshield\"}, {\"id\": 18646, \"name\": \"the red hoodie\"}, {\"id\": 18647, \"name\": \"the 7-eleven store\"}, {\"id\": 18648, \"name\": \"a black headscarf\"}, {\"id\": 18649, \"name\": \"a rustic building\"}, {\"id\": 18650, \"name\": \"a black frame\"}, {\"id\": 18651, \"name\": \"short haircut\"}, {\"id\": 18652, \"name\": \"the yellow and red logo\"}, {\"id\": 18653, \"name\": \"yellow creature\"}, {\"id\": 18654, \"name\": \"the ice\"}, {\"id\": 18655, \"name\": \"a gray vest\"}, {\"id\": 18656, \"name\": \"the wooden platform\"}, {\"id\": 18657, \"name\": \"username\"}, {\"id\": 18658, \"name\": \"a man in white\"}, {\"id\": 18659, \"name\": \"pink sweatshirt\"}, {\"id\": 18660, \"name\": \"white floral top\"}, {\"id\": 18661, \"name\": \"a small icon of a bicycle\"}, {\"id\": 18662, \"name\": \"red facade\"}, {\"id\": 18663, \"name\": \"an ocean's white wave\"}, {\"id\": 18664, \"name\": \"a black bicycle\"}, {\"id\": 18665, \"name\": \"triangular support\"}, {\"id\": 18666, \"name\": \"the pink stripe\"}, {\"id\": 18667, \"name\": \"a rung\"}, {\"id\": 18668, \"name\": \"a scooters' red light\"}, {\"id\": 18669, \"name\": \"a platform's red pillar\"}, {\"id\": 18670, \"name\": \"the orange basket\"}, {\"id\": 18671, \"name\": \"red rim\"}, {\"id\": 18672, \"name\": \"brown outline\"}, {\"id\": 18673, \"name\": \"pink sheet\"}, {\"id\": 18674, \"name\": \"a red fin\"}, {\"id\": 18675, \"name\": \"a smoke\"}, {\"id\": 18676, \"name\": \"black metal beam\"}, {\"id\": 18677, \"name\": \"car's number\"}, {\"id\": 18678, \"name\": \"a side door\"}, {\"id\": 18679, \"name\": \"white window frame\"}, {\"id\": 18680, \"name\": \"the chelsea football club jersey\"}, {\"id\": 18681, \"name\": \"a tv antenna\"}, {\"id\": 18682, \"name\": \"a blue door\"}, {\"id\": 18683, \"name\": \"the orange arm\"}, {\"id\": 18684, \"name\": \"green infield\"}, {\"id\": 18685, \"name\": \"a waist\"}, {\"id\": 18686, \"name\": \"the rein\"}, {\"id\": 18687, \"name\": \"the blue and white air jordan logo\"}, {\"id\": 18688, \"name\": \"prominent metal scaffolding structure\"}, {\"id\": 18689, \"name\": \"a black stick\"}, {\"id\": 18690, \"name\": \"a large quantity of food\"}, {\"id\": 18691, \"name\": \"the dirt bike\"}, {\"id\": 18692, \"name\": \"winding track\"}, {\"id\": 18693, \"name\": \"a black and white dog bone-pattern\"}, {\"id\": 18694, \"name\": \"white horse logo\"}, {\"id\": 18695, \"name\": \"blurred wheel\"}, {\"id\": 18696, \"name\": \"a black case\"}, {\"id\": 18697, \"name\": \"the arabic script\"}, {\"id\": 18698, \"name\": \"website address\"}, {\"id\": 18699, \"name\": \"beige purse\"}, {\"id\": 18700, \"name\": \"the red bar\"}, {\"id\": 18701, \"name\": \"red brick\"}, {\"id\": 18702, \"name\": \"yellow cab\"}, {\"id\": 18703, \"name\": \"a golden-brown food\"}, {\"id\": 18704, \"name\": \"blue and white polka dot shirt\"}, {\"id\": 18705, \"name\": \"the white leg\"}, {\"id\": 18706, \"name\": \"the yellow license plate\"}, {\"id\": 18707, \"name\": \"a blue arrow\"}, {\"id\": 18708, \"name\": \"prominent headline overlay\"}, {\"id\": 18709, \"name\": \"the man in a red shirt and short\"}, {\"id\": 18710, \"name\": \"the brown handle\"}, {\"id\": 18711, \"name\": \"blue machinery\"}, {\"id\": 18712, \"name\": \"gray suv\"}, {\"id\": 18713, \"name\": \"a taxi's rear window\"}, {\"id\": 18714, \"name\": \"a tall wooden pole\"}, {\"id\": 18715, \"name\": \"a winding path\"}, {\"id\": 18716, \"name\": \"white metal structure\"}, {\"id\": 18717, \"name\": \"adrift\"}, {\"id\": 18718, \"name\": \"curved track\"}, {\"id\": 18719, \"name\": \"a flag of australia\"}, {\"id\": 18720, \"name\": \"distinctive red and white grill\"}, {\"id\": 18721, \"name\": \"the green visor\"}, {\"id\": 18722, \"name\": \"the tan cowboy hat\"}, {\"id\": 18723, \"name\": \"a tailgate\"}, {\"id\": 18724, \"name\": \"large red and white lollipop sculpture\"}, {\"id\": 18725, \"name\": \"the white and gray striped canopy\"}, {\"id\": 18726, \"name\": \"the one way sign\"}, {\"id\": 18727, \"name\": \"the snack\"}, {\"id\": 18728, \"name\": \"palm\"}, {\"id\": 18729, \"name\": \"the white skull logo\"}, {\"id\": 18730, \"name\": \"green barrier\"}, {\"id\": 18731, \"name\": \"a white nypd police car\"}, {\"id\": 18732, \"name\": \"orange training equipment\"}, {\"id\": 18733, \"name\": \"green traffic light\"}, {\"id\": 18734, \"name\": \"black and red line\"}, {\"id\": 18735, \"name\": \"silver suv\"}, {\"id\": 18736, \"name\": \"grid of cardboard divider\"}, {\"id\": 18737, \"name\": \"white horse\"}, {\"id\": 18738, \"name\": \"a woman in a black coat and jean\"}, {\"id\": 18739, \"name\": \"paved oval\"}, {\"id\": 18740, \"name\": \"the yellow and black attire\"}, {\"id\": 18741, \"name\": \"a wall-mounted display\"}, {\"id\": 18742, \"name\": \"orange car\"}, {\"id\": 18743, \"name\": \"the kitchen utensil\"}, {\"id\": 18744, \"name\": \"the white box truck\"}, {\"id\": 18745, \"name\": \"a green wire fence\"}, {\"id\": 18746, \"name\": \"the aon logo\"}, {\"id\": 18747, \"name\": \"the red outline\"}, {\"id\": 18748, \"name\": \"vibrant pink and white patterned shirt\"}, {\"id\": 18749, \"name\": \"racing helmet\"}, {\"id\": 18750, \"name\": \"the black wing\"}, {\"id\": 18751, \"name\": \"crate of fruit\"}, {\"id\": 18752, \"name\": \"the orange and white logo\"}, {\"id\": 18753, \"name\": \"the gray and red striped section\"}, {\"id\": 18754, \"name\": \"the and team 11im production\"}, {\"id\": 18755, \"name\": \"yellow trim\"}, {\"id\": 18756, \"name\": \"white and yellow dot\"}, {\"id\": 18757, \"name\": \"a blue and white object\"}, {\"id\": 18758, \"name\": \"a bottle of soda\"}, {\"id\": 18759, \"name\": \"white ruffled collar\"}, {\"id\": 18760, \"name\": \"a xorit logo\"}, {\"id\": 18761, \"name\": \"the thumb\"}, {\"id\": 18762, \"name\": \"a baton\"}, {\"id\": 18763, \"name\": \"large, ornate chandelier\"}, {\"id\": 18764, \"name\": \"loft area\"}, {\"id\": 18765, \"name\": \"black gear\"}, {\"id\": 18766, \"name\": \"well\"}, {\"id\": 18767, \"name\": \"a red lawn mower\"}, {\"id\": 18768, \"name\": \"small green triangle\"}, {\"id\": 18769, \"name\": \"cracked windshield\"}, {\"id\": 18770, \"name\": \". 1 for adult orthopedic in the u.s.\"}, {\"id\": 18771, \"name\": \"a creature's face\"}, {\"id\": 18772, \"name\": \"stone column\"}, {\"id\": 18773, \"name\": \"the observation deck\"}, {\"id\": 18774, \"name\": \"blue patch\"}, {\"id\": 18775, \"name\": \"a van's side window\"}, {\"id\": 18776, \"name\": \"the south terminal\"}, {\"id\": 18777, \"name\": \"a rig\"}, {\"id\": 18778, \"name\": \"a red bow\"}, {\"id\": 18779, \"name\": \"the green water gun\"}, {\"id\": 18780, \"name\": \"the white outline\"}, {\"id\": 18781, \"name\": \"dr zig\"}, {\"id\": 18782, \"name\": \"a stream of water\"}, {\"id\": 18783, \"name\": \"the distinctive red tuft of fur\"}, {\"id\": 18784, \"name\": \"a cluttered table\"}, {\"id\": 18785, \"name\": \"the large metal trash can\"}, {\"id\": 18786, \"name\": \"an orange jacket\"}, {\"id\": 18787, \"name\": \"white bumper\"}, {\"id\": 18788, \"name\": \"the vehicle's open door\"}, {\"id\": 18789, \"name\": \"a bubble\"}, {\"id\": 18790, \"name\": \"a green saddle\"}, {\"id\": 18791, \"name\": \"blurred crowd\"}, {\"id\": 18792, \"name\": \"small video screen\"}, {\"id\": 18793, \"name\": \"small nbc logo\"}, {\"id\": 18794, \"name\": \"the rack of white clothing\"}, {\"id\": 18795, \"name\": \"the black and yellow handlebar\"}, {\"id\": 18796, \"name\": \"the rocky shoreline\"}, {\"id\": 18797, \"name\": \"the blurry sign\"}, {\"id\": 18798, \"name\": \"black and gray blanket\"}, {\"id\": 18799, \"name\": \"the chain link fence\"}, {\"id\": 18800, \"name\": \"a sleek metal railing\"}, {\"id\": 18801, \"name\": \"colorful boat\"}, {\"id\": 18802, \"name\": \"horizontal wire\"}, {\"id\": 18803, \"name\": \"the silver breastplate\"}, {\"id\": 18804, \"name\": \"the blue case\"}, {\"id\": 18805, \"name\": \"camcorder\"}, {\"id\": 18806, \"name\": \"a red arm\"}, {\"id\": 18807, \"name\": \"high chair\"}, {\"id\": 18808, \"name\": \"a wheelchair accessible entrance\"}, {\"id\": 18809, \"name\": \"light-blue flip-flop\"}, {\"id\": 18810, \"name\": \"red and yellow stripe\"}, {\"id\": 18811, \"name\": \"the blue sedan\"}, {\"id\": 18812, \"name\": \"a binoculars\"}, {\"id\": 18813, \"name\": \"an exposed red beam\"}, {\"id\": 18814, \"name\": \"the small square\"}, {\"id\": 18815, \"name\": \"a platform\"}, {\"id\": 18816, \"name\": \"a vertical column of japanese character\"}, {\"id\": 18817, \"name\": \"lush green field\"}, {\"id\": 18818, \"name\": \"black boat\"}, {\"id\": 18819, \"name\": \"long skirt\"}, {\"id\": 18820, \"name\": \"a blue trash can\"}, {\"id\": 18821, \"name\": \"a black and red stripe\"}, {\"id\": 18822, \"name\": \"the large umbrella\"}, {\"id\": 18823, \"name\": \"a passenger seat\"}, {\"id\": 18824, \"name\": \"white police car\"}, {\"id\": 18825, \"name\": \"the small round mirror\"}, {\"id\": 18826, \"name\": \"red and white striped bumper\"}, {\"id\": 18827, \"name\": \"the rounded head\"}, {\"id\": 18828, \"name\": \"a grey patterned dress\"}, {\"id\": 18829, \"name\": \"red cleat\"}, {\"id\": 18830, \"name\": \"a green hedge\"}, {\"id\": 18831, \"name\": \"the weed\"}, {\"id\": 18832, \"name\": \"wpri 12\"}, {\"id\": 18833, \"name\": \"a museum\"}, {\"id\": 18834, \"name\": \"the green bar\"}, {\"id\": 18835, \"name\": \"the flame\"}, {\"id\": 18836, \"name\": \"a abc15 logo\"}, {\"id\": 18837, \"name\": \"yellow and red design\"}, {\"id\": 18838, \"name\": \"a prominent purple advertisement\"}, {\"id\": 18839, \"name\": \"dark blue water\"}, {\"id\": 18840, \"name\": \"the dark blue short\"}, {\"id\": 18841, \"name\": \"a tire's tread pattern\"}, {\"id\": 18842, \"name\": \"skull head\"}, {\"id\": 18843, \"name\": \"the vienna.info logo\"}, {\"id\": 18844, \"name\": \"a silver hubcap\"}, {\"id\": 18845, \"name\": \"the score of 3-0\"}, {\"id\": 18846, \"name\": \"a white ghost-like figure\"}, {\"id\": 18847, \"name\": \"the blue and green vehicle\"}, {\"id\": 18848, \"name\": \"shoulder-length black hair\"}, {\"id\": 18849, \"name\": \"the bottom-right image\"}, {\"id\": 18850, \"name\": \"motorcycle's handlebar\"}, {\"id\": 18851, \"name\": \"the orange foot\"}, {\"id\": 18852, \"name\": \"wide, toothy smile\"}, {\"id\": 18853, \"name\": \"the tab\"}, {\"id\": 18854, \"name\": \"black bar\"}, {\"id\": 18855, \"name\": \"the bare branch\"}, {\"id\": 18856, \"name\": \"the soccer goalie\"}, {\"id\": 18857, \"name\": \"a white and blue sign\"}, {\"id\": 18858, \"name\": \"black graffiti\"}, {\"id\": 18859, \"name\": \"yellow barrier\"}, {\"id\": 18860, \"name\": \"circle of small star\"}, {\"id\": 18861, \"name\": \"the kobe bryant's face\"}, {\"id\": 18862, \"name\": \"firefighter\"}, {\"id\": 18863, \"name\": \"the checkered flag\"}, {\"id\": 18864, \"name\": \"white trailer train label\"}, {\"id\": 18865, \"name\": \"a tall multi-story structure\"}, {\"id\": 18866, \"name\": \"the festive headband\"}, {\"id\": 18867, \"name\": \"weight\"}, {\"id\": 18868, \"name\": \"a license plate 8825\"}, {\"id\": 18869, \"name\": \"the quote or phrase\"}, {\"id\": 18870, \"name\": \"a gray mitsubishi suv\"}, {\"id\": 18871, \"name\": \"large white and blue semi-truck\"}, {\"id\": 18872, \"name\": \"red cross\"}, {\"id\": 18873, \"name\": \"footpeg\"}, {\"id\": 18874, \"name\": \"the white purse\"}, {\"id\": 18875, \"name\": \"white piece of paper\"}, {\"id\": 18876, \"name\": \"a yellow and purple path\"}, {\"id\": 18877, \"name\": \"a perfume bottle\"}, {\"id\": 18878, \"name\": \"the large white ship\"}, {\"id\": 18879, \"name\": \"the black folder\"}, {\"id\": 18880, \"name\": \"the green grassy area\"}, {\"id\": 18881, \"name\": \"the red and white 12 symbol\"}, {\"id\": 18882, \"name\": \"a player's arm\"}, {\"id\": 18883, \"name\": \"instagram\"}, {\"id\": 18884, \"name\": \"a budweiser advertisement\"}, {\"id\": 18885, \"name\": \"woman's left arm\"}, {\"id\": 18886, \"name\": \"the black bar\"}, {\"id\": 18887, \"name\": \"a dark-colored vehicle\"}, {\"id\": 18888, \"name\": \"the white net\"}, {\"id\": 18889, \"name\": \"a gold shield\"}, {\"id\": 18890, \"name\": \"a rocky shoreline\"}, {\"id\": 18891, \"name\": \"dark gray surface\"}, {\"id\": 18892, \"name\": \"raw burger\"}, {\"id\": 18893, \"name\": \"cymbal\"}, {\"id\": 18894, \"name\": \"the emergency responder\"}, {\"id\": 18895, \"name\": \"a man's hand\"}, {\"id\": 18896, \"name\": \"white pickup truck\"}, {\"id\": 18897, \"name\": \"dark grey tie\"}, {\"id\": 18898, \"name\": \"the table's surface\"}, {\"id\": 18899, \"name\": \"the camel\"}, {\"id\": 18900, \"name\": \"a green and orange jersey\"}, {\"id\": 18901, \"name\": \"small black banner\"}, {\"id\": 18902, \"name\": \"vibrant mural\"}, {\"id\": 18903, \"name\": \"a glas storefront\"}, {\"id\": 18904, \"name\": \"a still frame\"}, {\"id\": 18905, \"name\": \"a blue p\"}, {\"id\": 18906, \"name\": \"a white puffy coat\"}, {\"id\": 18907, \"name\": \"a white interior of the car\"}, {\"id\": 18908, \"name\": \"casebolt\"}, {\"id\": 18909, \"name\": \"a cut-out shoulder\"}, {\"id\": 18910, \"name\": \"the blue scooter\"}, {\"id\": 18911, \"name\": \"a denim jacket\"}, {\"id\": 18912, \"name\": \"the gear\"}, {\"id\": 18913, \"name\": \"yellow lanyard\"}, {\"id\": 18914, \"name\": \"crown\"}, {\"id\": 18915, \"name\": \"the filter\"}, {\"id\": 18916, \"name\": \"red shelf\"}, {\"id\": 18917, \"name\": \"the colored dot\"}, {\"id\": 18918, \"name\": \"large white eye\"}, {\"id\": 18919, \"name\": \"grey t-shirt\"}, {\"id\": 18920, \"name\": \"a team's logo\"}, {\"id\": 18921, \"name\": \"a dark gray car\"}, {\"id\": 18922, \"name\": \"a blue and black helmet\"}, {\"id\": 18923, \"name\": \"the large armored vehicle\"}, {\"id\": 18924, \"name\": \"the bodyworx machine\"}, {\"id\": 18925, \"name\": \"black sweatshirt\"}, {\"id\": 18926, \"name\": \"a dirt arena\"}, {\"id\": 18927, \"name\": \"pink and purple gift bag\"}, {\"id\": 18928, \"name\": \"a plus500\"}, {\"id\": 18929, \"name\": \"a dented rear bumper\"}, {\"id\": 18930, \"name\": \"the digital leaderboard\"}, {\"id\": 18931, \"name\": \"the dark long-sleeve shirt\"}, {\"id\": 18932, \"name\": \"the red patch\"}, {\"id\": 18933, \"name\": \"a nigerian flag\"}, {\"id\": 18934, \"name\": \"caption\"}, {\"id\": 18935, \"name\": \"gray border\"}, {\"id\": 18936, \"name\": \"the prominent red brick structure\"}, {\"id\": 18937, \"name\": \"the dreadlock\"}, {\"id\": 18938, \"name\": \"the green and yellow sign\"}, {\"id\": 18939, \"name\": \"the multi-lane highway\"}, {\"id\": 18940, \"name\": \"the bike lane\"}, {\"id\": 18941, \"name\": \"blue and white logo\"}, {\"id\": 18942, \"name\": \"an older man\"}, {\"id\": 18943, \"name\": \"a blue neon sign\"}, {\"id\": 18944, \"name\": \"a yellow fence\"}, {\"id\": 18945, \"name\": \"the black headband\"}, {\"id\": 18946, \"name\": \"long, dark fur\"}, {\"id\": 18947, \"name\": \"dark blue tie\"}, {\"id\": 18948, \"name\": \"the green taxi\"}, {\"id\": 18949, \"name\": \"the water level\"}, {\"id\": 18950, \"name\": \"overhead fluorescent light\"}, {\"id\": 18951, \"name\": \"an amoeba\"}, {\"id\": 18952, \"name\": \"yellow tail\"}, {\"id\": 18953, \"name\": \"486 m\"}, {\"id\": 18954, \"name\": \"the pink and blue piece of equipment\"}, {\"id\": 18955, \"name\": \"the yellow rugby jersey\"}, {\"id\": 18956, \"name\": \"smiling face\"}, {\"id\": 18957, \"name\": \"a yellow drawer\"}, {\"id\": 18958, \"name\": \"a rider's legs\"}, {\"id\": 18959, \"name\": \"a tan hoodie\"}, {\"id\": 18960, \"name\": \"the yellow and white surfboard\"}, {\"id\": 18961, \"name\": \"the photo of a deer\"}, {\"id\": 18962, \"name\": \"the gray vest\"}, {\"id\": 18963, \"name\": \"orange uniform\"}, {\"id\": 18964, \"name\": \"rounded headlight\"}, {\"id\": 18965, \"name\": \"the mirror's reflection\"}, {\"id\": 18966, \"name\": \"white and orange safety vest\"}, {\"id\": 18967, \"name\": \"a robot\"}, {\"id\": 18968, \"name\": \"the thumbs-up gesture\"}, {\"id\": 18969, \"name\": \"the blue rectangle\"}, {\"id\": 18970, \"name\": \"the pine tree\"}, {\"id\": 18971, \"name\": \"the black fur\"}, {\"id\": 18972, \"name\": \"car crash\"}, {\"id\": 18973, \"name\": \"a blue cart\"}, {\"id\": 18974, \"name\": \"a presence of the motorcycle\"}, {\"id\": 18975, \"name\": \"the purple tent\"}, {\"id\": 18976, \"name\": \"forever 21 billboard\"}, {\"id\": 18977, \"name\": \"red and yellow star pattern\"}, {\"id\": 18978, \"name\": \"a stretcher\"}, {\"id\": 18979, \"name\": \"television news banner\"}, {\"id\": 18980, \"name\": \"a black metal bench\"}, {\"id\": 18981, \"name\": \"the aquatic animal\"}, {\"id\": 18982, \"name\": \"a gma logo\"}, {\"id\": 18983, \"name\": \"a long fluorescent light fixture\"}, {\"id\": 18984, \"name\": \"a model train crossing\"}, {\"id\": 18985, \"name\": \"white symbol\"}, {\"id\": 18986, \"name\": \"glas ceiling\"}, {\"id\": 18987, \"name\": \"yellow cartoon character\"}, {\"id\": 18988, \"name\": \"the white and red marking\"}, {\"id\": 18989, \"name\": \"black handlebar\"}, {\"id\": 18990, \"name\": \"a red and white striped tape\"}, {\"id\": 18991, \"name\": \"green banner\"}, {\"id\": 18992, \"name\": \"horse's neck\"}, {\"id\": 18993, \"name\": \"a high-visibility orange vest\"}, {\"id\": 18994, \"name\": \"dark purple car\"}, {\"id\": 18995, \"name\": \"the large hair clip\"}, {\"id\": 18996, \"name\": \"a signpost\"}, {\"id\": 18997, \"name\": \"a person in a red striped shirt\"}, {\"id\": 18998, \"name\": \"an orange and blue design\"}, {\"id\": 18999, \"name\": \"the silk\"}, {\"id\": 19000, \"name\": \"the beige shoe\"}, {\"id\": 19001, \"name\": \"a white horse graphic\"}, {\"id\": 19002, \"name\": \"a red plastic barrier\"}, {\"id\": 19003, \"name\": \"a small red flag\"}, {\"id\": 19004, \"name\": \"the line of large white truck\"}, {\"id\": 19005, \"name\": \"a black wristband\"}, {\"id\": 19006, \"name\": \"a red door\"}, {\"id\": 19007, \"name\": \"black sunglass\"}, {\"id\": 19008, \"name\": \"the blue frame\"}, {\"id\": 19009, \"name\": \"the red and green wavy line\"}, {\"id\": 19010, \"name\": \"a closed shuttered door\"}, {\"id\": 19011, \"name\": \"a contact information\"}, {\"id\": 19012, \"name\": \"the waistband\"}, {\"id\": 19013, \"name\": \"shipping container\"}, {\"id\": 19014, \"name\": \"carousel's canopy\"}, {\"id\": 19015, \"name\": \"a dark, curly hair\"}, {\"id\": 19016, \"name\": \"a blurred wall\"}, {\"id\": 19017, \"name\": \"mountainou backdrop\"}, {\"id\": 19018, \"name\": \"the earbud\"}, {\"id\": 19019, \"name\": \"long, curved stem\"}, {\"id\": 19020, \"name\": \"glas table\"}, {\"id\": 19021, \"name\": \"closed metal shutter\"}, {\"id\": 19022, \"name\": \"the dedicated bike lane\"}, {\"id\": 19023, \"name\": \"a fender\"}, {\"id\": 19024, \"name\": \"yellow outline\"}, {\"id\": 19025, \"name\": \"shelf of electronic cigarette\"}, {\"id\": 19026, \"name\": \"the pink item\"}, {\"id\": 19027, \"name\": \"a bulletproof vest\"}, {\"id\": 19028, \"name\": \"flat surface\"}, {\"id\": 19029, \"name\": \"a protesters disrupt operation\"}, {\"id\": 19030, \"name\": \"a black and yellow design\"}, {\"id\": 19031, \"name\": \"a white watermark\"}, {\"id\": 19032, \"name\": \"a dark gray surface\"}, {\"id\": 19033, \"name\": \"copper-colored frame\"}, {\"id\": 19034, \"name\": \"the checkered scarf\"}, {\"id\": 19035, \"name\": \"a blue and gold harness\"}, {\"id\": 19036, \"name\": \"the white, spire-topped structure\"}, {\"id\": 19037, \"name\": \"blue star-shaped banner\"}, {\"id\": 19038, \"name\": \"blue vehicle\"}, {\"id\": 19039, \"name\": \"the yellow subtitle\"}, {\"id\": 19040, \"name\": \"gray banner\"}, {\"id\": 19041, \"name\": \"combine harvester\"}, {\"id\": 19042, \"name\": \"the brown icon\"}, {\"id\": 19043, \"name\": \"the longboard\"}, {\"id\": 19044, \"name\": \"the stage light\"}, {\"id\": 19045, \"name\": \"white window\"}, {\"id\": 19046, \"name\": \"yellow hair\"}, {\"id\": 19047, \"name\": \"the reflective material\"}, {\"id\": 19048, \"name\": \"a white jersey\"}, {\"id\": 19049, \"name\": \"the blue bar\"}, {\"id\": 19050, \"name\": \"the black trouser\"}, {\"id\": 19051, \"name\": \"rack of bicycle\"}, {\"id\": 19052, \"name\": \"blurry image of a sign or advertisement\"}, {\"id\": 19053, \"name\": \"a blurred wheel\"}, {\"id\": 19054, \"name\": \"purple label\"}, {\"id\": 19055, \"name\": \"a yellow and white banner\"}, {\"id\": 19056, \"name\": \"the white l logo\"}, {\"id\": 19057, \"name\": \"a cluttered shelf\"}, {\"id\": 19058, \"name\": \"orange frame\"}, {\"id\": 19059, \"name\": \"a sport jersey\"}, {\"id\": 19060, \"name\": \"the portable toilet\"}, {\"id\": 19061, \"name\": \"the white triangle\"}, {\"id\": 19062, \"name\": \"man in a black shirt and pant\"}, {\"id\": 19063, \"name\": \"a small screen\"}, {\"id\": 19064, \"name\": \"black and purple racing suit\"}, {\"id\": 19065, \"name\": \"a cricket network\"}, {\"id\": 19066, \"name\": \"a box of pastry\"}, {\"id\": 19067, \"name\": \"the oil\"}, {\"id\": 19068, \"name\": \"teal dress\"}, {\"id\": 19069, \"name\": \"a race car\"}, {\"id\": 19070, \"name\": \"red megaphone\"}, {\"id\": 19071, \"name\": \"a car mirror\"}, {\"id\": 19072, \"name\": \"the pause button\"}, {\"id\": 19073, \"name\": \"the purple light fixture\"}, {\"id\": 19074, \"name\": \"the green frog\"}, {\"id\": 19075, \"name\": \"the white booth\"}, {\"id\": 19076, \"name\": \"86 minutes and 8 second\"}, {\"id\": 19077, \"name\": \"grinder\"}, {\"id\": 19078, \"name\": \"a pink hair clip\"}, {\"id\": 19079, \"name\": \"central square orange tag\"}, {\"id\": 19080, \"name\": \"a white hood\"}, {\"id\": 19081, \"name\": \"man's foot\"}, {\"id\": 19082, \"name\": \"red and white striped tent\"}, {\"id\": 19083, \"name\": \"a tall brick structure\"}, {\"id\": 19084, \"name\": \"grey shelving unit\"}, {\"id\": 19085, \"name\": \"a small wooden box\"}, {\"id\": 19086, \"name\": \"the guizhou renhe\"}, {\"id\": 19087, \"name\": \"distinctive green rim\"}, {\"id\": 19088, \"name\": \"metal chain\"}, {\"id\": 19089, \"name\": \"blue-painted toenail\"}, {\"id\": 19090, \"name\": \"jeep\"}, {\"id\": 19091, \"name\": \"the blue bridle\"}, {\"id\": 19092, \"name\": \"a line of yellow school bus\"}, {\"id\": 19093, \"name\": \"a blonde-haired mermaid\"}, {\"id\": 19094, \"name\": \"the geox storefront\"}, {\"id\": 19095, \"name\": \"the neon green logo\"}, {\"id\": 19096, \"name\": \"a subscribe to our channel\"}, {\"id\": 19097, \"name\": \"the barricade\"}, {\"id\": 19098, \"name\": \"the small orange box\"}, {\"id\": 19099, \"name\": \"small piece of plastic\"}, {\"id\": 19100, \"name\": \"red and yellow plastic piece\"}, {\"id\": 19101, \"name\": \"green cap\"}, {\"id\": 19102, \"name\": \"green and white stripe\"}, {\"id\": 19103, \"name\": \"a clear plastic water bottle\"}, {\"id\": 19104, \"name\": \"central, dark brown or black circle\"}, {\"id\": 19105, \"name\": \"the blue graphic\"}, {\"id\": 19106, \"name\": \"the yellow traffic cone\"}, {\"id\": 19107, \"name\": \"man in a black shirt and short\"}, {\"id\": 19108, \"name\": \"the bottom row\"}, {\"id\": 19109, \"name\": \"the glas jar\"}, {\"id\": 19110, \"name\": \"a large, triangular structure\"}, {\"id\": 19111, \"name\": \"orange safety vest\"}, {\"id\": 19112, \"name\": \"the floral pant\"}, {\"id\": 19113, \"name\": \"west philadelphia firehouse\"}, {\"id\": 19114, \"name\": \"a blurred green field\"}, {\"id\": 19115, \"name\": \"review on it for\"}, {\"id\": 19116, \"name\": \"gravel\"}, {\"id\": 19117, \"name\": \"the middle-aged man\"}, {\"id\": 19118, \"name\": \"a reporter's name\"}, {\"id\": 19119, \"name\": \"the vibrant umbrella\"}, {\"id\": 19120, \"name\": \"human\"}, {\"id\": 19121, \"name\": \"white arrow\"}, {\"id\": 19122, \"name\": \"blue-framed glass\"}, {\"id\": 19123, \"name\": \"blurry, colorful image\"}, {\"id\": 19124, \"name\": \"the blue stadium\"}, {\"id\": 19125, \"name\": \"the wooden floor\"}, {\"id\": 19126, \"name\": \"black and red bicycle helmet\"}, {\"id\": 19127, \"name\": \"abc9\"}, {\"id\": 19128, \"name\": \"the date\"}, {\"id\": 19129, \"name\": \"the 22.74 second\"}, {\"id\": 19130, \"name\": \"a natural and rustic backdrop\"}, {\"id\": 19131, \"name\": \"a blue tablecloth\"}, {\"id\": 19132, \"name\": \"red and yellow middle border\"}, {\"id\": 19133, \"name\": \"chinese sign\"}, {\"id\": 19134, \"name\": \"race track\"}, {\"id\": 19135, \"name\": \"a gray strip of flooring\"}, {\"id\": 19136, \"name\": \"colorful headscarf\"}, {\"id\": 19137, \"name\": \"a gray bar\"}, {\"id\": 19138, \"name\": \"loading dock 3\"}, {\"id\": 19139, \"name\": \"cartoon face\"}, {\"id\": 19140, \"name\": \"dotted pattern\"}, {\"id\": 19141, \"name\": \"a caption\"}, {\"id\": 19142, \"name\": \"a red cap\"}, {\"id\": 19143, \"name\": \"the reflective vest\"}, {\"id\": 19144, \"name\": \"the plastic jack-o-lantern\"}, {\"id\": 19145, \"name\": \"a timer\"}, {\"id\": 19146, \"name\": \"large tarp\"}, {\"id\": 19147, \"name\": \"parking\"}, {\"id\": 19148, \"name\": \"the tan shell\"}, {\"id\": 19149, \"name\": \"stadium's stands\"}, {\"id\": 19150, \"name\": \"the red arrow\"}, {\"id\": 19151, \"name\": \"the shirt collar\"}, {\"id\": 19152, \"name\": \"the hashtag\"}, {\"id\": 19153, \"name\": \"live\"}, {\"id\": 19154, \"name\": \"a blue lego piece\"}, {\"id\": 19155, \"name\": \"the score of 377688272\"}, {\"id\": 19156, \"name\": \"a sign for a halal pizza shop\"}, {\"id\": 19157, \"name\": \"red and white rbn\"}, {\"id\": 19158, \"name\": \"a blue ga tank\"}, {\"id\": 19159, \"name\": \"white buoy\"}, {\"id\": 19160, \"name\": \"silver canister\"}, {\"id\": 19161, \"name\": \"striped dress\"}, {\"id\": 19162, \"name\": \"black bar stool\"}, {\"id\": 19163, \"name\": \"eat\"}, {\"id\": 19164, \"name\": \"red and blue tuk-tuk\"}, {\"id\": 19165, \"name\": \"a chat window\"}, {\"id\": 19166, \"name\": \"a black outfit\"}, {\"id\": 19167, \"name\": \"carpeted surface\"}, {\"id\": 19168, \"name\": \"the colorful blanket\"}, {\"id\": 19169, \"name\": \"black riding gear\"}, {\"id\": 19170, \"name\": \"the white and blue cap\"}, {\"id\": 19171, \"name\": \"tank\"}, {\"id\": 19172, \"name\": \"a black suv\"}, {\"id\": 19173, \"name\": \"a shadowy figure\"}, {\"id\": 19174, \"name\": \"a grassy track\"}, {\"id\": 19175, \"name\": \"partially obscured license plate\"}, {\"id\": 19176, \"name\": \"a green and white umbrella\"}, {\"id\": 19177, \"name\": \"the cycist's name\"}, {\"id\": 19178, \"name\": \"a moving box\"}, {\"id\": 19179, \"name\": \"lead cyclist\"}, {\"id\": 19180, \"name\": \"window or glas door\"}, {\"id\": 19181, \"name\": \"a teal and yellow booth\"}, {\"id\": 19182, \"name\": \"the graffiti\"}, {\"id\": 19183, \"name\": \"wrc logo\"}, {\"id\": 19184, \"name\": \"a white gear\"}, {\"id\": 19185, \"name\": \"the v-neckline\"}, {\"id\": 19186, \"name\": \"chain-link\"}, {\"id\": 19187, \"name\": \"the blue and white sticker\"}, {\"id\": 19188, \"name\": \"a restaurant\"}, {\"id\": 19189, \"name\": \"hood of the car\"}, {\"id\": 19190, \"name\": \"a title\"}, {\"id\": 19191, \"name\": \"a ring finger\"}, {\"id\": 19192, \"name\": \"the long, curly hair\"}, {\"id\": 19193, \"name\": \"an orange toy car\"}, {\"id\": 19194, \"name\": \"the turret\"}, {\"id\": 19195, \"name\": \"a keep clear\"}, {\"id\": 19196, \"name\": \"a black microphone stand\"}, {\"id\": 19197, \"name\": \"silver head\"}, {\"id\": 19198, \"name\": \"the dragon or dinosaur\"}, {\"id\": 19199, \"name\": \"a radio or gp screen\"}, {\"id\": 19200, \"name\": \"smile dental clinic\"}, {\"id\": 19201, \"name\": \"the green design\"}, {\"id\": 19202, \"name\": \"the green shopping bag\"}, {\"id\": 19203, \"name\": \"the cluttered bed\"}, {\"id\": 19204, \"name\": \"larger metal starting gate\"}, {\"id\": 19205, \"name\": \"chrome hardware\"}, {\"id\": 19206, \"name\": \"red cargo train\"}, {\"id\": 19207, \"name\": \"the short, light-blonde hair\"}, {\"id\": 19208, \"name\": \"the large tree trunk\"}, {\"id\": 19209, \"name\": \"the race track\"}, {\"id\": 19210, \"name\": \"the small fountain\"}, {\"id\": 19211, \"name\": \"a coat pocket\"}, {\"id\": 19212, \"name\": \"a white bar\"}, {\"id\": 19213, \"name\": \"blue and white body\"}, {\"id\": 19214, \"name\": \"blue and black sign\"}, {\"id\": 19215, \"name\": \"the small yellow rectangle\"}, {\"id\": 19216, \"name\": \"a dark opening\"}, {\"id\": 19217, \"name\": \"the shirt with black and grey vertical stripe\"}, {\"id\": 19218, \"name\": \"a can of soda\"}, {\"id\": 19219, \"name\": \"blue handle\"}, {\"id\": 19220, \"name\": \"the walkie-talkie\"}, {\"id\": 19221, \"name\": \"the sd card\"}, {\"id\": 19222, \"name\": \"a large catapult\"}, {\"id\": 19223, \"name\": \"strip of green grass\"}, {\"id\": 19224, \"name\": \"the motorcycle's rear tire\"}, {\"id\": 19225, \"name\": \"a green pole\"}, {\"id\": 19226, \"name\": \"the yellow and orange logo\"}, {\"id\": 19227, \"name\": \"blue cap\"}, {\"id\": 19228, \"name\": \"neon-colored attire\"}, {\"id\": 19229, \"name\": \"a starting gate\"}, {\"id\": 19230, \"name\": \"a tray of assorted pastry\"}, {\"id\": 19231, \"name\": \"logo of nbc affiliate 41 action news\"}, {\"id\": 19232, \"name\": \"the semi-transparent watermark\"}, {\"id\": 19233, \"name\": \"a grey roof\"}, {\"id\": 19234, \"name\": \"yellow dress\"}, {\"id\": 19235, \"name\": \"metal grill\"}, {\"id\": 19236, \"name\": \"neon green hat\"}, {\"id\": 19237, \"name\": \"tackle diabetes\"}, {\"id\": 19238, \"name\": \"prominent white crosswalk\"}, {\"id\": 19239, \"name\": \"a red and white barrier\"}, {\"id\": 19240, \"name\": \"a curved walkway\"}, {\"id\": 19241, \"name\": \"the purple vest\"}, {\"id\": 19242, \"name\": \"orange bollard\"}, {\"id\": 19243, \"name\": \"a back of the carriage\"}, {\"id\": 19244, \"name\": \"bran\"}, {\"id\": 19245, \"name\": \"the white arch\"}, {\"id\": 19246, \"name\": \"a red and gold cloth\"}, {\"id\": 19247, \"name\": \"the cartoon horse jockey\"}, {\"id\": 19248, \"name\": \"the rack of colorful clothing\"}, {\"id\": 19249, \"name\": \"yellow and maroon jersey\"}, {\"id\": 19250, \"name\": \"the shadowy figure\"}, {\"id\": 19251, \"name\": \"a warm, beige-colored wall\"}, {\"id\": 19252, \"name\": \"a saddle\"}, {\"id\": 19253, \"name\": \"rafter\"}, {\"id\": 19254, \"name\": \"the white water bottle\"}, {\"id\": 19255, \"name\": \"the interior of the vehicle\"}, {\"id\": 19256, \"name\": \"congested urban road\"}, {\"id\": 19257, \"name\": \"the truck bed\"}, {\"id\": 19258, \"name\": \"pink t-shirt\"}, {\"id\": 19259, \"name\": \"a black silhouette of a person walking\"}, {\"id\": 19260, \"name\": \"multicolored shirt\"}, {\"id\": 19261, \"name\": \"a grooming station\"}, {\"id\": 19262, \"name\": \"a black apron\"}, {\"id\": 19263, \"name\": \"a clear liquid\"}, {\"id\": 19264, \"name\": \"the long-sleeved dress\"}, {\"id\": 19265, \"name\": \"the long neck\"}, {\"id\": 19266, \"name\": \"the driver's rearview mirror\"}, {\"id\": 19267, \"name\": \"a small wing\"}, {\"id\": 19268, \"name\": \"a white hubcap\"}, {\"id\": 19269, \"name\": \"a brand\"}, {\"id\": 19270, \"name\": \"hyundai logo\"}, {\"id\": 19271, \"name\": \"a motorcycle's rearview mirror\"}, {\"id\": 19272, \"name\": \"the decorative black lamppost\"}, {\"id\": 19273, \"name\": \"a gold emblem\"}, {\"id\": 19274, \"name\": \"the person on a bicycle\"}, {\"id\": 19275, \"name\": \"a flowing script\"}, {\"id\": 19276, \"name\": \"the curved guardrail\"}, {\"id\": 19277, \"name\": \"police lineup\"}, {\"id\": 19278, \"name\": \"pedestrian walkway\"}, {\"id\": 19279, \"name\": \"the pink patch\"}, {\"id\": 19280, \"name\": \"the blue mane\"}, {\"id\": 19281, \"name\": \"stage's backdrop\"}, {\"id\": 19282, \"name\": \"a dark gray pant\"}, {\"id\": 19283, \"name\": \"the blurry image\"}, {\"id\": 19284, \"name\": \"purple bracelet\"}, {\"id\": 19285, \"name\": \"a curved track\"}, {\"id\": 19286, \"name\": \"an orb\"}, {\"id\": 19287, \"name\": \"the green traffic light pole\"}, {\"id\": 19288, \"name\": \"african continent logo\"}, {\"id\": 19289, \"name\": \"a teal border\"}, {\"id\": 19290, \"name\": \"blue line\"}, {\"id\": 19291, \"name\": \"the small icon of a white flower\"}, {\"id\": 19292, \"name\": \"the black and white striped object\"}, {\"id\": 19293, \"name\": \"time\"}, {\"id\": 19294, \"name\": \"the black fedora\"}, {\"id\": 19295, \"name\": \"the long, flat blade\"}, {\"id\": 19296, \"name\": \"a white pom-pom\"}, {\"id\": 19297, \"name\": \"a gray square\"}, {\"id\": 19298, \"name\": \"the small device\"}, {\"id\": 19299, \"name\": \"the headphones\"}, {\"id\": 19300, \"name\": \"the black harness\"}, {\"id\": 19301, \"name\": \"a large blue umbrella\"}, {\"id\": 19302, \"name\": \"a swimsuit\"}, {\"id\": 19303, \"name\": \"a grassy hillside\"}, {\"id\": 19304, \"name\": \"an armband\"}, {\"id\": 19305, \"name\": \"a red crate\"}, {\"id\": 19306, \"name\": \"a man with dark hair\"}, {\"id\": 19307, \"name\": \"red rectangle\"}, {\"id\": 19308, \"name\": \"the white 22 logo\"}, {\"id\": 19309, \"name\": \"the roadway\"}, {\"id\": 19310, \"name\": \"the bright blue body of water\"}, {\"id\": 19311, \"name\": \"the camouflage backpack strap\"}, {\"id\": 19312, \"name\": \"neon yellow sock\"}, {\"id\": 19313, \"name\": \"the maroon bar\"}, {\"id\": 19314, \"name\": \"the matching black t-shirt\"}, {\"id\": 19315, \"name\": \"white bench\"}, {\"id\": 19316, \"name\": \"the kia\"}, {\"id\": 19317, \"name\": \"a large, rectangular object\"}, {\"id\": 19318, \"name\": \"the man in a blue and white patterned outfit\"}, {\"id\": 19319, \"name\": \"the tote bag\"}, {\"id\": 19320, \"name\": \"a paw print\"}, {\"id\": 19321, \"name\": \"the tan brick\"}, {\"id\": 19322, \"name\": \"the play/pause button\"}, {\"id\": 19323, \"name\": \"an uniform\"}, {\"id\": 19324, \"name\": \"the large cartoon character\"}, {\"id\": 19325, \"name\": \"close-up of a fish's mouth\"}, {\"id\": 19326, \"name\": \"a shore\"}, {\"id\": 19327, \"name\": \"the dishwasher\"}, {\"id\": 19328, \"name\": \"a silver handlebar\"}, {\"id\": 19329, \"name\": \"digital display screen\"}, {\"id\": 19330, \"name\": \"a white-bordered rectangle\"}, {\"id\": 19331, \"name\": \"dog on a leash\"}, {\"id\": 19332, \"name\": \"a person in a navy hoodie\"}, {\"id\": 19333, \"name\": \"a yellow sticker\"}, {\"id\": 19334, \"name\": \"a blue and purple creature\"}, {\"id\": 19335, \"name\": \"a green t-shirt\"}, {\"id\": 19336, \"name\": \"long wooden stick\"}, {\"id\": 19337, \"name\": \"a large concrete slab\"}, {\"id\": 19338, \"name\": \"a concrete halfpipe\"}, {\"id\": 19339, \"name\": \"the weather icon\"}, {\"id\": 19340, \"name\": \"the large black sign\"}, {\"id\": 19341, \"name\": \"car trunk\"}, {\"id\": 19342, \"name\": \"a brown writing or symbol\"}, {\"id\": 19343, \"name\": \"the white door frame\"}, {\"id\": 19344, \"name\": \"the chart\"}, {\"id\": 19345, \"name\": \"a green horse\"}, {\"id\": 19346, \"name\": \"koala bear\"}, {\"id\": 19347, \"name\": \"a red and green label\"}, {\"id\": 19348, \"name\": \"the woman's short, platinum-blonde hair\"}, {\"id\": 19349, \"name\": \"gray concrete foundation\"}, {\"id\": 19350, \"name\": \"a gray banner\"}, {\"id\": 19351, \"name\": \"dark doorway\"}, {\"id\": 19352, \"name\": \"the carousel\"}, {\"id\": 19353, \"name\": \"white and black jersey\"}, {\"id\": 19354, \"name\": \"signpost\"}, {\"id\": 19355, \"name\": \"the goalpost\"}, {\"id\": 19356, \"name\": \"white-outlined rectangle\"}, {\"id\": 19357, \"name\": \"woman with dark hair\"}, {\"id\": 19358, \"name\": \"yellow beaded necklace\"}, {\"id\": 19359, \"name\": \"rectangular cutout\"}, {\"id\": 19360, \"name\": \"a green and purple light\"}, {\"id\": 19361, \"name\": \"a ac\"}, {\"id\": 19362, \"name\": \"the blue and white stripe\"}, {\"id\": 19363, \"name\": \"post office\"}, {\"id\": 19364, \"name\": \"the red honda civic\"}, {\"id\": 19365, \"name\": \"a man in a gray shirt\"}, {\"id\": 19366, \"name\": \"the dark, black stage\"}, {\"id\": 19367, \"name\": \"a light blue and white patch\"}, {\"id\": 19368, \"name\": \"motorist\"}, {\"id\": 19369, \"name\": \"red suv\"}, {\"id\": 19370, \"name\": \"the lush grassy area\"}, {\"id\": 19371, \"name\": \"the white sheet or blanket\"}, {\"id\": 19372, \"name\": \"the black and white shirt\"}, {\"id\": 19373, \"name\": \"large metal structure\"}, {\"id\": 19374, \"name\": \"neon light\"}, {\"id\": 19375, \"name\": \"motorcycle rider\"}, {\"id\": 19376, \"name\": \"a predominantly white wall\"}, {\"id\": 19377, \"name\": \"long counter of food and drink\"}, {\"id\": 19378, \"name\": \"orange pant\"}, {\"id\": 19379, \"name\": \"yellow school bu\"}, {\"id\": 19380, \"name\": \"large pool of water\"}, {\"id\": 19381, \"name\": \"a lap to go counter\"}, {\"id\": 19382, \"name\": \"a red and white cricket wicket\"}, {\"id\": 19383, \"name\": \"the pit area\"}, {\"id\": 19384, \"name\": \"the festive christma hat\"}, {\"id\": 19385, \"name\": \"car interior\"}, {\"id\": 19386, \"name\": \"snow-covered ledge\"}, {\"id\": 19387, \"name\": \"orange backpack\"}, {\"id\": 19388, \"name\": \"green stone\"}, {\"id\": 19389, \"name\": \"a red cone\"}, {\"id\": 19390, \"name\": \"the jordan brand\"}, {\"id\": 19391, \"name\": \"the white and red logo\"}, {\"id\": 19392, \"name\": \"a front tire\"}, {\"id\": 19393, \"name\": \"a small globe\"}, {\"id\": 19394, \"name\": \"plaid hat\"}, {\"id\": 19395, \"name\": \"blurred bicycle wheel\"}, {\"id\": 19396, \"name\": \"runner's world\"}, {\"id\": 19397, \"name\": \"fire pit\"}, {\"id\": 19398, \"name\": \"a black front grill\"}, {\"id\": 19399, \"name\": \"the rear wing\"}, {\"id\": 19400, \"name\": \"a long, pointed ear\"}, {\"id\": 19401, \"name\": \"a mercedes-benz logo\"}, {\"id\": 19402, \"name\": \"rear seat of a vehicle\"}, {\"id\": 19403, \"name\": \"a train's window\"}, {\"id\": 19404, \"name\": \"black lid\"}, {\"id\": 19405, \"name\": \"long mane\"}, {\"id\": 19406, \"name\": \"the red 95\"}, {\"id\": 19407, \"name\": \"white window shutter\"}, {\"id\": 19408, \"name\": \"the nbcsn\"}, {\"id\": 19409, \"name\": \"a left lane\"}, {\"id\": 19410, \"name\": \"player in a red jersey\"}, {\"id\": 19411, \"name\": \"metal roof\"}, {\"id\": 19412, \"name\": \"the snowman\"}, {\"id\": 19413, \"name\": \"the orange logo\"}, {\"id\": 19414, \"name\": \"health bar\"}, {\"id\": 19415, \"name\": \"a gold lettering\"}, {\"id\": 19416, \"name\": \"black and yellow train\"}, {\"id\": 19417, \"name\": \"the smokestack\"}, {\"id\": 19418, \"name\": \"a replay logo\"}, {\"id\": 19419, \"name\": \"granite top\"}, {\"id\": 19420, \"name\": \"the red and white marking\"}, {\"id\": 19421, \"name\": \"the black and white striped jersey\"}, {\"id\": 19422, \"name\": \"a fire escape\"}, {\"id\": 19423, \"name\": \"abcnews.com logo\"}, {\"id\": 19424, \"name\": \"yellow and red flower\"}, {\"id\": 19425, \"name\": \"blond hair\"}, {\"id\": 19426, \"name\": \"a girl's face\"}, {\"id\": 19427, \"name\": \"the large yellow arm\"}, {\"id\": 19428, \"name\": \"tall ship\"}, {\"id\": 19429, \"name\": \"long tail\"}, {\"id\": 19430, \"name\": \"goalpost\"}, {\"id\": 19431, \"name\": \"red and black plaid shirt\"}, {\"id\": 19432, \"name\": \"a yellow life\"}, {\"id\": 19433, \"name\": \"blue strap\"}, {\"id\": 19434, \"name\": \"the red hard hat\"}, {\"id\": 19435, \"name\": \"a maroon sweater\"}, {\"id\": 19436, \"name\": \"paper airplane\"}, {\"id\": 19437, \"name\": \"a red bird silhouette\"}, {\"id\": 19438, \"name\": \"a pink outline\"}, {\"id\": 19439, \"name\": \"a small machine\"}, {\"id\": 19440, \"name\": \"silver rim\"}, {\"id\": 19441, \"name\": \"the roll of paper towel\"}, {\"id\": 19442, \"name\": \"a wooden pier\"}, {\"id\": 19443, \"name\": \"a man's arm\"}, {\"id\": 19444, \"name\": \"yellow truck\"}, {\"id\": 19445, \"name\": \"the grassy bank\"}, {\"id\": 19446, \"name\": \"a black foam cover\"}, {\"id\": 19447, \"name\": \"emoji\"}, {\"id\": 19448, \"name\": \"sandals with strap\"}, {\"id\": 19449, \"name\": \"tray of makeup brush\"}, {\"id\": 19450, \"name\": \"a white beam\"}, {\"id\": 19451, \"name\": \"the karts\"}, {\"id\": 19452, \"name\": \"the character's face\"}, {\"id\": 19453, \"name\": \"the red cuff\"}, {\"id\": 19454, \"name\": \"steam\"}, {\"id\": 19455, \"name\": \"staff\"}, {\"id\": 19456, \"name\": \"a blue pattern\"}, {\"id\": 19457, \"name\": \"a driver's forearms\"}, {\"id\": 19458, \"name\": \"the bull\"}, {\"id\": 19459, \"name\": \"white suv\"}, {\"id\": 19460, \"name\": \"large white sign\"}, {\"id\": 19461, \"name\": \"the concrete path\"}, {\"id\": 19462, \"name\": \"the metal roof\"}, {\"id\": 19463, \"name\": \"the pharmacy\"}, {\"id\": 19464, \"name\": \"the white and red helmet\"}, {\"id\": 19465, \"name\": \"green hat\"}, {\"id\": 19466, \"name\": \"a gold-colored serving dish\"}, {\"id\": 19467, \"name\": \"the red and white striped body\"}, {\"id\": 19468, \"name\": \"the bright window\"}, {\"id\": 19469, \"name\": \"the black bull\"}, {\"id\": 19470, \"name\": \"red taxi\"}, {\"id\": 19471, \"name\": \"a valley national bank\"}, {\"id\": 19472, \"name\": \"the black sail\"}, {\"id\": 19473, \"name\": \"the massage therapist\"}, {\"id\": 19474, \"name\": \"dimly lit stage\"}, {\"id\": 19475, \"name\": \"an oar\"}, {\"id\": 19476, \"name\": \"red buoy\"}, {\"id\": 19477, \"name\": \"the pink bag\"}, {\"id\": 19478, \"name\": \"a logo resembling a fish\"}, {\"id\": 19479, \"name\": \"blue van\"}, {\"id\": 19480, \"name\": \"long black pole\"}, {\"id\": 19481, \"name\": \"the herd of cattle\"}, {\"id\": 19482, \"name\": \"white flip-flop\"}, {\"id\": 19483, \"name\": \"black swimsuit\"}, {\"id\": 19484, \"name\": \"a soccer ball graphic\"}, {\"id\": 19485, \"name\": \"a recycling bin\"}, {\"id\": 19486, \"name\": \"gold pendant\"}, {\"id\": 19487, \"name\": \"cartoon cat\"}, {\"id\": 19488, \"name\": \"black letterman jacket\"}, {\"id\": 19489, \"name\": \"small black rectangle\"}, {\"id\": 19490, \"name\": \"red chinese lantern\"}, {\"id\": 19491, \"name\": \"a red and white frame\"}, {\"id\": 19492, \"name\": \"blue scarf\"}, {\"id\": 19493, \"name\": \"the green banner\"}, {\"id\": 19494, \"name\": \"the large red firetruck\"}, {\"id\": 19495, \"name\": \"roland logo\"}, {\"id\": 19496, \"name\": \"the tricycle\"}, {\"id\": 19497, \"name\": \"top hat\"}, {\"id\": 19498, \"name\": \"a candelabrum\"}, {\"id\": 19499, \"name\": \"treat\"}, {\"id\": 19500, \"name\": \"a pattern of stripe\"}, {\"id\": 19501, \"name\": \"the white taxi\"}, {\"id\": 19502, \"name\": \"the red button\"}, {\"id\": 19503, \"name\": \"the gray hair\"}, {\"id\": 19504, \"name\": \"the wheelbarrow\"}, {\"id\": 19505, \"name\": \"the shoulder-length dark hair\"}, {\"id\": 19506, \"name\": \"the bin\"}, {\"id\": 19507, \"name\": \"a back seat\"}, {\"id\": 19508, \"name\": \"rolled-up sleeve\"}, {\"id\": 19509, \"name\": \"small, tiered fountain\"}, {\"id\": 19510, \"name\": \"a green box\"}, {\"id\": 19511, \"name\": \"a white police vehicle\"}, {\"id\": 19512, \"name\": \"smaller piece of wood\"}, {\"id\": 19513, \"name\": \"blue and red sign\"}, {\"id\": 19514, \"name\": \"the red and yellow striped awning\"}, {\"id\": 19515, \"name\": \"white structure\"}, {\"id\": 19516, \"name\": \"the hoyer (2)\"}, {\"id\": 19517, \"name\": \"blue map of africa\"}, {\"id\": 19518, \"name\": \"a parking sign\"}, {\"id\": 19519, \"name\": \"a boy's head\"}, {\"id\": 19520, \"name\": \"large white box\"}, {\"id\": 19521, \"name\": \"a green character\"}, {\"id\": 19522, \"name\": \"handle of a suitcase\"}, {\"id\": 19523, \"name\": \"the small grey box\"}, {\"id\": 19524, \"name\": \"the pink purse\"}, {\"id\": 19525, \"name\": \"yellow inflatable pool toy\"}, {\"id\": 19526, \"name\": \"wooden building\"}, {\"id\": 19527, \"name\": \"person in an orange safety vest\"}, {\"id\": 19528, \"name\": \"purple leg\"}, {\"id\": 19529, \"name\": \"the brown wooden panel\"}, {\"id\": 19530, \"name\": \"a tall blue dumpster\"}, {\"id\": 19531, \"name\": \"a chest plate\"}, {\"id\": 19532, \"name\": \"a woman in an orange vest\"}, {\"id\": 19533, \"name\": \"a golden arch\"}, {\"id\": 19534, \"name\": \"backdrop of palm tree\"}, {\"id\": 19535, \"name\": \"a star-shaped pattern\"}, {\"id\": 19536, \"name\": \"the television news graphic\"}, {\"id\": 19537, \"name\": \"bin\"}, {\"id\": 19538, \"name\": \"a metal bike rack\"}, {\"id\": 19539, \"name\": \"a purple light\"}, {\"id\": 19540, \"name\": \"white crosswalk\"}, {\"id\": 19541, \"name\": \"a semi-transparent grey rectangle\"}, {\"id\": 19542, \"name\": \"the red and white striped tent\"}, {\"id\": 19543, \"name\": \"the bmw welt sign\"}, {\"id\": 19544, \"name\": \"the red and white striped curb\"}, {\"id\": 19545, \"name\": \"the brown box\"}, {\"id\": 19546, \"name\": \"a snow-capped mountain\"}, {\"id\": 19547, \"name\": \"a date\"}, {\"id\": 19548, \"name\": \"a green outline\"}, {\"id\": 19549, \"name\": \"the pliers\"}, {\"id\": 19550, \"name\": \"a white metal post\"}, {\"id\": 19551, \"name\": \"a circular hole\"}, {\"id\": 19552, \"name\": \"the white rail\"}, {\"id\": 19553, \"name\": \"the athletic shoe\"}, {\"id\": 19554, \"name\": \"the 7 to go\"}, {\"id\": 19555, \"name\": \"person on bike\"}, {\"id\": 19556, \"name\": \"the stop sign\"}, {\"id\": 19557, \"name\": \"white belt\"}, {\"id\": 19558, \"name\": \"the clock tower\"}, {\"id\": 19559, \"name\": \"cereal\"}, {\"id\": 19560, \"name\": \"grandstand\"}, {\"id\": 19561, \"name\": \"date\"}, {\"id\": 19562, \"name\": \"the beige surface\"}, {\"id\": 19563, \"name\": \"the matching green t-shirt\"}, {\"id\": 19564, \"name\": \"the black-and-white striped pant\"}, {\"id\": 19565, \"name\": \"a black license plate\"}, {\"id\": 19566, \"name\": \"conductor\"}, {\"id\": 19567, \"name\": \"the colorful graphic design\"}, {\"id\": 19568, \"name\": \"blue truck\"}, {\"id\": 19569, \"name\": \"a horse racing track\"}, {\"id\": 19570, \"name\": \"a raised arm\"}, {\"id\": 19571, \"name\": \"sadlife sticker\"}, {\"id\": 19572, \"name\": \"the green floor\"}, {\"id\": 19573, \"name\": \"a glowing green light\"}, {\"id\": 19574, \"name\": \"caption overlay\"}, {\"id\": 19575, \"name\": \"the large, green, cylindrical structure\"}, {\"id\": 19576, \"name\": \"american flag\"}, {\"id\": 19577, \"name\": \"the underwater mural\"}, {\"id\": 19578, \"name\": \"green poncho\"}, {\"id\": 19579, \"name\": \"prominent blue logo\"}, {\"id\": 19580, \"name\": \"model train conductor\"}, {\"id\": 19581, \"name\": \"the woman in a black dress\"}, {\"id\": 19582, \"name\": \"black outline\"}, {\"id\": 19583, \"name\": \"metal shelf\"}, {\"id\": 19584, \"name\": \"white water\"}, {\"id\": 19585, \"name\": \"large orange banner\"}, {\"id\": 19586, \"name\": \"headphones\"}, {\"id\": 19587, \"name\": \"the tan military uniform\"}, {\"id\": 19588, \"name\": \"large black machine\"}, {\"id\": 19589, \"name\": \"the thin strap\"}, {\"id\": 19590, \"name\": \"man in a gray t-shirt\"}, {\"id\": 19591, \"name\": \"long, thin tail\"}, {\"id\": 19592, \"name\": \"an easel\"}, {\"id\": 19593, \"name\": \"a prominent watermark\"}, {\"id\": 19594, \"name\": \"a stand of a stadium\"}, {\"id\": 19595, \"name\": \"hashtag\"}, {\"id\": 19596, \"name\": \"the boat's deck\"}, {\"id\": 19597, \"name\": \"social media link\"}, {\"id\": 19598, \"name\": \"a hs logo\"}, {\"id\": 19599, \"name\": \"a ship's name\"}, {\"id\": 19600, \"name\": \"a truck's trailer\"}, {\"id\": 19601, \"name\": \"vela pan\"}, {\"id\": 19602, \"name\": \"a black face mask\"}, {\"id\": 19603, \"name\": \"the yellow interior\"}, {\"id\": 19604, \"name\": \"warehouse floor\"}, {\"id\": 19605, \"name\": \"white wire\"}, {\"id\": 19606, \"name\": \"the gopro\"}, {\"id\": 19607, \"name\": \"the dslr\"}, {\"id\": 19608, \"name\": \"yellow sun\"}, {\"id\": 19609, \"name\": \"a turret\"}, {\"id\": 19610, \"name\": \"a distinctive horn\"}, {\"id\": 19611, \"name\": \"a central white line\"}, {\"id\": 19612, \"name\": \"a fan-shaped window\"}, {\"id\": 19613, \"name\": \"a yellow and red truck\"}, {\"id\": 19614, \"name\": \"a cardboard sign\"}, {\"id\": 19615, \"name\": \"a dark, wet, and reflective road\"}, {\"id\": 19616, \"name\": \"a grandstand\"}, {\"id\": 19617, \"name\": \"gold belt\"}, {\"id\": 19618, \"name\": \"a red sunburst design\"}, {\"id\": 19619, \"name\": \"a right shoulder\"}, {\"id\": 19620, \"name\": \"large stone structure\"}, {\"id\": 19621, \"name\": \"boundary\"}, {\"id\": 19622, \"name\": \"a blue and red sign\"}, {\"id\": 19623, \"name\": \"a trtworld\"}, {\"id\": 19624, \"name\": \"a purple and white jockey's cap\"}, {\"id\": 19625, \"name\": \"the teal border\"}, {\"id\": 19626, \"name\": \"an orange shape\"}, {\"id\": 19627, \"name\": \"van's windshield\"}, {\"id\": 19628, \"name\": \"metal barrier\"}, {\"id\": 19629, \"name\": \"a yellow, green, and blue object\"}, {\"id\": 19630, \"name\": \"a tall light pole\"}, {\"id\": 19631, \"name\": \"the red and white umbrella\"}, {\"id\": 19632, \"name\": \"a red brick structure\"}, {\"id\": 19633, \"name\": \"guitar amplifier\"}, {\"id\": 19634, \"name\": \"a large, silver-colored machine\"}, {\"id\": 19635, \"name\": \"tricycle\"}, {\"id\": 19636, \"name\": \"blue liquid\"}, {\"id\": 19637, \"name\": \"small white fireplace\"}, {\"id\": 19638, \"name\": \"a winged figure\"}, {\"id\": 19639, \"name\": \"the red mailbox\"}, {\"id\": 19640, \"name\": \"the blue water bottle\"}, {\"id\": 19641, \"name\": \"the purple skirt\"}, {\"id\": 19642, \"name\": \"residential dwelling\"}, {\"id\": 19643, \"name\": \"grassy infield\"}, {\"id\": 19644, \"name\": \"black hair dryer\"}, {\"id\": 19645, \"name\": \"a small portion of a red banner\"}, {\"id\": 19646, \"name\": \"a black leash\"}, {\"id\": 19647, \"name\": \"the url\"}, {\"id\": 19648, \"name\": \"horse track\"}, {\"id\": 19649, \"name\": \"the high-waisted short\"}, {\"id\": 19650, \"name\": \"a black bollard fence\"}, {\"id\": 19651, \"name\": \"a white plate of toasted bun\"}, {\"id\": 19652, \"name\": \"the deck of a boat\"}, {\"id\": 19653, \"name\": \"the purple staining\"}, {\"id\": 19654, \"name\": \"a woman in a red shirt\"}, {\"id\": 19655, \"name\": \"red double-decker bus\"}, {\"id\": 19656, \"name\": \"green, orange, and yellow pole\"}, {\"id\": 19657, \"name\": \"translucent gray bar\"}, {\"id\": 19658, \"name\": \"a long, sloping roof\"}, {\"id\": 19659, \"name\": \"rainbow hat\"}, {\"id\": 19660, \"name\": \"sleek silver bike\"}, {\"id\": 19661, \"name\": \"a warehouse\"}, {\"id\": 19662, \"name\": \"the dj booth\"}, {\"id\": 19663, \"name\": \"the orange cleat\"}, {\"id\": 19664, \"name\": \"a claw crane\"}, {\"id\": 19665, \"name\": \"a stop sign\"}, {\"id\": 19666, \"name\": \"the individual running\"}, {\"id\": 19667, \"name\": \"the bright orange head\"}, {\"id\": 19668, \"name\": \"clothing rack\"}, {\"id\": 19669, \"name\": \"a square headlight\"}, {\"id\": 19670, \"name\": \"the smaller red banner\"}, {\"id\": 19671, \"name\": \"witch's hat\"}, {\"id\": 19672, \"name\": \"the white shirt with red stripe\"}, {\"id\": 19673, \"name\": \"the framed photograph\"}, {\"id\": 19674, \"name\": \"the nah\"}, {\"id\": 19675, \"name\": \"a white sandbag\"}, {\"id\": 19676, \"name\": \"an outdoor pavilion\"}, {\"id\": 19677, \"name\": \"wooden box\"}, {\"id\": 19678, \"name\": \"the black barrier\"}, {\"id\": 19679, \"name\": \"the brown truck\"}, {\"id\": 19680, \"name\": \"yellow circle logo\"}, {\"id\": 19681, \"name\": \"the sliding door\"}, {\"id\": 19682, \"name\": \"a shelter\"}, {\"id\": 19683, \"name\": \"a red, blue, and white facade\"}, {\"id\": 19684, \"name\": \"long-sleeved green shirt\"}, {\"id\": 19685, \"name\": \"a wooden bongo drumstick\"}, {\"id\": 19686, \"name\": \"a www.wionews.com\"}, {\"id\": 19687, \"name\": \"the lime green vehicle\"}, {\"id\": 19688, \"name\": \"bank\"}, {\"id\": 19689, \"name\": \"a clear plastic shelf\"}, {\"id\": 19690, \"name\": \"the blue hatchback\"}, {\"id\": 19691, \"name\": \"a horizontal slat\"}, {\"id\": 19692, \"name\": \"black bulletproof vest\"}, {\"id\": 19693, \"name\": \"luggage\"}, {\"id\": 19694, \"name\": \"a multicolored shirt\"}, {\"id\": 19695, \"name\": \"a wide-brimmed hat\"}, {\"id\": 19696, \"name\": \"a green td bank sign\"}, {\"id\": 19697, \"name\": \"autofrey\"}, {\"id\": 19698, \"name\": \"long, curved tail\"}, {\"id\": 19699, \"name\": \"a blue shorts with white polka dot\"}, {\"id\": 19700, \"name\": \"motorcycle's front wheel\"}, {\"id\": 19701, \"name\": \"the large american flag\"}, {\"id\": 19702, \"name\": \"a blue sphere\"}, {\"id\": 19703, \"name\": \"the curved mouth\"}, {\"id\": 19704, \"name\": \"the black-and-white sign\"}, {\"id\": 19705, \"name\": \"the turn on red sign\"}, {\"id\": 19706, \"name\": \"the yellow hula hoop\"}, {\"id\": 19707, \"name\": \"gray bar\"}, {\"id\": 19708, \"name\": \"individual on bicycle\"}, {\"id\": 19709, \"name\": \"a yellow sock\"}, {\"id\": 19710, \"name\": \"interior roof\"}, {\"id\": 19711, \"name\": \"ntn24 logo\"}, {\"id\": 19712, \"name\": \"dock or pier\"}, {\"id\": 19713, \"name\": \"a green and red sign\"}, {\"id\": 19714, \"name\": \"a blurry pole\"}, {\"id\": 19715, \"name\": \"white dresser\"}, {\"id\": 19716, \"name\": \"white swirl design\"}, {\"id\": 19717, \"name\": \"game boy\"}, {\"id\": 19718, \"name\": \"a yellow mcdonald's sign\"}, {\"id\": 19719, \"name\": \"the man in a white shirt and short\"}, {\"id\": 19720, \"name\": \"a green sock\"}, {\"id\": 19721, \"name\": \"the deck\"}, {\"id\": 19722, \"name\": \"the brick accent\"}, {\"id\": 19723, \"name\": \"a rectangular box\"}, {\"id\": 19724, \"name\": \"a flatbed truck\"}, {\"id\": 19725, \"name\": \"a blue circle\"}, {\"id\": 19726, \"name\": \"the yellow lion graphic\"}, {\"id\": 19727, \"name\": \"the flatbed area\"}, {\"id\": 19728, \"name\": \"blonde wig\"}, {\"id\": 19729, \"name\": \"a black bmx bike\"}, {\"id\": 19730, \"name\": \"the green short\"}, {\"id\": 19731, \"name\": \"a patio or deck\"}, {\"id\": 19732, \"name\": \"small triangular logo\"}, {\"id\": 19733, \"name\": \"a cleat\"}, {\"id\": 19734, \"name\": \"the blue flag\"}, {\"id\": 19735, \"name\": \"cliff\"}, {\"id\": 19736, \"name\": \"yellow design\"}, {\"id\": 19737, \"name\": \"the white headlight\"}, {\"id\": 19738, \"name\": \"the red traffic light\"}, {\"id\": 19739, \"name\": \"the long whip\"}, {\"id\": 19740, \"name\": \"the backdrop of palm tree\"}, {\"id\": 19741, \"name\": \"green alga\"}, {\"id\": 19742, \"name\": \"a prominent red sign\"}, {\"id\": 19743, \"name\": \"red hood\"}, {\"id\": 19744, \"name\": \"the central tower\"}, {\"id\": 19745, \"name\": \"the distinctive red tail\"}, {\"id\": 19746, \"name\": \"a green hooded sweatshirt\"}, {\"id\": 19747, \"name\": \"the red, yellow, and black flag\"}, {\"id\": 19748, \"name\": \"a wooden drum\"}, {\"id\": 19749, \"name\": \"a yellow rope\"}, {\"id\": 19750, \"name\": \"ski slope\"}, {\"id\": 19751, \"name\": \"a twitter logo\"}, {\"id\": 19752, \"name\": \"the brown dirt track\"}, {\"id\": 19753, \"name\": \"headset\"}, {\"id\": 19754, \"name\": \"a blue and black big 10 logo\"}, {\"id\": 19755, \"name\": \"patterned tie\"}, {\"id\": 19756, \"name\": \"dirt track\"}, {\"id\": 19757, \"name\": \"a race bib\"}, {\"id\": 19758, \"name\": \"a blue plastic handle\"}, {\"id\": 19759, \"name\": \"small blue and red triangle\"}, {\"id\": 19760, \"name\": \"a bow and arrow\"}, {\"id\": 19761, \"name\": \"striped awning\"}, {\"id\": 19762, \"name\": \"a large white heart\"}, {\"id\": 19763, \"name\": \"the black muzzle\"}, {\"id\": 19764, \"name\": \"white box truck\"}, {\"id\": 19765, \"name\": \"the walking stick\"}, {\"id\": 19766, \"name\": \"the black and yellow checkered stripe\"}, {\"id\": 19767, \"name\": \"the trophy\"}, {\"id\": 19768, \"name\": \"a small tree\"}, {\"id\": 19769, \"name\": \"a hollywood walk of fame\"}, {\"id\": 19770, \"name\": \"the black tire\"}, {\"id\": 19771, \"name\": \"food stand\"}, {\"id\": 19772, \"name\": \"medium-length blonde hair\"}, {\"id\": 19773, \"name\": \"the orange dress\"}, {\"id\": 19774, \"name\": \"a sign in japanese character\"}, {\"id\": 19775, \"name\": \"dark brown leg\"}, {\"id\": 19776, \"name\": \"a plastic sheeting\"}, {\"id\": 19777, \"name\": \"gray rock\"}, {\"id\": 19778, \"name\": \"bright lighting\"}, {\"id\": 19779, \"name\": \"yellow and blue checkered pattern\"}, {\"id\": 19780, \"name\": \"the clean and neutral backdrop\"}, {\"id\": 19781, \"name\": \"the dark gray asphalt\"}, {\"id\": 19782, \"name\": \"the black and silver head\"}, {\"id\": 19783, \"name\": \"the sodor clock tower\"}, {\"id\": 19784, \"name\": \"a white tank top\"}, {\"id\": 19785, \"name\": \"dark bridle\"}, {\"id\": 19786, \"name\": \"a brown baseball cap\"}, {\"id\": 19787, \"name\": \"blue and white shirt\"}, {\"id\": 19788, \"name\": \"red extension cord\"}, {\"id\": 19789, \"name\": \"a green field\"}, {\"id\": 19790, \"name\": \"a forested area\"}, {\"id\": 19791, \"name\": \"the black base\"}, {\"id\": 19792, \"name\": \"a green crane or excavator\"}, {\"id\": 19793, \"name\": \"the cartoon chef\"}, {\"id\": 19794, \"name\": \"the short, white hair\"}, {\"id\": 19795, \"name\": \"a black metal frame\"}, {\"id\": 19796, \"name\": \"the hive's white wooden frame\"}, {\"id\": 19797, \"name\": \"the large light fixture\"}, {\"id\": 19798, \"name\": \"the high-visibility jacket\"}, {\"id\": 19799, \"name\": \"the casual western attire\"}, {\"id\": 19800, \"name\": \"red and white striped shirt\"}, {\"id\": 19801, \"name\": \"person's wrist\"}, {\"id\": 19802, \"name\": \"the musical staff\"}, {\"id\": 19803, \"name\": \"purple cuff\"}, {\"id\": 19804, \"name\": \"the silver border\"}, {\"id\": 19805, \"name\": \"the santa's right hand\"}, {\"id\": 19806, \"name\": \"the white corrugated wall\"}, {\"id\": 19807, \"name\": \"large ear\"}, {\"id\": 19808, \"name\": \"large brown shopping bag\"}, {\"id\": 19809, \"name\": \"the white, toothy grin\"}, {\"id\": 19810, \"name\": \"sign or marker\"}, {\"id\": 19811, \"name\": \"small blue cone\"}, {\"id\": 19812, \"name\": \"wicker basket\"}, {\"id\": 19813, \"name\": \"the vine\"}, {\"id\": 19814, \"name\": \"a silver microphone\"}, {\"id\": 19815, \"name\": \"small candy cane\"}, {\"id\": 19816, \"name\": \"the time elapsed\"}, {\"id\": 19817, \"name\": \"the pegboard\"}, {\"id\": 19818, \"name\": \"vehicle's side door\"}, {\"id\": 19819, \"name\": \"a jockey\"}, {\"id\": 19820, \"name\": \"large vertical strip of blue and black\"}, {\"id\": 19821, \"name\": \"the red and black tool box\"}, {\"id\": 19822, \"name\": \"the brick median\"}, {\"id\": 19823, \"name\": \"a roof of the station\"}, {\"id\": 19824, \"name\": \"the curved leg\"}, {\"id\": 19825, \"name\": \"black bag strap\"}, {\"id\": 19826, \"name\": \"a computer equipment\"}, {\"id\": 19827, \"name\": \"a silver chain\"}, {\"id\": 19828, \"name\": \"the skull\"}, {\"id\": 19829, \"name\": \"the black and red go-kart\"}, {\"id\": 19830, \"name\": \"a colorful costume\"}, {\"id\": 19831, \"name\": \"silver mercedes-benz logo\"}, {\"id\": 19832, \"name\": \"a score\"}, {\"id\": 19833, \"name\": \"a credit card terminal\"}, {\"id\": 19834, \"name\": \"a neon yellow and black outfit\"}, {\"id\": 19835, \"name\": \"a white archway\"}, {\"id\": 19836, \"name\": \"the small white logo\"}, {\"id\": 19837, \"name\": \"an orange uniform\"}, {\"id\": 19838, \"name\": \"harmonium\"}, {\"id\": 19839, \"name\": \"a tan hat\"}, {\"id\": 19840, \"name\": \"the prominent white boat\"}, {\"id\": 19841, \"name\": \"black train\"}, {\"id\": 19842, \"name\": \"the graphic of a maple leaf\"}, {\"id\": 19843, \"name\": \"the large, green pool float\"}, {\"id\": 19844, \"name\": \"the blue material\"}, {\"id\": 19845, \"name\": \"a gentle arch\"}, {\"id\": 19846, \"name\": \"blue cushion\"}, {\"id\": 19847, \"name\": \"red and yellow border\"}, {\"id\": 19848, \"name\": \"red tongue\"}, {\"id\": 19849, \"name\": \"a ram 1500 logo\"}, {\"id\": 19850, \"name\": \"a gray lego brick\"}, {\"id\": 19851, \"name\": \"golf green\"}, {\"id\": 19852, \"name\": \"a vibrant, graffiti-style mural\"}, {\"id\": 19853, \"name\": \"the pink ball\"}, {\"id\": 19854, \"name\": \"the green jeep wrangler\"}, {\"id\": 19855, \"name\": \"the display of wine bottle\"}, {\"id\": 19856, \"name\": \"a balloon stick\"}, {\"id\": 19857, \"name\": \"phrase\"}, {\"id\": 19858, \"name\": \"a yellow traffic light\"}, {\"id\": 19859, \"name\": \"the flexible rod\"}, {\"id\": 19860, \"name\": \"a dotted white line\"}, {\"id\": 19861, \"name\": \"a bottle of orange liquid\"}, {\"id\": 19862, \"name\": \"a large, rectangular mirror\"}, {\"id\": 19863, \"name\": \"the sign on a pole\"}, {\"id\": 19864, \"name\": \"a red postbox\"}, {\"id\": 19865, \"name\": \"a red suv\"}, {\"id\": 19866, \"name\": \"a nbcsn logo\"}, {\"id\": 19867, \"name\": \"a news channel's name\"}, {\"id\": 19868, \"name\": \"white and red canoe\"}, {\"id\": 19869, \"name\": \"a hood of a vehicle\"}, {\"id\": 19870, \"name\": \"an orange rim\"}, {\"id\": 19871, \"name\": \"chemical irritant\"}, {\"id\": 19872, \"name\": \"the network of crosswalk\"}, {\"id\": 19873, \"name\": \"pink octopus-like creature\"}, {\"id\": 19874, \"name\": \"gbr 19 logo\"}, {\"id\": 19875, \"name\": \"lay potato chip logo\"}, {\"id\": 19876, \"name\": \"a purple roof\"}, {\"id\": 19877, \"name\": \"the flagpole\"}, {\"id\": 19878, \"name\": \"the bike's front fork\"}, {\"id\": 19879, \"name\": \"large, dark hairdo\"}, {\"id\": 19880, \"name\": \"a dark polo shirt\"}, {\"id\": 19881, \"name\": \"a mara\"}, {\"id\": 19882, \"name\": \"a black hull\"}, {\"id\": 19883, \"name\": \"the red and green cube\"}, {\"id\": 19884, \"name\": \"a driver's head\"}, {\"id\": 19885, \"name\": \"hot wheel\"}, {\"id\": 19886, \"name\": \"the metal bolt\"}, {\"id\": 19887, \"name\": \"the flat rooftop\"}, {\"id\": 19888, \"name\": \"an orange and white traffic cone\"}, {\"id\": 19889, \"name\": \"a toy vehicle\"}, {\"id\": 19890, \"name\": \"red circle\"}, {\"id\": 19891, \"name\": \"woman holding a \\\" parking\\\" sign\"}, {\"id\": 19892, \"name\": \"october 6, 2018\"}, {\"id\": 19893, \"name\": \"a red triangle\"}, {\"id\": 19894, \"name\": \"a i am sorry message\"}, {\"id\": 19895, \"name\": \"the bald man\"}, {\"id\": 19896, \"name\": \"the blue and green label\"}, {\"id\": 19897, \"name\": \"a white window\"}, {\"id\": 19898, \"name\": \"a small blue hold\"}, {\"id\": 19899, \"name\": \"a dump truck\"}, {\"id\": 19900, \"name\": \"small mouth\"}, {\"id\": 19901, \"name\": \"orange arrow\"}, {\"id\": 19902, \"name\": \"distant shoreline\"}, {\"id\": 19903, \"name\": \"road barrier\"}, {\"id\": 19904, \"name\": \"the navy blue t-shirt\"}, {\"id\": 19905, \"name\": \"black seat\"}, {\"id\": 19906, \"name\": \"the red rim\"}, {\"id\": 19907, \"name\": \"panda logo\"}, {\"id\": 19908, \"name\": \"the red outfit\"}, {\"id\": 19909, \"name\": \"brown fence\"}, {\"id\": 19910, \"name\": \"the beige-colored grandstand\"}, {\"id\": 19911, \"name\": \"dark purple border\"}, {\"id\": 19912, \"name\": \"the vertical plank\"}, {\"id\": 19913, \"name\": \"choker\"}, {\"id\": 19914, \"name\": \"the wire\"}, {\"id\": 19915, \"name\": \"a green coral\"}, {\"id\": 19916, \"name\": \"a coil of wire\"}, {\"id\": 19917, \"name\": \"checkered flag icon\"}, {\"id\": 19918, \"name\": \"red wooden beam\"}, {\"id\": 19919, \"name\": \"the moving vehicle\"}, {\"id\": 19920, \"name\": \"parking sign\"}, {\"id\": 19921, \"name\": \"blue team's goalkeeper\"}, {\"id\": 19922, \"name\": \"the 46 minutes and 35 second\"}, {\"id\": 19923, \"name\": \"a large archway\"}, {\"id\": 19924, \"name\": \"the blue-and-white jordan t-shirt\"}, {\"id\": 19925, \"name\": \"the yellow reflective strip\"}, {\"id\": 19926, \"name\": \"the stovetop\"}, {\"id\": 19927, \"name\": \"a lens\"}, {\"id\": 19928, \"name\": \"the makeup\"}, {\"id\": 19929, \"name\": \"can of red bull\"}, {\"id\": 19930, \"name\": \"a prominent grip\"}, {\"id\": 19931, \"name\": \"pool toy\"}, {\"id\": 19932, \"name\": \"the red cape\"}, {\"id\": 19933, \"name\": \"the backpack strap\"}, {\"id\": 19934, \"name\": \"light blue cup\"}, {\"id\": 19935, \"name\": \"a blue pickup truck\"}, {\"id\": 19936, \"name\": \"white and black checkered scarf\"}, {\"id\": 19937, \"name\": \"the lamp logo\"}, {\"id\": 19938, \"name\": \"a stork\"}, {\"id\": 19939, \"name\": \"a date stamp\"}, {\"id\": 19940, \"name\": \"a denim short\"}, {\"id\": 19941, \"name\": \"a drop cloth\"}, {\"id\": 19942, \"name\": \"the crisp white tablecloth\"}, {\"id\": 19943, \"name\": \"a wavy green railing\"}, {\"id\": 19944, \"name\": \"the white wall of the kitchen\"}, {\"id\": 19945, \"name\": \"the subway door\"}, {\"id\": 19946, \"name\": \"a blue and white facade\"}, {\"id\": 19947, \"name\": \"arched doorway\"}, {\"id\": 19948, \"name\": \"the tree or pole\"}, {\"id\": 19949, \"name\": \"laughing emoji\"}, {\"id\": 19950, \"name\": \"a wooden trough\"}, {\"id\": 19951, \"name\": \"the large white bowl\"}, {\"id\": 19952, \"name\": \"a tan sheet\"}, {\"id\": 19953, \"name\": \"a toasted bread\"}, {\"id\": 19954, \"name\": \"a blue and silver tube\"}, {\"id\": 19955, \"name\": \"the black backboard\"}, {\"id\": 19956, \"name\": \"a motorcycle's frame\"}, {\"id\": 19957, \"name\": \"grey tail\"}, {\"id\": 19958, \"name\": \"clown's mouth\"}, {\"id\": 19959, \"name\": \"a green metal pole\"}, {\"id\": 19960, \"name\": \"the white toy\"}, {\"id\": 19961, \"name\": \"hand with finger\"}, {\"id\": 19962, \"name\": \"tarp\"}, {\"id\": 19963, \"name\": \"blue and white umbrella\"}, {\"id\": 19964, \"name\": \"a small white star\"}, {\"id\": 19965, \"name\": \"the small potted plant\"}, {\"id\": 19966, \"name\": \"a long, curly blonde hair\"}, {\"id\": 19967, \"name\": \"a small, yellow object\"}, {\"id\": 19968, \"name\": \"a medieval-style sword fight\"}, {\"id\": 19969, \"name\": \"the white and black grid pattern\"}, {\"id\": 19970, \"name\": \"the numbered sign\"}, {\"id\": 19971, \"name\": \"blue back fender\"}, {\"id\": 19972, \"name\": \"the neon pink stripe\"}, {\"id\": 19973, \"name\": \"the brown door mat\"}, {\"id\": 19974, \"name\": \"distinctive red and blue stripe\"}, {\"id\": 19975, \"name\": \"the blue and black glove\"}, {\"id\": 19976, \"name\": \"a black gutter\"}, {\"id\": 19977, \"name\": \"a white bicycle icon\"}, {\"id\": 19978, \"name\": \"a red truck\"}, {\"id\": 19979, \"name\": \"catapult\"}, {\"id\": 19980, \"name\": \"white soccer net\"}, {\"id\": 19981, \"name\": \"the white house\"}, {\"id\": 19982, \"name\": \"the blue box\"}, {\"id\": 19983, \"name\": \"stop\"}, {\"id\": 19984, \"name\": \"beige pole\"}, {\"id\": 19985, \"name\": \"the wrt logo\"}, {\"id\": 19986, \"name\": \"a light grey sky\"}, {\"id\": 19987, \"name\": \"an abc15 arizona\"}, {\"id\": 19988, \"name\": \"the red and white traffic cone\"}, {\"id\": 19989, \"name\": \"blue arrow\"}, {\"id\": 19990, \"name\": \"the gray tank top\"}, {\"id\": 19991, \"name\": \"a bright headlight\"}, {\"id\": 19992, \"name\": \"a small, square table\"}, {\"id\": 19993, \"name\": \"red ear protector\"}, {\"id\": 19994, \"name\": \"the shirt pocket\"}, {\"id\": 19995, \"name\": \"a blue stadium seating\"}, {\"id\": 19996, \"name\": \"a large arched doorway\"}, {\"id\": 19997, \"name\": \"the blurry, rainbow-colored object\"}, {\"id\": 19998, \"name\": \"the yellow ferrari f50\"}, {\"id\": 19999, \"name\": \"the yellow cap\"}, {\"id\": 20000, \"name\": \"the red legging\"}, {\"id\": 20001, \"name\": \"black and white racing seat\"}, {\"id\": 20002, \"name\": \"the dock\"}, {\"id\": 20003, \"name\": \"the crisp white wall\"}, {\"id\": 20004, \"name\": \"red or yellow hat\"}, {\"id\": 20005, \"name\": \"the gold chain\"}, {\"id\": 20006, \"name\": \"bag of chip\"}, {\"id\": 20007, \"name\": \"a black packaging\"}, {\"id\": 20008, \"name\": \"green skirt\"}, {\"id\": 20009, \"name\": \"the green glass\"}, {\"id\": 20010, \"name\": \"gray stone platform\"}, {\"id\": 20011, \"name\": \"a dark hat\"}, {\"id\": 20012, \"name\": \"the bicycle wheel\"}, {\"id\": 20013, \"name\": \"a wetsuit\"}, {\"id\": 20014, \"name\": \"the image of food\"}, {\"id\": 20015, \"name\": \"the artificial lighting\"}, {\"id\": 20016, \"name\": \"player's shoe\"}, {\"id\": 20017, \"name\": \"yellow caution tape line\"}, {\"id\": 20018, \"name\": \"the rifle\"}, {\"id\": 20019, \"name\": \"tortoiseshell sunglass\"}, {\"id\": 20020, \"name\": \"the small picture\"}, {\"id\": 20021, \"name\": \"the red and orange box\"}, {\"id\": 20022, \"name\": \"the pool deck\"}, {\"id\": 20023, \"name\": \"the street or sidewalk\"}, {\"id\": 20024, \"name\": \"a small, black table\"}, {\"id\": 20025, \"name\": \"dark wooden table\"}, {\"id\": 20026, \"name\": \"black and white shoe\"}, {\"id\": 20027, \"name\": \"the black-and-white striped pillow\"}, {\"id\": 20028, \"name\": \"jeepney\"}, {\"id\": 20029, \"name\": \"rocky hillside\"}, {\"id\": 20030, \"name\": \"a patterned skirt\"}, {\"id\": 20031, \"name\": \"silver and red pompom\"}, {\"id\": 20032, \"name\": \"white surfboard\"}, {\"id\": 20033, \"name\": \"a green table or shelf\"}, {\"id\": 20034, \"name\": \"black desk\"}, {\"id\": 20035, \"name\": \"the small bubble\"}, {\"id\": 20036, \"name\": \"the tissue\"}, {\"id\": 20037, \"name\": \"short, spiky hair\"}, {\"id\": 20038, \"name\": \"the orange and black bead\"}, {\"id\": 20039, \"name\": \"a central handle\"}, {\"id\": 20040, \"name\": \"a green dress\"}, {\"id\": 20041, \"name\": \"the cymbal\"}, {\"id\": 20042, \"name\": \"a black wetsuit\"}, {\"id\": 20043, \"name\": \"the light-brown concrete surface\"}, {\"id\": 20044, \"name\": \"the dark green border\"}, {\"id\": 20045, \"name\": \"a pink flower\"}, {\"id\": 20046, \"name\": \"person's right leg\"}, {\"id\": 20047, \"name\": \"wooden picnic table\"}, {\"id\": 20048, \"name\": \"the bag of snack\"}, {\"id\": 20049, \"name\": \"a green plastic basket\"}, {\"id\": 20050, \"name\": \"the balding head\"}, {\"id\": 20051, \"name\": \"truck's windshield\"}, {\"id\": 20052, \"name\": \"shoe rack\"}, {\"id\": 20053, \"name\": \"a small boat\"}, {\"id\": 20054, \"name\": \"a navy collar\"}, {\"id\": 20055, \"name\": \"a white lace tablecloth\"}, {\"id\": 20056, \"name\": \"black wetsuit\"}, {\"id\": 20057, \"name\": \"the white lane\"}, {\"id\": 20058, \"name\": \"black and yellow sticker\"}, {\"id\": 20059, \"name\": \"the blurry hand\"}, {\"id\": 20060, \"name\": \"a red knob\"}, {\"id\": 20061, \"name\": \"a horizontal groove\"}, {\"id\": 20062, \"name\": \"the wing\"}, {\"id\": 20063, \"name\": \"light-colored jacket\"}, {\"id\": 20064, \"name\": \"desk\"}, {\"id\": 20065, \"name\": \"the leopard-print fabric\"}, {\"id\": 20066, \"name\": \"a long, dark brown hair\"}, {\"id\": 20067, \"name\": \"the curved white counter\"}, {\"id\": 20068, \"name\": \"orange snowboard\"}, {\"id\": 20069, \"name\": \"small yellow archway\"}, {\"id\": 20070, \"name\": \"the man in a blue jacket and jean\"}, {\"id\": 20071, \"name\": \"a black and white tag\"}, {\"id\": 20072, \"name\": \"notre dame fighting irish logo\"}, {\"id\": 20073, \"name\": \"the green and blue mat\"}, {\"id\": 20074, \"name\": \"an entrance of an elementary school\"}, {\"id\": 20075, \"name\": \"the small bowl\"}, {\"id\": 20076, \"name\": \"the yellow and blue umbrella\"}, {\"id\": 20077, \"name\": \"black pouch\"}, {\"id\": 20078, \"name\": \"the rope or cord\"}, {\"id\": 20079, \"name\": \"brown terrain\"}, {\"id\": 20080, \"name\": \"the rear spoiler\"}, {\"id\": 20081, \"name\": \"child in a white shirt and jean\"}, {\"id\": 20082, \"name\": \"large, worn-out track\"}, {\"id\": 20083, \"name\": \"oval-shaped opening\"}, {\"id\": 20084, \"name\": \"red and black striped shawl\"}, {\"id\": 20085, \"name\": \"a blonde woman\"}, {\"id\": 20086, \"name\": \"backdrop of tree\"}, {\"id\": 20087, \"name\": \"the yellow blouse\"}, {\"id\": 20088, \"name\": \"the car available\"}, {\"id\": 20089, \"name\": \"yellow hook\"}, {\"id\": 20090, \"name\": \"a colorful frozen yogurt dessert\"}, {\"id\": 20091, \"name\": \"the black sweatshirt\"}, {\"id\": 20092, \"name\": \"the large chandelier\"}, {\"id\": 20093, \"name\": \"the bright yellow b logo\"}, {\"id\": 20094, \"name\": \"the dark wooden floor\"}, {\"id\": 20095, \"name\": \"a front grille\"}, {\"id\": 20096, \"name\": \"the bird's leg\"}, {\"id\": 20097, \"name\": \"a raised left rear wheel\"}, {\"id\": 20098, \"name\": \"the red pickup truck\"}, {\"id\": 20099, \"name\": \"car roof\"}, {\"id\": 20100, \"name\": \"a colorful track\"}, {\"id\": 20101, \"name\": \"the clothing\"}, {\"id\": 20102, \"name\": \"the transparent shelf\"}, {\"id\": 20103, \"name\": \"a light-brown background\"}, {\"id\": 20104, \"name\": \"the left handlebar\"}, {\"id\": 20105, \"name\": \"curly dark hair\"}, {\"id\": 20106, \"name\": \"tiered seating area\"}, {\"id\": 20107, \"name\": \"gray and teal facade\"}, {\"id\": 20108, \"name\": \"the man's hands\"}, {\"id\": 20109, \"name\": \"the casino floor\"}, {\"id\": 20110, \"name\": \"a gold or gold-colored item\"}, {\"id\": 20111, \"name\": \"a tong\"}, {\"id\": 20112, \"name\": \"gold sword\"}, {\"id\": 20113, \"name\": \"rear of a black car\"}, {\"id\": 20114, \"name\": \"double-barreled shotgun\"}, {\"id\": 20115, \"name\": \"truck's logo\"}, {\"id\": 20116, \"name\": \"the thin white border\"}, {\"id\": 20117, \"name\": \"a yellow and orange dress\"}, {\"id\": 20118, \"name\": \"close-up shot of a man's face\"}, {\"id\": 20119, \"name\": \"white vase\"}, {\"id\": 20120, \"name\": \"the bras button\"}, {\"id\": 20121, \"name\": \"a red christma tree\"}, {\"id\": 20122, \"name\": \"a silver spoon\"}, {\"id\": 20123, \"name\": \"the hemi badge\"}, {\"id\": 20124, \"name\": \"cannon wheel\"}, {\"id\": 20125, \"name\": \"a small black and white object\"}, {\"id\": 20126, \"name\": \"a white satellite dish\"}, {\"id\": 20127, \"name\": \"a red metal structure\"}, {\"id\": 20128, \"name\": \"a red deck\"}, {\"id\": 20129, \"name\": \"red bowtie\"}, {\"id\": 20130, \"name\": \"the sephora storefront\"}, {\"id\": 20131, \"name\": \"black apron\"}, {\"id\": 20132, \"name\": \"the electrical outlet\"}, {\"id\": 20133, \"name\": \"the white paper towel\"}, {\"id\": 20134, \"name\": \"infotainment screen\"}, {\"id\": 20135, \"name\": \"the swimmer's attire\"}, {\"id\": 20136, \"name\": \"wide staircase\"}, {\"id\": 20137, \"name\": \"motorcycle's brake lever\"}, {\"id\": 20138, \"name\": \"a international baccalaureate (ib) logo\"}, {\"id\": 20139, \"name\": \"a sound system\"}, {\"id\": 20140, \"name\": \"yellow auto-rickshaw\"}, {\"id\": 20141, \"name\": \"grid of small photos of people's faces\"}, {\"id\": 20142, \"name\": \"the circular design\"}, {\"id\": 20143, \"name\": \"a black wood or plastic\"}, {\"id\": 20144, \"name\": \"a red and white shirt\"}, {\"id\": 20145, \"name\": \"the blue carpeted floor\"}, {\"id\": 20146, \"name\": \"the orange cap\"}, {\"id\": 20147, \"name\": \"small red ball\"}, {\"id\": 20148, \"name\": \"a blurry building\"}, {\"id\": 20149, \"name\": \"the circular concrete planter\"}, {\"id\": 20150, \"name\": \"the shiny interior\"}, {\"id\": 20151, \"name\": \"a doorway of a house\"}, {\"id\": 20152, \"name\": \"black armband\"}, {\"id\": 20153, \"name\": \"the white plastic bottle\"}, {\"id\": 20154, \"name\": \"the yellow and blue jersey\"}, {\"id\": 20155, \"name\": \"clear container\"}, {\"id\": 20156, \"name\": \"the large, white, cylindrical device\"}, {\"id\": 20157, \"name\": \"black goggle\"}, {\"id\": 20158, \"name\": \"the dirt infield\"}, {\"id\": 20159, \"name\": \"the microphone stand\"}, {\"id\": 20160, \"name\": \"the red rash guard\"}, {\"id\": 20161, \"name\": \"a storage bin\"}, {\"id\": 20162, \"name\": \"woven brown mat\"}, {\"id\": 20163, \"name\": \"a bright yellow vest\"}, {\"id\": 20164, \"name\": \"a thick, red-orange liquid\"}, {\"id\": 20165, \"name\": \"a black sole\"}, {\"id\": 20166, \"name\": \"pointy ear\"}, {\"id\": 20167, \"name\": \"a vibrant bridge or walkway\"}, {\"id\": 20168, \"name\": \"the boulder\"}, {\"id\": 20169, \"name\": \"a red switch\"}, {\"id\": 20170, \"name\": \"the triptych\"}, {\"id\": 20171, \"name\": \"mobil\"}, {\"id\": 20172, \"name\": \"a green rectangle\"}, {\"id\": 20173, \"name\": \"brown leather office chair\"}, {\"id\": 20174, \"name\": \"a black leather pant\"}, {\"id\": 20175, \"name\": \"a green and yellow object\"}, {\"id\": 20176, \"name\": \"red nozzle\"}, {\"id\": 20177, \"name\": \"the black phone\"}, {\"id\": 20178, \"name\": \"the yellow curb\"}, {\"id\": 20179, \"name\": \"the overgrown vegetation\"}, {\"id\": 20180, \"name\": \"purple base\"}, {\"id\": 20181, \"name\": \"a circular illustration of a woman's head\"}, {\"id\": 20182, \"name\": \"a five-spoke design\"}, {\"id\": 20183, \"name\": \"a truck's bed\"}, {\"id\": 20184, \"name\": \"a box of food\"}, {\"id\": 20185, \"name\": \"the sbt logo\"}, {\"id\": 20186, \"name\": \"a styrofoam\"}, {\"id\": 20187, \"name\": \"the toy race track\"}, {\"id\": 20188, \"name\": \"the trunk\"}, {\"id\": 20189, \"name\": \"the blue and yellow frame\"}, {\"id\": 20190, \"name\": \"passenger seat\"}, {\"id\": 20191, \"name\": \"motorcyclist's handlebar\"}, {\"id\": 20192, \"name\": \"a red and white checkered flag design\"}, {\"id\": 20193, \"name\": \"the black and white square\"}, {\"id\": 20194, \"name\": \"the patterned sock\"}, {\"id\": 20195, \"name\": \"red tablecloth\"}, {\"id\": 20196, \"name\": \"spike\"}, {\"id\": 20197, \"name\": \"driver's helmet visor\"}, {\"id\": 20198, \"name\": \"a plastic hook\"}, {\"id\": 20199, \"name\": \"the white notepad\"}, {\"id\": 20200, \"name\": \"a message\"}, {\"id\": 20201, \"name\": \"a small ladder\"}, {\"id\": 20202, \"name\": \"the vertical support column\"}, {\"id\": 20203, \"name\": \"the green stripe\"}, {\"id\": 20204, \"name\": \"the black flip-flop\"}, {\"id\": 20205, \"name\": \"the black hexagon\"}, {\"id\": 20206, \"name\": \"large circle\"}, {\"id\": 20207, \"name\": \"bird-like creature\"}, {\"id\": 20208, \"name\": \"the atv\"}, {\"id\": 20209, \"name\": \"the bead\"}, {\"id\": 20210, \"name\": \"a mast\"}, {\"id\": 20211, \"name\": \"the red dashboard\"}, {\"id\": 20212, \"name\": \"a man's hands\"}, {\"id\": 20213, \"name\": \"the gauge\"}, {\"id\": 20214, \"name\": \"the shadow of a person\"}, {\"id\": 20215, \"name\": \"the bright white star\"}, {\"id\": 20216, \"name\": \"the prominent white line\"}, {\"id\": 20217, \"name\": \"the yellow spout\"}, {\"id\": 20218, \"name\": \"a red frame\"}, {\"id\": 20219, \"name\": \"red v\"}, {\"id\": 20220, \"name\": \"red van with yellow marking\"}, {\"id\": 20221, \"name\": \"large black steering wheel\"}, {\"id\": 20222, \"name\": \"the black line\"}, {\"id\": 20223, \"name\": \"a red sneaker\"}, {\"id\": 20224, \"name\": \"new york license plate\"}, {\"id\": 20225, \"name\": \"a small, rectangular piece of white cardstock or paper\"}, {\"id\": 20226, \"name\": \"the red nose\"}, {\"id\": 20227, \"name\": \"white face\"}, {\"id\": 20228, \"name\": \"a small bush\"}, {\"id\": 20229, \"name\": \"the ga station\"}, {\"id\": 20230, \"name\": \"the gray pegboard\"}, {\"id\": 20231, \"name\": \"the blurred image\"}, {\"id\": 20232, \"name\": \"a red and white arrow\"}, {\"id\": 20233, \"name\": \"a purple border\"}, {\"id\": 20234, \"name\": \"short, light-brown hair\"}, {\"id\": 20235, \"name\": \"an orange and silver reflective stripe\"}, {\"id\": 20236, \"name\": \"a thick, stitched border\"}, {\"id\": 20237, \"name\": \"the yellow roller coaster\"}, {\"id\": 20238, \"name\": \"yellow wheel\"}, {\"id\": 20239, \"name\": \"a long white pole\"}, {\"id\": 20240, \"name\": \"sphere\"}, {\"id\": 20241, \"name\": \"a white paper product box\"}, {\"id\": 20242, \"name\": \"the man's left hand\"}, {\"id\": 20243, \"name\": \"kobalt logo\"}, {\"id\": 20244, \"name\": \"the cane's handle\"}, {\"id\": 20245, \"name\": \"the control tower\"}, {\"id\": 20246, \"name\": \"the glas and metal object\"}, {\"id\": 20247, \"name\": \"the small puddle of water\"}, {\"id\": 20248, \"name\": \"the tap\"}, {\"id\": 20249, \"name\": \"the red and green camouflage pattern\"}, {\"id\": 20250, \"name\": \"six-lane highway\"}, {\"id\": 20251, \"name\": \"a fern\"}, {\"id\": 20252, \"name\": \"the wispy white cloud\"}, {\"id\": 20253, \"name\": \"the christma tree\"}, {\"id\": 20254, \"name\": \"the rear quarter panel\"}, {\"id\": 20255, \"name\": \"the metallic armor\"}, {\"id\": 20256, \"name\": \"red and white wristband\"}, {\"id\": 20257, \"name\": \"circular opening\"}, {\"id\": 20258, \"name\": \"the green baseball field\"}, {\"id\": 20259, \"name\": \"green item\"}, {\"id\": 20260, \"name\": \"open field\"}, {\"id\": 20261, \"name\": \"the photograph of person in military uniform\"}, {\"id\": 20262, \"name\": \"a face of dog\"}, {\"id\": 20263, \"name\": \"the round gray table\"}, {\"id\": 20264, \"name\": \"a small plastic cup\"}, {\"id\": 20265, \"name\": \"horn\"}, {\"id\": 20266, \"name\": \"partially visible person's face\"}, {\"id\": 20267, \"name\": \"a bright light of the streetlight\"}, {\"id\": 20268, \"name\": \"the red strawberry-shaped ruler\"}, {\"id\": 20269, \"name\": \"pink short\"}, {\"id\": 20270, \"name\": \"truck's license plate\"}, {\"id\": 20271, \"name\": \"the red cardigan\"}, {\"id\": 20272, \"name\": \"black-and-white sock\"}, {\"id\": 20273, \"name\": \"the tan top\"}, {\"id\": 20274, \"name\": \"the matching hat\"}, {\"id\": 20275, \"name\": \"a large pillar\"}, {\"id\": 20276, \"name\": \"a glass dish\"}, {\"id\": 20277, \"name\": \"an overgrown vegetation\"}, {\"id\": 20278, \"name\": \"a left monk\"}, {\"id\": 20279, \"name\": \"the shuttered storefront\"}, {\"id\": 20280, \"name\": \"cardboard\"}, {\"id\": 20281, \"name\": \"blue bicycle\"}, {\"id\": 20282, \"name\": \"the black puck\"}, {\"id\": 20283, \"name\": \"green headband\"}, {\"id\": 20284, \"name\": \"the black boot\"}, {\"id\": 20285, \"name\": \"the tan, feathered headdress\"}, {\"id\": 20286, \"name\": \"black and white plaid pant\"}, {\"id\": 20287, \"name\": \"a red and white banner\"}, {\"id\": 20288, \"name\": \"a large bouquet of red flower\"}, {\"id\": 20289, \"name\": \"a purple backpack\"}, {\"id\": 20290, \"name\": \"smokestack\"}, {\"id\": 20291, \"name\": \"a place of worship\"}, {\"id\": 20292, \"name\": \"flat-screen tv\"}, {\"id\": 20293, \"name\": \"the red net\"}, {\"id\": 20294, \"name\": \"a large metal wok\"}, {\"id\": 20295, \"name\": \"a dark t-shirt\"}, {\"id\": 20296, \"name\": \"a sauce\"}, {\"id\": 20297, \"name\": \"a tenni racket\"}, {\"id\": 20298, \"name\": \"the red ponytail\"}, {\"id\": 20299, \"name\": \"the coach storefront\"}, {\"id\": 20300, \"name\": \"the blue luggage cart\"}, {\"id\": 20301, \"name\": \"the red chinese lantern\"}, {\"id\": 20302, \"name\": \"the blue horse\"}, {\"id\": 20303, \"name\": \"the blue metal pole\"}, {\"id\": 20304, \"name\": \"the vibrant flock of parrot\"}, {\"id\": 20305, \"name\": \"red plastic sheet\"}, {\"id\": 20306, \"name\": \"the white and blue checkered sheet\"}, {\"id\": 20307, \"name\": \"blue lanyard\"}, {\"id\": 20308, \"name\": \"tall tower\"}, {\"id\": 20309, \"name\": \"the single lane\"}, {\"id\": 20310, \"name\": \"elaborate costume\"}, {\"id\": 20311, \"name\": \"the small ear\"}, {\"id\": 20312, \"name\": \"a blue clothing\"}, {\"id\": 20313, \"name\": \"the white ceiling fan\"}, {\"id\": 20314, \"name\": \"a large, red seat\"}, {\"id\": 20315, \"name\": \"a large screw\"}, {\"id\": 20316, \"name\": \"metal spatula\"}, {\"id\": 20317, \"name\": \"scuba diving gear\"}, {\"id\": 20318, \"name\": \"red raft\"}, {\"id\": 20319, \"name\": \"orange tarp\"}, {\"id\": 20320, \"name\": \"pink dress\"}, {\"id\": 20321, \"name\": \"concrete divider\"}, {\"id\": 20322, \"name\": \"the light bulb\"}, {\"id\": 20323, \"name\": \"ride\"}, {\"id\": 20324, \"name\": \"the wooden awning\"}, {\"id\": 20325, \"name\": \"the goat\"}, {\"id\": 20326, \"name\": \"glass dome structure\"}, {\"id\": 20327, \"name\": \"a small beak\"}, {\"id\": 20328, \"name\": \"distant red flag\"}, {\"id\": 20329, \"name\": \"a dolphin-shaped slide\"}, {\"id\": 20330, \"name\": \"the concrete ledge\"}, {\"id\": 20331, \"name\": \"the large thatched umbrella\"}, {\"id\": 20332, \"name\": \"bowls of noodle\"}, {\"id\": 20333, \"name\": \"the long-sleeved, button-up plaid shirt\"}, {\"id\": 20334, \"name\": \"the orange safety vest\"}, {\"id\": 20335, \"name\": \"a black backpack strap\"}, {\"id\": 20336, \"name\": \"the red and white costume\"}, {\"id\": 20337, \"name\": \"a concrete planter\"}, {\"id\": 20338, \"name\": \"a large load of good\"}, {\"id\": 20339, \"name\": \"the plank\"}, {\"id\": 20340, \"name\": \"the bright yellow light\"}, {\"id\": 20341, \"name\": \"the blade\"}, {\"id\": 20342, \"name\": \"the concrete space\"}, {\"id\": 20343, \"name\": \"the green item of clothing\"}, {\"id\": 20344, \"name\": \"the long white skirt\"}, {\"id\": 20345, \"name\": \"the black tarp\"}, {\"id\": 20346, \"name\": \"the white canopy\"}, {\"id\": 20347, \"name\": \"dark grey t-shirt\"}, {\"id\": 20348, \"name\": \"the small soccer goal\"}, {\"id\": 20349, \"name\": \"the green stool\"}, {\"id\": 20350, \"name\": \"the glass facade\"}, {\"id\": 20351, \"name\": \"the large, multi-level structure\"}, {\"id\": 20352, \"name\": \"white and black floral shirt\"}, {\"id\": 20353, \"name\": \"the tall black pole\"}, {\"id\": 20354, \"name\": \"a blue surgical mask\"}, {\"id\": 20355, \"name\": \"a children's attire\"}, {\"id\": 20356, \"name\": \"blue go-kart\"}, {\"id\": 20357, \"name\": \"the metal ladder\"}, {\"id\": 20358, \"name\": \"the man in a blue shirt and black hat\"}, {\"id\": 20359, \"name\": \"a red and white sneaker\"}, {\"id\": 20360, \"name\": \"a wet-looking surface\"}, {\"id\": 20361, \"name\": \"the indian flag\"}, {\"id\": 20362, \"name\": \"a black cloak\"}, {\"id\": 20363, \"name\": \"vibrant orange traffic cone\"}, {\"id\": 20364, \"name\": \"the partially visible white car\"}, {\"id\": 20365, \"name\": \"the blue safety fence\"}, {\"id\": 20366, \"name\": \"the piece of copper\"}, {\"id\": 20367, \"name\": \"the cylindrical metal piece\"}, {\"id\": 20368, \"name\": \"a partially obscured sign\"}, {\"id\": 20369, \"name\": \"the neon green vest\"}, {\"id\": 20370, \"name\": \"the red circle\"}, {\"id\": 20371, \"name\": \"the white light strip\"}, {\"id\": 20372, \"name\": \"large, empty parking\"}, {\"id\": 20373, \"name\": \"red taillight\"}, {\"id\": 20374, \"name\": \"light-colored robe\"}, {\"id\": 20375, \"name\": \"the white tarp\"}, {\"id\": 20376, \"name\": \"the vibrant arcade game\"}, {\"id\": 20377, \"name\": \"the black fanny pack\"}, {\"id\": 20378, \"name\": \"a yellow and green auto-rickshaw\"}, {\"id\": 20379, \"name\": \"low wooden fence\"}, {\"id\": 20380, \"name\": \"gas station's canopy\"}, {\"id\": 20381, \"name\": \"the yellow barrier\"}, {\"id\": 20382, \"name\": \"a bottle of milk\"}, {\"id\": 20383, \"name\": \"red and white checkered bag\"}, {\"id\": 20384, \"name\": \"a blue partition\"}, {\"id\": 20385, \"name\": \"the small yellow icon\"}, {\"id\": 20386, \"name\": \"yellow and black car\"}, {\"id\": 20387, \"name\": \"the large menu board\"}, {\"id\": 20388, \"name\": \"the white skirt\"}, {\"id\": 20389, \"name\": \"a floral skirt\"}, {\"id\": 20390, \"name\": \"a pink and white vehicle\"}, {\"id\": 20391, \"name\": \"jet\"}, {\"id\": 20392, \"name\": \"a dark blue athletic uniform\"}, {\"id\": 20393, \"name\": \"a white tile ceiling\"}, {\"id\": 20394, \"name\": \"a brown cross-body bag\"}, {\"id\": 20395, \"name\": \"yellow turban\"}, {\"id\": 20396, \"name\": \"the brown collar\"}, {\"id\": 20397, \"name\": \"the yellow outfit\"}, {\"id\": 20398, \"name\": \"reddish-brown wood\"}, {\"id\": 20399, \"name\": \"a metal tong\"}, {\"id\": 20400, \"name\": \"the red and white racing suit\"}, {\"id\": 20401, \"name\": \"the black animal\"}, {\"id\": 20402, \"name\": \"the gray house\"}, {\"id\": 20403, \"name\": \"thick black border\"}, {\"id\": 20404, \"name\": \"the long, silver conveyor belt\"}, {\"id\": 20405, \"name\": \"the white scoreboard\"}, {\"id\": 20406, \"name\": \"the yellow bird costume\"}, {\"id\": 20407, \"name\": \"the illuminated signage\"}, {\"id\": 20408, \"name\": \"the red litter box\"}, {\"id\": 20409, \"name\": \"a trousers\"}, {\"id\": 20410, \"name\": \"the traditional middle eastern scarf\"}, {\"id\": 20411, \"name\": \"the denim vest\"}, {\"id\": 20412, \"name\": \"the large, wrapped object\"}, {\"id\": 20413, \"name\": \"a white balloon\"}, {\"id\": 20414, \"name\": \"a hood of one of the taxi\"}, {\"id\": 20415, \"name\": \"a distinctive black top hat\"}, {\"id\": 20416, \"name\": \"a large, oversized teacup\"}, {\"id\": 20417, \"name\": \"a red metal component\"}, {\"id\": 20418, \"name\": \"red cap\"}, {\"id\": 20419, \"name\": \"a warning or hazard symbol\"}, {\"id\": 20420, \"name\": \"the wrapped item\"}, {\"id\": 20421, \"name\": \"a black plaque\"}, {\"id\": 20422, \"name\": \"a red apron\"}, {\"id\": 20423, \"name\": \"vibrant green and yellow striped lanyard\"}, {\"id\": 20424, \"name\": \"a woman in a black tank top and short\"}, {\"id\": 20425, \"name\": \"orange tail feather\"}, {\"id\": 20426, \"name\": \"a light blue noodle\"}, {\"id\": 20427, \"name\": \"small, brightly colored target\"}, {\"id\": 20428, \"name\": \"a white blanket\"}, {\"id\": 20429, \"name\": \"red belt\"}, {\"id\": 20430, \"name\": \"lush, grassy area\"}, {\"id\": 20431, \"name\": \"the pink skirt\"}, {\"id\": 20432, \"name\": \"the oversized wheel\"}, {\"id\": 20433, \"name\": \"an orange cat's tail\"}, {\"id\": 20434, \"name\": \"the scrap metal\"}, {\"id\": 20435, \"name\": \"a green bathing suit\"}, {\"id\": 20436, \"name\": \"the square\"}, {\"id\": 20437, \"name\": \"the grey helmet\"}, {\"id\": 20438, \"name\": \"a red drum\"}, {\"id\": 20439, \"name\": \"ukraine flag\"}, {\"id\": 20440, \"name\": \"a yellow and orange fish\"}, {\"id\": 20441, \"name\": \"a vibrant red sari\"}, {\"id\": 20442, \"name\": \"the wooden ledge\"}, {\"id\": 20443, \"name\": \"a light-colored concrete surface\"}, {\"id\": 20444, \"name\": \"the orange fish\"}, {\"id\": 20445, \"name\": \"a blue and yellow pillar\"}, {\"id\": 20446, \"name\": \"green shawl\"}, {\"id\": 20447, \"name\": \"the woman in a red top and jean\"}, {\"id\": 20448, \"name\": \"a colorful circular surface\"}, {\"id\": 20449, \"name\": \"the green, grassy area\"}, {\"id\": 20450, \"name\": \"large cartoon animal costume\"}, {\"id\": 20451, \"name\": \"the green, blocky head\"}, {\"id\": 20452, \"name\": \"large, glowing sign\"}, {\"id\": 20453, \"name\": \"the barrier of red and white striped concrete bollard\"}, {\"id\": 20454, \"name\": \"the corrugated metal\"}, {\"id\": 20455, \"name\": \"bicycle rider\"}, {\"id\": 20456, \"name\": \"an illuminated sign\"}, {\"id\": 20457, \"name\": \"vibrant balloon arch\"}, {\"id\": 20458, \"name\": \"an orange robe\"}, {\"id\": 20459, \"name\": \"a white surgical mask\"}, {\"id\": 20460, \"name\": \"a black athletic pant\"}, {\"id\": 20461, \"name\": \"the headstone\"}, {\"id\": 20462, \"name\": \"a stainles steel surface\"}, {\"id\": 20463, \"name\": \"vendor's cart\"}, {\"id\": 20464, \"name\": \"extensive bottom toolbar\"}, {\"id\": 20465, \"name\": \"a white pipe\"}, {\"id\": 20466, \"name\": \"beige shirt\"}, {\"id\": 20467, \"name\": \"large metal spoon\"}, {\"id\": 20468, \"name\": \"eleven hotel sign\"}, {\"id\": 20469, \"name\": \"colorful, neon-lit archway\"}, {\"id\": 20470, \"name\": \"the tall metal tower\"}, {\"id\": 20471, \"name\": \"the life jacket\"}, {\"id\": 20472, \"name\": \"gray funnel\"}, {\"id\": 20473, \"name\": \"the yellow fire hydrant\"}, {\"id\": 20474, \"name\": \"red bench\"}, {\"id\": 20475, \"name\": \"an orange handle\"}, {\"id\": 20476, \"name\": \"brown wooden paneling\"}, {\"id\": 20477, \"name\": \"white head covering\"}, {\"id\": 20478, \"name\": \"the green substance\"}, {\"id\": 20479, \"name\": \"large white banner\"}, {\"id\": 20480, \"name\": \"purple toy car\"}, {\"id\": 20481, \"name\": \"blue sleeveles shirt\"}, {\"id\": 20482, \"name\": \"the large yellow banner\"}, {\"id\": 20483, \"name\": \"the green clothing\"}, {\"id\": 20484, \"name\": \"the blue and white shirt\"}, {\"id\": 20485, \"name\": \"a grey short\"}, {\"id\": 20486, \"name\": \"a yellow fish\"}, {\"id\": 20487, \"name\": \"large, dark green bin\"}, {\"id\": 20488, \"name\": \"a green toy\"}, {\"id\": 20489, \"name\": \"a colorful arrangement of puppet\"}, {\"id\": 20490, \"name\": \"white lifeguard tower\"}, {\"id\": 20491, \"name\": \"the bow\"}, {\"id\": 20492, \"name\": \"black induction stove\"}, {\"id\": 20493, \"name\": \"the blue vest\"}, {\"id\": 20494, \"name\": \"the orange fish design\"}, {\"id\": 20495, \"name\": \"open back door\"}, {\"id\": 20496, \"name\": \"the small, yellow circle\"}, {\"id\": 20497, \"name\": \"the large wooden wheelbarrow\"}, {\"id\": 20498, \"name\": \"the large white bag\"}, {\"id\": 20499, \"name\": \"a small meatball\"}, {\"id\": 20500, \"name\": \"pink floral shirt\"}, {\"id\": 20501, \"name\": \"soda\"}, {\"id\": 20502, \"name\": \"the bright orange safety vest\"}, {\"id\": 20503, \"name\": \"a purple sphere\"}, {\"id\": 20504, \"name\": \"the black stripe\"}, {\"id\": 20505, \"name\": \"the frog-themed design\"}, {\"id\": 20506, \"name\": \"a white grille\"}, {\"id\": 20507, \"name\": \"the brown and black chicken\"}, {\"id\": 20508, \"name\": \"a large grey bucket\"}, {\"id\": 20509, \"name\": \"prominent white structure\"}, {\"id\": 20510, \"name\": \"intricately designed red vest\"}, {\"id\": 20511, \"name\": \"beige curtain\"}, {\"id\": 20512, \"name\": \"a smaller drum\"}, {\"id\": 20513, \"name\": \"the square concrete slab\"}, {\"id\": 20514, \"name\": \"the brown-framed window\"}, {\"id\": 20515, \"name\": \"a red, yellow, and blue life jacket\"}, {\"id\": 20516, \"name\": \"the tan trash can\"}, {\"id\": 20517, \"name\": \"blue lid\"}, {\"id\": 20518, \"name\": \"the elegant red and black dress\"}, {\"id\": 20519, \"name\": \"the silver knob\"}, {\"id\": 20520, \"name\": \"a tall, sparkly crystal-like fixture\"}, {\"id\": 20521, \"name\": \"a yellow diamond-shaped sign\"}, {\"id\": 20522, \"name\": \"red, blue, green, and yellow metal\"}, {\"id\": 20523, \"name\": \"the jet\"}, {\"id\": 20524, \"name\": \"the dark cloud\"}, {\"id\": 20525, \"name\": \"a driver's hand\"}, {\"id\": 20526, \"name\": \"the stoplight\"}, {\"id\": 20527, \"name\": \"the blue and red swim trunk\"}, {\"id\": 20528, \"name\": \"the blue slipper\"}, {\"id\": 20529, \"name\": \"orange orb\"}, {\"id\": 20530, \"name\": \"smaller, dark blue boat\"}, {\"id\": 20531, \"name\": \"red dress\"}, {\"id\": 20532, \"name\": \"a sandy foreground\"}, {\"id\": 20533, \"name\": \"the long, brown dress\"}, {\"id\": 20534, \"name\": \"the blue semi-truck\"}, {\"id\": 20535, \"name\": \"black and yellow epaulet\"}, {\"id\": 20536, \"name\": \"red and white jacket\"}, {\"id\": 20537, \"name\": \"a large sword\"}, {\"id\": 20538, \"name\": \"the round, yellow-lit structure\"}, {\"id\": 20539, \"name\": \"a blouse\"}, {\"id\": 20540, \"name\": \"van's roof rack\"}, {\"id\": 20541, \"name\": \"a long wooden boat\"}, {\"id\": 20542, \"name\": \"the cross-body bag\"}, {\"id\": 20543, \"name\": \"a green grassy area\"}, {\"id\": 20544, \"name\": \"purple jacket\"}, {\"id\": 20545, \"name\": \"the horizontal beam\"}, {\"id\": 20546, \"name\": \"the small shop\"}, {\"id\": 20547, \"name\": \"a pale yellow metal panel\"}, {\"id\": 20548, \"name\": \"a long green leaf\"}, {\"id\": 20549, \"name\": \"a steeple\"}, {\"id\": 20550, \"name\": \"a white police car\"}, {\"id\": 20551, \"name\": \"a traditional indian attire\"}, {\"id\": 20552, \"name\": \"a lit-up billboard\"}, {\"id\": 20553, \"name\": \"a purple and yellow top\"}, {\"id\": 20554, \"name\": \"small school of clownfish\"}, {\"id\": 20555, \"name\": \"a brown garage door\"}, {\"id\": 20556, \"name\": \"the vibrant orange balloon\"}, {\"id\": 20557, \"name\": \"a truck's side panel\"}, {\"id\": 20558, \"name\": \"the red train car\"}, {\"id\": 20559, \"name\": \"ride's bright blue base\"}, {\"id\": 20560, \"name\": \"a brown scarf\"}, {\"id\": 20561, \"name\": \"a bright green tablecloth\"}, {\"id\": 20562, \"name\": \"a distant kite\"}, {\"id\": 20563, \"name\": \"wooden shovel handle\"}, {\"id\": 20564, \"name\": \"a large, metal platform\"}, {\"id\": 20565, \"name\": \"the parked motorcycle\"}, {\"id\": 20566, \"name\": \"a cacti\"}, {\"id\": 20567, \"name\": \"the large beach ball\"}, {\"id\": 20568, \"name\": \"large, blue and white mural\"}, {\"id\": 20569, \"name\": \"the pigtail\"}, {\"id\": 20570, \"name\": \"the ship\"}, {\"id\": 20571, \"name\": \"empty bleacher\"}, {\"id\": 20572, \"name\": \"the green sneaker\"}, {\"id\": 20573, \"name\": \"small clear plastic container\"}, {\"id\": 20574, \"name\": \"the front of the truck\"}, {\"id\": 20575, \"name\": \"the white surgical mask\"}, {\"id\": 20576, \"name\": \"red panel\"}, {\"id\": 20577, \"name\": \"a scooter's throttle\"}, {\"id\": 20578, \"name\": \"a white storage unit\"}, {\"id\": 20579, \"name\": \"a large red ball\"}, {\"id\": 20580, \"name\": \"a yellow slide\"}, {\"id\": 20581, \"name\": \"blue flip-flop\"}, {\"id\": 20582, \"name\": \"the large black tire\"}, {\"id\": 20583, \"name\": \"an oversized black shoe\"}, {\"id\": 20584, \"name\": \"the pink shopping bag\"}, {\"id\": 20585, \"name\": \"the kitten's face\"}, {\"id\": 20586, \"name\": \"fire escape ladder\"}, {\"id\": 20587, \"name\": \"the wooden board\"}, {\"id\": 20588, \"name\": \"the cross\"}, {\"id\": 20589, \"name\": \"a sleek black wheel\"}, {\"id\": 20590, \"name\": \"a slide structure\"}, {\"id\": 20591, \"name\": \"blue metal fence\"}, {\"id\": 20592, \"name\": \"the tan puppy\"}, {\"id\": 20593, \"name\": \"the pink top\"}, {\"id\": 20594, \"name\": \"the large bouquet of flower\"}, {\"id\": 20595, \"name\": \"a float\"}, {\"id\": 20596, \"name\": \"the backseat\"}, {\"id\": 20597, \"name\": \"stick's black blade\"}, {\"id\": 20598, \"name\": \"blue button\"}, {\"id\": 20599, \"name\": \"a light grey pant\"}, {\"id\": 20600, \"name\": \"blue jean short\"}, {\"id\": 20601, \"name\": \"long, vertical blue streak\"}, {\"id\": 20602, \"name\": \"a light wood table\"}, {\"id\": 20603, \"name\": \"an instructor\"}, {\"id\": 20604, \"name\": \"green and yellow shirt\"}, {\"id\": 20605, \"name\": \"light-colored short\"}, {\"id\": 20606, \"name\": \"the green tent\"}, {\"id\": 20607, \"name\": \"a stone walkway\"}, {\"id\": 20608, \"name\": \"reflective vest\"}, {\"id\": 20609, \"name\": \"a yellow skirt\"}, {\"id\": 20610, \"name\": \"officer\"}, {\"id\": 20611, \"name\": \"the large inflatable ball\"}, {\"id\": 20612, \"name\": \"sign with an arrow pointing up\"}, {\"id\": 20613, \"name\": \"a green canopy\"}, {\"id\": 20614, \"name\": \"a sports equipment\"}, {\"id\": 20615, \"name\": \"blue pop-up tent\"}, {\"id\": 20616, \"name\": \"a large white cloud\"}, {\"id\": 20617, \"name\": \"a sailboat's hull\"}, {\"id\": 20618, \"name\": \"a shopfront\"}, {\"id\": 20619, \"name\": \"a vintage car\"}, {\"id\": 20620, \"name\": \"the blue long-sleeved shirt\"}, {\"id\": 20621, \"name\": \"blue bench\"}, {\"id\": 20622, \"name\": \"the yellow hurdle\"}, {\"id\": 20623, \"name\": \"teal roof\"}, {\"id\": 20624, \"name\": \"the branding\"}, {\"id\": 20625, \"name\": \"perforated top\"}, {\"id\": 20626, \"name\": \"a dozen of peeled orange\"}, {\"id\": 20627, \"name\": \"a yellow cord\"}, {\"id\": 20628, \"name\": \"red and white checkered belt\"}, {\"id\": 20629, \"name\": \"blue head covering\"}, {\"id\": 20630, \"name\": \"bottle of liquid\"}, {\"id\": 20631, \"name\": \"brick-paved surface\"}, {\"id\": 20632, \"name\": \"the yellow tapestry or poster\"}, {\"id\": 20633, \"name\": \"the yellow, green, and blue slide\"}, {\"id\": 20634, \"name\": \"man wearing a straw hat\"}, {\"id\": 20635, \"name\": \"a large church or cathedral\"}, {\"id\": 20636, \"name\": \"colored scooter\"}, {\"id\": 20637, \"name\": \"pink arm\"}, {\"id\": 20638, \"name\": \"the black speaker\"}, {\"id\": 20639, \"name\": \"a red headband\"}, {\"id\": 20640, \"name\": \"yellow wristband\"}, {\"id\": 20641, \"name\": \"red bar\"}, {\"id\": 20642, \"name\": \"the distinctive black and white striped facade\"}, {\"id\": 20643, \"name\": \"orange and black top\"}, {\"id\": 20644, \"name\": \"a red plastic cup\"}, {\"id\": 20645, \"name\": \"a large brown woven bag\"}, {\"id\": 20646, \"name\": \"pigeon\"}, {\"id\": 20647, \"name\": \"the open flame\"}, {\"id\": 20648, \"name\": \"illuminated sign\"}, {\"id\": 20649, \"name\": \"a white fish\"}, {\"id\": 20650, \"name\": \"the large blue truck bed\"}, {\"id\": 20651, \"name\": \"the brown garage door\"}, {\"id\": 20652, \"name\": \"a vertical green bar\"}, {\"id\": 20653, \"name\": \"yellow toy car\"}, {\"id\": 20654, \"name\": \"the distinctive license plate\"}, {\"id\": 20655, \"name\": \"the yellow, green, and red emblem\"}, {\"id\": 20656, \"name\": \"dark post\"}, {\"id\": 20657, \"name\": \"red entry sign\"}, {\"id\": 20658, \"name\": \"an officer\"}, {\"id\": 20659, \"name\": \"the yellow and grey helmet\"}, {\"id\": 20660, \"name\": \"the orange tail\"}, {\"id\": 20661, \"name\": \"red crate\"}, {\"id\": 20662, \"name\": \"the dark-colored suv\"}, {\"id\": 20663, \"name\": \"the blue flower design\"}, {\"id\": 20664, \"name\": \"a large, dark-brown bucket\"}, {\"id\": 20665, \"name\": \"a black shopping bag\"}, {\"id\": 20666, \"name\": \"small white speedboat\"}, {\"id\": 20667, \"name\": \"yellow lantern\"}, {\"id\": 20668, \"name\": \"the pagoda-style roof\"}, {\"id\": 20669, \"name\": \"the spiral decoration\"}, {\"id\": 20670, \"name\": \"a sedan\"}, {\"id\": 20671, \"name\": \"tuk-tuk vehicle\"}, {\"id\": 20672, \"name\": \"steep hillside\"}, {\"id\": 20673, \"name\": \"a sports car\"}, {\"id\": 20674, \"name\": \"a machinery\"}, {\"id\": 20675, \"name\": \"safety vest\"}, {\"id\": 20676, \"name\": \"the sweatpants\"}, {\"id\": 20677, \"name\": \"a large industrial machine\"}, {\"id\": 20678, \"name\": \"large blue creature\"}, {\"id\": 20679, \"name\": \"orange and red bleachers\"}, {\"id\": 20680, \"name\": \"the cycling jersey\"}, {\"id\": 20681, \"name\": \"the brown object\"}, {\"id\": 20682, \"name\": \"grassy verge\"}, {\"id\": 20683, \"name\": \"the large, yellow, saucer-shaped object\"}, {\"id\": 20684, \"name\": \"distinctive, slanted roof\"}, {\"id\": 20685, \"name\": \"duck's head\"}, {\"id\": 20686, \"name\": \"a square cutout\"}, {\"id\": 20687, \"name\": \"the sandy ground\"}, {\"id\": 20688, \"name\": \"the grassy side\"}, {\"id\": 20689, \"name\": \"blue wheel\"}, {\"id\": 20690, \"name\": \"player's weapon\"}, {\"id\": 20691, \"name\": \"the black ceiling fan\"}, {\"id\": 20692, \"name\": \"gas mask\"}, {\"id\": 20693, \"name\": \"the grassy foreground\"}, {\"id\": 20694, \"name\": \"a neon-lit ring\"}, {\"id\": 20695, \"name\": \"kayaker's paddle\"}, {\"id\": 20696, \"name\": \"the large, grayish-green cannon-like object\"}, {\"id\": 20697, \"name\": \"long braid\"}, {\"id\": 20698, \"name\": \"the traditional indian clothing\"}, {\"id\": 20699, \"name\": \"circular shape\"}, {\"id\": 20700, \"name\": \"a green feather\"}, {\"id\": 20701, \"name\": \"red band\"}, {\"id\": 20702, \"name\": \"the large white truck\"}, {\"id\": 20703, \"name\": \"a single white lane marker\"}, {\"id\": 20704, \"name\": \"the punching bag\"}, {\"id\": 20705, \"name\": \"the blue scarf\"}, {\"id\": 20706, \"name\": \"a white and gray marble\"}, {\"id\": 20707, \"name\": \"the white ticket counter\"}, {\"id\": 20708, \"name\": \"a green, cellophane-wrapped item\"}, {\"id\": 20709, \"name\": \"the toddler\"}, {\"id\": 20710, \"name\": \"rear of the truck\"}, {\"id\": 20711, \"name\": \"a blue lanyard\"}, {\"id\": 20712, \"name\": \"the red and white tunic\"}, {\"id\": 20713, \"name\": \"a bikini top\"}, {\"id\": 20714, \"name\": \"black ball\"}, {\"id\": 20715, \"name\": \"blue barrel\"}, {\"id\": 20716, \"name\": \"black hockey puck\"}, {\"id\": 20717, \"name\": \"a large inflatable toy\"}, {\"id\": 20718, \"name\": \"person in yellow vest\"}, {\"id\": 20719, \"name\": \"dark t-shirt\"}, {\"id\": 20720, \"name\": \"the roll of plastic tubing\"}, {\"id\": 20721, \"name\": \"cardboard sign\"}, {\"id\": 20722, \"name\": \"the pepper\"}, {\"id\": 20723, \"name\": \"gold emblem\"}, {\"id\": 20724, \"name\": \"large, glowing, green circle\"}, {\"id\": 20725, \"name\": \"a white glow ring\"}, {\"id\": 20726, \"name\": \"a black scarf\"}, {\"id\": 20727, \"name\": \"a woman in a blue dress\"}, {\"id\": 20728, \"name\": \"the blue hat\"}, {\"id\": 20729, \"name\": \"the bras instrument\"}, {\"id\": 20730, \"name\": \"robot's leash\"}, {\"id\": 20731, \"name\": \"the blue-lit structure\"}, {\"id\": 20732, \"name\": \"green toy\"}, {\"id\": 20733, \"name\": \"the long, dark blue skirt\"}, {\"id\": 20734, \"name\": \"an orange and yellow wrapped object\"}, {\"id\": 20735, \"name\": \"a black pig\"}, {\"id\": 20736, \"name\": \"large, white pillar\"}, {\"id\": 20737, \"name\": \"the glass front\"}, {\"id\": 20738, \"name\": \"the large silver truck\"}, {\"id\": 20739, \"name\": \"the side mirror\"}, {\"id\": 20740, \"name\": \"the large, open red machine or structure\"}, {\"id\": 20741, \"name\": \"a large, green creature\"}, {\"id\": 20742, \"name\": \"an utensil\"}, {\"id\": 20743, \"name\": \"the red, plastic backrest\"}, {\"id\": 20744, \"name\": \"racing atv\"}, {\"id\": 20745, \"name\": \"the small, yellow, circular symbol\"}, {\"id\": 20746, \"name\": \"black lamppost\"}, {\"id\": 20747, \"name\": \"personnel\"}, {\"id\": 20748, \"name\": \"green moss\"}, {\"id\": 20749, \"name\": \"the pot's handle\"}, {\"id\": 20750, \"name\": \"a large red sign\"}, {\"id\": 20751, \"name\": \"a red digital sign\"}, {\"id\": 20752, \"name\": \"the headpiece\"}, {\"id\": 20753, \"name\": \"the lone player\"}, {\"id\": 20754, \"name\": \"the gray flip-flop\"}, {\"id\": 20755, \"name\": \"car's rear passenger window\"}, {\"id\": 20756, \"name\": \"the overpass\"}, {\"id\": 20757, \"name\": \"the brown pot\"}, {\"id\": 20758, \"name\": \"the beige column\"}, {\"id\": 20759, \"name\": \"a large white container\"}, {\"id\": 20760, \"name\": \"a green frog logo\"}, {\"id\": 20761, \"name\": \"the exposed concrete\"}, {\"id\": 20762, \"name\": \"palestinian flag\"}, {\"id\": 20763, \"name\": \"colorful animal-shaped ride\"}, {\"id\": 20764, \"name\": \"the large gray aircraft\"}, {\"id\": 20765, \"name\": \"the fluffy fur\"}, {\"id\": 20766, \"name\": \"the swim trunk\"}, {\"id\": 20767, \"name\": \"the weapon\"}, {\"id\": 20768, \"name\": \"dark helmet\"}, {\"id\": 20769, \"name\": \"the large metal object\"}, {\"id\": 20770, \"name\": \"teal headscarf\"}, {\"id\": 20771, \"name\": \"the three-quarter-length sleeve\"}, {\"id\": 20772, \"name\": \"large concrete column\"}, {\"id\": 20773, \"name\": \"small boat\"}, {\"id\": 20774, \"name\": \"helium balloon\"}, {\"id\": 20775, \"name\": \"a person on horseback\"}, {\"id\": 20776, \"name\": \"a red and white seat\"}, {\"id\": 20777, \"name\": \"the circular platform\"}, {\"id\": 20778, \"name\": \"a black metal plate\"}, {\"id\": 20779, \"name\": \"a bear design\"}, {\"id\": 20780, \"name\": \"plastic bag of food\"}, {\"id\": 20781, \"name\": \"the orange stripe\"}, {\"id\": 20782, \"name\": \"a silver and black helmet\"}, {\"id\": 20783, \"name\": \"the silver spoon\"}, {\"id\": 20784, \"name\": \"a green wristband\"}, {\"id\": 20785, \"name\": \"a red and white table\"}, {\"id\": 20786, \"name\": \"the yellow light\"}, {\"id\": 20787, \"name\": \"light blue pant\"}, {\"id\": 20788, \"name\": \"kayaker's blue boat\"}, {\"id\": 20789, \"name\": \"purple top hat\"}, {\"id\": 20790, \"name\": \"dark nail\"}, {\"id\": 20791, \"name\": \"small planter\"}, {\"id\": 20792, \"name\": \"a mallet\"}, {\"id\": 20793, \"name\": \"the smoke\"}, {\"id\": 20794, \"name\": \"the large orange plastic container\"}, {\"id\": 20795, \"name\": \"an orange barricade\"}, {\"id\": 20796, \"name\": \"colorful costume\"}, {\"id\": 20797, \"name\": \"the yellow shutter\"}, {\"id\": 20798, \"name\": \"atv\"}, {\"id\": 20799, \"name\": \"red and white flag\"}, {\"id\": 20800, \"name\": \"a red wheel\"}, {\"id\": 20801, \"name\": \"sitting person\"}, {\"id\": 20802, \"name\": \"a large grey wheel\"}, {\"id\": 20803, \"name\": \"roof of the truck\"}, {\"id\": 20804, \"name\": \"the white pigeon\"}, {\"id\": 20805, \"name\": \"concrete basketball hoop\"}, {\"id\": 20806, \"name\": \"the starbuck sign\"}, {\"id\": 20807, \"name\": \"a colorful butterfly design\"}, {\"id\": 20808, \"name\": \"keyboardist\"}, {\"id\": 20809, \"name\": \"human hand\"}, {\"id\": 20810, \"name\": \"a presence of person\"}, {\"id\": 20811, \"name\": \"a prominent orange mark\"}, {\"id\": 20812, \"name\": \"train's car\"}, {\"id\": 20813, \"name\": \"brown truck\"}, {\"id\": 20814, \"name\": \"vertical post\"}, {\"id\": 20815, \"name\": \"three-wheeled vehicle\"}, {\"id\": 20816, \"name\": \"red or white shirt\"}, {\"id\": 20817, \"name\": \"blue hull\"}, {\"id\": 20818, \"name\": \"the blue saddle\"}, {\"id\": 20819, \"name\": \"a small fountain\"}, {\"id\": 20820, \"name\": \"the monster truck\"}, {\"id\": 20821, \"name\": \"pink hat\"}, {\"id\": 20822, \"name\": \"the messy bun\"}, {\"id\": 20823, \"name\": \"the hanging laundry\"}, {\"id\": 20824, \"name\": \"red head\"}, {\"id\": 20825, \"name\": \"person's left leg\"}, {\"id\": 20826, \"name\": \"the large bucket\"}, {\"id\": 20827, \"name\": \"tank top\"}, {\"id\": 20828, \"name\": \"a white ramp\"}, {\"id\": 20829, \"name\": \"the wooden fence or gate\"}, {\"id\": 20830, \"name\": \"the light brown rectangle\"}, {\"id\": 20831, \"name\": \"the large, dark-colored bumper car\"}, {\"id\": 20832, \"name\": \"the tan-colored tail\"}, {\"id\": 20833, \"name\": \"a yellowish grain\"}, {\"id\": 20834, \"name\": \"white and black motorcycle\"}, {\"id\": 20835, \"name\": \"soap bubble\"}, {\"id\": 20836, \"name\": \"a blue shopping cart\"}, {\"id\": 20837, \"name\": \"a yellow puffer jacket\"}, {\"id\": 20838, \"name\": \"a round light\"}, {\"id\": 20839, \"name\": \"the white picture frame\"}, {\"id\": 20840, \"name\": \"reddish-brown head\"}, {\"id\": 20841, \"name\": \"the red pad\"}, {\"id\": 20842, \"name\": \"large dial\"}, {\"id\": 20843, \"name\": \"a patterned shirt\"}, {\"id\": 20844, \"name\": \"white metal gate\"}, {\"id\": 20845, \"name\": \"a long brown skirt\"}, {\"id\": 20846, \"name\": \"orange t-shirt\"}, {\"id\": 20847, \"name\": \"large open door\"}, {\"id\": 20848, \"name\": \"an ornate white column\"}, {\"id\": 20849, \"name\": \"the panda toy\"}, {\"id\": 20850, \"name\": \"the knee-high white sock\"}, {\"id\": 20851, \"name\": \"a hammock\"}, {\"id\": 20852, \"name\": \"a dome-shaped roof\"}, {\"id\": 20853, \"name\": \"avatar\"}, {\"id\": 20854, \"name\": \"long stick\"}, {\"id\": 20855, \"name\": \"an orange pot\"}, {\"id\": 20856, \"name\": \"white short-sleeved shirt\"}, {\"id\": 20857, \"name\": \"the crisp white chef's coat\"}, {\"id\": 20858, \"name\": \"grassy strip\"}, {\"id\": 20859, \"name\": \"green light\"}, {\"id\": 20860, \"name\": \"blue carpet\"}, {\"id\": 20861, \"name\": \"red backpack strap\"}, {\"id\": 20862, \"name\": \"the red and white pant\"}, {\"id\": 20863, \"name\": \"a child's bare feet\"}, {\"id\": 20864, \"name\": \"the floral shirt\"}, {\"id\": 20865, \"name\": \"the small, white sign\"}, {\"id\": 20866, \"name\": \"green mountain\"}, {\"id\": 20867, \"name\": \"the white bleacher\"}, {\"id\": 20868, \"name\": \"motorcycle delivery driver\"}, {\"id\": 20869, \"name\": \"the gray octagonal box\"}, {\"id\": 20870, \"name\": \"rickshaw-style vehicle\"}, {\"id\": 20871, \"name\": \"stone pile\"}, {\"id\": 20872, \"name\": \"toy rc car\"}, {\"id\": 20873, \"name\": \"a white bucket\"}, {\"id\": 20874, \"name\": \"a yellow and green vehicle\"}, {\"id\": 20875, \"name\": \"orange cap\"}, {\"id\": 20876, \"name\": \"large blue machine\"}, {\"id\": 20877, \"name\": \"neon yellow t-shirt\"}, {\"id\": 20878, \"name\": \"blue and white top\"}, {\"id\": 20879, \"name\": \"the large, greenish-brown rock\"}, {\"id\": 20880, \"name\": \"yellow sash\"}, {\"id\": 20881, \"name\": \"a bottle of champagne\"}, {\"id\": 20882, \"name\": \"green license plate\"}, {\"id\": 20883, \"name\": \"blue truck model\"}, {\"id\": 20884, \"name\": \"plastic bucket\"}, {\"id\": 20885, \"name\": \"the yellow paper\"}, {\"id\": 20886, \"name\": \"rough, uneven surface\"}, {\"id\": 20887, \"name\": \"the thin strip of purple light\"}, {\"id\": 20888, \"name\": \"large metal tower\"}, {\"id\": 20889, \"name\": \"large black head\"}, {\"id\": 20890, \"name\": \"dark brown pant\"}, {\"id\": 20891, \"name\": \"the black and white striped barrier\"}, {\"id\": 20892, \"name\": \"sea turtle\"}, {\"id\": 20893, \"name\": \"teacup\"}, {\"id\": 20894, \"name\": \"the red head\"}, {\"id\": 20895, \"name\": \"blue unicorn\"}, {\"id\": 20896, \"name\": \"clear plastic bottle\"}, {\"id\": 20897, \"name\": \"the diver's head\"}, {\"id\": 20898, \"name\": \"the orange mane\"}, {\"id\": 20899, \"name\": \"brown rooster\"}, {\"id\": 20900, \"name\": \"colorful necklace\"}, {\"id\": 20901, \"name\": \"the small orange bucket\"}, {\"id\": 20902, \"name\": \"a stacks of box\"}, {\"id\": 20903, \"name\": \"a black bin\"}, {\"id\": 20904, \"name\": \"the colorful playground equipment\"}, {\"id\": 20905, \"name\": \"red pipe\"}, {\"id\": 20906, \"name\": \"a dark baseball cap\"}, {\"id\": 20907, \"name\": \"a long white robe\"}, {\"id\": 20908, \"name\": \"a pigtail\"}, {\"id\": 20909, \"name\": \"a tan tarp or sheet\"}, {\"id\": 20910, \"name\": \"the black and white floral pant\"}, {\"id\": 20911, \"name\": \"worker's hand\"}, {\"id\": 20912, \"name\": \"the small beach umbrella\"}, {\"id\": 20913, \"name\": \"a white jet ski\"}, {\"id\": 20914, \"name\": \"coil of blue wire\"}, {\"id\": 20915, \"name\": \"the vibrant, multi-colored, neon-lit structure\"}, {\"id\": 20916, \"name\": \"a green metal design\"}, {\"id\": 20917, \"name\": \"brown liquid\"}, {\"id\": 20918, \"name\": \"a green produce bag\"}, {\"id\": 20919, \"name\": \"the music player\"}, {\"id\": 20920, \"name\": \"the car's side mirror\"}, {\"id\": 20921, \"name\": \"the black rear window\"}, {\"id\": 20922, \"name\": \"a large, white, circular structure\"}, {\"id\": 20923, \"name\": \"the green and white ball\"}, {\"id\": 20924, \"name\": \"the green highway sign\"}, {\"id\": 20925, \"name\": \"a red and white striped section\"}, {\"id\": 20926, \"name\": \"a bulletin board\"}, {\"id\": 20927, \"name\": \"wispy white cloud\"}, {\"id\": 20928, \"name\": \"large semi-truck\"}, {\"id\": 20929, \"name\": \"the matching saucer\"}, {\"id\": 20930, \"name\": \"a blue wing\"}, {\"id\": 20931, \"name\": \"the yellow base\"}, {\"id\": 20932, \"name\": \"a horseback rider\"}, {\"id\": 20933, \"name\": \"the large glass\"}, {\"id\": 20934, \"name\": \"the brown purse strap\"}, {\"id\": 20935, \"name\": \"flatbed\"}, {\"id\": 20936, \"name\": \"the grassy patch\"}, {\"id\": 20937, \"name\": \"a red and orange design\"}, {\"id\": 20938, \"name\": \"a weapon\"}, {\"id\": 20939, \"name\": \"information booth\"}, {\"id\": 20940, \"name\": \"green curtain\"}, {\"id\": 20941, \"name\": \"the dark blue t-shirt\"}, {\"id\": 20942, \"name\": \"an autumnal foliage\"}, {\"id\": 20943, \"name\": \"the lush green hillside\"}, {\"id\": 20944, \"name\": \"the vintage race car\"}, {\"id\": 20945, \"name\": \"the gray, corrugated metal structure\"}, {\"id\": 20946, \"name\": \"green balloon\"}, {\"id\": 20947, \"name\": \"the large black sombrero\"}, {\"id\": 20948, \"name\": \"blue creature\"}, {\"id\": 20949, \"name\": \"multicolored pole\"}, {\"id\": 20950, \"name\": \"the brown dresser or cabinet\"}, {\"id\": 20951, \"name\": \"large monument\"}, {\"id\": 20952, \"name\": \"a white top hat\"}, {\"id\": 20953, \"name\": \"the man in a blue shirt and baseball cap\"}, {\"id\": 20954, \"name\": \"the buzz cut\"}, {\"id\": 20955, \"name\": \"a white face mask\"}, {\"id\": 20956, \"name\": \"the dark skirt\"}, {\"id\": 20957, \"name\": \"car-shaped ride\"}, {\"id\": 20958, \"name\": \"passenger car\"}, {\"id\": 20959, \"name\": \"rpg\"}, {\"id\": 20960, \"name\": \"a smaller white boat\"}, {\"id\": 20961, \"name\": \"a large machine\"}, {\"id\": 20962, \"name\": \"a small toy car\"}, {\"id\": 20963, \"name\": \"the boy in green\"}, {\"id\": 20964, \"name\": \"a grid of small, evenly spaced light\"}, {\"id\": 20965, \"name\": \"calf\"}, {\"id\": 20966, \"name\": \"a coral\"}, {\"id\": 20967, \"name\": \"the black clutch purse\"}, {\"id\": 20968, \"name\": \"anthropomorphic animal\"}, {\"id\": 20969, \"name\": \"the exposed red beam\"}, {\"id\": 20970, \"name\": \"the black metal bird cage\"}, {\"id\": 20971, \"name\": \"the long, green hose\"}, {\"id\": 20972, \"name\": \"red storefront\"}, {\"id\": 20973, \"name\": \"the blue balloon\"}, {\"id\": 20974, \"name\": \"the classic car\"}, {\"id\": 20975, \"name\": \"wheel of a vehicle\"}, {\"id\": 20976, \"name\": \"the cart laden with produce\"}, {\"id\": 20977, \"name\": \"red loincloth\"}, {\"id\": 20978, \"name\": \"large bucket\"}, {\"id\": 20979, \"name\": \"the patch of greenery\"}, {\"id\": 20980, \"name\": \"blue progres bar\"}, {\"id\": 20981, \"name\": \"the blue plastic kiddie pool\"}, {\"id\": 20982, \"name\": \"green rug\"}, {\"id\": 20983, \"name\": \"a brown plaid shirt\"}, {\"id\": 20984, \"name\": \"a green container\"}, {\"id\": 20985, \"name\": \"the orange sign\"}, {\"id\": 20986, \"name\": \"small black screen\"}, {\"id\": 20987, \"name\": \"the glass of amber liquid\"}, {\"id\": 20988, \"name\": \"green jersey\"}, {\"id\": 20989, \"name\": \"hello kitty design\"}, {\"id\": 20990, \"name\": \"the car's rear light\"}, {\"id\": 20991, \"name\": \"the white station wagon\"}, {\"id\": 20992, \"name\": \"red elmo t-shirt\"}, {\"id\": 20993, \"name\": \"red cylinder\"}, {\"id\": 20994, \"name\": \"yellow, orange, and green pattern\"}, {\"id\": 20995, \"name\": \"the blue and white scarf\"}, {\"id\": 20996, \"name\": \"smaller pool\"}, {\"id\": 20997, \"name\": \"the brown hat\"}, {\"id\": 20998, \"name\": \"a dark grey t-shirt\"}, {\"id\": 20999, \"name\": \"a fighter jet\"}, {\"id\": 21000, \"name\": \"a dark hull\"}, {\"id\": 21001, \"name\": \"black suit jacket\"}, {\"id\": 21002, \"name\": \"purse or bag\"}, {\"id\": 21003, \"name\": \"the yellow shoe\"}, {\"id\": 21004, \"name\": \"a white plastic-wrapped item\"}, {\"id\": 21005, \"name\": \"a light-colored wood top\"}, {\"id\": 21006, \"name\": \"the large red shipping container\"}, {\"id\": 21007, \"name\": \"small white car\"}, {\"id\": 21008, \"name\": \"red-and-white striped barrier\"}, {\"id\": 21009, \"name\": \"a blue tire\"}, {\"id\": 21010, \"name\": \"the circular roof\"}, {\"id\": 21011, \"name\": \"large blue tarp\"}, {\"id\": 21012, \"name\": \"large load of wooden plank\"}, {\"id\": 21013, \"name\": \"the man in a gray jacket\"}, {\"id\": 21014, \"name\": \"red tiled roof\"}, {\"id\": 21015, \"name\": \"a green apron\"}, {\"id\": 21016, \"name\": \"light-colored hat\"}, {\"id\": 21017, \"name\": \"an orange truck\"}, {\"id\": 21018, \"name\": \"a metal spoon\"}, {\"id\": 21019, \"name\": \"small turquoise stool\"}, {\"id\": 21020, \"name\": \"substantial load of cardboard box\"}, {\"id\": 21021, \"name\": \"a red horse\"}, {\"id\": 21022, \"name\": \"a woman with a backpack\"}, {\"id\": 21023, \"name\": \"the white and blue race car\"}, {\"id\": 21024, \"name\": \"the brown and white dog\"}, {\"id\": 21025, \"name\": \"large concrete pillar\"}, {\"id\": 21026, \"name\": \"a shelf unit\"}, {\"id\": 21027, \"name\": \"a red beach umbrella\"}, {\"id\": 21028, \"name\": \"an american flag\"}, {\"id\": 21029, \"name\": \"the white hoodie\"}, {\"id\": 21030, \"name\": \"the light brown pickup truck\"}, {\"id\": 21031, \"name\": \"green hose\"}, {\"id\": 21032, \"name\": \"a large bowl of food\"}, {\"id\": 21033, \"name\": \"the distinctive green nozzle\"}, {\"id\": 21034, \"name\": \"silver collar\"}, {\"id\": 21035, \"name\": \"the tan couch\"}, {\"id\": 21036, \"name\": \"smiley face\"}, {\"id\": 21037, \"name\": \"the drummer\"}, {\"id\": 21038, \"name\": \"a vibrant, multicolored duck\"}, {\"id\": 21039, \"name\": \"a white, egg-shaped container\"}, {\"id\": 21040, \"name\": \"the white floral pattern\"}, {\"id\": 21041, \"name\": \"the luggage cart\"}, {\"id\": 21042, \"name\": \"the green barrier\"}, {\"id\": 21043, \"name\": \"large, dark-colored shoe\"}, {\"id\": 21044, \"name\": \"brown jersey\"}, {\"id\": 21045, \"name\": \"the red traffic cone\"}, {\"id\": 21046, \"name\": \"the yellow triangle\"}, {\"id\": 21047, \"name\": \"white bow\"}, {\"id\": 21048, \"name\": \"a large, round, and illuminated object\"}, {\"id\": 21049, \"name\": \"large red wheel\"}, {\"id\": 21050, \"name\": \"the large metal pot\"}, {\"id\": 21051, \"name\": \"the larger boat\"}, {\"id\": 21052, \"name\": \"the black face mask\"}, {\"id\": 21053, \"name\": \"black hooded jacket\"}, {\"id\": 21054, \"name\": \"the tan bucket hat\"}, {\"id\": 21055, \"name\": \"the muddy riverbank\"}, {\"id\": 21056, \"name\": \"large, yellow curb\"}, {\"id\": 21057, \"name\": \"large green bag or container\"}, {\"id\": 21058, \"name\": \"far shore\"}, {\"id\": 21059, \"name\": \"a large, round, silver knob\"}, {\"id\": 21060, \"name\": \"the minaret\"}, {\"id\": 21061, \"name\": \"the worker\"}, {\"id\": 21062, \"name\": \"a dark-colored canopy\"}, {\"id\": 21063, \"name\": \"the brick structure\"}, {\"id\": 21064, \"name\": \"mountain range\"}, {\"id\": 21065, \"name\": \"a commercial or industrial structure\"}, {\"id\": 21066, \"name\": \"a large utility pole\"}, {\"id\": 21067, \"name\": \"a blue tail\"}, {\"id\": 21068, \"name\": \"blue polo shirt\"}, {\"id\": 21069, \"name\": \"the large inflatable soccer ball\"}, {\"id\": 21070, \"name\": \"the tall, white metal structure\"}, {\"id\": 21071, \"name\": \"the yellow component\"}, {\"id\": 21072, \"name\": \"the girl's attire\"}, {\"id\": 21073, \"name\": \"large yellow slide\"}, {\"id\": 21074, \"name\": \"a white cab\"}, {\"id\": 21075, \"name\": \"pink bar\"}, {\"id\": 21076, \"name\": \"the ornate green carving\"}, {\"id\": 21077, \"name\": \"the steeple\"}, {\"id\": 21078, \"name\": \"a man in a striped shirt and dark pant\"}, {\"id\": 21079, \"name\": \"the red light post\"}, {\"id\": 21080, \"name\": \"updo\"}, {\"id\": 21081, \"name\": \"police officer\"}, {\"id\": 21082, \"name\": \"the light-colored hat\"}, {\"id\": 21083, \"name\": \"purple stripe\"}, {\"id\": 21084, \"name\": \"long, flowing robe\"}, {\"id\": 21085, \"name\": \"the black radio\"}, {\"id\": 21086, \"name\": \"a colorful shirt\"}, {\"id\": 21087, \"name\": \"glass of red liquid\"}, {\"id\": 21088, \"name\": \"the blue cowboy hat\"}, {\"id\": 21089, \"name\": \"colorful traditional attire\"}, {\"id\": 21090, \"name\": \"the bank of a river\"}, {\"id\": 21091, \"name\": \"large, colorful ball\"}, {\"id\": 21092, \"name\": \"a blue plaid shirt\"}, {\"id\": 21093, \"name\": \"a torn pakistani flag\"}, {\"id\": 21094, \"name\": \"long white robe\"}, {\"id\": 21095, \"name\": \"large, irregularly shaped rock\"}, {\"id\": 21096, \"name\": \"the red sweatshirt\"}, {\"id\": 21097, \"name\": \"sidewalk curb\"}, {\"id\": 21098, \"name\": \"the stone pedestal\"}, {\"id\": 21099, \"name\": \"orange cat\"}, {\"id\": 21100, \"name\": \"the small hut\"}, {\"id\": 21101, \"name\": \"the white jumpsuit\"}, {\"id\": 21102, \"name\": \"interior of the vehicle\"}, {\"id\": 21103, \"name\": \"the white x\"}, {\"id\": 21104, \"name\": \"large pot of brown food\"}, {\"id\": 21105, \"name\": \"the ghost figure\"}, {\"id\": 21106, \"name\": \"large, blue, rounded cylinder\"}, {\"id\": 21107, \"name\": \"green tuk-tuk\"}, {\"id\": 21108, \"name\": \"the tan jacket\"}, {\"id\": 21109, \"name\": \"minaret\"}, {\"id\": 21110, \"name\": \"small, colorful step\"}, {\"id\": 21111, \"name\": \"corrugated metal\"}, {\"id\": 21112, \"name\": \"the lone blue motorbike\"}, {\"id\": 21113, \"name\": \"pool area\"}, {\"id\": 21114, \"name\": \"a dark mane\"}, {\"id\": 21115, \"name\": \"yellow and white sleeveless dress\"}, {\"id\": 21116, \"name\": \"wheel well\"}, {\"id\": 21117, \"name\": \"a guard shack\"}, {\"id\": 21118, \"name\": \"piece of equipment\"}, {\"id\": 21119, \"name\": \"white or light pink attire\"}, {\"id\": 21120, \"name\": \"the red leg\"}, {\"id\": 21121, \"name\": \"the blue shirt with neon green stripe\"}, {\"id\": 21122, \"name\": \"long dress\"}, {\"id\": 21123, \"name\": \"the plaid top\"}, {\"id\": 21124, \"name\": \"blue boxing glove\"}, {\"id\": 21125, \"name\": \"the red-and-white striped pattern\"}, {\"id\": 21126, \"name\": \"the caribbean flag\"}, {\"id\": 21127, \"name\": \"red pagoda-style structure\"}, {\"id\": 21128, \"name\": \"partially visible sign\"}, {\"id\": 21129, \"name\": \"a traffic or pedestrian signal\"}, {\"id\": 21130, \"name\": \"the baseball batting cage\"}, {\"id\": 21131, \"name\": \"piece of fencing\"}, {\"id\": 21132, \"name\": \"a bucket of cement\"}, {\"id\": 21133, \"name\": \"the blue jean short\"}, {\"id\": 21134, \"name\": \"dark cap\"}, {\"id\": 21135, \"name\": \"red and white paper\"}, {\"id\": 21136, \"name\": \"densely forested area\"}, {\"id\": 21137, \"name\": \"grey rock\"}, {\"id\": 21138, \"name\": \"colorful design\"}, {\"id\": 21139, \"name\": \"the small patch of greenery\"}, {\"id\": 21140, \"name\": \"the yellow train\"}, {\"id\": 21141, \"name\": \"large yellow flag\"}, {\"id\": 21142, \"name\": \"the yellow and white striped shirt\"}, {\"id\": 21143, \"name\": \"light blue, short-sleeved button-up shirt\"}, {\"id\": 21144, \"name\": \"red and white striped fabric\"}, {\"id\": 21145, \"name\": \"a red bloom\"}, {\"id\": 21146, \"name\": \"the blue jumpsuit\"}, {\"id\": 21147, \"name\": \"the bulldozer\"}, {\"id\": 21148, \"name\": \"a sandy area\"}, {\"id\": 21149, \"name\": \"small toy soccer ball\"}, {\"id\": 21150, \"name\": \"a dark brown cake or bread\"}, {\"id\": 21151, \"name\": \"the white fan\"}, {\"id\": 21152, \"name\": \"wooden cage\"}, {\"id\": 21153, \"name\": \"laborer\"}, {\"id\": 21154, \"name\": \"the large, multi-story apartment complex\"}, {\"id\": 21155, \"name\": \"the small, green, glowing orb\"}, {\"id\": 21156, \"name\": \"blue watch\"}, {\"id\": 21157, \"name\": \"black and white steering wheel\"}, {\"id\": 21158, \"name\": \"man in black vest over white shirt\"}, {\"id\": 21159, \"name\": \"tiled roof\"}, {\"id\": 21160, \"name\": \"a green awning\"}, {\"id\": 21161, \"name\": \"black polo shirt\"}, {\"id\": 21162, \"name\": \"bottle of sauce or seasoning\"}, {\"id\": 21163, \"name\": \"red traffic light\"}, {\"id\": 21164, \"name\": \"hawaiian-style shirt\"}, {\"id\": 21165, \"name\": \"the blue and white panel\"}, {\"id\": 21166, \"name\": \"the large, cylindrical metal component\"}, {\"id\": 21167, \"name\": \"large exercise ball\"}, {\"id\": 21168, \"name\": \"a large, blue and green structure\"}, {\"id\": 21169, \"name\": \"white trainer\"}, {\"id\": 21170, \"name\": \"yellow hose\"}, {\"id\": 21171, \"name\": \"a yellow toy with wheel\"}, {\"id\": 21172, \"name\": \"the tall light post\"}, {\"id\": 21173, \"name\": \"red metal cylinder\"}, {\"id\": 21174, \"name\": \"the front license plate\"}, {\"id\": 21175, \"name\": \"an orange and white fish\"}, {\"id\": 21176, \"name\": \"archway\"}, {\"id\": 21177, \"name\": \"brown house\"}, {\"id\": 21178, \"name\": \"the red post\"}, {\"id\": 21179, \"name\": \"the blue tent\"}, {\"id\": 21180, \"name\": \"the blue bucket hat\"}, {\"id\": 21181, \"name\": \"the barefoot\"}, {\"id\": 21182, \"name\": \"a gray section of the stage\"}, {\"id\": 21183, \"name\": \"ornate piece\"}, {\"id\": 21184, \"name\": \"large, white basketball hoop\"}, {\"id\": 21185, \"name\": \"the white reindeer lawn ornament\"}, {\"id\": 21186, \"name\": \"grey and white floral pattern\"}, {\"id\": 21187, \"name\": \"close-up of a person's arm\"}, {\"id\": 21188, \"name\": \"a small head\"}, {\"id\": 21189, \"name\": \"a black headphone\"}, {\"id\": 21190, \"name\": \"the game's circular surface\"}, {\"id\": 21191, \"name\": \"red gravel surface\"}, {\"id\": 21192, \"name\": \"yellow metal frame\"}, {\"id\": 21193, \"name\": \"black plastic base\"}, {\"id\": 21194, \"name\": \"pull-up bar\"}, {\"id\": 21195, \"name\": \"the blue and red feathered tail\"}, {\"id\": 21196, \"name\": \"the blue facade\"}, {\"id\": 21197, \"name\": \"long, flat surface\"}, {\"id\": 21198, \"name\": \"the red fence\"}, {\"id\": 21199, \"name\": \"the blue bucket\"}, {\"id\": 21200, \"name\": \"white basketball\"}, {\"id\": 21201, \"name\": \"the checkered black and white pattern\"}, {\"id\": 21202, \"name\": \"a piece of sushi\"}, {\"id\": 21203, \"name\": \"a white crosswalk marking\"}, {\"id\": 21204, \"name\": \"blue bow\"}, {\"id\": 21205, \"name\": \"food truck\"}, {\"id\": 21206, \"name\": \"a brown vehicle\"}, {\"id\": 21207, \"name\": \"the red spotlight\"}, {\"id\": 21208, \"name\": \"white mask\"}, {\"id\": 21209, \"name\": \"a long, vertical, blue and white structure resembling a rocket\"}, {\"id\": 21210, \"name\": \"the white seat\"}, {\"id\": 21211, \"name\": \"a green bird\"}, {\"id\": 21212, \"name\": \"a japanese flag\"}, {\"id\": 21213, \"name\": \"tan-colored animal\"}, {\"id\": 21214, \"name\": \"the blue attire\"}, {\"id\": 21215, \"name\": \"the tray of coconut-covered brownies or bar\"}, {\"id\": 21216, \"name\": \"the officer\"}, {\"id\": 21217, \"name\": \"the distinctive white hat\"}, {\"id\": 21218, \"name\": \"a large mickey mouse head\"}, {\"id\": 21219, \"name\": \"the pink play mat\"}, {\"id\": 21220, \"name\": \"a red and blue basketball hoop\"}, {\"id\": 21221, \"name\": \"a prominent white banner\"}, {\"id\": 21222, \"name\": \"purple shoe\"}, {\"id\": 21223, \"name\": \"bright yellow outfit\"}, {\"id\": 21224, \"name\": \"pink facade\"}, {\"id\": 21225, \"name\": \"large fountain\"}, {\"id\": 21226, \"name\": \"red and white overpass\"}, {\"id\": 21227, \"name\": \"pink balloon\"}, {\"id\": 21228, \"name\": \"a black-and-white striped flag\"}, {\"id\": 21229, \"name\": \"inflatable tube\"}, {\"id\": 21230, \"name\": \"the large, dark blue umbrella\"}, {\"id\": 21231, \"name\": \"a white and orange traffic cone\"}, {\"id\": 21232, \"name\": \"a menu bar\"}, {\"id\": 21233, \"name\": \"the pink horse\"}, {\"id\": 21234, \"name\": \"the colorful tents and stall\"}, {\"id\": 21235, \"name\": \"a baby's head\"}, {\"id\": 21236, \"name\": \"a man in the tan shirt\"}, {\"id\": 21237, \"name\": \"person in the yellow shirt\"}, {\"id\": 21238, \"name\": \"a dark-colored horse\"}, {\"id\": 21239, \"name\": \"vibrant parade float\"}, {\"id\": 21240, \"name\": \"distinctive gray headrest\"}, {\"id\": 21241, \"name\": \"the white tire barrier\"}, {\"id\": 21242, \"name\": \"pointed ear\"}, {\"id\": 21243, \"name\": \"the dolphin\"}, {\"id\": 21244, \"name\": \"the large, red, egg-shaped object\"}, {\"id\": 21245, \"name\": \"large black dog\"}, {\"id\": 21246, \"name\": \"the smiling blue creature\"}, {\"id\": 21247, \"name\": \"long brown dress\"}, {\"id\": 21248, \"name\": \"an open garage door\"}, {\"id\": 21249, \"name\": \"the yellow balloon\"}, {\"id\": 21250, \"name\": \"the animal figure\"}, {\"id\": 21251, \"name\": \"driver of a vehicle\"}, {\"id\": 21252, \"name\": \"the red container\"}, {\"id\": 21253, \"name\": \"a blue and white fish\"}, {\"id\": 21254, \"name\": \"a beach umbrella\"}, {\"id\": 21255, \"name\": \"yellow and brown boat-shaped seat\"}, {\"id\": 21256, \"name\": \"red sedan\"}, {\"id\": 21257, \"name\": \"head covering\"}, {\"id\": 21258, \"name\": \"the stainless-steel counter\"}, {\"id\": 21259, \"name\": \"a rear window of the vehicle\"}, {\"id\": 21260, \"name\": \"an orange spaceship\"}, {\"id\": 21261, \"name\": \"small section of green foliage\"}, {\"id\": 21262, \"name\": \"a white birthday cake\"}, {\"id\": 21263, \"name\": \"an updo\"}, {\"id\": 21264, \"name\": \"light blue short\"}, {\"id\": 21265, \"name\": \"the blue and white striped column\"}, {\"id\": 21266, \"name\": \"closed white shutter door\"}, {\"id\": 21267, \"name\": \"the squat or lunge exercise\"}, {\"id\": 21268, \"name\": \"a black bucket\"}, {\"id\": 21269, \"name\": \"small hot plate or stove\"}, {\"id\": 21270, \"name\": \"long, pink platform\"}, {\"id\": 21271, \"name\": \"the green backpack\"}, {\"id\": 21272, \"name\": \"the large white canopy\"}, {\"id\": 21273, \"name\": \"a black and yellow shirt\"}, {\"id\": 21274, \"name\": \"a prominent tower\"}, {\"id\": 21275, \"name\": \"the tan-colored hill or mountain\"}, {\"id\": 21276, \"name\": \"distant hill\"}, {\"id\": 21277, \"name\": \"curved tail\"}, {\"id\": 21278, \"name\": \"a closed storefront\"}, {\"id\": 21279, \"name\": \"advertising banner\"}, {\"id\": 21280, \"name\": \"a public transportation\"}, {\"id\": 21281, \"name\": \"a large woven basket\"}, {\"id\": 21282, \"name\": \"a small fan\"}, {\"id\": 21283, \"name\": \"a toy's body\"}, {\"id\": 21284, \"name\": \"a pink structure\"}, {\"id\": 21285, \"name\": \"black tarp or cover\"}, {\"id\": 21286, \"name\": \"small brown cup\"}, {\"id\": 21287, \"name\": \"ladle\"}, {\"id\": 21288, \"name\": \"large, round, blue light\"}, {\"id\": 21289, \"name\": \"a hull\"}, {\"id\": 21290, \"name\": \"cargo area\"}, {\"id\": 21291, \"name\": \"orange roof\"}, {\"id\": 21292, \"name\": \"the large orange plastic object\"}, {\"id\": 21293, \"name\": \"yellow sandal\"}, {\"id\": 21294, \"name\": \"a large, blue ribbon\"}, {\"id\": 21295, \"name\": \"a black, partially covered ski lift\"}, {\"id\": 21296, \"name\": \"a food vendor\"}, {\"id\": 21297, \"name\": \"a pink legging\"}, {\"id\": 21298, \"name\": \"a yellow name tag\"}, {\"id\": 21299, \"name\": \"the metal sheet\"}, {\"id\": 21300, \"name\": \"small sign\"}, {\"id\": 21301, \"name\": \"signage\"}, {\"id\": 21302, \"name\": \"a smaller boat\"}, {\"id\": 21303, \"name\": \"brown purse\"}, {\"id\": 21304, \"name\": \"the yellow button\"}, {\"id\": 21305, \"name\": \"a shield\"}, {\"id\": 21306, \"name\": \"long fluorescent light fixture\"}, {\"id\": 21307, \"name\": \"sign at the ga station\"}, {\"id\": 21308, \"name\": \"black vent\"}, {\"id\": 21309, \"name\": \"orange rooster\"}, {\"id\": 21310, \"name\": \"kong thmor drum\"}, {\"id\": 21311, \"name\": \"red harem pant\"}, {\"id\": 21312, \"name\": \"four-wheeler\"}, {\"id\": 21313, \"name\": \"the red and white flag\"}, {\"id\": 21314, \"name\": \"a mcdonald's restaurant\"}, {\"id\": 21315, \"name\": \"the police motorcycle\"}, {\"id\": 21316, \"name\": \"black and white striped awning\"}, {\"id\": 21317, \"name\": \"the green umbrella\"}, {\"id\": 21318, \"name\": \"the red handle\"}, {\"id\": 21319, \"name\": \"a large, round eye\"}, {\"id\": 21320, \"name\": \"the red and gold jacket\"}, {\"id\": 21321, \"name\": \"a large beam\"}, {\"id\": 21322, \"name\": \"black and white striped top\"}, {\"id\": 21323, \"name\": \"the wooden horse\"}, {\"id\": 21324, \"name\": \"vibrant sign\"}, {\"id\": 21325, \"name\": \"the medical mask\"}, {\"id\": 21326, \"name\": \"yellow safety barrier\"}, {\"id\": 21327, \"name\": \"a large, colorful, red and blue dragon statue\"}, {\"id\": 21328, \"name\": \"the white umbrella\"}, {\"id\": 21329, \"name\": \"the large blue scaffolding structure\"}, {\"id\": 21330, \"name\": \"dark wood pillar\"}, {\"id\": 21331, \"name\": \"the orange vest\"}, {\"id\": 21332, \"name\": \"the large grey truck\"}, {\"id\": 21333, \"name\": \"the player's character\"}, {\"id\": 21334, \"name\": \"the corrugated metal ceiling\"}, {\"id\": 21335, \"name\": \"the black and silver car\"}, {\"id\": 21336, \"name\": \"a pink headscarf\"}, {\"id\": 21337, \"name\": \"the blue and white sign\"}, {\"id\": 21338, \"name\": \"large, open-sided truck\"}, {\"id\": 21339, \"name\": \"the light brown van\"}, {\"id\": 21340, \"name\": \"the red, white, and yellow flag\"}, {\"id\": 21341, \"name\": \"a construction worker\"}, {\"id\": 21342, \"name\": \"a red and white facade\"}, {\"id\": 21343, \"name\": \"a large black punching bag\"}, {\"id\": 21344, \"name\": \"mast\"}, {\"id\": 21345, \"name\": \"the red star\"}, {\"id\": 21346, \"name\": \"white planter\"}, {\"id\": 21347, \"name\": \"filing cabinet\"}, {\"id\": 21348, \"name\": \"pink pipe\"}, {\"id\": 21349, \"name\": \"an orange sari\"}, {\"id\": 21350, \"name\": \"the grey sneaker\"}, {\"id\": 21351, \"name\": \"the shirtless\"}, {\"id\": 21352, \"name\": \"the purple horse\"}, {\"id\": 21353, \"name\": \"the pointed top\"}, {\"id\": 21354, \"name\": \"square shape\"}, {\"id\": 21355, \"name\": \"a blue and yellow headdress\"}, {\"id\": 21356, \"name\": \"cell tower\"}, {\"id\": 21357, \"name\": \"the red head covering\"}, {\"id\": 21358, \"name\": \"stoplight\"}, {\"id\": 21359, \"name\": \"large red bag\"}, {\"id\": 21360, \"name\": \"the long wooden plank\"}, {\"id\": 21361, \"name\": \"the malaysian flag\"}, {\"id\": 21362, \"name\": \"a large black flag\"}, {\"id\": 21363, \"name\": \"the construction worker\"}, {\"id\": 21364, \"name\": \"a silver chopstick\"}, {\"id\": 21365, \"name\": \"the bubble wand\"}, {\"id\": 21366, \"name\": \"the black, circular pattern\"}, {\"id\": 21367, \"name\": \"the camouflage sleeve\"}, {\"id\": 21368, \"name\": \"the green bicycle\"}, {\"id\": 21369, \"name\": \"the canal or river\"}, {\"id\": 21370, \"name\": \"brown bag\"}, {\"id\": 21371, \"name\": \"the blue and yellow truck\"}, {\"id\": 21372, \"name\": \"a stone facade\"}, {\"id\": 21373, \"name\": \"beach ball\"}, {\"id\": 21374, \"name\": \"the badminton racket\"}, {\"id\": 21375, \"name\": \"the large white light\"}, {\"id\": 21376, \"name\": \"black and white decal\"}, {\"id\": 21377, \"name\": \"large military-style truck\"}, {\"id\": 21378, \"name\": \"a colorful inflatable\"}, {\"id\": 21379, \"name\": \"the driver of the motorcycle\"}, {\"id\": 21380, \"name\": \"green shutter\"}, {\"id\": 21381, \"name\": \"the raft\"}, {\"id\": 21382, \"name\": \"person in a green and white shirt\"}, {\"id\": 21383, \"name\": \"a black circle\"}, {\"id\": 21384, \"name\": \"the large, black, inflatable character\"}, {\"id\": 21385, \"name\": \"an ornament\"}, {\"id\": 21386, \"name\": \"roll of white tape\"}, {\"id\": 21387, \"name\": \"orange bowl\"}, {\"id\": 21388, \"name\": \"a black scope\"}, {\"id\": 21389, \"name\": \"corrugated metal exterior\"}, {\"id\": 21390, \"name\": \"the white pickup truck\"}, {\"id\": 21391, \"name\": \"a blue billboard\"}, {\"id\": 21392, \"name\": \"the small, red, motorized scooter\"}, {\"id\": 21393, \"name\": \"toy gun\"}, {\"id\": 21394, \"name\": \"a black, white, and brown cattle\"}, {\"id\": 21395, \"name\": \"a black spotlight\"}, {\"id\": 21396, \"name\": \"the yellow metal grate platform\"}, {\"id\": 21397, \"name\": \"logo for a2z live\"}, {\"id\": 21398, \"name\": \"the wrapped object\"}, {\"id\": 21399, \"name\": \"beach umbrella\"}, {\"id\": 21400, \"name\": \"yellow hat\"}, {\"id\": 21401, \"name\": \"a digital display screen\"}, {\"id\": 21402, \"name\": \"a black snowmobile\"}, {\"id\": 21403, \"name\": \"the short skirt\"}, {\"id\": 21404, \"name\": \"young boy in a grey sweatshirt and black pant\"}, {\"id\": 21405, \"name\": \"a tray of food\"}, {\"id\": 21406, \"name\": \"red plastic stool\"}, {\"id\": 21407, \"name\": \"the small toy car\"}, {\"id\": 21408, \"name\": \"a large white cake\"}, {\"id\": 21409, \"name\": \"a steaming hot mixture of vegetable\"}, {\"id\": 21410, \"name\": \"the pool\"}, {\"id\": 21411, \"name\": \"bright yellow crop top\"}, {\"id\": 21412, \"name\": \"matching navy blue shirt\"}, {\"id\": 21413, \"name\": \"the blue clothing\"}, {\"id\": 21414, \"name\": \"the parked scooters and motorcycle\"}, {\"id\": 21415, \"name\": \"the tan, patterned cap\"}, {\"id\": 21416, \"name\": \"a yellow storefront\"}, {\"id\": 21417, \"name\": \"a red traffic light\"}, {\"id\": 21418, \"name\": \"the blue canopy tent\"}, {\"id\": 21419, \"name\": \"purple pant\"}, {\"id\": 21420, \"name\": \"a dark blue cap\"}, {\"id\": 21421, \"name\": \"a bright blue spotlight\"}, {\"id\": 21422, \"name\": \"a striped hammock\"}, {\"id\": 21423, \"name\": \"the striking blue fish\"}, {\"id\": 21424, \"name\": \"a purple square\"}, {\"id\": 21425, \"name\": \"a cloud of smoke\"}, {\"id\": 21426, \"name\": \"dark-colored pickup truck\"}, {\"id\": 21427, \"name\": \"a green face\"}, {\"id\": 21428, \"name\": \"the brown pant\"}, {\"id\": 21429, \"name\": \"a large, decorated trash can\"}, {\"id\": 21430, \"name\": \"roll of blue painter's tape\"}, {\"id\": 21431, \"name\": \"a large quantity of red produce\"}, {\"id\": 21432, \"name\": \"hill or mountain\"}, {\"id\": 21433, \"name\": \"an exercise rope\"}, {\"id\": 21434, \"name\": \"the hose\"}, {\"id\": 21435, \"name\": \"a gray helmet\"}, {\"id\": 21436, \"name\": \"an open blind\"}, {\"id\": 21437, \"name\": \"a machine or tool\"}, {\"id\": 21438, \"name\": \"a wooden bookshelf\"}, {\"id\": 21439, \"name\": \"tall, red metal frame\"}, {\"id\": 21440, \"name\": \"gold top\"}, {\"id\": 21441, \"name\": \"cloudy\"}, {\"id\": 21442, \"name\": \"metal panel\"}, {\"id\": 21443, \"name\": \"wild turkey\"}, {\"id\": 21444, \"name\": \"the white sphere\"}, {\"id\": 21445, \"name\": \"white spaceship\"}, {\"id\": 21446, \"name\": \"the yellow head\"}, {\"id\": 21447, \"name\": \"the low bun\"}, {\"id\": 21448, \"name\": \"green mesh object\"}, {\"id\": 21449, \"name\": \"large bowl of green\"}, {\"id\": 21450, \"name\": \"the small, white, fuzzy ball\"}, {\"id\": 21451, \"name\": \"the yellow tank top\"}, {\"id\": 21452, \"name\": \"a bowling pin\"}, {\"id\": 21453, \"name\": \"a tall crane\"}, {\"id\": 21454, \"name\": \"green mat\"}, {\"id\": 21455, \"name\": \"a small yellow object\"}, {\"id\": 21456, \"name\": \"yellow motorbike\"}, {\"id\": 21457, \"name\": \"large, blue, cylindrical object\"}, {\"id\": 21458, \"name\": \"a humanoid creature\"}, {\"id\": 21459, \"name\": \"a white cylinder\"}, {\"id\": 21460, \"name\": \"a snow-covered embankment\"}, {\"id\": 21461, \"name\": \"the crown\"}, {\"id\": 21462, \"name\": \"a purple bottle\"}, {\"id\": 21463, \"name\": \"red lantern\"}, {\"id\": 21464, \"name\": \"rider's shirt\"}, {\"id\": 21465, \"name\": \"the grey cooler\"}, {\"id\": 21466, \"name\": \"pink sandal\"}, {\"id\": 21467, \"name\": \"prominent white pedestrian crossing\"}, {\"id\": 21468, \"name\": \"the white plastic part\"}, {\"id\": 21469, \"name\": \"the thick pink border\"}, {\"id\": 21470, \"name\": \"a vibrant yellow dress\"}, {\"id\": 21471, \"name\": \"a dark cloud\"}, {\"id\": 21472, \"name\": \"a red, black, and white jersey\"}, {\"id\": 21473, \"name\": \"blue column\"}, {\"id\": 21474, \"name\": \"the orange lace\"}, {\"id\": 21475, \"name\": \"the man in the striped shirt\"}, {\"id\": 21476, \"name\": \"white and blue design\"}, {\"id\": 21477, \"name\": \"game's title\"}, {\"id\": 21478, \"name\": \"blue jersey with white stripe\"}, {\"id\": 21479, \"name\": \"a glass facade\"}, {\"id\": 21480, \"name\": \"white metal fence\"}, {\"id\": 21481, \"name\": \"white tofu\"}, {\"id\": 21482, \"name\": \"a yellow and green auto rickshaw\"}, {\"id\": 21483, \"name\": \"a small pink container\"}, {\"id\": 21484, \"name\": \"a dark vest\"}, {\"id\": 21485, \"name\": \"gravel area\"}, {\"id\": 21486, \"name\": \"small black pigeon\"}, {\"id\": 21487, \"name\": \"a well-groomed horse\"}, {\"id\": 21488, \"name\": \"the large tub\"}, {\"id\": 21489, \"name\": \"a brick walkway\"}, {\"id\": 21490, \"name\": \"the snorkeler\"}, {\"id\": 21491, \"name\": \"the lighting fixture\"}, {\"id\": 21492, \"name\": \"a vibrant red and orange garland\"}, {\"id\": 21493, \"name\": \"vertical piece of wood\"}, {\"id\": 21494, \"name\": \"a black horse\"}, {\"id\": 21495, \"name\": \"the gray rock\"}, {\"id\": 21496, \"name\": \"the winter toy\"}, {\"id\": 21497, \"name\": \"green inflatable slide\"}, {\"id\": 21498, \"name\": \"distinctive orange roof\"}, {\"id\": 21499, \"name\": \"burner\"}, {\"id\": 21500, \"name\": \"the green pillar\"}, {\"id\": 21501, \"name\": \"a blue pillar\"}, {\"id\": 21502, \"name\": \"the white motorcycle\"}, {\"id\": 21503, \"name\": \"the concrete ground\"}, {\"id\": 21504, \"name\": \"a tan dress\"}, {\"id\": 21505, \"name\": \"the coral formation\"}, {\"id\": 21506, \"name\": \"string of white light\"}, {\"id\": 21507, \"name\": \"rear windshield\"}, {\"id\": 21508, \"name\": \"a tank\"}, {\"id\": 21509, \"name\": \"the long, narrow boat\"}, {\"id\": 21510, \"name\": \"the matching red and yellow jacket\"}, {\"id\": 21511, \"name\": \"tall pole\"}, {\"id\": 21512, \"name\": \"the silver sedan\"}, {\"id\": 21513, \"name\": \"a yellow couch\"}, {\"id\": 21514, \"name\": \"a neon green t-shirt\"}, {\"id\": 21515, \"name\": \"athletic short\"}, {\"id\": 21516, \"name\": \"the traditional vietnamese hat\"}, {\"id\": 21517, \"name\": \"green glow\"}, {\"id\": 21518, \"name\": \"the meat\"}, {\"id\": 21519, \"name\": \"a tall grey pole\"}, {\"id\": 21520, \"name\": \"a game controller\"}, {\"id\": 21521, \"name\": \"yellow robe\"}, {\"id\": 21522, \"name\": \"the colorful birdcage\"}, {\"id\": 21523, \"name\": \"the ripped jean\"}, {\"id\": 21524, \"name\": \"truck bed\"}, {\"id\": 21525, \"name\": \"the teal backpack\"}, {\"id\": 21526, \"name\": \"a player's character\"}, {\"id\": 21527, \"name\": \"the large, blue, hexagonal icon\"}, {\"id\": 21528, \"name\": \"a tube sponge\"}, {\"id\": 21529, \"name\": \"the muddy terrain\"}, {\"id\": 21530, \"name\": \"high fence\"}, {\"id\": 21531, \"name\": \"the red awning or canopy\"}, {\"id\": 21532, \"name\": \"matching headpiece\"}, {\"id\": 21533, \"name\": \"green creature resembling a stick figure\"}, {\"id\": 21534, \"name\": \"bright yellow dress\"}, {\"id\": 21535, \"name\": \"slender tower\"}, {\"id\": 21536, \"name\": \"the home plate\"}, {\"id\": 21537, \"name\": \"white hard hat\"}, {\"id\": 21538, \"name\": \"black basketball\"}, {\"id\": 21539, \"name\": \"dark wooden fence\"}, {\"id\": 21540, \"name\": \"red and green light\"}, {\"id\": 21541, \"name\": \"the black grate\"}, {\"id\": 21542, \"name\": \"smaller dark-colored car\"}, {\"id\": 21543, \"name\": \"a patterned rug or carpet\"}, {\"id\": 21544, \"name\": \"a white robe\"}, {\"id\": 21545, \"name\": \"the large blue tarp or blanket\"}, {\"id\": 21546, \"name\": \"the woman in the yellow dress\"}, {\"id\": 21547, \"name\": \"a long, horizontal bar\"}, {\"id\": 21548, \"name\": \"the tall, black lamppost\"}, {\"id\": 21549, \"name\": \"the brown tank top\"}, {\"id\": 21550, \"name\": \"smaller wheel\"}, {\"id\": 21551, \"name\": \"gray wooden plank\"}, {\"id\": 21552, \"name\": \"the red boot\"}, {\"id\": 21553, \"name\": \"the white police car\"}, {\"id\": 21554, \"name\": \"a white hull\"}, {\"id\": 21555, \"name\": \"the pink leash\"}, {\"id\": 21556, \"name\": \"yellow feed\"}, {\"id\": 21557, \"name\": \"a blue and white dress\"}, {\"id\": 21558, \"name\": \"the metal surface\"}, {\"id\": 21559, \"name\": \"a metal sheet\"}, {\"id\": 21560, \"name\": \"a steep, rocky mountain slope\"}, {\"id\": 21561, \"name\": \"exercise bike\"}, {\"id\": 21562, \"name\": \"a purple hoodie\"}, {\"id\": 21563, \"name\": \"a curved glass front\"}, {\"id\": 21564, \"name\": \"a red slipper\"}, {\"id\": 21565, \"name\": \"the dark gray hull\"}, {\"id\": 21566, \"name\": \"the rapunzel\"}, {\"id\": 21567, \"name\": \"tutu\"}, {\"id\": 21568, \"name\": \"a bus's windshield\"}, {\"id\": 21569, \"name\": \"red ornament\"}, {\"id\": 21570, \"name\": \"a cartoon-like animal statue\"}, {\"id\": 21571, \"name\": \"yellow bar\"}, {\"id\": 21572, \"name\": \"white volleyball\"}, {\"id\": 21573, \"name\": \"the sheer curtain\"}, {\"id\": 21574, \"name\": \"the yellow circle\"}, {\"id\": 21575, \"name\": \"the small, blue toy car\"}, {\"id\": 21576, \"name\": \"the white superstructure\"}, {\"id\": 21577, \"name\": \"the green pot with a white rim\"}, {\"id\": 21578, \"name\": \"a light-colored curtain\"}, {\"id\": 21579, \"name\": \"the plaid pant\"}, {\"id\": 21580, \"name\": \"monk\"}, {\"id\": 21581, \"name\": \"the black ceiling\"}, {\"id\": 21582, \"name\": \"circular swing\"}, {\"id\": 21583, \"name\": \"the large yellow tractor\"}, {\"id\": 21584, \"name\": \"the red pitchfork\"}, {\"id\": 21585, \"name\": \"a red, white, and blue striped flag\"}, {\"id\": 21586, \"name\": \"a person in a blue shirt\"}, {\"id\": 21587, \"name\": \"a large metal wheel\"}, {\"id\": 21588, \"name\": \"the vibrant pink object\"}, {\"id\": 21589, \"name\": \"purple clothing\"}, {\"id\": 21590, \"name\": \"a small light\"}, {\"id\": 21591, \"name\": \"the yellow container\"}, {\"id\": 21592, \"name\": \"a tall red pant\"}, {\"id\": 21593, \"name\": \"a team uniform\"}, {\"id\": 21594, \"name\": \"a small white ball\"}, {\"id\": 21595, \"name\": \"the yellow and red clothing\"}, {\"id\": 21596, \"name\": \"the large speaker\"}, {\"id\": 21597, \"name\": \"orange flag\"}, {\"id\": 21598, \"name\": \"blue and black helmet\"}, {\"id\": 21599, \"name\": \"silver minivan\"}, {\"id\": 21600, \"name\": \"the brown surface\"}, {\"id\": 21601, \"name\": \"the red and white hockey stick\"}, {\"id\": 21602, \"name\": \"the dusty parking\"}, {\"id\": 21603, \"name\": \"a small motorcycle\"}, {\"id\": 21604, \"name\": \"a tan bucket hat\"}, {\"id\": 21605, \"name\": \"a cone\"}, {\"id\": 21606, \"name\": \"a blurry sign\"}, {\"id\": 21607, \"name\": \"dark-colored jacket or coat\"}, {\"id\": 21608, \"name\": \"the blue folder\"}, {\"id\": 21609, \"name\": \"denim short\"}, {\"id\": 21610, \"name\": \"blue toy car\"}, {\"id\": 21611, \"name\": \"large spotlight\"}, {\"id\": 21612, \"name\": \"a notebook or book\"}, {\"id\": 21613, \"name\": \"the paintbrush\"}, {\"id\": 21614, \"name\": \"the triangle\"}, {\"id\": 21615, \"name\": \"a traditional vietnamese conical hat\"}, {\"id\": 21616, \"name\": \"colorful umbrella\"}, {\"id\": 21617, \"name\": \"the pink shredded vegetable\"}, {\"id\": 21618, \"name\": \"the large, cylindrical machine\"}, {\"id\": 21619, \"name\": \"a white garment\"}, {\"id\": 21620, \"name\": \"white pop-up tent\"}, {\"id\": 21621, \"name\": \"a black truck\"}, {\"id\": 21622, \"name\": \"smaller sign\"}, {\"id\": 21623, \"name\": \"small bowl of sauce\"}, {\"id\": 21624, \"name\": \"a race schedule\"}, {\"id\": 21625, \"name\": \"metal handle\"}, {\"id\": 21626, \"name\": \"the cart's wheel\"}, {\"id\": 21627, \"name\": \"plastic tube\"}, {\"id\": 21628, \"name\": \"a green dinosaur-shaped rollercoaster car\"}, {\"id\": 21629, \"name\": \"black toy car\"}, {\"id\": 21630, \"name\": \"large orange and blue tarp\"}, {\"id\": 21631, \"name\": \"person in a red life jacket\"}, {\"id\": 21632, \"name\": \"pink face mask\"}, {\"id\": 21633, \"name\": \"the white church\"}, {\"id\": 21634, \"name\": \"gray hat\"}, {\"id\": 21635, \"name\": \"a red brick walkway\"}, {\"id\": 21636, \"name\": \"unique wreath\"}, {\"id\": 21637, \"name\": \"the glass of sake\"}, {\"id\": 21638, \"name\": \"the camouflage outfit\"}, {\"id\": 21639, \"name\": \"mountain bike\"}, {\"id\": 21640, \"name\": \"black stool\"}, {\"id\": 21641, \"name\": \"the red tablecloth\"}, {\"id\": 21642, \"name\": \"a woman on the bicycle\"}, {\"id\": 21643, \"name\": \"camouflage hat\"}, {\"id\": 21644, \"name\": \"the police car\"}, {\"id\": 21645, \"name\": \"a tall yellow sign\"}, {\"id\": 21646, \"name\": \"the black scarf\"}, {\"id\": 21647, \"name\": \"yellow front\"}, {\"id\": 21648, \"name\": \"a white satchel strap\"}, {\"id\": 21649, \"name\": \"a parquet-style plank\"}, {\"id\": 21650, \"name\": \"man's back\"}, {\"id\": 21651, \"name\": \"white wrist cuff\"}, {\"id\": 21652, \"name\": \"a yellow kayak\"}, {\"id\": 21653, \"name\": \"the pink headscarf\"}, {\"id\": 21654, \"name\": \"the teal flip-flop\"}, {\"id\": 21655, \"name\": \"the colorful toy bar\"}, {\"id\": 21656, \"name\": \"blue beanie\"}, {\"id\": 21657, \"name\": \"a red cable car\"}, {\"id\": 21658, \"name\": \"a large black wheel\"}, {\"id\": 21659, \"name\": \"white lion\"}, {\"id\": 21660, \"name\": \"black fish\"}, {\"id\": 21661, \"name\": \"the red glove\"}, {\"id\": 21662, \"name\": \"gray fish\"}, {\"id\": 21663, \"name\": \"the blue mask\"}, {\"id\": 21664, \"name\": \"the multi-domed structure\"}, {\"id\": 21665, \"name\": \"blue stool\"}, {\"id\": 21666, \"name\": \"a large gray electrical box\"}, {\"id\": 21667, \"name\": \"a person in a tan uniform\"}, {\"id\": 21668, \"name\": \"a blue storage bin\"}, {\"id\": 21669, \"name\": \"tan cargo area\"}, {\"id\": 21670, \"name\": \"large, multicolored flag\"}, {\"id\": 21671, \"name\": \"pink and green mat\"}, {\"id\": 21672, \"name\": \"a man on the truck\"}, {\"id\": 21673, \"name\": \"yellow umbrella\"}, {\"id\": 21674, \"name\": \"the light brown ear\"}, {\"id\": 21675, \"name\": \"a rubber boot\"}, {\"id\": 21676, \"name\": \"a basket of fruit\"}, {\"id\": 21677, \"name\": \"a food scrap\"}, {\"id\": 21678, \"name\": \"a yellow marking\"}, {\"id\": 21679, \"name\": \"the yellow crane\"}, {\"id\": 21680, \"name\": \"the fuel tank\"}, {\"id\": 21681, \"name\": \"a yellow basket\"}, {\"id\": 21682, \"name\": \"the gray and black motorcycle\"}, {\"id\": 21683, \"name\": \"the headgear\"}, {\"id\": 21684, \"name\": \"white limousine\"}, {\"id\": 21685, \"name\": \"a corrugated metal panel\"}, {\"id\": 21686, \"name\": \"the large, dark-colored truck\"}, {\"id\": 21687, \"name\": \"a guitar stand\"}, {\"id\": 21688, \"name\": \"the swirling yellow design\"}, {\"id\": 21689, \"name\": \"orange sari\"}, {\"id\": 21690, \"name\": \"colorful bumper car\"}, {\"id\": 21691, \"name\": \"light floral dress\"}, {\"id\": 21692, \"name\": \"the toy frog\"}, {\"id\": 21693, \"name\": \"distinctive red roof\"}, {\"id\": 21694, \"name\": \"a man with the saw\"}, {\"id\": 21695, \"name\": \"a patient bed\"}, {\"id\": 21696, \"name\": \"a light post\"}, {\"id\": 21697, \"name\": \"the large, armored creature\"}, {\"id\": 21698, \"name\": \"yellow basket\"}, {\"id\": 21699, \"name\": \"black and white baseball cap\"}, {\"id\": 21700, \"name\": \"the dark blue cap\"}, {\"id\": 21701, \"name\": \"a digital billboard\"}, {\"id\": 21702, \"name\": \"the white ear\"}, {\"id\": 21703, \"name\": \"a blue floatation device\"}, {\"id\": 21704, \"name\": \"a metal bowl or container\"}, {\"id\": 21705, \"name\": \"the concrete step\"}, {\"id\": 21706, \"name\": \"prominent white sign\"}, {\"id\": 21707, \"name\": \"the tongs\"}, {\"id\": 21708, \"name\": \"a red gemstone\"}, {\"id\": 21709, \"name\": \"the man in a red robe\"}, {\"id\": 21710, \"name\": \"man in the gray shirt\"}, {\"id\": 21711, \"name\": \"the operator\"}, {\"id\": 21712, \"name\": \"church steeple\"}, {\"id\": 21713, \"name\": \"a small italian flag\"}, {\"id\": 21714, \"name\": \"small, makeshift table\"}, {\"id\": 21715, \"name\": \"white plastic sheet or tarp\"}, {\"id\": 21716, \"name\": \"the beige fedora\"}, {\"id\": 21717, \"name\": \"the large crack\"}, {\"id\": 21718, \"name\": \"an interior of the bus\"}, {\"id\": 21719, \"name\": \"white cardigan\"}, {\"id\": 21720, \"name\": \"the white goat\"}, {\"id\": 21721, \"name\": \"a small black and green circle\"}, {\"id\": 21722, \"name\": \"a floral display\"}, {\"id\": 21723, \"name\": \"hanging basket\"}, {\"id\": 21724, \"name\": \"the long, blonde ponytail\"}, {\"id\": 21725, \"name\": \"the prominent red flag\"}, {\"id\": 21726, \"name\": \"boy in a blue shirt\"}, {\"id\": 21727, \"name\": \"grey backpack\"}, {\"id\": 21728, \"name\": \"a green fabric covering\"}, {\"id\": 21729, \"name\": \"the black traffic signal\"}, {\"id\": 21730, \"name\": \"the red and white santa hat\"}, {\"id\": 21731, \"name\": \"black and red uniform\"}, {\"id\": 21732, \"name\": \"a turquoise stuffed animal\"}, {\"id\": 21733, \"name\": \"the wooden handle\"}, {\"id\": 21734, \"name\": \"a blue engine\"}, {\"id\": 21735, \"name\": \"the horse-drawn rickshaw\"}, {\"id\": 21736, \"name\": \"a slide\"}, {\"id\": 21737, \"name\": \"the plastic sheet\"}, {\"id\": 21738, \"name\": \"a long, red dress\"}, {\"id\": 21739, \"name\": \"black-framed window\"}, {\"id\": 21740, \"name\": \"green legging\"}, {\"id\": 21741, \"name\": \"large plastic bag\"}, {\"id\": 21742, \"name\": \"a sandbar\"}, {\"id\": 21743, \"name\": \"man in a high-visibility vest\"}, {\"id\": 21744, \"name\": \"a mint green skirt\"}, {\"id\": 21745, \"name\": \"distinct colored wheel\"}, {\"id\": 21746, \"name\": \"white t-shirt with a logo\"}, {\"id\": 21747, \"name\": \"printing plate\"}, {\"id\": 21748, \"name\": \"black head\"}, {\"id\": 21749, \"name\": \"a colorful skirt\"}, {\"id\": 21750, \"name\": \"glowing blue sword\"}, {\"id\": 21751, \"name\": \"small animal\"}, {\"id\": 21752, \"name\": \"the black television\"}, {\"id\": 21753, \"name\": \"a green neon light\"}, {\"id\": 21754, \"name\": \"bright blue material\"}, {\"id\": 21755, \"name\": \"a glass display case\"}, {\"id\": 21756, \"name\": \"light pink hijab\"}, {\"id\": 21757, \"name\": \"picture of a plate of food\"}, {\"id\": 21758, \"name\": \"a large white tile\"}, {\"id\": 21759, \"name\": \"the large golden object\"}, {\"id\": 21760, \"name\": \"a can of drink\"}, {\"id\": 21761, \"name\": \"the large, white plastic swan swing\"}, {\"id\": 21762, \"name\": \"orange top\"}, {\"id\": 21763, \"name\": \"white platform\"}, {\"id\": 21764, \"name\": \"the bucket or container\"}, {\"id\": 21765, \"name\": \"placard\"}, {\"id\": 21766, \"name\": \"a weathered metal material\"}, {\"id\": 21767, \"name\": \"conveyor belt\"}, {\"id\": 21768, \"name\": \"a high ponytail\"}, {\"id\": 21769, \"name\": \"a pad\"}, {\"id\": 21770, \"name\": \"the blue slide\"}, {\"id\": 21771, \"name\": \"large pipe or tube\"}, {\"id\": 21772, \"name\": \"miniature white picket fence\"}, {\"id\": 21773, \"name\": \"dark brown roof\"}, {\"id\": 21774, \"name\": \"a gucci backpack\"}, {\"id\": 21775, \"name\": \"a large metal structure\"}, {\"id\": 21776, \"name\": \"a lamppost\"}, {\"id\": 21777, \"name\": \"orange tire\"}, {\"id\": 21778, \"name\": \"a snorkeler's shadow\"}, {\"id\": 21779, \"name\": \"faint plume of white smoke\"}, {\"id\": 21780, \"name\": \"large patch\"}, {\"id\": 21781, \"name\": \"the large arched window\"}, {\"id\": 21782, \"name\": \"the amplifier\"}, {\"id\": 21783, \"name\": \"bright yellow ball\"}, {\"id\": 21784, \"name\": \"cycling attire\"}, {\"id\": 21785, \"name\": \"a striped pole\"}, {\"id\": 21786, \"name\": \"neon green safety vest\"}, {\"id\": 21787, \"name\": \"green and white seat\"}, {\"id\": 21788, \"name\": \"a residential or commercial structure\"}, {\"id\": 21789, \"name\": \"large tank\"}, {\"id\": 21790, \"name\": \"paraglider\"}, {\"id\": 21791, \"name\": \"a yellow and blue jacket\"}, {\"id\": 21792, \"name\": \"the blue strap\"}, {\"id\": 21793, \"name\": \"a red and white skirt\"}, {\"id\": 21794, \"name\": \"black suv\"}, {\"id\": 21795, \"name\": \"a similar black lamppost\"}, {\"id\": 21796, \"name\": \"the white tail\"}, {\"id\": 21797, \"name\": \"dark-colored hat\"}, {\"id\": 21798, \"name\": \"a green tarp\"}, {\"id\": 21799, \"name\": \"fire dancer\"}, {\"id\": 21800, \"name\": \"the yellow toy car\"}, {\"id\": 21801, \"name\": \"a bright red comb\"}, {\"id\": 21802, \"name\": \"the red circular object\"}, {\"id\": 21803, \"name\": \"the small blue object\"}, {\"id\": 21804, \"name\": \"pink toy\"}, {\"id\": 21805, \"name\": \"the construction equipment\"}, {\"id\": 21806, \"name\": \"a yellow headband\"}, {\"id\": 21807, \"name\": \"green bin\"}, {\"id\": 21808, \"name\": \"the red and white sock\"}, {\"id\": 21809, \"name\": \"a large white canopy\"}, {\"id\": 21810, \"name\": \"white, sparkly skirt\"}, {\"id\": 21811, \"name\": \"blue shipping container\"}, {\"id\": 21812, \"name\": \"left handle\"}, {\"id\": 21813, \"name\": \"a truck's wheel\"}, {\"id\": 21814, \"name\": \"metal trash can\"}, {\"id\": 21815, \"name\": \"white blind\"}, {\"id\": 21816, \"name\": \"a bangle\"}, {\"id\": 21817, \"name\": \"navy undershirt\"}, {\"id\": 21818, \"name\": \"the red can\"}, {\"id\": 21819, \"name\": \"green drum\"}, {\"id\": 21820, \"name\": \"dumpling\"}, {\"id\": 21821, \"name\": \"the sloping red roof\"}, {\"id\": 21822, \"name\": \"the bouquet of flower\"}, {\"id\": 21823, \"name\": \"the circular metal object\"}, {\"id\": 21824, \"name\": \"a brown horse\"}, {\"id\": 21825, \"name\": \"sledding\"}, {\"id\": 21826, \"name\": \"the store's sign\"}, {\"id\": 21827, \"name\": \"a circular icon\"}, {\"id\": 21828, \"name\": \"the black storage bin\"}, {\"id\": 21829, \"name\": \"a white soccer ball\"}, {\"id\": 21830, \"name\": \"a pearl earring\"}, {\"id\": 21831, \"name\": \"man on a bicycle\"}, {\"id\": 21832, \"name\": \"the hanging flower pot\"}, {\"id\": 21833, \"name\": \"the white and black jacket\"}, {\"id\": 21834, \"name\": \"white tower\"}, {\"id\": 21835, \"name\": \"a plastic package of egg\"}, {\"id\": 21836, \"name\": \"the crab meat\"}, {\"id\": 21837, \"name\": \"large hole or trench\"}, {\"id\": 21838, \"name\": \"an orange vest\"}, {\"id\": 21839, \"name\": \"white rectangular box\"}, {\"id\": 21840, \"name\": \"colorful image\"}, {\"id\": 21841, \"name\": \"brown woven wood panel\"}, {\"id\": 21842, \"name\": \"the red game controller\"}, {\"id\": 21843, \"name\": \"a dark doorway\"}, {\"id\": 21844, \"name\": \"the rectangular tag\"}, {\"id\": 21845, \"name\": \"the laptop screen\"}, {\"id\": 21846, \"name\": \"cross\"}, {\"id\": 21847, \"name\": \"a gray stone\"}, {\"id\": 21848, \"name\": \"the dark-colored cap\"}, {\"id\": 21849, \"name\": \"a teal lid\"}, {\"id\": 21850, \"name\": \"an arrow\"}, {\"id\": 21851, \"name\": \"the metal scaffolding structure\"}, {\"id\": 21852, \"name\": \"a leopard print vest\"}, {\"id\": 21853, \"name\": \"the creature\"}, {\"id\": 21854, \"name\": \"large fabric banner\"}, {\"id\": 21855, \"name\": \"tall, black pole\"}, {\"id\": 21856, \"name\": \"a dirt embankment\"}, {\"id\": 21857, \"name\": \"the gray stone structure\"}, {\"id\": 21858, \"name\": \"the black watch\"}, {\"id\": 21859, \"name\": \"the bird's head\"}, {\"id\": 21860, \"name\": \"a dark interior\"}, {\"id\": 21861, \"name\": \"a puma logo\"}, {\"id\": 21862, \"name\": \"the large, ornate statue\"}, {\"id\": 21863, \"name\": \"an orange and white striped plastic barrier\"}, {\"id\": 21864, \"name\": \"red and white barrier\"}, {\"id\": 21865, \"name\": \"a large white handle\"}, {\"id\": 21866, \"name\": \"large green battery pack\"}, {\"id\": 21867, \"name\": \"the large teal christma ornament\"}, {\"id\": 21868, \"name\": \"the prominent green sign\"}, {\"id\": 21869, \"name\": \"a small red motorcycle\"}, {\"id\": 21870, \"name\": \"large, red, star-shaped decoration\"}, {\"id\": 21871, \"name\": \"a large, round object\"}, {\"id\": 21872, \"name\": \"the blue, yellow, and white helmet\"}, {\"id\": 21873, \"name\": \"a turquoise post\"}, {\"id\": 21874, \"name\": \"the white flower\"}, {\"id\": 21875, \"name\": \"a white wave\"}, {\"id\": 21876, \"name\": \"a red tent\"}, {\"id\": 21877, \"name\": \"green and yellow sticker\"}, {\"id\": 21878, \"name\": \"the yellow and green logo\"}, {\"id\": 21879, \"name\": \"a colorful bandana\"}, {\"id\": 21880, \"name\": \"the tall, black pole\"}, {\"id\": 21881, \"name\": \"the human\"}, {\"id\": 21882, \"name\": \"a white saddle pad\"}, {\"id\": 21883, \"name\": \"a large front windshield\"}, {\"id\": 21884, \"name\": \"long, yellow, feathered boa\"}, {\"id\": 21885, \"name\": \"large air vent\"}, {\"id\": 21886, \"name\": \"the large, white, rectangular package\"}, {\"id\": 21887, \"name\": \"the pink outfit\"}, {\"id\": 21888, \"name\": \"the red and white striped barrier\"}, {\"id\": 21889, \"name\": \"the blue stand\"}, {\"id\": 21890, \"name\": \"black post\"}, {\"id\": 21891, \"name\": \"a pink and white cartoon character\"}, {\"id\": 21892, \"name\": \"bright green sash\"}, {\"id\": 21893, \"name\": \"parked scooter\"}, {\"id\": 21894, \"name\": \"blue bridle accent\"}, {\"id\": 21895, \"name\": \"a floating structure\"}, {\"id\": 21896, \"name\": \"a grey hat\"}, {\"id\": 21897, \"name\": \"the blue and white striped awning\"}, {\"id\": 21898, \"name\": \"a toy motorcycle\"}, {\"id\": 21899, \"name\": \"the white fur-trimmed hood\"}, {\"id\": 21900, \"name\": \"a dark gray carpeting\"}, {\"id\": 21901, \"name\": \"the large, white pole\"}, {\"id\": 21902, \"name\": \"the digital timer\"}, {\"id\": 21903, \"name\": \"blue sleeve\"}, {\"id\": 21904, \"name\": \"the man in a black jacket\"}, {\"id\": 21905, \"name\": \"a small tan dog\"}, {\"id\": 21906, \"name\": \"white poster\"}, {\"id\": 21907, \"name\": \"red robe\"}, {\"id\": 21908, \"name\": \"the large helicopter\"}, {\"id\": 21909, \"name\": \"a hanging light\"}, {\"id\": 21910, \"name\": \"colorful streamer\"}, {\"id\": 21911, \"name\": \"the long, fluffy tail\"}, {\"id\": 21912, \"name\": \"the vietnamese flag\"}, {\"id\": 21913, \"name\": \"the black fan\"}, {\"id\": 21914, \"name\": \"the red cab\"}, {\"id\": 21915, \"name\": \"the bundle of green plant\"}, {\"id\": 21916, \"name\": \"the white area\"}, {\"id\": 21917, \"name\": \"the prominent red banner\"}, {\"id\": 21918, \"name\": \"bicycle tire\"}, {\"id\": 21919, \"name\": \"wooden pillar\"}, {\"id\": 21920, \"name\": \"pepsi stand\"}, {\"id\": 21921, \"name\": \"the large, rust-colored metal object\"}, {\"id\": 21922, \"name\": \"soccer jersey\"}, {\"id\": 21923, \"name\": \"the black athletic attire\"}, {\"id\": 21924, \"name\": \"a black tail\"}, {\"id\": 21925, \"name\": \"a white and black patterned top\"}, {\"id\": 21926, \"name\": \"yellow circle\"}, {\"id\": 21927, \"name\": \"the green vehicle\"}, {\"id\": 21928, \"name\": \"a pool deck\"}, {\"id\": 21929, \"name\": \"black capri pant\"}, {\"id\": 21930, \"name\": \"black traffic light\"}, {\"id\": 21931, \"name\": \"a yellow umbrella\"}, {\"id\": 21932, \"name\": \"the red cone\"}, {\"id\": 21933, \"name\": \"red canopy tent\"}, {\"id\": 21934, \"name\": \"the large quantity of fish\"}, {\"id\": 21935, \"name\": \"game's map\"}, {\"id\": 21936, \"name\": \"the pink beak\"}, {\"id\": 21937, \"name\": \"a blue train car\"}, {\"id\": 21938, \"name\": \"wooden handle\"}, {\"id\": 21939, \"name\": \"a red can\"}, {\"id\": 21940, \"name\": \"pink baseball cap\"}, {\"id\": 21941, \"name\": \"surfing\"}, {\"id\": 21942, \"name\": \"narrow strip of brown material\"}, {\"id\": 21943, \"name\": \"navy blue sleeve\"}, {\"id\": 21944, \"name\": \"the blue weight\"}, {\"id\": 21945, \"name\": \"an aquatic plant\"}, {\"id\": 21946, \"name\": \"orange outfit\"}, {\"id\": 21947, \"name\": \"tambourine\"}, {\"id\": 21948, \"name\": \"a yellow cap\"}, {\"id\": 21949, \"name\": \"a matching headscarf\"}, {\"id\": 21950, \"name\": \"the bollard\"}, {\"id\": 21951, \"name\": \"the young goat\"}, {\"id\": 21952, \"name\": \"orange traffic cone\"}, {\"id\": 21953, \"name\": \"the large gray pot\"}, {\"id\": 21954, \"name\": \"fire\"}, {\"id\": 21955, \"name\": \"the red sash\"}, {\"id\": 21956, \"name\": \"a driver's hands\"}, {\"id\": 21957, \"name\": \"the pavilion\"}, {\"id\": 21958, \"name\": \"low, green hedge\"}, {\"id\": 21959, \"name\": \"an orange and yellow safety vest\"}, {\"id\": 21960, \"name\": \"colorful slide\"}, {\"id\": 21961, \"name\": \"blue bandana\"}, {\"id\": 21962, \"name\": \"the concrete divider\"}, {\"id\": 21963, \"name\": \"the quad bike\"}, {\"id\": 21964, \"name\": \"yellow face mask\"}, {\"id\": 21965, \"name\": \"prominent white and black jet ski\"}, {\"id\": 21966, \"name\": \"vertical toolbar\"}, {\"id\": 21967, \"name\": \"colorful sash\"}, {\"id\": 21968, \"name\": \"a pots and pan\"}, {\"id\": 21969, \"name\": \"dark grey vest\"}, {\"id\": 21970, \"name\": \"an orange fish\"}, {\"id\": 21971, \"name\": \"the light-brown wooden structure\"}, {\"id\": 21972, \"name\": \"the pole of the horse seat\"}, {\"id\": 21973, \"name\": \"the dark, metal roller shutter\"}, {\"id\": 21974, \"name\": \"the white oval\"}, {\"id\": 21975, \"name\": \"a yellow and white taxi\"}, {\"id\": 21976, \"name\": \"the black barber's cape\"}, {\"id\": 21977, \"name\": \"the denim jacket\"}, {\"id\": 21978, \"name\": \"car's headlights\"}, {\"id\": 21979, \"name\": \"a hand gesture\"}, {\"id\": 21980, \"name\": \"the red heart eye\"}, {\"id\": 21981, \"name\": \"the light-blue jean\"}, {\"id\": 21982, \"name\": \"red umbrella\"}, {\"id\": 21983, \"name\": \"red chicken\"}, {\"id\": 21984, \"name\": \"black paddle\"}, {\"id\": 21985, \"name\": \"the large, white airplane\"}, {\"id\": 21986, \"name\": \"low ponytail\"}, {\"id\": 21987, \"name\": \"the red and yellow flag\"}, {\"id\": 21988, \"name\": \"the red square\"}, {\"id\": 21989, \"name\": \"blue outfit\"}, {\"id\": 21990, \"name\": \"the netting\"}, {\"id\": 21991, \"name\": \"a bottle of cleaning solution\"}, {\"id\": 21992, \"name\": \"a white net\"}, {\"id\": 21993, \"name\": \"large, furry creature\"}, {\"id\": 21994, \"name\": \"a red and white striped curb\"}, {\"id\": 21995, \"name\": \"vibrant and colorful outfit\"}, {\"id\": 21996, \"name\": \"the large white support structure\"}, {\"id\": 21997, \"name\": \"a blue hard hat\"}, {\"id\": 21998, \"name\": \"the red and gold patterned carpet or rug\"}, {\"id\": 21999, \"name\": \"rectangular opening\"}, {\"id\": 22000, \"name\": \"a traffic signal post\"}, {\"id\": 22001, \"name\": \"the orange pickup truck\"}, {\"id\": 22002, \"name\": \"gray mailbox\"}, {\"id\": 22003, \"name\": \"blue surface\"}, {\"id\": 22004, \"name\": \"a bright plumage\"}, {\"id\": 22005, \"name\": \"a light-colored table\"}, {\"id\": 22006, \"name\": \"a steep cliff\"}, {\"id\": 22007, \"name\": \"light-colored suv\"}, {\"id\": 22008, \"name\": \"blue raft\"}, {\"id\": 22009, \"name\": \"the colorful barrier\"}, {\"id\": 22010, \"name\": \"the white and grey fish\"}, {\"id\": 22011, \"name\": \"dark blue long-sleeve shirt\"}, {\"id\": 22012, \"name\": \"a corrugated roof\"}, {\"id\": 22013, \"name\": \"the racket\"}, {\"id\": 22014, \"name\": \"a blue wristband\"}, {\"id\": 22015, \"name\": \"the excavator's bucket\"}, {\"id\": 22016, \"name\": \"a white paper roll\"}, {\"id\": 22017, \"name\": \"an individual in military uniform\"}, {\"id\": 22018, \"name\": \"the yellow face covering\"}, {\"id\": 22019, \"name\": \"a gold roof\"}, {\"id\": 22020, \"name\": \"the small brown stuffed animal\"}, {\"id\": 22021, \"name\": \"yellow leg\"}, {\"id\": 22022, \"name\": \"the traditional hat\"}, {\"id\": 22023, \"name\": \"the antenna\"}, {\"id\": 22024, \"name\": \"the blue-gloved hand\"}, {\"id\": 22025, \"name\": \"oversized tire\"}, {\"id\": 22026, \"name\": \"red costume\"}, {\"id\": 22027, \"name\": \"red swing\"}, {\"id\": 22028, \"name\": \"a yellow, inflatable tube\"}, {\"id\": 22029, \"name\": \"a blue scaffolding structure\"}, {\"id\": 22030, \"name\": \"the colorful object\"}, {\"id\": 22031, \"name\": \"a red wrapper\"}, {\"id\": 22032, \"name\": \"a black and yellow flag\"}, {\"id\": 22033, \"name\": \"white fan\"}, {\"id\": 22034, \"name\": \"green tube\"}, {\"id\": 22035, \"name\": \"the dark-colored t-shirt\"}, {\"id\": 22036, \"name\": \"beige umbrella\"}, {\"id\": 22037, \"name\": \"a large white roll of material\"}, {\"id\": 22038, \"name\": \"the small bicycle\"}, {\"id\": 22039, \"name\": \"the pink and white fish\"}, {\"id\": 22040, \"name\": \"lemon print\"}, {\"id\": 22041, \"name\": \"the brown barrel\"}, {\"id\": 22042, \"name\": \"plastic vegetable\"}, {\"id\": 22043, \"name\": \"the large wooden sign\"}, {\"id\": 22044, \"name\": \"a truck's headlights\"}, {\"id\": 22045, \"name\": \"sleek white, blue, and red train\"}, {\"id\": 22046, \"name\": \"the parked scooter\"}, {\"id\": 22047, \"name\": \"the round mirror\"}, {\"id\": 22048, \"name\": \"a green car\"}, {\"id\": 22049, \"name\": \"the blue recycling bin\"}, {\"id\": 22050, \"name\": \"an individual's arm\"}, {\"id\": 22051, \"name\": \"a yacht\"}, {\"id\": 22052, \"name\": \"a truck's front bumper\"}, {\"id\": 22053, \"name\": \"the red long-sleeved button-up shirt\"}, {\"id\": 22054, \"name\": \"black tactical uniform\"}, {\"id\": 22055, \"name\": \"a black sweatshirt\"}, {\"id\": 22056, \"name\": \"rv\"}, {\"id\": 22057, \"name\": \"a lush tropical vegetation\"}, {\"id\": 22058, \"name\": \"a gray bowl\"}, {\"id\": 22059, \"name\": \"white short with a black and white pattern\"}, {\"id\": 22060, \"name\": \"distinctive yellow baseball cap\"}, {\"id\": 22061, \"name\": \"the blue conveyor belt\"}, {\"id\": 22062, \"name\": \"a child's hands\"}, {\"id\": 22063, \"name\": \"a drummer's hand\"}, {\"id\": 22064, \"name\": \"the reddish-brown body\"}, {\"id\": 22065, \"name\": \"the bright red pillar\"}, {\"id\": 22066, \"name\": \"brown basket\"}, {\"id\": 22067, \"name\": \"light blue sweatshirt\"}, {\"id\": 22068, \"name\": \"the green sock\"}, {\"id\": 22069, \"name\": \"the police suv\"}, {\"id\": 22070, \"name\": \"thick, bright yellow link\"}, {\"id\": 22071, \"name\": \"a blue handle\"}, {\"id\": 22072, \"name\": \"a sea urchin\"}, {\"id\": 22073, \"name\": \"a pink sign\"}, {\"id\": 22074, \"name\": \"a man in a white shirt\"}, {\"id\": 22075, \"name\": \"wire mesh\"}, {\"id\": 22076, \"name\": \"low concrete step\"}, {\"id\": 22077, \"name\": \"a black marker\"}, {\"id\": 22078, \"name\": \"a firetruck\"}, {\"id\": 22079, \"name\": \"a yellow glass object\"}, {\"id\": 22080, \"name\": \"construction equipment\"}, {\"id\": 22081, \"name\": \"largest crab\"}, {\"id\": 22082, \"name\": \"black bench\"}, {\"id\": 22083, \"name\": \"overhead microphone\"}, {\"id\": 22084, \"name\": \"the vibrant blue dress\"}, {\"id\": 22085, \"name\": \"the rake\"}, {\"id\": 22086, \"name\": \"a large dragon puppet\"}, {\"id\": 22087, \"name\": \"the barber\"}, {\"id\": 22088, \"name\": \"patterned curtain\"}, {\"id\": 22089, \"name\": \"black mary jane shoe\"}, {\"id\": 22090, \"name\": \"muddy puddle\"}, {\"id\": 22091, \"name\": \"metal platform\"}, {\"id\": 22092, \"name\": \"white steeple\"}, {\"id\": 22093, \"name\": \"the man in a green shirt\"}, {\"id\": 22094, \"name\": \"central door\"}, {\"id\": 22095, \"name\": \"deer antler design\"}, {\"id\": 22096, \"name\": \"a covered roof\"}, {\"id\": 22097, \"name\": \"the yellow bollard\"}, {\"id\": 22098, \"name\": \"a large metal pipe\"}, {\"id\": 22099, \"name\": \"the yellow piece of equipment\"}, {\"id\": 22100, \"name\": \"the black and silver motorcycle\"}, {\"id\": 22101, \"name\": \"a mop head\"}, {\"id\": 22102, \"name\": \"cartoonish image of a cloud\"}, {\"id\": 22103, \"name\": \"the black fish\"}, {\"id\": 22104, \"name\": \"vast expanse of the ocean\"}, {\"id\": 22105, \"name\": \"the stage or platform\"}, {\"id\": 22106, \"name\": \"curved facade\"}, {\"id\": 22107, \"name\": \"a dome roof\"}, {\"id\": 22108, \"name\": \"the yellow machine\"}, {\"id\": 22109, \"name\": \"the semi-truck\"}, {\"id\": 22110, \"name\": \"a brick base\"}, {\"id\": 22111, \"name\": \"gray metal beam\"}, {\"id\": 22112, \"name\": \"central table\"}, {\"id\": 22113, \"name\": \"a man's yellow t-shirt\"}, {\"id\": 22114, \"name\": \"a stamina bar\"}, {\"id\": 22115, \"name\": \"blue basket\"}, {\"id\": 22116, \"name\": \"ornate decoration\"}, {\"id\": 22117, \"name\": \"a red backpack\"}, {\"id\": 22118, \"name\": \"a large potted plant\"}, {\"id\": 22119, \"name\": \"the prominent red cable or tube\"}, {\"id\": 22120, \"name\": \"a back of their head\"}, {\"id\": 22121, \"name\": \"the rubble\"}, {\"id\": 22122, \"name\": \"woman in a pink hijab\"}, {\"id\": 22123, \"name\": \"section of red carpet\"}, {\"id\": 22124, \"name\": \"the car's wheel\"}, {\"id\": 22125, \"name\": \"large, transparent beach ball\"}, {\"id\": 22126, \"name\": \"the Qatar Airways logo\"}, {\"id\": 22127, \"name\": \"distinctive red chef's hat\"}, {\"id\": 22128, \"name\": \"the large barrel\"}, {\"id\": 22129, \"name\": \"a light-colored hardwood\"}, {\"id\": 22130, \"name\": \"a station's roof\"}, {\"id\": 22131, \"name\": \"sandy arena\"}, {\"id\": 22132, \"name\": \"prominent pink toy car\"}, {\"id\": 22133, \"name\": \"the tv screen\"}, {\"id\": 22134, \"name\": \"red-tiled roof\"}, {\"id\": 22135, \"name\": \"large, red, box-like object\"}, {\"id\": 22136, \"name\": \"a tall, white light pole\"}, {\"id\": 22137, \"name\": \"a spoon's handle\"}, {\"id\": 22138, \"name\": \"a beehive\"}, {\"id\": 22139, \"name\": \"large, ornate dragon head\"}, {\"id\": 22140, \"name\": \"beige mat\"}, {\"id\": 22141, \"name\": \"the large white plastic bag\"}, {\"id\": 22142, \"name\": \"the green and white tuk-tuk\"}, {\"id\": 22143, \"name\": \"the black wire\"}, {\"id\": 22144, \"name\": \"yellow raft\"}, {\"id\": 22145, \"name\": \"the large red flag\"}, {\"id\": 22146, \"name\": \"a cement mixer truck\"}, {\"id\": 22147, \"name\": \"the yellow floatie\"}, {\"id\": 22148, \"name\": \"the garbage can\"}, {\"id\": 22149, \"name\": \"the gun icon\"}, {\"id\": 22150, \"name\": \"child in a white tank top\"}, {\"id\": 22151, \"name\": \"large pikachu character\"}, {\"id\": 22152, \"name\": \"costumed adult\"}, {\"id\": 22153, \"name\": \"the white and red car\"}, {\"id\": 22154, \"name\": \"the long fluorescent light\"}, {\"id\": 22155, \"name\": \"blind\"}, {\"id\": 22156, \"name\": \"a tall, pointed spire\"}, {\"id\": 22157, \"name\": \"white flower design\"}, {\"id\": 22158, \"name\": \"white boat\"}, {\"id\": 22159, \"name\": \"the black flag\"}, {\"id\": 22160, \"name\": \"the model airplane\"}, {\"id\": 22161, \"name\": \"blue rectangle\"}, {\"id\": 22162, \"name\": \"a rear of the vehicle\"}, {\"id\": 22163, \"name\": \"futuristic-looking scooter\"}, {\"id\": 22164, \"name\": \"a white pillow\"}, {\"id\": 22165, \"name\": \"the gearshift\"}, {\"id\": 22166, \"name\": \"the bright orange and yellow garment\"}, {\"id\": 22167, \"name\": \"the brown leather jacket\"}, {\"id\": 22168, \"name\": \"the snow pant\"}, {\"id\": 22169, \"name\": \"the red bas drum\"}, {\"id\": 22170, \"name\": \"canopy tent\"}, {\"id\": 22171, \"name\": \"red traffic cone\"}, {\"id\": 22172, \"name\": \"a blue vest\"}, {\"id\": 22173, \"name\": \"a woman in bikini\"}, {\"id\": 22174, \"name\": \"large hill\"}, {\"id\": 22175, \"name\": \"an aloe vera\"}, {\"id\": 22176, \"name\": \"the large purple planet\"}, {\"id\": 22177, \"name\": \"the white lace top\"}, {\"id\": 22178, \"name\": \"a small orange cone\"}, {\"id\": 22179, \"name\": \"a vibrant floral shirt\"}, {\"id\": 22180, \"name\": \"the long-handled tool\"}, {\"id\": 22181, \"name\": \"rickshaw\"}, {\"id\": 22182, \"name\": \"sleek, glass exterior\"}, {\"id\": 22183, \"name\": \"the floating dock\"}, {\"id\": 22184, \"name\": \"ornate gold accent\"}, {\"id\": 22185, \"name\": \"the striking red lighthouse\"}, {\"id\": 22186, \"name\": \"a large black storage box\"}, {\"id\": 22187, \"name\": \"the sheet of metal\"}, {\"id\": 22188, \"name\": \"the piece of slate\"}, {\"id\": 22189, \"name\": \"the striking pink dress\"}, {\"id\": 22190, \"name\": \"large black bowl\"}, {\"id\": 22191, \"name\": \"a plastic bag of food\"}, {\"id\": 22192, \"name\": \"white heart-shaped sparkle\"}, {\"id\": 22193, \"name\": \"the bell tower\"}, {\"id\": 22194, \"name\": \"gold-colored roof\"}, {\"id\": 22195, \"name\": \"the yellow package\"}, {\"id\": 22196, \"name\": \"gray couch\"}, {\"id\": 22197, \"name\": \"a yellow motor\"}, {\"id\": 22198, \"name\": \"large glass door\"}, {\"id\": 22199, \"name\": \"blue yoga mat\"}, {\"id\": 22200, \"name\": \"the statue of a man on horseback\"}, {\"id\": 22201, \"name\": \"wheelbarrow\"}, {\"id\": 22202, \"name\": \"the cargo\"}, {\"id\": 22203, \"name\": \"race car\"}, {\"id\": 22204, \"name\": \"large green bowl\"}, {\"id\": 22205, \"name\": \"blue flag\"}, {\"id\": 22206, \"name\": \"a cartoon dog\"}, {\"id\": 22207, \"name\": \"a tan seat\"}, {\"id\": 22208, \"name\": \"a healthcare professional\"}, {\"id\": 22209, \"name\": \"a yellow clothing\"}, {\"id\": 22210, \"name\": \"a rusty corrugated metal\"}, {\"id\": 22211, \"name\": \"the dark red coat\"}, {\"id\": 22212, \"name\": \"a black bridle\"}, {\"id\": 22213, \"name\": \"the car's windshield\"}, {\"id\": 22214, \"name\": \"the white outlet\"}, {\"id\": 22215, \"name\": \"black game controller\"}, {\"id\": 22216, \"name\": \"a dark blue and orange plumage\"}, {\"id\": 22217, \"name\": \"a white and blue adida shirt\"}, {\"id\": 22218, \"name\": \"red reflector\"}, {\"id\": 22219, \"name\": \"paragliding equipment\"}, {\"id\": 22220, \"name\": \"a driver or passenger\"}, {\"id\": 22221, \"name\": \"the yellow shuttered storefront\"}, {\"id\": 22222, \"name\": \"the surveying equipment\"}, {\"id\": 22223, \"name\": \"a red residue\"}, {\"id\": 22224, \"name\": \"purple and white shell\"}, {\"id\": 22225, \"name\": \"peace sign\"}, {\"id\": 22226, \"name\": \"flip flop\"}, {\"id\": 22227, \"name\": \"a red bicycle\"}, {\"id\": 22228, \"name\": \"a black and white outfit\"}, {\"id\": 22229, \"name\": \"inner forearm\"}, {\"id\": 22230, \"name\": \"the red and black motorcycle\"}, {\"id\": 22231, \"name\": \"an orange hat\"}, {\"id\": 22232, \"name\": \"green hijab\"}, {\"id\": 22233, \"name\": \"white napkin\"}, {\"id\": 22234, \"name\": \"the gray suv\"}, {\"id\": 22235, \"name\": \"large black speaker\"}, {\"id\": 22236, \"name\": \"the red tank top\"}, {\"id\": 22237, \"name\": \"the lush green area\"}, {\"id\": 22238, \"name\": \"the prominent blue sign\"}, {\"id\": 22239, \"name\": \"black light fixture\"}, {\"id\": 22240, \"name\": \"the red honda banner\"}, {\"id\": 22241, \"name\": \"black coleman brand bag\"}, {\"id\": 22242, \"name\": \"the red and white striped curbing\"}, {\"id\": 22243, \"name\": \"the long, large truck\"}, {\"id\": 22244, \"name\": \"red and yellow structure\"}, {\"id\": 22245, \"name\": \"an orange foot\"}, {\"id\": 22246, \"name\": \"the red harness\"}, {\"id\": 22247, \"name\": \"a black bowl\"}, {\"id\": 22248, \"name\": \"pink floatie\"}, {\"id\": 22249, \"name\": \"a prominent race track\"}, {\"id\": 22250, \"name\": \"a large drum\"}, {\"id\": 22251, \"name\": \"the vehicle's dashboard\"}, {\"id\": 22252, \"name\": \"a santa hat\"}, {\"id\": 22253, \"name\": \"nut\"}, {\"id\": 22254, \"name\": \"the large metal bowl\"}, {\"id\": 22255, \"name\": \"the intricately patterned skirt\"}, {\"id\": 22256, \"name\": \"driver's window\"}, {\"id\": 22257, \"name\": \"a large wooden pillar\"}, {\"id\": 22258, \"name\": \"yellow metal structure\"}, {\"id\": 22259, \"name\": \"black-and-white blouse\"}, {\"id\": 22260, \"name\": \"the camouflage baseball cap\"}, {\"id\": 22261, \"name\": \"a brick structure\"}, {\"id\": 22262, \"name\": \"the small, rectangular object\"}, {\"id\": 22263, \"name\": \"the drone\"}, {\"id\": 22264, \"name\": \"the white and black polka-dot top\"}, {\"id\": 22265, \"name\": \"an ornamental topiary\"}, {\"id\": 22266, \"name\": \"the blue legging\"}, {\"id\": 22267, \"name\": \"a person in a red jacket\"}, {\"id\": 22268, \"name\": \"the pixelated icon\"}, {\"id\": 22269, \"name\": \"the yellow steering wheel\"}, {\"id\": 22270, \"name\": \"a music sheet\"}, {\"id\": 22271, \"name\": \"a red sash\"}, {\"id\": 22272, \"name\": \"the large blue and yellow truck\"}, {\"id\": 22273, \"name\": \"the green boot\"}, {\"id\": 22274, \"name\": \"a yellow crane\"}, {\"id\": 22275, \"name\": \"reflective glass facade\"}, {\"id\": 22276, \"name\": \"the rainbow-colored glow stick\"}, {\"id\": 22277, \"name\": \"the red floppy hat\"}, {\"id\": 22278, \"name\": \"a white croc\"}, {\"id\": 22279, \"name\": \"a tall gray pole\"}, {\"id\": 22280, \"name\": \"a festive decoration\"}, {\"id\": 22281, \"name\": \"red and white drum\"}, {\"id\": 22282, \"name\": \"the Iron Man's arm\"}, {\"id\": 22283, \"name\": \"the large rope\"}, {\"id\": 22284, \"name\": \"turkey\"}, {\"id\": 22285, \"name\": \"statue of a man\"}, {\"id\": 22286, \"name\": \"pink tutu\"}, {\"id\": 22287, \"name\": \"the woman holding a shopping bag\"}, {\"id\": 22288, \"name\": \"the small white cup\"}, {\"id\": 22289, \"name\": \"a circular pavilion\"}, {\"id\": 22290, \"name\": \"braids\"}, {\"id\": 22291, \"name\": \"the blue tail\"}, {\"id\": 22292, \"name\": \"the blue bracelet\"}, {\"id\": 22293, \"name\": \"the rectangular vent\"}, {\"id\": 22294, \"name\": \"the person in traditional clothing\"}, {\"id\": 22295, \"name\": \"the green elastic band\"}, {\"id\": 22296, \"name\": \"red t-shirt with white lettering\"}, {\"id\": 22297, \"name\": \"green top\"}, {\"id\": 22298, \"name\": \"the black handrail\"}, {\"id\": 22299, \"name\": \"a metal tool\"}, {\"id\": 22300, \"name\": \"large puddle\"}, {\"id\": 22301, \"name\": \"black cubicle\"}, {\"id\": 22302, \"name\": \"a yellow toy\"}, {\"id\": 22303, \"name\": \"a blue scrub\"}, {\"id\": 22304, \"name\": \"mushroom\"}, {\"id\": 22305, \"name\": \"the purple trim\"}, {\"id\": 22306, \"name\": \"a beige short\"}, {\"id\": 22307, \"name\": \"large blue sign\"}, {\"id\": 22308, \"name\": \"a mini-map\"}, {\"id\": 22309, \"name\": \"small music stand\"}, {\"id\": 22310, \"name\": \"the cooking utensil\"}, {\"id\": 22311, \"name\": \"the bowl of food\"}, {\"id\": 22312, \"name\": \"the cement mixer\"}, {\"id\": 22313, \"name\": \"the black lamppost\"}, {\"id\": 22314, \"name\": \"BMW vehicle\"}, {\"id\": 22315, \"name\": \"the white hijab\"}, {\"id\": 22316, \"name\": \"the blue section\"}, {\"id\": 22317, \"name\": \"corrugated roof\"}, {\"id\": 22318, \"name\": \"a man in a plaid shirt and gray pant\"}, {\"id\": 22319, \"name\": \"gold dragon costume\"}, {\"id\": 22320, \"name\": \"crab's shell\"}, {\"id\": 22321, \"name\": \"teacher\"}, {\"id\": 22322, \"name\": \"the player's avatar\"}, {\"id\": 22323, \"name\": \"a large, intricately designed gold bowl\"}, {\"id\": 22324, \"name\": \"a large planter\"}, {\"id\": 22325, \"name\": \"bright reflection\"}, {\"id\": 22326, \"name\": \"a light blue short-sleeved shirt\"}, {\"id\": 22327, \"name\": \"the black metal stool\"}, {\"id\": 22328, \"name\": \"small drum\"}, {\"id\": 22329, \"name\": \"the red headband\"}, {\"id\": 22330, \"name\": \"a dragon's head\"}, {\"id\": 22331, \"name\": \"the large, green, circular symbol\"}, {\"id\": 22332, \"name\": \"red toy shaped like a chili pepper\"}, {\"id\": 22333, \"name\": \"the tan baseball cap\"}, {\"id\": 22334, \"name\": \"the bright yellow romper\"}, {\"id\": 22335, \"name\": \"the red motorbike\"}, {\"id\": 22336, \"name\": \"a blue arm of the excavator\"}, {\"id\": 22337, \"name\": \"tv stand\"}, {\"id\": 22338, \"name\": \"dark blue pant\"}, {\"id\": 22339, \"name\": \"semi-trucks\"}, {\"id\": 22340, \"name\": \"antler\"}, {\"id\": 22341, \"name\": \"a green truck\"}, {\"id\": 22342, \"name\": \"a white coral\"}, {\"id\": 22343, \"name\": \"the large statue of a person with horn and a long robe\"}, {\"id\": 22344, \"name\": \"motorcycle rider's side mirror\"}, {\"id\": 22345, \"name\": \"black sleeveless dress\"}, {\"id\": 22346, \"name\": \"the rider's helmet\"}, {\"id\": 22347, \"name\": \"a blue creature\"}, {\"id\": 22348, \"name\": \"the traditional islamic clothing\"}, {\"id\": 22349, \"name\": \"a gray bollard\"}, {\"id\": 22350, \"name\": \"the life preserver\"}, {\"id\": 22351, \"name\": \"the yellow canopy\"}, {\"id\": 22352, \"name\": \"maroon dress\"}, {\"id\": 22353, \"name\": \"the brown cue\"}, {\"id\": 22354, \"name\": \"a grey bag\"}, {\"id\": 22355, \"name\": \"church or cathedral\"}, {\"id\": 22356, \"name\": \"white bag strap\"}, {\"id\": 22357, \"name\": \"green sign with chinese character\"}, {\"id\": 22358, \"name\": \"hay or grass\"}, {\"id\": 22359, \"name\": \"purple sneaker\"}, {\"id\": 22360, \"name\": \"a red name tag\"}, {\"id\": 22361, \"name\": \"black athletic attire\"}, {\"id\": 22362, \"name\": \"dark blue backpack\"}, {\"id\": 22363, \"name\": \"a blade of the stick\"}, {\"id\": 22364, \"name\": \"the small blue roof\"}, {\"id\": 22365, \"name\": \"the large black statue\"}, {\"id\": 22366, \"name\": \"red canopy\"}, {\"id\": 22367, \"name\": \"a colorful train car\"}, {\"id\": 22368, \"name\": \"a sign for the tenga shop\"}, {\"id\": 22369, \"name\": \"the red shipping container\"}, {\"id\": 22370, \"name\": \"a chinese temple\"}, {\"id\": 22371, \"name\": \"the calendar\"}, {\"id\": 22372, \"name\": \"blue, pixelated character\"}, {\"id\": 22373, \"name\": \"a red health bar\"}, {\"id\": 22374, \"name\": \"the closed metal shutter\"}, {\"id\": 22375, \"name\": \"snowboarding\"}, {\"id\": 22376, \"name\": \"a blue surface\"}, {\"id\": 22377, \"name\": \"a truck's cab\"}, {\"id\": 22378, \"name\": \"red support beam\"}, {\"id\": 22379, \"name\": \"the paint can\"}, {\"id\": 22380, \"name\": \"a large screen tv\"}, {\"id\": 22381, \"name\": \"the grassy section\"}, {\"id\": 22382, \"name\": \"a light blue bowl\"}, {\"id\": 22383, \"name\": \"the smaller, gold-colored pot\"}, {\"id\": 22384, \"name\": \"a window with a black frame\"}, {\"id\": 22385, \"name\": \"a short-sleeved button-down shirt\"}, {\"id\": 22386, \"name\": \"the small red light\"}, {\"id\": 22387, \"name\": \"blue diamond-shaped icon\"}, {\"id\": 22388, \"name\": \"the tan hat\"}, {\"id\": 22389, \"name\": \"small white icon of a person\"}, {\"id\": 22390, \"name\": \"a male runner\"}, {\"id\": 22391, \"name\": \"a large, red and white ride\"}, {\"id\": 22392, \"name\": \"the light-colored goat\"}, {\"id\": 22393, \"name\": \"herringbone pattern\"}, {\"id\": 22394, \"name\": \"a green hose\"}, {\"id\": 22395, \"name\": \"the children's hand\"}, {\"id\": 22396, \"name\": \"large, dark gray square\"}, {\"id\": 22397, \"name\": \"a large, white, cylindrical structure\"}, {\"id\": 22398, \"name\": \"a yellow and black shirt\"}, {\"id\": 22399, \"name\": \"large, rusty tractor wheel\"}, {\"id\": 22400, \"name\": \"the small gray and white cat\"}, {\"id\": 22401, \"name\": \"the veil\"}, {\"id\": 22402, \"name\": \"a black and yellow ear\"}, {\"id\": 22403, \"name\": \"passenger's arm\"}, {\"id\": 22404, \"name\": \"the black cylindrical object\"}, {\"id\": 22405, \"name\": \"a pink long-sleeve shirt\"}, {\"id\": 22406, \"name\": \"blue vest\"}, {\"id\": 22407, \"name\": \"a gray overpass\"}, {\"id\": 22408, \"name\": \"the black furry boot\"}, {\"id\": 22409, \"name\": \"black spoiler\"}, {\"id\": 22410, \"name\": \"a rugged, four-wheel drive vehicle\"}, {\"id\": 22411, \"name\": \"a pink tag or badge\"}, {\"id\": 22412, \"name\": \"an adult woman\"}, {\"id\": 22413, \"name\": \"the man's hat\"}, {\"id\": 22414, \"name\": \"a small step\"}, {\"id\": 22415, \"name\": \"a small garden\"}, {\"id\": 22416, \"name\": \"a dark blue baseball cap\"}, {\"id\": 22417, \"name\": \"blue boat\"}, {\"id\": 22418, \"name\": \"a cowboy hat\"}, {\"id\": 22419, \"name\": \"the yellow ramp\"}, {\"id\": 22420, \"name\": \"shark's mouth\"}, {\"id\": 22421, \"name\": \"exposed pipe\"}, {\"id\": 22422, \"name\": \"penguin-shaped ride-on toy\"}, {\"id\": 22423, \"name\": \"curved horn\"}, {\"id\": 22424, \"name\": \"the dark blue backpack\"}, {\"id\": 22425, \"name\": \"a white headband\"}, {\"id\": 22426, \"name\": \"yellow sneaker\"}, {\"id\": 22427, \"name\": \"gearshift\"}, {\"id\": 22428, \"name\": \"mirror reflection\"}, {\"id\": 22429, \"name\": \"a paper decoration\"}, {\"id\": 22430, \"name\": \"the gray leggings\"}, {\"id\": 22431, \"name\": \"a rider's bike\"}, {\"id\": 22432, \"name\": \"the white chef's hat\"}, {\"id\": 22433, \"name\": \"a tall white boot\"}, {\"id\": 22434, \"name\": \"the blue puffer jacket\"}, {\"id\": 22435, \"name\": \"broccoli\"}, {\"id\": 22436, \"name\": \"a pink top\"}, {\"id\": 22437, \"name\": \"red string\"}, {\"id\": 22438, \"name\": \"green, inflatable ride\"}, {\"id\": 22439, \"name\": \"a saddlebag\"}, {\"id\": 22440, \"name\": \"the jet ski\"}, {\"id\": 22441, \"name\": \"the white conical hat\"}, {\"id\": 22442, \"name\": \"the small white chicken\"}, {\"id\": 22443, \"name\": \"blue barrier\"}, {\"id\": 22444, \"name\": \"the red-and-white striped pole\"}, {\"id\": 22445, \"name\": \"a green headband\"}, {\"id\": 22446, \"name\": \"blue and white plaid shirt\"}, {\"id\": 22447, \"name\": \"a white head covering\"}, {\"id\": 22448, \"name\": \"a yellow exercise ball\"}, {\"id\": 22449, \"name\": \"a red polo shirt\"}, {\"id\": 22450, \"name\": \"white plaid pant\"}, {\"id\": 22451, \"name\": \"a straw hat\"}, {\"id\": 22452, \"name\": \"a large swimming pool\"}, {\"id\": 22453, \"name\": \"the boat's hull\"}, {\"id\": 22454, \"name\": \"the game's menu bar\"}, {\"id\": 22455, \"name\": \"black fan\"}, {\"id\": 22456, \"name\": \"blue swimsuit\"}, {\"id\": 22457, \"name\": \"a dark blue or black surface\"}, {\"id\": 22458, \"name\": \"chicken's head\"}, {\"id\": 22459, \"name\": \"yellow handle\"}, {\"id\": 22460, \"name\": \"small potted plant\"}, {\"id\": 22461, \"name\": \"a white bollard\"}, {\"id\": 22462, \"name\": \"a colorful traditional attire\"}, {\"id\": 22463, \"name\": \"a white painted crosswalk\"}, {\"id\": 22464, \"name\": \"the fire hydrant\"}, {\"id\": 22465, \"name\": \"the pink logo\"}, {\"id\": 22466, \"name\": \"a brick-patterned design\"}, {\"id\": 22467, \"name\": \"gold trim\"}, {\"id\": 22468, \"name\": \"partially obscured sign\"}, {\"id\": 22469, \"name\": \"large green object\"}, {\"id\": 22470, \"name\": \"large off-white drum\"}, {\"id\": 22471, \"name\": \"yellow stuffed animal\"}, {\"id\": 22472, \"name\": \"gray sneaker\"}, {\"id\": 22473, \"name\": \"red tractor\"}, {\"id\": 22474, \"name\": \"the large bas drum\"}, {\"id\": 22475, \"name\": \"the long green robe\"}, {\"id\": 22476, \"name\": \"piece of corrugated metal\"}, {\"id\": 22477, \"name\": \"collection of cardboard boxes\"}, {\"id\": 22478, \"name\": \"a white, red, and black jacket\"}, {\"id\": 22479, \"name\": \"a blade\"}, {\"id\": 22480, \"name\": \"double-decker vehicle\"}, {\"id\": 22481, \"name\": \"green and white sneaker\"}, {\"id\": 22482, \"name\": \"the sausage\"}, {\"id\": 22483, \"name\": \"a red and white headscarf\"}, {\"id\": 22484, \"name\": \"a traditional orange robe\"}, {\"id\": 22485, \"name\": \"the large yellow and grey metal container\"}, {\"id\": 22486, \"name\": \"balance board\"}, {\"id\": 22487, \"name\": \"a dark grey ceiling\"}, {\"id\": 22488, \"name\": \"a puck\"}, {\"id\": 22489, \"name\": \"blue rain jacket\"}, {\"id\": 22490, \"name\": \"a tow truck\"}, {\"id\": 22491, \"name\": \"a lane of traffic\"}, {\"id\": 22492, \"name\": \"white pipe structure\"}, {\"id\": 22493, \"name\": \"the metal beam\"}, {\"id\": 22494, \"name\": \"large, arched structure\"}, {\"id\": 22495, \"name\": \"the round yellow and white pin\"}, {\"id\": 22496, \"name\": \"the yellow bicycle\"}, {\"id\": 22497, \"name\": \"motorcycle's wheel\"}, {\"id\": 22498, \"name\": \"light brown dog\"}, {\"id\": 22499, \"name\": \"prominent digital billboard\"}, {\"id\": 22500, \"name\": \"the electric guitar\"}, {\"id\": 22501, \"name\": \"blue circle\"}, {\"id\": 22502, \"name\": \"the tall power pole\"}, {\"id\": 22503, \"name\": \"a green flag\"}, {\"id\": 22504, \"name\": \"a vibrant purple carpet\"}, {\"id\": 22505, \"name\": \"pink bow\"}, {\"id\": 22506, \"name\": \"large ferrari logo\"}, {\"id\": 22507, \"name\": \"the matching red jacket\"}, {\"id\": 22508, \"name\": \"the white metal structure\"}, {\"id\": 22509, \"name\": \"a flat-screen tv\"}, {\"id\": 22510, \"name\": \"yellow base\"}, {\"id\": 22511, \"name\": \"a green bin\"}, {\"id\": 22512, \"name\": \"orange scarf\"}, {\"id\": 22513, \"name\": \"dark-colored swimsuit\"}, {\"id\": 22514, \"name\": \"an orange orb\"}, {\"id\": 22515, \"name\": \"diorama\"}, {\"id\": 22516, \"name\": \"the person in an orange safety vest\"}, {\"id\": 22517, \"name\": \"the orange tank top\"}, {\"id\": 22518, \"name\": \"the purple and yellow trim\"}, {\"id\": 22519, \"name\": \"reception desk\"}, {\"id\": 22520, \"name\": \"the gray sign\"}, {\"id\": 22521, \"name\": \"the large dog\"}, {\"id\": 22522, \"name\": \"a tall basketball hoop\"}, {\"id\": 22523, \"name\": \"the blue blade\"}, {\"id\": 22524, \"name\": \"the green-framed window\"}, {\"id\": 22525, \"name\": \"beige, triangular overpass\"}, {\"id\": 22526, \"name\": \"a car's dashboard\"}, {\"id\": 22527, \"name\": \"the neon yellow and black jacket\"}, {\"id\": 22528, \"name\": \"the rollerblade\"}, {\"id\": 22529, \"name\": \"glass display case\"}, {\"id\": 22530, \"name\": \"a man in a yellow vest\"}, {\"id\": 22531, \"name\": \"the beige shirt\"}, {\"id\": 22532, \"name\": \"the oversized bow\"}, {\"id\": 22533, \"name\": \"distinctive brown patterned hat\"}, {\"id\": 22534, \"name\": \"the dark-colored vehicle\"}, {\"id\": 22535, \"name\": \"grey vest\"}, {\"id\": 22536, \"name\": \"a red, white, and blue striped pole\"}, {\"id\": 22537, \"name\": \"a blue latex glove\"}, {\"id\": 22538, \"name\": \"white rowboat\"}, {\"id\": 22539, \"name\": \"green costume\"}, {\"id\": 22540, \"name\": \"large disco ball\"}, {\"id\": 22541, \"name\": \"a heavy-duty machine\"}, {\"id\": 22542, \"name\": \"the pink bicycle\"}, {\"id\": 22543, \"name\": \"a round black knob\"}, {\"id\": 22544, \"name\": \"the blue swim trunks\"}, {\"id\": 22545, \"name\": \"the black snake\"}, {\"id\": 22546, \"name\": \"a tan backpack strap\"}, {\"id\": 22547, \"name\": \"black nozzle\"}, {\"id\": 22548, \"name\": \"a small pendant\"}, {\"id\": 22549, \"name\": \"circular sign\"}, {\"id\": 22550, \"name\": \"green billboard\"}, {\"id\": 22551, \"name\": \"yellow shoe\"}, {\"id\": 22552, \"name\": \"large, curved marble planter\"}, {\"id\": 22553, \"name\": \"the large white house\"}, {\"id\": 22554, \"name\": \"truck's cab\"}, {\"id\": 22555, \"name\": \"the yellow traffic light\"}, {\"id\": 22556, \"name\": \"a traditional thai headdress\"}, {\"id\": 22557, \"name\": \"red brick structure\"}, {\"id\": 22558, \"name\": \"a small, plastic basketball hoop\"}, {\"id\": 22559, \"name\": \"a vibrant red tram\"}, {\"id\": 22560, \"name\": \"a tuk-tuks\"}, {\"id\": 22561, \"name\": \"the smaller raft\"}, {\"id\": 22562, \"name\": \"the red and orange striped sash\"}, {\"id\": 22563, \"name\": \"the small grey car\"}, {\"id\": 22564, \"name\": \"the sailboat\"}, {\"id\": 22565, \"name\": \"tennis ball\"}, {\"id\": 22566, \"name\": \"purple button with a square\"}, {\"id\": 22567, \"name\": \"green wheelbarrow\"}, {\"id\": 22568, \"name\": \"the red tinsel\"}, {\"id\": 22569, \"name\": \"the black-and-white striped shirt\"}, {\"id\": 22570, \"name\": \"the yellow plastic container\"}, {\"id\": 22571, \"name\": \"the large, yellow, cartoonish head\"}, {\"id\": 22572, \"name\": \"light-colored wooden drum\"}, {\"id\": 22573, \"name\": \"juice box\"}, {\"id\": 22574, \"name\": \"the green metal object\"}, {\"id\": 22575, \"name\": \"the person's bare leg\"}, {\"id\": 22576, \"name\": \"the stainles steel counter\"}, {\"id\": 22577, \"name\": \"light-colored car\"}, {\"id\": 22578, \"name\": \"the large orange banner\"}, {\"id\": 22579, \"name\": \"a small icon\"}, {\"id\": 22580, \"name\": \"a blue uniform with white lettering\"}, {\"id\": 22581, \"name\": \"a large yellow and black dump truck\"}, {\"id\": 22582, \"name\": \"white fabric\"}, {\"id\": 22583, \"name\": \"a carnival ride\"}, {\"id\": 22584, \"name\": \"the white fish\"}, {\"id\": 22585, \"name\": \"marine life\"}, {\"id\": 22586, \"name\": \"ox\"}, {\"id\": 22587, \"name\": \"the flat, green roof\"}, {\"id\": 22588, \"name\": \"the colorful, multi-level slide\"}, {\"id\": 22589, \"name\": \"black and white polka-dot blouse\"}, {\"id\": 22590, \"name\": \"the passenger-side window\"}, {\"id\": 22591, \"name\": \"large ferri wheel\"}, {\"id\": 22592, \"name\": \"a brown and black species\"}, {\"id\": 22593, \"name\": \"referee\"}, {\"id\": 22594, \"name\": \"a row of white tents\"}, {\"id\": 22595, \"name\": \"storefront window\"}, {\"id\": 22596, \"name\": \"the brown and black cabinet\"}, {\"id\": 22597, \"name\": \"navy blue long-sleeved shirt\"}, {\"id\": 22598, \"name\": \"the person in the yellow shirt\"}, {\"id\": 22599, \"name\": \"the stringed instrument\"}, {\"id\": 22600, \"name\": \"a large pan\"}, {\"id\": 22601, \"name\": \"red top with a hello kitty design\"}, {\"id\": 22602, \"name\": \"small tan cardboard box\"}, {\"id\": 22603, \"name\": \"a black crate\"}, {\"id\": 22604, \"name\": \"rider's leg\"}, {\"id\": 22605, \"name\": \"the straw hat\"}, {\"id\": 22606, \"name\": \"dark green long-sleeved shirt\"}, {\"id\": 22607, \"name\": \"the overturned vehicle\"}, {\"id\": 22608, \"name\": \"the green metal gate\"}, {\"id\": 22609, \"name\": \"a balding head\"}, {\"id\": 22610, \"name\": \"a white bowl of food\"}, {\"id\": 22611, \"name\": \"food stall\"}, {\"id\": 22612, \"name\": \"a yellow graphic\"}, {\"id\": 22613, \"name\": \"a grey surface\"}, {\"id\": 22614, \"name\": \"an excavator's bucket\"}, {\"id\": 22615, \"name\": \"a gray lid\"}, {\"id\": 22616, \"name\": \"white and black striped barber cape\"}, {\"id\": 22617, \"name\": \"red carpet\"}, {\"id\": 22618, \"name\": \"small white cap\"}, {\"id\": 22619, \"name\": \"a white dial\"}, {\"id\": 22620, \"name\": \"a racecar\"}, {\"id\": 22621, \"name\": \"yellow scarf\"}, {\"id\": 22622, \"name\": \"bright yellow slide\"}, {\"id\": 22623, \"name\": \"a tan headrest\"}, {\"id\": 22624, \"name\": \"the large pot of noodle\"}, {\"id\": 22625, \"name\": \"the pink lanyard\"}, {\"id\": 22626, \"name\": \"the red and black jacket\"}, {\"id\": 22627, \"name\": \"a prominent basketball hoop\"}, {\"id\": 22628, \"name\": \"a blue and yellow truck\"}, {\"id\": 22629, \"name\": \"black and red exercise ball\"}, {\"id\": 22630, \"name\": \"a gold-colored object\"}, {\"id\": 22631, \"name\": \"a musical toy\"}, {\"id\": 22632, \"name\": \"the tarmac of an airport\"}, {\"id\": 22633, \"name\": \"the smaller red bar\"}, {\"id\": 22634, \"name\": \"the green shirt sleeve\"}, {\"id\": 22635, \"name\": \"the conical hat\"}, {\"id\": 22636, \"name\": \"blue squiggle\"}, {\"id\": 22637, \"name\": \"a directional pad\"}, {\"id\": 22638, \"name\": \"sarong\"}, {\"id\": 22639, \"name\": \"the egg-shaped design\"}, {\"id\": 22640, \"name\": \"gp or radio display\"}, {\"id\": 22641, \"name\": \"a woman in a red headscarf\"}, {\"id\": 22642, \"name\": \"the boat's wake\"}, {\"id\": 22643, \"name\": \"tackled player\"}, {\"id\": 22644, \"name\": \"a horseback\"}, {\"id\": 22645, \"name\": \"cream-colored cushion\"}, {\"id\": 22646, \"name\": \"the red hatchback\"}, {\"id\": 22647, \"name\": \"the vehicle's infotainment system\"}, {\"id\": 22648, \"name\": \"a red and white soccer jersey\"}, {\"id\": 22649, \"name\": \"a truck's driver\"}, {\"id\": 22650, \"name\": \"the white short-sleeved top\"}, {\"id\": 22651, \"name\": \"blue and black collar\"}, {\"id\": 22652, \"name\": \"the orange kite\"}, {\"id\": 22653, \"name\": \"the red and orange sash\"}, {\"id\": 22654, \"name\": \"star of david\"}, {\"id\": 22655, \"name\": \"a colorful facade\"}, {\"id\": 22656, \"name\": \"striking red and white headdress\"}, {\"id\": 22657, \"name\": \"a wicker handle\"}, {\"id\": 22658, \"name\": \"blue cylindrical object\"}, {\"id\": 22659, \"name\": \"yellow and white tent-like structure\"}, {\"id\": 22660, \"name\": \"the metal clamp\"}, {\"id\": 22661, \"name\": \"the large yellow drum\"}, {\"id\": 22662, \"name\": \"small yellow slide\"}, {\"id\": 22663, \"name\": \"a magenta border\"}, {\"id\": 22664, \"name\": \"white post\"}, {\"id\": 22665, \"name\": \"a red ox cart\"}, {\"id\": 22666, \"name\": \"a dark cap\"}, {\"id\": 22667, \"name\": \"sliced fish ball\"}, {\"id\": 22668, \"name\": \"a large american flag\"}, {\"id\": 22669, \"name\": \"a bright yellow shirt\"}, {\"id\": 22670, \"name\": \"the orange block\"}, {\"id\": 22671, \"name\": \"white piping\"}, {\"id\": 22672, \"name\": \"blue vertical pole\"}, {\"id\": 22673, \"name\": \"green toy soldier\"}, {\"id\": 22674, \"name\": \"the blue and black plaid short\"}, {\"id\": 22675, \"name\": \"long-handled tool\"}, {\"id\": 22676, \"name\": \"single lane\"}, {\"id\": 22677, \"name\": \"a plaid sarong\"}, {\"id\": 22678, \"name\": \"the gray and white plumage\"}, {\"id\": 22679, \"name\": \"a woman in pink shirt\"}, {\"id\": 22680, \"name\": \"red and white striped barrier\"}, {\"id\": 22681, \"name\": \"the camouflage short\"}, {\"id\": 22682, \"name\": \"the prominent red and white shark\"}, {\"id\": 22683, \"name\": \"green swim vest\"}, {\"id\": 22684, \"name\": \"the large warehouse\"}, {\"id\": 22685, \"name\": \"the golden spire\"}, {\"id\": 22686, \"name\": \"a distinctive yellow stripe\"}, {\"id\": 22687, \"name\": \"the white face mask\"}, {\"id\": 22688, \"name\": \"the puffy white cloud\"}, {\"id\": 22689, \"name\": \"a red digital display\"}, {\"id\": 22690, \"name\": \"large red and white flag\"}, {\"id\": 22691, \"name\": \"a parked vehicle\"}, {\"id\": 22692, \"name\": \"the green dumbbell\"}, {\"id\": 22693, \"name\": \"the cobblestone driveway\"}, {\"id\": 22694, \"name\": \"the black shirt with yellow lettering\"}, {\"id\": 22695, \"name\": \"a blue cap\"}, {\"id\": 22696, \"name\": \"red and black race car\"}, {\"id\": 22697, \"name\": \"a load of yellow bag\"}, {\"id\": 22698, \"name\": \"a green tuk-tuk\"}, {\"id\": 22699, \"name\": \"the small stool\"}, {\"id\": 22700, \"name\": \"the purple sleeve\"}, {\"id\": 22701, \"name\": \"river bank\"}, {\"id\": 22702, \"name\": \"a large golden retriever\"}, {\"id\": 22703, \"name\": \"the green picnic table\"}, {\"id\": 22704, \"name\": \"the brown pole\"}, {\"id\": 22705, \"name\": \"the combine harvester\"}, {\"id\": 22706, \"name\": \"bright pink flag\"}, {\"id\": 22707, \"name\": \"the blue tuk-tuk\"}, {\"id\": 22708, \"name\": \"the red or gray hoodie\"}, {\"id\": 22709, \"name\": \"the blue and white flag\"}, {\"id\": 22710, \"name\": \"a yellow short\"}, {\"id\": 22711, \"name\": \"yellow taxi\"}, {\"id\": 22712, \"name\": \"the neon orange vest\"}, {\"id\": 22713, \"name\": \"a gray platform\"}, {\"id\": 22714, \"name\": \"prominent red pipe or tube\"}, {\"id\": 22715, \"name\": \"dark brickwork\"}, {\"id\": 22716, \"name\": \"a red barstool\"}, {\"id\": 22717, \"name\": \"red jockey\"}, {\"id\": 22718, \"name\": \"turquoise stripe\"}, {\"id\": 22719, \"name\": \"wooden fence post\"}, {\"id\": 22720, \"name\": \"the train platform\"}, {\"id\": 22721, \"name\": \"a green and white helmet\"}, {\"id\": 22722, \"name\": \"rider's motorcycle\"}, {\"id\": 22723, \"name\": \"a nascar\"}, {\"id\": 22724, \"name\": \"a white belly\"}, {\"id\": 22725, \"name\": \"a brown backpack strap\"}, {\"id\": 22726, \"name\": \"the silver chafing dish\"}, {\"id\": 22727, \"name\": \"a black plastic basin\"}, {\"id\": 22728, \"name\": \"small white ball\"}, {\"id\": 22729, \"name\": \"a packaging machine\"}, {\"id\": 22730, \"name\": \"a small, white icon\"}, {\"id\": 22731, \"name\": \"man dressed as santa clau\"}, {\"id\": 22732, \"name\": \"bowl of soup\"}, {\"id\": 22733, \"name\": \"the large monster truck\"}, {\"id\": 22734, \"name\": \"the man in the orange shirt\"}, {\"id\": 22735, \"name\": \"red shirt with a rainbow-colored design\"}, {\"id\": 22736, \"name\": \"a green strip\"}, {\"id\": 22737, \"name\": \"spearhead\"}, {\"id\": 22738, \"name\": \"a yacht or cruise ship\"}, {\"id\": 22739, \"name\": \"gray curtain\"}, {\"id\": 22740, \"name\": \"a large blue beam\"}, {\"id\": 22741, \"name\": \"pinata\"}, {\"id\": 22742, \"name\": \"the large, ornate wheel\"}, {\"id\": 22743, \"name\": \"a larger ship\"}, {\"id\": 22744, \"name\": \"light pink t-shirt\"}, {\"id\": 22745, \"name\": \"the present\"}, {\"id\": 22746, \"name\": \"a layer of white frosting\"}, {\"id\": 22747, \"name\": \"a herringbone pattern\"}, {\"id\": 22748, \"name\": \"large screen or banner\"}, {\"id\": 22749, \"name\": \"a mosque or community center\"}, {\"id\": 22750, \"name\": \"a striking pastel rainbow-colored tent\"}, {\"id\": 22751, \"name\": \"a red mark\"}, {\"id\": 22752, \"name\": \"the dome-shaped roof\"}, {\"id\": 22753, \"name\": \"the vehicle's headlights\"}, {\"id\": 22754, \"name\": \"map of the game's terrain\"}, {\"id\": 22755, \"name\": \"the large drum set\"}, {\"id\": 22756, \"name\": \"mud-covered tire\"}, {\"id\": 22757, \"name\": \"a large motorboat\"}, {\"id\": 22758, \"name\": \"a large stone archway\"}, {\"id\": 22759, \"name\": \"the black surface\"}, {\"id\": 22760, \"name\": \"a green produce\"}, {\"id\": 22761, \"name\": \"the large, black tire\"}, {\"id\": 22762, \"name\": \"green stool\"}, {\"id\": 22763, \"name\": \"a neon-colored pant\"}, {\"id\": 22764, \"name\": \"the river's surface\"}, {\"id\": 22765, \"name\": \"a warning symbol\"}, {\"id\": 22766, \"name\": \"the yellow surfboard\"}, {\"id\": 22767, \"name\": \"the indoor bumper car ride\"}, {\"id\": 22768, \"name\": \"the yellow garment\"}, {\"id\": 22769, \"name\": \"a black curtain\"}, {\"id\": 22770, \"name\": \"tall, white coral structure\"}, {\"id\": 22771, \"name\": \"the yellow cartoon face\"}, {\"id\": 22772, \"name\": \"a wire cage\"}, {\"id\": 22773, \"name\": \"a blue and white jersey\"}, {\"id\": 22774, \"name\": \"orange netting\"}, {\"id\": 22775, \"name\": \"large engine\"}, {\"id\": 22776, \"name\": \"a light blue rectangle\"}, {\"id\": 22777, \"name\": \"the green slipper\"}, {\"id\": 22778, \"name\": \"red and yellow flag\"}, {\"id\": 22779, \"name\": \"a red, white, and blue plastic clothespin\"}, {\"id\": 22780, \"name\": \"a pink and white backpack\"}, {\"id\": 22781, \"name\": \"the large green leaf\"}, {\"id\": 22782, \"name\": \"the blue hoodie\"}, {\"id\": 22783, \"name\": \"piece of machinery\"}, {\"id\": 22784, \"name\": \"a green shoe\"}, {\"id\": 22785, \"name\": \"light blue jean\"}, {\"id\": 22786, \"name\": \"white plastic tub\"}, {\"id\": 22787, \"name\": \"the car's rear window\"}, {\"id\": 22788, \"name\": \"the tan pig\"}, {\"id\": 22789, \"name\": \"a stove's control panel\"}, {\"id\": 22790, \"name\": \"the globe emblem\"}, {\"id\": 22791, \"name\": \"a green legging\"}, {\"id\": 22792, \"name\": \"dark blue sneaker\"}, {\"id\": 22793, \"name\": \"the large, round, yellow and red object\"}, {\"id\": 22794, \"name\": \"food item\"}, {\"id\": 22795, \"name\": \"white motorbike\"}, {\"id\": 22796, \"name\": \"large red umbrella\"}, {\"id\": 22797, \"name\": \"the numerou open umbrella\"}, {\"id\": 22798, \"name\": \"digital scoreboard\"}, {\"id\": 22799, \"name\": \"snowy decoration\"}, {\"id\": 22800, \"name\": \"a black knob\"}, {\"id\": 22801, \"name\": \"a brown baseboard\"}, {\"id\": 22802, \"name\": \"thin black metal fence\"}, {\"id\": 22803, \"name\": \"a tall, metal light pole\"}, {\"id\": 22804, \"name\": \"a red, white, and blue design\"}, {\"id\": 22805, \"name\": \"the TV screen\"}, {\"id\": 22806, \"name\": \"blue playground structure\"}, {\"id\": 22807, \"name\": \"white tuk-tuk\"}, {\"id\": 22808, \"name\": \"the red sweater\"}, {\"id\": 22809, \"name\": \"colorful, multi-colored kite\"}, {\"id\": 22810, \"name\": \"a blue butterfly wing\"}, {\"id\": 22811, \"name\": \"glowing blue orb\"}, {\"id\": 22812, \"name\": \"orange bandana\"}, {\"id\": 22813, \"name\": \"a large white bowl\"}, {\"id\": 22814, \"name\": \"the teacher\"}, {\"id\": 22815, \"name\": \"a red and white boat\"}, {\"id\": 22816, \"name\": \"the pink bus\"}, {\"id\": 22817, \"name\": \"bright orange robe\"}, {\"id\": 22818, \"name\": \"a sleek escalator\"}, {\"id\": 22819, \"name\": \"the blue elephant toy\"}, {\"id\": 22820, \"name\": \"the green soccer ball\"}, {\"id\": 22821, \"name\": \"a man in a green shirt\"}, {\"id\": 22822, \"name\": \"the white watch\"}, {\"id\": 22823, \"name\": \"black trim\"}, {\"id\": 22824, \"name\": \"blue and red forklift\"}, {\"id\": 22825, \"name\": \"a prominent fan\"}, {\"id\": 22826, \"name\": \"a brown barrel\"}, {\"id\": 22827, \"name\": \"a direction of traffic flow\"}, {\"id\": 22828, \"name\": \"blue plastic lid\"}, {\"id\": 22829, \"name\": \"a large tv screen\"}, {\"id\": 22830, \"name\": \"the black long-sleeve shirt\"}, {\"id\": 22831, \"name\": \"long, thin rope or branch\"}, {\"id\": 22832, \"name\": \"a green plant\"}, {\"id\": 22833, \"name\": \"the piece of cardboard or paper\"}, {\"id\": 22834, \"name\": \"the large red mushroom cap\"}, {\"id\": 22835, \"name\": \"the large fire truck\"}, {\"id\": 22836, \"name\": \"a teal-colored cabinet\"}, {\"id\": 22837, \"name\": \"large green pole\"}, {\"id\": 22838, \"name\": \"the blue and white bumper car\"}, {\"id\": 22839, \"name\": \"the white ice cream\"}, {\"id\": 22840, \"name\": \"blue plastic bench\"}, {\"id\": 22841, \"name\": \"pink headband\"}, {\"id\": 22842, \"name\": \"the large round mirror\"}, {\"id\": 22843, \"name\": \"the messy ponytail\"}, {\"id\": 22844, \"name\": \"the wire mesh fence\"}, {\"id\": 22845, \"name\": \"the blue and red sneaker\"}, {\"id\": 22846, \"name\": \"headgear\"}, {\"id\": 22847, \"name\": \"the volleyball\"}, {\"id\": 22848, \"name\": \"green camouflage t-shirt\"}, {\"id\": 22849, \"name\": \"bright white light\"}, {\"id\": 22850, \"name\": \"black and red racing glove\"}, {\"id\": 22851, \"name\": \"the circular attachment\"}, {\"id\": 22852, \"name\": \"blue chalkboard\"}, {\"id\": 22853, \"name\": \"a large, white, curved tube\"}, {\"id\": 22854, \"name\": \"a blue rollerblade\"}, {\"id\": 22855, \"name\": \"the gray cap\"}, {\"id\": 22856, \"name\": \"the red vest\"}, {\"id\": 22857, \"name\": \"the truck or tractor\"}, {\"id\": 22858, \"name\": \"the long yellow skirt\"}, {\"id\": 22859, \"name\": \"surrounding mountain\"}, {\"id\": 22860, \"name\": \"a green plastic handle\"}, {\"id\": 22861, \"name\": \"silver scooter\"}, {\"id\": 22862, \"name\": \"a white train\"}, {\"id\": 22863, \"name\": \"the large orange wheel\"}, {\"id\": 22864, \"name\": \"the large, purple and pink structure\"}, {\"id\": 22865, \"name\": \"red cross-body strap\"}, {\"id\": 22866, \"name\": \"the fire\"}, {\"id\": 22867, \"name\": \"red arrow\"}, {\"id\": 22868, \"name\": \"the orange flag\"}, {\"id\": 22869, \"name\": \"the rainbow-colored backpack\"}, {\"id\": 22870, \"name\": \"dark gray awning\"}, {\"id\": 22871, \"name\": \"rainbow-colored kite\"}, {\"id\": 22872, \"name\": \"blue tube\"}, {\"id\": 22873, \"name\": \"a lacrosse stick\"}, {\"id\": 22874, \"name\": \"the red and yellow balloon\"}, {\"id\": 22875, \"name\": \"the large engine\"}, {\"id\": 22876, \"name\": \"the black martial arts uniform\"}, {\"id\": 22877, \"name\": \"a silver vertical rod\"}, {\"id\": 22878, \"name\": \"sandbag\"}, {\"id\": 22879, \"name\": \"the circular bubble wand\"}, {\"id\": 22880, \"name\": \"black net\"}, {\"id\": 22881, \"name\": \"the group of spectators\"}, {\"id\": 22882, \"name\": \"lead motorcycle\"}, {\"id\": 22883, \"name\": \"the transparent plastic material\"}, {\"id\": 22884, \"name\": \"red item\"}, {\"id\": 22885, \"name\": \"a cluttered workbench\"}, {\"id\": 22886, \"name\": \"a gray cabinet\"}, {\"id\": 22887, \"name\": \"the metal workbench\"}, {\"id\": 22888, \"name\": \"orange and black jacket\"}, {\"id\": 22889, \"name\": \"a navy blue t-shirt\"}, {\"id\": 22890, \"name\": \"the red hose\"}, {\"id\": 22891, \"name\": \"the silver metal surface\"}, {\"id\": 22892, \"name\": \"the strip of green grass\"}, {\"id\": 22893, \"name\": \"driver's legs\"}, {\"id\": 22894, \"name\": \"the white lane marking\"}, {\"id\": 22895, \"name\": \"a plastic storage container\"}, {\"id\": 22896, \"name\": \"the large, covered grandstand\"}, {\"id\": 22897, \"name\": \"a gold foil label\"}, {\"id\": 22898, \"name\": \"an elevated highway bridge\"}, {\"id\": 22899, \"name\": \"white lacrosse stick\"}, {\"id\": 22900, \"name\": \"military plane\"}, {\"id\": 22901, \"name\": \"assortment of plants\"}, {\"id\": 22902, \"name\": \"the tall tower\"}, {\"id\": 22903, \"name\": \"small white and black dirt bike\"}, {\"id\": 22904, \"name\": \"purple and pink leaf\"}, {\"id\": 22905, \"name\": \"the large, white machine\"}, {\"id\": 22906, \"name\": \"the horse race\"}, {\"id\": 22907, \"name\": \"the white rope barrier\"}, {\"id\": 22908, \"name\": \"the blurred drum set\"}, {\"id\": 22909, \"name\": \"large, dark eye\"}, {\"id\": 22910, \"name\": \"bright red frisbee\"}, {\"id\": 22911, \"name\": \"the black and clear paintball mask\"}, {\"id\": 22912, \"name\": \"large orange deer statue\"}, {\"id\": 22913, \"name\": \"paved track\"}, {\"id\": 22914, \"name\": \"the windshield frame\"}, {\"id\": 22915, \"name\": \"spectators' seating area\"}, {\"id\": 22916, \"name\": \"the purple bead\"}, {\"id\": 22917, \"name\": \"a light-colored stone facade\"}, {\"id\": 22918, \"name\": \"large pink and purple design\"}, {\"id\": 22919, \"name\": \"the group of go-kart drivers\"}, {\"id\": 22920, \"name\": \"shore\"}, {\"id\": 22921, \"name\": \"a power line tower\"}, {\"id\": 22922, \"name\": \"numerous colorful tents\"}, {\"id\": 22923, \"name\": \"the white air conditioning unit\"}, {\"id\": 22924, \"name\": \"a Turkish flag\"}, {\"id\": 22925, \"name\": \"a colorful lei\"}, {\"id\": 22926, \"name\": \"the black T-shirt\"}, {\"id\": 22927, \"name\": \"blue soccer ball\"}, {\"id\": 22928, \"name\": \"the stack of similar boxes\"}, {\"id\": 22929, \"name\": \"a dirt bike rider\"}, {\"id\": 22930, \"name\": \"a blue sock\"}, {\"id\": 22931, \"name\": \"a pagoda-style roof\"}, {\"id\": 22932, \"name\": \"a piece of exercise equipment\"}, {\"id\": 22933, \"name\": \"the blurry white line\"}, {\"id\": 22934, \"name\": \"a store interior\"}, {\"id\": 22935, \"name\": \"dark substrate\"}, {\"id\": 22936, \"name\": \"the large digital billboard\"}, {\"id\": 22937, \"name\": \"red brake light\"}, {\"id\": 22938, \"name\": \"the crowd of onlooker\"}, {\"id\": 22939, \"name\": \"a black racing suit\"}, {\"id\": 22940, \"name\": \"a cluster of houses\"}, {\"id\": 22941, \"name\": \"the bag of dried fish\"}, {\"id\": 22942, \"name\": \"a white facade\"}, {\"id\": 22943, \"name\": \"line of cyclists\"}, {\"id\": 22944, \"name\": \"the concrete ramp\"}, {\"id\": 22945, \"name\": \"the band member\"}, {\"id\": 22946, \"name\": \"the blue sock\"}, {\"id\": 22947, \"name\": \"child's hands\"}, {\"id\": 22948, \"name\": \"puffy sleeve\"}, {\"id\": 22949, \"name\": \"the red lid\"}, {\"id\": 22950, \"name\": \"a gear\"}, {\"id\": 22951, \"name\": \"red partition\"}, {\"id\": 22952, \"name\": \"a bumblebee\"}, {\"id\": 22953, \"name\": \"a paddleboarder\"}, {\"id\": 22954, \"name\": \"a traffic signal\"}, {\"id\": 22955, \"name\": \"glowing green light\"}, {\"id\": 22956, \"name\": \"the white rectangular object\"}, {\"id\": 22957, \"name\": \"the black leather tank top\"}, {\"id\": 22958, \"name\": \"a truck's ladder\"}, {\"id\": 22959, \"name\": \"the large white cloud\"}, {\"id\": 22960, \"name\": \"black-and-white poster\"}, {\"id\": 22961, \"name\": \"a gravel-covered track\"}, {\"id\": 22962, \"name\": \"the heavy machinery\"}, {\"id\": 22963, \"name\": \"a turtle's foot\"}, {\"id\": 22964, \"name\": \"the green and black striped pattern\"}, {\"id\": 22965, \"name\": \"glass roof\"}, {\"id\": 22966, \"name\": \"the row of banners and signs\"}, {\"id\": 22967, \"name\": \"kayaker\"}, {\"id\": 22968, \"name\": \"the large bouquet of flowers\"}, {\"id\": 22969, \"name\": \"a white tower\"}, {\"id\": 22970, \"name\": \"flat, rectangular head\"}, {\"id\": 22971, \"name\": \"the character icon\"}, {\"id\": 22972, \"name\": \"the tan facade\"}, {\"id\": 22973, \"name\": \"the vibrant bumper car ride\"}, {\"id\": 22974, \"name\": \"the yellow and black paraglider\"}, {\"id\": 22975, \"name\": \"the red trash can\"}, {\"id\": 22976, \"name\": \"synthesizer\"}, {\"id\": 22977, \"name\": \"the cluster of shrimp\"}, {\"id\": 22978, \"name\": \"a packed grandstand\"}, {\"id\": 22979, \"name\": \"a scooter's seat\"}, {\"id\": 22980, \"name\": \"black racing suit\"}, {\"id\": 22981, \"name\": \"the long white beard\"}, {\"id\": 22982, \"name\": \"an anemone\"}, {\"id\": 22983, \"name\": \"a large mountain\"}, {\"id\": 22984, \"name\": \"a gray metal structure\"}, {\"id\": 22985, \"name\": \"a puffy skirt\"}, {\"id\": 22986, \"name\": \"cluster of pink balloon\"}, {\"id\": 22987, \"name\": \"rack of boxing glove\"}, {\"id\": 22988, \"name\": \"gray asphalt\"}, {\"id\": 22989, \"name\": \"a cluster of trees\"}, {\"id\": 22990, \"name\": \"a stone post\"}, {\"id\": 22991, \"name\": \"a stand-up paddleboard\"}, {\"id\": 22992, \"name\": \"the bare feet\"}, {\"id\": 22993, \"name\": \"training equipment\"}, {\"id\": 22994, \"name\": \"the race bib\"}, {\"id\": 22995, \"name\": \"sea of spectators\"}, {\"id\": 22996, \"name\": \"the dark blue and calm water\"}, {\"id\": 22997, \"name\": \"small, white, circular dog bed\"}, {\"id\": 22998, \"name\": \"player character\"}, {\"id\": 22999, \"name\": \"the commercial or office structure\"}, {\"id\": 23000, \"name\": \"silver trash can\"}, {\"id\": 23001, \"name\": \"the athlete\"}, {\"id\": 23002, \"name\": \"a beach towel\"}, {\"id\": 23003, \"name\": \"a white skirt\"}, {\"id\": 23004, \"name\": \"tall, narrow water jet\"}, {\"id\": 23005, \"name\": \"a yellow block of material\"}, {\"id\": 23006, \"name\": \"a black fanny pack\"}, {\"id\": 23007, \"name\": \"a race track\"}, {\"id\": 23008, \"name\": \"the rink's glass barrier\"}, {\"id\": 23009, \"name\": \"horse's tail\"}, {\"id\": 23010, \"name\": \"a scooter's wheel\"}, {\"id\": 23011, \"name\": \"a blue layer\"}, {\"id\": 23012, \"name\": \"a locomotive\"}, {\"id\": 23013, \"name\": \"distant mountain range\"}, {\"id\": 23014, \"name\": \"the large Red Bull advertisement\"}, {\"id\": 23015, \"name\": \"a sandy terrain\"}, {\"id\": 23016, \"name\": \"the vibrant red canopy\"}, {\"id\": 23017, \"name\": \"the large black speaker\"}, {\"id\": 23018, \"name\": \"the metal file brush\"}, {\"id\": 23019, \"name\": \"smaller arch\"}, {\"id\": 23020, \"name\": \"a wooden toy stand\"}, {\"id\": 23021, \"name\": \"black straw\"}, {\"id\": 23022, \"name\": \"green and white surface\"}, {\"id\": 23023, \"name\": \"a red shorts\"}, {\"id\": 23024, \"name\": \"a stack of red and white paint buckets\"}, {\"id\": 23025, \"name\": \"the cormorant\"}, {\"id\": 23026, \"name\": \"light-colored shorts\"}, {\"id\": 23027, \"name\": \"a black and white car\"}, {\"id\": 23028, \"name\": \"a train's platform\"}, {\"id\": 23029, \"name\": \"a white and red barrier\"}, {\"id\": 23030, \"name\": \"the blue and purple tassel\"}, {\"id\": 23031, \"name\": \"a gray hat\"}, {\"id\": 23032, \"name\": \"boxing ring\"}, {\"id\": 23033, \"name\": \"the white rope\"}, {\"id\": 23034, \"name\": \"a large purple cooler\"}, {\"id\": 23035, \"name\": \"the blue skirt\"}, {\"id\": 23036, \"name\": \"the pink and white horn\"}, {\"id\": 23037, \"name\": \"a snow-covered sidewalk\"}, {\"id\": 23038, \"name\": \"a silver drain plug\"}, {\"id\": 23039, \"name\": \"white top section\"}, {\"id\": 23040, \"name\": \"black fire escape\"}, {\"id\": 23041, \"name\": \"purple object\"}, {\"id\": 23042, \"name\": \"7-Eleven store\"}, {\"id\": 23043, \"name\": \"the yellow wristband\"}, {\"id\": 23044, \"name\": \"a group of onlookers\"}, {\"id\": 23045, \"name\": \"large tan dog\"}, {\"id\": 23046, \"name\": \"the motorcycle helmet\"}, {\"id\": 23047, \"name\": \"bird's head\"}, {\"id\": 23048, \"name\": \"a gray sidewalk\"}, {\"id\": 23049, \"name\": \"a cluttered white shelving unit\"}, {\"id\": 23050, \"name\": \"the athletic clothing\"}, {\"id\": 23051, \"name\": \"dirt bike\"}, {\"id\": 23052, \"name\": \"wooden table or shelf\"}, {\"id\": 23053, \"name\": \"a kite\"}, {\"id\": 23054, \"name\": \"small mechanical device\"}, {\"id\": 23055, \"name\": \"a group of go-kart drivers\"}, {\"id\": 23056, \"name\": \"the red pair of shorts\"}, {\"id\": 23057, \"name\": \"small, white and blue structure\"}, {\"id\": 23058, \"name\": \"the ramp\"}, {\"id\": 23059, \"name\": \"the road or track\"}, {\"id\": 23060, \"name\": \"a racing gear\"}, {\"id\": 23061, \"name\": \"a McDonald's restaurant\"}, {\"id\": 23062, \"name\": \"the cleat\"}, {\"id\": 23063, \"name\": \"colorful component\"}, {\"id\": 23064, \"name\": \"yellow and neon green t-shirt\"}, {\"id\": 23065, \"name\": \"lit-up sign\"}, {\"id\": 23066, \"name\": \"the long, thin, orange and yellow flower\"}, {\"id\": 23067, \"name\": \"light grey t-shirt\"}, {\"id\": 23068, \"name\": \"the chainring\"}, {\"id\": 23069, \"name\": \"a pack of dogs\"}, {\"id\": 23070, \"name\": \"a rear of the truck\"}, {\"id\": 23071, \"name\": \"small gray square\"}, {\"id\": 23072, \"name\": \"a metal bucket\"}, {\"id\": 23073, \"name\": \"the punching bag stand\"}, {\"id\": 23074, \"name\": \"the elevated road\"}, {\"id\": 23075, \"name\": \"a bright pink nail polish\"}, {\"id\": 23076, \"name\": \"propeller\"}, {\"id\": 23077, \"name\": \"a green netting\"}, {\"id\": 23078, \"name\": \"the large, black metal structure\"}, {\"id\": 23079, \"name\": \"the red and black object\"}, {\"id\": 23080, \"name\": \"black sleeve\"}, {\"id\": 23081, \"name\": \"the brown rug\"}, {\"id\": 23082, \"name\": \"black and white helmet\"}, {\"id\": 23083, \"name\": \"a central drainage grate\"}, {\"id\": 23084, \"name\": \"the large, brown object\"}, {\"id\": 23085, \"name\": \"a tray's metal frame\"}, {\"id\": 23086, \"name\": \"the metal bench\"}, {\"id\": 23087, \"name\": \"kayak\"}, {\"id\": 23088, \"name\": \"a mountainous backdrop\"}, {\"id\": 23089, \"name\": \"a muscular arm\"}, {\"id\": 23090, \"name\": \"a purple plastic handle\"}, {\"id\": 23091, \"name\": \"white cushion\"}, {\"id\": 23092, \"name\": \"a red path\"}, {\"id\": 23093, \"name\": \"pink and white ruffled dress\"}, {\"id\": 23094, \"name\": \"a red bucket\"}, {\"id\": 23095, \"name\": \"green hedge\"}, {\"id\": 23096, \"name\": \"a racquet\"}, {\"id\": 23097, \"name\": \"a vibrant collection of plush toys\"}, {\"id\": 23098, \"name\": \"black circular weight\"}, {\"id\": 23099, \"name\": \"a large rectangular light\"}, {\"id\": 23100, \"name\": \"the pink helmet\"}, {\"id\": 23101, \"name\": \"the red and white checkered barrier\"}, {\"id\": 23102, \"name\": \"wooden toy diorama\"}, {\"id\": 23103, \"name\": \"car's window\"}, {\"id\": 23104, \"name\": \"yellow concrete overpass\"}, {\"id\": 23105, \"name\": \"rocky surface\"}, {\"id\": 23106, \"name\": \"the round balloon\"}, {\"id\": 23107, \"name\": \"brightly illuminated red light\"}, {\"id\": 23108, \"name\": \"trolley track\"}, {\"id\": 23109, \"name\": \"rack of exercise equipment\"}, {\"id\": 23110, \"name\": \"red and white striped facade\"}, {\"id\": 23111, \"name\": \"a dark grey surface\"}, {\"id\": 23112, \"name\": \"the red cap\"}, {\"id\": 23113, \"name\": \"the white tube\"}, {\"id\": 23114, \"name\": \"the red SUBSCRIBE button\"}, {\"id\": 23115, \"name\": \"the red and gray baseball cap\"}, {\"id\": 23116, \"name\": \"the blue and yellow storefront\"}, {\"id\": 23117, \"name\": \"a blue cone\"}, {\"id\": 23118, \"name\": \"a large glass facade\"}, {\"id\": 23119, \"name\": \"the blue shield\"}, {\"id\": 23120, \"name\": \"the white lanyard\"}, {\"id\": 23121, \"name\": \"the vibrant display of yellow-green foliage\"}, {\"id\": 23122, \"name\": \"goal\"}, {\"id\": 23123, \"name\": \"yellow boxing glove\"}, {\"id\": 23124, \"name\": \"the front door\"}, {\"id\": 23125, \"name\": \"a blue plastic bin\"}, {\"id\": 23126, \"name\": \"a black-and-white sneaker\"}, {\"id\": 23127, \"name\": \"red boxing glove logo\"}, {\"id\": 23128, \"name\": \"a yellow-painted parking space demarcation\"}, {\"id\": 23129, \"name\": \"the piece of white string\"}, {\"id\": 23130, \"name\": \"red ladder\"}, {\"id\": 23131, \"name\": \"small train\"}, {\"id\": 23132, \"name\": \"the phone screen\"}, {\"id\": 23133, \"name\": \"the blue paddleboard\"}, {\"id\": 23134, \"name\": \"a yellow driving glove\"}, {\"id\": 23135, \"name\": \"purple slipper\"}, {\"id\": 23136, \"name\": \"male player\"}, {\"id\": 23137, \"name\": \"red rope\"}, {\"id\": 23138, \"name\": \"the bodice\"}, {\"id\": 23139, \"name\": \"a wooden block\"}, {\"id\": 23140, \"name\": \"a purple creature\"}, {\"id\": 23141, \"name\": \"orange candy\"}, {\"id\": 23142, \"name\": \"a blue disc\"}, {\"id\": 23143, \"name\": \"the water jetpack\"}, {\"id\": 23144, \"name\": \"black and red sneaker\"}, {\"id\": 23145, \"name\": \"the bat\"}, {\"id\": 23146, \"name\": \"the large, blue metal plate\"}, {\"id\": 23147, \"name\": \"the number 7\"}, {\"id\": 23148, \"name\": \"a long neck\"}, {\"id\": 23149, \"name\": \"the red and black camouflage pattern\"}, {\"id\": 23150, \"name\": \"a brightly lit carnival ride\"}, {\"id\": 23151, \"name\": \"a potted tree\"}, {\"id\": 23152, \"name\": \"a vibrant helmet\"}, {\"id\": 23153, \"name\": \"a blue weight\"}, {\"id\": 23154, \"name\": \"the large, off-white bag\"}, {\"id\": 23155, \"name\": \"black boxing bag\"}, {\"id\": 23156, \"name\": \"a blurry orange ball\"}, {\"id\": 23157, \"name\": \"the statue of a man\"}, {\"id\": 23158, \"name\": \"a black metal structure\"}, {\"id\": 23159, \"name\": \"an orange paper\"}, {\"id\": 23160, \"name\": \"an exercise attire\"}, {\"id\": 23161, \"name\": \"the white bandana\"}, {\"id\": 23162, \"name\": \"the Barclays Center\"}, {\"id\": 23163, \"name\": \"the high-rise apartment complex\"}, {\"id\": 23164, \"name\": \"a black ceiling\"}, {\"id\": 23165, \"name\": \"the group of stormtroopers\"}, {\"id\": 23166, \"name\": \"the yellow and orange tank top\"}, {\"id\": 23167, \"name\": \"a white goal\"}, {\"id\": 23168, \"name\": \"the wide-brimmed straw hat\"}, {\"id\": 23169, \"name\": \"shield\"}, {\"id\": 23170, \"name\": \"the red boxing glove\"}, {\"id\": 23171, \"name\": \"a man holding a microphone\"}, {\"id\": 23172, \"name\": \"pinkish-orange coral\"}, {\"id\": 23173, \"name\": \"large, yellow, circular fan\"}, {\"id\": 23174, \"name\": \"a pitcher's glove\"}, {\"id\": 23175, \"name\": \"a large scoreboard\"}, {\"id\": 23176, \"name\": \"the yarn or string\"}, {\"id\": 23177, \"name\": \"a skatepark ramp\"}, {\"id\": 23178, \"name\": \"small block of green material\"}, {\"id\": 23179, \"name\": \"a middle screenshot\"}, {\"id\": 23180, \"name\": \"brown bridge\"}, {\"id\": 23181, \"name\": \"short sleeve\"}, {\"id\": 23182, \"name\": \"a thatched roof structure\"}, {\"id\": 23183, \"name\": \"the green sweater\"}, {\"id\": 23184, \"name\": \"the man in a blue shirt\"}, {\"id\": 23185, \"name\": \"the red leather jacket\"}, {\"id\": 23186, \"name\": \"the weapon's barrel\"}, {\"id\": 23187, \"name\": \"a small wooden stool\"}, {\"id\": 23188, \"name\": \"red dot\"}, {\"id\": 23189, \"name\": \"the pink and blue fabric\"}, {\"id\": 23190, \"name\": \"the bus window\"}, {\"id\": 23191, \"name\": \"black sports bra\"}, {\"id\": 23192, \"name\": \"the heart garland\"}, {\"id\": 23193, \"name\": \"the neon yellow shirt\"}, {\"id\": 23194, \"name\": \"the green and red circular platform\"}, {\"id\": 23195, \"name\": \"an ice block\"}, {\"id\": 23196, \"name\": \"maroon curtain\"}, {\"id\": 23197, \"name\": \"a blue bench\"}, {\"id\": 23198, \"name\": \"a black waistband\"}, {\"id\": 23199, \"name\": \"the light blue headscarf\"}, {\"id\": 23200, \"name\": \"green foot\"}, {\"id\": 23201, \"name\": \"the checkered flag motif\"}, {\"id\": 23202, \"name\": \"racket\"}, {\"id\": 23203, \"name\": \"a large digital scoreboard\"}, {\"id\": 23204, \"name\": \"the hexagonal board\"}, {\"id\": 23205, \"name\": \"the black Bauer hockey jersey\"}, {\"id\": 23206, \"name\": \"the white metal staircase\"}, {\"id\": 23207, \"name\": \"the small white ball\"}, {\"id\": 23208, \"name\": \"small ball\"}, {\"id\": 23209, \"name\": \"large, cylindrical object\"}, {\"id\": 23210, \"name\": \"colorful illustration of a dog's face\"}, {\"id\": 23211, \"name\": \"a black punching bag\"}, {\"id\": 23212, \"name\": \"the golden-yellow caramel popcorn\"}, {\"id\": 23213, \"name\": \"a brown horse with a white saddle\"}, {\"id\": 23214, \"name\": \"black T-shirt\"}, {\"id\": 23215, \"name\": \"dark-colored bicycle\"}, {\"id\": 23216, \"name\": \"a gray T-shirt\"}, {\"id\": 23217, \"name\": \"bone-shaped chew toy\"}, {\"id\": 23218, \"name\": \"a cluster of motorcycles\"}, {\"id\": 23219, \"name\": \"display of color and light\"}, {\"id\": 23220, \"name\": \"group of young martial artists\"}, {\"id\": 23221, \"name\": \"the small yellow hurdle\"}, {\"id\": 23222, \"name\": \"circular bed of yellow and red foliage\"}, {\"id\": 23223, \"name\": \"the rice\"}, {\"id\": 23224, \"name\": \"a blue and white sign with a white triangle\"}, {\"id\": 23225, \"name\": \"a black diving gear\"}, {\"id\": 23226, \"name\": \"the leg press\"}, {\"id\": 23227, \"name\": \"small toy car\"}, {\"id\": 23228, \"name\": \"a large, brown and orange boulder\"}, {\"id\": 23229, \"name\": \"dog's bed\"}, {\"id\": 23230, \"name\": \"the light post\"}, {\"id\": 23231, \"name\": \"red, yellow, and white sign\"}, {\"id\": 23232, \"name\": \"the red platform\"}, {\"id\": 23233, \"name\": \"the sign reading \\\"BOOKS\\\"\"}, {\"id\": 23234, \"name\": \"a toy marble run\"}, {\"id\": 23235, \"name\": \"light blue sneaker\"}, {\"id\": 23236, \"name\": \"camouflage shorts\"}, {\"id\": 23237, \"name\": \"a handle of the cart\"}, {\"id\": 23238, \"name\": \"gray plastic stool\"}, {\"id\": 23239, \"name\": \"wheelie\"}, {\"id\": 23240, \"name\": \"a rack of blue and black punching bag\"}, {\"id\": 23241, \"name\": \"obstacle course\"}, {\"id\": 23242, \"name\": \"red and white net\"}, {\"id\": 23243, \"name\": \"the orange star-shaped balloon\"}, {\"id\": 23244, \"name\": \"long black asphalt track\"}, {\"id\": 23245, \"name\": \"black mane\"}, {\"id\": 23246, \"name\": \"the orange-red creature\"}, {\"id\": 23247, \"name\": \"a bangs\"}, {\"id\": 23248, \"name\": \"the white and orange-striped jump\"}, {\"id\": 23249, \"name\": \"the wiper\"}, {\"id\": 23250, \"name\": \"white cross-body bag\"}, {\"id\": 23251, \"name\": \"the sea of spectators\"}, {\"id\": 23252, \"name\": \"the black trash can\"}, {\"id\": 23253, \"name\": \"the green game board\"}, {\"id\": 23254, \"name\": \"the white upper deck\"}, {\"id\": 23255, \"name\": \"a dark horse\"}, {\"id\": 23256, \"name\": \"pile of debris\"}, {\"id\": 23257, \"name\": \"head of a brown and white duck\"}, {\"id\": 23258, \"name\": \"a crowd of spectators\"}, {\"id\": 23259, \"name\": \"cream-colored body\"}, {\"id\": 23260, \"name\": \"a green billboard\"}, {\"id\": 23261, \"name\": \"red lamp\"}, {\"id\": 23262, \"name\": \"blue plastic bag\"}, {\"id\": 23263, \"name\": \"the green weight\"}, {\"id\": 23264, \"name\": \"large tan tent\"}, {\"id\": 23265, \"name\": \"white knee-high sock\"}, {\"id\": 23266, \"name\": \"handwritten sign\"}, {\"id\": 23267, \"name\": \"large punching bag\"}, {\"id\": 23268, \"name\": \"large piece of coral\"}, {\"id\": 23269, \"name\": \"metal stand\"}, {\"id\": 23270, \"name\": \"small, colorful object\"}, {\"id\": 23271, \"name\": \"a small, orange-red fish\"}, {\"id\": 23272, \"name\": \"the light blue exterior\"}, {\"id\": 23273, \"name\": \"yellow tool\"}, {\"id\": 23274, \"name\": \"red tube\"}, {\"id\": 23275, \"name\": \"the white or black martial arts uniform\"}, {\"id\": 23276, \"name\": \"line of cars\"}, {\"id\": 23277, \"name\": \"buoy\"}, {\"id\": 23278, \"name\": \"red and black motorbike\"}, {\"id\": 23279, \"name\": \"large white dome-shaped structure\"}, {\"id\": 23280, \"name\": \"black and grey motorcycle\"}, {\"id\": 23281, \"name\": \"a gray spool of thread\"}, {\"id\": 23282, \"name\": \"a bright orange t-shirt\"}, {\"id\": 23283, \"name\": \"the yellow boxing glove\"}, {\"id\": 23284, \"name\": \"an orange hoop\"}, {\"id\": 23285, \"name\": \"the large black trailer\"}, {\"id\": 23286, \"name\": \"a black puck holder\"}, {\"id\": 23287, \"name\": \"vibrant purple stage\"}, {\"id\": 23288, \"name\": \"row of TV screen\"}, {\"id\": 23289, \"name\": \"canine's head\"}, {\"id\": 23290, \"name\": \"red or orange hoodie\"}, {\"id\": 23291, \"name\": \"the forefinger\"}, {\"id\": 23292, \"name\": \"the diverse array of colorful bumper car\"}, {\"id\": 23293, \"name\": \"the rider's armor\"}, {\"id\": 23294, \"name\": \"an open, clear container\"}, {\"id\": 23295, \"name\": \"the partial view of a red car\"}, {\"id\": 23296, \"name\": \"the large, white tower\"}, {\"id\": 23297, \"name\": \"the large, robotic figure\"}, {\"id\": 23298, \"name\": \"bangs\"}, {\"id\": 23299, \"name\": \"red eye\"}, {\"id\": 23300, \"name\": \"a creature's body\"}, {\"id\": 23301, \"name\": \"the red component\"}, {\"id\": 23302, \"name\": \"a flower-like design\"}, {\"id\": 23303, \"name\": \"the blue circle\"}, {\"id\": 23304, \"name\": \"red cylindrical object\"}, {\"id\": 23305, \"name\": \"the large, black metal object\"}, {\"id\": 23306, \"name\": \"raised barrier\"}, {\"id\": 23307, \"name\": \"a player statistic\"}, {\"id\": 23308, \"name\": \"the tennis racket\"}, {\"id\": 23309, \"name\": \"a red and black rifle\"}, {\"id\": 23310, \"name\": \"a grandstand's facade\"}, {\"id\": 23311, \"name\": \"a shamrock logo\"}, {\"id\": 23312, \"name\": \"the variety of balloons\"}, {\"id\": 23313, \"name\": \"a small metal bolt or screw\"}, {\"id\": 23314, \"name\": \"the metal shutter\"}, {\"id\": 23315, \"name\": \"a white birdhouse\"}, {\"id\": 23316, \"name\": \"a matching green and red ensemble\"}, {\"id\": 23317, \"name\": \"the brightly colored jersey\"}, {\"id\": 23318, \"name\": \"a dark-colored van\"}, {\"id\": 23319, \"name\": \"the Starbucks coffee shop\"}, {\"id\": 23320, \"name\": \"silver rod\"}, {\"id\": 23321, \"name\": \"blue health bar\"}, {\"id\": 23322, \"name\": \"erect ear\"}, {\"id\": 23323, \"name\": \"pink skirt\"}, {\"id\": 23324, \"name\": \"blue vertical light\"}, {\"id\": 23325, \"name\": \"the parking lot\"}, {\"id\": 23326, \"name\": \"beige hat\"}, {\"id\": 23327, \"name\": \"a school bus\"}, {\"id\": 23328, \"name\": \"set of stairs\"}, {\"id\": 23329, \"name\": \"a bed of the truck\"}, {\"id\": 23330, \"name\": \"the black punching bag\"}, {\"id\": 23331, \"name\": \"large, rusty metal padlock\"}, {\"id\": 23332, \"name\": \"a large traffic light\"}, {\"id\": 23333, \"name\": \"a white T-shirt\"}, {\"id\": 23334, \"name\": \"large scoreboard\"}, {\"id\": 23335, \"name\": \"steep, rocky hill\"}, {\"id\": 23336, \"name\": \"a first-person shooter\"}, {\"id\": 23337, \"name\": \"a tall pair of stilts\"}, {\"id\": 23338, \"name\": \"the blue and red dry suit\"}, {\"id\": 23339, \"name\": \"a vehicle's license plate\"}, {\"id\": 23340, \"name\": \"a training aid\"}, {\"id\": 23341, \"name\": \"the saddlebag\"}, {\"id\": 23342, \"name\": \"paddle\"}, {\"id\": 23343, \"name\": \"green net\"}, {\"id\": 23344, \"name\": \"the school uniform\"}, {\"id\": 23345, \"name\": \"the green exterior\"}, {\"id\": 23346, \"name\": \"pink circle\"}, {\"id\": 23347, \"name\": \"red and black pant\"}, {\"id\": 23348, \"name\": \"an orange shorts\"}, {\"id\": 23349, \"name\": \"the gray remote control\"}, {\"id\": 23350, \"name\": \"puppy's shadow\"}, {\"id\": 23351, \"name\": \"covered awning\"}, {\"id\": 23352, \"name\": \"the blue image\"}, {\"id\": 23353, \"name\": \"the gray concrete surface\"}, {\"id\": 23354, \"name\": \"the red and white van\"}, {\"id\": 23355, \"name\": \"the lime green ball\"}, {\"id\": 23356, \"name\": \"the black kettlebell\"}, {\"id\": 23357, \"name\": \"antenna\"}, {\"id\": 23358, \"name\": \"the racket's strings\"}, {\"id\": 23359, \"name\": \"blue convertible\"}, {\"id\": 23360, \"name\": \"a large, ornate chandelier\"}, {\"id\": 23361, \"name\": \"a chain strap purse\"}, {\"id\": 23362, \"name\": \"a row of parked cars\"}, {\"id\": 23363, \"name\": \"black drum set\"}, {\"id\": 23364, \"name\": \"blue ocean\"}, {\"id\": 23365, \"name\": \"dark-colored vehicle\"}, {\"id\": 23366, \"name\": \"the circular manhole cover\"}, {\"id\": 23367, \"name\": \"red and yellow clothing\"}, {\"id\": 23368, \"name\": \"a boat or ship\"}, {\"id\": 23369, \"name\": \"the weight lifting barbell\"}, {\"id\": 23370, \"name\": \"the single solid white line\"}, {\"id\": 23371, \"name\": \"Red Bull logo\"}, {\"id\": 23372, \"name\": \"large red box\"}, {\"id\": 23373, \"name\": \"the red pipe\"}, {\"id\": 23374, \"name\": \"metal tray\"}, {\"id\": 23375, \"name\": \"a pair of blue flip-flops\"}, {\"id\": 23376, \"name\": \"a tan pant\"}, {\"id\": 23377, \"name\": \"stack of black tires\"}, {\"id\": 23378, \"name\": \"a DJ booth\"}, {\"id\": 23379, \"name\": \"large black tube\"}, {\"id\": 23380, \"name\": \"a gray storage bin\"}, {\"id\": 23381, \"name\": \"a gray body of water\"}, {\"id\": 23382, \"name\": \"large red fire truck\"}, {\"id\": 23383, \"name\": \"the crustacean\"}, {\"id\": 23384, \"name\": \"green surface\"}, {\"id\": 23385, \"name\": \"the batting helmet\"}, {\"id\": 23386, \"name\": \"the top right image\"}, {\"id\": 23387, \"name\": \"the large, metal cage\"}, {\"id\": 23388, \"name\": \"the pink and gold head\"}, {\"id\": 23389, \"name\": \"large black Nike logo\"}, {\"id\": 23390, \"name\": \"an aqua-colored leggings\"}, {\"id\": 23391, \"name\": \"a yellow and brown cylinder\"}, {\"id\": 23392, \"name\": \"black shorts with white stripes\"}, {\"id\": 23393, \"name\": \"the small white dog\"}, {\"id\": 23394, \"name\": \"pallet of paper product\"}, {\"id\": 23395, \"name\": \"a collection of soccer balls\"}, {\"id\": 23396, \"name\": \"the circular symbol\"}, {\"id\": 23397, \"name\": \"audience member\"}, {\"id\": 23398, \"name\": \"a white fur skirt\"}, {\"id\": 23399, \"name\": \"small bowl of red sauce\"}, {\"id\": 23400, \"name\": \"the username\"}, {\"id\": 23401, \"name\": \"red paint marking\"}, {\"id\": 23402, \"name\": \"black motorcycle suit\"}, {\"id\": 23403, \"name\": \"a large white cylindrical body\"}, {\"id\": 23404, \"name\": \"a pile of papers\"}, {\"id\": 23405, \"name\": \"a fluffy fur\"}, {\"id\": 23406, \"name\": \"the colorful sock\"}, {\"id\": 23407, \"name\": \"group of whales\"}, {\"id\": 23408, \"name\": \"a construction equipment\"}, {\"id\": 23409, \"name\": \"parked truck\"}, {\"id\": 23410, \"name\": \"a messy, curly afro\"}, {\"id\": 23411, \"name\": \"black rollerblade\"}, {\"id\": 23412, \"name\": \"a stack of white objects\"}, {\"id\": 23413, \"name\": \"a rewind button\"}, {\"id\": 23414, \"name\": \"center line\"}, {\"id\": 23415, \"name\": \"heart shape\"}, {\"id\": 23416, \"name\": \"a pink pool noodle\"}, {\"id\": 23417, \"name\": \"tall, thin pole\"}, {\"id\": 23418, \"name\": \"the blue motorcycle\"}, {\"id\": 23419, \"name\": \"the ocean wave\"}, {\"id\": 23420, \"name\": \"the young baseball player\"}, {\"id\": 23421, \"name\": \"a yellow dot\"}, {\"id\": 23422, \"name\": \"a white watch\"}, {\"id\": 23423, \"name\": \"blue sedan\"}, {\"id\": 23424, \"name\": \"the socks\"}, {\"id\": 23425, \"name\": \"driver's view of the race track\"}, {\"id\": 23426, \"name\": \"yellow skirt\"}, {\"id\": 23427, \"name\": \"the tentacle\"}, {\"id\": 23428, \"name\": \"large, round concrete ball\"}, {\"id\": 23429, \"name\": \"spool holder\"}, {\"id\": 23430, \"name\": \"a large hull\"}, {\"id\": 23431, \"name\": \"a medicine ball\"}, {\"id\": 23432, \"name\": \"the beef\"}, {\"id\": 23433, \"name\": \"a tan-colored face\"}, {\"id\": 23434, \"name\": \"a trolley car\"}, {\"id\": 23435, \"name\": \"the stir-fried vegetable\"}, {\"id\": 23436, \"name\": \"silver food bowl\"}, {\"id\": 23437, \"name\": \"a small, rectangular, silver-colored device\"}, {\"id\": 23438, \"name\": \"power line tower\"}, {\"id\": 23439, \"name\": \"a large, stylized mural\"}, {\"id\": 23440, \"name\": \"the small cluster of bird decoys\"}, {\"id\": 23441, \"name\": \"the red hockey goal\"}, {\"id\": 23442, \"name\": \"a plastic wrap\"}, {\"id\": 23443, \"name\": \"large metal shutter\"}, {\"id\": 23444, \"name\": \"child in a red hat\"}, {\"id\": 23445, \"name\": \"the grey T-shirt\"}, {\"id\": 23446, \"name\": \"the concrete block\"}, {\"id\": 23447, \"name\": \"brass plate\"}, {\"id\": 23448, \"name\": \"a pink table\"}, {\"id\": 23449, \"name\": \"a series of white tubes\"}, {\"id\": 23450, \"name\": \"a plush toy squirrel\"}, {\"id\": 23451, \"name\": \"the red cylinder\"}, {\"id\": 23452, \"name\": \"blurred white box\"}, {\"id\": 23453, \"name\": \"a racer\"}, {\"id\": 23454, \"name\": \"the red and green object\"}, {\"id\": 23455, \"name\": \"large, industrial machine\"}, {\"id\": 23456, \"name\": \"yellow door\"}, {\"id\": 23457, \"name\": \"a tennis net\"}, {\"id\": 23458, \"name\": \"a CAUTION\"}, {\"id\": 23459, \"name\": \"Jungle Runner\"}, {\"id\": 23460, \"name\": \"bushes\"}, {\"id\": 23461, \"name\": \"a tennis player\"}, {\"id\": 23462, \"name\": \"the aluminum\"}, {\"id\": 23463, \"name\": \"black and white target\"}, {\"id\": 23464, \"name\": \"the yellow rectangular block\"}, {\"id\": 23465, \"name\": \"a black glove\"}, {\"id\": 23466, \"name\": \"the red basketball\"}, {\"id\": 23467, \"name\": \"the boxing ring\"}, {\"id\": 23468, \"name\": \"small, plastic container\"}, {\"id\": 23469, \"name\": \"a sleek, high ponytail\"}, {\"id\": 23470, \"name\": \"the USOpen.org logo\"}, {\"id\": 23471, \"name\": \"the 10-story edifice\"}, {\"id\": 23472, \"name\": \"trail of smoke\"}, {\"id\": 23473, \"name\": \"the child's arm\"}, {\"id\": 23474, \"name\": \"a rustic wooden cabin\"}, {\"id\": 23475, \"name\": \"home plate\"}, {\"id\": 23476, \"name\": \"the trail of water\"}, {\"id\": 23477, \"name\": \"the jewelry\"}, {\"id\": 23478, \"name\": \"white net\"}, {\"id\": 23479, \"name\": \"driver of the car\"}, {\"id\": 23480, \"name\": \"tall plastic bag\"}, {\"id\": 23481, \"name\": \"a hockey goal\"}, {\"id\": 23482, \"name\": \"an orange club\"}, {\"id\": 23483, \"name\": \"a skateboarding\"}, {\"id\": 23484, \"name\": \"the man dressed in a white karate uniform and black belt\"}, {\"id\": 23485, \"name\": \"miniature house\"}, {\"id\": 23486, \"name\": \"start/finish line\"}, {\"id\": 23487, \"name\": \"the VICTORY banner\"}, {\"id\": 23488, \"name\": \"the red and blue motorcycle\"}, {\"id\": 23489, \"name\": \"red box of Peanuts brand snacks\"}, {\"id\": 23490, \"name\": \"the BMX\"}, {\"id\": 23491, \"name\": \"a white billboard\"}, {\"id\": 23492, \"name\": \"the purple, circular platform\"}, {\"id\": 23493, \"name\": \"a silver keyboard\"}, {\"id\": 23494, \"name\": \"the clear and rose-gold gemstone\"}, {\"id\": 23495, \"name\": \"blue and red plaid shirt\"}, {\"id\": 23496, \"name\": \"black cape\"}, {\"id\": 23497, \"name\": \"black and white patterned seat cushion\"}, {\"id\": 23498, \"name\": \"a child's leg\"}, {\"id\": 23499, \"name\": \"the neon yellow sock\"}, {\"id\": 23500, \"name\": \"the lead ATV\"}, {\"id\": 23501, \"name\": \"white and black shirt\"}, {\"id\": 23502, \"name\": \"pink yarn\"}, {\"id\": 23503, \"name\": \"the green shoe\"}, {\"id\": 23504, \"name\": \"the shipping container\"}, {\"id\": 23505, \"name\": \"brown sneaker\"}, {\"id\": 23506, \"name\": \"a white paper towel roll\"}, {\"id\": 23507, \"name\": \"the person in a red shirt\"}, {\"id\": 23508, \"name\": \"the pink weight\"}, {\"id\": 23509, \"name\": \"the surrounding water\"}, {\"id\": 23510, \"name\": \"small, white gear\"}, {\"id\": 23511, \"name\": \"the goalie's leg pad\"}, {\"id\": 23512, \"name\": \"the brick stairs\"}, {\"id\": 23513, \"name\": \"a grey marbling effect\"}, {\"id\": 23514, \"name\": \"yellow fence\"}, {\"id\": 23515, \"name\": \"tall metal tower\"}, {\"id\": 23516, \"name\": \"black and red jersey\"}, {\"id\": 23517, \"name\": \"the colorful tuk-tuk\"}, {\"id\": 23518, \"name\": \"dark brown horse's tail\"}, {\"id\": 23519, \"name\": \"a light-colored cowboy hat\"}, {\"id\": 23520, \"name\": \"a blue race car\"}, {\"id\": 23521, \"name\": \"the reddish-brown, fern-like plant\"}, {\"id\": 23522, \"name\": \"the blue butterfly wing\"}, {\"id\": 23523, \"name\": \"Hilton sign\"}, {\"id\": 23524, \"name\": \"the hockey pant\"}, {\"id\": 23525, \"name\": \"the black and white stripe\"}, {\"id\": 23526, \"name\": \"the animal head\"}, {\"id\": 23527, \"name\": \"white vest\"}, {\"id\": 23528, \"name\": \"a smaller, dark blue banner\"}, {\"id\": 23529, \"name\": \"red circular base\"}, {\"id\": 23530, \"name\": \"a commercial or office complex\"}, {\"id\": 23531, \"name\": \"the rider's helmeted head\"}, {\"id\": 23532, \"name\": \"the small girl\"}, {\"id\": 23533, \"name\": \"the white or red flag\"}, {\"id\": 23534, \"name\": \"the dark-framed glasses\"}, {\"id\": 23535, \"name\": \"vibrant pink and red patterned shirt\"}, {\"id\": 23536, \"name\": \"large Pepsi logo\"}, {\"id\": 23537, \"name\": \"a red and white traffic cone\"}, {\"id\": 23538, \"name\": \"small silver dog bowl stand\"}, {\"id\": 23539, \"name\": \"a roll of green string\"}, {\"id\": 23540, \"name\": \"the tan bandage\"}, {\"id\": 23541, \"name\": \"a small speaker\"}, {\"id\": 23542, \"name\": \"a red harness\"}, {\"id\": 23543, \"name\": \"a red Ferrari\"}, {\"id\": 23544, \"name\": \"driver's cockpit\"}, {\"id\": 23545, \"name\": \"the VP COMP CAMS sticker\"}, {\"id\": 23546, \"name\": \"heart\"}, {\"id\": 23547, \"name\": \"a loose curl\"}, {\"id\": 23548, \"name\": \"the orange pole\"}, {\"id\": 23549, \"name\": \"a small, round, blue, wool-like object\"}, {\"id\": 23550, \"name\": \"a red, white, and blue striped border\"}, {\"id\": 23551, \"name\": \"the colorful, cartoon-like face\"}, {\"id\": 23552, \"name\": \"a red and white car\"}, {\"id\": 23553, \"name\": \"the blue and white car\"}, {\"id\": 23554, \"name\": \"a stack of plastic cups\"}, {\"id\": 23555, \"name\": \"the distant shoreline\"}, {\"id\": 23556, \"name\": \"a red and blue neon outline\"}, {\"id\": 23557, \"name\": \"a number 57 jersey\"}, {\"id\": 23558, \"name\": \"pink base\"}, {\"id\": 23559, \"name\": \"the pink gem\"}, {\"id\": 23560, \"name\": \"blue small paint container\"}, {\"id\": 23561, \"name\": \"row of storefronts\"}, {\"id\": 23562, \"name\": \"mural of the ocean\"}, {\"id\": 23563, \"name\": \"the taxi's right rear door\"}, {\"id\": 23564, \"name\": \"large metal pillar\"}, {\"id\": 23565, \"name\": \"large, blue, humanoid character\"}, {\"id\": 23566, \"name\": \"the tree's trunk\"}, {\"id\": 23567, \"name\": \"a tall apartment complex\"}, {\"id\": 23568, \"name\": \"red dirt inlay\"}, {\"id\": 23569, \"name\": \"the light-colored wood countertop\"}, {\"id\": 23570, \"name\": \"the girl's face\"}, {\"id\": 23571, \"name\": \"a long tan coat\"}, {\"id\": 23572, \"name\": \"a blue workbench\"}, {\"id\": 23573, \"name\": \"the large excavator\"}, {\"id\": 23574, \"name\": \"the kitten\"}, {\"id\": 23575, \"name\": \"a purple vase\"}, {\"id\": 23576, \"name\": \"a purple fender\"}, {\"id\": 23577, \"name\": \"a white and black rock\"}, {\"id\": 23578, \"name\": \"light gray sweatshirt\"}, {\"id\": 23579, \"name\": \"a red, white, and blue flag\"}, {\"id\": 23580, \"name\": \"grey shorts\"}, {\"id\": 23581, \"name\": \"the dugout\"}, {\"id\": 23582, \"name\": \"a small red knob\"}, {\"id\": 23583, \"name\": \"a black gun\"}, {\"id\": 23584, \"name\": \"a camouflage cap\"}, {\"id\": 23585, \"name\": \"a rooftop platform\"}, {\"id\": 23586, \"name\": \"a large red flag\"}, {\"id\": 23587, \"name\": \"pink shoe\"}, {\"id\": 23588, \"name\": \"green metal object\"}, {\"id\": 23589, \"name\": \"bright yellow pole\"}, {\"id\": 23590, \"name\": \"riding gear\"}, {\"id\": 23591, \"name\": \"a green mosque illustration\"}, {\"id\": 23592, \"name\": \"the large cake\"}, {\"id\": 23593, \"name\": \"the green overpass\"}, {\"id\": 23594, \"name\": \"small, colored object\"}, {\"id\": 23595, \"name\": \"the yellow caution cone\"}, {\"id\": 23596, \"name\": \"a long barrel\"}, {\"id\": 23597, \"name\": \"small green ball of yarn\"}, {\"id\": 23598, \"name\": \"white batting glove\"}, {\"id\": 23599, \"name\": \"black dolly\"}, {\"id\": 23600, \"name\": \"the gray grill\"}, {\"id\": 23601, \"name\": \"a dual exhaust pipe\"}, {\"id\": 23602, \"name\": \"cycling short\"}, {\"id\": 23603, \"name\": \"blue motorbike\"}, {\"id\": 23604, \"name\": \"black exercise ball\"}, {\"id\": 23605, \"name\": \"blue and white paddleboard\"}, {\"id\": 23606, \"name\": \"a rainbow-colored object\"}, {\"id\": 23607, \"name\": \"lever\"}, {\"id\": 23608, \"name\": \"the tube\"}, {\"id\": 23609, \"name\": \"a collection of buckets\"}, {\"id\": 23610, \"name\": \"tiki figure\"}, {\"id\": 23611, \"name\": \"the pink backpack strap\"}, {\"id\": 23612, \"name\": \"a silver speaker\"}, {\"id\": 23613, \"name\": \"swim cap\"}, {\"id\": 23614, \"name\": \"large metal gate\"}, {\"id\": 23615, \"name\": \"the central stainless steel cylinder\"}, {\"id\": 23616, \"name\": \"a metal guardrail\"}, {\"id\": 23617, \"name\": \"the breaded and fried food\"}, {\"id\": 23618, \"name\": \"a foamy wave\"}, {\"id\": 23619, \"name\": \"the airport runway\"}, {\"id\": 23620, \"name\": \"the large, partially obscured sign\"}, {\"id\": 23621, \"name\": \"wooden bat\"}, {\"id\": 23622, \"name\": \"the stack of shipping container\"}, {\"id\": 23623, \"name\": \"a large punching bag\"}, {\"id\": 23624, \"name\": \"the stack of yellow boxes\"}, {\"id\": 23625, \"name\": \"stack of paper towels\"}, {\"id\": 23626, \"name\": \"a large bow\"}, {\"id\": 23627, \"name\": \"the green hoodie\"}, {\"id\": 23628, \"name\": \"a row of traffic lights\"}, {\"id\": 23629, \"name\": \"the group of people\"}, {\"id\": 23630, \"name\": \"tall structure\"}, {\"id\": 23631, \"name\": \"people on bicycles\"}, {\"id\": 23632, \"name\": \"orange kayak\"}, {\"id\": 23633, \"name\": \"a gold wheel\"}, {\"id\": 23634, \"name\": \"the green turf surface\"}, {\"id\": 23635, \"name\": \"large blue exercise ball\"}, {\"id\": 23636, \"name\": \"man in the white shirt\"}, {\"id\": 23637, \"name\": \"the pink lollipop\"}, {\"id\": 23638, \"name\": \"a large white bus\"}, {\"id\": 23639, \"name\": \"red cone\"}, {\"id\": 23640, \"name\": \"a black rubber mat\"}, {\"id\": 23641, \"name\": \"roll of paper towels\"}, {\"id\": 23642, \"name\": \"a player's status\"}, {\"id\": 23643, \"name\": \"a black and yellow racket\"}, {\"id\": 23644, \"name\": \"blue medicine ball\"}, {\"id\": 23645, \"name\": \"tray of chocolate-covered strawberries\"}, {\"id\": 23646, \"name\": \"the small orange cone\"}, {\"id\": 23647, \"name\": \"driver's hands\"}, {\"id\": 23648, \"name\": \"a forested hill\"}, {\"id\": 23649, \"name\": \"a bright green shelf\"}, {\"id\": 23650, \"name\": \"cartoon-style astronaut\"}, {\"id\": 23651, \"name\": \"the blue and white striped towel\"}, {\"id\": 23652, \"name\": \"the cartoon eye\"}, {\"id\": 23653, \"name\": \"the red forklift\"}, {\"id\": 23654, \"name\": \"the blue lamppost\"}, {\"id\": 23655, \"name\": \"forklift\"}, {\"id\": 23656, \"name\": \"the vibrant yellow and turquoise costume\"}, {\"id\": 23657, \"name\": \"a black banner\"}, {\"id\": 23658, \"name\": \"a large, light-blue exercise ball\"}, {\"id\": 23659, \"name\": \"blue train\"}, {\"id\": 23660, \"name\": \"the traditional drum\"}, {\"id\": 23661, \"name\": \"the dog's eye\"}, {\"id\": 23662, \"name\": \"blue siding\"}, {\"id\": 23663, \"name\": \"the blue or orange cap\"}, {\"id\": 23664, \"name\": \"white creature\"}, {\"id\": 23665, \"name\": \"a colorful arch\"}, {\"id\": 23666, \"name\": \"the row of billboard\"}, {\"id\": 23667, \"name\": \"large gun\"}, {\"id\": 23668, \"name\": \"goal net\"}, {\"id\": 23669, \"name\": \"the rear of a police car\"}, {\"id\": 23670, \"name\": \"tall wooden cabinet\"}, {\"id\": 23671, \"name\": \"the stack of light-colored wooden blocks\"}, {\"id\": 23672, \"name\": \"dual exhaust pipe\"}, {\"id\": 23673, \"name\": \"large digital billboard\"}, {\"id\": 23674, \"name\": \"collection of wooden blocks\"}, {\"id\": 23675, \"name\": \"the black apron\"}, {\"id\": 23676, \"name\": \"grid of light blue squares\"}, {\"id\": 23677, \"name\": \"a row of spectators\"}, {\"id\": 23678, \"name\": \"the compass icon\"}, {\"id\": 23679, \"name\": \"spool of white thread\"}, {\"id\": 23680, \"name\": \"the impressive array of large vases, bowls, and other decorative item\"}, {\"id\": 23681, \"name\": \"tall green net\"}, {\"id\": 23682, \"name\": \"the yellow flower design\"}, {\"id\": 23683, \"name\": \"black gun\"}, {\"id\": 23684, \"name\": \"the grassy terrain\"}, {\"id\": 23685, \"name\": \"the smaller purple billboard\"}, {\"id\": 23686, \"name\": \"the racket head\"}, {\"id\": 23687, \"name\": \"the metalworking machinery\"}, {\"id\": 23688, \"name\": \"a socks\"}, {\"id\": 23689, \"name\": \"the black cable\"}, {\"id\": 23690, \"name\": \"dragon\"}, {\"id\": 23691, \"name\": \"the row of fluorescent lights\"}, {\"id\": 23692, \"name\": \"the red rectangle\"}, {\"id\": 23693, \"name\": \"a white chef's outfit\"}, {\"id\": 23694, \"name\": \"a blue fin\"}, {\"id\": 23695, \"name\": \"the sword\"}, {\"id\": 23696, \"name\": \"concrete drainage channel\"}, {\"id\": 23697, \"name\": \"a person on a motorcycle\"}, {\"id\": 23698, \"name\": \"gray stuffed animal\"}, {\"id\": 23699, \"name\": \"the white belt\"}, {\"id\": 23700, \"name\": \"boat's wake\"}, {\"id\": 23701, \"name\": \"a wooden crate\"}, {\"id\": 23702, \"name\": \"the tall, multi-story structure\"}, {\"id\": 23703, \"name\": \"the bamboo pole\"}, {\"id\": 23704, \"name\": \"the laughing face\"}, {\"id\": 23705, \"name\": \"the pile of dirt and mud\"}, {\"id\": 23706, \"name\": \"the sewing equipment\"}, {\"id\": 23707, \"name\": \"a black racket\"}, {\"id\": 23708, \"name\": \"wrestler\"}, {\"id\": 23709, \"name\": \"the dark brown line\"}, {\"id\": 23710, \"name\": \"mirror's reflection\"}, {\"id\": 23711, \"name\": \"a man in a black suit\"}, {\"id\": 23712, \"name\": \"a boat's white hull\"}, {\"id\": 23713, \"name\": \"row of dirt bikes\"}, {\"id\": 23714, \"name\": \"a red hatchback\"}, {\"id\": 23715, \"name\": \"the character from The Incredibles\"}, {\"id\": 23716, \"name\": \"a brass tap\"}, {\"id\": 23717, \"name\": \"large gate\"}, {\"id\": 23718, \"name\": \"the sizzling hot frying pan\"}, {\"id\": 23719, \"name\": \"the scaffolding\"}, {\"id\": 23720, \"name\": \"machinery\"}, {\"id\": 23721, \"name\": \"a gray apron\"}, {\"id\": 23722, \"name\": \"the long, thin, wooden stick\"}, {\"id\": 23723, \"name\": \"a dirt track\"}, {\"id\": 23724, \"name\": \"a cobblestone road\"}, {\"id\": 23725, \"name\": \"the person's lower body\"}, {\"id\": 23726, \"name\": \"blue T-shirt\"}, {\"id\": 23727, \"name\": \"blue apron\"}, {\"id\": 23728, \"name\": \"a taxi's side door\"}, {\"id\": 23729, \"name\": \"the thread\"}, {\"id\": 23730, \"name\": \"an aircraft\"}, {\"id\": 23731, \"name\": \"the white hard hat\"}, {\"id\": 23732, \"name\": \"a dark grey mat\"}, {\"id\": 23733, \"name\": \"the ringed celestial body\"}, {\"id\": 23734, \"name\": \"the purple bar\"}, {\"id\": 23735, \"name\": \"a foamy white water\"}, {\"id\": 23736, \"name\": \"white or green label\"}, {\"id\": 23737, \"name\": \"distinctive red sign\"}, {\"id\": 23738, \"name\": \"bottom robot\"}, {\"id\": 23739, \"name\": \"light-colored wooden structure\"}, {\"id\": 23740, \"name\": \"a rope barrier\"}, {\"id\": 23741, \"name\": \"the woman in swimsuit\"}, {\"id\": 23742, \"name\": \"grass area\"}, {\"id\": 23743, \"name\": \"black and white Nike sneaker\"}, {\"id\": 23744, \"name\": \"white painted crosswalk\"}, {\"id\": 23745, \"name\": \"black and white striped curb\"}, {\"id\": 23746, \"name\": \"a white bag or container\"}, {\"id\": 23747, \"name\": \"a black puck\"}, {\"id\": 23748, \"name\": \"the pipe system\"}, {\"id\": 23749, \"name\": \"a white shipping container\"}, {\"id\": 23750, \"name\": \"Car Door\"}, {\"id\": 23751, \"name\": \"the large tote bag\"}, {\"id\": 23752, \"name\": \"light-colored T-shirt\"}, {\"id\": 23753, \"name\": \"a large sandbag\"}, {\"id\": 23754, \"name\": \"the armed character\"}, {\"id\": 23755, \"name\": \"small cobblestone planter\"}, {\"id\": 23756, \"name\": \"blue backpack\"}, {\"id\": 23757, \"name\": \"the white protective suit\"}, {\"id\": 23758, \"name\": \"the large dirt pile\"}, {\"id\": 23759, \"name\": \"the red and black rack\"}, {\"id\": 23760, \"name\": \"the small white dog with black spots\"}, {\"id\": 23761, \"name\": \"orange jersey\"}, {\"id\": 23762, \"name\": \"a smaller truck\"}, {\"id\": 23763, \"name\": \"fishing pole\"}, {\"id\": 23764, \"name\": \"yellow metal barrier\"}, {\"id\": 23765, \"name\": \"the large green billboard\"}, {\"id\": 23766, \"name\": \"the green crocheted item\"}, {\"id\": 23767, \"name\": \"a health bar\"}, {\"id\": 23768, \"name\": \"marine creature\"}, {\"id\": 23769, \"name\": \"dark grey shorts\"}, {\"id\": 23770, \"name\": \"orange tent\"}, {\"id\": 23771, \"name\": \"translucent floral sleeve\"}, {\"id\": 23772, \"name\": \"sun\"}, {\"id\": 23773, \"name\": \"a central bike lane\"}, {\"id\": 23774, \"name\": \"the car's front license plate\"}, {\"id\": 23775, \"name\": \"a yellow warning label\"}, {\"id\": 23776, \"name\": \"white sand\"}, {\"id\": 23777, \"name\": \"a Angora rabbit\"}, {\"id\": 23778, \"name\": \"lead motorcyclist\"}, {\"id\": 23779, \"name\": \"Artist Brush\"}, {\"id\": 23780, \"name\": \"middle finger\"}, {\"id\": 23781, \"name\": \"a large, deep pan\"}, {\"id\": 23782, \"name\": \"green line\"}, {\"id\": 23783, \"name\": \"the purple-leafed variety\"}, {\"id\": 23784, \"name\": \"the barbed wire fence\"}, {\"id\": 23785, \"name\": \"the red capri leggings\"}, {\"id\": 23786, \"name\": \"a grey trousers\"}, {\"id\": 23787, \"name\": \"the lever\"}, {\"id\": 23788, \"name\": \"the number 18\"}, {\"id\": 23789, \"name\": \"the white knit hat\"}, {\"id\": 23790, \"name\": \"a shrub\"}, {\"id\": 23791, \"name\": \"collection of colorful kites\"}, {\"id\": 23792, \"name\": \"a game's clock\"}, {\"id\": 23793, \"name\": \"small container of white rice\"}, {\"id\": 23794, \"name\": \"a prominent blue advertisement banner\"}, {\"id\": 23795, \"name\": \"interior of the rickshaw\"}, {\"id\": 23796, \"name\": \"the small, green plant\"}, {\"id\": 23797, \"name\": \"the heavy bag\"}, {\"id\": 23798, \"name\": \"the white and orange strip\"}, {\"id\": 23799, \"name\": \"a smaller black punching bag\"}, {\"id\": 23800, \"name\": \"a keychain holder\"}, {\"id\": 23801, \"name\": \"gold bezel\"}, {\"id\": 23802, \"name\": \"the camouflage clothing\"}, {\"id\": 23803, \"name\": \"red circle with a line through it\"}, {\"id\": 23804, \"name\": \"the blue paddle\"}, {\"id\": 23805, \"name\": \"the large cluster of coral\"}, {\"id\": 23806, \"name\": \"the blue icon\"}, {\"id\": 23807, \"name\": \"plywood\"}, {\"id\": 23808, \"name\": \"white switch\"}, {\"id\": 23809, \"name\": \"boat's railing\"}, {\"id\": 23810, \"name\": \"the yellow wheel\"}, {\"id\": 23811, \"name\": \"the orc\"}, {\"id\": 23812, \"name\": \"vehicle's front wheel\"}, {\"id\": 23813, \"name\": \"rear end of a bus\"}, {\"id\": 23814, \"name\": \"pink robot\"}, {\"id\": 23815, \"name\": \"a tall gray metal cabinet\"}, {\"id\": 23816, \"name\": \"a cycist\"}, {\"id\": 23817, \"name\": \"yellow kettlebell\"}, {\"id\": 23818, \"name\": \"the prominent yellow Volvo front-end loader\"}, {\"id\": 23819, \"name\": \"the paintbrush handle\"}, {\"id\": 23820, \"name\": \"a large, white concrete sphere\"}, {\"id\": 23821, \"name\": \"black punching bag\"}, {\"id\": 23822, \"name\": \"brown and black fur\"}, {\"id\": 23823, \"name\": \"the green gate\"}, {\"id\": 23824, \"name\": \"jumping rope\"}, {\"id\": 23825, \"name\": \"red and white painted curb\"}, {\"id\": 23826, \"name\": \"the pink collared shirt\"}, {\"id\": 23827, \"name\": \"no parking sign\"}, {\"id\": 23828, \"name\": \"an orange pillow\"}, {\"id\": 23829, \"name\": \"the green sleeve\"}, {\"id\": 23830, \"name\": \"a metal fence\"}, {\"id\": 23831, \"name\": \"glimpse of greenery\"}, {\"id\": 23832, \"name\": \"the red and brown patterned rug\"}, {\"id\": 23833, \"name\": \"a bus interior\"}, {\"id\": 23834, \"name\": \"a Castrol EDGE logo\"}, {\"id\": 23835, \"name\": \"the gray door\"}, {\"id\": 23836, \"name\": \"the duck's head\"}, {\"id\": 23837, \"name\": \"a white and red stripe\"}, {\"id\": 23838, \"name\": \"the red mouth\"}, {\"id\": 23839, \"name\": \"muddy motorcycle\"}, {\"id\": 23840, \"name\": \"neon yellow shoe\"}, {\"id\": 23841, \"name\": \"the large black helicopter\"}, {\"id\": 23842, \"name\": \"auto-rickshaw\"}, {\"id\": 23843, \"name\": \"a stacked wood plank\"}, {\"id\": 23844, \"name\": \"sandals\"}, {\"id\": 23845, \"name\": \"labradoodle\"}, {\"id\": 23846, \"name\": \"an athletic pant\"}, {\"id\": 23847, \"name\": \"the sunglasses\"}, {\"id\": 23848, \"name\": \"the V-neckline\"}, {\"id\": 23849, \"name\": \"the black and white diving mask\"}, {\"id\": 23850, \"name\": \"a Big Ben clock tower\"}, {\"id\": 23851, \"name\": \"police car\"}, {\"id\": 23852, \"name\": \"blue crown\"}, {\"id\": 23853, \"name\": \"the piece of bacon\"}, {\"id\": 23854, \"name\": \"the unique license plate\"}, {\"id\": 23855, \"name\": \"traditional conical hat\"}, {\"id\": 23856, \"name\": \"a tall silver ladder\"}, {\"id\": 23857, \"name\": \"Mastercard logo\"}, {\"id\": 23858, \"name\": \"the white hamster\"}, {\"id\": 23859, \"name\": \"the purple piece\"}, {\"id\": 23860, \"name\": \"orange Frisbee\"}, {\"id\": 23861, \"name\": \"a corded power tool\"}, {\"id\": 23862, \"name\": \"a cigarette\"}, {\"id\": 23863, \"name\": \"the mound\"}, {\"id\": 23864, \"name\": \"sleeveless top\"}, {\"id\": 23865, \"name\": \"a black medicine ball\"}, {\"id\": 23866, \"name\": \"the gray and black shirt\"}, {\"id\": 23867, \"name\": \"the crosswalk sign\"}, {\"id\": 23868, \"name\": \"red painted curb\"}, {\"id\": 23869, \"name\": \"a bench press\"}, {\"id\": 23870, \"name\": \"the transparent plastic piece\"}, {\"id\": 23871, \"name\": \"the gray circular eye\"}, {\"id\": 23872, \"name\": \"a small floating platform\"}, {\"id\": 23873, \"name\": \"a red and gold vest\"}, {\"id\": 23874, \"name\": \"a yellow sports car\"}, {\"id\": 23875, \"name\": \"orange plastic barrier\"}, {\"id\": 23876, \"name\": \"white chef's hat\"}, {\"id\": 23877, \"name\": \"a lush, green grassy area\"}, {\"id\": 23878, \"name\": \"the square shape\"}, {\"id\": 23879, \"name\": \"a colored ring\"}, {\"id\": 23880, \"name\": \"the orange handle\"}, {\"id\": 23881, \"name\": \"a row of toy vehicles\"}, {\"id\": 23882, \"name\": \"a black tag\"}, {\"id\": 23883, \"name\": \"the large, white barrel\"}, {\"id\": 23884, \"name\": \"yellow and white striped crosswalk\"}, {\"id\": 23885, \"name\": \"the athletic short\"}, {\"id\": 23886, \"name\": \"red and silver ball\"}, {\"id\": 23887, \"name\": \"a yellow and black motorcycle\"}, {\"id\": 23888, \"name\": \"the tram's number 3\"}, {\"id\": 23889, \"name\": \"white water tank\"}, {\"id\": 23890, \"name\": \"the grid-like pattern\"}, {\"id\": 23891, \"name\": \"a cartoon condom character\"}, {\"id\": 23892, \"name\": \"a weights\"}, {\"id\": 23893, \"name\": \"tram's roof\"}, {\"id\": 23894, \"name\": \"small side table\"}, {\"id\": 23895, \"name\": \"a red candy\"}, {\"id\": 23896, \"name\": \"pectoral muscle\"}, {\"id\": 23897, \"name\": \"man holding a bicycle\"}, {\"id\": 23898, \"name\": \"large blue and orange paraglider\"}, {\"id\": 23899, \"name\": \"the red SUV\"}, {\"id\": 23900, \"name\": \"a pair of tan tights\"}, {\"id\": 23901, \"name\": \"the white hand icon\"}, {\"id\": 23902, \"name\": \"an illuminated sail\"}, {\"id\": 23903, \"name\": \"the red hoop\"}, {\"id\": 23904, \"name\": \"the large shipping container\"}, {\"id\": 23905, \"name\": \"a wooden fence post\"}, {\"id\": 23906, \"name\": \"white marble or ceramic\"}, {\"id\": 23907, \"name\": \"large white house\"}, {\"id\": 23908, \"name\": \"red tank top\"}, {\"id\": 23909, \"name\": \"the small garden bed\"}, {\"id\": 23910, \"name\": \"the illuminated sign\"}, {\"id\": 23911, \"name\": \"collage of characters\"}, {\"id\": 23912, \"name\": \"an illustration of dog\"}, {\"id\": 23913, \"name\": \"cluster of 20 golf balls\"}, {\"id\": 23914, \"name\": \"the white center line\"}, {\"id\": 23915, \"name\": \"red and blue metal structure\"}, {\"id\": 23916, \"name\": \"red and white marking\"}, {\"id\": 23917, \"name\": \"a white ladder\"}, {\"id\": 23918, \"name\": \"black storage container\"}, {\"id\": 23919, \"name\": \"the store's frontage\"}, {\"id\": 23920, \"name\": \"bicycle component\"}, {\"id\": 23921, \"name\": \"the black brush\"}, {\"id\": 23922, \"name\": \"prominent crane\"}, {\"id\": 23923, \"name\": \"black knob\"}, {\"id\": 23924, \"name\": \"plastic\"}, {\"id\": 23925, \"name\": \"white SUV police car\"}, {\"id\": 23926, \"name\": \"the TikTok logo\"}, {\"id\": 23927, \"name\": \"the colorful paper lantern\"}, {\"id\": 23928, \"name\": \"baby toy\"}, {\"id\": 23929, \"name\": \"the fan coral\"}, {\"id\": 23930, \"name\": \"the row of colorful balloons\"}, {\"id\": 23931, \"name\": \"the character in blue\"}, {\"id\": 23932, \"name\": \"small stump\"}, {\"id\": 23933, \"name\": \"a long dress\"}, {\"id\": 23934, \"name\": \"red and black control panel\"}, {\"id\": 23935, \"name\": \"weight bench\"}, {\"id\": 23936, \"name\": \"red and white striped column\"}, {\"id\": 23937, \"name\": \"the large green grassy area\"}, {\"id\": 23938, \"name\": \"a dark brown coat\"}, {\"id\": 23939, \"name\": \"the dark blue hull\"}, {\"id\": 23940, \"name\": \"four dogs\"}, {\"id\": 23941, \"name\": \"a spool of thread\"}, {\"id\": 23942, \"name\": \"the blue section of the track\"}, {\"id\": 23943, \"name\": \"white goal net\"}, {\"id\": 23944, \"name\": \"a small, toy-like vehicle\"}, {\"id\": 23945, \"name\": \"a long handle\"}, {\"id\": 23946, \"name\": \"a green candy\"}, {\"id\": 23947, \"name\": \"the large metal ball\"}, {\"id\": 23948, \"name\": \"pedestrian crossing sign\"}, {\"id\": 23949, \"name\": \"a gray tank top\"}, {\"id\": 23950, \"name\": \"the bright orange traffic cone\"}, {\"id\": 23951, \"name\": \"a red surface\"}, {\"id\": 23952, \"name\": \"the large tarp or plastic sheet\"}, {\"id\": 23953, \"name\": \"a hockey puck\"}, {\"id\": 23954, \"name\": \"the large fire\"}, {\"id\": 23955, \"name\": \"yellow sports car\"}, {\"id\": 23956, \"name\": \"the darker brown nose\"}, {\"id\": 23957, \"name\": \"the statue of a person on horseback\"}, {\"id\": 23958, \"name\": \"white bowling ball\"}, {\"id\": 23959, \"name\": \"the icing drip\"}, {\"id\": 23960, \"name\": \"blue section of the bus\"}, {\"id\": 23961, \"name\": \"a lit sign\"}, {\"id\": 23962, \"name\": \"a smaller punching bag\"}, {\"id\": 23963, \"name\": \"a carved elephant design\"}, {\"id\": 23964, \"name\": \"small crab or crustacean\"}, {\"id\": 23965, \"name\": \"the small orange head\"}, {\"id\": 23966, \"name\": \"a dark blue hoodie\"}, {\"id\": 23967, \"name\": \"a large crowd of people\"}, {\"id\": 23968, \"name\": \"the blue block\"}, {\"id\": 23969, \"name\": \"the prominent portrait of a man's face\"}, {\"id\": 23970, \"name\": \"a gray door\"}, {\"id\": 23971, \"name\": \"a driver's window\"}, {\"id\": 23972, \"name\": \"a grey asphalt surface\"}, {\"id\": 23973, \"name\": \"the rack of bars\"}, {\"id\": 23974, \"name\": \"the group of puppies\"}, {\"id\": 23975, \"name\": \"man in a plaid shirt\"}, {\"id\": 23976, \"name\": \"a square piece of tan-colored material\"}, {\"id\": 23977, \"name\": \"a QR code sign\"}, {\"id\": 23978, \"name\": \"a woman's left arm\"}, {\"id\": 23979, \"name\": \"white hockey stick\"}, {\"id\": 23980, \"name\": \"red star\"}, {\"id\": 23981, \"name\": \"the mountainous area\"}, {\"id\": 23982, \"name\": \"the hurdle\"}, {\"id\": 23983, \"name\": \"the red and white shopping bag\"}, {\"id\": 23984, \"name\": \"a dark line of trees\"}, {\"id\": 23985, \"name\": \"tall metal power line tower\"}, {\"id\": 23986, \"name\": \"the chopstick\"}, {\"id\": 23987, \"name\": \"a waistband\"}, {\"id\": 23988, \"name\": \"player's leg\"}, {\"id\": 23989, \"name\": \"a large seat\"}, {\"id\": 23990, \"name\": \"blue pom-pom\"}, {\"id\": 23991, \"name\": \"the white tractor\"}, {\"id\": 23992, \"name\": \"shallow, clear water\"}, {\"id\": 23993, \"name\": \"the gold dome\"}, {\"id\": 23994, \"name\": \"the large, round engine\"}, {\"id\": 23995, \"name\": \"the dark-colored hat\"}, {\"id\": 23996, \"name\": \"a large white teapot sculpture\"}, {\"id\": 23997, \"name\": \"the athletic wear\"}, {\"id\": 23998, \"name\": \"the pink hat\"}, {\"id\": 23999, \"name\": \"the black hockey stick\"}, {\"id\": 24000, \"name\": \"red-framed archway\"}, {\"id\": 24001, \"name\": \"the silver serving spoon\"}, {\"id\": 24002, \"name\": \"the large halfpipe\"}, {\"id\": 24003, \"name\": \"green sock\"}, {\"id\": 24004, \"name\": \"a crocheted object\"}, {\"id\": 24005, \"name\": \"a white handle\"}, {\"id\": 24006, \"name\": \"a green plastic crate\"}, {\"id\": 24007, \"name\": \"red wicket\"}, {\"id\": 24008, \"name\": \"green and yellow saddle\"}, {\"id\": 24009, \"name\": \"a Hello Kitty\"}, {\"id\": 24010, \"name\": \"a hockey net\"}, {\"id\": 24011, \"name\": \"a series of colored rings\"}, {\"id\": 24012, \"name\": \"clamp\"}, {\"id\": 24013, \"name\": \"the roadside\"}, {\"id\": 24014, \"name\": \"small tennis racket\"}, {\"id\": 24015, \"name\": \"a white camper\"}, {\"id\": 24016, \"name\": \"thick, grey cloud\"}, {\"id\": 24017, \"name\": \"man on a bike\"}, {\"id\": 24018, \"name\": \"orange square\"}, {\"id\": 24019, \"name\": \"a drummer's arm\"}, {\"id\": 24020, \"name\": \"a black water bottle\"}, {\"id\": 24021, \"name\": \"red shorts\"}, {\"id\": 24022, \"name\": \"the large, orange truck\"}, {\"id\": 24023, \"name\": \"small island or peninsula\"}, {\"id\": 24024, \"name\": \"the large yellow garage door\"}, {\"id\": 24025, \"name\": \"row of cartoon goldfish\"}, {\"id\": 24026, \"name\": \"an orange traffic barrel\"}, {\"id\": 24027, \"name\": \"the long black fringe\"}, {\"id\": 24028, \"name\": \"kettlebell workout\"}, {\"id\": 24029, \"name\": \"a pitcher's mound\"}, {\"id\": 24030, \"name\": \"the backboard\"}, {\"id\": 24031, \"name\": \"a yellow cone\"}, {\"id\": 24032, \"name\": \"a red brick border\"}, {\"id\": 24033, \"name\": \"the raised stage\"}, {\"id\": 24034, \"name\": \"the skein of wool\"}, {\"id\": 24035, \"name\": \"a colorful game board design\"}, {\"id\": 24036, \"name\": \"BMX bike\"}, {\"id\": 24037, \"name\": \"adult instructor\"}, {\"id\": 24038, \"name\": \"Emergency Instructions\"}, {\"id\": 24039, \"name\": \"black easel\"}, {\"id\": 24040, \"name\": \"the large, black, metal pan or grill\"}, {\"id\": 24041, \"name\": \"the teal car\"}, {\"id\": 24042, \"name\": \"the blue cap\"}, {\"id\": 24043, \"name\": \"tiger's shadow\"}, {\"id\": 24044, \"name\": \"gray shirt with blue flowers\"}, {\"id\": 24045, \"name\": \"red air compressor\"}, {\"id\": 24046, \"name\": \"smaller billboard\"}, {\"id\": 24047, \"name\": \"a red play button\"}, {\"id\": 24048, \"name\": \"the long pant\"}, {\"id\": 24049, \"name\": \"the large, round, silver metal bowl or container\"}, {\"id\": 24050, \"name\": \"black scuba tank\"}, {\"id\": 24051, \"name\": \"red digital display\"}, {\"id\": 24052, \"name\": \"the tan spot\"}, {\"id\": 24053, \"name\": \"a rear window of the taxi\"}, {\"id\": 24054, \"name\": \"the blue and white striped barrier\"}, {\"id\": 24055, \"name\": \"white and yellow flower backdrop\"}, {\"id\": 24056, \"name\": \"five drums\"}, {\"id\": 24057, \"name\": \"yellow and orange object\"}, {\"id\": 24058, \"name\": \"the thick, murky water\"}, {\"id\": 24059, \"name\": \"game's HUD\"}, {\"id\": 24060, \"name\": \"a sunglasses\"}, {\"id\": 24061, \"name\": \"the yellow headband\"}, {\"id\": 24062, \"name\": \"the tall utility pole\"}, {\"id\": 24063, \"name\": \"a dark grey train car\"}, {\"id\": 24064, \"name\": \"TikTok\"}, {\"id\": 24065, \"name\": \"red light bar\"}, {\"id\": 24066, \"name\": \"a large, red object\"}, {\"id\": 24067, \"name\": \"the white square object\"}, {\"id\": 24068, \"name\": \"large cargo ship\"}, {\"id\": 24069, \"name\": \"a distinctive dome\"}, {\"id\": 24070, \"name\": \"the white food truck\"}, {\"id\": 24071, \"name\": \"the white and red uniform\"}, {\"id\": 24072, \"name\": \"orange helmet\"}, {\"id\": 24073, \"name\": \"the Nike logo\"}, {\"id\": 24074, \"name\": \"a blue overall\"}, {\"id\": 24075, \"name\": \"a creature resembling a salmon\"}, {\"id\": 24076, \"name\": \"a pink tank top\"}, {\"id\": 24077, \"name\": \"vibrant red and white parachute\"}, {\"id\": 24078, \"name\": \"stack of partially completed skateboards\"}, {\"id\": 24079, \"name\": \"metal rolling stool\"}, {\"id\": 24080, \"name\": \"close-up of a weightlifting barbell\"}, {\"id\": 24081, \"name\": \"the dark brown saddle\"}, {\"id\": 24082, \"name\": \"a muddy water\"}, {\"id\": 24083, \"name\": \"a crowd of onlookers\"}, {\"id\": 24084, \"name\": \"silver Ferrari logo\"}, {\"id\": 24085, \"name\": \"the row of lights\"}, {\"id\": 24086, \"name\": \"white paneling\"}, {\"id\": 24087, \"name\": \"the red goal\"}, {\"id\": 24088, \"name\": \"a green platform\"}, {\"id\": 24089, \"name\": \"the small dirt bike\"}, {\"id\": 24090, \"name\": \"the navy blue T-shirt\"}, {\"id\": 24091, \"name\": \"the large gray dump truck\"}, {\"id\": 24092, \"name\": \"the blue and yellow fish\"}, {\"id\": 24093, \"name\": \"the purple dumbbell\"}, {\"id\": 24094, \"name\": \"a red marking\"}, {\"id\": 24095, \"name\": \"the bus's destination sign\"}, {\"id\": 24096, \"name\": \"a red kayak\"}, {\"id\": 24097, \"name\": \"the red cooler\"}, {\"id\": 24098, \"name\": \"the large, round, teal target\"}, {\"id\": 24099, \"name\": \"a blue motor\"}, {\"id\": 24100, \"name\": \"a clear plastic cap\"}, {\"id\": 24101, \"name\": \"the festive red and yellow lanterns\"}, {\"id\": 24102, \"name\": \"tan-colored car\"}, {\"id\": 24103, \"name\": \"curved ramp\"}, {\"id\": 24104, \"name\": \"a small pile of green and blue objects\"}, {\"id\": 24105, \"name\": \"player's score\"}, {\"id\": 24106, \"name\": \"the light green shirt\"}, {\"id\": 24107, \"name\": \"the blue crate\"}, {\"id\": 24108, \"name\": \"pedestrian crosswalk\"}, {\"id\": 24109, \"name\": \"silver earring\"}, {\"id\": 24110, \"name\": \"transparent, circular, plastic stand\"}, {\"id\": 24111, \"name\": \"the brown saddle\"}, {\"id\": 24112, \"name\": \"the large ship\"}, {\"id\": 24113, \"name\": \"a white taxi cab\"}, {\"id\": 24114, \"name\": \"the mosque\"}, {\"id\": 24115, \"name\": \"a piece of dough\"}, {\"id\": 24116, \"name\": \"large digital scoreboard\"}, {\"id\": 24117, \"name\": \"the red and blue striped shirt\"}, {\"id\": 24118, \"name\": \"the rear of the bus\"}, {\"id\": 24119, \"name\": \"the goalie's pad\"}, {\"id\": 24120, \"name\": \"a black label\"}, {\"id\": 24121, \"name\": \"a red and orange pillow\"}, {\"id\": 24122, \"name\": \"brown toy\"}, {\"id\": 24123, \"name\": \"red brick accent\"}, {\"id\": 24124, \"name\": \"blue coral\"}, {\"id\": 24125, \"name\": \"red and white lid\"}, {\"id\": 24126, \"name\": \"yellow boot\"}, {\"id\": 24127, \"name\": \"hookah\"}, {\"id\": 24128, \"name\": \"the back of his head\"}, {\"id\": 24129, \"name\": \"blue cleat\"}, {\"id\": 24130, \"name\": \"a stone lantern\"}, {\"id\": 24131, \"name\": \"the large firework\"}, {\"id\": 24132, \"name\": \"a dog's face\"}, {\"id\": 24133, \"name\": \"red weight\"}, {\"id\": 24134, \"name\": \"the drainage grate\"}, {\"id\": 24135, \"name\": \"the score counter\"}, {\"id\": 24136, \"name\": \"a rock face\"}, {\"id\": 24137, \"name\": \"small drainage channel\"}, {\"id\": 24138, \"name\": \"the energy drink\"}, {\"id\": 24139, \"name\": \"assortment of eyeshadow colors\"}, {\"id\": 24140, \"name\": \"the exercise machine\"}, {\"id\": 24141, \"name\": \"the yellow house\"}, {\"id\": 24142, \"name\": \"a white advertisement\"}, {\"id\": 24143, \"name\": \"the white paper towel roll\"}, {\"id\": 24144, \"name\": \"the bald head\"}, {\"id\": 24145, \"name\": \"the caution tape\"}, {\"id\": 24146, \"name\": \"large door\"}, {\"id\": 24147, \"name\": \"red hockey jersey\"}, {\"id\": 24148, \"name\": \"the dirt bike rider\"}, {\"id\": 24149, \"name\": \"the white and black plaid shirt\"}, {\"id\": 24150, \"name\": \"the airplane\"}, {\"id\": 24151, \"name\": \"the breathing apparatus\"}, {\"id\": 24152, \"name\": \"black and white outfit\"}, {\"id\": 24153, \"name\": \"a neon orange safety vest\"}, {\"id\": 24154, \"name\": \"the large NBA logo\"}, {\"id\": 24155, \"name\": \"the stick's handle\"}, {\"id\": 24156, \"name\": \"a massive, fire-breathing dragon-like creature\"}, {\"id\": 24157, \"name\": \"a wooden paneling\"}, {\"id\": 24158, \"name\": \"the white jeans\"}, {\"id\": 24159, \"name\": \"tall, multi-story structure\"}, {\"id\": 24160, \"name\": \"the dumpster\"}, {\"id\": 24161, \"name\": \"the dark blue sedan\"}, {\"id\": 24162, \"name\": \"yellow tarp\"}, {\"id\": 24163, \"name\": \"brake lever of a bicycle\"}, {\"id\": 24164, \"name\": \"a chew toy\"}, {\"id\": 24165, \"name\": \"dark-colored skateboard\"}, {\"id\": 24166, \"name\": \"a front license plate\"}, {\"id\": 24167, \"name\": \"racing wheel\"}, {\"id\": 24168, \"name\": \"a red brick surface\"}, {\"id\": 24169, \"name\": \"the green shrub\"}, {\"id\": 24170, \"name\": \"the rink's board\"}, {\"id\": 24171, \"name\": \"an exposed wooden beam\"}, {\"id\": 24172, \"name\": \"gray leggings\"}, {\"id\": 24173, \"name\": \"a gray body\"}, {\"id\": 24174, \"name\": \"an intricate stone and brickwork\"}, {\"id\": 24175, \"name\": \"the child's right arm\"}, {\"id\": 24176, \"name\": \"blue cone\"}, {\"id\": 24177, \"name\": \"the black treadmill\"}, {\"id\": 24178, \"name\": \"a large, messy, and dirty frying pan\"}, {\"id\": 24179, \"name\": \"a large wok\"}, {\"id\": 24180, \"name\": \"a pink swirl\"}, {\"id\": 24181, \"name\": \"the abs\"}, {\"id\": 24182, \"name\": \"red lace\"}, {\"id\": 24183, \"name\": \"a large, black, cylindrical object\"}, {\"id\": 24184, \"name\": \"Hot Wheels packaging box\"}, {\"id\": 24185, \"name\": \"snow-covered sidewalk\"}, {\"id\": 24186, \"name\": \"a lead rider\"}, {\"id\": 24187, \"name\": \"an orange and white barricade\"}, {\"id\": 24188, \"name\": \"the guppy\"}, {\"id\": 24189, \"name\": \"the black mask\"}, {\"id\": 24190, \"name\": \"shaft of the stick\"}, {\"id\": 24191, \"name\": \"the silver bicycle\"}, {\"id\": 24192, \"name\": \"a row of shops and restaurant\"}, {\"id\": 24193, \"name\": \"the small clear container\"}, {\"id\": 24194, \"name\": \"the black paddle\"}, {\"id\": 24195, \"name\": \"row of exercise machine\"}, {\"id\": 24196, \"name\": \"a black and white square-shaped component\"}, {\"id\": 24197, \"name\": \"orange-colored structure\"}, {\"id\": 24198, \"name\": \"red and yellow hockey goal net\"}, {\"id\": 24199, \"name\": \"dial or gauge\"}, {\"id\": 24200, \"name\": \"the dark baseboard\"}, {\"id\": 24201, \"name\": \"a bright orange forklift\"}, {\"id\": 24202, \"name\": \"Ford emblem\"}, {\"id\": 24203, \"name\": \"large blue door\"}, {\"id\": 24204, \"name\": \"the dark brown ear\"}, {\"id\": 24205, \"name\": \"the metallic bra\"}, {\"id\": 24206, \"name\": \"the men\"}, {\"id\": 24207, \"name\": \"the hood of the car\"}, {\"id\": 24208, \"name\": \"the white pot\"}, {\"id\": 24209, \"name\": \"the red column\"}, {\"id\": 24210, \"name\": \"the seated position\"}, {\"id\": 24211, \"name\": \"the black hockey stick holder\"}, {\"id\": 24212, \"name\": \"red-hot metal\"}, {\"id\": 24213, \"name\": \"thumbs-up gesture\"}, {\"id\": 24214, \"name\": \"a black suspenders\"}, {\"id\": 24215, \"name\": \"a plastic roll\"}, {\"id\": 24216, \"name\": \"the surfboard\"}, {\"id\": 24217, \"name\": \"a blurred drummer\"}, {\"id\": 24218, \"name\": \"orange bar\"}, {\"id\": 24219, \"name\": \"yellow, industrial-sized object\"}, {\"id\": 24220, \"name\": \"the yellow pants\"}, {\"id\": 24221, \"name\": \"small, brown creature\"}, {\"id\": 24222, \"name\": \"a black, circular metal object\"}, {\"id\": 24223, \"name\": \"blue hose\"}, {\"id\": 24224, \"name\": \"a large red and white sign\"}, {\"id\": 24225, \"name\": \"the go-kart track\"}, {\"id\": 24226, \"name\": \"a large, red and green mural\"}, {\"id\": 24227, \"name\": \"a mountain bike\"}, {\"id\": 24228, \"name\": \"vibrant orange trolley train\"}, {\"id\": 24229, \"name\": \"a gray snow pant\"}, {\"id\": 24230, \"name\": \"the loader's window\"}, {\"id\": 24231, \"name\": \"a dark blue or black pant\"}, {\"id\": 24232, \"name\": \"the cluster of blue creatures\"}, {\"id\": 24233, \"name\": \"colorful coral\"}, {\"id\": 24234, \"name\": \"turquoise waistband\"}, {\"id\": 24235, \"name\": \"the yellow tape roll\"}, {\"id\": 24236, \"name\": \"a concrete drain\"}, {\"id\": 24237, \"name\": \"a Union Jack flag\"}, {\"id\": 24238, \"name\": \"motor\"}, {\"id\": 24239, \"name\": \"the cathedral\"}, {\"id\": 24240, \"name\": \"smaller white circle\"}, {\"id\": 24241, \"name\": \"the paved with alternating light and dark gray blocks\"}, {\"id\": 24242, \"name\": \"lush green grass surface\"}, {\"id\": 24243, \"name\": \"ocean's shoreline\"}, {\"id\": 24244, \"name\": \"the thick grey cloud\"}, {\"id\": 24245, \"name\": \"the glass storefront\"}, {\"id\": 24246, \"name\": \"glowing eye\"}, {\"id\": 24247, \"name\": \"small, yellow barrier\"}, {\"id\": 24248, \"name\": \"a gear shifter\"}, {\"id\": 24249, \"name\": \"the cat's tail\"}, {\"id\": 24250, \"name\": \"a blue divider\"}, {\"id\": 24251, \"name\": \"diver's head\"}, {\"id\": 24252, \"name\": \"an orange and black life jacket\"}, {\"id\": 24253, \"name\": \"pair of silver pliers\"}, {\"id\": 24254, \"name\": \"red, white, and yellow go-kart\"}, {\"id\": 24255, \"name\": \"the small garden area\"}, {\"id\": 24256, \"name\": \"small screen or monitor\"}, {\"id\": 24257, \"name\": \"the row of tents\"}, {\"id\": 24258, \"name\": \"black plastic food container\"}, {\"id\": 24259, \"name\": \"the forested area\"}, {\"id\": 24260, \"name\": \"a long hose\"}, {\"id\": 24261, \"name\": \"a concession stand\"}, {\"id\": 24262, \"name\": \"helm\"}, {\"id\": 24263, \"name\": \"the moss\"}, {\"id\": 24264, \"name\": \"a red and black control panel\"}, {\"id\": 24265, \"name\": \"yellowish net\"}, {\"id\": 24266, \"name\": \"dark blue sweater\"}, {\"id\": 24267, \"name\": \"the steeples\"}, {\"id\": 24268, \"name\": \"the small sticker\"}, {\"id\": 24269, \"name\": \"large white tile\"}, {\"id\": 24270, \"name\": \"the residential or commercial structure\"}, {\"id\": 24271, \"name\": \"a row of colorful toy ducks\"}, {\"id\": 24272, \"name\": \"the white and black soccer ball\"}, {\"id\": 24273, \"name\": \"the crowded grandstand\"}, {\"id\": 24274, \"name\": \"the camel's head\"}, {\"id\": 24275, \"name\": \"beetle\"}, {\"id\": 24276, \"name\": \"the large headlight\"}, {\"id\": 24277, \"name\": \"the white Nike logo\"}, {\"id\": 24278, \"name\": \"the white figure\"}, {\"id\": 24279, \"name\": \"food cart\"}, {\"id\": 24280, \"name\": \"yellow pedestrian sign\"}, {\"id\": 24281, \"name\": \"the backdrop of trees\"}, {\"id\": 24282, \"name\": \"the boat's engine\"}, {\"id\": 24283, \"name\": \"the small white circle\"}, {\"id\": 24284, \"name\": \"yellow and orange wing\"}, {\"id\": 24285, \"name\": \"large red banner\"}, {\"id\": 24286, \"name\": \"a human\"}, {\"id\": 24287, \"name\": \"dark-colored cap\"}, {\"id\": 24288, \"name\": \"black and white boxing glove\"}, {\"id\": 24289, \"name\": \"a row of hedges\"}, {\"id\": 24290, \"name\": \"the star-shaped cutout\"}, {\"id\": 24291, \"name\": \"red and black Western train\"}, {\"id\": 24292, \"name\": \"the folded white card\"}, {\"id\": 24293, \"name\": \"a row of card\"}, {\"id\": 24294, \"name\": \"a bike's wheel\"}, {\"id\": 24295, \"name\": \"the small icon\"}, {\"id\": 24296, \"name\": \"the yellow lemon-shaped logo\"}, {\"id\": 24297, \"name\": \"furry animal's back\"}, {\"id\": 24298, \"name\": \"red line\"}, {\"id\": 24299, \"name\": \"rash guard\"}, {\"id\": 24300, \"name\": \"the cartoon police officer\"}, {\"id\": 24301, \"name\": \"the circular indentation\"}, {\"id\": 24302, \"name\": \"the steam\"}, {\"id\": 24303, \"name\": \"a four-lane road\"}, {\"id\": 24304, \"name\": \"the blue and white icon\"}, {\"id\": 24305, \"name\": \"the karate gi\"}, {\"id\": 24306, \"name\": \"gold foil label\"}, {\"id\": 24307, \"name\": \"kickboxing pad\"}, {\"id\": 24308, \"name\": \"cream-colored exterior\"}, {\"id\": 24309, \"name\": \"a lone surfer\"}, {\"id\": 24310, \"name\": \"grid-like facade\"}, {\"id\": 24311, \"name\": \"yellow school bus\"}, {\"id\": 24312, \"name\": \"a white frosting\"}, {\"id\": 24313, \"name\": \"the dog bed\"}, {\"id\": 24314, \"name\": \"motorcycle's license plate\"}, {\"id\": 24315, \"name\": \"the red metal post\"}, {\"id\": 24316, \"name\": \"sausage or meat\"}, {\"id\": 24317, \"name\": \"axe head\"}, {\"id\": 24318, \"name\": \"a blue and white shoe\"}, {\"id\": 24319, \"name\": \"a two motorcycles\"}, {\"id\": 24320, \"name\": \"sliced mushroom\"}, {\"id\": 24321, \"name\": \"the Thai license plate\"}, {\"id\": 24322, \"name\": \"row of golf balls\"}, {\"id\": 24323, \"name\": \"a German Shepherd\"}, {\"id\": 24324, \"name\": \"telephone tower\"}, {\"id\": 24325, \"name\": \"the green leafy green\"}, {\"id\": 24326, \"name\": \"the orange bus\"}, {\"id\": 24327, \"name\": \"the large trench\"}, {\"id\": 24328, \"name\": \"a large tan shipping container\"}, {\"id\": 24329, \"name\": \"aerial view of a road\"}, {\"id\": 24330, \"name\": \"a small garden bed\"}, {\"id\": 24331, \"name\": \"the black saddlebag\"}, {\"id\": 24332, \"name\": \"red and black ATV\"}, {\"id\": 24333, \"name\": \"the player's left arm\"}, {\"id\": 24334, \"name\": \"the club head\"}, {\"id\": 24335, \"name\": \"brightly colored life jacket\"}, {\"id\": 24336, \"name\": \"the competitor\"}, {\"id\": 24337, \"name\": \"the tall white observation tower\"}, {\"id\": 24338, \"name\": \"white saddle blanket\"}, {\"id\": 24339, \"name\": \"the blue badminton racket\"}, {\"id\": 24340, \"name\": \"green tent\"}, {\"id\": 24341, \"name\": \"the truck's rear door\"}, {\"id\": 24342, \"name\": \"a blue rectangle\"}, {\"id\": 24343, \"name\": \"the green and black uniform\"}, {\"id\": 24344, \"name\": \"metal ceiling\"}, {\"id\": 24345, \"name\": \"a creature\"}, {\"id\": 24346, \"name\": \"a red toolbox\"}, {\"id\": 24347, \"name\": \"a smaller coral\"}, {\"id\": 24348, \"name\": \"a crossing signal\"}, {\"id\": 24349, \"name\": \"the brick walkway\"}, {\"id\": 24350, \"name\": \"the row of dumbbells\"}, {\"id\": 24351, \"name\": \"the red and white barrier\"}, {\"id\": 24352, \"name\": \"a brown border\"}, {\"id\": 24353, \"name\": \"a sleek silver sports car\"}, {\"id\": 24354, \"name\": \"a steep incline\"}, {\"id\": 24355, \"name\": \"the black weight\"}, {\"id\": 24356, \"name\": \"red test tube holder\"}, {\"id\": 24357, \"name\": \"the cycist\"}, {\"id\": 24358, \"name\": \"row of motorcycles\"}, {\"id\": 24359, \"name\": \"large black fan\"}, {\"id\": 24360, \"name\": \"green towel\"}, {\"id\": 24361, \"name\": \"the white and blue motorcycle\"}, {\"id\": 24362, \"name\": \"a wooden dowel\"}, {\"id\": 24363, \"name\": \"horseback\"}, {\"id\": 24364, \"name\": \"a Hot Wheels sign\"}, {\"id\": 24365, \"name\": \"large, yellow semi-truck\"}, {\"id\": 24366, \"name\": \"circular gauge\"}, {\"id\": 24367, \"name\": \"gray sock\"}, {\"id\": 24368, \"name\": \"a pink, circular object\"}, {\"id\": 24369, \"name\": \"the row of orange traffic cones\"}, {\"id\": 24370, \"name\": \"the large brown paper bag\"}, {\"id\": 24371, \"name\": \"the sponsor banner\"}, {\"id\": 24372, \"name\": \"gold seashell\"}, {\"id\": 24373, \"name\": \"the red and black bikini top\"}, {\"id\": 24374, \"name\": \"the paraglider\"}, {\"id\": 24375, \"name\": \"the trumpet's bell\"}, {\"id\": 24376, \"name\": \"the black and white puppy\"}, {\"id\": 24377, \"name\": \"the COOKIE\"}, {\"id\": 24378, \"name\": \"large green eye\"}, {\"id\": 24379, \"name\": \"wooden rattle\"}, {\"id\": 24380, \"name\": \"a vibrant and intricate miniature world\"}, {\"id\": 24381, \"name\": \"a small planter box\"}, {\"id\": 24382, \"name\": \"a dirt race track\"}, {\"id\": 24383, \"name\": \"the white and pink paisley design\"}, {\"id\": 24384, \"name\": \"a tall black metal structure\"}, {\"id\": 24385, \"name\": \"the black, pyramid-shaped structure\"}, {\"id\": 24386, \"name\": \"a red exercise bike\"}, {\"id\": 24387, \"name\": \"vibrant red scarf\"}, {\"id\": 24388, \"name\": \"sunglasses\"}, {\"id\": 24389, \"name\": \"the white life preserver\"}, {\"id\": 24390, \"name\": \"red wire\"}, {\"id\": 24391, \"name\": \"the large vehicle\"}, {\"id\": 24392, \"name\": \"blue, reflective surface\"}, {\"id\": 24393, \"name\": \"the black and neon green jersey\"}, {\"id\": 24394, \"name\": \"a yellow yoga mat\"}, {\"id\": 24395, \"name\": \"a large blue canopy\"}, {\"id\": 24396, \"name\": \"free weight\"}, {\"id\": 24397, \"name\": \"the glowing, iridescent circle\"}, {\"id\": 24398, \"name\": \"the red-shirted cyclist\"}, {\"id\": 24399, \"name\": \"metal bell\"}, {\"id\": 24400, \"name\": \"a floatie\"}, {\"id\": 24401, \"name\": \"a silver metal surface\"}, {\"id\": 24402, \"name\": \"a brown handle\"}, {\"id\": 24403, \"name\": \"a long, rectangular fluorescent light\"}, {\"id\": 24404, \"name\": \"a yoga pose\"}, {\"id\": 24405, \"name\": \"pink fabric with black polka dot\"}, {\"id\": 24406, \"name\": \"the blue bleacher\"}, {\"id\": 24407, \"name\": \"a black rollerblade\"}, {\"id\": 24408, \"name\": \"large shipping container\"}, {\"id\": 24409, \"name\": \"a small, white, spherical object\"}, {\"id\": 24410, \"name\": \"the framed jersey\"}, {\"id\": 24411, \"name\": \"the silver cross necklace\"}, {\"id\": 24412, \"name\": \"small blue bucket\"}, {\"id\": 24413, \"name\": \"a tall yellow stack of dog toys\"}, {\"id\": 24414, \"name\": \"eel's tail\"}, {\"id\": 24415, \"name\": \"aircraft's wing\"}, {\"id\": 24416, \"name\": \"a wheelie\"}, {\"id\": 24417, \"name\": \"the blue shoe cover\"}, {\"id\": 24418, \"name\": \"the yellow flag\"}, {\"id\": 24419, \"name\": \"white and orange kitten\"}, {\"id\": 24420, \"name\": \"short white dress\"}, {\"id\": 24421, \"name\": \"a tall, dark green pole\"}, {\"id\": 24422, \"name\": \"dark shadow\"}, {\"id\": 24423, \"name\": \"the row of teacup\"}, {\"id\": 24424, \"name\": \"a purple fairy costume\"}, {\"id\": 24425, \"name\": \"white earbud\"}, {\"id\": 24426, \"name\": \"the tropical headpiece\"}, {\"id\": 24427, \"name\": \"molten metal\"}, {\"id\": 24428, \"name\": \"plaid blanket\"}, {\"id\": 24429, \"name\": \"an exercise machine\"}, {\"id\": 24430, \"name\": \"the power line tower\"}, {\"id\": 24431, \"name\": \"large black piece of fabric\"}, {\"id\": 24432, \"name\": \"a middle image\"}, {\"id\": 24433, \"name\": \"orange exercise machine\"}, {\"id\": 24434, \"name\": \"small piece of blue tape\"}, {\"id\": 24435, \"name\": \"the red and white fence\"}, {\"id\": 24436, \"name\": \"the brown stripe\"}, {\"id\": 24437, \"name\": \"the row of tricycles\"}, {\"id\": 24438, \"name\": \"the yellow cab\"}, {\"id\": 24439, \"name\": \"the row of yellow posts\"}, {\"id\": 24440, \"name\": \"the rider's motorcycle\"}, {\"id\": 24441, \"name\": \"grey helmet\"}, {\"id\": 24442, \"name\": \"a bowling alley\"}, {\"id\": 24443, \"name\": \"a long metal blade\"}, {\"id\": 24444, \"name\": \"a mountainous area\"}, {\"id\": 24445, \"name\": \"a black inner ring\"}, {\"id\": 24446, \"name\": \"white box or bag\"}, {\"id\": 24447, \"name\": \"dog's nose\"}, {\"id\": 24448, \"name\": \"a distant boat\"}, {\"id\": 24449, \"name\": \"stack of paper\"}, {\"id\": 24450, \"name\": \"a purple section\"}, {\"id\": 24451, \"name\": \"silver strap\"}, {\"id\": 24452, \"name\": \"the shotgun\"}, {\"id\": 24453, \"name\": \"a small stack of blue brick\"}, {\"id\": 24454, \"name\": \"a yellow crosswalk\"}, {\"id\": 24455, \"name\": \"green golf mat\"}, {\"id\": 24456, \"name\": \"a snakes' head\"}, {\"id\": 24457, \"name\": \"red sucker\"}, {\"id\": 24458, \"name\": \"the scaffolding piece\"}, {\"id\": 24459, \"name\": \"large arched entryway\"}, {\"id\": 24460, \"name\": \"the closed blind\"}, {\"id\": 24461, \"name\": \"a metal claw\"}, {\"id\": 24462, \"name\": \"the black knee-high sock\"}, {\"id\": 24463, \"name\": \"white crown\"}, {\"id\": 24464, \"name\": \"the silver blade\"}, {\"id\": 24465, \"name\": \"high-rise structure\"}, {\"id\": 24466, \"name\": \"an electric-powered skateboard\"}, {\"id\": 24467, \"name\": \"a snorkeler\"}, {\"id\": 24468, \"name\": \"the headset\"}, {\"id\": 24469, \"name\": \"yellow crown\"}, {\"id\": 24470, \"name\": \"prominent goalpost\"}, {\"id\": 24471, \"name\": \"a green concrete platform\"}, {\"id\": 24472, \"name\": \"the white V\"}, {\"id\": 24473, \"name\": \"the white number 47\"}, {\"id\": 24474, \"name\": \"red drawstring backpack\"}, {\"id\": 24475, \"name\": \"the small square base\"}, {\"id\": 24476, \"name\": \"large gray shark\"}, {\"id\": 24477, \"name\": \"red chest protector\"}, {\"id\": 24478, \"name\": \"the ear protection\"}, {\"id\": 24479, \"name\": \"a brown belt\"}, {\"id\": 24480, \"name\": \"vibrant orange backdrop\"}, {\"id\": 24481, \"name\": \"train's front car\"}, {\"id\": 24482, \"name\": \"the light gray fur\"}, {\"id\": 24483, \"name\": \"the scattered tennis ball\"}, {\"id\": 24484, \"name\": \"a blue umbrella\"}, {\"id\": 24485, \"name\": \"green bench or table\"}, {\"id\": 24486, \"name\": \"a purple sari\"}, {\"id\": 24487, \"name\": \"the white wake\"}, {\"id\": 24488, \"name\": \"a black hammer\"}, {\"id\": 24489, \"name\": \"pile of rocks\"}, {\"id\": 24490, \"name\": \"blue and yellow bus\"}, {\"id\": 24491, \"name\": \"large set of bleachers\"}, {\"id\": 24492, \"name\": \"dirt ramp\"}, {\"id\": 24493, \"name\": \"the red and green train\"}, {\"id\": 24494, \"name\": \"kickboard\"}, {\"id\": 24495, \"name\": \"the purple bag\"}, {\"id\": 24496, \"name\": \"the plane\"}, {\"id\": 24497, \"name\": \"the pier\"}, {\"id\": 24498, \"name\": \"a mirror's reflection\"}, {\"id\": 24499, \"name\": \"the blue rim\"}, {\"id\": 24500, \"name\": \"a colorful toy\"}, {\"id\": 24501, \"name\": \"a purple design\"}, {\"id\": 24502, \"name\": \"small screw\"}, {\"id\": 24503, \"name\": \"black tights\"}, {\"id\": 24504, \"name\": \"US Open logo\"}, {\"id\": 24505, \"name\": \"the white surface of the workbench\"}, {\"id\": 24506, \"name\": \"a grey metal surface\"}, {\"id\": 24507, \"name\": \"brown planter box\"}, {\"id\": 24508, \"name\": \"a red handwritten number\"}, {\"id\": 24509, \"name\": \"the man in a red jacket\"}, {\"id\": 24510, \"name\": \"a driver in a car\"}, {\"id\": 24511, \"name\": \"the blue medical face mask\"}, {\"id\": 24512, \"name\": \"navy blue polo shirt\"}, {\"id\": 24513, \"name\": \"yellow food truck\"}, {\"id\": 24514, \"name\": \"the Jeep\"}, {\"id\": 24515, \"name\": \"spring\"}, {\"id\": 24516, \"name\": \"the tennis net\"}, {\"id\": 24517, \"name\": \"the small vent\"}, {\"id\": 24518, \"name\": \"the oxygen tank\"}, {\"id\": 24519, \"name\": \"thick, dark grey metal beam\"}, {\"id\": 24520, \"name\": \"the neon green helmet\"}, {\"id\": 24521, \"name\": \"large, thick rope\"}, {\"id\": 24522, \"name\": \"the white train car\"}, {\"id\": 24523, \"name\": \"the row of treadmill\"}, {\"id\": 24524, \"name\": \"the small, white, feathery sea fan\"}, {\"id\": 24525, \"name\": \"the metal piece of hardware\"}, {\"id\": 24526, \"name\": \"red and yellow graphic\"}, {\"id\": 24527, \"name\": \"a purple tank top\"}, {\"id\": 24528, \"name\": \"a small dirt mound\"}, {\"id\": 24529, \"name\": \"the surface of the water\"}, {\"id\": 24530, \"name\": \"man on the ATV\"}, {\"id\": 24531, \"name\": \"the blue headband\"}, {\"id\": 24532, \"name\": \"blue fence\"}, {\"id\": 24533, \"name\": \"the blurred blue curtain\"}, {\"id\": 24534, \"name\": \"a white bottle\"}, {\"id\": 24535, \"name\": \"the yellow motorcycle\"}, {\"id\": 24536, \"name\": \"large white statue of an angel\"}, {\"id\": 24537, \"name\": \"wet and dirty surface\"}, {\"id\": 24538, \"name\": \"the large video screen\"}, {\"id\": 24539, \"name\": \"orange ring\"}, {\"id\": 24540, \"name\": \"the vibrant red fabric\"}, {\"id\": 24541, \"name\": \"a dark uniform\"}, {\"id\": 24542, \"name\": \"the horseback\"}, {\"id\": 24543, \"name\": \"white basket\"}, {\"id\": 24544, \"name\": \"the pink traffic cone\"}, {\"id\": 24545, \"name\": \"a silver SUV\"}, {\"id\": 24546, \"name\": \"the large, metal spatula\"}, {\"id\": 24547, \"name\": \"bowl of nuts\"}, {\"id\": 24548, \"name\": \"the golf bag\"}, {\"id\": 24549, \"name\": \"a duck's beak\"}, {\"id\": 24550, \"name\": \"the black horse's head\"}, {\"id\": 24551, \"name\": \"white base plate\"}, {\"id\": 24552, \"name\": \"the tall red-and-white tower\"}, {\"id\": 24553, \"name\": \"white llama\"}, {\"id\": 24554, \"name\": \"the dirt track\"}, {\"id\": 24555, \"name\": \"plastic ring\"}, {\"id\": 24556, \"name\": \"the white flag\"}, {\"id\": 24557, \"name\": \"yellow playpen\"}, {\"id\": 24558, \"name\": \"large purple rock\"}, {\"id\": 24559, \"name\": \"medicine ball\"}, {\"id\": 24560, \"name\": \"the yellow flipper\"}, {\"id\": 24561, \"name\": \"short tail\"}, {\"id\": 24562, \"name\": \"the white goggles\"}, {\"id\": 24563, \"name\": \"dark tank top\"}, {\"id\": 24564, \"name\": \"red straw\"}, {\"id\": 24565, \"name\": \"the black and red machine\"}, {\"id\": 24566, \"name\": \"concrete bench\"}, {\"id\": 24567, \"name\": \"a black tripod\"}, {\"id\": 24568, \"name\": \"white and silver motor\"}, {\"id\": 24569, \"name\": \"the red pillar\"}, {\"id\": 24570, \"name\": \"light blue pole\"}, {\"id\": 24571, \"name\": \"fireplace\"}, {\"id\": 24572, \"name\": \"a pair of black, padded arm guard\"}, {\"id\": 24573, \"name\": \"the white gate\"}, {\"id\": 24574, \"name\": \"brown cat's head\"}, {\"id\": 24575, \"name\": \"the bouquet of red and white flowers\"}, {\"id\": 24576, \"name\": \"white and black racing suit\"}, {\"id\": 24577, \"name\": \"horns\"}, {\"id\": 24578, \"name\": \"a black-and-white information board\"}, {\"id\": 24579, \"name\": \"black and red Deere machine\"}, {\"id\": 24580, \"name\": \"a vibrant orange and black body\"}, {\"id\": 24581, \"name\": \"the colorful toy duck\"}, {\"id\": 24582, \"name\": \"the presence of adults\"}, {\"id\": 24583, \"name\": \"the small white tent\"}, {\"id\": 24584, \"name\": \"a machine's pipe\"}, {\"id\": 24585, \"name\": \"the navy collar\"}, {\"id\": 24586, \"name\": \"a distinctive brown nose\"}, {\"id\": 24587, \"name\": \"blurry red car\"}, {\"id\": 24588, \"name\": \"the large, open parking lot\"}, {\"id\": 24589, \"name\": \"a green plastic piece\"}, {\"id\": 24590, \"name\": \"yellow short-sleeved t-shirt\"}, {\"id\": 24591, \"name\": \"the black tripod\"}, {\"id\": 24592, \"name\": \"a DJ\"}, {\"id\": 24593, \"name\": \"granite-topped table\"}, {\"id\": 24594, \"name\": \"a brown nose\"}, {\"id\": 24595, \"name\": \"a concrete beam\"}, {\"id\": 24596, \"name\": \"the roll of black and gold tape\"}, {\"id\": 24597, \"name\": \"a black weightlifting belt\"}, {\"id\": 24598, \"name\": \"rider's black shirt\"}, {\"id\": 24599, \"name\": \"concrete patio\"}, {\"id\": 24600, \"name\": \"the large, blue, cylindrical machine\"}, {\"id\": 24601, \"name\": \"aquarium's base\"}, {\"id\": 24602, \"name\": \"black hood\"}, {\"id\": 24603, \"name\": \"large orange and yellow graphic\"}, {\"id\": 24604, \"name\": \"dark brown boot\"}, {\"id\": 24605, \"name\": \"the black pigtails\"}, {\"id\": 24606, \"name\": \"the black and white patterned pillow\"}, {\"id\": 24607, \"name\": \"the black nose\"}, {\"id\": 24608, \"name\": \"the black metal bridge\"}, {\"id\": 24609, \"name\": \"brown stuffed animal\"}, {\"id\": 24610, \"name\": \"a white and red line\"}, {\"id\": 24611, \"name\": \"the large white monument\"}, {\"id\": 24612, \"name\": \"person wearing a red hooded jacket\"}, {\"id\": 24613, \"name\": \"light blue object\"}, {\"id\": 24614, \"name\": \"the brown shorts\"}, {\"id\": 24615, \"name\": \"cratered surface\"}, {\"id\": 24616, \"name\": \"a vibrant red and pink light display\"}, {\"id\": 24617, \"name\": \"large, gray machine\"}, {\"id\": 24618, \"name\": \"the Balls\"}, {\"id\": 24619, \"name\": \"red shipping container\"}, {\"id\": 24620, \"name\": \"the pink bucket\"}, {\"id\": 24621, \"name\": \"red and yellow banner\"}, {\"id\": 24622, \"name\": \"tall cell tower\"}, {\"id\": 24623, \"name\": \"orange basketball\"}, {\"id\": 24624, \"name\": \"a blue dish\"}, {\"id\": 24625, \"name\": \"a leopard print body\"}, {\"id\": 24626, \"name\": \"large orange inflatable barrier\"}, {\"id\": 24627, \"name\": \"bus stop\"}, {\"id\": 24628, \"name\": \"dark wood paneling\"}, {\"id\": 24629, \"name\": \"collection of strawberries\"}, {\"id\": 24630, \"name\": \"a large side mirror\"}, {\"id\": 24631, \"name\": \"blue robot\"}, {\"id\": 24632, \"name\": \"the ambulance\"}, {\"id\": 24633, \"name\": \"a small, blue, square-shaped object\"}, {\"id\": 24634, \"name\": \"the black post\"}, {\"id\": 24635, \"name\": \"paper bag\"}, {\"id\": 24636, \"name\": \"a cartoon palm tree\"}, {\"id\": 24637, \"name\": \"a game's logo\"}, {\"id\": 24638, \"name\": \"a grey tank top\"}, {\"id\": 24639, \"name\": \"the gold barrel\"}, {\"id\": 24640, \"name\": \"the FedEx logo\"}, {\"id\": 24641, \"name\": \"a birds' plumage\"}, {\"id\": 24642, \"name\": \"brown pole\"}, {\"id\": 24643, \"name\": \"black punching pad\"}, {\"id\": 24644, \"name\": \"partially assembled Lego train\"}, {\"id\": 24645, \"name\": \"the yellow and black jersey\"}, {\"id\": 24646, \"name\": \"a large metal hood\"}, {\"id\": 24647, \"name\": \"driftwood\"}, {\"id\": 24648, \"name\": \"a full skirt\"}, {\"id\": 24649, \"name\": \"the slightly older child\"}, {\"id\": 24650, \"name\": \"red broom\"}, {\"id\": 24651, \"name\": \"a yellow bus\"}, {\"id\": 24652, \"name\": \"large crowd of spectators\"}, {\"id\": 24653, \"name\": \"the sleeveless top\"}, {\"id\": 24654, \"name\": \"workout attire\"}, {\"id\": 24655, \"name\": \"a black and teal bullseye design\"}, {\"id\": 24656, \"name\": \"a guitar amplifier\"}, {\"id\": 24657, \"name\": \"the weightlifting bench\"}, {\"id\": 24658, \"name\": \"a safety cone\"}, {\"id\": 24659, \"name\": \"a green screen\"}, {\"id\": 24660, \"name\": \"gray surface\"}, {\"id\": 24661, \"name\": \"tennis instructor\"}, {\"id\": 24662, \"name\": \"a white bikini bottom\"}, {\"id\": 24663, \"name\": \"yellow pad\"}, {\"id\": 24664, \"name\": \"the red and white pole\"}, {\"id\": 24665, \"name\": \"white rain boot\"}, {\"id\": 24666, \"name\": \"the white baseboard\"}, {\"id\": 24667, \"name\": \"a skull icon\"}, {\"id\": 24668, \"name\": \"the medley of vegetables and meat\"}, {\"id\": 24669, \"name\": \"a large jet of water\"}, {\"id\": 24670, \"name\": \"the black platform\"}, {\"id\": 24671, \"name\": \"the neck of the guitar\"}, {\"id\": 24672, \"name\": \"a rounded bell shape\"}, {\"id\": 24673, \"name\": \"a rider's leg\"}, {\"id\": 24674, \"name\": \"multiple spools of white thread\"}, {\"id\": 24675, \"name\": \"the stone-paved walkway\"}, {\"id\": 24676, \"name\": \"a blue-gray paneling\"}, {\"id\": 24677, \"name\": \"rectangular hole\"}, {\"id\": 24678, \"name\": \"the tag\"}, {\"id\": 24679, \"name\": \"large, black tire\"}, {\"id\": 24680, \"name\": \"the bottle of Samuel Adams Holiday White Ale beer\"}, {\"id\": 24681, \"name\": \"group of motorcyclists\"}, {\"id\": 24682, \"name\": \"the pink bandana\"}, {\"id\": 24683, \"name\": \"a game's name\"}, {\"id\": 24684, \"name\": \"the white RVCA logo\"}, {\"id\": 24685, \"name\": \"a circular grassy area\"}, {\"id\": 24686, \"name\": \"the large round headlight\"}, {\"id\": 24687, \"name\": \"the yellow metal ruler or measuring tool\"}, {\"id\": 24688, \"name\": \"fingernail\"}, {\"id\": 24689, \"name\": \"the camper\"}, {\"id\": 24690, \"name\": \"the yellow and black striped border\"}, {\"id\": 24691, \"name\": \"the blue and white trailer\"}, {\"id\": 24692, \"name\": \"the stack of four light-colored wooden board\"}, {\"id\": 24693, \"name\": \"the red wagon\"}, {\"id\": 24694, \"name\": \"the piece of ice\"}, {\"id\": 24695, \"name\": \"suit jacket\"}, {\"id\": 24696, \"name\": \"a white bat\"}, {\"id\": 24697, \"name\": \"a black athletic attire\"}, {\"id\": 24698, \"name\": \"a large pink umbrella\"}, {\"id\": 24699, \"name\": \"the green and white bollard\"}, {\"id\": 24700, \"name\": \"a cartoonish depiction of a Native American warrior's head\"}, {\"id\": 24701, \"name\": \"white paw\"}, {\"id\": 24702, \"name\": \"the device\"}, {\"id\": 24703, \"name\": \"the rusted metal\"}, {\"id\": 24704, \"name\": \"blue, white, and red striped lane divider\"}, {\"id\": 24705, \"name\": \"black pom-pom\"}, {\"id\": 24706, \"name\": \"pinwheel\"}, {\"id\": 24707, \"name\": \"the large, yellowish-brown food item\"}, {\"id\": 24708, \"name\": \"a long white beard\"}, {\"id\": 24709, \"name\": \"red stop sign post\"}, {\"id\": 24710, \"name\": \"cartoon drawing of a dog collar\"}, {\"id\": 24711, \"name\": \"the pink border\"}, {\"id\": 24712, \"name\": \"a vibrant graffiti\"}, {\"id\": 24713, \"name\": \"dark eye\"}, {\"id\": 24714, \"name\": \"a metal square plate\"}, {\"id\": 24715, \"name\": \"the purple icon\"}, {\"id\": 24716, \"name\": \"the stone church\"}, {\"id\": 24717, \"name\": \"black-and-white flag\"}, {\"id\": 24718, \"name\": \"decorative sconce\"}, {\"id\": 24719, \"name\": \"a white barrier\"}, {\"id\": 24720, \"name\": \"gray water heater\"}, {\"id\": 24721, \"name\": \"the red K.O.\"}, {\"id\": 24722, \"name\": \"blue and white checkered shirt\"}, {\"id\": 24723, \"name\": \"black racket\"}, {\"id\": 24724, \"name\": \"the green trainer\"}, {\"id\": 24725, \"name\": \"the orange astronaut costume\"}, {\"id\": 24726, \"name\": \"a clear hose\"}, {\"id\": 24727, \"name\": \"a large white number 00\"}, {\"id\": 24728, \"name\": \"small rearview mirror\"}, {\"id\": 24729, \"name\": \"row of illuminated fountains\"}, {\"id\": 24730, \"name\": \"the green bottom layer\"}, {\"id\": 24731, \"name\": \"the butterfly-shaped ring\"}, {\"id\": 24732, \"name\": \"the black vertical line\"}, {\"id\": 24733, \"name\": \"large green buoy\"}, {\"id\": 24734, \"name\": \"the person in a blue shirt\"}, {\"id\": 24735, \"name\": \"okra\"}, {\"id\": 24736, \"name\": \"woman in a green headscarf\"}, {\"id\": 24737, \"name\": \"a brown smokestack\"}, {\"id\": 24738, \"name\": \"the parade float\"}, {\"id\": 24739, \"name\": \"the large blue box\"}, {\"id\": 24740, \"name\": \"striking orange roof\"}, {\"id\": 24741, \"name\": \"the red and black helmet\"}, {\"id\": 24742, \"name\": \"the stack of large bags of cement or sand\"}, {\"id\": 24743, \"name\": \"the white undershirt\"}, {\"id\": 24744, \"name\": \"backdrop of cars\"}, {\"id\": 24745, \"name\": \"the yellow train car\"}, {\"id\": 24746, \"name\": \"the large yellow crane\"}, {\"id\": 24747, \"name\": \"red sauce or condiment\"}, {\"id\": 24748, \"name\": \"the white megaphone\"}, {\"id\": 24749, \"name\": \"the long dock\"}, {\"id\": 24750, \"name\": \"black, patterned vest\"}, {\"id\": 24751, \"name\": \"a small red bucket\"}, {\"id\": 24752, \"name\": \"the row of trash cans\"}, {\"id\": 24753, \"name\": \"TISSOT logo\"}, {\"id\": 24754, \"name\": \"marine animal\"}, {\"id\": 24755, \"name\": \"the blue flip-flop\"}, {\"id\": 24756, \"name\": \"red curb\"}, {\"id\": 24757, \"name\": \"black BMW steering wheel cover\"}, {\"id\": 24758, \"name\": \"a red-and-white curb\"}, {\"id\": 24759, \"name\": \"the pink sign\"}, {\"id\": 24760, \"name\": \"the grey cap\"}, {\"id\": 24761, \"name\": \"the cylindrical white base\"}, {\"id\": 24762, \"name\": \"the yellow sneaker\"}, {\"id\": 24763, \"name\": \"the large red umbrella\"}, {\"id\": 24764, \"name\": \"the fighter jet\"}, {\"id\": 24765, \"name\": \"red flip-flop\"}, {\"id\": 24766, \"name\": \"bright yellow paddle\"}, {\"id\": 24767, \"name\": \"large instrument\"}, {\"id\": 24768, \"name\": \"large black bird\"}, {\"id\": 24769, \"name\": \"a red trough\"}, {\"id\": 24770, \"name\": \"yellow horn\"}, {\"id\": 24771, \"name\": \"a grey pole\"}, {\"id\": 24772, \"name\": \"an orange wire\"}, {\"id\": 24773, \"name\": \"a dark shorts\"}, {\"id\": 24774, \"name\": \"minimap\"}, {\"id\": 24775, \"name\": \"the recyclable material\"}, {\"id\": 24776, \"name\": \"the grey and white kitten\"}, {\"id\": 24777, \"name\": \"vertical pole\"}, {\"id\": 24778, \"name\": \"the distinctive red roof\"}, {\"id\": 24779, \"name\": \"white scarf\"}, {\"id\": 24780, \"name\": \"a long leg\"}, {\"id\": 24781, \"name\": \"person in a blue shirt\"}, {\"id\": 24782, \"name\": \"a large wooden barn\"}, {\"id\": 24783, \"name\": \"person's bare foot\"}, {\"id\": 24784, \"name\": \"a keyboard player\"}, {\"id\": 24785, \"name\": \"the human-like figure\"}, {\"id\": 24786, \"name\": \"the black crate\"}, {\"id\": 24787, \"name\": \"the blue spiderman short\"}, {\"id\": 24788, \"name\": \"the vibrant arrangement of potted plants\"}, {\"id\": 24789, \"name\": \"the yellow tic-tac-toe board\"}, {\"id\": 24790, \"name\": \"ride-on toy\"}, {\"id\": 24791, \"name\": \"the castle quarter\"}, {\"id\": 24792, \"name\": \"yellow dhl sign\"}, {\"id\": 24793, \"name\": \"the large overhang\"}, {\"id\": 24794, \"name\": \"small white object\"}, {\"id\": 24795, \"name\": \"the circular icon\"}, {\"id\": 24796, \"name\": \"a yellow wheel\"}, {\"id\": 24797, \"name\": \"a matching white shirt\"}, {\"id\": 24798, \"name\": \"the small, white table\"}, {\"id\": 24799, \"name\": \"the blue and yellow rattle\"}, {\"id\": 24800, \"name\": \"the light-colored fabric\"}, {\"id\": 24801, \"name\": \"vertical light\"}, {\"id\": 24802, \"name\": \"drone\"}, {\"id\": 24803, \"name\": \"yellow parachute\"}, {\"id\": 24804, \"name\": \"the blurred light\"}, {\"id\": 24805, \"name\": \"a red coat\"}, {\"id\": 24806, \"name\": \"black armrest\"}, {\"id\": 24807, \"name\": \"the congested highway\"}, {\"id\": 24808, \"name\": \"rider's helmet\"}, {\"id\": 24809, \"name\": \"a stainles steel pan\"}, {\"id\": 24810, \"name\": \"red, white, and blue canopy\"}, {\"id\": 24811, \"name\": \"blue plastic container\"}, {\"id\": 24812, \"name\": \"the canoe\"}, {\"id\": 24813, \"name\": \"a person in a white shirt and blue pant\"}, {\"id\": 24814, \"name\": \"an orange-and-black patterned pant\"}, {\"id\": 24815, \"name\": \"inflatable\"}, {\"id\": 24816, \"name\": \"the large cargo\"}, {\"id\": 24817, \"name\": \"green creature\"}, {\"id\": 24818, \"name\": \"light blue dress\"}, {\"id\": 24819, \"name\": \"child's left arm\"}, {\"id\": 24820, \"name\": \"large gold statue of a dragon\"}, {\"id\": 24821, \"name\": \"blue and white swim trunk\"}, {\"id\": 24822, \"name\": \"a blue parasol\"}, {\"id\": 24823, \"name\": \"light-colored top\"}, {\"id\": 24824, \"name\": \"the large cloud of smoke and ash\"}, {\"id\": 24825, \"name\": \"the chili pepper\"}, {\"id\": 24826, \"name\": \"blue plastic cooler\"}, {\"id\": 24827, \"name\": \"a vibrant firework display\"}, {\"id\": 24828, \"name\": \"a red heart emoji\"}, {\"id\": 24829, \"name\": \"a parked motorcycle\"}, {\"id\": 24830, \"name\": \"black air vent\"}, {\"id\": 24831, \"name\": \"the black ear\"}, {\"id\": 24832, \"name\": \"a sled\"}, {\"id\": 24833, \"name\": \"green aquatic plant\"}, {\"id\": 24834, \"name\": \"a large, light-colored rock\"}, {\"id\": 24835, \"name\": \"a dark helmet\"}, {\"id\": 24836, \"name\": \"motorbike's mirror\"}, {\"id\": 24837, \"name\": \"blue button-up shirt\"}, {\"id\": 24838, \"name\": \"breed\"}, {\"id\": 24839, \"name\": \"brown lattice design\"}, {\"id\": 24840, \"name\": \"blue plastic tarp\"}, {\"id\": 24841, \"name\": \"purple t-shirt\"}, {\"id\": 24842, \"name\": \"the small patch of grass\"}, {\"id\": 24843, \"name\": \"a silver taxi\"}, {\"id\": 24844, \"name\": \"the jellyfish\"}, {\"id\": 24845, \"name\": \"ambulance's light\"}, {\"id\": 24846, \"name\": \"a denim overall\"}, {\"id\": 24847, \"name\": \"a white stick\"}, {\"id\": 24848, \"name\": \"gold light\"}, {\"id\": 24849, \"name\": \"the gray locker\"}, {\"id\": 24850, \"name\": \"yellow scooter\"}, {\"id\": 24851, \"name\": \"a woman in blue\"}, {\"id\": 24852, \"name\": \"a brown hat\"}, {\"id\": 24853, \"name\": \"the large graphic of a bowling pin\"}, {\"id\": 24854, \"name\": \"pink cardigan\"}, {\"id\": 24855, \"name\": \"a vibrant blue and red umbrella\"}, {\"id\": 24856, \"name\": \"large, plastic bag\"}, {\"id\": 24857, \"name\": \"red or orange shirt\"}, {\"id\": 24858, \"name\": \"yellow and white car\"}, {\"id\": 24859, \"name\": \"the metal fencing\"}, {\"id\": 24860, \"name\": \"a blue spot\"}, {\"id\": 24861, \"name\": \"a vibrant purple and gold flag\"}, {\"id\": 24862, \"name\": \"truck's bumper\"}, {\"id\": 24863, \"name\": \"a coop's design\"}, {\"id\": 24864, \"name\": \"a red and white headdress\"}, {\"id\": 24865, \"name\": \"the purple sash\"}, {\"id\": 24866, \"name\": \"the majestic mountain\"}, {\"id\": 24867, \"name\": \"the wide-brimmed hat\"}, {\"id\": 24868, \"name\": \"larger black patch\"}, {\"id\": 24869, \"name\": \"square hole\"}, {\"id\": 24870, \"name\": \"the red metal framing\"}, {\"id\": 24871, \"name\": \"the white and blue collared shirt\"}, {\"id\": 24872, \"name\": \"brown-and-white labrador\"}, {\"id\": 24873, \"name\": \"the bright red light\"}, {\"id\": 24874, \"name\": \"rickshaw's body\"}, {\"id\": 24875, \"name\": \"the light-up wand toy\"}, {\"id\": 24876, \"name\": \"bright green and yellow rickshaw\"}, {\"id\": 24877, \"name\": \"blue and red shirt\"}, {\"id\": 24878, \"name\": \"pirate character\"}, {\"id\": 24879, \"name\": \"large, red billboard\"}, {\"id\": 24880, \"name\": \"a small fish\"}, {\"id\": 24881, \"name\": \"a white ceramic mug\"}, {\"id\": 24882, \"name\": \"the green and black plaid shirt\"}, {\"id\": 24883, \"name\": \"the white cab\"}, {\"id\": 24884, \"name\": \"the white trash bag\"}, {\"id\": 24885, \"name\": \"a large military truck\"}, {\"id\": 24886, \"name\": \"the dark wooden table\"}, {\"id\": 24887, \"name\": \"a yellow pedestrian crossing sign\"}, {\"id\": 24888, \"name\": \"wooden rack\"}, {\"id\": 24889, \"name\": \"a distinctive red base\"}, {\"id\": 24890, \"name\": \"the gray roof\"}, {\"id\": 24891, \"name\": \"a player's inventory\"}, {\"id\": 24892, \"name\": \"the tall cell tower\"}, {\"id\": 24893, \"name\": \"floral skirt\"}, {\"id\": 24894, \"name\": \"tilapia\"}, {\"id\": 24895, \"name\": \"the Hello Kitty statue\"}, {\"id\": 24896, \"name\": \"a swim trunk\"}, {\"id\": 24897, \"name\": \"a multitude of person\"}, {\"id\": 24898, \"name\": \"yellow and orange shirt\"}, {\"id\": 24899, \"name\": \"the metal handle\"}, {\"id\": 24900, \"name\": \"a man in a pink shirt and short\"}, {\"id\": 24901, \"name\": \"a long strand of pink and white balloon\"}, {\"id\": 24902, \"name\": \"the fish feeder\"}, {\"id\": 24903, \"name\": \"red and black robe\"}, {\"id\": 24904, \"name\": \"red saddle\"}, {\"id\": 24905, \"name\": \"animal's long neck\"}, {\"id\": 24906, \"name\": \"the man's motorcycle\"}, {\"id\": 24907, \"name\": \"the black rubber boot\"}, {\"id\": 24908, \"name\": \"the snowball\"}, {\"id\": 24909, \"name\": \"a woman in a red shirt and headscarf\"}, {\"id\": 24910, \"name\": \"a pink head\"}, {\"id\": 24911, \"name\": \"a green pom-pom\"}, {\"id\": 24912, \"name\": \"a clear plastic cylinder\"}, {\"id\": 24913, \"name\": \"red polo shirt\"}, {\"id\": 24914, \"name\": \"a teal shoe\"}, {\"id\": 24915, \"name\": \"a woman wearing a blue hat and white t-shirt\"}, {\"id\": 24916, \"name\": \"a knee pad\"}, {\"id\": 24917, \"name\": \"bundle of money\"}, {\"id\": 24918, \"name\": \"signal\"}, {\"id\": 24919, \"name\": \"the orange cone barrier\"}, {\"id\": 24920, \"name\": \"long strap\"}, {\"id\": 24921, \"name\": \"the white wing\"}, {\"id\": 24922, \"name\": \"the long red floral skirt\"}, {\"id\": 24923, \"name\": \"brown and white horse\"}, {\"id\": 24924, \"name\": \"white panda head\"}, {\"id\": 24925, \"name\": \"an unicorn swing\"}, {\"id\": 24926, \"name\": \"the grid of window\"}, {\"id\": 24927, \"name\": \"blue plastic stool\"}, {\"id\": 24928, \"name\": \"large peacock feather design\"}, {\"id\": 24929, \"name\": \"yellow speck\"}, {\"id\": 24930, \"name\": \"the vibrant blue pole\"}, {\"id\": 24931, \"name\": \"heavy-duty truck\"}, {\"id\": 24932, \"name\": \"the kayaker\"}, {\"id\": 24933, \"name\": \"the paper bag\"}, {\"id\": 24934, \"name\": \"a silver convertible\"}, {\"id\": 24935, \"name\": \"a large pot of food\"}, {\"id\": 24936, \"name\": \"tall, lighted pole\"}, {\"id\": 24937, \"name\": \"a colorful column\"}, {\"id\": 24938, \"name\": \"a red crane arm\"}, {\"id\": 24939, \"name\": \"the black tuk-tuk\"}, {\"id\": 24940, \"name\": \"an orange and blue inflatable ring\"}, {\"id\": 24941, \"name\": \"a person holding the sign\"}, {\"id\": 24942, \"name\": \"colorful paper fan\"}, {\"id\": 24943, \"name\": \"a Dior sign\"}, {\"id\": 24944, \"name\": \"a white fabric item\"}, {\"id\": 24945, \"name\": \"a red place mat\"}, {\"id\": 24946, \"name\": \"the red-and-white jersey\"}, {\"id\": 24947, \"name\": \"colorful box\"}, {\"id\": 24948, \"name\": \"the red metal panel\"}, {\"id\": 24949, \"name\": \"a red bar\"}, {\"id\": 24950, \"name\": \"the black and white top\"}, {\"id\": 24951, \"name\": \"a lobster\"}, {\"id\": 24952, \"name\": \"the yellow sandal\"}, {\"id\": 24953, \"name\": \"a black trim\"}, {\"id\": 24954, \"name\": \"red sandal\"}, {\"id\": 24955, \"name\": \"neon-lit display\"}, {\"id\": 24956, \"name\": \"shadowy figure\"}, {\"id\": 24957, \"name\": \"the flaming torch\"}, {\"id\": 24958, \"name\": \"red arcade seat\"}, {\"id\": 24959, \"name\": \"red health bar\"}, {\"id\": 24960, \"name\": \"a yellow, plastic, honeycomb-like object\"}, {\"id\": 24961, \"name\": \"the billowy sleeve\"}, {\"id\": 24962, \"name\": \"the large open red canopy structure\"}, {\"id\": 24963, \"name\": \"the ride\"}, {\"id\": 24964, \"name\": \"bright red comb\"}, {\"id\": 24965, \"name\": \"dark blue puffer jacket\"}, {\"id\": 24966, \"name\": \"the conveyor belt\"}, {\"id\": 24967, \"name\": \"gray bird\"}, {\"id\": 24968, \"name\": \"the blue sign with a white arrow pointing up\"}, {\"id\": 24969, \"name\": \"the light blue scarf\"}, {\"id\": 24970, \"name\": \"a surveyor\"}, {\"id\": 24971, \"name\": \"the black and green long-sleeved shirt\"}, {\"id\": 24972, \"name\": \"goat's head\"}, {\"id\": 24973, \"name\": \"decorative trim\"}, {\"id\": 24974, \"name\": \"the dead leaf\"}, {\"id\": 24975, \"name\": \"a yellow foam handle\"}, {\"id\": 24976, \"name\": \"the white seating\"}, {\"id\": 24977, \"name\": \"black-and-white patterned shirt\"}, {\"id\": 24978, \"name\": \"the scuba diver\"}, {\"id\": 24979, \"name\": \"a dragon-shaped decoration\"}, {\"id\": 24980, \"name\": \"woman with a pink baseball cap\"}, {\"id\": 24981, \"name\": \"the dark metal bollard\"}, {\"id\": 24982, \"name\": \"a gravel bed\"}, {\"id\": 24983, \"name\": \"the large crane\"}, {\"id\": 24984, \"name\": \"back of a person\"}, {\"id\": 24985, \"name\": \"red and white traffic cone\"}, {\"id\": 24986, \"name\": \"a black and grey patterned shirt\"}, {\"id\": 24987, \"name\": \"a light gray air vent\"}, {\"id\": 24988, \"name\": \"a sliced onion\"}, {\"id\": 24989, \"name\": \"the white strap\"}, {\"id\": 24990, \"name\": \"large, red ladder\"}, {\"id\": 24991, \"name\": \"yellow cap\"}, {\"id\": 24992, \"name\": \"a yellow structure\"}, {\"id\": 24993, \"name\": \"the large grey boulder\"}, {\"id\": 24994, \"name\": \"yellow exercise ball\"}, {\"id\": 24995, \"name\": \"the small, yellow fish\"}, {\"id\": 24996, \"name\": \"the beachgoer\"}, {\"id\": 24997, \"name\": \"the string of white light\"}, {\"id\": 24998, \"name\": \"the smaller blue bar\"}, {\"id\": 24999, \"name\": \"the orange head covering\"}, {\"id\": 25000, \"name\": \"the orange fin\"}, {\"id\": 25001, \"name\": \"the smaller metal tool\"}, {\"id\": 25002, \"name\": \"vehicle or piece of equipment\"}, {\"id\": 25003, \"name\": \"the black truck\"}, {\"id\": 25004, \"name\": \"a pink ear\"}, {\"id\": 25005, \"name\": \"blue swim trunks\"}, {\"id\": 25006, \"name\": \"a yellow and gray curb\"}, {\"id\": 25007, \"name\": \"the red bin\"}, {\"id\": 25008, \"name\": \"a yellow salwar kameez\"}, {\"id\": 25009, \"name\": \"the black step\"}, {\"id\": 25010, \"name\": \"larger fish\"}, {\"id\": 25011, \"name\": \"the red and black flag\"}, {\"id\": 25012, \"name\": \"the play equipment\"}, {\"id\": 25013, \"name\": \"a parked scooter\"}, {\"id\": 25014, \"name\": \"pool's green bottom\"}, {\"id\": 25015, \"name\": \"the bouquet\"}, {\"id\": 25016, \"name\": \"a small black sticker\"}, {\"id\": 25017, \"name\": \"national flag\"}, {\"id\": 25018, \"name\": \"the gray concrete barrier\"}, {\"id\": 25019, \"name\": \"yellow short\"}, {\"id\": 25020, \"name\": \"a canopy of pink and yellow curtain\"}, {\"id\": 25021, \"name\": \"tan t-shirt\"}, {\"id\": 25022, \"name\": \"a light grey tablecloth\"}, {\"id\": 25023, \"name\": \"sleek white and red design\"}, {\"id\": 25024, \"name\": \"a yellow section\"}, {\"id\": 25025, \"name\": \"the short, dark blue dress\"}, {\"id\": 25026, \"name\": \"the blue-tiled pillar\"}, {\"id\": 25027, \"name\": \"yellow fin\"}, {\"id\": 25028, \"name\": \"large piece of wood\"}, {\"id\": 25029, \"name\": \"the large concrete pillar\"}, {\"id\": 25030, \"name\": \"the red safety net\"}, {\"id\": 25031, \"name\": \"the tall, pointed tower\"}, {\"id\": 25032, \"name\": \"the man in a yellow jersey\"}, {\"id\": 25033, \"name\": \"a smaller green sign\"}, {\"id\": 25034, \"name\": \"the bull mask\"}, {\"id\": 25035, \"name\": \"thick, rough metal\"}, {\"id\": 25036, \"name\": \"upper deck\"}, {\"id\": 25037, \"name\": \"the american flag\"}, {\"id\": 25038, \"name\": \"a yellow tang\"}, {\"id\": 25039, \"name\": \"a tall metal tower\"}, {\"id\": 25040, \"name\": \"health and mana bar\"}, {\"id\": 25041, \"name\": \"a flash\"}, {\"id\": 25042, \"name\": \"large metal bowl\"}, {\"id\": 25043, \"name\": \"red creature\"}, {\"id\": 25044, \"name\": \"the glow stick\"}, {\"id\": 25045, \"name\": \"the yellow and black fish\"}, {\"id\": 25046, \"name\": \"man in a yellow shirt with blue lettering\"}, {\"id\": 25047, \"name\": \"a brightly colored tube\"}, {\"id\": 25048, \"name\": \"the small, white tag\"}, {\"id\": 25049, \"name\": \"red boxing glove\"}, {\"id\": 25050, \"name\": \"a small blue bar\"}, {\"id\": 25051, \"name\": \"red headscarf\"}, {\"id\": 25052, \"name\": \"white unicorn statue\"}, {\"id\": 25053, \"name\": \"a traditional red attire\"}, {\"id\": 25054, \"name\": \"an inflatable tube\"}, {\"id\": 25055, \"name\": \"the orange sleeve\"}, {\"id\": 25056, \"name\": \"the white polka dot\"}, {\"id\": 25057, \"name\": \"a central hole\"}, {\"id\": 25058, \"name\": \"the motorcycle rider\"}, {\"id\": 25059, \"name\": \"a colorful ride\"}, {\"id\": 25060, \"name\": \"dark-colored marking\"}, {\"id\": 25061, \"name\": \"the bag or backpack\"}, {\"id\": 25062, \"name\": \"the black metal scaffolding structure\"}, {\"id\": 25063, \"name\": \"a sea creature\"}, {\"id\": 25064, \"name\": \"red projectile\"}, {\"id\": 25065, \"name\": \"large military plane\"}, {\"id\": 25066, \"name\": \"open-face helmet\"}, {\"id\": 25067, \"name\": \"the small raft\"}, {\"id\": 25068, \"name\": \"navy blue hijab\"}, {\"id\": 25069, \"name\": \"the grey, rock-like object\"}, {\"id\": 25070, \"name\": \"a green star\"}, {\"id\": 25071, \"name\": \"orange hue\"}, {\"id\": 25072, \"name\": \"an elaborate costume\"}, {\"id\": 25073, \"name\": \"blue and white floral dress\"}, {\"id\": 25074, \"name\": \"the softball\"}, {\"id\": 25075, \"name\": \"a colorful top\"}, {\"id\": 25076, \"name\": \"the pink head covering\"}, {\"id\": 25077, \"name\": \"the wooden perch\"}, {\"id\": 25078, \"name\": \"black, three-light traffic light\"}, {\"id\": 25079, \"name\": \"alligator\"}, {\"id\": 25080, \"name\": \"the black stand\"}, {\"id\": 25081, \"name\": \"a white triangle\"}, {\"id\": 25082, \"name\": \"the utensil\"}, {\"id\": 25083, \"name\": \"bright orange sari\"}, {\"id\": 25084, \"name\": \"a buoy\"}, {\"id\": 25085, \"name\": \"large awning\"}, {\"id\": 25086, \"name\": \"instrumentalist\"}, {\"id\": 25087, \"name\": \"the elaborate headdress\"}, {\"id\": 25088, \"name\": \"the triangular wooden structure\"}, {\"id\": 25089, \"name\": \"a red kite\"}, {\"id\": 25090, \"name\": \"drumstick\"}, {\"id\": 25091, \"name\": \"large light\"}, {\"id\": 25092, \"name\": \"spatula or spoon\"}, {\"id\": 25093, \"name\": \"the matching uniform\"}, {\"id\": 25094, \"name\": \"a gray seat\"}, {\"id\": 25095, \"name\": \"pot of food\"}, {\"id\": 25096, \"name\": \"rear of a truck\"}, {\"id\": 25097, \"name\": \"the surfer\"}, {\"id\": 25098, \"name\": \"a red and white motorcycle\"}, {\"id\": 25099, \"name\": \"wooden piece\"}, {\"id\": 25100, \"name\": \"the patterned section\"}, {\"id\": 25101, \"name\": \"blue glass facade\"}, {\"id\": 25102, \"name\": \"the blue curtain\"}, {\"id\": 25103, \"name\": \"the tiered seating area\"}, {\"id\": 25104, \"name\": \"polo shirt\"}, {\"id\": 25105, \"name\": \"the dj's laptop computer\"}, {\"id\": 25106, \"name\": \"the circular metal component\"}, {\"id\": 25107, \"name\": \"dark trousers\"}, {\"id\": 25108, \"name\": \"outfit\"}, {\"id\": 25109, \"name\": \"a beige hat\"}, {\"id\": 25110, \"name\": \"double-decker tram\"}, {\"id\": 25111, \"name\": \"a surrounding red object\"}, {\"id\": 25112, \"name\": \"large drum\"}, {\"id\": 25113, \"name\": \"black habit\"}, {\"id\": 25114, \"name\": \"a white club\"}, {\"id\": 25115, \"name\": \"green uniform\"}, {\"id\": 25116, \"name\": \"red light strip\"}, {\"id\": 25117, \"name\": \"bright orange shirt\"}, {\"id\": 25118, \"name\": \"a white speedometer\"}, {\"id\": 25119, \"name\": \"bumper car ride\"}, {\"id\": 25120, \"name\": \"black balloon\"}, {\"id\": 25121, \"name\": \"a human figure\"}, {\"id\": 25122, \"name\": \"small cone\"}, {\"id\": 25123, \"name\": \"purple basketball\"}, {\"id\": 25124, \"name\": \"the distinctive domed roof\"}, {\"id\": 25125, \"name\": \"the red and white decoration\"}, {\"id\": 25126, \"name\": \"boat's canopy\"}, {\"id\": 25127, \"name\": \"modern structure\"}, {\"id\": 25128, \"name\": \"black wire\"}, {\"id\": 25129, \"name\": \"large blue plastic bin\"}, {\"id\": 25130, \"name\": \"the white and red padded striking pad\"}, {\"id\": 25131, \"name\": \"the red tractor\"}, {\"id\": 25132, \"name\": \"gondola ride\"}, {\"id\": 25133, \"name\": \"the bike's front wheel\"}, {\"id\": 25134, \"name\": \"small red object\"}, {\"id\": 25135, \"name\": \"the large, lit-up, orange and white figure\"}, {\"id\": 25136, \"name\": \"small can\"}, {\"id\": 25137, \"name\": \"the dark brown, ornate lamppost\"}, {\"id\": 25138, \"name\": \"black pant leg\"}, {\"id\": 25139, \"name\": \"navy blue jersey\"}, {\"id\": 25140, \"name\": \"cooking tool\"}, {\"id\": 25141, \"name\": \"a red saddle blanket\"}, {\"id\": 25142, \"name\": \"a golden horn\"}, {\"id\": 25143, \"name\": \"the gray glove\"}, {\"id\": 25144, \"name\": \"a landscaped area\"}, {\"id\": 25145, \"name\": \"a red tiled roof\"}, {\"id\": 25146, \"name\": \"a colorful headpiece\"}, {\"id\": 25147, \"name\": \"the large dump truck\"}, {\"id\": 25148, \"name\": \"the goldfish\"}, {\"id\": 25149, \"name\": \"a snack or treat\"}, {\"id\": 25150, \"name\": \"a right lane\"}, {\"id\": 25151, \"name\": \"the red metal fence\"}, {\"id\": 25152, \"name\": \"white lattice pattern\"}, {\"id\": 25153, \"name\": \"the black umbrella\"}, {\"id\": 25154, \"name\": \"the flat-screen tv\"}, {\"id\": 25155, \"name\": \"the brown body\"}, {\"id\": 25156, \"name\": \"boat's dark blue canopy\"}, {\"id\": 25157, \"name\": \"matching blue t-shirt\"}, {\"id\": 25158, \"name\": \"the large brown shipping container\"}, {\"id\": 25159, \"name\": \"circular icon\"}, {\"id\": 25160, \"name\": \"the large red container\"}, {\"id\": 25161, \"name\": \"white pom-pom\"}, {\"id\": 25162, \"name\": \"a blue and white striped design\"}, {\"id\": 25163, \"name\": \"the group of characters\"}, {\"id\": 25164, \"name\": \"the blue vehicle\"}, {\"id\": 25165, \"name\": \"the green rickshaw\"}, {\"id\": 25166, \"name\": \"blue crate\"}, {\"id\": 25167, \"name\": \"the white slipper\"}, {\"id\": 25168, \"name\": \"red and yellow checkered pattern\"}, {\"id\": 25169, \"name\": \"a clear plastic bottle\"}, {\"id\": 25170, \"name\": \"the tall white structure\"}, {\"id\": 25171, \"name\": \"tall palm frond\"}, {\"id\": 25172, \"name\": \"row of vehicles\"}, {\"id\": 25173, \"name\": \"light blue bag\"}, {\"id\": 25174, \"name\": \"the banner or flag\"}, {\"id\": 25175, \"name\": \"the puppy\"}, {\"id\": 25176, \"name\": \"white light\"}, {\"id\": 25177, \"name\": \"the top hat\"}, {\"id\": 25178, \"name\": \"the green metal door\"}, {\"id\": 25179, \"name\": \"the smaller gear\"}, {\"id\": 25180, \"name\": \"the light blue border\"}, {\"id\": 25181, \"name\": \"a man in an orange sash\"}, {\"id\": 25182, \"name\": \"the yellow and blue object\"}, {\"id\": 25183, \"name\": \"the cab of a yellow excavator\"}, {\"id\": 25184, \"name\": \"large fish\"}, {\"id\": 25185, \"name\": \"a Malaysian flag\"}, {\"id\": 25186, \"name\": \"a brown, ornate carousel horse\"}, {\"id\": 25187, \"name\": \"the long red and brown fabric\"}, {\"id\": 25188, \"name\": \"the helicopter\"}, {\"id\": 25189, \"name\": \"the black jeep wrangler\"}, {\"id\": 25190, \"name\": \"the pink light saber\"}, {\"id\": 25191, \"name\": \"the yellow weight\"}, {\"id\": 25192, \"name\": \"the taxi's back window\"}, {\"id\": 25193, \"name\": \"the knee brace\"}, {\"id\": 25194, \"name\": \"a blue and white keyboard instrument\"}, {\"id\": 25195, \"name\": \"a black and white motorcycle\"}, {\"id\": 25196, \"name\": \"blue emblem\"}, {\"id\": 25197, \"name\": \"blue sword\"}, {\"id\": 25198, \"name\": \"ornate pillar\"}, {\"id\": 25199, \"name\": \"the pink bucket hat\"}, {\"id\": 25200, \"name\": \"the large white banner\"}, {\"id\": 25201, \"name\": \"white taxi\"}, {\"id\": 25202, \"name\": \"the woman in a light blue shirt\"}, {\"id\": 25203, \"name\": \"a large, illuminated tower\"}, {\"id\": 25204, \"name\": \"the colorful clown decoration\"}, {\"id\": 25205, \"name\": \"game's character\"}, {\"id\": 25206, \"name\": \"a black metal park bench\"}, {\"id\": 25207, \"name\": \"a decorative banner\"}, {\"id\": 25208, \"name\": \"the yellow fish toy\"}, {\"id\": 25209, \"name\": \"brown tail\"}, {\"id\": 25210, \"name\": \"the team\"}, {\"id\": 25211, \"name\": \"a light saber\"}, {\"id\": 25212, \"name\": \"yellow and white sneaker\"}, {\"id\": 25213, \"name\": \"a ATV\"}, {\"id\": 25214, \"name\": \"the sparkler's handle\"}, {\"id\": 25215, \"name\": \"muted green hue\"}, {\"id\": 25216, \"name\": \"the center monitor\"}, {\"id\": 25217, \"name\": \"blue mask\"}, {\"id\": 25218, \"name\": \"the military aircraft\"}, {\"id\": 25219, \"name\": \"a side of the truck\"}, {\"id\": 25220, \"name\": \"light blue car\"}, {\"id\": 25221, \"name\": \"the yamaha atv\"}, {\"id\": 25222, \"name\": \"the white billboard\"}, {\"id\": 25223, \"name\": \"crab meat\"}, {\"id\": 25224, \"name\": \"large screen tv\"}, {\"id\": 25225, \"name\": \"the duckling\"}, {\"id\": 25226, \"name\": \"light blue polo shirt\"}, {\"id\": 25227, \"name\": \"a music instrument\"}, {\"id\": 25228, \"name\": \"red backpack\"}, {\"id\": 25229, \"name\": \"orange sash\"}, {\"id\": 25230, \"name\": \"rounded top\"}, {\"id\": 25231, \"name\": \"black metal grate\"}, {\"id\": 25232, \"name\": \"the gray polo shirt\"}, {\"id\": 25233, \"name\": \"a game's chat window\"}, {\"id\": 25234, \"name\": \"the gold headpiece\"}, {\"id\": 25235, \"name\": \"blue beach umbrella\"}, {\"id\": 25236, \"name\": \"red and gold banner\"}, {\"id\": 25237, \"name\": \"multi-story structure\"}, {\"id\": 25238, \"name\": \"the yellow triangular sign\"}, {\"id\": 25239, \"name\": \"a balloon arrangement\"}, {\"id\": 25240, \"name\": \"cat's ear\"}, {\"id\": 25241, \"name\": \"a tall structure\"}, {\"id\": 25242, \"name\": \"the blue cone\"}, {\"id\": 25243, \"name\": \"raised concrete barrier\"}, {\"id\": 25244, \"name\": \"the sandbag\"}, {\"id\": 25245, \"name\": \"an abundance of pizza box\"}, {\"id\": 25246, \"name\": \"glass of ice tea\"}, {\"id\": 25247, \"name\": \"the small square icon\"}, {\"id\": 25248, \"name\": \"the hindu deity\"}, {\"id\": 25249, \"name\": \"7-eleven store\"}, {\"id\": 25250, \"name\": \"white jug\"}, {\"id\": 25251, \"name\": \"the gray fish\"}, {\"id\": 25252, \"name\": \"slice of white vegetable\"}, {\"id\": 25253, \"name\": \"the large, black bag\"}, {\"id\": 25254, \"name\": \"the wire cage\"}, {\"id\": 25255, \"name\": \"the large, orange boot\"}, {\"id\": 25256, \"name\": \"blue and yellow life jacket\"}, {\"id\": 25257, \"name\": \"a purple hat\"}, {\"id\": 25258, \"name\": \"the green decorative panel\"}, {\"id\": 25259, \"name\": \"a rough, gray skin\"}, {\"id\": 25260, \"name\": \"thai flag\"}, {\"id\": 25261, \"name\": \"a closed shutter door\"}, {\"id\": 25262, \"name\": \"grey sock\"}, {\"id\": 25263, \"name\": \"a man in an orange vest and blue jean\"}, {\"id\": 25264, \"name\": \"the yellow short\"}, {\"id\": 25265, \"name\": \"the glass vase\"}, {\"id\": 25266, \"name\": \"white head wrap\"}, {\"id\": 25267, \"name\": \"the wooden column\"}, {\"id\": 25268, \"name\": \"a yellow and black bird\"}, {\"id\": 25269, \"name\": \"the gold triangle\"}, {\"id\": 25270, \"name\": \"the yellow basketball\"}, {\"id\": 25271, \"name\": \"a breed\"}, {\"id\": 25272, \"name\": \"the traditional red and gold attire\"}, {\"id\": 25273, \"name\": \"the bottle of condiment\"}, {\"id\": 25274, \"name\": \"cement bag\"}, {\"id\": 25275, \"name\": \"a pink and white graphic t-shirt\"}, {\"id\": 25276, \"name\": \"a pink hat\"}, {\"id\": 25277, \"name\": \"the large ornate ceiling\"}, {\"id\": 25278, \"name\": \"the large teepee\"}, {\"id\": 25279, \"name\": \"camouflage green uniform\"}, {\"id\": 25280, \"name\": \"orange balloon\"}, {\"id\": 25281, \"name\": \"the colorful fan-shaped decoration\"}, {\"id\": 25282, \"name\": \"the pane\"}, {\"id\": 25283, \"name\": \"emergency vehicle\"}, {\"id\": 25284, \"name\": \"the yellow and white bag\"}, {\"id\": 25285, \"name\": \"a vibrant red tablecloth\"}, {\"id\": 25286, \"name\": \"white spotlight\"}, {\"id\": 25287, \"name\": \"a blue ramp\"}, {\"id\": 25288, \"name\": \"the red plastic stool\"}, {\"id\": 25289, \"name\": \"a red x\"}, {\"id\": 25290, \"name\": \"violin case\"}, {\"id\": 25291, \"name\": \"a striking red flower\"}, {\"id\": 25292, \"name\": \"vegetation\"}, {\"id\": 25293, \"name\": \"the young chick\"}, {\"id\": 25294, \"name\": \"a large ferri wheel\"}, {\"id\": 25295, \"name\": \"a small wooden post\"}, {\"id\": 25296, \"name\": \"a beer bottle\"}, {\"id\": 25297, \"name\": \"a prop\"}, {\"id\": 25298, \"name\": \"black panel\"}, {\"id\": 25299, \"name\": \"square-shaped vent\"}, {\"id\": 25300, \"name\": \"a curved pipe\"}, {\"id\": 25301, \"name\": \"the light on the ceiling\"}, {\"id\": 25302, \"name\": \"backpack strap\"}, {\"id\": 25303, \"name\": \"a large crane\"}, {\"id\": 25304, \"name\": \"the orange and white fish\"}, {\"id\": 25305, \"name\": \"hi-hat\"}, {\"id\": 25306, \"name\": \"a woman in a red shirt and green short\"}, {\"id\": 25307, \"name\": \"the black metal rod\"}, {\"id\": 25308, \"name\": \"gray awning\"}, {\"id\": 25309, \"name\": \"a parasail\"}, {\"id\": 25310, \"name\": \"red and white umbrella\"}, {\"id\": 25311, \"name\": \"red and black helmet\"}, {\"id\": 25312, \"name\": \"a gray rock\"}, {\"id\": 25313, \"name\": \"a dried red pepper\"}, {\"id\": 25314, \"name\": \"a long red skirt\"}, {\"id\": 25315, \"name\": \"the red suspender\"}, {\"id\": 25316, \"name\": \"the covered roof\"}, {\"id\": 25317, \"name\": \"dark mark\"}, {\"id\": 25318, \"name\": \"the delivery truck\"}, {\"id\": 25319, \"name\": \"a large, glowing jack-o'-lantern\"}, {\"id\": 25320, \"name\": \"a young child's head\"}, {\"id\": 25321, \"name\": \"a large piece of cheese\"}, {\"id\": 25322, \"name\": \"small white fish\"}, {\"id\": 25323, \"name\": \"a large inflatable animal\"}, {\"id\": 25324, \"name\": \"the iconic white swoosh logo\"}, {\"id\": 25325, \"name\": \"the toilet paper roll\"}, {\"id\": 25326, \"name\": \"a white, egg-shaped object\"}, {\"id\": 25327, \"name\": \"the empty seating area\"}, {\"id\": 25328, \"name\": \"mural of car\"}, {\"id\": 25329, \"name\": \"a black shoulder strap\"}, {\"id\": 25330, \"name\": \"the denim short\"}, {\"id\": 25331, \"name\": \"the large green and white bag of rice\"}, {\"id\": 25332, \"name\": \"a large orange ball\"}, {\"id\": 25333, \"name\": \"the child in orange shirt\"}, {\"id\": 25334, \"name\": \"the gathering of motorcycle enthusiasts\"}, {\"id\": 25335, \"name\": \"a light blue license plate\"}, {\"id\": 25336, \"name\": \"woman in a yellow dress\"}, {\"id\": 25337, \"name\": \"small toyota logo\"}, {\"id\": 25338, \"name\": \"the yellow and gray canopy\"}, {\"id\": 25339, \"name\": \"the festive decoration\"}, {\"id\": 25340, \"name\": \"the blue power washer\"}, {\"id\": 25341, \"name\": \"the trousers\"}, {\"id\": 25342, \"name\": \"the truck or jeep\"}, {\"id\": 25343, \"name\": \"a person in a black shirt\"}, {\"id\": 25344, \"name\": \"a white drum\"}, {\"id\": 25345, \"name\": \"the orange safety cone\"}, {\"id\": 25346, \"name\": \"the top of tent\"}, {\"id\": 25347, \"name\": \"suitcase handle\"}, {\"id\": 25348, \"name\": \"the canadian flag\"}, {\"id\": 25349, \"name\": \"orange canopy\"}, {\"id\": 25350, \"name\": \"reflective firefighter coat\"}, {\"id\": 25351, \"name\": \"the prominent white sign\"}, {\"id\": 25352, \"name\": \"a dark trash can\"}, {\"id\": 25353, \"name\": \"reflective surface\"}, {\"id\": 25354, \"name\": \"large yellow arm\"}, {\"id\": 25355, \"name\": \"a white boat's stern\"}, {\"id\": 25356, \"name\": \"smaller umbrella\"}, {\"id\": 25357, \"name\": \"large red fabric canopy\"}, {\"id\": 25358, \"name\": \"a witch hat\"}, {\"id\": 25359, \"name\": \"the white screen\"}, {\"id\": 25360, \"name\": \"yellow top\"}, {\"id\": 25361, \"name\": \"a blue medical mask\"}, {\"id\": 25362, \"name\": \"a styrofoam ball\"}, {\"id\": 25363, \"name\": \"small house\"}, {\"id\": 25364, \"name\": \"piece of art\"}, {\"id\": 25365, \"name\": \"the white vest\"}, {\"id\": 25366, \"name\": \"the large, illuminated chinese dragon lantern\"}, {\"id\": 25367, \"name\": \"the motorboat\"}, {\"id\": 25368, \"name\": \"the illuminated headlight\"}, {\"id\": 25369, \"name\": \"orange fin\"}, {\"id\": 25370, \"name\": \"a large blue tarp\"}, {\"id\": 25371, \"name\": \"a large, flat, grayish-brown coral head\"}, {\"id\": 25372, \"name\": \"orange weight\"}, {\"id\": 25373, \"name\": \"white record\"}, {\"id\": 25374, \"name\": \"panda face\"}, {\"id\": 25375, \"name\": \"the light-colored short-sleeved collared shirt\"}, {\"id\": 25376, \"name\": \"a handball\"}, {\"id\": 25377, \"name\": \"flying kite\"}, {\"id\": 25378, \"name\": \"a large black stove\"}, {\"id\": 25379, \"name\": \"headpiece\"}, {\"id\": 25380, \"name\": \"a prominent green awning\"}, {\"id\": 25381, \"name\": \"the gray coat\"}, {\"id\": 25382, \"name\": \"a patterned scarf\"}, {\"id\": 25383, \"name\": \"the tine\"}, {\"id\": 25384, \"name\": \"a dark-colored object\"}, {\"id\": 25385, \"name\": \"a minnie mouse design\"}, {\"id\": 25386, \"name\": \"the red agility ladder piece\"}, {\"id\": 25387, \"name\": \"a black roof\"}, {\"id\": 25388, \"name\": \"a car's side mirror\"}, {\"id\": 25389, \"name\": \"the tram's roof\"}, {\"id\": 25390, \"name\": \"concrete column\"}, {\"id\": 25391, \"name\": \"blue tablecloth\"}, {\"id\": 25392, \"name\": \"a single white button\"}, {\"id\": 25393, \"name\": \"vibrant outfit\"}, {\"id\": 25394, \"name\": \"a teal tank top\"}, {\"id\": 25395, \"name\": \"red stole\"}, {\"id\": 25396, \"name\": \"a rider's passenger\"}, {\"id\": 25397, \"name\": \"the bright yellow shirt\"}, {\"id\": 25398, \"name\": \"the yellow dinosaur\"}, {\"id\": 25399, \"name\": \"pink light\"}, {\"id\": 25400, \"name\": \"a brown fish\"}, {\"id\": 25401, \"name\": \"a pool toy\"}, {\"id\": 25402, \"name\": \"the traditional craftwork\"}, {\"id\": 25403, \"name\": \"a mickey mouse cartoon character\"}, {\"id\": 25404, \"name\": \"the blue symbol\"}, {\"id\": 25405, \"name\": \"small opening\"}, {\"id\": 25406, \"name\": \"a colorful flag\"}, {\"id\": 25407, \"name\": \"the crisp white uniform\"}, {\"id\": 25408, \"name\": \"the white bumper car\"}, {\"id\": 25409, \"name\": \"green rope toy\"}, {\"id\": 25410, \"name\": \"a red digital\"}, {\"id\": 25411, \"name\": \"the white puppy\"}, {\"id\": 25412, \"name\": \"a player's head\"}, {\"id\": 25413, \"name\": \"a cathedral's intricate architecture\"}, {\"id\": 25414, \"name\": \"the musical instrument\"}, {\"id\": 25415, \"name\": \"a chinese lantern\"}, {\"id\": 25416, \"name\": \"striking red dress\"}, {\"id\": 25417, \"name\": \"small orange bag\"}, {\"id\": 25418, \"name\": \"grey post\"}, {\"id\": 25419, \"name\": \"large, round, woven light fixture\"}, {\"id\": 25420, \"name\": \"the second kart\"}, {\"id\": 25421, \"name\": \"the flotation device\"}, {\"id\": 25422, \"name\": \"the striking red banner\"}, {\"id\": 25423, \"name\": \"the black diving suit\"}, {\"id\": 25424, \"name\": \"the covered parking structure\"}, {\"id\": 25425, \"name\": \"the woman in a yellow shirt\"}, {\"id\": 25426, \"name\": \"white hen\"}, {\"id\": 25427, \"name\": \"large overpass\"}, {\"id\": 25428, \"name\": \"large glass-fronted structure\"}, {\"id\": 25429, \"name\": \"a small, green piece\"}, {\"id\": 25430, \"name\": \"blue and white jersey\"}, {\"id\": 25431, \"name\": \"a model vehicle\"}, {\"id\": 25432, \"name\": \"the wooden tray\"}, {\"id\": 25433, \"name\": \"a wooden stepping platform\"}, {\"id\": 25434, \"name\": \"the small froglet\"}, {\"id\": 25435, \"name\": \"the hoop\"}, {\"id\": 25436, \"name\": \"messenger bag\"}, {\"id\": 25437, \"name\": \"the large pink and blue paper flower\"}, {\"id\": 25438, \"name\": \"sweatpants\"}, {\"id\": 25439, \"name\": \"the carriage\"}, {\"id\": 25440, \"name\": \"the large, multicolored tire\"}, {\"id\": 25441, \"name\": \"the lit candle\"}, {\"id\": 25442, \"name\": \"a vehicle's steering wheel\"}, {\"id\": 25443, \"name\": \"a string of bright light\"}, {\"id\": 25444, \"name\": \"a blue long-sleeved top\"}, {\"id\": 25445, \"name\": \"white bracelet\"}, {\"id\": 25446, \"name\": \"vertical stick\"}, {\"id\": 25447, \"name\": \"yellow soup\"}, {\"id\": 25448, \"name\": \"the chimney\"}, {\"id\": 25449, \"name\": \"the large green canopy\"}, {\"id\": 25450, \"name\": \"a white wheel\"}, {\"id\": 25451, \"name\": \"the gray, rocky cliff\"}, {\"id\": 25452, \"name\": \"overhead bar\"}, {\"id\": 25453, \"name\": \"a red and gold saddle\"}, {\"id\": 25454, \"name\": \"the dark green plaid shirt\"}, {\"id\": 25455, \"name\": \"open yellow umbrella\"}, {\"id\": 25456, \"name\": \"elaborate up-do\"}, {\"id\": 25457, \"name\": \"a green and black machinery\"}, {\"id\": 25458, \"name\": \"the brown horse\"}, {\"id\": 25459, \"name\": \"the black bird\"}, {\"id\": 25460, \"name\": \"the tall, cream-colored structure\"}, {\"id\": 25461, \"name\": \"blue bowling pin\"}, {\"id\": 25462, \"name\": \"a dragon's pole\"}, {\"id\": 25463, \"name\": \"a large orange canopy\"}, {\"id\": 25464, \"name\": \"the airport vehicle\"}, {\"id\": 25465, \"name\": \"the silver pan\"}, {\"id\": 25466, \"name\": \"person in a blue and white striped snowman costume\"}, {\"id\": 25467, \"name\": \"a matching skirt\"}, {\"id\": 25468, \"name\": \"lightbulb\"}, {\"id\": 25469, \"name\": \"a windsurfer's sail\"}, {\"id\": 25470, \"name\": \"striking blue-and-white striped pole\"}, {\"id\": 25471, \"name\": \"the white semi-truck\"}, {\"id\": 25472, \"name\": \"the monk's right hand\"}, {\"id\": 25473, \"name\": \"the eagle's wings\"}, {\"id\": 25474, \"name\": \"an atv\"}, {\"id\": 25475, \"name\": \"the lush, grassy area\"}, {\"id\": 25476, \"name\": \"the game piece\"}, {\"id\": 25477, \"name\": \"a solar panel\"}, {\"id\": 25478, \"name\": \"person in the hard hat\"}, {\"id\": 25479, \"name\": \"a green metal frame\"}, {\"id\": 25480, \"name\": \"the bright blue spotlight\"}, {\"id\": 25481, \"name\": \"small black fish\"}, {\"id\": 25482, \"name\": \"a deep purple sari\"}, {\"id\": 25483, \"name\": \"the brown wooden crate\"}, {\"id\": 25484, \"name\": \"blue tuk-tuk\"}, {\"id\": 25485, \"name\": \"a pink toy\"}, {\"id\": 25486, \"name\": \"khaki hat\"}, {\"id\": 25487, \"name\": \"a sparkler\"}, {\"id\": 25488, \"name\": \"white and black jet ski\"}, {\"id\": 25489, \"name\": \"the red fabric\"}, {\"id\": 25490, \"name\": \"vibrant hat\"}, {\"id\": 25491, \"name\": \"overhead lighting\"}, {\"id\": 25492, \"name\": \"a large mechanical creature\"}, {\"id\": 25493, \"name\": \"a large bubble\"}, {\"id\": 25494, \"name\": \"a glass of beer\"}, {\"id\": 25495, \"name\": \"neon pink light post\"}, {\"id\": 25496, \"name\": \"the large teacup\"}, {\"id\": 25497, \"name\": \"the yellow and blue shirt\"}, {\"id\": 25498, \"name\": \"passenger van\"}, {\"id\": 25499, \"name\": \"a blue wheel\"}, {\"id\": 25500, \"name\": \"parrotfish\"}, {\"id\": 25501, \"name\": \"a pole of the carousel horse\"}, {\"id\": 25502, \"name\": \"the white goalpost\"}, {\"id\": 25503, \"name\": \"a puffy white cloud\"}, {\"id\": 25504, \"name\": \"a checkered flag\"}, {\"id\": 25505, \"name\": \"red book\"}, {\"id\": 25506, \"name\": \"a red plastic bag\"}, {\"id\": 25507, \"name\": \"traditional turban\"}, {\"id\": 25508, \"name\": \"white drumhead\"}, {\"id\": 25509, \"name\": \"the yellow circular object\"}, {\"id\": 25510, \"name\": \"police van\"}, {\"id\": 25511, \"name\": \"green and yellow tuk-tuk\"}, {\"id\": 25512, \"name\": \"a silver ramp\"}, {\"id\": 25513, \"name\": \"the white wire mesh fence\"}, {\"id\": 25514, \"name\": \"yellow and white fish\"}, {\"id\": 25515, \"name\": \"a black headband\"}, {\"id\": 25516, \"name\": \"small blue control stick\"}, {\"id\": 25517, \"name\": \"a dark shutter\"}, {\"id\": 25518, \"name\": \"a black-and-white baseball cap\"}, {\"id\": 25519, \"name\": \"the large, dark blue fish\"}, {\"id\": 25520, \"name\": \"the large roll of plastic wrap\"}, {\"id\": 25521, \"name\": \"a white bench\"}, {\"id\": 25522, \"name\": \"the blue fish\"}, {\"id\": 25523, \"name\": \"a red santa hat\"}, {\"id\": 25524, \"name\": \"a long fluorescent light\"}, {\"id\": 25525, \"name\": \"the white suzuki truck\"}, {\"id\": 25526, \"name\": \"a score tab\"}, {\"id\": 25527, \"name\": \"white baseball pant\"}, {\"id\": 25528, \"name\": \"a car's windshield\"}, {\"id\": 25529, \"name\": \"father\"}, {\"id\": 25530, \"name\": \"the red bike\"}, {\"id\": 25531, \"name\": \"the small, dark-colored animal\"}, {\"id\": 25532, \"name\": \"green toy car\"}, {\"id\": 25533, \"name\": \"distinctive green basket\"}, {\"id\": 25534, \"name\": \"a large, dilapidated kennel structure\"}, {\"id\": 25535, \"name\": \"the block\"}, {\"id\": 25536, \"name\": \"large yellow sack\"}, {\"id\": 25537, \"name\": \"the plate of pasta salad\"}, {\"id\": 25538, \"name\": \"a cartoon bowling pin\"}, {\"id\": 25539, \"name\": \"bottle of beverage\"}, {\"id\": 25540, \"name\": \"the red lighting\"}, {\"id\": 25541, \"name\": \"large concrete structure\"}, {\"id\": 25542, \"name\": \"early pre-teen\"}, {\"id\": 25543, \"name\": \"the man in the group\"}, {\"id\": 25544, \"name\": \"a black pillar\"}, {\"id\": 25545, \"name\": \"the purple umbrella\"}, {\"id\": 25546, \"name\": \"a purple gem\"}, {\"id\": 25547, \"name\": \"large bus\"}, {\"id\": 25548, \"name\": \"cartoon lion\"}, {\"id\": 25549, \"name\": \"a large inflatable head of mario\"}, {\"id\": 25550, \"name\": \"yellow and black tuk-tuk\"}, {\"id\": 25551, \"name\": \"a blue and red jacket\"}, {\"id\": 25552, \"name\": \"white trash can\"}, {\"id\": 25553, \"name\": \"an intricate carving\"}, {\"id\": 25554, \"name\": \"the church\"}, {\"id\": 25555, \"name\": \"a white head\"}, {\"id\": 25556, \"name\": \"the wooden roof\"}, {\"id\": 25557, \"name\": \"a red tuk-tuk\"}, {\"id\": 25558, \"name\": \"blue metal bench\"}, {\"id\": 25559, \"name\": \"a raised bed of soil and plant\"}, {\"id\": 25560, \"name\": \"an intricate latticework design\"}, {\"id\": 25561, \"name\": \"the black military helicopter\"}, {\"id\": 25562, \"name\": \"a badminton player\"}, {\"id\": 25563, \"name\": \"toy construction vehicle\"}, {\"id\": 25564, \"name\": \"smaller slide\"}, {\"id\": 25565, \"name\": \"pink lid\"}, {\"id\": 25566, \"name\": \"a blue dome\"}, {\"id\": 25567, \"name\": \"toddler girl\"}, {\"id\": 25568, \"name\": \"white head\"}, {\"id\": 25569, \"name\": \"the metal tower\"}, {\"id\": 25570, \"name\": \"a red -terrain vehicle\"}, {\"id\": 25571, \"name\": \"large white cylindrical tank\"}, {\"id\": 25572, \"name\": \"the vibrant green sign\"}, {\"id\": 25573, \"name\": \"a large basketball hoop\"}, {\"id\": 25574, \"name\": \"large white and blue stick\"}, {\"id\": 25575, \"name\": \"yellow belt\"}, {\"id\": 25576, \"name\": \"fan blade\"}, {\"id\": 25577, \"name\": \"a raised garden bed\"}, {\"id\": 25578, \"name\": \"a silver top\"}, {\"id\": 25579, \"name\": \"the exit sign\"}, {\"id\": 25580, \"name\": \"tall metal pole\"}, {\"id\": 25581, \"name\": \"the muddy\"}, {\"id\": 25582, \"name\": \"grey object\"}, {\"id\": 25583, \"name\": \"the bag of produce\"}, {\"id\": 25584, \"name\": \"child in a white shirt\"}, {\"id\": 25585, \"name\": \"a green stool\"}, {\"id\": 25586, \"name\": \"the red canopy tent\"}, {\"id\": 25587, \"name\": \"a blue orb\"}, {\"id\": 25588, \"name\": \"the vibrant lime green\"}, {\"id\": 25589, \"name\": \"the NBA Finals logo\"}, {\"id\": 25590, \"name\": \"a large green leaf\"}, {\"id\": 25591, \"name\": \"yellow awning\"}, {\"id\": 25592, \"name\": \"an orange flag\"}, {\"id\": 25593, \"name\": \"large white bear headpiece\"}, {\"id\": 25594, \"name\": \"diamond-shaped window\"}, {\"id\": 25595, \"name\": \"a large tanker truck\"}, {\"id\": 25596, \"name\": \"the child's outfit\"}, {\"id\": 25597, \"name\": \"the physical activity\"}, {\"id\": 25598, \"name\": \"a floating walkway\"}, {\"id\": 25599, \"name\": \"the large, yellow dump truck\"}, {\"id\": 25600, \"name\": \"the dark horse\"}, {\"id\": 25601, \"name\": \"a large, intricately designed lantern\"}, {\"id\": 25602, \"name\": \"blue cable\"}, {\"id\": 25603, \"name\": \"small handle\"}, {\"id\": 25604, \"name\": \"circular drain grate\"}, {\"id\": 25605, \"name\": \"navy blue waistband\"}, {\"id\": 25606, \"name\": \"the red gerbera daisy\"}, {\"id\": 25607, \"name\": \"buddhist nun\"}, {\"id\": 25608, \"name\": \"the ventilation system\"}, {\"id\": 25609, \"name\": \"the burgundy skirt\"}, {\"id\": 25610, \"name\": \"a small black bag\"}, {\"id\": 25611, \"name\": \"man in the black shirt\"}, {\"id\": 25612, \"name\": \"matching attire\"}, {\"id\": 25613, \"name\": \"a large red lantern\"}, {\"id\": 25614, \"name\": \"curved wooden bridge\"}, {\"id\": 25615, \"name\": \"the bare foot\"}, {\"id\": 25616, \"name\": \"the white folding table\"}, {\"id\": 25617, \"name\": \"green pepper\"}, {\"id\": 25618, \"name\": \"the yellow cone\"}, {\"id\": 25619, \"name\": \"the long skirt\"}, {\"id\": 25620, \"name\": \"yellow salwar kameez\"}, {\"id\": 25621, \"name\": \"the colorful turtle-shaped car\"}, {\"id\": 25622, \"name\": \"the small and white dog\"}, {\"id\": 25623, \"name\": \"a blue and white striped snowsuit\"}, {\"id\": 25624, \"name\": \"the tall structure\"}, {\"id\": 25625, \"name\": \"the white soccer goal\"}, {\"id\": 25626, \"name\": \"the orange barrier\"}, {\"id\": 25627, \"name\": \"backyard swimming pool\"}, {\"id\": 25628, \"name\": \"a colorful bag\"}, {\"id\": 25629, \"name\": \"the cartoon thumb-up emoji\"}, {\"id\": 25630, \"name\": \"large truck tire\"}, {\"id\": 25631, \"name\": \"the white dot\"}, {\"id\": 25632, \"name\": \"the lead motorcycle\"}, {\"id\": 25633, \"name\": \"small, round wheel\"}, {\"id\": 25634, \"name\": \"black base\"}, {\"id\": 25635, \"name\": \"orange bucket\"}, {\"id\": 25636, \"name\": \"the light-colored cap\"}, {\"id\": 25637, \"name\": \"large blue shipping container\"}, {\"id\": 25638, \"name\": \"headrest\"}, {\"id\": 25639, \"name\": \"the directions\"}, {\"id\": 25640, \"name\": \"dark hat\"}, {\"id\": 25641, \"name\": \"a black and white dog\"}, {\"id\": 25642, \"name\": \"the silver boot\"}, {\"id\": 25643, \"name\": \"elaborate golden crown\"}, {\"id\": 25644, \"name\": \"red character\"}, {\"id\": 25645, \"name\": \"candy wrapper\"}, {\"id\": 25646, \"name\": \"bright blue sign\"}, {\"id\": 25647, \"name\": \"a snowboard\"}, {\"id\": 25648, \"name\": \"a leg of person\"}, {\"id\": 25649, \"name\": \"the drag queen\"}, {\"id\": 25650, \"name\": \"the large white rock\"}, {\"id\": 25651, \"name\": \"the concrete overpass\"}, {\"id\": 25652, \"name\": \"a black-and-white checkered flag\"}, {\"id\": 25653, \"name\": \"a cat bowl\"}, {\"id\": 25654, \"name\": \"grey octagon\"}, {\"id\": 25655, \"name\": \"a large, worn-out basket\"}, {\"id\": 25656, \"name\": \"the lorry\"}, {\"id\": 25657, \"name\": \"bowl of noodle\"}, {\"id\": 25658, \"name\": \"the winding highway\"}, {\"id\": 25659, \"name\": \"the large, ornate gate\"}, {\"id\": 25660, \"name\": \"tenni racket\"}, {\"id\": 25661, \"name\": \"foreground car\"}, {\"id\": 25662, \"name\": \"stainles steel frying pan\"}, {\"id\": 25663, \"name\": \"a large red and white poster\"}, {\"id\": 25664, \"name\": \"red and yellow toy\"}, {\"id\": 25665, \"name\": \"red ping-pong paddle\"}, {\"id\": 25666, \"name\": \"the orange wing\"}, {\"id\": 25667, \"name\": \"a red octagonal stop sign\"}, {\"id\": 25668, \"name\": \"jet ski\"}, {\"id\": 25669, \"name\": \"red badminton racket\"}, {\"id\": 25670, \"name\": \"a riverbank\"}, {\"id\": 25671, \"name\": \"a blue rash guard\"}, {\"id\": 25672, \"name\": \"green sheet of material\"}, {\"id\": 25673, \"name\": \"green area\"}, {\"id\": 25674, \"name\": \"blue denim short\"}, {\"id\": 25675, \"name\": \"yellow hoodie\"}, {\"id\": 25676, \"name\": \"green inflatable object\"}, {\"id\": 25677, \"name\": \"the white bottle\"}, {\"id\": 25678, \"name\": \"an abundance of clothing\"}, {\"id\": 25679, \"name\": \"an orange life jacket\"}, {\"id\": 25680, \"name\": \"an obstacle course\"}, {\"id\": 25681, \"name\": \"large metal screw\"}, {\"id\": 25682, \"name\": \"the motorcycle's front wheel\"}, {\"id\": 25683, \"name\": \"the blue crystal\"}, {\"id\": 25684, \"name\": \"a ferry\"}, {\"id\": 25685, \"name\": \"yellow glow\"}, {\"id\": 25686, \"name\": \"a white ball or object\"}, {\"id\": 25687, \"name\": \"black rear windshield\"}, {\"id\": 25688, \"name\": \"the cape\"}, {\"id\": 25689, \"name\": \"pink cat-like creature\"}, {\"id\": 25690, \"name\": \"pile of trash\"}, {\"id\": 25691, \"name\": \"the whitewater kayaker\"}, {\"id\": 25692, \"name\": \"temple or shrine\"}, {\"id\": 25693, \"name\": \"an orange object\"}, {\"id\": 25694, \"name\": \"the large military plane\"}, {\"id\": 25695, \"name\": \"the large trash can\"}, {\"id\": 25696, \"name\": \"a small orange traffic cone\"}, {\"id\": 25697, \"name\": \"the small, green, open-air vehicle\"}, {\"id\": 25698, \"name\": \"red tent\"}, {\"id\": 25699, \"name\": \"a yellow liquid\"}, {\"id\": 25700, \"name\": \"man wearing an orange shirt\"}, {\"id\": 25701, \"name\": \"a woman wearing a red hat and a blue plastic bag\"}, {\"id\": 25702, \"name\": \"the white neon animal figure\"}, {\"id\": 25703, \"name\": \"pink hijab\"}, {\"id\": 25704, \"name\": \"a plate of sliced meat\"}, {\"id\": 25705, \"name\": \"orange sleeve\"}, {\"id\": 25706, \"name\": \"a silver sedan\"}, {\"id\": 25707, \"name\": \"a green bar\"}, {\"id\": 25708, \"name\": \"the knee-high sock\"}, {\"id\": 25709, \"name\": \"white fin\"}, {\"id\": 25710, \"name\": \"red plastic bowl\"}, {\"id\": 25711, \"name\": \"pale green dress\"}, {\"id\": 25712, \"name\": \"the stainles steel pan\"}, {\"id\": 25713, \"name\": \"dragon's structure\"}, {\"id\": 25714, \"name\": \"a small gray dog\"}, {\"id\": 25715, \"name\": \"the artwork\"}, {\"id\": 25716, \"name\": \"the red graffiti design\"}, {\"id\": 25717, \"name\": \"beef\"}, {\"id\": 25718, \"name\": \"a black cape\"}, {\"id\": 25719, \"name\": \"a black weighted object\"}, {\"id\": 25720, \"name\": \"blue bike\"}, {\"id\": 25721, \"name\": \"a small black microphone\"}, {\"id\": 25722, \"name\": \"the colorful hat\"}, {\"id\": 25723, \"name\": \"large, rectangular sign\"}, {\"id\": 25724, \"name\": \"red basket\"}, {\"id\": 25725, \"name\": \"the large pineapple decoration\"}, {\"id\": 25726, \"name\": \"spring roll\"}, {\"id\": 25727, \"name\": \"a white taxi\"}, {\"id\": 25728, \"name\": \"striking red and yellow dragon\"}, {\"id\": 25729, \"name\": \"German flag\"}, {\"id\": 25730, \"name\": \"large red and white truck\"}, {\"id\": 25731, \"name\": \"the yellow boot\"}, {\"id\": 25732, \"name\": \"the green kayak\"}, {\"id\": 25733, \"name\": \"red and black fish\"}, {\"id\": 25734, \"name\": \"a pomegranate\"}, {\"id\": 25735, \"name\": \"purple mane\"}, {\"id\": 25736, \"name\": \"the yellow and black go-kart\"}, {\"id\": 25737, \"name\": \"yellow hard hat\"}, {\"id\": 25738, \"name\": \"the orange food scraps\"}, {\"id\": 25739, \"name\": \"red and white train car\"}, {\"id\": 25740, \"name\": \"a light blue cloth\"}, {\"id\": 25741, \"name\": \"distinctive headlight\"}, {\"id\": 25742, \"name\": \"a pink bear-shaped rattle\"}, {\"id\": 25743, \"name\": \"the large motor\"}, {\"id\": 25744, \"name\": \"the yellow and orange coral-like decoration\"}, {\"id\": 25745, \"name\": \"small orange cone\"}, {\"id\": 25746, \"name\": \"colorful balloon\"}, {\"id\": 25747, \"name\": \"the bright orange vest\"}, {\"id\": 25748, \"name\": \"the hanging flower box\"}, {\"id\": 25749, \"name\": \"the red stool\"}, {\"id\": 25750, \"name\": \"the pink short\"}, {\"id\": 25751, \"name\": \"the dolphin statue\"}, {\"id\": 25752, \"name\": \"mug\"}, {\"id\": 25753, \"name\": \"the red sari\"}, {\"id\": 25754, \"name\": \"the cocktail\"}, {\"id\": 25755, \"name\": \"a long cardboard box\"}, {\"id\": 25756, \"name\": \"the wrapped package\"}, {\"id\": 25757, \"name\": \"miniature train\"}, {\"id\": 25758, \"name\": \"small purple heart\"}, {\"id\": 25759, \"name\": \"red can\"}, {\"id\": 25760, \"name\": \"rim\"}, {\"id\": 25761, \"name\": \"purple, glowing creature\"}, {\"id\": 25762, \"name\": \"the square hole\"}, {\"id\": 25763, \"name\": \"the yellow ribbon\"}, {\"id\": 25764, \"name\": \"a white traffic cone\"}, {\"id\": 25765, \"name\": \"white campervan\"}, {\"id\": 25766, \"name\": \"a pink floral top\"}, {\"id\": 25767, \"name\": \"a direction\"}, {\"id\": 25768, \"name\": \"the purple mat\"}, {\"id\": 25769, \"name\": \"a large piece of pink fabric\"}, {\"id\": 25770, \"name\": \"the green traffic cone\"}, {\"id\": 25771, \"name\": \"the large, cylindrical body\"}, {\"id\": 25772, \"name\": \"the large blue oval logo\"}, {\"id\": 25773, \"name\": \"large, gold-colored cabinet\"}, {\"id\": 25774, \"name\": \"cartoon airplane\"}, {\"id\": 25775, \"name\": \"the large, white, oval-shaped object\"}, {\"id\": 25776, \"name\": \"a multitude of kite\"}, {\"id\": 25777, \"name\": \"large bubble wand\"}, {\"id\": 25778, \"name\": \"red suspender\"}, {\"id\": 25779, \"name\": \"the white dial\"}, {\"id\": 25780, \"name\": \"broken furniture\"}, {\"id\": 25781, \"name\": \"the green balloon\"}, {\"id\": 25782, \"name\": \"the vintage plane\"}, {\"id\": 25783, \"name\": \"the light gray top\"}, {\"id\": 25784, \"name\": \"green plastic container\"}, {\"id\": 25785, \"name\": \"the starburst shape\"}, {\"id\": 25786, \"name\": \"a white semi-truck\"}, {\"id\": 25787, \"name\": \"the dark blue bar\"}, {\"id\": 25788, \"name\": \"rider of the red motorcycle\"}, {\"id\": 25789, \"name\": \"the blue pallet\"}, {\"id\": 25790, \"name\": \"red and white necktie\"}, {\"id\": 25791, \"name\": \"the white mazda hatchback car\"}, {\"id\": 25792, \"name\": \"the blue and black flip-flop\"}, {\"id\": 25793, \"name\": \"a diver\"}, {\"id\": 25794, \"name\": \"the small white boat\"}, {\"id\": 25795, \"name\": \"row of flag\"}, {\"id\": 25796, \"name\": \"large kite\"}, {\"id\": 25797, \"name\": \"black and white patterned cape\"}, {\"id\": 25798, \"name\": \"the rough, unfinished texture\"}, {\"id\": 25799, \"name\": \"large, brightly colored column\"}, {\"id\": 25800, \"name\": \"the bright red sari\"}, {\"id\": 25801, \"name\": \"men\"}, {\"id\": 25802, \"name\": \"red, illuminated sign\"}, {\"id\": 25803, \"name\": \"a large red hoop\"}, {\"id\": 25804, \"name\": \"the costumed character\"}, {\"id\": 25805, \"name\": \"the large white sign or billboard\"}, {\"id\": 25806, \"name\": \"orange piece\"}, {\"id\": 25807, \"name\": \"black stripe\"}, {\"id\": 25808, \"name\": \"large, dark awning\"}, {\"id\": 25809, \"name\": \"the cartoon face\"}, {\"id\": 25810, \"name\": \"a brick border\"}, {\"id\": 25811, \"name\": \"white and red striped cloth\"}, {\"id\": 25812, \"name\": \"the pink flip-flop\"}, {\"id\": 25813, \"name\": \"a truck's windshield\"}, {\"id\": 25814, \"name\": \"the prominent red and white sign\"}, {\"id\": 25815, \"name\": \"the high-rise structure\"}, {\"id\": 25816, \"name\": \"a vibrant red and blue border\"}, {\"id\": 25817, \"name\": \"the matching skirt\"}, {\"id\": 25818, \"name\": \"a triangular-shaped grip\"}, {\"id\": 25819, \"name\": \"a white moving truck\"}, {\"id\": 25820, \"name\": \"smaller car\"}, {\"id\": 25821, \"name\": \"a yellow and black tool\"}, {\"id\": 25822, \"name\": \"a brown short\"}, {\"id\": 25823, \"name\": \"the colorful graffiti-style artwork\"}, {\"id\": 25824, \"name\": \"the light brown dog\"}, {\"id\": 25825, \"name\": \"pink toy with a handle\"}, {\"id\": 25826, \"name\": \"the bright orange and yellow hue\"}, {\"id\": 25827, \"name\": \"a large, white structure\"}, {\"id\": 25828, \"name\": \"the white and black checkered sleeve\"}, {\"id\": 25829, \"name\": \"the colorful slide\"}, {\"id\": 25830, \"name\": \"the grey short\"}, {\"id\": 25831, \"name\": \"white feather-like decoration\"}, {\"id\": 25832, \"name\": \"the large white and red truck\"}, {\"id\": 25833, \"name\": \"yellow bracket\"}, {\"id\": 25834, \"name\": \"the large blue piece of equipment\"}, {\"id\": 25835, \"name\": \"a piece of driftwood\"}, {\"id\": 25836, \"name\": \"multicolored flag\"}, {\"id\": 25837, \"name\": \"a white sarong\"}, {\"id\": 25838, \"name\": \"the spaceship or aircraft\"}, {\"id\": 25839, \"name\": \"man in a dark suit\"}, {\"id\": 25840, \"name\": \"anemone\"}, {\"id\": 25841, \"name\": \"gray raft\"}, {\"id\": 25842, \"name\": \"the tall gray pole\"}, {\"id\": 25843, \"name\": \"green auto rickshaw\"}, {\"id\": 25844, \"name\": \"small, round table\"}, {\"id\": 25845, \"name\": \"an orange content\"}, {\"id\": 25846, \"name\": \"the black elbow and knee pad\"}, {\"id\": 25847, \"name\": \"the blue face mask\"}, {\"id\": 25848, \"name\": \"red legging\"}, {\"id\": 25849, \"name\": \"a cylindrical protrusion\"}, {\"id\": 25850, \"name\": \"the long white robe\"}, {\"id\": 25851, \"name\": \"a suspender\"}, {\"id\": 25852, \"name\": \"cat cage\"}, {\"id\": 25853, \"name\": \"the matching gold headpiece\"}, {\"id\": 25854, \"name\": \"the blue and black life jacket\"}, {\"id\": 25855, \"name\": \"silver cup\"}, {\"id\": 25856, \"name\": \"the whimsical cartoon character\"}, {\"id\": 25857, \"name\": \"car's rear wheel\"}, {\"id\": 25858, \"name\": \"orange facade\"}, {\"id\": 25859, \"name\": \"a tall spire\"}, {\"id\": 25860, \"name\": \"a green swim trunk\"}, {\"id\": 25861, \"name\": \"matching purple vest\"}, {\"id\": 25862, \"name\": \"teal leg\"}, {\"id\": 25863, \"name\": \"black-and-yellow striped insect\"}, {\"id\": 25864, \"name\": \"bike's front wheel\"}, {\"id\": 25865, \"name\": \"a green shell\"}, {\"id\": 25866, \"name\": \"the traffic light pole\"}, {\"id\": 25867, \"name\": \"a black balloon\"}, {\"id\": 25868, \"name\": \"large ball\"}, {\"id\": 25869, \"name\": \"the brown disc\"}, {\"id\": 25870, \"name\": \"the large, silver metal object\"}, {\"id\": 25871, \"name\": \"larger red sign\"}, {\"id\": 25872, \"name\": \"decorated rickshaw\"}, {\"id\": 25873, \"name\": \"brown wooden gate\"}, {\"id\": 25874, \"name\": \"the rickshaw or carriage\"}, {\"id\": 25875, \"name\": \"the red lever\"}, {\"id\": 25876, \"name\": \"the glass of amber-colored liquid\"}, {\"id\": 25877, \"name\": \"a small, light-colored wicker cage\"}, {\"id\": 25878, \"name\": \"the yellow and white fish\"}, {\"id\": 25879, \"name\": \"the white wooden fence\"}, {\"id\": 25880, \"name\": \"large, light blue, inflatable tube\"}, {\"id\": 25881, \"name\": \"an orange light\"}, {\"id\": 25882, \"name\": \"the mussel\"}, {\"id\": 25883, \"name\": \"the cyclist's arm\"}, {\"id\": 25884, \"name\": \"the gold belt\"}, {\"id\": 25885, \"name\": \"the large, sloping grandstand\"}, {\"id\": 25886, \"name\": \"arrow\"}, {\"id\": 25887, \"name\": \"black metal ladder\"}, {\"id\": 25888, \"name\": \"large fishing structure\"}, {\"id\": 25889, \"name\": \"the pink figure\"}, {\"id\": 25890, \"name\": \"the yellow graphic\"}, {\"id\": 25891, \"name\": \"teal-colored table\"}, {\"id\": 25892, \"name\": \"large, white, and gold-colored structure\"}, {\"id\": 25893, \"name\": \"a red tutu\"}, {\"id\": 25894, \"name\": \"a small bag\"}, {\"id\": 25895, \"name\": \"the crouched position\"}, {\"id\": 25896, \"name\": \"the large cardboard box\"}, {\"id\": 25897, \"name\": \"the pink or purple rock\"}, {\"id\": 25898, \"name\": \"man holding the bucket\"}, {\"id\": 25899, \"name\": \"the safety vest\"}, {\"id\": 25900, \"name\": \"the central white crosswalk\"}, {\"id\": 25901, \"name\": \"the brown balcony\"}, {\"id\": 25902, \"name\": \"the distinctive black fur hat\"}, {\"id\": 25903, \"name\": \"young person\"}, {\"id\": 25904, \"name\": \"the floral headpiece\"}, {\"id\": 25905, \"name\": \"a safety gear\"}, {\"id\": 25906, \"name\": \"an antenna\"}, {\"id\": 25907, \"name\": \"a trumpet player\"}, {\"id\": 25908, \"name\": \"the lone surfer\"}, {\"id\": 25909, \"name\": \"the elaborate gold head\"}, {\"id\": 25910, \"name\": \"the yellow fringed top\"}, {\"id\": 25911, \"name\": \"the support beam\"}, {\"id\": 25912, \"name\": \"the small yellow dinosaur figurine\"}, {\"id\": 25913, \"name\": \"large, tan-colored facade\"}, {\"id\": 25914, \"name\": \"the orange and red clothing\"}, {\"id\": 25915, \"name\": \"yellowish-brown feather\"}, {\"id\": 25916, \"name\": \"a buzz cut\"}, {\"id\": 25917, \"name\": \"a red writing\"}, {\"id\": 25918, \"name\": \"the light-brown fur\"}, {\"id\": 25919, \"name\": \"the house with a blue fence\"}, {\"id\": 25920, \"name\": \"a large white column\"}, {\"id\": 25921, \"name\": \"strip of light\"}, {\"id\": 25922, \"name\": \"cowboy\"}, {\"id\": 25923, \"name\": \"a snowmaking equipment\"}, {\"id\": 25924, \"name\": \"yellow rim\"}, {\"id\": 25925, \"name\": \"large, red and gold lion puppet\"}, {\"id\": 25926, \"name\": \"the child's hands\"}, {\"id\": 25927, \"name\": \"the pink sleeve\"}, {\"id\": 25928, \"name\": \"the metal siding\"}, {\"id\": 25929, \"name\": \"a rafter\"}, {\"id\": 25930, \"name\": \"yellow t-shirt\"}, {\"id\": 25931, \"name\": \"the large, industrial machine\"}, {\"id\": 25932, \"name\": \"blaster\"}, {\"id\": 25933, \"name\": \"a passerby\"}, {\"id\": 25934, \"name\": \"hind leg\"}, {\"id\": 25935, \"name\": \"a giraffe picture\"}, {\"id\": 25936, \"name\": \"the blue knee brace\"}, {\"id\": 25937, \"name\": \"a glass pane\"}, {\"id\": 25938, \"name\": \"red and white seat\"}, {\"id\": 25939, \"name\": \"screw or bolt\"}, {\"id\": 25940, \"name\": \"the vibrant red attire\"}, {\"id\": 25941, \"name\": \"drainage pipe\"}, {\"id\": 25942, \"name\": \"the dark suv\"}, {\"id\": 25943, \"name\": \"the rickshaw\"}, {\"id\": 25944, \"name\": \"the large brown bag of grain\"}, {\"id\": 25945, \"name\": \"the black breed\"}, {\"id\": 25946, \"name\": \"a red life preserver\"}, {\"id\": 25947, \"name\": \"variety of food items\"}, {\"id\": 25948, \"name\": \"a tall can\"}, {\"id\": 25949, \"name\": \"a breathing apparatus\"}, {\"id\": 25950, \"name\": \"the red lettering\"}, {\"id\": 25951, \"name\": \"the chef's hand\"}, {\"id\": 25952, \"name\": \"red hijab\"}, {\"id\": 25953, \"name\": \"the black corrugated metal\"}, {\"id\": 25954, \"name\": \"a vertical rod\"}, {\"id\": 25955, \"name\": \"a small, white ceramic bowl\"}, {\"id\": 25956, \"name\": \"blue hawaiian shirt\"}, {\"id\": 25957, \"name\": \"a black wrought iron fence\"}, {\"id\": 25958, \"name\": \"the biker\"}, {\"id\": 25959, \"name\": \"pink wheel\"}, {\"id\": 25960, \"name\": \"black pendant light\"}, {\"id\": 25961, \"name\": \"the papers or pamphlet\"}, {\"id\": 25962, \"name\": \"the rear section\"}, {\"id\": 25963, \"name\": \"chicken feeder\"}, {\"id\": 25964, \"name\": \"large green box\"}, {\"id\": 25965, \"name\": \"pink and white plaid shirt\"}, {\"id\": 25966, \"name\": \"a large scuba diver\"}, {\"id\": 25967, \"name\": \"the red section\"}, {\"id\": 25968, \"name\": \"short, rounded tail\"}, {\"id\": 25969, \"name\": \"vibrant, multicolored outfit\"}, {\"id\": 25970, \"name\": \"the gold jewelry\"}, {\"id\": 25971, \"name\": \"the thai writing\"}, {\"id\": 25972, \"name\": \"a row of horse-drawn carriages\"}, {\"id\": 25973, \"name\": \"large white coral formation\"}, {\"id\": 25974, \"name\": \"the tan-colored sandal\"}, {\"id\": 25975, \"name\": \"the circular table\"}, {\"id\": 25976, \"name\": \"prop\"}, {\"id\": 25977, \"name\": \"the child wearing blue short\"}, {\"id\": 25978, \"name\": \"small sailboat\"}, {\"id\": 25979, \"name\": \"green tiled roof\"}, {\"id\": 25980, \"name\": \"metal cone\"}, {\"id\": 25981, \"name\": \"the clay pot\"}, {\"id\": 25982, \"name\": \"a small tv screen\"}, {\"id\": 25983, \"name\": \"a riding lawnmower\"}, {\"id\": 25984, \"name\": \"menu bar\"}, {\"id\": 25985, \"name\": \"yellow bag\"}, {\"id\": 25986, \"name\": \"a green rickshaw\"}, {\"id\": 25987, \"name\": \"large, grey, stone-like creature\"}, {\"id\": 25988, \"name\": \"the purple flip-flop\"}, {\"id\": 25989, \"name\": \"a vibrant display of hat\"}, {\"id\": 25990, \"name\": \"yellow buckle\"}, {\"id\": 25991, \"name\": \"the red and yellow umbrella\"}, {\"id\": 25992, \"name\": \"a bundle of wood\"}, {\"id\": 25993, \"name\": \"the yellow parachute\"}, {\"id\": 25994, \"name\": \"the bustling bumper car ride\"}, {\"id\": 25995, \"name\": \"blue and purple banner\"}, {\"id\": 25996, \"name\": \"mobile device\"}, {\"id\": 25997, \"name\": \"the white karate uniform\"}, {\"id\": 25998, \"name\": \"vibrant decoration\"}, {\"id\": 25999, \"name\": \"the large, black, curved object\"}, {\"id\": 26000, \"name\": \"the pink hijab\"}, {\"id\": 26001, \"name\": \"a black pulley wheel\"}, {\"id\": 26002, \"name\": \"a headdress\"}, {\"id\": 26003, \"name\": \"a black cup\"}, {\"id\": 26004, \"name\": \"blue bollard\"}, {\"id\": 26005, \"name\": \"tall, metal electrical pole\"}, {\"id\": 26006, \"name\": \"the dark helmet\"}, {\"id\": 26007, \"name\": \"purple balloon\"}, {\"id\": 26008, \"name\": \"cobblestone paving\"}, {\"id\": 26009, \"name\": \"shark\"}, {\"id\": 26010, \"name\": \"light-blue jean\"}, {\"id\": 26011, \"name\": \"the driver's head\"}, {\"id\": 26012, \"name\": \"large metal wheel\"}, {\"id\": 26013, \"name\": \"blue kite\"}, {\"id\": 26014, \"name\": \"a line of police vehicles\"}, {\"id\": 26015, \"name\": \"the piece of torn cardboard\"}, {\"id\": 26016, \"name\": \"traditional vietnamese hat\"}, {\"id\": 26017, \"name\": \"the terracotta pot\"}, {\"id\": 26018, \"name\": \"the bright pink border\"}, {\"id\": 26019, \"name\": \"the gold crown or tiara\"}, {\"id\": 26020, \"name\": \"yellow fish\"}, {\"id\": 26021, \"name\": \"the colorful tent\"}, {\"id\": 26022, \"name\": \"the yellow crosswalk marking\"}, {\"id\": 26023, \"name\": \"the large, golden-colored giraffe sculpture\"}, {\"id\": 26024, \"name\": \"a graphic of a bowling ball\"}, {\"id\": 26025, \"name\": \"green umbrella\"}, {\"id\": 26026, \"name\": \"blue roll of paper\"}, {\"id\": 26027, \"name\": \"man in a black t-shirt\"}, {\"id\": 26028, \"name\": \"yellow flower-shaped light\"}, {\"id\": 26029, \"name\": \"a beige castle\"}, {\"id\": 26030, \"name\": \"the blue tang\"}, {\"id\": 26031, \"name\": \"red and gold accent\"}, {\"id\": 26032, \"name\": \"a white tile section\"}, {\"id\": 26033, \"name\": \"the cartoon snowman\"}, {\"id\": 26034, \"name\": \"the hanging light bulb\"}, {\"id\": 26035, \"name\": \"the blue-lit bar or counter\"}, {\"id\": 26036, \"name\": \"the windshield of the front car\"}, {\"id\": 26037, \"name\": \"a stadium's facade\"}, {\"id\": 26038, \"name\": \"a purple and grey uniform\"}, {\"id\": 26039, \"name\": \"fluffy tail\"}, {\"id\": 26040, \"name\": \"the black writing\"}, {\"id\": 26041, \"name\": \"a large mario head\"}, {\"id\": 26042, \"name\": \"tall, illuminated light pole\"}, {\"id\": 26043, \"name\": \"a blue power tool\"}, {\"id\": 26044, \"name\": \"large green melon\"}, {\"id\": 26045, \"name\": \"brightly colored arcade game\"}, {\"id\": 26046, \"name\": \"a pink strap\"}, {\"id\": 26047, \"name\": \"tall antenna\"}, {\"id\": 26048, \"name\": \"the crowd of onlookers\"}, {\"id\": 26049, \"name\": \"the red plastic container\"}, {\"id\": 26050, \"name\": \"a black tuxedo\"}, {\"id\": 26051, \"name\": \"small blue sign\"}, {\"id\": 26052, \"name\": \"large, orange dragon\"}, {\"id\": 26053, \"name\": \"ceremonial weapon\"}, {\"id\": 26054, \"name\": \"the purple and pink orb\"}, {\"id\": 26055, \"name\": \"a blue sash\"}, {\"id\": 26056, \"name\": \"a vibrant green and white ball\"}, {\"id\": 26057, \"name\": \"the colorful balloon tube\"}, {\"id\": 26058, \"name\": \"seated woman\"}, {\"id\": 26059, \"name\": \"the step stool\"}, {\"id\": 26060, \"name\": \"soccer net\"}, {\"id\": 26061, \"name\": \"the colored shirt\"}, {\"id\": 26062, \"name\": \"identical yellow shirt\"}, {\"id\": 26063, \"name\": \"ceramic\"}, {\"id\": 26064, \"name\": \"passing vehicle\"}, {\"id\": 26065, \"name\": \"orange, yellow, and red traffic cone\"}, {\"id\": 26066, \"name\": \"silver metal piece\"}, {\"id\": 26067, \"name\": \"the weapon information\"}, {\"id\": 26068, \"name\": \"the partially filled bottle of beer\"}, {\"id\": 26069, \"name\": \"the colorful neon light\"}, {\"id\": 26070, \"name\": \"the white tower\"}, {\"id\": 26071, \"name\": \"the chat box\"}, {\"id\": 26072, \"name\": \"large rear window\"}, {\"id\": 26073, \"name\": \"horse statue\"}, {\"id\": 26074, \"name\": \"black purse strap\"}, {\"id\": 26075, \"name\": \"the blue tie\"}, {\"id\": 26076, \"name\": \"white gate\"}, {\"id\": 26077, \"name\": \"a small square\"}, {\"id\": 26078, \"name\": \"a red parasail\"}, {\"id\": 26079, \"name\": \"mickey mouse emblem\"}, {\"id\": 26080, \"name\": \"prominent yellow sign\"}, {\"id\": 26081, \"name\": \"an ear protection\"}, {\"id\": 26082, \"name\": \"a terracotta roof\"}, {\"id\": 26083, \"name\": \"red fin\"}, {\"id\": 26084, \"name\": \"the traditional thai attire\"}, {\"id\": 26085, \"name\": \"large industrial structure\"}, {\"id\": 26086, \"name\": \"a front window of the tuk-tuk\"}, {\"id\": 26087, \"name\": \"the sarong\"}, {\"id\": 26088, \"name\": \"crutch\"}, {\"id\": 26089, \"name\": \"a square icon\"}, {\"id\": 26090, \"name\": \"an ice skate\"}, {\"id\": 26091, \"name\": \"portable toilet\"}, {\"id\": 26092, \"name\": \"the sketch\"}, {\"id\": 26093, \"name\": \"diver\"}, {\"id\": 26094, \"name\": \"a rider of the motorcycle\"}, {\"id\": 26095, \"name\": \"a red and gold writing\"}, {\"id\": 26096, \"name\": \"the concrete piece\"}, {\"id\": 26097, \"name\": \"glass bowl of cookies\"}, {\"id\": 26098, \"name\": \"large, colorful wing\"}, {\"id\": 26099, \"name\": \"the red atv\"}, {\"id\": 26100, \"name\": \"a plastic poncho\"}, {\"id\": 26101, \"name\": \"a metal nut\"}, {\"id\": 26102, \"name\": \"the large gray rock\"}, {\"id\": 26103, \"name\": \"exposed white beam\"}, {\"id\": 26104, \"name\": \"the discarded item\"}, {\"id\": 26105, \"name\": \"the colorful helmet\"}, {\"id\": 26106, \"name\": \"the sewing machine\"}, {\"id\": 26107, \"name\": \"mana bar\"}, {\"id\": 26108, \"name\": \"the head of one rooster\"}, {\"id\": 26109, \"name\": \"the sniper rifle\"}, {\"id\": 26110, \"name\": \"cooking vessel\"}, {\"id\": 26111, \"name\": \"the black vent\"}, {\"id\": 26112, \"name\": \"raised garden bed\"}, {\"id\": 26113, \"name\": \"a parked cars and bicycle\"}, {\"id\": 26114, \"name\": \"the projector\"}, {\"id\": 26115, \"name\": \"17 jersey\"}, {\"id\": 26116, \"name\": \"a small brown animal\"}, {\"id\": 26117, \"name\": \"a red flip-flop\"}, {\"id\": 26118, \"name\": \"neon yellow helmet\"}, {\"id\": 26119, \"name\": \"the rear wheel of the motorcycle\"}, {\"id\": 26120, \"name\": \"the garden area\"}, {\"id\": 26121, \"name\": \"the bottle of alcohol\"}, {\"id\": 26122, \"name\": \"the dome-shaped structure\"}, {\"id\": 26123, \"name\": \"the pink legging\"}, {\"id\": 26124, \"name\": \"the stylized good morning sign\"}, {\"id\": 26125, \"name\": \"a puppy\"}, {\"id\": 26126, \"name\": \"the gray concrete\"}, {\"id\": 26127, \"name\": \"the diver\"}, {\"id\": 26128, \"name\": \"blue and white tanker\"}, {\"id\": 26129, \"name\": \"a black motorized unicycle\"}, {\"id\": 26130, \"name\": \"the blue sash\"}, {\"id\": 26131, \"name\": \"the mini-map\"}, {\"id\": 26132, \"name\": \"a purple and white ball-shaped candy\"}, {\"id\": 26133, \"name\": \"gravel patch\"}, {\"id\": 26134, \"name\": \"mural of a cityscape\"}, {\"id\": 26135, \"name\": \"a black traffic signal\"}, {\"id\": 26136, \"name\": \"black bike\"}, {\"id\": 26137, \"name\": \"the tassel\"}, {\"id\": 26138, \"name\": \"the pink tube top\"}, {\"id\": 26139, \"name\": \"game's menu system\"}, {\"id\": 26140, \"name\": \"reflective jacket\"}, {\"id\": 26141, \"name\": \"a person in a white shirt\"}, {\"id\": 26142, \"name\": \"a long, narrow, concrete slab\"}, {\"id\": 26143, \"name\": \"the purple lightsaber\"}, {\"id\": 26144, \"name\": \"the large, irregularly shaped rock\"}, {\"id\": 26145, \"name\": \"the cargo container\"}, {\"id\": 26146, \"name\": \"tow truck\"}, {\"id\": 26147, \"name\": \"the gray leather seat\"}, {\"id\": 26148, \"name\": \"tricycles' wheel\"}, {\"id\": 26149, \"name\": \"a kart\"}, {\"id\": 26150, \"name\": \"a small white rat\"}, {\"id\": 26151, \"name\": \"front license plate\"}, {\"id\": 26152, \"name\": \"gold coin icon\"}, {\"id\": 26153, \"name\": \"yellow and black flag\"}, {\"id\": 26154, \"name\": \"a clown head\"}, {\"id\": 26155, \"name\": \"tan cowboy hat\"}, {\"id\": 26156, \"name\": \"the children's colorful uniform\"}, {\"id\": 26157, \"name\": \"the light blue helmet\"}, {\"id\": 26158, \"name\": \"gray pickup truck\"}, {\"id\": 26159, \"name\": \"black cross-body bag\"}, {\"id\": 26160, \"name\": \"a short, fluffy tail\"}, {\"id\": 26161, \"name\": \"a silver pot\"}, {\"id\": 26162, \"name\": \"motorized rickshaw\"}, {\"id\": 26163, \"name\": \"the yellow headpiece\"}, {\"id\": 26164, \"name\": \"bus's headlights\"}, {\"id\": 26165, \"name\": \"large, dark-colored roof\"}, {\"id\": 26166, \"name\": \"bundle of long, thin bamboo stick\"}, {\"id\": 26167, \"name\": \"a small, round brown food item\"}, {\"id\": 26168, \"name\": \"a large slice of green vegetable\"}, {\"id\": 26169, \"name\": \"a vibrant, pixelated character\"}, {\"id\": 26170, \"name\": \"the white hood\"}, {\"id\": 26171, \"name\": \"yellow flag\"}, {\"id\": 26172, \"name\": \"dark blue paraglider\"}, {\"id\": 26173, \"name\": \"the large, blue and white tent\"}, {\"id\": 26174, \"name\": \"the bright, glowing orb\"}, {\"id\": 26175, \"name\": \"the blue exercise bench\"}, {\"id\": 26176, \"name\": \"red flower design\"}, {\"id\": 26177, \"name\": \"a yellow and green bag\"}, {\"id\": 26178, \"name\": \"the brown tag\"}, {\"id\": 26179, \"name\": \"metal stool\"}, {\"id\": 26180, \"name\": \"a dark liquid\"}, {\"id\": 26181, \"name\": \"an individual's legs\"}, {\"id\": 26182, \"name\": \"military uniform\"}, {\"id\": 26183, \"name\": \"the rectangular, light grey feeding tray\"}, {\"id\": 26184, \"name\": \"pink sleeve\"}, {\"id\": 26185, \"name\": \"gray van\"}, {\"id\": 26186, \"name\": \"large, arched mirror\"}, {\"id\": 26187, \"name\": \"a light-colored dress\"}, {\"id\": 26188, \"name\": \"a majestic church\"}, {\"id\": 26189, \"name\": \"wide-brimmed hat\"}, {\"id\": 26190, \"name\": \"white long-sleeve shirt\"}, {\"id\": 26191, \"name\": \"the coral\"}, {\"id\": 26192, \"name\": \"the glowing yellow orb\"}, {\"id\": 26193, \"name\": \"a staff\"}, {\"id\": 26194, \"name\": \"a light-colored hat\"}, {\"id\": 26195, \"name\": \"snorkeler\"}, {\"id\": 26196, \"name\": \"a yellow shoe\"}, {\"id\": 26197, \"name\": \"ham\"}, {\"id\": 26198, \"name\": \"the green cross\"}, {\"id\": 26199, \"name\": \"bowl of raw meat\"}, {\"id\": 26200, \"name\": \"the pink cloth\"}, {\"id\": 26201, \"name\": \"small tail fin\"}, {\"id\": 26202, \"name\": \"the large gray boulder\"}, {\"id\": 26203, \"name\": \"the large inflatable animal\"}, {\"id\": 26204, \"name\": \"a sliced apple\"}, {\"id\": 26205, \"name\": \"person wearing a high-visibility vest\"}, {\"id\": 26206, \"name\": \"the traditional islamic attire\"}, {\"id\": 26207, \"name\": \"the dark bottle of beer\"}, {\"id\": 26208, \"name\": \"the purple circle\"}, {\"id\": 26209, \"name\": \"a bowling lane\"}, {\"id\": 26210, \"name\": \"a black pipe\"}, {\"id\": 26211, \"name\": \"white styrofoam tray\"}, {\"id\": 26212, \"name\": \"a people on bicycle\"}, {\"id\": 26213, \"name\": \"the empty seat\"}, {\"id\": 26214, \"name\": \"large, circular icon\"}, {\"id\": 26215, \"name\": \"a central arched entryway\"}, {\"id\": 26216, \"name\": \"a red and white flag\"}, {\"id\": 26217, \"name\": \"boat or vessel\"}, {\"id\": 26218, \"name\": \"the cooking or frying food\"}, {\"id\": 26219, \"name\": \"the red drawstring bag\"}, {\"id\": 26220, \"name\": \"a stack\"}, {\"id\": 26221, \"name\": \"a large, curved dirt ramp\"}, {\"id\": 26222, \"name\": \"a tall lamppost\"}, {\"id\": 26223, \"name\": \"the large wooden pillar\"}, {\"id\": 26224, \"name\": \"a small, silver, blue, and red train engine\"}, {\"id\": 26225, \"name\": \"tan dog\"}, {\"id\": 26226, \"name\": \"a toyota dealership sign\"}, {\"id\": 26227, \"name\": \"sledge\"}, {\"id\": 26228, \"name\": \"sea lion\"}, {\"id\": 26229, \"name\": \"small fire\"}, {\"id\": 26230, \"name\": \"larger, dark-colored fish\"}, {\"id\": 26231, \"name\": \"the blue and white cloud-like design\"}, {\"id\": 26232, \"name\": \"a distinctive red crown\"}, {\"id\": 26233, \"name\": \"an orange wig\"}, {\"id\": 26234, \"name\": \"glass of amber-colored drink\"}, {\"id\": 26235, \"name\": \"a large column\"}, {\"id\": 26236, \"name\": \"the yellow marking\"}, {\"id\": 26237, \"name\": \"the colorful plastic shape\"}, {\"id\": 26238, \"name\": \"parked motorbike\"}, {\"id\": 26239, \"name\": \"large horn\"}, {\"id\": 26240, \"name\": \"the tall, ornate headpiece\"}, {\"id\": 26241, \"name\": \"the white cabana\"}, {\"id\": 26242, \"name\": \"red and white object\"}, {\"id\": 26243, \"name\": \"the yellow orb\"}, {\"id\": 26244, \"name\": \"trouser\"}, {\"id\": 26245, \"name\": \"a large red drum\"}, {\"id\": 26246, \"name\": \"a large, open umbrella\"}, {\"id\": 26247, \"name\": \"truck's front bumper\"}, {\"id\": 26248, \"name\": \"the adult woman\"}, {\"id\": 26249, \"name\": \"a yellow vertical surface\"}, {\"id\": 26250, \"name\": \"man in a light blue shirt\"}, {\"id\": 26251, \"name\": \"the brown and black striped cat\"}, {\"id\": 26252, \"name\": \"orange hat\"}, {\"id\": 26253, \"name\": \"a rider's arm\"}, {\"id\": 26254, \"name\": \"the sedan's license plate\"}, {\"id\": 26255, \"name\": \"chicken's leg\"}, {\"id\": 26256, \"name\": \"bolt's threaded portion\"}, {\"id\": 26257, \"name\": \"the bright orange lever\"}, {\"id\": 26258, \"name\": \"large black punching bag\"}, {\"id\": 26259, \"name\": \"glass of beer\"}, {\"id\": 26260, \"name\": \"model train car\"}, {\"id\": 26261, \"name\": \"a doberman breed\"}, {\"id\": 26262, \"name\": \"the dark stain\"}, {\"id\": 26263, \"name\": \"a yellow base\"}, {\"id\": 26264, \"name\": \"a large brown furry hat\"}, {\"id\": 26265, \"name\": \"human's foot\"}, {\"id\": 26266, \"name\": \"the cartoon-style, two-dimensional character\"}, {\"id\": 26267, \"name\": \"a green boat\"}, {\"id\": 26268, \"name\": \"large bowl of salad\"}, {\"id\": 26269, \"name\": \"the slanted beam\"}, {\"id\": 26270, \"name\": \"an uniformed security guard\"}, {\"id\": 26271, \"name\": \"the boat or raft\"}, {\"id\": 26272, \"name\": \"the black bikini top\"}, {\"id\": 26273, \"name\": \"neon green ear muff\"}, {\"id\": 26274, \"name\": \"a lone rider\"}, {\"id\": 26275, \"name\": \"blue and green umbrella\"}, {\"id\": 26276, \"name\": \"the parked cars and truck\"}, {\"id\": 26277, \"name\": \"construction crew\"}, {\"id\": 26278, \"name\": \"the green skirt\"}, {\"id\": 26279, \"name\": \"the blue square\"}, {\"id\": 26280, \"name\": \"the large blue balloon\"}, {\"id\": 26281, \"name\": \"the cartoon dog design\"}, {\"id\": 26282, \"name\": \"the red lollipop\"}, {\"id\": 26283, \"name\": \"white vehicle's hood\"}, {\"id\": 26284, \"name\": \"a corrugated metal siding\"}, {\"id\": 26285, \"name\": \"the traditional attire\"}, {\"id\": 26286, \"name\": \"a rainbow umbrella\"}, {\"id\": 26287, \"name\": \"a button-down shirt\"}, {\"id\": 26288, \"name\": \"a purple balloon\"}, {\"id\": 26289, \"name\": \"a black headrest\"}, {\"id\": 26290, \"name\": \"the skateboarder\"}, {\"id\": 26291, \"name\": \"a white wing\"}, {\"id\": 26292, \"name\": \"orange fish\"}, {\"id\": 26293, \"name\": \"short-sleeved shirt\"}, {\"id\": 26294, \"name\": \"large red lantern\"}, {\"id\": 26295, \"name\": \"a vibrant red flag\"}, {\"id\": 26296, \"name\": \"the ROBUSSEN BUS\"}, {\"id\": 26297, \"name\": \"a colorful table\"}, {\"id\": 26298, \"name\": \"cartoon alligator head\"}, {\"id\": 26299, \"name\": \"the white, oval-shaped object\"}, {\"id\": 26300, \"name\": \"a turquoise collar\"}, {\"id\": 26301, \"name\": \"the metal wheel\"}, {\"id\": 26302, \"name\": \"truck's white cab\"}, {\"id\": 26303, \"name\": \"cartoon girl\"}, {\"id\": 26304, \"name\": \"the green spire\"}, {\"id\": 26305, \"name\": \"a carousel's horse\"}, {\"id\": 26306, \"name\": \"the warm yellow glow\"}, {\"id\": 26307, \"name\": \"a silver bollard\"}, {\"id\": 26308, \"name\": \"a large school of fish\"}, {\"id\": 26309, \"name\": \"the gatorade cooler\"}, {\"id\": 26310, \"name\": \"amusement park bumper car ride\"}, {\"id\": 26311, \"name\": \"lace\"}, {\"id\": 26312, \"name\": \"a tall utility pole\"}, {\"id\": 26313, \"name\": \"white door frame\"}, {\"id\": 26314, \"name\": \"a yellow crescent moon\"}, {\"id\": 26315, \"name\": \"majestic cruise ship\"}, {\"id\": 26316, \"name\": \"the raindrop\"}, {\"id\": 26317, \"name\": \"yellow hoop\"}, {\"id\": 26318, \"name\": \"tan romper\"}, {\"id\": 26319, \"name\": \"the matching red overall\"}, {\"id\": 26320, \"name\": \"a person in a red jacket and brown cap\"}, {\"id\": 26321, \"name\": \"large, white, rectangular sign or screen\"}, {\"id\": 26322, \"name\": \"a white-painted fingernail\"}, {\"id\": 26323, \"name\": \"square fluorescent light\"}, {\"id\": 26324, \"name\": \"a yellow dress\"}, {\"id\": 26325, \"name\": \"an orange and black fish\"}, {\"id\": 26326, \"name\": \"the vibrant pink dress\"}, {\"id\": 26327, \"name\": \"large bowl of food\"}, {\"id\": 26328, \"name\": \"the small terracotta pot\"}, {\"id\": 26329, \"name\": \"white tent or canopy\"}, {\"id\": 26330, \"name\": \"a sea turtle\"}, {\"id\": 26331, \"name\": \"the round, translucent plastic ball\"}, {\"id\": 26332, \"name\": \"man in white robes\"}, {\"id\": 26333, \"name\": \"a crouching position\"}, {\"id\": 26334, \"name\": \"blurred yellow blanket\"}, {\"id\": 26335, \"name\": \"a colored helmet\"}, {\"id\": 26336, \"name\": \"the blurred speedometer\"}, {\"id\": 26337, \"name\": \"black-and-white image\"}, {\"id\": 26338, \"name\": \"a stone cutting facility\"}, {\"id\": 26339, \"name\": \"red candle\"}, {\"id\": 26340, \"name\": \"a cork\"}, {\"id\": 26341, \"name\": \"a red and white outfit\"}, {\"id\": 26342, \"name\": \"a black rubber tube or hose\"}, {\"id\": 26343, \"name\": \"the red tuk-tuk\"}, {\"id\": 26344, \"name\": \"the yellow and red vehicle\"}, {\"id\": 26345, \"name\": \"large, square, gray stone\"}, {\"id\": 26346, \"name\": \"the box of canned beverages\"}, {\"id\": 26347, \"name\": \"the red, white, and blue sneaker\"}, {\"id\": 26348, \"name\": \"large black and red pad\"}, {\"id\": 26349, \"name\": \"pair of silver tongs\"}, {\"id\": 26350, \"name\": \"the handle of the suitcase\"}, {\"id\": 26351, \"name\": \"mobility scooter\"}, {\"id\": 26352, \"name\": \"the purse strap\"}, {\"id\": 26353, \"name\": \"the set of tracks\"}, {\"id\": 26354, \"name\": \"the sailboat mast\"}, {\"id\": 26355, \"name\": \"a pink shoe\"}, {\"id\": 26356, \"name\": \"large white plate\"}, {\"id\": 26357, \"name\": \"a fireball\"}, {\"id\": 26358, \"name\": \"the hull\"}, {\"id\": 26359, \"name\": \"the ribbed surface\"}, {\"id\": 26360, \"name\": \"the black pickup truck\"}, {\"id\": 26361, \"name\": \"the shrimp\"}, {\"id\": 26362, \"name\": \"a red rear light\"}, {\"id\": 26363, \"name\": \"the temple's roof\"}, {\"id\": 26364, \"name\": \"a tall window\"}, {\"id\": 26365, \"name\": \"gray hexagon\"}, {\"id\": 26366, \"name\": \"large yellow hoop\"}, {\"id\": 26367, \"name\": \"large garage door\"}, {\"id\": 26368, \"name\": \"a light-colored wooden surface\"}, {\"id\": 26369, \"name\": \"a character's shadow\"}, {\"id\": 26370, \"name\": \"the medley of food items\"}, {\"id\": 26371, \"name\": \"the large clay pot\"}, {\"id\": 26372, \"name\": \"spurrlng, multi-level Lego play area\"}, {\"id\": 26373, \"name\": \"yellow component\"}, {\"id\": 26374, \"name\": \"a black plastic bin\"}, {\"id\": 26375, \"name\": \"white light fixture\"}, {\"id\": 26376, \"name\": \"a blue can\"}, {\"id\": 26377, \"name\": \"throw pillow\"}, {\"id\": 26378, \"name\": \"the monkey head\"}, {\"id\": 26379, \"name\": \"cartoon cat image\"}, {\"id\": 26380, \"name\": \"light blue flip-flop\"}, {\"id\": 26381, \"name\": \"a large yellow object\"}, {\"id\": 26382, \"name\": \"metal shutter door\"}, {\"id\": 26383, \"name\": \"the large, greenish-yellow object\"}, {\"id\": 26384, \"name\": \"boy's head\"}, {\"id\": 26385, \"name\": \"a red base\"}, {\"id\": 26386, \"name\": \"a propeller\"}, {\"id\": 26387, \"name\": \"ticket counter\"}, {\"id\": 26388, \"name\": \"the ballet attire\"}, {\"id\": 26389, \"name\": \"the blue screen\"}, {\"id\": 26390, \"name\": \"a pop it\"}, {\"id\": 26391, \"name\": \"the row of motorcycles\"}, {\"id\": 26392, \"name\": \"blue long-sleeved shirt\"}, {\"id\": 26393, \"name\": \"brown bench\"}, {\"id\": 26394, \"name\": \"the pink umbrella\"}, {\"id\": 26395, \"name\": \"purple and yellow shirt\"}, {\"id\": 26396, \"name\": \"a green progres bar\"}, {\"id\": 26397, \"name\": \"dark gray meat\"}, {\"id\": 26398, \"name\": \"the large orange trash can\"}, {\"id\": 26399, \"name\": \"a blue plastic tub\"}, {\"id\": 26400, \"name\": \"vase of yellow flower\"}, {\"id\": 26401, \"name\": \"barber\"}, {\"id\": 26402, \"name\": \"a truck's license plate\"}, {\"id\": 26403, \"name\": \"the prominent green whale tail\"}, {\"id\": 26404, \"name\": \"a red and white train\"}, {\"id\": 26405, \"name\": \"the blue and white striped umbrella\"}, {\"id\": 26406, \"name\": \"saddlebag\"}, {\"id\": 26407, \"name\": \"the brick pillar\"}, {\"id\": 26408, \"name\": \"large orange and blue tent\"}, {\"id\": 26409, \"name\": \"striking gold statue of a dragon\"}, {\"id\": 26410, \"name\": \"the rear of a white car\"}, {\"id\": 26411, \"name\": \"the colorful hula hoop\"}, {\"id\": 26412, \"name\": \"the large yellow and blue inflatable tube\"}, {\"id\": 26413, \"name\": \"the bowling lane\"}, {\"id\": 26414, \"name\": \"circular white mark\"}, {\"id\": 26415, \"name\": \"a raw frog leg\"}, {\"id\": 26416, \"name\": \"brick foundation\"}, {\"id\": 26417, \"name\": \"-terrain vehicle (atv)\"}, {\"id\": 26418, \"name\": \"a blue figure\"}, {\"id\": 26419, \"name\": \"the white airplane\"}, {\"id\": 26420, \"name\": \"wooden dowel\"}, {\"id\": 26421, \"name\": \"a distinctive red roof\"}, {\"id\": 26422, \"name\": \"an elderly\"}, {\"id\": 26423, \"name\": \"the dark blue wing\"}, {\"id\": 26424, \"name\": \"intricate carving\"}, {\"id\": 26425, \"name\": \"the grey sidewalk\"}, {\"id\": 26426, \"name\": \"a grey gradient overlay\"}, {\"id\": 26427, \"name\": \"a white and black curb\"}, {\"id\": 26428, \"name\": \"the orange ramp\"}, {\"id\": 26429, \"name\": \"the bright floodlight\"}, {\"id\": 26430, \"name\": \"medical professional\"}, {\"id\": 26431, \"name\": \"the dark blue suv\"}, {\"id\": 26432, \"name\": \"flat gray surface\"}, {\"id\": 26433, \"name\": \"a shark statue\"}, {\"id\": 26434, \"name\": \"roll of material\"}, {\"id\": 26435, \"name\": \"plane's tail\"}, {\"id\": 26436, \"name\": \"the blue and white bus\"}, {\"id\": 26437, \"name\": \"red and blue train\"}, {\"id\": 26438, \"name\": \"the small, black and brown pig\"}, {\"id\": 26439, \"name\": \"decorative floral design\"}, {\"id\": 26440, \"name\": \"the red toy car\"}, {\"id\": 26441, \"name\": \"a small black nose\"}, {\"id\": 26442, \"name\": \"a handler\"}, {\"id\": 26443, \"name\": \"red, yellow, and blue train car\"}, {\"id\": 26444, \"name\": \"the striking red roofed structure\"}, {\"id\": 26445, \"name\": \"a blue and yellow fish\"}, {\"id\": 26446, \"name\": \"concrete ramp\"}, {\"id\": 26447, \"name\": \"a black bra\"}, {\"id\": 26448, \"name\": \"tv screen\"}, {\"id\": 26449, \"name\": \"the white cup and saucer\"}, {\"id\": 26450, \"name\": \"a white plastic lid\"}, {\"id\": 26451, \"name\": \"a man in a white t-shirt and baseball cap\"}, {\"id\": 26452, \"name\": \"man in a white robe\"}, {\"id\": 26453, \"name\": \"a pink and white headband\"}, {\"id\": 26454, \"name\": \"a green spire\"}, {\"id\": 26455, \"name\": \"the vibrant yellow and green taxi\"}, {\"id\": 26456, \"name\": \"traditional chinese decoration\"}, {\"id\": 26457, \"name\": \"the vibrant curtain\"}, {\"id\": 26458, \"name\": \"bag of cat litter\"}, {\"id\": 26459, \"name\": \"pink firefighter hat\"}, {\"id\": 26460, \"name\": \"large, dark-colored structure\"}, {\"id\": 26461, \"name\": \"an orange clothing\"}, {\"id\": 26462, \"name\": \"a green banner\"}, {\"id\": 26463, \"name\": \"a black bumper\"}, {\"id\": 26464, \"name\": \"a man in a baseball cap\"}, {\"id\": 26465, \"name\": \"the large, colorful sculpture\"}, {\"id\": 26466, \"name\": \"the seat cushion\"}, {\"id\": 26467, \"name\": \"a green dome\"}, {\"id\": 26468, \"name\": \"orange and white train\"}, {\"id\": 26469, \"name\": \"the driver's hands\"}, {\"id\": 26470, \"name\": \"cat feeder\"}, {\"id\": 26471, \"name\": \"rubber mat\"}, {\"id\": 26472, \"name\": \"aquatic display\"}, {\"id\": 26473, \"name\": \"the matching orange outfit\"}, {\"id\": 26474, \"name\": \"a purple feathered headpiece\"}, {\"id\": 26475, \"name\": \"the low shrub\"}, {\"id\": 26476, \"name\": \"the orange support pillar\"}, {\"id\": 26477, \"name\": \"the yellow figure\"}, {\"id\": 26478, \"name\": \"the large, white aircraft\"}, {\"id\": 26479, \"name\": \"silver duct\"}, {\"id\": 26480, \"name\": \"a corrugated metal covering\"}, {\"id\": 26481, \"name\": \"a green lego mat\"}, {\"id\": 26482, \"name\": \"player's champion character\"}, {\"id\": 26483, \"name\": \"a karate uniform\"}, {\"id\": 26484, \"name\": \"tall white pole\"}, {\"id\": 26485, \"name\": \"the large cymbal\"}, {\"id\": 26486, \"name\": \"a can of beer\"}, {\"id\": 26487, \"name\": \"game's menu\"}, {\"id\": 26488, \"name\": \"red circular object\"}, {\"id\": 26489, \"name\": \"a red shipping container\"}, {\"id\": 26490, \"name\": \"a large kite\"}, {\"id\": 26491, \"name\": \"yellow and black helmet\"}, {\"id\": 26492, \"name\": \"a blue bicycle\"}, {\"id\": 26493, \"name\": \"the direction\"}, {\"id\": 26494, \"name\": \"the shutter\"}, {\"id\": 26495, \"name\": \"blue saddle\"}, {\"id\": 26496, \"name\": \"trousers\"}, {\"id\": 26497, \"name\": \"the red drum\"}, {\"id\": 26498, \"name\": \"the blue barrier\"}, {\"id\": 26499, \"name\": \"vibrant display of orange fruit\"}, {\"id\": 26500, \"name\": \"a plaid short\"}, {\"id\": 26501, \"name\": \"honda civic\"}, {\"id\": 26502, \"name\": \"the red and white crosswalk\"}, {\"id\": 26503, \"name\": \"a pixelated figure\"}, {\"id\": 26504, \"name\": \"the multicolored pennant flag\"}, {\"id\": 26505, \"name\": \"a central light source\"}, {\"id\": 26506, \"name\": \"the gray headscarf\"}, {\"id\": 26507, \"name\": \"illuminated signage\"}, {\"id\": 26508, \"name\": \"the pink and blue cup\"}, {\"id\": 26509, \"name\": \"the green flag\"}, {\"id\": 26510, \"name\": \"pearl necklace\"}, {\"id\": 26511, \"name\": \"a tattooed skin\"}, {\"id\": 26512, \"name\": \"the pink pot\"}, {\"id\": 26513, \"name\": \"the red corrugated metal surface\"}, {\"id\": 26514, \"name\": \"black barbell\"}, {\"id\": 26515, \"name\": \"the blue, circular shape\"}, {\"id\": 26516, \"name\": \"the blue tractor\"}, {\"id\": 26517, \"name\": \"a white hen\"}, {\"id\": 26518, \"name\": \"white attire\"}, {\"id\": 26519, \"name\": \"the vehicle's canopy\"}, {\"id\": 26520, \"name\": \"a red rooster\"}, {\"id\": 26521, \"name\": \"a large green bag\"}, {\"id\": 26522, \"name\": \"the green orb\"}, {\"id\": 26523, \"name\": \"a red fire truck\"}, {\"id\": 26524, \"name\": \"black and yellow sign\"}, {\"id\": 26525, \"name\": \"the teal t-shirt\"}, {\"id\": 26526, \"name\": \"the red brick pillar\"}, {\"id\": 26527, \"name\": \"white costume\"}, {\"id\": 26528, \"name\": \"a spring\"}, {\"id\": 26529, \"name\": \"a passenger on a scooter\"}, {\"id\": 26530, \"name\": \"larger bird\"}, {\"id\": 26531, \"name\": \"blue support column\"}, {\"id\": 26532, \"name\": \"the metal scoop\"}, {\"id\": 26533, \"name\": \"a security personnel\"}, {\"id\": 26534, \"name\": \"a concrete overpass\"}, {\"id\": 26535, \"name\": \"a shark's body\"}, {\"id\": 26536, \"name\": \"the horse racing\"}, {\"id\": 26537, \"name\": \"the purple choker\"}, {\"id\": 26538, \"name\": \"a blue kayak\"}, {\"id\": 26539, \"name\": \"the colorful canopy\"}, {\"id\": 26540, \"name\": \"a white coral-like object\"}, {\"id\": 26541, \"name\": \"a black gate\"}, {\"id\": 26542, \"name\": \"back of the net\"}, {\"id\": 26543, \"name\": \"the elaborate costume\"}, {\"id\": 26544, \"name\": \"the large gray elephant statue\"}, {\"id\": 26545, \"name\": \"a tan dog\"}, {\"id\": 26546, \"name\": \"string of light\"}, {\"id\": 26547, \"name\": \"white and red vehicle\"}, {\"id\": 26548, \"name\": \"a long, gold skirt\"}, {\"id\": 26549, \"name\": \"the saxophone player\"}, {\"id\": 26550, \"name\": \"sword\"}, {\"id\": 26551, \"name\": \"a large piece of metal\"}, {\"id\": 26552, \"name\": \"the yellow fish with black marking\"}, {\"id\": 26553, \"name\": \"a white tent or canopy\"}, {\"id\": 26554, \"name\": \"mechanic\"}, {\"id\": 26555, \"name\": \"the cone\"}, {\"id\": 26556, \"name\": \"thatched roof\"}, {\"id\": 26557, \"name\": \"statue of animal\"}, {\"id\": 26558, \"name\": \"a subtle floral design\"}, {\"id\": 26559, \"name\": \"circular, textured design\"}, {\"id\": 26560, \"name\": \"the wooden or brown surface\"}, {\"id\": 26561, \"name\": \"blue blind\"}, {\"id\": 26562, \"name\": \"basket of block\"}, {\"id\": 26563, \"name\": \"red paddle\"}, {\"id\": 26564, \"name\": \"large red object\"}, {\"id\": 26565, \"name\": \"central spire\"}, {\"id\": 26566, \"name\": \"black-and-white sandal\"}, {\"id\": 26567, \"name\": \"red mark\"}, {\"id\": 26568, \"name\": \"the yellow tube\"}, {\"id\": 26569, \"name\": \"the blue and white striped long-sleeved shirt\"}, {\"id\": 26570, \"name\": \"a bold green sign\"}, {\"id\": 26571, \"name\": \"the pink flag\"}, {\"id\": 26572, \"name\": \"the four-wheeler\"}, {\"id\": 26573, \"name\": \"piece of green netting\"}, {\"id\": 26574, \"name\": \"a golden yellow metal base\"}, {\"id\": 26575, \"name\": \"a blue robe\"}, {\"id\": 26576, \"name\": \"the large, intricately decorated lantern\"}, {\"id\": 26577, \"name\": \"a clear jar\"}, {\"id\": 26578, \"name\": \"a green frame\"}, {\"id\": 26579, \"name\": \"partially poured concrete slab\"}, {\"id\": 26580, \"name\": \"a wooden step\"}, {\"id\": 26581, \"name\": \"additional yellow object\"}, {\"id\": 26582, \"name\": \"black and white striped crosswalk\"}, {\"id\": 26583, \"name\": \"red metal barrier\"}, {\"id\": 26584, \"name\": \"green kite\"}, {\"id\": 26585, \"name\": \"a black and white t-shirt\"}, {\"id\": 26586, \"name\": \"boulder\"}, {\"id\": 26587, \"name\": \"plastic slide\"}, {\"id\": 26588, \"name\": \"red and white cloth\"}, {\"id\": 26589, \"name\": \"the bowling ball\"}, {\"id\": 26590, \"name\": \"the child in a grey shirt and denim short\"}, {\"id\": 26591, \"name\": \"the white flip-flop\"}, {\"id\": 26592, \"name\": \"boogie board\"}, {\"id\": 26593, \"name\": \"soundboard\"}, {\"id\": 26594, \"name\": \"the green lamppost\"}, {\"id\": 26595, \"name\": \"kiteboarder\"}, {\"id\": 26596, \"name\": \"a large, flat rock\"}, {\"id\": 26597, \"name\": \"the glass of clear liquid\"}, {\"id\": 26598, \"name\": \"the presentation\"}, {\"id\": 26599, \"name\": \"large american flag\"}, {\"id\": 26600, \"name\": \"sausage\"}, {\"id\": 26601, \"name\": \"large crane\"}, {\"id\": 26602, \"name\": \"the blue laptop computer\"}, {\"id\": 26603, \"name\": \"wok\"}, {\"id\": 26604, \"name\": \"lead performer\"}, {\"id\": 26605, \"name\": \"the white and black scooter\"}, {\"id\": 26606, \"name\": \"long, thin piece of dough or clay\"}, {\"id\": 26607, \"name\": \"mural of a large dinosaur skeleton\"}, {\"id\": 26608, \"name\": \"a blue tent\"}, {\"id\": 26609, \"name\": \"turquoise plaid shirt\"}, {\"id\": 26610, \"name\": \"black rubber boot\"}, {\"id\": 26611, \"name\": \"a red and white bead\"}, {\"id\": 26612, \"name\": \"a blue and white barrier\"}, {\"id\": 26613, \"name\": \"the large yellow taxi\"}, {\"id\": 26614, \"name\": \"green light fixture\"}, {\"id\": 26615, \"name\": \"dark grey cloud\"}, {\"id\": 26616, \"name\": \"black bracelet\"}, {\"id\": 26617, \"name\": \"the orange and white cat\"}, {\"id\": 26618, \"name\": \"the small car\"}, {\"id\": 26619, \"name\": \"a handle on a cabinet\"}, {\"id\": 26620, \"name\": \"a blue and white van\"}, {\"id\": 26621, \"name\": \"a concrete ledge\"}, {\"id\": 26622, \"name\": \"bright yellow and green uniform\"}, {\"id\": 26623, \"name\": \"the red foot\"}, {\"id\": 26624, \"name\": \"the tall sign\"}, {\"id\": 26625, \"name\": \"large orange exercise ball\"}, {\"id\": 26626, \"name\": \"the red bus\"}, {\"id\": 26627, \"name\": \"the white jersey with red stripes and black short\"}, {\"id\": 26628, \"name\": \"a man in a high-visibility vest\"}, {\"id\": 26629, \"name\": \"the large white yacht\"}, {\"id\": 26630, \"name\": \"bollard\"}, {\"id\": 26631, \"name\": \"the compass-like icon\"}, {\"id\": 26632, \"name\": \"a black pvc pipe\"}, {\"id\": 26633, \"name\": \"a green shamrock emblem\"}, {\"id\": 26634, \"name\": \"the sedan's windshield\"}, {\"id\": 26635, \"name\": \"person in a bear costume\"}, {\"id\": 26636, \"name\": \"small amount of liquid\"}, {\"id\": 26637, \"name\": \"the small suitcase\"}, {\"id\": 26638, \"name\": \"the ATV\"}, {\"id\": 26639, \"name\": \"construction worker\"}, {\"id\": 26640, \"name\": \"a green ball\"}, {\"id\": 26641, \"name\": \"the motorcycle mirror\"}, {\"id\": 26642, \"name\": \"the large black backpack\"}, {\"id\": 26643, \"name\": \"a checkered flag pattern\"}, {\"id\": 26644, \"name\": \"the small blue toy car\"}, {\"id\": 26645, \"name\": \"a large, arched sign\"}, {\"id\": 26646, \"name\": \"the front step\"}, {\"id\": 26647, \"name\": \"the large black circle\"}, {\"id\": 26648, \"name\": \"a white pointy hat\"}, {\"id\": 26649, \"name\": \"the red stick\"}, {\"id\": 26650, \"name\": \"red and pink outfit\"}, {\"id\": 26651, \"name\": \"the thick gray and black fur\"}, {\"id\": 26652, \"name\": \"the silver fish\"}, {\"id\": 26653, \"name\": \"the metal control panel\"}, {\"id\": 26654, \"name\": \"the white number 30\"}, {\"id\": 26655, \"name\": \"large, red cat-like creature\"}, {\"id\": 26656, \"name\": \"the white short-sleeved shirt\"}, {\"id\": 26657, \"name\": \"the long, green skirt\"}, {\"id\": 26658, \"name\": \"orange costume\"}, {\"id\": 26659, \"name\": \"a black short-sleeved shirt\"}, {\"id\": 26660, \"name\": \"the sticker on the rear window\"}, {\"id\": 26661, \"name\": \"a large, clear plastic fan\"}, {\"id\": 26662, \"name\": \"a racer's colorful attire\"}, {\"id\": 26663, \"name\": \"large, round, red and white structure\"}, {\"id\": 26664, \"name\": \"the ornate gold-colored decoration\"}, {\"id\": 26665, \"name\": \"a large gray door\"}, {\"id\": 26666, \"name\": \"prominent red light\"}, {\"id\": 26667, \"name\": \"a person in a red shirt\"}, {\"id\": 26668, \"name\": \"the overhead fixture\"}, {\"id\": 26669, \"name\": \"a round object\"}, {\"id\": 26670, \"name\": \"blue flute\"}, {\"id\": 26671, \"name\": \"a central screen\"}, {\"id\": 26672, \"name\": \"the chrome bumper\"}, {\"id\": 26673, \"name\": \"the tall, pointed structure\"}, {\"id\": 26674, \"name\": \"black cymbal\"}, {\"id\": 26675, \"name\": \"a large, brown object\"}, {\"id\": 26676, \"name\": \"a grayish-blue hexagon\"}, {\"id\": 26677, \"name\": \"a small wooden bench\"}, {\"id\": 26678, \"name\": \"white or light-colored shirt\"}, {\"id\": 26679, \"name\": \"yellow crate\"}, {\"id\": 26680, \"name\": \"an ice cube\"}, {\"id\": 26681, \"name\": \"the green button\"}, {\"id\": 26682, \"name\": \"brick structure\"}, {\"id\": 26683, \"name\": \"a white drumhead\"}, {\"id\": 26684, \"name\": \"a barn or coop\"}, {\"id\": 26685, \"name\": \"long red and yellow flag\"}, {\"id\": 26686, \"name\": \"the blue toy gun\"}, {\"id\": 26687, \"name\": \"a motor\"}, {\"id\": 26688, \"name\": \"rubber duck\"}, {\"id\": 26689, \"name\": \"a light blue jacket\"}, {\"id\": 26690, \"name\": \"the paddleboarder\"}, {\"id\": 26691, \"name\": \"large black and gray fish\"}, {\"id\": 26692, \"name\": \"pink glow\"}, {\"id\": 26693, \"name\": \"the pedicab\"}, {\"id\": 26694, \"name\": \"a minnie mouse ear\"}, {\"id\": 26695, \"name\": \"the large, green, spherical structure\"}, {\"id\": 26696, \"name\": \"a traditional pakistani clothing\"}, {\"id\": 26697, \"name\": \"the man in a black shirt and jean\"}, {\"id\": 26698, \"name\": \"cycling gear\"}, {\"id\": 26699, \"name\": \"the white cat with black spot\"}, {\"id\": 26700, \"name\": \"the blue pole\"}, {\"id\": 26701, \"name\": \"the score of 14-57\"}, {\"id\": 26702, \"name\": \"purple seat\"}, {\"id\": 26703, \"name\": \"a tent setup\"}, {\"id\": 26704, \"name\": \"a cup holder\"}, {\"id\": 26705, \"name\": \"glass pane\"}, {\"id\": 26706, \"name\": \"rider's head\"}, {\"id\": 26707, \"name\": \"a large, fluffy cloud\"}, {\"id\": 26708, \"name\": \"a ruffled skirt\"}, {\"id\": 26709, \"name\": \"colorful array of confetti\"}, {\"id\": 26710, \"name\": \"the white chandelier\"}, {\"id\": 26711, \"name\": \"a low ponytail\"}, {\"id\": 26712, \"name\": \"the garland of yellow and red flowers\"}, {\"id\": 26713, \"name\": \"large box\"}, {\"id\": 26714, \"name\": \"a clownfish\"}, {\"id\": 26715, \"name\": \"the hoverboard's wheel\"}, {\"id\": 26716, \"name\": \"a dark blue shorts\"}, {\"id\": 26717, \"name\": \"blue and white poster\"}, {\"id\": 26718, \"name\": \"the beige truck\"}, {\"id\": 26719, \"name\": \"spoon's handle\"}, {\"id\": 26720, \"name\": \"the large white and blue sewing machine\"}, {\"id\": 26721, \"name\": \"tan headscarf\"}, {\"id\": 26722, \"name\": \"a driver's red shirt\"}, {\"id\": 26723, \"name\": \"a dock or pier\"}, {\"id\": 26724, \"name\": \"the orange segment\"}, {\"id\": 26725, \"name\": \"the white head\"}, {\"id\": 26726, \"name\": \"miniature car\"}, {\"id\": 26727, \"name\": \"the white circular table\"}, {\"id\": 26728, \"name\": \"the large, dark-colored vehicle\"}, {\"id\": 26729, \"name\": \"the red and white plane\"}, {\"id\": 26730, \"name\": \"the black top hat\"}, {\"id\": 26731, \"name\": \"the speedboat\"}, {\"id\": 26732, \"name\": \"vibrant, oversized costume\"}, {\"id\": 26733, \"name\": \"a red robot\"}, {\"id\": 26734, \"name\": \"the white and blue banner\"}, {\"id\": 26735, \"name\": \"a gray box\"}, {\"id\": 26736, \"name\": \"silver pickup truck\"}, {\"id\": 26737, \"name\": \"the light-colored wood panel\"}, {\"id\": 26738, \"name\": \"the small section of a blue tarp\"}, {\"id\": 26739, \"name\": \"a stunning floral display\"}, {\"id\": 26740, \"name\": \"large fire\"}, {\"id\": 26741, \"name\": \"large, blue tent\"}, {\"id\": 26742, \"name\": \"a rectangular piece of paper\"}, {\"id\": 26743, \"name\": \"the circular blue object\"}, {\"id\": 26744, \"name\": \"the white basketball hoop\"}, {\"id\": 26745, \"name\": \"black and red striped vest\"}, {\"id\": 26746, \"name\": \"boat's hull\"}, {\"id\": 26747, \"name\": \"a yellow oar\"}, {\"id\": 26748, \"name\": \"white partition\"}, {\"id\": 26749, \"name\": \"partially cooked piece of meat\"}, {\"id\": 26750, \"name\": \"the small red bowl\"}, {\"id\": 26751, \"name\": \"blue and black striped shirt\"}, {\"id\": 26752, \"name\": \"large, dark-colored bag\"}, {\"id\": 26753, \"name\": \"bulletin board\"}, {\"id\": 26754, \"name\": \"large, red and white quarter pipe\"}, {\"id\": 26755, \"name\": \"unique license plate\"}, {\"id\": 26756, \"name\": \"the gray tarp\"}, {\"id\": 26757, \"name\": \"small american flag\"}, {\"id\": 26758, \"name\": \"large gray boulder\"}, {\"id\": 26759, \"name\": \"grassy patch\"}, {\"id\": 26760, \"name\": \"the rollerblader\"}, {\"id\": 26761, \"name\": \"the trombone\"}, {\"id\": 26762, \"name\": \"market stall\"}, {\"id\": 26763, \"name\": \"a yellow circle\"}, {\"id\": 26764, \"name\": \"a low hedge\"}, {\"id\": 26765, \"name\": \"a colorful outfit\"}, {\"id\": 26766, \"name\": \"the large light\"}, {\"id\": 26767, \"name\": \"a large, flat surface\"}, {\"id\": 26768, \"name\": \"the circular object\"}, {\"id\": 26769, \"name\": \"a red light bar\"}, {\"id\": 26770, \"name\": \"athletic wear\"}, {\"id\": 26771, \"name\": \"dark grey column\"}, {\"id\": 26772, \"name\": \"stone-tiled walkway\"}, {\"id\": 26773, \"name\": \"dark grey car\"}, {\"id\": 26774, \"name\": \"pole with wire\"}, {\"id\": 26775, \"name\": \"a dark brown horse\"}, {\"id\": 26776, \"name\": \"a matching blue shirt\"}, {\"id\": 26777, \"name\": \"the white paper lantern\"}, {\"id\": 26778, \"name\": \"blue screen\"}, {\"id\": 26779, \"name\": \"the orange-and-gray striped traffic barrier\"}, {\"id\": 26780, \"name\": \"a small stall\"}, {\"id\": 26781, \"name\": \"long oar\"}, {\"id\": 26782, \"name\": \"the excavator's arm\"}, {\"id\": 26783, \"name\": \"a paint cup\"}, {\"id\": 26784, \"name\": \"the rear wheel\"}, {\"id\": 26785, \"name\": \"belgian flag\"}, {\"id\": 26786, \"name\": \"the wire mesh\"}, {\"id\": 26787, \"name\": \"oar\"}, {\"id\": 26788, \"name\": \"dark-colored suv\"}, {\"id\": 26789, \"name\": \"the game's machinery\"}, {\"id\": 26790, \"name\": \"collared shirt\"}, {\"id\": 26791, \"name\": \"the large white boat\"}, {\"id\": 26792, \"name\": \"the black strip\"}, {\"id\": 26793, \"name\": \"long white table\"}, {\"id\": 26794, \"name\": \"yellow structure\"}, {\"id\": 26795, \"name\": \"circle with a yellow icon\"}, {\"id\": 26796, \"name\": \"blue and black design\"}, {\"id\": 26797, \"name\": \"a white skate\"}, {\"id\": 26798, \"name\": \"a halloween costume\"}, {\"id\": 26799, \"name\": \"a small red stool\"}, {\"id\": 26800, \"name\": \"cab of the vehicle\"}, {\"id\": 26801, \"name\": \"the people dressed in traditional muslim attire\"}, {\"id\": 26802, \"name\": \"the sliced sausage\"}, {\"id\": 26803, \"name\": \"pink dot\"}, {\"id\": 26804, \"name\": \"triangular roof\"}, {\"id\": 26805, \"name\": \"a helmet's visor\"}, {\"id\": 26806, \"name\": \"black duck\"}, {\"id\": 26807, \"name\": \"exercise ball\"}, {\"id\": 26808, \"name\": \"a black and gray motorcycle\"}, {\"id\": 26809, \"name\": \"the light grey fabric object\"}, {\"id\": 26810, \"name\": \"the orange roof\"}, {\"id\": 26811, \"name\": \"the blue tube\"}, {\"id\": 26812, \"name\": \"light bulb\"}, {\"id\": 26813, \"name\": \"the stacked cardboard box\"}, {\"id\": 26814, \"name\": \"the stunning one-shoulder gown\"}, {\"id\": 26815, \"name\": \"left beam\"}, {\"id\": 26816, \"name\": \"a white gate\"}, {\"id\": 26817, \"name\": \"the strapless white dress\"}, {\"id\": 26818, \"name\": \"a speaker or amplifier\"}, {\"id\": 26819, \"name\": \"person in a red shirt\"}, {\"id\": 26820, \"name\": \"the large white structure\"}, {\"id\": 26821, \"name\": \"a yellow suspension strap system\"}, {\"id\": 26822, \"name\": \"the large, grey megaphone\"}, {\"id\": 26823, \"name\": \"large silver pot\"}, {\"id\": 26824, \"name\": \"yellow crane\"}, {\"id\": 26825, \"name\": \"the large glass jar\"}, {\"id\": 26826, \"name\": \"a riders' bike\"}, {\"id\": 26827, \"name\": \"bell tower\"}, {\"id\": 26828, \"name\": \"red carpeted stage\"}, {\"id\": 26829, \"name\": \"colorful ride\"}, {\"id\": 26830, \"name\": \"the large tent\"}, {\"id\": 26831, \"name\": \"low metal fence\"}, {\"id\": 26832, \"name\": \"yellow nozzle\"}, {\"id\": 26833, \"name\": \"the blue hooded jacket\"}, {\"id\": 26834, \"name\": \"red bumper\"}, {\"id\": 26835, \"name\": \"a closed blind\"}, {\"id\": 26836, \"name\": \"the young player\"}, {\"id\": 26837, \"name\": \"a smaller, tan cat\"}, {\"id\": 26838, \"name\": \"the small animal\"}, {\"id\": 26839, \"name\": \"large winged creature\"}, {\"id\": 26840, \"name\": \"control panel\"}, {\"id\": 26841, \"name\": \"the firetruck\"}, {\"id\": 26842, \"name\": \"large, cream-colored plastic container\"}, {\"id\": 26843, \"name\": \"a cooking vessel\"}, {\"id\": 26844, \"name\": \"a colorful traditional clothing\"}, {\"id\": 26845, \"name\": \"a character's top hat\"}, {\"id\": 26846, \"name\": \"the tachometer\"}, {\"id\": 26847, \"name\": \"a vibrant, multicolored dress\"}, {\"id\": 26848, \"name\": \"power cord\"}, {\"id\": 26849, \"name\": \"the small, blue structure\"}, {\"id\": 26850, \"name\": \"the corrugated metal structure\"}, {\"id\": 26851, \"name\": \"tall black flag\"}, {\"id\": 26852, \"name\": \"the cliff\"}, {\"id\": 26853, \"name\": \"the car's brake light\"}, {\"id\": 26854, \"name\": \"a blue punching bag\"}, {\"id\": 26855, \"name\": \"a purple and yellow orb\"}, {\"id\": 26856, \"name\": \"the light blue dress\"}, {\"id\": 26857, \"name\": \"a tutu\"}, {\"id\": 26858, \"name\": \"the patio furniture\"}, {\"id\": 26859, \"name\": \"bright red conveyor belt\"}, {\"id\": 26860, \"name\": \"a yellow flipper\"}, {\"id\": 26861, \"name\": \"the large arched entranceway\"}, {\"id\": 26862, \"name\": \"the tan horse\"}, {\"id\": 26863, \"name\": \"a pamphlet\"}, {\"id\": 26864, \"name\": \"the tiara headband\"}, {\"id\": 26865, \"name\": \"a large basket\"}, {\"id\": 26866, \"name\": \"white string\"}, {\"id\": 26867, \"name\": \"a hanging light bulb\"}, {\"id\": 26868, \"name\": \"orange hen\"}, {\"id\": 26869, \"name\": \"black-and-white kitten\"}, {\"id\": 26870, \"name\": \"the matching blue short\"}, {\"id\": 26871, \"name\": \"a distinctive roof\"}, {\"id\": 26872, \"name\": \"a frozen-themed item\"}, {\"id\": 26873, \"name\": \"yellow boat\"}, {\"id\": 26874, \"name\": \"the flip flop\"}, {\"id\": 26875, \"name\": \"Game button\"}, {\"id\": 26876, \"name\": \"the table or bench\"}, {\"id\": 26877, \"name\": \"blue uniform\"}, {\"id\": 26878, \"name\": \"the maroon skirt\"}, {\"id\": 26879, \"name\": \"the red, green, and white umbrella\"}, {\"id\": 26880, \"name\": \"yellow baseball cap\"}, {\"id\": 26881, \"name\": \"intricate white design\"}, {\"id\": 26882, \"name\": \"the beige planter\"}, {\"id\": 26883, \"name\": \"the red canopy\"}, {\"id\": 26884, \"name\": \"the grayish-blue fish\"}, {\"id\": 26885, \"name\": \"yellow kite\"}, {\"id\": 26886, \"name\": \"purple flower design\"}, {\"id\": 26887, \"name\": \"a dark blue sneaker\"}, {\"id\": 26888, \"name\": \"the ruffled skirt\"}, {\"id\": 26889, \"name\": \"a pinecone\"}, {\"id\": 26890, \"name\": \"a registration plate\"}, {\"id\": 26891, \"name\": \"a dark-colored bag\"}, {\"id\": 26892, \"name\": \"a joystick\"}, {\"id\": 26893, \"name\": \"the red chicken\"}, {\"id\": 26894, \"name\": \"a catch\"}, {\"id\": 26895, \"name\": \"the interior of the cup\"}, {\"id\": 26896, \"name\": \"a row of cars and trucks\"}, {\"id\": 26897, \"name\": \"the swimming on their back\"}, {\"id\": 26898, \"name\": \"a helicopter\"}, {\"id\": 26899, \"name\": \"traditional middle eastern attire\"}, {\"id\": 26900, \"name\": \"yellow inflatable tube\"}, {\"id\": 26901, \"name\": \"a knee-high sock\"}, {\"id\": 26902, \"name\": \"the large blue orb\"}, {\"id\": 26903, \"name\": \"the thick column\"}, {\"id\": 26904, \"name\": \"a dark blue exterior\"}, {\"id\": 26905, \"name\": \"a large, clear tube\"}, {\"id\": 26906, \"name\": \"a cart of produce\"}, {\"id\": 26907, \"name\": \"the light brown head\"}, {\"id\": 26908, \"name\": \"the large disco ball\"}, {\"id\": 26909, \"name\": \"a character's\"}, {\"id\": 26910, \"name\": \"balcony or rooftop\"}, {\"id\": 26911, \"name\": \"red and yellow attire\"}, {\"id\": 26912, \"name\": \"blue crystal\"}, {\"id\": 26913, \"name\": \"a woman in a white jacket\"}, {\"id\": 26914, \"name\": \"a lush green grassy area\"}, {\"id\": 26915, \"name\": \"the yellow and black striped barrier\"}, {\"id\": 26916, \"name\": \"a red and white striped monster\"}, {\"id\": 26917, \"name\": \"the weightlifter\"}, {\"id\": 26918, \"name\": \"the substantial quantity of green produce\"}, {\"id\": 26919, \"name\": \"a green trash bin\"}, {\"id\": 26920, \"name\": \"the black legging\"}, {\"id\": 26921, \"name\": \"the round, black table\"}, {\"id\": 26922, \"name\": \"a light gray hijab\"}, {\"id\": 26923, \"name\": \"storage facility\"}, {\"id\": 26924, \"name\": \"beverage\"}, {\"id\": 26925, \"name\": \"a grey plaid skirt\"}, {\"id\": 26926, \"name\": \"blue license plate\"}, {\"id\": 26927, \"name\": \"a green substance\"}, {\"id\": 26928, \"name\": \"pink and maroon uniform\"}, {\"id\": 26929, \"name\": \"a vaulting box\"}, {\"id\": 26930, \"name\": \"a cartoon image of a young girl\"}, {\"id\": 26931, \"name\": \"large, glowing, orange and yellow dragon sculpture\"}, {\"id\": 26932, \"name\": \"the dhl storefront\"}, {\"id\": 26933, \"name\": \"a gift basket\"}, {\"id\": 26934, \"name\": \"a strap of a bag\"}, {\"id\": 26935, \"name\": \"the red substance\"}, {\"id\": 26936, \"name\": \"a large shuttered storefront\"}, {\"id\": 26937, \"name\": \"a tram's track\"}, {\"id\": 26938, \"name\": \"the yellow plastic crate\"}, {\"id\": 26939, \"name\": \"a lead motorcyclist\"}, {\"id\": 26940, \"name\": \"dome-like roof\"}, {\"id\": 26941, \"name\": \"large, colorful costume\"}, {\"id\": 26942, \"name\": \"a vehicle's front wheel\"}, {\"id\": 26943, \"name\": \"white edge\"}, {\"id\": 26944, \"name\": \"an arched opening\"}, {\"id\": 26945, \"name\": \"the black and silver motorbike\"}, {\"id\": 26946, \"name\": \"metal component\"}, {\"id\": 26947, \"name\": \"a small, red, round object\"}, {\"id\": 26948, \"name\": \"a player's avatar\"}, {\"id\": 26949, \"name\": \"a white rim\"}, {\"id\": 26950, \"name\": \"white sport car\"}, {\"id\": 26951, \"name\": \"a snowmobile\"}, {\"id\": 26952, \"name\": \"a large, covered outdoor sawmill\"}, {\"id\": 26953, \"name\": \"a red crane\"}, {\"id\": 26954, \"name\": \"the colorful plastic piece\"}, {\"id\": 26955, \"name\": \"woman in scrubs\"}, {\"id\": 26956, \"name\": \"the pigeon hole\"}, {\"id\": 26957, \"name\": \"the luggage handle\"}, {\"id\": 26958, \"name\": \"the black and white patterned top\"}, {\"id\": 26959, \"name\": \"a scaffolding\"}, {\"id\": 26960, \"name\": \"large white car\"}, {\"id\": 26961, \"name\": \"the small screw\"}, {\"id\": 26962, \"name\": \"a pop-up tent\"}, {\"id\": 26963, \"name\": \"a large orange flag\"}, {\"id\": 26964, \"name\": \"the white short-sleeved button-up shirt\"}, {\"id\": 26965, \"name\": \"a yellow and blue canopy\"}, {\"id\": 26966, \"name\": \"pink pant\"}, {\"id\": 26967, \"name\": \"single post\"}, {\"id\": 26968, \"name\": \"a red plant\"}, {\"id\": 26969, \"name\": \"a gold curtain\"}, {\"id\": 26970, \"name\": \"blue square of paper\"}, {\"id\": 26971, \"name\": \"a vertical stripe\"}, {\"id\": 26972, \"name\": \"white awning\"}, {\"id\": 26973, \"name\": \"the drummer's right hand\"}, {\"id\": 26974, \"name\": \"green stick\"}, {\"id\": 26975, \"name\": \"chopstick\"}, {\"id\": 26976, \"name\": \"the white material\"}, {\"id\": 26977, \"name\": \"square picture\"}, {\"id\": 26978, \"name\": \"black windshield wiper\"}, {\"id\": 26979, \"name\": \"the bucket hat\"}, {\"id\": 26980, \"name\": \"the caution tape barrier\"}, {\"id\": 26981, \"name\": \"the long, light pink robe\"}, {\"id\": 26982, \"name\": \"the yellow diagonal stripe\"}, {\"id\": 26983, \"name\": \"the parked bicycle\"}, {\"id\": 26984, \"name\": \"white and blue geometric pattern\"}, {\"id\": 26985, \"name\": \"the prominent golden statue or sculpture\"}, {\"id\": 26986, \"name\": \"the player in black uniform\"}, {\"id\": 26987, \"name\": \"the auto\"}, {\"id\": 26988, \"name\": \"a taxi van\"}, {\"id\": 26989, \"name\": \"the light green backpack strap\"}, {\"id\": 26990, \"name\": \"a corrugated metal exterior\"}, {\"id\": 26991, \"name\": \"the muddy atv\"}, {\"id\": 26992, \"name\": \"a large bundle of black plastic or rubber tubing\"}, {\"id\": 26993, \"name\": \"speedboat\"}, {\"id\": 26994, \"name\": \"the large, beige, rectangular sign\"}, {\"id\": 26995, \"name\": \"colorful hat\"}, {\"id\": 26996, \"name\": \"the raccetrack's blue and yellow barrier\"}, {\"id\": 26997, \"name\": \"large tent or canopy\"}, {\"id\": 26998, \"name\": \"a large purple and white flower-shaped ride\"}, {\"id\": 26999, \"name\": \"a tiny creature\"}, {\"id\": 27000, \"name\": \"a construction vehicle\"}, {\"id\": 27001, \"name\": \"the white suit\"}, {\"id\": 27002, \"name\": \"a distinctive silver grill\"}, {\"id\": 27003, \"name\": \"a lattice work\"}, {\"id\": 27004, \"name\": \"a white sandal\"}, {\"id\": 27005, \"name\": \"the driver's face\"}, {\"id\": 27006, \"name\": \"brown and white dog\"}, {\"id\": 27007, \"name\": \"a knights on horseback\"}, {\"id\": 27008, \"name\": \"the suit jacket\"}, {\"id\": 27009, \"name\": \"the large saw blade\"}, {\"id\": 27010, \"name\": \"the small blue plastic container\"}, {\"id\": 27011, \"name\": \"a slide's edge\"}, {\"id\": 27012, \"name\": \"a male player\"}, {\"id\": 27013, \"name\": \"a colorful horse\"}, {\"id\": 27014, \"name\": \"the metal roller\"}, {\"id\": 27015, \"name\": \"a white medical mask\"}, {\"id\": 27016, \"name\": \"the tall control tower\"}, {\"id\": 27017, \"name\": \"an orange head covering\"}, {\"id\": 27018, \"name\": \"a floral garland\"}, {\"id\": 27019, \"name\": \"black plastic sheeting\"}, {\"id\": 27020, \"name\": \"latex glove\"}, {\"id\": 27021, \"name\": \"the red and white chew toy\"}, {\"id\": 27022, \"name\": \"the wooden cane\"}, {\"id\": 27023, \"name\": \"large flower sculpture\"}, {\"id\": 27024, \"name\": \"a barbell\"}, {\"id\": 27025, \"name\": \"a stainles steel pot\"}, {\"id\": 27026, \"name\": \"the nun\"}, {\"id\": 27027, \"name\": \"trio\"}, {\"id\": 27028, \"name\": \"beach toy\"}, {\"id\": 27029, \"name\": \"the pink and white cartoon pig\"}, {\"id\": 27030, \"name\": \"a large, yellow, cylindrical machine\"}, {\"id\": 27031, \"name\": \"the blue drap\"}, {\"id\": 27032, \"name\": \"thick gray cloud\"}, {\"id\": 27033, \"name\": \"beige headband\"}, {\"id\": 27034, \"name\": \"the vibrant floral or tropical-patterned shirt\"}, {\"id\": 27035, \"name\": \"the large blue table\"}, {\"id\": 27036, \"name\": \"uniformed officer\"}, {\"id\": 27037, \"name\": \"a dark-colored attire\"}, {\"id\": 27038, \"name\": \"a covered area\"}, {\"id\": 27039, \"name\": \"rough, concrete-like material\"}, {\"id\": 27040, \"name\": \"a small silver bowl\"}, {\"id\": 27041, \"name\": \"the red plastic bowl\"}, {\"id\": 27042, \"name\": \"white stool\"}, {\"id\": 27043, \"name\": \"purple circle\"}, {\"id\": 27044, \"name\": \"a palestinian flag\"}, {\"id\": 27045, \"name\": \"pink column\"}, {\"id\": 27046, \"name\": \"tall, vertical column of smoke\"}, {\"id\": 27047, \"name\": \"green sack\"}, {\"id\": 27048, \"name\": \"a purple pant\"}, {\"id\": 27049, \"name\": \"an orange leash\"}, {\"id\": 27050, \"name\": \"stock car\"}, {\"id\": 27051, \"name\": \"shell-like design\"}, {\"id\": 27052, \"name\": \"a white figure\"}, {\"id\": 27053, \"name\": \"red carpeted section\"}, {\"id\": 27054, \"name\": \"a small, gold-colored object\"}, {\"id\": 27055, \"name\": \"a yellow and black stripe\"}, {\"id\": 27056, \"name\": \"blue hippo\"}, {\"id\": 27057, \"name\": \"the man riding a bicycle\"}, {\"id\": 27058, \"name\": \"a brown puppy\"}, {\"id\": 27059, \"name\": \"a prominent, fiery breath\"}, {\"id\": 27060, \"name\": \"horse rider\"}, {\"id\": 27061, \"name\": \"the light-colored short\"}, {\"id\": 27062, \"name\": \"a soda can\"}, {\"id\": 27063, \"name\": \"a white and black top\"}, {\"id\": 27064, \"name\": \"a truck's rear light\"}, {\"id\": 27065, \"name\": \"pink jersey\"}, {\"id\": 27066, \"name\": \"vibrant plumage\"}, {\"id\": 27067, \"name\": \"a man in a white shirt and black pant\"}, {\"id\": 27068, \"name\": \"a brightly colored mat\"}, {\"id\": 27069, \"name\": \"a white mast\"}, {\"id\": 27070, \"name\": \"person in a white shirt\"}, {\"id\": 27071, \"name\": \"a blue rickshaw\"}, {\"id\": 27072, \"name\": \"orange and white striped cone\"}, {\"id\": 27073, \"name\": \"the light-brown tent\"}, {\"id\": 27074, \"name\": \"a bag of trash\"}, {\"id\": 27075, \"name\": \"the blue shade canopy\"}, {\"id\": 27076, \"name\": \"the transmission tower\"}, {\"id\": 27077, \"name\": \"a semi-truck\"}, {\"id\": 27078, \"name\": \"the large black banner\"}, {\"id\": 27079, \"name\": \"the kittens' eye\"}, {\"id\": 27080, \"name\": \"the red brick\"}, {\"id\": 27081, \"name\": \"the blue plastic container\"}, {\"id\": 27082, \"name\": \"a bikini\"}, {\"id\": 27083, \"name\": \"the rope barrier\"}, {\"id\": 27084, \"name\": \"a green dot\"}, {\"id\": 27085, \"name\": \"a black-and-white jacket\"}, {\"id\": 27086, \"name\": \"the glass of orange juice\"}, {\"id\": 27087, \"name\": \"a biking gear\"}, {\"id\": 27088, \"name\": \"green and red striped awning\"}, {\"id\": 27089, \"name\": \"a smaller, dark symbol\"}, {\"id\": 27090, \"name\": \"gray boulder\"}, {\"id\": 27091, \"name\": \"horse's head\"}, {\"id\": 27092, \"name\": \"the orange cushion\"}, {\"id\": 27093, \"name\": \"the small green bowl\"}, {\"id\": 27094, \"name\": \"dark-colored SUV\"}, {\"id\": 27095, \"name\": \"dark-colored seat\"}, {\"id\": 27096, \"name\": \"the white scooter\"}, {\"id\": 27097, \"name\": \"the yellow bowling pin\"}, {\"id\": 27098, \"name\": \"the large digital display board\"}, {\"id\": 27099, \"name\": \"a white hooded sweatshirt\"}, {\"id\": 27100, \"name\": \"the red seat\"}, {\"id\": 27101, \"name\": \"the small white surface\"}, {\"id\": 27102, \"name\": \"the black and white striped curb\"}, {\"id\": 27103, \"name\": \"a white and red button\"}, {\"id\": 27104, \"name\": \"large, round, orange light\"}, {\"id\": 27105, \"name\": \"ski\"}, {\"id\": 27106, \"name\": \"rainbow-colored arch\"}, {\"id\": 27107, \"name\": \"the tan headrest\"}, {\"id\": 27108, \"name\": \"striking red and black racing suit\"}, {\"id\": 27109, \"name\": \"the large, white curtain\"}, {\"id\": 27110, \"name\": \"a blue machine\"}, {\"id\": 27111, \"name\": \"semi-truck\"}, {\"id\": 27112, \"name\": \"a white silhouette of character\"}, {\"id\": 27113, \"name\": \"the small shellfish\"}, {\"id\": 27114, \"name\": \"small, light-colored fish\"}, {\"id\": 27115, \"name\": \"green substance\"}, {\"id\": 27116, \"name\": \"a pencil cup\"}, {\"id\": 27117, \"name\": \"large green plant\"}, {\"id\": 27118, \"name\": \"the tall, dry grass\"}, {\"id\": 27119, \"name\": \"a blue polo shirt\"}, {\"id\": 27120, \"name\": \"the back of the person's head\"}, {\"id\": 27121, \"name\": \"bucket hat\"}, {\"id\": 27122, \"name\": \"the grey tank top\"}, {\"id\": 27123, \"name\": \"matching pant\"}, {\"id\": 27124, \"name\": \"the top deck\"}, {\"id\": 27125, \"name\": \"a blue fence\"}, {\"id\": 27126, \"name\": \"the small amount of white\"}, {\"id\": 27127, \"name\": \"blue ribbon\"}, {\"id\": 27128, \"name\": \"winnie-the-pooh stuffed animal\"}, {\"id\": 27129, \"name\": \"closed beige shutter\"}, {\"id\": 27130, \"name\": \"neon-colored safety vest\"}, {\"id\": 27131, \"name\": \"pink and blue light display\"}, {\"id\": 27132, \"name\": \"the red swing\"}, {\"id\": 27133, \"name\": \"a large suitcase\"}, {\"id\": 27134, \"name\": \"the warning symbol\"}, {\"id\": 27135, \"name\": \"the orange and green garment\"}, {\"id\": 27136, \"name\": \"a large stovetop\"}, {\"id\": 27137, \"name\": \"the distant parachute\"}, {\"id\": 27138, \"name\": \"a red cylindrical object\"}, {\"id\": 27139, \"name\": \"the bowling ramp\"}, {\"id\": 27140, \"name\": \"the buddha statue\"}, {\"id\": 27141, \"name\": \"a large, umbrella-like canopy\"}, {\"id\": 27142, \"name\": \"the boy's toy\"}, {\"id\": 27143, \"name\": \"small wooden platform\"}, {\"id\": 27144, \"name\": \"the matching red and black uniform\"}, {\"id\": 27145, \"name\": \"shredded vegetable\"}, {\"id\": 27146, \"name\": \"large, cartoonish, white, stone-like structure\"}, {\"id\": 27147, \"name\": \"light blue clothing\"}, {\"id\": 27148, \"name\": \"yellow gown\"}, {\"id\": 27149, \"name\": \"a brown satchel strap\"}, {\"id\": 27150, \"name\": \"the large white balloon\"}, {\"id\": 27151, \"name\": \"a black stroller\"}, {\"id\": 27152, \"name\": \"an orange sarong\"}, {\"id\": 27153, \"name\": \"row of flags on poles\"}, {\"id\": 27154, \"name\": \"the dark-colored drink\"}, {\"id\": 27155, \"name\": \"the speedometer\"}, {\"id\": 27156, \"name\": \"person's gloved hand\"}, {\"id\": 27157, \"name\": \"disco ball\"}, {\"id\": 27158, \"name\": \"gray cross-body bag\"}, {\"id\": 27159, \"name\": \"a yellow school bus\"}, {\"id\": 27160, \"name\": \"atv adventure\"}, {\"id\": 27161, \"name\": \"a matress\"}, {\"id\": 27162, \"name\": \"a large red tail\"}, {\"id\": 27163, \"name\": \"red and white box\"}, {\"id\": 27164, \"name\": \"the black steering wheel\"}, {\"id\": 27165, \"name\": \"umbrella hat\"}, {\"id\": 27166, \"name\": \"the rider's black motorcycle\"}, {\"id\": 27167, \"name\": \"a light-blue coat\"}, {\"id\": 27168, \"name\": \"piece of bone\"}, {\"id\": 27169, \"name\": \"a short tail\"}, {\"id\": 27170, \"name\": \"a person in a yellow shirt\"}, {\"id\": 27171, \"name\": \"a purple heart\"}, {\"id\": 27172, \"name\": \"a red basket\"}, {\"id\": 27173, \"name\": \"small metal cylinder\"}, {\"id\": 27174, \"name\": \"the bundle of white flower\"}, {\"id\": 27175, \"name\": \"tachometer\"}, {\"id\": 27176, \"name\": \"cushioned seating area\"}, {\"id\": 27177, \"name\": \"the dog's white paw\"}, {\"id\": 27178, \"name\": \"the truck wheel\"}, {\"id\": 27179, \"name\": \"a large inflatable archway\"}, {\"id\": 27180, \"name\": \"yellow and blue fish\"}, {\"id\": 27181, \"name\": \"dark-colored bus\"}, {\"id\": 27182, \"name\": \"the blue fan\"}, {\"id\": 27183, \"name\": \"partially collapsed roof\"}, {\"id\": 27184, \"name\": \"seated individual\"}, {\"id\": 27185, \"name\": \"the green and yellow uniform\"}, {\"id\": 27186, \"name\": \"dome-shaped top\"}, {\"id\": 27187, \"name\": \"a person in an orange shirt and jean\"}, {\"id\": 27188, \"name\": \"a blue icon\"}, {\"id\": 27189, \"name\": \"the hammer's head\"}, {\"id\": 27190, \"name\": \"black floral top\"}, {\"id\": 27191, \"name\": \"long black ponytail\"}, {\"id\": 27192, \"name\": \"the elderly woman\"}, {\"id\": 27193, \"name\": \"white pin\"}, {\"id\": 27194, \"name\": \"a tall telephone pole\"}, {\"id\": 27195, \"name\": \"a white teapot\"}, {\"id\": 27196, \"name\": \"a bird's body\"}, {\"id\": 27197, \"name\": \"yellow sack\"}, {\"id\": 27198, \"name\": \"the blue stool\"}, {\"id\": 27199, \"name\": \"a brown chicken\"}, {\"id\": 27200, \"name\": \"the blue metal pipe\"}, {\"id\": 27201, \"name\": \"a Flag\"}, {\"id\": 27202, \"name\": \"tassel\"}, {\"id\": 27203, \"name\": \"a large, illuminated clamshell\"}, {\"id\": 27204, \"name\": \"the blue and white uniform\"}, {\"id\": 27205, \"name\": \"beer bottle\"}, {\"id\": 27206, \"name\": \"the -terrain vehicles (atvs)\"}, {\"id\": 27207, \"name\": \"a dark car\"}, {\"id\": 27208, \"name\": \"the red tape\"}, {\"id\": 27209, \"name\": \"red structure\"}, {\"id\": 27210, \"name\": \"a curved horn\"}, {\"id\": 27211, \"name\": \"large gray roll-up door\"}, {\"id\": 27212, \"name\": \"a set of marbles\"}, {\"id\": 27213, \"name\": \"large orange Spiderman character\"}, {\"id\": 27214, \"name\": \"red-lit doorway\"}, {\"id\": 27215, \"name\": \"can of beer\"}, {\"id\": 27216, \"name\": \"the beach vendor\"}, {\"id\": 27217, \"name\": \"the grey sport car\"}, {\"id\": 27218, \"name\": \"a reddish sausage\"}, {\"id\": 27219, \"name\": \"the red race car\"}, {\"id\": 27220, \"name\": \"larger, dark blue fish\"}, {\"id\": 27221, \"name\": \"the red and white motorcycle\"}, {\"id\": 27222, \"name\": \"chef's hand\"}, {\"id\": 27223, \"name\": \"the moving car\"}, {\"id\": 27224, \"name\": \"the red slide\"}, {\"id\": 27225, \"name\": \"cattle-herding\"}, {\"id\": 27226, \"name\": \"a light blue shoe\"}, {\"id\": 27227, \"name\": \"a roof of the train station\"}, {\"id\": 27228, \"name\": \"the red structure\"}, {\"id\": 27229, \"name\": \"the yellow and green sign\"}, {\"id\": 27230, \"name\": \"large white bag or package\"}, {\"id\": 27231, \"name\": \"gray mask\"}, {\"id\": 27232, \"name\": \"blue and white section\"}, {\"id\": 27233, \"name\": \"a large, transparent, egg-shaped object\"}, {\"id\": 27234, \"name\": \"the corrugated roofing\"}, {\"id\": 27235, \"name\": \"a rainbow design\"}, {\"id\": 27236, \"name\": \"the large portrait\"}, {\"id\": 27237, \"name\": \"the stamina bar\"}, {\"id\": 27238, \"name\": \"the small ball of dough\"}, {\"id\": 27239, \"name\": \"white bridge\"}, {\"id\": 27240, \"name\": \"white toyota police car\"}, {\"id\": 27241, \"name\": \"miniature replica of a classic convertible\"}, {\"id\": 27242, \"name\": \"grey running surface\"}, {\"id\": 27243, \"name\": \"a green turf area\"}, {\"id\": 27244, \"name\": \"spectator's head\"}, {\"id\": 27245, \"name\": \"a light blue food cart\"}, {\"id\": 27246, \"name\": \"a person in a red vest\"}, {\"id\": 27247, \"name\": \"the starbuck storefront\"}, {\"id\": 27248, \"name\": \"a white plastic container\"}, {\"id\": 27249, \"name\": \"the red food truck\"}, {\"id\": 27250, \"name\": \"a camper\"}, {\"id\": 27251, \"name\": \"a white z900 rider sticker\"}, {\"id\": 27252, \"name\": \"gray bollard\"}, {\"id\": 27253, \"name\": \"the green chain link fence\"}, {\"id\": 27254, \"name\": \"the long ear\"}, {\"id\": 27255, \"name\": \"white headrest\"}, {\"id\": 27256, \"name\": \"a larger truck\"}, {\"id\": 27257, \"name\": \"the PRIDE banner\"}, {\"id\": 27258, \"name\": \"the pink feathered object\"}, {\"id\": 27259, \"name\": \"the small piece of orange debris\"}, {\"id\": 27260, \"name\": \"the patio's furnishing\"}, {\"id\": 27261, \"name\": \"the black and white car\"}, {\"id\": 27262, \"name\": \"Hong Kong flag\"}, {\"id\": 27263, \"name\": \"foreground kayak\"}, {\"id\": 27264, \"name\": \"the leech\"}, {\"id\": 27265, \"name\": \"the child in a white shirt and blue short\"}, {\"id\": 27266, \"name\": \"a dark blue top\"}, {\"id\": 27267, \"name\": \"the large, cylindrical propane tank\"}, {\"id\": 27268, \"name\": \"the black basin\"}, {\"id\": 27269, \"name\": \"the white and blue short-sleeved shirt\"}, {\"id\": 27270, \"name\": \"green vehicle\"}, {\"id\": 27271, \"name\": \"teal patterned curtain\"}, {\"id\": 27272, \"name\": \"top\"}, {\"id\": 27273, \"name\": \"the yellow top\"}, {\"id\": 27274, \"name\": \"a bucket of metal shaving\"}, {\"id\": 27275, \"name\": \"a man in the white shirt\"}, {\"id\": 27276, \"name\": \"the blue plastic cup\"}, {\"id\": 27277, \"name\": \"a muddy liquid\"}, {\"id\": 27278, \"name\": \"a post\"}, {\"id\": 27279, \"name\": \"plaid skirt\"}, {\"id\": 27280, \"name\": \"white and black tarp\"}, {\"id\": 27281, \"name\": \"the red carpeted area\"}, {\"id\": 27282, \"name\": \"the large, white pillar\"}, {\"id\": 27283, \"name\": \"the white ray\"}, {\"id\": 27284, \"name\": \"the small, white machine\"}, {\"id\": 27285, \"name\": \"brown hen\"}, {\"id\": 27286, \"name\": \"the black-and-white Converse shoe\"}, {\"id\": 27287, \"name\": \"the seated audience member\"}, {\"id\": 27288, \"name\": \"round float\"}, {\"id\": 27289, \"name\": \"yellow and red kayak\"}, {\"id\": 27290, \"name\": \"white speaker\"}, {\"id\": 27291, \"name\": \"the framed painting\"}, {\"id\": 27292, \"name\": \"a yellow and white tuk-tuk\"}, {\"id\": 27293, \"name\": \"the large silver column\"}, {\"id\": 27294, \"name\": \"a barber's cape\"}, {\"id\": 27295, \"name\": \"truck's mirror\"}, {\"id\": 27296, \"name\": \"a crane's head\"}, {\"id\": 27297, \"name\": \"vibrant bumper car ride\"}, {\"id\": 27298, \"name\": \"dark-colored bucket hat\"}, {\"id\": 27299, \"name\": \"the large white airplane\"}, {\"id\": 27300, \"name\": \"wrapped gift\"}, {\"id\": 27301, \"name\": \"the black trousers\"}, {\"id\": 27302, \"name\": \"the teal-sailed board\"}, {\"id\": 27303, \"name\": \"the large gold headpiece\"}, {\"id\": 27304, \"name\": \"the small, white object\"}, {\"id\": 27305, \"name\": \"a blue fish\"}, {\"id\": 27306, \"name\": \"yellow material\"}, {\"id\": 27307, \"name\": \"character's health bar\"}, {\"id\": 27308, \"name\": \"river\"}, {\"id\": 27309, \"name\": \"a navy and light blue raglan sleeve\"}, {\"id\": 27310, \"name\": \"orange robe\"}, {\"id\": 27311, \"name\": \"the patient\"}, {\"id\": 27312, \"name\": \"a large, round wheel\"}, {\"id\": 27313, \"name\": \"a sandy-colored terrain\"}, {\"id\": 27314, \"name\": \"the dark hat\"}, {\"id\": 27315, \"name\": \"a small white boat\"}, {\"id\": 27316, \"name\": \"a jollibee uniform\"}, {\"id\": 27317, \"name\": \"the orange and yellow patterned shirt\"}, {\"id\": 27318, \"name\": \"the cellphone\"}, {\"id\": 27319, \"name\": \"large bundle of wooden stick\"}, {\"id\": 27320, \"name\": \"black spotlight\"}, {\"id\": 27321, \"name\": \"large wiper blade\"}, {\"id\": 27322, \"name\": \"a white gravel substrate\"}, {\"id\": 27323, \"name\": \"large white semi-truck\"}, {\"id\": 27324, \"name\": \"large white cloud\"}, {\"id\": 27325, \"name\": \"reflection of light\"}, {\"id\": 27326, \"name\": \"the metal tong\"}, {\"id\": 27327, \"name\": \"white headscarf\"}, {\"id\": 27328, \"name\": \"green table\"}, {\"id\": 27329, \"name\": \"distinctive license plate\"}, {\"id\": 27330, \"name\": \"a flip flop\"}, {\"id\": 27331, \"name\": \"a life ring\"}, {\"id\": 27332, \"name\": \"the blue lettering\"}, {\"id\": 27333, \"name\": \"orange dot\"}, {\"id\": 27334, \"name\": \"the green fan\"}, {\"id\": 27335, \"name\": \"star decoration\"}, {\"id\": 27336, \"name\": \"the dhoti\"}, {\"id\": 27337, \"name\": \"the shuttered window\"}, {\"id\": 27338, \"name\": \"the red bow\"}, {\"id\": 27339, \"name\": \"a red plastic basket\"}, {\"id\": 27340, \"name\": \"the ornate architecture\"}, {\"id\": 27341, \"name\": \"a red glove\"}, {\"id\": 27342, \"name\": \"brown button with a white x\"}, {\"id\": 27343, \"name\": \"a combination of the colorful decoration\"}, {\"id\": 27344, \"name\": \"the vibrant yellow flag\"}, {\"id\": 27345, \"name\": \"white race car\"}, {\"id\": 27346, \"name\": \"logs or branch\"}, {\"id\": 27347, \"name\": \"a partially visible car\"}, {\"id\": 27348, \"name\": \"yellow mat\"}, {\"id\": 27349, \"name\": \"large, dark grey pipe\"}, {\"id\": 27350, \"name\": \"the blue and white fish\"}, {\"id\": 27351, \"name\": \"the silver nissan logo\"}, {\"id\": 27352, \"name\": \"vibrant balloon archway\"}, {\"id\": 27353, \"name\": \"black insect\"}, {\"id\": 27354, \"name\": \"boat's motor\"}, {\"id\": 27355, \"name\": \"the distinctive green jacket\"}, {\"id\": 27356, \"name\": \"a large metal spoon\"}, {\"id\": 27357, \"name\": \"a red and yellow color scheme\"}, {\"id\": 27358, \"name\": \"glass of yellow liquid\"}, {\"id\": 27359, \"name\": \"a white and black sneaker\"}, {\"id\": 27360, \"name\": \"the orange head\"}, {\"id\": 27361, \"name\": \"soccer goalie\"}, {\"id\": 27362, \"name\": \"the stadium light\"}, {\"id\": 27363, \"name\": \"the construction vehicle\"}, {\"id\": 27364, \"name\": \"the green strip of artificial turf\"}, {\"id\": 27365, \"name\": \"the black TV or monitor\"}, {\"id\": 27366, \"name\": \"the red and white attire\"}, {\"id\": 27367, \"name\": \"the yellow garland\"}, {\"id\": 27368, \"name\": \"the quarter pipe\"}, {\"id\": 27369, \"name\": \"green panel\"}, {\"id\": 27370, \"name\": \"the glass container\"}, {\"id\": 27371, \"name\": \"red and white jersey\"}, {\"id\": 27372, \"name\": \"the brick facade\"}, {\"id\": 27373, \"name\": \"the black and red seat\"}, {\"id\": 27374, \"name\": \"ornate white statue of mythical creature\"}, {\"id\": 27375, \"name\": \"a large digital display board\"}, {\"id\": 27376, \"name\": \"a green mountain\"}, {\"id\": 27377, \"name\": \"the streamer\"}, {\"id\": 27378, \"name\": \"brown top hat\"}, {\"id\": 27379, \"name\": \"the black honda cbr\"}, {\"id\": 27380, \"name\": \"the map\"}, {\"id\": 27381, \"name\": \"the simple orange robe\"}, {\"id\": 27382, \"name\": \"an orange sticker\"}, {\"id\": 27383, \"name\": \"a dark blue sail\"}, {\"id\": 27384, \"name\": \"a traditional mexican clothing\"}, {\"id\": 27385, \"name\": \"French flag\"}, {\"id\": 27386, \"name\": \"a black puppy\"}, {\"id\": 27387, \"name\": \"the dark grey dog\"}, {\"id\": 27388, \"name\": \"license plate on the motorcycle\"}, {\"id\": 27389, \"name\": \"tuk-tuk's roof\"}, {\"id\": 27390, \"name\": \"red and black motorcycle\"}, {\"id\": 27391, \"name\": \"a black-framed picture\"}, {\"id\": 27392, \"name\": \"the kayaking\"}, {\"id\": 27393, \"name\": \"long, bright orange tail\"}, {\"id\": 27394, \"name\": \"game's controls\"}, {\"id\": 27395, \"name\": \"lehenga\"}, {\"id\": 27396, \"name\": \"a red floral dress\"}, {\"id\": 27397, \"name\": \"the large, black wheel\"}, {\"id\": 27398, \"name\": \"a steep mountain\"}, {\"id\": 27399, \"name\": \"the machine's arm\"}, {\"id\": 27400, \"name\": \"the beach ball\"}, {\"id\": 27401, \"name\": \"the black xbox controller\"}, {\"id\": 27402, \"name\": \"the bright white light\"}, {\"id\": 27403, \"name\": \"a lilac dress\"}, {\"id\": 27404, \"name\": \"oversized teacup\"}, {\"id\": 27405, \"name\": \"the metal luggage cart\"}, {\"id\": 27406, \"name\": \"red chili pepper\"}, {\"id\": 27407, \"name\": \"red floral top\"}, {\"id\": 27408, \"name\": \"the red do not enter sign\"}, {\"id\": 27409, \"name\": \"the long paddle\"}, {\"id\": 27410, \"name\": \"traditional alphorn\"}, {\"id\": 27411, \"name\": \"a bridge's support column\"}, {\"id\": 27412, \"name\": \"red and yellow slide\"}, {\"id\": 27413, \"name\": \"a blue and white striped fence\"}, {\"id\": 27414, \"name\": \"the motorcyclist's white jacket\"}, {\"id\": 27415, \"name\": \"red writing\"}, {\"id\": 27416, \"name\": \"light-colored button-down shirt\"}, {\"id\": 27417, \"name\": \"a boat's roof\"}, {\"id\": 27418, \"name\": \"small, dark brown gazebo\"}, {\"id\": 27419, \"name\": \"the yellow glove\"}, {\"id\": 27420, \"name\": \"the black battery charger\"}, {\"id\": 27421, \"name\": \"the white under armour logo\"}, {\"id\": 27422, \"name\": \"a person in traditional islamic attire\"}, {\"id\": 27423, \"name\": \"a blue head\"}, {\"id\": 27424, \"name\": \"the rainbow flag\"}, {\"id\": 27425, \"name\": \"a red poster\"}, {\"id\": 27426, \"name\": \"stacked egg crate\"}, {\"id\": 27427, \"name\": \"sandy brown hill\"}, {\"id\": 27428, \"name\": \"dragon's head\"}, {\"id\": 27429, \"name\": \"black and gold floral-patterned top\"}, {\"id\": 27430, \"name\": \"a floral short\"}, {\"id\": 27431, \"name\": \"a man in a light blue shirt\"}, {\"id\": 27432, \"name\": \"transportation\"}, {\"id\": 27433, \"name\": \"large, ornate drum\"}, {\"id\": 27434, \"name\": \"a red and green umbrella\"}, {\"id\": 27435, \"name\": \"the small, white convertible car\"}, {\"id\": 27436, \"name\": \"a large dark blue curtain\"}, {\"id\": 27437, \"name\": \"the dark polo shirt\"}, {\"id\": 27438, \"name\": \"rider's hand\"}, {\"id\": 27439, \"name\": \"red inflatable obstacle course\"}, {\"id\": 27440, \"name\": \"the brown outfit\"}, {\"id\": 27441, \"name\": \"gray table\"}, {\"id\": 27442, \"name\": \"a gas can\"}, {\"id\": 27443, \"name\": \"a pink and white neon light\"}, {\"id\": 27444, \"name\": \"a silver belt\"}, {\"id\": 27445, \"name\": \"yellow lid\"}, {\"id\": 27446, \"name\": \"the black component\"}, {\"id\": 27447, \"name\": \"long, metal counter\"}, {\"id\": 27448, \"name\": \"long wooden plank\"}, {\"id\": 27449, \"name\": \"small yellow rectangle\"}, {\"id\": 27450, \"name\": \"the dark curtain\"}, {\"id\": 27451, \"name\": \"the white and black plumage\"}, {\"id\": 27452, \"name\": \"backseat\"}, {\"id\": 27453, \"name\": \"the multitude of car\"}, {\"id\": 27454, \"name\": \"a person on a bicycle\"}, {\"id\": 27455, \"name\": \"the man with the microphone\"}, {\"id\": 27456, \"name\": \"colorful flip-flop\"}, {\"id\": 27457, \"name\": \"elderly person\"}, {\"id\": 27458, \"name\": \"transport truck\"}, {\"id\": 27459, \"name\": \"drummer's equipment\"}, {\"id\": 27460, \"name\": \"black conveyor belt\"}, {\"id\": 27461, \"name\": \"metal stick\"}, {\"id\": 27462, \"name\": \"white block\"}, {\"id\": 27463, \"name\": \"rainbow-colored wig\"}, {\"id\": 27464, \"name\": \"black pig\"}, {\"id\": 27465, \"name\": \"man wearing a large straw hat\"}, {\"id\": 27466, \"name\": \"a prominent red banner\"}, {\"id\": 27467, \"name\": \"vibrant display of tropical fruit\"}, {\"id\": 27468, \"name\": \"a large red barn\"}, {\"id\": 27469, \"name\": \"the scissors\"}, {\"id\": 27470, \"name\": \"black parrot\"}, {\"id\": 27471, \"name\": \"the large metal plate\"}, {\"id\": 27472, \"name\": \"a vibrant, multicolored flag\"}, {\"id\": 27473, \"name\": \"the blue bmw\"}, {\"id\": 27474, \"name\": \"the front hood\"}, {\"id\": 27475, \"name\": \"gas tank\"}, {\"id\": 27476, \"name\": \"a chanel interlocking cc logo\"}, {\"id\": 27477, \"name\": \"the distinctive red jacket\"}, {\"id\": 27478, \"name\": \"the small grassy area\"}, {\"id\": 27479, \"name\": \"the bright yellow fish\"}, {\"id\": 27480, \"name\": \"the small bowl of sauce or dip\"}, {\"id\": 27481, \"name\": \"the large silver oxygen tank\"}, {\"id\": 27482, \"name\": \"the blue rickshaw\"}, {\"id\": 27483, \"name\": \"vibrant floral arrangement\"}, {\"id\": 27484, \"name\": \"the purple sign\"}, {\"id\": 27485, \"name\": \"square opening\"}, {\"id\": 27486, \"name\": \"the crane's long arm\"}, {\"id\": 27487, \"name\": \"an exposed brick archway\"}, {\"id\": 27488, \"name\": \"blue tail\"}, {\"id\": 27489, \"name\": \"the blue bowl\"}, {\"id\": 27490, \"name\": \"large white covering\"}, {\"id\": 27491, \"name\": \"the orange rope\"}, {\"id\": 27492, \"name\": \"bunting\"}, {\"id\": 27493, \"name\": \"canopy's pole\"}, {\"id\": 27494, \"name\": \"low white fence\"}, {\"id\": 27495, \"name\": \"the small, dark brown nose\"}, {\"id\": 27496, \"name\": \"the trombone player\"}, {\"id\": 27497, \"name\": \"the game's graphics\"}, {\"id\": 27498, \"name\": \"the blue and white can\"}, {\"id\": 27499, \"name\": \"lit yellow flame\"}, {\"id\": 27500, \"name\": \"a white crab leg\"}, {\"id\": 27501, \"name\": \"the blue spire\"}, {\"id\": 27502, \"name\": \"the sliced orange\"}, {\"id\": 27503, \"name\": \"the lush grass\"}, {\"id\": 27504, \"name\": \"the orange claw\"}, {\"id\": 27505, \"name\": \"bright pink jacket\"}, {\"id\": 27506, \"name\": \"an orange support\"}, {\"id\": 27507, \"name\": \"the young girl in a blue dress\"}, {\"id\": 27508, \"name\": \"large cross\"}, {\"id\": 27509, \"name\": \"a recessed ceiling light\"}, {\"id\": 27510, \"name\": \"dark green t-shirt\"}, {\"id\": 27511, \"name\": \"the large rocky cliff\"}, {\"id\": 27512, \"name\": \"the tall, white and red striped hat\"}, {\"id\": 27513, \"name\": \"taxi's license plate\"}, {\"id\": 27514, \"name\": \"a -terrain vehicle (atv)\"}, {\"id\": 27515, \"name\": \"the round metal plate\"}, {\"id\": 27516, \"name\": \"red and orange flag\"}, {\"id\": 27517, \"name\": \"black-and-white striped shirt\"}, {\"id\": 27518, \"name\": \"monkey's head\"}, {\"id\": 27519, \"name\": \"the large, green, triangular sign\"}, {\"id\": 27520, \"name\": \"stage or lighting rig\"}, {\"id\": 27521, \"name\": \"a great pyramid of giza\"}, {\"id\": 27522, \"name\": \"a black skate\"}, {\"id\": 27523, \"name\": \"rider's shadow\"}, {\"id\": 27524, \"name\": \"distinctive white, cylindrical column\"}, {\"id\": 27525, \"name\": \"dark-rimmed glass\"}, {\"id\": 27526, \"name\": \"a large rocky outcrop\"}, {\"id\": 27527, \"name\": \"a two-lane highway\"}, {\"id\": 27528, \"name\": \"the brown brick trim\"}, {\"id\": 27529, \"name\": \"a corrugated metal structure\"}, {\"id\": 27530, \"name\": \"a grey turtleneck\"}, {\"id\": 27531, \"name\": \"pedestal\"}, {\"id\": 27532, \"name\": \"a gray, concrete pillar\"}, {\"id\": 27533, \"name\": \"slide's surface\"}, {\"id\": 27534, \"name\": \"yellow section\"}, {\"id\": 27535, \"name\": \"the flags or banner\"}, {\"id\": 27536, \"name\": \"the wooden shed\"}, {\"id\": 27537, \"name\": \"multi-level overpass\"}, {\"id\": 27538, \"name\": \"a parked motorbike\"}, {\"id\": 27539, \"name\": \"white graphic t-shirt\"}, {\"id\": 27540, \"name\": \"a conch shell\"}, {\"id\": 27541, \"name\": \"large silver bowl\"}, {\"id\": 27542, \"name\": \"a pink tie\"}, {\"id\": 27543, \"name\": \"a burner\"}, {\"id\": 27544, \"name\": \"the flap\"}, {\"id\": 27545, \"name\": \"the traditional clothing\"}, {\"id\": 27546, \"name\": \"stunt rider\"}, {\"id\": 27547, \"name\": \"grey border\"}, {\"id\": 27548, \"name\": \"the red long-sleeve shirt\"}, {\"id\": 27549, \"name\": \"a checkered shirt\"}, {\"id\": 27550, \"name\": \"diving mask\"}, {\"id\": 27551, \"name\": \"the metal-framed canopy\"}, {\"id\": 27552, \"name\": \"red fish\"}, {\"id\": 27553, \"name\": \"a motorcycle ride\"}, {\"id\": 27554, \"name\": \"flowing brown mane\"}, {\"id\": 27555, \"name\": \"round wheel\"}, {\"id\": 27556, \"name\": \"a long, thin string\"}, {\"id\": 27557, \"name\": \"the prop\"}, {\"id\": 27558, \"name\": \"the beige pant\"}, {\"id\": 27559, \"name\": \"a worker's hand\"}, {\"id\": 27560, \"name\": \"a yellow piece of metal\"}, {\"id\": 27561, \"name\": \"red go-kart\"}, {\"id\": 27562, \"name\": \"black and red jacket\"}, {\"id\": 27563, \"name\": \"the prominent yellow and black object\"}, {\"id\": 27564, \"name\": \"a shot put ball\"}, {\"id\": 27565, \"name\": \"the long, white table\"}, {\"id\": 27566, \"name\": \"a pier\"}, {\"id\": 27567, \"name\": \"the striking red and white costume\"}, {\"id\": 27568, \"name\": \"the red and blue life jacket\"}, {\"id\": 27569, \"name\": \"orange billboard\"}, {\"id\": 27570, \"name\": \"a bald man\"}, {\"id\": 27571, \"name\": \"the blue tarp-covered vehicle\"}, {\"id\": 27572, \"name\": \"cymbal stand\"}, {\"id\": 27573, \"name\": \"a blue plumage\"}, {\"id\": 27574, \"name\": \"green smoke bomb\"}, {\"id\": 27575, \"name\": \"the ornate headdress\"}, {\"id\": 27576, \"name\": \"the small piece of food\"}, {\"id\": 27577, \"name\": \"a dark brown trunk\"}, {\"id\": 27578, \"name\": \"a kayaker\"}, {\"id\": 27579, \"name\": \"a brown basket\"}, {\"id\": 27580, \"name\": \"the dark jean\"}, {\"id\": 27581, \"name\": \"a blue cuff\"}, {\"id\": 27582, \"name\": \"white motor scooter\"}, {\"id\": 27583, \"name\": \"a red rope\"}, {\"id\": 27584, \"name\": \"the cowboy hat\"}, {\"id\": 27585, \"name\": \"the black bag strap\"}, {\"id\": 27586, \"name\": \"crane's cab\"}, {\"id\": 27587, \"name\": \"large, arched bridge structure\"}, {\"id\": 27588, \"name\": \"the red lanyard\"}, {\"id\": 27589, \"name\": \"a phone screen\"}, {\"id\": 27590, \"name\": \"the riders' attire\"}, {\"id\": 27591, \"name\": \"a small green awning\"}, {\"id\": 27592, \"name\": \"the large winged statue\"}, {\"id\": 27593, \"name\": \"blue hard hat\"}, {\"id\": 27594, \"name\": \"the purple table\"}, {\"id\": 27595, \"name\": \"the dark grey head\"}, {\"id\": 27596, \"name\": \"a tall, illuminated pole\"}, {\"id\": 27597, \"name\": \"blue and white box\"}, {\"id\": 27598, \"name\": \"cheerleader\"}, {\"id\": 27599, \"name\": \"yellow and orange explosion\"}, {\"id\": 27600, \"name\": \"gray metal pole\"}, {\"id\": 27601, \"name\": \"red runner\"}, {\"id\": 27602, \"name\": \"the vibrant, multicolored tent\"}, {\"id\": 27603, \"name\": \"a brown plumage\"}, {\"id\": 27604, \"name\": \"large white package\"}, {\"id\": 27605, \"name\": \"the child's left arm\"}, {\"id\": 27606, \"name\": \"the colorful barrel train\"}, {\"id\": 27607, \"name\": \"the stainless-steel control panel\"}, {\"id\": 27608, \"name\": \"the small platform\"}, {\"id\": 27609, \"name\": \"a red long-sleeve shirt\"}, {\"id\": 27610, \"name\": \"the large pink and red balloon\"}, {\"id\": 27611, \"name\": \"the large, white, pointed structure\"}, {\"id\": 27612, \"name\": \"the black ankle support\"}, {\"id\": 27613, \"name\": \"a plate of sushi\"}, {\"id\": 27614, \"name\": \"the black and red motorcycle\"}, {\"id\": 27615, \"name\": \"the red and yellow hat\"}, {\"id\": 27616, \"name\": \"the large, ornate object\"}, {\"id\": 27617, \"name\": \"white and blue striped shirt\"}, {\"id\": 27618, \"name\": \"a bright orange shirt\"}, {\"id\": 27619, \"name\": \"a silver metal top\"}, {\"id\": 27620, \"name\": \"small mirror\"}, {\"id\": 27621, \"name\": \"the white structure\"}, {\"id\": 27622, \"name\": \"a beige shirt\"}, {\"id\": 27623, \"name\": \"plastic tablecloth\"}, {\"id\": 27624, \"name\": \"the blue claw\"}, {\"id\": 27625, \"name\": \"matching white outfit\"}, {\"id\": 27626, \"name\": \"tan minivan\"}, {\"id\": 27627, \"name\": \"yellow and blue dump truck\"}, {\"id\": 27628, \"name\": \"the gray pillar\"}, {\"id\": 27629, \"name\": \"yellow and white flags or banner\"}, {\"id\": 27630, \"name\": \"the white dres with a floral pattern\"}, {\"id\": 27631, \"name\": \"a patterned pant\"}, {\"id\": 27632, \"name\": \"the man in a blue shirt and jean\"}, {\"id\": 27633, \"name\": \"a blue boat\"}, {\"id\": 27634, \"name\": \"the sash\"}, {\"id\": 27635, \"name\": \"the red apron\"}, {\"id\": 27636, \"name\": \"a yellow dumpster\"}, {\"id\": 27637, \"name\": \"a silver shoe\"}, {\"id\": 27638, \"name\": \"a yellow roofed structure\"}, {\"id\": 27639, \"name\": \"people in orange vest\"}, {\"id\": 27640, \"name\": \"a white breeches\"}, {\"id\": 27641, \"name\": \"black drum\"}, {\"id\": 27642, \"name\": \"a tall blue pole\"}, {\"id\": 27643, \"name\": \"the large gray fish\"}, {\"id\": 27644, \"name\": \"the dark blue baseball cap\"}, {\"id\": 27645, \"name\": \"the grinder\"}, {\"id\": 27646, \"name\": \"the red and yellow sign\"}, {\"id\": 27647, \"name\": \"a headshot of a man\"}, {\"id\": 27648, \"name\": \"a visible vent\"}, {\"id\": 27649, \"name\": \"woman's outstretched arm\"}, {\"id\": 27650, \"name\": \"the wok\"}, {\"id\": 27651, \"name\": \"the open-air structure\"}, {\"id\": 27652, \"name\": \"red toy car\"}, {\"id\": 27653, \"name\": \"brown fedora\"}, {\"id\": 27654, \"name\": \"the yellow surface\"}, {\"id\": 27655, \"name\": \"a red swim trunk\"}, {\"id\": 27656, \"name\": \"red and black shirt\"}, {\"id\": 27657, \"name\": \"yellow tricycle\"}, {\"id\": 27658, \"name\": \"a green metal\"}, {\"id\": 27659, \"name\": \"the blue trash bin\"}, {\"id\": 27660, \"name\": \"the large black helmet\"}, {\"id\": 27661, \"name\": \"the vertical sign\"}, {\"id\": 27662, \"name\": \"a dark gray shirt\"}, {\"id\": 27663, \"name\": \"large blue tail\"}, {\"id\": 27664, \"name\": \"the tall radio tower\"}, {\"id\": 27665, \"name\": \"speed limit sign\"}, {\"id\": 27666, \"name\": \"the gold-colored statue\"}, {\"id\": 27667, \"name\": \"the weight\"}, {\"id\": 27668, \"name\": \"a race boat\"}, {\"id\": 27669, \"name\": \"the white checkered van logo\"}, {\"id\": 27670, \"name\": \"crossbody bag\"}, {\"id\": 27671, \"name\": \"black-and-white shirt\"}, {\"id\": 27672, \"name\": \"a traffic pole\"}, {\"id\": 27673, \"name\": \"large, red, and yellow mask\"}, {\"id\": 27674, \"name\": \"the small black animal\"}, {\"id\": 27675, \"name\": \"a playground area\"}, {\"id\": 27676, \"name\": \"the occupant\"}, {\"id\": 27677, \"name\": \"a white-painted metal ceiling\"}, {\"id\": 27678, \"name\": \"large, yellow spaceship\"}, {\"id\": 27679, \"name\": \"a blue component\"}, {\"id\": 27680, \"name\": \"the bright yellow sign\"}, {\"id\": 27681, \"name\": \"the mirror reflection\"}, {\"id\": 27682, \"name\": \"prominent light\"}, {\"id\": 27683, \"name\": \"a red canopy tent\"}, {\"id\": 27684, \"name\": \"grey tie\"}, {\"id\": 27685, \"name\": \"green head covering\"}, {\"id\": 27686, \"name\": \"black framing\"}, {\"id\": 27687, \"name\": \"the large, fluffy tail\"}, {\"id\": 27688, \"name\": \"light blue and white striped cloth\"}, {\"id\": 27689, \"name\": \"red and orange koi\"}, {\"id\": 27690, \"name\": \"the rider's bike\"}, {\"id\": 27691, \"name\": \"a large, flat, metal platform\"}, {\"id\": 27692, \"name\": \"the statue of the virgin of guadalupe\"}, {\"id\": 27693, \"name\": \"black flag\"}, {\"id\": 27694, \"name\": \"the rear of the vehicle\"}, {\"id\": 27695, \"name\": \"red spiderman design\"}, {\"id\": 27696, \"name\": \"pink toy shopping cart\"}, {\"id\": 27697, \"name\": \"person's blue flip-flop\"}, {\"id\": 27698, \"name\": \"orange headband\"}, {\"id\": 27699, \"name\": \"light pink barricade\"}, {\"id\": 27700, \"name\": \"the bottle of beer\"}, {\"id\": 27701, \"name\": \"a busy crosswalk\"}, {\"id\": 27702, \"name\": \"an orange skirt\"}, {\"id\": 27703, \"name\": \"a thin leg\"}, {\"id\": 27704, \"name\": \"the fist\"}, {\"id\": 27705, \"name\": \"the brown egg\"}, {\"id\": 27706, \"name\": \"a ski pole\"}, {\"id\": 27707, \"name\": \"a park bench\"}, {\"id\": 27708, \"name\": \"a dark-colored cap\"}, {\"id\": 27709, \"name\": \"a metal coil\"}, {\"id\": 27710, \"name\": \"a side mirror of a car\"}, {\"id\": 27711, \"name\": \"a gold headdress\"}, {\"id\": 27712, \"name\": \"a sliced mushroom\"}, {\"id\": 27713, \"name\": \"a large, yellow bag\"}, {\"id\": 27714, \"name\": \"the multi-level yellow slide\"}, {\"id\": 27715, \"name\": \"a white spoon\"}, {\"id\": 27716, \"name\": \"the traditional indian dress\"}, {\"id\": 27717, \"name\": \"the bright white ceiling\"}, {\"id\": 27718, \"name\": \"the phone's screen\"}, {\"id\": 27719, \"name\": \"plaque\"}, {\"id\": 27720, \"name\": \"the large swimming pool\"}, {\"id\": 27721, \"name\": \"the large white bird\"}, {\"id\": 27722, \"name\": \"blue billboard\"}, {\"id\": 27723, \"name\": \"a matching hat\"}, {\"id\": 27724, \"name\": \"red and black jacket\"}, {\"id\": 27725, \"name\": \"a racing car\"}, {\"id\": 27726, \"name\": \"large structure\"}, {\"id\": 27727, \"name\": \"the river\"}, {\"id\": 27728, \"name\": \"platform\"}, {\"id\": 27729, \"name\": \"finch\"}, {\"id\": 27730, \"name\": \"the brown leather bag\"}, {\"id\": 27731, \"name\": \"the large drum\"}, {\"id\": 27732, \"name\": \"a pearl necklace\"}, {\"id\": 27733, \"name\": \"a dark green plant or decoration\"}, {\"id\": 27734, \"name\": \"the yellow belt\"}, {\"id\": 27735, \"name\": \"a clear bottle\"}, {\"id\": 27736, \"name\": \"the red and green flag\"}, {\"id\": 27737, \"name\": \"a long ponytail\"}, {\"id\": 27738, \"name\": \"the tall, ornate lamp post\"}, {\"id\": 27739, \"name\": \"circular black stage\"}, {\"id\": 27740, \"name\": \"red and blue rope-like material\"}, {\"id\": 27741, \"name\": \"the white motorbike\"}, {\"id\": 27742, \"name\": \"the blue and green tentacle-shaped ride\"}, {\"id\": 27743, \"name\": \"green fence\"}, {\"id\": 27744, \"name\": \"a lead kart\"}, {\"id\": 27745, \"name\": \"small shop\"}, {\"id\": 27746, \"name\": \"long pant\"}, {\"id\": 27747, \"name\": \"a camouflage backpack\"}, {\"id\": 27748, \"name\": \"heavy machinery\"}, {\"id\": 27749, \"name\": \"long front window\"}, {\"id\": 27750, \"name\": \"the long green conveyor belt\"}, {\"id\": 27751, \"name\": \"a white clothing\"}, {\"id\": 27752, \"name\": \"yellow and black shirt\"}, {\"id\": 27753, \"name\": \"a gold dome\"}, {\"id\": 27754, \"name\": \"the large punching bag\"}, {\"id\": 27755, \"name\": \"the dog toy\"}, {\"id\": 27756, \"name\": \"black jelly cube\"}, {\"id\": 27757, \"name\": \"a sedan's license plate\"}, {\"id\": 27758, \"name\": \"the white vending machine\"}, {\"id\": 27759, \"name\": \"striking red archway\"}, {\"id\": 27760, \"name\": \"the red thai sign\"}, {\"id\": 27761, \"name\": \"the central manhole cover\"}, {\"id\": 27762, \"name\": \"a sequined dress\"}, {\"id\": 27763, \"name\": \"the load of good\"}, {\"id\": 27764, \"name\": \"a red beach bag\"}, {\"id\": 27765, \"name\": \"blue jewel\"}, {\"id\": 27766, \"name\": \"the tall, white, illuminated structure\"}, {\"id\": 27767, \"name\": \"a fish's silhouettes\"}, {\"id\": 27768, \"name\": \"a beige uniform\"}, {\"id\": 27769, \"name\": \"the deck of playing card\"}, {\"id\": 27770, \"name\": \"the front wheel of the motorcycle\"}, {\"id\": 27771, \"name\": \"the white graffiti\"}, {\"id\": 27772, \"name\": \"a colorful bunting flag\"}, {\"id\": 27773, \"name\": \"person on a motorbike\"}, {\"id\": 27774, \"name\": \"black, brown, and white cattle\"}, {\"id\": 27775, \"name\": \"a black metal door\"}, {\"id\": 27776, \"name\": \"the red, yellow, and white ribbon\"}, {\"id\": 27777, \"name\": \"dark blue suv\"}, {\"id\": 27778, \"name\": \"tall, brown stone structure\"}, {\"id\": 27779, \"name\": \"a stone base\"}, {\"id\": 27780, \"name\": \"the green display board\"}, {\"id\": 27781, \"name\": \"a large yellow truck\"}, {\"id\": 27782, \"name\": \"a large green tarp\"}, {\"id\": 27783, \"name\": \"a silver toyota emblem\"}, {\"id\": 27784, \"name\": \"clear glass bowl\"}, {\"id\": 27785, \"name\": \"a child on the rope swing\"}, {\"id\": 27786, \"name\": \"a light-blue jean\"}, {\"id\": 27787, \"name\": \"high-visibility clothing\"}, {\"id\": 27788, \"name\": \"large metal fan\"}, {\"id\": 27789, \"name\": \"the yellow and blue sandal\"}, {\"id\": 27790, \"name\": \"matching blue shirt\"}, {\"id\": 27791, \"name\": \"the large digital screen\"}, {\"id\": 27792, \"name\": \"matching navy blue tank top\"}, {\"id\": 27793, \"name\": \"the large rectangular shape\"}, {\"id\": 27794, \"name\": \"a lead motorcycle\"}, {\"id\": 27795, \"name\": \"grey head\"}, {\"id\": 27796, \"name\": \"the broomstick\"}, {\"id\": 27797, \"name\": \"a large dome\"}, {\"id\": 27798, \"name\": \"the large, yellow and pink plastic structure\"}, {\"id\": 27799, \"name\": \"a woman in a pink dress\"}, {\"id\": 27800, \"name\": \"the right-hand lane\"}, {\"id\": 27801, \"name\": \"an emergency vehicle\"}, {\"id\": 27802, \"name\": \"the red straw\"}, {\"id\": 27803, \"name\": \"blue visor\"}, {\"id\": 27804, \"name\": \"an ornate design\"}, {\"id\": 27805, \"name\": \"the large, hexagonal icon\"}, {\"id\": 27806, \"name\": \"long, flowing yellow dress\"}, {\"id\": 27807, \"name\": \"the metal ceiling\"}, {\"id\": 27808, \"name\": \"red sweatshirt\"}, {\"id\": 27809, \"name\": \"the white tile surface\"}, {\"id\": 27810, \"name\": \"the bus's window\"}, {\"id\": 27811, \"name\": \"a purple, red, and blue feather\"}, {\"id\": 27812, \"name\": \"golden dome\"}, {\"id\": 27813, \"name\": \"the selfie stick\"}, {\"id\": 27814, \"name\": \"the colorful lantern\"}, {\"id\": 27815, \"name\": \"the blue knee pad\"}, {\"id\": 27816, \"name\": \"a gray metal bridge\"}, {\"id\": 27817, \"name\": \"a beach volleyball\"}, {\"id\": 27818, \"name\": \"a tall antenna\"}, {\"id\": 27819, \"name\": \"the guitar case\"}, {\"id\": 27820, \"name\": \"a gold chain\"}, {\"id\": 27821, \"name\": \"the brown wooden plank\"}, {\"id\": 27822, \"name\": \"child's face\"}, {\"id\": 27823, \"name\": \"the man in a high-visibility vest\"}, {\"id\": 27824, \"name\": \"the yellow-lit sign\"}, {\"id\": 27825, \"name\": \"a drum-like instrument\"}, {\"id\": 27826, \"name\": \"small fish\"}, {\"id\": 27827, \"name\": \"the purple baseball cap\"}, {\"id\": 27828, \"name\": \"the beach umbrella\"}, {\"id\": 27829, \"name\": \"yellow life jacket\"}, {\"id\": 27830, \"name\": \"the gray pickup truck\"}, {\"id\": 27831, \"name\": \"the saw blade\"}, {\"id\": 27832, \"name\": \"a large banner or sign\"}, {\"id\": 27833, \"name\": \"the black lever\"}, {\"id\": 27834, \"name\": \"a rider's hands\"}, {\"id\": 27835, \"name\": \"domed roof\"}, {\"id\": 27836, \"name\": \"the traditional venetian gondola\"}, {\"id\": 27837, \"name\": \"the green parrot\"}, {\"id\": 27838, \"name\": \"an underwear\"}, {\"id\": 27839, \"name\": \"a bike's front wheel\"}, {\"id\": 27840, \"name\": \"the large planter box\"}, {\"id\": 27841, \"name\": \"the red book emblem\"}, {\"id\": 27842, \"name\": \"the black and white motorcycle helmet\"}, {\"id\": 27843, \"name\": \"the light-colored button-up shirt\"}, {\"id\": 27844, \"name\": \"a decorative star-shaped structure\"}, {\"id\": 27845, \"name\": \"the pres box\"}, {\"id\": 27846, \"name\": \"a large, colorful tent or canopy\"}, {\"id\": 27847, \"name\": \"red boot\"}, {\"id\": 27848, \"name\": \"the large silver lid\"}, {\"id\": 27849, \"name\": \"a gray suv\"}, {\"id\": 27850, \"name\": \"a prominent circular window\"}, {\"id\": 27851, \"name\": \"the yellow and gray tiled patio\"}, {\"id\": 27852, \"name\": \"the tall staff\"}, {\"id\": 27853, \"name\": \"the cabin\"}, {\"id\": 27854, \"name\": \"black floral shirt\"}, {\"id\": 27855, \"name\": \"the red metal pole\"}, {\"id\": 27856, \"name\": \"red carpeted platform\"}, {\"id\": 27857, \"name\": \"the black canopy\"}, {\"id\": 27858, \"name\": \"the shallow silver bowl\"}, {\"id\": 27859, \"name\": \"a smaller, circular playground\"}, {\"id\": 27860, \"name\": \"the boy's head\"}, {\"id\": 27861, \"name\": \"the large black and white poster\"}, {\"id\": 27862, \"name\": \"the tan trench coat\"}, {\"id\": 27863, \"name\": \"a park ranger\"}, {\"id\": 27864, \"name\": \"blurred scooter\"}, {\"id\": 27865, \"name\": \"the large, red boat\"}, {\"id\": 27866, \"name\": \"a blue and white swim trunk\"}, {\"id\": 27867, \"name\": \"the black tablecloth\"}, {\"id\": 27868, \"name\": \"yellow and black striped pole\"}, {\"id\": 27869, \"name\": \"a silver suzuki logo\"}, {\"id\": 27870, \"name\": \"the shelter\"}, {\"id\": 27871, \"name\": \"the partially obscured man in a white jacket\"}, {\"id\": 27872, \"name\": \"red stool\"}, {\"id\": 27873, \"name\": \"the ride vehicle\"}, {\"id\": 27874, \"name\": \"a black dj booth\"}, {\"id\": 27875, \"name\": \"a close-up of a person's hand\"}, {\"id\": 27876, \"name\": \"a floral shirt\"}, {\"id\": 27877, \"name\": \"the player character\"}, {\"id\": 27878, \"name\": \"black choker\"}, {\"id\": 27879, \"name\": \"a red suspender\"}, {\"id\": 27880, \"name\": \"beach towel\"}, {\"id\": 27881, \"name\": \"the load of large brown box\"}, {\"id\": 27882, \"name\": \"dark blue and white boat\"}, {\"id\": 27883, \"name\": \"long, thin, silver metal box or machine\"}, {\"id\": 27884, \"name\": \"gray concrete ground\"}, {\"id\": 27885, \"name\": \"metal sheet\"}, {\"id\": 27886, \"name\": \"the double-decker tram\"}, {\"id\": 27887, \"name\": \"the white cage\"}, {\"id\": 27888, \"name\": \"a brown bench\"}, {\"id\": 27889, \"name\": \"a black sash\"}, {\"id\": 27890, \"name\": \"the white nike logo\"}, {\"id\": 27891, \"name\": \"large black seatbelt\"}, {\"id\": 27892, \"name\": \"toe\"}, {\"id\": 27893, \"name\": \"an exercise mat\"}, {\"id\": 27894, \"name\": \"yellow body\"}, {\"id\": 27895, \"name\": \"a gold tablecloth\"}, {\"id\": 27896, \"name\": \"canopy's metal pole\"}, {\"id\": 27897, \"name\": \"the crocs\"}, {\"id\": 27898, \"name\": \"a speedboat\"}, {\"id\": 27899, \"name\": \"a large, silver pillar\"}, {\"id\": 27900, \"name\": \"the aircraft\"}, {\"id\": 27901, \"name\": \"the gray bag\"}, {\"id\": 27902, \"name\": \"the curved ramp\"}, {\"id\": 27903, \"name\": \"round light\"}, {\"id\": 27904, \"name\": \"long gray coat\"}, {\"id\": 27905, \"name\": \"the maroon sleeve\"}, {\"id\": 27906, \"name\": \"the stump\"}, {\"id\": 27907, \"name\": \"bright stage light\"}, {\"id\": 27908, \"name\": \"the large, square metal piece\"}, {\"id\": 27909, \"name\": \"the diver's face\"}, {\"id\": 27910, \"name\": \"the orange-colored object\"}, {\"id\": 27911, \"name\": \"blue icon\"}, {\"id\": 27912, \"name\": \"a purple jacket\"}, {\"id\": 27913, \"name\": \"the post\"}, {\"id\": 27914, \"name\": \"the colorful arcade game\"}, {\"id\": 27915, \"name\": \"metal lever\"}, {\"id\": 27916, \"name\": \"the strainer or filter\"}, {\"id\": 27917, \"name\": \"a large, silver horn\"}, {\"id\": 27918, \"name\": \"blue, red, and white checkerboard pattern\"}, {\"id\": 27919, \"name\": \"red container\"}, {\"id\": 27920, \"name\": \"a make-shift machine\"}, {\"id\": 27921, \"name\": \"the orange wheel\"}, {\"id\": 27922, \"name\": \"the small screen or display\"}, {\"id\": 27923, \"name\": \"a muddy puddle\"}, {\"id\": 27924, \"name\": \"green napkin\"}, {\"id\": 27925, \"name\": \"beige apron\"}, {\"id\": 27926, \"name\": \"the colorful ride vehicle\"}, {\"id\": 27927, \"name\": \"the large, white sign\"}, {\"id\": 27928, \"name\": \"ride vehicle\"}, {\"id\": 27929, \"name\": \"black and silver balloon\"}, {\"id\": 27930, \"name\": \"a high visibility vest\"}, {\"id\": 27931, \"name\": \"a ride-on toy\"}, {\"id\": 27932, \"name\": \"brown cabin\"}, {\"id\": 27933, \"name\": \"the large orange traffic cone\"}, {\"id\": 27934, \"name\": \"an orange flags and banner\"}, {\"id\": 27935, \"name\": \"the prominent red, white, and blue flag\"}, {\"id\": 27936, \"name\": \"the diagonal beam\"}, {\"id\": 27937, \"name\": \"silver suitcase\"}, {\"id\": 27938, \"name\": \"a magenta jacket\"}, {\"id\": 27939, \"name\": \"the light blue center\"}, {\"id\": 27940, \"name\": \"the yellow nose\"}, {\"id\": 27941, \"name\": \"a children's toy\"}, {\"id\": 27942, \"name\": \"the blue apron\"}, {\"id\": 27943, \"name\": \"a small red circle\"}, {\"id\": 27944, \"name\": \"white fedex truck\"}, {\"id\": 27945, \"name\": \"the lush hillside\"}, {\"id\": 27946, \"name\": \"blue dumpster\"}, {\"id\": 27947, \"name\": \"mattress\"}, {\"id\": 27948, \"name\": \"large, silver ornament\"}, {\"id\": 27949, \"name\": \"the dark gray pickup truck\"}, {\"id\": 27950, \"name\": \"yellow taxi cab\"}, {\"id\": 27951, \"name\": \"large grey cement bag\"}, {\"id\": 27952, \"name\": \"the green hose pipe\"}, {\"id\": 27953, \"name\": \"distinctive green dome\"}, {\"id\": 27954, \"name\": \"the silver fan\"}, {\"id\": 27955, \"name\": \"the large gold skirt\"}, {\"id\": 27956, \"name\": \"a blue plastic stool\"}, {\"id\": 27957, \"name\": \"yellow bowl\"}, {\"id\": 27958, \"name\": \"a small white ceramic animal\"}, {\"id\": 27959, \"name\": \"green, toy soldier costume\"}, {\"id\": 27960, \"name\": \"the trombonist\"}, {\"id\": 27961, \"name\": \"the long, narrow, green and blue machine\"}, {\"id\": 27962, \"name\": \"fire personnel\"}, {\"id\": 27963, \"name\": \"an orange plastic pumpkin-shaped bucket\"}, {\"id\": 27964, \"name\": \"the closed door\"}, {\"id\": 27965, \"name\": \"stove's back burner\"}, {\"id\": 27966, \"name\": \"the stone walkway\"}, {\"id\": 27967, \"name\": \"decorative stick\"}, {\"id\": 27968, \"name\": \"the green auto rickshaw\"}, {\"id\": 27969, \"name\": \"the square icon\"}, {\"id\": 27970, \"name\": \"the pink floral shawl\"}, {\"id\": 27971, \"name\": \"the green column\"}, {\"id\": 27972, \"name\": \"blue canopy tent\"}, {\"id\": 27973, \"name\": \"the bright orange comb\"}, {\"id\": 27974, \"name\": \"tall smokestack\"}, {\"id\": 27975, \"name\": \"colorful floral pattern\"}, {\"id\": 27976, \"name\": \"the lens\"}, {\"id\": 27977, \"name\": \"blue teddy bear\"}, {\"id\": 27978, \"name\": \"a red slide\"}, {\"id\": 27979, \"name\": \"the blue storefront\"}, {\"id\": 27980, \"name\": \"the red and pink attire\"}, {\"id\": 27981, \"name\": \"hexagonal nut\"}, {\"id\": 27982, \"name\": \"the yellow sack\"}, {\"id\": 27983, \"name\": \"the tripod stand\"}, {\"id\": 27984, \"name\": \"a green band\"}, {\"id\": 27985, \"name\": \"the skull mask\"}, {\"id\": 27986, \"name\": \"a tallest structure\"}, {\"id\": 27987, \"name\": \"the short-sleeved, striped shirt\"}, {\"id\": 27988, \"name\": \"the brown, thatched-roof structure\"}, {\"id\": 27989, \"name\": \"gray platform\"}, {\"id\": 27990, \"name\": \"the red dot\"}, {\"id\": 27991, \"name\": \"a pajama\"}, {\"id\": 27992, \"name\": \"purple pool float\"}, {\"id\": 27993, \"name\": \"the blue snowflake\"}, {\"id\": 27994, \"name\": \"a flag of color\"}, {\"id\": 27995, \"name\": \"the yellow roof\"}, {\"id\": 27996, \"name\": \"the clear visor\"}, {\"id\": 27997, \"name\": \"black toy gun\"}, {\"id\": 27998, \"name\": \"the black and white motorcycle\"}, {\"id\": 27999, \"name\": \"a mickey mouse design\"}, {\"id\": 28000, \"name\": \"blue and white soccer ball\"}, {\"id\": 28001, \"name\": \"a monk's vibrant attire\"}, {\"id\": 28002, \"name\": \"large wooden stump\"}, {\"id\": 28003, \"name\": \"child's dress\"}, {\"id\": 28004, \"name\": \"a vibrant multicolored blouse\"}, {\"id\": 28005, \"name\": \"character's avatar\"}, {\"id\": 28006, \"name\": \"yellow and white shirt\"}, {\"id\": 28007, \"name\": \"the tall banner\"}, {\"id\": 28008, \"name\": \"the yellow hoodie\"}, {\"id\": 28009, \"name\": \"green cartoon frog\"}, {\"id\": 28010, \"name\": \"a man in an orange jacket\"}, {\"id\": 28011, \"name\": \"a red dot\"}, {\"id\": 28012, \"name\": \"large, wrapped mattress\"}, {\"id\": 28013, \"name\": \"a layer of fallen leaf\"}, {\"id\": 28014, \"name\": \"the yellow traffic signal\"}, {\"id\": 28015, \"name\": \"child's leg\"}, {\"id\": 28016, \"name\": \"white shopping bag\"}, {\"id\": 28017, \"name\": \"matching red and white shirt\"}, {\"id\": 28018, \"name\": \"a humanoid robot\"}, {\"id\": 28019, \"name\": \"large gray rock\"}, {\"id\": 28020, \"name\": \"body slide\"}, {\"id\": 28021, \"name\": \"yellow fan\"}, {\"id\": 28022, \"name\": \"the leopard-print tablecloth\"}, {\"id\": 28023, \"name\": \"dark blue t-shirt\"}, {\"id\": 28024, \"name\": \"a blurred light\"}, {\"id\": 28025, \"name\": \"the large yellow and red iron man head\"}, {\"id\": 28026, \"name\": \"a yellow archway\"}, {\"id\": 28027, \"name\": \"the rear of the scooter\"}, {\"id\": 28028, \"name\": \"a small character\"}, {\"id\": 28029, \"name\": \"the motor\"}, {\"id\": 28030, \"name\": \"race car graphic\"}, {\"id\": 28031, \"name\": \"a small tripod\"}, {\"id\": 28032, \"name\": \"yellow storefront\"}, {\"id\": 28033, \"name\": \"the large soundboard\"}, {\"id\": 28034, \"name\": \"high concrete barrier\"}, {\"id\": 28035, \"name\": \"yellow traffic cone\"}, {\"id\": 28036, \"name\": \"black shirt with a white pattern\"}, {\"id\": 28037, \"name\": \"the light-colored tail feather\"}, {\"id\": 28038, \"name\": \"a dark blue hull\"}, {\"id\": 28039, \"name\": \"the green strip\"}, {\"id\": 28040, \"name\": \"the small, white, round object\"}, {\"id\": 28041, \"name\": \"a vibrant multi-colored staircase\"}, {\"id\": 28042, \"name\": \"a curled tail\"}, {\"id\": 28043, \"name\": \"the red, yellow, and brown toy\"}, {\"id\": 28044, \"name\": \"green chalkboard\"}, {\"id\": 28045, \"name\": \"white hooded jacket\"}, {\"id\": 28046, \"name\": \"the dark-colored surface\"}, {\"id\": 28047, \"name\": \"the advertisement or announcement for an event\"}, {\"id\": 28048, \"name\": \"the man wearing a yellow shirt\"}, {\"id\": 28049, \"name\": \"the black shirt sleeve\"}, {\"id\": 28050, \"name\": \"a front of the truck\"}, {\"id\": 28051, \"name\": \"a grey helmet\"}, {\"id\": 28052, \"name\": \"a structure's roof\"}, {\"id\": 28053, \"name\": \"beige exterior\"}, {\"id\": 28054, \"name\": \"small white boat\"}, {\"id\": 28055, \"name\": \"a silver truck\"}, {\"id\": 28056, \"name\": \"the sea anemone\"}, {\"id\": 28057, \"name\": \"repeating red design\"}, {\"id\": 28058, \"name\": \"blue star emblem\"}, {\"id\": 28059, \"name\": \"a yellow logo\"}, {\"id\": 28060, \"name\": \"blue post\"}, {\"id\": 28061, \"name\": \"large metal box\"}, {\"id\": 28062, \"name\": \"a chalkboard sign\"}, {\"id\": 28063, \"name\": \"the small, round white object\"}, {\"id\": 28064, \"name\": \"a gold mane\"}, {\"id\": 28065, \"name\": \"white feathered tail\"}, {\"id\": 28066, \"name\": \"a barge\"}, {\"id\": 28067, \"name\": \"the green stuffed toy\"}, {\"id\": 28068, \"name\": \"concrete block\"}, {\"id\": 28069, \"name\": \"the blue cup\"}, {\"id\": 28070, \"name\": \"teal shirt\"}, {\"id\": 28071, \"name\": \"exercise mat\"}, {\"id\": 28072, \"name\": \"a blue metal fence\"}, {\"id\": 28073, \"name\": \"a red jewel\"}, {\"id\": 28074, \"name\": \"colorful pom-pom\"}, {\"id\": 28075, \"name\": \"rider in the red jacket\"}, {\"id\": 28076, \"name\": \"the yellow emblem\"}, {\"id\": 28077, \"name\": \"the prominent red traffic light\"}, {\"id\": 28078, \"name\": \"smiling mickey mouse head\"}, {\"id\": 28079, \"name\": \"the wheelie\"}, {\"id\": 28080, \"name\": \"sousaphone\"}, {\"id\": 28081, \"name\": \"gift bag\"}, {\"id\": 28082, \"name\": \"the yellow, airplane-shaped ride\"}, {\"id\": 28083, \"name\": \"a blurry arm\"}, {\"id\": 28084, \"name\": \"a crane's sign\"}, {\"id\": 28085, \"name\": \"the light brown hen\"}, {\"id\": 28086, \"name\": \"a red plastic lid\"}, {\"id\": 28087, \"name\": \"a player's health bar\"}, {\"id\": 28088, \"name\": \"individual in a red shirt\"}, {\"id\": 28089, \"name\": \"festive birthday cake\"}, {\"id\": 28090, \"name\": \"a pot of food\"}, {\"id\": 28091, \"name\": \"a brown bark\"}, {\"id\": 28092, \"name\": \"the young adult\"}, {\"id\": 28093, \"name\": \"the lead bus's headlights\"}, {\"id\": 28094, \"name\": \"a red hazard button\"}, {\"id\": 28095, \"name\": \"plate of bread\"}, {\"id\": 28096, \"name\": \"the green and black helmet\"}, {\"id\": 28097, \"name\": \"a smaller, green raft\"}, {\"id\": 28098, \"name\": \"dilapidated structure\"}, {\"id\": 28099, \"name\": \"gray curb\"}, {\"id\": 28100, \"name\": \"the blurry face\"}, {\"id\": 28101, \"name\": \"prominent red lantern\"}, {\"id\": 28102, \"name\": \"the small, yellow object\"}, {\"id\": 28103, \"name\": \"black wire cage\"}, {\"id\": 28104, \"name\": \"smaller piece of coral\"}, {\"id\": 28105, \"name\": \"a pinkish-white substance\"}, {\"id\": 28106, \"name\": \"large basket\"}, {\"id\": 28107, \"name\": \"stadium seating\"}, {\"id\": 28108, \"name\": \"large cooler\"}, {\"id\": 28109, \"name\": \"the large blue and orange train car\"}, {\"id\": 28110, \"name\": \"the cartoon monkey\"}, {\"id\": 28111, \"name\": \"paper roll\"}, {\"id\": 28112, \"name\": \"small red circle\"}, {\"id\": 28113, \"name\": \"middle truck\"}, {\"id\": 28114, \"name\": \"the red and white saddle blanket\"}, {\"id\": 28115, \"name\": \"green coral formation\"}, {\"id\": 28116, \"name\": \"the green gem\"}, {\"id\": 28117, \"name\": \"a stainles steel\"}, {\"id\": 28118, \"name\": \"the utility box\"}, {\"id\": 28119, \"name\": \"weightlifting bench\"}, {\"id\": 28120, \"name\": \"red and black bar\"}, {\"id\": 28121, \"name\": \"the saucer\"}, {\"id\": 28122, \"name\": \"a small house or shed\"}, {\"id\": 28123, \"name\": \"a reflective stripe\"}, {\"id\": 28124, \"name\": \"a pillow or bag\"}, {\"id\": 28125, \"name\": \"the white christma light\"}, {\"id\": 28126, \"name\": \"black and green car\"}, {\"id\": 28127, \"name\": \"the satellite dish\"}, {\"id\": 28128, \"name\": \"a red and white santa hat\"}, {\"id\": 28129, \"name\": \"white and black helmet\"}, {\"id\": 28130, \"name\": \"the taxi sign\"}, {\"id\": 28131, \"name\": \"a logo for kshb.com\"}, {\"id\": 28132, \"name\": \"an old monk logo\"}, {\"id\": 28133, \"name\": \"the news channel's logo\"}, {\"id\": 28134, \"name\": \"the purple jellyfish\"}, {\"id\": 28135, \"name\": \"a scissor\"}, {\"id\": 28136, \"name\": \"the suited man\"}, {\"id\": 28137, \"name\": \"the large wooden door\"}, {\"id\": 28138, \"name\": \"a puppet\"}, {\"id\": 28139, \"name\": \"the oar\"}, {\"id\": 28140, \"name\": \"the purple accent\"}, {\"id\": 28141, \"name\": \"a knight\"}, {\"id\": 28142, \"name\": \"beak\"}, {\"id\": 28143, \"name\": \"a teal sari\"}, {\"id\": 28144, \"name\": \"white angora rabbit\"}, {\"id\": 28145, \"name\": \"silver faucet\"}, {\"id\": 28146, \"name\": \"string\"}, {\"id\": 28147, \"name\": \"woman in a colorful dres with a skull design\"}, {\"id\": 28148, \"name\": \"a red attire\"}, {\"id\": 28149, \"name\": \"a rider's gloved hand\"}, {\"id\": 28150, \"name\": \"a vaping device\"}, {\"id\": 28151, \"name\": \"pink tank top\"}, {\"id\": 28152, \"name\": \"blue eye\"}, {\"id\": 28153, \"name\": \"small black antenna\"}, {\"id\": 28154, \"name\": \"blue and white wetsuit\"}, {\"id\": 28155, \"name\": \"the white toyota logo\"}, {\"id\": 28156, \"name\": \"large, black, open-front thong\"}, {\"id\": 28157, \"name\": \"the flashing light\"}, {\"id\": 28158, \"name\": \"white engine\"}, {\"id\": 28159, \"name\": \"track's surface\"}, {\"id\": 28160, \"name\": \"the large glass door\"}, {\"id\": 28161, \"name\": \"the khaki uniform\"}, {\"id\": 28162, \"name\": \"a yellow caption\"}, {\"id\": 28163, \"name\": \"clown face\"}, {\"id\": 28164, \"name\": \"bicycle logo\"}, {\"id\": 28165, \"name\": \"stainles steel oven\"}, {\"id\": 28166, \"name\": \"the arched doorway\"}, {\"id\": 28167, \"name\": \"a green accent\"}, {\"id\": 28168, \"name\": \"the name\"}, {\"id\": 28169, \"name\": \"the black and white attire\"}, {\"id\": 28170, \"name\": \"orange trim\"}, {\"id\": 28171, \"name\": \"the light-colored clothing\"}, {\"id\": 28172, \"name\": \"a musical note symbol\"}, {\"id\": 28173, \"name\": \"a rough and muddy surface\"}, {\"id\": 28174, \"name\": \"graphic of a horse's head\"}, {\"id\": 28175, \"name\": \"carousel\"}, {\"id\": 28176, \"name\": \"a blurred image of people riding segway\"}, {\"id\": 28177, \"name\": \"m pro racing logo\"}, {\"id\": 28178, \"name\": \"smaller logo\"}, {\"id\": 28179, \"name\": \"yellow buoy\"}, {\"id\": 28180, \"name\": \"green and red balloon\"}, {\"id\": 28181, \"name\": \"leafy top\"}, {\"id\": 28182, \"name\": \"a yellow train crossing sign\"}, {\"id\": 28183, \"name\": \"a fountain's edge\"}, {\"id\": 28184, \"name\": \"the yellow paint\"}, {\"id\": 28185, \"name\": \"rider's body\"}, {\"id\": 28186, \"name\": \"a yellow plastic support\"}, {\"id\": 28187, \"name\": \"glass case\"}, {\"id\": 28188, \"name\": \"a green shutter\"}, {\"id\": 28189, \"name\": \"a quarterpipe\"}, {\"id\": 28190, \"name\": \"a discarded cup\"}, {\"id\": 28191, \"name\": \"the black and red toy\"}, {\"id\": 28192, \"name\": \"a bright blue pool noodle\"}, {\"id\": 28193, \"name\": \"red pouch or bag\"}, {\"id\": 28194, \"name\": \"the black windshield\"}, {\"id\": 28195, \"name\": \"a black propeller\"}, {\"id\": 28196, \"name\": \"store sign\"}, {\"id\": 28197, \"name\": \"a light blue polo shirt\"}, {\"id\": 28198, \"name\": \"a blue and white checkered pattern\"}, {\"id\": 28199, \"name\": \"pink strap\"}, {\"id\": 28200, \"name\": \"a colorful dress\"}, {\"id\": 28201, \"name\": \"a white writing\"}, {\"id\": 28202, \"name\": \"decorative object\"}, {\"id\": 28203, \"name\": \"a red decoration\"}, {\"id\": 28204, \"name\": \"the diamond-shaped icon\"}, {\"id\": 28205, \"name\": \"the red and black chain saw\"}, {\"id\": 28206, \"name\": \"the red cardboard\"}, {\"id\": 28207, \"name\": \"the epaulet\"}, {\"id\": 28208, \"name\": \"a cyclist's number\"}, {\"id\": 28209, \"name\": \"the usa team\"}, {\"id\": 28210, \"name\": \"the beverage\"}, {\"id\": 28211, \"name\": \"canvas-covered cargo area\"}, {\"id\": 28212, \"name\": \"white cabinet door\"}, {\"id\": 28213, \"name\": \"the bold line\"}, {\"id\": 28214, \"name\": \"sponsor logo\"}, {\"id\": 28215, \"name\": \"the large bowl of fish\"}, {\"id\": 28216, \"name\": \"the large, leafy canopy\"}, {\"id\": 28217, \"name\": \"the woman in a white dress\"}, {\"id\": 28218, \"name\": \"a waiting area\"}, {\"id\": 28219, \"name\": \"the small plastic figurine\"}, {\"id\": 28220, \"name\": \"light blue lane\"}, {\"id\": 28221, \"name\": \"a fire truck\"}, {\"id\": 28222, \"name\": \"the open back door\"}, {\"id\": 28223, \"name\": \"the disposable coffee cup\"}, {\"id\": 28224, \"name\": \"mcdonald's restaurant\"}, {\"id\": 28225, \"name\": \"a small plastic container\"}, {\"id\": 28226, \"name\": \"the antler\"}, {\"id\": 28227, \"name\": \"dark brown mane\"}, {\"id\": 28228, \"name\": \"the hello kitty\"}, {\"id\": 28229, \"name\": \"red brick exterior\"}, {\"id\": 28230, \"name\": \"a assorted magic snake\"}, {\"id\": 28231, \"name\": \"white and grey patterned surface\"}, {\"id\": 28232, \"name\": \"the blue and white marking\"}, {\"id\": 28233, \"name\": \"wooden roof\"}, {\"id\": 28234, \"name\": \"the red eye\"}, {\"id\": 28235, \"name\": \"the white bristle\"}, {\"id\": 28236, \"name\": \"a tall beige structure\"}, {\"id\": 28237, \"name\": \"landing gear\"}, {\"id\": 28238, \"name\": \"the black and white mesh surface\"}, {\"id\": 28239, \"name\": \"muddy trail\"}, {\"id\": 28240, \"name\": \"the paper confetti piece\"}, {\"id\": 28241, \"name\": \"aircraft\"}, {\"id\": 28242, \"name\": \"a yellow soccer\"}, {\"id\": 28243, \"name\": \"the ski lift tower\"}, {\"id\": 28244, \"name\": \"gray jumpsuit\"}, {\"id\": 28245, \"name\": \"the rider's left hand\"}, {\"id\": 28246, \"name\": \"the large, tan-colored exterior\"}, {\"id\": 28247, \"name\": \"a light blue banner\"}, {\"id\": 28248, \"name\": \"the logo's design\"}, {\"id\": 28249, \"name\": \"the large gray tent\"}, {\"id\": 28250, \"name\": \"a field of green grass\"}, {\"id\": 28251, \"name\": \"a long dreadlock\"}, {\"id\": 28252, \"name\": \"the yellow embroidery\"}, {\"id\": 28253, \"name\": \"rice sack\"}, {\"id\": 28254, \"name\": \"the light from the pole\"}, {\"id\": 28255, \"name\": \"the purple and white uniform\"}, {\"id\": 28256, \"name\": \"large white awning\"}, {\"id\": 28257, \"name\": \"mountain biker\"}, {\"id\": 28258, \"name\": \"a throttle\"}, {\"id\": 28259, \"name\": \"blue border\"}, {\"id\": 28260, \"name\": \"gold tinsel\"}, {\"id\": 28261, \"name\": \"the red and green scarf\"}, {\"id\": 28262, \"name\": \"the tent canopy\"}, {\"id\": 28263, \"name\": \"a small, plastic doll\"}, {\"id\": 28264, \"name\": \"a clear plastic tray\"}, {\"id\": 28265, \"name\": \"green logo\"}, {\"id\": 28266, \"name\": \"flat, rectangular deck\"}, {\"id\": 28267, \"name\": \"white and black backdrop\"}, {\"id\": 28268, \"name\": \"the shiny, reflective surface\"}, {\"id\": 28269, \"name\": \"exposed ductwork\"}, {\"id\": 28270, \"name\": \"a teal bike pant\"}, {\"id\": 28271, \"name\": \"the black outline\"}, {\"id\": 28272, \"name\": \"semi-trailer truck\"}, {\"id\": 28273, \"name\": \"the matching blue shirt\"}, {\"id\": 28274, \"name\": \"swirling white pattern\"}, {\"id\": 28275, \"name\": \"stretcher\"}, {\"id\": 28276, \"name\": \"the thick black border\"}, {\"id\": 28277, \"name\": \"a striking green motorcycle\"}, {\"id\": 28278, \"name\": \"yellow plastic object\"}, {\"id\": 28279, \"name\": \"woman in a blue outfit\"}, {\"id\": 28280, \"name\": \"a blue toy\"}, {\"id\": 28281, \"name\": \"a white signature\"}, {\"id\": 28282, \"name\": \"black and silver frame\"}, {\"id\": 28283, \"name\": \"rear license plate\"}, {\"id\": 28284, \"name\": \"the black, tree-like structure\"}, {\"id\": 28285, \"name\": \"shadow of the aircraft\"}, {\"id\": 28286, \"name\": \"a red balcony\"}, {\"id\": 28287, \"name\": \"a license plate fhe 7022\"}, {\"id\": 28288, \"name\": \"the dark window\"}, {\"id\": 28289, \"name\": \"the lapel\"}, {\"id\": 28290, \"name\": \"decal\"}, {\"id\": 28291, \"name\": \"a circular logo\"}, {\"id\": 28292, \"name\": \"white decal\"}, {\"id\": 28293, \"name\": \"black script\"}, {\"id\": 28294, \"name\": \"the silver robot\"}, {\"id\": 28295, \"name\": \"players' name\"}, {\"id\": 28296, \"name\": \"lace pattern\"}, {\"id\": 28297, \"name\": \"neon green uniform\"}, {\"id\": 28298, \"name\": \"long, thin, black body\"}, {\"id\": 28299, \"name\": \"the commercial establishment\"}, {\"id\": 28300, \"name\": \"the thick frame\"}, {\"id\": 28301, \"name\": \"yellow block\"}, {\"id\": 28302, \"name\": \"the light brown base\"}, {\"id\": 28303, \"name\": \"a blurred view of the road\"}, {\"id\": 28304, \"name\": \"the blue and black costume\"}, {\"id\": 28305, \"name\": \"the black spoiler\"}, {\"id\": 28306, \"name\": \"a yellow arrow\"}, {\"id\": 28307, \"name\": \"a wooden log obstacle\"}, {\"id\": 28308, \"name\": \"a tan sectional couch\"}, {\"id\": 28309, \"name\": \"the large fairy wing\"}, {\"id\": 28310, \"name\": \"a silver stripe\"}, {\"id\": 28311, \"name\": \"brown and black legging\"}, {\"id\": 28312, \"name\": \"the pink ornament\"}, {\"id\": 28313, \"name\": \"the card of baby accessory\"}, {\"id\": 28314, \"name\": \"a white race bib\"}, {\"id\": 28315, \"name\": \"a rider's bike handlebar\"}, {\"id\": 28316, \"name\": \"a visually appealing display\"}, {\"id\": 28317, \"name\": \"a black shelf\"}, {\"id\": 28318, \"name\": \"a scrambler motorcycle\"}, {\"id\": 28319, \"name\": \"a wooden stake\"}, {\"id\": 28320, \"name\": \"yellow pant\"}, {\"id\": 28321, \"name\": \"a yellow eight\"}, {\"id\": 28322, \"name\": \"the grey material\"}, {\"id\": 28323, \"name\": \"red metal bar\"}, {\"id\": 28324, \"name\": \"the pink marking\"}, {\"id\": 28325, \"name\": \"small, two-door hatchback\"}, {\"id\": 28326, \"name\": \"the long white tusk\"}, {\"id\": 28327, \"name\": \"a gray carpet\"}, {\"id\": 28328, \"name\": \"yellow label\"}, {\"id\": 28329, \"name\": \"a black plastic component\"}, {\"id\": 28330, \"name\": \"the large screen or tv\"}, {\"id\": 28331, \"name\": \"a blue mark\"}, {\"id\": 28332, \"name\": \"teal interior\"}, {\"id\": 28333, \"name\": \"of car\"}, {\"id\": 28334, \"name\": \"a plastic toy\"}, {\"id\": 28335, \"name\": \"white handlebar\"}, {\"id\": 28336, \"name\": \"a yellow cabinet\"}, {\"id\": 28337, \"name\": \"the guitar amplifier\"}, {\"id\": 28338, \"name\": \"a large inflatable cactu\"}, {\"id\": 28339, \"name\": \"a corkscrew design\"}, {\"id\": 28340, \"name\": \"the small brown or orange tag\"}, {\"id\": 28341, \"name\": \"shadow of the hand\"}, {\"id\": 28342, \"name\": \"lawn mower\"}, {\"id\": 28343, \"name\": \"yellow star\"}, {\"id\": 28344, \"name\": \"the screenshot\"}, {\"id\": 28345, \"name\": \"the headband\"}, {\"id\": 28346, \"name\": \"a vibrant yellow headscarf\"}, {\"id\": 28347, \"name\": \"surprise daily logo\"}, {\"id\": 28348, \"name\": \"camouflage uniform\"}, {\"id\": 28349, \"name\": \"a child's face\"}, {\"id\": 28350, \"name\": \"the hello kitty head\"}, {\"id\": 28351, \"name\": \"the ace angler\"}, {\"id\": 28352, \"name\": \"a light blue stripe\"}, {\"id\": 28353, \"name\": \"the driver's leg\"}, {\"id\": 28354, \"name\": \"the red brick archway\"}, {\"id\": 28355, \"name\": \"the bib\"}, {\"id\": 28356, \"name\": \"green crayon\"}, {\"id\": 28357, \"name\": \"a gift bag\"}, {\"id\": 28358, \"name\": \"a white horn\"}, {\"id\": 28359, \"name\": \"gold cap\"}, {\"id\": 28360, \"name\": \"the red can of soup\"}, {\"id\": 28361, \"name\": \"the thick layer of dust\"}, {\"id\": 28362, \"name\": \"a barbie\"}, {\"id\": 28363, \"name\": \"a conveyor belt\"}, {\"id\": 28364, \"name\": \"a tail of a chariot\"}, {\"id\": 28365, \"name\": \"patch of wet grass\"}, {\"id\": 28366, \"name\": \"the plate of spaghetti and meat sauce\"}, {\"id\": 28367, \"name\": \"a front basket\"}, {\"id\": 28368, \"name\": \"the orange title\"}, {\"id\": 28369, \"name\": \"a silver stanchion\"}, {\"id\": 28370, \"name\": \"the man in a brown vest and jean\"}, {\"id\": 28371, \"name\": \"a woman in a dress\"}, {\"id\": 28372, \"name\": \"a military personnel\"}, {\"id\": 28373, \"name\": \"the rope bridge\"}, {\"id\": 28374, \"name\": \"a burning vehicle\"}, {\"id\": 28375, \"name\": \"the lime green shirt\"}, {\"id\": 28376, \"name\": \"black grille\"}, {\"id\": 28377, \"name\": \"a circular marker\"}, {\"id\": 28378, \"name\": \"the female character\"}, {\"id\": 28379, \"name\": \"a large white house\"}, {\"id\": 28380, \"name\": \"a blurred view of a shelf\"}, {\"id\": 28381, \"name\": \"the green plastic object\"}, {\"id\": 28382, \"name\": \"the white snowflake design\"}, {\"id\": 28383, \"name\": \"a barn or stable\"}, {\"id\": 28384, \"name\": \"a machine's body\"}, {\"id\": 28385, \"name\": \"the light-colored headscarf\"}, {\"id\": 28386, \"name\": \"a red and blue writing\"}, {\"id\": 28387, \"name\": \"the snow-covered ground\"}, {\"id\": 28388, \"name\": \"the blue knee-high sock\"}, {\"id\": 28389, \"name\": \"red 17\"}, {\"id\": 28390, \"name\": \"a market stall\"}, {\"id\": 28391, \"name\": \"a yellow safety jacket\"}, {\"id\": 28392, \"name\": \"a gold shoe\"}, {\"id\": 28393, \"name\": \"a yellow design\"}, {\"id\": 28394, \"name\": \"pink handle\"}, {\"id\": 28395, \"name\": \"a window or partition\"}, {\"id\": 28396, \"name\": \"a white bike\"}, {\"id\": 28397, \"name\": \"owner\"}, {\"id\": 28398, \"name\": \"large pink ball\"}, {\"id\": 28399, \"name\": \"the tripadvisor banner\"}, {\"id\": 28400, \"name\": \"the decorative metalwork\"}, {\"id\": 28401, \"name\": \"the black lace sleeve\"}, {\"id\": 28402, \"name\": \"side door\"}, {\"id\": 28403, \"name\": \"the navy blue zip-up hoodie\"}, {\"id\": 28404, \"name\": \"the glowing yellow eye\"}, {\"id\": 28405, \"name\": \"the black bracket\"}, {\"id\": 28406, \"name\": \"boston red sox jersey\"}, {\"id\": 28407, \"name\": \"a teal\"}, {\"id\": 28408, \"name\": \"a blue marking\"}, {\"id\": 28409, \"name\": \"black metal fencing\"}, {\"id\": 28410, \"name\": \"the blurred figure\"}, {\"id\": 28411, \"name\": \"the dark blue banner\"}, {\"id\": 28412, \"name\": \"the large furnace or forge\"}, {\"id\": 28413, \"name\": \"large white inflatable swan\"}, {\"id\": 28414, \"name\": \"a yellow patch\"}, {\"id\": 28415, \"name\": \"the chin strap\"}, {\"id\": 28416, \"name\": \"a two-lane\"}, {\"id\": 28417, \"name\": \"the small yellow sign\"}, {\"id\": 28418, \"name\": \"a yellow and green shirt\"}, {\"id\": 28419, \"name\": \"the light blue skin\"}, {\"id\": 28420, \"name\": \"bright yellow and black color scheme\"}, {\"id\": 28421, \"name\": \"a silver tag\"}, {\"id\": 28422, \"name\": \"the small chimney\"}, {\"id\": 28423, \"name\": \"a pink elephant\"}, {\"id\": 28424, \"name\": \"small patch of green grass\"}, {\"id\": 28425, \"name\": \"a red bike\"}, {\"id\": 28426, \"name\": \"a white dove logo\"}, {\"id\": 28427, \"name\": \"wide, black asphalt surface\"}, {\"id\": 28428, \"name\": \"white 6\"}, {\"id\": 28429, \"name\": \"a black ankle boot\"}, {\"id\": 28430, \"name\": \"a person in a white jacket\"}, {\"id\": 28431, \"name\": \"red sleeve\"}, {\"id\": 28432, \"name\": \"white spot\"}, {\"id\": 28433, \"name\": \"a large gray wheel\"}, {\"id\": 28434, \"name\": \"a black arrow\"}, {\"id\": 28435, \"name\": \"the small bush\"}, {\"id\": 28436, \"name\": \"gentle hand\"}, {\"id\": 28437, \"name\": \"the low, white, horizontal barrier\"}, {\"id\": 28438, \"name\": \"neon green helmet\"}, {\"id\": 28439, \"name\": \"the yellow buoy\"}, {\"id\": 28440, \"name\": \"the long-sleeved\"}, {\"id\": 28441, \"name\": \"a rider's right hand\"}, {\"id\": 28442, \"name\": \"gear-like shape\"}, {\"id\": 28443, \"name\": \"the green harness\"}, {\"id\": 28444, \"name\": \"a large stone statue\"}, {\"id\": 28445, \"name\": \"red canoe\"}, {\"id\": 28446, \"name\": \"a top panel\"}, {\"id\": 28447, \"name\": \"a yellow boxcar\"}, {\"id\": 28448, \"name\": \"the large blue eye\"}, {\"id\": 28449, \"name\": \"gold-colored design\"}, {\"id\": 28450, \"name\": \"portable stove\"}, {\"id\": 28451, \"name\": \"a checkered floor\"}, {\"id\": 28452, \"name\": \"the large brown ball\"}, {\"id\": 28453, \"name\": \"prominent black tower\"}, {\"id\": 28454, \"name\": \"the brown tiled floor\"}, {\"id\": 28455, \"name\": \"a green frog-shaped balloon\"}, {\"id\": 28456, \"name\": \"red lawn mower\"}, {\"id\": 28457, \"name\": \"a strappy heel\"}, {\"id\": 28458, \"name\": \"red spot\"}, {\"id\": 28459, \"name\": \"a silver latch\"}, {\"id\": 28460, \"name\": \"a food packaging\"}, {\"id\": 28461, \"name\": \"circular black metal casing\"}, {\"id\": 28462, \"name\": \"the vibrant purple design\"}, {\"id\": 28463, \"name\": \"a front windshield\"}, {\"id\": 28464, \"name\": \"team in white\"}, {\"id\": 28465, \"name\": \"white metal stand\"}, {\"id\": 28466, \"name\": \"the broken front bumper\"}, {\"id\": 28467, \"name\": \"a silver metal tray\"}, {\"id\": 28468, \"name\": \"the black and white spot\"}, {\"id\": 28469, \"name\": \"bowling pin\"}, {\"id\": 28470, \"name\": \"yellow dragon\"}, {\"id\": 28471, \"name\": \"country's emblem\"}, {\"id\": 28472, \"name\": \"the white bouquet\"}, {\"id\": 28473, \"name\": \"tattered clothing\"}, {\"id\": 28474, \"name\": \"the matching black baseball cap\"}, {\"id\": 28475, \"name\": \"a front fork\"}, {\"id\": 28476, \"name\": \"a silver bumper\"}, {\"id\": 28477, \"name\": \"image of orange\"}, {\"id\": 28478, \"name\": \"black and silver handlebar\"}, {\"id\": 28479, \"name\": \"the front left wheel\"}, {\"id\": 28480, \"name\": \"a tuk-tuk driver\"}, {\"id\": 28481, \"name\": \"a lego-themed game\"}, {\"id\": 28482, \"name\": \"good\"}, {\"id\": 28483, \"name\": \"large black stripe\"}, {\"id\": 28484, \"name\": \"the yellow shelf\"}, {\"id\": 28485, \"name\": \"red marker\"}, {\"id\": 28486, \"name\": \"red platform\"}, {\"id\": 28487, \"name\": \"the orange construction barrier\"}, {\"id\": 28488, \"name\": \"the black bollard\"}, {\"id\": 28489, \"name\": \"the white switch plate\"}, {\"id\": 28490, \"name\": \"white horn\"}, {\"id\": 28491, \"name\": \"red table\"}, {\"id\": 28492, \"name\": \"the green hillside\"}, {\"id\": 28493, \"name\": \"a green and white banner\"}, {\"id\": 28494, \"name\": \"a dark blue marking\"}, {\"id\": 28495, \"name\": \"the orange hand\"}, {\"id\": 28496, \"name\": \"gray overhang\"}, {\"id\": 28497, \"name\": \"muddy area\"}, {\"id\": 28498, \"name\": \"a logo resembling a spiral\"}, {\"id\": 28499, \"name\": \"pink umbrella\"}, {\"id\": 28500, \"name\": \"central sign\"}, {\"id\": 28501, \"name\": \"the red and white stripe\"}, {\"id\": 28502, \"name\": \"the yellow 61\"}, {\"id\": 28503, \"name\": \"the cracker\"}, {\"id\": 28504, \"name\": \"an orange spot\"}, {\"id\": 28505, \"name\": \"the ability option\"}, {\"id\": 28506, \"name\": \"a cross-body bag\"}, {\"id\": 28507, \"name\": \"the orange decoration\"}, {\"id\": 28508, \"name\": \"patio heater\"}, {\"id\": 28509, \"name\": \"red team's jersey\"}, {\"id\": 28510, \"name\": \"bag of snack\"}, {\"id\": 28511, \"name\": \"the red wig\"}, {\"id\": 28512, \"name\": \"white safety barrier\"}, {\"id\": 28513, \"name\": \"the large white eye\"}, {\"id\": 28514, \"name\": \"a beige hangar\"}, {\"id\": 28515, \"name\": \"the small colored circle\"}, {\"id\": 28516, \"name\": \"the cluttered surface\"}, {\"id\": 28517, \"name\": \"the white plastic barrier\"}, {\"id\": 28518, \"name\": \"the glas barrier\"}, {\"id\": 28519, \"name\": \"the orange-and-white striped ball\"}, {\"id\": 28520, \"name\": \"the brown paneling\"}, {\"id\": 28521, \"name\": \"the thin gold necklace\"}, {\"id\": 28522, \"name\": \"a red rollerblade\"}, {\"id\": 28523, \"name\": \"purple paper flower\"}, {\"id\": 28524, \"name\": \"a gopro\"}, {\"id\": 28525, \"name\": \"a table's surface\"}, {\"id\": 28526, \"name\": \"the blue outline\"}, {\"id\": 28527, \"name\": \"large ice cream cone\"}, {\"id\": 28528, \"name\": \"neon yellow accent\"}, {\"id\": 28529, \"name\": \"a blurred airport runway\"}, {\"id\": 28530, \"name\": \"the modern, multi-story structure\"}, {\"id\": 28531, \"name\": \"oxygen\"}, {\"id\": 28532, \"name\": \"a pink sash\"}, {\"id\": 28533, \"name\": \"the large arrow\"}, {\"id\": 28534, \"name\": \"a large crevasse\"}, {\"id\": 28535, \"name\": \"a red short\"}, {\"id\": 28536, \"name\": \"residential unit\"}, {\"id\": 28537, \"name\": \"garage interior\"}, {\"id\": 28538, \"name\": \"a large chrome bumper\"}, {\"id\": 28539, \"name\": \"temperature reading\"}, {\"id\": 28540, \"name\": \"yoga pose\"}, {\"id\": 28541, \"name\": \"white jumpsuit\"}, {\"id\": 28542, \"name\": \"a turtle sculpture\"}, {\"id\": 28543, \"name\": \"brick facade\"}, {\"id\": 28544, \"name\": \"a shuttlecock\"}, {\"id\": 28545, \"name\": \"flashing light\"}, {\"id\": 28546, \"name\": \"a colorful lego city\"}, {\"id\": 28547, \"name\": \"a yellow princess dress\"}, {\"id\": 28548, \"name\": \"the jewel\"}, {\"id\": 28549, \"name\": \"the game's control panel\"}, {\"id\": 28550, \"name\": \"a man in a yellow hoodie\"}, {\"id\": 28551, \"name\": \"a red belt\"}, {\"id\": 28552, \"name\": \"the white scrub\"}, {\"id\": 28553, \"name\": \"intricate stone carving\"}, {\"id\": 28554, \"name\": \"a large piece of cardboard\"}, {\"id\": 28555, \"name\": \"the large black pole\"}, {\"id\": 28556, \"name\": \"oval-shaped track\"}, {\"id\": 28557, \"name\": \"arm tattoo\"}, {\"id\": 28558, \"name\": \"a tree-lined area\"}, {\"id\": 28559, \"name\": \"cream-colored table\"}, {\"id\": 28560, \"name\": \"a top of the machine\"}, {\"id\": 28561, \"name\": \"hook\"}, {\"id\": 28562, \"name\": \"a blue ball pit\"}, {\"id\": 28563, \"name\": \"the child's right hand\"}, {\"id\": 28564, \"name\": \"small orange traffic cone\"}, {\"id\": 28565, \"name\": \"green strip of grass\"}, {\"id\": 28566, \"name\": \"a splatoon\"}, {\"id\": 28567, \"name\": \"the purple stroller\"}, {\"id\": 28568, \"name\": \"lock\"}, {\"id\": 28569, \"name\": \"a yellow beam\"}, {\"id\": 28570, \"name\": \"the large grill\"}, {\"id\": 28571, \"name\": \"the green rectangle\"}, {\"id\": 28572, \"name\": \"colorful pattern\"}, {\"id\": 28573, \"name\": \"a facility\"}, {\"id\": 28574, \"name\": \"the twine\"}, {\"id\": 28575, \"name\": \"a black glass\"}, {\"id\": 28576, \"name\": \"a yellow-painted curb\"}, {\"id\": 28577, \"name\": \"the beige bag\"}, {\"id\": 28578, \"name\": \"a large black basket\"}, {\"id\": 28579, \"name\": \"brown animal head\"}, {\"id\": 28580, \"name\": \"a large stuffed animal\"}, {\"id\": 28581, \"name\": \"black paint splatter design\"}, {\"id\": 28582, \"name\": \"the large, cylindrical pipe\"}, {\"id\": 28583, \"name\": \"white metal frame\"}, {\"id\": 28584, \"name\": \"a glowing eye\"}, {\"id\": 28585, \"name\": \"the face shield\"}, {\"id\": 28586, \"name\": \"jcb logo\"}, {\"id\": 28587, \"name\": \"yellow jumpsuit\"}, {\"id\": 28588, \"name\": \"the shield\"}, {\"id\": 28589, \"name\": \"a navy blue sock\"}, {\"id\": 28590, \"name\": \"a thick black cable\"}, {\"id\": 28591, \"name\": \"the white and purple line\"}, {\"id\": 28592, \"name\": \"superhero\"}, {\"id\": 28593, \"name\": \"black lever\"}, {\"id\": 28594, \"name\": \"the red, white, and blue car\"}, {\"id\": 28595, \"name\": \"a ferri wheel\"}, {\"id\": 28596, \"name\": \"racecar\"}, {\"id\": 28597, \"name\": \"a police station\"}, {\"id\": 28598, \"name\": \"broken windshield\"}, {\"id\": 28599, \"name\": \"the sandy terrain\"}, {\"id\": 28600, \"name\": \"a neon green cleat\"}, {\"id\": 28601, \"name\": \"the white surfboard\"}, {\"id\": 28602, \"name\": \"a wire basket\"}, {\"id\": 28603, \"name\": \"the horse's harness\"}, {\"id\": 28604, \"name\": \"silver buckle\"}, {\"id\": 28605, \"name\": \"the blue panel\"}, {\"id\": 28606, \"name\": \"an entrance of a store\"}, {\"id\": 28607, \"name\": \"a blood-stained pant\"}, {\"id\": 28608, \"name\": \"yellow and black license plate\"}, {\"id\": 28609, \"name\": \"a circular button\"}, {\"id\": 28610, \"name\": \"the blue sign with chinese character and a diamond symbol\"}, {\"id\": 28611, \"name\": \"a black color\"}, {\"id\": 28612, \"name\": \"a brown house\"}, {\"id\": 28613, \"name\": \"the small sailboat\"}, {\"id\": 28614, \"name\": \"the track or racing surface\"}, {\"id\": 28615, \"name\": \"surrounding grass\"}, {\"id\": 28616, \"name\": \"the white and black hull\"}, {\"id\": 28617, \"name\": \"flame\"}, {\"id\": 28618, \"name\": \"teal color\"}, {\"id\": 28619, \"name\": \"an orange and blue color scheme\"}, {\"id\": 28620, \"name\": \"long tunic\"}, {\"id\": 28621, \"name\": \"the motorcycle's handlebar\"}, {\"id\": 28622, \"name\": \"the gold spike\"}, {\"id\": 28623, \"name\": \"the minion character\"}, {\"id\": 28624, \"name\": \"the white tank\"}, {\"id\": 28625, \"name\": \"a large brown pipe\"}, {\"id\": 28626, \"name\": \"a red and green cherry design\"}, {\"id\": 28627, \"name\": \"pink fingernail\"}, {\"id\": 28628, \"name\": \"yellow caution sign\"}, {\"id\": 28629, \"name\": \"white mesh back\"}, {\"id\": 28630, \"name\": \"cylinder head\"}, {\"id\": 28631, \"name\": \"baltimore raven logo\"}, {\"id\": 28632, \"name\": \"a gold balloon\"}, {\"id\": 28633, \"name\": \"a red and gold accent\"}, {\"id\": 28634, \"name\": \"a red diamond-shaped decal\"}, {\"id\": 28635, \"name\": \"draft king\"}, {\"id\": 28636, \"name\": \"desert mountain\"}, {\"id\": 28637, \"name\": \"a yellow tenni ball\"}, {\"id\": 28638, \"name\": \"a small white circle\"}, {\"id\": 28639, \"name\": \"large antenna\"}, {\"id\": 28640, \"name\": \"a white toy\"}, {\"id\": 28641, \"name\": \"the harry winston store\"}, {\"id\": 28642, \"name\": \"a cockpit\"}, {\"id\": 28643, \"name\": \"a lego ninjago movie\"}, {\"id\": 28644, \"name\": \"paved\"}, {\"id\": 28645, \"name\": \"a facebook logo\"}, {\"id\": 28646, \"name\": \"a rider's helmet\"}, {\"id\": 28647, \"name\": \"a black stem\"}, {\"id\": 28648, \"name\": \"the person in a yellow safety suit\"}, {\"id\": 28649, \"name\": \"a thin red and black bar\"}, {\"id\": 28650, \"name\": \"a white and red label\"}, {\"id\": 28651, \"name\": \"segway's black wheel\"}, {\"id\": 28652, \"name\": \"a rower's face\"}, {\"id\": 28653, \"name\": \"kite's lines\"}, {\"id\": 28654, \"name\": \"a black lid\"}, {\"id\": 28655, \"name\": \"the rocky trail\"}, {\"id\": 28656, \"name\": \"white paddleboard\"}, {\"id\": 28657, \"name\": \"the white sleeve\"}, {\"id\": 28658, \"name\": \"a minion graphic\"}, {\"id\": 28659, \"name\": \"rectangular card\"}, {\"id\": 28660, \"name\": \"parked bicycle\"}, {\"id\": 28661, \"name\": \"white and black baseball cap\"}, {\"id\": 28662, \"name\": \"the lego creation\"}, {\"id\": 28663, \"name\": \"vibrant orange shirt\"}, {\"id\": 28664, \"name\": \"the antennae\"}, {\"id\": 28665, \"name\": \"motorcycle's handlebars\"}, {\"id\": 28666, \"name\": \"wide, open mouth\"}, {\"id\": 28667, \"name\": \"a thin white line\"}, {\"id\": 28668, \"name\": \"a lock or gate\"}, {\"id\": 28669, \"name\": \"white peg\"}, {\"id\": 28670, \"name\": \"the black arm\"}, {\"id\": 28671, \"name\": \"small, teal-colored vase\"}, {\"id\": 28672, \"name\": \"black jumpsuit\"}, {\"id\": 28673, \"name\": \"small baby\"}, {\"id\": 28674, \"name\": \"yellow band\"}, {\"id\": 28675, \"name\": \"a black switch\"}, {\"id\": 28676, \"name\": \"long leg\"}, {\"id\": 28677, \"name\": \"the dirt and debris\"}, {\"id\": 28678, \"name\": \"large metal dumpster\"}, {\"id\": 28679, \"name\": \"a white polka dot pattern\"}, {\"id\": 28680, \"name\": \"purple character\"}, {\"id\": 28681, \"name\": \"a green patch\"}, {\"id\": 28682, \"name\": \"the small bale of hay\"}, {\"id\": 28683, \"name\": \"highway's median\"}, {\"id\": 28684, \"name\": \"cup of breaded and fried cheese stick\"}, {\"id\": 28685, \"name\": \"dark-colored go-kart\"}, {\"id\": 28686, \"name\": \"a dark-colored hat\"}, {\"id\": 28687, \"name\": \"subtitle\"}, {\"id\": 28688, \"name\": \"the orange, yellow, and gray cloud\"}, {\"id\": 28689, \"name\": \"a fried pickle\"}, {\"id\": 28690, \"name\": \"the rudder\"}, {\"id\": 28691, \"name\": \"a gold hardware\"}, {\"id\": 28692, \"name\": \"a purple bib\"}, {\"id\": 28693, \"name\": \"garage\"}, {\"id\": 28694, \"name\": \"the decorative molding pattern\"}, {\"id\": 28695, \"name\": \"a thick layer of snow\"}, {\"id\": 28696, \"name\": \"side of the train\"}, {\"id\": 28697, \"name\": \"a phrase\"}, {\"id\": 28698, \"name\": \"matching gold headpiece\"}, {\"id\": 28699, \"name\": \"a dry dock\"}, {\"id\": 28700, \"name\": \"wetsuit\"}, {\"id\": 28701, \"name\": \"the robot\"}, {\"id\": 28702, \"name\": \"the rider's black sleeve\"}, {\"id\": 28703, \"name\": \"rainbow border\"}, {\"id\": 28704, \"name\": \"the white skate\"}, {\"id\": 28705, \"name\": \"the white sail\"}, {\"id\": 28706, \"name\": \"white shade\"}, {\"id\": 28707, \"name\": \"distinctive spire\"}, {\"id\": 28708, \"name\": \"an interior of the container\"}, {\"id\": 28709, \"name\": \"a steep trail\"}, {\"id\": 28710, \"name\": \"the abc good morning america\"}, {\"id\": 28711, \"name\": \"the small, green structure\"}, {\"id\": 28712, \"name\": \"red, white, and blue logo\"}, {\"id\": 28713, \"name\": \"a yellow face\"}, {\"id\": 28714, \"name\": \"blurry object\"}, {\"id\": 28715, \"name\": \"the red lamppost\"}, {\"id\": 28716, \"name\": \"the dirt section\"}, {\"id\": 28717, \"name\": \"camping setting\"}, {\"id\": 28718, \"name\": \"the ride's blue light\"}, {\"id\": 28719, \"name\": \"an adidas\"}, {\"id\": 28720, \"name\": \"the green and red accent\"}, {\"id\": 28721, \"name\": \"translucent strip\"}, {\"id\": 28722, \"name\": \"white and blue sport bra\"}, {\"id\": 28723, \"name\": \"the niner\"}, {\"id\": 28724, \"name\": \"a ground level of the barn\"}, {\"id\": 28725, \"name\": \"the metal pot lid\"}, {\"id\": 28726, \"name\": \"a green stripe\"}, {\"id\": 28727, \"name\": \"the bright green and purple seat\"}, {\"id\": 28728, \"name\": \"a matching black shirt\"}, {\"id\": 28729, \"name\": \"a high-visibility safety vest\"}, {\"id\": 28730, \"name\": \"a black and green stripe\"}, {\"id\": 28731, \"name\": \"the palm\"}, {\"id\": 28732, \"name\": \"a light-colored dirt\"}, {\"id\": 28733, \"name\": \"the stone facade\"}, {\"id\": 28734, \"name\": \"white button\"}, {\"id\": 28735, \"name\": \"the sharp knife\"}, {\"id\": 28736, \"name\": \"the large, round base\"}, {\"id\": 28737, \"name\": \"tub's rim\"}, {\"id\": 28738, \"name\": \"the cartoon train\"}, {\"id\": 28739, \"name\": \"the small, round knob\"}, {\"id\": 28740, \"name\": \"a british\"}, {\"id\": 28741, \"name\": \"the metal piece\"}, {\"id\": 28742, \"name\": \"a small yellow light\"}, {\"id\": 28743, \"name\": \"a six flag\"}, {\"id\": 28744, \"name\": \"a black drawstring\"}, {\"id\": 28745, \"name\": \"a single car\"}, {\"id\": 28746, \"name\": \"purple flower\"}, {\"id\": 28747, \"name\": \"a center console\"}, {\"id\": 28748, \"name\": \"striking yellow stripe\"}, {\"id\": 28749, \"name\": \"machine's component\"}, {\"id\": 28750, \"name\": \"wooden platform\"}, {\"id\": 28751, \"name\": \"a table or countertop\"}, {\"id\": 28752, \"name\": \"large open mouth\"}, {\"id\": 28753, \"name\": \"the central bolt\"}, {\"id\": 28754, \"name\": \"a white bike frame\"}, {\"id\": 28755, \"name\": \"a black wooden fence\"}, {\"id\": 28756, \"name\": \"the brake lever\"}, {\"id\": 28757, \"name\": \"maroon top\"}, {\"id\": 28758, \"name\": \"dark-colored sedan\"}, {\"id\": 28759, \"name\": \"the yellow cord or wire\"}, {\"id\": 28760, \"name\": \"a foam\"}, {\"id\": 28761, \"name\": \"the right lane\"}, {\"id\": 28762, \"name\": \"a distant traffic light\"}, {\"id\": 28763, \"name\": \"a black metal tree-like stand\"}, {\"id\": 28764, \"name\": \"the neck of the bottle\"}, {\"id\": 28765, \"name\": \"the chair or table leg\"}, {\"id\": 28766, \"name\": \"the white cursive script\"}, {\"id\": 28767, \"name\": \"the distinctive hairstyle\"}, {\"id\": 28768, \"name\": \"a colored circle\"}, {\"id\": 28769, \"name\": \"a yellow and black protective gear\"}, {\"id\": 28770, \"name\": \"a rounded roof\"}, {\"id\": 28771, \"name\": \"the -terrain vehicle (atv)\"}, {\"id\": 28772, \"name\": \"roof of the house\"}, {\"id\": 28773, \"name\": \"vehicle's column\"}, {\"id\": 28774, \"name\": \"bag of french fry\"}, {\"id\": 28775, \"name\": \"a long, sloping roofline\"}, {\"id\": 28776, \"name\": \"black and white fence\"}, {\"id\": 28777, \"name\": \"a white fletching\"}, {\"id\": 28778, \"name\": \"lush and green grassy area\"}, {\"id\": 28779, \"name\": \"the large dent\"}, {\"id\": 28780, \"name\": \"a picture of a red convertible car\"}, {\"id\": 28781, \"name\": \"bookcase\"}, {\"id\": 28782, \"name\": \"the central hole\"}, {\"id\": 28783, \"name\": \"a highbrew coffee\"}, {\"id\": 28784, \"name\": \"the sleek black, orange, and white go-kart\"}, {\"id\": 28785, \"name\": \"red water tower\"}, {\"id\": 28786, \"name\": \"the large, black box\"}, {\"id\": 28787, \"name\": \"glas case\"}, {\"id\": 28788, \"name\": \"elbow\"}, {\"id\": 28789, \"name\": \"the purple clothing\"}, {\"id\": 28790, \"name\": \"brown and grey pattern\"}, {\"id\": 28791, \"name\": \"the small keyhole\"}, {\"id\": 28792, \"name\": \"the spark\"}, {\"id\": 28793, \"name\": \"red and yellow pole\"}, {\"id\": 28794, \"name\": \"the rollercoaster\"}, {\"id\": 28795, \"name\": \"brown paper bag\"}, {\"id\": 28796, \"name\": \"a corrugated cardboard\"}, {\"id\": 28797, \"name\": \"a turkish flag\"}, {\"id\": 28798, \"name\": \"plastic sheet\"}, {\"id\": 28799, \"name\": \"the dark smoke\"}, {\"id\": 28800, \"name\": \"the yellow-brown burner\"}, {\"id\": 28801, \"name\": \"a white and tan paint\"}, {\"id\": 28802, \"name\": \"distinctive grill\"}, {\"id\": 28803, \"name\": \"a long, curved tail\"}, {\"id\": 28804, \"name\": \"the dark blue outline\"}, {\"id\": 28805, \"name\": \"an orange outline of a kangaroo's head\"}, {\"id\": 28806, \"name\": \"black microphone stand\"}, {\"id\": 28807, \"name\": \"the short, buzzed hair\"}, {\"id\": 28808, \"name\": \"purple and pink graphic\"}, {\"id\": 28809, \"name\": \"the flour\"}, {\"id\": 28810, \"name\": \"the tire track\"}, {\"id\": 28811, \"name\": \"large, multi-colored fountain\"}, {\"id\": 28812, \"name\": \"a white bmw\"}, {\"id\": 28813, \"name\": \"a red and white net fence\"}, {\"id\": 28814, \"name\": \"a red and green object\"}, {\"id\": 28815, \"name\": \"flying cloud\"}, {\"id\": 28816, \"name\": \"blue writing\"}, {\"id\": 28817, \"name\": \"the brown vest\"}, {\"id\": 28818, \"name\": \"green, white, and black stripe design\"}, {\"id\": 28819, \"name\": \"the large, sealed box\"}, {\"id\": 28820, \"name\": \"the outer lane\"}, {\"id\": 28821, \"name\": \"hot air balloon\"}, {\"id\": 28822, \"name\": \"a metal clip\"}, {\"id\": 28823, \"name\": \"the sand trap\"}, {\"id\": 28824, \"name\": \"a sleek, white grandstand\"}, {\"id\": 28825, \"name\": \"the glass window\"}, {\"id\": 28826, \"name\": \"the black swim trunk\"}, {\"id\": 28827, \"name\": \"mud-splattered tire\"}, {\"id\": 28828, \"name\": \"support beam\"}, {\"id\": 28829, \"name\": \"the red chinese character\"}, {\"id\": 28830, \"name\": \"a sandy shore\"}, {\"id\": 28831, \"name\": \"a blue cord or strap\"}, {\"id\": 28832, \"name\": \"a silver button\"}, {\"id\": 28833, \"name\": \"monster energy logo\"}, {\"id\": 28834, \"name\": \"a clear plastic window\"}, {\"id\": 28835, \"name\": \"the red strap\"}, {\"id\": 28836, \"name\": \"a vast field\"}, {\"id\": 28837, \"name\": \"a gray base\"}, {\"id\": 28838, \"name\": \"the adjacent sign\"}, {\"id\": 28839, \"name\": \"spoke\"}, {\"id\": 28840, \"name\": \"the khaki shirt\"}, {\"id\": 28841, \"name\": \"a dark green tinted water\"}, {\"id\": 28842, \"name\": \"close-up of a bicycle wheel\"}, {\"id\": 28843, \"name\": \"the truck's license plate\"}, {\"id\": 28844, \"name\": \"cluttered white table\"}, {\"id\": 28845, \"name\": \"dark gray pole\"}, {\"id\": 28846, \"name\": \"box of makeup\"}, {\"id\": 28847, \"name\": \"the wooden workbench\"}, {\"id\": 28848, \"name\": \"square window\"}, {\"id\": 28849, \"name\": \"hanging bulb\"}, {\"id\": 28850, \"name\": \"face shield\"}, {\"id\": 28851, \"name\": \"white airplane icon\"}, {\"id\": 28852, \"name\": \"solid white line\"}, {\"id\": 28853, \"name\": \"front of a car\"}, {\"id\": 28854, \"name\": \"the rider's hand\"}, {\"id\": 28855, \"name\": \"a patch of brown\"}, {\"id\": 28856, \"name\": \"the left lane\"}, {\"id\": 28857, \"name\": \"the speed limit sign\"}, {\"id\": 28858, \"name\": \"the white and brown structure\"}, {\"id\": 28859, \"name\": \"a grey work surface\"}, {\"id\": 28860, \"name\": \"black and gray baseplate\"}, {\"id\": 28861, \"name\": \"a gray baseboard\"}, {\"id\": 28862, \"name\": \"the visible seat\"}, {\"id\": 28863, \"name\": \"the rack of yellow and white dress\"}, {\"id\": 28864, \"name\": \"tall, pointed structure\"}, {\"id\": 28865, \"name\": \"a large xp logo\"}, {\"id\": 28866, \"name\": \"a stern\"}, {\"id\": 28867, \"name\": \"a large nose\"}, {\"id\": 28868, \"name\": \"the man in a dark suit and coat\"}, {\"id\": 28869, \"name\": \"the neon green accent\"}, {\"id\": 28870, \"name\": \"the large, black and red tire\"}, {\"id\": 28871, \"name\": \"the ripple\"}, {\"id\": 28872, \"name\": \"a free weight\"}, {\"id\": 28873, \"name\": \"company's name\"}, {\"id\": 28874, \"name\": \"a grey bar\"}, {\"id\": 28875, \"name\": \"large archway\"}, {\"id\": 28876, \"name\": \"the cros bush farm shop\"}, {\"id\": 28877, \"name\": \"the model train track\"}, {\"id\": 28878, \"name\": \"black fingernail\"}, {\"id\": 28879, \"name\": \"the blury and dark image\"}, {\"id\": 28880, \"name\": \"the red and white tent\"}, {\"id\": 28881, \"name\": \"the wood shaving\"}, {\"id\": 28882, \"name\": \"interior of the truck\"}, {\"id\": 28883, \"name\": \"a blue and white striped border\"}, {\"id\": 28884, \"name\": \"the window frame\"}, {\"id\": 28885, \"name\": \"the brown nose\"}, {\"id\": 28886, \"name\": \"silver air vent\"}, {\"id\": 28887, \"name\": \"a grassy section\"}, {\"id\": 28888, \"name\": \"stomach\"}, {\"id\": 28889, \"name\": \"a gray asphalt track\"}, {\"id\": 28890, \"name\": \"the rotating drum\"}, {\"id\": 28891, \"name\": \"a picture of a yellow cube\"}, {\"id\": 28892, \"name\": \"the black iron lantern\"}, {\"id\": 28893, \"name\": \"mirror's black frame\"}, {\"id\": 28894, \"name\": \"the white frosting\"}, {\"id\": 28895, \"name\": \"the white long-sleeved top\"}, {\"id\": 28896, \"name\": \"yellow and white beam\"}, {\"id\": 28897, \"name\": \"yellow battery pack\"}, {\"id\": 28898, \"name\": \"green accent\"}, {\"id\": 28899, \"name\": \"the blue overall\"}, {\"id\": 28900, \"name\": \"the blue wire\"}, {\"id\": 28901, \"name\": \"brown leather arm guard\"}, {\"id\": 28902, \"name\": \"gray concrete surface\"}, {\"id\": 28903, \"name\": \"pink flip-flop\"}, {\"id\": 28904, \"name\": \"an orange and white graphic\"}, {\"id\": 28905, \"name\": \"large mouth\"}, {\"id\": 28906, \"name\": \"reindeer\"}, {\"id\": 28907, \"name\": \"a white bicycle\"}, {\"id\": 28908, \"name\": \"a store aisle\"}, {\"id\": 28909, \"name\": \"the purple wheel\"}, {\"id\": 28910, \"name\": \"the grape\"}, {\"id\": 28911, \"name\": \"the tan puffer jacket\"}, {\"id\": 28912, \"name\": \"dark-colored spider\"}, {\"id\": 28913, \"name\": \"a shay mitchell-themed snapchat sticker\"}, {\"id\": 28914, \"name\": \"colorful teacup ride vehicle\"}, {\"id\": 28915, \"name\": \"a small metal piece\"}, {\"id\": 28916, \"name\": \"a red and yellow sign\"}, {\"id\": 28917, \"name\": \"person's finger\"}, {\"id\": 28918, \"name\": \"a flower-adorned headband\"}, {\"id\": 28919, \"name\": \"black body\"}, {\"id\": 28920, \"name\": \"a purple suitcase\"}, {\"id\": 28921, \"name\": \"an employee's attire\"}, {\"id\": 28922, \"name\": \"the vibrant blue, red, and black helmet\"}, {\"id\": 28923, \"name\": \"crew\"}, {\"id\": 28924, \"name\": \"yellow and white volleyball\"}, {\"id\": 28925, \"name\": \"a blue and yellow design\"}, {\"id\": 28926, \"name\": \"large black and white m logo\"}, {\"id\": 28927, \"name\": \"a dormer window\"}, {\"id\": 28928, \"name\": \"white ford sedan\"}, {\"id\": 28929, \"name\": \"a purple blob\"}, {\"id\": 28930, \"name\": \"black and white sign\"}, {\"id\": 28931, \"name\": \"the white shin guard\"}, {\"id\": 28932, \"name\": \"the plane's wing\"}, {\"id\": 28933, \"name\": \"the forest trail\"}, {\"id\": 28934, \"name\": \"red and white trim\"}, {\"id\": 28935, \"name\": \"a small orange machine\"}, {\"id\": 28936, \"name\": \"the white fencing or barrier\"}, {\"id\": 28937, \"name\": \"the blue and purple object\"}, {\"id\": 28938, \"name\": \"silver door\"}, {\"id\": 28939, \"name\": \"air conditioning control\"}, {\"id\": 28940, \"name\": \"red and silver wire\"}, {\"id\": 28941, \"name\": \"the black eye\"}, {\"id\": 28942, \"name\": \"beige and tan patterned rug\"}, {\"id\": 28943, \"name\": \"black umbrella\"}, {\"id\": 28944, \"name\": \"dark-colored roof\"}, {\"id\": 28945, \"name\": \"a pink and white striped dress\"}, {\"id\": 28946, \"name\": \"yellow lightning bolt\"}, {\"id\": 28947, \"name\": \"cockpit\"}, {\"id\": 28948, \"name\": \"rear of the crane\"}, {\"id\": 28949, \"name\": \"a winding black asphalt path\"}, {\"id\": 28950, \"name\": \"the white and red striped curb\"}, {\"id\": 28951, \"name\": \"a blue and black bar\"}, {\"id\": 28952, \"name\": \"a green wheel\"}, {\"id\": 28953, \"name\": \"a 11 parkade\"}, {\"id\": 28954, \"name\": \"sleek, dark-colored sedan\"}, {\"id\": 28955, \"name\": \"a winding track\"}, {\"id\": 28956, \"name\": \"a black spring\"}, {\"id\": 28957, \"name\": \"kona\"}, {\"id\": 28958, \"name\": \"the yellow lotu elise\"}, {\"id\": 28959, \"name\": \"yellow battery\"}, {\"id\": 28960, \"name\": \"envelope\"}, {\"id\": 28961, \"name\": \"large, furry head\"}, {\"id\": 28962, \"name\": \"the white or gray roof\"}, {\"id\": 28963, \"name\": \"the steering column\"}, {\"id\": 28964, \"name\": \"a blue hair\"}, {\"id\": 28965, \"name\": \"the camouflage pattern\"}, {\"id\": 28966, \"name\": \"wattle\"}, {\"id\": 28967, \"name\": \"a left bank of the river\"}, {\"id\": 28968, \"name\": \"the yellow wing\"}, {\"id\": 28969, \"name\": \"green metal surface\"}, {\"id\": 28970, \"name\": \"the index and middle finger\"}, {\"id\": 28971, \"name\": \"the small patio\"}, {\"id\": 28972, \"name\": \"the subscribe to wionew\"}, {\"id\": 28973, \"name\": \"a white doily or lace\"}, {\"id\": 28974, \"name\": \"the blue collar\"}, {\"id\": 28975, \"name\": \"a brick-paved sidewalk\"}, {\"id\": 28976, \"name\": \"furry material\"}, {\"id\": 28977, \"name\": \"a black car's rear window\"}, {\"id\": 28978, \"name\": \"the reflective orange and white stripe\"}, {\"id\": 28979, \"name\": \"black and red toy chest\"}, {\"id\": 28980, \"name\": \"silver wheel\"}, {\"id\": 28981, \"name\": \"the red and white curb\"}, {\"id\": 28982, \"name\": \"brown briefcase\"}, {\"id\": 28983, \"name\": \"valve\"}, {\"id\": 28984, \"name\": \"military aircraft\"}, {\"id\": 28985, \"name\": \"green grassy infield\"}, {\"id\": 28986, \"name\": \"phone mount\"}, {\"id\": 28987, \"name\": \"park's name\"}, {\"id\": 28988, \"name\": \"title spider man\"}, {\"id\": 28989, \"name\": \"a rocky hillside\"}, {\"id\": 28990, \"name\": \"a fuel\"}, {\"id\": 28991, \"name\": \"the hot wheel toy car\"}, {\"id\": 28992, \"name\": \"corrugated cardboard\"}, {\"id\": 28993, \"name\": \"ammunition\"}, {\"id\": 28994, \"name\": \"the red and black weed whacker\"}, {\"id\": 28995, \"name\": \"the middle finger\"}, {\"id\": 28996, \"name\": \"the teal handlebar\"}, {\"id\": 28997, \"name\": \"the railing or partition\"}, {\"id\": 28998, \"name\": \"jet engine\"}, {\"id\": 28999, \"name\": \"small, light-colored monkey\"}, {\"id\": 29000, \"name\": \"brown ball\"}, {\"id\": 29001, \"name\": \"the black dot\"}, {\"id\": 29002, \"name\": \"blue valve\"}, {\"id\": 29003, \"name\": \"tiara\"}, {\"id\": 29004, \"name\": \"a small figurine\"}, {\"id\": 29005, \"name\": \"a blue and black shoe\"}, {\"id\": 29006, \"name\": \"a landmass\"}, {\"id\": 29007, \"name\": \"blue, rocky, and sandy area\"}, {\"id\": 29008, \"name\": \"grassy bank\"}, {\"id\": 29009, \"name\": \"terms and condition\"}, {\"id\": 29010, \"name\": \"the black drawstring\"}, {\"id\": 29011, \"name\": \"a plane's body\"}, {\"id\": 29012, \"name\": \"a wooden sawhorse\"}, {\"id\": 29013, \"name\": \"metal bit\"}, {\"id\": 29014, \"name\": \"a phone holder\"}, {\"id\": 29015, \"name\": \"the blurry view of a kitchen\"}, {\"id\": 29016, \"name\": \"black racing stripe\"}, {\"id\": 29017, \"name\": \"subscribe\"}, {\"id\": 29018, \"name\": \"trophy\"}, {\"id\": 29019, \"name\": \"the riverfront\"}, {\"id\": 29020, \"name\": \"red rock\"}, {\"id\": 29021, \"name\": \"the yellow hull\"}, {\"id\": 29022, \"name\": \"the cloak\"}, {\"id\": 29023, \"name\": \"reading of 106.9\"}, {\"id\": 29024, \"name\": \"large, blue, prehistoric-looking creature\"}, {\"id\": 29025, \"name\": \"faint, curved line\"}, {\"id\": 29026, \"name\": \"black decal\"}, {\"id\": 29027, \"name\": \"the yellow crosshatch pattern\"}, {\"id\": 29028, \"name\": \"a red and white surface\"}, {\"id\": 29029, \"name\": \"dark brown coat\"}, {\"id\": 29030, \"name\": \"the driver's side door\"}, {\"id\": 29031, \"name\": \"trailer's floor\"}, {\"id\": 29032, \"name\": \"red and white checkered paper\"}, {\"id\": 29033, \"name\": \"colored handhold\"}, {\"id\": 29034, \"name\": \"the large purple sign\"}, {\"id\": 29035, \"name\": \"yellow lightning bolt design\"}, {\"id\": 29036, \"name\": \"a black square\"}, {\"id\": 29037, \"name\": \"a small orange icon\"}, {\"id\": 29038, \"name\": \"the yellow tail\"}, {\"id\": 29039, \"name\": \"large cabin\"}, {\"id\": 29040, \"name\": \"a square head\"}, {\"id\": 29041, \"name\": \"a white oven\"}, {\"id\": 29042, \"name\": \"light box\"}, {\"id\": 29043, \"name\": \"small glas jar\"}, {\"id\": 29044, \"name\": \"a chrome wheel\"}, {\"id\": 29045, \"name\": \"a black control panel\"}, {\"id\": 29046, \"name\": \"red and white ingredient\"}, {\"id\": 29047, \"name\": \"the white and gray long-sleeved shirt\"}, {\"id\": 29048, \"name\": \"large bmw power sign\"}, {\"id\": 29049, \"name\": \"large metal arm\"}, {\"id\": 29050, \"name\": \"logo for the radio station\"}, {\"id\": 29051, \"name\": \"a high-speed oval track\"}, {\"id\": 29052, \"name\": \"a bike's stem\"}, {\"id\": 29053, \"name\": \"the blue component\"}, {\"id\": 29054, \"name\": \"the central circle\"}, {\"id\": 29055, \"name\": \"a storm logo\"}, {\"id\": 29056, \"name\": \"a logo for new china\"}, {\"id\": 29057, \"name\": \"a galvanized steel trash can\"}, {\"id\": 29058, \"name\": \"the black fireplace screen\"}, {\"id\": 29059, \"name\": \"a white nike swoosh\"}, {\"id\": 29060, \"name\": \"the yellow speedometer\"}, {\"id\": 29061, \"name\": \"a large white headlight\"}, {\"id\": 29062, \"name\": \"the dark grey metal structure\"}, {\"id\": 29063, \"name\": \"clip\"}, {\"id\": 29064, \"name\": \"the menacing open mouth\"}, {\"id\": 29065, \"name\": \"a red and green glove\"}, {\"id\": 29066, \"name\": \"the low metal fence\"}, {\"id\": 29067, \"name\": \"red curtain backdrop\"}, {\"id\": 29068, \"name\": \"a small, round, black button\"}, {\"id\": 29069, \"name\": \"dark blue hoodie\"}, {\"id\": 29070, \"name\": \"white comforter\"}, {\"id\": 29071, \"name\": \"the tall light\"}, {\"id\": 29072, \"name\": \"plane's wing\"}, {\"id\": 29073, \"name\": \"a white pad\"}, {\"id\": 29074, \"name\": \"dark blue exterior\"}, {\"id\": 29075, \"name\": \"the pink pufferfish\"}, {\"id\": 29076, \"name\": \"silver tube\"}, {\"id\": 29077, \"name\": \"the small room\"}, {\"id\": 29078, \"name\": \"the biceps\"}, {\"id\": 29079, \"name\": \"gold line\"}, {\"id\": 29080, \"name\": \"green and white plastic sheeting\"}, {\"id\": 29081, \"name\": \"a line of parked car\"}, {\"id\": 29082, \"name\": \"a paved track\"}, {\"id\": 29083, \"name\": \"green and black mountain bike\"}, {\"id\": 29084, \"name\": \"a zig-zag pattern\"}, {\"id\": 29085, \"name\": \"a curved blue track\"}, {\"id\": 29086, \"name\": \"the brown table\"}, {\"id\": 29087, \"name\": \"an aquarium tank\"}, {\"id\": 29088, \"name\": \"the distinctive dome\"}, {\"id\": 29089, \"name\": \"the tall metal power line tower\"}, {\"id\": 29090, \"name\": \"the light blue lining\"}, {\"id\": 29091, \"name\": \"a motorcycle's black dashboard\"}, {\"id\": 29092, \"name\": \"prominent silver grill\"}, {\"id\": 29093, \"name\": \"a teenage boy\"}, {\"id\": 29094, \"name\": \"blue dot\"}, {\"id\": 29095, \"name\": \"boat's name\"}, {\"id\": 29096, \"name\": \"monkey enclosure\"}, {\"id\": 29097, \"name\": \"the white knob\"}, {\"id\": 29098, \"name\": \"a red, yellow, and orange stripe\"}, {\"id\": 29099, \"name\": \"hiking pole\"}, {\"id\": 29100, \"name\": \"a red eye\"}, {\"id\": 29101, \"name\": \"long, red tail\"}, {\"id\": 29102, \"name\": \"simple, monochromatic graphic\"}, {\"id\": 29103, \"name\": \"a directional sign\"}, {\"id\": 29104, \"name\": \"a red fruit\"}, {\"id\": 29105, \"name\": \"the parking garage\"}, {\"id\": 29106, \"name\": \"wardrobe and make up abbey sweeney\"}, {\"id\": 29107, \"name\": \"the train's wheel\"}, {\"id\": 29108, \"name\": \"the stethoscope\"}, {\"id\": 29109, \"name\": \"the yellow support pole\"}, {\"id\": 29110, \"name\": \"a red and blue railing\"}, {\"id\": 29111, \"name\": \"a transparent and rectangular shape\"}, {\"id\": 29112, \"name\": \"dark blue border\"}, {\"id\": 29113, \"name\": \"the stylized, hand-drawn map\"}, {\"id\": 29114, \"name\": \"a headphones\"}, {\"id\": 29115, \"name\": \"red 197\"}, {\"id\": 29116, \"name\": \"dirt barrier\"}, {\"id\": 29117, \"name\": \"silver metal arm\"}, {\"id\": 29118, \"name\": \"the gold clasp\"}, {\"id\": 29119, \"name\": \"the wheel cover\"}, {\"id\": 29120, \"name\": \"the large, white structure\"}, {\"id\": 29121, \"name\": \"bike lane\"}, {\"id\": 29122, \"name\": \"a colorful map of a city or town\"}, {\"id\": 29123, \"name\": \"red and orange toy car\"}, {\"id\": 29124, \"name\": \"white interior\"}, {\"id\": 29125, \"name\": \"a small yellow and orange sticker\"}, {\"id\": 29126, \"name\": \"the driveway\"}, {\"id\": 29127, \"name\": \"rating\"}, {\"id\": 29128, \"name\": \"cluttered shelf\"}, {\"id\": 29129, \"name\": \"sand dune\"}, {\"id\": 29130, \"name\": \"wheel or axle\"}, {\"id\": 29131, \"name\": \"white wsj watermark\"}, {\"id\": 29132, \"name\": \"a blue and red light bar\"}, {\"id\": 29133, \"name\": \"ownline logo\"}, {\"id\": 29134, \"name\": \"welcome to\"}, {\"id\": 29135, \"name\": \"dark cargo pant\"}, {\"id\": 29136, \"name\": \"the brown infield\"}, {\"id\": 29137, \"name\": \"red and white tail section\"}, {\"id\": 29138, \"name\": \"the sleek white hull\"}, {\"id\": 29139, \"name\": \"smudge of green polish\"}, {\"id\": 29140, \"name\": \"rocket launcher\"}, {\"id\": 29141, \"name\": \"the video game controller\"}, {\"id\": 29142, \"name\": \"the white shipping container\"}, {\"id\": 29143, \"name\": \"a racing line logo\"}, {\"id\": 29144, \"name\": \"distinctive green roof\"}, {\"id\": 29145, \"name\": \"the floor of a store\"}, {\"id\": 29146, \"name\": \"a church\"}, {\"id\": 29147, \"name\": \"the small, white screen\"}, {\"id\": 29148, \"name\": \"a yellow mountain range graphic\"}, {\"id\": 29149, \"name\": \"the motorcycle's windshield\"}, {\"id\": 29150, \"name\": \"a double-decker\"}, {\"id\": 29151, \"name\": \"bowl of oil\"}, {\"id\": 29152, \"name\": \"black card\"}, {\"id\": 29153, \"name\": \"tire obstacle course\"}, {\"id\": 29154, \"name\": \"back of the car\"}, {\"id\": 29155, \"name\": \"the small metal screw\"}, {\"id\": 29156, \"name\": \"the tan canopy\"}, {\"id\": 29157, \"name\": \"a parallel line\"}, {\"id\": 29158, \"name\": \"a thin orange line\"}, {\"id\": 29159, \"name\": \"the green logo\"}, {\"id\": 29160, \"name\": \"model water tower\"}, {\"id\": 29161, \"name\": \"the colorful image of a flower\"}, {\"id\": 29162, \"name\": \"a gray track\"}, {\"id\": 29163, \"name\": \"engine\"}, {\"id\": 29164, \"name\": \"the dashboard of a motorbike\"}, {\"id\": 29165, \"name\": \"large gray backpack\"}, {\"id\": 29166, \"name\": \"black and white camouflage jacket\"}, {\"id\": 29167, \"name\": \"a pedestrian crossing sign\"}, {\"id\": 29168, \"name\": \"the bright yellow plate\"}, {\"id\": 29169, \"name\": \"a green and pink pattern\"}, {\"id\": 29170, \"name\": \"white and yellow tape strip\"}, {\"id\": 29171, \"name\": \"a black and white design\"}, {\"id\": 29172, \"name\": \"a red undercarriage\"}, {\"id\": 29173, \"name\": \"the red and gold logo\"}, {\"id\": 29174, \"name\": \"white and red boat\"}, {\"id\": 29175, \"name\": \"a red leg\"}, {\"id\": 29176, \"name\": \"pink drink\"}, {\"id\": 29177, \"name\": \"a yellow and black crash test dummy\"}, {\"id\": 29178, \"name\": \"the vehicle's rear axle seal\"}, {\"id\": 29179, \"name\": \"the central white circle\"}, {\"id\": 29180, \"name\": \"a three-story garage\"}, {\"id\": 29181, \"name\": \"brown arch\"}, {\"id\": 29182, \"name\": \"a dark-colored table or countertop\"}, {\"id\": 29183, \"name\": \"red and blue helmet\"}, {\"id\": 29184, \"name\": \"wbz\"}, {\"id\": 29185, \"name\": \"the red and yellow marking\"}, {\"id\": 29186, \"name\": \"a neon yellow jacket\"}, {\"id\": 29187, \"name\": \"the tarmac\"}, {\"id\": 29188, \"name\": \"black and white checkered flag\"}, {\"id\": 29189, \"name\": \"an olive branch\"}, {\"id\": 29190, \"name\": \"a light-colored powder or granule\"}, {\"id\": 29191, \"name\": \"the black straw\"}, {\"id\": 29192, \"name\": \"a roller\"}, {\"id\": 29193, \"name\": \"a large engine\"}, {\"id\": 29194, \"name\": \"the spinx6\"}, {\"id\": 29195, \"name\": \"the pink glove\"}, {\"id\": 29196, \"name\": \"the green liquid\"}, {\"id\": 29197, \"name\": \"the blue band\"}, {\"id\": 29198, \"name\": \"a union station\"}, {\"id\": 29199, \"name\": \"the black and beige dashboard\"}, {\"id\": 29200, \"name\": \"the rocky cliff\"}, {\"id\": 29201, \"name\": \"a green plant illustration\"}, {\"id\": 29202, \"name\": \"transparent plastic packaging\"}, {\"id\": 29203, \"name\": \"the grill\"}, {\"id\": 29204, \"name\": \"a red and blue stripe\"}, {\"id\": 29205, \"name\": \"a fuzzy pom-pom\"}, {\"id\": 29206, \"name\": \"the green scarf\"}, {\"id\": 29207, \"name\": \"small white spot\"}, {\"id\": 29208, \"name\": \"the air intake\"}, {\"id\": 29209, \"name\": \"a blue and gold sword\"}, {\"id\": 29210, \"name\": \"the cardboard divider\"}, {\"id\": 29211, \"name\": \"the jacket or shirt\"}, {\"id\": 29212, \"name\": \"medical glove\"}, {\"id\": 29213, \"name\": \"person's index finger\"}, {\"id\": 29214, \"name\": \"a dark dashboard\"}, {\"id\": 29215, \"name\": \"a fight watch\"}, {\"id\": 29216, \"name\": \"a red nintendo logo\"}, {\"id\": 29217, \"name\": \"patient\"}, {\"id\": 29218, \"name\": \"the small black screen\"}, {\"id\": 29219, \"name\": \"gopro\"}, {\"id\": 29220, \"name\": \"the empty stand\"}, {\"id\": 29221, \"name\": \"the thomas\"}, {\"id\": 29222, \"name\": \"a pink brick\"}, {\"id\": 29223, \"name\": \"the usb connector\"}, {\"id\": 29224, \"name\": \"a large, white, geometric column\"}, {\"id\": 29225, \"name\": \"the tall, gray pole\"}, {\"id\": 29226, \"name\": \"rear end\"}, {\"id\": 29227, \"name\": \"ribbed fabric\"}, {\"id\": 29228, \"name\": \"pink, purple, and red flower\"}, {\"id\": 29229, \"name\": \"a concrete or cement floor\"}, {\"id\": 29230, \"name\": \"the dark gray car\"}, {\"id\": 29231, \"name\": \"a large construction crane\"}, {\"id\": 29232, \"name\": \"the white menu board\"}, {\"id\": 29233, \"name\": \"a white towel or cloth\"}, {\"id\": 29234, \"name\": \"car's headlight\"}, {\"id\": 29235, \"name\": \"the long, black antenna\"}, {\"id\": 29236, \"name\": \"a sleek black track\"}, {\"id\": 29237, \"name\": \"the grey metal body\"}, {\"id\": 29238, \"name\": \"a light blue, orange, and brown pattern\"}, {\"id\": 29239, \"name\": \"black bicycle frame\"}, {\"id\": 29240, \"name\": \"car on the right\"}, {\"id\": 29241, \"name\": \"the small green basket\"}, {\"id\": 29242, \"name\": \"person's body\"}, {\"id\": 29243, \"name\": \"a cd drive\"}, {\"id\": 29244, \"name\": \"the small black box\"}, {\"id\": 29245, \"name\": \"a dashcam\"}, {\"id\": 29246, \"name\": \"the metal platform\"}, {\"id\": 29247, \"name\": \"a screwdriver\"}, {\"id\": 29248, \"name\": \"the snow-covered roof\"}, {\"id\": 29249, \"name\": \"the green and black jersey\"}, {\"id\": 29250, \"name\": \"five own goal in one match\"}, {\"id\": 29251, \"name\": \"the white dome-shaped light fixture\"}, {\"id\": 29252, \"name\": \"trunk of a car\"}, {\"id\": 29253, \"name\": \"a colorful butterfly pattern\"}, {\"id\": 29254, \"name\": \"a red fish\"}, {\"id\": 29255, \"name\": \"small black chimney\"}, {\"id\": 29256, \"name\": \"a tall tower\"}, {\"id\": 29257, \"name\": \"the pink, wavy border\"}, {\"id\": 29258, \"name\": \"crumpled hood\"}, {\"id\": 29259, \"name\": \"the long metal tool\"}, {\"id\": 29260, \"name\": \"the red dirt bike\"}, {\"id\": 29261, \"name\": \"the large ear\"}, {\"id\": 29262, \"name\": \"the pink container\"}, {\"id\": 29263, \"name\": \"ncaaf logo\"}, {\"id\": 29264, \"name\": \"the soil\"}, {\"id\": 29265, \"name\": \"a phone mount\"}, {\"id\": 29266, \"name\": \"the phone holder\"}, {\"id\": 29267, \"name\": \"the distinctive \\\"70\\\" logo\"}, {\"id\": 29268, \"name\": \"black and red glove\"}, {\"id\": 29269, \"name\": \"the red lip\"}, {\"id\": 29270, \"name\": \"bill of the front cap\"}, {\"id\": 29271, \"name\": \"a silver nissan\"}, {\"id\": 29272, \"name\": \"a drainage grate\"}, {\"id\": 29273, \"name\": \"green and white circular design\"}, {\"id\": 29274, \"name\": \"orange reflector\"}, {\"id\": 29275, \"name\": \"dark-colored canopy\"}, {\"id\": 29276, \"name\": \"the blue denim fabric\"}, {\"id\": 29277, \"name\": \"a hourglas tattoo\"}, {\"id\": 29278, \"name\": \"the circular emblem\"}, {\"id\": 29279, \"name\": \"date of august 15th\"}, {\"id\": 29280, \"name\": \"the zipper of a garment\"}, {\"id\": 29281, \"name\": \"a black and orange x-shaped design\"}, {\"id\": 29282, \"name\": \"a tall, yellow metal structure\"}, {\"id\": 29283, \"name\": \"the screw\"}, {\"id\": 29284, \"name\": \"a hip\"}, {\"id\": 29285, \"name\": \"the black j\"}, {\"id\": 29286, \"name\": \"a red wire\"}, {\"id\": 29287, \"name\": \"the kayak's footrest\"}, {\"id\": 29288, \"name\": \"an individual's left hand\"}, {\"id\": 29289, \"name\": \"a red body\"}, {\"id\": 29290, \"name\": \"dark wood flooring\"}, {\"id\": 29291, \"name\": \"red engine cover\"}, {\"id\": 29292, \"name\": \"the rider on the track\"}, {\"id\": 29293, \"name\": \"vibrant teal and yellow track\"}, {\"id\": 29294, \"name\": \"a gold light\"}, {\"id\": 29295, \"name\": \"the vehicle's front grill\"}, {\"id\": 29296, \"name\": \"train's wheel\"}, {\"id\": 29297, \"name\": \"large red truck\"}, {\"id\": 29298, \"name\": \"orange robot\"}, {\"id\": 29299, \"name\": \"well-defined physique\"}, {\"id\": 29300, \"name\": \"roller coaster's track\"}, {\"id\": 29301, \"name\": \"a wolf's head\"}, {\"id\": 29302, \"name\": \"the ride's sign\"}, {\"id\": 29303, \"name\": \"general tire\"}, {\"id\": 29304, \"name\": \"yellow and black sign\"}, {\"id\": 29305, \"name\": \"the blue engine\"}, {\"id\": 29306, \"name\": \"black oval\"}, {\"id\": 29307, \"name\": \"black and white hood\"}, {\"id\": 29308, \"name\": \"the burner\"}, {\"id\": 29309, \"name\": \"the white watch or bracelet\"}, {\"id\": 29310, \"name\": \"black and red helmet\"}, {\"id\": 29311, \"name\": \"the driver's gloved hands\"}, {\"id\": 29312, \"name\": \"small portion of a black microphone\"}, {\"id\": 29313, \"name\": \"yellow and white graphic\"}, {\"id\": 29314, \"name\": \"a green handle\"}, {\"id\": 29315, \"name\": \"the black throttle\"}, {\"id\": 29316, \"name\": \"the metal rim\"}, {\"id\": 29317, \"name\": \"the red and white checkered pattern\"}, {\"id\": 29318, \"name\": \"large yellow flame\"}, {\"id\": 29319, \"name\": \"a manchester city logo\"}, {\"id\": 29320, \"name\": \"the control room\"}, {\"id\": 29321, \"name\": \"back of the bike\"}, {\"id\": 29322, \"name\": \"a grille\"}, {\"id\": 29323, \"name\": \"rider's right arm\"}, {\"id\": 29324, \"name\": \"eldora\"}, {\"id\": 29325, \"name\": \"the player name\"}, {\"id\": 29326, \"name\": \"tall cabinet\"}, {\"id\": 29327, \"name\": \"the cluttered shelf\"}, {\"id\": 29328, \"name\": \"the white interior\"}, {\"id\": 29329, \"name\": \"the large red nose\"}, {\"id\": 29330, \"name\": \"a purple tag\"}, {\"id\": 29331, \"name\": \"a large gray structure\"}, {\"id\": 29332, \"name\": \"the numbered item\"}, {\"id\": 29333, \"name\": \"a red, yellow, and blue stripe\"}, {\"id\": 29334, \"name\": \"the track's surface\"}, {\"id\": 29335, \"name\": \"a small, circular object\"}, {\"id\": 29336, \"name\": \"the foundation\"}, {\"id\": 29337, \"name\": \"a corkscrew\"}, {\"id\": 29338, \"name\": \"black and white lane divider\"}, {\"id\": 29339, \"name\": \"a small white rectangle\"}, {\"id\": 29340, \"name\": \"a silo\"}, {\"id\": 29341, \"name\": \"the score 17\"}, {\"id\": 29342, \"name\": \"green and silver head\"}, {\"id\": 29343, \"name\": \"open door\"}, {\"id\": 29344, \"name\": \"a cowtown indian center\"}, {\"id\": 29345, \"name\": \"a small black screen\"}, {\"id\": 29346, \"name\": \"white couch\"}, {\"id\": 29347, \"name\": \"a black and brown floral pattern\"}, {\"id\": 29348, \"name\": \"the yellow arrow\"}, {\"id\": 29349, \"name\": \"a 47.54\"}, {\"id\": 29350, \"name\": \"dirt hill\"}, {\"id\": 29351, \"name\": \"left handlebar\"}, {\"id\": 29352, \"name\": \"a white-framed window\"}, {\"id\": 29353, \"name\": \"the green and black toy\"}, {\"id\": 29354, \"name\": \"the long, curved neck\"}, {\"id\": 29355, \"name\": \"the blue and black suit\"}, {\"id\": 29356, \"name\": \"a shirt's neckline\"}, {\"id\": 29357, \"name\": \"a pink plastic container\"}, {\"id\": 29358, \"name\": \"white wood\"}, {\"id\": 29359, \"name\": \"blue and white outfit\"}, {\"id\": 29360, \"name\": \"a yellow body\"}, {\"id\": 29361, \"name\": \"the yellow and black uniform\"}, {\"id\": 29362, \"name\": \"the wxyz detro\"}, {\"id\": 29363, \"name\": \"a black face guard\"}, {\"id\": 29364, \"name\": \"large trailer\"}, {\"id\": 29365, \"name\": \"drill press\"}, {\"id\": 29366, \"name\": \"subtle watermark\"}, {\"id\": 29367, \"name\": \"the green and yellow logo\"}, {\"id\": 29368, \"name\": \"the white sandy floor\"}, {\"id\": 29369, \"name\": \"second-story door\"}, {\"id\": 29370, \"name\": \"the brown feather\"}, {\"id\": 29371, \"name\": \"blue and green label\"}, {\"id\": 29372, \"name\": \"the table or desk\"}, {\"id\": 29373, \"name\": \"the box of silicone baking mold\"}, {\"id\": 29374, \"name\": \"a red and white track\"}, {\"id\": 29375, \"name\": \"a dark-colored floor or table\"}, {\"id\": 29376, \"name\": \"black plastic piece\"}, {\"id\": 29377, \"name\": \"an animal's head\"}, {\"id\": 29378, \"name\": \"the terex\"}, {\"id\": 29379, \"name\": \"white protective suit\"}, {\"id\": 29380, \"name\": \"an airport control tower\"}, {\"id\": 29381, \"name\": \"a large white garage door\"}, {\"id\": 29382, \"name\": \"a zipper closure\"}, {\"id\": 29383, \"name\": \"furniture\"}, {\"id\": 29384, \"name\": \"a large blue bin\"}, {\"id\": 29385, \"name\": \"the green and yellow striped jersey\"}, {\"id\": 29386, \"name\": \"a california license plate\"}, {\"id\": 29387, \"name\": \"a blue banner at the top\"}, {\"id\": 29388, \"name\": \"the four-way traffic circle\"}, {\"id\": 29389, \"name\": \"a non-english language\"}, {\"id\": 29390, \"name\": \"a white c\"}, {\"id\": 29391, \"name\": \"vertical slot\"}, {\"id\": 29392, \"name\": \"red and yellow logo\"}, {\"id\": 29393, \"name\": \"the foreground diver\"}, {\"id\": 29394, \"name\": \"red and white cord\"}, {\"id\": 29395, \"name\": \"a long tusk\"}, {\"id\": 29396, \"name\": \"railroad crossing\"}, {\"id\": 29397, \"name\": \"the tank's track\"}, {\"id\": 29398, \"name\": \"scuff\"}, {\"id\": 29399, \"name\": \"the rider's right hand\"}, {\"id\": 29400, \"name\": \"the pink seashell bra\"}, {\"id\": 29401, \"name\": \"digital speedometer\"}, {\"id\": 29402, \"name\": \"the black armband\"}, {\"id\": 29403, \"name\": \"the smoke stack\"}, {\"id\": 29404, \"name\": \"blurred hand\"}, {\"id\": 29405, \"name\": \"plant or bouquet\"}, {\"id\": 29406, \"name\": \"the person's left ring finger\"}, {\"id\": 29407, \"name\": \"a small hole\"}, {\"id\": 29408, \"name\": \"an image of person\"}, {\"id\": 29409, \"name\": \"computer expres sign\"}, {\"id\": 29410, \"name\": \"a wooden track system\"}, {\"id\": 29411, \"name\": \"a southeast nerf club\"}, {\"id\": 29412, \"name\": \"motorcycle's instrument panel\"}, {\"id\": 29413, \"name\": \"a temperature reading\"}, {\"id\": 29414, \"name\": \"the black oval\"}, {\"id\": 29415, \"name\": \"the sun visor\"}, {\"id\": 29416, \"name\": \"a model train bridge\"}, {\"id\": 29417, \"name\": \"a blurred landscape\"}, {\"id\": 29418, \"name\": \"yellow protective suit\"}, {\"id\": 29419, \"name\": \"yellow and red vehicle\"}, {\"id\": 29420, \"name\": \"black metal latch\"}, {\"id\": 29421, \"name\": \"curved section of the track\"}, {\"id\": 29422, \"name\": \"the plate of brown and white food\"}, {\"id\": 29423, \"name\": \"high bun\"}, {\"id\": 29424, \"name\": \"a vertical column\"}, {\"id\": 29425, \"name\": \"surveillance footage\"}, {\"id\": 29426, \"name\": \"a blurred person\"}, {\"id\": 29427, \"name\": \"pyramid\"}, {\"id\": 29428, \"name\": \"a large tractor tire\"}, {\"id\": 29429, \"name\": \"man's torso\"}, {\"id\": 29430, \"name\": \"a yellow mountain graphic\"}, {\"id\": 29431, \"name\": \"the large pit\"}, {\"id\": 29432, \"name\": \"yellow and black grinder\"}, {\"id\": 29433, \"name\": \"blue and black striped pole\"}, {\"id\": 29434, \"name\": \"a mixture of seed and other food item\"}, {\"id\": 29435, \"name\": \"the mount\"}, {\"id\": 29436, \"name\": \"industrial park\"}, {\"id\": 29437, \"name\": \"the large plume of white water\"}, {\"id\": 29438, \"name\": \"the cart's handle\"}, {\"id\": 29439, \"name\": \"a small portion of a house\"}, {\"id\": 29440, \"name\": \"the white envelope\"}, {\"id\": 29441, \"name\": \"white and black stripe\"}, {\"id\": 29442, \"name\": \"steel\"}, {\"id\": 29443, \"name\": \"a small box with a white label\"}, {\"id\": 29444, \"name\": \"a blurry figure of a person\"}, {\"id\": 29445, \"name\": \"corrugated metal facade\"}, {\"id\": 29446, \"name\": \"a black and yellow stripe\"}, {\"id\": 29447, \"name\": \"the toy track\"}, {\"id\": 29448, \"name\": \"slow sign\"}, {\"id\": 29449, \"name\": \"public gathering\"}, {\"id\": 29450, \"name\": \"blue and white train\"}, {\"id\": 29451, \"name\": \"black and gray racing suit\"}, {\"id\": 29452, \"name\": \"the green alien\"}, {\"id\": 29453, \"name\": \"the yellow race car\"}, {\"id\": 29454, \"name\": \"large blue bud light advertisement\"}, {\"id\": 29455, \"name\": \"a black gear shifter\"}, {\"id\": 29456, \"name\": \"the white airplane icon\"}, {\"id\": 29457, \"name\": \"the play button\"}, {\"id\": 29458, \"name\": \"the nebula\"}, {\"id\": 29459, \"name\": \"the dark blue and yellow toy track piece\"}, {\"id\": 29460, \"name\": \"the long snout\"}, {\"id\": 29461, \"name\": \"a racing information\"}, {\"id\": 29462, \"name\": \"the quote\"}, {\"id\": 29463, \"name\": \"red pompom\"}, {\"id\": 29464, \"name\": \"a blue hatchback\"}, {\"id\": 29465, \"name\": \"the red and white tower\"}, {\"id\": 29466, \"name\": \"red oval\"}, {\"id\": 29467, \"name\": \"a red structure\"}, {\"id\": 29468, \"name\": \"the red and yellow object\"}, {\"id\": 29469, \"name\": \"a person in a yellow vest\"}, {\"id\": 29470, \"name\": \"driver's right arm\"}, {\"id\": 29471, \"name\": \"white tire\"}, {\"id\": 29472, \"name\": \"shipwrecked vessel\"}, {\"id\": 29473, \"name\": \"small, red wire\"}, {\"id\": 29474, \"name\": \"blue and yellow saddle blanket\"}, {\"id\": 29475, \"name\": \"the green circle\"}, {\"id\": 29476, \"name\": \"a high-heeled sandal\"}, {\"id\": 29477, \"name\": \"brown brick fireplace\"}, {\"id\": 29478, \"name\": \"victorian-era attire\"}, {\"id\": 29479, \"name\": \"a compass\"}, {\"id\": 29480, \"name\": \"cutting board\"}, {\"id\": 29481, \"name\": \"the blue train\"}, {\"id\": 29482, \"name\": \"the vibrant blue sport car\"}, {\"id\": 29483, \"name\": \"the yellow number\"}, {\"id\": 29484, \"name\": \"powerdirector\"}, {\"id\": 29485, \"name\": \"a white signpost\"}, {\"id\": 29486, \"name\": \"the blue and yellow helmet\"}, {\"id\": 29487, \"name\": \"a red seatbelt\"}, {\"id\": 29488, \"name\": \"a metal roller coaster track\"}, {\"id\": 29489, \"name\": \"the brown fence\"}, {\"id\": 29490, \"name\": \"red mouth\"}, {\"id\": 29491, \"name\": \"the red handrail\"}, {\"id\": 29492, \"name\": \"white board\"}, {\"id\": 29493, \"name\": \"the purple and white stripe\"}, {\"id\": 29494, \"name\": \"the black handgun\"}, {\"id\": 29495, \"name\": \"brown paper label\"}, {\"id\": 29496, \"name\": \"a green and yellow elf dress\"}, {\"id\": 29497, \"name\": \"the stylized outline of new zealand\"}, {\"id\": 29498, \"name\": \"the cartoonish graphic\"}, {\"id\": 29499, \"name\": \"construction site\"}, {\"id\": 29500, \"name\": \"the rider's shadow\"}, {\"id\": 29501, \"name\": \"a white 13\"}, {\"id\": 29502, \"name\": \"the black and white bag\"}, {\"id\": 29503, \"name\": \"the side of the house\"}, {\"id\": 29504, \"name\": \"a miniature race track\"}, {\"id\": 29505, \"name\": \"purchase logo\"}, {\"id\": 29506, \"name\": \"an orange safety cone\"}, {\"id\": 29507, \"name\": \"the green teapot\"}, {\"id\": 29508, \"name\": \"black radiator\"}, {\"id\": 29509, \"name\": \"the yellow and red flame design\"}, {\"id\": 29510, \"name\": \"the orange sneaker\"}, {\"id\": 29511, \"name\": \"the trail of white foam\"}, {\"id\": 29512, \"name\": \"the toy town\"}, {\"id\": 29513, \"name\": \"people's feet\"}, {\"id\": 29514, \"name\": \"the large grassy area\"}, {\"id\": 29515, \"name\": \"brown paper package\"}, {\"id\": 29516, \"name\": \"a cyclist's handlebar\"}, {\"id\": 29517, \"name\": \"industrial airport\"}, {\"id\": 29518, \"name\": \"a black ink\"}, {\"id\": 29519, \"name\": \"lion\"}, {\"id\": 29520, \"name\": \"a grey road\"}, {\"id\": 29521, \"name\": \"a green train car\"}, {\"id\": 29522, \"name\": \"a shell name\"}, {\"id\": 29523, \"name\": \"transparent wing\"}, {\"id\": 29524, \"name\": \"a rider's left hand\"}, {\"id\": 29525, \"name\": \"the large blade\"}, {\"id\": 29526, \"name\": \"the red and gray shirt\"}, {\"id\": 29527, \"name\": \"a middle finger\"}, {\"id\": 29528, \"name\": \"squid or octopu\"}, {\"id\": 29529, \"name\": \"line of motorcycle\"}, {\"id\": 29530, \"name\": \"the caption in spanish\"}, {\"id\": 29531, \"name\": \"the green decoration\"}, {\"id\": 29532, \"name\": \"the large, light-colored seal\"}, {\"id\": 29533, \"name\": \"the toaster or similar appliance\"}, {\"id\": 29534, \"name\": \"a daytona logo\"}, {\"id\": 29535, \"name\": \"blue trailer\"}, {\"id\": 29536, \"name\": \"the circular fiat logo\"}, {\"id\": 29537, \"name\": \"a thumb nail\"}, {\"id\": 29538, \"name\": \"a structure's slat\"}, {\"id\": 29539, \"name\": \"the blue oval\"}, {\"id\": 29540, \"name\": \"the turquoise nail polish\"}, {\"id\": 29541, \"name\": \"vertical white line\"}, {\"id\": 29542, \"name\": \"the black clothing\"}, {\"id\": 29543, \"name\": \"the bank of cylinder\"}, {\"id\": 29544, \"name\": \"a circular red dirt pitcher's mound\"}, {\"id\": 29545, \"name\": \"the concrete or tile floor\"}, {\"id\": 29546, \"name\": \"makeup brush\"}, {\"id\": 29547, \"name\": \"the red-painted fingernail\"}, {\"id\": 29548, \"name\": \"the bell\"}, {\"id\": 29549, \"name\": \"the wrench\"}, {\"id\": 29550, \"name\": \"a chrome tail light\"}, {\"id\": 29551, \"name\": \"a reflection of the road\"}, {\"id\": 29552, \"name\": \"a ppe zone\"}, {\"id\": 29553, \"name\": \"the leopard-printed seat cover\"}, {\"id\": 29554, \"name\": \"the mileage\"}, {\"id\": 29555, \"name\": \"utility truck\"}, {\"id\": 29556, \"name\": \"a white and blue bu\"}, {\"id\": 29557, \"name\": \"the small yellow light\"}, {\"id\": 29558, \"name\": \"the mixed fruit\"}, {\"id\": 29559, \"name\": \"digital timestamp\"}, {\"id\": 29560, \"name\": \"the yellow price tag\"}, {\"id\": 29561, \"name\": \"a truck's gearshift\"}, {\"id\": 29562, \"name\": \"a blue and red logo\"}, {\"id\": 29563, \"name\": \"brown square head\"}, {\"id\": 29564, \"name\": \"the vibrant green plant\"}, {\"id\": 29565, \"name\": \"an orange tag\"}, {\"id\": 29566, \"name\": \"the blue shelf\"}, {\"id\": 29567, \"name\": \"the long, narrow red and gold-patterned rug\"}, {\"id\": 29568, \"name\": \"tesla logo\"}, {\"id\": 29569, \"name\": \"vehicle's front grille\"}, {\"id\": 29570, \"name\": \"the yellow bp logo\"}, {\"id\": 29571, \"name\": \"a cut-out handle\"}, {\"id\": 29572, \"name\": \"a youtube url\"}, {\"id\": 29573, \"name\": \"a black air vent\"}, {\"id\": 29574, \"name\": \"the long red hose\"}, {\"id\": 29575, \"name\": \"camouflage-print bag\"}, {\"id\": 29576, \"name\": \"the green cylinder\"}, {\"id\": 29577, \"name\": \"a baseball umpire\"}, {\"id\": 29578, \"name\": \"a uci logo\"}, {\"id\": 29579, \"name\": \"dark spot\"}, {\"id\": 29580, \"name\": \"a black and white rug\"}, {\"id\": 29581, \"name\": \"an ice rink\"}, {\"id\": 29582, \"name\": \"the lot\"}, {\"id\": 29583, \"name\": \"logo for abc2\"}, {\"id\": 29584, \"name\": \"a tan-colored concrete deck\"}, {\"id\": 29585, \"name\": \"stage or platform\"}, {\"id\": 29586, \"name\": \"a brown finish\"}, {\"id\": 29587, \"name\": \"icon\"}, {\"id\": 29588, \"name\": \"a blue and white striped pillow\"}, {\"id\": 29589, \"name\": \"a long, spiky tail\"}, {\"id\": 29590, \"name\": \"a mechanical device\"}, {\"id\": 29591, \"name\": \"a film's sprocket\"}, {\"id\": 29592, \"name\": \"the black plastic panel\"}, {\"id\": 29593, \"name\": \"cloud of smoke\"}, {\"id\": 29594, \"name\": \"the spiky horn\"}, {\"id\": 29595, \"name\": \"a bike's handlebars\"}, {\"id\": 29596, \"name\": \"river or stream\"}, {\"id\": 29597, \"name\": \"a cranberry\"}, {\"id\": 29598, \"name\": \"the wood chipper\"}, {\"id\": 29599, \"name\": \"multicolored line\"}, {\"id\": 29600, \"name\": \"round air vent\"}, {\"id\": 29601, \"name\": \"a small circular object\"}, {\"id\": 29602, \"name\": \"a red ladder\"}, {\"id\": 29603, \"name\": \"small, silver, cylindrical object\"}, {\"id\": 29604, \"name\": \"the back of their head\"}, {\"id\": 29605, \"name\": \"a golden liquid\"}, {\"id\": 29606, \"name\": \"a green shipping container\"}, {\"id\": 29607, \"name\": \"a thin black line\"}, {\"id\": 29608, \"name\": \"the marker\"}, {\"id\": 29609, \"name\": \"red and black line\"}, {\"id\": 29610, \"name\": \"a paved surface\"}, {\"id\": 29611, \"name\": \"the white tray\"}, {\"id\": 29612, \"name\": \"the handlebar of the bike\"}, {\"id\": 29613, \"name\": \"the fat truck logo\"}, {\"id\": 29614, \"name\": \"black brake lever\"}, {\"id\": 29615, \"name\": \"a grocery store\"}, {\"id\": 29616, \"name\": \"the user named bryancasella\"}, {\"id\": 29617, \"name\": \"the white wire\"}, {\"id\": 29618, \"name\": \"the earplug\"}, {\"id\": 29619, \"name\": \"dark tree line\"}, {\"id\": 29620, \"name\": \"a zigzag pattern\"}, {\"id\": 29621, \"name\": \"a blue reflector\"}, {\"id\": 29622, \"name\": \"a driver's dashboard\"}, {\"id\": 29623, \"name\": \"the dog's owner\"}, {\"id\": 29624, \"name\": \"a dark gray fabric case\"}, {\"id\": 29625, \"name\": \"the purple hat\"}, {\"id\": 29626, \"name\": \"the grey and black jacket\"}, {\"id\": 29627, \"name\": \"address\"}, {\"id\": 29628, \"name\": \"the eyebrow\"}, {\"id\": 29629, \"name\": \"the car's roof\"}, {\"id\": 29630, \"name\": \"large, dark-colored dragon\"}, {\"id\": 29631, \"name\": \"makeup table\"}, {\"id\": 29632, \"name\": \"a percentage indicator\"}, {\"id\": 29633, \"name\": \"the 20\"}, {\"id\": 29634, \"name\": \"blue tape\"}, {\"id\": 29635, \"name\": \"the phone mount\"}, {\"id\": 29636, \"name\": \"bandaged eye\"}, {\"id\": 29637, \"name\": \"lobby\"}, {\"id\": 29638, \"name\": \"black and white feathered wristband\"}, {\"id\": 29639, \"name\": \"a steep drop\"}, {\"id\": 29640, \"name\": \"the boat's registration number\"}, {\"id\": 29641, \"name\": \"diagonal line\"}, {\"id\": 29642, \"name\": \"an orange and yellow wire\"}, {\"id\": 29643, \"name\": \"a light-colored ceiling\"}, {\"id\": 29644, \"name\": \"green coating\"}, {\"id\": 29645, \"name\": \"a red and blue table\"}, {\"id\": 29646, \"name\": \"a red handlebar\"}, {\"id\": 29647, \"name\": \"a sleek black and red sport car\"}, {\"id\": 29648, \"name\": \"a white phone\"}, {\"id\": 29649, \"name\": \"the long, curved leg\"}, {\"id\": 29650, \"name\": \"large bridge\"}, {\"id\": 29651, \"name\": \"oil derrick\"}, {\"id\": 29652, \"name\": \"bag of green grape\"}, {\"id\": 29653, \"name\": \"the subtle watermark\"}, {\"id\": 29654, \"name\": \"the yellow railing\"}, {\"id\": 29655, \"name\": \"dotted border\"}, {\"id\": 29656, \"name\": \"the yellowish light\"}, {\"id\": 29657, \"name\": \"a dark red and black chest piece\"}, {\"id\": 29658, \"name\": \"the wooden panel\"}, {\"id\": 29659, \"name\": \"an orange cloud of fire\"}, {\"id\": 29660, \"name\": \"dark gray isuzu d-max\"}, {\"id\": 29661, \"name\": \"the red and white cooler\"}, {\"id\": 29662, \"name\": \"vibrant red circle\"}, {\"id\": 29663, \"name\": \"the nail technician\"}, {\"id\": 29664, \"name\": \"the track's metal rail\"}, {\"id\": 29665, \"name\": \"a dark green roof\"}, {\"id\": 29666, \"name\": \"a brown and green box\"}, {\"id\": 29667, \"name\": \"rope ladder\"}, {\"id\": 29668, \"name\": \"a light blue carpet\"}, {\"id\": 29669, \"name\": \"a handlebar of the bicycle\"}, {\"id\": 29670, \"name\": \"helmeted individual\"}, {\"id\": 29671, \"name\": \"red lid\"}, {\"id\": 29672, \"name\": \"a yellow hook\"}, {\"id\": 29673, \"name\": \"the pit lane\"}, {\"id\": 29674, \"name\": \"university name\"}, {\"id\": 29675, \"name\": \"a autism society\"}, {\"id\": 29676, \"name\": \"metal scaffolding\"}, {\"id\": 29677, \"name\": \"a car's roof\"}, {\"id\": 29678, \"name\": \"gold and silver bowling ball\"}, {\"id\": 29679, \"name\": \"a green and black object\"}, {\"id\": 29680, \"name\": \"a warning message\"}, {\"id\": 29681, \"name\": \"the faint watermark\"}, {\"id\": 29682, \"name\": \"a white grip\"}, {\"id\": 29683, \"name\": \"a black whisker\"}, {\"id\": 29684, \"name\": \"a black mesh floor\"}, {\"id\": 29685, \"name\": \"a white and red sleeve\"}, {\"id\": 29686, \"name\": \"yellow cloth\"}, {\"id\": 29687, \"name\": \"dark-colored jersey\"}, {\"id\": 29688, \"name\": \"an armored personnel carrier\"}, {\"id\": 29689, \"name\": \"the black rearview mirror\"}, {\"id\": 29690, \"name\": \"a handheld power tool\"}, {\"id\": 29691, \"name\": \"the green curve\"}, {\"id\": 29692, \"name\": \"a black ladder\"}, {\"id\": 29693, \"name\": \"a tree-lined road\"}, {\"id\": 29694, \"name\": \"rfd\"}, {\"id\": 29695, \"name\": \"a black road\"}, {\"id\": 29696, \"name\": \"a white, red, and blue label\"}, {\"id\": 29697, \"name\": \"tree-lined sidewalk\"}, {\"id\": 29698, \"name\": \"gold tag\"}, {\"id\": 29699, \"name\": \"red long-sleeve shirt\"}, {\"id\": 29700, \"name\": \"the roof of the house\"}, {\"id\": 29701, \"name\": \"football pitch\"}, {\"id\": 29702, \"name\": \"blue and white feather\"}, {\"id\": 29703, \"name\": \"lip gloss\"}, {\"id\": 29704, \"name\": \"warehouse\"}, {\"id\": 29705, \"name\": \"blue water bottle holder\"}, {\"id\": 29706, \"name\": \"white bottle\"}, {\"id\": 29707, \"name\": \"green couch\"}, {\"id\": 29708, \"name\": \"a small flame\"}, {\"id\": 29709, \"name\": \"the red and white light\"}, {\"id\": 29710, \"name\": \"the red and yellow horizontal stripe\"}, {\"id\": 29711, \"name\": \"white gravel\"}, {\"id\": 29712, \"name\": \"the tagline\"}, {\"id\": 29713, \"name\": \"danone\"}, {\"id\": 29714, \"name\": \"the dark gray roof\"}, {\"id\": 29715, \"name\": \"dark mane\"}, {\"id\": 29716, \"name\": \"a pechter's breads-bagels 718-439-9600\"}, {\"id\": 29717, \"name\": \"the colorful banner\"}, {\"id\": 29718, \"name\": \"the yellow and light blue stripe\"}, {\"id\": 29719, \"name\": \"challenge\"}, {\"id\": 29720, \"name\": \"yellow perimeter\"}, {\"id\": 29721, \"name\": \"a hat or helmet\"}, {\"id\": 29722, \"name\": \"the gray asphalt surface\"}, {\"id\": 29723, \"name\": \"the uae flag\"}, {\"id\": 29724, \"name\": \"black metal grid\"}, {\"id\": 29725, \"name\": \"a large white rectangle\"}, {\"id\": 29726, \"name\": \"black metal ring\"}, {\"id\": 29727, \"name\": \"the 2019 chevro logo\"}, {\"id\": 29728, \"name\": \"a black and red logo\"}, {\"id\": 29729, \"name\": \"the cup holder\"}, {\"id\": 29730, \"name\": \"single window\"}, {\"id\": 29731, \"name\": \"christma tree\"}, {\"id\": 29732, \"name\": \"yellow flame design\"}, {\"id\": 29733, \"name\": \"the time stamp\"}, {\"id\": 29734, \"name\": \"a grassy infield\"}, {\"id\": 29735, \"name\": \"a black and red go-kart\"}, {\"id\": 29736, \"name\": \"the zebra's head\"}, {\"id\": 29737, \"name\": \"the red and white striped pole\"}, {\"id\": 29738, \"name\": \"the blue cable\"}, {\"id\": 29739, \"name\": \"orange motorcycle\"}, {\"id\": 29740, \"name\": \"a red and yellow object\"}, {\"id\": 29741, \"name\": \"a water bottle holder\"}, {\"id\": 29742, \"name\": \"a small tablet\"}, {\"id\": 29743, \"name\": \"the large, black control panel\"}, {\"id\": 29744, \"name\": \"a pink string\"}, {\"id\": 29745, \"name\": \"older man's face\"}, {\"id\": 29746, \"name\": \"the ship's hull\"}, {\"id\": 29747, \"name\": \"a logo for cctv news\"}, {\"id\": 29748, \"name\": \"dark blue bead\"}, {\"id\": 29749, \"name\": \"the red, white, and yellow light\"}, {\"id\": 29750, \"name\": \"saw blade\"}, {\"id\": 29751, \"name\": \"phil 480\"}, {\"id\": 29752, \"name\": \"the large, octagonal rearview mirror\"}, {\"id\": 29753, \"name\": \"a large grassy area\"}, {\"id\": 29754, \"name\": \"the tree-like structure\"}, {\"id\": 29755, \"name\": \"epcot ball\"}, {\"id\": 29756, \"name\": \"white speedometer graphic\"}, {\"id\": 29757, \"name\": \"a climbing frame\"}, {\"id\": 29758, \"name\": \"green and black rearview mirror\"}, {\"id\": 29759, \"name\": \"a sun visor\"}, {\"id\": 29760, \"name\": \"large exhaust pipe\"}, {\"id\": 29761, \"name\": \"white and blue beam\"}, {\"id\": 29762, \"name\": \"a mint\"}, {\"id\": 29763, \"name\": \"blue and yellow vest\"}, {\"id\": 29764, \"name\": \"the sign or plaque\"}, {\"id\": 29765, \"name\": \"a yellow kernel\"}, {\"id\": 29766, \"name\": \"a live feed\"}, {\"id\": 29767, \"name\": \"a driver's side\"}, {\"id\": 29768, \"name\": \"a large red and black tail\"}, {\"id\": 29769, \"name\": \"the blue and black tool\"}, {\"id\": 29770, \"name\": \"the white folder\"}, {\"id\": 29771, \"name\": \"the black atv\"}, {\"id\": 29772, \"name\": \"the white, red, and blue painted curb\"}, {\"id\": 29773, \"name\": \"the game's title\"}, {\"id\": 29774, \"name\": \"small storage compartment\"}, {\"id\": 29775, \"name\": \"renthal logo\"}, {\"id\": 29776, \"name\": \"red tray\"}, {\"id\": 29777, \"name\": \"a tuning peg\"}, {\"id\": 29778, \"name\": \"a train yard\"}, {\"id\": 29779, \"name\": \"the cluttered table\"}, {\"id\": 29780, \"name\": \"tall, dark column\"}, {\"id\": 29781, \"name\": \"sawhorse\"}, {\"id\": 29782, \"name\": \"a company logo\"}, {\"id\": 29783, \"name\": \"the open garage door\"}, {\"id\": 29784, \"name\": \"bottle or container\"}, {\"id\": 29785, \"name\": \"the piece of fabric or leather\"}, {\"id\": 29786, \"name\": \"a gray trash can\"}, {\"id\": 29787, \"name\": \"a red toy car\"}, {\"id\": 29788, \"name\": \"green arrow\"}, {\"id\": 29789, \"name\": \"red mailbox\"}, {\"id\": 29790, \"name\": \"the orange trigger\"}, {\"id\": 29791, \"name\": \"toyota truck\"}, {\"id\": 29792, \"name\": \"a black and red grip\"}, {\"id\": 29793, \"name\": \"a black lever\"}, {\"id\": 29794, \"name\": \"a rider's feet\"}, {\"id\": 29795, \"name\": \"a high-visibility yellow vest\"}, {\"id\": 29796, \"name\": \"white wave\"}, {\"id\": 29797, \"name\": \"the mower\"}, {\"id\": 29798, \"name\": \"rowing boat\"}, {\"id\": 29799, \"name\": \"a multicolored jersey\"}, {\"id\": 29800, \"name\": \"the lower half of a person\"}, {\"id\": 29801, \"name\": \"the white racing logo\"}, {\"id\": 29802, \"name\": \"a vibrant orange claw\"}, {\"id\": 29803, \"name\": \"exhaust pipe\"}, {\"id\": 29804, \"name\": \"a white carousel horse\"}, {\"id\": 29805, \"name\": \"a white and black logo\"}, {\"id\": 29806, \"name\": \"the seaweed\"}, {\"id\": 29807, \"name\": \"an office complex\"}, {\"id\": 29808, \"name\": \"reporter\"}, {\"id\": 29809, \"name\": \"vibrant amusement park ride\"}, {\"id\": 29810, \"name\": \"the golfer\"}, {\"id\": 29811, \"name\": \"a black brim\"}, {\"id\": 29812, \"name\": \"a brown spot\"}, {\"id\": 29813, \"name\": \"the pink polo shirt\"}, {\"id\": 29814, \"name\": \"the dark gray uniform\"}, {\"id\": 29815, \"name\": \"the safety official\"}, {\"id\": 29816, \"name\": \"black-framed glass\"}, {\"id\": 29817, \"name\": \"the lawn mower's wheel\"}, {\"id\": 29818, \"name\": \"a japanese writing\"}, {\"id\": 29819, \"name\": \"black and red sign\"}, {\"id\": 29820, \"name\": \"a yellow crosswalk marking\"}, {\"id\": 29821, \"name\": \"the packed audience\"}, {\"id\": 29822, \"name\": \"the maroon banner\"}, {\"id\": 29823, \"name\": \"the parking area\"}, {\"id\": 29824, \"name\": \"a red and white striped barrier\"}, {\"id\": 29825, \"name\": \"a pumpkin\"}, {\"id\": 29826, \"name\": \"silver volkswagen\"}, {\"id\": 29827, \"name\": \"monster\"}, {\"id\": 29828, \"name\": \"the karting miami\"}, {\"id\": 29829, \"name\": \"a silhouette of a person in a costume\"}, {\"id\": 29830, \"name\": \"brown umbrella\"}, {\"id\": 29831, \"name\": \"the dark area\"}, {\"id\": 29832, \"name\": \"the black and red banner\"}, {\"id\": 29833, \"name\": \"table or other flat surface\"}, {\"id\": 29834, \"name\": \"a bow tie\"}, {\"id\": 29835, \"name\": \"caption or title\"}, {\"id\": 29836, \"name\": \"the and flyboy zombie\"}, {\"id\": 29837, \"name\": \"the comfortable outdoor seating\"}, {\"id\": 29838, \"name\": \"the man in a yellow shirt and black pant\"}, {\"id\": 29839, \"name\": \"a dark blue sedan\"}, {\"id\": 29840, \"name\": \"zootopia logo\"}, {\"id\": 29841, \"name\": \"the tent or awning\"}, {\"id\": 29842, \"name\": \"a computer\"}, {\"id\": 29843, \"name\": \"a yellow and white uniform\"}, {\"id\": 29844, \"name\": \"black and white car\"}, {\"id\": 29845, \"name\": \"an open blue umbrella\"}, {\"id\": 29846, \"name\": \"a large nesting doll\"}, {\"id\": 29847, \"name\": \"humanoid robot\"}, {\"id\": 29848, \"name\": \"the red metal structure\"}, {\"id\": 29849, \"name\": \"child's shadow\"}, {\"id\": 29850, \"name\": \"a long wooden bar\"}, {\"id\": 29851, \"name\": \"brown volcano\"}, {\"id\": 29852, \"name\": \"beige tile\"}, {\"id\": 29853, \"name\": \"yellow and black uniform\"}, {\"id\": 29854, \"name\": \"man in a white hat and black jacket\"}, {\"id\": 29855, \"name\": \"a tropical-print tank top\"}, {\"id\": 29856, \"name\": \"the stunning architectural ensemble\"}, {\"id\": 29857, \"name\": \"vibrant toy train set\"}, {\"id\": 29858, \"name\": \"bow's yellow arrowhead\"}, {\"id\": 29859, \"name\": \"a neon green and blue uniform\"}, {\"id\": 29860, \"name\": \"the apartment\"}, {\"id\": 29861, \"name\": \"the red traffic sign\"}, {\"id\": 29862, \"name\": \"green cone\"}, {\"id\": 29863, \"name\": \"yellow hatchback\"}, {\"id\": 29864, \"name\": \"the sombrero\"}, {\"id\": 29865, \"name\": \"the large toothy grin\"}, {\"id\": 29866, \"name\": \"the gras hill\"}, {\"id\": 29867, \"name\": \"the colorful piece\"}, {\"id\": 29868, \"name\": \"the top of an ambulance\"}, {\"id\": 29869, \"name\": \"the yellow school bu sign\"}, {\"id\": 29870, \"name\": \"man in a blue shirt and tie\"}, {\"id\": 29871, \"name\": \"pointed snout\"}, {\"id\": 29872, \"name\": \"red flannel shirt\"}, {\"id\": 29873, \"name\": \"red brick house\"}, {\"id\": 29874, \"name\": \"a mouthpiece\"}, {\"id\": 29875, \"name\": \"the dark green cap\"}, {\"id\": 29876, \"name\": \"rectangular image\"}, {\"id\": 29877, \"name\": \"light orange chicken\"}, {\"id\": 29878, \"name\": \"kart's tire\"}, {\"id\": 29879, \"name\": \"a jewelry\"}, {\"id\": 29880, \"name\": \"the underside of a surfboard\"}, {\"id\": 29881, \"name\": \"dark gray suv\"}, {\"id\": 29882, \"name\": \"the cowboy\"}, {\"id\": 29883, \"name\": \"a large mound of sand\"}, {\"id\": 29884, \"name\": \"man's arm\"}, {\"id\": 29885, \"name\": \"small circular object\"}, {\"id\": 29886, \"name\": \"the large suv\"}, {\"id\": 29887, \"name\": \"the red hexagon icon\"}, {\"id\": 29888, \"name\": \"teal handle\"}, {\"id\": 29889, \"name\": \"the black and blue checkered flag design\"}, {\"id\": 29890, \"name\": \"white flag\"}, {\"id\": 29891, \"name\": \"asphalt surface\"}, {\"id\": 29892, \"name\": \"the small red lego block\"}, {\"id\": 29893, \"name\": \"white sleeveless dres or tunic\"}, {\"id\": 29894, \"name\": \"blue and yellow design\"}, {\"id\": 29895, \"name\": \"yellow writing\"}, {\"id\": 29896, \"name\": \"a white apron\"}, {\"id\": 29897, \"name\": \"a red and white santa clau figurine\"}, {\"id\": 29898, \"name\": \"the round white face\"}, {\"id\": 29899, \"name\": \"dark hoodie\"}, {\"id\": 29900, \"name\": \"metallic armor\"}, {\"id\": 29901, \"name\": \"the bright light source\"}, {\"id\": 29902, \"name\": \"the blue and yellow checkered stripe\"}, {\"id\": 29903, \"name\": \"the minion statue\"}, {\"id\": 29904, \"name\": \"a large, heavily modified vehicle\"}, {\"id\": 29905, \"name\": \"red or orange life jacket\"}, {\"id\": 29906, \"name\": \"a large pink ear\"}, {\"id\": 29907, \"name\": \"one-way sign\"}, {\"id\": 29908, \"name\": \"the earring\"}, {\"id\": 29909, \"name\": \"a coffee maker\"}, {\"id\": 29910, \"name\": \"a red japanese character\"}, {\"id\": 29911, \"name\": \"a white shorts with black stripe\"}, {\"id\": 29912, \"name\": \"the light blue plastic figurine\"}, {\"id\": 29913, \"name\": \"maroon banner\"}, {\"id\": 29914, \"name\": \"the pink glow\"}, {\"id\": 29915, \"name\": \"the halo of light\"}, {\"id\": 29916, \"name\": \"gray step\"}, {\"id\": 29917, \"name\": \"the blue hard hat\"}, {\"id\": 29918, \"name\": \"a graphic of a horse's head\"}, {\"id\": 29919, \"name\": \"large purse\"}, {\"id\": 29920, \"name\": \"a thin tire\"}, {\"id\": 29921, \"name\": \"a stocking\"}, {\"id\": 29922, \"name\": \"white-painted crosswalk\"}, {\"id\": 29923, \"name\": \"the metal stand\"}, {\"id\": 29924, \"name\": \"a blurry green sign\"}, {\"id\": 29925, \"name\": \"a lead car\"}, {\"id\": 29926, \"name\": \"news banner\"}, {\"id\": 29927, \"name\": \"the white sack\"}, {\"id\": 29928, \"name\": \"the lively drumming\"}, {\"id\": 29929, \"name\": \"a thai writing\"}, {\"id\": 29930, \"name\": \"the marionette\"}, {\"id\": 29931, \"name\": \"a lapel\"}, {\"id\": 29932, \"name\": \"black opening\"}, {\"id\": 29933, \"name\": \"the white circle sign\"}, {\"id\": 29934, \"name\": \"knitted item\"}, {\"id\": 29935, \"name\": \"a yellow cable\"}, {\"id\": 29936, \"name\": \"the balding\"}, {\"id\": 29937, \"name\": \"a black harness\"}, {\"id\": 29938, \"name\": \"a passenger's head\"}, {\"id\": 29939, \"name\": \"the vertical pillar\"}, {\"id\": 29940, \"name\": \"the gray bottle\"}, {\"id\": 29941, \"name\": \"a man in purple\"}, {\"id\": 29942, \"name\": \"stone tower\"}, {\"id\": 29943, \"name\": \"a white undershirt\"}, {\"id\": 29944, \"name\": \"the large green truck\"}, {\"id\": 29945, \"name\": \"an official\"}, {\"id\": 29946, \"name\": \"the white eye\"}, {\"id\": 29947, \"name\": \"the large tan structure\"}, {\"id\": 29948, \"name\": \"the orange headlight\"}, {\"id\": 29949, \"name\": \"skull\"}, {\"id\": 29950, \"name\": \"red suit\"}, {\"id\": 29951, \"name\": \"the devil's bowl speedway\"}, {\"id\": 29952, \"name\": \"red and green slide\"}, {\"id\": 29953, \"name\": \"the large blue door\"}, {\"id\": 29954, \"name\": \"closed shutter\"}, {\"id\": 29955, \"name\": \"the prominent logo\"}, {\"id\": 29956, \"name\": \"lance\"}, {\"id\": 29957, \"name\": \"quaker state logo\"}, {\"id\": 29958, \"name\": \"red vertical stripe\"}, {\"id\": 29959, \"name\": \"a clear glass pitcher\"}, {\"id\": 29960, \"name\": \"a blue and yellow circle\"}, {\"id\": 29961, \"name\": \"the large glass storefront\"}, {\"id\": 29962, \"name\": \"a red and green lantern\"}, {\"id\": 29963, \"name\": \"a green perimeter\"}, {\"id\": 29964, \"name\": \"a white folder\"}, {\"id\": 29965, \"name\": \"a speed\"}, {\"id\": 29966, \"name\": \"large castle-like structure\"}, {\"id\": 29967, \"name\": \"the black-and-white car\"}, {\"id\": 29968, \"name\": \"the delta logo\"}, {\"id\": 29969, \"name\": \"coin\"}, {\"id\": 29970, \"name\": \"the silver food bowl\"}, {\"id\": 29971, \"name\": \"the small red banner\"}, {\"id\": 29972, \"name\": \"a green leafy canopy\"}, {\"id\": 29973, \"name\": \"the small, round mouth\"}, {\"id\": 29974, \"name\": \"a potion bottle\"}, {\"id\": 29975, \"name\": \"an infrastructure\"}, {\"id\": 29976, \"name\": \"a green area\"}, {\"id\": 29977, \"name\": \"a red brick median\"}, {\"id\": 29978, \"name\": \"a large white crate\"}, {\"id\": 29979, \"name\": \"a yellow wig\"}, {\"id\": 29980, \"name\": \"the racing helmet\"}, {\"id\": 29981, \"name\": \"a small white card\"}, {\"id\": 29982, \"name\": \"the throw pillow\"}, {\"id\": 29983, \"name\": \"white and yellow racing jacket\"}, {\"id\": 29984, \"name\": \"a black and gray frame\"}, {\"id\": 29985, \"name\": \"the large, white, cylindrical tank\"}, {\"id\": 29986, \"name\": \"pink and purple design\"}, {\"id\": 29987, \"name\": \"a red and white construction cone\"}, {\"id\": 29988, \"name\": \"black fin\"}, {\"id\": 29989, \"name\": \"the blue and black swim trunk\"}, {\"id\": 29990, \"name\": \"star-shaped decoration\"}, {\"id\": 29991, \"name\": \"red and pink seating area\"}, {\"id\": 29992, \"name\": \"checkered pattern\"}, {\"id\": 29993, \"name\": \"the colorful fish\"}, {\"id\": 29994, \"name\": \"the distinctive red cross\"}, {\"id\": 29995, \"name\": \"dark green exterior\"}, {\"id\": 29996, \"name\": \"small patch of gravel\"}, {\"id\": 29997, \"name\": \"a sledge\"}, {\"id\": 29998, \"name\": \"the tooth\"}, {\"id\": 29999, \"name\": \"a green feathered shoulder piece\"}, {\"id\": 30000, \"name\": \"the large red archway\"}, {\"id\": 30001, \"name\": \"the american flag pattern\"}, {\"id\": 30002, \"name\": \"an oversized shoe\"}, {\"id\": 30003, \"name\": \"blue band\"}, {\"id\": 30004, \"name\": \"the large, cylindrical object\"}, {\"id\": 30005, \"name\": \"sign or poster\"}, {\"id\": 30006, \"name\": \"the white hood of a car\"}, {\"id\": 30007, \"name\": \"a bar stool\"}, {\"id\": 30008, \"name\": \"woman in a white shirt and tan skirt\"}, {\"id\": 30009, \"name\": \"the pedicab's canopy\"}, {\"id\": 30010, \"name\": \"an orange attire\"}, {\"id\": 30011, \"name\": \"the blue thai writing\"}, {\"id\": 30012, \"name\": \"a high collar\"}, {\"id\": 30013, \"name\": \"red and yellow uniform\"}, {\"id\": 30014, \"name\": \"black oar\"}, {\"id\": 30015, \"name\": \"the pineapple\"}, {\"id\": 30016, \"name\": \"the light green panel\"}, {\"id\": 30017, \"name\": \"earbud\"}, {\"id\": 30018, \"name\": \"blue and green hat\"}, {\"id\": 30019, \"name\": \"the brick\"}, {\"id\": 30020, \"name\": \"a silver oxygen tank\"}, {\"id\": 30021, \"name\": \"the raw meat\"}, {\"id\": 30022, \"name\": \"a prominent crosswalk\"}, {\"id\": 30023, \"name\": \"the white plastic table\"}, {\"id\": 30024, \"name\": \"a back arrow\"}, {\"id\": 30025, \"name\": \"a red and yellow chevron\"}, {\"id\": 30026, \"name\": \"person in a blue jacket\"}, {\"id\": 30027, \"name\": \"a wide, arched entryway\"}, {\"id\": 30028, \"name\": \"a yellow and white shirt\"}, {\"id\": 30029, \"name\": \"a green coupe\"}, {\"id\": 30030, \"name\": \"the person in a green shirt\"}, {\"id\": 30031, \"name\": \"suit and tie\"}, {\"id\": 30032, \"name\": \"the yellow rowing boat\"}, {\"id\": 30033, \"name\": \"a large surfboard\"}, {\"id\": 30034, \"name\": \"the black and red car\"}, {\"id\": 30035, \"name\": \"man's left hand\"}, {\"id\": 30036, \"name\": \"a blue poster\"}, {\"id\": 30037, \"name\": \"the padres' logo\"}, {\"id\": 30038, \"name\": \"parade participant\"}, {\"id\": 30039, \"name\": \"the light cardigan\"}, {\"id\": 30040, \"name\": \"a light-colored food item\"}, {\"id\": 30041, \"name\": \"a tractor's tire\"}, {\"id\": 30042, \"name\": \"brown, cartoon mammoth\"}, {\"id\": 30043, \"name\": \"a red and white striped sign\"}, {\"id\": 30044, \"name\": \"the windmill\"}, {\"id\": 30045, \"name\": \"green plastic bag\"}, {\"id\": 30046, \"name\": \"black tent\"}, {\"id\": 30047, \"name\": \"the large bowl of candy\"}, {\"id\": 30048, \"name\": \"a food stand\"}, {\"id\": 30049, \"name\": \"black motor\"}, {\"id\": 30050, \"name\": \"the thick white sole\"}, {\"id\": 30051, \"name\": \"a dark blue car\"}, {\"id\": 30052, \"name\": \"the vibrant red canoe\"}, {\"id\": 30053, \"name\": \"a large gray trash can\"}, {\"id\": 30054, \"name\": \"the large red buoy\"}, {\"id\": 30055, \"name\": \"the orange toy car\"}, {\"id\": 30056, \"name\": \"green lettering\"}, {\"id\": 30057, \"name\": \"sponsor\"}, {\"id\": 30058, \"name\": \"the white dollar sign\"}, {\"id\": 30059, \"name\": \"a red and white graphic\"}, {\"id\": 30060, \"name\": \"one way sign\"}, {\"id\": 30061, \"name\": \"channel's logo\"}, {\"id\": 30062, \"name\": \"a khaki cargo pant\"}, {\"id\": 30063, \"name\": \"a gaffa logo\"}, {\"id\": 30064, \"name\": \"a loincloth\"}, {\"id\": 30065, \"name\": \"the small hoop earring\"}, {\"id\": 30066, \"name\": \"rider's left leg\"}, {\"id\": 30067, \"name\": \"green, yellow, and red clothing\"}, {\"id\": 30068, \"name\": \"a restaurant's sign\"}, {\"id\": 30069, \"name\": \"the round hole\"}, {\"id\": 30070, \"name\": \"small orange buoy\"}, {\"id\": 30071, \"name\": \"the pink bikini\"}, {\"id\": 30072, \"name\": \"slat\"}, {\"id\": 30073, \"name\": \"an universal studio logo\"}, {\"id\": 30074, \"name\": \"the social media icon\"}, {\"id\": 30075, \"name\": \"the man in a white t-shirt\"}, {\"id\": 30076, \"name\": \"the light blue jersey\"}, {\"id\": 30077, \"name\": \"blue ink\"}, {\"id\": 30078, \"name\": \"a copper trim\"}, {\"id\": 30079, \"name\": \"a large orange handbag\"}, {\"id\": 30080, \"name\": \"the white and pink pillow\"}, {\"id\": 30081, \"name\": \"the peace sign\"}, {\"id\": 30082, \"name\": \"white mat\"}, {\"id\": 30083, \"name\": \"the dinosaur's face\"}, {\"id\": 30084, \"name\": \"red ny logo\"}, {\"id\": 30085, \"name\": \"a small green smudge\"}, {\"id\": 30086, \"name\": \"a white eye\"}, {\"id\": 30087, \"name\": \"sleeve of tattoo\"}, {\"id\": 30088, \"name\": \"a handle of a black suitcase\"}, {\"id\": 30089, \"name\": \"ga burner\"}, {\"id\": 30090, \"name\": \"the muscular torso\"}, {\"id\": 30091, \"name\": \"the white beanie\"}, {\"id\": 30092, \"name\": \"a large mural\"}, {\"id\": 30093, \"name\": \"striking white fish\"}, {\"id\": 30094, \"name\": \"a white beanie\"}, {\"id\": 30095, \"name\": \"a green logo\"}, {\"id\": 30096, \"name\": \"the bottle of hand sanitizer\"}, {\"id\": 30097, \"name\": \"the rower\"}, {\"id\": 30098, \"name\": \"a large green rectangle\"}, {\"id\": 30099, \"name\": \"umpire\"}, {\"id\": 30100, \"name\": \"blue gem\"}, {\"id\": 30101, \"name\": \"logo for i24 news\"}, {\"id\": 30102, \"name\": \"small monkey\"}, {\"id\": 30103, \"name\": \"yellow and black taxi\"}, {\"id\": 30104, \"name\": \"a blue antenna\"}, {\"id\": 30105, \"name\": \"green section of the board\"}, {\"id\": 30106, \"name\": \"japanese character\"}, {\"id\": 30107, \"name\": \"the mazda logo\"}, {\"id\": 30108, \"name\": \"a vase of flower\"}, {\"id\": 30109, \"name\": \"small white horn\"}, {\"id\": 30110, \"name\": \"scratcher\"}, {\"id\": 30111, \"name\": \"the reindeer head\"}, {\"id\": 30112, \"name\": \"pmc logo\"}, {\"id\": 30113, \"name\": \"dark red car\"}, {\"id\": 30114, \"name\": \"a black metal base\"}, {\"id\": 30115, \"name\": \"the blue police van\"}, {\"id\": 30116, \"name\": \"the surrounding light\"}, {\"id\": 30117, \"name\": \"dark object\"}, {\"id\": 30118, \"name\": \"the wooden countertop\"}, {\"id\": 30119, \"name\": \"the orange balloon\"}, {\"id\": 30120, \"name\": \"a dark-colored tank top\"}, {\"id\": 30121, \"name\": \"player's hand\"}, {\"id\": 30122, \"name\": \"a muscular physique\"}, {\"id\": 30123, \"name\": \"a green vest\"}, {\"id\": 30124, \"name\": \"the one-way sign\"}, {\"id\": 30125, \"name\": \"a lush green landscape\"}, {\"id\": 30126, \"name\": \"a gray cow\"}, {\"id\": 30127, \"name\": \"a red square\"}, {\"id\": 30128, \"name\": \"the green eye\"}, {\"id\": 30129, \"name\": \"an american girl doll\"}, {\"id\": 30130, \"name\": \"the mcdonald's\"}, {\"id\": 30131, \"name\": \"the yellow barrel\"}, {\"id\": 30132, \"name\": \"a large gear\"}, {\"id\": 30133, \"name\": \"a vibrant orange color\"}, {\"id\": 30134, \"name\": \"the rainbow-colored bird\"}, {\"id\": 30135, \"name\": \"a silver and green label\"}, {\"id\": 30136, \"name\": \"a red live banner\"}, {\"id\": 30137, \"name\": \"large opening\"}, {\"id\": 30138, \"name\": \"the green attire\"}, {\"id\": 30139, \"name\": \"a tan purse\"}, {\"id\": 30140, \"name\": \"the red cart\"}, {\"id\": 30141, \"name\": \"the woman in a blue shirt and pink skirt\"}, {\"id\": 30142, \"name\": \"the red and yellow tassel\"}, {\"id\": 30143, \"name\": \"the black and white pennant flag\"}, {\"id\": 30144, \"name\": \"yellow foliage\"}, {\"id\": 30145, \"name\": \"circular cutout\"}, {\"id\": 30146, \"name\": \"colorful graffiti\"}, {\"id\": 30147, \"name\": \"athletic shoe\"}, {\"id\": 30148, \"name\": \"the green lego piece\"}, {\"id\": 30149, \"name\": \"the white icon\"}, {\"id\": 30150, \"name\": \"dog tag\"}, {\"id\": 30151, \"name\": \"the bank of america sign\"}, {\"id\": 30152, \"name\": \"the light blue skirt\"}, {\"id\": 30153, \"name\": \"black-and-orange sneaker\"}, {\"id\": 30154, \"name\": \"the black side mirror\"}, {\"id\": 30155, \"name\": \"a small logo of a yellow and red fruit\"}, {\"id\": 30156, \"name\": \"a bike ride\"}, {\"id\": 30157, \"name\": \"the large green crane\"}, {\"id\": 30158, \"name\": \"red and yellow sign\"}, {\"id\": 30159, \"name\": \"a small microphone\"}, {\"id\": 30160, \"name\": \"lone orange traffic cone\"}, {\"id\": 30161, \"name\": \"a colorful pool toy\"}, {\"id\": 30162, \"name\": \"an eye\"}, {\"id\": 30163, \"name\": \"piece of cheese\"}, {\"id\": 30164, \"name\": \"lebanese flag\"}, {\"id\": 30165, \"name\": \"matching blue short\"}, {\"id\": 30166, \"name\": \"colorful rock\"}, {\"id\": 30167, \"name\": \"the black sport bra\"}, {\"id\": 30168, \"name\": \"loop\"}, {\"id\": 30169, \"name\": \"the hula hoop\"}, {\"id\": 30170, \"name\": \"large red heart\"}, {\"id\": 30171, \"name\": \"the hanging basket of flower\"}, {\"id\": 30172, \"name\": \"green and black uniform\"}, {\"id\": 30173, \"name\": \"the burgundy vest\"}, {\"id\": 30174, \"name\": \"a jake\"}, {\"id\": 30175, \"name\": \"the ingredient\"}, {\"id\": 30176, \"name\": \"the white and red color scheme\"}, {\"id\": 30177, \"name\": \"the blue coat\"}, {\"id\": 30178, \"name\": \"long dreadlock\"}, {\"id\": 30179, \"name\": \"the black polka dot\"}, {\"id\": 30180, \"name\": \"the small cloud\"}, {\"id\": 30181, \"name\": \"a white cabin\"}, {\"id\": 30182, \"name\": \"the brown doll\"}, {\"id\": 30183, \"name\": \"blue and white dock\"}, {\"id\": 30184, \"name\": \"the title\"}, {\"id\": 30185, \"name\": \"baseball glove\"}, {\"id\": 30186, \"name\": \"a large, dangling earring\"}, {\"id\": 30187, \"name\": \"the large, steaming pot\"}, {\"id\": 30188, \"name\": \"white 1\"}, {\"id\": 30189, \"name\": \"the brown handbag\"}, {\"id\": 30190, \"name\": \"a small green rectangle\"}, {\"id\": 30191, \"name\": \"the white skull face paint\"}, {\"id\": 30192, \"name\": \"a red and yellow jersey\"}, {\"id\": 30193, \"name\": \"a pro logo\"}, {\"id\": 30194, \"name\": \"a red barn\"}, {\"id\": 30195, \"name\": \"black and white fur\"}, {\"id\": 30196, \"name\": \"the gray bar\"}, {\"id\": 30197, \"name\": \"green go-kart\"}, {\"id\": 30198, \"name\": \"the pen or marker\"}, {\"id\": 30199, \"name\": \"a pink saddle blanket\"}, {\"id\": 30200, \"name\": \"the olympic ring\"}, {\"id\": 30201, \"name\": \"a memphis grizzly logo\"}, {\"id\": 30202, \"name\": \"purple barrette\"}, {\"id\": 30203, \"name\": \"the crew of person\"}, {\"id\": 30204, \"name\": \"large piece of construction equipment\"}, {\"id\": 30205, \"name\": \"a dark blue lettering\"}, {\"id\": 30206, \"name\": \"the light green headscarf\"}, {\"id\": 30207, \"name\": \"blue and red car\"}, {\"id\": 30208, \"name\": \"the central cyclist\"}, {\"id\": 30209, \"name\": \"raccoon\"}, {\"id\": 30210, \"name\": \"the red and white striped shirt\"}, {\"id\": 30211, \"name\": \"black, orange, and yellow car\"}, {\"id\": 30212, \"name\": \"the green and yellow creature\"}, {\"id\": 30213, \"name\": \"the sign advertising sale\"}, {\"id\": 30214, \"name\": \"a small digital display\"}, {\"id\": 30215, \"name\": \"a colorful button\"}, {\"id\": 30216, \"name\": \"the tattoo supply\"}, {\"id\": 30217, \"name\": \"large, dark gray stain\"}, {\"id\": 30218, \"name\": \"the large, dark green beam\"}, {\"id\": 30219, \"name\": \"red and white -entry sign\"}, {\"id\": 30220, \"name\": \"the woman in a blue swimsuit and a light blue bandana\"}, {\"id\": 30221, \"name\": \"green paper\"}, {\"id\": 30222, \"name\": \"a columned structure\"}, {\"id\": 30223, \"name\": \"white racing suit\"}, {\"id\": 30224, \"name\": \"green face\"}, {\"id\": 30225, \"name\": \"the large breed\"}, {\"id\": 30226, \"name\": \"right knee\"}, {\"id\": 30227, \"name\": \"the white stomach\"}, {\"id\": 30228, \"name\": \"a colorful polka dot\"}, {\"id\": 30229, \"name\": \"the pink lid\"}, {\"id\": 30230, \"name\": \"blue team's jersey\"}, {\"id\": 30231, \"name\": \"the stadium's light\"}, {\"id\": 30232, \"name\": \"a long blue barrier\"}, {\"id\": 30233, \"name\": \"black riding boot\"}, {\"id\": 30234, \"name\": \"the current score\"}, {\"id\": 30235, \"name\": \"a green oxygen tank\"}, {\"id\": 30236, \"name\": \"a shot glass\"}, {\"id\": 30237, \"name\": \"a black segway\"}, {\"id\": 30238, \"name\": \"a center of the ride\"}, {\"id\": 30239, \"name\": \"a light blue short\"}, {\"id\": 30240, \"name\": \"red and white structure\"}, {\"id\": 30241, \"name\": \"black compression sock\"}, {\"id\": 30242, \"name\": \"the bulletin board\"}, {\"id\": 30243, \"name\": \"a purple bikini\"}, {\"id\": 30244, \"name\": \"circular manhole cover\"}, {\"id\": 30245, \"name\": \"a black london taxi\"}, {\"id\": 30246, \"name\": \"the alcoholic beverage\"}, {\"id\": 30247, \"name\": \"graffiti mural\"}, {\"id\": 30248, \"name\": \"the the anime dude\"}, {\"id\": 30249, \"name\": \"the nc military\"}, {\"id\": 30250, \"name\": \"a rectangular hole\"}, {\"id\": 30251, \"name\": \"the yellow poncho\"}, {\"id\": 30252, \"name\": \"korean character\"}, {\"id\": 30253, \"name\": \"a balding crown\"}, {\"id\": 30254, \"name\": \"a man with a backpack\"}, {\"id\": 30255, \"name\": \"a toy military tank\"}, {\"id\": 30256, \"name\": \"green and brown vest\"}, {\"id\": 30257, \"name\": \"the multicolored border\"}, {\"id\": 30258, \"name\": \"the glowing green object\"}, {\"id\": 30259, \"name\": \"girl on the left\"}, {\"id\": 30260, \"name\": \"front window\"}, {\"id\": 30261, \"name\": \"the anchovy\"}, {\"id\": 30262, \"name\": \"a location\"}, {\"id\": 30263, \"name\": \"horse's rope\"}, {\"id\": 30264, \"name\": \"a large black truck\"}, {\"id\": 30265, \"name\": \"the muscular man\"}, {\"id\": 30266, \"name\": \"the sheet of music\"}, {\"id\": 30267, \"name\": \"small white and blue van\"}, {\"id\": 30268, \"name\": \"the black sport car\"}, {\"id\": 30269, \"name\": \"black and white checkered pattern\"}, {\"id\": 30270, \"name\": \"brown mustache\"}, {\"id\": 30271, \"name\": \"a gold armband\"}, {\"id\": 30272, \"name\": \"yellow 8\"}, {\"id\": 30273, \"name\": \"a black car seat\"}, {\"id\": 30274, \"name\": \"a red and white striped design\"}, {\"id\": 30275, \"name\": \"a spare tire\"}, {\"id\": 30276, \"name\": \"horse-drawn chariot\"}, {\"id\": 30277, \"name\": \"the long arm\"}, {\"id\": 30278, \"name\": \"the cycling metric\"}, {\"id\": 30279, \"name\": \"the red and white color scheme\"}, {\"id\": 30280, \"name\": \"the white and blue stripe\"}, {\"id\": 30281, \"name\": \"string of purple lantern\"}, {\"id\": 30282, \"name\": \"a brick exterior\"}, {\"id\": 30283, \"name\": \"woman in a floral top\"}, {\"id\": 30284, \"name\": \"a cab\"}, {\"id\": 30285, \"name\": \"spaghetti\"}, {\"id\": 30286, \"name\": \"light blue shoe\"}, {\"id\": 30287, \"name\": \"a silver and white pattern\"}, {\"id\": 30288, \"name\": \"matching black, red, and white jacket\"}, {\"id\": 30289, \"name\": \"the dark gray base\"}, {\"id\": 30290, \"name\": \"a blue and white striped tent\"}, {\"id\": 30291, \"name\": \"a color-coded square\"}, {\"id\": 30292, \"name\": \"the green creature\"}, {\"id\": 30293, \"name\": \"a stone or concrete\"}, {\"id\": 30294, \"name\": \"pink and yellow life preserver design\"}, {\"id\": 30295, \"name\": \"dora the explorer balloon\"}, {\"id\": 30296, \"name\": \"a truck's logo\"}, {\"id\": 30297, \"name\": \"the neon green hat\"}, {\"id\": 30298, \"name\": \"white curb\"}, {\"id\": 30299, \"name\": \"distinct attire\"}, {\"id\": 30300, \"name\": \"stylized logo\"}, {\"id\": 30301, \"name\": \"wooden crate\"}, {\"id\": 30302, \"name\": \"an ornate lamppost\"}, {\"id\": 30303, \"name\": \"the small screen device\"}, {\"id\": 30304, \"name\": \"a black mouth\"}, {\"id\": 30305, \"name\": \"the white marble backsplash\"}, {\"id\": 30306, \"name\": \"silver and black scooter\"}, {\"id\": 30307, \"name\": \"a stone pedestal\"}, {\"id\": 30308, \"name\": \"a front paw\"}, {\"id\": 30309, \"name\": \"a snowy median\"}, {\"id\": 30310, \"name\": \"the blue and green umbrella\"}, {\"id\": 30311, \"name\": \"a classical architectural style\"}, {\"id\": 30312, \"name\": \"the purple horn\"}, {\"id\": 30313, \"name\": \"a black lettering\"}, {\"id\": 30314, \"name\": \"the brown bench\"}, {\"id\": 30315, \"name\": \"large-scale robot model\"}, {\"id\": 30316, \"name\": \"the blue barricade\"}, {\"id\": 30317, \"name\": \"the curtain-like design\"}, {\"id\": 30318, \"name\": \"vibrant yellow and green patterned dress\"}, {\"id\": 30319, \"name\": \"red and orange cart\"}, {\"id\": 30320, \"name\": \"the white stick\"}, {\"id\": 30321, \"name\": \"the glowing yellow light\"}, {\"id\": 30322, \"name\": \"the plaid pattern\"}, {\"id\": 30323, \"name\": \"a dark wood leg\"}, {\"id\": 30324, \"name\": \"the pink and blue butterfly\"}, {\"id\": 30325, \"name\": \"a green map\"}, {\"id\": 30326, \"name\": \"a man in a suit\"}, {\"id\": 30327, \"name\": \"the blue long-sleeve shirt\"}, {\"id\": 30328, \"name\": \"clear plastic dome\"}, {\"id\": 30329, \"name\": \"the white shopping bag\"}, {\"id\": 30330, \"name\": \"the white fur trim\"}, {\"id\": 30331, \"name\": \"a clear shield\"}, {\"id\": 30332, \"name\": \"a white nike sock\"}, {\"id\": 30333, \"name\": \"the red, white, and blue stripe\"}, {\"id\": 30334, \"name\": \"orange cleat\"}, {\"id\": 30335, \"name\": \"a wooden slat\"}, {\"id\": 30336, \"name\": \"a paint splatter\"}, {\"id\": 30337, \"name\": \"a brown donkey\"}, {\"id\": 30338, \"name\": \"a gold button\"}, {\"id\": 30339, \"name\": \"a saw blade\"}, {\"id\": 30340, \"name\": \"a hatchback model\"}, {\"id\": 30341, \"name\": \"the bike's seat\"}, {\"id\": 30342, \"name\": \"woman in a black dres and headscarf\"}, {\"id\": 30343, \"name\": \"the yellow chinese character\"}, {\"id\": 30344, \"name\": \"a pink car\"}, {\"id\": 30345, \"name\": \"the brown beanie hat\"}, {\"id\": 30346, \"name\": \"a city dweller\"}, {\"id\": 30347, \"name\": \"a colorful lanyard\"}, {\"id\": 30348, \"name\": \"the red tent\"}, {\"id\": 30349, \"name\": \"a blue and red striped jersey\"}, {\"id\": 30350, \"name\": \"the r2-d2\"}, {\"id\": 30351, \"name\": \"blue creature with a long neck and tail\"}, {\"id\": 30352, \"name\": \"the metal bridge\"}, {\"id\": 30353, \"name\": \"snow white and the seven dwarf\"}, {\"id\": 30354, \"name\": \"blue and white base\"}, {\"id\": 30355, \"name\": \"muddy patch\"}, {\"id\": 30356, \"name\": \"a red and white striped pole\"}, {\"id\": 30357, \"name\": \"green support pillar\"}, {\"id\": 30358, \"name\": \"lit-up skeleton\"}, {\"id\": 30359, \"name\": \"purple backpack\"}, {\"id\": 30360, \"name\": \"the man wearing a white shirt with a red logo\"}, {\"id\": 30361, \"name\": \"the blue plastic crate or box\"}, {\"id\": 30362, \"name\": \"the giraffe seat\"}, {\"id\": 30363, \"name\": \"white embroidered star\"}, {\"id\": 30364, \"name\": \"modern advertisement\"}, {\"id\": 30365, \"name\": \"the nike\"}, {\"id\": 30366, \"name\": \"light green paneling\"}, {\"id\": 30367, \"name\": \"the green leg\"}, {\"id\": 30368, \"name\": \"the military jet\"}, {\"id\": 30369, \"name\": \"yellow smile\"}, {\"id\": 30370, \"name\": \"a grand stone structure\"}, {\"id\": 30371, \"name\": \"the young hockey player\"}, {\"id\": 30372, \"name\": \"wxyz detroit logo\"}, {\"id\": 30373, \"name\": \"a green lane marker\"}, {\"id\": 30374, \"name\": \"a news channel's logo\"}, {\"id\": 30375, \"name\": \"an inscription\"}, {\"id\": 30376, \"name\": \"the black shirt with white stripe\"}, {\"id\": 30377, \"name\": \"blue rain poncho\"}, {\"id\": 30378, \"name\": \"a tan-colored area\"}, {\"id\": 30379, \"name\": \"an orange and white sneaker\"}, {\"id\": 30380, \"name\": \"central picnic table\"}, {\"id\": 30381, \"name\": \"a plastic storage bin\"}, {\"id\": 30382, \"name\": \"a white symbol of a bicycle\"}, {\"id\": 30383, \"name\": \"small plastic horse\"}, {\"id\": 30384, \"name\": \"the floral top\"}, {\"id\": 30385, \"name\": \"a curved tusk\"}, {\"id\": 30386, \"name\": \"blood\"}, {\"id\": 30387, \"name\": \"a floatation device\"}, {\"id\": 30388, \"name\": \"colorful jewel\"}, {\"id\": 30389, \"name\": \"tourist\"}, {\"id\": 30390, \"name\": \"the pitcher's mound\"}, {\"id\": 30391, \"name\": \"a green rescue tube\"}, {\"id\": 30392, \"name\": \"the fruit stand\"}, {\"id\": 30393, \"name\": \"a pink boot\"}, {\"id\": 30394, \"name\": \"rider's left foot\"}, {\"id\": 30395, \"name\": \"the small dark blue banner\"}, {\"id\": 30396, \"name\": \"a main character\"}, {\"id\": 30397, \"name\": \"the sleeveless dress\"}, {\"id\": 30398, \"name\": \"storage basket\"}, {\"id\": 30399, \"name\": \"the black bu\"}, {\"id\": 30400, \"name\": \"a small white tent\"}, {\"id\": 30401, \"name\": \"blue latex glove\"}, {\"id\": 30402, \"name\": \"tights\"}, {\"id\": 30403, \"name\": \"the bubble\"}, {\"id\": 30404, \"name\": \"a reporter\"}, {\"id\": 30405, \"name\": \"spiral staircase\"}, {\"id\": 30406, \"name\": \"a curved branch\"}, {\"id\": 30407, \"name\": \"arm of the cyclist\"}, {\"id\": 30408, \"name\": \"a bright pink knee-high sock\"}, {\"id\": 30409, \"name\": \"the capsized green kayak\"}, {\"id\": 30410, \"name\": \"the prominent black m logo\"}, {\"id\": 30411, \"name\": \"the grey panel\"}, {\"id\": 30412, \"name\": \"a white polka dot\"}, {\"id\": 30413, \"name\": \"a social media platform\"}, {\"id\": 30414, \"name\": \"the sharp tooth\"}, {\"id\": 30415, \"name\": \"footprint\"}, {\"id\": 30416, \"name\": \"a pink pig figurine\"}, {\"id\": 30417, \"name\": \"the black drill\"}, {\"id\": 30418, \"name\": \"the green and black jacket\"}, {\"id\": 30419, \"name\": \"black lettering\"}, {\"id\": 30420, \"name\": \"black case\"}, {\"id\": 30421, \"name\": \"personal belonging\"}, {\"id\": 30422, \"name\": \"the black belly\"}, {\"id\": 30423, \"name\": \"a white creature\"}, {\"id\": 30424, \"name\": \"a blue curved line\"}, {\"id\": 30425, \"name\": \"blurry, green and gray area\"}, {\"id\": 30426, \"name\": \"printer\"}, {\"id\": 30427, \"name\": \"the individual in costume\"}, {\"id\": 30428, \"name\": \"the bird head costume\"}, {\"id\": 30429, \"name\": \"small rock\"}, {\"id\": 30430, \"name\": \"cylindrical piece\"}, {\"id\": 30431, \"name\": \"indoor go-kart\"}, {\"id\": 30432, \"name\": \"black and yellow striped tape\"}, {\"id\": 30433, \"name\": \"a rhino\"}, {\"id\": 30434, \"name\": \"a laptop stand\"}, {\"id\": 30435, \"name\": \"brown breed\"}, {\"id\": 30436, \"name\": \"a camouflage jacket\"}, {\"id\": 30437, \"name\": \"the white metal frame\"}, {\"id\": 30438, \"name\": \"the round head\"}, {\"id\": 30439, \"name\": \"wooden ceiling\"}, {\"id\": 30440, \"name\": \"the small silver box\"}, {\"id\": 30441, \"name\": \"the cartoon-style illustration\"}, {\"id\": 30442, \"name\": \"the black front bumper\"}, {\"id\": 30443, \"name\": \"deck of a cruise ship\"}, {\"id\": 30444, \"name\": \"the tree's silhouette\"}, {\"id\": 30445, \"name\": \"the truck's front tire\"}, {\"id\": 30446, \"name\": \"waterfall\"}, {\"id\": 30447, \"name\": \"glas jar\"}, {\"id\": 30448, \"name\": \"a black motorcycle helmet\"}, {\"id\": 30449, \"name\": \"blue-and-white striped barrier\"}, {\"id\": 30450, \"name\": \"a seat tube\"}, {\"id\": 30451, \"name\": \"a windshield of the car\"}, {\"id\": 30452, \"name\": \"a small, green fish-shaped lure\"}, {\"id\": 30453, \"name\": \"black pickup truck\"}, {\"id\": 30454, \"name\": \"a green and brown landscape\"}, {\"id\": 30455, \"name\": \"zipper\"}, {\"id\": 30456, \"name\": \"lush canopy of tree\"}, {\"id\": 30457, \"name\": \"a black shaft\"}, {\"id\": 30458, \"name\": \"a beer\"}, {\"id\": 30459, \"name\": \"a gras area\"}, {\"id\": 30460, \"name\": \"diagonal white stripe\"}, {\"id\": 30461, \"name\": \"a silver cone\"}, {\"id\": 30462, \"name\": \"eyeshadow palette\"}, {\"id\": 30463, \"name\": \"low blue fence\"}, {\"id\": 30464, \"name\": \"a character's leg\"}, {\"id\": 30465, \"name\": \"the tvg logo\"}, {\"id\": 30466, \"name\": \"the undercarriage\"}, {\"id\": 30467, \"name\": \"a dark metal\"}, {\"id\": 30468, \"name\": \"rider's handlebar\"}, {\"id\": 30469, \"name\": \"long eyelash\"}, {\"id\": 30470, \"name\": \"the cat's nose\"}, {\"id\": 30471, \"name\": \"blue frame\"}, {\"id\": 30472, \"name\": \"the dark blue paint\"}, {\"id\": 30473, \"name\": \"a white trader joe's logo\"}, {\"id\": 30474, \"name\": \"a person's body\"}, {\"id\": 30475, \"name\": \"a black and white frame\"}, {\"id\": 30476, \"name\": \"a leith logo\"}, {\"id\": 30477, \"name\": \"silver wedding band\"}, {\"id\": 30478, \"name\": \"purple suit\"}, {\"id\": 30479, \"name\": \"a red-and-white light rail train\"}, {\"id\": 30480, \"name\": \"a yellow and green sign\"}, {\"id\": 30481, \"name\": \"down tube\"}, {\"id\": 30482, \"name\": \"a moving vehicle\"}, {\"id\": 30483, \"name\": \"yellow wire\"}, {\"id\": 30484, \"name\": \"blue medical symbol\"}, {\"id\": 30485, \"name\": \"screw\"}, {\"id\": 30486, \"name\": \"the horse's number\"}, {\"id\": 30487, \"name\": \"the pink shawl\"}, {\"id\": 30488, \"name\": \"the name of a racer\"}, {\"id\": 30489, \"name\": \"tube of sunscreen\"}, {\"id\": 30490, \"name\": \"the green bead\"}, {\"id\": 30491, \"name\": \"the yellow eye\"}, {\"id\": 30492, \"name\": \"an arched entryway\"}, {\"id\": 30493, \"name\": \"the gemini logo\"}, {\"id\": 30494, \"name\": \"a mountain biker\"}, {\"id\": 30495, \"name\": \"light-colored brick exterior\"}, {\"id\": 30496, \"name\": \"a silver ultra logo\"}, {\"id\": 30497, \"name\": \"a list of vehicle\"}, {\"id\": 30498, \"name\": \"catamaran's deck\"}, {\"id\": 30499, \"name\": \"red nose\"}, {\"id\": 30500, \"name\": \"a boxpark.co.uk\"}, {\"id\": 30501, \"name\": \"the trail of smoke\"}, {\"id\": 30502, \"name\": \"stylized orange motorcycle\"}, {\"id\": 30503, \"name\": \"small white button\"}, {\"id\": 30504, \"name\": \"a red and white vehicle\"}, {\"id\": 30505, \"name\": \"desert terrain\"}, {\"id\": 30506, \"name\": \"the distant mountain range\"}, {\"id\": 30507, \"name\": \"a top floor\"}, {\"id\": 30508, \"name\": \"sturdy frame\"}, {\"id\": 30509, \"name\": \"the large hatch\"}, {\"id\": 30510, \"name\": \"navigation screen\"}, {\"id\": 30511, \"name\": \"wingtips\"}, {\"id\": 30512, \"name\": \"the road map\"}, {\"id\": 30513, \"name\": \"a silver skull\"}, {\"id\": 30514, \"name\": \"black hoodie\"}, {\"id\": 30515, \"name\": \"a highway bridge\"}, {\"id\": 30516, \"name\": \"a large, shimmering blue and purple light display\"}, {\"id\": 30517, \"name\": \"black office chair\"}, {\"id\": 30518, \"name\": \"aconis\"}, {\"id\": 30519, \"name\": \"vehicle's license plate\"}, {\"id\": 30520, \"name\": \"the yellow vertical line\"}, {\"id\": 30521, \"name\": \"a large, red and white boat\"}, {\"id\": 30522, \"name\": \"the yellow knob\"}, {\"id\": 30523, \"name\": \"the thick layer of cloud\"}, {\"id\": 30524, \"name\": \"white chalk marking\"}, {\"id\": 30525, \"name\": \"vehicle interior\"}, {\"id\": 30526, \"name\": \"white beam\"}, {\"id\": 30527, \"name\": \"the blurred finger\"}, {\"id\": 30528, \"name\": \"long blonde ponytail\"}, {\"id\": 30529, \"name\": \"the red and blue outfit\"}, {\"id\": 30530, \"name\": \"the white spray bottle of febreze\"}, {\"id\": 30531, \"name\": \"the small patch of dirt\"}, {\"id\": 30532, \"name\": \"radio antenna\"}, {\"id\": 30533, \"name\": \"a small green box\"}, {\"id\": 30534, \"name\": \"the red super bowl\"}, {\"id\": 30535, \"name\": \"the distinctive light bar\"}, {\"id\": 30536, \"name\": \"low fence\"}, {\"id\": 30537, \"name\": \"a blue jacket sleeve\"}, {\"id\": 30538, \"name\": \"black bell\"}, {\"id\": 30539, \"name\": \"a dark stripe\"}, {\"id\": 30540, \"name\": \"a clear plastic insert\"}, {\"id\": 30541, \"name\": \"long, pointed snout\"}, {\"id\": 30542, \"name\": \"green metal gate\"}, {\"id\": 30543, \"name\": \"a list of rating\"}, {\"id\": 30544, \"name\": \"pink and gray terminal\"}, {\"id\": 30545, \"name\": \"a yellow and red coat\"}, {\"id\": 30546, \"name\": \"small green sign\"}, {\"id\": 30547, \"name\": \"red headlight\"}, {\"id\": 30548, \"name\": \"the driver's hand\"}, {\"id\": 30549, \"name\": \"a motorcycle's dashboard\"}, {\"id\": 30550, \"name\": \"a larger display\"}, {\"id\": 30551, \"name\": \"the white storage unit\"}, {\"id\": 30552, \"name\": \"the orange, white, and black body\"}, {\"id\": 30553, \"name\": \"yellow and black tool\"}, {\"id\": 30554, \"name\": \"racetrack\"}, {\"id\": 30555, \"name\": \"a 49ers\"}, {\"id\": 30556, \"name\": \"yellow sunburst motif\"}, {\"id\": 30557, \"name\": \"a green and red light display\"}, {\"id\": 30558, \"name\": \"a square-shaped mirror\"}, {\"id\": 30559, \"name\": \"the large white trailer\"}, {\"id\": 30560, \"name\": \"a jar of grain\"}, {\"id\": 30561, \"name\": \"a gray car dashboard\"}, {\"id\": 30562, \"name\": \"figure with outstretched arm\"}, {\"id\": 30563, \"name\": \"a conifer\"}, {\"id\": 30564, \"name\": \"the vehicle's interior design\"}, {\"id\": 30565, \"name\": \"black mirror\"}, {\"id\": 30566, \"name\": \"a spiral marking\"}, {\"id\": 30567, \"name\": \"red plastic crate or container\"}, {\"id\": 30568, \"name\": \"the highway or expressway\"}, {\"id\": 30569, \"name\": \"an orange\"}, {\"id\": 30570, \"name\": \"the long, cylindrical barrel\"}, {\"id\": 30571, \"name\": \"a silver ladder\"}, {\"id\": 30572, \"name\": \"a purple and black spacesuit\"}, {\"id\": 30573, \"name\": \"a large barn\"}, {\"id\": 30574, \"name\": \"the desert landscape\"}, {\"id\": 30575, \"name\": \"a union pacific\"}, {\"id\": 30576, \"name\": \"the white pegboard\"}, {\"id\": 30577, \"name\": \"launch enable\"}, {\"id\": 30578, \"name\": \"yacht's upper deck\"}, {\"id\": 30579, \"name\": \"ntt indycar series\"}, {\"id\": 30580, \"name\": \"a small red and white striped structure\"}, {\"id\": 30581, \"name\": \"light on the ceiling\"}, {\"id\": 30582, \"name\": \"a miniature train track\"}, {\"id\": 30583, \"name\": \"the silver and black handlebar\"}, {\"id\": 30584, \"name\": \"creature's head\"}, {\"id\": 30585, \"name\": \"a driver's body\"}, {\"id\": 30586, \"name\": \"a pink and orange floral pattern\"}, {\"id\": 30587, \"name\": \"a rear seat\"}, {\"id\": 30588, \"name\": \"rear brake light\"}, {\"id\": 30589, \"name\": \"the orange rim\"}, {\"id\": 30590, \"name\": \"the white and gold striped rope handle\"}, {\"id\": 30591, \"name\": \"the spanish-language headline\"}, {\"id\": 30592, \"name\": \"beach\"}, {\"id\": 30593, \"name\": \"black rope\"}, {\"id\": 30594, \"name\": \"long, flowing white hair\"}, {\"id\": 30595, \"name\": \"the www.goldcreekfilms.com\"}, {\"id\": 30596, \"name\": \"the small silver screw\"}, {\"id\": 30597, \"name\": \"dark brown hull\"}, {\"id\": 30598, \"name\": \"a back leg\"}, {\"id\": 30599, \"name\": \"pegboard\"}, {\"id\": 30600, \"name\": \"a car's tail light\"}, {\"id\": 30601, \"name\": \"the thought bubble\"}, {\"id\": 30602, \"name\": \"the red and black netted slide\"}, {\"id\": 30603, \"name\": \"red and white polka dot dress\"}, {\"id\": 30604, \"name\": \"small napa logo\"}, {\"id\": 30605, \"name\": \"long white hair\"}, {\"id\": 30606, \"name\": \"a red and white striped cone\"}, {\"id\": 30607, \"name\": \"the raised eyebrow\"}, {\"id\": 30608, \"name\": \"red wheel hub and axle\"}, {\"id\": 30609, \"name\": \"the yellow and red banner\"}, {\"id\": 30610, \"name\": \"a runway\"}, {\"id\": 30611, \"name\": \"an interior of a white vehicle\"}, {\"id\": 30612, \"name\": \"a white dragon\"}, {\"id\": 30613, \"name\": \"the refrigerator's door\"}, {\"id\": 30614, \"name\": \"a china shipping\"}, {\"id\": 30615, \"name\": \"the white storage container\"}, {\"id\": 30616, \"name\": \"a fri, may\"}, {\"id\": 30617, \"name\": \"the aluminum foil\"}, {\"id\": 30618, \"name\": \"stripe\"}, {\"id\": 30619, \"name\": \"the dark brown interior lining\"}, {\"id\": 30620, \"name\": \"the black and white checkered flag\"}, {\"id\": 30621, \"name\": \"observation deck\"}, {\"id\": 30622, \"name\": \"the green vertical column\"}, {\"id\": 30623, \"name\": \"the large, dark-colored wagon\"}, {\"id\": 30624, \"name\": \"a gray line\"}, {\"id\": 30625, \"name\": \"toolbox\"}, {\"id\": 30626, \"name\": \"the rider's right arm\"}, {\"id\": 30627, \"name\": \"the blurred image of a man in a yellow shirt\"}, {\"id\": 30628, \"name\": \"roadwork\"}, {\"id\": 30629, \"name\": \"vessel\"}, {\"id\": 30630, \"name\": \"a brown suv\"}, {\"id\": 30631, \"name\": \"large container for agricultural material\"}, {\"id\": 30632, \"name\": \"red, plastic, retro-style radio\"}, {\"id\": 30633, \"name\": \"the diver's left leg\"}, {\"id\": 30634, \"name\": \"snowmobile rider\"}, {\"id\": 30635, \"name\": \"facial feature\"}, {\"id\": 30636, \"name\": \"a long, thin handle\"}, {\"id\": 30637, \"name\": \"a black hand grip\"}, {\"id\": 30638, \"name\": \"a snow\"}, {\"id\": 30639, \"name\": \"white goalpost\"}, {\"id\": 30640, \"name\": \"small car\"}, {\"id\": 30641, \"name\": \"the hydrogen\"}, {\"id\": 30642, \"name\": \"the clear plastic clip\"}, {\"id\": 30643, \"name\": \"the logo for onc noticia\"}, {\"id\": 30644, \"name\": \"black hole\"}, {\"id\": 30645, \"name\": \"the chocolate fountain\"}, {\"id\": 30646, \"name\": \"black gear shifter\"}, {\"id\": 30647, \"name\": \"a small porch\"}, {\"id\": 30648, \"name\": \"old-fashioned wooden roller coaster\"}, {\"id\": 30649, \"name\": \"gate or fence\"}, {\"id\": 30650, \"name\": \"industrial oven\"}, {\"id\": 30651, \"name\": \"holder\"}, {\"id\": 30652, \"name\": \"red tag\"}, {\"id\": 30653, \"name\": \"the winding trail\"}, {\"id\": 30654, \"name\": \"long, thin metal pole or handle\"}, {\"id\": 30655, \"name\": \"a small graphic of a castle\"}, {\"id\": 30656, \"name\": \"a blue and orange toy gun\"}, {\"id\": 30657, \"name\": \"a digital speedometer\"}, {\"id\": 30658, \"name\": \"blue hair\"}, {\"id\": 30659, \"name\": \"a dirty rag\"}, {\"id\": 30660, \"name\": \"green plant graphic\"}, {\"id\": 30661, \"name\": \"a white and orange logo\"}, {\"id\": 30662, \"name\": \"the green road sign\"}, {\"id\": 30663, \"name\": \"the front passenger seat\"}, {\"id\": 30664, \"name\": \"white xxl logo\"}, {\"id\": 30665, \"name\": \"falken logo\"}, {\"id\": 30666, \"name\": \"yellow and black symbol\"}, {\"id\": 30667, \"name\": \"an utility line\"}, {\"id\": 30668, \"name\": \"an instrument panel\"}, {\"id\": 30669, \"name\": \"the wooden tent pole\"}, {\"id\": 30670, \"name\": \"the black and silver podium\"}, {\"id\": 30671, \"name\": \"a green track\"}, {\"id\": 30672, \"name\": \"traditional asian conical hat\"}, {\"id\": 30673, \"name\": \"a front car\"}, {\"id\": 30674, \"name\": \"a golden helmet\"}, {\"id\": 30675, \"name\": \"a back of a person\"}, {\"id\": 30676, \"name\": \"the right handlebar\"}, {\"id\": 30677, \"name\": \"a smaller icon of a book\"}, {\"id\": 30678, \"name\": \"the neon green visor\"}, {\"id\": 30679, \"name\": \"yellow bell\"}, {\"id\": 30680, \"name\": \"tan and brown fabric seat\"}, {\"id\": 30681, \"name\": \"the white and red barrier\"}, {\"id\": 30682, \"name\": \"canon sign\"}, {\"id\": 30683, \"name\": \"a black and red wire\"}, {\"id\": 30684, \"name\": \"tan high heel\"}, {\"id\": 30685, \"name\": \"a dark suit\"}, {\"id\": 30686, \"name\": \"the pink long-sleeved shirt\"}, {\"id\": 30687, \"name\": \"the red and white box of frozen food\"}, {\"id\": 30688, \"name\": \"the black and yellow wetsuit\"}, {\"id\": 30689, \"name\": \"transparent plastic tube\"}, {\"id\": 30690, \"name\": \"the boeing logo\"}, {\"id\": 30691, \"name\": \"curved line\"}, {\"id\": 30692, \"name\": \"the black pen\"}, {\"id\": 30693, \"name\": \"the yellow belly\"}, {\"id\": 30694, \"name\": \"an identification\"}, {\"id\": 30695, \"name\": \"a jagged line\"}, {\"id\": 30696, \"name\": \"a driver's seat of a vehicle\"}, {\"id\": 30697, \"name\": \"the lemon slice\"}, {\"id\": 30698, \"name\": \"the red, white, and blue tail\"}, {\"id\": 30699, \"name\": \"blue location pin icon\"}, {\"id\": 30700, \"name\": \"large empty stadium\"}, {\"id\": 30701, \"name\": \"a car's brake light\"}, {\"id\": 30702, \"name\": \"player's hands\"}, {\"id\": 30703, \"name\": \"a blue and white bag\"}, {\"id\": 30704, \"name\": \"the rosy cheek\"}, {\"id\": 30705, \"name\": \"central silver sphere\"}, {\"id\": 30706, \"name\": \"large, bright light source\"}, {\"id\": 30707, \"name\": \"blurred, black, and white object\"}, {\"id\": 30708, \"name\": \"tan top\"}, {\"id\": 30709, \"name\": \"a driver's door\"}, {\"id\": 30710, \"name\": \"the florida gulf coast university\"}, {\"id\": 30711, \"name\": \"pier\"}, {\"id\": 30712, \"name\": \"the chevrolet logo\"}, {\"id\": 30713, \"name\": \"the red and green surface\"}, {\"id\": 30714, \"name\": \"the white and yellow accent\"}, {\"id\": 30715, \"name\": \"a red wristband\"}, {\"id\": 30716, \"name\": \"a blue button\"}, {\"id\": 30717, \"name\": \"the pink platform\"}, {\"id\": 30718, \"name\": \"black machine\"}, {\"id\": 30719, \"name\": \"the child's leg\"}, {\"id\": 30720, \"name\": \"a green folder\"}, {\"id\": 30721, \"name\": \"police light\"}, {\"id\": 30722, \"name\": \"the light-brown surface\"}, {\"id\": 30723, \"name\": \"the bottle of deodorant\"}, {\"id\": 30724, \"name\": \"rider's black glove\"}, {\"id\": 30725, \"name\": \"a black phone holder\"}, {\"id\": 30726, \"name\": \"the colorful mural or artwork\"}, {\"id\": 30727, \"name\": \"a motorcyclist's gloved hand\"}, {\"id\": 30728, \"name\": \"a central white pole\"}, {\"id\": 30729, \"name\": \"front fork\"}, {\"id\": 30730, \"name\": \"the turquoise skin\"}, {\"id\": 30731, \"name\": \"a dark red dashboard\"}, {\"id\": 30732, \"name\": \"bird's right arm\"}, {\"id\": 30733, \"name\": \"the grey asphalt track\"}, {\"id\": 30734, \"name\": \"a american flag design\"}, {\"id\": 30735, \"name\": \"the large white circle\"}, {\"id\": 30736, \"name\": \"yellow and orange stripe\"}, {\"id\": 30737, \"name\": \"teal logo\"}, {\"id\": 30738, \"name\": \"pc direct store\"}, {\"id\": 30739, \"name\": \"tall, dark brown fence\"}, {\"id\": 30740, \"name\": \"a construction scaffolding\"}, {\"id\": 30741, \"name\": \"white spiral\"}, {\"id\": 30742, \"name\": \"blue and white striped cloth\"}, {\"id\": 30743, \"name\": \"a red and yellow flag\"}, {\"id\": 30744, \"name\": \"the cartoon character's ear\"}, {\"id\": 30745, \"name\": \"yellow gumball\"}, {\"id\": 30746, \"name\": \"cartoon woman's face\"}, {\"id\": 30747, \"name\": \"the tube of toothpaste\"}, {\"id\": 30748, \"name\": \"the tractor cab\"}, {\"id\": 30749, \"name\": \"the toyota logo\"}, {\"id\": 30750, \"name\": \"gray grip\"}, {\"id\": 30751, \"name\": \"a minnie mouse\"}, {\"id\": 30752, \"name\": \"a train's smokestack\"}, {\"id\": 30753, \"name\": \"red bag with a zipper\"}, {\"id\": 30754, \"name\": \"distinctive circular roof\"}, {\"id\": 30755, \"name\": \"the bridge or overpass\"}, {\"id\": 30756, \"name\": \"black chain\"}, {\"id\": 30757, \"name\": \"a small, red, white, and blue logo\"}, {\"id\": 30758, \"name\": \"mural of cartoon character\"}, {\"id\": 30759, \"name\": \"a cluttered countertop\"}, {\"id\": 30760, \"name\": \"a black rear wheel\"}, {\"id\": 30761, \"name\": \"the driver's door\"}, {\"id\": 30762, \"name\": \"fuselage\"}, {\"id\": 30763, \"name\": \"brvm\"}, {\"id\": 30764, \"name\": \"wide, red smile\"}, {\"id\": 30765, \"name\": \"the wide, toothy grin\"}, {\"id\": 30766, \"name\": \"the dark liquid\"}, {\"id\": 30767, \"name\": \"the plane's body\"}, {\"id\": 30768, \"name\": \"the vibrant blue and pink light\"}, {\"id\": 30769, \"name\": \"the white bottom\"}, {\"id\": 30770, \"name\": \"red and blue jacket\"}, {\"id\": 30771, \"name\": \"the black and red bumper\"}, {\"id\": 30772, \"name\": \"the warehouse\"}, {\"id\": 30773, \"name\": \"wet surface\"}, {\"id\": 30774, \"name\": \"broadcasting network\"}, {\"id\": 30775, \"name\": \"suspension bridge\"}, {\"id\": 30776, \"name\": \"red partner rental logo\"}, {\"id\": 30777, \"name\": \"a left side of the image\"}, {\"id\": 30778, \"name\": \"a front of the go-kart\"}, {\"id\": 30779, \"name\": \"turtle's head\"}, {\"id\": 30780, \"name\": \"blue oxygen mask\"}, {\"id\": 30781, \"name\": \"a white and green racing suit\"}, {\"id\": 30782, \"name\": \"the scotrail logo\"}, {\"id\": 30783, \"name\": \"iconic bmw logo\"}, {\"id\": 30784, \"name\": \"a darkened arena\"}, {\"id\": 30785, \"name\": \"driver's gloved hand\"}, {\"id\": 30786, \"name\": \"brown pastry\"}, {\"id\": 30787, \"name\": \"chevrolet logo\"}, {\"id\": 30788, \"name\": \"a sign featuring a red and white koi fish\"}, {\"id\": 30789, \"name\": \"the circle\"}, {\"id\": 30790, \"name\": \"the white and red tent\"}, {\"id\": 30791, \"name\": \"a long, thin, white support beam\"}, {\"id\": 30792, \"name\": \"a right handlebar\"}, {\"id\": 30793, \"name\": \"a red and black outfit\"}, {\"id\": 30794, \"name\": \"thi way\"}, {\"id\": 30795, \"name\": \"a blue lug nut\"}, {\"id\": 30796, \"name\": \"a yellow 2315\"}, {\"id\": 30797, \"name\": \"blue hood\"}, {\"id\": 30798, \"name\": \"a bird's head\"}, {\"id\": 30799, \"name\": \"social media platform\"}, {\"id\": 30800, \"name\": \"the load of reed\"}, {\"id\": 30801, \"name\": \"green and white tag\"}, {\"id\": 30802, \"name\": \"a green strip of grass\"}, {\"id\": 30803, \"name\": \"rider's black helmet\"}, {\"id\": 30804, \"name\": \"undertide\"}, {\"id\": 30805, \"name\": \"a rollercoaster's red and white car\"}, {\"id\": 30806, \"name\": \"a black container\"}, {\"id\": 30807, \"name\": \"a supercell\"}, {\"id\": 30808, \"name\": \"a white field\"}, {\"id\": 30809, \"name\": \"red cursive\"}, {\"id\": 30810, \"name\": \"the arco logo\"}, {\"id\": 30811, \"name\": \"a large godzilla toy\"}, {\"id\": 30812, \"name\": \"a central divider\"}, {\"id\": 30813, \"name\": \"the green plastic\"}, {\"id\": 30814, \"name\": \"a autism walk-a-thon logo\"}, {\"id\": 30815, \"name\": \"a phone's screen\"}, {\"id\": 30816, \"name\": \"3d printer\"}, {\"id\": 30817, \"name\": \"blue glow effect\"}, {\"id\": 30818, \"name\": \"a small, white label\"}, {\"id\": 30819, \"name\": \"a fox logo\"}, {\"id\": 30820, \"name\": \"the small circular white knob\"}, {\"id\": 30821, \"name\": \"the red and white youtube logo\"}, {\"id\": 30822, \"name\": \"the black hexagon outline\"}, {\"id\": 30823, \"name\": \"a mint leaf\"}, {\"id\": 30824, \"name\": \"a right grip\"}, {\"id\": 30825, \"name\": \"large white 8\"}, {\"id\": 30826, \"name\": \"helmet's visor\"}, {\"id\": 30827, \"name\": \"a large snow blade\"}, {\"id\": 30828, \"name\": \"the green checkered pattern\"}, {\"id\": 30829, \"name\": \"red and yellow exterior\"}, {\"id\": 30830, \"name\": \"roller coaster's structure\"}, {\"id\": 30831, \"name\": \"a vehicle's dashboard\"}, {\"id\": 30832, \"name\": \"the sidewalk or curb\"}, {\"id\": 30833, \"name\": \"a white orb\"}, {\"id\": 30834, \"name\": \"a logo for fox 5 news\"}, {\"id\": 30835, \"name\": \"a headstock\"}, {\"id\": 30836, \"name\": \"the long, yellow arm\"}, {\"id\": 30837, \"name\": \"a nozzle\"}, {\"id\": 30838, \"name\": \"the 668\"}, {\"id\": 30839, \"name\": \"fiat\"}, {\"id\": 30840, \"name\": \"green kart\"}, {\"id\": 30841, \"name\": \"rear axle\"}, {\"id\": 30842, \"name\": \"grey maple leaf logo\"}, {\"id\": 30843, \"name\": \"the silver chevrolet hhr\"}, {\"id\": 30844, \"name\": \"a silver body\"}, {\"id\": 30845, \"name\": \"a prominent front grille\"}, {\"id\": 30846, \"name\": \"a large, multicolored rocket ship\"}, {\"id\": 30847, \"name\": \"the yellow and blue accent\"}, {\"id\": 30848, \"name\": \"the turquoise rim\"}, {\"id\": 30849, \"name\": \"a 8oz\"}, {\"id\": 30850, \"name\": \"the dark circle\"}, {\"id\": 30851, \"name\": \"white tray\"}, {\"id\": 30852, \"name\": \"red engine\"}, {\"id\": 30853, \"name\": \"glas barrier\"}, {\"id\": 30854, \"name\": \"a red and orange bumper\"}, {\"id\": 30855, \"name\": \"white fence post\"}, {\"id\": 30856, \"name\": \"the trout\"}, {\"id\": 30857, \"name\": \"sbt esporte\"}, {\"id\": 30858, \"name\": \"padlock\"}, {\"id\": 30859, \"name\": \"squiggly line\"}, {\"id\": 30860, \"name\": \"protective screen\"}, {\"id\": 30861, \"name\": \"a pink border\"}, {\"id\": 30862, \"name\": \"red rearview mirror\"}, {\"id\": 30863, \"name\": \"the white track\"}, {\"id\": 30864, \"name\": \"the green stand\"}, {\"id\": 30865, \"name\": \"small, square, maroon-colored device\"}, {\"id\": 30866, \"name\": \"the pink orb\"}, {\"id\": 30867, \"name\": \"the boat's bow\"}, {\"id\": 30868, \"name\": \"a stihl logo\"}, {\"id\": 30869, \"name\": \"a small yellow star emblem\"}, {\"id\": 30870, \"name\": \"the curly coat\"}, {\"id\": 30871, \"name\": \"teal mug\"}, {\"id\": 30872, \"name\": \"display rack\"}, {\"id\": 30873, \"name\": \"central bridge or overpass\"}, {\"id\": 30874, \"name\": \"a short red-and-white pole\"}, {\"id\": 30875, \"name\": \"a blue crossbar\"}, {\"id\": 30876, \"name\": \"yellow and black checkered short\"}, {\"id\": 30877, \"name\": \"a dark blue stripe\"}, {\"id\": 30878, \"name\": \"the black, red, blue, and white wire\"}, {\"id\": 30879, \"name\": \"orange storage unit\"}, {\"id\": 30880, \"name\": \"the black pipe\"}, {\"id\": 30881, \"name\": \"blue pickup truck\"}, {\"id\": 30882, \"name\": \"the ring finger\"}, {\"id\": 30883, \"name\": \"medical oxygen mask\"}, {\"id\": 30884, \"name\": \"the blurred image of a mountain bike\"}, {\"id\": 30885, \"name\": \"a black tread\"}, {\"id\": 30886, \"name\": \"a motorcycle's handlebar\"}, {\"id\": 30887, \"name\": \"a polar\"}, {\"id\": 30888, \"name\": \"a car's radio dial\"}, {\"id\": 30889, \"name\": \"a white x\"}, {\"id\": 30890, \"name\": \"prominent headline\"}, {\"id\": 30891, \"name\": \"the blue ramp\"}, {\"id\": 30892, \"name\": \"nail polish\"}, {\"id\": 30893, \"name\": \"the house fire\"}, {\"id\": 30894, \"name\": \"a black and red jacket\"}, {\"id\": 30895, \"name\": \"the snowy\"}, {\"id\": 30896, \"name\": \"the front of a green john deere tractor\"}, {\"id\": 30897, \"name\": \"the large hook\"}, {\"id\": 30898, \"name\": \"a white spot\"}, {\"id\": 30899, \"name\": \"black-framed mirror\"}, {\"id\": 30900, \"name\": \"gray metal cabinet\"}, {\"id\": 30901, \"name\": \"the black button\"}, {\"id\": 30902, \"name\": \"green mask\"}, {\"id\": 30903, \"name\": \"the hot tub\"}, {\"id\": 30904, \"name\": \"red and yellow brim\"}, {\"id\": 30905, \"name\": \"puddle of water\"}, {\"id\": 30906, \"name\": \"orange and white body\"}, {\"id\": 30907, \"name\": \"a tree line\"}, {\"id\": 30908, \"name\": \"the gray button\"}, {\"id\": 30909, \"name\": \"yellow and black racecar\"}, {\"id\": 30910, \"name\": \"the distinctive spoiler\"}, {\"id\": 30911, \"name\": \"the yellow and black logo\"}, {\"id\": 30912, \"name\": \"a wbz\"}, {\"id\": 30913, \"name\": \"a black and red box\"}, {\"id\": 30914, \"name\": \"the road's shoulder\"}, {\"id\": 30915, \"name\": \"ship's wing\"}, {\"id\": 30916, \"name\": \"a cedar point logo\"}, {\"id\": 30917, \"name\": \"the white rv\"}, {\"id\": 30918, \"name\": \"the railroad crossing\"}, {\"id\": 30919, \"name\": \"blue item of clothing\"}, {\"id\": 30920, \"name\": \"a blue inner ring\"}, {\"id\": 30921, \"name\": \"a white outline of a person riding a bike\"}, {\"id\": 30922, \"name\": \"the front of a combine harvester\"}, {\"id\": 30923, \"name\": \"a small yellow tag\"}, {\"id\": 30924, \"name\": \"the dark-colored suit\"}, {\"id\": 30925, \"name\": \"the bold, white lettering\"}, {\"id\": 30926, \"name\": \"the metal rack\"}, {\"id\": 30927, \"name\": \"the tile floor\"}, {\"id\": 30928, \"name\": \"the pink and blue design\"}, {\"id\": 30929, \"name\": \"headline\"}, {\"id\": 30930, \"name\": \"vase of white flower\"}, {\"id\": 30931, \"name\": \"the small cart\"}, {\"id\": 30932, \"name\": \"a white horse\"}, {\"id\": 30933, \"name\": \"a sleek black and silver motorcycle\"}, {\"id\": 30934, \"name\": \"the prominent red advertisement\"}, {\"id\": 30935, \"name\": \"the lone individual\"}, {\"id\": 30936, \"name\": \"sailor\"}, {\"id\": 30937, \"name\": \"a white paper bag\"}, {\"id\": 30938, \"name\": \"orange and white striped section\"}, {\"id\": 30939, \"name\": \"the red control panel\"}, {\"id\": 30940, \"name\": \"the silver metal body\"}, {\"id\": 30941, \"name\": \"field of tall, golden-colored crop\"}, {\"id\": 30942, \"name\": \"the shoreline\"}, {\"id\": 30943, \"name\": \"the arched opening\"}, {\"id\": 30944, \"name\": \"the broken windshield\"}, {\"id\": 30945, \"name\": \"the large, red dump truck\"}, {\"id\": 30946, \"name\": \"a black hood\"}, {\"id\": 30947, \"name\": \"green cable\"}, {\"id\": 30948, \"name\": \"the white dress with black polka dot\"}, {\"id\": 30949, \"name\": \"the larger purple circle\"}, {\"id\": 30950, \"name\": \"the black asphalt surface\"}, {\"id\": 30951, \"name\": \"the black metal\"}, {\"id\": 30952, \"name\": \"the cockpit\"}, {\"id\": 30953, \"name\": \"a rim of the mug\"}, {\"id\": 30954, \"name\": \"the cobblestone road\"}, {\"id\": 30955, \"name\": \"roundabout\"}, {\"id\": 30956, \"name\": \"a green tube\"}, {\"id\": 30957, \"name\": \"light blue concrete barrier\"}, {\"id\": 30958, \"name\": \"spray\"}, {\"id\": 30959, \"name\": \"the line of orange traffic cone\"}, {\"id\": 30960, \"name\": \"the paper or cardstock\"}, {\"id\": 30961, \"name\": \"actor\"}, {\"id\": 30962, \"name\": \"the prominent brick staircase\"}, {\"id\": 30963, \"name\": \"large metal plate\"}, {\"id\": 30964, \"name\": \"a red backdrop\"}, {\"id\": 30965, \"name\": \"cityscape backdrop\"}, {\"id\": 30966, \"name\": \"a front light\"}, {\"id\": 30967, \"name\": \"the black, blue, and gray handlebar\"}, {\"id\": 30968, \"name\": \"an image of strawberry\"}, {\"id\": 30969, \"name\": \"the go-kart driver\"}, {\"id\": 30970, \"name\": \"large warehouse\"}, {\"id\": 30971, \"name\": \"the green shipping container\"}, {\"id\": 30972, \"name\": \"snowy road\"}, {\"id\": 30973, \"name\": \"a red metal frame\"}, {\"id\": 30974, \"name\": \"orange outline\"}, {\"id\": 30975, \"name\": \"the single red light\"}, {\"id\": 30976, \"name\": \"translucent white watermark\"}, {\"id\": 30977, \"name\": \"bagpipe\"}, {\"id\": 30978, \"name\": \"natural canopy\"}, {\"id\": 30979, \"name\": \"silver chain\"}, {\"id\": 30980, \"name\": \"the amgen logo\"}, {\"id\": 30981, \"name\": \"a red taxi\"}, {\"id\": 30982, \"name\": \"a lego character\"}, {\"id\": 30983, \"name\": \"the green three-wheeled vehicle\"}, {\"id\": 30984, \"name\": \"a large white tooth\"}, {\"id\": 30985, \"name\": \"yellow and black body\"}, {\"id\": 30986, \"name\": \"the clown-themed backdrop\"}, {\"id\": 30987, \"name\": \"a black bracket\"}, {\"id\": 30988, \"name\": \"black convertible top\"}, {\"id\": 30989, \"name\": \"the busy highway\"}, {\"id\": 30990, \"name\": \"the beige pegboard\"}, {\"id\": 30991, \"name\": \"the basket of produce\"}, {\"id\": 30992, \"name\": \"a toy car racing track\"}, {\"id\": 30993, \"name\": \"a white outline\"}, {\"id\": 30994, \"name\": \"the stylized s\"}, {\"id\": 30995, \"name\": \"orange fringe\"}, {\"id\": 30996, \"name\": \"white organizer box\"}, {\"id\": 30997, \"name\": \"thin, white line\"}, {\"id\": 30998, \"name\": \"the metal guardrail\"}, {\"id\": 30999, \"name\": \"a white towel or bag\"}, {\"id\": 31000, \"name\": \"a red pathway\"}, {\"id\": 31001, \"name\": \"a large brick structure\"}, {\"id\": 31002, \"name\": \"a blue weapon\"}, {\"id\": 31003, \"name\": \"a cluttered wooden shelf\"}, {\"id\": 31004, \"name\": \"a light-colored wood surface\"}, {\"id\": 31005, \"name\": \"a wooden surface\"}, {\"id\": 31006, \"name\": \"gray suit jacket\"}, {\"id\": 31007, \"name\": \"red bull formula 1 race car\"}, {\"id\": 31008, \"name\": \"the person's black pant\"}, {\"id\": 31009, \"name\": \"a colorful display of balloon\"}, {\"id\": 31010, \"name\": \"the lush garden bed\"}, {\"id\": 31011, \"name\": \"a ripped knee\"}, {\"id\": 31012, \"name\": \"the small credit\"}, {\"id\": 31013, \"name\": \"the purple border\"}, {\"id\": 31014, \"name\": \"an indoor rowing machine\"}, {\"id\": 31015, \"name\": \"the drummer's face\"}, {\"id\": 31016, \"name\": \"natural beach hotel\"}, {\"id\": 31017, \"name\": \"a white boxing ring\"}, {\"id\": 31018, \"name\": \"the waving gesture\"}, {\"id\": 31019, \"name\": \"a roadside\"}, {\"id\": 31020, \"name\": \"a blue and white tuk-tuk\"}, {\"id\": 31021, \"name\": \"the celery\"}, {\"id\": 31022, \"name\": \"event's detail\"}, {\"id\": 31023, \"name\": \"protective case\"}, {\"id\": 31024, \"name\": \"red and white on your side logo\"}, {\"id\": 31025, \"name\": \"the gray nike shirt\"}, {\"id\": 31026, \"name\": \"a small gray bar\"}, {\"id\": 31027, \"name\": \"a light blue cap\"}, {\"id\": 31028, \"name\": \"a blue streaked hair\"}, {\"id\": 31029, \"name\": \"the large garage door\"}, {\"id\": 31030, \"name\": \"the dark-colored trolley\"}, {\"id\": 31031, \"name\": \"the black and white truck\"}, {\"id\": 31032, \"name\": \"man in a blue and green striped shirt\"}, {\"id\": 31033, \"name\": \"the quicksilver pro logo\"}, {\"id\": 31034, \"name\": \"a yellow flying disc\"}, {\"id\": 31035, \"name\": \"the 7 now logo\"}, {\"id\": 31036, \"name\": \"the horse-drawn carriage\"}, {\"id\": 31037, \"name\": \"a blue horse\"}, {\"id\": 31038, \"name\": \"the single bicycle\"}, {\"id\": 31039, \"name\": \"a fox sport live logo\"}, {\"id\": 31040, \"name\": \"the black and gray top\"}, {\"id\": 31041, \"name\": \"palette of white, yellow, and blue\"}, {\"id\": 31042, \"name\": \"the red motorcycle handlebar\"}, {\"id\": 31043, \"name\": \"a vein\"}, {\"id\": 31044, \"name\": \"a white ticket or card\"}, {\"id\": 31045, \"name\": \"a white ambulance\"}, {\"id\": 31046, \"name\": \"a mammoth\"}, {\"id\": 31047, \"name\": \"a black toyota sedan\"}, {\"id\": 31048, \"name\": \"the white outline of a square\"}, {\"id\": 31049, \"name\": \"blue atm machine\"}, {\"id\": 31050, \"name\": \"a speaker system\"}, {\"id\": 31051, \"name\": \"a tire track\"}, {\"id\": 31052, \"name\": \"red and black graphic design\"}, {\"id\": 31053, \"name\": \"red and yellow bag\"}, {\"id\": 31054, \"name\": \"the tile or linoleum\"}, {\"id\": 31055, \"name\": \"the died the star of the baloncesto kobe bryant\"}, {\"id\": 31056, \"name\": \"the yellow cheese wedge\"}, {\"id\": 31057, \"name\": \"a blue pool\"}, {\"id\": 31058, \"name\": \"dark grey dashboard\"}, {\"id\": 31059, \"name\": \"the black and white leopard-print blouse\"}, {\"id\": 31060, \"name\": \"the shop window\"}, {\"id\": 31061, \"name\": \"a red and yellow life jacket\"}, {\"id\": 31062, \"name\": \"the channel name\"}, {\"id\": 31063, \"name\": \"yellow and red jacket\"}, {\"id\": 31064, \"name\": \"orange and white barrier\"}, {\"id\": 31065, \"name\": \"the woman in a white shirt and short\"}, {\"id\": 31066, \"name\": \"the vibrant red backpack\"}, {\"id\": 31067, \"name\": \"the blue shirt with white stripe\"}, {\"id\": 31068, \"name\": \"small box or container\"}, {\"id\": 31069, \"name\": \"the orange, blue, and white section\"}, {\"id\": 31070, \"name\": \"white and red patterned object\"}, {\"id\": 31071, \"name\": \"a small white tab\"}, {\"id\": 31072, \"name\": \"the colorful flower box\"}, {\"id\": 31073, \"name\": \"white ceramic tile\"}, {\"id\": 31074, \"name\": \"a motorcycle dashboard\"}, {\"id\": 31075, \"name\": \"miniature railroad track\"}, {\"id\": 31076, \"name\": \"a plume of smoke\"}, {\"id\": 31077, \"name\": \"the sideswipe\"}, {\"id\": 31078, \"name\": \"the trail of dust\"}, {\"id\": 31079, \"name\": \"the white front bumper\"}, {\"id\": 31080, \"name\": \"the rear tire\"}, {\"id\": 31081, \"name\": \"the rocky hillside\"}, {\"id\": 31082, \"name\": \"red interior lining\"}, {\"id\": 31083, \"name\": \"product name\"}, {\"id\": 31084, \"name\": \"the woman's arm\"}, {\"id\": 31085, \"name\": \"a large bowl of yellow corn on the cob\"}, {\"id\": 31086, \"name\": \"the motorcycle driver\"}, {\"id\": 31087, \"name\": \"a gray backpack strap\"}, {\"id\": 31088, \"name\": \"the rider's right leg\"}, {\"id\": 31089, \"name\": \"blue and yellow gate\"}, {\"id\": 31090, \"name\": \"yellow clamp\"}, {\"id\": 31091, \"name\": \"the front deck\"}, {\"id\": 31092, \"name\": \"the large, rectangular metal chafing dish\"}, {\"id\": 31093, \"name\": \"rider's view of the sedan's license plate\"}, {\"id\": 31094, \"name\": \"a round head\"}, {\"id\": 31095, \"name\": \"high, vaulted roof\"}, {\"id\": 31096, \"name\": \"a brake lever\"}, {\"id\": 31097, \"name\": \"an utility belt\"}, {\"id\": 31098, \"name\": \"gold logo\"}, {\"id\": 31099, \"name\": \"the recording device\"}, {\"id\": 31100, \"name\": \"wide, dark belt\"}, {\"id\": 31101, \"name\": \"the x-shaped support\"}, {\"id\": 31102, \"name\": \"blue streak\"}, {\"id\": 31103, \"name\": \"the measurement marking\"}, {\"id\": 31104, \"name\": \"bike's handlebars\"}, {\"id\": 31105, \"name\": \"plant in a red pot\"}, {\"id\": 31106, \"name\": \"the white and blue jersey\"}, {\"id\": 31107, \"name\": \"dark water\"}, {\"id\": 31108, \"name\": \"rear wheel of the bike\"}, {\"id\": 31109, \"name\": \"a green landscape\"}, {\"id\": 31110, \"name\": \"a n license plate\"}, {\"id\": 31111, \"name\": \"a small hill\"}, {\"id\": 31112, \"name\": \"a brick pathway\"}, {\"id\": 31113, \"name\": \"white plastic basket\"}, {\"id\": 31114, \"name\": \"player's uniform\"}, {\"id\": 31115, \"name\": \"driver's silhouette\"}, {\"id\": 31116, \"name\": \"a rollercoaster\"}, {\"id\": 31117, \"name\": \"vertical black line\"}, {\"id\": 31118, \"name\": \"a bungee jumping platform\"}, {\"id\": 31119, \"name\": \"the motorcycle's handlebars\"}, {\"id\": 31120, \"name\": \"orange section\"}, {\"id\": 31121, \"name\": \"a beige stone floor\"}, {\"id\": 31122, \"name\": \"neon yellow stripe\"}, {\"id\": 31123, \"name\": \"a large orange banner\"}, {\"id\": 31124, \"name\": \"the tabletop\"}, {\"id\": 31125, \"name\": \"small green light\"}, {\"id\": 31126, \"name\": \"a product name\"}, {\"id\": 31127, \"name\": \"a dashed line\"}, {\"id\": 31128, \"name\": \"a dark brown wooden frame\"}, {\"id\": 31129, \"name\": \"the driver's black and yellow racing glove\"}, {\"id\": 31130, \"name\": \"the utility line\"}, {\"id\": 31131, \"name\": \"steel track\"}, {\"id\": 31132, \"name\": \"purple kite\"}, {\"id\": 31133, \"name\": \"the pink and yellow cloud\"}, {\"id\": 31134, \"name\": \"a large windowed deck\"}, {\"id\": 31135, \"name\": \"a flap\"}, {\"id\": 31136, \"name\": \"the tall, brown brick sign\"}, {\"id\": 31137, \"name\": \"red and light blue fence\"}, {\"id\": 31138, \"name\": \"the metal framework\"}, {\"id\": 31139, \"name\": \"red bin\"}, {\"id\": 31140, \"name\": \"the pack of 3m removable label\"}, {\"id\": 31141, \"name\": \"large white pole\"}, {\"id\": 31142, \"name\": \"school of small, brownish-pink fish\"}, {\"id\": 31143, \"name\": \"the clapboard\"}, {\"id\": 31144, \"name\": \"pack of wolf\"}, {\"id\": 31145, \"name\": \"the dark brown wing\"}, {\"id\": 31146, \"name\": \"a trail\"}, {\"id\": 31147, \"name\": \"a lego person\"}, {\"id\": 31148, \"name\": \"the pink handbag\"}, {\"id\": 31149, \"name\": \"white short-sleeved uniform\"}, {\"id\": 31150, \"name\": \"a black stone border\"}, {\"id\": 31151, \"name\": \"the canvas\"}, {\"id\": 31152, \"name\": \"the white tape\"}, {\"id\": 31153, \"name\": \"the short fingernail\"}, {\"id\": 31154, \"name\": \"red tail\"}, {\"id\": 31155, \"name\": \"long pole or hose\"}, {\"id\": 31156, \"name\": \"a green grassy field\"}, {\"id\": 31157, \"name\": \"a yellow, red, and blue rainbow design\"}, {\"id\": 31158, \"name\": \"black tv stand\"}, {\"id\": 31159, \"name\": \"a faint trail\"}, {\"id\": 31160, \"name\": \"a csx intermodal\"}, {\"id\": 31161, \"name\": \"the long leg\"}, {\"id\": 31162, \"name\": \"a white painted marking\"}, {\"id\": 31163, \"name\": \"a wet pavement\"}, {\"id\": 31164, \"name\": \"a cyclist's speedometer\"}, {\"id\": 31165, \"name\": \"stem of the bike\"}, {\"id\": 31166, \"name\": \"the dark blue surface\"}, {\"id\": 31167, \"name\": \"a yellow and white advertisement\"}, {\"id\": 31168, \"name\": \"a small green bar\"}, {\"id\": 31169, \"name\": \"a right-side mirror\"}, {\"id\": 31170, \"name\": \"blue floor\"}, {\"id\": 31171, \"name\": \"the newspaper or magazine page\"}, {\"id\": 31172, \"name\": \"a rear tire of a black car\"}, {\"id\": 31173, \"name\": \"the plate of donut\"}, {\"id\": 31174, \"name\": \"a lower deck\"}, {\"id\": 31175, \"name\": \"a single lane of traffic\"}, {\"id\": 31176, \"name\": \"a gray highlight\"}, {\"id\": 31177, \"name\": \"a large wing\"}, {\"id\": 31178, \"name\": \"the sleek black and blue open-wheel car\"}, {\"id\": 31179, \"name\": \"a black-and-white racing suit\"}, {\"id\": 31180, \"name\": \"pale yellow bottle\"}, {\"id\": 31181, \"name\": \"a silver mesh covering\"}, {\"id\": 31182, \"name\": \"the person's finger\"}, {\"id\": 31183, \"name\": \"mcdonald's sign\"}, {\"id\": 31184, \"name\": \"a steep slope\"}, {\"id\": 31185, \"name\": \"the white spot\"}, {\"id\": 31186, \"name\": \"the small side mirror\"}, {\"id\": 31187, \"name\": \"cake stand\"}, {\"id\": 31188, \"name\": \"the factory\"}, {\"id\": 31189, \"name\": \"a lush forest\"}, {\"id\": 31190, \"name\": \"stylized white wave-like symbol\"}, {\"id\": 31191, \"name\": \"a brown saddle\"}, {\"id\": 31192, \"name\": \"the yellow cord\"}, {\"id\": 31193, \"name\": \"a yellow passenger train\"}, {\"id\": 31194, \"name\": \"the vehicle's handlebar\"}, {\"id\": 31195, \"name\": \"the vark logo\"}, {\"id\": 31196, \"name\": \"the lake or swamp\"}, {\"id\": 31197, \"name\": \"the smaller wing\"}, {\"id\": 31198, \"name\": \"a pink ground\"}, {\"id\": 31199, \"name\": \"the frame of the vehicle\"}, {\"id\": 31200, \"name\": \"the yellow grip\"}, {\"id\": 31201, \"name\": \"carnaval 2020\"}, {\"id\": 31202, \"name\": \"small circular window\"}, {\"id\": 31203, \"name\": \"orange and red reflective strip\"}, {\"id\": 31204, \"name\": \"a white striped wristband\"}, {\"id\": 31205, \"name\": \"a white baseball glove\"}, {\"id\": 31206, \"name\": \"a truck's body\"}, {\"id\": 31207, \"name\": \"charging station\"}, {\"id\": 31208, \"name\": \"a name\"}, {\"id\": 31209, \"name\": \"the blue and white logo\"}, {\"id\": 31210, \"name\": \"a roll of white tape\"}, {\"id\": 31211, \"name\": \"the white and black stripe\"}, {\"id\": 31212, \"name\": \"bee hive\"}, {\"id\": 31213, \"name\": \"the green zipper\"}, {\"id\": 31214, \"name\": \"clear visor\"}, {\"id\": 31215, \"name\": \"large metal cylinder\"}, {\"id\": 31216, \"name\": \"the red and white striped bag\"}, {\"id\": 31217, \"name\": \"the gold and purple necklace\"}, {\"id\": 31218, \"name\": \"clear windshield\"}, {\"id\": 31219, \"name\": \"the grid of purple and red brick\"}, {\"id\": 31220, \"name\": \"a white logo of griffith university\"}, {\"id\": 31221, \"name\": \"the roof rack\"}, {\"id\": 31222, \"name\": \"black plastic box\"}, {\"id\": 31223, \"name\": \"female volleyball player\"}, {\"id\": 31224, \"name\": \"the white medical glove\"}, {\"id\": 31225, \"name\": \"the digital gauge\"}, {\"id\": 31226, \"name\": \"a white and black design\"}, {\"id\": 31227, \"name\": \"a large, dark-colored boat\"}, {\"id\": 31228, \"name\": \"small cabin\"}, {\"id\": 31229, \"name\": \"a black rope\"}, {\"id\": 31230, \"name\": \"round glas face\"}, {\"id\": 31231, \"name\": \"white and orange cable\"}, {\"id\": 31232, \"name\": \"a written and directed by david ayer\"}, {\"id\": 31233, \"name\": \"an orange and white striped traffic cone\"}, {\"id\": 31234, \"name\": \"a closet\"}, {\"id\": 31235, \"name\": \"blue shadow effect\"}, {\"id\": 31236, \"name\": \"the boat's name\"}, {\"id\": 31237, \"name\": \"the blue and gold design\"}, {\"id\": 31238, \"name\": \"a dark-colored long-sleeved shirt\"}, {\"id\": 31239, \"name\": \"the cartoon hand\"}, {\"id\": 31240, \"name\": \"a black track\"}, {\"id\": 31241, \"name\": \"red and black design\"}, {\"id\": 31242, \"name\": \"the vehicle's frame\"}, {\"id\": 31243, \"name\": \"beige pot\"}, {\"id\": 31244, \"name\": \"the large, white, triangular structure\"}, {\"id\": 31245, \"name\": \"a coe moto logo\"}, {\"id\": 31246, \"name\": \"the blue and white tag\"}, {\"id\": 31247, \"name\": \"the blue and white label\"}, {\"id\": 31248, \"name\": \"a caterpillar brand\"}, {\"id\": 31249, \"name\": \"the snorkel\"}, {\"id\": 31250, \"name\": \"a large blue crane\"}, {\"id\": 31251, \"name\": \"large, wooden frame\"}, {\"id\": 31252, \"name\": \"the yellow fletching\"}, {\"id\": 31253, \"name\": \"mango\"}, {\"id\": 31254, \"name\": \"a black and yellow uniform\"}, {\"id\": 31255, \"name\": \"the yellow packaging\"}, {\"id\": 31256, \"name\": \"the round speedometer\"}, {\"id\": 31257, \"name\": \"the small brush\"}, {\"id\": 31258, \"name\": \"a fuel tank\"}, {\"id\": 31259, \"name\": \"red axle\"}, {\"id\": 31260, \"name\": \"the toaster\"}, {\"id\": 31261, \"name\": \"a hand's fingers\"}, {\"id\": 31262, \"name\": \"the blue and black wetsuit\"}, {\"id\": 31263, \"name\": \"dark purple body\"}, {\"id\": 31264, \"name\": \"a green and brown design\"}, {\"id\": 31265, \"name\": \"yellow, red, and white uniform\"}, {\"id\": 31266, \"name\": \"distinctive curved roof\"}, {\"id\": 31267, \"name\": \"cosco\"}, {\"id\": 31268, \"name\": \"the marker tip\"}, {\"id\": 31269, \"name\": \"the star-shaped wand\"}, {\"id\": 31270, \"name\": \"a black leaf design\"}, {\"id\": 31271, \"name\": \"a caption overlay\"}, {\"id\": 31272, \"name\": \"the patreon link\"}, {\"id\": 31273, \"name\": \"the green track surface\"}, {\"id\": 31274, \"name\": \"white tube\"}, {\"id\": 31275, \"name\": \"a brand's logo\"}, {\"id\": 31276, \"name\": \"horse trailer\"}, {\"id\": 31277, \"name\": \"small tin can\"}, {\"id\": 31278, \"name\": \"metal workbench\"}, {\"id\": 31279, \"name\": \"the white bicycle symbol\"}, {\"id\": 31280, \"name\": \"the digital speedometer\"}, {\"id\": 31281, \"name\": \"grayish-blue body of water\"}, {\"id\": 31282, \"name\": \"finish line\"}, {\"id\": 31283, \"name\": \"the silver cap\"}, {\"id\": 31284, \"name\": \"the lone rider\"}, {\"id\": 31285, \"name\": \"the purple mask\"}, {\"id\": 31286, \"name\": \"the tan pedal\"}, {\"id\": 31287, \"name\": \"horizontal, black, plastic handle\"}, {\"id\": 31288, \"name\": \"the metallic blue car\"}, {\"id\": 31289, \"name\": \"a white cylindrical item\"}, {\"id\": 31290, \"name\": \"white semi-truck\"}, {\"id\": 31291, \"name\": \"the brick exterior\"}, {\"id\": 31292, \"name\": \"the fluffy white neck\"}, {\"id\": 31293, \"name\": \"a truck's rear window\"}, {\"id\": 31294, \"name\": \"box of wooden-handled tool\"}, {\"id\": 31295, \"name\": \"a small graphic of a sun rising over a mountain range\"}, {\"id\": 31296, \"name\": \"david ayer\"}, {\"id\": 31297, \"name\": \"bicycle's shadow\"}, {\"id\": 31298, \"name\": \"the dirty bird toy already!\"}, {\"id\": 31299, \"name\": \"a motorcyclist's rearview mirror\"}, {\"id\": 31300, \"name\": \"a rodomaior\"}, {\"id\": 31301, \"name\": \"red clamp\"}, {\"id\": 31302, \"name\": \"a red engine\"}, {\"id\": 31303, \"name\": \"a vibrant blue track\"}, {\"id\": 31304, \"name\": \"two-door coupe\"}, {\"id\": 31305, \"name\": \"a spaceship\"}, {\"id\": 31306, \"name\": \"a circular design\"}, {\"id\": 31307, \"name\": \"knot\"}, {\"id\": 31308, \"name\": \"a black, red, and yellow component\"}, {\"id\": 31309, \"name\": \"a prominent orange ramp\"}, {\"id\": 31310, \"name\": \"alta logo\"}, {\"id\": 31311, \"name\": \"the red and black accent\"}, {\"id\": 31312, \"name\": \"the black and yellow jacket\"}, {\"id\": 31313, \"name\": \"red cape\"}, {\"id\": 31314, \"name\": \"a yellow and blue handhold\"}, {\"id\": 31315, \"name\": \"a red keychain\"}, {\"id\": 31316, \"name\": \"the green gras strip\"}, {\"id\": 31317, \"name\": \"a green toy car\"}, {\"id\": 31318, \"name\": \"large white tooth-like design\"}, {\"id\": 31319, \"name\": \"a rocky trail\"}, {\"id\": 31320, \"name\": \"large fuel tank\"}, {\"id\": 31321, \"name\": \"a jelly bean\"}, {\"id\": 31322, \"name\": \"bike's tire\"}, {\"id\": 31323, \"name\": \"kona banner\"}, {\"id\": 31324, \"name\": \"a go-kart's steering wheel\"}, {\"id\": 31325, \"name\": \"lift hill\"}, {\"id\": 31326, \"name\": \"a light-colored short\"}, {\"id\": 31327, \"name\": \"a white wire\"}, {\"id\": 31328, \"name\": \"a train's white front\"}, {\"id\": 31329, \"name\": \"the coffee machine\"}, {\"id\": 31330, \"name\": \"a left handlebar\"}, {\"id\": 31331, \"name\": \"a glas or plastic barrier\"}, {\"id\": 31332, \"name\": \"the blue body\"}, {\"id\": 31333, \"name\": \"a speed demon\"}, {\"id\": 31334, \"name\": \"the circular white component\"}, {\"id\": 31335, \"name\": \"yellow pedestrian crossing sign\"}, {\"id\": 31336, \"name\": \"a white icing\"}, {\"id\": 31337, \"name\": \"a white, futuristic-looking vehicle\"}, {\"id\": 31338, \"name\": \"a downspout\"}, {\"id\": 31339, \"name\": \"the long hose\"}, {\"id\": 31340, \"name\": \"the blue and white advertisement\"}, {\"id\": 31341, \"name\": \"a st. clare\"}, {\"id\": 31342, \"name\": \"the red tailgate\"}, {\"id\": 31343, \"name\": \"a prominent caption\"}, {\"id\": 31344, \"name\": \"large anchor\"}, {\"id\": 31345, \"name\": \"spout\"}, {\"id\": 31346, \"name\": \"the prominent wheel\"}, {\"id\": 31347, \"name\": \"stylized white br logo\"}, {\"id\": 31348, \"name\": \"a handle of the stretcher\"}, {\"id\": 31349, \"name\": \"a passenger seat of a car\"}, {\"id\": 31350, \"name\": \"dark-colored stone or concrete\"}, {\"id\": 31351, \"name\": \"a green glow\"}, {\"id\": 31352, \"name\": \"the cotton ball\"}, {\"id\": 31353, \"name\": \"thin orange stripe\"}, {\"id\": 31354, \"name\": \"residential setting\"}, {\"id\": 31355, \"name\": \"vice\"}, {\"id\": 31356, \"name\": \"the small orange figurine\"}, {\"id\": 31357, \"name\": \"welch's logo\"}, {\"id\": 31358, \"name\": \"slot for inserting coin\"}, {\"id\": 31359, \"name\": \"car's rear window\"}, {\"id\": 31360, \"name\": \"the vertical panel\"}, {\"id\": 31361, \"name\": \"the black crane\"}, {\"id\": 31362, \"name\": \"a black and green seat\"}, {\"id\": 31363, \"name\": \"grassy shoulder\"}, {\"id\": 31364, \"name\": \"glas bowl\"}, {\"id\": 31365, \"name\": \"the white post\"}, {\"id\": 31366, \"name\": \"the driver's helmet\"}, {\"id\": 31367, \"name\": \"wolf tooth logo\"}, {\"id\": 31368, \"name\": \"the green paint splatter\"}, {\"id\": 31369, \"name\": \"a small blue circle\"}, {\"id\": 31370, \"name\": \"a red needle\"}, {\"id\": 31371, \"name\": \"the white lock symbol\"}, {\"id\": 31372, \"name\": \"a green and red item\"}, {\"id\": 31373, \"name\": \"small trash can\"}, {\"id\": 31374, \"name\": \"the scope\"}, {\"id\": 31375, \"name\": \"the handlebar of the motorcycle\"}, {\"id\": 31376, \"name\": \"name\"}, {\"id\": 31377, \"name\": \"dark brown or black surface\"}, {\"id\": 31378, \"name\": \"small white rabbit character\"}, {\"id\": 31379, \"name\": \"white, rectangular beam\"}, {\"id\": 31380, \"name\": \"a wooden walkway\"}, {\"id\": 31381, \"name\": \"a racing stripe\"}, {\"id\": 31382, \"name\": \"a kitchen countertop\"}, {\"id\": 31383, \"name\": \"ferri wheel\"}, {\"id\": 31384, \"name\": \"the back of her head\"}, {\"id\": 31385, \"name\": \"a subscribed button\"}, {\"id\": 31386, \"name\": \"the store aisle\"}, {\"id\": 31387, \"name\": \"low-lying cloud\"}, {\"id\": 31388, \"name\": \"negative terminal\"}, {\"id\": 31389, \"name\": \"the shock absorber\"}, {\"id\": 31390, \"name\": \"a clean backdrop\"}, {\"id\": 31391, \"name\": \"dark brown base\"}, {\"id\": 31392, \"name\": \"the red and black metal object\"}, {\"id\": 31393, \"name\": \"pink cable\"}, {\"id\": 31394, \"name\": \"the stainles steel sink bowl\"}, {\"id\": 31395, \"name\": \"bike rack\"}, {\"id\": 31396, \"name\": \"smaller orange light\"}, {\"id\": 31397, \"name\": \"the large belly\"}, {\"id\": 31398, \"name\": \"the white-framed window\"}, {\"id\": 31399, \"name\": \"the small black bag\"}, {\"id\": 31400, \"name\": \"the black and green handlebar\"}, {\"id\": 31401, \"name\": \"red stripe design\"}, {\"id\": 31402, \"name\": \"genesis\"}, {\"id\": 31403, \"name\": \"a nose ring\"}, {\"id\": 31404, \"name\": \"a motorcycle's rear\"}, {\"id\": 31405, \"name\": \"a driver's windshield\"}, {\"id\": 31406, \"name\": \"white packaging\"}, {\"id\": 31407, \"name\": \"large, white, half-circle shape\"}, {\"id\": 31408, \"name\": \"a yellow buckle\"}, {\"id\": 31409, \"name\": \"the gold foil\"}, {\"id\": 31410, \"name\": \"black racing helmet\"}, {\"id\": 31411, \"name\": \"mountain biker's right leg\"}, {\"id\": 31412, \"name\": \"red top loop\"}, {\"id\": 31413, \"name\": \"radio station frequency\"}, {\"id\": 31414, \"name\": \"black and white striped bag\"}, {\"id\": 31415, \"name\": \"the red and white checkered flag\"}, {\"id\": 31416, \"name\": \"red and blue component\"}, {\"id\": 31417, \"name\": \"a large, black, geometric-shaped muzzle\"}, {\"id\": 31418, \"name\": \"a white interior\"}, {\"id\": 31419, \"name\": \"the hammer\"}, {\"id\": 31420, \"name\": \"streak of white light\"}, {\"id\": 31421, \"name\": \"green purse\"}, {\"id\": 31422, \"name\": \"rear deck lid\"}, {\"id\": 31423, \"name\": \"a gold clasp\"}, {\"id\": 31424, \"name\": \"the black tank\"}, {\"id\": 31425, \"name\": \"a white hair\"}, {\"id\": 31426, \"name\": \"a small metal base\"}, {\"id\": 31427, \"name\": \"the subscribe banner\"}, {\"id\": 31428, \"name\": \"a cautionary note\"}, {\"id\": 31429, \"name\": \"a yellow and black device\"}, {\"id\": 31430, \"name\": \"the green and yellow elf hat\"}, {\"id\": 31431, \"name\": \"the amusement park\"}, {\"id\": 31432, \"name\": \"small patch of grass and weed\"}, {\"id\": 31433, \"name\": \"a adida logo\"}, {\"id\": 31434, \"name\": \"the kayaker's paddle\"}, {\"id\": 31435, \"name\": \"black and red dashboard\"}, {\"id\": 31436, \"name\": \"a light brown exterior\"}, {\"id\": 31437, \"name\": \"black horn\"}, {\"id\": 31438, \"name\": \"the black car seat base\"}, {\"id\": 31439, \"name\": \"black car's side mirror\"}, {\"id\": 31440, \"name\": \"the coil of green and red wire\"}, {\"id\": 31441, \"name\": \"a dark green and white helmet\"}, {\"id\": 31442, \"name\": \"the white trash can\"}, {\"id\": 31443, \"name\": \"black smoke stack\"}, {\"id\": 31444, \"name\": \"the list of benefit\"}, {\"id\": 31445, \"name\": \"the phone receiver\"}, {\"id\": 31446, \"name\": \"geico logo\"}, {\"id\": 31447, \"name\": \"neon green grip\"}, {\"id\": 31448, \"name\": \"a al jazeera logo\"}, {\"id\": 31449, \"name\": \"dark wood trim\"}, {\"id\": 31450, \"name\": \"a blue wrapper\"}, {\"id\": 31451, \"name\": \"the rear brake light\"}, {\"id\": 31452, \"name\": \"the hood of the toy car\"}, {\"id\": 31453, \"name\": \"the pink ruffled skirt\"}, {\"id\": 31454, \"name\": \"case\"}, {\"id\": 31455, \"name\": \"green and black package of a car accessory\"}, {\"id\": 31456, \"name\": \"a clear windshield\"}, {\"id\": 31457, \"name\": \"a boeing logo\"}, {\"id\": 31458, \"name\": \"a white shelf\"}, {\"id\": 31459, \"name\": \"a red q symbol\"}, {\"id\": 31460, \"name\": \"the gull-wing door\"}, {\"id\": 31461, \"name\": \"black film strip border\"}, {\"id\": 31462, \"name\": \"a red and black short\"}, {\"id\": 31463, \"name\": \"the dark green stripe\"}, {\"id\": 31464, \"name\": \"a score counter\"}, {\"id\": 31465, \"name\": \"a white and red sticker\"}, {\"id\": 31466, \"name\": \"the green cord\"}, {\"id\": 31467, \"name\": \"the roof of a vehicle\"}, {\"id\": 31468, \"name\": \"translucent watermark\"}, {\"id\": 31469, \"name\": \"a green paint\"}, {\"id\": 31470, \"name\": \"a blue and yellow label\"}, {\"id\": 31471, \"name\": \"the fruit design\"}, {\"id\": 31472, \"name\": \"an oxygen symbol\"}, {\"id\": 31473, \"name\": \"bright green sweater\"}, {\"id\": 31474, \"name\": \"an artist's fingernail\"}, {\"id\": 31475, \"name\": \"colorful shelving unit\"}, {\"id\": 31476, \"name\": \"the yellow and red train car\"}, {\"id\": 31477, \"name\": \"curly brown hair\"}, {\"id\": 31478, \"name\": \"the advertisement board\"}, {\"id\": 31479, \"name\": \"the red and white taxi\"}, {\"id\": 31480, \"name\": \"the cartoon clown\"}, {\"id\": 31481, \"name\": \"the long, flowing tail\"}, {\"id\": 31482, \"name\": \"passenger seat of a car\"}, {\"id\": 31483, \"name\": \"black-and-white striped curb\"}, {\"id\": 31484, \"name\": \"the driver's window\"}, {\"id\": 31485, \"name\": \"the small volvo logo\"}, {\"id\": 31486, \"name\": \"a large pile\"}, {\"id\": 31487, \"name\": \"a large, dark, blue, rectangular shape\"}, {\"id\": 31488, \"name\": \"the red and gray metal beam\"}, {\"id\": 31489, \"name\": \"black and white camouflage patterned outfit\"}, {\"id\": 31490, \"name\": \"the small yellow plane\"}, {\"id\": 31491, \"name\": \"wooden bleacher\"}, {\"id\": 31492, \"name\": \"white and blue stripe\"}, {\"id\": 31493, \"name\": \"the long, pointed tail\"}, {\"id\": 31494, \"name\": \"side of the cab\"}, {\"id\": 31495, \"name\": \"freightliner semi-trailer truck\"}, {\"id\": 31496, \"name\": \"an user's name\"}, {\"id\": 31497, \"name\": \"grey bridge\"}, {\"id\": 31498, \"name\": \"gray fabric\"}, {\"id\": 31499, \"name\": \"the brown wooden cabinet\"}, {\"id\": 31500, \"name\": \"the key\"}, {\"id\": 31501, \"name\": \"the red bell\"}, {\"id\": 31502, \"name\": \"a red and green design\"}, {\"id\": 31503, \"name\": \"green gras patch\"}, {\"id\": 31504, \"name\": \"a black and red hood\"}, {\"id\": 31505, \"name\": \"stylized m symbol\"}, {\"id\": 31506, \"name\": \"spaceship\"}, {\"id\": 31507, \"name\": \"the vw logo\"}, {\"id\": 31508, \"name\": \"small section of green water\"}, {\"id\": 31509, \"name\": \"rocky and uneven ground\"}, {\"id\": 31510, \"name\": \"a high back\"}, {\"id\": 31511, \"name\": \"dark-colored engine block\"}, {\"id\": 31512, \"name\": \"a atv\"}, {\"id\": 31513, \"name\": \"a large deck\"}, {\"id\": 31514, \"name\": \"silver ring\"}, {\"id\": 31515, \"name\": \"gray console\"}, {\"id\": 31516, \"name\": \"gonetothesnowdogs.com\"}, {\"id\": 31517, \"name\": \"a stove top\"}, {\"id\": 31518, \"name\": \"a black and white striped band\"}, {\"id\": 31519, \"name\": \"red brake lever\"}, {\"id\": 31520, \"name\": \"a purple surface\"}, {\"id\": 31521, \"name\": \"a large white oval\"}, {\"id\": 31522, \"name\": \"blue star\"}, {\"id\": 31523, \"name\": \"the long yellow cord\"}, {\"id\": 31524, \"name\": \"large house\"}, {\"id\": 31525, \"name\": \"the round black logo\"}, {\"id\": 31526, \"name\": \"white fuselage\"}, {\"id\": 31527, \"name\": \"roof of the car\"}, {\"id\": 31528, \"name\": \"orange line\"}, {\"id\": 31529, \"name\": \"heavy-duty vehicle\"}, {\"id\": 31530, \"name\": \"a light blue car\"}, {\"id\": 31531, \"name\": \"the red jersey with white stripe\"}, {\"id\": 31532, \"name\": \"wide, toothy grin\"}, {\"id\": 31533, \"name\": \"a large, round head\"}, {\"id\": 31534, \"name\": \"the tattooed arm\"}, {\"id\": 31535, \"name\": \"a red tail\"}, {\"id\": 31536, \"name\": \"the green and red plate\"}, {\"id\": 31537, \"name\": \"the vertical silver pole\"}, {\"id\": 31538, \"name\": \"cyrillic writing\"}, {\"id\": 31539, \"name\": \"a car seat\"}, {\"id\": 31540, \"name\": \"a brown wooden door\"}, {\"id\": 31541, \"name\": \"the rodeo arena\"}, {\"id\": 31542, \"name\": \"airbag compartment\"}, {\"id\": 31543, \"name\": \"thin, yellow line\"}, {\"id\": 31544, \"name\": \"the red and yellow stripe\"}, {\"id\": 31545, \"name\": \"the turn signal lever\"}, {\"id\": 31546, \"name\": \"a storefront light\"}, {\"id\": 31547, \"name\": \"a production logo\"}, {\"id\": 31548, \"name\": \"the person's gloved hand\"}, {\"id\": 31549, \"name\": \"black hand grip\"}, {\"id\": 31550, \"name\": \"the large orange eye\"}, {\"id\": 31551, \"name\": \"horizontal line\"}, {\"id\": 31552, \"name\": \"truck's headlights\"}, {\"id\": 31553, \"name\": \"white and orange line\"}, {\"id\": 31554, \"name\": \"a jug\"}, {\"id\": 31555, \"name\": \"the mercedes-benz\"}, {\"id\": 31556, \"name\": \"yellow tinted windshield\"}, {\"id\": 31557, \"name\": \"the field of golden corn\"}, {\"id\": 31558, \"name\": \"leaderboard\"}, {\"id\": 31559, \"name\": \"a convertible car\"}, {\"id\": 31560, \"name\": \"the perch\"}, {\"id\": 31561, \"name\": \"median strip\"}, {\"id\": 31562, \"name\": \"an arrow tip\"}, {\"id\": 31563, \"name\": \"an orange flower design\"}, {\"id\": 31564, \"name\": \"the white train\"}, {\"id\": 31565, \"name\": \"a motorcycle's mirror\"}, {\"id\": 31566, \"name\": \"the train's headlights\"}, {\"id\": 31567, \"name\": \"author's name\"}, {\"id\": 31568, \"name\": \"a colorful rock\"}, {\"id\": 31569, \"name\": \"a cursive white signature\"}, {\"id\": 31570, \"name\": \"the black plastic vent\"}, {\"id\": 31571, \"name\": \"a white tissue box\"}, {\"id\": 31572, \"name\": \"the topeak\"}, {\"id\": 31573, \"name\": \"the black, paved surface\"}, {\"id\": 31574, \"name\": \"the 22\"}, {\"id\": 31575, \"name\": \"a distinctive red stripe\"}, {\"id\": 31576, \"name\": \"colorful character token\"}, {\"id\": 31577, \"name\": \"a character's arm\"}, {\"id\": 31578, \"name\": \"white paper label\"}, {\"id\": 31579, \"name\": \"xbox\"}, {\"id\": 31580, \"name\": \"black, red, and white jersey\"}, {\"id\": 31581, \"name\": \"the starting line of a running track\"}, {\"id\": 31582, \"name\": \"a small white and gray lego object\"}, {\"id\": 31583, \"name\": \"tree root\"}, {\"id\": 31584, \"name\": \"a metal grate drain\"}, {\"id\": 31585, \"name\": \"a deck of the aircraft carrier\"}, {\"id\": 31586, \"name\": \"a gras hill\"}, {\"id\": 31587, \"name\": \"a triangular shape\"}, {\"id\": 31588, \"name\": \"the red tag\"}, {\"id\": 31589, \"name\": \"yellow and black plastic component\"}, {\"id\": 31590, \"name\": \"a short-sleeved shirt\"}, {\"id\": 31591, \"name\": \"a blue and purple bikini top\"}, {\"id\": 31592, \"name\": \"the yellow clasp\"}, {\"id\": 31593, \"name\": \"the yellow and black construction vehicle\"}, {\"id\": 31594, \"name\": \"a golden corral restaurant\"}, {\"id\": 31595, \"name\": \"large, circular structure\"}, {\"id\": 31596, \"name\": \"the red and white title\"}, {\"id\": 31597, \"name\": \"the gray countertop\"}, {\"id\": 31598, \"name\": \"the roller coaster\"}, {\"id\": 31599, \"name\": \"white cros symbol\"}, {\"id\": 31600, \"name\": \"the black, spiky helmet\"}, {\"id\": 31601, \"name\": \"the character's gun\"}, {\"id\": 31602, \"name\": \"numerical datum\"}, {\"id\": 31603, \"name\": \"a stylized eagle's head\"}, {\"id\": 31604, \"name\": \"a black converse-style shoe\"}, {\"id\": 31605, \"name\": \"a controller's logo\"}, {\"id\": 31606, \"name\": \"the axle component\"}, {\"id\": 31607, \"name\": \"pink line\"}, {\"id\": 31608, \"name\": \"the black scuba diving mask\"}, {\"id\": 31609, \"name\": \"a shadow of the rider\"}, {\"id\": 31610, \"name\": \"a track's surface\"}, {\"id\": 31611, \"name\": \"yellow car illustration\"}, {\"id\": 31612, \"name\": \"wide grin\"}, {\"id\": 31613, \"name\": \"a wine bottle\"}, {\"id\": 31614, \"name\": \"white p logo\"}, {\"id\": 31615, \"name\": \"a red pin\"}, {\"id\": 31616, \"name\": \"the prominent 1\"}, {\"id\": 31617, \"name\": \"a grassy outfield\"}, {\"id\": 31618, \"name\": \"a decorated yellow motorcycle\"}, {\"id\": 31619, \"name\": \"short dirt path\"}, {\"id\": 31620, \"name\": \"the diagonal banner\"}, {\"id\": 31621, \"name\": \"a driver's legs\"}, {\"id\": 31622, \"name\": \"jet-skiing\"}, {\"id\": 31623, \"name\": \"the construction site\"}, {\"id\": 31624, \"name\": \"a green and black graphic\"}, {\"id\": 31625, \"name\": \"blue window\"}, {\"id\": 31626, \"name\": \"the black metal bench\"}, {\"id\": 31627, \"name\": \"metal wheel\"}, {\"id\": 31628, \"name\": \"the white rug\"}, {\"id\": 31629, \"name\": \"the instrument panel\"}, {\"id\": 31630, \"name\": \"the blurred view of the go-kart track\"}, {\"id\": 31631, \"name\": \"the white character\"}, {\"id\": 31632, \"name\": \"the black cylindrical body\"}, {\"id\": 31633, \"name\": \"the mud-covered tire\"}, {\"id\": 31634, \"name\": \"clear plastic\"}, {\"id\": 31635, \"name\": \"yellow-and-black shoe\"}, {\"id\": 31636, \"name\": \"the miniature train bridge\"}, {\"id\": 31637, \"name\": \"an indoor go-kart\"}, {\"id\": 31638, \"name\": \"the red and black diagonal-cutting plier\"}, {\"id\": 31639, \"name\": \"black and red go-kart\"}, {\"id\": 31640, \"name\": \"a sleeve of a black jacket\"}, {\"id\": 31641, \"name\": \"the tan doormat\"}, {\"id\": 31642, \"name\": \"a black body bag\"}, {\"id\": 31643, \"name\": \"a gold square window\"}, {\"id\": 31644, \"name\": \"the large black eye\"}, {\"id\": 31645, \"name\": \"yellow tape barrier\"}, {\"id\": 31646, \"name\": \"runway\"}, {\"id\": 31647, \"name\": \"the yellow door frame\"}, {\"id\": 31648, \"name\": \"front fairing\"}, {\"id\": 31649, \"name\": \"dotted line circle\"}, {\"id\": 31650, \"name\": \"a thin black antenna\"}, {\"id\": 31651, \"name\": \"red and yellow design\"}, {\"id\": 31652, \"name\": \"the flat deck\"}, {\"id\": 31653, \"name\": \"a dashboard of the motorcycle\"}, {\"id\": 31654, \"name\": \"muddy, snowy terrain\"}, {\"id\": 31655, \"name\": \"the wwlp.com\"}, {\"id\": 31656, \"name\": \"dock\"}, {\"id\": 31657, \"name\": \"the silver engine\"}, {\"id\": 31658, \"name\": \"the menacing red eye\"}, {\"id\": 31659, \"name\": \"a truck's license plate number\"}, {\"id\": 31660, \"name\": \"the cartoon alien\"}, {\"id\": 31661, \"name\": \"surfboard\"}, {\"id\": 31662, \"name\": \"a white candle\"}, {\"id\": 31663, \"name\": \"a blue, orange, and yellow sticker\"}, {\"id\": 31664, \"name\": \"the rider's vehicle\"}, {\"id\": 31665, \"name\": \"hiker's hiking pole\"}, {\"id\": 31666, \"name\": \"the red p logo\"}, {\"id\": 31667, \"name\": \"the driver's right hand\"}, {\"id\": 31668, \"name\": \"the blue reflector\"}, {\"id\": 31669, \"name\": \"a mustache\"}, {\"id\": 31670, \"name\": \"the green field\"}, {\"id\": 31671, \"name\": \"a black and red rear light\"}, {\"id\": 31672, \"name\": \"prominent mustache\"}, {\"id\": 31673, \"name\": \"the colorful car\"}, {\"id\": 31674, \"name\": \"bright sun\"}, {\"id\": 31675, \"name\": \"a time 49.03\"}, {\"id\": 31676, \"name\": \"the curved railing\"}, {\"id\": 31677, \"name\": \"the taxiway\"}, {\"id\": 31678, \"name\": \"bright pink swimsuit\"}, {\"id\": 31679, \"name\": \"a yellow rectangle\"}, {\"id\": 31680, \"name\": \"the green house\"}, {\"id\": 31681, \"name\": \"a gold\"}, {\"id\": 31682, \"name\": \"the pink shoe\"}, {\"id\": 31683, \"name\": \"track's red and white barrier\"}, {\"id\": 31684, \"name\": \"blue horn\"}, {\"id\": 31685, \"name\": \"the burgundy upholstery\"}, {\"id\": 31686, \"name\": \"long, grey trunk\"}, {\"id\": 31687, \"name\": \"yellow canister\"}, {\"id\": 31688, \"name\": \"the partially visible person\"}, {\"id\": 31689, \"name\": \"ship's stat\"}, {\"id\": 31690, \"name\": \"the black spartan helmet logo\"}, {\"id\": 31691, \"name\": \"green floor\"}, {\"id\": 31692, \"name\": \"civilian\"}, {\"id\": 31693, \"name\": \"a white, plastic tray\"}, {\"id\": 31694, \"name\": \"the passport logo\"}, {\"id\": 31695, \"name\": \"prominent yellow sign or billboard\"}, {\"id\": 31696, \"name\": \"the news headline\"}, {\"id\": 31697, \"name\": \"factory\"}, {\"id\": 31698, \"name\": \"silver hatchback\"}, {\"id\": 31699, \"name\": \"the pink storefront\"}, {\"id\": 31700, \"name\": \"a black toolbox\"}, {\"id\": 31701, \"name\": \"a dark gray dashboard\"}, {\"id\": 31702, \"name\": \"the toy cowboy\"}, {\"id\": 31703, \"name\": \"a gold icon\"}, {\"id\": 31704, \"name\": \"a red and black frame\"}, {\"id\": 31705, \"name\": \"a blurry green figure\"}, {\"id\": 31706, \"name\": \"the brown leather handle\"}, {\"id\": 31707, \"name\": \"the zippered top\"}, {\"id\": 31708, \"name\": \"a yellow and white cloud\"}, {\"id\": 31709, \"name\": \"right turn sign\"}, {\"id\": 31710, \"name\": \"a neon green stripe\"}, {\"id\": 31711, \"name\": \"brown object\"}, {\"id\": 31712, \"name\": \"a car's body\"}, {\"id\": 31713, \"name\": \"red and black car\"}, {\"id\": 31714, \"name\": \"a covered entrance\"}, {\"id\": 31715, \"name\": \"a white paper towel\"}, {\"id\": 31716, \"name\": \"a ball or marble\"}, {\"id\": 31717, \"name\": \"mouthwash\"}, {\"id\": 31718, \"name\": \"a red brick sidewalk\"}, {\"id\": 31719, \"name\": \"12 logo\"}, {\"id\": 31720, \"name\": \"arrow pointing upward\"}, {\"id\": 31721, \"name\": \"orange bead\"}, {\"id\": 31722, \"name\": \"the black racing suit\"}, {\"id\": 31723, \"name\": \"lush hillside\"}, {\"id\": 31724, \"name\": \"the cartoon logo\"}, {\"id\": 31725, \"name\": \"water bottle holder\"}, {\"id\": 31726, \"name\": \"a box containing diapers and wipe\"}, {\"id\": 31727, \"name\": \"forest of tree\"}, {\"id\": 31728, \"name\": \"the calculator\"}, {\"id\": 31729, \"name\": \"the orange and white chair\"}, {\"id\": 31730, \"name\": \"a polaris\"}, {\"id\": 31731, \"name\": \"thrilling roller coaster\"}, {\"id\": 31732, \"name\": \"a white clutch bag\"}, {\"id\": 31733, \"name\": \"muddy path\"}, {\"id\": 31734, \"name\": \"a rectangular platform\"}, {\"id\": 31735, \"name\": \"small shelf\"}, {\"id\": 31736, \"name\": \"a kite surfer\"}, {\"id\": 31737, \"name\": \"the paved surface\"}, {\"id\": 31738, \"name\": \"flat, blue tool\"}, {\"id\": 31739, \"name\": \"the person's left arm\"}, {\"id\": 31740, \"name\": \"fuel tank\"}, {\"id\": 31741, \"name\": \"the hood ornament\"}, {\"id\": 31742, \"name\": \"the large white ball\"}, {\"id\": 31743, \"name\": \"black screen\"}, {\"id\": 31744, \"name\": \"khaki clothing\"}, {\"id\": 31745, \"name\": \"the small black bird\"}, {\"id\": 31746, \"name\": \"blue and black mountain bike\"}, {\"id\": 31747, \"name\": \"a red pen\"}, {\"id\": 31748, \"name\": \"the silver gauge\"}, {\"id\": 31749, \"name\": \"a stadium's bleachers\"}, {\"id\": 31750, \"name\": \"a white m\"}, {\"id\": 31751, \"name\": \"the blue and red bag\"}, {\"id\": 31752, \"name\": \"a driver's side door\"}, {\"id\": 31753, \"name\": \"the train's front car\"}, {\"id\": 31754, \"name\": \"red and yellow car\"}, {\"id\": 31755, \"name\": \"the white backboard\"}, {\"id\": 31756, \"name\": \"an orange circle\"}, {\"id\": 31757, \"name\": \"the white board\"}, {\"id\": 31758, \"name\": \"the black tight\"}, {\"id\": 31759, \"name\": \"numbered banner\"}, {\"id\": 31760, \"name\": \"red and yellow object\"}, {\"id\": 31761, \"name\": \"a red grip\"}, {\"id\": 31762, \"name\": \"the plane's nose\"}, {\"id\": 31763, \"name\": \"the partially visible sign\"}, {\"id\": 31764, \"name\": \"silver hook\"}, {\"id\": 31765, \"name\": \"the dashcam\"}, {\"id\": 31766, \"name\": \"the 40.1\"}, {\"id\": 31767, \"name\": \"the blue-framed door\"}, {\"id\": 31768, \"name\": \"a yellow metal machine\"}, {\"id\": 31769, \"name\": \"the cityscape backdrop\"}, {\"id\": 31770, \"name\": \"a road infrastructure\"}, {\"id\": 31771, \"name\": \"yellow chevron\"}, {\"id\": 31772, \"name\": \"white eye\"}, {\"id\": 31773, \"name\": \"the smaller circular top\"}, {\"id\": 31774, \"name\": \"a danger\"}, {\"id\": 31775, \"name\": \"speed 21 mph\"}, {\"id\": 31776, \"name\": \"the car key fob\"}, {\"id\": 31777, \"name\": \"large front grill\"}, {\"id\": 31778, \"name\": \"gray cowling\"}, {\"id\": 31779, \"name\": \"the 48/100\"}, {\"id\": 31780, \"name\": \"a waterway\"}, {\"id\": 31781, \"name\": \"the tow truck\"}, {\"id\": 31782, \"name\": \"the red mane and tail\"}, {\"id\": 31783, \"name\": \"the frog's mouth\"}, {\"id\": 31784, \"name\": \"a small rectangular opening\"}, {\"id\": 31785, \"name\": \"vast expanse of the runway\"}, {\"id\": 31786, \"name\": \"the gras median\"}, {\"id\": 31787, \"name\": \"small red and yellow logo\"}, {\"id\": 31788, \"name\": \"a red circle with a line through it\"}, {\"id\": 31789, \"name\": \"intake manifold\"}, {\"id\": 31790, \"name\": \"a dark brown wooden table\"}, {\"id\": 31791, \"name\": \"a youtube logo\"}, {\"id\": 31792, \"name\": \"the gray lamppost\"}, {\"id\": 31793, \"name\": \"heart-shaped tray\"}, {\"id\": 31794, \"name\": \"ibtimes news\"}, {\"id\": 31795, \"name\": \"a dark apron\"}, {\"id\": 31796, \"name\": \"a white side mirror\"}, {\"id\": 31797, \"name\": \"a predominantly white hull\"}, {\"id\": 31798, \"name\": \"a white cross\"}, {\"id\": 31799, \"name\": \"white bill\"}, {\"id\": 31800, \"name\": \"the silver handlebar\"}, {\"id\": 31801, \"name\": \"a boulder\"}, {\"id\": 31802, \"name\": \"a steering wheel of a vehicle\"}, {\"id\": 31803, \"name\": \"the vibrant blue frame\"}, {\"id\": 31804, \"name\": \"picture of a man's face\"}, {\"id\": 31805, \"name\": \"engine compartment\"}, {\"id\": 31806, \"name\": \"a green start button\"}, {\"id\": 31807, \"name\": \"the pink my pony stuffed animal\"}, {\"id\": 31808, \"name\": \"a white lane marking\"}, {\"id\": 31809, \"name\": \"a cartoon image of a motorcyclist riding a bike\"}, {\"id\": 31810, \"name\": \"the vanity handicap\"}, {\"id\": 31811, \"name\": \"the red and blue circle\"}, {\"id\": 31812, \"name\": \"the white and black device\"}, {\"id\": 31813, \"name\": \"tiered stand\"}, {\"id\": 31814, \"name\": \"the capybara\"}, {\"id\": 31815, \"name\": \"the vibrant red fin\"}, {\"id\": 31816, \"name\": \"a hangar door\"}, {\"id\": 31817, \"name\": \"dotted outline\"}, {\"id\": 31818, \"name\": \"a supernovum\"}, {\"id\": 31819, \"name\": \"a smaller, cylindrical motor\"}, {\"id\": 31820, \"name\": \"ridgid logo\"}, {\"id\": 31821, \"name\": \"black gearshift\"}, {\"id\": 31822, \"name\": \"a white diagonal stripe\"}, {\"id\": 31823, \"name\": \"a picture of a baby\"}, {\"id\": 31824, \"name\": \"the interior of the cab\"}, {\"id\": 31825, \"name\": \"the yellow neck gaiter\"}, {\"id\": 31826, \"name\": \"transparent plastic cover\"}, {\"id\": 31827, \"name\": \"distinctive red stripe\"}, {\"id\": 31828, \"name\": \"the green and yellow cloud\"}, {\"id\": 31829, \"name\": \"green john deere combine\"}, {\"id\": 31830, \"name\": \"blue gun\"}, {\"id\": 31831, \"name\": \"the black wolf's silhouette\"}, {\"id\": 31832, \"name\": \"publication name\"}, {\"id\": 31833, \"name\": \"the white paper cone\"}, {\"id\": 31834, \"name\": \"the narrow dirt path\"}, {\"id\": 31835, \"name\": \"beige surface\"}, {\"id\": 31836, \"name\": \"fleet of car\"}, {\"id\": 31837, \"name\": \"rusty, corrugated metal roof\"}, {\"id\": 31838, \"name\": \"a cluttered makeup table\"}, {\"id\": 31839, \"name\": \"the whisk\"}, {\"id\": 31840, \"name\": \"the wooden boat\"}, {\"id\": 31841, \"name\": \"the black convertible\"}, {\"id\": 31842, \"name\": \"a website address\"}, {\"id\": 31843, \"name\": \"light blue rectangle\"}, {\"id\": 31844, \"name\": \"the grey plastic vent\"}, {\"id\": 31845, \"name\": \"the large wing\"}, {\"id\": 31846, \"name\": \"orange and black coozie\"}, {\"id\": 31847, \"name\": \"a leather cuff\"}, {\"id\": 31848, \"name\": \"white f\"}, {\"id\": 31849, \"name\": \"the muddy-brown water\"}, {\"id\": 31850, \"name\": \"rider's left arm\"}, {\"id\": 31851, \"name\": \"vintage yellow car\"}, {\"id\": 31852, \"name\": \"grassy hillside\"}, {\"id\": 31853, \"name\": \"white and orange label\"}, {\"id\": 31854, \"name\": \"black fur coat\"}, {\"id\": 31855, \"name\": \"the blue vase\"}, {\"id\": 31856, \"name\": \"a paint\"}, {\"id\": 31857, \"name\": \"the black motorcycle handlebar\"}, {\"id\": 31858, \"name\": \"'oo look cooking school!\"}, {\"id\": 31859, \"name\": \"bed of hay or straw\"}, {\"id\": 31860, \"name\": \"thin strap\"}, {\"id\": 31861, \"name\": \"the yellow spool of twine or cable\"}, {\"id\": 31862, \"name\": \"pattern of large polka dot\"}, {\"id\": 31863, \"name\": \"the floodlight\"}, {\"id\": 31864, \"name\": \"green base\"}, {\"id\": 31865, \"name\": \"a red post\"}, {\"id\": 31866, \"name\": \"a red rash guard\"}, {\"id\": 31867, \"name\": \"a logo storm\"}, {\"id\": 31868, \"name\": \"horse herd\"}, {\"id\": 31869, \"name\": \"a gray cursive\"}, {\"id\": 31870, \"name\": \"the orange and white striped traffic cone\"}, {\"id\": 31871, \"name\": \"a red metal detail\"}, {\"id\": 31872, \"name\": \"the light purple wristwatch\"}, {\"id\": 31873, \"name\": \"the blue pump\"}, {\"id\": 31874, \"name\": \"a prominent white staircase\"}, {\"id\": 31875, \"name\": \"the graphic overlay\"}, {\"id\": 31876, \"name\": \"fireball\"}, {\"id\": 31877, \"name\": \"the beige stove\"}, {\"id\": 31878, \"name\": \"white archway\"}, {\"id\": 31879, \"name\": \"a black and orange leather glove\"}, {\"id\": 31880, \"name\": \"a tree-lined shoreline\"}, {\"id\": 31881, \"name\": \"the bullet hole\"}, {\"id\": 31882, \"name\": \"a white oval\"}, {\"id\": 31883, \"name\": \"a raised eyebrow\"}, {\"id\": 31884, \"name\": \"a large gray and white circle\"}, {\"id\": 31885, \"name\": \"a driver's bare feet\"}, {\"id\": 31886, \"name\": \"the middle ground\"}, {\"id\": 31887, \"name\": \"the diesel logo\"}, {\"id\": 31888, \"name\": \"mud\"}, {\"id\": 31889, \"name\": \"the white bow\"}, {\"id\": 31890, \"name\": \"white-painted fingernail\"}, {\"id\": 31891, \"name\": \"a black and yellow striped pole\"}, {\"id\": 31892, \"name\": \"the gray concrete wall\"}, {\"id\": 31893, \"name\": \"the ferri wheel\"}, {\"id\": 31894, \"name\": \"the miller\"}, {\"id\": 31895, \"name\": \"the red and green barrier\"}, {\"id\": 31896, \"name\": \"cable\"}, {\"id\": 31897, \"name\": \"the partly cloudy\"}, {\"id\": 31898, \"name\": \"the pink baby comb\"}, {\"id\": 31899, \"name\": \"a large, dark-colored boat or ship\"}, {\"id\": 31900, \"name\": \"a gold banner\"}, {\"id\": 31901, \"name\": \"driver's body\"}, {\"id\": 31902, \"name\": \"the red and yellow outline\"}, {\"id\": 31903, \"name\": \"a gravel platform\"}, {\"id\": 31904, \"name\": \"the blue mural\"}, {\"id\": 31905, \"name\": \"the glowing orb\"}, {\"id\": 31906, \"name\": \"the white cord\"}, {\"id\": 31907, \"name\": \"the brown ponytail\"}, {\"id\": 31908, \"name\": \"a winding asphalt road\"}, {\"id\": 31909, \"name\": \"a blurry fence\"}, {\"id\": 31910, \"name\": \"the dark blue uniform\"}, {\"id\": 31911, \"name\": \"red and black sail\"}, {\"id\": 31912, \"name\": \"a dark brown boot\"}, {\"id\": 31913, \"name\": \"a sleeve of her coat\"}, {\"id\": 31914, \"name\": \"shops and restaurant\"}, {\"id\": 31915, \"name\": \"the red and white container\"}, {\"id\": 31916, \"name\": \"red and yellow base\"}, {\"id\": 31917, \"name\": \"large dent\"}, {\"id\": 31918, \"name\": \"a white and green striped side panel\"}, {\"id\": 31919, \"name\": \"the black dvd player\"}, {\"id\": 31920, \"name\": \"globe\"}, {\"id\": 31921, \"name\": \"the off-road wheel\"}, {\"id\": 31922, \"name\": \"scrap metal\"}, {\"id\": 31923, \"name\": \"a green tent\"}, {\"id\": 31924, \"name\": \"landing pad\"}, {\"id\": 31925, \"name\": \"the prominent title\"}, {\"id\": 31926, \"name\": \"sleek black vehicle\"}, {\"id\": 31927, \"name\": \"the large boat\"}, {\"id\": 31928, \"name\": \"a time of the report\"}, {\"id\": 31929, \"name\": \"the yard\"}, {\"id\": 31930, \"name\": \"a rider's right leg\"}, {\"id\": 31931, \"name\": \"a bom logo\"}, {\"id\": 31932, \"name\": \"a large white and black wheel\"}, {\"id\": 31933, \"name\": \"dark blue racing suit\"}, {\"id\": 31934, \"name\": \"black wolf's head silhouette\"}, {\"id\": 31935, \"name\": \"narrow neck\"}, {\"id\": 31936, \"name\": \"the fire engine\"}, {\"id\": 31937, \"name\": \"a yellow lettering\"}, {\"id\": 31938, \"name\": \"brim\"}, {\"id\": 31939, \"name\": \"black ga tank\"}, {\"id\": 31940, \"name\": \"the figure in a black and yellow costume\"}, {\"id\": 31941, \"name\": \"the red lower section\"}, {\"id\": 31942, \"name\": \"the yellow landing stage\"}, {\"id\": 31943, \"name\": \"a gray pillow\"}, {\"id\": 31944, \"name\": \"the navigation equipment\"}, {\"id\": 31945, \"name\": \"the matching blue and yellow vest\"}, {\"id\": 31946, \"name\": \"the curved white line\"}, {\"id\": 31947, \"name\": \"a seat of a motorcycle\"}, {\"id\": 31948, \"name\": \"small white spoon\"}, {\"id\": 31949, \"name\": \"a blue metal box\"}, {\"id\": 31950, \"name\": \"the yellow caption\"}, {\"id\": 31951, \"name\": \"the dark wood grain pattern\"}, {\"id\": 31952, \"name\": \"front of the bike\"}, {\"id\": 31953, \"name\": \"a wooden ring\"}, {\"id\": 31954, \"name\": \"a patio\"}, {\"id\": 31955, \"name\": \"the rider's seat\"}, {\"id\": 31956, \"name\": \"boat's dark hull\"}, {\"id\": 31957, \"name\": \"keyhole\"}, {\"id\": 31958, \"name\": \"black gear shift knob\"}, {\"id\": 31959, \"name\": \"a small, silver box\"}, {\"id\": 31960, \"name\": \"the iv drip\"}, {\"id\": 31961, \"name\": \"a sharp claw\"}, {\"id\": 31962, \"name\": \"shelf of the grocery store\"}, {\"id\": 31963, \"name\": \"a large 20\"}, {\"id\": 31964, \"name\": \"the gray asphalt track\"}, {\"id\": 31965, \"name\": \"the small red circle\"}, {\"id\": 31966, \"name\": \"the truck's windshield\"}, {\"id\": 31967, \"name\": \"a storage unit\"}, {\"id\": 31968, \"name\": \"a driver's feet\"}, {\"id\": 31969, \"name\": \"the black and green stripe\"}, {\"id\": 31970, \"name\": \"person in the foreground\"}, {\"id\": 31971, \"name\": \"the clothing rack\"}, {\"id\": 31972, \"name\": \"the large seat\"}, {\"id\": 31973, \"name\": \"level of shelving\"}, {\"id\": 31974, \"name\": \"the translucent white watermark\"}, {\"id\": 31975, \"name\": \"a large front grille\"}, {\"id\": 31976, \"name\": \"a colorful statue\"}, {\"id\": 31977, \"name\": \"a gold trim\"}, {\"id\": 31978, \"name\": \"the white spade symbol\"}, {\"id\": 31979, \"name\": \"a dorsal fin\"}, {\"id\": 31980, \"name\": \"the red and white bandana\"}, {\"id\": 31981, \"name\": \"black taxi\"}, {\"id\": 31982, \"name\": \"person in a spacesuit\"}, {\"id\": 31983, \"name\": \"decorative mirror\"}, {\"id\": 31984, \"name\": \"a black and white striped dress\"}, {\"id\": 31985, \"name\": \"urgente: trasladan a amado boudou\"}, {\"id\": 31986, \"name\": \"an abandoned bicycle\"}, {\"id\": 31987, \"name\": \"dark gray wood plank\"}, {\"id\": 31988, \"name\": \"a bu stop\"}, {\"id\": 31989, \"name\": \"a labrador\"}, {\"id\": 31990, \"name\": \"silver tire\"}, {\"id\": 31991, \"name\": \"a yellow bow\"}, {\"id\": 31992, \"name\": \"a tall concrete barrier\"}, {\"id\": 31993, \"name\": \"a rivet\"}, {\"id\": 31994, \"name\": \"a large, tan-colored vehicle\"}, {\"id\": 31995, \"name\": \"scoreboard with light\"}, {\"id\": 31996, \"name\": \"the large hood scoop\"}, {\"id\": 31997, \"name\": \"a red marker\"}, {\"id\": 31998, \"name\": \"the audience member's head\"}, {\"id\": 31999, \"name\": \"gold hoop earring\"}, {\"id\": 32000, \"name\": \"a right front wheel\"}, {\"id\": 32001, \"name\": \"a tall fence\"}, {\"id\": 32002, \"name\": \"the metal security door\"}, {\"id\": 32003, \"name\": \"brown desk\"}, {\"id\": 32004, \"name\": \"metal sheeting\"}, {\"id\": 32005, \"name\": \"the line of white car\"}, {\"id\": 32006, \"name\": \"a blury face\"}, {\"id\": 32007, \"name\": \"animal skin cape\"}, {\"id\": 32008, \"name\": \"monkey's right arm\"}, {\"id\": 32009, \"name\": \"the white spoiler\"}, {\"id\": 32010, \"name\": \"the bike's handlebar\"}, {\"id\": 32011, \"name\": \"a single-file line\"}, {\"id\": 32012, \"name\": \"a neon light\"}, {\"id\": 32013, \"name\": \"a black-framed glass\"}, {\"id\": 32014, \"name\": \"the green crosswalk\"}, {\"id\": 32015, \"name\": \"red jeep\"}, {\"id\": 32016, \"name\": \"a thin wooden stick\"}, {\"id\": 32017, \"name\": \"black basket\"}, {\"id\": 32018, \"name\": \"the brightly colored jacket\"}, {\"id\": 32019, \"name\": \"a red nail polish\"}, {\"id\": 32020, \"name\": \"navy blue helmet\"}, {\"id\": 32021, \"name\": \"the lone white ball\"}, {\"id\": 32022, \"name\": \"the grom logo\"}, {\"id\": 32023, \"name\": \"a time and date of image\"}, {\"id\": 32024, \"name\": \"a white icon\"}, {\"id\": 32025, \"name\": \"yellow target symbol\"}, {\"id\": 32026, \"name\": \"brown metal\"}, {\"id\": 32027, \"name\": \"tall, beige-colored structure\"}, {\"id\": 32028, \"name\": \"a blue headgear\"}, {\"id\": 32029, \"name\": \"orange shoe\"}, {\"id\": 32030, \"name\": \"a blue trash bin\"}, {\"id\": 32031, \"name\": \"a large, red and white striped pole\"}, {\"id\": 32032, \"name\": \"tree or bush\"}, {\"id\": 32033, \"name\": \"the collage of person\"}, {\"id\": 32034, \"name\": \"the large black bag\"}, {\"id\": 32035, \"name\": \"a cement block\"}, {\"id\": 32036, \"name\": \"the prominent orange construction barrel\"}, {\"id\": 32037, \"name\": \"a long arm\"}, {\"id\": 32038, \"name\": \"green chat bubble\"}, {\"id\": 32039, \"name\": \"the teal glove\"}, {\"id\": 32040, \"name\": \"a black and yellow boxing glove\"}, {\"id\": 32041, \"name\": \"yellow marker\"}, {\"id\": 32042, \"name\": \"a military group\"}, {\"id\": 32043, \"name\": \"a red and black border\"}, {\"id\": 32044, \"name\": \"the curved windshield\"}, {\"id\": 32045, \"name\": \"prominent arched window\"}, {\"id\": 32046, \"name\": \"the large white column\"}, {\"id\": 32047, \"name\": \"the black racecar\"}, {\"id\": 32048, \"name\": \"blurred car\"}, {\"id\": 32049, \"name\": \"a channel\"}, {\"id\": 32050, \"name\": \"white observation tower\"}, {\"id\": 32051, \"name\": \"a small black banner\"}, {\"id\": 32052, \"name\": \"the close-up shot of a person working on a hose\"}, {\"id\": 32053, \"name\": \"the white propeller\"}, {\"id\": 32054, \"name\": \"heap on the ground\"}, {\"id\": 32055, \"name\": \"a white circular\"}, {\"id\": 32056, \"name\": \"the large, multi-colored tube slide\"}, {\"id\": 32057, \"name\": \"a field of dry grass\"}, {\"id\": 32058, \"name\": \"a green strip of gras or turf\"}, {\"id\": 32059, \"name\": \"the red bull straight rhythm 100 yard dash\"}, {\"id\": 32060, \"name\": \"a streamer\"}, {\"id\": 32061, \"name\": \"glas facade\"}, {\"id\": 32062, \"name\": \"the steeply pitched roof\"}, {\"id\": 32063, \"name\": \"a large docked ship\"}, {\"id\": 32064, \"name\": \"a blue base\"}, {\"id\": 32065, \"name\": \"a hospital strike\"}, {\"id\": 32066, \"name\": \"a uk license plate\"}, {\"id\": 32067, \"name\": \"a white and red truck\"}, {\"id\": 32068, \"name\": \"fur-trimmed cuff\"}, {\"id\": 32069, \"name\": \"a multicolored block structure\"}, {\"id\": 32070, \"name\": \"the green screen\"}, {\"id\": 32071, \"name\": \"a dark gray line\"}, {\"id\": 32072, \"name\": \"a person in a white shirt with a on the back\"}, {\"id\": 32073, \"name\": \"a blue metal toolbox\"}, {\"id\": 32074, \"name\": \"discraft logo\"}, {\"id\": 32075, \"name\": \"the pink earbud\"}, {\"id\": 32076, \"name\": \"the small, white light fixture\"}, {\"id\": 32077, \"name\": \"a french flag\"}, {\"id\": 32078, \"name\": \"the white hat with black spot\"}, {\"id\": 32079, \"name\": \"strip\"}, {\"id\": 32080, \"name\": \"a dark-colored dresser\"}, {\"id\": 32081, \"name\": \"a silver snare drum\"}, {\"id\": 32082, \"name\": \"the browser's address bar\"}, {\"id\": 32083, \"name\": \"a long, orange tail\"}, {\"id\": 32084, \"name\": \"a yellow wing\"}, {\"id\": 32085, \"name\": \"a human head\"}, {\"id\": 32086, \"name\": \"the traffic\"}, {\"id\": 32087, \"name\": \"turquoise door\"}, {\"id\": 32088, \"name\": \"suspect\"}, {\"id\": 32089, \"name\": \"a large, purple rectangle\"}, {\"id\": 32090, \"name\": \"the intricate carving\"}, {\"id\": 32091, \"name\": \"vibrant purple couch\"}, {\"id\": 32092, \"name\": \"the light-colored wooden desk or table\"}, {\"id\": 32093, \"name\": \"yellow strap or handle\"}, {\"id\": 32094, \"name\": \"purple creature\"}, {\"id\": 32095, \"name\": \"a striped pattern\"}, {\"id\": 32096, \"name\": \"a boy in red robe\"}, {\"id\": 32097, \"name\": \"train platform\"}, {\"id\": 32098, \"name\": \"a cliff face\"}, {\"id\": 32099, \"name\": \"brown facade\"}, {\"id\": 32100, \"name\": \"arched door\"}, {\"id\": 32101, \"name\": \"notable sign\"}, {\"id\": 32102, \"name\": \"the colorful tassel\"}, {\"id\": 32103, \"name\": \"the dark silhouette of a person\"}, {\"id\": 32104, \"name\": \"a red outfit\"}, {\"id\": 32105, \"name\": \"the green scooter\"}, {\"id\": 32106, \"name\": \"a large, black arcade machine\"}, {\"id\": 32107, \"name\": \"roof rack\"}, {\"id\": 32108, \"name\": \"orange and white toy gun\"}, {\"id\": 32109, \"name\": \"the dark, curly hair\"}, {\"id\": 32110, \"name\": \"carousel ride\"}, {\"id\": 32111, \"name\": \"wooden cutting board\"}, {\"id\": 32112, \"name\": \"brown monkey-like creature\"}, {\"id\": 32113, \"name\": \"the jet's nose\"}, {\"id\": 32114, \"name\": \"translucent pink rectangle\"}, {\"id\": 32115, \"name\": \"a stylized g\"}, {\"id\": 32116, \"name\": \"the tan vest\"}, {\"id\": 32117, \"name\": \"person in a red wetsuit\"}, {\"id\": 32118, \"name\": \"the large, red stripe\"}, {\"id\": 32119, \"name\": \"the carnival cruise ship\"}, {\"id\": 32120, \"name\": \"the dark blue sneaker\"}, {\"id\": 32121, \"name\": \"blue and white cloud\"}, {\"id\": 32122, \"name\": \"the blue and yellow racing suit\"}, {\"id\": 32123, \"name\": \"long, dark grill\"}, {\"id\": 32124, \"name\": \"kitchen\"}, {\"id\": 32125, \"name\": \"a cloudy ocean floor\"}, {\"id\": 32126, \"name\": \"the striking black statue of a man on horseback\"}, {\"id\": 32127, \"name\": \"a purple, gold, and red costume\"}, {\"id\": 32128, \"name\": \"a mcdonald's\"}, {\"id\": 32129, \"name\": \"a torch\"}, {\"id\": 32130, \"name\": \"a silhouette of a person's face\"}, {\"id\": 32131, \"name\": \"a crossed-out red circle\"}, {\"id\": 32132, \"name\": \"wizard hat\"}, {\"id\": 32133, \"name\": \"metal barricade\"}, {\"id\": 32134, \"name\": \"the green storefront\"}, {\"id\": 32135, \"name\": \"the rainbow-colored mural\"}, {\"id\": 32136, \"name\": \"green marker\"}, {\"id\": 32137, \"name\": \"a single file line\"}, {\"id\": 32138, \"name\": \"the white bu\"}, {\"id\": 32139, \"name\": \"a concrete platform\"}, {\"id\": 32140, \"name\": \"stained glass\"}, {\"id\": 32141, \"name\": \"a talking clock\"}, {\"id\": 32142, \"name\": \"green and black striped pipe\"}, {\"id\": 32143, \"name\": \"the hollywood tower hotel\"}, {\"id\": 32144, \"name\": \"the black rubber mat\"}, {\"id\": 32145, \"name\": \"the white circular logo\"}, {\"id\": 32146, \"name\": \"cartoon image of a man's face\"}, {\"id\": 32147, \"name\": \"large crosswalk\"}, {\"id\": 32148, \"name\": \"clay pot\"}, {\"id\": 32149, \"name\": \"the tan cap\"}, {\"id\": 32150, \"name\": \"a small, brown box\"}, {\"id\": 32151, \"name\": \"a small plastic cow figurine\"}, {\"id\": 32152, \"name\": \"dark-colored door\"}, {\"id\": 32153, \"name\": \"a cauldron\"}, {\"id\": 32154, \"name\": \"light-brown hair\"}, {\"id\": 32155, \"name\": \"a blue, yellow, and green object\"}, {\"id\": 32156, \"name\": \"the shelves of product\"}, {\"id\": 32157, \"name\": \"the large anchor\"}, {\"id\": 32158, \"name\": \"green bag\"}, {\"id\": 32159, \"name\": \"the flag of the united state\"}, {\"id\": 32160, \"name\": \"the red phone booth\"}, {\"id\": 32161, \"name\": \"an orange basket\"}, {\"id\": 32162, \"name\": \"a prominent green and white sign\"}, {\"id\": 32163, \"name\": \"carrick picnic area\"}, {\"id\": 32164, \"name\": \"male character\"}, {\"id\": 32165, \"name\": \"a green tank top\"}, {\"id\": 32166, \"name\": \"blue ring toy\"}, {\"id\": 32167, \"name\": \"the snapple logo\"}, {\"id\": 32168, \"name\": \"black and red flag\"}, {\"id\": 32169, \"name\": \"the black and yellow truck\"}, {\"id\": 32170, \"name\": \"a large chest\"}, {\"id\": 32171, \"name\": \"a ford truck\"}, {\"id\": 32172, \"name\": \"tuxedo\"}, {\"id\": 32173, \"name\": \"kenyan flag\"}, {\"id\": 32174, \"name\": \"a red and gold light\"}, {\"id\": 32175, \"name\": \"the black seatbelt\"}, {\"id\": 32176, \"name\": \"toll booth or gate\"}, {\"id\": 32177, \"name\": \"an amoeba and bacterium\"}, {\"id\": 32178, \"name\": \"a overhead crane remote\"}, {\"id\": 32179, \"name\": \"a yellow and black jersey\"}, {\"id\": 32180, \"name\": \"a patient's vital sign\"}, {\"id\": 32181, \"name\": \"a large blue tent\"}, {\"id\": 32182, \"name\": \"man in the red shirt\"}, {\"id\": 32183, \"name\": \"a black metal leg\"}, {\"id\": 32184, \"name\": \"a purple star\"}, {\"id\": 32185, \"name\": \"a light hair\"}, {\"id\": 32186, \"name\": \"a leopard-print coat\"}, {\"id\": 32187, \"name\": \"the barstools\"}, {\"id\": 32188, \"name\": \"a velvet\"}, {\"id\": 32189, \"name\": \"a short red hair\"}, {\"id\": 32190, \"name\": \"white flower logo\"}, {\"id\": 32191, \"name\": \"bulgari\"}, {\"id\": 32192, \"name\": \"red metal frame\"}, {\"id\": 32193, \"name\": \"a disc\"}, {\"id\": 32194, \"name\": \"the bright orange pant\"}, {\"id\": 32195, \"name\": \"a prominent red and white logo\"}, {\"id\": 32196, \"name\": \"a green surfboard or paddleboard\"}, {\"id\": 32197, \"name\": \"the red extension cord\"}, {\"id\": 32198, \"name\": \"a yellow and black striped wooden barrier\"}, {\"id\": 32199, \"name\": \"a neon green helmet\"}, {\"id\": 32200, \"name\": \"a parsley\"}, {\"id\": 32201, \"name\": \"a handshake\"}, {\"id\": 32202, \"name\": \"the forehead\"}, {\"id\": 32203, \"name\": \"a blue tarp or banner\"}, {\"id\": 32204, \"name\": \"yellow section of the wall\"}, {\"id\": 32205, \"name\": \"cat's bed\"}, {\"id\": 32206, \"name\": \"a brown leg\"}, {\"id\": 32207, \"name\": \"a blue cord\"}, {\"id\": 32208, \"name\": \"orange trigger\"}, {\"id\": 32209, \"name\": \"prominent black suv\"}, {\"id\": 32210, \"name\": \"a rectangular section\"}, {\"id\": 32211, \"name\": \"black tight\"}, {\"id\": 32212, \"name\": \"a red and white logo\"}, {\"id\": 32213, \"name\": \"a purple dreadlock\"}, {\"id\": 32214, \"name\": \"large pink, red, and yellow striped fabric\"}, {\"id\": 32215, \"name\": \"dark green shirt\"}, {\"id\": 32216, \"name\": \"the red and white striped awning\"}, {\"id\": 32217, \"name\": \"a display of shoe\"}, {\"id\": 32218, \"name\": \"blue pen\"}, {\"id\": 32219, \"name\": \"a team\"}, {\"id\": 32220, \"name\": \"the small green box\"}, {\"id\": 32221, \"name\": \"the yellow bookshelf\"}, {\"id\": 32222, \"name\": \"large, boxy vehicle\"}, {\"id\": 32223, \"name\": \"the green sign with white lettering\"}, {\"id\": 32224, \"name\": \"the large, open-air structure\"}, {\"id\": 32225, \"name\": \"the large, curved arm\"}, {\"id\": 32226, \"name\": \"the vibrant flower\"}, {\"id\": 32227, \"name\": \"the tan purse strap\"}, {\"id\": 32228, \"name\": \"an announcer's booth\"}, {\"id\": 32229, \"name\": \"the lolly\"}, {\"id\": 32230, \"name\": \"rodeo arena\"}, {\"id\": 32231, \"name\": \"the red and white checkered stripe\"}, {\"id\": 32232, \"name\": \"a red metal framework\"}, {\"id\": 32233, \"name\": \"a large, cartoon-style float\"}, {\"id\": 32234, \"name\": \"a protective eyewear\"}, {\"id\": 32235, \"name\": \"gray tight\"}, {\"id\": 32236, \"name\": \"red license plate\"}, {\"id\": 32237, \"name\": \"a zebra-print dress\"}, {\"id\": 32238, \"name\": \"large tree trunk\"}, {\"id\": 32239, \"name\": \"the decorative crown\"}, {\"id\": 32240, \"name\": \"maroon-colored track\"}, {\"id\": 32241, \"name\": \"the large tusk\"}, {\"id\": 32242, \"name\": \"a pedestrian's path\"}, {\"id\": 32243, \"name\": \"the horn\"}, {\"id\": 32244, \"name\": \"blurry tan field\"}, {\"id\": 32245, \"name\": \"pink and white striped pillow\"}, {\"id\": 32246, \"name\": \"the gant logo\"}, {\"id\": 32247, \"name\": \"large yellow and silver wing\"}, {\"id\": 32248, \"name\": \"the large overpass\"}, {\"id\": 32249, \"name\": \"a vibrant pink garland\"}, {\"id\": 32250, \"name\": \"a black and yellow taxi\"}, {\"id\": 32251, \"name\": \"a smoking sign\"}, {\"id\": 32252, \"name\": \"the white smartphone\"}, {\"id\": 32253, \"name\": \"the gray drawer\"}, {\"id\": 32254, \"name\": \"the yellow and orange streak of light\"}, {\"id\": 32255, \"name\": \"paint\"}, {\"id\": 32256, \"name\": \"the drumstick\"}, {\"id\": 32257, \"name\": \"the carousel ride\"}, {\"id\": 32258, \"name\": \"the man in a tan jacket\"}, {\"id\": 32259, \"name\": \"the red and yellow structure\"}, {\"id\": 32260, \"name\": \"the prominent red awning\"}, {\"id\": 32261, \"name\": \"long white pole\"}, {\"id\": 32262, \"name\": \"a green eye\"}, {\"id\": 32263, \"name\": \"channel\"}, {\"id\": 32264, \"name\": \"a fur-lined hood\"}, {\"id\": 32265, \"name\": \"the black earbud\"}, {\"id\": 32266, \"name\": \"porch\"}, {\"id\": 32267, \"name\": \"a red ear protector\"}, {\"id\": 32268, \"name\": \"gray bean bag chair\"}, {\"id\": 32269, \"name\": \"a full-length mirror\"}, {\"id\": 32270, \"name\": \"the vendor's hand\"}, {\"id\": 32271, \"name\": \"the ash\"}, {\"id\": 32272, \"name\": \"a person wearing red outfit\"}, {\"id\": 32273, \"name\": \"tall apartment\"}, {\"id\": 32274, \"name\": \"a canadian flag\"}, {\"id\": 32275, \"name\": \"a messenger bag\"}, {\"id\": 32276, \"name\": \"rope swing\"}, {\"id\": 32277, \"name\": \"dark gray beanie\"}, {\"id\": 32278, \"name\": \"a large pink feather\"}, {\"id\": 32279, \"name\": \"green and yellow striped shirt\"}, {\"id\": 32280, \"name\": \"blue medical gown\"}, {\"id\": 32281, \"name\": \"the white electric guitar\"}, {\"id\": 32282, \"name\": \"the dark-colored granite\"}, {\"id\": 32283, \"name\": \"a large television screen\"}, {\"id\": 32284, \"name\": \"a black and white water bottle\"}, {\"id\": 32285, \"name\": \"the blue body of water\"}, {\"id\": 32286, \"name\": \"the metal cage\"}, {\"id\": 32287, \"name\": \"small, circular mirror\"}, {\"id\": 32288, \"name\": \"a rear cargo door\"}, {\"id\": 32289, \"name\": \"the green graduation cap\"}, {\"id\": 32290, \"name\": \"the ea logo\"}, {\"id\": 32291, \"name\": \"a white desk\"}, {\"id\": 32292, \"name\": \"black volkswagen polo\"}, {\"id\": 32293, \"name\": \"blue counter\"}, {\"id\": 32294, \"name\": \"dark gray concrete\"}, {\"id\": 32295, \"name\": \"a yellow patch of hair\"}, {\"id\": 32296, \"name\": \"a blue sundress\"}, {\"id\": 32297, \"name\": \"yellow metal part\"}, {\"id\": 32298, \"name\": \"a rainbow-striped roof\"}, {\"id\": 32299, \"name\": \"the western union office\"}, {\"id\": 32300, \"name\": \"a gold-colored frame\"}, {\"id\": 32301, \"name\": \"an username\"}, {\"id\": 32302, \"name\": \"blue paw\"}, {\"id\": 32303, \"name\": \"the video's settings bar\"}, {\"id\": 32304, \"name\": \"700 tiny mattress in one\"}, {\"id\": 32305, \"name\": \"brown purse strap\"}, {\"id\": 32306, \"name\": \"a granite or marble\"}, {\"id\": 32307, \"name\": \"purple bridge\"}, {\"id\": 32308, \"name\": \"large blue bottle\"}, {\"id\": 32309, \"name\": \"the lion's head\"}, {\"id\": 32310, \"name\": \"grey metal fence\"}, {\"id\": 32311, \"name\": \"a large, colorful balloon\"}, {\"id\": 32312, \"name\": \"a red play bar\"}, {\"id\": 32313, \"name\": \"a lever\"}, {\"id\": 32314, \"name\": \"a 77\"}, {\"id\": 32315, \"name\": \"a red convertible\"}, {\"id\": 32316, \"name\": \"dicky\"}, {\"id\": 32317, \"name\": \"a small, dark-colored table\"}, {\"id\": 32318, \"name\": \"the purple tank top\"}, {\"id\": 32319, \"name\": \"the bang\"}, {\"id\": 32320, \"name\": \"a large green awning\"}, {\"id\": 32321, \"name\": \"kettlebell\"}, {\"id\": 32322, \"name\": \"colored hat\"}, {\"id\": 32323, \"name\": \"a blue gown\"}, {\"id\": 32324, \"name\": \"small patch of oil\"}, {\"id\": 32325, \"name\": \"the orange and white outfit\"}, {\"id\": 32326, \"name\": \"the armored car\"}, {\"id\": 32327, \"name\": \"a fountain or water feature\"}, {\"id\": 32328, \"name\": \"the partition\"}, {\"id\": 32329, \"name\": \"large plastic tube\"}, {\"id\": 32330, \"name\": \"coffee shop sign\"}, {\"id\": 32331, \"name\": \"a brown headband\"}, {\"id\": 32332, \"name\": \"prominent black sign\"}, {\"id\": 32333, \"name\": \"dark, spiky hairstyle\"}, {\"id\": 32334, \"name\": \"a dark tie\"}, {\"id\": 32335, \"name\": \"the front passenger window\"}, {\"id\": 32336, \"name\": \"red and white truck\"}, {\"id\": 32337, \"name\": \"a yellow belly\"}, {\"id\": 32338, \"name\": \"a cartoon mammoth\"}, {\"id\": 32339, \"name\": \"a large bas drum\"}, {\"id\": 32340, \"name\": \"the figure in a red costume\"}, {\"id\": 32341, \"name\": \"blue wave design\"}, {\"id\": 32342, \"name\": \"a blue outline of africa\"}, {\"id\": 32343, \"name\": \"the red and white drink\"}, {\"id\": 32344, \"name\": \"red and green logo\"}, {\"id\": 32345, \"name\": \"crane's arm\"}, {\"id\": 32346, \"name\": \"the large white sculpture\"}, {\"id\": 32347, \"name\": \"a doll's head\"}, {\"id\": 32348, \"name\": \"the toy crane\"}, {\"id\": 32349, \"name\": \"the blue backpack strap\"}, {\"id\": 32350, \"name\": \"the large, red construction fence\"}, {\"id\": 32351, \"name\": \"a pergola\"}, {\"id\": 32352, \"name\": \"the red stop sign\"}, {\"id\": 32353, \"name\": \"the blue crop top\"}, {\"id\": 32354, \"name\": \"vibrant carousel horse\"}, {\"id\": 32355, \"name\": \"the bright sun\"}, {\"id\": 32356, \"name\": \"the warning\"}, {\"id\": 32357, \"name\": \"a rider's shadow\"}, {\"id\": 32358, \"name\": \"the vendor's head\"}, {\"id\": 32359, \"name\": \"a blue-framed shelving unit\"}, {\"id\": 32360, \"name\": \"a weightlifting equipment\"}, {\"id\": 32361, \"name\": \"a dark wood trim\"}, {\"id\": 32362, \"name\": \"a prominent nbc logo\"}, {\"id\": 32363, \"name\": \"a road's white center line\"}, {\"id\": 32364, \"name\": \"a large metal workbench\"}, {\"id\": 32365, \"name\": \"a cannon-like mechanism\"}, {\"id\": 32366, \"name\": \"the atm\"}, {\"id\": 32367, \"name\": \"a white dashed line\"}, {\"id\": 32368, \"name\": \"red and black section\"}, {\"id\": 32369, \"name\": \"the open fire\"}, {\"id\": 32370, \"name\": \"a restaurant's entrance\"}, {\"id\": 32371, \"name\": \"dark green baseball cap\"}, {\"id\": 32372, \"name\": \"the red crosswalk\"}, {\"id\": 32373, \"name\": \"ground floor\"}, {\"id\": 32374, \"name\": \"the red and green brick\"}, {\"id\": 32375, \"name\": \"a dark gray sweater\"}, {\"id\": 32376, \"name\": \"the blue, swirling design\"}, {\"id\": 32377, \"name\": \"colorful silk\"}, {\"id\": 32378, \"name\": \"dark blue wetsuit\"}, {\"id\": 32379, \"name\": \"the bottle of dye\"}, {\"id\": 32380, \"name\": \"brown awning\"}, {\"id\": 32381, \"name\": \"a prominent blue banner\"}, {\"id\": 32382, \"name\": \"a price of the pastry\"}, {\"id\": 32383, \"name\": \"the white name tag\"}, {\"id\": 32384, \"name\": \"the man wearing a black hat and shirt\"}, {\"id\": 32385, \"name\": \"a brown dachshund\"}, {\"id\": 32386, \"name\": \"blue square\"}, {\"id\": 32387, \"name\": \"grey and white patterned t-shirt\"}, {\"id\": 32388, \"name\": \"a white vest\"}, {\"id\": 32389, \"name\": \"long black skirt\"}, {\"id\": 32390, \"name\": \"the partially visible person's knee\"}, {\"id\": 32391, \"name\": \"the dark dress\"}, {\"id\": 32392, \"name\": \"the woman wearing a white shirt and a colorful headscarf\"}, {\"id\": 32393, \"name\": \"a white multi-story structure\"}, {\"id\": 32394, \"name\": \"an arena's dirt floor\"}, {\"id\": 32395, \"name\": \"silver rivet\"}, {\"id\": 32396, \"name\": \"man in a white shirt and yellow tie\"}, {\"id\": 32397, \"name\": \"large poster or banner\"}, {\"id\": 32398, \"name\": \"the large flock of pigeon\"}, {\"id\": 32399, \"name\": \"the large, ornate hook\"}, {\"id\": 32400, \"name\": \"the large white graphic\"}, {\"id\": 32401, \"name\": \"the couple\"}, {\"id\": 32402, \"name\": \"the red and white striped sock\"}, {\"id\": 32403, \"name\": \"pause button\"}, {\"id\": 32404, \"name\": \"large yellow umbrella\"}, {\"id\": 32405, \"name\": \"a black and white striped pattern\"}, {\"id\": 32406, \"name\": \"large, brown, spiral-shaped object\"}, {\"id\": 32407, \"name\": \"yellow shelf\"}, {\"id\": 32408, \"name\": \"a small toy\"}, {\"id\": 32409, \"name\": \"a yellow and blue striped hood\"}, {\"id\": 32410, \"name\": \"a red chevrolet hatchback\"}, {\"id\": 32411, \"name\": \"the red watermark\"}, {\"id\": 32412, \"name\": \"company name\"}, {\"id\": 32413, \"name\": \"the white path\"}, {\"id\": 32414, \"name\": \"the roundabout\"}, {\"id\": 32415, \"name\": \"a matching brown and white checkered pajama\"}, {\"id\": 32416, \"name\": \"a child's seat\"}, {\"id\": 32417, \"name\": \"red supreme sticker\"}, {\"id\": 32418, \"name\": \"the tall, ornate lamppost\"}, {\"id\": 32419, \"name\": \"drummer's cymbal\"}, {\"id\": 32420, \"name\": \"a traffic ramp\"}, {\"id\": 32421, \"name\": \"the cranky\"}, {\"id\": 32422, \"name\": \"sport jersey\"}, {\"id\": 32423, \"name\": \"a large, black sign\"}, {\"id\": 32424, \"name\": \"the green hill\"}, {\"id\": 32425, \"name\": \"the white grid pattern\"}, {\"id\": 32426, \"name\": \"the penguin\"}, {\"id\": 32427, \"name\": \"the pink, yellow, and blue tie-dye shirt\"}, {\"id\": 32428, \"name\": \"a long, black grill\"}, {\"id\": 32429, \"name\": \"the damaged road barrier\"}, {\"id\": 32430, \"name\": \"the convoy of armed individual\"}, {\"id\": 32431, \"name\": \"a small christma tree\"}, {\"id\": 32432, \"name\": \"phone or device\"}, {\"id\": 32433, \"name\": \"a white feathered headpiece\"}, {\"id\": 32434, \"name\": \"a graphic of a blonde woman\"}, {\"id\": 32435, \"name\": \"a yellow helmeted rider\"}, {\"id\": 32436, \"name\": \"a large, multi-colored bird or dragon-like creature\"}, {\"id\": 32437, \"name\": \"a silver watch\"}, {\"id\": 32438, \"name\": \"wooden horse\"}, {\"id\": 32439, \"name\": \"the pink toy\"}, {\"id\": 32440, \"name\": \"man in a white shirt and purple tie\"}, {\"id\": 32441, \"name\": \"zipper closure\"}, {\"id\": 32442, \"name\": \"beige countertop\"}, {\"id\": 32443, \"name\": \"a matching armchair\"}, {\"id\": 32444, \"name\": \"a crane's base\"}, {\"id\": 32445, \"name\": \"the large white watermark\"}, {\"id\": 32446, \"name\": \"the black track system\"}, {\"id\": 32447, \"name\": \"a middle ground\"}, {\"id\": 32448, \"name\": \"the striking red tablecloth\"}, {\"id\": 32449, \"name\": \"prominent palm tree\"}, {\"id\": 32450, \"name\": \"a red octopu character\"}, {\"id\": 32451, \"name\": \"a black mouse\"}, {\"id\": 32452, \"name\": \"the parking anytime sign\"}, {\"id\": 32453, \"name\": \"the pink pole\"}, {\"id\": 32454, \"name\": \"a grey hoodie\"}, {\"id\": 32455, \"name\": \"blue grip\"}, {\"id\": 32456, \"name\": \"curved wooden bench\"}, {\"id\": 32457, \"name\": \"a brown curtain\"}, {\"id\": 32458, \"name\": \"the statue of a person\"}, {\"id\": 32459, \"name\": \"light-colored zip-up sweatshirt\"}, {\"id\": 32460, \"name\": \"red spring\"}, {\"id\": 32461, \"name\": \"green net or sheet\"}, {\"id\": 32462, \"name\": \"the gray metal stand\"}, {\"id\": 32463, \"name\": \"a palm\"}, {\"id\": 32464, \"name\": \"the brown backpack\"}, {\"id\": 32465, \"name\": \"the white oven\"}, {\"id\": 32466, \"name\": \"a long, horizontal metal bar\"}, {\"id\": 32467, \"name\": \"yellow horizontal line\"}, {\"id\": 32468, \"name\": \"game's floor\"}, {\"id\": 32469, \"name\": \"skeleton\"}, {\"id\": 32470, \"name\": \"the seasoning\"}, {\"id\": 32471, \"name\": \"a curly cord\"}, {\"id\": 32472, \"name\": \"pony\"}, {\"id\": 32473, \"name\": \"light-colored wooden cabinet\"}, {\"id\": 32474, \"name\": \"the large, ancient-looking stone statue\"}, {\"id\": 32475, \"name\": \"the large white sunhat\"}, {\"id\": 32476, \"name\": \"a f6f hellcat\"}, {\"id\": 32477, \"name\": \"dark grey tiled floor\"}, {\"id\": 32478, \"name\": \"the dark purple short\"}, {\"id\": 32479, \"name\": \"traffic light pole\"}, {\"id\": 32480, \"name\": \"green and red stick\"}, {\"id\": 32481, \"name\": \"a red and white marking\"}, {\"id\": 32482, \"name\": \"large window display\"}, {\"id\": 32483, \"name\": \"long red hair\"}, {\"id\": 32484, \"name\": \"man in a camouflage hat\"}, {\"id\": 32485, \"name\": \"the red dish\"}, {\"id\": 32486, \"name\": \"the decorative sculpture\"}, {\"id\": 32487, \"name\": \"gras track\"}, {\"id\": 32488, \"name\": \"large, round, red button\"}, {\"id\": 32489, \"name\": \"the green star\"}, {\"id\": 32490, \"name\": \"the snow white\"}, {\"id\": 32491, \"name\": \"a red and black jacket\"}, {\"id\": 32492, \"name\": \"the green and white santa hat\"}, {\"id\": 32493, \"name\": \"large metal end\"}, {\"id\": 32494, \"name\": \"a blue tubing\"}, {\"id\": 32495, \"name\": \"a large grill\"}, {\"id\": 32496, \"name\": \"a tropical print\"}, {\"id\": 32497, \"name\": \"a green short\"}, {\"id\": 32498, \"name\": \"a clear plastic storage bin\"}, {\"id\": 32499, \"name\": \"a large mast\"}, {\"id\": 32500, \"name\": \"the paved\"}, {\"id\": 32501, \"name\": \"yellow bumper\"}, {\"id\": 32502, \"name\": \"a live watermark\"}, {\"id\": 32503, \"name\": \"white signature\"}, {\"id\": 32504, \"name\": \"a black silhouette of a person riding a bicycle around a globe\"}, {\"id\": 32505, \"name\": \"dark wall\"}, {\"id\": 32506, \"name\": \"a guitar amp\"}, {\"id\": 32507, \"name\": \"small braid\"}, {\"id\": 32508, \"name\": \"hasbro logo\"}, {\"id\": 32509, \"name\": \"the red b symbol\"}, {\"id\": 32510, \"name\": \"a stylized hf logo\"}, {\"id\": 32511, \"name\": \"a large stone statue of a dragon\"}, {\"id\": 32512, \"name\": \"the real vehicle\"}, {\"id\": 32513, \"name\": \"small, white cat\"}, {\"id\": 32514, \"name\": \"a name of the horse\"}, {\"id\": 32515, \"name\": \"a right side of the road\"}, {\"id\": 32516, \"name\": \"the pink and blue border\"}, {\"id\": 32517, \"name\": \"white blanket\"}, {\"id\": 32518, \"name\": \"a large body of water\"}, {\"id\": 32519, \"name\": \"a rug's road\"}, {\"id\": 32520, \"name\": \"the small bottle of spray\"}, {\"id\": 32521, \"name\": \"a white circular marking\"}, {\"id\": 32522, \"name\": \"a kawasaki\"}, {\"id\": 32523, \"name\": \"white oval sticker\"}, {\"id\": 32524, \"name\": \"green and brown landscape\"}, {\"id\": 32525, \"name\": \"blue tie\"}, {\"id\": 32526, \"name\": \"the visor\"}, {\"id\": 32527, \"name\": \"the white mantle\"}, {\"id\": 32528, \"name\": \"the decorative metal grate\"}, {\"id\": 32529, \"name\": \"the large, blue-green rectangular object\"}, {\"id\": 32530, \"name\": \"a gray long-sleeve shirt\"}, {\"id\": 32531, \"name\": \"construction crane\"}, {\"id\": 32532, \"name\": \"bar area\"}, {\"id\": 32533, \"name\": \"elaborate red robe\"}, {\"id\": 32534, \"name\": \"visa credit card machine\"}, {\"id\": 32535, \"name\": \"a grey umbrella\"}, {\"id\": 32536, \"name\": \"yellow display screen\"}, {\"id\": 32537, \"name\": \"gmc logo\"}, {\"id\": 32538, \"name\": \"green can\"}, {\"id\": 32539, \"name\": \"the tattoo sleeve\"}, {\"id\": 32540, \"name\": \"black license plate\"}, {\"id\": 32541, \"name\": \"a red coca-cola t-shirt\"}, {\"id\": 32542, \"name\": \"low, white tv cabinet\"}, {\"id\": 32543, \"name\": \"smartwatch\"}, {\"id\": 32544, \"name\": \"black nose\"}, {\"id\": 32545, \"name\": \"the pink-painted fingernail\"}, {\"id\": 32546, \"name\": \"elegant white gown\"}, {\"id\": 32547, \"name\": \"the tram's front door\"}, {\"id\": 32548, \"name\": \"a red rope barrier\"}, {\"id\": 32549, \"name\": \"a black and white sign\"}, {\"id\": 32550, \"name\": \"a purple and white logo\"}, {\"id\": 32551, \"name\": \"the sturdy bar\"}, {\"id\": 32552, \"name\": \"a white athletic shoe\"}, {\"id\": 32553, \"name\": \"yellow play-doh container\"}, {\"id\": 32554, \"name\": \"the festive sweater\"}, {\"id\": 32555, \"name\": \"the short grey hair\"}, {\"id\": 32556, \"name\": \"the graphic collage of photograph\"}, {\"id\": 32557, \"name\": \"the shiny, reflective material\"}, {\"id\": 32558, \"name\": \"the small blue light\"}, {\"id\": 32559, \"name\": \"deep blue sky\"}, {\"id\": 32560, \"name\": \"a prominent nike air logo\"}, {\"id\": 32561, \"name\": \"the casserole dish\"}, {\"id\": 32562, \"name\": \"the rack of weight\"}, {\"id\": 32563, \"name\": \"hand of the photographer\"}, {\"id\": 32564, \"name\": \"the yellow end cap\"}, {\"id\": 32565, \"name\": \"a blue metal object\"}, {\"id\": 32566, \"name\": \"black seatbelt\"}, {\"id\": 32567, \"name\": \"an engine block\"}, {\"id\": 32568, \"name\": \"the black container\"}, {\"id\": 32569, \"name\": \"the yard line\"}, {\"id\": 32570, \"name\": \"green foam tube\"}, {\"id\": 32571, \"name\": \"a bare leg\"}, {\"id\": 32572, \"name\": \"blue and purple stripe\"}, {\"id\": 32573, \"name\": \"the vehicle's passenger side door\"}, {\"id\": 32574, \"name\": \"starting block\"}, {\"id\": 32575, \"name\": \"bleacher section\"}, {\"id\": 32576, \"name\": \"peach-colored t-shirt\"}, {\"id\": 32577, \"name\": \"boy's left foot\"}, {\"id\": 32578, \"name\": \"yellow lighting\"}, {\"id\": 32579, \"name\": \"the person's white t-shirt\"}, {\"id\": 32580, \"name\": \"young football player\"}, {\"id\": 32581, \"name\": \"the fox sport florida logo\"}, {\"id\": 32582, \"name\": \"a white football jersey\"}, {\"id\": 32583, \"name\": \"green and black face paint design\"}, {\"id\": 32584, \"name\": \"a thin white border\"}, {\"id\": 32585, \"name\": \"men's attire\"}, {\"id\": 32586, \"name\": \"the yamaha logo\"}, {\"id\": 32587, \"name\": \"a colorful scarf\"}, {\"id\": 32588, \"name\": \"the percussion instrument\"}, {\"id\": 32589, \"name\": \"the court's wall\"}, {\"id\": 32590, \"name\": \"red graphic\"}, {\"id\": 32591, \"name\": \"stylish hat\"}, {\"id\": 32592, \"name\": \"the vibrant red flower\"}, {\"id\": 32593, \"name\": \"a pink decoration\"}, {\"id\": 32594, \"name\": \"a tassel\"}, {\"id\": 32595, \"name\": \"the tropical print\"}, {\"id\": 32596, \"name\": \"a yellow ladder\"}, {\"id\": 32597, \"name\": \"blue hockey uniform\"}, {\"id\": 32598, \"name\": \"a yellow splatter\"}, {\"id\": 32599, \"name\": \"a snow-covered verge\"}, {\"id\": 32600, \"name\": \"a dark, black curtain\"}, {\"id\": 32601, \"name\": \"the hurling stick\"}, {\"id\": 32602, \"name\": \"the training device\"}, {\"id\": 32603, \"name\": \"the yellow sport bra\"}, {\"id\": 32604, \"name\": \"a small white tag\"}, {\"id\": 32605, \"name\": \"white nipple\"}, {\"id\": 32606, \"name\": \"the colorful tarp\"}, {\"id\": 32607, \"name\": \"a teal design\"}, {\"id\": 32608, \"name\": \"a flowing dress\"}, {\"id\": 32609, \"name\": \"a player in the red uniform\"}, {\"id\": 32610, \"name\": \"toy and knick-knack\"}, {\"id\": 32611, \"name\": \"verdant grassy area\"}, {\"id\": 32612, \"name\": \"purple, fuzzy material\"}, {\"id\": 32613, \"name\": \"regalia\"}, {\"id\": 32614, \"name\": \"a pink swim cap\"}, {\"id\": 32615, \"name\": \"tan-colored garment\"}, {\"id\": 32616, \"name\": \"grey weathered board\"}, {\"id\": 32617, \"name\": \"muddy\"}, {\"id\": 32618, \"name\": \"the high-visibility yellow jacket\"}, {\"id\": 32619, \"name\": \"yellow headband\"}, {\"id\": 32620, \"name\": \"the mirror's frame\"}, {\"id\": 32621, \"name\": \"a large light\"}, {\"id\": 32622, \"name\": \"the bud light logo\"}, {\"id\": 32623, \"name\": \"red and blue striped shirt\"}, {\"id\": 32624, \"name\": \"person in a red jacket\"}, {\"id\": 32625, \"name\": \"vibrant ferri wheel\"}, {\"id\": 32626, \"name\": \"the train's window\"}, {\"id\": 32627, \"name\": \"a strip of comic book-style panel\"}, {\"id\": 32628, \"name\": \"a green metal gate\"}, {\"id\": 32629, \"name\": \"circular gray mat\"}, {\"id\": 32630, \"name\": \"the man in a black coat\"}, {\"id\": 32631, \"name\": \"green blind\"}, {\"id\": 32632, \"name\": \"the gray trim\"}, {\"id\": 32633, \"name\": \"black and red leg guard\"}, {\"id\": 32634, \"name\": \"mlb logo\"}, {\"id\": 32635, \"name\": \"tricycle's seat\"}, {\"id\": 32636, \"name\": \"gray squiggly line\"}, {\"id\": 32637, \"name\": \"a maroon tank top\"}, {\"id\": 32638, \"name\": \"a grotesque, decaying face\"}, {\"id\": 32639, \"name\": \"figure's raised hand\"}, {\"id\": 32640, \"name\": \"a yellow and gray sail\"}, {\"id\": 32641, \"name\": \"a menacing face\"}, {\"id\": 32642, \"name\": \"a veil\"}, {\"id\": 32643, \"name\": \"stylish updo\"}, {\"id\": 32644, \"name\": \"a gold metal\"}, {\"id\": 32645, \"name\": \"a dominican republic flag\"}, {\"id\": 32646, \"name\": \"raspberry\"}, {\"id\": 32647, \"name\": \"a mint green plastic container\"}, {\"id\": 32648, \"name\": \"a blue star\"}, {\"id\": 32649, \"name\": \"raised ramp\"}, {\"id\": 32650, \"name\": \"black wing\"}, {\"id\": 32651, \"name\": \"the sound system\"}, {\"id\": 32652, \"name\": \"carriage's body\"}, {\"id\": 32653, \"name\": \"the person's black and red glove\"}, {\"id\": 32654, \"name\": \"black outfit\"}, {\"id\": 32655, \"name\": \"the dark-colored jersey\"}, {\"id\": 32656, \"name\": \"the large, colorful handprint\"}, {\"id\": 32657, \"name\": \"cardboard roll\"}, {\"id\": 32658, \"name\": \"a patch of tall grass\"}, {\"id\": 32659, \"name\": \"the blue floor\"}, {\"id\": 32660, \"name\": \"a large metal frame\"}, {\"id\": 32661, \"name\": \"a distinctive silver helmet\"}, {\"id\": 32662, \"name\": \"the small square component\"}, {\"id\": 32663, \"name\": \"the booth\"}, {\"id\": 32664, \"name\": \"a colorful saddle\"}, {\"id\": 32665, \"name\": \"a small awning\"}, {\"id\": 32666, \"name\": \"creature's mouth\"}, {\"id\": 32667, \"name\": \"paintball player\"}, {\"id\": 32668, \"name\": \"a yellowish, possibly cheesy, substance\"}, {\"id\": 32669, \"name\": \"person's foot\"}, {\"id\": 32670, \"name\": \"driver's head\"}, {\"id\": 32671, \"name\": \"a diagonal line\"}, {\"id\": 32672, \"name\": \"the large, round, white and purple dial\"}, {\"id\": 32673, \"name\": \"a purple horn\"}, {\"id\": 32674, \"name\": \"prominent white 10\"}, {\"id\": 32675, \"name\": \"short, curly hair\"}, {\"id\": 32676, \"name\": \"the black crack\"}, {\"id\": 32677, \"name\": \"the red cartoon character\"}, {\"id\": 32678, \"name\": \"concrete circle\"}, {\"id\": 32679, \"name\": \"a white decorative plate\"}, {\"id\": 32680, \"name\": \"an earring\"}, {\"id\": 32681, \"name\": \"a black and gray baseball cap\"}, {\"id\": 32682, \"name\": \"large black suitcase\"}, {\"id\": 32683, \"name\": \"the silver metal panel\"}, {\"id\": 32684, \"name\": \"gray and black uniform\"}, {\"id\": 32685, \"name\": \"small white plane\"}, {\"id\": 32686, \"name\": \"the pile\"}, {\"id\": 32687, \"name\": \"blue and yellow balloon\"}, {\"id\": 32688, \"name\": \"the red bench seat\"}, {\"id\": 32689, \"name\": \"the pink trampoline\"}, {\"id\": 32690, \"name\": \"the large silver balloon\"}, {\"id\": 32691, \"name\": \"silver knob\"}, {\"id\": 32692, \"name\": \"large audience\"}, {\"id\": 32693, \"name\": \"toy's body\"}, {\"id\": 32694, \"name\": \"vibrant upholstery\"}, {\"id\": 32695, \"name\": \"the image of item\"}, {\"id\": 32696, \"name\": \"purple rock formation\"}, {\"id\": 32697, \"name\": \"a zero\"}, {\"id\": 32698, \"name\": \"the white chair or seat\"}, {\"id\": 32699, \"name\": \"dark grey tunic\"}, {\"id\": 32700, \"name\": \"vibrant green plant\"}, {\"id\": 32701, \"name\": \"a pediment\"}, {\"id\": 32702, \"name\": \"the ornate window\"}, {\"id\": 32703, \"name\": \"picture of a woman\"}, {\"id\": 32704, \"name\": \"the purple plant\"}, {\"id\": 32705, \"name\": \"a batter\"}, {\"id\": 32706, \"name\": \"the black whip\"}, {\"id\": 32707, \"name\": \"passenger-side window\"}, {\"id\": 32708, \"name\": \"a hood of vehicle\"}, {\"id\": 32709, \"name\": \"multicolored seat\"}, {\"id\": 32710, \"name\": \"the round silver goggle\"}, {\"id\": 32711, \"name\": \"leather jacket\"}, {\"id\": 32712, \"name\": \"teal-colored wall\"}, {\"id\": 32713, \"name\": \"the store window\"}, {\"id\": 32714, \"name\": \"the gray legging\"}, {\"id\": 32715, \"name\": \"a gold jewelry\"}, {\"id\": 32716, \"name\": \"the yellow fin\"}, {\"id\": 32717, \"name\": \"an illuminated window\"}, {\"id\": 32718, \"name\": \"the red and black table tenni paddle\"}, {\"id\": 32719, \"name\": \"a tiger\"}, {\"id\": 32720, \"name\": \"woman in a colorful sari\"}, {\"id\": 32721, \"name\": \"the packed grandstand\"}, {\"id\": 32722, \"name\": \"a balding\"}, {\"id\": 32723, \"name\": \"a blue metal structure\"}, {\"id\": 32724, \"name\": \"a woolly mammoth\"}, {\"id\": 32725, \"name\": \"the newborn\"}, {\"id\": 32726, \"name\": \"the colorful illustration\"}, {\"id\": 32727, \"name\": \"a dark-colored uniform\"}, {\"id\": 32728, \"name\": \"stylish black hat\"}, {\"id\": 32729, \"name\": \"colorful saddle\"}, {\"id\": 32730, \"name\": \"kitchen utensil\"}, {\"id\": 32731, \"name\": \"a planter box\"}, {\"id\": 32732, \"name\": \"large wooden oar\"}, {\"id\": 32733, \"name\": \"the belmont stake\"}, {\"id\": 32734, \"name\": \"the black robe\"}, {\"id\": 32735, \"name\": \"a leather vest\"}, {\"id\": 32736, \"name\": \"a white 10\"}, {\"id\": 32737, \"name\": \"the glass barrier\"}, {\"id\": 32738, \"name\": \"the bag or purse\"}, {\"id\": 32739, \"name\": \"black band\"}, {\"id\": 32740, \"name\": \"the black lettering\"}, {\"id\": 32741, \"name\": \"the red and orange race car\"}, {\"id\": 32742, \"name\": \"thin metal leg\"}, {\"id\": 32743, \"name\": \"the white and blue design\"}, {\"id\": 32744, \"name\": \"traditional korean updo\"}, {\"id\": 32745, \"name\": \"a woman's feet\"}, {\"id\": 32746, \"name\": \"a middle lane\"}, {\"id\": 32747, \"name\": \"the rider's face\"}, {\"id\": 32748, \"name\": \"rider's right foot\"}, {\"id\": 32749, \"name\": \"black or striped shirt\"}, {\"id\": 32750, \"name\": \"a glas pane\"}, {\"id\": 32751, \"name\": \"adult male\"}, {\"id\": 32752, \"name\": \"the glass with a straw\"}, {\"id\": 32753, \"name\": \"the grey surface\"}, {\"id\": 32754, \"name\": \"a snack packet\"}, {\"id\": 32755, \"name\": \"triangular flag\"}, {\"id\": 32756, \"name\": \"the squat rack\"}, {\"id\": 32757, \"name\": \"a black eye-like feature\"}, {\"id\": 32758, \"name\": \"rear end of the car\"}, {\"id\": 32759, \"name\": \"large blue shutter\"}, {\"id\": 32760, \"name\": \"a gray horizontal stripe\"}, {\"id\": 32761, \"name\": \"white emblem\"}, {\"id\": 32762, \"name\": \"the profile picture\"}, {\"id\": 32763, \"name\": \"green gemstone\"}, {\"id\": 32764, \"name\": \"the red cheek\"}, {\"id\": 32765, \"name\": \"the comfortable seating area\"}, {\"id\": 32766, \"name\": \"a cardboard cutout\"}, {\"id\": 32767, \"name\": \"the monkey's head\"}, {\"id\": 32768, \"name\": \"a rugby team\"}, {\"id\": 32769, \"name\": \"small white triangle\"}, {\"id\": 32770, \"name\": \"a tan body\"}, {\"id\": 32771, \"name\": \"a camouflage tarp\"}, {\"id\": 32772, \"name\": \"yellow dot\"}, {\"id\": 32773, \"name\": \"the yellow tuk-tuk\"}, {\"id\": 32774, \"name\": \"an person\"}, {\"id\": 32775, \"name\": \"crease\"}, {\"id\": 32776, \"name\": \"the short neck\"}, {\"id\": 32777, \"name\": \"black and gray bowling ball return\"}, {\"id\": 32778, \"name\": \"the matching bracelet\"}, {\"id\": 32779, \"name\": \"the black metal stand\"}, {\"id\": 32780, \"name\": \"the purple t-shirt\"}, {\"id\": 32781, \"name\": \"tall mast\"}, {\"id\": 32782, \"name\": \"numbered marker\"}, {\"id\": 32783, \"name\": \"rainbow-colored border\"}, {\"id\": 32784, \"name\": \"the rider's body\"}, {\"id\": 32785, \"name\": \"red lightsaber\"}, {\"id\": 32786, \"name\": \"a large metal sculpture of leaf\"}, {\"id\": 32787, \"name\": \"a large black boat\"}, {\"id\": 32788, \"name\": \"a lorry\"}, {\"id\": 32789, \"name\": \"a white tooth\"}, {\"id\": 32790, \"name\": \"the pan with a golden-brown dish\"}, {\"id\": 32791, \"name\": \"large, colorful character\"}, {\"id\": 32792, \"name\": \"the man seated\"}, {\"id\": 32793, \"name\": \"a green tank\"}, {\"id\": 32794, \"name\": \"the handcuff\"}, {\"id\": 32795, \"name\": \"black pipe\"}, {\"id\": 32796, \"name\": \"a lucas oil motocros logo\"}, {\"id\": 32797, \"name\": \"rugged red rock formation\"}, {\"id\": 32798, \"name\": \"dinosaur pattern\"}, {\"id\": 32799, \"name\": \"a small orange tag\"}, {\"id\": 32800, \"name\": \"a golf glove\"}, {\"id\": 32801, \"name\": \"the black long-sleeve top\"}, {\"id\": 32802, \"name\": \"canoe\"}, {\"id\": 32803, \"name\": \"a cycling shoe\"}, {\"id\": 32804, \"name\": \"the right headlight\"}, {\"id\": 32805, \"name\": \"the matching white and black jacket\"}, {\"id\": 32806, \"name\": \"the -terrain vehicle\"}, {\"id\": 32807, \"name\": \"the black and gold uniform\"}, {\"id\": 32808, \"name\": \"white and blue tiled column\"}, {\"id\": 32809, \"name\": \"headphone\"}, {\"id\": 32810, \"name\": \"bike's wheel\"}, {\"id\": 32811, \"name\": \"a sponsor\"}, {\"id\": 32812, \"name\": \"seahawk logo\"}, {\"id\": 32813, \"name\": \"nascar logo\"}, {\"id\": 32814, \"name\": \"red wing\"}, {\"id\": 32815, \"name\": \"the man in gold uniform\"}, {\"id\": 32816, \"name\": \"the red plastic cup\"}, {\"id\": 32817, \"name\": \"the neon green kitten-heeled shoe\"}, {\"id\": 32818, \"name\": \"the yellow and black outfit\"}, {\"id\": 32819, \"name\": \"the white and red vehicle\"}, {\"id\": 32820, \"name\": \"the orange cherry tomato\"}, {\"id\": 32821, \"name\": \"a lady\"}, {\"id\": 32822, \"name\": \"blue and red jersey\"}, {\"id\": 32823, \"name\": \"a gray cushion\"}, {\"id\": 32824, \"name\": \"a landing gear\"}, {\"id\": 32825, \"name\": \"a close-up of a player's face\"}, {\"id\": 32826, \"name\": \"the black tent\"}, {\"id\": 32827, \"name\": \"white lantern\"}, {\"id\": 32828, \"name\": \"a cowboy\"}, {\"id\": 32829, \"name\": \"the black light post\"}, {\"id\": 32830, \"name\": \"a large wooden structure\"}, {\"id\": 32831, \"name\": \"black tarp\"}, {\"id\": 32832, \"name\": \"large indoor climbing wall\"}, {\"id\": 32833, \"name\": \"a nasa insignia\"}, {\"id\": 32834, \"name\": \"gray, paved road\"}, {\"id\": 32835, \"name\": \"a blue and orange shirt\"}, {\"id\": 32836, \"name\": \"a small amount of water\"}, {\"id\": 32837, \"name\": \"the multicolored pattern\"}, {\"id\": 32838, \"name\": \"small black ball\"}, {\"id\": 32839, \"name\": \"a black bracelet\"}, {\"id\": 32840, \"name\": \"black t logo\"}, {\"id\": 32841, \"name\": \"a vibrant pink dress\"}, {\"id\": 32842, \"name\": \"the grassy\"}, {\"id\": 32843, \"name\": \"the toy wooden marble run\"}, {\"id\": 32844, \"name\": \"the bacon\"}, {\"id\": 32845, \"name\": \"sperm\"}, {\"id\": 32846, \"name\": \"a rear quarter panel\"}, {\"id\": 32847, \"name\": \"black bow tie\"}, {\"id\": 32848, \"name\": \"the older man\"}, {\"id\": 32849, \"name\": \"cartoon image\"}, {\"id\": 32850, \"name\": \"the red and white racing uniform\"}, {\"id\": 32851, \"name\": \"white signpost\"}, {\"id\": 32852, \"name\": \"a large logo\"}, {\"id\": 32853, \"name\": \"the i24 news logo\"}, {\"id\": 32854, \"name\": \"the woman in a blue and pink top and jean\"}, {\"id\": 32855, \"name\": \"a pres option\"}, {\"id\": 32856, \"name\": \"the football team\"}, {\"id\": 32857, \"name\": \"a prominent building\"}, {\"id\": 32858, \"name\": \"blood splatter\"}, {\"id\": 32859, \"name\": \"a pink clothing\"}, {\"id\": 32860, \"name\": \"a large, white object\"}, {\"id\": 32861, \"name\": \"brightly colored parrot\"}, {\"id\": 32862, \"name\": \"the bright blue horse\"}, {\"id\": 32863, \"name\": \"colorful fruit illustration\"}, {\"id\": 32864, \"name\": \"a small vehicle\"}, {\"id\": 32865, \"name\": \"the green camouflage hat\"}, {\"id\": 32866, \"name\": \"striking striped pattern\"}, {\"id\": 32867, \"name\": \"walkway's awning\"}, {\"id\": 32868, \"name\": \"the dark-colored jacket\"}, {\"id\": 32869, \"name\": \"a triangular pediment\"}, {\"id\": 32870, \"name\": \"dark-colored polo shirt\"}, {\"id\": 32871, \"name\": \"saxophone\"}, {\"id\": 32872, \"name\": \"a yellow tip\"}, {\"id\": 32873, \"name\": \"cartoon-style mammoth\"}, {\"id\": 32874, \"name\": \"long snout\"}, {\"id\": 32875, \"name\": \"the footprint\"}, {\"id\": 32876, \"name\": \"white and blue jockey\"}, {\"id\": 32877, \"name\": \"a camouflage fatigue\"}, {\"id\": 32878, \"name\": \"a tall black pole\"}, {\"id\": 32879, \"name\": \"the man in a yellow hoodie\"}, {\"id\": 32880, \"name\": \"a light blue barrier\"}, {\"id\": 32881, \"name\": \"orange and white striped traffic cone\"}, {\"id\": 32882, \"name\": \"the headphone\"}, {\"id\": 32883, \"name\": \"bubble wand\"}, {\"id\": 32884, \"name\": \"server\"}, {\"id\": 32885, \"name\": \"green and blue accent\"}, {\"id\": 32886, \"name\": \"the multicolored shirt\"}, {\"id\": 32887, \"name\": \"cargo\"}, {\"id\": 32888, \"name\": \"a yellow bar\"}, {\"id\": 32889, \"name\": \"the dumbbell\"}, {\"id\": 32890, \"name\": \"orange and white striped barrier\"}, {\"id\": 32891, \"name\": \"a dark-colored sedan\"}, {\"id\": 32892, \"name\": \"the red metal object\"}, {\"id\": 32893, \"name\": \"the red cleat\"}, {\"id\": 32894, \"name\": \"green neon sign\"}, {\"id\": 32895, \"name\": \"the american flag shirt\"}, {\"id\": 32896, \"name\": \"a black storefront\"}, {\"id\": 32897, \"name\": \"a yellow auto-rickshaw\"}, {\"id\": 32898, \"name\": \"a white and green jersey\"}, {\"id\": 32899, \"name\": \"the striped pattern\"}, {\"id\": 32900, \"name\": \"the black eyebrow\"}, {\"id\": 32901, \"name\": \"dark-colored long-sleeved shirt\"}, {\"id\": 32902, \"name\": \"a blue long-sleeved shirt\"}, {\"id\": 32903, \"name\": \"blue leash\"}, {\"id\": 32904, \"name\": \"the thai license plate\"}, {\"id\": 32905, \"name\": \"the happy expression\"}, {\"id\": 32906, \"name\": \"the colorful wheel\"}, {\"id\": 32907, \"name\": \"man in the grey hoodie\"}, {\"id\": 32908, \"name\": \"team\"}, {\"id\": 32909, \"name\": \"large planter\"}, {\"id\": 32910, \"name\": \"a white and green striped awning\"}, {\"id\": 32911, \"name\": \"the colorful advertisement\"}, {\"id\": 32912, \"name\": \"large lily pad\"}, {\"id\": 32913, \"name\": \"an action figure\"}, {\"id\": 32914, \"name\": \"the light pink shirt\"}, {\"id\": 32915, \"name\": \"vibrant rainbow stripe\"}, {\"id\": 32916, \"name\": \"red fire extinguisher\"}, {\"id\": 32917, \"name\": \"a clear plastic wrapper\"}, {\"id\": 32918, \"name\": \"large front windshield\"}, {\"id\": 32919, \"name\": \"a dark gray jacket\"}, {\"id\": 32920, \"name\": \"police\"}, {\"id\": 32921, \"name\": \"a yellow buoy\"}, {\"id\": 32922, \"name\": \"game option\"}, {\"id\": 32923, \"name\": \"the gray long-sleeved shirt\"}, {\"id\": 32924, \"name\": \"entrance\"}, {\"id\": 32925, \"name\": \"shelves of supply\"}, {\"id\": 32926, \"name\": \"beige stone facade\"}, {\"id\": 32927, \"name\": \"the pink lipstick\"}, {\"id\": 32928, \"name\": \"the spatula\"}, {\"id\": 32929, \"name\": \"a long snout\"}, {\"id\": 32930, \"name\": \"a black metal post\"}, {\"id\": 32931, \"name\": \"logo for channel 41\"}, {\"id\": 32932, \"name\": \"the white x symbol\"}, {\"id\": 32933, \"name\": \"small outcropping of rock\"}, {\"id\": 32934, \"name\": \"the sleek, black vehicle\"}, {\"id\": 32935, \"name\": \"a red lipstick\"}, {\"id\": 32936, \"name\": \"large red trash can\"}, {\"id\": 32937, \"name\": \"upper level of the ship\"}, {\"id\": 32938, \"name\": \"a yellow and orange jersey\"}, {\"id\": 32939, \"name\": \"the large, lit-up sign\"}, {\"id\": 32940, \"name\": \"the man in a blue sweatshirt\"}, {\"id\": 32941, \"name\": \"matching pink shirt\"}, {\"id\": 32942, \"name\": \"the red and white christma item\"}, {\"id\": 32943, \"name\": \"the tan seat\"}, {\"id\": 32944, \"name\": \"long red and white barrier\"}, {\"id\": 32945, \"name\": \"a boom lift\"}, {\"id\": 32946, \"name\": \"a rickshaw's passenger\"}, {\"id\": 32947, \"name\": \"a striking green and white striped pattern\"}, {\"id\": 32948, \"name\": \"a minion character\"}, {\"id\": 32949, \"name\": \"the man in a gray shirt\"}, {\"id\": 32950, \"name\": \"ornate lamppost\"}, {\"id\": 32951, \"name\": \"a gold helmet\"}, {\"id\": 32952, \"name\": \"board's trucks\"}, {\"id\": 32953, \"name\": \"shorts with white stripe\"}, {\"id\": 32954, \"name\": \"the rack of club\"}, {\"id\": 32955, \"name\": \"the long white bench\"}, {\"id\": 32956, \"name\": \"entrance sign\"}, {\"id\": 32957, \"name\": \"twisted branch\"}, {\"id\": 32958, \"name\": \"a surgical mask\"}, {\"id\": 32959, \"name\": \"the blue pane\"}, {\"id\": 32960, \"name\": \"a small post\"}, {\"id\": 32961, \"name\": \"a player's name\"}, {\"id\": 32962, \"name\": \"the bowling green\"}, {\"id\": 32963, \"name\": \"a tan trim\"}, {\"id\": 32964, \"name\": \"a chef's hands\"}, {\"id\": 32965, \"name\": \"the black taxi\"}, {\"id\": 32966, \"name\": \"a black caster\"}, {\"id\": 32967, \"name\": \"white cycling outfit\"}, {\"id\": 32968, \"name\": \"a bustling storefront\"}, {\"id\": 32969, \"name\": \"the red sport car\"}, {\"id\": 32970, \"name\": \"black converse shoe\"}, {\"id\": 32971, \"name\": \"a logo on the chest\"}, {\"id\": 32972, \"name\": \"a white cylindrical structure\"}, {\"id\": 32973, \"name\": \"brown skirt\"}, {\"id\": 32974, \"name\": \"a black and white train car\"}, {\"id\": 32975, \"name\": \"gold watch\"}, {\"id\": 32976, \"name\": \"white lane\"}, {\"id\": 32977, \"name\": \"lush green lawn\"}, {\"id\": 32978, \"name\": \"a large windowed frontage\"}, {\"id\": 32979, \"name\": \"the checkered flag decal\"}, {\"id\": 32980, \"name\": \"a red ferri wheel\"}, {\"id\": 32981, \"name\": \"sombrero\"}, {\"id\": 32982, \"name\": \"yellow rectangular sticker\"}, {\"id\": 32983, \"name\": \"stylish and functional luggage\"}, {\"id\": 32984, \"name\": \"a distinct logo\"}, {\"id\": 32985, \"name\": \"the natural wood\"}, {\"id\": 32986, \"name\": \"tip of the dropper\"}, {\"id\": 32987, \"name\": \"silver doorknob\"}, {\"id\": 32988, \"name\": \"blue and white banner\"}, {\"id\": 32989, \"name\": \"blue and gold collar\"}, {\"id\": 32990, \"name\": \"large bowl\"}, {\"id\": 32991, \"name\": \"teal ribbon\"}, {\"id\": 32992, \"name\": \"green attire\"}, {\"id\": 32993, \"name\": \"the black and white mesh\"}, {\"id\": 32994, \"name\": \"tan snare drum\"}, {\"id\": 32995, \"name\": \"green baseplate\"}, {\"id\": 32996, \"name\": \"green progres bar\"}, {\"id\": 32997, \"name\": \"the ray-ban-branded\"}, {\"id\": 32998, \"name\": \"a man in suits\"}, {\"id\": 32999, \"name\": \"the display case or shelf\"}, {\"id\": 33000, \"name\": \"a rounded fender\"}, {\"id\": 33001, \"name\": \"a traditional architectural style\"}, {\"id\": 33002, \"name\": \"a construction\"}, {\"id\": 33003, \"name\": \"qr code\"}, {\"id\": 33004, \"name\": \"the gray body\"}, {\"id\": 33005, \"name\": \"off-the-shoulder sleeve\"}, {\"id\": 33006, \"name\": \"the distinct section\"}, {\"id\": 33007, \"name\": \"a player's vehicle\"}, {\"id\": 33008, \"name\": \"the heron\"}, {\"id\": 33009, \"name\": \"the red and green vegetable\"}, {\"id\": 33010, \"name\": \"light brown object\"}, {\"id\": 33011, \"name\": \"black and white top\"}, {\"id\": 33012, \"name\": \"the blue and white boat\"}, {\"id\": 33013, \"name\": \"a blue advertisement banner\"}, {\"id\": 33014, \"name\": \"pinata stick\"}, {\"id\": 33015, \"name\": \"the center lane\"}, {\"id\": 33016, \"name\": \"an orange kilt\"}, {\"id\": 33017, \"name\": \"sign in italian\"}, {\"id\": 33018, \"name\": \"the soccer uniform\"}, {\"id\": 33019, \"name\": \"a large orange object\"}, {\"id\": 33020, \"name\": \"inscription\"}, {\"id\": 33021, \"name\": \"the black wig\"}, {\"id\": 33022, \"name\": \"running shoe\"}, {\"id\": 33023, \"name\": \"a blue body\"}, {\"id\": 33024, \"name\": \"the white ruffle\"}, {\"id\": 33025, \"name\": \"small white train car\"}, {\"id\": 33026, \"name\": \"thick tire\"}, {\"id\": 33027, \"name\": \"large white counter\"}, {\"id\": 33028, \"name\": \"figure's face\"}, {\"id\": 33029, \"name\": \"the driver's side window\"}, {\"id\": 33030, \"name\": \"a left rear wheel\"}, {\"id\": 33031, \"name\": \"light green shirt\"}, {\"id\": 33032, \"name\": \"the gray electrical panel\"}, {\"id\": 33033, \"name\": \"company's logo\"}, {\"id\": 33034, \"name\": \"the fish's mouth\"}, {\"id\": 33035, \"name\": \"a raised white lettering\"}, {\"id\": 33036, \"name\": \"blue dog bowl\"}, {\"id\": 33037, \"name\": \"pink spiky wig\"}, {\"id\": 33038, \"name\": \"a white honda civic\"}, {\"id\": 33039, \"name\": \"truck's side panel\"}, {\"id\": 33040, \"name\": \"a gray stone sidewalk\"}, {\"id\": 33041, \"name\": \"matching purple shirt\"}, {\"id\": 33042, \"name\": \"colorful top\"}, {\"id\": 33043, \"name\": \"snow-covered awning\"}, {\"id\": 33044, \"name\": \"black furry object\"}, {\"id\": 33045, \"name\": \"a long wooden counter\"}, {\"id\": 33046, \"name\": \"a location marker\"}, {\"id\": 33047, \"name\": \"colorful action figure\"}, {\"id\": 33048, \"name\": \"a teal-colored section\"}, {\"id\": 33049, \"name\": \"brightly colored shirt\"}, {\"id\": 33050, \"name\": \"the hoop earring\"}, {\"id\": 33051, \"name\": \"large poster or advertisement\"}, {\"id\": 33052, \"name\": \"the ride car\"}, {\"id\": 33053, \"name\": \"the cartoon alpaca design\"}, {\"id\": 33054, \"name\": \"large orange creature\"}, {\"id\": 33055, \"name\": \"sharp tooth\"}, {\"id\": 33056, \"name\": \"the pink mane\"}, {\"id\": 33057, \"name\": \"a vertical wooden plank\"}, {\"id\": 33058, \"name\": \"the orange and red flower\"}, {\"id\": 33059, \"name\": \"plane's shadow\"}, {\"id\": 33060, \"name\": \"the bottle of paint\"}, {\"id\": 33061, \"name\": \"an exposed wood\"}, {\"id\": 33062, \"name\": \"the black and blue design\"}, {\"id\": 33063, \"name\": \"the blue cow logo\"}, {\"id\": 33064, \"name\": \"yellow tape\"}, {\"id\": 33065, \"name\": \"a crossbody bag\"}, {\"id\": 33066, \"name\": \"the orange and white decal\"}, {\"id\": 33067, \"name\": \"fiery explosion\"}, {\"id\": 33068, \"name\": \"an ornate ironwork\"}, {\"id\": 33069, \"name\": \"a snowflake\"}, {\"id\": 33070, \"name\": \"the red base\"}, {\"id\": 33071, \"name\": \"a large wooden post\"}, {\"id\": 33072, \"name\": \"rusted texture\"}, {\"id\": 33073, \"name\": \"white pattern\"}, {\"id\": 33074, \"name\": \"a protruding spike\"}, {\"id\": 33075, \"name\": \"a black and white patterned blouse\"}, {\"id\": 33076, \"name\": \"red football\"}, {\"id\": 33077, \"name\": \"the mural or graffiti\"}, {\"id\": 33078, \"name\": \"the white cuff\"}, {\"id\": 33079, \"name\": \"songbook\"}, {\"id\": 33080, \"name\": \"a blue and white package\"}, {\"id\": 33081, \"name\": \"the hornets' player\"}, {\"id\": 33082, \"name\": \"center lane\"}, {\"id\": 33083, \"name\": \"a vintage green motorcycle\"}, {\"id\": 33084, \"name\": \"grey trim\"}, {\"id\": 33085, \"name\": \"a garden bed\"}, {\"id\": 33086, \"name\": \"tan vest\"}, {\"id\": 33087, \"name\": \"white hurdle\"}, {\"id\": 33088, \"name\": \"a partially visible sign\"}, {\"id\": 33089, \"name\": \"large, red and yellow sign\"}, {\"id\": 33090, \"name\": \"limb\"}, {\"id\": 33091, \"name\": \"numbered pole\"}, {\"id\": 33092, \"name\": \"the colorful glass ball\"}, {\"id\": 33093, \"name\": \"dashcam\"}, {\"id\": 33094, \"name\": \"brown wood trim accent\"}, {\"id\": 33095, \"name\": \"a yellow and red excavator\"}, {\"id\": 33096, \"name\": \"a case\"}, {\"id\": 33097, \"name\": \"the dark blue sock\"}, {\"id\": 33098, \"name\": \"a large digital display screen\"}, {\"id\": 33099, \"name\": \"brown barn or shed\"}, {\"id\": 33100, \"name\": \"a tall black microphone stand\"}, {\"id\": 33101, \"name\": \"a t-70 x-wing\"}, {\"id\": 33102, \"name\": \"the residential structure\"}, {\"id\": 33103, \"name\": \"uka logo\"}, {\"id\": 33104, \"name\": \"brown or grey exterior\"}, {\"id\": 33105, \"name\": \"the metal base\"}, {\"id\": 33106, \"name\": \"yellow reflective vest\"}, {\"id\": 33107, \"name\": \"ear protection\"}, {\"id\": 33108, \"name\": \"a gold stripe\"}, {\"id\": 33109, \"name\": \"glass and concrete facade\"}, {\"id\": 33110, \"name\": \"gray stone accent\"}, {\"id\": 33111, \"name\": \"a green attire\"}, {\"id\": 33112, \"name\": \"a red nose\"}, {\"id\": 33113, \"name\": \"a gray exterior\"}, {\"id\": 33114, \"name\": \"the thoma the tank engine\"}, {\"id\": 33115, \"name\": \"brown brick structure\"}, {\"id\": 33116, \"name\": \"the red light bar\"}, {\"id\": 33117, \"name\": \"small stall\"}, {\"id\": 33118, \"name\": \"promotional material\"}, {\"id\": 33119, \"name\": \"a patch of green grass\"}, {\"id\": 33120, \"name\": \"the standing person\"}, {\"id\": 33121, \"name\": \"a multicolored design\"}, {\"id\": 33122, \"name\": \"moss\"}, {\"id\": 33123, \"name\": \"the black elbow pad\"}, {\"id\": 33124, \"name\": \"a red sock\"}, {\"id\": 33125, \"name\": \"green outfit\"}, {\"id\": 33126, \"name\": \"a white design\"}, {\"id\": 33127, \"name\": \"the barstool\"}, {\"id\": 33128, \"name\": \"round head\"}, {\"id\": 33129, \"name\": \"a bright green light\"}, {\"id\": 33130, \"name\": \"man in a white shirt and short\"}, {\"id\": 33131, \"name\": \"the bold lettering\"}, {\"id\": 33132, \"name\": \"the jockey attire\"}, {\"id\": 33133, \"name\": \"white lego brick\"}, {\"id\": 33134, \"name\": \"the blue writing\"}, {\"id\": 33135, \"name\": \"yellow pillar\"}, {\"id\": 33136, \"name\": \"a brown platform\"}, {\"id\": 33137, \"name\": \"colored shirt\"}, {\"id\": 33138, \"name\": \"a beetle\"}, {\"id\": 33139, \"name\": \"athlete\"}, {\"id\": 33140, \"name\": \"rear spoiler\"}, {\"id\": 33141, \"name\": \"the large bolt\"}, {\"id\": 33142, \"name\": \"plastic bag or sheet\"}, {\"id\": 33143, \"name\": \"glass window\"}, {\"id\": 33144, \"name\": \"the large white tooth\"}, {\"id\": 33145, \"name\": \"a bright green neon light\"}, {\"id\": 33146, \"name\": \"the small opening\"}, {\"id\": 33147, \"name\": \"the tray of roasted nut\"}, {\"id\": 33148, \"name\": \"train's face\"}, {\"id\": 33149, \"name\": \"pink backpack\"}, {\"id\": 33150, \"name\": \"the black wine bottle\"}, {\"id\": 33151, \"name\": \"brown stitching\"}, {\"id\": 33152, \"name\": \"a green stem\"}, {\"id\": 33153, \"name\": \"an image of a person\"}, {\"id\": 33154, \"name\": \"the advertising banner\"}, {\"id\": 33155, \"name\": \"green health bar\"}, {\"id\": 33156, \"name\": \"bustling factory floor\"}, {\"id\": 33157, \"name\": \"the highway's center lane\"}, {\"id\": 33158, \"name\": \"low barrier\"}, {\"id\": 33159, \"name\": \"a mural of a cartoon character\"}, {\"id\": 33160, \"name\": \"the three-wheeled auto rickshaw\"}, {\"id\": 33161, \"name\": \"the summer attire\"}, {\"id\": 33162, \"name\": \"the purple octopu\"}, {\"id\": 33163, \"name\": \"tan wheel\"}, {\"id\": 33164, \"name\": \"red vodafone umbrella\"}, {\"id\": 33165, \"name\": \"the blurry pole\"}, {\"id\": 33166, \"name\": \"a colorful image\"}, {\"id\": 33167, \"name\": \"paw patrol series\"}, {\"id\": 33168, \"name\": \"a colleague\"}, {\"id\": 33169, \"name\": \"white metal pole\"}, {\"id\": 33170, \"name\": \"a multicolored balloon\"}, {\"id\": 33171, \"name\": \"small gray object\"}, {\"id\": 33172, \"name\": \"an elbow\"}, {\"id\": 33173, \"name\": \"the brown seat\"}, {\"id\": 33174, \"name\": \"a head of person\"}, {\"id\": 33175, \"name\": \"the gray skull\"}, {\"id\": 33176, \"name\": \"the orange and navy blue uniform\"}, {\"id\": 33177, \"name\": \"a brown shelf\"}, {\"id\": 33178, \"name\": \"a tunic\"}, {\"id\": 33179, \"name\": \"a green wheelbarrow\"}, {\"id\": 33180, \"name\": \"iconic red suit\"}, {\"id\": 33181, \"name\": \"the gray exterior\"}, {\"id\": 33182, \"name\": \"a smile\"}, {\"id\": 33183, \"name\": \"triangular head\"}, {\"id\": 33184, \"name\": \"the white bikini\"}, {\"id\": 33185, \"name\": \"the yellow warning sign\"}, {\"id\": 33186, \"name\": \"stable\"}, {\"id\": 33187, \"name\": \"blue and black tool\"}, {\"id\": 33188, \"name\": \"the small black tattoo\"}, {\"id\": 33189, \"name\": \"the cartoon flamingo design\"}, {\"id\": 33190, \"name\": \"an omega logo\"}, {\"id\": 33191, \"name\": \"the small bump\"}, {\"id\": 33192, \"name\": \"the blue plaid shirt\"}, {\"id\": 33193, \"name\": \"a man in a white shirt and black backpack\"}, {\"id\": 33194, \"name\": \"a black-and-white patterned top\"}, {\"id\": 33195, \"name\": \"the rider's left arm\"}, {\"id\": 33196, \"name\": \"navy blue trim\"}, {\"id\": 33197, \"name\": \"a mirror's surface\"}, {\"id\": 33198, \"name\": \"gray wheel\"}, {\"id\": 33199, \"name\": \"human head\"}, {\"id\": 33200, \"name\": \"the black box with a white circle and a blue arrow\"}, {\"id\": 33201, \"name\": \"a small debris\"}, {\"id\": 33202, \"name\": \"the blue arrow\"}, {\"id\": 33203, \"name\": \"an orange and white cone\"}, {\"id\": 33204, \"name\": \"large black and silver tanker truck\"}, {\"id\": 33205, \"name\": \"the yellow, round object\"}, {\"id\": 33206, \"name\": \"the white and red painted curb\"}, {\"id\": 33207, \"name\": \"a large white balloon\"}, {\"id\": 33208, \"name\": \"round white face\"}, {\"id\": 33209, \"name\": \"a red stove\"}, {\"id\": 33210, \"name\": \"the beard\"}, {\"id\": 33211, \"name\": \"the single eye\"}, {\"id\": 33212, \"name\": \"a robotic arm\"}, {\"id\": 33213, \"name\": \"racing silk\"}, {\"id\": 33214, \"name\": \"a green circle\"}, {\"id\": 33215, \"name\": \"purple and white nutcracker\"}, {\"id\": 33216, \"name\": \"a parked golf cart\"}, {\"id\": 33217, \"name\": \"purple cloth\"}, {\"id\": 33218, \"name\": \"person's lap\"}, {\"id\": 33219, \"name\": \"dark gray asphalt\"}, {\"id\": 33220, \"name\": \"the horse icon\"}, {\"id\": 33221, \"name\": \"a commercial or retail area\"}, {\"id\": 33222, \"name\": \"a schwan logo\"}, {\"id\": 33223, \"name\": \"the red and white chevrolet logo\"}, {\"id\": 33224, \"name\": \"a spalding logo\"}, {\"id\": 33225, \"name\": \"a red semi-truck\"}, {\"id\": 33226, \"name\": \"a jean short\"}, {\"id\": 33227, \"name\": \"the white and orange striped pole\"}, {\"id\": 33228, \"name\": \"dark purple sweater\"}, {\"id\": 33229, \"name\": \"a large, dark brown log\"}, {\"id\": 33230, \"name\": \"the cork coaster\"}, {\"id\": 33231, \"name\": \"a distinctive black and white marking\"}, {\"id\": 33232, \"name\": \"the distinctive logo of honda\"}, {\"id\": 33233, \"name\": \"teal long-sleeved shirt\"}, {\"id\": 33234, \"name\": \"a green play dough\"}, {\"id\": 33235, \"name\": \"a host\"}, {\"id\": 33236, \"name\": \"a green grassy surface\"}, {\"id\": 33237, \"name\": \"a man in a blue jersey and white short\"}, {\"id\": 33238, \"name\": \"silver armor suit\"}, {\"id\": 33239, \"name\": \"red detail\"}, {\"id\": 33240, \"name\": \"red cab\"}, {\"id\": 33241, \"name\": \"the long, thin neck\"}, {\"id\": 33242, \"name\": \"large windowed storefront\"}, {\"id\": 33243, \"name\": \"the digital menu\"}, {\"id\": 33244, \"name\": \"the circular track\"}, {\"id\": 33245, \"name\": \"the miniature race track\"}, {\"id\": 33246, \"name\": \"a blue shelf\"}, {\"id\": 33247, \"name\": \"distinctive red top\"}, {\"id\": 33248, \"name\": \"a beige tent\"}, {\"id\": 33249, \"name\": \"the red honda flag\"}, {\"id\": 33250, \"name\": \"the speech bubble\"}, {\"id\": 33251, \"name\": \"grey bollard\"}, {\"id\": 33252, \"name\": \"a white gift bag\"}, {\"id\": 33253, \"name\": \"the bottle of liquor\"}, {\"id\": 33254, \"name\": \"green wheel\"}, {\"id\": 33255, \"name\": \"the marking\"}, {\"id\": 33256, \"name\": \"the silver tractor\"}, {\"id\": 33257, \"name\": \"light blue tank top\"}, {\"id\": 33258, \"name\": \"loudspeaker\"}, {\"id\": 33259, \"name\": \"the patch of snow\"}, {\"id\": 33260, \"name\": \"a beef\"}, {\"id\": 33261, \"name\": \"the white base\"}, {\"id\": 33262, \"name\": \"infinix logo\"}, {\"id\": 33263, \"name\": \"restaurant's sign\"}, {\"id\": 33264, \"name\": \"the blue race car\"}, {\"id\": 33265, \"name\": \"yellow siding\"}, {\"id\": 33266, \"name\": \"the blue towel\"}, {\"id\": 33267, \"name\": \"a colorful game booth\"}, {\"id\": 33268, \"name\": \"the purple tile\"}, {\"id\": 33269, \"name\": \"a pink flower arrangement\"}, {\"id\": 33270, \"name\": \"a silver grille\"}, {\"id\": 33271, \"name\": \"a purple circle\"}, {\"id\": 33272, \"name\": \"white arrow pattern\"}, {\"id\": 33273, \"name\": \"the metal drum\"}, {\"id\": 33274, \"name\": \"the exaggerated architecture\"}, {\"id\": 33275, \"name\": \"large military vehicle\"}, {\"id\": 33276, \"name\": \"a metal lattice\"}, {\"id\": 33277, \"name\": \"horse name\"}, {\"id\": 33278, \"name\": \"blurred sign\"}, {\"id\": 33279, \"name\": \"purple color scheme\"}, {\"id\": 33280, \"name\": \"a dark sweater\"}, {\"id\": 33281, \"name\": \"a large, arched doorway\"}, {\"id\": 33282, \"name\": \"the blue and white vehicle\"}, {\"id\": 33283, \"name\": \"the large house\"}, {\"id\": 33284, \"name\": \"large handle\"}, {\"id\": 33285, \"name\": \"the orange umbrella\"}, {\"id\": 33286, \"name\": \"white feather\"}, {\"id\": 33287, \"name\": \"colored square\"}, {\"id\": 33288, \"name\": \"visible fingernail\"}, {\"id\": 33289, \"name\": \"a window or door\"}, {\"id\": 33290, \"name\": \"a bmw logo\"}, {\"id\": 33291, \"name\": \"horizontal white stripe\"}, {\"id\": 33292, \"name\": \"a ballet outfit\"}, {\"id\": 33293, \"name\": \"a dust\"}, {\"id\": 33294, \"name\": \"a small white mark\"}, {\"id\": 33295, \"name\": \"the basket of item\"}, {\"id\": 33296, \"name\": \"foreground truck\"}, {\"id\": 33297, \"name\": \"the stacks of cash\"}, {\"id\": 33298, \"name\": \"yellow tube\"}, {\"id\": 33299, \"name\": \"the red and black car\"}, {\"id\": 33300, \"name\": \"the vibrant rainbow\"}, {\"id\": 33301, \"name\": \"the small structure\"}, {\"id\": 33302, \"name\": \"a picture of person\"}, {\"id\": 33303, \"name\": \"festina sign\"}, {\"id\": 33304, \"name\": \"a hbc\"}, {\"id\": 33305, \"name\": \"black heel\"}, {\"id\": 33306, \"name\": \"joyful face\"}, {\"id\": 33307, \"name\": \"the white or blue tank top\"}, {\"id\": 33308, \"name\": \"red or pink shirt\"}, {\"id\": 33309, \"name\": \"inflatable ring\"}, {\"id\": 33310, \"name\": \"a traditional clothing\"}, {\"id\": 33311, \"name\": \"the wires and cable\"}, {\"id\": 33312, \"name\": \"the large front tire\"}, {\"id\": 33313, \"name\": \"the casual, athletic clothing\"}, {\"id\": 33314, \"name\": \"small parking space\"}, {\"id\": 33315, \"name\": \"the comic book panel\"}, {\"id\": 33316, \"name\": \"a headset\"}, {\"id\": 33317, \"name\": \"the mickey mouse ear\"}, {\"id\": 33318, \"name\": \"menacing grin\"}, {\"id\": 33319, \"name\": \"a rolled-up item\"}, {\"id\": 33320, \"name\": \"the green military-style uniform\"}, {\"id\": 33321, \"name\": \"small shed\"}, {\"id\": 33322, \"name\": \"a yoga block\"}, {\"id\": 33323, \"name\": \"turquoise fingernail polish\"}, {\"id\": 33324, \"name\": \"the large black object\"}, {\"id\": 33325, \"name\": \"a large, illuminated sign\"}, {\"id\": 33326, \"name\": \"the large, tall, dark beer bottle\"}, {\"id\": 33327, \"name\": \"oil container\"}, {\"id\": 33328, \"name\": \"a large smile\"}, {\"id\": 33329, \"name\": \"sharp blade\"}, {\"id\": 33330, \"name\": \"the large, black, and red sail\"}, {\"id\": 33331, \"name\": \"the icon\"}, {\"id\": 33332, \"name\": \"dark liquid\"}, {\"id\": 33333, \"name\": \"an orange bloom\"}, {\"id\": 33334, \"name\": \"large pipe\"}, {\"id\": 33335, \"name\": \"company\"}, {\"id\": 33336, \"name\": \"close-up of a person's face\"}, {\"id\": 33337, \"name\": \"beige house\"}, {\"id\": 33338, \"name\": \"the middle lane\"}, {\"id\": 33339, \"name\": \"a driver's face\"}, {\"id\": 33340, \"name\": \"the soap\"}, {\"id\": 33341, \"name\": \"the red string\"}, {\"id\": 33342, \"name\": \"neatly trimmed bush\"}, {\"id\": 33343, \"name\": \"a main banner\"}, {\"id\": 33344, \"name\": \"a roll of paper towel\"}, {\"id\": 33345, \"name\": \"a gold and red saddle\"}, {\"id\": 33346, \"name\": \"officer's face\"}, {\"id\": 33347, \"name\": \"red clothing\"}, {\"id\": 33348, \"name\": \"a van's window\"}, {\"id\": 33349, \"name\": \"the silver can of diet coke\"}, {\"id\": 33350, \"name\": \"the blue oar\"}, {\"id\": 33351, \"name\": \"wooden broomstick\"}, {\"id\": 33352, \"name\": \"dunlop logo\"}, {\"id\": 33353, \"name\": \"a dark brown fur\"}, {\"id\": 33354, \"name\": \"a horse's ear\"}, {\"id\": 33355, \"name\": \"toy action figure\"}, {\"id\": 33356, \"name\": \"the small restaurant\"}, {\"id\": 33357, \"name\": \"blurred white garment with polka dot\"}, {\"id\": 33358, \"name\": \"a small red light\"}, {\"id\": 33359, \"name\": \"phanatic's large head\"}, {\"id\": 33360, \"name\": \"the commercial property\"}, {\"id\": 33361, \"name\": \"a brown liquid\"}, {\"id\": 33362, \"name\": \"a vehicle's white body\"}, {\"id\": 33363, \"name\": \"a red facade\"}, {\"id\": 33364, \"name\": \"a bright yellow safety vest\"}, {\"id\": 33365, \"name\": \"an arched door\"}, {\"id\": 33366, \"name\": \"a small green frog figurine\"}, {\"id\": 33367, \"name\": \"movie\"}, {\"id\": 33368, \"name\": \"action figures and toy\"}, {\"id\": 33369, \"name\": \"a colored light\"}, {\"id\": 33370, \"name\": \"the rear\"}, {\"id\": 33371, \"name\": \"bowl of pasta\"}, {\"id\": 33372, \"name\": \"the pink fuzzy item\"}, {\"id\": 33373, \"name\": \"fruit stand\"}, {\"id\": 33374, \"name\": \"a warm black coat\"}, {\"id\": 33375, \"name\": \"the food cart\"}, {\"id\": 33376, \"name\": \"striped jacket\"}, {\"id\": 33377, \"name\": \"the large illuminated horse sculpture\"}, {\"id\": 33378, \"name\": \"large, yellow nose\"}, {\"id\": 33379, \"name\": \"a green body\"}, {\"id\": 33380, \"name\": \"unicycle\"}, {\"id\": 33381, \"name\": \"a square base\"}, {\"id\": 33382, \"name\": \"pink toad plush toy\"}, {\"id\": 33383, \"name\": \"blue lane rope\"}, {\"id\": 33384, \"name\": \"a large white and red bag\"}, {\"id\": 33385, \"name\": \"yellow polka dot\"}, {\"id\": 33386, \"name\": \"large white canopy tent\"}, {\"id\": 33387, \"name\": \"a large, blue-green fish\"}, {\"id\": 33388, \"name\": \"the white symbol\"}, {\"id\": 33389, \"name\": \"the restaurant or gift shop\"}, {\"id\": 33390, \"name\": \"festive paper\"}, {\"id\": 33391, \"name\": \"purple orb\"}, {\"id\": 33392, \"name\": \"residential property\"}, {\"id\": 33393, \"name\": \"a green mane and tail\"}, {\"id\": 33394, \"name\": \"a robot's body\"}, {\"id\": 33395, \"name\": \"a green icon\"}, {\"id\": 33396, \"name\": \"the swan\"}, {\"id\": 33397, \"name\": \"a pork\"}, {\"id\": 33398, \"name\": \"woman in a black tank top\"}, {\"id\": 33399, \"name\": \"red and yellow jumpsuit\"}, {\"id\": 33400, \"name\": \"the runner\"}, {\"id\": 33401, \"name\": \"small raccoon\"}, {\"id\": 33402, \"name\": \"a brown deck\"}, {\"id\": 33403, \"name\": \"the stall's red canopy\"}, {\"id\": 33404, \"name\": \"the colorful and ornate design\"}, {\"id\": 33405, \"name\": \"an illegible writing\"}, {\"id\": 33406, \"name\": \"a brown bead\"}, {\"id\": 33407, \"name\": \"the yellow bulldozer\"}, {\"id\": 33408, \"name\": \"single eye\"}, {\"id\": 33409, \"name\": \"a white tire barrier\"}, {\"id\": 33410, \"name\": \"a red fire extinguisher\"}, {\"id\": 33411, \"name\": \"large wooden barrel\"}, {\"id\": 33412, \"name\": \"silver leg\"}, {\"id\": 33413, \"name\": \"sabrett logo\"}, {\"id\": 33414, \"name\": \"road or path\"}, {\"id\": 33415, \"name\": \"a string light\"}, {\"id\": 33416, \"name\": \"a red and green plaid vest\"}, {\"id\": 33417, \"name\": \"white numeral\"}, {\"id\": 33418, \"name\": \"the bear\"}, {\"id\": 33419, \"name\": \"a large, round light\"}, {\"id\": 33420, \"name\": \"a black blazer\"}, {\"id\": 33421, \"name\": \"the yellow backhoe loader\"}, {\"id\": 33422, \"name\": \"purple skirt\"}, {\"id\": 33423, \"name\": \"the 7-eleven\"}, {\"id\": 33424, \"name\": \"planet\"}, {\"id\": 33425, \"name\": \"yellowish and transparent substance\"}, {\"id\": 33426, \"name\": \"viking hat\"}, {\"id\": 33427, \"name\": \"the purple cape\"}, {\"id\": 33428, \"name\": \"the archer's feet\"}, {\"id\": 33429, \"name\": \"bald\"}, {\"id\": 33430, \"name\": \"the striped long-sleeve shirt\"}, {\"id\": 33431, \"name\": \"a purple fringe\"}, {\"id\": 33432, \"name\": \"white splash\"}, {\"id\": 33433, \"name\": \"spool of blue material\"}, {\"id\": 33434, \"name\": \"the mound of brown dirt\"}, {\"id\": 33435, \"name\": \"the black gate\"}, {\"id\": 33436, \"name\": \"the ancient column\"}, {\"id\": 33437, \"name\": \"purple and green design\"}, {\"id\": 33438, \"name\": \"a performer's face\"}, {\"id\": 33439, \"name\": \"the similar barrel\"}, {\"id\": 33440, \"name\": \"the symmetrical facade\"}, {\"id\": 33441, \"name\": \"green swirl\"}, {\"id\": 33442, \"name\": \"a red-and-white striped vest\"}, {\"id\": 33443, \"name\": \"black section\"}, {\"id\": 33444, \"name\": \"green zero\"}, {\"id\": 33445, \"name\": \"the hand-drawn cartoon character\"}, {\"id\": 33446, \"name\": \"a large foot\"}, {\"id\": 33447, \"name\": \"the brown and black horse\"}, {\"id\": 33448, \"name\": \"tailpipe\"}, {\"id\": 33449, \"name\": \"the distinctive black nose\"}, {\"id\": 33450, \"name\": \"gray canopy\"}, {\"id\": 33451, \"name\": \"rider's seat\"}, {\"id\": 33452, \"name\": \"meatball\"}, {\"id\": 33453, \"name\": \"single boat\"}, {\"id\": 33454, \"name\": \"the white component\"}, {\"id\": 33455, \"name\": \"a speech bubble\"}, {\"id\": 33456, \"name\": \"the blue visor\"}, {\"id\": 33457, \"name\": \"mascot\"}, {\"id\": 33458, \"name\": \"chef's hat\"}, {\"id\": 33459, \"name\": \"the high-visibility yellow clothing\"}, {\"id\": 33460, \"name\": \"a blue-clad figure\"}, {\"id\": 33461, \"name\": \"logo for the tour de france\"}, {\"id\": 33462, \"name\": \"chestnut horse with a green and white jockey\"}, {\"id\": 33463, \"name\": \"a retail store\"}, {\"id\": 33464, \"name\": \"large tooth\"}, {\"id\": 33465, \"name\": \"a brown sled\"}, {\"id\": 33466, \"name\": \"a stainles steel bowl\"}, {\"id\": 33467, \"name\": \"red bracelet\"}, {\"id\": 33468, \"name\": \"the plywood\"}, {\"id\": 33469, \"name\": \"the spiral design\"}, {\"id\": 33470, \"name\": \"the silver vw logo\"}, {\"id\": 33471, \"name\": \"the distinctive grille\"}, {\"id\": 33472, \"name\": \"a string of light\"}, {\"id\": 33473, \"name\": \"checkered flag\"}, {\"id\": 33474, \"name\": \"hoove\"}, {\"id\": 33475, \"name\": \"a blue train\"}, {\"id\": 33476, \"name\": \"a black puffy jacket\"}, {\"id\": 33477, \"name\": \"a lush green area\"}, {\"id\": 33478, \"name\": \"the black awning\"}, {\"id\": 33479, \"name\": \"the small image\"}, {\"id\": 33480, \"name\": \"thrilling dirt track race\"}, {\"id\": 33481, \"name\": \"white yoshi plush toy\"}, {\"id\": 33482, \"name\": \"large metal pipe\"}, {\"id\": 33483, \"name\": \"glass barrier\"}, {\"id\": 33484, \"name\": \"a rust stain\"}, {\"id\": 33485, \"name\": \"dark blue container\"}, {\"id\": 33486, \"name\": \"white locker\"}, {\"id\": 33487, \"name\": \"title or caption\"}, {\"id\": 33488, \"name\": \"the yellow brake caliper\"}, {\"id\": 33489, \"name\": \"blue and red saddle\"}, {\"id\": 33490, \"name\": \"silver pot\"}, {\"id\": 33491, \"name\": \"the traditional indian attire\"}, {\"id\": 33492, \"name\": \"a black tight\"}, {\"id\": 33493, \"name\": \"a man in a white shirt and dark jacket\"}, {\"id\": 33494, \"name\": \"the small white rectangle\"}, {\"id\": 33495, \"name\": \"man's portrait\"}, {\"id\": 33496, \"name\": \"a sleek silver pole\"}, {\"id\": 33497, \"name\": \"the wooden top\"}, {\"id\": 33498, \"name\": \"a man in a black shirt and glass\"}, {\"id\": 33499, \"name\": \"a slender green body\"}, {\"id\": 33500, \"name\": \"digital leaderboard\"}, {\"id\": 33501, \"name\": \"the sperm cell\"}, {\"id\": 33502, \"name\": \"the blurry logo\"}, {\"id\": 33503, \"name\": \"a fresh strawberry\"}, {\"id\": 33504, \"name\": \"a black piece of material\"}, {\"id\": 33505, \"name\": \"the prominent yellow banner\"}, {\"id\": 33506, \"name\": \"perch\"}, {\"id\": 33507, \"name\": \"the yellow lanyard\"}, {\"id\": 33508, \"name\": \"white hose\"}, {\"id\": 33509, \"name\": \"the mercedes-benz logo\"}, {\"id\": 33510, \"name\": \"food and drink stall\"}, {\"id\": 33511, \"name\": \"the red suit\"}, {\"id\": 33512, \"name\": \"tent's canopy\"}, {\"id\": 33513, \"name\": \"dark blue canopy\"}, {\"id\": 33514, \"name\": \"a black wing\"}, {\"id\": 33515, \"name\": \"a large military vehicle\"}, {\"id\": 33516, \"name\": \"the k24 logo\"}, {\"id\": 33517, \"name\": \"a news channel logo\"}, {\"id\": 33518, \"name\": \"the vibrant attire\"}, {\"id\": 33519, \"name\": \"a dark wooden pillar\"}, {\"id\": 33520, \"name\": \"a spear\"}, {\"id\": 33521, \"name\": \"the green writing\"}, {\"id\": 33522, \"name\": \"the funko pop box\"}, {\"id\": 33523, \"name\": \"the heart\"}, {\"id\": 33524, \"name\": \"wood chip\"}, {\"id\": 33525, \"name\": \"a distinctive puppet\"}, {\"id\": 33526, \"name\": \"white snowflake design\"}, {\"id\": 33527, \"name\": \"a red and white lane divider\"}, {\"id\": 33528, \"name\": \"the black outboard motor\"}, {\"id\": 33529, \"name\": \"the bangle\"}, {\"id\": 33530, \"name\": \"the white turban\"}, {\"id\": 33531, \"name\": \"the green-shirted person\"}, {\"id\": 33532, \"name\": \"large yellow and black sign\"}, {\"id\": 33533, \"name\": \"yoga mat\"}, {\"id\": 33534, \"name\": \"an orange turban\"}, {\"id\": 33535, \"name\": \"a light-colored pant\"}, {\"id\": 33536, \"name\": \"a large poster or advertisement\"}, {\"id\": 33537, \"name\": \"a golden garland\"}, {\"id\": 33538, \"name\": \"a prominent red square\"}, {\"id\": 33539, \"name\": \"a rear left turn signal\"}, {\"id\": 33540, \"name\": \"telephone wire\"}, {\"id\": 33541, \"name\": \"small black object\"}, {\"id\": 33542, \"name\": \"the distinctive yellow stripe\"}, {\"id\": 33543, \"name\": \"a black fabric\"}, {\"id\": 33544, \"name\": \"the robot face\"}, {\"id\": 33545, \"name\": \"a man in yellow swim trunk\"}, {\"id\": 33546, \"name\": \"a glass doors and window\"}, {\"id\": 33547, \"name\": \"a white wood\"}, {\"id\": 33548, \"name\": \"toy figure\"}, {\"id\": 33549, \"name\": \"light blue jersey\"}, {\"id\": 33550, \"name\": \"the lumber\"}, {\"id\": 33551, \"name\": \"the green box\"}, {\"id\": 33552, \"name\": \"the medication\"}, {\"id\": 33553, \"name\": \"the large base\"}, {\"id\": 33554, \"name\": \"green and black label\"}, {\"id\": 33555, \"name\": \"the large outdoor dining area\"}, {\"id\": 33556, \"name\": \"a purple helmet\"}, {\"id\": 33557, \"name\": \"the prominent red life preserver\"}, {\"id\": 33558, \"name\": \"a yellow and blue attire\"}, {\"id\": 33559, \"name\": \"the translucent material\"}, {\"id\": 33560, \"name\": \"a yellow bollard\"}, {\"id\": 33561, \"name\": \"exclamation mark icon\"}, {\"id\": 33562, \"name\": \"a white tunic\"}, {\"id\": 33563, \"name\": \"an image of food\"}, {\"id\": 33564, \"name\": \"a green hand\"}, {\"id\": 33565, \"name\": \"hood of a classic car\"}, {\"id\": 33566, \"name\": \"hood of a taxi\"}, {\"id\": 33567, \"name\": \"a bas drum\"}, {\"id\": 33568, \"name\": \"the player or game host\"}, {\"id\": 33569, \"name\": \"the black and white leopard print design\"}, {\"id\": 33570, \"name\": \"a gray sleeve\"}, {\"id\": 33571, \"name\": \"company's phone number\"}, {\"id\": 33572, \"name\": \"black north face jacket\"}, {\"id\": 33573, \"name\": \"a roll of wire or cable\"}, {\"id\": 33574, \"name\": \"the large exercise ball\"}, {\"id\": 33575, \"name\": \"the subtle blue streak\"}, {\"id\": 33576, \"name\": \"the closed shutter\"}, {\"id\": 33577, \"name\": \"a grand facade\"}, {\"id\": 33578, \"name\": \"the train's number\"}, {\"id\": 33579, \"name\": \"player's shadow\"}, {\"id\": 33580, \"name\": \"black rod holder\"}, {\"id\": 33581, \"name\": \"the blue and black motorcycle\"}, {\"id\": 33582, \"name\": \"light-colored concrete surface\"}, {\"id\": 33583, \"name\": \"a neon green jersey\"}, {\"id\": 33584, \"name\": \"pink, fan-like structure\"}, {\"id\": 33585, \"name\": \"a tall, ornate structure\"}, {\"id\": 33586, \"name\": \"the white party hat\"}, {\"id\": 33587, \"name\": \"duck-like appearance\"}, {\"id\": 33588, \"name\": \"trinket\"}, {\"id\": 33589, \"name\": \"the red and white banner\"}, {\"id\": 33590, \"name\": \"the pink label\"}, {\"id\": 33591, \"name\": \"the black metal bollard\"}, {\"id\": 33592, \"name\": \"the dragon-like creature\"}, {\"id\": 33593, \"name\": \"grassy lawn\"}, {\"id\": 33594, \"name\": \"a blue visa sign\"}, {\"id\": 33595, \"name\": \"the red crane\"}, {\"id\": 33596, \"name\": \"child's legs\"}, {\"id\": 33597, \"name\": \"a colored icon\"}, {\"id\": 33598, \"name\": \"a mountainou terrain\"}, {\"id\": 33599, \"name\": \"silver metal container\"}, {\"id\": 33600, \"name\": \"a right turn sign\"}, {\"id\": 33601, \"name\": \"bottle of cleaning solution\"}, {\"id\": 33602, \"name\": \"the well-lit store\"}, {\"id\": 33603, \"name\": \"gold-colored film reel\"}, {\"id\": 33604, \"name\": \"a square opening\"}, {\"id\": 33605, \"name\": \"a white iphone\"}, {\"id\": 33606, \"name\": \"the gray strap\"}, {\"id\": 33607, \"name\": \"circular footrest\"}, {\"id\": 33608, \"name\": \"a fedex logo\"}, {\"id\": 33609, \"name\": \"the blue and yellow section\"}, {\"id\": 33610, \"name\": \"white clock\"}, {\"id\": 33611, \"name\": \"the rectangular box\"}, {\"id\": 33612, \"name\": \"the menacing grin\"}, {\"id\": 33613, \"name\": \"the white balloon\"}, {\"id\": 33614, \"name\": \"social media icon\"}, {\"id\": 33615, \"name\": \"large purple square\"}, {\"id\": 33616, \"name\": \"yellow cloud\"}, {\"id\": 33617, \"name\": \"a green base\"}, {\"id\": 33618, \"name\": \"the mixture of vegetable and meat\"}, {\"id\": 33619, \"name\": \"half-timbered facade\"}, {\"id\": 33620, \"name\": \"white foot\"}, {\"id\": 33621, \"name\": \"a car's wheel\"}, {\"id\": 33622, \"name\": \"the bright yellow helmet\"}, {\"id\": 33623, \"name\": \"couch or bed\"}, {\"id\": 33624, \"name\": \"race's logo\"}, {\"id\": 33625, \"name\": \"a black windshield wiper\"}, {\"id\": 33626, \"name\": \"the long brown beard\"}, {\"id\": 33627, \"name\": \"a large, dark-colored object\"}, {\"id\": 33628, \"name\": \"colorful display\"}, {\"id\": 33629, \"name\": \"leash\"}, {\"id\": 33630, \"name\": \"a rusty metal wheel\"}, {\"id\": 33631, \"name\": \"a gray ticket machine\"}, {\"id\": 33632, \"name\": \"air fryer\"}, {\"id\": 33633, \"name\": \"a man in the foreground\"}, {\"id\": 33634, \"name\": \"a stern of a large yacht\"}, {\"id\": 33635, \"name\": \"the narrow strip of brown dirt\"}, {\"id\": 33636, \"name\": \"a bottom left image\"}, {\"id\": 33637, \"name\": \"the stone structure\"}, {\"id\": 33638, \"name\": \"thin black cable\"}, {\"id\": 33639, \"name\": \"a white tape\"}, {\"id\": 33640, \"name\": \"small windshield\"}, {\"id\": 33641, \"name\": \"the yellow and green vest\"}, {\"id\": 33642, \"name\": \"the large, knobby tire\"}, {\"id\": 33643, \"name\": \"a yellow eye\"}, {\"id\": 33644, \"name\": \"the silver roof\"}, {\"id\": 33645, \"name\": \"title\"}, {\"id\": 33646, \"name\": \"a time elapsed\"}, {\"id\": 33647, \"name\": \"marking\"}, {\"id\": 33648, \"name\": \"the company's logo\"}, {\"id\": 33649, \"name\": \"dark-colored carpet\"}, {\"id\": 33650, \"name\": \"handlebar's grip\"}, {\"id\": 33651, \"name\": \"the yellow and black label\"}, {\"id\": 33652, \"name\": \"horse's mane\"}, {\"id\": 33653, \"name\": \"a small dark dot\"}, {\"id\": 33654, \"name\": \"cylindrical garden ornament\"}, {\"id\": 33655, \"name\": \"the left lapel\"}, {\"id\": 33656, \"name\": \"a riverside rent-a-car\"}, {\"id\": 33657, \"name\": \"a blue sweater\"}, {\"id\": 33658, \"name\": \"emirate logo\"}, {\"id\": 33659, \"name\": \"pet\"}, {\"id\": 33660, \"name\": \"curved white line\"}, {\"id\": 33661, \"name\": \"the brown foot\"}, {\"id\": 33662, \"name\": \"the faint gray squiggle\"}, {\"id\": 33663, \"name\": \"the colorful floral pattern\"}, {\"id\": 33664, \"name\": \"a vash garage\"}, {\"id\": 33665, \"name\": \"pole of the carousel horse\"}, {\"id\": 33666, \"name\": \"the box's side\"}, {\"id\": 33667, \"name\": \"a large white dog with black spot\"}, {\"id\": 33668, \"name\": \"showroom window\"}, {\"id\": 33669, \"name\": \"the large blue bin\"}, {\"id\": 33670, \"name\": \"a matching bow\"}, {\"id\": 33671, \"name\": \"a large metal cylinder\"}, {\"id\": 33672, \"name\": \"the rectangular sign\"}, {\"id\": 33673, \"name\": \"the colored top\"}, {\"id\": 33674, \"name\": \"the public or commercial structure\"}, {\"id\": 33675, \"name\": \"the horse name\"}, {\"id\": 33676, \"name\": \"the taxi's front grille\"}, {\"id\": 33677, \"name\": \"box of popcorn bag\"}, {\"id\": 33678, \"name\": \"a handlebar of the bike\"}, {\"id\": 33679, \"name\": \"orange gas cylinder\"}, {\"id\": 33680, \"name\": \"the commercial structure\"}, {\"id\": 33681, \"name\": \"large blue suitcase\"}, {\"id\": 33682, \"name\": \"densely wooded area\"}, {\"id\": 33683, \"name\": \"a beer can\"}, {\"id\": 33684, \"name\": \"the cyclist's name\"}, {\"id\": 33685, \"name\": \"the sturdy white tripod\"}, {\"id\": 33686, \"name\": \"a dirt oval track\"}, {\"id\": 33687, \"name\": \"a denim shirt\"}, {\"id\": 33688, \"name\": \"blue and white striped dress\"}, {\"id\": 33689, \"name\": \"a red and yellow stripe\"}, {\"id\": 33690, \"name\": \"the black and white checkered sign\"}, {\"id\": 33691, \"name\": \"motorcycle handlebar\"}, {\"id\": 33692, \"name\": \"the man in a dark blue shirt\"}, {\"id\": 33693, \"name\": \"light post\"}, {\"id\": 33694, \"name\": \"a white chef coat\"}, {\"id\": 33695, \"name\": \"blue and white helmet\"}, {\"id\": 33696, \"name\": \"a small, colorful beak\"}, {\"id\": 33697, \"name\": \"woman in a gray sweater and black pant\"}, {\"id\": 33698, \"name\": \"a player in black\"}, {\"id\": 33699, \"name\": \"cargo short\"}, {\"id\": 33700, \"name\": \"the person's neck\"}, {\"id\": 33701, \"name\": \"the multicolored blouse\"}, {\"id\": 33702, \"name\": \"a colorful handle\"}, {\"id\": 33703, \"name\": \"red and white top\"}, {\"id\": 33704, \"name\": \"the granite or marble\"}, {\"id\": 33705, \"name\": \"maroon baseball cap\"}, {\"id\": 33706, \"name\": \"an ethiopian flag\"}, {\"id\": 33707, \"name\": \"the yellow crown\"}, {\"id\": 33708, \"name\": \"a yellow center line\"}, {\"id\": 33709, \"name\": \"the teal bike\"}, {\"id\": 33710, \"name\": \"a blue suit\"}, {\"id\": 33711, \"name\": \"the yellow-colored object\"}, {\"id\": 33712, \"name\": \"a kilt\"}, {\"id\": 33713, \"name\": \"distinctive tattoo\"}, {\"id\": 33714, \"name\": \"the distinctive white and red bird-shaped hat\"}, {\"id\": 33715, \"name\": \"the white or blue helmet\"}, {\"id\": 33716, \"name\": \"sturdy blue metal shelving unit\"}, {\"id\": 33717, \"name\": \"blue claw\"}, {\"id\": 33718, \"name\": \"blue and yellow light\"}, {\"id\": 33719, \"name\": \"brow\"}, {\"id\": 33720, \"name\": \"commercial property\"}, {\"id\": 33721, \"name\": \"a glass front\"}, {\"id\": 33722, \"name\": \"a white and blue striped tank top\"}, {\"id\": 33723, \"name\": \"the red pot\"}, {\"id\": 33724, \"name\": \"a screen of the object\"}, {\"id\": 33725, \"name\": \"the tan seating\"}, {\"id\": 33726, \"name\": \"white tooth\"}, {\"id\": 33727, \"name\": \"an image of men's face\"}, {\"id\": 33728, \"name\": \"metal pipe\"}, {\"id\": 33729, \"name\": \"the grooming table\"}, {\"id\": 33730, \"name\": \"the rear end\"}, {\"id\": 33731, \"name\": \"crew of soldiers and sailor\"}, {\"id\": 33732, \"name\": \"a brick-paved area\"}, {\"id\": 33733, \"name\": \"large purple front\"}, {\"id\": 33734, \"name\": \"the silver leg\"}, {\"id\": 33735, \"name\": \"a starry galaxy\"}, {\"id\": 33736, \"name\": \"the chevrolet emblem\"}, {\"id\": 33737, \"name\": \"a 95 car\"}, {\"id\": 33738, \"name\": \"the piece of metal or debris\"}, {\"id\": 33739, \"name\": \"a powder\"}, {\"id\": 33740, \"name\": \"a light-colored stone structure\"}, {\"id\": 33741, \"name\": \"a brown interior\"}, {\"id\": 33742, \"name\": \"clear glass square top\"}, {\"id\": 33743, \"name\": \"green auto-rickshaw\"}, {\"id\": 33744, \"name\": \"a monument's base\"}, {\"id\": 33745, \"name\": \"the dark-colored wood\"}, {\"id\": 33746, \"name\": \"white, arched tunnel\"}, {\"id\": 33747, \"name\": \"the red dirt border\"}, {\"id\": 33748, \"name\": \"the visible scratch\"}, {\"id\": 33749, \"name\": \"a wooden tray\"}, {\"id\": 33750, \"name\": \"a neon green lettering\"}, {\"id\": 33751, \"name\": \"briefcase\"}, {\"id\": 33752, \"name\": \"large pole\"}, {\"id\": 33753, \"name\": \"the back door\"}, {\"id\": 33754, \"name\": \"large front tire\"}, {\"id\": 33755, \"name\": \"purple, black, and orange fletching\"}, {\"id\": 33756, \"name\": \"a pink writing\"}, {\"id\": 33757, \"name\": \"a berm\"}, {\"id\": 33758, \"name\": \"a large, modern structure\"}, {\"id\": 33759, \"name\": \"the balance board\"}, {\"id\": 33760, \"name\": \"distinctive hat\"}, {\"id\": 33761, \"name\": \"the red boxing boot\"}, {\"id\": 33762, \"name\": \"the large, silver mixing bowl\"}, {\"id\": 33763, \"name\": \"vine\"}, {\"id\": 33764, \"name\": \"the waist up\"}, {\"id\": 33765, \"name\": \"a slipper\"}, {\"id\": 33766, \"name\": \"the black bicycle helmet\"}, {\"id\": 33767, \"name\": \"the red backpack\"}, {\"id\": 33768, \"name\": \"bulldog\"}, {\"id\": 33769, \"name\": \"serving window\"}, {\"id\": 33770, \"name\": \"the bacterium\"}, {\"id\": 33771, \"name\": \"ornate architecture\"}, {\"id\": 33772, \"name\": \"black tray\"}, {\"id\": 33773, \"name\": \"the large open doorway\"}, {\"id\": 33774, \"name\": \"the small white table\"}, {\"id\": 33775, \"name\": \"a yellow team\"}, {\"id\": 33776, \"name\": \"a light blue archway\"}, {\"id\": 33777, \"name\": \"man in a yellow safety vest and black jacket\"}, {\"id\": 33778, \"name\": \"black burner\"}, {\"id\": 33779, \"name\": \"a data display\"}, {\"id\": 33780, \"name\": \"pink feathered headdress\"}, {\"id\": 33781, \"name\": \"a large green dinosaur-like creature\"}, {\"id\": 33782, \"name\": \"a purple and yellow car\"}, {\"id\": 33783, \"name\": \"the passenger's face\"}, {\"id\": 33784, \"name\": \"a red fencing\"}, {\"id\": 33785, \"name\": \"a white tail\"}, {\"id\": 33786, \"name\": \"a brightly colored pant\"}, {\"id\": 33787, \"name\": \"sign reading \\\"west\\\"\"}, {\"id\": 33788, \"name\": \"a large, toothy grin\"}, {\"id\": 33789, \"name\": \"a rosy cheek\"}, {\"id\": 33790, \"name\": \"the spike\"}, {\"id\": 33791, \"name\": \"the large, angry-looking eye\"}, {\"id\": 33792, \"name\": \"skateboarder's lower body\"}, {\"id\": 33793, \"name\": \"a cartoon-like character\"}, {\"id\": 33794, \"name\": \"makeshift shelter\"}, {\"id\": 33795, \"name\": \"metal-framed table\"}, {\"id\": 33796, \"name\": \"long metal rod\"}, {\"id\": 33797, \"name\": \"modern art installation\"}, {\"id\": 33798, \"name\": \"brick exterior\"}, {\"id\": 33799, \"name\": \"a monster energy logo\"}, {\"id\": 33800, \"name\": \"toothy grin\"}, {\"id\": 33801, \"name\": \"a residential neighborhood\"}, {\"id\": 33802, \"name\": \"a train's cargo car\"}, {\"id\": 33803, \"name\": \"the commercial or industrial structure\"}, {\"id\": 33804, \"name\": \"a ride's operator\"}, {\"id\": 33805, \"name\": \"a white and blue checkered pattern\"}, {\"id\": 33806, \"name\": \"the distinctive red-lit side panel\"}, {\"id\": 33807, \"name\": \"a purple skein of yarn\"}, {\"id\": 33808, \"name\": \"panel\"}, {\"id\": 33809, \"name\": \"philadelphia 76ers' logo\"}, {\"id\": 33810, \"name\": \"plastic bin\"}, {\"id\": 33811, \"name\": \"a long white metal arm\"}, {\"id\": 33812, \"name\": \"stack\"}, {\"id\": 33813, \"name\": \"dark brown tail\"}, {\"id\": 33814, \"name\": \"circular component\"}, {\"id\": 33815, \"name\": \"wooden storage box\"}, {\"id\": 33816, \"name\": \"an identical white and yellow jersey\"}, {\"id\": 33817, \"name\": \"the turquoise face\"}, {\"id\": 33818, \"name\": \"dhl logo\"}, {\"id\": 33819, \"name\": \"a glass or plastic dome\"}, {\"id\": 33820, \"name\": \"bustling sidewalk\"}, {\"id\": 33821, \"name\": \"the tinted window\"}, {\"id\": 33822, \"name\": \"a white and red patch\"}, {\"id\": 33823, \"name\": \"large boulder\"}, {\"id\": 33824, \"name\": \"overlapping circle\"}, {\"id\": 33825, \"name\": \"the colored horse\"}, {\"id\": 33826, \"name\": \"a golden decoration\"}, {\"id\": 33827, \"name\": \"a wooden halfpipe\"}, {\"id\": 33828, \"name\": \"the baked good\"}, {\"id\": 33829, \"name\": \"a red and white striped awning\"}, {\"id\": 33830, \"name\": \"metal roll-up door\"}, {\"id\": 33831, \"name\": \"the large piece of machinery\"}, {\"id\": 33832, \"name\": \"the bull's tail rope\"}, {\"id\": 33833, \"name\": \"babytrend logo\"}, {\"id\": 33834, \"name\": \"the creature's mouth\"}, {\"id\": 33835, \"name\": \"chicken nugget\"}, {\"id\": 33836, \"name\": \"a red sleeve\"}, {\"id\": 33837, \"name\": \"a aqua lung logo\"}, {\"id\": 33838, \"name\": \"a large digital sign\"}, {\"id\": 33839, \"name\": \"grey suit jacket\"}, {\"id\": 33840, \"name\": \"the surrounding grass\"}, {\"id\": 33841, \"name\": \"a blue play structure\"}, {\"id\": 33842, \"name\": \"a white siding\"}, {\"id\": 33843, \"name\": \"person's hand and arm\"}, {\"id\": 33844, \"name\": \"large gray pipe\"}, {\"id\": 33845, \"name\": \"the snare drum\"}, {\"id\": 33846, \"name\": \"a large, curved overpass\"}, {\"id\": 33847, \"name\": \"a blue and black uniform\"}, {\"id\": 33848, \"name\": \"the budweiser logo\"}, {\"id\": 33849, \"name\": \"a white support cable\"}, {\"id\": 33850, \"name\": \"snowflake decoration\"}, {\"id\": 33851, \"name\": \"a large leaf\"}, {\"id\": 33852, \"name\": \"plate of bun\"}, {\"id\": 33853, \"name\": \"crossfit game logo\"}, {\"id\": 33854, \"name\": \"the front tire\"}, {\"id\": 33855, \"name\": \"the grandstand\"}, {\"id\": 33856, \"name\": \"dog's face\"}, {\"id\": 33857, \"name\": \"the thin layer of snow and ice\"}, {\"id\": 33858, \"name\": \"the direction of traffic\"}, {\"id\": 33859, \"name\": \"puppet's head\"}, {\"id\": 33860, \"name\": \"the large parking\"}, {\"id\": 33861, \"name\": \"a dark mark\"}, {\"id\": 33862, \"name\": \"the pink goggle\"}, {\"id\": 33863, \"name\": \"a pink boat\"}, {\"id\": 33864, \"name\": \"the black and white checkered decal\"}, {\"id\": 33865, \"name\": \"single blue eye\"}, {\"id\": 33866, \"name\": \"large red flag\"}, {\"id\": 33867, \"name\": \"a large glass structure\"}, {\"id\": 33868, \"name\": \"a warm clothing\"}, {\"id\": 33869, \"name\": \"a denim vest\"}, {\"id\": 33870, \"name\": \"the llama design\"}, {\"id\": 33871, \"name\": \"a hockey player\"}, {\"id\": 33872, \"name\": \"a brown dirt\"}, {\"id\": 33873, \"name\": \"a purple seat\"}, {\"id\": 33874, \"name\": \"the string\"}, {\"id\": 33875, \"name\": \"cartoon-like face\"}, {\"id\": 33876, \"name\": \"the rain-soaked window\"}, {\"id\": 33877, \"name\": \"grey metal framework\"}, {\"id\": 33878, \"name\": \"the autumnal decoration\"}, {\"id\": 33879, \"name\": \"the single go-kart driver\"}, {\"id\": 33880, \"name\": \"weather symbol\"}, {\"id\": 33881, \"name\": \"blue and white design\"}, {\"id\": 33882, \"name\": \"a black cab\"}, {\"id\": 33883, \"name\": \"the plastic cup\"}, {\"id\": 33884, \"name\": \"a 1950s-style dress\"}, {\"id\": 33885, \"name\": \"stacks of box\"}, {\"id\": 33886, \"name\": \"the comic book image\"}, {\"id\": 33887, \"name\": \"the bright orange reflective clothing\"}, {\"id\": 33888, \"name\": \"a white lamp\"}, {\"id\": 33889, \"name\": \"the red band\"}, {\"id\": 33890, \"name\": \"the large, circular planter\"}, {\"id\": 33891, \"name\": \"the dark swirl\"}, {\"id\": 33892, \"name\": \"window of a store\"}, {\"id\": 33893, \"name\": \"the black lobster\"}, {\"id\": 33894, \"name\": \"armor\"}, {\"id\": 33895, \"name\": \"the white symbol or logo\"}, {\"id\": 33896, \"name\": \"the wine bottle\"}, {\"id\": 33897, \"name\": \"a rear\"}, {\"id\": 33898, \"name\": \"the black toyota camry\"}, {\"id\": 33899, \"name\": \"a large, three-story gray house\"}, {\"id\": 33900, \"name\": \"a green pizza box\"}, {\"id\": 33901, \"name\": \"gray backpack\"}, {\"id\": 33902, \"name\": \"the claw\"}, {\"id\": 33903, \"name\": \"a mirrored interior\"}, {\"id\": 33904, \"name\": \"blue star symbol\"}, {\"id\": 33905, \"name\": \"a cartoonish orange face\"}, {\"id\": 33906, \"name\": \"the van's open back door\"}, {\"id\": 33907, \"name\": \"the checkered flag pattern\"}, {\"id\": 33908, \"name\": \"black tile\"}, {\"id\": 33909, \"name\": \"the social media symbol\"}, {\"id\": 33910, \"name\": \"the mirror of a motorcycle\"}, {\"id\": 33911, \"name\": \"the tan blanket\"}, {\"id\": 33912, \"name\": \"a green fish\"}, {\"id\": 33913, \"name\": \"prominent orange ladder\"}, {\"id\": 33914, \"name\": \"a dark trousers\"}, {\"id\": 33915, \"name\": \"the small round fuse\"}, {\"id\": 33916, \"name\": \"a prominent orange traffic cone\"}, {\"id\": 33917, \"name\": \"tvg logo\"}, {\"id\": 33918, \"name\": \"a grassy landscape\"}, {\"id\": 33919, \"name\": \"the santa hat\"}, {\"id\": 33920, \"name\": \"the wooden pedestal\"}, {\"id\": 33921, \"name\": \"a black window\"}, {\"id\": 33922, \"name\": \"large steering wheel\"}, {\"id\": 33923, \"name\": \"the dark blue skirt\"}, {\"id\": 33924, \"name\": \"a quiver of arrow\"}, {\"id\": 33925, \"name\": \"person on a skateboard\"}, {\"id\": 33926, \"name\": \"woman in a red shirt\"}, {\"id\": 33927, \"name\": \"the surrounding grassy area\"}, {\"id\": 33928, \"name\": \"long, ornate staff\"}, {\"id\": 33929, \"name\": \"a question mark symbol\"}, {\"id\": 33930, \"name\": \"yellow and white sticker\"}, {\"id\": 33931, \"name\": \"the rack of dish\"}, {\"id\": 33932, \"name\": \"a regal-looking red and black uniform\"}, {\"id\": 33933, \"name\": \"a mountainou backdrop\"}, {\"id\": 33934, \"name\": \"a man with a spear\"}, {\"id\": 33935, \"name\": \"rolling green hill\"}, {\"id\": 33936, \"name\": \"a red helicopter\"}, {\"id\": 33937, \"name\": \"large windshield wiper\"}, {\"id\": 33938, \"name\": \"a black grip\"}, {\"id\": 33939, \"name\": \"character's face\"}, {\"id\": 33940, \"name\": \"red tub\"}, {\"id\": 33941, \"name\": \"the person's hand holding a cell phone\"}, {\"id\": 33942, \"name\": \"an entrance of the store\"}, {\"id\": 33943, \"name\": \"the blue fin\"}, {\"id\": 33944, \"name\": \"a knight's face\"}, {\"id\": 33945, \"name\": \"the rolling stone logo\"}, {\"id\": 33946, \"name\": \"the large front grill\"}, {\"id\": 33947, \"name\": \"metal utensil\"}, {\"id\": 33948, \"name\": \"a small audience\"}, {\"id\": 33949, \"name\": \"circular top\"}, {\"id\": 33950, \"name\": \"a white globe\"}, {\"id\": 33951, \"name\": \"a driftwood\"}, {\"id\": 33952, \"name\": \"a brown and red saddle\"}, {\"id\": 33953, \"name\": \"a man in an orange shirt\"}, {\"id\": 33954, \"name\": \"a car's license plate\"}, {\"id\": 33955, \"name\": \"the yellow kayak\"}, {\"id\": 33956, \"name\": \"red, triangular piece\"}, {\"id\": 33957, \"name\": \"blue and green helmet\"}, {\"id\": 33958, \"name\": \"an upper deck\"}, {\"id\": 33959, \"name\": \"the black and white hoove\"}, {\"id\": 33960, \"name\": \"the red and orange light\"}, {\"id\": 33961, \"name\": \"a yellow and white police car\"}, {\"id\": 33962, \"name\": \"a roller coaster\"}, {\"id\": 33963, \"name\": \"the table with a black tablecloth\"}, {\"id\": 33964, \"name\": \"yellow and red logo\"}, {\"id\": 33965, \"name\": \"a commercial structure\"}, {\"id\": 33966, \"name\": \"the splatter\"}, {\"id\": 33967, \"name\": \"the poc tent\"}, {\"id\": 33968, \"name\": \"dark brown surface\"}, {\"id\": 33969, \"name\": \"a circular indentation\"}, {\"id\": 33970, \"name\": \"a robot's arm\"}, {\"id\": 33971, \"name\": \"a yellow and black striped line\"}, {\"id\": 33972, \"name\": \"a numbered racing silk\"}, {\"id\": 33973, \"name\": \"the vendor's table\"}, {\"id\": 33974, \"name\": \"a statue of a man\"}, {\"id\": 33975, \"name\": \"the yellow oval\"}, {\"id\": 33976, \"name\": \"the large pink and white ear\"}, {\"id\": 33977, \"name\": \"a vintage airplane\"}, {\"id\": 33978, \"name\": \"the model bridge\"}, {\"id\": 33979, \"name\": \"the white and black helmet\"}, {\"id\": 33980, \"name\": \"hexagonal control panel\"}, {\"id\": 33981, \"name\": \"the chinese character\"}, {\"id\": 33982, \"name\": \"a creepy doll\"}, {\"id\": 33983, \"name\": \"the small yellow license plate\"}, {\"id\": 33984, \"name\": \"a blue and orange car\"}, {\"id\": 33985, \"name\": \"prominent white banner\"}, {\"id\": 33986, \"name\": \"clear plastic package or bag\"}, {\"id\": 33987, \"name\": \"a green stand\"}, {\"id\": 33988, \"name\": \"formal outfit\"}, {\"id\": 33989, \"name\": \"a lower jawbone\"}, {\"id\": 33990, \"name\": \"the sec network logo\"}, {\"id\": 33991, \"name\": \"the villager\"}, {\"id\": 33992, \"name\": \"tyler polamalu\"}, {\"id\": 33993, \"name\": \"the star\"}, {\"id\": 33994, \"name\": \"plunging neckline\"}, {\"id\": 33995, \"name\": \"the black panda ear\"}, {\"id\": 33996, \"name\": \"silver bar\"}, {\"id\": 33997, \"name\": \"the green saddle blanket\"}, {\"id\": 33998, \"name\": \"a small silver object\"}, {\"id\": 33999, \"name\": \"the black bikini bottom\"}, {\"id\": 34000, \"name\": \"cash\"}, {\"id\": 34001, \"name\": \"the horse's neck\"}, {\"id\": 34002, \"name\": \"the open road ahead\"}, {\"id\": 34003, \"name\": \"the distinctive red awning\"}, {\"id\": 34004, \"name\": \"a white, red, and yellow marking\"}, {\"id\": 34005, \"name\": \"the canopy tent\"}, {\"id\": 34006, \"name\": \"the shutter-style door\"}, {\"id\": 34007, \"name\": \"a snow on the ground\"}, {\"id\": 34008, \"name\": \"a dark body\"}, {\"id\": 34009, \"name\": \"the character from the toy story franchise\"}, {\"id\": 34010, \"name\": \"a violin\"}, {\"id\": 34011, \"name\": \"red ramp\"}, {\"id\": 34012, \"name\": \"red diamond\"}, {\"id\": 34013, \"name\": \"a car or van\"}, {\"id\": 34014, \"name\": \"crescent moon\"}, {\"id\": 34015, \"name\": \"a colorful puppet\"}, {\"id\": 34016, \"name\": \"the purple lighting\"}, {\"id\": 34017, \"name\": \"the partially visible woman\"}, {\"id\": 34018, \"name\": \"the dark tint\"}, {\"id\": 34019, \"name\": \"the reflective clothing\"}, {\"id\": 34020, \"name\": \"gray cylinder\"}, {\"id\": 34021, \"name\": \"the pencil holder\"}, {\"id\": 34022, \"name\": \"a health level\"}, {\"id\": 34023, \"name\": \"the black fender\"}, {\"id\": 34024, \"name\": \"a yellow and black bike\"}, {\"id\": 34025, \"name\": \"the rugged trail\"}, {\"id\": 34026, \"name\": \"gray lego plate\"}, {\"id\": 34027, \"name\": \"rene simo\"}, {\"id\": 34028, \"name\": \"a porthole\"}, {\"id\": 34029, \"name\": \"wooden interior\"}, {\"id\": 34030, \"name\": \"large flower pot\"}, {\"id\": 34031, \"name\": \"closed shutter door\"}, {\"id\": 34032, \"name\": \"traditional skirt\"}, {\"id\": 34033, \"name\": \"colorful round ball\"}, {\"id\": 34034, \"name\": \"a white motorcycle\"}, {\"id\": 34035, \"name\": \"a large white multi-story structure\"}, {\"id\": 34036, \"name\": \"an airline's name\"}, {\"id\": 34037, \"name\": \"the dark wooden shelf\"}, {\"id\": 34038, \"name\": \"a wide grin\"}, {\"id\": 34039, \"name\": \"a man in a white shirt and white short\"}, {\"id\": 34040, \"name\": \"silver bracelet\"}, {\"id\": 34041, \"name\": \"the empanadas\"}, {\"id\": 34042, \"name\": \"the pagoda-style design\"}, {\"id\": 34043, \"name\": \"home\"}, {\"id\": 34044, \"name\": \"the yellow and orange marking\"}, {\"id\": 34045, \"name\": \"the product's name\"}, {\"id\": 34046, \"name\": \"purple grip\"}, {\"id\": 34047, \"name\": \"display of mickey mouse plush toy\"}, {\"id\": 34048, \"name\": \"the hiking boot\"}, {\"id\": 34049, \"name\": \"a central pillar\"}, {\"id\": 34050, \"name\": \"colorful section\"}, {\"id\": 34051, \"name\": \"a striking red and yellow logo\"}, {\"id\": 34052, \"name\": \"a colorful cat\"}, {\"id\": 34053, \"name\": \"the stilt\"}, {\"id\": 34054, \"name\": \"a large orange flame\"}, {\"id\": 34055, \"name\": \"the brown trash can\"}, {\"id\": 34056, \"name\": \"the coaster\"}, {\"id\": 34057, \"name\": \"yellow lego piece\"}, {\"id\": 34058, \"name\": \"the prominent red and white striped barrier\"}, {\"id\": 34059, \"name\": \"the black tractor\"}, {\"id\": 34060, \"name\": \"vibrant umbrella\"}, {\"id\": 34061, \"name\": \"the purple rollerblade\"}, {\"id\": 34062, \"name\": \"the packed stand\"}, {\"id\": 34063, \"name\": \"black arched window\"}, {\"id\": 34064, \"name\": \"a small white bag\"}, {\"id\": 34065, \"name\": \"the rainbow-colored worm\"}, {\"id\": 34066, \"name\": \"man with glass\"}, {\"id\": 34067, \"name\": \"the sleek glass structure\"}, {\"id\": 34068, \"name\": \"a yellow and blue platform\"}, {\"id\": 34069, \"name\": \"green and black jersey\"}, {\"id\": 34070, \"name\": \"utility belt\"}, {\"id\": 34071, \"name\": \"the arched and spiral design\"}, {\"id\": 34072, \"name\": \"a beige couch\"}, {\"id\": 34073, \"name\": \"beige border\"}, {\"id\": 34074, \"name\": \"a white goalpost\"}, {\"id\": 34075, \"name\": \"the black and yellow shoe\"}, {\"id\": 34076, \"name\": \"an allianz logo\"}, {\"id\": 34077, \"name\": \"player's profile\"}, {\"id\": 34078, \"name\": \"the lit torch\"}, {\"id\": 34079, \"name\": \"yellow and blue seat\"}, {\"id\": 34080, \"name\": \"the vertical column\"}, {\"id\": 34081, \"name\": \"the ancient egyptian attire\"}, {\"id\": 34082, \"name\": \"child's tutu\"}, {\"id\": 34083, \"name\": \"character's attire\"}, {\"id\": 34084, \"name\": \"instructor\"}, {\"id\": 34085, \"name\": \"small microphone\"}, {\"id\": 34086, \"name\": \"table tenni table\"}, {\"id\": 34087, \"name\": \"a california wildfire\"}, {\"id\": 34088, \"name\": \"a woman in a red jacket\"}, {\"id\": 34089, \"name\": \"the small tan object\"}, {\"id\": 34090, \"name\": \"the white and red truck\"}, {\"id\": 34091, \"name\": \"a jack's restaurant sign\"}, {\"id\": 34092, \"name\": \"pack of cigarette\"}, {\"id\": 34093, \"name\": \"the rider's legs\"}, {\"id\": 34094, \"name\": \"the black ramp\"}, {\"id\": 34095, \"name\": \"the white tire\"}, {\"id\": 34096, \"name\": \"the black, red, and white sticker\"}, {\"id\": 34097, \"name\": \"a red reflector\"}, {\"id\": 34098, \"name\": \"purple light\"}, {\"id\": 34099, \"name\": \"bag or container\"}, {\"id\": 34100, \"name\": \"potato\"}, {\"id\": 34101, \"name\": \"a stream\"}, {\"id\": 34102, \"name\": \"a circular area\"}, {\"id\": 34103, \"name\": \"distinctive exhaust pipe\"}, {\"id\": 34104, \"name\": \"the panda\"}, {\"id\": 34105, \"name\": \"a black and red helmet\"}, {\"id\": 34106, \"name\": \"a fallen tree trunk\"}, {\"id\": 34107, \"name\": \"a neon yellow vest\"}, {\"id\": 34108, \"name\": \"the bright white sign or billboard\"}, {\"id\": 34109, \"name\": \"a red and black handle\"}, {\"id\": 34110, \"name\": \"a large flame\"}, {\"id\": 34111, \"name\": \"the man in a white shirt and gray pant\"}, {\"id\": 34112, \"name\": \"a large brown object\"}, {\"id\": 34113, \"name\": \"the green saddle\"}, {\"id\": 34114, \"name\": \"pile\"}, {\"id\": 34115, \"name\": \"the small green circle\"}, {\"id\": 34116, \"name\": \"a spiral shape\"}, {\"id\": 34117, \"name\": \"the bank\"}, {\"id\": 34118, \"name\": \"yellow object resembling a rubber band\"}, {\"id\": 34119, \"name\": \"black manhole cover\"}, {\"id\": 34120, \"name\": \"red warning sign\"}, {\"id\": 34121, \"name\": \"the red and green logo\"}, {\"id\": 34122, \"name\": \"mane\"}, {\"id\": 34123, \"name\": \"a green glowing light\"}, {\"id\": 34124, \"name\": \"the dark brown wooden beam\"}, {\"id\": 34125, \"name\": \"large black bumper\"}, {\"id\": 34126, \"name\": \"the vertical wooden plank\"}, {\"id\": 34127, \"name\": \"bright green color\"}, {\"id\": 34128, \"name\": \"the kart's front\"}, {\"id\": 34129, \"name\": \"the blue backrest\"}, {\"id\": 34130, \"name\": \"the floral headband\"}, {\"id\": 34131, \"name\": \"a time remaining\"}, {\"id\": 34132, \"name\": \"a ripped blue jean\"}, {\"id\": 34133, \"name\": \"a white mask\"}, {\"id\": 34134, \"name\": \"a tan cloth\"}, {\"id\": 34135, \"name\": \"the prominent column\"}, {\"id\": 34136, \"name\": \"the left turn sign\"}, {\"id\": 34137, \"name\": \"cluttered wooden surface\"}, {\"id\": 34138, \"name\": \"a chocolate-covered pretzel stick\"}, {\"id\": 34139, \"name\": \"a vibrant neon light\"}, {\"id\": 34140, \"name\": \"festival-goer\"}, {\"id\": 34141, \"name\": \"a residential house\"}, {\"id\": 34142, \"name\": \"the coca-cola\"}, {\"id\": 34143, \"name\": \"a puppet character\"}, {\"id\": 34144, \"name\": \"creature\"}, {\"id\": 34145, \"name\": \"the moose head\"}, {\"id\": 34146, \"name\": \"turbulent water\"}, {\"id\": 34147, \"name\": \"black and white book or magazine\"}, {\"id\": 34148, \"name\": \"the long, thin, metal tube\"}, {\"id\": 34149, \"name\": \"white graphic\"}, {\"id\": 34150, \"name\": \"a large soap bubble\"}, {\"id\": 34151, \"name\": \"a storm\"}, {\"id\": 34152, \"name\": \"a garland of rainbow-colored streamer\"}, {\"id\": 34153, \"name\": \"a dwarf\"}, {\"id\": 34154, \"name\": \"a black x symbol\"}, {\"id\": 34155, \"name\": \"sleeveles shirt\"}, {\"id\": 34156, \"name\": \"door frame\"}, {\"id\": 34157, \"name\": \"the floating book\"}, {\"id\": 34158, \"name\": \"green van\"}, {\"id\": 34159, \"name\": \"a purple rectangle\"}, {\"id\": 34160, \"name\": \"woman in a pink shirt\"}, {\"id\": 34161, \"name\": \"white bentley\"}, {\"id\": 34162, \"name\": \"a grotesque, monstrou creature\"}, {\"id\": 34163, \"name\": \"center circle\"}, {\"id\": 34164, \"name\": \"a cone-shaped object\"}, {\"id\": 34165, \"name\": \"a vendor's stand\"}, {\"id\": 34166, \"name\": \"the tinted visor\"}, {\"id\": 34167, \"name\": \"bright yellow helmet\"}, {\"id\": 34168, \"name\": \"blue oval\"}, {\"id\": 34169, \"name\": \"the green carpet\"}, {\"id\": 34170, \"name\": \"the truck's logo\"}, {\"id\": 34171, \"name\": \"the white snow\"}, {\"id\": 34172, \"name\": \"a roller coaster's structure\"}, {\"id\": 34173, \"name\": \"the lift here\"}, {\"id\": 34174, \"name\": \"the passenger car\"}, {\"id\": 34175, \"name\": \"the lego person\"}, {\"id\": 34176, \"name\": \"the alphabet block\"}, {\"id\": 34177, \"name\": \"yellow or red collar\"}, {\"id\": 34178, \"name\": \"black candleholder\"}, {\"id\": 34179, \"name\": \"the large white floatie\"}, {\"id\": 34180, \"name\": \"a wood paneling\"}, {\"id\": 34181, \"name\": \"a goodyear brand tire\"}, {\"id\": 34182, \"name\": \"a small orange and green object\"}, {\"id\": 34183, \"name\": \"the blue short-sleeved button-down shirt\"}, {\"id\": 34184, \"name\": \"purple square\"}, {\"id\": 34185, \"name\": \"boom\"}, {\"id\": 34186, \"name\": \"large white sailboat\"}, {\"id\": 34187, \"name\": \"substantial quantity of crab\"}, {\"id\": 34188, \"name\": \"a large screen or projection\"}, {\"id\": 34189, \"name\": \"tassel tie\"}, {\"id\": 34190, \"name\": \"an elegant stone architecture\"}, {\"id\": 34191, \"name\": \"the black and white striped line\"}, {\"id\": 34192, \"name\": \"the wooden object\"}, {\"id\": 34193, \"name\": \"rope or cable\"}, {\"id\": 34194, \"name\": \"a people's winter attire\"}, {\"id\": 34195, \"name\": \"a black and white cleat\"}, {\"id\": 34196, \"name\": \"a grey stone\"}, {\"id\": 34197, \"name\": \"a champion icon\"}, {\"id\": 34198, \"name\": \"blue and orange vehicle\"}, {\"id\": 34199, \"name\": \"truck's roof\"}, {\"id\": 34200, \"name\": \"automated check-in kiosk\"}, {\"id\": 34201, \"name\": \"green hillside\"}, {\"id\": 34202, \"name\": \"large purple orb\"}, {\"id\": 34203, \"name\": \"the red team\"}, {\"id\": 34204, \"name\": \"the man's leg\"}, {\"id\": 34205, \"name\": \"the audi's logo\"}, {\"id\": 34206, \"name\": \"a neon yellow cleat\"}, {\"id\": 34207, \"name\": \"tall church steeple\"}, {\"id\": 34208, \"name\": \"the bug bunny plush toy\"}, {\"id\": 34209, \"name\": \"the single boat\"}, {\"id\": 34210, \"name\": \"the brick house\"}, {\"id\": 34211, \"name\": \"the face covering\"}, {\"id\": 34212, \"name\": \"small black pupil\"}, {\"id\": 34213, \"name\": \"green space\"}, {\"id\": 34214, \"name\": \"the steep hillside\"}, {\"id\": 34215, \"name\": \"white and red striped shirt\"}, {\"id\": 34216, \"name\": \"a green and white lighted frame\"}, {\"id\": 34217, \"name\": \"the large purple device\"}, {\"id\": 34218, \"name\": \"the great smoky mountain national park\"}, {\"id\": 34219, \"name\": \"a shopping mall\"}, {\"id\": 34220, \"name\": \"a red buoy\"}, {\"id\": 34221, \"name\": \"bold red and white lettering\"}, {\"id\": 34222, \"name\": \"a delta logo\"}, {\"id\": 34223, \"name\": \"darker patch\"}, {\"id\": 34224, \"name\": \"a yellow 02 sign\"}, {\"id\": 34225, \"name\": \"the wooden base\"}, {\"id\": 34226, \"name\": \"green and white fabric bundle\"}, {\"id\": 34227, \"name\": \"horizontal stack\"}, {\"id\": 34228, \"name\": \"the truck's license plate number\"}, {\"id\": 34229, \"name\": \"a circular burner\"}, {\"id\": 34230, \"name\": \"black beam\"}, {\"id\": 34231, \"name\": \"the blue and red uniform\"}, {\"id\": 34232, \"name\": \"the prominent pumpkin\"}, {\"id\": 34233, \"name\": \"a goggles\"}, {\"id\": 34234, \"name\": \"a packed stand\"}, {\"id\": 34235, \"name\": \"a neon green lace\"}, {\"id\": 34236, \"name\": \"a light blue top\"}, {\"id\": 34237, \"name\": \"a reflective yellow safety vest\"}, {\"id\": 34238, \"name\": \"the rv\"}, {\"id\": 34239, \"name\": \"white and yellow tube\"}, {\"id\": 34240, \"name\": \"the red and blue jacket\"}, {\"id\": 34241, \"name\": \"small kiosk\"}, {\"id\": 34242, \"name\": \"a border of stone block\"}, {\"id\": 34243, \"name\": \"the tall hat\"}, {\"id\": 34244, \"name\": \"blue suv\"}, {\"id\": 34245, \"name\": \"the cartoon sperm cell\"}, {\"id\": 34246, \"name\": \"the red necklace\"}, {\"id\": 34247, \"name\": \"the low fence\"}, {\"id\": 34248, \"name\": \"layered arrangement\"}, {\"id\": 34249, \"name\": \"a red and yellow light\"}, {\"id\": 34250, \"name\": \"a large bin\"}, {\"id\": 34251, \"name\": \"an antennae\"}, {\"id\": 34252, \"name\": \"the gold rim\"}, {\"id\": 34253, \"name\": \"the jean short\"}, {\"id\": 34254, \"name\": \"a distinctive front grille\"}, {\"id\": 34255, \"name\": \"the black-and-white curb\"}, {\"id\": 34256, \"name\": \"broken window\"}, {\"id\": 34257, \"name\": \"transparent plastic window\"}, {\"id\": 34258, \"name\": \"the small light fixture\"}, {\"id\": 34259, \"name\": \"the dark mane\"}, {\"id\": 34260, \"name\": \"the race course\"}, {\"id\": 34261, \"name\": \"a purple strap\"}, {\"id\": 34262, \"name\": \"a colorful wire\"}, {\"id\": 34263, \"name\": \"a large cloud\"}, {\"id\": 34264, \"name\": \"the red-and-white striped barrier\"}, {\"id\": 34265, \"name\": \"a speed of 34.4 mph\"}, {\"id\": 34266, \"name\": \"rectangular and square shape\"}, {\"id\": 34267, \"name\": \"a kentucky sign\"}, {\"id\": 34268, \"name\": \"herd\"}, {\"id\": 34269, \"name\": \"a silver payphone\"}, {\"id\": 34270, \"name\": \"a blue-shirted man\"}, {\"id\": 34271, \"name\": \"the small, robot-like toy\"}, {\"id\": 34272, \"name\": \"soccer uniform\"}, {\"id\": 34273, \"name\": \"small tv\"}, {\"id\": 34274, \"name\": \"a mannequin display\"}, {\"id\": 34275, \"name\": \"an orange safety barrier\"}, {\"id\": 34276, \"name\": \"the yellow exercise ball\"}, {\"id\": 34277, \"name\": \"carousel's central pole\"}, {\"id\": 34278, \"name\": \"the grey overall\"}, {\"id\": 34279, \"name\": \"the back\"}, {\"id\": 34280, \"name\": \"the large yellow eye\"}, {\"id\": 34281, \"name\": \"a bottle of lotion\"}, {\"id\": 34282, \"name\": \"white toy\"}, {\"id\": 34283, \"name\": \"a large grey stone\"}, {\"id\": 34284, \"name\": \"green highway sign\"}, {\"id\": 34285, \"name\": \"the white button\"}, {\"id\": 34286, \"name\": \"a circle symbol\"}, {\"id\": 34287, \"name\": \"a woman's left hand\"}, {\"id\": 34288, \"name\": \"the white glow\"}, {\"id\": 34289, \"name\": \"white paper item\"}, {\"id\": 34290, \"name\": \"the residential unit\"}, {\"id\": 34291, \"name\": \"black eye\"}, {\"id\": 34292, \"name\": \"a maroon jacket\"}, {\"id\": 34293, \"name\": \"a yellow warning sign\"}, {\"id\": 34294, \"name\": \"the monster's left hand\"}, {\"id\": 34295, \"name\": \"long tusk\"}, {\"id\": 34296, \"name\": \"a calf's ear\"}, {\"id\": 34297, \"name\": \"a large black spoiler\"}, {\"id\": 34298, \"name\": \"an individual's right hand\"}, {\"id\": 34299, \"name\": \"dark tail\"}, {\"id\": 34300, \"name\": \"white fencing\"}, {\"id\": 34301, \"name\": \"a white popsicle\"}, {\"id\": 34302, \"name\": \"a logo featuring a cartoon fox in a watch cap and goggle\"}, {\"id\": 34303, \"name\": \"the gray roll-up door\"}, {\"id\": 34304, \"name\": \"the black and white clothing\"}, {\"id\": 34305, \"name\": \"a presence of fan\"}, {\"id\": 34306, \"name\": \"the display of book\"}, {\"id\": 34307, \"name\": \"a light blue lining\"}, {\"id\": 34308, \"name\": \"a logo of the united nation foundation\"}, {\"id\": 34309, \"name\": \"a yellow pant\"}, {\"id\": 34310, \"name\": \"the red tip\"}, {\"id\": 34311, \"name\": \"the white team\"}, {\"id\": 34312, \"name\": \"horse's pole\"}, {\"id\": 34313, \"name\": \"the brown strap\"}, {\"id\": 34314, \"name\": \"yellow square\"}, {\"id\": 34315, \"name\": \"a large tarp\"}, {\"id\": 34316, \"name\": \"a green monster\"}, {\"id\": 34317, \"name\": \"the light blue frame\"}, {\"id\": 34318, \"name\": \"a black leather glove\"}, {\"id\": 34319, \"name\": \"blue boot\"}, {\"id\": 34320, \"name\": \"a bright orange safety vest\"}, {\"id\": 34321, \"name\": \"blue and white tent\"}, {\"id\": 34322, \"name\": \"the imposing, fluted column\"}, {\"id\": 34323, \"name\": \"the black mirror\"}, {\"id\": 34324, \"name\": \"the yellow, horizontal, t-shaped piece\"}, {\"id\": 34325, \"name\": \"the small blue box\"}, {\"id\": 34326, \"name\": \"the red heart-shaped design\"}, {\"id\": 34327, \"name\": \"plastic or vinyl\"}, {\"id\": 34328, \"name\": \"striking red, yellow, and green striped design\"}, {\"id\": 34329, \"name\": \"purple body\"}, {\"id\": 34330, \"name\": \"a yellow and black marking\"}, {\"id\": 34331, \"name\": \"the stylized gold design\"}, {\"id\": 34332, \"name\": \"the dark blue van\"}, {\"id\": 34333, \"name\": \"a bright green cycling suit\"}, {\"id\": 34334, \"name\": \"ripped jean\"}, {\"id\": 34335, \"name\": \"red bull logo\"}, {\"id\": 34336, \"name\": \"the black bracelet\"}, {\"id\": 34337, \"name\": \"a dark grey asphalt\"}, {\"id\": 34338, \"name\": \"brown spot\"}, {\"id\": 34339, \"name\": \"vehicle's window frame\"}, {\"id\": 34340, \"name\": \"the ipho logo\"}, {\"id\": 34341, \"name\": \"the transit police\"}, {\"id\": 34342, \"name\": \"the black metal gate\"}, {\"id\": 34343, \"name\": \"a stadium's roof\"}, {\"id\": 34344, \"name\": \"hanging lantern\"}, {\"id\": 34345, \"name\": \"green and yellow flower\"}, {\"id\": 34346, \"name\": \"a fish scale pattern\"}, {\"id\": 34347, \"name\": \"a pink ice cream cone\"}, {\"id\": 34348, \"name\": \"the easel\"}, {\"id\": 34349, \"name\": \"the red heat sink\"}, {\"id\": 34350, \"name\": \"the predominantly pink and purple color scheme\"}, {\"id\": 34351, \"name\": \"the right sign\"}, {\"id\": 34352, \"name\": \"rocket ship\"}, {\"id\": 34353, \"name\": \"a black mane\"}, {\"id\": 34354, \"name\": \"the large black bin\"}, {\"id\": 34355, \"name\": \"the dvds and blu-ray\"}, {\"id\": 34356, \"name\": \"racing suit\"}, {\"id\": 34357, \"name\": \"the horizontal siding\"}, {\"id\": 34358, \"name\": \"a taxi stand\"}, {\"id\": 34359, \"name\": \"blue bottom\"}, {\"id\": 34360, \"name\": \"the stone pillar\"}, {\"id\": 34361, \"name\": \"large doorway\"}, {\"id\": 34362, \"name\": \"purple metal\"}, {\"id\": 34363, \"name\": \"a picture of an avocado\"}, {\"id\": 34364, \"name\": \"a large silver bowl\"}, {\"id\": 34365, \"name\": \"a small red buoy\"}, {\"id\": 34366, \"name\": \"a ride\"}, {\"id\": 34367, \"name\": \"man's arm and hand\"}, {\"id\": 34368, \"name\": \"the white bandage\"}, {\"id\": 34369, \"name\": \"the recessed area\"}, {\"id\": 34370, \"name\": \"a red military uniform\"}, {\"id\": 34371, \"name\": \"black shutter\"}, {\"id\": 34372, \"name\": \"a ga stove\"}, {\"id\": 34373, \"name\": \"foreground bowl\"}, {\"id\": 34374, \"name\": \"the digital destination sign\"}, {\"id\": 34375, \"name\": \"sign or advertisement\"}, {\"id\": 34376, \"name\": \"cherry\"}, {\"id\": 34377, \"name\": \"large gray rectangle\"}, {\"id\": 34378, \"name\": \"tall, tan-colored structure\"}, {\"id\": 34379, \"name\": \"a black turtleneck\"}, {\"id\": 34380, \"name\": \"a tall metal structure\"}, {\"id\": 34381, \"name\": \"a blue window\"}, {\"id\": 34382, \"name\": \"a yellow and white boot\"}, {\"id\": 34383, \"name\": \"the dark shadow\"}, {\"id\": 34384, \"name\": \"the volleyball net\"}, {\"id\": 34385, \"name\": \"a leopard print pattern\"}, {\"id\": 34386, \"name\": \"a black dot\"}, {\"id\": 34387, \"name\": \"circular symbol\"}, {\"id\": 34388, \"name\": \"nurse\"}, {\"id\": 34389, \"name\": \"a beige and cream-colored facade\"}, {\"id\": 34390, \"name\": \"a face of individual\"}, {\"id\": 34391, \"name\": \"the small rowing boat\"}, {\"id\": 34392, \"name\": \"a red and orange advertisement\"}, {\"id\": 34393, \"name\": \"the man in a light blue shirt\"}, {\"id\": 34394, \"name\": \"a tuba\"}, {\"id\": 34395, \"name\": \"a bright orange vest\"}, {\"id\": 34396, \"name\": \"large, ornate white vase\"}, {\"id\": 34397, \"name\": \"forehead\"}, {\"id\": 34398, \"name\": \"the coal\"}, {\"id\": 34399, \"name\": \"stainles steel\"}, {\"id\": 34400, \"name\": \"hotel or casino\"}, {\"id\": 34401, \"name\": \"ornate molding\"}, {\"id\": 34402, \"name\": \"a flowing mane\"}, {\"id\": 34403, \"name\": \"the music equipment\"}, {\"id\": 34404, \"name\": \"a camouflage print top\"}, {\"id\": 34405, \"name\": \"a gold fringe\"}, {\"id\": 34406, \"name\": \"the bag of pretzel\"}, {\"id\": 34407, \"name\": \"the dialogue bubble\"}, {\"id\": 34408, \"name\": \"brown balcony\"}, {\"id\": 34409, \"name\": \"a white shutter\"}, {\"id\": 34410, \"name\": \"the stock car\"}, {\"id\": 34411, \"name\": \"gras area\"}, {\"id\": 34412, \"name\": \"human torso\"}, {\"id\": 34413, \"name\": \"trench coat\"}, {\"id\": 34414, \"name\": \"a large mouth full of tooth\"}, {\"id\": 34415, \"name\": \"the large blue object\"}, {\"id\": 34416, \"name\": \"people wearing yellow jacket\"}, {\"id\": 34417, \"name\": \"blue inflatable raft\"}, {\"id\": 34418, \"name\": \"blue cros emblem\"}, {\"id\": 34419, \"name\": \"the white and gray polka-dot cushion\"}, {\"id\": 34420, \"name\": \"the large red tray\"}, {\"id\": 34421, \"name\": \"yellow fragile sticker\"}, {\"id\": 34422, \"name\": \"the bearded man\"}, {\"id\": 34423, \"name\": \"man in a plaid shirt and helmet\"}, {\"id\": 34424, \"name\": \"white flower pattern\"}, {\"id\": 34425, \"name\": \"the large object\"}, {\"id\": 34426, \"name\": \"the star rating\"}, {\"id\": 34427, \"name\": \"black wire rack\"}, {\"id\": 34428, \"name\": \"gold and green leaf\"}, {\"id\": 34429, \"name\": \"a dark green accent\"}, {\"id\": 34430, \"name\": \"a multicolored christma light\"}, {\"id\": 34431, \"name\": \"the red safety cone\"}, {\"id\": 34432, \"name\": \"a large, yellow, metallic object\"}, {\"id\": 34433, \"name\": \"the scoop of white ice cream\"}, {\"id\": 34434, \"name\": \"blue horizontal stripe\"}, {\"id\": 34435, \"name\": \"red plastic\"}, {\"id\": 34436, \"name\": \"the striking red and black uniform\"}, {\"id\": 34437, \"name\": \"album\"}, {\"id\": 34438, \"name\": \"ride's base\"}, {\"id\": 34439, \"name\": \"a shuttered door\"}, {\"id\": 34440, \"name\": \"an adjacent grassy area\"}, {\"id\": 34441, \"name\": \"a nike swoosh logo\"}, {\"id\": 34442, \"name\": \"the subway restaurant\"}, {\"id\": 34443, \"name\": \"an additional shelf\"}, {\"id\": 34444, \"name\": \"yellow and orange safety vest\"}, {\"id\": 34445, \"name\": \"red lever\"}, {\"id\": 34446, \"name\": \"an aztec warrior\"}, {\"id\": 34447, \"name\": \"logo of fc bayern munchen\"}, {\"id\": 34448, \"name\": \"a large feathered headdress\"}, {\"id\": 34449, \"name\": \"the black and orange leg\"}, {\"id\": 34450, \"name\": \"man in a yellow jacket\"}, {\"id\": 34451, \"name\": \"the machine gun\"}, {\"id\": 34452, \"name\": \"a pepsi store\"}, {\"id\": 34453, \"name\": \"a price in euro\"}, {\"id\": 34454, \"name\": \"the wooden shopfront\"}, {\"id\": 34455, \"name\": \"the truck's side panel\"}, {\"id\": 34456, \"name\": \"prominent white rv\"}, {\"id\": 34457, \"name\": \"dark feather\"}, {\"id\": 34458, \"name\": \"a gold dot\"}, {\"id\": 34459, \"name\": \"a red sport jersey\"}, {\"id\": 34460, \"name\": \"light blue t-shirt\"}, {\"id\": 34461, \"name\": \"darker brown tip\"}, {\"id\": 34462, \"name\": \"the honda bike\"}, {\"id\": 34463, \"name\": \"a navy blue tank top\"}, {\"id\": 34464, \"name\": \"box of produce\"}, {\"id\": 34465, \"name\": \"a blurred license plate\"}, {\"id\": 34466, \"name\": \"the white and yellow racing gear\"}, {\"id\": 34467, \"name\": \"a blue and grey jacket\"}, {\"id\": 34468, \"name\": \"the red and white item\"}, {\"id\": 34469, \"name\": \"the white napkin holder\"}, {\"id\": 34470, \"name\": \"a pres badge\"}, {\"id\": 34471, \"name\": \"colorful advertisement\"}, {\"id\": 34472, \"name\": \"the red kayak\"}, {\"id\": 34473, \"name\": \"the ornament\"}, {\"id\": 34474, \"name\": \"dark blue cloth\"}, {\"id\": 34475, \"name\": \"the exposed metal framing\"}, {\"id\": 34476, \"name\": \"large brown canopy\"}, {\"id\": 34477, \"name\": \"the rider of horse 6\"}, {\"id\": 34478, \"name\": \"central black cylinder\"}, {\"id\": 34479, \"name\": \"a skate\"}, {\"id\": 34480, \"name\": \"a matching uniform\"}, {\"id\": 34481, \"name\": \"the shiny finish\"}, {\"id\": 34482, \"name\": \"back bumper\"}, {\"id\": 34483, \"name\": \"a light blue wheel\"}, {\"id\": 34484, \"name\": \"the front wheel of the bike\"}, {\"id\": 34485, \"name\": \"smaller container of lighter brown sauce\"}, {\"id\": 34486, \"name\": \"the large wing tip\"}, {\"id\": 34487, \"name\": \"a single eye\"}, {\"id\": 34488, \"name\": \"a men's face\"}, {\"id\": 34489, \"name\": \"the apartment window\"}, {\"id\": 34490, \"name\": \"the pink and red pixelated landscape\"}, {\"id\": 34491, \"name\": \"the colorful cushion\"}, {\"id\": 34492, \"name\": \"a woman wearing a red headscarf\"}, {\"id\": 34493, \"name\": \"mark\"}, {\"id\": 34494, \"name\": \"a black rubber disc\"}, {\"id\": 34495, \"name\": \"a packed audience\"}, {\"id\": 34496, \"name\": \"a human face\"}, {\"id\": 34497, \"name\": \"a pink nose\"}, {\"id\": 34498, \"name\": \"the light blue logo\"}, {\"id\": 34499, \"name\": \"a philly logo\"}, {\"id\": 34500, \"name\": \"the red padding\"}, {\"id\": 34501, \"name\": \"white cowboy hat\"}, {\"id\": 34502, \"name\": \"official\"}, {\"id\": 34503, \"name\": \"a yellow paddle\"}, {\"id\": 34504, \"name\": \"bright floodlight\"}, {\"id\": 34505, \"name\": \"a black car's bumper\"}, {\"id\": 34506, \"name\": \"small horn\"}, {\"id\": 34507, \"name\": \"the cyclist's jersey number\"}, {\"id\": 34508, \"name\": \"packed bleacher section\"}, {\"id\": 34509, \"name\": \"the work clothe\"}, {\"id\": 34510, \"name\": \"a foldable bicycle\"}, {\"id\": 34511, \"name\": \"man in a dark blue jacket and white hat\"}, {\"id\": 34512, \"name\": \"a wooden leg\"}, {\"id\": 34513, \"name\": \"black and white vertical stripe\"}, {\"id\": 34514, \"name\": \"light yellow shirt\"}, {\"id\": 34515, \"name\": \"a white and red car\"}, {\"id\": 34516, \"name\": \"the paw patrol character\"}, {\"id\": 34517, \"name\": \"wide smile\"}, {\"id\": 34518, \"name\": \"white and pink flower\"}, {\"id\": 34519, \"name\": \"the colorful graphic\"}, {\"id\": 34520, \"name\": \"illuminated storefront\"}, {\"id\": 34521, \"name\": \"small patch of red flower\"}, {\"id\": 34522, \"name\": \"a black goggle\"}, {\"id\": 34523, \"name\": \"the pink cone\"}, {\"id\": 34524, \"name\": \"the red balcony\"}, {\"id\": 34525, \"name\": \"the house or apartment complex\"}, {\"id\": 34526, \"name\": \"a shirtles man\"}, {\"id\": 34527, \"name\": \"the distinctive white tuft\"}, {\"id\": 34528, \"name\": \"paw patrol\"}, {\"id\": 34529, \"name\": \"metal object with a long handle\"}, {\"id\": 34530, \"name\": \"the dark blue motor\"}, {\"id\": 34531, \"name\": \"the high-visibility yellow and blue uniform\"}, {\"id\": 34532, \"name\": \"the maroon jersey\"}, {\"id\": 34533, \"name\": \"a chrome grill\"}, {\"id\": 34534, \"name\": \"a towering structure\"}, {\"id\": 34535, \"name\": \"dark gray head and neck\"}, {\"id\": 34536, \"name\": \"warm lighting\"}, {\"id\": 34537, \"name\": \"the square opening\"}, {\"id\": 34538, \"name\": \"the blue toy train\"}, {\"id\": 34539, \"name\": \"dog's owner\"}, {\"id\": 34540, \"name\": \"dark red liquid\"}, {\"id\": 34541, \"name\": \"the large expanse of green grass\"}, {\"id\": 34542, \"name\": \"large black and white billboard\"}, {\"id\": 34543, \"name\": \"the news banner\"}, {\"id\": 34544, \"name\": \"the stainles steel kitchen equipment\"}, {\"id\": 34545, \"name\": \"a small wheel\"}, {\"id\": 34546, \"name\": \"a gray knit hat with a pompom\"}, {\"id\": 34547, \"name\": \"a red paddle\"}, {\"id\": 34548, \"name\": \"a blue table tenni table\"}, {\"id\": 34549, \"name\": \"the military presence\"}, {\"id\": 34550, \"name\": \"a rowing team\"}, {\"id\": 34551, \"name\": \"the side of the car\"}, {\"id\": 34552, \"name\": \"a piece of fruit\"}, {\"id\": 34553, \"name\": \"the leather jacket\"}, {\"id\": 34554, \"name\": \"the abstract design\"}, {\"id\": 34555, \"name\": \"the d bank\"}, {\"id\": 34556, \"name\": \"a lace\"}, {\"id\": 34557, \"name\": \"a light blue dress\"}, {\"id\": 34558, \"name\": \"pointed black cap\"}, {\"id\": 34559, \"name\": \"bottom-left image\"}, {\"id\": 34560, \"name\": \"a he\"}, {\"id\": 34561, \"name\": \"the baby in a stroller\"}, {\"id\": 34562, \"name\": \"the logo of the surfboard company billabong\"}, {\"id\": 34563, \"name\": \"a black and blue cart\"}, {\"id\": 34564, \"name\": \"a kitchen item\"}, {\"id\": 34565, \"name\": \"the slash\"}, {\"id\": 34566, \"name\": \"the thin black band\"}, {\"id\": 34567, \"name\": \"the driver's shadow\"}, {\"id\": 34568, \"name\": \"the yellow dot\"}, {\"id\": 34569, \"name\": \"the racing kart\"}, {\"id\": 34570, \"name\": \"crane's white sign\"}, {\"id\": 34571, \"name\": \"the yellow front end\"}, {\"id\": 34572, \"name\": \"white butterfly\"}, {\"id\": 34573, \"name\": \"a car's front wheel\"}, {\"id\": 34574, \"name\": \"large column\"}, {\"id\": 34575, \"name\": \"the vibrant surfboard\"}, {\"id\": 34576, \"name\": \"the tall, silver pole\"}, {\"id\": 34577, \"name\": \"red and yellow striped sign\"}, {\"id\": 34578, \"name\": \"the matching jewelry\"}, {\"id\": 34579, \"name\": \"white fur trim\"}, {\"id\": 34580, \"name\": \"prominent yellow and black striped tube\"}, {\"id\": 34581, \"name\": \"a player image\"}, {\"id\": 34582, \"name\": \"blurred and distorted representation of a room or office\"}, {\"id\": 34583, \"name\": \"the black and white costume\"}, {\"id\": 34584, \"name\": \"large cushion\"}, {\"id\": 34585, \"name\": \"a distinctive red and white uniform\"}, {\"id\": 34586, \"name\": \"a white bowling ball\"}, {\"id\": 34587, \"name\": \"red double-decker bu\"}, {\"id\": 34588, \"name\": \"a circular black hole or vent\"}, {\"id\": 34589, \"name\": \"the prominent red wooden boat\"}, {\"id\": 34590, \"name\": \"the race information\"}, {\"id\": 34591, \"name\": \"a statistic\"}, {\"id\": 34592, \"name\": \"sleeveles white shirt\"}, {\"id\": 34593, \"name\": \"dorsal fin\"}, {\"id\": 34594, \"name\": \"cast-iron pan\"}, {\"id\": 34595, \"name\": \"a purple cape\"}, {\"id\": 34596, \"name\": \"a long robe\"}, {\"id\": 34597, \"name\": \"black skateboard\"}, {\"id\": 34598, \"name\": \"product's release date\"}, {\"id\": 34599, \"name\": \"wine bottle\"}, {\"id\": 34600, \"name\": \"fur coat\"}, {\"id\": 34601, \"name\": \"brown structure\"}, {\"id\": 34602, \"name\": \"the brightly colored cycling outfit\"}, {\"id\": 34603, \"name\": \"a prominent red double-decker bu\"}, {\"id\": 34604, \"name\": \"a blue and white bicycle sign\"}, {\"id\": 34605, \"name\": \"the teal cleat\"}, {\"id\": 34606, \"name\": \"a yard\"}, {\"id\": 34607, \"name\": \"a cream-colored structure\"}, {\"id\": 34608, \"name\": \"the blue swim belt\"}, {\"id\": 34609, \"name\": \"a yellow traffic cone\"}, {\"id\": 34610, \"name\": \"the yellow exterior\"}, {\"id\": 34611, \"name\": \"the cloud of white smoke\"}, {\"id\": 34612, \"name\": \"single mammoth\"}, {\"id\": 34613, \"name\": \"the yum! logo\"}, {\"id\": 34614, \"name\": \"a toy figure\"}, {\"id\": 34615, \"name\": \"the black scuba gear\"}, {\"id\": 34616, \"name\": \"the sleeve of the child's arm\"}, {\"id\": 34617, \"name\": \"a red car-shaped seat\"}, {\"id\": 34618, \"name\": \"the white head covering\"}, {\"id\": 34619, \"name\": \"a multi-car crash\"}, {\"id\": 34620, \"name\": \"a large insect-like figure\"}, {\"id\": 34621, \"name\": \"red and white striped object\"}, {\"id\": 34622, \"name\": \"the athletic headband\"}, {\"id\": 34623, \"name\": \"the vibrant graffiti\"}, {\"id\": 34624, \"name\": \"the teal bar\"}, {\"id\": 34625, \"name\": \"a large black curtain\"}, {\"id\": 34626, \"name\": \"unique display of snowboard\"}, {\"id\": 34627, \"name\": \"a green netting enclosure\"}, {\"id\": 34628, \"name\": \"large tusk\"}, {\"id\": 34629, \"name\": \"the white ola cab\"}, {\"id\": 34630, \"name\": \"white icon\"}, {\"id\": 34631, \"name\": \"player's name\"}, {\"id\": 34632, \"name\": \"minifigure\"}, {\"id\": 34633, \"name\": \"woman in a gray tank top\"}, {\"id\": 34634, \"name\": \"green skin\"}, {\"id\": 34635, \"name\": \"a blue bowling ball\"}, {\"id\": 34636, \"name\": \"black tie\"}, {\"id\": 34637, \"name\": \"white-painted concrete pillar\"}, {\"id\": 34638, \"name\": \"the glass double door\"}, {\"id\": 34639, \"name\": \"dark blue or black car\"}, {\"id\": 34640, \"name\": \"large orange and brown rock\"}, {\"id\": 34641, \"name\": \"the checkout counter\"}, {\"id\": 34642, \"name\": \"the child's bike\"}, {\"id\": 34643, \"name\": \"colorful snack\"}, {\"id\": 34644, \"name\": \"a large gray umbrella\"}, {\"id\": 34645, \"name\": \"the -white attire\"}, {\"id\": 34646, \"name\": \"ornate column\"}, {\"id\": 34647, \"name\": \"the colorful uniform\"}, {\"id\": 34648, \"name\": \"long blade\"}, {\"id\": 34649, \"name\": \"the gray purse\"}, {\"id\": 34650, \"name\": \"brown saddle\"}, {\"id\": 34651, \"name\": \"a striking yellow flower\"}, {\"id\": 34652, \"name\": \"children's face\"}, {\"id\": 34653, \"name\": \"a vertical black sign\"}, {\"id\": 34654, \"name\": \"an urban outfitter store\"}, {\"id\": 34655, \"name\": \"the storage container\"}, {\"id\": 34656, \"name\": \"the inflatable black and white french bulldog\"}, {\"id\": 34657, \"name\": \"curly brown fur\"}, {\"id\": 34658, \"name\": \"player's left arm\"}, {\"id\": 34659, \"name\": \"a bouquet of pink roses and baby's breath\"}, {\"id\": 34660, \"name\": \"the blue cylinder\"}, {\"id\": 34661, \"name\": \"charcoal\"}, {\"id\": 34662, \"name\": \"the black-and-gray jersey\"}, {\"id\": 34663, \"name\": \"starting line\"}, {\"id\": 34664, \"name\": \"black lantern\"}, {\"id\": 34665, \"name\": \"the green electrical box\"}, {\"id\": 34666, \"name\": \"snare drum\"}, {\"id\": 34667, \"name\": \"an orange and black high-visibility clothing\"}, {\"id\": 34668, \"name\": \"the white horn\"}, {\"id\": 34669, \"name\": \"character from the game\"}, {\"id\": 34670, \"name\": \"a blue ford logo\"}, {\"id\": 34671, \"name\": \"colorful ball\"}, {\"id\": 34672, \"name\": \"black bridle\"}, {\"id\": 34673, \"name\": \"a black ball\"}, {\"id\": 34674, \"name\": \"the ribbon\"}, {\"id\": 34675, \"name\": \"yellow 77\"}, {\"id\": 34676, \"name\": \"commercial or industrial structure\"}, {\"id\": 34677, \"name\": \"a pink suitcase\"}, {\"id\": 34678, \"name\": \"lush green bush\"}, {\"id\": 34679, \"name\": \"the pink inflatable ball\"}, {\"id\": 34680, \"name\": \"the small plastic umbrella\"}, {\"id\": 34681, \"name\": \"the orange section\"}, {\"id\": 34682, \"name\": \"a face paint\"}, {\"id\": 34683, \"name\": \"small white stool\"}, {\"id\": 34684, \"name\": \"small white symbol\"}, {\"id\": 34685, \"name\": \"a short skirt\"}, {\"id\": 34686, \"name\": \"barefoot child\"}, {\"id\": 34687, \"name\": \"orange and blue saddle\"}, {\"id\": 34688, \"name\": \"numbered post\"}, {\"id\": 34689, \"name\": \"a white foam block\"}, {\"id\": 34690, \"name\": \"breakfast dish\"}, {\"id\": 34691, \"name\": \"the white and grey jersey\"}, {\"id\": 34692, \"name\": \"the hockey net\"}, {\"id\": 34693, \"name\": \"light blue ripped jean\"}, {\"id\": 34694, \"name\": \"the large pink handle\"}, {\"id\": 34695, \"name\": \"bold black lettering\"}, {\"id\": 34696, \"name\": \"a gold design\"}, {\"id\": 34697, \"name\": \"a scuba gear\"}, {\"id\": 34698, \"name\": \"the dog's black nose\"}, {\"id\": 34699, \"name\": \"the small box\"}, {\"id\": 34700, \"name\": \"souvenir\"}, {\"id\": 34701, \"name\": \"a white metal\"}, {\"id\": 34702, \"name\": \"large roll of paper or plastic\"}, {\"id\": 34703, \"name\": \"a red circular object\"}, {\"id\": 34704, \"name\": \"black menu bar\"}, {\"id\": 34705, \"name\": \"the credit card reader\"}, {\"id\": 34706, \"name\": \"trader joe's storefront\"}, {\"id\": 34707, \"name\": \"a foreground boat\"}, {\"id\": 34708, \"name\": \"gray structure\"}, {\"id\": 34709, \"name\": \"white scrub\"}, {\"id\": 34710, \"name\": \"a lime green jersey\"}, {\"id\": 34711, \"name\": \"the large, gray, stone-like structure\"}, {\"id\": 34712, \"name\": \"a red team's player\"}, {\"id\": 34713, \"name\": \"the white window\"}, {\"id\": 34714, \"name\": \"a crash barrier\"}, {\"id\": 34715, \"name\": \"large plume of dark smoke\"}, {\"id\": 34716, \"name\": \"boat's number\"}, {\"id\": 34717, \"name\": \"a company name\"}, {\"id\": 34718, \"name\": \"a white and blue uniform\"}, {\"id\": 34719, \"name\": \"the navy hoodie\"}, {\"id\": 34720, \"name\": \"black and white striped pole\"}, {\"id\": 34721, \"name\": \"the logo of a spiral and a dot\"}, {\"id\": 34722, \"name\": \"vibrant game wheel\"}, {\"id\": 34723, \"name\": \"computer\"}, {\"id\": 34724, \"name\": \"a brown tile\"}, {\"id\": 34725, \"name\": \"social media logo\"}, {\"id\": 34726, \"name\": \"boarded up window\"}, {\"id\": 34727, \"name\": \"dark gray horizontal stripe\"}, {\"id\": 34728, \"name\": \"an empty stand\"}, {\"id\": 34729, \"name\": \"the rightmost glass\"}, {\"id\": 34730, \"name\": \"blue, red, and white vehicle\"}, {\"id\": 34731, \"name\": \"a red short-sleeved shirt\"}, {\"id\": 34732, \"name\": \"white counter top\"}, {\"id\": 34733, \"name\": \"large glass and steel structure\"}, {\"id\": 34734, \"name\": \"lower tooth\"}, {\"id\": 34735, \"name\": \"tall black hat\"}, {\"id\": 34736, \"name\": \"a box or crate\"}, {\"id\": 34737, \"name\": \"ornate balcony\"}, {\"id\": 34738, \"name\": \"the open white shutter\"}, {\"id\": 34739, \"name\": \"the white ceramic cup\"}, {\"id\": 34740, \"name\": \"a square format\"}, {\"id\": 34741, \"name\": \"the small green toy\"}, {\"id\": 34742, \"name\": \"the red and white striped border\"}, {\"id\": 34743, \"name\": \"a blue section\"}, {\"id\": 34744, \"name\": \"a blue and white highway sign\"}, {\"id\": 34745, \"name\": \"a peace gesture\"}, {\"id\": 34746, \"name\": \"the tan pillar\"}, {\"id\": 34747, \"name\": \"black beak\"}, {\"id\": 34748, \"name\": \"the red reflector\"}, {\"id\": 34749, \"name\": \"woman in a black hoodie\"}, {\"id\": 34750, \"name\": \"a white emblem\"}, {\"id\": 34751, \"name\": \"popcorn\"}, {\"id\": 34752, \"name\": \"a silver toyota logo\"}, {\"id\": 34753, \"name\": \"the piece of music equipment\"}, {\"id\": 34754, \"name\": \"the blonde wig\"}, {\"id\": 34755, \"name\": \"the silver volkswagen logo\"}, {\"id\": 34756, \"name\": \"a pink crop top\"}, {\"id\": 34757, \"name\": \"a lit window\"}, {\"id\": 34758, \"name\": \"a sombrero\"}, {\"id\": 34759, \"name\": \"dark beanie\"}, {\"id\": 34760, \"name\": \"gray bubble\"}, {\"id\": 34761, \"name\": \"a fish-shaped object\"}, {\"id\": 34762, \"name\": \"a grey rectangle\"}, {\"id\": 34763, \"name\": \"the rowing boat\"}, {\"id\": 34764, \"name\": \"a white surfboard\"}, {\"id\": 34765, \"name\": \"a fragment of cardboard box\"}, {\"id\": 34766, \"name\": \"the blue boot\"}, {\"id\": 34767, \"name\": \"menacing face\"}, {\"id\": 34768, \"name\": \"toy dog's head\"}, {\"id\": 34769, \"name\": \"yellow foot\"}, {\"id\": 34770, \"name\": \"a grey machine\"}, {\"id\": 34771, \"name\": \"the blurred view of the grassy track\"}, {\"id\": 34772, \"name\": \"a green shape\"}, {\"id\": 34773, \"name\": \"the red mushroom house\"}, {\"id\": 34774, \"name\": \"the orange life jacket\"}, {\"id\": 34775, \"name\": \"sweet potato\"}, {\"id\": 34776, \"name\": \"a wide eye\"}, {\"id\": 34777, \"name\": \"the colorful pedal\"}, {\"id\": 34778, \"name\": \"blue and white van\"}, {\"id\": 34779, \"name\": \"the red cargo car\"}, {\"id\": 34780, \"name\": \"round yellow eye\"}, {\"id\": 34781, \"name\": \"a man in a similar safety vest\"}, {\"id\": 34782, \"name\": \"white and orange barrier\"}, {\"id\": 34783, \"name\": \"the game's logo\"}, {\"id\": 34784, \"name\": \"a blue collared shirt\"}, {\"id\": 34785, \"name\": \"a caption or title\"}, {\"id\": 34786, \"name\": \"prominent stone column\"}, {\"id\": 34787, \"name\": \"black netting material\"}, {\"id\": 34788, \"name\": \"mural of a man\"}, {\"id\": 34789, \"name\": \"a white goal post\"}, {\"id\": 34790, \"name\": \"the thin border\"}, {\"id\": 34791, \"name\": \"the green and yellow arm\"}, {\"id\": 34792, \"name\": \"a police logo\"}, {\"id\": 34793, \"name\": \"green pipe\"}, {\"id\": 34794, \"name\": \"a warm yellow glow\"}, {\"id\": 34795, \"name\": \"car tire\"}, {\"id\": 34796, \"name\": \"yellow placemat\"}, {\"id\": 34797, \"name\": \"a small pink creature\"}, {\"id\": 34798, \"name\": \"smaller image\"}, {\"id\": 34799, \"name\": \"a yellow inflatable raft\"}, {\"id\": 34800, \"name\": \"diver's left hand\"}, {\"id\": 34801, \"name\": \"a neon green hula hoop\"}, {\"id\": 34802, \"name\": \"purple border\"}, {\"id\": 34803, \"name\": \"time indicator\"}, {\"id\": 34804, \"name\": \"the white tie\"}, {\"id\": 34805, \"name\": \"a long eyelash\"}, {\"id\": 34806, \"name\": \"gold and brown mushroom shape\"}, {\"id\": 34807, \"name\": \"distinctive red door\"}, {\"id\": 34808, \"name\": \"purple lanyard\"}, {\"id\": 34809, \"name\": \"a yellow or red shirt\"}, {\"id\": 34810, \"name\": \"a comic book panel\"}, {\"id\": 34811, \"name\": \"colorful mat or rug\"}, {\"id\": 34812, \"name\": \"the brown tunic\"}, {\"id\": 34813, \"name\": \"the black face shield\"}, {\"id\": 34814, \"name\": \"the white knee-high sock\"}, {\"id\": 34815, \"name\": \"the front of the vehicle\"}, {\"id\": 34816, \"name\": \"the flash's facial expression\"}, {\"id\": 34817, \"name\": \"a subaru sign\"}, {\"id\": 34818, \"name\": \"a silver vehicle\"}, {\"id\": 34819, \"name\": \"the black knobs and switch\"}, {\"id\": 34820, \"name\": \"a prominent stack\"}, {\"id\": 34821, \"name\": \"a cartoon person's legs\"}, {\"id\": 34822, \"name\": \"the speed\"}, {\"id\": 34823, \"name\": \"a green and yellow light\"}, {\"id\": 34824, \"name\": \"red antenna\"}, {\"id\": 34825, \"name\": \"elderly woman\"}, {\"id\": 34826, \"name\": \"the yellowish glow\"}, {\"id\": 34827, \"name\": \"red paw\"}, {\"id\": 34828, \"name\": \"a storage space\"}, {\"id\": 34829, \"name\": \"red pom-pom\"}, {\"id\": 34830, \"name\": \"a bottom image\"}, {\"id\": 34831, \"name\": \"the gold crown\"}, {\"id\": 34832, \"name\": \"a large arched brick structure\"}, {\"id\": 34833, \"name\": \"head of the toothbrush\"}, {\"id\": 34834, \"name\": \"a yellow and red stripe\"}, {\"id\": 34835, \"name\": \"the top-right image\"}, {\"id\": 34836, \"name\": \"a walking path\"}, {\"id\": 34837, \"name\": \"the prominent black backpack\"}, {\"id\": 34838, \"name\": \"cast of character\"}, {\"id\": 34839, \"name\": \"an orange leg\"}, {\"id\": 34840, \"name\": \"close-up of a man's face\"}, {\"id\": 34841, \"name\": \"a red chinese character\"}, {\"id\": 34842, \"name\": \"a golden-brown substance\"}, {\"id\": 34843, \"name\": \"foreground boat\"}, {\"id\": 34844, \"name\": \"the item for sale\"}, {\"id\": 34845, \"name\": \"large roll of white material\"}, {\"id\": 34846, \"name\": \"the festive green and red attire\"}, {\"id\": 34847, \"name\": \"the food counter\"}, {\"id\": 34848, \"name\": \"a large audience\"}, {\"id\": 34849, \"name\": \"fire department uniform\"}, {\"id\": 34850, \"name\": \"gray metal structure\"}, {\"id\": 34851, \"name\": \"the small blue sign\"}, {\"id\": 34852, \"name\": \"a baggage carousel\"}, {\"id\": 34853, \"name\": \"a small house\"}, {\"id\": 34854, \"name\": \"screenshot\"}, {\"id\": 34855, \"name\": \"a green metal barrier\"}, {\"id\": 34856, \"name\": \"a left bank\"}, {\"id\": 34857, \"name\": \"a third floor\"}, {\"id\": 34858, \"name\": \"branch or metal rod\"}, {\"id\": 34859, \"name\": \"black and gray area\"}, {\"id\": 34860, \"name\": \"a green highway sign\"}, {\"id\": 34861, \"name\": \"the ventilation duct\"}, {\"id\": 34862, \"name\": \"the black leather backpack strap\"}, {\"id\": 34863, \"name\": \"the dark-colored shark\"}, {\"id\": 34864, \"name\": \"the young girl in a white hijab\"}, {\"id\": 34865, \"name\": \"patterned top\"}, {\"id\": 34866, \"name\": \"a man in a black tuxedo and top hat\"}, {\"id\": 34867, \"name\": \"a colored cleat\"}, {\"id\": 34868, \"name\": \"the green blanket\"}, {\"id\": 34869, \"name\": \"a black cleat\"}, {\"id\": 34870, \"name\": \"gloved hand\"}, {\"id\": 34871, \"name\": \"sb logo\"}, {\"id\": 34872, \"name\": \"the large red trailer\"}, {\"id\": 34873, \"name\": \"light purple color\"}, {\"id\": 34874, \"name\": \"gold embroidery\"}, {\"id\": 34875, \"name\": \"the bag of popcorn\"}, {\"id\": 34876, \"name\": \"a white nba logo\"}, {\"id\": 34877, \"name\": \"yellow pipe\"}, {\"id\": 34878, \"name\": \"the lightning\"}, {\"id\": 34879, \"name\": \"a brick-paved square\"}, {\"id\": 34880, \"name\": \"headwrap\"}, {\"id\": 34881, \"name\": \"the whimsical costume\"}, {\"id\": 34882, \"name\": \"the woman in a pink shirt\"}, {\"id\": 34883, \"name\": \"small section of sidewalk\"}, {\"id\": 34884, \"name\": \"the white backsplash\"}, {\"id\": 34885, \"name\": \"creature's eyes\"}, {\"id\": 34886, \"name\": \"url\"}, {\"id\": 34887, \"name\": \"the black mane\"}, {\"id\": 34888, \"name\": \"a black-and-white striped top\"}, {\"id\": 34889, \"name\": \"inflatable pool tube\"}, {\"id\": 34890, \"name\": \"a price sticker\"}, {\"id\": 34891, \"name\": \"a blue trim\"}, {\"id\": 34892, \"name\": \"a green streak\"}, {\"id\": 34893, \"name\": \"a light blue jersey\"}, {\"id\": 34894, \"name\": \"rider's uniform\"}, {\"id\": 34895, \"name\": \"a giraffe's raised arm\"}, {\"id\": 34896, \"name\": \"the large semi-truck\"}, {\"id\": 34897, \"name\": \"the black advertisement\"}, {\"id\": 34898, \"name\": \"cart's awning\"}, {\"id\": 34899, \"name\": \"a black and yellow striped body\"}, {\"id\": 34900, \"name\": \"display stand\"}, {\"id\": 34901, \"name\": \"the patch of dried grass\"}, {\"id\": 34902, \"name\": \"boat's registration number\"}, {\"id\": 34903, \"name\": \"the man in short\"}, {\"id\": 34904, \"name\": \"long escalator\"}, {\"id\": 34905, \"name\": \"red hanger\"}, {\"id\": 34906, \"name\": \"the man in a dark jacket\"}, {\"id\": 34907, \"name\": \"the black hull\"}, {\"id\": 34908, \"name\": \"hot dog\"}, {\"id\": 34909, \"name\": \"the green monster energy logo\"}, {\"id\": 34910, \"name\": \"a white and black stripe\"}, {\"id\": 34911, \"name\": \"a white ford logo\"}, {\"id\": 34912, \"name\": \"blue swim cap\"}, {\"id\": 34913, \"name\": \"neon green stripe\"}, {\"id\": 34914, \"name\": \"the subway store\"}, {\"id\": 34915, \"name\": \"the pink grip\"}, {\"id\": 34916, \"name\": \"an oakley logo\"}, {\"id\": 34917, \"name\": \"a construction material\"}, {\"id\": 34918, \"name\": \"bucking bull\"}, {\"id\": 34919, \"name\": \"yellow and green umbrella\"}, {\"id\": 34920, \"name\": \"gnarled trunk\"}, {\"id\": 34921, \"name\": \"a priefert logo\"}, {\"id\": 34922, \"name\": \"a police motorcycle\"}, {\"id\": 34923, \"name\": \"blue safety vest\"}, {\"id\": 34924, \"name\": \"front facade\"}, {\"id\": 34925, \"name\": \"bike seat\"}, {\"id\": 34926, \"name\": \"long neck\"}, {\"id\": 34927, \"name\": \"the trainer\"}, {\"id\": 34928, \"name\": \"a green and blue section\"}, {\"id\": 34929, \"name\": \"the game's score\"}, {\"id\": 34930, \"name\": \"the maroon shirt\"}, {\"id\": 34931, \"name\": \"the white wave\"}, {\"id\": 34932, \"name\": \"a white beard\"}, {\"id\": 34933, \"name\": \"the orange box\"}, {\"id\": 34934, \"name\": \"the small shop or stall\"}, {\"id\": 34935, \"name\": \"a miniature carrot\"}, {\"id\": 34936, \"name\": \"the partially obscured person\"}, {\"id\": 34937, \"name\": \"an identification tag\"}, {\"id\": 34938, \"name\": \"colorful pennant flag\"}, {\"id\": 34939, \"name\": \"bowl of chopped green and white vegetable\"}, {\"id\": 34940, \"name\": \"the electronic screen\"}, {\"id\": 34941, \"name\": \"vibrant green face\"}, {\"id\": 34942, \"name\": \"the small brown boat\"}, {\"id\": 34943, \"name\": \"dark wood finish\"}, {\"id\": 34944, \"name\": \"the brown lower section\"}, {\"id\": 34945, \"name\": \"the white and blue circular logo\"}, {\"id\": 34946, \"name\": \"the small microphone\"}, {\"id\": 34947, \"name\": \"black and white striped jersey\"}, {\"id\": 34948, \"name\": \"a large yellow and blue inflatable minion\"}, {\"id\": 34949, \"name\": \"brown handbag\"}, {\"id\": 34950, \"name\": \"the green accent\"}, {\"id\": 34951, \"name\": \"a red and black traffic light\"}, {\"id\": 34952, \"name\": \"orange team\"}, {\"id\": 34953, \"name\": \"large, black eye\"}, {\"id\": 34954, \"name\": \"the monster\"}, {\"id\": 34955, \"name\": \"plain black shirt\"}, {\"id\": 34956, \"name\": \"a soap sud\"}, {\"id\": 34957, \"name\": \"anthropomorphic character\"}, {\"id\": 34958, \"name\": \"score counter\"}, {\"id\": 34959, \"name\": \"large metal shield\"}, {\"id\": 34960, \"name\": \"black chaise lounge\"}, {\"id\": 34961, \"name\": \"stovetop\"}, {\"id\": 34962, \"name\": \"the display of cycling datum\"}, {\"id\": 34963, \"name\": \"bright yellow vest\"}, {\"id\": 34964, \"name\": \"a lunch\"}, {\"id\": 34965, \"name\": \"a black graphic t-shirt\"}, {\"id\": 34966, \"name\": \"a red warning label\"}, {\"id\": 34967, \"name\": \"an entrance of a motorcycle resort\"}, {\"id\": 34968, \"name\": \"grid-like pattern\"}, {\"id\": 34969, \"name\": \"the bright orange shirt\"}, {\"id\": 34970, \"name\": \"yellow exterior\"}, {\"id\": 34971, \"name\": \"the red shelving unit\"}, {\"id\": 34972, \"name\": \"a green and white pillar\"}, {\"id\": 34973, \"name\": \"red plastic base\"}, {\"id\": 34974, \"name\": \"black seat cushion\"}, {\"id\": 34975, \"name\": \"a large metal container or pit\"}, {\"id\": 34976, \"name\": \"a weather-related information\"}, {\"id\": 34977, \"name\": \"a large front grill\"}, {\"id\": 34978, \"name\": \"figurine dressed in white robe\"}, {\"id\": 34979, \"name\": \"the latch\"}, {\"id\": 34980, \"name\": \"a cart's wheel\"}, {\"id\": 34981, \"name\": \"blurred black object\"}, {\"id\": 34982, \"name\": \"a colorful plastic disc\"}, {\"id\": 34983, \"name\": \"red or blue vest\"}, {\"id\": 34984, \"name\": \"the black and grey checkered coat\"}, {\"id\": 34985, \"name\": \"pink metal frame\"}, {\"id\": 34986, \"name\": \"stadium's stand\"}, {\"id\": 34987, \"name\": \"brown plaid shirt\"}, {\"id\": 34988, \"name\": \"tall silver pole\"}, {\"id\": 34989, \"name\": \"a table or booth\"}, {\"id\": 34990, \"name\": \"the red, white, and blue ribbon\"}, {\"id\": 34991, \"name\": \"white yacht\"}, {\"id\": 34992, \"name\": \"armored personnel carrier\"}, {\"id\": 34993, \"name\": \"small white square\"}, {\"id\": 34994, \"name\": \"metal stanchion\"}, {\"id\": 34995, \"name\": \"blue and yellow stripe\"}, {\"id\": 34996, \"name\": \"a wake of the boat\"}, {\"id\": 34997, \"name\": \"a man in a blue football jersey\"}, {\"id\": 34998, \"name\": \"the silo\"}, {\"id\": 34999, \"name\": \"colorful star\"}, {\"id\": 35000, \"name\": \"a boat's name\"}, {\"id\": 35001, \"name\": \"a curved metal body\"}, {\"id\": 35002, \"name\": \"the gray front bumper\"}, {\"id\": 35003, \"name\": \"a glowing green eye\"}, {\"id\": 35004, \"name\": \"a long strap\"}, {\"id\": 35005, \"name\": \"raised wing\"}, {\"id\": 35006, \"name\": \"wooden pier\"}, {\"id\": 35007, \"name\": \"the open top\"}, {\"id\": 35008, \"name\": \"the large, weathered, silver-colored metal object\"}, {\"id\": 35009, \"name\": \"the truck's front bumper\"}, {\"id\": 35010, \"name\": \"a player in a yellow uniform\"}, {\"id\": 35011, \"name\": \"purple jersey\"}, {\"id\": 35012, \"name\": \"large black device\"}, {\"id\": 35013, \"name\": \"colorful bookshelf\"}, {\"id\": 35014, \"name\": \"the gold gauntlet\"}, {\"id\": 35015, \"name\": \"punching bag stand\"}, {\"id\": 35016, \"name\": \"the gray metal frame\"}, {\"id\": 35017, \"name\": \"the small figurine\"}, {\"id\": 35018, \"name\": \"bullet point\"}, {\"id\": 35019, \"name\": \"the man in a dark brown jacket and black pant\"}, {\"id\": 35020, \"name\": \"harness\"}, {\"id\": 35021, \"name\": \"the pink treat\"}, {\"id\": 35022, \"name\": \"the horse-drawn tram\"}, {\"id\": 35023, \"name\": \"a rectangular sign\"}, {\"id\": 35024, \"name\": \"red and orange uniform\"}, {\"id\": 35025, \"name\": \"a dreadlock\"}, {\"id\": 35026, \"name\": \"a yellow light\"}, {\"id\": 35027, \"name\": \"big ten logo\"}, {\"id\": 35028, \"name\": \"still from a movie or tv show\"}, {\"id\": 35029, \"name\": \"the blue piece of paper\"}, {\"id\": 35030, \"name\": \"the purple cabin\"}, {\"id\": 35031, \"name\": \"the girl's body\"}, {\"id\": 35032, \"name\": \"a small train\"}, {\"id\": 35033, \"name\": \"the black skate\"}, {\"id\": 35034, \"name\": \"a red and yellow damselfish\"}, {\"id\": 35035, \"name\": \"the black and white checkered pattern\"}, {\"id\": 35036, \"name\": \"a swim goggle\"}, {\"id\": 35037, \"name\": \"the small orange object\"}, {\"id\": 35038, \"name\": \"a purple and white striped shirt\"}, {\"id\": 35039, \"name\": \"black-and-white striped pole\"}, {\"id\": 35040, \"name\": \"wooden plank of wood\"}, {\"id\": 35041, \"name\": \"the dark red outer layer\"}, {\"id\": 35042, \"name\": \"roller coaster's support\"}, {\"id\": 35043, \"name\": \"bus's side mirror\"}, {\"id\": 35044, \"name\": \"the sharp claw\"}, {\"id\": 35045, \"name\": \"a type of bread\"}, {\"id\": 35046, \"name\": \"the black and white patterned design\"}, {\"id\": 35047, \"name\": \"the books or magazine\"}, {\"id\": 35048, \"name\": \"a blue label\"}, {\"id\": 35049, \"name\": \"an outdoor concrete step\"}, {\"id\": 35050, \"name\": \"tinted windshield\"}, {\"id\": 35051, \"name\": \"a flying kite\"}, {\"id\": 35052, \"name\": \"a taxi's windshield\"}, {\"id\": 35053, \"name\": \"large gray branch\"}, {\"id\": 35054, \"name\": \"the blue storage unit\"}, {\"id\": 35055, \"name\": \"the bend\"}, {\"id\": 35056, \"name\": \"a ring of green light\"}, {\"id\": 35057, \"name\": \"clutter\"}, {\"id\": 35058, \"name\": \"the alpinestar logo\"}, {\"id\": 35059, \"name\": \"large tent or pavilion\"}, {\"id\": 35060, \"name\": \"badge\"}, {\"id\": 35061, \"name\": \"a bmw\"}, {\"id\": 35062, \"name\": \"a smiling white rabbit\"}, {\"id\": 35063, \"name\": \"a large conveyor belt\"}, {\"id\": 35064, \"name\": \"the senoh logo\"}, {\"id\": 35065, \"name\": \"the trash pile\"}, {\"id\": 35066, \"name\": \"the dark red or black car\"}, {\"id\": 35067, \"name\": \"product description\"}, {\"id\": 35068, \"name\": \"a green patterned bib\"}, {\"id\": 35069, \"name\": \"the red branch\"}, {\"id\": 35070, \"name\": \"main creature\"}, {\"id\": 35071, \"name\": \"a white and red sign\"}, {\"id\": 35072, \"name\": \"brown cardboard box\"}, {\"id\": 35073, \"name\": \"white guard tower\"}, {\"id\": 35074, \"name\": \"robot's head\"}, {\"id\": 35075, \"name\": \"brown seat\"}, {\"id\": 35076, \"name\": \"yellow and black bollard\"}, {\"id\": 35077, \"name\": \"black buoy\"}, {\"id\": 35078, \"name\": \"the purple creature\"}, {\"id\": 35079, \"name\": \"fire escape\"}, {\"id\": 35080, \"name\": \"an air conditioning vent\"}, {\"id\": 35081, \"name\": \"brown hijab\"}, {\"id\": 35082, \"name\": \"large brown purse\"}, {\"id\": 35083, \"name\": \"a patterned tank top\"}, {\"id\": 35084, \"name\": \"gray section\"}, {\"id\": 35085, \"name\": \"the metal gears and lever\"}, {\"id\": 35086, \"name\": \"a large floral arrangement\"}, {\"id\": 35087, \"name\": \"a large paddle or oar\"}, {\"id\": 35088, \"name\": \"a small white helicopter\"}, {\"id\": 35089, \"name\": \"the large, white vehicle\"}, {\"id\": 35090, \"name\": \"black hinge\"}, {\"id\": 35091, \"name\": \"a segway\"}, {\"id\": 35092, \"name\": \"the distant hill\"}, {\"id\": 35093, \"name\": \"the horse's mouth\"}, {\"id\": 35094, \"name\": \"the back leg\"}, {\"id\": 35095, \"name\": \"a grassy surface\"}, {\"id\": 35096, \"name\": \"a white pompom\"}, {\"id\": 35097, \"name\": \"a bull's horn\"}, {\"id\": 35098, \"name\": \"hexagonal symbol\"}, {\"id\": 35099, \"name\": \"wiener dog\"}, {\"id\": 35100, \"name\": \"hood of their coat\"}, {\"id\": 35101, \"name\": \"a red chinese lantern\"}, {\"id\": 35102, \"name\": \"top-left screen\"}, {\"id\": 35103, \"name\": \"vintage car\"}, {\"id\": 35104, \"name\": \"a kettlebell\"}, {\"id\": 35105, \"name\": \"a central square image\"}, {\"id\": 35106, \"name\": \"the green train car\"}, {\"id\": 35107, \"name\": \"black and white patterned top\"}, {\"id\": 35108, \"name\": \"a taxi's license plate\"}, {\"id\": 35109, \"name\": \"stylized dragonfly logo\"}, {\"id\": 35110, \"name\": \"the yellow splat\"}, {\"id\": 35111, \"name\": \"a mechanic\"}, {\"id\": 35112, \"name\": \"a white twig\"}, {\"id\": 35113, \"name\": \"green metal arm\"}, {\"id\": 35114, \"name\": \"an aqua short\"}, {\"id\": 35115, \"name\": \"park-goer\"}, {\"id\": 35116, \"name\": \"black and white color scheme\"}, {\"id\": 35117, \"name\": \"a wake\"}, {\"id\": 35118, \"name\": \"a long tube\"}, {\"id\": 35119, \"name\": \"white hood\"}, {\"id\": 35120, \"name\": \"small blue rectangle\"}, {\"id\": 35121, \"name\": \"an orange kickboard\"}, {\"id\": 35122, \"name\": \"the motorcycle wheel\"}, {\"id\": 35123, \"name\": \"a silver honda\"}, {\"id\": 35124, \"name\": \"a cyclist's leg\"}, {\"id\": 35125, \"name\": \"a boy's feet\"}, {\"id\": 35126, \"name\": \"a cook\"}, {\"id\": 35127, \"name\": \"the black front fender\"}, {\"id\": 35128, \"name\": \"the green grassy surface\"}, {\"id\": 35129, \"name\": \"a speed limit of 30 mile per hour\"}, {\"id\": 35130, \"name\": \"the arched entrance\"}, {\"id\": 35131, \"name\": \"a firefighter\"}, {\"id\": 35132, \"name\": \"black and neon green armband\"}, {\"id\": 35133, \"name\": \"the stone castle\"}, {\"id\": 35134, \"name\": \"black pattern\"}, {\"id\": 35135, \"name\": \"macaque\"}, {\"id\": 35136, \"name\": \"the layered skirt\"}, {\"id\": 35137, \"name\": \"pink star\"}, {\"id\": 35138, \"name\": \"the bike's tire\"}, {\"id\": 35139, \"name\": \"the aluminum food container\"}, {\"id\": 35140, \"name\": \"a rhinoceros\"}, {\"id\": 35141, \"name\": \"the score of the game\"}, {\"id\": 35142, \"name\": \"the white mane\"}, {\"id\": 35143, \"name\": \"a plaid pattern\"}, {\"id\": 35144, \"name\": \"a white punching bag stand\"}, {\"id\": 35145, \"name\": \"the pattern of gold squares and dot\"}, {\"id\": 35146, \"name\": \"bright yellow star\"}, {\"id\": 35147, \"name\": \"the complex network of roads and intersection\"}, {\"id\": 35148, \"name\": \"a red and white santa clau figure\"}, {\"id\": 35149, \"name\": \"the white reflective strip\"}, {\"id\": 35150, \"name\": \"left wing\"}, {\"id\": 35151, \"name\": \"festive christma decoration\"}, {\"id\": 35152, \"name\": \"the maryland terrapin logo\"}, {\"id\": 35153, \"name\": \"a concrete mixer truck\"}, {\"id\": 35154, \"name\": \"couple\"}, {\"id\": 35155, \"name\": \"a brown wig\"}, {\"id\": 35156, \"name\": \"a light brown wooden cabinet\"}, {\"id\": 35157, \"name\": \"a vibrant red and white flower\"}, {\"id\": 35158, \"name\": \"the long gun\"}, {\"id\": 35159, \"name\": \"a bull's rope\"}, {\"id\": 35160, \"name\": \"the briefcase\"}, {\"id\": 35161, \"name\": \"a matching outfit\"}, {\"id\": 35162, \"name\": \"traditional african clothing\"}, {\"id\": 35163, \"name\": \"the wooden table or countertop\"}, {\"id\": 35164, \"name\": \"the dark grey sweater\"}, {\"id\": 35165, \"name\": \"a yellow boat\"}, {\"id\": 35166, \"name\": \"the grassy strip\"}, {\"id\": 35167, \"name\": \"a competitor\"}, {\"id\": 35168, \"name\": \"the model horse display\"}, {\"id\": 35169, \"name\": \"the silver body\"}, {\"id\": 35170, \"name\": \"a vibrant attire\"}, {\"id\": 35171, \"name\": \"patron\"}, {\"id\": 35172, \"name\": \"the white plush toy rabbit\"}, {\"id\": 35173, \"name\": \"a puffy black coat\"}, {\"id\": 35174, \"name\": \"tall, grey pole\"}, {\"id\": 35175, \"name\": \"black and blue banner\"}, {\"id\": 35176, \"name\": \"the warm yellow light\"}, {\"id\": 35177, \"name\": \"a window display\"}, {\"id\": 35178, \"name\": \"white and red stripe\"}, {\"id\": 35179, \"name\": \"a camouflage pant\"}, {\"id\": 35180, \"name\": \"the paddleboard\"}, {\"id\": 35181, \"name\": \"the coach\"}, {\"id\": 35182, \"name\": \"the large stone column\"}, {\"id\": 35183, \"name\": \"rider's orange helmet\"}, {\"id\": 35184, \"name\": \"round top\"}, {\"id\": 35185, \"name\": \"the distinctive black stripe\"}, {\"id\": 35186, \"name\": \"an alien design\"}, {\"id\": 35187, \"name\": \"team in red and blue uniform\"}, {\"id\": 35188, \"name\": \"an olive\"}, {\"id\": 35189, \"name\": \"the red and orange flag\"}, {\"id\": 35190, \"name\": \"the person in a gym\"}, {\"id\": 35191, \"name\": \"the man with a backpack\"}, {\"id\": 35192, \"name\": \"black fedora\"}, {\"id\": 35193, \"name\": \"a fiery explosion\"}, {\"id\": 35194, \"name\": \"american flag design\"}, {\"id\": 35195, \"name\": \"a blue buoy\"}, {\"id\": 35196, \"name\": \"a red and yellow lego vehicle\"}, {\"id\": 35197, \"name\": \"the truck's wheel\"}, {\"id\": 35198, \"name\": \"appliance\"}, {\"id\": 35199, \"name\": \"the storefront's facade\"}, {\"id\": 35200, \"name\": \"silver bolt\"}, {\"id\": 35201, \"name\": \"a modi clearance center sign\"}, {\"id\": 35202, \"name\": \"mint chocolate chip\"}, {\"id\": 35203, \"name\": \"the blue train car\"}, {\"id\": 35204, \"name\": \"the cycling gear\"}, {\"id\": 35205, \"name\": \"the single, white tooth\"}, {\"id\": 35206, \"name\": \"patterned pillow\"}, {\"id\": 35207, \"name\": \"the clear plastic front\"}, {\"id\": 35208, \"name\": \"lego logo\"}, {\"id\": 35209, \"name\": \"the woman in a green dress\"}, {\"id\": 35210, \"name\": \"prominent purple banner\"}, {\"id\": 35211, \"name\": \"graphic\"}, {\"id\": 35212, \"name\": \"the horse's tail\"}, {\"id\": 35213, \"name\": \"the yellow metal object\"}, {\"id\": 35214, \"name\": \"cycling uniform\"}, {\"id\": 35215, \"name\": \"a red step\"}, {\"id\": 35216, \"name\": \"the vibrant pink gown\"}, {\"id\": 35217, \"name\": \"the central white line\"}, {\"id\": 35218, \"name\": \"a pet store shelf\"}, {\"id\": 35219, \"name\": \"the red sole\"}, {\"id\": 35220, \"name\": \"a yellow and blue base\"}, {\"id\": 35221, \"name\": \"a large bundle of hay\"}, {\"id\": 35222, \"name\": \"original packaging\"}, {\"id\": 35223, \"name\": \"the -black attire\"}, {\"id\": 35224, \"name\": \"woman with a white head covering\"}, {\"id\": 35225, \"name\": \"a rider's uniform\"}, {\"id\": 35226, \"name\": \"a pineapple decoration\"}, {\"id\": 35227, \"name\": \"the anthropomorphic polar bear\"}, {\"id\": 35228, \"name\": \"the metal armrest\"}, {\"id\": 35229, \"name\": \"iconic green and black attire\"}, {\"id\": 35230, \"name\": \"red circle or ring\"}, {\"id\": 35231, \"name\": \"the red-and-white striped curb\"}, {\"id\": 35232, \"name\": \"the stylized f symbol\"}, {\"id\": 35233, \"name\": \"a prominent yellow pole\"}, {\"id\": 35234, \"name\": \"white lg logo\"}, {\"id\": 35235, \"name\": \"surgical mask\"}, {\"id\": 35236, \"name\": \"forested area\"}, {\"id\": 35237, \"name\": \"the green wood\"}, {\"id\": 35238, \"name\": \"the bristle\"}, {\"id\": 35239, \"name\": \"black dot\"}, {\"id\": 35240, \"name\": \"large bucket of liquid\"}, {\"id\": 35241, \"name\": \"a man on a bicycle\"}, {\"id\": 35242, \"name\": \"a colorful monster\"}, {\"id\": 35243, \"name\": \"black shoebox\"}, {\"id\": 35244, \"name\": \"surprise doll\"}, {\"id\": 35245, \"name\": \"toy's left ear\"}, {\"id\": 35246, \"name\": \"a red neon light\"}, {\"id\": 35247, \"name\": \"a yellow symbol\"}, {\"id\": 35248, \"name\": \"a small shops or market stall\"}, {\"id\": 35249, \"name\": \"a red casket\"}, {\"id\": 35250, \"name\": \"the large blue banner\"}, {\"id\": 35251, \"name\": \"the dc comic logo\"}, {\"id\": 35252, \"name\": \"patient's hand\"}, {\"id\": 35253, \"name\": \"a patch of snow\"}, {\"id\": 35254, \"name\": \"the prominent red and black tuk-tuk\"}, {\"id\": 35255, \"name\": \"a pink butterfly graphic\"}, {\"id\": 35256, \"name\": \"the small black dot\"}, {\"id\": 35257, \"name\": \"blue diamond-shaped sign\"}, {\"id\": 35258, \"name\": \"a wood shutter\"}, {\"id\": 35259, \"name\": \"the toasted bread\"}, {\"id\": 35260, \"name\": \"couple's head\"}, {\"id\": 35261, \"name\": \"the mast\"}, {\"id\": 35262, \"name\": \"large yellow 6\"}, {\"id\": 35263, \"name\": \"the spiderman\"}, {\"id\": 35264, \"name\": \"the player's score\"}, {\"id\": 35265, \"name\": \"the small green bush\"}, {\"id\": 35266, \"name\": \"a predominantly black base\"}, {\"id\": 35267, \"name\": \"a large, brown, curly tendril\"}, {\"id\": 35268, \"name\": \"the blurred leg\"}, {\"id\": 35269, \"name\": \"the man in a white shirt with black short\"}, {\"id\": 35270, \"name\": \"the light-colored wooden shelf\"}, {\"id\": 35271, \"name\": \"an orange construction cone\"}, {\"id\": 35272, \"name\": \"the black hooded cloak\"}, {\"id\": 35273, \"name\": \"prominent yellow m logo\"}, {\"id\": 35274, \"name\": \"the white cycling shoe\"}, {\"id\": 35275, \"name\": \"large hand\"}, {\"id\": 35276, \"name\": \"a small white pole\"}, {\"id\": 35277, \"name\": \"long pier\"}, {\"id\": 35278, \"name\": \"the white onesie\"}, {\"id\": 35279, \"name\": \"a wion logo\"}, {\"id\": 35280, \"name\": \"a picture of character from toy story franchise\"}, {\"id\": 35281, \"name\": \"large orange sign\"}, {\"id\": 35282, \"name\": \"the fuel gauge\"}, {\"id\": 35283, \"name\": \"the large red and blue poster\"}, {\"id\": 35284, \"name\": \"the colorful cupcake\"}, {\"id\": 35285, \"name\": \"the brown exterior\"}, {\"id\": 35286, \"name\": \"the fox news logo\"}, {\"id\": 35287, \"name\": \"a small black wheel\"}, {\"id\": 35288, \"name\": \"ornate carving\"}, {\"id\": 35289, \"name\": \"character's head\"}, {\"id\": 35290, \"name\": \"granite countertop\"}, {\"id\": 35291, \"name\": \"a large quantity of sheep's wool\"}, {\"id\": 35292, \"name\": \"the purple planet\"}, {\"id\": 35293, \"name\": \"touch of greenery\"}, {\"id\": 35294, \"name\": \"green garnish\"}, {\"id\": 35295, \"name\": \"the 10 player\"}, {\"id\": 35296, \"name\": \"outdoor furniture\"}, {\"id\": 35297, \"name\": \"the team uniform\"}, {\"id\": 35298, \"name\": \"a red and yellow saddle\"}, {\"id\": 35299, \"name\": \"pediment\"}, {\"id\": 35300, \"name\": \"the time display\"}, {\"id\": 35301, \"name\": \"the yellow pillar\"}, {\"id\": 35302, \"name\": \"a horizontal slat of metal\"}, {\"id\": 35303, \"name\": \"a nose of the cow\"}, {\"id\": 35304, \"name\": \"the black-and-white striped fabric\"}, {\"id\": 35305, \"name\": \"a prominent sandstone mountain range\"}, {\"id\": 35306, \"name\": \"the toy train\"}, {\"id\": 35307, \"name\": \"red lego piece\"}, {\"id\": 35308, \"name\": \"the dangling earring\"}, {\"id\": 35309, \"name\": \"a large tooth\"}, {\"id\": 35310, \"name\": \"the lush green bush\"}, {\"id\": 35311, \"name\": \"a translucent blue wing\"}, {\"id\": 35312, \"name\": \"a small wooden block\"}, {\"id\": 35313, \"name\": \"the numbered starting block\"}, {\"id\": 35314, \"name\": \"red and white striped pattern\"}, {\"id\": 35315, \"name\": \"white and red striped pole\"}, {\"id\": 35316, \"name\": \"red and yellow newspaper stand\"}, {\"id\": 35317, \"name\": \"blue fish logo\"}, {\"id\": 35318, \"name\": \"dark hull\"}, {\"id\": 35319, \"name\": \"the colorful rope toy\"}, {\"id\": 35320, \"name\": \"the large white vessel\"}, {\"id\": 35321, \"name\": \"a stainles steel sink\"}, {\"id\": 35322, \"name\": \"the cereal\"}, {\"id\": 35323, \"name\": \"the key feature\"}, {\"id\": 35324, \"name\": \"metal luggage cart\"}, {\"id\": 35325, \"name\": \"small white star\"}, {\"id\": 35326, \"name\": \"vendor's setup\"}, {\"id\": 35327, \"name\": \"a red accountant logo\"}, {\"id\": 35328, \"name\": \"the white machine\"}, {\"id\": 35329, \"name\": \"tan-colored structure\"}, {\"id\": 35330, \"name\": \"store with a red awning\"}, {\"id\": 35331, \"name\": \"black athletic short\"}, {\"id\": 35332, \"name\": \"a yellow crane or lift\"}, {\"id\": 35333, \"name\": \"lighter brown patch\"}, {\"id\": 35334, \"name\": \"the unicorn horn\"}, {\"id\": 35335, \"name\": \"an image of dish\"}, {\"id\": 35336, \"name\": \"lush grassy hillside\"}, {\"id\": 35337, \"name\": \"silver pole\"}, {\"id\": 35338, \"name\": \"front of the truck\"}, {\"id\": 35339, \"name\": \"a cra logo\"}, {\"id\": 35340, \"name\": \"the clear plastic part\"}, {\"id\": 35341, \"name\": \"the blue life jacket\"}, {\"id\": 35342, \"name\": \"a prominent yellow planter\"}, {\"id\": 35343, \"name\": \"the ironing board\"}, {\"id\": 35344, \"name\": \"the pub's name\"}, {\"id\": 35345, \"name\": \"the ship's sail\"}, {\"id\": 35346, \"name\": \"wooden shelving unit\"}, {\"id\": 35347, \"name\": \"a mickey mouse ear headband\"}, {\"id\": 35348, \"name\": \"a silver hatchback vehicle\"}, {\"id\": 35349, \"name\": \"the prominent picnic table\"}, {\"id\": 35350, \"name\": \"person's blue pant\"}, {\"id\": 35351, \"name\": \"an apartment\"}, {\"id\": 35352, \"name\": \"the dark-colored column\"}, {\"id\": 35353, \"name\": \"grey cardigan\"}, {\"id\": 35354, \"name\": \"a bottom panel\"}, {\"id\": 35355, \"name\": \"ancient architecture\"}, {\"id\": 35356, \"name\": \"the large, grey, stone-like structure\"}, {\"id\": 35357, \"name\": \"black tablecloth\"}, {\"id\": 35358, \"name\": \"the weathered stone block\"}, {\"id\": 35359, \"name\": \"the yellow platform\"}, {\"id\": 35360, \"name\": \"the blue and yellow cart\"}, {\"id\": 35361, \"name\": \"rainbow umbrella\"}, {\"id\": 35362, \"name\": \"a black rear bumper\"}, {\"id\": 35363, \"name\": \"the white-clad team\"}, {\"id\": 35364, \"name\": \"a gray bin\"}, {\"id\": 35365, \"name\": \"the north carolina team\"}, {\"id\": 35366, \"name\": \"red, white, and blue cycling outfit\"}, {\"id\": 35367, \"name\": \"a large plant with long, thin leaf\"}, {\"id\": 35368, \"name\": \"crucifix\"}, {\"id\": 35369, \"name\": \"large black pole\"}, {\"id\": 35370, \"name\": \"a small cartoon character\"}, {\"id\": 35371, \"name\": \"the yellow-painted parking space\"}, {\"id\": 35372, \"name\": \"the small brown object\"}, {\"id\": 35373, \"name\": \"a long, dark ponytail\"}, {\"id\": 35374, \"name\": \"rich dark red finish\"}, {\"id\": 35375, \"name\": \"the individual's arm\"}, {\"id\": 35376, \"name\": \"the small bucket\"}, {\"id\": 35377, \"name\": \"silver button\"}, {\"id\": 35378, \"name\": \"the yellow foot\"}, {\"id\": 35379, \"name\": \"the dark blue racing suit\"}, {\"id\": 35380, \"name\": \"uc logo\"}, {\"id\": 35381, \"name\": \"white siding\"}, {\"id\": 35382, \"name\": \"large grassy area\"}, {\"id\": 35383, \"name\": \"large minion character\"}, {\"id\": 35384, \"name\": \"the khaki\"}, {\"id\": 35385, \"name\": \"a wagon's wheel\"}, {\"id\": 35386, \"name\": \"the long, thin, white and blue eye\"}, {\"id\": 35387, \"name\": \"beverage can\"}, {\"id\": 35388, \"name\": \"blue and red icing\"}, {\"id\": 35389, \"name\": \"a grey block\"}, {\"id\": 35390, \"name\": \"spurs' player\"}, {\"id\": 35391, \"name\": \"dark grey jacket\"}, {\"id\": 35392, \"name\": \"the black, red, and white racing suit\"}, {\"id\": 35393, \"name\": \"blue rope barrier\"}, {\"id\": 35394, \"name\": \"the l sign\"}, {\"id\": 35395, \"name\": \"flying ant\"}, {\"id\": 35396, \"name\": \"the plate of cookie\"}, {\"id\": 35397, \"name\": \"the blue flag or banner\"}, {\"id\": 35398, \"name\": \"a van's license plate\"}, {\"id\": 35399, \"name\": \"a dragon's wing\"}, {\"id\": 35400, \"name\": \"broken glass shard\"}, {\"id\": 35401, \"name\": \"white trunk\"}, {\"id\": 35402, \"name\": \"tall, rectangular lantern\"}, {\"id\": 35403, \"name\": \"ski lift\"}, {\"id\": 35404, \"name\": \"the silver stripe\"}, {\"id\": 35405, \"name\": \"the green oar\"}, {\"id\": 35406, \"name\": \"storefront sign\"}, {\"id\": 35407, \"name\": \"brown pattern\"}, {\"id\": 35408, \"name\": \"racecourse's logo\"}, {\"id\": 35409, \"name\": \"a brown cushion\"}, {\"id\": 35410, \"name\": \"a f sign\"}, {\"id\": 35411, \"name\": \"black strip\"}, {\"id\": 35412, \"name\": \"a small yellow sign\"}, {\"id\": 35413, \"name\": \"a man with pointed ear\"}, {\"id\": 35414, \"name\": \"a black jean\"}, {\"id\": 35415, \"name\": \"a large yellow banner\"}, {\"id\": 35416, \"name\": \"the white lab coat\"}, {\"id\": 35417, \"name\": \"a soft, diffused light\"}, {\"id\": 35418, \"name\": \"large, illuminated sign\"}, {\"id\": 35419, \"name\": \"an ambulance door\"}, {\"id\": 35420, \"name\": \"the larevista logo\"}, {\"id\": 35421, \"name\": \"dark red exterior\"}, {\"id\": 35422, \"name\": \"the blue exterior\"}, {\"id\": 35423, \"name\": \"colorful, fuzzy leg\"}, {\"id\": 35424, \"name\": \"silver blade\"}, {\"id\": 35425, \"name\": \"the gray cell phone\"}, {\"id\": 35426, \"name\": \"a dead leaf\"}, {\"id\": 35427, \"name\": \"camouflage pattern\"}, {\"id\": 35428, \"name\": \"the green vine-like structure\"}, {\"id\": 35429, \"name\": \"a white platter\"}, {\"id\": 35430, \"name\": \"the neon green sign\"}, {\"id\": 35431, \"name\": \"a black side mirror\"}, {\"id\": 35432, \"name\": \"scotch brand name\"}, {\"id\": 35433, \"name\": \"the dark-colored baseball cap\"}, {\"id\": 35434, \"name\": \"a large blue barrel\"}, {\"id\": 35435, \"name\": \"a yellow support beam\"}, {\"id\": 35436, \"name\": \"the band\"}, {\"id\": 35437, \"name\": \"large electronic device\"}, {\"id\": 35438, \"name\": \"the basket of food item\"}, {\"id\": 35439, \"name\": \"puffy white cloud\"}, {\"id\": 35440, \"name\": \"car's front right headlight\"}, {\"id\": 35441, \"name\": \"the do not enter sign\"}, {\"id\": 35442, \"name\": \"dark-colored sign\"}, {\"id\": 35443, \"name\": \"the grid\"}, {\"id\": 35444, \"name\": \"the reddit logo\"}, {\"id\": 35445, \"name\": \"the gray sport bra\"}, {\"id\": 35446, \"name\": \"the saw\"}, {\"id\": 35447, \"name\": \"sign in spanish\"}, {\"id\": 35448, \"name\": \"the fox news channel news graphic\"}, {\"id\": 35449, \"name\": \"a rainbow-colored hat\"}, {\"id\": 35450, \"name\": \"a large light fixture\"}, {\"id\": 35451, \"name\": \"blue and white stripe\"}, {\"id\": 35452, \"name\": \"grey stone accent\"}, {\"id\": 35453, \"name\": \"a partially open door\"}, {\"id\": 35454, \"name\": \"a blue tuk-tuk\"}, {\"id\": 35455, \"name\": \"black-and-white striped barrier\"}, {\"id\": 35456, \"name\": \"the small yellow arrow\"}, {\"id\": 35457, \"name\": \"a purple hue\"}, {\"id\": 35458, \"name\": \"a saddle horn\"}, {\"id\": 35459, \"name\": \"the brown window shutter\"}, {\"id\": 35460, \"name\": \"the red and gold suit\"}, {\"id\": 35461, \"name\": \"bag of potting soil\"}, {\"id\": 35462, \"name\": \"the stylized t logo\"}, {\"id\": 35463, \"name\": \"a diamond-shaped design\"}, {\"id\": 35464, \"name\": \"a black-and-white drawing of a face\"}, {\"id\": 35465, \"name\": \"a large mouth\"}, {\"id\": 35466, \"name\": \"the red pom-pom nose\"}, {\"id\": 35467, \"name\": \"a reflection of a fan\"}, {\"id\": 35468, \"name\": \"black silhouette of a child and adult\"}, {\"id\": 35469, \"name\": \"stone construction\"}, {\"id\": 35470, \"name\": \"hole in it trunk\"}, {\"id\": 35471, \"name\": \"blue and grey striped shirt\"}, {\"id\": 35472, \"name\": \"a pink circle\"}, {\"id\": 35473, \"name\": \"a red decorative structure\"}, {\"id\": 35474, \"name\": \"the brightly colored helmet\"}, {\"id\": 35475, \"name\": \"the car's number\"}, {\"id\": 35476, \"name\": \"the curved visor\"}, {\"id\": 35477, \"name\": \"a contraption\"}, {\"id\": 35478, \"name\": \"a large, cylindrical component\"}, {\"id\": 35479, \"name\": \"a blue-framed window\"}, {\"id\": 35480, \"name\": \"the red and blue surface\"}, {\"id\": 35481, \"name\": \"large bundle of produce\"}, {\"id\": 35482, \"name\": \"the blue wetsuit pant\"}, {\"id\": 35483, \"name\": \"the top right screenshot\"}, {\"id\": 35484, \"name\": \"the thick coat\"}, {\"id\": 35485, \"name\": \"patterned headscarf\"}, {\"id\": 35486, \"name\": \"a cart's seat\"}, {\"id\": 35487, \"name\": \"a character's face\"}, {\"id\": 35488, \"name\": \"vibrant pink base\"}, {\"id\": 35489, \"name\": \"a yellow and blue stripe\"}, {\"id\": 35490, \"name\": \"blue swim trunk\"}, {\"id\": 35491, \"name\": \"a blue luggage cart\"}, {\"id\": 35492, \"name\": \"the beach toy\"}, {\"id\": 35493, \"name\": \"the woman in a red shirt\"}, {\"id\": 35494, \"name\": \"the load of box\"}, {\"id\": 35495, \"name\": \"a storefront's name\"}, {\"id\": 35496, \"name\": \"large rv\"}, {\"id\": 35497, \"name\": \"the green and white sign\"}, {\"id\": 35498, \"name\": \"modern attire\"}, {\"id\": 35499, \"name\": \"a prominent red and black logo\"}, {\"id\": 35500, \"name\": \"the red pom-pom\"}, {\"id\": 35501, \"name\": \"beauty product\"}, {\"id\": 35502, \"name\": \"light blue scrub\"}, {\"id\": 35503, \"name\": \"green life jacket\"}, {\"id\": 35504, \"name\": \"the red and yellow uniform\"}, {\"id\": 35505, \"name\": \"paved lane\"}, {\"id\": 35506, \"name\": \"the stylized x logo\"}, {\"id\": 35507, \"name\": \"the red body\"}, {\"id\": 35508, \"name\": \"snow pant\"}, {\"id\": 35509, \"name\": \"traditional vietnamese conical hat\"}, {\"id\": 35510, \"name\": \"a red running surface\"}, {\"id\": 35511, \"name\": \"small red plastic container\"}, {\"id\": 35512, \"name\": \"race official\"}, {\"id\": 35513, \"name\": \"a large parking\"}, {\"id\": 35514, \"name\": \"a bobblehead\"}, {\"id\": 35515, \"name\": \"grey boot\"}, {\"id\": 35516, \"name\": \"red metal structure\"}, {\"id\": 35517, \"name\": \"the large, black pillar\"}, {\"id\": 35518, \"name\": \"bike's grip\"}, {\"id\": 35519, \"name\": \"a tan loincloth\"}, {\"id\": 35520, \"name\": \"the oval-shaped tag\"}, {\"id\": 35521, \"name\": \"a small blue patch\"}, {\"id\": 35522, \"name\": \"a suspension\"}, {\"id\": 35523, \"name\": \"the dirt or grassy terrain\"}, {\"id\": 35524, \"name\": \"the tall window\"}, {\"id\": 35525, \"name\": \"a brown, rectangular object\"}, {\"id\": 35526, \"name\": \"a ride's car\"}, {\"id\": 35527, \"name\": \"a red metal object\"}, {\"id\": 35528, \"name\": \"the white pipe\"}, {\"id\": 35529, \"name\": \"a green or yellow cap\"}, {\"id\": 35530, \"name\": \"a factory worker\"}, {\"id\": 35531, \"name\": \"the red brick accent\"}, {\"id\": 35532, \"name\": \"a heavy machinery\"}, {\"id\": 35533, \"name\": \"the image of person\"}, {\"id\": 35534, \"name\": \"a gold logo\"}, {\"id\": 35535, \"name\": \"a paddle board\"}, {\"id\": 35536, \"name\": \"the decorative balcony\"}, {\"id\": 35537, \"name\": \"spider-man\"}, {\"id\": 35538, \"name\": \"orange pole\"}, {\"id\": 35539, \"name\": \"a yellow and red seat\"}, {\"id\": 35540, \"name\": \"a red and yellow track\"}, {\"id\": 35541, \"name\": \"a grey box\"}, {\"id\": 35542, \"name\": \"the large glove\"}, {\"id\": 35543, \"name\": \"the white and black striped barrier\"}, {\"id\": 35544, \"name\": \"a white garage\"}, {\"id\": 35545, \"name\": \"a colorful silhouette\"}, {\"id\": 35546, \"name\": \"a piece of white material\"}, {\"id\": 35547, \"name\": \"a protest sign\"}, {\"id\": 35548, \"name\": \"baby stroller\"}, {\"id\": 35549, \"name\": \"a brown bottle\"}, {\"id\": 35550, \"name\": \"large tan military vehicle\"}, {\"id\": 35551, \"name\": \"black eye socket\"}, {\"id\": 35552, \"name\": \"a pipe or tube\"}, {\"id\": 35553, \"name\": \"orange border\"}, {\"id\": 35554, \"name\": \"black bottom\"}, {\"id\": 35555, \"name\": \"circular formation\"}, {\"id\": 35556, \"name\": \"a lightsaber\"}, {\"id\": 35557, \"name\": \"the city's architecture\"}, {\"id\": 35558, \"name\": \"the drawing of a gun firing\"}, {\"id\": 35559, \"name\": \"a small blue object\"}, {\"id\": 35560, \"name\": \"black and white checkered tile\"}, {\"id\": 35561, \"name\": \"branch\"}, {\"id\": 35562, \"name\": \"round base\"}, {\"id\": 35563, \"name\": \"a yellow hand\"}, {\"id\": 35564, \"name\": \"a medieval costume\"}, {\"id\": 35565, \"name\": \"a small bottle of paint\"}, {\"id\": 35566, \"name\": \"the white and yellow logo\"}, {\"id\": 35567, \"name\": \"a police vehicle\"}, {\"id\": 35568, \"name\": \"wind chime\"}, {\"id\": 35569, \"name\": \"a highway's median\"}, {\"id\": 35570, \"name\": \"black and white striped police tape\"}, {\"id\": 35571, \"name\": \"boxy, high roofline\"}, {\"id\": 35572, \"name\": \"the lake or river\"}, {\"id\": 35573, \"name\": \"the intricate lattice work\"}, {\"id\": 35574, \"name\": \"branding element\"}, {\"id\": 35575, \"name\": \"central pole\"}, {\"id\": 35576, \"name\": \"sponsorship\"}, {\"id\": 35577, \"name\": \"thick, black tire\"}, {\"id\": 35578, \"name\": \"las vega logo\"}, {\"id\": 35579, \"name\": \"antennae\"}, {\"id\": 35580, \"name\": \"a white and gray structure\"}, {\"id\": 35581, \"name\": \"the green metal beam\"}, {\"id\": 35582, \"name\": \"a red and yellow striped pattern\"}, {\"id\": 35583, \"name\": \"a glass storefront\"}, {\"id\": 35584, \"name\": \"a purple design element\"}, {\"id\": 35585, \"name\": \"the black and green jersey\"}, {\"id\": 35586, \"name\": \"a large load of cargo\"}, {\"id\": 35587, \"name\": \"the address\"}, {\"id\": 35588, \"name\": \"blue table surface\"}, {\"id\": 35589, \"name\": \"large parking\"}, {\"id\": 35590, \"name\": \"a large pendant\"}, {\"id\": 35591, \"name\": \"a yellow and white painted curb\"}, {\"id\": 35592, \"name\": \"an overhead sign\"}, {\"id\": 35593, \"name\": \"the metal vent\"}, {\"id\": 35594, \"name\": \"a subtitle\"}, {\"id\": 35595, \"name\": \"the matching top hat\"}, {\"id\": 35596, \"name\": \"a thick layer of mud\"}, {\"id\": 35597, \"name\": \"the large brown spot\"}, {\"id\": 35598, \"name\": \"blue nose\"}, {\"id\": 35599, \"name\": \"the score of\"}, {\"id\": 35600, \"name\": \"racing kart\"}, {\"id\": 35601, \"name\": \"small black bird\"}, {\"id\": 35602, \"name\": \"a caution tape\"}, {\"id\": 35603, \"name\": \"shiny green tile\"}, {\"id\": 35604, \"name\": \"the purple plastic basket\"}, {\"id\": 35605, \"name\": \"a brightly colored clothing\"}, {\"id\": 35606, \"name\": \"black propeller\"}, {\"id\": 35607, \"name\": \"blue and yellow costume\"}, {\"id\": 35608, \"name\": \"black lamp\"}, {\"id\": 35609, \"name\": \"sleek black and white wetsuit\"}, {\"id\": 35610, \"name\": \"red and yellow striped barrier\"}, {\"id\": 35611, \"name\": \"a kfc restaurant\"}, {\"id\": 35612, \"name\": \"gray siding\"}, {\"id\": 35613, \"name\": \"blue section of the banner\"}, {\"id\": 35614, \"name\": \"a teal uniform\"}, {\"id\": 35615, \"name\": \"silver detail\"}, {\"id\": 35616, \"name\": \"a blue trouser\"}, {\"id\": 35617, \"name\": \"a red and white advertisement\"}, {\"id\": 35618, \"name\": \"a black and white floral top\"}, {\"id\": 35619, \"name\": \"the flower box\"}, {\"id\": 35620, \"name\": \"a players' face\"}, {\"id\": 35621, \"name\": \"a silver stud\"}, {\"id\": 35622, \"name\": \"the red and black uniform\"}, {\"id\": 35623, \"name\": \"the blue nose\"}, {\"id\": 35624, \"name\": \"a smiling face\"}, {\"id\": 35625, \"name\": \"buck\"}, {\"id\": 35626, \"name\": \"silver bag\"}, {\"id\": 35627, \"name\": \"a superhero\"}, {\"id\": 35628, \"name\": \"a snowmobilers\"}, {\"id\": 35629, \"name\": \"the large pink structure\"}, {\"id\": 35630, \"name\": \"the white and light blue logo\"}, {\"id\": 35631, \"name\": \"a sleek, high-glos white cabinetry\"}, {\"id\": 35632, \"name\": \"the man in a suit jacket\"}, {\"id\": 35633, \"name\": \"the large front wheel\"}, {\"id\": 35634, \"name\": \"a large blue bowl\"}, {\"id\": 35635, \"name\": \"the bottle of cold drink\"}, {\"id\": 35636, \"name\": \"a tuxedo\"}, {\"id\": 35637, \"name\": \"a brown robe\"}, {\"id\": 35638, \"name\": \"goggles\"}, {\"id\": 35639, \"name\": \"wossner banner\"}, {\"id\": 35640, \"name\": \"running person\"}, {\"id\": 35641, \"name\": \"grey wetsuit\"}, {\"id\": 35642, \"name\": \"an orange banner\"}, {\"id\": 35643, \"name\": \"the beige satchel\"}, {\"id\": 35644, \"name\": \"a white graphic design\"}, {\"id\": 35645, \"name\": \"the purple ball\"}, {\"id\": 35646, \"name\": \"prominent cylindrical structure\"}, {\"id\": 35647, \"name\": \"yellow and black jacket\"}, {\"id\": 35648, \"name\": \"the ornate column\"}, {\"id\": 35649, \"name\": \"the round headlight\"}, {\"id\": 35650, \"name\": \"the garage\"}, {\"id\": 35651, \"name\": \"top-center image\"}, {\"id\": 35652, \"name\": \"the white and yellow canoe or kayak\"}, {\"id\": 35653, \"name\": \"the blue foot\"}, {\"id\": 35654, \"name\": \"turquoise bird\"}, {\"id\": 35655, \"name\": \"bus's license plate\"}, {\"id\": 35656, \"name\": \"a prominent black litter bin\"}, {\"id\": 35657, \"name\": \"a red medicine ball\"}, {\"id\": 35658, \"name\": \"the tube of lipstick\"}, {\"id\": 35659, \"name\": \"blue lamppost\"}, {\"id\": 35660, \"name\": \"the game clock\"}, {\"id\": 35661, \"name\": \"small flag\"}, {\"id\": 35662, \"name\": \"fire truck's flashing lights\"}, {\"id\": 35663, \"name\": \"the buckle\"}, {\"id\": 35664, \"name\": \"towering dalek\"}, {\"id\": 35665, \"name\": \"a red and yellow ball\"}, {\"id\": 35666, \"name\": \"a leaderboard\"}, {\"id\": 35667, \"name\": \"rocky path\"}, {\"id\": 35668, \"name\": \"white bunny ear\"}, {\"id\": 35669, \"name\": \"blue and white car\"}, {\"id\": 35670, \"name\": \"the cyclist's uniform\"}, {\"id\": 35671, \"name\": \"the lush bush\"}, {\"id\": 35672, \"name\": \"a distinctive brown horn\"}, {\"id\": 35673, \"name\": \"traditional headscarf\"}, {\"id\": 35674, \"name\": \"the checkered flag icon\"}, {\"id\": 35675, \"name\": \"small square window\"}, {\"id\": 35676, \"name\": \"the dwarves from snow white and the seven dwarf\"}, {\"id\": 35677, \"name\": \"the small black hole\"}, {\"id\": 35678, \"name\": \"a logo of a person riding a bicycle around a globe\"}, {\"id\": 35679, \"name\": \"a bright yellow eye\"}, {\"id\": 35680, \"name\": \"red and green stripe\"}, {\"id\": 35681, \"name\": \"white and black front end\"}, {\"id\": 35682, \"name\": \"snowflake\"}, {\"id\": 35683, \"name\": \"the pink eye\"}, {\"id\": 35684, \"name\": \"a gray suit jacket\"}, {\"id\": 35685, \"name\": \"framed picture of leaf\"}, {\"id\": 35686, \"name\": \"the brown, humanoid creature\"}, {\"id\": 35687, \"name\": \"a light-colored facade\"}, {\"id\": 35688, \"name\": \"a dagashi mart sign\"}, {\"id\": 35689, \"name\": \"red dragon\"}, {\"id\": 35690, \"name\": \"a white volkswagen beetle\"}, {\"id\": 35691, \"name\": \"dark green peterbilt truck\"}, {\"id\": 35692, \"name\": \"the additional storage compartment\"}, {\"id\": 35693, \"name\": \"a rocky mountain\"}, {\"id\": 35694, \"name\": \"the yellow hose\"}, {\"id\": 35695, \"name\": \"the striped skirt\"}, {\"id\": 35696, \"name\": \"the cubby\"}, {\"id\": 35697, \"name\": \"a server\"}, {\"id\": 35698, \"name\": \"a large stove\"}, {\"id\": 35699, \"name\": \"the maroon uniform\"}, {\"id\": 35700, \"name\": \"a gold jersey\"}, {\"id\": 35701, \"name\": \"a blue swimsuit\"}, {\"id\": 35702, \"name\": \"the colorful saddle\"}, {\"id\": 35703, \"name\": \"an open window\"}, {\"id\": 35704, \"name\": \"gray shopping cart\"}, {\"id\": 35705, \"name\": \"large train car\"}, {\"id\": 35706, \"name\": \"the subtle grid pattern\"}, {\"id\": 35707, \"name\": \"back pocket\"}, {\"id\": 35708, \"name\": \"a red leaf\"}, {\"id\": 35709, \"name\": \"distinctive snout\"}, {\"id\": 35710, \"name\": \"a pink mouse\"}, {\"id\": 35711, \"name\": \"the aqua-boutique\"}, {\"id\": 35712, \"name\": \"the twitter icon\"}, {\"id\": 35713, \"name\": \"the colorful overlay\"}, {\"id\": 35714, \"name\": \"red and yellow flower\"}, {\"id\": 35715, \"name\": \"red and yellow pant\"}, {\"id\": 35716, \"name\": \"the stomach\"}, {\"id\": 35717, \"name\": \"the market stall\"}, {\"id\": 35718, \"name\": \"a red plume\"}, {\"id\": 35719, \"name\": \"an orange fleck\"}, {\"id\": 35720, \"name\": \"a light-colored wood plank\"}, {\"id\": 35721, \"name\": \"a rightmost panel\"}, {\"id\": 35722, \"name\": \"the coffee cup\"}, {\"id\": 35723, \"name\": \"the green divider\"}, {\"id\": 35724, \"name\": \"expo's logo\"}, {\"id\": 35725, \"name\": \"blue and gold striped design\"}, {\"id\": 35726, \"name\": \"purple bike\"}, {\"id\": 35727, \"name\": \"the teal pegboard\"}, {\"id\": 35728, \"name\": \"the pink heart\"}, {\"id\": 35729, \"name\": \"vendor tent\"}, {\"id\": 35730, \"name\": \"the dark denim capri pant\"}, {\"id\": 35731, \"name\": \"a red, white, and blue banner\"}, {\"id\": 35732, \"name\": \"orange and black helmet\"}, {\"id\": 35733, \"name\": \"a large, round, white eye\"}, {\"id\": 35734, \"name\": \"an apple\"}, {\"id\": 35735, \"name\": \"a dark grey carpeting\"}, {\"id\": 35736, \"name\": \"man in military fatigue\"}, {\"id\": 35737, \"name\": \"the blue bow\"}, {\"id\": 35738, \"name\": \"circular emblem\"}, {\"id\": 35739, \"name\": \"a green metal cart\"}, {\"id\": 35740, \"name\": \"the scenic dirt road\"}, {\"id\": 35741, \"name\": \"a blue sleeve\"}, {\"id\": 35742, \"name\": \"the tall, narrow glass cylinder\"}, {\"id\": 35743, \"name\": \"the makeup kit\"}, {\"id\": 35744, \"name\": \"cylindrical tower\"}, {\"id\": 35745, \"name\": \"the sauce\"}, {\"id\": 35746, \"name\": \"the black bridge\"}, {\"id\": 35747, \"name\": \"the furrowed eyebrow\"}, {\"id\": 35748, \"name\": \"the man in a blue uniform\"}, {\"id\": 35749, \"name\": \"percussion instrument\"}, {\"id\": 35750, \"name\": \"a lush green hillside\"}, {\"id\": 35751, \"name\": \"pink inner tube\"}, {\"id\": 35752, \"name\": \"a rectangular image\"}, {\"id\": 35753, \"name\": \"stylized graphic\"}, {\"id\": 35754, \"name\": \"the tall, narrow sign\"}, {\"id\": 35755, \"name\": \"red beak\"}, {\"id\": 35756, \"name\": \"a hood of a black jacket\"}, {\"id\": 35757, \"name\": \"the low ponytail\"}, {\"id\": 35758, \"name\": \"the toy horse\"}, {\"id\": 35759, \"name\": \"long, winding tube\"}, {\"id\": 35760, \"name\": \"black and gray gravel or crushed material\"}, {\"id\": 35761, \"name\": \"children's blue shirt\"}, {\"id\": 35762, \"name\": \"bike's front end\"}, {\"id\": 35763, \"name\": \"a metal barricade\"}, {\"id\": 35764, \"name\": \"truck's side door\"}, {\"id\": 35765, \"name\": \"long white ribbon\"}, {\"id\": 35766, \"name\": \"dark-colored carriage\"}, {\"id\": 35767, \"name\": \"flower bed\"}, {\"id\": 35768, \"name\": \"bouquet of flower\"}, {\"id\": 35769, \"name\": \"the smile\"}, {\"id\": 35770, \"name\": \"a hand of the woman\"}, {\"id\": 35771, \"name\": \"a red packaging\"}, {\"id\": 35772, \"name\": \"a tarmac\"}, {\"id\": 35773, \"name\": \"a brim\"}, {\"id\": 35774, \"name\": \"the sole\"}, {\"id\": 35775, \"name\": \"the neon green safety vest\"}, {\"id\": 35776, \"name\": \"a sleek white boat\"}, {\"id\": 35777, \"name\": \"the bubble drawing\"}, {\"id\": 35778, \"name\": \"the neon yellow sneaker\"}, {\"id\": 35779, \"name\": \"the user's profile picture\"}, {\"id\": 35780, \"name\": \"the distant light\"}, {\"id\": 35781, \"name\": \"distinctive yellow s emblem\"}, {\"id\": 35782, \"name\": \"yellow traffic light\"}, {\"id\": 35783, \"name\": \"brick arch\"}, {\"id\": 35784, \"name\": \"a commercial establishment\"}, {\"id\": 35785, \"name\": \"a temple entrance\"}, {\"id\": 35786, \"name\": \"a large, white tooth\"}, {\"id\": 35787, \"name\": \"a blue and yellow stripe\"}, {\"id\": 35788, \"name\": \"the white 07\"}, {\"id\": 35789, \"name\": \"emergency responder\"}, {\"id\": 35790, \"name\": \"the large white box truck\"}, {\"id\": 35791, \"name\": \"a large windshield wiper\"}, {\"id\": 35792, \"name\": \"the brown face\"}, {\"id\": 35793, \"name\": \"an eataly sign\"}, {\"id\": 35794, \"name\": \"the blue platform\"}, {\"id\": 35795, \"name\": \"a distinctive red nose\"}, {\"id\": 35796, \"name\": \"small gold star\"}, {\"id\": 35797, \"name\": \"the gold hoop earring\"}, {\"id\": 35798, \"name\": \"long-sleeved top\"}, {\"id\": 35799, \"name\": \"the lottery board game\"}, {\"id\": 35800, \"name\": \"silver badge\"}, {\"id\": 35801, \"name\": \"the pink sweater\"}, {\"id\": 35802, \"name\": \"a red and green grassy area\"}, {\"id\": 35803, \"name\": \"the cart of toy\"}, {\"id\": 35804, \"name\": \"a large gray box\"}, {\"id\": 35805, \"name\": \"green laser light\"}, {\"id\": 35806, \"name\": \"champions cup series logo\"}, {\"id\": 35807, \"name\": \"spark\"}, {\"id\": 35808, \"name\": \"the glass orb\"}, {\"id\": 35809, \"name\": \"the white square marker\"}, {\"id\": 35810, \"name\": \"espn logo\"}, {\"id\": 35811, \"name\": \"festive red apron\"}, {\"id\": 35812, \"name\": \"a green-clad team\"}, {\"id\": 35813, \"name\": \"a model train set\"}, {\"id\": 35814, \"name\": \"small white card\"}, {\"id\": 35815, \"name\": \"the trinket\"}, {\"id\": 35816, \"name\": \"carousel's base\"}, {\"id\": 35817, \"name\": \"a brown window frame\"}, {\"id\": 35818, \"name\": \"whipped cream\"}, {\"id\": 35819, \"name\": \"the barber cape\"}, {\"id\": 35820, \"name\": \"red, white, and black uniform\"}, {\"id\": 35821, \"name\": \"colorful gummy candy\"}, {\"id\": 35822, \"name\": \"vise\"}, {\"id\": 35823, \"name\": \"a brick garage\"}, {\"id\": 35824, \"name\": \"the badge\"}, {\"id\": 35825, \"name\": \"the small mirror\"}, {\"id\": 35826, \"name\": \"the coffee table\"}, {\"id\": 35827, \"name\": \"yellow spot\"}, {\"id\": 35828, \"name\": \"the red cylindrical object\"}, {\"id\": 35829, \"name\": \"the pink star\"}, {\"id\": 35830, \"name\": \"a male soccer player\"}, {\"id\": 35831, \"name\": \"a small, robot-like toy\"}, {\"id\": 35832, \"name\": \"distinctive curved shape\"}, {\"id\": 35833, \"name\": \"a tall, arched structure\"}, {\"id\": 35834, \"name\": \"the white and black horse\"}, {\"id\": 35835, \"name\": \"a rabbit headpiece\"}, {\"id\": 35836, \"name\": \"flag's design\"}, {\"id\": 35837, \"name\": \"a sign with chinese character\"}, {\"id\": 35838, \"name\": \"dark-colored handle\"}, {\"id\": 35839, \"name\": \"the yellow and black lego vehicle\"}, {\"id\": 35840, \"name\": \"the man in a yellow shirt and orange short\"}, {\"id\": 35841, \"name\": \"red and white tent\"}, {\"id\": 35842, \"name\": \"navy blue t-shirt\"}, {\"id\": 35843, \"name\": \"rider's face\"}, {\"id\": 35844, \"name\": \"yellow and gray object\"}, {\"id\": 35845, \"name\": \"the small, round hole\"}, {\"id\": 35846, \"name\": \"the blue, red, yellow, and white species\"}, {\"id\": 35847, \"name\": \"a man on a motorbike wearing a red helmet\"}, {\"id\": 35848, \"name\": \"a pink and black striped leg wrap\"}, {\"id\": 35849, \"name\": \"large, dark-colored panel\"}, {\"id\": 35850, \"name\": \"a red hand signal\"}, {\"id\": 35851, \"name\": \"the four-lane thoroughfare\"}, {\"id\": 35852, \"name\": \"the dark, shadowy unicorn\"}, {\"id\": 35853, \"name\": \"hurley store sign\"}, {\"id\": 35854, \"name\": \"plow\"}, {\"id\": 35855, \"name\": \"foamy wave\"}, {\"id\": 35856, \"name\": \"white wing\"}, {\"id\": 35857, \"name\": \"a silver sphere\"}, {\"id\": 35858, \"name\": \"rib\"}, {\"id\": 35859, \"name\": \"the dark interior\"}, {\"id\": 35860, \"name\": \"a red parasol\"}, {\"id\": 35861, \"name\": \"a panther logo\"}, {\"id\": 35862, \"name\": \"a wooden platform\"}, {\"id\": 35863, \"name\": \"the colleague\"}, {\"id\": 35864, \"name\": \"fork\"}, {\"id\": 35865, \"name\": \"an outstretched arm\"}, {\"id\": 35866, \"name\": \"narrow lane\"}, {\"id\": 35867, \"name\": \"vibrant, tie-dyed shirt\"}, {\"id\": 35868, \"name\": \"woman in blue jean and gray jacket\"}, {\"id\": 35869, \"name\": \"red jacket-clad individual\"}, {\"id\": 35870, \"name\": \"the green and white umbrella\"}, {\"id\": 35871, \"name\": \"the small, blurry object\"}, {\"id\": 35872, \"name\": \"the wagon\"}, {\"id\": 35873, \"name\": \"nike logo\"}, {\"id\": 35874, \"name\": \"the lush green grassy hillside\"}, {\"id\": 35875, \"name\": \"a black bikini\"}, {\"id\": 35876, \"name\": \"brown robot's arm\"}, {\"id\": 35877, \"name\": \"silver balloon\"}, {\"id\": 35878, \"name\": \"a dark red brick\"}, {\"id\": 35879, \"name\": \"muddy ground\"}, {\"id\": 35880, \"name\": \"a phone model\"}, {\"id\": 35881, \"name\": \"a neon green or yellow life jacket\"}, {\"id\": 35882, \"name\": \"blue saddle blanket\"}, {\"id\": 35883, \"name\": \"paper plate\"}, {\"id\": 35884, \"name\": \"a small white star symbol\"}, {\"id\": 35885, \"name\": \"the green train engine\"}, {\"id\": 35886, \"name\": \"the large piece of wood\"}, {\"id\": 35887, \"name\": \"the man's back\"}, {\"id\": 35888, \"name\": \"a yellow and black glove\"}, {\"id\": 35889, \"name\": \"a yellow ramp\"}, {\"id\": 35890, \"name\": \"the basket of folded yoga mat\"}, {\"id\": 35891, \"name\": \"large orange bin\"}, {\"id\": 35892, \"name\": \"white boutonniere\"}, {\"id\": 35893, \"name\": \"a light jean\"}, {\"id\": 35894, \"name\": \"the large wheel or drum\"}, {\"id\": 35895, \"name\": \"a central doorway\"}, {\"id\": 35896, \"name\": \"yellow sedan\"}, {\"id\": 35897, \"name\": \"an indy logo\"}, {\"id\": 35898, \"name\": \"pink aura\"}, {\"id\": 35899, \"name\": \"the jack-o-lantern\"}, {\"id\": 35900, \"name\": \"a muddy chicken coop\"}, {\"id\": 35901, \"name\": \"the large yellow object\"}, {\"id\": 35902, \"name\": \"the player's shoe\"}, {\"id\": 35903, \"name\": \"green flame character\"}, {\"id\": 35904, \"name\": \"a yellow and black sign\"}, {\"id\": 35905, \"name\": \"bottom-middle screenshot\"}, {\"id\": 35906, \"name\": \"an ornate pillar\"}, {\"id\": 35907, \"name\": \"a wooden sled\"}, {\"id\": 35908, \"name\": \"a large black bin\"}, {\"id\": 35909, \"name\": \"a green lattice-like structure\"}, {\"id\": 35910, \"name\": \"multicolored polka dot\"}, {\"id\": 35911, \"name\": \"man in a red military uniform\"}, {\"id\": 35912, \"name\": \"the catcher's gear\"}, {\"id\": 35913, \"name\": \"the msnbc logo\"}, {\"id\": 35914, \"name\": \"the black microphone\"}, {\"id\": 35915, \"name\": \"a white smartphone\"}, {\"id\": 35916, \"name\": \"large purple eye\"}, {\"id\": 35917, \"name\": \"a vibrant mixture of vegetable and meat\"}, {\"id\": 35918, \"name\": \"a countdown timer\"}, {\"id\": 35919, \"name\": \"the yellowish material\"}, {\"id\": 35920, \"name\": \"the tan suit jacket\"}, {\"id\": 35921, \"name\": \"blue foot\"}, {\"id\": 35922, \"name\": \"colorful writing\"}, {\"id\": 35923, \"name\": \"a yellow sphere\"}, {\"id\": 35924, \"name\": \"hybrid bike\"}, {\"id\": 35925, \"name\": \"large metal body\"}, {\"id\": 35926, \"name\": \"the cartoon drawing\"}, {\"id\": 35927, \"name\": \"the pink light\"}, {\"id\": 35928, \"name\": \"a faint brown outline\"}, {\"id\": 35929, \"name\": \"the large, black rearview mirror\"}, {\"id\": 35930, \"name\": \"a red and black motorcycle\"}, {\"id\": 35931, \"name\": \"pink streak\"}, {\"id\": 35932, \"name\": \"a front of the bike\"}, {\"id\": 35933, \"name\": \"green and yellow vehicle\"}, {\"id\": 35934, \"name\": \"the colored hat\"}, {\"id\": 35935, \"name\": \"a metal countertop\"}, {\"id\": 35936, \"name\": \"a red-tinted glass\"}, {\"id\": 35937, \"name\": \"the police van\"}, {\"id\": 35938, \"name\": \"the large red and white drum\"}, {\"id\": 35939, \"name\": \"blue volunteer shirt\"}, {\"id\": 35940, \"name\": \"a dark grey jacket\"}, {\"id\": 35941, \"name\": \"a wheel-like object\"}, {\"id\": 35942, \"name\": \"the red robe\"}, {\"id\": 35943, \"name\": \"the black joystick\"}, {\"id\": 35944, \"name\": \"the dust cloud\"}, {\"id\": 35945, \"name\": \"black and white hat\"}, {\"id\": 35946, \"name\": \"the brown rocky structure\"}, {\"id\": 35947, \"name\": \"cartoon fish\"}, {\"id\": 35948, \"name\": \"vibrant flower\"}, {\"id\": 35949, \"name\": \"silhouette\"}, {\"id\": 35950, \"name\": \"the orange lighting\"}, {\"id\": 35951, \"name\": \"large, orange rock formation\"}, {\"id\": 35952, \"name\": \"a prominent green sign\"}, {\"id\": 35953, \"name\": \"excavator\"}, {\"id\": 35954, \"name\": \"the gray suit jacket\"}, {\"id\": 35955, \"name\": \"the gray hood\"}, {\"id\": 35956, \"name\": \"a rifle scope\"}, {\"id\": 35957, \"name\": \"the casual summer attire\"}, {\"id\": 35958, \"name\": \"the light blue surgical mask\"}, {\"id\": 35959, \"name\": \"the circular logo\"}, {\"id\": 35960, \"name\": \"the lead car\"}, {\"id\": 35961, \"name\": \"head wrap\"}, {\"id\": 35962, \"name\": \"the armored man\"}, {\"id\": 35963, \"name\": \"batter's box\"}, {\"id\": 35964, \"name\": \"red rubber toy\"}, {\"id\": 35965, \"name\": \"the large metal pan\"}, {\"id\": 35966, \"name\": \"a red and green car\"}, {\"id\": 35967, \"name\": \"a red and yellow striped vest\"}, {\"id\": 35968, \"name\": \"the yellow flame design\"}, {\"id\": 35969, \"name\": \"the decorative ironwork\"}, {\"id\": 35970, \"name\": \"the light-colored saddle\"}, {\"id\": 35971, \"name\": \"the cardboard item\"}, {\"id\": 35972, \"name\": \"the blue light bar\"}, {\"id\": 35973, \"name\": \"a woman in costume\"}, {\"id\": 35974, \"name\": \"the black lanyard\"}, {\"id\": 35975, \"name\": \"crew of man\"}, {\"id\": 35976, \"name\": \"small icon\"}, {\"id\": 35977, \"name\": \"the orange finger\"}, {\"id\": 35978, \"name\": \"the black track\"}, {\"id\": 35979, \"name\": \"the blue suit jacket\"}, {\"id\": 35980, \"name\": \"the blue genie from aladdin\"}, {\"id\": 35981, \"name\": \"the toy box\"}, {\"id\": 35982, \"name\": \"the large suitcase\"}, {\"id\": 35983, \"name\": \"the large roll of material\"}, {\"id\": 35984, \"name\": \"patient's arm\"}, {\"id\": 35985, \"name\": \"the green gras median\"}, {\"id\": 35986, \"name\": \"silver grid pattern\"}, {\"id\": 35987, \"name\": \"the blue and red logo\"}, {\"id\": 35988, \"name\": \"the blue and white item\"}, {\"id\": 35989, \"name\": \"a striking white van\"}, {\"id\": 35990, \"name\": \"the yellow ring\"}, {\"id\": 35991, \"name\": \"zigzag pattern\"}, {\"id\": 35992, \"name\": \"the colorful costume\"}, {\"id\": 35993, \"name\": \"the dark blue vest\"}, {\"id\": 35994, \"name\": \"bright brake light\"}, {\"id\": 35995, \"name\": \"a red cord\"}, {\"id\": 35996, \"name\": \"the buffet-style arrangement of dessert\"}, {\"id\": 35997, \"name\": \"neon pink light\"}, {\"id\": 35998, \"name\": \"large yellow and green pole\"}, {\"id\": 35999, \"name\": \"the white and red striped pole\"}, {\"id\": 36000, \"name\": \"the forested shoreline\"}, {\"id\": 36001, \"name\": \"the layer of white sand\"}, {\"id\": 36002, \"name\": \"large pink and yellow wing\"}, {\"id\": 36003, \"name\": \"the large white and gold balloon\"}, {\"id\": 36004, \"name\": \"a red spoiler\"}, {\"id\": 36005, \"name\": \"person's black sleeve\"}, {\"id\": 36006, \"name\": \"a green and blue sea plant\"}, {\"id\": 36007, \"name\": \"the car's red brake light\"}, {\"id\": 36008, \"name\": \"small plush toy or doll\"}, {\"id\": 36009, \"name\": \"the menu of service offered\"}, {\"id\": 36010, \"name\": \"the red and white tractor\"}, {\"id\": 36011, \"name\": \"the white nike swoosh\"}, {\"id\": 36012, \"name\": \"the black tube\"}, {\"id\": 36013, \"name\": \"a large red sphere\"}, {\"id\": 36014, \"name\": \"a black and gray tank top\"}, {\"id\": 36015, \"name\": \"a black cone\"}, {\"id\": 36016, \"name\": \"white and gold pole\"}, {\"id\": 36017, \"name\": \"the small, yellow license plate\"}, {\"id\": 36018, \"name\": \"a silver stem\"}, {\"id\": 36019, \"name\": \"the grey and white sneaker\"}, {\"id\": 36020, \"name\": \"the navy baseball cap\"}, {\"id\": 36021, \"name\": \"the dark uniform\"}, {\"id\": 36022, \"name\": \"a dark gray asphalt\"}, {\"id\": 36023, \"name\": \"green bike lane\"}, {\"id\": 36024, \"name\": \"the yellow horn\"}, {\"id\": 36025, \"name\": \"the short leg\"}, {\"id\": 36026, \"name\": \"a faint glow\"}, {\"id\": 36027, \"name\": \"colorful vehicle\"}, {\"id\": 36028, \"name\": \"metal bollard\"}, {\"id\": 36029, \"name\": \"a fruit bowl\"}, {\"id\": 36030, \"name\": \"large, damaged tire or wheel cover\"}, {\"id\": 36031, \"name\": \"black short-sleeved shirt\"}, {\"id\": 36032, \"name\": \"a candy\"}, {\"id\": 36033, \"name\": \"cream-colored stone pillar\"}, {\"id\": 36034, \"name\": \"an emergency responder\"}, {\"id\": 36035, \"name\": \"tattooed arm\"}, {\"id\": 36036, \"name\": \"black utility belt\"}, {\"id\": 36037, \"name\": \"large hose\"}, {\"id\": 36038, \"name\": \"the brick surface\"}, {\"id\": 36039, \"name\": \"complex arrangement of metallic pipe\"}, {\"id\": 36040, \"name\": \"a front windshield of a vehicle\"}, {\"id\": 36041, \"name\": \"the white hatchback\"}, {\"id\": 36042, \"name\": \"matching black shirt\"}, {\"id\": 36043, \"name\": \"the large warehouse or garage\"}, {\"id\": 36044, \"name\": \"an arched doorway\"}, {\"id\": 36045, \"name\": \"a white or blue detail\"}, {\"id\": 36046, \"name\": \"a footwear\"}, {\"id\": 36047, \"name\": \"a flexi logo\"}, {\"id\": 36048, \"name\": \"a blue paint splatter design\"}, {\"id\": 36049, \"name\": \"the matching striped pattern\"}, {\"id\": 36050, \"name\": \"yellow couch\"}, {\"id\": 36051, \"name\": \"the yellow and blue flower\"}, {\"id\": 36052, \"name\": \"a white symbol of bicycle\"}, {\"id\": 36053, \"name\": \"golden spire\"}, {\"id\": 36054, \"name\": \"cockpit of a race car\"}, {\"id\": 36055, \"name\": \"white stone facade\"}, {\"id\": 36056, \"name\": \"red and black vacuum cleaner\"}, {\"id\": 36057, \"name\": \"the large rv\"}, {\"id\": 36058, \"name\": \"the players' foot\"}, {\"id\": 36059, \"name\": \"front hood\"}, {\"id\": 36060, \"name\": \"the black checkered pattern\"}, {\"id\": 36061, \"name\": \"tan-colored block\"}, {\"id\": 36062, \"name\": \"an eliminated sign\"}, {\"id\": 36063, \"name\": \"a lime green car\"}, {\"id\": 36064, \"name\": \"a white pedestrian crossing\"}, {\"id\": 36065, \"name\": \"the rubber duck\"}, {\"id\": 36066, \"name\": \"white chinese character\"}, {\"id\": 36067, \"name\": \"a white board\"}, {\"id\": 36068, \"name\": \"the brightly colored bench\"}, {\"id\": 36069, \"name\": \"an interlocking gray tile\"}, {\"id\": 36070, \"name\": \"a strip\"}, {\"id\": 36071, \"name\": \"large red light\"}, {\"id\": 36072, \"name\": \"a silver sequin design\"}, {\"id\": 36073, \"name\": \"the bright yellow body\"}, {\"id\": 36074, \"name\": \"the high-ranking military official\"}, {\"id\": 36075, \"name\": \"the small metal wheel\"}, {\"id\": 36076, \"name\": \"ivory tusk\"}, {\"id\": 36077, \"name\": \"the front fender\"}, {\"id\": 36078, \"name\": \"the large, brown cliff\"}, {\"id\": 36079, \"name\": \"the rocky terrain\"}, {\"id\": 36080, \"name\": \"a formally dressed man\"}, {\"id\": 36081, \"name\": \"excavator's arm\"}, {\"id\": 36082, \"name\": \"the internal organ\"}, {\"id\": 36083, \"name\": \"blue or red scarf\"}, {\"id\": 36084, \"name\": \"whimsical gold-colored metal design\"}, {\"id\": 36085, \"name\": \"silver ornament\"}, {\"id\": 36086, \"name\": \"the brown cushion\"}, {\"id\": 36087, \"name\": \"the bicycle rider\"}, {\"id\": 36088, \"name\": \"a white and red logo\"}, {\"id\": 36089, \"name\": \"the red metal frame\"}, {\"id\": 36090, \"name\": \"police logo\"}, {\"id\": 36091, \"name\": \"a bridle\"}, {\"id\": 36092, \"name\": \"the slogan\"}, {\"id\": 36093, \"name\": \"barn's exterior\"}, {\"id\": 36094, \"name\": \"the brown dirt path\"}, {\"id\": 36095, \"name\": \"the white caravan\"}, {\"id\": 36096, \"name\": \"distinctive orange stripe\"}, {\"id\": 36097, \"name\": \"insect\"}, {\"id\": 36098, \"name\": \"a pot of broth\"}, {\"id\": 36099, \"name\": \"green horse\"}, {\"id\": 36100, \"name\": \"green and yellow safety vest\"}, {\"id\": 36101, \"name\": \"a large white satellite dish\"}, {\"id\": 36102, \"name\": \"the woman in a gray jacket\"}, {\"id\": 36103, \"name\": \"colored sign\"}, {\"id\": 36104, \"name\": \"waistband\"}, {\"id\": 36105, \"name\": \"the yellow curtain or divider\"}, {\"id\": 36106, \"name\": \"the red and blue banner\"}, {\"id\": 36107, \"name\": \"an oxen-drawn cart\"}, {\"id\": 36108, \"name\": \"a yellow and black striped design\"}, {\"id\": 36109, \"name\": \"the tall, thin pole\"}, {\"id\": 36110, \"name\": \"a light-colored wood grain pattern\"}, {\"id\": 36111, \"name\": \"a die\"}, {\"id\": 36112, \"name\": \"vast expanse of sand\"}, {\"id\": 36113, \"name\": \"a fish's mouth\"}, {\"id\": 36114, \"name\": \"stone pillar\"}, {\"id\": 36115, \"name\": \"an artist's hands\"}, {\"id\": 36116, \"name\": \"image of food\"}, {\"id\": 36117, \"name\": \"a sport bra\"}, {\"id\": 36118, \"name\": \"long, rectangular blue structure\"}, {\"id\": 36119, \"name\": \"the yellow sleeve\"}, {\"id\": 36120, \"name\": \"a yellow tunnel\"}, {\"id\": 36121, \"name\": \"hiking boot\"}, {\"id\": 36122, \"name\": \"the bottom panel\"}, {\"id\": 36123, \"name\": \"an exposed root\"}, {\"id\": 36124, \"name\": \"flat, rectangular base\"}, {\"id\": 36125, \"name\": \"the monkey or sloth\"}, {\"id\": 36126, \"name\": \"a bathroom counter\"}, {\"id\": 36127, \"name\": \"unique, layered facade\"}, {\"id\": 36128, \"name\": \"orange plastic bag\"}, {\"id\": 36129, \"name\": \"a blue headband\"}, {\"id\": 36130, \"name\": \"van's side door\"}, {\"id\": 36131, \"name\": \"a multicolored lego block\"}, {\"id\": 36132, \"name\": \"the large commercial vehicle\"}, {\"id\": 36133, \"name\": \"white and blue uniform\"}, {\"id\": 36134, \"name\": \"a pet\"}, {\"id\": 36135, \"name\": \"oversized prop\"}, {\"id\": 36136, \"name\": \"brown cardboard tube\"}, {\"id\": 36137, \"name\": \"the blue and purple diamond pattern\"}, {\"id\": 36138, \"name\": \"toy baseball bat\"}, {\"id\": 36139, \"name\": \"a red and white semi-truck\"}, {\"id\": 36140, \"name\": \"an illegible red writing\"}, {\"id\": 36141, \"name\": \"the person's shoulder\"}, {\"id\": 36142, \"name\": \"the american flag decal\"}, {\"id\": 36143, \"name\": \"a red elf on the shelf\"}, {\"id\": 36144, \"name\": \"a striking blue skin\"}, {\"id\": 36145, \"name\": \"a city sidewalk\"}, {\"id\": 36146, \"name\": \"large metal trash can\"}, {\"id\": 36147, \"name\": \"a dark blue uniform\"}, {\"id\": 36148, \"name\": \"a woman carrying a bag of grocery\"}, {\"id\": 36149, \"name\": \"red and white guardrail\"}, {\"id\": 36150, \"name\": \"blue and green jersey\"}, {\"id\": 36151, \"name\": \"wooden bookshelf\"}, {\"id\": 36152, \"name\": \"a low bun\"}, {\"id\": 36153, \"name\": \"black-and-white mural\"}, {\"id\": 36154, \"name\": \"streamer\"}, {\"id\": 36155, \"name\": \"the brown suit\"}, {\"id\": 36156, \"name\": \"a bottle of oil\"}, {\"id\": 36157, \"name\": \"blue and white striped hull\"}, {\"id\": 36158, \"name\": \"hood scoop\"}, {\"id\": 36159, \"name\": \"a large black sign\"}, {\"id\": 36160, \"name\": \"vibrant purple t-shirt\"}, {\"id\": 36161, \"name\": \"the zippered closure\"}, {\"id\": 36162, \"name\": \"faded paint\"}, {\"id\": 36163, \"name\": \"the bicyclist\"}, {\"id\": 36164, \"name\": \"an image of a lobster\"}, {\"id\": 36165, \"name\": \"a long coat\"}, {\"id\": 36166, \"name\": \"orange dog\"}, {\"id\": 36167, \"name\": \"the glowing light\"}, {\"id\": 36168, \"name\": \"the silver side mirror\"}, {\"id\": 36169, \"name\": \"curved roofline\"}, {\"id\": 36170, \"name\": \"the taxi's logo\"}, {\"id\": 36171, \"name\": \"a cyclist's attire\"}, {\"id\": 36172, \"name\": \"the ne logo\"}, {\"id\": 36173, \"name\": \"beaded necklace\"}, {\"id\": 36174, \"name\": \"red signage\"}, {\"id\": 36175, \"name\": \"the navy polo shirt\"}, {\"id\": 36176, \"name\": \"the red shell\"}, {\"id\": 36177, \"name\": \"coiled spring\"}, {\"id\": 36178, \"name\": \"an uc logo\"}, {\"id\": 36179, \"name\": \"indistinct blue writing\"}, {\"id\": 36180, \"name\": \"police officer in uniform\"}, {\"id\": 36181, \"name\": \"dark gray stripe\"}, {\"id\": 36182, \"name\": \"the gamerhub logo\"}, {\"id\": 36183, \"name\": \"the parallel, white metal rod\"}, {\"id\": 36184, \"name\": \"the clear backboard\"}, {\"id\": 36185, \"name\": \"the xtreme logo\"}, {\"id\": 36186, \"name\": \"a black-and-white stripe\"}, {\"id\": 36187, \"name\": \"a picture of a baby's face\"}, {\"id\": 36188, \"name\": \"a fire personnel\"}, {\"id\": 36189, \"name\": \"dark wood\"}, {\"id\": 36190, \"name\": \"chelsea\"}, {\"id\": 36191, \"name\": \"blue and black shirt\"}, {\"id\": 36192, \"name\": \"black and yellow glove\"}, {\"id\": 36193, \"name\": \"a blue and orange banner\"}, {\"id\": 36194, \"name\": \"black saddle\"}, {\"id\": 36195, \"name\": \"the candy\"}, {\"id\": 36196, \"name\": \"left turn sign\"}, {\"id\": 36197, \"name\": \"a slice of cheese\"}, {\"id\": 36198, \"name\": \"the hay\"}, {\"id\": 36199, \"name\": \"woman in black pant\"}, {\"id\": 36200, \"name\": \"player's current speed\"}, {\"id\": 36201, \"name\": \"the decal\"}, {\"id\": 36202, \"name\": \"a large, red and yellow striped object\"}, {\"id\": 36203, \"name\": \"the prominent blue banner\"}, {\"id\": 36204, \"name\": \"a window's design\"}, {\"id\": 36205, \"name\": \"a blue austin morri 1100\"}, {\"id\": 36206, \"name\": \"the child in a pink dress\"}, {\"id\": 36207, \"name\": \"large beam\"}, {\"id\": 36208, \"name\": \"a speed limit\"}, {\"id\": 36209, \"name\": \"the white tile\"}, {\"id\": 36210, \"name\": \"the black bandana\"}, {\"id\": 36211, \"name\": \"a brown shutter\"}, {\"id\": 36212, \"name\": \"the pink lettering\"}, {\"id\": 36213, \"name\": \"blue droplet\"}, {\"id\": 36214, \"name\": \"a thick beard\"}, {\"id\": 36215, \"name\": \"middle lane\"}, {\"id\": 36216, \"name\": \"mussel\"}, {\"id\": 36217, \"name\": \"a tv station's logo\"}, {\"id\": 36218, \"name\": \"ornate design\"}, {\"id\": 36219, \"name\": \"the tripadvisor logo\"}, {\"id\": 36220, \"name\": \"a dark-colored jacket\"}, {\"id\": 36221, \"name\": \"the single, red eye\"}, {\"id\": 36222, \"name\": \"the white arrow pointing upward\"}, {\"id\": 36223, \"name\": \"white beach ball\"}, {\"id\": 36224, \"name\": \"beige cover\"}, {\"id\": 36225, \"name\": \"the blue stanchion\"}, {\"id\": 36226, \"name\": \"a yellow and blue jersey\"}, {\"id\": 36227, \"name\": \"the wide-brimmed white hat\"}, {\"id\": 36228, \"name\": \"white pillar or column\"}, {\"id\": 36229, \"name\": \"a recording device\"}, {\"id\": 36230, \"name\": \"a red and black plaid tube\"}, {\"id\": 36231, \"name\": \"the animal-shaped seat\"}, {\"id\": 36232, \"name\": \"large metal pole\"}, {\"id\": 36233, \"name\": \"the rainbow\"}, {\"id\": 36234, \"name\": \"an attendee\"}, {\"id\": 36235, \"name\": \"the hornet logo\"}, {\"id\": 36236, \"name\": \"a white and orange striped barrier\"}, {\"id\": 36237, \"name\": \"dark-colored t-shirt\"}, {\"id\": 36238, \"name\": \"a mermaid's tail\"}, {\"id\": 36239, \"name\": \"the white pvc pipe\"}, {\"id\": 36240, \"name\": \"blue bikini top\"}, {\"id\": 36241, \"name\": \"the short, brown bob\"}, {\"id\": 36242, \"name\": \"long shadow\"}, {\"id\": 36243, \"name\": \"the reformer equipment\"}, {\"id\": 36244, \"name\": \"a neon pink light\"}, {\"id\": 36245, \"name\": \"pink tassel earring\"}, {\"id\": 36246, \"name\": \"a navy blue top\"}, {\"id\": 36247, \"name\": \"a cartoon of spiderman's arm\"}, {\"id\": 36248, \"name\": \"the high-visibility safety vest\"}, {\"id\": 36249, \"name\": \"the bike handlebar\"}, {\"id\": 36250, \"name\": \"the fireplace\"}, {\"id\": 36251, \"name\": \"the green drawer\"}, {\"id\": 36252, \"name\": \"the smoking tire\"}, {\"id\": 36253, \"name\": \"the grey aircraft\"}, {\"id\": 36254, \"name\": \"yellow and blue stripe\"}, {\"id\": 36255, \"name\": \"left bicep\"}, {\"id\": 36256, \"name\": \"a woman's red-painted fingernail\"}, {\"id\": 36257, \"name\": \"the wide smile\"}, {\"id\": 36258, \"name\": \"small picture frame\"}, {\"id\": 36259, \"name\": \"the radio\"}, {\"id\": 36260, \"name\": \"a red and black lettering\"}, {\"id\": 36261, \"name\": \"a presence of a leaderboard\"}, {\"id\": 36262, \"name\": \"a brown strap\"}, {\"id\": 36263, \"name\": \"the bus's front grille\"}, {\"id\": 36264, \"name\": \"a silver front light\"}, {\"id\": 36265, \"name\": \"race's participants\"}, {\"id\": 36266, \"name\": \"green color scheme\"}, {\"id\": 36267, \"name\": \"boy on a bike\"}, {\"id\": 36268, \"name\": \"a large bird of prey\"}, {\"id\": 36269, \"name\": \"dark blue tank top\"}, {\"id\": 36270, \"name\": \"the patch of white paint\"}, {\"id\": 36271, \"name\": \"a ride's track\"}, {\"id\": 36272, \"name\": \"the pixelated face\"}, {\"id\": 36273, \"name\": \"the red fuel tank\"}, {\"id\": 36274, \"name\": \"a white and red striped barrier\"}, {\"id\": 36275, \"name\": \"the black item\"}, {\"id\": 36276, \"name\": \"dark gray pant\"}, {\"id\": 36277, \"name\": \"bicycle symbol\"}, {\"id\": 36278, \"name\": \"the disney character\"}, {\"id\": 36279, \"name\": \"a raccoon's shadow\"}, {\"id\": 36280, \"name\": \"a spire\"}, {\"id\": 36281, \"name\": \"the silver metal piece\"}, {\"id\": 36282, \"name\": \"a distance and speed indicator\"}, {\"id\": 36283, \"name\": \"congested highway\"}, {\"id\": 36284, \"name\": \"a red and white pattern\"}, {\"id\": 36285, \"name\": \"beige leather passenger door panel\"}, {\"id\": 36286, \"name\": \"a dashboard of the vehicle or bike\"}, {\"id\": 36287, \"name\": \"large, dark stone basin\"}, {\"id\": 36288, \"name\": \"the large quantity of onion\"}, {\"id\": 36289, \"name\": \"the small, silver eye\"}, {\"id\": 36290, \"name\": \"the circular target\"}, {\"id\": 36291, \"name\": \"a distant forested area\"}, {\"id\": 36292, \"name\": \"nose cone\"}, {\"id\": 36293, \"name\": \"the blurred windshield\"}, {\"id\": 36294, \"name\": \"a red saddle pad\"}, {\"id\": 36295, \"name\": \"grey couch\"}, {\"id\": 36296, \"name\": \"large beige structure\"}, {\"id\": 36297, \"name\": \"a distinctive teal stripe\"}, {\"id\": 36298, \"name\": \"landing\"}, {\"id\": 36299, \"name\": \"fishing vessel\"}, {\"id\": 36300, \"name\": \"a black and white stripe\"}, {\"id\": 36301, \"name\": \"a muddy platform\"}, {\"id\": 36302, \"name\": \"conveyer belt\"}, {\"id\": 36303, \"name\": \"transparent black box\"}, {\"id\": 36304, \"name\": \"neon green sport car\"}, {\"id\": 36305, \"name\": \"the emergency personnel\"}, {\"id\": 36306, \"name\": \"shoulder-length blonde hair\"}, {\"id\": 36307, \"name\": \"bicycle rack\"}, {\"id\": 36308, \"name\": \"a tortoiseshell frame\"}, {\"id\": 36309, \"name\": \"short black tail\"}, {\"id\": 36310, \"name\": \"a green wetsuit\"}, {\"id\": 36311, \"name\": \"bare-chested man\"}, {\"id\": 36312, \"name\": \"dark, vertical line\"}, {\"id\": 36313, \"name\": \"brown and tan granite countertop\"}, {\"id\": 36314, \"name\": \"the large, light blue advertisement\"}, {\"id\": 36315, \"name\": \"the red oval banner\"}, {\"id\": 36316, \"name\": \"the red double-decker bu\"}, {\"id\": 36317, \"name\": \"the maroon and white jersey\"}, {\"id\": 36318, \"name\": \"the black and white crosswalk\"}, {\"id\": 36319, \"name\": \"white silhouette of africa\"}, {\"id\": 36320, \"name\": \"white fender\"}, {\"id\": 36321, \"name\": \"the wooden booth\"}, {\"id\": 36322, \"name\": \"the yellow table\"}, {\"id\": 36323, \"name\": \"a red atv\"}, {\"id\": 36324, \"name\": \"a yea sport logo\"}, {\"id\": 36325, \"name\": \"pink bra-like top\"}, {\"id\": 36326, \"name\": \"the black and white floral top\"}, {\"id\": 36327, \"name\": \"a spx roninair fin straighten\"}, {\"id\": 36328, \"name\": \"the small, brown clay pots and cup\"}, {\"id\": 36329, \"name\": \"people's leg\"}, {\"id\": 36330, \"name\": \"a streetlight pole\"}, {\"id\": 36331, \"name\": \"a busine attire\"}, {\"id\": 36332, \"name\": \"the barber's chair\"}, {\"id\": 36333, \"name\": \"protester\"}, {\"id\": 36334, \"name\": \"a man in suit\"}, {\"id\": 36335, \"name\": \"the exercise mat\"}, {\"id\": 36336, \"name\": \"small, open-wheeled vehicle\"}, {\"id\": 36337, \"name\": \"playground's equipment\"}, {\"id\": 36338, \"name\": \"the dark car\"}, {\"id\": 36339, \"name\": \"photograph of a motorcycle\"}, {\"id\": 36340, \"name\": \"a potted succulent\"}, {\"id\": 36341, \"name\": \"the painting or drawing\"}, {\"id\": 36342, \"name\": \"the red-painted symbol\"}, {\"id\": 36343, \"name\": \"a store's shelf\"}, {\"id\": 36344, \"name\": \"a statue of an angel\"}, {\"id\": 36345, \"name\": \"a salt and pepper shaker\"}, {\"id\": 36346, \"name\": \"blurred image of a yellow taxi cab\"}, {\"id\": 36347, \"name\": \"panel's metal surface\"}, {\"id\": 36348, \"name\": \"the open page\"}, {\"id\": 36349, \"name\": \"a blue paper\"}, {\"id\": 36350, \"name\": \"a condiment\"}, {\"id\": 36351, \"name\": \"a black tattoo\"}, {\"id\": 36352, \"name\": \"the counter area\"}, {\"id\": 36353, \"name\": \"the white cable\"}, {\"id\": 36354, \"name\": \"a grocery store produce section\"}, {\"id\": 36355, \"name\": \"a stainles steel appliance\"}, {\"id\": 36356, \"name\": \"an eyeglass\"}, {\"id\": 36357, \"name\": \"jean\"}, {\"id\": 36358, \"name\": \"the horizontal wooden slat\"}, {\"id\": 36359, \"name\": \"the purple tape\"}, {\"id\": 36360, \"name\": \"snowflake design\"}, {\"id\": 36361, \"name\": \"the coffee-making equipment\"}, {\"id\": 36362, \"name\": \"the security guard\"}, {\"id\": 36363, \"name\": \"the rack of clothe\"}, {\"id\": 36364, \"name\": \"image of drink\"}, {\"id\": 36365, \"name\": \"top shelf\"}, {\"id\": 36366, \"name\": \"the person's backpack\"}, {\"id\": 36367, \"name\": \"a silver finish\"}, {\"id\": 36368, \"name\": \"piece of artwork\"}, {\"id\": 36369, \"name\": \"a dark blue handle\"}, {\"id\": 36370, \"name\": \"holiday decoration\"}, {\"id\": 36371, \"name\": \"mickey mouse graphic\"}, {\"id\": 36372, \"name\": \"a black pedal\"}, {\"id\": 36373, \"name\": \"blue and white patterned rim\"}, {\"id\": 36374, \"name\": \"the silver doorknob\"}, {\"id\": 36375, \"name\": \"soap dispenser\"}, {\"id\": 36376, \"name\": \"the individual's fingers\"}, {\"id\": 36377, \"name\": \"a red compartment\"}, {\"id\": 36378, \"name\": \"a white metal rack\"}, {\"id\": 36379, \"name\": \"person's image\"}, {\"id\": 36380, \"name\": \"the remote\"}, {\"id\": 36381, \"name\": \"the grey backpack\"}, {\"id\": 36382, \"name\": \"the green gardening tool\"}, {\"id\": 36383, \"name\": \"chip\"}, {\"id\": 36384, \"name\": \"the console's screen\"}, {\"id\": 36385, \"name\": \"black wire stand\"}, {\"id\": 36386, \"name\": \"a framed piece of art\"}, {\"id\": 36387, \"name\": \"the white stone pillar\"}, {\"id\": 36388, \"name\": \"a black metal grille\"}, {\"id\": 36389, \"name\": \"green apron\"}, {\"id\": 36390, \"name\": \"black metal\"}, {\"id\": 36391, \"name\": \"a carved detail\"}, {\"id\": 36392, \"name\": \"the light-colored finish\"}, {\"id\": 36393, \"name\": \"open book\"}, {\"id\": 36394, \"name\": \"the neatly trimmed bush\"}, {\"id\": 36395, \"name\": \"the festive holiday decoration\"}, {\"id\": 36396, \"name\": \"the yellow graffiti symbol\"}, {\"id\": 36397, \"name\": \"a leg of a black piece of furniture\"}, {\"id\": 36398, \"name\": \"stationary bicycle\"}, {\"id\": 36399, \"name\": \"black nail polish\"}, {\"id\": 36400, \"name\": \"black knife\"}, {\"id\": 36401, \"name\": \"bus's rear\"}, {\"id\": 36402, \"name\": \"the orange velvet\"}, {\"id\": 36403, \"name\": \"the art piece\"}, {\"id\": 36404, \"name\": \"the trash receptacle\"}, {\"id\": 36405, \"name\": \"the colorful menu\"}, {\"id\": 36406, \"name\": \"the sweet treat\"}, {\"id\": 36407, \"name\": \"pink nail\"}, {\"id\": 36408, \"name\": \"a blue-handled knife\"}, {\"id\": 36409, \"name\": \"the product label\"}, {\"id\": 36410, \"name\": \"white snowflake\"}, {\"id\": 36411, \"name\": \"a chocolate cake slice\"}, {\"id\": 36412, \"name\": \"blue rim\"}, {\"id\": 36413, \"name\": \"the nail\"}, {\"id\": 36414, \"name\": \"the batman poster\"}, {\"id\": 36415, \"name\": \"a wooden planter box\"}, {\"id\": 36416, \"name\": \"the storefront of a restaurant\"}, {\"id\": 36417, \"name\": \"the white desk\"}, {\"id\": 36418, \"name\": \"a bottle of hand soap\"}, {\"id\": 36419, \"name\": \"a gold-framed picture\"}, {\"id\": 36420, \"name\": \"an uniformed worker\"}, {\"id\": 36421, \"name\": \"a gold stanchion\"}, {\"id\": 36422, \"name\": \"the yoga pose\"}, {\"id\": 36423, \"name\": \"controller\"}, {\"id\": 36424, \"name\": \"the ceiling's design\"}, {\"id\": 36425, \"name\": \"a glass-fronted refrigerator\"}, {\"id\": 36426, \"name\": \"the dark brown granite\"}, {\"id\": 36427, \"name\": \"energy drink\"}, {\"id\": 36428, \"name\": \"a yellow bloom\"}, {\"id\": 36429, \"name\": \"a menu board\"}, {\"id\": 36430, \"name\": \"black iron fence\"}, {\"id\": 36431, \"name\": \"glass entranceway\"}, {\"id\": 36432, \"name\": \"a plant pot\"}, {\"id\": 36433, \"name\": \"a red canister\"}, {\"id\": 36434, \"name\": \"black glass surface\"}, {\"id\": 36435, \"name\": \"a green bell pepper\"}, {\"id\": 36436, \"name\": \"a ticket counter\"}, {\"id\": 36437, \"name\": \"gray carpeted area\"}, {\"id\": 36438, \"name\": \"a grogu\"}, {\"id\": 36439, \"name\": \"white charging pad\"}, {\"id\": 36440, \"name\": \"black pole or beam\"}, {\"id\": 36441, \"name\": \"beige granite\"}, {\"id\": 36442, \"name\": \"a tan-colored panel\"}, {\"id\": 36443, \"name\": \"statue of a woman\"}, {\"id\": 36444, \"name\": \"the colorful painting\"}, {\"id\": 36445, \"name\": \"the black control panel\"}, {\"id\": 36446, \"name\": \"well-stocked grocery store shelf\"}, {\"id\": 36447, \"name\": \"orange power tool\"}, {\"id\": 36448, \"name\": \"the cleaning supply\"}, {\"id\": 36449, \"name\": \"a decorative garland\"}, {\"id\": 36450, \"name\": \"the front card\"}, {\"id\": 36451, \"name\": \"a yellow spot\"}, {\"id\": 36452, \"name\": \"a black stanchion\"}, {\"id\": 36453, \"name\": \"a brick pillar\"}, {\"id\": 36454, \"name\": \"a ricola logo\"}, {\"id\": 36455, \"name\": \"pink tablecloth\"}, {\"id\": 36456, \"name\": \"the purse or bag\"}, {\"id\": 36457, \"name\": \"the festive wreath\"}, {\"id\": 36458, \"name\": \"decorative molding\"}, {\"id\": 36459, \"name\": \"funko pop figure\"}, {\"id\": 36460, \"name\": \"the glass case\"}, {\"id\": 36461, \"name\": \"an open book\"}, {\"id\": 36462, \"name\": \"stainless-steel appliance\"}, {\"id\": 36463, \"name\": \"black and silver bench press\"}, {\"id\": 36464, \"name\": \"a ceiling's design\"}, {\"id\": 36465, \"name\": \"the statue of a woman\"}, {\"id\": 36466, \"name\": \"the dark wood or laminate\"}, {\"id\": 36467, \"name\": \"an orange graffiti\"}, {\"id\": 36468, \"name\": \"light-colored cabinet\"}, {\"id\": 36469, \"name\": \"a yellow creature\"}, {\"id\": 36470, \"name\": \"bottle of hand sanitizer\"}, {\"id\": 36471, \"name\": \"the round air vent\"}, {\"id\": 36472, \"name\": \"a white and red shopping bag\"}, {\"id\": 36473, \"name\": \"textile\"}, {\"id\": 36474, \"name\": \"ductwork\"}, {\"id\": 36475, \"name\": \"the black-and-white drawing\"}, {\"id\": 36476, \"name\": \"the kitchen countertop\"}, {\"id\": 36477, \"name\": \"colorful artwork\"}, {\"id\": 36478, \"name\": \"the white crown molding\"}, {\"id\": 36479, \"name\": \"bottle of shampoo\"}, {\"id\": 36480, \"name\": \"a festive christma decoration\"}, {\"id\": 36481, \"name\": \"a raw chicken piece\"}, {\"id\": 36482, \"name\": \"tablet\"}, {\"id\": 36483, \"name\": \"the red or blue box\"}, {\"id\": 36484, \"name\": \"a concrete stair\"}, {\"id\": 36485, \"name\": \"the decorative pumpkin\"}, {\"id\": 36486, \"name\": \"the cab\"}, {\"id\": 36487, \"name\": \"a lace cuff\"}, {\"id\": 36488, \"name\": \"sleeveless\"}, {\"id\": 36489, \"name\": \"the station's red support column\"}, {\"id\": 36490, \"name\": \"the decorative molding\"}, {\"id\": 36491, \"name\": \"office tower\"}, {\"id\": 36492, \"name\": \"dark green bag\"}, {\"id\": 36493, \"name\": \"a black keyboard\"}, {\"id\": 36494, \"name\": \"the stove's control panel\"}, {\"id\": 36495, \"name\": \"yellow mixture\"}, {\"id\": 36496, \"name\": \"the white platform\"}, {\"id\": 36497, \"name\": \"vase with flower\"}, {\"id\": 36498, \"name\": \"the black awning or overhang\"}, {\"id\": 36499, \"name\": \"japanese flag\"}, {\"id\": 36500, \"name\": \"blender\"}, {\"id\": 36501, \"name\": \"a decorative vase\"}, {\"id\": 36502, \"name\": \"black blade\"}, {\"id\": 36503, \"name\": \"dark leather couch\"}, {\"id\": 36504, \"name\": \"a stainles steel finish\"}, {\"id\": 36505, \"name\": \"a stone pillar\"}, {\"id\": 36506, \"name\": \"the yellow bird logo\"}, {\"id\": 36507, \"name\": \"a black mailbox\"}, {\"id\": 36508, \"name\": \"christma wreath\"}, {\"id\": 36509, \"name\": \"raw chicken part\"}, {\"id\": 36510, \"name\": \"the gold doorknob\"}, {\"id\": 36511, \"name\": \"a starbuck storefront\"}, {\"id\": 36512, \"name\": \"a large, metal horse statue\"}, {\"id\": 36513, \"name\": \"circular metal emblem\"}, {\"id\": 36514, \"name\": \"statue of person\"}, {\"id\": 36515, \"name\": \"red and white kiosk\"}, {\"id\": 36516, \"name\": \"a yellow banana\"}, {\"id\": 36517, \"name\": \"the bottle of soap\"}, {\"id\": 36518, \"name\": \"the vanilla cake\"}, {\"id\": 36519, \"name\": \"light-colored hardwood\"}, {\"id\": 36520, \"name\": \"white pipe\"}, {\"id\": 36521, \"name\": \"a sink drain\"}, {\"id\": 36522, \"name\": \"the store employee\"}, {\"id\": 36523, \"name\": \"cacti\"}, {\"id\": 36524, \"name\": \"green canopy tent\"}, {\"id\": 36525, \"name\": \"a light-brown scarf\"}, {\"id\": 36526, \"name\": \"the metal pegboard\"}, {\"id\": 36527, \"name\": \"bag of bread\"}, {\"id\": 36528, \"name\": \"the green potted plant\"}, {\"id\": 36529, \"name\": \"the wooden pencil\"}, {\"id\": 36530, \"name\": \"the oven's control panel\"}, {\"id\": 36531, \"name\": \"the brown base\"}, {\"id\": 36532, \"name\": \"green and silver trash can\"}, {\"id\": 36533, \"name\": \"a black burner\"}, {\"id\": 36534, \"name\": \"green tray\"}, {\"id\": 36535, \"name\": \"white crown molding\"}, {\"id\": 36536, \"name\": \"the colorful pen\"}, {\"id\": 36537, \"name\": \"a silver clasp\"}, {\"id\": 36538, \"name\": \"black stanchion\"}, {\"id\": 36539, \"name\": \"the glass entranceway\"}, {\"id\": 36540, \"name\": \"the usp price sticker\"}, {\"id\": 36541, \"name\": \"the burgundy pant\"}, {\"id\": 36542, \"name\": \"a well-organized greeting card section\"}, {\"id\": 36543, \"name\": \"onion\"}, {\"id\": 36544, \"name\": \"off-white wood\"}, {\"id\": 36545, \"name\": \"a clear roof\"}, {\"id\": 36546, \"name\": \"a yellow food truck\"}, {\"id\": 36547, \"name\": \"the red oven mitt\"}, {\"id\": 36548, \"name\": \"a bottle of condiment\"}, {\"id\": 36549, \"name\": \"a store's entrance\"}, {\"id\": 36550, \"name\": \"the playing card\"}, {\"id\": 36551, \"name\": \"shopper's hands\"}, {\"id\": 36552, \"name\": \"the papers and note\"}, {\"id\": 36553, \"name\": \"a digital tablet\"}, {\"id\": 36554, \"name\": \"the black tattoo\"}, {\"id\": 36555, \"name\": \"a lion statue\"}, {\"id\": 36556, \"name\": \"a yellow card\"}, {\"id\": 36557, \"name\": \"the cream-colored pumpkin-shaped container\"}, {\"id\": 36558, \"name\": \"white sink\"}, {\"id\": 36559, \"name\": \"gold stanchion\"}, {\"id\": 36560, \"name\": \"a festive christma wreath\"}, {\"id\": 36561, \"name\": \"a fire hydrant\"}, {\"id\": 36562, \"name\": \"the silver metal grate\"}, {\"id\": 36563, \"name\": \"purple long-sleeve shirt\"}, {\"id\": 36564, \"name\": \"the bottle of soda\"}, {\"id\": 36565, \"name\": \"the bottle of lotion\"}, {\"id\": 36566, \"name\": \"the iphone 15 model\"}, {\"id\": 36567, \"name\": \"a deck of card\"}, {\"id\": 36568, \"name\": \"a white mannequin head\"}, {\"id\": 36569, \"name\": \"the updo\"}, {\"id\": 36570, \"name\": \"black-and-white painting\"}, {\"id\": 36571, \"name\": \"a large white dog logo\"}, {\"id\": 36572, \"name\": \"a yellow and black bag\"}, {\"id\": 36573, \"name\": \"the pie\"}, {\"id\": 36574, \"name\": \"black control panel\"}, {\"id\": 36575, \"name\": \"brown backpack\"}, {\"id\": 36576, \"name\": \"the piece of butter\"}, {\"id\": 36577, \"name\": \"the top right burner\"}, {\"id\": 36578, \"name\": \"light-colored wooden shelf\"}, {\"id\": 36579, \"name\": \"a blue plastic bottle\"}, {\"id\": 36580, \"name\": \"the orange construction cone\"}, {\"id\": 36581, \"name\": \"bowl of raw chicken wing\"}, {\"id\": 36582, \"name\": \"the silver faucet\"}, {\"id\": 36583, \"name\": \"box of pasta\"}, {\"id\": 36584, \"name\": \"gift box\"}, {\"id\": 36585, \"name\": \"sink drain\"}, {\"id\": 36586, \"name\": \"wooden nightstand\"}, {\"id\": 36587, \"name\": \"stacked red and blue package\"}, {\"id\": 36588, \"name\": \"the sandwich or roll\"}, {\"id\": 36589, \"name\": \"a colorful food cart\"}, {\"id\": 36590, \"name\": \"stainles steel stove\"}, {\"id\": 36591, \"name\": \"white plastic dish rack\"}, {\"id\": 36592, \"name\": \"colorful bag of gummy worm\"}, {\"id\": 36593, \"name\": \"left cabinet door\"}, {\"id\": 36594, \"name\": \"switch plate\"}, {\"id\": 36595, \"name\": \"yellow penske truck\"}, {\"id\": 36596, \"name\": \"oak\"}, {\"id\": 36597, \"name\": \"brown finish\"}, {\"id\": 36598, \"name\": \"the stainles steel, single-bowl model\"}, {\"id\": 36599, \"name\": \"a the shop\"}, {\"id\": 36600, \"name\": \"clear glass container\"}, {\"id\": 36601, \"name\": \"the blue picture\"}, {\"id\": 36602, \"name\": \"a rack of clothe\"}, {\"id\": 36603, \"name\": \"a white bloom\"}, {\"id\": 36604, \"name\": \"a note\"}, {\"id\": 36605, \"name\": \"rack of plaid shirt\"}, {\"id\": 36606, \"name\": \"crayola pencil\"}, {\"id\": 36607, \"name\": \"paper shopping bag\"}, {\"id\": 36608, \"name\": \"a black minivan\"}, {\"id\": 36609, \"name\": \"a dark nail polish\"}, {\"id\": 36610, \"name\": \"a light-colored sneaker\"}, {\"id\": 36611, \"name\": \"a white nail\"}, {\"id\": 36612, \"name\": \"a fluted ionic column\"}, {\"id\": 36613, \"name\": \"a white platform\"}, {\"id\": 36614, \"name\": \"the black cardigan\"}, {\"id\": 36615, \"name\": \"a festive garland\"}, {\"id\": 36616, \"name\": \"usb-c port\"}, {\"id\": 36617, \"name\": \"the green plush toy\"}, {\"id\": 36618, \"name\": \"a metal whisk\"}, {\"id\": 36619, \"name\": \"festive christma wreath\"}, {\"id\": 36620, \"name\": \"bowl of banana\"}, {\"id\": 36621, \"name\": \"a personal care product\"}, {\"id\": 36622, \"name\": \"a dark blue step\"}, {\"id\": 36623, \"name\": \"the snack or treat\"}, {\"id\": 36624, \"name\": \"a garment's front side\"}, {\"id\": 36625, \"name\": \"black-painted nail\"}, {\"id\": 36626, \"name\": \"the train door\"}, {\"id\": 36627, \"name\": \"a blue toothbrush\"}, {\"id\": 36628, \"name\": \"a silver burner\"}, {\"id\": 36629, \"name\": \"white pot\"}, {\"id\": 36630, \"name\": \"a clear vase\"}, {\"id\": 36631, \"name\": \"dunkin' logo\"}, {\"id\": 36632, \"name\": \"the pink rag\"}, {\"id\": 36633, \"name\": \"warning sign\"}, {\"id\": 36634, \"name\": \"the yellowish liquid\"}, {\"id\": 36635, \"name\": \"the black tv\"}, {\"id\": 36636, \"name\": \"the sliding glass door\"}, {\"id\": 36637, \"name\": \"a toothbrush holder\"}, {\"id\": 36638, \"name\": \"the sink area\"}, {\"id\": 36639, \"name\": \"metal tong\"}, {\"id\": 36640, \"name\": \"left burner\"}, {\"id\": 36641, \"name\": \"the jar of peanut butter\"}, {\"id\": 36642, \"name\": \"iphone 16 pro\"}, {\"id\": 36643, \"name\": \"a box of crayola crayon\"}, {\"id\": 36644, \"name\": \"a bottle of syrup\"}, {\"id\": 36645, \"name\": \"yellow token\"}, {\"id\": 36646, \"name\": \"a statue of notable figure\"}, {\"id\": 36647, \"name\": \"white mug\"}, {\"id\": 36648, \"name\": \"a column's base\"}, {\"id\": 36649, \"name\": \"the red soda can\"}, {\"id\": 36650, \"name\": \"glass jar\"}, {\"id\": 36651, \"name\": \"a white liquid\"}, {\"id\": 36652, \"name\": \"a purple nail polish\"}, {\"id\": 36653, \"name\": \"a gold bag\"}, {\"id\": 36654, \"name\": \"the blue cartoon character\"}, {\"id\": 36655, \"name\": \"a wooden entertainment unit\"}, {\"id\": 36656, \"name\": \"the black burner\"}, {\"id\": 36657, \"name\": \"the sponge\"}, {\"id\": 36658, \"name\": \"blue fabric cover\"}, {\"id\": 36659, \"name\": \"a yellow blanket or jacket\"}, {\"id\": 36660, \"name\": \"the black bottle\"}, {\"id\": 36661, \"name\": \"the bottle of oil\"}, {\"id\": 36662, \"name\": \"the brown finish\"}, {\"id\": 36663, \"name\": \"blue and white striped flag\"}, {\"id\": 36664, \"name\": \"the blue construction vehicle\"}, {\"id\": 36665, \"name\": \"the man in a dark suit and tie\"}, {\"id\": 36666, \"name\": \"the yellow debris\"}, {\"id\": 36667, \"name\": \"silver spout\"}, {\"id\": 36668, \"name\": \"a crumpled paper towel\"}, {\"id\": 36669, \"name\": \"denim jacket\"}, {\"id\": 36670, \"name\": \"the month of october 2024\"}, {\"id\": 36671, \"name\": \"the lime\"}, {\"id\": 36672, \"name\": \"egg carton\"}, {\"id\": 36673, \"name\": \"the blackberry design\"}, {\"id\": 36674, \"name\": \"black and silver oven\"}, {\"id\": 36675, \"name\": \"brown lid\"}, {\"id\": 36676, \"name\": \"tan bowl\"}, {\"id\": 36677, \"name\": \"shelf of product\"}, {\"id\": 36678, \"name\": \"colorful board game\"}, {\"id\": 36679, \"name\": \"a silver dog bowl\"}, {\"id\": 36680, \"name\": \"the storage\"}, {\"id\": 36681, \"name\": \"a condiment bottle\"}, {\"id\": 36682, \"name\": \"stainles steel sink\"}, {\"id\": 36683, \"name\": \"silver drain\"}, {\"id\": 36684, \"name\": \"the pink and blue bag\"}, {\"id\": 36685, \"name\": \"a yellow taxi cab\"}, {\"id\": 36686, \"name\": \"the hanging greenery\"}, {\"id\": 36687, \"name\": \"picture of a pizza\"}, {\"id\": 36688, \"name\": \"the beige pillow\"}, {\"id\": 36689, \"name\": \"the yellow lemon\"}, {\"id\": 36690, \"name\": \"the orange and black balloon\"}, {\"id\": 36691, \"name\": \"a box of tissue\"}, {\"id\": 36692, \"name\": \"the black nail polish\"}, {\"id\": 36693, \"name\": \"the orange liquid\"}, {\"id\": 36694, \"name\": \"dark gray keyboard\"}, {\"id\": 36695, \"name\": \"the dark brown finish\"}, {\"id\": 36696, \"name\": \"a red and white bag\"}, {\"id\": 36697, \"name\": \"the light-colored granite\"}, {\"id\": 36698, \"name\": \"clear glass vase\"}, {\"id\": 36699, \"name\": \"a silver doorknob\"}, {\"id\": 36700, \"name\": \"the tie-dye shirt\"}, {\"id\": 36701, \"name\": \"the brown plastic bag\"}, {\"id\": 36702, \"name\": \"red onion\"}, {\"id\": 36703, \"name\": \"a white hanger\"}, {\"id\": 36704, \"name\": \"a white pedestal\"}, {\"id\": 36705, \"name\": \"a cooking utensil\"}, {\"id\": 36706, \"name\": \"tan knit cap\"}, {\"id\": 36707, \"name\": \"cow mask\"}, {\"id\": 36708, \"name\": \"an individual's hands\"}, {\"id\": 36709, \"name\": \"a red toy motorcycle\"}, {\"id\": 36710, \"name\": \"bottle of nail polish\"}, {\"id\": 36711, \"name\": \"the glass roof\"}, {\"id\": 36712, \"name\": \"the black key\"}, {\"id\": 36713, \"name\": \"a blue bottle of dish soap\"}, {\"id\": 36714, \"name\": \"a square vent\"}, {\"id\": 36715, \"name\": \"the yoga mat\"}, {\"id\": 36716, \"name\": \"the brown wooden post\"}, {\"id\": 36717, \"name\": \"a right burner\"}, {\"id\": 36718, \"name\": \"white trash bag\"}, {\"id\": 36719, \"name\": \"planter box\"}, {\"id\": 36720, \"name\": \"a green liquid\"}, {\"id\": 36721, \"name\": \"a cosmetic\"}, {\"id\": 36722, \"name\": \"the sculpture\"}, {\"id\": 36723, \"name\": \"game's logo\"}, {\"id\": 36724, \"name\": \"the glass measuring cup\"}, {\"id\": 36725, \"name\": \"a black phone\"}, {\"id\": 36726, \"name\": \"a dreamcatcher\"}, {\"id\": 36727, \"name\": \"beige book\"}, {\"id\": 36728, \"name\": \"a blue canister\"}, {\"id\": 36729, \"name\": \"apple\"}, {\"id\": 36730, \"name\": \"a metal baking tray\"}, {\"id\": 36731, \"name\": \"a xumo tv logo\"}, {\"id\": 36732, \"name\": \"the black lid\"}, {\"id\": 36733, \"name\": \"glass measuring cup\"}, {\"id\": 36734, \"name\": \"decorative pediment\"}, {\"id\": 36735, \"name\": \"pink duffel bag\"}, {\"id\": 36736, \"name\": \"the digital clock\"}, {\"id\": 36737, \"name\": \"a french bulldog\"}, {\"id\": 36738, \"name\": \"a red and white striped bag\"}, {\"id\": 36739, \"name\": \"the bottle of salt and pepper\"}, {\"id\": 36740, \"name\": \"a black post\"}, {\"id\": 36741, \"name\": \"a gold door\"}, {\"id\": 36742, \"name\": \"dark grey hoodie\"}, {\"id\": 36743, \"name\": \"a blue joy-con controller\"}, {\"id\": 36744, \"name\": \"grey beanie hat\"}, {\"id\": 36745, \"name\": \"a man in a black suit and top hat\"}, {\"id\": 36746, \"name\": \"the green bottle of hand soap\"}, {\"id\": 36747, \"name\": \"the glass bottle of liquid soap\"}, {\"id\": 36748, \"name\": \"festive wreath\"}, {\"id\": 36749, \"name\": \"a streaming service logo\"}, {\"id\": 36750, \"name\": \"magnet\"}, {\"id\": 36751, \"name\": \"a brown wooden leg of a table\"}, {\"id\": 36752, \"name\": \"a gray paint\"}, {\"id\": 36753, \"name\": \"a stainles steel oven\"}, {\"id\": 36754, \"name\": \"the sticky note\"}, {\"id\": 36755, \"name\": \"a top shelf\"}, {\"id\": 36756, \"name\": \"metal lid\"}, {\"id\": 36757, \"name\": \"white soap dispenser\"}, {\"id\": 36758, \"name\": \"the jean\"}, {\"id\": 36759, \"name\": \"individual's hands\"}, {\"id\": 36760, \"name\": \"an orange pencil\"}, {\"id\": 36761, \"name\": \"the grocery store aisle\"}, {\"id\": 36762, \"name\": \"a right page\"}, {\"id\": 36763, \"name\": \"the cream-colored shade\"}, {\"id\": 36764, \"name\": \"a vintage books and magazine\"}, {\"id\": 36765, \"name\": \"the metal hinge\"}, {\"id\": 36766, \"name\": \"a gray drawer\"}, {\"id\": 36767, \"name\": \"cartoon\"}, {\"id\": 36768, \"name\": \"the gray carpet\"}, {\"id\": 36769, \"name\": \"sculpture\"}, {\"id\": 36770, \"name\": \"a bottle of shampoo\"}, {\"id\": 36771, \"name\": \"a white dresser drawer\"}, {\"id\": 36772, \"name\": \"blue tin\"}, {\"id\": 36773, \"name\": \"a canopy tent\"}, {\"id\": 36774, \"name\": \"a black-and-white image\"}, {\"id\": 36775, \"name\": \"a red and white gift box\"}, {\"id\": 36776, \"name\": \"the jar of spice\"}, {\"id\": 36777, \"name\": \"a sheet of paper\"}, {\"id\": 36778, \"name\": \"a black hook\"}, {\"id\": 36779, \"name\": \"a pond\"}, {\"id\": 36780, \"name\": \"a cereal box\"}, {\"id\": 36781, \"name\": \"box of chicken nugget\"}, {\"id\": 36782, \"name\": \"the black-and-white striped pillar\"}, {\"id\": 36783, \"name\": \"a framed piece of artwork\"}, {\"id\": 36784, \"name\": \"brisa colombian restaurant\"}, {\"id\": 36785, \"name\": \"a frosted glass panel\"}, {\"id\": 36786, \"name\": \"a vase of red flower\"}, {\"id\": 36787, \"name\": \"an entranceway\"}, {\"id\": 36788, \"name\": \"the foreground table\"}, {\"id\": 36789, \"name\": \"the white stick figure\"}, {\"id\": 36790, \"name\": \"the touch of greenery\"}, {\"id\": 36791, \"name\": \"the individual's leg\"}, {\"id\": 36792, \"name\": \"a wreath\"}, {\"id\": 36793, \"name\": \"black and silver stove\"}, {\"id\": 36794, \"name\": \"the gift bag\"}, {\"id\": 36795, \"name\": \"the grey and white pattern\"}, {\"id\": 36796, \"name\": \"dark wood panel\"}, {\"id\": 36797, \"name\": \"a black sink\"}, {\"id\": 36798, \"name\": \"the glass pane\"}, {\"id\": 36799, \"name\": \"a dark-colored tarp\"}, {\"id\": 36800, \"name\": \"white marble backsplash\"}, {\"id\": 36801, \"name\": \"a green and yellow plush toy\"}, {\"id\": 36802, \"name\": \"blue pillow\"}, {\"id\": 36803, \"name\": \"the stove top\"}, {\"id\": 36804, \"name\": \"the colorful knit cuff\"}, {\"id\": 36805, \"name\": \"the burberry handbag\"}, {\"id\": 36806, \"name\": \"the can of soda\"}, {\"id\": 36807, \"name\": \"a textured finish\"}, {\"id\": 36808, \"name\": \"the bottle of vitamin\"}, {\"id\": 36809, \"name\": \"the coffee maker\"}, {\"id\": 36810, \"name\": \"the brown leather backpack\"}, {\"id\": 36811, \"name\": \"an oven door\"}, {\"id\": 36812, \"name\": \"black stroller\"}, {\"id\": 36813, \"name\": \"the black coffee maker\"}, {\"id\": 36814, \"name\": \"a glass vase\"}, {\"id\": 36815, \"name\": \"the note\"}, {\"id\": 36816, \"name\": \"a pegboard\"}, {\"id\": 36817, \"name\": \"a blue digital sign\"}, {\"id\": 36818, \"name\": \"a silver escalator\"}, {\"id\": 36819, \"name\": \"black monitor\"}, {\"id\": 36820, \"name\": \"cosmetic\"}, {\"id\": 36821, \"name\": \"white tablet\"}, {\"id\": 36822, \"name\": \"the individual's hands\"}, {\"id\": 36823, \"name\": \"a yellow cling seal package\"}, {\"id\": 36824, \"name\": \"the white blow dryer icon\"}, {\"id\": 36825, \"name\": \"a black picture frame\"}, {\"id\": 36826, \"name\": \"a purse or bag\"}, {\"id\": 36827, \"name\": \"the blackboard sign\"}, {\"id\": 36828, \"name\": \"the stainless-steel appliance\"}, {\"id\": 36829, \"name\": \"glass-topped table\"}, {\"id\": 36830, \"name\": \"a bottle of liquid soap\"}, {\"id\": 36831, \"name\": \"seasoning\"}, {\"id\": 36832, \"name\": \"the storefront sign\"}, {\"id\": 36833, \"name\": \"black makeup brush\"}, {\"id\": 36834, \"name\": \"a touch of greenery\"}, {\"id\": 36835, \"name\": \"the bag of bread\"}, {\"id\": 36836, \"name\": \"white rug\"}, {\"id\": 36837, \"name\": \"the christma wreath\"}, {\"id\": 36838, \"name\": \"the festive garland\"}, {\"id\": 36839, \"name\": \"the yellow and black leopard-print design\"}, {\"id\": 36840, \"name\": \"wooden spoon\"}, {\"id\": 36841, \"name\": \"a glass roof\"}, {\"id\": 36842, \"name\": \"the framed piece of artwork\"}, {\"id\": 36843, \"name\": \"blender's handle\"}, {\"id\": 36844, \"name\": \"the beige carpeting\"}, {\"id\": 36845, \"name\": \"a brown paper package\"}, {\"id\": 36846, \"name\": \"a white pot\"}, {\"id\": 36847, \"name\": \"kitchenaid stand mixer\"}, {\"id\": 36848, \"name\": \"a blue search bar\"}, {\"id\": 36849, \"name\": \"the white mannequin\"}, {\"id\": 36850, \"name\": \"spice or condiment container\"}, {\"id\": 36851, \"name\": \"an orange bracelet\"}, {\"id\": 36852, \"name\": \"the black side table\"}, {\"id\": 36853, \"name\": \"the bottle of spice\"}, {\"id\": 36854, \"name\": \"the blue trash can\"}, {\"id\": 36855, \"name\": \"man in military uniform\"}, {\"id\": 36856, \"name\": \"a black remote control\"}, {\"id\": 36857, \"name\": \"a gray and white backsplash\"}, {\"id\": 36858, \"name\": \"a white cabinet door\"}, {\"id\": 36859, \"name\": \"the vegetable mixture\"}, {\"id\": 36860, \"name\": \"the pink painting\"}, {\"id\": 36861, \"name\": \"white stand\"}, {\"id\": 36862, \"name\": \"the brown cardboard box\"}, {\"id\": 36863, \"name\": \"a dark brown entertainment center\"}, {\"id\": 36864, \"name\": \"white laundry basket\"}, {\"id\": 36865, \"name\": \"a painted red fingernail\"}, {\"id\": 36866, \"name\": \"black beaded bracelet\"}, {\"id\": 36867, \"name\": \"black bottle\"}, {\"id\": 36868, \"name\": \"a roll of red tape\"}, {\"id\": 36869, \"name\": \"the yellow basketball hoop\"}, {\"id\": 36870, \"name\": \"the start button\"}, {\"id\": 36871, \"name\": \"brown plate\"}, {\"id\": 36872, \"name\": \"bottle of soap\"}, {\"id\": 36873, \"name\": \"a brown and white dog\"}, {\"id\": 36874, \"name\": \"the raw chicken piece\"}, {\"id\": 36875, \"name\": \"the convenience store\"}, {\"id\": 36876, \"name\": \"blue hooded jacket\"}, {\"id\": 36877, \"name\": \"the bottle of dish soap\"}, {\"id\": 36878, \"name\": \"the round knob\"}, {\"id\": 36879, \"name\": \"the pegboard panel\"}, {\"id\": 36880, \"name\": \"blue pencil\"}, {\"id\": 36881, \"name\": \"a white trash bag\"}, {\"id\": 36882, \"name\": \"the apple macbook\"}, {\"id\": 36883, \"name\": \"the bottle of nail polish\"}, {\"id\": 36884, \"name\": \"the gravel\"}, {\"id\": 36885, \"name\": \"the self-service kiosk\"}, {\"id\": 36886, \"name\": \"white sticky note\"}, {\"id\": 36887, \"name\": \"a brown wood paneling\"}, {\"id\": 36888, \"name\": \"blue sponge\"}, {\"id\": 36889, \"name\": \"a white plastic egg\"}, {\"id\": 36890, \"name\": \"the glass-front refrigerator door\"}, {\"id\": 36891, \"name\": \"glass shelf\"}, {\"id\": 36892, \"name\": \"the stainles steel handle\"}, {\"id\": 36893, \"name\": \"black fabric wristband\"}, {\"id\": 36894, \"name\": \"white marble\"}, {\"id\": 36895, \"name\": \"the sheet of white paper\"}, {\"id\": 36896, \"name\": \"the black and white painting\"}, {\"id\": 36897, \"name\": \"the blue-and-white abstract painting\"}, {\"id\": 36898, \"name\": \"a gray desk\"}, {\"id\": 36899, \"name\": \"the silver rod\"}, {\"id\": 36900, \"name\": \"the check-in counter\"}, {\"id\": 36901, \"name\": \"the left mannequin\"}, {\"id\": 36902, \"name\": \"white paper towel roll\"}, {\"id\": 36903, \"name\": \"decorative wreath\"}, {\"id\": 36904, \"name\": \"a teal-colored couch\"}, {\"id\": 36905, \"name\": \"the yellow bar of soap\"}, {\"id\": 36906, \"name\": \"delivery truck\"}, {\"id\": 36907, \"name\": \"a webpage\"}, {\"id\": 36908, \"name\": \"a foil\"}, {\"id\": 36909, \"name\": \"an entertainment unit\"}, {\"id\": 36910, \"name\": \"a vase of red poinsettia\"}, {\"id\": 36911, \"name\": \"the wooden leg\"}, {\"id\": 36912, \"name\": \"the bottle of vitamin d\"}, {\"id\": 36913, \"name\": \"a white crown molding\"}, {\"id\": 36914, \"name\": \"pink packaging\"}, {\"id\": 36915, \"name\": \"piece of butter\"}, {\"id\": 36916, \"name\": \"a red package of chocolate-covered nut\"}, {\"id\": 36917, \"name\": \"the faucet handle\"}, {\"id\": 36918, \"name\": \"digital control panel\"}, {\"id\": 36919, \"name\": \"the stainless-steel finish\"}, {\"id\": 36920, \"name\": \"a gold statue of a human head\"}, {\"id\": 36921, \"name\": \"the butter\"}, {\"id\": 36922, \"name\": \"paper towel\"}, {\"id\": 36923, \"name\": \"a black book\"}, {\"id\": 36924, \"name\": \"a twix candy bar\"}, {\"id\": 36925, \"name\": \"green cup\"}, {\"id\": 36926, \"name\": \"the open notebook\"}, {\"id\": 36927, \"name\": \"orange cushion\"}, {\"id\": 36928, \"name\": \"the tan fabric\"}, {\"id\": 36929, \"name\": \"individual's hand\"}, {\"id\": 36930, \"name\": \"woman's reflection\"}, {\"id\": 36931, \"name\": \"a white folding table\"}, {\"id\": 36932, \"name\": \"a yellow shopping bag\"}, {\"id\": 36933, \"name\": \"the black air fryer\"}, {\"id\": 36934, \"name\": \"a white bed frame\"}, {\"id\": 36935, \"name\": \"an advertisement kiosk\"}, {\"id\": 36936, \"name\": \"a white speaker\"}, {\"id\": 36937, \"name\": \"nail polish brush\"}, {\"id\": 36938, \"name\": \"the white plastic bin\"}, {\"id\": 36939, \"name\": \"a blue bottle of mouthwash\"}, {\"id\": 36940, \"name\": \"the white styrofoam cup\"}, {\"id\": 36941, \"name\": \"the clear lid\"}, {\"id\": 36942, \"name\": \"the black elevator door\"}, {\"id\": 36943, \"name\": \"the vanilla ice cream\"}, {\"id\": 36944, \"name\": \"a stainles steel trash can\"}, {\"id\": 36945, \"name\": \"a player's right hand\"}, {\"id\": 36946, \"name\": \"white plastic bottle\"}, {\"id\": 36947, \"name\": \"a front entrance of gregory's coffee\"}, {\"id\": 36948, \"name\": \"dog's paw\"}, {\"id\": 36949, \"name\": \"the yellow stucco finish\"}, {\"id\": 36950, \"name\": \"a bottle of blue sport drink\"}, {\"id\": 36951, \"name\": \"white top shelf\"}, {\"id\": 36952, \"name\": \"the stone fireplace\"}, {\"id\": 36953, \"name\": \"wood or laminate\"}, {\"id\": 36954, \"name\": \"a black and gray backsplash\"}, {\"id\": 36955, \"name\": \"white knob\"}, {\"id\": 36956, \"name\": \"a charging pad\"}, {\"id\": 36957, \"name\": \"black mobile payment device\"}, {\"id\": 36958, \"name\": \"image of an orange\"}, {\"id\": 36959, \"name\": \"the black-and-white painting of a cow's head\"}, {\"id\": 36960, \"name\": \"a faucet handle\"}, {\"id\": 36961, \"name\": \"cluttered wooden shelf\"}, {\"id\": 36962, \"name\": \"white pencil\"}, {\"id\": 36963, \"name\": \"a colorful jacket\"}, {\"id\": 36964, \"name\": \"the cabinet drawer\"}, {\"id\": 36965, \"name\": \"the white marble\"}, {\"id\": 36966, \"name\": \"a decorative molding\"}, {\"id\": 36967, \"name\": \"the red velvet rope\"}, {\"id\": 36968, \"name\": \"the colorful circular image\"}, {\"id\": 36969, \"name\": \"the mixture\"}, {\"id\": 36970, \"name\": \"a colorful toothbrush\"}, {\"id\": 36971, \"name\": \"the blue pot\"}, {\"id\": 36972, \"name\": \"a wooden cabinet or bookshelf\"}, {\"id\": 36973, \"name\": \"framed piece of art\"}, {\"id\": 36974, \"name\": \"green plate\"}, {\"id\": 36975, \"name\": \"white compartment\"}, {\"id\": 36976, \"name\": \"a manufacturer information\"}, {\"id\": 36977, \"name\": \"beige and black granite\"}, {\"id\": 36978, \"name\": \"soil\"}, {\"id\": 36979, \"name\": \"a brown leather case\"}, {\"id\": 36980, \"name\": \"white usp truck\"}, {\"id\": 36981, \"name\": \"a white barstool\"}, {\"id\": 36982, \"name\": \"the left burner\"}, {\"id\": 36983, \"name\": \"the makeup brush\"}, {\"id\": 36984, \"name\": \"the decorative ball\"}, {\"id\": 36985, \"name\": \"white backpack\"}, {\"id\": 36986, \"name\": \"the green sweater sleeve\"}, {\"id\": 36987, \"name\": \"the colorful arrangement of fruit\"}, {\"id\": 36988, \"name\": \"the blender\"}, {\"id\": 36989, \"name\": \"the wooden step\"}, {\"id\": 36990, \"name\": \"the green bottle opener\"}, {\"id\": 36991, \"name\": \"a small french bulldog\"}, {\"id\": 36992, \"name\": \"a roll of wrapping paper\"}, {\"id\": 36993, \"name\": \"a marble\"}, {\"id\": 36994, \"name\": \"the festive red and white striped paper\"}, {\"id\": 36995, \"name\": \"a pointed arch\"}, {\"id\": 36996, \"name\": \"a black coffee maker\"}, {\"id\": 36997, \"name\": \"cat's left ear\"}, {\"id\": 36998, \"name\": \"a yellow pencil\"}, {\"id\": 36999, \"name\": \"a wooden grandfather clock\"}, {\"id\": 37000, \"name\": \"the beige canopy\"}, {\"id\": 37001, \"name\": \"a blue glass ball\"}, {\"id\": 37002, \"name\": \"toy guitar\"}, {\"id\": 37003, \"name\": \"a black and white bag\"}, {\"id\": 37004, \"name\": \"the rack of hats and glove\"}, {\"id\": 37005, \"name\": \"a green lego piece\"}, {\"id\": 37006, \"name\": \"white adida logo\"}, {\"id\": 37007, \"name\": \"red gift box\"}, {\"id\": 37008, \"name\": \"a hinge\"}, {\"id\": 37009, \"name\": \"iphone 14 pro model\"}, {\"id\": 37010, \"name\": \"a brown dog food\"}, {\"id\": 37011, \"name\": \"the brown onion\"}, {\"id\": 37012, \"name\": \"the store's food section\"}, {\"id\": 37013, \"name\": \"white mug with a black design\"}, {\"id\": 37014, \"name\": \"the silver finish\"}, {\"id\": 37015, \"name\": \"white pump\"}, {\"id\": 37016, \"name\": \"an aloe\"}, {\"id\": 37017, \"name\": \"a white laptop\"}, {\"id\": 37018, \"name\": \"brown and white geometric patterned cushion\"}, {\"id\": 37019, \"name\": \"a white cardboard box\"}, {\"id\": 37020, \"name\": \"red-handled scissors\"}, {\"id\": 37021, \"name\": \"the computer mouse\"}, {\"id\": 37022, \"name\": \"white plastic lid\"}, {\"id\": 37023, \"name\": \"a silver hinge\"}, {\"id\": 37024, \"name\": \"the red onion\"}, {\"id\": 37025, \"name\": \"the lion\"}, {\"id\": 37026, \"name\": \"the yellow sticky note\"}, {\"id\": 37027, \"name\": \"switch\"}, {\"id\": 37028, \"name\": \"the metal griddle\"}, {\"id\": 37029, \"name\": \"the red bird design\"}, {\"id\": 37030, \"name\": \"the black-and-white image\"}, {\"id\": 37031, \"name\": \"the soda can\"}, {\"id\": 37032, \"name\": \"a clear soap dispenser\"}, {\"id\": 37033, \"name\": \"milk chocolate\"}, {\"id\": 37034, \"name\": \"hinge\"}, {\"id\": 37035, \"name\": \"the gold stanchion\"}, {\"id\": 37036, \"name\": \"the individual's right hand\"}, {\"id\": 37037, \"name\": \"sausage link\"}, {\"id\": 37038, \"name\": \"a mural of a heron\"}, {\"id\": 37039, \"name\": \"a nail polish\"}, {\"id\": 37040, \"name\": \"the brightly lit sign\"}, {\"id\": 37041, \"name\": \"rack of black jacket\"}, {\"id\": 37042, \"name\": \"key\"}, {\"id\": 37043, \"name\": \"deck of blue and white patterned card\"}, {\"id\": 37044, \"name\": \"the purple flag\"}, {\"id\": 37045, \"name\": \"a white ribbon\"}, {\"id\": 37046, \"name\": \"a container of purell surface disinfecting wipe\"}, {\"id\": 37047, \"name\": \"grocery store aisle\"}, {\"id\": 37048, \"name\": \"the frosted glass panel\"}, {\"id\": 37049, \"name\": \"the interior of the fridge door\"}, {\"id\": 37050, \"name\": \"a small white rabbit figurine\"}, {\"id\": 37051, \"name\": \"box of flu relief medicine\"}, {\"id\": 37052, \"name\": \"a bottle of hand sanitizer\"}, {\"id\": 37053, \"name\": \"the paper towel holder\"}, {\"id\": 37054, \"name\": \"red kettle\"}, {\"id\": 37055, \"name\": \"a right basin\"}, {\"id\": 37056, \"name\": \"the white cabinet door\"}, {\"id\": 37057, \"name\": \"the dark gray suv\"}, {\"id\": 37058, \"name\": \"gold plaque\"}, {\"id\": 37059, \"name\": \"nightstand\"}, {\"id\": 37060, \"name\": \"the red box of organic jasmine rice\"}, {\"id\": 37061, \"name\": \"blue bottle\"}, {\"id\": 37062, \"name\": \"a black smartphone\"}, {\"id\": 37063, \"name\": \"the gardening tool\"}, {\"id\": 37064, \"name\": \"a silver screw\"}, {\"id\": 37065, \"name\": \"colorful eyeshadow\"}, {\"id\": 37066, \"name\": \"the crossword puzzle\"}, {\"id\": 37067, \"name\": \"white and black bag\"}, {\"id\": 37068, \"name\": \"gray pillow\"}, {\"id\": 37069, \"name\": \"the sewer cover\"}, {\"id\": 37070, \"name\": \"the soap dispenser\"}, {\"id\": 37071, \"name\": \"a ventilation\"}, {\"id\": 37072, \"name\": \"red square\"}, {\"id\": 37073, \"name\": \"a white device\"}, {\"id\": 37074, \"name\": \"the mcdonald's restaurant\"}, {\"id\": 37075, \"name\": \"black metal trash can\"}, {\"id\": 37076, \"name\": \"the coffee\"}, {\"id\": 37077, \"name\": \"a left burner\"}, {\"id\": 37078, \"name\": \"a container of sliced mushroom\"}, {\"id\": 37079, \"name\": \"the nail care kit\"}, {\"id\": 37080, \"name\": \"the black computer mouse\"}, {\"id\": 37081, \"name\": \"the craft project\"}, {\"id\": 37082, \"name\": \"a scrubber\"}, {\"id\": 37083, \"name\": \"gray hood\"}, {\"id\": 37084, \"name\": \"the wooden door frame\"}, {\"id\": 37085, \"name\": \"metal strainer\"}, {\"id\": 37086, \"name\": \"red and green decoration\"}, {\"id\": 37087, \"name\": \"the statue of person\"}, {\"id\": 37088, \"name\": \"brown mixture\"}, {\"id\": 37089, \"name\": \"close-up of a person's left arm\"}, {\"id\": 37090, \"name\": \"departure board\"}, {\"id\": 37091, \"name\": \"a white vase\"}, {\"id\": 37092, \"name\": \"a towel rack\"}, {\"id\": 37093, \"name\": \"brown wooden tv stand\"}, {\"id\": 37094, \"name\": \"stone walkway\"}, {\"id\": 37095, \"name\": \"the yellow book\"}, {\"id\": 37096, \"name\": \"the stone step\"}, {\"id\": 37097, \"name\": \"oven's control panel\"}, {\"id\": 37098, \"name\": \"red and orange poster\"}, {\"id\": 37099, \"name\": \"circular starbuck logo\"}, {\"id\": 37100, \"name\": \"blue cross-body bag\"}, {\"id\": 37101, \"name\": \"a red joy-con\"}, {\"id\": 37102, \"name\": \"the gold hinge\"}, {\"id\": 37103, \"name\": \"man in the orange vest\"}, {\"id\": 37104, \"name\": \"black appliance\"}, {\"id\": 37105, \"name\": \"the black stove\"}, {\"id\": 37106, \"name\": \"green poster\"}, {\"id\": 37107, \"name\": \"stall's counter\"}, {\"id\": 37108, \"name\": \"dark-colored knob\"}, {\"id\": 37109, \"name\": \"a grandfather clock\"}, {\"id\": 37110, \"name\": \"silver burner\"}, {\"id\": 37111, \"name\": \"an orange backpack\"}, {\"id\": 37112, \"name\": \"the lemon\"}, {\"id\": 37113, \"name\": \"walkway sign\"}, {\"id\": 37114, \"name\": \"bottle of juice\"}, {\"id\": 37115, \"name\": \"the blue sponge\"}, {\"id\": 37116, \"name\": \"the pink cover\"}, {\"id\": 37117, \"name\": \"the timer setting\"}, {\"id\": 37118, \"name\": \"a white bottle of shampoo\"}, {\"id\": 37119, \"name\": \"the blue advertisement\"}, {\"id\": 37120, \"name\": \"decorative flower\"}, {\"id\": 37121, \"name\": \"butter\"}, {\"id\": 37122, \"name\": \"the bottle of hand soap\"}, {\"id\": 37123, \"name\": \"a green wreath\"}, {\"id\": 37124, \"name\": \"gold shopping bag\"}, {\"id\": 37125, \"name\": \"the decorative glass vase\"}, {\"id\": 37126, \"name\": \"a bunny ear\"}, {\"id\": 37127, \"name\": \"the white speaker\"}, {\"id\": 37128, \"name\": \"bag of candy\"}, {\"id\": 37129, \"name\": \"orange button\"}, {\"id\": 37130, \"name\": \"can of green soda\"}, {\"id\": 37131, \"name\": \"the bottle of syrup\"}, {\"id\": 37132, \"name\": \"the piece of brown paper\"}, {\"id\": 37133, \"name\": \"closed book\"}, {\"id\": 37134, \"name\": \"the white stove\"}, {\"id\": 37135, \"name\": \"red heart-shaped ceramic decoration\"}, {\"id\": 37136, \"name\": \"red and black scarf\"}, {\"id\": 37137, \"name\": \"the white poster\"}, {\"id\": 37138, \"name\": \"red pepper flake\"}, {\"id\": 37139, \"name\": \"black and pink fuzzy sweater\"}, {\"id\": 37140, \"name\": \"bag of banana\"}, {\"id\": 37141, \"name\": \"a toaster oven\"}, {\"id\": 37142, \"name\": \"a brown mulch\"}, {\"id\": 37143, \"name\": \"surface of the stove\"}, {\"id\": 37144, \"name\": \"a teal scrub\"}, {\"id\": 37145, \"name\": \"the stovetop burner\"}, {\"id\": 37146, \"name\": \"the metal grille\"}, {\"id\": 37147, \"name\": \"dark gray carpeting\"}, {\"id\": 37148, \"name\": \"self-service kiosk\"}, {\"id\": 37149, \"name\": \"the lip gloss\"}, {\"id\": 37150, \"name\": \"a zara logo\"}, {\"id\": 37151, \"name\": \"a cabinet space\"}, {\"id\": 37152, \"name\": \"red and white shopping bag\"}, {\"id\": 37153, \"name\": \"reflection of the mirror\"}, {\"id\": 37154, \"name\": \"a red archway\"}, {\"id\": 37155, \"name\": \"heater or air conditioning unit\"}, {\"id\": 37156, \"name\": \"stone urn\"}, {\"id\": 37157, \"name\": \"lone horse\"}, {\"id\": 37158, \"name\": \"the stone staircase\"}, {\"id\": 37159, \"name\": \"metal wire shelf\"}, {\"id\": 37160, \"name\": \"the red basket of banana\"}, {\"id\": 37161, \"name\": \"a teal-colored sign\"}, {\"id\": 37162, \"name\": \"a neon green ball\"}, {\"id\": 37163, \"name\": \"image of person in old-fashioned clothing\"}, {\"id\": 37164, \"name\": \"the khaki cargo pant\"}, {\"id\": 37165, \"name\": \"the paper towel\"}, {\"id\": 37166, \"name\": \"black pot\"}, {\"id\": 37167, \"name\": \"white book\"}, {\"id\": 37168, \"name\": \"the orange ball\"}, {\"id\": 37169, \"name\": \"tan carpeting\"}, {\"id\": 37170, \"name\": \"the cutting board\"}, {\"id\": 37171, \"name\": \"the beige sweater\"}, {\"id\": 37172, \"name\": \"the piece of art\"}, {\"id\": 37173, \"name\": \"an empty page\"}, {\"id\": 37174, \"name\": \"the bag of chip\"}, {\"id\": 37175, \"name\": \"the blue liquid\"}, {\"id\": 37176, \"name\": \"a pink bottle of lotion\"}, {\"id\": 37177, \"name\": \"a blue stair\"}, {\"id\": 37178, \"name\": \"a white spice container\"}, {\"id\": 37179, \"name\": \"the bottle of soy sauce\"}, {\"id\": 37180, \"name\": \"dark purple nail polish\"}, {\"id\": 37181, \"name\": \"the package of butter\"}, {\"id\": 37182, \"name\": \"gray stone border\"}, {\"id\": 37183, \"name\": \"the white shoe box\"}, {\"id\": 37184, \"name\": \"a vase of white flower\"}, {\"id\": 37185, \"name\": \"kitchen tool\"}, {\"id\": 37186, \"name\": \"the volume control\"}, {\"id\": 37187, \"name\": \"the red rug\"}, {\"id\": 37188, \"name\": \"bottle of dish soap\"}, {\"id\": 37189, \"name\": \"a mirror's frame\"}, {\"id\": 37190, \"name\": \"comb\"}, {\"id\": 37191, \"name\": \"the roll of wrapping paper\"}, {\"id\": 37192, \"name\": \"the vendor's menu board\"}, {\"id\": 37193, \"name\": \"white plastic rod\"}, {\"id\": 37194, \"name\": \"the person's arms\"}, {\"id\": 37195, \"name\": \"a blank page\"}, {\"id\": 37196, \"name\": \"round gray knob\"}, {\"id\": 37197, \"name\": \"the turquoise spray bottle\"}, {\"id\": 37198, \"name\": \"the cupcake\"}, {\"id\": 37199, \"name\": \"power outlet\"}, {\"id\": 37200, \"name\": \"gray cord\"}, {\"id\": 37201, \"name\": \"a see option\"}, {\"id\": 37202, \"name\": \"bowl of egg\"}, {\"id\": 37203, \"name\": \"the foot locker sign\"}, {\"id\": 37204, \"name\": \"a blue, purple, and white tie-dyed shirt\"}, {\"id\": 37205, \"name\": \"the open box of banana game card\"}, {\"id\": 37206, \"name\": \"the white mouse\"}, {\"id\": 37207, \"name\": \"a pink backpack\"}, {\"id\": 37208, \"name\": \"the shake shack restaurant\"}, {\"id\": 37209, \"name\": \"the white rectangular sign\"}, {\"id\": 37210, \"name\": \"sheet of white paper\"}, {\"id\": 37211, \"name\": \"product card\"}, {\"id\": 37212, \"name\": \"the white hook\"}, {\"id\": 37213, \"name\": \"the individual's hand\"}, {\"id\": 37214, \"name\": \"the bottom shelf\"}, {\"id\": 37215, \"name\": \"a right index finger\"}, {\"id\": 37216, \"name\": \"a grey countertop\"}, {\"id\": 37217, \"name\": \"a silver lid\"}, {\"id\": 37218, \"name\": \"search bar\"}, {\"id\": 37219, \"name\": \"black tv\"}, {\"id\": 37220, \"name\": \"the black utensil\"}, {\"id\": 37221, \"name\": \"a store's red sign\"}, {\"id\": 37222, \"name\": \"the shelves of cosmetics product\"}, {\"id\": 37223, \"name\": \"a white bottle of seasoning\"}, {\"id\": 37224, \"name\": \"a wrapped package\"}, {\"id\": 37225, \"name\": \"well-stocked refrigerator\"}, {\"id\": 37226, \"name\": \"a wooden spoon\"}, {\"id\": 37227, \"name\": \"green packet\"}, {\"id\": 37228, \"name\": \"a toaster's cord\"}, {\"id\": 37229, \"name\": \"a washer's lid\"}, {\"id\": 37230, \"name\": \"supreme logo\"}, {\"id\": 37231, \"name\": \"light-brown wood\"}, {\"id\": 37232, \"name\": \"the colorful food cart\"}, {\"id\": 37233, \"name\": \"the curved brown couch\"}, {\"id\": 37234, \"name\": \"a black screen\"}, {\"id\": 37235, \"name\": \"a white macbook\"}, {\"id\": 37236, \"name\": \"the silver laptop\"}, {\"id\": 37237, \"name\": \"a bottle of dish soap\"}, {\"id\": 37238, \"name\": \"black plastic bag\"}, {\"id\": 37239, \"name\": \"red menu board\"}, {\"id\": 37240, \"name\": \"the red and white ornament\"}, {\"id\": 37241, \"name\": \"the blue blanket\"}, {\"id\": 37242, \"name\": \"a gold-colored metal spoon\"}, {\"id\": 37243, \"name\": \"the red van\"}, {\"id\": 37244, \"name\": \"clear glass\"}, {\"id\": 37245, \"name\": \"gray backsplash\"}, {\"id\": 37246, \"name\": \"man in a blue jacket and black pant\"}, {\"id\": 37247, \"name\": \"red pedestal\"}, {\"id\": 37248, \"name\": \"can of butter\"}, {\"id\": 37249, \"name\": \"a crumpled brown paper bag\"}, {\"id\": 37250, \"name\": \"a the lion king\"}, {\"id\": 37251, \"name\": \"a purple coat\"}, {\"id\": 37252, \"name\": \"dark curtain\"}, {\"id\": 37253, \"name\": \"white and black nike sneaker\"}, {\"id\": 37254, \"name\": \"the black glass surface\"}, {\"id\": 37255, \"name\": \"reflection of a person's face\"}, {\"id\": 37256, \"name\": \"a rider's hand\"}, {\"id\": 37257, \"name\": \"the art\"}, {\"id\": 37258, \"name\": \"a canvas\"}, {\"id\": 37259, \"name\": \"a black fire hydrant\"}, {\"id\": 37260, \"name\": \"round black knob\"}, {\"id\": 37261, \"name\": \"a pink paper\"}, {\"id\": 37262, \"name\": \"image of woman\"}, {\"id\": 37263, \"name\": \"bottle of pepto bismol\"}, {\"id\": 37264, \"name\": \"a burgundy hijab\"}, {\"id\": 37265, \"name\": \"a sticky note\"}, {\"id\": 37266, \"name\": \"a white paint\"}, {\"id\": 37267, \"name\": \"container of spice\"}, {\"id\": 37268, \"name\": \"white stove\"}, {\"id\": 37269, \"name\": \"a statue of person\"}, {\"id\": 37270, \"name\": \"the remote control\"}, {\"id\": 37271, \"name\": \"the care bear\"}, {\"id\": 37272, \"name\": \"gold nail polish\"}, {\"id\": 37273, \"name\": \"the white tissue\"}, {\"id\": 37274, \"name\": \"the metal shelf\"}, {\"id\": 37275, \"name\": \"a green and white geometric pattern\"}, {\"id\": 37276, \"name\": \"the red apple\"}, {\"id\": 37277, \"name\": \"the metal clothing rack\"}, {\"id\": 37278, \"name\": \"a blue strap\"}, {\"id\": 37279, \"name\": \"score of the game\"}, {\"id\": 37280, \"name\": \"a box of uno card\"}, {\"id\": 37281, \"name\": \"a grey hood\"}, {\"id\": 37282, \"name\": \"a straw\"}, {\"id\": 37283, \"name\": \"white tissue\"}, {\"id\": 37284, \"name\": \"the plush toy wolf\"}, {\"id\": 37285, \"name\": \"a bottle of vitamin\"}, {\"id\": 37286, \"name\": \"a woman's hand\"}, {\"id\": 37287, \"name\": \"the black baseboard\"}, {\"id\": 37288, \"name\": \"a search bar\"}, {\"id\": 37289, \"name\": \"a dog leash\"}, {\"id\": 37290, \"name\": \"the black granite\"}, {\"id\": 37291, \"name\": \"the warning label\"}, {\"id\": 37292, \"name\": \"stainles steel basin\"}, {\"id\": 37293, \"name\": \"green dinosaur logo\"}, {\"id\": 37294, \"name\": \"a white bottle of lotion\"}, {\"id\": 37295, \"name\": \"the vertical blind\"}, {\"id\": 37296, \"name\": \"beige sofa\"}, {\"id\": 37297, \"name\": \"the white vertical blind\"}, {\"id\": 37298, \"name\": \"a decorative nail piece\"}, {\"id\": 37299, \"name\": \"a white shopping bag\"}, {\"id\": 37300, \"name\": \"colorful parrot design\"}, {\"id\": 37301, \"name\": \"the bottle of liquid\"}, {\"id\": 37302, \"name\": \"black remote control\"}, {\"id\": 37303, \"name\": \"a beige purse\"}, {\"id\": 37304, \"name\": \"black joystick\"}, {\"id\": 37305, \"name\": \"the stone base\"}, {\"id\": 37306, \"name\": \"the red piece of art\"}, {\"id\": 37307, \"name\": \"the white wooden paneling\"}, {\"id\": 37308, \"name\": \"concrete platform\"}, {\"id\": 37309, \"name\": \"blue scoreboard\"}, {\"id\": 37310, \"name\": \"the white leash\"}, {\"id\": 37311, \"name\": \"frosted glass panel\"}, {\"id\": 37312, \"name\": \"the red patterned curtain\"}, {\"id\": 37313, \"name\": \"pencil cup\"}, {\"id\": 37314, \"name\": \"a black paper\"}, {\"id\": 37315, \"name\": \"statue of a person\"}, {\"id\": 37316, \"name\": \"the apple\"}, {\"id\": 37317, \"name\": \"the box of tissue\"}, {\"id\": 37318, \"name\": \"white smartphone\"}, {\"id\": 37319, \"name\": \"a metal door\"}, {\"id\": 37320, \"name\": \"the white tablet\"}, {\"id\": 37321, \"name\": \"stainles steel faucet\"}, {\"id\": 37322, \"name\": \"blue smartphone\"}, {\"id\": 37323, \"name\": \"dark-colored granite\"}, {\"id\": 37324, \"name\": \"the white pedestal\"}, {\"id\": 37325, \"name\": \"the gold-colored horse\"}, {\"id\": 37326, \"name\": \"the clear glass bottle\"}, {\"id\": 37327, \"name\": \"a decorative pumpkin\"}, {\"id\": 37328, \"name\": \"the metal blender\"}, {\"id\": 37329, \"name\": \"game jenga\"}, {\"id\": 37330, \"name\": \"the dog graphic\"}, {\"id\": 37331, \"name\": \"sink's faucet\"}, {\"id\": 37332, \"name\": \"the bottle of shampoo\"}, {\"id\": 37333, \"name\": \"the gray sedan\"}, {\"id\": 37334, \"name\": \"floral patterned cloth\"}, {\"id\": 37335, \"name\": \"the blue canister\"}, {\"id\": 37336, \"name\": \"perforated back panel\"}, {\"id\": 37337, \"name\": \"red dog toy\"}, {\"id\": 37338, \"name\": \"the red decorative paper\"}, {\"id\": 37339, \"name\": \"the subject's face\"}, {\"id\": 37340, \"name\": \"the purple flower\"}, {\"id\": 37341, \"name\": \"the clear soap dispenser\"}, {\"id\": 37342, \"name\": \"left-hand page\"}, {\"id\": 37343, \"name\": \"black rug\"}, {\"id\": 37344, \"name\": \"art piece\"}, {\"id\": 37345, \"name\": \"the black leather couch\"}, {\"id\": 37346, \"name\": \"black iron gate\"}, {\"id\": 37347, \"name\": \"white notebook\"}, {\"id\": 37348, \"name\": \"a brown nike shopping bag\"}, {\"id\": 37349, \"name\": \"black and white backsplash\"}, {\"id\": 37350, \"name\": \"the green paper\"}, {\"id\": 37351, \"name\": \"the jar of green sauce\"}, {\"id\": 37352, \"name\": \"a cylindrical container\"}, {\"id\": 37353, \"name\": \"white plastic cup\"}, {\"id\": 37354, \"name\": \"a statue of woman\"}, {\"id\": 37355, \"name\": \"a red gate\"}, {\"id\": 37356, \"name\": \"the display of dog toy\"}, {\"id\": 37357, \"name\": \"person's reflection\"}, {\"id\": 37358, \"name\": \"a surrounding sidewalk\"}, {\"id\": 37359, \"name\": \"black stove top\"}, {\"id\": 37360, \"name\": \"the thumb of a person's right hand\"}, {\"id\": 37361, \"name\": \"the blank page\"}, {\"id\": 37362, \"name\": \"box of glove\"}, {\"id\": 37363, \"name\": \"the laptop's screen\"}, {\"id\": 37364, \"name\": \"the magnet\"}, {\"id\": 37365, \"name\": \"the plastic shopping bag\"}, {\"id\": 37366, \"name\": \"the right burner\"}, {\"id\": 37367, \"name\": \"the bottle of blue liquid soap\"}, {\"id\": 37368, \"name\": \"white coffee maker\"}, {\"id\": 37369, \"name\": \"the black, puffy slipper\"}, {\"id\": 37370, \"name\": \"the bag of yellow chip\"}, {\"id\": 37371, \"name\": \"purple advertisement\"}, {\"id\": 37372, \"name\": \"the kitchen's stainless-steel backsplash\"}, {\"id\": 37373, \"name\": \"a box of butter\"}, {\"id\": 37374, \"name\": \"the stainles steel sink\"}, {\"id\": 37375, \"name\": \"the poinsettia plant\"}, {\"id\": 37376, \"name\": \"a gold decoration\"}, {\"id\": 37377, \"name\": \"a white basket\"}, {\"id\": 37378, \"name\": \"the qr code\"}, {\"id\": 37379, \"name\": \"an orange image\"}, {\"id\": 37380, \"name\": \"a dark brown soil\"}, {\"id\": 37381, \"name\": \"a fluffy blue material\"}, {\"id\": 37382, \"name\": \"subway sandwich shop\"}, {\"id\": 37383, \"name\": \"a frigidaire brand logo\"}, {\"id\": 37384, \"name\": \"the black door\"}, {\"id\": 37385, \"name\": \"the silver stanchion\"}, {\"id\": 37386, \"name\": \"the pediment\"}, {\"id\": 37387, \"name\": \"the stainles steel mixing bowl\"}, {\"id\": 37388, \"name\": \"bottle of soy sauce\"}, {\"id\": 37389, \"name\": \"the ticket counter\"}, {\"id\": 37390, \"name\": \"the gold-colored metal\"}, {\"id\": 37391, \"name\": \"crown molding\"}, {\"id\": 37392, \"name\": \"the pink flower pot\"}, {\"id\": 37393, \"name\": \"a pink face roller\"}, {\"id\": 37394, \"name\": \"frosted panel\"}, {\"id\": 37395, \"name\": \"colorful strap\"}, {\"id\": 37396, \"name\": \"stainles steel surface\"}, {\"id\": 37397, \"name\": \"cleaning supply\"}, {\"id\": 37398, \"name\": \"the parked blue and white bicycle\"}, {\"id\": 37399, \"name\": \"pterodactyl\"}, {\"id\": 37400, \"name\": \"a black blender\"}, {\"id\": 37401, \"name\": \"blue soap dispenser\"}, {\"id\": 37402, \"name\": \"a toaster\"}, {\"id\": 37403, \"name\": \"the coffee bag\"}, {\"id\": 37404, \"name\": \"heart-shaped card\"}, {\"id\": 37405, \"name\": \"a peeled banana\"}, {\"id\": 37406, \"name\": \"a black notebook\"}, {\"id\": 37407, \"name\": \"the puma logo\"}, {\"id\": 37408, \"name\": \"the clear jar\"}, {\"id\": 37409, \"name\": \"the top drawer\"}, {\"id\": 37410, \"name\": \"the grey stone base\"}, {\"id\": 37411, \"name\": \"the red marker\"}, {\"id\": 37412, \"name\": \"a wooden-handled spoon\"}, {\"id\": 37413, \"name\": \"a scrambled egg\"}, {\"id\": 37414, \"name\": \"black menu board\"}, {\"id\": 37415, \"name\": \"a pink and blue umbrella\"}, {\"id\": 37416, \"name\": \"a paper shopping bag\"}, {\"id\": 37417, \"name\": \"white and black paint\"}, {\"id\": 37418, \"name\": \"the gray granite\"}, {\"id\": 37419, \"name\": \"the box of butter\"}, {\"id\": 37420, \"name\": \"black ottoman\"}, {\"id\": 37421, \"name\": \"purple carnival ride\"}, {\"id\": 37422, \"name\": \"the racing track\"}, {\"id\": 37423, \"name\": \"a green, metal panel\"}, {\"id\": 37424, \"name\": \"the carousel horse\"}, {\"id\": 37425, \"name\": \"a pink and gold sequined suit\"}, {\"id\": 37426, \"name\": \"a racing helmet\"}, {\"id\": 37427, \"name\": \"the black train\"}, {\"id\": 37428, \"name\": \"the brass-colored sphere\"}, {\"id\": 37429, \"name\": \"shelter\"}, {\"id\": 37430, \"name\": \"dirt trackside barrier\"}, {\"id\": 37431, \"name\": \"the red pallet\"}, {\"id\": 37432, \"name\": \"back of a horse\"}, {\"id\": 37433, \"name\": \"the red curb\"}, {\"id\": 37434, \"name\": \"the dark gray carpeted floor\"}, {\"id\": 37435, \"name\": \"a passenger-side window\"}, {\"id\": 37436, \"name\": \"gray lego piece\"}, {\"id\": 37437, \"name\": \"a black long-sleeved shirt\"}, {\"id\": 37438, \"name\": \"a red curb\"}, {\"id\": 37439, \"name\": \"the wooden walkway\"}, {\"id\": 37440, \"name\": \"a dark sneaker\"}, {\"id\": 37441, \"name\": \"a black smokestack\"}, {\"id\": 37442, \"name\": \"a green track surface\"}, {\"id\": 37443, \"name\": \"rear wing\"}, {\"id\": 37444, \"name\": \"the glas facade\"}, {\"id\": 37445, \"name\": \"a red bumper\"}, {\"id\": 37446, \"name\": \"the black and white striped pattern\"}, {\"id\": 37447, \"name\": \"bright yellow, blue, and purple ride\"}, {\"id\": 37448, \"name\": \"a metal arch\"}, {\"id\": 37449, \"name\": \"yellow and green feathered sleeve\"}, {\"id\": 37450, \"name\": \"a shoreline\"}, {\"id\": 37451, \"name\": \"clear plastic rain bonnet\"}, {\"id\": 37452, \"name\": \"white lighting\"}, {\"id\": 37453, \"name\": \"a vessel\"}, {\"id\": 37454, \"name\": \"suspended seat\"}, {\"id\": 37455, \"name\": \"a stainles steel counter\"}, {\"id\": 37456, \"name\": \"the black hand wrap\"}, {\"id\": 37457, \"name\": \"red plastic chair\"}, {\"id\": 37458, \"name\": \"a yellow chain\"}, {\"id\": 37459, \"name\": \"the toy train set\"}, {\"id\": 37460, \"name\": \"a cluttered work table\"}, {\"id\": 37461, \"name\": \"horse-drawn carriage\"}, {\"id\": 37462, \"name\": \"the red and yellow striped pattern\"}, {\"id\": 37463, \"name\": \"an epaulet\"}, {\"id\": 37464, \"name\": \"lit sign\"}, {\"id\": 37465, \"name\": \"the dark-colored coat\"}, {\"id\": 37466, \"name\": \"colorful cycling attire\"}, {\"id\": 37467, \"name\": \"spacesuit\"}, {\"id\": 37468, \"name\": \"the vendor's stand\"}, {\"id\": 37469, \"name\": \"a red bu\"}, {\"id\": 37470, \"name\": \"a pattern of white and red mask\"}, {\"id\": 37471, \"name\": \"a white boxing glove\"}, {\"id\": 37472, \"name\": \"colorful attire\"}, {\"id\": 37473, \"name\": \"the black puffer jacket\"}, {\"id\": 37474, \"name\": \"luggage conveyor\"}, {\"id\": 37475, \"name\": \"the grey hat\"}, {\"id\": 37476, \"name\": \"the safety gear\"}, {\"id\": 37477, \"name\": \"a curved front end\"}, {\"id\": 37478, \"name\": \"pit bull\"}, {\"id\": 37479, \"name\": \"the neon green material\"}, {\"id\": 37480, \"name\": \"a covered wooden structure\"}, {\"id\": 37481, \"name\": \"the tan slide\"}, {\"id\": 37482, \"name\": \"the 14\"}, {\"id\": 37483, \"name\": \"the white metal stand\"}, {\"id\": 37484, \"name\": \"white, green, and red graphic\"}, {\"id\": 37485, \"name\": \"a jumpsuit\"}, {\"id\": 37486, \"name\": \"blue train track\"}, {\"id\": 37487, \"name\": \"an earbud\"}, {\"id\": 37488, \"name\": \"the white plastic hairnet\"}, {\"id\": 37489, \"name\": \"a truck's bucket lift\"}, {\"id\": 37490, \"name\": \"a black front grille\"}, {\"id\": 37491, \"name\": \"busy highway\"}, {\"id\": 37492, \"name\": \"a gray siding\"}, {\"id\": 37493, \"name\": \"foamy white wave\"}, {\"id\": 37494, \"name\": \"the colorful light\"}, {\"id\": 37495, \"name\": \"a distant shoreline\"}, {\"id\": 37496, \"name\": \"the red and white bu\"}, {\"id\": 37497, \"name\": \"the back panel\"}, {\"id\": 37498, \"name\": \"a colorful stall\"}, {\"id\": 37499, \"name\": \"vibrant yellow structure\"}, {\"id\": 37500, \"name\": \"the subway train\"}, {\"id\": 37501, \"name\": \"a protective glove\"}, {\"id\": 37502, \"name\": \"yellow cord\"}, {\"id\": 37503, \"name\": \"the blury, red-and-white fence\"}, {\"id\": 37504, \"name\": \"the black-and-white striped crosswalk\"}, {\"id\": 37505, \"name\": \"the covered patio area\"}, {\"id\": 37506, \"name\": \"a streak of light\"}, {\"id\": 37507, \"name\": \"the prominent breakwater\"}, {\"id\": 37508, \"name\": \"gray cobblestone\"}, {\"id\": 37509, \"name\": \"a lush uniform\"}, {\"id\": 37510, \"name\": \"explosion\"}, {\"id\": 37511, \"name\": \"the purple label\"}, {\"id\": 37512, \"name\": \"an aquatic creature\"}, {\"id\": 37513, \"name\": \"military attire\"}, {\"id\": 37514, \"name\": \"a gold spike\"}, {\"id\": 37515, \"name\": \"the explosion\"}, {\"id\": 37516, \"name\": \"tan and orange jacket\"}, {\"id\": 37517, \"name\": \"the grey hair\"}, {\"id\": 37518, \"name\": \"a yellow metal object\"}, {\"id\": 37519, \"name\": \"fur-trimmed cape\"}, {\"id\": 37520, \"name\": \"a baby trend logo\"}, {\"id\": 37521, \"name\": \"a distinctive blue stripe\"}, {\"id\": 37522, \"name\": \"a ateneo\"}, {\"id\": 37523, \"name\": \"a black-and-white sign\"}, {\"id\": 37524, \"name\": \"the metal column\"}, {\"id\": 37525, \"name\": \"metal rack\"}, {\"id\": 37526, \"name\": \"the ruffled hem\"}, {\"id\": 37527, \"name\": \"wooden shack\"}, {\"id\": 37528, \"name\": \"the chef's hands\"}, {\"id\": 37529, \"name\": \"black and yellow design\"}, {\"id\": 37530, \"name\": \"a blue and purple fence\"}, {\"id\": 37531, \"name\": \"the porch\"}, {\"id\": 37532, \"name\": \"a rider's black helmet\"}, {\"id\": 37533, \"name\": \"gray track\"}, {\"id\": 37534, \"name\": \"the red fire truck\"}, {\"id\": 37535, \"name\": \"mascot cart\"}, {\"id\": 37536, \"name\": \"shadow of a microphone\"}, {\"id\": 37537, \"name\": \"black puffy jacket\"}, {\"id\": 37538, \"name\": \"tire barrier\"}, {\"id\": 37539, \"name\": \"red, white, and blue jersey\"}, {\"id\": 37540, \"name\": \"black and white graphic\"}, {\"id\": 37541, \"name\": \"pink and blue headdress\"}, {\"id\": 37542, \"name\": \"a vertical mullion\"}, {\"id\": 37543, \"name\": \"the red sphere\"}, {\"id\": 37544, \"name\": \"bright orange outfit\"}, {\"id\": 37545, \"name\": \"a pattern of round hole\"}, {\"id\": 37546, \"name\": \"a red-framed entrance\"}, {\"id\": 37547, \"name\": \"pink and white fairy costume\"}, {\"id\": 37548, \"name\": \"pony toy\"}, {\"id\": 37549, \"name\": \"the double door\"}, {\"id\": 37550, \"name\": \"rainbow-colored blanket\"}, {\"id\": 37551, \"name\": \"a circular, gray platform\"}, {\"id\": 37552, \"name\": \"station wagon\"}, {\"id\": 37553, \"name\": \"the red and white barricade\"}, {\"id\": 37554, \"name\": \"a crowded escalator\"}, {\"id\": 37555, \"name\": \"blurred trailer\"}, {\"id\": 37556, \"name\": \"a dlsu team\"}, {\"id\": 37557, \"name\": \"the wooden nightstand\"}, {\"id\": 37558, \"name\": \"a vibrant parade float\"}, {\"id\": 37559, \"name\": \"an ornate white stone detail\"}, {\"id\": 37560, \"name\": \"rocky breakwater\"}, {\"id\": 37561, \"name\": \"sliding glas door\"}, {\"id\": 37562, \"name\": \"a yellow and black bumper\"}, {\"id\": 37563, \"name\": \"the double-decker bu\"}, {\"id\": 37564, \"name\": \"the conveyer belt system\"}, {\"id\": 37565, \"name\": \"a red cooler\"}, {\"id\": 37566, \"name\": \"the left front wheel\"}, {\"id\": 37567, \"name\": \"fedcom\"}, {\"id\": 37568, \"name\": \"blue and orange striped canopy\"}, {\"id\": 37569, \"name\": \"winch\"}, {\"id\": 37570, \"name\": \"the white and blue racecar\"}, {\"id\": 37571, \"name\": \"a vertical wire\"}, {\"id\": 37572, \"name\": \"the string of car\"}, {\"id\": 37573, \"name\": \"a white chest\"}, {\"id\": 37574, \"name\": \"the yellow cable\"}, {\"id\": 37575, \"name\": \"lego block\"}, {\"id\": 37576, \"name\": \"a raised train track\"}, {\"id\": 37577, \"name\": \"the orange pallet\"}, {\"id\": 37578, \"name\": \"the elbow\"}, {\"id\": 37579, \"name\": \"the black-and-white graphic\"}, {\"id\": 37580, \"name\": \"the bright glow\"}, {\"id\": 37581, \"name\": \"reflective silver stripe\"}, {\"id\": 37582, \"name\": \"the boat's mast\"}, {\"id\": 37583, \"name\": \"a black and white body\"}, {\"id\": 37584, \"name\": \"vehicle's headlight\"}, {\"id\": 37585, \"name\": \"the black wrought-iron balcony\"}, {\"id\": 37586, \"name\": \"open rear door\"}, {\"id\": 37587, \"name\": \"a red grill\"}, {\"id\": 37588, \"name\": \"a large blue arm\"}, {\"id\": 37589, \"name\": \"a janisee\"}, {\"id\": 37590, \"name\": \"red or blue uniform\"}, {\"id\": 37591, \"name\": \"a blue sail\"}, {\"id\": 37592, \"name\": \"light brown coat\"}, {\"id\": 37593, \"name\": \"a red and pink gummy\"}, {\"id\": 37594, \"name\": \"a brick floor\"}, {\"id\": 37595, \"name\": \"a light blue surface\"}, {\"id\": 37596, \"name\": \"red and white striped awning\"}, {\"id\": 37597, \"name\": \"the lush green hill\"}, {\"id\": 37598, \"name\": \"a colorful float\"}, {\"id\": 37599, \"name\": \"factory production line\"}, {\"id\": 37600, \"name\": \"field of hay\"}, {\"id\": 37601, \"name\": \"silver hoop earring\"}, {\"id\": 37602, \"name\": \"white cartoon face\"}, {\"id\": 37603, \"name\": \"a dark brown couch\"}, {\"id\": 37604, \"name\": \"a woman in a blue tank top\"}, {\"id\": 37605, \"name\": \"silver tricycle\"}, {\"id\": 37606, \"name\": \"the church's steeple\"}, {\"id\": 37607, \"name\": \"the lush field of leafy green\"}, {\"id\": 37608, \"name\": \"tile floor\"}, {\"id\": 37609, \"name\": \"glass front\"}, {\"id\": 37610, \"name\": \"the dark asphalt\"}, {\"id\": 37611, \"name\": \"the green border\"}, {\"id\": 37612, \"name\": \"box of dog toy\"}, {\"id\": 37613, \"name\": \"a 167.5k\"}, {\"id\": 37614, \"name\": \"the women soccer player\"}, {\"id\": 37615, \"name\": \"the white carousel horse\"}, {\"id\": 37616, \"name\": \"the white hull\"}, {\"id\": 37617, \"name\": \"the graphic of a bicycle\"}, {\"id\": 37618, \"name\": \"the cage-like structure\"}, {\"id\": 37619, \"name\": \"a car's front bumper\"}, {\"id\": 37620, \"name\": \"the light brown horse\"}, {\"id\": 37621, \"name\": \"the red and yellow border\"}, {\"id\": 37622, \"name\": \"central yellow line\"}, {\"id\": 37623, \"name\": \"gray road\"}, {\"id\": 37624, \"name\": \"white front door\"}, {\"id\": 37625, \"name\": \"open-air top of a vehicle\"}, {\"id\": 37626, \"name\": \"an engine's red frame\"}, {\"id\": 37627, \"name\": \"a grid of dark brown block\"}, {\"id\": 37628, \"name\": \"red and white news banner\"}, {\"id\": 37629, \"name\": \"yellow and white material\"}, {\"id\": 37630, \"name\": \"silver container\"}, {\"id\": 37631, \"name\": \"horizontal plank\"}, {\"id\": 37632, \"name\": \"a racer's number\"}, {\"id\": 37633, \"name\": \"a vibrant blue material\"}, {\"id\": 37634, \"name\": \"the makeup product\"}, {\"id\": 37635, \"name\": \"black and white bag\"}, {\"id\": 37636, \"name\": \"dirt oval\"}, {\"id\": 37637, \"name\": \"a red metal railing\"}, {\"id\": 37638, \"name\": \"a blue school uniform\"}, {\"id\": 37639, \"name\": \"a green playground structure\"}, {\"id\": 37640, \"name\": \"a festive christma light\"}, {\"id\": 37641, \"name\": \"side table\"}, {\"id\": 37642, \"name\": \"the yellow grill\"}, {\"id\": 37643, \"name\": \"black metal bike rack\"}, {\"id\": 37644, \"name\": \"the blurred forest path\"}, {\"id\": 37645, \"name\": \"the yellow mane\"}, {\"id\": 37646, \"name\": \"a silver rail\"}, {\"id\": 37647, \"name\": \"driver's side of the rearview mirror\"}, {\"id\": 37648, \"name\": \"a blurred hand\"}, {\"id\": 37649, \"name\": \"dry and barren field\"}, {\"id\": 37650, \"name\": \"a yellow pedicab\"}, {\"id\": 37651, \"name\": \"the long blue skirt\"}, {\"id\": 37652, \"name\": \"the pink stroller\"}, {\"id\": 37653, \"name\": \"yellow star logo\"}, {\"id\": 37654, \"name\": \"black-and-white checkered floor\"}, {\"id\": 37655, \"name\": \"model train set\"}, {\"id\": 37656, \"name\": \"a gray concrete curb\"}, {\"id\": 37657, \"name\": \"a knight in armor\"}, {\"id\": 37658, \"name\": \"the bright, colorful light\"}, {\"id\": 37659, \"name\": \"a white fur-lined coat\"}, {\"id\": 37660, \"name\": \"the parking sign\"}, {\"id\": 37661, \"name\": \"the airport gate\"}, {\"id\": 37662, \"name\": \"a department of labour\"}, {\"id\": 37663, \"name\": \"a red scissor lift\"}, {\"id\": 37664, \"name\": \"red bikini top\"}, {\"id\": 37665, \"name\": \"a silver metal piece\"}, {\"id\": 37666, \"name\": \"a red-roofed structure\"}, {\"id\": 37667, \"name\": \"red crayon mark\"}, {\"id\": 37668, \"name\": \"blue front panel\"}, {\"id\": 37669, \"name\": \"the right shoulder\"}, {\"id\": 37670, \"name\": \"the evan logo\"}, {\"id\": 37671, \"name\": \"the metal spikes or blade\"}, {\"id\": 37672, \"name\": \"a yellow substance\"}, {\"id\": 37673, \"name\": \"green wire\"}, {\"id\": 37674, \"name\": \"black pug\"}, {\"id\": 37675, \"name\": \"a npr one logo\"}, {\"id\": 37676, \"name\": \"a person in the wheelchair\"}, {\"id\": 37677, \"name\": \"green tie\"}, {\"id\": 37678, \"name\": \"the white o\"}, {\"id\": 37679, \"name\": \"a pink pedestal\"}, {\"id\": 37680, \"name\": \"a skate ramp\"}, {\"id\": 37681, \"name\": \"a light-colored guitar\"}, {\"id\": 37682, \"name\": \"rotor blade\"}, {\"id\": 37683, \"name\": \"green scooter\"}, {\"id\": 37684, \"name\": \"castle\"}, {\"id\": 37685, \"name\": \"a dark grey tile\"}, {\"id\": 37686, \"name\": \"silver arm\"}, {\"id\": 37687, \"name\": \"wix\"}, {\"id\": 37688, \"name\": \"website url\"}, {\"id\": 37689, \"name\": \"the gummy bear\"}, {\"id\": 37690, \"name\": \"the shovel\"}, {\"id\": 37691, \"name\": \"the abc\"}, {\"id\": 37692, \"name\": \"the brown pillar\"}, {\"id\": 37693, \"name\": \"the rider's left foot\"}, {\"id\": 37694, \"name\": \"tail feather\"}, {\"id\": 37695, \"name\": \"black and white windshield\"}, {\"id\": 37696, \"name\": \"the rowing course\"}, {\"id\": 37697, \"name\": \"gray reflective stripe\"}, {\"id\": 37698, \"name\": \"the blue and white striped long-sleeve button-up shirt\"}, {\"id\": 37699, \"name\": \"oxygen tank\"}, {\"id\": 37700, \"name\": \"red and orange logo\"}, {\"id\": 37701, \"name\": \"busy crosswalk\"}, {\"id\": 37702, \"name\": \"news headline\"}, {\"id\": 37703, \"name\": \"a game's title\"}, {\"id\": 37704, \"name\": \"black and purple outfit\"}, {\"id\": 37705, \"name\": \"black counter\"}, {\"id\": 37706, \"name\": \"orange reflective stripe\"}, {\"id\": 37707, \"name\": \"a framed document\"}, {\"id\": 37708, \"name\": \"bright-colored jacket\"}, {\"id\": 37709, \"name\": \"yellow painted line\"}, {\"id\": 37710, \"name\": \"blue and white patterned shirt\"}, {\"id\": 37711, \"name\": \"the gray pole\"}, {\"id\": 37712, \"name\": \"well-manicured gra field\"}, {\"id\": 37713, \"name\": \"an inflatable water slide\"}, {\"id\": 37714, \"name\": \"a view of the ceiling\"}, {\"id\": 37715, \"name\": \"black gear and leaf design\"}, {\"id\": 37716, \"name\": \"the mighty hammer\"}, {\"id\": 37717, \"name\": \"pothole\"}, {\"id\": 37718, \"name\": \"a black-and-white coat\"}, {\"id\": 37719, \"name\": \"jamaican flag\"}, {\"id\": 37720, \"name\": \"a white floral pattern\"}, {\"id\": 37721, \"name\": \"a racing wheel chair\"}, {\"id\": 37722, \"name\": \"a elevated corridor\"}, {\"id\": 37723, \"name\": \"snow-covered hood\"}, {\"id\": 37724, \"name\": \"a neon yellow shoe\"}, {\"id\": 37725, \"name\": \"the large potted plant\"}, {\"id\": 37726, \"name\": \"tropical-print dress\"}, {\"id\": 37727, \"name\": \"a dark red sauce\"}, {\"id\": 37728, \"name\": \"a the body shop\"}, {\"id\": 37729, \"name\": \"tray of ice cube\"}, {\"id\": 37730, \"name\": \"a dark surface\"}, {\"id\": 37731, \"name\": \"a archivo difilm watermark\"}, {\"id\": 37732, \"name\": \"the gold mirror\"}, {\"id\": 37733, \"name\": \"fire van\"}, {\"id\": 37734, \"name\": \"a red and white decoration\"}, {\"id\": 37735, \"name\": \"a hinged lid\"}, {\"id\": 37736, \"name\": \"the disc\"}, {\"id\": 37737, \"name\": \"a canon printer\"}, {\"id\": 37738, \"name\": \"the target\"}, {\"id\": 37739, \"name\": \"abc action news logo\"}, {\"id\": 37740, \"name\": \"the bike's number plate\"}, {\"id\": 37741, \"name\": \"the white and red mascot\"}, {\"id\": 37742, \"name\": \"an utility vehicle\"}, {\"id\": 37743, \"name\": \"a red track\"}, {\"id\": 37744, \"name\": \"an ice cream treat\"}, {\"id\": 37745, \"name\": \"a roof of the vehicle\"}, {\"id\": 37746, \"name\": \"convoy\"}, {\"id\": 37747, \"name\": \"neon green wheel\"}, {\"id\": 37748, \"name\": \"the bottom aquarium\"}, {\"id\": 37749, \"name\": \"a carousel ride\"}, {\"id\": 37750, \"name\": \"the hairnet\"}, {\"id\": 37751, \"name\": \"a oust\"}, {\"id\": 37752, \"name\": \"a carousel's base\"}, {\"id\": 37753, \"name\": \"the pinata\"}, {\"id\": 37754, \"name\": \"paved section\"}, {\"id\": 37755, \"name\": \"the instrument's string\"}, {\"id\": 37756, \"name\": \"the white side\"}, {\"id\": 37757, \"name\": \"a red sign with a white stripe\"}, {\"id\": 37758, \"name\": \"blue and white logo for 2 wmar\"}, {\"id\": 37759, \"name\": \"green tank top\"}, {\"id\": 37760, \"name\": \"hollywood first look feature\"}, {\"id\": 37761, \"name\": \"a large stone column\"}, {\"id\": 37762, \"name\": \"brown, speckled surface\"}, {\"id\": 37763, \"name\": \"a black stereo system\"}, {\"id\": 37764, \"name\": \"a red band\"}, {\"id\": 37765, \"name\": \"the tailgate\"}, {\"id\": 37766, \"name\": \"the noticias\"}, {\"id\": 37767, \"name\": \"a tan puppy\"}, {\"id\": 37768, \"name\": \"the dark blue stripe\"}, {\"id\": 37769, \"name\": \"red and white patterned cover\"}, {\"id\": 37770, \"name\": \"a sonica mountain\"}, {\"id\": 37771, \"name\": \"man in a black robe\"}, {\"id\": 37772, \"name\": \"the white and orange striped barrier\"}, {\"id\": 37773, \"name\": \"blue zipper\"}, {\"id\": 37774, \"name\": \"a white bicycle lane symbol\"}, {\"id\": 37775, \"name\": \"the blue twitter logo\"}, {\"id\": 37776, \"name\": \"the prominent watermark\"}, {\"id\": 37777, \"name\": \"a gray and orange shirt\"}, {\"id\": 37778, \"name\": \"glas cup\"}, {\"id\": 37779, \"name\": \"gray truck\"}, {\"id\": 37780, \"name\": \"the paper mache\"}, {\"id\": 37781, \"name\": \"cartoon alien\"}, {\"id\": 37782, \"name\": \"a partially visible license plate\"}, {\"id\": 37783, \"name\": \"the red and yellow curb\"}, {\"id\": 37784, \"name\": \"stainles steel countertop\"}, {\"id\": 37785, \"name\": \"a vertical bar\"}, {\"id\": 37786, \"name\": \"the bu stop\"}, {\"id\": 37787, \"name\": \"a yield sign\"}, {\"id\": 37788, \"name\": \"a green machine\"}, {\"id\": 37789, \"name\": \"a gray backrest\"}, {\"id\": 37790, \"name\": \"yellow water bottle\"}, {\"id\": 37791, \"name\": \"a bread or pastry\"}, {\"id\": 37792, \"name\": \"taxi's dashboard\"}, {\"id\": 37793, \"name\": \"the large wooden board\"}, {\"id\": 37794, \"name\": \"the yellow and black barrier\"}, {\"id\": 37795, \"name\": \"the black-framed glass\"}, {\"id\": 37796, \"name\": \"a bib\"}, {\"id\": 37797, \"name\": \"2016 justin bieber world tour logo\"}, {\"id\": 37798, \"name\": \"blue bag strap\"}, {\"id\": 37799, \"name\": \"blue and gold costume\"}, {\"id\": 37800, \"name\": \"the white emblem\"}, {\"id\": 37801, \"name\": \"white x\"}, {\"id\": 37802, \"name\": \"9 on your side at 6pm banner\"}, {\"id\": 37803, \"name\": \"red and white curb\"}, {\"id\": 37804, \"name\": \"abc2 logo\"}, {\"id\": 37805, \"name\": \"the red and white striped object\"}, {\"id\": 37806, \"name\": \"a white price tag\"}, {\"id\": 37807, \"name\": \"a yellow band\"}, {\"id\": 37808, \"name\": \"red electrical cord\"}, {\"id\": 37809, \"name\": \"alligator's mouth\"}, {\"id\": 37810, \"name\": \"the yellow ear protection\"}, {\"id\": 37811, \"name\": \"a rocky coastline\"}, {\"id\": 37812, \"name\": \"a white kimono\"}, {\"id\": 37813, \"name\": \"the bbc report\"}, {\"id\": 37814, \"name\": \"the cursive script\"}, {\"id\": 37815, \"name\": \"a green design\"}, {\"id\": 37816, \"name\": \"black and red stripe\"}, {\"id\": 37817, \"name\": \"a trolley track\"}, {\"id\": 37818, \"name\": \"a yellow seat\"}, {\"id\": 37819, \"name\": \"the metal pan\"}, {\"id\": 37820, \"name\": \"outdoor dining area\"}, {\"id\": 37821, \"name\": \"a silver knee pad\"}, {\"id\": 37822, \"name\": \"a forehead\"}, {\"id\": 37823, \"name\": \"the car seat\"}, {\"id\": 37824, \"name\": \"the red and white line\"}, {\"id\": 37825, \"name\": \"the destination sign\"}, {\"id\": 37826, \"name\": \"the black, spiky fence\"}, {\"id\": 37827, \"name\": \"a crate of pineapple\"}, {\"id\": 37828, \"name\": \"the scale\"}, {\"id\": 37829, \"name\": \"the white blanket\"}, {\"id\": 37830, \"name\": \"a grey sweatshirt\"}, {\"id\": 37831, \"name\": \"a red and white tail light\"}, {\"id\": 37832, \"name\": \"yellow and black pole\"}, {\"id\": 37833, \"name\": \"a news broadcast logo\"}, {\"id\": 37834, \"name\": \"the large white curtain\"}, {\"id\": 37835, \"name\": \"expert\"}, {\"id\": 37836, \"name\": \"a man in a striped tank top\"}, {\"id\": 37837, \"name\": \"the navy blue and white striped shirt\"}, {\"id\": 37838, \"name\": \"red, white, and green light\"}, {\"id\": 37839, \"name\": \"clown\"}, {\"id\": 37840, \"name\": \"a red and white chevron pattern\"}, {\"id\": 37841, \"name\": \"green and black frame\"}, {\"id\": 37842, \"name\": \"the person in a yellow jacket\"}, {\"id\": 37843, \"name\": \"a white jp logo\"}, {\"id\": 37844, \"name\": \"gray hair\"}, {\"id\": 37845, \"name\": \"the black grille\"}, {\"id\": 37846, \"name\": \"the green and white scoreboard\"}, {\"id\": 37847, \"name\": \"the brown guitar\"}, {\"id\": 37848, \"name\": \"red hatchback car\"}, {\"id\": 37849, \"name\": \"the green capri pant\"}, {\"id\": 37850, \"name\": \"rider's feet\"}, {\"id\": 37851, \"name\": \"a man in a yellow and green jacket\"}, {\"id\": 37852, \"name\": \"a green mask\"}, {\"id\": 37853, \"name\": \"red, yellow, and green design\"}, {\"id\": 37854, \"name\": \"a man's left arm\"}, {\"id\": 37855, \"name\": \"a buffet-style food service area\"}, {\"id\": 37856, \"name\": \"yellow screen\"}, {\"id\": 37857, \"name\": \"the white zipper\"}, {\"id\": 37858, \"name\": \"sponsor's name\"}, {\"id\": 37859, \"name\": \"cigarette\"}, {\"id\": 37860, \"name\": \"sign on the roof\"}, {\"id\": 37861, \"name\": \"a rider of the black motorcycle\"}, {\"id\": 37862, \"name\": \"round, white head\"}, {\"id\": 37863, \"name\": \"red beanie\"}, {\"id\": 37864, \"name\": \"amir figueroa\"}, {\"id\": 37865, \"name\": \"blue patterned shirt\"}, {\"id\": 37866, \"name\": \"paved pathway\"}, {\"id\": 37867, \"name\": \"the shining\"}, {\"id\": 37868, \"name\": \"red-shirted person\"}, {\"id\": 37869, \"name\": \"wildly stronger!\"}, {\"id\": 37870, \"name\": \"green handlebar\"}, {\"id\": 37871, \"name\": \"a casket or coffin\"}, {\"id\": 37872, \"name\": \"pink awning\"}, {\"id\": 37873, \"name\": \"a white outline of africa\"}, {\"id\": 37874, \"name\": \"a large hot air balloon\"}, {\"id\": 37875, \"name\": \"wooden workbench\"}, {\"id\": 37876, \"name\": \"the yellow chevron\"}, {\"id\": 37877, \"name\": \"a white sill\"}, {\"id\": 37878, \"name\": \"a red and green ornament\"}, {\"id\": 37879, \"name\": \"the colorful floral lei\"}, {\"id\": 37880, \"name\": \"a green support beam\"}, {\"id\": 37881, \"name\": \"a busy thoroughfare\"}, {\"id\": 37882, \"name\": \"cartoon character's face\"}, {\"id\": 37883, \"name\": \"vibrant blue track\"}, {\"id\": 37884, \"name\": \"a grid\"}, {\"id\": 37885, \"name\": \"dark grey suv\"}, {\"id\": 37886, \"name\": \"the prime time\"}, {\"id\": 37887, \"name\": \"the black golf cart\"}, {\"id\": 37888, \"name\": \"person on a motorcycle\"}, {\"id\": 37889, \"name\": \"a dark ceiling\"}, {\"id\": 37890, \"name\": \"a parque villa\"}, {\"id\": 37891, \"name\": \"a subway platform\"}, {\"id\": 37892, \"name\": \"the striped pant\"}, {\"id\": 37893, \"name\": \"distinctive red and white patterned cable or cord\"}, {\"id\": 37894, \"name\": \"purple and white checkered blanket\"}, {\"id\": 37895, \"name\": \"the orange and silver handle\"}, {\"id\": 37896, \"name\": \"a guinness world record museum\"}, {\"id\": 37897, \"name\": \"the fresh cinnamon roasted nut\"}, {\"id\": 37898, \"name\": \"the food stall's counter\"}, {\"id\": 37899, \"name\": \"a white blood cell\"}, {\"id\": 37900, \"name\": \"model car\"}, {\"id\": 37901, \"name\": \"an instrument's headstock\"}, {\"id\": 37902, \"name\": \"the timer\"}, {\"id\": 37903, \"name\": \"the vibrant red hat\"}, {\"id\": 37904, \"name\": \"a black knit headband\"}, {\"id\": 37905, \"name\": \"blue and purple costume\"}, {\"id\": 37906, \"name\": \"the forest of tree\"}, {\"id\": 37907, \"name\": \"white lane marking\"}, {\"id\": 37908, \"name\": \"the red spool\"}, {\"id\": 37909, \"name\": \"the red, yellow, and blue stripe\"}, {\"id\": 37910, \"name\": \"the dirt oval track\"}, {\"id\": 37911, \"name\": \"police personnel\"}, {\"id\": 37912, \"name\": \"roland\"}, {\"id\": 37913, \"name\": \"lift\"}, {\"id\": 37914, \"name\": \"red and yellow bow\"}, {\"id\": 37915, \"name\": \"the silver grille\"}, {\"id\": 37916, \"name\": \"the lindsay wing\"}, {\"id\": 37917, \"name\": \"the black wheelbarrow\"}, {\"id\": 37918, \"name\": \"a portal or gateway\"}, {\"id\": 37919, \"name\": \"honey logo\"}, {\"id\": 37920, \"name\": \"a gold border\"}, {\"id\": 37921, \"name\": \"clown-like figure\"}, {\"id\": 37922, \"name\": \"the dark, textured surface\"}, {\"id\": 37923, \"name\": \"a polly dash\"}, {\"id\": 37924, \"name\": \"red and yellow striped roof\"}, {\"id\": 37925, \"name\": \"the santa clau\"}, {\"id\": 37926, \"name\": \"the decorative star pattern\"}, {\"id\": 37927, \"name\": \"man in a brown suit\"}, {\"id\": 37928, \"name\": \"the diamond-shaped pattern\"}, {\"id\": 37929, \"name\": \"car's front grille\"}, {\"id\": 37930, \"name\": \"a date and time of the report\"}, {\"id\": 37931, \"name\": \"the al jazeera logo\"}, {\"id\": 37932, \"name\": \"table of good\"}, {\"id\": 37933, \"name\": \"5-hour energy\"}, {\"id\": 37934, \"name\": \"the neon green and black pant\"}, {\"id\": 37935, \"name\": \"a metal grate floor\"}, {\"id\": 37936, \"name\": \"the white and yellow taxi sign\"}, {\"id\": 37937, \"name\": \"a silver panel\"}, {\"id\": 37938, \"name\": \"prominent red canopy\"}, {\"id\": 37939, \"name\": \"the pedestrian path\"}, {\"id\": 37940, \"name\": \"the carousel's structure\"}, {\"id\": 37941, \"name\": \"a silver machine\"}, {\"id\": 37942, \"name\": \"a cbsla logo\"}, {\"id\": 37943, \"name\": \"woman in a red jacket\"}, {\"id\": 37944, \"name\": \"the plaid jacket\"}, {\"id\": 37945, \"name\": \"the luca oil logo\"}, {\"id\": 37946, \"name\": \"the gray metal box\"}, {\"id\": 37947, \"name\": \"airport's exterior\"}, {\"id\": 37948, \"name\": \"frog toy\"}, {\"id\": 37949, \"name\": \"neon yellow safety vest\"}, {\"id\": 37950, \"name\": \"pink and purple flower-shaped decoration\"}, {\"id\": 37951, \"name\": \"sync logo\"}, {\"id\": 37952, \"name\": \"a line of motorcycle\"}, {\"id\": 37953, \"name\": \"white front panel\"}, {\"id\": 37954, \"name\": \"the white and red striped barrier\"}, {\"id\": 37955, \"name\": \"the dark blue body\"}, {\"id\": 37956, \"name\": \"the blue support structure\"}, {\"id\": 37957, \"name\": \"a track's infield\"}, {\"id\": 37958, \"name\": \"the signage\"}, {\"id\": 37959, \"name\": \"a red telephone box\"}, {\"id\": 37960, \"name\": \"minnie mouse design\"}, {\"id\": 37961, \"name\": \"snow-covered yard\"}, {\"id\": 37962, \"name\": \"the blue, purple, and yellow tail\"}, {\"id\": 37963, \"name\": \"the blue checkered stripe\"}, {\"id\": 37964, \"name\": \"dark interior of the car\"}, {\"id\": 37965, \"name\": \"the pearl earring\"}, {\"id\": 37966, \"name\": \"black and white striped sock\"}, {\"id\": 37967, \"name\": \"a yellow digital clock\"}, {\"id\": 37968, \"name\": \"the teal banner\"}, {\"id\": 37969, \"name\": \"a dark blue suv\"}, {\"id\": 37970, \"name\": \"the the louvre\"}, {\"id\": 37971, \"name\": \"bridge or walkway\"}, {\"id\": 37972, \"name\": \"a blue floor\"}, {\"id\": 37973, \"name\": \"the tan hooded jacket\"}, {\"id\": 37974, \"name\": \"a ump modified a-feature\"}, {\"id\": 37975, \"name\": \"shops or business\"}, {\"id\": 37976, \"name\": \"rock cafe\"}, {\"id\": 37977, \"name\": \"black silhouette of arrow\"}, {\"id\": 37978, \"name\": \"red and white striped hat\"}, {\"id\": 37979, \"name\": \"black and tan striped top\"}, {\"id\": 37980, \"name\": \"metal post\"}, {\"id\": 37981, \"name\": \"the cardboard box of tim hortons donut\"}, {\"id\": 37982, \"name\": \"truck's tire\"}, {\"id\": 37983, \"name\": \"a red sticker\"}, {\"id\": 37984, \"name\": \"the roller\"}, {\"id\": 37985, \"name\": \"a distinctive black hat\"}, {\"id\": 37986, \"name\": \"the rower's oar\"}, {\"id\": 37987, \"name\": \"a green and yellow hat\"}, {\"id\": 37988, \"name\": \"blue plastic tray\"}, {\"id\": 37989, \"name\": \"a black line\"}, {\"id\": 37990, \"name\": \"a fidget spinner\"}, {\"id\": 37991, \"name\": \"pink ribbon headband\"}, {\"id\": 37992, \"name\": \"the legally blind paddler make debut in crown point\"}, {\"id\": 37993, \"name\": \"the sawhorse\"}, {\"id\": 37994, \"name\": \"colorful, arched structure\"}, {\"id\": 37995, \"name\": \"white forky plush toy\"}, {\"id\": 37996, \"name\": \"a bull or elephant\"}, {\"id\": 37997, \"name\": \"dirt bike rider\"}, {\"id\": 37998, \"name\": \"the orange cylinder\"}, {\"id\": 37999, \"name\": \"the palm tree graphic\"}, {\"id\": 38000, \"name\": \"neon yellow and black cycling outfit\"}, {\"id\": 38001, \"name\": \"race leaderboard\"}, {\"id\": 38002, \"name\": \"an armchair\"}, {\"id\": 38003, \"name\": \"the vehicle's rear bumper\"}, {\"id\": 38004, \"name\": \"africa live\"}, {\"id\": 38005, \"name\": \"a atm\"}, {\"id\": 38006, \"name\": \"the yellow and black backpack\"}, {\"id\": 38007, \"name\": \"gray athletic shoe\"}, {\"id\": 38008, \"name\": \"the yellow and red diagonal stripe\"}, {\"id\": 38009, \"name\": \"a silver gun\"}, {\"id\": 38010, \"name\": \"woman in a white shirt and black pant\"}, {\"id\": 38011, \"name\": \"a black couch\"}, {\"id\": 38012, \"name\": \"the light brown wood chip\"}, {\"id\": 38013, \"name\": \"a silver necklace\"}, {\"id\": 38014, \"name\": \"the outdoor stage\"}, {\"id\": 38015, \"name\": \"the wpri symbol\"}, {\"id\": 38016, \"name\": \"the mountaineer\"}, {\"id\": 38017, \"name\": \"an emergency response vehicle\"}, {\"id\": 38018, \"name\": \"the dark cape\"}, {\"id\": 38019, \"name\": \"panda head\"}, {\"id\": 38020, \"name\": \"the black flooring\"}, {\"id\": 38021, \"name\": \"the female\"}, {\"id\": 38022, \"name\": \"glowing blue circle\"}, {\"id\": 38023, \"name\": \"the red and white graphic\"}, {\"id\": 38024, \"name\": \"a maroon banner\"}, {\"id\": 38025, \"name\": \"the blue bicycle\"}, {\"id\": 38026, \"name\": \"the iconic red and blue costume\"}, {\"id\": 38027, \"name\": \"the black and white banner\"}, {\"id\": 38028, \"name\": \"black and red checkered pattern\"}, {\"id\": 38029, \"name\": \"the waffle\"}, {\"id\": 38030, \"name\": \"a white and red bag\"}, {\"id\": 38031, \"name\": \"red bandana\"}, {\"id\": 38032, \"name\": \"a logo of ntn24 and youtube/mtn24\"}, {\"id\": 38033, \"name\": \"a family of four\"}, {\"id\": 38034, \"name\": \"red and white checkered flag\"}, {\"id\": 38035, \"name\": \"a yellow lantern\"}, {\"id\": 38036, \"name\": \"grassy path\"}, {\"id\": 38037, \"name\": \"a white hard hat\"}, {\"id\": 38038, \"name\": \"chrome bumper\"}, {\"id\": 38039, \"name\": \"the long red hair\"}, {\"id\": 38040, \"name\": \"team's name\"}, {\"id\": 38041, \"name\": \"muddy object\"}, {\"id\": 38042, \"name\": \"red x mark\"}, {\"id\": 38043, \"name\": \"blue and black bar\"}, {\"id\": 38044, \"name\": \"the purple car\"}, {\"id\": 38045, \"name\": \"the transparent gray rectangle overlay\"}, {\"id\": 38046, \"name\": \"white and green ribbon\"}, {\"id\": 38047, \"name\": \"the white ford logo\"}, {\"id\": 38048, \"name\": \"a black len\"}, {\"id\": 38049, \"name\": \"the black trigger\"}, {\"id\": 38050, \"name\": \"the sabc news logo\"}, {\"id\": 38051, \"name\": \"abc\"}, {\"id\": 38052, \"name\": \"red electric bas guitar\"}, {\"id\": 38053, \"name\": \"the black netting\"}, {\"id\": 38054, \"name\": \"the warehouse or store aisle\"}, {\"id\": 38055, \"name\": \"a light grey sweatshirt\"}, {\"id\": 38056, \"name\": \"a you have the ball!\"}, {\"id\": 38057, \"name\": \"the exposed wooden beam\"}, {\"id\": 38058, \"name\": \"red and white striped clothing\"}, {\"id\": 38059, \"name\": \"a efrain perlaza\"}, {\"id\": 38060, \"name\": \"a green and blue car\"}, {\"id\": 38061, \"name\": \"a yellow step\"}, {\"id\": 38062, \"name\": \"the crossing\"}, {\"id\": 38063, \"name\": \"the black front panel\"}, {\"id\": 38064, \"name\": \"a red drawstring\"}, {\"id\": 38065, \"name\": \"the neon green strap\"}, {\"id\": 38066, \"name\": \"blue, red, and white pattern\"}, {\"id\": 38067, \"name\": \"a white snowflake ornament\"}, {\"id\": 38068, \"name\": \"the gray top\"}, {\"id\": 38069, \"name\": \"an exclamation mark\"}, {\"id\": 38070, \"name\": \"a black disc\"}, {\"id\": 38071, \"name\": \"red and white fletching\"}, {\"id\": 38072, \"name\": \"utility box\"}, {\"id\": 38073, \"name\": \"clock icon\"}, {\"id\": 38074, \"name\": \"a red and yellow hat\"}, {\"id\": 38075, \"name\": \"red and white striped sign\"}, {\"id\": 38076, \"name\": \"a black car with a yellow license plate\"}, {\"id\": 38077, \"name\": \"white rock\"}, {\"id\": 38078, \"name\": \"the black metal table\"}, {\"id\": 38079, \"name\": \"a blue hooded sweatshirt\"}, {\"id\": 38080, \"name\": \"a lunchbox\"}, {\"id\": 38081, \"name\": \"roll bar\"}, {\"id\": 38082, \"name\": \"the line of truck\"}, {\"id\": 38083, \"name\": \"the subtitle\"}, {\"id\": 38084, \"name\": \"a chalet\"}, {\"id\": 38085, \"name\": \"backdrop of green grass and tree\"}, {\"id\": 38086, \"name\": \"sale a sol\"}, {\"id\": 38087, \"name\": \"a blue-and-black striped jersey\"}, {\"id\": 38088, \"name\": \"yellow and white boat\"}, {\"id\": 38089, \"name\": \"the orange inflatable ring\"}, {\"id\": 38090, \"name\": \"a white costume\"}, {\"id\": 38091, \"name\": \"square tile\"}, {\"id\": 38092, \"name\": \"weekend top5\"}, {\"id\": 38093, \"name\": \"a black and white polka-dot scarf\"}, {\"id\": 38094, \"name\": \"yellow double-decker bu\"}, {\"id\": 38095, \"name\": \"a blue and white striped creature\"}, {\"id\": 38096, \"name\": \"the german shepherd dog\"}, {\"id\": 38097, \"name\": \"a follow us:\"}, {\"id\": 38098, \"name\": \"the man in a white shirt and jean\"}, {\"id\": 38099, \"name\": \"blue conveyor belt\"}, {\"id\": 38100, \"name\": \"quois '14\"}, {\"id\": 38101, \"name\": \"a nightstand\"}, {\"id\": 38102, \"name\": \"the red hatchback car\"}, {\"id\": 38103, \"name\": \"a black and white striped pole\"}, {\"id\": 38104, \"name\": \"blue head\"}, {\"id\": 38105, \"name\": \"the mustache\"}, {\"id\": 38106, \"name\": \"cal fire logo\"}, {\"id\": 38107, \"name\": \"dragon-like creature\"}, {\"id\": 38108, \"name\": \"restaurant or bar setting\"}, {\"id\": 38109, \"name\": \"a generator\"}, {\"id\": 38110, \"name\": \"a woman with blonde hair\"}, {\"id\": 38111, \"name\": \"a rough surface\"}, {\"id\": 38112, \"name\": \"the roadwork\"}, {\"id\": 38113, \"name\": \"the women's volleyball\"}, {\"id\": 38114, \"name\": \"yellow and green dress\"}, {\"id\": 38115, \"name\": \"mack truck\"}, {\"id\": 38116, \"name\": \"a cartoon thumb-up icon\"}, {\"id\": 38117, \"name\": \"a black block\"}, {\"id\": 38118, \"name\": \"black overall\"}, {\"id\": 38119, \"name\": \"a black taxi cab\"}, {\"id\": 38120, \"name\": \"the person in the foreground\"}, {\"id\": 38121, \"name\": \"the teal pant\"}, {\"id\": 38122, \"name\": \"the 1 sign\"}, {\"id\": 38123, \"name\": \"al jazeera logo\"}, {\"id\": 38124, \"name\": \"graphene logo\"}, {\"id\": 38125, \"name\": \"stuffed alligator\"}, {\"id\": 38126, \"name\": \"michelin logo\"}, {\"id\": 38127, \"name\": \"a glas front\"}, {\"id\": 38128, \"name\": \"the party hat\"}, {\"id\": 38129, \"name\": \"the the animal must be housebroken\"}, {\"id\": 38130, \"name\": \"the whale's mouth\"}, {\"id\": 38131, \"name\": \"a dark blue life jacket\"}, {\"id\": 38132, \"name\": \"enemy reaper incoming\"}, {\"id\": 38133, \"name\": \"a green trees and bush\"}, {\"id\": 38134, \"name\": \"a miniature tree\"}, {\"id\": 38135, \"name\": \"a green ferrari logo\"}, {\"id\": 38136, \"name\": \"the light green bottom\"}, {\"id\": 38137, \"name\": \"the news logo\"}, {\"id\": 38138, \"name\": \"the brown metal grate top\"}, {\"id\": 38139, \"name\": \"a makeshift wooden platform\"}, {\"id\": 38140, \"name\": \"the pink cross-body bag\"}, {\"id\": 38141, \"name\": \"the green and brown metal bar\"}, {\"id\": 38142, \"name\": \"a brick-paved patio\"}, {\"id\": 38143, \"name\": \"the rainbow-colored heart\"}, {\"id\": 38144, \"name\": \"tan exterior\"}, {\"id\": 38145, \"name\": \"the roller skate\"}, {\"id\": 38146, \"name\": \"the black and white patterned band\"}, {\"id\": 38147, \"name\": \"a dark-colored seal\"}, {\"id\": 38148, \"name\": \"woman in a pink dress\"}, {\"id\": 38149, \"name\": \"a white and red scooter\"}, {\"id\": 38150, \"name\": \"the pay station\"}, {\"id\": 38151, \"name\": \"stylized p within a circle\"}, {\"id\": 38152, \"name\": \"the projector screen\"}, {\"id\": 38153, \"name\": \"a white lanyard\"}, {\"id\": 38154, \"name\": \"the back paw\"}, {\"id\": 38155, \"name\": \"the straight track with a small gap in the middle\"}, {\"id\": 38156, \"name\": \"red drawstring bag\"}, {\"id\": 38157, \"name\": \"pedicab\"}, {\"id\": 38158, \"name\": \"fox business\"}, {\"id\": 38159, \"name\": \"patio or porch\"}, {\"id\": 38160, \"name\": \"a barbed-wire fence\"}, {\"id\": 38161, \"name\": \"the red and white striped cone\"}, {\"id\": 38162, \"name\": \"a logo of a television channel\"}, {\"id\": 38163, \"name\": \"miami dolphin logo\"}, {\"id\": 38164, \"name\": \"the yellow safety jacket\"}, {\"id\": 38165, \"name\": \"a sporting good\"}, {\"id\": 38166, \"name\": \"large, white, spinning wheel\"}, {\"id\": 38167, \"name\": \"the brown and white train car\"}, {\"id\": 38168, \"name\": \"an animal horse\"}, {\"id\": 38169, \"name\": \"a green and yellow net\"}, {\"id\": 38170, \"name\": \"a miri ben-ari project logo\"}, {\"id\": 38171, \"name\": \"a tan costume\"}, {\"id\": 38172, \"name\": \"the red crown\"}, {\"id\": 38173, \"name\": \"a black crack\"}, {\"id\": 38174, \"name\": \"tan helmet\"}, {\"id\": 38175, \"name\": \"small white refrigerator\"}, {\"id\": 38176, \"name\": \"a deserve kindness\"}, {\"id\": 38177, \"name\": \"the red and white tag\"}, {\"id\": 38178, \"name\": \"yellow and white border\"}, {\"id\": 38179, \"name\": \"blue and white mannequin head\"}, {\"id\": 38180, \"name\": \"the maslow's army\"}, {\"id\": 38181, \"name\": \"a brown brick structure\"}, {\"id\": 38182, \"name\": \"red brick median\"}, {\"id\": 38183, \"name\": \"a green and white canopy\"}, {\"id\": 38184, \"name\": \"the red shopping cart\"}, {\"id\": 38185, \"name\": \"the navy blue hoodie\"}, {\"id\": 38186, \"name\": \"a production equipment\"}, {\"id\": 38187, \"name\": \"a purple eye\"}, {\"id\": 38188, \"name\": \"the metal door\"}, {\"id\": 38189, \"name\": \"white with a red outline\"}, {\"id\": 38190, \"name\": \"a green countertop\"}, {\"id\": 38191, \"name\": \"a flower bed\"}, {\"id\": 38192, \"name\": \"brown blanket\"}, {\"id\": 38193, \"name\": \"whip\"}, {\"id\": 38194, \"name\": \"guitar case\"}, {\"id\": 38195, \"name\": \"a whole food\"}, {\"id\": 38196, \"name\": \"the small section of a wooden floor\"}, {\"id\": 38197, \"name\": \"a hard rock cafe\"}, {\"id\": 38198, \"name\": \"a circular window\"}, {\"id\": 38199, \"name\": \"the white voa logo\"}, {\"id\": 38200, \"name\": \"the dirt track race\"}, {\"id\": 38201, \"name\": \"green, ruffled glove\"}, {\"id\": 38202, \"name\": \"a confetti popper\"}, {\"id\": 38203, \"name\": \"blue and white toy\"}, {\"id\": 38204, \"name\": \"time stamp\"}, {\"id\": 38205, \"name\": \"a light blue scooter\"}, {\"id\": 38206, \"name\": \"bail hearing\"}, {\"id\": 38207, \"name\": \"the blue and yellow banner\"}, {\"id\": 38208, \"name\": \"an individuals' face\"}, {\"id\": 38209, \"name\": \"the m eg 2107\"}, {\"id\": 38210, \"name\": \"an orange swim cap\"}, {\"id\": 38211, \"name\": \"the blue speech bubble\"}, {\"id\": 38212, \"name\": \"blue and white striped swimsuit\"}, {\"id\": 38213, \"name\": \"a white divider\"}, {\"id\": 38214, \"name\": \"the vibrant striped towel\"}, {\"id\": 38215, \"name\": \"the distinctive brown hairstyle\"}, {\"id\": 38216, \"name\": \"bright yellow safety vest\"}, {\"id\": 38217, \"name\": \"band of drummer\"}, {\"id\": 38218, \"name\": \"the silver cylinder\"}, {\"id\": 38219, \"name\": \"green and yellow banner\"}, {\"id\": 38220, \"name\": \"a white script\"}, {\"id\": 38221, \"name\": \"red and white patterned headscarf\"}, {\"id\": 38222, \"name\": \"silhouette of an audience\"}, {\"id\": 38223, \"name\": \"a black and white skirt\"}, {\"id\": 38224, \"name\": \"townsend sign\"}, {\"id\": 38225, \"name\": \"the daisy\"}, {\"id\": 38226, \"name\": \"an astronaut\"}, {\"id\": 38227, \"name\": \"the yellow center line\"}, {\"id\": 38228, \"name\": \"the teal dress\"}, {\"id\": 38229, \"name\": \"yellow and white floatie\"}, {\"id\": 38230, \"name\": \"factory floor\"}, {\"id\": 38231, \"name\": \"a maroon t-shirt\"}, {\"id\": 38232, \"name\": \"stainles steel counter\"}, {\"id\": 38233, \"name\": \"a back seat of a taxi\"}, {\"id\": 38234, \"name\": \"glowing blue light\"}, {\"id\": 38235, \"name\": \"a purple and yellow helmet\"}, {\"id\": 38236, \"name\": \"a yellow horn\"}, {\"id\": 38237, \"name\": \"a timecode\"}, {\"id\": 38238, \"name\": \"current time\"}, {\"id\": 38239, \"name\": \"the brown shuttered facade\"}, {\"id\": 38240, \"name\": \"the solid yellow line\"}, {\"id\": 38241, \"name\": \"a tie-dye shirt\"}, {\"id\": 38242, \"name\": \"a thousands of athlete compete in canoe race\"}, {\"id\": 38243, \"name\": \"the red-tiled floor\"}, {\"id\": 38244, \"name\": \"the white and silver patterned top\"}, {\"id\": 38245, \"name\": \"the roller coaster's tracks\"}, {\"id\": 38246, \"name\": \"a grey banner\"}, {\"id\": 38247, \"name\": \"dark, long-sleeved clothing\"}, {\"id\": 38248, \"name\": \"white mustache\"}, {\"id\": 38249, \"name\": \"a small pink bottle\"}, {\"id\": 38250, \"name\": \"-black attire\"}, {\"id\": 38251, \"name\": \"clear bag of small toy\"}, {\"id\": 38252, \"name\": \"the green and black motorcycle\"}, {\"id\": 38253, \"name\": \"the child in a black shirt\"}, {\"id\": 38254, \"name\": \"the box of clearwire earbud\"}, {\"id\": 38255, \"name\": \"the green diamond\"}, {\"id\": 38256, \"name\": \"the yellow cup\"}, {\"id\": 38257, \"name\": \"the red and white peppermint candy\"}, {\"id\": 38258, \"name\": \"open compartment\"}, {\"id\": 38259, \"name\": \"a player's hair\"}, {\"id\": 38260, \"name\": \"black, sleeveles shirt\"}, {\"id\": 38261, \"name\": \"an alien head\"}, {\"id\": 38262, \"name\": \"blue metal wire\"}, {\"id\": 38263, \"name\": \"the shooters life through the len\"}, {\"id\": 38264, \"name\": \"the green triangle-shaped monster\"}, {\"id\": 38265, \"name\": \"a brown harness\"}, {\"id\": 38266, \"name\": \"circular yellow logo\"}, {\"id\": 38267, \"name\": \"purple and gold costume\"}, {\"id\": 38268, \"name\": \"the game's menu\"}, {\"id\": 38269, \"name\": \"a purple frame\"}, {\"id\": 38270, \"name\": \"the red and white logo for wthr\"}, {\"id\": 38271, \"name\": \"a black star\"}, {\"id\": 38272, \"name\": \"transparent overlay\"}, {\"id\": 38273, \"name\": \"gray bowl\"}, {\"id\": 38274, \"name\": \"small, lit-up storefront\"}, {\"id\": 38275, \"name\": \"a yellow bib\"}, {\"id\": 38276, \"name\": \"blue and white polka-dot pattern\"}, {\"id\": 38277, \"name\": \"a pink object with stripe\"}, {\"id\": 38278, \"name\": \"the line of tree\"}, {\"id\": 38279, \"name\": \"the nut or bolt head\"}, {\"id\": 38280, \"name\": \"red hockey net\"}, {\"id\": 38281, \"name\": \"blue and red marking\"}, {\"id\": 38282, \"name\": \"a red and white fire truck\"}, {\"id\": 38283, \"name\": \"the black and gold car\"}, {\"id\": 38284, \"name\": \"the our women are so strong, they are basically like man\"}, {\"id\": 38285, \"name\": \"grey minivan\"}, {\"id\": 38286, \"name\": \"a blue trailer\"}, {\"id\": 38287, \"name\": \"a race information\"}, {\"id\": 38288, \"name\": \"a yellow sweater\"}, {\"id\": 38289, \"name\": \"silver component\"}, {\"id\": 38290, \"name\": \"gray-shirted man\"}, {\"id\": 38291, \"name\": \"white pvc pipe frame\"}, {\"id\": 38292, \"name\": \"white front grille\"}, {\"id\": 38293, \"name\": \"a khaki cargo short\"}, {\"id\": 38294, \"name\": \"the mailbox\"}, {\"id\": 38295, \"name\": \"large green tuk-tuk\"}, {\"id\": 38296, \"name\": \"a white fur lining\"}, {\"id\": 38297, \"name\": \"bob\"}, {\"id\": 38298, \"name\": \"sedan's front grille\"}, {\"id\": 38299, \"name\": \"a red lip\"}, {\"id\": 38300, \"name\": \"baseball jersey\"}, {\"id\": 38301, \"name\": \"the live score\"}, {\"id\": 38302, \"name\": \"open bed\"}, {\"id\": 38303, \"name\": \"a gray fighter jet\"}, {\"id\": 38304, \"name\": \"a black-and-white mask\"}, {\"id\": 38305, \"name\": \"a gorilla costume\"}, {\"id\": 38306, \"name\": \"destroyed vehicle\"}, {\"id\": 38307, \"name\": \"the yellow window\"}, {\"id\": 38308, \"name\": \"dark grey curtain\"}, {\"id\": 38309, \"name\": \"graphic of a football helmet and football\"}, {\"id\": 38310, \"name\": \"the drill bit\"}, {\"id\": 38311, \"name\": \"a blue panel\"}, {\"id\": 38312, \"name\": \"the white camper van\"}, {\"id\": 38313, \"name\": \"the white gift bag\"}, {\"id\": 38314, \"name\": \"the black headboard\"}, {\"id\": 38315, \"name\": \"ginaz cold store\"}, {\"id\": 38316, \"name\": \"the yellow and red taxi\"}, {\"id\": 38317, \"name\": \"the busines attire\"}, {\"id\": 38318, \"name\": \"black garbage bag\"}, {\"id\": 38319, \"name\": \"a blury car\"}, {\"id\": 38320, \"name\": \"a mjolnir\"}, {\"id\": 38321, \"name\": \"dark blue knit cap\"}, {\"id\": 38322, \"name\": \"the white tape line\"}, {\"id\": 38323, \"name\": \"foxtel\"}, {\"id\": 38324, \"name\": \"a black high-heeled boot\"}, {\"id\": 38325, \"name\": \"back of a person's head\"}, {\"id\": 38326, \"name\": \"happening tonight\"}, {\"id\": 38327, \"name\": \"the woman's shoulder\"}, {\"id\": 38328, \"name\": \"flood\"}, {\"id\": 38329, \"name\": \"the image's frame\"}, {\"id\": 38330, \"name\": \"a red and black cable\"}, {\"id\": 38331, \"name\": \"red and blue plastic bucket\"}, {\"id\": 38332, \"name\": \"a white rug\"}, {\"id\": 38333, \"name\": \"an anthropomorphic monster\"}, {\"id\": 38334, \"name\": \"a brown cap\"}, {\"id\": 38335, \"name\": \"a gold headband\"}, {\"id\": 38336, \"name\": \"a wooden deck or patio\"}, {\"id\": 38337, \"name\": \"a rider's foot\"}, {\"id\": 38338, \"name\": \"distant hill or mountain range\"}, {\"id\": 38339, \"name\": \"the framed\"}, {\"id\": 38340, \"name\": \"the dark gray garage door\"}, {\"id\": 38341, \"name\": \"the cctv america logo\"}, {\"id\": 38342, \"name\": \"a picture of a fruit drink\"}, {\"id\": 38343, \"name\": \"red and white checkered pattern\"}, {\"id\": 38344, \"name\": \"taxi's yellow roof sign\"}, {\"id\": 38345, \"name\": \"the filing cabinet\"}, {\"id\": 38346, \"name\": \"green tractor\"}, {\"id\": 38347, \"name\": \"a bad ben 2016\"}, {\"id\": 38348, \"name\": \"abundance of bread roll\"}, {\"id\": 38349, \"name\": \"dvd\"}, {\"id\": 38350, \"name\": \"the tenni ball\"}, {\"id\": 38351, \"name\": \"the silver ring pendant\"}, {\"id\": 38352, \"name\": \"a parked bicycle\"}, {\"id\": 38353, \"name\": \"a hole in the ground\"}, {\"id\": 38354, \"name\": \"blue and white pole\"}, {\"id\": 38355, \"name\": \"a white metal shelving unit\"}, {\"id\": 38356, \"name\": \"red surface\"}, {\"id\": 38357, \"name\": \"the news ticker\"}, {\"id\": 38358, \"name\": \"a train's wheel\"}, {\"id\": 38359, \"name\": \"a red vertical line\"}, {\"id\": 38360, \"name\": \"the gray longboard\"}, {\"id\": 38361, \"name\": \"news ticker\"}, {\"id\": 38362, \"name\": \"a distinctive red and black logo\"}, {\"id\": 38363, \"name\": \"a boat docked\"}, {\"id\": 38364, \"name\": \"a red and white football jersey\"}, {\"id\": 38365, \"name\": \"the gray and white panel\"}, {\"id\": 38366, \"name\": \"green rope\"}, {\"id\": 38367, \"name\": \"black leather seat\"}, {\"id\": 38368, \"name\": \"the pink swimsuit\"}, {\"id\": 38369, \"name\": \"a doll's face\"}, {\"id\": 38370, \"name\": \"the pink substance\"}, {\"id\": 38371, \"name\": \"white tight\"}, {\"id\": 38372, \"name\": \"the red and white cafe teria sign\"}, {\"id\": 38373, \"name\": \"the ferrari sport car\"}, {\"id\": 38374, \"name\": \"a white left turn sign\"}, {\"id\": 38375, \"name\": \"the prominent red and yellow banner\"}, {\"id\": 38376, \"name\": \"the rounded shape\"}, {\"id\": 38377, \"name\": \"the outboard motor\"}, {\"id\": 38378, \"name\": \"the vibrant design of red, green, and gold snowflakes, stars, and leaf\"}, {\"id\": 38379, \"name\": \"a gray teddy bear\"}, {\"id\": 38380, \"name\": \"an open-air balcony\"}, {\"id\": 38381, \"name\": \"the bright pink bow\"}, {\"id\": 38382, \"name\": \"a red creature\"}, {\"id\": 38383, \"name\": \"the table format\"}, {\"id\": 38384, \"name\": \"light grey undercarriage\"}, {\"id\": 38385, \"name\": \"the tan sweater\"}, {\"id\": 38386, \"name\": \"white cros pattern\"}, {\"id\": 38387, \"name\": \"a sewing machine\"}, {\"id\": 38388, \"name\": \"the black wetsuit\"}, {\"id\": 38389, \"name\": \"person in a wheelchair\"}, {\"id\": 38390, \"name\": \"the black label\"}, {\"id\": 38391, \"name\": \"joy news\"}, {\"id\": 38392, \"name\": \"line of green and yellow tuk-tuk\"}, {\"id\": 38393, \"name\": \"the woman with a drum\"}, {\"id\": 38394, \"name\": \"a white cursive\"}, {\"id\": 38395, \"name\": \"an open white drawer\"}, {\"id\": 38396, \"name\": \"the standing wolf\"}, {\"id\": 38397, \"name\": \"the image of individual wearing head covering\"}, {\"id\": 38398, \"name\": \"the white rabbit\"}, {\"id\": 38399, \"name\": \"the electronic device\"}, {\"id\": 38400, \"name\": \"gray tiled floor\"}, {\"id\": 38401, \"name\": \"a black and white duffel bag\"}, {\"id\": 38402, \"name\": \"colorful serape blanket\"}, {\"id\": 38403, \"name\": \"wooden slat\"}, {\"id\": 38404, \"name\": \"a white tissue paper\"}, {\"id\": 38405, \"name\": \"a black and white face\"}, {\"id\": 38406, \"name\": \"a camouflage short\"}, {\"id\": 38407, \"name\": \"a abc 10 logo\"}, {\"id\": 38408, \"name\": \"a green vine\"}, {\"id\": 38409, \"name\": \"the gold helmet\"}, {\"id\": 38410, \"name\": \"the character from the movie 'the nightmare before christmas'\"}, {\"id\": 38411, \"name\": \"short or skirt\"}, {\"id\": 38412, \"name\": \"the black light stand\"}, {\"id\": 38413, \"name\": \"the white musical notation\"}, {\"id\": 38414, \"name\": \"green engine\"}, {\"id\": 38415, \"name\": \"a chrome bumper\"}, {\"id\": 38416, \"name\": \"grey hair\"}, {\"id\": 38417, \"name\": \"blurry image of a person or object\"}, {\"id\": 38418, \"name\": \"owner's hand\"}, {\"id\": 38419, \"name\": \"the prominent gold seal\"}, {\"id\": 38420, \"name\": \"a new thi morning\"}, {\"id\": 38421, \"name\": \"a yellow globe\"}, {\"id\": 38422, \"name\": \"the orange rectangle\"}, {\"id\": 38423, \"name\": \"orange and white striped construction barrier\"}, {\"id\": 38424, \"name\": \"the black and yellow license plate\"}, {\"id\": 38425, \"name\": \"the white spool of thread\"}, {\"id\": 38426, \"name\": \"the white double line\"}, {\"id\": 38427, \"name\": \"a bright orange dress\"}, {\"id\": 38428, \"name\": \"a halloween attire\"}, {\"id\": 38429, \"name\": \"glowing red circle\"}, {\"id\": 38430, \"name\": \"the silver chain choker\"}, {\"id\": 38431, \"name\": \"pink and blue patterned pant\"}, {\"id\": 38432, \"name\": \"a passenger car\"}, {\"id\": 38433, \"name\": \"a cream-colored crate\"}, {\"id\": 38434, \"name\": \"pliers\"}, {\"id\": 38435, \"name\": \"mondy logo\"}, {\"id\": 38436, \"name\": \"a central double yellow line\"}, {\"id\": 38437, \"name\": \"a loui vuitton store\"}, {\"id\": 38438, \"name\": \"construction barrier\"}, {\"id\": 38439, \"name\": \"spool of green thread\"}, {\"id\": 38440, \"name\": \"the world today logo\"}, {\"id\": 38441, \"name\": \"a live broadcast logo\"}, {\"id\": 38442, \"name\": \"padded seat\"}, {\"id\": 38443, \"name\": \"the black machine\"}, {\"id\": 38444, \"name\": \"a ruffled cuff\"}, {\"id\": 38445, \"name\": \"the yellow v\"}, {\"id\": 38446, \"name\": \"blue and white tile pattern\"}, {\"id\": 38447, \"name\": \"the red leather chair\"}, {\"id\": 38448, \"name\": \"the rusty brown trailer\"}, {\"id\": 38449, \"name\": \"concrete traffic cone\"}, {\"id\": 38450, \"name\": \"pink-hooded figure\"}, {\"id\": 38451, \"name\": \"a white turban\"}, {\"id\": 38452, \"name\": \"a bakery storefront\"}, {\"id\": 38453, \"name\": \"the food and water bowl\"}, {\"id\": 38454, \"name\": \"single lit candle\"}, {\"id\": 38455, \"name\": \"a pirate logo\"}, {\"id\": 38456, \"name\": \"a current score\"}, {\"id\": 38457, \"name\": \"a black horse silhouette\"}, {\"id\": 38458, \"name\": \"dark-colored wood or paneling\"}, {\"id\": 38459, \"name\": \"a black valance\"}, {\"id\": 38460, \"name\": \"the black pot\"}, {\"id\": 38461, \"name\": \"a red tablecloth-covered table\"}, {\"id\": 38462, \"name\": \"a numbered marker\"}, {\"id\": 38463, \"name\": \"the woman in a tan coat\"}, {\"id\": 38464, \"name\": \"the white, blue, and red logo\"}, {\"id\": 38465, \"name\": \"a crafting tool\"}, {\"id\": 38466, \"name\": \"a local television station\"}, {\"id\": 38467, \"name\": \"the open sign\"}, {\"id\": 38468, \"name\": \"a knee guard\"}, {\"id\": 38469, \"name\": \"diamond-patterned tile backsplash\"}, {\"id\": 38470, \"name\": \"white 60 sticker\"}, {\"id\": 38471, \"name\": \"a dark blue leg guard\"}, {\"id\": 38472, \"name\": \"a cartoon cell phone\"}, {\"id\": 38473, \"name\": \"the man standing behind a counter\"}, {\"id\": 38474, \"name\": \"the green produce\"}, {\"id\": 38475, \"name\": \"a center lane\"}, {\"id\": 38476, \"name\": \"small checkered flag\"}, {\"id\": 38477, \"name\": \"a formal dress\"}, {\"id\": 38478, \"name\": \"maroon bar\"}, {\"id\": 38479, \"name\": \"the bumper\"}, {\"id\": 38480, \"name\": \"a welding machine\"}, {\"id\": 38481, \"name\": \"the yellow and black folding chair\"}, {\"id\": 38482, \"name\": \"a yellow cable machine\"}, {\"id\": 38483, \"name\": \"a front seat\"}, {\"id\": 38484, \"name\": \"red coca-cola sign\"}, {\"id\": 38485, \"name\": \"multicolored swirl\"}, {\"id\": 38486, \"name\": \"a yellow police tape\"}, {\"id\": 38487, \"name\": \"blue and yellow cleat\"}, {\"id\": 38488, \"name\": \"a red do not enter sign\"}, {\"id\": 38489, \"name\": \"the parking space\"}, {\"id\": 38490, \"name\": \"the green and blue design\"}, {\"id\": 38491, \"name\": \"the red massey harri tractor\"}, {\"id\": 38492, \"name\": \"the cluttered wooden table\"}, {\"id\": 38493, \"name\": \"a person in white\"}, {\"id\": 38494, \"name\": \"a white lab coat\"}, {\"id\": 38495, \"name\": \"a teal head covering\"}, {\"id\": 38496, \"name\": \"bag of dog food\"}, {\"id\": 38497, \"name\": \"the gray bag strap\"}, {\"id\": 38498, \"name\": \"blue-and-green brooch\"}, {\"id\": 38499, \"name\": \"a black trailer\"}, {\"id\": 38500, \"name\": \"shutter\"}, {\"id\": 38501, \"name\": \"a black collar\"}, {\"id\": 38502, \"name\": \"a white and yellow striped umbrella\"}, {\"id\": 38503, \"name\": \"vibrant pink shirt\"}, {\"id\": 38504, \"name\": \"the 6abc\"}, {\"id\": 38505, \"name\": \"recording\"}, {\"id\": 38506, \"name\": \"a yellow-handled scissor\"}, {\"id\": 38507, \"name\": \"a usa store\"}, {\"id\": 38508, \"name\": \"cristiano ronaldo\"}, {\"id\": 38509, \"name\": \"shark-tooth pattern\"}, {\"id\": 38510, \"name\": \"multicolored pattern\"}, {\"id\": 38511, \"name\": \"red fire truck\"}, {\"id\": 38512, \"name\": \"a red shoulder strap\"}, {\"id\": 38513, \"name\": \"trt world logo\"}, {\"id\": 38514, \"name\": \"the earphone\"}, {\"id\": 38515, \"name\": \"the pink section\"}, {\"id\": 38516, \"name\": \"white garage door\"}, {\"id\": 38517, \"name\": \"the blue-green t-shirt\"}, {\"id\": 38518, \"name\": \"a blue scarf\"}, {\"id\": 38519, \"name\": \"person in red shirt\"}, {\"id\": 38520, \"name\": \"a gray reflective strip\"}, {\"id\": 38521, \"name\": \"an earphone\"}, {\"id\": 38522, \"name\": \"handmade jewelry\"}, {\"id\": 38523, \"name\": \"the citibank branch\"}, {\"id\": 38524, \"name\": \"the crate of banana\"}, {\"id\": 38525, \"name\": \"the track and field area\"}, {\"id\": 38526, \"name\": \"nutcracker\"}, {\"id\": 38527, \"name\": \"the blue archway\"}, {\"id\": 38528, \"name\": \"bottom step\"}, {\"id\": 38529, \"name\": \"black collared shirt\"}, {\"id\": 38530, \"name\": \"water droplet\"}, {\"id\": 38531, \"name\": \"a large, multicolored umbrella\"}, {\"id\": 38532, \"name\": \"a colorful head wrap\"}, {\"id\": 38533, \"name\": \"striped pant\"}, {\"id\": 38534, \"name\": \"an ice cream cone\"}, {\"id\": 38535, \"name\": \"silver and black armor\"}, {\"id\": 38536, \"name\": \"a red, white, and orange helmet\"}, {\"id\": 38537, \"name\": \"stylized illustration of a cyclist riding a bicycle\"}, {\"id\": 38538, \"name\": \"a cd case\"}, {\"id\": 38539, \"name\": \"a church steeple\"}, {\"id\": 38540, \"name\": \"the brown and white cape\"}, {\"id\": 38541, \"name\": \"the orange border\"}, {\"id\": 38542, \"name\": \"the wristband\"}, {\"id\": 38543, \"name\": \"ramrod\"}, {\"id\": 38544, \"name\": \"the dtimothy\"}, {\"id\": 38545, \"name\": \"cooper tire\"}, {\"id\": 38546, \"name\": \"blue and white striped shirt\"}, {\"id\": 38547, \"name\": \"the sleek, futuristic spaceship\"}, {\"id\": 38548, \"name\": \"the beige structure\"}, {\"id\": 38549, \"name\": \"the castle door\"}, {\"id\": 38550, \"name\": \"an open cockpit\"}, {\"id\": 38551, \"name\": \"the rat\"}, {\"id\": 38552, \"name\": \"pink liquid\"}, {\"id\": 38553, \"name\": \"white crosswalk pattern\"}, {\"id\": 38554, \"name\": \"meat grinder\"}, {\"id\": 38555, \"name\": \"white stud\"}, {\"id\": 38556, \"name\": \"a swiss tie after immigration vote\"}, {\"id\": 38557, \"name\": \"the glas bottle\"}, {\"id\": 38558, \"name\": \"usama mir\"}, {\"id\": 38559, \"name\": \"tan canopy\"}, {\"id\": 38560, \"name\": \"beige cap\"}, {\"id\": 38561, \"name\": \"the white block\"}, {\"id\": 38562, \"name\": \"muller\"}, {\"id\": 38563, \"name\": \"white toyota logo\"}, {\"id\": 38564, \"name\": \"a man in blue shirt\"}, {\"id\": 38565, \"name\": \"a turquoise stripe\"}, {\"id\": 38566, \"name\": \"a plane's tail\"}, {\"id\": 38567, \"name\": \"a white arm sleeve\"}, {\"id\": 38568, \"name\": \"the front left leg\"}, {\"id\": 38569, \"name\": \"the red and gold icon\"}, {\"id\": 38570, \"name\": \"the black icon\"}, {\"id\": 38571, \"name\": \"the blue vespa scooter\"}, {\"id\": 38572, \"name\": \"the dark brown eye\"}, {\"id\": 38573, \"name\": \"yellow watermark\"}, {\"id\": 38574, \"name\": \"the spear\"}, {\"id\": 38575, \"name\": \"blue lantern\"}, {\"id\": 38576, \"name\": \"march 12, 2017\"}, {\"id\": 38577, \"name\": \"the black towel\"}, {\"id\": 38578, \"name\": \"the green slide\"}, {\"id\": 38579, \"name\": \"white beard\"}, {\"id\": 38580, \"name\": \"blue crescent\"}, {\"id\": 38581, \"name\": \"the metal tub\"}, {\"id\": 38582, \"name\": \"the news reporter\"}, {\"id\": 38583, \"name\": \"nerf gun\"}, {\"id\": 38584, \"name\": \"a pink heart\"}, {\"id\": 38585, \"name\": \"the kaleidoscope of light\"}, {\"id\": 38586, \"name\": \"a black and green dirt bike\"}, {\"id\": 38587, \"name\": \"stall's interior\"}, {\"id\": 38588, \"name\": \"a red-handled screwdriver\"}, {\"id\": 38589, \"name\": \"the live on the go\"}, {\"id\": 38590, \"name\": \"the black and red hat\"}, {\"id\": 38591, \"name\": \"the white attire\"}, {\"id\": 38592, \"name\": \"blue overall\"}, {\"id\": 38593, \"name\": \"the cash register\"}, {\"id\": 38594, \"name\": \"red and yellow striped sweater\"}, {\"id\": 38595, \"name\": \"dark window\"}, {\"id\": 38596, \"name\": \"toy box\"}, {\"id\": 38597, \"name\": \"grey scarf\"}, {\"id\": 38598, \"name\": \"a xbox one console\"}, {\"id\": 38599, \"name\": \"the time 259\"}, {\"id\": 38600, \"name\": \"black and white bug-like creature\"}, {\"id\": 38601, \"name\": \"a black-painted nail\"}, {\"id\": 38602, \"name\": \"a hiking boot\"}, {\"id\": 38603, \"name\": \"sliver of light\"}, {\"id\": 38604, \"name\": \"the white and blue plaid shirt\"}, {\"id\": 38605, \"name\": \"a dirt or debris\"}, {\"id\": 38606, \"name\": \"a white latex glove\"}, {\"id\": 38607, \"name\": \"the silver monster truck\"}, {\"id\": 38608, \"name\": \"score bar\"}, {\"id\": 38609, \"name\": \"a white barricade\"}, {\"id\": 38610, \"name\": \"the image of meat\"}, {\"id\": 38611, \"name\": \"the artist's name\"}, {\"id\": 38612, \"name\": \"the christian\"}, {\"id\": 38613, \"name\": \"new image kitchen\"}, {\"id\": 38614, \"name\": \"white cane\"}, {\"id\": 38615, \"name\": \"canon\"}, {\"id\": 38616, \"name\": \"the reporter\"}, {\"id\": 38617, \"name\": \"a city name\"}, {\"id\": 38618, \"name\": \"two-story structure\"}, {\"id\": 38619, \"name\": \"a decorative knot\"}, {\"id\": 38620, \"name\": \"the indoor shopping mall\"}, {\"id\": 38621, \"name\": \"white target symbol\"}, {\"id\": 38622, \"name\": \"the black guitar strap\"}, {\"id\": 38623, \"name\": \"toy airplane\"}, {\"id\": 38624, \"name\": \"the time of the broadcast\"}, {\"id\": 38625, \"name\": \"dark blue sea\"}, {\"id\": 38626, \"name\": \"a blue swim trunk\"}, {\"id\": 38627, \"name\": \"compass\"}, {\"id\": 38628, \"name\": \"a yellow leg\"}, {\"id\": 38629, \"name\": \"a gold and black suit\"}, {\"id\": 38630, \"name\": \"a light grey sweater\"}, {\"id\": 38631, \"name\": \"wooden storage cube\"}, {\"id\": 38632, \"name\": \"the concrete skate bowl\"}, {\"id\": 38633, \"name\": \"the gas station's storefront\"}, {\"id\": 38634, \"name\": \"the circular window\"}, {\"id\": 38635, \"name\": \"earth\"}, {\"id\": 38636, \"name\": \"the bicycle rack\"}, {\"id\": 38637, \"name\": \"the ground zero\"}, {\"id\": 38638, \"name\": \"a red and white clothing\"}, {\"id\": 38639, \"name\": \"a blind\"}, {\"id\": 38640, \"name\": \"the sailor\"}, {\"id\": 38641, \"name\": \"the blue monster\"}, {\"id\": 38642, \"name\": \"teal city bu\"}, {\"id\": 38643, \"name\": \"www.citizennews.co\"}, {\"id\": 38644, \"name\": \"a live joy news\"}, {\"id\": 38645, \"name\": \"a white hurdle\"}, {\"id\": 38646, \"name\": \"pink hair\"}, {\"id\": 38647, \"name\": \"red and black scooter\"}, {\"id\": 38648, \"name\": \"87 degree\"}, {\"id\": 38649, \"name\": \"the box of vermeftin\"}, {\"id\": 38650, \"name\": \"glowing arc reactor\"}, {\"id\": 38651, \"name\": \"vibrant, multicolored skirt\"}, {\"id\": 38652, \"name\": \"the caption bar\"}, {\"id\": 38653, \"name\": \"yellow ladder\"}, {\"id\": 38654, \"name\": \"the packed grandstand of spectator\"}, {\"id\": 38655, \"name\": \"the golden snake's tail\"}, {\"id\": 38656, \"name\": \"open\"}, {\"id\": 38657, \"name\": \"pink croc\"}, {\"id\": 38658, \"name\": \"a map of the course\"}, {\"id\": 38659, \"name\": \"the red and white icing detail\"}, {\"id\": 38660, \"name\": \"a url\"}, {\"id\": 38661, \"name\": \"the blue and yellow striped towel\"}, {\"id\": 38662, \"name\": \"the flotrack\"}, {\"id\": 38663, \"name\": \"the brown patch\"}, {\"id\": 38664, \"name\": \"a green goalkeeper attire\"}, {\"id\": 38665, \"name\": \"a red and white striped cushion\"}, {\"id\": 38666, \"name\": \"the purple unicorn\"}, {\"id\": 38667, \"name\": \"the brown mustache\"}, {\"id\": 38668, \"name\": \"military formation\"}, {\"id\": 38669, \"name\": \"the bright green vest\"}, {\"id\": 38670, \"name\": \"a thick layer of water\"}, {\"id\": 38671, \"name\": \"a fire hose\"}, {\"id\": 38672, \"name\": \"the grey beanie\"}, {\"id\": 38673, \"name\": \"the bright red headdress\"}, {\"id\": 38674, \"name\": \"a left eyebrow\"}, {\"id\": 38675, \"name\": \"a wine glas with a frog design\"}, {\"id\": 38676, \"name\": \"green bracelet\"}, {\"id\": 38677, \"name\": \"white hand sculpture\"}, {\"id\": 38678, \"name\": \"the carnival\"}, {\"id\": 38679, \"name\": \"the yellow suv\"}, {\"id\": 38680, \"name\": \"race entrance sign\"}, {\"id\": 38681, \"name\": \"a hammer icon\"}, {\"id\": 38682, \"name\": \"the 16 sign\"}, {\"id\": 38683, \"name\": \"a youtube\"}, {\"id\": 38684, \"name\": \"a bigg darklighter becoming an x-wing pilot\"}, {\"id\": 38685, \"name\": \"blue backpack strap\"}, {\"id\": 38686, \"name\": \"woman with long red hair and purple outfit\"}, {\"id\": 38687, \"name\": \"boat's white hull\"}, {\"id\": 38688, \"name\": \"a blue motorbike\"}, {\"id\": 38689, \"name\": \"a yellow teacup\"}, {\"id\": 38690, \"name\": \"purple spike\"}, {\"id\": 38691, \"name\": \"lush surrounding\"}, {\"id\": 38692, \"name\": \"man in suits\"}, {\"id\": 38693, \"name\": \"a turquoise tank top\"}, {\"id\": 38694, \"name\": \"a warehouse entrance\"}, {\"id\": 38695, \"name\": \"the person in a pink hoodie\"}, {\"id\": 38696, \"name\": \"the blue and orange coat\"}, {\"id\": 38697, \"name\": \"a stone structure\"}, {\"id\": 38698, \"name\": \"the fedex truck\"}, {\"id\": 38699, \"name\": \"outdoor table\"}, {\"id\": 38700, \"name\": \"time limit\"}, {\"id\": 38701, \"name\": \"a construction site\"}, {\"id\": 38702, \"name\": \"person dressed a a zebra\"}, {\"id\": 38703, \"name\": \"church\"}, {\"id\": 38704, \"name\": \"the red swim trunk\"}, {\"id\": 38705, \"name\": \"a black triangular object\"}, {\"id\": 38706, \"name\": \"teal top\"}, {\"id\": 38707, \"name\": \"grey pillar\"}, {\"id\": 38708, \"name\": \"fishing rod\"}, {\"id\": 38709, \"name\": \"blue blanket or towel\"}, {\"id\": 38710, \"name\": \"purple rod\"}, {\"id\": 38711, \"name\": \"the police boat\"}, {\"id\": 38712, \"name\": \"the yellow and green police vehicle\"}, {\"id\": 38713, \"name\": \"the black marker\"}, {\"id\": 38714, \"name\": \"a cooper tire\"}, {\"id\": 38715, \"name\": \"yellow front bumper\"}, {\"id\": 38716, \"name\": \"the shadow of the motorcyclist\"}, {\"id\": 38717, \"name\": \"news segment\"}, {\"id\": 38718, \"name\": \"the orange and white traffic cone\"}, {\"id\": 38719, \"name\": \"the distinctive red stripe\"}, {\"id\": 38720, \"name\": \"a transmission or gear box part\"}, {\"id\": 38721, \"name\": \"the warehouse floor\"}, {\"id\": 38722, \"name\": \"a bc bird\"}, {\"id\": 38723, \"name\": \"a woman in pink short\"}, {\"id\": 38724, \"name\": \"vibrant green headscarf\"}, {\"id\": 38725, \"name\": \"an athletic wear\"}, {\"id\": 38726, \"name\": \"a cafe\"}, {\"id\": 38727, \"name\": \"a blue bag strap\"}, {\"id\": 38728, \"name\": \"wooden porch\"}, {\"id\": 38729, \"name\": \"the black jacket and pant\"}, {\"id\": 38730, \"name\": \"cheek\"}, {\"id\": 38731, \"name\": \"u-haul truck\"}, {\"id\": 38732, \"name\": \"the blue ribbon\"}, {\"id\": 38733, \"name\": \"pink boat\"}, {\"id\": 38734, \"name\": \"a wig\"}, {\"id\": 38735, \"name\": \"piece of debris\"}, {\"id\": 38736, \"name\": \"the gray banner\"}, {\"id\": 38737, \"name\": \"jockey\"}, {\"id\": 38738, \"name\": \"purple short\"}, {\"id\": 38739, \"name\": \"badder ben\"}, {\"id\": 38740, \"name\": \"large, darkened structure\"}, {\"id\": 38741, \"name\": \"the coca-cola advertisement\"}, {\"id\": 38742, \"name\": \"metal shutter\"}, {\"id\": 38743, \"name\": \"a yellow sticky note\"}, {\"id\": 38744, \"name\": \"sea\"}, {\"id\": 38745, \"name\": \"the carousel's ticket booth\"}, {\"id\": 38746, \"name\": \"a purple and green component\"}, {\"id\": 38747, \"name\": \"the metal barricade\"}, {\"id\": 38748, \"name\": \"a white t\"}, {\"id\": 38749, \"name\": \"the coolie\"}, {\"id\": 38750, \"name\": \"black entertainment center\"}, {\"id\": 38751, \"name\": \"the small metal scoop\"}, {\"id\": 38752, \"name\": \"the map of africa\"}, {\"id\": 38753, \"name\": \"the grid of item\"}, {\"id\": 38754, \"name\": \"the blue design\"}, {\"id\": 38755, \"name\": \"the green and yellow stripe\"}, {\"id\": 38756, \"name\": \"fx\"}, {\"id\": 38757, \"name\": \"the short red hair\"}, {\"id\": 38758, \"name\": \"a brown tail\"}, {\"id\": 38759, \"name\": \"a boy's face\"}, {\"id\": 38760, \"name\": \"the vendor stall\"}, {\"id\": 38761, \"name\": \"the blue gutter\"}, {\"id\": 38762, \"name\": \"a silver hook\"}, {\"id\": 38763, \"name\": \"the rollerblade's wheel\"}, {\"id\": 38764, \"name\": \"red convertible\"}, {\"id\": 38765, \"name\": \"a sail's mesh material\"}, {\"id\": 38766, \"name\": \"the blue and white dress\"}, {\"id\": 38767, \"name\": \"the black cushion\"}, {\"id\": 38768, \"name\": \"the front grill\"}, {\"id\": 38769, \"name\": \"red wreath\"}, {\"id\": 38770, \"name\": \"black platform\"}, {\"id\": 38771, \"name\": \"black front bumper\"}, {\"id\": 38772, \"name\": \"black fender\"}, {\"id\": 38773, \"name\": \"a white and blue jersey\"}, {\"id\": 38774, \"name\": \"the white circular design\"}, {\"id\": 38775, \"name\": \"the horizontal strip of light\"}, {\"id\": 38776, \"name\": \"the yellow rear spoiler\"}, {\"id\": 38777, \"name\": \"a white sash\"}, {\"id\": 38778, \"name\": \"the game\"}, {\"id\": 38779, \"name\": \"the bikini\"}, {\"id\": 38780, \"name\": \"blue neon light\"}, {\"id\": 38781, \"name\": \"a green tag\"}, {\"id\": 38782, \"name\": \"muppet\"}, {\"id\": 38783, \"name\": \"a crew of soldier\"}, {\"id\": 38784, \"name\": \"blurred red and white race car\"}, {\"id\": 38785, \"name\": \"rink's white wall\"}, {\"id\": 38786, \"name\": \"the sleek white sailboat\"}, {\"id\": 38787, \"name\": \"the driver's attire\"}, {\"id\": 38788, \"name\": \"a white support\"}, {\"id\": 38789, \"name\": \"a man in short\"}, {\"id\": 38790, \"name\": \"the golf cart\"}, {\"id\": 38791, \"name\": \"the orange fencing\"}, {\"id\": 38792, \"name\": \"the park bench\"}, {\"id\": 38793, \"name\": \"quadcopter\"}, {\"id\": 38794, \"name\": \"an individual on bicycle\"}, {\"id\": 38795, \"name\": \"woman in a black shirt and jean\"}, {\"id\": 38796, \"name\": \"pink border\"}, {\"id\": 38797, \"name\": \"the female player\"}, {\"id\": 38798, \"name\": \"corn\"}, {\"id\": 38799, \"name\": \"the grey floral design\"}, {\"id\": 38800, \"name\": \"the blaster\"}, {\"id\": 38801, \"name\": \"the patterned tie\"}, {\"id\": 38802, \"name\": \"cargo door\"}, {\"id\": 38803, \"name\": \"a stylized r\"}, {\"id\": 38804, \"name\": \"neon yellow vest\"}, {\"id\": 38805, \"name\": \"a clapboard siding\"}, {\"id\": 38806, \"name\": \"the red wing tip\"}, {\"id\": 38807, \"name\": \"a red and yellow diagonal stripe pattern\"}, {\"id\": 38808, \"name\": \"the shoulder of a child\"}, {\"id\": 38809, \"name\": \"the small, cylindrical item\"}, {\"id\": 38810, \"name\": \"a table or other flat surface\"}, {\"id\": 38811, \"name\": \"a gymnastic ring\"}, {\"id\": 38812, \"name\": \"a vehicle's number plate\"}, {\"id\": 38813, \"name\": \"the cable car or ski lift\"}, {\"id\": 38814, \"name\": \"colorful flower\"}, {\"id\": 38815, \"name\": \"a yellow piece of equipment\"}, {\"id\": 38816, \"name\": \"formal black uniform\"}, {\"id\": 38817, \"name\": \"a neon green or blue vest\"}, {\"id\": 38818, \"name\": \"the glowing blue or red light\"}, {\"id\": 38819, \"name\": \"yellow or white shirt\"}, {\"id\": 38820, \"name\": \"the black pupil\"}, {\"id\": 38821, \"name\": \"the black and red hull\"}, {\"id\": 38822, \"name\": \"a front of car\"}, {\"id\": 38823, \"name\": \"the roll of pastry or bread\"}, {\"id\": 38824, \"name\": \"the blue ear tag\"}, {\"id\": 38825, \"name\": \"a black grille\"}, {\"id\": 38826, \"name\": \"the bee box\"}, {\"id\": 38827, \"name\": \"a white race car\"}, {\"id\": 38828, \"name\": \"dark windshield\"}, {\"id\": 38829, \"name\": \"pumpkin\"}, {\"id\": 38830, \"name\": \"a pattern of white dot\"}, {\"id\": 38831, \"name\": \"a wispy white cloud\"}, {\"id\": 38832, \"name\": \"the clear plastic cup\"}, {\"id\": 38833, \"name\": \"the white kayak\"}, {\"id\": 38834, \"name\": \"the mysterious, glowing purple flower\"}, {\"id\": 38835, \"name\": \"leg of person\"}, {\"id\": 38836, \"name\": \"boy's right arm\"}, {\"id\": 38837, \"name\": \"a black tuxedo jacket\"}, {\"id\": 38838, \"name\": \"lattice-like design\"}, {\"id\": 38839, \"name\": \"a black mirror\"}, {\"id\": 38840, \"name\": \"a colorful sculpture\"}, {\"id\": 38841, \"name\": \"the black-and-yellow starburst graphic\"}, {\"id\": 38842, \"name\": \"the concrete bollard\"}, {\"id\": 38843, \"name\": \"a question\"}, {\"id\": 38844, \"name\": \"yellow binder\"}, {\"id\": 38845, \"name\": \"a pink short\"}, {\"id\": 38846, \"name\": \"prominent yellow stripe\"}, {\"id\": 38847, \"name\": \"an open cargo bed\"}, {\"id\": 38848, \"name\": \"a black asphalt surface\"}, {\"id\": 38849, \"name\": \"the colorful bunting\"}, {\"id\": 38850, \"name\": \"a red or blue shirt\"}, {\"id\": 38851, \"name\": \"a princes costume\"}, {\"id\": 38852, \"name\": \"the back left tire\"}, {\"id\": 38853, \"name\": \"a neon green tenni ball\"}, {\"id\": 38854, \"name\": \"children in stroller\"}, {\"id\": 38855, \"name\": \"a yellow high-visibility jacket\"}, {\"id\": 38856, \"name\": \"the green advertisement\"}, {\"id\": 38857, \"name\": \"silver trim\"}, {\"id\": 38858, \"name\": \"bright yellow skin\"}, {\"id\": 38859, \"name\": \"the autumnal attire\"}, {\"id\": 38860, \"name\": \"the silver hatchback car\"}, {\"id\": 38861, \"name\": \"a wiring harness\"}, {\"id\": 38862, \"name\": \"the green post\"}, {\"id\": 38863, \"name\": \"the brown edge\"}, {\"id\": 38864, \"name\": \"the blue bridge\"}, {\"id\": 38865, \"name\": \"brand's logo\"}, {\"id\": 38866, \"name\": \"a yellow metal\"}, {\"id\": 38867, \"name\": \"a blue hub\"}, {\"id\": 38868, \"name\": \"the stadium's advertising board\"}, {\"id\": 38869, \"name\": \"the royal enfield\"}, {\"id\": 38870, \"name\": \"an orange wheel\"}, {\"id\": 38871, \"name\": \"a dark-colored table or bench\"}, {\"id\": 38872, \"name\": \"the green body\"}, {\"id\": 38873, \"name\": \"a chocolate-like cube\"}, {\"id\": 38874, \"name\": \"a vibrant dress\"}, {\"id\": 38875, \"name\": \"a -right-turn sign\"}, {\"id\": 38876, \"name\": \"an illuminated entrance\"}, {\"id\": 38877, \"name\": \"white unlock symbol\"}, {\"id\": 38878, \"name\": \"the goalie glove\"}, {\"id\": 38879, \"name\": \"the red headlight\"}, {\"id\": 38880, \"name\": \"red sticker\"}, {\"id\": 38881, \"name\": \"a gray long-sleeved shirt\"}, {\"id\": 38882, \"name\": \"single seat\"}, {\"id\": 38883, \"name\": \"a yellow starting block\"}, {\"id\": 38884, \"name\": \"storage or repair facility\"}, {\"id\": 38885, \"name\": \"a blurred rider\"}, {\"id\": 38886, \"name\": \"silver hatchback car\"}, {\"id\": 38887, \"name\": \"the colorful jockey cap\"}, {\"id\": 38888, \"name\": \"the prominent green slide\"}, {\"id\": 38889, \"name\": \"white 32\"}, {\"id\": 38890, \"name\": \"a red or dark blue jacket\"}, {\"id\": 38891, \"name\": \"a black metal beam\"}, {\"id\": 38892, \"name\": \"a saucer\"}, {\"id\": 38893, \"name\": \"the white knee brace\"}, {\"id\": 38894, \"name\": \"a prominent red logo\"}, {\"id\": 38895, \"name\": \"red bottom\"}, {\"id\": 38896, \"name\": \"a training equipment\"}, {\"id\": 38897, \"name\": \"white drumstick\"}, {\"id\": 38898, \"name\": \"pink bikini top\"}, {\"id\": 38899, \"name\": \"the beak\"}, {\"id\": 38900, \"name\": \"radio or communication antenna\"}, {\"id\": 38901, \"name\": \"the melon\"}, {\"id\": 38902, \"name\": \"the black and white checkered barrier\"}, {\"id\": 38903, \"name\": \"san jose player\"}, {\"id\": 38904, \"name\": \"a white saddle\"}, {\"id\": 38905, \"name\": \"blue and yellow accent\"}, {\"id\": 38906, \"name\": \"the predominantly white body\"}, {\"id\": 38907, \"name\": \"the white and blue advertisement\"}, {\"id\": 38908, \"name\": \"brown and black horse\"}, {\"id\": 38909, \"name\": \"woman in a black coat and dark pant\"}, {\"id\": 38910, \"name\": \"hoverboard\"}, {\"id\": 38911, \"name\": \"the riders' lower body\"}, {\"id\": 38912, \"name\": \"the neon green light\"}, {\"id\": 38913, \"name\": \"rocky, dirt path\"}, {\"id\": 38914, \"name\": \"black pedal\"}, {\"id\": 38915, \"name\": \"a police light\"}, {\"id\": 38916, \"name\": \"a dump bed\"}, {\"id\": 38917, \"name\": \"a sling\"}, {\"id\": 38918, \"name\": \"the boxing attire\"}, {\"id\": 38919, \"name\": \"a yellow concrete barrier\"}, {\"id\": 38920, \"name\": \"the child-sized bicycle\"}, {\"id\": 38921, \"name\": \"a cymbal stand\"}, {\"id\": 38922, \"name\": \"a horizontal metal bar\"}, {\"id\": 38923, \"name\": \"a black bollard\"}, {\"id\": 38924, \"name\": \"the blue lining\"}, {\"id\": 38925, \"name\": \"a serving tray\"}, {\"id\": 38926, \"name\": \"an exercise equipment\"}, {\"id\": 38927, \"name\": \"the blurry road\"}, {\"id\": 38928, \"name\": \"white, heavy-duty vehicle\"}, {\"id\": 38929, \"name\": \"a green, metal, vertical barrier\"}, {\"id\": 38930, \"name\": \"a blue and white jockey\"}, {\"id\": 38931, \"name\": \"the swirl\"}, {\"id\": 38932, \"name\": \"a lid of the toolbox\"}, {\"id\": 38933, \"name\": \"the large heart-shaped decoration\"}, {\"id\": 38934, \"name\": \"the white 14\"}, {\"id\": 38935, \"name\": \"white fork\"}, {\"id\": 38936, \"name\": \"the odometer\"}, {\"id\": 38937, \"name\": \"a white and green sticker\"}, {\"id\": 38938, \"name\": \"cluttered makeup table\"}, {\"id\": 38939, \"name\": \"portable ga grill\"}, {\"id\": 38940, \"name\": \"the silver mirror\"}, {\"id\": 38941, \"name\": \"display shelf\"}, {\"id\": 38942, \"name\": \"watch face\"}, {\"id\": 38943, \"name\": \"the white outfit\"}, {\"id\": 38944, \"name\": \"the pink knee-high sock\"}, {\"id\": 38945, \"name\": \"miniature ferri wheel\"}, {\"id\": 38946, \"name\": \"a yellow reflective jacket\"}, {\"id\": 38947, \"name\": \"white pedestrian crossing\"}, {\"id\": 38948, \"name\": \"a silver knob\"}, {\"id\": 38949, \"name\": \"driver's door\"}, {\"id\": 38950, \"name\": \"dark-colored car windshield\"}, {\"id\": 38951, \"name\": \"rider's black leather jacket sleeve\"}, {\"id\": 38952, \"name\": \"bike exchange logo\"}, {\"id\": 38953, \"name\": \"a man in a gray t-shirt and short\"}, {\"id\": 38954, \"name\": \"blue oval logo\"}, {\"id\": 38955, \"name\": \"a blurred front tire\"}, {\"id\": 38956, \"name\": \"a black-and-white checkered pattern\"}, {\"id\": 38957, \"name\": \"white bottle of condiment\"}, {\"id\": 38958, \"name\": \"green grid\"}, {\"id\": 38959, \"name\": \"the brim\"}, {\"id\": 38960, \"name\": \"the rickshaw's windshield\"}, {\"id\": 38961, \"name\": \"front of the car\"}, {\"id\": 38962, \"name\": \"swim trunk\"}, {\"id\": 38963, \"name\": \"a banner or sign\"}, {\"id\": 38964, \"name\": \"yellow support beam\"}, {\"id\": 38965, \"name\": \"the pink design element\"}, {\"id\": 38966, \"name\": \"toy car race track\"}, {\"id\": 38967, \"name\": \"goalkeeper's gloves\"}, {\"id\": 38968, \"name\": \"a lounge\"}, {\"id\": 38969, \"name\": \"booth\"}, {\"id\": 38970, \"name\": \"shoulder of a small child\"}, {\"id\": 38971, \"name\": \"the advertisment\"}, {\"id\": 38972, \"name\": \"orange tail\"}, {\"id\": 38973, \"name\": \"bridge's red frame\"}, {\"id\": 38974, \"name\": \"a racing bike\"}, {\"id\": 38975, \"name\": \"a black cloth\"}, {\"id\": 38976, \"name\": \"highway's side barrier\"}, {\"id\": 38977, \"name\": \"distinctive black hood\"}, {\"id\": 38978, \"name\": \"a black zipper\"}, {\"id\": 38979, \"name\": \"the teal-colored glove\"}, {\"id\": 38980, \"name\": \"outdoor light\"}, {\"id\": 38981, \"name\": \"woman in a floral dress\"}, {\"id\": 38982, \"name\": \"the yellow clamp\"}, {\"id\": 38983, \"name\": \"the prominent political banner\"}, {\"id\": 38984, \"name\": \"woman in a blue hijab\"}, {\"id\": 38985, \"name\": \"a toy model\"}, {\"id\": 38986, \"name\": \"the black gear\"}, {\"id\": 38987, \"name\": \"a wooden display case\"}, {\"id\": 38988, \"name\": \"corkboard section\"}, {\"id\": 38989, \"name\": \"the barcode sticker\"}, {\"id\": 38990, \"name\": \"the long fur\"}, {\"id\": 38991, \"name\": \"a silver headlight\"}, {\"id\": 38992, \"name\": \"a yellow metal frame\"}, {\"id\": 38993, \"name\": \"a brown and yellow beaver\"}, {\"id\": 38994, \"name\": \"a white hoove\"}, {\"id\": 38995, \"name\": \"passing lane\"}, {\"id\": 38996, \"name\": \"a green and black uniform\"}, {\"id\": 38997, \"name\": \"a white guard tower\"}, {\"id\": 38998, \"name\": \"the rugged cliff\"}, {\"id\": 38999, \"name\": \"the horse 3\"}, {\"id\": 39000, \"name\": \"a dark blue balloon\"}, {\"id\": 39001, \"name\": \"prominent green flag\"}, {\"id\": 39002, \"name\": \"a blue harness\"}, {\"id\": 39003, \"name\": \"team uniform\"}, {\"id\": 39004, \"name\": \"a green climbing hold\"}, {\"id\": 39005, \"name\": \"girl's face\"}, {\"id\": 39006, \"name\": \"long, flowing yellow string\"}, {\"id\": 39007, \"name\": \"a volume knob\"}, {\"id\": 39008, \"name\": \"white staircase\"}, {\"id\": 39009, \"name\": \"a green line\"}, {\"id\": 39010, \"name\": \"the red and yellow sticker\"}, {\"id\": 39011, \"name\": \"a green bench\"}, {\"id\": 39012, \"name\": \"a rotax logo\"}, {\"id\": 39013, \"name\": \"a back bumper of a white truck\"}, {\"id\": 39014, \"name\": \"the jet-powered surfboard\"}, {\"id\": 39015, \"name\": \"the white checkmark\"}, {\"id\": 39016, \"name\": \"small, white cake\"}, {\"id\": 39017, \"name\": \"top of the horse\"}, {\"id\": 39018, \"name\": \"the window of the car\"}, {\"id\": 39019, \"name\": \"taxi's rearview mirror\"}, {\"id\": 39020, \"name\": \"the lap to go 4\"}, {\"id\": 39021, \"name\": \"a polari branded, black and red vacuum cleaner\"}, {\"id\": 39022, \"name\": \"the railing or fence\"}, {\"id\": 39023, \"name\": \"the ski lift\"}, {\"id\": 39024, \"name\": \"anime character\"}, {\"id\": 39025, \"name\": \"the rear of the car\"}, {\"id\": 39026, \"name\": \"the red and yellow dragon\"}, {\"id\": 39027, \"name\": \"yellow and white spot\"}, {\"id\": 39028, \"name\": \"a crocheted bunting\"}, {\"id\": 39029, \"name\": \"dense foliage\"}, {\"id\": 39030, \"name\": \"white crosswalk stripe\"}, {\"id\": 39031, \"name\": \"the black locomotive\"}, {\"id\": 39032, \"name\": \"the pixelated character\"}, {\"id\": 39033, \"name\": \"the green signpost\"}, {\"id\": 39034, \"name\": \"the dog's face\"}, {\"id\": 39035, \"name\": \"red crane\"}, {\"id\": 39036, \"name\": \"white and red license plate\"}, {\"id\": 39037, \"name\": \"blue life jacket\"}, {\"id\": 39038, \"name\": \"blurry pole\"}, {\"id\": 39039, \"name\": \"a puppy's nose\"}, {\"id\": 39040, \"name\": \"a carriage wheel\"}, {\"id\": 39041, \"name\": \"a beige or light-colored tile\"}, {\"id\": 39042, \"name\": \"the light blue outfit\"}, {\"id\": 39043, \"name\": \"green lego piece\"}, {\"id\": 39044, \"name\": \"a passenger-side mirror\"}, {\"id\": 39045, \"name\": \"the christma decoration\"}, {\"id\": 39046, \"name\": \"a tan toy tank\"}, {\"id\": 39047, \"name\": \"dark blue cylindrical body\"}, {\"id\": 39048, \"name\": \"a yellow and white vehicle\"}, {\"id\": 39049, \"name\": \"the military personnel\"}, {\"id\": 39050, \"name\": \"a cityscape backdrop\"}, {\"id\": 39051, \"name\": \"large bow\"}, {\"id\": 39052, \"name\": \"a makehift awning\"}, {\"id\": 39053, \"name\": \"a red or white frame\"}, {\"id\": 39054, \"name\": \"modern glass tower\"}, {\"id\": 39055, \"name\": \"the porsche logo\"}, {\"id\": 39056, \"name\": \"wooden beam or post\"}, {\"id\": 39057, \"name\": \"a wet road\"}, {\"id\": 39058, \"name\": \"the yellow support beam\"}, {\"id\": 39059, \"name\": \"red crash test dummy\"}, {\"id\": 39060, \"name\": \"the shadowy silhouette of the hulk\"}, {\"id\": 39061, \"name\": \"golden-brown food\"}, {\"id\": 39062, \"name\": \"a white d\"}, {\"id\": 39063, \"name\": \"the orange and white striped barrier\"}, {\"id\": 39064, \"name\": \"a simple map of new zealand\"}, {\"id\": 39065, \"name\": \"black speedometer\"}, {\"id\": 39066, \"name\": \"a torch on a stick\"}, {\"id\": 39067, \"name\": \"a red cross\"}, {\"id\": 39068, \"name\": \"a yellow jug\"}, {\"id\": 39069, \"name\": \"large blue and white logo\"}, {\"id\": 39070, \"name\": \"a tropical plant\"}, {\"id\": 39071, \"name\": \"a dark, reflective surface of the water\"}, {\"id\": 39072, \"name\": \"a yellow and black jacket\"}, {\"id\": 39073, \"name\": \"cell phone tower\"}, {\"id\": 39074, \"name\": \"interior of a metal cabinet\"}, {\"id\": 39075, \"name\": \"yellow woven vest\"}, {\"id\": 39076, \"name\": \"the black and white race car\"}, {\"id\": 39077, \"name\": \"a cat toy\"}, {\"id\": 39078, \"name\": \"person wearing mask\"}, {\"id\": 39079, \"name\": \"the pink spot\"}, {\"id\": 39080, \"name\": \"the pink tunic\"}, {\"id\": 39081, \"name\": \"the red and white reflective stripe\"}, {\"id\": 39082, \"name\": \"a circular gauge\"}, {\"id\": 39083, \"name\": \"a green and white striped seat\"}, {\"id\": 39084, \"name\": \"light brown wooden pole\"}, {\"id\": 39085, \"name\": \"a red sedan\"}, {\"id\": 39086, \"name\": \"the overhead compartment\"}, {\"id\": 39087, \"name\": \"patriot player\"}, {\"id\": 39088, \"name\": \"blue chevrolet\"}, {\"id\": 39089, \"name\": \"the colorful sign\"}, {\"id\": 39090, \"name\": \"gray cloud\"}, {\"id\": 39091, \"name\": \"colorful piece of art or fabric\"}, {\"id\": 39092, \"name\": \"blue highlight\"}, {\"id\": 39093, \"name\": \"time remaining in the period\"}, {\"id\": 39094, \"name\": \"light blue top\"}, {\"id\": 39095, \"name\": \"a red and black rope\"}, {\"id\": 39096, \"name\": \"a pink rectangle\"}, {\"id\": 39097, \"name\": \"visible vein\"}, {\"id\": 39098, \"name\": \"the tall black boot\"}, {\"id\": 39099, \"name\": \"krone\"}, {\"id\": 39100, \"name\": \"orange and yellow barrier\"}, {\"id\": 39101, \"name\": \"chang beer\"}, {\"id\": 39102, \"name\": \"the smiling mouth\"}, {\"id\": 39103, \"name\": \"blue handlebar\"}, {\"id\": 39104, \"name\": \"patterned short\"}, {\"id\": 39105, \"name\": \"a pink surface\"}, {\"id\": 39106, \"name\": \"black ramp\"}, {\"id\": 39107, \"name\": \"a car's passenger window\"}, {\"id\": 39108, \"name\": \"a small door\"}, {\"id\": 39109, \"name\": \"the concrete signpost\"}, {\"id\": 39110, \"name\": \"yellow or orange license plate\"}, {\"id\": 39111, \"name\": \"a blue tunic\"}, {\"id\": 39112, \"name\": \"brown, frothy mixture\"}, {\"id\": 39113, \"name\": \"the orange ear\"}, {\"id\": 39114, \"name\": \"a dewalt logo\"}, {\"id\": 39115, \"name\": \"black dumbbell\"}, {\"id\": 39116, \"name\": \"a white scalloped design\"}, {\"id\": 39117, \"name\": \"the harvester\"}, {\"id\": 39118, \"name\": \"the gold pole\"}, {\"id\": 39119, \"name\": \"large pink nose\"}, {\"id\": 39120, \"name\": \"the handle of the oar\"}, {\"id\": 39121, \"name\": \"bmx bike\"}, {\"id\": 39122, \"name\": \"a rustic hut\"}, {\"id\": 39123, \"name\": \"the yellow and black sneaker\"}, {\"id\": 39124, \"name\": \"yellow plastic\"}, {\"id\": 39125, \"name\": \"teal short\"}, {\"id\": 39126, \"name\": \"the single tooth\"}, {\"id\": 39127, \"name\": \"yellow button\"}, {\"id\": 39128, \"name\": \"a red head covering\"}, {\"id\": 39129, \"name\": \"character from a disney movie or show\"}, {\"id\": 39130, \"name\": \"a chicago bear logo\"}, {\"id\": 39131, \"name\": \"a black and grey tie-dye shirt\"}, {\"id\": 39132, \"name\": \"the grey stone brick\"}, {\"id\": 39133, \"name\": \"weathered wood\"}, {\"id\": 39134, \"name\": \"a prominent blue character\"}, {\"id\": 39135, \"name\": \"gold-colored rectangular section\"}, {\"id\": 39136, \"name\": \"the catamaran\"}, {\"id\": 39137, \"name\": \"green component\"}, {\"id\": 39138, \"name\": \"red and white striped traffic cone\"}, {\"id\": 39139, \"name\": \"fantasy character\"}, {\"id\": 39140, \"name\": \"the green splatter design\"}, {\"id\": 39141, \"name\": \"the pointed helmet\"}, {\"id\": 39142, \"name\": \"a maroon short\"}, {\"id\": 39143, \"name\": \"a black face\"}, {\"id\": 39144, \"name\": \"gray metal post\"}, {\"id\": 39145, \"name\": \"a green kimono\"}, {\"id\": 39146, \"name\": \"team's oars\"}, {\"id\": 39147, \"name\": \"a minnie mouse headband\"}, {\"id\": 39148, \"name\": \"the french flag\"}, {\"id\": 39149, \"name\": \"a black police car\"}, {\"id\": 39150, \"name\": \"a blue tub\"}, {\"id\": 39151, \"name\": \"a neatly folded garment\"}, {\"id\": 39152, \"name\": \"the red and blue accent\"}, {\"id\": 39153, \"name\": \"a pointed tail\"}, {\"id\": 39154, \"name\": \"white boxing glove\"}, {\"id\": 39155, \"name\": \"a black and pink striped shirt\"}, {\"id\": 39156, \"name\": \"the game's scoreboard\"}, {\"id\": 39157, \"name\": \"the metal post\"}, {\"id\": 39158, \"name\": \"an olive green backpack strap\"}, {\"id\": 39159, \"name\": \"a cream-colored curtain\"}, {\"id\": 39160, \"name\": \"yellow police tape\"}, {\"id\": 39161, \"name\": \"balding head\"}, {\"id\": 39162, \"name\": \"a groomer\"}, {\"id\": 39163, \"name\": \"large, multi-paned window\"}, {\"id\": 39164, \"name\": \"can or bottle\"}, {\"id\": 39165, \"name\": \"the yellow and blue outfit\"}, {\"id\": 39166, \"name\": \"a woman with a brown purse\"}, {\"id\": 39167, \"name\": \"bright green eye\"}, {\"id\": 39168, \"name\": \"a bank\"}, {\"id\": 39169, \"name\": \"the blue, orange, and yellow saddle\"}, {\"id\": 39170, \"name\": \"a blue pajamas\"}, {\"id\": 39171, \"name\": \"a glass-fronted display case\"}, {\"id\": 39172, \"name\": \"the miniature diorama\"}, {\"id\": 39173, \"name\": \"the male soccer player\"}, {\"id\": 39174, \"name\": \"colorful horse\"}, {\"id\": 39175, \"name\": \"the character from the animated series my pony\"}, {\"id\": 39176, \"name\": \"the large painting or mural\"}, {\"id\": 39177, \"name\": \"the light-brown body\"}, {\"id\": 39178, \"name\": \"pink pony\"}, {\"id\": 39179, \"name\": \"the black and gray helmet\"}, {\"id\": 39180, \"name\": \"a colorful backpack\"}, {\"id\": 39181, \"name\": \"the large brown awning\"}, {\"id\": 39182, \"name\": \"a grey dress\"}, {\"id\": 39183, \"name\": \"white dome\"}, {\"id\": 39184, \"name\": \"blue-and-white checkered pattern\"}, {\"id\": 39185, \"name\": \"the yellow stadium seat\"}, {\"id\": 39186, \"name\": \"a neon green attire\"}, {\"id\": 39187, \"name\": \"the colored block\"}, {\"id\": 39188, \"name\": \"white and brown hexagonal base\"}, {\"id\": 39189, \"name\": \"the wetsuit\"}, {\"id\": 39190, \"name\": \"rainbow stripe\"}, {\"id\": 39191, \"name\": \"the fedora\"}, {\"id\": 39192, \"name\": \"a wire mesh enclosure\"}, {\"id\": 39193, \"name\": \"a tall plant\"}, {\"id\": 39194, \"name\": \"yellow, blue, and red flag\"}, {\"id\": 39195, \"name\": \"a dark asphalt\"}, {\"id\": 39196, \"name\": \"prominent black suitcase\"}, {\"id\": 39197, \"name\": \"the woman in a black and white polka-dot shirt\"}, {\"id\": 39198, \"name\": \"white card\"}, {\"id\": 39199, \"name\": \"the red and yellow accent\"}, {\"id\": 39200, \"name\": \"lime green shirt\"}, {\"id\": 39201, \"name\": \"the pink cap\"}, {\"id\": 39202, \"name\": \"the tan conical hat\"}, {\"id\": 39203, \"name\": \"cooked bread roll\"}, {\"id\": 39204, \"name\": \"woman in a red shirt and blue short\"}, {\"id\": 39205, \"name\": \"an individuals wearing backpack\"}, {\"id\": 39206, \"name\": \"the team wearing yellow uniform\"}, {\"id\": 39207, \"name\": \"the vibrant yellow taxi\"}, {\"id\": 39208, \"name\": \"a white and blue one\"}, {\"id\": 39209, \"name\": \"the vegetable-based dish\"}, {\"id\": 39210, \"name\": \"the silver microphone\"}, {\"id\": 39211, \"name\": \"yellow and green taxi\"}, {\"id\": 39212, \"name\": \"toy story alien swirling saucer ride\"}, {\"id\": 39213, \"name\": \"neon pink light installation\"}, {\"id\": 39214, \"name\": \"a decorative rock\"}, {\"id\": 39215, \"name\": \"a quote\"}, {\"id\": 39216, \"name\": \"a white fur trim\"}, {\"id\": 39217, \"name\": \"the neon blue sign\"}, {\"id\": 39218, \"name\": \"a large orange traffic cone\"}, {\"id\": 39219, \"name\": \"a yellow or orange clothing\"}, {\"id\": 39220, \"name\": \"the advertisements and sign\"}, {\"id\": 39221, \"name\": \"the green ramp\"}, {\"id\": 39222, \"name\": \"a substantial cargo beam\"}, {\"id\": 39223, \"name\": \"an outer lane\"}, {\"id\": 39224, \"name\": \"a rowing boat\"}, {\"id\": 39225, \"name\": \"the bottle of amber liquid\"}, {\"id\": 39226, \"name\": \"the colorful ornament\"}, {\"id\": 39227, \"name\": \"the grid-patterned window\"}, {\"id\": 39228, \"name\": \"a brick or siding exterior\"}, {\"id\": 39229, \"name\": \"the dark grill\"}, {\"id\": 39230, \"name\": \"a traditional mexican attire\"}, {\"id\": 39231, \"name\": \"a home\"}, {\"id\": 39232, \"name\": \"blue energy blast\"}, {\"id\": 39233, \"name\": \"the blue thoma the tank engine toy\"}, {\"id\": 39234, \"name\": \"gray-colored outer lane\"}, {\"id\": 39235, \"name\": \"a bottom shelf\"}, {\"id\": 39236, \"name\": \"the bright orange and yellow handle\"}, {\"id\": 39237, \"name\": \"the lush green hedge\"}, {\"id\": 39238, \"name\": \"a guppy\"}, {\"id\": 39239, \"name\": \"a mickey mouse ear\"}, {\"id\": 39240, \"name\": \"a lush green lawn\"}, {\"id\": 39241, \"name\": \"the multicolored top\"}, {\"id\": 39242, \"name\": \"the blurry, out-of-focus yellow car\"}, {\"id\": 39243, \"name\": \"boy in a blue jacket\"}, {\"id\": 39244, \"name\": \"the pink creature\"}, {\"id\": 39245, \"name\": \"a blue and red jersey\"}, {\"id\": 39246, \"name\": \"red and white sleeve\"}, {\"id\": 39247, \"name\": \"the pink and blue costume\"}, {\"id\": 39248, \"name\": \"a white and gray wall\"}, {\"id\": 39249, \"name\": \"the red dirt strip\"}, {\"id\": 39250, \"name\": \"yellow decal\"}, {\"id\": 39251, \"name\": \"people's face\"}, {\"id\": 39252, \"name\": \"a rower\"}, {\"id\": 39253, \"name\": \"boat's oar\"}, {\"id\": 39254, \"name\": \"beer brand\"}, {\"id\": 39255, \"name\": \"a brown wooden bench\"}, {\"id\": 39256, \"name\": \"the snowflake design\"}, {\"id\": 39257, \"name\": \"the disney-themed headband\"}, {\"id\": 39258, \"name\": \"matching blue hat\"}, {\"id\": 39259, \"name\": \"a yellow snout\"}, {\"id\": 39260, \"name\": \"an electric scooter\"}, {\"id\": 39261, \"name\": \"the table or shelf\"}, {\"id\": 39262, \"name\": \"a news banner overlay\"}, {\"id\": 39263, \"name\": \"a large, blue eye\"}, {\"id\": 39264, \"name\": \"the tan fedora\"}, {\"id\": 39265, \"name\": \"a bulldozer's body\"}, {\"id\": 39266, \"name\": \"a large, tinted windshield\"}, {\"id\": 39267, \"name\": \"a black circular base\"}, {\"id\": 39268, \"name\": \"a purple bracelet\"}, {\"id\": 39269, \"name\": \"green diamond\"}, {\"id\": 39270, \"name\": \"a tyrannosauru rex\"}, {\"id\": 39271, \"name\": \"the cartoon character's face\"}, {\"id\": 39272, \"name\": \"stand-up paddleboard\"}, {\"id\": 39273, \"name\": \"the cycling short\"}, {\"id\": 39274, \"name\": \"a corrugated metal sheet\"}, {\"id\": 39275, \"name\": \"a circular opening\"}, {\"id\": 39276, \"name\": \"a rack of clothing and accessory\"}, {\"id\": 39277, \"name\": \"light blue long-sleeved shirt\"}, {\"id\": 39278, \"name\": \"yellow oval-shaped hold\"}, {\"id\": 39279, \"name\": \"a bicep\"}, {\"id\": 39280, \"name\": \"the green crate\"}, {\"id\": 39281, \"name\": \"the character's head\"}, {\"id\": 39282, \"name\": \"a purple crystal\"}, {\"id\": 39283, \"name\": \"a residential property\"}, {\"id\": 39284, \"name\": \"the white bike\"}, {\"id\": 39285, \"name\": \"the dark blue, rounded rectangle\"}, {\"id\": 39286, \"name\": \"large red logo\"}, {\"id\": 39287, \"name\": \"the yellow and dark blue lane line\"}, {\"id\": 39288, \"name\": \"a large yellow umbrella\"}, {\"id\": 39289, \"name\": \"cave-like opening\"}, {\"id\": 39290, \"name\": \"a black front bumper\"}, {\"id\": 39291, \"name\": \"event staff\"}, {\"id\": 39292, \"name\": \"the purple design\"}, {\"id\": 39293, \"name\": \"the distinctive red mask\"}, {\"id\": 39294, \"name\": \"the textured brown wall\"}, {\"id\": 39295, \"name\": \"man in a plaid shirt and jean\"}, {\"id\": 39296, \"name\": \"vibrant, multi-colored rug or blanket\"}, {\"id\": 39297, \"name\": \"the green tunic\"}, {\"id\": 39298, \"name\": \"a blue handicap symbol\"}, {\"id\": 39299, \"name\": \"red and black 5 car\"}, {\"id\": 39300, \"name\": \"the basket of flower\"}, {\"id\": 39301, \"name\": \"the emergency vehicle\"}, {\"id\": 39302, \"name\": \"asteroid\"}, {\"id\": 39303, \"name\": \"small white tag\"}, {\"id\": 39304, \"name\": \"colorful puppet\"}, {\"id\": 39305, \"name\": \"the neon green shoe\"}, {\"id\": 39306, \"name\": \"the rider's vantage point\"}, {\"id\": 39307, \"name\": \"the long dreadlock\"}, {\"id\": 39308, \"name\": \"orange paw\"}, {\"id\": 39309, \"name\": \"the circular window or archway\"}, {\"id\": 39310, \"name\": \"a yellow antennae\"}, {\"id\": 39311, \"name\": \"a motosport sign\"}, {\"id\": 39312, \"name\": \"the small house\"}, {\"id\": 39313, \"name\": \"a glass frontage\"}, {\"id\": 39314, \"name\": \"a ticket booth\"}, {\"id\": 39315, \"name\": \"an excavator's cab\"}, {\"id\": 39316, \"name\": \"mustache\"}, {\"id\": 39317, \"name\": \"range of colorful train\"}, {\"id\": 39318, \"name\": \"blue train car\"}, {\"id\": 39319, \"name\": \"golden-brown dinner roll\"}, {\"id\": 39320, \"name\": \"white and yellow facade\"}, {\"id\": 39321, \"name\": \"star symbol\"}, {\"id\": 39322, \"name\": \"colorful trolley\"}, {\"id\": 39323, \"name\": \"the large black crate\"}, {\"id\": 39324, \"name\": \"the stainles steel appliance\"}, {\"id\": 39325, \"name\": \"the blurry banner\"}, {\"id\": 39326, \"name\": \"title or heading\"}, {\"id\": 39327, \"name\": \"a temperature\"}, {\"id\": 39328, \"name\": \"the yellow and red striped chevron\"}, {\"id\": 39329, \"name\": \"the light blue spaceship\"}, {\"id\": 39330, \"name\": \"black and gray ramp\"}, {\"id\": 39331, \"name\": \"red and yellow striped border\"}, {\"id\": 39332, \"name\": \"a white bull\"}, {\"id\": 39333, \"name\": \"distinctive red brick pillar\"}, {\"id\": 39334, \"name\": \"the black scoreboard\"}, {\"id\": 39335, \"name\": \"the purple container\"}, {\"id\": 39336, \"name\": \"orange hand\"}, {\"id\": 39337, \"name\": \"prominent yellow goalpost\"}, {\"id\": 39338, \"name\": \"youtube logo\"}, {\"id\": 39339, \"name\": \"the black brim\"}, {\"id\": 39340, \"name\": \"a reflective windshield\"}, {\"id\": 39341, \"name\": \"a pink and white label\"}, {\"id\": 39342, \"name\": \"toy story land sign\"}, {\"id\": 39343, \"name\": \"a crew\"}, {\"id\": 39344, \"name\": \"the black center\"}, {\"id\": 39345, \"name\": \"black decal of a flying saucer\"}, {\"id\": 39346, \"name\": \"the white and black striped pattern\"}, {\"id\": 39347, \"name\": \"a blue butterfly design\"}, {\"id\": 39348, \"name\": \"blue overhang\"}, {\"id\": 39349, \"name\": \"the white 16\"}, {\"id\": 39350, \"name\": \"the pink canister\"}, {\"id\": 39351, \"name\": \"the horse statue\"}, {\"id\": 39352, \"name\": \"a gangnam style\"}, {\"id\": 39353, \"name\": \"a yellow and brown head\"}, {\"id\": 39354, \"name\": \"a woman's arm\"}, {\"id\": 39355, \"name\": \"black bin\"}, {\"id\": 39356, \"name\": \"gold writing\"}, {\"id\": 39357, \"name\": \"the prominent yellow crosswalk\"}, {\"id\": 39358, \"name\": \"the giraffe's left hand\"}, {\"id\": 39359, \"name\": \"the large piece of furniture\"}, {\"id\": 39360, \"name\": \"the reflective visor\"}, {\"id\": 39361, \"name\": \"brown spindle\"}, {\"id\": 39362, \"name\": \"the wooden barrier\"}, {\"id\": 39363, \"name\": \"the orange train car\"}, {\"id\": 39364, \"name\": \"the blue and white tire\"}, {\"id\": 39365, \"name\": \"a car's bumper\"}, {\"id\": 39366, \"name\": \"the colorful floral design\"}, {\"id\": 39367, \"name\": \"the red, yellow, and blue trim\"}, {\"id\": 39368, \"name\": \"a brown and black horse\"}, {\"id\": 39369, \"name\": \"abema news logo\"}, {\"id\": 39370, \"name\": \"the teal awning\"}, {\"id\": 39371, \"name\": \"trombone\"}, {\"id\": 39372, \"name\": \"a presence of camel\"}, {\"id\": 39373, \"name\": \"a tugboat\"}, {\"id\": 39374, \"name\": \"gold rim\"}, {\"id\": 39375, \"name\": \"kneeling person\"}, {\"id\": 39376, \"name\": \"a sharp tooth\"}, {\"id\": 39377, \"name\": \"the yellow gift tag\"}, {\"id\": 39378, \"name\": \"man in a yellow shirt and dark pant\"}, {\"id\": 39379, \"name\": \"blue conductor's hat\"}, {\"id\": 39380, \"name\": \"the kaleidoscope of colorful light\"}, {\"id\": 39381, \"name\": \"a dark square\"}, {\"id\": 39382, \"name\": \"a women's face\"}, {\"id\": 39383, \"name\": \"a hello kitty\"}, {\"id\": 39384, \"name\": \"a red pallet\"}, {\"id\": 39385, \"name\": \"a disney character\"}, {\"id\": 39386, \"name\": \"the brown grass\"}, {\"id\": 39387, \"name\": \"speaker setup\"}, {\"id\": 39388, \"name\": \"pink hippopotamu\"}, {\"id\": 39389, \"name\": \"a patterned design\"}, {\"id\": 39390, \"name\": \"dark-colored object\"}, {\"id\": 39391, \"name\": \"basket of fruit or vegetable\"}, {\"id\": 39392, \"name\": \"a subway restaurant\"}, {\"id\": 39393, \"name\": \"man in a black hoodie\"}, {\"id\": 39394, \"name\": \"a prominent blue pole\"}, {\"id\": 39395, \"name\": \"the large stone structure\"}, {\"id\": 39396, \"name\": \"male performer\"}, {\"id\": 39397, \"name\": \"light blue apron\"}, {\"id\": 39398, \"name\": \"large, clear plastic panel\"}, {\"id\": 39399, \"name\": \"the snare drum's head\"}, {\"id\": 39400, \"name\": \"colorful illustration of a lizard\"}, {\"id\": 39401, \"name\": \"a visible gap\"}, {\"id\": 39402, \"name\": \"yellow board\"}, {\"id\": 39403, \"name\": \"brown wooden side\"}, {\"id\": 39404, \"name\": \"a gray column\"}, {\"id\": 39405, \"name\": \"red punching bag\"}, {\"id\": 39406, \"name\": \"a large black eye\"}, {\"id\": 39407, \"name\": \"a packed crowd\"}, {\"id\": 39408, \"name\": \"the rope or twine\"}, {\"id\": 39409, \"name\": \"the truck's side door\"}, {\"id\": 39410, \"name\": \"a red tassel\"}, {\"id\": 39411, \"name\": \"the soccer cleat\"}, {\"id\": 39412, \"name\": \"yellow seat\"}, {\"id\": 39413, \"name\": \"an image of fish\"}, {\"id\": 39414, \"name\": \"an unicorn's tail\"}, {\"id\": 39415, \"name\": \"the residential property\"}, {\"id\": 39416, \"name\": \"the older structure\"}, {\"id\": 39417, \"name\": \"a black tent\"}, {\"id\": 39418, \"name\": \"a gray eye\"}, {\"id\": 39419, \"name\": \"the upper torso\"}, {\"id\": 39420, \"name\": \"competitors' name\"}, {\"id\": 39421, \"name\": \"the hood scoop\"}, {\"id\": 39422, \"name\": \"a yellow and black ear protector\"}, {\"id\": 39423, \"name\": \"a beige-colored panel\"}, {\"id\": 39424, \"name\": \"the blue wheel\"}, {\"id\": 39425, \"name\": \"the lower half of a person's legs\"}, {\"id\": 39426, \"name\": \"a black and white lamppost\"}, {\"id\": 39427, \"name\": \"vintage orange convertible\"}, {\"id\": 39428, \"name\": \"the gray blazer\"}, {\"id\": 39429, \"name\": \"boat location information\"}, {\"id\": 39430, \"name\": \"the white grid line\"}, {\"id\": 39431, \"name\": \"the yellow handkerchief\"}, {\"id\": 39432, \"name\": \"a vintage tractor\"}, {\"id\": 39433, \"name\": \"a large yellow ramp or obstacle\"}, {\"id\": 39434, \"name\": \"peg\"}, {\"id\": 39435, \"name\": \"storage area\"}, {\"id\": 39436, \"name\": \"a large white wheel\"}, {\"id\": 39437, \"name\": \"hanging light\"}, {\"id\": 39438, \"name\": \"an of colorful pin\"}, {\"id\": 39439, \"name\": \"historic architecture\"}, {\"id\": 39440, \"name\": \"wooden wagon\"}, {\"id\": 39441, \"name\": \"a netting\"}, {\"id\": 39442, \"name\": \"the gray coverall\"}, {\"id\": 39443, \"name\": \"white unicorn\"}, {\"id\": 39444, \"name\": \"an english sign\"}, {\"id\": 39445, \"name\": \"a coconut\"}, {\"id\": 39446, \"name\": \"au logo\"}, {\"id\": 39447, \"name\": \"white flower\"}, {\"id\": 39448, \"name\": \"the small boat or ship\"}, {\"id\": 39449, \"name\": \"the distinctive blue stripe\"}, {\"id\": 39450, \"name\": \"large, toothy mouth\"}, {\"id\": 39451, \"name\": \"a red balloon\"}, {\"id\": 39452, \"name\": \"a people's hand\"}, {\"id\": 39453, \"name\": \"a dark-colored countertop\"}, {\"id\": 39454, \"name\": \"the car's side window\"}, {\"id\": 39455, \"name\": \"man's shirt\"}, {\"id\": 39456, \"name\": \"the rowing team\"}, {\"id\": 39457, \"name\": \"a purple accent\"}, {\"id\": 39458, \"name\": \"woman in a light blue shirt and khaki pant\"}, {\"id\": 39459, \"name\": \"an image of a person's face\"}, {\"id\": 39460, \"name\": \"the folder\"}, {\"id\": 39461, \"name\": \"the california license plate\"}, {\"id\": 39462, \"name\": \"the blue and red glove\"}, {\"id\": 39463, \"name\": \"the orange and green racing stripe\"}, {\"id\": 39464, \"name\": \"lush green hillside\"}, {\"id\": 39465, \"name\": \"the volunteers' red shirt\"}, {\"id\": 39466, \"name\": \"additional storage compartment\"}, {\"id\": 39467, \"name\": \"a purple trim\"}, {\"id\": 39468, \"name\": \"the brown-speckled granite countertop\"}, {\"id\": 39469, \"name\": \"red track\"}, {\"id\": 39470, \"name\": \"the lush, green grassy area\"}, {\"id\": 39471, \"name\": \"the white and blue jacket\"}, {\"id\": 39472, \"name\": \"a black-and-white, fish-eye len view\"}, {\"id\": 39473, \"name\": \"a storage container\"}, {\"id\": 39474, \"name\": \"farmer\"}, {\"id\": 39475, \"name\": \"a nascar racecar\"}, {\"id\": 39476, \"name\": \"a light blue and dark blue uniform\"}, {\"id\": 39477, \"name\": \"large machine\"}, {\"id\": 39478, \"name\": \"a cyclist's arm\"}, {\"id\": 39479, \"name\": \"the red block\"}, {\"id\": 39480, \"name\": \"faded tan facade\"}, {\"id\": 39481, \"name\": \"white paper towel\"}, {\"id\": 39482, \"name\": \"the black and red wetsuit\"}, {\"id\": 39483, \"name\": \"the dirt ramp\"}, {\"id\": 39484, \"name\": \"calm ocean water\"}, {\"id\": 39485, \"name\": \"passenger door\"}, {\"id\": 39486, \"name\": \"the curved bay window\"}, {\"id\": 39487, \"name\": \"the black cycling attire\"}, {\"id\": 39488, \"name\": \"the pink and white tent\"}, {\"id\": 39489, \"name\": \"tall black object\"}, {\"id\": 39490, \"name\": \"the large white pole\"}, {\"id\": 39491, \"name\": \"a large, gold roman numeral\"}, {\"id\": 39492, \"name\": \"the seated girl\"}, {\"id\": 39493, \"name\": \"a ga burner\"}, {\"id\": 39494, \"name\": \"blue and yellow headpiece\"}, {\"id\": 39495, \"name\": \"the room's white wall\"}, {\"id\": 39496, \"name\": \"pink and yellow logo\"}, {\"id\": 39497, \"name\": \"a vibrant floral-patterned dress\"}, {\"id\": 39498, \"name\": \"sleek black van\"}, {\"id\": 39499, \"name\": \"large shadow\"}, {\"id\": 39500, \"name\": \"white catamaran sailboat\"}, {\"id\": 39501, \"name\": \"the purple surface\"}, {\"id\": 39502, \"name\": \"the concrete ramp or half-pipe\"}, {\"id\": 39503, \"name\": \"a swarm\"}, {\"id\": 39504, \"name\": \"central hub\"}, {\"id\": 39505, \"name\": \"cluttered interior\"}, {\"id\": 39506, \"name\": \"the gold top hat\"}, {\"id\": 39507, \"name\": \"boxcar\"}, {\"id\": 39508, \"name\": \"the red leash\"}, {\"id\": 39509, \"name\": \"scuba diving tank\"}, {\"id\": 39510, \"name\": \"purple antennae\"}, {\"id\": 39511, \"name\": \"a prominent news banner\"}, {\"id\": 39512, \"name\": \"the crack in the concrete pavement\"}, {\"id\": 39513, \"name\": \"black cloth\"}, {\"id\": 39514, \"name\": \"a purple or blue cover\"}, {\"id\": 39515, \"name\": \"blue and orange uniform\"}, {\"id\": 39516, \"name\": \"the funko pop figure\"}, {\"id\": 39517, \"name\": \"the neon-colored shirt\"}, {\"id\": 39518, \"name\": \"a small vessel\"}, {\"id\": 39519, \"name\": \"golfer's name\"}, {\"id\": 39520, \"name\": \"the orange taxi\"}, {\"id\": 39521, \"name\": \"sports car\"}, {\"id\": 39522, \"name\": \"an intricate stone carving\"}, {\"id\": 39523, \"name\": \"the court's markings\"}, {\"id\": 39524, \"name\": \"a white swan\"}, {\"id\": 39525, \"name\": \"the pixelated representation of the popular arcade game space invader\"}, {\"id\": 39526, \"name\": \"a wooden dining table\"}, {\"id\": 39527, \"name\": \"the monkey's shadow\"}, {\"id\": 39528, \"name\": \"the colorful ball\"}, {\"id\": 39529, \"name\": \"a blue mirrored len\"}, {\"id\": 39530, \"name\": \"light blue gear\"}, {\"id\": 39531, \"name\": \"the crosswalk's white square\"}, {\"id\": 39532, \"name\": \"red and green object\"}, {\"id\": 39533, \"name\": \"the coat rack\"}, {\"id\": 39534, \"name\": \"the uniform with white accent\"}, {\"id\": 39535, \"name\": \"man in the black hoodie\"}, {\"id\": 39536, \"name\": \"the white hockey net\"}, {\"id\": 39537, \"name\": \"white inflatable dome\"}, {\"id\": 39538, \"name\": \"the white tent or canopy\"}, {\"id\": 39539, \"name\": \"the 25\"}, {\"id\": 39540, \"name\": \"a chat box\"}, {\"id\": 39541, \"name\": \"the blue, white, and gray swirl\"}, {\"id\": 39542, \"name\": \"woman in a gray shirt and jean holding a cell phone\"}, {\"id\": 39543, \"name\": \"white and red striped barrier\"}, {\"id\": 39544, \"name\": \"front passenger door\"}, {\"id\": 39545, \"name\": \"football goalpost\"}, {\"id\": 39546, \"name\": \"a vertical panel\"}, {\"id\": 39547, \"name\": \"a flat area\"}, {\"id\": 39548, \"name\": \"yellow oar\"}, {\"id\": 39549, \"name\": \"boxer\"}, {\"id\": 39550, \"name\": \"a red and white dirt bike\"}, {\"id\": 39551, \"name\": \"a tan armchair\"}, {\"id\": 39552, \"name\": \"a large bundle of green vegetation\"}, {\"id\": 39553, \"name\": \"north pole sign\"}, {\"id\": 39554, \"name\": \"the yellow and red stripe\"}, {\"id\": 39555, \"name\": \"the traveler\"}, {\"id\": 39556, \"name\": \"the green leaf\"}, {\"id\": 39557, \"name\": \"an olympic logo\"}, {\"id\": 39558, \"name\": \"the black metal grate bottom\"}, {\"id\": 39559, \"name\": \"the open brown shutter\"}, {\"id\": 39560, \"name\": \"the image of a woman's face\"}, {\"id\": 39561, \"name\": \"a long black hose\"}, {\"id\": 39562, \"name\": \"the vehicle's hood\"}, {\"id\": 39563, \"name\": \"the black and yellow car\"}, {\"id\": 39564, \"name\": \"dark blue outfit\"}, {\"id\": 39565, \"name\": \"soft purple hue\"}, {\"id\": 39566, \"name\": \"a registration\"}, {\"id\": 39567, \"name\": \"auto\"}, {\"id\": 39568, \"name\": \"a black metal barrier\"}, {\"id\": 39569, \"name\": \"a disneyland-style teacup ride\"}, {\"id\": 39570, \"name\": \"the orange ring\"}, {\"id\": 39571, \"name\": \"floral-patterned top\"}, {\"id\": 39572, \"name\": \"white or black jersey\"}, {\"id\": 39573, \"name\": \"rubble\"}, {\"id\": 39574, \"name\": \"colorful character\"}, {\"id\": 39575, \"name\": \"the step platform\"}, {\"id\": 39576, \"name\": \"large, multi-level lego display\"}, {\"id\": 39577, \"name\": \"the black bottom\"}, {\"id\": 39578, \"name\": \"a photo of the man\"}, {\"id\": 39579, \"name\": \"a tub of crab\"}, {\"id\": 39580, \"name\": \"the green piece of debris\"}, {\"id\": 39581, \"name\": \"the girl's right hand\"}, {\"id\": 39582, \"name\": \"traffic lane\"}, {\"id\": 39583, \"name\": \"a blue and white banner\"}, {\"id\": 39584, \"name\": \"a square tile\"}, {\"id\": 39585, \"name\": \"large ramp\"}, {\"id\": 39586, \"name\": \"the children's toy\"}, {\"id\": 39587, \"name\": \"green moon\"}, {\"id\": 39588, \"name\": \"large, white, and round eye\"}, {\"id\": 39589, \"name\": \"the bright yellow uniform\"}, {\"id\": 39590, \"name\": \"the display stand\"}, {\"id\": 39591, \"name\": \"white and black fence\"}, {\"id\": 39592, \"name\": \"the pink house\"}, {\"id\": 39593, \"name\": \"the drawing\"}, {\"id\": 39594, \"name\": \"the apartment complex\"}, {\"id\": 39595, \"name\": \"gold ribbon\"}, {\"id\": 39596, \"name\": \"red and black train\"}, {\"id\": 39597, \"name\": \"a colorful awning\"}, {\"id\": 39598, \"name\": \"a blue 11\"}, {\"id\": 39599, \"name\": \"the front of the car\"}, {\"id\": 39600, \"name\": \"lawton speedway logo\"}, {\"id\": 39601, \"name\": \"blue light bar\"}, {\"id\": 39602, \"name\": \"the white and yellow banner\"}, {\"id\": 39603, \"name\": \"the man holding an open black umbrella\"}, {\"id\": 39604, \"name\": \"the gray blade\"}, {\"id\": 39605, \"name\": \"the turquoise short\"}, {\"id\": 39606, \"name\": \"neon sign\"}, {\"id\": 39607, \"name\": \"the modern apartment complex\"}, {\"id\": 39608, \"name\": \"a socks with red stripe\"}, {\"id\": 39609, \"name\": \"a clothing item\"}, {\"id\": 39610, \"name\": \"a snowflake decoration\"}, {\"id\": 39611, \"name\": \"light hair\"}, {\"id\": 39612, \"name\": \"lush green hedge\"}, {\"id\": 39613, \"name\": \"the stone archway\"}, {\"id\": 39614, \"name\": \"the a/n logo\"}, {\"id\": 39615, \"name\": \"the covered dock\"}, {\"id\": 39616, \"name\": \"a gingerbread cookie\"}, {\"id\": 39617, \"name\": \"clear plastic bin\"}, {\"id\": 39618, \"name\": \"the large satellite dish\"}, {\"id\": 39619, \"name\": \"a pineapple\"}, {\"id\": 39620, \"name\": \"the camouflage pant\"}, {\"id\": 39621, \"name\": \"patio set\"}, {\"id\": 39622, \"name\": \"man in blue uniform\"}, {\"id\": 39623, \"name\": \"man in a blue and white striped shirt\"}, {\"id\": 39624, \"name\": \"the english title\"}, {\"id\": 39625, \"name\": \"a sea of spectator\"}, {\"id\": 39626, \"name\": \"the yellow rectangle\"}, {\"id\": 39627, \"name\": \"players' leg\"}, {\"id\": 39628, \"name\": \"a large purple bush\"}, {\"id\": 39629, \"name\": \"a tan uniform\"}, {\"id\": 39630, \"name\": \"the horse's mane\"}, {\"id\": 39631, \"name\": \"facebook logo\"}, {\"id\": 39632, \"name\": \"green and white banner\"}, {\"id\": 39633, \"name\": \"large cylindrical object\"}, {\"id\": 39634, \"name\": \"black and yellow border\"}, {\"id\": 39635, \"name\": \"a blue and yellow robot\"}, {\"id\": 39636, \"name\": \"red and blue banner\"}, {\"id\": 39637, \"name\": \"silver bollard\"}, {\"id\": 39638, \"name\": \"the white police vehicle\"}, {\"id\": 39639, \"name\": \"textured shell\"}, {\"id\": 39640, \"name\": \"brown block\"}, {\"id\": 39641, \"name\": \"a warren\"}, {\"id\": 39642, \"name\": \"a dior logo\"}, {\"id\": 39643, \"name\": \"the yellow and black life jacket\"}, {\"id\": 39644, \"name\": \"the white cloud graphic\"}, {\"id\": 39645, \"name\": \"white receiver\"}, {\"id\": 39646, \"name\": \"a formal busine attire\"}, {\"id\": 39647, \"name\": \"corrugated metal fence\"}, {\"id\": 39648, \"name\": \"blue and yellow sleeve\"}, {\"id\": 39649, \"name\": \"lush forested hillside\"}, {\"id\": 39650, \"name\": \"the dark blue scrub top\"}, {\"id\": 39651, \"name\": \"the taiyaki\"}, {\"id\": 39652, \"name\": \"black cowboy hat\"}, {\"id\": 39653, \"name\": \"the kali center\"}, {\"id\": 39654, \"name\": \"camelbak\"}, {\"id\": 39655, \"name\": \"a blue yoga mat\"}, {\"id\": 39656, \"name\": \"a yellow forklift\"}, {\"id\": 39657, \"name\": \"a black and white striped jersey\"}, {\"id\": 39658, \"name\": \"the wheel of the truck\"}, {\"id\": 39659, \"name\": \"the jockey's number\"}, {\"id\": 39660, \"name\": \"a fc-redcat-racing.com\"}, {\"id\": 39661, \"name\": \"orange box\"}, {\"id\": 39662, \"name\": \"dark purple area\"}, {\"id\": 39663, \"name\": \"the logo for nbc news\"}, {\"id\": 39664, \"name\": \"short red hair\"}, {\"id\": 39665, \"name\": \"the shelf of product\"}, {\"id\": 39666, \"name\": \"the red sock\"}, {\"id\": 39667, \"name\": \"vibrant yellow and red jersey\"}, {\"id\": 39668, \"name\": \"vertical wire\"}, {\"id\": 39669, \"name\": \"a floral bikini\"}, {\"id\": 39670, \"name\": \"fieldhouse\"}, {\"id\": 39671, \"name\": \"display of cycling metric\"}, {\"id\": 39672, \"name\": \"the colorful candy bag\"}, {\"id\": 39673, \"name\": \"green vine\"}, {\"id\": 39674, \"name\": \"a red and yellow safety harness\"}, {\"id\": 39675, \"name\": \"character's wide smile\"}, {\"id\": 39676, \"name\": \"a floating platform\"}, {\"id\": 39677, \"name\": \"the red and white striped jersey\"}, {\"id\": 39678, \"name\": \"a gold up logo\"}, {\"id\": 39679, \"name\": \"a large white eye\"}, {\"id\": 39680, \"name\": \"a red bracelet\"}, {\"id\": 39681, \"name\": \"a central median strip\"}, {\"id\": 39682, \"name\": \"the channel\"}, {\"id\": 39683, \"name\": \"the blue and silver medal\"}, {\"id\": 39684, \"name\": \"bright red reflective safety triangle\"}, {\"id\": 39685, \"name\": \"the plastic wrap\"}, {\"id\": 39686, \"name\": \"dark glove\"}, {\"id\": 39687, \"name\": \"gold metal arm\"}, {\"id\": 39688, \"name\": \"the black and yellow cat\"}, {\"id\": 39689, \"name\": \"blue and white striped awning\"}, {\"id\": 39690, \"name\": \"brown booth-style bench\"}, {\"id\": 39691, \"name\": \"lush green surface\"}, {\"id\": 39692, \"name\": \"grilled squid\"}, {\"id\": 39693, \"name\": \"the health level\"}, {\"id\": 39694, \"name\": \"the yellow and white bus\"}, {\"id\": 39695, \"name\": \"snake-like object\"}, {\"id\": 39696, \"name\": \"the blue and gold armor\"}, {\"id\": 39697, \"name\": \"large white boat\"}, {\"id\": 39698, \"name\": \"the person dressed in a blue costume\"}, {\"id\": 39699, \"name\": \"a small toy airplane\"}, {\"id\": 39700, \"name\": \"the truck's driver\"}, {\"id\": 39701, \"name\": \"backward black cap\"}, {\"id\": 39702, \"name\": \"wooden window\"}, {\"id\": 39703, \"name\": \"a chef's hat\"}, {\"id\": 39704, \"name\": \"the pink ear\"}, {\"id\": 39705, \"name\": \"the equal sign\"}, {\"id\": 39706, \"name\": \"a charcoal\"}, {\"id\": 39707, \"name\": \"the wbz 4 cbsboston.com\"}, {\"id\": 39708, \"name\": \"the diver's feet\"}, {\"id\": 39709, \"name\": \"the sail gp\"}, {\"id\": 39710, \"name\": \"vibrant purple banner\"}, {\"id\": 39711, \"name\": \"taxi's interior\"}, {\"id\": 39712, \"name\": \"short blonde hair\"}, {\"id\": 39713, \"name\": \"yellow reflective strip\"}, {\"id\": 39714, \"name\": \"the overhead wire\"}, {\"id\": 39715, \"name\": \"yellow tie\"}, {\"id\": 39716, \"name\": \"the black and white plastic-wrapped object\"}, {\"id\": 39717, \"name\": \"source of light\"}, {\"id\": 39718, \"name\": \"a green slide\"}, {\"id\": 39719, \"name\": \"yellow cabinet\"}, {\"id\": 39720, \"name\": \"the ride's seat\"}, {\"id\": 39721, \"name\": \"the spherical light fixture\"}, {\"id\": 39722, \"name\": \"a man in a black hat and shirt\"}, {\"id\": 39723, \"name\": \"a austin\"}, {\"id\": 39724, \"name\": \"a industrial equipment\"}, {\"id\": 39725, \"name\": \"a gold ring\"}, {\"id\": 39726, \"name\": \"thor\"}, {\"id\": 39727, \"name\": \"illuminated headlight\"}, {\"id\": 39728, \"name\": \"logo for nbc sport philadelphia\"}, {\"id\": 39729, \"name\": \"la tarde\"}, {\"id\": 39730, \"name\": \"a green hull\"}, {\"id\": 39731, \"name\": \"the dark sunglass\"}, {\"id\": 39732, \"name\": \"flat cap\"}, {\"id\": 39733, \"name\": \"a 13:54:15\"}, {\"id\": 39734, \"name\": \"maroon jeepney\"}, {\"id\": 39735, \"name\": \"blue float\"}, {\"id\": 39736, \"name\": \"bold red square outline\"}, {\"id\": 39737, \"name\": \"the semi-transparent, reflective surface\"}, {\"id\": 39738, \"name\": \"a tan saddle\"}, {\"id\": 39739, \"name\": \"a small boy\"}, {\"id\": 39740, \"name\": \"herd of white horse\"}, {\"id\": 39741, \"name\": \"the w.b. sign\"}, {\"id\": 39742, \"name\": \"center stage\"}, {\"id\": 39743, \"name\": \"the pink spatula\"}, {\"id\": 39744, \"name\": \"the rolled-up piece of paper or cardboard\"}, {\"id\": 39745, \"name\": \"the .com\"}, {\"id\": 39746, \"name\": \"a blue and red banner\"}, {\"id\": 39747, \"name\": \"a purple speech bubble\"}, {\"id\": 39748, \"name\": \"a white and black carriage\"}, {\"id\": 39749, \"name\": \"woman in a black jacket and tan pant\"}, {\"id\": 39750, \"name\": \"black and yellow trim\"}, {\"id\": 39751, \"name\": \"a white cart\"}, {\"id\": 39752, \"name\": \"a cb 5 kpix logo\"}, {\"id\": 39753, \"name\": \"a left eye\"}, {\"id\": 39754, \"name\": \"the racing uk\"}, {\"id\": 39755, \"name\": \"small, red reflector\"}, {\"id\": 39756, \"name\": \"horse racing cart\"}, {\"id\": 39757, \"name\": \"the buffet table\"}, {\"id\": 39758, \"name\": \"green and yellow flag\"}, {\"id\": 39759, \"name\": \"mother\"}, {\"id\": 39760, \"name\": \"the character head\"}, {\"id\": 39761, \"name\": \"a low black fence\"}, {\"id\": 39762, \"name\": \"a black hair band\"}, {\"id\": 39763, \"name\": \"the storage unit\"}, {\"id\": 39764, \"name\": \"white headband\"}, {\"id\": 39765, \"name\": \"a dense vegetation\"}, {\"id\": 39766, \"name\": \"soccer cleat\"}, {\"id\": 39767, \"name\": \"a controller\"}, {\"id\": 39768, \"name\": \"a green jersey\"}, {\"id\": 39769, \"name\": \"bikini top\"}, {\"id\": 39770, \"name\": \"a small white hat\"}, {\"id\": 39771, \"name\": \"the racinboy website url\"}, {\"id\": 39772, \"name\": \"truck's headlight\"}, {\"id\": 39773, \"name\": \"a white parasol\"}, {\"id\": 39774, \"name\": \"the brown bleacher\"}, {\"id\": 39775, \"name\": \"a striped dress\"}, {\"id\": 39776, \"name\": \"the orange and black shock absorber\"}, {\"id\": 39777, \"name\": \"rear side\"}, {\"id\": 39778, \"name\": \"purple and white balloon\"}, {\"id\": 39779, \"name\": \"gold clasp\"}, {\"id\": 39780, \"name\": \"a background of tree\"}, {\"id\": 39781, \"name\": \"the white car with a red and yellow design\"}, {\"id\": 39782, \"name\": \"a baby doll\"}, {\"id\": 39783, \"name\": \"a tan-colored surface\"}, {\"id\": 39784, \"name\": \"person holding a camera\"}, {\"id\": 39785, \"name\": \"grey stripe\"}, {\"id\": 39786, \"name\": \"the blue-framed store\"}, {\"id\": 39787, \"name\": \"dark gray car\"}, {\"id\": 39788, \"name\": \"the vertical post\"}, {\"id\": 39789, \"name\": \"red and white body\"}, {\"id\": 39790, \"name\": \"the crew of rower\"}, {\"id\": 39791, \"name\": \"the large antler\"}, {\"id\": 39792, \"name\": \"silver grill\"}, {\"id\": 39793, \"name\": \"a maroon jersey\"}, {\"id\": 39794, \"name\": \"the white downspout\"}, {\"id\": 39795, \"name\": \"black fishnet stocking\"}, {\"id\": 39796, \"name\": \"light-colored door\"}, {\"id\": 39797, \"name\": \"a pink feathered top\"}, {\"id\": 39798, \"name\": \"the swim cap\"}, {\"id\": 39799, \"name\": \"a back muscle\"}, {\"id\": 39800, \"name\": \"the fighter's attire\"}, {\"id\": 39801, \"name\": \"the large white umbrella\"}, {\"id\": 39802, \"name\": \"the smooth fabric\"}, {\"id\": 39803, \"name\": \"the bright green outfit\"}, {\"id\": 39804, \"name\": \"front of the plane\"}, {\"id\": 39805, \"name\": \"a traditional egyptian attire\"}, {\"id\": 39806, \"name\": \"the large dark object\"}, {\"id\": 39807, \"name\": \"a large, blue and white target\"}, {\"id\": 39808, \"name\": \"the clipboard\"}, {\"id\": 39809, \"name\": \"a channel's logo\"}, {\"id\": 39810, \"name\": \"light green jersey\"}, {\"id\": 39811, \"name\": \"colorful shirt\"}, {\"id\": 39812, \"name\": \"buoy or marker\"}, {\"id\": 39813, \"name\": \"large open doorway\"}, {\"id\": 39814, \"name\": \"a black dump bed\"}, {\"id\": 39815, \"name\": \"the table tenni paddle\"}, {\"id\": 39816, \"name\": \"a large gray suv\"}, {\"id\": 39817, \"name\": \"the red or orange roof\"}, {\"id\": 39818, \"name\": \"carpeted floor\"}, {\"id\": 39819, \"name\": \"a left shoulder\"}, {\"id\": 39820, \"name\": \"man's right arm\"}, {\"id\": 39821, \"name\": \"a red and white helmet\"}, {\"id\": 39822, \"name\": \"a hummingbird's wing\"}, {\"id\": 39823, \"name\": \"the brown brick wall\"}, {\"id\": 39824, \"name\": \"the starting gate\"}, {\"id\": 39825, \"name\": \"a ranking\"}, {\"id\": 39826, \"name\": \"the blue and white suit\"}, {\"id\": 39827, \"name\": \"the tan uniform\"}, {\"id\": 39828, \"name\": \"player's equipment\"}, {\"id\": 39829, \"name\": \"the distinctive red and white hood\"}, {\"id\": 39830, \"name\": \"a small flag\"}, {\"id\": 39831, \"name\": \"creature's body\"}, {\"id\": 39832, \"name\": \"a white bikini top\"}, {\"id\": 39833, \"name\": \"a baseball\"}, {\"id\": 39834, \"name\": \"toy dragon's head\"}, {\"id\": 39835, \"name\": \"the seahorse-shaped obstacle\"}, {\"id\": 39836, \"name\": \"officer's right foot\"}, {\"id\": 39837, \"name\": \"the golden-brown food\"}, {\"id\": 39838, \"name\": \"a graceful swan\"}, {\"id\": 39839, \"name\": \"the large cliff\"}, {\"id\": 39840, \"name\": \"the open laptop\"}, {\"id\": 39841, \"name\": \"white c logo\"}, {\"id\": 39842, \"name\": \"the tan snout\"}, {\"id\": 39843, \"name\": \"image of a plate of food\"}, {\"id\": 39844, \"name\": \"pink bunny ear\"}, {\"id\": 39845, \"name\": \"an orange, wavy tail\"}, {\"id\": 39846, \"name\": \"the lead kart\"}, {\"id\": 39847, \"name\": \"a pink balloon\"}, {\"id\": 39848, \"name\": \"the baggage claim conveyor belt\"}, {\"id\": 39849, \"name\": \"a circular couch\"}, {\"id\": 39850, \"name\": \"dog's leash\"}, {\"id\": 39851, \"name\": \"the person's reflection\"}, {\"id\": 39852, \"name\": \"the japanese news channel logo\"}, {\"id\": 39853, \"name\": \"clear plastic visor\"}, {\"id\": 39854, \"name\": \"the lower leg\"}, {\"id\": 39855, \"name\": \"the skull icon\"}, {\"id\": 39856, \"name\": \"a colorful suitcase\"}, {\"id\": 39857, \"name\": \"tan backpack\"}, {\"id\": 39858, \"name\": \"a driver's side mirror\"}, {\"id\": 39859, \"name\": \"dark brown foot\"}, {\"id\": 39860, \"name\": \"pink stripe\"}, {\"id\": 39861, \"name\": \"large, round purple rock\"}, {\"id\": 39862, \"name\": \"pole vaulting bar\"}, {\"id\": 39863, \"name\": \"the blue 51\"}, {\"id\": 39864, \"name\": \"the vibrant purple gown\"}, {\"id\": 39865, \"name\": \"the white bucket or container\"}, {\"id\": 39866, \"name\": \"white dot\"}, {\"id\": 39867, \"name\": \"racing vest\"}, {\"id\": 39868, \"name\": \"a clear magazine\"}, {\"id\": 39869, \"name\": \"red exercise ball\"}, {\"id\": 39870, \"name\": \"the white lego structure\"}, {\"id\": 39871, \"name\": \"a faint line\"}, {\"id\": 39872, \"name\": \"a black car with a white stripe\"}, {\"id\": 39873, \"name\": \"patterned blouse\"}, {\"id\": 39874, \"name\": \"a woman in a white shirt and black pant\"}, {\"id\": 39875, \"name\": \"black leather backrest\"}, {\"id\": 39876, \"name\": \"a rein\"}, {\"id\": 39877, \"name\": \"a small oval-shaped illustration\"}, {\"id\": 39878, \"name\": \"question\"}, {\"id\": 39879, \"name\": \"a large roll of white wrapping paper\"}, {\"id\": 39880, \"name\": \"a red and white braided cord\"}, {\"id\": 39881, \"name\": \"red, white, and blue flag\"}, {\"id\": 39882, \"name\": \"a distinctive white marking\"}, {\"id\": 39883, \"name\": \"the vintage vehicle\"}, {\"id\": 39884, \"name\": \"silver panel\"}, {\"id\": 39885, \"name\": \"the blue and yellow design\"}, {\"id\": 39886, \"name\": \"the green backrest\"}, {\"id\": 39887, \"name\": \"a lit sparkler\"}, {\"id\": 39888, \"name\": \"the central image\"}, {\"id\": 39889, \"name\": \"pixelated screenshot\"}, {\"id\": 39890, \"name\": \"a blue and red striped shirt\"}, {\"id\": 39891, \"name\": \"the thick branch\"}, {\"id\": 39892, \"name\": \"pink and grey shirt\"}, {\"id\": 39893, \"name\": \"rower\"}, {\"id\": 39894, \"name\": \"the green truck or tractor\"}, {\"id\": 39895, \"name\": \"blue gift bag\"}, {\"id\": 39896, \"name\": \"sergio\"}, {\"id\": 39897, \"name\": \"a vehicle's window\"}, {\"id\": 39898, \"name\": \"sheer top\"}, {\"id\": 39899, \"name\": \"a cartoon donkey\"}, {\"id\": 39900, \"name\": \"the passenger side window\"}, {\"id\": 39901, \"name\": \"a vibrant pink costume\"}, {\"id\": 39902, \"name\": \"jag logo\"}, {\"id\": 39903, \"name\": \"a red and white van off the wall logo\"}, {\"id\": 39904, \"name\": \"white iphone\"}, {\"id\": 39905, \"name\": \"yellow step\"}, {\"id\": 39906, \"name\": \"green bikini\"}, {\"id\": 39907, \"name\": \"doll's face\"}, {\"id\": 39908, \"name\": \"triangle\"}, {\"id\": 39909, \"name\": \"black and white airplane icon\"}, {\"id\": 39910, \"name\": \"the brick section\"}, {\"id\": 39911, \"name\": \"a green backpack\"}, {\"id\": 39912, \"name\": \"hydration pack\"}, {\"id\": 39913, \"name\": \"the open door of the suv\"}, {\"id\": 39914, \"name\": \"colorful clothing item\"}, {\"id\": 39915, \"name\": \"the red brick border\"}, {\"id\": 39916, \"name\": \"the ferrari 360 modena\"}, {\"id\": 39917, \"name\": \"silver pole with lights\"}, {\"id\": 39918, \"name\": \"the gold ribbon\"}, {\"id\": 39919, \"name\": \"square light\"}, {\"id\": 39920, \"name\": \"the purple led light\"}, {\"id\": 39921, \"name\": \"the dark-colored helmet\"}, {\"id\": 39922, \"name\": \"a back of a man's head\"}, {\"id\": 39923, \"name\": \"a bichon frise\"}, {\"id\": 39924, \"name\": \"a blue chest protector\"}, {\"id\": 39925, \"name\": \"red boom\"}, {\"id\": 39926, \"name\": \"a square fluorescent light\"}, {\"id\": 39927, \"name\": \"the partially visible white banner\"}, {\"id\": 39928, \"name\": \"the black rim\"}, {\"id\": 39929, \"name\": \"the rider's jersey\"}, {\"id\": 39930, \"name\": \"maid of honor\"}, {\"id\": 39931, \"name\": \"dog's mouth\"}, {\"id\": 39932, \"name\": \"a rich, orange-colored dish\"}, {\"id\": 39933, \"name\": \"a platform or stage\"}, {\"id\": 39934, \"name\": \"teal stripe\"}, {\"id\": 39935, \"name\": \"a small nbc logo\"}, {\"id\": 39936, \"name\": \"a front-left wheel\"}, {\"id\": 39937, \"name\": \"ladle's bowl\"}, {\"id\": 39938, \"name\": \"a dark blue and white uniform\"}, {\"id\": 39939, \"name\": \"the cycling helmet\"}, {\"id\": 39940, \"name\": \"the purple name tag\"}, {\"id\": 39941, \"name\": \"toy aisle\"}, {\"id\": 39942, \"name\": \"prominent green and yellow checkered stripe\"}, {\"id\": 39943, \"name\": \"breastplate\"}, {\"id\": 39944, \"name\": \"the car's headlight\"}, {\"id\": 39945, \"name\": \"a sponsor's name\"}, {\"id\": 39946, \"name\": \"a spit logo\"}, {\"id\": 39947, \"name\": \"the male performer\"}, {\"id\": 39948, \"name\": \"a dark-colored wood\"}, {\"id\": 39949, \"name\": \"dark brown beam\"}, {\"id\": 39950, \"name\": \"neon green light strip\"}, {\"id\": 39951, \"name\": \"red triangle\"}, {\"id\": 39952, \"name\": \"a dog's legs\"}, {\"id\": 39953, \"name\": \"a dye brush\"}, {\"id\": 39954, \"name\": \"packed stand\"}, {\"id\": 39955, \"name\": \"large black and red display stand\"}, {\"id\": 39956, \"name\": \"a race detail\"}, {\"id\": 39957, \"name\": \"a standard orange basketball\"}, {\"id\": 39958, \"name\": \"the white feather\"}, {\"id\": 39959, \"name\": \"a small body\"}, {\"id\": 39960, \"name\": \"metal beam\"}, {\"id\": 39961, \"name\": \"gray reflective strip\"}, {\"id\": 39962, \"name\": \"a rear of a vehicle's windshield\"}, {\"id\": 39963, \"name\": \"the magneto's costume\"}, {\"id\": 39964, \"name\": \"the surrounding wall\"}, {\"id\": 39965, \"name\": \"white strip\"}, {\"id\": 39966, \"name\": \"camouflage jacket\"}, {\"id\": 39967, \"name\": \"the brick and concrete facade\"}, {\"id\": 39968, \"name\": \"the white fur-trimmed coat\"}, {\"id\": 39969, \"name\": \"the blue animal\"}, {\"id\": 39970, \"name\": \"the white tuxedo\"}, {\"id\": 39971, \"name\": \"a red and purple ribbon\"}, {\"id\": 39972, \"name\": \"passenger side window\"}, {\"id\": 39973, \"name\": \"a numbered bib\"}, {\"id\": 39974, \"name\": \"white dash\"}, {\"id\": 39975, \"name\": \"a disco ball\"}, {\"id\": 39976, \"name\": \"a blue saddle blanket\"}, {\"id\": 39977, \"name\": \"the loudspeaker\"}, {\"id\": 39978, \"name\": \"rosy cheek\"}, {\"id\": 39979, \"name\": \"red, pink, or green bowl\"}, {\"id\": 39980, \"name\": \"silver cylinder\"}, {\"id\": 39981, \"name\": \"colorful drawer\"}, {\"id\": 39982, \"name\": \"the orange label\"}, {\"id\": 39983, \"name\": \"white hijab\"}, {\"id\": 39984, \"name\": \"a large orange barrier\"}, {\"id\": 39985, \"name\": \"a rear wing\"}, {\"id\": 39986, \"name\": \"a gray fabric\"}, {\"id\": 39987, \"name\": \"the fence or barrier\"}, {\"id\": 39988, \"name\": \"the dark-colored stage\"}, {\"id\": 39989, \"name\": \"the illustration of a pink flamingo\"}, {\"id\": 39990, \"name\": \"gray asphalt highway\"}, {\"id\": 39991, \"name\": \"a man in a black t-shirt\"}, {\"id\": 39992, \"name\": \"gp route\"}, {\"id\": 39993, \"name\": \"person's right foot\"}, {\"id\": 39994, \"name\": \"the wooden framework\"}, {\"id\": 39995, \"name\": \"table or shelf\"}, {\"id\": 39996, \"name\": \"the money\"}, {\"id\": 39997, \"name\": \"motorized scooter\"}, {\"id\": 39998, \"name\": \"the host of the video\"}, {\"id\": 39999, \"name\": \"a white light bar\"}, {\"id\": 40000, \"name\": \"the blurry green landscape\"}, {\"id\": 40001, \"name\": \"the tall, white, and black pole\"}, {\"id\": 40002, \"name\": \"the green taxi sign\"}, {\"id\": 40003, \"name\": \"the gold bridle\"}, {\"id\": 40004, \"name\": \"a child's red bike\"}, {\"id\": 40005, \"name\": \"the kite string\"}, {\"id\": 40006, \"name\": \"the multicolored light\"}, {\"id\": 40007, \"name\": \"the smaller black car\"}, {\"id\": 40008, \"name\": \"a dappled shadow\"}, {\"id\": 40009, \"name\": \"a green bleacher\"}, {\"id\": 40010, \"name\": \"a vehicle's windshield\"}, {\"id\": 40011, \"name\": \"hurdle\"}, {\"id\": 40012, \"name\": \"riverbank\"}, {\"id\": 40013, \"name\": \"a horizontal orange stripe\"}, {\"id\": 40014, \"name\": \"yellow graphic\"}, {\"id\": 40015, \"name\": \"heart-shaped design\"}, {\"id\": 40016, \"name\": \"an anime-style cartoon character\"}, {\"id\": 40017, \"name\": \"a child's arm\"}, {\"id\": 40018, \"name\": \"a steep, rocky hillside\"}, {\"id\": 40019, \"name\": \"a muddy tire\"}, {\"id\": 40020, \"name\": \"carriage\"}, {\"id\": 40021, \"name\": \"the minion\"}, {\"id\": 40022, \"name\": \"bird's beak\"}, {\"id\": 40023, \"name\": \"the fin\"}, {\"id\": 40024, \"name\": \"dark sleeve\"}, {\"id\": 40025, \"name\": \"the beige uniform\"}, {\"id\": 40026, \"name\": \"a front of the vehicle\"}, {\"id\": 40027, \"name\": \"the individuals' costume\"}, {\"id\": 40028, \"name\": \"the red interior\"}, {\"id\": 40029, \"name\": \"a pink fabric\"}, {\"id\": 40030, \"name\": \"the white sash\"}, {\"id\": 40031, \"name\": \"a traffic lane\"}, {\"id\": 40032, \"name\": \"black fedora-style hat\"}, {\"id\": 40033, \"name\": \"the pointed ear\"}, {\"id\": 40034, \"name\": \"a carousel horse\"}, {\"id\": 40035, \"name\": \"black motorbike\"}, {\"id\": 40036, \"name\": \"red and white costume\"}, {\"id\": 40037, \"name\": \"yellow heart\"}, {\"id\": 40038, \"name\": \"a hindu deity\"}, {\"id\": 40039, \"name\": \"the black motorbike\"}, {\"id\": 40040, \"name\": \"ruffled hem\"}, {\"id\": 40041, \"name\": \"surfer\"}, {\"id\": 40042, \"name\": \"the white horizontal beam\"}, {\"id\": 40043, \"name\": \"gold circular pattern\"}, {\"id\": 40044, \"name\": \"cylindrical shaft\"}, {\"id\": 40045, \"name\": \"a commercial vehicle\"}, {\"id\": 40046, \"name\": \"a girl's feet\"}, {\"id\": 40047, \"name\": \"a pig's head\"}, {\"id\": 40048, \"name\": \"the unicorn\"}, {\"id\": 40049, \"name\": \"colored helmet\"}, {\"id\": 40050, \"name\": \"orange flame decal\"}, {\"id\": 40051, \"name\": \"party hat\"}, {\"id\": 40052, \"name\": \"a feet and leg of the bird\"}, {\"id\": 40053, \"name\": \"motorboat\"}, {\"id\": 40054, \"name\": \"the clown's arm\"}, {\"id\": 40055, \"name\": \"a school uniform\"}, {\"id\": 40056, \"name\": \"the black hose\"}, {\"id\": 40057, \"name\": \"the truck's rear end\"}, {\"id\": 40058, \"name\": \"red beam\"}, {\"id\": 40059, \"name\": \"rocky shore\"}, {\"id\": 40060, \"name\": \"the trucks' tire\"}, {\"id\": 40061, \"name\": \"the open doorway\"}, {\"id\": 40062, \"name\": \"a dark-colored rock\"}, {\"id\": 40063, \"name\": \"the toolbar\"}, {\"id\": 40064, \"name\": \"blue robe\"}, {\"id\": 40065, \"name\": \"the harness\"}, {\"id\": 40066, \"name\": \"woman in dress\"}, {\"id\": 40067, \"name\": \"the stylish outfit\"}, {\"id\": 40068, \"name\": \"yellow jet skier\"}, {\"id\": 40069, \"name\": \"grayish-black skin\"}, {\"id\": 40070, \"name\": \"goose\"}, {\"id\": 40071, \"name\": \"the prominent gray stripe\"}, {\"id\": 40072, \"name\": \"a black opening\"}, {\"id\": 40073, \"name\": \"a brightly colored jersey\"}, {\"id\": 40074, \"name\": \"vertical beam\"}, {\"id\": 40075, \"name\": \"the blue surface\"}, {\"id\": 40076, \"name\": \"the truck's rear tire\"}, {\"id\": 40077, \"name\": \"high-waisted bottom\"}, {\"id\": 40078, \"name\": \"the grey tail\"}, {\"id\": 40079, \"name\": \"a camel's halter\"}, {\"id\": 40080, \"name\": \"machinery part\"}, {\"id\": 40081, \"name\": \"gray material\"}, {\"id\": 40082, \"name\": \"the neon-colored clothing\"}, {\"id\": 40083, \"name\": \"blue rhinestone\"}, {\"id\": 40084, \"name\": \"train's roof\"}, {\"id\": 40085, \"name\": \"colorful ride animal\"}, {\"id\": 40086, \"name\": \"cargo ship\"}, {\"id\": 40087, \"name\": \"a carousel's metal pole\"}, {\"id\": 40088, \"name\": \"a gold scrollwork\"}, {\"id\": 40089, \"name\": \"the mermaids' tail\"}, {\"id\": 40090, \"name\": \"the shoulder strap\"}, {\"id\": 40091, \"name\": \"a colorful ball\"}, {\"id\": 40092, \"name\": \"pail\"}, {\"id\": 40093, \"name\": \"an outdoor seating\"}, {\"id\": 40094, \"name\": \"a green army costume\"}, {\"id\": 40095, \"name\": \"the yellow star\"}, {\"id\": 40096, \"name\": \"the black pad\"}, {\"id\": 40097, \"name\": \"the rounded end\"}, {\"id\": 40098, \"name\": \"the brightly colored shirt\"}, {\"id\": 40099, \"name\": \"surrounding vehicle\"}, {\"id\": 40100, \"name\": \"a translucent plastic\"}, {\"id\": 40101, \"name\": \"paddleboarding\"}, {\"id\": 40102, \"name\": \"the vibrant headpiece\"}, {\"id\": 40103, \"name\": \"the motorcyclist's bike\"}, {\"id\": 40104, \"name\": \"the gray folding table\"}, {\"id\": 40105, \"name\": \"the mascot's arm\"}, {\"id\": 40106, \"name\": \"orange and white jersey\"}, {\"id\": 40107, \"name\": \"the multicolored jersey\"}, {\"id\": 40108, \"name\": \"faded white crosswalk marking\"}, {\"id\": 40109, \"name\": \"a human performer\"}, {\"id\": 40110, \"name\": \"gray wood plank\"}, {\"id\": 40111, \"name\": \"a black paw print\"}, {\"id\": 40112, \"name\": \"car's front end\"}, {\"id\": 40113, \"name\": \"the bright yellow flag\"}, {\"id\": 40114, \"name\": \"blue jumpsuit\"}, {\"id\": 40115, \"name\": \"the green food\"}, {\"id\": 40116, \"name\": \"a black speaker system\"}, {\"id\": 40117, \"name\": \"a black framing\"}, {\"id\": 40118, \"name\": \"the gold embellishment\"}, {\"id\": 40119, \"name\": \"the blue stove\"}, {\"id\": 40120, \"name\": \"a band member\"}, {\"id\": 40121, \"name\": \"the red bumper\"}, {\"id\": 40122, \"name\": \"blue metal siding\"}, {\"id\": 40123, \"name\": \"a boogie board\"}, {\"id\": 40124, \"name\": \"the asphalt section\"}, {\"id\": 40125, \"name\": \"a silver ball-shaped post\"}, {\"id\": 40126, \"name\": \"black metal rod\"}, {\"id\": 40127, \"name\": \"a colorful tent\"}, {\"id\": 40128, \"name\": \"purse strap\"}, {\"id\": 40129, \"name\": \"a paved\"}, {\"id\": 40130, \"name\": \"the dolly\"}, {\"id\": 40131, \"name\": \"vertical black bar\"}, {\"id\": 40132, \"name\": \"the white foam of the wave\"}, {\"id\": 40133, \"name\": \"the diver's legs\"}, {\"id\": 40134, \"name\": \"a gravel shoulder\"}, {\"id\": 40135, \"name\": \"the instrument's neck\"}, {\"id\": 40136, \"name\": \"a girl's legs\"}, {\"id\": 40137, \"name\": \"pillar of the temple\"}, {\"id\": 40138, \"name\": \"an orange and blue jersey\"}, {\"id\": 40139, \"name\": \"yellow strip of paint\"}, {\"id\": 40140, \"name\": \"the wooden barre\"}, {\"id\": 40141, \"name\": \"colorful teacup-shaped vehicle\"}, {\"id\": 40142, \"name\": \"the red, blue, and white sign\"}, {\"id\": 40143, \"name\": \"the teal sweater\"}, {\"id\": 40144, \"name\": \"the blurry license plate\"}, {\"id\": 40145, \"name\": \"a blue shelving unit\"}, {\"id\": 40146, \"name\": \"the white grout\"}, {\"id\": 40147, \"name\": \"silver vehicle\"}, {\"id\": 40148, \"name\": \"red and blue zig-zag pattern\"}, {\"id\": 40149, \"name\": \"a lawn mower\"}, {\"id\": 40150, \"name\": \"the sack\"}, {\"id\": 40151, \"name\": \"blue and yellow seat\"}, {\"id\": 40152, \"name\": \"costumed individual\"}, {\"id\": 40153, \"name\": \"rough-hewn wooden plank\"}, {\"id\": 40154, \"name\": \"the bright-colored helmet\"}, {\"id\": 40155, \"name\": \"a curved tube\"}, {\"id\": 40156, \"name\": \"an intricate headdress\"}, {\"id\": 40157, \"name\": \"the blue basketball jersey\"}, {\"id\": 40158, \"name\": \"the vibrant inflatable raft\"}, {\"id\": 40159, \"name\": \"a rectangular container\"}, {\"id\": 40160, \"name\": \"a lush hillside\"}, {\"id\": 40161, \"name\": \"bundle of hay\"}, {\"id\": 40162, \"name\": \"the small, round ear\"}, {\"id\": 40163, \"name\": \"fluffy white tail\"}, {\"id\": 40164, \"name\": \"a wavy, red and green design\"}, {\"id\": 40165, \"name\": \"the yellow skirt\"}, {\"id\": 40166, \"name\": \"a peninsula\"}, {\"id\": 40167, \"name\": \"dark gray head\"}, {\"id\": 40168, \"name\": \"a bright pink frame\"}, {\"id\": 40169, \"name\": \"dark patch\"}, {\"id\": 40170, \"name\": \"the onlooker\"}, {\"id\": 40171, \"name\": \"the black front grill\"}, {\"id\": 40172, \"name\": \"the white boxcar\"}, {\"id\": 40173, \"name\": \"the colorful mat\"}, {\"id\": 40174, \"name\": \"vibrant, colorful children's ride\"}, {\"id\": 40175, \"name\": \"a tan helmet\"}, {\"id\": 40176, \"name\": \"a silver metal pole\"}, {\"id\": 40177, \"name\": \"a dark green banner\"}, {\"id\": 40178, \"name\": \"dark wood frame\"}, {\"id\": 40179, \"name\": \"a school child\"}, {\"id\": 40180, \"name\": \"the raised section\"}, {\"id\": 40181, \"name\": \"red and yellow roller coaster\"}, {\"id\": 40182, \"name\": \"the spool of red wire with white edge\"}, {\"id\": 40183, \"name\": \"the snout\"}, {\"id\": 40184, \"name\": \"hanging disco ball\"}, {\"id\": 40185, \"name\": \"the blue rod\"}, {\"id\": 40186, \"name\": \"blue avatar\"}, {\"id\": 40187, \"name\": \"the jungle gym\"}, {\"id\": 40188, \"name\": \"a manual labor\"}, {\"id\": 40189, \"name\": \"the white paint\"}, {\"id\": 40190, \"name\": \"single wheel\"}, {\"id\": 40191, \"name\": \"lane marking\"}, {\"id\": 40192, \"name\": \"a rear of a pickup truck\"}, {\"id\": 40193, \"name\": \"a christmas wreath\"}, {\"id\": 40194, \"name\": \"green sari\"}, {\"id\": 40195, \"name\": \"the tiger's coat\"}, {\"id\": 40196, \"name\": \"the piece of farm equipment\"}, {\"id\": 40197, \"name\": \"black and gray fish\"}, {\"id\": 40198, \"name\": \"a patio furniture\"}, {\"id\": 40199, \"name\": \"the red spot\"}, {\"id\": 40200, \"name\": \"person in blue\"}, {\"id\": 40201, \"name\": \"flipper\"}, {\"id\": 40202, \"name\": \"the green sari\"}, {\"id\": 40203, \"name\": \"brown panel design\"}, {\"id\": 40204, \"name\": \"a colorful, multi-colored, balloon-shaped ride car\"}, {\"id\": 40205, \"name\": \"the transparent plastic container\"}, {\"id\": 40206, \"name\": \"a bag's handle\"}, {\"id\": 40207, \"name\": \"a black carpeted section\"}, {\"id\": 40208, \"name\": \"a bowl of snack\"}, {\"id\": 40209, \"name\": \"a stainless steel bowl\"}, {\"id\": 40210, \"name\": \"the uneven bar\"}, {\"id\": 40211, \"name\": \"the green army man costume\"}, {\"id\": 40212, \"name\": \"mossy and lichen-covered surface\"}, {\"id\": 40213, \"name\": \"motorbike rider\"}, {\"id\": 40214, \"name\": \"a plastic tarp\"}, {\"id\": 40215, \"name\": \"a colorful clothing\"}, {\"id\": 40216, \"name\": \"a predominantly white interior\"}, {\"id\": 40217, \"name\": \"a blue hose\"}, {\"id\": 40218, \"name\": \"a white and brown cat\"}, {\"id\": 40219, \"name\": \"rocky riverbank\"}, {\"id\": 40220, \"name\": \"the model train\"}, {\"id\": 40221, \"name\": \"golden mane\"}, {\"id\": 40222, \"name\": \"bang\"}, {\"id\": 40223, \"name\": \"the yellow inflatable raft\"}, {\"id\": 40224, \"name\": \"the orange and black tank top\"}, {\"id\": 40225, \"name\": \"pink feathered headpiece\"}, {\"id\": 40226, \"name\": \"human's pink-painted toenail\"}, {\"id\": 40227, \"name\": \"the mountainous landscape\"}, {\"id\": 40228, \"name\": \"a white metal bar\"}, {\"id\": 40229, \"name\": \"a distant white sailboat\"}, {\"id\": 40230, \"name\": \"piglet\"}, {\"id\": 40231, \"name\": \"a metal framework\"}, {\"id\": 40232, \"name\": \"a black jet ski\"}, {\"id\": 40233, \"name\": \"woman in a red jacket and black pant\"}, {\"id\": 40234, \"name\": \"the round base\"}, {\"id\": 40235, \"name\": \"a dark-colored boat\"}, {\"id\": 40236, \"name\": \"the yellow rim\"}, {\"id\": 40237, \"name\": \"the metal ramp\"}, {\"id\": 40238, \"name\": \"the black air bag\"}, {\"id\": 40239, \"name\": \"a stacked metal cage\"}, {\"id\": 40240, \"name\": \"a brown shell\"}, {\"id\": 40241, \"name\": \"black exercise machine\"}, {\"id\": 40242, \"name\": \"a front wheel of a motorcycle\"}, {\"id\": 40243, \"name\": \"the bowl of soup\"}, {\"id\": 40244, \"name\": \"yellow timestamp\"}, {\"id\": 40245, \"name\": \"lush plant\"}, {\"id\": 40246, \"name\": \"the exposed metal truss\"}, {\"id\": 40247, \"name\": \"a marching unit\"}, {\"id\": 40248, \"name\": \"elephant statue\"}, {\"id\": 40249, \"name\": \"a rope swing\"}, {\"id\": 40250, \"name\": \"dove\"}, {\"id\": 40251, \"name\": \"the green and yellow tractor\"}, {\"id\": 40252, \"name\": \"a child's left leg\"}, {\"id\": 40253, \"name\": \"a prominent blue arch\"}, {\"id\": 40254, \"name\": \"a green square\"}, {\"id\": 40255, \"name\": \"red and white life jacket\"}, {\"id\": 40256, \"name\": \"the yellow and white streamer\"}, {\"id\": 40257, \"name\": \"the vibrant, colorful mural\"}, {\"id\": 40258, \"name\": \"a toy's base\"}, {\"id\": 40259, \"name\": \"dark uniform\"}, {\"id\": 40260, \"name\": \"fiery mouth\"}, {\"id\": 40261, \"name\": \"upper level of the stadium\"}, {\"id\": 40262, \"name\": \"the pedestrian walkway\"}, {\"id\": 40263, \"name\": \"the patch of brown mulch\"}, {\"id\": 40264, \"name\": \"black vertical pipe\"}, {\"id\": 40265, \"name\": \"dark grey asphalt surface\"}, {\"id\": 40266, \"name\": \"the black cover\"}, {\"id\": 40267, \"name\": \"the piece of metal siding\"}, {\"id\": 40268, \"name\": \"the vehicle's wheel\"}, {\"id\": 40269, \"name\": \"the cargo bed\"}, {\"id\": 40270, \"name\": \"an opposite shore\"}, {\"id\": 40271, \"name\": \"monument\"}, {\"id\": 40272, \"name\": \"black and white dog\"}, {\"id\": 40273, \"name\": \"a mode of transportation\"}, {\"id\": 40274, \"name\": \"a soccer cleat\"}, {\"id\": 40275, \"name\": \"the blue and white wheel\"}, {\"id\": 40276, \"name\": \"a stuntman\"}, {\"id\": 40277, \"name\": \"the tenni racket\"}, {\"id\": 40278, \"name\": \"a badminton racket\"}, {\"id\": 40279, \"name\": \"black ear\"}, {\"id\": 40280, \"name\": \"a green edge\"}, {\"id\": 40281, \"name\": \"gray container\"}, {\"id\": 40282, \"name\": \"purple basket\"}, {\"id\": 40283, \"name\": \"a back wheel\"}, {\"id\": 40284, \"name\": \"white squiggly face\"}, {\"id\": 40285, \"name\": \"stuffed sheep\"}, {\"id\": 40286, \"name\": \"black hoof\"}, {\"id\": 40287, \"name\": \"blue, red, and black clothing\"}, {\"id\": 40288, \"name\": \"a black harley\"}, {\"id\": 40289, \"name\": \"striking wooden pattern\"}, {\"id\": 40290, \"name\": \"a medical professional\"}, {\"id\": 40291, \"name\": \"a distinctive black front windshield\"}, {\"id\": 40292, \"name\": \"bucket of green vegetable\"}, {\"id\": 40293, \"name\": \"a woman in red dress\"}, {\"id\": 40294, \"name\": \"elaborate headpiece\"}, {\"id\": 40295, \"name\": \"black sound hole\"}, {\"id\": 40296, \"name\": \"the plane's shadow\"}, {\"id\": 40297, \"name\": \"wagon\"}, {\"id\": 40298, \"name\": \"the miniature metal cylinder bundle\"}, {\"id\": 40299, \"name\": \"yellow front loader\"}, {\"id\": 40300, \"name\": \"gold ornament\"}, {\"id\": 40301, \"name\": \"the blue feeder\"}, {\"id\": 40302, \"name\": \"a modern architecture\"}, {\"id\": 40303, \"name\": \"the yellow and blue stripe\"}, {\"id\": 40304, \"name\": \"the racing gear\"}, {\"id\": 40305, \"name\": \"prominent advertisement\"}, {\"id\": 40306, \"name\": \"bright pink dress\"}, {\"id\": 40307, \"name\": \"a pool's surface\"}, {\"id\": 40308, \"name\": \"dark-colored stone\"}, {\"id\": 40309, \"name\": \"the blue cycling jersey\"}, {\"id\": 40310, \"name\": \"a piece of machinery\"}, {\"id\": 40311, \"name\": \"an illuminated monument\"}, {\"id\": 40312, \"name\": \"the teal scarf\"}, {\"id\": 40313, \"name\": \"green and red bar\"}, {\"id\": 40314, \"name\": \"the web-like pattern\"}, {\"id\": 40315, \"name\": \"truck's rear panel\"}, {\"id\": 40316, \"name\": \"the white kimono\"}, {\"id\": 40317, \"name\": \"vibrant red dress\"}, {\"id\": 40318, \"name\": \"the drum kit\"}, {\"id\": 40319, \"name\": \"the dragon\"}, {\"id\": 40320, \"name\": \"beige jacket\"}, {\"id\": 40321, \"name\": \"the brown hull\"}, {\"id\": 40322, \"name\": \"rider in black and red\"}, {\"id\": 40323, \"name\": \"ruffled bodice\"}, {\"id\": 40324, \"name\": \"airplane in the air\"}, {\"id\": 40325, \"name\": \"the flying jet\"}, {\"id\": 40326, \"name\": \"the yellow clothing\"}, {\"id\": 40327, \"name\": \"wire mesh enclosure\"}, {\"id\": 40328, \"name\": \"the white pith helmet\"}, {\"id\": 40329, \"name\": \"orange leg\"}, {\"id\": 40330, \"name\": \"a wave's white foam\"}, {\"id\": 40331, \"name\": \"yellow shipping container\"}, {\"id\": 40332, \"name\": \"a beige jacket\"}, {\"id\": 40333, \"name\": \"the red and green costume\"}, {\"id\": 40334, \"name\": \"a dove\"}, {\"id\": 40335, \"name\": \"a black and red motorcycle\"}, {\"id\": 40336, \"name\": \"a box wrapped in plastic\"}, {\"id\": 40337, \"name\": \"the red tail\"}, {\"id\": 40338, \"name\": \"white duck\"}, {\"id\": 40339, \"name\": \"a black and yellow belt\"}, {\"id\": 40340, \"name\": \"a lush green plant\"}, {\"id\": 40341, \"name\": \"a weight label\"}, {\"id\": 40342, \"name\": \"a circular gold emblem\"}, {\"id\": 40343, \"name\": \"a concrete divider\"}, {\"id\": 40344, \"name\": \"a windshield of the vehicle\"}, {\"id\": 40345, \"name\": \"black and pink backpack\"}, {\"id\": 40346, \"name\": \"a black lanyard\"}, {\"id\": 40347, \"name\": \"the grey metal panel\"}, {\"id\": 40348, \"name\": \"rolled-up blue material\"}, {\"id\": 40349, \"name\": \"a steep hillside\"}, {\"id\": 40350, \"name\": \"the vehicle's number\"}, {\"id\": 40351, \"name\": \"the black athletic shoe\"}, {\"id\": 40352, \"name\": \"the bright orange spot\"}, {\"id\": 40353, \"name\": \"vibrant orange flag\"}, {\"id\": 40354, \"name\": \"beige dashboard\"}, {\"id\": 40355, \"name\": \"the orange door\"}, {\"id\": 40356, \"name\": \"the ride's body\"}, {\"id\": 40357, \"name\": \"the gym equipment\"}, {\"id\": 40358, \"name\": \"the orange flower\"}, {\"id\": 40359, \"name\": \"the blue and white t-shirt\"}, {\"id\": 40360, \"name\": \"a metal surface\"}, {\"id\": 40361, \"name\": \"wake\"}, {\"id\": 40362, \"name\": \"a yellow and blue pole\"}, {\"id\": 40363, \"name\": \"the stem\"}, {\"id\": 40364, \"name\": \"muddy terrain\"}, {\"id\": 40365, \"name\": \"the green signboard\"}, {\"id\": 40366, \"name\": \"11 military helicopter\"}, {\"id\": 40367, \"name\": \"the colorful logo\"}, {\"id\": 40368, \"name\": \"black shirt and short\"}, {\"id\": 40369, \"name\": \"the brown short\"}, {\"id\": 40370, \"name\": \"the fluorescent\"}, {\"id\": 40371, \"name\": \"green plastic stool\"}, {\"id\": 40372, \"name\": \"silver dot\"}, {\"id\": 40373, \"name\": \"isuzu logo\"}, {\"id\": 40374, \"name\": \"a trumpet\"}, {\"id\": 40375, \"name\": \"the white police van\"}, {\"id\": 40376, \"name\": \"the orange ride\"}, {\"id\": 40377, \"name\": \"the goal net\"}, {\"id\": 40378, \"name\": \"octopus' tentacle\"}, {\"id\": 40379, \"name\": \"the black sleeveless top\"}, {\"id\": 40380, \"name\": \"a red bar stool\"}, {\"id\": 40381, \"name\": \"long black dress\"}, {\"id\": 40382, \"name\": \"gold circle\"}, {\"id\": 40383, \"name\": \"the gable-style design\"}, {\"id\": 40384, \"name\": \"a black circular platform\"}, {\"id\": 40385, \"name\": \"black and red design\"}, {\"id\": 40386, \"name\": \"blue metal bar\"}, {\"id\": 40387, \"name\": \"the black embroidery\"}, {\"id\": 40388, \"name\": \"the brown cardboard\"}, {\"id\": 40389, \"name\": \"a brown storefront\"}, {\"id\": 40390, \"name\": \"a male musician\"}, {\"id\": 40391, \"name\": \"an orange cap\"}, {\"id\": 40392, \"name\": \"the white plastic tube\"}, {\"id\": 40393, \"name\": \"a kayak's hull\"}, {\"id\": 40394, \"name\": \"a black and silver scooter\"}, {\"id\": 40395, \"name\": \"black lamp post\"}, {\"id\": 40396, \"name\": \"blue kayak\"}, {\"id\": 40397, \"name\": \"the gray garage door\"}, {\"id\": 40398, \"name\": \"a greenish material\"}, {\"id\": 40399, \"name\": \"a raised concrete bench\"}, {\"id\": 40400, \"name\": \"blue and green castle\"}, {\"id\": 40401, \"name\": \"vibrant green table\"}, {\"id\": 40402, \"name\": \"the brick platform\"}, {\"id\": 40403, \"name\": \"metal rafter\"}, {\"id\": 40404, \"name\": \"worker's face\"}, {\"id\": 40405, \"name\": \"cartoon animal\"}, {\"id\": 40406, \"name\": \"a brown flip-flop\"}, {\"id\": 40407, \"name\": \"a colored t-shirt\"}, {\"id\": 40408, \"name\": \"red and white striped fence\"}, {\"id\": 40409, \"name\": \"a green basket\"}, {\"id\": 40410, \"name\": \"a black gas stove burner\"}, {\"id\": 40411, \"name\": \"woman with a backpack\"}, {\"id\": 40412, \"name\": \"the speaker system\"}, {\"id\": 40413, \"name\": \"central hole\"}, {\"id\": 40414, \"name\": \"the military helicopter\"}, {\"id\": 40415, \"name\": \"playhouse\"}, {\"id\": 40416, \"name\": \"the black bas drum\"}, {\"id\": 40417, \"name\": \"a blue side\"}, {\"id\": 40418, \"name\": \"shawl\"}, {\"id\": 40419, \"name\": \"the stage setup\"}, {\"id\": 40420, \"name\": \"large, round yellow base\"}, {\"id\": 40421, \"name\": \"traditional indian garb\"}, {\"id\": 40422, \"name\": \"green pot\"}, {\"id\": 40423, \"name\": \"kart\"}, {\"id\": 40424, \"name\": \"white ring\"}, {\"id\": 40425, \"name\": \"a white cushion\"}, {\"id\": 40426, \"name\": \"a white grid-like pattern\"}, {\"id\": 40427, \"name\": \"metal percussion instrument\"}, {\"id\": 40428, \"name\": \"a green and red skirt\"}, {\"id\": 40429, \"name\": \"a double-decker bus\"}, {\"id\": 40430, \"name\": \"person's fingertip\"}, {\"id\": 40431, \"name\": \"a yellow loves gas station sign\"}, {\"id\": 40432, \"name\": \"training wheel\"}, {\"id\": 40433, \"name\": \"a flat body\"}, {\"id\": 40434, \"name\": \"white stone pillar\"}, {\"id\": 40435, \"name\": \"a blue striped shirt\"}, {\"id\": 40436, \"name\": \"the hamster\"}, {\"id\": 40437, \"name\": \"a small, square stone\"}, {\"id\": 40438, \"name\": \"the paved lane\"}, {\"id\": 40439, \"name\": \"the gray and black puffer vest\"}, {\"id\": 40440, \"name\": \"a black capri pant\"}, {\"id\": 40441, \"name\": \"the drumming harness\"}, {\"id\": 40442, \"name\": \"blue divider\"}, {\"id\": 40443, \"name\": \"the white curb\"}, {\"id\": 40444, \"name\": \"the dark blue border\"}, {\"id\": 40445, \"name\": \"yellow and green tuk-tuk\"}, {\"id\": 40446, \"name\": \"the illumination\"}, {\"id\": 40447, \"name\": \"a bare head\"}, {\"id\": 40448, \"name\": \"black and yellow flame design\"}, {\"id\": 40449, \"name\": \"cat's head\"}, {\"id\": 40450, \"name\": \"sign in a foreign language\"}, {\"id\": 40451, \"name\": \"gray vertical panel\"}, {\"id\": 40452, \"name\": \"an orange poncho\"}, {\"id\": 40453, \"name\": \"lush forested area\"}, {\"id\": 40454, \"name\": \"lace sleeve\"}, {\"id\": 40455, \"name\": \"colorful beach umbrella\"}, {\"id\": 40456, \"name\": \"children's head\"}, {\"id\": 40457, \"name\": \"a snowy terrain\"}, {\"id\": 40458, \"name\": \"a reddish-brown spike\"}, {\"id\": 40459, \"name\": \"orange boom\"}, {\"id\": 40460, \"name\": \"green and cream-colored train car\"}, {\"id\": 40461, \"name\": \"stainless steel rack\"}, {\"id\": 40462, \"name\": \"the dark gray swim trunk\"}, {\"id\": 40463, \"name\": \"black and white striped cape\"}, {\"id\": 40464, \"name\": \"a suited man\"}, {\"id\": 40465, \"name\": \"an orange hard hat\"}, {\"id\": 40466, \"name\": \"a stacked box\"}, {\"id\": 40467, \"name\": \"gray vehicle\"}, {\"id\": 40468, \"name\": \"a smooth surface\"}, {\"id\": 40469, \"name\": \"the blue and white feathered skirt\"}, {\"id\": 40470, \"name\": \"black elbow pad\"}, {\"id\": 40471, \"name\": \"the tropical shirt\"}, {\"id\": 40472, \"name\": \"a red and white border\"}, {\"id\": 40473, \"name\": \"olympic logo\"}, {\"id\": 40474, \"name\": \"colorful square\"}, {\"id\": 40475, \"name\": \"a maroon capri pant\"}, {\"id\": 40476, \"name\": \"the horse-mounted soldier\"}, {\"id\": 40477, \"name\": \"dark blue strap\"}, {\"id\": 40478, \"name\": \"cap's brim\"}, {\"id\": 40479, \"name\": \"the circular yellow logo\"}, {\"id\": 40480, \"name\": \"white cycling jersey\"}, {\"id\": 40481, \"name\": \"a dull and grey surface\"}, {\"id\": 40482, \"name\": \"brown beam\"}, {\"id\": 40483, \"name\": \"the thai flag\"}, {\"id\": 40484, \"name\": \"a red cape\"}, {\"id\": 40485, \"name\": \"grey tank top\"}, {\"id\": 40486, \"name\": \"a round hole\"}, {\"id\": 40487, \"name\": \"the matching blue t-shirt\"}, {\"id\": 40488, \"name\": \"the electrical pole\"}, {\"id\": 40489, \"name\": \"pennant\"}, {\"id\": 40490, \"name\": \"the performer's body\"}, {\"id\": 40491, \"name\": \"bird feeder\"}, {\"id\": 40492, \"name\": \"kayak's hull\"}, {\"id\": 40493, \"name\": \"the athletic pant\"}, {\"id\": 40494, \"name\": \"red embroidered design\"}, {\"id\": 40495, \"name\": \"a yellow safety net\"}, {\"id\": 40496, \"name\": \"air force plane\"}, {\"id\": 40497, \"name\": \"a distinctive black front bumper\"}, {\"id\": 40498, \"name\": \"a character from the toy story franchise\"}, {\"id\": 40499, \"name\": \"hula hoop\"}, {\"id\": 40500, \"name\": \"single white fish\"}, {\"id\": 40501, \"name\": \"the blue hull\"}, {\"id\": 40502, \"name\": \"a man on the scooter\"}, {\"id\": 40503, \"name\": \"a black headdress\"}, {\"id\": 40504, \"name\": \"a blue and black striped shirt\"}, {\"id\": 40505, \"name\": \"a distinctive black stripe\"}, {\"id\": 40506, \"name\": \"an orange mat\"}, {\"id\": 40507, \"name\": \"red kayak\"}, {\"id\": 40508, \"name\": \"the red end\"}, {\"id\": 40509, \"name\": \"the green-colored character\"}, {\"id\": 40510, \"name\": \"the black and red t-shirt\"}, {\"id\": 40511, \"name\": \"a blue and green floral pattern\"}, {\"id\": 40512, \"name\": \"the green vine\"}, {\"id\": 40513, \"name\": \"a silver bar\"}, {\"id\": 40514, \"name\": \"a metal support beam\"}, {\"id\": 40515, \"name\": \"yellow table\"}, {\"id\": 40516, \"name\": \"a blue basketball uniform\"}, {\"id\": 40517, \"name\": \"the median strip\"}, {\"id\": 40518, \"name\": \"black symbol\"}, {\"id\": 40519, \"name\": \"dark blue jersey\"}, {\"id\": 40520, \"name\": \"the dark brown shutter\"}, {\"id\": 40521, \"name\": \"the green kart\"}, {\"id\": 40522, \"name\": \"the tuba\"}, {\"id\": 40523, \"name\": \"white foamy wave\"}, {\"id\": 40524, \"name\": \"brown cushion\"}, {\"id\": 40525, \"name\": \"yellow and green auto rickshaw\"}, {\"id\": 40526, \"name\": \"hood of the white car\"}, {\"id\": 40527, \"name\": \"the top of the bus\"}, {\"id\": 40528, \"name\": \"a presence of flag\"}, {\"id\": 40529, \"name\": \"dragon's mouth\"}, {\"id\": 40530, \"name\": \"arched opening\"}, {\"id\": 40531, \"name\": \"the rocky cliff face\"}, {\"id\": 40532, \"name\": \"carnival tent\"}, {\"id\": 40533, \"name\": \"red lane divider\"}, {\"id\": 40534, \"name\": \"a bus's advertisement\"}, {\"id\": 40535, \"name\": \"man in brown robe\"}, {\"id\": 40536, \"name\": \"black claw\"}, {\"id\": 40537, \"name\": \"a green and yellow paintwork\"}, {\"id\": 40538, \"name\": \"the rocky area\"}, {\"id\": 40539, \"name\": \"a blue metal arm\"}, {\"id\": 40540, \"name\": \"exposed hull\"}, {\"id\": 40541, \"name\": \"a race official\"}, {\"id\": 40542, \"name\": \"camel\"}, {\"id\": 40543, \"name\": \"a blue shape\"}, {\"id\": 40544, \"name\": \"the beige cap\"}, {\"id\": 40545, \"name\": \"a black wire\"}, {\"id\": 40546, \"name\": \"colorful can\"}, {\"id\": 40547, \"name\": \"aquatic decoration\"}, {\"id\": 40548, \"name\": \"a passenger-side door\"}, {\"id\": 40549, \"name\": \"colorful skirt\"}, {\"id\": 40550, \"name\": \"a red pad\"}, {\"id\": 40551, \"name\": \"child's colorful outfit\"}, {\"id\": 40552, \"name\": \"boy's shadow\"}, {\"id\": 40553, \"name\": \"a dark-colored tail\"}, {\"id\": 40554, \"name\": \"head of insect\"}, {\"id\": 40555, \"name\": \"the red, blue, and black uniform\"}, {\"id\": 40556, \"name\": \"casual outfit\"}, {\"id\": 40557, \"name\": \"a hood of a black vehicle\"}, {\"id\": 40558, \"name\": \"the person in a white shirt and black vest\"}, {\"id\": 40559, \"name\": \"the guitar neck\"}, {\"id\": 40560, \"name\": \"orange flotation device\"}, {\"id\": 40561, \"name\": \"crawdad\"}, {\"id\": 40562, \"name\": \"a sand trap\"}, {\"id\": 40563, \"name\": \"red and blue slide tower\"}, {\"id\": 40564, \"name\": \"stacks of green and blue crate\"}, {\"id\": 40565, \"name\": \"the lower shelf\"}, {\"id\": 40566, \"name\": \"the red and gold archway\"}, {\"id\": 40567, \"name\": \"piece of the mural\"}, {\"id\": 40568, \"name\": \"the checkered cushion\"}, {\"id\": 40569, \"name\": \"a yellow x sign\"}, {\"id\": 40570, \"name\": \"the black and blue jacket\"}, {\"id\": 40571, \"name\": \"a child's left arm\"}, {\"id\": 40572, \"name\": \"yellow-and-black striped caution tape\"}, {\"id\": 40573, \"name\": \"red train car\"}, {\"id\": 40574, \"name\": \"low bun\"}, {\"id\": 40575, \"name\": \"yellow and black tire\"}, {\"id\": 40576, \"name\": \"a black metal spring\"}, {\"id\": 40577, \"name\": \"a dark gray t-shirt\"}, {\"id\": 40578, \"name\": \"a front truck\"}, {\"id\": 40579, \"name\": \"the black cylinder\"}, {\"id\": 40580, \"name\": \"a blue and white boat\"}, {\"id\": 40581, \"name\": \"exposed red metal beam\"}, {\"id\": 40582, \"name\": \"a black metal arm\"}, {\"id\": 40583, \"name\": \"kia logo\"}, {\"id\": 40584, \"name\": \"the multicolored logo\"}, {\"id\": 40585, \"name\": \"black-and-white striped umbrella\"}, {\"id\": 40586, \"name\": \"a traditional headdress\"}, {\"id\": 40587, \"name\": \"the helm\"}, {\"id\": 40588, \"name\": \"the pink heart-shaped balloon\"}, {\"id\": 40589, \"name\": \"red rooster\"}, {\"id\": 40590, \"name\": \"vibrant red outfit\"}, {\"id\": 40591, \"name\": \"a red prop\"}, {\"id\": 40592, \"name\": \"the yellow and blue pole\"}, {\"id\": 40593, \"name\": \"the vehicle's front windshield\"}, {\"id\": 40594, \"name\": \"a yellow and white sign\"}, {\"id\": 40595, \"name\": \"the green pepper\"}, {\"id\": 40596, \"name\": \"a single blue motorcycle\"}, {\"id\": 40597, \"name\": \"plants and pot\"}, {\"id\": 40598, \"name\": \"brown, wooden-framed mirror\"}, {\"id\": 40599, \"name\": \"the grey head covering\"}, {\"id\": 40600, \"name\": \"the hanging ornament\"}, {\"id\": 40601, \"name\": \"blonde boy\"}, {\"id\": 40602, \"name\": \"the checkered shirt\"}, {\"id\": 40603, \"name\": \"the white flagpole\"}, {\"id\": 40604, \"name\": \"a beige clothing\"}, {\"id\": 40605, \"name\": \"a cartoon face\"}, {\"id\": 40606, \"name\": \"the man in a blue shirt and black short\"}, {\"id\": 40607, \"name\": \"dark gray pillar\"}, {\"id\": 40608, \"name\": \"the red and yellow lantern\"}, {\"id\": 40609, \"name\": \"black metal grating\"}, {\"id\": 40610, \"name\": \"a model car\"}, {\"id\": 40611, \"name\": \"a blue couch\"}, {\"id\": 40612, \"name\": \"white goat\"}, {\"id\": 40613, \"name\": \"a stone step\"}, {\"id\": 40614, \"name\": \"advertisement for badminton\"}, {\"id\": 40615, \"name\": \"the sliced lemon\"}, {\"id\": 40616, \"name\": \"a man in a grey shirt\"}, {\"id\": 40617, \"name\": \"red folder\"}, {\"id\": 40618, \"name\": \"a red and white vest\"}, {\"id\": 40619, \"name\": \"a crisp white border\"}, {\"id\": 40620, \"name\": \"a cyclist's bike\"}, {\"id\": 40621, \"name\": \"the scratch\"}, {\"id\": 40622, \"name\": \"the striking golden statue\"}, {\"id\": 40623, \"name\": \"an orange and yellow tent\"}, {\"id\": 40624, \"name\": \"the partially cooked egg\"}, {\"id\": 40625, \"name\": \"a ground-level storefront\"}, {\"id\": 40626, \"name\": \"the fencing\"}, {\"id\": 40627, \"name\": \"black circle with a white arrow\"}, {\"id\": 40628, \"name\": \"the covered stage\"}, {\"id\": 40629, \"name\": \"a pursel\"}, {\"id\": 40630, \"name\": \"towering apartment complex\"}, {\"id\": 40631, \"name\": \"a murky liquid\"}, {\"id\": 40632, \"name\": \"green metal frame\"}, {\"id\": 40633, \"name\": \"the blue horizontal stripe\"}, {\"id\": 40634, \"name\": \"the rocky bank\"}, {\"id\": 40635, \"name\": \"black and white checkered stripe\"}, {\"id\": 40636, \"name\": \"the red and yellow skirt\"}, {\"id\": 40637, \"name\": \"tiered formation\"}, {\"id\": 40638, \"name\": \"a colorful children's ride\"}, {\"id\": 40639, \"name\": \"the lush green plant\"}, {\"id\": 40640, \"name\": \"an ornate architecture\"}, {\"id\": 40641, \"name\": \"a child's shadow\"}, {\"id\": 40642, \"name\": \"gray purse\"}, {\"id\": 40643, \"name\": \"a braided texture\"}, {\"id\": 40644, \"name\": \"pig's dark shadow\"}, {\"id\": 40645, \"name\": \"green fatigue\"}, {\"id\": 40646, \"name\": \"a yellowish beak\"}, {\"id\": 40647, \"name\": \"a ski gondola\"}, {\"id\": 40648, \"name\": \"lawnmower\"}, {\"id\": 40649, \"name\": \"a machine component\"}, {\"id\": 40650, \"name\": \"a white logo on the chest\"}, {\"id\": 40651, \"name\": \"the glowing eye\"}, {\"id\": 40652, \"name\": \"the pink tutu\"}, {\"id\": 40653, \"name\": \"the mane\"}, {\"id\": 40654, \"name\": \"a plant-like pattern\"}, {\"id\": 40655, \"name\": \"yellow horse\"}, {\"id\": 40656, \"name\": \"a red and white patterned belt\"}, {\"id\": 40657, \"name\": \"a transparent surface\"}, {\"id\": 40658, \"name\": \"the yellow symbol\"}, {\"id\": 40659, \"name\": \"the green, yellow, and purple balloon\"}, {\"id\": 40660, \"name\": \"the pink body\"}, {\"id\": 40661, \"name\": \"a yellow hull\"}, {\"id\": 40662, \"name\": \"the black-and-yellow crossing arm\"}, {\"id\": 40663, \"name\": \"the landing gear\"}, {\"id\": 40664, \"name\": \"the green cube\"}, {\"id\": 40665, \"name\": \"the black blouse\"}, {\"id\": 40666, \"name\": \"a metal wire mesh\"}, {\"id\": 40667, \"name\": \"the blue plastic stool\"}, {\"id\": 40668, \"name\": \"a motorbike rider\"}, {\"id\": 40669, \"name\": \"dark-colored truck\"}, {\"id\": 40670, \"name\": \"a yellow and orange cone\"}, {\"id\": 40671, \"name\": \"gray and white circle\"}, {\"id\": 40672, \"name\": \"a colorful ribbon\"}, {\"id\": 40673, \"name\": \"steeple\"}, {\"id\": 40674, \"name\": \"red sport car\"}, {\"id\": 40675, \"name\": \"the monk\"}, {\"id\": 40676, \"name\": \"the camouflage shirt\"}, {\"id\": 40677, \"name\": \"the blue house\"}, {\"id\": 40678, \"name\": \"a pedal bike\"}, {\"id\": 40679, \"name\": \"the man's legs\"}, {\"id\": 40680, \"name\": \"a playful and imaginative toy\"}, {\"id\": 40681, \"name\": \"the gold, ornate design\"}, {\"id\": 40682, \"name\": \"pool inflatable\"}, {\"id\": 40683, \"name\": \"a white speck\"}, {\"id\": 40684, \"name\": \"the yellow and black striped curb\"}, {\"id\": 40685, \"name\": \"a vertical black handle\"}, {\"id\": 40686, \"name\": \"the dark-colored tire\"}, {\"id\": 40687, \"name\": \"purple design\"}, {\"id\": 40688, \"name\": \"fingertip\"}, {\"id\": 40689, \"name\": \"firetruck\"}, {\"id\": 40690, \"name\": \"the white music note\"}, {\"id\": 40691, \"name\": \"a red trash can\"}, {\"id\": 40692, \"name\": \"the rear seat of a motorcycle\"}, {\"id\": 40693, \"name\": \"gold helmet\"}, {\"id\": 40694, \"name\": \"ice skating\"}, {\"id\": 40695, \"name\": \"white lamp\"}, {\"id\": 40696, \"name\": \"red and white step\"}, {\"id\": 40697, \"name\": \"bodyboard\"}, {\"id\": 40698, \"name\": \"twig\"}, {\"id\": 40699, \"name\": \"white drape\"}, {\"id\": 40700, \"name\": \"prominent orange fish\"}, {\"id\": 40701, \"name\": \"red neckerchief\"}, {\"id\": 40702, \"name\": \"landmass\"}, {\"id\": 40703, \"name\": \"a military truck\"}, {\"id\": 40704, \"name\": \"a slatted wooden plank\"}, {\"id\": 40705, \"name\": \"brim of hi hat\"}, {\"id\": 40706, \"name\": \"the blue shell-shaped seat\"}, {\"id\": 40707, \"name\": \"hello kitty-themed decoration\"}, {\"id\": 40708, \"name\": \"a tattoo sleeve\"}, {\"id\": 40709, \"name\": \"motorcycle's mirror\"}, {\"id\": 40710, \"name\": \"diamond pattern\"}, {\"id\": 40711, \"name\": \"ornate hello kitty-themed tricycle\"}, {\"id\": 40712, \"name\": \"green suitcase\"}, {\"id\": 40713, \"name\": \"a white go-kart\"}, {\"id\": 40714, \"name\": \"the square manhole\"}, {\"id\": 40715, \"name\": \"recycling symbol\"}, {\"id\": 40716, \"name\": \"the glare effect\"}, {\"id\": 40717, \"name\": \"the teal cup\"}, {\"id\": 40718, \"name\": \"the black circular end\"}, {\"id\": 40719, \"name\": \"a red and white striped barricade\"}, {\"id\": 40720, \"name\": \"ornate gold detailing\"}, {\"id\": 40721, \"name\": \"a man with the bandana\"}, {\"id\": 40722, \"name\": \"a parked bike\"}, {\"id\": 40723, \"name\": \"a glowing white eye\"}, {\"id\": 40724, \"name\": \"a tenni ball\"}, {\"id\": 40725, \"name\": \"orange garland\"}, {\"id\": 40726, \"name\": \"an open doorway\"}, {\"id\": 40727, \"name\": \"prominent metal hook\"}, {\"id\": 40728, \"name\": \"a blue and white curb\"}, {\"id\": 40729, \"name\": \"the person on horseback\"}, {\"id\": 40730, \"name\": \"a blue support pole\"}, {\"id\": 40731, \"name\": \"a white checkered stripe\"}, {\"id\": 40732, \"name\": \"a knee-high black sock\"}, {\"id\": 40733, \"name\": \"the sleek sport car\"}, {\"id\": 40734, \"name\": \"a mariachi outfit\"}, {\"id\": 40735, \"name\": \"a long golden tail\"}, {\"id\": 40736, \"name\": \"the black rope\"}, {\"id\": 40737, \"name\": \"the chinese lantern\"}, {\"id\": 40738, \"name\": \"bumper car\"}, {\"id\": 40739, \"name\": \"a colorful paper streamer\"}, {\"id\": 40740, \"name\": \"maroon jersey\"}, {\"id\": 40741, \"name\": \"the triangular sign\"}, {\"id\": 40742, \"name\": \"the black planter box\"}, {\"id\": 40743, \"name\": \"toothpick\"}, {\"id\": 40744, \"name\": \"patch of green shrub\"}, {\"id\": 40745, \"name\": \"the black trolley cart\"}, {\"id\": 40746, \"name\": \"a framed painting\"}, {\"id\": 40747, \"name\": \"the high-flying aircraft\"}, {\"id\": 40748, \"name\": \"the laborer\"}, {\"id\": 40749, \"name\": \"the truck's side mirror\"}, {\"id\": 40750, \"name\": \"the brown driftwood piece\"}, {\"id\": 40751, \"name\": \"pink head covering\"}, {\"id\": 40752, \"name\": \"the smooth, curved surface\"}, {\"id\": 40753, \"name\": \"tan statue\"}, {\"id\": 40754, \"name\": \"a wooden hut\"}, {\"id\": 40755, \"name\": \"badminton player\"}, {\"id\": 40756, \"name\": \"a green canopy tent\"}, {\"id\": 40757, \"name\": \"colorful cartoon character\"}, {\"id\": 40758, \"name\": \"warm orange glow\"}, {\"id\": 40759, \"name\": \"the bean\"}, {\"id\": 40760, \"name\": \"a red vertical beam\"}, {\"id\": 40761, \"name\": \"blurry, brown creature\"}, {\"id\": 40762, \"name\": \"the blue and yellow umbrella\"}, {\"id\": 40763, \"name\": \"white sheet of paper\"}, {\"id\": 40764, \"name\": \"the blue back door\"}, {\"id\": 40765, \"name\": \"the gold frame\"}, {\"id\": 40766, \"name\": \"bright, white dot\"}, {\"id\": 40767, \"name\": \"a black and white cat\"}, {\"id\": 40768, \"name\": \"thai script\"}, {\"id\": 40769, \"name\": \"the yellow and red toy\"}, {\"id\": 40770, \"name\": \"colorful inflatable toy\"}, {\"id\": 40771, \"name\": \"a red ship's wheel logo\"}, {\"id\": 40772, \"name\": \"patterned long-sleeved shirt\"}, {\"id\": 40773, \"name\": \"the rainbow-colored garland\"}, {\"id\": 40774, \"name\": \"the beige hijab\"}, {\"id\": 40775, \"name\": \"the metal tool\"}, {\"id\": 40776, \"name\": \"the toy doll\"}, {\"id\": 40777, \"name\": \"the black floral dress\"}, {\"id\": 40778, \"name\": \"a grey car\"}, {\"id\": 40779, \"name\": \"the green marble\"}, {\"id\": 40780, \"name\": \"outlet\"}, {\"id\": 40781, \"name\": \"the prominent yellow excavator\"}, {\"id\": 40782, \"name\": \"the schoolgirl\"}, {\"id\": 40783, \"name\": \"a blue and white ball\"}, {\"id\": 40784, \"name\": \"the bouquet of white flower\"}, {\"id\": 40785, \"name\": \"a red and white suit\"}, {\"id\": 40786, \"name\": \"cactus\"}, {\"id\": 40787, \"name\": \"the strip of land\"}, {\"id\": 40788, \"name\": \"the curved windshield of a motorcycle\"}, {\"id\": 40789, \"name\": \"the pink neon light\"}, {\"id\": 40790, \"name\": \"a sketch\"}, {\"id\": 40791, \"name\": \"blue aerosol can\"}, {\"id\": 40792, \"name\": \"the red roller coaster\"}, {\"id\": 40793, \"name\": \"a sitting person\"}, {\"id\": 40794, \"name\": \"red gas cylinder stand\"}, {\"id\": 40795, \"name\": \"the black bat\"}, {\"id\": 40796, \"name\": \"the gray curtain\"}, {\"id\": 40797, \"name\": \"section of empty seat\"}, {\"id\": 40798, \"name\": \"a colorful beach ball\"}, {\"id\": 40799, \"name\": \"a yellow horizontal stripe\"}, {\"id\": 40800, \"name\": \"silver chain-link gate\"}, {\"id\": 40801, \"name\": \"a yellow and red auto-rickshaw\"}, {\"id\": 40802, \"name\": \"a pink vase\"}, {\"id\": 40803, \"name\": \"the silver mesh cage\"}, {\"id\": 40804, \"name\": \"a green plastic stool\"}, {\"id\": 40805, \"name\": \"the dark wood door\"}, {\"id\": 40806, \"name\": \"the black electrical cord\"}, {\"id\": 40807, \"name\": \"chain-link design\"}, {\"id\": 40808, \"name\": \"a yellow platform\"}, {\"id\": 40809, \"name\": \"long-handled shovel\"}, {\"id\": 40810, \"name\": \"the standing child\"}, {\"id\": 40811, \"name\": \"a green rim\"}, {\"id\": 40812, \"name\": \"a support column\"}, {\"id\": 40813, \"name\": \"the white-painted crosswalk\"}, {\"id\": 40814, \"name\": \"green turf area\"}, {\"id\": 40815, \"name\": \"a yellow framing\"}, {\"id\": 40816, \"name\": \"the square grid pattern\"}, {\"id\": 40817, \"name\": \"the striped pole\"}, {\"id\": 40818, \"name\": \"a truck's tank\"}, {\"id\": 40819, \"name\": \"red, humanoid figure\"}, {\"id\": 40820, \"name\": \"a gymnastic ball\"}, {\"id\": 40821, \"name\": \"dark-colored rock\"}, {\"id\": 40822, \"name\": \"blue drap\"}, {\"id\": 40823, \"name\": \"a white sack\"}, {\"id\": 40824, \"name\": \"type of fern\"}, {\"id\": 40825, \"name\": \"colorful glow stick\"}, {\"id\": 40826, \"name\": \"hutch\"}, {\"id\": 40827, \"name\": \"an orange and white uniform\"}, {\"id\": 40828, \"name\": \"military vehicle\"}, {\"id\": 40829, \"name\": \"a yellow spring\"}, {\"id\": 40830, \"name\": \"a goat's head\"}, {\"id\": 40831, \"name\": \"a white knob\"}, {\"id\": 40832, \"name\": \"the prominent red metal frame\"}, {\"id\": 40833, \"name\": \"a busy sidewalk\"}, {\"id\": 40834, \"name\": \"orange and black sign\"}, {\"id\": 40835, \"name\": \"a golden dragon\"}, {\"id\": 40836, \"name\": \"a lush green mountain range\"}, {\"id\": 40837, \"name\": \"a cooking surface\"}, {\"id\": 40838, \"name\": \"the blue color of the container\"}, {\"id\": 40839, \"name\": \"black and yellow baseball cap\"}, {\"id\": 40840, \"name\": \"white hatchback car\"}, {\"id\": 40841, \"name\": \"outstretched wing\"}, {\"id\": 40842, \"name\": \"a brown sign\"}, {\"id\": 40843, \"name\": \"the pink scooter\"}, {\"id\": 40844, \"name\": \"the orange and white ocellaris clownfish\"}, {\"id\": 40845, \"name\": \"the flame on the pole\"}, {\"id\": 40846, \"name\": \"gray concrete overpass\"}, {\"id\": 40847, \"name\": \"the green and purple circular area\"}, {\"id\": 40848, \"name\": \"a yellow fish head\"}, {\"id\": 40849, \"name\": \"a red lever\"}, {\"id\": 40850, \"name\": \"back of the truck\"}, {\"id\": 40851, \"name\": \"the dark rock\"}, {\"id\": 40852, \"name\": \"anime-style character\"}, {\"id\": 40853, \"name\": \"the gray mat\"}, {\"id\": 40854, \"name\": \"a green seaweed\"}, {\"id\": 40855, \"name\": \"orange marking\"}, {\"id\": 40856, \"name\": \"a brown pot\"}, {\"id\": 40857, \"name\": \"the orange bikini top\"}, {\"id\": 40858, \"name\": \"a folded clothe\"}, {\"id\": 40859, \"name\": \"the red scooter\"}, {\"id\": 40860, \"name\": \"the dark red cab\"}, {\"id\": 40861, \"name\": \"a piglet\"}, {\"id\": 40862, \"name\": \"a single go-kart\"}, {\"id\": 40863, \"name\": \"a dark jean\"}, {\"id\": 40864, \"name\": \"the brick planter\"}, {\"id\": 40865, \"name\": \"the yellow and blue saddle\"}, {\"id\": 40866, \"name\": \"hard material\"}, {\"id\": 40867, \"name\": \"a yellow porsche 911\"}, {\"id\": 40868, \"name\": \"the rocky brown peak\"}, {\"id\": 40869, \"name\": \"the white football\"}, {\"id\": 40870, \"name\": \"the white archway\"}, {\"id\": 40871, \"name\": \"the pink and white shoe\"}, {\"id\": 40872, \"name\": \"grey hijab\"}, {\"id\": 40873, \"name\": \"a metal tower\"}, {\"id\": 40874, \"name\": \"a blue and white car\"}, {\"id\": 40875, \"name\": \"boy's legs\"}, {\"id\": 40876, \"name\": \"the white dish\"}, {\"id\": 40877, \"name\": \"the tan-colored carpet\"}, {\"id\": 40878, \"name\": \"a chain-link net\"}, {\"id\": 40879, \"name\": \"a gray umbrella\"}, {\"id\": 40880, \"name\": \"the green and orange mat\"}, {\"id\": 40881, \"name\": \"green tag\"}, {\"id\": 40882, \"name\": \"a blue tray\"}, {\"id\": 40883, \"name\": \"a playing area\"}, {\"id\": 40884, \"name\": \"red sari\"}, {\"id\": 40885, \"name\": \"a pool float\"}, {\"id\": 40886, \"name\": \"a rack of free weight\"}, {\"id\": 40887, \"name\": \"white bird\"}, {\"id\": 40888, \"name\": \"playground equipment\"}, {\"id\": 40889, \"name\": \"the pink hello kitty-themed tricycle\"}, {\"id\": 40890, \"name\": \"a curved backrest\"}, {\"id\": 40891, \"name\": \"a pink interior\"}, {\"id\": 40892, \"name\": \"the yellow and orange character\"}, {\"id\": 40893, \"name\": \"purple fish\"}, {\"id\": 40894, \"name\": \"white drum\"}, {\"id\": 40895, \"name\": \"the man in a white robe\"}, {\"id\": 40896, \"name\": \"a crisp white jersey\"}, {\"id\": 40897, \"name\": \"the blue bumper\"}, {\"id\": 40898, \"name\": \"a compartment\"}, {\"id\": 40899, \"name\": \"the round fan\"}, {\"id\": 40900, \"name\": \"red semi-truck\"}, {\"id\": 40901, \"name\": \"a gray uniform\"}, {\"id\": 40902, \"name\": \"the red heart-shaped eye\"}, {\"id\": 40903, \"name\": \"lone monster truck\"}, {\"id\": 40904, \"name\": \"the dark pickup truck\"}, {\"id\": 40905, \"name\": \"the bumper car ride\"}, {\"id\": 40906, \"name\": \"the lone balloon\"}, {\"id\": 40907, \"name\": \"the triangular formation\"}, {\"id\": 40908, \"name\": \"a blue mask\"}, {\"id\": 40909, \"name\": \"a red van\"}, {\"id\": 40910, \"name\": \"the left side wheel\"}, {\"id\": 40911, \"name\": \"a metal shelf\"}, {\"id\": 40912, \"name\": \"gardening tool\"}, {\"id\": 40913, \"name\": \"a purple and red design\"}, {\"id\": 40914, \"name\": \"a green plastic mesh\"}, {\"id\": 40915, \"name\": \"decorative stone\"}, {\"id\": 40916, \"name\": \"dark jersey\"}, {\"id\": 40917, \"name\": \"concrete bank\"}, {\"id\": 40918, \"name\": \"green garbage truck\"}, {\"id\": 40919, \"name\": \"blue fin\"}, {\"id\": 40920, \"name\": \"black hooded cloak\"}, {\"id\": 40921, \"name\": \"a ship's upper deck\"}, {\"id\": 40922, \"name\": \"a back of a person's head\"}, {\"id\": 40923, \"name\": \"red stone\"}, {\"id\": 40924, \"name\": \"a yellow rubber boot\"}, {\"id\": 40925, \"name\": \"the blue and orange scooter\"}, {\"id\": 40926, \"name\": \"the child's toy scooter\"}, {\"id\": 40927, \"name\": \"the green and black patterned shirt\"}, {\"id\": 40928, \"name\": \"a blue and gray shirt\"}, {\"id\": 40929, \"name\": \"blue heart\"}, {\"id\": 40930, \"name\": \"a purple and blue logo\"}, {\"id\": 40931, \"name\": \"red and blue uniform\"}, {\"id\": 40932, \"name\": \"the gray ramp\"}, {\"id\": 40933, \"name\": \"a bag of metal\"}, {\"id\": 40934, \"name\": \"white plastic jug\"}, {\"id\": 40935, \"name\": \"the formation of military jet\"}, {\"id\": 40936, \"name\": \"red and white short\"}, {\"id\": 40937, \"name\": \"the prominent dental advertisement\"}, {\"id\": 40938, \"name\": \"the white bird\"}, {\"id\": 40939, \"name\": \"man on the stool\"}, {\"id\": 40940, \"name\": \"a red shirt with white stripe\"}, {\"id\": 40941, \"name\": \"a teacher\"}, {\"id\": 40942, \"name\": \"a red and white striped border\"}, {\"id\": 40943, \"name\": \"a black dog crate\"}, {\"id\": 40944, \"name\": \"a gray croc\"}, {\"id\": 40945, \"name\": \"a kurta\"}, {\"id\": 40946, \"name\": \"the dark red surface\"}, {\"id\": 40947, \"name\": \"white vessel\"}, {\"id\": 40948, \"name\": \"bicycles and tricycle\"}, {\"id\": 40949, \"name\": \"silver vw logo\"}, {\"id\": 40950, \"name\": \"a pink drap\"}, {\"id\": 40951, \"name\": \"closed metal security shutter\"}, {\"id\": 40952, \"name\": \"the concrete pole\"}, {\"id\": 40953, \"name\": \"grey sneaker\"}, {\"id\": 40954, \"name\": \"a white hose\"}, {\"id\": 40955, \"name\": \"a lush green patch of vegetation\"}, {\"id\": 40956, \"name\": \"a black chinese character\"}, {\"id\": 40957, \"name\": \"brown rug\"}, {\"id\": 40958, \"name\": \"red kfc logo\"}, {\"id\": 40959, \"name\": \"red and yellow canister\"}, {\"id\": 40960, \"name\": \"the white trainer\"}, {\"id\": 40961, \"name\": \"the black air conditioning unit\"}, {\"id\": 40962, \"name\": \"a red and gold floral pattern\"}, {\"id\": 40963, \"name\": \"yellow spring\"}, {\"id\": 40964, \"name\": \"tractor's front tire\"}, {\"id\": 40965, \"name\": \"a flat base\"}, {\"id\": 40966, \"name\": \"the prairie dog\"}, {\"id\": 40967, \"name\": \"snowy surface\"}, {\"id\": 40968, \"name\": \"a blue dolly\"}, {\"id\": 40969, \"name\": \"a brown cowboy hat\"}, {\"id\": 40970, \"name\": \"metal communication tower\"}, {\"id\": 40971, \"name\": \"tan stripe\"}, {\"id\": 40972, \"name\": \"the white and black checkered shirt\"}, {\"id\": 40973, \"name\": \"the multicolored umbrella\"}, {\"id\": 40974, \"name\": \"the purple sarong\"}, {\"id\": 40975, \"name\": \"a rear of the handlebar\"}, {\"id\": 40976, \"name\": \"dark blue stripe\"}, {\"id\": 40977, \"name\": \"cargo container\"}, {\"id\": 40978, \"name\": \"an orange glow\"}, {\"id\": 40979, \"name\": \"an additional person\"}, {\"id\": 40980, \"name\": \"a foreground kart\"}, {\"id\": 40981, \"name\": \"a striking red and white tram\"}, {\"id\": 40982, \"name\": \"tarpaulin\"}, {\"id\": 40983, \"name\": \"the silhouette of person\"}, {\"id\": 40984, \"name\": \"a yellow and blue machine\"}, {\"id\": 40985, \"name\": \"the dark blue car\"}, {\"id\": 40986, \"name\": \"the worm\"}, {\"id\": 40987, \"name\": \"a red bowl\"}, {\"id\": 40988, \"name\": \"adult pig\"}, {\"id\": 40989, \"name\": \"a green arrow\"}, {\"id\": 40990, \"name\": \"the grey stone\"}, {\"id\": 40991, \"name\": \"a marble-like pattern\"}, {\"id\": 40992, \"name\": \"white and black pigeon\"}, {\"id\": 40993, \"name\": \"a red cycling shoe\"}, {\"id\": 40994, \"name\": \"an orange paint\"}, {\"id\": 40995, \"name\": \"lime-green bag\"}, {\"id\": 40996, \"name\": \"the white speck\"}, {\"id\": 40997, \"name\": \"the round body\"}, {\"id\": 40998, \"name\": \"an orange jumpsuit\"}, {\"id\": 40999, \"name\": \"a green weed\"}, {\"id\": 41000, \"name\": \"a silver-colored leg\"}, {\"id\": 41001, \"name\": \"colorful curtain\"}, {\"id\": 41002, \"name\": \"softball bat\"}, {\"id\": 41003, \"name\": \"an orange brick\"}, {\"id\": 41004, \"name\": \"the yellow baseboard\"}, {\"id\": 41005, \"name\": \"the black-and-white vertical stripe\"}, {\"id\": 41006, \"name\": \"white utility pole\"}, {\"id\": 41007, \"name\": \"the player's inventory\"}, {\"id\": 41008, \"name\": \"the spare part\"}, {\"id\": 41009, \"name\": \"the green knight\"}, {\"id\": 41010, \"name\": \"the vibrant flag\"}, {\"id\": 41011, \"name\": \"plant material\"}, {\"id\": 41012, \"name\": \"a blue and teal embellishment\"}, {\"id\": 41013, \"name\": \"a pointed top\"}, {\"id\": 41014, \"name\": \"tier of seating\"}, {\"id\": 41015, \"name\": \"a batting helmet\"}, {\"id\": 41016, \"name\": \"bright blue glow\"}, {\"id\": 41017, \"name\": \"the bus's body\"}, {\"id\": 41018, \"name\": \"the wooden barn\"}, {\"id\": 41019, \"name\": \"a red counter\"}, {\"id\": 41020, \"name\": \"the chain-link mesh\"}, {\"id\": 41021, \"name\": \"a metal rack\"}, {\"id\": 41022, \"name\": \"a pink cushion\"}, {\"id\": 41023, \"name\": \"colorful floatie\"}, {\"id\": 41024, \"name\": \"red, yellow, and blue striped awning\"}, {\"id\": 41025, \"name\": \"an individual's face\"}, {\"id\": 41026, \"name\": \"the dark brown beam\"}, {\"id\": 41027, \"name\": \"a pigeon's head\"}, {\"id\": 41028, \"name\": \"the striking yellow flower\"}, {\"id\": 41029, \"name\": \"a tire manufacturer\"}, {\"id\": 41030, \"name\": \"dense forested hillside\"}, {\"id\": 41031, \"name\": \"the black and white animal\"}, {\"id\": 41032, \"name\": \"the white back door\"}, {\"id\": 41033, \"name\": \"green bowtie\"}, {\"id\": 41034, \"name\": \"the black bucket with a yellow handle\"}, {\"id\": 41035, \"name\": \"silver-colored metal frame\"}, {\"id\": 41036, \"name\": \"a traditional indonesian skirt\"}, {\"id\": 41037, \"name\": \"pink floral top\"}, {\"id\": 41038, \"name\": \"red tarp\"}, {\"id\": 41039, \"name\": \"white stove top\"}, {\"id\": 41040, \"name\": \"the white debris\"}, {\"id\": 41041, \"name\": \"a silver scooter\"}, {\"id\": 41042, \"name\": \"large, black fan\"}, {\"id\": 41043, \"name\": \"the prominent white crosswalk\"}, {\"id\": 41044, \"name\": \"dark gray border\"}, {\"id\": 41045, \"name\": \"a plate of vegetable\"}, {\"id\": 41046, \"name\": \"a metal lid\"}, {\"id\": 41047, \"name\": \"the beige vest\"}, {\"id\": 41048, \"name\": \"a woman in a purple shirt\"}, {\"id\": 41049, \"name\": \"a food bowl\"}, {\"id\": 41050, \"name\": \"the piece of printing equipment\"}, {\"id\": 41051, \"name\": \"blue-painted fingernail\"}, {\"id\": 41052, \"name\": \"the shell\"}, {\"id\": 41053, \"name\": \"the cartoon-style mural\"}, {\"id\": 41054, \"name\": \"green handle\"}, {\"id\": 41055, \"name\": \"round wooden piece\"}, {\"id\": 41056, \"name\": \"circular mirror\"}, {\"id\": 41057, \"name\": \"a red billboard\"}, {\"id\": 41058, \"name\": \"rear-facing license plate\"}, {\"id\": 41059, \"name\": \"the black appliance\"}, {\"id\": 41060, \"name\": \"dark blue bag\"}, {\"id\": 41061, \"name\": \"the darker patch\"}, {\"id\": 41062, \"name\": \"the gray gate\"}, {\"id\": 41063, \"name\": \"a blue rain boot\"}, {\"id\": 41064, \"name\": \"orange seat\"}, {\"id\": 41065, \"name\": \"a yellow cube\"}, {\"id\": 41066, \"name\": \"tail section\"}, {\"id\": 41067, \"name\": \"image of other player\"}, {\"id\": 41068, \"name\": \"the volleyball short\"}, {\"id\": 41069, \"name\": \"a white basketball hoop\"}, {\"id\": 41070, \"name\": \"a lifeboat\"}, {\"id\": 41071, \"name\": \"wooden boat\"}, {\"id\": 41072, \"name\": \"direction of traffic flow\"}, {\"id\": 41073, \"name\": \"oversized red and yellow shoe\"}, {\"id\": 41074, \"name\": \"red-brown patch\"}, {\"id\": 41075, \"name\": \"the dark blue sweatshirt\"}, {\"id\": 41076, \"name\": \"a windsurfing board\"}, {\"id\": 41077, \"name\": \"an orange box\"}, {\"id\": 41078, \"name\": \"a yellow front\"}, {\"id\": 41079, \"name\": \"person's legs and feet\"}, {\"id\": 41080, \"name\": \"white globe\"}, {\"id\": 41081, \"name\": \"the yellow-painted crosswalk\"}, {\"id\": 41082, \"name\": \"the traditional thai footwear\"}, {\"id\": 41083, \"name\": \"red and white taxi\"}, {\"id\": 41084, \"name\": \"front of the vehicle\"}, {\"id\": 41085, \"name\": \"a disposable face mask\"}, {\"id\": 41086, \"name\": \"single sphere\"}, {\"id\": 41087, \"name\": \"the camouflage hat\"}, {\"id\": 41088, \"name\": \"the yellow toe\"}, {\"id\": 41089, \"name\": \"a piece of heavy machinery\"}, {\"id\": 41090, \"name\": \"the white lattice-patterned mesh\"}, {\"id\": 41091, \"name\": \"the black-and-white shoe\"}, {\"id\": 41092, \"name\": \"a woman in a pink top\"}, {\"id\": 41093, \"name\": \"a green sport car\"}, {\"id\": 41094, \"name\": \"lone race car\"}, {\"id\": 41095, \"name\": \"the ornate design\"}, {\"id\": 41096, \"name\": \"the blue tentacle-like decoration\"}, {\"id\": 41097, \"name\": \"the pink and white shirt\"}, {\"id\": 41098, \"name\": \"yellow head\"}, {\"id\": 41099, \"name\": \"a floral top\"}, {\"id\": 41100, \"name\": \"front tire\"}, {\"id\": 41101, \"name\": \"a beautiful bouquet of flower\"}, {\"id\": 41102, \"name\": \"gold crown\"}, {\"id\": 41103, \"name\": \"a red and white cone\"}, {\"id\": 41104, \"name\": \"brown cowboy hat\"}, {\"id\": 41105, \"name\": \"checkered flag logo\"}, {\"id\": 41106, \"name\": \"a black button-up shirt\"}, {\"id\": 41107, \"name\": \"the women's head\"}, {\"id\": 41108, \"name\": \"pigeon coop\"}, {\"id\": 41109, \"name\": \"red and orange patterned sash\"}, {\"id\": 41110, \"name\": \"paved asphalt\"}, {\"id\": 41111, \"name\": \"thick trunk\"}, {\"id\": 41112, \"name\": \"inflatable boat\"}, {\"id\": 41113, \"name\": \"the blue dinosaur\"}, {\"id\": 41114, \"name\": \"a red and white tip\"}, {\"id\": 41115, \"name\": \"the man wearing a white face mask\"}, {\"id\": 41116, \"name\": \"a grey slat\"}, {\"id\": 41117, \"name\": \"a large, blue, plastic tube\"}, {\"id\": 41118, \"name\": \"the white windshield\"}, {\"id\": 41119, \"name\": \"metal truss\"}, {\"id\": 41120, \"name\": \"the yellow collar\"}, {\"id\": 41121, \"name\": \"a character jinx\"}, {\"id\": 41122, \"name\": \"white legging\"}, {\"id\": 41123, \"name\": \"gingerbread man\"}, {\"id\": 41124, \"name\": \"gold dragon\"}, {\"id\": 41125, \"name\": \"yellow tank top\"}, {\"id\": 41126, \"name\": \"the red center\"}, {\"id\": 41127, \"name\": \"the red plastic basket\"}, {\"id\": 41128, \"name\": \"the statue of buddha\"}, {\"id\": 41129, \"name\": \"the sound system setup\"}, {\"id\": 41130, \"name\": \"guppy\"}, {\"id\": 41131, \"name\": \"a bright, white glare\"}, {\"id\": 41132, \"name\": \"yellow marking\"}, {\"id\": 41133, \"name\": \"the yellow hexagonal hole\"}, {\"id\": 41134, \"name\": \"the red gas can\"}, {\"id\": 41135, \"name\": \"a gray brick\"}, {\"id\": 41136, \"name\": \"a black-and-white jersey\"}, {\"id\": 41137, \"name\": \"neck of the violin\"}, {\"id\": 41138, \"name\": \"a man in a gray hoodie\"}, {\"id\": 41139, \"name\": \"black and yellow coil spring\"}, {\"id\": 41140, \"name\": \"the elaborate floral arrangement\"}, {\"id\": 41141, \"name\": \"blurring of the face\"}, {\"id\": 41142, \"name\": \"pebble\"}, {\"id\": 41143, \"name\": \"the cat litter box\"}, {\"id\": 41144, \"name\": \"a white circular shape\"}, {\"id\": 41145, \"name\": \"a blue mouth\"}, {\"id\": 41146, \"name\": \"dark stripe\"}, {\"id\": 41147, \"name\": \"intricate peacock design\"}, {\"id\": 41148, \"name\": \"the orange chopstick\"}, {\"id\": 41149, \"name\": \"the faint haze of smoke\"}, {\"id\": 41150, \"name\": \"colorful kite\"}, {\"id\": 41151, \"name\": \"the black and orange stripe\"}, {\"id\": 41152, \"name\": \"the bundle of wire mesh\"}, {\"id\": 41153, \"name\": \"the red and gold costume\"}, {\"id\": 41154, \"name\": \"a yellow caution cone\"}, {\"id\": 41155, \"name\": \"woman's shirt\"}, {\"id\": 41156, \"name\": \"purple cup\"}, {\"id\": 41157, \"name\": \"lattice pattern\"}, {\"id\": 41158, \"name\": \"the spring\"}, {\"id\": 41159, \"name\": \"an electrical box\"}, {\"id\": 41160, \"name\": \"a dark green section\"}, {\"id\": 41161, \"name\": \"a safety marker\"}, {\"id\": 41162, \"name\": \"the certificate\"}, {\"id\": 41163, \"name\": \"flat brim\"}, {\"id\": 41164, \"name\": \"a distant cityscape\"}, {\"id\": 41165, \"name\": \"colorful striped design\"}, {\"id\": 41166, \"name\": \"yellow pedal\"}, {\"id\": 41167, \"name\": \"black and blue motorcycle\"}, {\"id\": 41168, \"name\": \"a vibrant red headscarf\"}, {\"id\": 41169, \"name\": \"the temple's architecture\"}, {\"id\": 41170, \"name\": \"black long-sleeve shirt\"}, {\"id\": 41171, \"name\": \"an athletic short\"}, {\"id\": 41172, \"name\": \"a red and black boxing glove\"}, {\"id\": 41173, \"name\": \"the brown toy dump truck\"}, {\"id\": 41174, \"name\": \"open garage\"}, {\"id\": 41175, \"name\": \"a white globe lamp\"}, {\"id\": 41176, \"name\": \"the salmon\"}, {\"id\": 41177, \"name\": \"the giraffe's neck\"}, {\"id\": 41178, \"name\": \"the pulley system\"}, {\"id\": 41179, \"name\": \"a red and green litter box\"}, {\"id\": 41180, \"name\": \"rear end of the bus\"}, {\"id\": 41181, \"name\": \"colorful paper parrot\"}, {\"id\": 41182, \"name\": \"wood plank\"}, {\"id\": 41183, \"name\": \"a purple character\"}, {\"id\": 41184, \"name\": \"the bed of white and black pebble\"}, {\"id\": 41185, \"name\": \"the muddy parking\"}, {\"id\": 41186, \"name\": \"a pink horse\"}, {\"id\": 41187, \"name\": \"a white and blue shirt\"}, {\"id\": 41188, \"name\": \"yellow tire\"}, {\"id\": 41189, \"name\": \"a white and yellow motorcycle\"}, {\"id\": 41190, \"name\": \"the blue, slatted surface\"}, {\"id\": 41191, \"name\": \"pink and red inflatable tube\"}, {\"id\": 41192, \"name\": \"the dense forested hillside\"}, {\"id\": 41193, \"name\": \"the vertical tube\"}, {\"id\": 41194, \"name\": \"dark purple cylindrical piece of suet\"}, {\"id\": 41195, \"name\": \"the vibrant red and gold patterned carpet\"}, {\"id\": 41196, \"name\": \"pink swimsuit\"}, {\"id\": 41197, \"name\": \"bright sign\"}, {\"id\": 41198, \"name\": \"the orange warning label\"}, {\"id\": 41199, \"name\": \"the orange cord\"}, {\"id\": 41200, \"name\": \"the oversized cartoon character\"}, {\"id\": 41201, \"name\": \"a pointed peak\"}, {\"id\": 41202, \"name\": \"a raceway\"}, {\"id\": 41203, \"name\": \"the patterned pant\"}, {\"id\": 41204, \"name\": \"the camping gear\"}, {\"id\": 41205, \"name\": \"the maroon seat\"}, {\"id\": 41206, \"name\": \"a dot\"}, {\"id\": 41207, \"name\": \"bottom of the tank\"}, {\"id\": 41208, \"name\": \"an inflatable figure\"}, {\"id\": 41209, \"name\": \"a black crop top\"}, {\"id\": 41210, \"name\": \"car in the foreground\"}, {\"id\": 41211, \"name\": \"a black-framed, white sign\"}, {\"id\": 41212, \"name\": \"a black and red shirt\"}, {\"id\": 41213, \"name\": \"a blue table\"}, {\"id\": 41214, \"name\": \"the reflective glass surface\"}, {\"id\": 41215, \"name\": \"the plume of white smoke\"}, {\"id\": 41216, \"name\": \"the long, curved ramp\"}, {\"id\": 41217, \"name\": \"rider's white shirt\"}, {\"id\": 41218, \"name\": \"the blue and black jacket\"}, {\"id\": 41219, \"name\": \"statue of mythological creature\"}, {\"id\": 41220, \"name\": \"copper-colored tray\"}, {\"id\": 41221, \"name\": \"yellow spike\"}, {\"id\": 41222, \"name\": \"the roll of yellow material\"}, {\"id\": 41223, \"name\": \"black-and-white striped barber's cape\"}, {\"id\": 41224, \"name\": \"yellow and black checkered marking\"}, {\"id\": 41225, \"name\": \"blue sandal\"}, {\"id\": 41226, \"name\": \"the pink and blue pole\"}, {\"id\": 41227, \"name\": \"double-decker bus\"}, {\"id\": 41228, \"name\": \"the wooden sign\"}, {\"id\": 41229, \"name\": \"a brown feather\"}, {\"id\": 41230, \"name\": \"green fencing\"}, {\"id\": 41231, \"name\": \"cotton ball\"}, {\"id\": 41232, \"name\": \"neon yellow kayak\"}, {\"id\": 41233, \"name\": \"the green handle\"}, {\"id\": 41234, \"name\": \"a pink advertisement\"}, {\"id\": 41235, \"name\": \"a dragon head\"}, {\"id\": 41236, \"name\": \"hole in the center\"}, {\"id\": 41237, \"name\": \"a black cooking surface\"}, {\"id\": 41238, \"name\": \"an orange curtain\"}, {\"id\": 41239, \"name\": \"the neon green shirt\"}, {\"id\": 41240, \"name\": \"traffic signal post\"}, {\"id\": 41241, \"name\": \"the border\"}, {\"id\": 41242, \"name\": \"walmart logo\"}, {\"id\": 41243, \"name\": \"grey cap\"}, {\"id\": 41244, \"name\": \"a man with the cart\"}, {\"id\": 41245, \"name\": \"the yellow and blue tower\"}, {\"id\": 41246, \"name\": \"a red sculpture\"}, {\"id\": 41247, \"name\": \"blue, collared shirt\"}, {\"id\": 41248, \"name\": \"cat food\"}, {\"id\": 41249, \"name\": \"blue hyundai sedan\"}, {\"id\": 41250, \"name\": \"the brown bollard\"}, {\"id\": 41251, \"name\": \"the polished concrete\"}, {\"id\": 41252, \"name\": \"blue and green swim trunk\"}, {\"id\": 41253, \"name\": \"the metal cooking surface\"}, {\"id\": 41254, \"name\": \"soda bottle\"}, {\"id\": 41255, \"name\": \"the white goal post\"}, {\"id\": 41256, \"name\": \"the orange basketball\"}, {\"id\": 41257, \"name\": \"the ice cube\"}, {\"id\": 41258, \"name\": \"a cartoonish bear's head\"}, {\"id\": 41259, \"name\": \"the denim-style outfit\"}, {\"id\": 41260, \"name\": \"a blue boxing glove\"}, {\"id\": 41261, \"name\": \"a front of the bus\"}, {\"id\": 41262, \"name\": \"a darkened border\"}, {\"id\": 41263, \"name\": \"a white sheet of paper\"}, {\"id\": 41264, \"name\": \"raised mound\"}, {\"id\": 41265, \"name\": \"a gravel pit\"}, {\"id\": 41266, \"name\": \"a green fin\"}, {\"id\": 41267, \"name\": \"brown head\"}, {\"id\": 41268, \"name\": \"yellow stair\"}, {\"id\": 41269, \"name\": \"dark jeans\"}, {\"id\": 41270, \"name\": \"red turban\"}, {\"id\": 41271, \"name\": \"the person on bicycle\"}, {\"id\": 41272, \"name\": \"a Motorcyclist\"}, {\"id\": 41273, \"name\": \"vibrant red comb\"}, {\"id\": 41274, \"name\": \"blue and white surgical mask\"}, {\"id\": 41275, \"name\": \"the thatched umbrella\"}, {\"id\": 41276, \"name\": \"the blue and yellow circle\"}, {\"id\": 41277, \"name\": \"the registration number\"}, {\"id\": 41278, \"name\": \"wooden bar\"}, {\"id\": 41279, \"name\": \"yellow and black striped concrete barrier\"}, {\"id\": 41280, \"name\": \"wool processing tool\"}, {\"id\": 41281, \"name\": \"the gold bodice\"}, {\"id\": 41282, \"name\": \"a monk\"}, {\"id\": 41283, \"name\": \"a cyclist in a blue shirt\"}, {\"id\": 41284, \"name\": \"an orange, green, and white flag\"}, {\"id\": 41285, \"name\": \"a plates\"}, {\"id\": 41286, \"name\": \"picture of a person\"}, {\"id\": 41287, \"name\": \"a pink flip-flop\"}, {\"id\": 41288, \"name\": \"a light blue knee-high sock\"}, {\"id\": 41289, \"name\": \"the stacks of plastic bottle\"}, {\"id\": 41290, \"name\": \"dark-colored awning\"}, {\"id\": 41291, \"name\": \"purple flag\"}, {\"id\": 41292, \"name\": \"rollerblade\"}, {\"id\": 41293, \"name\": \"red padding\"}, {\"id\": 41294, \"name\": \"blue and white marching band uniform\"}, {\"id\": 41295, \"name\": \"the red digital display\"}, {\"id\": 41296, \"name\": \"the low concrete curb\"}, {\"id\": 41297, \"name\": \"the inner tube\"}, {\"id\": 41298, \"name\": \"brain coral\"}, {\"id\": 41299, \"name\": \"the kitchenware\"}, {\"id\": 41300, \"name\": \"the golden horse\"}, {\"id\": 41301, \"name\": \"brown marking\"}, {\"id\": 41302, \"name\": \"the seated woman\"}, {\"id\": 41303, \"name\": \"the tiered seating\"}, {\"id\": 41304, \"name\": \"bright pink pant\"}, {\"id\": 41305, \"name\": \"the line of cars\"}, {\"id\": 41306, \"name\": \"the white chalk drawing\"}, {\"id\": 41307, \"name\": \"a yellow and silver safety vest\"}, {\"id\": 41308, \"name\": \"a gold headpiece\"}, {\"id\": 41309, \"name\": \"a participant\"}, {\"id\": 41310, \"name\": \"dominoes\"}, {\"id\": 41311, \"name\": \"a carriage's wheel\"}, {\"id\": 41312, \"name\": \"orange and white traffic cone\"}, {\"id\": 41313, \"name\": \"the castle\"}, {\"id\": 41314, \"name\": \"the men on scooters\"}, {\"id\": 41315, \"name\": \"black tutu\"}, {\"id\": 41316, \"name\": \"the yellow headscarf\"}, {\"id\": 41317, \"name\": \"a green pedestrian signal\"}, {\"id\": 41318, \"name\": \"the seated individual\"}, {\"id\": 41319, \"name\": \"the pink piece of paper\"}, {\"id\": 41320, \"name\": \"a white short-sleeved shirt\"}, {\"id\": 41321, \"name\": \"windsurfer\"}, {\"id\": 41322, \"name\": \"white, circular table\"}, {\"id\": 41323, \"name\": \"the orange and green scarf\"}, {\"id\": 41324, \"name\": \"sedan's rear window\"}, {\"id\": 41325, \"name\": \"distinctive orange tiled awning\"}, {\"id\": 41326, \"name\": \"a martial arts practitioner\"}, {\"id\": 41327, \"name\": \"the traffic lane\"}, {\"id\": 41328, \"name\": \"a red robe\"}, {\"id\": 41329, \"name\": \"hockey gear\"}, {\"id\": 41330, \"name\": \"the foot of a chicken or turkey\"}, {\"id\": 41331, \"name\": \"an ornate red and gold structure\"}, {\"id\": 41332, \"name\": \"otter\"}, {\"id\": 41333, \"name\": \"a blue bumper car\"}, {\"id\": 41334, \"name\": \"the woman in a long dress\"}, {\"id\": 41335, \"name\": \"orange life jacket\"}, {\"id\": 41336, \"name\": \"the parked cars and vehicle\"}, {\"id\": 41337, \"name\": \"long train car\"}, {\"id\": 41338, \"name\": \"a knobby tread pattern\"}, {\"id\": 41339, \"name\": \"blue bucket hat\"}, {\"id\": 41340, \"name\": \"the person in a yellow top\"}, {\"id\": 41341, \"name\": \"a blue ear\"}, {\"id\": 41342, \"name\": \"the blue-dressed woman\"}, {\"id\": 41343, \"name\": \"a woman in a black top\"}, {\"id\": 41344, \"name\": \"red and white tail\"}, {\"id\": 41345, \"name\": \"the inflatable\"}, {\"id\": 41346, \"name\": \"an airport tarmac\"}, {\"id\": 41347, \"name\": \"the orange running shoe\"}, {\"id\": 41348, \"name\": \"the green and blue striped sock\"}, {\"id\": 41349, \"name\": \"a salwar kameez outfit\"}, {\"id\": 41350, \"name\": \"the brown rooftop\"}, {\"id\": 41351, \"name\": \"the racer's kart\"}, {\"id\": 41352, \"name\": \"a black trouser\"}, {\"id\": 41353, \"name\": \"the brown leather seat\"}, {\"id\": 41354, \"name\": \"a navy blue skirt\"}, {\"id\": 41355, \"name\": \"a green ramp\"}, {\"id\": 41356, \"name\": \"plastic basket\"}, {\"id\": 41357, \"name\": \"a spanish-style tile roof\"}, {\"id\": 41358, \"name\": \"a vehicle's body\"}, {\"id\": 41359, \"name\": \"play structure\"}, {\"id\": 41360, \"name\": \"the Red Bull logo\"}, {\"id\": 41361, \"name\": \"van's tail light\"}, {\"id\": 41362, \"name\": \"the chrome exhaust pipe\"}, {\"id\": 41363, \"name\": \"metal piece\"}, {\"id\": 41364, \"name\": \"the green and blue component\"}, {\"id\": 41365, \"name\": \"pile of dirt and debris\"}, {\"id\": 41366, \"name\": \"large yellow construction vehicle\"}, {\"id\": 41367, \"name\": \"the silver hatchback\"}, {\"id\": 41368, \"name\": \"the large, arched structure\"}, {\"id\": 41369, \"name\": \"the raceway\"}, {\"id\": 41370, \"name\": \"concrete dock\"}, {\"id\": 41371, \"name\": \"red and white saddle blanket\"}, {\"id\": 41372, \"name\": \"cargo stack\"}, {\"id\": 41373, \"name\": \"an excavator's boom\"}, {\"id\": 41374, \"name\": \"red backboard\"}, {\"id\": 41375, \"name\": \"hull\"}, {\"id\": 41376, \"name\": \"sith trooper\"}, {\"id\": 41377, \"name\": \"a red or neon yellow shirt\"}, {\"id\": 41378, \"name\": \"the head of a seated individual\"}, {\"id\": 41379, \"name\": \"the accordion-like instrument\"}, {\"id\": 41380, \"name\": \"the green neon light\"}, {\"id\": 41381, \"name\": \"the black clarinet\"}, {\"id\": 41382, \"name\": \"the yellow flower headpiece\"}, {\"id\": 41383, \"name\": \"odometer\"}, {\"id\": 41384, \"name\": \"the smaller figure of jesu christ\"}, {\"id\": 41385, \"name\": \"long, green bamboo piece\"}, {\"id\": 41386, \"name\": \"a sauce or ingredient\"}, {\"id\": 41387, \"name\": \"a gold horn\"}, {\"id\": 41388, \"name\": \"a green lighting\"}, {\"id\": 41389, \"name\": \"the scattered yellow ball\"}, {\"id\": 41390, \"name\": \"blue and black mechanism\"}, {\"id\": 41391, \"name\": \"a dark green panel\"}, {\"id\": 41392, \"name\": \"car or motorcycle\"}, {\"id\": 41393, \"name\": \"the white beaded belt\"}, {\"id\": 41394, \"name\": \"a green mat or rug\"}, {\"id\": 41395, \"name\": \"the ornate pink column\"}, {\"id\": 41396, \"name\": \"a tractor parade\"}, {\"id\": 41397, \"name\": \"lumber\"}, {\"id\": 41398, \"name\": \"the car or vehicle\"}, {\"id\": 41399, \"name\": \"the narrow, two-lane road\"}, {\"id\": 41400, \"name\": \"red directional sign\"}, {\"id\": 41401, \"name\": \"aquarium's lighting\"}, {\"id\": 41402, \"name\": \"a tall boot\"}, {\"id\": 41403, \"name\": \"a purple and gray step platform\"}, {\"id\": 41404, \"name\": \"the distinctive red hull\"}, {\"id\": 41405, \"name\": \"the dog pajama\"}, {\"id\": 41406, \"name\": \"a white drumstick\"}, {\"id\": 41407, \"name\": \"a classic car\"}, {\"id\": 41408, \"name\": \"a large, dark blue train engine\"}, {\"id\": 41409, \"name\": \"yellow eye\"}, {\"id\": 41410, \"name\": \"traditional white attire\"}, {\"id\": 41411, \"name\": \"the large black gear\"}, {\"id\": 41412, \"name\": \"blue marking\"}, {\"id\": 41413, \"name\": \"the open red umbrella\"}, {\"id\": 41414, \"name\": \"red lion costume\"}, {\"id\": 41415, \"name\": \"a top tray\"}, {\"id\": 41416, \"name\": \"the red traffic signal\"}, {\"id\": 41417, \"name\": \"brightly colored step\"}, {\"id\": 41418, \"name\": \"floral arrangement\"}, {\"id\": 41419, \"name\": \"humanoid creature\"}, {\"id\": 41420, \"name\": \"a black golf cart\"}, {\"id\": 41421, \"name\": \"pattern of blue snowflake\"}, {\"id\": 41422, \"name\": \"the white piece\"}, {\"id\": 41423, \"name\": \"the digital traffic light\"}, {\"id\": 41424, \"name\": \"an ornate column\"}, {\"id\": 41425, \"name\": \"the pointed hood\"}, {\"id\": 41426, \"name\": \"the sale\"}, {\"id\": 41427, \"name\": \"man on horseback\"}, {\"id\": 41428, \"name\": \"rocket\"}, {\"id\": 41429, \"name\": \"seamstress\"}, {\"id\": 41430, \"name\": \"wooden deck\"}, {\"id\": 41431, \"name\": \"straight section\"}, {\"id\": 41432, \"name\": \"a light brown cow\"}, {\"id\": 41433, \"name\": \"the blue tarp or bag\"}, {\"id\": 41434, \"name\": \"a lush, grassy area\"}, {\"id\": 41435, \"name\": \"the beige curtain\"}, {\"id\": 41436, \"name\": \"blue shield\"}, {\"id\": 41437, \"name\": \"motorcycle's rear light\"}, {\"id\": 41438, \"name\": \"the large glass container\"}, {\"id\": 41439, \"name\": \"black drumhead\"}, {\"id\": 41440, \"name\": \"blue, curved cover\"}, {\"id\": 41441, \"name\": \"the outstretched tentacle\"}, {\"id\": 41442, \"name\": \"the tall glass-fronted structure\"}, {\"id\": 41443, \"name\": \"the green material\"}, {\"id\": 41444, \"name\": \"the red bar stool\"}, {\"id\": 41445, \"name\": \"the player's vehicle\"}, {\"id\": 41446, \"name\": \"front of each bus\"}, {\"id\": 41447, \"name\": \"the large yellow ball\"}, {\"id\": 41448, \"name\": \"a back of the head\"}, {\"id\": 41449, \"name\": \"the flower crown\"}, {\"id\": 41450, \"name\": \"the row of colorful advertisements\"}, {\"id\": 41451, \"name\": \"a designated pedestrian crossing area\"}, {\"id\": 41452, \"name\": \"the large, colorful head\"}, {\"id\": 41453, \"name\": \"long spoon\"}, {\"id\": 41454, \"name\": \"barricade\"}, {\"id\": 41455, \"name\": \"the large, out-of-focus human hand\"}, {\"id\": 41456, \"name\": \"woman in a red swimsuit\"}, {\"id\": 41457, \"name\": \"a gummy worm\"}, {\"id\": 41458, \"name\": \"the black and white checkered accent\"}, {\"id\": 41459, \"name\": \"mode of transportation\"}, {\"id\": 41460, \"name\": \"the light purple shirt\"}, {\"id\": 41461, \"name\": \"the lead rider\"}, {\"id\": 41462, \"name\": \"the yellow concrete barrier\"}, {\"id\": 41463, \"name\": \"a blue and yellow inflatable toy\"}, {\"id\": 41464, \"name\": \"majestic ferri wheel\"}, {\"id\": 41465, \"name\": \"the large heart-shaped design\"}, {\"id\": 41466, \"name\": \"white and blue vehicle\"}, {\"id\": 41467, \"name\": \"the red, blue, and gold umbrella\"}, {\"id\": 41468, \"name\": \"the gray boot\"}, {\"id\": 41469, \"name\": \"the man on horseback\"}, {\"id\": 41470, \"name\": \"person in a white robe\"}, {\"id\": 41471, \"name\": \"a tall green net\"}, {\"id\": 41472, \"name\": \"the small, blue and yellow structure\"}, {\"id\": 41473, \"name\": \"person on motorcycle\"}, {\"id\": 41474, \"name\": \"the teal-colored triangular awning\"}, {\"id\": 41475, \"name\": \"the school child\"}, {\"id\": 41476, \"name\": \"colorful, animal-shaped vehicle\"}, {\"id\": 41477, \"name\": \"the green carpeting\"}, {\"id\": 41478, \"name\": \"the large, green cab\"}, {\"id\": 41479, \"name\": \"the red and black tractor\"}, {\"id\": 41480, \"name\": \"woman in pink\"}, {\"id\": 41481, \"name\": \"woman's raised hand\"}, {\"id\": 41482, \"name\": \"large arched structure\"}, {\"id\": 41483, \"name\": \"a large bus\"}, {\"id\": 41484, \"name\": \"the navy jacket\"}, {\"id\": 41485, \"name\": \"orange sarong\"}, {\"id\": 41486, \"name\": \"flower headpiece\"}, {\"id\": 41487, \"name\": \"large, white sign\"}, {\"id\": 41488, \"name\": \"the gray goose\"}, {\"id\": 41489, \"name\": \"an insect\"}, {\"id\": 41490, \"name\": \"light-colored horse\"}, {\"id\": 41491, \"name\": \"diner\"}, {\"id\": 41492, \"name\": \"a long white rope\"}, {\"id\": 41493, \"name\": \"opaque yellow liquid\"}, {\"id\": 41494, \"name\": \"the large bowling alley\"}, {\"id\": 41495, \"name\": \"a large black knob\"}, {\"id\": 41496, \"name\": \"the safety label\"}, {\"id\": 41497, \"name\": \"bouquet of yellow flower\"}, {\"id\": 41498, \"name\": \"ornate robe\"}, {\"id\": 41499, \"name\": \"the dock or sidewalk\"}, {\"id\": 41500, \"name\": \"the colorful paper decoration\"}, {\"id\": 41501, \"name\": \"black blind\"}, {\"id\": 41502, \"name\": \"the tan, sandy surface\"}, {\"id\": 41503, \"name\": \"the gold dragon design\"}, {\"id\": 41504, \"name\": \"the brown wood paneling trim\"}, {\"id\": 41505, \"name\": \"stacked wooden crate\"}, {\"id\": 41506, \"name\": \"a blue and white jacket\"}, {\"id\": 41507, \"name\": \"the blue and yellow costume\"}, {\"id\": 41508, \"name\": \"the green net\"}, {\"id\": 41509, \"name\": \"a vibrant yellow attire\"}, {\"id\": 41510, \"name\": \"a large, orange, flame-shaped light fixture\"}, {\"id\": 41511, \"name\": \"the beige head covering\"}, {\"id\": 41512, \"name\": \"dragon statue\"}, {\"id\": 41513, \"name\": \"long-sleeved striped shirt\"}, {\"id\": 41514, \"name\": \"green leaf-shaped base\"}, {\"id\": 41515, \"name\": \"a circular cutout\"}, {\"id\": 41516, \"name\": \"the bowl of brightly colored ice cream ball\"}, {\"id\": 41517, \"name\": \"a white fur\"}, {\"id\": 41518, \"name\": \"the round gauge\"}, {\"id\": 41519, \"name\": \"the large metal bell\"}, {\"id\": 41520, \"name\": \"an individual wearing a blue hat\"}, {\"id\": 41521, \"name\": \"the small, white and reddish-brown egg\"}, {\"id\": 41522, \"name\": \"a curved vertical support\"}, {\"id\": 41523, \"name\": \"a boat's hull\"}, {\"id\": 41524, \"name\": \"a sleek black tuxedo\"}, {\"id\": 41525, \"name\": \"a large green vegetable\"}, {\"id\": 41526, \"name\": \"the small, silver metal piece\"}, {\"id\": 41527, \"name\": \"a yellow corn cob\"}, {\"id\": 41528, \"name\": \"the type of percussion instrument\"}, {\"id\": 41529, \"name\": \"the content of the pot\"}, {\"id\": 41530, \"name\": \"a red and white tennis racket\"}, {\"id\": 41531, \"name\": \"a blue and white figure\"}, {\"id\": 41532, \"name\": \"brown animal\"}, {\"id\": 41533, \"name\": \"the yellow front loader\"}, {\"id\": 41534, \"name\": \"green and yellow fish\"}, {\"id\": 41535, \"name\": \"tall light\"}, {\"id\": 41536, \"name\": \"dark brown cloth\"}, {\"id\": 41537, \"name\": \"blue shutter\"}, {\"id\": 41538, \"name\": \"red and gold vest\"}, {\"id\": 41539, \"name\": \"black ribbon\"}, {\"id\": 41540, \"name\": \"artificial lighting\"}, {\"id\": 41541, \"name\": \"the black chain-link fence\"}, {\"id\": 41542, \"name\": \"a clear plastic cup\"}, {\"id\": 41543, \"name\": \"a clear, flexible tube\"}, {\"id\": 41544, \"name\": \"white or light-brown fur\"}, {\"id\": 41545, \"name\": \"industrial machine\"}, {\"id\": 41546, \"name\": \"ornate roof\"}, {\"id\": 41547, \"name\": \"the plaid scarf\"}, {\"id\": 41548, \"name\": \"woman in a black tank top and white short\"}, {\"id\": 41549, \"name\": \"a large black bird\"}, {\"id\": 41550, \"name\": \"a pink soccer ball\"}, {\"id\": 41551, \"name\": \"toy track\"}, {\"id\": 41552, \"name\": \"a blue outfit\"}, {\"id\": 41553, \"name\": \"red thigh-high boot\"}, {\"id\": 41554, \"name\": \"the blue and white jacket\"}, {\"id\": 41555, \"name\": \"red and black bucket hat\"}, {\"id\": 41556, \"name\": \"small kitten\"}, {\"id\": 41557, \"name\": \"a vibrant balloon arch\"}, {\"id\": 41558, \"name\": \"the wooden partition\"}, {\"id\": 41559, \"name\": \"the gray door panel\"}, {\"id\": 41560, \"name\": \"large brown bowl\"}, {\"id\": 41561, \"name\": \"large rectangular picture frame\"}, {\"id\": 41562, \"name\": \"the stainles steel cooking surface\"}, {\"id\": 41563, \"name\": \"the large mural\"}, {\"id\": 41564, \"name\": \"the yellow water gun\"}, {\"id\": 41565, \"name\": \"the rainbow-colored flag\"}, {\"id\": 41566, \"name\": \"large green trash bin\"}, {\"id\": 41567, \"name\": \"the concrete step or block\"}, {\"id\": 41568, \"name\": \"a vibrant orange jacket\"}, {\"id\": 41569, \"name\": \"a large white bucket\"}, {\"id\": 41570, \"name\": \"the small white string\"}, {\"id\": 41571, \"name\": \"the green bowling pin\"}, {\"id\": 41572, \"name\": \"an orange balloon\"}, {\"id\": 41573, \"name\": \"instrument cases and bag\"}, {\"id\": 41574, \"name\": \"the green panel\"}, {\"id\": 41575, \"name\": \"tall, rectangular light fixture\"}, {\"id\": 41576, \"name\": \"the aquatic life\"}, {\"id\": 41577, \"name\": \"scenic highway\"}, {\"id\": 41578, \"name\": \"a game's menu\"}, {\"id\": 41579, \"name\": \"a drum and cymbal set\"}, {\"id\": 41580, \"name\": \"a blue boot\"}, {\"id\": 41581, \"name\": \"a dark gray truck\"}, {\"id\": 41582, \"name\": \"rusty appearance\"}, {\"id\": 41583, \"name\": \"the gold-colored cymbal\"}, {\"id\": 41584, \"name\": \"bright yellow skirt\"}, {\"id\": 41585, \"name\": \"the green wheelbarrow\"}, {\"id\": 41586, \"name\": \"a red and yellow skirt\"}, {\"id\": 41587, \"name\": \"white foam\"}, {\"id\": 41588, \"name\": \"a red tie\"}, {\"id\": 41589, \"name\": \"the bird's feet\"}, {\"id\": 41590, \"name\": \"striking blue light\"}, {\"id\": 41591, \"name\": \"the high-visibility yellow vest\"}, {\"id\": 41592, \"name\": \"small grassy area\"}, {\"id\": 41593, \"name\": \"brown strap\"}, {\"id\": 41594, \"name\": \"curtain or partition\"}, {\"id\": 41595, \"name\": \"person in a striped shirt\"}, {\"id\": 41596, \"name\": \"a large arched opening\"}, {\"id\": 41597, \"name\": \"the traditional indonesian clothing\"}, {\"id\": 41598, \"name\": \"the red and white striped crosswalk\"}, {\"id\": 41599, \"name\": \"bright orange vest\"}, {\"id\": 41600, \"name\": \"tall clock tower\"}, {\"id\": 41601, \"name\": \"green and black tuk-tuk\"}, {\"id\": 41602, \"name\": \"a small metal bowl\"}, {\"id\": 41603, \"name\": \"the large metal spatula\"}, {\"id\": 41604, \"name\": \"the slice of cake\"}, {\"id\": 41605, \"name\": \"the curved track\"}, {\"id\": 41606, \"name\": \"the small hamster\"}, {\"id\": 41607, \"name\": \"a tiered seating area\"}, {\"id\": 41608, \"name\": \"small, pixelated icon\"}, {\"id\": 41609, \"name\": \"the green boat\"}, {\"id\": 41610, \"name\": \"the black controller\"}, {\"id\": 41611, \"name\": \"brown teddy bear\"}, {\"id\": 41612, \"name\": \"a wire chicken coop\"}, {\"id\": 41613, \"name\": \"the predominantly orange body\"}, {\"id\": 41614, \"name\": \"ferry\"}, {\"id\": 41615, \"name\": \"yellow claw mechanism\"}, {\"id\": 41616, \"name\": \"black key\"}, {\"id\": 41617, \"name\": \"purple block\"}, {\"id\": 41618, \"name\": \"blue house\"}, {\"id\": 41619, \"name\": \"distinctive blue and gold onion dome\"}, {\"id\": 41620, \"name\": \"dark awning\"}, {\"id\": 41621, \"name\": \"the yellow cylinder\"}, {\"id\": 41622, \"name\": \"the large machine\"}, {\"id\": 41623, \"name\": \"a british flag\"}, {\"id\": 41624, \"name\": \"the spiderman shirt\"}, {\"id\": 41625, \"name\": \"human figure\"}, {\"id\": 41626, \"name\": \"colorful green and blue monster head\"}, {\"id\": 41627, \"name\": \"large, blue and gold crown\"}, {\"id\": 41628, \"name\": \"a large amount of food\"}, {\"id\": 41629, \"name\": \"a large yellow slide\"}, {\"id\": 41630, \"name\": \"blue and yellow basketball\"}, {\"id\": 41631, \"name\": \"a white poodle\"}, {\"id\": 41632, \"name\": \"a ferry's window\"}, {\"id\": 41633, \"name\": \"the colorful foam block toy\"}, {\"id\": 41634, \"name\": \"bernese mountain dog\"}, {\"id\": 41635, \"name\": \"a woman in a blue top\"}, {\"id\": 41636, \"name\": \"the duck's body\"}, {\"id\": 41637, \"name\": \"the divided compartment\"}, {\"id\": 41638, \"name\": \"red soccer jersey\"}, {\"id\": 41639, \"name\": \"the maroon object\"}, {\"id\": 41640, \"name\": \"red metal shutter door\"}, {\"id\": 41641, \"name\": \"the copper-colored metal disc\"}, {\"id\": 41642, \"name\": \"the worker or craftsman\"}, {\"id\": 41643, \"name\": \"a person's hand or arm\"}, {\"id\": 41644, \"name\": \"a black flip flop\"}, {\"id\": 41645, \"name\": \"the green and white vehicle\"}, {\"id\": 41646, \"name\": \"a pepsi vending machine\"}, {\"id\": 41647, \"name\": \"hay bale\"}, {\"id\": 41648, \"name\": \"a white symbol\"}, {\"id\": 41649, \"name\": \"the red bow tie\"}, {\"id\": 41650, \"name\": \"the stone column\"}, {\"id\": 41651, \"name\": \"dark blue sleeveles blouse\"}, {\"id\": 41652, \"name\": \"the gray cat\"}, {\"id\": 41653, \"name\": \"the dark blue tank top\"}, {\"id\": 41654, \"name\": \"large, teardrop-shaped object\"}, {\"id\": 41655, \"name\": \"a pale yellow t-shirt\"}, {\"id\": 41656, \"name\": \"a person in a green shirt\"}, {\"id\": 41657, \"name\": \"the grey sweatpant\"}, {\"id\": 41658, \"name\": \"mascot's head\"}, {\"id\": 41659, \"name\": \"a large, white tarp\"}, {\"id\": 41660, \"name\": \"a person in a blue cap\"}, {\"id\": 41661, \"name\": \"the orange bucket\"}, {\"id\": 41662, \"name\": \"the tan blazer\"}, {\"id\": 41663, \"name\": \"a purple plaid shirt\"}, {\"id\": 41664, \"name\": \"a statue of a winged man\"}, {\"id\": 41665, \"name\": \"flowing skirt\"}, {\"id\": 41666, \"name\": \"the white hose\"}, {\"id\": 41667, \"name\": \"the red beanie\"}, {\"id\": 41668, \"name\": \"navy blue jacket\"}, {\"id\": 41669, \"name\": \"a protective suit\"}, {\"id\": 41670, \"name\": \"a blue triangle\"}, {\"id\": 41671, \"name\": \"the lit-up menorah\"}, {\"id\": 41672, \"name\": \"procession of ant\"}, {\"id\": 41673, \"name\": \"a light pink shirt\"}, {\"id\": 41674, \"name\": \"black flip-flop\"}, {\"id\": 41675, \"name\": \"small red arrow\"}, {\"id\": 41676, \"name\": \"white clip\"}, {\"id\": 41677, \"name\": \"the white slip-on shoe\"}, {\"id\": 41678, \"name\": \"john deere\"}, {\"id\": 41679, \"name\": \"a drummer's hands\"}, {\"id\": 41680, \"name\": \"yellow strap\"}, {\"id\": 41681, \"name\": \"tan tarp\"}, {\"id\": 41682, \"name\": \"black and white striped tote bag\"}, {\"id\": 41683, \"name\": \"a long spatula\"}, {\"id\": 41684, \"name\": \"cart's handle\"}, {\"id\": 41685, \"name\": \"the red rooster\"}, {\"id\": 41686, \"name\": \"the large green canopy tent\"}, {\"id\": 41687, \"name\": \"the striped cardigan\"}, {\"id\": 41688, \"name\": \"slender trunk\"}, {\"id\": 41689, \"name\": \"white safety bar\"}, {\"id\": 41690, \"name\": \"a colorful vehicle\"}, {\"id\": 41691, \"name\": \"the black cross\"}, {\"id\": 41692, \"name\": \"an illuminated taillight\"}, {\"id\": 41693, \"name\": \"a blue cooler\"}, {\"id\": 41694, \"name\": \"the light pink dress\"}, {\"id\": 41695, \"name\": \"a vibrant beach ball\"}, {\"id\": 41696, \"name\": \"gray, square-shaped exercise step\"}, {\"id\": 41697, \"name\": \"the small ball pit\"}, {\"id\": 41698, \"name\": \"the plastic bag of food\"}, {\"id\": 41699, \"name\": \"the large, curved white object\"}, {\"id\": 41700, \"name\": \"an orange or red clothing\"}, {\"id\": 41701, \"name\": \"red hooded cape\"}, {\"id\": 41702, \"name\": \"the standing bird\"}, {\"id\": 41703, \"name\": \"red sail\"}, {\"id\": 41704, \"name\": \"the pink swim cap\"}, {\"id\": 41705, \"name\": \"a tan tote bag\"}, {\"id\": 41706, \"name\": \"the large red dragon carousel horse\"}, {\"id\": 41707, \"name\": \"the large, brown door\"}, {\"id\": 41708, \"name\": \"woman wearing a white hoodie\"}, {\"id\": 41709, \"name\": \"green kawasaki motorcycle\"}, {\"id\": 41710, \"name\": \"helicopter\"}, {\"id\": 41711, \"name\": \"a basque flag\"}, {\"id\": 41712, \"name\": \"a safety glass\"}, {\"id\": 41713, \"name\": \"black and white striped shirt\"}, {\"id\": 41714, \"name\": \"the green table\"}, {\"id\": 41715, \"name\": \"black and yellow helmet\"}, {\"id\": 41716, \"name\": \"red float or vehicle\"}, {\"id\": 41717, \"name\": \"a gray ceiling\"}, {\"id\": 41718, \"name\": \"a green foot\"}, {\"id\": 41719, \"name\": \"the yellow basket\"}, {\"id\": 41720, \"name\": \"brown cap\"}, {\"id\": 41721, \"name\": \"the diverse array of toys and accessories\"}, {\"id\": 41722, \"name\": \"spiderman theme\"}, {\"id\": 41723, \"name\": \"the large, red, white, and blue ride\"}, {\"id\": 41724, \"name\": \"long, thin metal rod\"}, {\"id\": 41725, \"name\": \"a vibrant bumper car ride\"}, {\"id\": 41726, \"name\": \"a muddy and wet ground\"}, {\"id\": 41727, \"name\": \"the blue base\"}, {\"id\": 41728, \"name\": \"large, cartoonish character\"}, {\"id\": 41729, \"name\": \"the silver ferrule\"}, {\"id\": 41730, \"name\": \"top of the tank\"}, {\"id\": 41731, \"name\": \"a white cone\"}, {\"id\": 41732, \"name\": \"row of red hearts\"}, {\"id\": 41733, \"name\": \"brown ear\"}, {\"id\": 41734, \"name\": \"the black race car\"}, {\"id\": 41735, \"name\": \"a metal bleacher\"}, {\"id\": 41736, \"name\": \"tan overalls\"}, {\"id\": 41737, \"name\": \"a hockey goalie\"}, {\"id\": 41738, \"name\": \"a blurred audience\"}, {\"id\": 41739, \"name\": \"a white electric stove\"}, {\"id\": 41740, \"name\": \"dark-colored leg of a piece of furniture\"}, {\"id\": 41741, \"name\": \"a green and yellow auto rickshaw\"}, {\"id\": 41742, \"name\": \"the red metal bar\"}, {\"id\": 41743, \"name\": \"blue hockey pant\"}, {\"id\": 41744, \"name\": \"green turf surface\"}, {\"id\": 41745, \"name\": \"the ice hockey goalie\"}, {\"id\": 41746, \"name\": \"a red leg wrap\"}, {\"id\": 41747, \"name\": \"tan couch\"}, {\"id\": 41748, \"name\": \"the clear bubble wand\"}, {\"id\": 41749, \"name\": \"the magnifying glass\"}, {\"id\": 41750, \"name\": \"a scooter's handlebar\"}, {\"id\": 41751, \"name\": \"a pink tent\"}, {\"id\": 41752, \"name\": \"the red and white cone\"}, {\"id\": 41753, \"name\": \"a silver exhaust pipe\"}, {\"id\": 41754, \"name\": \"a skateboarder's head\"}, {\"id\": 41755, \"name\": \"bright yellow agility hurdle\"}, {\"id\": 41756, \"name\": \"arm armor\"}, {\"id\": 41757, \"name\": \"the raised curb\"}, {\"id\": 41758, \"name\": \"the vehicle's front tire\"}, {\"id\": 41759, \"name\": \"gray and black backpack\"}, {\"id\": 41760, \"name\": \"the dark blue line\"}, {\"id\": 41761, \"name\": \"highway's center divider\"}, {\"id\": 41762, \"name\": \"the faded green paint\"}, {\"id\": 41763, \"name\": \"the blue plate\"}, {\"id\": 41764, \"name\": \"a rooftop\"}, {\"id\": 41765, \"name\": \"a colorful striped awning\"}, {\"id\": 41766, \"name\": \"exposed black beam\"}, {\"id\": 41767, \"name\": \"a dark wetsuit\"}, {\"id\": 41768, \"name\": \"a black hoove\"}, {\"id\": 41769, \"name\": \"the lush forested hillside\"}, {\"id\": 41770, \"name\": \"the sequined top\"}, {\"id\": 41771, \"name\": \"small white box\"}, {\"id\": 41772, \"name\": \"the motorist\"}, {\"id\": 41773, \"name\": \"drummer's stick\"}, {\"id\": 41774, \"name\": \"a long, elegant dress\"}, {\"id\": 41775, \"name\": \"the white clothing\"}, {\"id\": 41776, \"name\": \"the dense greenery\"}, {\"id\": 41777, \"name\": \"partially open white curtain\"}, {\"id\": 41778, \"name\": \"the parrot's perch\"}, {\"id\": 41779, \"name\": \"white dhoti\"}, {\"id\": 41780, \"name\": \"a tall stadium light\"}, {\"id\": 41781, \"name\": \"cat's tail\"}, {\"id\": 41782, \"name\": \"the yellow beam\"}, {\"id\": 41783, \"name\": \"gold base\"}, {\"id\": 41784, \"name\": \"the stretcher\"}, {\"id\": 41785, \"name\": \"blue, rolling, open-sided tube car\"}, {\"id\": 41786, \"name\": \"a casually dressed person\"}, {\"id\": 41787, \"name\": \"large white cartoon\"}, {\"id\": 41788, \"name\": \"a matching light blue outfit\"}, {\"id\": 41789, \"name\": \"a long wooden stick\"}, {\"id\": 41790, \"name\": \"a room's beige carpet\"}, {\"id\": 41791, \"name\": \"the orange fabric\"}, {\"id\": 41792, \"name\": \"the skates' wheels\"}, {\"id\": 41793, \"name\": \"the black beam\"}, {\"id\": 41794, \"name\": \"tripod stand\"}, {\"id\": 41795, \"name\": \"yellow and purple ball\"}, {\"id\": 41796, \"name\": \"a gray pillar\"}, {\"id\": 41797, \"name\": \"the small bowl of birdseed\"}, {\"id\": 41798, \"name\": \"the grey and white striped shirt\"}, {\"id\": 41799, \"name\": \"a vibrant red costume\"}, {\"id\": 41800, \"name\": \"large, ornate object\"}, {\"id\": 41801, \"name\": \"slot car\"}, {\"id\": 41802, \"name\": \"long-handled broom\"}, {\"id\": 41803, \"name\": \"a long strap or bag\"}, {\"id\": 41804, \"name\": \"the brown trim\"}, {\"id\": 41805, \"name\": \"a small aircraft\"}, {\"id\": 41806, \"name\": \"a red protective pad\"}, {\"id\": 41807, \"name\": \"coil of red and blue wire\"}, {\"id\": 41808, \"name\": \"an off-road vehicle\"}, {\"id\": 41809, \"name\": \"bright blue shirt\"}, {\"id\": 41810, \"name\": \"the snow-covered hillside\"}, {\"id\": 41811, \"name\": \"a small, well-manicured flowerbed\"}, {\"id\": 41812, \"name\": \"the black planter\"}, {\"id\": 41813, \"name\": \"the black and silver cart\"}, {\"id\": 41814, \"name\": \"a cardio equipment\"}, {\"id\": 41815, \"name\": \"orb\"}, {\"id\": 41816, \"name\": \"a large white boat\"}, {\"id\": 41817, \"name\": \"woman in a patterned dress\"}, {\"id\": 41818, \"name\": \"a truck's rear wheel\"}, {\"id\": 41819, \"name\": \"a divers' head\"}, {\"id\": 41820, \"name\": \"the dark mountain\"}, {\"id\": 41821, \"name\": \"girl's arm\"}, {\"id\": 41822, \"name\": \"beach cart\"}, {\"id\": 41823, \"name\": \"a suitcase handle\"}, {\"id\": 41824, \"name\": \"a black suspender\"}, {\"id\": 41825, \"name\": \"the designated crosswalk\"}, {\"id\": 41826, \"name\": \"a large emblem\"}, {\"id\": 41827, \"name\": \"a large, dark shadow\"}, {\"id\": 41828, \"name\": \"a large, industrial machine\"}, {\"id\": 41829, \"name\": \"the dark green metal body\"}, {\"id\": 41830, \"name\": \"a large pane of glass\"}, {\"id\": 41831, \"name\": \"the dragon's body\"}, {\"id\": 41832, \"name\": \"the white control panel\"}, {\"id\": 41833, \"name\": \"the long red pole\"}, {\"id\": 41834, \"name\": \"an upper deck of a double-decker bus\"}, {\"id\": 41835, \"name\": \"a stormtrooper character\"}, {\"id\": 41836, \"name\": \"a rider of the atv\"}, {\"id\": 41837, \"name\": \"the boy in a blue shirt\"}, {\"id\": 41838, \"name\": \"traditional attire\"}, {\"id\": 41839, \"name\": \"a tall steeple\"}, {\"id\": 41840, \"name\": \"a large planter box\"}, {\"id\": 41841, \"name\": \"brown train\"}, {\"id\": 41842, \"name\": \"a warm lighting\"}, {\"id\": 41843, \"name\": \"black and red wheel\"}, {\"id\": 41844, \"name\": \"a pole with a sign\"}, {\"id\": 41845, \"name\": \"blue and white hat\"}, {\"id\": 41846, \"name\": \"a stepping stone\"}, {\"id\": 41847, \"name\": \"inner tube\"}, {\"id\": 41848, \"name\": \"a lion-like face\"}, {\"id\": 41849, \"name\": \"the black suit jacket\"}, {\"id\": 41850, \"name\": \"vibrant, colorful costume\"}, {\"id\": 41851, \"name\": \"the light grey t-shirt\"}, {\"id\": 41852, \"name\": \"the yellow cartoon character\"}, {\"id\": 41853, \"name\": \"the car's rear bumper\"}, {\"id\": 41854, \"name\": \"ornate structure\"}, {\"id\": 41855, \"name\": \"the long, white robe\"}, {\"id\": 41856, \"name\": \"a video game character\"}, {\"id\": 41857, \"name\": \"underwear\"}, {\"id\": 41858, \"name\": \"a colorful kite\"}, {\"id\": 41859, \"name\": \"red shutter\"}, {\"id\": 41860, \"name\": \"small wheel\"}, {\"id\": 41861, \"name\": \"large metal gear or cog\"}, {\"id\": 41862, \"name\": \"the teal sari\"}, {\"id\": 41863, \"name\": \"the colorful tent or canopy\"}, {\"id\": 41864, \"name\": \"small picture\"}, {\"id\": 41865, \"name\": \"the large roof\"}, {\"id\": 41866, \"name\": \"the yellow cloth\"}, {\"id\": 41867, \"name\": \"the industrial equipment\"}, {\"id\": 41868, \"name\": \"the leopard print clothing\"}, {\"id\": 41869, \"name\": \"the red and white floral arrangement\"}, {\"id\": 41870, \"name\": \"the tall spire\"}, {\"id\": 41871, \"name\": \"the military-style hat\"}, {\"id\": 41872, \"name\": \"a weighted ball\"}, {\"id\": 41873, \"name\": \"a red tarp\"}, {\"id\": 41874, \"name\": \"gear icon\"}, {\"id\": 41875, \"name\": \"the tailgate of a white pickup truck\"}, {\"id\": 41876, \"name\": \"a leopard-print top\"}, {\"id\": 41877, \"name\": \"the colorful coral\"}, {\"id\": 41878, \"name\": \"the blue section of the game's interface\"}, {\"id\": 41879, \"name\": \"a small, red-roofed canopy\"}, {\"id\": 41880, \"name\": \"inhabitant\"}, {\"id\": 41881, \"name\": \"circular cage\"}, {\"id\": 41882, \"name\": \"a black rat-like animal\"}, {\"id\": 41883, \"name\": \"a gray tarp\"}, {\"id\": 41884, \"name\": \"performer's hand\"}, {\"id\": 41885, \"name\": \"a large, brown rock\"}, {\"id\": 41886, \"name\": \"a brown sash\"}, {\"id\": 41887, \"name\": \"the long dark skirt\"}, {\"id\": 41888, \"name\": \"a flowing red cape\"}, {\"id\": 41889, \"name\": \"bongo\"}, {\"id\": 41890, \"name\": \"the blue dragon\"}, {\"id\": 41891, \"name\": \"the large brown purse\"}, {\"id\": 41892, \"name\": \"a yellow crate\"}, {\"id\": 41893, \"name\": \"a tan-colored rock\"}, {\"id\": 41894, \"name\": \"the green glow\"}, {\"id\": 41895, \"name\": \"white house with a balcony\"}, {\"id\": 41896, \"name\": \"the clear headlight\"}, {\"id\": 41897, \"name\": \"responder\"}, {\"id\": 41898, \"name\": \"bright pink robe\"}, {\"id\": 41899, \"name\": \"the silver circular component\"}, {\"id\": 41900, \"name\": \"the companion\"}, {\"id\": 41901, \"name\": \"the gold cymbal\"}, {\"id\": 41902, \"name\": \"the resource bar\"}, {\"id\": 41903, \"name\": \"the vibrant decoration\"}, {\"id\": 41904, \"name\": \"a large boulder\"}, {\"id\": 41905, \"name\": \"the image of a highway\"}, {\"id\": 41906, \"name\": \"the yellow and green fruit\"}, {\"id\": 41907, \"name\": \"a glass of wine\"}, {\"id\": 41908, \"name\": \"the character's health bar\"}, {\"id\": 41909, \"name\": \"cartoon pineapple design\"}, {\"id\": 41910, \"name\": \"white paper object\"}, {\"id\": 41911, \"name\": \"bright blue attire\"}, {\"id\": 41912, \"name\": \"a small white bowl\"}, {\"id\": 41913, \"name\": \"the black front wheel\"}, {\"id\": 41914, \"name\": \"orange head covering\"}, {\"id\": 41915, \"name\": \"the back of a truck\"}, {\"id\": 41916, \"name\": \"the black number\"}, {\"id\": 41917, \"name\": \"the colorful, cartoon-themed vehicle\"}, {\"id\": 41918, \"name\": \"white diving gear\"}, {\"id\": 41919, \"name\": \"child's head\"}, {\"id\": 41920, \"name\": \"the white rectangular license plate\"}, {\"id\": 41921, \"name\": \"a blue, graphic t-shirt\"}, {\"id\": 41922, \"name\": \"large white light\"}, {\"id\": 41923, \"name\": \"an utv\"}, {\"id\": 41924, \"name\": \"the yellow sari\"}, {\"id\": 41925, \"name\": \"a blue pen\"}, {\"id\": 41926, \"name\": \"a -terrain vehicle\"}, {\"id\": 41927, \"name\": \"lawn tractor\"}, {\"id\": 41928, \"name\": \"colorful orb\"}, {\"id\": 41929, \"name\": \"blue orb\"}, {\"id\": 41930, \"name\": \"central wooden pillar\"}, {\"id\": 41931, \"name\": \"the sea\"}, {\"id\": 41932, \"name\": \"bowl of dumpling\"}, {\"id\": 41933, \"name\": \"the person's lap\"}, {\"id\": 41934, \"name\": \"a red mane\"}, {\"id\": 41935, \"name\": \"a vibrant red tractor\"}, {\"id\": 41936, \"name\": \"colorful plastic piece\"}, {\"id\": 41937, \"name\": \"the striking large billboard\"}, {\"id\": 41938, \"name\": \"an of flower and foliage\"}, {\"id\": 41939, \"name\": \"an atv's front wheel\"}, {\"id\": 41940, \"name\": \"red plastic tub\"}, {\"id\": 41941, \"name\": \"white and blue shirt\"}, {\"id\": 41942, \"name\": \"player's champion's health bar\"}, {\"id\": 41943, \"name\": \"yellow food item\"}, {\"id\": 41944, \"name\": \"surrounding rock\"}, {\"id\": 41945, \"name\": \"a casual athletic attire\"}, {\"id\": 41946, \"name\": \"the mana bar\"}, {\"id\": 41947, \"name\": \"the foreground bumper car\"}, {\"id\": 41948, \"name\": \"the dense green foliage\"}, {\"id\": 41949, \"name\": \"the passing car\"}, {\"id\": 41950, \"name\": \"a temple\"}, {\"id\": 41951, \"name\": \"the small flagpole\"}, {\"id\": 41952, \"name\": \"tall metal structure\"}, {\"id\": 41953, \"name\": \"bright yellow vehicle\"}, {\"id\": 41954, \"name\": \"dragon car\"}, {\"id\": 41955, \"name\": \"the parked airplane\"}, {\"id\": 41956, \"name\": \"large load of wooden planks\"}, {\"id\": 41957, \"name\": \"the soft coral\"}, {\"id\": 41958, \"name\": \"the city sidewalk\"}, {\"id\": 41959, \"name\": \"scooter's wheel\"}, {\"id\": 41960, \"name\": \"scuba tank\"}, {\"id\": 41961, \"name\": \"yardage marker\"}, {\"id\": 41962, \"name\": \"smaller circular mirror\"}, {\"id\": 41963, \"name\": \"distinctive red metal structure\"}, {\"id\": 41964, \"name\": \"the dark-colored horse\"}, {\"id\": 41965, \"name\": \"cambodian flag\"}, {\"id\": 41966, \"name\": \"small, multicolored ride car\"}, {\"id\": 41967, \"name\": \"a colorful inflatable tube\"}, {\"id\": 41968, \"name\": \"the black basket\"}, {\"id\": 41969, \"name\": \"a floating ball\"}, {\"id\": 41970, \"name\": \"wheeled stand\"}, {\"id\": 41971, \"name\": \"the motorized scooter\"}, {\"id\": 41972, \"name\": \"truck's side mirror\"}, {\"id\": 41973, \"name\": \"large, tan, drum-like structure\"}, {\"id\": 41974, \"name\": \"a tractor or vehicle\"}, {\"id\": 41975, \"name\": \"a yellow and white jacket\"}, {\"id\": 41976, \"name\": \"the vertical, blue panel\"}, {\"id\": 41977, \"name\": \"the yellow accent\"}, {\"id\": 41978, \"name\": \"large rectangular screen\"}, {\"id\": 41979, \"name\": \"a large pole\"}, {\"id\": 41980, \"name\": \"a tuk-tuk's canopy\"}, {\"id\": 41981, \"name\": \"a large orange pole\"}, {\"id\": 41982, \"name\": \"blue-and-white striped fabric\"}, {\"id\": 41983, \"name\": \"toy vehicle\"}, {\"id\": 41984, \"name\": \"large, weathered metal object\"}, {\"id\": 41985, \"name\": \"rickshaw's wheel\"}, {\"id\": 41986, \"name\": \"dark-colored baseball cap\"}, {\"id\": 41987, \"name\": \"the long, thin light\"}, {\"id\": 41988, \"name\": \"a paraglider backpack\"}, {\"id\": 41989, \"name\": \"an illuminated billboard\"}, {\"id\": 41990, \"name\": \"a white fencing\"}, {\"id\": 41991, \"name\": \"the large, white, cylindrical object\"}, {\"id\": 41992, \"name\": \"the matching blue tutu\"}, {\"id\": 41993, \"name\": \"the raincoat\"}, {\"id\": 41994, \"name\": \"recessed lighting fixture\"}, {\"id\": 41995, \"name\": \"prominent yellow banner\"}, {\"id\": 41996, \"name\": \"the blue and white ride\"}, {\"id\": 41997, \"name\": \"the large, clear, ornate object\"}, {\"id\": 41998, \"name\": \"a health bar or other game element\"}, {\"id\": 41999, \"name\": \"man in a black suit jacket and white shirt\"}, {\"id\": 42000, \"name\": \"tall, slender pole\"}, {\"id\": 42001, \"name\": \"the orange trash bag\"}, {\"id\": 42002, \"name\": \"the calico pattern\"}, {\"id\": 42003, \"name\": \"the bucket of gravel\"}, {\"id\": 42004, \"name\": \"large amount of white foam or snow-like material\"}, {\"id\": 42005, \"name\": \"black-and-white striped median\"}, {\"id\": 42006, \"name\": \"the amber-colored drink\"}, {\"id\": 42007, \"name\": \"basket of offering\"}, {\"id\": 42008, \"name\": \"the round silver disc\"}, {\"id\": 42009, \"name\": \"small hill\"}, {\"id\": 42010, \"name\": \"the green coral\"}, {\"id\": 42011, \"name\": \"the blue leg\"}, {\"id\": 42012, \"name\": \"person playing a drum\"}, {\"id\": 42013, \"name\": \"a brown figure\"}, {\"id\": 42014, \"name\": \"large yellow object\"}, {\"id\": 42015, \"name\": \"the prominent blue and white striped umbrella\"}, {\"id\": 42016, \"name\": \"the matching headpiece\"}, {\"id\": 42017, \"name\": \"a multicolored striped dress\"}, {\"id\": 42018, \"name\": \"the bowl of broth\"}, {\"id\": 42019, \"name\": \"beige sandal\"}, {\"id\": 42020, \"name\": \"the gloved hand\"}, {\"id\": 42021, \"name\": \"large, multi-colored sign\"}, {\"id\": 42022, \"name\": \"a long, striped skirt\"}, {\"id\": 42023, \"name\": \"the large blue cylinder\"}, {\"id\": 42024, \"name\": \"vibrant flag\"}, {\"id\": 42025, \"name\": \"a small gift box\"}, {\"id\": 42026, \"name\": \"the child's feet\"}, {\"id\": 42027, \"name\": \"a single red light\"}, {\"id\": 42028, \"name\": \"a large red balloon\"}, {\"id\": 42029, \"name\": \"large, silver lever\"}, {\"id\": 42030, \"name\": \"the vibrant red and white inflatable object\"}, {\"id\": 42031, \"name\": \"yellow shock absorber\"}, {\"id\": 42032, \"name\": \"the umbrella's pole\"}, {\"id\": 42033, \"name\": \"large piece of equipment\"}, {\"id\": 42034, \"name\": \"a mana bar\"}, {\"id\": 42035, \"name\": \"a cargo\"}, {\"id\": 42036, \"name\": \"large suitcase\"}, {\"id\": 42037, \"name\": \"the navy blue athletic uniform\"}, {\"id\": 42038, \"name\": \"bright yellow jumpsuit\"}, {\"id\": 42039, \"name\": \"a green engine with a blue and red stripe\"}, {\"id\": 42040, \"name\": \"the concrete edge\"}, {\"id\": 42041, \"name\": \"the outstretched wing\"}, {\"id\": 42042, \"name\": \"the rainbow-colored, fuzzy hat\"}, {\"id\": 42043, \"name\": \"blue inner tube\"}, {\"id\": 42044, \"name\": \"the small white speedboat\"}, {\"id\": 42045, \"name\": \"a moving car\"}, {\"id\": 42046, \"name\": \"the matching t-shirt\"}, {\"id\": 42047, \"name\": \"the hookah\"}, {\"id\": 42048, \"name\": \"a foreground truck\"}, {\"id\": 42049, \"name\": \"large circular light fixture\"}, {\"id\": 42050, \"name\": \"red light of a traffic signal\"}, {\"id\": 42051, \"name\": \"prominent dome\"}, {\"id\": 42052, \"name\": \"the blue-painted wooden beam\"}, {\"id\": 42053, \"name\": \"a vibrant yellow lion dance costume\"}, {\"id\": 42054, \"name\": \"light tan facade\"}, {\"id\": 42055, \"name\": \"the blue and green component\"}, {\"id\": 42056, \"name\": \"a large green chalkboard\"}, {\"id\": 42057, \"name\": \"black and white striped barrier\"}, {\"id\": 42058, \"name\": \"green house\"}, {\"id\": 42059, \"name\": \"a distinctive red and white plaid shirt\"}, {\"id\": 42060, \"name\": \"a vendor stall\"}, {\"id\": 42061, \"name\": \"colored pencil\"}, {\"id\": 42062, \"name\": \"ornate black ironwork\"}, {\"id\": 42063, \"name\": \"the blue circle with a white arrow pointing to the right\"}, {\"id\": 42064, \"name\": \"the red bean\"}, {\"id\": 42065, \"name\": \"the rickshaw's wheel\"}, {\"id\": 42066, \"name\": \"the white floral detail\"}, {\"id\": 42067, \"name\": \"the small, red and black duck statue\"}, {\"id\": 42068, \"name\": \"the light blue cup\"}, {\"id\": 42069, \"name\": \"a yellow food stand\"}, {\"id\": 42070, \"name\": \"large, white sack\"}, {\"id\": 42071, \"name\": \"vintage vehicle\"}, {\"id\": 42072, \"name\": \"blurred goal\"}, {\"id\": 42073, \"name\": \"the blurred palm frond\"}, {\"id\": 42074, \"name\": \"the planter box\"}, {\"id\": 42075, \"name\": \"median barrier\"}, {\"id\": 42076, \"name\": \"string light\"}, {\"id\": 42077, \"name\": \"map of the game world\"}, {\"id\": 42078, \"name\": \"red kilt\"}, {\"id\": 42079, \"name\": \"children's car\"}, {\"id\": 42080, \"name\": \"the taxi's rear light\"}, {\"id\": 42081, \"name\": \"the vibrant red head\"}, {\"id\": 42082, \"name\": \"the colorful lanyard\"}, {\"id\": 42083, \"name\": \"a blue emblem\"}, {\"id\": 42084, \"name\": \"a person's hands and arms\"}, {\"id\": 42085, \"name\": \"brown sack\"}, {\"id\": 42086, \"name\": \"an out-of-focus license plate\"}, {\"id\": 42087, \"name\": \"the intricate gold carving\"}, {\"id\": 42088, \"name\": \"the piece of machinery\"}, {\"id\": 42089, \"name\": \"the central pillar\"}, {\"id\": 42090, \"name\": \"prominent yellow robotic arm\"}, {\"id\": 42091, \"name\": \"red electronic sign\"}, {\"id\": 42092, \"name\": \"yellow bollard\"}, {\"id\": 42093, \"name\": \"a tom-tom\"}, {\"id\": 42094, \"name\": \"bucket of plant\"}, {\"id\": 42095, \"name\": \"the yellow and red cone\"}, {\"id\": 42096, \"name\": \"the motor scooter\"}, {\"id\": 42097, \"name\": \"the thumb and forefinger\"}, {\"id\": 42098, \"name\": \"a large orange tarp\"}, {\"id\": 42099, \"name\": \"dark gray cloud\"}, {\"id\": 42100, \"name\": \"a red tongue\"}, {\"id\": 42101, \"name\": \"orange tile\"}, {\"id\": 42102, \"name\": \"the chinese-style roof\"}, {\"id\": 42103, \"name\": \"large, yellow, circular object\"}, {\"id\": 42104, \"name\": \"the intricately decorated basket\"}, {\"id\": 42105, \"name\": \"a martial art uniform\"}, {\"id\": 42106, \"name\": \"a badminton\"}, {\"id\": 42107, \"name\": \"the pumpkin\"}, {\"id\": 42108, \"name\": \"a tulle skirt\"}, {\"id\": 42109, \"name\": \"yellow bucket hat\"}, {\"id\": 42110, \"name\": \"the distinctive arched window\"}, {\"id\": 42111, \"name\": \"the grey suit\"}, {\"id\": 42112, \"name\": \"a yellow spray bottle\"}, {\"id\": 42113, \"name\": \"the red bollard\"}, {\"id\": 42114, \"name\": \"map\"}, {\"id\": 42115, \"name\": \"metal cart\"}, {\"id\": 42116, \"name\": \"the dark wetsuit\"}, {\"id\": 42117, \"name\": \"purple and blue orb\"}, {\"id\": 42118, \"name\": \"teal facade\"}, {\"id\": 42119, \"name\": \"a lone player\"}, {\"id\": 42120, \"name\": \"fence line\"}, {\"id\": 42121, \"name\": \"the red rope barrier\"}, {\"id\": 42122, \"name\": \"the largest teacup\"}, {\"id\": 42123, \"name\": \"the large, colorful lantern\"}, {\"id\": 42124, \"name\": \"orange traffic light\"}, {\"id\": 42125, \"name\": \"a large, empty billboard\"}, {\"id\": 42126, \"name\": \"large banner or advertisement\"}, {\"id\": 42127, \"name\": \"a white sweater\"}, {\"id\": 42128, \"name\": \"the bright green artificial turf\"}, {\"id\": 42129, \"name\": \"the green houseboat\"}, {\"id\": 42130, \"name\": \"the racing car\"}, {\"id\": 42131, \"name\": \"row of parked motorbikes\"}, {\"id\": 42132, \"name\": \"white cape\"}, {\"id\": 42133, \"name\": \"a baseball player\"}, {\"id\": 42134, \"name\": \"a large black tire\"}, {\"id\": 42135, \"name\": \"a blue and red ribbon\"}, {\"id\": 42136, \"name\": \"vendor's tent\"}, {\"id\": 42137, \"name\": \"two cats\"}, {\"id\": 42138, \"name\": \"a tan cat\"}, {\"id\": 42139, \"name\": \"the elevated highway\"}, {\"id\": 42140, \"name\": \"dark blue, rocky surface\"}, {\"id\": 42141, \"name\": \"metalworking equipment\"}, {\"id\": 42142, \"name\": \"large, winged creature\"}, {\"id\": 42143, \"name\": \"green ramp\"}, {\"id\": 42144, \"name\": \"the red painted border\"}, {\"id\": 42145, \"name\": \"a diamond-shaped marking\"}, {\"id\": 42146, \"name\": \"a mosaic border\"}, {\"id\": 42147, \"name\": \"the green metal pillar\"}, {\"id\": 42148, \"name\": \"animal sculpture\"}, {\"id\": 42149, \"name\": \"the tan rock\"}, {\"id\": 42150, \"name\": \"the pink, white, and red top\"}, {\"id\": 42151, \"name\": \"the cartoon dinosaur design\"}, {\"id\": 42152, \"name\": \"the jagged peak\"}, {\"id\": 42153, \"name\": \"a man playing a drum\"}, {\"id\": 42154, \"name\": \"the red and white striped tube\"}, {\"id\": 42155, \"name\": \"the striped poncho\"}, {\"id\": 42156, \"name\": \"curve\"}, {\"id\": 42157, \"name\": \"pink flag\"}, {\"id\": 42158, \"name\": \"a gold waistband\"}, {\"id\": 42159, \"name\": \"black silhouette\"}, {\"id\": 42160, \"name\": \"the bright green and orange bristle\"}, {\"id\": 42161, \"name\": \"a stick's shaft\"}, {\"id\": 42162, \"name\": \"black gutter\"}, {\"id\": 42163, \"name\": \"a biker's bike\"}, {\"id\": 42164, \"name\": \"a teacup\"}, {\"id\": 42165, \"name\": \"blue door\"}, {\"id\": 42166, \"name\": \"orange curtain\"}, {\"id\": 42167, \"name\": \"a riding pant\"}, {\"id\": 42168, \"name\": \"the yellow decal\"}, {\"id\": 42169, \"name\": \"front of the bus\"}, {\"id\": 42170, \"name\": \"a blue streak\"}, {\"id\": 42171, \"name\": \"foothold\"}, {\"id\": 42172, \"name\": \"black cooking station\"}, {\"id\": 42173, \"name\": \"an orange panel\"}, {\"id\": 42174, \"name\": \"biker\"}, {\"id\": 42175, \"name\": \"the red and yellow striped section\"}, {\"id\": 42176, \"name\": \"patch of snow\"}, {\"id\": 42177, \"name\": \"a yellow and red flag\"}, {\"id\": 42178, \"name\": \"orange bowling ball\"}, {\"id\": 42179, \"name\": \"inflatable pool\"}, {\"id\": 42180, \"name\": \"cylindrical bollard\"}, {\"id\": 42181, \"name\": \"a monster truck\"}, {\"id\": 42182, \"name\": \"a horse's tail\"}, {\"id\": 42183, \"name\": \"black and yellow outfit\"}, {\"id\": 42184, \"name\": \"marionette\"}, {\"id\": 42185, \"name\": \"the grey base\"}, {\"id\": 42186, \"name\": \"a white clutch purse\"}, {\"id\": 42187, \"name\": \"an anthropomorphic toy\"}, {\"id\": 42188, \"name\": \"disc\"}, {\"id\": 42189, \"name\": \"a white streamer\"}, {\"id\": 42190, \"name\": \"a red hunting jacket\"}, {\"id\": 42191, \"name\": \"green cord\"}, {\"id\": 42192, \"name\": \"the blue triangle\"}, {\"id\": 42193, \"name\": \"the white obstacle\"}, {\"id\": 42194, \"name\": \"a toy dump truck\"}, {\"id\": 42195, \"name\": \"passenger compartment\"}, {\"id\": 42196, \"name\": \"a black spout\"}, {\"id\": 42197, \"name\": \"a van's front bumper\"}, {\"id\": 42198, \"name\": \"translucent material\"}, {\"id\": 42199, \"name\": \"a dark blue bar\"}, {\"id\": 42200, \"name\": \"a round seat\"}, {\"id\": 42201, \"name\": \"the parked bus\"}, {\"id\": 42202, \"name\": \"the white metal beam\"}, {\"id\": 42203, \"name\": \"the pointed peak\"}, {\"id\": 42204, \"name\": \"yellow and red striped pattern\"}, {\"id\": 42205, \"name\": \"floating, multicolored sphere\"}, {\"id\": 42206, \"name\": \"a yellow metal piece\"}, {\"id\": 42207, \"name\": \"white and yellow jersey\"}, {\"id\": 42208, \"name\": \"the long-sleeved uniform\"}, {\"id\": 42209, \"name\": \"lush green shrub\"}, {\"id\": 42210, \"name\": \"the carrot-shaped patch\"}, {\"id\": 42211, \"name\": \"rounded, black opening\"}, {\"id\": 42212, \"name\": \"a white rowing boat\"}, {\"id\": 42213, \"name\": \"a brick paver\"}, {\"id\": 42214, \"name\": \"a white balcony\"}, {\"id\": 42215, \"name\": \"a blue horizontal stripe\"}, {\"id\": 42216, \"name\": \"yellow costume\"}, {\"id\": 42217, \"name\": \"blue metal panel\"}, {\"id\": 42218, \"name\": \"corrugated metal design\"}, {\"id\": 42219, \"name\": \"a bright red shirt\"}, {\"id\": 42220, \"name\": \"a blue and yellow vehicle\"}, {\"id\": 42221, \"name\": \"a crate of soda\"}, {\"id\": 42222, \"name\": \"toy's surface\"}, {\"id\": 42223, \"name\": \"a yellow top\"}, {\"id\": 42224, \"name\": \"the traditional nepalese hat\"}, {\"id\": 42225, \"name\": \"the black heel\"}, {\"id\": 42226, \"name\": \"the white stone monument\"}, {\"id\": 42227, \"name\": \"the minaret of a mosque\"}, {\"id\": 42228, \"name\": \"the pink flower crown\"}, {\"id\": 42229, \"name\": \"the teal section\"}, {\"id\": 42230, \"name\": \"a patterned shell\"}, {\"id\": 42231, \"name\": \"the horseback rider\"}, {\"id\": 42232, \"name\": \"an intricate silver embellishment\"}, {\"id\": 42233, \"name\": \"the green military costume\"}, {\"id\": 42234, \"name\": \"the animal's front legs\"}, {\"id\": 42235, \"name\": \"a boat-shaped vessel\"}, {\"id\": 42236, \"name\": \"the windshield of a vehicle\"}, {\"id\": 42237, \"name\": \"the concrete bench\"}, {\"id\": 42238, \"name\": \"the green base\"}, {\"id\": 42239, \"name\": \"a white foot\"}, {\"id\": 42240, \"name\": \"the boom\"}, {\"id\": 42241, \"name\": \"glass of orange juice\"}, {\"id\": 42242, \"name\": \"a black and white striped bollard\"}, {\"id\": 42243, \"name\": \"the metal disc\"}, {\"id\": 42244, \"name\": \"steak\"}, {\"id\": 42245, \"name\": \"the gold center\"}, {\"id\": 42246, \"name\": \"the colorful dot\"}, {\"id\": 42247, \"name\": \"the shiny chrome ball\"}, {\"id\": 42248, \"name\": \"the person on the platform\"}, {\"id\": 42249, \"name\": \"the ride's colorful base\"}, {\"id\": 42250, \"name\": \"a blue and yellow canopy\"}, {\"id\": 42251, \"name\": \"the black metal pole\"}, {\"id\": 42252, \"name\": \"a metallic gold body\"}, {\"id\": 42253, \"name\": \"the green front wheel\"}, {\"id\": 42254, \"name\": \"honeycomb shape\"}, {\"id\": 42255, \"name\": \"dark red and black striped skirt\"}, {\"id\": 42256, \"name\": \"the white paper filter\"}, {\"id\": 42257, \"name\": \"the craft\"}, {\"id\": 42258, \"name\": \"the ride's vehicle\"}, {\"id\": 42259, \"name\": \"the yellow and blue uniform\"}, {\"id\": 42260, \"name\": \"a play table\"}, {\"id\": 42261, \"name\": \"a black riding helmet\"}, {\"id\": 42262, \"name\": \"the teal-colored rubber ball\"}, {\"id\": 42263, \"name\": \"a subtle wood grain pattern\"}, {\"id\": 42264, \"name\": \"brick base\"}, {\"id\": 42265, \"name\": \"the van's rear end\"}, {\"id\": 42266, \"name\": \"decorative gold trim\"}, {\"id\": 42267, \"name\": \"the black plastic sheet\"}, {\"id\": 42268, \"name\": \"colorful ruffle\"}, {\"id\": 42269, \"name\": \"the black smartphone\"}, {\"id\": 42270, \"name\": \"flowing white dress\"}, {\"id\": 42271, \"name\": \"the sliced onion\"}, {\"id\": 42272, \"name\": \"the plastic bowling pin\"}, {\"id\": 42273, \"name\": \"an oversized head\"}, {\"id\": 42274, \"name\": \"a plastic glove\"}, {\"id\": 42275, \"name\": \"a dark-brown shutter\"}, {\"id\": 42276, \"name\": \"the woven bamboo basket\"}, {\"id\": 42277, \"name\": \"the rider's arm\"}, {\"id\": 42278, \"name\": \"an orange and white metal component\"}, {\"id\": 42279, \"name\": \"a black and white betta fish\"}, {\"id\": 42280, \"name\": \"the hood of a black car\"}, {\"id\": 42281, \"name\": \"the yellow and blue truck\"}, {\"id\": 42282, \"name\": \"a catfish\"}, {\"id\": 42283, \"name\": \"a Red Bull logo\"}, {\"id\": 42284, \"name\": \"a man in a white and red uniform\"}, {\"id\": 42285, \"name\": \"safety cone\"}, {\"id\": 42286, \"name\": \"distinctive black helmet\"}, {\"id\": 42287, \"name\": \"the single rod\"}, {\"id\": 42288, \"name\": \"the black and white fish\"}, {\"id\": 42289, \"name\": \"the circular track design\"}, {\"id\": 42290, \"name\": \"an assortment of books and magazines\"}, {\"id\": 42291, \"name\": \"a yellow seat cushion\"}, {\"id\": 42292, \"name\": \"full skirt\"}, {\"id\": 42293, \"name\": \"distinctive red and orange canopy\"}, {\"id\": 42294, \"name\": \"the mint green sneaker\"}, {\"id\": 42295, \"name\": \"the white plastic drums with black design\"}, {\"id\": 42296, \"name\": \"the green and black shirt\"}, {\"id\": 42297, \"name\": \"a blue top hat\"}, {\"id\": 42298, \"name\": \"large red and yellow box\"}, {\"id\": 42299, \"name\": \"a yellow ring toy\"}, {\"id\": 42300, \"name\": \"red hand wrap\"}, {\"id\": 42301, \"name\": \"the silver wheel\"}, {\"id\": 42302, \"name\": \"pin\"}, {\"id\": 42303, \"name\": \"a white couch\"}, {\"id\": 42304, \"name\": \"truck's back window\"}, {\"id\": 42305, \"name\": \"a base of the rocket\"}, {\"id\": 42306, \"name\": \"the black and yellow tool\"}, {\"id\": 42307, \"name\": \"a white chicken\"}, {\"id\": 42308, \"name\": \"the traditional thai clothing\"}, {\"id\": 42309, \"name\": \"dog food\"}, {\"id\": 42310, \"name\": \"a yellow fin\"}, {\"id\": 42311, \"name\": \"the work boot\"}, {\"id\": 42312, \"name\": \"a tiara\"}, {\"id\": 42313, \"name\": \"the blue shoulder wrap\"}, {\"id\": 42314, \"name\": \"concrete pipe\"}, {\"id\": 42315, \"name\": \"the tram's track\"}, {\"id\": 42316, \"name\": \"white bikini top\"}, {\"id\": 42317, \"name\": \"the small tire\"}, {\"id\": 42318, \"name\": \"an athletic outfit\"}, {\"id\": 42319, \"name\": \"a small black animal\"}, {\"id\": 42320, \"name\": \"the green motorcycle\"}, {\"id\": 42321, \"name\": \"the shark\"}, {\"id\": 42322, \"name\": \"a large, circular light fixture\"}, {\"id\": 42323, \"name\": \"the brown cat's head\"}, {\"id\": 42324, \"name\": \"a tan apron\"}, {\"id\": 42325, \"name\": \"a model train track\"}, {\"id\": 42326, \"name\": \"an operator's seat\"}, {\"id\": 42327, \"name\": \"the sedan's red brake light\"}, {\"id\": 42328, \"name\": \"a patient's bed\"}, {\"id\": 42329, \"name\": \"a large portico\"}, {\"id\": 42330, \"name\": \"the child's bicycle\"}, {\"id\": 42331, \"name\": \"the kite's tail\"}, {\"id\": 42332, \"name\": \"the dog's black tail\"}, {\"id\": 42333, \"name\": \"large, multi-story structure\"}, {\"id\": 42334, \"name\": \"the vibrant traditional attire\"}, {\"id\": 42335, \"name\": \"the gray, rocky hillside\"}, {\"id\": 42336, \"name\": \"large, colorful, circular seat\"}, {\"id\": 42337, \"name\": \"a bright green cone\"}, {\"id\": 42338, \"name\": \"the plastic crate\"}, {\"id\": 42339, \"name\": \"the small, furry animal\"}, {\"id\": 42340, \"name\": \"the gold finial\"}, {\"id\": 42341, \"name\": \"the black and yellow striped barrier\"}, {\"id\": 42342, \"name\": \"counter of the machine\"}, {\"id\": 42343, \"name\": \"a boat's bow\"}, {\"id\": 42344, \"name\": \"the taco\"}, {\"id\": 42345, \"name\": \"reflective gear\"}, {\"id\": 42346, \"name\": \"a trolley's roof\"}, {\"id\": 42347, \"name\": \"a traditional scottish kilt\"}, {\"id\": 42348, \"name\": \"the blue creature\"}, {\"id\": 42349, \"name\": \"a wire mesh\"}, {\"id\": 42350, \"name\": \"the large red object\"}, {\"id\": 42351, \"name\": \"a silver drum\"}, {\"id\": 42352, \"name\": \"rider's hands\"}, {\"id\": 42353, \"name\": \"large tube\"}, {\"id\": 42354, \"name\": \"a blue hoof\"}, {\"id\": 42355, \"name\": \"a blue guardrail\"}, {\"id\": 42356, \"name\": \"a rocky outcrop\"}, {\"id\": 42357, \"name\": \"the full riot gear\"}, {\"id\": 42358, \"name\": \"a tram's tracks\"}, {\"id\": 42359, \"name\": \"the green plastic piece\"}, {\"id\": 42360, \"name\": \"tram track\"}, {\"id\": 42361, \"name\": \"colorful koi fish\"}, {\"id\": 42362, \"name\": \"a black hockey stick\"}, {\"id\": 42363, \"name\": \"black plastic reel\"}, {\"id\": 42364, \"name\": \"horse head\"}, {\"id\": 42365, \"name\": \"an orange sock\"}, {\"id\": 42366, \"name\": \"the weighted medicine ball\"}, {\"id\": 42367, \"name\": \"the black shoulder strap\"}, {\"id\": 42368, \"name\": \"large conical hat\"}, {\"id\": 42369, \"name\": \"a red bikini top\"}, {\"id\": 42370, \"name\": \"a gold-colored machine part\"}, {\"id\": 42371, \"name\": \"a blue surfboard\"}, {\"id\": 42372, \"name\": \"a gray gear\"}, {\"id\": 42373, \"name\": \"rider's pants\"}, {\"id\": 42374, \"name\": \"a dark-colored SUV\"}, {\"id\": 42375, \"name\": \"rear of the vehicle\"}, {\"id\": 42376, \"name\": \"the cartoon frog design\"}, {\"id\": 42377, \"name\": \"the lit-up facade\"}, {\"id\": 42378, \"name\": \"a chrome front fender\"}, {\"id\": 42379, \"name\": \"dark-colored short\"}, {\"id\": 42380, \"name\": \"wooden disk\"}, {\"id\": 42381, \"name\": \"a glowing light stick\"}, {\"id\": 42382, \"name\": \"orange wheel\"}, {\"id\": 42383, \"name\": \"the ice skate\"}, {\"id\": 42384, \"name\": \"a X symbol\"}, {\"id\": 42385, \"name\": \"the carousel's bench\"}, {\"id\": 42386, \"name\": \"broomstick\"}, {\"id\": 42387, \"name\": \"a small stool\"}, {\"id\": 42388, \"name\": \"stack of model trains\"}, {\"id\": 42389, \"name\": \"the blue and white package\"}, {\"id\": 42390, \"name\": \"a colorful, toy-like vehicle\"}, {\"id\": 42391, \"name\": \"the yellow saddle bag\"}, {\"id\": 42392, \"name\": \"a red dirt bike\"}, {\"id\": 42393, \"name\": \"the white go-kart\"}, {\"id\": 42394, \"name\": \"yellow or green shirt\"}, {\"id\": 42395, \"name\": \"a dark-colored metal\"}, {\"id\": 42396, \"name\": \"large, green military vehicle\"}, {\"id\": 42397, \"name\": \"a tall light post\"}, {\"id\": 42398, \"name\": \"a purple plant\"}, {\"id\": 42399, \"name\": \"the blue string\"}, {\"id\": 42400, \"name\": \"the small brown puppy\"}, {\"id\": 42401, \"name\": \"the canopy's support pole\"}, {\"id\": 42402, \"name\": \"a tattered t-shirt\"}, {\"id\": 42403, \"name\": \"the rollerblades\"}, {\"id\": 42404, \"name\": \"the yellow rubber boot\"}, {\"id\": 42405, \"name\": \"a blue and white costume\"}, {\"id\": 42406, \"name\": \"shiny metal surface\"}, {\"id\": 42407, \"name\": \"the western-style suit\"}, {\"id\": 42408, \"name\": \"the shop sign\"}, {\"id\": 42409, \"name\": \"a large circular door\"}, {\"id\": 42410, \"name\": \"elevated roadway\"}, {\"id\": 42411, \"name\": \"the green instrument\"}, {\"id\": 42412, \"name\": \"the black marking\"}, {\"id\": 42413, \"name\": \"the brightly lit, multi-colored machine\"}, {\"id\": 42414, \"name\": \"a tall hat\"}, {\"id\": 42415, \"name\": \"a portion of the motorcycle's body\"}, {\"id\": 42416, \"name\": \"the open rear door\"}, {\"id\": 42417, \"name\": \"a small, grassy area\"}, {\"id\": 42418, \"name\": \"man sitting on a motorbike\"}, {\"id\": 42419, \"name\": \"a scratching post\"}, {\"id\": 42420, \"name\": \"a tall, slender minaret\"}, {\"id\": 42421, \"name\": \"patterned dress\"}, {\"id\": 42422, \"name\": \"a dark gray pattern of large circle\"}, {\"id\": 42423, \"name\": \"yellow bird\"}, {\"id\": 42424, \"name\": \"a large, wide-brimmed straw hat\"}, {\"id\": 42425, \"name\": \"the tall metal pole\"}, {\"id\": 42426, \"name\": \"a brown stripe\"}, {\"id\": 42427, \"name\": \"a front of the car\"}, {\"id\": 42428, \"name\": \"maroon hat\"}, {\"id\": 42429, \"name\": \"a kittens' eye\"}, {\"id\": 42430, \"name\": \"a dedicated pedestrian crossing\"}, {\"id\": 42431, \"name\": \"a small platform\"}, {\"id\": 42432, \"name\": \"a hanging floral arrangement\"}, {\"id\": 42433, \"name\": \"black, coiled pipe\"}, {\"id\": 42434, \"name\": \"a headwear\"}, {\"id\": 42435, \"name\": \"a dollar sign\"}, {\"id\": 42436, \"name\": \"the orange sarong\"}, {\"id\": 42437, \"name\": \"the white drumhead\"}, {\"id\": 42438, \"name\": \"a brown camel\"}, {\"id\": 42439, \"name\": \"rectangular grassy area\"}, {\"id\": 42440, \"name\": \"the small black puppy\"}, {\"id\": 42441, \"name\": \"large yacht\"}, {\"id\": 42442, \"name\": \"blurred grassy area\"}, {\"id\": 42443, \"name\": \"the large blue tassel\"}, {\"id\": 42444, \"name\": \"a red saddle\"}, {\"id\": 42445, \"name\": \"a blue-and-white striped barrel\"}, {\"id\": 42446, \"name\": \"a black lantern\"}, {\"id\": 42447, \"name\": \"the yellow raft\"}, {\"id\": 42448, \"name\": \"car's door handle\"}, {\"id\": 42449, \"name\": \"the group of motorcycles\"}, {\"id\": 42450, \"name\": \"a rider's head\"}, {\"id\": 42451, \"name\": \"a vibrant electronic billboard\"}, {\"id\": 42452, \"name\": \"a traditional African attire\"}, {\"id\": 42453, \"name\": \"tall steeple\"}, {\"id\": 42454, \"name\": \"a collection of toy cars\"}, {\"id\": 42455, \"name\": \"yellow and white barrier\"}, {\"id\": 42456, \"name\": \"a large orange balloon column\"}, {\"id\": 42457, \"name\": \"small white figure\"}, {\"id\": 42458, \"name\": \"the tan and orange facade\"}, {\"id\": 42459, \"name\": \"green metal\"}, {\"id\": 42460, \"name\": \"dragon motif\"}, {\"id\": 42461, \"name\": \"a white airplane\"}, {\"id\": 42462, \"name\": \"the white top surface\"}, {\"id\": 42463, \"name\": \"the yellow claw attachment\"}, {\"id\": 42464, \"name\": \"distinctive blue racing stripe\"}, {\"id\": 42465, \"name\": \"traditional pakistani clothing\"}, {\"id\": 42466, \"name\": \"a bike's handlebar\"}, {\"id\": 42467, \"name\": \"the man in the black shirt\"}, {\"id\": 42468, \"name\": \"yellow parasol\"}, {\"id\": 42469, \"name\": \"the yellow design\"}, {\"id\": 42470, \"name\": \"a gold epaulet\"}, {\"id\": 42471, \"name\": \"the open awning\"}, {\"id\": 42472, \"name\": \"white taxi cab\"}, {\"id\": 42473, \"name\": \"the traditional scottish attire\"}, {\"id\": 42474, \"name\": \"a plastic bag of clothe\"}, {\"id\": 42475, \"name\": \"the black and white watch or strap\"}, {\"id\": 42476, \"name\": \"the member\"}, {\"id\": 42477, \"name\": \"the cart of luggage\"}, {\"id\": 42478, \"name\": \"scattered tennis ball\"}, {\"id\": 42479, \"name\": \"a nutrition facts table\"}, {\"id\": 42480, \"name\": \"a blue kiddie pool\"}, {\"id\": 42481, \"name\": \"a cylindrical head\"}, {\"id\": 42482, \"name\": \"a chest pocket\"}, {\"id\": 42483, \"name\": \"a racetrack\"}, {\"id\": 42484, \"name\": \"the black baseball uniform\"}, {\"id\": 42485, \"name\": \"the figurine of a person\"}, {\"id\": 42486, \"name\": \"a cooking equipment\"}, {\"id\": 42487, \"name\": \"woven wicker basket\"}, {\"id\": 42488, \"name\": \"a multi-story structure\"}, {\"id\": 42489, \"name\": \"a tall red flag\"}, {\"id\": 42490, \"name\": \"a beige curtain\"}, {\"id\": 42491, \"name\": \"the small, open cart\"}, {\"id\": 42492, \"name\": \"the ornate white stone balustrade\"}, {\"id\": 42493, \"name\": \"plane's door\"}, {\"id\": 42494, \"name\": \"the goggles\"}, {\"id\": 42495, \"name\": \"barstool\"}, {\"id\": 42496, \"name\": \"rider's helmeted head\"}, {\"id\": 42497, \"name\": \"the black and white skate\"}, {\"id\": 42498, \"name\": \"a dark green polo shirt\"}, {\"id\": 42499, \"name\": \"ornate headdress\"}, {\"id\": 42500, \"name\": \"the white stable door\"}, {\"id\": 42501, \"name\": \"the red and white checkered pole\"}, {\"id\": 42502, \"name\": \"dark grey roof\"}, {\"id\": 42503, \"name\": \"the red knob\"}, {\"id\": 42504, \"name\": \"a long, thin, yellow flag\"}, {\"id\": 42505, \"name\": \"a red and white lightsaber\"}, {\"id\": 42506, \"name\": \"the intricate lace shirt\"}, {\"id\": 42507, \"name\": \"cylindrical metal piece\"}, {\"id\": 42508, \"name\": \"a white storage container\"}, {\"id\": 42509, \"name\": \"the blue orb\"}, {\"id\": 42510, \"name\": \"a white partition\"}, {\"id\": 42511, \"name\": \"the small white kitten\"}, {\"id\": 42512, \"name\": \"large gun barrel\"}, {\"id\": 42513, \"name\": \"person's hat\"}, {\"id\": 42514, \"name\": \"a performers' attire\"}, {\"id\": 42515, \"name\": \"a blue hawaiian-style shirt\"}, {\"id\": 42516, \"name\": \"the slope of a mountain\"}, {\"id\": 42517, \"name\": \"a snorkeling gear\"}, {\"id\": 42518, \"name\": \"the black signpost\"}, {\"id\": 42519, \"name\": \"the horse and rider\"}, {\"id\": 42520, \"name\": \"the large, shiny metal column\"}, {\"id\": 42521, \"name\": \"sponsor banner\"}, {\"id\": 42522, \"name\": \"the dark purple stripe\"}, {\"id\": 42523, \"name\": \"blue hand pad\"}, {\"id\": 42524, \"name\": \"dog's left paw\"}, {\"id\": 42525, \"name\": \"the black hooded jacket\"}, {\"id\": 42526, \"name\": \"a green bollard\"}, {\"id\": 42527, \"name\": \"driver's side mirror\"}, {\"id\": 42528, \"name\": \"red no symbol\"}, {\"id\": 42529, \"name\": \"the large egg\"}, {\"id\": 42530, \"name\": \"blue pallet\"}, {\"id\": 42531, \"name\": \"metal part\"}, {\"id\": 42532, \"name\": \"unique saddle design\"}, {\"id\": 42533, \"name\": \"the woman wearing a face mask\"}, {\"id\": 42534, \"name\": \"yellow icon\"}, {\"id\": 42535, \"name\": \"a matching headpiece\"}, {\"id\": 42536, \"name\": \"a michelin logo\"}, {\"id\": 42537, \"name\": \"the silver bolt\"}, {\"id\": 42538, \"name\": \"a brown tiled roof\"}, {\"id\": 42539, \"name\": \"the tiled ceiling\"}, {\"id\": 42540, \"name\": \"bag of material\"}, {\"id\": 42541, \"name\": \"a large red brake light\"}, {\"id\": 42542, \"name\": \"the intersection's traffic light\"}, {\"id\": 42543, \"name\": \"a large, colorful decoration\"}, {\"id\": 42544, \"name\": \"the white, circular opening\"}, {\"id\": 42545, \"name\": \"the ornate decoration\"}, {\"id\": 42546, \"name\": \"a lifebuoy\"}, {\"id\": 42547, \"name\": \"the plant pot\"}, {\"id\": 42548, \"name\": \"a tall billboard\"}, {\"id\": 42549, \"name\": \"a white rowing machine\"}, {\"id\": 42550, \"name\": \"a small pot\"}, {\"id\": 42551, \"name\": \"a colorful arcade game\"}, {\"id\": 42552, \"name\": \"the light blue mask\"}, {\"id\": 42553, \"name\": \"the socket wrench\"}, {\"id\": 42554, \"name\": \"the stanchion\"}, {\"id\": 42555, \"name\": \"a black and white traffic cone\"}, {\"id\": 42556, \"name\": \"the gold earring\"}, {\"id\": 42557, \"name\": \"a domed roof\"}, {\"id\": 42558, \"name\": \"gray T-shirt\"}, {\"id\": 42559, \"name\": \"the round lantern\"}, {\"id\": 42560, \"name\": \"a dock's white pole\"}, {\"id\": 42561, \"name\": \"a dark blue and white lane divider\"}, {\"id\": 42562, \"name\": \"a pink awning\"}, {\"id\": 42563, \"name\": \"the white and black banner\"}, {\"id\": 42564, \"name\": \"the pencil sharpener\"}, {\"id\": 42565, \"name\": \"a load of item\"}, {\"id\": 42566, \"name\": \"broccoli stalk\"}, {\"id\": 42567, \"name\": \"large, multicolored balloon\"}, {\"id\": 42568, \"name\": \"a vibrant umbrella\"}, {\"id\": 42569, \"name\": \"small purple ball\"}, {\"id\": 42570, \"name\": \"safety boot\"}, {\"id\": 42571, \"name\": \"a vendor's booth\"}, {\"id\": 42572, \"name\": \"engine-powered concrete mixer\"}, {\"id\": 42573, \"name\": \"a light gray leg\"}, {\"id\": 42574, \"name\": \"a red marching band uniform\"}, {\"id\": 42575, \"name\": \"T-shirt\"}, {\"id\": 42576, \"name\": \"window's white frame\"}, {\"id\": 42577, \"name\": \"red and yellow decoration\"}, {\"id\": 42578, \"name\": \"small tan-colored animal\"}, {\"id\": 42579, \"name\": \"white yarmulke\"}, {\"id\": 42580, \"name\": \"an orange barrier\"}, {\"id\": 42581, \"name\": \"a main road\"}, {\"id\": 42582, \"name\": \"red race car\"}, {\"id\": 42583, \"name\": \"the laughing emoji\"}, {\"id\": 42584, \"name\": \"the british flag\"}, {\"id\": 42585, \"name\": \"the fluffy, curly light-brown coat\"}, {\"id\": 42586, \"name\": \"car's rear light\"}, {\"id\": 42587, \"name\": \"Monster Energy logo\"}, {\"id\": 42588, \"name\": \"neon orange sign\"}, {\"id\": 42589, \"name\": \"white, red, and blue trim\"}, {\"id\": 42590, \"name\": \"the large, colorful rickshaw\"}, {\"id\": 42591, \"name\": \"white flipper\"}, {\"id\": 42592, \"name\": \"the bright overhead light\"}, {\"id\": 42593, \"name\": \"girl's head\"}, {\"id\": 42594, \"name\": \"black cylindrical bead\"}, {\"id\": 42595, \"name\": \"a green stair\"}, {\"id\": 42596, \"name\": \"a Chilean flag\"}, {\"id\": 42597, \"name\": \"a small, white, rectangular table\"}, {\"id\": 42598, \"name\": \"small indian flag\"}, {\"id\": 42599, \"name\": \"licence plate\"}, {\"id\": 42600, \"name\": \"the raised flowerbed\"}, {\"id\": 42601, \"name\": \"the blue and white barrier\"}, {\"id\": 42602, \"name\": \"a large, yellow banner\"}, {\"id\": 42603, \"name\": \"a pink unicorn\"}, {\"id\": 42604, \"name\": \"the protective helmet\"}, {\"id\": 42605, \"name\": \"a large exercise ball\"}, {\"id\": 42606, \"name\": \"the bright yellow yolk\"}, {\"id\": 42607, \"name\": \"playing card\"}, {\"id\": 42608, \"name\": \"motorized rickshaw or tuk-tuk\"}, {\"id\": 42609, \"name\": \"pink bowl\"}, {\"id\": 42610, \"name\": \"the dark hole\"}, {\"id\": 42611, \"name\": \"the top sheet\"}, {\"id\": 42612, \"name\": \"plane's engine\"}, {\"id\": 42613, \"name\": \"a floating blue platform\"}, {\"id\": 42614, \"name\": \"iv pole\"}, {\"id\": 42615, \"name\": \"recessed circular air vent\"}, {\"id\": 42616, \"name\": \"yellow hand\"}, {\"id\": 42617, \"name\": \"the large, open-sided tent\"}, {\"id\": 42618, \"name\": \"the yellow and silver train car\"}, {\"id\": 42619, \"name\": \"the brown ceiling\"}, {\"id\": 42620, \"name\": \"the green rope\"}, {\"id\": 42621, \"name\": \"a black front tire\"}, {\"id\": 42622, \"name\": \"nose of the plane\"}, {\"id\": 42623, \"name\": \"yellow cleat\"}, {\"id\": 42624, \"name\": \"the small head\"}, {\"id\": 42625, \"name\": \"a black and red hat\"}, {\"id\": 42626, \"name\": \"palette of paint\"}, {\"id\": 42627, \"name\": \"a red neon sign\"}, {\"id\": 42628, \"name\": \"camel's harness\"}, {\"id\": 42629, \"name\": \"white capri pant\"}, {\"id\": 42630, \"name\": \"white and black chicken\"}, {\"id\": 42631, \"name\": \"orange and yellow flame\"}, {\"id\": 42632, \"name\": \"a camel's head\"}, {\"id\": 42633, \"name\": \"small red bar\"}, {\"id\": 42634, \"name\": \"the orange extension cord\"}, {\"id\": 42635, \"name\": \"a vibrant orange beak\"}, {\"id\": 42636, \"name\": \"the small, plastic bag\"}, {\"id\": 42637, \"name\": \"the smaller bowl\"}, {\"id\": 42638, \"name\": \"a white skull icon\"}, {\"id\": 42639, \"name\": \"horse-like figure\"}, {\"id\": 42640, \"name\": \"the wire mesh structure\"}, {\"id\": 42641, \"name\": \"wooden partition\"}, {\"id\": 42642, \"name\": \"the bowl of bolt\"}, {\"id\": 42643, \"name\": \"a dark brown door\"}, {\"id\": 42644, \"name\": \"Stormtrooper\"}, {\"id\": 42645, \"name\": \"brown shipping container\"}, {\"id\": 42646, \"name\": \"a colorful pattern\"}, {\"id\": 42647, \"name\": \"the yellow spot\"}, {\"id\": 42648, \"name\": \"the blue barrel\"}, {\"id\": 42649, \"name\": \"the glowing pink light\"}, {\"id\": 42650, \"name\": \"lone cowboy\"}, {\"id\": 42651, \"name\": \"a red string\"}, {\"id\": 42652, \"name\": \"a belmont park logo\"}, {\"id\": 42653, \"name\": \"metal base\"}, {\"id\": 42654, \"name\": \"a players' attire\"}, {\"id\": 42655, \"name\": \"the central strip of raised metal grating\"}, {\"id\": 42656, \"name\": \"the single white cloud\"}, {\"id\": 42657, \"name\": \"camel's head\"}, {\"id\": 42658, \"name\": \"the tan substance\"}, {\"id\": 42659, \"name\": \"wood-chopping block\"}, {\"id\": 42660, \"name\": \"the muscular arm\"}, {\"id\": 42661, \"name\": \"the hour marker\"}, {\"id\": 42662, \"name\": \"pink basketball\"}, {\"id\": 42663, \"name\": \"white shelving\"}, {\"id\": 42664, \"name\": \"a gold coin\"}, {\"id\": 42665, \"name\": \"red rose\"}, {\"id\": 42666, \"name\": \"rack of wedding dress\"}, {\"id\": 42667, \"name\": \"the toy fire ladder\"}, {\"id\": 42668, \"name\": \"a white cane\"}, {\"id\": 42669, \"name\": \"grotesque face\"}, {\"id\": 42670, \"name\": \"the baby brezza logo\"}, {\"id\": 42671, \"name\": \"the wooden foot\"}, {\"id\": 42672, \"name\": \"the player in the white jersey\"}, {\"id\": 42673, \"name\": \"plastic wrap\"}, {\"id\": 42674, \"name\": \"a bottle of hemp oil\"}, {\"id\": 42675, \"name\": \"the colorful chair\"}, {\"id\": 42676, \"name\": \"bright colored helmet\"}, {\"id\": 42677, \"name\": \"grey guardrail\"}, {\"id\": 42678, \"name\": \"the bra top\"}, {\"id\": 42679, \"name\": \"red garment\"}, {\"id\": 42680, \"name\": \"black snout\"}, {\"id\": 42681, \"name\": \"red-and-white striped candy cane\"}, {\"id\": 42682, \"name\": \"an orange vehicle\"}, {\"id\": 42683, \"name\": \"blue racecar\"}, {\"id\": 42684, \"name\": \"brightly colored seat\"}, {\"id\": 42685, \"name\": \"coxswain\"}, {\"id\": 42686, \"name\": \"black motorcycle mirror\"}, {\"id\": 42687, \"name\": \"neon green sock\"}, {\"id\": 42688, \"name\": \"murky liquid\"}, {\"id\": 42689, \"name\": \"go-kart driver\"}, {\"id\": 42690, \"name\": \"a rain poncho\"}, {\"id\": 42691, \"name\": \"the swimmer\"}, {\"id\": 42692, \"name\": \"a light-colored car\"}, {\"id\": 42693, \"name\": \"the polka dot\"}, {\"id\": 42694, \"name\": \"a celery stick\"}, {\"id\": 42695, \"name\": \"a blue wave design\"}, {\"id\": 42696, \"name\": \"a lightning bolt\"}, {\"id\": 42697, \"name\": \"the light-colored surface\"}, {\"id\": 42698, \"name\": \"zebra crossing\"}, {\"id\": 42699, \"name\": \"the yellow auto rickshaw\"}, {\"id\": 42700, \"name\": \"the colored shoe\"}, {\"id\": 42701, \"name\": \"the grid pattern of metal beam\"}, {\"id\": 42702, \"name\": \"the silver barrier\"}, {\"id\": 42703, \"name\": \"the dark brown lamppost\"}, {\"id\": 42704, \"name\": \"the man in black uniform\"}, {\"id\": 42705, \"name\": \"rear\"}, {\"id\": 42706, \"name\": \"the white and black jersey\"}, {\"id\": 42707, \"name\": \"the red wattle\"}, {\"id\": 42708, \"name\": \"woman in a black dres and hat\"}, {\"id\": 42709, \"name\": \"ticket strip\"}, {\"id\": 42710, \"name\": \"peep toy\"}, {\"id\": 42711, \"name\": \"a tiled facade\"}, {\"id\": 42712, \"name\": \"blue platform\"}, {\"id\": 42713, \"name\": \"a yellow component\"}, {\"id\": 42714, \"name\": \"a human player\"}, {\"id\": 42715, \"name\": \"an ornate facade\"}, {\"id\": 42716, \"name\": \"the white storefront\"}, {\"id\": 42717, \"name\": \"a conveyor belt system\"}, {\"id\": 42718, \"name\": \"the hand's fingers\"}, {\"id\": 42719, \"name\": \"the clear face shield\"}, {\"id\": 42720, \"name\": \"a light jacket\"}, {\"id\": 42721, \"name\": \"playpen\"}, {\"id\": 42722, \"name\": \"a black snout\"}, {\"id\": 42723, \"name\": \"a dark brown wicker leg\"}, {\"id\": 42724, \"name\": \"dark paint\"}, {\"id\": 42725, \"name\": \"the red and white patterned sign\"}, {\"id\": 42726, \"name\": \"casual athletic attire\"}, {\"id\": 42727, \"name\": \"the english signage\"}, {\"id\": 42728, \"name\": \"a person in a white hoodie\"}, {\"id\": 42729, \"name\": \"the person's wrist\"}, {\"id\": 42730, \"name\": \"the brown and black striped kitten\"}, {\"id\": 42731, \"name\": \"a red-and-white striped pole\"}, {\"id\": 42732, \"name\": \"a blonde doll\"}, {\"id\": 42733, \"name\": \"gold head\"}, {\"id\": 42734, \"name\": \"the stock price\"}, {\"id\": 42735, \"name\": \"a yellow-framed window\"}, {\"id\": 42736, \"name\": \"a dark-colored umbrella\"}, {\"id\": 42737, \"name\": \"the white handle\"}, {\"id\": 42738, \"name\": \"a field's white line\"}, {\"id\": 42739, \"name\": \"closed eye\"}, {\"id\": 42740, \"name\": \"white and blue logo\"}, {\"id\": 42741, \"name\": \"purple sport car\"}, {\"id\": 42742, \"name\": \"a wooden basket\"}, {\"id\": 42743, \"name\": \"the golden mane\"}, {\"id\": 42744, \"name\": \"a gray head covering\"}, {\"id\": 42745, \"name\": \"a red brick exterior\"}, {\"id\": 42746, \"name\": \"a black and white attire\"}, {\"id\": 42747, \"name\": \"black side mirror\"}, {\"id\": 42748, \"name\": \"the rain poncho\"}, {\"id\": 42749, \"name\": \"the toe\"}, {\"id\": 42750, \"name\": \"the gray cobblestone\"}, {\"id\": 42751, \"name\": \"the black circular object\"}, {\"id\": 42752, \"name\": \"a game's screen\"}, {\"id\": 42753, \"name\": \"a small, round base\"}, {\"id\": 42754, \"name\": \"the black and white bikini top\"}, {\"id\": 42755, \"name\": \"orange, spiral-shaped object\"}, {\"id\": 42756, \"name\": \"a cctv\"}, {\"id\": 42757, \"name\": \"food product\"}, {\"id\": 42758, \"name\": \"multicoloured scarf\"}, {\"id\": 42759, \"name\": \"stylized rainbow\"}, {\"id\": 42760, \"name\": \"bull's head\"}, {\"id\": 42761, \"name\": \"white loop\"}, {\"id\": 42762, \"name\": \"the players' uniform\"}, {\"id\": 42763, \"name\": \"purple accent\"}, {\"id\": 42764, \"name\": \"the red plastic tray\"}, {\"id\": 42765, \"name\": \"pagoda-style roof\"}, {\"id\": 42766, \"name\": \"the neon green cleat\"}, {\"id\": 42767, \"name\": \"green railing\"}, {\"id\": 42768, \"name\": \"a red-painted fingernail\"}, {\"id\": 42769, \"name\": \"a yellow arm\"}, {\"id\": 42770, \"name\": \"law enforcement officer\"}, {\"id\": 42771, \"name\": \"a passenger's arm\"}, {\"id\": 42772, \"name\": \"a red railing\"}, {\"id\": 42773, \"name\": \"the wooden stand\"}, {\"id\": 42774, \"name\": \"a mane\"}, {\"id\": 42775, \"name\": \"the sleeveles top\"}, {\"id\": 42776, \"name\": \"a gold badge\"}, {\"id\": 42777, \"name\": \"player in black jersey\"}, {\"id\": 42778, \"name\": \"individual bag\"}, {\"id\": 42779, \"name\": \"the wicker\"}, {\"id\": 42780, \"name\": \"the bikini top\"}, {\"id\": 42781, \"name\": \"a light-brown substance\"}, {\"id\": 42782, \"name\": \"a prominent black logo\"}, {\"id\": 42783, \"name\": \"a teal rectangle\"}, {\"id\": 42784, \"name\": \"a gray sail\"}, {\"id\": 42785, \"name\": \"a closed metal shutter\"}, {\"id\": 42786, \"name\": \"silver screw\"}, {\"id\": 42787, \"name\": \"the red puffy decoration\"}, {\"id\": 42788, \"name\": \"a red fingernail\"}, {\"id\": 42789, \"name\": \"a red-and-white umbrella\"}, {\"id\": 42790, \"name\": \"blue base\"}, {\"id\": 42791, \"name\": \"the pink circle\"}, {\"id\": 42792, \"name\": \"the person in military uniform\"}, {\"id\": 42793, \"name\": \"the gray concrete curb\"}, {\"id\": 42794, \"name\": \"a spike\"}, {\"id\": 42795, \"name\": \"partially filled glass\"}, {\"id\": 42796, \"name\": \"the superimposed message\"}, {\"id\": 42797, \"name\": \"black and blue uniform\"}, {\"id\": 42798, \"name\": \"the five logo\"}, {\"id\": 42799, \"name\": \"train's body\"}, {\"id\": 42800, \"name\": \"a cylindrical rod\"}, {\"id\": 42801, \"name\": \"a round logo\"}, {\"id\": 42802, \"name\": \"the person in a yellow helmet\"}, {\"id\": 42803, \"name\": \"the longhorn cow\"}, {\"id\": 42804, \"name\": \"a silver motorcycle\"}, {\"id\": 42805, \"name\": \"the character's skin\"}, {\"id\": 42806, \"name\": \"black and white cleat\"}, {\"id\": 42807, \"name\": \"red-painted fingernail\"}, {\"id\": 42808, \"name\": \"the orange item\"}, {\"id\": 42809, \"name\": \"the green wreath\"}, {\"id\": 42810, \"name\": \"the white motor\"}, {\"id\": 42811, \"name\": \"white and gray material\"}, {\"id\": 42812, \"name\": \"an intricate scrollwork\"}, {\"id\": 42813, \"name\": \"black light\"}, {\"id\": 42814, \"name\": \"the yellow-and-black striped barrier\"}, {\"id\": 42815, \"name\": \"gray umbrella\"}, {\"id\": 42816, \"name\": \"a lush grassy bank\"}, {\"id\": 42817, \"name\": \"the yellow puffer jacket\"}, {\"id\": 42818, \"name\": \"the bundle of good\"}, {\"id\": 42819, \"name\": \"the sharp spike\"}, {\"id\": 42820, \"name\": \"the colorful board\"}, {\"id\": 42821, \"name\": \"man on a motorcycle\"}, {\"id\": 42822, \"name\": \"a green and yellow tuk-tuk\"}, {\"id\": 42823, \"name\": \"the paper lantern\"}, {\"id\": 42824, \"name\": \"a player in a yellow shirt\"}, {\"id\": 42825, \"name\": \"pocket\"}, {\"id\": 42826, \"name\": \"a dark wood finish\"}, {\"id\": 42827, \"name\": \"ornate leg\"}, {\"id\": 42828, \"name\": \"green running surface\"}, {\"id\": 42829, \"name\": \"gray concrete barrier\"}, {\"id\": 42830, \"name\": \"the colorful label\"}, {\"id\": 42831, \"name\": \"black paper\"}, {\"id\": 42832, \"name\": \"a black plate or bowl\"}, {\"id\": 42833, \"name\": \"a chinese signage\"}, {\"id\": 42834, \"name\": \"pink and yellow sign\"}, {\"id\": 42835, \"name\": \"field's white line\"}, {\"id\": 42836, \"name\": \"a barefoot or wearing sandal\"}, {\"id\": 42837, \"name\": \"the reindeer's head\"}, {\"id\": 42838, \"name\": \"the paddler\"}, {\"id\": 42839, \"name\": \"contact information\"}, {\"id\": 42840, \"name\": \"the red and grey stripe\"}, {\"id\": 42841, \"name\": \"a pink-painted fingernail\"}, {\"id\": 42842, \"name\": \"a knick-knack\"}, {\"id\": 42843, \"name\": \"the circular yellow light\"}, {\"id\": 42844, \"name\": \"the firemen's face\"}, {\"id\": 42845, \"name\": \"a cupcake\"}, {\"id\": 42846, \"name\": \"the black and white police car\"}, {\"id\": 42847, \"name\": \"a white allianz logo\"}, {\"id\": 42848, \"name\": \"a red bus\"}, {\"id\": 42849, \"name\": \"a red and blue light bar\"}, {\"id\": 42850, \"name\": \"the rubber\"}, {\"id\": 42851, \"name\": \"glas partition\"}, {\"id\": 42852, \"name\": \"an animal figure\"}, {\"id\": 42853, \"name\": \"the purple eye\"}, {\"id\": 42854, \"name\": \"the individual in a white shirt and jean\"}, {\"id\": 42855, \"name\": \"white tape barrier\"}, {\"id\": 42856, \"name\": \"the gold screw\"}, {\"id\": 42857, \"name\": \"a monster\"}, {\"id\": 42858, \"name\": \"a white pillow or cushion\"}, {\"id\": 42859, \"name\": \"the red metal chair\"}, {\"id\": 42860, \"name\": \"a green and yellow stripe\"}, {\"id\": 42861, \"name\": \"black team's uniform\"}, {\"id\": 42862, \"name\": \"the dark apron strap\"}, {\"id\": 42863, \"name\": \"a small red shed\"}, {\"id\": 42864, \"name\": \"vibrant inflatable raft\"}, {\"id\": 42865, \"name\": \"a white track suit\"}, {\"id\": 42866, \"name\": \"person figure\"}, {\"id\": 42867, \"name\": \"mr\"}, {\"id\": 42868, \"name\": \"tall stone tower\"}, {\"id\": 42869, \"name\": \"the pet\"}, {\"id\": 42870, \"name\": \"a rainbow-colored stream of water\"}, {\"id\": 42871, \"name\": \"the merman costume\"}, {\"id\": 42872, \"name\": \"fly racing logo\"}, {\"id\": 42873, \"name\": \"mathematical symbol\"}, {\"id\": 42874, \"name\": \"minnie mouse ear headband\"}, {\"id\": 42875, \"name\": \"kitten\"}, {\"id\": 42876, \"name\": \"the red loop\"}, {\"id\": 42877, \"name\": \"the basket of dog toys and treat\"}, {\"id\": 42878, \"name\": \"the white patterned design\"}, {\"id\": 42879, \"name\": \"the social media handle\"}, {\"id\": 42880, \"name\": \"black and white body\"}, {\"id\": 42881, \"name\": \"light green rain poncho\"}, {\"id\": 42882, \"name\": \"the computer tower\"}, {\"id\": 42883, \"name\": \"rack of colorful clothing\"}, {\"id\": 42884, \"name\": \"female employee\"}, {\"id\": 42885, \"name\": \"handwritten number\"}, {\"id\": 42886, \"name\": \"the black and white gate\"}, {\"id\": 42887, \"name\": \"a spark\"}, {\"id\": 42888, \"name\": \"the red double-decker bus\"}, {\"id\": 42889, \"name\": \"grey tile backsplash\"}, {\"id\": 42890, \"name\": \"a green tail\"}, {\"id\": 42891, \"name\": \"an orange pole\"}, {\"id\": 42892, \"name\": \"circular structure\"}, {\"id\": 42893, \"name\": \"a bunting flag\"}, {\"id\": 42894, \"name\": \"a watermark overlay\"}, {\"id\": 42895, \"name\": \"the large flag\"}, {\"id\": 42896, \"name\": \"lure\"}, {\"id\": 42897, \"name\": \"the red exterior\"}, {\"id\": 42898, \"name\": \"colourful short\"}, {\"id\": 42899, \"name\": \"the droid depot\"}, {\"id\": 42900, \"name\": \"a black motor\"}, {\"id\": 42901, \"name\": \"the gray t-shirt sleeve\"}, {\"id\": 42902, \"name\": \"tangent logo\"}, {\"id\": 42903, \"name\": \"the silver metal leg\"}, {\"id\": 42904, \"name\": \"an orange horizontal line\"}, {\"id\": 42905, \"name\": \"the brick-paved patio\"}, {\"id\": 42906, \"name\": \"the polo shirt\"}, {\"id\": 42907, \"name\": \"restaurant's outdoor menu board\"}, {\"id\": 42908, \"name\": \"a navigation bar\"}, {\"id\": 42909, \"name\": \"a wooden stand\"}, {\"id\": 42910, \"name\": \"a yellow guardrail\"}, {\"id\": 42911, \"name\": \"gold faceplate\"}, {\"id\": 42912, \"name\": \"paddler\"}, {\"id\": 42913, \"name\": \"a reddish-brown coat\"}, {\"id\": 42914, \"name\": \"the large, green awning or tent\"}, {\"id\": 42915, \"name\": \"the gold detailing\"}, {\"id\": 42916, \"name\": \"a purple spot\"}, {\"id\": 42917, \"name\": \"a team's score\"}, {\"id\": 42918, \"name\": \"the cylindrical shape\"}, {\"id\": 42919, \"name\": \"the red coca-cola branding\"}, {\"id\": 42920, \"name\": \"a feathered accessory\"}, {\"id\": 42921, \"name\": \"a floodlight\"}, {\"id\": 42922, \"name\": \"the foamy beer\"}, {\"id\": 42923, \"name\": \"a green pillar\"}, {\"id\": 42924, \"name\": \"the movie title accountant\"}, {\"id\": 42925, \"name\": \"the buzzed haircut\"}, {\"id\": 42926, \"name\": \"donkey\"}, {\"id\": 42927, \"name\": \"a yellow and white jersey\"}, {\"id\": 42928, \"name\": \"a fxr logo\"}, {\"id\": 42929, \"name\": \"a metal bar stool\"}, {\"id\": 42930, \"name\": \"the purple ribbon\"}, {\"id\": 42931, \"name\": \"a metal base\"}, {\"id\": 42932, \"name\": \"the red, white, and green jersey\"}, {\"id\": 42933, \"name\": \"a booth\"}, {\"id\": 42934, \"name\": \"the watermelon\"}, {\"id\": 42935, \"name\": \"the van's front bumper\"}, {\"id\": 42936, \"name\": \"the vertical black stripe\"}, {\"id\": 42937, \"name\": \"the corrugated steel\"}, {\"id\": 42938, \"name\": \"the client\"}, {\"id\": 42939, \"name\": \"a silver pole\"}, {\"id\": 42940, \"name\": \"a left ear\"}, {\"id\": 42941, \"name\": \"a pink wheel\"}, {\"id\": 42942, \"name\": \"a large organ\"}, {\"id\": 42943, \"name\": \"a steve's speech bubble\"}, {\"id\": 42944, \"name\": \"serving utensil\"}, {\"id\": 42945, \"name\": \"black and blue wetsuit\"}, {\"id\": 42946, \"name\": \"the red oar\"}, {\"id\": 42947, \"name\": \"beige bag\"}, {\"id\": 42948, \"name\": \"shiny stainles steel surface\"}, {\"id\": 42949, \"name\": \"small, intricately painted ceramic figurine\"}, {\"id\": 42950, \"name\": \"a black character\"}, {\"id\": 42951, \"name\": \"visor\"}, {\"id\": 42952, \"name\": \"an aquatic animal\"}, {\"id\": 42953, \"name\": \"the grey patterned design\"}, {\"id\": 42954, \"name\": \"the person in street clothe\"}, {\"id\": 42955, \"name\": \"hood of a dark-colored car\"}, {\"id\": 42956, \"name\": \"beach bag\"}, {\"id\": 42957, \"name\": \"a woman in a beige dress\"}, {\"id\": 42958, \"name\": \"a reel\"}, {\"id\": 42959, \"name\": \"yellow surface\"}, {\"id\": 42960, \"name\": \"a children's face\"}, {\"id\": 42961, \"name\": \"the cable\"}, {\"id\": 42962, \"name\": \"the blurry image of a person\"}, {\"id\": 42963, \"name\": \"the multicolored paper flower\"}, {\"id\": 42964, \"name\": \"the medical device\"}, {\"id\": 42965, \"name\": \"a long hood\"}, {\"id\": 42966, \"name\": \"a colorful marble\"}, {\"id\": 42967, \"name\": \"rider's blue jersey\"}, {\"id\": 42968, \"name\": \"the gray sail\"}, {\"id\": 42969, \"name\": \"the central circular design\"}, {\"id\": 42970, \"name\": \"the clear glass bowl\"}, {\"id\": 42971, \"name\": \"a glas window\"}, {\"id\": 42972, \"name\": \"a central table\"}, {\"id\": 42973, \"name\": \"a graphic\"}, {\"id\": 42974, \"name\": \"a large tan purse or bag\"}, {\"id\": 42975, \"name\": \"the ornate black iron balcony\"}, {\"id\": 42976, \"name\": \"a horse's rider\"}, {\"id\": 42977, \"name\": \"the pump or other device\"}, {\"id\": 42978, \"name\": \"the boy's face\"}, {\"id\": 42979, \"name\": \"a carved stone arch\"}, {\"id\": 42980, \"name\": \"the pool cleaning tool\"}, {\"id\": 42981, \"name\": \"the navy-blue short\"}, {\"id\": 42982, \"name\": \"pizza shop\"}, {\"id\": 42983, \"name\": \"the red suv\"}, {\"id\": 42984, \"name\": \"a glowing blue and pink flame\"}, {\"id\": 42985, \"name\": \"black and white logo\"}, {\"id\": 42986, \"name\": \"a green pepper\"}, {\"id\": 42987, \"name\": \"large smokestack\"}, {\"id\": 42988, \"name\": \"the auto club logo\"}, {\"id\": 42989, \"name\": \"pen or pencil\"}, {\"id\": 42990, \"name\": \"man-made structure\"}, {\"id\": 42991, \"name\": \"tan-colored stone or brick\"}, {\"id\": 42992, \"name\": \"a mouse character\"}, {\"id\": 42993, \"name\": \"the black chain link fence\"}, {\"id\": 42994, \"name\": \"jets' logo\"}, {\"id\": 42995, \"name\": \"a green skin\"}, {\"id\": 42996, \"name\": \"back seat\"}, {\"id\": 42997, \"name\": \"the shop's name\"}, {\"id\": 42998, \"name\": \"the red sedan\"}, {\"id\": 42999, \"name\": \"a red or green bar\"}, {\"id\": 43000, \"name\": \"a slide with a curved top\"}, {\"id\": 43001, \"name\": \"the yellow and black plastic barrier\"}, {\"id\": 43002, \"name\": \"a vertical black bar\"}, {\"id\": 43003, \"name\": \"the distinctive tower\"}, {\"id\": 43004, \"name\": \"the white cone\"}, {\"id\": 43005, \"name\": \"establishment\"}, {\"id\": 43006, \"name\": \"the checkered flag pole\"}, {\"id\": 43007, \"name\": \"the yellow name tag\"}, {\"id\": 43008, \"name\": \"a small wooden structure\"}, {\"id\": 43009, \"name\": \"black and green tractor\"}, {\"id\": 43010, \"name\": \"the beige attire\"}, {\"id\": 43011, \"name\": \"a parked black car\"}, {\"id\": 43012, \"name\": \"a festive string of light\"}, {\"id\": 43013, \"name\": \"a man's waist\"}, {\"id\": 43014, \"name\": \"the carving\"}, {\"id\": 43015, \"name\": \"a stadium light\"}, {\"id\": 43016, \"name\": \"the black bike\"}, {\"id\": 43017, \"name\": \"a system specification\"}, {\"id\": 43018, \"name\": \"a disney-themed signage\"}, {\"id\": 43019, \"name\": \"price of item\"}, {\"id\": 43020, \"name\": \"the colorful skirt\"}, {\"id\": 43021, \"name\": \"the orange body\"}, {\"id\": 43022, \"name\": \"a sweatpants\"}, {\"id\": 43023, \"name\": \"sled's red fabric\"}, {\"id\": 43024, \"name\": \"wooden obstacle\"}, {\"id\": 43025, \"name\": \"the young girls and boy\"}, {\"id\": 43026, \"name\": \"model airplane\"}, {\"id\": 43027, \"name\": \"dark grey metal\"}, {\"id\": 43028, \"name\": \"a large aluminum serving dish\"}, {\"id\": 43029, \"name\": \"the passersby\"}, {\"id\": 43030, \"name\": \"the red machine\"}, {\"id\": 43031, \"name\": \"a silver metal bolt\"}, {\"id\": 43032, \"name\": \"the glittery red bead\"}, {\"id\": 43033, \"name\": \"a large storage compartment\"}, {\"id\": 43034, \"name\": \"a nintendo logo\"}, {\"id\": 43035, \"name\": \"the tall black banner\"}, {\"id\": 43036, \"name\": \"a green pattern\"}, {\"id\": 43037, \"name\": \"a long red cable\"}, {\"id\": 43038, \"name\": \"basket of food\"}, {\"id\": 43039, \"name\": \"the container of makeup powder\"}, {\"id\": 43040, \"name\": \"glass of wine\"}, {\"id\": 43041, \"name\": \"game's scoreboard\"}, {\"id\": 43042, \"name\": \"a light blue kayak\"}, {\"id\": 43043, \"name\": \"the small black cat\"}, {\"id\": 43044, \"name\": \"a lush, tropical vegetation\"}, {\"id\": 43045, \"name\": \"the black and white cleat\"}, {\"id\": 43046, \"name\": \"large, irregularly shaped patch of lime green\"}, {\"id\": 43047, \"name\": \"a list of instructions or specification\"}, {\"id\": 43048, \"name\": \"a backdrop of green seat\"}, {\"id\": 43049, \"name\": \"a volkswagen logo\"}, {\"id\": 43050, \"name\": \"the car's window\"}, {\"id\": 43051, \"name\": \"numerous car\"}, {\"id\": 43052, \"name\": \"brown, black, and white longhorn cattle\"}, {\"id\": 43053, \"name\": \"the herb\"}, {\"id\": 43054, \"name\": \"large crate\"}, {\"id\": 43055, \"name\": \"a gray concrete structure\"}, {\"id\": 43056, \"name\": \"the prominent black banner\"}, {\"id\": 43057, \"name\": \"silver or gold embellishment\"}, {\"id\": 43058, \"name\": \"white-clad player\"}, {\"id\": 43059, \"name\": \"person in a red and white outfit\"}, {\"id\": 43060, \"name\": \"a female player\"}, {\"id\": 43061, \"name\": \"a man in a white hat\"}, {\"id\": 43062, \"name\": \"the pink bar\"}, {\"id\": 43063, \"name\": \"the multitude of white tent\"}, {\"id\": 43064, \"name\": \"small red bucket\"}, {\"id\": 43065, \"name\": \"a traffic light pole\"}, {\"id\": 43066, \"name\": \"a tall smokestack\"}, {\"id\": 43067, \"name\": \"red hawaiian-style shirt\"}, {\"id\": 43068, \"name\": \"a minnie mouse figurine\"}, {\"id\": 43069, \"name\": \"quaint, one- to two-story building\"}, {\"id\": 43070, \"name\": \"the greenish-brown material\"}, {\"id\": 43071, \"name\": \"a large, shallow, silver serving dish\"}, {\"id\": 43072, \"name\": \"a metal tank\"}, {\"id\": 43073, \"name\": \"the cup of drink\"}, {\"id\": 43074, \"name\": \"the ski lodge\"}, {\"id\": 43075, \"name\": \"the male athlete\"}, {\"id\": 43076, \"name\": \"an illustration\"}, {\"id\": 43077, \"name\": \"a sink faucet\"}, {\"id\": 43078, \"name\": \"white turban\"}, {\"id\": 43079, \"name\": \"black suitcase on wheel\"}, {\"id\": 43080, \"name\": \"a red and white lettering\"}, {\"id\": 43081, \"name\": \"a long, pointed tail\"}, {\"id\": 43082, \"name\": \"the lasso\"}, {\"id\": 43083, \"name\": \"a large wooden floor\"}, {\"id\": 43084, \"name\": \"a wood chip\"}, {\"id\": 43085, \"name\": \"silver metal stand\"}, {\"id\": 43086, \"name\": \"the piece of lumber\"}, {\"id\": 43087, \"name\": \"the electronic\"}, {\"id\": 43088, \"name\": \"the orange shoe\"}, {\"id\": 43089, \"name\": \"the teams' name\"}, {\"id\": 43090, \"name\": \"donald trump mask\"}, {\"id\": 43091, \"name\": \"a baking sheet\"}, {\"id\": 43092, \"name\": \"a purple flag\"}, {\"id\": 43093, \"name\": \"the black leather chair\"}, {\"id\": 43094, \"name\": \"black and white jersey\"}, {\"id\": 43095, \"name\": \"a track worker\"}, {\"id\": 43096, \"name\": \"green track\"}, {\"id\": 43097, \"name\": \"large black sign\"}, {\"id\": 43098, \"name\": \"a pink wristband\"}, {\"id\": 43099, \"name\": \"bike's blue and silver frame\"}, {\"id\": 43100, \"name\": \"a large headlight\"}, {\"id\": 43101, \"name\": \"an orange and red uniform\"}, {\"id\": 43102, \"name\": \"a cluttered bathroom counter\"}, {\"id\": 43103, \"name\": \"the purple attire\"}, {\"id\": 43104, \"name\": \"thin white stripe\"}, {\"id\": 43105, \"name\": \"the roof of a building\"}, {\"id\": 43106, \"name\": \"a red cursive writing\"}, {\"id\": 43107, \"name\": \"white cone\"}, {\"id\": 43108, \"name\": \"dark blue uniform\"}, {\"id\": 43109, \"name\": \"the distinctive black patch\"}, {\"id\": 43110, \"name\": \"the small leg\"}, {\"id\": 43111, \"name\": \"a green and yellow question mark\"}, {\"id\": 43112, \"name\": \"the cooked ground meat\"}, {\"id\": 43113, \"name\": \"a silhouette of a factory\"}, {\"id\": 43114, \"name\": \"well-manicured lawn\"}, {\"id\": 43115, \"name\": \"the lifeguard stand\"}, {\"id\": 43116, \"name\": \"distinctive silver horn\"}, {\"id\": 43117, \"name\": \"concrete or asphalt\"}, {\"id\": 43118, \"name\": \"blue bas drum\"}, {\"id\": 43119, \"name\": \"a bundle of electrical wire\"}, {\"id\": 43120, \"name\": \"the sleek, high-end sports car\"}, {\"id\": 43121, \"name\": \"black harness\"}, {\"id\": 43122, \"name\": \"a large price tag\"}, {\"id\": 43123, \"name\": \"turquoise graphic\"}, {\"id\": 43124, \"name\": \"a rocky formation\"}, {\"id\": 43125, \"name\": \"a neon green jacket\"}, {\"id\": 43126, \"name\": \"a red and white stp logo\"}, {\"id\": 43127, \"name\": \"the white horse's head\"}, {\"id\": 43128, \"name\": \"automatic rifle\"}, {\"id\": 43129, \"name\": \"distant spectator\"}, {\"id\": 43130, \"name\": \"the long, black handle\"}, {\"id\": 43131, \"name\": \"white and brown brick structure\"}, {\"id\": 43132, \"name\": \"the face of a man\"}, {\"id\": 43133, \"name\": \"the takeout container\"}, {\"id\": 43134, \"name\": \"small green piece\"}, {\"id\": 43135, \"name\": \"a red brick accent\"}, {\"id\": 43136, \"name\": \"the blue and white light\"}, {\"id\": 43137, \"name\": \"the large, dark-colored, cylindrical object\"}, {\"id\": 43138, \"name\": \"large roof\"}, {\"id\": 43139, \"name\": \"the shark swimming in water\"}, {\"id\": 43140, \"name\": \"a vibrant bouquet of flower\"}, {\"id\": 43141, \"name\": \"the film strip\"}, {\"id\": 43142, \"name\": \"black support beam\"}, {\"id\": 43143, \"name\": \"a man in a black shirt and jean\"}, {\"id\": 43144, \"name\": \"black pedestal\"}, {\"id\": 43145, \"name\": \"blue shell shape\"}, {\"id\": 43146, \"name\": \"a white plastic component\"}, {\"id\": 43147, \"name\": \"a mulch\"}, {\"id\": 43148, \"name\": \"a small, plaid clutch purse\"}, {\"id\": 43149, \"name\": \"diamond necklace\"}, {\"id\": 43150, \"name\": \"a gray puffer jacket\"}, {\"id\": 43151, \"name\": \"subway platform\"}, {\"id\": 43152, \"name\": \"the ford ranger\"}, {\"id\": 43153, \"name\": \"green conveyor belt\"}, {\"id\": 43154, \"name\": \"the vehicle's rear tire\"}, {\"id\": 43155, \"name\": \"the short gray hair\"}, {\"id\": 43156, \"name\": \"a silver embellishment\"}, {\"id\": 43157, \"name\": \"wooden\"}, {\"id\": 43158, \"name\": \"the large, dark-colored light fixture\"}, {\"id\": 43159, \"name\": \"a large vase of flower\"}, {\"id\": 43160, \"name\": \"the white piece of paper or cardboard\"}, {\"id\": 43161, \"name\": \"the large red ball\"}, {\"id\": 43162, \"name\": \"white tiled facade\"}, {\"id\": 43163, \"name\": \"a stone deck\"}, {\"id\": 43164, \"name\": \"single door\"}, {\"id\": 43165, \"name\": \"the white, multi-story hotel\"}, {\"id\": 43166, \"name\": \"the black v-neck shirt\"}, {\"id\": 43167, \"name\": \"pompadour\"}, {\"id\": 43168, \"name\": \"a racing-themed clothing\"}, {\"id\": 43169, \"name\": \"the blue steel\"}, {\"id\": 43170, \"name\": \"white fish\"}, {\"id\": 43171, \"name\": \"a digital timer\"}, {\"id\": 43172, \"name\": \"a prominent blue gantry crane\"}, {\"id\": 43173, \"name\": \"a gold or silver hardware\"}, {\"id\": 43174, \"name\": \"the man in a black t-shirt\"}, {\"id\": 43175, \"name\": \"the blue metal\"}, {\"id\": 43176, \"name\": \"pink plastic egg holder\"}, {\"id\": 43177, \"name\": \"a table with merchandise\"}, {\"id\": 43178, \"name\": \"a manchester united's player\"}, {\"id\": 43179, \"name\": \"the dim lighting\"}, {\"id\": 43180, \"name\": \"a gray dress\"}, {\"id\": 43181, \"name\": \"the loadout\"}, {\"id\": 43182, \"name\": \"the player's attire\"}, {\"id\": 43183, \"name\": \"the large, blue shipping container\"}, {\"id\": 43184, \"name\": \"woman in a black coat\"}, {\"id\": 43185, \"name\": \"a can of spray paint\"}, {\"id\": 43186, \"name\": \"the red crop top\"}, {\"id\": 43187, \"name\": \"the white button-down shirt\"}, {\"id\": 43188, \"name\": \"a red lanyard\"}, {\"id\": 43189, \"name\": \"the commercial vehicle\"}, {\"id\": 43190, \"name\": \"prominent pink circle\"}, {\"id\": 43191, \"name\": \"cycling-related metric\"}, {\"id\": 43192, \"name\": \"a indian policeman\"}, {\"id\": 43193, \"name\": \"woman's right arm\"}, {\"id\": 43194, \"name\": \"the spoke\"}, {\"id\": 43195, \"name\": \"an orange shock absorber\"}, {\"id\": 43196, \"name\": \"white cab\"}, {\"id\": 43197, \"name\": \"banana leaf\"}, {\"id\": 43198, \"name\": \"the pink gift bag\"}, {\"id\": 43199, \"name\": \"receipt or other document\"}, {\"id\": 43200, \"name\": \"a soup or sauce\"}, {\"id\": 43201, \"name\": \"a food or service vehicle\"}, {\"id\": 43202, \"name\": \"the dark, black wall\"}, {\"id\": 43203, \"name\": \"a dark-colored sport car\"}, {\"id\": 43204, \"name\": \"a pink column\"}, {\"id\": 43205, \"name\": \"police tape barrier\"}, {\"id\": 43206, \"name\": \"tiled exterior\"}, {\"id\": 43207, \"name\": \"the ornate molding\"}, {\"id\": 43208, \"name\": \"the game board\"}, {\"id\": 43209, \"name\": \"silver cylindrical trash can\"}, {\"id\": 43210, \"name\": \"the large, rectangular block of stone\"}, {\"id\": 43211, \"name\": \"yellow ford suv\"}, {\"id\": 43212, \"name\": \"the modern and traditional architecture\"}, {\"id\": 43213, \"name\": \"the beige carpet\"}, {\"id\": 43214, \"name\": \"group ride\"}, {\"id\": 43215, \"name\": \"the brown and gray horse\"}, {\"id\": 43216, \"name\": \"metal target\"}, {\"id\": 43217, \"name\": \"the beer tap\"}, {\"id\": 43218, \"name\": \"the small ramp\"}, {\"id\": 43219, \"name\": \"grey top\"}, {\"id\": 43220, \"name\": \"the medical team\"}, {\"id\": 43221, \"name\": \"passing car\"}, {\"id\": 43222, \"name\": \"neon green accent\"}, {\"id\": 43223, \"name\": \"the autumnal foliage\"}, {\"id\": 43224, \"name\": \"dark or black object\"}, {\"id\": 43225, \"name\": \"a blue sequined top\"}, {\"id\": 43226, \"name\": \"the floral patterned cushion\"}, {\"id\": 43227, \"name\": \"rice\"}, {\"id\": 43228, \"name\": \"crashed car\"}, {\"id\": 43229, \"name\": \"a kitchen appliance\"}, {\"id\": 43230, \"name\": \"the visible accessory\"}, {\"id\": 43231, \"name\": \"a single window\"}, {\"id\": 43232, \"name\": \"a yellow lanyard\"}, {\"id\": 43233, \"name\": \"a rich brown sauce\"}, {\"id\": 43234, \"name\": \"a wooden pier or dock\"}, {\"id\": 43235, \"name\": \"a certificate\"}, {\"id\": 43236, \"name\": \"large black sail\"}, {\"id\": 43237, \"name\": \"a bottle of conditioner\"}, {\"id\": 43238, \"name\": \"a homemade wheelbarrow\"}, {\"id\": 43239, \"name\": \"piece of office equipment\"}, {\"id\": 43240, \"name\": \"orange hurdle\"}, {\"id\": 43241, \"name\": \"a gray key\"}, {\"id\": 43242, \"name\": \"the seagull\"}, {\"id\": 43243, \"name\": \"rear passenger-side taillight\"}, {\"id\": 43244, \"name\": \"the writing\"}, {\"id\": 43245, \"name\": \"a luggage conveyor belt\"}, {\"id\": 43246, \"name\": \"a character from the marvel universe\"}, {\"id\": 43247, \"name\": \"the large black curtain\"}, {\"id\": 43248, \"name\": \"a gold tray\"}, {\"id\": 43249, \"name\": \"a brick facade\"}, {\"id\": 43250, \"name\": \"the tamiya brand paint container\"}, {\"id\": 43251, \"name\": \"indoor turf field\"}, {\"id\": 43252, \"name\": \"a dark and light-colored facade\"}, {\"id\": 43253, \"name\": \"the man with white hair\"}, {\"id\": 43254, \"name\": \"the white light switch\"}, {\"id\": 43255, \"name\": \"vibrant red shirt\"}, {\"id\": 43256, \"name\": \"modern signage\"}, {\"id\": 43257, \"name\": \"a striking red brick facade\"}, {\"id\": 43258, \"name\": \"an electrical or mechanical device\"}, {\"id\": 43259, \"name\": \"the dark jacket or coat\"}, {\"id\": 43260, \"name\": \"large blue rectangle\"}, {\"id\": 43261, \"name\": \"the small outdoor bar\"}, {\"id\": 43262, \"name\": \"a fremantle player\"}, {\"id\": 43263, \"name\": \"black writing\"}, {\"id\": 43264, \"name\": \"a digital signage\"}, {\"id\": 43265, \"name\": \"the white step\"}, {\"id\": 43266, \"name\": \"the yellow tenni ball\"}, {\"id\": 43267, \"name\": \"white circular object\"}, {\"id\": 43268, \"name\": \"the player in a blue uniform\"}, {\"id\": 43269, \"name\": \"bottle of product\"}, {\"id\": 43270, \"name\": \"the green sign with a white arrow pointing down\"}, {\"id\": 43271, \"name\": \"the white ferrari\"}, {\"id\": 43272, \"name\": \"a tall black streetlight\"}, {\"id\": 43273, \"name\": \"the napkin\"}, {\"id\": 43274, \"name\": \"man in a referee's uniform\"}, {\"id\": 43275, \"name\": \"black, blue, and white jersey\"}, {\"id\": 43276, \"name\": \"the racing silk\"}, {\"id\": 43277, \"name\": \"the black and yellow swimsuit\"}, {\"id\": 43278, \"name\": \"writing utensil\"}, {\"id\": 43279, \"name\": \"silver spoke\"}, {\"id\": 43280, \"name\": \"the dark shell\"}, {\"id\": 43281, \"name\": \"the tan block\"}, {\"id\": 43282, \"name\": \"a colorful boat\"}, {\"id\": 43283, \"name\": \"a floral motif\"}, {\"id\": 43284, \"name\": \"large gray truck\"}, {\"id\": 43285, \"name\": \"ornate gold detail\"}, {\"id\": 43286, \"name\": \"the wooden utility pole\"}, {\"id\": 43287, \"name\": \"the hanging lantern\"}, {\"id\": 43288, \"name\": \"the woman's smile\"}, {\"id\": 43289, \"name\": \"small digital screen\"}, {\"id\": 43290, \"name\": \"a gold-painted nail\"}, {\"id\": 43291, \"name\": \"boy in the red shirt\"}, {\"id\": 43292, \"name\": \"the black and yellow motorcycle\"}, {\"id\": 43293, \"name\": \"the rocky hill\"}, {\"id\": 43294, \"name\": \"stern\"}, {\"id\": 43295, \"name\": \"wooden table or desk\"}, {\"id\": 43296, \"name\": \"a large spotlight\"}, {\"id\": 43297, \"name\": \"male athlete\"}, {\"id\": 43298, \"name\": \"a large, ornate structure\"}, {\"id\": 43299, \"name\": \"the translucent black banner\"}, {\"id\": 43300, \"name\": \"the red and white glove\"}, {\"id\": 43301, \"name\": \"a red and green costume\"}, {\"id\": 43302, \"name\": \"a lush bush\"}, {\"id\": 43303, \"name\": \"four-wheel-drive car\"}, {\"id\": 43304, \"name\": \"a rooster's head\"}, {\"id\": 43305, \"name\": \"kitten's ear\"}, {\"id\": 43306, \"name\": \"the circular opening\"}, {\"id\": 43307, \"name\": \"kid\"}, {\"id\": 43308, \"name\": \"an intricate lace detail\"}, {\"id\": 43309, \"name\": \"metal surface\"}, {\"id\": 43310, \"name\": \"a khaki clothing\"}, {\"id\": 43311, \"name\": \"a horse rider\"}, {\"id\": 43312, \"name\": \"multicolored pennant flag\"}, {\"id\": 43313, \"name\": \"distinctive green surface\"}, {\"id\": 43314, \"name\": \"the man in a turban\"}, {\"id\": 43315, \"name\": \"a red-painted crosswalk\"}, {\"id\": 43316, \"name\": \"an outboard motor\"}, {\"id\": 43317, \"name\": \"bright yellow top\"}, {\"id\": 43318, \"name\": \"the front wheel of a green tractor\"}, {\"id\": 43319, \"name\": \"green metal bar\"}, {\"id\": 43320, \"name\": \"a yellow post\"}, {\"id\": 43321, \"name\": \"dark grey asphalt\"}, {\"id\": 43322, \"name\": \"bronze-colored metal object\"}, {\"id\": 43323, \"name\": \"the dark hull\"}, {\"id\": 43324, \"name\": \"the silver step\"}, {\"id\": 43325, \"name\": \"a green bike\"}, {\"id\": 43326, \"name\": \"monster energy helmet\"}, {\"id\": 43327, \"name\": \"the red-shirted individual\"}, {\"id\": 43328, \"name\": \"camper van\"}, {\"id\": 43329, \"name\": \"boy in swim trunk\"}, {\"id\": 43330, \"name\": \"the dark tinted windshield\"}, {\"id\": 43331, \"name\": \"yellow garage door\"}, {\"id\": 43332, \"name\": \"a large airplane\"}, {\"id\": 43333, \"name\": \"the truck's body\"}, {\"id\": 43334, \"name\": \"light-colored dress\"}, {\"id\": 43335, \"name\": \"bright pink top\"}, {\"id\": 43336, \"name\": \"small brown goat\"}, {\"id\": 43337, \"name\": \"a beige cabinet or storage unit\"}, {\"id\": 43338, \"name\": \"surrounding container\"}, {\"id\": 43339, \"name\": \"a pen or pencil\"}, {\"id\": 43340, \"name\": \"large metal panel\"}, {\"id\": 43341, \"name\": \"blue short or pant\"}, {\"id\": 43342, \"name\": \"a work boot\"}, {\"id\": 43343, \"name\": \"circular, shallow metal object\"}, {\"id\": 43344, \"name\": \"a black and white patterned top\"}, {\"id\": 43345, \"name\": \"advertising sign\"}, {\"id\": 43346, \"name\": \"seagull\"}, {\"id\": 43347, \"name\": \"long trousers\"}, {\"id\": 43348, \"name\": \"a gray pickup truck\"}, {\"id\": 43349, \"name\": \"hello kitty pillow\"}, {\"id\": 43350, \"name\": \"white dump truck\"}, {\"id\": 43351, \"name\": \"the green shutter door\"}, {\"id\": 43352, \"name\": \"the clear windshield\"}, {\"id\": 43353, \"name\": \"large white umbrella\"}, {\"id\": 43354, \"name\": \"a surgical cap\"}, {\"id\": 43355, \"name\": \"the instructor\"}, {\"id\": 43356, \"name\": \"a small red ball\"}, {\"id\": 43357, \"name\": \"folding table\"}, {\"id\": 43358, \"name\": \"distinctive blue design\"}, {\"id\": 43359, \"name\": \"a blue Volkswagen\"}, {\"id\": 43360, \"name\": \"a bright yellow hexagon\"}, {\"id\": 43361, \"name\": \"the blue kayak\"}, {\"id\": 43362, \"name\": \"large bush\"}, {\"id\": 43363, \"name\": \"a plate of noodle\"}, {\"id\": 43364, \"name\": \"a metal security gate\"}, {\"id\": 43365, \"name\": \"a feathered headpiece\"}, {\"id\": 43366, \"name\": \"the bright pink, glowing object\"}, {\"id\": 43367, \"name\": \"the corrugated metal exterior\"}, {\"id\": 43368, \"name\": \"a small orange pepper slice\"}, {\"id\": 43369, \"name\": \"prominent pink sign\"}, {\"id\": 43370, \"name\": \"the small black table\"}, {\"id\": 43371, \"name\": \"people on motorcycle\"}, {\"id\": 43372, \"name\": \"black bird\"}, {\"id\": 43373, \"name\": \"the grey hood\"}, {\"id\": 43374, \"name\": \"a pink attire\"}, {\"id\": 43375, \"name\": \"the blue float\"}, {\"id\": 43376, \"name\": \"decorative facade\"}, {\"id\": 43377, \"name\": \"the snapping turtle\"}, {\"id\": 43378, \"name\": \"the cotton candy maker\"}, {\"id\": 43379, \"name\": \"clear, plastic stem\"}, {\"id\": 43380, \"name\": \"ball or puck\"}, {\"id\": 43381, \"name\": \"a black curtain or partition\"}, {\"id\": 43382, \"name\": \"a tranquil river\"}, {\"id\": 43383, \"name\": \"hen\"}, {\"id\": 43384, \"name\": \"the exposed red piping\"}, {\"id\": 43385, \"name\": \"the glass of beer\"}, {\"id\": 43386, \"name\": \"the brown camel\"}, {\"id\": 43387, \"name\": \"a large green shrub\"}, {\"id\": 43388, \"name\": \"the staff\"}, {\"id\": 43389, \"name\": \"silver pompom\"}, {\"id\": 43390, \"name\": \"a blue and white patterned shirt\"}, {\"id\": 43391, \"name\": \"gray bag\"}, {\"id\": 43392, \"name\": \"red brick pillar\"}, {\"id\": 43393, \"name\": \"a distinctive orange mask\"}, {\"id\": 43394, \"name\": \"brown crossbody bag\"}, {\"id\": 43395, \"name\": \"small blue bar\"}, {\"id\": 43396, \"name\": \"a man in an orange vest\"}, {\"id\": 43397, \"name\": \"a purple top\"}, {\"id\": 43398, \"name\": \"a large portrait of a woman\"}, {\"id\": 43399, \"name\": \"busy sidewalk\"}, {\"id\": 43400, \"name\": \"orange jumpsuit\"}, {\"id\": 43401, \"name\": \"a red berry\"}, {\"id\": 43402, \"name\": \"brown plant material\"}, {\"id\": 43403, \"name\": \"close-up of a woman's face\"}, {\"id\": 43404, \"name\": \"a large bundle of white bag\"}, {\"id\": 43405, \"name\": \"red and white striped banner\"}, {\"id\": 43406, \"name\": \"a large black trash can\"}, {\"id\": 43407, \"name\": \"a drink dispenser\"}, {\"id\": 43408, \"name\": \"a dark grey sedan\"}, {\"id\": 43409, \"name\": \"dhoti\"}, {\"id\": 43410, \"name\": \"hockey puck\"}, {\"id\": 43411, \"name\": \"the white number\"}, {\"id\": 43412, \"name\": \"the rusty, corrugated metal roof\"}, {\"id\": 43413, \"name\": \"a colorful parachute\"}, {\"id\": 43414, \"name\": \"pile of bacon\"}, {\"id\": 43415, \"name\": \"the sports-related item\"}, {\"id\": 43416, \"name\": \"the white chalk marking\"}, {\"id\": 43417, \"name\": \"motorcycle's windshield\"}, {\"id\": 43418, \"name\": \"the large digital sign\"}, {\"id\": 43419, \"name\": \"small black and white striped fish\"}, {\"id\": 43420, \"name\": \"the black and white kitten's tail\"}, {\"id\": 43421, \"name\": \"a beer's name\"}, {\"id\": 43422, \"name\": \"a gas tank\"}, {\"id\": 43423, \"name\": \"pile of rocks and debris\"}, {\"id\": 43424, \"name\": \"red lip\"}, {\"id\": 43425, \"name\": \"a broken branch\"}, {\"id\": 43426, \"name\": \"red emergency vehicle\"}, {\"id\": 43427, \"name\": \"the training equipment\"}, {\"id\": 43428, \"name\": \"the large, flat, round object\"}, {\"id\": 43429, \"name\": \"a colorful tuk-tuk\"}, {\"id\": 43430, \"name\": \"a black display table\"}, {\"id\": 43431, \"name\": \"the engine part\"}, {\"id\": 43432, \"name\": \"the exercise equipment\"}, {\"id\": 43433, \"name\": \"a blue light fixture\"}, {\"id\": 43434, \"name\": \"the man in a police uniform\"}, {\"id\": 43435, \"name\": \"surrounding toy\"}, {\"id\": 43436, \"name\": \"black swim trunk\"}, {\"id\": 43437, \"name\": \"a brightly colored plastic piece\"}, {\"id\": 43438, \"name\": \"the red taxi\"}, {\"id\": 43439, \"name\": \"a maroon dress\"}, {\"id\": 43440, \"name\": \"a green kite\"}, {\"id\": 43441, \"name\": \"man in a yellow t-shirt\"}, {\"id\": 43442, \"name\": \"small clump of green gras or shrub\"}, {\"id\": 43443, \"name\": \"concrete planter\"}, {\"id\": 43444, \"name\": \"a patterned blouse\"}, {\"id\": 43445, \"name\": \"electronic display\"}, {\"id\": 43446, \"name\": \"woman in the group\"}, {\"id\": 43447, \"name\": \"the blow dryer\"}, {\"id\": 43448, \"name\": \"skier\"}, {\"id\": 43449, \"name\": \"a pretzel\"}, {\"id\": 43450, \"name\": \"an elevated highway\"}, {\"id\": 43451, \"name\": \"a yellow rope or cable\"}, {\"id\": 43452, \"name\": \"a wrought-iron fence\"}, {\"id\": 43453, \"name\": \"white cylindrical structure\"}, {\"id\": 43454, \"name\": \"the male character\"}, {\"id\": 43455, \"name\": \"small, light-colored surfboard\"}, {\"id\": 43456, \"name\": \"player's avatar\"}, {\"id\": 43457, \"name\": \"elaborate lion head\"}, {\"id\": 43458, \"name\": \"the large crowd of spectators\"}, {\"id\": 43459, \"name\": \"the stacks of packaged good\"}, {\"id\": 43460, \"name\": \"teacup-style seating\"}, {\"id\": 43461, \"name\": \"the gold headband\"}, {\"id\": 43462, \"name\": \"a long pink tail\"}, {\"id\": 43463, \"name\": \"the small, colorful, and plastic-looking bumper car\"}, {\"id\": 43464, \"name\": \"orange coloring\"}, {\"id\": 43465, \"name\": \"a bright red head\"}, {\"id\": 43466, \"name\": \"the light pink top\"}, {\"id\": 43467, \"name\": \"the frozen backpack\"}, {\"id\": 43468, \"name\": \"mermaid design\"}, {\"id\": 43469, \"name\": \"a man on a bike\"}, {\"id\": 43470, \"name\": \"the digital display board\"}, {\"id\": 43471, \"name\": \"large, transparent bubble\"}, {\"id\": 43472, \"name\": \"the small, pixelated creature\"}, {\"id\": 43473, \"name\": \"the row of bleachers\"}, {\"id\": 43474, \"name\": \"the red and white striped flag\"}, {\"id\": 43475, \"name\": \"the manual labor\"}, {\"id\": 43476, \"name\": \"the large circular object\"}, {\"id\": 43477, \"name\": \"the yellow rain poncho\"}, {\"id\": 43478, \"name\": \"the vibrant yellow or gold-colored clothing\"}, {\"id\": 43479, \"name\": \"a smaller blue circle\"}, {\"id\": 43480, \"name\": \"the circular light\"}, {\"id\": 43481, \"name\": \"large, multicolored shoe\"}, {\"id\": 43482, \"name\": \"a large military plane\"}, {\"id\": 43483, \"name\": \"traditional indian attire\"}, {\"id\": 43484, \"name\": \"a large, round, silver surface\"}, {\"id\": 43485, \"name\": \"X symbol\"}, {\"id\": 43486, \"name\": \"tall, white, cylindrical structure\"}, {\"id\": 43487, \"name\": \"the rounded top\"}, {\"id\": 43488, \"name\": \"the lone rower\"}, {\"id\": 43489, \"name\": \"yellow garland\"}, {\"id\": 43490, \"name\": \"fisherman\"}, {\"id\": 43491, \"name\": \"a bright lighting\"}, {\"id\": 43492, \"name\": \"the wooden award\"}, {\"id\": 43493, \"name\": \"neon yellow bike\"}, {\"id\": 43494, \"name\": \"the orange and white truck\"}, {\"id\": 43495, \"name\": \"mosque's illuminated tower\"}, {\"id\": 43496, \"name\": \"the wooden log\"}, {\"id\": 43497, \"name\": \"white target\"}, {\"id\": 43498, \"name\": \"metal awning\"}, {\"id\": 43499, \"name\": \"the safety warning\"}, {\"id\": 43500, \"name\": \"a piece of red packaging material\"}, {\"id\": 43501, \"name\": \"the foreground jet ski\"}, {\"id\": 43502, \"name\": \"the blue sail\"}, {\"id\": 43503, \"name\": \"pannier\"}, {\"id\": 43504, \"name\": \"tall, striped hat\"}, {\"id\": 43505, \"name\": \"the red and brown feather\"}, {\"id\": 43506, \"name\": \"the lone worker\"}, {\"id\": 43507, \"name\": \"the brown sign\"}, {\"id\": 43508, \"name\": \"the bowl of noodles\"}, {\"id\": 43509, \"name\": \"girl's shadow\"}, {\"id\": 43510, \"name\": \"large metal pot\"}, {\"id\": 43511, \"name\": \"brown woven fabric\"}, {\"id\": 43512, \"name\": \"a horizontal beam\"}, {\"id\": 43513, \"name\": \"large billboard or advertisement\"}, {\"id\": 43514, \"name\": \"dump truck\"}, {\"id\": 43515, \"name\": \"green and yellow taxi\"}, {\"id\": 43516, \"name\": \"vibrant red plumage\"}, {\"id\": 43517, \"name\": \"a dark blue tie\"}, {\"id\": 43518, \"name\": \"a viking-inspired attire\"}, {\"id\": 43519, \"name\": \"the white chicken\"}, {\"id\": 43520, \"name\": \"rickshaw driver\"}, {\"id\": 43521, \"name\": \"a white banner or sign\"}, {\"id\": 43522, \"name\": \"a bundle of hay\"}, {\"id\": 43523, \"name\": \"footwear\"}, {\"id\": 43524, \"name\": \"the bright orange light\"}, {\"id\": 43525, \"name\": \"the discarded plastic bottle\"}, {\"id\": 43526, \"name\": \"the worn tire\"}, {\"id\": 43527, \"name\": \"brown, segmented body\"}, {\"id\": 43528, \"name\": \"the cardboard sign\"}, {\"id\": 43529, \"name\": \"small object\"}, {\"id\": 43530, \"name\": \"a fireman\"}, {\"id\": 43531, \"name\": \"a red ingredient\"}, {\"id\": 43532, \"name\": \"large white cab\"}, {\"id\": 43533, \"name\": \"a bright white light\"}, {\"id\": 43534, \"name\": \"a blue umbrella or canopy\"}, {\"id\": 43535, \"name\": \"large, glowing red sword\"}, {\"id\": 43536, \"name\": \"the large, broken utility pole\"}, {\"id\": 43537, \"name\": \"the dragon or other mythical creature\"}, {\"id\": 43538, \"name\": \"the white plastic mat\"}, {\"id\": 43539, \"name\": \"a dark object\"}, {\"id\": 43540, \"name\": \"round leaf\"}, {\"id\": 43541, \"name\": \"white metal casing\"}, {\"id\": 43542, \"name\": \"a small, white lego figure\"}, {\"id\": 43543, \"name\": \"ornate costume\"}, {\"id\": 43544, \"name\": \"ornate stonework\"}, {\"id\": 43545, \"name\": \"orange cloth\"}, {\"id\": 43546, \"name\": \"the grey concrete sidewalk\"}, {\"id\": 43547, \"name\": \"the glass of drink\"}, {\"id\": 43548, \"name\": \"purple fabric\"}, {\"id\": 43549, \"name\": \"front poster\"}, {\"id\": 43550, \"name\": \"matching black pant\"}, {\"id\": 43551, \"name\": \"a bright blue and white hue\"}, {\"id\": 43552, \"name\": \"a shiny pink horse\"}, {\"id\": 43553, \"name\": \"stacked plastic container\"}, {\"id\": 43554, \"name\": \"the yellow and blue toy\"}, {\"id\": 43555, \"name\": \"an orange t-shirt\"}, {\"id\": 43556, \"name\": \"gray gutter\"}, {\"id\": 43557, \"name\": \"the red health bar\"}, {\"id\": 43558, \"name\": \"the black and tan dog\"}, {\"id\": 43559, \"name\": \"matching red bra strap\"}, {\"id\": 43560, \"name\": \"white marble or tile\"}, {\"id\": 43561, \"name\": \"yacht\"}, {\"id\": 43562, \"name\": \"the colorful balloon display\"}, {\"id\": 43563, \"name\": \"the black roller skate\"}, {\"id\": 43564, \"name\": \"the large white crane\"}, {\"id\": 43565, \"name\": \"blue dragon\"}, {\"id\": 43566, \"name\": \"a button-up shirt\"}, {\"id\": 43567, \"name\": \"blue or white gown\"}, {\"id\": 43568, \"name\": \"the brick-paved walkway\"}, {\"id\": 43569, \"name\": \"black puck\"}, {\"id\": 43570, \"name\": \"collared peccary\"}, {\"id\": 43571, \"name\": \"red brocket deer\"}, {\"id\": 43572, \"name\": \"wild cat\"}, {\"id\": 43573, \"name\": \"ocelot\"}, {\"id\": 43574, \"name\": \"paca\"}, {\"id\": 43575, \"name\": \"spider monkey\"}, {\"id\": 43576, \"name\": \"puma\"}, {\"id\": 43577, \"name\": \"crested guan\"}, {\"id\": 43578, \"name\": \"urial sheep\"}, {\"id\": 43579, \"name\": \"common opossum\"}, {\"id\": 43580, \"name\": \"gorilla\"}, {\"id\": 43581, \"name\": \"great curassow\"}, {\"id\": 43582, \"name\": \"jaguar\"}, {\"id\": 43583, \"name\": \"great tinamou\"}, {\"id\": 43584, \"name\": \"red-billed currasow\"}, {\"id\": 43585, \"name\": \"chimpanzee\"}, {\"id\": 43586, \"name\": \"red howler\"}, {\"id\": 43587, \"name\": \"crab-eating raccoon\"}, {\"id\": 43588, \"name\": \"capuchin monkey\"}, {\"id\": 43589, \"name\": \"agouti\"}, {\"id\": 43590, \"name\": \"persian leopard\"}, {\"id\": 43591, \"name\": \"bezoar goat\"}, {\"id\": 43592, \"name\": \"trumpeter\"}, {\"id\": 43593, \"name\": \"black howler\"}, {\"id\": 43594, \"name\": \"harpy eagle\"}, {\"id\": 43595, \"name\": \"squirrel\"}, {\"id\": 43596, \"name\": \"porcupine\"}, {\"id\": 43597, \"name\": \"northern tamandua\"}, {\"id\": 43598, \"name\": \"squirrel monkey\"}, {\"id\": 43599, \"name\": \"white-nosed coati\"}, {\"id\": 43600, \"name\": \"tayra\"}, {\"id\": 43601, \"name\": \"four-eyed opossum\"}, {\"id\": 43602, \"name\": \"night monkey\"}, {\"id\": 43603, \"name\": \"green iguana\"}, {\"id\": 43604, \"name\": \"striped hog-nosed skunk\"}, {\"id\": 43605, \"name\": \"marbled wood quail\"}, {\"id\": 43606, \"name\": \"fox\"}, {\"id\": 43607, \"name\": \"ninebanded armadillo\"}, {\"id\": 43608, \"name\": \"baird's tapir\"}, {\"id\": 43609, \"name\": \"margay\"}, {\"id\": 43610, \"name\": \"acouchy\"}, {\"id\": 43611, \"name\": \"hyena\"}, {\"id\": 43612, \"name\": \"bamboo rat\"}, {\"id\": 43613, \"name\": \"kinkajou\"}, {\"id\": 43614, \"name\": \"gray-necked wood rail\"}, {\"id\": 43615, \"name\": \"vulture\"}, {\"id\": 43616, \"name\": \"woolly monkey\"}, {\"id\": 43617, \"name\": \"peccary\"}, {\"id\": 43618, \"name\": \"african elephant\"}, {\"id\": 43619, \"name\": \"spix's guan\"}, {\"id\": 43620, \"name\": \"woolly opossum\"}, {\"id\": 43621, \"name\": \"blue-crowned motmot\"}, {\"id\": 43622, \"name\": \"jaguarundi\"}, {\"id\": 43623, \"name\": \"goitered gazelle\"}, {\"id\": 43624, \"name\": \"sloth\"}, {\"id\": 43625, \"name\": \"little tinamou\"}, {\"id\": 43626, \"name\": \"olingo\"}, {\"id\": 43627, \"name\": \"saddleback tamarin \"}, {\"id\": 43628, \"name\": \"red river hog\"}, {\"id\": 43629, \"name\": \"white-lipped peccary\"}, {\"id\": 43630, \"name\": \"olive oropendula\"}, {\"id\": 43631, \"name\": \"short crested hawk\"}, {\"id\": 43632, \"name\": \"lizard\"}, {\"id\": 43633, \"name\": \"gray-headed chachalaca\"}, {\"id\": 43634, \"name\": \"blue duiker\"}, {\"id\": 43635, \"name\": \"wolf\"}, {\"id\": 43636, \"name\": \"mustelid\"}, {\"id\": 43637, \"name\": \"coyote\"}, {\"id\": 43638, \"name\": \"black-faced antthrush\"}, {\"id\": 43639, \"name\": \"putty-nosed monkey\"}, {\"id\": 43640, \"name\": \"wild boar\"}, {\"id\": 43641, \"name\": \"iberian lynx\"}, {\"id\": 43642, \"name\": \"domestic cat\"}, {\"id\": 43643, \"name\": \"titi monkey\"}, {\"id\": 43644, \"name\": \"pacarana\"}, {\"id\": 43645, \"name\": \"greater grison\"}, {\"id\": 43646, \"name\": \"forest buffalo\"}, {\"id\": 43647, \"name\": \"peters's duiker\"}, {\"id\": 43648, \"name\": \"nunbird\"}, {\"id\": 43649, \"name\": \"emperor tamarin\"}, {\"id\": 43650, \"name\": \"indian crested porcupine\"}, {\"id\": 43651, \"name\": \"giant anteater\"}, {\"id\": 43652, \"name\": \"lettered aracari\"}, {\"id\": 43653, \"name\": \"eagle\"}, {\"id\": 43654, \"name\": \"giant armadillo\"}, {\"id\": 43655, \"name\": \"ground cuckoo\"}, {\"id\": 43656, \"name\": \"yellow-footed tortoise\"}, {\"id\": 43657, \"name\": \"yellow-headed vulture\"}, {\"id\": 43658, \"name\": \"capybara\"}, {\"id\": 43659, \"name\": \"great black hawk\"}, {\"id\": 43660, \"name\": \"short-eared dog\"}, {\"id\": 43661, \"name\": \"woodcreeper\"}, {\"id\": 43662, \"name\": \"hedgehog\"}, {\"id\": 43663, \"name\": \"roadside hawk\"}, {\"id\": 43664, \"name\": \"crested guinea fowl\"}, {\"id\": 43665, \"name\": \"moustached monkey\"}, {\"id\": 43666, \"name\": \"golden-crowned toucanet\"}, {\"id\": 43667, \"name\": \"badger\"}, {\"id\": 43668, \"name\": \"golden tegu\"}, {\"id\": 43669, \"name\": \"snow leopard\"}, {\"id\": 43670, \"name\": \"common mongoose\"}, {\"id\": 43671, \"name\": \"saki monkey\"}, {\"id\": 43672, \"name\": \"white-throated toucan\"}, {\"id\": 43673, \"name\": \"chukar partridge\"}, {\"id\": 43674, \"name\": \"the food residue\"}, {\"id\": 43675, \"name\": \"a wine glass\"}, {\"id\": 43676, \"name\": \"octopus\"}, {\"id\": 43677, \"name\": \"sparrow\"}, {\"id\": 43678, \"name\": \"a gauntlet\"}, {\"id\": 43679, \"name\": \"jogger\"}, {\"id\": 43680, \"name\": \"the moving box\"}, {\"id\": 43681, \"name\": \"the male\"}, {\"id\": 43682, \"name\": \"a red kettlebell\"}, {\"id\": 43683, \"name\": \"a red traffic sign\"}, {\"id\": 43684, \"name\": \"dolphin\"}, {\"id\": 43685, \"name\": \"the lock\"}, {\"id\": 43686, \"name\": \"a djembe drum\"}, {\"id\": 43687, \"name\": \"the solar panel\"}, {\"id\": 43688, \"name\": \"chef\"}, {\"id\": 43689, \"name\": \"peach\"}, {\"id\": 43690, \"name\": \"the party popper\"}, {\"id\": 43691, \"name\": \"the white wine\"}, {\"id\": 43692, \"name\": \"the wooden cutting board\"}, {\"id\": 43693, \"name\": \"a globe\"}, {\"id\": 43694, \"name\": \"the wasabi\"}, {\"id\": 43695, \"name\": \"a pwc\"}, {\"id\": 43696, \"name\": \"dough\"}, {\"id\": 43697, \"name\": \"the orb\"}, {\"id\": 43698, \"name\": \"the glass of white wine\"}, {\"id\": 43699, \"name\": \"man with a beard\"}, {\"id\": 43700, \"name\": \"party popper\"}, {\"id\": 43701, \"name\": \"the cup of soup\"}, {\"id\": 43702, \"name\": \"a seating for spectator\"}, {\"id\": 43703, \"name\": \"cylindrical silver component\"}, {\"id\": 43704, \"name\": \"a red icing\"}, {\"id\": 43705, \"name\": \"orangutan\"}, {\"id\": 43706, \"name\": \"a silhouetted figure\"}, {\"id\": 43707, \"name\": \"a green sled\"}, {\"id\": 43708, \"name\": \"the hiker\"}, {\"id\": 43709, \"name\": \"a glass bottle of juice\"}, {\"id\": 43710, \"name\": \"chicken piece\"}, {\"id\": 43711, \"name\": \"a pepper\"}, {\"id\": 43712, \"name\": \"a green frosting\"}, {\"id\": 43713, \"name\": \"the tray of chocolate\"}, {\"id\": 43714, \"name\": \"white kitchen cabinet\"}, {\"id\": 43715, \"name\": \"dancer\"}, {\"id\": 43716, \"name\": \"red vegetable\"}, {\"id\": 43717, \"name\": \"blurred woman\"}, {\"id\": 43718, \"name\": \"a white backboard\"}, {\"id\": 43719, \"name\": \"a black with white spot\"}, {\"id\": 43720, \"name\": \"the beer bottle\"}, {\"id\": 43721, \"name\": \"a cocktail\"}, {\"id\": 43722, \"name\": \"futuristic car\"}, {\"id\": 43723, \"name\": \"smartphone icon\"}, {\"id\": 43724, \"name\": \"silver fork\"}, {\"id\": 43725, \"name\": \"green beer bottle\"}, {\"id\": 43726, \"name\": \"dark-colored coconut\"}, {\"id\": 43727, \"name\": \"gear\"}, {\"id\": 43728, \"name\": \"a kayaker's paddle\"}, {\"id\": 43729, \"name\": \"a radio tower\"}, {\"id\": 43730, \"name\": \"the sliced avocado\"}, {\"id\": 43731, \"name\": \"top deck\"}, {\"id\": 43732, \"name\": \"grilled meat\"}, {\"id\": 43733, \"name\": \"pelican\"}, {\"id\": 43734, \"name\": \"a metal side\"}, {\"id\": 43735, \"name\": \"dancing woman\"}, {\"id\": 43736, \"name\": \"blue paddle\"}, {\"id\": 43737, \"name\": \"the powwow\"}, {\"id\": 43738, \"name\": \"gray shark\"}, {\"id\": 43739, \"name\": \"the taco shell\"}, {\"id\": 43740, \"name\": \"blueberry\"}, {\"id\": 43741, \"name\": \"the male chef\"}, {\"id\": 43742, \"name\": \"ice cube\"}, {\"id\": 43743, \"name\": \"the industrial-style lighting\"}, {\"id\": 43744, \"name\": \"the yellow and black feather\"}, {\"id\": 43745, \"name\": \"the swimming goggle\"}, {\"id\": 43746, \"name\": \"an overhead compartment\"}, {\"id\": 43747, \"name\": \"a rollerblade\"}, {\"id\": 43748, \"name\": \"pattern of white circle\"}, {\"id\": 43749, \"name\": \"an armed person\"}, {\"id\": 43750, \"name\": \"an orange butterfly\"}, {\"id\": 43751, \"name\": \"black stereo system\"}, {\"id\": 43752, \"name\": \"a kitchen shelf\"}, {\"id\": 43753, \"name\": \"the faint white circle\"}, {\"id\": 43754, \"name\": \"back paw\"}, {\"id\": 43755, \"name\": \"a runner\"}, {\"id\": 43756, \"name\": \"an orange vegetable\"}, {\"id\": 43757, \"name\": \"pink foot\"}, {\"id\": 43758, \"name\": \"the flipper\"}, {\"id\": 43759, \"name\": \"blue jockey\"}, {\"id\": 43760, \"name\": \"a pendant lighting\"}, {\"id\": 43761, \"name\": \"billiard ball\"}, {\"id\": 43762, \"name\": \"a surfer\"}, {\"id\": 43763, \"name\": \"bowl of salad\"}, {\"id\": 43764, \"name\": \"silverware\"}, {\"id\": 43765, \"name\": \"the scooter's wheel\"}, {\"id\": 43766, \"name\": \"front flipper\"}, {\"id\": 43767, \"name\": \"a pinkish-red fruit\"}, {\"id\": 43768, \"name\": \"grapefruit\"}, {\"id\": 43769, \"name\": \"runner\"}, {\"id\": 43770, \"name\": \"a white wakeboard\"}, {\"id\": 43771, \"name\": \"plyometric box\"}, {\"id\": 43772, \"name\": \"neon strip\"}, {\"id\": 43773, \"name\": \"a glass of red wine\"}, {\"id\": 43774, \"name\": \"a white painted bicycle symbol\"}, {\"id\": 43775, \"name\": \"caesar salad\"}, {\"id\": 43776, \"name\": \"the red wire\"}, {\"id\": 43777, \"name\": \"the wooden wheel\"}, {\"id\": 43778, \"name\": \"a black and silver machine\"}, {\"id\": 43779, \"name\": \"radish\"}, {\"id\": 43780, \"name\": \"falcon\"}, {\"id\": 43781, \"name\": \"swan\"}, {\"id\": 43782, \"name\": \"a pointed snout\"}, {\"id\": 43783, \"name\": \"the charcoal\"}, {\"id\": 43784, \"name\": \"the chalk powder\"}, {\"id\": 43785, \"name\": \"champagne\"}, {\"id\": 43786, \"name\": \"the head of rabbit\"}, {\"id\": 43787, \"name\": \"person wearing a mask\"}, {\"id\": 43788, \"name\": \"the sparkler\"}, {\"id\": 43789, \"name\": \"container truck\"}, {\"id\": 43790, \"name\": \"the pointed dorsal fin\"}, {\"id\": 43791, \"name\": \"a stethoscope\"}, {\"id\": 43792, \"name\": \"the fried shrimp tempura\"}, {\"id\": 43793, \"name\": \"a blue exercise ball\"}, {\"id\": 43794, \"name\": \"rack of exercise ball\"}, {\"id\": 43795, \"name\": \"the red, white, and blue medal\"}, {\"id\": 43796, \"name\": \"a yellow drink\"}, {\"id\": 43797, \"name\": \"the artichoke heart\"}, {\"id\": 43798, \"name\": \"black hoove\"}, {\"id\": 43799, \"name\": \"parrot\"}, {\"id\": 43800, \"name\": \"duckling\"}, {\"id\": 43801, \"name\": \"a ski\"}, {\"id\": 43802, \"name\": \"the white underbelly\"}, {\"id\": 43803, \"name\": \"a flipper\"}, {\"id\": 43804, \"name\": \"a sheep's face\"}, {\"id\": 43805, \"name\": \"rhinoceros\"}, {\"id\": 43806, \"name\": \"dark hoof\"}, {\"id\": 43807, \"name\": \"the pennant\"}, {\"id\": 43808, \"name\": \"a sliced tomato\"}, {\"id\": 43809, \"name\": \"the elephant\"}, {\"id\": 43810, \"name\": \"tenni ball\"}, {\"id\": 43811, \"name\": \"cable car\"}, {\"id\": 43812, \"name\": \"caveperson\"}, {\"id\": 43813, \"name\": \"sprig of mint\"}, {\"id\": 43814, \"name\": \"numbered lane\"}, {\"id\": 43815, \"name\": \"a ride's seat\"}, {\"id\": 43816, \"name\": \"a cooking supply\"}, {\"id\": 43817, \"name\": \"the glass of wine\"}, {\"id\": 43818, \"name\": \"the sharp beak\"}, {\"id\": 43819, \"name\": \"the maasai warrior\"}, {\"id\": 43820, \"name\": \"motor cycle\"}, {\"id\": 43821, \"name\": \"a lettuce garnish\"}, {\"id\": 43822, \"name\": \"zebra\"}, {\"id\": 43823, \"name\": \"a steak\"}, {\"id\": 43824, \"name\": \"the butcher's counter\"}, {\"id\": 43825, \"name\": \"the headlight of the vehicle\"}, {\"id\": 43826, \"name\": \"hiker\"}, {\"id\": 43827, \"name\": \"a white bunting\"}, {\"id\": 43828, \"name\": \"black resistance band\"}, {\"id\": 43829, \"name\": \"white-tipped tail\"}, {\"id\": 43830, \"name\": \"a yellow ear tag\"}, {\"id\": 43831, \"name\": \"a violin bow\"}, {\"id\": 43832, \"name\": \"a jar of coffee bean\"}, {\"id\": 43833, \"name\": \"lorikeet\"}, {\"id\": 43834, \"name\": \"glass of white wine\"}, {\"id\": 43835, \"name\": \"tray of sausage\"}, {\"id\": 43836, \"name\": \"juicy, red interior\"}, {\"id\": 43837, \"name\": \"coleslaw\"}, {\"id\": 43838, \"name\": \"gelato\"}, {\"id\": 43839, \"name\": \"weightlifting disc\"}, {\"id\": 43840, \"name\": \"a white and blue lane divider\"}, {\"id\": 43841, \"name\": \"the hood vent\"}, {\"id\": 43842, \"name\": \"the glass of red wine\"}, {\"id\": 43843, \"name\": \"yellow icing\"}, {\"id\": 43844, \"name\": \"jellyfish\"}, {\"id\": 43845, \"name\": \"a bright orange beak\"}, {\"id\": 43846, \"name\": \"the lime slice\"}, {\"id\": 43847, \"name\": \"a yellow tail\"}, {\"id\": 43848, \"name\": \"heron\"}, {\"id\": 43849, \"name\": \"the pickle\"}, {\"id\": 43850, \"name\": \"wooden ramp\"}, {\"id\": 43851, \"name\": \"a neck of the instrument\"}, {\"id\": 43852, \"name\": \"the white race bib\"}, {\"id\": 43853, \"name\": \"the bodyboard\"}, {\"id\": 43854, \"name\": \"juice\"}, {\"id\": 43855, \"name\": \"ski lift's cable\"}, {\"id\": 43856, \"name\": \"a red life vest\"}, {\"id\": 43857, \"name\": \"the technical equipment\"}, {\"id\": 43858, \"name\": \"a present\"}, {\"id\": 43859, \"name\": \"illuminated orb\"}, {\"id\": 43860, \"name\": \"basket of corn\"}, {\"id\": 43861, \"name\": \"a green parrot\"}, {\"id\": 43862, \"name\": \"a glass of champagne\"}, {\"id\": 43863, \"name\": \"blue player\"}, {\"id\": 43864, \"name\": \"a volunteer\"}, {\"id\": 43865, \"name\": \"olive\"}, {\"id\": 43866, \"name\": \"a cherry tomato\"}, {\"id\": 43867, \"name\": \"a brightly colored pool noodle\"}, {\"id\": 43868, \"name\": \"the ham\"}, {\"id\": 43869, \"name\": \"the vulture\"}, {\"id\": 43870, \"name\": \"the shirtless man\"}, {\"id\": 43871, \"name\": \"lunch box\"}, {\"id\": 43872, \"name\": \"the sled\"}, {\"id\": 43873, \"name\": \"brown beer bottle\"}, {\"id\": 43874, \"name\": \"the brown paw\"}, {\"id\": 43875, \"name\": \"the dollop of white filling\"}, {\"id\": 43876, \"name\": \"the green apple\"}, {\"id\": 43877, \"name\": \"a black dragon\"}, {\"id\": 43878, \"name\": \"the translucent white panel\"}, {\"id\": 43879, \"name\": \"a red cherry\"}, {\"id\": 43880, \"name\": \"the lightbulb\"}, {\"id\": 43881, \"name\": \"a lime wedge\"}, {\"id\": 43882, \"name\": \"sheep\"}, {\"id\": 43883, \"name\": \"crisp/chips\"}, {\"id\": 43884, \"name\": \"windsurfers\"}, {\"id\": 43885, \"name\": \"wine\"}, {\"id\": 43886, \"name\": \"a tray of pastry\"}, {\"id\": 43887, \"name\": \"the bottle of red wine\"}, {\"id\": 43888, \"name\": \"the black exercise ball\"}, {\"id\": 43889, \"name\": \"the lit sparkler\"}, {\"id\": 43890, \"name\": \"pepper\"}, {\"id\": 43891, \"name\": \"brown coffee cup\"}, {\"id\": 43892, \"name\": \"green grape\"}, {\"id\": 43893, \"name\": \"back of a seat\"}, {\"id\": 43894, \"name\": \"a black food\"}, {\"id\": 43895, \"name\": \"crocodile\"}, {\"id\": 43896, \"name\": \"the musket\"}, {\"id\": 43897, \"name\": \"the metal chain\"}, {\"id\": 43898, \"name\": \"a jet skier\"}, {\"id\": 43899, \"name\": \"a vegetable\"}, {\"id\": 43900, \"name\": \"white bike symbol\"}, {\"id\": 43901, \"name\": \"the flowing tail\"}, {\"id\": 43902, \"name\": \"metal food bowl\"}, {\"id\": 43903, \"name\": \"the circular prop\"}, {\"id\": 43904, \"name\": \"a cup of brown liquid\"}, {\"id\": 43905, \"name\": \"green lighting\"}, {\"id\": 43906, \"name\": \"an orange crate\"}, {\"id\": 43907, \"name\": \"the glass of lemonade\"}, {\"id\": 43908, \"name\": \"the brown wheel\"}, {\"id\": 43909, \"name\": \"the orange dot\"}, {\"id\": 43910, \"name\": \"tuft of white feather\"}, {\"id\": 43911, \"name\": \"raw egg\"}, {\"id\": 43912, \"name\": \"tan tail\"}, {\"id\": 43913, \"name\": \"a sliced cheese\"}, {\"id\": 43914, \"name\": \"a dvd\"}, {\"id\": 43915, \"name\": \"courgette\"}, {\"id\": 43916, \"name\": \"a white cable\"}, {\"id\": 43917, \"name\": \"a spice\"}, {\"id\": 43918, \"name\": \"the axe\"}, {\"id\": 43919, \"name\": \"the dorsal fin\"}, {\"id\": 43920, \"name\": \"the wristwatch\"}, {\"id\": 43921, \"name\": \"a breadstick\"}, {\"id\": 43922, \"name\": \"the putting green\"}, {\"id\": 43923, \"name\": \"the lime wedge\"}, {\"id\": 43924, \"name\": \"bowl of noodles and vegetable\"}, {\"id\": 43925, \"name\": \"green tray of food\"}, {\"id\": 43926, \"name\": \"juice carton\"}, {\"id\": 43927, \"name\": \"the teal-colored plate\"}, {\"id\": 43928, \"name\": \"the blurred circle\"}, {\"id\": 43929, \"name\": \"the gray mask\"}, {\"id\": 43930, \"name\": \"chocolate\"}, {\"id\": 43931, \"name\": \"the red gas tank\"}, {\"id\": 43932, \"name\": \"space shuttle\"}, {\"id\": 43933, \"name\": \"a pink paper pompom\"}, {\"id\": 43934, \"name\": \"green bottle of beer\"}, {\"id\": 43935, \"name\": \"a yellow race bib\"}, {\"id\": 43936, \"name\": \"a chef\"}, {\"id\": 43937, \"name\": \"a brown and green duck\"}, {\"id\": 43938, \"name\": \"a basketball player\"}, {\"id\": 43939, \"name\": \"a pool tube\"}, {\"id\": 43940, \"name\": \"snowshoer\"}, {\"id\": 43941, \"name\": \"the beige panel\"}, {\"id\": 43942, \"name\": \"pad thai\"}, {\"id\": 43943, \"name\": \"the teal wheel\"}, {\"id\": 43944, \"name\": \"clear face guard\"}, {\"id\": 43945, \"name\": \"the yolk\"}, {\"id\": 43946, \"name\": \"the obstacle course\"}, {\"id\": 43947, \"name\": \"a blue napkin\"}, {\"id\": 43948, \"name\": \"the gray wing\"}, {\"id\": 43949, \"name\": \"bagel\"}, {\"id\": 43950, \"name\": \"a black scuba diving tank\"}, {\"id\": 43951, \"name\": \"the barefoot and shirtless\"}, {\"id\": 43952, \"name\": \"a breads and pastry\"}, {\"id\": 43953, \"name\": \"brown bear\"}, {\"id\": 43954, \"name\": \"male\"}, {\"id\": 43955, \"name\": \"red front\"}, {\"id\": 43956, \"name\": \"a glass of orange juice\"}, {\"id\": 43957, \"name\": \"avacados\"}, {\"id\": 43958, \"name\": \"a green drink\"}, {\"id\": 43959, \"name\": \"the shuffleboard disc\"}, {\"id\": 43960, \"name\": \"a quail egg\"}, {\"id\": 43961, \"name\": \"the piece of chicken\"}, {\"id\": 43962, \"name\": \"the blue roller\"}, {\"id\": 43963, \"name\": \"a male\"}, {\"id\": 43964, \"name\": \"whale\"}, {\"id\": 43965, \"name\": \"the rolled-up yoga mat\"}, {\"id\": 43966, \"name\": \"a bowl of green vegetable\"}, {\"id\": 43967, \"name\": \"a yellow bell pepper\"}, {\"id\": 43968, \"name\": \"the rainbow-colored bubble popper\"}, {\"id\": 43969, \"name\": \"a commuter\"}, {\"id\": 43970, \"name\": \"pancake\"}, {\"id\": 43971, \"name\": \"the snowboard\"}, {\"id\": 43972, \"name\": \"chopping board\"}, {\"id\": 43973, \"name\": \"black hiking pole\"}, {\"id\": 43974, \"name\": \"the skewer\"}, {\"id\": 43975, \"name\": \"cucumber slice\"}, {\"id\": 43976, \"name\": \"white radiator\"}, {\"id\": 43977, \"name\": \"the spray paint can\"}, {\"id\": 43978, \"name\": \"an orange ring\"}, {\"id\": 43979, \"name\": \"a vr headset\"}, {\"id\": 43980, \"name\": \"the ski pole\"}, {\"id\": 43981, \"name\": \"an elevated obstacle course\"}, {\"id\": 43982, \"name\": \"a corn on the cob\"}, {\"id\": 43983, \"name\": \"a chocolate base\"}, {\"id\": 43984, \"name\": \"cooking utensil\"}, {\"id\": 43985, \"name\": \"the red sausage\"}, {\"id\": 43986, \"name\": \"a kayakers\"}, {\"id\": 43987, \"name\": \"glass of champagne\"}, {\"id\": 43988, \"name\": \"a silver hardware\"}, {\"id\": 43989, \"name\": \"the buoy\"}, {\"id\": 43990, \"name\": \"radiator\"}, {\"id\": 43991, \"name\": \"dromedary\"}, {\"id\": 43992, \"name\": \"white race bib\"}, {\"id\": 43993, \"name\": \"a glass of white wine\"}, {\"id\": 43994, \"name\": \"a wakeboarding ramp\"}, {\"id\": 43995, \"name\": \"food container\"}, {\"id\": 43996, \"name\": \"a dark-colored sail\"}, {\"id\": 43997, \"name\": \"red cocktail\"}, {\"id\": 43998, \"name\": \"blue lighting\"}, {\"id\": 43999, \"name\": \"a climbing tool\"}, {\"id\": 44000, \"name\": \"the cucumber\"}, {\"id\": 44001, \"name\": \"chocolate chip cookie\"}, {\"id\": 44002, \"name\": \"black tip\"}, {\"id\": 44003, \"name\": \"the food preparation equipment\"}, {\"id\": 44004, \"name\": \"the cookie\"}, {\"id\": 44005, \"name\": \"flamingo\"}, {\"id\": 44006, \"name\": \"lobster\"}, {\"id\": 44007, \"name\": \"the crumb\"}, {\"id\": 44008, \"name\": \"a silhouette of a person\"}, {\"id\": 44009, \"name\": \"wooden bowl\"}, {\"id\": 44010, \"name\": \"orange juice\"}, {\"id\": 44011, \"name\": \"the crop\"}, {\"id\": 44012, \"name\": \"the snow tube\"}, {\"id\": 44013, \"name\": \"a bag of nut\"}, {\"id\": 44014, \"name\": \"cake piece\"}, {\"id\": 44015, \"name\": \"wall-mounted pull-up bar\"}, {\"id\": 44016, \"name\": \"cheetah\"}, {\"id\": 44017, \"name\": \"the green pellet\"}, {\"id\": 44018, \"name\": \"the silver human-like figure\"}, {\"id\": 44019, \"name\": \"purple wheel\"}, {\"id\": 44020, \"name\": \"a yellow inflatable tube\"}, {\"id\": 44021, \"name\": \"snowboarder\"}, {\"id\": 44022, \"name\": \"a snowshoe\"}, {\"id\": 44023, \"name\": \"the spiral slide\"}, {\"id\": 44024, \"name\": \"the cherry tomato\"}, {\"id\": 44025, \"name\": \"a boxing ring's red rope\"}, {\"id\": 44026, \"name\": \"husky\"}, {\"id\": 44027, \"name\": \"a purple fin\"}, {\"id\": 44028, \"name\": \"blue and red group member\"}, {\"id\": 44029, \"name\": \"the flatbread\"}, {\"id\": 44030, \"name\": \"sliced kiwi\"}, {\"id\": 44031, \"name\": \"a broccoli floret\"}, {\"id\": 44032, \"name\": \"the boogie board\"}, {\"id\": 44033, \"name\": \"a green bike lane\"}, {\"id\": 44034, \"name\": \"a sea anemone\"}, {\"id\": 44035, \"name\": \"coffee\"}, {\"id\": 44036, \"name\": \"sparkler\"}, {\"id\": 44037, \"name\": \"a silver serving spoon\"}, {\"id\": 44038, \"name\": \"wildebeest\"}, {\"id\": 44039, \"name\": \"a lemon wedge\"}, {\"id\": 44040, \"name\": \"the pink wheel\"}, {\"id\": 44041, \"name\": \"the green saucer\"}, {\"id\": 44042, \"name\": \"the red exercise mat\"}, {\"id\": 44043, \"name\": \"slice of cucumber\"}, {\"id\": 44044, \"name\": \"the pink bowling ball\"}, {\"id\": 44045, \"name\": \"the cheese\"}, {\"id\": 44046, \"name\": \"the red and white striped sail\"}, {\"id\": 44047, \"name\": \"an orange traffic sign\"}, {\"id\": 44048, \"name\": \"cutlery\"}, {\"id\": 44049, \"name\": \"black headphone\"}, {\"id\": 44050, \"name\": \"the patron\"}, {\"id\": 44051, \"name\": \"the blonde\"}, {\"id\": 44052, \"name\": \"the bas guitar\"}, {\"id\": 44053, \"name\": \"virtual reality (vr) headset\"}, {\"id\": 44054, \"name\": \"the hoove\"}, {\"id\": 44055, \"name\": \"the grazing cattle\"}, {\"id\": 44056, \"name\": \"a glass bottle of orange juice\"}, {\"id\": 44057, \"name\": \"a red candy cane\"}, {\"id\": 44058, \"name\": \"the watermelon slice\"}, {\"id\": 44059, \"name\": \"the thyme\"}, {\"id\": 44060, \"name\": \"a black fish\"}, {\"id\": 44061, \"name\": \"a black paw\"}, {\"id\": 44062, \"name\": \"the portable electric stove\"}, {\"id\": 44063, \"name\": \"the lettuce\"}, {\"id\": 44064, \"name\": \"moving box\"}, {\"id\": 44065, \"name\": \"orange\"}, {\"id\": 44066, \"name\": \"the white circular vent\"}, {\"id\": 44067, \"name\": \"a lane divider\"}, {\"id\": 44068, \"name\": \"the meatball\"}, {\"id\": 44069, \"name\": \"a black fin\"}, {\"id\": 44070, \"name\": \"orange pepper\"}, {\"id\": 44071, \"name\": \"a cooked shrimp\"}, {\"id\": 44072, \"name\": \"a black spider\"}, {\"id\": 44073, \"name\": \"tiger\"}, {\"id\": 44074, \"name\": \"sea gull\"}, {\"id\": 44075, \"name\": \"a grey feather\"}, {\"id\": 44076, \"name\": \"sail boat\"}, {\"id\": 44077, \"name\": \"people's faces\"}, {\"id\": 44078, \"name\": \"tan and brown sheep\"}, {\"id\": 44079, \"name\": \"the distinctive white spot\"}, {\"id\": 44080, \"name\": \"a blade of helicopter\"}, {\"id\": 44081, \"name\": \"a brown horn\"}, {\"id\": 44082, \"name\": \"a black-tipped wing\"}, {\"id\": 44083, \"name\": \"the train's door\"}, {\"id\": 44084, \"name\": \"back leg\"}, {\"id\": 44085, \"name\": \"the white belly\"}, {\"id\": 44086, \"name\": \"the hoof\"}, {\"id\": 44087, \"name\": \"gray wing\"}, {\"id\": 44088, \"name\": \"sharp claw\"}, {\"id\": 44089, \"name\": \"wolf's head\"}, {\"id\": 44090, \"name\": \"the distinctive brown head\"}, {\"id\": 44091, \"name\": \"ant\"}, {\"id\": 44092, \"name\": \"a black antenna\"}, {\"id\": 44093, \"name\": \"pink snout\"}, {\"id\": 44094, \"name\": \"a rabbits' head\"}, {\"id\": 44095, \"name\": \"the bike rack\"}, {\"id\": 44096, \"name\": \"the dark wing\"}, {\"id\": 44097, \"name\": \"a curly tail\"}, {\"id\": 44098, \"name\": \"front fender\"}, {\"id\": 44099, \"name\": \"a sheep's head\"}, {\"id\": 44100, \"name\": \"a front flipper\"}, {\"id\": 44101, \"name\": \"racer\"}, {\"id\": 44102, \"name\": \"the tusk\"}, {\"id\": 44103, \"name\": \"a dark tail\"}, {\"id\": 44104, \"name\": \"a blurred tail of the zebra\"}, {\"id\": 44105, \"name\": \"a ski gear\"}, {\"id\": 44106, \"name\": \"a green wing\"}, {\"id\": 44107, \"name\": \"orange face\"}, {\"id\": 44108, \"name\": \"a dark gray-brown tail\"}, {\"id\": 44109, \"name\": \"white goat's head\"}, {\"id\": 44110, \"name\": \"the club\"}, {\"id\": 44111, \"name\": \"the leg of dog\"}, {\"id\": 44112, \"name\": \"a dark brown hoof\"}, {\"id\": 44113, \"name\": \"a black hoof\"}, {\"id\": 44114, \"name\": \"the pink face\"}, {\"id\": 44115, \"name\": \"distinctive black spot\"}, {\"id\": 44116, \"name\": \"deer's face\"}, {\"id\": 44117, \"name\": \"the chrome frame\"}, {\"id\": 44118, \"name\": \"white bike\"}, {\"id\": 44119, \"name\": \"a running gear\"}, {\"id\": 44120, \"name\": \"a pink face\"}, {\"id\": 44121, \"name\": \"the orange and black pompom\"}, {\"id\": 44122, \"name\": \"a tiger's face\"}, {\"id\": 44123, \"name\": \"dough ball\"}, {\"id\": 44124, \"name\": \"a rectangular metal piece\"}, {\"id\": 44125, \"name\": \"rear leg\"}, {\"id\": 44126, \"name\": \"the blue and red tail\"}, {\"id\": 44127, \"name\": \"a green-tipped wing\"}, {\"id\": 44128, \"name\": \"the african buffalo\"}, {\"id\": 44129, \"name\": \"the bike's rear wheel\"}, {\"id\": 44130, \"name\": \"a vehicle's hood\"}, {\"id\": 44131, \"name\": \"a hoove\"}, {\"id\": 44132, \"name\": \"an impala\"}, {\"id\": 44133, \"name\": \"the brown face with white patch\"}, {\"id\": 44134, \"name\": \"tusk\"}, {\"id\": 44135, \"name\": \"marine mammal\"}, {\"id\": 44136, \"name\": \"the yellow shape\"}, {\"id\": 44137, \"name\": \"the dark brown mane\"}, {\"id\": 44138, \"name\": \"the black hoof\"}, {\"id\": 44139, \"name\": \"a horse's face\"}, {\"id\": 44140, \"name\": \"an orange turn signal\"}, {\"id\": 44141, \"name\": \"a black duck\"}, {\"id\": 44142, \"name\": \"right front leg\"}, {\"id\": 44143, \"name\": \"the open beak\"}, {\"id\": 44144, \"name\": \"the dark tail\"}, {\"id\": 44145, \"name\": \"a white hoof\"}, {\"id\": 44146, \"name\": \"bushy tail\"}, {\"id\": 44147, \"name\": \"a handlebar grip\"}, {\"id\": 44148, \"name\": \"an australian shepherd dog\"}, {\"id\": 44149, \"name\": \"a back of a player\"}, {\"id\": 44150, \"name\": \"the pink snout\"}, {\"id\": 44151, \"name\": \"the left front leg\"}, {\"id\": 44152, \"name\": \"the distinctive black head\"}, {\"id\": 44153, \"name\": \"black sail\"}, {\"id\": 44154, \"name\": \"single mast\"}, {\"id\": 44155, \"name\": \"an orange and white traffic barrel\"}, {\"id\": 44156, \"name\": \"a striped abdoman\"}, {\"id\": 44157, \"name\": \"the ballet dancer\"}, {\"id\": 44158, \"name\": \"ear of cow\"}, {\"id\": 44159, \"name\": \"a rabbit's head\"}, {\"id\": 44160, \"name\": \"section of the parking\"}, {\"id\": 44161, \"name\": \"the white fin\"}, {\"id\": 44162, \"name\": \"a brightly colored gear\"}, {\"id\": 44163, \"name\": \"the chick\"}, {\"id\": 44164, \"name\": \"lion's head\"}, {\"id\": 44165, \"name\": \"the tan paw\"}, {\"id\": 44166, \"name\": \"a dark grey wing\"}, {\"id\": 44167, \"name\": \"a baseball gear\"}, {\"id\": 44168, \"name\": \"a black kickstand\"}, {\"id\": 44169, \"name\": \"the visible cabin\"}, {\"id\": 44170, \"name\": \"a dark-colored wing\"}, {\"id\": 44171, \"name\": \"the frog\"}, {\"id\": 44172, \"name\": \"the white trunk\"}, {\"id\": 44173, \"name\": \"a dark-tinted windshield\"}, {\"id\": 44174, \"name\": \"a foreleg\"}, {\"id\": 44175, \"name\": \"the grey wing\"}, {\"id\": 44176, \"name\": \"tan fur\"}, {\"id\": 44177, \"name\": \"front buffalo's face\"}, {\"id\": 44178, \"name\": \"a feathered leg\"}, {\"id\": 44179, \"name\": \"a white-tipped tail\"}, {\"id\": 44180, \"name\": \"a back paw\"}, {\"id\": 44181, \"name\": \"a juggling club\"}, {\"id\": 44182, \"name\": \"lioness\"}, {\"id\": 44183, \"name\": \"the snowboarder\"}, {\"id\": 44184, \"name\": \"the brownish mane\"}, {\"id\": 44185, \"name\": \"a red wattle\"}, {\"id\": 44186, \"name\": \"a pink snout\"}, {\"id\": 44187, \"name\": \"the zebra\"}, {\"id\": 44188, \"name\": \"a metal fencing\"}, {\"id\": 44189, \"name\": \"a distinctive black head\"}, {\"id\": 44190, \"name\": \"a white patch on wing\"}, {\"id\": 44191, \"name\": \"dark brown face\"}, {\"id\": 44192, \"name\": \"a black plumage\"}, {\"id\": 44193, \"name\": \"an orange face\"}, {\"id\": 44194, \"name\": \"yellow tread\"}, {\"id\": 44195, \"name\": \"the bass\"}, {\"id\": 44196, \"name\": \"a club\"}, {\"id\": 44197, \"name\": \"the men's shadow\"}, {\"id\": 44198, \"name\": \"a red comb and wattle\"}, {\"id\": 44199, \"name\": \"snowboard\"}, {\"id\": 44200, \"name\": \"a bike's rear wheel\"}, {\"id\": 44201, \"name\": \"teal balloon\"}, {\"id\": 44202, \"name\": \"zebras' head\"}, {\"id\": 44203, \"name\": \"a distinctive white patch\"}, {\"id\": 44204, \"name\": \"a turbine\"}, {\"id\": 44205, \"name\": \"metal bike rack\"}, {\"id\": 44206, \"name\": \"a rusty metal component\"}, {\"id\": 44207, \"name\": \"a tan tail\"}, {\"id\": 44208, \"name\": \"a head of brown dog\"}, {\"id\": 44209, \"name\": \"plane\"}, {\"id\": 44210, \"name\": \"the black paw\"}, {\"id\": 44211, \"name\": \"red knot\"}, {\"id\": 44212, \"name\": \"ear of the sheep\"}, {\"id\": 44213, \"name\": \"the orange ear tag\"}, {\"id\": 44214, \"name\": \"a distinctive black beak\"}, {\"id\": 44215, \"name\": \"the white rump\"}, {\"id\": 44216, \"name\": \"the running gear\"}, {\"id\": 44217, \"name\": \"a gray fur\"}, {\"id\": 44218, \"name\": \"a soft fur\"}, {\"id\": 44219, \"name\": \"an agricultural equipment\"}, {\"id\": 44220, \"name\": \"transport vehicle\"}, {\"id\": 44221, \"name\": \"black muzzle\"}, {\"id\": 44222, \"name\": \"the yellow-green wing\"}, {\"id\": 44223, \"name\": \"a silver ball\"}, {\"id\": 44224, \"name\": \"the muddy paw\"}, {\"id\": 44225, \"name\": \"a white underbelly\"}, {\"id\": 44226, \"name\": \"a red and white soccer ball\"}, {\"id\": 44227, \"name\": \"high-speed boat\"}, {\"id\": 44228, \"name\": \"a vibrant koi fish\"}, {\"id\": 44229, \"name\": \"the goat's head\"}, {\"id\": 44230, \"name\": \"a highway sign\"}, {\"id\": 44231, \"name\": \"the black handlebar-mounted mirror\"}, {\"id\": 44232, \"name\": \"a raised metal bar\"}, {\"id\": 44233, \"name\": \"lion cub\"}, {\"id\": 44234, \"name\": \"mantis shrimp\"}, {\"id\": 44235, \"name\": \"the spring suspension\"}, {\"id\": 44236, \"name\": \"rotor\"}, {\"id\": 44237, \"name\": \"striped abdoman\"}, {\"id\": 44238, \"name\": \"the white patch of fur\"}, {\"id\": 44239, \"name\": \"a white fender\"}, {\"id\": 44240, \"name\": \"the fuselage\"}, {\"id\": 44241, \"name\": \"a person in a brown costume\"}, {\"id\": 44242, \"name\": \"a lioness\"}, {\"id\": 44243, \"name\": \"a broken windshield\"}, {\"id\": 44244, \"name\": \"a brown wing\"}, {\"id\": 44245, \"name\": \"the exhaust pipe\"}, {\"id\": 44246, \"name\": \"a colorful, circular gondola\"}, {\"id\": 44247, \"name\": \"a head of a bird\"}, {\"id\": 44248, \"name\": \"the left flipper\"}, {\"id\": 44249, \"name\": \"a luggage rack\"}, {\"id\": 44250, \"name\": \"the wolf's head\"}, {\"id\": 44251, \"name\": \"rounded shape\"}, {\"id\": 44252, \"name\": \"a crocodile's head\"}, {\"id\": 44253, \"name\": \"grayish-white belly\"}, {\"id\": 44254, \"name\": \"toy animal\"}, {\"id\": 44255, \"name\": \"distinctive white patch\"}, {\"id\": 44256, \"name\": \"a panda's head\"}, {\"id\": 44257, \"name\": \"a seal pup\"}, {\"id\": 44258, \"name\": \"black and white kitten\"}, {\"id\": 44259, \"name\": \"the cheetah\"}, {\"id\": 44260, \"name\": \"the level of seating gondola\"}, {\"id\": 44261, \"name\": \"the gray horse\"}, {\"id\": 44262, \"name\": \"an enclosed, multi-colored gondola\"}, {\"id\": 44263, \"name\": \"the stable\"}, {\"id\": 44264, \"name\": \"a curved section of the highway\"}, {\"id\": 44265, \"name\": \"shirtless man\"}, {\"id\": 44266, \"name\": \"the deer's head\"}, {\"id\": 44267, \"name\": \"ballet dancer\"}, {\"id\": 44268, \"name\": \"a wild buffalo\"}, {\"id\": 44269, \"name\": \"the silver ball\"}, {\"id\": 44270, \"name\": \"fish's face\"}, {\"id\": 44271, \"name\": \"the open hood\"}, {\"id\": 44272, \"name\": \"a white fin\"}, {\"id\": 44273, \"name\": \"a ballerina\"}, {\"id\": 44274, \"name\": \"the silver inner circle\"}, {\"id\": 44275, \"name\": \"dark grey feather\"}, {\"id\": 44276, \"name\": \"black fur\"}, {\"id\": 44277, \"name\": \"a rusted metal\"}, {\"id\": 44278, \"name\": \"a sport ball\"}, {\"id\": 44279, \"name\": \"the white center\"}, {\"id\": 44280, \"name\": \"the rotor blade\"}, {\"id\": 44281, \"name\": \"the yellow dash\"}, {\"id\": 44282, \"name\": \"gray tail\"}, {\"id\": 44283, \"name\": \"a face of the duck\"}, {\"id\": 44284, \"name\": \"a black and tan puppy\"}, {\"id\": 44285, \"name\": \"bernese mountain puppy\"}, {\"id\": 44286, \"name\": \"pointed tail\"}, {\"id\": 44287, \"name\": \"a foreground goat\"}, {\"id\": 44288, \"name\": \"white pebble\"}, {\"id\": 44289, \"name\": \"a prominent tusk\"}, {\"id\": 44290, \"name\": \"a monkey's face\"}, {\"id\": 44291, \"name\": \"the dark-colored wing\"}, {\"id\": 44292, \"name\": \"the snowboard gear\"}, {\"id\": 44293, \"name\": \"the brown frog\"}, {\"id\": 44294, \"name\": \"red-eared slider\"}, {\"id\": 44295, \"name\": \"the dark patch of fur\"}, {\"id\": 44296, \"name\": \"a gondola\"}, {\"id\": 44297, \"name\": \"running gear\"}, {\"id\": 44298, \"name\": \"a red-eared slider\"}, {\"id\": 44299, \"name\": \"adult elephant\"}, {\"id\": 44300, \"name\": \"dark gray propeller\"}, {\"id\": 44301, \"name\": \"the silver ladder\"}, {\"id\": 44302, \"name\": \"the pedal\"}, {\"id\": 44303, \"name\": \"black tuft of fur\"}, {\"id\": 44304, \"name\": \"colorful gondola\"}, {\"id\": 44305, \"name\": \"the distinctive white tail\"}, {\"id\": 44306, \"name\": \"the golden retriever dog\"}, {\"id\": 44307, \"name\": \"a bumper car ride\"}, {\"id\": 44308, \"name\": \"a rotor\"}, {\"id\": 44309, \"name\": \"the front right leg of zebra\"}, {\"id\": 44310, \"name\": \"the metal bearing\"}, {\"id\": 44311, \"name\": \"the rotor\"}, {\"id\": 44312, \"name\": \"goats' head\"}, {\"id\": 44313, \"name\": \"the parachute\"}, {\"id\": 44314, \"name\": \"bicycle's frame\"}, {\"id\": 44315, \"name\": \"dark fin\"}, {\"id\": 44316, \"name\": \"a radio control\"}, {\"id\": 44317, \"name\": \"a stock car\"}, {\"id\": 44318, \"name\": \"a chicken's head\"}, {\"id\": 44319, \"name\": \"a central turtle's head\"}, {\"id\": 44320, \"name\": \"a stitching on a tenni ball\"}, {\"id\": 44321, \"name\": \"the alligator\"}, {\"id\": 44322, \"name\": \"red bowling ball\"}, {\"id\": 44323, \"name\": \"right hind leg\"}, {\"id\": 44324, \"name\": \"the blue sphere\"}, {\"id\": 44325, \"name\": \"the gull\"}, {\"id\": 44326, \"name\": \"black face\"}, {\"id\": 44327, \"name\": \"wild animal\"}, {\"id\": 44328, \"name\": \"the pink ear tag\"}, {\"id\": 44329, \"name\": \"a dark brown paw\"}, {\"id\": 44330, \"name\": \"bike's front tire\"}, {\"id\": 44331, \"name\": \"the bike shelter\"}, {\"id\": 44332, \"name\": \"a distinctive dark mane\"}, {\"id\": 44333, \"name\": \"a deer's head\"}, {\"id\": 44334, \"name\": \"black flipper\"}, {\"id\": 44335, \"name\": \"a black head and neck\"}, {\"id\": 44336, \"name\": \"a karts\"}, {\"id\": 44337, \"name\": \"the brown horn\"}, {\"id\": 44338, \"name\": \"black sheep\"}, {\"id\": 44339, \"name\": \"a white ear tag\"}, {\"id\": 44340, \"name\": \"a citibike logo\"}, {\"id\": 44341, \"name\": \"a white snout\"}, {\"id\": 44342, \"name\": \"a cat food\"}, {\"id\": 44343, \"name\": \"a distinctive black spot\"}, {\"id\": 44344, \"name\": \"black and white wing\"}, {\"id\": 44345, \"name\": \"a geese's face\"}, {\"id\": 44346, \"name\": \"the black snout\"}, {\"id\": 44347, \"name\": \"a zebra's head\"}, {\"id\": 44348, \"name\": \"the yellow face\"}, {\"id\": 44349, \"name\": \"a pink tenni ball\"}, {\"id\": 44350, \"name\": \"a feathered\"}, {\"id\": 44351, \"name\": \"the translucent wing\"}, {\"id\": 44352, \"name\": \"the white buoy\"}, {\"id\": 44353, \"name\": \"hoof\"}, {\"id\": 44354, \"name\": \"dark-colored wing\"}, {\"id\": 44355, \"name\": \"the rear tire of motorcycle\"}, {\"id\": 44356, \"name\": \"brown goat's head\"}, {\"id\": 44357, \"name\": \"a rear spoiler\"}, {\"id\": 44358, \"name\": \"dark tinted windshield\"}, {\"id\": 44359, \"name\": \"reddish-brown mane\"}, {\"id\": 44360, \"name\": \"the silver metal side\"}, {\"id\": 44361, \"name\": \"the distinctive black tail\"}, {\"id\": 44362, \"name\": \"the enclosed cabin\"}, {\"id\": 44363, \"name\": \"a lamb's head\"}, {\"id\": 44364, \"name\": \"the bright red eye\"}, {\"id\": 44365, \"name\": \"karts\"}, {\"id\": 44366, \"name\": \"a silhouette of animal\"}, {\"id\": 44367, \"name\": \"a spade\"}, {\"id\": 44368, \"name\": \"a jetway\"}, {\"id\": 44369, \"name\": \"the shiny, rounded surface\"}, {\"id\": 44370, \"name\": \"brown face\"}, {\"id\": 44371, \"name\": \"poultry\"}, {\"id\": 44372, \"name\": \"an open beak\"}, {\"id\": 44373, \"name\": \"colored ring\"}, {\"id\": 44374, \"name\": \"white, hexagonal-shaped ornament\"}, {\"id\": 44375, \"name\": \"sport ball\"}, {\"id\": 44376, \"name\": \"dark gray fin\"}, {\"id\": 44377, \"name\": \"a lion's head\"}, {\"id\": 44378, \"name\": \"pink face\"}, {\"id\": 44379, \"name\": \"the dark head and neck\"}, {\"id\": 44380, \"name\": \"black-and-white face\"}, {\"id\": 44381, \"name\": \"pedal\"}, {\"id\": 44382, \"name\": \"a fuselage\"}, {\"id\": 44383, \"name\": \"grayish-brown wing\"}, {\"id\": 44384, \"name\": \"paintball gun\"}, {\"id\": 44385, \"name\": \"black gas tank\"}, {\"id\": 44386, \"name\": \"a tan fur\"}, {\"id\": 44387, \"name\": \"the brown handlebar\"}, {\"id\": 44388, \"name\": \"reddish fin\"}, {\"id\": 44389, \"name\": \"a red and yellow chicken feeder\"}, {\"id\": 44390, \"name\": \"a joggers' face\"}, {\"id\": 44391, \"name\": \"dark face\"}, {\"id\": 44392, \"name\": \"icon of person\"}, {\"id\": 44393, \"name\": \"an orange ear tag\"}, {\"id\": 44394, \"name\": \"the tiger's head\"}, {\"id\": 44395, \"name\": \"a turtles' head\"}, {\"id\": 44396, \"name\": \"a tail of the helicopter\"}, {\"id\": 44397, \"name\": \"dark snout\"}, {\"id\": 44398, \"name\": \"colorful circle\"}, {\"id\": 44399, \"name\": \"a brown handlebar\"}, {\"id\": 44400, \"name\": \"a dark hoof\"}, {\"id\": 44401, \"name\": \"tiger's head\"}, {\"id\": 44402, \"name\": \"curly tail\"}, {\"id\": 44403, \"name\": \"the distinctive black spot\"}, {\"id\": 44404, \"name\": \"the tan horn\"}, {\"id\": 44405, \"name\": \"a rows of car\"}, {\"id\": 44406, \"name\": \"brown horn\"}, {\"id\": 44407, \"name\": \"the lion's face\"}, {\"id\": 44408, \"name\": \"green tail\"}, {\"id\": 44409, \"name\": \"distinctive red wattle\"}, {\"id\": 44410, \"name\": \"the dark fin\"}, {\"id\": 44411, \"name\": \"the dark brown face\"}, {\"id\": 44412, \"name\": \"parachute\"}, {\"id\": 44413, \"name\": \"the turquoise wing\"}, {\"id\": 44414, \"name\": \"the right paw\"}, {\"id\": 44415, \"name\": \"fender\"}, {\"id\": 44416, \"name\": \"the bike symbol\"}, {\"id\": 44417, \"name\": \"blurred wing\"}, {\"id\": 44418, \"name\": \"a bark\"}, {\"id\": 44419, \"name\": \"the motorcycle's rear wheel\"}, {\"id\": 44420, \"name\": \"a paintball gun\"}, {\"id\": 44421, \"name\": \"the brown mane\"}, {\"id\": 44422, \"name\": \"an orange dot\"}, {\"id\": 44423, \"name\": \"red and white plastic tire\"}, {\"id\": 44424, \"name\": \"blonde tail\"}, {\"id\": 44425, \"name\": \"black and brown spot\"}, {\"id\": 44426, \"name\": \"a stable\"}, {\"id\": 44427, \"name\": \"sturdy platform\"}, {\"id\": 44428, \"name\": \"carcass\"}, {\"id\": 44429, \"name\": \"the white goal net\"}, {\"id\": 44430, \"name\": \"a dark gray tail\"}, {\"id\": 44431, \"name\": \"the broken hood\"}, {\"id\": 44432, \"name\": \"the sedan's hood\"}, {\"id\": 44433, \"name\": \"a tan with white ear\"}, {\"id\": 44434, \"name\": \"the distinctive black and white striped face\"}, {\"id\": 44435, \"name\": \"a hangar\"}, {\"id\": 44436, \"name\": \"grey wing\"}, {\"id\": 44437, \"name\": \"a hind leg of the pig\"}, {\"id\": 44438, \"name\": \"the gray tail\"}, {\"id\": 44439, \"name\": \"a black-tipped tail\"}, {\"id\": 44440, \"name\": \"women's faces\"}, {\"id\": 44441, \"name\": \"a grayish-white wing\"}, {\"id\": 44442, \"name\": \"koi fish\"}, {\"id\": 44443, \"name\": \"front zebra's head\"}, {\"id\": 44444, \"name\": \"a distinctive black tail\"}, {\"id\": 44445, \"name\": \"dark wing\"}, {\"id\": 44446, \"name\": \"a white pebble\"}, {\"id\": 44447, \"name\": \"green tenni ball\"}, {\"id\": 44448, \"name\": \"the wolf's front paws\"}, {\"id\": 44449, \"name\": \"paved route\"}, {\"id\": 44450, \"name\": \"the head of a single duck\"}, {\"id\": 44451, \"name\": \"the bark\"}, {\"id\": 44452, \"name\": \"dark-colored face\"}, {\"id\": 44453, \"name\": \"the metal barre\"}, {\"id\": 44454, \"name\": \"the trekking pole\"}, {\"id\": 44455, \"name\": \"shoper\"}, {\"id\": 44456, \"name\": \"slice of orange\"}, {\"id\": 44457, \"name\": \"a pregnant woman\"}, {\"id\": 44458, \"name\": \"a fruit design\"}, {\"id\": 44459, \"name\": \"head of a brown horse\"}, {\"id\": 44460, \"name\": \"coworkers\"}, {\"id\": 44461, \"name\": \"plate of raw meat\"}, {\"id\": 44462, \"name\": \"white placemat\"}, {\"id\": 44463, \"name\": \"the camper van\"}, {\"id\": 44464, \"name\": \"a rosemary\"}, {\"id\": 44465, \"name\": \"person on the boat\"}, {\"id\": 44466, \"name\": \"the litter\"}, {\"id\": 44467, \"name\": \"a striped fish\"}, {\"id\": 44468, \"name\": \"white button mushroom\"}, {\"id\": 44469, \"name\": \"the kitchen cabinet\"}, {\"id\": 44470, \"name\": \"the green rind\"}, {\"id\": 44471, \"name\": \"the blue and yellow paddle\"}, {\"id\": 44472, \"name\": \"the natarajasana pose\"}, {\"id\": 44473, \"name\": \"the yellow bus stop marking\"}, {\"id\": 44474, \"name\": \"kangaroo\"}, {\"id\": 44475, \"name\": \"a red chili pepper\"}, {\"id\": 44476, \"name\": \"an afro-textured hairstyle\"}, {\"id\": 44477, \"name\": \"green shape\"}, {\"id\": 44478, \"name\": \"a man in a gray suit\"}, {\"id\": 44479, \"name\": \"the piece of dough\"}, {\"id\": 44480, \"name\": \"red napkin\"}, {\"id\": 44481, \"name\": \"the life ring\"}, {\"id\": 44482, \"name\": \"suspension strap\"}, {\"id\": 44483, \"name\": \"a lightbulb\"}, {\"id\": 44484, \"name\": \"the swim goggle\"}, {\"id\": 44485, \"name\": \"a red cylinder\"}, {\"id\": 44486, \"name\": \"the elderly\"}, {\"id\": 44487, \"name\": \"the mode of transportation\"}, {\"id\": 44488, \"name\": \"yellow cherry tomato\"}, {\"id\": 44489, \"name\": \"taxi's taillight\"}, {\"id\": 44490, \"name\": \"hippopotamus\"}, {\"id\": 44491, \"name\": \"bison\"}, {\"id\": 44492, \"name\": \"beige machine\"}, {\"id\": 44493, \"name\": \"the shirtless player\"}, {\"id\": 44494, \"name\": \"the green beer bottle\"}, {\"id\": 44495, \"name\": \"cooked rice\"}, {\"id\": 44496, \"name\": \"volunteer\"}, {\"id\": 44497, \"name\": \"inflated yellow and blue inner tube\"}, {\"id\": 44498, \"name\": \"the bus's license plate\"}, {\"id\": 44499, \"name\": \"a headlamp\"}, {\"id\": 44500, \"name\": \"candy cane\"}, {\"id\": 44501, \"name\": \"the athletic gear\"}, {\"id\": 44502, \"name\": \"the whipped cream\"}, {\"id\": 44503, \"name\": \"black skate\"}, {\"id\": 44504, \"name\": \"yellow melon\"}, {\"id\": 44505, \"name\": \"the yellow lane divider\"}, {\"id\": 44506, \"name\": \"the hump\"}, {\"id\": 44507, \"name\": \"a sliced lemon\"}, {\"id\": 44508, \"name\": \"the black belt student\"}, {\"id\": 44509, \"name\": \"a white egg\"}, {\"id\": 44510, \"name\": \"the wooden ballet barre\"}, {\"id\": 44511, \"name\": \"a white styrofoam plate of food\"}, {\"id\": 44512, \"name\": \"red plastic shape\"}, {\"id\": 44513, \"name\": \"piece of fish\"}, {\"id\": 44514, \"name\": \"a diver's tank\"}, {\"id\": 44515, \"name\": \"the whipped\"}, {\"id\": 44516, \"name\": \"the homemade pickled vegetable\"}, {\"id\": 44517, \"name\": \"the wooden crate of grape\"}, {\"id\": 44518, \"name\": \"a rope obstacle\"}, {\"id\": 44519, \"name\": \"the person in the group\"}, {\"id\": 44520, \"name\": \"scallop\"}, {\"id\": 44521, \"name\": \"horizontal cable\"}, {\"id\": 44522, \"name\": \"the pom-pom\"}, {\"id\": 44523, \"name\": \"a tail of the gray fish\"}, {\"id\": 44524, \"name\": \"the white button mushroom\"}, {\"id\": 44525, \"name\": \"a man in the black jacket\"}, {\"id\": 44526, \"name\": \"treadmill console\"}, {\"id\": 44527, \"name\": \"the shelf of alcohol\"}, {\"id\": 44528, \"name\": \"the sliced citru fruit\"}, {\"id\": 44529, \"name\": \"seal\"}, {\"id\": 44530, \"name\": \"the green lettuce\"}, {\"id\": 44531, \"name\": \"the patch of brown fur\"}, {\"id\": 44532, \"name\": \"a sharp fin\"}, {\"id\": 44533, \"name\": \"black medicine ball\"}, {\"id\": 44534, \"name\": \"male guitarist\"}, {\"id\": 44535, \"name\": \"a hiking gear\"}, {\"id\": 44536, \"name\": \"the front hoove\"}, {\"id\": 44537, \"name\": \"a silver hiking pole\"}, {\"id\": 44538, \"name\": \"a filling\"}, {\"id\": 44539, \"name\": \"the scuba gear\"}, {\"id\": 44540, \"name\": \"the surfer's board\"}, {\"id\": 44541, \"name\": \"the rugged tire\"}, {\"id\": 44542, \"name\": \"a percussion instrument\"}, {\"id\": 44543, \"name\": \"black boxing glove\"}, {\"id\": 44544, \"name\": \"the piece of equipment\"}, {\"id\": 44545, \"name\": \"a flaming baton\"}, {\"id\": 44546, \"name\": \"milk bottle\"}, {\"id\": 44547, \"name\": \"the white ice skate\"}, {\"id\": 44548, \"name\": \"residence\"}, {\"id\": 44549, \"name\": \"yellow sphere\"}, {\"id\": 44550, \"name\": \"chilli\"}, {\"id\": 44551, \"name\": \"black diving gear\"}, {\"id\": 44552, \"name\": \"a black baseball cleat\"}, {\"id\": 44553, \"name\": \"the reindeer\"}, {\"id\": 44554, \"name\": \"cosplayer\"}, {\"id\": 44555, \"name\": \"black and red boxing glove\"}, {\"id\": 44556, \"name\": \"black pane\"}, {\"id\": 44557, \"name\": \"a pasta piece\"}, {\"id\": 44558, \"name\": \"the wi-fi symbol\"}, {\"id\": 44559, \"name\": \"the seed\"}, {\"id\": 44560, \"name\": \"a white football helmet\"}, {\"id\": 44561, \"name\": \"the white plate of food\"}, {\"id\": 44562, \"name\": \"wooden spoke\"}, {\"id\": 44563, \"name\": \"the front flipper\"}, {\"id\": 44564, \"name\": \"green cutting board\"}, {\"id\": 44565, \"name\": \"round bulb\"}, {\"id\": 44566, \"name\": \"the boxing gear\"}, {\"id\": 44567, \"name\": \"the white dash\"}, {\"id\": 44568, \"name\": \"black and grey boxing glove\"}, {\"id\": 44569, \"name\": \"shirtless\"}, {\"id\": 44570, \"name\": \"a green bottle of beer\"}, {\"id\": 44571, \"name\": \"handlebar grip\"}, {\"id\": 44572, \"name\": \"plum\"}, {\"id\": 44573, \"name\": \"lemon half\"}, {\"id\": 44574, \"name\": \"the brown mushroom\"}, {\"id\": 44575, \"name\": \"the tan fur\"}, {\"id\": 44576, \"name\": \"the trampoline mat\"}, {\"id\": 44577, \"name\": \"star anise\"}, {\"id\": 44578, \"name\": \"white candle\"}, {\"id\": 44579, \"name\": \"the cherry\"}, {\"id\": 44580, \"name\": \"maki roll\"}, {\"id\": 44581, \"name\": \"basket of produce\"}, {\"id\": 44582, \"name\": \"a broccoli\"}, {\"id\": 44583, \"name\": \"the pinwheel\"}, {\"id\": 44584, \"name\": \"groundhog\"}, {\"id\": 44585, \"name\": \"bbq rib\"}, {\"id\": 44586, \"name\": \"a wooden plate of food\"}, {\"id\": 44587, \"name\": \"the red icing\"}, {\"id\": 44588, \"name\": \"black stick\"}, {\"id\": 44589, \"name\": \"a gray back\"}, {\"id\": 44590, \"name\": \"the loaf of bread\"}, {\"id\": 44591, \"name\": \"the party blower\"}, {\"id\": 44592, \"name\": \"a piece of red meat\"}, {\"id\": 44593, \"name\": \"muffin\"}, {\"id\": 44594, \"name\": \"a pipette\"}, {\"id\": 44595, \"name\": \"an impressive antler\"}, {\"id\": 44596, \"name\": \"an athletic gear\"}, {\"id\": 44597, \"name\": \"kitchen equipment\"}, {\"id\": 44598, \"name\": \"a cylindrical piece of dough\"}, {\"id\": 44599, \"name\": \"a freshly baked bread loaf\"}, {\"id\": 44600, \"name\": \"basil\"}, {\"id\": 44601, \"name\": \"an outstretched wing\"}, {\"id\": 44602, \"name\": \"the car design\"}, {\"id\": 44603, \"name\": \"golden fork\"}, {\"id\": 44604, \"name\": \"the corn on the cob\"}, {\"id\": 44605, \"name\": \"white frosting\"}, {\"id\": 44606, \"name\": \"a sliced pepper\"}, {\"id\": 44607, \"name\": \"a white rope of a boxing ring\"}, {\"id\": 44608, \"name\": \"the peach\"}, {\"id\": 44609, \"name\": \"a white radiator\"}, {\"id\": 44610, \"name\": \"rainbow-colored stacking toy\"}, {\"id\": 44611, \"name\": \"the sliced tomato\"}, {\"id\": 44612, \"name\": \"the turbine\"}, {\"id\": 44613, \"name\": \"a male basketball player\"}, {\"id\": 44614, \"name\": \"burrito\"}, {\"id\": 44615, \"name\": \"yellow surfboard\"}, {\"id\": 44616, \"name\": \"a stainless steel equipment\"}, {\"id\": 44617, \"name\": \"red pepper\"}, {\"id\": 44618, \"name\": \"a green grape\"}, {\"id\": 44619, \"name\": \"the bushy tail\"}, {\"id\": 44620, \"name\": \"a pompom\"}, {\"id\": 44621, \"name\": \"stylized human figure\"}, {\"id\": 44622, \"name\": \"the washing machine\"}, {\"id\": 44623, \"name\": \"a wakeboard\"}, {\"id\": 44624, \"name\": \"flowing tentacle\"}, {\"id\": 44625, \"name\": \"skewer\"}, {\"id\": 44626, \"name\": \"metal can\"}, {\"id\": 44627, \"name\": \"a cheese\"}, {\"id\": 44628, \"name\": \"brown car\"}, {\"id\": 44629, \"name\": \"bowling ball\"}, {\"id\": 44630, \"name\": \"a croissant\"}, {\"id\": 44631, \"name\": \"the green coffee cup\"}, {\"id\": 44632, \"name\": \"a kitesurfer\"}, {\"id\": 44633, \"name\": \"women's race bib\"}, {\"id\": 44634, \"name\": \"weightlifting platform\"}, {\"id\": 44635, \"name\": \"a yellow flotation device\"}, {\"id\": 44636, \"name\": \"the ballet bar\"}, {\"id\": 44637, \"name\": \"the fry\"}, {\"id\": 44638, \"name\": \"farm worker\"}, {\"id\": 44639, \"name\": \"pitta bread\"}, {\"id\": 44640, \"name\": \"the yellow life jacket\"}, {\"id\": 44641, \"name\": \"stems of the tomato\"}, {\"id\": 44642, \"name\": \"the reddish-brown feather\"}, {\"id\": 44643, \"name\": \"a yellow and orange garnish\"}, {\"id\": 44644, \"name\": \"black bike bag\"}, {\"id\": 44645, \"name\": \"marshmallow\"}, {\"id\": 44646, \"name\": \"suitcase's wheel\"}, {\"id\": 44647, \"name\": \"a black exercise mat\"}, {\"id\": 44648, \"name\": \"the pennant flag\"}, {\"id\": 44649, \"name\": \"a skewer\"}, {\"id\": 44650, \"name\": \"the lane for swimming lap\"}, {\"id\": 44651, \"name\": \"an overhead fluorescent\"}, {\"id\": 44652, \"name\": \"a magnet\"}, {\"id\": 44653, \"name\": \"a white paddle\"}, {\"id\": 44654, \"name\": \"a black reindeer design\"}, {\"id\": 44655, \"name\": \"kettle\"}, {\"id\": 44656, \"name\": \"a sandwich\"}, {\"id\": 44657, \"name\": \"the sleigh\"}, {\"id\": 44658, \"name\": \"a pannier\"}, {\"id\": 44659, \"name\": \"the nut\"}, {\"id\": 44660, \"name\": \"a ventilation system\"}, {\"id\": 44661, \"name\": \"a barbecue\"}, {\"id\": 44662, \"name\": \"a rope of the boxing ring\"}, {\"id\": 44663, \"name\": \"a baboon\"}, {\"id\": 44664, \"name\": \"the bread dough\"}, {\"id\": 44665, \"name\": \"black dial\"}, {\"id\": 44666, \"name\": \"a dried orange slice\"}, {\"id\": 44667, \"name\": \"the wooden boarder\"}, {\"id\": 44668, \"name\": \"red-shirted player\"}, {\"id\": 44669, \"name\": \"the yellow cheese slice\"}, {\"id\": 44670, \"name\": \"a female\"}, {\"id\": 44671, \"name\": \"a red bell pepper\"}, {\"id\": 44672, \"name\": \"uncooked, yellow, spiral-shaped pasta\"}, {\"id\": 44673, \"name\": \"the racer\"}, {\"id\": 44674, \"name\": \"the glowing red orb\"}, {\"id\": 44675, \"name\": \"the zucchini\"}, {\"id\": 44676, \"name\": \"a slice of starfruit\"}, {\"id\": 44677, \"name\": \"the black olive\"}, {\"id\": 44678, \"name\": \"the silver-colored hardware\"}, {\"id\": 44679, \"name\": \"black ski pole\"}, {\"id\": 44680, \"name\": \"a wildebeest\"}, {\"id\": 44681, \"name\": \"the black plastic rim\"}, {\"id\": 44682, \"name\": \"the red exercise ball\"}, {\"id\": 44683, \"name\": \"a chairlift\"}, {\"id\": 44684, \"name\": \"the buffalo\"}, {\"id\": 44685, \"name\": \"the motorcycle tire\"}, {\"id\": 44686, \"name\": \"the delectable dish of meat\"}, {\"id\": 44687, \"name\": \"the office worker\"}, {\"id\": 44688, \"name\": \"a tufted back\"}, {\"id\": 44689, \"name\": \"fig\"}, {\"id\": 44690, \"name\": \"trolley bus\"}, {\"id\": 44691, \"name\": \"the gymnastic ring\"}, {\"id\": 44692, \"name\": \"the red mirror\"}, {\"id\": 44693, \"name\": \"a multi-level highway interchange\"}, {\"id\": 44694, \"name\": \"the brown glass bottle of beer\"}, {\"id\": 44695, \"name\": \"metal tray of food\"}, {\"id\": 44696, \"name\": \"the science equipment\"}, {\"id\": 44697, \"name\": \"the glass of champagne\"}, {\"id\": 44698, \"name\": \"leisurely paddle\"}, {\"id\": 44699, \"name\": \"the tray of orange-colored pastry\"}, {\"id\": 44700, \"name\": \"a silver cylinder\"}, {\"id\": 44701, \"name\": \"the lane divider\"}, {\"id\": 44702, \"name\": \"yellow bubble wand\"}, {\"id\": 44703, \"name\": \"a green exercise ball\"}, {\"id\": 44704, \"name\": \"the red jeep\"}, {\"id\": 44705, \"name\": \"red butterfly\"}, {\"id\": 44706, \"name\": \"kitchen towel\"}, {\"id\": 44707, \"name\": \"metal carabiner\"}, {\"id\": 44708, \"name\": \"the snowcat\"}, {\"id\": 44709, \"name\": \"pizza base\"}, {\"id\": 44710, \"name\": \"a dessert\"}, {\"id\": 44711, \"name\": \"a head of the dog\"}, {\"id\": 44712, \"name\": \"the green exercise ball\"}, {\"id\": 44713, \"name\": \"paved highway\"}, {\"id\": 44714, \"name\": \"the impressive antler\"}, {\"id\": 44715, \"name\": \"pronghorn antelope\"}, {\"id\": 44716, \"name\": \"a head of a bee\"}, {\"id\": 44717, \"name\": \"flyboarder\"}, {\"id\": 44718, \"name\": \"guacamole\"}, {\"id\": 44719, \"name\": \"the table of diner\"}, {\"id\": 44720, \"name\": \"a windsurfers\"}, {\"id\": 44721, \"name\": \"metal serving utensil\"}, {\"id\": 44722, \"name\": \"a green lettuce\"}, {\"id\": 44723, \"name\": \"a green exercise mat\"}, {\"id\": 44724, \"name\": \"the plate of pastry\"}, {\"id\": 44725, \"name\": \"omelette\"}, {\"id\": 44726, \"name\": \"the yellow tractor\"}, {\"id\": 44727, \"name\": \"a yellow counter\"}, {\"id\": 44728, \"name\": \"meringue rose\"}, {\"id\": 44729, \"name\": \"green glass bottle of beer\"}, {\"id\": 44730, \"name\": \"a curler\"}, {\"id\": 44731, \"name\": \"yellow and black medicine ball\"}, {\"id\": 44732, \"name\": \"piece of bread\"}, {\"id\": 44733, \"name\": \"the jar of food\"}, {\"id\": 44734, \"name\": \"triangular dorsal fin\"}, {\"id\": 44735, \"name\": \"a potato pile\"}, {\"id\": 44736, \"name\": \"the black and white flipper\"}, {\"id\": 44737, \"name\": \"a hockey helmet\"}, {\"id\": 44738, \"name\": \"the volunteer\"}, {\"id\": 44739, \"name\": \"slice of cheese\"}, {\"id\": 44740, \"name\": \"plate of sushi\"}, {\"id\": 44741, \"name\": \"dessert\"}, {\"id\": 44742, \"name\": \"a sliced lotu root\"}, {\"id\": 44743, \"name\": \"the mechanical part\"}, {\"id\": 44744, \"name\": \"a glass bowl of salad\"}, {\"id\": 44745, \"name\": \"the onion\"}, {\"id\": 44746, \"name\": \"silver barware\"}, {\"id\": 44747, \"name\": \"cooking pot\"}, {\"id\": 44748, \"name\": \"a black flipper\"}, {\"id\": 44749, \"name\": \"the parasailing\"}, {\"id\": 44750, \"name\": \"a hump\"}, {\"id\": 44751, \"name\": \"a corn\"}, {\"id\": 44752, \"name\": \"rollerblading\"}, {\"id\": 44753, \"name\": \"a lemon slice\"}, {\"id\": 44754, \"name\": \"a slice of toasted bread\"}, {\"id\": 44755, \"name\": \"the brunette\"}, {\"id\": 44756, \"name\": \"a grazing animal\"}, {\"id\": 44757, \"name\": \"a hiking pole\"}, {\"id\": 44758, \"name\": \"brown sauce\"}, {\"id\": 44759, \"name\": \"the sliced red onion\"}, {\"id\": 44760, \"name\": \"juicy red flesh\"}, {\"id\": 44761, \"name\": \"a man on the sidewalk\"}, {\"id\": 44762, \"name\": \"bruschetta\"}, {\"id\": 44763, \"name\": \"scorpion\"}, {\"id\": 44764, \"name\": \"a blonde-haired woman\"}, {\"id\": 44765, \"name\": \"orange canoe\"}, {\"id\": 44766, \"name\": \"the black and red ski\"}, {\"id\": 44767, \"name\": \"a yellow shape\"}, {\"id\": 44768, \"name\": \"a van's taillight\"}, {\"id\": 44769, \"name\": \"the serving dish\"}, {\"id\": 44770, \"name\": \"the farmer\"}, {\"id\": 44771, \"name\": \"formula one car\"}, {\"id\": 44772, \"name\": \"the ball of twine\"}, {\"id\": 44773, \"name\": \"player in blue\"}, {\"id\": 44774, \"name\": \"a paddleboard\"}, {\"id\": 44775, \"name\": \"a white face guard\"}, {\"id\": 44776, \"name\": \"the bottle of an alcoholic beverage\"}, {\"id\": 44777, \"name\": \"the voluntee\"}, {\"id\": 44778, \"name\": \"piece of luggage\"}, {\"id\": 44779, \"name\": \"woman in a black shirt\"}, {\"id\": 44780, \"name\": \"brown egg\"}, {\"id\": 44781, \"name\": \"the stationary bike\"}, {\"id\": 44782, \"name\": \"the black ufc glove\"}, {\"id\": 44783, \"name\": \"an antelope\"}, {\"id\": 44784, \"name\": \"the woman in the white shirt\"}, {\"id\": 44785, \"name\": \"blue bowl\"}, {\"id\": 44786, \"name\": \"a meat skewer\"}, {\"id\": 44787, \"name\": \"sheet of dough\"}, {\"id\": 44788, \"name\": \"a wooden cooking utensil\"}, {\"id\": 44789, \"name\": \"a crumb\"}, {\"id\": 44790, \"name\": \"yellow-orange oval\"}, {\"id\": 44791, \"name\": \"a red guardrail\"}, {\"id\": 44792, \"name\": \"portion of roasted potato\"}, {\"id\": 44793, \"name\": \"the parallel cable\"}, {\"id\": 44794, \"name\": \"participant\"}, {\"id\": 44795, \"name\": \"rear quarter panel\"}, {\"id\": 44796, \"name\": \"a face guard\"}, {\"id\": 44797, \"name\": \"designated bus lane\"}, {\"id\": 44798, \"name\": \"a swing rope\"}, {\"id\": 44799, \"name\": \"a red lighting\"}, {\"id\": 44800, \"name\": \"construction vehicle\"}, {\"id\": 44801, \"name\": \"a black fencing mask\"}, {\"id\": 44802, \"name\": \"a boxing ring's rope\"}, {\"id\": 44803, \"name\": \"paddleball racket\"}, {\"id\": 44804, \"name\": \"a jogger\"}, {\"id\": 44805, \"name\": \"neon yellow cleat\"}, {\"id\": 44806, \"name\": \"a rolled-up yoga mat\"}, {\"id\": 44807, \"name\": \"gym equipment\"}, {\"id\": 44808, \"name\": \"an audience member\"}, {\"id\": 44809, \"name\": \"a pink wing\"}, {\"id\": 44810, \"name\": \"a suspension strap\"}, {\"id\": 44811, \"name\": \"a soccer net\"}, {\"id\": 44812, \"name\": \"black pan\"}, {\"id\": 44813, \"name\": \"the white duck\"}, {\"id\": 44814, \"name\": \"the baking supply\"}, {\"id\": 44815, \"name\": \"a rusty frame\"}, {\"id\": 44816, \"name\": \"the asparagus\"}, {\"id\": 44817, \"name\": \"a black kettlebell\"}, {\"id\": 44818, \"name\": \"the piping bag of icing\"}, {\"id\": 44819, \"name\": \"the aerial silk\"}, {\"id\": 44820, \"name\": \"black and white baby chicken\"}, {\"id\": 44821, \"name\": \"a cardamom pod\"}, {\"id\": 44822, \"name\": \"sailboard\"}, {\"id\": 44823, \"name\": \"the diving tank\"}, {\"id\": 44824, \"name\": \"the bowl of salad\"}, {\"id\": 44825, \"name\": \"the silver vehicle\"}, {\"id\": 44826, \"name\": \"the tail feather\"}, {\"id\": 44827, \"name\": \"lime\"}, {\"id\": 44828, \"name\": \"a shredded carrot\"}, {\"id\": 44829, \"name\": \"a picnic basket\"}, {\"id\": 44830, \"name\": \"the electrical box\"}, {\"id\": 44831, \"name\": \"the egg\"}, {\"id\": 44832, \"name\": \"meerkat\"}, {\"id\": 44833, \"name\": \"a bowl of dipping sauce\"}, {\"id\": 44834, \"name\": \"a white curb\"}, {\"id\": 44835, \"name\": \"the jet-ski\"}, {\"id\": 44836, \"name\": \"the bread roll\"}, {\"id\": 44837, \"name\": \"the yellow and red lane divider\"}, {\"id\": 44838, \"name\": \"the sprig of rosemary\"}, {\"id\": 44839, \"name\": \"the orange life preserver\"}, {\"id\": 44840, \"name\": \"the stadium's stand\"}, {\"id\": 44841, \"name\": \"yellow napkin\"}, {\"id\": 44842, \"name\": \"a silver serving dish\"}, {\"id\": 44843, \"name\": \"a bowl of cereal\"}, {\"id\": 44844, \"name\": \"the white mast\"}, {\"id\": 44845, \"name\": \"the talon\"}, {\"id\": 44846, \"name\": \"the squash\"}, {\"id\": 44847, \"name\": \"a red pebble\"}, {\"id\": 44848, \"name\": \"the blurred white car\"}, {\"id\": 44849, \"name\": \"coffee mug\"}, {\"id\": 44850, \"name\": \"a yellow and blue volleyball\"}, {\"id\": 44851, \"name\": \"traditional mexican dancer\"}, {\"id\": 44852, \"name\": \"the green bowling ball\"}, {\"id\": 44853, \"name\": \"sliced watermelon\"}, {\"id\": 44854, \"name\": \"a music equipment\"}, {\"id\": 44855, \"name\": \"the white football helmet\"}, {\"id\": 44856, \"name\": \"the silver serving tray\"}, {\"id\": 44857, \"name\": \"the blue race bib\"}, {\"id\": 44858, \"name\": \"party blower\"}, {\"id\": 44859, \"name\": \"a bottle of yellow liquid\"}, {\"id\": 44860, \"name\": \"the tortilla\"}, {\"id\": 44861, \"name\": \"the silver balloon\"}, {\"id\": 44862, \"name\": \"yellow tenni ball\"}, {\"id\": 44863, \"name\": \"the black rollerblade\"}, {\"id\": 44864, \"name\": \"a takeout food container\"}, {\"id\": 44865, \"name\": \"the tray of sushi\"}, {\"id\": 44866, \"name\": \"a sweet treat\"}, {\"id\": 44867, \"name\": \"rainbow-colored balloon\"}, {\"id\": 44868, \"name\": \"the jet skier\"}, {\"id\": 44869, \"name\": \"a tuft of white feather\"}, {\"id\": 44870, \"name\": \"the amber-colored liquid\"}, {\"id\": 44871, \"name\": \"the construction gear\"}, {\"id\": 44872, \"name\": \"the wildebeest\"}, {\"id\": 44873, \"name\": \"ring's ropes\"}, {\"id\": 44874, \"name\": \"the seatbelt\"}, {\"id\": 44875, \"name\": \"a root ball\"}, {\"id\": 44876, \"name\": \"silver ventilation system\"}, {\"id\": 44877, \"name\": \"a black dumbbell\"}, {\"id\": 44878, \"name\": \"a red grape\"}, {\"id\": 44879, \"name\": \"a desk lamp\"}, {\"id\": 44880, \"name\": \"the cinnamon stick\"}, {\"id\": 44881, \"name\": \"a shrimp spring roll\"}, {\"id\": 44882, \"name\": \"a white dough base\"}, {\"id\": 44883, \"name\": \"a red paper shape\"}, {\"id\": 44884, \"name\": \"the patty\"}, {\"id\": 44885, \"name\": \"white football helmet\"}, {\"id\": 44886, \"name\": \"a shredded lettuce\"}, {\"id\": 44887, \"name\": \"horseback rider\"}, {\"id\": 44888, \"name\": \"neon green bat\"}, {\"id\": 44889, \"name\": \"metal tool\"}, {\"id\": 44890, \"name\": \"an aluminum foil-wrapped tray of food\"}, {\"id\": 44891, \"name\": \"front bike's rider\"}, {\"id\": 44892, \"name\": \"silver appliance\"}, {\"id\": 44893, \"name\": \"the player in a red uniform\"}, {\"id\": 44894, \"name\": \"the lioness\"}, {\"id\": 44895, \"name\": \"black hood of a vehicle\"}, {\"id\": 44896, \"name\": \"savory crepe\"}, {\"id\": 44897, \"name\": \"a green rind\"}, {\"id\": 44898, \"name\": \"electrical cord\"}, {\"id\": 44899, \"name\": \"the dining table\"}, {\"id\": 44900, \"name\": \"a plate of bread\"}, {\"id\": 44901, \"name\": \"a valve\"}, {\"id\": 44902, \"name\": \"a computer desk\"}, {\"id\": 44903, \"name\": \"pink tail\"}, {\"id\": 44904, \"name\": \"garlic\"}, {\"id\": 44905, \"name\": \"rolling office chair\"}, {\"id\": 44906, \"name\": \"a herb\"}, {\"id\": 44907, \"name\": \"the vintage car\"}, {\"id\": 44908, \"name\": \"the yellow dye\"}, {\"id\": 44909, \"name\": \"feathered tail\"}, {\"id\": 44910, \"name\": \"the tan-colored ice skate\"}, {\"id\": 44911, \"name\": \"a paper star\"}, {\"id\": 44912, \"name\": \"the candy decoration\"}, {\"id\": 44913, \"name\": \"the fig\"}, {\"id\": 44914, \"name\": \"golf club\"}, {\"id\": 44915, \"name\": \"the lollipop\"}, {\"id\": 44916, \"name\": \"a boxing ring's white rope\"}, {\"id\": 44917, \"name\": \"a lighting in the room\"}, {\"id\": 44918, \"name\": \"a plastic container of sushi\"}, {\"id\": 44919, \"name\": \"cherry tomato\"}, {\"id\": 44920, \"name\": \"dollop of white cream\"}, {\"id\": 44921, \"name\": \"a graduate\"}, {\"id\": 44922, \"name\": \"the metal cable\"}, {\"id\": 44923, \"name\": \"green, alien-like creature\"}, {\"id\": 44924, \"name\": \"crumb\"}, {\"id\": 44925, \"name\": \"medieval warrior\"}, {\"id\": 44926, \"name\": \"the clear broth\"}, {\"id\": 44927, \"name\": \"the yak\"}, {\"id\": 44928, \"name\": \"an orange slice\"}, {\"id\": 44929, \"name\": \"the skewer of food\"}, {\"id\": 44930, \"name\": \"fridge\"}, {\"id\": 44931, \"name\": \"a barefoot child\"}, {\"id\": 44932, \"name\": \"office equipment\"}, {\"id\": 44933, \"name\": \"a stainless steel appliance\"}, {\"id\": 44934, \"name\": \"pink fish\"}, {\"id\": 44935, \"name\": \"a bowl of food\"}, {\"id\": 44936, \"name\": \"the stainless steel appliance\"}, {\"id\": 44937, \"name\": \"an artificial lighting\"}, {\"id\": 44938, \"name\": \"the beer mug\"}, {\"id\": 44939, \"name\": \"the shrimp tray\"}, {\"id\": 44940, \"name\": \"the silver front wheel\"}, {\"id\": 44941, \"name\": \"the pink pool float\"}, {\"id\": 44942, \"name\": \"a rear of the bike\"}, {\"id\": 44943, \"name\": \"slice of citru fruit\"}, {\"id\": 44944, \"name\": \"train's door\"}, {\"id\": 44945, \"name\": \"a square white plate\"}, {\"id\": 44946, \"name\": \"the kettlebell\"}, {\"id\": 44947, \"name\": \"the face guard\"}, {\"id\": 44948, \"name\": \"weightlifting bar\"}, {\"id\": 44949, \"name\": \"glass of drink\"}, {\"id\": 44950, \"name\": \"the brown coffee cup\"}, {\"id\": 44951, \"name\": \"a silver hitch ball\"}, {\"id\": 44952, \"name\": \"white egg\"}, {\"id\": 44953, \"name\": \"a white trunk\"}, {\"id\": 44954, \"name\": \"a reveler\"}, {\"id\": 44955, \"name\": \"the white egg\"}, {\"id\": 44956, \"name\": \"outdoor meal\"}, {\"id\": 44957, \"name\": \"the slice of carrot\"}, {\"id\": 44958, \"name\": \"metal serving spoon\"}, {\"id\": 44959, \"name\": \"the brown wing\"}, {\"id\": 44960, \"name\": \"the slice of meat\"}, {\"id\": 44961, \"name\": \"rowing oar\"}, {\"id\": 44962, \"name\": \"the hamburger bun\"}, {\"id\": 44963, \"name\": \"the digital billboard\"}, {\"id\": 44964, \"name\": \"a blue dumbbell\"}, {\"id\": 44965, \"name\": \"the grey pompom\"}, {\"id\": 44966, \"name\": \"a left taillight\"}, {\"id\": 44967, \"name\": \"a baking supply\"}, {\"id\": 44968, \"name\": \"the heavy-duty truck\"}, {\"id\": 44969, \"name\": \"a warehouse worker\"}, {\"id\": 44970, \"name\": \"the orange\"}, {\"id\": 44971, \"name\": \"parked suv\"}, {\"id\": 44972, \"name\": \"patagonian mara\"}, {\"id\": 44973, \"name\": \"the pompom\"}, {\"id\": 44974, \"name\": \"the white figure skate\"}, {\"id\": 44975, \"name\": \"a raw meat\"}, {\"id\": 44976, \"name\": \"back right hoof\"}, {\"id\": 44977, \"name\": \"a star anise pod\"}, {\"id\": 44978, \"name\": \"stems of the apple\"}, {\"id\": 44979, \"name\": \"visual aid\"}, {\"id\": 44980, \"name\": \"a lighting in the office\"}, {\"id\": 44981, \"name\": \"the metal carabiner\"}, {\"id\": 44982, \"name\": \"an employee\"}, {\"id\": 44983, \"name\": \"a pizza slice\"}, {\"id\": 44984, \"name\": \"a black and white tail\"}, {\"id\": 44985, \"name\": \"exercise gear\"}, {\"id\": 44986, \"name\": \"a metal food dish\"}, {\"id\": 44987, \"name\": \"a longboarding\"}, {\"id\": 44988, \"name\": \"the male dancer\"}, {\"id\": 44989, \"name\": \"a black and white race bib\"}, {\"id\": 44990, \"name\": \"the respirator\"}, {\"id\": 44991, \"name\": \"a red-shirted individual\"}, {\"id\": 44992, \"name\": \"the open lid of the machine\"}, {\"id\": 44993, \"name\": \"a person on bicycle\"}, {\"id\": 44994, \"name\": \"the humanoid alien\"}, {\"id\": 44995, \"name\": \"a food container\"}, {\"id\": 44996, \"name\": \"a boxing ring's ropes\"}, {\"id\": 44997, \"name\": \"the rugby player\"}, {\"id\": 44998, \"name\": \"crawfish\"}, {\"id\": 44999, \"name\": \"black engine\"}, {\"id\": 45000, \"name\": \"trail of bubble\"}, {\"id\": 45001, \"name\": \"the gazelle\"}, {\"id\": 45002, \"name\": \"a head of the woman\"}, {\"id\": 45003, \"name\": \"yellow silhouette of player\"}, {\"id\": 45004, \"name\": \"slice of yellow cheese\"}, {\"id\": 45005, \"name\": \"glass of red wine\"}, {\"id\": 45006, \"name\": \"an inflatable pool toy\"}, {\"id\": 45007, \"name\": \"a seagull\"}, {\"id\": 45008, \"name\": \"woman holding the beer\"}, {\"id\": 45009, \"name\": \"the boxing ring's black rope\"}, {\"id\": 45010, \"name\": \"black telephone receiver\"}, {\"id\": 45011, \"name\": \"a slice of fruit\"}, {\"id\": 45012, \"name\": \"companion\"}, {\"id\": 45013, \"name\": \"the white radiator\"}, {\"id\": 45014, \"name\": \"a signal\"}, {\"id\": 45015, \"name\": \"a black radiator\"}, {\"id\": 45016, \"name\": \"red and gray exercise equipment\"}, {\"id\": 45017, \"name\": \"red tuber\"}, {\"id\": 45018, \"name\": \"black and silver dial\"}, {\"id\": 45019, \"name\": \"the black figure\"}, {\"id\": 45020, \"name\": \"an inflatable ring\"}, {\"id\": 45021, \"name\": \"the red wine\"}, {\"id\": 45022, \"name\": \"the smiling woman\"}, {\"id\": 45023, \"name\": \"the vertical neon tube\"}, {\"id\": 45024, \"name\": \"a blue exercise mat\"}, {\"id\": 45025, \"name\": \"the yellow cheese\"}, {\"id\": 45026, \"name\": \"snowmobile\"}, {\"id\": 45027, \"name\": \"breadstick\"}, {\"id\": 45028, \"name\": \"red grape\"}, {\"id\": 45029, \"name\": \"a dwelling\"}, {\"id\": 45030, \"name\": \"a jar of spice\"}, {\"id\": 45031, \"name\": \"fighter jet\"}, {\"id\": 45032, \"name\": \"fidget spinner\"}, {\"id\": 45033, \"name\": \"stalks of green asparagu\"}, {\"id\": 45034, \"name\": \"taco al pastor\"}, {\"id\": 45035, \"name\": \"a bike's back wheel\"}, {\"id\": 45036, \"name\": \"the kiwi\"}, {\"id\": 45037, \"name\": \"hiking gear\"}, {\"id\": 45038, \"name\": \"white serving dish\"}, {\"id\": 45039, \"name\": \"the yellow pepper\"}, {\"id\": 45040, \"name\": \"a white serving dish\"}, {\"id\": 45041, \"name\": \"a melted white cheese\"}, {\"id\": 45042, \"name\": \"chain of the swing\"}, {\"id\": 45043, \"name\": \"the curved dorsal fin\"}, {\"id\": 45044, \"name\": \"the coconut\"}, {\"id\": 45045, \"name\": \"glass of iced tea\"}, {\"id\": 45046, \"name\": \"kettlebells\"}, {\"id\": 45047, \"name\": \"tomato slice\"}, {\"id\": 45048, \"name\": \"a silver laptop computer\"}, {\"id\": 45049, \"name\": \"silver metal component\"}, {\"id\": 45050, \"name\": \"the target board\"}, {\"id\": 45051, \"name\": \"a blue slide\"}, {\"id\": 45052, \"name\": \"the nose of the jet\"}, {\"id\": 45053, \"name\": \"multi-colored slide\"}, {\"id\": 45054, \"name\": \"the seafood\"}, {\"id\": 45055, \"name\": \"the black stick\"}, {\"id\": 45056, \"name\": \"the wedge of lemon\"}, {\"id\": 45057, \"name\": \"blue cheese\"}, {\"id\": 45058, \"name\": \"a glass of appetizer\"}, {\"id\": 45059, \"name\": \"a ballet barre\"}, {\"id\": 45060, \"name\": \"a pasta\"}, {\"id\": 45061, \"name\": \"a pennant flag\"}, {\"id\": 45062, \"name\": \"the circular stone\"}, {\"id\": 45063, \"name\": \"person in swimsuit\"}, {\"id\": 45064, \"name\": \"the music control\"}, {\"id\": 45065, \"name\": \"container of chocolate piece\"}, {\"id\": 45066, \"name\": \"a mountain goat\"}, {\"id\": 45067, \"name\": \"a woman in a black jacket and pant\"}, {\"id\": 45068, \"name\": \"a talon\"}, {\"id\": 45069, \"name\": \"a white bicycle symbol\"}, {\"id\": 45070, \"name\": \"tuning peg\"}, {\"id\": 45071, \"name\": \"aerial silk\"}, {\"id\": 45072, \"name\": \"a piece of orange fruit\"}, {\"id\": 45073, \"name\": \"nori seaweed sheet\"}, {\"id\": 45074, \"name\": \"an ultrasound image\"}, {\"id\": 45075, \"name\": \"the sliced ham\"}, {\"id\": 45076, \"name\": \"silhouette of a woman\"}, {\"id\": 45077, \"name\": \"section of a highway\"}, {\"id\": 45078, \"name\": \"place setting\"}, {\"id\": 45079, \"name\": \"the silver fork\"}, {\"id\": 45080, \"name\": \"a bunting\"}, {\"id\": 45081, \"name\": \"pink plate\"}, {\"id\": 45082, \"name\": \"a blonde\"}, {\"id\": 45083, \"name\": \"a red and white traffic sign\"}, {\"id\": 45084, \"name\": \"the knight's shield\"}, {\"id\": 45085, \"name\": \"a rosemary sprig\"}, {\"id\": 45086, \"name\": \"the cable car tower\"}, {\"id\": 45087, \"name\": \"family member\"}, {\"id\": 45088, \"name\": \"the orange food\"}, {\"id\": 45089, \"name\": \"grayish-white wing\"}, {\"id\": 45090, \"name\": \"the black and white panel\"}, {\"id\": 45091, \"name\": \"a blue life jacket\"}, {\"id\": 45092, \"name\": \"blue flipper\"}, {\"id\": 45093, \"name\": \"a visual aid\"}, {\"id\": 45094, \"name\": \"green liquid\"}, {\"id\": 45095, \"name\": \"hummus\"}, {\"id\": 45096, \"name\": \"an asparagus\"}, {\"id\": 45097, \"name\": \"cooker\"}, {\"id\": 45098, \"name\": \"trekking pole\"}, {\"id\": 45099, \"name\": \"the yellow bell pepper\"}, {\"id\": 45100, \"name\": \"a stainless steel ventilation system\"}, {\"id\": 45101, \"name\": \"the chopped white ingredient\"}, {\"id\": 45102, \"name\": \"a white beverage\"}, {\"id\": 45103, \"name\": \"a red and white curb\"}, {\"id\": 45104, \"name\": \"a yellow and black traffic sign\"}, {\"id\": 45105, \"name\": \"a glass of amber-colored liquid\"}, {\"id\": 45106, \"name\": \"hamburger\"}, {\"id\": 45107, \"name\": \"the diving gear\"}, {\"id\": 45108, \"name\": \"the ping-pong paddle\"}, {\"id\": 45109, \"name\": \"glass of orange liquid\"}, {\"id\": 45110, \"name\": \"the gray hatchback car\"}, {\"id\": 45111, \"name\": \"a stage lighting setup\"}, {\"id\": 45112, \"name\": \"bottle of alcohol\"}, {\"id\": 45113, \"name\": \"starburst\"}, {\"id\": 45114, \"name\": \"marble\"}, {\"id\": 45115, \"name\": \"red cabin\"}, {\"id\": 45116, \"name\": \"the yellow sled\"}, {\"id\": 45117, \"name\": \"a lock\"}, {\"id\": 45118, \"name\": \"pointed dorsal fin\"}, {\"id\": 45119, \"name\": \"plate of vegetable\"}, {\"id\": 45120, \"name\": \"a partygoer\"}, {\"id\": 45121, \"name\": \"bottle of condiment\"}, {\"id\": 45122, \"name\": \"a shadow of a person\"}, {\"id\": 45123, \"name\": \"a blueberry\"}, {\"id\": 45124, \"name\": \"a green bottle\"}, {\"id\": 45125, \"name\": \"the boxing glove\"}, {\"id\": 45126, \"name\": \"blue spotlight\"}, {\"id\": 45127, \"name\": \"a sliced cucumber\"}, {\"id\": 45128, \"name\": \"the white and orange box\"}, {\"id\": 45129, \"name\": \"orange dumbbell\"}, {\"id\": 45130, \"name\": \"the blue exercise mat\"}, {\"id\": 45131, \"name\": \"a tractor's wheel\"}, {\"id\": 45132, \"name\": \"a green olive\"}, {\"id\": 45133, \"name\": \"a weightlifting bar\"}, {\"id\": 45134, \"name\": \"a loaf of dough\"}, {\"id\": 45135, \"name\": \"a cup of coffee\"}, {\"id\": 45136, \"name\": \"the man in the white shirt\"}, {\"id\": 45137, \"name\": \"bald spectator\"}, {\"id\": 45138, \"name\": \"the pink tail\"}, {\"id\": 45139, \"name\": \"a blue and white striped hull\"}, {\"id\": 45140, \"name\": \"an industrial-style lighting\"}, {\"id\": 45141, \"name\": \"the chain of the swing\"}, {\"id\": 45142, \"name\": \"brown crust\"}, {\"id\": 45143, \"name\": \"a champagne\"}, {\"id\": 45144, \"name\": \"the red pepper\"}, {\"id\": 45145, \"name\": \"hummingbird\"}, {\"id\": 45146, \"name\": \"golden pole\"}, {\"id\": 45147, \"name\": \"a motorcycle gear\"}, {\"id\": 45148, \"name\": \"a piece of lettuce\"}, {\"id\": 45149, \"name\": \"leopard\"}, {\"id\": 45150, \"name\": \"a blue frosting\"}, {\"id\": 45151, \"name\": \"sliced tomato\"}, {\"id\": 45152, \"name\": \"the cucumber slice\"}, {\"id\": 45153, \"name\": \"blue napkin\"}, {\"id\": 45154, \"name\": \"white crockery jar\"}, {\"id\": 45155, \"name\": \"red tomato\"}, {\"id\": 45156, \"name\": \"a multi-level highway\"}, {\"id\": 45157, \"name\": \"an user\"}, {\"id\": 45158, \"name\": \"an inflatable raft\"}, {\"id\": 45159, \"name\": \"rear seat\"}, {\"id\": 45160, \"name\": \"a ship's hull\"}, {\"id\": 45161, \"name\": \"the roller coaster seat\"}, {\"id\": 45162, \"name\": \"the damselfish\"}, {\"id\": 45163, \"name\": \"the box of food\"}, {\"id\": 45164, \"name\": \"spinning blade\"}, {\"id\": 45165, \"name\": \"bike's rear wheel\"}, {\"id\": 45166, \"name\": \"the rind\"}, {\"id\": 45167, \"name\": \"battle rope\"}, {\"id\": 45168, \"name\": \"a cinnamon stick\"}, {\"id\": 45169, \"name\": \"a bowl of feed\"}, {\"id\": 45170, \"name\": \"the patch of blue feather\"}, {\"id\": 45171, \"name\": \"mid-skate\"}, {\"id\": 45172, \"name\": \"a lime slice\"}, {\"id\": 45173, \"name\": \"barefoot individual\"}, {\"id\": 45174, \"name\": \"an amber liquid\"}, {\"id\": 45175, \"name\": \"the green garnish\"}, {\"id\": 45176, \"name\": \"the blue and white patterned plate\"}, {\"id\": 45177, \"name\": \"zucchini\"}, {\"id\": 45178, \"name\": \"ostrich\"}, {\"id\": 45179, \"name\": \"a bottle of sauce\"}, {\"id\": 45180, \"name\": \"orange roller skate\"}, {\"id\": 45181, \"name\": \"a party blower\"}, {\"id\": 45182, \"name\": \"candy piece\"}, {\"id\": 45183, \"name\": \"training tool\"}, {\"id\": 45184, \"name\": \"hovercraft\"}, {\"id\": 45185, \"name\": \"a spoonful of dough\"}, {\"id\": 45186, \"name\": \"the red punching bag\"}, {\"id\": 45187, \"name\": \"a person on the riverbank\"}, {\"id\": 45188, \"name\": \"the ballet barre\"}, {\"id\": 45189, \"name\": \"the restaurant sign\"}, {\"id\": 45190, \"name\": \"neon green ring\"}, {\"id\": 45191, \"name\": \"the partygoer\"}, {\"id\": 45192, \"name\": \"kitesurfer\"}, {\"id\": 45193, \"name\": \"floatie\"}, {\"id\": 45194, \"name\": \"cheese\"}, {\"id\": 45195, \"name\": \"white yak\"}, {\"id\": 45196, \"name\": \"open-faced sandwich\"}, {\"id\": 45197, \"name\": \"the orange slice\"}, {\"id\": 45198, \"name\": \"a mace\"}, {\"id\": 45199, \"name\": \"rotting fruit\"}, {\"id\": 45200, \"name\": \"climbing equipment\"}, {\"id\": 45201, \"name\": \"a round metal surface\"}, {\"id\": 45202, \"name\": \"a construction crane\"}, {\"id\": 45203, \"name\": \"the men in blue shirt\"}, {\"id\": 45204, \"name\": \"the blue flipper\"}, {\"id\": 45205, \"name\": \"the brussel sprout\"}, {\"id\": 45206, \"name\": \"pipette\"}, {\"id\": 45207, \"name\": \"a dish of yellow food\"}, {\"id\": 45208, \"name\": \"lime wedge\"}, {\"id\": 45209, \"name\": \"silhouette of person\"}, {\"id\": 45210, \"name\": \"the inner white meat\"}, {\"id\": 45211, \"name\": \"the brown metal frame\"}, {\"id\": 45212, \"name\": \"a pole dancing pole\"}, {\"id\": 45213, \"name\": \"the black and yellow fin\"}, {\"id\": 45214, \"name\": \"the gold balloon\"}, {\"id\": 45215, \"name\": \"the person on a motorbike\"}, {\"id\": 45216, \"name\": \"trx suspension training device\"}, {\"id\": 45217, \"name\": \"a shadow of person\"}, {\"id\": 45218, \"name\": \"the cooking equipment\"}, {\"id\": 45219, \"name\": \"the yellow lighting\"}, {\"id\": 45220, \"name\": \"the hangar\"}, {\"id\": 45221, \"name\": \"the black ski pole\"}, {\"id\": 45222, \"name\": \"the black-tipped fin\"}, {\"id\": 45223, \"name\": \"a skydiver's parachute\"}, {\"id\": 45224, \"name\": \"the scythe\"}, {\"id\": 45225, \"name\": \"a green dinosaur\"}, {\"id\": 45226, \"name\": \"person in a red hat and white shirt\"}, {\"id\": 45227, \"name\": \"blue robotic arm\"}, {\"id\": 45228, \"name\": \"the black-tipped white tail\"}, {\"id\": 45229, \"name\": \"tortoise\"}, {\"id\": 45230, \"name\": \"a sliced carrot\"}, {\"id\": 45231, \"name\": \"the glass-enclosed cabin\"}, {\"id\": 45232, \"name\": \"the decorative saddle\"}, {\"id\": 45233, \"name\": \"a machinery's wheel\"}, {\"id\": 45234, \"name\": \"a goal post\"}, {\"id\": 45235, \"name\": \"the piece of meat\"}, {\"id\": 45236, \"name\": \"white part of the mower\"}, {\"id\": 45237, \"name\": \"white vent\"}, {\"id\": 45238, \"name\": \"yellow exercise machine\"}, {\"id\": 45239, \"name\": \"gray exercise mat\"}, {\"id\": 45240, \"name\": \"a yellow rind\"}, {\"id\": 45241, \"name\": \"a ski lift's cable\"}, {\"id\": 45242, \"name\": \"a trekking pole\"}, {\"id\": 45243, \"name\": \"a grocery store shelf\"}, {\"id\": 45244, \"name\": \"a hood of the vehicle\"}, {\"id\": 45245, \"name\": \"the ring's ropes\"}, {\"id\": 45246, \"name\": \"the confetti\"}, {\"id\": 45247, \"name\": \"the sleigh's rope\"}, {\"id\": 45248, \"name\": \"the carriage seat\"}, {\"id\": 45249, \"name\": \"the champagne\"}, {\"id\": 45250, \"name\": \"the bowling alley\"}, {\"id\": 45251, \"name\": \"a clove\"}, {\"id\": 45252, \"name\": \"a bungee cord\"}, {\"id\": 45253, \"name\": \"the surface of the slice\"}, {\"id\": 45254, \"name\": \"the white garage door\"}, {\"id\": 45255, \"name\": \"the two-story white house\"}, {\"id\": 45256, \"name\": \"the red-clad player\"}, {\"id\": 45257, \"name\": \"a thinly sliced meat\"}, {\"id\": 45258, \"name\": \"the okra\"}, {\"id\": 45259, \"name\": \"a cooked waffle\"}, {\"id\": 45260, \"name\": \"a suspension strap system\"}, {\"id\": 45261, \"name\": \"white ice skate\"}, {\"id\": 45262, \"name\": \"gray circle\"}, {\"id\": 45263, \"name\": \"a pink vehicle\"}, {\"id\": 45264, \"name\": \"the wooden pilate stick\"}, {\"id\": 45265, \"name\": \"the black ice skate\"}, {\"id\": 45266, \"name\": \"the painted lane divider\"}, {\"id\": 45267, \"name\": \"blue-tinted screen\"}, {\"id\": 45268, \"name\": \"artichoke\"}, {\"id\": 45269, \"name\": \"the pink aerial silk\"}, {\"id\": 45270, \"name\": \"a pink saddle\"}, {\"id\": 45271, \"name\": \"the white guardrail\"}, {\"id\": 45272, \"name\": \"sushi\"}, {\"id\": 45273, \"name\": \"the blue surfboard\"}, {\"id\": 45274, \"name\": \"the pipette\"}, {\"id\": 45275, \"name\": \"partygoer\"}, {\"id\": 45276, \"name\": \"a head of a turtle\"}, {\"id\": 45277, \"name\": \"a piece of red fruit\"}, {\"id\": 45278, \"name\": \"a black-and-white boxing glove\"}, {\"id\": 45279, \"name\": \"the brown coconut\"}, {\"id\": 45280, \"name\": \"a red boxing glove\"}, {\"id\": 45281, \"name\": \"food box\"}, {\"id\": 45282, \"name\": \"the green climbing hold\"}, {\"id\": 45283, \"name\": \"an aerial yoga silk\"}, {\"id\": 45284, \"name\": \"the silver hubcap\"}, {\"id\": 45285, \"name\": \"a spherical shape\"}, {\"id\": 45286, \"name\": \"washing machine\"}, {\"id\": 45287, \"name\": \"the inflatable noisemaker\"}, {\"id\": 45288, \"name\": \"a golden-brown pastry\"}, {\"id\": 45289, \"name\": \"open and empty crate\"}, {\"id\": 45290, \"name\": \"ball of dough\"}, {\"id\": 45291, \"name\": \"the white onion\"}, {\"id\": 45292, \"name\": \"the donkey\"}, {\"id\": 45293, \"name\": \"an eel\"}, {\"id\": 45294, \"name\": \"a kitchen utensil\"}, {\"id\": 45295, \"name\": \"a sport glove\"}, {\"id\": 45296, \"name\": \"the protective fencing gear\"}, {\"id\": 45297, \"name\": \"carrot package\"}, {\"id\": 45298, \"name\": \"the yellow arm floatie\"}, {\"id\": 45299, \"name\": \"a silver tank\"}, {\"id\": 45300, \"name\": \"a carabiner\"}, {\"id\": 45301, \"name\": \"the patrol\"}, {\"id\": 45302, \"name\": \"the white pompom\"}, {\"id\": 45303, \"name\": \"a black leopard print\"}, {\"id\": 45304, \"name\": \"female soccer player\"}, {\"id\": 45305, \"name\": \"the ice piece\"}, {\"id\": 45306, \"name\": \"clove of garlic\"}, {\"id\": 45307, \"name\": \"the red-tipped wing\"}, {\"id\": 45308, \"name\": \"the batch of pickle\"}, {\"id\": 45309, \"name\": \"elevator\"}, {\"id\": 45310, \"name\": \"the golden liquid\"}, {\"id\": 45311, \"name\": \"the controller\"}, {\"id\": 45312, \"name\": \"the bottle of champagne\"}, {\"id\": 45313, \"name\": \"the fidget spinner\"}, {\"id\": 45314, \"name\": \"black mask-like marking\"}, {\"id\": 45315, \"name\": \"lighting equipment\"}, {\"id\": 45316, \"name\": \"the hockey glove\"}, {\"id\": 45317, \"name\": \"a subway turnstile\"}, {\"id\": 45318, \"name\": \"pennant banner\"}, {\"id\": 45319, \"name\": \"barista\"}, {\"id\": 45320, \"name\": \"the white globe\"}, {\"id\": 45321, \"name\": \"can of food\"}, {\"id\": 45322, \"name\": \"the clown\"}, {\"id\": 45323, \"name\": \"the farm vehicle\"}, {\"id\": 45324, \"name\": \"the walnut\"}, {\"id\": 45325, \"name\": \"a flashlight\"}, {\"id\": 45326, \"name\": \"the blurred image of a dark-colored car\"}, {\"id\": 45327, \"name\": \"black bicycle helmet\"}, {\"id\": 45328, \"name\": \"the white washing machine\"}, {\"id\": 45329, \"name\": \"plastic bottle of clear liquid\"}, {\"id\": 45330, \"name\": \"the white and blue lane divider\"}, {\"id\": 45331, \"name\": \"the exercising individual\"}, {\"id\": 45332, \"name\": \"the yellow diamond\"}, {\"id\": 45333, \"name\": \"red reindeer\"}, {\"id\": 45334, \"name\": \"a wooden ball\"}, {\"id\": 45335, \"name\": \"orange frisbee\"}, {\"id\": 45336, \"name\": \"game piece\"}, {\"id\": 45337, \"name\": \"the food and drink\"}, {\"id\": 45338, \"name\": \"the plate of salad\"}, {\"id\": 45339, \"name\": \"red trailer\"}, {\"id\": 45340, \"name\": \"the scuba tank\"}, {\"id\": 45341, \"name\": \"the black and white horse\"}, {\"id\": 45342, \"name\": \"donut\"}, {\"id\": 45343, \"name\": \"the pull-up bar\"}, {\"id\": 45344, \"name\": \"a jar of ingredient\"}, {\"id\": 45345, \"name\": \"black crate\"}, {\"id\": 45346, \"name\": \"a black and brown boxing glove\"}, {\"id\": 45347, \"name\": \"a voluntee\"}, {\"id\": 45348, \"name\": \"the red raft\"}, {\"id\": 45349, \"name\": \"base of tomato sauce\"}, {\"id\": 45350, \"name\": \"a slice of bread\"}, {\"id\": 45351, \"name\": \"gold foil balloon\"}, {\"id\": 45352, \"name\": \"a floating present\"}, {\"id\": 45353, \"name\": \"a piece of corn on the cob\"}, {\"id\": 45354, \"name\": \"a male chef\"}, {\"id\": 45355, \"name\": \"wearer\"}, {\"id\": 45356, \"name\": \"the black paintball mask\"}, {\"id\": 45357, \"name\": \"a scientific equipment\"}, {\"id\": 45358, \"name\": \"red figure\"}, {\"id\": 45359, \"name\": \"dark-haired woman\"}, {\"id\": 45360, \"name\": \"patch of black fur\"}, {\"id\": 45361, \"name\": \"a jar of pickle\"}, {\"id\": 45362, \"name\": \"the coffee mug\"}, {\"id\": 45363, \"name\": \"a brown pom-pom\"}, {\"id\": 45364, \"name\": \"the orange buoy\"}, {\"id\": 45365, \"name\": \"red and yellow triangle\"}, {\"id\": 45366, \"name\": \"the tray of diced mango\"}, {\"id\": 45367, \"name\": \"a ski lift tower\"}, {\"id\": 45368, \"name\": \"the team member\"}, {\"id\": 45369, \"name\": \"the highway overpass\"}, {\"id\": 45370, \"name\": \"a red lane divider\"}, {\"id\": 45371, \"name\": \"residential home\"}, {\"id\": 45372, \"name\": \"cinnamon roll\"}, {\"id\": 45373, \"name\": \"the pasta\"}, {\"id\": 45374, \"name\": \"a group member\"}, {\"id\": 45375, \"name\": \"mercedes\"}, {\"id\": 45376, \"name\": \"the icing\"}, {\"id\": 45377, \"name\": \"a paddler\"}, {\"id\": 45378, \"name\": \"a hanging punching bag\"}, {\"id\": 45379, \"name\": \"the violin\"}, {\"id\": 45380, \"name\": \"rein\"}, {\"id\": 45381, \"name\": \"the gymnastics ring\"}, {\"id\": 45382, \"name\": \"the flying kite\"}, {\"id\": 45383, \"name\": \"a burger\"}, {\"id\": 45384, \"name\": \"a clown\"}, {\"id\": 45385, \"name\": \"a bodyboard\"}, {\"id\": 45386, \"name\": \"a halved kiwi\"}, {\"id\": 45387, \"name\": \"a bowl of brown food\"}, {\"id\": 45388, \"name\": \"the black rubber wheel\"}, {\"id\": 45389, \"name\": \"a sliced pickle\"}, {\"id\": 45390, \"name\": \"the chocolate\"}, {\"id\": 45391, \"name\": \"pink candle\"}, {\"id\": 45392, \"name\": \"the bay mare\"}, {\"id\": 45393, \"name\": \"the golden-brown crust\"}, {\"id\": 45394, \"name\": \"the brown sauce\"}, {\"id\": 45395, \"name\": \"the neon green bat\"}, {\"id\": 45396, \"name\": \"a center circle\"}, {\"id\": 45397, \"name\": \"a blue aerial silk\"}, {\"id\": 45398, \"name\": \"female participant\"}, {\"id\": 45399, \"name\": \"alpaca\"}, {\"id\": 45400, \"name\": \"the black baseball helmet\"}, {\"id\": 45401, \"name\": \"a striking black beak\"}, {\"id\": 45402, \"name\": \"the white blaze\"}, {\"id\": 45403, \"name\": \"a vehicle's tire\"}, {\"id\": 45404, \"name\": \"a taco shell\"}, {\"id\": 45405, \"name\": \"blue climbing hold\"}, {\"id\": 45406, \"name\": \"white coffee cup\"}, {\"id\": 45407, \"name\": \"the religiou figure\"}, {\"id\": 45408, \"name\": \"a translucent wing\"}, {\"id\": 45409, \"name\": \"the pink diamond shape\"}, {\"id\": 45410, \"name\": \"bowl of fruit\"}, {\"id\": 45411, \"name\": \"tractor tire\"}, {\"id\": 45412, \"name\": \"the fruit bowl\"}, {\"id\": 45413, \"name\": \"topping\"}, {\"id\": 45414, \"name\": \"the bridle\"}, {\"id\": 45415, \"name\": \"a man in the red shirt\"}, {\"id\": 45416, \"name\": \"a crate of produce\"}, {\"id\": 45417, \"name\": \"the brown seed\"}, {\"id\": 45418, \"name\": \"a white serving platter\"}, {\"id\": 45419, \"name\": \"the yellow, plastic toy\"}, {\"id\": 45420, \"name\": \"the coworker\"}, {\"id\": 45421, \"name\": \"a tail of jet\"}, {\"id\": 45422, \"name\": \"a black lamp\"}, {\"id\": 45423, \"name\": \"slice of kiwi fruit\"}, {\"id\": 45424, \"name\": \"the metal serving spoon\"}, {\"id\": 45425, \"name\": \"a red wine\"}, {\"id\": 45426, \"name\": \"a fried chicken breast\"}, {\"id\": 45427, \"name\": \"a mussel\"}, {\"id\": 45428, \"name\": \"the garlic\"}, {\"id\": 45429, \"name\": \"the bottom crosswalk\"}, {\"id\": 45430, \"name\": \"a bird's black and white plumage\"}, {\"id\": 45431, \"name\": \"a plane's engine\"}, {\"id\": 45432, \"name\": \"the boxing ring's white rope\"}, {\"id\": 45433, \"name\": \"a seed\"}, {\"id\": 45434, \"name\": \"the pole dancing pole\"}, {\"id\": 45435, \"name\": \"a white electrical box\"}, {\"id\": 45436, \"name\": \"figure skater\"}, {\"id\": 45437, \"name\": \"an ice piece\"}, {\"id\": 45438, \"name\": \"the soda bottle\"}, {\"id\": 45439, \"name\": \"the head of the bird\"}, {\"id\": 45440, \"name\": \"dog sled\"}, {\"id\": 45441, \"name\": \"the laser tag gun\"}, {\"id\": 45442, \"name\": \"paper pompom\"}, {\"id\": 45443, \"name\": \"the sprig of mint\"}, {\"id\": 45444, \"name\": \"the orange excavator\"}, {\"id\": 45445, \"name\": \"white animal\"}, {\"id\": 45446, \"name\": \"pool-goer\"}, {\"id\": 45447, \"name\": \"the yellow oar\"}, {\"id\": 45448, \"name\": \"a green garnish\"}, {\"id\": 45449, \"name\": \"red berry\"}, {\"id\": 45450, \"name\": \"the appetizer\"}, {\"id\": 45451, \"name\": \"the yellow robotic arm\"}, {\"id\": 45452, \"name\": \"the shelf of bread\"}, {\"id\": 45453, \"name\": \"the portion of food\"}, {\"id\": 45454, \"name\": \"pink cupcake liner\"}, {\"id\": 45455, \"name\": \"a female athlete\"}, {\"id\": 45456, \"name\": \"wooden wheel\"}, {\"id\": 45457, \"name\": \"the blueberry\"}, {\"id\": 45458, \"name\": \"a windsurfer\"}, {\"id\": 45459, \"name\": \"the orange sauce\"}, {\"id\": 45460, \"name\": \"plate of fruit\"}, {\"id\": 45461, \"name\": \"glowing neon tube\"}, {\"id\": 45462, \"name\": \"the slice of lime\"}, {\"id\": 45463, \"name\": \"the dollop of butter\"}, {\"id\": 45464, \"name\": \"the red meat\"}, {\"id\": 45465, \"name\": \"pink surfboard\"}, {\"id\": 45466, \"name\": \"the white bowl of food\"}, {\"id\": 45467, \"name\": \"gold balloon tube\"}, {\"id\": 45468, \"name\": \"margherita pizza\"}, {\"id\": 45469, \"name\": \"an air tank\"}, {\"id\": 45470, \"name\": \"the silver tank\"}, {\"id\": 45471, \"name\": \"the animal hide\"}, {\"id\": 45472, \"name\": \"person in a pink bikini\"}, {\"id\": 45473, \"name\": \"the woman in a purple shirt\"}, {\"id\": 45474, \"name\": \"a yellow lighting\"}, {\"id\": 45475, \"name\": \"surfboarder\"}, {\"id\": 45476, \"name\": \"the lettuce head\"}, {\"id\": 45477, \"name\": \"a black and blue helmet\"}, {\"id\": 45478, \"name\": \"the orange dump truck\"}, {\"id\": 45479, \"name\": \"the brown tail\"}, {\"id\": 45480, \"name\": \"yellow and black flipper\"}, {\"id\": 45481, \"name\": \"a beekeeper\"}, {\"id\": 45482, \"name\": \"the raspberry\"}, {\"id\": 45483, \"name\": \"the beige screen\"}, {\"id\": 45484, \"name\": \"yellow drink\"}, {\"id\": 45485, \"name\": \"an orange carrot\"}, {\"id\": 45486, \"name\": \"a red, blue and white lane divider\"}, {\"id\": 45487, \"name\": \"sashimi\"}, {\"id\": 45488, \"name\": \"the spice\"}, {\"id\": 45489, \"name\": \"the toast\"}, {\"id\": 45490, \"name\": \"a person in black uniforms\"}, {\"id\": 45491, \"name\": \"the circular slice\"}, {\"id\": 45492, \"name\": \"the paper shopping bag\"}, {\"id\": 45493, \"name\": \"the back of a seat\"}, {\"id\": 45494, \"name\": \"a skiier\"}, {\"id\": 45495, \"name\": \"handle of the kettlebell\"}, {\"id\": 45496, \"name\": \"irregular, circular shape\"}, {\"id\": 45497, \"name\": \"the participant\"}, {\"id\": 45498, \"name\": \"the black, foam-covered dumbbell\"}, {\"id\": 45499, \"name\": \"black life jacket\"}, {\"id\": 45500, \"name\": \"the white and black post\"}, {\"id\": 45501, \"name\": \"the bicycle's tire\"}, {\"id\": 45502, \"name\": \"broccoli floret\"}, {\"id\": 45503, \"name\": \"a white icon of a person\"}, {\"id\": 45504, \"name\": \"the sphere\"}, {\"id\": 45505, \"name\": \"judo logo\"}, {\"id\": 45506, \"name\": \"the warehouse worker\"}, {\"id\": 45507, \"name\": \"plate of food with a burger\"}, {\"id\": 45508, \"name\": \"a rope toy\"}, {\"id\": 45509, \"name\": \"red boxing ring\"}, {\"id\": 45510, \"name\": \"an elliptical handle\"}, {\"id\": 45511, \"name\": \"the brown meat dish\"}, {\"id\": 45512, \"name\": \"a marshmallow\"}, {\"id\": 45513, \"name\": \"dancing person\"}, {\"id\": 45514, \"name\": \"blue-green wing\"}, {\"id\": 45515, \"name\": \"black rifle\"}, {\"id\": 45516, \"name\": \"slice of food\"}, {\"id\": 45517, \"name\": \"metal enclosure\"}, {\"id\": 45518, \"name\": \"stainless-steel serving dish\"}, {\"id\": 45519, \"name\": \"person in a black suit\"}, {\"id\": 45520, \"name\": \"the pool float\"}, {\"id\": 45521, \"name\": \"a woman in hijab\"}, {\"id\": 45522, \"name\": \"the rosemary sprig\"}, {\"id\": 45523, \"name\": \"a balloon's envelope\"}, {\"id\": 45524, \"name\": \"front of the train\"}, {\"id\": 45525, \"name\": \"a banana slice\"}, {\"id\": 45526, \"name\": \"a black olive\"}, {\"id\": 45527, \"name\": \"a green chili pepper\"}, {\"id\": 45528, \"name\": \"a baby chick\"}, {\"id\": 45529, \"name\": \"a hiker\"}, {\"id\": 45530, \"name\": \"paddleball\"}, {\"id\": 45531, \"name\": \"a sailboard\"}, {\"id\": 45532, \"name\": \"stationary bike\"}, {\"id\": 45533, \"name\": \"the name of the produce\"}, {\"id\": 45534, \"name\": \"the silver serving dish\"}, {\"id\": 45535, \"name\": \"the sailboard\"}, {\"id\": 45536, \"name\": \"the black hockey glove\"}, {\"id\": 45537, \"name\": \"a wooden sword\"}, {\"id\": 45538, \"name\": \"the santa\"}, {\"id\": 45539, \"name\": \"the black and silver cleat\"}, {\"id\": 45540, \"name\": \"the chopped red vegetable\"}, {\"id\": 45541, \"name\": \"a bowl of salad\"}, {\"id\": 45542, \"name\": \"red nose cone\"}, {\"id\": 45543, \"name\": \"yellow bottle of condiment\"}, {\"id\": 45544, \"name\": \"the white tenni racket\"}, {\"id\": 45545, \"name\": \"the basket of pastry\"}, {\"id\": 45546, \"name\": \"the hockey helmet\"}, {\"id\": 45547, \"name\": \"bus door\"}, {\"id\": 45548, \"name\": \"overhead compartment\"}, {\"id\": 45549, \"name\": \"wire cable\"}, {\"id\": 45550, \"name\": \"safety equipment\"}, {\"id\": 45551, \"name\": \"the hockey skate\"}, {\"id\": 45552, \"name\": \"a headlight of car\"}, {\"id\": 45553, \"name\": \"yellow animal\"}, {\"id\": 45554, \"name\": \"the wheel of toy car\"}, {\"id\": 45555, \"name\": \"the black peppercorn\"}, {\"id\": 45556, \"name\": \"a kite's bar\"}, {\"id\": 45557, \"name\": \"the husky\"}, {\"id\": 45558, \"name\": \"the deer\"}, {\"id\": 45559, \"name\": \"bottle of liquor\"}, {\"id\": 45560, \"name\": \"the front wheel of a bicycle\"}, {\"id\": 45561, \"name\": \"the zebras' head\"}, {\"id\": 45562, \"name\": \"the dark-haired woman\"}, {\"id\": 45563, \"name\": \"a clear signage\"}, {\"id\": 45564, \"name\": \"jet-powered watercraft\"}, {\"id\": 45565, \"name\": \"a white, circular base\"}, {\"id\": 45566, \"name\": \"white square plate\"}, {\"id\": 45567, \"name\": \"croissant\"}, {\"id\": 45568, \"name\": \"red-painted metal frame\"}, {\"id\": 45569, \"name\": \"the pink paw\"}, {\"id\": 45570, \"name\": \"the metal hardware\"}, {\"id\": 45571, \"name\": \"the dark-brown wing\"}, {\"id\": 45572, \"name\": \"a ringed tail\"}, {\"id\": 45573, \"name\": \"bumper boat\"}, {\"id\": 45574, \"name\": \"the piece of red meat\"}, {\"id\": 45575, \"name\": \"gray ice skate\"}, {\"id\": 45576, \"name\": \"the humpback whale\"}, {\"id\": 45577, \"name\": \"the pastry\"}, {\"id\": 45578, \"name\": \"the red book\"}, {\"id\": 45579, \"name\": \"a warrior\"}, {\"id\": 45580, \"name\": \"the gazelle-like animal\"}, {\"id\": 45581, \"name\": \"a human-like figure\"}, {\"id\": 45582, \"name\": \"band member\"}, {\"id\": 45583, \"name\": \"a basket of plant\"}, {\"id\": 45584, \"name\": \"a piece of office equipment\"}, {\"id\": 45585, \"name\": \"hockey glove\"}, {\"id\": 45586, \"name\": \"the leafy green garnish\"}, {\"id\": 45587, \"name\": \"the spherical shape\"}, {\"id\": 45588, \"name\": \"chopped green vegetable\"}, {\"id\": 45589, \"name\": \"source of illumination\"}, {\"id\": 45590, \"name\": \"a decorative plate\"}, {\"id\": 45591, \"name\": \"hamster\"}, {\"id\": 45592, \"name\": \"a white cupcake container\"}, {\"id\": 45593, \"name\": \"descending person\"}, {\"id\": 45594, \"name\": \"solitary tire\"}, {\"id\": 45595, \"name\": \"the gauntlet\"}, {\"id\": 45596, \"name\": \"a black and red wheel\"}, {\"id\": 45597, \"name\": \"a confetti\"}, {\"id\": 45598, \"name\": \"a white headlight\"}, {\"id\": 45599, \"name\": \"a grey seal\"}, {\"id\": 45600, \"name\": \"a piece of boxing equipment\"}, {\"id\": 45601, \"name\": \"a responder\"}, {\"id\": 45602, \"name\": \"the yellow sparkler\"}, {\"id\": 45603, \"name\": \"yellow paddle\"}, {\"id\": 45604, \"name\": \"the yellow plastic box\"}, {\"id\": 45605, \"name\": \"the child in motion\"}, {\"id\": 45606, \"name\": \"a residence\"}, {\"id\": 45607, \"name\": \"mozzarella\"}, {\"id\": 45608, \"name\": \"the martial artist\"}, {\"id\": 45609, \"name\": \"a dark gray fin\"}, {\"id\": 45610, \"name\": \"the exhaust\"}, {\"id\": 45611, \"name\": \"the shelf of produce\"}, {\"id\": 45612, \"name\": \"a military aircraft\"}, {\"id\": 45613, \"name\": \"a rind\"}, {\"id\": 45614, \"name\": \"a shelf of dumbbell\"}, {\"id\": 45615, \"name\": \"the food delivery logo\"}, {\"id\": 45616, \"name\": \"box of pizza\"}, {\"id\": 45617, \"name\": \"silver serving tray\"}, {\"id\": 45618, \"name\": \"the bottle of orange liquid\"}, {\"id\": 45619, \"name\": \"orange slice\"}, {\"id\": 45620, \"name\": \"the red jam\"}, {\"id\": 45621, \"name\": \"the climbing gear\"}, {\"id\": 45622, \"name\": \"a yellow sea sponge\"}, {\"id\": 45623, \"name\": \"a built-in microwave\"}, {\"id\": 45624, \"name\": \"a brown egg\"}, {\"id\": 45625, \"name\": \"white team\"}, {\"id\": 45626, \"name\": \"the wine\"}, {\"id\": 45627, \"name\": \"the gray fin\"}, {\"id\": 45628, \"name\": \"a park's playground equipment\"}, {\"id\": 45629, \"name\": \"blurred figure of person\"}, {\"id\": 45630, \"name\": \"cinnamon stick\"}, {\"id\": 45631, \"name\": \"a bird nest\"}, {\"id\": 45632, \"name\": \"a white patch of fur\"}, {\"id\": 45633, \"name\": \"occupant\"}, {\"id\": 45634, \"name\": \"a crushed soda can\"}, {\"id\": 45635, \"name\": \"spherical shape\"}, {\"id\": 45636, \"name\": \"a tuft of fur\"}, {\"id\": 45637, \"name\": \"a metal component\"}, {\"id\": 45638, \"name\": \"the grocery\"}, {\"id\": 45639, \"name\": \"the hiking gear\"}, {\"id\": 45640, \"name\": \"the sheep's face\"}, {\"id\": 45641, \"name\": \"the brown dough cutout\"}, {\"id\": 45642, \"name\": \"a blurred figure of a man\"}, {\"id\": 45643, \"name\": \"car's taillight\"}, {\"id\": 45644, \"name\": \"the green lighting\"}, {\"id\": 45645, \"name\": \"plate of what appear to be pasta\"}, {\"id\": 45646, \"name\": \"the feta cheese\"}, {\"id\": 45647, \"name\": \"a sangria\"}, {\"id\": 45648, \"name\": \"the basket of fruit\"}, {\"id\": 45649, \"name\": \"a cartoonish orange onion\"}, {\"id\": 45650, \"name\": \"red topping\"}, {\"id\": 45651, \"name\": \"the head of musician\"}, {\"id\": 45652, \"name\": \"the red topping\"}, {\"id\": 45653, \"name\": \"the slice of cucumber\"}, {\"id\": 45654, \"name\": \"a blue icing\"}, {\"id\": 45655, \"name\": \"the pan of bread\"}, {\"id\": 45656, \"name\": \"green cube\"}, {\"id\": 45657, \"name\": \"a yellow pompom\"}, {\"id\": 45658, \"name\": \"yellow float\"}, {\"id\": 45659, \"name\": \"the brown plate\"}, {\"id\": 45660, \"name\": \"piece of fruit\"}, {\"id\": 45661, \"name\": \"the blue stadium seat\"}, {\"id\": 45662, \"name\": \"the dried herb\"}, {\"id\": 45663, \"name\": \"dispenser\"}, {\"id\": 45664, \"name\": \"the plate of cupcake\"}, {\"id\": 45665, \"name\": \"a paraglider\"}, {\"id\": 45666, \"name\": \"santa\"}, {\"id\": 45667, \"name\": \"a sliced jalapeno\"}, {\"id\": 45668, \"name\": \"the grazing cow\"}, {\"id\": 45669, \"name\": \"white ski pole\"}, {\"id\": 45670, \"name\": \"the white food container\"}, {\"id\": 45671, \"name\": \"the brown and black checkered tablecloth\"}, {\"id\": 45672, \"name\": \"a banana stem\"}, {\"id\": 45673, \"name\": \"a red and yellow circle\"}, {\"id\": 45674, \"name\": \"a white spread\"}, {\"id\": 45675, \"name\": \"a highlighter\"}, {\"id\": 45676, \"name\": \"a trainer\"}, {\"id\": 45677, \"name\": \"the patch of black fur\"}, {\"id\": 45678, \"name\": \"baking supply\"}, {\"id\": 45679, \"name\": \"a black snowboard\"}, {\"id\": 45680, \"name\": \"piece of dough\"}, {\"id\": 45681, \"name\": \"bulldozer\"}, {\"id\": 45682, \"name\": \"red oar\"}, {\"id\": 45683, \"name\": \"a salmon sashimi\"}, {\"id\": 45684, \"name\": \"a padel\"}, {\"id\": 45685, \"name\": \"the red and white checkered tablecloth\"}, {\"id\": 45686, \"name\": \"a donut\"}, {\"id\": 45687, \"name\": \"a coxswain\"}, {\"id\": 45688, \"name\": \"brown pot\"}, {\"id\": 45689, \"name\": \"the artichoke\"}, {\"id\": 45690, \"name\": \"metal rim\"}, {\"id\": 45691, \"name\": \"soy sauce\"}, {\"id\": 45692, \"name\": \"yellowish-orange flesh\"}, {\"id\": 45693, \"name\": \"the sled's wheel\"}, {\"id\": 45694, \"name\": \"black component\"}, {\"id\": 45695, \"name\": \"computer screen\"}, {\"id\": 45696, \"name\": \"the yellow landing gear\"}, {\"id\": 45697, \"name\": \"a white cup of coffee\"}, {\"id\": 45698, \"name\": \"the fencing weapon\"}, {\"id\": 45699, \"name\": \"a golden-brown chicken wing\"}, {\"id\": 45700, \"name\": \"the red stadium-style seat\"}, {\"id\": 45701, \"name\": \"the sliced strawberry\"}, {\"id\": 45702, \"name\": \"a fried chicken\"}, {\"id\": 45703, \"name\": \"a chain of the swing\"}, {\"id\": 45704, \"name\": \"pizza\"}, {\"id\": 45705, \"name\": \"the heavy load\"}, {\"id\": 45706, \"name\": \"a kiwi slice\"}, {\"id\": 45707, \"name\": \"a left-turn arrow\"}, {\"id\": 45708, \"name\": \"the pizza crust\"}, {\"id\": 45709, \"name\": \"a black tip\"}, {\"id\": 45710, \"name\": \"the chocolate shaving\"}, {\"id\": 45711, \"name\": \"a hanging equipment\"}, {\"id\": 45712, \"name\": \"the medley of sliced fruit\"}, {\"id\": 45713, \"name\": \"service vehicle\"}, {\"id\": 45714, \"name\": \"the metal filing cabinet\"}, {\"id\": 45715, \"name\": \"a goalie mask\"}, {\"id\": 45716, \"name\": \"an elderly adult\"}, {\"id\": 45717, \"name\": \"the head of broccoli\"}, {\"id\": 45718, \"name\": \"a gray tail\"}, {\"id\": 45719, \"name\": \"the man in a tuxedo\"}, {\"id\": 45720, \"name\": \"the white boxing glove\"}, {\"id\": 45721, \"name\": \"the black figure skate\"}, {\"id\": 45722, \"name\": \"fluffy white ball\"}, {\"id\": 45723, \"name\": \"the sliced bread\"}, {\"id\": 45724, \"name\": \"a stems of the pepper\"}, {\"id\": 45725, \"name\": \"a yellow teapot\"}, {\"id\": 45726, \"name\": \"black-gloved opponent\"}, {\"id\": 45727, \"name\": \"the red chili pepper\"}, {\"id\": 45728, \"name\": \"floating toy\"}, {\"id\": 45729, \"name\": \"a red inflatable ring\"}, {\"id\": 45730, \"name\": \"the black deck\"}, {\"id\": 45731, \"name\": \"a train door\"}, {\"id\": 45732, \"name\": \"plantain\"}, {\"id\": 45733, \"name\": \"metal grille\"}, {\"id\": 45734, \"name\": \"english muffin\"}, {\"id\": 45735, \"name\": \"the swing chain\"}, {\"id\": 45736, \"name\": \"the blurry person\"}, {\"id\": 45737, \"name\": \"stir fry\"}, {\"id\": 45738, \"name\": \"the blue punching bag\"}, {\"id\": 45739, \"name\": \"black gridded pane\"}, {\"id\": 45740, \"name\": \"orange bell pepper\"}, {\"id\": 45741, \"name\": \"boiled egg\"}, {\"id\": 45742, \"name\": \"the fencing gear\"}, {\"id\": 45743, \"name\": \"brown paw\"}, {\"id\": 45744, \"name\": \"the bocce ball\"}, {\"id\": 45745, \"name\": \"a white rice\"}, {\"id\": 45746, \"name\": \"the loose circle\"}, {\"id\": 45747, \"name\": \"a yellow lemon\"}, {\"id\": 45748, \"name\": \"the beige box\"}, {\"id\": 45749, \"name\": \"blue and black boxing glove\"}, {\"id\": 45750, \"name\": \"a blue liquid\"}, {\"id\": 45751, \"name\": \"close-up of a piglet's face\"}, {\"id\": 45752, \"name\": \"an american football player\"}, {\"id\": 45753, \"name\": \"the teenager\"}, {\"id\": 45754, \"name\": \"a dollop of white sauce\"}, {\"id\": 45755, \"name\": \"the yellow garnish\"}, {\"id\": 45756, \"name\": \"the fidget toy\"}, {\"id\": 45757, \"name\": \"the gold cutlery\"}, {\"id\": 45758, \"name\": \"a skateboard deck\"}, {\"id\": 45759, \"name\": \"an aerial yoga swing\"}, {\"id\": 45760, \"name\": \"orange sphere\"}, {\"id\": 45761, \"name\": \"the purple aerial silk\"}, {\"id\": 45762, \"name\": \"emergency personnel\"}, {\"id\": 45763, \"name\": \"the pool inflatable\"}, {\"id\": 45764, \"name\": \"a black serving spoon\"}, {\"id\": 45765, \"name\": \"the round shield\"}, {\"id\": 45766, \"name\": \"a black panel\"}, {\"id\": 45767, \"name\": \"industrial equipment\"}, {\"id\": 45768, \"name\": \"black-and-white goalie gear\"}, {\"id\": 45769, \"name\": \"the electrical discharge\"}, {\"id\": 45770, \"name\": \"the gray horn\"}, {\"id\": 45771, \"name\": \"a mug of beer\"}, {\"id\": 45772, \"name\": \"inflatable raft\"}, {\"id\": 45773, \"name\": \"a dance equipment\"}, {\"id\": 45774, \"name\": \"orange shape\"}, {\"id\": 45775, \"name\": \"the glass of beverage\"}, {\"id\": 45776, \"name\": \"green sea anemone\"}, {\"id\": 45777, \"name\": \"a red topping\"}, {\"id\": 45778, \"name\": \"a paint-covered individual\"}, {\"id\": 45779, \"name\": \"graduate\"}, {\"id\": 45780, \"name\": \"the yellow helicopter ride\"}, {\"id\": 45781, \"name\": \"a white ice skate\"}, {\"id\": 45782, \"name\": \"pink car\"}, {\"id\": 45783, \"name\": \"the parallel bar\"}, {\"id\": 45784, \"name\": \"the blue and white lane divider\"}, {\"id\": 45785, \"name\": \"a barefoot woman\"}, {\"id\": 45786, \"name\": \"a humanoid figure\"}, {\"id\": 45787, \"name\": \"the brown crust\"}, {\"id\": 45788, \"name\": \"the left-hand escalator\"}, {\"id\": 45789, \"name\": \"the black firearm\"}, {\"id\": 45790, \"name\": \"the climbing rope\"}, {\"id\": 45791, \"name\": \"the electronic sign\"}, {\"id\": 45792, \"name\": \"the ketchup bottle\"}, {\"id\": 45793, \"name\": \"emu\"}, {\"id\": 45794, \"name\": \"moose\"}, {\"id\": 45795, \"name\": \"a martial artist\"}, {\"id\": 45796, \"name\": \"black olive\"}, {\"id\": 45797, \"name\": \"chairlift\"}, {\"id\": 45798, \"name\": \"the ballerina\"}, {\"id\": 45799, \"name\": \"the white mushroom\"}, {\"id\": 45800, \"name\": \"african grey parrot\"}, {\"id\": 45801, \"name\": \"a curved metal frame\"}, {\"id\": 45802, \"name\": \"a sprig of rosemary\"}, {\"id\": 45803, \"name\": \"barware\"}, {\"id\": 45804, \"name\": \"a lane of the pool\"}, {\"id\": 45805, \"name\": \"yellow circle with cutouts\"}, {\"id\": 45806, \"name\": \"subway\"}, {\"id\": 45807, \"name\": \"the silver refrigerator\"}, {\"id\": 45808, \"name\": \"the pancake\"}, {\"id\": 45809, \"name\": \"the clear sail\"}, {\"id\": 45810, \"name\": \"a front wheel of their bike\"}, {\"id\": 45811, \"name\": \"the gray windshield\"}, {\"id\": 45812, \"name\": \"a blue paw print\"}, {\"id\": 45813, \"name\": \"gray fur\"}, {\"id\": 45814, \"name\": \"the red sail\"}, {\"id\": 45815, \"name\": \"boat's crew\"}, {\"id\": 45816, \"name\": \"the foreground boat\"}, {\"id\": 45817, \"name\": \"gray fin\"}, {\"id\": 45818, \"name\": \"the baseball glove\"}, {\"id\": 45819, \"name\": \"the round shape\"}, {\"id\": 45820, \"name\": \"a bowling shoe\"}, {\"id\": 45821, \"name\": \"black hockey stick\"}, {\"id\": 45822, \"name\": \"cow's face\"}, {\"id\": 45823, \"name\": \"lower wing\"}, {\"id\": 45824, \"name\": \"motorcycle brand\"}, {\"id\": 45825, \"name\": \"sheep's face\"}, {\"id\": 45826, \"name\": \"the cue stick\"}, {\"id\": 45827, \"name\": \"a driver-side door\"}, {\"id\": 45828, \"name\": \"a pink dot\"}, {\"id\": 45829, \"name\": \"a ski showboard\"}, {\"id\": 45830, \"name\": \"the dark brown paw\"}, {\"id\": 45831, \"name\": \"the chrome front wheel\"}, {\"id\": 45832, \"name\": \"the motorcycle gear\"}, {\"id\": 45833, \"name\": \"metal serving dish\"}, {\"id\": 45834, \"name\": \"basket of french fry\"}, {\"id\": 45835, \"name\": \"the grocery store shelf\"}, {\"id\": 45836, \"name\": \"a windshield area\"}, {\"id\": 45837, \"name\": \"a bike's tire\"}, {\"id\": 45838, \"name\": \"bamboo raft\"}, {\"id\": 45839, \"name\": \"a black fender flare\"}, {\"id\": 45840, \"name\": \"bottle of sauce\"}, {\"id\": 45841, \"name\": \"white mushroom\"}, {\"id\": 45842, \"name\": \"the shelf of packaged food\"}, {\"id\": 45843, \"name\": \"the white circular platform\"}, {\"id\": 45844, \"name\": \"a tray of donut\"}, {\"id\": 45845, \"name\": \"ferrari\"}, {\"id\": 45846, \"name\": \"a dark brown wing\"}, {\"id\": 45847, \"name\": \"brown wing\"}, {\"id\": 45848, \"name\": \"orange skate\"}, {\"id\": 45849, \"name\": \"athletic gear\"}, {\"id\": 45850, \"name\": \"a hood scoop\"}, {\"id\": 45851, \"name\": \"the engine\"}, {\"id\": 45852, \"name\": \"stainless steel serving dish\"}, {\"id\": 45853, \"name\": \"a white silhouette of a man\"}, {\"id\": 45854, \"name\": \"pink napkin\"}, {\"id\": 45855, \"name\": \"the pool table\"}, {\"id\": 45856, \"name\": \"parachutist\"}, {\"id\": 45857, \"name\": \"a white paw print\"}, {\"id\": 45858, \"name\": \"a yellow dunlop logo\"}, {\"id\": 45859, \"name\": \"sugar cookie\"}, {\"id\": 45860, \"name\": \"an orange cleat\"}, {\"id\": 45861, \"name\": \"the gray back\"}, {\"id\": 45862, \"name\": \"the tail section\"}, {\"id\": 45863, \"name\": \"a white circular logo\"}, {\"id\": 45864, \"name\": \"distinctive pointy ear\"}, {\"id\": 45865, \"name\": \"a distinctive blue tail\"}, {\"id\": 45866, \"name\": \"chimpanzee's head\"}, {\"id\": 45867, \"name\": \"the extended crane\"}, {\"id\": 45868, \"name\": \"foreground cat's face\"}, {\"id\": 45869, \"name\": \"the perch for bird\"}, {\"id\": 45870, \"name\": \"a jet's cockpit\"}, {\"id\": 45871, \"name\": \"the broken eggshell\"}, {\"id\": 45872, \"name\": \"a gray horn\"}, {\"id\": 45873, \"name\": \"a dark gray wing\"}, {\"id\": 45874, \"name\": \"the man in black hoodie\"}, {\"id\": 45875, \"name\": \"plate of gingerbread cookie\"}, {\"id\": 45876, \"name\": \"a dark blue tail\"}, {\"id\": 45877, \"name\": \"spare tire\"}, {\"id\": 45878, \"name\": \"leg of the cow\"}, {\"id\": 45879, \"name\": \"the rear fender\"}, {\"id\": 45880, \"name\": \"the white ufc logo\"}, {\"id\": 45881, \"name\": \"the horizontal metal bar\"}, {\"id\": 45882, \"name\": \"a seat post\"}, {\"id\": 45883, \"name\": \"grocery\"}, {\"id\": 45884, \"name\": \"ford\"}, {\"id\": 45885, \"name\": \"the writing utensil\"}, {\"id\": 45886, \"name\": \"jerry can\"}, {\"id\": 45887, \"name\": \"lamborghini\"}, {\"id\": 45888, \"name\": \"the metal cabinet\"}, {\"id\": 45889, \"name\": \"a boxing gear\"}, {\"id\": 45890, \"name\": \"the team logo\"}, {\"id\": 45891, \"name\": \"the tambourine\"}, {\"id\": 45892, \"name\": \"container of food\"}, {\"id\": 45893, \"name\": \"an anthropomorphic animal\"}, {\"id\": 45894, \"name\": \"bicycle dutch\"}, {\"id\": 45895, \"name\": \"eggplant\"}, {\"id\": 45896, \"name\": \"a bottle of liquor\"}, {\"id\": 45897, \"name\": \"the red switch\"}, {\"id\": 45898, \"name\": \"the flyer\"}, {\"id\": 45899, \"name\": \"an orange hold\"}, {\"id\": 45900, \"name\": \"the door panel\"}, {\"id\": 45901, \"name\": \"the price of the donut\"}, {\"id\": 45902, \"name\": \"the tesla supercharger station\"}, {\"id\": 45903, \"name\": \"the red ink\"}, {\"id\": 45904, \"name\": \"the red inflatable bag\"}, {\"id\": 45905, \"name\": \"the red budweiser beer bottle\"}, {\"id\": 45906, \"name\": \"toasted bun\"}, {\"id\": 45907, \"name\": \"the function key\"}, {\"id\": 45908, \"name\": \"the food dish\"}, {\"id\": 45909, \"name\": \"yellow race bib\"}, {\"id\": 45910, \"name\": \"a green swing\"}, {\"id\": 45911, \"name\": \"a popsicle\"}, {\"id\": 45912, \"name\": \"a black roller skate\"}, {\"id\": 45913, \"name\": \"tail of the helicopter\"}, {\"id\": 45914, \"name\": \"a round piece of dough\"}, {\"id\": 45915, \"name\": \"a bicycle's front wheel\"}, {\"id\": 45916, \"name\": \"the pepperoni\"}, {\"id\": 45917, \"name\": \"back wheel\"}, {\"id\": 45918, \"name\": \"the sliced sweet potato\"}, {\"id\": 45919, \"name\": \"an atv's tire\"}, {\"id\": 45920, \"name\": \"a barware\"}, {\"id\": 45921, \"name\": \"metal serving tray\"}, {\"id\": 45922, \"name\": \"a luxury car\"}, {\"id\": 45923, \"name\": \"recording device\"}, {\"id\": 45924, \"name\": \"the pickleball paddle\"}, {\"id\": 45925, \"name\": \"keypad\"}, {\"id\": 45926, \"name\": \"front-right wheel\"}, {\"id\": 45927, \"name\": \"dial knob\"}, {\"id\": 45928, \"name\": \"the batting glove\"}, {\"id\": 45929, \"name\": \"rally pizza\"}, {\"id\": 45930, \"name\": \"the food option\"}, {\"id\": 45931, \"name\": \"outboard motor\"}, {\"id\": 45932, \"name\": \"the yellow race bib\"}, {\"id\": 45933, \"name\": \"pink placemat\"}, {\"id\": 45934, \"name\": \"the staff member\"}, {\"id\": 45935, \"name\": \"the interior of boat\"}, {\"id\": 45936, \"name\": \"the american league champion banner\"}, {\"id\": 45937, \"name\": \"glass of liquid\"}, {\"id\": 45938, \"name\": \"the back of the seat\"}, {\"id\": 45939, \"name\": \"a yellow pastry\"}, {\"id\": 45940, \"name\": \"a red turn signal\"}, {\"id\": 45941, \"name\": \"a white rv\"}, {\"id\": 45942, \"name\": \"horse race starting gate\"}, {\"id\": 45943, \"name\": \"the black propeller\"}, {\"id\": 45944, \"name\": \"a rope of the boat\"}, {\"id\": 45945, \"name\": \"the boxer\"}, {\"id\": 45946, \"name\": \"livestock\"}, {\"id\": 45947, \"name\": \"distinctive black head\"}, {\"id\": 45948, \"name\": \"a prominent white patch\"}, {\"id\": 45949, \"name\": \"a larva\"}, {\"id\": 45950, \"name\": \"the leg of a giraffe\"}, {\"id\": 45951, \"name\": \"the dolphin's head\"}, {\"id\": 45952, \"name\": \"the dodgeball\"}, {\"id\": 45953, \"name\": \"translucent wing\"}, {\"id\": 45954, \"name\": \"the people's face\"}, {\"id\": 45955, \"name\": \"red player\"}, {\"id\": 45956, \"name\": \"a pet carrier\"}, {\"id\": 45957, \"name\": \"a colorful, decorative sphere\"}, {\"id\": 45958, \"name\": \"white mane\"}, {\"id\": 45959, \"name\": \"clear windscreen\"}, {\"id\": 45960, \"name\": \"a white sail\"}, {\"id\": 45961, \"name\": \"tall metal frame\"}, {\"id\": 45962, \"name\": \"cockpit canopy\"}, {\"id\": 45963, \"name\": \"a white circular sticker\"}, {\"id\": 45964, \"name\": \"avocado\"}, {\"id\": 45965, \"name\": \"black silhouette of a fish\"}, {\"id\": 45966, \"name\": \"a blue training equipment\"}, {\"id\": 45967, \"name\": \"an open hood\"}, {\"id\": 45968, \"name\": \"the swept-back wing\"}, {\"id\": 45969, \"name\": \"a round shell\"}, {\"id\": 45970, \"name\": \"a dark sail\"}, {\"id\": 45971, \"name\": \"the fender\"}, {\"id\": 45972, \"name\": \"a cartoon animal\"}, {\"id\": 45973, \"name\": \"a yellow and orange ball\"}, {\"id\": 45974, \"name\": \"game controller\"}, {\"id\": 45975, \"name\": \"the distinctive black fur\"}, {\"id\": 45976, \"name\": \"the hood of the vehicle\"}, {\"id\": 45977, \"name\": \"fawn's head\"}, {\"id\": 45978, \"name\": \"a propeller blade\"}, {\"id\": 45979, \"name\": \"leg of horse\"}, {\"id\": 45980, \"name\": \"black and white face\"}, {\"id\": 45981, \"name\": \"distinctive red leg\"}, {\"id\": 45982, \"name\": \"an elephant's head\"}, {\"id\": 45983, \"name\": \"red and yellow lamp\"}, {\"id\": 45984, \"name\": \"a maserati logo\"}, {\"id\": 45985, \"name\": \"the red golf ball\"}, {\"id\": 45986, \"name\": \"a grapefruit\"}, {\"id\": 45987, \"name\": \"a black metal cage\"}, {\"id\": 45988, \"name\": \"the tailfin\"}, {\"id\": 45989, \"name\": \"dark brown plumage\"}, {\"id\": 45990, \"name\": \"the single mast\"}, {\"id\": 45991, \"name\": \"a gold charger plate\"}, {\"id\": 45992, \"name\": \"the cartoon blue bird\"}, {\"id\": 45993, \"name\": \"a sausage patty\"}, {\"id\": 45994, \"name\": \"nose of one plane\"}, {\"id\": 45995, \"name\": \"the the person\"}, {\"id\": 45996, \"name\": \"nose of goat\"}, {\"id\": 45997, \"name\": \"the racing stripe\"}, {\"id\": 45998, \"name\": \"the boat's occupants\"}, {\"id\": 45999, \"name\": \"white underbelly\"}, {\"id\": 46000, \"name\": \"a distinctive black grill\"}, {\"id\": 46001, \"name\": \"a front fender\"}, {\"id\": 46002, \"name\": \"a horse-like head\"}, {\"id\": 46003, \"name\": \"the white hoof\"}, {\"id\": 46004, \"name\": \"a tail section\"}, {\"id\": 46005, \"name\": \"the white fairing\"}, {\"id\": 46006, \"name\": \"pig's face\"}, {\"id\": 46007, \"name\": \"the distinctive white patch\"}, {\"id\": 46008, \"name\": \"the ski equipment\"}, {\"id\": 46009, \"name\": \"boat's mast\"}, {\"id\": 46010, \"name\": \"protective helmet\"}, {\"id\": 46011, \"name\": \"chrome rim\"}, {\"id\": 46012, \"name\": \"a penguin's head\"}, {\"id\": 46013, \"name\": \"sleek black bicycle\"}, {\"id\": 46014, \"name\": \"a dark wing\"}, {\"id\": 46015, \"name\": \"the fender flare\"}, {\"id\": 46016, \"name\": \"the metal plank\"}, {\"id\": 46017, \"name\": \"the tennis player\"}, {\"id\": 46018, \"name\": \"a single mast\"}, {\"id\": 46019, \"name\": \"chrome engine\"}, {\"id\": 46020, \"name\": \"a black spot on it head\"}, {\"id\": 46021, \"name\": \"the paw print\"}, {\"id\": 46022, \"name\": \"wheel of a white sport car\"}, {\"id\": 46023, \"name\": \"the striking red hull\"}, {\"id\": 46024, \"name\": \"white tuft of fur\"}, {\"id\": 46025, \"name\": \"single piece of metal\"}, {\"id\": 46026, \"name\": \"the passenger-side door\"}, {\"id\": 46027, \"name\": \"white soccer goal\"}, {\"id\": 46028, \"name\": \"an animal's face\"}, {\"id\": 46029, \"name\": \"the green garage door\"}, {\"id\": 46030, \"name\": \"a foot of duck\"}, {\"id\": 46031, \"name\": \"a gray tone of the rabbit\"}, {\"id\": 46032, \"name\": \"a cycling equipment\"}, {\"id\": 46033, \"name\": \"the hatch\"}, {\"id\": 46034, \"name\": \"blue and white parakeet\"}, {\"id\": 46035, \"name\": \"blurred propeller\"}, {\"id\": 46036, \"name\": \"elk\"}, {\"id\": 46037, \"name\": \"vibrant parachute\"}, {\"id\": 46038, \"name\": \"the red-brown face\"}, {\"id\": 46039, \"name\": \"headlamp\"}, {\"id\": 46040, \"name\": \"a tail rotor\"}, {\"id\": 46041, \"name\": \"airport equipment\"}, {\"id\": 46042, \"name\": \"face of an orangutan\"}, {\"id\": 46043, \"name\": \"a colorful dot\"}, {\"id\": 46044, \"name\": \"the fawn\"}, {\"id\": 46045, \"name\": \"a distinctive yellow and black head\"}, {\"id\": 46046, \"name\": \"flyer\"}, {\"id\": 46047, \"name\": \"a black and brown wing\"}, {\"id\": 46048, \"name\": \"the leg of an elephant\"}, {\"id\": 46049, \"name\": \"the spoiler\"}, {\"id\": 46050, \"name\": \"a brown snout\"}, {\"id\": 46051, \"name\": \"the muddy hoof\"}, {\"id\": 46052, \"name\": \"the cow's white head\"}, {\"id\": 46053, \"name\": \"the pool cue\"}, {\"id\": 46054, \"name\": \"antelope\"}, {\"id\": 46055, \"name\": \"a brown rabbit\"}, {\"id\": 46056, \"name\": \"a helicopter's tail rotor\"}, {\"id\": 46057, \"name\": \"hangar\"}, {\"id\": 46058, \"name\": \"a prominent float\"}, {\"id\": 46059, \"name\": \"the horizontal pipe\"}, {\"id\": 46060, \"name\": \"the gold-colored shock absorber\"}, {\"id\": 46061, \"name\": \"the black front lip spoiler\"}, {\"id\": 46062, \"name\": \"a panda's white fur\"}, {\"id\": 46063, \"name\": \"the silver hood stripe\"}, {\"id\": 46064, \"name\": \"an ivory tusk\"}, {\"id\": 46065, \"name\": \"a chrome rim\"}, {\"id\": 46066, \"name\": \"young adult\"}, {\"id\": 46067, \"name\": \"bridle\"}, {\"id\": 46068, \"name\": \"large trunk\"}, {\"id\": 46069, \"name\": \"brown metal frame\"}, {\"id\": 46070, \"name\": \"white eagle\"}, {\"id\": 46071, \"name\": \"the white snout\"}, {\"id\": 46072, \"name\": \"formation of fighter jet\"}, {\"id\": 46073, \"name\": \"orange turn signal\"}, {\"id\": 46074, \"name\": \"a military gear\"}, {\"id\": 46075, \"name\": \"black-and-white spotted dog\"}, {\"id\": 46076, \"name\": \"the portion of the plane's white fuselage\"}, {\"id\": 46077, \"name\": \"the deer head\"}, {\"id\": 46078, \"name\": \"motorcycle club member\"}, {\"id\": 46079, \"name\": \"a right wing\"}, {\"id\": 46080, \"name\": \"a springbok\"}, {\"id\": 46081, \"name\": \"harley-davidson sign\"}, {\"id\": 46082, \"name\": \"a turtle's body\"}, {\"id\": 46083, \"name\": \"duck hunter\"}, {\"id\": 46084, \"name\": \"a panda head\"}, {\"id\": 46085, \"name\": \"a distinctive black mane\"}, {\"id\": 46086, \"name\": \"the tan muzzle\"}, {\"id\": 46087, \"name\": \"the left horse's legs\"}, {\"id\": 46088, \"name\": \"the ski lift cable\"}, {\"id\": 46089, \"name\": \"the silver renault logo\"}, {\"id\": 46090, \"name\": \"a wing of plane\"}, {\"id\": 46091, \"name\": \"black lab\"}, {\"id\": 46092, \"name\": \"a wolves' eye\"}, {\"id\": 46093, \"name\": \"a head of the bear\"}, {\"id\": 46094, \"name\": \"the wolf's face\"}, {\"id\": 46095, \"name\": \"a large chrome handlebar\"}, {\"id\": 46096, \"name\": \"the blurred front wheel\"}, {\"id\": 46097, \"name\": \"the distinctive ear tag\"}, {\"id\": 46098, \"name\": \"small yellow turn signal\"}, {\"id\": 46099, \"name\": \"name of the bird species\"}, {\"id\": 46100, \"name\": \"a black gas tank\"}, {\"id\": 46101, \"name\": \"the game controller\"}, {\"id\": 46102, \"name\": \"the metal bucket\"}, {\"id\": 46103, \"name\": \"the black rear fender\"}, {\"id\": 46104, \"name\": \"purple fur\"}, {\"id\": 46105, \"name\": \"a reddish-brown tail\"}, {\"id\": 46106, \"name\": \"black and white rabbit\"}, {\"id\": 46107, \"name\": \"wheel rim\"}, {\"id\": 46108, \"name\": \"sleek white hull\"}, {\"id\": 46109, \"name\": \"top box\"}, {\"id\": 46110, \"name\": \"a parked black van\"}, {\"id\": 46111, \"name\": \"a distinctive dark stripe\"}, {\"id\": 46112, \"name\": \"the hot air balloon\"}, {\"id\": 46113, \"name\": \"a green felt top\"}, {\"id\": 46114, \"name\": \"an unicorn-costumed individual\"}, {\"id\": 46115, \"name\": \"a gold-colored wheel\"}, {\"id\": 46116, \"name\": \"white spotted coat\"}, {\"id\": 46117, \"name\": \"the single post\"}, {\"id\": 46118, \"name\": \"the white slide\"}, {\"id\": 46119, \"name\": \"bustling parking\"}, {\"id\": 46120, \"name\": \"flock of goose\"}, {\"id\": 46121, \"name\": \"a distinctive grill\"}, {\"id\": 46122, \"name\": \"a yellow and blue wing\"}, {\"id\": 46123, \"name\": \"a professional pool table\"}, {\"id\": 46124, \"name\": \"bighorn sheep\"}, {\"id\": 46125, \"name\": \"a chrome chevrolet logo\"}, {\"id\": 46126, \"name\": \"the black silhouette of animal\"}, {\"id\": 46127, \"name\": \"crocodile's head\"}, {\"id\": 46128, \"name\": \"white hoof\"}, {\"id\": 46129, \"name\": \"a logo the boat show\"}, {\"id\": 46130, \"name\": \"the dark grey head and neck\"}, {\"id\": 46131, \"name\": \"the dark face\"}, {\"id\": 46132, \"name\": \"the green patch of fur\"}, {\"id\": 46133, \"name\": \"chrome gas tank\"}, {\"id\": 46134, \"name\": \"the dark brown head and neck\"}, {\"id\": 46135, \"name\": \"gray metal rack\"}, {\"id\": 46136, \"name\": \"the left wing\"}, {\"id\": 46137, \"name\": \"a gray wing\"}, {\"id\": 46138, \"name\": \"distinctive black tail\"}, {\"id\": 46139, \"name\": \"the shadow of the standing duckling\"}, {\"id\": 46140, \"name\": \"a single wheel\"}, {\"id\": 46141, \"name\": \"an aircraft's wing\"}, {\"id\": 46142, \"name\": \"a prominent bmw logo\"}, {\"id\": 46143, \"name\": \"the white handlebar\"}, {\"id\": 46144, \"name\": \"the red and white circle\"}, {\"id\": 46145, \"name\": \"the dark-colored animal\"}, {\"id\": 46146, \"name\": \"the tenni net\"}, {\"id\": 46147, \"name\": \"a gray feather\"}, {\"id\": 46148, \"name\": \"a brown breed\"}, {\"id\": 46149, \"name\": \"the package of tenni ball\"}, {\"id\": 46150, \"name\": \"a small snout\"}, {\"id\": 46151, \"name\": \"the circular target design\"}, {\"id\": 46152, \"name\": \"a red, yellow, and orange tail\"}, {\"id\": 46153, \"name\": \"the silver gas tank\"}, {\"id\": 46154, \"name\": \"a florida license plate\"}, {\"id\": 46155, \"name\": \"the eye of monkey\"}, {\"id\": 46156, \"name\": \"the paintball mask\"}, {\"id\": 46157, \"name\": \"tiger's gaze\"}, {\"id\": 46158, \"name\": \"vibrant red and blue inflatable obstacle\"}, {\"id\": 46159, \"name\": \"a head of lizard\"}, {\"id\": 46160, \"name\": \"white foam head\"}, {\"id\": 46161, \"name\": \"a dark gray rotor blade\"}, {\"id\": 46162, \"name\": \"a helm\"}, {\"id\": 46163, \"name\": \"the nose of plane\"}, {\"id\": 46164, \"name\": \"the right wing\"}, {\"id\": 46165, \"name\": \"a simple drawing of a person\"}, {\"id\": 46166, \"name\": \"the ski bag\"}, {\"id\": 46167, \"name\": \"jetway\"}, {\"id\": 46168, \"name\": \"the round logo\"}, {\"id\": 46169, \"name\": \"a black ant\"}, {\"id\": 46170, \"name\": \"the iguana head\"}, {\"id\": 46171, \"name\": \"cartoon hot air balloon design\"}, {\"id\": 46172, \"name\": \"a swept-back wing\"}, {\"id\": 46173, \"name\": \"white antelope\"}, {\"id\": 46174, \"name\": \"a piercing blue eye\"}, {\"id\": 46175, \"name\": \"crew member\"}, {\"id\": 46176, \"name\": \"the head of the giraffe\"}, {\"id\": 46177, \"name\": \"the foreground dolphin\"}, {\"id\": 46178, \"name\": \"the mural of a rider on a motorcycle\"}, {\"id\": 46179, \"name\": \"a cartoon soldier\"}, {\"id\": 46180, \"name\": \"dead bee\"}, {\"id\": 46181, \"name\": \"piercing blue eye\"}, {\"id\": 46182, \"name\": \"the white cow\"}, {\"id\": 46183, \"name\": \"the cheerful smile\"}, {\"id\": 46184, \"name\": \"person/body\"}, {\"id\": 46185, \"name\": \"clothing/pants\"}, {\"id\": 46186, \"name\": \"flan \"}, {\"id\": 46187, \"name\": \"martini \"}, {\"id\": 46188, \"name\": \"hump\"}, {\"id\": 46189, \"name\": \"a white coffee cup\"}, {\"id\": 46190, \"name\": \"the red tomato\"}, {\"id\": 46191, \"name\": \"the frill\"}, {\"id\": 46192, \"name\": \"patch of tan fur\"}, {\"id\": 46193, \"name\": \"a yellow diamond shape\"}, {\"id\": 46194, \"name\": \"the grill mark\"}, {\"id\": 46195, \"name\": \"red drink\"}, {\"id\": 46196, \"name\": \"the cheerleader\"}, {\"id\": 46197, \"name\": \"a fluorescent\"}, {\"id\": 46198, \"name\": \"the microscope\"}, {\"id\": 46199, \"name\": \"a black and pink foam dumbbell\"}, {\"id\": 46200, \"name\": \"a black and gray boxing glove\"}, {\"id\": 46201, \"name\": \"a diver's breathing apparatus\"}, {\"id\": 46202, \"name\": \"the white butterfly\"}, {\"id\": 46203, \"name\": \"a brightly colored ball\"}, {\"id\": 46204, \"name\": \"a round bulb\"}, {\"id\": 46205, \"name\": \"protective equipment\"}, {\"id\": 46206, \"name\": \"a white hub\"}, {\"id\": 46207, \"name\": \"a blue swing\"}, {\"id\": 46208, \"name\": \"a green chili\"}, {\"id\": 46209, \"name\": \"a silver fork\"}, {\"id\": 46210, \"name\": \"lime slice\"}, {\"id\": 46211, \"name\": \"brussels sprout\"}, {\"id\": 46212, \"name\": \"glasses of drink\"}, {\"id\": 46213, \"name\": \"a yellow fruit\"}, {\"id\": 46214, \"name\": \"a rounded orb\"}, {\"id\": 46215, \"name\": \"the pointed antenna\"}, {\"id\": 46216, \"name\": \"the feather\"}, {\"id\": 46217, \"name\": \"a seated group member\"}, {\"id\": 46218, \"name\": \"green rind\"}, {\"id\": 46219, \"name\": \"a gray fin\"}, {\"id\": 46220, \"name\": \"the bob\"}, {\"id\": 46221, \"name\": \"rusty brown metal frame\"}, {\"id\": 46222, \"name\": \"a crocodile's open mouth\"}, {\"id\": 46223, \"name\": \"the playground equipment\"}, {\"id\": 46224, \"name\": \"kitchenware\"}, {\"id\": 46225, \"name\": \"the maraca\"}, {\"id\": 46226, \"name\": \"a glass of an orange-colored beverage\"}, {\"id\": 46227, \"name\": \"a reformer machine\"}, {\"id\": 46228, \"name\": \"the triangular dorsal fin\"}, {\"id\": 46229, \"name\": \"the flyboard\"}, {\"id\": 46230, \"name\": \"a plate of salad\"}, {\"id\": 46231, \"name\": \"a beach toy\"}, {\"id\": 46232, \"name\": \"yellow seasoning\"}, {\"id\": 46233, \"name\": \"the jeep's hood\"}, {\"id\": 46234, \"name\": \"green, pixelated human figure\"}, {\"id\": 46235, \"name\": \"the tail fin\"}, {\"id\": 46236, \"name\": \"the noodle\"}, {\"id\": 46237, \"name\": \"the atv's front wheel\"}, {\"id\": 46238, \"name\": \"a bike wheel\"}, {\"id\": 46239, \"name\": \"a ski helmet\"}, {\"id\": 46240, \"name\": \"a neon tube\"}, {\"id\": 46241, \"name\": \"a slice of pizza\"}, {\"id\": 46242, \"name\": \"a black life jacket\"}, {\"id\": 46243, \"name\": \"glass of lemonade\"}, {\"id\": 46244, \"name\": \"sound equipment\"}, {\"id\": 46245, \"name\": \"the curler\"}, {\"id\": 46246, \"name\": \"a zombie\"}, {\"id\": 46247, \"name\": \"air tank\"}, {\"id\": 46248, \"name\": \"an extraterrestrial creature\"}, {\"id\": 46249, \"name\": \"the spotted tail\"}, {\"id\": 46250, \"name\": \"the teacup ride\"}, {\"id\": 46251, \"name\": \"slice of kiwi\"}, {\"id\": 46252, \"name\": \"the outfield\"}, {\"id\": 46253, \"name\": \"a polo mallet\"}, {\"id\": 46254, \"name\": \"tan paw\"}, {\"id\": 46255, \"name\": \"a people ice-skating outdoor\"}, {\"id\": 46256, \"name\": \"muddy tire\"}, {\"id\": 46257, \"name\": \"the black and white pig\"}, {\"id\": 46258, \"name\": \"a ride's metal base\"}, {\"id\": 46259, \"name\": \"the military gear\"}, {\"id\": 46260, \"name\": \"bicycle's front wheel\"}, {\"id\": 46261, \"name\": \"a lead rope\"}, {\"id\": 46262, \"name\": \"an armed individual\"}, {\"id\": 46263, \"name\": \"a white-tipped fin\"}, {\"id\": 46264, \"name\": \"an off-ramp\"}, {\"id\": 46265, \"name\": \"black and white soccer cleat\"}, {\"id\": 46266, \"name\": \"skewer of food\"}, {\"id\": 46267, \"name\": \"a blue stadium-style seat\"}, {\"id\": 46268, \"name\": \"a life vest\"}, {\"id\": 46269, \"name\": \"a pastry\"}, {\"id\": 46270, \"name\": \"goalie gear\"}, {\"id\": 46271, \"name\": \"the sprinkler\"}, {\"id\": 46272, \"name\": \"the hockey equipment\"}, {\"id\": 46273, \"name\": \"a triangular dorsal fin\"}, {\"id\": 46274, \"name\": \"a white rump\"}, {\"id\": 46275, \"name\": \"a pennant\"}, {\"id\": 46276, \"name\": \"jump rope\"}, {\"id\": 46277, \"name\": \"black and white cow\"}, {\"id\": 46278, \"name\": \"a sharp beak\"}, {\"id\": 46279, \"name\": \"the yellow chanterelle mushroom\"}, {\"id\": 46280, \"name\": \"wheel of the carriage\"}, {\"id\": 46281, \"name\": \"a star anise\"}, {\"id\": 46282, \"name\": \"a brown mane\"}, {\"id\": 46283, \"name\": \"a sliced ham\"}, {\"id\": 46284, \"name\": \"a tan paw\"}, {\"id\": 46285, \"name\": \"the black go-kart\"}, {\"id\": 46286, \"name\": \"the chairlift\"}, {\"id\": 46287, \"name\": \"handle of the racket\"}, {\"id\": 46288, \"name\": \"a brown paw\"}, {\"id\": 46289, \"name\": \"tail stripe\"}, {\"id\": 46290, \"name\": \"a ticket gate\"}, {\"id\": 46291, \"name\": \"a yellow tortilla\"}, {\"id\": 46292, \"name\": \"green portion\"}, {\"id\": 46293, \"name\": \"an orange handlebar\"}, {\"id\": 46294, \"name\": \"fire hoop\"}, {\"id\": 46295, \"name\": \"flaming baton\"}, {\"id\": 46296, \"name\": \"woman in swimsuit\"}, {\"id\": 46297, \"name\": \"the blurred motorcyclist\"}, {\"id\": 46298, \"name\": \"people wearing face mask\"}, {\"id\": 46299, \"name\": \"a helmeted rider\"}, {\"id\": 46300, \"name\": \"an orange piece\"}, {\"id\": 46301, \"name\": \"skewer of meat\"}, {\"id\": 46302, \"name\": \"the stingray\"}, {\"id\": 46303, \"name\": \"a plastic ball\"}, {\"id\": 46304, \"name\": \"a jockey gear\"}, {\"id\": 46305, \"name\": \"cart's metal frame\"}, {\"id\": 46306, \"name\": \"cartoon penguin\"}, {\"id\": 46307, \"name\": \"the stalk of wheat\"}, {\"id\": 46308, \"name\": \"the wooden club\"}, {\"id\": 46309, \"name\": \"woman in the gold dress\"}, {\"id\": 46310, \"name\": \"the hockey player\"}, {\"id\": 46311, \"name\": \"a patty\"}, {\"id\": 46312, \"name\": \"a wooden fencing\"}, {\"id\": 46313, \"name\": \"green belt\"}, {\"id\": 46314, \"name\": \"person on a bicycle\"}, {\"id\": 46315, \"name\": \"the sloping hood\"}, {\"id\": 46316, \"name\": \"a dark-brown wing\"}, {\"id\": 46317, \"name\": \"the red liquid\"}, {\"id\": 46318, \"name\": \"a gondola's car\"}, {\"id\": 46319, \"name\": \"a container of food\"}, {\"id\": 46320, \"name\": \"the yellow talon\"}, {\"id\": 46321, \"name\": \"a yellow and black striped fish\"}, {\"id\": 46322, \"name\": \"the tan figure skate\"}, {\"id\": 46323, \"name\": \"bungee cord\"}, {\"id\": 46324, \"name\": \"a glowing circle\"}, {\"id\": 46325, \"name\": \"a hatchback\"}, {\"id\": 46326, \"name\": \"white device\"}, {\"id\": 46327, \"name\": \"the outdoor table\"}, {\"id\": 46328, \"name\": \"racing gear\"}, {\"id\": 46329, \"name\": \"the sheet of dough\"}, {\"id\": 46330, \"name\": \"a ballet dancer\"}, {\"id\": 46331, \"name\": \"a primate\"}, {\"id\": 46332, \"name\": \"folded white napkin\"}, {\"id\": 46333, \"name\": \"a cartoon-style santa claus\"}, {\"id\": 46334, \"name\": \"the person in the room\"}, {\"id\": 46335, \"name\": \"a back wheel of a black mountain bike\"}, {\"id\": 46336, \"name\": \"a fencing gear\"}, {\"id\": 46337, \"name\": \"the map pin icon\"}, {\"id\": 46338, \"name\": \"a glass of drink\"}, {\"id\": 46339, \"name\": \"a cross-country skier\"}, {\"id\": 46340, \"name\": \"a red foam finger\"}, {\"id\": 46341, \"name\": \"a baseball cleat\"}, {\"id\": 46342, \"name\": \"dim sum\"}, {\"id\": 46343, \"name\": \"a gymnastic equipment\"}, {\"id\": 46344, \"name\": \"a horse-drawn sleigh\"}, {\"id\": 46345, \"name\": \"the white front panel\"}, {\"id\": 46346, \"name\": \"a virtual reality (vr) headset\"}, {\"id\": 46347, \"name\": \"blue tang\"}, {\"id\": 46348, \"name\": \"skateboard's wheel\"}, {\"id\": 46349, \"name\": \"a beachgoer\"}, {\"id\": 46350, \"name\": \"a celery\"}, {\"id\": 46351, \"name\": \"silver circle\"}, {\"id\": 46352, \"name\": \"the black bowling lane divider\"}, {\"id\": 46353, \"name\": \"a pink boxing glove\"}, {\"id\": 46354, \"name\": \"the meat skewer\"}, {\"id\": 46355, \"name\": \"the wild horse\"}, {\"id\": 46356, \"name\": \"child's skates\"}, {\"id\": 46357, \"name\": \"the snorkel gear\"}, {\"id\": 46358, \"name\": \"yellow ball pit\"}, {\"id\": 46359, \"name\": \"the round opening\"}, {\"id\": 46360, \"name\": \"a viking warrior\"}, {\"id\": 46361, \"name\": \"surf\"}, {\"id\": 46362, \"name\": \"silhouette of a man\"}, {\"id\": 46363, \"name\": \"yellow talon\"}, {\"id\": 46364, \"name\": \"the icon of person\"}, {\"id\": 46365, \"name\": \"the stick with marshmallow\"}, {\"id\": 46366, \"name\": \"circuit board\"}, {\"id\": 46367, \"name\": \"grey fin\"}, {\"id\": 46368, \"name\": \"the box of white rose-shaped pastry\"}, {\"id\": 46369, \"name\": \"a kitchen tool\"}, {\"id\": 46370, \"name\": \"the kayakers\"}, {\"id\": 46371, \"name\": \"the rolling office chair\"}, {\"id\": 46372, \"name\": \"toucan\"}, {\"id\": 46373, \"name\": \"cabbage\"}, {\"id\": 46374, \"name\": \"a fruit garnish\"}, {\"id\": 46375, \"name\": \"a yellow wire\"}, {\"id\": 46376, \"name\": \"the pot of plant\"}, {\"id\": 46377, \"name\": \"the greenish-yellow fin\"}, {\"id\": 46378, \"name\": \"the brown mohawk\"}, {\"id\": 46379, \"name\": \"a white helmet with a cage\"}, {\"id\": 46380, \"name\": \"a zucchini\"}, {\"id\": 46381, \"name\": \"a parkway\"}, {\"id\": 46382, \"name\": \"scuba gear\"}, {\"id\": 46383, \"name\": \"a blade of their skate\"}, {\"id\": 46384, \"name\": \"yellow pom-pom\"}, {\"id\": 46385, \"name\": \"green bumper\"}, {\"id\": 46386, \"name\": \"the boxing ring's red rope\"}, {\"id\": 46387, \"name\": \"writing instrument\"}, {\"id\": 46388, \"name\": \"the bicycle handlebar\"}, {\"id\": 46389, \"name\": \"the bungee cord\"}, {\"id\": 46390, \"name\": \"the dark dorsal fin\"}, {\"id\": 46391, \"name\": \"a green wrap\"}, {\"id\": 46392, \"name\": \"the paper pompom\"}, {\"id\": 46393, \"name\": \"a head of one swimmer\"}, {\"id\": 46394, \"name\": \"a red connector\"}, {\"id\": 46395, \"name\": \"the olive\"}, {\"id\": 46396, \"name\": \"the file cabinet\"}, {\"id\": 46397, \"name\": \"diver's blue fin\"}, {\"id\": 46398, \"name\": \"rear of a dark-colored car\"}, {\"id\": 46399, \"name\": \"roller skate\"}, {\"id\": 46400, \"name\": \"the uniformed rider\"}, {\"id\": 46401, \"name\": \"a copper-colored cylindrical component\"}, {\"id\": 46402, \"name\": \"red placemat\"}, {\"id\": 46403, \"name\": \"player in the blue uniform\"}, {\"id\": 46404, \"name\": \"black-and-white skate\"}, {\"id\": 46405, \"name\": \"takoyaki ball\"}, {\"id\": 46406, \"name\": \"heavily armed and armored individual\"}, {\"id\": 46407, \"name\": \"the ride's handle\"}, {\"id\": 46408, \"name\": \"pennant flag\"}, {\"id\": 46409, \"name\": \"an orange cocktail\"}, {\"id\": 46410, \"name\": \"person in camouflage\"}, {\"id\": 46411, \"name\": \"the blurred tail\"}, {\"id\": 46412, \"name\": \"a front of the boot\"}, {\"id\": 46413, \"name\": \"the left rear tire\"}, {\"id\": 46414, \"name\": \"a black bike\"}, {\"id\": 46415, \"name\": \"white goose\"}, {\"id\": 46416, \"name\": \"an atv rider\"}, {\"id\": 46417, \"name\": \"a kneeling woman\"}, {\"id\": 46418, \"name\": \"the air tank\"}, {\"id\": 46419, \"name\": \"white wine\"}, {\"id\": 46420, \"name\": \"yellow dumpling\"}, {\"id\": 46421, \"name\": \"speckled neck\"}, {\"id\": 46422, \"name\": \"cookware\"}, {\"id\": 46423, \"name\": \"the train's side door\"}, {\"id\": 46424, \"name\": \"a people on motorbike\"}, {\"id\": 46425, \"name\": \"the film reel\"}, {\"id\": 46426, \"name\": \"a gym's lighting\"}, {\"id\": 46427, \"name\": \"the garnish\"}, {\"id\": 46428, \"name\": \"the chain belt\"}, {\"id\": 46429, \"name\": \"a red team\"}, {\"id\": 46430, \"name\": \"the glass bottle of beer\"}, {\"id\": 46431, \"name\": \"covered back seat\"}, {\"id\": 46432, \"name\": \"the black rectangular pad\"}, {\"id\": 46433, \"name\": \"the black computer monitor\"}, {\"id\": 46434, \"name\": \"man in armor\"}, {\"id\": 46435, \"name\": \"the protester\"}, {\"id\": 46436, \"name\": \"the hour and minute hand\"}, {\"id\": 46437, \"name\": \"the cooking ingredient\"}, {\"id\": 46438, \"name\": \"the industrial-style lighting fixture\"}, {\"id\": 46439, \"name\": \"pink piglet\"}, {\"id\": 46440, \"name\": \"the breadstick\"}, {\"id\": 46441, \"name\": \"the chain of a swing\"}, {\"id\": 46442, \"name\": \"the rainbow pinwheel\"}, {\"id\": 46443, \"name\": \"the round, golden-brown pastry\"}, {\"id\": 46444, \"name\": \"an animal-themed ride\"}, {\"id\": 46445, \"name\": \"a red chap\"}, {\"id\": 46446, \"name\": \"the cup of coffee\"}, {\"id\": 46447, \"name\": \"grabber\"}, {\"id\": 46448, \"name\": \"a boxing shoe\"}, {\"id\": 46449, \"name\": \"talon\"}, {\"id\": 46450, \"name\": \"the rim\"}, {\"id\": 46451, \"name\": \"the yellow exercise mat\"}, {\"id\": 46452, \"name\": \"the blue boxing glove\"}, {\"id\": 46453, \"name\": \"a control button\"}, {\"id\": 46454, \"name\": \"the red swing bar\"}, {\"id\": 46455, \"name\": \"a plunging back\"}, {\"id\": 46456, \"name\": \"the sprinkle\"}, {\"id\": 46457, \"name\": \"the clear wing\"}, {\"id\": 46458, \"name\": \"the neon yellow cleat\"}, {\"id\": 46459, \"name\": \"the aviator gear\"}, {\"id\": 46460, \"name\": \"pink lighting\"}, {\"id\": 46461, \"name\": \"muddy paw\"}, {\"id\": 46462, \"name\": \"silver serving dish\"}, {\"id\": 46463, \"name\": \"vr controller\"}, {\"id\": 46464, \"name\": \"a safety equipment\"}, {\"id\": 46465, \"name\": \"red ink\"}, {\"id\": 46466, \"name\": \"face of a brown and black brindle puppy\"}, {\"id\": 46467, \"name\": \"man in a gray shirt\"}, {\"id\": 46468, \"name\": \"neon yellow and blue cleat\"}, {\"id\": 46469, \"name\": \"the silver sword\"}, {\"id\": 46470, \"name\": \"a white tire\"}, {\"id\": 46471, \"name\": \"black paintball mask\"}, {\"id\": 46472, \"name\": \"an elegantly dressed individual\"}, {\"id\": 46473, \"name\": \"pink filling\"}, {\"id\": 46474, \"name\": \"cafeteria table\"}, {\"id\": 46475, \"name\": \"a pineapple garnish\"}, {\"id\": 46476, \"name\": \"black and white rollerblade\"}, {\"id\": 46477, \"name\": \"a boat with a blue-and-white striped awning\"}, {\"id\": 46478, \"name\": \"the park's playground equipment\"}, {\"id\": 46479, \"name\": \"festive table\"}, {\"id\": 46480, \"name\": \"blonde\"}, {\"id\": 46481, \"name\": \"clear plastic container of sushi\"}, {\"id\": 46482, \"name\": \"wrestling ring's red rope\"}, {\"id\": 46483, \"name\": \"the metal tray of food\"}, {\"id\": 46484, \"name\": \"the plate of meat\"}, {\"id\": 46485, \"name\": \"black and gray checkerboard circle\"}, {\"id\": 46486, \"name\": \"sound system\"}, {\"id\": 46487, \"name\": \"a potato chip\"}, {\"id\": 46488, \"name\": \"the sliced cheese\"}, {\"id\": 46489, \"name\": \"the bar graph\"}, {\"id\": 46490, \"name\": \"a black and white boxing glove\"}, {\"id\": 46491, \"name\": \"dough piece\"}, {\"id\": 46492, \"name\": \"the plastic fruit\"}, {\"id\": 46493, \"name\": \"female dancer\"}, {\"id\": 46494, \"name\": \"black and white tail\"}, {\"id\": 46495, \"name\": \"the men in the stand\"}, {\"id\": 46496, \"name\": \"the rider's tire\"}, {\"id\": 46497, \"name\": \"the red pompom\"}, {\"id\": 46498, \"name\": \"the black and white wheel\"}, {\"id\": 46499, \"name\": \"person in red shoe\"}, {\"id\": 46500, \"name\": \"a black muzzle\"}, {\"id\": 46501, \"name\": \"a silver cleat\"}, {\"id\": 46502, \"name\": \"the curved horn\"}, {\"id\": 46503, \"name\": \"the red and black go-kart\"}, {\"id\": 46504, \"name\": \"the safety cone\"}, {\"id\": 46505, \"name\": \"the green bottle of beer\"}, {\"id\": 46506, \"name\": \"metal burner\"}, {\"id\": 46507, \"name\": \"cartoon horse\"}, {\"id\": 46508, \"name\": \"a numbered race bib\"}, {\"id\": 46509, \"name\": \"a pink pinwheel\"}, {\"id\": 46510, \"name\": \"red wine\"}, {\"id\": 46511, \"name\": \"kiwi fruit slice\"}, {\"id\": 46512, \"name\": \"the led strip\"}, {\"id\": 46513, \"name\": \"child's sled\"}, {\"id\": 46514, \"name\": \"teammate\"}, {\"id\": 46515, \"name\": \"the woman in a wedding dress\"}, {\"id\": 46516, \"name\": \"a back tire\"}, {\"id\": 46517, \"name\": \"black and silver cart\"}, {\"id\": 46518, \"name\": \"a white cylindrical component\"}, {\"id\": 46519, \"name\": \"single-person cart\"}, {\"id\": 46520, \"name\": \"a file\"}, {\"id\": 46521, \"name\": \"the beer pong cup\"}, {\"id\": 46522, \"name\": \"a blue stadium seat\"}, {\"id\": 46523, \"name\": \"vr headset\"}, {\"id\": 46524, \"name\": \"the white equipment\"}, {\"id\": 46525, \"name\": \"a bale of hay\"}, {\"id\": 46526, \"name\": \"the pizza base\"}, {\"id\": 46527, \"name\": \"crate of produce\"}, {\"id\": 46528, \"name\": \"red food\"}, {\"id\": 46529, \"name\": \"the athletic equipment\"}, {\"id\": 46530, \"name\": \"the red pumpkin\"}, {\"id\": 46531, \"name\": \"cannoli\"}, {\"id\": 46532, \"name\": \"person in the group\"}, {\"id\": 46533, \"name\": \"the gym-goer\"}, {\"id\": 46534, \"name\": \"a wheel of cheese\"}, {\"id\": 46535, \"name\": \"the gymnastic mat\"}, {\"id\": 46536, \"name\": \"the blue bodywork\"}, {\"id\": 46537, \"name\": \"voluntee\"}, {\"id\": 46538, \"name\": \"the business professional\"}, {\"id\": 46539, \"name\": \"an office worker\"}, {\"id\": 46540, \"name\": \"a medieval knight\"}, {\"id\": 46541, \"name\": \"the woman in a bikini\"}, {\"id\": 46542, \"name\": \"climbing shoe\"}, {\"id\": 46543, \"name\": \"the fire staff\"}, {\"id\": 46544, \"name\": \"pepperoni\"}, {\"id\": 46545, \"name\": \"a purple wheel\"}, {\"id\": 46546, \"name\": \"a recessed lighting fixture\"}, {\"id\": 46547, \"name\": \"the barrel of the cannon\"}, {\"id\": 46548, \"name\": \"the boxing pad\"}, {\"id\": 46549, \"name\": \"the led lighting\"}, {\"id\": 46550, \"name\": \"brown goat\"}, {\"id\": 46551, \"name\": \"white shape\"}, {\"id\": 46552, \"name\": \"the handrail of the escalator\"}, {\"id\": 46553, \"name\": \"flowing golden tail\"}, {\"id\": 46554, \"name\": \"a piece of green fruit\"}, {\"id\": 46555, \"name\": \"the diving board\"}, {\"id\": 46556, \"name\": \"the raw, uncooked bread dough\"}, {\"id\": 46557, \"name\": \"brown tire\"}, {\"id\": 46558, \"name\": \"the green, humanoid figure\"}, {\"id\": 46559, \"name\": \"the hind flipper\"}, {\"id\": 46560, \"name\": \"roll\"}, {\"id\": 46561, \"name\": \"a black seat belt\"}, {\"id\": 46562, \"name\": \"yellow and black drone\"}, {\"id\": 46563, \"name\": \"a black and white skate\"}, {\"id\": 46564, \"name\": \"cooking appliance\"}, {\"id\": 46565, \"name\": \"a person ice skating\"}, {\"id\": 46566, \"name\": \"a bamboo sword\"}, {\"id\": 46567, \"name\": \"the white foam finger\"}, {\"id\": 46568, \"name\": \"a children riding bike\"}, {\"id\": 46569, \"name\": \"slice of bread\"}, {\"id\": 46570, \"name\": \"man clad in black\"}, {\"id\": 46571, \"name\": \"a sliced avocado\"}, {\"id\": 46572, \"name\": \"sliced meat\"}, {\"id\": 46573, \"name\": \"a barracuda\"}, {\"id\": 46574, \"name\": \"a black-and-white cleat\"}, {\"id\": 46575, \"name\": \"a platter\"}, {\"id\": 46576, \"name\": \"sleigh\"}, {\"id\": 46577, \"name\": \"a glass of yellow liquid\"}, {\"id\": 46578, \"name\": \"a white spray tank\"}, {\"id\": 46579, \"name\": \"beef brisket\"}, {\"id\": 46580, \"name\": \"a van's tire\"}, {\"id\": 46581, \"name\": \"a sliced meat\"}, {\"id\": 46582, \"name\": \"combatant\"}, {\"id\": 46583, \"name\": \"the wooden grip\"}, {\"id\": 46584, \"name\": \"white silhouette of a person\"}, {\"id\": 46585, \"name\": \"an animal hide\"}, {\"id\": 46586, \"name\": \"the people's faces\"}, {\"id\": 46587, \"name\": \"the ride's metal arm\"}, {\"id\": 46588, \"name\": \"schoolchild\"}, {\"id\": 46589, \"name\": \"the car's front tire\"}, {\"id\": 46590, \"name\": \"the stirrup\"}, {\"id\": 46591, \"name\": \"metal handlebar\"}, {\"id\": 46592, \"name\": \"the stationary equipment\"}, {\"id\": 46593, \"name\": \"a green plate\"}, {\"id\": 46594, \"name\": \"bike wheel\"}, {\"id\": 46595, \"name\": \"the salami\"}, {\"id\": 46596, \"name\": \"the cooked food\"}, {\"id\": 46597, \"name\": \"cylindrical component\"}, {\"id\": 46598, \"name\": \"overhead fluorescent\"}, {\"id\": 46599, \"name\": \"diving gear\"}, {\"id\": 46600, \"name\": \"black cockpit\"}, {\"id\": 46601, \"name\": \"the pink glow stick\"}, {\"id\": 46602, \"name\": \"a black lighting fixture\"}, {\"id\": 46603, \"name\": \"a type of chanterelle\"}, {\"id\": 46604, \"name\": \"the dispenser\"}, {\"id\": 46605, \"name\": \"a neon strip\"}, {\"id\": 46606, \"name\": \"a round loaf of bread\"}, {\"id\": 46607, \"name\": \"the bowstring\"}, {\"id\": 46608, \"name\": \"the scoop of pink ice cream\"}, {\"id\": 46609, \"name\": \"red skate\"}, {\"id\": 46610, \"name\": \"a red and white checkered tablecloth\"}, {\"id\": 46611, \"name\": \"the glass of food\"}, {\"id\": 46612, \"name\": \"a train's handle\"}, {\"id\": 46613, \"name\": \"the grey dumbbell\"}, {\"id\": 46614, \"name\": \"goalie's equipment\"}, {\"id\": 46615, \"name\": \"a rope of the swing\"}, {\"id\": 46616, \"name\": \"amber liquid\"}, {\"id\": 46617, \"name\": \"the black patch of fur\"}, {\"id\": 46618, \"name\": \"a roller skate\"}, {\"id\": 46619, \"name\": \"packaged meat\"}, {\"id\": 46620, \"name\": \"young ballet dancer\"}, {\"id\": 46621, \"name\": \"a plastic bag of fruit\"}, {\"id\": 46622, \"name\": \"the windsurfer\"}, {\"id\": 46623, \"name\": \"the fried flatbread\"}, {\"id\": 46624, \"name\": \"the wooden car\"}, {\"id\": 46625, \"name\": \"the cookware\"}, {\"id\": 46626, \"name\": \"gymnastics ring\"}, {\"id\": 46627, \"name\": \"the metal bike rack\"}, {\"id\": 46628, \"name\": \"the green salad\"}, {\"id\": 46629, \"name\": \"front mower's wheel\"}, {\"id\": 46630, \"name\": \"a fencing sword\"}, {\"id\": 46631, \"name\": \"the grey cabinet\"}, {\"id\": 46632, \"name\": \"the gingerbread cookie\"}, {\"id\": 46633, \"name\": \"a woman with a purple bikini top\"}, {\"id\": 46634, \"name\": \"birthday candle\"}, {\"id\": 46635, \"name\": \"the zombie\"}, {\"id\": 46636, \"name\": \"a silhouetted person\"}, {\"id\": 46637, \"name\": \"the green shell\"}, {\"id\": 46638, \"name\": \"the person in a black jacket\"}, {\"id\": 46639, \"name\": \"woodworking tool\"}, {\"id\": 46640, \"name\": \"pregnant woman\"}, {\"id\": 46641, \"name\": \"toy sword\"}, {\"id\": 46642, \"name\": \"the chicken wing\"}, {\"id\": 46643, \"name\": \"a black and white soccer cleat\"}, {\"id\": 46644, \"name\": \"a cycling gear\"}, {\"id\": 46645, \"name\": \"sesame seed bun\"}, {\"id\": 46646, \"name\": \"supporting woman\"}, {\"id\": 46647, \"name\": \"a pink wrapper\"}, {\"id\": 46648, \"name\": \"a green metal fencing\"}, {\"id\": 46649, \"name\": \"the metal tray of raw meat\"}, {\"id\": 46650, \"name\": \"glass jar of cereal\"}, {\"id\": 46651, \"name\": \"tray of dough\"}, {\"id\": 46652, \"name\": \"the food ingredient\"}, {\"id\": 46653, \"name\": \"brunette\"}, {\"id\": 46654, \"name\": \"the cream cheese\"}, {\"id\": 46655, \"name\": \"rock climber\"}, {\"id\": 46656, \"name\": \"the christmas-themed cookie\"}, {\"id\": 46657, \"name\": \"car windshield\"}, {\"id\": 46658, \"name\": \"a blue player\"}, {\"id\": 46659, \"name\": \"the can of food\"}, {\"id\": 46660, \"name\": \"the sliced meat\"}, {\"id\": 46661, \"name\": \"the boy in the red shirt\"}, {\"id\": 46662, \"name\": \"coffee option\"}, {\"id\": 46663, \"name\": \"the motorized vehicle\"}, {\"id\": 46664, \"name\": \"foosball handle\"}, {\"id\": 46665, \"name\": \"a sliced bell pepper\"}, {\"id\": 46666, \"name\": \"the green egg\"}, {\"id\": 46667, \"name\": \"a blue oar\"}, {\"id\": 46668, \"name\": \"a gazelle\"}, {\"id\": 46669, \"name\": \"the shirtless individual\"}, {\"id\": 46670, \"name\": \"the dollop of white spread\"}, {\"id\": 46671, \"name\": \"white gondola\"}, {\"id\": 46672, \"name\": \"a chocolate glaze\"}, {\"id\": 46673, \"name\": \"black ice skate\"}, {\"id\": 46674, \"name\": \"glass of amber liquid\"}, {\"id\": 46675, \"name\": \"the ice cream case\"}, {\"id\": 46676, \"name\": \"black and white hockey glove\"}, {\"id\": 46677, \"name\": \"the controller's knobs\"}, {\"id\": 46678, \"name\": \"a fencing mask\"}, {\"id\": 46679, \"name\": \"round, flat bread\"}, {\"id\": 46680, \"name\": \"a white wine\"}, {\"id\": 46681, \"name\": \"the white hen\"}, {\"id\": 46682, \"name\": \"a hockey glove\"}, {\"id\": 46683, \"name\": \"a piece of orange sushi\"}, {\"id\": 46684, \"name\": \"stationary exercise bike\"}, {\"id\": 46685, \"name\": \"brown hoof\"}, {\"id\": 46686, \"name\": \"black wheel well\"}, {\"id\": 46687, \"name\": \"a people in the stand\"}, {\"id\": 46688, \"name\": \"the wooden oar\"}, {\"id\": 46689, \"name\": \"a chili pepper\"}, {\"id\": 46690, \"name\": \"the bike's silver frame\"}, {\"id\": 46691, \"name\": \"a flat handlebar\"}, {\"id\": 46692, \"name\": \"sliced banana\"}, {\"id\": 46693, \"name\": \"the hartebeest\"}, {\"id\": 46694, \"name\": \"ferret\"}, {\"id\": 46695, \"name\": \"purple wing\"}, {\"id\": 46696, \"name\": \"a red pompom\"}, {\"id\": 46697, \"name\": \"flowing tail\"}, {\"id\": 46698, \"name\": \"the green, alien-like creature\"}, {\"id\": 46699, \"name\": \"karate practitioner\"}, {\"id\": 46700, \"name\": \"a people's faces\"}, {\"id\": 46701, \"name\": \"a white wing patch\"}, {\"id\": 46702, \"name\": \"man in a white shirt and black trousers\"}, {\"id\": 46703, \"name\": \"a serving dish\"}, {\"id\": 46704, \"name\": \"flour\"}, {\"id\": 46705, \"name\": \"the logo featuring a fork and knife\"}, {\"id\": 46706, \"name\": \"the dancing person\"}, {\"id\": 46707, \"name\": \"golden-brown crust\"}, {\"id\": 46708, \"name\": \"buffalo\"}, {\"id\": 46709, \"name\": \"green ingredient\"}, {\"id\": 46710, \"name\": \"white pull-up bar\"}, {\"id\": 46711, \"name\": \"the grabber tool\"}, {\"id\": 46712, \"name\": \"polar bear\"}, {\"id\": 46713, \"name\": \"bread roll\"}, {\"id\": 46714, \"name\": \"a tray of pastries and cake\"}, {\"id\": 46715, \"name\": \"a game's equipment\"}, {\"id\": 46716, \"name\": \"the blue climbing hold\"}, {\"id\": 46717, \"name\": \"the circular handhold\"}, {\"id\": 46718, \"name\": \"brown bird\"}, {\"id\": 46719, \"name\": \"the red cocktail\"}, {\"id\": 46720, \"name\": \"the transportation infrastructure\"}, {\"id\": 46721, \"name\": \"blurred figure of a man\"}, {\"id\": 46722, \"name\": \"the green onion\"}, {\"id\": 46723, \"name\": \"the orange foam roller\"}, {\"id\": 46724, \"name\": \"the blonde girl\"}, {\"id\": 46725, \"name\": \"the laboratory equipment\"}, {\"id\": 46726, \"name\": \"hood of a white car\"}, {\"id\": 46727, \"name\": \"the marble\"}, {\"id\": 46728, \"name\": \"sport equipment\"}, {\"id\": 46729, \"name\": \"green olive garnish\"}, {\"id\": 46730, \"name\": \"a plastic container of food\"}, {\"id\": 46731, \"name\": \"the kitesurfer\"}, {\"id\": 46732, \"name\": \"an artist\"}, {\"id\": 46733, \"name\": \"coffee-making equipment\"}, {\"id\": 46734, \"name\": \"the stems of the cherry\"}, {\"id\": 46735, \"name\": \"the tan tail\"}, {\"id\": 46736, \"name\": \"an atv's rear tire\"}, {\"id\": 46737, \"name\": \"metal circle\"}, {\"id\": 46738, \"name\": \"a refreshment\"}, {\"id\": 46739, \"name\": \"the train's seat\"}, {\"id\": 46740, \"name\": \"the golden brown liquid\"}, {\"id\": 46741, \"name\": \"a piece of chocolate cake\"}, {\"id\": 46742, \"name\": \"plane's wheel\"}, {\"id\": 46743, \"name\": \"the bottle of ketchup\"}, {\"id\": 46744, \"name\": \"a bay horse\"}, {\"id\": 46745, \"name\": \"a guinea fowl\"}, {\"id\": 46746, \"name\": \"a bowl of lettuce\"}, {\"id\": 46747, \"name\": \"the safety bar\"}, {\"id\": 46748, \"name\": \"pink dumbbell\"}, {\"id\": 46749, \"name\": \"the black seed\"}, {\"id\": 46750, \"name\": \"the slice of kiwi\"}, {\"id\": 46751, \"name\": \"a white figure skate\"}, {\"id\": 46752, \"name\": \"a pink-topped treat\"}, {\"id\": 46753, \"name\": \"a metal serving spoon\"}, {\"id\": 46754, \"name\": \"a person in costume\"}, {\"id\": 46755, \"name\": \"the taxi's side door\"}, {\"id\": 46756, \"name\": \"the kiwi slice\"}, {\"id\": 46757, \"name\": \"the brown tuft\"}, {\"id\": 46758, \"name\": \"the pickaxe\"}, {\"id\": 46759, \"name\": \"box truck\"}, {\"id\": 46760, \"name\": \"a roll\"}, {\"id\": 46761, \"name\": \"the goalie's equipment\"}, {\"id\": 46762, \"name\": \"blue goblet\"}, {\"id\": 46763, \"name\": \"the man in white\"}, {\"id\": 46764, \"name\": \"a metal serving pan\"}, {\"id\": 46765, \"name\": \"the man in wrestling singlet\"}, {\"id\": 46766, \"name\": \"a hole in the dough\"}, {\"id\": 46767, \"name\": \"gold-colored orb\"}, {\"id\": 46768, \"name\": \"the marshmallow\"}, {\"id\": 46769, \"name\": \"fish ball\"}, {\"id\": 46770, \"name\": \"white bread\"}, {\"id\": 46771, \"name\": \"gymnastic ring\"}, {\"id\": 46772, \"name\": \"neck of the instrument\"}, {\"id\": 46773, \"name\": \"a sliced banana\"}, {\"id\": 46774, \"name\": \"a white slide\"}, {\"id\": 46775, \"name\": \"the black lane divider\"}, {\"id\": 46776, \"name\": \"the cabbage roll\"}, {\"id\": 46777, \"name\": \"the woman in a black top\"}, {\"id\": 46778, \"name\": \"orange napkin\"}, {\"id\": 46779, \"name\": \"the puck\"}, {\"id\": 46780, \"name\": \"white patch of feather\"}, {\"id\": 46781, \"name\": \"a head of the puppy\"}, {\"id\": 46782, \"name\": \"the kettlebells\"}, {\"id\": 46783, \"name\": \"a string of white balloon\"}, {\"id\": 46784, \"name\": \"a round shape\"}, {\"id\": 46785, \"name\": \"a rustic, crusty bread\"}, {\"id\": 46786, \"name\": \"scientific equipment\"}, {\"id\": 46787, \"name\": \"a white and black goalie gear\"}, {\"id\": 46788, \"name\": \"crisp white tablecloth\"}, {\"id\": 46789, \"name\": \"dark-colored olive\"}, {\"id\": 46790, \"name\": \"the people's raised fist\"}, {\"id\": 46791, \"name\": \"jet-ski\"}, {\"id\": 46792, \"name\": \"a meat patty\"}, {\"id\": 46793, \"name\": \"sliced salami\"}, {\"id\": 46794, \"name\": \"honeycomb\"}, {\"id\": 46795, \"name\": \"a fishing gear\"}, {\"id\": 46796, \"name\": \"a neon green flipper\"}, {\"id\": 46797, \"name\": \"a black rubber ring\"}, {\"id\": 46798, \"name\": \"the yellow police barricade\"}, {\"id\": 46799, \"name\": \"the pannier\"}, {\"id\": 46800, \"name\": \"silhouetted figure\"}, {\"id\": 46801, \"name\": \"bread slice\"}, {\"id\": 46802, \"name\": \"machine's handlebar\"}, {\"id\": 46803, \"name\": \"dumpling wrapper\"}, {\"id\": 46804, \"name\": \"a blade of the skate\"}, {\"id\": 46805, \"name\": \"the person in a sparkly dress\"}, {\"id\": 46806, \"name\": \"yellow and black suspension strap\"}, {\"id\": 46807, \"name\": \"the gray exercise mat\"}, {\"id\": 46808, \"name\": \"hind flipper\"}, {\"id\": 46809, \"name\": \"the glass of liquid\"}, {\"id\": 46810, \"name\": \"toasted english muffin\"}, {\"id\": 46811, \"name\": \"the bowl of blueberry\"}, {\"id\": 46812, \"name\": \"the bowl of chopped vegetable\"}, {\"id\": 46813, \"name\": \"the kitchen's equipment\"}, {\"id\": 46814, \"name\": \"bike's frame\"}, {\"id\": 46815, \"name\": \"the circular button\"}, {\"id\": 46816, \"name\": \"glass of amber-colored liquid\"}, {\"id\": 46817, \"name\": \"black plumage\"}, {\"id\": 46818, \"name\": \"a bowl of fruit\"}, {\"id\": 46819, \"name\": \"oryx\"}, {\"id\": 46820, \"name\": \"a cinnamon\"}, {\"id\": 46821, \"name\": \"cookie dough\"}, {\"id\": 46822, \"name\": \"the sledge\"}, {\"id\": 46823, \"name\": \"a corgi\"}, {\"id\": 46824, \"name\": \"the lane rope\"}, {\"id\": 46825, \"name\": \"black firearm\"}, {\"id\": 46826, \"name\": \"the black-tipped wing\"}, {\"id\": 46827, \"name\": \"green icing\"}, {\"id\": 46828, \"name\": \"the golden-colored liquid\"}, {\"id\": 46829, \"name\": \"an elevated roadway\"}, {\"id\": 46830, \"name\": \"the coworkers\"}, {\"id\": 46831, \"name\": \"a gray dinosaur\"}, {\"id\": 46832, \"name\": \"factory worker\"}, {\"id\": 46833, \"name\": \"silver serving utensil\"}, {\"id\": 46834, \"name\": \"a rapier\"}, {\"id\": 46835, \"name\": \"ticket gate\"}, {\"id\": 46836, \"name\": \"gymnast\"}, {\"id\": 46837, \"name\": \"blurred tail\"}, {\"id\": 46838, \"name\": \"the circular top\"}, {\"id\": 46839, \"name\": \"neon green and black skate\"}, {\"id\": 46840, \"name\": \"man in a purple shirt\"}, {\"id\": 46841, \"name\": \"white patch of fur\"}, {\"id\": 46842, \"name\": \"the yellow hoove\"}, {\"id\": 46843, \"name\": \"the red parking sign\"}, {\"id\": 46844, \"name\": \"black seed\"}, {\"id\": 46845, \"name\": \"the rectangular table\"}, {\"id\": 46846, \"name\": \"blue berry\"}, {\"id\": 46847, \"name\": \"the candy cane\"}, {\"id\": 46848, \"name\": \"the filling\"}, {\"id\": 46849, \"name\": \"the black exercise step\"}, {\"id\": 46850, \"name\": \"the fantastical, humanoid creature\"}, {\"id\": 46851, \"name\": \"decorative circle\"}, {\"id\": 46852, \"name\": \"a hand of musician\"}, {\"id\": 46853, \"name\": \"the woman in the group\"}, {\"id\": 46854, \"name\": \"a head of a woman\"}, {\"id\": 46855, \"name\": \"the santa claus\"}, {\"id\": 46856, \"name\": \"the crusty bread base\"}, {\"id\": 46857, \"name\": \"a santa\"}, {\"id\": 46858, \"name\": \"churros\"}, {\"id\": 46859, \"name\": \"rider's board\"}, {\"id\": 46860, \"name\": \"black and white boxing pad\"}, {\"id\": 46861, \"name\": \"jet-ski rider\"}, {\"id\": 46862, \"name\": \"the stadium seat\"}, {\"id\": 46863, \"name\": \"brown wheel\"}, {\"id\": 46864, \"name\": \"an orange jack-o-lantern\"}, {\"id\": 46865, \"name\": \"an african wild dog\"}, {\"id\": 46866, \"name\": \"a scuba tank\"}, {\"id\": 46867, \"name\": \"a wooden feeding trough\"}, {\"id\": 46868, \"name\": \"an arm guard\"}, {\"id\": 46869, \"name\": \"falafel\"}, {\"id\": 46870, \"name\": \"a curved form\"}, {\"id\": 46871, \"name\": \"the white coffee cup\"}, {\"id\": 46872, \"name\": \"a security tape\"}, {\"id\": 46873, \"name\": \"a bear-costumed individual\"}, {\"id\": 46874, \"name\": \"glass bottle of beer\"}, {\"id\": 46875, \"name\": \"a blue pill\"}, {\"id\": 46876, \"name\": \"glass of juice\"}, {\"id\": 46877, \"name\": \"red party blower\"}, {\"id\": 46878, \"name\": \"the english muffin\"}, {\"id\": 46879, \"name\": \"a cape buffalo\"}, {\"id\": 46880, \"name\": \"a silver rectangle\"}, {\"id\": 46881, \"name\": \"a toasted bread slice\"}, {\"id\": 46882, \"name\": \"neon green flipper\"}, {\"id\": 46883, \"name\": \"the red kettlebell\"}, {\"id\": 46884, \"name\": \"the man in a blue and white jersey\"}, {\"id\": 46885, \"name\": \"young ballerina\"}, {\"id\": 46886, \"name\": \"the gray feather\"}, {\"id\": 46887, \"name\": \"the white and black vehicle\"}, {\"id\": 46888, \"name\": \"a food residue\"}, {\"id\": 46889, \"name\": \"a basil\"}, {\"id\": 46890, \"name\": \"a plate of chocolate cake\"}, {\"id\": 46891, \"name\": \"the jogger\"}, {\"id\": 46892, \"name\": \"white and black tail\"}, {\"id\": 46893, \"name\": \"sliced cucumber\"}, {\"id\": 46894, \"name\": \"blue stadium seat\"}, {\"id\": 46895, \"name\": \"a speckled quail egg\"}, {\"id\": 46896, \"name\": \"the blue egg\"}, {\"id\": 46897, \"name\": \"a chocolate cake\"}, {\"id\": 46898, \"name\": \"-terrain vehicle\"}, {\"id\": 46899, \"name\": \"white propeller\"}, {\"id\": 46900, \"name\": \"nose of the aircraft\"}, {\"id\": 46901, \"name\": \"the white foam kickboard\"}, {\"id\": 46902, \"name\": \"the paintball gun\"}, {\"id\": 46903, \"name\": \"white labrador puppy\"}, {\"id\": 46904, \"name\": \"the crushed can\"}, {\"id\": 46905, \"name\": \"the striped fish\"}, {\"id\": 46906, \"name\": \"an orange paddle\"}, {\"id\": 46907, \"name\": \"the control knob\"}, {\"id\": 46908, \"name\": \"top bun\"}, {\"id\": 46909, \"name\": \"the player in the red jersey\"}, {\"id\": 46910, \"name\": \"a woman in a bikini\"}, {\"id\": 46911, \"name\": \"the slice of orange\"}, {\"id\": 46912, \"name\": \"a green weight\"}, {\"id\": 46913, \"name\": \"a woman in white\"}, {\"id\": 46914, \"name\": \"zombie\"}, {\"id\": 46915, \"name\": \"sandwich's filling\"}, {\"id\": 46916, \"name\": \"the fried shrimp\"}, {\"id\": 46917, \"name\": \"bowl of brown sauce\"}, {\"id\": 46918, \"name\": \"a white-tipped paw\"}, {\"id\": 46919, \"name\": \"the submachine gun\"}, {\"id\": 46920, \"name\": \"the basket of lemon\"}, {\"id\": 46921, \"name\": \"the curved handlebar\"}, {\"id\": 46922, \"name\": \"a model airplane\"}, {\"id\": 46923, \"name\": \"the slice of cheese\"}, {\"id\": 46924, \"name\": \"plate of rice\"}, {\"id\": 46925, \"name\": \"the yellow egg\"}, {\"id\": 46926, \"name\": \"a silver circle\"}, {\"id\": 46927, \"name\": \"an inflatable\"}, {\"id\": 46928, \"name\": \"a martial art weapon\"}, {\"id\": 46929, \"name\": \"the mower's wheel\"}, {\"id\": 46930, \"name\": \"pear\"}, {\"id\": 46931, \"name\": \"the purple exercise mat\"}, {\"id\": 46932, \"name\": \"a blue pom-pom\"}, {\"id\": 46933, \"name\": \"the medical professional\"}, {\"id\": 46934, \"name\": \"a virtual reality (vr) device\"}, {\"id\": 46935, \"name\": \"an edible purple and white flower\"}, {\"id\": 46936, \"name\": \"the fencing mask\"}, {\"id\": 46937, \"name\": \"a metal support\"}, {\"id\": 46938, \"name\": \"bikers' equipment\"}, {\"id\": 46939, \"name\": \"the yellow tank\"}, {\"id\": 46940, \"name\": \"sliced pepper\"}, {\"id\": 46941, \"name\": \"the tournament logo\"}, {\"id\": 46942, \"name\": \"the oryx\"}, {\"id\": 46943, \"name\": \"a round white bulb\"}, {\"id\": 46944, \"name\": \"a head of a peacock\"}, {\"id\": 46945, \"name\": \"a man in armor\"}, {\"id\": 46946, \"name\": \"a white racket\"}, {\"id\": 46947, \"name\": \"the golden brown bun\"}, {\"id\": 46948, \"name\": \"gray horn\"}, {\"id\": 46949, \"name\": \"red onion slice\"}, {\"id\": 46950, \"name\": \"roasted chicken piece\"}, {\"id\": 46951, \"name\": \"a brown animals with curved horn\"}, {\"id\": 46952, \"name\": \"pani puri\"}, {\"id\": 46953, \"name\": \"the piece of office equipment\"}, {\"id\": 46954, \"name\": \"black-and-white volleyball\"}, {\"id\": 46955, \"name\": \"a white goalie mask\"}, {\"id\": 46956, \"name\": \"the red sauce\"}, {\"id\": 46957, \"name\": \"the close-up of a person's face\"}, {\"id\": 46958, \"name\": \"a wooden ramp\"}, {\"id\": 46959, \"name\": \"bicycle's wheel\"}, {\"id\": 46960, \"name\": \"the blue powder\"}, {\"id\": 46961, \"name\": \"gray suitcase\"}, {\"id\": 46962, \"name\": \"red spatula\"}, {\"id\": 46963, \"name\": \"white marshmallow\"}, {\"id\": 46964, \"name\": \"volleyball logo\"}, {\"id\": 46965, \"name\": \"a tan wheel\"}, {\"id\": 46966, \"name\": \"red and black boxing glove\"}, {\"id\": 46967, \"name\": \"swirl of white frosting\"}, {\"id\": 46968, \"name\": \"a brown seed\"}, {\"id\": 46969, \"name\": \"a toast\"}, {\"id\": 46970, \"name\": \"the close-up of a man's face\"}, {\"id\": 46971, \"name\": \"a red container of food\"}, {\"id\": 46972, \"name\": \"the diver's air tank\"}, {\"id\": 46973, \"name\": \"head of a teammate\"}, {\"id\": 46974, \"name\": \"the butcher\"}, {\"id\": 46975, \"name\": \"an elderly woman\"}, {\"id\": 46976, \"name\": \"the yellow maraca\"}, {\"id\": 46977, \"name\": \"a lion's paws\"}, {\"id\": 46978, \"name\": \"the red plate\"}, {\"id\": 46979, \"name\": \"the swing rope\"}, {\"id\": 46980, \"name\": \"rolled pancake\"}, {\"id\": 46981, \"name\": \"green ring\"}, {\"id\": 46982, \"name\": \"hand-drawn circle\"}, {\"id\": 46983, \"name\": \"the cooked meat\"}, {\"id\": 46984, \"name\": \"the metal serving pan\"}, {\"id\": 46985, \"name\": \"a white electrical panel\"}, {\"id\": 46986, \"name\": \"the apple design\"}, {\"id\": 46987, \"name\": \"a string of black and white checkered flag\"}, {\"id\": 46988, \"name\": \"an eggplant\"}, {\"id\": 46989, \"name\": \"the pink wing\"}, {\"id\": 46990, \"name\": \"black snowshoe\"}, {\"id\": 46991, \"name\": \"a brown beer bottle\"}, {\"id\": 46992, \"name\": \"the cooked patty\"}, {\"id\": 46993, \"name\": \"the blurred license plate\"}, {\"id\": 46994, \"name\": \"female player\"}, {\"id\": 46995, \"name\": \"a green beer bottle\"}, {\"id\": 46996, \"name\": \"a dog's bowl\"}, {\"id\": 46997, \"name\": \"a calf's white face\"}, {\"id\": 46998, \"name\": \"the speckled brown tail\"}, {\"id\": 46999, \"name\": \"yellow connector\"}, {\"id\": 47000, \"name\": \"a buffet-style table\"}, {\"id\": 47001, \"name\": \"lasso\"}, {\"id\": 47002, \"name\": \"a black hockey skate\"}, {\"id\": 47003, \"name\": \"the food container\"}, {\"id\": 47004, \"name\": \"a vehicle's front end\"}, {\"id\": 47005, \"name\": \"green salad\"}, {\"id\": 47006, \"name\": \"milkshake\"}, {\"id\": 47007, \"name\": \"the mountain goat\"}, {\"id\": 47008, \"name\": \"a carcass's meat\"}, {\"id\": 47009, \"name\": \"the laboratory instrument\"}, {\"id\": 47010, \"name\": \"popsicle\"}, {\"id\": 47011, \"name\": \"a cleaning equipment\"}, {\"id\": 47012, \"name\": \"white and black skate\"}, {\"id\": 47013, \"name\": \"an overhead handle\"}, {\"id\": 47014, \"name\": \"a hubcap\"}, {\"id\": 47015, \"name\": \"the lizard pattern\"}, {\"id\": 47016, \"name\": \"the springbok\"}, {\"id\": 47017, \"name\": \"an app's layout\"}, {\"id\": 47018, \"name\": \"clear bulb\"}, {\"id\": 47019, \"name\": \"the human figure\"}, {\"id\": 47020, \"name\": \"the bread slice\"}, {\"id\": 47021, \"name\": \"the whip\"}, {\"id\": 47022, \"name\": \"blue and yellow paddle\"}, {\"id\": 47023, \"name\": \"a sled's curved frame\"}, {\"id\": 47024, \"name\": \"the croissant\"}, {\"id\": 47025, \"name\": \"a sliced vegetable\"}, {\"id\": 47026, \"name\": \"the mobile phone screen\"}, {\"id\": 47027, \"name\": \"an airfield\"}, {\"id\": 47028, \"name\": \"bag of produce\"}, {\"id\": 47029, \"name\": \"a silver spoke\"}, {\"id\": 47030, \"name\": \"a red and green croc\"}, {\"id\": 47031, \"name\": \"blue, circular weight\"}, {\"id\": 47032, \"name\": \"brownie\"}, {\"id\": 47033, \"name\": \"the pink dumbbell\"}, {\"id\": 47034, \"name\": \"a bowl of sliced tomato\"}, {\"id\": 47035, \"name\": \"a red onion ring\"}, {\"id\": 47036, \"name\": \"an orange pumpkin\"}, {\"id\": 47037, \"name\": \"tuna\"}, {\"id\": 47038, \"name\": \"the scientific equipment\"}, {\"id\": 47039, \"name\": \"the red swimsuit-wearing woman\"}, {\"id\": 47040, \"name\": \"a clay animal\"}, {\"id\": 47041, \"name\": \"a blue cheese cube\"}, {\"id\": 47042, \"name\": \"a blue kickboard\"}, {\"id\": 47043, \"name\": \"a bowl of noodle\"}, {\"id\": 47044, \"name\": \"the roman soldier\"}, {\"id\": 47045, \"name\": \"a bicycle symbol\"}, {\"id\": 47046, \"name\": \"the dog design\"}, {\"id\": 47047, \"name\": \"face of the fox\"}, {\"id\": 47048, \"name\": \"the curved bar\"}, {\"id\": 47049, \"name\": \"the red bell pepper\"}, {\"id\": 47050, \"name\": \"a white bowl of fruit\"}, {\"id\": 47051, \"name\": \"red kite\"}, {\"id\": 47052, \"name\": \"a parked vintage car\"}, {\"id\": 47053, \"name\": \"the rainbow-colored toy\"}, {\"id\": 47054, \"name\": \"pink boxing glove\"}, {\"id\": 47055, \"name\": \"a shredded cabbage and carrot\"}, {\"id\": 47056, \"name\": \"black marker\"}, {\"id\": 47057, \"name\": \"a brown glass bottle of beer\"}, {\"id\": 47058, \"name\": \"the black amplifier\"}, {\"id\": 47059, \"name\": \"a gray knob\"}, {\"id\": 47060, \"name\": \"a metal vent\"}, {\"id\": 47061, \"name\": \"a laboratory equipment\"}, {\"id\": 47062, \"name\": \"the dark-haired man\"}, {\"id\": 47063, \"name\": \"flatbread\"}, {\"id\": 47064, \"name\": \"the cut-out handle\"}, {\"id\": 47065, \"name\": \"brown bottle of beer\"}, {\"id\": 47066, \"name\": \"the boxing ring's ropes\"}, {\"id\": 47067, \"name\": \"a blue skate\"}, {\"id\": 47068, \"name\": \"a slice of tomato\"}, {\"id\": 47069, \"name\": \"strawberry garnish\"}, {\"id\": 47070, \"name\": \"pink mixture\"}, {\"id\": 47071, \"name\": \"a black paddle\"}, {\"id\": 47072, \"name\": \"a gray exercise equipment\"}, {\"id\": 47073, \"name\": \"a kettlebell handle\"}, {\"id\": 47074, \"name\": \"red and yellow streamer\"}, {\"id\": 47075, \"name\": \"the green tomato\"}, {\"id\": 47076, \"name\": \"tempura\"}, {\"id\": 47077, \"name\": \"a network of wi-fi symbol\"}, {\"id\": 47078, \"name\": \"a medieval archer\"}, {\"id\": 47079, \"name\": \"man in a green shirt\"}, {\"id\": 47080, \"name\": \"baking equipment\"}, {\"id\": 47081, \"name\": \"gray placemat\"}, {\"id\": 47082, \"name\": \"the keg\"}, {\"id\": 47083, \"name\": \"the burro\"}, {\"id\": 47084, \"name\": \"the blue swing pole\"}, {\"id\": 47085, \"name\": \"the pregnant woman\"}, {\"id\": 47086, \"name\": \"a head of a chicken\"}, {\"id\": 47087, \"name\": \"the gray croc\"}, {\"id\": 47088, \"name\": \"a green icing\"}, {\"id\": 47089, \"name\": \"the glass of tea\"}, {\"id\": 47090, \"name\": \"a crate of apple\"}, {\"id\": 47091, \"name\": \"a crispy onion ring\"}, {\"id\": 47092, \"name\": \"an onion\"}, {\"id\": 47093, \"name\": \"a person in pink\"}, {\"id\": 47094, \"name\": \"the schoolchild\"}, {\"id\": 47095, \"name\": \"a man in the helmet\"}, {\"id\": 47096, \"name\": \"llama\"}, {\"id\": 47097, \"name\": \"the robotic arm\"}, {\"id\": 47098, \"name\": \"the blurry figure of a person\"}, {\"id\": 47099, \"name\": \"an orange drink\"}, {\"id\": 47100, \"name\": \"the hiking pole\"}, {\"id\": 47101, \"name\": \"a kid\"}, {\"id\": 47102, \"name\": \"a blue exercise tool\"}, {\"id\": 47103, \"name\": \"the glowing yellow bulb\"}, {\"id\": 47104, \"name\": \"neon-colored goggle\"}, {\"id\": 47105, \"name\": \"a circular top\"}, {\"id\": 47106, \"name\": \"red circular toy\"}, {\"id\": 47107, \"name\": \"the lion design\"}, {\"id\": 47108, \"name\": \"white led lighting\"}, {\"id\": 47109, \"name\": \"the blue dumbbell\"}, {\"id\": 47110, \"name\": \"boxing ring's black rope\"}, {\"id\": 47111, \"name\": \"kiwi slice\"}, {\"id\": 47112, \"name\": \"player in a red-and-white jersey\"}, {\"id\": 47113, \"name\": \"a white plate of food\"}, {\"id\": 47114, \"name\": \"plate of pizza\"}, {\"id\": 47115, \"name\": \"a snorkel gear\"}, {\"id\": 47116, \"name\": \"a bird's white head\"}, {\"id\": 47117, \"name\": \"chopped onion\"}, {\"id\": 47118, \"name\": \"yellow bell pepper\"}, {\"id\": 47119, \"name\": \"electronic device\"}, {\"id\": 47120, \"name\": \"the leather shield\"}, {\"id\": 47121, \"name\": \"white onion\"}, {\"id\": 47122, \"name\": \"the red and yellow net\"}, {\"id\": 47123, \"name\": \"a black earbud\"}, {\"id\": 47124, \"name\": \"orange buoy\"}, {\"id\": 47125, \"name\": \"salami\"}, {\"id\": 47126, \"name\": \"a respirator\"}, {\"id\": 47127, \"name\": \"a top rope\"}, {\"id\": 47128, \"name\": \"the quail egg\"}, {\"id\": 47129, \"name\": \"the orange turn signal\"}, {\"id\": 47130, \"name\": \"charcuterie board\"}, {\"id\": 47131, \"name\": \"sliced strawberry\"}, {\"id\": 47132, \"name\": \"the black metal fencing\"}, {\"id\": 47133, \"name\": \"the blurred white van\"}, {\"id\": 47134, \"name\": \"the crate of produce\"}, {\"id\": 47135, \"name\": \"a savory dish\"}, {\"id\": 47136, \"name\": \"a female artist\"}, {\"id\": 47137, \"name\": \"the worn wheel\"}, {\"id\": 47138, \"name\": \"atv rider\"}, {\"id\": 47139, \"name\": \"a red exercise ball\"}, {\"id\": 47140, \"name\": \"waffle\"}, {\"id\": 47141, \"name\": \"a black firearm\"}, {\"id\": 47142, \"name\": \"male figure\"}, {\"id\": 47143, \"name\": \"automatic sliding glass door\"}, {\"id\": 47144, \"name\": \"the red dumbbell\"}, {\"id\": 47145, \"name\": \"the oval-shaped shell\"}, {\"id\": 47146, \"name\": \"white teacup\"}, {\"id\": 47147, \"name\": \"a lime half\"}, {\"id\": 47148, \"name\": \"a pickle\"}, {\"id\": 47149, \"name\": \"floodlight\"}, {\"id\": 47150, \"name\": \"an orange sled\"}, {\"id\": 47151, \"name\": \"a black barbell\"}, {\"id\": 47152, \"name\": \"the raw chicken\"}, {\"id\": 47153, \"name\": \"a bird's brown head\"}, {\"id\": 47154, \"name\": \"wooden ballet barre\"}, {\"id\": 47155, \"name\": \"a white and blue race bib\"}, {\"id\": 47156, \"name\": \"the back seat of a car\"}, {\"id\": 47157, \"name\": \"the garlic braid\"}, {\"id\": 47158, \"name\": \"a cylindrical bulb\"}, {\"id\": 47159, \"name\": \"the bowl of seasoning\"}, {\"id\": 47160, \"name\": \"the brown bagel\"}, {\"id\": 47161, \"name\": \"the blurred yellow car\"}, {\"id\": 47162, \"name\": \"an email icon\"}, {\"id\": 47163, \"name\": \"the red filling\"}, {\"id\": 47164, \"name\": \"a tomato on the vine\"}, {\"id\": 47165, \"name\": \"a man in glass\"}, {\"id\": 47166, \"name\": \"the glass of golden-colored liquid\"}, {\"id\": 47167, \"name\": \"a white semicircle\"}, {\"id\": 47168, \"name\": \"a circular manhole cover\"}, {\"id\": 47169, \"name\": \"the black serving dish\"}, {\"id\": 47170, \"name\": \"the built-in microwave\"}, {\"id\": 47171, \"name\": \"the blue hula hoop\"}, {\"id\": 47172, \"name\": \"the brass-colored hardware\"}, {\"id\": 47173, \"name\": \"square white plate\"}, {\"id\": 47174, \"name\": \"dried orange slice\"}, {\"id\": 47175, \"name\": \"sliced bread\"}, {\"id\": 47176, \"name\": \"a parallel cable\"}, {\"id\": 47177, \"name\": \"mixing bowl\"}, {\"id\": 47178, \"name\": \"the tray of breaded food\"}, {\"id\": 47179, \"name\": \"a cooked fish\"}, {\"id\": 47180, \"name\": \"the mace\"}, {\"id\": 47181, \"name\": \"stainless-steel equipment\"}, {\"id\": 47182, \"name\": \"red exercise mat\"}, {\"id\": 47183, \"name\": \"a pink weight\"}, {\"id\": 47184, \"name\": \"bartender\"}, {\"id\": 47185, \"name\": \"the serving table\"}, {\"id\": 47186, \"name\": \"the stainless steel equipment\"}, {\"id\": 47187, \"name\": \"the sliced kiwi fruit\"}, {\"id\": 47188, \"name\": \"brown beverage\"}, {\"id\": 47189, \"name\": \"the green, circular obstacle\"}, {\"id\": 47190, \"name\": \"club sandwich\"}, {\"id\": 47191, \"name\": \"refreshment\"}, {\"id\": 47192, \"name\": \"the horse-shaped ride\"}, {\"id\": 47193, \"name\": \"a brown baseball mitt\"}, {\"id\": 47194, \"name\": \"a pasture\"}, {\"id\": 47195, \"name\": \"the silver component\"}, {\"id\": 47196, \"name\": \"kite surfer\"}, {\"id\": 47197, \"name\": \"a bob\"}, {\"id\": 47198, \"name\": \"a red oar\"}, {\"id\": 47199, \"name\": \"a black and red boxing glove\"}, {\"id\": 47200, \"name\": \"brown tentacle\"}, {\"id\": 47201, \"name\": \"a yellowish fin\"}, {\"id\": 47202, \"name\": \"the anemone\"}, {\"id\": 47203, \"name\": \"person in a yellow safety vest\"}, {\"id\": 47204, \"name\": \"black and white puppy\"}, {\"id\": 47205, \"name\": \"a red drink\"}, {\"id\": 47206, \"name\": \"the battle rope\"}, {\"id\": 47207, \"name\": \"the cupcake design\"}, {\"id\": 47208, \"name\": \"a curved metal ladder\"}, {\"id\": 47209, \"name\": \"the fruit skewer\"}, {\"id\": 47210, \"name\": \"a lego wheel\"}, {\"id\": 47211, \"name\": \"plate of cake\"}, {\"id\": 47212, \"name\": \"a motorcycle's license plate\"}, {\"id\": 47213, \"name\": \"a black ufc glove\"}, {\"id\": 47214, \"name\": \"a red and white striped candy cane\"}, {\"id\": 47215, \"name\": \"a piece of raw meat\"}, {\"id\": 47216, \"name\": \"the black kitchen tool\"}, {\"id\": 47217, \"name\": \"gyro wrap\"}, {\"id\": 47218, \"name\": \"a yellow-colored vegetable\"}, {\"id\": 47219, \"name\": \"turquoise spatula\"}, {\"id\": 47220, \"name\": \"a soft drink\"}, {\"id\": 47221, \"name\": \"patty\"}, {\"id\": 47222, \"name\": \"the bowl of fruit\"}, {\"id\": 47223, \"name\": \"purple exercise mat\"}, {\"id\": 47224, \"name\": \"rectangular table\"}, {\"id\": 47225, \"name\": \"mud-covered black tire\"}, {\"id\": 47226, \"name\": \"dinosaur\"}, {\"id\": 47227, \"name\": \"wi-fi symbol\"}, {\"id\": 47228, \"name\": \"the dark-colored wheel\"}, {\"id\": 47229, \"name\": \"the sparkling wine\"}, {\"id\": 47230, \"name\": \"a food can\"}, {\"id\": 47231, \"name\": \"the festive meal\"}, {\"id\": 47232, \"name\": \"the man in swimwear\"}, {\"id\": 47233, \"name\": \"yellow dorsal fin\"}, {\"id\": 47234, \"name\": \"red boxing ring rope\"}, {\"id\": 47235, \"name\": \"lollipop\"}, {\"id\": 47236, \"name\": \"the blue rollerblade\"}, {\"id\": 47237, \"name\": \"a person dancing\"}, {\"id\": 47238, \"name\": \"a garlic\"}, {\"id\": 47239, \"name\": \"the bmx bicycle\"}, {\"id\": 47240, \"name\": \"space-suited figure\"}, {\"id\": 47241, \"name\": \"a black mast\"}, {\"id\": 47242, \"name\": \"black roller skate\"}, {\"id\": 47243, \"name\": \"the white bean\"}, {\"id\": 47244, \"name\": \"the gray tire\"}, {\"id\": 47245, \"name\": \"a gym's equipment\"}, {\"id\": 47246, \"name\": \"a weightlifting machine\"}, {\"id\": 47247, \"name\": \"honey\"}, {\"id\": 47248, \"name\": \"a man in a white shirt and pant\"}, {\"id\": 47249, \"name\": \"brown mane\"}, {\"id\": 47250, \"name\": \"yellow food\"}, {\"id\": 47251, \"name\": \"a black lane divider\"}, {\"id\": 47252, \"name\": \"a diving hose\"}, {\"id\": 47253, \"name\": \"well-dressed individual\"}, {\"id\": 47254, \"name\": \"a food particle\"}, {\"id\": 47255, \"name\": \"cracker\"}, {\"id\": 47256, \"name\": \"the sliced cucumber\"}, {\"id\": 47257, \"name\": \"a pointed wing\"}, {\"id\": 47258, \"name\": \"a white filling\"}, {\"id\": 47259, \"name\": \"cart's wheel\"}, {\"id\": 47260, \"name\": \"a ballet bar\"}, {\"id\": 47261, \"name\": \"parallel bar\"}, {\"id\": 47262, \"name\": \"climbing gear\"}, {\"id\": 47263, \"name\": \"the golf club\"}, {\"id\": 47264, \"name\": \"red and white striped cracker\"}, {\"id\": 47265, \"name\": \"the piece of leafy green broccoli\"}, {\"id\": 47266, \"name\": \"the treadmill's controls\"}, {\"id\": 47267, \"name\": \"a rear hatch\"}, {\"id\": 47268, \"name\": \"tray of assorted dessert\"}, {\"id\": 47269, \"name\": \"the safety chain\"}, {\"id\": 47270, \"name\": \"a red tomato\"}, {\"id\": 47271, \"name\": \"pole vaulting pole\"}, {\"id\": 47272, \"name\": \"a white goat\"}, {\"id\": 47273, \"name\": \"a kettlebells\"}, {\"id\": 47274, \"name\": \"the yellow paddle\"}, {\"id\": 47275, \"name\": \"an aerial silk\"}, {\"id\": 47276, \"name\": \"a black ice skate\"}, {\"id\": 47277, \"name\": \"crate of apple\"}, {\"id\": 47278, \"name\": \"a lunge position\"}, {\"id\": 47279, \"name\": \"the safety equipment\"}, {\"id\": 47280, \"name\": \"boxing ring's ropes\"}, {\"id\": 47281, \"name\": \"a piece of farm equipment\"}, {\"id\": 47282, \"name\": \"the clear safety net\"}, {\"id\": 47283, \"name\": \"the bottle of mustard\"}, {\"id\": 47284, \"name\": \"a battle rope\"}, {\"id\": 47285, \"name\": \"inflatable pool toy\"}, {\"id\": 47286, \"name\": \"circular slice of bread\"}, {\"id\": 47287, \"name\": \"man with the beard\"}, {\"id\": 47288, \"name\": \"garnish\"}, {\"id\": 47289, \"name\": \"black foam roller\"}, {\"id\": 47290, \"name\": \"a gym-goer\"}, {\"id\": 47291, \"name\": \"a black circular center\"}, {\"id\": 47292, \"name\": \"rope of the boxing ring\"}, {\"id\": 47293, \"name\": \"the hanging punching bag\"}, {\"id\": 47294, \"name\": \"a plate of raw seafood\"}, {\"id\": 47295, \"name\": \"a camouflage military gear\"}, {\"id\": 47296, \"name\": \"kimchi\"}, {\"id\": 47297, \"name\": \"the black and white boxing glove\"}, {\"id\": 47298, \"name\": \"white swirl\"}, {\"id\": 47299, \"name\": \"a red decorative ball\"}, {\"id\": 47300, \"name\": \"the silver metal component\"}, {\"id\": 47301, \"name\": \"the pasture\"}, {\"id\": 47302, \"name\": \"black kitchen appliance\"}, {\"id\": 47303, \"name\": \"a cured meat\"}, {\"id\": 47304, \"name\": \"white tuft of feather\"}, {\"id\": 47305, \"name\": \"an orange liquid\"}, {\"id\": 47306, \"name\": \"college graduate\"}, {\"id\": 47307, \"name\": \"the rope swing\"}, {\"id\": 47308, \"name\": \"a stainless steel vent\"}, {\"id\": 47309, \"name\": \"the divers' tank\"}, {\"id\": 47310, \"name\": \"the floating platform\"}, {\"id\": 47311, \"name\": \"elevated highway\"}, {\"id\": 47312, \"name\": \"the blurred person's face\"}, {\"id\": 47313, \"name\": \"a pointed dorsal fin\"}, {\"id\": 47314, \"name\": \"a spoiler\"}, {\"id\": 47315, \"name\": \"the pilate mat\"}, {\"id\": 47316, \"name\": \"black exercise equipment\"}, {\"id\": 47317, \"name\": \"digital billboard\"}, {\"id\": 47318, \"name\": \"a rounded helmet\"}, {\"id\": 47319, \"name\": \"the bottled liquid\"}, {\"id\": 47320, \"name\": \"purple tail\"}, {\"id\": 47321, \"name\": \"seat of the bike\"}, {\"id\": 47322, \"name\": \"a cut hay\"}, {\"id\": 47323, \"name\": \"red skid plate\"}, {\"id\": 47324, \"name\": \"hour and minute hand\"}, {\"id\": 47325, \"name\": \"a balance beam\"}, {\"id\": 47326, \"name\": \"a tuft of feather\"}, {\"id\": 47327, \"name\": \"diving equipment\"}, {\"id\": 47328, \"name\": \"a cooking ingredient\"}, {\"id\": 47329, \"name\": \"viking warrior\"}, {\"id\": 47330, \"name\": \"a logo of a person with their arm raised above their head\"}, {\"id\": 47331, \"name\": \"the formally dressed individual\"}, {\"id\": 47332, \"name\": \"the male player\"}, {\"id\": 47333, \"name\": \"walnut\"}, {\"id\": 47334, \"name\": \"potato package\"}, {\"id\": 47335, \"name\": \"the circular hoop\"}, {\"id\": 47336, \"name\": \"the ice hockey player\"}, {\"id\": 47337, \"name\": \"the black and white striped wing\"}, {\"id\": 47338, \"name\": \"a white plate of bread\"}, {\"id\": 47339, \"name\": \"the ski lift's cable\"}, {\"id\": 47340, \"name\": \"a layer of rice\"}, {\"id\": 47341, \"name\": \"red pepperoni\"}, {\"id\": 47342, \"name\": \"the piece of luggage\"}, {\"id\": 47343, \"name\": \"a plastic-wrapped bag of green vegetable\"}, {\"id\": 47344, \"name\": \"the paper airplane\"}, {\"id\": 47345, \"name\": \"the blue ice skate\"}, {\"id\": 47346, \"name\": \"orange liquid\"}, {\"id\": 47347, \"name\": \"the grid of bubble\"}, {\"id\": 47348, \"name\": \"a raspberry\"}, {\"id\": 47349, \"name\": \"the food preparation container\"}, {\"id\": 47350, \"name\": \"a blue racket\"}, {\"id\": 47351, \"name\": \"a radish slice\"}, {\"id\": 47352, \"name\": \"a driverless\"}, {\"id\": 47353, \"name\": \"nutmeg\"}, {\"id\": 47354, \"name\": \"a yellow arch\"}, {\"id\": 47355, \"name\": \"a top lane\"}, {\"id\": 47356, \"name\": \"the handlebar bag\"}, {\"id\": 47357, \"name\": \"black cable\"}, {\"id\": 47358, \"name\": \"a grain chute\"}, {\"id\": 47359, \"name\": \"seafood\"}, {\"id\": 47360, \"name\": \"the yellow drink\"}, {\"id\": 47361, \"name\": \"silver handlebar\"}, {\"id\": 47362, \"name\": \"the yellow swing\"}, {\"id\": 47363, \"name\": \"the iridescent wing\"}, {\"id\": 47364, \"name\": \"the star anise pod\"}, {\"id\": 47365, \"name\": \"the swimming lap\"}, {\"id\": 47366, \"name\": \"a circular bulb\"}, {\"id\": 47367, \"name\": \"white ambulance\"}, {\"id\": 47368, \"name\": \"the woman playing soccer\"}, {\"id\": 47369, \"name\": \"laboratory equipment\"}, {\"id\": 47370, \"name\": \"the silver dumbbell\"}, {\"id\": 47371, \"name\": \"the statue of musical instrument\"}, {\"id\": 47372, \"name\": \"a metal-framed picture\"}, {\"id\": 47373, \"name\": \"a cufflink\"}, {\"id\": 47374, \"name\": \"the ankle\"}, {\"id\": 47375, \"name\": \"the mitten\"}, {\"id\": 47376, \"name\": \"an ambulance\"}, {\"id\": 47377, \"name\": \"triangular top\"}, {\"id\": 47378, \"name\": \"wallet\"}, {\"id\": 47379, \"name\": \"the orange luggage\"}, {\"id\": 47380, \"name\": \"the purple jacket\"}, {\"id\": 47381, \"name\": \"metal sculpture\"}, {\"id\": 47382, \"name\": \"purple tattoo\"}, {\"id\": 47383, \"name\": \"the green purse\"}, {\"id\": 47384, \"name\": \"an iron door\"}, {\"id\": 47385, \"name\": \"lattice\"}, {\"id\": 47386, \"name\": \"green canister\"}, {\"id\": 47387, \"name\": \"the sideburn\"}, {\"id\": 47388, \"name\": \"the plastic cord\"}, {\"id\": 47389, \"name\": \"a dishwasher\"}, {\"id\": 47390, \"name\": \"a kitchen table\"}, {\"id\": 47391, \"name\": \"bronze section\"}, {\"id\": 47392, \"name\": \"the paper snowflake\"}, {\"id\": 47393, \"name\": \"a binder\"}, {\"id\": 47394, \"name\": \"shoe hook\"}, {\"id\": 47395, \"name\": \"a steel column\"}, {\"id\": 47396, \"name\": \"an iron nail\"}, {\"id\": 47397, \"name\": \"the metal statue\"}, {\"id\": 47398, \"name\": \"a bag of plum\"}, {\"id\": 47399, \"name\": \"a post office\"}, {\"id\": 47400, \"name\": \"engineer\"}, {\"id\": 47401, \"name\": \"the purple mulch\"}, {\"id\": 47402, \"name\": \"the alley\"}, {\"id\": 47403, \"name\": \"the iron\"}, {\"id\": 47404, \"name\": \"white purse\"}, {\"id\": 47405, \"name\": \"the eraser\"}, {\"id\": 47406, \"name\": \"a bronze packaging\"}, {\"id\": 47407, \"name\": \"saucepan\"}, {\"id\": 47408, \"name\": \"the fresco\"}, {\"id\": 47409, \"name\": \"gold sign\"}, {\"id\": 47410, \"name\": \"fabric banner\"}, {\"id\": 47411, \"name\": \"the metal figurine\"}, {\"id\": 47412, \"name\": \"aluminum barrier\"}, {\"id\": 47413, \"name\": \"wooden charging stand\"}, {\"id\": 47414, \"name\": \"fabric cover\"}, {\"id\": 47415, \"name\": \"the flower stem\"}, {\"id\": 47416, \"name\": \"the lithograph\"}, {\"id\": 47417, \"name\": \"a blush\"}, {\"id\": 47418, \"name\": \"a can of soup\"}, {\"id\": 47419, \"name\": \"a sideburn\"}, {\"id\": 47420, \"name\": \"the gold sticker\"}, {\"id\": 47421, \"name\": \"cityscape\"}, {\"id\": 47422, \"name\": \"leather-framed picture\"}, {\"id\": 47423, \"name\": \"a wooden vase\"}, {\"id\": 47424, \"name\": \"bread crumb\"}, {\"id\": 47425, \"name\": \"a paperweight\"}, {\"id\": 47426, \"name\": \"the bronze statue\"}, {\"id\": 47427, \"name\": \"the bookcase\"}, {\"id\": 47428, \"name\": \"a gold handbag\"}, {\"id\": 47429, \"name\": \"aluminum\"}, {\"id\": 47430, \"name\": \"a brown and beige timberland boot\"}, {\"id\": 47431, \"name\": \"a blue ornament\"}, {\"id\": 47432, \"name\": \"an orange advertisement banner\"}, {\"id\": 47433, \"name\": \"valley\"}, {\"id\": 47434, \"name\": \"silver vase\"}, {\"id\": 47435, \"name\": \"a square trash can\"}, {\"id\": 47436, \"name\": \"the statue of a superhero\"}, {\"id\": 47437, \"name\": \"petal\"}, {\"id\": 47438, \"name\": \"a fabric pedestal\"}, {\"id\": 47439, \"name\": \"tape\"}, {\"id\": 47440, \"name\": \"the black paper\"}, {\"id\": 47441, \"name\": \"a veranda\"}, {\"id\": 47442, \"name\": \"the steel fence\"}, {\"id\": 47443, \"name\": \"the newspaper stand\"}, {\"id\": 47444, \"name\": \"the orange oval\"}, {\"id\": 47445, \"name\": \"the e-reader\"}, {\"id\": 47446, \"name\": \"clutch\"}, {\"id\": 47447, \"name\": \"fabric tapestry\"}, {\"id\": 47448, \"name\": \"the brown nail polish\"}, {\"id\": 47449, \"name\": \"cookie jar\"}, {\"id\": 47450, \"name\": \"an oven mitt\"}, {\"id\": 47451, \"name\": \"ponytail holder\"}, {\"id\": 47452, \"name\": \"the gold bag\"}, {\"id\": 47453, \"name\": \"radio station logo\"}, {\"id\": 47454, \"name\": \"the flower pot\"}, {\"id\": 47455, \"name\": \"the multimeter\"}, {\"id\": 47456, \"name\": \"a copper plug\"}, {\"id\": 47457, \"name\": \"the detour sign\"}, {\"id\": 47458, \"name\": \"kitchen island\"}, {\"id\": 47459, \"name\": \"a metal pedestal\"}, {\"id\": 47460, \"name\": \"the sugar packet\"}, {\"id\": 47461, \"name\": \"a paper sack\"}, {\"id\": 47462, \"name\": \"a gaming console\"}, {\"id\": 47463, \"name\": \"tapestry\"}, {\"id\": 47464, \"name\": \"stone shelf\"}, {\"id\": 47465, \"name\": \"a purple and pink van sneaker\"}, {\"id\": 47466, \"name\": \"marble statue\"}, {\"id\": 47467, \"name\": \"the metal pier\"}, {\"id\": 47468, \"name\": \"the nail glue\"}, {\"id\": 47469, \"name\": \"an artificial flower\"}, {\"id\": 47470, \"name\": \"a caution\"}, {\"id\": 47471, \"name\": \"the fuse box\"}, {\"id\": 47472, \"name\": \"landscape\"}, {\"id\": 47473, \"name\": \"the silver taillight\"}, {\"id\": 47474, \"name\": \"the plastic table\"}, {\"id\": 47475, \"name\": \"a root\"}, {\"id\": 47476, \"name\": \"the iron cabinet\"}, {\"id\": 47477, \"name\": \"a solid blue background\"}, {\"id\": 47478, \"name\": \"the bust\"}, {\"id\": 47479, \"name\": \"the dump truck\"}, {\"id\": 47480, \"name\": \"the silver clip\"}, {\"id\": 47481, \"name\": \"the image of car\"}, {\"id\": 47482, \"name\": \"a purple van\"}, {\"id\": 47483, \"name\": \"a silhouette of a dog\"}, {\"id\": 47484, \"name\": \"the fabric label\"}, {\"id\": 47485, \"name\": \"ceramic mug\"}, {\"id\": 47486, \"name\": \"the stone door\"}, {\"id\": 47487, \"name\": \"a video game cover\"}, {\"id\": 47488, \"name\": \"an italian flag\"}, {\"id\": 47489, \"name\": \"the hand truck\"}, {\"id\": 47490, \"name\": \"orange-colored sign\"}, {\"id\": 47491, \"name\": \"fabric pouch\"}, {\"id\": 47492, \"name\": \"woman's hat\"}, {\"id\": 47493, \"name\": \"the concrete foundation\"}, {\"id\": 47494, \"name\": \"a coffee table\"}, {\"id\": 47495, \"name\": \"an alien's hands\"}, {\"id\": 47496, \"name\": \"the brooch\"}, {\"id\": 47497, \"name\": \"copper price tag\"}, {\"id\": 47498, \"name\": \"the metal colander\"}, {\"id\": 47499, \"name\": \"silver purse\"}, {\"id\": 47500, \"name\": \"external hard drive\"}, {\"id\": 47501, \"name\": \"statue of sport equipment\"}, {\"id\": 47502, \"name\": \"the marble box\"}, {\"id\": 47503, \"name\": \"the valance\"}, {\"id\": 47504, \"name\": \"a gerbil\"}, {\"id\": 47505, \"name\": \"the paper cup\"}, {\"id\": 47506, \"name\": \"plastic pedestal\"}, {\"id\": 47507, \"name\": \"a purple sign\"}, {\"id\": 47508, \"name\": \"the bank sign\"}, {\"id\": 47509, \"name\": \"book title\"}, {\"id\": 47510, \"name\": \"litter box\"}, {\"id\": 47511, \"name\": \"steel door\"}, {\"id\": 47512, \"name\": \"the tile wall\"}, {\"id\": 47513, \"name\": \"a seatbelt\"}, {\"id\": 47514, \"name\": \"image of animal\"}, {\"id\": 47515, \"name\": \"statue of a griffin\"}, {\"id\": 47516, \"name\": \"the roll of label\"}, {\"id\": 47517, \"name\": \"a gold sign\"}, {\"id\": 47518, \"name\": \"the plum\"}, {\"id\": 47519, \"name\": \"the stone mannequin\"}, {\"id\": 47520, \"name\": \"the living room couch\"}, {\"id\": 47521, \"name\": \"piano\"}, {\"id\": 47522, \"name\": \"a bronze arrow\"}, {\"id\": 47523, \"name\": \"a blue outlet\"}, {\"id\": 47524, \"name\": \"a bedroom\"}, {\"id\": 47525, \"name\": \"the mosaic\"}, {\"id\": 47526, \"name\": \"the book's reflection\"}, {\"id\": 47527, \"name\": \"the gray sock\"}, {\"id\": 47528, \"name\": \"the petal\"}, {\"id\": 47529, \"name\": \"the copper plaque\"}, {\"id\": 47530, \"name\": \"silver box\"}, {\"id\": 47531, \"name\": \"a gold necklace\"}, {\"id\": 47532, \"name\": \"binoculars\"}, {\"id\": 47533, \"name\": \"the tunnel\"}, {\"id\": 47534, \"name\": \"stadium\"}, {\"id\": 47535, \"name\": \"the tile roof\"}, {\"id\": 47536, \"name\": \"a flute\"}, {\"id\": 47537, \"name\": \"hair clip\"}, {\"id\": 47538, \"name\": \"the metal lamp post\"}, {\"id\": 47539, \"name\": \"festive christmas wreath\"}, {\"id\": 47540, \"name\": \"the kitchen island\"}, {\"id\": 47541, \"name\": \"concrete plaque\"}, {\"id\": 47542, \"name\": \"a statue of a god\"}, {\"id\": 47543, \"name\": \"the paper wall\"}, {\"id\": 47544, \"name\": \"a ceramic bottle opener\"}, {\"id\": 47545, \"name\": \"silver archway\"}, {\"id\": 47546, \"name\": \"a porcelain doll\"}, {\"id\": 47547, \"name\": \"the wallet\"}, {\"id\": 47548, \"name\": \"the pink and purple purse\"}, {\"id\": 47549, \"name\": \"the plastic tube\"}, {\"id\": 47550, \"name\": \"the purple satchel\"}, {\"id\": 47551, \"name\": \"heater\"}, {\"id\": 47552, \"name\": \"the wooden house\"}, {\"id\": 47553, \"name\": \"a diamond shape\"}, {\"id\": 47554, \"name\": \"a mist\"}, {\"id\": 47555, \"name\": \"a leather belt\"}, {\"id\": 47556, \"name\": \"a dining table\"}, {\"id\": 47557, \"name\": \"a nail strengthener\"}, {\"id\": 47558, \"name\": \"persimmon\"}, {\"id\": 47559, \"name\": \"a revolving gate\"}, {\"id\": 47560, \"name\": \"a gray motorcycle\"}, {\"id\": 47561, \"name\": \"airplane wing\"}, {\"id\": 47562, \"name\": \"the fabric banner\"}, {\"id\": 47563, \"name\": \"a wool sweater\"}, {\"id\": 47564, \"name\": \"the notebook cover\"}, {\"id\": 47565, \"name\": \"a gray sock\"}, {\"id\": 47566, \"name\": \"velvet pillow\"}, {\"id\": 47567, \"name\": \"goatee\"}, {\"id\": 47568, \"name\": \"a fabric-framed picture\"}, {\"id\": 47569, \"name\": \"bronze plaque\"}, {\"id\": 47570, \"name\": \"saucer\"}, {\"id\": 47571, \"name\": \"blue and orange new balance sneaker\"}, {\"id\": 47572, \"name\": \"a living room furniture\"}, {\"id\": 47573, \"name\": \"the blue wallet\"}, {\"id\": 47574, \"name\": \"the razor\"}, {\"id\": 47575, \"name\": \"the parked taxi\"}, {\"id\": 47576, \"name\": \"cotton shirt\"}, {\"id\": 47577, \"name\": \"the subway\"}, {\"id\": 47578, \"name\": \"the russian flag\"}, {\"id\": 47579, \"name\": \"a statue of electronic device\"}, {\"id\": 47580, \"name\": \"a chinese flag\"}, {\"id\": 47581, \"name\": \"stainless steel fork\"}, {\"id\": 47582, \"name\": \"a ceramic pedestal\"}, {\"id\": 47583, \"name\": \"roll of bill\"}, {\"id\": 47584, \"name\": \"yellow poster\"}, {\"id\": 47585, \"name\": \"a rubber band\"}, {\"id\": 47586, \"name\": \"the iron beam\"}, {\"id\": 47587, \"name\": \"metal shopping bag\"}, {\"id\": 47588, \"name\": \"the silver and gold jewelry\"}, {\"id\": 47589, \"name\": \"the museum\"}, {\"id\": 47590, \"name\": \"the glass table\"}, {\"id\": 47591, \"name\": \"parked gray helicopter\"}, {\"id\": 47592, \"name\": \"a leather wallet\"}, {\"id\": 47593, \"name\": \"the copper mannequin\"}, {\"id\": 47594, \"name\": \"the iron pedestal\"}, {\"id\": 47595, \"name\": \"a leather couch\"}, {\"id\": 47596, \"name\": \"a silver door\"}, {\"id\": 47597, \"name\": \"a gray backpack\"}, {\"id\": 47598, \"name\": \"purple box\"}, {\"id\": 47599, \"name\": \"stainless steel countertop\"}, {\"id\": 47600, \"name\": \"the purple price tag\"}, {\"id\": 47601, \"name\": \"the octagonal top\"}, {\"id\": 47602, \"name\": \"the eyeliner\"}, {\"id\": 47603, \"name\": \"granite floor\"}, {\"id\": 47604, \"name\": \"a webcam\"}, {\"id\": 47605, \"name\": \"metal mannequin\"}, {\"id\": 47606, \"name\": \"the webcam\"}, {\"id\": 47607, \"name\": \"the metal clip\"}, {\"id\": 47608, \"name\": \"hdmi cable\"}, {\"id\": 47609, \"name\": \"the leather shopping bag\"}, {\"id\": 47610, \"name\": \"a stone box\"}, {\"id\": 47611, \"name\": \"the silhouette of a dog\"}, {\"id\": 47612, \"name\": \"plastic bottle opener\"}, {\"id\": 47613, \"name\": \"gym\"}, {\"id\": 47614, \"name\": \"grapefruit slice\"}, {\"id\": 47615, \"name\": \"a phone booth\"}, {\"id\": 47616, \"name\": \"the backhoe\"}, {\"id\": 47617, \"name\": \"a steel rod\"}, {\"id\": 47618, \"name\": \"the juice box\"}, {\"id\": 47619, \"name\": \"the copper sign\"}, {\"id\": 47620, \"name\": \"elliptical cutout\"}, {\"id\": 47621, \"name\": \"the astronaut\"}, {\"id\": 47622, \"name\": \"the plastic chair\"}, {\"id\": 47623, \"name\": \"the pompadour\"}, {\"id\": 47624, \"name\": \"a paper plate\"}, {\"id\": 47625, \"name\": \"brown and beige geometric pattern\"}, {\"id\": 47626, \"name\": \"a beer mug\"}, {\"id\": 47627, \"name\": \"tile wall\"}, {\"id\": 47628, \"name\": \"paper sign\"}, {\"id\": 47629, \"name\": \"a sharpener\"}, {\"id\": 47630, \"name\": \"a statue of athlete\"}, {\"id\": 47631, \"name\": \"a wooden appliance\"}, {\"id\": 47632, \"name\": \"the lizard\"}, {\"id\": 47633, \"name\": \"the metal curtain\"}, {\"id\": 47634, \"name\": \"the hat hook\"}, {\"id\": 47635, \"name\": \"the purple sedan\"}, {\"id\": 47636, \"name\": \"packet of sugar\"}, {\"id\": 47637, \"name\": \"silver bell\"}, {\"id\": 47638, \"name\": \"teacher's hand\"}, {\"id\": 47639, \"name\": \"a russian flag\"}, {\"id\": 47640, \"name\": \"metal vase\"}, {\"id\": 47641, \"name\": \"desert\"}, {\"id\": 47642, \"name\": \"purple sign\"}, {\"id\": 47643, \"name\": \"a silver crown\"}, {\"id\": 47644, \"name\": \"veranda\"}, {\"id\": 47645, \"name\": \"a quiff\"}, {\"id\": 47646, \"name\": \"the pink phone\"}, {\"id\": 47647, \"name\": \"apple logo\"}, {\"id\": 47648, \"name\": \"a journal\"}, {\"id\": 47649, \"name\": \"a jar of jam\"}, {\"id\": 47650, \"name\": \"the glass shelf\"}, {\"id\": 47651, \"name\": \"a stained glass\"}, {\"id\": 47652, \"name\": \"bookend\"}, {\"id\": 47653, \"name\": \"velvet bow\"}, {\"id\": 47654, \"name\": \"submarine\"}, {\"id\": 47655, \"name\": \"a luggage cart\"}, {\"id\": 47656, \"name\": \"the turquoise table\"}, {\"id\": 47657, \"name\": \"a subway entrance\"}, {\"id\": 47658, \"name\": \"a wooden cane\"}, {\"id\": 47659, \"name\": \"fabric ribbon\"}, {\"id\": 47660, \"name\": \"silver nail polish\"}, {\"id\": 47661, \"name\": \"metal column\"}, {\"id\": 47662, \"name\": \"a fig\"}, {\"id\": 47663, \"name\": \"wooden cane\"}, {\"id\": 47664, \"name\": \"a stainle steel sink\"}, {\"id\": 47665, \"name\": \"a telephone booth\"}, {\"id\": 47666, \"name\": \"the stand mixer\"}, {\"id\": 47667, \"name\": \"an iron skillet\"}, {\"id\": 47668, \"name\": \"the green-and-yellow abstract painting\"}, {\"id\": 47669, \"name\": \"the glass cabinet\"}, {\"id\": 47670, \"name\": \"a watercolor\"}, {\"id\": 47671, \"name\": \"trapezoidal top\"}, {\"id\": 47672, \"name\": \"the baby's face\"}, {\"id\": 47673, \"name\": \"a purple bus\"}, {\"id\": 47674, \"name\": \"the pinterest logo\"}, {\"id\": 47675, \"name\": \"a tea infuser\"}, {\"id\": 47676, \"name\": \"choir\"}, {\"id\": 47677, \"name\": \"the copper packaging\"}, {\"id\": 47678, \"name\": \"a stand mixer\"}, {\"id\": 47679, \"name\": \"hurricane\"}, {\"id\": 47680, \"name\": \"ribcage\"}, {\"id\": 47681, \"name\": \"the saltshaker\"}, {\"id\": 47682, \"name\": \"the leather wallet\"}, {\"id\": 47683, \"name\": \"gold traffic cone\"}, {\"id\": 47684, \"name\": \"gold limousine\"}, {\"id\": 47685, \"name\": \"a collage\"}, {\"id\": 47686, \"name\": \"purple awning\"}, {\"id\": 47687, \"name\": \"the paper plate\"}, {\"id\": 47688, \"name\": \"papaya\"}, {\"id\": 47689, \"name\": \"glass coffee machine\"}, {\"id\": 47690, \"name\": \"a stamp\"}, {\"id\": 47691, \"name\": \"a wall art\"}, {\"id\": 47692, \"name\": \"a parked boat\"}, {\"id\": 47693, \"name\": \"a silver candle\"}, {\"id\": 47694, \"name\": \"drain pipe\"}, {\"id\": 47695, \"name\": \"the roll of vinyl\"}, {\"id\": 47696, \"name\": \"a wooden fork\"}, {\"id\": 47697, \"name\": \"toaster\"}, {\"id\": 47698, \"name\": \"the hot dog vendor\"}, {\"id\": 47699, \"name\": \"a soloist\"}, {\"id\": 47700, \"name\": \"a mosaic\"}, {\"id\": 47701, \"name\": \"a stewed chicken part\"}, {\"id\": 47702, \"name\": \"bronze statue\"}, {\"id\": 47703, \"name\": \"a gray glove\"}, {\"id\": 47704, \"name\": \"the fabric box\"}, {\"id\": 47705, \"name\": \"a hot chocolate\"}, {\"id\": 47706, \"name\": \"revolving door\"}, {\"id\": 47707, \"name\": \"a silhouette\"}, {\"id\": 47708, \"name\": \"the engineer\"}, {\"id\": 47709, \"name\": \"pink purse\"}, {\"id\": 47710, \"name\": \"a peach slice\"}, {\"id\": 47711, \"name\": \"the statue of food\"}, {\"id\": 47712, \"name\": \"a stainles steel bottless opener\"}, {\"id\": 47713, \"name\": \"silk fabric\"}, {\"id\": 47714, \"name\": \"a metal staircase\"}, {\"id\": 47715, \"name\": \"museum\"}, {\"id\": 47716, \"name\": \"a brown van\"}, {\"id\": 47717, \"name\": \"stamp\"}, {\"id\": 47718, \"name\": \"a woodcut\"}, {\"id\": 47719, \"name\": \"the wooden plant\"}, {\"id\": 47720, \"name\": \"brown belt\"}, {\"id\": 47721, \"name\": \"leather belt\"}, {\"id\": 47722, \"name\": \"the bronze wallet\"}, {\"id\": 47723, \"name\": \"wooden statue\"}, {\"id\": 47724, \"name\": \"a reader\"}, {\"id\": 47725, \"name\": \"the kitchen table\"}, {\"id\": 47726, \"name\": \"the octagon shape\"}, {\"id\": 47727, \"name\": \"a telehandler\"}, {\"id\": 47728, \"name\": \"gold nail\"}, {\"id\": 47729, \"name\": \"copper wire\"}, {\"id\": 47730, \"name\": \"a desktop\"}, {\"id\": 47731, \"name\": \"a calculator\"}, {\"id\": 47732, \"name\": \"archipelago\"}, {\"id\": 47733, \"name\": \"a bag of coffee\"}, {\"id\": 47734, \"name\": \"leather wallet\"}, {\"id\": 47735, \"name\": \"the bus stop\"}, {\"id\": 47736, \"name\": \"purple taxi\"}, {\"id\": 47737, \"name\": \"a leather backpack\"}, {\"id\": 47738, \"name\": \"an envelope\"}, {\"id\": 47739, \"name\": \"lead screw\"}, {\"id\": 47740, \"name\": \"plastic shelf\"}, {\"id\": 47741, \"name\": \"the gas station\"}, {\"id\": 47742, \"name\": \"the bathroom\"}, {\"id\": 47743, \"name\": \"the glue\"}, {\"id\": 47744, \"name\": \"the hot dog cart\"}, {\"id\": 47745, \"name\": \"green backpack\"}, {\"id\": 47746, \"name\": \"a fresco\"}, {\"id\": 47747, \"name\": \"statue of a demon\"}, {\"id\": 47748, \"name\": \"pizza parlor\"}, {\"id\": 47749, \"name\": \"the hospital\"}, {\"id\": 47750, \"name\": \"a tile wall\"}, {\"id\": 47751, \"name\": \"tile door frame\"}, {\"id\": 47752, \"name\": \"leather strap\"}, {\"id\": 47753, \"name\": \"a sleet\"}, {\"id\": 47754, \"name\": \"a silver price tag\"}, {\"id\": 47755, \"name\": \"a velvet coat\"}, {\"id\": 47756, \"name\": \"the silver briefcase\"}, {\"id\": 47757, \"name\": \"a glass box\"}, {\"id\": 47758, \"name\": \"elf\"}, {\"id\": 47759, \"name\": \"a copper sticker\"}, {\"id\": 47760, \"name\": \"tea infuser\"}, {\"id\": 47761, \"name\": \"door hook\"}, {\"id\": 47762, \"name\": \"the office supply\"}, {\"id\": 47763, \"name\": \"horse saddle\"}, {\"id\": 47764, \"name\": \"the fabric couch\"}, {\"id\": 47765, \"name\": \"a red license plate\"}, {\"id\": 47766, \"name\": \"the sushi roll\"}, {\"id\": 47767, \"name\": \"shower\"}, {\"id\": 47768, \"name\": \"an end table\"}, {\"id\": 47769, \"name\": \"the mexican flag\"}, {\"id\": 47770, \"name\": \"a bicycle rack\"}, {\"id\": 47771, \"name\": \"the bicycle helmet\"}, {\"id\": 47772, \"name\": \"a fabric napkin\"}, {\"id\": 47773, \"name\": \"the lyricist\"}, {\"id\": 47774, \"name\": \"a fabric railing\"}, {\"id\": 47775, \"name\": \"silver plate\"}, {\"id\": 47776, \"name\": \"the yellow purse\"}, {\"id\": 47777, \"name\": \"tunnel\"}, {\"id\": 47778, \"name\": \"the grey elephant\"}, {\"id\": 47779, \"name\": \"the orange bicycle\"}, {\"id\": 47780, \"name\": \"a professor\"}, {\"id\": 47781, \"name\": \"the red keyboard\"}, {\"id\": 47782, \"name\": \"the stone floor\"}, {\"id\": 47783, \"name\": \"taco bell\"}, {\"id\": 47784, \"name\": \"the electrical tape\"}, {\"id\": 47785, \"name\": \"parking meter\"}, {\"id\": 47786, \"name\": \"the stencil art\"}, {\"id\": 47787, \"name\": \"blue stone\"}, {\"id\": 47788, \"name\": \"the green packaging\"}, {\"id\": 47789, \"name\": \"french flag\"}, {\"id\": 47790, \"name\": \"a stone door frame\"}, {\"id\": 47791, \"name\": \"the paperclip\"}, {\"id\": 47792, \"name\": \"a heart-shaped cutout\"}, {\"id\": 47793, \"name\": \"a bronze door\"}, {\"id\": 47794, \"name\": \"the statue of a dragon\"}, {\"id\": 47795, \"name\": \"parked ambulance\"}, {\"id\": 47796, \"name\": \"a port\"}, {\"id\": 47797, \"name\": \"the oval cutout\"}, {\"id\": 47798, \"name\": \"stone arch\"}, {\"id\": 47799, \"name\": \"the paper pedestal\"}, {\"id\": 47800, \"name\": \"a pentagon shape\"}, {\"id\": 47801, \"name\": \"black price tag\"}, {\"id\": 47802, \"name\": \"the conceptual art\"}, {\"id\": 47803, \"name\": \"a metal column\"}, {\"id\": 47804, \"name\": \"the airbag\"}, {\"id\": 47805, \"name\": \"the box of cereal\"}, {\"id\": 47806, \"name\": \"the roll of stamp\"}, {\"id\": 47807, \"name\": \"plastic fork\"}, {\"id\": 47808, \"name\": \"a pink and purple geometric pattern\"}, {\"id\": 47809, \"name\": \"a gray bag\"}, {\"id\": 47810, \"name\": \"the glass bowl\"}, {\"id\": 47811, \"name\": \"red advertisement banner\"}, {\"id\": 47812, \"name\": \"a yellow brick\"}, {\"id\": 47813, \"name\": \"a foundation\"}, {\"id\": 47814, \"name\": \"the paper napkin\"}, {\"id\": 47815, \"name\": \"the silver sign\"}, {\"id\": 47816, \"name\": \"a gold screw\"}, {\"id\": 47817, \"name\": \"pink sock\"}, {\"id\": 47818, \"name\": \"the nail buffer\"}, {\"id\": 47819, \"name\": \"a pink phone case\"}, {\"id\": 47820, \"name\": \"mexican flag\"}, {\"id\": 47821, \"name\": \"the tree stump\"}, {\"id\": 47822, \"name\": \"the ruler\"}, {\"id\": 47823, \"name\": \"the pear\"}, {\"id\": 47824, \"name\": \"ankle\"}, {\"id\": 47825, \"name\": \"the rhombu top\"}, {\"id\": 47826, \"name\": \"the abdoman\"}, {\"id\": 47827, \"name\": \"concrete vase\"}, {\"id\": 47828, \"name\": \"the ambulance light\"}, {\"id\": 47829, \"name\": \"the orange airplane\"}, {\"id\": 47830, \"name\": \"a pear\"}, {\"id\": 47831, \"name\": \"roadblock\"}, {\"id\": 47832, \"name\": \"radio\"}, {\"id\": 47833, \"name\": \"a copper plate\"}, {\"id\": 47834, \"name\": \"the file\"}, {\"id\": 47835, \"name\": \"a concrete cabinet\"}, {\"id\": 47836, \"name\": \"the steel gate\"}, {\"id\": 47837, \"name\": \"a metal basket\"}, {\"id\": 47838, \"name\": \"the kitchen shears\"}, {\"id\": 47839, \"name\": \"the orange chair\"}, {\"id\": 47840, \"name\": \"greek flag\"}, {\"id\": 47841, \"name\": \"wool\"}, {\"id\": 47842, \"name\": \"an opaque vase\"}, {\"id\": 47843, \"name\": \"the moon\"}, {\"id\": 47844, \"name\": \"modem\"}, {\"id\": 47845, \"name\": \"a green and yellow reebok sneaker\"}, {\"id\": 47846, \"name\": \"the solid blue background\"}, {\"id\": 47847, \"name\": \"the bronze sign\"}, {\"id\": 47848, \"name\": \"a nurse\"}, {\"id\": 47849, \"name\": \"wire nut\"}, {\"id\": 47850, \"name\": \"a purple motorcycle\"}, {\"id\": 47851, \"name\": \"orange compost\"}, {\"id\": 47852, \"name\": \"raindrop\"}, {\"id\": 47853, \"name\": \"the silver traffic cone\"}, {\"id\": 47854, \"name\": \"the sport drink\"}, {\"id\": 47855, \"name\": \"gold taillight\"}, {\"id\": 47856, \"name\": \"plastic spoon\"}, {\"id\": 47857, \"name\": \"image of child\"}, {\"id\": 47858, \"name\": \"a green moss\"}, {\"id\": 47859, \"name\": \"a mexican flag\"}, {\"id\": 47860, \"name\": \"slow cooker\"}, {\"id\": 47861, \"name\": \"a silver box\"}, {\"id\": 47862, \"name\": \"the stapler\"}, {\"id\": 47863, \"name\": \"plastic wheel\"}, {\"id\": 47864, \"name\": \"the gray and blue backpack\"}, {\"id\": 47865, \"name\": \"a dirty trash can\"}, {\"id\": 47866, \"name\": \"stone cabinet\"}, {\"id\": 47867, \"name\": \"a metal colander\"}, {\"id\": 47868, \"name\": \"gray nail polish\"}, {\"id\": 47869, \"name\": \"router\"}, {\"id\": 47870, \"name\": \"the back button\"}, {\"id\": 47871, \"name\": \"a hexagon shape\"}, {\"id\": 47872, \"name\": \"bronze nail\"}, {\"id\": 47873, \"name\": \"the wastebasket\"}, {\"id\": 47874, \"name\": \"a silk cloth\"}, {\"id\": 47875, \"name\": \"the porcelain sink\"}, {\"id\": 47876, \"name\": \"purple heart\"}, {\"id\": 47877, \"name\": \"a picture hook\"}, {\"id\": 47878, \"name\": \"the gray console\"}, {\"id\": 47879, \"name\": \"iron cabinet\"}, {\"id\": 47880, \"name\": \"a spark plug\"}, {\"id\": 47881, \"name\": \"a baby's hand\"}, {\"id\": 47882, \"name\": \"a german flag\"}, {\"id\": 47883, \"name\": \"the leather belt\"}, {\"id\": 47884, \"name\": \"copper sink\"}, {\"id\": 47885, \"name\": \"fabric bag\"}, {\"id\": 47886, \"name\": \"glass pipe\"}, {\"id\": 47887, \"name\": \"a printer\"}, {\"id\": 47888, \"name\": \"the silver curtain\"}, {\"id\": 47889, \"name\": \"dishwasher\"}, {\"id\": 47890, \"name\": \"the metal floor\"}, {\"id\": 47891, \"name\": \"a tonic water\"}, {\"id\": 47892, \"name\": \"a glass cabinet\"}, {\"id\": 47893, \"name\": \"copper barrier\"}, {\"id\": 47894, \"name\": \"a silver sticker\"}, {\"id\": 47895, \"name\": \"a billboard advertisement\"}, {\"id\": 47896, \"name\": \"the cafe\"}, {\"id\": 47897, \"name\": \"the cassette player\"}, {\"id\": 47898, \"name\": \"a black and white hat\"}, {\"id\": 47899, \"name\": \"a centerpiece\"}, {\"id\": 47900, \"name\": \"fabric napkin\"}, {\"id\": 47901, \"name\": \"voltage tester\"}, {\"id\": 47902, \"name\": \"nail file\"}, {\"id\": 47903, \"name\": \"a wooden countertop\"}, {\"id\": 47904, \"name\": \"the gold statue\"}, {\"id\": 47905, \"name\": \"the mute button\"}, {\"id\": 47906, \"name\": \"the italian flag\"}, {\"id\": 47907, \"name\": \"wooden column\"}, {\"id\": 47908, \"name\": \"a velvet fabric\"}, {\"id\": 47909, \"name\": \"the valley\"}, {\"id\": 47910, \"name\": \"the purple taxi\"}, {\"id\": 47911, \"name\": \"the silver handbag\"}, {\"id\": 47912, \"name\": \"artist\"}, {\"id\": 47913, \"name\": \"an apricot\"}, {\"id\": 47914, \"name\": \"the paper door frame\"}, {\"id\": 47915, \"name\": \"the fabric tablecloth\"}, {\"id\": 47916, \"name\": \"an eraser\"}, {\"id\": 47917, \"name\": \"parked police car\"}, {\"id\": 47918, \"name\": \"metal briefcase\"}, {\"id\": 47919, \"name\": \"table's reflection\"}, {\"id\": 47920, \"name\": \"the scanner\"}, {\"id\": 47921, \"name\": \"the birthday cake\"}, {\"id\": 47922, \"name\": \"velvet packaging\"}, {\"id\": 47923, \"name\": \"the television stand\"}, {\"id\": 47924, \"name\": \"blue police car\"}, {\"id\": 47925, \"name\": \"the glass cup\"}, {\"id\": 47926, \"name\": \"a kfc\"}, {\"id\": 47927, \"name\": \"a silver nail\"}, {\"id\": 47928, \"name\": \"the binder\"}, {\"id\": 47929, \"name\": \"an animal's hands\"}, {\"id\": 47930, \"name\": \"a metal sink\"}, {\"id\": 47931, \"name\": \"a statue of a phoenix\"}, {\"id\": 47932, \"name\": \"the tree's reflection\"}, {\"id\": 47933, \"name\": \"the blue taxi\"}, {\"id\": 47934, \"name\": \"a glass door frame\"}, {\"id\": 47935, \"name\": \"the music critic\"}, {\"id\": 47936, \"name\": \"hotel room\"}, {\"id\": 47937, \"name\": \"a roll of sticker\"}, {\"id\": 47938, \"name\": \"the satellite\"}, {\"id\": 47939, \"name\": \"a book store\"}, {\"id\": 47940, \"name\": \"calendar\"}, {\"id\": 47941, \"name\": \"a turquoise and teal geometric pattern\"}, {\"id\": 47942, \"name\": \"a silk fabric\"}, {\"id\": 47943, \"name\": \"concrete cabinet\"}, {\"id\": 47944, \"name\": \"red purse\"}, {\"id\": 47945, \"name\": \"drapery\"}, {\"id\": 47946, \"name\": \"diary\"}, {\"id\": 47947, \"name\": \"a volume button\"}, {\"id\": 47948, \"name\": \"glass baseboard\"}, {\"id\": 47949, \"name\": \"silver backpack\"}, {\"id\": 47950, \"name\": \"green compartment\"}, {\"id\": 47951, \"name\": \"the gray ambulance\"}, {\"id\": 47952, \"name\": \"the bird's claw\"}, {\"id\": 47953, \"name\": \"engraving\"}, {\"id\": 47954, \"name\": \"gold crescent\"}, {\"id\": 47955, \"name\": \"the milk carton\"}, {\"id\": 47956, \"name\": \"a tea bag\"}, {\"id\": 47957, \"name\": \"a velvet cloth\"}, {\"id\": 47958, \"name\": \"statue of child\"}, {\"id\": 47959, \"name\": \"wooden plate\"}, {\"id\": 47960, \"name\": \"a beach\"}, {\"id\": 47961, \"name\": \"wooden bottle opener\"}, {\"id\": 47962, \"name\": \"steel mannequin\"}, {\"id\": 47963, \"name\": \"white plaque\"}, {\"id\": 47964, \"name\": \"the roll of wire\"}, {\"id\": 47965, \"name\": \"baby's hands\"}, {\"id\": 47966, \"name\": \"house door\"}, {\"id\": 47967, \"name\": \"wooden comb\"}, {\"id\": 47968, \"name\": \"the bird's wing\"}, {\"id\": 47969, \"name\": \"food processor\"}, {\"id\": 47970, \"name\": \"statue of a child\"}, {\"id\": 47971, \"name\": \"the toilet\"}, {\"id\": 47972, \"name\": \"glass bottle opener\"}, {\"id\": 47973, \"name\": \"wooden carving\"}, {\"id\": 47974, \"name\": \"the toothbrush holder\"}, {\"id\": 47975, \"name\": \"copper box\"}, {\"id\": 47976, \"name\": \"the gray cone\"}, {\"id\": 47977, \"name\": \"the tea infuser\"}, {\"id\": 47978, \"name\": \"the phone booth\"}, {\"id\": 47979, \"name\": \"gold backpack\"}, {\"id\": 47980, \"name\": \"green scarf\"}, {\"id\": 47981, \"name\": \"person's gift box\"}, {\"id\": 47982, \"name\": \"plastic chair\"}, {\"id\": 47983, \"name\": \"the afro\"}, {\"id\": 47984, \"name\": \"the german flag\"}, {\"id\": 47985, \"name\": \"the spine\"}, {\"id\": 47986, \"name\": \"the brown briefcase\"}, {\"id\": 47987, \"name\": \"a wooden picture frame\"}, {\"id\": 47988, \"name\": \"the piano\"}, {\"id\": 47989, \"name\": \"bag of pear\"}, {\"id\": 47990, \"name\": \"the plastic railing\"}, {\"id\": 47991, \"name\": \"the bathtub\"}, {\"id\": 47992, \"name\": \"ethernet cable\"}, {\"id\": 47993, \"name\": \"a copper sign\"}, {\"id\": 47994, \"name\": \"a whipped cream\"}, {\"id\": 47995, \"name\": \"a wooden curtain\"}, {\"id\": 47996, \"name\": \"glass mug\"}, {\"id\": 47997, \"name\": \"a plastic chair\"}, {\"id\": 47998, \"name\": \"the reset button\"}, {\"id\": 47999, \"name\": \"the plaque\"}, {\"id\": 48000, \"name\": \"the bedroom dresser\"}, {\"id\": 48001, \"name\": \"the modem\"}, {\"id\": 48002, \"name\": \"curtain hook\"}, {\"id\": 48003, \"name\": \"blue nail polish\"}, {\"id\": 48004, \"name\": \"a purple and pink geometric pattern\"}, {\"id\": 48005, \"name\": \"mullet\"}, {\"id\": 48006, \"name\": \"bird's reflection\"}, {\"id\": 48007, \"name\": \"deli\"}, {\"id\": 48008, \"name\": \"chinese flag\"}, {\"id\": 48009, \"name\": \"blueberry slice\"}, {\"id\": 48010, \"name\": \"bath mat\"}, {\"id\": 48011, \"name\": \"flower pot\"}, {\"id\": 48012, \"name\": \"the stone patio\"}, {\"id\": 48013, \"name\": \"the chair leg\"}, {\"id\": 48014, \"name\": \"the australian flag\"}, {\"id\": 48015, \"name\": \"the food processor\"}, {\"id\": 48016, \"name\": \"the glass of milk\"}, {\"id\": 48017, \"name\": \"an athlete's hand\"}, {\"id\": 48018, \"name\": \"silver bottle opener\"}, {\"id\": 48019, \"name\": \"a glass tube\"}, {\"id\": 48020, \"name\": \"a ruler\"}, {\"id\": 48021, \"name\": \"fabric display\"}, {\"id\": 48022, \"name\": \"silk scarf\"}, {\"id\": 48023, \"name\": \"sport team logo\"}, {\"id\": 48024, \"name\": \"green book\"}, {\"id\": 48025, \"name\": \"fabric panel\"}, {\"id\": 48026, \"name\": \"the orange diamond\"}, {\"id\": 48027, \"name\": \"silver briefcase\"}, {\"id\": 48028, \"name\": \"gray-colored sign\"}, {\"id\": 48029, \"name\": \"a metal trunk\"}, {\"id\": 48030, \"name\": \"the orange bag\"}, {\"id\": 48031, \"name\": \"orange marquee\"}, {\"id\": 48032, \"name\": \"galaxy\"}, {\"id\": 48033, \"name\": \"a garden chair\"}, {\"id\": 48034, \"name\": \"the patio\"}, {\"id\": 48035, \"name\": \"a bronze medal\"}, {\"id\": 48036, \"name\": \"the wooden advertisement banner\"}, {\"id\": 48037, \"name\": \"the broken bench\"}, {\"id\": 48038, \"name\": \"the hospital corridor\"}, {\"id\": 48039, \"name\": \"marble table\"}, {\"id\": 48040, \"name\": \"the paperweight\"}, {\"id\": 48041, \"name\": \"fabric book\"}, {\"id\": 48042, \"name\": \"the marimba\"}, {\"id\": 48043, \"name\": \"gold box\"}, {\"id\": 48044, \"name\": \"beige nail polish\"}, {\"id\": 48045, \"name\": \"a garden glove\"}, {\"id\": 48046, \"name\": \"stone baseboard\"}, {\"id\": 48047, \"name\": \"a stapler\"}, {\"id\": 48048, \"name\": \"iron shelf\"}, {\"id\": 48049, \"name\": \"forest\"}, {\"id\": 48050, \"name\": \"a broken chair\"}, {\"id\": 48051, \"name\": \"an alien\"}, {\"id\": 48052, \"name\": \"a stem\"}, {\"id\": 48053, \"name\": \"green parrot logo\"}, {\"id\": 48054, \"name\": \"embroidery\"}, {\"id\": 48055, \"name\": \"ceramic railing\"}, {\"id\": 48056, \"name\": \"a torn umbrella\"}, {\"id\": 48057, \"name\": \"wooden baseboard\"}, {\"id\": 48058, \"name\": \"the grass-covered roof\"}, {\"id\": 48059, \"name\": \"a tornado\"}, {\"id\": 48060, \"name\": \"a broken vase\"}, {\"id\": 48061, \"name\": \"the silk tie\"}, {\"id\": 48062, \"name\": \"a paper plane\"}, {\"id\": 48063, \"name\": \"the music festival\"}, {\"id\": 48064, \"name\": \"dining table\"}, {\"id\": 48065, \"name\": \"the japanese flag\"}, {\"id\": 48066, \"name\": \"the restaurant menu\"}, {\"id\": 48067, \"name\": \"brown bicycle\"}, {\"id\": 48068, \"name\": \"a subway\"}, {\"id\": 48069, \"name\": \"the stone door frame\"}, {\"id\": 48070, \"name\": \"metal drawer\"}, {\"id\": 48071, \"name\": \"the slow cooker\"}, {\"id\": 48072, \"name\": \"the revolving door\"}, {\"id\": 48073, \"name\": \"a paper curtain\"}, {\"id\": 48074, \"name\": \"the hailstone\"}, {\"id\": 48075, \"name\": \"a pelvis\"}, {\"id\": 48076, \"name\": \"the jewelry store\"}, {\"id\": 48077, \"name\": \"tile roof\"}, {\"id\": 48078, \"name\": \"an iron lamp\"}, {\"id\": 48079, \"name\": \"the bowl of cereal\"}, {\"id\": 48080, \"name\": \"the purple van\"}, {\"id\": 48081, \"name\": \"blue and white shopping bag\"}, {\"id\": 48082, \"name\": \"the flavored water\"}, {\"id\": 48083, \"name\": \"the pink nail polish\"}, {\"id\": 48084, \"name\": \"a blood pressure monitor\"}, {\"id\": 48085, \"name\": \"the stoop\"}, {\"id\": 48086, \"name\": \"the image of house\"}, {\"id\": 48087, \"name\": \"a white nail polish\"}, {\"id\": 48088, \"name\": \"octagonal cutout\"}, {\"id\": 48089, \"name\": \"a performance art\"}, {\"id\": 48090, \"name\": \"the wooden picture frame\"}, {\"id\": 48091, \"name\": \"a bag of tea\"}, {\"id\": 48092, \"name\": \"brooch\"}, {\"id\": 48093, \"name\": \"the plastic shelf\"}, {\"id\": 48094, \"name\": \"a statue of a queen\"}, {\"id\": 48095, \"name\": \"a wooden shopping bag\"}, {\"id\": 48096, \"name\": \"leather case\"}, {\"id\": 48097, \"name\": \"an orange tree\"}, {\"id\": 48098, \"name\": \"the woven basket\"}, {\"id\": 48099, \"name\": \"stapler\"}, {\"id\": 48100, \"name\": \"glass pedestal\"}, {\"id\": 48101, \"name\": \"a blue pillow\"}, {\"id\": 48102, \"name\": \"the sinkhole\"}, {\"id\": 48103, \"name\": \"the silver wallet\"}, {\"id\": 48104, \"name\": \"ceramic plaque\"}, {\"id\": 48105, \"name\": \"a glass shelf\"}, {\"id\": 48106, \"name\": \"metal sink\"}, {\"id\": 48107, \"name\": \"a sparkling water\"}, {\"id\": 48108, \"name\": \"the spanish flag\"}, {\"id\": 48109, \"name\": \"the korean flag\"}, {\"id\": 48110, \"name\": \"a lip liner\"}, {\"id\": 48111, \"name\": \"the pilot\"}, {\"id\": 48112, \"name\": \"the yellow advertisement banner\"}, {\"id\": 48113, \"name\": \"a glass shopping bag\"}, {\"id\": 48114, \"name\": \"a bronze statue\"}, {\"id\": 48115, \"name\": \"the gold nail\"}, {\"id\": 48116, \"name\": \"the desktop computer\"}, {\"id\": 48117, \"name\": \"metal hand\"}, {\"id\": 48118, \"name\": \"an image of book\"}, {\"id\": 48119, \"name\": \"an iron box\"}, {\"id\": 48120, \"name\": \"a fishbowl\"}, {\"id\": 48121, \"name\": \"wooden vent\"}, {\"id\": 48122, \"name\": \"the cocktail glass\"}, {\"id\": 48123, \"name\": \"the dragonfruit\"}, {\"id\": 48124, \"name\": \"the ceramic bowl\"}, {\"id\": 48125, \"name\": \"fabric cabinet\"}, {\"id\": 48126, \"name\": \"a bronze price tag\"}, {\"id\": 48127, \"name\": \"the concrete box\"}, {\"id\": 48128, \"name\": \"the universe\"}, {\"id\": 48129, \"name\": \"muffler\"}, {\"id\": 48130, \"name\": \"gold clutch\"}, {\"id\": 48131, \"name\": \"birdbath\"}, {\"id\": 48132, \"name\": \"the tv stand\"}, {\"id\": 48133, \"name\": \"an alley\"}, {\"id\": 48134, \"name\": \"fax machine\"}, {\"id\": 48135, \"name\": \"the gray traffic cone\"}, {\"id\": 48136, \"name\": \"the road roller\"}, {\"id\": 48137, \"name\": \"a statue of a fairy\"}, {\"id\": 48138, \"name\": \"bird cage\"}, {\"id\": 48139, \"name\": \"the saxophone\"}, {\"id\": 48140, \"name\": \"dog's reflection\"}, {\"id\": 48141, \"name\": \"wooden stove\"}, {\"id\": 48142, \"name\": \"the statue of a minotaur\"}, {\"id\": 48143, \"name\": \"a wooden comb\"}, {\"id\": 48144, \"name\": \"speed bump\"}, {\"id\": 48145, \"name\": \"toilet paper roll\"}, {\"id\": 48146, \"name\": \"the mascara\"}, {\"id\": 48147, \"name\": \"the accordion\"}, {\"id\": 48148, \"name\": \"a purple sticker\"}, {\"id\": 48149, \"name\": \"a copper screw\"}, {\"id\": 48150, \"name\": \"the stone bust\"}, {\"id\": 48151, \"name\": \"the bag of strawberry\"}, {\"id\": 48152, \"name\": \"a glitter nail polish\"}, {\"id\": 48153, \"name\": \"iron pendant\"}, {\"id\": 48154, \"name\": \"a cardboard tube\"}, {\"id\": 48155, \"name\": \"the cat's paw\"}, {\"id\": 48156, \"name\": \"plastic stick\"}, {\"id\": 48157, \"name\": \"bronze box\"}, {\"id\": 48158, \"name\": \"a silk tablecloth\"}, {\"id\": 48159, \"name\": \"an er\"}, {\"id\": 48160, \"name\": \"a green forklift\"}, {\"id\": 48161, \"name\": \"the pet store\"}, {\"id\": 48162, \"name\": \"a hot dog cart\"}, {\"id\": 48163, \"name\": \"roll of yarn\"}, {\"id\": 48164, \"name\": \"calculator\"}, {\"id\": 48165, \"name\": \"speaker wire\"}, {\"id\": 48166, \"name\": \"a flower vase\"}, {\"id\": 48167, \"name\": \"russian flag\"}, {\"id\": 48168, \"name\": \"the blizzard\"}, {\"id\": 48169, \"name\": \"the stainless steel refrigerator\"}, {\"id\": 48170, \"name\": \"the black sticky note\"}, {\"id\": 48171, \"name\": \"the menu button\"}, {\"id\": 48172, \"name\": \"the collage\"}, {\"id\": 48173, \"name\": \"white joystick\"}, {\"id\": 48174, \"name\": \"a blue taillight\"}, {\"id\": 48175, \"name\": \"an iron door frame\"}, {\"id\": 48176, \"name\": \"an oxygen tank\"}, {\"id\": 48177, \"name\": \"the wendy's\"}, {\"id\": 48178, \"name\": \"rubber ball\"}, {\"id\": 48179, \"name\": \"metal ruler\"}, {\"id\": 48180, \"name\": \"the purple pavilion\"}, {\"id\": 48181, \"name\": \"the orange juice\"}, {\"id\": 48182, \"name\": \"root\"}, {\"id\": 48183, \"name\": \"the green mouse\"}, {\"id\": 48184, \"name\": \"cornrows\"}, {\"id\": 48185, \"name\": \"a power bank\"}, {\"id\": 48186, \"name\": \"statue of king\"}, {\"id\": 48187, \"name\": \"a petal\"}, {\"id\": 48188, \"name\": \"a harmonica\"}, {\"id\": 48189, \"name\": \"electrical panel\"}, {\"id\": 48190, \"name\": \"a copper faucet\"}, {\"id\": 48191, \"name\": \"hair dryer\"}, {\"id\": 48192, \"name\": \"charcoal drawing\"}, {\"id\": 48193, \"name\": \"the pink controller\"}, {\"id\": 48194, \"name\": \"red and white adidas sneaker\"}, {\"id\": 48195, \"name\": \"the orange nail polish\"}, {\"id\": 48196, \"name\": \"the bronze bottle opener\"}, {\"id\": 48197, \"name\": \"the paper clip\"}, {\"id\": 48198, \"name\": \"a purple purse\"}, {\"id\": 48199, \"name\": \"bakery\"}, {\"id\": 48200, \"name\": \"the banjo\"}, {\"id\": 48201, \"name\": \"a cooked chicken part\"}, {\"id\": 48202, \"name\": \"silver airplane\"}, {\"id\": 48203, \"name\": \"a black television\"}, {\"id\": 48204, \"name\": \"ironing board\"}, {\"id\": 48205, \"name\": \"the orange and yellow geometric pattern\"}, {\"id\": 48206, \"name\": \"an apartment building\"}, {\"id\": 48207, \"name\": \"the wine opener\"}, {\"id\": 48208, \"name\": \"a desk calendar\"}, {\"id\": 48209, \"name\": \"recording studio\"}, {\"id\": 48210, \"name\": \"the belt hook\"}, {\"id\": 48211, \"name\": \"the heater\"}, {\"id\": 48212, \"name\": \"a purple sticky note\"}, {\"id\": 48213, \"name\": \"a parked train\"}, {\"id\": 48214, \"name\": \"the kitchen door\"}, {\"id\": 48215, \"name\": \"the guava\"}, {\"id\": 48216, \"name\": \"an office chair\"}, {\"id\": 48217, \"name\": \"a wooden stanchion\"}, {\"id\": 48218, \"name\": \"pink sticky note\"}, {\"id\": 48219, \"name\": \"wooden sculpture\"}, {\"id\": 48220, \"name\": \"the copier\"}, {\"id\": 48221, \"name\": \"a portrait\"}, {\"id\": 48222, \"name\": \"parked train\"}, {\"id\": 48223, \"name\": \"the leather apron\"}, {\"id\": 48224, \"name\": \"library sign\"}, {\"id\": 48225, \"name\": \"diamond-shaped cutout\"}, {\"id\": 48226, \"name\": \"the cave\"}, {\"id\": 48227, \"name\": \"grey motorcycle\"}, {\"id\": 48228, \"name\": \"boat sail\"}, {\"id\": 48229, \"name\": \"a fabric door frame\"}, {\"id\": 48230, \"name\": \"the brazilian flag\"}, {\"id\": 48231, \"name\": \"the parked black boat\"}, {\"id\": 48232, \"name\": \"silhouette of a cat\"}, {\"id\": 48233, \"name\": \"wooden picture frame\"}, {\"id\": 48234, \"name\": \"the jug\"}, {\"id\": 48235, \"name\": \"a chair's reflection\"}, {\"id\": 48236, \"name\": \"office desk\"}, {\"id\": 48237, \"name\": \"the wool railing\"}, {\"id\": 48238, \"name\": \"headphone cord\"}, {\"id\": 48239, \"name\": \"orange door\"}, {\"id\": 48240, \"name\": \"purple nail polish\"}, {\"id\": 48241, \"name\": \"a treehouse\"}, {\"id\": 48242, \"name\": \"leather bag\"}, {\"id\": 48243, \"name\": \"the gold medal\"}, {\"id\": 48244, \"name\": \"the iron skillet\"}, {\"id\": 48245, \"name\": \"surgical glove\"}, {\"id\": 48246, \"name\": \"the construction sign\"}, {\"id\": 48247, \"name\": \"book page\"}, {\"id\": 48248, \"name\": \"a blue bag of coffee\"}, {\"id\": 48249, \"name\": \"the icicle\"}, {\"id\": 48250, \"name\": \"a fabric tablecloth\"}, {\"id\": 48251, \"name\": \"indian flag\"}, {\"id\": 48252, \"name\": \"whatsapp logo\"}, {\"id\": 48253, \"name\": \"apple store\"}, {\"id\": 48254, \"name\": \"spanish flag\"}, {\"id\": 48255, \"name\": \"a kitchen island\"}, {\"id\": 48256, \"name\": \"a gray helicopter\"}, {\"id\": 48257, \"name\": \"hearing aid\"}, {\"id\": 48258, \"name\": \"the parked green motorcycle\"}, {\"id\": 48259, \"name\": \"wineglass\"}, {\"id\": 48260, \"name\": \"the iron plaque\"}, {\"id\": 48261, \"name\": \"the electric stove\"}, {\"id\": 48262, \"name\": \"statue of a mermaid\"}, {\"id\": 48263, \"name\": \"an airbag\"}, {\"id\": 48264, \"name\": \"a watering can\"}, {\"id\": 48265, \"name\": \"the spiral staircase\"}, {\"id\": 48266, \"name\": \"the flower shop\"}, {\"id\": 48267, \"name\": \"gold earring\"}, {\"id\": 48268, \"name\": \"steel door frame\"}, {\"id\": 48269, \"name\": \"pineapple slice\"}, {\"id\": 48270, \"name\": \"australian flag\"}, {\"id\": 48271, \"name\": \"school\"}, {\"id\": 48272, \"name\": \"the music teacher\"}, {\"id\": 48273, \"name\": \"gold wire\"}, {\"id\": 48274, \"name\": \"the post office\"}, {\"id\": 48275, \"name\": \"the purple taillight\"}, {\"id\": 48276, \"name\": \"the gold tattoo\"}, {\"id\": 48277, \"name\": \"the green nail polish\"}, {\"id\": 48278, \"name\": \"orange nail polish\"}, {\"id\": 48279, \"name\": \"a taxi window\"}, {\"id\": 48280, \"name\": \"the image of chair\"}, {\"id\": 48281, \"name\": \"clipboard\"}, {\"id\": 48282, \"name\": \"german flag\"}, {\"id\": 48283, \"name\": \"a gray nail polish\"}, {\"id\": 48284, \"name\": \"the car's reflection\"}, {\"id\": 48285, \"name\": \"the black limousine\"}, {\"id\": 48286, \"name\": \"laundry basket\"}, {\"id\": 48287, \"name\": \"a yellow pillow\"}, {\"id\": 48288, \"name\": \"plastic table\"}, {\"id\": 48289, \"name\": \"electric mixer\"}, {\"id\": 48290, \"name\": \"the desert\"}, {\"id\": 48291, \"name\": \"copper roof\"}, {\"id\": 48292, \"name\": \"gray sign\"}, {\"id\": 48293, \"name\": \"palette\"}, {\"id\": 48294, \"name\": \"the green nail\"}, {\"id\": 48295, \"name\": \"bronze license plate\"}, {\"id\": 48296, \"name\": \"a fire station\"}, {\"id\": 48297, \"name\": \"a ratchet\"}, {\"id\": 48298, \"name\": \"the violin bow\"}, {\"id\": 48299, \"name\": \"the library bookshelf\"}, {\"id\": 48300, \"name\": \"pottery\"}, {\"id\": 48301, \"name\": \"a metal packaging\"}, {\"id\": 48302, \"name\": \"linkedin logo\"}, {\"id\": 48303, \"name\": \"the trail\"}, {\"id\": 48304, \"name\": \"the school sign\"}, {\"id\": 48305, \"name\": \"a silver badge\"}, {\"id\": 48306, \"name\": \"drill\"}, {\"id\": 48307, \"name\": \"a leather cabinet\"}, {\"id\": 48308, \"name\": \"a glacier\"}, {\"id\": 48309, \"name\": \"ceramic coffee maker\"}, {\"id\": 48310, \"name\": \"garlic press\"}, {\"id\": 48311, \"name\": \"a copper finish\"}, {\"id\": 48312, \"name\": \"a black helicopter\"}, {\"id\": 48313, \"name\": \"wooden bead\"}, {\"id\": 48314, \"name\": \"bronze taillight\"}, {\"id\": 48315, \"name\": \"the concrete door frame\"}, {\"id\": 48316, \"name\": \"a gold nail polish\"}, {\"id\": 48317, \"name\": \"a paper airplane\"}, {\"id\": 48318, \"name\": \"piano key\"}, {\"id\": 48319, \"name\": \"the fish's fin\"}, {\"id\": 48320, \"name\": \"q-tips\"}, {\"id\": 48321, \"name\": \"a modem\"}, {\"id\": 48322, \"name\": \"a straw basket\"}, {\"id\": 48323, \"name\": \"a marble door\"}, {\"id\": 48324, \"name\": \"the white traffic light\"}, {\"id\": 48325, \"name\": \"pastry brush\"}, {\"id\": 48326, \"name\": \"purple scarf\"}, {\"id\": 48327, \"name\": \"a metal door frame\"}, {\"id\": 48328, \"name\": \"the steel refrigerator\"}, {\"id\": 48329, \"name\": \"an indian flag\"}, {\"id\": 48330, \"name\": \"a blue sunglass\"}, {\"id\": 48331, \"name\": \"white tiger\"}, {\"id\": 48332, \"name\": \"an olive garden\"}, {\"id\": 48333, \"name\": \"the cat's reflection\"}, {\"id\": 48334, \"name\": \"the cauliflower\"}, {\"id\": 48335, \"name\": \"the silver bag\"}, {\"id\": 48336, \"name\": \"black hexagon\"}, {\"id\": 48337, \"name\": \"wooden electrical outlet\"}, {\"id\": 48338, \"name\": \"guitar string\"}, {\"id\": 48339, \"name\": \"a moon\"}, {\"id\": 48340, \"name\": \"phone booth\"}, {\"id\": 48341, \"name\": \"the pink taxi\"}, {\"id\": 48342, \"name\": \"the sculptor\"}, {\"id\": 48343, \"name\": \"the silver vase\"}, {\"id\": 48344, \"name\": \"the hexagonal cutout\"}, {\"id\": 48345, \"name\": \"the subway map\"}, {\"id\": 48346, \"name\": \"the glass wall\"}, {\"id\": 48347, \"name\": \"silhouette of a dog\"}, {\"id\": 48348, \"name\": \"a contact len\"}, {\"id\": 48349, \"name\": \"a gray elephant\"}, {\"id\": 48350, \"name\": \"peninsula\"}, {\"id\": 48351, \"name\": \"a living room couch\"}, {\"id\": 48352, \"name\": \"a pizza hut\"}, {\"id\": 48353, \"name\": \"performance\"}, {\"id\": 48354, \"name\": \"rain\"}, {\"id\": 48355, \"name\": \"an usb flash drive\"}, {\"id\": 48356, \"name\": \"the birdhouse\"}, {\"id\": 48357, \"name\": \"yellow taillight\"}, {\"id\": 48358, \"name\": \"the leather packaging\"}, {\"id\": 48359, \"name\": \"earring\"}, {\"id\": 48360, \"name\": \"old newspaper\"}, {\"id\": 48361, \"name\": \"the harp\"}, {\"id\": 48362, \"name\": \"the wooden mirror frame\"}, {\"id\": 48363, \"name\": \"the wooden sink\"}, {\"id\": 48364, \"name\": \"silk curtain\"}, {\"id\": 48365, \"name\": \"a movie theater seat\"}, {\"id\": 48366, \"name\": \"silver cone\"}, {\"id\": 48367, \"name\": \"the white backpack\"}, {\"id\": 48368, \"name\": \"a chair's leg\"}, {\"id\": 48369, \"name\": \"a leather railing\"}, {\"id\": 48370, \"name\": \"hair tie\"}, {\"id\": 48371, \"name\": \"a mascara\"}, {\"id\": 48372, \"name\": \"plastic tree\"}, {\"id\": 48373, \"name\": \"the iron mannequin\"}, {\"id\": 48374, \"name\": \"a statue of a president\"}, {\"id\": 48375, \"name\": \"parked fire truck\"}, {\"id\": 48376, \"name\": \"glacier\"}, {\"id\": 48377, \"name\": \"a lead nail\"}, {\"id\": 48378, \"name\": \"the copper nail\"}, {\"id\": 48379, \"name\": \"a banjo\"}, {\"id\": 48380, \"name\": \"the copper door\"}, {\"id\": 48381, \"name\": \"a plastic ruler\"}, {\"id\": 48382, \"name\": \"turquoise truck\"}, {\"id\": 48383, \"name\": \"a toilet brush\"}, {\"id\": 48384, \"name\": \"the silk napkin\"}, {\"id\": 48385, \"name\": \"stand mixer\"}, {\"id\": 48386, \"name\": \"a lipstick\"}, {\"id\": 48387, \"name\": \"a grass-covered roof\"}, {\"id\": 48388, \"name\": \"the blue traffic light\"}, {\"id\": 48389, \"name\": \"a parked police car\"}, {\"id\": 48390, \"name\": \"a pink train\"}, {\"id\": 48391, \"name\": \"yellow nail polish\"}, {\"id\": 48392, \"name\": \"the canyon\"}, {\"id\": 48393, \"name\": \"glass countertop\"}, {\"id\": 48394, \"name\": \"an electric kettle\"}, {\"id\": 48395, \"name\": \"the yellow-orange flower\"}, {\"id\": 48396, \"name\": \"beer can\"}, {\"id\": 48397, \"name\": \"the black pebble\"}, {\"id\": 48398, \"name\": \"the turquoise van\"}, {\"id\": 48399, \"name\": \"a bird bath\"}, {\"id\": 48400, \"name\": \"a black cat\"}, {\"id\": 48401, \"name\": \"the silver packaging\"}, {\"id\": 48402, \"name\": \"a fish market\"}, {\"id\": 48403, \"name\": \"concrete pedestal\"}, {\"id\": 48404, \"name\": \"the hair dryer\"}, {\"id\": 48405, \"name\": \"the turquoise earring\"}, {\"id\": 48406, \"name\": \"a tunnel\"}, {\"id\": 48407, \"name\": \"yellow and orange geometric pattern\"}, {\"id\": 48408, \"name\": \"a garden gate\"}, {\"id\": 48409, \"name\": \"foam cup\"}, {\"id\": 48410, \"name\": \"a cotton ball\"}, {\"id\": 48411, \"name\": \"parked pink limousine\"}, {\"id\": 48412, \"name\": \"the coffee shop counter\"}, {\"id\": 48413, \"name\": \"the panama hat\"}, {\"id\": 48414, \"name\": \"an electric toothbrush\"}, {\"id\": 48415, \"name\": \"the copper vase\"}, {\"id\": 48416, \"name\": \"a green scarf\"}, {\"id\": 48417, \"name\": \"the metal keychain\"}, {\"id\": 48418, \"name\": \"a house key\"}, {\"id\": 48419, \"name\": \"the hospital sign\"}, {\"id\": 48420, \"name\": \"the green taillight\"}, {\"id\": 48421, \"name\": \"a glass table\"}, {\"id\": 48422, \"name\": \"the garbage truck\"}, {\"id\": 48423, \"name\": \"a statue of a king\"}, {\"id\": 48424, \"name\": \"a bronze mannequin\"}, {\"id\": 48425, \"name\": \"a park sign\"}, {\"id\": 48426, \"name\": \"the towel rack\"}, {\"id\": 48427, \"name\": \"iceberg\"}, {\"id\": 48428, \"name\": \"a bowl of mixed green\"}, {\"id\": 48429, \"name\": \"a purple and orange shopping bag\"}, {\"id\": 48430, \"name\": \"street performer\"}, {\"id\": 48431, \"name\": \"the bag of kiwi\"}, {\"id\": 48432, \"name\": \"bookstore\"}, {\"id\": 48433, \"name\": \"the hotel lobby\"}, {\"id\": 48434, \"name\": \"a bowler hat\"}, {\"id\": 48435, \"name\": \"a wooden lamp\"}, {\"id\": 48436, \"name\": \"a green menu board\"}, {\"id\": 48437, \"name\": \"a yellow canister\"}, {\"id\": 48438, \"name\": \"the doctor\"}, {\"id\": 48439, \"name\": \"copper handle\"}, {\"id\": 48440, \"name\": \"the school\"}, {\"id\": 48441, \"name\": \"a silicone spatula\"}, {\"id\": 48442, \"name\": \"shin\"}, {\"id\": 48443, \"name\": \"a parked bus\"}, {\"id\": 48444, \"name\": \"binder\"}, {\"id\": 48445, \"name\": \"the reading lamp\"}, {\"id\": 48446, \"name\": \"the air filter\"}, {\"id\": 48447, \"name\": \"music album cover\"}, {\"id\": 48448, \"name\": \"roll of carpet\"}, {\"id\": 48449, \"name\": \"hexagonal top\"}, {\"id\": 48450, \"name\": \"telescope\"}, {\"id\": 48451, \"name\": \"a white rabbit\"}, {\"id\": 48452, \"name\": \"a museum exhibit\"}, {\"id\": 48453, \"name\": \"a speed bump\"}, {\"id\": 48454, \"name\": \"tea cup\"}, {\"id\": 48455, \"name\": \"the catalytic converter\"}, {\"id\": 48456, \"name\": \"a shower\"}, {\"id\": 48457, \"name\": \"the stop button\"}, {\"id\": 48458, \"name\": \"the star shape\"}, {\"id\": 48459, \"name\": \"silver train\"}, {\"id\": 48460, \"name\": \"the music producer\"}, {\"id\": 48461, \"name\": \"a purple shelf\"}, {\"id\": 48462, \"name\": \"blue nail\"}, {\"id\": 48463, \"name\": \"a plate of cooked chicken\"}, {\"id\": 48464, \"name\": \"the yellow backpack\"}, {\"id\": 48465, \"name\": \"a metal ruler\"}, {\"id\": 48466, \"name\": \"the house door\"}, {\"id\": 48467, \"name\": \"a purple bush\"}, {\"id\": 48468, \"name\": \"forward button\"}, {\"id\": 48469, \"name\": \"a copper mug\"}, {\"id\": 48470, \"name\": \"the parked white train\"}, {\"id\": 48471, \"name\": \"plant hook\"}, {\"id\": 48472, \"name\": \"white bicycle\"}, {\"id\": 48473, \"name\": \"the bronze nail\"}, {\"id\": 48474, \"name\": \"the crystal chandelier\"}, {\"id\": 48475, \"name\": \"the metal spoon\"}, {\"id\": 48476, \"name\": \"the plate of egg\"}, {\"id\": 48477, \"name\": \"a copper bottle opener\"}, {\"id\": 48478, \"name\": \"plastic charm\"}, {\"id\": 48479, \"name\": \"gold section\"}, {\"id\": 48480, \"name\": \"the turtle\"}, {\"id\": 48481, \"name\": \"a white sand\"}, {\"id\": 48482, \"name\": \"a plastic plate\"}, {\"id\": 48483, \"name\": \"the purple traffic cone\"}, {\"id\": 48484, \"name\": \"the envelope\"}, {\"id\": 48485, \"name\": \"the eggplant\"}, {\"id\": 48486, \"name\": \"piano keyboard\"}, {\"id\": 48487, \"name\": \"the globe\"}, {\"id\": 48488, \"name\": \"the wooden spoon rest\"}, {\"id\": 48489, \"name\": \"vacuum cleaner\"}, {\"id\": 48490, \"name\": \"snowman\"}, {\"id\": 48491, \"name\": \"a cookbook\"}, {\"id\": 48492, \"name\": \"the router\"}, {\"id\": 48493, \"name\": \"a blue wallet\"}, {\"id\": 48494, \"name\": \"a volcano\"}, {\"id\": 48495, \"name\": \"folder\"}, {\"id\": 48496, \"name\": \"the alien\"}, {\"id\": 48497, \"name\": \"a pink nail polish\"}, {\"id\": 48498, \"name\": \"turquoise earring\"}, {\"id\": 48499, \"name\": \"the crutch\"}, {\"id\": 48500, \"name\": \"the singer\"}, {\"id\": 48501, \"name\": \"an orange bicycle\"}, {\"id\": 48502, \"name\": \"razor\"}, {\"id\": 48503, \"name\": \"a pink scooter\"}, {\"id\": 48504, \"name\": \"the waffle iron\"}, {\"id\": 48505, \"name\": \"tweezer\"}, {\"id\": 48506, \"name\": \"seascape\"}, {\"id\": 48507, \"name\": \"a snapchat logo\"}, {\"id\": 48508, \"name\": \"arabic flag\"}, {\"id\": 48509, \"name\": \"the steel railing\"}, {\"id\": 48510, \"name\": \"the black nail\"}, {\"id\": 48511, \"name\": \"a tenni court\"}, {\"id\": 48512, \"name\": \"a dark-colored cabinet\"}, {\"id\": 48513, \"name\": \"a hair dryer\"}, {\"id\": 48514, \"name\": \"the hairpin\"}, {\"id\": 48515, \"name\": \"the image of man\"}, {\"id\": 48516, \"name\": \"a purple traffic cone\"}, {\"id\": 48517, \"name\": \"curling iron\"}, {\"id\": 48518, \"name\": \"the nail brush\"}, {\"id\": 48519, \"name\": \"the butcher shop\"}, {\"id\": 48520, \"name\": \"orange sunglass\"}, {\"id\": 48521, \"name\": \"milk carton\"}, {\"id\": 48522, \"name\": \"a clock radio\"}, {\"id\": 48523, \"name\": \"a brown truck\"}, {\"id\": 48524, \"name\": \"the latte\"}, {\"id\": 48525, \"name\": \"fabric packaging\"}, {\"id\": 48526, \"name\": \"the forest\"}, {\"id\": 48527, \"name\": \"the purple diamond\"}, {\"id\": 48528, \"name\": \"a printer ink\"}, {\"id\": 48529, \"name\": \"the toilet brush\"}, {\"id\": 48530, \"name\": \"parked rv\"}, {\"id\": 48531, \"name\": \"a fish's fin\"}, {\"id\": 48532, \"name\": \"wooden lever\"}, {\"id\": 48533, \"name\": \"purple license plate\"}, {\"id\": 48534, \"name\": \"a cat's paw\"}, {\"id\": 48535, \"name\": \"the prosthetic limb\"}, {\"id\": 48536, \"name\": \"metal clip\"}, {\"id\": 48537, \"name\": \"a statue of vehicle\"}, {\"id\": 48538, \"name\": \"the gym\"}, {\"id\": 48539, \"name\": \"the nail growth serum\"}, {\"id\": 48540, \"name\": \"school bus\"}, {\"id\": 48541, \"name\": \"the trellis\"}, {\"id\": 48542, \"name\": \"the gold door\"}, {\"id\": 48543, \"name\": \"a black boat\"}, {\"id\": 48544, \"name\": \"pink scarf\"}, {\"id\": 48545, \"name\": \"a wooden rocking chair\"}, {\"id\": 48546, \"name\": \"a spanish flag\"}, {\"id\": 48547, \"name\": \"a headphone\"}, {\"id\": 48548, \"name\": \"the electric kettle\"}, {\"id\": 48549, \"name\": \"black pillow\"}, {\"id\": 48550, \"name\": \"the burger king\"}, {\"id\": 48551, \"name\": \"parked brown taxi\"}, {\"id\": 48552, \"name\": \"the composer\"}, {\"id\": 48553, \"name\": \"the toilet seat\"}, {\"id\": 48554, \"name\": \"the paper railing\"}, {\"id\": 48555, \"name\": \"poached chicken part\"}, {\"id\": 48556, \"name\": \"google logo\"}, {\"id\": 48557, \"name\": \"metal wheelchair\"}, {\"id\": 48558, \"name\": \"orange airplane\"}, {\"id\": 48559, \"name\": \"socket wrench\"}, {\"id\": 48560, \"name\": \"white advertisement banner\"}, {\"id\": 48561, \"name\": \"the green box of quinoa\"}, {\"id\": 48562, \"name\": \"the egg carton\"}, {\"id\": 48563, \"name\": \"a trencher\"}, {\"id\": 48564, \"name\": \"the garden bench\"}, {\"id\": 48565, \"name\": \"book cover\"}, {\"id\": 48566, \"name\": \"a bicycle helmet\"}, {\"id\": 48567, \"name\": \"the writer\"}, {\"id\": 48568, \"name\": \"glass plaque\"}, {\"id\": 48569, \"name\": \"an ukulele\"}, {\"id\": 48570, \"name\": \"yellow and orange umbrella\"}, {\"id\": 48571, \"name\": \"green flower\"}, {\"id\": 48572, \"name\": \"the glass staircase\"}, {\"id\": 48573, \"name\": \"the mountain trail\"}, {\"id\": 48574, \"name\": \"bronze brooch\"}, {\"id\": 48575, \"name\": \"fedora\"}, {\"id\": 48576, \"name\": \"bread loaf\"}, {\"id\": 48577, \"name\": \"the green juice\"}, {\"id\": 48578, \"name\": \"pink fertilizer\"}, {\"id\": 48579, \"name\": \"a parked silver airplane\"}, {\"id\": 48580, \"name\": \"the velvet cloth\"}, {\"id\": 48581, \"name\": \"the silver buckle\"}, {\"id\": 48582, \"name\": \"a metal briefcase\"}, {\"id\": 48583, \"name\": \"easel\"}, {\"id\": 48584, \"name\": \"lawyer\"}, {\"id\": 48585, \"name\": \"a green suitcase\"}, {\"id\": 48586, \"name\": \"a grey airplane\"}, {\"id\": 48587, \"name\": \"perfume\"}, {\"id\": 48588, \"name\": \"a sap\"}, {\"id\": 48589, \"name\": \"metal colander\"}, {\"id\": 48590, \"name\": \"the copper kettle\"}, {\"id\": 48591, \"name\": \"the school desk\"}, {\"id\": 48592, \"name\": \"a yellow nail polish\"}, {\"id\": 48593, \"name\": \"bathroom\"}, {\"id\": 48594, \"name\": \"wooden bookcase\"}, {\"id\": 48595, \"name\": \"the silver plaque\"}, {\"id\": 48596, \"name\": \"the greek flag\"}, {\"id\": 48597, \"name\": \"a copper earring\"}, {\"id\": 48598, \"name\": \"the bag of cherry\"}, {\"id\": 48599, \"name\": \"the concrete shelf\"}, {\"id\": 48600, \"name\": \"the blue menu board\"}, {\"id\": 48601, \"name\": \"wooden mug\"}, {\"id\": 48602, \"name\": \"a black paper towel\"}, {\"id\": 48603, \"name\": \"the eyeshadow\"}, {\"id\": 48604, \"name\": \"conference room\"}, {\"id\": 48605, \"name\": \"black flower\"}, {\"id\": 48606, \"name\": \"a mohawk\"}, {\"id\": 48607, \"name\": \"a bathroom mirror\"}, {\"id\": 48608, \"name\": \"a gas station\"}, {\"id\": 48609, \"name\": \"the pastry brush\"}, {\"id\": 48610, \"name\": \"parked yellow bus\"}, {\"id\": 48611, \"name\": \"a clipboard\"}, {\"id\": 48612, \"name\": \"the abandoned car\"}, {\"id\": 48613, \"name\": \"a korean flag\"}, {\"id\": 48614, \"name\": \"the parked limousine\"}, {\"id\": 48615, \"name\": \"wire stripper\"}, {\"id\": 48616, \"name\": \"the fish's reflection\"}, {\"id\": 48617, \"name\": \"car wheel\"}, {\"id\": 48618, \"name\": \"spiral ramp\"}, {\"id\": 48619, \"name\": \"a submarine\"}, {\"id\": 48620, \"name\": \"metal cabinet\"}, {\"id\": 48621, \"name\": \"vegetable stand\"}, {\"id\": 48622, \"name\": \"gray sticky note\"}, {\"id\": 48623, \"name\": \"the image of computer\"}, {\"id\": 48624, \"name\": \"a gallery\"}, {\"id\": 48625, \"name\": \"an accelerator pedal\"}, {\"id\": 48626, \"name\": \"a stainless steel sink\"}, {\"id\": 48627, \"name\": \"the stone bench\"}, {\"id\": 48628, \"name\": \"an iron table\"}, {\"id\": 48629, \"name\": \"a black and silver shopping bag\"}, {\"id\": 48630, \"name\": \"a metal helmet\"}, {\"id\": 48631, \"name\": \"the silver and gray geometric pattern\"}, {\"id\": 48632, \"name\": \"plastic cabinet\"}, {\"id\": 48633, \"name\": \"a copper heart\"}, {\"id\": 48634, \"name\": \"canyon\"}, {\"id\": 48635, \"name\": \"purple nail\"}, {\"id\": 48636, \"name\": \"the orange pillow\"}, {\"id\": 48637, \"name\": \"the turkey\"}, {\"id\": 48638, \"name\": \"fabric towel\"}, {\"id\": 48639, \"name\": \"the statue of a unicorn\"}, {\"id\": 48640, \"name\": \"a school hallway\"}, {\"id\": 48641, \"name\": \"the metal desk\"}, {\"id\": 48642, \"name\": \"microscope\"}, {\"id\": 48643, \"name\": \"nail clipper\"}, {\"id\": 48644, \"name\": \"the strawberry slice\"}, {\"id\": 48645, \"name\": \"a stone fireplace\"}, {\"id\": 48646, \"name\": \"a gold plaque\"}, {\"id\": 48647, \"name\": \"an aquarium\"}, {\"id\": 48648, \"name\": \"a grocery cart\"}, {\"id\": 48649, \"name\": \"the doctor's hand\"}, {\"id\": 48650, \"name\": \"a ventriloquist's dummy\"}, {\"id\": 48651, \"name\": \"the circuit breaker\"}, {\"id\": 48652, \"name\": \"an earbuds\"}, {\"id\": 48653, \"name\": \"copper motorcycle\"}, {\"id\": 48654, \"name\": \"turquoise motorcycle\"}, {\"id\": 48655, \"name\": \"a yellow tattoo\"}, {\"id\": 48656, \"name\": \"the silhouette of a cat\"}, {\"id\": 48657, \"name\": \"the rabbit's ear\"}, {\"id\": 48658, \"name\": \"the ceramic figurine\"}, {\"id\": 48659, \"name\": \"british flag\"}, {\"id\": 48660, \"name\": \"newsboy cap\"}, {\"id\": 48661, \"name\": \"the wooden shoe\"}, {\"id\": 48662, \"name\": \"the metal vase\"}, {\"id\": 48663, \"name\": \"the ceramic box\"}, {\"id\": 48664, \"name\": \"doorbell\"}, {\"id\": 48665, \"name\": \"a highway ramp\"}, {\"id\": 48666, \"name\": \"the starfruit\"}, {\"id\": 48667, \"name\": \"a silver-framed picture\"}, {\"id\": 48668, \"name\": \"a bag of orange\"}, {\"id\": 48669, \"name\": \"the white tattoo\"}, {\"id\": 48670, \"name\": \"the chinese flag\"}, {\"id\": 48671, \"name\": \"turquoise necklace\"}, {\"id\": 48672, \"name\": \"soap\"}, {\"id\": 48673, \"name\": \"blue sticky note\"}, {\"id\": 48674, \"name\": \"bag of pineapple\"}, {\"id\": 48675, \"name\": \"an atm machine\"}, {\"id\": 48676, \"name\": \"the orange nail\"}, {\"id\": 48677, \"name\": \"the powder\"}, {\"id\": 48678, \"name\": \"the blue traffic cone\"}, {\"id\": 48679, \"name\": \"an image of tree\"}, {\"id\": 48680, \"name\": \"a piano keyboard\"}, {\"id\": 48681, \"name\": \"the rice cooker\"}, {\"id\": 48682, \"name\": \"the fuel injector\"}, {\"id\": 48683, \"name\": \"a plum\"}, {\"id\": 48684, \"name\": \"the purple pillow\"}, {\"id\": 48685, \"name\": \"white nail polish\"}, {\"id\": 48686, \"name\": \"the glass coffee table\"}, {\"id\": 48687, \"name\": \"the ceramic mug\"}, {\"id\": 48688, \"name\": \"starbuck coffee\"}, {\"id\": 48689, \"name\": \"a car wheel\"}, {\"id\": 48690, \"name\": \"a glass kettle\"}, {\"id\": 48691, \"name\": \"a fade\"}, {\"id\": 48692, \"name\": \"a roll of film\"}, {\"id\": 48693, \"name\": \"the gym sign\"}, {\"id\": 48694, \"name\": \"sand beach\"}, {\"id\": 48695, \"name\": \"black stove\"}, {\"id\": 48696, \"name\": \"aerial lift\"}, {\"id\": 48697, \"name\": \"the traffic circle\"}, {\"id\": 48698, \"name\": \"the birdcage\"}, {\"id\": 48699, \"name\": \"the purple cone\"}, {\"id\": 48700, \"name\": \"metal whisk\"}, {\"id\": 48701, \"name\": \"a green traffic cone\"}, {\"id\": 48702, \"name\": \"yellow-orange flower\"}, {\"id\": 48703, \"name\": \"a bicycle's pedal\"}, {\"id\": 48704, \"name\": \"gray elephant\"}, {\"id\": 48705, \"name\": \"an exhibition\"}, {\"id\": 48706, \"name\": \"the gutter\"}, {\"id\": 48707, \"name\": \"cufflink\"}, {\"id\": 48708, \"name\": \"a silver-colored sign\"}, {\"id\": 48709, \"name\": \"a glass packaging\"}, {\"id\": 48710, \"name\": \"a wooden screw\"}, {\"id\": 48711, \"name\": \"the silk tablecloth\"}, {\"id\": 48712, \"name\": \"bag of popcorn\"}, {\"id\": 48713, \"name\": \"brown cone\"}, {\"id\": 48714, \"name\": \"parked bus\"}, {\"id\": 48715, \"name\": \"nail clippers\"}, {\"id\": 48716, \"name\": \"a leather pedestal\"}, {\"id\": 48717, \"name\": \"a guitar string\"}, {\"id\": 48718, \"name\": \"red airplane\"}, {\"id\": 48719, \"name\": \"metal utensil holder\"}, {\"id\": 48720, \"name\": \"the domino pizza\"}, {\"id\": 48721, \"name\": \"wooden ruler\"}, {\"id\": 48722, \"name\": \"gold handle\"}, {\"id\": 48723, \"name\": \"a copper fountain\"}, {\"id\": 48724, \"name\": \"hospital bed\"}, {\"id\": 48725, \"name\": \"the toilet paper\"}, {\"id\": 48726, \"name\": \"a silver outlet\"}, {\"id\": 48727, \"name\": \"a hot air balloon\"}, {\"id\": 48728, \"name\": \"metal scissors\"}, {\"id\": 48729, \"name\": \"computer's screen\"}, {\"id\": 48730, \"name\": \"gold lamppost\"}, {\"id\": 48731, \"name\": \"a conductor\"}, {\"id\": 48732, \"name\": \"a yellow plug\"}, {\"id\": 48733, \"name\": \"meat counter\"}, {\"id\": 48734, \"name\": \"the white phone\"}, {\"id\": 48735, \"name\": \"black dishwasher\"}, {\"id\": 48736, \"name\": \"a heart shape\"}, {\"id\": 48737, \"name\": \"paperweight\"}, {\"id\": 48738, \"name\": \"the tree's branch\"}, {\"id\": 48739, \"name\": \"a glass floor\"}, {\"id\": 48740, \"name\": \"black silhouette of a dog\"}, {\"id\": 48741, \"name\": \"the roll of paper\"}, {\"id\": 48742, \"name\": \"wooden packaging\"}, {\"id\": 48743, \"name\": \"a kiwi\"}, {\"id\": 48744, \"name\": \"a champagne flute\"}, {\"id\": 48745, \"name\": \"the copper rim\"}, {\"id\": 48746, \"name\": \"white tote\"}, {\"id\": 48747, \"name\": \"a map\"}, {\"id\": 48748, \"name\": \"a blue helicopter\"}, {\"id\": 48749, \"name\": \"the green and yellow shopping bag\"}, {\"id\": 48750, \"name\": \"a wooden dock\"}, {\"id\": 48751, \"name\": \"a straightening iron\"}, {\"id\": 48752, \"name\": \"the poinsettia\"}, {\"id\": 48753, \"name\": \"the black traffic cone\"}, {\"id\": 48754, \"name\": \"a showerhead\"}, {\"id\": 48755, \"name\": \"a cookie jar\"}, {\"id\": 48756, \"name\": \"copper lid\"}, {\"id\": 48757, \"name\": \"the roll of coin\"}, {\"id\": 48758, \"name\": \"a bronze rim\"}, {\"id\": 48759, \"name\": \"amazon logo\"}, {\"id\": 48760, \"name\": \"pink lettering\"}, {\"id\": 48761, \"name\": \"the gold nail polish\"}, {\"id\": 48762, \"name\": \"the image of table\"}, {\"id\": 48763, \"name\": \"a movie theater sign\"}, {\"id\": 48764, \"name\": \"an architect\"}, {\"id\": 48765, \"name\": \"a blue nail polish\"}, {\"id\": 48766, \"name\": \"cafe\"}, {\"id\": 48767, \"name\": \"treehouse\"}, {\"id\": 48768, \"name\": \"the house's door\"}, {\"id\": 48769, \"name\": \"horse's hoof\"}, {\"id\": 48770, \"name\": \"blackberry slice\"}, {\"id\": 48771, \"name\": \"the concert hall\"}, {\"id\": 48772, \"name\": \"an anklet\"}, {\"id\": 48773, \"name\": \"the clarinet\"}, {\"id\": 48774, \"name\": \"a green tape\"}, {\"id\": 48775, \"name\": \"a parked fire truck\"}, {\"id\": 48776, \"name\": \"a light nail polish\"}, {\"id\": 48777, \"name\": \"the curling iron\"}, {\"id\": 48778, \"name\": \"a rubber mat\"}, {\"id\": 48779, \"name\": \"the anklet\"}, {\"id\": 48780, \"name\": \"a raspberry slice\"}, {\"id\": 48781, \"name\": \"a parked ambulance\"}, {\"id\": 48782, \"name\": \"statue of a sphinx\"}, {\"id\": 48783, \"name\": \"can opener\"}, {\"id\": 48784, \"name\": \"a razor\"}, {\"id\": 48785, \"name\": \"a copier\"}, {\"id\": 48786, \"name\": \"the passionfruit\"}, {\"id\": 48787, \"name\": \"a purple canister\"}, {\"id\": 48788, \"name\": \"grader\"}, {\"id\": 48789, \"name\": \"the wooden rim\"}, {\"id\": 48790, \"name\": \"the houseplant\"}, {\"id\": 48791, \"name\": \"the fish tank\"}, {\"id\": 48792, \"name\": \"etching\"}, {\"id\": 48793, \"name\": \"parked airplane\"}, {\"id\": 48794, \"name\": \"a silver bow\"}, {\"id\": 48795, \"name\": \"the van mirror\"}, {\"id\": 48796, \"name\": \"the fabric napkin\"}, {\"id\": 48797, \"name\": \"bag of mango\"}, {\"id\": 48798, \"name\": \"black and white geometric pattern\"}, {\"id\": 48799, \"name\": \"a white pentagon\"}, {\"id\": 48800, \"name\": \"a yellow nail\"}, {\"id\": 48801, \"name\": \"a bathroom trash can\"}, {\"id\": 48802, \"name\": \"paper towel roll\"}, {\"id\": 48803, \"name\": \"a blue price tag\"}, {\"id\": 48804, \"name\": \"red handbag\"}, {\"id\": 48805, \"name\": \"a bookcase\"}, {\"id\": 48806, \"name\": \"plastic box\"}, {\"id\": 48807, \"name\": \"a stone shelf\"}, {\"id\": 48808, \"name\": \"a book page\"}, {\"id\": 48809, \"name\": \"a grey duffel\"}, {\"id\": 48810, \"name\": \"an orange nail polish\"}, {\"id\": 48811, \"name\": \"the round vent\"}, {\"id\": 48812, \"name\": \"the glass rim\"}, {\"id\": 48813, \"name\": \"the steel girder\"}, {\"id\": 48814, \"name\": \"the velvet pillow\"}, {\"id\": 48815, \"name\": \"gold ring\"}, {\"id\": 48816, \"name\": \"a parked taxi\"}, {\"id\": 48817, \"name\": \"a metal wheelchair\"}, {\"id\": 48818, \"name\": \"metal can opener\"}, {\"id\": 48819, \"name\": \"an athlete's hands\"}, {\"id\": 48820, \"name\": \"a paperclip\"}, {\"id\": 48821, \"name\": \"the glass mannequin\"}, {\"id\": 48822, \"name\": \"museum sign\"}, {\"id\": 48823, \"name\": \"harp\"}, {\"id\": 48824, \"name\": \"orange bus\"}, {\"id\": 48825, \"name\": \"a metal pen\"}, {\"id\": 48826, \"name\": \"island\"}, {\"id\": 48827, \"name\": \"brown wallet\"}, {\"id\": 48828, \"name\": \"a yellow dog\"}, {\"id\": 48829, \"name\": \"a glove compartment\"}, {\"id\": 48830, \"name\": \"quince\"}, {\"id\": 48831, \"name\": \"a bronze screw\"}, {\"id\": 48832, \"name\": \"an undercut\"}, {\"id\": 48833, \"name\": \"the spinach\"}, {\"id\": 48834, \"name\": \"purple truck\"}, {\"id\": 48835, \"name\": \"an office\"}, {\"id\": 48836, \"name\": \"image of bicycle\"}, {\"id\": 48837, \"name\": \"child with a ball\"}, {\"id\": 48838, \"name\": \"the glacier\"}, {\"id\": 48839, \"name\": \"a turquoise sunglass\"}, {\"id\": 48840, \"name\": \"glass door frame\"}, {\"id\": 48841, \"name\": \"a silver airplane\"}, {\"id\": 48842, \"name\": \"the gold curtain\"}, {\"id\": 48843, \"name\": \"silver laptop\"}, {\"id\": 48844, \"name\": \"mule\"}, {\"id\": 48845, \"name\": \"green basket\"}, {\"id\": 48846, \"name\": \"a bag of watermelon\"}, {\"id\": 48847, \"name\": \"the glockenspiel\"}, {\"id\": 48848, \"name\": \"statue of furniture\"}, {\"id\": 48849, \"name\": \"the statue of an animal\"}, {\"id\": 48850, \"name\": \"the calf\"}, {\"id\": 48851, \"name\": \"aluminum foil\"}, {\"id\": 48852, \"name\": \"the compass\"}, {\"id\": 48853, \"name\": \"a coat hook\"}, {\"id\": 48854, \"name\": \"yellow gravel\"}, {\"id\": 48855, \"name\": \"a red nail\"}, {\"id\": 48856, \"name\": \"an australian flag\"}, {\"id\": 48857, \"name\": \"cement mixer\"}, {\"id\": 48858, \"name\": \"the lemonade\"}, {\"id\": 48859, \"name\": \"the fax machine\"}, {\"id\": 48860, \"name\": \"the thermometer\"}, {\"id\": 48861, \"name\": \"a green house\"}, {\"id\": 48862, \"name\": \"a silver luggage\"}, {\"id\": 48863, \"name\": \"the musician's hand\"}, {\"id\": 48864, \"name\": \"the streetcar\"}, {\"id\": 48865, \"name\": \"the purple glove\"}, {\"id\": 48866, \"name\": \"a gray suitcase\"}, {\"id\": 48867, \"name\": \"an office desk\"}, {\"id\": 48868, \"name\": \"the porcelain coffee maker\"}, {\"id\": 48869, \"name\": \"the rubber tire\"}, {\"id\": 48870, \"name\": \"computer's reflection\"}, {\"id\": 48871, \"name\": \"the pink limousine\"}, {\"id\": 48872, \"name\": \"a statue of plant\"}, {\"id\": 48873, \"name\": \"bag of grape\"}, {\"id\": 48874, \"name\": \"pink limousine\"}, {\"id\": 48875, \"name\": \"triangular cutout\"}, {\"id\": 48876, \"name\": \"a green glove\"}, {\"id\": 48877, \"name\": \"newspaper headline\"}, {\"id\": 48878, \"name\": \"a bicycle's reflection\"}, {\"id\": 48879, \"name\": \"blue canister\"}, {\"id\": 48880, \"name\": \"the parked orange truck\"}, {\"id\": 48881, \"name\": \"the statue of a centaur\"}, {\"id\": 48882, \"name\": \"the silicone spatula\"}, {\"id\": 48883, \"name\": \"a clutch pedal\"}, {\"id\": 48884, \"name\": \"a parked purple van\"}, {\"id\": 48885, \"name\": \"israeli flag\"}, {\"id\": 48886, \"name\": \"a garden bench\"}, {\"id\": 48887, \"name\": \"a sketchbook\"}, {\"id\": 48888, \"name\": \"the purple wallet\"}, {\"id\": 48889, \"name\": \"the yellow sunglass\"}, {\"id\": 48890, \"name\": \"santa claus\"}, {\"id\": 48891, \"name\": \"silver star\"}, {\"id\": 48892, \"name\": \"a turquoise clutch\"}, {\"id\": 48893, \"name\": \"bicycle seat\"}, {\"id\": 48894, \"name\": \"a man's reflection\"}, {\"id\": 48895, \"name\": \"a steel sink\"}, {\"id\": 48896, \"name\": \"a parked red car\"}, {\"id\": 48897, \"name\": \"the brake pedal\"}, {\"id\": 48898, \"name\": \"the gray computer\"}, {\"id\": 48899, \"name\": \"the plastic fork\"}, {\"id\": 48900, \"name\": \"a horse's mane\"}, {\"id\": 48901, \"name\": \"the purple nail polish\"}, {\"id\": 48902, \"name\": \"silver bird\"}, {\"id\": 48903, \"name\": \"a yellow curtain\"}, {\"id\": 48904, \"name\": \"the silver nail polish\"}, {\"id\": 48905, \"name\": \"a copper nail\"}, {\"id\": 48906, \"name\": \"the parked fire truck\"}, {\"id\": 48907, \"name\": \"aluminum nail\"}, {\"id\": 48908, \"name\": \"rice cooker\"}, {\"id\": 48909, \"name\": \"black television\"}, {\"id\": 48910, \"name\": \"a xylophone\"}, {\"id\": 48911, \"name\": \"the black helicopter\"}, {\"id\": 48912, \"name\": \"the skid-steer loader\"}, {\"id\": 48913, \"name\": \"a parked turquoise ambulance\"}, {\"id\": 48914, \"name\": \"the brick door frame\"}, {\"id\": 48915, \"name\": \"the gold sign\"}, {\"id\": 48916, \"name\": \"the hair straightener\"}, {\"id\": 48917, \"name\": \"a clarinet\"}, {\"id\": 48918, \"name\": \"fabric sofa\"}, {\"id\": 48919, \"name\": \"the waterfall\"}, {\"id\": 48920, \"name\": \"bicycle pedal\"}, {\"id\": 48921, \"name\": \"a green price tag\"}, {\"id\": 48922, \"name\": \"a yellow bike\"}, {\"id\": 48923, \"name\": \"racing plate\"}, {\"id\": 48924, \"name\": \"the ray\"}, {\"id\": 48925, \"name\": \"the fly\"}, {\"id\": 48926, \"name\": \"the rowboat\"}, {\"id\": 48927, \"name\": \"the paddleboat\"}, {\"id\": 48928, \"name\": \"the actor\"}, {\"id\": 48929, \"name\": \"a water ski\"}, {\"id\": 48930, \"name\": \"the robin\"}, {\"id\": 48931, \"name\": \"the metal cone\"}, {\"id\": 48932, \"name\": \"jungle gym\"}, {\"id\": 48933, \"name\": \"a cricket ball\"}, {\"id\": 48934, \"name\": \"the soccer jersey\"}, {\"id\": 48935, \"name\": \"the leopard\"}, {\"id\": 48936, \"name\": \"a squash racket\"}, {\"id\": 48937, \"name\": \"taco\"}, {\"id\": 48938, \"name\": \"the copper race bib\"}, {\"id\": 48939, \"name\": \"the ferry\"}, {\"id\": 48940, \"name\": \"the submarine\"}, {\"id\": 48941, \"name\": \"the volleyball player\"}, {\"id\": 48942, \"name\": \"the book on table\"}, {\"id\": 48943, \"name\": \"the teapot\"}, {\"id\": 48944, \"name\": \"houseboat\"}, {\"id\": 48945, \"name\": \"rowboat\"}, {\"id\": 48946, \"name\": \"an iron race bib\"}, {\"id\": 48947, \"name\": \"whisker\"}, {\"id\": 48948, \"name\": \"glass screen\"}, {\"id\": 48949, \"name\": \"grilled cheese\"}, {\"id\": 48950, \"name\": \"a silver plate\"}, {\"id\": 48951, \"name\": \"a blimp\"}, {\"id\": 48952, \"name\": \"a placemat\"}, {\"id\": 48953, \"name\": \"barge\"}, {\"id\": 48954, \"name\": \"the boat seat\"}, {\"id\": 48955, \"name\": \"purple tree\"}, {\"id\": 48956, \"name\": \"the hockey puck\"}, {\"id\": 48957, \"name\": \"the centerpiece\"}, {\"id\": 48958, \"name\": \"the silver whip\"}, {\"id\": 48959, \"name\": \"doctor\"}, {\"id\": 48960, \"name\": \"strawberry tart\"}, {\"id\": 48961, \"name\": \"a pontoon\"}, {\"id\": 48962, \"name\": \"the iron wheel\"}, {\"id\": 48963, \"name\": \"a falcon\"}, {\"id\": 48964, \"name\": \"purple cone\"}, {\"id\": 48965, \"name\": \"tenni player\"}, {\"id\": 48966, \"name\": \"a flea\"}, {\"id\": 48967, \"name\": \"bus seat\"}, {\"id\": 48968, \"name\": \"a dog in green collar\"}, {\"id\": 48969, \"name\": \"cardinal\"}, {\"id\": 48970, \"name\": \"a canoe\"}, {\"id\": 48971, \"name\": \"the pepper grinder\"}, {\"id\": 48972, \"name\": \"whiskey decanter\"}, {\"id\": 48973, \"name\": \"the lighthouse\"}, {\"id\": 48974, \"name\": \"a swallow\"}, {\"id\": 48975, \"name\": \"a bird cage\"}, {\"id\": 48976, \"name\": \"conductor's baton\"}, {\"id\": 48977, \"name\": \"cricket bat\"}, {\"id\": 48978, \"name\": \"the giraffe\"}, {\"id\": 48979, \"name\": \"frigate\"}, {\"id\": 48980, \"name\": \"a lighthouse\"}, {\"id\": 48981, \"name\": \"steel shaft\"}, {\"id\": 48982, \"name\": \"halter\"}, {\"id\": 48983, \"name\": \"a lawyer\"}, {\"id\": 48984, \"name\": \"a writer\"}, {\"id\": 48985, \"name\": \"the wolf\"}, {\"id\": 48986, \"name\": \"the spider\"}, {\"id\": 48987, \"name\": \"purple glider\"}, {\"id\": 48988, \"name\": \"an airship\"}, {\"id\": 48989, \"name\": \"the fishing boat\"}, {\"id\": 48990, \"name\": \"the squirrel\"}, {\"id\": 48991, \"name\": \"the blimp\"}, {\"id\": 48992, \"name\": \"a swim cap\"}, {\"id\": 48993, \"name\": \"a bear keepsake\"}, {\"id\": 48994, \"name\": \"the judge\"}, {\"id\": 48995, \"name\": \"the plastic wheel\"}, {\"id\": 48996, \"name\": \"a roasted chicken\"}, {\"id\": 48997, \"name\": \"a fabric upholstery\"}, {\"id\": 48998, \"name\": \"architect\"}, {\"id\": 48999, \"name\": \"a red bicycle helmet\"}, {\"id\": 49000, \"name\": \"table leg\"}, {\"id\": 49001, \"name\": \"the tenni player\"}, {\"id\": 49002, \"name\": \"motorcycle helmet\"}, {\"id\": 49003, \"name\": \"purple canopy\"}, {\"id\": 49004, \"name\": \"a ping pong ball\"}, {\"id\": 49005, \"name\": \"the tennis shoe\"}, {\"id\": 49006, \"name\": \"the snorkeling mask\"}, {\"id\": 49007, \"name\": \"a starfish\"}, {\"id\": 49008, \"name\": \"a tenni shoe\"}, {\"id\": 49009, \"name\": \"the falcon\"}, {\"id\": 49010, \"name\": \"a snorkeling mask\"}, {\"id\": 49011, \"name\": \"firefly\"}, {\"id\": 49012, \"name\": \"the journal\"}, {\"id\": 49013, \"name\": \"purple race bib\"}, {\"id\": 49014, \"name\": \"a chair with wooden leg\"}, {\"id\": 49015, \"name\": \"purple sock\"}, {\"id\": 49016, \"name\": \"a sparrow\"}, {\"id\": 49017, \"name\": \"green tea ice cream\"}, {\"id\": 49018, \"name\": \"squid\"}, {\"id\": 49019, \"name\": \"the lawyer\"}, {\"id\": 49020, \"name\": \"a wooden carriage\"}, {\"id\": 49021, \"name\": \"a ceramic wheel\"}, {\"id\": 49022, \"name\": \"the office chair\"}, {\"id\": 49023, \"name\": \"a sailor\"}, {\"id\": 49024, \"name\": \"a metal cone\"}, {\"id\": 49025, \"name\": \"the farm tractor\"}, {\"id\": 49026, \"name\": \"a metal race bib\"}, {\"id\": 49027, \"name\": \"glider\"}, {\"id\": 49028, \"name\": \"a tenni player\"}, {\"id\": 49029, \"name\": \"a black forest cake\"}, {\"id\": 49030, \"name\": \"the cricket bat\"}, {\"id\": 49031, \"name\": \"basketball shoe\"}, {\"id\": 49032, \"name\": \"a mule\"}, {\"id\": 49033, \"name\": \"train rider\"}, {\"id\": 49034, \"name\": \"horse figurine\"}, {\"id\": 49035, \"name\": \"a grasshopper\"}, {\"id\": 49036, \"name\": \"woodpecker\"}, {\"id\": 49037, \"name\": \"the toad\"}, {\"id\": 49038, \"name\": \"wooden cone\"}, {\"id\": 49039, \"name\": \"an engineer\"}, {\"id\": 49040, \"name\": \"the pink rabbit\"}, {\"id\": 49041, \"name\": \"a plastic hubcap\"}, {\"id\": 49042, \"name\": \"a boxer\"}, {\"id\": 49043, \"name\": \"a golf ball\"}, {\"id\": 49044, \"name\": \"fishing boat\"}, {\"id\": 49045, \"name\": \"painter\"}, {\"id\": 49046, \"name\": \"the emu\"}, {\"id\": 49047, \"name\": \"the purple fish\"}, {\"id\": 49048, \"name\": \"car driver\"}, {\"id\": 49049, \"name\": \"an octopus\"}, {\"id\": 49050, \"name\": \"fish ornament\"}, {\"id\": 49051, \"name\": \"the fisherman\"}, {\"id\": 49052, \"name\": \"the poached salmon\"}, {\"id\": 49053, \"name\": \"a silver bicycle\"}, {\"id\": 49054, \"name\": \"the octopus\"}, {\"id\": 49055, \"name\": \"gymnastic mat\"}, {\"id\": 49056, \"name\": \"a soup\"}, {\"id\": 49057, \"name\": \"the mosquito\"}, {\"id\": 49058, \"name\": \"the monkey bar\"}, {\"id\": 49059, \"name\": \"a couch cushion\"}, {\"id\": 49060, \"name\": \"submarine periscope\"}, {\"id\": 49061, \"name\": \"pontoon\"}, {\"id\": 49062, \"name\": \"cocktail shaker\"}, {\"id\": 49063, \"name\": \"an owl\"}, {\"id\": 49064, \"name\": \"zeppelin\"}, {\"id\": 49065, \"name\": \"crispy bacon\"}, {\"id\": 49066, \"name\": \"the sparrow\"}, {\"id\": 49067, \"name\": \"dragonfly\"}, {\"id\": 49068, \"name\": \"a fishing boat\"}, {\"id\": 49069, \"name\": \"ping pong ball\"}, {\"id\": 49070, \"name\": \"a bronze race bib\"}, {\"id\": 49071, \"name\": \"an orange biplane\"}, {\"id\": 49072, \"name\": \"a gold bicycle helmet\"}, {\"id\": 49073, \"name\": \"green race bib\"}, {\"id\": 49074, \"name\": \"the woman in red dress\"}, {\"id\": 49075, \"name\": \"the ping pong paddle\"}, {\"id\": 49076, \"name\": \"the hawk\"}, {\"id\": 49077, \"name\": \"a rabbit toy\"}, {\"id\": 49078, \"name\": \"cricket\"}, {\"id\": 49079, \"name\": \"the golf ball\"}, {\"id\": 49080, \"name\": \"wearing wedge\"}, {\"id\": 49081, \"name\": \"spacecraft\"}, {\"id\": 49082, \"name\": \"the lobster\"}, {\"id\": 49083, \"name\": \"truck mirror\"}, {\"id\": 49084, \"name\": \"the paper cone\"}, {\"id\": 49085, \"name\": \"golf course\"}, {\"id\": 49086, \"name\": \"a wearing sock\"}, {\"id\": 49087, \"name\": \"paddleboard\"}, {\"id\": 49088, \"name\": \"a robin\"}, {\"id\": 49089, \"name\": \"the cargo ship\"}, {\"id\": 49090, \"name\": \"gray cat\"}, {\"id\": 49091, \"name\": \"the copper wheel\"}, {\"id\": 49092, \"name\": \"the basketball shoe\"}, {\"id\": 49093, \"name\": \"the rhino\"}, {\"id\": 49094, \"name\": \"a zip line\"}, {\"id\": 49095, \"name\": \"the eagle\"}, {\"id\": 49096, \"name\": \"the whale\"}, {\"id\": 49097, \"name\": \"a purple cone\"}, {\"id\": 49098, \"name\": \"an orange marmalade\"}, {\"id\": 49099, \"name\": \"the mule\"}, {\"id\": 49100, \"name\": \"the gymnast\"}, {\"id\": 49101, \"name\": \"a wolf\"}, {\"id\": 49102, \"name\": \"the muzzle\"}, {\"id\": 49103, \"name\": \"sheet music\"}, {\"id\": 49104, \"name\": \"silver bicycle helmet\"}, {\"id\": 49105, \"name\": \"computer with black screen\"}, {\"id\": 49106, \"name\": \"gold race bib\"}, {\"id\": 49107, \"name\": \"a camel\"}, {\"id\": 49108, \"name\": \"the sun hat\"}, {\"id\": 49109, \"name\": \"the milk\"}, {\"id\": 49110, \"name\": \"a green cone\"}, {\"id\": 49111, \"name\": \"the red race bib\"}, {\"id\": 49112, \"name\": \"a cruise ship\"}, {\"id\": 49113, \"name\": \"the crocodile\"}, {\"id\": 49114, \"name\": \"the crab\"}, {\"id\": 49115, \"name\": \"a singer\"}, {\"id\": 49116, \"name\": \"the kangaroo\"}, {\"id\": 49117, \"name\": \"wearing flip flop\"}, {\"id\": 49118, \"name\": \"lacrosse stick\"}, {\"id\": 49119, \"name\": \"a blue bird\"}, {\"id\": 49120, \"name\": \"the scientist\"}, {\"id\": 49121, \"name\": \"the silver race bib\"}, {\"id\": 49122, \"name\": \"a brown kite\"}, {\"id\": 49123, \"name\": \"a sidecar\"}, {\"id\": 49124, \"name\": \"a squid\"}, {\"id\": 49125, \"name\": \"the fox\"}, {\"id\": 49126, \"name\": \"soup\"}, {\"id\": 49127, \"name\": \"mountain lion\"}, {\"id\": 49128, \"name\": \"the tree with yellow leaf\"}, {\"id\": 49129, \"name\": \"the bicycle with red wheel\"}, {\"id\": 49130, \"name\": \"a table tenni paddle\"}, {\"id\": 49131, \"name\": \"a grey blimp\"}, {\"id\": 49132, \"name\": \"the ostrich\"}, {\"id\": 49133, \"name\": \"sugar bowl\"}, {\"id\": 49134, \"name\": \"purple bicycle helmet\"}, {\"id\": 49135, \"name\": \"an aircraft carrier\"}, {\"id\": 49136, \"name\": \"rat\"}, {\"id\": 49137, \"name\": \"a brown bear\"}, {\"id\": 49138, \"name\": \"the rhinoceros\"}, {\"id\": 49139, \"name\": \"a brochure\"}, {\"id\": 49140, \"name\": \"the coral reef\"}, {\"id\": 49141, \"name\": \"the horse rider\"}, {\"id\": 49142, \"name\": \"a seahorse\"}, {\"id\": 49143, \"name\": \"the orange bicycle helmet\"}, {\"id\": 49144, \"name\": \"seesaw\"}, {\"id\": 49145, \"name\": \"a skunk\"}, {\"id\": 49146, \"name\": \"a fisherman\"}, {\"id\": 49147, \"name\": \"librarian\"}, {\"id\": 49148, \"name\": \"tenni court\"}, {\"id\": 49149, \"name\": \"a painter\"}, {\"id\": 49150, \"name\": \"yellow plane\"}, {\"id\": 49151, \"name\": \"the goose\"}, {\"id\": 49152, \"name\": \"toad\"}, {\"id\": 49153, \"name\": \"table tenni paddle\"}, {\"id\": 49154, \"name\": \"a fried egg\"}, {\"id\": 49155, \"name\": \"the tea\"}, {\"id\": 49156, \"name\": \"a cockroach\"}, {\"id\": 49157, \"name\": \"the black race bib\"}, {\"id\": 49158, \"name\": \"a leaflet\"}, {\"id\": 49159, \"name\": \"giraffe souvenir\"}, {\"id\": 49160, \"name\": \"satellite\"}, {\"id\": 49161, \"name\": \"a finch\"}, {\"id\": 49162, \"name\": \"the wearing heel\"}, {\"id\": 49163, \"name\": \"baked potato\"}, {\"id\": 49164, \"name\": \"a silver jet\"}, {\"id\": 49165, \"name\": \"a silver bike\"}, {\"id\": 49166, \"name\": \"owl\"}, {\"id\": 49167, \"name\": \"blimp\"}, {\"id\": 49168, \"name\": \"ladybug\"}, {\"id\": 49169, \"name\": \"an emu\"}, {\"id\": 49170, \"name\": \"ship anchor\"}, {\"id\": 49171, \"name\": \"boat propeller\"}, {\"id\": 49172, \"name\": \"the monkey trinket\"}, {\"id\": 49173, \"name\": \"squash racket\"}, {\"id\": 49174, \"name\": \"tugboat\"}, {\"id\": 49175, \"name\": \"the architect\"}, {\"id\": 49176, \"name\": \"the wearing loafer\"}, {\"id\": 49177, \"name\": \"scale\"}, {\"id\": 49178, \"name\": \"hunter\"}, {\"id\": 49179, \"name\": \"a rabbit hutch\"}, {\"id\": 49180, \"name\": \"speed boat\"}, {\"id\": 49181, \"name\": \"the owl\"}, {\"id\": 49182, \"name\": \"a leather sofa\"}, {\"id\": 49183, \"name\": \"a wearing sneaker\"}, {\"id\": 49184, \"name\": \"the artist\"}, {\"id\": 49185, \"name\": \"a green pasture\"}, {\"id\": 49186, \"name\": \"a house with white door\"}, {\"id\": 49187, \"name\": \"lighthouse\"}, {\"id\": 49188, \"name\": \"a crow\"}, {\"id\": 49189, \"name\": \"a tourist\"}, {\"id\": 49190, \"name\": \"paper cone\"}, {\"id\": 49191, \"name\": \"the braised lamb\"}, {\"id\": 49192, \"name\": \"trampoline\"}, {\"id\": 49193, \"name\": \"stingray\"}, {\"id\": 49194, \"name\": \"robin\"}, {\"id\": 49195, \"name\": \"the apple pie\"}, {\"id\": 49196, \"name\": \"boiled carrot\"}, {\"id\": 49197, \"name\": \"a red bird\"}, {\"id\": 49198, \"name\": \"opossum\"}, {\"id\": 49199, \"name\": \"the rocket\"}, {\"id\": 49200, \"name\": \"a bus passenger\"}, {\"id\": 49201, \"name\": \"the crow\"}, {\"id\": 49202, \"name\": \"a houseboat\"}, {\"id\": 49203, \"name\": \"the wrestler\"}, {\"id\": 49204, \"name\": \"a whale\"}, {\"id\": 49205, \"name\": \"a battleship\"}, {\"id\": 49206, \"name\": \"wooden race bib\"}, {\"id\": 49207, \"name\": \"the spaceship\"}, {\"id\": 49208, \"name\": \"the painter\"}, {\"id\": 49209, \"name\": \"an orange race bib\"}, {\"id\": 49210, \"name\": \"grass-covered roof\"}, {\"id\": 49211, \"name\": \"green tree\"}, {\"id\": 49212, \"name\": \"a black candle\"}, {\"id\": 49213, \"name\": \"the black cone\"}, {\"id\": 49214, \"name\": \"the yellow bicycle helmet\"}, {\"id\": 49215, \"name\": \"elephant model\"}, {\"id\": 49216, \"name\": \"the peacock\"}, {\"id\": 49217, \"name\": \"a jellyfish\"}, {\"id\": 49218, \"name\": \"the raccoon\"}, {\"id\": 49219, \"name\": \"hawk\"}, {\"id\": 49220, \"name\": \"an oct\"}, {\"id\": 49221, \"name\": \"writer\"}, {\"id\": 49222, \"name\": \"the starfish\"}, {\"id\": 49223, \"name\": \"raven\"}, {\"id\": 49224, \"name\": \"car seat\"}, {\"id\": 49225, \"name\": \"pilot\"}, {\"id\": 49226, \"name\": \"the turquoise parachute\"}, {\"id\": 49227, \"name\": \"the sushi\"}, {\"id\": 49228, \"name\": \"a child in blue shirt\"}, {\"id\": 49229, \"name\": \"the pink macaron\"}, {\"id\": 49230, \"name\": \"the green helicopter\"}, {\"id\": 49231, \"name\": \"a gymnastic mat\"}, {\"id\": 49232, \"name\": \"the ant\"}, {\"id\": 49233, \"name\": \"a merry-go-round\"}, {\"id\": 49234, \"name\": \"rugby pitch\"}, {\"id\": 49235, \"name\": \"a resistance band\"}, {\"id\": 49236, \"name\": \"boat sailor\"}, {\"id\": 49237, \"name\": \"wearing dress shoe\"}, {\"id\": 49238, \"name\": \"a flamingo\"}, {\"id\": 49239, \"name\": \"a bus seat\"}, {\"id\": 49240, \"name\": \"a castle\"}, {\"id\": 49241, \"name\": \"koala\"}, {\"id\": 49242, \"name\": \"a train seat\"}, {\"id\": 49243, \"name\": \"the climbing wall\"}, {\"id\": 49244, \"name\": \"golf ball\"}, {\"id\": 49245, \"name\": \"the helicopter blade\"}, {\"id\": 49246, \"name\": \"a wrestler\"}, {\"id\": 49247, \"name\": \"a tiger collectible\"}, {\"id\": 49248, \"name\": \"brown bicycle helmet\"}, {\"id\": 49249, \"name\": \"pink bicycle helmet\"}, {\"id\": 49250, \"name\": \"the tenni court\"}, {\"id\": 49251, \"name\": \"the grey sheep\"}, {\"id\": 49252, \"name\": \"the cardinal\"}, {\"id\": 49253, \"name\": \"the professor\"}, {\"id\": 49254, \"name\": \"car in driveway\"}, {\"id\": 49255, \"name\": \"the airplane wing\"}, {\"id\": 49256, \"name\": \"an ostrich\"}, {\"id\": 49257, \"name\": \"the blue seaplane\"}, {\"id\": 49258, \"name\": \"a gymnast\"}, {\"id\": 49259, \"name\": \"rhino\"}, {\"id\": 49260, \"name\": \"an exercise bike\"}, {\"id\": 49261, \"name\": \"a salamander\"}, {\"id\": 49262, \"name\": \"an airplane seat\"}, {\"id\": 49263, \"name\": \"a train wheel\"}, {\"id\": 49264, \"name\": \"a gym\"}, {\"id\": 49265, \"name\": \"the parrot\"}, {\"id\": 49266, \"name\": \"a tank tread\"}, {\"id\": 49267, \"name\": \"the flamingo\"}, {\"id\": 49268, \"name\": \"the pontoon boat\"}, {\"id\": 49269, \"name\": \"reader\"}, {\"id\": 49270, \"name\": \"court\"}, {\"id\": 49271, \"name\": \"an airplane passenger\"}, {\"id\": 49272, \"name\": \"air jordan\"}, {\"id\": 49273, \"name\": \"the white electrical cord\"}, {\"id\": 49274, \"name\": \"a 13\"}, {\"id\": 49275, \"name\": \"human's leg\"}, {\"id\": 49276, \"name\": \"a red gate label\"}, {\"id\": 49277, \"name\": \"a colorful effigy\"}, {\"id\": 49278, \"name\": \"black wok\"}, {\"id\": 49279, \"name\": \"the silver bollard\"}, {\"id\": 49280, \"name\": \"the dark blue sweater\"}, {\"id\": 49281, \"name\": \"the quickmart sign\"}, {\"id\": 49282, \"name\": \"a multicolored stripe\"}, {\"id\": 49283, \"name\": \"the man's left arm\"}, {\"id\": 49284, \"name\": \"pane of glass\"}, {\"id\": 49285, \"name\": \"wicket\"}, {\"id\": 49286, \"name\": \"colored button\"}, {\"id\": 49287, \"name\": \"the white molding\"}, {\"id\": 49288, \"name\": \"the payment terminal\"}, {\"id\": 49289, \"name\": \"stuffed santa claus\"}, {\"id\": 49290, \"name\": \"the red upholstery\"}, {\"id\": 49291, \"name\": \"the open shoe box\"}, {\"id\": 49292, \"name\": \"barrow\"}, {\"id\": 49293, \"name\": \"the black smartwatch\"}, {\"id\": 49294, \"name\": \"the yellow cart\"}, {\"id\": 49295, \"name\": \"a white and yellow design\"}, {\"id\": 49296, \"name\": \"striking orange flag\"}, {\"id\": 49297, \"name\": \"white m within a circle\"}, {\"id\": 49298, \"name\": \"a brown and white curtain\"}, {\"id\": 49299, \"name\": \"the drawstring waist\"}, {\"id\": 49300, \"name\": \"power strip\"}, {\"id\": 49301, \"name\": \"the green and white label\"}, {\"id\": 49302, \"name\": \"racquetball\"}, {\"id\": 49303, \"name\": \"the gold leg\"}, {\"id\": 49304, \"name\": \"the grab-n-win!\"}, {\"id\": 49305, \"name\": \"the stone surface\"}, {\"id\": 49306, \"name\": \"the brown basket\"}, {\"id\": 49307, \"name\": \"the green paint\"}, {\"id\": 49308, \"name\": \"the silver stud\"}, {\"id\": 49309, \"name\": \"shelved storage area\"}, {\"id\": 49310, \"name\": \"the crisp white, long-sleeved dress shirt\"}, {\"id\": 49311, \"name\": \"pink bulletin board\"}, {\"id\": 49312, \"name\": \"the colorful flower\"}, {\"id\": 49313, \"name\": \"a gradient effect\"}, {\"id\": 49314, \"name\": \"the person's left foot\"}, {\"id\": 49315, \"name\": \"a kcy\"}, {\"id\": 49316, \"name\": \"a subscribe and bell notification\"}, {\"id\": 49317, \"name\": \"a pao de queljo\"}, {\"id\": 49318, \"name\": \"the red and white moped\"}, {\"id\": 49319, \"name\": \"a marked crosswalk\"}, {\"id\": 49320, \"name\": \"a left ring finger\"}, {\"id\": 49321, \"name\": \"a woman's shoulder\"}, {\"id\": 49322, \"name\": \"a white trash can\"}, {\"id\": 49323, \"name\": \"a gas stove\"}, {\"id\": 49324, \"name\": \"a brown sandal\"}, {\"id\": 49325, \"name\": \"a cauliflower\"}, {\"id\": 49326, \"name\": \"a white headset\"}, {\"id\": 49327, \"name\": \"the brownish liquid\"}, {\"id\": 49328, \"name\": \"the animated program\"}, {\"id\": 49329, \"name\": \"the soundbar\"}, {\"id\": 49330, \"name\": \"the nighttime image\"}, {\"id\": 49331, \"name\": \"the bleacher area\"}, {\"id\": 49332, \"name\": \"a brown and white swag\"}, {\"id\": 49333, \"name\": \"red chili pepper cutout\"}, {\"id\": 49334, \"name\": \"the maroon pant\"}, {\"id\": 49335, \"name\": \"white tissue box\"}, {\"id\": 49336, \"name\": \"the bright orange tie-dye shirt\"}, {\"id\": 49337, \"name\": \"the bottle cap\"}, {\"id\": 49338, \"name\": \"the shower curtain rod\"}, {\"id\": 49339, \"name\": \"a dark gray cobblestone\"}, {\"id\": 49340, \"name\": \"the red netflix logo\"}, {\"id\": 49341, \"name\": \"the bottom of a wooden dresser\"}, {\"id\": 49342, \"name\": \"a white ironing cloth\"}, {\"id\": 49343, \"name\": \"new plymouth\"}, {\"id\": 49344, \"name\": \"the black rear spoiler\"}, {\"id\": 49345, \"name\": \"the mop handle\"}, {\"id\": 49346, \"name\": \"a silver blowtorch\"}, {\"id\": 49347, \"name\": \"a red and black sandal\"}, {\"id\": 49348, \"name\": \"the tear in the paint\"}, {\"id\": 49349, \"name\": \"a cracked surface\"}, {\"id\": 49350, \"name\": \"covered porch\"}, {\"id\": 49351, \"name\": \"the brown image\"}, {\"id\": 49352, \"name\": \"a purple floral patterned cushion\"}, {\"id\": 49353, \"name\": \"right bicep\"}, {\"id\": 49354, \"name\": \"a spreading canopy\"}, {\"id\": 49355, \"name\": \"a black and red toy\"}, {\"id\": 49356, \"name\": \"the illuminated, silver sea creature\"}, {\"id\": 49357, \"name\": \"white donkey\"}, {\"id\": 49358, \"name\": \"electrical component\"}, {\"id\": 49359, \"name\": \"li-ning logo\"}, {\"id\": 49360, \"name\": \"the hardware sign\"}, {\"id\": 49361, \"name\": \"a bright red hat\"}, {\"id\": 49362, \"name\": \"kiara\"}, {\"id\": 49363, \"name\": \"the dark brown marble strip\"}, {\"id\": 49364, \"name\": \"the left knee\"}, {\"id\": 49365, \"name\": \"a beige heel\"}, {\"id\": 49366, \"name\": \"a blue plastic cape\"}, {\"id\": 49367, \"name\": \"a cardio machine\"}, {\"id\": 49368, \"name\": \"the silver metal strip\"}, {\"id\": 49369, \"name\": \"the horizontal crossbar\"}, {\"id\": 49370, \"name\": \"the curved, clear plastic straw\"}, {\"id\": 49371, \"name\": \"the man's left shoulder\"}, {\"id\": 49372, \"name\": \"the black ductwork\"}, {\"id\": 49373, \"name\": \"the heel\"}, {\"id\": 49374, \"name\": \"a black metal framing\"}, {\"id\": 49375, \"name\": \"a sequin\"}, {\"id\": 49376, \"name\": \"right forearm\"}, {\"id\": 49377, \"name\": \"a grey singlet\"}, {\"id\": 49378, \"name\": \"a child's right hand\"}, {\"id\": 49379, \"name\": \"the blue, yellow, and green design\"}, {\"id\": 49380, \"name\": \"price guarantee\"}, {\"id\": 49381, \"name\": \"the black desk\"}, {\"id\": 49382, \"name\": \"a keyboard stand\"}, {\"id\": 49383, \"name\": \"cream-colored plastic bin\"}, {\"id\": 49384, \"name\": \"the brown, speckled casing\"}, {\"id\": 49385, \"name\": \"the covered porch\"}, {\"id\": 49386, \"name\": \"a white feather\"}, {\"id\": 49387, \"name\": \"a greenery stand\"}, {\"id\": 49388, \"name\": \"white outer ring\"}, {\"id\": 49389, \"name\": \"a red and yellow paint\"}, {\"id\": 49390, \"name\": \"the white kiosk\"}, {\"id\": 49391, \"name\": \"frayed hem\"}, {\"id\": 49392, \"name\": \"the tan button-up shirt\"}, {\"id\": 49393, \"name\": \"the turquoise broom\"}, {\"id\": 49394, \"name\": \"round loaf\"}, {\"id\": 49395, \"name\": \"the outdoor enclosure\"}, {\"id\": 49396, \"name\": \"dog's eye\"}, {\"id\": 49397, \"name\": \"nike box\"}, {\"id\": 49398, \"name\": \"a blue-framed door\"}, {\"id\": 49399, \"name\": \"intricate gold design\"}, {\"id\": 49400, \"name\": \"a brown seat\"}, {\"id\": 49401, \"name\": \"a yellow snowsuit\"}, {\"id\": 49402, \"name\": \"the 00:08:06.7\"}, {\"id\": 49403, \"name\": \"the yellow and green metal frame\"}, {\"id\": 49404, \"name\": \"the red and yellow arcade game\"}, {\"id\": 49405, \"name\": \"a basket of tomato\"}, {\"id\": 49406, \"name\": \"a ribbon\"}, {\"id\": 49407, \"name\": \"a black wooden beam\"}, {\"id\": 49408, \"name\": \"a wooden overhang\"}, {\"id\": 49409, \"name\": \"the purple-painted fingernail\"}, {\"id\": 49410, \"name\": \"exposed insulation\"}, {\"id\": 49411, \"name\": \"skewer of potato\"}, {\"id\": 49412, \"name\": \"the dark table\"}, {\"id\": 49413, \"name\": \"the metal ring\"}, {\"id\": 49414, \"name\": \"black metal grill\"}, {\"id\": 49415, \"name\": \"a black granite table\"}, {\"id\": 49416, \"name\": \"the left elbow\"}, {\"id\": 49417, \"name\": \"the white advertisement\"}, {\"id\": 49418, \"name\": \"the animated cartoon\"}, {\"id\": 49419, \"name\": \"the wavy design\"}, {\"id\": 49420, \"name\": \"the clippers\"}, {\"id\": 49421, \"name\": \"a vibrant red and gold ornament\"}, {\"id\": 49422, \"name\": \"pink braid\"}, {\"id\": 49423, \"name\": \"the stone border\"}, {\"id\": 49424, \"name\": \"a cardboard takeout box\"}, {\"id\": 49425, \"name\": \"red and white advertisement\"}, {\"id\": 49426, \"name\": \"a red floral design\"}, {\"id\": 49427, \"name\": \"the decorative stone border\"}, {\"id\": 49428, \"name\": \"a machine's front panel\"}, {\"id\": 49429, \"name\": \"a white and blue toy\"}, {\"id\": 49430, \"name\": \"bright curtain\"}, {\"id\": 49431, \"name\": \"wetland\"}, {\"id\": 49432, \"name\": \"yellow, arcade-style machine\"}, {\"id\": 49433, \"name\": \"the boy's chest\"}, {\"id\": 49434, \"name\": \"the white ledge\"}, {\"id\": 49435, \"name\": \"the orange base\"}, {\"id\": 49436, \"name\": \"the yellow and green platform\"}, {\"id\": 49437, \"name\": \"suspended bridge\"}, {\"id\": 49438, \"name\": \"a sheer white curtain\"}, {\"id\": 49439, \"name\": \"a wooden paneling section\"}, {\"id\": 49440, \"name\": \"loaf of bread\"}, {\"id\": 49441, \"name\": \"the red and blue logo\"}, {\"id\": 49442, \"name\": \"a yellow and white clothing\"}, {\"id\": 49443, \"name\": \"a decorative ironwork\"}, {\"id\": 49444, \"name\": \"a diamond pattern design\"}, {\"id\": 49445, \"name\": \"an exposed wooden stud\"}, {\"id\": 49446, \"name\": \"a grey floral pattern\"}, {\"id\": 49447, \"name\": \"the vibrant red, black, yellow, and white patterned cover\"}, {\"id\": 49448, \"name\": \"a zig-zag design\"}, {\"id\": 49449, \"name\": \"the raised, white, walkway\"}, {\"id\": 49450, \"name\": \"dark brown border\"}, {\"id\": 49451, \"name\": \"a white and purple flower\"}, {\"id\": 49452, \"name\": \"a yellow and red diagonal stripe\"}, {\"id\": 49453, \"name\": \"the black support beam\"}, {\"id\": 49454, \"name\": \"dark red nail polish\"}, {\"id\": 49455, \"name\": \"the exposed root\"}, {\"id\": 49456, \"name\": \"a wet area\"}, {\"id\": 49457, \"name\": \"the lone scooter rider\"}, {\"id\": 49458, \"name\": \"grey shoulder strap\"}, {\"id\": 49459, \"name\": \"a lower portion of their leg\"}, {\"id\": 49460, \"name\": \"a carport\"}, {\"id\": 49461, \"name\": \"an exposed white support beam\"}, {\"id\": 49462, \"name\": \"stone bridge\"}, {\"id\": 49463, \"name\": \"yellow and black machine\"}, {\"id\": 49464, \"name\": \"metal drying rack\"}, {\"id\": 49465, \"name\": \"the avocado\"}, {\"id\": 49466, \"name\": \"drum kit\"}, {\"id\": 49467, \"name\": \"a handle of the polisher\"}, {\"id\": 49468, \"name\": \"an intricate gold embroidery\"}, {\"id\": 49469, \"name\": \"white signage\"}, {\"id\": 49470, \"name\": \"a translucent white panel\"}, {\"id\": 49471, \"name\": \"the green and black short\"}, {\"id\": 49472, \"name\": \"a person's left shoulder\"}, {\"id\": 49473, \"name\": \"a vendor's setup\"}, {\"id\": 49474, \"name\": \"back of her right calf\"}, {\"id\": 49475, \"name\": \"green platform\"}, {\"id\": 49476, \"name\": \"windshield wiper blade\"}, {\"id\": 49477, \"name\": \"a yellow character\"}, {\"id\": 49478, \"name\": \"decorative pattern of gold-colored shape\"}, {\"id\": 49479, \"name\": \"the red game machine\"}, {\"id\": 49480, \"name\": \"an electrical cord\"}, {\"id\": 49481, \"name\": \"alley\"}, {\"id\": 49482, \"name\": \"the grid pattern of wooden beam\"}, {\"id\": 49483, \"name\": \"exposed metal beam\"}, {\"id\": 49484, \"name\": \"blind's control cord\"}, {\"id\": 49485, \"name\": \"the orange support\"}, {\"id\": 49486, \"name\": \"playstation\"}, {\"id\": 49487, \"name\": \"karaoke machine\"}, {\"id\": 49488, \"name\": \"the tan sandal\"}, {\"id\": 49489, \"name\": \"the white plastic armrest\"}, {\"id\": 49490, \"name\": \"a blue pod\"}, {\"id\": 49491, \"name\": \"a spectacle\"}, {\"id\": 49492, \"name\": \"glass-enclosed balcony\"}, {\"id\": 49493, \"name\": \"back of her right thigh\"}, {\"id\": 49494, \"name\": \"the dark-colored material\"}, {\"id\": 49495, \"name\": \"white cardboard box\"}, {\"id\": 49496, \"name\": \"a cooking station\"}, {\"id\": 49497, \"name\": \"round shape\"}, {\"id\": 49498, \"name\": \"a grey wooden deck\"}, {\"id\": 49499, \"name\": \"a glass of a yellow beverage\"}, {\"id\": 49500, \"name\": \"a black controller\"}, {\"id\": 49501, \"name\": \"a dark-colored stripe\"}, {\"id\": 49502, \"name\": \"a checkout counter\"}, {\"id\": 49503, \"name\": \"a distinctive striped pattern\"}, {\"id\": 49504, \"name\": \"the nike air jordan logo\"}, {\"id\": 49505, \"name\": \"a tier\"}, {\"id\": 49506, \"name\": \"crate of white onion\"}, {\"id\": 49507, \"name\": \"a white nike logo\"}, {\"id\": 49508, \"name\": \"a checkout machine\"}, {\"id\": 49509, \"name\": \"yellow edge\"}, {\"id\": 49510, \"name\": \"the hockey\"}, {\"id\": 49511, \"name\": \"a sloping bottom\"}, {\"id\": 49512, \"name\": \"covered poolside bar\"}, {\"id\": 49513, \"name\": \"halfpipe\"}, {\"id\": 49514, \"name\": \"the sleek silver metal box\"}, {\"id\": 49515, \"name\": \"a dark spot\"}, {\"id\": 49516, \"name\": \"a juego playstation 3\"}, {\"id\": 49517, \"name\": \"a mic stand\"}, {\"id\": 49518, \"name\": \"the right knee\"}, {\"id\": 49519, \"name\": \"brown nose\"}, {\"id\": 49520, \"name\": \"vibrant, colorful decor\"}, {\"id\": 49521, \"name\": \"the gas tank\"}, {\"id\": 49522, \"name\": \"top of the windshield\"}, {\"id\": 49523, \"name\": \"a cardboard package\"}, {\"id\": 49524, \"name\": \"the gold jersey\"}, {\"id\": 49525, \"name\": \"a front door\"}, {\"id\": 49526, \"name\": \"the shiny, silver rim\"}, {\"id\": 49527, \"name\": \"a white fabric border\"}, {\"id\": 49528, \"name\": \"charcoal base\"}, {\"id\": 49529, \"name\": \"musician's hands\"}, {\"id\": 49530, \"name\": \"a dark stain\"}, {\"id\": 49531, \"name\": \"a second-story balcony\"}, {\"id\": 49532, \"name\": \"blonde tip\"}, {\"id\": 49533, \"name\": \"a bright bead\"}, {\"id\": 49534, \"name\": \"drying rack\"}, {\"id\": 49535, \"name\": \"a green and yellow fruit\"}, {\"id\": 49536, \"name\": \"the wet, paved sidewalk\"}, {\"id\": 49537, \"name\": \"a red and green surface\"}, {\"id\": 49538, \"name\": \"the plastic toy mouse\"}, {\"id\": 49539, \"name\": \"the brown and black striped coat pattern\"}, {\"id\": 49540, \"name\": \"a red fire hydrant\"}, {\"id\": 49541, \"name\": \"a shiny, round, silver rim\"}, {\"id\": 49542, \"name\": \"pump it up prime\"}, {\"id\": 49543, \"name\": \"black and orange singlet\"}, {\"id\": 49544, \"name\": \"a pink ledge\"}, {\"id\": 49545, \"name\": \"clothing store\"}, {\"id\": 49546, \"name\": \"the white goal\"}, {\"id\": 49547, \"name\": \"a blue swim goggle\"}, {\"id\": 49548, \"name\": \"prominent circular white border\"}, {\"id\": 49549, \"name\": \"the folded cloth\"}, {\"id\": 49550, \"name\": \"claw grabber\"}, {\"id\": 49551, \"name\": \"the metal scaffolding\"}, {\"id\": 49552, \"name\": \"a red legging\"}, {\"id\": 49553, \"name\": \"a pink and white sandal\"}, {\"id\": 49554, \"name\": \"the moving walkway\"}, {\"id\": 49555, \"name\": \"the wire mesh bulletin board\"}, {\"id\": 49556, \"name\": \"a stomach\"}, {\"id\": 49557, \"name\": \"the white examination table\"}, {\"id\": 49558, \"name\": \"a sketch of a house\"}, {\"id\": 49559, \"name\": \"the left calf\"}, {\"id\": 49560, \"name\": \"shiny surface\"}, {\"id\": 49561, \"name\": \"a striped top\"}, {\"id\": 49562, \"name\": \"the blurry black border\"}, {\"id\": 49563, \"name\": \"a cartoon character design\"}, {\"id\": 49564, \"name\": \"multicolored balloon\"}, {\"id\": 49565, \"name\": \"a wave pool\"}, {\"id\": 49566, \"name\": \"a gold watch\"}, {\"id\": 49567, \"name\": \"the orange robe\"}, {\"id\": 49568, \"name\": \"the front of the machine\"}, {\"id\": 49569, \"name\": \"the silver stove top\"}, {\"id\": 49570, \"name\": \"the ballet dancer emoji\"}, {\"id\": 49571, \"name\": \"the barley\"}, {\"id\": 49572, \"name\": \"metal grating\"}, {\"id\": 49573, \"name\": \"a black-and-white backsplash\"}, {\"id\": 49574, \"name\": \"red and white polka dot\"}, {\"id\": 49575, \"name\": \"a sleek, curved glass design\"}, {\"id\": 49576, \"name\": \"an iron boot\"}, {\"id\": 49577, \"name\": \"a pump\"}, {\"id\": 49578, \"name\": \"dark pillar\"}, {\"id\": 49579, \"name\": \"orange hood\"}, {\"id\": 49580, \"name\": \"green finish\"}, {\"id\": 49581, \"name\": \"the dark gray mat\"}, {\"id\": 49582, \"name\": \"a grey collar\"}, {\"id\": 49583, \"name\": \"a striped sock\"}, {\"id\": 49584, \"name\": \"the white nike sneaker\"}, {\"id\": 49585, \"name\": \"delicate silver necklace\"}, {\"id\": 49586, \"name\": \"a sleek black base\"}, {\"id\": 49587, \"name\": \"a square black screen\"}, {\"id\": 49588, \"name\": \"white cruise ship\"}, {\"id\": 49589, \"name\": \"a wooden door frame\"}, {\"id\": 49590, \"name\": \"a yellow sponge\"}, {\"id\": 49591, \"name\": \"the teal archway\"}, {\"id\": 49592, \"name\": \"the brown turtleneck\"}, {\"id\": 49593, \"name\": \"the fringe necklace\"}, {\"id\": 49594, \"name\": \"gray singlet\"}, {\"id\": 49595, \"name\": \"distinctive blue stripe\"}, {\"id\": 49596, \"name\": \"sole\"}, {\"id\": 49597, \"name\": \"a central walkway\"}, {\"id\": 49598, \"name\": \"the pink sequin\"}, {\"id\": 49599, \"name\": \"the gold pattern\"}, {\"id\": 49600, \"name\": \"a purple skirt\"}, {\"id\": 49601, \"name\": \"a power cord\"}, {\"id\": 49602, \"name\": \"a woman's glasses\"}, {\"id\": 49603, \"name\": \"buckle strap\"}, {\"id\": 49604, \"name\": \"the hindquarter\"}, {\"id\": 49605, \"name\": \"a red and orange scarf\"}, {\"id\": 49606, \"name\": \"the crown molding design\"}, {\"id\": 49607, \"name\": \"muddy appearance\"}, {\"id\": 49608, \"name\": \"verge\"}, {\"id\": 49609, \"name\": \"a trunk of a white car\"}, {\"id\": 49610, \"name\": \"bar counter\"}, {\"id\": 49611, \"name\": \"a prominent black sign\"}, {\"id\": 49612, \"name\": \"a barcode reader\"}, {\"id\": 49613, \"name\": \"yellow net\"}, {\"id\": 49614, \"name\": \"yellow dial\"}, {\"id\": 49615, \"name\": \"the black metal support beam\"}, {\"id\": 49616, \"name\": \"a clear plastic panel\"}, {\"id\": 49617, \"name\": \"raised concrete step\"}, {\"id\": 49618, \"name\": \"the prominent circular pattern\"}, {\"id\": 49619, \"name\": \"the jl.dagen\"}, {\"id\": 49620, \"name\": \"a red brick house\"}, {\"id\": 49621, \"name\": \"top bar\"}, {\"id\": 49622, \"name\": \"the shower stall\"}, {\"id\": 49623, \"name\": \"a gray pattern\"}, {\"id\": 49624, \"name\": \"black wrestling headgear\"}, {\"id\": 49625, \"name\": \"the yellow and red sign\"}, {\"id\": 49626, \"name\": \"bottom box\"}, {\"id\": 49627, \"name\": \"the covered walkway\"}, {\"id\": 49628, \"name\": \"the sloping side\"}, {\"id\": 49629, \"name\": \"a pilgrim sign\"}, {\"id\": 49630, \"name\": \"a white drawstring\"}, {\"id\": 49631, \"name\": \"a yellow and black polishing machine\"}, {\"id\": 49632, \"name\": \"a cartoon snowman\"}, {\"id\": 49633, \"name\": \"red and green laser beam\"}, {\"id\": 49634, \"name\": \"gray and white diamond pattern\"}, {\"id\": 49635, \"name\": \"an illegible symbol\"}, {\"id\": 49636, \"name\": \"a game pro sign\"}, {\"id\": 49637, \"name\": \"a yellow and black polisher\"}, {\"id\": 49638, \"name\": \"white strap\"}, {\"id\": 49639, \"name\": \"cello\"}, {\"id\": 49640, \"name\": \"a restroom\"}, {\"id\": 49641, \"name\": \"a dark brown paint\"}, {\"id\": 49642, \"name\": \"red and yellow license plate\"}, {\"id\": 49643, \"name\": \"the cream-colored curtain\"}, {\"id\": 49644, \"name\": \"yellow, pink, blue, and green checkered pattern\"}, {\"id\": 49645, \"name\": \"a busy hallway\"}, {\"id\": 49646, \"name\": \"a black and orange design\"}, {\"id\": 49647, \"name\": \"the yellow arcade game machine\"}, {\"id\": 49648, \"name\": \"a headgear\"}, {\"id\": 49649, \"name\": \"a green archway\"}, {\"id\": 49650, \"name\": \"the purple tag\"}, {\"id\": 49651, \"name\": \"the beige gravel substrate\"}, {\"id\": 49652, \"name\": \"metal checkout counter\"}, {\"id\": 49653, \"name\": \"scuff mark\"}, {\"id\": 49654, \"name\": \"a leg of dog\"}, {\"id\": 49655, \"name\": \"the machine's wheel\"}, {\"id\": 49656, \"name\": \"mystic skatepark\"}, {\"id\": 49657, \"name\": \"brown platform\"}, {\"id\": 49658, \"name\": \"red and orange dried pepper\"}, {\"id\": 49659, \"name\": \"la mer store\"}, {\"id\": 49660, \"name\": \"a striped cushion\"}, {\"id\": 49661, \"name\": \"the green steering wheel\"}, {\"id\": 49662, \"name\": \"mirror's surface\"}, {\"id\": 49663, \"name\": \"the pink paw pad\"}, {\"id\": 49664, \"name\": \"blue mountain range\"}, {\"id\": 49665, \"name\": \"a silver tape\"}, {\"id\": 49666, \"name\": \"white tissue paper\"}, {\"id\": 49667, \"name\": \"a hem\"}, {\"id\": 49668, \"name\": \"the mamma ticket game\"}, {\"id\": 49669, \"name\": \"the black and yellow polisher\"}, {\"id\": 49670, \"name\": \"a metal grill\"}, {\"id\": 49671, \"name\": \"a wooden porch\"}, {\"id\": 49672, \"name\": \"peeling paint\"}, {\"id\": 49673, \"name\": \"a gray and beige panel\"}, {\"id\": 49674, \"name\": \"leg pres machine\"}, {\"id\": 49675, \"name\": \"a silver base\"}, {\"id\": 49676, \"name\": \"a blue and white striped banner\"}, {\"id\": 49677, \"name\": \"the yellow head covering\"}, {\"id\": 49678, \"name\": \"a yellow smiley face\"}, {\"id\": 49679, \"name\": \"present\"}, {\"id\": 49680, \"name\": \"a spice rack\"}, {\"id\": 49681, \"name\": \"gray handle\"}, {\"id\": 49682, \"name\": \"a tan short\"}, {\"id\": 49683, \"name\": \"a cluttered surface\"}, {\"id\": 49684, \"name\": \"red and gold ornament\"}, {\"id\": 49685, \"name\": \"the main grill\"}, {\"id\": 49686, \"name\": \"a tangled cord\"}, {\"id\": 49687, \"name\": \"beige-colored canopy\"}, {\"id\": 49688, \"name\": \"the altar\"}, {\"id\": 49689, \"name\": \"a blue bikini\"}, {\"id\": 49690, \"name\": \"white, two-story loft area\"}, {\"id\": 49691, \"name\": \"a list of option\"}, {\"id\": 49692, \"name\": \"a blury image\"}, {\"id\": 49693, \"name\": \"a leg press\"}, {\"id\": 49694, \"name\": \"the vibrant orange marking\"}, {\"id\": 49695, \"name\": \"vinyl\"}, {\"id\": 49696, \"name\": \"a black entertainment center\"}, {\"id\": 49697, \"name\": \"a black metal security gate\"}, {\"id\": 49698, \"name\": \"the closet\"}, {\"id\": 49699, \"name\": \"blue pattern\"}, {\"id\": 49700, \"name\": \"a flowers and plant\"}, {\"id\": 49701, \"name\": \"a dark-colored base\"}, {\"id\": 49702, \"name\": \"drilling machine\"}, {\"id\": 49703, \"name\": \"a metal cutting saw\"}, {\"id\": 49704, \"name\": \"a charger\"}, {\"id\": 49705, \"name\": \"glass aquarium\"}, {\"id\": 49706, \"name\": \"dog's white fur\"}, {\"id\": 49707, \"name\": \"a stone area\"}, {\"id\": 49708, \"name\": \"a red, purple, and beige floral pattern\"}, {\"id\": 49709, \"name\": \"white vr headset\"}, {\"id\": 49710, \"name\": \"the grey dress\"}, {\"id\": 49711, \"name\": \"the white riser\"}, {\"id\": 49712, \"name\": \"the stain\"}, {\"id\": 49713, \"name\": \"room's decor\"}, {\"id\": 49714, \"name\": \"the bali's cornet\"}, {\"id\": 49715, \"name\": \"a white cotton swab\"}, {\"id\": 49716, \"name\": \"a white and red design\"}, {\"id\": 49717, \"name\": \"a merry christmas\"}, {\"id\": 49718, \"name\": \"the mold\"}, {\"id\": 49719, \"name\": \"a metal grid pattern\"}, {\"id\": 49720, \"name\": \"studio thi way\"}, {\"id\": 49721, \"name\": \"head of cabbage\"}, {\"id\": 49722, \"name\": \"the red and blue stripe\"}, {\"id\": 49723, \"name\": \"the entrance of the church\"}, {\"id\": 49724, \"name\": \"the rowing machine\"}, {\"id\": 49725, \"name\": \"red-framed storefront\"}, {\"id\": 49726, \"name\": \"toe of a shoe\"}, {\"id\": 49727, \"name\": \"a happy christmas\"}, {\"id\": 49728, \"name\": \"a silver pipe\"}, {\"id\": 49729, \"name\": \"cd\"}, {\"id\": 49730, \"name\": \"a colorful, cartoon-style game\"}, {\"id\": 49731, \"name\": \"tangled coil of cable\"}, {\"id\": 49732, \"name\": \"green bead\"}, {\"id\": 49733, \"name\": \"yellow stucco\"}, {\"id\": 49734, \"name\": \"buy-two-get-one-free promotion\"}, {\"id\": 49735, \"name\": \"a maroon long-sleeved shirt\"}, {\"id\": 49736, \"name\": \"the new york 1698\"}, {\"id\": 49737, \"name\": \"a curved metal handle\"}, {\"id\": 49738, \"name\": \"a semi-circle\"}, {\"id\": 49739, \"name\": \"a bag of grocery\"}, {\"id\": 49740, \"name\": \"ball-making machine\"}, {\"id\": 49741, \"name\": \"a yellow handle\"}, {\"id\": 49742, \"name\": \"the game's controls\"}, {\"id\": 49743, \"name\": \"the bright yellow fabric\"}, {\"id\": 49744, \"name\": \"the kebab\"}, {\"id\": 49745, \"name\": \"the seating for spectator\"}, {\"id\": 49746, \"name\": \"the blue and silver streamer\"}, {\"id\": 49747, \"name\": \"wavy, tan and orange pattern\"}, {\"id\": 49748, \"name\": \"the key icon\"}, {\"id\": 49749, \"name\": \"a snow-covered mountain slope\"}, {\"id\": 49750, \"name\": \"a dog's chest\"}, {\"id\": 49751, \"name\": \"a vibrant head wrap\"}, {\"id\": 49752, \"name\": \"a blue scooter\"}, {\"id\": 49753, \"name\": \"a sparse scattering of bush\"}, {\"id\": 49754, \"name\": \"red and yellow toy car\"}, {\"id\": 49755, \"name\": \"red and blue sign\"}, {\"id\": 49756, \"name\": \"beauty store\"}, {\"id\": 49757, \"name\": \"a tan-colored bottom\"}, {\"id\": 49758, \"name\": \"red and blue logo\"}, {\"id\": 49759, \"name\": \"a nintendo switch\"}, {\"id\": 49760, \"name\": \"the gray button-down shirt\"}, {\"id\": 49761, \"name\": \"a stone foundation\"}, {\"id\": 49762, \"name\": \"a smooth, glossy finish\"}, {\"id\": 49763, \"name\": \"the silver top\"}, {\"id\": 49764, \"name\": \"blue ping pong table\"}, {\"id\": 49765, \"name\": \"the li-ning logo\"}, {\"id\": 49766, \"name\": \"the dark red door\"}, {\"id\": 49767, \"name\": \"top grill\"}, {\"id\": 49768, \"name\": \"a mcdonald's sign\"}, {\"id\": 49769, \"name\": \"the wooden slat\"}, {\"id\": 49770, \"name\": \"an image of fried chicken\"}, {\"id\": 49771, \"name\": \"blue, yellow, and white stripe\"}, {\"id\": 49772, \"name\": \"a japanese supermarket checkout machine\"}, {\"id\": 49773, \"name\": \"delicate white veil\"}, {\"id\": 49774, \"name\": \"black and yellow vest\"}, {\"id\": 49775, \"name\": \"clear, shallow pool\"}, {\"id\": 49776, \"name\": \"a concrete center divider\"}, {\"id\": 49777, \"name\": \"the soccer game\"}, {\"id\": 49778, \"name\": \"a pink center\"}, {\"id\": 49779, \"name\": \"a black vr headset\"}, {\"id\": 49780, \"name\": \"the paw patrol logo\"}, {\"id\": 49781, \"name\": \"bedspread\"}, {\"id\": 49782, \"name\": \"maersk\"}, {\"id\": 49783, \"name\": \"the elephant print\"}, {\"id\": 49784, \"name\": \"the red and blue design\"}, {\"id\": 49785, \"name\": \"pavilion\"}, {\"id\": 49786, \"name\": \"plastic sheeting\"}, {\"id\": 49787, \"name\": \"canvas-covered back\"}, {\"id\": 49788, \"name\": \"the white, cylindrical container\"}, {\"id\": 49789, \"name\": \"a gold label\"}, {\"id\": 49790, \"name\": \"the wooden seat\"}, {\"id\": 49791, \"name\": \"gray legging\"}, {\"id\": 49792, \"name\": \"an illuminated landing pad\"}, {\"id\": 49793, \"name\": \"green and red label\"}, {\"id\": 49794, \"name\": \"hardware\"}, {\"id\": 49795, \"name\": \"red plastic covering\"}, {\"id\": 49796, \"name\": \"tropical fruit fresh\"}, {\"id\": 49797, \"name\": \"the slat\"}, {\"id\": 49798, \"name\": \"stainless steel conveyor belt\"}, {\"id\": 49799, \"name\": \"a left ankle\"}, {\"id\": 49800, \"name\": \"the cargo pocket\"}, {\"id\": 49801, \"name\": \"black neck\"}, {\"id\": 49802, \"name\": \"the colorful float\"}, {\"id\": 49803, \"name\": \"the black dvd case\"}, {\"id\": 49804, \"name\": \"a yellow and green metalwork\"}, {\"id\": 49805, \"name\": \"cracked asphalt\"}, {\"id\": 49806, \"name\": \"blue and white striped\"}, {\"id\": 49807, \"name\": \"black headset\"}, {\"id\": 49808, \"name\": \"a white middle ring\"}, {\"id\": 49809, \"name\": \"man's waist\"}, {\"id\": 49810, \"name\": \"the smiling jellyfish\"}, {\"id\": 49811, \"name\": \"an exit\"}, {\"id\": 49812, \"name\": \"the purple base\"}, {\"id\": 49813, \"name\": \"the red bull\"}, {\"id\": 49814, \"name\": \"the white veil\"}, {\"id\": 49815, \"name\": \"the swing ride\"}, {\"id\": 49816, \"name\": \"the drawstring\"}, {\"id\": 49817, \"name\": \"a blurred reflection\"}, {\"id\": 49818, \"name\": \"a brooklyn new york 1998\"}, {\"id\": 49819, \"name\": \"a rough, uneven surface\"}, {\"id\": 49820, \"name\": \"a firewood\"}, {\"id\": 49821, \"name\": \"the green and white graphic\"}, {\"id\": 49822, \"name\": \"an aluminum boot\"}, {\"id\": 49823, \"name\": \"the white bas drum\"}, {\"id\": 49824, \"name\": \"black folder\"}, {\"id\": 49825, \"name\": \"the stairs\"}, {\"id\": 49826, \"name\": \"beige and grey paint\"}, {\"id\": 49827, \"name\": \"a wrapped gift\"}, {\"id\": 49828, \"name\": \"nipple\"}, {\"id\": 49829, \"name\": \"intricate white embroidery\"}, {\"id\": 49830, \"name\": \"vibrant painting\"}, {\"id\": 49831, \"name\": \"a sosrokusuman\"}, {\"id\": 49832, \"name\": \"car's trunk\"}, {\"id\": 49833, \"name\": \"the red, blue, yellow, and green bar\"}, {\"id\": 49834, \"name\": \"a man with a long white beard\"}, {\"id\": 49835, \"name\": \"a yellow and white cloth\"}, {\"id\": 49836, \"name\": \"the pink mat\"}, {\"id\": 49837, \"name\": \"sheer white curtain\"}, {\"id\": 49838, \"name\": \"the white controller\"}, {\"id\": 49839, \"name\": \"a dark brown bristle\"}, {\"id\": 49840, \"name\": \"nyc\"}, {\"id\": 49841, \"name\": \"a rectangular shape\"}, {\"id\": 49842, \"name\": \"a blue waistband\"}, {\"id\": 49843, \"name\": \"hanging streamer\"}, {\"id\": 49844, \"name\": \"a black pad\"}, {\"id\": 49845, \"name\": \"silver headphone\"}, {\"id\": 49846, \"name\": \"white bookshelf\"}, {\"id\": 49847, \"name\": \"a deciduous\"}, {\"id\": 49848, \"name\": \"a cursive writing\"}, {\"id\": 49849, \"name\": \"the decorative fan-shaped design\"}, {\"id\": 49850, \"name\": \"a vinyl\"}, {\"id\": 49851, \"name\": \"a yellow, green, pink, and orange stripe\"}, {\"id\": 49852, \"name\": \"a yellow dress form\"}, {\"id\": 49853, \"name\": \"a tray of white turnip\"}, {\"id\": 49854, \"name\": \"neon yellow skirt\"}, {\"id\": 49855, \"name\": \"the windshield wiper blade\"}, {\"id\": 49856, \"name\": \"an orange, white, and black body\"}, {\"id\": 49857, \"name\": \"central altar\"}, {\"id\": 49858, \"name\": \"the black-framed door\"}, {\"id\": 49859, \"name\": \"black headband\"}, {\"id\": 49860, \"name\": \"a black stage\"}, {\"id\": 49861, \"name\": \"the blue dot\"}, {\"id\": 49862, \"name\": \"a brown take-out container\"}, {\"id\": 49863, \"name\": \"a blue microphone\"}, {\"id\": 49864, \"name\": \"lunch\"}, {\"id\": 49865, \"name\": \"the green knee brace\"}, {\"id\": 49866, \"name\": \"the white bowl of green\"}, {\"id\": 49867, \"name\": \"a crown molding\"}, {\"id\": 49868, \"name\": \"a grey and white patterned rug\"}, {\"id\": 49869, \"name\": \"brown and tan pattern\"}, {\"id\": 49870, \"name\": \"the standing cat\"}, {\"id\": 49871, \"name\": \"green piece of paper\"}, {\"id\": 49872, \"name\": \"a work surface\"}, {\"id\": 49873, \"name\": \"the black and white striped bag\"}, {\"id\": 49874, \"name\": \"black and white bench\"}, {\"id\": 49875, \"name\": \"stem of the bouquet\"}, {\"id\": 49876, \"name\": \"the rose-gold frame\"}, {\"id\": 49877, \"name\": \"the orange, yellow, and white koi\"}, {\"id\": 49878, \"name\": \"the black and white leg\"}, {\"id\": 49879, \"name\": \"the blue, yellow, green, red, and black stripe\"}, {\"id\": 49880, \"name\": \"the green pipe\"}, {\"id\": 49881, \"name\": \"the kifiria\"}, {\"id\": 49882, \"name\": \"the red and black graphic\"}, {\"id\": 49883, \"name\": \"the pink area\"}, {\"id\": 49884, \"name\": \"a dark red motorbike\"}, {\"id\": 49885, \"name\": \"tooth\"}, {\"id\": 49886, \"name\": \"the pink bead\"}, {\"id\": 49887, \"name\": \"elliptical machine\"}, {\"id\": 49888, \"name\": \"a xbox logo\"}, {\"id\": 49889, \"name\": \"a gray metal desk\"}, {\"id\": 49890, \"name\": \"a yellow collar\"}, {\"id\": 49891, \"name\": \"a vertical beige panel\"}, {\"id\": 49892, \"name\": \"black, glossy surface\"}, {\"id\": 49893, \"name\": \"dark-colored stone ledge\"}, {\"id\": 49894, \"name\": \"brown short\"}, {\"id\": 49895, \"name\": \"blue cord\"}, {\"id\": 49896, \"name\": \"the prominent red and blue banner\"}, {\"id\": 49897, \"name\": \"a show's logo\"}, {\"id\": 49898, \"name\": \"the black and red spot\"}, {\"id\": 49899, \"name\": \"the tan underbelly\"}, {\"id\": 49900, \"name\": \"bent left elbow\"}, {\"id\": 49901, \"name\": \"a bag of food\"}, {\"id\": 49902, \"name\": \"brown bookshelf\"}, {\"id\": 49903, \"name\": \"the front seat of a vehicle\"}, {\"id\": 49904, \"name\": \"the woman's left hand\"}, {\"id\": 49905, \"name\": \"shoulder strap\"}, {\"id\": 49906, \"name\": \"the soil and mulch\"}, {\"id\": 49907, \"name\": \"the p.g barudwale event\"}, {\"id\": 49908, \"name\": \"a red and black squash racket\"}, {\"id\": 49909, \"name\": \"the tan carpeted surface\"}, {\"id\": 49910, \"name\": \"blue door frame\"}, {\"id\": 49911, \"name\": \"white-and-black zebra-print rug\"}, {\"id\": 49912, \"name\": \"the green and red column\"}, {\"id\": 49913, \"name\": \"glass elevator shaft\"}, {\"id\": 49914, \"name\": \"green kiosk\"}, {\"id\": 49915, \"name\": \"usb end\"}, {\"id\": 49916, \"name\": \"the red circular button\"}, {\"id\": 49917, \"name\": \"rocky hill\"}, {\"id\": 49918, \"name\": \"patterned rug\"}, {\"id\": 49919, \"name\": \"rainbow of color\"}, {\"id\": 49920, \"name\": \"a form-fitting, dark dress\"}, {\"id\": 49921, \"name\": \"a playstation 3\"}, {\"id\": 49922, \"name\": \"the red dial\"}, {\"id\": 49923, \"name\": \"the white-speckled terrazzo\"}, {\"id\": 49924, \"name\": \"black and red tomy logo\"}, {\"id\": 49925, \"name\": \"black netting\"}, {\"id\": 49926, \"name\": \"rough, cracked surface\"}, {\"id\": 49927, \"name\": \"a child in pink shirt and black pant\"}, {\"id\": 49928, \"name\": \"the pink swirl\"}, {\"id\": 49929, \"name\": \"tan boot\"}, {\"id\": 49930, \"name\": \"a purple triangle\"}, {\"id\": 49931, \"name\": \"a yellow and black pad\"}, {\"id\": 49932, \"name\": \"corrugated metal panel\"}, {\"id\": 49933, \"name\": \"a blue square\"}, {\"id\": 49934, \"name\": \"the shelf of bag\"}, {\"id\": 49935, \"name\": \"red slide\"}, {\"id\": 49936, \"name\": \"the brown wooden door\"}, {\"id\": 49937, \"name\": \"a worn, raised curb\"}, {\"id\": 49938, \"name\": \"the red and black post\"}, {\"id\": 49939, \"name\": \"the white key icon\"}, {\"id\": 49940, \"name\": \"a red and blue advertisement\"}, {\"id\": 49941, \"name\": \"white patterned design\"}, {\"id\": 49942, \"name\": \"round, white top\"}, {\"id\": 49943, \"name\": \"a red food cart\"}, {\"id\": 49944, \"name\": \"a matching red blouse\"}, {\"id\": 49945, \"name\": \"a curved gray counter\"}, {\"id\": 49946, \"name\": \"blue foam roller\"}, {\"id\": 49947, \"name\": \"the red purse\"}, {\"id\": 49948, \"name\": \"oferta\"}, {\"id\": 49949, \"name\": \"the pipe organ\"}, {\"id\": 49950, \"name\": \"a person wearing a blue hat\"}, {\"id\": 49951, \"name\": \"the white steering wheel\"}, {\"id\": 49952, \"name\": \"a white patch of feather\"}, {\"id\": 49953, \"name\": \"a dark green paint\"}, {\"id\": 49954, \"name\": \"yellow and grey polishing wheel\"}, {\"id\": 49955, \"name\": \"tiger's face\"}, {\"id\": 49956, \"name\": \"a black and yellow polishing pad\"}, {\"id\": 49957, \"name\": \"bystander\"}, {\"id\": 49958, \"name\": \"a ductwork\"}, {\"id\": 49959, \"name\": \"the top box\"}, {\"id\": 49960, \"name\": \"a marble top\"}, {\"id\": 49961, \"name\": \"a blue, curved escalator\"}, {\"id\": 49962, \"name\": \"an electronic keyboard\"}, {\"id\": 49963, \"name\": \"the wrestling singlet\"}, {\"id\": 49964, \"name\": \"hand of a person\"}, {\"id\": 49965, \"name\": \"the woman in a purple sari\"}, {\"id\": 49966, \"name\": \"pit\"}, {\"id\": 49967, \"name\": \"a black ring\"}, {\"id\": 49968, \"name\": \"the gray stone border\"}, {\"id\": 49969, \"name\": \"a grinding tool\"}, {\"id\": 49970, \"name\": \"a silver canister\"}, {\"id\": 49971, \"name\": \"a swirling pattern of brown and yellow\"}, {\"id\": 49972, \"name\": \"the bottle of blue liquid\"}, {\"id\": 49973, \"name\": \"the portable stove\"}, {\"id\": 49974, \"name\": \"a white and brown pattern\"}, {\"id\": 49975, \"name\": \"the adult's hand\"}, {\"id\": 49976, \"name\": \"lb\"}, {\"id\": 49977, \"name\": \"a teal-colored vehicle\"}, {\"id\": 49978, \"name\": \"the single seat\"}, {\"id\": 49979, \"name\": \"the plastic surface\"}, {\"id\": 49980, \"name\": \"a colorful tattoo design\"}, {\"id\": 49981, \"name\": \"a black and yellow shoe\"}, {\"id\": 49982, \"name\": \"united state\"}, {\"id\": 49983, \"name\": \"the red and pink border\"}, {\"id\": 49984, \"name\": \"white shutter\"}, {\"id\": 49985, \"name\": \"handball\"}, {\"id\": 49986, \"name\": \"the catalog\"}, {\"id\": 49987, \"name\": \"vibrant, multicolored dress\"}, {\"id\": 49988, \"name\": \"red apron\"}, {\"id\": 49989, \"name\": \"yellow and green pole\"}, {\"id\": 49990, \"name\": \"self-service checkout\"}, {\"id\": 49991, \"name\": \"the corner of a wooden desk\"}, {\"id\": 49992, \"name\": \"steel boot\"}, {\"id\": 49993, \"name\": \"the blue machine\"}, {\"id\": 49994, \"name\": \"the pink and white flower\"}, {\"id\": 49995, \"name\": \"a dark border\"}, {\"id\": 49996, \"name\": \"church aisle\"}, {\"id\": 49997, \"name\": \"a silver pendant\"}, {\"id\": 49998, \"name\": \"the adjacent sidewalk\"}, {\"id\": 49999, \"name\": \"the partially open sliding door\"}, {\"id\": 50000, \"name\": \"a prominent green machine\"}, {\"id\": 50001, \"name\": \"a pink, blue, yellow, and white balloon\"}, {\"id\": 50002, \"name\": \"the spruce\"}, {\"id\": 50003, \"name\": \"silverware set\"}, {\"id\": 50004, \"name\": \"a select beverage\"}, {\"id\": 50005, \"name\": \"the prominent blue and white sign\"}, {\"id\": 50006, \"name\": \"a blue conveyor belt\"}, {\"id\": 50007, \"name\": \"the cement\"}, {\"id\": 50008, \"name\": \"the woven placemat\"}, {\"id\": 50009, \"name\": \"blue medical mask\"}, {\"id\": 50010, \"name\": \"makehift stove\"}, {\"id\": 50011, \"name\": \"wide-angle len\"}, {\"id\": 50012, \"name\": \"the flat, round base\"}, {\"id\": 50013, \"name\": \"the vibrant red image\"}, {\"id\": 50014, \"name\": \"a wii controller\"}, {\"id\": 50015, \"name\": \"3x2\"}, {\"id\": 50016, \"name\": \"the reddish-brown ground\"}, {\"id\": 50017, \"name\": \"white streak\"}, {\"id\": 50018, \"name\": \"pantry\"}, {\"id\": 50019, \"name\": \"zinvo\"}, {\"id\": 50020, \"name\": \"red and black graphic\"}, {\"id\": 50021, \"name\": \"the blue, red, and purple circular logo\"}, {\"id\": 50022, \"name\": \"a game board\"}, {\"id\": 50023, \"name\": \"a beige carpeting\"}, {\"id\": 50024, \"name\": \"red and black machine\"}, {\"id\": 50025, \"name\": \"a fish food\"}, {\"id\": 50026, \"name\": \"the yellow start button\"}, {\"id\": 50027, \"name\": \"a blue and purple patterned border\"}, {\"id\": 50028, \"name\": \"a happy birthday\"}, {\"id\": 50029, \"name\": \"an acrylic\"}, {\"id\": 50030, \"name\": \"the red pod\"}, {\"id\": 50031, \"name\": \"pink wooden top\"}, {\"id\": 50032, \"name\": \"the blue tag\"}, {\"id\": 50033, \"name\": \"a yellow, red, and black bus\"}, {\"id\": 50034, \"name\": \"nintendo\"}, {\"id\": 50035, \"name\": \"the green vest\"}, {\"id\": 50036, \"name\": \"a self-service checkout counter\"}, {\"id\": 50037, \"name\": \"multicolored metal bar\"}, {\"id\": 50038, \"name\": \"the blue and white floral sheet\"}, {\"id\": 50039, \"name\": \"a silver and black arm\"}, {\"id\": 50040, \"name\": \"a blue-and-white striped spiral\"}, {\"id\": 50041, \"name\": \"the green bridge\"}, {\"id\": 50042, \"name\": \"a load of box\"}, {\"id\": 50043, \"name\": \"the tan-colored back panel\"}, {\"id\": 50044, \"name\": \"the purple lanyard\"}, {\"id\": 50045, \"name\": \"red and black stripe\"}, {\"id\": 50046, \"name\": \"the drink special\"}, {\"id\": 50047, \"name\": \"a blue and gold balloon arch\"}, {\"id\": 50048, \"name\": \"the flowing white dress\"}, {\"id\": 50049, \"name\": \"green square\"}, {\"id\": 50050, \"name\": \"the blue and yellow striped pole\"}, {\"id\": 50051, \"name\": \"white plastic cone\"}, {\"id\": 50052, \"name\": \"the antelope\"}, {\"id\": 50053, \"name\": \"the bed of beige rock\"}, {\"id\": 50054, \"name\": \"a brown sauce\"}, {\"id\": 50055, \"name\": \"a movie's credits\"}, {\"id\": 50056, \"name\": \"an image of wine glass\"}, {\"id\": 50057, \"name\": \"the burning wood\"}, {\"id\": 50058, \"name\": \"a white kettle\"}, {\"id\": 50059, \"name\": \"the child's seat\"}, {\"id\": 50060, \"name\": \"distinctive purple logo\"}, {\"id\": 50061, \"name\": \"metal tube\"}, {\"id\": 50062, \"name\": \"colorful drawing\"}, {\"id\": 50063, \"name\": \"the house's front door\"}, {\"id\": 50064, \"name\": \"a red, blue, and yellow balloon\"}, {\"id\": 50065, \"name\": \"the pink, purple, and white flower design\"}, {\"id\": 50066, \"name\": \"a pink handlebar\"}, {\"id\": 50067, \"name\": \"a black rolling bag\"}, {\"id\": 50068, \"name\": \"yellow tip\"}, {\"id\": 50069, \"name\": \"man's shadow\"}, {\"id\": 50070, \"name\": \"the elephant print design\"}, {\"id\": 50071, \"name\": \"a small section of the sandy beach\"}, {\"id\": 50072, \"name\": \"spoon handle\"}, {\"id\": 50073, \"name\": \"a red bralette\"}, {\"id\": 50074, \"name\": \"a workshop\"}, {\"id\": 50075, \"name\": \"the green cushion\"}, {\"id\": 50076, \"name\": \"camel's nose ring\"}, {\"id\": 50077, \"name\": \"the 31\"}, {\"id\": 50078, \"name\": \"the red and black design\"}, {\"id\": 50079, \"name\": \"a brown and white patterned blanket\"}, {\"id\": 50080, \"name\": \"the orange and white patterned top\"}, {\"id\": 50081, \"name\": \"the yellow construction vest\"}, {\"id\": 50082, \"name\": \"a dark top\"}, {\"id\": 50083, \"name\": \"smudge\"}, {\"id\": 50084, \"name\": \"the top bowl\"}, {\"id\": 50085, \"name\": \"the diamond\"}, {\"id\": 50086, \"name\": \"white and brown patterned seat\"}, {\"id\": 50087, \"name\": \"the white diamond-shaped sign\"}, {\"id\": 50088, \"name\": \"a green apple\"}, {\"id\": 50089, \"name\": \"the hinged lid\"}, {\"id\": 50090, \"name\": \"splice\"}, {\"id\": 50091, \"name\": \"a takeaway\"}, {\"id\": 50092, \"name\": \"a bali's comet\"}, {\"id\": 50093, \"name\": \"the decorative gold pattern\"}, {\"id\": 50094, \"name\": \"a black power cord\"}, {\"id\": 50095, \"name\": \"a lap of a person\"}, {\"id\": 50096, \"name\": \"the red zipper\"}, {\"id\": 50097, \"name\": \"a blue kurta\"}, {\"id\": 50098, \"name\": \"the gudal\"}, {\"id\": 50099, \"name\": \"an open fire\"}, {\"id\": 50100, \"name\": \"salt\"}, {\"id\": 50101, \"name\": \"a maroon pant\"}, {\"id\": 50102, \"name\": \"air conditioning vent\"}, {\"id\": 50103, \"name\": \"the gourmet marshmallow\"}, {\"id\": 50104, \"name\": \"slender green trunk\"}, {\"id\": 50105, \"name\": \"a 3 x 2 juego playstation 3\"}, {\"id\": 50106, \"name\": \"a dark-colored carpet\"}, {\"id\": 50107, \"name\": \"person running\"}, {\"id\": 50108, \"name\": \"a laser pointer\"}, {\"id\": 50109, \"name\": \"the white sole\"}, {\"id\": 50110, \"name\": \"the silver tube\"}, {\"id\": 50111, \"name\": \"the pink headband\"}, {\"id\": 50112, \"name\": \"a blue sofa\"}, {\"id\": 50113, \"name\": \"the order summary\"}, {\"id\": 50114, \"name\": \"a striking yellow, pink, and red figurehead\"}, {\"id\": 50115, \"name\": \"the gray seat\"}, {\"id\": 50116, \"name\": \"a red and yellow patterned tablecloth\"}, {\"id\": 50117, \"name\": \"a royale\"}, {\"id\": 50118, \"name\": \"a white bracelet\"}, {\"id\": 50119, \"name\": \"the stove's handle\"}, {\"id\": 50120, \"name\": \"vinyl record\"}, {\"id\": 50121, \"name\": \"the brown beaded necklace\"}, {\"id\": 50122, \"name\": \"a green, purple, and white led strip\"}, {\"id\": 50123, \"name\": \"brows. bureau de change\"}, {\"id\": 50124, \"name\": \"a 1698 new york\"}, {\"id\": 50125, \"name\": \"a white tabletop\"}, {\"id\": 50126, \"name\": \"a step machine\"}, {\"id\": 50127, \"name\": \"a security checkpoint\"}, {\"id\": 50128, \"name\": \"the red and white striped marking\"}, {\"id\": 50129, \"name\": \"the white piping\"}, {\"id\": 50130, \"name\": \"a blue and white striped sandal\"}, {\"id\": 50131, \"name\": \"the 28\"}, {\"id\": 50132, \"name\": \"a chart\"}, {\"id\": 50133, \"name\": \"visible tattoo\"}, {\"id\": 50134, \"name\": \"the white and blue box\"}, {\"id\": 50135, \"name\": \"gold bar\"}, {\"id\": 50136, \"name\": \"a purple folder\"}, {\"id\": 50137, \"name\": \"red and white striped seat\"}, {\"id\": 50138, \"name\": \"the purple handle\"}, {\"id\": 50139, \"name\": \"a game of dart\"}, {\"id\": 50140, \"name\": \"colosseum\"}, {\"id\": 50141, \"name\": \"black and yellow stripe\"}, {\"id\": 50142, \"name\": \"cartoon princess\"}, {\"id\": 50143, \"name\": \"arm of the couch\"}, {\"id\": 50144, \"name\": \"a rusty chain\"}, {\"id\": 50145, \"name\": \"a white island\"}, {\"id\": 50146, \"name\": \"the prabhu shree ram aagaman\"}, {\"id\": 50147, \"name\": \"green drink\"}, {\"id\": 50148, \"name\": \"a nintendo sign\"}, {\"id\": 50149, \"name\": \"a pick-a-size\"}, {\"id\": 50150, \"name\": \"a fish head\"}, {\"id\": 50151, \"name\": \"app\"}, {\"id\": 50152, \"name\": \"stem\"}, {\"id\": 50153, \"name\": \"the dark wooden doorway\"}, {\"id\": 50154, \"name\": \"the cream-colored seat\"}, {\"id\": 50155, \"name\": \"blue bead\"}, {\"id\": 50156, \"name\": \"stone alcove\"}, {\"id\": 50157, \"name\": \"a central speaker\"}, {\"id\": 50158, \"name\": \"a store's interior\"}, {\"id\": 50159, \"name\": \"yellow a sign\"}, {\"id\": 50160, \"name\": \"the individual's left arm\"}, {\"id\": 50161, \"name\": \"wicker back\"}, {\"id\": 50162, \"name\": \"the souvenir\"}, {\"id\": 50163, \"name\": \"maroon blind\"}, {\"id\": 50164, \"name\": \"a green knit hat\"}, {\"id\": 50165, \"name\": \"a brow las nail\"}, {\"id\": 50166, \"name\": \"sprinkler system\"}, {\"id\": 50167, \"name\": \"the pink vein\"}, {\"id\": 50168, \"name\": \"the hammock\"}, {\"id\": 50169, \"name\": \"the peach-colored tank top\"}, {\"id\": 50170, \"name\": \"the blue and green toy\"}, {\"id\": 50171, \"name\": \"a purple cliffside\"}, {\"id\": 50172, \"name\": \"dresser\"}, {\"id\": 50173, \"name\": \"the red and black rectangle\"}, {\"id\": 50174, \"name\": \"white terra logo\"}, {\"id\": 50175, \"name\": \"an elephant design\"}, {\"id\": 50176, \"name\": \"the black and white nike sneaker\"}, {\"id\": 50177, \"name\": \"orange label\"}, {\"id\": 50178, \"name\": \"the orange graffiti\"}, {\"id\": 50179, \"name\": \"yellow, green, and blue umbrella\"}, {\"id\": 50180, \"name\": \"the white ring\"}, {\"id\": 50181, \"name\": \"a 2024 6 season hd 5.1 ad 5.1 english\"}, {\"id\": 50182, \"name\": \"the victory\"}, {\"id\": 50183, \"name\": \"the brown book\"}, {\"id\": 50184, \"name\": \"gray running surface\"}, {\"id\": 50185, \"name\": \"second-story area\"}, {\"id\": 50186, \"name\": \"the red and yellow wheel\"}, {\"id\": 50187, \"name\": \"an isuzu logo\"}, {\"id\": 50188, \"name\": \"reed\"}, {\"id\": 50189, \"name\": \"a blue and green slide\"}, {\"id\": 50190, \"name\": \"a camel's nose\"}, {\"id\": 50191, \"name\": \"the retail setting\"}, {\"id\": 50192, \"name\": \"a grab-a-win!\"}, {\"id\": 50193, \"name\": \"start\"}, {\"id\": 50194, \"name\": \"the red stocking\"}, {\"id\": 50195, \"name\": \"smiling moon\"}, {\"id\": 50196, \"name\": \"scanner\"}, {\"id\": 50197, \"name\": \"a drawing\"}, {\"id\": 50198, \"name\": \"smog in the air\"}, {\"id\": 50199, \"name\": \"a top of the hill\"}, {\"id\": 50200, \"name\": \"floral patterned skirt\"}, {\"id\": 50201, \"name\": \"a grayish-brown panel\"}, {\"id\": 50202, \"name\": \"the yellow folder\"}, {\"id\": 50203, \"name\": \"a white stove\"}, {\"id\": 50204, \"name\": \"the red and white striped tablecloth\"}, {\"id\": 50205, \"name\": \"the butterfly print\"}, {\"id\": 50206, \"name\": \"boy's left hand\"}, {\"id\": 50207, \"name\": \"historic amphitheater\"}, {\"id\": 50208, \"name\": \"the pink mattress\"}, {\"id\": 50209, \"name\": \"the blue and white checkered pattern\"}, {\"id\": 50210, \"name\": \"the brown and yellow patch\"}, {\"id\": 50211, \"name\": \"the green board\"}, {\"id\": 50212, \"name\": \"a loose thread\"}, {\"id\": 50213, \"name\": \"a flecked pattern\"}, {\"id\": 50214, \"name\": \"santa claus figurine\"}, {\"id\": 50215, \"name\": \"the green lace\"}, {\"id\": 50216, \"name\": \"a golf game\"}, {\"id\": 50217, \"name\": \"raised curb\"}, {\"id\": 50218, \"name\": \"black and red control stick\"}, {\"id\": 50219, \"name\": \"ape\"}, {\"id\": 50220, \"name\": \"a gray sweatpant\"}, {\"id\": 50221, \"name\": \"a brooklyn\"}, {\"id\": 50222, \"name\": \"a red and black stripe\"}, {\"id\": 50223, \"name\": \"the right bicep\"}, {\"id\": 50224, \"name\": \"a straw wrapper\"}, {\"id\": 50225, \"name\": \"the dark frame\"}, {\"id\": 50226, \"name\": \"a black jogger\"}, {\"id\": 50227, \"name\": \"the red and yellow brick\"}, {\"id\": 50228, \"name\": \"a white lifeboat\"}, {\"id\": 50229, \"name\": \"the glass circle\"}, {\"id\": 50230, \"name\": \"a yellow and orange curtain\"}, {\"id\": 50231, \"name\": \"a fashion mall\"}, {\"id\": 50232, \"name\": \"a yellow and red pole\"}, {\"id\": 50233, \"name\": \"a gourmet marinated olive\"}, {\"id\": 50234, \"name\": \"the dark green stain\"}, {\"id\": 50235, \"name\": \"black metal shelf\"}, {\"id\": 50236, \"name\": \"an elk\"}, {\"id\": 50237, \"name\": \"the cobblestone surface\"}, {\"id\": 50238, \"name\": \"black and white striped sweater\"}, {\"id\": 50239, \"name\": \"the red, green, and yellow stripe\"}, {\"id\": 50240, \"name\": \"arena\"}, {\"id\": 50241, \"name\": \"the red and black plaid shirt\"}, {\"id\": 50242, \"name\": \"red and green striped fabric\"}, {\"id\": 50243, \"name\": \"furnishing\"}, {\"id\": 50244, \"name\": \"a blue and green tarp\"}, {\"id\": 50245, \"name\": \"the strappy sandal\"}, {\"id\": 50246, \"name\": \"a white and gray marble-like pattern\"}, {\"id\": 50247, \"name\": \"the vibrant green area\"}, {\"id\": 50248, \"name\": \"a black bowl of food\"}, {\"id\": 50249, \"name\": \"the central divider\"}, {\"id\": 50250, \"name\": \"the mark\"}, {\"id\": 50251, \"name\": \"a prominent electronic billboard\"}, {\"id\": 50252, \"name\": \"the wood and concrete\"}, {\"id\": 50253, \"name\": \"the red guitar\"}, {\"id\": 50254, \"name\": \"the green and tan diamond\"}, {\"id\": 50255, \"name\": \"a red dinosaur\"}, {\"id\": 50256, \"name\": \"the blue and gold balloon\"}, {\"id\": 50257, \"name\": \"the yellow and black design\"}, {\"id\": 50258, \"name\": \"seat's armrest\"}, {\"id\": 50259, \"name\": \"grab-n-win! logo\"}, {\"id\": 50260, \"name\": \"wrestling mat\"}, {\"id\": 50261, \"name\": \"the orange stool\"}, {\"id\": 50262, \"name\": \"the purple rim\"}, {\"id\": 50263, \"name\": \"silver metal tray\"}, {\"id\": 50264, \"name\": \"the orange earbud\"}, {\"id\": 50265, \"name\": \"black phone case\"}, {\"id\": 50266, \"name\": \"long, flowing veil\"}, {\"id\": 50267, \"name\": \"plush mauve mat\"}, {\"id\": 50268, \"name\": \"colorful drum\"}, {\"id\": 50269, \"name\": \"a blue and gray frame\"}, {\"id\": 50270, \"name\": \"the dark-colored head covering\"}, {\"id\": 50271, \"name\": \"the red and yellow pole\"}, {\"id\": 50272, \"name\": \"black nike slide\"}, {\"id\": 50273, \"name\": \"dashboard of a car\"}, {\"id\": 50274, \"name\": \"the dark grey tank top\"}, {\"id\": 50275, \"name\": \"silver connector\"}, {\"id\": 50276, \"name\": \"the striped tablecloth\"}, {\"id\": 50277, \"name\": \"black wire basket\"}, {\"id\": 50278, \"name\": \"front panel\"}, {\"id\": 50279, \"name\": \"the frayed hem\"}, {\"id\": 50280, \"name\": \"a blue and red frame\"}, {\"id\": 50281, \"name\": \"a green, red, and yellow toy\"}, {\"id\": 50282, \"name\": \"the white wireless earbud\"}, {\"id\": 50283, \"name\": \"the shallow, rectangular container\"}, {\"id\": 50284, \"name\": \"the 1698 newyork\"}, {\"id\": 50285, \"name\": \"kitchen countertop\"}, {\"id\": 50286, \"name\": \"a brow lasi nail\"}, {\"id\": 50287, \"name\": \"the brown top\"}, {\"id\": 50288, \"name\": \"jl dagen\"}, {\"id\": 50289, \"name\": \"the white paneling\"}, {\"id\": 50290, \"name\": \"an elephant toy\"}, {\"id\": 50291, \"name\": \"the exit\"}, {\"id\": 50292, \"name\": \"the red digital clock\"}, {\"id\": 50293, \"name\": \"the stone\"}, {\"id\": 50294, \"name\": \"the dark blue card\"}, {\"id\": 50295, \"name\": \"nearby lamp\"}, {\"id\": 50296, \"name\": \"red and gold bauble\"}, {\"id\": 50297, \"name\": \"the long-handled mop\"}, {\"id\": 50298, \"name\": \"the dark wood dresser\"}, {\"id\": 50299, \"name\": \"a moonlit night\"}, {\"id\": 50300, \"name\": \"a white e\"}, {\"id\": 50301, \"name\": \"a dusk\"}, {\"id\": 50302, \"name\": \"the white hyundai logo\"}, {\"id\": 50303, \"name\": \"the top tier\"}, {\"id\": 50304, \"name\": \"tan short-sleeved shirt\"}, {\"id\": 50305, \"name\": \"pink rectangle\"}, {\"id\": 50306, \"name\": \"a dark beverage\"}, {\"id\": 50307, \"name\": \"white slide\"}, {\"id\": 50308, \"name\": \"a black and silver bracelet\"}, {\"id\": 50309, \"name\": \"white rickshaw\"}, {\"id\": 50310, \"name\": \"vibrant, multicolored plastic cup\"}, {\"id\": 50311, \"name\": \"a need a key? safety\"}, {\"id\": 50312, \"name\": \"the end credit\"}, {\"id\": 50313, \"name\": \"a special drink deal\"}, {\"id\": 50314, \"name\": \"fresh seafood\"}, {\"id\": 50315, \"name\": \"yellow paint\"}, {\"id\": 50316, \"name\": \"the green sweatshirt\"}, {\"id\": 50317, \"name\": \"the crown molding pattern\"}, {\"id\": 50318, \"name\": \"the white outlet plug\"}, {\"id\": 50319, \"name\": \"an address\"}, {\"id\": 50320, \"name\": \"an ark\"}, {\"id\": 50321, \"name\": \"blue and green plastic container\"}, {\"id\": 50322, \"name\": \"glass boot\"}, {\"id\": 50323, \"name\": \"the green and white debris\"}, {\"id\": 50324, \"name\": \"clear plastic cone\"}, {\"id\": 50325, \"name\": \"the white neck\"}, {\"id\": 50326, \"name\": \"a blue and yellow striped sandal\"}, {\"id\": 50327, \"name\": \"a metal blowtorch\"}, {\"id\": 50328, \"name\": \"gold lock\"}, {\"id\": 50329, \"name\": \"the white air conditioner unit\"}, {\"id\": 50330, \"name\": \"prominent green kiosk\"}, {\"id\": 50331, \"name\": \"the alleyway\"}, {\"id\": 50332, \"name\": \"a grey and white patterned carpet\"}, {\"id\": 50333, \"name\": \"cheese cloth\"}, {\"id\": 50334, \"name\": \"brown leather couch\"}, {\"id\": 50335, \"name\": \"a rupes\"}, {\"id\": 50336, \"name\": \"the gray beanie\"}, {\"id\": 50337, \"name\": \"pink frosting\"}, {\"id\": 50338, \"name\": \"the la mer storefront\"}, {\"id\": 50339, \"name\": \"the small black clip\"}, {\"id\": 50340, \"name\": \"a black, shadowy drone\"}, {\"id\": 50341, \"name\": \"a starbucks\"}, {\"id\": 50342, \"name\": \"a white graphic\"}, {\"id\": 50343, \"name\": \"a black and white sandal\"}, {\"id\": 50344, \"name\": \"a parsnip\"}, {\"id\": 50345, \"name\": \"a lord rama\"}, {\"id\": 50346, \"name\": \"the happy birthday banner\"}, {\"id\": 50347, \"name\": \"a red and black plaid jacket\"}, {\"id\": 50348, \"name\": \"central passageway\"}, {\"id\": 50349, \"name\": \"the yellow and green rope\"}, {\"id\": 50350, \"name\": \"the historic roman amphitheater\"}, {\"id\": 50351, \"name\": \"a broken plastic mat\"}, {\"id\": 50352, \"name\": \"a wooden interior\"}, {\"id\": 50353, \"name\": \"the brain board cover industry\"}, {\"id\": 50354, \"name\": \"a beige cardigan\"}, {\"id\": 50355, \"name\": \"the adidas logo\"}, {\"id\": 50356, \"name\": \"black couch\"}, {\"id\": 50357, \"name\": \"the black and red singlet\"}, {\"id\": 50358, \"name\": \"the decorated altar\"}, {\"id\": 50359, \"name\": \"gold-colored nike logo\"}, {\"id\": 50360, \"name\": \"the mosaic pattern\"}, {\"id\": 50361, \"name\": \"red bandage\"}, {\"id\": 50362, \"name\": \"the sturdy wooden frame\"}, {\"id\": 50363, \"name\": \"ozarks\"}, {\"id\": 50364, \"name\": \"a dark baseboard\"}, {\"id\": 50365, \"name\": \"red picnic table\"}, {\"id\": 50366, \"name\": \"the description of the show\"}, {\"id\": 50367, \"name\": \"the sheer pink curtain\"}, {\"id\": 50368, \"name\": \"striped tablecloth\"}, {\"id\": 50369, \"name\": \"the blue goggle\"}, {\"id\": 50370, \"name\": \"the cashier's face\"}, {\"id\": 50371, \"name\": \"the wooden shelving unit\"}, {\"id\": 50372, \"name\": \"the bottom stack\"}, {\"id\": 50373, \"name\": \"the ticket\"}, {\"id\": 50374, \"name\": \"clock face\"}, {\"id\": 50375, \"name\": \"a sole of the slipper\"}, {\"id\": 50376, \"name\": \"green and white checkered area\"}, {\"id\": 50377, \"name\": \"vertical red, yellow, and blue bar\"}, {\"id\": 50378, \"name\": \"the switch\"}, {\"id\": 50379, \"name\": \"a gray tattoo\"}, {\"id\": 50380, \"name\": \"a round metal plate\"}, {\"id\": 50381, \"name\": \"the teal-colored mop handle\"}, {\"id\": 50382, \"name\": \"the dark brown candle holder\"}, {\"id\": 50383, \"name\": \"the dark brown leg\"}, {\"id\": 50384, \"name\": \"a white new york yankee logo\"}, {\"id\": 50385, \"name\": \"rugby\"}, {\"id\": 50386, \"name\": \"the vibrant orange tie-dyed t-shirt\"}, {\"id\": 50387, \"name\": \"the vertical strip\"}, {\"id\": 50388, \"name\": \"metal fan\"}, {\"id\": 50389, \"name\": \"a wind\"}, {\"id\": 50390, \"name\": \"gravel surface\"}, {\"id\": 50391, \"name\": \"a helicopter pad\"}, {\"id\": 50392, \"name\": \"a rear windshield wiper\"}, {\"id\": 50393, \"name\": \"the hanging pot\"}, {\"id\": 50394, \"name\": \"a gg sosrokusuman\"}, {\"id\": 50395, \"name\": \"red pebble\"}, {\"id\": 50396, \"name\": \"neon yellow-colored athletic sock\"}, {\"id\": 50397, \"name\": \"the red wristband\"}, {\"id\": 50398, \"name\": \"the front of the toilet\"}, {\"id\": 50399, \"name\": \"the red and yellow ticket machine\"}, {\"id\": 50400, \"name\": \"a greenish-yellow eye\"}, {\"id\": 50401, \"name\": \"a tommy logo\"}, {\"id\": 50402, \"name\": \"the need a key?\"}, {\"id\": 50403, \"name\": \"yellow frame\"}, {\"id\": 50404, \"name\": \"basket of present\"}, {\"id\": 50405, \"name\": \"a chinese sport brand\"}, {\"id\": 50406, \"name\": \"closed\"}, {\"id\": 50407, \"name\": \"the bus interior\"}, {\"id\": 50408, \"name\": \"a birthday\"}, {\"id\": 50409, \"name\": \"the checkered red and white bumper\"}, {\"id\": 50410, \"name\": \"white silhouettes of person dancing\"}, {\"id\": 50411, \"name\": \"a red train car\"}, {\"id\": 50412, \"name\": \"the red heart symbol\"}, {\"id\": 50413, \"name\": \"a dark brown frame\"}, {\"id\": 50414, \"name\": \"drawing\"}, {\"id\": 50415, \"name\": \"the blue mop\"}, {\"id\": 50416, \"name\": \"bed of wood\"}, {\"id\": 50417, \"name\": \"sandy ground\"}, {\"id\": 50418, \"name\": \"a bag of lemon\"}, {\"id\": 50419, \"name\": \"the yellow curtain\"}, {\"id\": 50420, \"name\": \"the brown mat\"}, {\"id\": 50421, \"name\": \"a red and clear liquid\"}, {\"id\": 50422, \"name\": \"the jump! command\"}, {\"id\": 50423, \"name\": \"a woman in a pink jacket\"}, {\"id\": 50424, \"name\": \"the gold watch\"}, {\"id\": 50425, \"name\": \"the white mat\"}, {\"id\": 50426, \"name\": \"a bulb\"}, {\"id\": 50427, \"name\": \"a pink tight\"}, {\"id\": 50428, \"name\": \"white boulder\"}, {\"id\": 50429, \"name\": \"metal stove\"}, {\"id\": 50430, \"name\": \"the orange smiley face\"}, {\"id\": 50431, \"name\": \"a silver logo\"}, {\"id\": 50432, \"name\": \"a messy bob\"}, {\"id\": 50433, \"name\": \"a yellow food cart\"}, {\"id\": 50434, \"name\": \"a textured glass pane\"}, {\"id\": 50435, \"name\": \"blue and orange bus stop sign\"}, {\"id\": 50436, \"name\": \"a silver checkout counter\"}, {\"id\": 50437, \"name\": \"a yellow t4 sign\"}, {\"id\": 50438, \"name\": \"the blue plaid vest\"}, {\"id\": 50439, \"name\": \"an unseen person\"}, {\"id\": 50440, \"name\": \"red sleeveless top\"}, {\"id\": 50441, \"name\": \"nike\"}, {\"id\": 50442, \"name\": \"an open flame\"}, {\"id\": 50443, \"name\": \"the green lamp\"}, {\"id\": 50444, \"name\": \"blue suit\"}, {\"id\": 50445, \"name\": \"black scarf\"}, {\"id\": 50446, \"name\": \"green and white checkered archway\"}, {\"id\": 50447, \"name\": \"glass enclosure\"}, {\"id\": 50448, \"name\": \"the gourmet manuka oyster\"}, {\"id\": 50449, \"name\": \"a white shoe box\"}, {\"id\": 50450, \"name\": \"red tube top\"}, {\"id\": 50451, \"name\": \"the yellow and blue polisher pad\"}, {\"id\": 50452, \"name\": \"a black bristle\"}, {\"id\": 50453, \"name\": \"black doorknob\"}, {\"id\": 50454, \"name\": \"the black and yellow design\"}, {\"id\": 50455, \"name\": \"the corrugated metal sheet\"}, {\"id\": 50456, \"name\": \"the grey stripe\"}, {\"id\": 50457, \"name\": \"a yellow, blue, pink, and green checkered pattern\"}, {\"id\": 50458, \"name\": \"the jump ju banner\"}, {\"id\": 50459, \"name\": \"kitchen torch\"}, {\"id\": 50460, \"name\": \"a small seat\"}, {\"id\": 50461, \"name\": \"the square silver buckle\"}, {\"id\": 50462, \"name\": \"the white throat\"}, {\"id\": 50463, \"name\": \"a blue air conditioning unit\"}, {\"id\": 50464, \"name\": \"a bottle of cleaning product\"}, {\"id\": 50465, \"name\": \"the upper level of the mall\"}, {\"id\": 50466, \"name\": \"a pink and purple cap\"}, {\"id\": 50467, \"name\": \"asparagu\"}, {\"id\": 50468, \"name\": \"the right elbow\"}, {\"id\": 50469, \"name\": \"a warung\"}, {\"id\": 50470, \"name\": \"a 2006\"}, {\"id\": 50471, \"name\": \"a drill\"}, {\"id\": 50472, \"name\": \"the quickmart store\"}, {\"id\": 50473, \"name\": \"a table lamp\"}, {\"id\": 50474, \"name\": \"white doorway\"}, {\"id\": 50475, \"name\": \"the maroon dress\"}, {\"id\": 50476, \"name\": \"a white bowl of vegetable\"}, {\"id\": 50477, \"name\": \"the backyard\"}, {\"id\": 50478, \"name\": \"the green door frame\"}, {\"id\": 50479, \"name\": \"a circular marking\"}, {\"id\": 50480, \"name\": \"the green broom\"}, {\"id\": 50481, \"name\": \"the paw patrol design\"}, {\"id\": 50482, \"name\": \"the black grill rack\"}, {\"id\": 50483, \"name\": \"a red gas tank\"}, {\"id\": 50484, \"name\": \"brain 24 hour board cover industry\"}, {\"id\": 50485, \"name\": \"the red nintendo logo\"}, {\"id\": 50486, \"name\": \"the white ruffled sock\"}, {\"id\": 50487, \"name\": \"red wristband\"}, {\"id\": 50488, \"name\": \"green emergency exit sign\"}, {\"id\": 50489, \"name\": \"the dark room\"}, {\"id\": 50490, \"name\": \"the banner in hindi\"}, {\"id\": 50491, \"name\": \"a gourmet market deli\"}, {\"id\": 50492, \"name\": \"purple section\"}, {\"id\": 50493, \"name\": \"yellow and blue design\"}, {\"id\": 50494, \"name\": \"a white flip-flop\"}, {\"id\": 50495, \"name\": \"red cello\"}, {\"id\": 50496, \"name\": \"a special drink\"}, {\"id\": 50497, \"name\": \"a teepee\"}, {\"id\": 50498, \"name\": \"golden star\"}, {\"id\": 50499, \"name\": \"a human's left hand\"}, {\"id\": 50500, \"name\": \"the green waistband\"}, {\"id\": 50501, \"name\": \"the loom\"}, {\"id\": 50502, \"name\": \"the diagonal striped pattern\"}, {\"id\": 50503, \"name\": \"a distinctive blue and white sash\"}, {\"id\": 50504, \"name\": \"a partially covered porch\"}, {\"id\": 50505, \"name\": \"a black fedora\"}, {\"id\": 50506, \"name\": \"green sandal\"}, {\"id\": 50507, \"name\": \"a cash register\"}, {\"id\": 50508, \"name\": \"the black heating lamp\"}, {\"id\": 50509, \"name\": \"the check\"}, {\"id\": 50510, \"name\": \"a tapa\"}, {\"id\": 50511, \"name\": \"the tan scarf\"}, {\"id\": 50512, \"name\": \"upper level of the amphitheater\"}, {\"id\": 50513, \"name\": \"gray apron\"}, {\"id\": 50514, \"name\": \"the red drumstick\"}, {\"id\": 50515, \"name\": \"the silver outlet\"}, {\"id\": 50516, \"name\": \"green sweatband\"}, {\"id\": 50517, \"name\": \"the brown-framed picture\"}, {\"id\": 50518, \"name\": \"a buy 2 get 1 free\"}, {\"id\": 50519, \"name\": \"an outdoor table\"}, {\"id\": 50520, \"name\": \"a teal-colored door\"}, {\"id\": 50521, \"name\": \"the plaid long-sleeve shirt\"}, {\"id\": 50522, \"name\": \"black earbud\"}, {\"id\": 50523, \"name\": \"prominent blue sign\"}, {\"id\": 50524, \"name\": \"room's interior\"}, {\"id\": 50525, \"name\": \"a dark wood door\"}, {\"id\": 50526, \"name\": \"the white sofa\"}, {\"id\": 50527, \"name\": \"vibrant blue collar\"}, {\"id\": 50528, \"name\": \"brown curtain\"}, {\"id\": 50529, \"name\": \"the hay travel\"}, {\"id\": 50530, \"name\": \"red and gray rectangle\"}, {\"id\": 50531, \"name\": \"yellow short-sleeved shirt\"}, {\"id\": 50532, \"name\": \"countdown timer\"}, {\"id\": 50533, \"name\": \"the colorful game\"}, {\"id\": 50534, \"name\": \"the small, round, gold-framed clock\"}, {\"id\": 50535, \"name\": \"a nintendo\"}, {\"id\": 50536, \"name\": \"green alligator\"}, {\"id\": 50537, \"name\": \"the floral patterned back\"}, {\"id\": 50538, \"name\": \"a green plastic cup\"}, {\"id\": 50539, \"name\": \"the frame of the image\"}, {\"id\": 50540, \"name\": \"wooden china cabinet\"}, {\"id\": 50541, \"name\": \"terracotta chimney\"}, {\"id\": 50542, \"name\": \"a colorful keyboard\"}, {\"id\": 50543, \"name\": \"market\"}, {\"id\": 50544, \"name\": \"a financial institution\"}, {\"id\": 50545, \"name\": \"the white cat\"}, {\"id\": 50546, \"name\": \"the pointed arch\"}, {\"id\": 50547, \"name\": \"an orange and white design\"}, {\"id\": 50548, \"name\": \"piece of concrete\"}, {\"id\": 50549, \"name\": \"the leek\"}, {\"id\": 50550, \"name\": \"white dial\"}, {\"id\": 50551, \"name\": \"the 90\"}, {\"id\": 50552, \"name\": \"flat ground area\"}, {\"id\": 50553, \"name\": \"a rainfall\"}, {\"id\": 50554, \"name\": \"thick gold chain\"}, {\"id\": 50555, \"name\": \"white dove\"}, {\"id\": 50556, \"name\": \"buy 1 get 1 free\"}, {\"id\": 50557, \"name\": \"a liquid, possibly oil\"}, {\"id\": 50558, \"name\": \"a yellow ring\"}, {\"id\": 50559, \"name\": \"a red and black logo\"}, {\"id\": 50560, \"name\": \"red price tag\"}, {\"id\": 50561, \"name\": \"black eraser\"}, {\"id\": 50562, \"name\": \"resident\"}, {\"id\": 50563, \"name\": \"the blue and white footrest\"}, {\"id\": 50564, \"name\": \"white and black striped shirt\"}, {\"id\": 50565, \"name\": \"a blue belt\"}, {\"id\": 50566, \"name\": \"a white paint chip\"}, {\"id\": 50567, \"name\": \"the black bicycle seat\"}, {\"id\": 50568, \"name\": \"a partially open white screen\"}, {\"id\": 50569, \"name\": \"a red and green tablecloth\"}, {\"id\": 50570, \"name\": \"a silver pen\"}, {\"id\": 50571, \"name\": \"the boxing\"}, {\"id\": 50572, \"name\": \"pink ruffled tablecloth\"}, {\"id\": 50573, \"name\": \"an advertisement for beer\"}, {\"id\": 50574, \"name\": \"a red gas canister\"}, {\"id\": 50575, \"name\": \"a happy hour special\"}, {\"id\": 50576, \"name\": \"the tiga jega\"}, {\"id\": 50577, \"name\": \"red and white cushion\"}, {\"id\": 50578, \"name\": \"the patch of gray\"}, {\"id\": 50579, \"name\": \"wheat stalk\"}, {\"id\": 50580, \"name\": \"a brown plastic bottle\"}, {\"id\": 50581, \"name\": \"the black bra\"}, {\"id\": 50582, \"name\": \"a brown and white cushion\"}, {\"id\": 50583, \"name\": \"pink bench\"}, {\"id\": 50584, \"name\": \"a red coat hanger\"}, {\"id\": 50585, \"name\": \"a white fur collar\"}, {\"id\": 50586, \"name\": \"a white vein\"}, {\"id\": 50587, \"name\": \"green metal awning\"}, {\"id\": 50588, \"name\": \"the orange and black singlet\"}, {\"id\": 50589, \"name\": \"the green and white food truck\"}, {\"id\": 50590, \"name\": \"a cheza kama wewe!\"}, {\"id\": 50591, \"name\": \"a hanna ticket\"}, {\"id\": 50592, \"name\": \"an usb drive\"}, {\"id\": 50593, \"name\": \"primark\"}, {\"id\": 50594, \"name\": \"blue footrest\"}, {\"id\": 50595, \"name\": \"man's clothing\"}, {\"id\": 50596, \"name\": \"white paper napkin\"}, {\"id\": 50597, \"name\": \"a movie theater screen\"}, {\"id\": 50598, \"name\": \"the bump\"}, {\"id\": 50599, \"name\": \"an adult's hands\"}, {\"id\": 50600, \"name\": \"number 3 and 2\"}, {\"id\": 50601, \"name\": \"the ironed yellow t-shirt\"}, {\"id\": 50602, \"name\": \"the white lantern\"}, {\"id\": 50603, \"name\": \"elliptical trainer\"}, {\"id\": 50604, \"name\": \"napkin holder\"}, {\"id\": 50605, \"name\": \"the white and pink sheet\"}, {\"id\": 50606, \"name\": \"a golden door\"}, {\"id\": 50607, \"name\": \"the white crosswalk design\"}, {\"id\": 50608, \"name\": \"the 3-for-2 deal on playstation 3 game\"}, {\"id\": 50609, \"name\": \"a habiknit\"}, {\"id\": 50610, \"name\": \"a blue band\"}, {\"id\": 50611, \"name\": \"firewood\"}, {\"id\": 50612, \"name\": \"the black television screen\"}, {\"id\": 50613, \"name\": \"a ram\"}, {\"id\": 50614, \"name\": \"a quickmart logo\"}, {\"id\": 50615, \"name\": \"the red collared shirt\"}, {\"id\": 50616, \"name\": \"tailor shop\"}, {\"id\": 50617, \"name\": \"a wooden bed frame\"}, {\"id\": 50618, \"name\": \"black croc shoe\"}, {\"id\": 50619, \"name\": \"a multicolored smiley face graphic\"}, {\"id\": 50620, \"name\": \"circular air vent\"}, {\"id\": 50621, \"name\": \"an image of a deity\"}, {\"id\": 50622, \"name\": \"the white dresser\"}, {\"id\": 50623, \"name\": \"a weed\"}, {\"id\": 50624, \"name\": \"the silver turtle design\"}, {\"id\": 50625, \"name\": \"a white and blue striped turban\"}, {\"id\": 50626, \"name\": \"the brown bowtie\"}, {\"id\": 50627, \"name\": \"the red and blue slide\"}, {\"id\": 50628, \"name\": \"green slide\"}, {\"id\": 50629, \"name\": \"the white w\"}, {\"id\": 50630, \"name\": \"a gray singlet\"}, {\"id\": 50631, \"name\": \"the blue feather\"}, {\"id\": 50632, \"name\": \"mandia ticket\"}, {\"id\": 50633, \"name\": \"a bright blue eye\"}, {\"id\": 50634, \"name\": \"central well\"}, {\"id\": 50635, \"name\": \"the white artist tag\"}, {\"id\": 50636, \"name\": \"the brownish-gray leg\"}, {\"id\": 50637, \"name\": \"gold chain\"}, {\"id\": 50638, \"name\": \"green and red surface\"}, {\"id\": 50639, \"name\": \"the spin radio\"}, {\"id\": 50640, \"name\": \"a colorful bracelet\"}, {\"id\": 50641, \"name\": \"the beige and blue paint\"}, {\"id\": 50642, \"name\": \"a red and green apple\"}, {\"id\": 50643, \"name\": \"an idol\"}, {\"id\": 50644, \"name\": \"a white \\\"i\\\"\"}, {\"id\": 50645, \"name\": \"the white swim cap\"}, {\"id\": 50646, \"name\": \"dollar store\"}, {\"id\": 50647, \"name\": \"the blue cord\"}, {\"id\": 50648, \"name\": \"a bright blue flame\"}, {\"id\": 50649, \"name\": \"the black and yellow bollard\"}, {\"id\": 50650, \"name\": \"a gray bedspread\"}, {\"id\": 50651, \"name\": \"the plume of smoke\"}, {\"id\": 50652, \"name\": \"a rusty surface\"}, {\"id\": 50653, \"name\": \"the large arrangement of candle\"}, {\"id\": 50654, \"name\": \"a black and blue motorcycle\"}, {\"id\": 50655, \"name\": \"the tan-colored pillow\"}, {\"id\": 50656, \"name\": \"the bright pink jacket\"}, {\"id\": 50657, \"name\": \"the green cover\"}, {\"id\": 50658, \"name\": \"brown and white reindeer\"}, {\"id\": 50659, \"name\": \"a black utensil holder\"}, {\"id\": 50660, \"name\": \"a camouflage headgear\"}, {\"id\": 50661, \"name\": \"a grey baseboard\"}, {\"id\": 50662, \"name\": \"the 1848\"}, {\"id\": 50663, \"name\": \"the cornrow\"}, {\"id\": 50664, \"name\": \"image of beverage\"}, {\"id\": 50665, \"name\": \"the white corded phone\"}, {\"id\": 50666, \"name\": \"a black electrical outlet\"}, {\"id\": 50667, \"name\": \"a purple bedspread\"}, {\"id\": 50668, \"name\": \"power tool\"}, {\"id\": 50669, \"name\": \"the green-tinted glass\"}, {\"id\": 50670, \"name\": \"the white bookshelf\"}, {\"id\": 50671, \"name\": \"white swim goggle strap\"}, {\"id\": 50672, \"name\": \"a red, blue, yellow, and white pole\"}, {\"id\": 50673, \"name\": \"a purple, pink, and white square\"}, {\"id\": 50674, \"name\": \"a dark electrical cord\"}, {\"id\": 50675, \"name\": \"a pink sandal\"}, {\"id\": 50676, \"name\": \"the white and brown patterned bowl\"}, {\"id\": 50677, \"name\": \"the blue pool table\"}, {\"id\": 50678, \"name\": \"the white and gray sheet\"}, {\"id\": 50679, \"name\": \"a bottom of a bed\"}, {\"id\": 50680, \"name\": \"second-story balcony\"}, {\"id\": 50681, \"name\": \"vertical white panel\"}, {\"id\": 50682, \"name\": \"a blue bracelet\"}, {\"id\": 50683, \"name\": \"a payment card\"}, {\"id\": 50684, \"name\": \"the round manhole cover\"}, {\"id\": 50685, \"name\": \"the central escalator\"}, {\"id\": 50686, \"name\": \"a suzuki logo\"}, {\"id\": 50687, \"name\": \"hazy image\"}, {\"id\": 50688, \"name\": \"the white 3\"}, {\"id\": 50689, \"name\": \"a yellow cart\"}, {\"id\": 50690, \"name\": \"new\"}, {\"id\": 50691, \"name\": \"the vertical beige curtain\"}, {\"id\": 50692, \"name\": \"a purple container\"}, {\"id\": 50693, \"name\": \"metal vent\"}, {\"id\": 50694, \"name\": \"the blue denim jacket\"}, {\"id\": 50695, \"name\": \"the ventilation grille\"}, {\"id\": 50696, \"name\": \"pink tip\"}, {\"id\": 50697, \"name\": \"black and red vehicle\"}, {\"id\": 50698, \"name\": \"the black nike logo\"}, {\"id\": 50699, \"name\": \"the black cue stick\"}, {\"id\": 50700, \"name\": \"the bright blue eye\"}, {\"id\": 50701, \"name\": \"a gray deck\"}, {\"id\": 50702, \"name\": \"the happy hour\"}, {\"id\": 50703, \"name\": \"a purple box\"}, {\"id\": 50704, \"name\": \"diagram\"}, {\"id\": 50705, \"name\": \"white fur lining\"}, {\"id\": 50706, \"name\": \"blue stick\"}, {\"id\": 50707, \"name\": \"a maroon fabric\"}, {\"id\": 50708, \"name\": \"green and red pillar\"}, {\"id\": 50709, \"name\": \"the central table\"}, {\"id\": 50710, \"name\": \"the yellow pencil\"}, {\"id\": 50711, \"name\": \"a silver cros earring\"}, {\"id\": 50712, \"name\": \"the cigarette\"}, {\"id\": 50713, \"name\": \"white mosaic pattern\"}, {\"id\": 50714, \"name\": \"the piece of sushi\"}, {\"id\": 50715, \"name\": \"picture of a key\"}, {\"id\": 50716, \"name\": \"the white metal grate door\"}, {\"id\": 50717, \"name\": \"victory logo\"}, {\"id\": 50718, \"name\": \"a mannequin head\"}, {\"id\": 50719, \"name\": \"studio\"}, {\"id\": 50720, \"name\": \"brown shelf\"}, {\"id\": 50721, \"name\": \"the propane tank\"}, {\"id\": 50722, \"name\": \"tern\"}, {\"id\": 50723, \"name\": \"the blue bottle\"}, {\"id\": 50724, \"name\": \"flowing veil\"}, {\"id\": 50725, \"name\": \"token\"}, {\"id\": 50726, \"name\": \"the new york\"}, {\"id\": 50727, \"name\": \"prominent yellow and green frame\"}, {\"id\": 50728, \"name\": \"low hill\"}, {\"id\": 50729, \"name\": \"the yellow and green bag\"}, {\"id\": 50730, \"name\": \"a rocky surface\"}, {\"id\": 50731, \"name\": \"a left upper arm\"}, {\"id\": 50732, \"name\": \"the left ear\"}, {\"id\": 50733, \"name\": \"speckled pattern\"}, {\"id\": 50734, \"name\": \"gucci\"}, {\"id\": 50735, \"name\": \"tan slipper\"}, {\"id\": 50736, \"name\": \"neon yellow design\"}, {\"id\": 50737, \"name\": \"game one\"}, {\"id\": 50738, \"name\": \"delicate veil\"}, {\"id\": 50739, \"name\": \"a brown and white mouse\"}, {\"id\": 50740, \"name\": \"the blue and white striped design\"}, {\"id\": 50741, \"name\": \"the red halter\"}, {\"id\": 50742, \"name\": \"the image of a woman\"}, {\"id\": 50743, \"name\": \"the green wristband\"}, {\"id\": 50744, \"name\": \"electrical wire\"}, {\"id\": 50745, \"name\": \"a circle with star\"}, {\"id\": 50746, \"name\": \"the black and white striped cushion\"}, {\"id\": 50747, \"name\": \"orange and white sneaker\"}, {\"id\": 50748, \"name\": \"blue rock formation\"}, {\"id\": 50749, \"name\": \"the white cardboard box\"}, {\"id\": 50750, \"name\": \"the nyc\"}, {\"id\": 50751, \"name\": \"vibrant, cartoon-style game\"}, {\"id\": 50752, \"name\": \"shipping dock\"}, {\"id\": 50753, \"name\": \"wrestling\"}, {\"id\": 50754, \"name\": \"black marble candleholder\"}, {\"id\": 50755, \"name\": \"the sheriff\"}, {\"id\": 50756, \"name\": \"a mezzanine\"}, {\"id\": 50757, \"name\": \"low table\"}, {\"id\": 50758, \"name\": \"a dark gray steering wheel\"}, {\"id\": 50759, \"name\": \"dark gray long-sleeved shirt\"}, {\"id\": 50760, \"name\": \"vibrant collar\"}, {\"id\": 50761, \"name\": \"a black dress pant\"}, {\"id\": 50762, \"name\": \"a white-painted toenail\"}, {\"id\": 50763, \"name\": \"red, white, and blue american flag design\"}, {\"id\": 50764, \"name\": \"make-shift tripod\"}, {\"id\": 50765, \"name\": \"a hall\"}, {\"id\": 50766, \"name\": \"the 12:36:55\"}, {\"id\": 50767, \"name\": \"haberdashery\"}, {\"id\": 50768, \"name\": \"turquoise mop\"}, {\"id\": 50769, \"name\": \"a tan comforter\"}, {\"id\": 50770, \"name\": \"the gold-framed clock\"}, {\"id\": 50771, \"name\": \"the brown reindeer\"}, {\"id\": 50772, \"name\": \"a yellow and black bollard\"}, {\"id\": 50773, \"name\": \"a white coat rack\"}, {\"id\": 50774, \"name\": \"an entrance to the mall\"}, {\"id\": 50775, \"name\": \"transparent mat\"}, {\"id\": 50776, \"name\": \"a blue ribbon\"}, {\"id\": 50777, \"name\": \"child's feet\"}, {\"id\": 50778, \"name\": \"a trash receptacle\"}, {\"id\": 50779, \"name\": \"meat case\"}, {\"id\": 50780, \"name\": \"the pink counter\"}, {\"id\": 50781, \"name\": \"the black and white hat\"}, {\"id\": 50782, \"name\": \"the white clothe drying rack\"}, {\"id\": 50783, \"name\": \"silhouette of the musician\"}, {\"id\": 50784, \"name\": \"purple and yellow writing\"}, {\"id\": 50785, \"name\": \"yellow and green sneaker\"}, {\"id\": 50786, \"name\": \"cork\"}, {\"id\": 50787, \"name\": \"the wooden, metal, and rope element\"}, {\"id\": 50788, \"name\": \"the barbecue\"}, {\"id\": 50789, \"name\": \"nintendo logo\"}, {\"id\": 50790, \"name\": \"dj booth\"}, {\"id\": 50791, \"name\": \"upper body\"}, {\"id\": 50792, \"name\": \"a man's left hand\"}, {\"id\": 50793, \"name\": \"the me logo\"}, {\"id\": 50794, \"name\": \"black pocket\"}, {\"id\": 50795, \"name\": \"a red throat patch\"}, {\"id\": 50796, \"name\": \"grey air conditioning unit\"}, {\"id\": 50797, \"name\": \"the pink and white candle\"}, {\"id\": 50798, \"name\": \"brown and tan patterned blanket\"}, {\"id\": 50799, \"name\": \"a silver straw\"}, {\"id\": 50800, \"name\": \"baton\"}, {\"id\": 50801, \"name\": \"the yellow game box\"}, {\"id\": 50802, \"name\": \"the triangular metal grate\"}, {\"id\": 50803, \"name\": \"the board cover industry\"}, {\"id\": 50804, \"name\": \"the picture of a person\"}, {\"id\": 50805, \"name\": \"the red and white bar\"}, {\"id\": 50806, \"name\": \"gray boot\"}, {\"id\": 50807, \"name\": \"blue plastic bottle\"}, {\"id\": 50808, \"name\": \"the fried garlic\"}, {\"id\": 50809, \"name\": \"the red and black striped jacket\"}, {\"id\": 50810, \"name\": \"the white and red bag\"}, {\"id\": 50811, \"name\": \"the blue and white pet carrier\"}, {\"id\": 50812, \"name\": \"the dark-colored marble\"}, {\"id\": 50813, \"name\": \"the wire mesh top\"}, {\"id\": 50814, \"name\": \"the black shawl\"}, {\"id\": 50815, \"name\": \"black luggage rack\"}, {\"id\": 50816, \"name\": \"the pale blue-green eye\"}, {\"id\": 50817, \"name\": \"a green grooming table\"}, {\"id\": 50818, \"name\": \"the white shirt sleeve\"}, {\"id\": 50819, \"name\": \"a pink sari\"}, {\"id\": 50820, \"name\": \"pizza box\"}, {\"id\": 50821, \"name\": \"a red rope leash\"}, {\"id\": 50822, \"name\": \"the brooklyn new york 1988\"}, {\"id\": 50823, \"name\": \"self-service checkout machine\"}, {\"id\": 50824, \"name\": \"a hemp\"}, {\"id\": 50825, \"name\": \"ziplock bag\"}, {\"id\": 50826, \"name\": \"luggage carousel\"}, {\"id\": 50827, \"name\": \"music\"}, {\"id\": 50828, \"name\": \"a house's brown wooden door\"}, {\"id\": 50829, \"name\": \"a pillow with a colorful design\"}, {\"id\": 50830, \"name\": \"a burning log\"}, {\"id\": 50831, \"name\": \"an uk 15 c0465\"}, {\"id\": 50832, \"name\": \"a tommy hilfiger logo\"}, {\"id\": 50833, \"name\": \"soan\"}, {\"id\": 50834, \"name\": \"the white bus illustration\"}, {\"id\": 50835, \"name\": \"the pampers\"}, {\"id\": 50836, \"name\": \"the blurred, rocky surface\"}, {\"id\": 50837, \"name\": \"the orange button\"}, {\"id\": 50838, \"name\": \"beige stucco\"}, {\"id\": 50839, \"name\": \"bead\"}, {\"id\": 50840, \"name\": \"splash of red\"}, {\"id\": 50841, \"name\": \"a harbor\"}, {\"id\": 50842, \"name\": \"a white stand\"}, {\"id\": 50843, \"name\": \"green game box\"}, {\"id\": 50844, \"name\": \"the red fez hat\"}, {\"id\": 50845, \"name\": \"koole brand name\"}, {\"id\": 50846, \"name\": \"a blue turban\"}, {\"id\": 50847, \"name\": \"yellow and black logo\"}, {\"id\": 50848, \"name\": \"dark gray short\"}, {\"id\": 50849, \"name\": \"an orange and blue plaid shirt\"}, {\"id\": 50850, \"name\": \"box of tea\"}, {\"id\": 50851, \"name\": \"the bright yellow safety vest\"}, {\"id\": 50852, \"name\": \"haberdashery store\"}, {\"id\": 50853, \"name\": \"the person's black shoe\"}, {\"id\": 50854, \"name\": \"a glass bowl\"}, {\"id\": 50855, \"name\": \"the black and white floral skirt\"}, {\"id\": 50856, \"name\": \"the frosted glass\"}, {\"id\": 50857, \"name\": \"a red, yellow, and black vehicle\"}, {\"id\": 50858, \"name\": \"the blowtorch\"}, {\"id\": 50859, \"name\": \"a stairs\"}, {\"id\": 50860, \"name\": \"the black and gray keypad\"}, {\"id\": 50861, \"name\": \"the blue and yellow tent\"}, {\"id\": 50862, \"name\": \"the woman's right hand\"}, {\"id\": 50863, \"name\": \"a gate 2\"}, {\"id\": 50864, \"name\": \"the white and brown striped tablecloth\"}, {\"id\": 50865, \"name\": \"a grey and orange singlet\"}, {\"id\": 50866, \"name\": \"the green signage\"}, {\"id\": 50867, \"name\": \"the small white bowl\"}, {\"id\": 50868, \"name\": \"polished wood\"}, {\"id\": 50869, \"name\": \"illuminated dial\"}, {\"id\": 50870, \"name\": \"a red and white checkered hat\"}, {\"id\": 50871, \"name\": \"the wad of cash\"}, {\"id\": 50872, \"name\": \"foot of the bed\"}, {\"id\": 50873, \"name\": \"pink couch\"}, {\"id\": 50874, \"name\": \"a black jacket with a star design\"}, {\"id\": 50875, \"name\": \"round, decorative embellishment\"}, {\"id\": 50876, \"name\": \"the gradient of pink and blue hue\"}, {\"id\": 50877, \"name\": \"bright yellow bag\"}, {\"id\": 50878, \"name\": \"the ramayana\"}, {\"id\": 50879, \"name\": \"a white cordless phone\"}, {\"id\": 50880, \"name\": \"white toilet\"}, {\"id\": 50881, \"name\": \"cluttered desk\"}, {\"id\": 50882, \"name\": \"black marble candle holder\"}, {\"id\": 50883, \"name\": \"the bundle of wheat\"}, {\"id\": 50884, \"name\": \"linoleum\"}, {\"id\": 50885, \"name\": \"a white and black athletic sandal\"}, {\"id\": 50886, \"name\": \"the green and white checkered pot\"}, {\"id\": 50887, \"name\": \"the self-service key machine\"}, {\"id\": 50888, \"name\": \"a knitting store\"}, {\"id\": 50889, \"name\": \"a bureau de change\"}, {\"id\": 50890, \"name\": \"mangia ticket\"}, {\"id\": 50891, \"name\": \"a black and white camouflage tank top\"}, {\"id\": 50892, \"name\": \"a pink clutch bag\"}, {\"id\": 50893, \"name\": \"a wooden spice rack\"}, {\"id\": 50894, \"name\": \"TV\"}, {\"id\": 50895, \"name\": \"a bk 169 new york\"}, {\"id\": 50896, \"name\": \"the zing\"}, {\"id\": 50897, \"name\": \"the ancient roman colosseum\"}, {\"id\": 50898, \"name\": \"an architectural feature\"}, {\"id\": 50899, \"name\": \"the house icon\"}, {\"id\": 50900, \"name\": \"a red and black shelving unit\"}, {\"id\": 50901, \"name\": \"the green tray\"}, {\"id\": 50902, \"name\": \"a white onesie\"}, {\"id\": 50903, \"name\": \"the german\"}, {\"id\": 50904, \"name\": \"the uk15 c0465\"}, {\"id\": 50905, \"name\": \"red and white label\"}, {\"id\": 50906, \"name\": \"a left forearm\"}, {\"id\": 50907, \"name\": \"the 328\"}, {\"id\": 50908, \"name\": \"paint splatter\"}, {\"id\": 50909, \"name\": \"a gas range\"}, {\"id\": 50910, \"name\": \"wet asphalt surface\"}, {\"id\": 50911, \"name\": \"the yellow and orange striped cloth\"}, {\"id\": 50912, \"name\": \"an image of bottle\"}, {\"id\": 50913, \"name\": \"a distinctive dark brown knot\"}, {\"id\": 50914, \"name\": \"an adidas logo\"}, {\"id\": 50915, \"name\": \"a red and green sign\"}, {\"id\": 50916, \"name\": \"red and yellow vehicle\"}, {\"id\": 50917, \"name\": \"a rainbow flag\"}, {\"id\": 50918, \"name\": \"swift\"}, {\"id\": 50919, \"name\": \"yellow pillow\"}, {\"id\": 50920, \"name\": \"usb-c plug\"}, {\"id\": 50921, \"name\": \"the black-and-white checkered pattern\"}, {\"id\": 50922, \"name\": \"a welcome\"}, {\"id\": 50923, \"name\": \"green and red stripe\"}, {\"id\": 50924, \"name\": \"blue and white cake stand\"}, {\"id\": 50925, \"name\": \"nightclub\"}, {\"id\": 50926, \"name\": \"a black fingerless glove\"}, {\"id\": 50927, \"name\": \"black walking stick\"}, {\"id\": 50928, \"name\": \"cowl\"}, {\"id\": 50929, \"name\": \"red base\"}, {\"id\": 50930, \"name\": \"a child's shoulder\"}, {\"id\": 50931, \"name\": \"the milestone\"}, {\"id\": 50932, \"name\": \"person's blue and white striped sock\"}, {\"id\": 50933, \"name\": \"a bobbin\"}, {\"id\": 50934, \"name\": \"jenga game\"}, {\"id\": 50935, \"name\": \"food option\"}, {\"id\": 50936, \"name\": \"a blue laptop\"}, {\"id\": 50937, \"name\": \"the fish market\"}, {\"id\": 50938, \"name\": \"the blue and green barrel\"}, {\"id\": 50939, \"name\": \"a dark door\"}, {\"id\": 50940, \"name\": \"blue len\"}, {\"id\": 50941, \"name\": \"an indoor ice rink\"}, {\"id\": 50942, \"name\": \"the liman\"}, {\"id\": 50943, \"name\": \"thin black bracelet\"}, {\"id\": 50944, \"name\": \"a black electronic device\"}, {\"id\": 50945, \"name\": \"the beige skirt\"}, {\"id\": 50946, \"name\": \"a decorative carving\"}, {\"id\": 50947, \"name\": \"the wavy pattern\"}, {\"id\": 50948, \"name\": \"red and yellow instrument panel\"}, {\"id\": 50949, \"name\": \"the gray slipper\"}, {\"id\": 50950, \"name\": \"black ripped jean\"}, {\"id\": 50951, \"name\": \"brown suspender\"}, {\"id\": 50952, \"name\": \"the beige roll-up blind\"}, {\"id\": 50953, \"name\": \"the white charging cable\"}, {\"id\": 50954, \"name\": \"a red emergency exit sign\"}, {\"id\": 50955, \"name\": \"car's rear trunk\"}, {\"id\": 50956, \"name\": \"the shoulder cutout\"}, {\"id\": 50957, \"name\": \"the welcome logo\"}, {\"id\": 50958, \"name\": \"the black quarter-zip top\"}, {\"id\": 50959, \"name\": \"a black plastic crate\"}, {\"id\": 50960, \"name\": \"the metal dish of food\"}, {\"id\": 50961, \"name\": \"the person's right foot\"}, {\"id\": 50962, \"name\": \"the white volleyball\"}, {\"id\": 50963, \"name\": \"a 99\"}, {\"id\": 50964, \"name\": \"green turf\"}, {\"id\": 50965, \"name\": \"a blue shipping container\"}, {\"id\": 50966, \"name\": \"pale yellow house\"}, {\"id\": 50967, \"name\": \"a bright green top\"}, {\"id\": 50968, \"name\": \"mall's upper level\"}, {\"id\": 50969, \"name\": \"a wooden headboard\"}, {\"id\": 50970, \"name\": \"a chunky chain necklace\"}, {\"id\": 50971, \"name\": \"a stair stepper\"}, {\"id\": 50972, \"name\": \"a make-shift stove\"}, {\"id\": 50973, \"name\": \"the muddy and uneven ground\"}, {\"id\": 50974, \"name\": \"pink notebook\"}, {\"id\": 50975, \"name\": \"the mixer\"}, {\"id\": 50976, \"name\": \"the long, orange beak\"}, {\"id\": 50977, \"name\": \"human's hand\"}, {\"id\": 50978, \"name\": \"the white plastic bar\"}, {\"id\": 50979, \"name\": \"a blue, yellow, and green panel\"}, {\"id\": 50980, \"name\": \"red and white ice resurfacer\"}, {\"id\": 50981, \"name\": \"a woman's right arm\"}, {\"id\": 50982, \"name\": \"the copper-colored tag\"}, {\"id\": 50983, \"name\": \"red pot\"}, {\"id\": 50984, \"name\": \"the bald spot\"}, {\"id\": 50985, \"name\": \"a clothe rack\"}, {\"id\": 50986, \"name\": \"the barn\"}, {\"id\": 50987, \"name\": \"the grey animal-print headrest\"}, {\"id\": 50988, \"name\": \"white horizontal slat\"}, {\"id\": 50989, \"name\": \"the timer reading\"}, {\"id\": 50990, \"name\": \"a large hoop earring\"}, {\"id\": 50991, \"name\": \"blue broom\"}, {\"id\": 50992, \"name\": \"the back of a motorcycle\"}, {\"id\": 50993, \"name\": \"motorcycle seat\"}, {\"id\": 50994, \"name\": \"the long-sleeved grey top\"}, {\"id\": 50995, \"name\": \"the single braid\"}, {\"id\": 50996, \"name\": \"4.95 euro\"}, {\"id\": 50997, \"name\": \"the brown and white diamond-patterned curtain\"}, {\"id\": 50998, \"name\": \"purple sari\"}, {\"id\": 50999, \"name\": \"white, long-sleeved dress shirt\"}, {\"id\": 51000, \"name\": \"the black rectangular patch\"}, {\"id\": 51001, \"name\": \"a 1000\"}, {\"id\": 51002, \"name\": \"a darker knot\"}, {\"id\": 51003, \"name\": \"auto rickshaw\"}, {\"id\": 51004, \"name\": \"a silver foil\"}, {\"id\": 51005, \"name\": \"the multicolored collar\"}, {\"id\": 51006, \"name\": \"blue and white polka dot napkin\"}, {\"id\": 51007, \"name\": \"the turquoise skirt\"}, {\"id\": 51008, \"name\": \"a netflix logo\"}, {\"id\": 51009, \"name\": \"the long-sleeved plaid shirt\"}, {\"id\": 51010, \"name\": \"mountainous landmass\"}, {\"id\": 51011, \"name\": \"petrovila\"}, {\"id\": 51012, \"name\": \"a transparent glass shelf\"}, {\"id\": 51013, \"name\": \"a brows. bureau de change\"}, {\"id\": 51014, \"name\": \"desolate, sandy area\"}, {\"id\": 51015, \"name\": \"beige wave\"}, {\"id\": 51016, \"name\": \"a white bus graphic\"}, {\"id\": 51017, \"name\": \"the keychain\"}, {\"id\": 51018, \"name\": \"port\"}, {\"id\": 51019, \"name\": \"an adult's hand\"}, {\"id\": 51020, \"name\": \"pharmacy\"}, {\"id\": 51021, \"name\": \"the discount\"}, {\"id\": 51022, \"name\": \"3x2 juego playstation 3\"}, {\"id\": 51023, \"name\": \"a blue and red bar\"}, {\"id\": 51024, \"name\": \"pool's brown-speckled concrete deck\"}, {\"id\": 51025, \"name\": \"loose curl\"}, {\"id\": 51026, \"name\": \"red bull\"}, {\"id\": 51027, \"name\": \"a silver sneaker\"}, {\"id\": 51028, \"name\": \"red and yellow top\"}, {\"id\": 51029, \"name\": \"clutch purse\"}, {\"id\": 51030, \"name\": \"metal cage\"}, {\"id\": 51031, \"name\": \"cityscape projection\"}, {\"id\": 51032, \"name\": \"a popcorn machine\"}, {\"id\": 51033, \"name\": \"the pink baby walker\"}, {\"id\": 51034, \"name\": \"a gold handle\"}, {\"id\": 51035, \"name\": \"a star design\"}, {\"id\": 51036, \"name\": \"dark-colored dashboard\"}, {\"id\": 51037, \"name\": \"a black cross-body bag strap\"}, {\"id\": 51038, \"name\": \"the neon yellow pant\"}, {\"id\": 51039, \"name\": \"a black heart\"}, {\"id\": 51040, \"name\": \"brake\"}, {\"id\": 51041, \"name\": \"a paper cone\"}, {\"id\": 51042, \"name\": \"the fish fillet\"}, {\"id\": 51043, \"name\": \"the bronze shield\"}, {\"id\": 51044, \"name\": \"dragon onesie\"}, {\"id\": 51045, \"name\": \"the dragonfly\"}, {\"id\": 51046, \"name\": \"an alligator\"}, {\"id\": 51047, \"name\": \"a transparent balloon\"}, {\"id\": 51048, \"name\": \"a trombone\"}, {\"id\": 51049, \"name\": \"water slide\"}, {\"id\": 51050, \"name\": \"a purple egg\"}, {\"id\": 51051, \"name\": \"the archer\"}, {\"id\": 51052, \"name\": \"a fabric cone\"}, {\"id\": 51053, \"name\": \"the wooden cone\"}, {\"id\": 51054, \"name\": \"the boat sailor\"}, {\"id\": 51055, \"name\": \"a concrete cone\"}, {\"id\": 51056, \"name\": \"living room couch\"}, {\"id\": 51057, \"name\": \"the seahorse\"}, {\"id\": 51058, \"name\": \"a powerlifter\"}, {\"id\": 51059, \"name\": \"superhero cape\"}, {\"id\": 51060, \"name\": \"the pink player\"}, {\"id\": 51061, \"name\": \"a cluttered desk\"}, {\"id\": 51062, \"name\": \"the torn napkin\"}, {\"id\": 51063, \"name\": \"tick\"}, {\"id\": 51064, \"name\": \"pet food pantry\"}, {\"id\": 51065, \"name\": \"the orange triangle\"}, {\"id\": 51066, \"name\": \"yellow lemonade pitcher\"}, {\"id\": 51067, \"name\": \"the purple oval\"}, {\"id\": 51068, \"name\": \"scientist\"}, {\"id\": 51069, \"name\": \"the plate of soup\"}, {\"id\": 51070, \"name\": \"the silver star\"}, {\"id\": 51071, \"name\": \"an opossum\"}, {\"id\": 51072, \"name\": \"the parked train\"}, {\"id\": 51073, \"name\": \"blue diamond\"}, {\"id\": 51074, \"name\": \"soccer field\"}, {\"id\": 51075, \"name\": \"a scientist\"}, {\"id\": 51076, \"name\": \"an orange tiger\"}, {\"id\": 51077, \"name\": \"the glass of water\"}, {\"id\": 51078, \"name\": \"ping pong paddle\"}, {\"id\": 51079, \"name\": \"purple book\"}, {\"id\": 51080, \"name\": \"an old newspaper\"}, {\"id\": 51081, \"name\": \"a stone cone\"}, {\"id\": 51082, \"name\": \"martini glass\"}, {\"id\": 51083, \"name\": \"a birdhouse\"}, {\"id\": 51084, \"name\": \"wooden hurdle\"}, {\"id\": 51085, \"name\": \"the pirate costume\"}, {\"id\": 51086, \"name\": \"the gold crescent\"}, {\"id\": 51087, \"name\": \"the squid\"}, {\"id\": 51088, \"name\": \"the white muzzle\"}, {\"id\": 51089, \"name\": \"a gold player\"}, {\"id\": 51090, \"name\": \"a black diamond\"}, {\"id\": 51091, \"name\": \"the creamer\"}, {\"id\": 51092, \"name\": \"orange rectangle\"}, {\"id\": 51093, \"name\": \"the green snake\"}, {\"id\": 51094, \"name\": \"the ghost sheet\"}, {\"id\": 51095, \"name\": \"a bacon strip\"}, {\"id\": 51096, \"name\": \"cricket ball\"}, {\"id\": 51097, \"name\": \"the train conductor\"}, {\"id\": 51098, \"name\": \"a delivery truck\"}, {\"id\": 51099, \"name\": \"bus driver\"}, {\"id\": 51100, \"name\": \"the llama\"}, {\"id\": 51101, \"name\": \"the blue player\"}, {\"id\": 51102, \"name\": \"the walrus\"}, {\"id\": 51103, \"name\": \"starfish\"}, {\"id\": 51104, \"name\": \"the plate of cake\"}, {\"id\": 51105, \"name\": \"the parked police car\"}, {\"id\": 51106, \"name\": \"the clown nose\"}, {\"id\": 51107, \"name\": \"the rectangular frame\"}, {\"id\": 51108, \"name\": \"oval painting\"}, {\"id\": 51109, \"name\": \"a diamond ring\"}, {\"id\": 51110, \"name\": \"striped balloon\"}, {\"id\": 51111, \"name\": \"a turquoise pentagon\"}, {\"id\": 51112, \"name\": \"a magnesium\"}, {\"id\": 51113, \"name\": \"yellow player\"}, {\"id\": 51114, \"name\": \"the star-shaped decoration\"}, {\"id\": 51115, \"name\": \"the bronze heart\"}, {\"id\": 51116, \"name\": \"porcelain doll\"}, {\"id\": 51117, \"name\": \"claw of a tiger\"}, {\"id\": 51118, \"name\": \"whiskey barrel\"}, {\"id\": 51119, \"name\": \"the airplane pilot\"}, {\"id\": 51120, \"name\": \"the portrait of a historical figure\"}, {\"id\": 51121, \"name\": \"the violinist\"}, {\"id\": 51122, \"name\": \"the bronze arrow\"}, {\"id\": 51123, \"name\": \"a koala\"}, {\"id\": 51124, \"name\": \"a batman mask\"}, {\"id\": 51125, \"name\": \"a rusty fork\"}, {\"id\": 51126, \"name\": \"a blue inflatable bag\"}, {\"id\": 51127, \"name\": \"a horse cart\"}, {\"id\": 51128, \"name\": \"the puppeteer\"}, {\"id\": 51129, \"name\": \"a burnt toast\"}, {\"id\": 51130, \"name\": \"a moldy bread\"}, {\"id\": 51131, \"name\": \"the grasshopper\"}, {\"id\": 51132, \"name\": \"eel\"}, {\"id\": 51133, \"name\": \"a hexagonal tile\"}, {\"id\": 51134, \"name\": \"road sign\"}, {\"id\": 51135, \"name\": \"a marimba\"}, {\"id\": 51136, \"name\": \"the car driver\"}, {\"id\": 51137, \"name\": \"kelp\"}, {\"id\": 51138, \"name\": \"the mermaid tail\"}, {\"id\": 51139, \"name\": \"the gong\"}, {\"id\": 51140, \"name\": \"the sandbox\"}, {\"id\": 51141, \"name\": \"sculptor\"}, {\"id\": 51142, \"name\": \"the red squirrel\"}, {\"id\": 51143, \"name\": \"a whisker of a cat\"}, {\"id\": 51144, \"name\": \"the papaya\"}, {\"id\": 51145, \"name\": \"an apartment complex\"}, {\"id\": 51146, \"name\": \"gray scarf\"}, {\"id\": 51147, \"name\": \"the gerbil\"}, {\"id\": 51148, \"name\": \"a ceramic brake\"}, {\"id\": 51149, \"name\": \"a teenager\"}, {\"id\": 51150, \"name\": \"black cone\"}, {\"id\": 51151, \"name\": \"an excavator\"}, {\"id\": 51152, \"name\": \"fin of a shark\"}, {\"id\": 51153, \"name\": \"octagonal table\"}, {\"id\": 51154, \"name\": \"green player\"}, {\"id\": 51155, \"name\": \"nectarine\"}, {\"id\": 51156, \"name\": \"black cylinder\"}, {\"id\": 51157, \"name\": \"cube\"}, {\"id\": 51158, \"name\": \"the boat captain\"}, {\"id\": 51159, \"name\": \"fairy wing\"}, {\"id\": 51160, \"name\": \"the airplane propeller\"}, {\"id\": 51161, \"name\": \"a train conductor\"}, {\"id\": 51162, \"name\": \"the boat anchor\"}, {\"id\": 51163, \"name\": \"purple water bottle\"}, {\"id\": 51164, \"name\": \"the water ski\"}, {\"id\": 51165, \"name\": \"the taxi cab\"}, {\"id\": 51166, \"name\": \"spider\"}, {\"id\": 51167, \"name\": \"an airport gate\"}, {\"id\": 51168, \"name\": \"the school bus\"}, {\"id\": 51169, \"name\": \"beak of a parrot\"}, {\"id\": 51170, \"name\": \"a green wine bottle\"}, {\"id\": 51171, \"name\": \"the fishing rod\"}, {\"id\": 51172, \"name\": \"a pink hexagon\"}, {\"id\": 51173, \"name\": \"turquoise star\"}, {\"id\": 51174, \"name\": \"airplane pilot\"}, {\"id\": 51175, \"name\": \"a car driver\"}, {\"id\": 51176, \"name\": \"the garden hose\"}, {\"id\": 51177, \"name\": \"the black horse\"}, {\"id\": 51178, \"name\": \"square pillow\"}, {\"id\": 51179, \"name\": \"the parked crane\"}, {\"id\": 51180, \"name\": \"taxi driver\"}, {\"id\": 51181, \"name\": \"the hexagonal balloon\"}, {\"id\": 51182, \"name\": \"a nose of a pig\"}, {\"id\": 51183, \"name\": \"the photograph of a city\"}, {\"id\": 51184, \"name\": \"the veggie burger\"}, {\"id\": 51185, \"name\": \"the broken chair\"}, {\"id\": 51186, \"name\": \"purple elephant\"}, {\"id\": 51187, \"name\": \"a juggler\"}, {\"id\": 51188, \"name\": \"the alpaca\"}, {\"id\": 51189, \"name\": \"skyscraper\"}, {\"id\": 51190, \"name\": \"an orange diamond\"}, {\"id\": 51191, \"name\": \"the blue bird\"}, {\"id\": 51192, \"name\": \"the blue pom-pom\"}, {\"id\": 51193, \"name\": \"seahorse\"}, {\"id\": 51194, \"name\": \"a cocktail shaker\"}, {\"id\": 51195, \"name\": \"the octagon\"}, {\"id\": 51196, \"name\": \"the placemat\"}, {\"id\": 51197, \"name\": \"the painting of a landscape\"}, {\"id\": 51198, \"name\": \"a firefly\"}, {\"id\": 51199, \"name\": \"a pirate\"}, {\"id\": 51200, \"name\": \"an alpaca\"}, {\"id\": 51201, \"name\": \"an ironing board\"}, {\"id\": 51202, \"name\": \"the green cone\"}, {\"id\": 51203, \"name\": \"silver sphere\"}, {\"id\": 51204, \"name\": \"the tofu block\"}, {\"id\": 51205, \"name\": \"a hoof of a horse\"}, {\"id\": 51206, \"name\": \"bodybuilder\"}, {\"id\": 51207, \"name\": \"the guitarist\"}, {\"id\": 51208, \"name\": \"the sculpture of an animal\"}, {\"id\": 51209, \"name\": \"the rectangular balloon\"}, {\"id\": 51210, \"name\": \"a chicken breast\"}, {\"id\": 51211, \"name\": \"parked tractor\"}, {\"id\": 51212, \"name\": \"an arm of a human\"}, {\"id\": 51213, \"name\": \"a fishing net\"}, {\"id\": 51214, \"name\": \"the whiskey decanter\"}, {\"id\": 51215, \"name\": \"oval balloon\"}, {\"id\": 51216, \"name\": \"a pink rabbit\"}, {\"id\": 51217, \"name\": \"the sugar cube\"}, {\"id\": 51218, \"name\": \"the vacuum cleaner\"}, {\"id\": 51219, \"name\": \"the dirty tablecloth\"}, {\"id\": 51220, \"name\": \"the silver fox\"}, {\"id\": 51221, \"name\": \"a bus driver\"}, {\"id\": 51222, \"name\": \"a hippo\"}, {\"id\": 51223, \"name\": \"the wing of a bird\"}, {\"id\": 51224, \"name\": \"a metal airplane\"}, {\"id\": 51225, \"name\": \"a plate of muffin\"}, {\"id\": 51226, \"name\": \"infant\"}, {\"id\": 51227, \"name\": \"a star-shaped balloon\"}, {\"id\": 51228, \"name\": \"a pink pig\"}, {\"id\": 51229, \"name\": \"a golden bear\"}, {\"id\": 51230, \"name\": \"the copper oval\"}, {\"id\": 51231, \"name\": \"fencer\"}, {\"id\": 51232, \"name\": \"the sketch of a building\"}, {\"id\": 51233, \"name\": \"a circular mirror\"}, {\"id\": 51234, \"name\": \"the horn of a rhino\"}, {\"id\": 51235, \"name\": \"the equestrian\"}, {\"id\": 51236, \"name\": \"magician\"}, {\"id\": 51237, \"name\": \"ship wheel\"}, {\"id\": 51238, \"name\": \"grey cat\"}, {\"id\": 51239, \"name\": \"the flute\"}, {\"id\": 51240, \"name\": \"xylophone\"}, {\"id\": 51241, \"name\": \"the ping pong ball\"}, {\"id\": 51242, \"name\": \"a bus stop bench\"}, {\"id\": 51243, \"name\": \"plate of pasta\"}, {\"id\": 51244, \"name\": \"green obstacle course\"}, {\"id\": 51245, \"name\": \"a brown coffee pot\"}, {\"id\": 51246, \"name\": \"banjo\"}, {\"id\": 51247, \"name\": \"the wizard\"}, {\"id\": 51248, \"name\": \"the cannon\"}], \"video_np_pairs\": [{\"id\": 123, \"video_id\": 0, \"category_id\": 50554, \"noun_phrase\": \"thick gold chain\", \"num_masklets\": 0}, {\"id\": 977, \"video_id\": 0, \"category_id\": 1390, \"noun_phrase\": \"a braid\", \"num_masklets\": 1}, {\"id\": 1336, \"video_id\": 0, \"category_id\": 3827, \"noun_phrase\": \"the face\", \"num_masklets\": 4}, {\"id\": 1369, \"video_id\": 0, \"category_id\": 847, \"noun_phrase\": \"a neck\", \"num_masklets\": 4}, {\"id\": 1375, \"video_id\": 0, \"category_id\": 49504, \"noun_phrase\": \"the nike air jordan logo\", \"num_masklets\": 4}, {\"id\": 1380, \"video_id\": 0, \"category_id\": 1985, \"noun_phrase\": \"the right hand\", \"num_masklets\": 4}, {\"id\": 1390, \"video_id\": 0, \"category_id\": 3802, \"noun_phrase\": \"the left hand\", \"num_masklets\": 4}, {\"id\": 1446, \"video_id\": 0, \"category_id\": 49272, \"noun_phrase\": \"air jordan\", \"num_masklets\": 12}]}"
  },
  {
    "path": "assets/veval/toy_gt_and_pred/toy_saco_veval_sav_test_pred.json",
    "content": "[{\"video_id\": 0, \"category_id\": 847, \"bboxes\": [[592, 702, 34, 23], [597, 703, 35, 23], [602, 701, 35, 24], [605, 701, 38, 23], [608, 699, 37, 25], [611, 700, 37, 24], [617, 701, 37, 25], [634, 703, 27, 24], [638, 704, 28, 24], [637, 704, 28, 23], [639, 703, 23, 24], [642, 703, 19, 24], [648, 704, 13, 23], [650, 703, 13, 26], [649, 704, 10, 21], [650, 707, 6, 16], [0, 0, 0, 0], [0, 0, 0, 0], [634, 700, 14, 22], [630, 696, 19, 26], [618, 699, 30, 24], [614, 701, 35, 23], [616, 698, 38, 25], [619, 696, 38, 26], [623, 694, 38, 27], [626, 697, 38, 25], [625, 698, 37, 24], [624, 697, 39, 25], [625, 697, 39, 25], [630, 695, 37, 26], [635, 694, 39, 27], [636, 697, 39, 25], [639, 698, 38, 27], [639, 701, 38, 28], [638, 703, 37, 28], [638, 704, 37, 28], [638, 703, 38, 29], [636, 703, 37, 29], [632, 701, 35, 32], [636, 702, 35, 30], [645, 702, 35, 30], [651, 701, 37, 30], [658, 703, 37, 26], [661, 695, 39, 29], [660, 696, 38, 27], [650, 699, 38, 27], [640, 690, 39, 24], [628, 668, 46, 20], [628, 639, 45, 20], [628, 613, 44, 20], [630, 602, 45, 22], [635, 600, 46, 20], [640, 598, 47, 21], [645, 597, 49, 20], [650, 597, 49, 19], [655, 596, 49, 18], [663, 594, 45, 19], [665, 597, 43, 18], [665, 596, 43, 20], [662, 598, 47, 21], [664, 596, 49, 21], [673, 587, 46, 32], [686, 594, 33, 27], [678, 598, 41, 21], [667, 590, 42, 23], [665, 586, 41, 23], [666, 586, 38, 21], [665, 583, 36, 20], [662, 582, 37, 20], [665, 577, 39, 26], [674, 586, 36, 20], [676, 586, 40, 23], [683, 588, 35, 25], [683, 588, 36, 27], [674, 588, 42, 26], [674, 588, 40, 26], [665, 588, 45, 23], [656, 590, 49, 24], [656, 598, 45, 25], [650, 605, 45, 24], [646, 607, 48, 26], [651, 607, 48, 24], [658, 606, 48, 24], [659, 605, 52, 25], [663, 607, 50, 25], [664, 609, 50, 26], [662, 611, 52, 28], [662, 623, 46, 20], [665, 623, 44, 21], [667, 622, 44, 20], [668, 610, 46, 25], [671, 607, 44, 25], [679, 607, 40, 20], [687, 595, 32, 26], [696, 595, 23, 25], [696, 593, 23, 23], [695, 588, 24, 25], [698, 583, 21, 27], [705, 608, 14, 12], [702, 608, 17, 26], [700, 625, 19, 15], [699, 622, 20, 18], [691, 606, 28, 27], [694, 604, 25, 20], [692, 589, 27, 29], [692, 589, 27, 24], [692, 588, 27, 21], [695, 587, 24, 21], [696, 582, 23, 21], [695, 593, 24, 6], [695, 577, 24, 20], [695, 588, 24, 19], [688, 596, 31, 23], [682, 601, 37, 23], [676, 604, 43, 21], [671, 600, 44, 23], [668, 596, 42, 22], [667, 595, 41, 21], [667, 594, 39, 22], [661, 588, 42, 24], [662, 586, 43, 24], [665, 581, 43, 26], [670, 581, 43, 25], [675, 583, 43, 25], [683, 593, 36, 21], [685, 588, 34, 28], [679, 588, 40, 24], [668, 583, 42, 23], [656, 581, 42, 21], [645, 580, 41, 20], [646, 580, 31, 22], [0, 0, 0, 0], [655, 584, 12, 14], [649, 587, 16, 16], [654, 586, 13, 17], [654, 590, 14, 20], [0, 0, 0, 0], [655, 605, 15, 20], [651, 607, 19, 25], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]], \"score\": 0.7064220309257507, \"segmentations\": [{\"size\": [1280, 720], \"counts\": \"\\\\fTg06gW15M2N1O100O2N1O100O1O1O1O1000O01000O1000000O10000O100O100O3N2M3N3K3Mjac3\"}, {\"size\": [1280, 720], \"counts\": \"^nZg06hW13N1N201N101N100O1O1O100O100O1O1O10O01000O100O10000O10001N2N2O2M3M3N1NhQ\\\\3\"}, {\"size\": [1280, 720], \"counts\": \"\\\\Vag06iW13L3N1O2N100O100O100O1000O0100O1O10000O001O1000O2O000O2O0O100O3M4L4L3KjiU3\"}, {\"size\": [1280, 720], \"counts\": \"]ndg02lW16J2O2O0O101N1O100O100O1O100O1O100O10O0100000O10001NO2000O1O101N2N3M3M2N3M4MgYn2\"}, {\"size\": [1280, 720], \"counts\": \"Zfhg09fW11O1O2N101N1O10000O100O100O1O1O10000O10O0100O00100000O100O2N101N2O1N3M3M3Mlik2\"}, {\"size\": [1280, 720], \"counts\": \"\\\\^lg03kW14L4M2O0O2N100O100O100O1O10000O0010000O100O10O0100001N100O2N100O2O3L3L3M6LgQh2\"}, {\"size\": [1280, 720], \"counts\": \"\\\\nSh06iW12N2N1O101O001N100O1O100O2O0O010O1O10O0100O11N01O1O10O10000O2N1O2O2M2N3N3Kka`2\"}, {\"size\": [1280, 720], \"counts\": \"aVih06hW12O2M2O1N2O100O010O1O1O1O1O0011N01O1O0100O101N4M3L3M2NhiW2\"}, {\"size\": [1280, 720], \"counts\": \"aVnh04fW19K2O2N10O10O100O100O100O10O01O10O100O002O000O3N2N3L3L4MeaQ2\"}, {\"size\": [1280, 720], \"counts\": \"bnlh05bW1:N1O100O100O100O10O10O10000O0010O01000O101N2O2N3L2N4K4MdiR2\"}, {\"size\": [1280, 720], \"counts\": \"a^oh02eW1=J3N1O1000O0100O0100O01O1O010000O3N2M4L2N3M3MfaV2\"}, {\"size\": [1280, 720], \"counts\": \"UVSi08gW15K5L3M0O1O0100O1O01O0101N2O4K3M2N3M4LdiW2\"}, {\"size\": [1280, 720], \"counts\": \"UfZi0<aW16L3N2M01O101N2N4L3N1N3L4MeiW2\"}, {\"size\": [1280, 720], \"counts\": \"[V]i0:aW18I5L4OO1O2N2M4M4M1N2M4M3MeYU2\"}, {\"size\": [1280, 720], \"counts\": \"_n[i05aW1<K3O0002N3N2M4L3M3MeYZ2\"}, {\"size\": [1280, 720], \"counts\": \"ZV]i09cW15M1003M5J6KdQ^2\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"XVih0;_W16L4O001001N2O001N3N3L4M1N1MkQh2\"}, {\"size\": [1280, 720], \"counts\": \"WVdh0<_W15L4L4N20O100O101N2O1N2O1O3L4M2L4M2N2Ogif2\"}, {\"size\": [1280, 720], \"counts\": \"`VUh03eW18N3N100O1O100O1O1O1000000O00100O1O10O10O01O2O0O2O2M4L3M2N3MjQh2\"}, {\"size\": [1280, 720], \"counts\": \"\\\\VPh06hW13N2N1000000O2O000O1O1O1O100O1O1O100O00100O10O100O100O1O101N3N1N3L3N4Ljif2\"}, {\"size\": [1280, 720], \"counts\": \"[fRh05iW13N2O1O0O100O1O2N1O100O1O1O100O1O100O100O010000O10O10O010O101N2O2M3M3M2N2O1Nla`2\"}, {\"size\": [1280, 720], \"counts\": \"X^Vh06iW12N2O1N2N1O1O2O0O1O1O10O02O0O1O100O010000O10O100O0100O01001N3M2N3M2O2M3M2M2Omi\\\\2\"}, {\"size\": [1280, 720], \"counts\": \"X^[h06iW12N2N2N1O1O1O1O1O2O0O100O100O100O01000O00100O1O0010O2O0O100O3M3N2M3M2O1N3M2NmiW2\"}, {\"size\": [1280, 720], \"counts\": \"YV_h06iW12N2N2N1O101O0O1O100O1O100O100O1O100O1O10O010O100O10O0101O0O2O1N3M3M3M2O1N2LnQT2\"}, {\"size\": [1280, 720], \"counts\": \"[n]h04jW14M2N2N1O1O10000O1O100O1O100O100O1O100O1O10O100O1O10O10O101N103K3O2M3M2N1NnaV2\"}, {\"size\": [1280, 720], \"counts\": \"]f\\\\h01kW16M2N1N3O0O1O1O101N100O100O10O01O1O100O010O10O011O0O10O0101N2N2N3N1N2O2L3N2N10lYU2\"}, {\"size\": [1280, 720], \"counts\": \"]n]h01kW15N2N2M2O2N10000O1O2O0O1O10O01O1O1O100O10O01000O100O10000O101N2N2N3N2M2N2N2N2NmQT2\"}, {\"size\": [1280, 720], \"counts\": \"YVdh05iW13O1N2N2N100O100O1N2O1O100O1O1O10O01000O10O10O10O10O10O02N3M2O2M2O2M3L3N2NmYP2\"}, {\"size\": [1280, 720], \"counts\": \"X^jh07hW11O2N1O2N1O100O1O2O0O100O100O001O1O1O001O100O0100O2OO10O1O2O1N3N2M3N1M3O2M2N2Nmag1\"}, {\"size\": [1280, 720], \"counts\": \"]fkh01lW14N3M10001N1O101N1O100O1O1O1O1O100O100O001000O010O10O10O01000O2O1N3M2N2O2M2O2LnYf1\"}, {\"size\": [1280, 720], \"counts\": \"`^oh03jW13N3O0O2N101O0O1O100O1O1O100O1O10O01O1O1O1O001O1O10O101O0O2N2N2O2M3N2M3M3L3Nhic1\"}, {\"size\": [1280, 720], \"counts\": \"b^oh03lW13L3N1O2O0O101N100O1O010O100O1O1O1O1O001O1O1N101000O100O2O0O2O2M5L2N2L4M2N2Ndic1\"}, {\"size\": [1280, 720], \"counts\": \"eVnh03kW12N3N2O1N1O2O0O100O100O1O1O100O1O001O1O10O10O0O2O0011O000O102M4L3N2M2O2M2NdYf1\"}, {\"size\": [1280, 720], \"counts\": \"eVnh03kW14L3N2O0O101N1O100O1O1O1O100O1O1N10100O10O10O0O20O10000O2O1N5L1N3M3N1N2O1NcYf1\"}, {\"size\": [1280, 720], \"counts\": \"fVnh01nW13K5M2N101N1O1O100O100O1O1O1O1O1O1O1O001O1O1O0010O10000O2O0O5L2M3N2M2N2N2N5K`Qe1\"}, {\"size\": [1280, 720], \"counts\": \"ffkh02lW13M4M1O2N1O100O2N100O1O1O001O1O1O1O1O1O001O10O100O010O2O1N3N4L1N2N3M2O0O2Ocih1\"}, {\"size\": [1280, 720], \"counts\": \"effh05hW15M1O2N1O1O1O100O2N1O1O1N101O1O1O10OO2O1N20O100O2O0O2N101N5L3L3M3M2N2NdYP2\"}, {\"size\": [1280, 720], \"counts\": \"efkh05iW13M3N1O1O2N100O1O1O100O1O1N2O1O001N2O1O010O11N1O2O0O012M4M3L2N2O2M2N2OcYk1\"}, {\"size\": [1280, 720], \"counts\": \"dnVi07gW13N1O1O1O2M200O1O1O100O1O1O001O1O1M3O10O010O101N2O0O1O104K4L3M2O1N2O1LfQ`1\"}, {\"size\": [1280, 720], \"counts\": \"e^^i02kW15M2N2N1O2N1O100N2O100O1O100N2O1O10O01O1N10001000O101O0O2O1N2N3N4K4L2N2M3MeQV1\"}, {\"size\": [1280, 720], \"counts\": \"aVgi05jW12N2O0O101O0O1O2O0O1O1O1O001O1O1O100O1O1O01000O001000O2O0O2O1N3N1N3N1N2N4KfYm0\"}, {\"size\": [1280, 720], \"counts\": \"\\\\nji03lW13M3M2N101N1O1O1O1O1O1O1O1O100O1O1O1O10O01O100O10O010O102M1O2O0O4M2M2N2N2N3M2NlQg0\"}, {\"size\": [1280, 720], \"counts\": \"\\\\fii02kW14N2O2N1N1O101N1O10OO200N200O100O1O100O100O0010O010O010O1O2N1O3M3N1N3M2N3M2Nmai0\"}, {\"size\": [1280, 720], \"counts\": \"[V]i06iW13N1N2O1O1O000O10000O1O010O1O100O1O00100O001O001O000100O1O2N2N2N101N2N3L3M3NmQV1\"}, {\"size\": [1280, 720], \"counts\": \"QfPi05jW13N2M10001O00000O100N20000O1O10000O1001N10O10O01O01O010O10O1O000011M3N2N1N3N2NZZa1\"}, {\"size\": [1280, 720], \"counts\": \"Teah03mW1001N4M1O001N2N20O01O000O100O2O0O10000001O1O00000O10O1000OO2001N10O01O100O1O00001O100O1O1N5LRcg1\"}, {\"size\": [1280, 720], \"counts\": \"Ydah03lW12N2O1O1O000O2N1001OO2O000O2O000O101O0O010000O10O1000O0100O02O000O10O0100O100O1O1O1O1O1O2LSlh1\"}, {\"size\": [1280, 720], \"counts\": \"_cah04kW13M2N101O000O10001O001O0O1O2O0000000O1O11O1O1OO1O10O100OO2O02O1N2OO01O2N100O00001O1O2N4LgTj1\"}, {\"size\": [1280, 720], \"counts\": \"RSdh07gW14M2N2O001O00001O00001O001O000O011O000000O10000000O010O10O101OO10O1000O01O1O100O100O1O1N4LV]f1\"}, {\"size\": [1280, 720], \"counts\": \"S[jh01lW16K3N2N2O001O010O0O101O00000O100O100O100000000001O000O1000O10O10O0101O0O10000O100N101O1O2M3KZm^1\"}, {\"size\": [1280, 720], \"counts\": \"mbPi07hW13N1N2O1O00001O000O2O0O10001N1000000001O1O00O01000O10OO21O000O100000000O10O01O2N100O1O1O2N1N3LZ]W1\"}, {\"size\": [1280, 720], \"counts\": \"njVi01kW17M2N1O001O1O1O000O2O000000001O000O10000O100O11O1O0000000000O10O0100000O10000O1O2OO0002M3N1O2M3M3NWen0\"}, {\"size\": [1280, 720], \"counts\": \"jR]i05jW12O1O1O1O1O001O1O001O0O2O0000000O1010O001O0000O10000O1O1000O1000O11O0000O0100O101N010O1O2N2M3M2N3NX]h0\"}, {\"size\": [1280, 720], \"counts\": \"jZci01nW12O003M001O000O2O1O001N2O0010O0001N1000001N100001O000000O10000O1O010001N01000O2O00O01O2N1O2O1N2M3MYUb0\"}, {\"size\": [1280, 720], \"counts\": \"iZmi02mW12O1O1O2N0O2O001N101O1O000O101O01O0000O1001O01M101000000O10000001O0O10001O0O1O011N2N1N3M3NZU=\"}, {\"size\": [1280, 720], \"counts\": \"ijoi03mW13M1O001O1O0O2O1O0O2O00001O01O01O000O2O000O1O1001O0O0100001O00O1000O101OO0100O1O2M3N2L[U=\"}, {\"size\": [1280, 720], \"counts\": \"jjoi05jW1200O1O1N2N2O001O1N101O00000001O01N1000000O1O1000000O1000O10000000001N2OO01O100O2M3M2OZU=\"}, {\"size\": [1280, 720], \"counts\": \"nRli01nW12O1N2O2N1O1O1O1N101O0O100O20O000001O0001OO1O101N01000001OO100000000O0011O001N01000O1O1O2M2O3LXm;\"}, {\"size\": [1280, 720], \"counts\": \"nbni01nW12N2N101O10O01O1O1N2N2N101O000O101N100000000O1O1001O000000000000O1O10O101OO1001N2O0OO11O1O3N1M3M4MWm6\"}, {\"size\": [1280, 720], \"counts\": \"njYj02nW14K3N2M101O00000O2N1O101N10000010O000000O1O100O1O1O10000O1O1O010O10O10O1N20O01O10O1O1O2N1O1Oe]O\"}, {\"size\": [1280, 720], \"counts\": \"[Sjj01kW14M4O0O10001N1O1O100N2000000000000O100O1O010O1N200O0010000O0O2O10O_]O\"}, {\"size\": [1280, 720], \"counts\": \"VS`j02mW1101N101O0O2L3N3O0O1000000O1N2O10000O1000O10O100O02O0O100O10001N100O001O1O002M4J4OX]O\"}, {\"size\": [1280, 720], \"counts\": \"nZRj03lW12O1O001O0O1O2N101M2O1O1O100O1O100O1O1O1N2001O00O010000O10000O100O1O2O0O1O1O1O2M4N3J^m;\"}, {\"size\": [1280, 720], \"counts\": \"njoi01nW11N3O00000O1N3M2O101N1N200N2N2O1O1O100000000000O0100001O0O100O100O100O1O2O0O2M5L5J^e?\"}, {\"size\": [1280, 720], \"counts\": \"jRQj01oW10O2N101N3M1O1O1O1N3N1N2O02OO10000O100O10O1000O010001N1O010O1O2N01000O100O3KgUb0\"}, {\"size\": [1280, 720], \"counts\": \"gjoi04hW15N1O11O000000O1N2N2O100O1O100O01000O1O1000O102M100O100O100O1O1O1O2N1N4Lfme0\"}, {\"size\": [1280, 720], \"counts\": \"eRli01nW12O1M3N2N2L31O2N00O1O1O1O1O1O10O01O1O1000O01000O100O2O000O2O0O1O1O1O1N3M4Mf]h0\"}, {\"size\": [1280, 720], \"counts\": \"bjoi06gW14M3N10010O0O100O1O2N1O1O1O1O1O1O001000000O0100O2O0O2O0O2O1N1O001O100O100N2O4LjUb0\"}, {\"size\": [1280, 720], \"counts\": \"iR[j02lW13N2M2O10001N101N1O100O0O2O1000O10O100O010000O10000O100O1O2N100O1N2O2N10de:\"}, {\"size\": [1280, 720], \"counts\": \"jb]j02mW14L10001O0O1O2O00001L3O10000O1O1O1O1O10OO200O1000O1000000O2N1O1O100O1O2N1O1O2N3LcU3\"}, {\"size\": [1280, 720], \"counts\": \"nZfj04kW13L3N100N2000000O1O100O2O0O001O100O1O1O1O010O10O00101N100O012M3M2M4M3J_e0\"}, {\"size\": [1280, 720], \"counts\": \"P[fj04jW13O1N2N1O101N1O1O1N20000O1O0010000N2O0010000O0100O011O0O00011N4L2N2N3L3N\\\\]O\"}, {\"size\": [1280, 720], \"counts\": \"nR[j02mW13N2M101O0O2O000O2N1O100N2O1O10000O1O1O01000O100O10000O010O10O100O100O1O100O002N5J5K^U3\"}, {\"size\": [1280, 720], \"counts\": \"nR[j01nW15K2O001N10001O0O1N201N1O1N2000O10O1O1O10O0100N20O2O00O10O1O1O100O1O1O1O1O00007H`e5\"}, {\"size\": [1280, 720], \"counts\": \"njoi01oW10O2O0O101N101N1O2O0O2N1O1O100O100O100O100000O01O100O1O100O010O0100O100O100O1N3N1O1N2O2M4Kce:\"}, {\"size\": [1280, 720], \"counts\": \"nbdi01nW12O0O2O2N1O0O2N1O2O001O0O10000O1O1000000N2O1000000O1O0010000000O100000O1O0100O100O2N1O100O1N2O1O3Jcm`0\"}, {\"size\": [1280, 720], \"counts\": \"[cdi01lW13N2O2M2O2O000O2N100O1O2O0001OO1000O10O100O100O100000O01O10O10O010000O10O0002O0O1N2O2N2N1NYme0\"}, {\"size\": [1280, 720], \"counts\": \"[S]i05kW11N2N101O001N2N10000O100O101M200000000O10000O1000O0100O10O1000O010O10O1O100O1O1O1O1O2M3M4KR]m0\"}, {\"size\": [1280, 720], \"counts\": \"^SXi03mW11N2O0O3N1N101N101N100O110O00000O2O00O10N2O2OO1000N2O1000O100O011O000O0100O01O1O1O011N101M2O1O3Lodn0\"}, {\"size\": [1280, 720], \"counts\": \"[[^i03mW13M1N2O1O1N101O001N100O2O01O00N2O1001O0000O1O1O100000O010000O100O0100O100O1O100O00100N2O1O2L4N2LQ]h0\"}, {\"size\": [1280, 720], \"counts\": \"YSgi04jW15M1N2O1O001O1N101O0000010N10000O1O10000000000O100O10O1000000O10000O100O1O010O002N1O2NO1001O2M5IRe?\"}, {\"size\": [1280, 720], \"counts\": \"W[hi02mW15K3N2N1O0N3O1O10O01N1O10001O0O10O10O11O10O000O0100000O1000O101O0O10O10O0010O0100O1O001O1O1O1O2M2O1O2N3MP]9\"}, {\"size\": [1280, 720], \"counts\": \"Y[mi08gW12O1O1O1N101N2O1O001O000O10000001O0000O1000000O10000000O0100O10O10O0100O100O00100O1O2N1O2N001O1O0000Rm6\"}, {\"size\": [1280, 720], \"counts\": \"[cni07gW14M2O1N2O1N2O1O1O000O1O20O00001O00000000O10000O1001O0000O10OO2000O012N1N2OO0010O1O2N2N1O0001O01O3M2Mmd5\"}, {\"size\": [1280, 720], \"counts\": \"]Sli04kW13M4L3N2N1O1N2N101O0O2O000O2O0001O001O0O01001O00N2000000000O1000O1N12N101N101OO0010N2O2N1O1O1O101N100O2Oid5\"}, {\"size\": [1280, 720], \"counts\": \"cSli07fW15N20O00N1O20O01O001O000000000N2010O1OO100O0100000O11N100O010O100O100O1O1O10000O1O1N2N2O1N2ObT=\"}, {\"size\": [1280, 720], \"counts\": \"ekoi08fW13N2O1O1N2O1O0010O0000O1O2O01O0000O100000O10O100000O10000O100O1O100O100O1O1O1O1O1N2O1O2Nal;\"}, {\"size\": [1280, 720], \"counts\": \"d[Rj01jW1:K1O3M100O010O001N1O1010O0000O100000000O11O000O10000O10O0101N1000000O1N200O1O1O1N3O001N`\\\\9\"}, {\"size\": [1280, 720], \"counts\": \"]cSj05gW17K4N1O2OO01O000O1O2N100O10000O00100001O1O001O0000O100O02O0O2O1N2OO0102M2N1O0000100O1O2N1N5Lfd5\"}, {\"size\": [1280, 720], \"counts\": \"W[Wj08fW15M2M3N1O001O0O1O1O2O00000000000O11O1O1OO1O10O1000O0012N1N2OO1O0100O1O2N1O100N2O1N2N2O3Mm\\\\4\"}, {\"size\": [1280, 720], \"counts\": \"V[aj04jW16K3N1O1O0000001N1000010N10O1001O0000O10000000O10O10000O10001N1O100O00010O001N20Q]O\"}, {\"size\": [1280, 720], \"counts\": \"mZkj08gW15K2O2M2O010N1N200001O00O100O1000000O1O1O11O001O000O1000O1O1O010]]O\"}, {\"size\": [1280, 720], \"counts\": \"jbVk0>cW11O0N2N1O1O10001N10000O10000001O0000O100N10100]]O\"}, {\"size\": [1280, 720], \"counts\": \"jbVk01oW15J4L3M2N2O1O001N1O10000000000002N0000N1O2O100_]O\"}, {\"size\": [1280, 720], \"counts\": \"eZUk06hW15M1N2O1N2O1N2O001O00000O2O0O1000000000000O1O100d]O\"}, {\"size\": [1280, 720], \"counts\": \"\\\\RYk05kW15L1IGdhN;YW19M2N001N20O1O0000O20N10O101O0O10000i]O\"}, {\"size\": [1280, 720], \"counts\": \"Tkak04kW13N0O2O001O0000000O2O000O100P]O\"}, {\"size\": [1280, 720], \"counts\": \"`S^k04kW13M2O1N2O0O2N1M3N2O1O2O0O100O1N200P]O\"}, {\"size\": [1280, 720], \"counts\": \"ic[k01nW12N3M2O1N1O2O00000O1000001N100O1000000_\\\\O\"}, {\"size\": [1280, 720], \"counts\": \"i[Zk01nW13M3N0O2N101N1O100O101N1000O02N100O10000b\\\\O\"}, {\"size\": [1280, 720], \"counts\": \"`[Pk01oW11N2O2M2O0O2O1O0O1O2N1N3O0O1M3O1O1000000O100O10000O1O100R]O\"}, {\"size\": [1280, 720], \"counts\": \"[STk01lW15M101N101N100O2N1O100O100O100O1000O0100O100O10O10T]O\"}, {\"size\": [1280, 720], \"counts\": \"ScQk02mW14L1O2N1O2O00000O2O0O100O1O1N2O1O1O1O1O1N2O010O100O1O1c]O\"}, {\"size\": [1280, 720], \"counts\": \"nbQk01oW11O2M3N0O100O2O000O100O1O2N1O1O1O10O01O1O100O1O002N100c]O\"}, {\"size\": [1280, 720], \"counts\": \"kbQk04kW1101N100O2N10000O101N100O1O100O1O1O01000O10000O001O100d]O\"}, {\"size\": [1280, 720], \"counts\": \"jZUk05jW12O0O1000000O2O0O10000N2O1O1O01000O100O1O1O1O1O1e]O\"}, {\"size\": [1280, 720], \"counts\": \"ebVk06iW11O1000000O2O0O1O100O1N2000000O100O1N2O100O1O1j]O\"}, {\"size\": [1280, 720], \"counts\": \"aZUk01oW14L1O00000001O01O0O1001O1O00001O00001N10PX10PhN000[]O\"}, {\"size\": [1280, 720], \"counts\": \"]ZUk02mW14M0O200O000O101N2O0O1O100000000O1O1O10O01N200O1o]O\"}, {\"size\": [1280, 720], \"counts\": \"jZUk02mW12N3M1000000N201N100O1O100O100O10000O10O01O1O100d]O\"}, {\"size\": [1280, 720], \"counts\": \"Wclj01nW1100O2O000O2N1O101M2000000O1O10001N1O010O10O01O1O100O100N2O100\\\\]O\"}, {\"size\": [1280, 720], \"counts\": \"[Sej01mW13O001O0O2N101O1O0O1O100O1O10000O1O100O010O1O010O10O10O1O10O10O10O10O10O10W]O\"}, {\"size\": [1280, 720], \"counts\": \"[c]j02nW1001N2O2M101N100O100O1O100O1O1O100O1O1O100O0100000O0100O100O100O100O002N1O10000O1O1N2OT]O\"}, {\"size\": [1280, 720], \"counts\": \"W[Wj05kW11N101O000O101N100O2N100O10000O10000O00100O1000O10O100O001O1O01O1O100O2N100O1O100O1O2M6IU]4\"}, {\"size\": [1280, 720], \"counts\": \"RcSj02mW13N2M101O0O1O2O0O10001O000O100O1O1O010O100O100O001O1000O100O1O2O0O1O101N2N1O1O1O2L5MWe:\"}, {\"size\": [1280, 720], \"counts\": \"Q[Rj03lW13M2O001O000O100O2O0O100O100O1O1O1O10O01O1000O2OO001000O100O010O1O1O1O1O2O0N2N3N1L_U=\"}, {\"size\": [1280, 720], \"counts\": \"S[Rj02mW14M0O10000O2N1O100O1O10000O100N2O010O1O1O10O100O10O010001N01O01O1O001O2N1O2M3M]e?\"}, {\"size\": [1280, 720], \"counts\": \"njji02nW11O2M2O0O100O20O0000O100O1O1O1O1O1O1O1O10O01O1O10O01O0100O100O100O100N200O00001N3N2Nc]c0\"}, {\"size\": [1280, 720], \"counts\": \"lRli03kW13O0O101O0O100O2O0O1O10001N10O0100O1O1O010O1O1O10O01000O100O010O1O100O10O01O1O1O2O0N4Kfm`0\"}, {\"size\": [1280, 720], \"counts\": \"ijoi01mW13M3O0O2O0O2O1N1O101N10000O100O10000O10O010000O10O10O010O010O01000O00010O01O101M3N2M4KiU=\"}, {\"size\": [1280, 720], \"counts\": \"eRVj06jW1001N101O1N101N10000O100O100O10000O100O10O10O10O01O01000O01000O0O11O100O1O001O2N1O2M3Mjm6\"}, {\"size\": [1280, 720], \"counts\": \"iZ\\\\j02mW12O1O0O3N0O2N1000000O101N100O100O100O010O100000O1000O010O01O01O10O10O010N100101N2N2N6Hde0\"}, {\"size\": [1280, 720], \"counts\": \"nZfj02mW13N2N001O000000001O0O100O2O000000O0100O1O100O1O00100000O0100O010O0010O10_]O\"}, {\"size\": [1280, 720], \"counts\": \"Pkhj04jW13N2N3N0O101N100O100O1O100O1O1N2O1O100O1O100N11000000O010O1000000O10d]O\"}, {\"size\": [1280, 720], \"counts\": \"mZaj03kW13N3M1O2O0O2O0O100O1O100O1O100O100O10O10000000O10O010O10O0100O1O1000O01O1O1O100Oe]O\"}, {\"size\": [1280, 720], \"counts\": \"ebSj01nW13M4M1N1O2N101N1O100O10001N1000O0100O100000O01000O01000O01000O2N1O101N100O1O1N2O101Mhe:\"}, {\"size\": [1280, 720], \"counts\": \"ebdi01kW15O1N10001N101N100O2N10000O100O10OO200O100O01000000O01000O100O100O2N1O1O100N3N1O1M4Mjei0\"}, {\"size\": [1280, 720], \"counts\": \"`jVi04kW13N1O0O101O0O100O101O0O1O100O100O10O01O100O1O02O001OO010000O1O00100O1O1O1O1O2N2M4LjeX1\"}, {\"size\": [1280, 720], \"counts\": \"ZRXi04lW13M3M2N1N2O1N2O0O2OO00011N2O1OO1O01000O010O02N101N1O1O1N2O1O2Llmc1\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"XZci02nW14L3N2M2M2O01O0O1N2O1O3Ji]P2\"}, {\"size\": [1280, 720], \"counts\": \"]j[i0237^W17O00O100O010O1O100O100O1O1N2O2LgmR2\"}, {\"size\": [1280, 720], \"counts\": \"[Rbi04kW18I2N2O1N00O1O1O100O1N2O2Mf]P2\"}, {\"size\": [1280, 720], \"counts\": \"eRbi02jW1:H6L1N20N2N1O100O1O1O2N1N4L_Uo1\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"V[ci08eW16L1O2N0000010O02O001N1O2M3N3JQel1\"}, {\"size\": [1280, 720], \"counts\": \"d[^i05gW14K5L4K5O001O12M3N1N2O000O2N1O1N2O1N3Njdl1\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}], \"areas\": [620, 607, 610, 626, 691, 641, 636, 443, 486, 480, 398, 325, 224, 233, 147, 72, 0, 0, 232, 345, 519, 577, 658, 670, 673, 655, 628, 630, 658, 627, 681, 631, 635, 637, 634, 666, 694, 664, 723, 676, 678, 700, 618, 744, 634, 601, 560, 612, 601, 600, 776, 734, 761, 745, 721, 632, 623, 617, 697, 718, 775, 971, 551, 577, 686, 660, 541, 501, 491, 718, 491, 621, 573, 628, 732, 646, 635, 793, 730, 756, 805, 798, 807, 903, 888, 1004, 1077, 705, 750, 705, 842, 858, 614, 715, 503, 429, 508, 505, 148, 315, 232, 273, 509, 371, 491, 388, 376, 318, 324, 110, 311, 315, 391, 518, 581, 624, 590, 547, 513, 576, 616, 678, 638, 585, 445, 710, 687, 656, 581, 555, 454, 0, 131, 205, 180, 207, 0, 218, 313, 0, 0, 0, 0, 0, 0, 0]}, {\"video_id\": 0, \"category_id\": 847, \"bboxes\": [[175, 396, 34, 51], [152, 394, 32, 48], [125, 387, 36, 51], [105, 373, 43, 53], [101, 374, 50, 45], [105, 375, 54, 44], [111, 375, 55, 37], [109, 372, 56, 35], [98, 385, 54, 34], [78, 382, 53, 38], [65, 357, 49, 48], [54, 351, 52, 58], [51, 372, 47, 53], [53, 373, 48, 50], [48, 371, 51, 44], [50, 388, 54, 37], [60, 389, 53, 37], [62, 367, 57, 39], [64, 376, 54, 36], [62, 405, 54, 32], [60, 386, 54, 30], [65, 373, 56, 28], [83, 405, 57, 24], [106, 393, 53, 25], [121, 378, 47, 24], [118, 394, 57, 26], [109, 389, 52, 24], [99, 377, 50, 25], [98, 385, 52, 27], [105, 411, 54, 37], [110, 374, 49, 25], [105, 366, 55, 32], [102, 385, 53, 29], [99, 380, 52, 27], [102, 384, 50, 20], [114, 415, 50, 18], [148, 423, 59, 23], [180, 386, 52, 29], [195, 379, 52, 31], [215, 427, 42, 29], [205, 405, 47, 34], [174, 391, 59, 39], [141, 405, 65, 42], [107, 404, 60, 31], [76, 371, 59, 33], [68, 373, 56, 47], [77, 382, 60, 50], [99, 346, 59, 41], [126, 357, 61, 43], [175, 411, 51, 33], [202, 382, 54, 42], [216, 372, 58, 39], [241, 391, 55, 38], [238, 408, 56, 41], [219, 392, 57, 43], [192, 390, 60, 39], [166, 428, 49, 42], [113, 412, 62, 34], [95, 382, 56, 42], [88, 388, 57, 38], [94, 371, 57, 32], [105, 338, 65, 38], [127, 351, 69, 34], [160, 375, 55, 33], [155, 336, 49, 46], [110, 339, 59, 20], [101, 357, 55, 30], [71, 338, 46, 29], [78, 347, 59, 36], [87, 352, 63, 31], [103, 357, 65, 45], [134, 322, 31, 49], [101, 344, 44, 38], [105, 367, 24, 23], [77, 322, 52, 50], [77, 332, 59, 39], [95, 357, 60, 42], [98, 344, 58, 48], [0, 0, 0, 0], [0, 0, 0, 0], [111, 375, 36, 40], [88, 366, 46, 41], [103, 374, 57, 43], [120, 374, 57, 48], [107, 319, 58, 67], [78, 335, 50, 32], [73, 355, 69, 54], [95, 357, 62, 52], [90, 352, 69, 52], [96, 410, 63, 42], [95, 402, 78, 42], [120, 415, 52, 37], [136, 467, 32, 16], [141, 448, 65, 38], [145, 417, 62, 40], [156, 446, 27, 17], [146, 429, 67, 42], [148, 392, 59, 35], [145, 402, 61, 30], [151, 427, 58, 35], [148, 413, 56, 30], [129, 378, 66, 46], [116, 392, 69, 55], [120, 396, 62, 39], [111, 342, 61, 36], [110, 355, 58, 30], [111, 373, 63, 45], [126, 398, 54, 24], [129, 410, 54, 22], [126, 412, 59, 45], [113, 396, 63, 48], [99, 370, 63, 38], [90, 374, 63, 33], [85, 400, 67, 34], [80, 375, 64, 32], [78, 375, 63, 31], [66, 410, 60, 24], [90, 384, 27, 24], [42, 369, 36, 24], [60, 339, 41, 43], [14, 350, 35, 33], [7, 357, 50, 38], [48, 339, 47, 46], [37, 358, 72, 33], [60, 356, 77, 43], [88, 339, 77, 44], [90, 318, 76, 42], [125, 303, 69, 52], [135, 309, 66, 46], [130, 292, 70, 47], [137, 307, 74, 49], [123, 320, 75, 46], [103, 320, 81, 37], [80, 326, 85, 42], [58, 322, 96, 60], [43, 319, 98, 42], [23, 320, 85, 49], [11, 331, 96, 42], [27, 333, 94, 37], [42, 302, 106, 58], [87, 304, 87, 62], [73, 323, 119, 71], [123, 351, 84, 49], [138, 357, 95, 31], [150, 344, 105, 57], [203, 318, 58, 79]], \"score\": 0.675126850605011, \"segmentations\": [{\"size\": [1280, 720], \"counts\": \"nTk64jW15K5L3M2M5L2N3L4M2N3M3M2N2N2O1O10000000O001O001O1N2N2N1O2O2M3M8H4K5JlRmc0\"}, {\"size\": [1280, 720], \"counts\": \"j\\\\n56gW15M3M3M3L4L3N3M2N2N2N2N3N1O1O1O1000000O0100O2N1O1N4M1O101N2M:E7IhZld0\"}, {\"size\": [1280, 720], \"counts\": \"edl47fW16J5J6K4L4M4N2N1O1O2O0001ON2N2O11N2O000O100O3M2N2O1N3M2N1O2M2O6J4L2M2N3McRie0\"}, {\"size\": [1280, 720], \"counts\": \"UdS4;bW15M3K4L5J5M31O01N100O20O1O000O2O011NO1N2O12M10O01N101O1N2O1O1O5K1O2N2N1N2O1N3N100O2M2NoZYf0\"}, {\"size\": [1280, 720], \"counts\": \"ncn38fW16J5L4L3M30O01M3L2O201N1O1N3N1O100O00O1001N11O001O004LN2000001N13M1O1O1O0000O1N12O010N3N1O1O2N2M3^OkhN3[W1EVdUf0\"}, {\"size\": [1280, 720], \"counts\": \"QdS4;cW15M2M4K4L4L3O2N2N1N3N1O1O1O00O1001O0000O1001O0000000000001O0000O1000001O00O20O001O003L101N1000001O1N3N3L4M3^OjhNLTkle0\"}, {\"size\": [1280, 720], \"counts\": \"nS[44iW17J6L2M4L3N2N2N2N1O1O001O1O1O1OO100O100010O00000000000000001O0000000000000000000000O1000O10000000O1O2O2M4L5L4Jmkbe0\"}, {\"size\": [1280, 720], \"counts\": \"gcX47hW14M4L2N3L2O2M3N1O1O1O1O1O010O0000000000001O000000001O00000001O000000000001O000000O10001O00O100000O100O100O101M8AihNE^[ee0\"}, {\"size\": [1280, 720], \"counts\": \"Wlj33kW14K6L3N2N2N2O1N1N3N1O101N10000O1O000001N1O101O000000000010O000001O0001O000000000000000001O000000001O0O2N2M7_Ok[Tf0\"}, {\"size\": [1280, 720], \"counts\": \"UlQ35hW14L5M3N1O10010N1N2N3N1O1N102N11O00O1O1O1000O1O0001O0001O0000100O00001O00000000001O001O1O001O1O1O1O001N2N5K\\\\cnf0\"}, {\"size\": [1280, 720], \"counts\": \"_ca28_W1>J2O2N1O1O01000O1O2N10O01O100O001O1O1O1000000000O100O100O010O0010O01O001O000000001O1O1O2N2M4M3M8Gfkcg0\"}, {\"size\": [1280, 720], \"counts\": \"UkS21iW1<J:I4J1N1O1O010O1O1O2O0O0010000O010O1O1O1O11N2OO10O010000O100O11O1N01O01O0010O000001O002N2N2N001O0O3M2NPlmg0\"}, {\"size\": [1280, 720], \"counts\": \"jSP23hW1`0D7J1O101O0O1O1000000O010O1004K10000O10001OO1N20000O100O010O100O0100O5K002N2N1O1O1O0O10001N2MWkWh0\"}, {\"size\": [1280, 720], \"counts\": \"fcR2<cW1:G2N100O1O10000O100O1O100001N10O10O2O00O100O11O00O10O10O10O01O1O010O01O001O00001O001O1O1O1N2N4K]STh0\"}, {\"size\": [1280, 720], \"counts\": \"g[l1<dW12M3M2N2O1O1N2O11OO1O10O0010O1O10O01O1O10000O001O1O1O010001OO000010O002OO01O001N3N1O1N2O00001O001O3L6J_cVh0\"}, {\"size\": [1280, 720], \"counts\": \"Zln19aW1700O1O2M2O100O1O1O0002OO1O1O1O100O10O010O01O100O10O100O1O1O1O001O0O2O002N1N11O00@fhN:ZW1FfhN9`W115J11O01N10000O10001N2OmZPh0\"}, {\"size\": [1280, 720], \"counts\": \"X\\\\[2:cW15N2N1O2N1O101N1O0O2O100O001O100O100O00100O10O01O010O01O1O0O2O0O101O1O2N1O1N3N2N1N101O002N001O001O0O2N2O2NnReg0\"}, {\"size\": [1280, 720], \"counts\": \"bk]28eW14O2O2M1O1N2001OO100O2N1N2000O10O1O10O10O1O10O01O100O010O1O0001O000O3N1O001O00000O2O010O000O2O001N2O001O001O001O2Mhc]g0\"}, {\"size\": [1280, 720], \"counts\": \"T\\\\`21hW19K4N3M2O10001N1O1O1O1O1O1000000O1O001O10O010O01O01M2O10001O00000000001N1000000000000010OO01001O1N101O2M2O3Mek^g0\"}, {\"size\": [1280, 720], \"counts\": \"jl]26iW12N3N2N2N2N2M2O1N2O2N1O2M2O10O1O10O01O001O00000000000O2O00000000000O10000000O1000O2O000000000O010O100O1O3M4GW[ag0\"}, {\"size\": [1280, 720], \"counts\": \"Y\\\\[21mW14M3N1N3N2L4N2N2N1O1N2O2N1O1O1O00O2M2O2O1O1O0000000O101O00O100000000000000001O0O101O0000O10O2O000O001N2O1O3Ilkcg0\"}, {\"size\": [1280, 720], \"counts\": \"gca24kW14M1N3N2O2M2N2N2N2M2O10000N0100O1000000000001O00000000000000000000002M10000000000000001O0O100000001N101O1N00NO2JaT[g0\"}, {\"size\": [1280, 720], \"counts\": \"jTX34lW12N2M2O2M3N2N1O1N1O101N100001O001O00000000O1001O01O01O1O0000O100O12N100N2O000000O100000000O1O10O02O00O1N3N1L5L3N3LX[cf0\"}, {\"size\": [1280, 720], \"counts\": \"elT42nW1000L5M3N1O3N1O1O2N2O000N1M2O20001O01O00O100002N1OO10000001O002N001O0000O10000001O0000O1O0010N2O1N3L4N2M4Jccke0\"}, {\"size\": [1280, 720], \"counts\": \"Tdg41mW13M300O000O2N3M2M4L201O001O1O0101ON1O100O1000000001O2M2O001O0O01000O1000000O10000O001O2L3N2O1NT\\\\`e0\"}, {\"size\": [1280, 720], \"counts\": \"blc41nW12N3M2O2N1N2O0O200O1N2O1N2O001O1O1O1O01O0O0010O1O1001O0001O01O1O00O100000000001O0O100000001O00001O0O2O0O1O0N3O3L5J_cWe0\"}, {\"size\": [1280, 720], \"counts\": \"]dX41nW13N3K4L4M2O1O2N1N20O01O0O010O10000001O001O00O10000O10000001O1O0000O1000000000000000O10O01N2N3M2N2O1O1O2MkSie0\"}, {\"size\": [1280, 720], \"counts\": \"TTl32jW18K2O2N2N1N3M20O00000O01O1N2O1001O3M1OO10O10O2N1O1O1002N1O0000000000O10000001O00O0100N1O3M2N3M3N3L3LSTXf0\"}, {\"size\": [1280, 720], \"counts\": \"Xlj37iW13L5L2M2O1O1N2O1O0000000000000000O011O00000000O1O10000000000001O0000000000O2O000O10O1O02O01M2N2M4L4L5L4LjkVf0\"}, {\"size\": [1280, 720], \"counts\": \"XeS41jW18K4M3M3L4N2N3M2N2M3N1O0010N1O1O10O10000001O000O1000O100O1001O00001O0O1000000O1O1000000O2O0000000N3M3N3L4K7Gmbke0\"}, {\"size\": [1280, 720], \"counts\": \"nkY45hW16M3M3L3N1O1N100O100O11O0000O1001O0000000O11O0O1000O1000000000001OO10000O100000O1O100O1O1N2N3N109BUdke0\"}, {\"size\": [1280, 720], \"counts\": \"gcS4`0_W13N2N2M2N2O0001O00O10O10000000O10O0100000000000O1000000000000O0101OO010001O1O00O001O001O2O1N100O1O101N1N3N5J6JX\\\\je0\"}, {\"size\": [1280, 720], \"counts\": \"Ylo3:eW14M2M2N2O1O1O1O1O1O000O010O11O00000000O10000O100000O2O000000O100001O000000O01000O1001N1O01O02O0O2N1N3N2L4JocPf0\"}, {\"size\": [1280, 720], \"counts\": \"WTl33jW14M4M3M3N1O1O1N2O1O1O0O2OO0001001O1O1O0O101OO10000001OO10000000000O10010O00000001O1N010O101N10OO2O1O2N2LQdUf0\"}, {\"size\": [1280, 720], \"counts\": \"Wlo34kW13M3N1N2N2N2N101O000O11N10000O101O001O000000000010O00O1000O2O00000000001O0000001N11O0O1O2O0O3M2O0O3Li[Tf0\"}, {\"size\": [1280, 720], \"counts\": \"Xm^41mW13M4M3N1N2N2N10000O10000001O000000000001O01O000O11N1O10000001O001O001O1O1N11O0001N1001N11N3N1N1O2O1NgZee0\"}, {\"size\": [1280, 720], \"counts\": \"\\\\]i52Sh3Oj_M2M4O3M1O1O2N2N1N101O00000000000000001O0O10000O100001O00000O10O2N10000001O000O100000O100O1000O2O1N100O1N3M2O2L5Jgboc0\"}, {\"size\": [1280, 720], \"counts\": \"b\\\\Q75jW12N2N3M2N2O2OO01N1N101O1000001O0O100000000O1O100O10000001O001O00O1O1O1000O012N1N10O1000O0O2N1O1M4N4M2N4Il[Pc0\"}, {\"size\": [1280, 720], \"counts\": \"\\\\Td72lW14L4M3N1N1O2N1O100000001O00010O0O10000O101O0O11O1O00O2N1000001N1O1O10O10O10O011O00000000O0001M4L3N3L5K4LSd]b0\"}, {\"size\": [1280, 720], \"counts\": \"\\\\U]82mW14M101N1O2N2N2N1O1N2O1O100O1O2N1O001O001O001O001O00000000000O11O0000000O101O0O2N2N2NaRQb0\"}, {\"size\": [1280, 720], \"counts\": \"fdP86jW12N2O0N3M101O2N1O2N1O2N1O10001N1O001O1O1O10O0001O001O000000O1001O01O000000000000001O0O1O1N3Na0[OjZWb0\"}, {\"size\": [1280, 720], \"counts\": \"Yli62mW17J3M3L3N2O2M2N1O1O001O001O1O0010O01O1O1O1O001O1O00001O001O000000001ON200O2O0000O100000000000O101OO1000000000O1O1O8F=]O_Sob0\"}, {\"size\": [1280, 720], \"counts\": \"fd`5260`W1:N2M4M2N2N101N2N3L3N2N1O2N2N10O10O100O00000000000001O000000000000O11O0000001O0000000000O11O0000000O01000O1O2N1O1N2N2M3N2N2O2M2L7IVkPd0\"}, {\"size\": [1280, 720], \"counts\": \"kTV44jW17K1O3L2N2O2N2M3O00O0010O00000O2O0000000000000000000O2O0O11O1O001O1OO100O1O1001O000O1O1O0O101O1N2O101N00000O3O000O1N2O5G\\\\cae0\"}, {\"size\": [1280, 720], \"counts\": \"T\\\\o22lW16H5N4K4N1O2N1O00000001OO2O000O10O010001OO101O10O000O100O100001O0O2O0000O10O100O1O2OO0O101OO2O1O2N2N1O000O100M4O0M4N2Kbdif0\"}, {\"size\": [1280, 720], \"counts\": \"Y\\\\e27iW14K9H5J2O1O1N10001O0000000O10000O100001O00O100O10000000000002N3L1O11N2N1N11O00O1001O0N2O2N1O1O1N4N1N01N11N3L3N4L]\\\\Wg0\"}, {\"size\": [1280, 720], \"counts\": \"bdP38gW16J5L6I3N1N2O1O0O2O01O0001O000N20001O00000000000O100O10000000000001OO1O010O10000O100000O0010OO1O2O1K40O010ON4M3M3N2M4L8ERTgf0\"}, {\"size\": [1280, 720], \"counts\": \"XSl35jW12N2N3M3N2N2N2N2N3M1O2N2N1O00000000O2O0001O000O1O10000001O001O0000O100O1O100001O0O10O1O1O100O1O001OO2O1N1L5M3L3N3M3M4LXmle0\"}, {\"size\": [1280, 720], \"counts\": \"bkm43nW12M2N1O3L2N4M3L4M3M2M3N1O00001N01000O10000001O001OO10O010000000000000000O1O1O10O2O01OO10000O010O1N1O3L3N1O00101M202M2N3M4Gndhd0\"}, {\"size\": [1280, 720], \"counts\": \"SUk62kW17L2N3M2M4M2M2O2N001O1O00000O2O0O10000000001N12N1O00O101O0000000001O1N1000O100001N100N2O0O2N3L3N2N2N2NSkWc0\"}, {\"size\": [1280, 720], \"counts\": \"]ll71jW1:K2M3N1O2N1O2M2N3N100N2O2M3N000N2O100O1000010O0O100O2N100000O10000000000O1O100O2O001O00O0100O2M2O2M3L3M4K6HQ\\\\Rb0\"}, {\"size\": [1280, 720], \"counts\": \"l[^81PX15J1L5K5N2N1O2M2O2N1O1O2N1O2O0O0O2O0000001O1O00010N1000000O1001OO10000000000O1001O000O100O10001N1N2O1N2N2N3N1N2M4L6JXl[a0\"}, {\"size\": [1280, 720], \"counts\": \"]d]91mW14K7L3L3N3N2N1N10001N2N2N1O1O01O1O00000000O2O00000100O000010O01OO1N2O11O2N1O1O1O0000001O000O010N3J5M3O2M2N3K4N][``0\"}, {\"size\": [1280, 720], \"counts\": \"jlY93mW15K4K4M2M4M2N2N2O1N2N1O2N1O1O1O100O000000000000000000001O01OO101O000000000000000001O0O100000001O0O2OO00M4G9M3O4HWkb`0\"}, {\"size\": [1280, 720], \"counts\": \"]Tb8:eW13M3M4M2M3N2N3M2N2M3N1O1O1O00001O00001O0000001O0000000000001O01O00000000000000001N1000001O00001O000O1O1M3L4L6K5K6Gc[Ya0\"}, {\"size\": [1280, 720], \"counts\": \"[\\\\`75hW19J2N3M3L3N2N1O2N2N2N1N2O1O1OO1O2O0000O100000001O010O00000000000O2O01O000000O11O0000O1001O000O1000000O1000000O2O0O1L4I7GQ\\\\Wb0\"}, {\"size\": [1280, 720], \"counts\": \"Sn_62nW14M1K6J6M100O1O1N2O1O0000000O100000000000000000000000000O100000O01O1O0010O010O010O01O0O1L3110O2Na0WO\\\\bec0\"}, {\"size\": [1280, 720], \"counts\": \"We]42mW12O1O0N4L6K10K41O7J1O2N101OO01N2O1N1O2N10100O10N2OO0O2O11O000001O0001O000O10O101O01O2M3N00000O10O1O1001O1N10002N3L3N0M3N2M5JibWe0\"}, {\"size\": [1280, 720], \"counts\": \"bTg33kW15K2M4M3O001O2M3N1N2O1O101N1O1N2O0O100O2O0001O0O1000000O10O100001O0000O1O001000000O1N20O1O1O10000O2M1O1N2O3M7D;FncUf0\"}, {\"size\": [1280, 720], \"counts\": \"a\\\\^34kW13N2N2N3N1N2N2L4O1N1M3N1O1N2O101O00000O1001O1OO1O10000000000000000000O10O1001O000O100O10O0001M3O1N2N2O1M201M2N3M3JRT]f0\"}, {\"size\": [1280, 720], \"counts\": \"ike38gW15K3N2M5L2M3N2N001O0O100001O000000O100O10000001O1O001OO10000O10000001O0O10001OO1000O10000O100N1O101N2M3O1M4M3M2O1N]dUf0\"}, {\"size\": [1280, 720], \"counts\": \"RcS45iW13N3L4K6J8J2O1O1O0O10001O00001O00000O100O10000000000000000O1001O001O002M1000000000001N100O10000O01000O2O1N2N2M2N2N1O2O1O2M2O2M2O2LWm]e0\"}, {\"size\": [1280, 720], \"counts\": \"TSo49fW14L2N3N1N101O2M2O101O0O1N2O00100O0O2O0100O00O100O100000000001O1O0000001OO100001O1N100000000000000O10001O0O1000000O1O1O1O1O1O100O100O1O2M6Ii\\\\]d0\"}, {\"size\": [1280, 720], \"counts\": \"P\\\\X64jW14N2M3N010O1N2O1M3N1O101O1O1N2N101O0011O0OO1O101O0100O3NO000O1O1O11O1O1O1O000O10001O0O2O1O00O1001O1N3M100O2O5Ihcec0\"}, {\"size\": [1280, 720], \"counts\": \"gRR62mW12N2N6L00O01M3N2N2N2N5M1O1N2N3M1O1O1O1O1O100O10O2OO001M2101N001O10OO01O11O000O10002M1O1O0O2O3M3L5^O_]Sd0\"}, {\"size\": [1280, 720], \"counts\": \"ojY42gW1800O0O1O100O2M30O2M1001O1N10000O1N2002N10N1O2O0O10O11O00O3K4OO101O01O001O000O100O02N2O00O0100002M1O2OO1N3O0001O1N5K2MWU_e0\"}, {\"size\": [1280, 720], \"counts\": \"Ycn3350cW17]hNGTW11lhN>UW1AjhNa0VW15O1N2N2O01O01O0000010O000002N01O0000O10001O1O1O1O1O1O00000000000O2O1O0O2OO1000O100010O01N6J2N10M24N0OIMahN3lcPf0\"}, {\"size\": [1280, 720], \"counts\": \"hRi22j_2O\\\\`M3_W1O]hN;]W16M2L2O2O1O01OO10001O01OO1O2O01O1OO10O101O0000002N1O1N101OO100003L2N2N2I_hNMbW11ahNM`W1371M0001OZhNNaW10XU`g0\"}, {\"size\": [1280, 720], \"counts\": \"UkQ32mW14K4J5N2M4N001O1O1O001000O01O1O10O01O1O001O10O01O00000010O001O0000010O00O2O01O2N00O100001OO10O1O1O2N1O2N1O1N2N4M3L5K4McTgf0\"}, {\"size\": [1280, 720], \"counts\": \"SS]34kW12O1M4N001O1O2N001000O01N2O0O2O001O001O10000O00100O1O1O000010O01N1000000001O001O000001O000000000002N1O1O1O1N02O0001N2N1N3L3M5LclVf0\"}, {\"size\": [1280, 720], \"counts\": \"VSQ41nW1:H3L1O1N201N3N0N101O2O0O1O001O100O1O1N2O1O1O1O0012N1O0O01N22M0010N1000000001O01O01O001O1O10O01O00000000001O000O2N100O1O1O3J7K2N3LV\\\\`e0\"}, {\"size\": [1280, 720], \"counts\": \"\\\\jW56cW1?chN]OlV1f0PiN^O6N\\\\V1m0biNUO]V1o0_iNRO`V1X1O1O000N21O1O0000NbNeiNZ1_V12O1000O011N2O0O3N1N1O2O3L100O2H8LeUde0\"}, {\"size\": [1280, 720], \"counts\": \"\\\\cn34dW19L3O3N0O2O1N1N3N1N3O1N2O1O1O100O000O2O0003M01LPOXiNn0hV1ROXiNn0lV11000O2N2O0O2O0O2N1O2M3N1O00101N1N3N2HSU]f0\"}, {\"size\": [1280, 720], \"counts\": \"ncS43iW1401L3O1O2L4O00001N101N1O11O1O10O00000000O2N2L5FbTQg0\"}, {\"size\": [1280, 720], \"counts\": \"cbP3<cW13M2J6K5N2L4O001O1M3O1O1O100O001O010O1001N101N2O0O1O010O001O1O0000001O00010O001O1O2N0O2O6I2M2M4L3M5M3N1NlTQg0\"}, {\"size\": [1280, 720], \"counts\": \"abP38hW13L2M3N2N2O1O100O00110M2N101O100O1O1O10O10O1O001N101O001O0100O001O0000000000010O1O0000000000001O1O1O2N1O2N3L100O2N2N3FU]hf0\"}, {\"size\": [1280, 720], \"counts\": \"YSg3510dW17N3M2N2N3N1O1O1O00101M101O10000O1N101O1O001O1000O01N2O1O0100O1O2OO01O1O00O12N1O1O00002N1O0000001O001O1O1N101N100O2O2L6GUdPf0\"}, {\"size\": [1280, 720], \"counts\": \"Pkj3?aW111ON2M3N2O010H[OSiNe0TW11M20001O001N12O00O1O02O00O1O001O010O010O10O010O1O01O0011O3MO001O01O1O1O010O1O1O1O00001N1O101O0N3M5G\\\\\\\\oe0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"eT[42lW13M3M4K3O2M3N1O2N1N201N1O2HUOYiNk0fV1WOXiNj0hV17MmN\\\\iNP1iV1M3N211NMVOTiNh0nV1311OO1OOUOSiNd0VW1M120O3L3L4M4MLK`hN3iW1MmcZf0\"}, {\"size\": [1280, 720], \"counts\": \"k[^35gW16L3M3M3M3O001O0O2O1O1O001O10O01O1O100O001O1O1O010O1O001O11N1O001O000010O01O00004K8I3M1N2N2Nikjf0\"}, {\"size\": [1280, 720], \"counts\": \"fSQ44mW1OWhN7_W15N1O2M2M3N2O001O1O1O1O1O001O1000OO2O2N010O0101O0O0000O100O101O03M10O03M2N000001O0002N1O1O0000010O5J2O1N2M3L5L4L\\\\[je0\"}, {\"size\": [1280, 720], \"counts\": \"j[f48eW18K5J3N100O1O1O1O1O100O1O1O1O2O0N2O101O0O0O2O10O00003M2N010O1O2N1O0001O01O1O001O01O0001O001O011N1N101N10002M4L3K4HbSUe0\"}, {\"size\": [1280, 720], \"counts\": \"USV41\\\\W15lhN4gV1h0H7M310O0010O10O1O04M1N2N2N100O01O10O010O10O010O1O10O2O8H001O00O10000N101N12O0O0000000010O01O2N4L1O1N2O2K4K5N2N2N[Tde0\"}, {\"size\": [1280, 720], \"counts\": \"jjQ38dW19J3M3M2N2O1O010N100001O10N1O1O1N2003M1O000000O100001O1O00001O1O0O02O0000O2N3N3NO01O2N00010N1N2H`hN0aW1O_hN0fW1NQ]Rg0\"}, {\"size\": [1280, 720], \"counts\": \"Uck21oW1050I;F2M4M4M5J2O1O0001O2N0O0M5O1JihNAXW1=jhNCTW1?601O001F_hN3eW112N5JRib01mV]O5M0O0JN_hN4_W18N1O101N100001O01O0O1O11O000O101O1N2N1O1O2N4Kgk`f0\"}, {\"size\": [1280, 720], \"counts\": \"\\\\Sg34jW16L2O3J5J4N2O1O1O001O001O1O02O1NO2O1O010O12N000O100001N1O1O001001OO100O1O10OO2O0001O1O01OO2O00001O00001O2N1O1O1O1O2N1N2O1N4GnSne0\"}, {\"size\": [1280, 720], \"counts\": \"Vk`3=aW13N2O0O1O1O11O1O1O2N1N20O0JEfhN<ZW15001O4L1N100000O2O000001N10000000N2O2OO3N1O00N22N1H`hNN`W12`hNN_W13ahNL^W1:4O01NJ^hNNcW165JRiNNRV13liN1QV12YiNb0fV1@WiNa0jV1:1O100O00O1N3OO102POViNf0kV1YOUiNg0kV1ZOTiNf0mV1YOSiNf0nV1ZORiNf0oV1YOQiNf0UW1O6J7Hocke0\"}, {\"size\": [1280, 720], \"counts\": \"U]h35iW14K6L3M2N2O3M2N2N1N3O0O1N2N2N110O2N001N1001O0O1O10000O100000001O00O100000000000000000O100O100000O1001O1O0O100O2O001O2M5K2M4K7Dmbke0\"}, {\"size\": [1280, 720], \"counts\": \"cTg36iW12O2N42OL8H0O1O2N0O2O02O0O01O2N1O002M2O0000O102N10O0O1O2O01O00001O0O1000O2M21O1O1O0000000O1001M22N1O001O0000O10000O20N2O1O1O001O001O1O000000001O1N2O2N1N2O00dRZe0\"}, {\"size\": [1280, 720], \"counts\": \"R]f48gW15L4L3M5J3O000O2N2M3L3O00O1000002M100O1O1000001N1000000O1000O02O0O2O1O0O1000000O101O001O001N3M2O0O2N2M3LfZ[e0\"}, {\"size\": [1280, 720], \"counts\": \"m^Z51mW12N2O2O2M200O1N1O10000000O10O10O0100O02O1O1O1O1N3M2O1O0000001O0OTY`e0\"}, {\"size\": [1280, 720], \"counts\": \"Uf`55jW15K3ghNEhV1>TiNEjV1j0N2O1O11O00O1000O00N200001O001O1N100000N2O11O1O0O2O1O00001O000000O1O100O011O0001O01OO1O02N1N20O100O2O0O2O1O0O1O2N2O1N2M3M4AQjPd0\"}, {\"size\": [1280, 720], \"counts\": \"Xee51lW17J>D2M2N2O011O0O0O2O10O01N100O20O1O0000O11O00001O2N0010O00000001O000000010OO101O00010O0001O01O0000001O0000O2M2O1O2N1O2M5J7K]boc0\"}, {\"size\": [1280, 720], \"counts\": \"n]S63mW13M1O2O00OO2N3N1O2N2NN2O10000000001O01O01O2O0O1O5J101Ngamd0\"}, {\"size\": [1280, 720], \"counts\": \"emf55gW15M21O1O000001N100001O001O1O2NO101N1N2O11O2NO2N1^hNJYW1`0N2N2O0001O011NO3N1O00010O000001O1OO1N3O0O10000000010O0O10002M2N102O000N2O00O1N2J5O2UO^Shc0\"}, {\"size\": [1280, 720], \"counts\": \"]\\\\i52nW11N1001011M0N201O2O0O0O2N2N3M2O1ghN_ORW1h0O2O01NM4N101O0O2O0000000000O3N1OO100O1O1001O1O00O1O01O1O1O010O2O0O1O1O1O1O100O3L7I6I_coc0\"}, {\"size\": [1280, 720], \"counts\": \"fde51nW13XhNM_W1?K1O1O100N2O1N3N1O1O1O01O1O1OO1O100O10000O11O1O01N1000000O10000001O000000000000O1O1001O1OO100001O0000000O1O1O2N100N3N<DPkPd0\"}, {\"size\": [1280, 720], \"counts\": \"eUm56iW15K4L3M3M2N3N2O0000O001ON2O2O0O1N24L000O110ON200O100N21O1O00O1O1001O00O1O1001O1OO100000000O101OO0100000O1O2N2M2N7I5L\\\\Rmc0\"}, {\"size\": [1280, 720], \"counts\": \"[]i57hW12N2N2N3N2N1N100O1O1010O01N10000KZOQiNe0oV1\\\\OPiNd0TW12O1000OO21O00N2O10XOnhNb0SW151O0000O100O100O10000001O1N0100O100101N0O2O0O0N3O1N4L6JlZSd0\"}, {\"size\": [1280, 720], \"counts\": \"adQ52mW12CMjhN5TW1MihN8TW1JghN>SW1DlhN=SW1CmhN?PW1BPiN?oV1:O13M00O2M2N2N1O1001N3M01O1000000O1000000NoNXiNn0hV15O100001OO1O100001O00O1003M001O0001N1O10O01O010NiN^iNU1fV10O1O01O1O0O1O1K50003M7[O]d^d0\"}, {\"size\": [1280, 720], \"counts\": \"f\\\\a41jW15O2O02M4MO5L3L8H4I6N001001001L4L5J5K4M2O0M31O1O00N3N1002N00O1O1001O10N1O11O1O1ON2O1002M2OO1O13M1O002N0000O1O001O11N2O002M2OO01M2J7M3O5G7L^Skd0\"}, {\"size\": [1280, 720], \"counts\": \"e\\\\f4;dW13N2N3L4M3M2N1N3M1O100O1N3O000001O1O0010O0O1N2001O001O000000O1O1O10000001O001O1O00O1O100001O1O00000000OO101O2M5K002N2N3M4L6H^knd0\"}, {\"size\": [1280, 720], \"counts\": \"kR[44lW14L5L2N9G0N2N2N2O0O2N1O1001O1O00O10000O1O1O100001O2N1O000000N2N21O1O2NO100O100O1O10000000000010O001OO1O11N1O3M1O101N2N3M3LS][e0\"}, {\"size\": [1280, 720], \"counts\": \"VkY4>aW14M7J2M0O101OO10000O1000001O0000001OO10000000000000000000O01000001O00O010001O00O1001O000O100000001O0000O100O2M201M:Ch\\\\`e0\"}, {\"size\": [1280, 720], \"counts\": \"PT[42nW1003M3L5K7J6I201N3M1N2M200101NN1100O10O10OO2001O0000N2001O00O1O1001O00O1000000000000000000O10001O00003M1OO1O100O0O3N1O2N1O1O8HSlXe0\"}, {\"size\": [1280, 720], \"counts\": \"flm42mW13M5K8H2O10N2O1O000000000000O1000000000000000O10000001O0000O10N2000000O1O1000000O2O00001O0O0100O2N2N3M3M;F1NR[Qe0\"}, {\"size\": [1280, 720], \"counts\": \"SeQ56iW12O10000O0O2O0O101M2N3N1O1O0101O1O00001O0000O100O1000000001O000000O1001O0000O11O000000O2N101N10N200O2N2M5K4LQcmd0\"}, {\"size\": [1280, 720], \"counts\": \"_mm4;^W18O0O3M3N4L2N2M2O1N100O100O101O0O010O1001O000000O100O1001O1O0O10O1O1O1O10000001O001O0O01O011N11O1O0O2N2N1O2L4M4L4K6J7HgRkd0\"}, {\"size\": [1280, 720], \"counts\": \"jd]44iW1`0B4L3N3N1M4L4L2O0000001O00O01000000O1000000O100001O000000O11O1O1ON2O100O1O1O1O010001O001O000O0100O1001O1N2M2O1N2N2N2N3N3K5M5G][Ve0\"}, {\"size\": [1280, 720], \"counts\": \"hSl3>aW14M3N2M1O3K5M3MO1000O1000000000000001OO1000O2O0000O2O01O0000O01000000001O00O1000O010O10O10000001N10010O00O1O1O2M2N3M2N3M3L7K6FUlge0\"}, {\"size\": [1280, 720], \"counts\": \"kk`3<bW19I1O1O2M2O2N0O2O000000O2O000001O000000O2O000001O01O00000000O100000000001O0000O100000000O100O101O00000000001N1000O2N1N2O3L7H9FPTSf0\"}, {\"size\": [1280, 720], \"counts\": \"jdZ3>aW13N1N2O2M2O2N001N10000001O0001O0O01000O100000000O100O10001O00000O011O00000000000000000O1000000O1001O0000001O000O10000O1N2O1O2N3M4K6J6IS[Tf0\"}, {\"size\": [1280, 720], \"counts\": \"T\\\\T3;bW14O2N1O1O2M3NO10O1O1O1O1O1O100001O1O0000O1O1O011O0000000000O01000000O10000001O1O1N01000000001O10O0O010O1000O010O2M3OOO100N3O1N;ER\\\\^f0\"}, {\"size\": [1280, 720], \"counts\": \"WlQ35jW14L3M2N3N1N2O1O000O1001O00000O1000O100O1O1O1N101M3001O00N200003M1ON2O1O1000O10006J1N10O100000001O01OO000N21O010O1O1O2M2O2M103EahNOf[cf0\"}, {\"size\": [1280, 720], \"counts\": \"olb21oW14L3L4M3M2N1O2M101O01O0O11O000O10000001O001OO1O1O100O10000001O1O0O1000O1000000001O000O00100000O10000001OO010O0100O2I7L5KUkTg0\"}, {\"size\": [1280, 720], \"counts\": \"dl`33hW16O0N2O1O1N200O2OO01O1O1O1O10000O1O1O100001O1N2O4K4L3JhS`g0\"}, {\"size\": [1280, 720], \"counts\": \"ckd13lW13N100O2O0O2N000N2O4M1O1O4L100O01O2M3M2O0O10N2O11N1O001O1O001N2O1O1O2L4N[lPi0\"}, {\"size\": [1280, 720], \"counts\": \"^[[2;\\\\W1<K2O0O2O1O1O1N3N1L4O100O10010O0O1O11O1O1OO100001O0001O0001O00O2O00000001O1N3M:G7WOihN3U\\\\Uh0\"}, {\"size\": [1280, 720], \"counts\": \"Uka02lW1:E400O1N1O2O1O1O2M2O001O1O2O00O01O1O1N10O1N2N3N2M2N2O20O1N0001O2L5M3GRUUj0\"}, {\"size\": [1280, 720], \"counts\": \"bS91bW1`0N1O001M2M4O010N1O1O2O000O1O2O0N2N1001O2N1N3N1N10000O0010000O2O1N2N010000O10O1O2O0O1O2N1O1O1O0O2O2M`Tki0\"}, {\"size\": [1280, 720], \"counts\": \"g[l12lW16@<L5N1O1O1O1O1O1N3M2L5L3O10001O000000000001O01O000001OO1O12N2N1O002N0000O11N10OO1O1J8M3I8I9FWe[h0\"}, {\"size\": [1280, 720], \"counts\": \"Vc^1:fW13NO1O1OO2O1N2O00011O00O2N10N2N2O100O001O10O01O001N1010O10O1O1O000001O1OO2O000O101N010O1O1N200001N1010OO100O01O100001O001O1O1N2O001O000O102L4L9GXTjg0\"}, {\"size\": [1280, 720], \"counts\": \"Z[[26iW19F4M3N1O1O1O1O1O1N2O1O1O1O1O1O1O1O1O1O2N001O10O0000O010000000O01000001N1000O100001O000O2O00O1000000000000001O0O10O100000O2O001O1O001O00000O1O2O2N2M3L7H8F[Tgf0\"}, {\"size\": [1280, 720], \"counts\": \"Q[^32mW17J4L3N1N2N1N3M3N1O2N1N2N101O1O00000O010001O00001O00O100O1N200O010O1N2000000O1O010000000O10O100000000O2O000000001O1O000O01O1O11O001N2O1O1O1O0O1N3M3M4L4K5LQUde0\"}, {\"size\": [1280, 720], \"counts\": \"dj`32lW16K101N2O00004L5K2M2K50N10000O10O1O1O1N1O200N200000000O1002N0O0100O100000000000O2OO100O100O11O0O100000001O00O1O110O000O01000O1O10O100O2N1O2M2O1N1O3M7I:Dgmbe0\"}, {\"size\": [1280, 720], \"counts\": \"]bl44kW12N4M2K5K5J6N2O1M3M2N1001OO10O01000O0100O10O2ON2N2O0101O0000O0010O01O1O010N110000O100O4M0O1O01O11O1N100000000N10O20O1O00000O2N2O2M2O1M3N=CWn_d0\"}, {\"size\": [1280, 720], \"counts\": \"[RY55iW15L4L4L4L6J4MO100000O02O1O0O010O100O010000O0100O100O10O01000O010000O010O100O10O1O001O1O00O20O001O100000001OO1O1N1O2O1O1N2O2N1N3N5K2LWVWd0\"}, {\"size\": [1280, 720], \"counts\": \"hiR58gW15L2O1N2N3L5L2N1O000000000000O1000001O0O2OO1O1N2M2102N2N000000O100N2O10000000000O1O000O10100O100O10O01O10O10O01O01O0100O001O1O2N3L3M4M5K3L5Ia^Xd0\"}, {\"size\": [1280, 720], \"counts\": \"]b[53`W1>O3M300M3M3GUOWiNl0hV16O20O1O00O100O1O1000000O2N1001N1000O2O0KjNaiNU1^V171O1O1O0000O1O1001O0000000000001O1O00M4O0002N000O01O2O0010O2N00000O00100N2N2O2N2M2N1O4L8G6KPfjc0\"}, {\"size\": [1280, 720], \"counts\": \"bRj49eW16J5L3N2N2M2M3O1OO1010O000O1O100O1O10000N2O100O1N200000000000010O0O1O11O002NO1O2O01O00N200002N1OO2N1002N00O1N31M5L000010O00O2O1N10O00O1O1O2O1O3L4L3L8HdmZd0\"}, {\"size\": [1280, 720], \"counts\": \"RRQ475L\\\\W1>O1N1N3N2N1O2N101N1O1O1N2O010O00000000000O100001O00O1000000O1000000000000O10000000000001O00001O0000001O00O2O03L3N0000O100001O1O1O00000O200O1O0O1O1N1N3N202M4K3M4K`]ld0\"}, {\"size\": [1280, 720], \"counts\": \"YZT3`0^W14M2O2N100O2M2O100O100N2N2O1O1O100O1O001N20O00O2O0001O1O001O1O0O2O0000010N100000001O0000000000O1O1O100010OO10000001OO1O0012N2N0000O2OO1000001O1O0000000O2N3M1OO10L6N2M2O1MdUde0\"}, {\"size\": [1280, 720], \"counts\": \"djX22jW1<_O;J6N1O101N1O1O2L3001O00O2M2O1O1O1O1N12OO010O010N2N101O0O110O0000O10001O00000000001O1O1O00O1O12N001O100O00000001N11O2N1OO101O0000000001O1O00010O0001O00O100O2N1O1O100O2O1N2N3M2N2N1O001M4J6L7HUmQf0\"}, {\"size\": [1280, 720], \"counts\": \"URf1;dW13M3M2O1O2N1O1N101O1O1O001O1O001N2O1O00001O0O20O10O0O101O001O001O2NO2N100O1000000000O11O000O2O1OO10000001O1O1OO1O1000000000000O1000001O00000001O00004L0000002N2OOO100O00100O101N2O1O0000001M5J4M2O2M_Ubf0\"}, {\"size\": [1280, 720], \"counts\": \"PRm09gW1:F4M4L0O10000O1O1O1O1O1O1O001O10O01N2O1O001O1O10O10O010M2O110O010N100O100O100000O10001O1O1OO1001O3M2N001O000O1000001O000000001O1O1O001O0O1000O1001O1O1O001O1O1O3M2N1O0EghNM\\\\W1O_]kg0\"}, {\"size\": [1280, 720], \"counts\": \"[R>2oW16I4L3M5K2N001O010O001O1O1O1O1O1O10000O1O1O1O1N2O1000O01O00001N100O1001O001O001N010000O1001O00001O00000000O1001O1O0000000000001O1OO100001N2O00O1001O001O00000000000000001O1O1O0O100O100O3N3L3J8GQelg0\"}, {\"size\": [1280, 720], \"counts\": \"jRR13fW19N2N1N1O2N1O2O0010000O0O1O1O2O1O1N2O100O10000O1N2O1O000001N2O1O001O000000O1001N100000O010000000001O0000000000000000000O100000000O10O100000O1000001O00000O2O0000002N2N2N2N10OO100O2O2N2L4L3OkT[g0\"}, {\"size\": [1280, 720], \"counts\": \"_jd11nW12N2N101N2N3N01O0003M2N2N1M4M2O1O10O0001O0M3O1001O2NO1O100O2O001O001O11ONO101O10O10000O1O1000O2N2OO100O1000000O1O000N30O100000000000N2N2O1O001O1010O1O1N0100O1O1O10001O01O0OO2N3N100O10O2O2N1hN_iNP1aV1mNeiNo0gV1O2N2N2N1O2N2N1M5J7Hb]Yf0\"}, {\"size\": [1280, 720], \"counts\": \"_R]31nW12L4M3\\\\hNKXW1e0J2N2O01O2N00O100O2O0O2M2001O2OO000O100TiNROeV1U1O2N101O1LfNaiNY1aV12001O1O00L400O1O2O00O3NO1N2O10000O1O10000O100N2O1O1O1000000O1O11O1N10O1O1100O1O1N3N2M5K3N1M2O2N1O2M3N3M3L3M4L3M8G4MamXe0\"}, {\"size\": [1280, 720], \"counts\": \"fck22mW12O0O2N2O02N1O1OO2N2O1O1O1N2O10O1O10N101O1O1O1N1010O1N2OO2O1O0000002N05J2N2N4N0OO100N210O1O02N3MO100000000001O2N001O1N10U^<1iaC1O2OOO2O2L<F5L0010O01O00100O1N2O2N2M3N1N2O1O1O1O2N1O2N2M3N3N10O0O1O1O2M3N1L5M3M3L4L5K3M3N2M4L5JR]bd0\"}, {\"size\": [1280, 720], \"counts\": \"bSj41oW11O00001O1N1O00111N2NO1O0002N7KO4L2NO1O100000000001O000O11O0000000O100O1O1O101O0O2O0O1O2O1O0O100N3N1O2N1O2O1O0101N10M3N1N3M2O2N2N21N5M1N2M2O1N3N0O1N2O102M2N3M1O2N1N2N:Fdcoc0\"}, {\"size\": [1280, 720], \"counts\": \"Vk\\\\58gW14M3M10000O002N3M1N2N2O2N1O0000000010O1N0100O1010O00001O0000001O01N10000002N1O001O1O1O001O1O0000O100O100O02O001O010ON200O10O01O1O11O2N1O0000001O000O2O000001O0O10002N00M211N2L3N1L410O00MM^hN0bW153LjTob0\"}, {\"size\": [1280, 720], \"counts\": \"Tkk59^W1;M3M2O2M2O1O00101O3L3ViNlNcV1Y103L3N3L1N010001O1N101N001O0M32N10O2O0O11O000O0O12O002N6J1O00O10000001N101O001O0000001O0O10OO4K5N001N100010O3NL4O01O1N2OO0O3O04L1OM3O01M3N2O00010010O0N3O0O00001O2O0O1N3N2O1NN3O10O1N2N2N3LndSb0\"}, {\"size\": [1280, 720], \"counts\": \"YTn71mW14L3L4L2O2O0N3N2M3M3O001J6M2000NJ340200O01N2O10O000NJPO_iNQ1aV172O2NO0O2000O2OO1O0O200001O1O0000O0O2N2N20000O1O0101O1N3ROdiNMhV1Kj0LiUla0\"}], \"areas\": [1230, 1095, 1158, 1189, 1810, 2099, 1846, 1706, 1415, 1398, 1525, 1730, 1217, 1317, 1360, 1076, 1179, 1253, 1267, 1316, 1114, 1214, 1042, 1020, 821, 1113, 1047, 988, 1164, 1540, 1086, 1369, 1246, 1078, 770, 742, 1036, 1128, 1136, 967, 1211, 1800, 2180, 1399, 1309, 1573, 2035, 1690, 1748, 1334, 1791, 1813, 1419, 1879, 2101, 2097, 1229, 1524, 1653, 1597, 1560, 1926, 1872, 1295, 1479, 878, 1097, 951, 1442, 1341, 1817, 1288, 1212, 418, 1658, 1623, 1726, 1420, 0, 0, 808, 1318, 1531, 1740, 1796, 1249, 935, 1794, 1372, 2156, 2054, 1377, 301, 1963, 1802, 326, 1606, 1429, 1601, 1715, 1363, 2297, 2877, 2096, 1933, 1553, 2219, 973, 916, 2155, 2520, 2001, 1814, 1955, 1688, 1418, 1186, 469, 507, 1489, 806, 1142, 1744, 1537, 2701, 2580, 2278, 2309, 1655, 2026, 2985, 2786, 2444, 2979, 4079, 3091, 2994, 2972, 2421, 3380, 3675, 2212, 1578, 2140, 3291, 2092]}, {\"video_id\": 0, \"category_id\": 847, \"bboxes\": [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [496, 315, 34, 33], [520, 303, 44, 40], [536, 304, 41, 46], [544, 295, 39, 48], [539, 289, 47, 47], [536, 308, 47, 32], [536, 314, 41, 28], [530, 307, 35, 39], [522, 322, 22, 29], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [428, 327, 21, 31], [412, 331, 45, 40], [410, 321, 50, 39], [430, 332, 27, 20], [433, 322, 22, 29], [421, 329, 40, 33], [424, 325, 41, 27], [442, 333, 35, 32], [455, 365, 51, 44], [458, 330, 52, 35], [466, 302, 45, 29], [468, 324, 46, 26], [476, 328, 37, 22], [483, 334, 38, 24], [493, 366, 38, 27], [491, 356, 42, 36], [493, 335, 23, 32], [491, 350, 12, 17], [493, 353, 18, 16], [486, 347, 13, 18], [486, 351, 11, 16], [493, 371, 20, 18], [502, 335, 19, 24], [513, 319, 20, 26], [526, 325, 18, 19], [525, 319, 25, 19], [514, 312, 30, 23], [508, 317, 26, 25], [501, 351, 23, 18], [506, 328, 18, 21], [506, 320, 20, 21], [506, 339, 17, 11], [508, 335, 20, 17], [504, 332, 22, 19], [494, 335, 19, 20], [495, 352, 23, 20], [504, 315, 20, 30], [509, 315, 25, 28], [505, 330, 30, 22], [499, 317, 40, 29], [502, 324, 49, 21], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [469, 301, 39, 33], [495, 293, 37, 41], [515, 302, 54, 38], [0, 0, 0, 0], [551, 318, 37, 23], [533, 348, 38, 23], [528, 332, 35, 30], [494, 313, 52, 34], [465, 316, 61, 36], [430, 314, 71, 49], [415, 313, 38, 24], [425, 325, 40, 35], [411, 339, 56, 36], [421, 327, 63, 38], [426, 328, 64, 43], [441, 381, 53, 41], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [488, 422, 33, 20], [517, 374, 22, 27], [514, 368, 25, 31], [485, 371, 41, 31], [485, 330, 38, 24], [496, 332, 28, 23], [488, 344, 38, 30], [482, 362, 42, 24], [483, 357, 47, 30], [494, 377, 43, 30], [495, 378, 44, 30], [485, 337, 45, 25], [482, 329, 45, 21], [483, 352, 44, 21], [483, 357, 46, 25], [482, 341, 52, 32], [494, 363, 50, 38], [483, 340, 54, 40], [476, 326, 48, 24], [470, 340, 45, 22], [464, 363, 47, 23], [459, 358, 47, 23], [470, 333, 36, 18], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [529, 299, 30, 27], [534, 308, 40, 33], [507, 296, 60, 36], [500, 295, 39, 26], [485, 289, 63, 34], [478, 282, 62, 36], [0, 0, 0, 0], [450, 279, 29, 16], [0, 0, 0, 0], [508, 263, 58, 31], [523, 254, 55, 30], [0, 0, 0, 0], [0, 0, 0, 0], [548, 266, 46, 31], [536, 274, 52, 37], [0, 0, 0, 0], [500, 266, 49, 22], [475, 283, 68, 33], [0, 0, 0, 0], [474, 290, 64, 25], [485, 269, 71, 54], [525, 268, 79, 62], [569, 308, 66, 34]], \"score\": 0.8324607610702515, \"segmentations\": [{\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"QZ\\\\c06iW1>C3M2N1N2O001O00000O2O00000O100000000O1000O1000O100000O10001N6K9F;F\\\\m[7\"}, {\"size\": [1280, 720], \"counts\": \"hYZd0f0ZW12N1O1N2O2N1O001N101O0O1O100000000O10000O011O0O100O10O1000O10O100000000000001N3N8H6J3L6Ij]Q6\"}, {\"size\": [1280, 720], \"counts\": \"nYnd0e0XW19H5L2O000O10001O000O1000000O100O100000001N100000000O1000O010O010O2OO10N2O2Nf0ZO4L6JcUa5\"}, {\"size\": [1280, 720], \"counts\": \"cYXe09dW17K4L3N2M3N1O2M2O1O2M2O1O2N1O1O001O000O2O0000000O10000O0010O1O1O10000O1O1N3POPjNJUV12egY5\"}, {\"size\": [1280, 720], \"counts\": \"_QRe06gW1:G5M2M4M3L4M1N2O0O2O000000001N1000001O0000000O10001O000O100O100O101O0000O10000O100O1O1O1N3UOiiNH[V13hoU5\"}, {\"size\": [1280, 720], \"counts\": \"QZnd03kW14M2M4M2N1O3N001O1O1O1N2O100O0O10O1000000001O000O10001N10000000000O10001N100000000001N1O2M3N3ZOUiNOVmZ5\"}, {\"size\": [1280, 720], \"counts\": \"UZnd03lW12N4L2O1N2O1N2O1O1N2O001O001N10000O10O1001N10000O100O1010O0000O11O001O00001O0O1M5@_Va5\"}, {\"size\": [1280, 720], \"counts\": \"Ujfd05jW12M5L3N2M2O2N1O1O1N2N2OO01O10001N1N200O1O1O2N1O100OoNYiNk0iV152N001O2M2O001O1YO\\\\iNOh\\\\Q6\"}, {\"size\": [1280, 720], \"counts\": \"_j\\\\d01oW13L4L4L4L2O1N2M2N20000N201N100001O0O1000NVORiNf0[W1Hb]j6\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"^Zg`06hW16K;F2M2O1O001N100O1000O10O10O100O101N1O4WO[Va:\"}, {\"size\": [1280, 720], \"counts\": \"cZS`02mW15L5K9F7J1O1O1O2N1O00000O2O00O1000000O1000O100000000O01000000O10O2O00O0100O01O11O01O0N2VO_iN4\\\\W1IlTW:\"}, {\"size\": [1280, 720], \"counts\": \"ajP`09_W1<K2N2N2N2O0000001O1O000O101O1O001O001N101O000000000O100000O10000000O1000000000O2N11O0000O2O1N2XOTiN:`W1G:GP]S:\"}, {\"size\": [1280, 720], \"counts\": \"kji`03iW16K2O200O2O1OO1O01O001O101O00001OO011O1N2O01O0000O2O6H\\\\UW:\"}, {\"size\": [1280, 720], \"counts\": \"[bm`01lW18I7K4L2M3N1N1O2OO1001O0000000001N10O1O02DnhNGVW1M]fY:\"}, {\"size\": [1280, 720], \"counts\": \"\\\\b^`0>aW13M3N2N2N2N1N200O001O000O1010O01O001O000O10000001OO1001O0000000000000000000@_iN@dV19g0HfUR:\"}, {\"size\": [1280, 720], \"counts\": \"ZZb`07fW1;G3N1O1N2O0000001O000000001O0000000000000000001O00O1000000001O00000001N1O101N3C<LiUm9\"}, {\"size\": [1280, 720], \"counts\": \"djXa05iW18I5K4L4M1N101O001N10001O00001O000000000001O0000000000000000000O2G8J<E`U^9\"}, {\"size\": [1280, 720], \"counts\": \"gSia0:dW1;F5L4K2O1O1N101O0O2O0001O01N10000000000000000000001O00000000000O1000001N1O010000000O11N1O3I41000M3M4YOVmY8\"}, {\"size\": [1280, 720], \"counts\": \"cjla04lW16J5K4L3L3N000O2O000O101N100000001N100000000000000000000000000O1000000000000O100O10O10O10000001N010O2N7FcmT8\"}, {\"size\": [1280, 720], \"counts\": \"iiVb09dW16L2N2N2O1N10001N10001O0O100000001O000000O10000O10000000000000000000000000O100000O011N6H<@ZfS8\"}, {\"size\": [1280, 720], \"counts\": \"aZYb07cW18M2N2O1N100O2O0001O00O100O101O0O1001O00O1000000O011O0001O00000O10O2O000000N2N3M2N3L3N3N1N4Limo7\"}, {\"size\": [1280, 720], \"counts\": \"aZcb09dW14N2N1O2N1O2O0000000O1001O000O11O00000000000000000O101O000001N1O1N4K4L4N2LeUQ8\"}, {\"size\": [1280, 720], \"counts\": \"dRlb03lW1:F3N1N101O1O1O000O10O10O1000000000001O00000000001O0000O0O2N2O1N2N2M3O1N3M4M`Ug7\"}, {\"size\": [1280, 720], \"counts\": \"jcXc07eW17L3M101O00001N100000O010000000001O00001O0O1000001O0O1000O010N2O1N1O2N4K5L6I]dZ7\"}, {\"size\": [1280, 720], \"counts\": \"iSVc08cW17J5M3N2N2O0O2O0O2O000O100O0100000O10001N100000000O1000O2O0000O1000O10O01O1M3J7F9J6KPUX7\"}, {\"size\": [1280, 720], \"counts\": \"UcXc08]W1=J4N3N1O100O10O10O100O1000O2M2M3K5M3M3N2N2N3Lb]m7\"}, {\"size\": [1280, 720], \"counts\": \"PSVc0?`W1101OO1O1N1N3O1O1O2M2O2NQe]8\"}, {\"size\": [1280, 720], \"counts\": \"\\\\cXc04gW16N2N1O0100O1O001N100O2N2O1N2N200000odS8\"}, {\"size\": [1280, 720], \"counts\": \"Xkob05`W1;O2N1000O1O1N2N2M3O1N3L3OUeb8\"}, {\"size\": [1280, 720], \"counts\": \"Tkob0;bW13O2O0O0100N2N3L3M3N2OQUe8\"}, {\"size\": [1280, 720], \"counts\": \"icXc07fW14N2N2O10OO2O001N10O10000O1N2O2L4N2L3N3OYTQ8\"}, {\"size\": [1280, 720], \"counts\": \"gjcc0;cW14M2N2N101N10001O00O001N1O2M4M2M3O1M5L`Ug7\"}, {\"size\": [1280, 720], \"counts\": \"XbQd09cW18J3N2O1N101O00001N1000O1N2M4L4M3M4L3K1ORVX7\"}, {\"size\": [1280, 720], \"counts\": \"]jad08eW14M3O0O2O1O0O10O010O1O2N2M3N2N1O1N3Mh]j6\"}, {\"size\": [1280, 720], \"counts\": \"Xb`d06gW15N1N1O2N1000000O10001O0O1001N1O001O1O001N3N1O2M3LQnb6\"}, {\"size\": [1280, 720], \"counts\": \"UjRd04hW17K3M3N1O2N2N1000O100001O0O100001N001N2O1N2O1O1N2O1O2N1N2O2NW^j6\"}, {\"size\": [1280, 720], \"counts\": \"XZkc0;aW16L3O0O10000O2O0O10O1O1O1O001O001O1O1N2O1O2N2N001N3NRnV7\"}, {\"size\": [1280, 720], \"counts\": \"Ycbc02lW13M5L2N101O000O1O2N100000O10O01O1N2O2N1O1N3N1LS]c7\"}, {\"size\": [1280, 720], \"counts\": \"bjhc08eW14K5N2N101O000000O002M2N2O1O2N1N2N2Og]c7\"}, {\"size\": [1280, 720], \"counts\": \"Yjhc08dW16K4O1O001N100000O1O1N2O1O1O1N1O2N2O2L3NQn`7\"}, {\"size\": [1280, 720], \"counts\": \"hjhc01oW1005J01O0101N2O01O10O000O00O2O0O1N`ed7\"}, {\"size\": [1280, 720], \"counts\": \"lZkc01k_21ShN0SX11kgN2M3O1O000O1O1O100L3O20BdhN7]W1HehN6cW1M5LZ]^7\"}, {\"size\": [1280, 720], \"counts\": \"hZfc01jW18L3M2O1M2O1O10000000000O100001N2N1N2N2N3L3Lem`7\"}, {\"size\": [1280, 720], \"counts\": \"hjYc07dW16N3N001O1O0O100O0001O1N2N3N1N3O0O1N2NaUQ8\"}, {\"size\": [1280, 720], \"counts\": \"VS[c0:eW12N3N1N1O2N10000O10O1001O00O100O1M3O1M3N2M3N3LRmj7\"}, {\"size\": [1280, 720], \"counts\": \"aZfc06_W1=I6N00101O0O100O01N2N101N1O3M3M2N2N2O3JU^c7\"}, {\"size\": [1280, 720], \"counts\": \"ablc04eW18G9N1N200O2O00O10000000O1O10O1O1O1N101O2M3M2M3N3JWnV7\"}, {\"size\": [1280, 720], \"counts\": \"dbgc0:dW13N1N2O2N1O2N001000001O0000000000O1O1O2N1N101O100O1O1O1O1M5LfeU7\"}, {\"size\": [1280, 720], \"counts\": \"bR`c03gW17J8J3N2O100O100O2O000000000O100O101O01O001OO1O1000O1O2N1O1O1O1O1O001N2O1N4L3L4LRfP7\"}, {\"size\": [1280, 720], \"counts\": \"_jcc01mW13O1N101O1M3O1M3N2O01OO101N11O1N02N1000O1000O1002N10N0O200000001N11O1O2M11O0O2OO1N3N1O2M3O0O2N4M1Nbea6\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"baZb0<bW15M2N2N1O1O1O0O101O00001O00000O2O1O001O0000001O0000001O0O11O001O0000002N6I7I8Gn]W8\"}, {\"size\": [1280, 720], \"counts\": \"[Q[c06jW12M3N1O2M3N2N2N3M1O1N2O1O1N2O001N2O001O001O1N2O01O00000001O0103L;E6J5J4L6Jh]Y7\"}, {\"size\": [1280, 720], \"counts\": \"_QTd0=cW15J4M2O1N1O1O010N1000001O1O00001O00001N2O0001O01O1O0000000O101O000010O01O000000O100000001N1O1O1N2M3L4J7L4L5L^Vk5\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PRae09iW10N11O000000O0O1M4K4O10001O00^OhhN<aW1M0001O1O1O0O1O1O1003M10O0001O001O0O10000O1Mi]S5\"}, {\"size\": [1280, 720], \"counts\": \"Tcjd03kW12O2O1O01OO2M3L4O0010O10O0O100O110O001O010O001O0O10010O001O00000000O1N3N3J6KPeh5\"}, {\"size\": [1280, 720], \"counts\": \"cZdd03lW12O3L3M2O2M2O1O1O001O001O000O1000001O001O1O1O001N101O1O000O11N2L4G8J7LceR6\"}, {\"size\": [1280, 720], \"counts\": \"miYc02nW11O3M1O1O001N2O001O1O2N1O1O2O0O1O1O1O2N1N2O2OO01N101O00001O00000000000O1000000O1000000O1000000000O1O1O2MXng6\"}, {\"size\": [1280, 720], \"counts\": \"VbUb01nW15J4L301O0O001N2O1O1N3N2N2N0O10001N3N001O1O00000O2N100O01001O01O0O2O0000001N01000000000000000000000000001O001O0O100O3L7H6Jim`7\"}, {\"size\": [1280, 720], \"counts\": \"Rji`01lW17L6I3N3M1O1O2N2N3M4L3L10001O00001O00001O000O2O0000001O1O1O2N1O00O1O1O10000O0O200O01000000000O1O2O0O2O0O01001O002N1N01000O2O1N101N3M2O4L6I4L5K\\\\U`8\"}, {\"size\": [1280, 720], \"counts\": \"mQW`01mW14M2N3N1O001O1O01000O1O1O100O100N101O1O0O2O0O2O01O00O1000O100O001O1N2N3L4L5MTV\\\\:\"}, {\"size\": [1280, 720], \"counts\": \"cbc`0431`W1;M1O1N3L4E:1N1ON2N20O4M1O0O1N20000O1000000O1O10O10O01O010O1001O1O1N101N2O0O2N5FgUm9\"}, {\"size\": [1280, 720], \"counts\": \"mRR`05jW16K3L5L2M3N1O1N1O2N1O101N10001O000001O01N10000O0101O00O1000000000000O100000O100O10000000000O101O1O00O1O01N3G8K6Iaej9\"}, {\"size\": [1280, 720], \"counts\": \"^b^`04lW13M2N2N2N3M1O2N1O2N0O2O2N00001O001O1O001O0O101O001O001O01O0000O100000O10O1000000O100000000000000O1000001O0O1000O1O1O010L4I7I;Dm]U9\"}, {\"size\": [1280, 720], \"counts\": \"\\\\jd`09eW17K4L4L3M2N1O2N1O1O1O1O1O10O0001O1O001O001O0000000000O10O1001N1000000O1000000001OO10O1000000O100000O1000000O1O1O001N2M3M3N3L3M4Ilmm8\"}, {\"size\": [1280, 720], \"counts\": \"TdWa0`0^W19G4N101N2M101O00000O2O1O001O01O01N100O10O1000O1001O001O1O00000O101O0000001O1O1N10000O10000O101N2O1N1N2M4Inkh8\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"[]Rc04kW12O1N1O100O10001O0O12000O2M00001N2O00N3N11O2N0000O10O01O100O2O3L3MaRg7\"}, {\"size\": [1280, 720], \"counts\": \"RdVd08eW1;HO00000100O100O01000O100O10000O10M2N3H9K4L]dP7\"}, {\"size\": [1280, 720], \"counts\": \"gkRd0>aW14L5L2N2M1000000O0100000O10000O10000O100000O100YOZiN4^W1HkcP7\"}, {\"size\": [1280, 720], \"counts\": \"kcnb06hW15M2M4M2M5L2N1O1O00000O1000O1000000000000000O1000000O101O00000O010001N2O00000O>B5Kkk`7\"}, {\"size\": [1280, 720], \"counts\": \"cbnb04kW15K4M2M2O1O0O1000000O2O000O100O10O001001O1O00O1000000O010000000O100O3M3N4K3N\\\\ed7\"}, {\"size\": [1280, 720], \"counts\": \"nZ\\\\c04gW16O0K5O1O1O1O1O10001O01OO100O010O100000000O02O2M1O2N2N3Gd]c7\"}, {\"size\": [1280, 720], \"counts\": \"^[Rc01jW16N2O1FLdhN8YW19O001O001M2O100O1O100O1000O3N1O001N100O10001OO100O11O2M3N1N2O0O2N2Oll`7\"}, {\"size\": [1280, 720], \"counts\": \"ckjb02nW13L3N2N1O1N2O2N001N2O0O100O1000000O10000O010000000000O01000O0100000000000O101N3N2M9D]\\\\c7\"}, {\"size\": [1280, 720], \"counts\": \"cSlb01nW15K2O1N2N2O001N101N2O1O2N1N101OO01O10000O10000000000O1000O1000O10000000O100000O10000O11N2O3L8H`l[7\"}, {\"size\": [1280, 720], \"counts\": \"TlYc03mW12M3N1N2O1N2O1O0O2O001O1O1N2O1O1N2O0O1000000O10000O1000O100001N11O000O10O010O100001M5_OaTS7\"}, {\"size\": [1280, 720], \"counts\": \"UT[c06iW15L1N2N3M2O1O1N3OO01O0O0100O100O10O100O100000000O1O1000O02O000000001O000O2O00000000N3L4H[dP7\"}, {\"size\": [1280, 720], \"counts\": \"Qcnb01lW15L4M2N2N1O2O001N101N10000000000O01000O2O0000O1000000000O1O1000O1000000000O2O00001OO1O2M<\\\\O^m[7\"}, {\"size\": [1280, 720], \"counts\": \"gjjb02kW14N201M2N101N1O100O1O10000000O011O01O000O1000000O100O10O0100O01000000O10001O1N02N12N2N4K4K^e_7\"}, {\"size\": [1280, 720], \"counts\": \"^Slb01nW13M3L3O10ON3N100O1O0100000O10O010O100O01000001OO1O10O100O100O101O00O100O2O0000001O0O3N2Lld_7\"}, {\"size\": [1280, 720], \"counts\": \"cSlb04kW12N2O1N2N2N2O1O001N1000000O10O10O1O10O10O1000O100O010O1000O10000O100000O010000000O1O1O2O2O3HhT]7\"}, {\"size\": [1280, 720], \"counts\": \"Ukjb05jW12O1N2O1O1N101O001N101N101O1O1N101N10000O10000O010O0100O0100O010O2O00O1000O010O1000000O11O002M3N3L6K2N4HllV7\"}, {\"size\": [1280, 720], \"counts\": \"okYc02lW14K5N100N2N2O0O2N2O2N2N1O1N2O01O00OO2N1101N1O1O00010O001O0100N2O00100O10O10O010O100O2N100O2M4L8I2M3M[\\\\j6\"}, {\"size\": [1280, 720], \"counts\": \"USlb02nW11O3M1N2O2N0O2O1N2N2O1O2N1O1O1O1O1O1N101O0O1000O0010O010O10O01000O10O100O1O100O10O01000000000O10O1O11O4K:F;BhTS7\"}, {\"size\": [1280, 720], \"counts\": \"gZcb02lW13M3N100O1O2N101O1O00001O0O1000000O1000000O010O100O10000O100000000O1O100O1000000000O2O0O2N200O7Ga]c7\"}, {\"size\": [1280, 720], \"counts\": \"Qk[b04kW12O1N2O001O1O1N1O100O1O1000000O10O1000O100O10000O1000O10O010000000O10000000000000O0100002M[en7\"}, {\"size\": [1280, 720], \"counts\": \"c[Tb05jW12O3M1N2O1O1O001O1N101O00000000O2OO10000O10O10O10O1000O100000O100000O10O010001O00O1000001N1O4HfdS8\"}, {\"size\": [1280, 720], \"counts\": \"^Sna02mW14L2O1O2N1N2N3O0O1O0O2O0O100000O100O100O10000O010000000O10O10O1000O1001N1000001N101N0100000O2OglY8\"}, {\"size\": [1280, 720], \"counts\": \"kj[b03iW15N1L4N2O2O0001O00000O0100O1000000000O100000O100000000000O2N1000001O0N3LcmY8\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"laed03kW15L2N2O001N1O2O001N1N200O001O1N2N200O11O001O00O11O001O2N7I5JUfW6\"}, {\"size\": [1280, 720], \"counts\": \"Zjkd02lW13N2O1O1M3M2O2O0O2O001N2O1O0O10O01O100O1000O01O2N001O100O100O1O1000000000O4N3L=Ajmd5\"}, {\"size\": [1280, 720], \"counts\": \"QRjc02mW12N2M3N2N101O010O1N101N2O0000001O00000O100O1000000O01001O000O100O100O1O10000O1000O10O101N010O100O10000O10O01O1M25L2M3M<CWfm5\"}, {\"size\": [1280, 720], \"counts\": \"jYac06hW14M001N2N2O1O1O100O1O10000O1O010O10000O100O1000000000001O000001N100O1000O0003KjfP7\"}, {\"size\": [1280, 720], \"counts\": \"_anb03lW13N1O1N2N2O1O1N200O1O1O1O1O100O1O000O2OO1001O0O100000O01000O100O10O10000O100000000O10O0100O010O100O1000001O000O010OO201O0O3M2]OPiN3lef6\"}, {\"size\": [1280, 720], \"counts\": \"Uieb06jW13L3M2O2O0N3M2O1O2M2O1O1O1O00001O0000O010O10O10O1000O1000O100000O10000000000O01000000O10O1000000000O10000O0100000O2O0O2N1O2NT_o6\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"ihba02lW1301N2N2O3L1O1O0001O0000000O100O10001N100O1O0010O01O1O1O2LYg[9\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"eXkc05jW16J3N2N1O1N2N2O0000000O1O1O1000000000O100O0100000000O1000O2O00000O1000O10000000000O01O02N2O1N1000O0O201O0O1N110N3N1DTPo5\"}, {\"size\": [1280, 720], \"counts\": \"VP^d03lW13M3M2O1O1N3N1O001N2O1O100O1O1O1O0O2O000000000001O00O10000O1000000001OO101O0O1O10O100000001O0O10O0100O1N3M3F;JRP`5\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"aX]e06iW13M3N1O1O1O1O1O1O1O2N0O2O1O001O1O001O00000000O1000O101O0000000000000O1000001N0100O3K4G5O3N5Flok4\"}, {\"size\": [1280, 720], \"counts\": \"nXnd08gW14L3N1N2O1N2O1O1N2O1O1O1O10O01N10001N100000O100000O10000001OO10000O100O1000000O1000O10O10O010O1N2I8H7L5Ja_S5\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"aXac01oW10000001O0000003MN2O1O100000O2O01O03L2N3M2O1O000010O00O100O100O2O1O0O10002N1O01O00O1O10O2N1N3N1O7GaWd6\"}, {\"size\": [1280, 720], \"counts\": \"UQbb05jW14M1O2N1N2N2O2N0O2O1N101N2O1O1O001O00001O00O2OO100000O100O1000O0100O10000000000O1000000O10O0101OO100000000O10O1000O2O0000O1O1O101N2N1N3NSgk6\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"[i`b02nW10O100O11O001O00O2O001N2O1O002M2O2N001O1O000O2O001O0O10000000000000000O10000O100O1001O000000O100O2O000000000000000000001O00O2N4LknQ7\"}, {\"size\": [1280, 720], \"counts\": \"l`nb07hW14M2N3M2N2M2O2N2N2M4M2N2N2O0O1O1N2O1N100000N20O100000O010O1000O01000O010000O0100000O10000O01O10000O100000O10O1O10000O100000O1O100O1N2K5N2K6E;Jf_[6\"}, {\"size\": [1280, 720], \"counts\": \"n``d01lW1:I3M4L3L3N3M2N2M3N3M3M2N3L2O1O1O1N101O00000O01001O0O0100000000O0100O10000O1000O011O00O0100000O100000O100000O01000O2O00O100000O10O1000O02K4L4G8M4N3K6J6I5N2O4J___4\"}, {\"size\": [1280, 720], \"counts\": \"RbWf03mW13M2N1N2O1O1O1O1O0O2O1O000O2O0O1001O00000000O100O1000010O001N1O100O100000000O1O101O0O101OO100000O10O0O1100O10O1000001N101N1N3N101N:^OXfX3\"}], \"areas\": [0, 0, 0, 0, 0, 0, 0, 988, 1523, 1621, 1501, 1860, 1203, 920, 1002, 487, 0, 0, 0, 0, 0, 563, 1438, 1678, 414, 542, 1181, 1037, 1010, 1912, 1576, 1117, 1036, 716, 732, 788, 1256, 557, 141, 143, 171, 133, 273, 332, 393, 253, 360, 529, 463, 301, 292, 308, 111, 154, 338, 278, 377, 427, 536, 541, 951, 729, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1084, 1045, 1673, 0, 493, 628, 735, 1319, 1738, 2505, 642, 1036, 1733, 1857, 2290, 1834, 0, 0, 0, 413, 404, 682, 1089, 762, 515, 867, 783, 1049, 989, 1058, 920, 733, 624, 796, 1101, 1104, 1364, 857, 729, 814, 761, 548, 0, 0, 0, 0, 0, 0, 0, 593, 910, 1320, 805, 1426, 1686, 0, 301, 0, 1319, 1323, 0, 0, 1066, 1484, 0, 545, 1637, 0, 1108, 2691, 3605, 1505]}, {\"video_id\": 0, \"category_id\": 847, \"bboxes\": [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [363, 548, 35, 24], [365, 562, 35, 22], [364, 561, 35, 22], [359, 539, 35, 18], [341, 538, 45, 21], [348, 562, 22, 15], [350, 561, 35, 16], [370, 561, 9, 15], [348, 568, 39, 16], [350, 557, 39, 14], [347, 547, 39, 16], [345, 544, 40, 17], [341, 547, 39, 17], [341, 547, 40, 13], [347, 545, 39, 13], [348, 549, 44, 15], [352, 549, 42, 15], [358, 547, 45, 15], [360, 549, 45, 15], [354, 545, 44, 19], [348, 535, 43, 21], [342, 534, 42, 17], [328, 535, 43, 22], [324, 534, 41, 14], [320, 534, 39, 17], [311, 542, 43, 18], [308, 544, 43, 19], [317, 544, 41, 17], [319, 549, 42, 17], [322, 558, 40, 18], [330, 559, 40, 17], [337, 561, 43, 16], [346, 561, 41, 18], [352, 563, 39, 17], [350, 575, 40, 18], [360, 580, 28, 14], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]], \"score\": 0.7264957427978516, \"segmentations\": [{\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"YYV>1mW17I6K3N2O1N1O1O1O0001O000000010O00000O01001O000O10O0100O1N102M2N2N3M3Klf`<\"}, {\"size\": [1280, 720], \"counts\": \"eiX>6iW13N1N2O2M2O1O1O2N010O001O01O01O00000000000000000O001000O101N1O0O2N3L6G`V^<\"}, {\"size\": [1280, 720], \"counts\": \"gaW>5iW13N3N1O2M101N2O10O01O00001O01O000001O00000000000O1O100O010001O1N3N1N4LX^_<\"}, {\"size\": [1280, 720], \"counts\": \"nXQ>6gW15N001O2N1O1O0010O010O01O00000001O00000000O10000000O1O2O000O1O1O2N2N4Lnfe<\"}, {\"size\": [1280, 720], \"counts\": \"khZ=2Q`22jgN1N100YhNMeW171OO11N20M2N101N101O000011N10O0O1010O0000000000001O0000000000001O0O102N0O2O002Kjfo<\"}, {\"size\": [1280, 720], \"counts\": \"jac=2iW15N200O2O1O010O10O010O010O001O1O0O2O000O2N20Tfc=\"}, {\"size\": [1280, 720], \"counts\": \"bQf=6jW1001N200O1O001O00100O1O010O00001O000000000001O000000000O101O00001N101MZnP=\"}, {\"size\": [1280, 720], \"counts\": \"mQ_>3fW18L3N2002M101O0O3M[^X=\"}, {\"size\": [1280, 720], \"counts\": \"kac=1nW1102M3N10O01O100O1O0O1O101O001O01O01N1O1010O00O10O1001O01O000O2O0O101O0O101N3MS^n<\"}, {\"size\": [1280, 720], \"counts\": \"]Qf=2nW12N2N2N1O100O001O00000010OO100001O01O0O1O11O01O0O1001O1OO0001O101OO0100O3L3N2N`nk<\"}, {\"size\": [1280, 720], \"counts\": \"XYb=2lW1201O1O2O0O001O1O001O1O00000O101O00000O100O100000000000000O01O100O100O101N1N3Mmfo<\"}, {\"size\": [1280, 720], \"counts\": \"Ti_=2nW11O3M1O010O001O1O1N101O000010O0000O2O0000000O100000O10O1001O0O01O0100O100O002N3LonP=\"}, {\"size\": [1280, 720], \"counts\": \"UiZ=5lW1O01O1O1O1O1O1O001O1O1N101O00000000001O000O10000000O01000O1000001N1O100O1N3M4LkVW=\"}, {\"size\": [1280, 720], \"counts\": \"TiZ=1oW11N2O2N100O1O001O1O1O00001O000000000001O000000O2OO1000000000000O1000O0100O2N2N3NinU=\"}, {\"size\": [1280, 720], \"counts\": \"TYb=2mW12O1O1O1O001O001O001O10O0001O0O1001O00O100000000O10000000O10O10O1O2O00O02O0O2LPgo<\"}, {\"size\": [1280, 720], \"counts\": \"Wac=3mW11O00001N2O1O100O001O001O0O2O001O00000000010O0000O11O0000O0100O100000O100O100O2N1O1O2M3MjVh<\"}, {\"size\": [1280, 720], \"counts\": \"Xah=2mW1101N2O1O1O1O001O001N2O001O01O0001O000000000000000000001O00O10O100000O2O0O2N100O1O2Nife<\"}, {\"size\": [1280, 720], \"counts\": \"YQP>1nW11N2N3O01OO2O1O2N010O00000O2O001O001O00000001O0000000000O10000000000O10O2O0O10O02O2M2N2M3Mi^Z<\"}, {\"size\": [1280, 720], \"counts\": \"YaR>1mW13N2N101O2N1O01O00000001O001O0010O000000O101O0000001N100000000000O100000001N10001N100O2N2NhnW<\"}, {\"size\": [1280, 720], \"counts\": \"TQk=3lW12N2O2M110O100O01O01O10O1O010O0001N2O0010O1OO1001O01O0O1000000001O0O1000000O100O1O1O1O2Ljf`<\"}, {\"size\": [1280, 720], \"counts\": \"g`c=1oW1024J101O1N1O010000O01N2O2N001O0O100100O00O1000010N1O1O11O1N100O00101OO01O101N100O0O1OW_i<\"}, {\"size\": [1280, 720], \"counts\": \"gP\\\\=5kW11O110N100O1O001O000O2O001O0010O01O000001OO10000O100001O0O01000O2O000N2O01N0101M3N3L\\\\WR=\"}, {\"size\": [1280, 720], \"counts\": \"g`j<2RX1OL2O2O2M1O1O1O1000O01O2N1O001N10000010O00O2O0001OO00100001O0O100O01000O2N1O2N1O1N010OX_b=\"}, {\"size\": [1280, 720], \"counts\": \"f`e<4lW13M1O1O2OO1O01O001N101N101O01O1O1N10O10001O0O1O11O00001O001O0O2O0O2ON1O2O10O1N3N2NYoi=\"}, {\"size\": [1280, 720], \"counts\": \"j``<3mW11N2M4O00000N2N1010O00O2N10010O0O1O11O1N2OO1O11O001OO1O100002M2O000O101N1N2O2KX_Q>\"}, {\"size\": [1280, 720], \"counts\": \"PYU<6iW12O3L110O0010O0001O10N1001O1O1O0001O001OO2O0000O1000O11O1O1O00000O1000O1000O1O1O2N2N4LifW>\"}, {\"size\": [1280, 720], \"counts\": \"TaQ<5jW1100O1O3M10101O0000O01O000010OO2O000100O00001O00000000001O1OO1000000O1000O100O101M4M4Ld^[>\"}, {\"size\": [1280, 720], \"counts\": \"Ti\\\\<3lW13N1N2O1O1O00002N0100OO2O000O10001O000O100001O1OO100000O01O10O1000000O2O1O0O2M2N2OnfR>\"}, {\"size\": [1280, 720], \"counts\": \"YY_<2nW13L2N101O1O10O01O1O0O10001N100O2O01O10O0001N10O1001O01N0100000000000000O101N100O3L4Lfnn=\"}, {\"size\": [1280, 720], \"counts\": \"aQc<3lW12N2N2O1O101N10O0100O0000001O0000001O000010O000O0100001O1O00001N100000O0100O2N2N\\\\fm=\"}, {\"size\": [1280, 720], \"counts\": \"bQm<1nW13N2M2O2M2O00100O0O2O0001O01O10O000001O0000000000000O100000O10001O001N2OO01O2O2L]fc=\"}, {\"size\": [1280, 720], \"counts\": \"fiU=1mW14L201N101O1O001O010O001O00001O00001O000O11O0010O0000000O1O0100000001O001N101N100O2N3MZVW=\"}, {\"size\": [1280, 720], \"counts\": \"hQa=4iW14N101O1O1O00O11N2O10O000000O10001O00000O1O1001O1O01O0000O1000O1000O2O1O1O1N2O1N3LX^n<\"}, {\"size\": [1280, 720], \"counts\": \"iah=3kW13N2O2N10O0001N1O100O2O001O000010O0O2OO2O0000000O100O11O0O1000000000000O3M4M2LW^i<\"}, {\"size\": [1280, 720], \"counts\": \"SRf=2mW12N2N2O1O1O1O100O0000000O2O0O1001O10O0O2N101O001O0000O2O0O1O2N1000001N1O101O1N3Mkej<\"}, {\"size\": [1280, 720], \"counts\": \"]bR>5fW15O2M20000000000001O0000O11O0000O1O1000O1000002M3M2O2M2NeUm<\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}], \"areas\": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 653, 643, 644, 514, 498, 224, 412, 117, 478, 427, 482, 470, 506, 403, 397, 497, 498, 530, 545, 492, 513, 476, 560, 468, 498, 527, 511, 557, 545, 496, 548, 554, 578, 549, 517, 349, 0, 0, 0]}, {\"video_id\": 0, \"category_id\": 1390, \"bboxes\": [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [262, 442, 108, 105], [262, 438, 117, 113], [265, 441, 119, 122], [265, 430, 126, 129], [268, 414, 131, 137], [283, 410, 126, 137], [296, 405, 132, 141], [315, 403, 134, 141], [326, 407, 133, 141], [318, 419, 130, 134], [301, 441, 120, 121], [290, 454, 116, 115], [291, 458, 102, 104], [298, 474, 102, 96], [305, 479, 103, 104], [299, 474, 116, 115], [292, 485, 108, 101], [286, 487, 81, 84], [285, 477, 51, 44], [286, 481, 83, 80], [286, 482, 64, 75], [282, 485, 43, 75], [278, 512, 21, 60], [281, 494, 70, 77], [291, 488, 71, 82], [301, 485, 41, 82], [308, 485, 68, 83], [308, 489, 69, 84], [308, 491, 70, 85], [314, 493, 65, 84], [315, 495, 66, 80], [317, 503, 64, 68], [314, 503, 68, 68], [311, 502, 67, 68], [313, 506, 68, 63], [322, 502, 66, 66], [345, 501, 51, 58], [329, 504, 70, 64], [337, 495, 67, 67], [345, 487, 67, 71], [344, 483, 68, 64], [340, 476, 72, 65], [359, 472, 59, 50], [342, 473, 73, 65], [342, 475, 67, 65], [338, 476, 70, 68], [335, 483, 70, 65], [328, 502, 33, 53], [332, 515, 26, 40], [331, 506, 21, 51], [362, 489, 36, 70], [351, 489, 48, 69], [336, 484, 65, 68], [333, 482, 69, 68], [332, 481, 67, 68], [330, 476, 54, 65], [331, 474, 61, 66], [325, 473, 72, 68], [318, 473, 75, 66], [313, 472, 76, 68], [313, 474, 75, 67], [310, 473, 74, 67], [309, 471, 73, 70], [306, 473, 81, 72], [310, 470, 43, 70], [318, 469, 35, 70], [321, 472, 33, 68], [323, 484, 22, 55], [325, 464, 30, 73], [324, 461, 43, 74], [319, 464, 43, 76], [316, 466, 72, 78], [311, 473, 70, 76], [310, 491, 69, 75], [305, 489, 65, 69], [306, 490, 64, 70], [307, 494, 69, 65], [314, 542, 64, 17], [322, 495, 70, 79], [320, 478, 72, 74], [316, 487, 69, 71], [313, 490, 57, 54], [335, 496, 43, 62], [307, 500, 29, 52], [308, 500, 51, 54], [308, 517, 43, 59], [306, 491, 67, 64], [314, 497, 69, 66], [320, 493, 65, 57], [325, 498, 65, 55], [325, 494, 66, 58], [332, 493, 55, 56], [326, 500, 67, 48], [330, 491, 67, 59], [380, 526, 16, 33], [0, 0, 0, 0], [329, 496, 58, 53], [330, 494, 65, 58], [331, 481, 61, 57], [387, 493, 26, 55], [393, 506, 15, 32], [347, 533, 69, 45], [348, 512, 72, 59], [348, 495, 75, 86], [345, 493, 74, 80], [342, 478, 68, 75], [335, 479, 63, 70], [331, 508, 63, 71], [333, 557, 13, 14], [331, 499, 68, 214], [334, 498, 67, 74], [333, 483, 73, 73], [329, 476, 73, 68], [326, 475, 75, 68], [323, 476, 73, 72], [324, 476, 73, 69], [327, 475, 76, 68], [331, 480, 76, 70], [335, 486, 75, 65], [342, 487, 74, 64], [345, 479, 73, 73], [342, 478, 69, 74], [336, 475, 67, 61], [326, 472, 67, 59], [316, 473, 65, 66], [310, 467, 65, 72], [330, 468, 43, 71], [298, 478, 68, 72], [297, 480, 66, 75], [300, 480, 70, 73], [304, 486, 68, 72], [307, 494, 67, 72], [314, 496, 68, 70], [323, 498, 69, 69], [331, 501, 71, 71], [336, 503, 70, 71], [335, 513, 71, 68], [353, 519, 50, 68], [388, 571, 19, 17], [0, 0, 0, 0], [384, 534, 20, 52]], \"score\": 0.7104072570800781, \"segmentations\": [{\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"VoW:>[W1:I6K4M2N1OF:M4M3O1O0101O001O00001O1O00100O010O1O100O10000O10O1000000000O1000001O0O10001O0O2O001O1O0010O01O001O10O01O0010O01O100O100O01000O100O010000000000O101O00000O101000diNUO`U1l0]jNXOaU1i0[jN\\\\OdU1a1000O1O2M2N4M3L4M2M2M5L<D5K3L4M2N3L3N4KXgc=\"}, {\"size\": [1280, 720], \"counts\": \"XoW::^W1<H6I6L4M2OK4I8K4O2O0011N101O001O010O001O010O10O01O100O10O010000000O10O10001N10000O101O001N101O00001O010O001O1O00001O000010O01O00100O100O100O10O010O01000O1000O10O101O000O1000001O01OO21giNmN`U1V1ZjNSOaU1n0ZjNXOdU1d1O0000O1O1O2M3N3L4M2M2N4L=C4L3L3N3L4M2M4M2N4IV_X=\"}, {\"size\": [1280, 720], \"counts\": \"^g[:;^W1:I7I6K4L4M3M00G:G8N3O0001000O100001O0010O01O010O010O010O100O10O01000000O100000000O010O101O0000001O0O10001O001O001O010O001O001O010O1O001O010O010O100O100O10000O01000000000O1001N10000O10000101N101liNPOSU1Q1ijNWOSU1h0jjN^OTU1b0fjNEYU1b10O1O2N3M2N2M3M5K3M3M4L>A5L4K4M5J6J=BeVR=\"}, {\"size\": [1280, 720], \"counts\": \"\\\\g[:5bW1?F8H6K5J7J4M4K5L2N10D;I7I8L3O2N2O100O11O000010O1O0001O10O01000O100O10O0100000O1000000O100O10000O100000O010000000000010O0001O001O1O0010O01O00010O1O1O0010O01000O0100O011O1O0O01000O1001OO1001OO100010O00010O0101O4fiNfND;_U1R1gjNZOVU1e0ijN@TU1`0ijNCWU1c1100O2M3N4K6J2N2M3N3Mb0]O5L4K4M4L3L6JU_i<\"}, {\"size\": [1280, 720], \"counts\": \"m^_:=`W17I7H7J5K5K5K6J5L3L8H3M4M2N2O1O00TOl0K5J6N2M3O1O1O2O0O2O000010OO2O001000O01000O010000O100O10O0100O1001N10000O100O100O1000000O10000000O100010O0000010O0010O01O010O1O010O001000O010O10O1000000O1000000O1000000000001O00010O10O1001O3`jNiNoS1Y1bkNYOYT1h0_kNB^T1>]kNHcT17XkNLkT1c13M3M4K5L5K3M2M3Ng0YO6I6K4K4L9^Oc__<\"}, {\"size\": [1280, 720], \"counts\": \"PWR;:aW1;F9H:G6I4L3K5M3N2M4L3N3M2O1N3MNfjNdMVU1o1YkNRNdT1l1akNWNZT1f1Q1O1N2O1L4N3N1O1O2N101O0O100010O01O00001O0010000O010000000O100000000000O10O100000O01001O1N1000000O11O0000000000000000000000000010O010O01000O10O010O100000000O010000O1000O10000001O1O010O1O2N2O001SkNbNTS1`1`lNaNhN3iT1`1VlNhNlS1]1ekNlN]T1Z22N3L4M4L7Ha0_O>hNYQS<\"}, {\"size\": [1280, 720], \"counts\": \"Z`b;1jg30j^M;ZjNG`U1d0ZjN\\\\OcU1j0e07[iNQOnU1W1hiNmNUV1a1M3M3L4M3L5L4N2M4L3N3L5L4L4LiNckNPOYT1j0ZlNnNaS1l0ilNTOSS1i0UmNUOhR1j0]mNUO`R1j0gmNnN\\\\R1P1Z20001N1O1O1O101O001O010O01O0100O10O1000O100000000O100000O01000O1O2O000O10000000000000000000O2O0000000000000010O001O0010O0100O001000O01000O1000000000O010000000000000O110O0O3OO02O001N3N0ojN]NcS1d1WlNiNaS1X1UlNoNmS1S1ZkNBhT1k11M2N3N2M5K`0PNdjN=kU1nNejNh0V^\\\\;\"}, {\"size\": [1280, 720], \"counts\": \"hVZ<9bW19I6I7J6I8J5J5K:F4L3M3M4N1N2N3N]OjjN^NUU1Q1dkNfN\\\\T1X1W1O1O2N1O1O1O10001O000010O001O10O01O10O010000O1000O10O100000000000000O100O1000O01000O1000O10000001O01O000010O01O1O001O010O001O001O10O010O00010O01O10O10O010001O00000000000O100010O000O100000001jjNkN_S1U1YlNTOfS1o0RlNWOmS1j0jkN^OUT1d0gkN]O]T1a0`kN@cT1?XkNDkT1g13M2N3M4K;F3M4L2N3XNTjNU1dV1H5K5K7H8Gao`:\"}, {\"size\": [1280, 720], \"counts\": \"bng<`0[W17I8J4K5L4N1M3OM2F:N3N2N10001O02N101O001O100O1O00100O010O100O100O101N100O2O0O100O100O100O10000000000000001O1N101O1O001O00001O0O2O1O001O000010O01O010O1O10O01O010O100O100O1O10O010O1000O10O10O10O100O10000000mjNZNkS1g1okNfNjS1[1PlNlNnS1T1okNPOoS1Q1nkNQORT1o0mkNROTT1n0jkNROWT1o0ekNSO[T1Y21O00002N4L4L5K4L4L2N2N6J>B4\\\\NoiNR1SV1iNVjNP1aV1M2M3M6J5K3M4K6HT_T:\"}, {\"size\": [1280, 720], \"counts\": \"mn]<5eW1:G7I7K3L30L2M5M3N101O10O10000O2O1O001O1O1O1O1O100O1O100O2O0O100O10000O10000O10000O101N2O0000001O0O2O1O001O1O001O001O001O001N2O0010O01O1O10O01O100O100O1O010O1O010O10O0100O10O01000O10O1000000000OfjNbNST1_1ikNgNUT1X1ikNlNUT1T1ikNnNXT1R1dkNRO\\\\T1P1^kNSOcT1U21N2N2N2N2N2N3M2N2N2N3M4M3L6J4L3M4L3N5dNdiNh0nV1L2N4L2N5K4L4K5KnVb:\"}, {\"size\": [1280, 720], \"counts\": \"Ygh;4gW19I5K2N1OO2O101O01O101O001O1O001O1O1O1O1O1O1O1O101N1O100O10000O10000O101N10001N2O1O0O2O1O001O001O001O1O001O1O001O1O001O001O001O100O1O100O100O1O1O100O010O1000O0100O0100O1000bjNWNdT1i1YkNcN^T1\\\\1bkNeN^T1[1akNgN^T1X1akNjN_T1U1`kNmN`T1S1_kNmNbT1R1\\\\kNQOeT1T21O2N3M2M4M2M2O1O2N3M5K3M5L4K7J:F2N3L6K4K2N3M3M5K4K5Ifnc;\"}, {\"size\": [1280, 720], \"counts\": \"XoZ;5hW14M3N1N2O1N3N1O1O1O1O1O1O100O2N1O1O1O2N1O2N1O2O0O2O0O101O000O2O1O0O2O000O2O0O2O001O1O1O001O1O1N2O1O1O1O1O1O0010O01O1O1O00100O010O0010O00010O010O010000O01000O1\\\\jNoMYU1`2K2O00100O0010O0O2N2N1O1O2N2N3M2O1N3M3N2M2N2O2MP1QO4K2O1N2N2N2O1O0O2N2M4M1N[fV<\"}, {\"size\": [1280, 720], \"counts\": \"XW\\\\;4iW14M3N1O1O1O1N201N1O2N1O1O2N101N1O2N101N2N2O0O101N2O1N101O0O2O1N2O001O1N101O1O1N2O001O1O1O1O001O1N101O10O01O001O001O100O2N10O0100O2O5J2N2O1O001N10001O00001O0001N100O1O2N1O2N2N2N2N2O2M4M2L5K3N2M4L5K6J4L4K6XOihN<gVh<\"}, {\"size\": [1280, 720], \"counts\": \"god;2jW16M2M2N3N2N1O2N1O2N2O1N1O1O2O0O101N2N2N101N2O2M1O2O1O0O2O1O0O2O1O001O0O2O1O001O1O0O2O001O1O001O1O1O1O001O010O2N2O2M1O100O100O101N10000O100000000000000000O2O0N2O2N3N1N2N3M3N3L2N3M2N2N1N3N2M3M2N3M3M2M4M3K7FWW^<\"}, {\"size\": [1280, 720], \"counts\": \"hgm;1jW16N2N2N1O2N1O2O1N1O2N2O0O2N101N1O101N101O1N2O1N101N2O1N101O1O0O2O1O1O001N2O1O1O001O1O001O001O1O1O101N2N3N1N00100O10001N102N0O10001O001O1O0000000001N101N2O1N3N3L3N1N3M2O1N2O1O0O2O0O2O0O101N2N2O1M3N2M>B7I4K6JTVT<\"}, {\"size\": [1280, 720], \"counts\": \"aWf;4iW13N3N1N2O2N1O101N2N101N1O2N100O2O1O0O2O0O101N101O0O2O1O1O0O2O1O1O1O001O001O1O001O001O1N200O1O2N1O101N2O1N1O100O2O001N2O1O00001O0000010O0001O1N101N2N2O2M2O2M2N2O2M2O2M2N2O0O10001N2O1O1N2O2M4L3N1O1N2O1N2O1N1O2O0O10000O101O11N2NO3O0N4Gm]k;\"}, {\"size\": [1280, 720], \"counts\": \"g_];4jW13M2O2N101N1O1O101N1O2O0O2N2O000O2O0O101N101O0O2O1O1O002N1N101O1O010N2O001O100O001O1O0010O02O0O2N3N3L2O1N101N101O1O1O1O1O000001O1N101O1O1N2N1O3N1N3N1N3N1N3N2M2O3L2O2M2O1N2N2O1N101N2N102M101N1O10001O0O100O100O1O101N2MjU^<\"}, {\"size\": [1280, 720], \"counts\": \"moU;1mW13L3O001N101O1O1O1O100O100O100O100O10001N10000O2O000O2O001O1O0O2O001O100O1O1O100O1O1O100O1O100O101O000O10004L8G4M2N2O0O01O1N101N2O2M2O1N4M1N3N3M4L5J2O1N2N2N4L3K5Jc^g=\"}, {\"size\": [1280, 720], \"counts\": \"mgT;;_W18K3N1O1ON200O2O1O100O1O10001N11O0100000O10O10O100000000O2O00000O100000001O00001O010O0010O010O010N2M4IQYn>\"}, {\"size\": [1280, 720], \"counts\": \"PPV;;aW18I6K6I?B6J6J4M2M3N2NkNejNF[U17mjNERU18TkNFkT18YkNFgT19^kNCcT1:ckNB]T1>fkN@ZT1>ikN@WT1a0jkN^OVT1a0d101O1O100O1000000O1000O100000000000001O0000010O0010O010000O011N101O00002N2N2O3L2YiNROYV1c1H2NO2M3N2N2M4M6I4L4M2M3M2N2O0O2O0O2N2O1N2N2Ncnd=\"}, {\"size\": [1280, 720], \"counts\": \"TPV;9bW1;F9H<C6L<D2N2O1N3N1O100O1OoNcjN@]U1=ljN^OSU1`0QkN^OPU1?SkNAlT1>XkN_OiT1?\\\\kN^OdT1?bkN]O_T1a0`101O1O1O1O1O1000O0100O100000000000000000010O000010O01O0100000O10O2O00100O0001O1O1N3M3LVh\\\\>\"}, {\"size\": [1280, 720], \"counts\": \"YPQ;=8HhV1m0J6I6L4ciNaNUV1g1M3N2M1O200O001O2NUO\\\\jN[OdU1b0bjN\\\\O^U1?ijN^OWU1?PkN]OQU1`0VkN[OjT1d0[kNWOgT1g0Y1O2N2N2O1O1O1O100O1000O0100O100000000O101O1N2NiP\\\\?\"}, {\"size\": [1280, 720], \"counts\": \"bPl:676iV1e0J8J5L:G5L000002M2O0O1O2N101N1jNeiNf0\\\\V1UOliNLLb0UW1N2N3M4Li^\\\\`0\"}, {\"size\": [1280, 720], \"counts\": \"aho:94OPW1f0I7J6J8I:G3N5K1N2O1O1O10OOXNXkN4PU1IYkNLkT12]kNFfT18b1O1O2M`P]1KfobN<E7XiND_U1a0SjNGoU1T11O2M4M5J8H=D3L5L4Ik^[>\"}, {\"size\": [1280, 720], \"counts\": \"WX\\\\;b07_OlV1P1H9I7H;E5L3N2M101NQO^jNDaU1;bjNC^U19hjNEXU17ojNFRU16SkNIlT16WkNHiT16[kNHfT15_kNHbT16ckNF]T1:e1O1O0O200O1O1O100O010O100000000000000001O01O0001O010O10000O01001O0O110O1O2O005J7TiNZOPV1Z12N2N2N4L6J7I<D3M3M1O4Kifm=\"}, {\"size\": [1280, 720], \"counts\": \"Uhh;<`W19ihN^OcV1X1F7I:G6J2N2O1N2O00lN\\\\jNMdU1OdjNM\\\\U10mjNKSU11SkNLmT13XkNJgT14^kNJbT14bkNI^T17ekNG[T17ikNDYT1<f1N2O010O1O10000O10000O10O10O10000O101N1O2Mkhf>\"}, {\"size\": [1280, 720], \"counts\": \"T`Q<<aW18ihNC]V1Z1F9G5J4M2O001NPOZjNIfU11cjNL]U10kjNLUU13ojNJPU15TkNIlT14ZkNJeT15_kNIaT15dkNG\\\\T18hkNDZT1;e1O2O00100O100O10000O010000000O100001O00000001O01O0010O10O010O2O00001O002N2N2O4K6L4K=DN3L3N6I`0@6J4L3LkV\\\\=\"}, {\"size\": [1280, 720], \"counts\": \"SaQ<3jW14L4^OKSiN;dV1g0Jb0_O3L4M2O0O2N1OmN]jNKdU13ajNJ_U15ejNHZU17kjNFUU18ojNGPU17VkNEjT1:ZkNCgT1;^kN@cT1?_1O2O0O2O1O1O0010000O100O100000000000000000000001O0001O010O10O01000O1000001O2O1N4M3M7TiNSOUV1^1ON3M3M8PO[iN5]W1K4LinZ=\"}, {\"size\": [1280, 720], \"counts\": \"UaQ<5iW13L4M3M8VOP1A5K3N2O1N1O001OlN]jNNcU12ajNK^U12gjNLXU13mjNKSU12SkNJmT15XkNGhT18_kNAbT1>`1O101O001O001O10O010000O10000O1000000000000001O001O0010O0010O010O1000001N2O1O1O5L4K=C9HO1O2M3M4L7RO[iN3_W1IjfY=\"}, {\"size\": [1280, 720], \"counts\": \"TQY<39>XV1AkiNP1kU1g0L4N100O2N10iNYjN6fU1K^jN1bU1NbjN0\\\\U1OijNNWU11njNLQU12VkNIjT15^kNBdT1?_1O0O1000001N2O010O1000O0100O100O100000000000001O01O001O001O01O01O1O011O0O101O1O1O2N3^iNXO]O5lU1k0SjNGoU1T1101N3M2N4L8Gd_X=\"}, {\"size\": [1280, 720], \"counts\": \"TYZ<52105PW1e0@TOdiNo0UV1b0M2O2N1O2N2OlNTjN5lU1JZjN1fU1O]jNNcU10ejNI\\\\U16W1N200O100O1O101O00001O0000100O1O100O1000000000000000000010O1O01O01O1O010O100O1O1miNBcT1?ZkNEdT1=QkNMnT15hjN2XU11ajN3`U1V11O2N1O1N3K6A`0B`0CloU=\"}, {\"size\": [1280, 720], \"counts\": \"ji\\\\<1TW1l0L3E;J6M3M3O1O0O1000O2M1YOh0M3N3N1O100O2N1O2N3Kmak0K[^TO2M2O2N1O1O1O100O1N2O100O1O0001O1M4JRoU=\"}, {\"size\": [1280, 720], \"counts\": \"XQY<c0YW15M2M2N3E;J5O2N2NWOkiNKTV12SjNJmU15ZjNFeU1:djN\\\\O]U1c0P11O00O011O000O10001O010O100O2O002M2O1O1O1O0O2O001O01O000010O01O001O1N101O1O1O1O2M2O2N2N3Mh0XO1O01O0O3M2N3L5J4J9@ogT=\"}, {\"size\": [1280, 720], \"counts\": \"PYU<4iW1:\\\\hNFPW1l0F6L3M4N0O2O1N10\\\\OdiNFZV19PjN_OPV1`0g0010000O10000O20O01O001000O010O10000O100000000O101O01O0000010O0001O00001O010O100O101O0O3N2N3M3M01N1O2M4L3M4J8BggY=\"}, {\"size\": [1280, 720], \"counts\": \"PiW<5iW14K4M3M?B3L3M3M2N2N2O@`iNB^V1>miNXORV1i0b00O1001OO20O001O0010O0010O0010O01000O10O10000000001O0001O001O001O0010O010O010O10O2OO010001O0O3N01O1O1M3N2M5J7F^oU=\"}, {\"size\": [1280, 720], \"counts\": \"nPc<8cW18J6J:G6J3L4M2M3M3O0OAniNTOQV1j0VjNQOjU1o0d0N1O1O2O00001O00001O010O010O100O1000000000000000O10001O0001O00010O01O010O100O010000001O0001001N4NO0N2N4J6H`Wm<\"}, {\"size\": [1280, 720], \"counts\": \"Ph_=2kW17K3M3M3M2N3M2O2M101O000O10O101O000O11O0O1000000010O00001O00010O00010O10O100001O01O00101N6K3M01M2M6I7G]Wc<\"}, {\"size\": [1280, 720], \"counts\": \"Xik<3kW14J5L3M3L5L3N3L7I5K5L3M2O1OAkiNUOTV1k0TjNlNmU1T1?00N201N100000001O0010O0010O1O100O10000000000000000000000010O010O00010O1O010O100000001O100O2N103M2NN2N3L5J9FX__<\"}, {\"size\": [1280, 720], \"counts\": \"jhU=:^W19K5L4L5K6J5L4M3L3N1O1O[OTjNVOmU1i0e00000O2N100O100000001O010O10000000000O1000000000000000010O00001O01O01O01O10000000000000101N1O2O2N5KO2M4L7H;@[WY<\"}, {\"size\": [1280, 720], \"counts\": \"hh_=6ZW1a0L4L5L4K6K5J6K4M1N2OYOUjN[OjU1e0ZjNXOeU1f0`jNWO`U1i0cjNTO^U1i0hjNROYU1m0m000O2O00001O001O1O00100000000O1000000000O1001O01O0001O0001O000010O010O11OO1001O10O02N102M7J3M01M3L5J>XOkWo;\"}, {\"size\": [1280, 720], \"counts\": \"^`^=2jW18H5M3L4N4mhNWOfV1U1L3M2M3N1O2O0OBniNROQV1m0TjNoNlU1P1b0O100O2O00001O00001O001O010O010O1000000O1000000001O0001O0001O01O0000010O0100O10O10O1010O01O2N3N6J00O2L4L6DWXo;\"}, {\"size\": [1280, 720], \"counts\": \"^`Y=2mW12M2M3M2O2L4M4L6K;E3M2N2M2O2O1NAjiNXOVV1e0PjNWOoU1j0TjNQOnU1o0a001O0O100O101O000001O01O010O10000O1000000O10000001O00000010O00000010O000100O0101O001O001O101N2O2N6JI9J6I;AXXo;\"}, {\"size\": [1280, 720], \"counts\": \"\\\\WQ>:cW19I6I4M2N00001OO100000001O0010O0001O010O1O1000000000000O1000O1000000010O0010O01O0010O00100O0100001O10O02N102NO2N2M4I;B[hg;\"}, {\"size\": [1280, 720], \"counts\": \"^P\\\\=2lW12L4M111N1N2O2M3N3M>B4L2N2N2N1O2N2OAoiNoNQV1R1?OO1001O000O1O2O0001OO2O0010O010O0100O101O000000000000000001O01O0000010O001O010O10O100000001O10001N3NN2N3K8E_`k;\"}, {\"size\": [1280, 720], \"counts\": \"YP\\\\=5gW15I6L5L4M9F7K2L3N2N1O@diN@[V1>SjNUOnU1j0d01O0000O10001O0O101O01O01O010O1O10000000000000O100000001O000001O010O010O01O010O100000001O1O00101N4N1WiNkNaV1[10F;L6F]PS<\"}, {\"size\": [1280, 720], \"counts\": \"bPW=6hW11N1N4F:L3N4M4L9G4L3N1O01O1BeiNVO\\\\V1e0b0L4M3N1M4O1O1N101O001O1O1O0100O001O1000O01N2O1M3N3N100O101O00010O000010O010O10O1000001O00100O2O2M6TiNlN`V1]1NO2N2M5^Od0^OZXT<\"}, {\"size\": [1280, 720], \"counts\": \"jXS=4jW12N0O1O3D:O2M5M2M5K:F7J2N2O1O^ORjNSOnU1l0XjNmNjU1P1d0O1O1O2N1O1O2O01O01O1N200O101O00001N101O00000O10000001O00000O101O0000001O10O010001O001O101N1O4N8F5MO1N2K8[O]PX<\"}, {\"size\": [1280, 720], \"counts\": \"o`j<2mW11M3N2N100J7J5N4L6J7I7J3M3O00000O[OjiNUOL7[V1b0UjNYOlU1c0YjNZOhU1d0[jN[OdU1d0l000O2O2M2O1O2M200O2N2N3MUPo=\"}, {\"size\": [1280, 720], \"counts\": \"]`o<<^W17M4N3L7J3N1N10O01N100000000O00AZiNFfV17d0G8M3O2N1O5L010O01egR>\"}, {\"size\": [1280, 720], \"counts\": \"iXn<3`W1>K5L4M6I6L5J4N0O2O1N01O010[OfiNC[V1;g0K4M4L5L3M3LSXZ>\"}, {\"size\": [1280, 720], \"counts\": \"[oT>5kW11N2O1O2N1O001O1N2O1O10O01O010O1O0100O10O010O11O01O000biNBYU1>RjN@M=PV14miN<SV1i01N2N2N2M5K9XOUh`<\"}, {\"size\": [1280, 720], \"counts\": \"_Wg=1mW13M201O1N2O1O1O1O1O1N2O1O002N001O00000000000000001O01O00010O100O11N10001O00003N1]iN_ObU1_11O2N3L3N3J7Db0@g__<\"}, {\"size\": [1280, 720], \"counts\": \"T`T=f0VW14M3M5K8H7J3M3N1O@liNVOSV1i0QjNUOnU1j0VjNTOiU1k0ZjNTOeU1i0ajNTO`U1h0m0O1O2O0O101N2O001O1O1O100O100O0100000O100000000001O0000010O01O0100O0100O11N2O002N001O2O2]iNZOfU1`11N2N2M4L6I<SOXP]<\"}, {\"size\": [1280, 720], \"counts\": \"khP=1mW12N2]O2RiNNlV1d0N3M3L5L5L8H5L3M1N^ORjNVOmU1h0WjNWOiU1f0\\\\jNWOdU1f0bjNVO_U1h0m0O1N2O2O1N101N10001O100O1000000O100000000000000000000001O00010O0100O0100000000O2O1O2N103[iN\\\\OeU1`11N2N3M3M4K6K;SOUh[<\"}, {\"size\": [1280, 720], \"counts\": \"^`o<1gW1:J5L5K4L6I=E3M3M3M2O_OjiNZOTV1g0PjNUOPV1j0UjNSOkU1l0WjNROiU1m0\\\\jNPOcU1n0ajNPO_U1o0i0O2N1O101O001N101O1O100O1O100O100000000000000000000000000010O01O10O0100O1001O1N2O2N000ciN\\\\O^U1f0ZjN_OgU1\\\\12N2N4K4L7XO_iNCbf`<\"}, {\"size\": [1280, 720], \"counts\": \"WPm<5hW14L3L5L5K7H7I9H5L2O1OAhiNXOWV1h0kiNWOTV1g0QjNWOnU1j0SjNTOmU1m0UjNQOiU1Q1ZjNlNeU1S1e0N1M3O2N1O101O1O0O2O1O1O1O1O100O1000000O100000000000001O00001O100O001O2N3LkXR=\"}, {\"size\": [1280, 720], \"counts\": \"VXn<5iW11M4M5J5M5J9F:H3L3N2O1N_OjiN[OVV1e0niNYOPV1g0SjNWOlU1i0VjNVOiU1k0YjNROgU1m0f0O1O2N1O1O2O001N101O1O001O1O100O10000O10000O10000000001O00000010O000100O10O01O100O2N1O2MjXh<\"}, {\"size\": [1280, 720], \"counts\": \"^hf<1kW14O01NO2O2M2M5M3L4L>B7I3M4M2N1O@hiN\\\\OWV1d0liNZOSV1f0PjNXOoU1h0TjNVOkU1k0WjNSOhU1m0e000N3N1O1O1O2O001O001O1O1O001O100O100O100O1000000O101O01O0001O001O01O01O10O0100000000001O1O3N3M002YiNXOQV1[1Q1eNchNMnVc<\"}, {\"size\": [1280, 720], \"counts\": \"^P^<2mW11M3O000O011O0O2L5M2M5K=C6K3K4N2N2N2N2O1N_OmiNYORV1g0TjNTOkU1k0ZjNROfU1m0]jNnNeU1S1c0O1N3N1O1O2O0O2O001O1O1O1O100O1O100O1000000O1000000000001O0010O0001O10O01000O10001O001O10001O5K4MI9J]Pg<\"}, {\"size\": [1280, 720], \"counts\": \"ghW<1nW11L202M110O1O0O20ON4M3M4L4L6J=C4L3N2N2N2O1O^OmiNYORV1f0TjNVOkU1j0XjNTOgU1l0\\\\jNROcU1n0`jNkNdU1T1d001M2O101O001N101N2O1O1O1O100O1O1O10000O010000001O0000001O001O010O0010O10O1000O010001O01O2O01004NFbPl<\"}, {\"size\": [1280, 720], \"counts\": \"fhW<3lW11M101O0001ON3N2O1N3N3K6K5K6I:G5L3N1N2O1OAjiNVOUV1h0TjNROkU1m0ZjNPOeU1P1_jNlNaU1T1e0O1O100O1O2O0O2O10O01O1O1O1O100O100O100O100O1000001O00000010O01O0010O0100O010O1O100O1O2O00100O102M6JVXm<\"}, {\"size\": [1280, 720], \"counts\": \"^PT<3kW13N001N1O010O2N1O2M4M3L6I7J7I7J3M2N2O1O^OjiN\\\\OUV1d0QjNWOmU1j0UjNUOjU1k0YjNSOgU1m0[jNPOeU1P1e0O1M3O100O2O001O001O1O1O1O1O1O10000O10000000000O1000001O010O0010O01O010O100O10O100001O0O200O11O2OM_XR=\"}, {\"size\": [1280, 720], \"counts\": \"^hR<5jW11N2N1O1O1O1N2M4M2O3L6J7H9H5L3M2O2M10]OmiN[ORV1d0RjNZOmU1e0YjNWOfU1i0]jNUObU1i0cjNUO\\\\U1j0kjNPOUU1o0n0O2O0O101O1N101O1O1O1O1O100O100O01000O100000001O000001O0001O00100O1O010000O01000000010O01O2O0O2MahT=\"}, {\"size\": [1280, 720], \"counts\": \"gPo;6fW1004NO20N200O1N2N2N3M5K5L6J8G5K4L3O1N2N2OZOoiN_OPV1`0UjN]OjU1c0[jNYOdU1g0^jNXOaU1g0cjNWO\\\\U1h0gjNWOXU1h0PkNROoT1m0S1N101N10001O001O1O1O1O100O100O100O1000000000000000001O010O010O010O100O01000001O001O103^iNZObU1d1ON4M2M3M5K6I8I6I;D[_n<\"}, {\"size\": [1280, 720], \"counts\": \"^PT<1nW13L2N1O2N1O1N3K4N3M4L5L6I9H4K4M2N2N2O^OmiNXOSV1d0TjNZOkU1f0YjNWOfU1h0_jNTOaU1k0cjNRO]U1m0k0O2N1O101O000O2O1O1O010O1O1O1O2N1O4L5KSQY>\"}, {\"size\": [1280, 720], \"counts\": \"^P^<5jW10O1O1O1O1N2E;N3M4L4M5J:G4L3L4N1O0O2O1OWOYjNZOgU1e0^jNXOaU1f0ejNWO[U1g0kjNTOUU1j0Q1O2N1O2N2O002M3N2M6K6HRQY>\"}, {\"size\": [1280, 720], \"counts\": \"^ha<8gW10O2N2N1O1O1K5J7L6L5J7J4L3L3N2N1O101N101OWOWjN\\\\OiU1b0^jNZOaU1e0djNXO[U1g0ojNPORU1n0Q1N2N3N2M4L5L3M5InhW>\"}, {\"size\": [1280, 720], \"counts\": \"^Xd<3lW12L4M2N1N2E<K5M4L4L5L6J5L11O1O1N4M2M4M3TOYiN8lV1B`iN4ZW1Kaob>\"}, {\"size\": [1280, 720], \"counts\": \"bhf<3iW15N0SO3fiNNYV1:`iNH]V1o0M3N3L6K3M3M3M101OPOdjN^O[U1`0ljN[OSU1c0XkNVOhT1g0Y1N2O2N101O001O1O101N103L7IWaV>\"}, {\"size\": [1280, 720], \"counts\": \"^`e<6iV17iiNLRV1=fiNFWV1S1M3N3M4K3O1N2O1NPO`jNC`U19jjNAVU1:TkN@mT1>\\\\1N1O101N101O010O10O10O10000000000000O100000000000000001O4J`ag=\"}, {\"size\": [1280, 720], \"counts\": \"bX_<7fW14SOLjiN7QV14fiNNVV1Q1L3M4M2N2N2N2N2O00mNdjND[U18ljNETU19QkNDoT1:XkNAhT1>^1N1O20O01O10O010000O10O10000000000000000O101OO101N3L_im=\"}, {\"size\": [1280, 720], \"counts\": \"c_[<:bW19ihNF[V1S1L4K6J3N3M3M2O0O2OlNjjN@UU1=RkN_OnT1`0WkN]OiT1`0\\\\kNZOgT1e0Z101O001O001000O01000000O100000O100000000010O1O1O1O000010O1O010O1O100O2N101N2O001N3fiNVOYU1n0XjN_OfU1]11O2O0O2N3N2M4L3N4L5J7I;E7H5KXWm<\"}, {\"size\": [1280, 720], \"counts\": \"^XU<2UW1=RiNJgV1o0H:F4L3M10VOoiNGoU19\\\\jN]OcU1b0djNYO\\\\U1d0kjNXOTU1f0SkNWOlT1h0V1O1O1O1O2N101O001O10O0100O01000000000000000000000001O00001O001O00100O100O2O0O2O1oiNYOeT1i0WkNZOiT1g0ojN_ORU1f0cjN@]U1a1100O2O0O1O2N2N2N2N3L6K9F<D?@ZoU=\"}, {\"size\": [1280, 720], \"counts\": \"WPT<><L]V1;[iN1ZV1o0K3N0OVOXjN_OhU1?`jNZOaU1e0l0O2N2N101O100O1O10001N110O00100O010O100001O000000000000001O00001O01O01O00100O1O100QjN]O_T1e0\\\\kN^OeT1d0TkNAlT1a0ojNARU1c0djNC[U1c0YjNChU1Z11O1O2N1O2N2M3N2M3N4L>A:G5J7I6If^X=\"}, {\"size\": [1280, 720], \"counts\": \"Whm;o0mV16L4L3NO0DbiNROeV1k08O2N2O001O1O1O10O1001O001O000010O0100O1000O01000000001O0000000000010O00001O010O1O101N1O102miNUOlT1m0ljNXOTU1n0SjNQO0=mU1^11O1O1O2M2O2M2O3M2M4L6hN\\\\iNN8<Xnd=\"}, {\"size\": [1280, 720], \"counts\": \"VPo;i0TW1a0@5L2M1O2OXOkiNGSV1:QjNCmU1=[jN\\\\OeU1b0l0O2M3O100O100O2O001O00010O100O010O100000000001O00000001O01O00001O001O00100O1O100O102kiNXOmT1j0kjN\\\\OUU1j0[jN_OfU1]11O2N1O2N1N3N2M3M3M>nNRiN1hnd=\"}, {\"size\": [1280, 720], \"counts\": \"UYP<2mW13M1N2O1O6J2N2N1002M2O2M5J\\\\f[1MiYdN6I7J3M2iiN@jT1b0RkN@nT1c0mjN_OSU1d0fjN@[U1e0[jN@eU1]11O1O1O1O2N1O2M3N3M3L<BgW\\\\=\"}, {\"size\": [1280, 720], \"counts\": \"TQY<5jW11ZhNK`W16]hNN`W1:O2OO1000O1000000010O1O1N4M2M2M3NRXT10mgkN5L1N3N1N2O11O001O001O001O2M3MifY=\"}, {\"size\": [1280, 720], \"counts\": \"\\\\Pc<>^W19Hh0YO9G3N1N2OmN]jNKaU14gjNGXU17njNERU18TkNFkT18YkNFgT19_kNAbT1=`110O2O00001O10O010O0100000000000000000000001O00001O00010O1O100O1O101VjN\\\\OTT1e0ckND]T1>^kNDcT1=XkNGhT1;SkNGnT1;mjNGTU1=ejNE[U1`0]jNCeU1[1100O3M2N2N2N3N4K9G4L3M2N5K4K5JgVh<\"}, {\"size\": [1280, 720], \"counts\": \"P``<2fW1a0D>Bd0]O3M2O2MSOoiNNQV1LbjNG^U17jjNCVU1;QkN\\\\ORU1d0V1L3O1O1000001O0010O010O1000O10000000000001O00000000001O01O01O1O100O1O2O0O101O1N2kiNWOPU1j0jjN^OSU1e0ejN@[U1b0`jN@aU1c0YjN_OhU1]10O2N2N2N2N2N4L4L:F=C4L5K2N3M2M3NlVh<\"}, {\"size\": [1280, 720], \"counts\": \"U`[<f0TW19I7J5L3M2N4M2N2N2N2N2O0O[OYjNTOfU1d0hjNVOWU1f0ojNYOQU1b0UkN]OjT1a0YkN^OhT1`0ZkN@fT1>_1N101O1O10O10O1000O10O10000000000001O01O0000001O0010O100000000O2O1O100O1O101_iNA\\\\U1c0\\\\jNAfU1b0QjNAQV1V13M2N4L5K5K;D3N3K:GjnP=\"}, {\"size\": [1280, 720], \"counts\": \"kgW<8fW15L5K4L5J8I3M4L3M2O2M01M2N3H8H7N3L4N101N2O1O001O100O10O11N100O10000000000O100010O01O1O100O10O10000010O00010O1O0O4KPhc=\"}, {\"size\": [1280, 720], \"counts\": \"kWS=2iW16M2O1O100O100O100001O00001O010O00100000O1000O10000000100O010O11O1O02M284ME01N3M2N1N2LjfY=\"}, {\"size\": [1280, 720], \"counts\": \"^XP<1jW19H8K3L3N3L4M3M3M4L3M3M1O1O10N2M2N3M3M3M3J6J5O2N2O1N1O3L4K^Xn>\"}, {\"size\": [1280, 720], \"counts\": \"e`Q<7eW18G8J5K4M3N2M3N2N1O2NO1K5O1K6J5M4M2O2N2N101N2O1O010O1O1O0100000O010000000000001O00001O01O010O1O0100O3LU`Q>\"}, {\"size\": [1280, 720], \"counts\": \"YaQ<6fW1:E<F4M4K5M1N2O1NO2H8I7L4M3L4N200O1O2O010O00100O010O1O010O10000000000000000O1000001N3Lj_[>\"}, {\"size\": [1280, 720], \"counts\": \"YPo;6fW1:G7J4L5K4M3L4L10D<L3M4N3N1N200O2O001O1O010O1O10O0100O100001O000O1000000000010O01O1O2MiQ?Iim@O[iN4dV1MXiN6hV1a01N2O1O1N2O3L5J8Hnn_=\"}, {\"size\": [1280, 720], \"counts\": \"_PY<5fW1;H5K4K7K1N1OM3M4M3M3N2N2O2O001N110O00100O0010O10O100000O1000000000001O000010O01O100O002O00O0100O2O001O2N2N4`iNUObU1l0UjNTOD3XV1`11O2N1N3N2N3M3L6K5J9H3L5J4Mj^S=\"}, {\"size\": [1280, 720], \"counts\": \"W``<=`W16K5K5L2M2NO2J5N3M3O1O1O1O101O0O20O0010O1O1O010O1000000001O0O1000001O0010O01O001O010O100O100O2O0O2O1O1O2N4L2N3N8GO1O2M3N3L3L6K6J9D^oP=\"}, {\"size\": [1280, 720], \"counts\": \"`hf<8dW19H5L6I4M4L3M3N2NN1H9L4M4L30001O0O2O1O010O1O010O100O10000000O101O0000001O0010O0001O010O010O101N101O001O1O001O2O2M3M6KN2N2N3K7J5J7H[gj<\"}, {\"size\": [1280, 720], \"counts\": \"Zhf<=`W17J5J7J3L5M3L2N4MM3A?J6N2O2N1O2O1O001O010O10O010000O0100000000000000000001O001O00100O01000O1O101O0O10001O2N102M1O2N3N8HN3M2L6K4L7H?AS_i<\"}, {\"size\": [1280, 720], \"counts\": \"k_o<3iW16L3M200O2N100O1O1000000O100000000000000O100000000000001O00010O00100O10O10O10000O2O1O1O2N2O1N013L5LM3M4L6I6J:E^_n<\"}, {\"size\": [1280, 720], \"counts\": \"YPh<6dW19J7J5K5K4L4L4N1N1O11N2\\\\Od0H8K6L4LSPX11logN4M4L3M2N4M3M4K7K9G00O2M3M5K5K5K4J8Hbof<\"}, {\"size\": [1280, 720], \"counts\": \"XPm<:cW17J5K4M3M3L5M1N2O0OEaiNTOaV1h0>I6M3N3N101N11O01O0010O00100O1O1O100O1O100O011N10001O001O000010O0100O100O1000000O2O1O1N2O3N2M3RiNTOaV1V11O1N1N4M3L5K6I9F`oa<\"}, {\"size\": [1280, 720], \"counts\": \"a`k>4jW13O2N2N2M4M3M8H4L2N0000O2O3K8H8GUWc<\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"^hk<9cW18K3L5K5K3M4M1N2NN3H8M2O3L3O1O2N101O001O010O1000O100000O1000O10001O00000O100001O01O010O1O10O010001N10000O1000O2N1N3JV`n<\"}, {\"size\": [1280, 720], \"counts\": \"]Pm<:aW18J6K5K4K6K3N2M4MC=N2L4K5N2O2N1O2O0010O01O010O10000O100000O1000000000001O000010O01O010O01000O100O10000O2O2N1O101N1O2O004M3LM5K6I9E__d<\"}, {\"size\": [1280, 720], \"counts\": \"PXn<8dW18I6J6J7J4L4L3M2O00I7H8L5L3N3N1O2N110O1O00100O100O01000O1000O1000001O000000000010O01O010O010O10O10O1000O100O10001N101N2N3L[Xh<\"}, {\"size\": [1280, 720], \"counts\": \"bWT?4kW12M4N1_iNGXU1<_jNL`U16ZjNOdU14XjNNiU12TjN0mU12oiNORV11hiN5ZV1KdiN4^V1KaiN5aV1GeiN5`V1EdiN8aV1@ciN`0oV1O1N200O1O1O1O1O1N2O2L3NQom;\"}, {\"size\": [1280, 720], \"counts\": \"Qh[?3kW14L4M4L4L5ghN[OoV1l000O11N2N3M3M3M5K7H]WT<\"}, {\"size\": [1280, 720], \"counts\": \"fXb=4lW14K3N3M3M2N4L101N100O16I2N3L3M3L\\\\P]10dobN5L2O1N1O2O1N3N001O000101N3M5J7IUVj;\"}, {\"size\": [1280, 720], \"counts\": \"U`c=3kW18H5M3M3L5L5K4L2N010O01WOZiN7YW1N1O6J7FXXY10igfN4K5N2N1O100000000O1001OO1000O1O100O1O001N4K]Ve;\"}, {\"size\": [1280, 720], \"counts\": \"]`c=:cW17I6K5ZiNUOoU1o0miNTOPV1P1jiNUOTV1\\\\1N2N2O_OliNYORV1>ZjN@dU1?`jN@_U1>ejNBYU1:mjNESU17TkNFkT17[kNFeT19_kNCbT1=a1O0O2O001O001O1O00100O10000O100000000000000001O0010O01O10O010O11N101O001fiNKfT15VkN0iT12RkN1nT13kjN1TU15bjNO_U18VjNJkU1V11O1O1N3N2M5L4K;F8H4L3L3N2M3M6J4Lm]a;\"}, {\"size\": [1280, 720], \"counts\": \"fi_=7^V16^jNL_U1:[jNIbU1=XjNFfU1?TjNDjU1a0niNEoU1W1O1N2OoNTjN0jU1NcjNI\\\\U16gjNIXU13mjNLSU11QkNOnT1NXkNOgT11]kNLcT12bkNK_T13fkNH[T18lkNATT1>h1O0O2O001O100O100O010O100000000000000001O0000001O00010O001000O1000000OhiNMeT11XkN5fT1JYkN9fT1FYkN<jT1@UkNb0TV1001O001O01O1O1O2O0O3M2O1N2N3L4LV^f;\"}, {\"size\": [1280, 720], \"counts\": \"mP\\\\=<ZW1;[O^OfiNf0YV1a0O100O17H3N5J2N2O2M4L4M3K8IVnW1LmQhN5diNNbT16ZkNOcT13XkN2fT12TkN0lT13ojN0PU19djNJ]U1]10O2N1O2M3N3M3M4L7H;Fd0[O6IWgQ<\"}, {\"size\": [1280, 720], \"counts\": \"gWS=j0SW15L3N1N10M2M4O100O1O1O1N3N1N20001N2N101O001O001O100O1O1O10000000000001O0O100010O0000010O01O1O010O2O0O101jiNBgT1`0SkNElT1>ojNCRU1g0TjNHmU1U11O2N2N3L3N4I7^Odh`<\"}, {\"size\": [1280, 720], \"counts\": \"iYn<8^W1<L3N000101N100O100O2O0O1O2O0O3N2M`fQ1HhYnN2O1M4M2N2O2N2N2O5K1O7I2ON2N2N3M4K7G`ge<\"}, {\"size\": [1280, 720], \"counts\": \"iiP=3dW19N2O10O2O000O2N100O2N1O2Mafa>\"}, {\"size\": [1280, 720], \"counts\": \"^Xn<7eW18PmNJlm09iQO6om0NhQOc0mm0_OZQO\\\\1bn0fN\\\\QO]1an0eN_QO\\\\1^n0fNbQO[1\\\\n0fNeQOW1[n0kNfQOR1[n0nNgQOo0Yn0SOiQOh0Yn0XOjQOe0Vn0[OlQOb0Vn0\\\\OoQOa0Pn0\\\\OUROb0km0]OYRO>jm0_O[RO>fm0_O_RO\\\\OkKj0hQ1HoROIXm04oROBWm0<X5N2O1N3N1N3KjQn01TnQO6J5L3N2N1E<N2001N2O1O1N2N4L;E5Hg^_<\"}, {\"size\": [1280, 720], \"counts\": \"bPR=7dW1;G8I7H7J7I8H5L2M2OUOXjN@gU1<bjN@]U1=ijNAVU1=PkN@oT1=VkNBjT1<]kN\\\\OeT1d0\\\\1N1O2O10O01O00100O100O10000000000000O101O000010O000001000O0100O10000O10101M3fiN[OTU1f0gjN_OYU1c0`jNB`U1^11O2N2N3M2N3L5L4Kc0]O:Dcn\\\\<\"}, {\"size\": [1280, 720], \"counts\": \"QhP=:cW17I6J6K4L9G5J4N2N^ORjNXOiU1c0^jN@]U1>ijN@UU1>RkN^OmT1a0WkN]OiT1`0\\\\kN]OdT1b0_kN\\\\OaT1d0]101O1O010O1000O1000000000000000000O1000001O01O01O00010O1O100O100O10001O001O1O2N3^iNYOeU1l0liN]OTV1X11N2N1N4M3M5K5K6J7I4L4K3N3M4K5LhfV<\"}, {\"size\": [1280, 720], \"counts\": \"jgk<:cW17I5L4J6L3M3M3NH8M3N2N2O1O1O2O1O010O010O0010O01000000000O10001O0000000000001O001O010O1O1O100O10000O1000010O1O2N1O3Na0^O01N2N2N3M3M4L5K4L3L5L3M4K4M2MZg[<\"}, {\"size\": [1280, 720], \"counts\": \"jog<;bW16J5K5K5K6K3N2M2OO1F:M3N2O2N101O001O1O01O10O1000O01000000000O10000000001O00001O010O00100O1O01000O011O001O00001O1O101N<D6KN2N3M3M4K4M4L3M5K2N4L3L3N2N2MZo\\\\<\"}, {\"size\": [1280, 720], \"counts\": \"lWd<;bW16I8I5K6J6L2M3N3M0000A?L4N2N3N101O001O010O1000O01000O10000000000O20O0000000001O001O010O100O10O10O101O00001O001O1O5Kb0_O0OO2O2M1N5L4L4L5K4L2N4K5L4L8FTWc<\"}, {\"size\": [1280, 720], \"counts\": \"k_e<;aW17I7J5K6K3M3N3L1001C<N2N2N2O101O1O0010O0100O10O1000O10O1000001O0000000000001O0010O01O1O10O10O01001OO11O001O00001O1O3N>B0000N2N3M4L6J4L3M3N3L4L:E3NQoa<\"}, {\"size\": [1280, 720], \"counts\": \"kWi<;aW17I6J6K5L4L4M2N2N1OA?M3N2O101N101O010O010O100O010O10000000O100000000000001O00010O00100O1O010O1000O11N1001OO2O00100O2O1O;D20O0O3L4L2M4M4L3M4L4L3M2N1O2M5JW_Z<\"}, {\"size\": [1280, 720], \"counts\": \"UXn<1iW1<F8I6I6L5K4L3N00O0O3G8M3O1N3O0O101O1N10100O011OO10O01O10000O101O0001O00000001O00001O10O010000O10O10O11O0001O001O1O103L8SiNiNaV1^1NO2M3M4L5K3M3N2M2N3M3M3M2N2N3LR_U<\"}, {\"size\": [1280, 720], \"counts\": \"RXS=7eW19I5K4M4L3M4M0000001OE;I7M3M2M4N2N2O1O010O01000O1O0011O0O10000O101M2O2N100N201N1O101N1O2N2O001O1O1O100O101O001O1O1O3N5SiNPO^V1^1LO1N3M3M5K4L3M4L4L4L3M3M4K3N3LofQ<\"}, {\"size\": [1280, 720], \"counts\": \"UP\\\\=2iW1;J3L4M3M6J1O1N2O1O2O2M10mNmiN8PW1N1Lenh0L`QWO1N3N1O2N1O2N2N2O0O2O1N2N110O1O101N20O01O101N9TiNnNXV1`1NO2M3N2M3L5L4L3M3M5K4L3L5L4KoVj;\"}, {\"size\": [1280, 720], \"counts\": \"Qh_=9cW18I6I7J6J6K4M3L5L4L4LWO\\\\jNYObU1b0gjNZOYU1e0kjNWOWU1g0Q1N201N101O001O010O100O10000000000O01000000O10001O01O0100O0010O0010000000O10001O0000100O3M3UiN]OTV1X1O02M4L3M7I6J4L3M4L4K4M5J4Lmfg;\"}, {\"size\": [1280, 720], \"counts\": \"QP\\\\=7cW1=F6J5K5K7J9H1N3NAkiNUOTV1g0SjNVOmU1h0]jNQObU1n0h0O1O1N201N101O001O010O10000O10000O10O101O0000O100001O00001O01O0100O0100O010000000001O1O100O5^iNSOgU1e1ON3M3M5K8H7I4L9G7H6Io^P<\"}, {\"size\": [1280, 720], \"counts\": \"k_T=9dW15J5M3L6I?D1N3OO1O1O1D<N2N2O1N2O1O1O1O1O1O1O10000O10000000000000O1O1O100O1O1O100O1O2N1N200O2N2O001O1000000000001O2O<D0O02M2N3M?A6J9G7H]_Z<\"}, {\"size\": [1280, 720], \"counts\": \"iog<5iW14M2M4L6hhN\\\\OjV1P1L2M3N2M2O2N10J7N2O1N2O1O100O1O1O100O1O100O01001O0O02O0000O10O2N100O100O101N1O1O101N10001O010O0010000000002O8G2NO2N2N3M3M5Ki0VOgof<\"}, {\"size\": [1280, 720], \"counts\": \"m_[<6hW13L4N3K`0B1N2M2O1ON2O1N3001N2O0001O01O001OO2O01O01O010O10000O1001OO100000000001O0000001O000100O010O0100O01000O2OO12N4^iNhNTV1c1O01N2O0N5K7I]PV=\"}, {\"size\": [1280, 720], \"counts\": \"loS<1gW1;J6Jd0\\\\O3N1O2N0O3OAliNROTV1n0>10O101O0O10001O001O0000010O10O01O1000000O100001O00000000001O00010O0010O0010O01000O101O001O2O1Nh0YOO1O1O1N5Kf0YOb0]O__]=\"}, {\"size\": [1280, 720], \"counts\": \"gnl<7gW15M4K4M1O001O001O000000001O0001O0000010O001O0100O100000000010OgiNZOWU1g0djN]O\\\\U1h0UjNBlU1Y11N1O3M5K7I:F6J<D5I\\\\o_=\"}, {\"size\": [1280, 720], \"counts\": \"^Pe;3lW11^Oa0N4N8F>C3L3M2O101NZORjN]OmU1c0[jNUOfU1k0h0N002N1O1O2N101O1O001O100O100O101N100O2O001O1N101O001O010O0010O01O010O0100O001O2O0O2N2O2\\\\iND_U1?VjNJhU1;kiNMVV1n02N3N2M4L8H9F?B8GQgh=\"}, {\"size\": [1280, 720], \"counts\": \"Shc;95JVW1d0K5K6I?C3M3M2N100000YOTjN[OlU1c0YjNZOgU1f0]jNUOcU1l0i0M201O0O2N1O10001O1O100O100O100000000000O100001O01O0000010O01000O010O1000001O001jiN\\\\OmT1d0ljNBVU1>ejNE\\\\U1?YjNFjU1W12N2O2M4L7I9G>A8HQ_l=\"}, {\"size\": [1280, 720], \"counts\": \"g`g;1ZW1e0K4N3M4L5L8G;G3L2O001OVOXjN]OhU1a0]jN\\\\OcU1d0`jNYO`U1f0cjNXO]U1f0hjNWOXU1f0njNWORU1h0QkNUOQU1i0S101O001O1O100O10000O1000O1000000000000001O00001O0010O0100O0100O11O01O1O1O100O1001h0WOO3L4L5L9F6J4L5J3N5Jnfc=\"}, {\"size\": [1280, 720], \"counts\": \"\\\\`l;4dW1?`hN^OPW1l0K4L4N3M>B6K2N1O1OWOYjNZOgU1d0^jNYObU1d0djNXO]U1f0gjNYOYU1e0ljNWOTU1h0R101N101O0O2O100O100O1O010O10000001O00000000001O01O0010O01O001O100O100001O0000010O101_iN[OcU1h0liNFUV1Q13M4M6I6J5K9G3L4LmVa=\"}, {\"size\": [1280, 720], \"counts\": \"`XP<:cW1>B3K4M5M8H<D2O1O1OXOniNCRV1;ZjN\\\\OeU1d0^jNYObU1f0cjNTO_U1k0k0O1O2N1O2O001N110O1O10000O01000O1000000000000001O01O00001O10O0100O010000000000010O0100_iN_OaU1h0kiNDVV1S11N3M5L8G7I7I5K6Icf^=\"}, {\"size\": [1280, 720], \"counts\": \"ePY<3fW1>F7G7K5L3M5K6K7I3M1OZOVjNXOiU1e0]jNXOcU1e0cjNWO^U1g0gjNVOZU1g0P100O2N2O1O1O1O010O100O1000O01001O0O100O100000000010O00100O010O1000O010000010O1O100O3N>nhNjN\\\\V1^11N4L4K6J5K4L7I4L4JcfT=\"}, {\"size\": [1280, 720], \"counts\": \"cXd<8cW19H8I6J5K4N2M4M4L5K4LZOTjN[OjU1a0`jNZO_U1e0ejNXO[U1d0ljNXOVU1f0mjNXOSU1g0T1O001O1O010O1000O10O100O01000000000001O00000001O010O0100O0100000O11O0000010O01O112M7I:GO2M3L3N4K4L4L5K4K4MaVh<\"}, {\"size\": [1280, 720], \"counts\": \"dXn<9bW1:H5K6J7J3M3N3M2N6J\\\\OSjN[OhU1a0_jN_O^U1`0fjN_OXU1?ljN_OUU1?PkN[ORU1d0V1O001O001O1O01000O10O10000O1000000010O000001O0000010O100O01000000000000O1001O101N6jhN\\\\OdV1W1JO1O2M4L3M5K7I2O3L2N3M5K3L6JXf[<\"}, {\"size\": [1280, 720], \"counts\": \"f`T=7eW18H7K5I7K6J4M2M2O@miNWOPV1f0XjNWOgU1e0k0O2N100O110O010O1O010O01000000000O100001O0O1000010O1O001O010O01000O1000O1001O00001O101O2Mf0[O00N3M2N3M4L4L5L4K5K3M2N3M3L[fV<\"}, {\"size\": [1280, 720], \"counts\": \"RYS=4fW1:H7J6J4L6L3L4M3M1O]OUjNUOhU1h0\\\\jNZObU1b0o0O10001O010O10O1000O10O10O10O10000000000000001O001O010O010O10000O0100001O001O1O00100Oa0_O3N1N03L3N2M4L5K4L4L4L2N4L3M3M3LoeV<\"}, {\"size\": [1280, 720], \"counts\": \"bhi=7gW13M3N2O0O2N1O2N100O10000O101O001O010O1O10001N1000000O201N00O13Ma0_O2OO1O1N3N1N3M3M3M3M4L2N4L5K5J4M3L4Li]Z<\"}, {\"size\": [1280, 720], \"counts\": \"SbU?5jW13M2N2N10O11N1000001O0O010O1O11O4L3M4Kj]U<\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"g`P?2nW17H6K2N4M2N5L3M4L3M01O1N1O1N2N3N4K5J7J4LmUY<\"}], \"areas\": [0, 0, 0, 0, 0, 3870, 4417, 4583, 4963, 5445, 5973, 6623, 6874, 7291, 6737, 5963, 5980, 5563, 5399, 4995, 4607, 3842, 2490, 990, 2168, 1717, 1427, 967, 1326, 2020, 1325, 1844, 1922, 2098, 1934, 1847, 1127, 1620, 2049, 2194, 2125, 1419, 2262, 2139, 2105, 2028, 2122, 1684, 2170, 2012, 1721, 1862, 765, 638, 674, 856, 1101, 1850, 1862, 1946, 1486, 1596, 1831, 1915, 1895, 1901, 1912, 1839, 2330, 1259, 1103, 1117, 757, 947, 1112, 1260, 2321, 2220, 2053, 1937, 1890, 1076, 451, 2163, 1897, 1820, 1063, 514, 885, 966, 802, 1023, 1531, 1420, 1607, 1556, 1240, 967, 1480, 364, 0, 1239, 1464, 1271, 552, 320, 587, 670, 1870, 1546, 1274, 1476, 659, 141, 1947, 1973, 2075, 2025, 2176, 2244, 2113, 2110, 2161, 1574, 1281, 2112, 2146, 1886, 2227, 2339, 2056, 1152, 1845, 2130, 1999, 1944, 1957, 1913, 1910, 1965, 1872, 1845, 1375, 249, 0, 511]}, {\"video_id\": 0, \"category_id\": 1985, \"bboxes\": [[277, 751, 21, 40], [269, 757, 22, 42], [252, 764, 23, 46], [238, 772, 18, 58], [235, 783, 22, 59], [243, 787, 25, 55], [0, 0, 0, 0], [262, 771, 17, 56], [256, 846, 39, 85], [236, 869, 39, 95], [258, 840, 59, 107], [324, 803, 75, 82], [370, 822, 76, 89], [335, 846, 66, 86], [282, 833, 37, 77], [260, 796, 34, 72], [238, 773, 31, 66], [228, 747, 24, 64], [247, 709, 27, 49], [0, 0, 0, 0], [0, 0, 0, 0], [293, 708, 16, 43], [271, 695, 20, 29], [281, 658, 17, 26], [285, 653, 16, 25], [0, 0, 0, 0], [288, 651, 11, 24], [286, 683, 15, 39], [287, 719, 24, 47], [281, 632, 25, 39], [303, 617, 65, 38], [288, 610, 44, 29], [224, 632, 70, 27], [236, 660, 30, 39], [271, 732, 19, 36], [0, 0, 0, 0], [269, 664, 27, 40], [0, 0, 0, 0], [393, 464, 36, 85], [403, 488, 30, 80], [401, 500, 35, 84], [406, 522, 36, 63], [410, 518, 41, 63], [421, 504, 29, 40], [265, 516, 56, 73], [280, 497, 46, 52], [291, 473, 27, 39], [265, 460, 23, 38], [266, 535, 50, 36], [273, 535, 37, 40], [0, 0, 0, 0], [299, 557, 13, 25], [302, 533, 28, 47], [312, 532, 18, 26], [297, 551, 27, 36], [289, 554, 17, 34], [272, 519, 34, 62], [251, 474, 57, 104], [248, 461, 61, 117], [258, 491, 48, 80], [253, 478, 52, 85], [236, 449, 69, 115], [252, 464, 49, 99], [263, 531, 34, 36], [248, 516, 46, 54], [248, 463, 48, 112], [246, 515, 52, 45], [242, 464, 48, 108], [241, 465, 50, 111], [246, 516, 58, 50], [250, 463, 53, 109], [263, 453, 49, 123], [265, 530, 71, 48], [254, 515, 62, 61], [247, 455, 56, 120], [245, 468, 57, 114], [242, 486, 64, 96], [227, 497, 75, 89], [226, 480, 73, 127], [235, 513, 52, 85], [235, 526, 53, 69], [241, 525, 49, 68], [248, 498, 47, 107], [260, 546, 45, 75], [254, 499, 47, 90], [254, 416, 60, 159], [286, 491, 36, 78], [297, 535, 24, 45], [252, 499, 67, 81], [237, 452, 73, 123], [246, 461, 57, 145], [238, 474, 66, 101], [251, 490, 56, 101], [268, 508, 48, 77], [291, 493, 39, 86], [291, 462, 38, 115], [306, 431, 26, 49], [298, 470, 34, 98], [300, 496, 35, 78], [307, 436, 33, 144], [301, 420, 34, 159], [300, 410, 34, 165], [289, 419, 45, 154], [283, 463, 42, 89], [269, 508, 50, 57], [278, 514, 46, 66], [292, 527, 31, 66], [297, 516, 31, 81], [291, 530, 41, 94], [276, 586, 61, 74], [274, 590, 75, 60], [337, 622, 29, 23], [325, 573, 54, 80], [296, 590, 70, 63], [272, 576, 46, 64], [259, 526, 46, 111], [252, 528, 58, 102], [272, 521, 44, 102], [270, 515, 40, 100], [254, 530, 48, 72], [252, 498, 52, 105], [259, 507, 52, 101], [267, 524, 46, 85], [267, 520, 47, 83], [265, 503, 59, 103], [271, 508, 53, 99], [267, 500, 45, 93], [291, 559, 14, 22], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [247, 533, 25, 61], [239, 520, 40, 88], [241, 515, 42, 99], [242, 526, 42, 87], [247, 551, 44, 67], [265, 518, 40, 105], [268, 544, 43, 79], [279, 550, 38, 73], [286, 535, 36, 99], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]], \"score\": 0.7387754917144775, \"segmentations\": [{\"size\": [1280, 720], \"counts\": \"doj:`0\\\\W18L2M2O2N3M2N2N1O1O10O2N3M2O3L4L3M4L3M2M5La_]`0\"}, {\"size\": [1280, 720], \"counts\": \"ko`:8eW1?C9G3M3N001O1O1O0010O1O2O0O4L5K5K2M4M2M4K8HZWf`0\"}, {\"size\": [1280, 720], \"counts\": \"Qhk9f0XW16K4M2M3O1N1O1O00001O1N101N1100O2N3N1N4L5J7Ic0\\\\OlVZa0\"}, {\"size\": [1280, 720], \"counts\": \"]XZ95eW1;J9G9H5J6K4L5K3M2N02N3M6J8H8H9F:E7I\\\\nQb0\"}, {\"size\": [1280, 720], \"counts\": \"j`V9j0oV1>H3L4N2N2N1O1O1O1O1O1O0O2O001O02N3M4L7I9F:F`fPb0\"}, {\"size\": [1280, 720], \"counts\": \"^a`97aW1=D;I6J5J5N2N2O1O001O0010O01000O1N2O1O2N4L?@6J7I4LUnba0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"WXX:2mW1;E6K8G6L3L3M2N4J:IO02N0O0102N009`N_iNh0Z^Va0\"}, {\"size\": [1280, 720], \"counts\": \"gjP:V1jV14L5K4N1M3L4L3O0O2O1M2O1M3L3N4OO01N1O1O2N2O001O0014K2N1O4L4L2N5J4L8H6I6I7G9H;EmSa`0\"}, {\"size\": [1280, 720], \"counts\": \"WlW9j0mV1=F:C=G9J6K3N2M2O1O100O001N1010O01O000O20O00O2O1N11O01O000101N3M3M3L5L6Jc0\\\\Od0^NciN:YW1EWRZa0\"}, {\"size\": [1280, 720], \"counts\": \"h[S:`0PW1i0YOc0E;I3M5K4M3M2O1O1O1O1O1O1N20O01O1O001N1O1O101N10010O010O0O101N10001O0002O0O1O001O1O1N2O2N2M2O2M3N4J5L6J3QOYjN@jU1<[jN_OkU1:ZjNAnU17[jN^OnU1<P1JXde?\"}, {\"size\": [1280, 720], \"counts\": \"oie<3jW17K6K5J3N5K4K4L3M3M3N2N1M4M3L4M2N2M3M201M2M4N101N1O2O001O000010O000000001O001O001O1O001O00001O1N2O1O001O1N101O1O1O001O0O2bNPjNj0QV1SOUjNi0mU1TOUjNk0mU1ROUjNl0PV1lNUjNS1\\\\V1O2N2O1N2O1O2M2N5K7GSU_<\"}, {\"size\": [1280, 720], \"counts\": \"bZ_>3iW18I6K5L4L5L2K5L4N2L4L5K3N4K3M4M3O0O2O0O2N10001N010O1O100O001O01000O01O100O001O1O010O1O1O1O1O1O010O1O1O1O1O1O1O1O1O1N200O1O1O1N2O101N2N2N5J<E2N2M2O2M3M4KV\\\\d:\"}, {\"size\": [1280, 720], \"counts\": \"kbS=<bW16L3L3N5L8G4K4K4N3M3M3L3M2O1M3N2O1O1O1N2O1N2N2O010O02O0O010O1O100O0010O01O1O100O1O100O1O2N1O1O2N1O2N100O2N2N2M2O1O2N2N2N2N1O2M2WOYiN6kV1C]iN8XW1L]c\\\\<\"}, {\"size\": [1280, 720], \"counts\": \"jZQ;g0VW1:F7E9F:M2N3N100O1O1O1N1O2N2O001O001O1O0000001O000001O10O01O002N3M3M4L7H>ClTc?\"}, {\"size\": [1280, 720], \"counts\": \"WiU:l0nV1:K7I5L3L4L4N1N1O1N2N2O1O00001O01O0010O000100O2N2O2M3M4L4L8H5K5K5K<B]]b`0\"}, {\"size\": [1280, 720], \"counts\": \"fXZ9a0UW1`0G7J5M4M1M3N1N2O1O1O1N2O010O10O01001O0O101N2N2N3M3M4L4L4K:G6XOofaa0\"}, {\"size\": [1280, 720], \"counts\": \"Shm8:dW19F8I5J6I6L4K5K4N2N2001O00O0O20O00001O2N4L?eNXiN`0_W1D6IQoVb0\"}, {\"size\": [1280, 720], \"counts\": \"n^e9?`W14L4M2N2M1N2J5O1L4O2N2O10O0011N11K5K4N3N1N2N2N2L4L4N3Ima[a0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"Xn^;;7HSW1f0K5L6N2L2M00N2O02O1O000100O?A;BPio?\"}, {\"size\": [1280, 720], \"counts\": \"Q^c:<bW14L3L4N2N10001O0O1000010O00001OO012K7J9_OVZf`0\"}, {\"size\": [1280, 720], \"counts\": \"jlo:9`W19M2O2N101N1O1O01O1ON1012M4M2M6I7Jnb]`0\"}, {\"size\": [1280, 720], \"counts\": \"hlT;1kW1=D4N4L1N11001O10O001O2N1O0O1O2N^kY`0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"`dX;7hW16J3M4M1M3000O0O2O2K5L`[\\\\`0\"}, {\"size\": [1280, 720], \"counts\": \"bUV;510eW18N2M3J6K4M3M3N1N2N12O004J4K9_O]jY`0\"}, {\"size\": [1280, 720], \"counts\": \"f^W;6gW17K6K5K3L6K2M3N1N3N2N1O1O1O010O02N3L4M3L3M4K7F<FgXm?\"}, {\"size\": [1280, 720], \"counts\": \"lko:442^W1;J4O4M3L3K3O1001O1N10000O1O2ON1000000001N2O4K4K5KdcS`0\"}, {\"size\": [1280, 720], \"counts\": \"Q\\\\k;1kW1211O101N6J10JchNJ[W1771N4MO0O11jhNEgV1>;7J1N1O10O0000010O1001N001O10O10O100O1000O1000O02O0O1O100000000000000001OO1O2N1O2N1O2N3K300K6O2O0OmW10cld=\"}, {\"size\": [1280, 720], \"counts\": \"_cX;6fW16K4N1O2O0O100O100O101O0O110O0O2O0000O10000001O001O1O1O000000001O01N2O001O0O101N10100O3]OjhN6k[T?\"}, {\"size\": [1280, 720], \"counts\": \"Vdh86gW17J3M2M4O001O10O1O00O02O001N100001O00000O100001O00002NO10O101O10L201001OO10001O0O10^OhhN<XW160O10O10000M^OjhN`0VW152N0_OihN9VW18O22N1O1_OhhN8YW164BchN3_W1JahN6cW111O00N21MOL^hN2aW1O`hNObW130K`hN3gW1NPdb`0\"}, {\"size\": [1280, 720], \"counts\": \"WeW9=aW16K3M2K5L3O10O01N2O010O0001O1O1O002N2L3N3N3N2M3M3M2M3N1M3N20obea0\"}, {\"size\": [1280, 720], \"counts\": \"__c:4cW1`0F5L4L3N2N1O101O01O0O100O100O2O1N4K8XOZag`0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"ll`:;eW19G4K5K4M2N1O1N101N1000001O00M3M3N3M3M4L4K4M3M3N1N2N2OPS``0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"of[?4gW15O01003M0O2N3L3O011M1O10015J4L2]jNoNQT1V1ZkNlN^ON12UU1W1YkNkN@M11TU1^1UkNfNNJnT12hjN]14lN0FWU10fjN_11kN3E]U1o1;O20000OKUNVjNi1kU1WNUjNi12WNgU1j162N01N2ASjNjNoU1T1c0_OYiN_OjV1N[iNNXX[;\"}, {\"size\": [1280, 720], \"counts\": \"XYh?2jW10hNOejN6UU1:_jNGBK\\\\U1d0njNC_U1d0ZjNB`U1f0YjN\\\\OeU1h0WjNUOMF2O^U1X1ajNkNN03NO0^U1m1<1XjNoMaU1P27]OTjNROnU1n0SjNPOnU1`11O101O010N1O2O1O011M3N001O3MOM3QO`iNGgWV;\"}, {\"size\": [1280, 720], \"counts\": \"]ie?1j_22ShN2VhNNbW1=H5M5K7H20O01ViNPObV1W11G[iNVOeV1S1N2N1O1O10N110K42NN3ON12M02O3NO01O3O0N12O2J6WOZiN1U_R;\"}, {\"size\": [1280, 720], \"counts\": \"\\\\Ql?3mW1OM2205ME1ahNOc_22\\\\`MO^W1<J8J4ihN[OmV1o0M4L3L4M1N2N02ON2OcNfiNV1[V160O2O2K401L4M3O00012M3N4K2M5K6KWgi:\"}, {\"size\": [1280, 720], \"counts\": \"TQQ`0424\\\\W1;N1O1N2N10002N2O01O1ON2M300O01ROSiNi0nV15O1N1N2O5YiNnNWV1a1L1O1N2N2O1N22N1O6I4M3M2N9F3L6L1N2O8H6BahNNje_:\"}, {\"size\": [1280, 720], \"counts\": \"^h^`085JXW1<43L2N2O1O2N1O1O2M3N100O010O100O102N2M2O0O10O1O100O2O1N3E]h_:\"}, {\"size\": [1280, 720], \"counts\": \"Zi[:=\\\\W18L4M3L4M2L6K4N1O2N3L3N2N2M3N1O2M2O20O3M1TNTjNO1[1cV1I3M2O2M2N3M2NO1O2N11O10O01O0001O000000001O01O1O0000O12N2N7I104K2N1M3Mkm`?\"}, {\"size\": [1280, 720], \"counts\": \"k`n:2mW12YhNNgW10@6hhNNXW16hhNFXW11hhN11MVW13ihN0]W12ahNN]W1921MGbhN6]W1KchN2`W19K3002CdhN2\\\\W1MehN1XW15fhNKZW12fhN2YW1:01O0000O10N20O2O0001O2O0O00100000000O02O001O0O2N3L3M3MZhZ?\"}, {\"size\": [1280, 720], \"counts\": \"RW\\\\;6gW1200]hNI^W1>N2N2N1O102N3M2N2N10O1O10O2O1OO01002N1O1N3M3I8K4Gchd?\"}, {\"size\": [1280, 720], \"counts\": \"cf[:<bW14L6L4K3N2N2N1O1O1O0O100O2OO10001O101M3N2N7G8]O\\\\Yj`0\"}, {\"size\": [1280, 720], \"counts\": \"RQ]:5eW1;J3M3N1O0O2M3000000N100001O01N0013M101N3M3M2N011N0001O01O1OO1O011N100000000101NM302NO2N3NO1O20N4L2F`hN1T^h?\"}, {\"size\": [1280, 720], \"counts\": \"Vie:`0\\\\W18J5L4L001O1O1O10O2OO101N2N1O010001O000O7J4M0WOohNa0RW162NO2N1O11O0O1YOmhNa0TW1\\\\OnhNe0VW101N3M;E3MXfn?\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"hYf;8`W1<I301O1O000010N2O2N4L1O8GSVl?\"}, {\"size\": [1280, 720], \"counts\": \"gQj;:_W1:F8I5O1M3K2300N3O20O10O2M2O1O1N2O000O2O1N2O1O2N1L5N2M6KcfU?\"}, {\"size\": [1280, 720], \"counts\": \"SaV<;`W15M3M4M2O1O1001O001O0O2O1L4L2O1M4N4LYgU?\"}, {\"size\": [1280, 720], \"counts\": \"Rjc;5bW1<K3M2O2L3O1O00AnhN2PW1MViNGL3lV17biND_V1;d0012M0N1002N1O2M03O0000O10001K[hNNdW10oV]?\"}, {\"size\": [1280, 720], \"counts\": \"biY;169TW1?K4N1O001O001O0O2000O1O2N1O1N>ZO_hN2lW1JjeS`0\"}, {\"size\": [1280, 720], \"counts\": \"``d:j0RW1a0B3L3N101N2O010O0011N10N1101N1O003N1N1O1O102M1O2O1O0O3N0O2O1N1O<WOehN4fW1KUfS`0\"}, {\"size\": [1280, 720], \"counts\": \"RWj98gW16H?Bc0^Oo0QO7K1N2N001O100O00000100O001O2eMmjNe1nU1I:F5K2N4M1N5L4K2O1N1O01O1O1O010O00001O000000O11O1O01O0001O0000010O0006AjhNHaW154I[VQ`0\"}, {\"size\": [1280, 720], \"counts\": \"e^f9>^W18J6K4M2M9I8G5Lf0YO9G4L4M0002N1O100O010N3Mg0YO6K2M6J5K1O3N1N4M001N4M1N10000O01O10O000010O01N2OO101O01O1O01O01O00010O2N1O2MM\\\\OWiN6\\\\W1NXno?\"}, {\"size\": [1280, 720], \"counts\": \"kPS:6aW1<E;H7I9F7K4M4M2O1O1O2N1O2O010O0O10001N2O0100O010O10OgNYjNWO4h0dU1NdjN2]U1LejN3\\\\U1JhjN4YU1JhjN7YU1FhjN:ZU1CgjN=^U1ZOgjNf0YV101O1O010O010O0000001O0N3\\\\Oc0OmfS`0\"}, {\"size\": [1280, 720], \"counts\": \"\\\\gl9c0RW1b0G:[iNbNWV1j1L0N11O5K1O0002NO11N10N2O101O2M3N1N3N2M6K2N2M101N10O00010O1O0100O000001O01O010O1OO2O010O02N000O2K=]OWoT`0\"}, {\"size\": [1280, 720], \"counts\": \"S^W97iW12N4L2N3M2N2M3N3L4M3M3N1O002M3M2O2015KO22J30O01OO3OOO1O012M1OO1O1O2N1O2M1O2O1N2O1O001O0O10O010O00100O01O01O001O001O01O1O01O001O01O0O106I8[OghN1YVV`0\"}, {\"size\": [1280, 720], \"counts\": \"i^k9a0ZW1;G8K6I;F7I6J4L4L4L4L2N2O0O1O0001mMQkNT1oT1jNYkNo0hT1nN]kNo0cT1nNdkNo0]T1nNhkNo0gU1M3N5K1N2O00O01O000000100OO2N10010O001O0001O1OO2O0004VOlhN?^W1JonY`0\"}, {\"size\": [1280, 720], \"counts\": \"PYY:1lW1<_O:L30O01O010O1O010O01O1OO2N20O000001O0001O10O0O2O0100O002N2N3^OhhN7YW1EjhN:cW1Gdn^`0\"}, {\"size\": [1280, 720], \"counts\": \"f`f9b0YW17H8H7N2O02O1N100O101N101N2O000O3N1OO0001O10O0000001O01O01O01O001O0100O0001O00001O001O1O2N;Cbfb`0\"}, {\"size\": [1280, 720], \"counts\": \"n`f99^W1=G8D:B>VOj0H8I7M3M3N2O1O001O001O010O001N11O3lMPlN2QT1D]lN8fS1\\\\OdlNb0`S1UOhlNh0[U1M2N0010O10O0000000001O0010O001OO1001N2O1N:DahNGlW1OQV``0\"}, {\"size\": [1280, 720], \"counts\": \"jPd94jW14M5K2O1N1O1O1O2O0O2N2M3N1O1N200O1O10000N2O2O00001O0O11O01O1N0100002M110O0001O000001O01O1O1O0O3J6I7H8K8G_g]`0\"}, {\"size\": [1280, 720], \"counts\": \"kP_9`0\\\\W16L3M3M2L5M2M4L4M3L3M4N1L5N1HVNWjNn1dU19N201N1O2K4K5N2N2N2OeN[kN^OcT1?ekN]OYT1a0okN[OPT1c0UlN\\\\OiS1a0[lN_OdS1`0^lNAaS1=blNB]S1=elNC[S1;glNEYS1:hlNGWS19ilNGWS17klNIWS14jlNLXS11ilNOYS1OglN1\\\\S1IglN7]S1BflN>aS1ZO`lNf0bS1VOalNi0[U10101]OkhN7dW1I^fg`0\"}, {\"size\": [1280, 720], \"counts\": \"Vi]94aW1`0H5K4L4M2M4L3M4M3M3M2N201N1O2M2N2M4BPNfjNT2RU1a0N2L5M2O1M3O2O1NoN\\\\kNhNcT1j0PlNROPT1l0SlNSOmS1j0VlNWOhS1h0ZlNXOfS1f0]lNZOcS1c0_lN^O`S1?clNA]S1=elNC\\\\S1:flNF[S17glNI[S11ilNOYS1LjlN4XS1CPmN<ZS1XOhlNg0YU11O0010O1O1O7I7H\\\\^f`0\"}, {\"size\": [1280, 720], \"counts\": \"jPd94iW16K3N2N101N1O1O101N1N101O1N200O0010001O3K5L200O1O1O000010O10N1O0110000O1O00000O1O2N101O001O1O000000N2K5M3N3L5L3L6L4JeWV`0\"}, {\"size\": [1280, 720], \"counts\": \"gPi9a0[W16K5K4M4J4M4J6K4N3L4L3M4L4N1K6K5L3M3O1M3O2M2O1N2O]NbkNF]T12okNKPT12WlNLgS14ZlNLfS12^lNLbS12flNIZS14ilNKWS14jlNMVS11llNOTS1NnlN2SS1LnlN4SS1JnlN6US1EmlN;US1AmlN?YS1XOjlNh0WU100000001O0000010O00001N<E<CW^W`0\"}, {\"size\": [1280, 720], \"counts\": \"fXY:d0YW15K7fM\\\\OilNm0RS1^OclNf0[S1^O]lNh0bS1_OPlNi0nS1_OakNl0^T1\\\\1O010O001O00001O0001O010O2N102M2WNgkNH^T1KmkN1UT1TO\\\\kNIn0Q1kU1I2N1O001O01O0000010O00000000001O0001O001N7J6I`Vl?\"}, {\"size\": [1280, 720], \"counts\": \"]i[::dW13L3N3N1O2N1N2O2O1N1O1O101N1O1O01000O10000O2N100000O02O000000001N101O0O2O000O101O000000000O10001O0001N110O1O010O1O1O1O3L2O001N111N1O0O103J7H?C_Vn>\"}, {\"size\": [1280, 720], \"counts\": \"TQn9>^W1:H4L4M2M2O1N2O2O0O1O101N100O1O1O2N1N201M2O1O1O1O1O1000O10O101O0O010000001O00000001OOTOPjNJPV15RjNJnU15UjNIlU16XjNEiU1;YjNCgU1=[jNAfU1>]jN^OdU1b0`jNYOaU1g0fjNTOYU1k0gjNUOYU1j0o001O1O1O1O1N3M2N4L`Wg?\"}, {\"size\": [1280, 720], \"counts\": \"jXe9c0UW1=I4E;^Oa0mNS1H8L4N1O2N1O20O001O000010O01O00100O2N1UNlkNHVT1E]lN9eS1C`lN;cS1[OflNb0_S1nNWkNO_1S1YU1N2N001O2N000O2O0001O01O0O10O1010O0O200O4LO10O6K1O4K6J3MW^W`0\"}, {\"size\": [1280, 720], \"counts\": \"ifb9?]W16M7J3M:diN^OfT1T2J3N2N2N1O2N1N10O2N100O2N1O2O0O1O4M7Il0SO5K7I6K2M1O2N4K02N1001O1O0000001O01N1O1002OO0000O2O0000002N1O0O3N3L]fX`0\"}, {\"size\": [1280, 720], \"counts\": \"Zo^97gW18I4L3N6J2O3L3L4M6K3M4K4K5M2N1O1O1O1O1O2M2O01O5mMSjNi1XV1J2N4L1N2O0O1O2O1N2O0O3M2O2M2O0O2O1N100OO2O001O01O00001O000000O11O1O002N1N2N3K5EefS`0\"}, {\"size\": [1280, 720], \"counts\": \"hWl81mW17I201N2N1O110O10ON3O001O1O2N2N2N2O1O2M3M3N2O0O10000O10O010001N10000O01O001O000O1O100001OO100O1001OO2O1O1O1O1O01O01O00O00000100O00001O001O0O101N3L3N3JafX`0\"}, {\"size\": [1280, 720], \"counts\": \"Soj87gW1:G6K2O1N2M2O2N1000OO3N2N101N2O2M4M2N1N5O0M2O02O2M111L2OOcNaiNZ1bV12N110O00O010O1O1O2N0000000001O000001O3M2ON1O03O0O00010O0000002M01001N10O01O1O1O2M2M3L9Hh]\\\\`0\"}, {\"size\": [1280, 720], \"counts\": \"_XV97bW1>G4L7K3M2M4L3N2M3N3M2N1O1O1O1O1O2N2O0100O1O3M6J7I2N2N1N103M1N101O1O000O010000O10O0100O010O01O1O1N2N3L5DZ^k`0\"}, {\"size\": [1280, 720], \"counts\": \"kXV9:cW16I9I5L3M3M4L3N2N100O1N101O02O0O01000O2O2M3N2M2O2N1N10001O1OO010O10O01O11OO100O10O01O01O10O01N101N1O2J6L5BcVj`0\"}, {\"size\": [1280, 720], \"counts\": \"jh]9e0VW16K6M3N4K3M3M2O100O001O1O010O001O1000O1O2O1O4K8I6J0O1O10O001000O01O010O1O10O001O01O01O0010ON3N2M9A[fg`0\"}, {\"size\": [1280, 720], \"counts\": \"gaf97_W1e0A8E;G7E<I5D;K6L4L4M3L3N3O0O2O001O000001O1O2N3L4jMPlN6ST1CTlN:nS1BUlN=nS1^OUlNa0lS1\\\\OVlNd0kS1ZOWlNe0bU100000001O001O0O100000001N2O1O4K9Fb]a`0\"}, {\"size\": [1280, 720], \"counts\": \"TbU:a03CiV1Y1^O<I5M3M3L5N000O101N1000000000O1000001O0[NgjNc0ZU1\\\\OhjNb0XU1^OmjN=YU1VOXjNJb0o0ZV1O00001O0000000O100O2O0O2N2O1N3N1N2O2M4L6IPmT`0\"}, {\"size\": [1280, 720], \"counts\": \"[Qn94cW1?ZOb0E:K5M2M4L4O1N2N2L4N3M1N4N001N11O01O01O1O1O1]NijN<ZU1ZOQkNa0TU1WORkNg0VV1M01O00100O1O0010000O01O1O00O0101O1O1N3M3FihNEdU[`0\"}, {\"size\": [1280, 720], \"counts\": \"[mm9?ZW1<I7J5L3L4M7J3M3L5L3N3L5L2N2M20O100O1O1N5L010N104L2M2O2N001N3OO00010O010O02O1O0O1N10O11N011O001N1O1O0O2N3J4N1O4J4N3K6I7JPgi?\"}, {\"size\": [1280, 720], \"counts\": \"coU;7hW13M4M1N4K4N2M3N2N3M7I5J8I9F9H5L0O1001N3M1O2N2M3M3M2N2N5K5I7K5K5J6K5J6J8Hof_?\"}, {\"size\": [1280, 720], \"counts\": \"Wic;a0\\\\W17J4M4M1N2O2N00O10O0010O1O010N1000000O1O1TORiNf0[W1I5L3Kin`?\"}, {\"size\": [1280, 720], \"counts\": \"W`k98fW14K5K5M4J5M3L3N3M3M1O2OO10O01O1O1O1O1O1N2O2N1O2N101N1100O02O00000000O10000O101O2M2O3M4M1M6J4L210N00010NO3M2N2N3M3M3M3M3L4M3L5L3L5K3L4Kl^c?\"}, {\"size\": [1280, 720], \"counts\": \"]fX94jW14J6N2M2O002M101N3O1N100O2O0O2O001O1O2N2N2N001O100O1O01O01O0010O01O2O000O10O02N0100000O2O0010O02M7J1O0kiNYNoU1l11N2N2O1N2O000O2N1N3N2O0N2L4M4L5L3K4M4L4L7Infn?\"}, {\"size\": [1280, 720], \"counts\": \"Roc91kW1:G7K8G6J7J6J8F6J9I6M1N2N1O1O1O1N2O0O33M4K4L5K3M200N1O1O3L2N3N002N001O1O0O0011OO10O2N001O1OO010M4K4K5L5K9I3L4K4N3KU^W`0\"}, {\"size\": [1280, 720], \"counts\": \"jnY9=cW14L5K4M4K3N0O2O02NO11N2O000010N01010N1O11O00O100O10010O0O101O010O100O1O100O1O101N1O00001O01OO2O10O01O010O1N01N2O2L4K4M2M5M2M4L3M3L6LoVV`0\"}, {\"size\": [1280, 720], \"counts\": \"UXj92eW1c0^O<H6J7K5N04L010O2N1O10O01O0002OO100O2N10000O10O02OO1O01O1O000O1O011O00O10O11O1N101O00010N0001N2K5M3K4K5N2K5N3Jh^R`0\"}, {\"size\": [1280, 720], \"counts\": \"U`_:6iW13N2N1N2O2N1N3M3N2M2N3M3M3M3N2N2O5L11M01O01000O010O01O01O010O001O2N01O0N2O2K5L3M3L5M2M2M5L4L5I6LeVg?\"}, {\"size\": [1280, 720], \"counts\": \"lW\\\\;<bW14K5K5M3L4M2M4M3N3N2M3N3M3O3L0O2N20OO1001O0O101O0O0O2N2M3M3M2M5L4L4K6J4L6J3N3KjfU?\"}, {\"size\": [1280, 720], \"counts\": \"WW\\\\;o0iV1=F7L5J6L5K5K3L3N4L5L3M3M30O1O2O3L8H5K5K4K4L4L2N002M1O2L4M3M3L3M4L4M4J6J7J4KlnV?\"}, {\"size\": [1280, 720], \"counts\": \"Qnn;5hW18H4M5L3L4L4M4L7I3M2OO100M2K6I7M3O1N2N2O2N2N2O1N3M3K\\\\ZS?\"}, {\"size\": [1280, 720], \"counts\": \"bod;;cW15L5K5J5L6J5F;D;I8H7L3L4M3N2M2O11O6H7J6I9G5K4L3L3M3L5L3L5L4K6J5L4K7GPWS?\"}, {\"size\": [1280, 720], \"counts\": \"k_g;3fW1>G7I6L4M5L3L1N6L1N2N2M3M3M3N0000001N12O0N4K4L3L5K4M3L5K3N3M3M3L5K5K5HQ_o>\"}, {\"size\": [1280, 720], \"counts\": \"hXP<9fW1;E4L4M3M4L4M1cMmNekNOo1V1VR1WOimNh0TR1_OjmN?TR1CmmN=oQ1FQnN9mQ1ITnN6jQ1KYnN3eQ10\\\\nNMbQ16_nNH`Q19anNE`Q15inNIUQ15PoNIQQ14RoNIQQ15QoNKoP14SoNIoP15ToNHmP19ToNEmP1:UoNDlP1;VoNBlP1=\\\\3N2O2O0O4L6GWZi>\"}, {\"size\": [1280, 720], \"counts\": \"[eh;`0]W14K401O6J2N1O2O000O101N2okNlNRQ1X1jnNmNRQ1W1inNQOoP1R1mnNVOnP1k0onNVOQQ1j0mnNYORQ1g0mnNYOSQ1i0knNXOWQ1g0gnNYO\\\\Q1e0cnNZOaQ1e0]nNZOiQ1VO^lNR1j1GUU1O1O1N2O1N2O2N1O1M4M5Jh^o>\"}, {\"size\": [1280, 720], \"counts\": \"__g;2]W1R1nNe0E;_OQNbjN\\\\2gT1T1A6K5J5L5J6J4M3M4M3M2M4L2O00O101VM[mN6hR1ZOYnN1jQ1DfnN5\\\\Q1BPoN8UQ1[OWoNb0lP1UO^oNg0QT1J6L3M5J4M3L6J8IhfP?\"}, {\"size\": [1280, 720], \"counts\": \"UhY;5`W1a0F7I6L5J4M5H7L3L6K3L6J4L5L4J6H8G8J6J6M3L6K4M2NYNdlNnNYS1j0RmNTOmR1g0[mNXOcR1g0`mNYO^R1c0imN\\\\OUR1a0PnN^OoQ1`0UnN_OjQ1>\\\\nN@cQ1?`nN_O`Q1=fnNAZQ1=knN@UQ1=PoN@RQ1=RoNAnP1>VoN_OjP1`0YoN[OkP1c0YoNYOiP1e0[oNVOhP1g0^oNSOeP1j0Z3M4K7]OYkP?\"}, {\"size\": [1280, 720], \"counts\": \"^WR;=_W17D;J6N1N2O1O1N2N2N2O1N2K5O1N3N2M3N2M2NUO_jNZO`U1`0jjN]OUU1=RkNCmT19XkNGgT17\\\\kNJaT16akNK]T14ekNL[T12gkNOWT1OkkN3UT1InkN7ST1FnkN;ST1APlN?iU10000O10OO100O2L4M3N4JZo[?\"}, {\"size\": [1280, 720], \"counts\": \"gh`:251]W1<L4M3M2N3L4M3K4N3M2O2O1O1O100000000O10O2O0O1O1O1O2jN[iNm0SW1I01O0001O01O0001O01O000010O1O00001N10002N1O0O2_OehN4bfd?\"}, {\"size\": [1280, 720], \"counts\": \"YQl:6iW13L4K4M3M4M2L4M3L4L4M3L3O2M2N2N3M3N1O1O20O010O0010O0100O101\\\\NUjNo0aV1iNZiNn0nV1N2NO1001O1O0000O1001L4]O^X1OVhNNhV]?\"}, {\"size\": [1280, 720], \"counts\": \"ia];=ZW1<J4L4H7M3N3N1M4M2M4M3N1O2N1O2O0001O001O0010O01Oj0VO7H4M1O001O0\\\\OTiN3Te_?\"}, {\"size\": [1280, 720], \"counts\": \"\\\\ic;o0jV1:H7J6K4K5M2M4L3K5O1O2N10000O1000O2O0RNfjNV1YU1gNojNU1QU1hN`kNk0_T1SOekNj0]T1ROjkNj0VT1ROPlNl0cU1O0O;F5K7I0O0LhUX?\"}, {\"size\": [1280, 720], \"counts\": \"hZ\\\\;?[W1;D8N2J6D<M2L5H6M4N2N1O2N2N2O1O100O1O1O1O1N2OnNnjNXOQU1OjkNOUT11mkNOST10okNOQT10PlNOQT10QlNOoS1OUlNOjS10YlNNhS12[lNKeS15]lNHeS16]lNHeS15_lNIbS14alNK`S13alNLaS12`lNN`S11blNN^S1OelN0\\\\S1OflNM]S1NfYS?\"}, {\"size\": [1280, 720], \"counts\": \"\\\\bi:?`W12N3N4M3L2M3N2N2N2N5K3M3N1O1O1O000011O1N200O1O3M0O1O001N101N1O001O1N2OO1N1101OO1N1100O010O2O0O2N2N3MO20O1O1O2O0O2N2N2N1N3Hcll>\"}, {\"size\": [1280, 720], \"counts\": \"cRg:4iW17K2O0O2O010O1OO1O11O1O010O010O1O2N101N1O102M01000000000000O2O2OM1O20O1000O100O1O010O100N2N2N2N2N20O10O1N1O2O10O01O0O20O10O0O2N1001O1N100O1O2M2M3M6I7ZOihN6WT_>\"}, {\"size\": [1280, 720], \"counts\": \"_kU=3lW14M2N2N3M1O2O0O010N101O1O2O0NO2O101N10N1100O1000000O1O100Hjdh=\"}, {\"size\": [1280, 720], \"counts\": \"Ykf<6eW1=H6J4K4L4M2N2O0N2O0O2O0O1O10000O1O100M3N2O0O100OM3O3N0O2N3L4L201O1OM5N11M3N2N2J5L5M21OO10001N2N2N2O1O2N1N3NR^X=\"}, {\"size\": [1280, 720], \"counts\": \"lcb;6fW19I3M3M4L3M2N3M2N2N3M2O1O0O2O0O101OO0110OO2OO1M21O3N10O01O00101N1M5J5L4M4M2^OkhN5XW1I\\\\P54moJ50L4M3NG2ahNM_W13ahNM_W14_hNMdW16M4N2O10N2OO011N11N1001O0001N101N1O1Mbeh=\"}, {\"size\": [1280, 720], \"counts\": \"`cd:=_W15J6I6N3I6L4O1N3M2O1M30000N2N1M301000O001O10O0100000O01O1O101N1O1O2M2O101N1M3H_iNPOcV1n0:M2L7J5L3L5J`ed?\"}, {\"size\": [1280, 720], \"counts\": \"QZT:6`W1m0XOc0]O<E9F8J4M4L3M3M3L3N3M2O011N100O1O10iMUlNkN]OX1_T1J`lN2aS1LclN1^S1MdlN1^S1MdlN2cS1F_lN8dS1E_lN8hS1@[lN?aU1110000O10000O2O2N1O2M3M101N3N1N2N2O1MflT`0\"}, {\"size\": [1280, 720], \"counts\": \"l`k98fW1`0A7G`0B6J4L5K4L4L5L002N2N4M1N3N2O0001N10NPNmjNT1SU1iNUkNS1oT1bNZkN[1hU1N1N3M2N3M2N1N2O1O001O0O1O2O001O00000O101N102N1N101N2N1O2O2M2O0O3M2N00ndn?\"}, {\"size\": [1280, 720], \"counts\": \"n`d:3<i0TV1j0F<D:G9I5L2M4L2O1N1O2O00001O000000kMdkNf0]T1WOjkNd0ZT1VOmkNe0UT1WOokNg0ST1TOSlNi0TT1lNTlNP1aU1L1O2O1N2O001N2N2O2N1N2O2M2O0O2N2N2N2N3N1NoTg?\"}, {\"size\": [1280, 720], \"counts\": \"bQb:Q1lV1<B9E:F9EhMfjN]2PU1>O2N1O1N20001N100O100001O001WN_kN0cT1MekNM_T1KhkN2\\\\T1FjkN7YT1DkkN;WT1_OokN?ST1]ORlN?VT1YOikNi0gU1O2O1O1O2M3N0O2O2M2O1O2M2NWen?\"}, {\"size\": [1280, 720], \"counts\": \"TQn93iW1=B7M2M7J7I5K6J4L4M2M4M2N1O1O1O001O001O01O102M3M;E4L3M1O3M5L4K3M1N3N001O001N101O2M3N4L001O3L2O3L^eX`0\"}, {\"size\": [1280, 720], \"counts\": \"g_k9c0ZW18J5K4M4L4L2O1N3Nb0^O5K7I4L4L2N001O01N2O2M2O2M2O4K9H2M3M7I5K2N3N4K5K3M1O2N1O1N101O00001O1O2M4M1N2O2N2M4L4L\\\\UV`0\"}, {\"size\": [1280, 720], \"counts\": \"YXT:2iW1;I4K5L4L4K5Li0XO5J8I6J8I3M3L11O01O01O0O1O101N2N6J3`McjNW2UV1^O6J3M4L3M6J2N00001O1N100000000O2O002N1N4L3N1O3L2N1O3K^]m?\"}, {\"size\": [1280, 720], \"counts\": \"hX^:f0WW1;F<E>A8H9H3N1N3N1O1O01O1O002N2N1O6J:F6J4L;E4L3M2N101N2N1O0O2O001O0O10001N3N2M111N2N1O1N3M3MYmj?\"}, {\"size\": [1280, 720], \"counts\": \"hX^:h0RW1e0_O6J8I4L4K4N2M3M3N1O100O10O2N2O1N3M?A;E3M4K2O6J1O1O001O010O1O0O2O1O00001O1O2N2M3N1O2M2O2M3M3N\\\\ei?\"}, {\"size\": [1280, 720], \"counts\": \"ng[:>\\\\W1:L2M3N2N3Mh0XO8H4L2N4M6I3N3M1O2O0O10O1O4L2\\\\MijN0OS2jU1L2O2M3M7I3M3M2N1N8I01NnNXiNn0mV101O0O101O0001O01O000O2O0O2O002M2O5K000O2O2N002M3M2M]U]?\"}, {\"size\": [1280, 720], \"counts\": \"VXc:9eW16I=Eh0XO8H8H7I3N5K5K3M2O0O100000O2O2M8I>A7I9H3L3M1N3N2N1O2M3N1O001O0O2O010O00001O1O1N2O2N1O3L101O2M2O2N2N1N]U]?\"}, {\"size\": [1280, 720], \"counts\": \"ZY^:>[W1;D:J7I6I8J5K5K5I7M1M4N1O1O1O2N1O1O1O2O0000QNakN<aT1^OfkN`0[T1]OmkN=TT1_OQlN?PT1]OTlNa0gU1O001O00010O1O1O001O1O2O2N2K4N1O3L2N4MfUl?\"}, {\"size\": [1280, 720], \"counts\": \"gY\\\\;=_W15M3N100O11O003M3M2N2M3M3M2LTnT`0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"gXe94jW19I5K2N2N4L2N2O1N3M1O1O2N2O3LD\\\\iN^OcV1a0_iNANEZV1i0UjN\\\\OjU1b0YjN@eU1=^jND`U1;fjN_O[U1`0T1O0O2M3AbW^a0\"}, {\"size\": [1280, 720], \"counts\": \"RZ[93fW1;G8I7L3K5J7J6I6K5M2M3N2M3N1O1M3N2O00001O1O1O00hNejNMYU10ojNMQU1N[kNKdT10ekNM[T11hkNNWT12mkNKST14nkNKST14nkNLRT13PlNKQT15PlNJQT14QlNJQT14RlNJUT1NmkN1RV1O3K3N2N\\\\]Ua0\"}, {\"size\": [1280, 720], \"counts\": \"^j]9=^W17H8L3L4M3J6L4L3M4L3L5L3N3L3L3N3E;M2O2N1010O02O0O10O1cNckNWO]T1c0ikN]OWT1<QlNBPT1;SlNEmS1:TlNFlS18VlNHkS15XlNJhS13^lNIcS15`lNJaS13clNJ`S11dlNN_S1JglN4dU1O1N3M3LY]Pa0\"}, {\"size\": [1280, 720], \"counts\": \"\\\\R_9;bW18E9J4J6M3L4K5J5N3L3N2N3M2O1N2N2O001N2O10OO2N3N10O0dNUkNEkT14\\\\kNLcT11bkNN^T1OfkN0YT10gkN1YT1NhkN2XT1MjkN2VT1NmkNOST10okNOPT12TlNIlS16WlNHjS18XlNEjS18YlNFhS18[lNFgS19^lNBbS1;T2JdWo`0\"}, {\"size\": [1280, 720], \"counts\": \"eZe97hW15K2N2M4N1L4N2L4I7L4M3M2N2N2N2N1O2N2N2O1O1O1N200O10000O10000000gN`jN1aU1LdjN2\\\\U1LfjN3[U1LfjN4ZU1LfjN4[U1JhjN4XU1LjjN1WU1NljNOVU1OnjNNRU10RkNNQU1IVkN5\\\\V1O1NS]f`0\"}, {\"size\": [1280, 720], \"counts\": \"cj[:b0ZW18H5N1N3L4M3N1N2N2N3J6K4K5M4B<O2N2O1N2O1N10100N2N2N101OPOTkNnNjT1l0akNQO^T1f0nkNXOQT1e0VlNXOjS1d0[lN[OdS1e0alNWO_S1g0dlNWO]S1f0glNYOYS1d0klN[OUS1b0PmNZOSS1c0QmNZOQS1`0UmN^OmR17ZRU`0\"}, {\"size\": [1280, 720], \"counts\": \"kb_:;bW15L4M2M200K6M3J6J5M4L3N2N2M4M2N2M3N1O2N2O10O01001O00000oNXjNJhU14[jNKeU11ajNM_U1KjjN4UU1ETkN:kT1EXkN:gT1C^kN<aT1DakN;_T1CdkN;]T1CgkN;XT1EnkN6RT1IokN6PT1ISlN6nS1IUlN4mU1O1No\\\\m?\"}, {\"size\": [1280, 720], \"counts\": \"nZm::eW14H7J4N3H7K6L3L4O1M3N2N2M3N2N2N2N2O2M2O1O100O010000mN^jNHbU15cjNI]U10ljNMVU1OmjN0TU1MRkN0nT1MWkN1jT1KZkN4gT1I]kN4fT1G_kN7WV1M2O0O4Kmle?\"}, {\"size\": [1280, 720], \"counts\": \"^SV;9bW17G8J5D<E;B?I6L4N2M202N1O1N101O1O1O00lNnjNaNMd0TU1d0_kNZOaT1d0ckN[O\\\\T1c0hkN\\\\OWT1b0nkN\\\\OQT1c0QlN\\\\OoS1d0SlN[OmS1c0VlN\\\\OiS1c0ZlN\\\\OfS1b0^lN\\\\OaS1d0blNZO^S1d0jlNUOZS1e0glN]O[S1=ilNBkU101M2N20`d_?\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}], \"areas\": [618, 688, 858, 710, 1144, 1074, 0, 732, 2528, 3117, 5225, 4456, 4508, 3943, 2529, 1998, 1596, 1167, 880, 0, 0, 570, 498, 367, 350, 0, 219, 423, 836, 712, 1211, 1062, 1290, 726, 584, 0, 764, 0, 1435, 1567, 1280, 1111, 1507, 912, 1901, 858, 641, 730, 1040, 1129, 0, 284, 792, 371, 433, 483, 1495, 2666, 2773, 2339, 1985, 2267, 2198, 877, 1408, 2707, 1698, 2756, 2962, 2034, 2879, 3197, 2605, 2829, 3385, 2979, 2565, 2366, 2960, 2129, 1888, 1855, 2879, 2148, 2334, 3340, 1621, 770, 2466, 2883, 3040, 2304, 2103, 1650, 1734, 2306, 771, 1811, 1502, 1365, 1429, 3372, 3468, 1650, 1662, 1739, 1509, 1712, 2213, 2209, 2082, 492, 1958, 1745, 2106, 2673, 2586, 2402, 2363, 1986, 2527, 2469, 2092, 2068, 2585, 2489, 2239, 237, 0, 0, 0, 0, 0, 734, 2039, 2421, 2176, 1931, 2555, 1997, 1787, 2007, 0, 0, 0, 0]}, {\"video_id\": 0, \"category_id\": 1985, \"bboxes\": [[555, 813, 14, 24], [555, 813, 18, 27], [553, 815, 23, 23], [551, 817, 24, 21], [562, 814, 16, 20], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [583, 793, 10, 34], [573, 802, 21, 26], [577, 805, 17, 24], [581, 804, 16, 24], [570, 810, 30, 23], [566, 810, 34, 24], [566, 809, 35, 24], [587, 805, 16, 25], [0, 0, 0, 0], [596, 800, 16, 28], [579, 810, 32, 24], [579, 812, 35, 26], [581, 818, 32, 23], [586, 820, 26, 20], [599, 819, 14, 23], [593, 820, 20, 21], [580, 822, 29, 21], [582, 818, 25, 21], [596, 815, 15, 22], [606, 815, 15, 18], [611, 814, 16, 20], [0, 0, 0, 0], [624, 790, 10, 25], [624, 788, 11, 22], [618, 783, 16, 29], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [611, 684, 50, 31], [623, 686, 43, 32], [626, 685, 48, 35], [627, 679, 49, 36], [621, 652, 27, 41], [0, 0, 0, 0], [603, 554, 27, 41], [602, 573, 19, 27], [603, 571, 24, 25], [605, 579, 26, 35], [610, 601, 36, 84], [617, 621, 44, 62], [622, 658, 36, 30], [613, 658, 36, 24], [603, 642, 42, 28], [594, 615, 47, 63], [589, 617, 50, 55], [590, 594, 46, 77], [602, 575, 28, 93], [601, 597, 41, 71], [610, 596, 40, 73], [613, 582, 33, 99], [615, 609, 31, 77], [625, 654, 25, 29], [590, 628, 57, 51], [592, 598, 47, 92], [592, 623, 47, 67], [620, 657, 22, 41], [609, 654, 25, 50], [636, 661, 28, 29], [636, 658, 29, 30], [648, 649, 33, 37], [655, 646, 27, 40], [642, 642, 27, 32], [618, 632, 26, 82], [616, 633, 20, 40], [640, 661, 25, 27], [626, 649, 25, 34], [618, 633, 24, 49], [643, 644, 23, 29], [648, 636, 22, 33], [640, 616, 22, 32], [665, 623, 20, 31], [678, 620, 25, 33], [655, 608, 19, 34], [654, 602, 17, 38], [679, 614, 25, 42], [655, 608, 21, 47], [635, 618, 27, 42], [658, 649, 26, 39], [642, 646, 28, 42], [628, 659, 33, 24], [621, 603, 24, 47], [631, 630, 30, 39], [631, 621, 13, 25], [620, 602, 25, 47], [620, 603, 28, 52], [634, 626, 32, 32], [627, 615, 33, 37], [623, 588, 30, 53], [634, 610, 26, 47], [640, 632, 24, 41], [624, 626, 30, 47], [621, 629, 25, 44], [632, 644, 22, 32], [635, 639, 22, 34], [621, 617, 21, 38], [612, 614, 14, 34], [613, 610, 23, 45], [613, 606, 25, 47], [622, 611, 14, 45], [613, 601, 26, 51], [637, 609, 20, 43], [627, 605, 24, 51], [619, 601, 26, 56], [630, 600, 14, 37], [630, 603, 17, 40], [609, 597, 17, 41], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]], \"score\": 0.5345454812049866, \"segmentations\": [{\"size\": [1280, 720], \"counts\": \"dafe08fW15L4K4M2O1N1000O2N2M3M3M3M3L`fj5\"}, {\"size\": [1280, 720], \"counts\": \"hafe02lW17J4L3M3M3N1M101O1O10O2N2N1M4M3M3N2M_fe5\"}, {\"size\": [1280, 720], \"counts\": \"fQde07hW14M3L2O1O1O1O0O10O10O100O002N1001O1N3M2O1L4L4M[na5\"}, {\"size\": [1280, 720], \"counts\": \"eaae06iW14M2N1O2N1N101O1O1O00000O01O10O010001N2O1O2N1N2L[Vc5\"}, {\"size\": [1280, 720], \"counts\": \"cYoe08gW15L2M2O1N10O10O10000O11O2N3M1L5L[^_5\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"caif03jW17A=F8O1M21O3M2J8K3KTgl4\"}, {\"size\": [1280, 720], \"counts\": \"bQ]f05iW14L4M2N1N3O0O100N101O1O000010O1N4K5L3O0O3Mh^k4\"}, {\"size\": [1280, 720], \"counts\": \"^Qbf0;dW13M2O1N2N1O1O0O10O3N2N2M3M2N3O0O2Nf^k4\"}, {\"size\": [1280, 720], \"counts\": \"_Qgf02kW1;F4N1M2N3N0011N2N2N2M3N1O1N2N3Kjfg4\"}, {\"size\": [1280, 720], \"counts\": \"cYYf08fW15M1N2N2N2O000O10O1O1O10O10O100000N2000O1O100O1O3M2OOO2O2K6Lcnc4\"}, {\"size\": [1280, 720], \"counts\": \"eYTf07hW13M2N101N101N101N101O000000O1O1O10O01000O1O10O01O0100O1O3M1O1O002M4Jfnc4\"}, {\"size\": [1280, 720], \"counts\": \"hYTf01oW1014E601N101N101N2N1000000000O1O1O010O1000O1O1O1O1O10O1O1O3M1O001O2L3Mhfb4\"}, {\"size\": [1280, 720], \"counts\": \"canf0:dW13M3N1O1N2O1N2N0102O2M2N2N2N2M2OeV`4\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"_iYg0;bW15K4M2O1O1M201O02N3NN11O1N3L5K9FjnT4\"}, {\"size\": [1280, 720], \"counts\": \"jadf04gW16N2N2N1O2N1O10001O00O100O1O1O10O011O000O1O1O1O01O2N2N1O2O1N2M4LaVV4\"}, {\"size\": [1280, 720], \"counts\": \"ladf02iW1:J2N2N101O000O2O0000O100N200O010O100001N100N2O1O01O0O3N2O1N1O1O2N3M2Ka^R4\"}, {\"size\": [1280, 720], \"counts\": \"PRgf02jW17K4L3N2O00001OO010O1O100O100O11O001N100O1O1O02M3O0O2N1O001O2N5KXfS4\"}, {\"size\": [1280, 720], \"counts\": \"mYmf07fW15M3N00000O10O1000O2O00001O0O1O1O0101M2O2N2N2O0O1O4JXnT4\"}, {\"size\": [1280, 720], \"counts\": \"Ub]g01eW1?G4O1N11O1M4N1O10O010O02L3IcfS4\"}, {\"size\": [1280, 720], \"counts\": \"lQVg0:eW12N1O2O1N100O2O0O0O20O1O1N3N101N1O2O2K5LWfS4\"}, {\"size\": [1280, 720], \"counts\": \"lief09eW14N1N2O1N2O1OO10000N200O01000000000000O10O3M2N1O00102M2M3MVfX4\"}, {\"size\": [1280, 720], \"counts\": \"lYhf06gW15M3M1O2O001OO10O01O1O1000O100O10000O11O2N0O2N1M5LZV[4\"}, {\"size\": [1280, 720], \"counts\": \"hiYg09fW11N3N1O2O1N2O1O0000N2J510J7L0NfVV4\"}, {\"size\": [1280, 720], \"counts\": \"gYfg08fW13M2O2N1O2O000O01N2M3O1M3O100Ncfi3\"}, {\"size\": [1280, 720], \"counts\": \"kalg07fW13N2N2N2O1O1O10001N01O1O1N2FahNOcW1N:NbVb3\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"Ui\\\\h07bW1:J4L1012O2N2N00002FehNI`fZ3\"}, {\"size\": [1280, 720], \"counts\": \"lh\\\\h04hW15K4ahNEZW1<41O1O3M3NO0KhhNBXW1=8I]WX3\"}, {\"size\": [1280, 720], \"counts\": \"RYUh01jW18J6I5O2H[OQiNf0RW1J6M5OO10O3O2OO01M5M2MY_Y3\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"]]lg0`0`W12M2O2N1O1ON200001O01O010O0O2O00000001O1O0001O01O01O0010N200O10O01O01O0001O0001O00001O1O0000002N3L4LRjW2\"}, {\"size\": [1280, 720], \"counts\": \"e][h07fW14N3M2N2O0O101O001O10O01O0000001O001O100O1O002N1O0010O01O01O0O2O001O001O1O1O002N2M5J5JQbQ2\"}, {\"size\": [1280, 720], \"counts\": \"jU_h02lW13N100GKhhN4XW1NghN1YW1OdhN5[W1LbhN9[W17O1O2M100010O01O001O1O1O1O1O1O001O10O1O01O00000001O00001O010O1O1O1N101O2M3M2L4L6KRbg1\"}, {\"size\": [1280, 720], \"counts\": \"g]`h01mW1200O2EMehN5[W19O1N3M2O1O1N111O0O1O1O001O001O01O0001O010O1O1O00010O01O001O1O1O001O001O001N2N1O3N1O6I5J5LmQe1\"}, {\"size\": [1280, 720], \"counts\": \"elXh0b0YW18J4N2O010O0010O0010O00100O1O10O01O1O10O1O0O3L4G:F8M2MXSh2\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"bYbg0<dW19F6J5L2N2NO0O2N2N2O000O101N2O001O1O1N1O2O2M2O0N3N3N3Ihf^3\"}, {\"size\": [1280, 720], \"counts\": \"SRag06hW18I5L3L4MO1000O01N2O1O1O0O2O1O1N2M3N3LUni3\"}, {\"size\": [1280, 720], \"counts\": \"SZbg02lW15L4L2O2N2M2O2M2O001OO1O10O0010O100O1O1O2K5L3N3MT^b3\"}, {\"size\": [1280, 720], \"counts\": \"fjdg0>_W14M10O11O5K1O2N1O001O01K401M2N201N101N101N2O1O1N2N3Nm]]3\"}, {\"size\": [1280, 720], \"counts\": \"VSkg0i0TW16L4K5M2M5L3L3M4M3M3M2M2O1N3M3N\\\\N^jN\\\\O0Y1aU1XOmjNd0RU1UOVkNk0hT1TO\\\\kNk0cT1ROckNk0]T1TOekNm0YT1QOkkNn0TT1POokNo0RT1oNQlNP1aU1O6IO1010O6I4M2N3M1O2M3Mjbj2\"}, {\"size\": [1280, 720], \"counts\": \"jkSh09dW17K3M5K3M3M3M2N4L3M2N2O1O100<D2N4L1O1N3N0O2N2O00O010O01O01O0O2O00001N2N110003L0O6J1O1L8IUkW2\"}, {\"size\": [1280, 720], \"counts\": \"lTZh07gW14L4L4M2N2N2O1O001O0010O1O01N1O11O4L10O0000000O1001OK500O0010O2N1O3M5I5KXc[2\"}, {\"size\": [1280, 720], \"counts\": \"nlng05iW14M3M2N1O101N100O101N101O01N10000000000000O1000O01001O00N2O11O0O3L2N5I8JVkf2\"}, {\"size\": [1280, 720], \"counts\": \"_\\\\bg05hW14K4M4N1O2O0O10O20O01O0010O1O010O001O01O01O000010O00000001O0000010O000001O00001N3M:D]kk2\"}, {\"size\": [1280, 720], \"counts\": \"bSWg08fW15K4L3M4M3L4N1O11O2N3M1O1O1N101O00000000O020O0O101O00O01O010O100O1O01001N010O10O101O0O3N0N3L7H[kP3\"}, {\"size\": [1280, 720], \"counts\": \"`kPg01nW17I5L4L2N1O2N1N2O2M2N2N2O2M2O002N11N4L6K4L2N1N10000O10N2O010O010O001O001O1O0010O0010O11N101N2M3N2M3M^[S3\"}, {\"size\": [1280, 720], \"counts\": \"oRRg09eW15K5K5L3M4L2N3M3N1N3N2M2O2N1O2N2N10`NhiNW1YV1dNXjNo0`V1M2OOO100001O01OO20N1001OO10001N10O1O13L2O1N1O101N4K5K[SW3\"}, {\"size\": [1280, 720], \"counts\": \"ZRag0S1jV1:H8G:G5K5K4M3L4L3N2N1O1O1O1O00QNUkNj0jT1SO^kNh0cT1UOckNg0]T1XOhkNd0YT1YOlkNd0TT1ZOYlN;kS1\\\\O\\\\lNb0eU1L2M4M5J2N2N_c^3\"}, {\"size\": [1280, 720], \"counts\": \"Qk_g07fW17I5L4M4L4L3M3N2N2N1O2N2N102M2O05L=B2N100N100001O0000000000O010O2O2M2O0O3N1N2N3L7G^co2\"}, {\"size\": [1280, 720], \"counts\": \"eRkg0;eW1e0ZO8I1O2N2N1O2O0O1O2N1012]NgiN2LQ1nV1L3M001O00001O0000O10000001N1O011N3N2N1N2O2M4L00002M7JUce2\"}, {\"size\": [1280, 720], \"counts\": \"`jng0d0ZW1:F:G5K6K6I5K6K4K3N3M4K3N2N2N2N1O2N2OjM_kNl0`T1POgkNn0XT1lNQlNQ1PT1cN^lN[1UU1O3L2N6I7J3M1O1O1N3M3LZcj2\"}, {\"size\": [1280, 720], \"counts\": \"T[Qh0o0nV18M0O02O2N2N1N2O001OO10O10O0010O00O2O00001O001OO1000001O2M2N:[OdhN1_jk2\"}, {\"size\": [1280, 720], \"counts\": \"ll]h07fW15L3M2O2M2M4O1N101O1O0001O2N1O100O1O1N101M3N3M2M4KVce2\"}, {\"size\": [1280, 720], \"counts\": \"fSRg06hW16L3N2L3N1O1O001O1O1O001O1O1O001O100O10O10002N1O2N1O000O100O1000O010O00100O1O0010O01O1000O2N2O0O010O2N1O1O0O100N6GZ[i2\"}, {\"size\": [1280, 720], \"counts\": \"ScTg0:`W1:G7L4N1O2O001N2O1N2O2N1O2N2M2O0000001O0020N5L7H4L2N1O1O10O000O2O002N001OO11O0100O1N2O1N1N4M7GQ[S3\"}, {\"size\": [1280, 720], \"counts\": \"_cTg0:fW14L3M7J0O2N1O2O001N1O1O1O2O0O2N10000O10000001O1O2N1O1O1O1O00000001N100002N0O2O00100N2N2N3K6K3KnZS3\"}, {\"size\": [1280, 720], \"counts\": \"ddWh04kW17I5L5J4M3M2N2N2O1N100O2N100O01OO0101N2M2M5]Oico2\"}, {\"size\": [1280, 720], \"counts\": \"blig0=aW18J4K3N1O2N2M3O0O2N1O2N1O10001N1000O110N3M3M9G7H;C]bY3\"}, {\"size\": [1280, 720], \"counts\": \"Zekh01j_21RhN4K4L6K3N2M3N2N2O0O1000000001O01O0001N1O2O1N2O0O10;]OSST2\"}, {\"size\": [1280, 720], \"counts\": \"Zekh01jg30T`M1K6N3L3O2L3M3N2N3L2O010O0100O1O2N1M3O1N3M2O1N3M3N3I]kR2\"}, {\"size\": [1280, 720], \"counts\": \"QeZi02nW10O1N2O2M5L9F2O1O1N10001O001O01O01O01OO110O0100O011M2O1O2L3O1N3K6Kek^1\"}, {\"size\": [1280, 720], \"counts\": \"o\\\\ci04dW18bhNJQW18ohNCTW1c004M1O100N20O00001OO2O01O0001O010O10O2N2M2O2M3M3Kic]1\"}, {\"size\": [1280, 720], \"counts\": \"fTSi04hW16L2O2M3O2L2O100O1O2N3M1O1O001O0100O2N100O2L3N2M3M4K7Jkkm1\"}, {\"size\": [1280, 720], \"counts\": \"YTUh05hW17H8G:I;E?C4L3M4K2O2N1100mMXjNk1hU1SNYjNo1kU11O1N2O100004K:E>B6J8F=\\\\OdRm2\"}, {\"size\": [1280, 720], \"counts\": \"VdRh0<_W18I6M3M3M2N3N3M01O0N3O002N6L0OO00000001HQTW3\"}, {\"size\": [1280, 720], \"counts\": \"PePi03iW16M3M3M3M2N1O101N3N1O1OO1000000O2N101N1O2N2N2N3L6ISkR2\"}, {\"size\": [1280, 720], \"counts\": \"mT_h06eW17L4L2N1N202M2O2N101O1N11O2N10O1000N1N4M2N2N2M5L3LY[d2\"}, {\"size\": [1280, 720], \"counts\": \"[TUh01kW19G6J6K5K5N1O1O2N1O1O2O1OO100O2N2N1OWiNPOcV1P1611M4L3M6ZORdo2\"}, {\"size\": [1280, 720], \"counts\": \"_\\\\Ti02kW1?B4L2O2N1O1000000O2O1O0000O2O0O2N1O3M2M4L5K5JbcQ2\"}, {\"size\": [1280, 720], \"counts\": \"ZdZi0=`W12O1O103M2M4M2N1O101O1O2N00N2O2M3L4N2N3M7H5Iicl1\"}, {\"size\": [1280, 720], \"counts\": \"hcPi07eW17J6I6L3N2N100O100001OO1001N2N3L5L4L2N2M4M2NZdV2\"}, {\"size\": [1280, 720], \"counts\": \"nkoi0:aW1<G1O2O0O2O1O10000O10O1O2N3M2M4M1N3L4M4J[lY1\"}, {\"size\": [1280, 720], \"counts\": \"RT`j01jW18I4O2N2N2M3N1N3N2O0O2O1N1O0010O1O2N3N0O4L3M3M4L4KV\\\\c0\"}, {\"size\": [1280, 720], \"counts\": \"d[ci01eW1`0G5K5L3N2O0O101O00000100O2N2L3N1O3M5Ejdg1\"}, {\"size\": [1280, 720], \"counts\": \"_Sbi02bW1b0D9L3M3M2O1O01O010O2N3M2N2N1N3M3Mg\\\\k1\"}, {\"size\": [1280, 720], \"counts\": \"Q\\\\aj0:[W1;L4N3L4M3M4M1O1O10001O01M2N2N3M3N1O3M2N2M5L3L4M4LZTb0\"}, {\"size\": [1280, 720], \"counts\": \"[[ci0:_W1>H4L3M2O3M3M2M3OO1N2O3M2N2O01O1NN2N3M5Ga0@ZTe1\"}, {\"size\": [1280, 720], \"counts\": \"\\\\[jh0:eW1=C4M2N1O2N100O20O3M01O00000O01O0O100O10001O011N4L6I9FgcV2\"}, {\"size\": [1280, 720], \"counts\": \"_Tgi0>^W17L3N1O1N3N2N2N1O0010O10000O1O001O002N1N2000010L5M5FVS[1\"}, {\"size\": [1280, 720], \"counts\": \"YTSi07hW1;E5L3L3O0O10O10O101O1O1O1N1O10O0000001N101O2M2N3N2N3L5DZcl1\"}, {\"size\": [1280, 720], \"counts\": \"cdah02nW1111OO0002N0O2N2OO02N1O0O102N1O00M`hNH^W17dhNI[W1=1N3O2C`hN8cW100N2O100MYhN0dW1O]hN2]W19004N2N:IdjW2\"}, {\"size\": [1280, 720], \"counts\": \"dkXh02kW17F8E:J7L3O2N101O1O01O01OO2L3N2N2N2O1O1N201N3M;@Zlk2\"}, {\"size\": [1280, 720], \"counts\": \"g[eh05kW12M4M5L4K4M1O1N101O0O1O1O100O1O1O1O01O00001O2M3M3M3M4L2N4J5IfkW2\"}, {\"size\": [1280, 720], \"counts\": \"][eh04lW14L3N2N2M2O0010O1O2N3L3N2MnSm2\"}, {\"size\": [1280, 720], \"counts\": \"RcWh06fW1:I6I7J3N1O100O1O101N2N2N2O0O1O01O101M4M3L6K7H7I4LUlk2\"}, {\"size\": [1280, 720], \"counts\": \"obWh07gW19H5K6K4L2N2OO10O101O1N2N3M2O0O1O100O10O1O2N1O3M6F8J8G7IVTh2\"}, {\"size\": [1280, 720], \"counts\": \"hSih02jW1:I5L2M3N1O001O1O100O100O01O0010O001O001O010O000001N2N3N1M4N4J5IocQ2\"}, {\"size\": [1280, 720], \"counts\": \"Y[`h06hW15M2N5K4L2N2N101O00O0100000O0100O001O00001O01O0000100O2M3L5K4J6L3LVTY2\"}, {\"size\": [1280, 720], \"counts\": \"\\\\Z[h0g0YW16J3M2N1O1002N1O10O00000O2N1O101N1O100O10O1O2M5J7H7M5K2M4J4LYla2\"}, {\"size\": [1280, 720], \"counts\": \"USih09eW1:G5L4L2N2N1O2N2N2N101O10O0O1000O01O010N2M3L4O1N2N9GYTY2\"}, {\"size\": [1280, 720], \"counts\": \"kcPi0b0]W14L3N3M2M3N1O1O1O1O010O1O20N100O2L3N4M2O1O2K4M4LbST2\"}, {\"size\": [1280, 720], \"counts\": \"fc\\\\h04jW1?B6K3M2N1O1O2M3N101N2O01O010N2O00OO100O2N1O2O1O2N3M3N3K5L8GZc`2\"}, {\"size\": [1280, 720], \"counts\": \"gkXh05jW17I;F4L2N2N2N1O2N1O2O0O2O0101M00001O1N2N1O101O2M8]OhhNNQkk2\"}, {\"size\": [1280, 720], \"counts\": \"[dfh01jW1:J6K4L3M2N1N3N1O1O0001O2N2N010O0O2O0O2O1O2Mbc`2\"}, {\"size\": [1280, 720], \"counts\": \"X\\\\jh0b0YW16M3N1O200O1O00000000000001000O10O1N3L3K6I7Hkk\\\\2\"}, {\"size\": [1280, 720], \"counts\": \"\\\\kXh0<cW15L5K4K6K3M1O1O001N100001N10001N2N2N3L6I8D_do2\"}, {\"size\": [1280, 720], \"counts\": \"Wcmg06iW15L3M4L4L2N3M2O1N2O0O10N2N2@Tec3\"}, {\"size\": [1280, 720], \"counts\": \"Vkng02kW17L5J6L2M2N2N2N1O2N3M2N2N1O1O01001O1OL4M3M4L6FgTW3\"}, {\"size\": [1280, 720], \"counts\": \"Rkng06iW15L6J4K4M3M1O3M1O2M4M2N1N2O0003N2O0O0M2N3N1N3J6M3JjdT3\"}, {\"size\": [1280, 720], \"counts\": \"SSZh05kW12N3M3M3N2N2N1O3ON02004OLN5_OchN3ON][X3\"}, {\"size\": [1280, 720], \"counts\": \"ljng0<cW15K4M2N3L3O1N3N20N0101N3M1O1N10O001O0O101N2N3M3M3M4Eg\\\\S3\"}, {\"size\": [1280, 720], \"counts\": \"aklh06dW1<E7K6L6J4M2N00O1000000010000N2N5J5J7G9Gal\\\\2\"}, {\"size\": [1280, 720], \"counts\": \"\\\\[`h0<aW16J5J7K5K4M4M2O1O11O0O1N2N1N2O0O2N100N3O002M3M5AmhNFbce2\"}, {\"size\": [1280, 720], \"counts\": \"`[Vh01fW1=H7J6I6M3M3M8J1N20O2N10OO10O10O001O1M201O1O2M6mN_iN<XW1J2Knkk2\"}, {\"size\": [1280, 720], \"counts\": \"iRdh03lW18I5K4N0O2O1N1N12OO003ML4O1HQUm2\"}, {\"size\": [1280, 720], \"counts\": \"XSdh0?^W17I5L9G2ON2O2N11ViNPOdV1P162N10N2O1N5K4I:Cl\\\\i2\"}, {\"size\": [1280, 720], \"counts\": \"ejig02nW16J3M4M3M3N3L3M1O2N0O101N0010N2J7ZObec3\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}], \"areas\": [266, 345, 407, 400, 269, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 253, 372, 272, 261, 496, 553, 540, 289, 0, 343, 518, 573, 502, 398, 225, 308, 443, 383, 212, 207, 240, 0, 195, 176, 211, 0, 0, 0, 0, 0, 1097, 987, 1152, 1164, 820, 0, 656, 370, 428, 506, 1487, 1192, 887, 662, 829, 1145, 1218, 1437, 1744, 1273, 1226, 1891, 1021, 539, 1316, 1712, 1429, 687, 896, 608, 495, 705, 661, 603, 1421, 648, 515, 599, 751, 539, 532, 554, 456, 580, 535, 537, 773, 706, 784, 761, 819, 312, 756, 739, 173, 834, 987, 749, 796, 947, 894, 761, 954, 826, 565, 622, 674, 351, 706, 813, 264, 849, 699, 861, 964, 298, 540, 401, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}, {\"video_id\": 0, \"category_id\": 1985, \"bboxes\": [[0, 652, 14, 53], [37, 680, 51, 72], [100, 668, 73, 72], [120, 658, 72, 65], [97, 673, 70, 68], [70, 685, 65, 72], [74, 672, 73, 72], [123, 624, 72, 71], [178, 592, 76, 65], [193, 575, 86, 69], [203, 555, 97, 71], [216, 544, 104, 69], [240, 548, 94, 60], [243, 556, 93, 54], [233, 549, 93, 54], [227, 546, 77, 48], [225, 551, 68, 40], [196, 584, 54, 48], [146, 650, 60, 63], [47, 690, 93, 69], [0, 641, 60, 72], [0, 635, 18, 29], [0, 0, 0, 0], [0, 524, 52, 41], [125, 551, 71, 48], [198, 606, 45, 49], [142, 570, 58, 43], [0, 522, 63, 41], [28, 601, 63, 71], [45, 558, 58, 62], [18, 290, 63, 69], [0, 140, 46, 60], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [24, 510, 46, 102], [86, 694, 58, 80], [99, 629, 41, 60], [102, 660, 44, 68], [152, 703, 56, 65], [208, 613, 32, 51], [211, 523, 62, 53], [200, 644, 61, 52], [85, 569, 81, 63], [61, 518, 70, 61], [106, 667, 51, 53], [54, 599, 51, 49], [31, 516, 49, 58], [8, 570, 66, 69], [74, 486, 70, 64], [166, 294, 67, 97], [201, 285, 50, 87], [106, 357, 55, 71], [97, 403, 55, 61], [88, 442, 56, 60], [42, 501, 68, 83], [56, 412, 40, 100], [65, 362, 57, 57], [15, 345, 50, 89], [0, 0, 0, 0], [0, 0, 0, 0], [0, 265, 74, 62], [61, 184, 96, 58], [33, 246, 40, 51], [0, 302, 29, 35], [165, 95, 106, 128], [240, 173, 66, 86], [259, 195, 79, 68], [209, 209, 114, 43], [124, 196, 94, 50], [45, 181, 55, 63], [120, 260, 47, 58], [180, 113, 105, 108], [174, 115, 86, 105], [169, 102, 98, 80], [100, 121, 121, 52], [24, 181, 47, 62], [8, 240, 70, 66], [12, 86, 86, 75], [220, 146, 76, 84], [225, 163, 96, 84], [221, 223, 92, 50], [155, 224, 73, 50], [84, 200, 79, 52], [0, 201, 43, 53], [44, 119, 116, 96], [221, 207, 81, 64], [128, 219, 109, 55], [72, 356, 85, 73], [51, 588, 53, 69], [0, 427, 34, 100], [0, 376, 31, 64], [8, 422, 37, 39], [63, 368, 65, 83], [131, 374, 107, 53], [160, 415, 77, 55], [88, 440, 118, 39], [54, 377, 112, 50], [64, 415, 86, 66], [81, 532, 98, 58], [96, 579, 97, 63], [97, 640, 79, 84], [56, 711, 65, 66], [3, 512, 82, 72], [0, 335, 54, 49], [3, 299, 63, 46], [0, 341, 67, 57], [0, 374, 48, 50], [0, 0, 0, 0], [67, 591, 90, 45], [59, 521, 88, 45], [0, 409, 64, 45], [0, 349, 30, 52], [0, 330, 20, 44], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [41, 321, 68, 100], [118, 251, 72, 81], [29, 205, 85, 166], [86, 225, 71, 86], [118, 256, 73, 80], [16, 226, 122, 112], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 195, 66, 84], [23, 195, 111, 83], [0, 403, 88, 106], [73, 209, 129, 87], [111, 179, 99, 109], [108, 191, 107, 104], [0, 162, 83, 136], [36, 192, 122, 128], [70, 210, 128, 119], [0, 180, 52, 138], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 457, 7, 21], [0, 439, 10, 30], [0, 420, 32, 91], [74, 367, 137, 97], [130, 233, 108, 196]], \"score\": 0.6483516097068787, \"segmentations\": [{\"size\": [1280, 720], \"counts\": \"\\\\d0f1ZV10000O1O1O1O1O1N2O1M4M2M4K5Jfc`k0\"}, {\"size\": [1280, 720], \"counts\": \"an^1`0VW1=EXOViNl0gV18N3M2N101O001O0O2N2L3O20O2N01L3N30O00K5O1O100O001O10O2O01N1N201O01O2M4L4L4L3M2O2N3M2M3M5K4L4K8I5I_Qdh0\"}, {\"size\": [1280, 720], \"counts\": \"Zem39bW18M1N2O2N3L2O1O2M4M2M3N2O1M3L4N1N2N2N3N2N1000000O1000O10O1O100O010O01O1O1O10O010O01O010O001O00001O00001N2O1O1N2O2M2O1N101N2O1N2O1N2N3M3N4K4L3M7I7I\\\\iYe0\"}, {\"size\": [1280, 720], \"counts\": \"Uef43kW16K2L5M2M4K5N1N2N3L3M3N3N1O100O1O100O1O2O0O100O1O100O100O1O10O010O10O0100O010N101O010O001O00100O001O00001O001O1N2O1O1N2N2N2N2O2M2N2N4M2M5L3M3L4KnQbd0\"}, {\"size\": [1280, 720], \"counts\": \"ami34iW17M1O1N2M4L3L4M2N3M3K5N1N3N1N3M3L3O2N1O1O100O1O100O10O01O010O0001O0010O010O01O001O010O0010O01O1O1O1O1N2O1N2O1N2O0O2O1N2O1N2O1O1O1N3M2N4L>A7H]Yae0\"}, {\"size\": [1280, 720], \"counts\": \"oUh23kW16K3K6L4M3L5K3M3N3M3M2M3M3N1N2O2N1N2M3N2N20O0100O010O0010O000010O01O10O01O1O100O1O1O1O100O1O1O1O1O1O1O1O2N1O1N2O0O3N1N2O2N2M2O2YOXiN0lV1L[iNMQ`jf0\"}, {\"size\": [1280, 720], \"counts\": \"^Um21mW16J4M4L30O11O1K4L5L3M4M3K4M3M3N1O2M2O1N2O2M3N1O1O1N2O10O010O1O10O01O0001O01O1O010O1O001O10O01O001O00100O001O1O001O1O001O1O1N2N3N1N2N2O1N2N2UOZiN9kV1@[iN<WW1M4K[YZf0\"}, {\"size\": [1280, 720], \"counts\": \"V\\\\j43eW1<J5L2J7L4L3N2N201N2O0O1O1O1O1O1O2N4L2N1O100O2N001O1O1O1O010O001O01O00010O000010O01N10010O001O1O1O010N2O1O1O000000001O1O0O2O2N2M2O2M6K2M3M4L9F8HnZ^d0\"}, {\"size\": [1280, 720], \"counts\": \"WSo66hW1201N2K5chNASW1k0K3M3N1O2N101N2O1N101O1O00100O1O1N2O002N1O00001O1O0O2O1N2O0O20O1O10O0010O0001O000000001O01O000000000000000O2O000000000O2O001O1O2M4M2M3M2M4L4M4GldTb0\"}, {\"size\": [1280, 720], \"counts\": \"mja72kW14L5VOGWiN36;aV1e0010O01O01000N101O10O01O1O10O01O100O00100O1O1O001O1O1O1O1O1O1O1O1N2O1N101O0O2O001O0O101N101O00001O00000000O1O1N2N2O1O1O2O000000001O00001N100N2N101N102O0O1^OdiN@\\\\V1LbiN65LZV1KhiN51MXV1IliN9OGkV17a0L[]Ua0\"}, {\"size\": [1280, 720], \"counts\": \"QZn78hW1010N1L5I8OO1O10L4G:L3N2O1N2O1O2O0O2N1O10O01O011N1O100O001O1O001O0010O01O001O1O1O1O001O1O001O00001O001O00100O0000000000O1O1O1N2O1O2N100O101O000000001O00000000000O101N100O2M2O1O1O1O2N1O2L4L5XOQiN7aW1K4JiU[`0\"}, {\"size\": [1280, 720], \"counts\": \"ja^81nW12N2N3H7M3M3F:K5O2N1O010000O0100O1O1O1O100O10O010O01O10O01O001O010O1O010O001O1O01O01O00001O000010O01O01O01O001O10O01O0010O01O0000001O01N1O100O2N100O101O0000001O0001O0001O001O0000001N2N1O2N101O0O101N1O3L4K6J9ARVb?\"}, {\"size\": [1280, 720], \"counts\": \"ga\\\\98gW12G9G8O2NXOohNc0QW1\\\\OPiNe0oV17O1O001O1O100O001O1O010O1O001O010O00001O001O01O01O001O010O00001O0010O0001O001O001O100O001O001O001O1O01O000O101N101O0000001O01O010O001O0010O0000001O0O1O101M2O2M201N2N2N2M3K5K;DTfP?\"}, {\"size\": [1280, 720], \"counts\": \"kY`95kW11O001M4M2O00100L4L4N1K6N101O001O001O001O00001O001O001O1O0010O0001O00001O00001O00001O001O001O001O001O001O010O1O001O0001O00000000010O0000000000010O0001O00001OO2O000O1N3N1O2N2N2M2N3M4J6H<ETVn>\"}, {\"size\": [1280, 720], \"counts\": \"fiS92mW11HOahN1^W12^hN1_W1;L5N11M1OH^OQiNd0nV1_OmhNc0SW1\\\\OnhNd0RW1[OohNf0PW1[OohNe0QW1501O00100N1010O01O0000010O001O01O00000001O00001O0000001O00001O01O01O1O010O001O1N2O001O00001O00001O001O1O0010O01O10O01O01O0000001O0O1O1N3N1O2M2O1N3M2N3M3M3M4J7J[fZ?\"}, {\"size\": [1280, 720], \"counts\": \"YYl8>_W14N2N2N2O0O2O001O001O001O0000000001N1001O0000O1000000001O01O01O001O00001O00100O1O001O1O1O1O00001O1O1O1O1O2N1O1O010O000000O2O000O1O100O1O2N1O1O2N2M3M4L3L8FbVV`0\"}, {\"size\": [1280, 720], \"counts\": \"\\\\ii83kW1202YhNL]W1=O2M2O2N3M1O001N10000000O100000001N1001O0000O2OO1000010O000O11O0001O1O1O3N1N1N2O1O1O1O1O00000001N10O02O000O100O2N2N101N1N4M2M3N3L5J]nc`0\"}, {\"size\": [1280, 720], \"counts\": \"fbe75aW1:O2O104MOK5N2O1N101O2N1O100O10O0001O100O1O010O10O01O010O010O00010O001O010OO10000O2OO0O1O13L4L2O1O1O2N2M5K6IYeYb0\"}, {\"size\": [1280, 720], \"counts\": \"RUg54eW18M5L201N2K4F\\\\OUiNf0jV19O1N2M3O1O2N10001O0O1000O0100O2N1O2O00000O010O01O001O1O001N102M2O0O10000O10001O0O2O1O1N3M2O0O2O0N3N2N3L8FgbPd0\"}, {\"size\": [1280, 720], \"counts\": \"j]k13mW11O2N2O2M110L4N002O1O1O1O2N1M2N2M3N3N3L3K40010GfNhiN\\\\1UV1gNgiN]1WV160100O1O101N1O002N1O0010O0001O00001O01O00001O00000010O01O1O001O001O10O01O1O001O1O001O1O001O001O001O0O2O1N101N1O2N2N2N1O2N2N2N2N2M4M4K6JXQcf0\"}, {\"size\": [1280, 720], \"counts\": \"Ud0l1TV10N2N30O2N1O10O010O1000O1O1O100O001O001O0010O01O1O001O00101N1O2O0O1O1O1O2N1O1O2M2O1O2N0O2O1O1O1O1O1O1O1N2O1O2N1O2M3N2M7FTRgi0\"}, {\"size\": [1280, 720], \"counts\": \"kc0n0RW101O0O1001O00N3O0O2O0O3M2N2N2O1N3L5Kic[k0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"\\\\`0Z1fV10001O00000O10000000000000000O100000001O1OO1001N101O00000100O00O1N3M2O1N3M3O0O201NO100001N1000003N1O2N4HoVQj0\"}, {\"size\": [1280, 720], \"counts\": \"kil41mW16L0O2L5I7K5N1O100O1O2I6N2N2O1O100O10O00010O10O0010O0000000010O0001O001O001O0O2O001O00000O100O10000O2N2O0O2O0O2N101O1N1O2N2N2N2O1O1N101O1N3N1NnU]d0\"}, {\"size\": [1280, 720], \"counts\": \"_Sh72lW12L5I7N3M2O1O2M2N3M3N2N2N2O001N100O00100O0O2000O010O100O1O01O0000O2O1N2N2N1O2N2N2N1N3M4K6I`\\\\bb0\"}, {\"size\": [1280, 720], \"counts\": \"_Rb53fW1O^hN3`W18O2N0M4H8M3N2O10O01O010O100O010O001O0010O01O001O0000010O000010O00001O0O1000001N10001N1O1O2N100O1O1O2N2N2O2M2N2M2NfUXd0\"}, {\"size\": [1280, 720], \"counts\": \"\\\\`0V1jV10000O10001O000000010O00000O1000O100O10000O1O11O0O101O0O110O0100O2N01N1O11O000001N11OIWiNWOiV1i070001O0HmhNCTW1<nhN_OUW1c04O0O2O0L5N1O2O00001O002M3MV_ci0\"}, {\"size\": [1280, 720], \"counts\": \"`cS16jW1111N4I4M3N2N1N3L3N2M3_OPOmiNU1PV1oNfiNY1_V12N24L1O01N101O000O010000O0100O01O01O010O010O000010N2O0000001O001M2O2N2N1O2O0O2O1M3N3M1O2O2M2N2M3M3KQ\\\\`h0\"}, {\"size\": [1280, 720], \"counts\": \"jjh11eW12_hN4ZW1<M3J6J5N2O1M4O010O0N3N1M3M4N101O1O1O1O001O000O101O000O2O001O00000O10O3M2N3N1N2N3M2N2N010O2N1O2N2N3N2M4L2O011O5H7Ib]Qh0\"}, {\"size\": [1280, 720], \"counts\": \"gif07dW19]hNNkV1i0J6L5K8H10000O2O0O100O101N100000001O001N2O1O0000O100O10000O100000000O10001N10000O10OO2O1O2O0O1N2O100O1O1M3N2M4L4M3M3N4K5K7HaVmh0\"}, {\"size\": [1280, 720], \"counts\": \"_4e1[V10O101O000O10001O001N101O1O00O10001OO101O000001N1O2N100O2O0O100N2O1N3M2N2N3N1N2N2N3M3M3L5K5J\\\\SYj0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"man04fV1?giNOlU1=jiNHPV1W1K5N10O10N5I5O5H:HZMnjNc2oT1800O0O2O0101N01L3O21M3NO1O1002N2N001O2NN11O3YMmjN]2UU1`MnjN]2TU1aMnjN3LS2_U1lMcjN1Oi1PV1O_OWNcjN1Lg1\\\\U1XNdjN33i00MZU1@fjNb03I[U1EajNa07CXU1BcjNT1\\\\V1lNdiN601RW1OThZi0\"}, {\"size\": [1280, 720], \"counts\": \"XV\\\\33lW18H9^Oc0F`0@;F1N3O1O0O1N1M30000O101O01O1O1OO1O100010O4L2N3M3M2O0O2N1O1O100O1O1O2N1O2N1O2N1O2N2M3N2N3M2M3N3M2M4M2M3M3L5KZP^f0\"}, {\"size\": [1280, 720], \"counts\": \"_\\\\l32_W1S1ZO6L3M1N2N2N10010N1O1O20O0000O1L42N00O100010O1O1O1N2O1O1O001O1O001O1N3N3L5L4K4K=_OZScf0\"}, {\"size\": [1280, 720], \"counts\": \"RUP47bW1<J7H;G6J4M4J4L4M2M2N101O1O000O1100O10N1O1001O001O1O2N1O1O1O2M3N3M2N2N3L2O2N3L3N3L4M5J8Fna[f0\"}, {\"size\": [1280, 720], \"counts\": \"`fn54eW1O^hN:ZW19M:G5H7K3M3M2N2M2O1N1N201N100O100O1O1O100001O0000001O1N101O1O2N2M2O1N2O2M2O1N2O1O1N2O1N2O1N2O0O3N1N2N3L4M4K6GoPnc0\"}, {\"size\": [1280, 720], \"counts\": \"ccT84gW1:D?G:G3L201N1O100O001O1O01O1O001N101O2N00O2001N2N1L8I4M5K5L4J7HiSfb0\"}, {\"size\": [1280, 720], \"counts\": \"jXX83lW11N2N4M3M2M4K4N2N3N5K1O1O1O0O11O01O00001O1000O100O0010O000001O1O10O0100O0001O103L0000100O10O001O1O0O1N3N1N3N2N1O1O2N1N3J6K7IPo\\\\a0\"}, {\"size\": [1280, 720], \"counts\": \"idj77^W1j0_OVOTiNe0dV1B]iN>bV1a0O2O0O1O001O000100O000001O00000O10000O11O00001O00O10O010000001N1000O010O1O0100O2N2O1N1O1O010O2M3N2N3M2M3N3M3L3N3L5J\\\\kka0\"}, {\"size\": [1280, 720], \"counts\": \"bjZ3=^W17O2M2K4N3M3J6K4L4O1N3M3N2N1O1O1N1O01000O10000O010000000001O000000001O000O10000O2O0O100N2O100O100O1O100O2M200O100O4LL41O10005KK400010000000M3M4POViNh0jV1WOZiNe0hV1[O[iN`0hV1@UiNN91cV11TiNN`W1OahN1cW12VX1NogNOhebe0\"}, {\"size\": [1280, 720], \"counts\": \"Ti\\\\2439YW18O4K2O0O2N3M2O1N1O1O101N001O002N1O001O0010O02O1N100O100N3N2O0O100O01O10O001O1O10000000O101O00O1O1O1001N100O101N1O2N2O1N3M2N2N2L5L4L4L3L:E:Hn^nf0\"}, {\"size\": [1280, 720], \"counts\": \"_UU49aW19I6M2N2O2L3N2O2M2O1N2N1O3M2O110O01N101O0O1O1O001O1O001O1O1O1O0O2O1O002M101N3N1N2N2O1N102N1N2O1N3M2N4Loime0\"}, {\"size\": [1280, 720], \"counts\": \"_ST27cW1;J5J3N3M2N2N2N1O2N1O2N2O0O1O1N2O101N100001O002N0000001O1O001O1O1O1O0000001O002N1O1N2N2O1M3N2M3N2N3M4Kalng0\"}, {\"size\": [1280, 720], \"counts\": \"iXW1b0UW1>H6K3L4N2N2O0O2O0O2O1O001000OO101O0O2O0001O010O1O1O2N1O3M4K3N2O1M4M2N2N1O2O1N3M3M1O1O1N3N1N2O1O3L\\\\Vnh0\"}, {\"size\": [1280, 720], \"counts\": \"`c:7gW14J5L4M2O2L4L4L3J7J5001O10N100O10O1000O100O010O1O1N2N2O1N1O2N10001O0001O100O10O010O0001O100O1O1O1N2O2M2M3N2O0O3N1N2O1M3M3O1O1N4M6J9EieUi0\"}, {\"size\": [1280, 720], \"counts\": \"cPm21nW1101O000O3L3M7`hNCQW1g0N2N3G2YiNSOeV1o0[iNQOcV1W1L4O100O100OOM5OMgiNbNXV1]1kiNaNTV1`160O5J5L2OO100N001O2O2N1O2N1O1M2K601N100O1O11O3M20M4M1N2M7J4K4L3O0O100M5LMJbhN1_W11dhNK\\\\W16:M5MM1H8000N2O2N]X^f0\"}, {\"size\": [1280, 720], \"counts\": \"ak_64dW1=J2ehN@QW1j0M3M2N2O1N2M3L4N2N2N2N3M2O1N2N2N2N2O1N2N3L3M4N2M3M2N2O010N2O1N3N1O0010O101N2O1O000O1N2O1O0100O0010O100kMijND4`1SU1hNPkND1b1PU1eNSkNH0a1nT1dNTkNK3[1SV1O1N2N1O2M?C5I5L3M4JeTob0\"}, {\"size\": [1280, 720], \"counts\": \"abk77gW1<E4K4L3M5J7J3N3L3M2N3N2N1N2O1N2N2O1M3L3O2M3N2N2N101O100O20O00001O1O000O100O10000O2L4M3M4K`0A5K4I7VO[iNITW10dfXb0\"}, {\"size\": [1280, 720], \"counts\": \"`kT47gW18H4L8I2O0020OL5M5[iNiNXV1Z1ciNjN[V1^100001001N1M^NjiN_1XV13000001jiN\\\\NnU1e1niN^NRV1i1O3M01O3L03M2N13M100O1N2O010O1O001O2N100N2N2N2M2O2N1N3M5K3M5J4K<DhSie0\"}, {\"size\": [1280, 720], \"counts\": \"edi34jW16L2N2O1N3M3N000O1O010O0000010O101N2N2nhNVOkV1P1O3M4M00O00001O001O100O02N10000O100O100O0100O10O01O002N1O0010O01K5L5J9CcZTf0\"}, {\"size\": [1280, 720], \"counts\": \"S^^31lW1:G;G?@3M2O1O1O0O2O1O1N2O2N2N1O00O101N2O0O101O001O10O010O00001O010O000001O1O10O010O01O010O010O1O100O1O1O1O1N2UOZiN9]W1]OWY^f0\"}, {\"size\": [1280, 720], \"counts\": \"iPe18eW19H>A8I5L3M4M1N2M3M2N2N2N2L4N101N2N3N100000001O1O0000000O2OO101O1O0O2O001N1O101N2O1O2N1O2N2OO011O00O2O1O2M102M2N2N2O1M3N4K9G7J2M3M6J5J3NVfhg0\"}, {\"size\": [1280, 720], \"counts\": \"R^V21gW1;K5I7D:K5M3L4N1N2O1O2O1N9I1N0001N1102[jNSNQU1n1ljNUNUU1\\\\2O2N1O3N00N4M4L4L1N1M4M5K2N2N2M3M4[Od0I8ImYZh0\"}, {\"size\": [1280, 720], \"counts\": \"dda23lW12B=J_OmhNa0RW1601O2N5K2O0O1O1N2O2L4M3N1O1N2001O001O10O0O2O0102MO1O1002N10OOM4J63L2O2MKaNliN[1WV1eNiiNX1cV1M2O00N3N12N2N0O2N3M001UOTiN<]W1EahN0aW1O`hN0XW1MkhN2N1WW1LmhN1N1[S[g0\"}, {\"size\": [1280, 720], \"counts\": \"lSc0241aW19K4O1N3M4L`0@010O2N10001O1O2M2N3N001O100O2[jNYNnT1h1e00O1bjNXNdT1h1jjNnNRU1S1ljNoNSU1P1njNnNSU1S1mjNlNTU1U1jjNlNVU1U1hjNnNVU1n10O1O100O101O002M5K8H7I5I6K4H9B=K6K4L6HPTai0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"a8m0SW100000O101O0N2O2N2O1N2O00001N3N2N1O1O1O1O1O1O1N2O0010O0000000000001O0O1000001O001O00001O000010000O01O0100O0101OO101N1O2N1O2M3M4L7J3M2M3N1N2M3M3N4K4LYnUi0\"}, {\"size\": [1280, 720], \"counts\": \"k^\\\\21k_22RhN1O2N1M6J3O1N4L9H1N102L2O2M2O1N1O2N2O000O2O1O1O1O1O001O10000000000O2O000001OO2O01O000000001O0O101O0000001N1000001N101O0O2O0O10000O100O10O1000O10O10O1000O01@\\\\iNDdV1<\\\\iNDdV1<\\\\iNDdV1<\\\\iNCeV1=[iNCeV1=[iNCeV15ciNK]V14eiNK[V14giNKYV13iiNMWV12kiNLVV13kiNMUV12miNMSV12oiNLSV12QjNHRV16P1KRZne0\"}, {\"size\": [1280, 720], \"counts\": \"i`Y12lW13H0]hN1_W1;K4N2N4M3M3M1O1O2O1N2N2N3M1O1N200O2N10O1O12N0000O1O1O1O100O101N2M3L4G9K8AehNNjW1LgWWi0\"}, {\"size\": [1280, 720], \"counts\": \"^9T1lV100O1000000O10001N1O1O100O1O1O1O2O0O1O1N102N1O1O1N2O1N114J_Vnj0\"}, {\"size\": [1280, 720], \"counts\": \"_[^69dW17L2N3NM4L4NO2N7I3K6K5RiNPOeV1Y1L4M000O002O2M:F4M2N3L4M00N20`jNnMRU1c2M6J000O2O0000O10O020O00000O011N12N001O01N3NO2N1000000008[kNmLmS1h3KL4O1N3L3N3N2M2N101N3M3L3N3L7J4L3M200O101O0O101N101N1O1O100O1N3N1N3M2O1N3N1O00100O1N2N3N1M5K7I5L3M3ZOWiNOoRaa0\"}, {\"size\": [1280, 720], \"counts\": \"bW\\\\95iW13N1M4K4L4oNGXjN<gU1IRjN;lU1o0O002N00O01O01O1O4M1O0O1O00O1O001N101O001O0O1O1010O0000000O2O001N10010O01O001O1N200O001O00gNUjN?jU1AXjN>hU1A[jN<eU1D]jN;cU1C`jN:aU1F`jN9aU1EbjN9_U1FdjN7]U1HejN5]U1JfjNO`U1NcjNGgU18Q1O2McRT`0\"}, {\"size\": [1280, 720], \"counts\": \"`oS:3kW14L:G5J5L1N2N2O2L4M2O2N1O1O2OO00O2O010O10O1O01O0100O10O0100000O10O0100O100O10O1000O0100000O1000O01O10O010O010O0010O01QOfiN7ZV1IhiN5YV1JjiN2XV1MkiNOVV12kiNKWV14liNITV18PjN@TV1?g0O1O100O1O100O1O1O101N2N1MnQl>\"}, {\"size\": [1280, 720], \"counts\": \"Y_U82mW15L0O1O1ANmhN5SW1LkhN6SW1KlhN6TW1;O1000000O100O10001O01OO100O101O001O1O1O001O00O0101O000000001O0001O0001O001O001O1O010O01O0000O101O0000000001O000O2O00001O00001N10001O000O2O000O100000000O101O000000000001O000001O0000001O001O1O001O1O1O1N2O1N1N4K6L4Leh^?\"}, {\"size\": [1280, 720], \"counts\": \"bVk41nW12HN_hN4`W16fhNGmV1:PiNKmV1:lhNISW1b001O1O001N2O1O0O2O1O1N2O0O2O001O0O2O001O000000000000001N11O0000001O01O00010O010OO2O0000100O001O00001N2O001O001O001O001O1O1N2O1N100O1O2N100O1N2N3N101N10001O002N1O1O1O1O1O1N2O1O3NhPbc0\"}, {\"size\": [1280, 720], \"counts\": \"l^h11lW13M4J6J6C<M3O1O10000O2O2M2O3L7J8H0O10O1000O010000000O010000O100O100O100O1O1O101N1O1M3N2O2O0O1O1O2M2M4I7L5J5M5JSbUh0\"}, {\"size\": [1280, 720], \"counts\": \"eXf492L21lV1n0L0O11O4MO001M2N4F:L30O10O0N2O26I1OO2L3O22M1O2N2OO00O1000O100M3001O2N2N1N2N1M10201N3N2H9L8H6Jmfae0\"}, {\"size\": [1280, 720], \"counts\": \"]VQ73jW14M4F9N1N3N1N3L4O000O1N2L4O2N1O1O1O2N4L2O1N1O1O00001N101N1O2M2O1WOSNRkN3JP2RU1d0O2001O1O2N101N101N2O1O0O2O0O101O10O0O3NM201O10O01N1N201N2O010O100N2O0100O1O10O010O1O0010O01000O10O1I6]Od0N2O1O100O101M201N2N2N3M2N2N2O0O3N1N2M6Ko[n`0\"}, {\"size\": [1280, 720], \"counts\": \"Zfi64gW17K5N1N2M1O101N4M3M1N2M3N3N1N4K4L4K5nN_NXkNm1eT1h0O10O00011N2N1O2O0O2N2N3M3L4M2N2N1O1O1O1O001O1O00100O000010O010O10O01O1O0010O0100O0010N101hNRjN<oU1BWjN9jU1FYjN6jU1JVjN4kU1LVjN2kU1NXjNNjU11YjNIjU17ZjNCiU1;n01OO2O00001O1O001O1O1N3N2L^dma0\"}, {\"size\": [1280, 720], \"counts\": \"l\\\\c6;bW17K4L4L3M2N2N1O2M3O0O1O10OO20O001O01O010O1O0001O0010O000001O01N2O001O001O1O001O001O0O2N2M3N2O100O1001O0O2O1O1O1O1N1000O01O10O1000O10O0001O1O1O1N2ZObiNH_V17fiNE[V1:jiNAWV1>f0O1O001O1O00100O00100O10000O001O100O1O002N2Mjlda0\"}, {\"size\": [1280, 720], \"counts\": \"eTm31nW12N2L?D1O3L5L1N2O0O1O100O0100000O100O10000O1O10000O100O100O1O100O100O100O1O2N00100O100O100O10O10000O0010000O2O000O1000001N10001O001N100O101O00001O1N101O1N101O0O1O1000000O2OO01O1O1N2N101O1O100O1O10000O1000O1000O100O1O100O1O10000O1O1O100O1O1O2MP\\\\^c0\"}, {\"size\": [1280, 720], \"counts\": \"bVn04jW17I5L3M6J7I9G2M101O1N101O1O1O1O1O1O1O00100O001O10O10O10000000O100O010N3M2N2N2N2M3N2N3L3K5K6K5K[jYi0\"}, {\"size\": [1280, 720], \"counts\": \"nX:3_W1O>2YhNN^W14^hNO\\\\W1=ViN_OSV1g0hiNZOXV1f0giN[OXV1f0`iNB`V1o0O1O1O1O1O1O1O1O1O1O2N100O1N3N1O10O01O1O2O000O1O1000000000000O10O1O1O0020O10N2M3N2M3M21O3M10O010N101N1O1M4M3M3N3K5FTiN\\\\OnV1b0TiNZOSW1`0:L3L2IkoPi0\"}, {\"size\": [1280, 720], \"counts\": \"VT?7_W1>H6J5N2N2O1N1O100N2M300N3N1O100O0O2O100O100O2O1N10001N1001O00O2N2O1O001O0O100OO1012O3L1N1N20N2@ZjN]N10fU1b1cjN]N\\\\U1e1?1O00O01miN[NN0gU1g1<1O1NMPjN[N3McU1h1ZjN\\\\N0OfU1i1]jNVNdU1i1[jNUNiU1k1VjNWNhU1R2ODWjNcNhU1g14H700KQjNZNnU1f16101NO2M4MiiN`NoU1b17DeiNTO[V1k0eiNTO]V1f0eiNRO22[V1j0eiNSO12ZV1l0liNSOVV1n0eiNPObV1m0_iNTObV17\\\\iN24HaV12_iN42KmV12e0KWTXh0\"}, {\"size\": [1280, 720], \"counts\": \"bVc89dW16L4L5K1N2N10001M4M3L4L4K4F:E:M4O0100O100O1O2N101N2O1N102N1N2OO01N10001O1O010N101O010O01O100O1000O01O001O10O1O000010O0001N2O1N1O2WOciNJbV14hiNA[V1<e002N1O1N2O1O2N1O3L\\\\c``0\"}, {\"size\": [1280, 720], \"counts\": \"U_i82mW12N3M4L1OM4O05L1N1M3dhNDSW1h0L0O20001O0N00011O001OO020O01O1O00001O000O2O1O0O2O00100O1O0000O2N02O0000001O010O010O100O1O0100000O01000O0100O01O010O01O1O001O010O001O1O001O1^OZiNHhV15b0O2O001O001O100O2N1O1O1N3N2LmZa?\"}, {\"size\": [1280, 720], \"counts\": \"]_d81oW11O2M1ZhNL57hV1MTiNL1:jV1KUiNMM;lV1KTiN;mV1<0001O0000000O10001O0000000000001O0O2O000O2O000O100O2O0O100O100O1O10000O1000001O001O001O0000000000O10001O0O10001O1N10001N2O1O1N2O0O2N101N101N2N2N1O2N2M2O2N2N1O2N1O001O1O2MaXk?\"}, {\"size\": [1280, 720], \"counts\": \"noQ67`W1;DDnhN?oV1;N2N2N101O000O101O1N101O001N101O0O10000O2O0O10000000000001O000000010O001O00001O001O1O1O1O1O0O2O1N2O1N2N2M3N2N1O2N2N1O2N101N1O1O4M2M2O1O000O0MY`Uc0\"}, {\"size\": [1280, 720], \"counts\": \"lVY31nW1100N4M4]hNIVW1:fhNI40hV1a0UiNAjV1k0O2N1O1O100O2M200O2N101N1O100O2O0O1000000000O101O0000001O1N101N2O001N101O1N2O1O2M2N2N2N2N101N2M3N101N2N1O2O1O001O000001O01N10001O01OO2O001O0O2O2Mjhfe0\"}, {\"size\": [1280, 720], \"counts\": \"^6P1PW1001N100O2O1O1O1N2O1O1O1O1O1N101O1O001O1O1N101OO1O1O1N3M2O10001L3M4N1N2M40O01N4K5M6K6Jnh\\\\j0\"}, {\"size\": [1280, 720], \"counts\": \"nTg1:dW14M4L1O2N101N1O2N2N2N3M2O0O2O0O2O0O2O1N1N200O2O001N010O2N101N1O100O10000O100O100O101N10O11N1O2O0O2O1O1O1N2O0000001N101O1O1N111N2M2O0O10O1O001000ejNbMTU1e2000N1GjjNhMXU1W29O1J600JYjNYN`U1g1bjNXN_U1f1>N23oiNXN31SU1h2G1O001OO1O100O2N10010O1M2NmjN\\\\MoT1c272N01M2L52M5K01M3I8J6J6\\\\OQjNROSV1j0PjNSOWV1=U1E2H]cje0\"}, {\"size\": [1280, 720], \"counts\": \"e_d87hW16I>B4K3N3M200O010O1O1O001O1O1O001O01O001O0000O101O001O0O1O101000O00100O10O01000001N1000001N100O1N201N1O1O1O1N2O1O1O1O001O1O100O1O2N1O1O1N2O1O2N2N1O1O2O0O2O1O1N4LVQY`0\"}, {\"size\": [1280, 720], \"counts\": \"XXP51oW10O1O1N2O01001N0N210O102M2N3M3M1O0O2O3M2N2N2N1O2O0O1O1O1O1O1O1N2O100N10100O100O00100O01O01000O010O100O1O00100O100N20O010O10O10O10O100O2O1N2O000O10000O2O000O100O1000000O100O100000001N2N2N101O001N101N2N10000000000N4M2M2NiXjb0\"}, {\"size\": [1280, 720], \"counts\": \"i\\\\j26<LlV1>nhNCnV1h0O2O000O20O1O0000K510O1N2OO2N10001O001O01O00010O00O1O101O1O0001N2O10000N3N2OO1OO2N3N2OO1O10O100O1O100O10O100000O2O000000000000000001O01O000000O2O00002N106I2N3L1O2M2N3]OTiNMX[oe0\"}, {\"size\": [1280, 720], \"counts\": \"W[P27eW16G:M102N1M2N2O1O2N1O001O1O2N001O2M2N2N3N0O2O1N2O1N2N1O2N1O2O0O2N10000000O01O01O1O1O1O1O1O2N1O00O11O2M5J>^O\\\\UPh0\"}, {\"size\": [1280, 720], \"counts\": \"`=c2]U10O2O1N1jjN\\\\MPU1j201N10002N1O1N1O11N1\\\\NYkNdN3n0eT1<kkN^OWT1`0okN[OST1c0SlNVOST1d0WlNROnS1i0f1O3M0O2N3M2NO2OBfhN8ZW1HhhN5]W1IdhN2_W1MbhN1cW12JMchN1hW1Mkigj0\"}, {\"size\": [1280, 720], \"counts\": \"i;P2PV100O002O1N101O1O1O001N1L5N2N2N2O1O1N2O2M2N100O3M3N1N4L2N4L6I6JUckj0\"}, {\"size\": [1280, 720], \"counts\": \"c]:8dW18I4M4M3M2N2O1O1N2O2N10O01O001O0001O0O2N2O1N2O10000O2HnhN]OTW1b062NO001N3N0O2N1O2K[RZj0\"}, {\"size\": [1280, 720], \"counts\": \"iU_22mW11N2N2N2M200O0M311N3M3B^OZiNg0PV1\\\\OkiN9O`0RV1l00001O2N1UjNUN`U1l1[jNWNfU1R20O0O2N12N2O1N1O10J510O0O200N20O01O1O001O1O100O1O100O1O1O100O2N2N2N1O2N1O2N10001N2N2J7L4L9DR\\\\Rg0\"}, {\"size\": [1280, 720], \"counts\": \"TTT52nW104OiW11ShN031K4N03M[hN1XW1NghN4TW11khNOSW15lhNITW1b0O10O110OO2N1O1O101N10O1001O1N100O1O1O20O000001O0O2O2N1N2O0000000O101O001O0O2O0O100O1001O001O01O0O2O0O10001O00O10000000O100000000000010OO2O1O1O1N2O1O1O100O1O1O1O2N2N1N2O2N2N1O2M4M2N1N5K4M2MRkhb0\"}, {\"size\": [1280, 720], \"counts\": \"j]X65hW14EHihN<TW19001O00N3O1N2O1L3O2O1N2M2O2O00002N1O1O0O20O01O001O001O01OO2O0010O01O0O2O01O01N100000001O0000000O10O1O10O01O1000000O101O0O101O001O0O2O2N1O1O2N2L6K6J6]OfhNNiYkb0\"}, {\"size\": [1280, 720], \"counts\": \"n]^34kW17J5I2O2O001O00000O2O001O00001N11O00O1000000000001O000000000010O0001O001O0001O010O001O00010O001O001O000000001O0000001O01O010O00000010O0010O0010O01O0001O001O00001O1O0010O000001O000010O01O001O0000001O0000001O0000001O001O00001N2O0O102M2N3LYiPd0\"}, {\"size\": [1280, 720], \"counts\": \"YlS21oW11N2N4L2IHdhN:WW1:N101N2O0O100000000O101O001O1O1O00001O0000001O100O10O010O0100O10O0010O0001O00001O1O00010O01O01O0001O010O0010O0100O001O01O000010O00O100N3N2O0O2O0O10001O1O1O010O000010000O01O01O001O1O00001O1O0O101N101O0O2O0O1O2M3N3LSkbe0\"}, {\"size\": [1280, 720], \"counts\": \"U]`26gW1:H4M3M2M3N2M5L0000001O100O010O010O1O1O1O001O1O2O0O6J6J0000100O1O00010O1O001O0001O000O2N1000000N2O2N1O100O10000O1000010O01O10O00010O0101O2NO2M2N2O000000000O101M4M3M7Ia0^O3M3NbiVf0\"}, {\"size\": [1280, 720], \"counts\": \"khU31k_21c`M2oV12jhN2UW1<01M3O1N2O001O001O001O1O1O1N101N2O1O1O2N1O1O1O1O0000001O0000100O2N1O1O0000000010O01O00010O00001O0010O1O1O001O101O2L3N2M2O1N2O1N101O1N110O001O001O1O01O00100O10O0O1O2O2L4N001O00O12L3K5M3N2O3KS^Re0\"}, {\"size\": [1280, 720], \"counts\": \"ibh32`W1>N2N3L6L2O1O1O1O1O101N1O00O101O1O01O001O00001O1O100O1O0O2O00100O0O2O001O1O1N101O001O0O2O1O1O1O001O1O1O1O001O1O0010O100O2M2N1_NiiNY1VV1fNmiNZ1nU1iNTjNW1mU1fNUjNW1mU1hNUjNV1mU1gNUjNX1[V1N100000001O00010O0000001O1O001O1O001O0O2O1O001O1N2O1N3L7Hhl`d0\"}, {\"size\": [1280, 720], \"counts\": \"]li38cW16N3O3L8G4G8N2O0O2L3O2N2O1O1M2O2O001O000O2O1N2O1O2M3N001OO010O1O1O1O1N2M2O2M3N101O00100000000001N100O010O10O001O1O1O001O1O00001O0O1O2N1O2N1O2M3M4M2M3M3M3L6J7H`RVe0\"}, {\"size\": [1280, 720], \"counts\": \"jfV2d0[W15K4J5J6M3K4N2N2O1O1M3O1O02O2MN3N2O000O1O101N101N200O00000001O010N2O1O2N2N2N2N2N2N1O1O2N1O2N1N3N2N2N2N2N101N2O1N2N2N2N1O1O2N1N3M3KShZg0\"}, {\"size\": [1280, 720], \"counts\": \"gX45jW15K9G3N1O1L3K310O3N1O10000O2O2M2N2O001O1O000O101O001N1O101N10001O001O001O001O10O01O010O1O001O100O1O1O0000O11O1O1O002N1O10O000O11O010O10O1O3M4K5K4J6L5L1O011N3L2N8F:Cengh0\"}, {\"size\": [1280, 720], \"counts\": \"`:R1nV10O1000001O000001O001O01O001O00100O1O00O2O0001O1O1N2O00012M2N1O000000O10001O000M4I7O001O1O2M110O1O25LO0O3L7_OehNKQTPj0\"}, {\"size\": [1280, 720], \"counts\": \"cR41T`20jgN2kW1OlgN603O1O2N3L1L4khN\\\\OoV1e0QiN\\\\OmV1e054M1N002TiNROeV1P143L02O0011N103M3L100O01000000O01000001O00010O0001O00010O00001O0O101O001N2N3N1M3M3M3L5I8HXn_i0\"}, {\"size\": [1280, 720], \"counts\": \"k;2oW1O26H1K40000N2O2N10O01N4L5MO0OohNYOlV1h0SiNZOjV1i044L1N2O1YiNnN_V1W132N00O11O1O2N100N110O010O01000O010000O10000010O01O00001O001N10001O001O0O2O1N2N2O1M3M4J6K4Knd^i0\"}, {\"size\": [1280, 720], \"counts\": \"f;a1_V110O0001O00O1O10000O100O100O1O1O100O1000000O10000O100O1O2O0O10000O2O0O2O1N2O1N1O2O1N2M3N3M2N3L4Mi[Vj0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"P[d25fW1?EImhNEoV13[iNMdV14\\\\iNMbV14]iNMbV17XiNMgV17SiNKmV18ohNIRW1b00010N1O1O100010O0001O001O001N101O00001O0O100O10000N3N1010O01O00O100001OO1000000000O10000000001OO101O001N1O1N3N1O100O2M2O1O1O1O2O0O10000000000O10000O001O2N1N3L4M4J\\\\mme0\"}, {\"size\": [1280, 720], \"counts\": \"aXZ23jW18K2M3N2N2N2M3N0O110O00001N100O101O0O200O1O001N1010O0001O1O000000O010O100O100O100O1O1010O0001OO2N12N1000O001N101O000O2O000O2OO1O101O000001O1O4L2N00N3H7M3O2N1O2N2O1N3N2M3M2N4L2MW_Zf0\"}, {\"size\": [1280, 720], \"counts\": \"i<i0WW100O100011N1O0O2OO2O0000O20O001OO2O1O010O1O001O10O01N1000001N110O1O001O00010O01O000001O010O0000001O100O01O001O1O2N1O1O2O1M5K4L7G\\\\Zbi0\"}, {\"size\": [1280, 720], \"counts\": \"m:Z1fV1001O10O0O2O0001O010O2N00001O2N10O1O1O010O1O011M2O1O2N2O3Km0ROeklj0\"}, {\"size\": [1280, 720], \"counts\": \"Z:\\\\1dV1001O000000O10000O2O0O101O1N1O2N2N3M3L6I\\\\]Yk0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"fcc17hW1101N2IHdhN:YW19F^ORiNd0kV1ATiN=jV1>D<ciNeNNOlU1o1N2N2OO100000O1000O1001O1O01N10O100O100^jNiMZU1Y2djNiM[U1]21N2O1N102O02K5H>F21I9K2M111N3N2M01L2201N2M3O1N3N010O2O0]NXjNn0hU1PObjNb0DROkU1<djN>bU1VOPjN6>c0iU1\\\\OYjNa0gU1AZjN;iU1EWjN9iV1O1O2GdhNKX\\\\kg0\"}, {\"size\": [1280, 720], \"counts\": \"hic42jg3OngN5ogN4M4K3M3M3Aa0G8M2N2N20O0100O2O2N00O00XjN\\\\NVU1f1_jNdN`U1n100O0100N2O1O100O001O1O1O10O01O10O0100O10O10O1O1O100O1O2M2O1O2N1O1N3N101L3N3M3M3M3N2M4L4L3N3L7Chodd0\"}, {\"size\": [1280, 720], \"counts\": \"P`T18ZW1a0ohN]Oe1KiR1l0]kN]OZ1KkNM[T1]1blNVO_S1l0[lNYOdS1i0ikN_N1j0UT1i0gkN`N0m0KQORT1d1nkNdN2P1oS1_20OO10M4N210OO0OOPlN^LfS1c3YlN`L`S1R40O21WlNPL_S1a4F5L1OO13M100O10O0^OTmNlKQS1n3c0L5O010O10000O1O1N1EUlN\\\\LRT1b37O100000000O1K5G9N20001N1000000O100001O0001N101O001K5M2L5K401O11ON2N2N2O1N2O1N2G:G9K9[OXQdg0\"}, {\"size\": [1280, 720], \"counts\": \"bg[34jW15K;F4K7J4L5Lc0\\\\O2O0O102N1O1O1N2O001N2O0000O1O12N2M101O2N2N2M2OO1001N2O00O001O2M2O100O2O000O2O0O10000O101N100O2N2N1O2M3N2M3N1N3N3M2M3M3K5L4J7I;B]Xne0\"}, {\"size\": [1280, 720], \"counts\": \"eic44kW12N1O3L4M2N2M2O1O1O2M2O2N1N6J2N4J<E4I5J7L3N3N20O00010O100O1O1O100O1O10000O1O10000O100O10001O00000O100O100O100O101N1O1N3N1O1N3M2N3M3M3N2N2N2N3L3L>B[gcd0\"}, {\"size\": [1280, 720], \"counts\": \"iYd01i_22^gN;PiNFnV1b016mhNYOcV1S101]iNkNVV1NkiNY1MkNSV1f1O2M2M3O2O001N3L5L1N2O2N1N3N4L3M3M2N100O2O0O1O10O01O101N1000000O0100000O100000O1000O1000O10O01001ON10100O1O11O2M3NN2N2O1O100N2O100001OO1O1O1N2O101O0003M2NO100O2N1O1N2O2O0O2N1O2M2O2M2O2M201N2N1O2M3N2N2N1O3K3O2M4M2N2L4M4I7L5J4K7JQPff0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"g6h0WW15I9H6K6I5M2M100O10000O101N10O10O100000001O2N1O00000O3N1O1N10001N3N00000000004L2M10O1O1N10100O1O1O00100O1O1O1O2N1O1O1N3M2N2N3K6J;_O`0E`Q`i0\"}, {\"size\": [1280, 720], \"counts\": \"Yol01mW1=@;F6K5K4M3M3N2N2N3L3N2O3L4L3M4L2N101O1N100O101M20O2O0O02N20O0O2OO1O1001N1O02O1O00O1000O11O0O100001O01O1OO1O02O1O1N01O0103M100N2O1ONkM\\\\jN?NQ1fU1`N]jN6L02Y1kU1a0OQNWjN5N]1lU1^NTjN51]1lU1cNWjN\\\\1kU1aNUjN`1UV11K51N4^NgiNV1TV1eNoiN5MU1WV1=O]NkiN[1UV1eNkiNZ1^V1OO21OKcNiiNZ1\\\\V14NeNciNU1]V1kNciNn0OSOaV1V1KgNhiNV1[V1hNfiN5Kk0hV154K3O0OM4HH\\\\iNCeV13^iNB29`V16hiNHXV1:`iN_ON6bV1:aiN@N4bV19kiNGWV17iiNHWV19hiNIVV19hiNHcV1K^iN6UW1L\\\\hNN\\\\W1MjhN4LNeW13BMlhN0O2VW1LlhN2N1nWlf0\"}, {\"size\": [1280, 720], \"counts\": \"c<Y3hT1O1N3M2O1O1O1O002O1O001N1O2N1O1]MjjNZ2]U11O12O0O002N2N1O001O002N0O1QNVjNh1jU1WNXjNh1jU1WNTjNj1oU1WNRjNc1WV1NF[N[jNe1eU1[N\\\\jN\\\\1IgNWV1Y1iiNeN[V1Z16O^iNgN]V1X1diNhN\\\\V1X1diNhN\\\\V1Y16oN\\\\iNf0cV1WO`iNf0aV1WOciNh0^V1WOdiNg0jV11K5JQOZiNo0eV1VOXiNj0gV18N2OTO^iN<bV1C^iN>cV1^O\\\\iNf0eV1XO]iNf0dV1;4L00O1101N0EUiN@lV1`0SiNAnV1h03^OQiNMlV1e0OFUiN@aV1JeiNe0M^O^V11^iNd0gV1XO\\\\iNP1gV13000QOUiNh0RW10001N2OL4I7N14CZiNZOgV1c0]iN[OdV1b0e0G3N2O1NjYdh0\"}, {\"size\": [1280, 720], \"counts\": \"o_k22nW11M2M3O2L4N2L4N2M7I5J4L4L4M3M3M4L3PjNXNcU1l1[jN^N[U1S20010O2N1000O10O2N01O2OO1O1O02O1O1O001O00000O1N102O03MO00002O10OM21000O2O001O0000000O1000000O2O101N0O01001O00O10O3N6I3J6M2N10O3NKbiNiN]V1W161O000N21O1OOOO2002O2N1N2O1N100000O0N32N1N1000001O1N1000O11O0O102N2M1O002O000HohNARW1?701L4NbhNE]W169M]PVd0\"}, {\"size\": [1280, 720], \"counts\": \"foZ41kW1g0\\\\O:F2WiNoN_V1Z1M3N2M3M4M4L3K5M20O10JkMajNU2^U17O2N1O010O1N3O00O01N3N1O1H8O011N1O100O100N3N1O010O10O100000000000O010O1O10000001O0000001O00001O0O2OO1O11O0O1000001O000O100O1O2O0O2O1N2O00002N3L2\\\\MkjN\\\\2aU1iMajNc1QV1N2L6eNbiNi0aV1TO_iNm0hV1M5ZOViN3oV1JRiN7ZW111G:Lookc0\"}, {\"size\": [1280, 720], \"counts\": \"oWW41mW13H:L3M4K<E3ViNoN`V1[1N1N4J6L2N2J61M1000O1N21O1OM3M3L4O001001O00N2N2L5L3O2OO010010O00O2OO101O0O0010O11O0O1N2N2O1N2000O1001O00O10O0O2O100O100000000000000O1000000000O101O000O10001O0N2N2O2O0O10O0101N1M4N3DhjNlMZU1d1ajNcN9GVU1e1djN`NjU1_1=0G]NSjNd1lU1\\\\NTjNf1kU1ZNRjNh1oU1CWjNjNSV12oiNKPW1NUjec0\"}, {\"size\": [1280, 720], \"counts\": \"o7n03XOfV1i0YiNWOdV1S1K4M4K5I8M6I5L6G8I6L3BQMckNV3kS1i0M3N2O001O110O0O010000000000000001OO1001O1O000O101O010O1O1O00001O1O0O2O1O1O1O1O001N101O001O00010O1O2N3L5L;E4L5K8G5L5K4L9F=]N\\\\iNR1nV1K8H3L3N2N2M4M3LUgjh0\"}, {\"size\": [1280, 720], \"counts\": \"XX]11oW12N2M3N1N9F6K3M2M5K3N3L4L6I4N6J1N4M3M1O1N2O1O2M3N1O1O1N2O1O1O1N3N1N2O1O1O1N3N1O2L3N3N2L3M3O1N2O100O2O0O2N1O100000001O000O100001O01O01O00000000001O010O00001O2N1O00001O001O1O1O1O00001N101O1O1O1O001OO02O0O2O0O1O1N3M2O1O2I7N1O1O3N3M3M2UOdjNmNcU1k0`jNnNjU1i0\\\\jNTOkU1d0k0_OQiNNRW1OnhN0aW10QPme0\"}, {\"size\": [1280, 720], \"counts\": \"Xhg22nW11ehNOdV13khNd0mV18O6J1O1O2N2M3M2M3N3I6O1N2O2N2O1N2N101N2M201O001O00001O1N2O0O10001O001N101N2O0O10000O2O0O1O10001N100O101OO01O2N1O2O0O2M2O1002N1OO1K6O0O02aLckNY3`T13O01O2N1O00001O0100O0001O1N10000001O1O1O01O0000010N1000O1000O10000000000000O1O03N1[O^kNiMcT1S2fkNgMYT1Y2h0M3M4L3J8M4L3M3H:H8G7K8E:F[P[d0\"}, {\"size\": [1280, 720], \"counts\": \"f5R4nS1OO20O101O0001O0010O01O010O01O001O1O0000001O1O00100O1O001O010O001O0100O01O000010O00O102N2N1O2M2M5^N_kNVN5m0_T1g0QlNPOZT18RkNWOm0;ZT1GilNJ^dRj0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"Y>f0ZW102N2N4L3M2N3MWaik0\"}, {\"size\": [1280, 720], \"counts\": \"g=o0QW103L3M3O2N0O4K3M4M3Lciek0\"}, {\"size\": [1280, 720], \"counts\": \"T=k2VU1O000010O01O1O2M101O1O1O1O2N4K4M6VNVjNR1eV1G3M2N1N4M2N2N2M2O2N1N3N2M3N2M^Xjj0\"}, {\"size\": [1280, 720], \"counts\": \"Rnl22mW11SO3eiNOVV14kiNMSV14niNJPV1b0giNFoU1h0diN]OWV1\\\\1H3N2N1O2I6O2M202M2N3M101O001O00100O01000O01O100O100000O0100000O10O1000000000000000000O1000000O10000000000O100000000000000000000O10001O010O1O1O2N1N2O1O2N1O1N2O1O2N3]NYjNg0kU1ROWjNn0^V1N2O1O1O2O1N100O001N2OJYOUiNf0hV1^OYiNa0dV1C[iN<dV1a0O0010O0O2O2M2O1000O01O10O01M301O0M3N2O11O1O000O11O1I:I301ONI_hN2aW1O`hN0`W10`hN0Tkkc0\"}, {\"size\": [1280, 720], \"counts\": \"gkR51`W12hhN6oV12lhN2]V1JliNi0nU1ZOoiNm0jU1UOXjNl0\\\\U1^OdjNe0b0gNiS1k0\\\\kNg0a0`NST1g2^kN`MWT1IgkN[3fS1oL[lNR3iS1b010100002N001O1OO0O2I72LTLSlNk3jS16N10013N1OJ5M3O11O2N0O10001O1L3M5L5L1O10233H3K1OM7M4N^OcKamNZ4bR1dKamNX4aR1gK`mNW4_R1lK_mNS4bR1nK]mNR4dR1nK[mNR4eR1oK]mNn3bR1TL`mNi3`R1XLWmNP4iR1c0^Oc0O01N2N5oJ_mN\\\\4cR1cK_mNY4dR1fK\\\\mNY4gR1dK[mNY4SS13010O1O1O1O2N2N3H]lNnKfS1m353M4O1ON2O10000N2M3ETlNaLmS1]3WlN_LYT1R3ikNlLYT1Q3<O2OOJ7N2^MkjNX2WU1gMhjNQ2cU147J3N2M3K3CniNfNWV1W1;J:XOXiNImV15`0Nnnhb0\"}], \"areas\": [688, 2804, 3381, 2949, 3199, 3226, 3576, 3675, 3812, 4328, 4774, 4467, 3888, 3521, 3249, 2487, 1882, 1794, 2469, 4214, 2749, 423, 0, 1697, 2218, 1461, 1721, 1909, 2330, 2625, 3639, 2369, 0, 0, 0, 3692, 3193, 2048, 2330, 2702, 1223, 1943, 2643, 3711, 3134, 1689, 1946, 2033, 2941, 2332, 4290, 3208, 2646, 1746, 2531, 4061, 2297, 2226, 2745, 0, 0, 3178, 3937, 1528, 776, 7740, 3249, 2921, 3609, 3125, 2510, 2045, 5887, 4351, 3675, 3898, 2111, 3397, 4527, 3539, 3288, 3312, 2648, 2459, 1757, 7304, 2814, 3279, 3130, 2631, 1973, 1385, 1065, 3283, 3854, 3311, 2604, 3038, 3871, 3479, 3958, 3963, 2973, 4019, 2002, 2035, 2360, 1819, 0, 3013, 2706, 2045, 1286, 825, 0, 0, 0, 4241, 3631, 8935, 4559, 3831, 10263, 0, 0, 0, 4349, 6509, 4698, 6902, 8334, 8543, 8475, 11324, 11046, 6371, 0, 0, 0, 0, 0, 122, 218, 1832, 8388, 13090]}, {\"video_id\": 0, \"category_id\": 1985, \"bboxes\": [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [371, 656, 40, 81], [291, 673, 65, 75], [198, 547, 64, 77], [366, 283, 112, 91], [557, 406, 56, 66], [436, 411, 77, 65], [256, 433, 73, 76], [306, 564, 64, 75], [371, 640, 43, 53], [354, 358, 53, 86], [335, 226, 45, 56], [286, 76, 42, 54], [245, 0, 51, 65], [208, 21, 65, 96], [248, 301, 82, 112], [361, 390, 97, 57], [359, 312, 51, 82], [306, 284, 42, 87], [274, 324, 55, 65], [324, 315, 48, 65], [361, 285, 49, 56], [422, 289, 24, 55], [408, 271, 39, 54], [326, 183, 47, 90], [250, 232, 64, 74], [287, 278, 49, 71], [343, 246, 48, 72], [394, 218, 56, 68], [444, 240, 28, 73], [441, 244, 23, 57], [380, 188, 58, 83], [289, 245, 61, 60], [309, 276, 46, 77], [320, 272, 61, 70], [347, 258, 59, 67], [404, 264, 41, 58], [398, 236, 55, 86], [324, 236, 43, 83], [282, 295, 77, 61], [379, 346, 71, 62], [394, 432, 57, 92], [416, 598, 67, 77], [431, 607, 73, 79], [461, 596, 74, 81], [473, 613, 63, 77], [459, 645, 65, 73], [446, 662, 69, 77], [467, 648, 75, 72], [469, 564, 96, 67], [473, 594, 89, 64], [449, 635, 85, 57], [432, 676, 83, 59], [444, 670, 78, 65], [440, 640, 80, 76], [449, 572, 80, 136], [498, 393, 121, 62], [544, 262, 109, 81], [541, 240, 97, 95], [336, 232, 129, 116], [249, 338, 103, 108], [287, 439, 70, 116], [380, 497, 66, 109], [460, 390, 104, 88], [478, 229, 120, 105], [452, 114, 92, 111], [215, 369, 97, 116], [215, 508, 65, 105], [314, 618, 62, 92], [369, 624, 61, 96], [355, 348, 58, 97], [334, 313, 55, 69], [378, 355, 56, 87], [513, 372, 93, 89], [581, 306, 89, 83], [591, 339, 85, 96], [516, 409, 130, 65], [401, 286, 108, 83], [378, 326, 129, 56], [395, 402, 121, 55], [430, 466, 114, 77], [510, 478, 79, 91], [603, 575, 68, 98], [479, 477, 104, 79], [372, 349, 95, 55], [351, 330, 75, 56], [353, 371, 62, 54], [337, 377, 46, 62], [396, 337, 62, 89], [487, 372, 99, 80], [376, 339, 101, 61], [324, 304, 84, 58], [319, 323, 70, 59], [311, 374, 55, 65], [276, 368, 52, 79], [382, 292, 81, 106], [542, 332, 78, 61], [592, 330, 65, 67], [573, 285, 84, 78], [402, 211, 91, 72], [416, 195, 121, 87], [525, 264, 106, 73], [463, 267, 102, 72], [294, 305, 76, 68], [282, 309, 79, 72], [400, 219, 69, 89], [472, 215, 110, 87], [378, 266, 94, 84], [361, 255, 89, 85], [472, 221, 118, 77], [524, 239, 118, 76], [558, 224, 151, 97], [379, 150, 127, 128], [305, 219, 131, 99], [546, 221, 130, 108], [506, 195, 117, 121], [111, 284, 214, 106], [156, 253, 190, 187], [395, 162, 212, 189], [445, 273, 102, 112], [362, 272, 115, 121], [499, 282, 140, 102], [445, 303, 123, 117], [260, 363, 126, 143], [458, 375, 147, 103], [566, 339, 153, 97]], \"score\": 0.859649121761322, \"segmentations\": [{\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"o]`>=`W15K4M3K7I7J8G7K3M3N2M2N2N3M2O1N2N2N2N2O1O100000O10000O2N1O100O1O100O2N1N2L4\\\\Oe0I6ZOS\\\\P<\"}, {\"size\": [1280, 720], \"counts\": \"a^\\\\;`0ZW17K5M3L5M1N2O1O2M2N3M3N1O2N1O2O000N2O2N1O1O1O2M2O1N2O1O1O0010000O100O1001O00000000000O10001N10000O101O0O1O2O0O101O0O100O001O9G7F8G^RU>\"}, {\"size\": [1280, 720], \"counts\": \"aQh74gW1?[hNAQW1P1H4M3L3M3M2N4M11OO1O2N101N2N001O1O1O2N2N2N10O01O001O0000000O100000000010O0O100O01O100O02O0O1O2N1O3N2M2N1O1O1O2N102M2O6F`0A9YOlhN5bW1M]eja0\"}, {\"size\": [1280, 720], \"counts\": \"akY>3\\\\W1ORiN2lV11SiNOgV18TiNMbV1=[iNE`V1P1N3N1M3M3O100O1M3L4L4L5N1O1O001O100O1O1O1O01M22O1O0O01L36K2M2N01N2000000N2N12O1N2OM3N1N2O2O101N01O2O00O1O1O010O1O1N2000O10O1O1O1O1O101N100N2L4O1O1000001N100O1000001O000O100O2O000O2O1O0O101N2O1N2N3L4L4J5N2O1N1O2N2L3Ldn\\\\9\"}, {\"size\": [1280, 720], \"counts\": \"\\\\ehe04iW1<ZhNGnV1j0H9K4M100O2O1O1O001O001N101O1N101N11O01O1O1O1O001O0O010001O01N1O2O010O0000O2N2N101O1O0M3O2M3K4N3K5M3L4M4I9I8FiRT4\"}, {\"size\": [1280, 720], \"counts\": \"n]Qa05kW1N3N03L3]hNLVW1`001O2N001N2O3L3N3M2N1N1O010O100O1O0100000O1O100O1O2O0O1O100O1O100O1O1O10O1OO3N2N10O0001O00100O1O1O2N1O10O1O1O101O001O000O20O1OO2M2O2L4L3N3M2M5L6ZORiNOiYR8\"}, {\"size\": [1280, 720], \"counts\": \"h^P:8:NjV18ohNMoV1d0M2M4K5M3N2M3M3N3L3M3M2N2O100O100O100O10001O1N2O0000O10O01000O010O1000O11O000001O001O001N100001O0000O201N1QNWjN0O]1lU1_NajN_1SV1M00001hN]iNQ1dV1lN_iNS1jV1M5K1O1O0000000O002O1EihNIZW10nhNLRXX?\"}, {\"size\": [1280, 720], \"counts\": \"SRo;6jW11M4L5L3N3K4L5M3N5FjN^iN[1^V16M2L3M_NkiN\\\\1TV1dNliN]1SV193M00K[NPjNc1oU1\\\\NSjNd1TV11O1N2I7O0101OO01O010O1O1O001O1O2O0O100O10O01O010O1O1O100O001O1O1N2O0O2I8I5F;L5M3N1N4L4JUec=\"}, {\"size\": [1280, 720], \"counts\": \"`\\\\`>b0\\\\W16J3L5L4K3O2N2O2M2O1O1O1O0O2O000000001O001O000000000001O1N2O0O10000O2O3L3N3L5L2M5L2M4LVcl;\"}, {\"size\": [1280, 720], \"counts\": \"mlj=<]W18K5]Ob0I7N3M5L<D2NKZjNRNeU1n1^jNPNaU1n1bjNRN]U1n1ejNQNZU1Q2:6I2N100O1N20M3O2N11O2N3M3M1N010O]OejN\\\\NcU1b1>N2O1O1O100O00100O1N2O2K5L21O2N10O00?@4G8G`0FSdU<\"}, {\"size\": [1280, 720], \"counts\": \"foR=1nW11O1N2M5L3M3N4M1M4M3L4N3L2O2N4L4K4M1N2O001O000010O01OO10000O100O100O2O0O2N2O1N1O2N2N3L4L6Hc`W=\"}, {\"size\": [1280, 720], \"counts\": \"kbU;74M\\\\W1>M5J3N1O1O1O1O1O2M3N100O10001N2O1O1O101N1O1O2O001N10O01OO2O0O2O002M2N2O1O1N3L3N4F;CWeX?\"}, {\"size\": [1280, 720], \"counts\": \"PXb98hW18H2N<D4L>B5K3M4L4L1O1O00O100O100000000O1000000O1000000O10000000000000000O10000GniNbNTV1Z1;M3M3N2K5L4L4M3M3K5IWh``0\"}, {\"size\": [1280, 720], \"counts\": \"kQT8o0mV19G8H6L4L4L3M3M4L3M3N1O2M2O2O1N2O1N10001N101N10000000O10O10O010O0010000O2O000O2O0O1O1O101N1O1O1O1N2O1O1N2N2M3M3L3M4L5K4M3L3N3L7I6UOPiN3NM`f^a0\"}, {\"size\": [1280, 720], \"counts\": \"YZf9>bW122M00I8H\\\\OPiNh0jV1=I4M5L1O3L2N102N102M1O0O6K1O2M1O00101O1M4K7I5N2N2N1O000O01O11N2O3M10O0O0WOVkNkM29hT1l1fkNmM[T1T2bkNmM_T1T2`kNjMbT1U2_kNkMbT1T2]kNlMdT1S2XkNoMNLdT1R2akN\\\\N_T1d1bkNWNBJiT1n1XkNQN3NXU1`2WkN_MST1a2mkNaMQT1_2PlN_MRT1a2mkN^MST1c2mkN]MST1c2e001O1N2O2GajNmM`U1P2cjNnM_U1R282M2N2N10O1IZjNQNkU1b1`0L3O1O100bNaiNX1eV1O001N:XOohNOYW1GlhN6fW1HalU?\"}, {\"size\": [1280, 720], \"counts\": \"odS>1j_23RhN2M3N1N3M1O2O1N2N1O1O2N2N2O0O2N100O100O2O00000O101O001N2O0O100000000O2O001N101N100000000000001O001O00001O00010O0O1001O01O01O000010O0100002M10O1N100O1001O001O1O000000O1O2O1N2N2O2L5L3L4L5K4M4KojU:\"}, {\"size\": [1280, 720], \"counts\": \"aSQ>a0UW1=H5M2N202N1O2N1O2N1O2M201N101N1O1O2N1O2N001O0O20O0000001N11O00O10O1000O2O001N1N200O1O2N2M4L4L6H8H6I:InmQ<\"}, {\"size\": [1280, 720], \"counts\": \"]jn;3fW1b0A9C<H7J6D^NRjNf1iU1<M3K4N2N2N101N1O0010O02O000000O100000001N2O101N1O1N4M2N3L4M2N2N2M5K3M6IS^_>\"}, {\"size\": [1280, 720], \"counts\": \"ojf:3jW18I6L3L5K3M3N2N100N2N3M2N1O2C=M3N110O010O0011N001O2O0O1O001O1O1O1O1O001O1O1O1O100O1O2N1O3L3M4K3M2O4L2L4M3M4L3NcTW?\"}, {\"size\": [1280, 720], \"counts\": \"nZe<?]W19I5K3M4L3CjNiiN]1lU1a0O0O2N1000000001N2O001O1N2O0O2O001N101O1N101O0O2N2O1O1N2N2O1M4L3M3M4L3M4L3M3N2M3N4KV]a=\"}, {\"size\": [1280, 720], \"counts\": \"jaS>9cW1;G5L3L4M2N2N2N2N100O101N100O100O1O1O1O1O1O1O001O10000O1O1O1001O1N101M2M4M3N2N3M3N2M2O3L3M3M3M3M5D_hNOjUS<\"}, {\"size\": [1280, 720], \"counts\": \"_i_`0>`W16K`0@4L2N1O2NO11O100O2O00000O101N2O1O1O1N11K8AbiNoNkV14]od:\"}, {\"size\": [1280, 720], \"counts\": \"dYn?3kW14L3M2M3L4O2G9L5K3M4M3N101N1O2O1O1O1O001N01O1O1000000O101O01OO001O10001O1O2^O_iN@SW1LUiNNhmd:\"}, {\"size\": [1280, 720], \"counts\": \"Ygg<7eW17I6L4K6F:G8I9I5F<J3M4M3M3N1N100O10001O000000000000O10001N101O001O1O1O1O2N2N1O2N1O1N4J7G<]Ob0Hd0UOmX`=\"}, {\"size\": [1280, 720], \"counts\": \"bhh9=aW16K2N2N3N1N2O2M2O0O2O1N100O2O0O2O0O2N1O1O1M3K5G9N2M3N2M300001O00012M1O1O2N1O1O3M1O2N1O1O1O100O1O1O1O1O1O1O:E4L3L3L4N2M3L4N2M3N2NWoi?\"}, {\"size\": [1280, 720], \"counts\": \"XRW;<XW1b0D8K4M3M2N2O1O100O1N1O2L3I701O10O010O01000O2O000O2O1O001N2O1O1N2N2O0O1O2N2M4M2N1O2G9K5L4M4M2M4K5Kd^n>\"}, {\"size\": [1280, 720], \"counts\": \"nP]=>`W15J7K4L4K4M2N2O1N2N100O100O100O1N2N1N3L4K5N2K500000O010O2O000O3M2M3N2L7K3K5M4L4K5L4M3L4K6K3M3M3MQgi<\"}, {\"size\": [1280, 720], \"counts\": \"Rh\\\\?:bW17J6K3M4M2M3M3N2O0O2O001N101N101O1N101O0000000000O100000O11O0O10000O01N1O1N2N3N2O001O1O010O10G801O1M3I8K4K5K6K7C[Q`:\"}, {\"size\": [1280, 720], \"counts\": \"`Y[a01fV13]jN2ZU1;\\\\jNJ]U1a0oiN_O00mU1U1QjNlNmU1V1QjNkNnU1d101N100ON2O1A?N2O2N1M301O2M2M3O1O2]Of0J03N2O4MFe`d9\"}, {\"size\": [1280, 720], \"counts\": \"RaWa01iW16M3M3L5I7K4K6L4K5M3M3N1N3N3N1O0000O1N3A?M2H;I[`n9\"}, {\"size\": [1280, 720], \"counts\": \"UWk>b0ZW17K6K5J7J8G3N2N1O1O1O1O1O001O100O1N2O1N2N2N2N2O1O1O001O1O01N101O0O2N2N2O2N1O1O1O1O2N101M3N2M4M2N4K6I6K3M3M3M4L4L3L4L\\\\Qo:\"}, {\"size\": [1280, 720], \"counts\": \"X`Y;1kW1:I4L1O2N2N2O2N1O1O2M2O2N1O2N3M3M4L00001O100N1000000000001O0000000O101O00001N1O1O1O2O00000000O1O2N00O1O13M2M3O0N3N3M6Jc0^O:ETo\\\\>\"}, {\"size\": [1280, 720], \"counts\": \"haR<?`W1:G4K5L2M3N0O2O1O0O2O0O2O001N1O1O2N0O2L4^Ob0N1100O10001N2O002M4L2N3N3L2M4L5J5L4K6L4K5L4L4J6M3KRfV>\"}, {\"size\": [1280, 720], \"counts\": \"VZ`<7fW15L3M2O1N2O1O1N2O1O1N2O2N1O1O2N1O2N1O2O0O2N100O2O0O100O2O0O1O1O001N2I7L4L4N1100O100O101N2M4L4K6J6M3L3M3M3N2N2K6K4L4L5M2M4MXVV=\"}, {\"size\": [1280, 720], \"counts\": \"dQb=5iW13M3N1N3N1N2O1N2O2N1O2N2N2N2N2N101N101N10001N101N10000O1000O1000O10000O1000O1000O1O0K5M3O1OO12O00O101O001O101N1O7Je0ZO8GmnV<\"}, {\"size\": [1280, 720], \"counts\": \"WYi?6gW17I5J6K5J6K5L5K4M3M1O2N1O1O10O010000000O1000001O001N2O2N1O1O1O1O1N2O1O1O1O0N2N3A`0G^Wf:\"}, {\"size\": [1280, 720], \"counts\": \"`ia?<aW16A=K4M4M2O1N2O1N2N3N1O1O002M3N001O1O1N2O001N10000O2M2N3N1N201N1001O1O10O01O2N101N1O2O0N3M3_OPjNlNTV1m0d0K5N2M3M3N3M3L6JTX\\\\:\"}, {\"size\": [1280, 720], \"counts\": \"RYe<1eW1>C<E:E:J6K6K4N2O2M2O1N3M3N1N3N2N1O0O10O2O0000001N2O1O1N2O1O2M2O2N1N3N2N1O3L3N3M2M8_Ol0[ORgg=\"}, {\"size\": [1280, 720], \"counts\": \"QjP;6gW14I6K5N2O2O0O10000000000O101O0O2O0O2O0O101N10000O2O1N10001N101O1O0010O01O001O1O00001O001O1O001O010O002N010O100O100000O10001N2M2O3M1N4M3L3N2M4K4N3M4K3L4M4K\\\\eQ>\"}, {\"size\": [1280, 720], \"counts\": \"kSj>:2HZW1l0_O9K3M3N2O2N1O10O11M200O100O2O000O2O0O10000O1000000000001O0000O0O2L4N2O100O1000O010001O1O0O2N2M3N1O1N2N3N2N1N2N2N3N2N1O1O1O01O10O1O1N3N1N4L5Kkl_:\"}, {\"size\": [1280, 720], \"counts\": \"Ro\\\\?1<6mV1g0C;G4K6O1N3L7I2O1N2N3M3M2M3N2M3N200M300O100O100N2O010001N001N2O100O100O101M3L3M3K6M3N2O001O4L2M4L8G4K4L4L5I7L3M6Haa^:\"}, {\"size\": [1280, 720], \"counts\": \"YcX`0g0oV1f0B5L3M3N2N1O2M2M3N200O1O1O00001O001N10000000000O11O000001O01O2N1O010O1O010O001O1O001O2N1O1O001O2N1O2N1O1O001O101N2N2N1O2N2N3M2N3L5K7Ie[V9\"}, {\"size\": [1280, 720], \"counts\": \"f[k`08aW18M3Ma0fhNWO\\\\V1[1L3M2N2O1N2O1N2O0O2O0O2N101O001O0O2O0000001O000001O01O0100O1N2O2N3M2O1N1O2N1O1O1N2O1O1O00010O1O100O1O1N2O001O100O001O1O1O2M3N2M2N3L4L4L4I7HbS\\\\8\"}, {\"size\": [1280, 720], \"counts\": \"SkPb05iW15L7I9khNWObV1e1^O5K2N2N2N2N2O1O0O2N100O2O00001N101O00001O000001O010O100O1O1O1O2N2N2N2N2O0O1O2N1O1O1N3N001O1O100O001O1N2O1O1O1N2O100O1N2O2M2N2N2N3L4M4K4M3M3Kd[U7\"}, {\"size\": [1280, 720], \"counts\": \"ek_b0=bW1P1PO5K3M2N2O0O2N2O1N2N2N101N2O0O1O1O1O10001O000O10001O01O01O01O2N2N2N2N2N2N2N1O2N1O1O1O1O1O1O1N2O1O2N1O2N1O1O1N2N2N2M3M3M4L8H7IWST7\"}, {\"size\": [1280, 720], \"counts\": \"c\\\\na0<bW18G:H7J4K6K4L2M3M2N2O1N2O0O2N1000001N10001O001O1O01O1O2O1N2N001O1O1O1O1O1O1O1O1O1O0010000O1N2N1O2O1N2N1O2O001N2N2N2M3M3N3L4L5L3L5JaRc7\"}, {\"size\": [1280, 720], \"counts\": \"WU^a07hW14L3M4K7I9I5J6K4K5K3M2N2O1N2N2O1N2O001O1O001O1O010M2O20O001O2N2O1N3M1O1O1O1O1O2N001O1O001O1O1N2O1O1N2O0O2O1N1O2O1O1O0O2O1N2O1N2M3N3K4M5J8IkYn7\"}, {\"size\": [1280, 720], \"counts\": \"P]Xb07iW14J4L4M4L3M4M1N3N2M2N3M3M3L4M3L4M3L3N2N2O1N1O101O0001O010O001O001O001O00001O00001O00001O000O1000001O0O101N10001O0O2O0O101N1O2M2O2N2N102M2M3M3N3L4K5K6Jmbl6\"}, {\"size\": [1280, 720], \"counts\": \"SjZb03jW1?B9H2N2O1N101O0WiNQOaV1o0^iNTO`V1m0_iNQOcV1S130001O0001O001O0100O000001O0O2O001O001O001O1N101O1N2O0O2O001O0O101O1N102N1O1N2O000O101N01000000O100O10O0100O100O1O101N1O100O101N1O100O101N1O1O1O2O1N2M3N3M2N2N3M4L4L4L4K6Efmo5\"}, {\"size\": [1280, 720], \"counts\": \"Wk_b06gW15J5L9I4L2O1N2N2O001N1O1010O00O1O2O0100O1O010O01O001O0O2O1O001O1O1O000O2O00001O1O0O2O001N101N2O1N101O000O2O00000O10O10000O100O100O1O101N100O100O1O1O2N1N2O1N201N1O2N2N2N3L4M3L6J:AmdS6\"}, {\"size\": [1280, 720], \"counts\": \"Ylaa0=_W1:H4M2O1N102M10000O1O2O0000000O2O1N101O00001O0O101O0010O0001O2N1O10O01O001O010O001O000000001O01O000000O2O00000001O01O0000O01000O1O2O0O1O1O2N1N2N2N2O1N2O2N1O1N3M2N3M3K4M5KmcV7\"}, {\"size\": [1280, 720], \"counts\": \"bel`03iW17L3L3N1O3N1N2O1O2M3N1O2M2O1O2M2O1O1N2O00002OO001O1O010O01O0000010O1O1O00101N1O1O010O010O0010O01O00010O0001O0O10001O0O1000001O0O10000O1O100O1O1O2O0O2M3M2N3N2M2O2N2I9HZZn7\"}, {\"size\": [1280, 720], \"counts\": \"^e[a02kW16K3N3L4M3N2M3N2N1N100O1O2O0O2O1O1N2O1O1N2O101N1000000000O0100O010O100O2O0O1O100O00010O010O010O010OO2O001N2O1O001O0O2O1N1O2O1N1O1O1O2N1O2N1O1O2N1O2N1N3M2O2MVbe7\"}, {\"size\": [1280, 720], \"counts\": \"bdVa0;cW14M3L5L4K5L4L5K3M4L2N5K101N3M3M3O0O1O2M2O100O0100O1O010O1O1O1O1O100O001O00010O0O2O0010O01N2O1O1N101O1O1N2O1O1O1O001O1N2O1N2O1O1O2O0O1O2N1M4M2N2N3M2O2M2N4M2N3M2NTRh7\"}, {\"size\": [1280, 720], \"counts\": \"hjaa07eW1;G8H<E:F;E7I3M2O2M4K5L3M3N1N3L5L3M3N2M2N3L4M2O1O101O0O1O1O2M2O2O0O10000001OO01000O10N2O1O001O1O1O1N2O1N2O001O1O1O1O1O1O002N2N1N3M4L2O1N3M2N3M2N3L4M6H8I5K6I9G:SO]l\\\\7\"}, {\"size\": [1280, 720], \"counts\": \"hm^c04hW14_Oa0O1O2O000000000000001O00001O1O1O1O0000O0N30000000O100000000O1000000O10000000002M4L5K010O10O001000O1NO2NfiNgNRV1Z191O1O100O1O1O100O100O1001O0O0100O2O5J20N1N2O001O1000000001O1O2O0O2N1O01N1000001O0O101O1N110O10O01O1O0O200O1O1O1N2O101O1N1N2N4L2N100ESiNCXW1MohNOeim3\"}, {\"size\": [1280, 720], \"counts\": \"lYXe03gW1:B>G6M4L101O1O2O000O10001O1O001O001O1N2O1N2O000O2O1N2N2O0O100O1O010N2O101N1000O10O10001O0O1O1O10000O101O000O1N2O2OO1000N2O03N1N01N2O02O2M10O0102M2O003M1N10NWNUjNa1TV1001005J2O00O0101O0O100O101N1000O2M7NB;M4M2O2POUiN:`W1N3L3OK40N5M3MSWb2\"}, {\"size\": [1280, 720], \"counts\": \"SaTe08^W1<J7K3M6J6J4L5L1O2N2N2O1O1O1N3N1O2M2M3N1O2O1N101M2N2O2N001O100O1O101N2OO10O1N3O00O1O1001N01N3N00100O1N2O1O1O1O100O00100O101N1O100N3M4L2O00102M2M201N1O3L2O2OO02N1O2N2N2N3M2M3N2O000O2N3L3N2M3N4J5GjoT3\"}, {\"size\": [1280, 720], \"counts\": \"RZT=1oW11O4J2aNJliN20O;OH8nU1>oiN5oU1n01O0200N2N1O2M1HSN\\\\jNP2cU17O1M3010O2ON1O12O0O0000N30O1O01O0O2O000O2M10100O2L4N1N31NJSMZkNm2eT1SM\\\\kNm2mT1O@UMbkN1Oj2_T1SMdkN10k2oT1N1O0O0001O0000O2N1010O0001O001O001O0O101O01O01O001O001N1O2O0100O2N10O1N2OO2O1ON2N200000001O000M210O4L2N000010000O00002O1N100N1O200O1O1M203L4M1N2M3N2O1N2M5M2WOohN2:KiV10ciNO]V10UXm9\"}, {\"size\": [1280, 720], \"counts\": \"idg98dW15K6M3L4L3N4L3L6K9G2N2N2N2N2M2O1O1O1N200O00010O1O1O1O1N2O1O1O001O2N101N100O1O01O0010O001O000O102O0O001O1O001O01O10O1O0100O1O0001O01O01O1O11O0O2N01O10O3M100N100010O01O100O1N2O0N3M3N2O1M3M3N2N2N2N2L4M4K6G;Db0_OT]Z>\"}, {\"size\": [1280, 720], \"counts\": \"cWW;:dW15L3_O`0K7J5L5L1O2N2M2O2N2N1O101O0O2N2O0O2O0N3N2N2N3L3O1N1O1N3N3N0O100O2O00000O105K4K1O10010O00000O101O1N1O1N31N2OOO2N2O1O003M4A\\\\kNZMkT1W2`kNfMhT1P2V1YO<G7I8G;DZPT>\"}, {\"size\": [1280, 720], \"counts\": \"k`k>3eW1i0ZO6L4L4K6K5M2O2L4L4M3L3N2O1N101M3O0O2M4M2O2N100O1O100O2N2O3L10O001O100O1O0101O000001N2N1O2N2M3M3N1N3M3N2L3N3M3M2N3M2O2M7H7G6K5J7C>Cgfd:\"}, {\"size\": [1280, 720], \"counts\": \"o\\\\oa0:cW14M2O1O2N2N3M5L2M2N3N3L2N2O1O1N3N0O11O1O001O0O1000O10001O001O1O001O001O0O2N2N2O5J2O0000000O1000O0100O4L01O10000O1000O2N4L`0AM3I7L4O1O100000000100O1O01N1O2N1O2O1O100O1O2N001O001O002N2N1O1O0N2N3L4L3N3J7K5L4K5K8GlZQ6\"}, {\"size\": [1280, 720], \"counts\": \"_ieb05ZW1NihN?SW1:K4M4FQO^iNQ1aV183NGfNhiN[1WV18O1N2N2M4EVNYjNo1eU1QNZjNQ2eU16O1O1O001O001O10O1O1N10000O10N2O1000000000000O1O100N2O10O0100O1O10000O10O01001OO100O10O1000O1000O10O10O100000O100O010O00000O2O100N2OO110O1000O0101O001O01O01N2O1O001O0O1O0LejNeMZU1[2501O0O2N1N3N2A?O2O1O003K3O3J5L5K7H;@WPg4\"}, {\"size\": [1280, 720], \"counts\": \"PVea02iW1:J4F<F8L2M2O2O1N1N3N1M3N3N1N200N3N2M5K2O1N2VOiM`kNb2^T1a0O2N2O000O1000O100001N101O0000001O1O0O1000001ON2N111O0M3001OK]kNmLcT1T341OL310O101OO100000FWkN\\\\MjT1c2;4gjNYMoT1m2O01O0ESkNbMoT1[2TkNdMmT1Y2VkNgMlT1U2VkNjMlT1T2b0N2M4aNUjNc0nU1oNfjNh0^V1J2O2N1N2O2M3N1N2O1N2O1O1NYbj6\"}, {\"size\": [1280, 720], \"counts\": \"mU]83aW1a0G=F8H5J4F:M2M3L5M2N2N1O2M101N10100O1N2O2N1O100O001N3M2O1O2N1O2O1OK5O1@QkNgM1NnT1a2:O1O0O1010000O100O1010N1O1O1O01000O2O0O1N3OO1O01O001OO2N2O1N2O02O7H100000]Od0J6QNSjN31X1SV1<0\\\\NliN\\\\1TV172N1ON3O04M3aNeiNS1]V1jNeiNU1\\\\V1hNgiNW1UV1iNQjNU1]V100O1O01O04VOTiNJ54iV1Mk0M2Mj[l?\"}, {\"size\": [1280, 720], \"counts\": \"\\\\Y]87bW1`0F4K5J5M2M5K3L4K5L6J5L4L3N2N2N2M3O2M2M2N300O0O2O1N101O0O2O0O20O01O1O00001N100000000002N2M2O2N1O2N3M2OO0O23L4L2N2O0O6H7F8I7F:I8H>\\\\OaVTa0\"}, {\"size\": [1280, 720], \"counts\": \"UUY<2jV1j0[iN]O_V1T1K3I8N2N2O101O2N1O2M3OO01O0O2O1O1O1O1N2N2O1O1O1O0O20000O001O0100000O10O4L6J2O1N2N1O2N3M2M4M2N100O1O1O2N3M3L5L4L2O1N6J6I8HYR\\\\=\"}, {\"size\": [1280, 720], \"counts\": \"[m]>f0eV1i0F8I6L4M2N3M201N2N2M2O1O1O2M2O100O1O1O101O0O1O1000000O2O0O1O10001O000001O1O00O1O1003M2N2N2N1O3M2M2O1O1N3N2M3L5J5M6J8G;D?ROTcX;\"}, {\"size\": [1280, 720], \"counts\": \"STl=f0UW1e0]O7J9G4M2M3N1O1O2N1O2N1O002N2M2N3N1O1O1O10000O1O101N101N10O100001O00000O1N20O2O2UMQkNa2PU1^MPkNa2RU1aMPkNY2PU1eMUkNY2jT1gMRkNNOX2nT1lMSkNLOV2oT1nM[kNP2fT1nM_kNm1cT1RN^kNl1\\\\U1M3N3K4L4M4K9F9F>CnSn;\"}, {\"size\": [1280, 720], \"counts\": \"ajQ=?]W1:G5K5L3O1M4M2N2N2O1N3M2O1O1N2O3M2N000O1000000O10000000O10O10001N0100001O000001O1O1O1O100N4M2N4L3N2M3M2O1L4BYiN]OmV1;YiNAn[m<\"}, {\"size\": [1280, 720], \"counts\": \"Zlh>>ZW1=G9H6K5K4L4M3M3M3M2N2N2N2O1N2N101N1O1O1O2N1O2N1O010001OO1000O10O11O000010O01O00O2O0001O1O1O2N1aNijN3XU1LmjNMVU11ojNITU15mjNKUU13kjNLXU11ijNM[U11ijNG]U16jjNB\\\\U18]mS;\"}, {\"size\": [1280, 720], \"counts\": \"XeQd01nW1100O101O0N2N6L2N5K1M2O2N101N1O2M4M2M3M3N2O1N2N2N3L3M6K6HO300000O11O1O0O1O1O1100O0100O10O01O000010O01O001O1O1O001N2O00010N2L4L4N10100O00100O10O0100000000O10000O100O1O2N2N101N101N3M3L4J6IUl\\\\4\"}, {\"size\": [1280, 720], \"counts\": \"obff08]W1<H8L3L5M3M3N2N2N1N3N2N3L4M2N2M4M2ObNVjNh0hU1oNXjNH3Z1cU1eN[jN04MOa1_U1iNhjNZ1VU1cN`jNM2d1\\\\U1aNajNj1]U1YNejNI1_1ZU1aNejNL132_1XU1bNmjNNL_1WU1cNkjNJ5b1QU1bNZkN^1fT1aN[kN_1eT1aN[kN_1bU1101M2F^NVjNb1jU1^NVjNb1kU1\\\\NWjNc1TV10N1O100O1N21NKZNSjNe1mU1[NSjNe1TV1O100O011O001N10001N101O0O101N1000001O0O100O2O00001N1O101N10000O2O001O001O0O1O2O1N2N2O3LRml1\"}, {\"size\": [1280, 720], \"counts\": \"hTSg05gW1;G8I2N2L4L3K5M4L3N3M2N2M3M4K4O1N3M101O1L4L4L4O1O1O1O2N2N101N1O2O000000O01001O00O10O101N1O11O02O0O002M3M202N0O3L2N3O1N100N4M5K4L3M3M2N1O1O1O1N2N2O01O02M2O001N3L3N3K5M3K>BW[e1\"}, {\"size\": [1280, 720], \"counts\": \"[^Ud01oW10000000O1O1N200O1O1M4M2M3I7M4N2N3M2O1O1N3M200O001O0O2O0O2N10000O001O1N2O0100000O100O2O000O100O1000O10O10O01O010O010O10O01000O2N3N1N1000000O10O10O2N10O100000O0100000001O0000000001O0000O1N2N201N10010O0101N4M2M2O0O001O1N2O0O2O0O100O101O0O101O0O2O2M6J4K5J4NJHfhN6ZW163N_jj2\"}, {\"size\": [1280, 720], \"counts\": \"Pce?6hW13M3M3M3M3M2O2N2M3N1N3M3N1O2N2N1O100O100O00010O1O010O1O1O1O1O2N1O1O2N100O1O00001OO100000001O2N000000100O1O1N101O2OO0O100100O10O01O010O10O101diNaNUV1e110ON2K4O2O1O0000O200O02O010N100O01M3O10000000O10000O2O:C6K3O1N1O1000010BnhNMRW1IXiN1]]W8\"}, {\"size\": [1280, 720], \"counts\": \"Vkh>2mW14L3M2N2N2N2N2M4M1O2N101N2N2N3N2N2M2O0O1O010O0100O1O00100O100O010O01O010O10O10O0100000O100O01000O1000O0100O0100000O10O1000000O10000O1O100000000001O0O11O001O000O20O001O01O001O00001O000000001O0O1N3N2N2N1O10O1000000001O001O0O101N1O100O1O1O1N3N1O0O2N3O0N2O1O1O2O\\\\eX8\"}, {\"size\": [1280, 720], \"counts\": \"XU^?5iW18H6J4M2M3N2M2O2O0O2O001N2O000O2O0O100O10000M43L1O1O00000000001O00O2N101O001O00O01000000O1O100O1O10000001OO2N101O00000001O01O000O2O001O000000000000001O00O100O1O10000001O10O1O1O1O001O0O100010O00O1O1O1N3N1O2M2O2N1O2M4J6L4M2M3O2M5M13L3L0O19G01O0[Zm7\"}, {\"size\": [1280, 720], \"counts\": \"Roi`08eW19H3M2N3N2O1N2N2N2O1O1N2O1O001N1000001O1N100000001O001O010O1O1O0000001O010O001O1O1O10O00001O0001O010O2N01O000001O01O01O1O0010O01O1O0010O01000O0100O100O2N010O00101N0000O1O1O1N3M2N2N3M2O1O200O1O101N1O1O2N1N3N3M3M3M2N2O0N3N2M3N2M4JjWj6\"}, {\"size\": [1280, 720], \"counts\": \"comc02hW1:G8L5G9K3M3N1N2O001N2O010N101O002OO01O100O10O01O1O100O1O100O1O2O0O101N100O102M:F10O0O2O010O1O1O1001N11OO01O001O00001O01000N2O1O2N1O1O1N2O1N2O1O1O1N3N2L5C<G:EjoQ5\"}, {\"size\": [1280, 720], \"counts\": \"nZbg01mW13H:I6N3N1O2M3M3N1N2M3N3L4N000O2N5K3M2M2O1N3L210O0O2N2O3L2O1N2N1O101O0N2J6L35L1O2N1O1O3M1O2O10\\\\jNgM^U1]22N1O001O1O2I6L4M3J6N2M2K7L5J4L5L3N4L6J6J^\\\\k1\"}, {\"size\": [1280, 720], \"counts\": \"_Wgb01iW17L4K5M4M2N2N3O1N1O13M1OL4M4O1OO10N3M2000000O2N10O10O001O1O1O001O00001N2N201N100O100O1O1001O001O000O01000O0010O00010O01OO100001O00O1001O00O1O100001N100000001O100O101O1O1O0O10O01O10O0001O001O001O1O1O2K5L5L4L6Eb_Y5\"}, {\"size\": [1280, 720], \"counts\": \"g[a>3lW13M3L5L3L3N2N2N101N1O2N2O0O1O2O1N2N101N2O001O1N1000001N3N2MO101O101OO0100000O10000000O101O00O101O00001N010O1000001O1O1O0O011O0000000001O00010O10O01O1N2O001O1O1N101O1N2O1O1N10101N1N3L3L5K5J9B^dj9\"}, {\"size\": [1280, 720], \"counts\": \"jSg=4kW11N2O2M2N2O2L4M2N2O1N3M2O1O2N1O1O1O1O1N2O1O001O2OO01O001N11O0001O00010O100O100O1O100000001OO010000000000001O000O10001N10001O0O1O1O2N1O2M200O2N2N3L=C8Iml];\"}, {\"size\": [1280, 720], \"counts\": \"Rei=4cW1:K6I5M3M3N3L3N2N2N2N2N2O1O1O1O1O100000000O10000000000O0100O1O010O1O100O10000O100O100O100O1O1O100O1O1O3M1O3M2N3M3M3M2N2N4K7Hkck;\"}, {\"size\": [1280, 720], \"counts\": \"YdU=a0ZW1e0]O7J5L4L2N3M101O000O100000O010O1O2O0O100O10000O010O101O0O1O1O2K4N2O1O1N3M2N2O2M3N2M3M3M5I8HlcS=\"}, {\"size\": [1280, 720], \"counts\": \"oZ_?6fW1k0VOQ1PO;F6J2O1O001N10000000000O10000000000000000000O10000000000O1000O100000O10000O1O1O2O000O10001N1O2M3N2K5N1O2N2M3L5K4K6QObiN3gV1H[iNNPW1MTiNMlSW:\"}, {\"size\": [1280, 720], \"counts\": \"XUQc03lW1;F4L5K2M1M4O000O2M2O2N2O0O1N3N100O100O1O1O1O10N12N10N2O010O01O1N101O1N21O2M10N2N20O0100O101O0O1O10O01O1O1N2N2O1O1O000010O01O00100O01000O010000000000000000O1O2N1O1O101O000O1O2N2N2N1O2N2N2N2N3L4J7H8HnkU5\"}, {\"size\": [1280, 720], \"counts\": \"g[f>6iW13N2M3M3M3M2N4L1N3N2N2N1O2N100O2N101N100O01O10O10O1O010O1O10O01O10O1O100O1000000O2O0O101O0O101O0O101N100O1O100O100O2O001N10000O2O1N100000000000O101O001O0O2O0O2O0O2O001O0O2O001O2M3N2N1N2O1N3N2N1N4M3L4M5\\\\OhT^9\"}, {\"size\": [1280, 720], \"counts\": \"gZe<7gW15L3M2O1O1N1O2M3N1O2N2N2N1O1O1O1O100O100O1O1O10O010O000001O01N100001O001O1O1O1O100O100O100O1O10000001N1000O11O00000O101O001O0O101O0O101N2O001O100O1O1O1O2N1O2N1N4L4M5J6J_]T<\"}, {\"size\": [1280, 720], \"counts\": \"fS_<4fW19H5N2N2M3M4N1N2N2M3N2N2O2M2N2O1O1O1O1O001O002N1O1000O010O010O1O001O100O1O1O100O10000O100O2N10001O0O2N10000O1O2O1N2O0O2O1N2N2N2O1N3M3L4M3M6GXUl<\"}, {\"size\": [1280, 720], \"counts\": \"[TU<g0SW1b0B8G5M3L2O0010O010O01000O1000O010000000O010000000000O100O0100000O1O1O1M3N2O1O1O1O2N1O1N2O2N1O2N2N1O2M2O3L4K5ITlh=\"}, {\"size\": [1280, 720], \"counts\": \"k[i:5fW1<G5L3M3N2M2O2N2N2N2N2N2M3N2N2N1O1O2N3M2N2N1O2N2N3M1O1O2N1O101N2OH7O2N1O2O1N1O2N2N1O5K7I4L7J6I7I6J3M3M3MP[X?\"}, {\"size\": [1280, 720], \"counts\": \"\\\\jm>4gW17K4L6I8K3K7K2L4M2N2O2M2O1O10001[jNfNaT1d1mjNhNoT1Z2K1NO2O010000O1000000O10000000O100000O20O00O1000000000001O0O101O0100N2O2M4M2N1M3L5M2O1N4M2N1N5C:M3L4N2N2N2M4N3L2N2M7I2N1M5L6M2M\\\\eo9\"}, {\"size\": [1280, 720], \"counts\": \"gjUe0512^W1;M2N1100000O2jhN[OnV1P1K2O001O010O001O001O1O100O1O001O001O00001O00001O010O00100O001O10O01O001O00100O1N101O002N00010O0000O1O101O001N101N1O2M2O2L3N3L3L5L4J6N2N1O4K4KR]k3\"}, {\"size\": [1280, 720], \"counts\": \"T[Tg06_W1<H8H8N2O2N1O002O0N3N2O0O1O2N1O100O1O1O100O001O1O1O010O001O001O1O100O001O0001O0O100001O1ON201O01O1OO101N100O10001O0O2NjiN\\\\NSV1c16N2L5E<G>CQU]2\"}, {\"size\": [1280, 720], \"counts\": \"^a\\\\f0n0kV19M3M2N100O2O00001O001N2O1O001N101O0O2O001N101O001N101O1O1O1O1N2O010O1O0O2O00010N2O001O00001O0000O101O00001O001O10O01O0000100O001O0010O01O1N200O1O1O2O0N3N2N1O3L6J8G:C`0]ObU]2\"}, {\"size\": [1280, 720], \"counts\": \"egf?=aW16J6K3L3N3L4L3L3N3M2M4N1O2N2N1O2N1O2O1N100O100O10000000000O100000O0100000001O00000000000001O000001N1001O00000000000001O000001O0000001O000001O01O000000001O001O001O1N1O3L3M3M3H7M3O4F=XOWiNKb_k8\"}, {\"size\": [1280, 720], \"counts\": \"kWX`02mW18H2N2M3N2N2N2N2N2N3M2M3N2N3L3M3M3N2N2M3N1O1N3M2N2N3N1O1O1O100O100O100O1000O010O10O01O010O001O1000000000O101O000O0101O0O0100000001N010000O10000O0101N100O011O000O01O010O010O100O2O0O1O2N11O1O01O1N2O1N2O2N1N2O100O1O2M3O1N3M2N2M3O2M3M3L3M4BohNGSW14RiNKnV13TiNLPW1OTiNNW_T7\"}, {\"size\": [1280, 720], \"counts\": \"_a`d03lW14L7J3K4M3M3M4L2N3N1N2N2M2O1O100O1O001O100O100O010O1O100O100O1O1O2O0O1O101N1O100O1O1O101N1O100000000O2O00000O2O0000001N10000000000000000001O00001O0001O000001O00001N102N1O1O1O2N1O2N2N3L3N1O2N2M3M3N2M3N2M4M1N6K6I7IPf]3\"}, {\"size\": [1280, 720], \"counts\": \"^QSb08eW17L5I5L3D<N1N2N2N2N2N2N2O100O1M2N3O1N1O11O0001O0O1O1000000O10000000000O1M3O11O001O0000000000O10010O0000000O10000O1O0101OO010000000000000001O001O1O001N10001O1O011N1O1O001N1000001O001O0O2O0O2N3N1N2N5K5I9Fc0\\\\OZVP6\"}, {\"size\": [1280, 720], \"counts\": \"fj_;3iW1:H5L1O2M3M3N2N3L4M2M3N1O2M3N101N1O1O1O1O1O100O2N101N2N101O1O001O000O100000000O1000000O1001O0000O2O000000O2O00001O001N101N2N1O2O1N3N2N3L4L4L4L7I4L4K8H7GUmc=\"}, {\"size\": [1280, 720], \"counts\": \"hjP;:cW18I4L4L4L4L3M3N2N2N101N1O101N1O1O2N1O1O1O2O0O1O1O1O100O2N101N101N101O1O00001O0000001O000000000001O0O2O0000001O000O2O0O2O1O2N1N2O1N2O1N4L4L5L3L4L4L3M2N3L4M4K4LQUo=\"}, {\"size\": [1280, 720], \"counts\": \"aXd?1kW1?_O7J7L6C=K5J3M2O2N1O2N1N3O0O2N2O0O2O0O1O100O100O2O000O100O100O101O0O10000O10O0100O0100O001O1O001O1O00100O1O100O100O1O101O0O2N3N1L5L5J:QOdiNYO17dV16ZYh9\"}, {\"size\": [1280, 720], \"counts\": \"]X^b08gW12O2M3N1N2O1O2M2N2N2N2O1O1O1N2O1L3J7M2O2N1N3O1N2O000M3N2L4O1O2O00001N100O1O1O0101O002M2O0O01M2O2M22O1N11N10000N1O3L4M3O0O020O0001M2O01N20O1O2N1O100O2O000001O0001N2O1O001O00000000001O001N101O1O1N2O1O2M3N2M3M4L3N3L3M4RO\\\\iN:XW1L5GdoZ5\"}, {\"size\": [1280, 720], \"counts\": \"Pjh>3`W1a0H7I6L3L4N4K5K3N2N2N2N2N2O0O2N2O0O2O0O100O2M200O100O100O10000O101O0O100000O010O10O2O1N01O0010O0100O10O0100O1O100O2O000000000000000O10001O00000O101O1O1O1N3N2M3M3N2M3M2O2M3M4POZiN`0iV1VOciNd0oV1M2N3L3N2N6HQ^d9\"}, {\"size\": [1280, 720], \"counts\": \"VaS>a0;@cV1i0WiN[OdV1j0XiNZOdV1S1M4L4M2M3N2N2N1O1O2O0O2O0O2O0O100O100O101N100O101N001O011N1O100O100O100O0100O00100O10O01O10000O10000O1O100000000001O00000001O0O10000000001O001O1O1N2O2M2N3N2M3M4K8Ic0]O5J4M2N4K_n_:\"}, {\"size\": [1280, 720], \"counts\": \"UX^b04kW12N4M3L5L3M2M2M4K4N3N1O1N2O2N001O0010OO2O1O0O2O0O10O02M2O100O00100O1O1N2O1N2O1O010O10O01O100N3N1O101O00000O100O100O10000000000000000O2O000O1000000000001O00000000000000000001O00000000010O001N2O1N101N101N2O1N2O1M3N2N2M3M3M4L3M3L5L4K5K7J:FooP5\"}, {\"size\": [1280, 720], \"counts\": \"`X_d06fW19J5L3N2M3M3L3N2N3L2O2N2M101N100O10000O10000O10000O1O100O010O1O2N00100O1O1O1O010O100O2O0O1O2O0O1O101N100O100O101N100000000O10001O0001O0000001O000001O0001O00000000001O00001O00001O00000O101O001O1O001O1O1O2M3N2N1O2N2M2O1N2N2N2N2M3N4K4L4K7^Ofoo2\"}, {\"size\": [1280, 720], \"counts\": \"`hie02jW1:H9G9I3L5L3M3L3N2N2N0O2N10000O1000000O10000O100O101N100O2O0O1O2N1O2O0O2N1O2O0O1O2O0O1O100O100O1O100O1O1O100O1O10000O100O1O100O100O100000000O100O100000001OO1000O10000000000O01000000O10O1000O1000000O010O10001N1O010O1O2M3N1O1O2N2O1O0O2O1N2N1O2N2M3M3L4N1O2M3N2O001O0O2O1O1O1O0O2O2N1O2M2N2M3M2M4O1N2M3I7M6JkW<\"}, {\"size\": [1280, 720], \"counts\": \"omi>6gW1`0A4N1O0N3M3N1N201M4M3I8M1O6I1N3N1N2M300O1O101N1000000000000O1O1O2O000O100000000O0010001OO1O0O2000O1000O100000000O10fjNfMnT1Z2PkNiM5NZT1Y2bkNhM40XT1Z2ckNeM70VT1[2dkNdM61WT1Z2]kNlM7M]T1c2bkN]M_T1h2ZkN[MeT1R3N4LO10000010O0dkNcLUT1^3ikNdLVT1c310O011000M2O1N11O100002N1N5J20O10O8H1O3L200N4M6I2O1O00000iM_jNn1cU1QN]jNo1eU143N1O1O1lMXjNn1oU1N0O2O2ZNmiN[1TV1cNniN[1SV1eNmiNV1YV1iNgiNV1eV1LKmNaiNP1]V1VO`iNj0[V1`0OQOciN=^V1AfiNB0<0D[V15aiN0=NaV10TiN0<MkV12c0OmPZ8\"}, {\"size\": [1280, 720], \"counts\": \"X`m;c0ZW14M2N2N3O2M7H3H8N2G`NoiNd1jU1<O0O100O010O2N2O0O2O0O100000001N1O2O0000O11O1O001N2O000000001N2O1O000O2O0O100O1010O000000000000000001O01O00000000001O01O1O01N11O00010O01O01O10O0O1100O0001O00000O2O1N1O2O0O2O0O2N2N2N2N1O2O0O2N2N1O2N101N1O2M3N2L3N3N1N2O2O1M3N2N2N2N3M2M4L3N2L7Hn_Q;\"}, {\"size\": [1280, 720], \"counts\": \"dhZe06QW1K_iNe0YV1]ObiNj0ZV1`03NL3M3N0O2N3M3N2N1N3M2O1O0O100O2N110O0N3L3O101N2O00000O2M2N2N2N3O0000000O101O0O2K4N3O02OO0O2O00001N1O2N100001O0O101O00O2O1N10000001O001O0O10O100001N100O1O2N2OO100O11N2N10O0102M2N2O1N101N1O100O1O2M2O1O1N2K5O2M2N2N2N2N3N1N1M5N1N3N2N2N2N2N1O2N2O1N3M3_OQiNLQW12RiNKoV1LPiNN43cW1Oe_e1\"}, {\"size\": [1280, 720], \"counts\": \"\\\\hhc0:ZW1a0E<G5@jNmiN\\\\1RV1:M3L3N4L3M4L3M3N2L5M2O000O2N2N11000N1N3N101N10001O0O100M4I6N30O0000O101O000000000100O1O1O1O2N1OO1N3N11N101N3M2N1O2O001O1O1O0O1000003L3M10O1001O1O0000001M2M4L4O1N3L6K2N1O1O0O2nMWjNl1jU15N1N33K4M3M2N1O5K3M2N3M2N3M3N1N1N3N6J2M3M3M3N6J4Jogg3\"}, {\"size\": [1280, 720], \"counts\": \"QS[41VX12C4K7KKfhN5nV1LRiN6lV1JQiN;lV1>M3N2N2M2O2O1O1O2M1O1O1O2F95KGVN\\\\jNj1cU1WN[jNk1eU1UNZjNl1_U1[NajNe1^U1?O1O1N3N1002MN3O2O0N1011O0000O1O0101O00O00010O01O100O10010N1N20M3M3O10O10000O01000O10O0101O1OO1N2O01O2N10O1001O1OO1O1O100000O1000O1N20001O000O10O2O00O1O1001O0000O11O1O1OO1000001O000O02O3M2_MdjNZ2_U12HbMnjN^2RU1aMojN_2WU13L6J0ojNZMmT1g2SkNZMlT1T3J0O0O100001O0O10O02K6D;N11M200000O01003M1O1O2N1OO1O1O1002N3M1N101O2N000000O1000J6F:O3N010O0O10O100O1M3O02O3MO1NDniNnNRV1R1oiNmNRV1Q1miNQOTV1n0kiNmNNOWV1S1`0O101O0000O0100000001O5KN2OTOWiN`0mV1_ORiNb0oV1\\\\OQiNf0jV1YOXiNb0KDPW1f02YOSiN9oV1JmhN5VW1KihNM`W19O00EdhN2_W1LbhN3gW1MZV\\\\?\"}, {\"size\": [1280, 720], \"counts\": \"gYS64jW15L3M2N2N6G5I6N5H7L5K4L3N2M7J1O2O0O3L5L2O2M2O0O2N2N3L3O1O1N3L3N3N1O2N1N2O2L3N2N3N1O3L2O1O10O06I4K3N4Mh0WO7J10O0eJbmNT5eR10OjJ\\\\mNS5bR17N3K4001OO^OgmN]KVR1FhmNa43]K92nQ1OgmNb4YS103MO0J51bKhlNT4ZS18100^KPmNR4PS1mKklN0NQ4iS1O6K5K1N1O2M3NQlNbL]S1]3blNiLZS1V3flNlLXS1X3elNjLYS1`3]lN`LaS1W3elNkL]S1T3dlNeLJKcS1_3dlNeLJLbS1_3\\\\lNfLM04MbS1]3\\\\lNmL2H`S1e3]lNdLdS1W3alNiL_S1X3d05J10L4N2O0OjkN]LST1c3lkN_LST1d3300N2G8N3O2L22O3M1OO2M200O1N2O100O1N20VkNRMbT1P362NI7N200001N10O0100000O1L4O1N20000NhjN_MUU1`2kjNbMTU1a2400O2K4O11O0000O1O101O000001O10OO0010O10000O1O101O01O0O0101O1N2O1N1M4L3O2M4LniN[NnU1c1SjN^NmU1a1<0NK6N4K2O1O1N3BRiNETW1KZiN1_Tc>\"}, {\"size\": [1280, 720], \"counts\": \"`o]?<`W17K5K3N2N1N2B?L4L3N200O1000000O1000000O2N10000O10O100000O1O010O101OO1O10N2O1000000XOTN\\\\kNk1dT1XNZkNh1fT1XNYkNi1iT1UNWkNk1jT1SNUkNn1iT1UNWkNk1hT1TNYkNm1fT1UNWkNm1hT1WNTkNj1kT1XNSkNi1lT1XNUkNg1kT1ZNVkNd1jT1\\\\NVkNc1kT1^NSkNc1mT1h000MVMRkNh2PU14O1O1N2N20000O1O0NnLYkNo2iT14O10O01N110O2N2N100]lNdL`R1\\\\3]mNiLaR1X3\\\\mNmLaR1T3VmNbLD=TS1Q3_mNRM^R1o2amNTM]R1n2_mNXM\\\\R1n2VmN^LKf0nR1o2SmNZMmR1m301N1N2O2N2N100O010O01O01O0002O1N1O10O001O000010O00010O1O10N1O20O2N2O0O1OO1N2010O2N00O1010O001O001O001O000O101O1O1O001O0O2O00001O0O1O2O1O2MO2O11O1O2M3M2O0O0O2O2N2N2M2N2O2N6I02M3O04M5J1bLfkNU3TT1PMnkNo2ST1nLPlNQ3ZT1kL`kNV3`T1oLbkNi2_T1VMbkNj2`T1TM_kNl2bT1TM^kNk2aT1VMckNe2^T1ZMfkNc2\\\\T1YMhkN2D\\\\2^U1M3O6I3M00O110>A2N3M2N2N2N2O1N2N3M3M3M4L3M2N4JWg[4\"}, {\"size\": [1280, 720], \"counts\": \"`a\\\\a0>YW1b0F5K5K3L3M3N3M3N1N2N2O1O1N3M2N2N10000O2O0O2O1O1O0O2O2N1N3N2N1O3M2N1N3N7I1N2O001O1O00001O1N11N100O100O100000O2O0001O000000000001O001O0001N1O101N1O10O01O2I6N3M2L5N101N2O001O1O1O1N2O3L3L3L2001O0d0ZO`0A4K4L7I5Foef6\"}, {\"size\": [1280, 720], \"counts\": \"cjT>4kW12D1chN6QW1c0I7J4N2M5K4I6L4M3M4L3M3N1O101N2M3N1O1N2O1N20O01O1O100O1O101O0O2O001N101O2N1N2O1O1O2N1O2N1O1O2O0O1O001O010O0000O100000000O2O000O100O1O1N2O1O1O1O2N1O1O1O2O000O2O0O101N101O1N2O1N101N101O1N2N1O2O1N2N2N3L3M4J6I6L5K4M4L3M3N4J6K5L5ITV^9\"}, {\"size\": [1280, 720], \"counts\": \"hQ`c09eW18I4M1N4M3M2M3N2N3M1O2N2M2O1O1N2N20O00O2N3N1N1O01000000000K5O1O1O010O100O100O100O100O2O0O2N2N2O1O2N1O1O001N2O1O00001O001N1O101O00001O0001O01O1O2N01O0001O010O01O00001O1O0001O01O0001O1O10O0000001O0001O01N101O0010O01O0O2O2N100O1O1N2O001N102M4K3\\\\Oe0L3N2O2M2O2N1N2O1O1N3N1O1O2N1N3N2N1O8C^eS3\"}, {\"size\": [1280, 720], \"counts\": \"Wb\\\\a083J[W1d0I8I4N1M4L2O3N2M2O1N1O3M3N1O1N2liNWNoU1n1N2O2N1N2O000O2O2N1O1O1N4ejNbMnT1j2N2N2N1O001O001O001O1O1O1O1O0000001O010O001O1O0011N3MO2O01O10O1O0O2O1N101O0001O0O1000010N101O1O001O000000000001N12M101N2O1O1O0O20O0100O1O0O101O0O2O00001O0O101N2O0O2L4M2N3L4L5K4J7G<A`0F>E2IP]l5\"}, {\"size\": [1280, 720], \"counts\": \"n]U:h0WW15J3N2H8N3M:F1N1O7H7J3L4M5K5J4N1N3N1O1O1M3O2N1O1O1O10O01O1O1O1O1O001O1O100O00000000010O00O1000O2O1O2M20O01N100001O010O10O10O0100O10O10O10M3J6O01000O100O10000O100000000001O0O101OO10001N1N2O1O1O1O1O1O1M3O101O0O100O2M3N2M3M4M2N3M2N2M4M2N2O2M2N3L5L3K7K8Fe0TOchNL_RQ=\"}, {\"size\": [1280, 720], \"counts\": \"Slla02kW1301O000O2O001bhNHRW19khNMQW1a01OO9G9H3M001O0O2O0O10007I4K3NO1001O001N2O1O2N8H3M0000001O1N10001O2M3N001O001O00001O001O1OO100O100000O10002N5KO10000O10000000000000000O1O1O1O2N1000001O01O015K1N00011N010O0001O0000001OO1000000000O2O0000001O1N2O001O0O10000O1O10O1000O1O1M4J5O2M3N2M3L3L6\\\\Od0J5N4L2O2M3M3N1N2N2O1N2N6KPS^4\"}, {\"size\": [1280, 720], \"counts\": \"TkSf03fW1a0E6I6J5L4M2M3N2N2N2M2O2N010O00100O001O1O001O00001O0O10001O0000001O00000O0101N1000000O10001O01O00000000000O101O1O2M4M1O1O1O1N2O0O2O000000001O00001O1O10O0001O2N1O00001N101O0001O000001O00001O0010O000001O00001O001O01O00000001O00001O1O0001O00000000001O00001O001O001O01O0001O0O2O1O1O0O2N2O0N3L4M2E_jNUNdU1f1=M4M2O1N3M2OkD\"}], \"areas\": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2571, 3983, 3923, 6626, 2944, 3394, 4179, 3016, 1941, 3128, 1810, 1632, 2646, 5010, 5542, 3665, 2680, 3041, 2298, 2160, 2010, 1079, 1649, 3407, 2928, 2411, 2227, 2502, 1114, 894, 3295, 2589, 2207, 2404, 2272, 1897, 2982, 2802, 2878, 3106, 3861, 4133, 4209, 4202, 3669, 3348, 3644, 4038, 4558, 4212, 3681, 3237, 2946, 3934, 7915, 5119, 5959, 6344, 9221, 7337, 5628, 4873, 5672, 8999, 6840, 7000, 5160, 4040, 4925, 4422, 3115, 3969, 3924, 4362, 5620, 5356, 4834, 4060, 5039, 5187, 4437, 3876, 4595, 3970, 2863, 2591, 2260, 4864, 5074, 4460, 3118, 2839, 2788, 2590, 6053, 3215, 3370, 5214, 5650, 7288, 5818, 6149, 3982, 4350, 4890, 6614, 6003, 6069, 6877, 6935, 10405, 9562, 9643, 10350, 9944, 15165, 18521, 23432, 8698, 10153, 9702, 10885, 12395, 10750, 11239]}, {\"video_id\": 0, \"category_id\": 3802, \"bboxes\": [[563, 779, 43, 24], [567, 781, 45, 25], [571, 785, 44, 27], [571, 795, 46, 27], [575, 798, 45, 27], [582, 803, 41, 24], [603, 805, 27, 26], [618, 807, 25, 26], [622, 808, 23, 26], [621, 809, 22, 25], [621, 808, 21, 25], [624, 808, 18, 25], [634, 809, 11, 24], [635, 810, 10, 23], [632, 808, 12, 23], [631, 808, 12, 24], [631, 807, 12, 22], [623, 808, 13, 24], [612, 809, 21, 27], [606, 809, 23, 28], [601, 808, 27, 30], [588, 808, 41, 29], [587, 807, 44, 28], [588, 805, 47, 29], [594, 805, 44, 30], [598, 806, 45, 30], [599, 806, 44, 30], [600, 805, 43, 30], [604, 805, 43, 30], [610, 804, 39, 29], [609, 802, 44, 29], [613, 804, 41, 27], [616, 809, 42, 28], [616, 812, 44, 29], [615, 814, 43, 28], [614, 814, 44, 29], [614, 815, 45, 29], [613, 814, 45, 29], [608, 816, 41, 28], [606, 815, 45, 25], [615, 816, 44, 23], [623, 813, 47, 23], [632, 808, 47, 28], [634, 800, 47, 33], [633, 798, 46, 27], [621, 786, 51, 34], [618, 770, 42, 33], [620, 740, 36, 38], [620, 725, 39, 29], [628, 707, 36, 32], [645, 701, 33, 32], [671, 686, 42, 43], [701, 685, 18, 35], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [703, 653, 16, 28], [700, 647, 19, 26], [697, 643, 22, 26], [695, 640, 24, 25], [691, 637, 28, 25], [689, 636, 28, 24], [0, 0, 0, 0], [702, 638, 17, 26], [705, 642, 14, 26], [706, 646, 13, 27], [706, 649, 13, 26], [706, 647, 13, 24], [699, 645, 20, 27], [698, 640, 21, 24], [697, 641, 22, 22], [0, 0, 0, 0], [686, 655, 28, 26], [0, 0, 0, 0], [0, 0, 0, 0], [697, 662, 22, 25], [699, 663, 20, 27], [701, 667, 18, 26], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [696, 674, 23, 25], [700, 672, 19, 27], [701, 667, 18, 25], [0, 0, 0, 0], [714, 660, 5, 19], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [707, 656, 12, 27], [705, 651, 14, 24], [700, 648, 19, 27], [699, 647, 20, 28], [696, 645, 23, 26], [699, 640, 20, 27], [0, 0, 0, 0], [706, 637, 13, 27], [713, 641, 6, 23], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [699, 636, 20, 28], [688, 633, 26, 25], [676, 632, 26, 24], [664, 633, 29, 32], [0, 0, 0, 0], [673, 638, 13, 20], [656, 637, 28, 24], [671, 641, 16, 21], [664, 645, 23, 23], [0, 0, 0, 0], [0, 0, 0, 0], [663, 665, 24, 27], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]], \"score\": 0.8128654956817627, \"segmentations\": [{\"size\": [1280, 720], \"counts\": \"c`Pf04iW15M3M3N1O1O1N101N101N2O0000000000000000001O00000001N101O00001O000O101O0000001O1N2O1N3L]_\\\\4\"}, {\"size\": [1280, 720], \"counts\": \"c`Uf08gW14L2O1N101O2N001N2O1O0O100O10001O00O2O00000000001O0000001O0O2O00001N110O000O2O00000O1O2N1N]oT4\"}, {\"size\": [1280, 720], \"counts\": \"i`Zf0;dW14L2N2N1O1O2O1N101O1N10000000O2O00001O00000000001O0O10001O0O2O001O000O2O00000O101O0000<ClVQ4\"}, {\"size\": [1280, 720], \"counts\": \"XaZf04hW16L4M2N2N1O2N1O2O1O2M01000000000000O1O1O1001O00O1000001O0O10000000000O2O00001N2O1O1N102M3M6Jgfn3\"}, {\"size\": [1280, 720], \"counts\": \"Za_f07gW13M3M3N2N2N2N2O0O10001O0O11O00001O0000000001O0000O100O100O100O10001N1O1O2O0O2O0O2OO0014L3Jjnj3\"}, {\"size\": [1280, 720], \"counts\": \"ZYhf08fW19G4M2N1000000000000000000000000000000O10000000O10000O1O100O1O2O0O100O100N3N1O101NkVg3\"}, {\"size\": [1280, 720], \"counts\": \"Yabg0c0\\\\W13M2N2O000O01000000O10000O10000O1O100O2O0O10000N3N1I`hNMaW12P_^3\"}, {\"size\": [1280, 720], \"counts\": \"cYUh04eW1?E5M2OO01000O100O100O1O1O2O0O100O100O101N1M3M4N1MiVn2\"}, {\"size\": [1280, 720], \"counts\": \"bYZh0=^W19J2O1O100O11O0O100O1O100O100O101N1O1O101N3M6Gbfk2\"}, {\"size\": [1280, 720], \"counts\": \"aQYh0a0[W15L300O10000000O10001N1O100O2N01000O1N3N102MdVn2\"}, {\"size\": [1280, 720], \"counts\": \"lQYh01cW1`0H5M2O1O100000O1O2O0O100O2N100O011N1O2M9Fa^o2\"}, {\"size\": [1280, 720], \"counts\": \"ei\\\\h0:`W19J3N2O1O02O0O100O100O100O100N3N1O2E_hN2YfP3\"}, {\"size\": [1280, 720], \"counts\": \"fYih09bW18J3M3N11O1M3N201N1O8Gafk2\"}, {\"size\": [1280, 720], \"counts\": \"gajh05gW1:F3N201O01N2O101L8Gdfk2\"}, {\"size\": [1280, 720], \"counts\": \"aifh0>^W15L3O1O100001N1N2O2M2O2CQol2\"}, {\"size\": [1280, 720], \"counts\": \"caeh0=^W16K4O10O010O2O0O1N3M2O2JjVn2\"}, {\"size\": [1280, 720], \"counts\": \"faeh05dW19J5M100002N1O1O2L4M2M4KiVn2\"}, {\"size\": [1280, 720], \"counts\": \"ea[h0:aW16J5N3N0100O1O2N1O1N3N2K6JgnV3\"}, {\"size\": [1280, 720], \"counts\": \"himg0;_W18L2N2N110O010O0100O1O10000O10O02N1O1N2O2LhfZ3\"}, {\"size\": [1280, 720], \"counts\": \"kYfg09bW17I5M3O1N11O010O10O0100O1O1O100O100O2N1O1O002Ngf_3\"}, {\"size\": [1280, 720], \"counts\": \"gQ`g0=]W17J7L200000O01000O1O100O100O100O001O1O100O1O2N100O1O1Nin`3\"}, {\"size\": [1280, 720], \"counts\": \"diof05jW15L2N2M2M3M3L3N3O000001O0001O0O100001O000O100O010O10000O1O010O1O1O100O1O2N100O1O2Off_3\"}, {\"size\": [1280, 720], \"counts\": \"danf06iW12O1O2M3N1N2M3N1O2M1O20001O0000000O100001N100000000O01000O10O0100O1O100O100O1O1O100O1O3LiV]3\"}, {\"size\": [1280, 720], \"counts\": \"giof02kW14O1N2N2N3M2N2N2N001O1O2O0O10000O2O00O1000O1000000O1000O10000O100O010O100O100O1O100O1O100O2M4MiVX3\"}, {\"size\": [1280, 720], \"counts\": \"cYWg05jW15K3M2N2M3N2M3N1O1O2O0O0100O11O000000O1000000000O010000O100O100O100O1O1O100O1O101N1O1O1Ok^T3\"}, {\"size\": [1280, 720], \"counts\": \"gY\\\\g02kW15M3M2N3M3L3N2N1N2O0110O00O101O0O10O2O000000O01000000O01000O00100O100O100O1O100O100O1O1O3LjVn2\"}, {\"size\": [1280, 720], \"counts\": \"ha]g01lW16L3M2M4L3N1O1O1O101O1O0O2O00000O1000O1000000O0100000O01000O1O10O0100O100O100O1O1O1O2O1EbhNOY^o2\"}, {\"size\": [1280, 720], \"counts\": \"hi^g01mW13M5K4L3N2N1O1O1O2O1O0O2O0O100000000000O10O10O10O100O0100O100O100O100O10O01O100O1O2N1AihN3S^o2\"}, {\"size\": [1280, 720], \"counts\": \"dicg06hW15L2M3M201N1O2N101N2O001OO01000000000O10O10000O10O1000O0100O100O100O1O1O1O100O101M3N3F`hNOX^j2\"}, {\"size\": [1280, 720], \"counts\": \"_Ykg0;aW17L3M3M2O0O100000000000000000000O10000000O010O100O100O100O1O1O1O10000O100O002Mmff2\"}, {\"size\": [1280, 720], \"counts\": \"dQjg01mW16J3M3M3M3N1O2N100000000O2O0000000O101O000000O10O1000O1000O01O100O100O100O10O02N1O1O2N2Kofa2\"}, {\"size\": [1280, 720], \"counts\": \"cQog06hW13M3N2M3M2O2O000O101O0O1000001O000O100000000000O01000O01000O101N1O10O0100O1N2O2N2Ml^`2\"}, {\"size\": [1280, 720], \"counts\": \"ciRh0:eW13M2M3N2O1N102M10000O2O00000000000000000O1000O10O0100O100O10000O1O1O100O100O2O1M5L5K_^[2\"}, {\"size\": [1280, 720], \"counts\": \"iiRh07fW15L3M3M3N2O1N2O000O20O000001O00000000000O10O100O01000O10000O100O1O1O100O2O0O1O2O0O1O2N4IbnX2\"}, {\"size\": [1280, 720], \"counts\": \"kaQh07gW13M3L5M2O0O101N2O00001O0000001O0000000O10000O0100O0010000O10000O100O2N1O101N2N1O2N2N3L]^[2\"}, {\"size\": [1280, 720], \"counts\": \"lYPh07gW13M3M3M3O001N101N101O0000001O000000001O0O10O10O100O01O1000O0100O2O000O1O2N1O2O0O2M2O2N2N]^[2\"}, {\"size\": [1280, 720], \"counts\": \"PZPh02jW15M3M3M3N1O2O0O2M3O1O010O00001O0000001O000000O0100O010O1000O100O1O100O2N1O1O1O2O0O1O1O2N:EWVZ2\"}, {\"size\": [1280, 720], \"counts\": \"lQog04jW14K4M3M4M2O1O0O2O0O2O0000010O00001O0000000O100000O010O1O10000O010O2O0O1O1O101N2N1O1O2N1O2N^^[2\"}, {\"size\": [1280, 720], \"counts\": \"nihg05iW15L5J3M2N2O2O0O100000001O00000000O1000O1000O010000O10O101N100O1O2O001N100O2O1N3M4LWff2\"}, {\"size\": [1280, 720], \"counts\": \"nYfg04fW17N2N3M101N1O101O0O2O0000O1000000000O101OO100O10000O010000O011O00O2N1O101M201N2N2N101N1N3N[Vd2\"}, {\"size\": [1280, 720], \"counts\": \"laQh07eW15O1M2O2N1O2O0O100O2O000000O100000O1000000000000000O1000000O1000000O1O2N1O2N2N2N2O1O1N2L[VZ2\"}, {\"size\": [1280, 720], \"counts\": \"ja[h03gW17N2N1N3N101O001N10000O101O01O01N100000O10000001O0000001OO1000000O10001N101N1O2N2N101N2O0O2M5LZ^l1\"}, {\"size\": [1280, 720], \"counts\": \"cifh06hW13N2N1O2N2M3O0O2O1O0O2O1O001O001O001O00000000O100001O000000O0N301O000O1O101O1O0O101O2M2N4L2N5KZVa1\"}, {\"size\": [1280, 720], \"counts\": \"YYih03eW1=J2O2O1N2O0O1N101O001O001O10O010O00O2O001O1O000000O1O1O100O1O1O10001N100O100O2N100O100O2N1N3Mlf^1\"}, {\"size\": [1280, 720], \"counts\": \"UQhh09cW17K3N2O000010O0001N101O1O1O001O000000000001O0000010N100O1O100O101O0O100O100O10001N101N2O5J5LcVa1\"}, {\"size\": [1280, 720], \"counts\": \"mPYh0;bW15L5L3L4L3O0O110O1O01O000000001O1O1O1N2O010O1O010O001O1O1O0000001O0O101O01OO1000001O000O101N1O2N10001IQoi1\"}, {\"size\": [1280, 720], \"counts\": \"bXUh08cW17H8L3O0O1O2O000O10000000000001O00010O1O1O010O2N1O2N1O2N010O1O1O10OO2O0O1O2N2N2N2N2MYoX2\"}, {\"size\": [1280, 720], \"counts\": \"_gWh0f0UW16L4M2O2O0001O0O2O1O1O1O100O001O100O2N2N100O1O2N010O1O001O1N101O0O2O1N2OQP^2\"}, {\"size\": [1280, 720], \"counts\": \"WgWh04cW11^hN5\\\\W1:K5N2O1N2O0001O001O0000001O001O1O001O1O00001O001O001O001O1O1O1O000O2OO1001EVYZ2\"}, {\"size\": [1280, 720], \"counts\": \"_fah0>\\\\W17L5L3N101O1O00010O01O0O101N101N10001O0O101N1O1000000O101O0000000O2O0O3ElQT2\"}, {\"size\": [1280, 720], \"counts\": \"\\\\nVi04kW14K4K:H101N101N10000O10O1O00001O010O010O1O00100O10000O2O1N5K4L3M2Nhab1\"}, {\"size\": [1280, 720], \"counts\": \"T^Wj02mW12N3L5M3M8G2O0O2N1O1O2N1000O0100O01O10O01O1O001O10O01O01O01O010O1O001O0O2N2O1O1N100Odj6\"}, {\"size\": [1280, 720], \"counts\": \"Pn\\\\k01jW1;H5K4L4M2N1O2O0O1O2O0O1000O0100O010cZO\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"l\\\\_k03lW15H5M3M3O1N2N2M3O000O010O1000000c[O\"}, {\"size\": [1280, 720], \"counts\": \"`d[k0<aW15M3M2N101N100O0010000000O1000000O100Oj[O\"}, {\"size\": [1280, 720], \"counts\": \"_lWk04iW16K5K4M2N2N10001O0O0100000000O011O0O10000000l[O\"}, {\"size\": [1280, 720], \"counts\": \"Z\\\\Uk07eW16N3L2O2M2N1O10000O100000000000000O11N1O10000O10o[O\"}, {\"size\": [1280, 720], \"counts\": \"W\\\\Pk09eW14L5L2O1N1O10000O10000O0100000000000O02O001O1O1O1N3M4L2Oh[O\"}, {\"size\": [1280, 720], \"counts\": \"Ylmj03iW17L3L3M3O1N2N10000O10O10O10000000000000O10001O2N1N3N3M4Jik1\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"VT^k0;dW14L3M3M100O101N1000O010000000O0100R\\\\O\"}, {\"size\": [1280, 720], \"counts\": \"_lak03jW17J5M0O2M3N2N2O0O10000O1O100n[O\"}, {\"size\": [1280, 720], \"counts\": \"cTck04hW18K3M2M3N2N101N101N10O010Ok[O\"}, {\"size\": [1280, 720], \"counts\": \"dTck09eW16J2O2M3N2N001000O01000O10g[O\"}, {\"size\": [1280, 720], \"counts\": \"bTck09dW15M2N2M2O2O0O10000O10O0100i[O\"}, {\"size\": [1280, 720], \"counts\": \"h\\\\Zk01jW18J4N2M3N1O2N2N10O02O0O1O10O1001O00O0100k[O\"}, {\"size\": [1280, 720], \"counts\": \"^TYk07fW13M4M3N101N100O1O10000O2O00O100001O00O0100P\\\\O\"}, {\"size\": [1280, 720], \"counts\": \"YlWk09eW14M2N2N2O0O2OO010000000000O100000001O00O1000o[O\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"gTjj0:eW12M3N2N2O1N101N10000O10O1001O000000000010O0001O10O1O1N4K\\\\c5\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"olWk08eW15M2N2M3O3L101O000O1000O10000O10000O2O000O10Y[O\"}, {\"size\": [1280, 720], \"counts\": \"P]Zk07fW17J5L3M2N2O1O0O01000000O100O2O000O1O2M20W[O\"}, {\"size\": [1280, 720], \"counts\": \"Um\\\\k08dW17L3L4M2N101O0O100000O100O10000O2O00T[O\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"beVk02jW16L4L3M4M2N1O1O100O10O01000000000O1000001O0O10mZO\"}, {\"size\": [1280, 720], \"counts\": \"ae[k03hW16L5L4L201N2O0O1O1O2O00O10O100001N1000oZO\"}, {\"size\": [1280, 720], \"counts\": \"Ym\\\\k04hW19I4L3N1O1O100O1O1O1000O100000000000U[O\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"jTmk09dW14N2O1N2N2[[O\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"k\\\\dk07fW13O3L5L4L2N1O101N1000000`[O\"}, {\"size\": [1280, 720], \"counts\": \"clak09eW18H201N1O2N100O1000000000000e[O\"}, {\"size\": [1280, 720], \"counts\": \"bd[k0=_W16M2M201N101N10000O010000001O000000000h[O\"}, {\"size\": [1280, 720], \"counts\": \"g\\\\Zk06cW1:J4N2N1O2O000O100O1000000O100010OO01000i[O\"}, {\"size\": [1280, 720], \"counts\": \"bdVk07fW17J3M3M3N1O1O1000000O100000000000O101O0O1O2N10i[O\"}, {\"size\": [1280, 720], \"counts\": \"]\\\\Zk09eW14K5L2O101N1O2O0O1000000O100000O1000O100P\\\\O\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"]Tck03kW13M3L4N2N2M3M3N2N1O2OO1000S\\\\O\"}, {\"size\": [1280, 720], \"counts\": \"[lkk09dW14L4N2M3N2O0o[O\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"^\\\\Zk02jW16J5L5L3N2N2O0O2O00000O10O1000000O10O100T\\\\O\"}, {\"size\": [1280, 720], \"counts\": \"Udlj08cW17K4N2O1O0O2O00000O100000000O10O100000000O2O1O1N3M9Dnc5\"}, {\"size\": [1280, 720], \"counts\": \"Pd]j0:cW16L2N2N2N2O00000000000000000O100001O00001O1N2O1N3M6Incd0\"}, {\"size\": [1280, 720], \"counts\": \"Qdni03kW1;E7K3L4M1N2N10N2O1O1O1O01000O100O10000000001O001O1N2O3L3Moko0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"okYj09fW15L5L1N01O0000000O2N2O2N5GkcX1\"}, {\"size\": [1280, 720], \"counts\": \"Vddi01iW19L3N2M3N2M2O2N1O0000000000O10001O0O10001N101O0O2O1N2O4IlS[1\"}, {\"size\": [1280, 720], \"counts\": \"R\\\\Wj01nW15L5K3M2N2N1O2N001O01O0O2O1N3L6Jh[W1\"}, {\"size\": [1280, 720], \"counts\": \"Vdni06jW14L4L3M3M1O1N2O000000000000O2O00000O101N3N1N5Hf[W1\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"^]mi02bW1`0I3N3N0010O0100000O0100000O10001O000O101N101N6IQ[W1\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}], \"areas\": [879, 911, 957, 970, 947, 884, 611, 489, 496, 493, 428, 380, 209, 174, 244, 251, 177, 249, 449, 506, 656, 963, 987, 1043, 1066, 1055, 1017, 976, 1002, 972, 986, 913, 986, 1053, 959, 972, 992, 1021, 920, 851, 833, 879, 1019, 1173, 997, 1200, 943, 903, 877, 887, 724, 1066, 525, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 372, 468, 503, 554, 599, 575, 0, 404, 305, 294, 302, 285, 455, 448, 459, 0, 631, 0, 0, 503, 482, 442, 0, 0, 0, 507, 442, 419, 0, 91, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 284, 332, 508, 519, 562, 493, 0, 266, 128, 0, 0, 0, 485, 590, 578, 719, 0, 224, 564, 268, 467, 0, 0, 543, 0, 0, 0, 0, 0, 0, 0]}, {\"video_id\": 0, \"category_id\": 3802, \"bboxes\": [[454, 591, 60, 106], [435, 570, 59, 110], [424, 571, 61, 113], [430, 598, 64, 106], [452, 618, 64, 105], [414, 589, 82, 94], [396, 570, 81, 95], [381, 552, 100, 76], [370, 552, 110, 70], [377, 541, 120, 75], [391, 538, 115, 74], [412, 521, 120, 88], [419, 527, 119, 85], [415, 535, 113, 77], [403, 540, 108, 74], [379, 552, 97, 65], [360, 570, 64, 51], [379, 563, 24, 49], [380, 571, 25, 42], [357, 621, 25, 80], [372, 634, 82, 123], [505, 590, 78, 132], [617, 528, 68, 84], [649, 365, 70, 100], [446, 360, 76, 83], [309, 515, 87, 51], [473, 490, 68, 58], [638, 419, 70, 52], [605, 533, 49, 62], [566, 597, 42, 54], [610, 238, 66, 127], [551, 175, 42, 62], [543, 98, 45, 67], [561, 4, 62, 80], [619, 0, 74, 62], [622, 193, 72, 110], [530, 229, 93, 120], [524, 140, 99, 66], [440, 76, 106, 75], [339, 182, 110, 97], [352, 155, 117, 102], [394, 121, 125, 81], [576, 148, 100, 66], [543, 0, 126, 131], [358, 8, 114, 106], [316, 140, 114, 78], [377, 172, 92, 117], [429, 90, 97, 111], [483, 75, 114, 82], [561, 140, 93, 61], [566, 82, 106, 75], [433, 32, 107, 83], [334, 150, 112, 82], [371, 186, 88, 116], [404, 150, 91, 126], [475, 134, 91, 61], [497, 130, 129, 82], [538, 62, 115, 75], [464, 41, 143, 93], [526, 186, 51, 128], [530, 328, 50, 109], [520, 419, 67, 93], [514, 620, 92, 85], [550, 654, 70, 75], [568, 619, 69, 63], [542, 631, 63, 68], [522, 631, 67, 63], [528, 531, 74, 86], [495, 534, 75, 85], [542, 646, 54, 93], [526, 627, 57, 82], [550, 606, 60, 88], [528, 651, 67, 77], [540, 637, 73, 72], [576, 560, 78, 72], [590, 474, 87, 122], [568, 292, 100, 77], [470, 178, 133, 98], [516, 117, 96, 121], [673, 193, 46, 56], [0, 0, 0, 0], [0, 0, 0, 0], [657, 397, 62, 120], [428, 300, 124, 75], [356, 228, 121, 86], [451, 114, 94, 108], [592, 220, 125, 98], [628, 386, 70, 125], [568, 564, 75, 85], [500, 614, 83, 89], [376, 343, 74, 67], [388, 342, 108, 57], [414, 393, 82, 49], [531, 371, 75, 58], [623, 342, 48, 43], [606, 339, 70, 94], [598, 325, 48, 82], [546, 271, 106, 83], [525, 309, 126, 40], [537, 386, 116, 75], [640, 443, 79, 124], [661, 444, 58, 136], [596, 553, 74, 113], [660, 473, 59, 79], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [671, 515, 48, 82], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [604, 203, 84, 80], [598, 166, 81, 80], [693, 109, 26, 77], [579, 132, 95, 88], [487, 192, 96, 79], [467, 237, 104, 81], [555, 174, 113, 83], [671, 123, 48, 91], [542, 179, 90, 84], [539, 175, 99, 90], [0, 0, 0, 0], [0, 0, 0, 0], [489, 88, 157, 108], [574, 0, 99, 141], [584, 19, 129, 130], [452, 117, 160, 98], [576, 23, 73, 165], [0, 0, 0, 0], [0, 0, 0, 0], [395, 161, 194, 114], [504, 38, 115, 143], [580, 0, 109, 166], [403, 178, 168, 123], [596, 76, 123, 154], [0, 0, 0, 0], [591, 116, 128, 147], [452, 194, 222, 135]], \"score\": 0.8012422323226929, \"segmentations\": [{\"size\": [1280, 720], \"counts\": \"eSha0=\\\\W1`0C:Db0A6J4K5K5K5L4O1O3M2N4L3M2N2N2N1O1O01O00O100000O101O0000010O0O1000O01O1O0O2O1O2N1O3N2L6K4K6K3L5K5K5L5J6J4M5J5L6J5I:Ggco7\"}, {\"size\": [1280, 720], \"counts\": \"T[Pa0X1cV17K5K8H6K9G5J5L3M3M2M4M2N2O1N101N2N2101O0O01N11O1O1O000O2N4L2N0000N3K4001O1O2N2N2N1N105K3M2N1O1O1N2O2N3M4L5K6J4L6I8H;Djdh8\"}, {\"size\": [1280, 720], \"counts\": \"Qdb`0=UW1b0F:Fa0B9G4M3M2N3M2N2M2O2N3M2N2O2N1N3M2N3M2N2N1O2O02N2N1O011N2O1N01O10O1O1O1O1O1O2N2N1O1O1O001O100O1O2M3N3M2N1O1O2M3N3Kd0[Od0oNohNGUTU9\"}, {\"size\": [1280, 720], \"counts\": \"mTj`06fW17F9Gg0ZO8H7L4M3L3N2N2M3O1N2O1N2N3M3M3N2M2O1N2O1O1N2N101O0101N2N100O100O1O010O1O1O1O1O1O101N1O2N3N1N1O2M201N1O2O0O1O1O2M4L3M2L4L8DQeh8\"}, {\"size\": [1280, 720], \"counts\": \"]eea0:aW17J5L:C`0A7J5M3L3N2N2N2O1N2N2N2O2M2N3N2M3N1O1O2N1N2N101O0O110N1100O2O1N2OO01O1O100O2N00101N100O0O2O2O1O1N2O1N1O101N2M2N3M2M4L5E;[OjTm7\"}, {\"size\": [1280, 720], \"counts\": \"dTV`09UW1NSiN7iV1b0N2N3L3N1N2O3N1N2N1N3N2N1N201N1O1O1O2N1O1O1N200O1O100O001O100O100O0010O0100O1O010O10000000000O010O00001O0001N101O000O1O2O0O2N1O101O001N4M5J5K3M5K5K5J6L1N4K6J3L5LjTf8\"}, {\"size\": [1280, 720], \"counts\": \"Qd_?b0XW19F8J5M4L3M3O1N2O2M3N1O1O0010OO2O100O1O0O2O0001O1N02O10O10O100O100O000010O01O0001N2O001N1O2N1010O01O000O2O1O0O20N11O001O001O0O2O001O010O2N5J3M4M2M4K4L4O6I2N3M4L3K^m]9\"}, {\"size\": [1280, 720], \"counts\": \"ljl>4iW16J4N2N2M3M3N1O2N1O2M3N1O2N2N3M3L4L2M3M3O1O2N1O2O00000O01000O2O00000O10000O10O1O0100O100O010000O1O1O1O10O00000001O1O2N1O1O10O0100O1O1O100O1O00100O010O00010O2N1O1O011N2N3M1O3M1O1O1N6K3M2N1O9G3M3L200O2OnmX9\"}, {\"size\": [1280, 720], \"counts\": \"[R_>2kW16^hNLRW14khN3PW1NmhN7PW1KnhN7QW1<01N1O1100O1OO2N2N2O0O2O001N1O2N1N2O1O1O100O10O010O01O001000000000000O2O001O002N1N2O0O10000O010O10O010O1O1O010O1O100O00100O10O11N1O100O100O10O01O10O010O10O100O010O001O1O1O10O01O1O1O1O1O1O2N3M2ROTiNe0VW1M3M1O2O1N2N2M3N4LRVZ9\"}, {\"size\": [1280, 720], \"counts\": \"nig>2kW15M2ehNKiV16UiNMhV15ViNMiV15RiNNmV1d0O00000O2O001O100N101N2O0O101O1O001O1N1O2O0O10O01O10O10O010000000000010O2N2N2N1N2O00O100000O10O0010O01O010O1N110O00100O1O010O1O01000O10O01O100O01000O10O100O0100O10O01O100O001O010O100O10O0100O100O100O1O001O002N1O2N2N2O2M4L000[OQiN:PW1DUiN8\\\\W1N\\\\nd8\"}, {\"size\": [1280, 720], \"counts\": \"jYY?5jW12N101ehNKiV15SiNOmV12RiNOlV14mhN2QW1?01O2N1O0O10001O0O2O0O2N1O2N1O2N3N001OO1000O01000O0101O00O10O11N100O101O0O2O1O000O4M2M10O10O0010O001O002N001O010O2O0O001O001O1O010O010O10O01000000O001O001O10O0100O10000O10000O10000O1O1O1O100O1O1O011N5K6J4L2N3M3M5K3M2N^fY8\"}, {\"size\": [1280, 720], \"counts\": \"XbS`07fW14_OKRiN9lV1HohN>mV1<N2O110O010N1O2O0O01001N3N0N2O2N100O2O3M0O10O1000000O0100O100O010O2O2M3N1N2O1N100O10000O2OO010O001N101O1N2O00001O1O1O1O001O001O1O001O0010O0100000000O100O10O01O1O100O10O0100O0010O01O10O01O010O1O00100O001O1O1O1O1O1O010O001O1O1O1_OTiNNmV1F^iN9VW1M8GPWY7\"}, {\"size\": [1280, 720], \"counts\": \"gY\\\\`05jW13chN0eV15SiN0kV1c0N3O0O1O1O10O1001O0O2N2O0O10001N2O0O2biNbNWV1e1O0O100O100O010O00010O01O1000000O0104L4L2M1O2O000O10O010O1O1O01O01O1O001N101N2O001O001O1O1O1O1O100O001O1O1O1O01000O100O010000O10O1O001O10O10O100O1O00100O00100O2N00100O1O010O1O1N4M101N5K5ZOihN:ZW1BihN<`W1M4L2NifQ7\"}, {\"size\": [1280, 720], \"counts\": \"aZW`01lW14L4^OJWiN8fV1MViN5hV1c0N2N100O100O1O1O10001O2N1N4L2O1O1N1O100000000O10O100O010O01O1O100O1000O0102N2M3N1N100O10O10O01O001O001O001O1O1O1O1O1O1O001O1O1O1O1N2O01000O010O100O0010000O10O100O10O10O01O10O01O100O1O100O1O100O1O1O1O2O0O4L`0@2N1O2O2L4LbV^7\"}, {\"size\": [1280, 720], \"counts\": \"aZh?6iW12M2@KTiN7jV1OmhN5RW1=O1O100O1O01001N100O101N4M1N4M2M101N1O0100O10O1000O010O010000O2O1O1N2O1O1O1N10O10O01O010O1O001O1O001O1O1O1O00100O1O001O100O001O10O01O100O01000O010O10O010000O01000000O10O010O1O001O1O1O1O1O100O1O6K1N1OKZOXiNLO<jV1DbiN;_V1DdiN:\\\\V1FgiN4`eT8\"}, {\"size\": [1280, 720], \"counts\": \"iZj>2jW15N2M2N3N1O1N2N3M3N3M4K3N2N1O1O1O1000O0100O1O100O010O2N101N2O1N1O2N100000O10000O001O0010O01O1O1O00100O1O1O010O1O10O010O010O0100O01000O100O010000O2O00001N10N2N2O1O001O1O2N5K7J3L3N1N1O101M3M2O1O3MTV_9\"}, {\"size\": [1280, 720], \"counts\": \"nbR>1nW15L1N1O2N2O0O2N2N1O2N1N3N1O2N1O1O1N200O100O1O1O1O101N100O2O1O0O100O1O1O00100O010O01O10O01O001O1O001N2N2N102N8H3N5I3N2N1O2M3N2N3N`U`;\"}, {\"size\": [1280, 720], \"counts\": \"WZj>a0ZW19H7K4J4O2N2O10O1O01K4O18H2N1N2O1N3K4L3K6J6L4M3Ll]Z<\"}, {\"size\": [1280, 720], \"counts\": \"ebk>>YW1<I4M3N101N1001O001O001OOPOYiNj0RW1L1O1N4L4M2N2M2N3M200O10dmW<\"}, {\"size\": [1280, 720], \"counts\": \"eln=>\\\\W1;F9I7H7J5M3L7J2N2L3M3M2N2010O010O2O2N6TOejNfN\\\\U1W1hjNhNZU1f0Z1^OlhNL]W12;LUdT=\"}, {\"size\": [1280, 720], \"counts\": \"bfa>?]W18J4K5M2M2O1O2N1O1N3M3L4L3N3M2M4M3M3N1N3N1N3M2O2M2O1O1O1O2M2N2O1N2N2N2O1O1N1O2N101O00000O10001O001000O10O0O2O10O101N101M2O1NM41mL]kNh2QU1L2N2M3M3M2M2M:G3N3M3K5L4L3M2O2D]iNZOdV1b0`0WOmhN6NOfj[:\"}, {\"size\": [1280, 720], \"counts\": \"dmgc0c0oV1d0C8D<L4K5L4M2M4L3N2N1M5L3O1M2N201N2M3N1O2N2M3O1N1O1O2M3N101O001O0001O01O1001N3N1O0O011N100OO2O03N3M1N10O0100000O0013L1O2N000010O0001N3N2M1O3N2M:E:G<D4J7eNciNe0eW1TOd[Y5\"}, {\"size\": [1280, 720], \"counts\": \"ijSh07fW14L3M3M3M3M4L4N1N2N2N2N3M2N2O1O1O1O1O1M3M3M3O1O1O1O1O1O1O1O1N2O1O1N3N1O1O1O1O1000000O1O11O0000000O1000O1N2N22N1N3N0O2N2O0O2O1N2M4N0O5J:ZOciNVOXU[1\"}, {\"size\": [1280, 720], \"counts\": \"ae[i05iW13L4K5N2M3M2O100O2N1@^O_iNf0\\\\V1b0J6I8K4G:H7M3N102N2N1O1O000010O00100O100O00100O1O0010O01O1O10O01O001O0O2O00001O001O0O101O001N2N2N2O1N3K4M4L4K5K6E;H6M^C\"}, {\"size\": [1280, 720], \"counts\": \"lk]a01PX13L2L3N1O2N3N2M101O1O1N2O0000003M2khNXOnV1n0N2O002M:G4K1M3O3N1N2N2O2N1N3M00O1011N2O2N2N3M2O0O01N1O101O01O2N001O00O1000000I8N1O1M3H9N100O2M4M3M3H_iNlNdV1Q18L4H7M5O1CdhN3]W1GehN;aW1Jgke7\"}, {\"size\": [1280, 720], \"counts\": \"jhR<6gW18I3M3N1O2N1N3O0O6I3M3M3O1M20100O0O100011N01O00000O101OO10000000O100O10000001O0000000O100000000000000O1000000O10O1000O2O0000O100000000O1001O00000O1O100O100O101N2M3N10OJYiNUOhV18ViNMOOYW1KWiN3\\\\W1OlVc<\"}, {\"size\": [1280, 720], \"counts\": \"dg_b0:eW16J4L4mhNZOeV1R1N10002M2O2M2O001O0O3N2N2N2N1O001N0100O010O1000000N3N2N101N1O2O0O1O101N100O1000000O1010O00001OO1N101O100O2N2M2O101N3M3M3^OehN;fW1J0O0O1OQPn6\"}, {\"size\": [1280, 720], \"counts\": \"emmh01kW17K5K5L4L4L2N1O2O4K2OO000102N1O0000000O11O010O01O0000000000001N1O3N1O0O101O01O01O010O00001O01O01O0001O001O00001O1O0O2O001O1O1O1N2O5K3L3N3K4MRZ=\"}, {\"size\": [1280, 720], \"counts\": \"Yidg01lW1b0@4L2M4L3N2O1N1O2L3O2N3M2N4M1N2O100000O001O0010O2N1O1O01O0O101M2J6N2N2N2N2O1O1O2N1O1N200O1N3J5K7JTg`2\"}, {\"size\": [1280, 720], \"counts\": \"SSTf01nW12O1O4XhNL\\\\W1`0K:G3M3L2O1N1O2N101O001N100O1O100O1O2N1O1O101O01O001N101N10001N11O01N2O2M2N3LRUZ4\"}, {\"size\": [1280, 720], \"counts\": \"jgjg0;bW16L3L5L2N3M3N2N2N1O2N2M3N2N2N3M2N2N2M3N2N1O2N2N1O3L3O2M3M2O2M3M102M2N5L2M3N2OO0O1O12N005LO12N00O1L4VOXkNeM]U1W2:M1KYjNRNgU1j1<AWjNbNiU1Z1XjNfNlU1W1b0L44RiNmNcV1W13M5J3N4Ic0^O4Md]e1\"}, {\"size\": [1280, 720], \"counts\": \"Qn`e0?]W17J4M3N7I6J2N1O3N1N2O1N102M3M3N1O001O0000000001O1O001O000O2N1000000_NmiNT1UV1jNmiNT1TV1lNmiNR1TV1nNRjNk0dV1L5I6F;K3LaYm4\"}, {\"size\": [1280, 720], \"counts\": \"kkVe0f0QW1b0D8I4M3M2N1N3N2N00100O1O100O0100O1N200O100O1O10001O0O2N2N2O1O1O1O2O001L4A>N4G7N3M2O1O3K3NTdS5\"}, {\"size\": [1280, 720], \"counts\": \"]Yme0:cW15L5J5L4L4L3L5M2N2N2N2M3N2O1N3M1O2O00000O101N100O101O0O10001M2M4N1N3M3O0002M2M3L201O1O2M2O1O1O2O01O0001O0O2O2M5L5J6L4K8H7ZOhhN2hW1IYng3\"}, {\"size\": [1280, 720], \"counts\": \"PhUh06jW19G6J4L4L2N4L3M1O2N2N2N3M1O2N2N2N2N1O1O1O1O1O00O1001O00O1001O001OO1000000000000000000001O00000000000000000000O1N2N20000O1O1O1O1O1O1N2N2N2M3N7H6F9I?Aa_P1\"}, {\"size\": [1280, 720], \"counts\": \"agYh0i0QW1;G8H;E<E7I5K4M2N3M2N3N2N1N3O1N2N001O1O101N10001O00O10O1O10001O0O010001N1L3M4N2N2N2N2O1O1N2N2M3O1O2N1O1O2N1O1N2N3N1N2N3N1O1N3L4M3N3M2N3M3M4C=G8DcQo0\"}, {\"size\": [1280, 720], \"counts\": \"_ifd0`0^W14J7J8H5J6L5J5I7M1O1O1O2M4M2N3M2N2N2N101N1O1N2O0010O00000O1M4N100001O001O1O1OO2M2N2M4N1N2N2O1O1O2N2M200000001O01O01O001N2O010O10O0010O1N2O1N101N2O1N2O1O1O2N1O001N3N1E\\\\iNZOeV1d0`iNWObV1g0=H7N3L4O10O10J5Nehg3\"}, {\"size\": [1280, 720], \"counts\": \"VU_d01kW18J5K3N1O2N1O2N2Na0_O1O2N100O010000000O01001N01N1O2N2O1O1O00100O10O010000N20O2O1O1O2M3_iNiNUV1c1N2N1O2M2O001O010O1O0000N2O1N2O1O1000001N100O1O1O100O2O001O00000N3L4I600O2M2O2M2M3N3O02O00O0K6N101N2N3M20O0003L2LRkg3\"}, {\"size\": [1280, 720], \"counts\": \"oRVa02mW14L2O1M3N2N2O0O2N101N2N2O2M2O4K>C5J6K2M2O2M3N1N3N1N100O101O000O100000N2M33M10N100K[jNoMeU1V2O0101O000O1O100001OO011O01O1O000000O2O01O1O010O010O10O1M3N2N2O1N1O2N3M2O0O2O2M10001O1O1O1N1N3O100O2L3K5N110M4K301O21OOOIihNCZW1<7M3M2OZTh6\"}, {\"size\": [1280, 720], \"counts\": \"goW=9bW18L4L2O1N2L4N1N2O2O00O10O101N01O010O0010O1O000010O1N11O2N2O0O1O2N3L2O3N2M2N4M2M3M2N3M1N3O00O01O2O1N01O0O2O000M4H7L4F:01N101O0000000O2O001O10O10O01O00010O000100O01000O010O001O10O0100N2O1O10000O2N10000M3I8FihNFZW1:61ON2O2N2M3M3NUZa:\"}, {\"size\": [1280, 720], \"counts\": \"kVh=3eW1<J5L2I\\\\OohNg0nV17M3O01O00N2O100000O010O1O2O0O10O10O100O10O10O1O10O1O2OO0100O3M2N2N2N2N3M2M4M2M4M2N2N2O2M3M2N3N2M2O2N1O001O1O000O10O001O0O101N2N101nNgjN[OZU1b0TkNQOnT1n0ZkNhNjT1V1P110O10O1O00100O10O1O000100O0010O1O1O10O01O100O100O01000O101N1TOSiNe0mV170XO]iN2dV1N\\\\iN2dV11ZiNNfV1KSiNO75hV10ZiN0fV1N\\\\iN2dV1K_iN5aV1IdiN5[V1JgiN5QW11O23G]Zh9\"}, {\"size\": [1280, 720], \"counts\": \"Re\\\\?1lW17J3N2N1O101ZOI_iN9_V1e000O1O010010O001O00000O10000O10000000001O0000000O101O0O101N1000001O0001O1O2O2M6J4K5L3N3L2M3N1O2N1N2O000O101O0O0010O1O001N2O0O2N2N101O1N2L4F9K5N3M3M3O1O010001N100O100000O0101OO10000000O10O100001O00O1000000000000001OO100O10000^ORiN2nV1NRiN2oV1LSiN1oV1J\\\\iNJjV16ViNJmV12dli7\"}, {\"size\": [1280, 720], \"counts\": \"oU`f04WW14mhN6mV1b0M1N3L302M20000O100O100000001N3NO1O1000000001O6J0000O1J511O01N<E5K1OM3N2N1O2O1N3O0O01000O1O10000000000000000000000010OO2O1O1O1O1O1O8H3M7H2O1O1O1O1N101O001O00001N110O0000001O0O10001O000O2O001O001N2OTbe1\"}, {\"size\": [1280, 720], \"counts\": \"SjVe078JlV10QiNo00VOTV1c1L3N1O10O01O10O01O2M200N2O101O1N2ajNlMoT1U2njNnMQU1S2kjNRNSU1^2ODQkNiMnT1X2RkNhMmT1[2PkNeMPU1f200O1O101N100N200O100O1O0O1O10O14L3M1O1O1O4L5K2OM3GnkNcLRT1^3PlN_LPT1a38O001O11O001OO001O100O10M2100O100O100000000O100O1O1000000O10000O103L2O1N2O1N2O1O1N3N2N2M3M5cMbjNo1oU1I2M3N2M2O1N3M2O1N2N2O1O1O1N2O1N2N2O2M2O2M2N3N1N3M3M2O4Je]n1\"}, {\"size\": [1280, 720], \"counts\": \"h`o=9cW19J2N2N3M2N2N101M3N3N1O2N2M6K7I3M6I5L3L6L1N2N2N1O10O100O1O2N1O0O2O000000001O00000O2O00O10000100O2O00000O01O1O100O1O001O01O01O1O1O1O0O2O1N2N1O1O1O1O1O2N1O1O1O2N2N0010O02N3N0001O102oL]kN_2XU1I6E:J5E<J6K4J5L3O1N2O2N1O1N103L4N2L1N4L3O04M01M^ed9\"}, {\"size\": [1280, 720], \"counts\": \"eU[<`0^W13K6J6L3N3L3M5M2OO010N1O2O010N00O0101000O2N2O1O0O10O01001N100000O10000O100O01000O101N1O2O0N2O2N101N2N1O2M2O2N1O2N2O0O2N2O01OO2O0010O00001O0O1O1O1M3L5K4L4O1O2N2O0O1O2N2N2O0O101O001N2O001O0O100000001N2OO101OO2O00O1O1001O000XOYiN8fV1FciN3aV1I_iN6gV1D[iN;cYZ;\"}, {\"size\": [1280, 720], \"counts\": \"\\\\`g>:bW18J3L4M4K4L4M3L3M4K4ROjNjjN^1UU1f01O001O001O0O1N2O101N3M101O001O1O1000O0100000O1O2O001N101O0O101N1O2N2O1N101N1O001O1O1O1N20N100010O00QOTkNjNnT1T1WkNdNmT1\\\\1l0O001O001O000O2O001O10O01O001O1O1O2N1O1O2O1N2OO01mNbiNc0RW1K2O1MnYh9\"}, {\"size\": [1280, 720], \"counts\": \"b]h`0<]W1;I5H8J6K4K4L5L2O2O0N2nN]NgkNd1XT1cN`kN_1`T1gNYkNZ1gT1o01O00001M2O1N3M3O0O2O2N1N202N1N2O1N101O1O00010O01O001O1O1O3M2N2N2N002M2O1N1O1O100O0100O001O1M3L3POQkNjNUU1U1l0O10O010O0100O010O01001N10O1O10O100O100000001J_iNlN`V1Q1eiNlN\\\\V1n0jiNROVV1l0a0L3O1M4K5M3N2OPUa7\"}, {\"size\": [1280, 720], \"counts\": \"Ylkb04iW15L4L4N2M3N3L1O2N100O1O001O1O1O1N2O001O001O001N10000O1O101M2O1O1O2N1O1000O1O11O010O100O3N1N2O2N2M4M2N2N1O1O1O0O2O1O001N101O001O0O10fNhjNKYU13ojNDTU1<X1O0100000000O1000001O000000001O0O11N100001O000000000O20N1N2K5N2K6N100GhhNIZW1<3J6005JHchNM^W182L6K4O0O2N[]h4\"}, {\"size\": [1280, 720], \"counts\": \"Y]me04jW12O2O1N2N2N1N2O1O2N1O2O001O000000010O001O001O001O001O001N10001N2N2N2N3M2O2M2M3N2N2O1N2N1O100O2OO01O01O10O0100O1O1O2M2O2N1O2N1N3M3M3M3M3O1O1O1O001N2O1N2N2N2000000O1L33NO2N2N1M41N6J0dhNBWW1<9O1GbRa2\"}, {\"size\": [1280, 720], \"counts\": \"YdSf04iW13_OLRiN8hV1c0E;M4M2N2N2O1O1O1O1O101N100O101O0O10O1O11O1O001N2OO2N10O10002N6J3MN2O1003M2NO101000O00N2N3O0O2N2N2N10100O1O0O3N1O1O1O1N2O1O1O1O1O001O1O1O1N1000000O1000000O10000O100000000O1000001N10001O1O1O1N3N2N2M3N2N4K4L4Kocj1\"}, {\"size\": [1280, 720], \"counts\": \"bYm`04iW1:ihNLRV1V1K3M2N101N1O2N1O2N101O1O1O0O2O1O001N2O001O001N101O00001O1O011N1O100OO1J7M2001OO2O0O2O0N2O101O0L4N200O100001ON3N1001O1ON2N2O10000001N2O0O10N2000O100O3N3M1N10O1L3O2N2N2O0N3N2N2N2N2N2M2N3N2M3N2M3M3N1O2N2N2O1O1N2N3LRgo6\"}, {\"size\": [1280, 720], \"counts\": \"`fQ=6gW1b0^O4M4L3M10O11O1O2N1O010O1N11O00000O100001O0010O01000O0100O2N100O2N2M4M2O1N2N2N2O2M2N101N10000O1O100O01O1YO[jNPOgU1o0fjNcN]U1\\\\1d0N2O1O01000O10O1O2N0100O01N2000O010O10O10O10O100000O10000O100O10000O10000O100000000O2O00001N10000_O[iNEhV1<=3ON1N1I8N1I7NUSe:\"}, {\"size\": [1280, 720], \"counts\": \"eP`>8cW17G9J4L4M4L3N2N2M3M3O2M3N3M2N4K4M2N2M2O1N3M2O0O101N100OO2L301000OO2O1L4N1101O1N2O2N1O2O1N1O2N2O1N3M2N2O1N2N2O1O1N101O_N`kNC^T1;kkN@TT1`0RlN[OmS1d0XlNXOhS1f0[lNYOeS1f0]lNYOcS1f0`lNXO`S1g0clNVO^S1i0flNSO[S1k0mlNmNUS1Q1n1O1N2O2O0O101O0_OYiNIgV15[iNKfV1N`iN2WW101O001O001O00002NWiT:\"}, {\"size\": [1280, 720], \"counts\": \"fWi?:aW17gNJ`jN<]U1G\\\\jN>bU1HTjN?jU1l000O2O00001O1N2O2N3M2N2N1N2O0O1O2N100O1O001O1O1O001O1N101N02N1O001M2M4H7O2O100000O10O101O001O000O101N1N2O110O1N1O1N3N2N1N3N101O1O0O2N2O1O1O1O10000O1O1O1O1O10001N1O2^OViNKkV12`iNEbV17g0J6MTkg8\"}, {\"size\": [1280, 720], \"counts\": \"_mab01kW16M1O2OO1O10O2O1N2N2O2N2N3M1O10OO2O3M000O100O1O1O1O1O2N2N1O001O1O2N1O1O1O1O1O1O1O100O000000101N100O100O100O10O001O001O001O1O10O0100O100O1O1O1N2O100O1O1O1O1O2O0O1O1001OO2N1O2_OViNIlV14XiNHkV16`000IlSo5\"}, {\"size\": [1280, 720], \"counts\": \"e]]c04jW14M2M2O2N1O1N2O1O100O100O10000O10O01000O010O00001O010000O010O1000O0002N2O0O2[iNYOnU1i0hiNDTV1U1N3M2N101N1O1O10O01O001OO101O001O0O2N1N3M201N2N1O2M2M4O1000OO2O1O10O01O1000000O01000000O100O10O100000O10O1O1000O10O10000000000000000001OO100L4O1O1M301O0O101O0O2K4O2O010O0N3L5M1L5NfSd3\"}, {\"size\": [1280, 720], \"counts\": \"]bPe06iW15J6K3N1N3N1N101O00001N1000000O10000iiNXOVU1h0fjN_OWU1b0fjNBXU1?djND\\\\U1>_jNF`U1]100O10000000000001O0O10O1000000O0001N2M2N3H7L5O1O100O010000000000001O00O10000000000000010O000001M2M4N1O100000001L3O100O1O1N2O1N3L3O101OO10000000000O010O100O00010O3N1N02N0O2O1O1O2N2N3M4Ll]b2\"}, {\"size\": [1280, 720], \"counts\": \"eRTb02iW18J5M1N2O1O1N2O1O1O1O1O2N1O100O010O1O100O100O1000000O010O100O10000000O100000ZjNQOZT1P1bkNVO\\\\T1j0bkNXO^T1g0akN[O_T1e0^kN^ObT1b0]kN^OdT1b0ZkN@fT1`0XkNAjT1>RkNEoT1;njNGRU1;jjNGWU19ejNJ\\\\U1\\\\11O1O100O1O100O010001N4M1O0O2O000O0100O001N101N1O2M2M4L4M3M3M3O1O10000O1000000000000000000000000001O00000O100O1O010O2O0O1000O1000000O2N2N2N4L3L3O1N3L201N2N1M4M3IghND[W1:7O001O0000002M2L40O1O12Lnm[4\"}, {\"size\": [1280, 720], \"counts\": \"Pgad09dW16K7I>B7I6J4M3M2N4L3M5J6K4L5J4L4M2N2N1O2N2O1O2M3N1N100O1O1O10000O2O1O1O1O1N2O1O10YNmkN@RT1;XlNAhS18blNE]S16llNGSS1@QlNZO50n0T1lR1YOTnNd0nQ1XOWnNf0iQ1XOZnNf0fQ1SOenNh0]Q1VORoN<`T1DbVa5\"}, {\"size\": [1280, 720], \"counts\": \"lkfd09`W1:I8H7I5K6J6K5J5M4K4M2N4L3N3M3L3N2M4N1000000O100O1O101N2M3M2O20N1O1O1O0O2O0O1QOWkNeNlT1Y1WkNcNkT1]1ZkN]NhT1b1YkN[NiT1d1\\\\kNTNiT16jjNV1lV1_O1N3M2M5L4EZ\\\\]5\"}, {\"size\": [1280, 720], \"counts\": \"R_Zd0>_W16J7I5L4L4J6L2O2M101N2O1N2N3M2N2N200N2N2O100O1O1O1N3N0O2N2O2M2O1O010O1O100O0100O10O0O0101O1N2M3N2N2N2L4K5H8M3K5M3N2N201O000O0N40OQO^iN7KIgV1NciN0L1bV1LSjN1ihU5\"}, {\"size\": [1280, 720], \"counts\": \"eSSd06gW1=D:I3L1O100O100O1O2N1O1O2N2O1O2M2N3L4M3M4L2M3N2N2N1O2M10000O2O01O000001O001O0O10001OO100O1O1O10O1000000000000O01000000O001O1O0O2000O01N2N2O101M4M2M2O001N2O2M3M8H3N2M8HLTOWiNl0eV19O03M8nNQiNb0XW1H7N5JYd\\\\4\"}, {\"size\": [1280, 720], \"counts\": \"YV`e06jW12M3N1N1N3M4K3L5M3L3N3N1N2O1O1O00100000000O100O100O100O002M2O1O1O1N2N2O1N1O2O001N2N100000O1O2N2K4N2K6L4M4M1O101N3M4L10O11N3N2N0O2O1N4L3K5K4M`Sk3\"}, {\"size\": [1280, 720], \"counts\": \"jdVf08eW15L4K5M1O2N1O2N2N1O100O1000000O1000000O001O1O010N2O100O1O1O0O2N2N2O1O1O0O2O0O2O00010O0000O2N1O2N200O1O1N2N3N2N1O1O0nNXiNj0nV112M3L3N2O2N2N2N2N2M7JblU3\"}, {\"size\": [1280, 720], \"counts\": \"VUVe0>aW12M4M2M4M1O2N100O100O100000000O10O01O1O1O1O1O1O1O1O1N2N2N2O2N1N2O1N2N2N3NK5N101N2O1O2O001O001N2N2O1N2O1O2N2M011N2O2M2O3L3L5L3IXl]4\"}, {\"size\": [1280, 720], \"counts\": \"TU]d03iW18I6J6J5L3N3M00001O1O2N1O1O1O2N100O1O2N1O100O1O011N100O01O010O1O10O01O00000O100O1000000O2O1O1O2N2N1O1O2N1O1N101O0O101N3N3M1O1N2O2L4M6HTlQ5\"}, {\"size\": [1280, 720], \"counts\": \"Xbdd04jW1;D6L2M4K5L4L5L3M3M2N2O1N1O1O101N1O1O1O0101O0O01N1101N10O1O1O10000L4O1O1O1O001M3O1O010N2O1O1O0001O1O010O1O001O10O02O3L4L10O1O02L4M3L5H8H6K5L4J7J5M5KQga4\"}, {\"size\": [1280, 720], \"counts\": \"SZ[c06bW1:J;G6J6K2N2M3N2M3N3L4N2M2N2O0O2N2O0O101N11O01O001O00000000000O0100000O010O01000O10O0100000O10O00100O1O001O01O1OOO2mM[jNk1hU1SNZjNj1PV1N3L3M6K1N2N1N5K3L5K6J5K3N103L4Kjfi5\"}, {\"size\": [1280, 720], \"counts\": \"oUVe0`0\\\\W17J6K4L4M2M4L3M4L3M2N2N3N0O3M2O1N2N2N3L3M3N2N2M3N2L4003M3MO1O1001O1N01N201O1O1O0O13L1O1N2L4N7J1O2N1N3M3WNSjN[1`V1Hf0jNmhNOcYj4\"}, {\"size\": [1280, 720], \"counts\": \"bUbd01aW1d0E7K5K3M4M2O2N1O2O1N3N1N2N1O1O1O100O1O2N001O1O1O1O1O1O1O100O001O0O2N101N2O1O1O010001N2N3N00001N100O100O1L4M4N6H6AciNRO_bZ5\"}, {\"size\": [1280, 720], \"counts\": \"^U`e06eW17K3O1O1K5L4M3N3M2N1O2M4L3O1M3N2N101O1N2N2O1N1O2O1N2O1O001N2O1O0O2M300O01000001O0O01O1O1N2O10N2O00100O00001N1O2N2M3K;\\\\Ol0XOhdW4\"}, {\"size\": [1280, 720], \"counts\": \"Yfdd01nW15J3N3M1O3M1O1O2N2N1O1O2N1O2M2O2O0O2N1N2O1O1O1N2O1O1O1O1O1N2O001N2O1N2O1O1N2O1N101O1N2000000001OOO020O2H7M3O1O2L4K4N1OO100O12K4M3M7F;Cg[j4\"}, {\"size\": [1280, 720], \"counts\": \"geSe01nW13K5L4K5M4K4M2N1O1N2O1O100O1O1O1O1O010O2M2O001O1O2N1O001O1O100O1N2O1O1O1O001O1O1O1O001O1O1O1O1O1O101N10O100N100O3M3L3N1O2M3M3N2M3M3M4K4K5L3M4L6J5MQlS4\"}, {\"size\": [1280, 720], \"counts\": \"jb`f0<cW12N1O2M2O2N2N1O2N2M3N2N2N2O2M2N2M3N1O2N2N100O2N1O101N1O10000O1O1O1O100O100O1O10000O100O010O1O100O10O100000000000O0100O1O1O1O1O1O1O2N2N1M3M2N3N1O2N101O1M3L<E[f`2\"}, {\"size\": [1280, 720], \"counts\": \"XRRg01iW1;G6BAQiNe0[V1^OdiNU1WV1=M2N3N1O00001O1N2O1N2N3L4M3N1N2N2O1O1O2N1O1O1N2N1N300O10O10O100O010M3M2N2O100011N000100O010O10O100O01O10N2M10002N3L3N3M2M3N2N3M2O1N2N3N1O2O0O1O2L4M3N1N4L6I9I4J5J7H9E]Pd1\"}, {\"size\": [1280, 720], \"counts\": \"UZVf02jW15M4L3L4L4M3N3M2M4M2O1N2O0O3N1N2N2O1N100O2O1N100N3O0O101O00001N101O000O2O001O001OO10000000001O0000001O1O0O2O001O1O0010000O010O02O2M01001O1O1NO2M3M3O1M30O1001O0O1000000K6L3N2JXiNROjV1k08N101O13L2NL6J5J8L3L3M3MR]o1\"}, {\"size\": [1280, 720], \"counts\": \"Tg[b02cW1d0C5M3N3L2O2N2N1O2N2N2N1O2L3O2N1O2N2J501N110O0O102M2O1O1N1O1O20O1OM4M3N2O001O01O00O10001O001N1010O1N1001N1103L1O1O002N4K2O1O10O0001O0O102N9HPjNUNmU1g1QjN[NPV1j1000000N22OO00000O12N0O11O001O10O2M2O1O2ML[NQjNb1PV1]NQjNd1VV101OO05K1O1O0O2O0O1O5K3M12N01IUiNWOkV1i08NO101OO2O1N4M2N00O10000000O10001N2L4L5L3NO2O0Wh`4\"}, {\"size\": [1280, 720], \"counts\": \"lUUd06iW13M2M5J6K3N2M3M5K5K4L3M9H6J3L3N2N1N3M2N2N2N2M3L3M4N2M4N1M2O2N3N100O00100O1O2O0O1000000O01000O2O00000O101OO2O000000010O00001O1100OO10001M4M2M4N2N6H6J6I<F5K;D4M1N3N101L4M3M3N1O1O2N1O2CmhNEXW199M5K202L_aU4\"}, {\"size\": [1280, 720], \"counts\": \"h^Yj09bW18L3M3M2M3O1O001O1N3N1O2M3M2M3O00000O2O1N2N1O10000000O0100O2O0O1O100001N2O0001O000O100010O00mI\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"neei05jW13L5^Oe0H9I2N2M3L4L8H3G8L5L4N2N2O0O2O1N2O00000O1000001O0O2O1O1O3M1N2OKXkNTMgT1Y3I1O1O0OJgkNfL[T1^3100O1O1N3N1O11O1O100O001O002N2N6J0000O2M2N2O`C\"}, {\"size\": [1280, 720], \"counts\": \"eZg`05iW16K2N3_ODXiNa0dV1a0L2O1N3NO1O101L3O12N1O010O2M1O10100O1O00001O000O3fiN_NQV1a1niN`NQV1b161O000000010O0O2O0O01J6M3N200O001O2O00OO1O110O0100O100O001O1O1O2N1N20000O10O0O2000O1000001O1O1OO010O1O101O0O1000001O000O2O0O100010O001O010O01000O01L4M3N1N3K5J6O1O1O1K4M4O0O2M6L0022L2OO1Lm]`6\"}, {\"size\": [1280, 720], \"counts\": \"XXm=`0]W14K5YiNXOQV1m0fiN]OUV1`1B9I4M3K5M3N201N100001OO01000000O100O1O100O100O100O1O2O000N2O1O100000O100001O0O101N2O000001O010O1O00O2N10O101O00000001O1O0000O1001O000010O1O10OO1001O1O1O00O2O0100O3L2O00O010101OOM3O1O2O1OO001002N1O0000O11O0O10O1002N0O101_OhiNPO`V1o08N3I7O9G2N1J^hNMa__9\"}, {\"size\": [1280, 720], \"counts\": \"Pnca02jW16K8D:J6K3N3L3N2O0O2M3M3M2N3N1O2N1O2M2O2M2O2M2G9]Oc0O2N2N1000000000O10O101O0000001O00000O1000O1N2N2N2O1O100O10O01O1N2O1N2N201N1O0N3N21O01O001O0010ON5M2M4M2M4^N\\\\jNb0gU1ROhjNg0]V1L2N1O1N2O1O2M2O1N2O1O1N101N2MZZi6\"}, {\"size\": [1280, 720], \"counts\": \"oWTg06dW1:I5K6L2N2O2M101O0O2N2N3L4M3N1N2N2O0O2O2M2N2N2O3M1O2N00O1M30O10O1O1002N1O00O010O01000O1003L2O1O1O1O1N2O000000O10O1000O1N2N1011O001O00001O0000O0101O10O0O1100O1O00001O0000001O001O3M1O001O2N001O1O011N1O1O00100O001O0001N1O2L4H9F:L5L3N2N2N2N2M2O2M3M4J:D>@kW2\"}, {\"size\": [1280, 720], \"counts\": \"[^ah0P1jV1:D=D;G8I6J5G8J7N1O1O100O2O000N2M3O100N101N2N2O1O10O10O100O1N2O100000O10O100001N010000000O1000000O100000001O1O000O100O2M3N1N3M3N1M4VNXkN7nT1ZOikN8[T1EokNNZT1KW2EZki0\"}, {\"size\": [1280, 720], \"counts\": \"ncVf07dW18K3L4M1O1O2L4M3N2O2N1O1O1O2N1O1O1O1O1O1O1O1O1O1N200O1O1O1O001N3N1N101N2O1O0O2O001O1O0O2N101N2O01000O000100O10O10O1O3L4L20O10N3L3N2N2L5K4L4N2O2A>M3A?MY^n2\"}, {\"size\": [1280, 720], \"counts\": \"]dac07fW17H6K5M3N1N3N0O2N2M201N2N3M2N2O2N1N3O0O001N2O1N1O3N0O2O1N101N1O2O1N101N2O1N2O0O2O0O2N1O2O0O100O1O1O1000O010000000O1000001N10O10O2O0O1O1O6J1O2N2N1O3dNWjN=lU1\\\\OdjN8^U1DhjN8[U1DPkN0SU1FPjN1^W10bhNO\\\\W15chNK\\\\W166N11`\\\\Y5\"}, {\"size\": [1280, 720], \"counts\": \"Y[f>4kW13M4J3N2O1O3L3O00O01O2O0O01O1O03N1O0O10000000O1O10O3M3N0000002N2O1N4M3L3M3N1O0O100000O2N1O100O0010O0100O1O1O0010O010000O10001N1N2O00102M100O2N10:D7Hfk_:\"}, {\"size\": [1280, 720], \"counts\": \"Y[U?2gW1`0_O<K6K01OO2O00000000O1O1M4O00000O00110OO10O1011N000000O110O00000001O00000000001O001O1OO2N11O001O1O1O1O1O0000001O001O00001O1O1O00N22N1O0O2O2M0N2002O1O10N1001O1OO1N8J2NO4K3O1O1O1O5VOgiNDXV1b0aiN_O]V1d0aiNXOMNaV1k0biNVOOO_V1k0a00TiNRO`V1P17SOYiNg0bV1ZO_iNf0bV1VObiNa0EBaW11=M\\\\O:lhNF02K1eV1:_iNC19bV1J[iNO?3WV1K]iN491cV1KaiN4_V1MbiNOaV11V]f8\"}, {\"size\": [1280, 720], \"counts\": \"jlU`02lW14G9O000K5N101N2N3N2L2O10001OO2N10O2O1O00001O000000O2OO101O01O0000010O001O1O010O1O1O10O10O01000N2O001O002N010O100OO10001001NN3O1O1O000000O1002OEQiNElV1:WiNEiV1:XiNEjV19WiNHiV17UiNLjV1LRiN01ON0oV10TiN07OcV10\\\\iN2PW1LSiN2ZW1040iZf8\"}, {\"size\": [1280, 720], \"counts\": \"jThd02lW13M2O1O1N201O0M1K223M4M4N00N200000O10O0102N3M00N20O101N102M3N0O011N3M2O1O1O4^iNcNYV1c1O0O10O0N3L4M3O001O10O0100O10O10O101OO10O1O10000O1O1O2N1O2N2O1N1O1O3M3L4JWl\\\\4\"}, {\"size\": [1280, 720], \"counts\": \"oS[h02kW14M2O100O1001N2OM30J]hNOcW170O10O010O1D<M4N1O10O2O1ON101O1O1GTO\\\\iNm0dV18O0100O100O2O000000000O2O0O101N2O1N3M=Chdk1\"}, {\"size\": [1280, 720], \"counts\": \"^keg09fW11N2N1O2L4N3L4M3N0O2O2N3M3N1N3M1O2O100O0O01O1O1O2OO1jjNeNgS1[1WlNiNgS1X1WlNiNiS1X1nkNoNTT1R1fkNROZT1n0bkNVO^T1j0akNXO^T1i0_kNZObT1e0WkNBiT1>UkNClT1>njNFTU1b11O3M1O2N2N2N2M2O1O102M5J5L1O6J1O1O1O1N2O1O001O1O1O0O3M2N2O1N3L4K7Gb[e1\"}, {\"size\": [1280, 720], \"counts\": \"gj[g0;YW1m0[O8K8I2M2O2N1O2N1O0000001O00101N2XjNmM_U1]2M010OO2A?N12N10N1O2N20O10O1N2O12NO1N2M4M20100O011OeiN`NUV1_1jiN`NXV1a15L<XOh0[O^lj2\"}, {\"size\": [1280, 720], \"counts\": \"ahZe08gW13M2O1O2N2O0O2N1O1O00100O010O010O1O100O001O100O01O02N100O101N0010N2O001000O1O2O0O100O01000O101N1O100O1O10O0010O100O1O2O0O1000O10O01000O0100O1O10O100O1000000O01O01000O10O010O10O010O1O1O001GZiNUOhV1h0:M3N2L4N4L2M2N20101O1Kh]c2\"}, {\"size\": [1280, 720], \"counts\": \"Ub`d0;bW15L3M2O2N1O2N2N2O1O1O1O1O000000000O100000000000O010000O10001N110O1O000000001O000000000000000000000000O100000O100000000000000001O0001O00000000O11O00O10000001O0000O10000000000000000O100O100000001O000O1000000000001O0O10000001O01O00001O1O001O1O002ClhNKUW10PiN0nV1OTiNLQW11a0Omed2\"}, {\"size\": [1280, 720], \"counts\": \"meod03jW19H4L3N2M201N2O1N100O100000000O100O2O0E;L3N3N1M3N201O101N100000000O1O1O2O0O1O10O0100000O010O100O100O101O1O001N10001O000O101O10O0O010000O1O010O1O1O1O010O01000O1000O01O010O1O010O100O1N1M4N1N3O1O00010O1N3N010O0010O0101O000O2O0O2H8M3H8MjSb2\"}, {\"size\": [1280, 720], \"counts\": \"S`Pi0=[W19M3H8G9M2L5K4M3M3N2M3J6M3M4M3N2N2M4M2O1N3L4M3M3N3L3N1O2N0O2O001O001O1O2N001O001O01O0O20O0000010O0O1010O001O5K101N3M1O1N2O1O1N3N5J3N3L3M3N2N1O1K6L:G6J3M3N2N00X@\"}, {\"size\": [1280, 720], \"counts\": \"khji06gW14C<ZOf0K4M4M3M3M3L5K4J6K5N3M2O2M4M4L6I8I7I4K4M4N1M2OO01O100O11N10000000O100O100O20O00010O0O2N1O001O1K6M4K5M3L3N2M7J2NdA\"}, {\"size\": [1280, 720], \"counts\": \"ebYg01kW1032YhN2ZW1NihNOWW11ihNOWW12hhNOVW13hhNNWW15fhNMZW1>O0O2N2N2N3M3M3L3N2N2O3L2N2O0O5K2M3N2M2O1kNTNSlNn1kS1TNTlNm1kS1SNUlNn1iS1RNXlNo1iS1PNVlNP2kS1PNSlNR2lS1nM]kNL`0X2ST1kM\\\\kNN?[2ST1gM]kN0=\\\\2UT1eM\\\\kN1?Y2UT1nM_kNE6]2ZT1VNdkNj1dT1mM\\\\kNT2iT1gMWkNY2iT1?O11O4K4L4N0O2O2M1O2N100O2O1N1O1O1N2N3L3L5L3M3J5N3M5J4M4L4L7I6Icdl1\"}, {\"size\": [1280, 720], \"counts\": \"S`ii0:`W1:I8D;J4L3L4N1N3O001O001N2N2O2N2M1O2O0O01001O1N100000O0100O0O200O010O100001N10O1O01O2OO1O0100O1O100O1O0100O0100O101N2OUA\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"UYWj0b0PW1?K7J5L5K3N3M2N2N2N2N2N1O4L4M1O0000000010O01O010O1O010O01O0001O01O0010OO1000000000000000000O0010h_O\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"SWcg0=`W19I6I4K5M2M3N3M2N2N2N3N1O1N101N1O2O1N101O001O001O1N10001O001O000O2O1O001N2O001OO1O1001O1O1O001O1O1O01N100O1O1O100O2N1O2O0O1000000O3L3N2000OK6N2O1O001O1O2M2O1N3M2J6L8H7Hk`V1\"}, {\"size\": [1280, 720], \"counts\": \"Uf[g0;aW18G9I7J4L3M4M3M3M2N2N2N2N2N2N101N2O1O1N101N2O001O0O2O00001O1O00001O0000001OO2N10001N1000000O1O2O00000000001O000O101O0O10001O001N101N1O2O0O2O1O001O1O1N3K5L4C>I:G:Dkia1\"}, {\"size\": [1280, 720], \"counts\": \"h\\\\Rk02gW1a0B9I:F3L6L2M4K4M2O1N2N2O1N3N1O1O1O1O100O100O1O101N1cL\"}, {\"size\": [1280, 720], \"counts\": \"flcf06gW1;E7K5K4L5L4K4L2O2M2N2O1N2N2O1N3N1O1N2O2N1N2O001O1O1O1O1O001O1O1O001O00001O0000001O00001O01O01O001O00001O00001O1O01N1O2O00000O2O0O1N3O0000001O00001O001N1O2N2N1001000O001O2N1N2N2M3O101N3K5J6D_iNQO`Yi1\"}, {\"size\": [1280, 720], \"counts\": \"anPc03`0NiV16RiNOmV12mhN6ME6N_V1Z1M2N10000O2kiNbNfU1l100O101O1N101N1000000O101N1N200O1O2O0O101N2O00O1N20000000010O01N2OO2M200001O00O1O20O1O10O010N2O1N1O2N2O1O100O10O01N2O2O0O1O100N3N1N20O01O100O100O1O1N1O2O1N101O0O3M2B[iNZOlV1d0UiNZOmV1i0QiNYOlV1f0UiNYOkV14[iNLdW1OUhY5\"}, {\"size\": [1280, 720], \"counts\": \"jPXb01lW14M3O2M2N1O2M3O2M1O2N2N2N2O001O1N2N1O2N2M3M2M4L3O2N3O0O100N3M3M100O011O01O0000O4M1O0O2O1OO100001N1000OM4NL4110O1010O00O2O1N101O3NJ6O001O05J1000M5H8K31O01O0O1O11O102N0O00O0O2M4N1O20O2NO002O4K2N2O1VObiN0`V1LjiNKXV10QjNKoU13TjNLmU11XjNLiU10V1Logh5\"}, {\"size\": [1280, 720], \"counts\": \"enee0;bW17K3M1O2O1N3M3M3N1N3M5K3N1N1O100O2N2N2N104K00002O1N1O2O0O2N100O101N1O1O100O100O00100O1O10000O10000O10000O101O00000O1000O100001O00O10O2O000000000000O100O100O100O1O1O1O2N1O1O1O1N3N2N1O2N101N2M3N2N102M2N2N3N1N3M3N2N2M3N2M3M3K5K6Jaao1\"}, {\"size\": [1280, 720], \"counts\": \"TmVj0<XW1`0I8F6L5L4M3M2M3O1N2O1N2O1N2O1N2N3N1O1N2O1N2O1N2O1N2O1O0000O02O0000000O1000000O10001OO101O000O10TL\"}, {\"size\": [1280, 720], \"counts\": \"^fUe06eW1:J9F8I5K6J8H4L4L4L3M2N2N101N1O100O100O1O2N1O1O2O000000000000000000001O0000000000O02O00000001O00000O1O2O0O1O11O0001O0O1O1O2N101N1N2O2O1O1O0O2N2N2O1N2O1N3L3O1N3K4M4M3M3I7M3M3M4L4M1MWa\\\\3\"}, {\"size\": [1280, 720], \"counts\": \"PoQe0244RW1i0I8H3N3M3M4L3L3N2M2N2O1N3N101N1O1O1O2O0O100O2N1O1O101N100O100O100000000O101O00001O00O10O010000O10O1000O100O1O10O02N1000O01000000O100O2O0O1O1N2O2N101N10001O0O2M2O2N2M3N2N2N2M4M2N3M3M3M3N1N4M2N2H8J7IbQU3\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"V\\\\Sc02iW16L4N3K4N100O1O1N3O0O1O1O2N2N1O1O1O2N2N1N3N1O3M2O1N1O2O1N1O2O0O2O3L2N10000O101O1N2O1O1N4M010O1N3N2N3M1O2N2M2O2N1N2O2N1N2O2N1O001N2O00001O0000000001O01N1001O000O100000001O0000001O001O001O00001O1N10001O001O00001N1000001O0O101N101N2O0O2O1O010N2N2J60O10J6N3M1O2N2O1M3N1N3L4O1N2M3N2N2M3N2O1N2N2O1O1N2H7O2O1M3N2L3N4N1O1NfSk2\"}, {\"size\": [1280, 720], \"counts\": \"`b]f0`0UW1a0E9H5J6L4L4N3L3N2N2M4M2N2N3M2N2N2N1O2N2N2N2N2N1O2O0O2N100O2N2N101N1O2N2N1O1O2O0N201N1O1O2N100O1O100O2N1O1O10001N10000O10000000001O0O1N2002N2N1N3N7Ia0_O=D3L3M3M6I5L4L4K5L3K4L3L6K5J5L4L4M2L5L3O001O5H:Hb\\\\i1\"}, {\"size\": [1280, 720], \"counts\": \"aRjf0:[W1`0F8K3N2M3N2N2M3N2N2M3N2N2N2N3M2N1O1O2O1M2O2N3M3M2N2O1N2N2N2N2N1O2N2N2N1O2O0O2N1O2O1N1O2O0O2O1N100O101O1O000010O01O01O00010OO1010O001O00000000001N101O010O0000100O1O101N1O2O0O1O2O1N2M2O2N2N1000O10O1O1N2M4L3O1N2N1O2O0O5I4M3O2K5K5L3M4M4L2N2M4M2N2M6J3M3O2N1N3L>De\\\\7\"}, {\"size\": [1280, 720], \"counts\": \"kTea04jW15^hNHUW1f0K3M3N1N101N3M2O0CUOciNm0[V1>N2N2O0O101O001O001O0000001O000000O11O00000O101O1N1M3M4N1N2000001O0O101O001O1O010O001O0O1001O01O0O3OO01O10O00010O1O00001O01O1O010O01O01O00100O00000100O001O0010O000001O001O001O0010O0001O001O001O001O001O1O001O1O001O001O001O100O001O1N101O00001O1O001O001O1N2O1O1O001N2O001N101M3BbiNSOcV1m0^iNPOdV1g0]iNZOjV1e092NIQiN^OPW1=;O2N2N3N3IibU4\"}, {\"size\": [1280, 720], \"counts\": \"WS`f02kW14K6DHhhN>TW19M3M<ROROUjNQ1YU1U1H7M4L5L5K4L2O1N3M2O1N2O1O1N7J4L2N0O101O1O2M2O1O1O001O2N1N2O1O1O2N001O1O1O1O002N00001O1N6LO000G[mNWKeR1i4:O1M3N2DglNnK]S1m3g0C5K4N2VMckNl1bT1fMmkNU2SU1I9G=E<B:E<F9Gl[g2\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"_o]??]W17K3L5M1H8F;N2M2N200O011O0O100O101O000O1O100000000O1000O0100O100O2OO1O0010O100O1XOVNZkNj1eT1XNZkNh1eT1ZNZkNe1fT1\\\\NZkNd1eT1^NZkNb1fT1_NYkNa1fT1aNXkN_1iT1bNTkN`1kT1bNSkN_1mT1j0O10000O10O10000001O1N2O1O1O100O1O001O1O010O001O100O2N00001O001O010O00O2OO100000O10O2O10M3I7N2O10O00O2N2O0O3N00000001OO101O1O1O1O0ImjNaMTU1V2jjNlM4MRU1\\\\290bjNfMYU1Y2gjNgMZU1Y2ejNgM\\\\U1[23O0020O1cjNcMUU1^2ijNcMWU1^252N01O0O2N102L3L3M32M20O1O00XjNnMcU1V21O2000O01O0000001O1O1N2N010O1O1M300O2M22N1OO1M3L46J1N100001000O000O11O00000O1O2001NN2N2O100UOkiNLTV13miNMRV1NniN@Kb0WV1LPjNBJb0VV1I[jN5eU1G`jN8cU1AdjN5ROLR`S5\"}, {\"size\": [1280, 720], \"counts\": \"fRfc02lW12N3N2L4AIQiN:jV1`0K6[iNlNZV1b1J4M2N2O4K:F9G1O0kjN]MoT1d2njN_MPU1l2M1N1O100O100O3N4L7H4M2M10001O0001O001O0010O011N001O3NO1N1OokNXLmS1l3100011O0000O110O00000O1O1O1O1O10O01O001O010O1O1O1O010O1O1O1N2O1N2O1N2O0O2O1N2O0O2O1O1N2O1O1N2O0O2N2O2M2M3N2M3N2N2L4L5L2M4M3M3K5M4I6I9@Qml3\"}, {\"size\": [1280, 720], \"counts\": \"bSef0;[W1a0D5M4L5K4N3L3N2N2N1O2N2N2N2N2M4L3M2O2M3M2N3N3L2eN[MYmNh2cR1eMQmN_2jR1kMllNY2QS1\\\\1O2N2O0O2N101N2N101N101O000O100O2O001N1N20O100000000001O00O10000O102N1N2O2N3M3L6K3M2M3N2M3N2N2M2O2N1N2O2M2O1N3N1N2O1O2M2N2N2N2N2N2N2N3L4M2O2I7L3M4K5L4K5L4M3L4K5L5]Ol]U1\"}, {\"size\": [1280, 720], \"counts\": \"iPh?;cW15oNDUjN`0eU1KPjN;mU1NkiN3TV1n0M2N2O011N1O1O010O2N100O100O1O1O010N110M2N3O001N1010O10000O10O1O001M3O1011N1OK6G8001O00N201O02OO2L3N2N1M2IjLekNU3cT12EhLjkNZ3VT1fLjkN[3XT18M0N3M21O2N1OM3O\\\\LlkN_3ST172M2ON2O1001O2NO100001O1OO1O11O2N1OO2N1000000O1O1000O1001O0000O10O1O1002N1NO200001O00O010002M2O0O102M3N1O0O0000001J70000N2N2O000O2O1N101O1O000O101N1O2N2O0O2O0O100O1O1O101N1N2O1O1O1N8I2N7J3]OmiNPOWV11aiN30:^W1M:Gbhh5\"}, {\"size\": [1280, 720], \"counts\": \"bUYg09cW19G6J5I7K5M4L3L4L5L4M2N1O3M2N2N2N2M3N1O2N1O2N2M3N2M3UOXMYlNk2dS1]MUlNf2fS1m0M3O00000O2O1N1N200O2N101N10000O100O2O1O0O200O10N2O0O10000O010O1O10O0010O0010O010O11N10001N1O00010O1O100O101N3N1N1O001O2N100N1010O00000001O3N1O001N2O1O1N2O1N10001N2O1N2O00001N103M3M2N1N3M1OlL\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"QmRg0?`W12N2N2J_OkhNa0TW16N3N1N4L9F8I3M2M4N2N1YjNQN[U1\\\\2M3O1N1O1O5L2M2N2O1N3N1N3N2M2O2M6K2M4M4L1O1O2M3N1O1O1N3N1O1O0O2O01O00O101O001O01O010O01O00001O001O0010O000000O0100000000000001N100000O100000O1O1000O010000000000O2O0O001O2N1O1O10O010001O0O10000O10000000000O101O0O10001O001O0000WL\"}, {\"size\": [1280, 720], \"counts\": \"[Yea0:dW14L4M4VOBiiNc0RV1CiiNa0SV1CjiN?SV1EjiN=TV1i0N2N3M2N2O1N2N1O10O010O0100O101N1O1O10O01O1O0O2N2H8L4M3O100O1O1O1O010O1O1O10O001O0100O10O01O01O100000O10O10O1O1O100O10O10O1O1O01O01O001O0001O1O01O10000O1O10O01O10O110OO10000O101O00001O00001O001O00001N10010O00001N20O01O000000N2O1001O1OO1O11O000000O10000001O00000O10O10000001OO2O000O1O2O1O0O0010000000O010001N100O011O000O100O10000O100O1001O000001O000000000N2N2N2O1O1O1O1O1O1O1O2OO01O1O1O1O100O1O1O1O1O100O2N1O100O100O1O2N2O1iM^jNm1oU1UOniNUO1MSV10TjN1nV1KViN3TXi1\"}], \"areas\": [5003, 5216, 5422, 5279, 5163, 5001, 4335, 4736, 4952, 5352, 5147, 5917, 5867, 5242, 4712, 3656, 1880, 828, 572, 1358, 6456, 7760, 4186, 5008, 3828, 3675, 2759, 2809, 2064, 1800, 4256, 2037, 2253, 3522, 3897, 5962, 5666, 4070, 5486, 4553, 5172, 4917, 3883, 10518, 7308, 5719, 5578, 6136, 4694, 2923, 5313, 5777, 4878, 5269, 5780, 3363, 4606, 4624, 6430, 4306, 3719, 4233, 5525, 2784, 2437, 2379, 2581, 4138, 4381, 3799, 3482, 3500, 2834, 3264, 4140, 6976, 4799, 7360, 7478, 2097, 0, 0, 5706, 5009, 8011, 6570, 8535, 7454, 4054, 5114, 2586, 3774, 2162, 2452, 1115, 3473, 2849, 3295, 4458, 5276, 7364, 6250, 4091, 3789, 0, 0, 0, 0, 0, 3420, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5221, 5212, 1594, 6695, 5895, 5607, 7117, 3680, 5976, 6850, 0, 0, 10954, 9447, 11383, 10690, 8432, 0, 0, 14808, 10910, 12374, 15834, 14332, 0, 15408, 22553]}, {\"video_id\": 0, \"category_id\": 3802, \"bboxes\": [[381, 739, 22, 44], [379, 743, 22, 49], [376, 737, 22, 51], [375, 745, 28, 65], [385, 770, 24, 59], [393, 769, 31, 63], [400, 788, 28, 59], [425, 818, 26, 58], [447, 835, 18, 54], [483, 818, 37, 82], [527, 812, 59, 79], [541, 815, 57, 81], [533, 825, 44, 71], [521, 825, 46, 68], [490, 819, 52, 66], [442, 810, 43, 58], [395, 782, 36, 56], [381, 752, 35, 53], [408, 734, 29, 47], [428, 700, 16, 48], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [560, 564, 49, 86], [330, 619, 66, 35], [550, 174, 66, 64], [580, 118, 73, 115], [585, 0, 66, 229], [628, 0, 65, 77], [403, 192, 291, 520], [271, 667, 18, 31], [530, 139, 107, 107], [0, 0, 0, 0], [286, 728, 8, 24], [0, 0, 0, 0], [0, 0, 0, 0], [571, 145, 106, 75], [604, 0, 65, 86], [358, 8, 119, 114], [0, 0, 0, 0], [321, 667, 36, 7], [445, 89, 98, 101], [488, 75, 114, 76], [214, 428, 59, 57], [225, 304, 44, 78], [304, 303, 31, 78], [0, 0, 0, 0], [381, 447, 6, 48], [0, 0, 0, 0], [0, 0, 0, 0], [228, 404, 20, 66], [145, 362, 125, 152], [181, 388, 8, 10], [604, 570, 23, 24], [0, 0, 0, 0], [0, 0, 0, 0], [251, 463, 24, 33], [621, 651, 98, 36], [612, 646, 107, 36], [602, 642, 99, 28], [594, 615, 122, 63], [589, 617, 50, 54], [590, 593, 103, 78], [691, 635, 28, 27], [601, 597, 102, 71], [610, 596, 40, 73], [613, 581, 34, 101], [614, 609, 92, 77], [625, 654, 25, 29], [591, 628, 57, 51], [593, 598, 46, 92], [592, 623, 47, 67], [690, 647, 29, 27], [611, 655, 104, 46], [685, 658, 29, 25], [689, 658, 30, 25], [615, 650, 64, 59], [621, 646, 61, 65], [623, 642, 45, 72], [699, 670, 20, 29], [698, 670, 21, 27], [695, 672, 24, 26], [623, 652, 27, 63], [619, 633, 25, 81], [628, 643, 38, 66], [705, 662, 14, 28], [635, 615, 28, 90], [646, 623, 39, 78], [654, 622, 48, 78], [0, 0, 0, 0], [0, 0, 0, 0], [663, 614, 41, 77], [655, 609, 24, 90], [635, 618, 41, 93], [658, 650, 27, 66], [642, 646, 37, 69], [0, 0, 0, 0], [621, 604, 49, 100], [631, 631, 41, 60], [0, 0, 0, 0], [0, 0, 0, 0], [620, 597, 49, 91], [635, 626, 46, 53], [627, 615, 53, 60], [623, 588, 51, 89], [0, 0, 0, 0], [640, 632, 31, 62], [625, 626, 37, 76], [621, 628, 34, 73], [633, 644, 23, 54], [633, 642, 24, 53], [621, 618, 26, 78], [612, 615, 27, 82], [614, 610, 28, 85], [614, 606, 32, 89], [702, 637, 17, 29], [613, 604, 43, 87], [638, 609, 24, 83], [627, 605, 39, 93], [0, 0, 0, 0], [0, 0, 0, 0], [630, 604, 22, 83], [687, 632, 28, 27], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]], \"score\": 0.8518518805503845, \"segmentations\": [{\"size\": [1280, 720], \"counts\": \"eol>d0XW18I5K3M3N3M10100000O10000O1O1O1O101N3M3L6J6IcXZ<\"}, {\"size\": [1280, 720], \"counts\": \"b_j>h0UW17I6K4M4L2O1N2OO100O1O100O101N4L3N3L3M7I6H:EPh\\\\<\"}, {\"size\": [1280, 720], \"counts\": \"mgf>`0XW1;H7L3K5K5M2N200O10O10001N101N5L2M4M5I7J8F8ET``<\"}, {\"size\": [1280, 720], \"counts\": \"e`e>:_W1=F7I6K4K4M3N2M3N2M2N3O1O001O1O010000O012M3M2N5K5J9F7H?_OjWZ<\"}, {\"size\": [1280, 720], \"counts\": \"SQR?`0YW1:H6K5M4K4L4N2L3O2N1O1000O10O1O100000O1O2N3L4L9FegR<\"}, {\"size\": [1280, 720], \"counts\": \"`Q\\\\?=RW1b0E;L4L3M3N3L3N2O1O100O1000000O10O10001O1O0O2O2M2N4L5J6K6J5J8ETo_;\"}, {\"size\": [1280, 720], \"counts\": \"Tjd?7dW17I6G8I7J7L3M4L3N2N2N20000O1O10O1O2O0O010O3N1N1O000O;F5HRoZ;\"}, {\"size\": [1280, 720], \"counts\": \"iRd`0>nV1JZiN;cV1c0L4K4M4M3O000O101N10000O100O100O12M5L2M4M3L4J6K7GiU^:\"}, {\"size\": [1280, 720], \"counts\": \"jb_a0d0nV1g0@9N1N1000O100O100O010O1O2N1N3L8E>Aeel9\"}, {\"size\": [1280, 720], \"counts\": \"lalb0<\\\\W1h0_Oh0XO=D5LO01O2N001N100000001O000000000001O001N10005J2N4L5L2M3M5J5K5L4K5J9EZmg7\"}, {\"size\": [1280, 720], \"counts\": \"hacd09eW14N3L4M2N2N1O1N3N1M3N3M2O1M4N1O2N1O2N1N3VjN]NSU1e1gjN`NXU1c1cjN`N[U1T20O2O001N1O1O1O0O2O1N3N1O001O2N1O1O1N2O1O1O1O1O1N3N1O2M3M3M3M3M3M2N3M4J7JS]U5\"}, {\"size\": [1280, 720], \"counts\": \"nQUe09gW12M4M3M3M2M2N3N1O2N2M2M3O1O2N2N2N1N2N2H`NmiNc1<_NTU1f1hjN^NUU1d1ijN]NYU1U20O1O2N2N1O1O1O001O2N1O1O1O1O1N2O1O1O1O1O2M2O1O2M3M2N3M3M3M3M4K4L6J5Jm\\\\f4\"}, {\"size\": [1280, 720], \"counts\": \"nQkd0;dW15K4M3M2N2N2M3N2M3N1N5L3liNgN[U1[1`jNjN^U1^1XjNeNgU1j1001O01N1000000002N002N1O2M2O1O2N1N2O2M2N3M3N2M2N3M3M4K6I9ETe`5\"}, {\"size\": [1280, 720], \"counts\": \"UR\\\\d01kW18J5L4M3M2M3N2N1N4M1liNQOTU1R1ijNoNVU1S1gjNoNYU1T1cjNmN]U1W1]jNkNcU1j1O000O10000000001O01O1O001O1O1N3N1O2N2M2O2M2N3M3M2O2L3N4K5K9F>AkTm5\"}, {\"size\": [1280, 720], \"counts\": \"SZUc06iW13J6M3M3K4N3N1O2N1O3M2M4L4diNfNlU1j1M0O100O101O0000000O110O01O1O1O1O1O1O1O1O2N101M2O2N1O1N3N1N2O2N1N3M3N2L6J6I;CR]l6\"}, {\"size\": [1280, 720], \"counts\": \"bYYa05hW18K3M4L2N4L4L=B5L1O1N2O001O0O2N1000001N1001O00100N2O1O1N101O0O2O1N2N3M3M4K6J5K4N4K4K5HgeS9\"}, {\"size\": [1280, 720], \"counts\": \"ka^?2jW15N3M2QO0jiN3RV14iiNNTV1R1N1O00001O0O100001O0000O1001O0O1000N1O20O100O2O5J4L4L3M5J6I:CkVW;\"}, {\"size\": [1280, 720], \"counts\": \"hPm>2jW17L3L3M3L5E9K6M2M4M2O1N2O1O2N100O11N2O00000O10001N100N2O1N3N1N3M3M3K;UOfPj;\"}, {\"size\": [1280, 720], \"counts\": \"ngn?7VW1NViN7aV1h0L4N1N3O1O1O0O101O00000000O1000000O2O0O1O100O1O2M4L3K8DQio:\"}, {\"size\": [1280, 720], \"counts\": \"Xfg`03?2hV1;jhNKmV1h0M9I00N_iNhN]V1Y15N1N4L4K4N2N3N2I6L6HTRg:\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"Xble01fW12]hN24LVW1:TiNK_V15XiNIK2lV15YiN5cV1MZiN8dV1H\\\\iNHL80FfV1:`iN3LD_V1O`iN;40NG`V1N^iN5482DLOlU10WjNON3O1N5b0FB0`U13ajNMO4N0O5k0GYU1i0l0WORiNf0nV1ZORiNe0QW1_OmhN=SW1BohN<ZW10N2102N00O01DbhN6\\\\1GgT1:ZkNFfT19a101N2N1O001O2O1O010O10O0010O1O01N1O1101O01O010O2^hNORTZ4\"}, {\"size\": [1280, 720], \"counts\": \"mSm<:eW12OOEGmhN<TW1ClhN?VW1FehN9YW1FihN:UW1:M30O2YOmhN>SW1AmhN?ZW10DASiN2M:XW1FhhN:VW1:N1002M02N1O\\\\OlhN?VW14IZOUiNd0mV1[ORiNf0QW130O10MYOPiNd0PW16O10000O02O0O100O10001O000O102N1N101O2N0O3N3I23NJ]hN1cW161N2N11M4K2OFchN2`W1O_hN0bW17MEdhN5fW1OEKlhN6QW1KSiN0c[d<\"}, {\"size\": [1280, 720], \"counts\": \"Yf_e02\\\\P5OgWL011KO00aV10QcM0_V10[N0cY10PhN0PP50QjI0PT10PlN0PT10P20QX10ogN10O010O00liN0XT11fkN0ZT10fkNO[T11ekNO\\\\T10S23C2HKchN6\\\\W1JdhN<WW18O2M2SiNZO\\\\V1i0_iNVOcV1l0YiNVOiV1P11N2N110O000000100O00000[OTiN6nV1ISiN6mV1LQiN3PW1JSiN5mV1IYiN4fV1L\\\\iN3dV1M\\\\iN1eV11YiNNiV10YiNOhV1Mg0OPbP4\"}, {\"size\": [1280, 720], \"counts\": \"^Tef0142jW1OiW13]gNLciN2mV1O^hN25OWW1OdhN241NMQW13e02\\\\hNK2OO2cV15R1K1Ojj`10S]`NO84R`MN`W13^hNN`W19OHahN1aW1O_hN1bW126MO3NNZhNO5MWW1;80N0M20O11NN3I_hN0dW143K^Yb2\"}, {\"size\": [1280, 720], \"counts\": \"_Xkf01nW10c_72\\\\`H1O2NNgjNNhR12XmNNhR12XmNM`M4SU11ZmN2jR1KSmN4kMJoT12RmN7nMIXW1=21O1ghN_OQW1a0PiNBmV1=RiNHkV1f00IRiN]OoV1>=M2AahN70M_W1;0EchN2]W1MehN3[W1JihN5VW1KkhN5UW1LjhN4cU1MckN2hNMoU10UkN1iNO3OPV11ojN0mN40NVV1MkjN1oN40NlV1M[iN=JEkV12TiN9_W1N4K7Kh\\\\`0Oib_O0ihN1O11OXW12<2[hNKWW12fhN24LUW17;10101NNOJ^hN3cW1KahN2VQf2\"}, {\"size\": [1280, 720], \"counts\": \"YQah01a`<0_gD00000O0O3NO2O00O3N010O100N10O100O1M32N00001O0010O00000O100001O001]hNKYW1682MO01O20O10O1O3M1OK500M2N2O1N1100iN2]jNOkU1NkiN3]V1M\\\\iN5hV14ihNMRgQ1\"}, {\"size\": [1280, 720], \"counts\": \"T^h?5iW12lNa0ciNB]V1P12M6JF:NO5H9SORiN6TT`8FkSaG1QP5Oo_M0o_M1O0UhN0fW124O00001O01N3N2N2[hNHPT1OZoN>TT1J7K4O001Nm_22o_M4jLJ`nN2oL1cW1M8000T]70a]G0kR10hl81gUG200bo9NYoE0]jNOk]20Zjm0\"}, {\"size\": [1280, 720], \"counts\": \"o\\\\c:=cW100M`hNKZW17dhNJ[W1?NDhhN3VW1a0KEPiNGPW18PiNHSW1MlhN014TW1GohN4M5XW1<N_OjhN:WW164K2L4K3O6Lhjh`0\"}, {\"size\": [1280, 720], \"counts\": \"jdfd01nW1000khN0Tnh00QYXO0P`70Q`H0oW10QhN000O0100000O10ORjN0nS10RlN0PV10mM0TlN0lS11Q201O00000000000000000010eY60ZfI0P`22n_M2L1N0M6iNEijN<VU1DijN>WU1BfjNa0YU1_OfjNa0\\\\U1_OajNc0_U1]O^jNe0]V11XOZOXjNf0hU1ZOZjNc0gU1^OYjNa0hU1_OWjNa0jU1^OQjNg0oU1ZOoiNf0RV1ZOniNf0SV1ZOkiNg0VV1YOhiNg0YV1[OdiNd0_V1\\\\OaiNf0^V1XObiNi09]OYU1I^jNj077dT1QOQkNi0<FSU1A]jNl0a0AUU1b0ljN^OVU1`0jjN@XU1>hjNUO^O7lU1b0kjN_OWU1?gjNCZU1<V10002L6K7I1N2O4LPZV3\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"`WV;1nW100000\\\\_20RaM3ZOMZab`0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"bnYf02nW10M3O100O1L4L302hNCkjNNoN32OZW1OehN030YW12jhNMWW13ehNN^W12bhNO]W1OehN1[W1NfhN2ZW1N<2NO1OVhN1WV1ORkN0gN2eW1N80M0TP51ioJ=LK^hNHZW1962M0[NDbkN`0PV1OaNA_kN?RV1O;GQNKTlN4hS1LYlN2hU101O1OVV10n10ogN0O0hiN0bT10^kN0bT10gY10PhN0eiN0fT10ZkN0fT10ea20eYL0fT10ZkN1eT1O[kN0dT12\\\\kNN[V1001[NOZkN0ZV1010^N0TkN0lT11SkNOmT12b1OO170XX6NbgI1ehNOfV10ZiN0gV10XiN0hV10XiN0iV10d`20`XL0nV10b00``20o^M0`00_hN0RW10n00A000O0E0<0\\\\hN0YW10fhN0[_20[hN004oYd1\"}, {\"size\": [1280, 720], \"counts\": \"PPcg03mW100000PXj00aiNNbnVO0\\\\`<0d_C010b00kN0QiN0RW10?012ThNNXeo1\"}, {\"size\": [1280, 720], \"counts\": \"d`o=4<NQW1O]P50TPK010N01010nW;0RhD000gj80XUG10Ok]f00VbYO0gom00XPRO00000U30gI0YnN2eQ1N]nN1kT1OfL0bnN0^Q10bnN1]Q10bnN1^Q1ObnN1^Q11akNOM0b20XR1O`kN0Q22SS1NblN8bS1HXlN9mS1FhkNc0hU13eN[OXkNf0lT1YOnjNl0QU1UOkjNo0RV1O010GRiN_OVV1K`jNb0nU1]OQjNb0QV1^OmiNc0hV12\\\\O[OhiNe0\\\\V1[ObiNb0bV1_OZiNb0gV1]OWiNd0jV1^ORiNa0RW1_OmhNb0RW1^ORiN?mV1BSiN7KJSW1OohN>TW161EihNKXW10ohNMRW11QiNMQW1Ok]^9\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"Qma<1ogb00Qh_O0ogN0no91RPFOQX10H6RhNO1O2N2NojS>\"}, {\"size\": [1280, 720], \"counts\": \"R[\\\\a01k_<0ThD0RX10ogN00000Q`20n_M0WjN0c[20jT:0hbD0cZ10jjN0WM000oW10oW10iV10fQK001fV1Ol00nW60ShI0O0a^20oZL0RU10njN0`f30PhN0QhN0aN0_10_iN1`V11aNNijN1lN1ZV1NgjN6lNM]V1MajN<ROG]V1M_jNc0aU1^O[jN`0jU1@UjN8ZOKcV1MPjNa0QV1@jiNb0XV1_OeiNb0]V1^OaiN`0cV1@\\\\iN?fV1<2N2O010O2N02NO2N1O2N3J5L2IbhNN1OUW13jhNM11WW10b01clk6\"}, {\"size\": [1280, 720], \"counts\": \"gTRc01UW60XgR30baiL0ViN1hN0K2O6N01NIIhhN7_W12K2FEViN:kV1FZiN5eV1KSiN0M5QW1KWiN7iV1ISiN;nV1FmhN=TW1CkhN=VW1BihN?XW1AfhN=^W121N4K5L[hNMaW1OahN1`\\\\c4\"}, {\"size\": [1280, 720], \"counts\": \"nm[81kW61QPK0K3RiN2QV1MRjN2jU1OZjN0fU1N^jNOcU10_jNOaU10X10PX10QhN1O0OO00SX10ngN0fYY10XffN12O10jP]a0\"}, {\"size\": [1280, 720], \"counts\": \"mai82lW13N2M2]hNI]W18`hNK\\\\W1MfhN6NNZW1MjhN6eW1MZhNKaW129OIO`hN0[W174N]hNO]W12fhNJ=9DKRV11ejNKA5^W1O2L]P:0i_<0lck`0\"}, {\"size\": [1280, 720], \"counts\": \"i[l;1SX60e]M0QZ60VXG0O0QX10P`20R`M0ogN0R`20n_M1Xfo>\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"Wfl>1mW1010O10O_iN1TU1OfjN1nNO]V10_cn<\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"l\\\\m81Si31nVL2PX10OM1010RX10kU10oaM0001000Qh30ZcXb0\"}, {\"size\": [1280, 720], \"counts\": \"oce53mW10[o4NVRK1aN1aNOkjN0cV1O_P50^oJ4O10M211O0OXfg0OYbC0eRg1:]dmM6N2O2M3M3M11O0N20ON21TiN[OJ0\\\\V1c0jiN]OJ0^V1`0iiND]V1<ZiN^OUW1a0hhNB[W1`01K5O1I7MRi`a0\"}, {\"size\": [1280, 720], \"counts\": \"TdR71YP:0X[]d0\"}, {\"size\": [1280, 720], \"counts\": \"Tbcg01nW15J3O3L3M2N3M2O0N12O1O00O1O0101O00OO111GihNE[W187N3MT^b3\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"dVj9>aW17G7L10L30O5K2O100O0O10O110O1HTiNYOlV1f08M4O1N1O2N3L2N2M^aZa0\"}, {\"size\": [1280, 720], \"counts\": \"PmXh02iW15O4L2L4O3K2N30O100ON3O[OkhN`0TW162N10M2OYOohNa0QW1^OPiNb0WW10O020O1O0000O11O00OO20O3L3L30MObhNH[W1OghN20N_W163H`hN2RgT2MghmM0PX10P`M1e[O\"}, {\"size\": [1280, 720], \"counts\": \"Remg01mW13M3M3M3O1N1O1N200O1M32N11O0ON11001O01N00020O001N0010O2O00N1011N2N3MO10O;Dg_n1N[`QN02000o_20R`M0i00WO0UO0fiN0ZV10fiN0Zn40kXL0h`20ZjL\"}, {\"size\": [1280, 720], \"counts\": \"aTag01lW16K2J5O2O3M100000O11O02N001O11OOO2O1O01O1O01N1O21N01O00000010O00O1010O00001O1O002N1N2CbhN5fW1McWR2LbhmM0ghN0^Rg0\"}, {\"size\": [1280, 720], \"counts\": \"cSWg06iW13M4I5N2M4M3L5NO103M3M1O002M101N20N01O02O03LO3N2N101O0O0010000O01000O1O100O100O0100O10002M3JZhY2@YXfM1OOXO0i00i00WO0VW10jhN0hX60XgI0XS3\"}, {\"size\": [1280, 720], \"counts\": \"`kPg02mW16J4M4L3N00001K4N2N2N2N2O2M3N002N03M3N7H5L1O1O000O1O11M1O11000O1O00001O00100O10O00011N101N1O1O2N2M2N_[S3\"}, {\"size\": [1280, 720], \"counts\": \"PSRg07fW17K3L6K2M5M2L3N3N1N3M3O1N2M2N4M1O02_NhiNO2n0VV1PO[jNj0`V1N10O01OO1O10010O00010O00O10000O2ON2N25K1N2O0O2N2N1O4K1O0HTho10YhRN0Y[m0\"}, {\"size\": [1280, 720], \"counts\": \"V\\\\Pk02lW16J5L5L000O3N1N1O2N1000O010O1000O1001O010N101O001O1O0000P\\\\O\"}, {\"size\": [1280, 720], \"counts\": \"Qk_g06gW17J4L5K4M5K2N4M1O2N2N1O3M102N1N05dNaiNo0nV1J2N11NO2O00O1000001O01OO11N1O101N2OO04M1M5JkoZ2FkPeM0IOZ[c0\"}, {\"size\": [1280, 720], \"counts\": \"fRkg081O4JTW1R1D4L1O2M2O100O2N102M1005`NdiNR1jV1K3N0O001O00001OO2OO10O1001N1O011N3N2N1N102M7IM3001O5JYce2\"}, {\"size\": [1280, 720], \"counts\": \"bjng0`0\\\\W1=G7H6I6K5K6J8H6K3M4K3N2N2N2M3O0O2NQNVkNj0hT1POdkNk0[T1ROkkNl0UT1lNTlNS1lS1bNalN\\\\1^S1bNflN]1RU1K107H6K2N2N1N2O3L2N7HU[i2\"}, {\"size\": [1280, 720], \"counts\": \"ZSPh0:3KSW1k0I5OO0O3M3N11ON20N101OO010O001O01O001O0O1O200O00O2O001O1O0O2N2ClhNIWgY2Lh`gMNPc?\"}, {\"size\": [1280, 720], \"counts\": \"kl]h08dW15O2K4N1O1M4O1O001N2O10N2O1O1O1O00001O2N1N1N4M2N3IYce2\"}, {\"size\": [1280, 720], \"counts\": \"d[Sg0<eW11O2M1M3O1O10OO2O1O1O100O0O3N0010000O11O011N1O2N1O0O002N0100000O01O00100O1O00100O002OO2N1O2OO10O1O1OO1001O2M1N2NOGbhN2oZi2\"}, {\"size\": [1280, 720], \"counts\": \"mjUg0c0ZW15K5M3O0O101O1N3N002N1O3N0M2O2N12ON20000O13aN`iNU1hV1N6J2N001O10O001O0O1O4NO0O10010O010O0O3N0O0O3N5IV[S3\"}, {\"size\": [1280, 720], \"counts\": \"`cTg05jW17K2N3M5J1M40O01N20O00O2L3O3N00O01N2001OO1001O102L011O2N1O01OO0101O0O1O03O0N20O01O2M1O3M2L5M2FT[S3\"}, {\"size\": [1280, 720], \"counts\": \"cToj07eW15O1N3M1O1O1O2O00001N1000000O1O20O000001O10O000000O11O0000g[O\"}, {\"size\": [1280, 720], \"counts\": \"g\\\\lg04R`23jgNMG0chNM02UW12QiN0lV12PiN2nV1MmhNM23NNoV12YiN0HOnV13ZiN0GMmV12^iNOFOkV12[iN=cV1A[iN5L3gV1KZiNb0hV1]OXiNMO30M2NdV15[iNL031L02cV13\\\\iN72L`V12[iN42JQW15ohNKSW1NQiN1WW1HihN2PoP2OgXPN1K67HC0ihN0TW10]`20QhN000QhN0k`a00]f_O0khN072eZ4\"}, {\"size\": [1280, 720], \"counts\": \"Pmhj02iW17L4M2N2M2O2N10O100000O1000001O000001O01O0000001O11N1N4L<Bob5\"}, {\"size\": [1280, 720], \"counts\": \"mlmj01lW1:G3O2M2N2O1O0O1O1O10O1000O0101O00O100010000N01O111N1O4H6K4JX[O\"}, {\"size\": [1280, 720], \"counts\": \"`]Qh07bW1>LN01N1O0O2O1O1N1O2N11O0O01O1000O10O0100OO1010O100O010O1O00001O000O10N210O1O0O1001OO101O00000010O00000000100N2O01000O1O1O2N1N4Jg[a1\"}, {\"size\": [1280, 720], \"counts\": \"amXh0<cW13L4N1N2N3N10OO2O0O101O000N110O0O100100O01O0101N00O11N010OO1101O00O10000O020O00001O0001O0001O0001O000010O0010O2N2N3L5K4K6Hcc]1\"}, {\"size\": [1280, 720], \"counts\": \"^][h0<cW13L6K3N101M2M3O1M3N3N00O01N1O2N0K7M2O10000O10O1N2O1N2N2O1N3M2O1M3N21O01O2O000N103L2N2N2L5NlSo1\"}, {\"size\": [1280, 720], \"counts\": \"]]Zk07eW15N3L6J2O1N2O2O0O10000O10N200O101N11O000Q[O\"}, {\"size\": [1280, 720], \"counts\": \"]UYk0;dW11M3N3M3N1O1O1O1O10O10O10000O11O0O10000O2OP[O\"}, {\"size\": [1280, 720], \"counts\": \"_]Uk06fW16M2N3K3O1O2N010O1O1O0100001O000000O2O0000000O10oZO\"}, {\"size\": [1280, 720], \"counts\": \"c][h06eW16G<D9L5L4K5L3L4M4M2N2O001N1002M2O0O3M1N3K6C<F8N3N6J4KYce2\"}, {\"size\": [1280, 720], \"counts\": \"Z\\\\Vh06dW18K4K8H9ViNPOZV1g1D4L4L2N1O20O02OO100O1OO1O1O04L4K3Mb0^O7Fc0XOeRm2\"}, {\"size\": [1280, 720], \"counts\": \"^eah02mW16I5K6L2O1L3N2N2M3M2O2M3J5IfNgiN[1SV1nNjiNR1TV1>N1O`NPjNS1QV1<2O0M3O2L4N2M3L3L5L4N112M2N1O4J3N3N3L4IgcQ2\"}, {\"size\": [1280, 720], \"counts\": \"Qmak0>^W15N2O0O2N1O2N10000O1000O0010Z[O\"}, {\"size\": [1280, 720], \"counts\": \"T]jh08dW16K5L4J7ROUO^jNQ1VU1ZOYjNE2Z1_U1o0L4M4M2NaMhjNX2XU19N2O01O00O1O03M2N3\\\\MhjNo016\\\\U1DRkN1RU1LWkNFSU1GQjN;P1B]U1<V1M5M1NX\\\\U2\"}, {\"size\": [1280, 720], \"counts\": \"PUXi04kW18G4N3N2M2M3M4L1O3L3N1M3O2N1N2L4M3K400H^NTjNa1kU1:0N0O3N2M3L3^NkiNZ1YV135oN`iN8fV1G[iN3jV1>2UOSiN<1CWW1=501M6M1N1O5JVlY1\"}, {\"size\": [1280, 720], \"counts\": \"UUbi01jW1:J4L5K3M2N3N1N2O1N2N2N2N0O2O010N1O2M2O1M3O001O1O1N100011N00010O001N3N3J5K4N201O4L2K22O07H3N4K5KVdd0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"k\\\\mi08fW15K7K1M3M4M2O1M3M2N3N1M4M2N1N3N2E:O2M2O1O0010TNVjNb1kU181O2L5M2L5J5K6M2J6TOTiNa0QW153M1N5K5K5L4M2MVTb0\"}, {\"size\": [1280, 720], \"counts\": \"d[ci02]W1;dhNMWW1b0M0M3N7YiN_OcU1b1M3M4M0O3M25J2ON1001NO2O01O3N1SN]jNY1YV1K7I4K_[a1\"}, {\"size\": [1280, 720], \"counts\": \"c[jh04dW1c0E3L3N1O1O2O1O0013L01N3N2N3L3M3N4K3M2M3N110O0012N5K3M3M2N3L2ON1006KO01N01O0:F;D2FbhNNeYf1\"}, {\"size\": [1280, 720], \"counts\": \"bTgi03fW1:M:E3N4PiNYO_V1X1L3M3M1O3M10100O001O0O0103LN2O02N3K4M4B?F:E;DXkY1\"}, {\"size\": [1280, 720], \"counts\": \"ZTSi05iW1:H6H6L1O1000O0110O1N2O1O1O2M2O0O1N3N2O1O0O3OO2NN3M34K3M2O2M000O4K6WOmhN;VW145J5AfhNOhab1\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"ckXh04jW15I6F:I7M2M4M3N200O0O3N100N2O20ON2O100O1O10N202M8bN\\\\iNP1iV121N10000O1O10O11N00011OO001O01O002N2L3N013L2N9C^bl1\"}, {\"size\": [1280, 720], \"counts\": \"h[eh02nW12M5M2M6K3L3M2O1O000O2N1O1O1O2N100O1O1O100O2O02N10N12N1O1O100O1O1O2NO10O2N1O2N3L?^OgRj1\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PcWh04cW1`0G4N4M2O2MUOPiNg0PW1401N10001M4M3N2N0O1O1N30N0100100O003N5J2N3M2N1O10N1O2O00000N2N3O010O10O2N2N1N6KO04L2LWkm1\"}, {\"size\": [1280, 720], \"counts\": \"h[jh06fW17M2M3N2M2O1O00100O100O02OO001O100O001N2O100O1O101O0000000004LO2N12N1O1O1O000O100ZOjhN?YW131M2N5J9HWk^1\"}, {\"size\": [1280, 720], \"counts\": \"Z[`h03jW17L1O5K2N4L2N20OO2O0000O1000O1O0011O0ON3N1010O10O001001O002N01OO100000O1O100OVOPiNe0SW14N12M3N00001O00O01O1N3L3N3L_S`1\"}, {\"size\": [1280, 720], \"counts\": \"\\\\Z[h0343_W1f0B3L2M101002M112MO20OO2O0O2N1O2N2O0O2N101N23L103L3M3N2M2N10O000O3M1O10001O01O1O011NO0102N0O1O3N4J4L8DYcg1\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"kcPi0a0]W16L2N3M1O2N1N2O2N1O100O2N200O2N3M0010O2N011N2N1O1kNXiNo0mV1O00N26I3M4LS[k1\"}, {\"size\": [1280, 720], \"counts\": \"dk]h0;dW1;G3L2M3N1O1N4M2O1N2O03ON010O0O2N2N2O2M2N101O0002O1O2M4L2N3N1N3L2O1N2M3M6HibV2\"}, {\"size\": [1280, 720], \"counts\": \"ekXh06kW13L7H9H3L3O001M3N2M300O1O12NO004K1O2O1N2N2N2O0004gNbiNh0kV112M1N2O1O0O2N3L5JiZ_2\"}, {\"size\": [1280, 720], \"counts\": \"Xlgh07gW18I6K2N4L7I1O4K3N2N200N2O10O00O10O1O000O3L8B<VOnhN1gZ_2\"}, {\"size\": [1280, 720], \"counts\": \"klgh0;aW16ECmhNc0fV1a0O2K5N110N1N2N2O11N0O01O11N2N2N2L5G7M3H:H8Gkk\\\\2\"}, {\"size\": [1280, 720], \"counts\": \"dkXh02iW19J7J6K9F7UiNkN_V1]1N5K8H4K3N2N2O0O2N10001OO2N6RNWjNY1lU1aN^jNX1[V1J9G;C4M6IeZi2\"}, {\"size\": [1280, 720], \"counts\": \"[cmg02lW17J3N5K5J3N3M4M4K6K2M3N4LSOdiN:VV1SOmiN18S1]V1202M1OO1O1O0O102M1O1O3L:FnZS3\"}, {\"size\": [1280, 720], \"counts\": \"VSPh03jW19I8J002M2N2M3O2M3RiNRO030OQV1j1K4M4M000O2O100O0O100000gM]jNS2hU11NjMgjN4LS1^V1N3N5J:ZOghNM^jP3\"}, {\"size\": [1280, 720], \"counts\": \"TSPh05iW17J9I002L3M3N3M<B<F4M4K4K3M35K1OO0O2N2N101N1O0011lMfjN_1VV1G4M5J6J3L5K8Hjbj2\"}, {\"size\": [1280, 720], \"counts\": \"_T^k01jW19J3L201N4M3M2N2N2ON2O0100000O1000S\\\\O\"}, {\"size\": [1280, 720], \"counts\": \"njng035OaW1;O1M2K6L3N23MO5M0M3M5K6J1O2O1N2N3N0O2N2N110O01O2]NgiN[1fV1J001O1N2N1O000ROTiNg0nV15000O2O2M2N3M6I7H3KkR^2\"}, {\"size\": [1280, 720], \"counts\": \"]Snh0>^W17G9L`0TiNgNSV1i1J5J3N2N2N1O20OO20O1OO02O1O5J0O0oMXjNl1QV1SNRjNb1`V1E3Mb0TO]cV2\"}, {\"size\": [1280, 720], \"counts\": \"`[`h06eW1:G5J7L4L6K5L3O3M0001O01O0N3N2M2N2N3N1O1N1010O4QNTjNb1XV1M4M1N1O0kN[iNm0iV15N10O3N1N3L7J3L5J3EahN1XjR2\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"[Sdh02jW1g0[hNZOgV1X1G9F7L6I4K4N3M1O2O0O1013K101nM[jNe1hU1aNXjNV1lU1`N`jNY1[V1I5J5Ld0XOPSc2\"}, {\"size\": [1280, 720], \"counts\": \"X\\\\kj07bW1MfhNOWW1NPiN1lV1OXiN0gV10[iN0cV11e01ihNO_V10ciNO]V11ciNO]V11ciNO]V11ciNO\\\\V11eiNO[V11k0O0000O0100000000000ihN0_V10`iN0`V11_iNOaV12]iNOcV13ZiNMmV15ghNN`W12o[4\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}], \"areas\": [845, 887, 895, 1379, 1234, 1657, 1368, 1276, 873, 2598, 2835, 2705, 2226, 2171, 2375, 1808, 1587, 1400, 1219, 503, 0, 0, 0, 0, 0, 0, 0, 0, 0, 651, 1364, 764, 281, 540, 334, 426, 327, 1037, 0, 10, 0, 0, 368, 29, 678, 0, 30, 606, 235, 61, 145, 13, 0, 13, 0, 0, 21, 476, 2, 393, 0, 0, 605, 752, 667, 823, 1075, 1166, 1409, 628, 1225, 1219, 1873, 1012, 548, 1308, 1685, 1372, 666, 402, 587, 605, 1690, 1674, 1606, 514, 500, 554, 1213, 1398, 1328, 366, 1620, 1419, 1773, 0, 0, 1646, 1517, 1630, 1237, 1285, 0, 1822, 1165, 0, 0, 1787, 1171, 1358, 1659, 0, 1163, 1440, 1260, 884, 994, 1308, 961, 1420, 1609, 391, 1547, 1482, 1682, 0, 0, 1321, 92, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}, {\"video_id\": 0, \"category_id\": 3802, \"bboxes\": [[269, 574, 28, 24], [256, 574, 32, 19], [236, 561, 48, 25], [252, 566, 34, 22], [260, 569, 28, 21], [260, 565, 23, 19], [255, 562, 20, 25], [0, 0, 0, 0], [0, 0, 0, 0], [239, 575, 42, 69], [203, 626, 35, 90], [185, 674, 23, 66], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [197, 651, 43, 42], [183, 521, 57, 30], [190, 498, 114, 98], [245, 642, 48, 120], [263, 571, 0, 0], [185, 468, 83, 107], [185, 479, 75, 141], [203, 539, 41, 104], [65, 443, 81, 53], [118, 542, 78, 60], [199, 610, 44, 45], [137, 571, 62, 41], [0, 521, 63, 40], [13, 533, 77, 140], [16, 558, 81, 62], [11, 290, 69, 73], [0, 140, 46, 62], [124, 76, 84, 318], [141, 67, 63, 324], [152, 197, 73, 214], [191, 428, 62, 121], [204, 526, 75, 134], [100, 617, 40, 72], [220, 509, 79, 193], [250, 563, 44, 197], [291, 535, 53, 188], [198, 600, 103, 55], [210, 549, 26, 35], [85, 565, 104, 72], [176, 442, 102, 197], [223, 495, 56, 232], [260, 474, 93, 200], [285, 426, 74, 124], [275, 469, 62, 83], [237, 429, 107, 138], [231, 302, 144, 187], [306, 303, 89, 181], [351, 417, 66, 122], [353, 440, 68, 118], [350, 438, 68, 109], [322, 486, 67, 75], [256, 420, 133, 145], [155, 365, 177, 107], [178, 333, 160, 110], [284, 408, 104, 65], [256, 414, 80, 65], [221, 349, 70, 93], [258, 259, 68, 149], [233, 255, 113, 157], [232, 258, 121, 161], [239, 221, 164, 201], [299, 229, 92, 191], [265, 227, 113, 180], [204, 236, 148, 149], [201, 247, 45, 138], [167, 241, 122, 173], [223, 240, 93, 172], [208, 276, 154, 127], [325, 215, 44, 89], [202, 239, 151, 151], [197, 224, 121, 143], [176, 248, 70, 151], [175, 301, 122, 119], [210, 225, 91, 197], [190, 284, 149, 151], [195, 342, 93, 95], [197, 266, 167, 146], [214, 253, 93, 168], [196, 262, 94, 175], [206, 227, 109, 196], [201, 214, 164, 204], [212, 298, 144, 142], [236, 281, 131, 156], [198, 335, 120, 134], [144, 505, 141, 79], [9, 417, 221, 77], [47, 374, 177, 72], [49, 425, 198, 62], [93, 275, 180, 228], [249, 298, 67, 214], [237, 453, 73, 140], [207, 381, 130, 187], [196, 386, 152, 74], [231, 418, 86, 89], [230, 485, 89, 116], [225, 471, 126, 164], [223, 475, 78, 221], [230, 504, 56, 250], [248, 494, 112, 220], [250, 437, 149, 181], [254, 443, 175, 141], [260, 464, 183, 82], [256, 459, 169, 64], [234, 461, 58, 113], [125, 495, 124, 166], [218, 486, 67, 198], [237, 451, 134, 172], [247, 466, 189, 123], [243, 483, 218, 93], [225, 453, 148, 137], [128, 446, 122, 167], [91, 446, 158, 130], [189, 291, 85, 231], [201, 267, 133, 229], [187, 249, 101, 241], [172, 241, 118, 251], [165, 322, 182, 193], [164, 311, 130, 199], [71, 273, 215, 182], [17, 281, 266, 166], [42, 204, 239, 222], [158, 180, 140, 249], [83, 196, 244, 185], [95, 134, 234, 248], [237, 63, 130, 355], [218, 123, 86, 372], [217, 173, 115, 342], [263, 215, 118, 274], [184, 210, 80, 108], [201, 242, 177, 181], [259, 226, 119, 190], [247, 401, 115, 71], [237, 428, 122, 70], [21, 241, 314, 239], [142, 258, 186, 295], [0, 304, 277, 266], [0, 276, 301, 295], [191, 317, 169, 290], [138, 412, 242, 226], [181, 385, 182, 168], [260, 232, 133, 399]], \"score\": 0.6363636255264282, \"segmentations\": [{\"size\": [1280, 720], \"counts\": \"Tj`::cW16K4M2O1O1O1O00000001O1N101N1O2O1N2O1N1O1O100O100O2N1O2Mkm^`0\"}, {\"size\": [1280, 720], \"counts\": \"XbP:1nW15E8N2O1O1O1O00000O1000O2O00000O1O100O100O1O1O100O1O100O1N200O10QVj`0\"}, {\"size\": [1280, 720], \"counts\": \"eaW93jW15]hNKVW1;dhNG[W1?01O1O0000001O1O0000010O000001O01O0000O101O0O10O02N100O100O2O000O1O100O001O1O1O1O001O1O2N2M]Vo`0\"}, {\"size\": [1280, 720], \"counts\": \"Wbk92^W15ehN3YW1;O\\\\OihN?\\\\W10L5O000000O1O100O100O100O1O1O100O1O10O01O1O1O1O010O1O100O1M3N\\\\fl`0\"}, {\"size\": [1280, 720], \"counts\": \"\\\\bU:2kW14AMlhN6TW1;0O101OO01O1O2O1N00100O1O010O10O0100O10O01O1O1O2M2NXVj`0\"}, {\"size\": [1280, 720], \"counts\": \"XbU:1nW11O1C2chN1]W1:ODchN8[W1IehN6]W151O10000O1O100O2O0O2N1O1O100O2N10W^Pa0\"}, {\"size\": [1280, 720], \"counts\": \"oYo9260_W12_hN0_W19LIbhN6\\\\W173L2OJ4M43M1O101N101N1O2N2O0O2N2OV^Za0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"WZ[936O11i00WU12miNO111L0371hU11oiNOO12M011ON3nU1e0XjN\\\\ON4hU13SjNINN41MO37iU13TjNO3O0OhU10UjN24NO4gU1LXjN11IMO2>gU1a12mNVjN^OOOjU1d0ZjNIgU12WjNWO03N8mU1>VjNVOO3O1NOPV1h0VjNKkU17RjNVON100RV1i0PjNVON:RV1<RjNMoU13oiNORV1n01001OROkiN1UV1OliN1TV1l01VOkiNIUV17kiNIUV1P11SOiiNOYV1k01ZOgiNA]V1?ciNA\\\\V1<diNIXV1:iiNEYV1;iiN_OYV1a0giN_OYV1a0hiN^OWV1NfiN56KTV10hiN46HWV1NjiN62JTV1MniN63IbV15g0N2MWmRa0\"}, {\"size\": [1280, 720], \"counts\": \"Q\\\\n73eW1O_hNe0mV1d0B40N2N4MO4L3ON10N1O2N7KO10Lh0TO9K42L4M6I4M2M5K3M2M4M2N3M2N4L7I3M2K9DVchb0\"}, {\"size\": [1280, 720], \"counts\": \"TmW77gW17M1M4M1N2O2N2N2N4L8I;E0O2ON2O1N2N5K2N4J3N4K4KkQnc0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"clf72mW16J3N3L5L2N3L2O3L2O2M3M101N2O1O00100O0O01O1001O01N101O1O001O1N200O1O001O1N2N2M4M3M2M5KSSfb0\"}, {\"size\": [1280, 720], \"counts\": \"eXU75kW10dW1Of`MN20WW1d0M10M4AehNN`W1181O3NK^X1NhgN104K1O1O010O0N2O2N1O1O100O100010O0000000O010000001O01O000001N100000000010O00O2O2N1O1N5LmVfb0\"}, {\"size\": [1280, 720], \"counts\": \"QP^71oW1000C0ehN2M0bW1112ZhNN6NTW14ghNN40RW1;mhNDSW10nhN9UW1GlhN8YW1FghN3MOXW10jhN0O1^W16GIkhN3L04MnV10QiN4MO3ORW16PiNNPW12PiN0kV15RiNMkV1ITiN91JjV1MViN82IlV1MTiN1OO02mV1MTiN10001SW15ohNOeV1HZiN5K037nV1IohN038jV1FTiN05>eV1CWiNN42K7kV1OViN;kV1HUiN3lV1MRiN5oV1KUiNHL0oV10SiNO21L1mV17WiN1hV1NWiN7hV1GXiN:jV1@WiNON113kV1KWiN1N014jV1=TiNDmV1NSiN71FmV12SiN5]W1GehN1_W1ObhN09OlV125OehN:1FTW196IchN0`W10`hN02NXW1>4NchNCXW1;:I4O2O32ThN1NNYW12ghN8VW1DlhNa0SW1^OnhNd0TW13N25KO\\\\OjhN>VW1BkhN>XW17J4N3N01ON2N2011NO01001O0011NL4O00101O3K010002N10N1O10100O0O1O20O1O1OO1O11O2N101N1O0O3N2M2O2M2M4L3M2O1DahN6fW1MQVV`0\"}, {\"size\": [1280, 720], \"counts\": \"Sob9:cW18K1O1N3M5K2N001K4M2O2N2O2N0EmNjiNU1SV1=1O1O1M3M20O1K20^NkiN`1TV1bNmiN[1bU1fN^jN0000X1bU1i03oMYjN:2k0hU1kNVjN93i0kU1ZOXjNc0mU1YOUjNd0mU1^ORjN>QV1EniN2YV1i0FcNSjN_1lU1dNniN_1UV13M\\\\NPjN`1ZV1M7F;H2N0WOQiN?NBQW1e042O01M3\\\\ORiN2]Re`0\"}, {\"size\": [1280, 720], \"counts\": \"kYY:1Tfja0\"}, {\"size\": [1280, 720], \"counts\": \"VgW75jW13N3IIbhN5\\\\W1MbhN4_W17JDhhN;WW19O0N2NZOohN;OEhV17ZiN2OHfV1Q13OL2OmNZiNd01_OeV1MZiNc03_OOJbV1[10E\\\\iNXOdV1S1O@_iNAbV1=_iNDaV1n0OC]iN_OcV1n01[O]iN@O2fV15ZiNH3OO20LaV1W13kN]iN9O1eV1FYiN<01hV1L\\\\iNN0IcV18]iNHM031bV18\\\\iNO2JdV19XiNO2FfV1g0]iNZOcV13ZiNL1O21LOfV12]iNM0110M0fV18\\\\iNJ1LM3hV13YiN51JhV10UiN54MlV1LPiN040K2PW1NQiN031L2QW13RiNKM2SW16RiNJoV16RiNFOMmV14RiNN100OmV11SiN0000OnV10[iN0PW13YiNL[V13diNN`V13mhNN23KNRW12ohNN42KORW17SiNOoV12QiNLPW1OUiNOmV1OViNNkV11ViNMlV13TiNLmV17PiNMlV11UiN0WW11ahN0TW14lhN1oV1OQiN2jV13ViNNnV1LQiN6QW1FohN80EnV12RiN7WW171N5N1F_hN2bW1M_hN2cW133M3O4LRP50noJ1O11O`Vca0\"}, {\"size\": [1280, 720], \"counts\": \"VgW75kW15K23NNOJIdhN31LWW11hhN21O]W11dhNM[W12`hN2cW13chNLoV18nhNNPW12PiN0mV13PiNNkV1IWiN81IhV1MZiN62KjV1LSiN380eV1NSiN0:1lV1NUiN1]V10ZiN0NO:2cV10e02ZhNOg0NQV13YiNO3071[V10[iNO4M92VV1OciN8^W1JMNbhN1_W1OahN0e_2060mgN1YhNN:0cV10a10VW10ShN1mW1OXX10]Y60^VG6G7PiNDXV17\\\\iN2IJkV13SjNJeU16fjNKmT16[kNJUT1O_jN6`1I[T14j1NO1O11N3O1Oma<0R^C0gTma0\"}, {\"size\": [1280, 720], \"counts\": \"eZn7=2JVW1k0D6K4L3M2O1nN`N\\\\kN0ZOb1XU1aNgjN05m1UU1ZNjjNg1oT1QNQkN9Me1YU1c0C^MWkNe2bT1ZM]kN10f2kT1[MRkNf2PU1]MQkN]2PU1bMPkN^2UU1aMjjN[2WU1dMkjN[2\\\\U11]ObMWkN32]2gT1eM\\\\kN\\\\2fT1aMWkNc2bT1]M[kN03c2iT1_MSkNb2oT1bMQkNY2PU1;0O2O2M3M8H3L3mM[jNh1hU155K5M2M2N3H9^OP1VOeUab0\"}, {\"size\": [1280, 720], \"counts\": \"Pfa2b0[W19J2M2O1N3N1O001O001O001O01O001O01O10O0101N0O20O1O001O0001O010O01O1O01000O001O1O00100O1O1O001O1O10O0010O01OO2O1O1O2M20O10O001O001O01O0001O001O1O1O1O3M3EahNOaW1M60]hN0kg30]XYf0\"}, {\"size\": [1280, 720], \"counts\": \"ZQd45hW14Ga0F200O1O2N2N1000O02O1O2NO00010CWiNBhV1=ZiNDeV1;ZiNGeV1l0OZO^iNMaV13`iNBL4bV19diNAL6`V17fiNAK8_V16miNKSV16liNKSV16liNJUV16jiNJVV15kiNLTV13kiN0UV1OmiNOSV1I_iNN`02_O0`V1M\\\\jN0VO4]V1O[jN6fU1NYjNOgU12XjNNiU12RjNH@3]V16RjNGB3[V16[jNJgU15XjNKiU15XjNKgU15QjNGB5]V18kiNEG3^V13YjNMiU11UjN0lU10UjN0jU1O]iNLg03mU1O]iNN=OG4_V11VjN0jU10VjNOkU11TjN0lU10miNM_O2eV10ZiNM=9[V1LiiN3XV1LkiN0VV11iiNOWV14biN0^V10\\\\iNEM;iV1L]iNIK:hV1L]iNKK8jV1L[iNLK7kV1NYiNKL7lV1N^iN1dV1M\\\\iN3fV1LPiNN08RW1JSiN7mV1KohN7RW1IlhN7VW181O2M4N1N2O2M2O1O1OmU]d0\"}, {\"size\": [1280, 720], \"counts\": \"_[i72bW1OchN8[W18O2N10000O2N2N2N1O2O20N10N0100O01O1L4H8O10O100O10O01O000001O02N1N1N3N1N4M2N1O2N1N4J7K^\\\\bb0\"}, {\"size\": [1280, 720], \"counts\": \"\\\\j[51mW14O1N100N1O2O002N1I6O2O15LN0B_O\\\\iNb0dV1ERiN?mV1:01O10O10O001O010O00001O10O0001O0010O001O0000001O00O1001O1N1000001M2O1O100O2M2O2N1N3M4M3M2Lg]Yd0\"}, {\"size\": [1280, 720], \"counts\": \"Z`0W1iV101O1N10O100001O01O0010O0000O010000000N20000O02O00001N1O20O10O001O0O1O2N01000O1M3O1O1M4N1O2N100O10001O001N10001O1O1O001O1N1O1M\\\\_ci0\"}, {\"size\": [1280, 720], \"counts\": \"gh`01S`22jgN1TX1MdhN0hN0TiN0bV13RiNO2O0Oh^22XaMNO30OgW11ZOMohN211LOQW10QiN012MNXW11mhN5fV1KYiN6OMhV1M[iN4M0:JM363aT1ISkN3155NE83DO?kT1BVkN010L15i0MXOMb0QU1]OmkNc0UO6bT1UOakN1o02lN84>ZV1AfiNa0bT1TOllN:cNd0`T1SOllNNaN61m0[V1SOhiNk0XT1oN^mN8YNi0XV1YOciNk0[V1?O21L2aNciNX1dV1O00O11N20N1O3M0100O01O010O010O001O1O10O01O10O001N11O01O1N2M2N3N2M3N1N2000001N2M3O1N2N3M2N2L4MPdah0\"}, {\"size\": [1280, 720], \"counts\": \"`bd0<\\\\W1GhhN;SW1OjhN1UW1:3\\\\OkhN1O0YW1MjhN1O0[W11hhN1VW10ghN6WW1IihN5`W101101HKehN5fW1K2N2O12^OOPiN1PW1MkhN1L2aW15O104I6O1M1_OZOiiN4A>WW151OK5N2L4O2I6M4L3O21M2N20O1000O00M3M4O1O0000001O10O01O0O2N2O00001N101OO2O00001O1N5K3M4L7I00N1101M2L50001O5L0N4@YnXh0\"}, {\"size\": [1280, 720], \"counts\": \"hR>3404N]W11K2khN2PW1NohN0QW16jhNKmV1h02O@[O_iNL23N:ODWV1]133liN^NgU1f1WjN\\\\NhU1l101001L3010OOQjNVNlU1l1210001O0O1O1001O1OO2N1010O1OO1O010000O2OO1001O1O00N2001N2O0O1O1O1N2N2O100O2N1O1O00002L3N3M2M4N2M3M6K6Hb^nh0\"}, {\"size\": [1280, 720], \"counts\": \"`4e1ZV1101N3NO1O1O03N3M1N10KPjNZNQV1i11O10O01O2O01O000001N10000O2N010O2O0O2N0N3O1N3N1O1M3O1O1O2M3L4M3L5L5I[SYj0\"}, {\"size\": [1280, 720], \"counts\": \"oSk41S`20UN0giN000Rj30nSL0QlN1lS10UlN1kS1NVlN2hS1NZlN2dS1N]lN2jU1OhMOXlN0XN1gW13I6kMF^lN>_U14PN^O]lNh0[U11SNWOblNl0[S1TOdlNo0ZS1QOdlNY1US1hNhlN3[NQ1WU1POokNX1UT1hNfkN_1YT1^N`kNe1dT1[NVkNm1iT1TNojNV2lT1jMPkN`2iT1^MVkNf2iT1914K035KNOOWkNnLeT1`3J0M3L312MM0OmkNdLgS1X4FWOalNmLO]OSS1h4mlNXKmR1Q5NXNVmNVNlR1a3M2OM3dNTmNgL1G000NlR1b3TmN`L0O2O0O2NlR1U4XmNPLPS1_3jlNgL10WS1S3ilNlL119DcR1T3WmNSM21NIM2mR1n2UmNRM61CO54MOSS1h2PmNTM1N8=GKRS1i2PmNVM140NoR1i2llNmM0]OVS1e2hlNgMO^O2O0OZS1e3elN]LO03M1OSS1W4TmNgKPS1d2flN[N2^O1F^S1\\\\2^lNaN3]O0GhS1Q3ZlNVMLOmS1?TlN70O2\\\\OO1RT1]30XLWlN]2E\\\\NbT1a21SN\\\\kN<iT1^OYkN_OO9nT12QkNi0TU1mNnjN\\\\OO3071`0bU1UOhjN2F81`0VV1SOkiN[1`V1kNbiNe0UW1H5@ehN1ecoc0\"}, {\"size\": [1280, 720], \"counts\": \"SZ`51QR50WV10bUL0]dM1_S1OclN0gU12O0O2N5J4L5K0mhNC3O1M\\\\U1_2XjNeMm0OTS1T4C6K6cM\\\\KQQOi4QQ13hMTKVQOm4YNUKJORR1i5iMVJPROl5Pn0TJnQOm5RNXJho0KUROV6SNmIeo0MTRO]6lm0fIPROo5[NZJRP15bPOR6TP1cI_oN^6jP1EonNUJWQ1a6I13H6CgnNjI]Q1R6dnNmIlQ1l54IRnNXJoQ1c5omNaJZR1OfmNi4NaKcR1N_mNn30SLdR1l41jJYmNS5lR1lJUmNh4OYKQS1d4RmN\\\\K1OgR1a4XmNaKOOXS1Y4hlNhK`S17PmNf2BRMcT1k2\\\\kNWMjT1n23SMSkN9MS12jN[U1U2:F3lMYjNj1PV1N<E:B5POWiNb0^W1_Ob\\\\Sd0\"}, {\"size\": [1280, 720], \"counts\": \"UVn51nPd00\\\\gD0g_H1N00000O10O1101M1N5I9I:F9TlNnNZP1T1flNlN]28jP1[1nnNgNgMFWS1l1hnN`NQNE4LgR1Y2lnNcNPQ1d1XmNdMY1MlNm0bR1i1]mNZMl0W1ROjN]R1Q3RnNfNmQ1d0bmNf0oQ1mLXnNf5jQ14:IemNaJWR1]5imNdJXR1a52O3N3K4J4nJYmNg4nR12007I2O2M6I5H8K4L3VLokN`3YT1OOaLhkNX3bT1O2O1M5K5J`0ZMcjNd1L[N[V16diN9Z\\\\Zc0\"}, {\"size\": [1280, 720], \"counts\": \"RV_72iW16FMchN:PW1EPiNf0nV1:L3M5K6J224I31M3MH^NniNf1nU19M42OK3N113L0O1JeMhjNX2_U100106LN10N01K6M03N2ON3N1O02K47J5J23IM4N2O2M3M12N101000OO00O20100ON100=D6oN[iN9jV1A[iN;iV1@ZiN?TW1DfhN0XWWb0\"}, {\"size\": [1280, 720], \"counts\": \"`co71nW10lg80T`H0QhN000PX10PhN0O1000000O01000O01O01O11O_iNOSU12ljNNTU12]110O111ON000O1O0XiNMcU14]jNL\\\\U1<cjNDZU1?fjNBTS15nkNN]1`0A\\\\ORS1j0PlN_O6H3m0<ROYS1m1PlNXOe0kN[S1V3clNjL_S1V3_lNkLaS1Y3ZlNgLfS1]3VlNdLjS1]3UlNbLlS1^3RlNcLoS1\\\\3QlNcLRT1\\\\3mkNcLVT1b32N1N1O2M4L4M3N1M3UMPkNe2RU113O2L3K5O2K4O2jM[jNn1oU1WNQjNZ1^V1L7I7mNSiNd0ZW1ZOjhNO]eVa0\"}, {\"size\": [1280, 720], \"counts\": \"Pdm3S1gV1=LN2O2LHjNgiNU1VV1nNjiNS1UV1nNiiNT1UV1<01K4O11OKWNWjNf1hU1[NUjNg1nU1400M3N2OTNVjNd1gU1]N[jNc1RV100O1O1O1N2O1O1O001O1O001N2O2N2M6K4K6G=^O]Scf0\"}, {\"size\": [1280, 720], \"counts\": \"nec81oW1000000000aN0njN0PU60aQK0O0010O0000001000ciN0jT10VkN0\\\\V10]V10^iN6LO]hNMbW1432liNH_T1<]kNDcT1`0ZkN@dT1e0WkN]OjT1d0SkN\\\\OhT1NYjNi0j0XOkT17WjNb0m0VOjT18YjNe0k0UOeT1>`jN=]V1h0RjN[N`T15YkN0Ib1;[NeT13SkNl16SNiT10PkN0Ob19`NjT1MnjN1O2O0Ol0=POnT1OijNR2LPNbU10`jN<0P1jU1POUjNo0PU1cNgkN;\\\\OR1nT1eNTkNOON1;OP1PU1hNakN;\\\\Oh0ZU1POQkN0D82`0kT1TOWkNOO80e1lT1UNTkN0Om0M8SU1jNRkN1Nd0O^ON20g0UU1TOTkNk0J9XU1nNijNj005cU1I^jN5cT1SO`kN4Od0O_OL6fT1G]kN21c00]ON3eT1OYkNN5d01ZOK3gT17XkNB013f01@OGhT13SkNi0014^OQU1HljN]2SU1bMnjN2N:0O2g0nT1d12ZMRkN800O1O`0SU1TOPkN814Ln0[U1POhjN0O2N`0[U1]OijN0O2O1NL\\\\U13hjNEL357N0bU1I[jN99GI63N_U1>bjN]O153IZU1e0]jNDdV1=]iNCcV1>]iN@cV13[iN2PW1OnhN0TW1IPiN8SW1FnhN9VW1DjhN;XW1J?Km_\\\\`0\"}, {\"size\": [1280, 720], \"counts\": \"TWi91=0PW11UiNOhV11ZiNOaV14aiNL[V14iiNLQV10XjN0mV10_L7inNIhP13RlN?T3^OeP1n0kmNROH0QR17blNk00TO2LR1N;0iQ1R2klNWN42Y1EiQ1_2olNkM\\\\T1J^kN`20fM[T1W3C`LTlNV3LWM_1BmP1Q4SoNoKkP1S4_12N2O1002OemNiKdP13RnNS4Z1jKcP14SnNR4YS1mKglNX4WS1iKilNW4XS173NnmN`KcP1a4]oN_KaP1c4_oN]K`P15YnNS4W1iKaP12[nNR4S1kKSQ11dmNa23dNU1jN[Q1`2`mNfNT1jNhQ1[1olNYO2C1j0U1oNmQ11PmNn0MB3D1k0fT1VO^kN0J:`0?RU1QO_jN5LNf0m0_V1UOkhN<^W104L8KfhNHdV1;i0GRhN1khc`0\"}, {\"size\": [1280, 720], \"counts\": \"RY\\\\;460ZW1;0NdhNHVW1c0KElhN2QW1OohN2QW1>2NPiNYOMh0mU1OSjN=iU1o004H4N3M2K8J6K2JmL]kNT3gT14I5^kNdLZT1R4kkNnK_S1P4>H<A6iL\\\\kNk2oT1M202N2O020N2M3M7I3M2mMYjNi1TV1K2O\\\\kNWNVS1`1P2D9HdjNTOST1`0kkN@]T1:l1L2M1OWiNN_U16[jNJgU10\\\\iN2j0NSV11Q1OQX10mgN001VRd>\"}, {\"size\": [1280, 720], \"counts\": \"WSh7=bW13M2N4M1O2N2N1O1O1O2M3M3M2O001O0O2O001O1O000O2O0000000001O000001O00O1O2O00000O2OO10002NO100001O2M01001N100O1O10000O10N3N100O100N1O2O10000O1O1O1N2O1O100O1O10OO2O10000O1N20O01WOnhNc0RW1\\\\OQiNc0nV180]OohN7RW1HnhN5YW1JhhNO^W1MdhN1[W1:0EdhN3^W1M`hN3dW14OJ^hNMaW162L^hNN^g3Od]W`0\"}, {\"size\": [1280, 720], \"counts\": \"RRW8132dW1O^hN0bW1O`hN0`W10`hN0eV11hiN4CKfV11eiN5DKgV16_iN0GLkV12^iN4FJlV12ZiN9IEnV11YiN9JFnV10YiN8KGnV1OWiN2J11NnV10TiN;NEnV1a0ViN^OlV1`0nhNETW16lhNOSW10ohNOSW14jhNF[W188N1L402N100N1O1O[Vkb0\"}, {\"size\": [1280, 720], \"counts\": \"\\\\jZ31aW17bhNIaW1:NM:J200OlW10UhN0oW;0UbC0hS10TlN4lS1LSlN5lU1N2M3000O10O100000000000000000O01000O0PX10QhN1gW;OXPF0PhN1O1mg3NUXL000c^70\\\\aH7J3M7I00NchNF<K^V1>WiNIUW1`0TiNYOaV1e0aiN[O^V1i0WiN_OhV1m0O2OO1O2_O[iNDhV1:[iNAhV1=ZiNAfV1`0=0001O00O10O102HahNL`W12`hNN^W1:1EchN3]W1NahN1cW106OL2102LZned0\"}, {\"size\": [1280, 720], \"counts\": \"ibl61ng30Q`M0O010O000010O0100O1N3N21OIMahN3]W1O`hN1U1JRU1`0ljNATU1`0liNBf0O_U1i0^jNWObU1k0[jNUOfU1k0YjNTOjU1i0XjNVOiU1j0WjNVOjU1k0e0AmhN1SW1a0QiNXO]V1V1NQObiNa0^V1`0N12N6fN]iNl0^V1ROeiN2N2N`0`V1\\\\OkiNj0ZV1YO_iN9LOcV1GdiNg0[V1\\\\ObiN6NIOM_V18diN1OJcV16\\\\iN21FhV19YiNMPW10SiNNdV1LWiN10013hV1LWiN11O03bV1K_iNOO7NJ45_V10`iN60M^V1OaiN4oV1IQiN61HgV14XiNNLO5OfV17ViNK00RW19TiNJfV19ZiNF^V1I`iN5K20N41`V1N^iN1000O20aV13ciN0KI33^V17biN03I[V16biNO7HmS13kmN5PN21F\\\\T15jkNMU19cN02F\\\\T14_mN7PN0eT1G_kNOL3g1:ZNL`T1GekN7a16^NFkT1MblNNcN4J23M2N^T1OkkN2HOS10mN1M30N2NoT10_lNOiN70JiT1OklN:ZNFiT13_lNOhN7oT11mjNHa0ND6K4OHmT19SkNIa0LD7K40GnT1:ojNHe0LC8K2[U12[kND_O<KNWU12_kNDB9KMUU12fjNJd0O_O1N20M11]U14cjNKb01^O00000O021QU13hkNL@N02RU17[kNL20_T12`kNM11`T12gjN0;LE72FM42MnT11mjN3352JK0381GM13OUU12fjN5?LC63FM4cU11_jNI1309OFN424^U1MjjNIM92LL2\\\\U1J`jN31N93GK2182_U10VjN03MO4ON1O:0gU12VjN31J30dU10\\\\jN9cV1LWiN33HUV19ciN30EN33LN3kU17VjN20N0IM1PV14oiN78FN3mU14liN1:HL1QV1`0SjN^OO2oU1KmiN;OGN181nU1KoiN3J23OM0;N\\\\V14aiNOnV14ohNMRW1OQiN1oV1NnhN6TW1GlhN:bW1K1NThVa0\"}, {\"size\": [1280, 720], \"counts\": \"TXg83nW1N44BLahN7\\\\W18OJahNM\\\\W1=OBfhN7U5Ghl02TnN1NOS5LTK0`Q1g0XSOYOTm0;omN0i4DeK0RQ1m0[5@TiN20Ea4Lam0=omN10H\\\\4Jfm0]1SROeNfLNXP1W1lnNQOm3J\\\\n0`1bQO_Nbn0a1o30eiNbNZ3MVo0m1hPORNYo05_mNl0N]O2HhU10VjNl1l2nMho01_mNb1MnNj2Ajo0O_mNa1NPO7@R22\\\\P1V3PoNiLSQ1`3[nNaLmNOkR1b3QnNhLQR1V3omNjLVR1R3PmNeL`08gR1\\\\3olNjLQS1JglN[2OeN[S1W32aKelNS4^S1kKdlNc2MhNgS1j2MlK\\\\lNR4hS101O1fLVlN^2lS1i03ULQlN90e2XT1b0NdLkkNP3UT1nLmkNQ3VT1mLhkNT3\\\\T1lLdkNo2o1kL_P10emNS3m1mLfP1Q3[oNoLiP1f0UmN^1R2lMZQ12PmNj1jU1NgkNVNiR12`kNn0b1POaS11kjNd0_1[OYT1h0WkNYO\\\\U1?YjNAjU10ciN0M1`0OfV11jhN8caVa0\"}, {\"size\": [1280, 720], \"counts\": \"h_U:1mW13MO2011N6G5L3N3M2F\\\\OTiNg0kV1;M2N2M3N012N00O03biNaNVV1e1O02N00102M01O0O205J2N7JO10N2O1O101010O24I1O1N10001O100O12N1O01OO001O5MekNhMiR1Z2QmNfMTS1\\\\2flNeM\\\\S1\\\\2`lNdMdS1`2QlNaMVT1^2bkNdMdT1\\\\2SkNeMUU1`24L5K4M4M3L3MlkNQN`R1h1`mNXNeR1e1n1L1O1OXkNaNPS1^1PmNbNQS1]1olNcNUS1Y1jlNgNYS1V1hlNkNXS1R1jlNnN\\\\S1k0dlNVO_S1g0alNYOaS1d0`lN\\\\OdS1a0[lN_OiS1>VlNBlS1=SlNCoS1<okNDUT1:jkNFWT1<ekNE\\\\T1<akNDnT1OQkN2RU1OjjN1fV1LB6bhNNlV1M`iN4iRZ>\"}, {\"size\": [1280, 720], \"counts\": \"PfT;7hW12K4M3O2L4L5M2J501N101O0001O1000N2O02O0001N01N21N100`kNmNRR1S1^21O1O000O100kiNhNaU1W1]jNiNeU1X1ZjNhNgU1Y1VjNgNmU1X1QjNhNPV1Y1niNhNSV1Z1fiNiN]V1[11O2O011N001O1N2N2O1N101N1O3N2N3M00005J2O1O]jN^OjS1`0TlN@PT1>okNBUT1<k1LmiNHbT15g1N0O1020O000N4lhNK_V14QiNLZgR>\"}, {\"size\": [1280, 720], \"counts\": \"oWh:2kW1OP`20S`M00010O010O0B4hhNNVW17ehNJZW1>100O10O1WjN_OST1a0nkN_OQT1a0g10O1N20O0100O1O2N1O1O1N21N2O00O00O4O00^jNYOiS1g0i1N^jN[OlS1c0k1OXjN^ORT1a0lkN_OVT1>kkNCWT1:jkNFYT16gkNJZT16fkNI^T14akNL`T15e10iiNKdT15ZkNKhT16a10biNJPU16^1OaiNKTU13_10O00032ihNK[V16YiNNoV12`Pm>\"}, {\"size\": [1280, 720], \"counts\": \"_eX91mW112OoW60RhI0^h=0bWB0Si=0lVB100N2O00:TOCaiN=^V1EaiN;`V1F]iN;gV1CViN?jV1BTiN>nV1@RiNa0mV1@RiNa0nV1@PiN`0PW1@PiNa0PW1_OnhNb0SW16021N0004J0002OO1]iNROSV1m0iiNYOVV1g0giN[OZV1U11O2M2O1O2O0O102MO10O27K9E00100ON2O0O20O2N1O1O00O101O11N12M1O020O000N4M1O2O0O3L4M1O2N2M3K6I5B]Wd>\"}, {\"size\": [1280, 720], \"counts\": \"YRQ93Sh3NUO2=3T`M0niNNXT1NmkNOTT10o10RjN3nU1M10YPS10folN2N1jMN]lN1fS1NWlN3kS1MPlN6iU15[NEckN<PV11aNCXkN?jT1ASkN`0oT1AljNa0XV11QO_O]jNe0]V12YOYOoiNm0RV1a00N10102MO2O01O00O1001O010N2N5LN2O23L000013L001O1OjN[iNQ1eV18M4L2giN_NoU1a1miNaNTV1e12KQjN`NfU1]1VjNiNjU1X1RjNjNnU1e10O10O11O000O002WNoiN_1SV180O00002M01O2O00001O01OO1100OO11N100O3M00O11O3M001O001O1O2M2O3M2M104K2N1M4M5L4M4K5K]a]=\"}, {\"size\": [1280, 720], \"counts\": \"min;1l10TV1000PX10ogN1O100O1O2N2N1O1N3M2N4M0dM^OYmNd0PU13M2O2M3N1O1O1O2N2O0SOmN\\\\jNN2X1]T1hNgkN1A3i00[OW1l0jNdR1LalN1P1b1GeNYT1\\\\1_kNkNaT1U1[kNnNfT1R1WkNoNjT1R1TkNoNmT1R21N2N11OO101N011N2OO12M10002NO20N2O0O1O2O0O2O001O1OO0003M100O10000O2N1N201N101O1N2N2N2M2O1O2N2N1N3M2N3M3M4K5Ikad<\"}, {\"size\": [1280, 720], \"counts\": \"ZWg=6iW1100O2L4N2N2N2N2O2N2M2O2M2hNUOYkNl0eT1VOXkNm0fT1UOYkNl0eT1@PkNa0nT1DmjN>oT1GnjN;oT1HnjN<nT1ImjN9RU1IkjN:SU1[1L4N2O1O1N2N2O2M2O1O1O1N10001O001N2O3M1O000O11O01OO1O100O100N2O1aMhkNlN3R2VT1oNUlNl0lS1QO^lNg0cS1YO_lNe0bS1YOdlNa0]S1\\\\OilN`0YS1_OllN7YS1IklN1XS1LWmN_ORS1?Y2_OfhN7fW1GeRi;\"}, {\"size\": [1280, 720], \"counts\": \"Qhi=1mW14M101M200O3N4L1N3N2M2M3O2N0O2N2O1O1N2N2O10001K4N200gNbNPlN2iNV1iU1hNRkNW1mT1jNQkNY1QU1hNjjNY1VU1gNajNb1^U1_N_jNd1`U1]N]jNe1dU1<jNkM_lNU2eT12jNiM`lNY2cT11jNfMdlN[2ZS1eMglN[2YS1eMglN[2XS1eMilN[2VS1eMjlN\\\\2]T1001N1jNbMjlN_2US1aMklN_2[T1000iNaMmlN_2[T1O00000O01ObMfjNX2eU1L@lMXkNP2eT1PNckNk1\\\\T1UNmkNa1UT1_NPlNS1YT1mNnkNh0VT1XOPlN=Ud3CTbM0[Qd;\"}, {\"size\": [1280, 720], \"counts\": \"hoe=2mW13M3KM\\\\hN2bW1700O1M2N3O0O3N1N2O0O2O2M2BQOfiNR1\\\\V1RO\\\\iNP1kV10kNlN]kNS1aT1mNakNS1fU100001N2O2O0cNhNPlNY1XU14M3N4fN\\\\NSlNe1SU11gNZNVlNg1QU1000hNYNWlNi1gS1WNYlNj1nT12eNTN]lNn1iT12O000gNPNalNQ2fT11hNnMblNS2eT1O1000hNnMclNQ2]S1oMclNQ2fT1O1OgNQNclN3mNa1aT1\\\\NblN0TO_1[T1aN`lN0YOZ1YT1fNSmNV1nR1jNWlN0MP1lS1QORlN26i0jS1TOnkN2d0>bS1@jmN0XR10ekN0^V10biN0^V10aiN0`V10h00fhN0dV11f00Xjg;\"}, {\"size\": [1280, 720], \"counts\": \"dob<1nW10eWe00[`^O0QXL000Rh30PhN000n_M0UjN0kU10O0Uf80nYG0g10Sf80of30o`_<\"}, {\"size\": [1280, 720], \"counts\": \"h^P:1oW100000P`20\\\\^M0XkN0\\\\V11N1011ObNNmjN2VV1OehN1[W1:O1O2WOBmiN?TV1AkiN?VV1B]iN01?cV1KZiN6hV1a0O0101N2OO2O000000000O010000000O1001O1OO00001O100O2O1O0O4M2N0O1O1O01O100O10O010O010O01000O10O01O0100000O10O01O1O1001OO10O1O1000O2O1OO100000O100O014K11N1O010O10O10101N0000O100O1O1000O010O1O1OO2N10005KM30O1gNaiNQ1`V1mNaiNR1gV102M5K3L2M4M4L2J7Fbok<\"}, {\"size\": [1280, 720], \"counts\": \"]SR63mW10Vh[1No_eN0ogN000mhN0SV12hiN1YV1OQiN434mV1b000000100N100010O0100O01O10O100O10TOQiNf0oV151O1O001N11O013M000O0O30N001O101OO0O4LKViNWOkV1m02N1O11O0010O01O101N0001000O2N10O00010O1000O10002M2]iNhNZV1Z1biNhN^V1^11OO01O1O0001O0100O01O0O12N01O0010O01O0000001O01O010O001N101O01001N1N200O10O01N200O001O10O0O1001O1O0000001O0000001N2O0O2O1O001N3L3N2N4K6HoYS?\"}, {\"size\": [1280, 720], \"counts\": \"Qln61[Ve00eQ\\\\O0oY10PfN0[V60YcH0\\\\V100000bN0ljN0VU10gjN1ZU10djN0^U1OajN2^U1OajN1aU1O\\\\jN2fU1MniN0_O7cV1JiiN5A3fV1HeiNe0]V1[OZiNm0fV1SOXiNn0hV151O1O010O100O10O01O01O01O1O011O1NN20O110O1O00O11O10O000001O01O0101OO0O10100O10O0MRiNWOkV1h0ViN@cV1`0`iN\\\\O`V1f0_iNYOaV1e0_iN^OaV1`0]iNCcV1=]iNDbV1<`iNBaV1=`iNB`V1?_iNAbV1n0]iNkN[V1U1diNlN\\\\V1V1610O0N\\\\iNmN_V1S1aiNnN_V1X12N11O100OO101O100OO1O11O1O10N1O12N0000010O000000101NO2N1011N1N20O0010O0000010O00O02O1O00001O001O001N2N2O0O2O1N1O3M2N1O4I6Mnjk>\"}, {\"size\": [1280, 720], \"counts\": \"l]S;5dW1?F3N2NIlhNETW19nhNCVW14ghN4]W1JjhN0VW11chN0aW1802MIehNNWW11lhNNTW12hhN1YW1NhhNN\\\\W10ghNNXW1167N4K5M3M2M4M5K2N2O1N3M3M3N2O1N1N2N3N2N100O1O0010O10N101O0001OO1010O1N1001N11O1O0O1O1O1O2N100010O00001OO2N100O10O1M32N1O004M0O001O00O2N10100O000O2OO10001O1O2N1O002M1O2N2N6G<G5^OghN0aan<\"}, {\"size\": [1280, 720], \"counts\": \"n^P:2oW1OaV`00[i_O0lW1050Ph80SPF000PX10PhN000PP50QPK0PX10ogN000RX10ogN1O001N1000002O0N000110O00M8J11N2O0?DNSiNXO^V13XiNa05^OiV1c0UiN^OkV1OTiNe0mV1[OSiNe0PW140O101O1N2O1N5J5K6HiYn>\"}, {\"size\": [1280, 720], \"counts\": \"Ued81PX1010cN0gjN0VU10njN0oT10^Y10naM0YT10hkN1RV13J5O012L2PNC[lN;eU102O0I_hN2^W1MchNO`W1OchN1fW1OUX11P]k0OkbYO0QhI0P`20P`M0_10\\\\N0lN0^jN2dU1NZjN4hV10POLRjN8iV12VOFkiN8\\\\V1HbiN9^V1GaiN4@MQW1O\\\\iN4mV1LnhN1YW11_hN1Ylg`0\"}, {\"size\": [1280, 720], \"counts\": \"UiR:1hW10nhN0R^70SQK0_aM0dV10100000oW10bfN0PkN0nT10SkN0lT10a10j10QiN0cP10RQO5gP1KZmN:fR1FXmN=mP1C_nN1`0?QQ1@_nN4:?WQ1]O_nN5KL`Nj0VS1UO`nN;]Ol0nS1c1UNVM^nNl2bQ1TM]nNl2[S1100ZNSMXnNn2\\\\S11O001O1O10O101O001OO0I8_Oa05\\\\LhkNN3n2lT1K4M3M2N5J3N4L3M3M3M2N4K5L4K4L4L4L4L5K5J6IYlZ?\"}, {\"size\": [1280, 720], \"counts\": \"o`S91PX60ooJ0000010O001O10O0010O010O001O1O2iNL^jN5cU1J[jN9eU1GVjN<jU1ETjN=lU1DPjN>RV1BkiN`0UV1BhiN?ZV1@diNb0\\\\V1^ObiNc0_V1]O^iNd0cV1^OZiNd0eV1\\\\OZiNe0gV1:01O01O010O00010O00O2N11O10N11O10O1O1O00011N100O1YjNeNfT1\\\\1WkNfNiT1[1RkNPOZOF59`T1R1mkNITT17hkNLYT15ckNM_T13^kNNcT12[kNOfT1e12M102N1N2O1N3M10002N1N2O2M2O1N2N2O1N2O1N2N3M1O101N2O2M2M3O1N2N2M3N2N3N0O2N2N3M102M2N2M3N3M3L4Ldla>\"}, {\"size\": [1280, 720], \"counts\": \"TYR91P`20PhN0O0RX10ogN0PX1000ogN001O0O01O10oo40QPK00000P`20P`M0QX10gfN0`jN1_U1OajN2gV10gNNbjN2^U1NcjN2]U1OajN1`U1O^jN4aU1L]jN6dU1JYjN8gU1HUjN<kU1DSjN>mU1ARjNa0nU1@kiNf0UV1ZOjiNg0e2]OUP1OSmNf0`2BaP1DllNl0`2CdP1CilNl0\\\\2GmP1^OclNn0Y2IUQ1>hnNDZQ1;_nNKbQ15\\\\nNKeQ17XnNJhQ17VnNIkQ19RnNHoQ18PnNHQR19mmNGSR1:kmNGWR19gmNG\\\\R18bmNG`R1;^mNDdR1=XmNDiR1>RmNDoR1>mlNCUS1=ilNCXS1>flNA^S1>`lNBbS1?[lNAgS1>XlNBjS1>SlNBPT1?mkNAUT1?hkNAYT1b0ckN_O^T1f0ZkN[OiT1m11O2M3N1O1O1N2O1O2M2O1N3N1N2O1O2N1N2O1O2M2O1N4M1N2O2M2O2M2O2N1N3N1N2N2O1N2N2N3M3N1N3L3N3L4K5K\\\\TY>\"}, {\"size\": [1280, 720], \"counts\": \"SU[92mW11O1INahN1XW17hhNHVW1;ghNHVW16ihN0PW16PiNJlV1g0K50HXiNWOiV1c0^iN\\\\ObV1b0aiN\\\\O`V1f0_iNXObV1;XiNOQW12mhNNSW1>QiN]OeV14ViNN1K53dV1;ZiNC03eV17^iNJMKeV1;^iNKLJeV1>`iNKaV1NXiNL93`V13`iN1bV1N[iN5eV1G`iN8`V1B^iN4O4^V1KciN202kV1NViN2`V1IYiN6:1]V1IYiN6:1YV1MaiN034[V1MdiNNO5_V17_iNNdV10YiN2fV1e0OXOXiN:gV1F[iN9fV10YiNGhV1EYiN940nV1KUiN3]W1O1ON2N2O0WOJbiN0F5iV1L_iN1F3kV1M_iNOG5gV1M]iNO14bV1M^iNNO5dV1M_iNLK9fV1L_iN;`V1FXiNb0gV1;10O1000O10O00001O001O1N2O1O1O1M201N101O1O1O001N101O0O1000O01000O1O001O001ON22N10M22N1N0101O000N31NN31N2N1OO100O100O2L3N2O2BTNgjNn1iT1oMWkN1L1N<0Y1QU1gNSkNLO[1nT1cNkkN\\\\1TT1^NTlN`1mS1_NUlN`1jS1aNXlN\\\\1iS1dNXlNZ1jS1dNYlNZ1eS1gN]lNW1dS1iN]lNV1cS1jN]lNT1eS1lN\\\\lNR1fS1nNZlNo0hS1ROXlNm0iS1SOXlNj0iS1VOYlNg0hS1ZOXlNd0jS1[OWlNd0iS1\\\\OXlNb0jS1]OVlNb0kS1^OVlN`0lS1@TlN?nS1AQlN<PT1EQlN;nS1ESlN:mS1FUlN6nS1ISlN5oS1JRlN4oS1MokN3ST1NkkN0WT11gkN0ZT1OekN3[T1LfkN3[T1LgkN3XT1NikN1WT1OjkN0WT1OgkN2SV10ZV1NjiN0W`70_XS<\"}, {\"size\": [1280, 720], \"counts\": \"oTf;5kW11N12NH0ahNO`W11^hN0fW1210L2011O2O1M2MO22NL31200L3002M10000O3NST`0MVl_O3NN^iNORU11ejN3]U1M`jN3dU1MUjN6oU1JmiN9UV1FWiN158HLRW1MmhN<3GQW1d04J4O0OWiNB^T1MalN>QOH_T1J`lNb0hNF_V1;]iNFeV1:YiNFRU1G_lNb0^NHRU1GalN`0\\\\NDYU1K]lNk0\\\\U1OSNVOelNh0[S1XOglNb0]S1^OflN>ZS1BhlN>WS1BklN<US1DllN;SS1EolN:\\\\U10cMFTmN8[U1O2N]MK]mN3dR1M]mN2cR1N^mN2aR1O_mN0bR1O^mN1XU1O]M0XmN1fR1O\\\\mN1dR1O\\\\mN1eR1OYmN2YU10100O100Om_20QhN000o^h<\"}, {\"size\": [1280, 720], \"counts\": \"kc[:2nW12M1M1200000O1O1^hN0RW15ihN16CdV1d0YiNBMNgV1b0[iNGdV1k0O1O10ON211M2N20O01O00001OkNeiNf0[V1ZOhiNb0ZV1_ObiNc0^V1ZObiNi0^V1<OiNdiNm0\\\\V1SOfiNj0[V1<1UObiN5^V1JgiN1YV1JdiNE4=ZV1GaiNM3N2=YV16hiNKXV17diNL\\\\V15ciNI_V10biND0M12[V1ObiN;2G8NUV1b0aiNA13`V11^iN53M]V10eiNM01[V1NaiNL4M11L6]V10eiNK1N12K1aV1=ciNA07^V17_iN6_V1N^iN3bV1HaiNG81WV18biNG70VV1e0biNB_V1<aiNF_V1;diN@]V1;`iN_O24]V1=diN^OO4\\\\V1k0aiNTOaV1U1O1N011bNeNXlN[1ZS1fNRmNZ1oR1eNZlNOF[1RT1eN_lN0VO]1iU198I4L2N2N^NkN\\\\lNS1gS1kNYlNS1iS1mNWlNR1]U1NdNQOlkNk0XT1UOfkNi0\\\\T1VOekNh0mU1K3O\\\\N^OkkN`0TT1@nkN>lU1M202LRNIWlN5iS1KZlN3iU101OkMN]lN1cS1O^lN1aS10`lNO`S10blN0fU11eMOflN1ZS1OflN1dU1000fMOglN0dU10fM0elN0[S10dlN0^S10alN0_S10alN0`S10^lN0dS10[lN0eS11ZlNOhS10VlN0mU1000O1]OOWiN1mV1OohN1VW1ObhN3nV[=\"}, {\"size\": [1280, 720], \"counts\": \"X[o7;bW1;G8H100O00000010O01O1O0010O01O0011N1O01O00001N2OO2O01O1O0000010N101O000O11O00O201N0O2O0010O00010OO2N1O110N1000001N011O0O1010O0000000010O01N010O1O1O100O1O101N10O10O101N1O1O1O1O2O00O2O00N21OO1N200O01O1L4LPOhiNa0XV1\\\\OmiNb0TV1[OPjNc0PV1^OQjN`0PV1_ORjN>PV1BRjN;oU1ESjN8nU1HSjN5oU1KQjN4oU1MQjN2oU1NRjN0oU11QjNNoU13QjNMoU12o01O10O1O00010O1N110O0000ooh00f`a=\"}, {\"size\": [1280, 720], \"counts\": \"Xck75iW13M3L5E<I6I8H:H6G7K4O1M3O1L3N2L4L401L3N3OO0101F94K3O0O2M2O1O1O2N1O1N3N1NRO_MblN^2_S1bMalN[2dT1K5ZN]jNIOb0hU1^Oh1O1K5M30^o^b0\"}, {\"size\": [1280, 720], \"counts\": \"ePa61XPi00ZVXO0jjN0WU10]10[iN0YU10fjN0eV11O1hNN`jN3`U1M_jN4fV11kNK]jN5dU1K[jN6eV13N2O0O2N2N3K4N2BXObiNi0_V1WO_iNj0bV1WOXiNm0iV16O1O01O0001O001O01O01O2OO000010O1O10O0010O3M10O0100O0_jNUOnNDfT1X1YlN@dS1a0ZlNBfS1=XlNDiS1;UlNHkS18SlNIoS16okNJST17ikNKXT15gkNJ[T16bkNL_T15\\\\kNNfT12XkNMjT15RkNLoT1b12M2N101N3N1N2O2N1N2N3N1N2N2O1N2O2M100O2N3N1N2N3M1O2N3M2N2O1N2N2N4L2N4L2N3M4K4M4J_Ti`0\"}, {\"size\": [1280, 720], \"counts\": \"aTg89bW17C;L5K4N110O100O1000000000000000O10O1dLjNnoNU1no0UOmoNk0ko0BooN?PP1CooN<PP1HnoN8QP1JnoN6QP1LnoN3RP1OloN2SP10loN0TP12joNNVP13ioNMVP16hoNIYP18eoNIZP19eoNG[P1;coNE\\\\P1=coNC]P1>boNA_P1`0_oNAaP1a0\\\\oN@cP1c0[oN\\\\OfP1g0WoNYOiP1i0UoNWOkP1l0QoNTOPQ1P1lnNPOTQ1R1jnNnNVQ1U1fnNlNZQ1W1cnNiN]Q1]1]nNbNdQ1b1XnN^NiQ1d1TnN\\\\NlQ1h1omNXNSR1i1kmNWNVR1k1emNVN]R1n1\\\\mNTNeR1m1WmNUNkR1l1olNWNRS1Z32O3M5J3N8H4K8I1O4K4M7H4QMUkNc2UU1N2O2N1N2O2N3M3L4M3M4L3L4M3L3N2L5L5K4L4L3M4K4M4J6Id\\\\g?\"}, {\"size\": [1280, 720], \"counts\": \"`\\\\T84gW15F:H8N2O1O1O1O1000000O10000O0100000O100O100O10000O10O010000000O2OO0100O01O100000O010O100O10O10O01O1000O10O10O100O01O10O010O01O10O01O1O1O0010O01N12O1ON1O2O1N1010O0001O01O0001O00010O00O2O001O00O100000O101O1N1000O11O0000O11O00000O2OO10000O1001O0O1O110O0001O00000001N1000000O1001N1001O0O10O10O11M24L2L4N2L6J3N3NSom=\"}, {\"size\": [1280, 720], \"counts\": \"n^f<1bh30a_M0PY12hfN4[OJZiN:gV1HRiN8UW1FchN=5DWW19ihNGYW18ehNJ[W1c0KO0K6OehNAVW1h0NO[NYOQlNe0eU1N[N]ORlNa0gU101N2N1N2O6JeMLllN3aU1N110O001O00O1000000OOPW12fhNO9O00PYe=\"}, {\"size\": [1280, 720], \"counts\": \"Zkl79bW1d0@4L2M3O01N0100O0100000O100O0100O01O10O1000O000100O0001O01O1O0100O001O1O01O001O01O0010O01O00010O00010O0O101N10001O001O01O01O0O2O001O000O110N1O20O00000000O1O1001O0001OO1001OO1O1000000000O11O00000O100O2O0O00100O011N1O100O01000O100O1000O1000O100000000O1000O1N1O22O2L4M2O1M2O3M2M4M2M4M2N2N3L3N1O2N1O1O1000lWY>\"}, {\"size\": [1280, 720], \"counts\": \"jbf73jW16J5L6J8J2M3L2O1O0O200O0O101O0O11000O0O010O1M3N120K410N1M3N3N100O1O010001O10N1O1O2N01O100000O00100O11O0O010N2O1O1O1O1N2O1O01O00N20O0:H3L3N2O1N2L4I7N2L3N3N1O1N2O1O1N2M210O001O001O01000O1000O0PX1000O010PX10O0YW;0iYW?\"}, {\"size\": [1280, 720], \"counts\": \"SXl61PX1010N1O1O2N3M1O2N2N1O1O3M1O4K9H2M4M2M4M2N2N2M3N3L7J5K4K5I8I6I[MRkNa2^U1J7HPkNmMTT1Q2fkNUNYT1k1ckNYN\\\\T1h1`kN]N^T1e1]kNaNaT1b2N0O01N2N3M2N2N2M5K3N2N3M2M3M3M4M1N4L3M3M2O1N3M2O2M4K4M5J5K4M4K5Gol^b0\"}, {\"size\": [1280, 720], \"counts\": \"`Qk6`0_W16K2N1N101O000O1010O0001N10010O001O0010000O10O100O10O11OO1O010O100O0100O100O010O010O10O01000O1O1O10O10liNVOA:aT1`0lkN5UT1LikN5YT1IekN9\\\\T1FckN;]T1F`kN;bT1D]kN=dT1CZkN=hT1CVkN>jT1DRkN>nT1DnjN>RU1AojN>SU1@njN`0RU1CjjN=WU1V1001N2N2O001N10001N1O2N2O0O2O1N100O1O2O2M101N101N100O3N1N2O1N2O1N1O101N101N100O1O101N100O1O101N1O2O1N2N2M3N2M3N3K6HfT_`0\"}, {\"size\": [1280, 720], \"counts\": \"olV86`W1;E:I8M2O0010000O1000O010000O1O10O10O10O0010O10O10O1O1O10O100O1O100O10O10O01XLmNcPOS1\\\\o0oNcPOP1\\\\o0TOaPOl0_o0UOaPOj0^o0XOaPOi0]o0ZObPOf0]o0\\\\OaPOd0^o0@_POa0_o0AaPO>_o0D^PO>ao0B`PO>^o0DaPO<^o0FaPO:_o0H`PO8_o0K_PO4ao0O\\\\PO2co02WPO1ho04RPOMoo09hoNJYP17coNK\\\\P1=WoNIgP1b0hnNEXQ1b0\\\\nNCeQ1\\\\30O100O101N100O1O100O10O10O100O1O1O1O2N1O1O1O2N1O3L5]NhlNTNeS1n0_2POk0kNRiN;Q_[`0\"}, {\"size\": [1280, 720], \"counts\": \"bl]7e0[W1<C101O0O001000O01O2N10O1O02O0O1O0001000O010O01000O00100OO10001O010O00010O10O1O000100000O10O01O1O0100O00001O000010000N1001O001O1O00001O1N1O101O001N10001O10O01O0010O1O0000000O2O000O1O100O20O0O11N20O0O10O101O00O10000000000000000000O1000O02O00001O0000O100O0101OO1O0100O100O10000000O100O10O1O2O2M4K6J6@Xgj>\"}, {\"size\": [1280, 720], \"counts\": \"fTd7914WW1c0G2O0O10O010000O1O1O10O10O1O1O01000O100O1O00010O010O001O010O01O010O00001O00100O0100O10O10O10O1O11N101N01K\\\\iNnNcV1j0>J5J70N2M2O1010OO20O0O10001N0000010000O0N3O11000O1N11N2O1O10OO101N2N4M4J4M2L40T]j`0\"}, {\"size\": [1280, 720], \"counts\": \"Tdf72lW16J6L2M4M4L2M3L3N2O00O0000010O010O1000N1001OO101O01O0000000010OO1001O01O00001OO10010O000O2O001N2O0O1O2N0100O100O0001OO200000O1000001OO2OO101O10N1001O1O1O010O1O010O1N20O00O11O1O0000001O0O10001O0O10000O2O000O011O001O0O2OO11O0O2O000O20O00O1000000000010O00000VORiNc0nV1YO[iNb0eV1^O]iNa0aV1_OdiN=PW1O00]ODeiN:SW1N1N1O1O2N1O100OP`20P`20mo>0W_W=\"}, {\"size\": [1280, 720], \"counts\": \"^l[85iW16K4K5L4L3M2L5N0O2N000N21O10M2N201O0N2N2L4N3L3M3M3M3M4M2M3N2O2N1O1K6N1O1O1O1N2N2M3M3N2L4N10iL_kNR3]T1oLikNm2XT1QMlkNm2ST1RMPlNm2mS1TMWlNi2aT1N2O1L5N1I7I8F9G9A?E:G9H8M300O10nNNXjN0lV1010O000PX1000PX10Q`M000O010O010oW10XgN0i00h00XO0XO0_10YO0[gR`0\"}, {\"size\": [1280, 720], \"counts\": \"VXe71RP50nWL0PX10ViN0dM0[jN0eU10[jN0eU10[jN0kV1000jN0ZjN2jV100001jNM[jN4eU1LZjN6gV12N1O2N1O1QOCYjN?aV13TO^OVjNe0jU1[OUjNh0iU1YOTjNj0kU1VOSjNl0mU1TORjNn0mU1RORjNP1mU1PORjNQ1nU1POQjNQ1nU1POPjNR1PV1oNkiNT1VV1<0O5LXkNXOWR1f0fmN^O[R1a0bmN@_R1`0`mNAbR1>[mNCgR1=WmNCkR1=RmNDoR1=nlNDTS1<jlNDXS1=dlND]S1=alNBaS1?\\\\lNBfS1>XlNBiS1`0SlN@oS1b0mkN_OTT1c0gkN^O\\\\T1c0_kN_ObT1c0YkN_OhT1k12O2N2M1O102M3M2O2M2O2M2N3M3N0O2O2M3M4L3N2M2N2N3M2N2O2M3M2N3M3M2N3M1O5J3M7Hhkg`0\"}, {\"size\": [1280, 720], \"counts\": \"`hQ81oW1000QX10P`20oWL0aV60__L1dYM=jKBRPO6eLb0YS1XOQPOR1_S1000cLnNkoNS1VP1mNhoNT1YP1lNfoNS1\\\\P1nNaoNS1`P1mN`oNR1aP1nN^oNR1cP1oN[oNR1eP1nNYoNR1iP1oNUoNQ1kP1QOSoNo0mP1RORoNn0mP1UOPoNl0oP1WOonNi0QQ1YOmnNg0RQ1\\\\OlnNd0mo0kNgnNc0Z1b0PP1kNdnNf0Z1?TP1kN`nNh0Z1<YQ1GenN9[P1lNYnNn0Z16[Q1MbnN3]Q11anNO]Q15anNK^Q18`nNG^Q1>`nNB\\\\Q1d0anN]OZQ1lNmlNl1g1XO`Q1i0]nNWOeQ1i0YnNWOlQ1f0RnNYOQR1h0kmNYOWR1h0emNYO]R1h0amNVObR1i0]mNWOeR1i0XmNXOjR1i0RmNXOPS1h0mlNYOTS1i0ilNVOZS1j0clNWO_S1j0]lNVOfS1n0SlNSOoS1o0kkNSOXT1X23N2M3N2N2M3N2N2M2O3L2N2O2M2O2N1N2O1N2O3L4M3M2N3L3N2N2M3M2O2M2O1N3M2O3L3M3M3M3M3M2M4M4JWdh?\"}, {\"size\": [1280, 720], \"counts\": \"Pdk7n0QW14L2O1M2O100O100O001O1000000O1O010O1O1O1O10O010O000100O01O00100O10O0010O1000O010O10O0100O001O0100O1O1O010OO11O1O1O010O001O00001O01O00O11O00000O11O01O1O00000N3N11O01O000001OO101N1N2O10O10000000000O0O200N111N2N0100000O10O01O2O00N0200OO20OO2N11O1O10N02O0O01M3N2O100L5OO20L04O20M2K4O2000O0O2N2L40O01101O0ON3L4M3M4K4M2O2L5L3O2L3M5K5E]Yj=\"}, {\"size\": [1280, 720], \"counts\": \"R]Y8>3ERW1j0H7N10O0O1L5M3L4N2N2M3N2M3N2N1O3N1L[g`15bX_N9H2N3M00O2JehNG[W195M`hNI_W1<O6PiN\\\\OZV1f0YiN^O2NcV1k0`iNSOaV1k0\\\\iNWOgV1i0YiNVOiV14SiN:3ElV10SiN2NO4NlV12PiN8XW1DjhN410VW1c0N1jhN[O3McV1Q1YiNROeV1U10N21OA\\\\iN]ONNoV13QiNN30ONkV1<YiNKgV1JUiN0:3YV1I`iNb01GXV10ciN;OJ\\\\V1d0diN]O[V1R11L5O2J5NO20001N5L1N1001OO1001N1O010N2000000O011O0O0100000O1O2M200N2O00100M2N3N2N2N2I70000O100O0010O1O1OO2M3Ni^U>\"}, {\"size\": [1280, 720], \"counts\": \"a]W91UW1m0L2O0O100O0100000000001OO010000O0110O00O010O1000O1000000O0100000000O010000OO200001OO1O10O01O1O100O001O1O1N1O2O001N2M3N1N3O1N2N101O000O2L4M2N3N1O2L4L3N200N2M3M200L3ONIXMZkNl2lT127H5J4M2M3N3M2N2N2M3L5K4L4M4H7M4J5L5N3J4N2L4M3M2L301O001O1000PX10PhN0ig30WXL000__70iW`=\"}, {\"size\": [1280, 720], \"counts\": \"mjg71d_<0YX60R`^O000O0RX10ogN0oW10QhN000diN0hT10d10ckN6dP1J]mN08<YR1D_mN04?eU15]N\\\\OekNf0]T1ZOakNg0`T1YO^kNh0kR1XObmN0@k0mR1UOdmN0\\\\Om0PS1SOdmN0^Ok0oR1UOjnNj0oR1VOYkNi0nR1WOknNj0TQ1VOemN0]Oj0oR1WOinNi0XQ1WOamNO]Ol0RS1UO`mN0]Ok0TS1UO_mNO^Ol0RS1UO`mNO^Ol0SS1UO_mNO^Ol0RS1VO_mNO]Ol0US1TO_mNO\\\\Om0WS1SO]mN0\\\\Ol0XS1TO[mN1\\\\Ok0YS1UOZmN0]Ok0[S1TOWmN2]Oj0]S1UOSmN3^Oi0aS1SOPmN5^Oh0cS1TOmlN6^Of0gS1TOilN7_Oe0kS1SOclN:Ac0mS1TO`lN:Bc0nS1SO_lN;Bb0QT1SO^lN;^Ob0UT1SOTlNf0C8ZT1ROnkNS1]OMgT1QOhkN[2YT1fMdkNZ2^T1hMYkNMJ[2nT1jMRkN^2PU173N2N1O2N2N3M100O2M4M2N1O3M2O00001N1N20O05KN200101N2N4L2N2N1O1O4L2N2M2O001O4L2L5L3M5K6Foid?\"}, {\"size\": [1280, 720], \"counts\": \"h`d51no40QXL0PX;0QXG0o_M000P`20o_M11OQQX10TngN0fiN0ZV11diN0[V11eiNOZV12fiNNZV11fiN0ZV10SiNO;2aV10SiNO:2dV1NSiN083eV1MSiN065gV1LQiN155kV1JnhN247nV1`01O0001N1O2]OROQjNP1mU1SOPjNo0mU1VOoiNl0oU1WOliNm0SV1a0O1O0M4N1O2O1N2N00100O2N10000O1O2O1O01O003M4L2N002O3L2N3M3M1O2N3NO0001O3M1O2N1O2N1O1O2N2N1O1O1O2N1O001N101N101N102M3L5L2N3MWnm`0\"}, {\"size\": [1280, 720], \"counts\": \"ae;1oW12N3K5JN_hNM\\\\W1=O@ohN3oV1LViN3hV1L]iN2aV1NbiN1YW1O21OOO1OOO0eV10_Ye00mgZO0ogN0QP:0P`H0o_M000000001O000ViNOfU10ZjN0fU10U10000000ViN0dU11[jNOfU10ZjN0kV10kN0U10U10gM0TY10PhN0TiN0hU10WZ10keN0TjN0lU10TjN0mU10RjN0PW10nN0RjN0oV10PO0PjN1oU10oiN1RV1OmiN0TV11kiNOVV11hiNOYV12eiNO\\\\V10diN0]V17[iNHgV1;UiNEkV1>RiNCmV1c0mhN]OSW1h00001N1O21O1N1000O002N1O01O0O1O1O1N30O00O1000010O00001N1001O01O1ON201O1O1O1O000010O10N2N2O010O2M101O10O1OO10001O100N2O1O01N2OO2N3N10001M010000001OO1000010O00001O1O00010O0000100N2O00010O001O1O01O01O000001O01N2O000O10000000O1001O1N100O1O2O1N1O2N3M00002^OfhN:cW1G`iRc0\"}, {\"size\": [1280, 720], \"counts\": \"mSk11oW10O102N3MM3O10O2O3nhNIUV14miNLUV10WiN0?0ZV19ciNI\\\\V1=^iNAeV16QiN100PW1f00OO00FQiNEoV1;QiNDPW1f01O0O100O101OO100000000O010O100N21O000000O11O1N2OO1N2001O00O2N1001O01O001N2O001O01O00100ON3OQOViNi0iV1UOZiNj0nV11O00100J4M21101O1OO100000000000000001OO100000001O000001N20N1kN\\\\iNm0lV10O1M4L4N6K5KN2L4N2O^iNiN]V1V1diNkN[V1V171O6J3M1O000001N1000O10100O1O0O02O0^NgiN\\\\1`V1OJeNgiN[1YV1gNdiNZ1\\\\V1502N3M00ZNliNa1[V1N1O3N2N0000O1003M2M1O1000O01O10O100O0100O001O00001O000100O0001O1N2O2OOO2O1N3M5_OhhN1eW1M^ZZc0\"}, {\"size\": [1280, 720], \"counts\": \"iem1111WX10I0O01Oj^a00Ta^O0QP:0PY`00nfUO10O0010UO0fiN0ZV11diN0\\\\V10diN0\\\\V11ciNO^V12QiNN70hV1;WiNEiV1<ViNCkV1=TiNDlV1<RiNFlV1=PiNGoV1e0O10001003M4K101NN4L3O10OJVOZiNh0eV1WO[iNj0hV14005M1ON2K4014K1OFUO^iNm0\\\\V1>01O00001O1O001O01N8F:H1000O1L3006MOO02N4K2OO1O10L4O25J0O12M2K42N2ON1N2004L000001N11O000000001O01O0001O00O20O2N3M1O1O0001O01O1O0000000000010O1O100O00000O3O0O01O010OO1O101O01OO00101O00N2M30O01005J3L1NKBnhN;TW1GkhN8VW192DchNO_W19005K2Lca]b0\"}, {\"size\": [1280, 720], \"counts\": \"kad31lW10b_<0``C000mg30SXL0O0XjN0kU1001O1O11NO1O10000O2O01N2O000O1O10O1O5J201[hNH_W1>N2N0O01O1O2N11O10OO2kL@hnN`0\\\\T10mL@enNa0\\\\Q1_OdnN3PM8eW1NjLGgnN7[Q1IbnN9_Q1G\\\\nN<cT12_MDZmN>RU13QN]O`lNc0dS1]OQlN3`Nb0`U1[OmkN2dNc0RW1]OnhNb0PW1^OQiNb0SW13F[OXiNj0dV1WOYiNk0hU1SOkjNOBT1eU1mNdjN3EP1gU1nNbjN^1_U1bN`jN^1cU1aN]jN_1bU1bNgjNV1WU1kNhjNU1XU1lNcjNX1]U1jNWjNMM\\\\1mU1a01O1N43KO1N4M1L5N1O1N2NfiNbNUV1]1jiNeNVV1`14M12M4M2O0000O2N2OO0O015L1N1O00N400O0O11O000O1O11O0O01O01000000O011NO2O1001N11N200O0O2O002M3SjNRNcU1W2O0OA]jNcNbU1n1000O0I7McjNnMTU1P2>01O21OOWjNTN\\\\U1l1`jNRNhU1S21OXjNnMaU1Y201O1O1O01M3N11O2NN4I5N4OO01N2M2N4L102L5K5K8G8SOUiN7_X^a0\"}, {\"size\": [1280, 720], \"counts\": \"Wgg95dW1>F:I1N2L4M4M3G7H8N2L4J6J6N1M5L3K5N2L3I8D;K6G8cN^1EcJkmN45T5dR1L2M4L2N2O0000000ON3iNTKYoNl4fP1UK[oNj4eP1VK^oNh4`P1WKeoNf4[P1ZKfoNf4YP1[KgoNe4XP1[KioNd4XP1[KioNd4WP1]KioNa4YP1^KgoNb4jQ1\\\\MklN>fQ1dMeoN^1\\\\ObNXOk1]S1^OhmN=\\\\R1[OomN0bR1MhmNXOUNK`T1k0_2L4G8MiNDRkN4XU1LejN2eV1OmNOZjN1iV10100OPX10QhN000N2I8k]g?\"}, {\"size\": [1280, 720], \"counts\": \"aiX93eW1b0D4L3J6M4L3K5M4M3M3M3N0O2N2M3N1O2M2M3M2O2O0N1O2O1O1L5K4L5L3K6M2M2M4L3K6OYLRlN_3XT1N2N3O2MN0201O2L5L1N2SOTkNXN5JjT1i1YkNVNYU1g1b0N2@SjNhNQV1m0g0G7J7N100O2N1L50OM4O0010O01OO2OO1O100000lin?\"}, {\"size\": [1280, 720], \"counts\": \"YTS81nW10ShN1oW111O1O00S_70m00m_H0VY60heI0UjN0nV1000000000PX10PhN0kN0YjN0m^20ifN0[jN0k^2000PhN0O010O001mNOUjN2mV100O0000O2O1QOLPjN5mV11N2O3SOEoiN=jV10VOCniN`0QV1AkiNb0UV1^OhiNg0^2XOiP11glNj0^2UOlP10flNm0\\\\2TOnP10clNn0^2ROoP12^lNR1`2lNSQ12ZlNV1`2iNWQ11ZlNV1\\\\2kNZQ10YlNW1X2kN`Q1NWlNX1m1fNWN7eS1\\\\1hmNWOYR1h0_mN@aR1`0^mN_OdR1b0VmNBmR1=PmNDRS1;mlNEUS1=dlNG^S1;[lNGgS19VlNHkS19RlNHnS19QlNGPT19nkNHUT1P21O3M1O1O2N100O3M2OOO10100O11M2O1002N1O1N2M7K1O1ZMijN_2^U1M6J2OO11O4K3L7K2M102N3L3N2N4K4M2M3M3M5K4KYol>\"}, {\"size\": [1280, 720], \"counts\": \"X]e73kW14M2N3M3N1N2O2N2L5J4M2O2N010O011N100000000O0100O10O10O02O000O10O100001N10000000O010000O1000O010O01O00100OO2N1000010O001N11O01O1O001O1O10O10O1O1O1N2O1N2O00100O1O101N1O2N1BehN6\\\\W1IehN8[W1FfhN:ZW1FfhN:ZW1FehN8^W1GchN9bW1N1Jgig0b0nUXO03J4L3N2K_hN0`_22ThN0M3NN^hNM`W12`hNNaW1450MO11]hNN[W14:NO0N3N2GFjhN:lV13QiNLoV16ohNKRW1a03IlhN]OTW14khN8WW1ImhN3QW1NUiNMkV13SiNMoV12QiNMQW14ohNJSW18:OLL5300O3I0dhNFcW155NgZ_>\"}, {\"size\": [1280, 720], \"counts\": \"iVQ9?_W16K4J6I6L4G:D;M1N2L4O100O1O10001O0O100000000000000O10O1000000O100O1O0N2N200N0O2O2O2N1O101N1O2O1O1M3N2L301O1O1O001N2N3L3O1O2N100O1O100O102N22L2M3OL4H8M2N2O1O3M6KKM45O0;MG12M]Rf?\"}, {\"size\": [1280, 720], \"counts\": \"bQP9750VW1m0@4CPOciNR1jU1iNYjNk1fU1[NXjNb1gU1_NYjNb1eU1=N3O0000O1000O010OO21O1OO1O2N10O010O01O1O100O1N2O1O1O1O1O1M3O010N1O2O1M2N3N2O1N1O101N2O0O2M3O1N2N2O1N101N2O100O1O1O1O2OO001001O0O01N200O1M200N2O2N1L5MWOXiN`0fV1@_iN;bV1DbiN2cV1EWiN42O0MmV14ZiNOmV12b00YhNM`W1Oo`c?\"}, {\"size\": [1280, 720], \"counts\": \"_ji8f0SW1<I4L4L3N3M2N3L3M3N2M2M4N1O2N1O2N1O1O1O2M2N2O1O1O1N3OO1N100O2O0O2O1O0O2N2L3L5H8G8N3O1N1N3M3M3L4N2M2O1O2N3M1O2N2O1O10O01O01N101O100O10O0ChhN4WW1:ROEXjN<cU1J\\\\jNE@9RV14^jN4`U1N`jN2^U10bjN0\\\\U12djNN[U14djNK\\\\U17cjNIZU1;fjNEXU1<gjNEWU1>hjNBVU1`0ijN@XU1`0ijN_OVU1c0ijN]OVU1d0hjN^OWU1c0fjN@XU1b0gjN_O[U1?ejN@]U1?cjNA^U1?`jNBaU1=^jNDiU15WjNKgU17XjNJgU17YjNHiU17VjNJPV1P1001O1O1N10001N2N2OO11O0N3N1O2O0O2N2O1O1O10ON2O2K5M2N3L4K5N3L3MV`[>\"}, {\"size\": [1280, 720], \"counts\": \"\\\\]g85aW1<C<K5M3J5M4J5M4K4L5L3K5N2L4K5N2N1O2L4M3O0O2N101O1O10000N2M3N2M2UMiLlPOY3So0nLgPOS3Wo0QMdPOQ3\\\\o0RMaPOo2^o0WMZPOk2go0YMPPOk2no0\\\\MmoNd2SP1ZLbnNl0Y1k2VP1_MeoNa2_P1cMYoNn1SOjLgQ1NYnNn0O]Oh0c2^OPMmQ1g1bnNk0VR1WO_mNg0mR1\\\\OolN<LjMWS1W4;M3K4O2N2O1M3M4L3O001N3L6iL\\\\kNj2hT1TMXkNk2nT1]MSkNS2oT1cMRkN31X2VU174K3eMajNQ2iU1O3L3K4M2M3^NiiNY1YV1fNhiNX1bV1K7J5L6K4K^oY`0\"}, {\"size\": [1280, 720], \"counts\": \"lVP9>WW1?I4fIQO\\\\UOU1cj0WOoTOl0Qk0B]TOb0]k0cNeoNR1e4>ek0LTTO6kk02kSO0Ul06dSOL\\\\l09]SOHcl0>VSOCkl0c0mRO_ORm0g0hROYOYm0j0aROYO_m0o0WROROim0b1aQO`N`n0g1coN\\\\L]1P2Po0g2loN_MUP1U5100O1000O1O1N22N1N2O00002N2N2N5J3N6I6K3aIanNW6gQ1M3M6J3L5L8Hg0QKilNg3bS155hK\\\\lNl3^T1]O=D=C`0_O6PNRjNc1aV1]N]iNQ1RW1E:Ggal`0\"}, {\"size\": [1280, 720], \"counts\": \"l_f96iW12L3N4N2LBehN<ZW16O4LHjhNGSW1e0N3M3M2O1O3L3N1O2N2N4M2M10001N2O1002NO2N1N2O0211NL2O2O101O1L3M3O02OO0001O01N3N11N1N12N2\\\\lNWNlP1j1nnN_NmP1b1PoNcNoP1_1jnNiNRQ1Z1hnNkNWQ1n3100O2M6K2N1O1M202M6J2cKglNQ4]S165K3N4K3M5K4M3L7J2N2N6J2N6K1N5K4L2O2M3M1O004K4M2N2N3L5L2M3N3M1N2O1N2M3N2N3M2M3N1O2L4N3KUSP>\"}, {\"size\": [1280, 720], \"counts\": \"Znh93iW16L4N1K4O3M3L2O1O2N2N2O1N3M2O2M20O01O2N101O00O11N110O1N21N2N0020N100N3N11OO00O2N1O2O1N010000O10000O1O10O10O002O01OO01000000001O0O2N1000O1000O10O010000000O10001O1O0O3N01N1O110O04L10O1O10000O1O2N8H216H6J2M0100O2N000O1O2O11OOO01O0O2N2N2N2O2N3M3L2N3L2O2N101O01O1O101N0001O0O100O101M4J6M2O1N2O1N3N4H5N2Gl]_<\"}, {\"size\": [1280, 720], \"counts\": \"`nm95hW16I5M3M3N1N3N3M2M201O1N1O2O1N2O1O1O01O020O0O01N30N2O001O1O00O10O101OO01O1O11O00001O2M2O1OO010O1O10O10O10O011N1O100O01O0000010O1O001O010O01O01000O1O100O10O0010O001O02N001O1O1O002OO010000O1O1O1O1O01O02N1O100O1000000001O000O0101OO010001O1O01O0O0010004K10002M100O100O12OO1N10001N010OO2O001000O01O1O000O101N100O10O2O2N01O010O01O0001O010O2N1O1M3M3N2M2M3^OihN9cW1ChnY;\"}, {\"size\": [1280, 720], \"counts\": \"T_U:=`W14M3M3L4M2O2N1O2N1N3O1N1O01100O1N100011OO0101OO0001000000O010O010000O0011O000O001O10O010000O0101NO1O2O11O0O001O1O0010O00001O000010O01O000000001N100001O0001N02O000001O00O2O01O1O0O2O1O0000001O1OO10010O01O00O11O000000001O001O00O20O00000O100010O01O010O001OO100O1N2O12N2OO1O0O100O20000O7JJ500O1O2N110010O0O11L3O0O1O1N21O1O0O1001O0O1N2N2O1000000010OO101O0O2OO01O10O1O12N1GbhNNXgi:\"}, {\"size\": [1280, 720], \"counts\": \"h^P:c0ZW14L4M2O1O1O2N101N2O00001O00001O001O1O0011O0O01O010O010O01000O0100O010O10O010O0002O000O01O01O01O000010N1000000O11O00000O10O1000010O00000000001O00000O101O000000000000001O0000000O2O00O100O1O1N10010O1O1000001N2N1O2O01O00O2O000O1O1O1O1O1O2O000000000O1N2N22N10O01O00000O1000000000001O000000002N1O1O100O0O2O1O1N3O2M3NWiNoNaV1Q1812K3M2N101O6J4M0O1N5K2O2MTP_;\"}, {\"size\": [1280, 720], \"counts\": \"jnT9c0XW1:lhNYON0]V1e1F4I:I8H3M3M3L4M2O1N3L1O2O101N1N3N2N1000ON2O11O1O3M2M5L3M2N5KO1001O5K2M5L2N1N4M3L2O4K2O3L3N4K2N5K2N4L3L5L3M4K4J6JXWe`0\"}, {\"size\": [1280, 720], \"counts\": \"Rll41oW19F101M2O2I6O1M3O1N3N100O100O10000O1010O00O100O1N3K42N2OO01O0001O001O001O00001N1001O0000000O10000O10O00001O0O01000M3O1O1O1O2N101M2N2O2N1M4L3O1L4M3M2M4L3L401O001M2O0O3M2M3O00O20N11001N2O01M4N100O2N1O1O2N2N2N3K4N2N2O1M3O2N100O1L4M4I6L4J6M4L4J6K5J9I5D^P[b0\"}, {\"size\": [1280, 720], \"counts\": \"oTa85`W1=QKAWROb0`m0IjmNE5Ok3i0Tn05fQONXn00mmNHW39kn0l0oPOUOPo0n0iPOWOVo0k0gPOWOYo0m0bPOTO^o0Q1\\\\POPOco0S1ZPOnNeo0W1UPOkNko0X1SnN_Mg1]1VP1X1ioNiNWP1V1ioNkNVP1[1eoNeNZP1]1coNeN]P1_1]oNcNcP1_1YoNcNhP1`1gnNnNXQ1T1enNmNZQ1W1cnNjN\\\\Q1]1]nNdNdQ1k31OO1O2N13M0O20O01O2N2M2O3M5dJemNi4iR10O07K1N2N3M2M5L4L2N3L9iKUlNh3[T1G<fLbkNY2cT1aMbkN\\\\2RU1M3N2kMZjNm1PV1L6J7I8H6K2M2N2N4K6K5Hgkm`0\"}, {\"size\": [1280, 720], \"counts\": \"ifX96fW18I4M3N3M2M3N2N2N3M2O1N2N2O1N3N2N0O4L2000O2O00001000O100O0O2N20O000001O0O2O2N1000OO010000O101O1OO2OO2OO10000O10001O0O100O01000O10000000011N01000O3N0O3N8H200N2NO10O100O00O3N1N101N100O1O1O1N101O010001000O1N1O5K100O10O01O1O2O1M101O010O001N10000O10100O000O3N1N2N2L4M6H8F`0D^]b=\"}, {\"size\": [1280, 720], \"counts\": \"WWe96eW17M2M3L4M3N2N2N1N2O2O1N101O1N01001O2OO011N0100O101OO010O100O10000O1O01000O20O1M2O10O1001O000O10000O101OO0100O1000O0001000O1O0010O100O01O01O0001O1O010O0100O1O01O010O001O1O010O100O100O1O001O10O01O2N100O02OO1O001O100O0001O10O01000O010000000O00001O1000O010O000010O1O11O2N1N01O00010O000010O1O2N010O1O1O0001OO1O2O00001O0O1000000O101O00001O00O10000000000001O0000O102M3N1N2N2O1N2O4J6I7FaVQ;\"}, {\"size\": [1280, 720], \"counts\": \"kW`9<aW14K5M3M2N3N1N3N1O1O101M2O2O0O2O1O1O1O1N10010O1O1N21OO0002O000O1O101O000O010O1O1O010O10O1O10O001O001000O1O01O10O01O010O01O01O1O010O000001O010O00000010O00001O0010OO2O1O00O11O001O000O110O00001O1O1O00010O0001O00100OO1O2O00010O001O0010O00001O00010O1O01O1O0O1O2O000010O10OO100010O01O000010O1OO1O101N1O20O1OO100O11O10O0010OO3N1O01O1ON3O0O20OO2O000O100O1001OO1N2O1O100001O01O0001O0000001OO100000O2OO2N100000O001O1O100O2O1O0O2O1O0O2N2O0O1O2L8F>^OUoQ:\"}, {\"size\": [1280, 720], \"counts\": \"nfi84bW1?H3M4N1N3N2N1O2N2N1O2O2N1O1O100O100O1O101OO1000O01010N10100O0O2O00000O11N1000000O101O0O1000O001001OO00100O100O11O00O1O2O00O010O001N2000O1004LM3O10O3NO11O01N2O100N0010O1O0101O0012M1N101O04M1N1O00O1O0aNeiNZ1[V1bNiiN^1[V11N22O1O00O0001O000000000001O1O001O000000000O1000O11O1O00O100O2OO01O010O2N101M2N3N2N3M7I:F:G2JZn_=\"}, {\"size\": [1280, 720], \"counts\": \"mbP53iW17I5N2N1O2M2N2O1O1O1O1O1N101O1O1O2N1N2L4O0O2O10000000010O0000000000000000000000000O1O100O1O1O00101N10O1O10O100O010O010O1OO10000001O10O10N2O1O1O1N3M1bMTNdlN0l1k1bQ1bNYnN_1gQ1fNQnN\\\\1nQ1jNmmNT1SR1POgmNS1[R1nNamNT1_R1nN_mNR1aR1oN^mNP1bR1SO[mNn0fR1XOSmNh0mR1\\\\OolNc0SS1CelN>[S1MYlN4fS1S21O1O1O0O101N2O0O2N2O1M106J3M3N4K4M3L3N6I011N3N2N3M4K7I6J8I9^NbiNo0jV1N5K4I`0@kgYb0\"}, {\"size\": [1280, 720], \"counts\": \"YVb34jW1201N2M2N2O3M5L1O1O3L2N20000O1O0N21000O5K1O10000O2N2O000O010O000001O01O0O1001O0001O01O1O002O1O1O1O1N102M010O1O10O11N4L2N1O2O2N1HhhNFWW18jhNIXW15fhNM\\\\W11bhN0aW1700O001N1101OO0100001N1000O1O0100O0O3M3N2N2M2O3M1O101N2O1N2O0O3M3L3N1M3M3N2M3M20001O0O1O1001N10O2O00001O2N000010O10N10001001O1N4L2O1N1O4MO10O3N0O3M2N10000O2M3M2N3N2M2M4K4M3N2M7J2L6IknZb0\"}, {\"size\": [1280, 720], \"counts\": \"ha\\\\77hW19F4M4K5K5L5J4N2[mNiN\\\\n0[1_QOiN_n0X1`QOiN^n0Z1_QOkN]n0W1_QOlN`n0U1`QOiN`n0[1^QObNcn0`1ZQOYNeL1nQ1j1XQOUNQMOdQ1n1WQOTNVMNbQ1P2VQOPNYM1bQ1Q2RQOkM^M2dQ1T2nPOhM`M2cQ1W2bQOiMZn0X2dQOjM^LOfQ1Y2PROjMnm0W2fQOfMlL5\\\\Q1W2gQOcMmL7ZQ1Y2kQObMgL4]Q1\\\\2jQO`MlL2YQ1`2URO_Mjm0e2cQOWM]M4PQ1f2SROZMlm0h2RROYMmm0i2RROVMnm0P3kQOQMUn0Q3hQOnLZn0U3eQOiLZn0[3VQOcLkM1mP1a3RQO_LQN0mP1d3^QO^Lbn0e3hPOYL[N3nP1g3dPOWL]N2oP1i3VPOWLcN35LRQ1l3RPOYLcN46HUQ1m3ooNTMPP1R3goNkKoNQ1ZQ1V3doNQM[P1Y3lnNbKL[1WQ1V5O1O2N0O1001003L2N01000O0100O1O1O1O100O3MO1002O1N2N1O1_JPoNPOJe4^Q1mK_oNBoN64W3^T1`L\\\\kNQ3TU1F4L3M8F<D=Dk0TO8F:E]h[a0\"}, {\"size\": [1280, 720], \"counts\": \"_fk79bW17K4N1N3N1O1N3M2O2N1O2N1N2O2N10001O0O10001000O001O01O001O1O0001O01O10O0010O010O00000100O1OO2O01O1O1O001O0001O0002N1O001O1N1000O2M200O2L3N2N2N3K4N3J5L4M4J5N2K4M4TLfMPRO\\\\2im0RNlQOS2Pn0RNlQOP2Pn0YNfQOl1Yn0[N^QOh1an0\\\\NXQOh1hn0XOioNW1WP1X32O0O001O1O1O2N0O1O2M4I6L4L3N4K4K5M3K5N2G9J6M3I7L4J7D<E:J5K7L4G9L3L5K4I8K4H:G7L5M3L5J6J7J7FUoP?\"}, {\"size\": [1280, 720], \"counts\": \"^VZ76eW16M5J4M2O2M2O1O1O1N3N1O1O101M2O2O1N1O1100O0000010O01O001`JgNXTOX1fk0kNYTOV1ek0SOTTOl0lk0XOPTOh0ok0]OmSOc0Sl0^OmSOa0Sl0BjSO?Ul0CjSO<Ul0GiSO9Vl0KgSO5Xl0NfSO3Zl0OdSO0\\\\l02cSON[l05cSOK]l07aSOJ^l09_SOH`l0:_SOE`l0=_SOCal0?^SO@al0e0\\\\SO[Ocl0g0[SOYOel0i0YSOWOgl0l0VSOTOjl0n0USOROjl0P1USOoNkl0S1TSOlNll0U1TSOjNkl0Z1TSOdNll0_1oROcNQm0a1gROcNXm0a1`ROdN`m0b1XRO`Nhm0d1RRO^Nnm0m1oPOfLkNb1VP1P2UPO]L[O`2`P1X1koNUOTP1\\\\40000O1000001O00000000000000000000000001N101N3N001N3[LjoN\\\\OVP1b0moN\\\\OUP1b0RPOVOQP1d0_POoLfNd1nP16XRO[Onm0?cROnNem0m0bROXNWn0a1Y4E=J2CQ`j`0\"}, {\"size\": [1280, 720], \"counts\": \"`^g61fW1;L3N4K3M3M3N1O2N2N2N2M2O2N10001N100001O01O001O0100O01O0001O1O0001O010O01O01O01O0O20O010OO011O002O0O1OO1hJkNeSOU1hk0GoSO:mk0JRTO6kk00STOOkk04STOMlk05RTOLnk06PTOJnk0;lSOHSl0<hSOFXl0<cSOG^l0;XSOMgl06SSOMll0:jROJUm0=VROdMaMX2YP1:nQONRn03lQONUn04eQOOYn08^QOM`n08noNQMg00SOj2XP1S1QPOWOno0^400O2O00000O10000O100O10O10O10O100O001O1N2N2M3K6I6L5I7SMknNeN^Q1W1gnN_L23OONNoQ1]3V2L4I9L3\\\\OZkNgMkT1P2h0K6J5M3L4J7L3I_iNlNdV1o0;J6H:Dhog`0\"}, {\"size\": [1280, 720], \"counts\": \"Zg^61jW17H7L5M1N3N1O2N2N1N3M2O2M2O1O2N100O100O2O2OO1O000001O01N11O01O1O000100O0000000100OO101O02M1010O00000010O2OO01O001000O0O1000010O00O1001O000O2M20000O1O001O1N3N100N2N2O1N1O2N2O1N2N2O1O1N2N2O0O10000O001N2O0010N1N2OO12OM31ON3O0O001O100O1O1O0O1[Oe0M41O1O1O00001M3M3N10002N2N1N2O1N3M2O1O2N1O2N2N2N1N2O2M4M1O1N3N2N2O1N101O1N2O001N2O1O001O1O1O1O011N2N101N2O1N2N2M4M2M4L4M4I9F]e`>\"}, {\"size\": [1280, 720], \"counts\": \"i^]67gW17I4K4M3N2N2M2N3M3N1O2N1O2N2N100010O1O01N101000O0001O01O0001O00010O000100O1O01O01O00010O00100O001O001O01O00001O01OO1O100cKdNTRO_1nm0bNlQO`1Un0aNgQOa1\\\\n0`N^QOb1dn0]NXQOf1in0\\\\NoPOi1So0]NbPOf1_o0[NYPOk1io0XNhoNChMY2aR1XN`oNP2bP1VNjnN[OaNf2eR1VN\\\\mN@OG2J2Q3aR1W22N2N00001O00O1N2O11O2N1O0O1000O10O10O02M3N1M3L4G9D=J5I6G:CdkNSM`T1f2`0K5I8I6M3A>EgiNlN^V1n0=K4M4J5L5N2K5N0M31O00N1O0O3O10O0gg30eV_`0\"}, {\"size\": [1280, 720], \"counts\": \"eQi232Nb_23k`MMmV14ghNO3OVW11hhNN33JLVW13WiN7dV1H_iN1@ORW1OeiN0]O0gV10UjNObU17S1M60KN]hN0bW171N1N11000O2M2O01ON32O0O0O1ON4O10iiN0]T10gkN0RT10o[11RdN0lkN1ST1OnkN1oS1NVlN1mU1OkM0[lN0dS10\\\\lN0aS11Y2OnW11ogN1002N\\\\jN0eU10dM0hlN0VS11klNOTS12llNNTS12^2O0001\\\\jNNYS11glNOYS11glNOYS11glNOZS10flN0ZS10flN0ZS10flN0eU10h]20YbM000QX10ogN00000oiN1oU10RNOmkN1TT1OkkN1VT1OjkN1WT1OgkN2YT1NgkN1TV1000O2YNM_kN3bT1M]kN4WV11]NKZkN5gT1KWkN5ZV110OaNKRkN7XV14M2kNCfjN?ZU1AdjN`0\\\\V13M3TOZOVjNl0kU1TORjNn0oU1ROoiNP1QV1POliNS1TV1oNfiNT1[V19O10O10000O1000O010O02O0000O010000O10000O100000hjNgNhS1Y1SlNXOaS1h0ZlN_OcS1c0ZlN_OfS1`0XlNDhS1<UlNFjS1;SlNHmS18PlNJRT16lkNJUT17gkNKYT16ekNL[T15`kNNaT13[kN0eT1e11O001O1O1O1N102N1O1O1O100O1O1O1O001O10O01O10O0001O1N2O2O0O0O013M0O2O2M2O1O001O1O1O1O010O2N1O1O100O1O1O1O010O2N1O001O1O1O1N2O101M2O1N2O1O1O2M2O1N2O1O3K4M5K3M2Lcjl`0\"}, {\"size\": [1280, 720], \"counts\": \"Pbe01aX10@0QX10]V10_aM0O0oW10RhN000liN0W\\\\20miN100O1O2O0001N2O000000000100N0100O2O0000O1010O01N01O2O00VjN0dS10\\\\lN0dS10\\\\lN0dS10\\\\lN0dS11[lNOfS11YlNOgS12XlNOgS10ZlN0eS10\\\\lN1dS1N\\\\lN1eS1O[lN0kU10kM0ZlN0fS10ZlN0fS11YlNOhS11WlNOiS12VlNNjS11WlNOiS10XlN1gS10XlNOjS10VlN0kS10TlN0kS10VlN0iS11T20RjNOmS10RlN0nS10RlN0nS11QlNOnS12QlNNST10lkN0RV100000UN0fkN0ZT11ekNO\\\\T10dkN0\\\\T10dkN0]T10bkN0WV1000[N0ZkN0[V10\\\\N0d11diNOhT10XkN0hT11WkNOkT10TkN0kT10UkN0kT11UkNOlT12QkNOPU12mjNOTU10kjN1TU10ljN0TU1OljN2UU1OhjN3ZU1LcjN5^U1J_jN9aU1HUjNNA:[V1GQjN4D3[V1IQjN5B3]V1HQjN5B3^V1HmiN8@4eV1DfiNk0ZV1UOeiNk0[V1UObiNn0^V1RO`iNP1`V1PO_iNR1aV1nN]iNS1dV15O00010O100O000O11O001O0010O1O01O100O10O01O00101N1O0100O10000O100O01001N0010O2OO0010000O000100O010O1N11O0011M2O01O102M10O10O010O2O0O1O0010000O01000O1O011O2M1O1O010giNdNmU1]1niNiNPV1d1O11@YjNcNgU1\\\\1ZjNdNfU1[1SjNfNNOPV1[1RjNlNoU1S1RjNcN30lU1\\\\1`0OeiNgNoU1Y1oiNjNQV1U1niNmNQV1S1QjNmNnU1Q1VjNmNiU1S1WjNmNjU1R1TjNoNLKiU1V1ZjNPOMKiU1T1[jNQOmU1o0TjNSOjU1l0TjNUOnU1l0jiNYO8CdU1c1ZjNbNdU1_1`jN^N^U1c1cjN^N[U1a1ejN_N]U1_1XjN_N71aU1`1XjN`N9M_U1f1ajN]N`U1a1_jNaNaU1_1^jNbNcU1^1YjNeNgU1[1XjNfNiU1X1]jNcNeU1Z1\\\\jNgNcU1Z1\\\\jNfNcU1^1ZjNbNfU1`1XjN`NjU1]1WjNcNiU1]1VjNdNkU1h1000O2O0N3O10OO2O1N101N2O100O002N1N101N2N1O2F^iNSOfV1j0[iNUOfV1l0\\\\iNTOaV1g0YiN[OoV1d0RiN]OnV1b0RiN_OnV1a0:O0N3M4K5M7HjbPa0\"}, {\"size\": [1280, 720], \"counts\": \"ehd11QX10cUj00[jUO000PX10Xhb00jWB0ngI000QX11ngN0002002N1N002M011N2NN2O5N5NM2ON1N000000111ON1O4QjNKkS16TlNKnS12QlN0oS1OPlN3oS1NQlN1oS11QlNMRT11nkNNQT13nkNNQT13okNNRT10nkN0ST10kkN0WT1OikN1YT1NUkN1UO4kV1OTiN2hU1IojN6XO2iU1HnjN8VO2lU1FkjN;XO0mU1FjjN;XO0mU1GijN:YOOnU1HhjNk0VU1UOljNl0RU1UOhjNR1WU1oNfjNT1YU1lNejNU1[U1lNdjNU1[U1kNejNU1[U1kNdjNX1[U1hNajNZ1bU1a0M5L3N30O002M1O2O0O011O1O011N1N1011N01O0O1O101N101N11O0000O0010000O2O000O2N100O100O1O011O0O10O2O3M0O01001N2O000O02O1ON3N11N1O1O20O0O2N2O002M;F1O001O2M2O01O10M2O10O2M101000O2N0100O1O001O1O2O0O001O2N1O001O1O1O001O1O00001N2O1O001N2O00001O001N2N3N1O2N1M2O4M1N2N4K3O4I6J9GfSSa0\"}, {\"size\": [1280, 720], \"counts\": \"deU61QX1000ZX60egI100`hb0Ocg_O0m_M0O1N2O3M2N3L3M3N1O2O1M2M2O2O2M6K2M3N2M3M4L2O2M3N3K4M2O3L3M4M3L5L4L1N3L4J6FkLdkNZ3ZT1gLakN^3_T13O1O020M211[mNjLYP1Y3_oNmLaP1Q3XoNWMjP1i2PoNZMPQ1k2hnNXMXQ1g2dnN^M^Q1[2kmNaLb0W1bQ1^2]nNdMdQ1Y2\\\\nNhMhQ1Q2ZnNQNiQ1k1lmNhLJa1]R1c1hmNfN[R1X1cmNjNaR1Y32NO1M26J2N2N3M2N3VKklNd4XS111O2M2O2M2N3N1N2N3M2O1PLUlNg3PT13N11O4L1O3N3L2N5J2O3N1N1O2M3N4L2N6J2M3M6K1O4K4L2N4K6K3L7I4L6H:\\\\OghN0XS_`0\"}, {\"size\": [1280, 720], \"counts\": \"TnW31oW10QX10_bM0PS10QmN1mR10SmN0nR10olN0TS1Obj30UbM0`cN0_lN0aS11]lN0cS1O\\\\lN0g[20UjN0miN0UT10kkN0TT10kkN0VT10m10kiN0UV10VN0j100000000010O0PX10bY10^^M0^N0TkN1kT1OUkN1kT1OUkN1kT1OUkN2jT1NVkN3iT1MWkN2jT1NVkN1lT1ORkN2QU1NljN1WU10ejN1[U1NejN3ZU1NfjN3ZU1MejN3\\\\U1LejN3[U1MejN3[U1NdjN2\\\\U1NdjN2]U1McjN3^U1N_jN3bU1M\\\\jN4eU1LYjN5hU1KWjN5iU1KVjN6jU1IWjN7iU1ITjN;lU1FTjN8nU1HQjN7oU1IPjN8PV1IniN8RV1IciNMK;bV1I[iN432cV18\\\\iNHdV19WiNLhV1f010O000010000O1O1O02O0O01N21N100O001000O1O100O001M201000000N1100O10O10001N01O10000000O11O00000O11N10O01000O01O0010O1O01O10O100O01O010O0010001O0O0O12O00O2O1O0O10O010O0001N1010N2O000002N5LO11O0O100O2O0O120M2N2N2O000O2N3OOO2N3N00O11M3OO2N0100O1O1O100O0100O010O1O0O5LO10100000O0O3N0O1O101O2N001O1N1O2O1N100O2O2M2O1O1N10001O2N1O1N3N1N2M3N3L3M3M4L6H7I\\\\eY?\"}, {\"size\": [1280, 720], \"counts\": \"cnf31oW100010N0QP:0PPF000QX10PhN0O000oW10QhN00000000010N010000001N10O101O01O00O10O1000000001O10O0O01000O100O100100O000000O100O1N3N1O2N10001M2kMCilN;_U1O210M[OK`iN6ZV1OeiN1[V1OeiN1PU1GgkN:YO0UV13kiNNSV13miNOQV12miN0PV12oiN0QV10liN3XU1\\\\OYkN_1iT1aNVkN_1jT1aNVkN_1jT1aNVkN^1kT1bNQkNb1oT1^NojNc1fU1101@[NbjNh1]U1XNbjNh1^U1[N_jNf1aU1]NZjNe1kU1WNRjNl1mU132O1O10N1O101O001O1O01N2O010O2M10010O10O10O10O1N11OO100O11O001O2OO10O1OO101O0100O002O00M3N11O1N101001M10000000O01O0101N010102M2M2O10O01O3_jNjMQU1b21MhjNdMPU1k2L2M200O0O1O0110OO01010O1N100000O010O1O10O0100O2N100O1O1N101N2M3N200O1O1N2O1N2O1N2N2N3N2M2O1O1O1N2O1O1O2N2M2O3M1N3M2M3N4K4L5J5K7J6_OdhN1m\\\\X?\"}, {\"size\": [1280, 720], \"counts\": \"PdX91fW1<\\\\hNHXW1b0O3A]OZiNf0aV1`0M3L5L4J3N3M3N2M2M3N3M3L3L4N3I6K5N3L3K5M3M3J6L5K4M3K5N2M3J6M3K4^JSKPXOP5Rm0>cNdJonN4j0[5XP1QKnnNa5TQ1e0O0N3QLUIgVOm6Xi0SIlVOh6Zm0L3MgK_IVWO_6jh0bIWWO[6Zm0M2NN1L5M33LPLdIaVOZ6bi0gI[VOW6hm0O[LmI_UOQ6gj0nIUUOR6Qk0kImTOn5\\\\k0RJbTOi5nn0J6G8E;J6G8A`0D<L4L4F:GUMVMSQO_2\\\\R1H8K5J6J5K5K6K4N3J6L4M2M3F9O2J402N101000O0100O1O10O1000O010O100000O0o_20PhN0RhN0O10O010O1000gg80X`H0icf=\"}, {\"size\": [1280, 720], \"counts\": \"Sg`82RX1O21NO11OOO11000M21OO0O2011N00O0O202L021OO1N20N2O1M3L4L5M3L4J5PlN]OWP1m0WoNH^P1d0lnN`NWNV1jR1>QnNf0]O`MUR1l1kmN[NN[2NeMUR1e5H7J3L4M1N1O2O100O001O0O1QK_I^XOc6_l00O100O101N10001cKXI`WOh6ll00fKXI\\\\WOh6ml01O100O1O1O2M2hKPI^WOR7fl0TInoNn5ok0lIXWO3[M1fN]5il0_JQWO2\\\\Nm4[n0SKPROe4]j0ZK_VO2^O]4Uj0`KYVO30R4hi0mKSVO0?k3bi0TLkUO0m0e3Zi0\\\\LbUO0f1T3kh0kL[UOOS2R3ch0oLWUOO`2h2\\\\h0YM^ZO^2fe0aMiTO0\\\\3S2Vj0mMUVOj1jg0WNPTOO_4c1fg0]NdSO1o4\\\\1ag0fNUSOMh5S1\\\\g0mNcQO34O35Y71eHOTaW`0\"}, {\"size\": [1280, 720], \"counts\": \"T__86iW1KahN0jW15UhNLoW10Qaa0Oh^^O1O1O3L01O2O0000N3L4K5M3N1K4M4M3O0O2N2N2N4L5hkNdNbQ1`1VnNgNhQ1Z1UnNjNhQ1W1TnNoNUm0^O^UOe1ZMQOiQ1R1SnNROXm0VOaUOi1UMTOWm0SOdUOk1RMUOWm0QOfUOS2hLPObm0PObUOS2hLoNfm0kNdUOZ2aLoNim0fNfUOb2YLkN_R1W1^mNkNSn0aNeUOi2RLiNXn0^NeUOm2fKWM2`1nR1\\\\3M2N1N2O2M3M3N1M2M6J4L7IUJZnNd5aQ1XJPoN`5gm0^JeTO3lM[5^P1cJloNY5Rm0fJbTO1bNY5ml0fJYTO1UOS5Qo0mJXQOP5dn0oJdQOn4Tl0RKcSO1c0h4kk0WK[SO3R1b4dP1KoJ`KmRO6S2Y4oj0aKjRO1b21XLV4^n0kKTRO3d3Q4^j0iKiQO3n3U4ao0I3M3L4L2M61OM2M4L2N4L4M1N3N2M2N4K3N3L2N5L2N2N4L3N2K5K5L8G7mNSiNg0VW1L9F5L5LeXS?\"}, {\"size\": [1280, 720], \"counts\": \"dPY:7gW14L7J9H3L4J7H8K4L4K4N2L3N4L4M3K3N4G8M4L3RnNUMbn0m2YQOXMen0k2WQOYMfn0o1dnNYN`2Mkn0g2QQO[Mon0g2mPO[MSo0f2jPO]MTo0c1knNaM4?k1`0So0a1`oNaM_O0k1Q1Wo0^1iQOdNWn0]1XoN_MO0\\\\1V1[o0JjnN>o2IWn0EnnN`0k2NVn0@QoN_OMe0k2?Vn0\\\\OToN_OIg0l2?Yn0YOYoN;_2<Wn0mNSoNGNNOU1h2;im0bN]oN<:BG_1i21gm0VOPPOi0Y22fm0VOmoNj0]21gm0VOioN]OEQ1i1kNVOc1`o0TOooN[ODP1i1POROb1`o0bNboN7i0c0U1TOoN`1`o0`NgoN6f0KYO:P2JeN[1io0\\\\NboN;o08Q1HbN[1mo0^NboN0n0?Q1J`NZ1lo0lNRPOD^O?W2H\\\\NY1eo0YNjoNZ2V2WNYNV1do0j0SROTNUNS1io0g0RROZNoMQ1jo0VNTPO\\\\2S2bNhMn0YP1`0PROa0Pn0]OQROc0om0XMioNh1\\\\2P1om0ROSROo0nm0lNVROT1gm0kN]ROV1]m0mNeROS1[m0]NUPOfNa2m2bm0YMgoNHk3o2oP1O110O10O3M1O1O00001O0010O000010O0O3O2NO100N2O1N3O1O0O2O00002M2FojN_MUU1_28N2O1O10O10O0N4M1O2N2M3N2N2N3L4M2M4L3M2N3M4L2N7I4C>ElQV=\"}, {\"size\": [1280, 720], \"counts\": \"nXV71mW15L3M2L4D=ROCniNf0hU1n0M3M4M1N3L4M3J6N2N3L3O000O2O00001O00001O00001O000000000000000001O00001O01O01O1O2N1O1O2N3M1O6K0O3M1O101O1N2N3M3N3L4L3M3M3M2N2N5J3M3L5L3M4K5I6J6N1O2Lj^ha0\"}, {\"size\": [1280, 720], \"counts\": \"Pbk71PP50PX10noJ01000QP50ooJ0O1aMOnlN2`U10O100O100O1WNKgkN5oU14_NGUkN=mT1DhjNe0YU1ZOfjNf0\\\\U1ZO^jN5XO>]V1]OXjNj0iU1VOmiN3Jg0YV1VOiiN7Md0[V1UOeiNY1]V1gN`iNZ1bV112O0O10O10O0100N2O100O10O00010O10O10O100000O1SkNTOfR1j0UmN]OjR1c0PmNDnR1<olNIPS17llNMTS14ilNOVS11glN1ZS1OclN3]S1NalN3`S1M^lN3dS1M[lN3eS1NYlN3hS1LXlN5gS1LXlN4iS1KVlN6jS1JVlN6kS1JTlN5nS1JSlN5nS1KPlN6PT1JPlN5RT1KlkN6TT1KkkN5VT1KikN5WT1LhkN4YT1LekN5[T1KekN5\\\\T1KckN5^T1L`kN4`T1M^kN3dT1MZkN4fT10ZkNLgT1f100001O1O1O001O0O2O000O2O1O001O1N2O1O2M4M2N1N101O1O001O2N2N001O1O001O0O100O100O101O1N10001O1O1O00010N100000001O1N100O101O000O2O001O00002M2O001N2N2O1N2O0O2N2N2N2N2M4L4L3M5J9FTlY=\"}, {\"size\": [1280, 720], \"counts\": \"ePT:84OVW1a0^Oc0]lNmNSP1W1goNmNWP1V1boNnN]P1X1ZoNmNcP1[1PoNlNgP1VO\\\\mNQ2g1nNmP1Z1knNnNSQ1T1hnNPOVQ1P1jnNSOTQ1m0mnNSORQ1m0onNTOPQ1l0PoNTOoP1n0onNSOQQ1g0amNkM_1^1oP16bmN^N4Nb1^1fP17hmNXNm1a1]P16hoNKXP1OdmNbNR2_1[P1JPPO5TP1FnoN:TP1CmoN=TP1CkoN=UP1CkoN=VP1AkoN>WP1AioN?YP1YOWmNYO`2^1WP1@joNa0UP1]OmoNc0QP1^OPPOb0eo0G]PO9[o0NfPO2\\\\o0IgPO7Zo0FhPO9Zo0DhPO=Yo0_OhPOb0Yo0\\\\OhPOe0Vo0[OkPOd0Ro0_OoPOa0on0[OWQOe0gn0[O[QOe0en0UOaQOk0`n0SOaQOm0an0nNbQOR1_n0hNeQOY1aR10O2OO1O10O1N200O1000O1000O11O1O1OO1O11O001O001OO1O010O100001O0000000001O000O11O0000000O10O10000001O0O101O001N2O1N2N2O1O1N2O1O2M2N2N2O1M3M3N3M2N3K7HQlY=\"}, {\"size\": [1280, 720], \"counts\": \"SVe95hW14N1N3M3K5M3M2M4L3L5H7L4O1O1O2N11O000001O000001N100001O00000100O2N00O1O2O01O1O00001O1O000001O00001O0000000O2N0O2M4M200O100O1O1O1O100O101N1000001O000000000000001O000O1001N1000O10001O01O002N2N1O1O1O1O1O2N3L4M2M4M3L3N2N3L3M4L2N3M6I5KPjm=\"}, {\"size\": [1280, 720], \"counts\": \"^fX93dW1:L4N2N2N2N10001O0000000O10001O0O101O00001O0O101O001O001O1N10001O00001O00001N1010OO1N3N1O2O00001N11O01O00000000000O2O00001M2N3N2O0O1010O01N10001N10001O001O00001O0O10001O001O0001OO1010N101O1O2N1O2N1O1O2M2O001O1O1O1O1N2O1N2N3M2O1N2M4M2N3L4L5K3M9D]aQ>\"}, {\"size\": [1280, 720], \"counts\": \"Uaj03`0OhV11d01XhN39KdV1c0WiN]OhV1l011ViNYO0JUV1o0eiNTO74PV1LjiN:2M30RV1FkiN5M0254ObV1=ciN[O\\\\V19[iN17E\\\\V1i0ciN[O[V14`iN41M_V1c0]iN_OdV1n001N1O1NBbiN^OUV1j0VjNlN\\\\U10^jNV17iNXU12]jN\\\\1=`NUU15^jN[17cN^U1f1cjNXN^U1m181O2NUNSjN^10eNjU1HYjNj1RV1L4N02N2N1OO20N4M1N11OO100O1O1M3N2O2E;O06J0OO2N2O100K61N1N11O1O00O10O2jjNZMmT1n2O20010L01L4O5M2M02N2N2000OAVMbkNQ3\\\\T1QMakNR3]T1PMbkNQ3]T1PMakNT3\\\\T19O0O00101O00O001O20000O1O1O4L1O2N1N2N30OO1O1O2M3O2N003L2O2N2M2O3M1O2M2O1N2M3N2N1O2O1O1N101N00100O1O010O010O010O10O010O010O10O10O1O02O0O011O0O10O10O10000O100O10O0101O00O00100000000O100O2O0O1O0100O000010O1O2N3NO1O0O101O0100O1000O10O1O10001OO0100O5L001O3L2O2M2O1O0O100O100O11O6J<C100O4M000O3M104K2N10O010000O1O1000O000O20O01O0010O1O100O2O01N001N1O2O0010OO1O1O20O01O001O10O01O0O2O2N1O1O1O00001O0000000001O0001O01O0000O010001O000O10000O2O1N2N2O0O2N2N2N3M2N3N1M3N3L5L4K7FTbo>\"}, {\"size\": [1280, 720], \"counts\": \"mia51kW19I5]OBXiNf0dV1>N2M4M1ciNeNRV1i1L4L1M3M3O1O1N3N3M010O0O2O0000100L4N2N2M3K50000N2J6N2O13MO1L4OhL^kNR3bT1lLakNT3eT11HlLbkN3Nj2^T1]MdkNc2ZT1^MfkNb2ZT1^MfkNc2TT1UMlkN30i2RT1bMnkN^2RT1cMlkN_2RT1PMnkNc3QT1eLQlNI0P3oS1SMUlNLMP3mS1PMQlN03O0R3kS1oLRlN21M2T3hS1nLUlN11L3V3fS1VMVlNF3U3gS1oL[lNMMV3eS1mL`lNLKX3dS1nLklNP3TS1kL]lNL>5@l2dS1SMPmNX3QS1gLflNMI2NT3aS1nLhlNLI3MT3`S1nL`lNN:_3SS1cLblN09`3RS1iLhlNB0f3WS1cLklNMJc3YS1aLjlNn3QS1f0K6J2N3MVM\\\\mN:aR1DhmN9UR1@TnN`0kQ1]O^nN?_Q1_OfnN`0YQ1\\\\OnnNc0SQ1WOQoNj0QQ1oNRoNS1VQ1aNnnN]1QQ1`NSoN`1lP1\\\\NcoN[1RP1kMmmNb0[2a1go0mMomN>k2Y1To0XNRnN9R3`1So0`NQQO^1mn0cNWQO[1Wn0ZN\\\\nNd0_3T1Qn0WNanNe0b3R1Sn0[NYnN:f3[1Qn0XN\\\\nN<d3[1om0[N\\\\nN9h3\\\\1jm0\\\\N]nN5l3a1em0ZN`nN0P4h1^m0VNmROk1cQ122OO0O1N1O1O4M1O1M3N2N2O0O1O00101N101N1O1O2N100N3N1O2O0O1O1O2N1O2N0O2O1O100O1O1O100O11O1O1O1O00010O00O01O2N2O1N1O1O1O2N2N1O2O00000O2N1N2N2N2N3M3N2M2N2N2N2O2N1N3M3M4M2M4L3L4L6J5K5K6EUXX?\"}, {\"size\": [1280, 720], \"counts\": \"e9c2]U101O01N1O010O2N1000010N2M21O10O0O1O12O1N005L0ON2M3O21N2N2N3N0O001O000001O0002N3M3M3M1ON2M4N11O2M2O3M4K010000O11PNTjNj1mU140O01K6NTNXjNb1hU1:10000TNXjN`1hU1_NYjNa1gU1<2WNUjNY1PV1bNPjN^1XV110O201NO2M2010O2O0O6K1ON1O2O22M0OL5N20O3L20N2N_NhiN]1XV161N01O2K[NmiNd1WV11O2N20OO001O1O010O01O2O2M10O0010O2NO6K11021L01O0O2O0O0110O2N1O001O00O12ON100O10000013MO0O1001O2L2O1O10010N2N1001O0010O02N10O1N00011N1O2O1N2O00000O11O01O1N0100O2O0010O0O0100000O1O1O102O0OO2O0O010O100O11O1O1O000O01001M2O2O000O100O100O2N2M2M30O2O1YMPkN[2oT1eMPkN]2TU16J6O2MWMUkN_2QU1XMRkNi2RU10KWMTkNh2kT1YMVkNg2iT1YMWkNh2oT11M40O6J1ON2OZMojN_2ZU1O004L000O102N1OO10O10AiMojN1MU2TU1kMljN3OQ2XU1>1aMfjNU2ZU1mMejNS2YU1VNijNa1WU1[NmjNe1TU1UNmjNn1WU1nMijN5Jc1_U1VNijN6Hc1lU1[NTjNf1mU1YNSjNf1UV1NFYNajNb1hU1bNkiN_1TV16oNWN\\\\kN2Mh1cT1VNRlNk1nS1VN]kN4Ni1fT1TN[kN6Ke1lT1WNPkN00V2RU1SNmjNk1UU1]OjjNaN2`0XU1_OgjNm0OSO=8ED[U14cjNl0m0ROjT1R1S1POViNb0YW1EYQXa0\"}, {\"size\": [1280, 720], \"counts\": \"c9<7FTW1:lhNFPW13khN6YW1LjhN4SW1NjhN4UW1:nhN@0L^V1f0`iNA1J^V1\\\\1N_OfiN]OYV1V11N3NN1O110O11OO0000100O000O20O100O00O11O1O00O1O2O03M0010O1M3M201O2N1N2O0010O100O10O0O1N1111OO1N00012N1N2N1O10001I74K01O0J5N32O2NM2O0011000L4M2O2N2N3N10O10M3N1001O00M3O1O2L5LnL_kNk2aT1:3M0N4K3L12N202MjL]kNS3gT11N2M21O3M1O010O1O001O001O4L3L3N0001O02N2N011N2N7J2M01O1O02O00001O1O6J4ML3O20O00O1O2LmMZjNQ2kU11N2O2OO01N3M100000002N2O1M2O2NO2O001N201N1O001O01O03M10O0002N1O2OO1N21N10N2O1N3N1O1O5L00O01O3NOO1O10010O1O00O10O10001O10O01O01O01O1O001O0001O00000O2O0001N11O01O1N101O0O10001O001O0O1O2N1O2O0N2O1O1O1O1O1M3O2N0O2N1O2O1N2N1O2N1O1O100O1O10O11N10O01N1101OO1N2O10O3NK51OO001OiK`lNo3aS1RL]lNo3fS140OM30mK]lNj3cS1_L_lNC:P3ZS1XMWmNa2kR1VMZlN2o0a2kR1`M]mN[2eR1eM^mNX2WT1M2N4L2N1N4G:F:DoPZ`0\"}, {\"size\": [1280, 720], \"counts\": \"hR_7=_W19I8J4M0N2N2N2N4J7J8liNWNdU1m1XjNTNgU1S2O1N20001N101O2N1O0O110O1O001N1001N1001N10100O0N3N3N01000M4M3N101N1M3O0O2O0O102O0O2N22MM3O2N10000N2O101O2N1M3M3N200N1N4L4M4N2L4L5M1NeLklNk1SS1QNUmNo1iR1jM^mNV2aR1fMdmNZ2[R1bMjmN_2UR1\\\\MomNi2mQ1RMWnNR3kQ1`L`nNb3nR1102N00003MO020M022NO2010M01N10O11OO022MM3N22ON010001OO3N11NN13`LokNj2QT1mL`lNk2]T1N2M4M100O01OXMljNc2VU1201002M10000O1O1O1O001O3M1O001O0000O10O2O1N1O010O100O0100001N2N1O2N3M3M3M3M2M3M7I3L4L5QOSiN`0k]Q>\"}, {\"size\": [1280, 720], \"counts\": \"em\\\\52lW14C<O4M0J[OPiN6L8RW1DQiNe0lV1_OTiN?kV1BTiN>lV1<0oNUiNk0QW1O0010OO101O01O0N2N2O1I7O1000000OWOZiN:fV1E[iN;eV1GYiN9hV1H\\\\iN2dV1M]iN4bV1IbiNGJ9cV1OdiNFM9eV1K`iN<aV1D^iN;dV1B^iN5C0`W1NchN1_W1KchN5\\\\W1NbhN2mV1M\\\\iN8HFkV10]iN`0[V1AfiNf0ZV1\\\\OdiNd0jV12J@TiN8mV1BPiN409WW1EjhN:VW1FlhN9SW1GkhN;RW1ASiN`0bV1BhiN?UV1FgiNKG8bV1NdiNKM6^V11hiN5YV1MeiN1]V1O[iNF1<bV1M_iNIM1O1eV15_iN0OKaV15`iNI54YV12RjN2nU1KjiNKH0O0aV13hiN01JXV14hiN4gV1JYiN:TW1H`hN30LYW12fhN21MYW18hhNHYW16hhNKZW1:4JBG\\\\iN:_V1N]iN2dV1N\\\\iNOgV13WiNOmV1KPiNMM6kV1LTiN228jV1IRiNO47OGdV1;[iNO1FeV19\\\\iN00DhV1:XiNNNKnV15TiN4PW1JQiN7oV1HRiN8oV1>1OMNO3SiNVOgV1h0ZiNXOfV1i092MM3N2010OHlhNESW1=khNCUW13lhN6TW1JlhN6TW1;0CnhNLSW13mhNLXW1OhhN0eW1O6NM2N0O1M34K2NmW10QhN52ONO00I0]hN1cW152GjRn02XmQO7K4M3M6K2N2N3M3M2N2N2N2N2M3O2M2M3M5K6J6J4L3N3L4L2O1N3N2N2N2N2N2O2N1O2N1O1N101O001O0O2O000O21N1O0000002N2N2N2M3N1O2M2O1N2O1N10002M100O2N2N1O1O2N1O1O1O2N1N2O1O1N101N2N2N2N2N1O2M2K6O1N101N101N1O4G:AQVW=\"}, {\"size\": [1280, 720], \"counts\": \"ceR77iW11O2M1O001N3M4M001O011O2L1H80100LUOWiNh0iV1UOXiNo0jV13N1N1I7O1L3OhNfiNn0\\\\V1:1O0OOL503O00N0L4100O3N001O0O2L4OM2O10010M42L1N20M4M4]iNgN[V1`100O1M2O2N2N1O2O000O100000O101N1O1O101N1000001O1N2O001O001O001O0000001N2O1O000001O0001N100000001O0001O0001O00001O000010O00001O0010O0100O1O10000O010000001O0O2O2N1O1N3N3M2N1N8I2N1O8H1O10O100O0002O0O2O1O0O1O20001M101O3MN31NO10001L1N4K5F9J7H8I6J6K5M4L3M3M3M3M3L5Ko`l=\"}, {\"size\": [1280, 720], \"counts\": \"_YU:2fW1?H5K5biNCPU1b0cjN3mS1\\\\OkkNJKc2kS1_1D8M2K4L7J6K2N3O2M2O000001O01O1N2O0O3M3M1O3L6I5L4L3O1O1N3L3O3O0N2O1N3M2O0106J1WIknNc6ZQ1015K5MM;F8PJWnNC3X5bR1oJfmN^4\\\\S1[K]lNm3lS10N3M3M12O0O5MN4M0004L09F5J4N0O0O100O1002OO1N12N00O0101N2O2Me0\\\\O2N4K003M3M1O001N101N1O1O010O2N100O1O1O000O101O0O10000O100O2O0O1000001N1O1O2N3N3L5L4K5K6I5K9F7I:A<DQnf<\"}], \"areas\": [488, 457, 868, 472, 340, 237, 288, 0, 0, 1948, 1907, 788, 0, 0, 0, 1370, 675, 2868, 2010, 1, 1803, 439, 2981, 2495, 1853, 1321, 1665, 1804, 2687, 2962, 3720, 2347, 6734, 6707, 5071, 3695, 3055, 2097, 2777, 3685, 3136, 3759, 342, 730, 2419, 4285, 4698, 2273, 863, 2579, 4524, 4410, 5109, 3240, 2610, 18, 5083, 5042, 4518, 4685, 499, 209, 3348, 4743, 5464, 5524, 775, 2984, 4253, 2991, 4721, 7213, 5737, 381, 5928, 3646, 3975, 5709, 7890, 6141, 2914, 4773, 3902, 4299, 6069, 7428, 3103, 5133, 4082, 3599, 4420, 6050, 4240, 7155, 5621, 4226, 5335, 3406, 4089, 4143, 6446, 7284, 9006, 8523, 6918, 6841, 6118, 6726, 4610, 6851, 8286, 7775, 7276, 8659, 6943, 7738, 6148, 11148, 11215, 12328, 12499, 10276, 8922, 8124, 7711, 9322, 10587, 9744, 11226, 10730, 9709, 9066, 11111, 6040, 9992, 7627, 6142, 5610, 19741, 16975, 20083, 25532, 16162, 9979, 9398, 18362]}, {\"video_id\": 0, \"category_id\": 3802, \"bboxes\": [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [258, 552, 53, 55], [261, 563, 54, 60], [250, 556, 43, 58], [237, 559, 29, 63], [203, 610, 39, 34], [63, 441, 80, 56], [0, 397, 186, 198], [5, 512, 72, 34], [55, 469, 60, 38], [150, 432, 69, 47], [176, 610, 52, 76], [175, 510, 62, 75], [135, 224, 43, 79], [120, 106, 43, 73], [109, 76, 49, 75], [122, 64, 53, 87], [136, 140, 61, 107], [191, 428, 40, 107], [200, 615, 56, 51], [218, 535, 51, 62], [213, 642, 49, 59], [250, 711, 43, 48], [310, 666, 34, 59], [196, 604, 62, 52], [146, 504, 76, 80], [83, 568, 81, 65], [128, 597, 76, 72], [231, 654, 47, 74], [313, 612, 40, 61], [311, 490, 48, 73], [274, 492, 62, 60], [215, 428, 71, 60], [231, 295, 59, 88], [297, 304, 38, 77], [364, 417, 56, 42], [378, 435, 55, 47], [365, 439, 56, 49], [321, 487, 69, 75], [228, 405, 48, 78], [142, 349, 56, 76], [156, 320, 63, 78], [300, 408, 88, 69], [256, 415, 68, 85], [216, 350, 74, 83], [258, 259, 46, 58], [205, 241, 61, 50], [226, 257, 55, 42], [356, 222, 53, 63], [338, 229, 55, 90], [327, 229, 51, 84], [306, 234, 52, 48], [225, 237, 53, 44], [152, 237, 57, 49], [243, 239, 43, 38], [353, 235, 14, 58], [325, 196, 46, 94], [329, 180, 52, 79], [264, 225, 78, 42], [138, 188, 69, 73], [173, 300, 20, 32], [247, 224, 52, 36], [334, 222, 66, 67], [327, 212, 58, 83], [325, 246, 73, 46], [260, 253, 61, 40], [179, 260, 53, 53], [192, 227, 64, 59], [332, 211, 35, 60], [0, 0, 0, 0], [318, 239, 49, 80], [193, 336, 81, 65], [128, 535, 85, 40], [15, 418, 88, 45], [20, 381, 46, 42], [49, 426, 65, 41], [93, 273, 80, 110], [274, 298, 41, 52], [274, 414, 42, 52], [208, 380, 71, 59], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [224, 554, 48, 84], [223, 670, 41, 31], [230, 740, 51, 22], [299, 670, 62, 47], [0, 0, 0, 0], [10, 300, 55, 54], [20, 340, 47, 49], [0, 374, 49, 49], [256, 572, 18, 3], [28, 590, 128, 46], [60, 522, 88, 44], [333, 580, 37, 41], [392, 543, 46, 44], [0, 329, 20, 45], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [2, 304, 111, 91], [2, 295, 139, 96], [190, 316, 88, 110], [135, 409, 126, 59], [175, 383, 118, 72], [260, 232, 52, 154]], \"score\": 0.7968254089355469, \"segmentations\": [{\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"dQS:4kW13M6J7F5N4L3N3L3M3M3N1O2N1O2N1O100O100O0100O001O2N1O1O001O1O1O00001N101O1O1N2O001O001N2O1N2O1N2N2M4M2M4K7Hj]m?\"}, {\"size\": [1280, 720], \"counts\": \"RjV:5iW15J6L2O3G8M2M4N2N3N1N3N1O1N10000O100O1O010O1O1O01000000O1O1O1O1N101N2N101O0O1O1O2N101N10001O001N2M3M3M3N4J8I]]h?\"}, {\"size\": [1280, 720], \"counts\": \"iQi94jW17I5J7L2M3M3L3M4N1O2N1O100O101O001O1O00001N100O01O01OO101O0000000000000O101N2N2L7A`0F9Ilmc`0\"}, {\"size\": [1280, 720], \"counts\": \"hiX99;IoV1n0E;E8H4L2N2N1O2M20O101O001O1O2M2O2N2N2N2M5K6J4L3L3M5K6I7H]eea0\"}, {\"size\": [1280, 720], \"counts\": \"R[n7271T`2Nlo40igI000PX10ogN0000000O0100000000000O10O10O0i_70V`H0jg31hl_b0\"}, {\"size\": [1280, 720], \"counts\": \"nU_2180^W1a0GAUiN1dV1Mi00O001RiNOlU11WjNOgU11ZjNOeU11[jNOfU10U10ViN0dU10V10WiN0jf30o_M1O01000hNO_jN2hV101NgN0bjN1gV10eNOejN1\\\\U1OcjN1fV10hNOajN1^U10bjN0_U1ObjN0gV10hN0ajN0gV1000iN0^jN0jV10hN0_jN0aU10^jN0iV10jN0\\\\jN0eU10ZjN0kV10QX10ogN001O00001PONQjN1oV1010O0POORjN0nV11000O10O1WOOdiN0WW10UO0diN0VW100000010O000000000000000000i_20mP]f0\"}, {\"size\": [1280, 720], \"counts\": \"^<_1aV10010OO2N2O0001O00O01010O001O0001O10OO2O1N11000O0000100O101N10O0011O00000O1O010000O01N110O100N1N400O2M00002N2N1OO1002M3O0O6I1O207H10gSe2K\\\\lZM2OOoW12PhN1[hNL[W16chNL[W16chNK\\\\W1<2O1O1OO1OO22FBmhN`02_OeV1l0XiNVOkV1h0:ONO1KlhN@QW1?ohNBPW1f01O11OHnhNBWW18khNGPW1g00N21NWOohNg0nV1ZORiN=OGQW1e0100O1IohN_OPW1OohN=[W14O3NO3I4J^hNM`W16`hNK^W15chNK]W15chNK^W14bhNL_W1:O01FahN0cW153L3NSfid0\"}, {\"size\": [1280, 720], \"counts\": \"Uh66jW1;D3M3N2M3M101N2O1O001O000000001O00000000O110O000O100000001O0000000001N10O1O1O10O020O0001O00000001O00000100N5L4L001_OghN31KXW1;ihNEWW19khNGUW17mhNITW19ihNG_W1443IN_hNO_W12dhNL\\\\W14ehNI]W185O1O100OcoQi0\"}, {\"size\": [1280, 720], \"counts\": \"lVU29dW1<F2O1N1O2N110O1N2O001O1O1O1O001O01N11O1O01O0O10001O00001O1O001O1O0010O02O0O1O1O1O1N2O1O00100O100O0O2O0AghN6bW10GKghN6VW1NghN4VW1NihN3XW1MghN2[W1MdhN2^W1MehN1\\\\W1MSabg0\"}, {\"size\": [1280, 720], \"counts\": \"imk57eW18J3N4L1O2O1O2O0O1O1O1O100N101N20O1000O01O010O10O01O1O2N1O0000100O1O2OO010O01O001O1O0001O0010O0010O010OO1001O001O0000O2O0JhhNDYW1`01N30O1O01N2M;B`a`c0\"}, {\"size\": [1280, 720], \"counts\": \"_dl68eW16K6J6K5J3N2M4M2N2N2N2O0O2N1O1O1O01000O1O010O100O0010O01O0O101O00001O01N00101N2N1L00>D5M1M4L4L3N3M9E4M4NYTUc0\"}, {\"size\": [1280, 720], \"counts\": \"fXk66iW13YOIaiN;ZV1GeiN;[V1GXiNON;kV1a01002M6J1O1O1013N2K6I4L32M3M1O3L3OO1O3M7I10N3L3O2O1O0N2N4MO2O1NO2O1010O01O1O00113L1L1010bNciNY1cV1O1OO1M31O0J`iNlN`V1OaiN5N8=AZV1i04WO\\\\iN0Mg0SW1^OlhN6aW1L4O3L\\\\nib0\"}, {\"size\": [1280, 720], \"counts\": \"ToX5`0^W18I3M2O1O1O1O2N2N3M4L2N2N3M2N2O1N1O2OO1000O10O200010OO10002N3MO02N1N1]Od0M4M4M4J3N3M1N4LXoSe0\"}, {\"size\": [1280, 720], \"counts\": \"`Sf4c0[W19H5K4M2M3N2N1N3N1O1O2N1O1O10O01O1O1O1O011N0011O1O012M4M1N2^OeiNYO^V1c0kiNUOYV1h0a01WiNVOXV1f0f0J4N1O1O2N2N2N2N3LSkfe0\"}, {\"size\": [1280, 720], \"counts\": \"eZX48fW16J5M2M5K3M3M5L1O3L2O2N2N1O3M002N2N101N1O1O2O001N1O1O101N2N1O2OHWjNXNhU1P20L5M2N3N10100O1N4MkiN[N_V1V1<K3I7J8H5L1OjSme0\"}, {\"size\": [1280, 720], \"counts\": \"Tbh49eW17J3N4L3M3L4N1N3M4L2N2N2O2M3M2N3N0O2O0O2O0O2O001N2O1N2O001N1O2N101N1O1O01O001N101N2O1N101O002N2N1O4L4Lo0oN=CjkWe0\"}, {\"size\": [1280, 720], \"counts\": \"_TZ5a0]W18J5J3N3M2N1O2N1O2N2O1N2N102M1O2N3M3N1N4L2N3N0O3N1N1O2O0O100O100O101N101N2O1N101N1O11O0O2O1O010N3L4J6L5K4M6H7J4I7F;L3L5K9D^Y\\\\d0\"}, {\"size\": [1280, 720], \"counts\": \"oU_732OYW1OohN0N3kV14RiNJ:0eV10fiN3UW1O02010L04WNLbkN1YT10jkNORT17kkNJRT1;kkNFRT14\\\\jNJf10nS14[lNLdS12`lNN\\\\S18clNIWS17llNMnR13SmNMRS1OQmNKPS12fjNNZ2OTS13mlNNRS15klNJTS16llNKiM0VU14RmN0nR11a221N2L2O01ONRX10PhN1O0N1ON476IL0l`Qc0\"}, {\"size\": [1280, 720], \"counts\": \"ocj7?ZW1:J4M4L3N2N2N2O1N1O1O101O0O101O000O11N100O10000O01000O01O0100O1O2O0O2N1O3N2M100000O0001O00108G3N1N2N5K4L2O0N101OTTRb0\"}, {\"size\": [1280, 720], \"counts\": \"aQa8`0^W18H5K3L5L5K3M3N2M1N3O2O1N1N2O2O00001O0001O00001O00001O001O2N1N2O001N2O001O1O0O2O002L3K5N1O2O002M1O3K7EihNChUca0\"}, {\"size\": [1280, 720], \"counts\": \"UmZ8426XW1`0H7K3M3N1N2O100O100O1O100O1000O010000O100O1O1O1O10O1O10O0010O1OLaNiiN^1XV141O4L4M3K4M4K2O0N3M7G5M2NO1029Cbcja0\"}, {\"size\": [1280, 720], \"counts\": \"UWi9=`W15M2N2L4F9L5L3N2O1N2O10000000O1000000000001N10001O00O10O1000000O2O0O1O2M3N4L2N3N2M7H6GXic`0\"}, {\"size\": [1280, 720], \"counts\": \"dUT<e0SW1>B9N2K5L4M201O01O3N0O0001O0000O1O2NO2N1002N2O4K4M3L6J3M3N3L3M3M2N3LXRd>\"}, {\"size\": [1280, 720], \"counts\": \"]ce74fW18MEjhN0SW10PiN0mV10UiN1hV1Oe00hhN0`V10biN0\\\\V10j0000nhN0TV10miN1QV1OQjN1lU1OWjN0nV10O0O010O0QX10cV10jZL0TU10ljN0cV10O0`N0PkN1oT1OQkN1oT1OQkN1oT1OQkN1oT10PkN0PU10PkN0QU1OojN0RU10_100000^iN0TU11kjNOVU10jjN0VU10]10\\\\iN0XU11[1O]iN0XU10hjN1WU1OkjN0TU10kjN0UU11kjNOUU11kjN0UU1OljN1SU1O^10aiN0PU10PkN0PU10_Y10caM0\\\\^70`fI0SkN0RjPb0\"}, {\"size\": [1280, 720], \"counts\": \"gPg57hW14M3K4I6K6I7M2M3M4M2N1O2M2N2O2O0O2O1O001O001O001O1O00001O010O1O10O100O010O100O1000000O00011N1O100O1O2N1O101O2M3N2M2N2O0O2O0O1O1H8M3O1O2I6N7JO01L4001O01Mdf\\\\c0\"}, {\"size\": [1280, 720], \"counts\": \"eZX31iW1<H6I5M3H8L3O3L3K4M4N1O2N3L2O1O1O100N10O10000O0100O1000O10001O0000000000001O0O10000O100O101N1O1O1O010O1O100O1O100O1O1O00100O1O1000000O1O10O20N2O6J3M7I0J4]OlhN<UW1EmhN9\\\\W10O23EgUee0\"}, {\"size\": [1280, 720], \"counts\": \"gcP5a0]W14M3M3L3N1O2N1O2N101N2N3M2O1N1O1O2O0O2N2N101N1O101N101O000O10001O0O1000000O10000O1000O0010O01O10O01O1N2O001O00001O001O1O1O2N2M3K6J3O2N2POXiNf0TW1M2M1OO2002M2O4DVUSd0\"}, {\"size\": [1280, 720], \"counts\": \"k]Q98eW16K3M4L3M4L2J8K3M4L3N3M2O2O1N2L4M2O2N2M210O001M2O11O2N00011N0O2O1O1O0000O001O1001O3L5K5K>@<E4J]bVa0\"}, {\"size\": [1280, 720], \"counts\": \"\\\\lW<>_W16H6L4N2M2L4L5I6N2N2N3N100O1O1002N00000O10000O01N2O010O11O1O1O1N2O3L3M2O6K300^O[iN]OoV1<XlX>\"}, {\"size\": [1280, 720], \"counts\": \"iXU<;bW19QOm0K3L3M3N2N2M20N10010O01O001001O000000O00100O10O01O01O1O1O1O1O00100N2N2O2O0N3O1N101N4K4L4K7H=@U`Q>\"}, {\"size\": [1280, 720], \"counts\": \"]Pg:6^W1>J6L2O2N2N2N2O1N2O1N4M3L2N2O1N1000001O1O00000O10O10001O00000O10O10000O1N21O1O00O0011N2NO101O0100O2O0O2N3M2N100O003J5N2N7H7DXXn>\"}, {\"size\": [1280, 720], \"counts\": \"QV]83kW13N2N5K3L4J5M3L4M4M2N2O0O2001N10N2N11000O002M2O0O2N2O001O01O100O1O3M2N2N2O00000000O1O10N2N2N200O010O1O0010OO2L5K4O100N3N3M001O2M3M3N001O2N001NUil`0\"}, {\"size\": [1280, 720], \"counts\": \"URQ94lW10M302N5J2N1GEkhN==@VV1k0]iNAaV1R1L5H6L3O2M4M1N2N2N2N2N3N1O1O0O1O110O1N101O1O000001N2O1O1O1N2O1O3M2N2N2N1O1O2O1M3K5J7M2N4K4M4K4L5J4M5HVmg`0\"}, {\"size\": [1280, 720], \"counts\": \"kbc;7aW1a0E5G:K6J2N2M2M4J5N3M3N2N1O100O2N1O1O1O1O10O1O1O010O1O1O100O10002N1O00101ROUjNnN48lU14TkNETkP?\"}, {\"size\": [1280, 720], \"counts\": \"h]W>8fW1J^hN6]W1JehN8WW1HkhN:QW1F[iN1aV10f0O2O001000mg30Q`20ShI0o_20Q`M000o_20Q`M000PX10PP50PhI000QX10ogN0QX1000ogN0100000ShN2gW16WhNJcW183M3MdZe;\"}, {\"size\": [1280, 720], \"counts\": \"dnh>1lW15H7L4M6K5K2M2O1O2N1O01O0100O100O1O010000O1000O10000O2O000O1O01O0O2M2N3O100000O11O1O1O1N2N2N1I7L4O2N100O3L3M3NSRU;\"}, {\"size\": [1280, 720], \"counts\": \"`fX>5fW1>E3M4M8H2M2O1O1O10O01O2O0ON3N2O11N11O001OO100O10OO110000O001000000001O0O10000000O1O1K5K4N3N2O100O1O1O2N2N2M3L4LWRd;\"}, {\"size\": [1280, 720], \"counts\": \"jga<b0^W14F]OnhNn0gV1a0E2M3M3N3L2N2N2N1O1O1O2O0O100O101O0000000O100001O00001O001O1O0O2O1O0O2O1N2O1O10O0001N2O1O2N1N3N1O1O1N1O2O1N2N100O1O2N2O1N1O2K5J5K7K6Hkgj<\"}, {\"size\": [1280, 720], \"counts\": \"m\\\\m85jW14M3M1hhNFNN_V1>_iN6]V1j0L2N3L4L3M4L2M4M3M2O1O1O1O2NO2O0M4N2N2N2M3N3M2N101O1O1O1O1O2N1O1N2O3O0O1D]iNWOeV1g0=L2N4L2O2CehN1gW1MjYYa0\"}, {\"size\": [1280, 720], \"counts\": \"Rla54lW12N1GMchN3[W10dhN1ZW1=L3N4GWOTiNR1cV17O001LfNbiNX1ZV1lNgiNS1XV1nNhiNR1XV1<N2O4L2O1NO100O01000000O2L9G4O01O1N2O1JYjNQNhU1n1510M2O2O1O2M2O2N1M3O1N2O2M3M3N1O2M3M3M4K4ZOnhN3YS\\\\d0\"}, {\"size\": [1280, 720], \"counts\": \"[ZS6g0XW11O2N3L:F7J2N2N2O0O200O1O1O2N1O1O001O001O1O1O1O1O1O1O2N101N1O2O00O101N2N1O2N2M2O1O1O1O1O2N1O1O1O2M2O1O2N2N2M3N1O1O2M2DSiN_ORW1<TiNAnV1;?G8Ncd`c0\"}, {\"size\": [1280, 720], \"counts\": \"\\\\]g;3f_2NTX10P`2030QPK10OQh30PXL000QX10PhN0N01000QX10oW10P`M10O00PX10QhN0O000O11O010O00010O00hiN0`T10h10O2giNNbT12^kNNcT11\\\\kN0cT11]kNNbT13_kNN`T1OckN1^T10`kN0bT1O]kN1cT1O]kN1eT11XkNNjT11UkN0jT10VkN0jT11UkNOjT12VkNNjT11XkNOgT10ZkN1cT11^kNO_T14akNN[T13ekNNYT13gkNLZT13hkNKYT15fkNL_T1OakN0`T10`kN0aT10^kN0bT11]kNNfT12XkNNkT10TkNOnT10RkN0PU10mjN0aV10eN0gjN0[U10cjN0_U14XjNNkU19liNGWV1;WiNFgan<\"}, {\"size\": [1280, 720], \"counts\": \"d^P::eW12N2M4K9H4K4M3M2M4L3N3L3N3L3M4K4L4L4N3N1O1O2O0O101O001O001O00000000000000O1000001O001M2O1O2M3N3M2M4M2O1N2O3L3M1O1O3M2N2M8J1N1N3Na0_O3M3K3NbY]?\"}, {\"size\": [1280, 720], \"counts\": \"^\\\\^8a0YW18L4L3M2N3N2N3N1M4M3L4M4L1N10M22O2M110O002O0O10O01O2N100O00100O1000001N010O01O0N2N3N1O2N101N101O1O100O0O2O1M3O100O1O1O100002NM3K6K4N2O2K4N3L5N1O2M4Kilg`0\"}, {\"size\": [1280, 720], \"counts\": \"ViR:<]W1:H7K4M1O2N1O2M3O0O101O100O1O012N1O2M4L200O00000000001O00N2O1O101N001O2N1O2N101O2O1N100N2G;TOX`V`0\"}, {\"size\": [1280, 720], \"counts\": \"X`P81iW17L3N3chNCTW1j0J3K4N2N101N101O1N10000O2O1O0000001N01000O10O100001O00000001O1O1O010O1O00010O10O0100O1O2N2N2N1N3K4N3L4M4M2N2N2M4M1OZoea0\"}, {\"size\": [1280, 720], \"counts\": \"jhj85eW18I6N2N2O0O101O001N102M101N2N101N100O100O001O10000O1000O10O11O01O00O1001O01O00010O011N1N2N3M2L4N1O3I6O3M1M4M4LYWSa0\"}, {\"size\": [1280, 720], \"counts\": \"XXm=2?0kV14QiNOgV1i0H8N2O2N1N3M2O1O001000001N2O0O1O1O001001OO1O1O2N1O10N3N1N1O11O2O1O1ON102M4M2N2N3M2N2N2N2O1N2N3M3M2L5K9F\\\\XS<\"}, {\"size\": [1280, 720], \"counts\": \"QhV=8fW17J7J5J3N3M3N3L8H2N3N0O01O10O1O1O1O1N2N2N2N2N3L2O2N1N2N2O1M3O2M2O1O100O01O1NO2001N1O2O2N2N3hM[jNP2TV1F<C<D5L3K4M3M3M3L^Wg<\"}, {\"size\": [1280, 720], \"counts\": \"hoh<:dW1?A8I7I5M5K2N0003MO000105M0NO1O3L2N2M2O2M2N4M1N3M101O0O101N2N10000O10010O01O0O2O001N2\\\\N^jNi0cU1UObjNg0_U1WOdjNf0^U1XOfjNe0]U1VOijNf0]V1J5K4L5HaoY=\"}, {\"size\": [1280, 720], \"counts\": \"Rhn;<`W15L4O1N2O2N2N1O000O10000O10000O001O100O1N3O0O2N1O101N101N2O0O1000000O100O100001N1O2O0O100O2mN^iNe0cV1XOciNe0]V1XOiiNc0nV1L3M4K6JgoR>\"}, {\"size\": [1280, 720], \"counts\": \"U`i8<bW15L2N101N1O1O1O2N1O1O2N101N0O200O000001O0010O0100O00100O02O0O100O101O1O2N1O1O10000O00O11O0011M2jNZiNo0nV1N2L5K4L5IQPWa0\"}, {\"size\": [1280, 720], \"counts\": \"gWn55hW15M3M2N2O2N2M3N2N3M1N3N1O1O2N1N3N1O2M2O1O001O0000000001O001O010O00001O1O100O1O1O1O2N1O1O1O1O1M4M2O1N2N3M2L4N3N2N3JeWmc0\"}, {\"size\": [1280, 720], \"counts\": \"VP`921O`W13`hNO:OnV10d000O1000og30oo40RhN0Q`H0000001O01000000001O0O1O311NO33NN:ITol`0\"}, {\"size\": [1280, 720], \"counts\": \"]_i=3lW1a0@<C<F0O100O3NO2O07JG9L5I8D\\\\hg=\"}, {\"size\": [1280, 720], \"counts\": \"g^f<7gW1;G5K5K4L5K4M2M3N2N4M3L11N1O1N2O0001N2M101N1O2M3M2M3N7I2M3M2O2M2O2O0O01000001N2N2oNojNbNaU1T1k0M4K6I6GPjb=\"}, {\"size\": [1280, 720], \"counts\": \"X^k<8fW1;F;F6J7I2O1O0O10O200O100O10O00001N1O0O2L3O1O1N2O1N101N2N101O0O2O001O00O10O2OO1N2K5N3N1O2N2M3N2L3N3M5XO[iNLlV1IR[V=\"}, {\"size\": [1280, 720], \"counts\": \"cWZ:9dW15L3N2O1N2N2N2N2O0O2N100O100O1O1O0100O100O100O100000000O100O100O1O1O100O10000O100O1O0100000O10O2O0000000000000000001O2OO02N2N2N3N1N2N100O03N2M2N1O2N2M4L5K<@RPg>\"}, {\"size\": [1280, 720], \"counts\": \"Wf\\\\5:fW1100N2khNKZV1:biNMVV1Q1N2N3N2M2N20O01O001N2O1O0O101O0002O0OO101O001001N100O001O000010O00001O2OO1O0000O100001O00001O1O0O100O101N2O1O001N2O0O4M3M3L7G;F;Dehoc0\"}, {\"size\": [1280, 720], \"counts\": \"_ah6<bW19I^OUiN3fV1M`iN1]V1OeiN1ZV1NgiN2WV1NjiN2VV1NkiN2TV1NliN2TV1NliN2TV1MmiN2TV1NmiN1SV10liN0TV10liN0TV10miNOSV10niN0RV10niN0RV11n001O00cVad0\"}, {\"size\": [1280, 720], \"counts\": \"lod95iW14NI^hN1`W1ObhN0]W1OghN0WW10jhN0cW10^W10`hN0oW10QhN10O0010000000Q`20o_M0ng30T`70UX60Q`^O2Yh\\\\`0\"}, {\"size\": [1280, 720], \"counts\": \"ghQ=5gW14M4L5L5K2J11301N3N0L40J[iNROdV1o06N3K4N3N2O1O2N2N3N1N2M4Mno>1PPA3M2O2M2N110N1N1O3O1O100O01O0O10001N101O1O1O1O1O2N1O2M5Ko`^<\"}, {\"size\": [1280, 720], \"counts\": \"jPi<1nW12L4K6J8@:K7M2M4M3M3N3L5M3N2M10O2O1O1O101N1O2N2N00O20N2O0OO2O1N2M3N1O2N2OO10000001O0000002M2O01O000000001aNTjNB2m0kU1@ajN;aU1CdjN:\\\\U1DhjN;WU1CSkN0SU1KnZQ=\"}, {\"size\": [1280, 720], \"counts\": \"e`f<2iW19K2M5L1O011N3M2N10001N1000000O10O10O1O1O1O001O1N2O1N2N101N200001O0O2O1N2O1O1N2O1O000O1000000000O10001O001O1O3M3L100O1000O01O01O1N1O2O1O1O1L6ZOPiN5TW1Hc0KZPa<\"}, {\"size\": [1280, 720], \"counts\": \"nXU:2jW16K4L3M4I6L4O1O2N1O1O2O0O1O1O0001O1O1O1O100O100O1O011N100O10000O10000O100O10000O2O00O02O000O1010O0001O001O1O001O1O1O1N3M4LeWa?\"}, {\"size\": [1280, 720], \"counts\": \"\\\\PP75iW15K5M1N2O2M2O2M3M3N1O1O2N100O101O0O100000O10O100O02O0000O1001N0100001N1O001O1O1O1O1O0N3M3O2M2N2O1O1O2O1N3Kf^Pc0\"}, {\"size\": [1280, 720], \"counts\": \"]W`72jW16L5L4L3lhNBaV1`0\\\\iNCcV1n0O1O1O1O001O001O0000001O0O20O00000001O0000000000001O0000001O100O001O1O1O1O10000000O3M2M3M3N2M3N1N3N0O3M3M3L2N2O3K5J9Hk_Rb0\"}, {\"size\": [1280, 720], \"counts\": \"fWo<2lW1000N3L031O2M01L4O20O10O0100001N1010hiNO`T12`kN0^T1O`kN1bT10ZkN2hT1NUkN1oT1OnjN0VU11ejN0^U10_jN0dU1O[jN1gU1OUjN3mU1NoiN2TV1NkiNOYV12ciNN`V12]iNOeV13ViNLQW13dhN2bW116JZig=\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"hh]<2aW1`0He0]O5M7I4L2M2O1O2N1O1O1N2O2O0O1O2O0O2N1O2O2N0O100000O0O3N1N3N3M2N2O1N2N3K5L3N3L4M3M3J5N2N3K7I5L4Jdgg=\"}, {\"size\": [1280, 720], \"counts\": \"Zca79cW1:H6I5M2M3M3M3N2N1N3N1O2N101O0O1000001O0O1000O10000000000O10000O01001N10O100000O10000010O2N001O100O1O010O001O02O0O1O10O0100O01000O1O1O2M2O2N2N2M3N1O21NG9K7L4M4G`hNJcW10]l[a0\"}, {\"size\": [1280, 720], \"counts\": \"SaP52kW14M2N3N3M8H2N3N4L0O11O1O0001O000O101OO1000O10O1001O000000O101O00001O05KO1OWiNPOcV1P1^iNPOcV1o07O10O2O002N1O0O01001O000000O2O000000001O010N2O1N2O1O1O001O0O1000O10000001N1000O01]OjhN;WW1DjhN:WW1GihN8XW1HhhN6[W1HghN5\\\\W1IehN4^Vic0\"}, {\"size\": [1280, 720], \"counts\": \"[Uc0;bW15L5L3M4L5K5L2N2N00001O001O00O1001O0001N100000001O01O0001O1O0010O1O00O1000000000001O00001O000000001O000O1001O001O00O2O000001O00O2O00000000001N10001O010O001O1N2O1O1N2N108H4L3M6JfaQh0\"}, {\"size\": [1280, 720], \"counts\": \"f\\\\i01cW15bhN1WW1?MIhhNIVW1g0I3M2N010O1O2N1O0100000O100O110O00000000000000O1O1O3M4KO2001N3N4LN2N\\\\OohN?ZW1M7J3FXh30PXL0ck_i0\"}, {\"size\": [1280, 720], \"counts\": \"jem11kW1>E4L3L5L3L3N2N2N2O0O2O0O100000000000000000O11O0000O2O000000000O100000000O1000001N1O01000O2O000O1O101N1O100O2O1O1N101O2_OghN7bW1O1O0O1N3LUjcg0\"}, {\"size\": [1280, 720], \"counts\": \"lad38eW16H5N3M3N2N1O1O1O1O2N3M5K5K6J5K3L4N1N1N3N2N2N2N2N4L1O1O2N2N101N3M3N1N2O1N2O0001N11N2O0000O100001O01O010N2O001N20TkNPMgT1V3000M3K:H>A5K8K4EmiN`NZV1Y1eiNhN]V1X1ciNhN\\\\V1V1diNkN]V1X14E_iNTOcV1k0^iNROhV1c0UiNBSW19QiNEPW19=O2009GO1K6DbhNM]\\\\[e0\"}, {\"size\": [1280, 720], \"counts\": \"Vjf:255WW1a0I5M4L2O1N100O1O1ON2001O3K5M100O1O100O0O020O1010O0O2O2N1N3N2M1O3M2M3N2N2N3M2N3M4JRfh?\"}, {\"size\": [1280, 720], \"counts\": \"Ynf:5cW18L4M4M4K011N3M4N3L1O10101N2N2N2N2N0010OO1100O10O0100O100O101M2O1O3M1O2N3N1N3M3L5K5KcZg?\"}, {\"size\": [1280, 720], \"counts\": \"Y\\\\T81fW11^hN4^W1<J6M2M3M1N3N100O10O01O010O000100O01O10O1O0010O010O10000O100000O010000O100000O100O010O010O1O1O01000O1O1N101O001N2O1O011M3L3M3N102M3K4M310O0OdbUa0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"ebh86_W1O8M6O\\\\Y10VM0ScM0[\\\\<0QjD0\\\\jN0YS10hlN0WS10jlN0US10\\\\20cjN0_U100001O100N1N2OK40\\\\nc00fWj`0\"}, {\"size\": [1280, 720], \"counts\": \"U]g833N21VW12ViNN^W1O10PX1000PhN1N10O01O2N000000M2O200000O10O0100000O10000001OO000010O0PX10PhN1OO10O0eRha0\"}, {\"size\": [1280, 720], \"counts\": \"ZWP91SX12K00O11OO10O0O012M5K2O0O100O1O100O00HdhNN\\\\W1MlhN0SW10?000000000oW10QhN00001O0000O0100O0100001O000O1000000O10O01002NjhRa0\"}, {\"size\": [1280, 720], \"counts\": \"Z]f;2^X11eNNj00ViN1PW11MNO101NO001Oo_20mW61SXG1n_2NTPd00aoJ0oo@0_[l=\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"_j<1fW12]hN24L02PW15WiNJjV19<OMM`hNNaW12ahNM]W16ahNJ]W1>K7M;lhNPO`V1Z10M4NN2010F^iNUObV1S1101000O10O002O003oNWiNd0fV1=00O10SO[iN`0dV1_O^iNa0aV1?1kN]iN0Ob0eV1b00WO]iN4eV1BYiNM4a0`V1d0011O0000000O20O01O1N3M2O0O3M2L3M4M6GXVai0\"}, {\"size\": [1280, 720], \"counts\": \"a[i03dW1;`hNETW1d0O1O4M3M1O2N6J1O1N3O00O01O1O010O10O100000O12N001O00010O00002M10001O000O2O001N2O1N2M3M4I7L3Knd^i0\"}, {\"size\": [1280, 720], \"counts\": \"g;`1`V100000010O00O0100000O01O100O2N1O010O10001O0O011O1O00O0002N2O1N1O011N2O001N2O1N101O1N2M3O1N2N3M3L4IlSUj0\"}, {\"size\": [1280, 720], \"counts\": \"oaP:1oW10moc00Wfg`0\"}, {\"size\": [1280, 720], \"counts\": \"VcS15gW17LI`hN03LVW14ghN03LSW17jhNOZW1NghNOO2XW1MjhN9]W1M41N4I=J]OkhN7YW1DjhN9oV1ESiN1O9`W1NCKSiN3nV1JTiN6VW13DGTiN8nV1HTiN9iV12mhN0oV11QiNNPW1HPiN3O0WW1MkhN2N1WW16jhNKVW11khN3SW1MkhN4UW1GohN9SW1GlhN9UW1:O000O_OjhN1O0WW1OmhNNN2VW14mhNOQW12mhN1RW1NmhN2UW1IihN;_W10L5O1N3I6N2OO1_OPiN2PW1>10000001O01O001O00O2O001N10001O010O0O101O000O2O0O1O2O0O100O2O1O0000O10O1001N1O11N2O1O0000O1000OO2O2O00010N2N1N2O1L5O000O100O2O0O101N10001N10O2O0001N1O1N2M3O100M4K5NZUoe0\"}, {\"size\": [1280, 720], \"counts\": \"_`[29gW13L3N1N3N3L3N000010O1OO101O0000001O1N1O2O1O001O00001O00000O2OO1N2O1O1O10001O0O01001O100O0000O10100O2O8GO10OO2N1100000000000O1O100O1002N1O3L5K11O000E;N2O2O0O2N3M3N1O2M2O2N2N1N3MUWYf0\"}, {\"size\": [1280, 720], \"counts\": \"VkP=1Q`20PhN1OO00O00000lWo00RhPO0oN1V10dec=\"}, {\"size\": [1280, 720], \"counts\": \"WbZ?1jf30UaM0QX10ViN0`U10YiN0ZiN0\\\\U10Z10O0100000o_70Q`H000VP?0ko@0000003jhNN\\\\V10]iN3eV1OQiN7RW1:3MYfn:\"}, {\"size\": [1280, 720], \"counts\": \"Y:^1bV101O00000000000O100O2O0O101O1N2N2N3M2N5H^]Yk0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"Xl21og30P`M0Q`20o_M0YM0_mN0Y]20ogN0XU60gjI105IL^hNOhW1O^hb00gW]O000F0_hN0mo90oWL0[Pi01`oQOO00SX12mgN0T`7OSh=1jgZOO0O]Teg0\"}, {\"size\": [1280, 720], \"counts\": \"Qj21QX12]ONPiN<SW1HfhN0XW1NkhN1[^2MZbM5kU1KU12O2NN2N02O0021MQX1OPhN1N1diNNkT11UkN0jT1OXkN0hT11WkNOjT11c10biNOlT10TkN0^V10^N0TkN0lT11SkNOmT11SkNOmT10TkN1jT1OXkN1[V10_NOSkN1^V10_N0SkNOlT12UkNNiT11ZkNO[V11XNO`kN2^T1NdkN1ZT10gkNOYT10l10miN0VT10n10liN0VT11jkNOVT11ikN0WT10ikN0WT1OikN2UT1NlkN2PV1110000OO1O12O0O10O000001O1O00QX10ngN10010aMOnlN0aU10O1001OR`20o_M0010ORX60mgI1O001O0101O1OP`20m_M1N301O02N2N1O10O000000010O000oW60QhI000]^f01ofke0\"}, {\"size\": [1280, 720], \"counts\": \"jj]76fW1LghN0oV1OZiN0`^20ihN1N00000[iNO[U12ijNNRU11^101000O1[jNN^S10dlN0[S11elNOZS12glNNXS11ilN0VS10klN0SS11olNNPS12_20O0djNNmR12`2000000O1001cjNNjR12VmNNiR13b2OgjNNfR12[mNNcR14d20ijNLbR12amNN^R11i20ljNOXR13hmNMWR14imNLVR12nmNNPR11SnNOjQ12R31N10N200O100O\\\\kN1YQ1OinN0VQ11jnNOUQ11^3O04MM201O01O1OO1010O1O1OO1O2O02N00O1O1002N00011O01N110ORiNOlU11PjN0SV11hiNO]V11^iNOgV16khNJL2\\\\W18;GbmVa0\"}, {\"size\": [1280, 720], \"counts\": \"`UY53hW1=G1MMfhNGVW1:nhNFoV10]iN0bV10biNM\\\\V15diNK[V10PiN36L81_V11UiN1e0MUV11ZiN2?MVV11\\\\iN1>NUV12WjNNhU13WiN0k0MmU1:RjNGkU1;UjNEjU1=VjNCiU1=VjNDiU1=WjNCjU1=UjNCkU1=VjNBjU1>VjNCiU1;YjNFeU19]jNGcU18^jNHbU11ejNO[U10fjNO[U11ejN0ZU10fjN0ZU10fjN0ZU1OmiNO:2jU1NkiN1;0jU10miNN92iU12oiN02NoU12niN14LoU12jiN6GJ9NUV12jiN7FM9JWV18diN3<EoU1:eiN0;HPV18biN25C04YV1g0eiNXO10[V1j0`iNXO5NZV1j0diNVO20YV1:diNO8HFN]V1<ciNN`0GnU11ciN9f0FgU11eiN7d0IfU1>\\\\jNBdU18liNG81lU19fiNC4094mU1`0hiN]O:3nU1`0hiN_O90oU1a0hiN]O;2lU1?eiND`0NlU11fiN;?CkU10kiN9KG81QV1OmiN6KM6MRV10oiN95HlU1OQjN71LnU1MQjNOF1<2mU1MPjN2IM1O15UV1MPjN20OK1UV1NPjN2I54JSV1NoiNc02^OnU11miN9KH0O01XV1OkiN901JH[V1NiiN:46SV1AgiN9:3oU14iiN]O30L8XV1;jiN\\\\O21M8WV1;QjN^OH7WV1<oiN^OK5UV1a0UjNAjU1b0PjNBPV1LgiN5:NQV1GmiN723QV1FmiN7O6TV1CoiN5O6RV1KiiNLMM6;TV1EhiNO228;nU1CkiNNO47OG5XV1JSjN0O1F5XV1KQjN100D6ZV1LPjNNNNK;XV1ImiNN4>nU1DliNO7=nU1CjiN18<nU1D`jN:`U1I_jN5aU1J]jN:cU1A`jN2VO6ZV1I^jN2XO5ZV1I_jN?aU1BajN;`U1D]jN>eU1AYjNa0gU1_OYjN`0iU1@WjN?iU1AoiN0E>]V1APjNOC`0^V1@eiN16?UV1@eiN16?VV1_OUjNa0kU1_OhiN4L=\\\\V1@giN3L>]V1CbiNO0a0\\\\V1LciN6]V1NaiNO_V11ciNN\\\\V12diNN[V12fiNNZV1OiiN1XV12ciN0\\\\V11biN3[V1i00POeiN;\\\\V1AiiN<XV1HgiN4[V1LdiN3_V1KaiN2cV1F]iNH1:KIkV14_iN2lV1KkhN8ZW109L4K4MaRla0\"}, {\"size\": [1280, 720], \"counts\": \"oTk61nW13NO00F;SiNHPV1;aiNA29ZV19biN_O3000ZV1j0biNXO5NWV1S1kiNlNSV1T1niNlNQV1T1oiNlNRV1S1oiNmNQV1Q1RjNoNkU1Q1YjNROdU1k0\\\\jNWOdU1g0]jN[ObU1c0^jN_O`U1b0ajN]O^U1d0djNZO\\\\U1d0ejN]O[U1:mjNGSU18njNHRU18njNHQU15TjNCh06TU19QjNBo02QU1`0RkN^OnT1b0SkN]OkT1d0VkN\\\\OiT1>^kNBcT1<^kNCcT19`kNHaT15WjNKn0OkT19_kNGaT1:^kNFbT1:]kNFeT17WkNNkT10UkNMnT16QkNJmT18SkNGmT18SkNHoT12UkNNlT12TkNMlT14SkNMlT16RkNJoT15QkNJQU16njNJSU15ljNLSU16ijNLWU16hjNJXU13jjNNVU10kjN1VU1MjjN4TU1NljN2TU1KPkN4PU1OljN2SU1OkjN4SU1MmjN4QU1JSkN5lT1JXkNMgN2PV11`kNN_T14akNK_T15f1NniNM[T12i1000QjNNQT12QlNNnS12RlNNnS12P2O100000QjNOoS11Q20O01O0001jiNO\\\\T10j10QX10PhN0fiN0cT10\\\\kN0dT10\\\\kN0eT10Z30[N0]V10da20`QK0oT10a10O000Qh30PX10Qh30ogI0_ca`0\"}, {\"size\": [1280, 720], \"counts\": \"\\\\YU:3ea2OYdNOfkN1jN2WU1N[kNOjT10ejN5JKc20jP14ePOLSo00`41hW6OZPK0SX10m_M000PX10ogN00000O1N11N100011000OS`20n_M010oj=0jUB0f[l?\"}], \"areas\": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2021, 2043, 1714, 1382, 30, 156, 3583, 1923, 1567, 1814, 2282, 2195, 1769, 1743, 2374, 3020, 3867, 295, 2089, 2518, 1971, 1726, 1542, 119, 3634, 3956, 3756, 2627, 1957, 2684, 2933, 2356, 3658, 2411, 118, 1629, 1909, 4012, 2482, 2799, 3383, 263, 4144, 3892, 2123, 2221, 1714, 2475, 3156, 2778, 1832, 1708, 1955, 88, 565, 2612, 2579, 2152, 3870, 114, 47, 1022, 3069, 2309, 1598, 1551, 2602, 208, 0, 2850, 3837, 2470, 3147, 1403, 2159, 5376, 1585, 1570, 2079, 0, 0, 0, 50, 78, 219, 33, 0, 1909, 1791, 1841, 3, 3588, 2588, 15, 88, 846, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 50, 284, 288, 3094, 1238, 60]}, {\"video_id\": 0, \"category_id\": 3802, \"bboxes\": [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [371, 415, 27, 37], [361, 445, 47, 32], [373, 508, 34, 24], [0, 0, 0, 0], [339, 574, 39, 29], [339, 572, 32, 34], [353, 623, 32, 32], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [321, 585, 50, 35], [288, 575, 31, 40], [0, 0, 0, 0], [89, 444, 61, 57], [188, 291, 73, 36], [281, 267, 52, 60], [215, 248, 73, 40], [229, 241, 59, 21], [328, 322, 19, 23], [222, 257, 76, 90], [71, 276, 99, 77], [17, 285, 110, 69], [36, 204, 111, 113], [142, 91, 84, 130], [24, 195, 130, 83], [67, 94, 125, 118], [271, 31, 115, 175], [213, 122, 91, 176], [218, 174, 80, 152], [260, 213, 63, 129], [383, 491, 27, 38], [185, 214, 53, 108], [391, 482, 25, 47], [380, 530, 36, 24], [386, 515, 32, 33], [406, 493, 24, 48], [403, 526, 33, 27], [420, 516, 17, 37], [405, 517, 23, 46], [403, 538, 26, 31], [419, 538, 17, 24], [422, 544, 18, 25], [415, 555, 23, 20]], \"score\": 0.7655677795410156, \"segmentations\": [{\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"ZU`>6dW1=F7K2N2O1O1O001O001O1O1O0100O00100N2O1O1N2N3M3M2O4I8H^j`<\"}, {\"size\": [1280, 720], \"counts\": \"SfS>1mW13M4M2O001N2O001O100O1N201N100N3N1O10O10N1O11O001OO1O1001O0001N20O1O0010O10O1O110M1O2N3M2M4L5JdYT<\"}, {\"size\": [1280, 720], \"counts\": \"^hb>5kW11JO\\\\hN0cW11]hN0`W19O1004K2OO0L5001OO2L3O1M3O100001O1O1O1O00001O001O2N3N0O0I^hNOcW10_hNObW10_hNOXgV<\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"WZX=3kW13L3O2N1O3M5L2O2N1OO1O001O01O000000O1000000000010O1O010O1O1O1O1O1O2N100O1O001N`eY=\"}, {\"size\": [1280, 720], \"counts\": \"WZX=6gW14M4M1O2N2N2N2O1N2O1O001O10O0100001N1O100O2O0O100N2O3L20000N3K5L^]b=\"}, {\"size\": [1280, 720], \"counts\": \"Qli=3lW12M3M2ahNIUW17fhNNYW13fhNNVW16jhNJRW1a00O34M2N100O00O106J4L1O00000O10001N101O1O1O0O2O1N2MPlP=\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"ija<2jW14O2N2L3O1O2O5K3M2N4LO0101N2O00O1O0O1O102N100O10000O10000O1O2O0000001OO2O00O10O02O001O0O2OOO2O2N2O3Ka]b=\"}, {\"size\": [1280, 720], \"counts\": \"ZbX;9`W1:L1O2O1O2N1N1O2O1O1O1O1O1O10O02N1O1O011NO1O1O3L3N3M3M4K4N2J8I`]c?\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"[f_32fW1:MKahNLM2\\\\W1OihN5XW1JihN5UW1LmhN4PW1LRiN5mV1IUiN7iV1JZiN5dV1KaiN3]V1MdiN5[V1IfiN7YV1IjiN4UV1LmiN4PV1MRjN2oU1NQjN1PW1O1OPO0PjN1mU1OUjN1lU1OTjN1PO4eV1K[jN>eV1KiNGhjN8WU1IjjN6WU1JijN5XU1KhjN4XU1LjjN2VU1NljN0SU12mjNMTU13]1O_iNNUU12]1O1O0001O0O1O02O100O]iN0VU10kb20b^M01000Wg=1jQie0\"}, {\"size\": [1280, 720], \"counts\": \"iY[72lW1O[hN3ZW1OohNNkV11b00N11O10O0200O010O0100O0001O10O0011O0001OO1000oW10QhN0o_20Q`M000PX10O0Q`20PXL010oW10PhN0O0100000Sh80n_H0PhN000O1000000WX60Tnea0\"}, {\"size\": [1280, 720], \"counts\": \"Pao:5dW1NjhN0iV1NeiN0oU14VjNMgU1OUY10kaM3[T1MnQS10RhUO070i^i>\"}, {\"size\": [1280, 720], \"counts\": \"VP]8=aW1EchN:XW1FkhN<QW1DXiN5gV1K]iN2bV1MciN0\\\\V10eiN0ZV10k00nW1020PhN0mo90RPF11Oog30QXL000oW10QhN000og30QXL0oW10QhN00000P`70P`H00000Q`20Uh30ngN0P`M0ehN0WVi`0\"}, {\"size\": [1280, 720], \"counts\": \"m_n81ko40Rh80Q`C010lg=0SPF0f`k00UWk?\"}, {\"size\": [1280, 720], \"counts\": \"RZj<1QX;0ooE00000RX10ngN01000TX12l_MNUma>\"}, {\"size\": [1280, 720], \"counts\": \"_ie87XW1f0F9I6H8K3L6K4M2L5M2N3M2O2N1O0O3N2OO0001O00O1N2O11O2N1O000000O01000O11O10O00000001N10O0101O00001O01O1N2O001N2O1N2O1O2N1M2N3M4L2M4K6L3M3L5K4M4J5L4N5F7N7CQo]`0\"}, {\"size\": [1280, 720], \"counts\": \"hQi22QX13UhNL^W16^hNLbW13`hNLYW12ehN33JWW1=43dhN@TW1g0O4J7G7O000O0O0O20000O1N3N100M41N1O0001L4O101O4I2N21N4M01L5M2O1O0N3O000O1O001O00100O1000000001N100000000000001O1O1O1O1O1O1O1O1O1N102N1O101N10O01O1N3N1O1O2O1N00012L2L4M4M2N1N3M4H7M3N2M5JRn]e0\"}, {\"size\": [1280, 720], \"counts\": \"dae0<`W1f0]O3M3L3N2M2O2N1N3M2O1O1O1N3N10000010O0001N2O00001O0010O1OO100O1000001O01O010OO1O100000000001O1O1OO1O100000000001O1O1OO1O11O001OO1010N2O001O0O2O001O002N1O00001O2N1O1O00100O001O1OO0102M3M1O10O03M3M1M4FQiNAoV1<ViNDhV1<YiNBhV1>XiNBiV1;ZiN@lV1;?O0M3N2O2NTfSg0\"}, {\"size\": [1280, 720], \"counts\": \"dW]19_W1d0\\\\O=H6J4M4M2K6M2M3N2N2O1N1O00102M2O1O2N1N2O9GN2K5O13ijNZMnT1m2O1O1O4L2M20O0N3J51O2NO1O2O12M3NO1M2N31N10N1M4O03M2N01M3N12N0000O3M12N11M5K4L211M002M3MO7J12O4M2L10OL[jNnMdU1W200002M3M2N5L1O0O1O2010O0OO10001N101^OkiNUOXV1g0kiNXOVV1g0kiNXOVV1g0liNVOVV1g0miNXOUV1g0liNXOVV1f0kiNYOVV1f0a020J7M2N2O000O2LfgZf0\"}, {\"size\": [1280, 720], \"counts\": \"Vda5<bW17H6K6G6M3M3N2N4L4L3L4M3L5L2M3N2N3M3M5K3M3M3N1N2O1N3N3L4M3L3N1N2O0O2O000000001N2O1O1O1O1O2M2O01N1000000O2N1000010N1N3N100O2O0O2O1N1O2N3N2M2O2L4M4M1N4L3N2M3N3M4K4K6lNcjNYOjU17ajNDQW1KQRXc0\"}, {\"size\": [1280, 720], \"counts\": \"XWn0=]W1;H6I6K5L2O2M3N2N2N2N4L3N2M5K3M2N2O1N1O101N100O1O100000000000O2OO2N1000O10000000000O11N11O00000000000010N10000001O0O1O10001O01001M2O0O2O2N1O01O02O1N0O1M4L4000OO1O1O1010ON2N20000O2O000000O0100N201O0000000000001N4M3L00101O01O01O00002M2N2N2O0O010M500OOO2N2EhhNKZW1MnhN3`W1O1KZPRf0\"}, {\"size\": [1280, 720], \"counts\": \"Rmc2:`W1:H6L4L4K6J4M3M3N3L3M3N1N2O2M4L5L3L4M3L4L4M1O1N2N4L4M1O0O2O1O2N00M3O21N102M2O2M100O1O101O001O0O1000O100000000001OO100000000O100010O00000001N2O001O000100O01N2O1O1N2O1N3N10001N2N2N1N3N2M3L4M3M3M3N2N1M40N110O2NZO\\\\jNoNeU1k0`jNTOaU1k0_jNUOcU1g0_jNYOcU1b0`jN^OaU1`0`jN@bU1>_jN@cU1=_jNBcU1>]jN_OgU1a0m0O00000L4N3M3N2M2O3M4K3Mbbbd0\"}, {\"size\": [1280, 720], \"counts\": \"[kb:a0]W1b0YOc0TOU1XOe0YO<G3N2OO1M2O14L4K4M2O1M4M2OM4I7K510O2M5K2WLnkN`3\\\\T1L3N2N4M1OO01O1O1N10O1O1000001N1O1O1N2O0L401O010O1O1N11O0O100N3O0O10000O010000001N01000O10O100O10O01N2N2N2O0O2O0O20O01001O1O1O001O1O1O1N1O2O2N2O1N3M2O1O14LO1O1F:K4N3M2K6L4M3M4H9]Oe0^OdVP=\"}, {\"size\": [1280, 720], \"counts\": \"[]Z8;dW1:G?@<F3M5K3L5K4M3M3M14NO1ON21OO0O3ON4K3M2N1O3K3J8_NhM`lNo2XS1R1I7J4N3N0O00011O00N3I7K501N3L5K4M0OO2N1L4J7M4L3O1O1O03M2O2M3N2M5L1N3N001O002N0O00010OO1001N2N2N3M4K4K8H5L6K4L5I5L4M3M3L4J9G6I;E9gN\\\\iN4O3jiW`0\"}, {\"size\": [1280, 720], \"counts\": \"Tg`8:cW18chNGgV1T1E8J4K4M2M4M1O101M1103MG9K6ZOd0J7K5L4M3M2O2M4M2M3N01N2M3O100L7J2ON4I6L4L4M3N2N2N3N100002OO00O5LN200002N1OO100002M100O2N2O0N2M5K5K5K5K:E3N3L7H6I6L6H7I6M4E;L9\\\\O`0K\\\\Q^`0\"}, {\"size\": [1280, 720], \"counts\": \"_XU:7gW14M4KKhhNJTW14PiNOiV11^iNM^V13kiNKoU15TjNLfU15^jNL^U11ijNMUU1OSkNOjT11b100OPjN0ST11okN0lS10WlN2dS1O`lN0\\\\S11hlNNTS14olNKnR14UmNLhR15a2K50O11O00O1cjN0kR11a21O1O0fjN1aM4hT1JhmNd0oQ1AQnN<oQ1FRnN0UR10ZlNF>:XS12VlNH`01`S18nkNI_OMe0OPT1=kkNI_OIg01nS1`0jkNM;ClS1?]kNGGNNOU1KkS13RkN0<:BG_1L`S12gkNb0iU1_OXjN=kU1DVjN9]OEQ1NlS12fkN>[ODP1MPT10TkN07i0c0VOUT11ojN46f0KYO:0fT12kjNN;o08oNcT17njNL0n0?nNaT19_kN8D^O?0aT13ijNOcW1163NKYhN4dW14010OahNITW1=fjNB\\\\S1?_lNEaS18^lNIgS10\\\\lNLlS12dkN6fNFkU1:[jNJH250MK21nU1:QjNFWP`?\"}, {\"size\": [1280, 720], \"counts\": \"bWo><aW17J3N2O2M3O001N2OO1000000O10000O1N2O2M2O1N2O001O1O1M3EchNO_oR<\"}, {\"size\": [1280, 720], \"counts\": \"aaW79dW16I5J6K4N3M2N3UNoNalNV1\\\\S1nN_lNU1_S1nNYkNFh0e1nS1SOhkNo0YT1ROakNS1_T1nN^kNT1aT1oNYkNU1fT1S1O0O3N20O0O20N2O0003L2N3N2N105J5K1O2N2N1N3M3N2M4M3L3N3L4M3J6I7E9O2JSiNXOmV1e08FihNJWW12mhNMUW10`0MWohb0\"}, {\"size\": [1280, 720], \"counts\": \"gWY?9dW15M3M3M4L3M3M3L3L3N3M3N1O0001O10002N3M2N1N3M3L6G9BdXj;\"}, {\"size\": [1280, 720], \"counts\": \"i`k><bW15M0O2O2M1O100000O010O10000O100O2O00001N10000000O00100O0100O1O2N3L2O2N4MWWj;\"}, {\"size\": [1280, 720], \"counts\": \"kPS?:aW15M1O3L4K4N3O10000000000001O1O1O100O1O1O1O1O1N2O1N101O1N101M2O1N`gg;\"}, {\"size\": [1280, 720], \"counts\": \"QPl?6iW13M3M2O2M2O2O0M4J6J5M2M4N2N2O100002N4K5K7I6J4K6J_gX;\"}, {\"size\": [1280, 720], \"counts\": \"^Xh?2nW14L100\\\\hN2SW1?O02O1N010O1O000001O00000000O110O00001O010O1O1N2O0O1O2N5L1JUWQ;\"}, {\"size\": [1280, 720], \"counts\": \"d`]`08eW14L5G9L3N2O2OO101N2O1N2O2N5K4K2N4JPoo:\"}, {\"size\": [1280, 720], \"counts\": \"Pij?2kW17L001N1^Ob0M202N201O1N2O0001O1O1O0O2O7H5K3L6K5KdV[;\"}, {\"size\": [1280, 720], \"counts\": \"[Yh?4iW14L4M3K5L4N1O2O001O001O0100N101O1O1O00100O10000N3K5LjnY;\"}, {\"size\": [1280, 720], \"counts\": \"]Y\\\\`05iW13M2N2M3O1M2O1000000010O2O2N1N2K6LQWQ;\"}, {\"size\": [1280, 720], \"counts\": \"]Q``04kW13M2M3N2N2N1N3N1O2N2N10O1O2N2L4N2O1JPWl:\"}, {\"size\": [1280, 720], \"counts\": \"`YW`08fW15L3N00000O1000000O2O00100ON3L3O2O001O1O1N2O2N\\\\fn:\"}], \"areas\": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 787, 952, 557, 0, 737, 692, 644, 0, 0, 0, 0, 1119, 892, 0, 286, 80, 30, 92, 8, 13, 5532, 5276, 6052, 8218, 8074, 7906, 10592, 12215, 10424, 8659, 1016, 676, 3595, 854, 658, 753, 699, 585, 407, 622, 600, 261, 323, 341]}, {\"video_id\": 0, \"category_id\": 3827, \"bboxes\": [[566, 649, 55, 63], [573, 649, 56, 65], [579, 646, 59, 66], [583, 645, 59, 67], [586, 644, 62, 67], [589, 642, 61, 68], [609, 642, 50, 71], [627, 650, 41, 62], [635, 653, 35, 57], [636, 654, 34, 56], [639, 660, 30, 50], [640, 660, 29, 51], [646, 671, 22, 38], [650, 673, 18, 30], [650, 678, 15, 24], [653, 679, 8, 25], [0, 0, 0, 0], [0, 0, 0, 0], [636, 676, 17, 23], [633, 674, 20, 25], [618, 651, 36, 58], [592, 642, 61, 71], [594, 641, 63, 70], [597, 639, 63, 71], [600, 638, 64, 71], [604, 640, 64, 70], [602, 641, 63, 69], [601, 641, 64, 68], [603, 640, 63, 69], [607, 640, 62, 70], [612, 639, 63, 70], [614, 642, 63, 70], [614, 645, 64, 70], [614, 648, 64, 70], [613, 651, 63, 70], [612, 653, 63, 68], [611, 653, 63, 68], [608, 651, 62, 69], [603, 651, 63, 70], [606, 650, 63, 71], [615, 650, 64, 70], [623, 648, 64, 72], [636, 646, 57, 72], [634, 641, 64, 72], [634, 640, 65, 72], [631, 638, 59, 76], [623, 631, 60, 74], [615, 606, 66, 71], [614, 575, 65, 72], [613, 552, 65, 67], [615, 538, 68, 69], [620, 537, 66, 66], [624, 537, 65, 64], [629, 536, 66, 64], [632, 537, 66, 64], [636, 537, 68, 64], [643, 538, 70, 61], [648, 539, 66, 62], [650, 539, 66, 63], [649, 539, 67, 64], [649, 541, 70, 63], [651, 546, 68, 69], [655, 552, 64, 61], [649, 548, 62, 64], [647, 540, 49, 66], [636, 539, 55, 64], [634, 535, 55, 65], [630, 532, 58, 64], [630, 530, 59, 63], [633, 531, 61, 63], [643, 533, 60, 62], [650, 534, 61, 66], [653, 539, 64, 64], [653, 540, 64, 65], [656, 538, 60, 66], [651, 535, 67, 70], [646, 535, 69, 68], [642, 537, 69, 67], [638, 542, 71, 69], [634, 549, 69, 69], [633, 553, 71, 68], [640, 553, 69, 67], [651, 554, 63, 62], [656, 554, 60, 61], [658, 556, 61, 61], [659, 558, 60, 61], [657, 561, 62, 60], [656, 567, 61, 57], [658, 567, 59, 59], [661, 566, 58, 57], [662, 560, 57, 58], [663, 556, 56, 58], [670, 553, 49, 57], [680, 547, 39, 58], [687, 543, 32, 68], [689, 545, 30, 60], [690, 539, 29, 59], [692, 534, 27, 64], [686, 551, 33, 55], [678, 557, 41, 65], [674, 569, 45, 61], [671, 574, 48, 57], [669, 560, 50, 64], [669, 553, 50, 61], [668, 541, 51, 67], [667, 538, 52, 65], [667, 535, 52, 64], [669, 532, 50, 66], [669, 530, 50, 64], [671, 518, 17, 70], [672, 522, 47, 65], [669, 533, 50, 64], [665, 542, 54, 66], [659, 550, 60, 62], [652, 551, 65, 63], [648, 549, 63, 64], [644, 543, 66, 64], [641, 542, 62, 64], [640, 540, 63, 66], [637, 539, 62, 64], [640, 536, 64, 65], [644, 527, 67, 68], [651, 525, 68, 70], [659, 524, 60, 73], [665, 538, 54, 64], [663, 540, 56, 62], [655, 537, 64, 62], [645, 531, 61, 64], [634, 526, 59, 64], [624, 526, 56, 65], [623, 528, 48, 57], [640, 534, 28, 45], [648, 540, 16, 45], [648, 555, 12, 33], [653, 548, 11, 35], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [655, 579, 12, 33], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]], \"score\": 0.8439024686813354, \"segmentations\": [{\"size\": [1280, 720], \"counts\": \"^UTf0:aW18H7I7H8L3L4M3M3M3N2N1O2N100O2O000001O001O1O1O00001N2O1N2O1N2O1N2N2N101N2N2O1N2N3N2M2O1O1N2O1O1O1O1O1N1O3M5K4L`ji3\"}, {\"size\": [1280, 720], \"counts\": \"[m\\\\f0?XW1=F9J4L4M3M4L3N1O2N2N1O1O1O2O000001O1O001O010O1O001N2N2O1O0O2O1N2O1N1O3N1N2N2O1N2M3O1N2O2N2N2N2M1000001O0O2O1N3Lej_3\"}, {\"size\": [1280, 720], \"counts\": \"Z]df0>[W19H9J5J6K4L4N2M3M3N2N100O2N100O1000001O01N2O001O1O002M101N2O1O1N2O1O1N2O1N101N2N3M2M3O2N1O1O2N2N001O2M101N100O2N1O3M3NdbT3\"}, {\"size\": [1280, 720], \"counts\": \"]]if03cW1`0C;H7J5K4M3M3M3N2N2N1O2N1O101N11OO1000010O0100N1O101O0O2O1N2O1N101N101N3M2N3M2O1N2O1O1O0O3M2N3N1O2N1O1O1N101O1N2N3M4Kfbo2\"}, {\"size\": [1280, 720], \"counts\": \"TUmf0>]W1;E7K5K5K5K4M3N2M3N1O2N2O0O101N10000010O0000001O1N10001O001O0O2N102N1N2O1N2O1N2O0O2O1N4J4N2O1O1O1O2N1O2N1O1N2N1O101N2N2M4M4MdRh2\"}, {\"size\": [1280, 720], \"counts\": \"QmPg0>]W1;F7J7I4M4L4M2M3M3N1O2N1O2N100O101N100001O1O00001O1O0O2O0O2O1O0O2O1O1N2O0O2O1N3M2N3M2O1N102N1N101O1O2M2O2N1O1N2N2N100O2N5Jibe2\"}, {\"size\": [1280, 720], \"counts\": \"dmig09gV1Y1\\\\O<L4M3N1O2O000003L2O1N101O001N3N001N2O2M2O1N2N2O0O2N2O003K4M3M2O002M2O1O2N001O1N2O0O1O1O2O0N4M2N4JfZZ2\"}, {\"size\": [1280, 720], \"counts\": \"\\\\]`h0<[W1d0\\\\O<G7J6M2M4O00O2O010O1O1N2N3N1N2O1N2O0O1O3M3L3N2O1O2N1O2M3N1N2N2N2N2N1O1O2M3N3L4KeRo1\"}, {\"size\": [1280, 720], \"counts\": \"a]jh03]W1NhhNe0iV1b0C:J3M4N11O1O1N2O2M100O3N4K3N0O1O2N3M3N2N101N1O1O0O1O1O1N3N1O102M4Jfbl1\"}, {\"size\": [1280, 720], \"counts\": \"Xekh0f0PW1b0C6I6M3N2001O1O2M2O0O103L3N2M2N2N2N3N1O2N1O2N1N2O0O1O2M2O1O1O2N3L7Ibbl1\"}, {\"size\": [1280, 720], \"counts\": \"^]oh0e0SW1;C;K3M42N2N2N0O1O2O2M2N2N2O1N2O1O1N2N1O2N2N1O1O2N1O2N2M4M3Jfjm1\"}, {\"size\": [1280, 720], \"counts\": \"]ePi0e0SW1<@=J61N2M101N102M3L3N2N2O1O1N2N2N1O2N1O2N1O1O2N1O1O3M2M6Iejm1\"}, {\"size\": [1280, 720], \"counts\": \"bUXi06_W1g0\\\\O:NO2M2N3O10OO2O0O2M2O2N2N2N1O1O1O2N1O3M2MgRo1\"}, {\"size\": [1280, 720], \"counts\": \"aU]i06eW1;E7H71M2O1O0O1O1O2N2N1O100O2M2O2N3KhRo1\"}, {\"size\": [1280, 720], \"counts\": \"cU]i05eW1<G4K4001N1O1O100O2N1O2N2N2L5KfjR2\"}, {\"size\": [1280, 720], \"counts\": \"gm`i0:aW12K7N22M3N2O2L5KcjW2\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"fekh04fW18J4L4N2O1O1000N3N100O100N4M4L4L2Leja2\"}, {\"size\": [1280, 720], \"counts\": \"bmgh08cW16I7K400O100001O0O2O0O1O011N1O1O2N2N3L5Kgja2\"}, {\"size\": [1280, 720], \"counts\": \"dUUh0>PW1d0D;I7L3L40O101O1N2O1N2M3M3O1O1O1N2O1O2M4L4L2N3N001N1O1O100O1O2N1N3N2N4Ikb`2\"}, {\"size\": [1280, 720], \"counts\": \"ldTg0`0ZW1a0B7H6K5L4M2N2N2N2N2N2O0O2O0O1011N1O10O01O0O101O001O0O2O001O0O2O2N1N2N2N2N2N3M2N2O2M2N2N3N2M3M2N2O1O000O1O100O1O2N2N3M2M6Jeja2\"}, {\"size\": [1280, 720], \"counts\": \"lTWg0e0UW1;G7K4L4L3M4L3N2M3N2N2N101N100100O00010O001O00001O1N10001O0O2N101M201N4L2O2M2N2O0O2N10004J5K5L2O010N2O1N10001N100O1O2O1M2N3N4Ikj\\\\2\"}, {\"size\": [1280, 720], \"counts\": \"QmZg0>ZW1<E:I6K5L4L3N3M2M3N2N101N2O1O0O101O010O000001N101O000O2O1N1O101N2N2O1N2O0O3M2O1O1O2L4M3L201O1O2M3N1O1O1O1N101N101N1O1O2M2O3M4InRY2\"}, {\"size\": [1280, 720], \"counts\": \"Ue^g01eW1a0D;E8J6I6L4L3N2N2M3N2N100O2O0O2O0000010O01O01O00O2O0O1O2O1O1N1O101N2N10001O0N3N1O2O1N2O2M3M2O1N2O7I3L110O0O2O0O100O2N2N1N3N3L3MnRT2\"}, {\"size\": [1280, 720], \"counts\": \"Qecg0=ZW1?E9I5K5L3M4L3N2N1O2N2N1O2N101O01OO100100O010N101O0O2O0O101O0O2O0O101N1O2N2O0O2O2M2N2O1O1N3L5L11O02N6I101N10001N1O101N1O1N4M2M3LnRo1\"}, {\"size\": [1280, 720], \"counts\": \"PUag0=\\\\W1=E8I7K4K5L3N3M2N2N1O2N2O0O2O0O20O0010O000001O00001N101N10000O2O1N101N101N1O2N2O2M2N2O1N101N2N4L4L102N2N1O1N2O0O101N1O2N1N4M2MmjR2\"}, {\"size\": [1280, 720], \"counts\": \"Qm_g0;]W1=E:H6K6K4L4L3O1N2N2N1O2O001O1O0O11O010O000000001O000O2O0O2O0O10001N2O0O101N1O2N1O2O2M2N3N1N2O2M6J3M2O1O001O000O2N101N1O2M2O3K5LmjR2\"}, {\"size\": [1280, 720], \"counts\": \"l\\\\bg0d0UW1;F9K5K4L3N2M4M2N1O2N2O0O2O0O100100O00000010O0001N101N1000001N101N1O2O0O2N1O2O1N2N2O2M2O1N3M4L4L2O1O1O1O1O000O2O0O1O2N1N3N2L6KmbQ2\"}, {\"size\": [1280, 720], \"counts\": \"Q]gg0:]W1=F:G9I5L5K4M2N3L3N1O2O001O1O00001O010O0001O1O000O101O0O2O0O1O2O0O2N1O2O0O2O0O2O001M4M3L5M1O1O1N4L3N1O001N2O001N1O1O2N1O2N3KQkm1\"}, {\"size\": [1280, 720], \"counts\": \"jdmg0c0TW1?F7J6K4M2N3M2N2N2N2N101N2O000O20O0010O000001O000O2O0O2O1O0O101O0O2O0O2O0O2N1O2N2O2M2N3M2O002M7H4M101O001O00000O1O2N1O2N2M2N3LR[f1\"}, {\"size\": [1280, 720], \"counts\": \"lTPh0>WW1b0F7J5K5L3L4M2N2N2O1N2O1N2O010O010O0000010O000001N101O000O2O001N1O2N1O2O0O3N1N2N2N2O001O1M5K3M3O011M3N1N2O1N100O2N1O101M3M4M2Kojc1\"}, {\"size\": [1280, 720], \"counts\": \"PUPh04cW1g0^O;G6J4L3M4M2N2N2N2N2O1O0O20O10O0010O0001O000001O1N2O000O2O1N2O001N101N2O1N2N101O1N1O102L4K4M3N3N1O2N1O1O000O2O0O1O1O2M3N1N4L5Ijbb1\"}, {\"size\": [1280, 720], \"counts\": \"PUPh0<]W1d0@8H6K3N3L3N3L3N2O1N2O1N2O0010O1O00001O01O000001O1N2O000O2O0O2O0O101O0O2O0O2O1N2N2O0O1O3M2K5N2N3N1N3N2N1O1O001O1N1O1O2M2O2M4L6Iebb1\"}, {\"size\": [1280, 720], \"counts\": \"Qmng0=]W1a0A:H5K4L3N3M2N2N3M2O1O1N20O01O01O001O00000100OO101O0O2O001O0O2O1N1O101N101N101O1N2N2N1O2O1N2N2O2M3M4L2O0O2N100O2N1O1N3M3N2M4LeRe1\"}, {\"size\": [1280, 720], \"counts\": \"mdmg0h0TW1<F6I5M3L3N3M2N2O1N2O2N1O001O000001O0000001O01O10N101O001O1O0O2O1N100O2N101N2O0O2O0O2N2N2N1O3N1N3kNYiNm0mV1O1O10000O1O1O2N1N3M3M3N2M4LdZf1\"}, {\"size\": [1280, 720], \"counts\": \"o\\\\lg0c0YW19G;G6J4M3L3N3M2O1O1O1N200O1O001O0001O0O101O0010O1O01O001N2O001N101N2O0O1O2O0O2O0O2O0O2N2N2N2N3M2O6I1O1O1O100O100O2N2M2N3M4L4Ldbg1\"}, {\"size\": [1280, 720], \"counts\": \"mdhg0b0[W1=D6I6K4L4L4M3M3N1M30O10O10O01O001O0001O0000001O1O0010O00O101N2O0O2O001N1O2N1O2O1N1O2M3M2N3N2N3M4L2O1O1O1O01O00O1O1N3N2N2M5Jgbl1\"}, {\"size\": [1280, 720], \"counts\": \"W]bg04`W1e0C:H6J5L2N3M3M3M2N3M2O1N2O1O0100O1O000010O001OO101O001O001O0O2N100O1O2N2O1N100O2N3L3N101N1N3N3L3N3N3L101N11O00O2O0N3N1O2M3M5KfbQ2\"}, {\"size\": [1280, 720], \"counts\": \"TUfg0;^W1=F:H6H7K4L4M3N2M2N2N2N1100O10O01O01O001O0000O2O001O0010O010OO1O2N101N1O2O0O2N2N101N2N2N2M3N1O3L3N3M2O1N2O1O1O000O2N1N3N2N2M6Jdjm1\"}, {\"size\": [1280, 720], \"counts\": \"P]Qh0`0[W1=F5I7K5K4L4M3M3M2N2N2O1O1O02OO01O0000010O0000001O0000100O001O0O1O1O2N1O2O1N101N2N1O1O3M2M3M3N2N3M3M2O1O1O0O2O0O101O0O1N3N3M2M7IcZa1\"}, {\"size\": [1280, 720], \"counts\": \"T][h0<[W1>G9H7J4M3L4M3M2N3L3O1N2O1O0O20O1O1O001O01O1O010O0001O0O2O001N100O1O1O2O1N1O2O0O2N2N1O2O0O3L3M4L3N3M2O2M2O001O001N1O101O1M3N3L6KdZW1\"}, {\"size\": [1280, 720], \"counts\": \"adkh0k0oV1`0E;E6J4L3M2O001O1O0010O001O1O00001O010O000O2O1N101N101N1N3N2O001N1O2N1O2N2O1N2N2O002L3L5M2O1O1O1N2N2O001N3L5L2N4Jfjo0\"}, {\"size\": [1280, 720], \"counts\": \"gTih0a0YW1b0A6K5K4M3L3N3M2N2O1N2N2O0O20O1O01O00010O00000010O001O001O0O101N100O2O0O101N1O2O1N2N101N3M2L5L3O002N3L2O1O1O0O2M201N2O1N2M4L4M3Klbi0\"}, {\"size\": [1280, 720], \"counts\": \"`Tih0i0SW1?B5L4M3M2N4L2N3M2O1O1N2O001O010O001O01O01O000001O1O000O2O00001N100O101O1N1O101N1O2N101O0N4K4N3N2N1N3N3L2O1O0O2M2O101N2M2N3L4M3M6JnZh0\"}, {\"size\": [1280, 720], \"counts\": \"h\\\\eh0V1XV1d0J5N3M2O1N2N1010000O001O10O0000001O1O0O11O01O0O2O00001N1O2O001N101N101O0O2M4L3M5M2N1N3M4M1N2N1M4M2N3O0O1O101N1O1O2L6HScS1\"}, {\"size\": [1280, 720], \"counts\": \"i[[h0R1mV1`0A6I3O1N1O1O2O1M3N1O1O0O2O1O10O1O0001O000001N11O0001N1O200O1O1N101O0O102M2N100O2O2N1O0O2O0N3K4O2N2N3L3L4L3N3N1O1O2N3M4K\\\\[\\\\1\"}, {\"size\": [1280, 720], \"counts\": \"T[Qh0c0ZW1;G5J8K4M3K5K3L4M2N1O1O01O01O1O1N101OO101N0101OO01000000001N1000O11O1O1O0001OO2O001O001O0O3N0M8J2O001O0O2O001N2O0N3N1J7J6M4M2M2N3L4LZl^1\"}, {\"size\": [1280, 720], \"counts\": \"\\\\RPh0;]W1=I:H4K6L4L2M3L3M4M2L4N2N001O1O2N001O000000000000O101M20O2OO1O2M22N00O1N201000OO1O2O00010N3M3N2O0O001N3M101O2L3N2M2M3O2K5I9K5L1O4GX]a1\"}, {\"size\": [1280, 720], \"counts\": \"Qjng0:_W1:K6K3L4K5M3M3L5L2O3L3N3M3L101O00000000000O1000O0100O1000O10001O00O01000100O00O020O01O0O100O2N2N11O01O1N3N0O102M5H5M4L2N3L4K6J6K6FTfb1\"}, {\"size\": [1280, 720], \"counts\": \"jYQh04iW1:E6L4M3L3L4L5L3M4K4M3O2N1N3N2N2N1N2N100O10001OO101OO100000O1000O20N100O1001OO2O000000000O2O001O001O1O0O104L2M2N10001N2M3M3M3N2K5L4L7I9GZ^\\\\1\"}, {\"size\": [1280, 720], \"counts\": \"iaWh07dW19J4M3L3K5N2L3M4M4L3N3M3L4N1N2O1O1N110O1O1O0O10000000O0020O000000O10010OO0100010O0000O1010OO101N2O1N2N2O3M1O1O1O1N2N2N2M2M4K4N4K6J;C\\\\fX1\"}, {\"size\": [1280, 720], \"counts\": \"aa\\\\h0?^W17J5L2M3K5L4M2O2N3M2N3M3N1O2N1O1O2N0O100000O10O0110O01OO011O0000O0101O1O01O0000000001N1O2O1N2N20O01O4L001O000O2N2O00001N2M5H6K6J9G_nT1\"}, {\"size\": [1280, 720], \"counts\": \"aibh07hW19F6J4K5K5J5M2O2O1N2N2O2M2N3N1N2O1O0O110O001O00000O10000O02O10O000000000000O101O0010OO2O001O001O0O2O1M3N3O1N00001N1O2O0M4J5N3K4M6K?^O[^m0\"}, {\"size\": [1280, 720], \"counts\": \"\\\\afh05hW17K5K5J5L4L3M3L4L4N1N3M2N3N2N1O2N00001O0O1000O1000O20O000000000001O00O10010O02NO1000001O0O2N101N1O2O1O003M1O00001N2N2N101O0K6H9J8I9H]fi0\"}, {\"size\": [1280, 720], \"counts\": \"\\\\akh04kW15J6I7I5L5L3M4M3L3M3M2O2N2N2N1N2O001O0O10000000001O000000000O1000010O0000000001O0000000001OO2O001O001N1O2N2N1O2O2N1O1O001N100N3I6L5I6L8HhVb0\"}, {\"size\": [1280, 720], \"counts\": \"VYTi0=bW16I7J5L2M3M3L4M3N1N3N1O1O2N1O1N101O1O001O00000O10000000000000000000001O000000000001N10001O0O2M2O2O001O10O01O1O1N2M3N200O0O2L3N2L4M4L3N3M5K5Hen6\"}, {\"size\": [1280, 720], \"counts\": \"\\\\aZi02iW1>D:I5J5L3L3M4L3N1N3M1O2N2O001O0O1000001O00000000000000000000001O0000000010O0O1O100O1O2O1O001O001O002M2N2N4M10OO2N2M3M2N3K5M3M2N104Laf5\"}, {\"size\": [1280, 720], \"counts\": \"ZQ]i0?]W1<G5K5K3M4L3N2M3M2N2N2O0O2O0000000O11O01O000O1001O0000O011O1O1OO101O00000O100O2O0O1O2O001O001O004L2L3N200O001N2M3M3L4L3M4N1N2O2N2K7GbV3\"}, {\"size\": [1280, 720], \"counts\": \"Yi[i0c0[W1:E8K2N3L3M4M2M2N3M2N2N2O0O2O0000O10O11O001O0000O10000000O11O000001O000000001O00001O0O2N103M2N1O0O2O1N2N2O1O1O1O2L2N3M2M3N3M2N2N3N5K4JZV3\"}, {\"size\": [1280, 720], \"counts\": \"]i[i0:dW1:G5I7J5M2M6K3M2N2L3M3N2O001O0O100O2O0001O000000000O10O100001O00000O101O00000O110O001O2M2O2N1O0O2O0O2N2N2O001O2N1O0O2N1O2N1L4M3N2N3N2M7I3L4NW^O\"}, {\"size\": [1280, 720], \"counts\": \"]Y^i0a0]W16I9I4L9G5L3M1O2M2N200O1O0O2O1O001O0O2O01O1O01O0000010N1O0100010O01O0001O00O1M3JSjNXNoU1j13O000N3N1001OO2N2O001O1N2O1O1O001N10001N1O2O000O2L4J5O]^O\"}, {\"size\": [1280, 720], \"counts\": \"aYci0i0SW1?C5K5M2O1N1N1O2O2O000O0O2O0O100000O101N1001O0O10O100O101O000O100O100N3N11O0001N2O00000O1000001O1N101O0O2O0O2O0O2N1N2O2O2K5M2M3OW^O\"}, {\"size\": [1280, 720], \"counts\": \"di[i0;6KmV1o0F5J5M4L2N3L3O1O1O1O1O1O01O001O01O01OO100001O00001O000O2L201O1O2O01OO2O1N1000001O1N2O1O1O1N2O1N2N2M3N1N3O1N2O1O1N4L4K5L4J3Oj]9\"}, {\"size\": [1280, 720], \"counts\": \"]ZYi0;aW16H7H8I7J5L5I6L4L4N2O100100OO1001O0O2O00100O1N102N2M101N100O2M21O0100M2N3N2N2N2N2M3M4L3N2N3N3J5J7JUVl0\"}, {\"size\": [1280, 720], \"counts\": \"dakh0f0nV1?J5K5K5L3N2N200O010O001N101O1O001O0001O00000000001O0O2O00100O1N2O010N2N2N2N11OO2N1O1N4L3N2N2M3N2M3N2M3N3M3L:DV^R1\"}, {\"size\": [1280, 720], \"counts\": \"[Qih0m0lV1:H7K5L4M2O1O1N2N2O10O10N1000000000000001O1O010O00001N10001O001O1O1N100N3O1N3N1N101N2M2O2M2O3M2O1N2O1N2M3M3M5J_nT1\"}, {\"size\": [1280, 720], \"counts\": \"fQdh01eW1i0YO;H7K5K5L3N2N2O1O001N1001O01O000000O00101O0O2O001O00000000000O2N2O1N1O101N10001O1O1O0O2N2O2M2O2N1N3M2N2M3N1O3M3L5JaVV1\"}, {\"size\": [1280, 720], \"counts\": \"_Qdh0d0RW1>F8J6K5L3N2N2N2O0O2O0001O001O000000O1O1001O1N10010O0O100O110N2N1O2N2N101O00001O001O1O1N2N3M2O1O2M2M4L201N2N1O2N3M4K8D`nT1\"}, {\"size\": [1280, 720], \"counts\": \"jigh04fW1<_O>G8I7K5L3M3N1O2N2O1N10000000001O01N101O01O00000O100001N1O2O001O0O1O101O1O0O101N2N2O001N101O1N2N3M2N2M4M2N2O1N2M3N2M3L5Mbfn0\"}, {\"size\": [1280, 720], \"counts\": \"`YTi0e0SW1;H9H6K4M3N2M3N2O0O10001O000000000001O0000001O00O10000001O001O1O0O101O00000O2O1N1O2O1N1O2N2O1N3M2O1N3M2O1O1N2M4K5K4K6L6I]^c0\"}, {\"size\": [1280, 720], \"counts\": \"cQ]i0d0WW18H7J6K5K6J4N2M4N0O100O1O11O2N1O00O20O0O10O101O01O1O00000001OO1O1O101O001N101N1O1O2N2N2N3N1N2N2N2N2O1N3N1O1N2O1O1N1N3L5G:H_^9\"}, {\"size\": [1280, 720], \"counts\": \"ji`i0`0ZW18F=G7K4L3L4M2O2N2N2O0001O00O2O0O10000001O0000001OO10000000O2O00001N1O1010O01M2O3M2N3N1O0O2N1O2N1O2O1N2O3L2N2O1N3M2O1N101N2M3M4JZn1\"}, {\"size\": [1280, 720], \"counts\": \"ki`i0`0ZW1:E;G8K3L4M2N3N3M1O2O0001OO2O00000O11O0000001O0001OO1000000O101O0O10001N101O1M3M4M2O00001N1O2M4M101O1O1N3N2N1N2N2N2O0O2O0O3M2M3KZn1\"}, {\"size\": [1280, 720], \"counts\": \"Xadi085j0XV1b0J6N2N2N2N1O100000001O0000O2O000000001O0O100000000O1O101O0O1O2O0O2M200O2O1O2M3N001N1O2N2N2O001N3N2N2N1N101N2O001N2M2M5CchNNk]4\"}, {\"size\": [1280, 720], \"counts\": \"PZ^i0<bW15J6K4K5L4L3M3M3N2N2M2O1O1O2N1O1N2N2N2O1N2O1O1N10100001O1O1O0O101O1O000O101O0O2O0M4N1O2O2N1O2M2O1N3M1O101N3M4L2O1O1O001O1O0O2N1N3M2J:F\\\\f0\"}, {\"size\": [1280, 720], \"counts\": \"fQXi0b0WW1:F:J5I8K3M3M3N2N2N3N00001O01OO100002N001O000001O00O1O101O000O1O100O2M201O0O101O00001M201N1O105K1N2N2O0O2O3L3N1O1O1N2O001O001N101N2M3N3L3KZ^4\"}, {\"size\": [1280, 720], \"counts\": \"RRSi07[W1b0E;F9K4K4L4N2N2N2N2N1000000O2O0000O2O00O02O001O0000001O00000000001N100O1O1M4N101N2O002N3M1O0O2M3O1O0O2O1O2N1O1O1O2M3N1N101N2O0O2O001N3L8HQ^9\"}, {\"size\": [1280, 720], \"counts\": \"VRnh0;\\\\W1>C;I6K4L4L4M3N2M3M3N2O1N100000O0010000O10000O1000001N1O110O0O101O001O1O0O2O001O0O2N101N2N101O1O00002N1O1O1N2O1O2M3N3M1O2N2M2N100O2N1O1N4N1N3Mmm;\"}, {\"size\": [1280, 720], \"counts\": \"SRih0g0PW1<J5K5K5L3M4M2N2M3N1O2N1O2O00000001N10001O000O1000000O1O101O001O00000O2N1O10001O1O001N2N1O100O2O1N2O0O2O5K2M2O3M3M1O1O1N101O0O2O1M3M3N4I5Lg]c0\"}, {\"size\": [1280, 720], \"counts\": \"]jgh0=ZW1<F;H7K4K5K4N2M3N2N2N101N1O1001O001O0O011O1O00000O101O0000O0101O000O2N100N2O1O101O001O00001N1N2O2O2N3M1O2N0O2O2N2N1O5K2M101N2O0O2O0O2N2N3L3N2LcUb0\"}, {\"size\": [1280, 720], \"counts\": \"VbPi0e0VW19H7I6L4K5K4M2N3O1N1O2N1O2O000000000000000000O101O0000000000000N102O1O1O0O100O2N1O101O001O001O001N3M3M3N1O1N2O001O1N5K3M4M2M2O1N2O1N100O2Khm;\"}, {\"size\": [1280, 720], \"counts\": \"ZZ^i0a0YW1:H7I6K4M3M3K4O1N2O1O2N1O1000001O0000O2N02O0001O0000000O2N1O1000001O0000001O0O100O10001O00001O0O1N3O1N6K1O2N3M2N1O2M8I2M2O1N5Jae5\"}, {\"size\": [1280, 720], \"counts\": \"Sbdi0i0RW18I8J3M3L4M2O1N2O1O1O2N1001O00O101O01N2OO100000O100000001O000O10001N1000O02O01O01N1O1O1O1N3O001O00010O1O2N2M5L1N1O6J4J6InU3\"}, {\"size\": [1280, 720], \"counts\": \"XRgi02iW1h0YO:G5L4L4L2N2N102M2O100O101O000000000O10O1000000O101O0000000O1O2O0000000O101O000O001O2O0O10001O00010O0100OO2N4K3M2O2N1N2OY^O\"}, {\"size\": [1280, 720], \"counts\": \"dZhi02aW1f0C9J4J5L3M3M2O1N2O1N3N10000O10000O10O02O0O1001OO1000000001O001O0O1O2O00000O1000O01O2O001O0O2O1O1O01N2O10N3N1N1N2N3L3N20W^O\"}, {\"size\": [1280, 720], \"counts\": \"^jei0=^W1:I7H6K4M2M4L2N101N2O1O2O0001O000000000000N2M3O11O10O00O10000000000000001N101O001O0O010O2N2O1O0010OO1001O1O10N100O1N3M2K5M3OV^O\"}, {\"size\": [1280, 720], \"counts\": \"^bdi0>_W15L8H7J4L3M2N2N10O00O2O1O2N2N2O0O1001N10O2N100O0011O001O0O0100O2OO100000001N2O1OO100001O2M01O11O100O0O101N3M2N2N1O1M3K6K;Bmm1\"}, {\"size\": [1280, 720], \"counts\": \"\\\\Rgi0a0^W16J6J5J4M3M1O1N2O1O101N2O2M1O10000O100O00100O100000001O0O001000O1O2O001O001N11N11O1N2OO10O110O10O0001N3M2O0O2N1O2L4I<Ekm1\"}, {\"size\": [1280, 720], \"counts\": \"]jji0<bW16J4L5K7H6L1M4O2M2N2N101OO100O0101N1N2O20O1O000O10O2O0O10O10001O0O2O000000O100O11O1O001O1OO1010O1O2NO1000O2N1N3L4M2OR^O\"}, {\"size\": [1280, 720], \"counts\": \"WRli0=_W18K4J6K6J5L4L2N2O1O000O01000O1O1N2O2N1000O2O01O0O001001O000O10001O00O100000N2O11O01O0010O0O11O010O1N2O1N2M3M1O2N2OY^O\"}, {\"size\": [1280, 720], \"counts\": \"XZmi01jW1<E7K5K4M4L4L4L4L2N3M10O01N2O1O100O1O100O10000000000001O000000000000000O101O000000000000000001O0001N100O2N1N2O1Oa^O\"}, {\"size\": [1280, 720], \"counts\": \"QRVj08dW17J6L3L4L6K3L4L5L3N2N1N1O1O1000O010O0101O1OO1001O0000O0100000O11O0001N2OO2N12N1O1OO1O11O10O00N2O10c^O\"}, {\"size\": [1280, 720], \"counts\": \"labj0;bW16J5L4L5K6I6L2M3O1O2N1N1000O100OO2N2O2O000100O1O0O10O2O0O1O1O1001O0O3N1OO101O0j^O\"}, {\"size\": [1280, 720], \"counts\": \"nYkj03gW1<G7I7J5K5K4M2M5L2O1N2N1O2N101O001N3N1N00001O10001O00001O01O0O10P_O\"}, {\"size\": [1280, 720], \"counts\": \"nimj02dW1a0F6K5K6J5L4L2N2O1N2N2N1O2O0O10000O101N100000000001O1O000000m^O\"}, {\"size\": [1280, 720], \"counts\": \"cQoj0c0YW18I7I6K3L5L3N1O2N1O010O2O0O10O0101O000000000000010O00O1000T_O\"}, {\"size\": [1280, 720], \"counts\": \"eaQk0b0ZW17H7J5L5M3K4M3M4L3N1O2O01N10O2M200001OO1O12N2N1O00O10OW_O\"}, {\"size\": [1280, 720], \"counts\": \"XRjj01iW1?_O<I5J5L5L3M3O1O00001N1O1O1N120O0001O1N02O0O100O11O001O1O1N11N100f^O\"}, {\"size\": [1280, 720], \"counts\": \"eR`j06eW19E:G8K5J6L3L4M3N2M3N1O1N20000010O00O10001N1000000001N10001N100001OO2O00001N10000O`^O\"}, {\"size\": [1280, 720], \"counts\": \"gR[j0:bW19E9I5H9K5M2M3N2N2O1O1O1O1N1001O01N1001O00000O100O010O1O100O101N100000000000000O3N2M2O0000Q^O\"}, {\"size\": [1280, 720], \"counts\": \"jZWj05hW16J5L5E;F9L3N3M2N3N20N100O100O00O2O001N1000000001O0O10O0100O2N1O2O0O100O010O101OO00100000O100000n]O\"}, {\"size\": [1280, 720], \"counts\": \"ejTj09bW1<B9H7I7L4L5L3N1N3O0O101N101O00001O000000O10000O10001OO10O10001O0O101O2N2N1O1N10010O0O2M3O11N00N2O1OU^O\"}, {\"size\": [1280, 720], \"counts\": \"WjTj0`0\\\\W18H5K5G:K3N4M2O1O1O1O00001O00001O0O1000000O100O10000000O01001N10O10O2O1O1N100O2N101N2O1O1N101N2O2N00[^O\"}, {\"size\": [1280, 720], \"counts\": \"SbSj0;`W1:B;G8J6M4L3N2N1N3N1O2N10001O1O001O00001N01000001N100O2O00000O2O1O001O1O1N2O1N1O101N1N2O2N101N2O00O10Oh^O\"}, {\"size\": [1280, 720], \"counts\": \"fYRj0c0TW1<I6J6L4K4N2N3M1O2N2O001O001O1N11O10O00000O1O100000O101O001O0O1010O00O2N2N101N2N2O1N1O2O1O1N2O1N2O0O1O10g^O\"}, {\"size\": [1280, 720], \"counts\": \"]YRj0h0QW1;H6K5L3N2N2N2O1O1N1O2O1O001N101O01O10O000001O00O1001O1O001N1000O0101N101N2O1N2O1N2O001N3N1N2O1N2N1O1000g^O\"}, {\"size\": [1280, 720], \"counts\": \"WiTj0m0lV19K5L4L3M4M3N1N3N1O000O20O0001O010O00000O1000010O01O00001O00O10O100O2O1O1N2O0O10O010001M3O0N3N101O00R_O\"}, {\"size\": [1280, 720], \"counts\": \"TiTj0g0QW1>G5L4L3N3N2M2O1O1N2O1O10O10O1O001O00000O1O11O1N11O0001O0O10O1000O2N101O0O1O100O2N10O1001O1N1000O010Y_O\"}, {\"size\": [1280, 720], \"counts\": \"fXWj0Q1kV15L4N4K4M4M1N[OmiNRON9QV1f0WjN[OhU1h0TjN\\\\OiU1`0RjNRO41L7mU1k0`jNUO^U1i0fjNWOZU1c0ljN]OUU1:TkNElT11WjNFQ18jT1N`kNO[V1MjUV1\"}, {\"size\": [1280, 720], \"counts\": \"WaXj0`0SW1a0G8J7I5M2M3O1N2O1N2O0010O01O00000000O10001O001OO100000O10000O20O01OO10O11O0001O0O2O4L1O000N2O]_O\"}, {\"size\": [1280, 720], \"counts\": \"fiTj02_W1l0^O8I6K4M4L3N3N1N2O1O1O00001O0010O000000O01O101O00010O0000000O10000O101O1O000O100O10000O2O1N101N1000T_O\"}, {\"size\": [1280, 720], \"counts\": \"nioi0=[W1=C;G8M2M4M2O2J5O1O1O0O2N2O0000000010O001N01000N200O20O01N2O1O002NO010001O1O001O000O2O0001N11N1O10100OO10O1Oh^O\"}, {\"size\": [1280, 720], \"counts\": \"TZhi0>WW1?G9I4L5L3M3M3O1M4N0010O0000001O000001O000O11O0O101O00000O2O01O00O101O0O2O0O10000O101O0O2N2O0O2O1N101N2O1N4M1O1O0O2N1O10V^O\"}, {\"size\": [1280, 720], \"counts\": \"Qb_i0c0VW1<G7I6K5L3M4M2N1O2O0O100100OO2O00000000000001O0000000O2O00000O2O001N100O1000001N100O2N2N101N1O100O2O1N4M2N1O1O1N3N2O0O0O2L3N3J6K6HRn1\"}, {\"size\": [1280, 720], \"counts\": \"RbZi0c0UW1;H8H6M3M3L4N2N3M2N101O00010O00010O0000O01000O101N100O2M2O1O11O1O1OO2O001N1O2O00001O0O101N2O1O1N2O1O1N2N2M4M3N1N101N2N2N2N4K5IP^9\"}, {\"size\": [1280, 720], \"counts\": \"QbUi0<YW1b0B9K4K5N2N2M3O1N2N100O2O0O100O1001O01O00010O00O10O1001O001N1O2O001N1000001N2O000O2O0O1O1O2M3N1O101O2N1O1O1O1O2M2O1N2N3M2M3N2O018G9Gde:\"}, {\"size\": [1280, 720], \"counts\": \"miQi0`0WW1=F9H6M4L2O2M3N2N2O1O1N110O0000001O0O0011O001O001O001O00O0101O1O1O1O0O2O0O10000O2N1O2N1O2N10002L4M2N2O100N2O1O2N1N3M3K6L2M6IQ^c0\"}, {\"size\": [1280, 720], \"counts\": \"kaPi0?WW1>G9H6L4M3L3M3M3N2O0O2O1N11O000001O00O010000001N10000010OO100000000O2O00001O0O10001N3M2O1O1N1O101N2M3N101O1O3M1O1N2O2M3K5L4M2KZ^c0\"}, {\"size\": [1280, 720], \"counts\": \"gilh09\\\\W1e0B6L6I5L3M4M2N2O1O0O2O0O2O1O0000010O01O00000O01000O2O00010N1O11O001OO1O101O0O2N1O101N2N2N3M2O1N2O1N2N3M3M2N101O001N101O0O4L\\\\^h0\"}, {\"size\": [1280, 720], \"counts\": \"_aPi0h0QW1;I5H8L3N3L3N2N2O010O1N101O01000O000O10O1O0100001O1OO10O2O000O10000O101O0O10000O2N1O2N101N1O2O0O2N2N2O3L3N001N101O1O2M3M3K4M3M4J`Vb0\"}, {\"size\": [1280, 720], \"counts\": \"eaUi03^W1k0_O8I6K4M3N2N2N2N1O2O2N1O000O1O2N11O2N01O000O10OO2O1000O2O0O2O1N001O100O101O1O1O0O1O10001O0O1O2O0O2O001N10002M3L3N2O1N4M3L2O1N2O1N4L5Jb^9\"}, {\"size\": [1280, 720], \"counts\": \"]Y^i0?[W1:H7I7J6J4M3M3N2N2N3N0O2N2O1N101N100O10000000000000O1O1000000O2N101N2O0O101N2N101N2O1O0O101O000O101M3L302M200O1N2O2N3M2N2M101N1N2N2O2M20g^O\"}, {\"size\": [1280, 720], \"counts\": \"\\\\Yhi0i0PW19I7L3L5L3M3N3M2N2O1M2O2O001O1O00000O1N2O1O0101N101O00O10O100O101N100O2O1N101O0O2M2O3N001N101O000O1O3L3M30O02N1O1N2N3M2No^O\"}, {\"size\": [1280, 720], \"counts\": \"eioi0a0WW1<I6J5L4L4K4N2N2N2O1N2O001O0O2O000O11O001O000O10O1000O1O10001O0O100O1O1O10001O00001N100O2N2O002N1O1N2O1O0000j^O\"}, {\"size\": [1280, 720], \"counts\": \"RZmi08aW1;F9I6K5K4L4N1N3N1N2N201N1O1N20001O000001O01O0000000O1000O101O0O100O2N10000O2N2N1O3N1N3M2O0O2O001O001O001O0O1000f^O\"}, {\"size\": [1280, 720], \"counts\": \"oYci01hW1a0B6I7K5K5L3M2M3N3N1N3M2O2O0000000O1100O01OO01000000O1001O000O100O2N101O0O101N101N101N2N101N2O2N1O2N1N2O001N2N101N2O0O2O0O3L3L30_^O\"}, {\"size\": [1280, 720], \"counts\": \"fiVi09aW1<F6J6L3K5M3M4K4N2M4M1O1O2N100O11O000010O00O10O10O101N100O2N100O1O101O00000O2O2M2O0O3N000O2O1O2N2M2N3N1N1O2N3M2N2N2M3L5K8I]f?\"}, {\"size\": [1280, 720], \"counts\": \"eQih03eW1a0D6J4L4K6K4L4L4M3N1O2N1O2N1O100O2O000000000000O101O00001O1N2O0O2O1N10001O0O2N2O1O1N1O2N2O0O2N2O1O1O1O1O2M3N2L4M3H8N3Lano0\"}, {\"size\": [1280, 720], \"counts\": \"\\\\a\\\\h07cW1=F7J5I7J5K6K4M2N2N2N2N101N1O02O00001OO100O010001O1N101O1O1N101N100O2O000O2O0O2N2O1N1O2O1N2N3N1O1O1N2O2M3M2O1K9FhV`1\"}, {\"size\": [1280, 720], \"counts\": \"mX[h08bW17N3M2O1N2N2N2N3O0O2M3N3L3N2O1N2N2N2O001O0O1O10O01O01O1O0O101O1O1O0O1O2N2N3M2O1O1N3N1N3N1N2M6G9Jb^k1\"}, {\"size\": [1280, 720], \"counts\": \"k`Pi0:cW17K4L3N2OO01N2O10O101OO100O2N10001N2001N002M4M0N1L5L7F8KaVo1\"}, {\"size\": [1280, 720], \"counts\": \"XaZi0>_W19H5K6J4K5N20N2OON2N4M3M7H3L4J7LdVT2\"}, {\"size\": [1280, 720], \"counts\": \"iaZi0<bW16I6J4N1N2O000001O2L8H8_OfVY2\"}, {\"size\": [1280, 720], \"counts\": \"oi`i06cW19E:K4L3N1201O1M6J4H7LfVT2\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"R[ci02iW16L3J5K6I7M300O04M5J4L4BhhNLidQ2\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}], \"areas\": [2416, 2568, 2707, 2734, 2940, 2963, 2227, 1624, 1258, 1227, 904, 877, 543, 368, 276, 128, 0, 0, 294, 410, 1285, 2866, 2929, 3063, 3111, 3072, 3055, 3084, 3056, 3095, 3116, 3085, 3046, 3130, 3095, 3102, 3070, 2995, 3032, 3072, 3100, 3167, 2901, 3312, 3428, 3248, 3411, 3636, 3645, 3490, 3677, 3449, 3405, 3382, 3329, 3523, 3476, 3322, 3287, 3430, 3396, 3735, 3276, 2968, 2318, 2772, 2836, 2899, 2920, 2993, 3024, 3020, 3169, 3137, 3061, 3209, 3384, 3359, 3530, 3500, 3455, 3565, 3141, 3097, 3208, 3198, 3171, 2939, 2935, 2929, 2907, 2873, 2500, 1984, 1797, 1574, 1584, 1530, 1640, 2372, 2441, 2233, 2797, 2672, 2856, 2921, 2832, 2953, 2890, 748, 2808, 2895, 3055, 3084, 3203, 3080, 3182, 3015, 3355, 3146, 3273, 3508, 3580, 3522, 2982, 2882, 3028, 2855, 2859, 2772, 1837, 723, 523, 310, 283, 0, 0, 0, 261, 0, 0, 0, 0, 0, 0, 0]}, {\"video_id\": 0, \"category_id\": 3827, \"bboxes\": [[205, 380, 72, 71], [181, 377, 78, 71], [145, 362, 83, 76], [98, 322, 96, 99], [76, 285, 90, 130], [79, 277, 89, 139], [88, 268, 90, 111], [88, 264, 89, 111], [76, 281, 90, 114], [58, 284, 97, 118], [43, 266, 98, 115], [37, 273, 99, 127], [39, 301, 98, 115], [35, 289, 100, 118], [31, 284, 98, 116], [35, 316, 98, 105], [44, 313, 99, 107], [43, 275, 91, 123], [42, 280, 90, 111], [41, 303, 89, 112], [39, 281, 89, 127], [43, 271, 88, 124], [58, 311, 89, 113], [76, 328, 90, 88], [93, 319, 86, 81], [99, 317, 89, 100], [84, 295, 88, 115], [73, 280, 87, 118], [74, 288, 86, 120], [85, 313, 83, 120], [88, 276, 81, 107], [88, 273, 81, 122], [83, 289, 83, 122], [80, 284, 85, 119], [81, 284, 86, 117], [93, 325, 87, 105], [122, 336, 91, 107], [138, 306, 100, 107], [154, 301, 99, 106], [183, 315, 92, 121], [176, 292, 93, 122], [150, 276, 95, 119], [127, 294, 99, 150], [81, 310, 102, 123], [47, 285, 101, 118], [33, 279, 101, 139], [42, 271, 102, 131], [67, 234, 98, 151], [100, 252, 98, 145], [145, 324, 95, 117], [201, 301, 58, 120], [221, 317, 72, 91], [222, 315, 92, 112], [220, 310, 93, 137], [198, 290, 92, 143], [173, 286, 90, 140], [129, 336, 93, 133], [83, 321, 103, 121], [57, 299, 98, 123], [59, 293, 96, 131], [68, 266, 98, 136], [82, 247, 102, 128], [108, 261, 101, 122], [127, 279, 101, 127], [119, 258, 90, 122], [89, 259, 86, 99], [79, 281, 99, 105], [55, 268, 101, 100], [78, 297, 67, 85], [63, 260, 99, 122], [83, 271, 98, 129], [72, 250, 98, 120], [60, 267, 98, 112], [55, 264, 94, 121], [48, 263, 98, 105], [58, 245, 100, 124], [70, 265, 104, 133], [86, 259, 90, 128], [53, 268, 74, 84], [49, 296, 94, 111], [52, 294, 97, 113], [89, 302, 71, 99], [85, 293, 101, 122], [101, 287, 103, 133], [100, 258, 87, 123], [66, 266, 89, 104], [63, 280, 88, 106], [91, 283, 75, 123], [80, 271, 90, 120], [78, 309, 91, 138], [92, 338, 91, 106], [101, 326, 88, 121], [113, 358, 88, 96], [128, 390, 81, 94], [127, 330, 80, 125], [134, 340, 86, 78], [136, 352, 83, 113], [129, 300, 87, 124], [128, 309, 86, 121], [130, 338, 87, 121], [125, 326, 88, 116], [112, 313, 90, 109], [103, 319, 90, 127], [104, 291, 91, 142], [98, 246, 89, 130], [94, 254, 87, 126], [93, 285, 91, 127], [96, 300, 92, 119], [98, 320, 92, 109], [102, 322, 89, 130], [97, 301, 89, 141], [83, 266, 89, 108], [74, 271, 89, 109], [72, 301, 91, 122], [64, 270, 92, 119], [54, 272, 95, 129], [38, 297, 100, 124], [27, 271, 102, 133], [15, 247, 100, 133], [8, 232, 103, 139], [0, 248, 89, 128], [0, 255, 100, 136], [0, 226, 138, 155], [15, 233, 105, 142], [33, 230, 111, 159], [53, 214, 108, 150], [59, 187, 116, 162], [83, 179, 112, 159], [92, 211, 109, 117], [98, 165, 114, 153], [122, 175, 104, 162], [112, 195, 107, 158], [79, 199, 120, 152], [81, 214, 107, 136], [50, 233, 119, 124], [52, 197, 108, 151], [0, 189, 138, 170], [0, 199, 115, 168], [0, 200, 101, 145], [0, 186, 146, 165], [0, 206, 168, 153], [3, 228, 175, 116], [52, 203, 168, 178], [114, 205, 156, 177], [128, 208, 169, 182], [115, 205, 154, 163]], \"score\": 0.7887788414955139, \"segmentations\": [{\"size\": [1280, 720], \"counts\": \"UdP84kW13M?A:G4YiNQOTV1a1L3M3N1O1N2O1O1N2O1O001O1N10000000000001N1O10001N100O10000O2N1O101N2O1O0O2O0O1O2O0O2O0O2O0O2N1O100O2M2N2O2M2O2M3M2O2M2O1O1N2N2O2N1N3M5IcSXa0\"}, {\"size\": [1280, 720], \"counts\": \"RdR76gW1d0]O`0B3L3N4K4M2M2O2M2O001O001O001O1O100O001O00010O00O10001N101O000O2O001N101N101N101O0O2O0O2O0O100O2O0O2O0O2N101N1O1O1N3N1N2O1O2N1O2M2O1O1N2O1O1N3N1N3M2O2N1Mecna0\"}, {\"size\": [1280, 720], \"counts\": \"jce546MWW1c0N3M7I2M3O0YiNnN_V1_1K3M3M4K4M2N2N1N200O1O001O00010O1O0000001O1O01O0000001O0000001O0010O0001O000000001O000O2O0O2N1O100O101M2O1O1O1O2N1O0O2O2M2O1O1N3M2O1N2N2N2M3N2O2M2N3M2L5I\\\\\\\\Uc0\"}, {\"size\": [1280, 720], \"counts\": \"ijj39aW1:L5J3M3N3M3K5K4M7_iN`NUV1g1N1O10001O3MO001003L4M1N2O0`jNgMYU1Y273M1O4L1O001ejNaMRU1`2njN`MRU1`2ojN_MPU1b2PkN^MPU1h21N20njNYMkT1g2TkNZMmT1f2QkN\\\\MmT1k21000O01O1000000000000000O0100000001O00O100000000001O00001N1001O0O010001O0O1N3L3N2O101N1L4N3M2N2M3M4N2L3L4L4K7H;I5]Omm_d0\"}, {\"size\": [1280, 720], \"counts\": \"^Zo2<^W19H7J6K4M3M3M5L2M4M3K4M4L4J5M4M3M3N1N3M4M3L2O3L2O1N2O1O1O004L1O1O1O6I2O01N11O001N01001O1O002N1O00O1O12N1OO1MTlNULiS1k3XlNTLiS1m341OO2M200010O00O1011N10O0N3N1O2N2L4J6M4J6M2M5L3L4I7I6I7L5K7I8H9H7H;F]mbe0\"}, {\"size\": [1280, 720], \"counts\": \"URS3<ZW1e0_O;I4K6J4K6K5K5L4M2M4M3M2N3N2M3M3N2N2N1O2M2O2M101O1O003L4M2N5J3N00001O001O001O1O2N2NO1O20O001O01O00010O0O1O20O1O01O001O001N10001O1O100N2O2N1O2M3M3M3N2M3L5K3I8D;I7B?H9J6J6H8I7K5Ih]`e0\"}, {\"size\": [1280, 720], \"counts\": \"YZ^33dW1c0XOb0I5L4I6L5J6L3L4M4L4L4L3O1N3M2O3L2O1N101N101ZkNjL`T1X322OO1N2000O100000002N00000000001O0O100001O0000001O00000001OO2O00010O001N101O001O1O0O2O1M400N2N1N4L3O1N1M4M3M4J6J4K6J7K4L5J7I6K4L7F<FdmSe0\"}, {\"size\": [1280, 720], \"counts\": \"QZ^3>[W1:C<K5L4J5M4L4L4K5H8K4M3M4M5K2N3N2M4L2O0O10000O1000001O0O1000000O10001O00000000000000000010O00001O0000000000010O0O1010O0000O2O00100N101O00100N3M3M3N2N2N4K3M4I5L5K5J6L5I5J;H7I7I7ImUUe0\"}, {\"size\": [1280, 720], \"counts\": \"\\\\Zo26fW19G6L4M3K4M2M4M4M2M5L3M3L5L3L4M3M4L3M2M4M4K4L3N1N3N100O100O101O001O1N2O1O00001O0O100000001O00000000001N20O00000001O01N10010O0000000010OO101O01O01O0N3N2N1O2N1O2N2L4L4K5H9J7K4L4D?Ff0XOQnbe0\"}, {\"size\": [1280, 720], \"counts\": \"ZjX22kW1;D9G6L3L4M3N2M3N2N2N2N2M4N3L6K3L3N2N0O2O1N6J2N1O100O101N101N1O2O0O1O2O0O2O0O2O0O2N2O0O2O0000001O0O101O0000001O00001O001O1O001O1O001O010O001O002N000001L3M301N2M2O2N100N3N1N3N2M3M4L5H7G9J6K5I;Ca0ASfPf0\"}, {\"size\": [1280, 720], \"counts\": \"jQf17gW14H;I6H4N4K4M2N3N1O2N101N1O2O1N2N100O3N3L5L2M3N1N2N2N2N2O1N101N101N100O2N2O0O1O2O001N2O1N101N2O1O1N101O000000001O00001O0000010O1O1N2O2N1O01O0O1O2N101N101N100O2O0O3M2O1N2O1N2N2M4M3L4L4L6G8H8I9F;F=AYVbf0\"}, {\"size\": [1280, 720], \"counts\": \"na^1;aW17G:J4L3M3M3N2O2L4M2N2N2N3N3L4L4M2M5L6I100O2O2M7I01OSkNTMhT1l2VkNVMiT1Q30000O100O0XkNnLdT1R3\\\\kNnLcT1S35O11N1YkNlLbT1T3]kNmLbT1Y300O1O2N2O0OOekNbLXT1b310O011N105J3N1OK50000O1N23M1O0000L42N00O1O1O101N100O100O2N1O1O2O0O2N1O10002M2N2N2N2N3L3N3L5K4I7K7G9F?B`0_OS^hf0\"}, {\"size\": [1280, 720], \"counts\": \"bRa15dW1=F7K4K5M2O1N4L3M3M3M5K7I5K5L2N4K101N101djN`MUU1h2N2N0O2O00004K1000001O000OO21O1O0000000O2O1N101N10O1000O2O0O6K1OO1O0100000001OO10000O101N1O100O100O1O100O100O1O2N1O1N2O100O1O2M2O1M4M3N1N2M4N1N3M3K6J6H<C=BSVgf0\"}, {\"size\": [1280, 720], \"counts\": \"^R\\\\12jW17I9H6K4L4L4M2L4O1N2O1N3N3L3M3M7I5L3M3M6I2O0O101N1O2ljNYMmT1h243N00N20O2O1O3L3M10O0O20O3N1N101N1O1O10000O2OO02O1O0O2O3M0000O11O1O0000O010000000O100O2N1000000000000O2O0O1O2O00000O1O2O0O2M4K5L5L3K4L6J7F9H:G8Gc0YOheif0\"}, {\"size\": [1280, 720], \"counts\": \"VRW18bW19K6H6L4L3M3O1M4M3M3M3M3N3M3L5L2N1N5L4K4M00000O10001N2O0O1O100O2O1O1N3N0O002O1N2ZkNmL\\\\T1U372M01O10]kNiL\\\\T1Y353L4M1N1000N21O1O1O00000O01O1010O00O10000000O010000O2O0O100O1000000O101O0O1O2O0O2O1N102M3L4L5C<H9E<D?Da0^OPVQg0\"}, {\"size\": [1280, 720], \"counts\": \"dR\\\\1`0_W17I2N3M3M3L3N3L4N1N2N3M4M5J3N3L:G1O2N001O3M0O10001O0000O11O00000O011O1kjN[MmT1f2PkN]MnT1j20O22N2N1O1N101O1N100000000O10000O10000000O1001O0000O10O10000001N1O1O1O1000O100001N100O1O10000000O02L4M2O1N2N2M4H8L4F=Aa0A>EfUlf0\"}, {\"size\": [1280, 720], \"counts\": \"fZg17eW1<F6K3L4L4M3M3M2O2M2O1N3M5L4K4M4K8I2M2O1O2N1O00001O0O2OO0O21O1O00000O1PkNXMhT1i2VkNYMiT1h2UkNYMjT1n21N22M3N1O001N100O101O0O10000O100000O1O1O1O100000000001O2N000000O1O1O10000000000O2N1O100O1001OO1O1O2L4M2N3M2F;K5I7H9D?D=A?Ebe_f0\"}, {\"size\": [1280, 720], \"counts\": \"nQf1d0WW17L4L5K3N2M4K4M3O2M3M2N3M4K5L5K3M3M2N2M3O0O1O1O3M2N2N3M2N10O010000O2O000000000000001O0O2O000000002N00001O2NO1001hkNaLnS1`3okNaLQT1g3000N3K4O1O11O01O00001O01OO2N101O001M2O2M3L4K5L4L4L5J5L5I8I:F8I<Ed0YOhmjf0\"}, {\"size\": [1280, 720], \"counts\": \"Xjd1<[W1`0E6K4N3K5L4K4J7L5K4L4M4L3M3N1N2N3L4M3N2M3N1N101O001N1000O1O10O101O1OO1001O1O1OO1001O00001O000000O20O1OO101O0000001N100001O01N1010O10N2N2O001O1O1O1O1O1N3L5H8J6H7L4L3M4L3K7K5I8H9H9Ge]mf0\"}, {\"size\": [1280, 720], \"counts\": \"Tcc18^W1?D:J4M4L3M4J6L3M3K6M2M4L4N1N2M3N4L6K4K4M1N2O1O001N101OO010O`kNgL[T1Z351O001O1O000O100000O101O01O2NO1O1000001O000010O00O101O000O2O001O000010O01N1O2O1O101M2N3M3M3N1N3K5J6I7L4L4L4M3L5J7I;CRmof0\"}, {\"size\": [1280, 720], \"counts\": \"ZRa1>XW1?G7L3L4L3L5L4L4I7K6K4L4M4L5K3M3N4K3M2N2N2O2N2M102N1N3N1O2N3M1O000O1000000000000010O000000001O00000O1000000O2O01O01O0O11O01O001N10100O0O2O1M3M3K5N3M3L4L6J4M3M2L4K5G;K5I7J8G8I6J>@^]Rg0\"}, {\"size\": [1280, 720], \"counts\": \"RRf11aW1g0C:H4L4L5J4M4L4K5L3N3L4L3N7I4L3M3M4L3N2N1N2O1N101O1O3M4K102N0000001O0O1000010O1O0000001O0000000000O2O01OO101O0001O01N10001N10010O0O2N2N20O01O2M2O2N2M2M4I7L4M3L4K6J4M3E=F;E:I:FTfnf0\"}, {\"size\": [1280, 720], \"counts\": \"cjX2>3DWW1j0H7I5K5K6J4L4M3M3L4L4M3M6K3L3N1N3N2M2O2N2N3M0000001N101O1O1O1O2N1N10000000010O0000000000000000000001O00000O1O1010O0001O001O001N2O1O10001M2N2O1O2N1O1O1O1N2M3N2M3M3L4L4N6XOZjNkNkU1>niN@c0MeU1>[1H5HjdZf0\"}, {\"size\": [1280, 720], \"counts\": \"cZo2g0WW15L3M3M3N2M4L4M2M3N1O2N1N3N1O2M2O1O1O0O2O1O100O1O001O1O1O001O001O1O1O1O1O1O1O1O1O1O1O001O000000001O000000000000O2O0000010N101O00001O001O00001O01OO100L5O000N2O1O2N1M4L4FPjN`NSV1i0n0J7K4M4K7JTmbe0\"}, {\"size\": [1280, 720], \"counts\": \"Xbd3<`W19I7K5J6K5J3N1O1O1N2O1O001O1O2N1O1O1O1O2N1O1O1O1O1O2N1O1O001O0000001O1O1O1O1O1O001O001O001O00000000000000000000O2O00000O10001O0O2O0O1O1M4O001N1O100N3M1N2O101M3N2M5D;D[iNVOlV1f0;J6KeeRe0\"}, {\"size\": [1280, 720], \"counts\": \"lRl34cW1b0D6K4L5L3M4L4K5L5K3L6K4L2N2O1O2M2O2M2O2N3M1O1N2O00001O1O001O1O2N001O000000000001O000001O0001O0000001O0000001O00001O00000O2O0000001O001O001N2M3M3N1O2N3M2N2M3M2N2M4I7I6I9E:M6K5BfhNJcW1NX]gd0\"}, {\"size\": [1280, 720], \"counts\": \"aZY3>WW1a0E8J6K4L3M5K4L4L4M3M3M3M3M3M3M3M3N0O2O1O3L3N00001O2M3N1O1O2N1O001O001O000001O000000000001O000001O0000001O0000001O001N11O01O1O001O1M4N1N2O1M3N2M4M3L3N2L5K6L2M3N2L4K5G9G:K5K7J8F^][e0\"}, {\"size\": [1280, 720], \"counts\": \"Pbk2?1GPW1P1D9G8I6K6J5M2N3M2N3M3N2M2N3N1N2O1N3N1N2O1O2N1N2O1O1O1O3M1O2N1O001O00000000O11O00000000000001N10000O1010O01O1O001O1O001O1O1O100O1O1N2M4M3M3M4K4M3M3K4N2N2M3M4H8K5J6I7I8I6L4K8Hd]je0\"}, {\"size\": [1280, 720], \"counts\": \"Wjl2l0jV1`0G6J5J6L4J6L4M3L5L2O2M3N2M2N3N2M3N1N2O001O0O2O002N2N2N1O2N1O000O10001O001OO2O0001O0000O101O00001N1001O01O0O110O010N1O20O02N1O1N3N2O1N2M2N3M2M3M5J4M4L3M4M3M3L4K5B>H9H7L6I9GY]je0\"}, {\"size\": [1280, 720], \"counts\": \"lbZ3Q1iV19F9J5M4K4N2N3L4M3N2M3M3M3M3N2M4M1N3N1O2N3M1N101O001OO1O11O3M0O01O1001O1O1O3MM3O10gkNaLQT1`383L00N2M34LJfkNgLZT1X3ekNhL\\\\T1Z35OdkNdLTT1Z3:0^kNiL\\\\T1V3ekNhL]T1W37O0N3O\\\\kNlL\\\\T1T39001OL4OXkNoLbT1P3^kNPMdT1n28N3N1O2N2N3M3L2O2C<L4K6J7H7I8K6J7I5Gf\\\\`e0\"}, {\"size\": [1280, 720], \"counts\": \"bY^3S1gV19H9J5K5K4M2N3M3M4L3L3N3N1N2O1N2O1N2O1O2M3N1O2M101O00O100000000O100O1O11O0010O000000000O100010O01N100O10001N101O1O0LUkNTMlT1m23O1O2M3N2O0O1O2N2L5M3L3M3K4M4K6K4I7G9J6K5J8IkU_e0\"}, {\"size\": [1280, 720], \"counts\": \"ZZ^3=TW1e0@<H7L5L5K4L4L5L3L4M3L5L4L3M3M3M4M2M2O003L3N2N0O2O00000000O10O100001O000O10000000001N1000000O101O001O0O2O1O1N2O010O1N2O1N3N3M1M4N1O1N3N3L4F9K6LXjNoMeU1n1:K5J6I7H6L5K6G8JjU_e0\"}, {\"size\": [1280, 720], \"counts\": \"aRX3i0oV1<F9G8K4L5L3M4M4K4K5M3M4L2N3M4L5K3M3N1N2O1O1N2O001O0O2O1O0O2OO2N1001N1000000001O000000001O00001O0000000O2O0010OO2N3N1O1O001N2O1O1N2N4L3M3L4L5K4K8H5M3M3L3N4L4L3L3M7H8HVmbe0\"}, {\"size\": [1280, 720], \"counts\": \"SZT3Q1kV1;F6J7J4K5L3L6J6K3L4N2N3M2N3N1N2O1N2O1N2N2N2O1O1O1O1N2O1O0000000O1010O000000000O10000000001O01O0000O101O001O01O01O001N2O1N2O1O1N2N2N3M3K4M4M4L3K5L3M4L5L4L3M3L4L5L7I5K6G<BVUde0\"}, {\"size\": [1280, 720], \"counts\": \"VbU3c0YW1;F7I4M5K6I6K5L3K5K5L7I3M4M4L3M2N1O2O1N2N2O1N2O2N1O0O2O0000000000000000000O1000000001O000000000000001O0O11O01O00001O00001O02OO0O2O0O2O1N2O2M3K5M2N4L3M3N3L2N4K4K5J6I<G7I4J;^Oeeae0\"}, {\"size\": [1280, 720], \"counts\": \"]cd37`W1a0C8H8K3M4N2M3M2N4K4M3L5L3M3L4N2M:F3N0O2O001O1N101O1O001O0O1001O00O101O000001O00000000000001O0000000000000001O0000000O2O00001O00010N101N101N2N2N3M2N3N1N3N2M2O2N1N3HYjNSNhU1j1:L4M3L;WO]iN_OYcRe0\"}, {\"size\": [1280, 720], \"counts\": \"mjh4>aW1>B7H:G5K4L4M2M4L4M2M4M2N6J2M2O001O1N2O001O0O101O001O2N2N1O001O1O1O1O1O1O1O1O1OO100001O000000000000000001O00000000001O0O1000100O1O1O1N2O1O1O0O200O1O1N2N1O1M3N3O0O2N2N1N4M1ROljNmNXU1h0Y1I6K5K7G`Thc0\"}, {\"size\": [1280, 720], \"counts\": \"Qj\\\\57fW17J6K4K5M2M5L6J4K6K3M2N4K3N4L2M3N2N1N2O001O1O1N2O1O1O001O1O2N2M2O2N2N1O001O00001O1O1O1O002N00010O00001O00000001O0000010O001O00001O001O001O00001O001O00001O00001O0O1O002N100O1O2N1K6M2M3I7M4L4PORjNSOhV1j0:L4M4J4Kjmhb0\"}, {\"size\": [1280, 720], \"counts\": \"liP69eW17J7I5J6L2M4M2M3N3M3M3L4M2M3N1O2M2O2N1O1N101O1O1O1O1N2O1O2N1O1O1O1O2N1N2O2N2N001O001O0O2O1O00001O00001O0001O0001O0000010O001O01O000000001O01O0001O00000O10000O10001O0O10000O1N2M4N2K5K5lNojNeND8cU1h0Y1L3O3M3L4L3MRVVb0\"}, {\"size\": [1280, 720], \"counts\": \"PSU7`0^W1;E9F8H6G9H8K3M4M2N3N2N1N3N1N2N2N2O0O2O1O1O0O2O1O2M2O002M3N1O1O0O3N001O001O1N1001O01O1OO100001O1OO1001O100OO100O1000000O110O001O001N2O1O100O1O1N2N2O1N2N2N1N3L4K5L4K6I7K5I7I:G9G6K4L6J9CjdZa0\"}, {\"size\": [1280, 720], \"counts\": \"`Zl6l0QW17I7J5I6I8J4L4M4M3L4M2M3N2M3N2N2N2O0O2O1O1N2O1N3M2O1N3N1O001O1N101O000000O1001O0000000O2O01O0000000001O1N1000000001O00001O0001O010OO2O1O1O002N1O1N2O2N1N3M2N3M2N3J6J6L3K6J6K5L6I6J6K6I8H:DVUba0\"}, {\"size\": [1280, 720], \"counts\": \"djk5;]W1=^O`0K4L4K5L4K5L4H8J6L4L4M2N2N2N2N3M2N1N3O2M2O1N100O101O0O10000O100000001N101O0000000000010O001O000000O10000000010O0000000O10001O0O1O20O1O01N101N101O1O1O1O1O1O2N2M2N3M4K4L5G8K5K4L4I8K7J7I8F9IgU`b0\"}, {\"size\": [1280, 720], \"counts\": \"USo44eW1a0@:G8I7G8I7K8I5K7H8H4M4M3L5L6J1O2O2M4L3M2N3N4K5K102N1N2O1O001N2O1O1OO0011O1O000000000000000000001O0001O000O100O20O000001O000O10001O001O0O2M2O2K4M4K4O2M2O2L4M3M3N2M4M3L4L5J5L6I5L4J6L3M3M4J6H8L4M9G>AjlWc0\"}, {\"size\": [1280, 720], \"counts\": \"cbU3=aW18F8H6J6L4M4L4L3M4L5J3M4M4M2M3N2N1O3M2N5J3N6J2N2N1N2O1O1O1O2N1O1O1O1O1O01O01O00001O00001O0000O11O1O0000O2O01O000000000001O000000001O000N3N1N2O2N1O100O2N100N2O2L3O2M3N1O2M3N2N2M4J5L4N2L4K5L4J7J6K5K4L5L6J6HWemd0\"}, {\"size\": [1280, 720], \"counts\": \"`Qk1b0\\\\W16J7I4M4K3N3M3N2M3L5M2M4L5K7J1N2O2N2N2N1O002N2N3M1N4M2O3L2N1O1O2N2N2N1O001O1O00001O00000001O0000010N100001O00O2O0000000000000O101O000O101N10000O1O1O2M2O1O2N1O1O2M2O2L3N3N2L4N2M3M3N2L3L4L4N3K4J8I8I6M3L5JX^Yf0\"}, {\"size\": [1280, 720], \"counts\": \"baY1d0WW1=E7K4K5J5L4L4L4M4L3M2O2N2M4M2N2M3N3M4K3N2N2N2N3M1O1O2M2O1O1O1O2N1OO1002N3M4L2N2N1O3M001O0010O00000O11O010O000000O2O000000001O00001O0O1O1O2N1O1O1O2O001N2N2N2N2M3M3M3N3L4K4M4K5H7N2N2M3M3K6J5J7G9J6J7I6K8Fnmjf0\"}, {\"size\": [1280, 720], \"counts\": \"eid18dW1=B>B=D:H6J4K6K4M3M2N2O1N2N3N2N1N3N2M3N2M2O2N2M3N3M1N2O2N3M1O1O1O001N10001O1O0001O000001N01001O000000O2O01O000000O101N100O2O00001O000O2O1O1N2O2N1O1O1N2N3N2N2N1O2M2N2M4N2O1O0L4K6J50000N3N2L5I6G9D<E<K5K4J9Ci]^f0\"}, {\"size\": [1280, 720], \"counts\": \"`Pd2l0oV1>B;E:H9H5J6L3L5L3M5K4L3M105J1O2O0O2O0O2O001O001N101N2O001O1O1O2M2O1O1O1O1O2N1O1O1O4L3M10O01O0000001O000001O0001O01O000000001O001O001O00001O001N3N1O1O1O1O2M2O2M3N1M3H8M4I7M3L3K6K4M3M4H8J6]Oc0G=E:G;GkVde0\"}, {\"size\": [1280, 720], \"counts\": \"`Ym32fW1g0UOb0F7I7I7J5K4M4L5L3L4N1N3N3L2O2M2O4K3N3M1O2M2O1O1O1O0O2O1O3M3M1N203L3M1N2O1O2N1O1OO1O110O01OO1000O10O2O0O10001O000000010O0O101O001O001O001N2N3N1N2N3M2N2M3M4L3M4K4M4M2M4M2K5M4K4L3L6E=E9I7I8I8I6J5L_nZd0\"}, {\"size\": [1280, 720], \"counts\": \"Uce53bW1f0@:I6J4L5K4M3L4M2O2M3N3L3N2N4L2M2O2N001O1N101O1O1O1N101O1O2N2N1O1O2N3M4L002N1O001O000000001O0000001O0000000000000000001O00O2N1O10001N101O1N2O1N2N2M3M3L4N2O1N2N2M3N2M2M4L3O2M2M5D<D<I9H8J5I:Eg\\\\fb0\"}, {\"size\": [1280, 720], \"counts\": \"Xdk76hW13O1O1O2N00I8N03\\\\hNLoV1k0J4M2M3N101O001O0[NkN^lNV1_S1QOTkNGS1Z1fS1XOXlNi0eS1YO[lNg0cS1^OZlNc0dS1AXlN`0hS1CUlN>iS1JokN7PT1JPlN6oS1JckNfN;_1QT15jkNLTT19fkNKXT1o1O10000000000O010O0010O1O2\\\\MekNWO9h1ST1nN\\\\lNn0fS1\\\\NbkN8V1P1\\\\U1M1O0O1J6N3M3L3N2N2N4L3H9KWdna0\"}, {\"size\": [1280, 720], \"counts\": \"kcd81kW140O16J2N3L10001fhN@RW1a0khNCTW1e0N102N1O1O1O0O2O0000000O1O2FnN`iNW1\\\\V18O1O1L4A?_Oa0O010O1O2O00O1000O1001N101N1O1O1O1N2N2M3N2N3N1N2N2M3N2N2O1O1O2N2O0O3M5L1N3fN]iNR1jV1N7I=A4M3KoTd`0\"}, {\"size\": [1280, 720], \"counts\": \"Rke8:XW1c0I5L2N3M3N3K3N3L4M2M3N3M3M1O2N2N3N3L3N1O2M4M2N2N2N2M3N2N1O1O3M1O0O1000001N101O00000000001O000O101O000000000001O00000000000000000O1O1N2L4O2N1O1O1N2N3L3N3M2O1O2N2N1O102M3M3L5I7G9D<I7I8HZmi?\"}, {\"size\": [1280, 720], \"counts\": \"\\\\[c8<YW1`0E8J6K5M3L4M2M4L5K6J4L3N2N2M4M3L4M3M5L3L4L3L3O2M3N2N1O2N1O1N5L00001O001N10O11O001O00000000001O001O0000001OO2O0O11O1OO10001O000O2O0O101N2L3I8I6O2L3N3K4O2N2N2N3N1N3M2N3M2N2N4L3J7H:E`0C9E=DdTk?\"}, {\"size\": [1280, 720], \"counts\": \"bjg7j0lV1=K4L4L4L3L5L3L5K6K4L4L4L4M3M3N1N2N2O1N3M4L6K2M3N2M3N2N1N3N3M1O2N1N2O001O00001O00001O0000001O01O000O11O1O00O2N1001OL^lNkKbS1X41O1001O10N1O1010O10N2N2M3J6L4M3L4M4L3K6K4L4M3M3L5J6K6J6I;E8H8G8J`0WOehN0]Ti`0\"}, {\"size\": [1280, 720], \"counts\": \"abh6f0UW1:^O`0K4J6J6L3L4M3M5K4M3M3M5K2N3M2O2M4L3N2N3L4M1O2M3N3L3N2N2N1N2O1O00O100001O0000000000001O0000001O00000001O0000001O001O00001O001O001O001N101O1O2N1O1N3M2K6J6C=J5L5L3L5M4L3GYjNSNjU1f1<K5K9E:Eleia0\"}, {\"size\": [1280, 720], \"counts\": \"^cQ5g0TW1`0B7G8K4L4M3M3M4K3O2M3M2O2M3N1O2M2O2M2O1O0O101O001N3N1O2N2N2N2M3N1O1O1O1O1O1O2N2N2N2N1O1O1O1O0001OO2O000001O000000001O000000001N1010O01N2O1N2O1N2O2M2O1O2M3N2N1M3N3L3N3L3L4_Ob0K6J5K6H:I9@b0Chk\\\\c0\"}, {\"size\": [1280, 720], \"counts\": \"oRX3a0ZW18F7N3N2M2M5M8H8H2N2N2O1[jNoMYU1T2ajNRN[U1[2OIejNjMYU1`2O001hjN\\\\MQU1j201O1N3N1O001O2N1O1O1O1O000O2O001O1O2N1O001O001O010O1O1O1O1O1O001O0001N100001O01O00000000001O001O001O001O001O001O1N2O1O1O1O1O1O1O0O2O1N2N3M1N3N2M3L5L3L6K4K6K4TOniNEUV1Q15gNgiND32M003]V12eiNJ0O02N2`V1LhiN3O1GNRW12ViN0PSkd0\"}, {\"size\": [1280, 720], \"counts\": \"hbW29bW19I8_Od0B9I6L4L4L4L4M5K3N1N2O1N2O00000O2O000000000O2O1O001O1O1N2O1O1O1O1O1O1O1O1O1O1O010O2N001O1O2OO01O1O1O1O1O10O000000001O000000000O2O001O00001O1O1O0O2O1O1O1O2N1N2N2O1N2O1N2N2M3L4M2N2L4K6G9I8G:I8IdePf0\"}, {\"size\": [1280, 720], \"counts\": \"cRZ2<]W1>D9F8I7K5L5K4K5L5K5L3L4M3M3N2M2O4K3N1O1O1O001O001N101O1O1O2N2N4K2O1O1O1O1O2N1O100O1O00000001O000000001O00001O00001O00001O00001N10001O001N101N1N3O0O2N2N2N3K4N2M4M3L4K5L4K5I7K5I6K5M3J6L7I8F9H8GYePf0\"}, {\"size\": [1280, 720], \"counts\": \"hYe2b0\\\\W14K5K5L2N3L4N2M4L4L4M3M4L4L5K5K4M4K4L3M2N4L4L3M2N101N3M3M2N4L4L4L3M101O0O101O0000001O00000000000000000O1001O0000000000000O100O2O0000001O0O101O0O2N1N3M2N3N1N2N3M2N3M2N3L3N3N1N3M4L4M2M4L4I7K4J7B?Ab0Bknbe0\"}, {\"size\": [1280, 720], \"counts\": \"ohV3<bW18I4J5J5L4M4L3N2M4M3M3M4M3L3N3M3L4L6J8I2M3N1O1N101O1N4M3M6I1O101O000O2N100O2O0O1O2O000O10001N100000000O10O1000000000000O100000001O000O1000000O101O0O100O1N2N2O2M2N2O2N1N3O0O2N2M2N3K4N3M2N3M3N3M3J7I6F;G:G=@^_ld0\"}, {\"size\": [1280, 720], \"counts\": \"XYW4c0XW1:I4L4K6K4M3M3K4O2N2M2O3L3N3L4M4K5L5K0O2O2N2N2N1N2O1O1O0O2O2N1O2N1N101O00001N10001N1O1000000O10000O100O1000000O10000O1000000000O1000000000000O100000001N002N1O100O100O101N1O1O2N2L4M2N3M3L6K4M4K7J5J6J6Fa0@eVmc0\"}, {\"size\": [1280, 720], \"counts\": \"^Ro42cW1=G9J:H5L4L3M2O1N2N101N3M2N2N3M2N2O2M2O1N3N2M3M6K1N2O2N1N2O1N2O1O0O2N2O1N101N101N101N3M101O0O2O001N2O1O0O101N10001O00001O0O10001OO10000000000000O10O101O001O002N1O0O2N1N3N1O2M3K7L2M4M4L4L3M3N3L9H7G7I6Cc0ZOg]Uc0\"}, {\"size\": [1280, 720], \"counts\": \"TRe44jW15M1O2N4L2N001O2N5KO1O2OO10002O1N0O10O11O002M0100002N1OO1001N2O1O1O2ZNoNYlNR1dS1PO[lNQ1dS1PO[lNS1bS1oN^lNQ1aS1oN_lNQ1`S1QOYlNW1eS1kNQlN^1mS1eN`kNC0R2YT1]NbkNE5l1[T1iN_kNUO3R2]T1RO_kNo0aT1Y1O1O00O02O001O00001O001O1O1N101O1O10O01O0001O1O1O001O1O1N2O1N2N3M3M2N3M2N4L3L5J5K6D<H8H>bNZiN?g]nc0\"}, {\"size\": [1280, 720], \"counts\": \"Ya_3b0ZW17J5M2L5M2O2L4N1N2N3N1N2O3L3M3N2M3N2M3N2M3N1N2O0O1000000O2O0O100O2O0O10000O100O100O2O0O2O0O010O100O10O0101O0O100000O1000000000000000000000O0100O11O00000001N100O1O2J6M4lNijNXO[U17ejNTOh02gT1O^jN3QoXe0\"}, {\"size\": [1280, 720], \"counts\": \"VRS34eW1a0C7I4N2M3N2N3M2N3M2N2N2O2M3M3N3L4M2M2O2M3M5L1N2N101O1N101N2O0O100O101N100O100O100O2N010O10000O100O10000O100000000000O10O10O10001O0000000000O10010O000001O001N1O2N1O3N1N3L4K5M3M3M3I8J5J6L4M5J7G7I8G7GPnSe0\"}, {\"size\": [1280, 720], \"counts\": \"jQU26fW1;F6J5J5N3L3M4M2N3N1N3M3M3M3N1N3M3M3N1N6K1O0O101N100O101N100O10001N1O1O100O100O101N1O1O100O100O10001O000O010000O01000000000000000O10000O100O2O0N2O2N1O1N20001O0O2O1O1N3N1N2M5K3N3N2N2M4F9L4M5K3L5J5L4M5H7G[^oe0\"}, {\"size\": [1280, 720], \"counts\": \"djQ34jW1a0ZO;B;N2N2N2I6M400O0N3M3N2N2N101O001O0O2O1O001O1N101O000010OO100O110O00000O11O00O1O2N1000O10O10O1O1O1N2N2O100O1O1O1O1O2N1N3M3N1M4K6G>XOkV]f0\"}, {\"size\": [1280, 720], \"counts\": \"gQ_2<^W18K5J9I4L4K4O1N3M4L2N3M3N2M2N2O1N2N101N2N2O0O2N2N2N2O1O1N2N101O1N2N1O2O0O2N1O2N101O1N2O0O2O1O0O2O1O1N101O1O1O000O101O0000000O100000000O20O0000001N101O00001O1N2O1N2N2M4M2N2N3M2N4L3M3M3L4M5K4L5K7H7CfiNgNhV19jnge0\"}, {\"size\": [1280, 720], \"counts\": \"kQX3b0[W16L3L4M3L4M3L4M2N2N2N3M2N2N2N2N3M3N3L3M3M3N2M2O1N1O2O3L3M2N1O2O1N101N2N1O2N3M2O0O2O1N101N2O1O1O1N101O1O001N100000001O0000000O10000010O0000000000001O0O100O2O1N2M3M5K4M2N2N2M3L4N3M4L5L5J4L3L5K8H6G>]ORVPe0\"}, {\"size\": [1280, 720], \"counts\": \"UYj2;`W1;G6K4K6K3M4N11000N2N2N2O2M10001N001O010O000001O0010O0000001O0001O0010O12M4L3J5O0O0001O1O10O01O2_NjNTlN[1fS1hNXlNW1hS1lNXkNCh0_1RT1oNSlNS1kS1nN[kN@b0b1RT1nN\\\\kNAa0a1ST1lNXlNT1hS1lNXlNT1gS1VOPlNl0mS1\\\\OmkNd0RT1^OlkNm0iS1SOVlNn0jS1SOTlNn0lS1QOUlNP1jS1oNXlNP1gS1QOYlNP1fS1POZlNo0gS1QOYlNo0gS1ROWlNo0iS1e1O1O00O1000000O1O1010O00O2O000O101N2N2O2N2M3N3L5G8I7H8D>Ca0[Oh0ZOkn]e0\"}, {\"size\": [1280, 720], \"counts\": \"]Y[2=^W17L5J6K4K5M3M2M3O1N3N1N3M3N0O10O10O01O1N200O1O1000O100000O1M2L50O100O10O001O1K4D=J6N10ijN]OSS1a0mlNGmR16UmNLjR1NTmN;kR1CWmN=jR1AWmN`0fR1^OXmNi0fR1WOZmNk0dR1UO\\\\mNm0dR1QO]mNP1bR1oN_mNQ1gT12O0O1001OO2J6K5O01OO2N2N1N100O2O1O01000O0O1O101O001N1O101O1O010O10O100O1N1N3J6N2J6F:J7F:Ejnle0\"}, {\"size\": [1280, 720], \"counts\": \"_QU2b0ZW17K5H7L3N3L3N101N2O1O1N101N2O1O1N2O1N3N2M2O1N10O000010O010O10O0L5N101M3N1O2O00000N3L3O2O0O101O010O1O1J6K4N3N2N2OdkNXObQ1f0m2NjkNZOVQ1d0gnNAYQ1=gnNDZQ19cnNAZM7RT16fnN0ZQ1OfnN3YQ1KgnN7YQ1HfnN:ZQ1EdnN=]Q1BdnN>\\\\Q1B^nNd0bQ1[O]nNf0dQ1XO[nNi0gQ1SO\\\\nNm0ZT1M401O01O100O1O1M2O2000O010O0L41N3OO001_OQiNH4KjV1:d0L6KjUXf0\"}, {\"size\": [1280, 720], \"counts\": \"SYl1;bW1;E5K5L2N2O010O00000001N100O2O1O000M4L5_Oaj=J\\\\n@6nV1j0J101N1J6N3O100M3I7N3M2O0O2O0O2O100H7N2N2O2M2O1O1000001N0N3L32O01O0O1O100O10O001OO2N101O0100O0100O01O01O0010O1O3N1N3M2N3H:K4E<H=[Oe0^Omn[f0\"}, {\"size\": [1280, 720], \"counts\": \"ZiX28_W1`0F7I5L3M3M4M2N2N3M2N2O1N3M2O2M4M2M4M2M2O1N3N0O2O1N2O1N2O1N1O2N2N2O1N2N2O1N2N2N1O2O0O2N2N101O0O2O00000O1000000O101O000000000O100000000000O100O1O002N100N3M2O2N2O0O2N101O1N3N2N2M3N4L2M3N1N4L4L6K6H9F7C>Cjnle0\"}, {\"size\": [1280, 720], \"counts\": \"mig21gW1<I:D:J5J5K4M3M3M3N2M2N2N2O1O1N2N2O1N3M3N2M3N2M3N2M2O0O2N2O1N3M2N1O2N2O1N101N2O1N2N2O1N101O0O2O1N101N2O1O001O0O101O001O000000000O100000000O101OO1000001O0O1O100O1N3M2N3M3M4M2N2O1N3M3M4M2M4M2M5K3N3L3M3M4K6H9E<AZnXe0\"}, {\"size\": [1280, 720], \"counts\": \"nh[31oW13ShN3`W1:K4Mg0ZO:E3N4K3N3L8H2N2O1N6J4M2M2N2O0O2O1N1O2O0O2N1O2O0O2O0O2O1N100O2O0O2O1O001N10001O001N101O001O1O0000000O02O0010O2OO0O2N2O001O1N1O2N2O2M3N1N2O2N1O3K4J6M4L4L3M3K5L5K5J4K6K9F=A8FV^Ve0\"}, {\"size\": [1280, 720], \"counts\": \"gaR26bW1=G8H7K5K3N4K4M4L3M4M3L2N2O0O1O1O2N1O2N10000O10O10O01000O100O01O10O101N10O0002O00O010010O1OJ6L302O1O1O1O2N1O1O1O2M3M3N100O1O2N3M2M3L5J:^O<O1O2N3L2O3LUfSg0\"}, {\"size\": [1280, 720], \"counts\": \"bbm1>]W1;G5L5K4M3M3M3M3M3M2N3N3L3M2N2N1O1O2O0O2O1O0O2N2O00000O1O1000000O2O0O100O2O000O100O10001N1000O02O000O1njNXMkT1Q3NO1M3005K2M010O2O2N2NO1F:1VkNSM_T1W30000000000O11O0O101O000O2O0000001N2N2N2O0J7O5Ja0eM]jNW1jV1ZO=A7I6K]d_f0\"}, {\"size\": [1280, 720], \"counts\": \"]ZQ2c0YW18J6K3L5L4K4M3M2N3M3M3N0O2N1O10O02O0O1O2O0O101O1N2O0O100O2O0O101N2O2NO0101O001N10O11O0O2O0O100O2O000O10000O1XkNSM]T1n2bkNRM]T1X313L3N00001O00000000000O100001O000O100O1O1001N11N101O001O1N2O2M3N1Ma0@3M>]O>E6J5K5J;@UUXf0\"}, {\"size\": [1280, 720], \"counts\": \"Vc_31kW1j0ZNAniNR1PV1a0M101N1O200O1O1N101N101N2O0O10001N3M3N1N1O2O1O0O2O00000OO20001O00O2O00000O010O00100O1000O1000O1O10O10000O0010000O1000O02N100O2N4L4K4M:YOc0oNf^je0\"}, {\"size\": [1280, 720], \"counts\": \"\\\\bZ3b0[W15K5L5L3M3L3N4L3M4L3L3N3M4M6I4M2N1N101N2O1N2O1O1O2M2O1O1N2O1O001N101N2N2O001N101N100O2N100O2O0O2O0O100O2O000O2O00001O001O0000O1001O00O1O100O1N2001OO1O011N100O100O100O1O010O2N3M3N1N3M3N2M4M2M4L6I5J8Eb0\\\\O`0A]mid0\"}, {\"size\": [1280, 720], \"counts\": \"^bn33iW17K6]hNEWW1e0L5K5K4L4L4M3L3O2M3M4L3M4L3N3L3N2M4M3L3N2M2N2O0O2O1O1N2O1N2N2O1N3M2O1N2N2O1N2O001N100O1O101O0O100O101O00001N100000001O000000000O11O0O1N200O1O1O1M4N100O1O10O0100O2O1O1N102N1N3N1N3M2O1G:K3M4K5J8G8G;E;C=WOc^Sd0\"}, {\"size\": [1280, 720], \"counts\": \"UYm3i0gV1l0D001N7J2N2M5L6I5K3N2N3L2O0O2O000O2O0O2N2O0O100O2O0O2O0O2N1O2O0O2O0O2O1N2O1O1O001N2O1O1O001O000000000001O0000000O1N200000000O2O0O101O0000001N1O2N2N2M3M3L5L4L4J6L5I7J7G;@?Cc0^Odfhd0\"}, {\"size\": [1280, 720], \"counts\": \"mib21eW1=H;G6J6K3N4L3M4L3M5L3L3M3M3N3L2O2M101N2O1N101N1O100O1O1O100O10000O2O000O010O2O000O100O100O2O0O100O2OO1000O2N10000000001OO1O100O1O2O001O002N4L1O3L6J4M1N3N4L2N3M4K3K7I9G<E2_OnhN1VW1HRiN3_W1O5LZePf0\"}, {\"size\": [1280, 720], \"counts\": \"RR_2149[W19K7I6J5K5N1N4L3M4L4L3N3L4L3N1N3N7H3M2O0O100O100O002O1O0O1O2O0O100O000010O00001O0101N10L3N3O001O010O100O1L4N2I7O001O1M3N2O2N1O1I7N2O1O002L3O1O1O1M4M3M2O1O2N1O2N2M2O2N3L3L4M4L4KlfUf0\"}, {\"size\": [1280, 720], \"counts\": \"USb33lW14L2M5L3L4L4K5I7K6L2M3M3N2M3M2O1O2L3O2N2N2O0O2N1O2N1N3M3K4L5O1N2O2N1N101O1M2O2M3O0O2N2N1N3O001N1N2N20O100O02O0000O01O01O1O101N1O2N2M4M4K5L5J6E:J8D>A`0\\\\OWnbe0\"}, {\"size\": [1280, 720], \"counts\": \"mYT3l0PW19I3M4K5L4L4K7H8J5J5L3N2M2O1O1O1O1O1O1O1O1O100O10000O100O1000000O100000000O10000000000000000000000000O10010O0000000010O1O7JN1N2101NH]kNSMdT1T30L55J3N1O01O0O2M3N2L3J7K5M2L5J6J7H8H:H7I7I<D;Cgm]e0\"}, {\"size\": [1280, 720], \"counts\": \"akQ3`0SW1`0H8I6K7J4L3L5K5K5L4L4L5L;E2M2O2N2N2N2O1N3M3M2O2M2N3N2N2M2O001O1O2N0O1000O1O11O1O0O1001O0000000000000000001O00000O2O00001O001N101N2O001O1O1N102M3N1N2N3N2M2M4H9K4K6K5J5E;K6K4K6I6J<F>_O\\\\T_e0\"}, {\"size\": [1280, 720], \"counts\": \"Z\\\\c35bW1a0@;F9J6J6L4L5I8J4L2N3N2N2N2N2N2N2O1N2O1N101O1N2O0000002N0O2O00000000001O00O1001O001OO10000000001O01O00000001O0000001N110O001O0O101O001O1O1O100N2O2N2N2M3N3M2N2N2M3M3J7F8L4K7I7J7J9G6Hgcmd0\"}, {\"size\": [1280, 720], \"counts\": \"\\\\cn3;[W1>G8K4L7I4L2O1O?@9H2N2N3M2N2O1N3N1N3M3N4L2M4M2N3L3N001O1O1O1O1N2O0000O11O0000O10001O000O10000O2O0O1000000O2O00001N1O2N1O200O0O2O002N1N2N2M4L3N3N2L3N2N4M3M2M3L4G9M3N2L5L3M5L5K5HRTfd0\"}, {\"size\": [1280, 720], \"counts\": \"ld]4>XW1;E;M2M3M3M6K6J6J3L6K2O1N2O1O1O101N1O2N2OO1000O101O0O104L000O3N1O1ON2M31O3M1O00O1100O0000L4O1O1M31O01O0OjjN]MQU1c2njN_MQU1b2601O0O10hjN]MTU1c2410N1OhjN^MTU1b2ljN^MUU1a2510O1N2O1O1O2N1O2N2N2O0N3N2N2M3N2M3N1N2O5G:D:J5KWSWd0\"}, {\"size\": [1280, 720], \"counts\": \"\\\\]P52^W1b0B?J6J6K5L3M4M3M2O1N3M2N6J6J5K1O001O010O010O1O10O0000101N2N1O2N1N2O1O00001O1O1O0000O14L0O10001O1O0000O1O00100001N3N3M100O7H2O000O10001N10001N4K6K4L4L2N2M2J7N2M3IXRmc0\"}, {\"size\": [1280, 720], \"counts\": \"PSo4g0VW16J6K3N3M1O1O1O1O1O2N2O1N1O101N2O00N10gjNeNoS1\\\\1nkNjNnS1V1QlNPOjS1Q1TlNROjS1o0UlNTOgS1m0XlNUOgS1l0XlNUOgS1k0XlNWOfS1j0ZlNVOfS1i0[lNXOdS1f0^lNZObS1f0^lNZObS1e0_lN[OaS1e0_lN\\\\O`S1d0`lN]O_S1b0blN^O^S1a0clN_O]S1a0clN_O]S1a0clN@\\\\S1`0dlN@\\\\S1a0clN_O]S1a0dlN^O\\\\S1b0elN]O[S1a0hlN^OYS1`0hlNAWS1>klNAUS1?klNAUS1?klNAUS1?llN@TS1a0llN^OUS1`0llN@TS1?mlNBSS1<nlNDRS1=mlNCTS1=llNBTS1>mlNATS1>mlNASS1?nlNARS1=olNCRS1<nlNDRS1;olNERS1:mlNHSS17llNJUS14nlNJTS13olNKSS12olNNQS11olNOSS1OmlN1TS1NllN2VS1KklN5VS1JjlN6XS1HilN8XS1FhlN:YS1DhlN;[S1CelN;_S1CalN<bS1B_lN=cS1A]lN=fS1A[lN=iS1AWlN<mS1CTlN;oS1BRlN2hhPd0\"}, {\"size\": [1280, 720], \"counts\": \"gkW51fW1j0YO:I6K4M2M4M3L2O2N1O100O101O000O100O10000O1000001N2O2N000O10000000O11O1O0001N100000000010O1O1O00O20O00000001O10O01O010O001O2N2N2N1O2O0N3N1O2N1O2N2N1N2O2M3N1O1N3M5L5J5L4J;D_[_c0\"}, {\"size\": [1280, 720], \"counts\": \"W\\\\Z5i0SW19E:E:H8K6I4N3M3M4L2N1O2O0O2N3N1N101O1N2O1O1N101O000O2O00001O001O001O00001O00000000001O00000010O00001O00010O000010O0001O1O1O1O101N1O2N1O2O1N3K3O1N3M4I5Gn0UO8J4H9J5K6J6KTc`c0\"}, {\"size\": [1280, 720], \"counts\": \"ibQ5f0oV1j0^O7G7L5J4L4L4L5M2N3M2O1N1O2N101N2N101N101N2O001O0O101O001O0O10001O001O1O2N2O2M1O1O1O1O001O01O0000001O0000001O1O001N101O1O1N2N2O2M2O2M4M1N3N2M3M3N2M3L4K6L4L3K4M4E;J6F_iNoNfV1j0=K8G9Gd\\\\dc0\"}, {\"size\": [1280, 720], \"counts\": \"lZP5U1eV1;D:K4K4M4L3M4L4L4M3M2N3M3N3L2N3M2N2O001N2O1O1O0O2O1O1N2O1O1O0000000000000000000001O00000000000010O01O0O101O001O1O100O1O1O1N2O1O2N1O2N1O2N101M3M4K4N2M3M3N2M3L4GZjNRNjU1f1<F;J9D;H8J`lfc0\"}, {\"size\": [1280, 720], \"counts\": \"]kR5P1lV1;F8F9J5K4M2N3M2N3M3N1N3N2N1N3N3L5L2N2M2O2M2O2N1O001N3N2N001O0000010O000000000000000O11O000000001O00001O001O00100O001O1O1O1N2O1O2N1O2N1O1O2N1N2N3N1N2N3L4M3L3L4H8G9I9G:H7F:J;BjScc0\"}, {\"size\": [1280, 720], \"counts\": \"nbl4g0SW1a0B<D7J5L3M2N3M2N4L3M2O2N1O7I3L2O0O2O1O001N3N1O2N1O1O1O1O1O0000001O001O1O000000001OO101O0000000000001O0000001O00001N10100O1O1O2M2O2M2O1O1O2O0O2M2O1N2M3N2M4M2M3N2M3M4H:F9B>E:K6H^Thc0\"}, {\"size\": [1280, 720], \"counts\": \"lZ\\\\4341WW1k0A:H7J5J6K5L3L6L6I2N2N2O2M2O1O1O1O001O1QkNTMgT1S301O00001O001N2O1O1O1O00O100O10O101O000001O000001O01N1000001O0000000000001O01O00O20O0001O1O1O1N2N2O100O2N1N2N3M1N3I7M3O1N2N2O0N4A`0E;H8F;J5JolUd0\"}, {\"size\": [1280, 720], \"counts\": \"USQ4e0TW1>E9G7J6I7K4L3N3L4M3M3M3N3L3N1N2O1N2O1O1N101O1O1O001N4M2N3M2N4L2M2O001O00000000000001O01O0000001O00O2O0000010O0000000O2O00001O010O0O2O0O3N1N2O1O2N3L2N2M4I6J7J5M4L3N2N3D;L5F:H:I9G7JaTad0\"}, {\"size\": [1280, 720], \"counts\": \"U[R4>XW1`0A<H7G8K6I7L3K7K4L5L5J5L4K6K3M3M3N2M3M2O2M4M2M3N1O000O2O000O101O00000O2O00O1O11O001OO2O00001O0O100001O10N1000010O001O1O1O001O1O1O1N2M2O2N2N2N2N3L3M4K6K5L5I6K5K4M3K6E;H6L5I7K5K5M5J<Cjd^d0\"}, {\"size\": [1280, 720], \"counts\": \"cij3=YW1?B;J6I7K5K4I9J5K7J5L3M4L5K5K4L6K5J2O0O2N101N101O0O10000O1000000000000O1000000001O000000000000001O01OO2O00001O001O00100O001N2N101O1N101N3N1N3N2L5I6I8I6M4K4L9C:I5L4L5K7I4L4M3L4L6E\\\\fhd0\"}, {\"size\": [1280, 720], \"counts\": \"mie32RW1\\\\1WO;K5K5L3N2N3L4L4M3M4L3M3N3L4L5K4L3N5K1N101N2O0000000O1000000000000O10000000000001O0000001O0O20O0001N1010O01O001N2O1O001O2N100O1EakNSMaT1U32_O^kNXM4LaT1l2>N3M2L6K4J5L4I8J6J7H8J5J5M2N2N3M3N4J8HjUPe0\"}, {\"size\": [1280, 720], \"counts\": \"iad3l0QW1?^O>E9I5K2M4L3N2N2N2O2M2O1N3N2M2O1O2M4M2N1N4M4L3M1N3N2N1O1N1000O1001O00000000000001O000000001N10001O001O00001O001O001O1O1O100O2N1O2N2M2N3N1O2N1N3N2N2N1O2M4M2M3M3L3B?E:J7E:L5M4K5L3M4JW]ld0\"}, {\"size\": [1280, 720], \"counts\": \"RZh3b0ZW19H:G9H6J4L6J5L2M3M3N1N3N1N2N2O2M2O1O2M2O1N3N1O1O2N1N3N2N1O1O1O2N4L3M1O0000000000000000000000000001O00001O0O2O010O1O1O1O1O1O1O1N2O1O2N1O1O2N100O1O1N2O2N1O1N3N2M2N2O1F:D<H9E;E<K4L4M3L4MQ]gd0\"}, {\"size\": [1280, 720], \"counts\": \"ajj3`0]W1:H7H7J5K4L3N3L3M3N2N2M3N2N2M3N1N2O1N3N2N1N3N2N3L2O2N3M2N1O1O1O2N1O1O000000000000001O0000000000O10000000001O001O001O1O001O001O1O1N200O1O1O1O0O2N1O2O1N2O10O01N2O0O2N2K7I6G9F:]Ob0K6L4M3J7Jjldd0\"}, {\"size\": [1280, 720], \"counts\": \"gjo3T1iV1:G8H5K6K2N3M3M3M3M4M2M3N1N3N2N3L2O2N2N2M4M2N2N1N2O001N4M3M1O1OO10000001O1O001O01N1001O2NO1O11O10O0O1O20O1O10N10001N2O1O100O1O1O2N1O2N1N2K6M2M3O2N1N3M2N2N2M4L4L4M2I9^Oa0I7E;K6L5I6LWdcd0\"}, {\"size\": [1280, 720], \"counts\": \"Rci3g0jV1c0F9G7M4K5K6K4L4M4L4K5L3M4M;D2N101O3L3N3L3N2M3M4M000O101O00000O101O01O0O11O001OO10000001OO2N1001O01O0O110O1O000O2O010O0O2O1N101O1N3N1O2N2N1M4M3L5I6K5M3L4M6H6J5H7F;I6K6J8J3M4L6K5Kclid0\"}, {\"size\": [1280, 720], \"counts\": \"PRX3l0dV1c0H8G9G8J7K3N1N3N2M4M4L3M2N2O1N10O10O100O10000O1000000O100000000000000000000000000000000001O000000001O000O20O01O1N101O1O001N101O1O1O2M3N1O1O2N2O1N2M3M3N4K4K5J7H6J7I7L4L2O0O3L8I6Gf][e0\"}, {\"size\": [1280, 720], \"counts\": \"Xjl2f0nV1>G9D<J7J7K4K4L4L4L3N2M3O0O3M01O10O100O1O100O1000001O0O101O000O2O00000O2O0000000010O0000001O001O0O11O010OO1O2O0O2N101O1O1M201O2N001O2N001O2N2M3N1O2O2M3M2N4J5L4I7H9I6L4L3L4M5K4M2M]efe0\"}, {\"size\": [1280, 720], \"counts\": \"Q[j2l0fV1a0H7J5K6L4K5J7K5L4K4M3M4L3M2O2M2N2N2O1N1O1000O10O2O00000000O100001N10001O00O1001O2N1O1O1OO1O101N1N21O01O000010O01N2N1O2O0O20O000100O1N2O2M2O2N1O3M2M3N2N2I7L5L8H5J4L5I6K5L4J6K9I6J5J5KZdfe0\"}, {\"size\": [1280, 720], \"counts\": \"\\\\Z`2=SW1g0@<C<K5K6L4K4M3K6K4M3L4M4M3L2N2N101N2O0O2O0O10O10O01N2000000001O0O2O001O1O0010O00000O1000001M2M300010O1O0001N1O1O2O001N101O01000N3M2O1O1O1O2N2N2N2N2M4M3M3K5K5K4M3L5L4I5L5K6L4K4M6I6K8EY]oe0\"}, {\"size\": [1280, 720], \"counts\": \"niS26\\\\W1o0XO=F6K5K5L4M3L4M3M3M3M3N2M3N2M3N4K3N2M4M3M1O1O002N2N3L101O0000000O012N1O001O000001O0000000O1O100O11O0001O0000010O00001O00001O001N2O1O1O1O1O1O2N1O2N2N1H]kNPMfT1l2:N2N2N2N3M2M3M2N2L4I8I7K5J6@ciNPOhV1j0<L4K5GkUXf0\"}, {\"size\": [1280, 720], \"counts\": \"Xj_1o0kV1=G7I6I8H6L4K5K4M3M4L3M2N2O1N3N2M3N1O1N2O001O1O1N10001O000O2O1O2N0000O11O00001O000000000000001O01O0001O1O0000O2O01O0001O000O101O00000010OO2O1O002M2O1N2O1O1O2N2O0N2M4M2M3N3M2N2N2O1N1N3D=I8J7F8J7F:J6K5IUmef0\"}, {\"size\": [1280, 720], \"counts\": \"`QR11kW1o0QO=C;E9I6J8H6K6J4L4M3L2N2N2O1N2O1O1N101N101O000O10001O001N0100O11O0000001O0000000000O100100O00O1001O1O001O3MO2O0006J2ON1O1O1O2N100O101O00001N1O2N2M4N1O1N3M2O1N3N1N2N3N1N2M3K5I7K4L5I7K5M3L5I7J7L5K3M4L6J5IlUQg0\"}, {\"size\": [1280, 720], \"counts\": \"jPc0m0PW1=@;G7H7K5L4M3L5M2M3M4M3L4M2M3N5J3N1N2O1O001O0O3N1O1O0O2O0000001N1000000SlNSLgS1n3YlNQLgS1Q43N2O10TlNSLeS1n3ZlNQLhS1R412N000000N2O100O1100O00N2N21O00O1O2O0O100O101N1O2N1O1O2M3M2N3L3N3N3M2O1N3L4M3L4L3M4K4M4M4L3M2M4M3K4M2M4K6L6K4J6K;Cafbg0\"}, {\"size\": [1280, 720], \"counts\": \"nX:d0SW1=J7I5I7F;J5K7I5L2N2M3L3O3L200O000001O0KRkNYMnT1e25O1mMkjN_1VU1^NkjNe1TU1[NmjNE1f1RU1eNojNA2i1nT1hNYkNW1gT1hN[kNV1gT1_NekN_1_T1\\\\NdkNa1^U1O3L5hN]iNk0bV1UOaiNj0_V1VOciNg0_V1YO`iNh0`V1ZOZiNm0nV1^OViNImV1Ni0Iih=0bo@3OL2:\\\\V1d0^iNMSV1R1N2M4M1O0000000O0010O00GnMejNT2YU1oMbjNU2]U170cMhjNQ2dU1OC>O01000O100010O0N2O0100O11O0000O01O2O0O1K3NN33J8J2N103L5J6M3K5Le0POjhNORnhg0\"}, {\"size\": [1280, 720], \"counts\": \"f8[1dV12L5L3M4K4K6L3M4L4L5M3L4M3L4M3L3N4L7I1N2O1O1O1N2O001O1O1O1N1000001OO0O200O1N20000O1O1O10000O1N3K400O1M30000O1O2N1O1N2O1N2O2N100N2N2M4M2N2N2O2N1N3N1O1N2L5J5M3N2I8N1O1O3K4L4L8H5J9EfWch0\"}, {\"size\": [1280, 720], \"counts\": \"Z8`2`U10O4K5L4L4L5L7I2M2O2M2O1O2N2N1N2O2N1O1O1O1O1O1O001O000O101O00O10O10000000O10000O100O2N1000001O0O101N1O1O101N1O1O1O1O1N2N200O2O0O101N1O1O2N1N2O2N1O2O0N3M3M3N2N2N3M3M3M3M3M3L5L3L3M4M2M5L4M3L4L3L6J4M5J9GQ^Uh0\"}, {\"size\": [1280, 720], \"counts\": \"a8j03ZO20aV1f0]iNZO20_V1[1LCbiNTO1N[V1W1diNjNZV1_1O1O1N3N1N2O1N3M3N2N2N102N2M2N4L2O0O003N0O2OO100001O0jjN`MkT1k200O1O00102M1O3M5K;E2N102M9H2M010O101N1alNdKZS1`4100O001002N1O1N101OO1000O1000001O0O0100000O10O1001OJklNaKUS1_4klNaKUS1_4klNbKSS1^4nlNaKSS1`4llN`KTS1`4mlN_KSS1`4PmN]KQS1b47N201J5O1H^lNPLcS1o39O000O1L4O1L5N1M2O2N3N1O1N2O1N3L4O0M3M3O2O0O1O2M3M2M3O2N2O0N3M3O1M3N1O3L3L4N2N3M2M3L4K6L5I5J9HQPff0\"}, {\"size\": [1280, 720], \"counts\": \"kPc0b0SW1f0C6J5J5L5K5K4M2M4M3L4M3M4J6K5L5K3L4N1N1O2O2M3N2N1O2M1O1000000O10O11O000O2O000000002M10001N20O0001O0000000000001O1O1OO100O2O000O10000010OO1hM^lN2bS1LalN3_S1JmlNLUS12nlNKSS14olNKQS16nlNJSS14olNKQS15PmNJQS15PmNLnR13UmNKkR1OWlNeNS1W1gR13fmNE[R1;fmNB\\\\R1=fmNB[R1=emNC[R1=fmNCYR1=hmNDWR1;jmNEWR19jmNISR17omNDUR1:omNBSR1=nmNmNSN>QT1d0SnNTOUR1j0mmNSOVR1l0kmNPOYR1o0\\\\201O1N2O2J5N3N3M2M9Fc_\\\\g0\"}, {\"size\": [1280, 720], \"counts\": \"i`Y12jW14M4L3H8L3M4N2M4L3N2O1O1O1N2O1O100O101N10000O010O100kkNaNiQ1_1WnNjN_Q1X1_nNmN]Q1S1cnNmN]Q1T1bnNlN^Q1T1bnNlN]Q1U1bnNlN^Q1T1bnNlN^Q1T1cnNkN]Q1T1dnNlN\\\\Q1U1cnNkN\\\\Q1V1dnNkN[Q1V1dnNjN\\\\Q1V1cnNkN]Q1U1cnNlN\\\\Q1V1bnNjN^Q1W1anNiN_Q1W1anNjN]Q1V1dnNjN\\\\Q1U1enNjN\\\\Q1X1bnNhN^Q1Y1anNgNSN^OWS1l1enNfNRNAXS1j1enNiN]Q1W1bnNiN_Q1W1anNiN_Q1X1_nNjNaQ1U1_nNmN_Q1S1anNlN`Q1S1anNlN`Q1U1`nNmN]Q1T1bnNmN]Q1T1cnNlNVNWOQS1n1hnNoNYQ1T1dnNkN]Q1U1cnNmN\\\\Q1R1dnNnN\\\\Q1R1cnNPO\\\\Q1Q1dnNnN\\\\Q1R1dnNnN\\\\Q1R1cnNnN^Q1R1bnNnN^Q1S1anNhNeQ1W1[nNjNdQ1W1[nNiNeQ1W1ZnNkNeQ1U1[nNlNeQ1S1ZnNnNfQ1R1ZnNmNhQ1R1XnNmNiQ1T1WnNlNiQ1U1VnNkNjQ1X1SnNiNkQ1W1UnNhNmQ1X1SnNfNnQ1Z1RnNgNnQ1X1QnNjNnQ1V1RnNjNoQ1U1RnNjNnQ1V1RnNiNPR1W1omNjNQR1V1nmNjNSR1V1lmNjNVR1T1jmNlNWR1S1jmNlNXR1R1hmNnNYR1Q1gmNoNZR1Q1emNPO\\\\R1n0dmNQO_R1n0`mNQObR1o0]mNoNgR1o0YmNmNlR1S1TmNkNoR1S1SmNkNoR1S1RmNkNQS1S1PmN[NjN8ZT1[1m1O2N2N2N3M3M7I8I4YOihN5LMQf_f0\"}, {\"size\": [1280, 720], \"counts\": \"SaR21iW1=C9I7I7K5K4M3M3N1M3N2M3N21O1O2N00O2O0O1O2M4M2O0001O1mM[jNj1mU12M2N1O2N2N1O1N200jMjM`nNW2^Q1TNXnNo1eQ1mM_nNU2_Q1kM`nNW2`Q1hMflNLd1\\\\2fQ1nMZnNQ2gQ1PNXnNP2hQ1PNXnNo1iQ1QNXnNn1hQ1UNUnNl1iQ1bNkmN^1TR1dNjmN\\\\1VR1dNkmNZ1VR1gNimNX1XR1hNhmNX1XR1hNhmNX1XR1hNimNW1WR1iNimNW1XR1hNhmNX1XR1kNQnNj0nQ1^OlmN`0TR1DhmN<XR1DhmN;YR1FfmN9[R1GemN:ZR1FfmN:ZR1GemN:ZR1FfmN:[R1EemN:\\\\R1GcmN:\\\\R1FdmN:\\\\R1FdmN:\\\\R1FdmN9^R1FbmN9_R1GbmN8_R1H`mN9_R1GamN:_R1DbmN;_R1EbmN:_R1EamN;`R1K]mN2bR1OgmNHYR15jmNJWR14kmNKVR13kmNMVR13hmNNYR1MkmN3WR1KjmN5WR1IjmN6WR1IimN6ZR1HfmN7\\\\R1HdmN3_NmNoS1n0cmN7`R1H`mN8bR1F^mN:cR1D^mN=bR1B_mN>bR1@_mN=fR1_O]mN>fR1A\\\\mN=gR1_O]mN=eR1B\\\\mN=eR1@^mN>cR1A^mN>dR1_O]mNa0dR1^O\\\\mNa0hR1[OZmNa0TU12OO0J7N8HVUie0\"}, {\"size\": [1280, 720], \"counts\": \"^PZ24iW18E9J6K5I8J4L4M3bNgNmkN_1lS1^1N3M3N2M3N2M3N3L3N2N3L6K3M2M3N1N2O1O1N3N001O2M2O0000000O10001O1O00000001O01O0000O1O100O1N201N1N23NO02NKllN^KTS1a4nlN^KRS1e4301O0N2011N100O2N100O1O01O0010O01O1N2N2O1JglNdKYS1Z4hlNfKYS1X4hlNhKYS1V4glNlKZS1T4clNmK_S1Y42N1E\\\\lNVLhS1g3YlNWLkS1m32M4L3L5N2N2N1N3N2N3L2O2L4B=@gjNQNaU1h1`0G:I6J6I<I3L4J;DegWe0\"}, {\"size\": [1280, 720], \"counts\": \"QnW31nW11njNOUR12hmN3TR1NlmN4RR1LnmN5PR1LPnN5nQ1NomN4oQ12emN7YR10_mN2_R10`mN1^R11amN1]R1OcmN2\\\\R1NdmN3ZR1LhmN4WR1OgmN3WR1NhmN3VR1NimN4VR1LimN7UR1JhmN9VR1HimN:VR1GhmN;WR1EimN<UR1EjmN>TR1BlmN?SR1AmmN`0QR1AomN`0PR1@PnNa0oQ1@omNa0QR1_OomNb0PR1^OPnNc0oQ1BlmN?SR1BkmN`0TR1AkmN`0TR1AkmN`0SR1@nmNb0PR1^OPnNd0nQ1\\\\ORnNe0mQ1]OPnNe0oQ1\\\\OmmNh0RR1YOmmNh0RR1[OlmNe0SR1[OlmNh0RR1XOnmNi0QR1WOnmNj0RR1TOQnNl0nQ1ROTnNn0lQ1UORnNj0nQ1WORnNi0mQ1XORnNh0nQ1XORnNh0nQ1XORnNh0oQ1WOQnNi0oQ1WOPnNj0PR1VOPnNj0PR1UORnNj0nQ1VOSnNi0nQ1VORnNi0oQ1VORnNi0oQ1WOQnNi0oQ1XOQnNh0nQ1YOQnNh0oQ1WOQnNi0oQ1WOQnNh0PR1XOPnNh0PR1XOPnNh0QR1WOPnNh0PR1XOPnNh0QR1WOomNi0RR1VOnmNj0RR1WOmmNi0TR1WOkmNi0UR1WOlmNh0UR1VOlmNj0TR1VOlmNi0VR1WOimNi0XR1VOhmNj0YR1VOgmNh0[R1XOfmNf0[R1\\\\OcmNc0^R1]OamNc0`R1]O_mNb0dR1\\\\O]mNc0dR1\\\\O]mNd0cR1ZO_mNe0cR1XO^mNh0cR1UO`mNi0bR1YO\\\\mNg0eR1XO\\\\mNg0dR1ZO[mNe0gR1YOZmNf0hR1XOZmNe0hR1ZOXmNf0jR1XOVmNi0kR1UOUmNk0mR1SOTmNk0oR1SORmNl0oR1TOPmNk0SS1TOllNk0WS1SOilNi0\\\\S1UOflNj0\\\\S1jNZkN1\\\\1U1[S1gN\\\\kN1[1W1VU100N2M3N3GWiNVOmV1e0;L2N2L4M3Mbf^d0\"}, {\"size\": [1280, 720], \"counts\": \"hVc38fW1a0@`0A?A7I2O2L3N001N2N3N101N2N1O10000O1O1O1O2N1O1O2N1O1O2M3N1O1O1O3M2N001O100O2M103M00L4O16J10N1O1N2O1000000001O00000000O2O00000O10010N100O2O010N2O1N2O001O1O1N3N1O001O1N2O000O2J5O2N101O0O2O1O2N001O00100O2N1M3I8M5J4[Od0Aa0ITXWd0\"}, {\"size\": [1280, 720], \"counts\": \"Wfj35bW1=I6J4M2O2M2O0O2O101N1O101N2N1O10000dkNeNRR1\\\\1nmNeNmM1lS1Z1WnNnNhQ1R1XnNoNgQ1U1TnNmNkQ1U1SnNkNlQ1W1SnNjNlQ1V1TnNkNjQ1W1UnNjNjQ1V1UnNlNjQ1U1UnNmNhQ1S1YnNnNfQ1Q1[nNRObQ1R1ZnNoNeQ1T1WnNoNgQ1R1omNYOoQ1h0PnNYOoQ1g0SnNXOlQ1h0RnNZOnQ1f0QnN[OoQ1e0PnN]OoQ1c0PnN]OPR1d0nmN]OTR1b0lmN^OTR1c0jmN^OVR1b0jmN^OVR1b0jmN^OVR1c0imN]OWR1c0hmN_OWR1a0imN_OWR1`0imNAXR1>hmNCWR1=imNCWR1>hmNBXR1g0^mNZObR1l0XmNTOhR1l0XmNTOhR1m0WmNTOhR1m0WmNSOjR1l0olNZORS1f0nlNZORS1f0olNYOQS1g0UmNSOlR1P3001N100O2O00001N100O2M2O2O001O0O2O1O1O1N2N3L4N1O1N2N3N2N3M1M4M3N2N2N1O1N2L5G9K4J6L4L4K5M3K8H6K6M4J5F8N9G5K\\\\`ic0\"}, {\"size\": [1280, 720], \"counts\": \"`fh46gW14\\\\hNI\\\\W18dhNG[W1;32N2M2RkNA[R1a0cmNCZR1=dmNOQR13nmN3lQ1NTnN3jQ1OUnN2jQ1NUnN5hQ1KYnN6fQ1I[nN8dQ1H\\\\nN9bQ1H^nN9aQ1EanN=\\\\Q1BfnNf0RQ1YOonNj0mP1VOToNj0lP1VOToNj0lP1UOUoNk0kP1UOToNl0lP1TOToNl0kP1UOUoNk0kP1TOVoNl0jP1QOYoNo0gP1mN^oNT1`P1kNaoNV1]P1kNboNV1^P1iNcoNX1\\\\P1iNboNX1^P1iNmlNE^2a1fP1jNklNG_2^1fP1gNeoNY1ZP1fNhoNZ1XP1fNgoN[1YP1eNgoN\\\\1XP1cNioN]1VP1aN[mNLHOP2e1lP1\\\\N\\\\mN7CL4Mg1f1UQ1ZN]mN?EC3L^1h1^Q1\\\\NYmNm0X1f0`Q1^NWmNm0X1e0aQ1_NVmN<LG^1^1`Q1aNSmN99_OU1h1^Q1XOQoNi0nP1WORoNi0oP1hNQmNJn1`1QQ1UOonNk0RQ1mNVoNS1iP1nNRmNXOl1l1RQ1SOlnNm0UQ1SOjnNn0UQ1oNonNQ1QQ1lNQoNU1oP1nNinNW1VQ1oNRmNWO335DJU2kR12UmNnMNQ2lR12TmNPNOo1kR13TmNlM0U2lR1f21N2O00001O1N101N1N3O2N1O2M2O1M4M2O1O2N1O3L3N1M4L3K5M4I7H9J5L6K5I8I7I6J6K6I6K3M4L6I5LkoWc0\"}, {\"size\": [1280, 720], \"counts\": \"]W\\\\41cW16`hNM]W1<O0O2N2fjN^OTS1?QmNDgR1a0XmNE`R1?_mNJUR1:imN1lQ1OUnN3gQ1NYnN5dQ1L\\\\nN6bQ1J^nN7`Q1J`nN7^Q1IcnN6^Q1JanN7^Q1IbnN;ZQ1FenN>XQ1AgnNc0WQ1^OhnNc0WQ1]OhnNd0WQ1]OinNc0WQ1\\\\OinNe0WQ1[OinNe0WQ1[OinNf0VQ1ZOjnNg0UQ1XOknNj0TQ1QOnlNWOm1j1TQ1mNSoNT1lP1mNRoNT1nP1lNQoNU1nP1lNRoNT1nP1lNSoNS1mP1mNSoNS1mP1kNUoNU1kP1eN[oN[1eP1dN]oN[1dP1dN\\\\oN\\\\1dP1dN\\\\oN[1dP1fN[oN[1eP1fNYoNZ1hP1eNYoN\\\\1fP1dNZoN\\\\1fP1dNZoN\\\\1fP1cN\\\\oN\\\\1dP1dN\\\\oN[1eP1dN\\\\oN\\\\1dP1cN\\\\oN^1dP1aN]oN_1cP1`N]oNa1cP1\\\\N`oNd1`P1\\\\N`oNd1`P1\\\\N`oNe1_P1ZNboNf1_P1XNboNg1_P1XNaoNh1`P1VNboNj1YS1001000000000000000O1O100000O010000O1O01F9_Oa0O101O000000000J3UOVMelNg2SS1iMXlNBM22a2nS1]MVlN34f2gS1XMVlN31d2lS1VMUlN40d2[T1WMikNg2oS1ZMYlNc2bT1O2OO1O11O3M3N5K3L?A7Ge0]O?Abg`c0\"}, {\"size\": [1280, 720], \"counts\": \"`oR3:\\\\W1i0^O8H6L2N4M3L3M2N3M4M5K4K3M3N2M3N2N2M3N3L5L4K4L3N1N3M4L2M3O1N2O0O00011N2O1N01001O1O0]lNiK\\\\S1Y451OO1O10_lNgK[S1^40102N3M00000010O01N101OO1O1N2O1O1001O0000001O1O1O00001O001O0000O1O1N3N1O2O0001O01O01O10O00001N1O1O2N1N3M2O1O1O2O0O2N1O1M4G8M4M3N2L4N2L4J7K5K5H?dN]jNCcnZd0\"}, {\"size\": [1280, 720], \"counts\": \"PbU3:fW11O1N2O001OO100010O1N4M3M0000001O000001J46K001O1O1O0O11O01O0000001N101O1O0O1F9N30O0101O01OO101N010N2GaNniNb1QV1aNliN`1SV18N1O2N0O2kMTNWlN0c1R2UR1YNYmNAVO[2aS1VN[lN>N_1gS1h1001O02N01N2N100O1000001O00001N1000001O001N1O2O0O1O2O0N2N2O1O2N1O1N2M4K5M3L4M3N2L3O2ROjjNnNYU1o0ljNkNZU1n0ljNkN^U1k0T1D``gd0\"}, {\"size\": [1280, 720], \"counts\": \"cgn16fW1?D1O1000000O10001O0\\\\jN\\\\O^N9oT1;`lN5aNXOaT1c0mlN;TS1FklN:VS1EklN;US1EilN=VS1DilN=WS1CilN<XS1EglN;YS1EglN<XS1DhlN>VS1BjlN?US1AklN>VS1AklN>VS1AklN?US1AjlNa0TS1_OmlNa0TS1^OllNb0TS1]OnlNb0TS1VORmNk0oR1oNTmNT1kT13NO01O1N2O01O0N21O10O10O01O001O1O1N20O01O0100O00O101O101N2M101O01O0O10O20O1OO1O100100OO010O20O00000001O01O0O0101O00000000001OO10O10001O000O10001N010N1O3L4M3M3L3N20002N1N100O100OhU_e0\"}, {\"size\": [1280, 720], \"counts\": \"_XQ2>ZW1=_O>SOjNhjN\\\\1SU1PO\\\\jN`1YU1g0Mc0]O2M2O3M3N2M3N2N1N10O10O10001N1O3N001N100O101O1O0O2OO100O02O1OO010001O1O000O101O001O1O1O1O001O1N2O00000O0100O1O10O1000O1O11O1O000000001O00O1001O1O00O100O1O100O1O1O010O1N3N1N4K5J6J6K5K6J5K5J9G;G9F:E=D8G:D>[OP`je0\"}, {\"size\": [1280, 720], \"counts\": \"U7W2iU11N101N2N2O001O1M3N2O1N10001N2O0O2O6I2N2N2N2N5L2N4L3L2O2M101N102M2O000O1O2O1N3N1N1O00O21N2O0O2N2N100O010O102M2O2NN11000O01klNZKPS1g44001O2N1N01O11N2O1O0O3NO1O100000O2O000O1000O10000000O2O1O00000000O100001O0O3N1OO100O1O1O100O1O1O2N2M3M3N2N3M2N3M3K4M3O1N3N3M2M4M3K6J6H8D:L5H8L5J7H9H7Gg0lNmoef0\"}, {\"size\": [1280, 720], \"counts\": \"d6\\\\3dT11N2O1N2O1O1N3N2N2N2N2N2M3N2N1N2O3L3N1O2M2O001O001O00000O2O000O10000O10000O101O000olNYKiR1h462O00O1ORmNVKhR1m44006J0O10O1M3001O1O2N1O00001OO100001O00000000000000O11O0000000001N100N2O2O00100N2M2101O0001N1O2N2N2N2N2M3L4M5I6M2L7I6K5K6H7A?I7J6D=Fa0\\\\Oe0WOSgbg0\"}, {\"size\": [1280, 720], \"counts\": \"]6Z3fT10O10003L3N2N1O5K1O7I1N10IUlN\\\\LhS1n300000001O001O1N2O1OO100O1001OO100O11O2NO100O1O10000O_lNjKZS1U4dlNoK[S1Z41AblN\\\\L^S1R4100hN`lNoM`S1R2alNoM]S1o1mlNiMSS1X2UmN`MjR1a2dlNlL;4H2YS1U3mlNgLO2TS1V3mlNiLM3VS1T3llNjLM2XS1T3mlNgLL5WS1n2clNmLf05hR1l2dlNnLe05gR1n2amNQM_R1o2bmNPM^R1m2fmNQM[R1f2omNWMSR1c2i1O001O001O1O001N1O2E;O100ISjNXNnU1h16N2O1O001O0O20O01N100O2O1O0O2O0O10100O1O2N9YOQiNGXW13=M4L3M\\\\XTh0\"}, {\"size\": [1280, 720], \"counts\": \"m5]3cT1002M2O00001O1O1O1N2O1O1O1O1O001O1O1O2N1O1O1O1O1O001O1O1O2M3N1O1O1O1O1O2N1O1O000010O0001O00001O00010O002N2N1O100O010O0000001O10O01O0001N11O010OO1O2O01O00O2O00000000001O00001O000O2O00000O2O0000000O2O00000O1O1000000001OO1KclNgK]S1X4601O000O2O1O0O2N100KVlNTLkS1k3510O100O1L4M3OO02O1L4N2M2J7H9M4L2dN\\\\1N2M5K4M5K5H:FbP\\\\f0\"}, {\"size\": [1280, 720], \"counts\": \"b6T2kU1100O6K001O1O001N<E4L1O1N1000000000001O6J5K00N22N2N1O00O11O1O1O0000001O00O1001O001dkNbLTT1d31O1O1N2N21O1O3M3M1O00002N3M01O00001O00000000010O0O1001O1O1O0001O00001O000011N3M10O0000000O2O03M3M10N1O12N2N01O001O10O01O010O010O001O01O1O2NO101N100000000O1O10O010000O100O1O1N2N2O2N1N2O1O1O101N1O1N3N1N3M3N1O2L4N1O2O0N3N101O0O2N1VOijNjNXU1R1njNkNTU1R1SkNgNPU1V1o0N2N3M3L6J6J6K6H7Hn_`e0\"}, {\"size\": [1280, 720], \"counts\": \"jo32dW1d0A=F7K6J3M2O5K3K1O012N1O1N01O12M2O1O1N2O000O100001O01N5L1O001O001OO001O1O1M35K1O001O3MK5004L4L1O001O0000000000O1000001O00O20O0J6K5N2N2O11O1OO1O1000001N1O1O11O01O000000000O100O100010O1O0000O2N1M3O10000013L1O01K43N1N0001O0010O0000001O6I5LO1001O5K0000001N2O1O1O001N2O1O001O3M3M2N2N001O00000001O0100O0001O1O0O2O010O03N2NL4N2M3M3N2N2M2O101N2N4M5QOfjNlNbU1i0R1G9I6H]oSe0\"}, {\"size\": [1280, 720], \"counts\": \"oVQ23iW1<D:I6J5L4K5L3L5K4M3L6K5K4K6K4L4K6K4L4K3N2N3M2N1N3N2N1O2N1O1O1O1O2N1O2M2O1O1O1O1O1O2N1O1O1O2N001O1O1N200O1O1O001O1O2N2N1O00010O001O001O1O2N1O100O00001O001O1O001O001O01O00000001O01N1000001O0000000000001O000O2O001O00000010O01O000010O1O100O00010O0001O00000000000O2L300M3O20O1ON2^OolNoKXS1`3l0O2O1N2N2O1N1O2M2N3M3M3L3N2N3N2N3H8E;K4L5G8L5M3M3N1N2M7]Oa0KP`_c0\"}, {\"size\": [1280, 720], \"counts\": \"mg^4c0WW1=E7K3L4N1N3M3N2N2O1N3L4N3K5L4L7I9G7J7H5K9G6J6K4K4M4K3N0O1O2O1N4MO0014K2N2O0O1O1O2N1000000000000O1000001]mNgJ]R1Y5cmNgJ]R1Y5cmNgJ]R1Y5amNjJ^R1]5O000000O1L4N25K1O100O00O1N2010O0001O01O0010O00001O00001O0000000O100010O000000000001O000O2O0010O00O2O0IXmNUKjR1o41O1M4M201N100O2O0J7O1O0N3N1N2N3N1O2O0O2N1O1O1N3M2M3M4N1N2O2M2M3N3M2M4L4M3N2N2M3N3M2N3M2L5K6I6Ag0iNjPaa0\"}, {\"size\": [1280, 720], \"counts\": \"hWP5e0XW16K5K4L3M3M3L4N2N1O2N100O2M3N2N3M4L:E9G;G;D9G>B8I4K2O2N2N00003L5L2N1O3M1O1O4L2N001O2N1O0O10001O00O1O11O1O1N10000O10O100000000000O1000000000O10000001O000000000000O10001O0001O01O00001N1000001O00001O0O2M2O2N1O101N101O000O2N1O2N1O1O1O2N100O1O2M2O1O2N1O2M2O2N1O2N1O2N1O2O0O2N2N101M2N3M2N2N3K4M4L3L5L3N3L3M4L3M4L4L4L3M4L3M4K5L3M4M3L4L4M3K6J5L5KWX_`0\"}, {\"size\": [1280, 720], \"counts\": \"ko_4`0\\\\W18I6J4M3M4M2M3N2N2M3N2O0O2M3M3O1N4L4L3L4M2N1O1OO1I7N30O100O10O1000O1O1N20000O10000O10O100000010OO10000000O100N3M2000000000O1000O101M2O1000O10O1O1O11O2N1OO1O100001O01O0N1K601O000000O11O0001O7I3MO1O10000002N1OO1OhlNmMeP1R2TmNQN3Ob0KXO106nR1n1TmNRN2Ne0a0UR1_1SmNSN3Me0`0WR1`1PmNSN4Mc0c0XR1^1olNSN6L`0g0ZR1[1olNSN6K=n0\\\\R1S1QmNTN6J:S1_R1o0QmNUN5G9Z1aR1Z1SmNZOnR1g0PmNZOQS1i2101O00001O0O1O2O00000O2O0O100O2N101O1N1O2O000O2M2O2N1N2YNZlNoNlS1l0flN]NcS1`1dlNXNaS1e1^1TORjNZO[V1>g0F;L3O2N3GSYba0\"}], \"areas\": [3627, 3875, 4689, 7442, 8673, 9517, 7918, 8093, 8038, 8526, 8235, 9328, 8766, 9033, 8855, 8282, 8417, 8344, 8079, 7983, 8888, 8694, 8165, 6281, 5601, 7091, 8038, 8185, 8269, 7596, 7040, 7769, 8141, 8186, 8241, 7674, 7986, 8305, 8040, 8734, 9161, 9373, 11499, 9664, 9073, 10529, 10417, 11764, 10808, 8515, 3578, 3781, 8089, 9773, 10179, 10202, 9713, 9782, 9594, 9685, 10255, 10190, 10144, 9676, 6473, 6964, 8092, 7645, 4721, 9169, 9537, 7158, 4758, 4049, 5622, 9479, 10395, 8743, 4441, 7895, 8254, 5842, 9691, 10390, 8785, 7030, 5864, 6343, 8183, 10032, 7991, 8201, 6697, 5316, 5370, 5253, 7517, 8389, 8565, 8514, 8367, 8107, 9118, 10173, 9082, 8482, 9122, 8497, 7903, 9030, 9812, 7990, 7741, 8779, 8647, 9764, 10225, 10373, 10092, 6292, 8247, 10548, 14403, 10570, 8186, 8293, 14475, 9568, 9745, 10567, 9241, 7803, 14950, 7390, 4686, 13618, 18555, 16815, 10128, 19648, 19038, 13467, 21912, 21463, 22724, 13152]}, {\"video_id\": 0, \"category_id\": 3827, \"bboxes\": [[292, 507, 55, 72], [283, 496, 57, 76], [280, 480, 63, 84], [278, 479, 68, 87], [275, 482, 72, 82], [268, 473, 80, 86], [268, 469, 87, 91], [272, 472, 92, 94], [277, 461, 95, 102], [285, 447, 99, 110], [300, 450, 101, 110], [320, 446, 99, 108], [332, 448, 101, 103], [335, 454, 97, 97], [324, 462, 91, 92], [305, 481, 81, 81], [289, 496, 76, 71], [288, 495, 62, 69], [293, 508, 53, 65], [301, 509, 48, 61], [295, 501, 53, 64], [289, 508, 53, 65], [285, 507, 55, 64], [287, 494, 47, 47], [295, 494, 52, 66], [299, 495, 46, 55], [295, 497, 48, 56], [293, 508, 49, 51], [292, 506, 49, 45], [301, 499, 50, 59], [310, 497, 39, 58], [317, 497, 50, 59], [321, 502, 48, 56], [321, 506, 48, 54], [322, 506, 49, 55], [324, 506, 48, 60], [328, 517, 35, 51], [323, 519, 48, 47], [321, 528, 48, 44], [323, 536, 50, 44], [332, 530, 49, 45], [356, 529, 33, 42], [342, 533, 51, 49], [348, 522, 49, 44], [355, 512, 50, 46], [355, 508, 51, 48], [355, 503, 51, 43], [362, 499, 51, 45], [358, 502, 52, 43], [353, 501, 50, 44], [352, 498, 49, 48], [350, 503, 49, 47], [344, 512, 47, 49], [344, 520, 35, 35], [343, 513, 45, 43], [363, 534, 28, 28], [368, 509, 24, 45], [345, 501, 50, 48], [345, 498, 50, 50], [343, 500, 51, 49], [343, 495, 49, 50], [343, 494, 51, 50], [343, 493, 51, 50], [337, 492, 53, 49], [334, 492, 53, 49], [333, 496, 53, 46], [330, 494, 53, 46], [326, 490, 54, 49], [325, 493, 53, 48], [329, 488, 37, 52], [336, 487, 37, 53], [340, 490, 35, 51], [341, 504, 20, 30], [338, 480, 41, 54], [334, 475, 40, 54], [331, 482, 42, 53], [325, 486, 49, 50], [317, 488, 50, 53], [314, 506, 49, 54], [309, 506, 50, 54], [311, 506, 48, 51], [320, 511, 44, 49], [328, 536, 37, 21], [328, 507, 48, 60], [327, 490, 48, 55], [328, 499, 47, 54], [326, 502, 47, 48], [324, 508, 46, 49], [323, 513, 12, 32], [321, 510, 45, 50], [318, 525, 33, 55], [314, 503, 50, 48], [320, 509, 49, 50], [324, 505, 51, 52], [333, 515, 49, 45], [333, 508, 50, 49], [335, 512, 44, 47], [336, 511, 46, 40], [338, 505, 49, 49], [342, 529, 45, 39], [0, 0, 0, 0], [337, 513, 50, 44], [338, 508, 50, 48], [340, 496, 50, 46], [348, 501, 48, 42], [377, 513, 22, 23], [353, 534, 43, 29], [358, 515, 41, 34], [356, 500, 51, 63], [353, 500, 52, 62], [348, 483, 51, 58], [340, 488, 51, 61], [358, 513, 30, 29], [0, 0, 0, 0], [344, 511, 45, 54], [343, 514, 48, 54], [340, 503, 50, 53], [336, 498, 50, 51], [334, 498, 51, 50], [332, 499, 48, 50], [333, 499, 48, 46], [335, 498, 51, 47], [340, 504, 50, 49], [344, 502, 50, 50], [353, 500, 47, 49], [353, 501, 51, 50], [350, 502, 50, 49], [345, 504, 49, 40], [336, 505, 48, 33], [325, 506, 49, 34], [318, 493, 49, 43], [319, 490, 45, 46], [308, 497, 49, 51], [305, 502, 51, 50], [309, 500, 50, 46], [312, 507, 51, 51], [316, 516, 49, 47], [324, 517, 48, 45], [333, 519, 48, 44], [340, 522, 47, 45], [344, 524, 47, 46], [343, 533, 47, 46], [359, 539, 26, 45], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]], \"score\": 0.8672199249267578, \"segmentations\": [{\"size\": [1280, 720], \"counts\": \"e`];f0UW1;G7I8I4K5M3M2M3N1O1O2O0O1O10000O2O00000000000000000000000000O2O0000001N2O1O001N2O1N2N3M2O2N2N2M2O2N2M4K8B<H9GQ_`>\"}, {\"size\": [1280, 720], \"counts\": \"gXR;1hW1d0]O;H7H6K5L4M3L4M2M4M100O2N100O1O1O101O0O1000O11N1000O1000010N10001O001O0O2O1O001O1N101O100N2O1O1N2N3N1O4L3L4J5Jb0XO_Wi>\"}, {\"size\": [1280, 720], \"counts\": \"\\\\`n:a0RW1b0F8J5L2M4L4M4L4M2N2N2N2M20001N101N100O1O100000000O2O001OO2N1O100010O01O00001N1O2O00001O1N2O00100N2O1N2O1O2N3M3L3M3M3M4K:E7G>Ba_e>\"}, {\"size\": [1280, 720], \"counts\": \"PPl:3fW1i0[O8G7J6K3M4L4M3M2N3M3N3L2N2O1N100O10001N10O10O2O001O000010O0001O1O00O2O01O00O2O0O2O1N1O2O001N2O100O1N2N2N102N1O2N2M3N2N1O1N3N5K3L4L3C?Aoga>\"}, {\"size\": [1280, 720], \"counts\": \"fWh:f0TW19J5L6K4L3M5K4L4M2M2O2M3N1O2N1N2O0O2O1O1O001O0000O10000000001O01O1OO100O2O001O1O001O010O001O0O2N101O1O001O1N1O2O001N2O2N1O2N1O1N2N2N3N3M2L4M3\\\\OWiNLkV10^``>\"}, {\"size\": [1280, 720], \"counts\": \"Z__:e0WW18K4K8H6K6J8H5K4M2M101O1N2O1O0O2O1O1O2M2O0000O100000000001O0000000001O010O00001N101O001N101O1O0O2O1O001O001N2O1N1O2N21OO1O0N3O1N2N101O1N2O1N2OO11O0N3N2O3L3N8SOYiNNmV1KYiN1e^`>\"}, {\"size\": [1280, 720], \"counts\": \"V__:b0\\\\W1:F5K5K5L4L3M4M3L4M3M2M3M2O2N2N1O2M2O1O1O1O1O0O2O2N1O0O100001O01O000001O01O01O001O0O101O001O001O1O001O00001O0O2O001O100O1O1O0O2O1N2O1O00100O0O2K4O2O1N1O2O1O2M2N2O100O1M5L3UObiNObV1IiiN0YfW>\"}, {\"size\": [1280, 720], \"counts\": \"U`d:1]W11ghN=oV1c0I:E<E7J4L2M3N2O3L3M2O1N2O2N1N100O2O0O2O001O00000000O1000O101O000000000000000001O001O00001O001O001O001O1O1O001O001O001O1O1O001O0O2O1O1N1O2O1O101N1N2O1N1O1O2O1O1N2O1N2O1O2N2N3M2UOciNO_V1MkiNKZV1MfXk=\"}, {\"size\": [1280, 720], \"counts\": \"bgj:i0lV1`0G:F=D6K4L3O1N1O2N2N3M2O2N2M1O3N1O0O2O000O100O10001O0000O101O00O01O11O0000001O0O10O1O1000010O0O2O01O01O0O2O10O01O0O2O0010OO2O1N101O1O1O1O1O001O1O1O1O1O2N1N2O1O1O1N101N201N1M4K4N4N2M3L4YOkiN[OZV12[iN1_W1LZXa=\"}, {\"size\": [1280, 720], \"counts\": \"ZgT;i0PW1>B;H7I7L4K5K5K4M2M4M3M2O1N1O2O0O2N1000000O10000O100O101N010O101O0001O0000010O0O10O101O1O1O1OO010O2N1O11O1O1O010O01O0O2O0O21OO01O01O0O2O1N2M3N21O0O01O1O1N102L3N2N3N1O1O001O2M2N3N2N2N2L5K4I8C>D=F8K7FYXR=\"}, {\"size\": [1280, 720], \"counts\": \"P`g;2\\\\W1c0K9G6SiNRO5KUV1e1L7H8J5K3L4N1N3N1N2O1O1N200O1O1O100O1O2O00000O101O00001N2O1O0000001O1N101O000O11O01O00010O0000000000O10000000O100001O0000001O001O001O1O001O1N101O1O1N2O001O1M3N2N2N2O1O1O1N2O2N1N4M3L5I6G8I8K6I8D<C]P]<\"}, {\"size\": [1280, 720], \"counts\": \"U_`<?1FRW1Q1C7K6J6J5L3M2M3M3M3M7I3N1O2O0O1O3N1N1000001O1N10O11O00001N2O1O0000000000001O001O00000000O100001O0O101N2O1OO1O2N11O0001O001O010N1O2O1O010O0O2O010O1N2N2O1N2M30O1000M2O3M3O0O1N2N2O1O2N2N2N5J5E9I8K4G<Ag`f;\"}, {\"size\": [1280, 720], \"counts\": \"i^o<i0TW19H:F8H5L3K6L3L6K5K4L2N2O00001N10001N100O100000001O001O00001N1001O0001O001O1N2O00O11O0O1000O11O1O000001O0O2O001O0010O01O010N2O001N2O00001O000O2O001O1M3N20O2OO1M3N200O1O1O1N1O3L3N101O2M3L3K6K5A?K6L4K4J9H`PU;\"}, {\"size\": [1280, 720], \"counts\": \"hVS=l0PW1a0A5K7I8H7J4L4L2O1O1N101O1OO01000002M1000010O1O00001O0000O100001O001O2N1O0000O2O00001O0010O10O001O0O2O1O1O00001O001O001O1N10001O1O001O0N3O100O1M3N2O0O2N11000O1O1O2M2O0N2O1O10100N3L3K5M4]Od0L3M2IahNK`W1OhhNMf_W;\"}, {\"size\": [1280, 720], \"counts\": \"Z_e<3aW1d0Ca0B4K:H6J6J4L4L3N1O1O1O1O1N2OO11O1O1O0010O000001O1O00001OO1000000001O01O010O001N101O00010O10O10O0O2N2N1100OO2N101O010N2O1N2O1N200O1O1O0N3O1O1O0N3N101O02OL4O1N2N2N1O2N2N2O1N2O0N3D?Bb`k;\"}, {\"size\": [1280, 720], \"counts\": \"`gm;d0XW1m0TO4M4M4L4K3N2O1O00000O2O1O001O1O00001O0010O01O00001O00O1000001O00010000O01O0O2O1O001O001O1O001O1O1O0O2O1O1N10001O001O2N1N2O001N2O1O1N2O1N2L4N2O1O00001O001O1N3M3VOQiNM84_nP=\"}, {\"size\": [1280, 720], \"counts\": \"kgY;4fW1m0VO?C6K3L2O2N2N001O10O01O00001O000010O0010O01O001O001O000010O1O01O00001O1O0O2O10O0100O0O2O1O1O1N2O10000O1N2N10100O1M3N10010O0N3N1O2N2O1N2M2O2M2O2N3M8F5KToi=\"}, {\"size\": [1280, 720], \"counts\": \"T`X;95NhV1T1D8K3M3M3N1O2N1O2N2N0001N11O01O010O010O0O2N2N10100N101O001O1O001O001O1O1O1O1O1O1N2O1O001O1O1O1N2O1N1000001O1N2N1M4M3M5L7H5IYg\\\\>\"}, {\"size\": [1280, 720], \"counts\": \"`h^;m0jV1=H6L3M3N2N2N101O01000O01O10O100O01O0010O01N2O1O010N2M3N2O1N101N2O001O1O1O1O1O001O1N2O0O2M20010LRiNVOmV1h07N3N1M7HRga>\"}, {\"size\": [1280, 720], \"counts\": \"Vhh;i0TW1>C4M2M4M2O2N3M1O10000O10O001O10O01O001O001O1N2O1N110O001O1O001O1O0O2O1N2O1O1O1O1O1N2M3L4M5J;G2Mnn]>\"}, {\"size\": [1280, 720], \"counts\": \"QXa;e0UW1e0^O5L3N201N3M1O1O010O1O100O01O0O110O001O1O001O10O0100O0O20O01O1O0O200O1O1N1O2N2O0O2N2N101N2O1N2M4M2M5K4K6IZW_>\"}, {\"size\": [1280, 720], \"counts\": \"\\\\hY;g0PW1>G;H3M3N1N4M10O01O1O010O100O01N1O20O10N1O2O00010N2N2O1O10O101OO1O1O0O2N2N101N2O1O1O1N2N2M5K5K4M4J6M2O1N2Ilff>\"}, {\"size\": [1280, 720], \"counts\": \"UhT;?_W1:G6J5J6J7J3N1O1N2O2N2N01O01O1O1O1O1OO1O2O00010O0O2O001O0O2O001O1O010O100O1O1O1N2O1O1O1O1N3N1N2O1N3N1N3L7J4K6JnVi>\"}, {\"size\": [1280, 720], \"counts\": \"UXW;7hW19E7I6K3K4N2O1O1O1O1O2N100O2O000O10O10000O100O1O2O0000000O2N1O1O1O101N10001N1O1O2O0O2O1OO01O3J^hP?\"}, {\"size\": [1280, 720], \"counts\": \"`Xa;7cW19H8F9L3M5L8G2O1O1N2O101N2N101N2O001O0O10001O000000001O01O0000001O000O2O001N2O1O2N1O1O1N2N2N3N3M3L6I6K5YOS``>\"}, {\"size\": [1280, 720], \"counts\": \"aXf;3fW19H=E5M3M4L3M4L2O2N2N2O0O01O001L4GciNkN`V1S18N2O1O1O00100O2N100O1O2N1O1O11O01O1N101N2O1N2O2M3N1O3L3MPPc>\"}, {\"size\": [1280, 720], \"counts\": \"bXa;3jW1;D5K7J4M4L4K4M3N1N2O1O100O1O101N10000O10O1001OO100001N20N2O5J4M4L4L4L4K3N2N2N1O1N3N2N1O2N1O3L3NP_e>\"}, {\"size\": [1280, 720], \"counts\": \"ah^;6fW18I8I7J2N2N2M4L4M2O1O000000001O0O10O11O00001O0000000O010000001O000O1O10000001O1O1N2O1N3L4M3M5J:F=AVgf>\"}, {\"size\": [1280, 720], \"counts\": \"^`];3dW1b0ahN^OlV1o0K4K3M20kN_iN`02AaV1LjiNOX_Z1=c`eNG`V1g0ViNZOgV1Q1O13M2N7H>A5IYog>\"}, {\"size\": [1280, 720], \"counts\": \"bhh;52O^W1?I6L3L5L2N3M2N4M2N3M2N1O100O010O100O2N2O00001O00000001O00010O001O001O1O001O1O1N2O1N3N2N2K5M4M3K<D7GZ_[>\"}, {\"size\": [1280, 720], \"counts\": \"`PT<6gW1:E5L5K5K5L4L2O2N2M3N4L0000010O100O1000000O2O1O00O1O10010N2N2O3M;F2L7I5L7I3N4JSo]>\"}, {\"size\": [1280, 720], \"counts\": \"_h\\\\<;`W18K3J7K4M5J5M2N2M3N100O2N3M2O0O100000000O1000O100001O0001O00000O101N101O1O1M3O10000N2N3M3N4L5K4K5L`0ZOV_g=\"}, {\"size\": [1280, 720], \"counts\": \"`ha<?^W15L5K3L4M3M4L5L2M3N1O2N100O10O0100O1000000O2O0000000001O000000001O001O0O2O0O2N2N2O2N1O2N3L5K3L5Kdod=\"}, {\"size\": [1280, 720], \"counts\": \"dha<<`W16K5L4L3M4L5L4L2M2O1O2N1O1O01001N1000O01000001O00000000O2O00001O00001O001O1O1N1O2O1N3N2M4J5M3M9F]od=\"}, {\"size\": [1280, 720], \"counts\": \"hPc<9dW16I5L5L3M4L6J4L3N1N2O2O0O1O1O1O10000O01000O2O00000000O1O1000001O01O01O001N2O1N2O001O2N1N3M4L2L6K8H__b=\"}, {\"size\": [1280, 720], \"counts\": \"k`e<6eW17K5K6J8I6J2O1N4M2M3N1O1O1O2N1O2N100O01001N10O100001O0000O1000001N1O2O001O1O1O1O001N2L5L4J7J5L4APXa=\"}, {\"size\": [1280, 720], \"counts\": \"j`j<=`W1;D9I4L2N2O1N3N1N2N3O01O2N:bN[iNm0oV1N2M2N1O1O1N2O1N2O000O2O00000O2N100O2N001Mj^l=\"}, {\"size\": [1280, 720], \"counts\": \"kXd<;dW13L7J5K4L4K2N2O1O1N200O1O1O1O10000O10000O1000000000000000O1O110O001N1O101M3N101O100O1O2M2O2M4J9B[_b=\"}, {\"size\": [1280, 720], \"counts\": \"Pia<9eW18I6I5K3N2N1O2N1O1O1O1O10000O100000001O001O000000O11O1OO1O1O101O0O101O00100M2O2N2N1O2N3N2N3K4L8Hlnd=\"}, {\"size\": [1280, 720], \"counts\": \"YYd<1mW17I6K6J2N3M2N2O2M2N2O1N1O3M101N10M3O01000000O1000000000000001O001O000010O0010M3N2N1N3M3N2O1M4L4J6M2Nhn_=\"}, {\"size\": [1280, 720], \"counts\": \"Vao<7gW14L4L5L3L4L3N2N2N1O2N2O1N3N0O100O01N110001O000001O000O1O1O110O000O2N11O2NO2M2O2O001N3N1O1N2O2L5J8HlnU=\"}, {\"size\": [1280, 720], \"counts\": \"c`m=8gW13N2N3L;F:F1O2N1O1O001O00N3N1000000000001O001N101O0O2O1N2O2N2L5L3JSok<\"}, {\"size\": [1280, 720], \"counts\": \"XQ\\\\=8gW15J5L3L2N3N3M2N2M3O1N2N1O2N101N3N0O0100001O00O1002N1O0000M31O01O000000001O00002M101M4M2O1N2M3M4L4M4L7Ehnf<\"}, {\"size\": [1280, 720], \"counts\": \"o`c=6fW16L3M4M6I4M2N2N2N1N3O0O1O100O100000000000001O1NO2000001O00000000000O101O001O1N102M2N2N2O2M2O3K5K6KQoa<\"}, {\"size\": [1280, 720], \"counts\": \"gXl=7hW14K4K5L3L4N2M3N3M2N2N2N1O2N0010O0100000000000O11O00001O0001O000O10001N1O2O00001N2N12O01L3N3M2O2M4L6J[oW<\"}, {\"size\": [1280, 720], \"counts\": \"fXl=3jW17I6L3L4L3M3N2N2M2O101N1O2N2N1O100O2OO1N110000O102N2NO10001N10010O0O1N3N101O00001O0O2O1N2O1N2M4M3M4L4DigV<\"}, {\"size\": [1280, 720], \"counts\": \"\\\\Xl=4hW18K2N3M3M3M3L3N3M2N1O1O1O100O2N1O10000O10000000001O0000O1001OO101N100O1000001O001O1N101N3M2M4N2M3M5J6HggV<\"}, {\"size\": [1280, 720], \"counts\": \"WPU>;bW16L2N2M4M3M2N2N100O2N3M1O100O2N100O010O1000000000000O100001O00001O0O20O000010O0O2O1N2O1N2N2O2M3K7J5J8Ggom;\"}, {\"size\": [1280, 720], \"counts\": \"ZPP>5jW15J4M3M3M3N1N2N2M3N3M1O001O2N1O1O010O2O001N011O000000000000000000O101O0O10001O001O0O2N101N2O1N3M3L4M3L4JmgQ<\"}, {\"size\": [1280, 720], \"counts\": \"Vhi=b0]W13N2M1O2M3N1N2N3O1N2N1O1O101N1O100O011O0000000000O100000001O0000001N10001O001O001O1O1O1N2N3M3N2M4K5Ij_Z<\"}, {\"size\": [1280, 720], \"counts\": \"[`h=:cW16K4J6L3M3M4M1N2O1O2N100O1O101N10001O000O10000001N10000000001O001N101O0O20O0001N2O2N1N2N3M3N2N4K6Ejo\\\\<\"}, {\"size\": [1280, 720], \"counts\": \"_Pf=;bW17I5L4L2O1N3N1O2M3N2N1O1O100O100O10000000001N1000O10000O20O0000O10001O001N1O2O100O1N3M2O2M3M4K5L5Gf__<\"}, {\"size\": [1280, 720], \"counts\": \"k`^=2iW1;G6J4L5L3N2N2M4L3N1O2N101N2OM20100000010O001O1O1O0O2O1O1O1O1O1O2M101O101N1M3N2N2N2O1O2M3N1N3MT_i<\"}, {\"size\": [1280, 720], \"counts\": \"g`^=;dW14K3M3K5M3M2O2O1N1001O00101N001O1O2M2O1O1O1M40N01O1O1O100O1N3M2N2N4L3Ml^X=\"}, {\"size\": [1280, 720], \"counts\": \"gX]=:dW15J5L3L4L5M2L3K60O10O0001O1O001O1O001O1O001O2N1O001O1N2O1O010N2N2N3N1O1O2M2O01001M2N2N2N3MnVm<\"}, {\"size\": [1280, 720], \"counts\": \"aYV>2nW10M3O011O000O01O02L3O1O010000O0100O1N1N3M2N3N01N3N2N5H7KX_i<\"}, {\"size\": [1280, 720], \"counts\": \"m_\\\\>1oW13M4L5K4L3M1O2N1O3M2PiNVOgV1j0WiNXOhV1m0ViNROiV1m0WiNTOhV1S100000O11ON2N3N2M4K:AcWh<\"}, {\"size\": [1280, 720], \"counts\": \"fh_=3iW17J4L5K4L4M4K4N3M1N2O002N1O1O100O100O100000O0100000000000010O00O1O2O01O01N1O2O11N11M2N3O002M4K3L4K9_Oj_d<\"}, {\"size\": [1280, 720], \"counts\": \"bh_=2jW18I5K6K4K4L4M3M4M1O1N2O2N100O1O100O01001O000O010000O10001O01O00O1O2O000O2O001O10000N2O1O101O3J6K5I8Ci_d<\"}, {\"size\": [1280, 720], \"counts\": \"_X]=6iW13L3M4L5K4L4L2N3N2N1N3N00100N3O0O2N2OO1O010000000000O1001N100000001N100000010N1O2O1O1O1N2O2N3M3M4I5I<Ecge<\"}, {\"size\": [1280, 720], \"counts\": \"[X]=9eW12N3N2M4K5M2L5L3N1N4N0O1O1O2N2N0000100O100000000O1000O100001OO100O20O00001O00O2O1O1N4L3M3N3M5J6J5GjWh<\"}, {\"size\": [1280, 720], \"counts\": \"\\\\X]=5gW16L3N2N2N3L4K5L3N1N3N2N1O2N2N1O00001O1O10000O01001OO10000001OO100O100001O001O01O0O2O001N2O001O2M7F8I<Clge<\"}, {\"size\": [1280, 720], \"counts\": \"VX]=>aW1101N2M3M5K3M3M2O2M3O0O2M2O1O2OO0001O100O100000000O10000000000000O2O0001O100O00O2O1O1N2N2O1N3M3N3K8G6Ioge<\"}, {\"size\": [1280, 720], \"counts\": \"YhU=3jW17K103M1M3M3N2M4L3M3M2O1O2N1N3N100O1O1O101N010O10001O000O1000O1001O01O0O10001O00001O011N1O002M3K5K5L3M4L:^OUhj<\"}, {\"size\": [1280, 720], \"counts\": \"ZPR=5hW17J3N1O2N2M3L5M3M2N1N2O1O1O2M2O2N1O1O100O100O10000O01001O0000O100000010O000O2O00010O0O2O1O1N3N1L4N3K5K5L6AahN0dgo<\"}, {\"size\": [1280, 720], \"counts\": \"]hP=5hW15L3M3N2N3M1N3L4N2M3O0O1O1O1O2N100O1O1O10000O100O2OO10O1000000001O0O1010O00000001O0O2O1O1O0O2M3M3N2O1L7I<Bmgo<\"}, {\"size\": [1280, 720], \"counts\": \"YPm<6hW16K2N2M3M3N3L2O2M2O1N3N1O2N1O1O1O2O0O1O100O10000000000O100000000001O0O101O001O1O0O101N2N2N3M2O0O3M2N3K9F7Jm_S=\"}, {\"size\": [1280, 720], \"counts\": \"YPh<1lW15M4L2N4J4M3M3N2M2O2N1N2O2M2O2N1O2N100O100O10O01O101O000000000000001O0001O001O0O2O001N2L301O1N2O2M3M2N3L3M4JXXW=\"}, {\"size\": [1280, 720], \"counts\": \"Yhf<7fW16L2N2N2M2N3M3M4M1O1O2N1O1O2M3N100O1O100O100O10000O011O00000000000001O0000001O0010O01N2O1O1O1N2O2I7M3M4K7DShY=\"}, {\"size\": [1280, 720], \"counts\": \"Vhk<6hW15J5L4L4L3N1N3N1O2N2M2O2N2N1O1O1O100O1O1O2N100O11O1O3M3N1N1O4K4M3L4L5K5H9Gggh=\"}, {\"size\": [1280, 720], \"counts\": \"V`T=2iW19J3M3L5K5L4L3M3M2O1O1N2O2N1O1O3N2N0010O3M6K0O002N2N2N002N3M2N3M1O1O3GjhNAZW1<8Mco_=\"}, {\"size\": [1280, 720], \"counts\": \"Q`Y=2kW1;F4L5K4L4M3L2N3N1N2O1N3N2OO1O2O2O10OO2O1O2M1O2N2N2O1N2O0O2M4M3M2M5F_hNMkW10T_]=\"}, {\"size\": [1280, 720], \"counts\": \"ogZ=54N]W1>K5M2O2N30M100O2N1O1O1O100O2N2N2N2N3M4I`on=\"}, {\"size\": [1280, 720], \"counts\": \"loV==]W18I7K4L6K4M2M3N2N1O1O1O100O100000000001O1O1O2N3M2O1M2O1O3M1O1O2M2O3M4L2N1O2N3M2N3Lg_X=\"}, {\"size\": [1280, 720], \"counts\": \"goQ==[W1<G8J4M4M2M2N201N2N1O1O011O000000O02O000010O000000O10O2N1000010O2N3L6K9F6J7K3M7Fkg^=\"}, {\"size\": [1280, 720], \"counts\": \"hWn<c0WW18J5M4L5K4M1O2N100O1O2O000O010000000000001O1O00O101O0000O02O4L4L5K3M4L4L3M5J5L2N1N3Ndo_=\"}, {\"size\": [1280, 720], \"counts\": \"Shf<9aW1:F9I5M3L4M4M2N1O10N1100O10001N101O00O1O10000000001O01O1O00O1O11O01O1N1O2O1O1O1O2O0O2N1N4L3N2L4J8GUh^=\"}, {\"size\": [1280, 720], \"counts\": \"Uh\\\\<;dW15J3K6K5L5K3N2L3O0O1O102N2N1O100O1000000000000O10000001O000000001O0000001O1O1O1N2O1O101N2L3M4L4L4M6GS`g=\"}, {\"size\": [1280, 720], \"counts\": \"kPY<9cW17L3K7H6K4M1O2N2N2O1N2O1O1O2N1O100O100O101O000000O101N11O000000000001O1N101N2O1N1O2M4N1N2N4L4M2L5@m_l=\"}, {\"size\": [1280, 720], \"counts\": \"hhR<153_W1>G6J5K4N1N2N1O2O1O2N1N3N1O1O1O1O101O0O0100000O10000001O01O001O0000O101O000O2O1O1N2O1N2N2M3N3M4L4K5Hf_Q>\"}, {\"size\": [1280, 720], \"counts\": \"gXU<6hW16I7`hNBTW1e0N102N2N3M3M3M4M1O1O1O1O100O100000O02O000000000001O01O00O100010O00O2O1O1N2O1M3N2N1O3N2M4J6Hh_Q>\"}, {\"size\": [1280, 720], \"counts\": \"n``<7gW15K5G9K201O02N1N01O1O1O100000000002OJ5O1O002O0O1O10001N1010OO1N1N3N2N1101N3L3K5N3M4J5K7GfWk=\"}, {\"size\": [1280, 720], \"counts\": \"o`j<3lW12O4K3N2N1N100O11N10O101O00000O2O1O00O010O1000000000O1001N100O2N1O100O3M3MSoi=\"}, {\"size\": [1280, 720], \"counts\": \"i`j<<`W18H7J5L4K4N2M4L4M1N2O1O2N2N100O10O011O00000O1O100000001O00001O1O001N1010000O1N2N3L4M3L4L5K5L5J6JWW\\\\=\"}, {\"size\": [1280, 720], \"counts\": \"WXi<?ZW1;I5K5L4M2L4M2O0O200O2N1O00011N2O000000000001N1O1001O01O001O001O0O2O1O001O101N2M2O1N3L4M5I5M5J6Jh_]=\"}, {\"size\": [1280, 720], \"counts\": \"f`j<4cW1>D:H8K3M2N3N1O2N1O3M01N11O101OO010O100001O000010O000O2O001O00001O001O1O1O1O2O1O1N3M2M3M3N3K8G`_]=\"}, {\"size\": [1280, 720], \"counts\": \"cPh<6aW1<I6L4K4M6K1N2O2N100O1O101OO0101N10O1000000000001O0001O00O10001N2O101N1O1O2N1O1N2O1N3N3L4L7I4M[o_=\"}, {\"size\": [1280, 720], \"counts\": \"c`e<173XW1c0H3M3M2O2M2O2M3M3N1O00102M2O00O1001O1O00O1001O01N2N2O00010O1O1N3N001N3M2O1O1O2M2O2N3M2O0M6IZgc=\"}, {\"size\": [1280, 720], \"counts\": \"aXd<67NTW1a0M2O1O01O100O1O1N3L5L5IQ`o>\"}, {\"size\": [1280, 720], \"counts\": \"mha<9]W1;K4N3M2N2N3N2N2M2O1O2O1N100O2O001O00O0101O001O0001O0O10000000001O1O1O1N2O1O3N2N3L4M2L6I:@Ugh=\"}, {\"size\": [1280, 720], \"counts\": \"YQ^<b0[W15I7K4M3L4N2M2O2N1O2N01N1100O2O0O1O100000000O020O0000000002M5Kc0\\\\Ob0YOa^[>\"}, {\"size\": [1280, 720], \"counts\": \"fPY<7bW1;H6K4M3M3M3M2O1O1O1O1O1O2O0O10000O1O11O0O10000000001O01O0001O0O2O001O1O2N2N100N2O2N2M2O2N1O2M5J4L5I]Wk=\"}, {\"size\": [1280, 720], \"counts\": \"g``<=aW15H8J5L2N3M3N2N2N1N101O100O100O101O000O0100001O000001O001N100O2O010O1O1O1O001N3N1O1O1N3N2L4L5M3L4KYod=\"}, {\"size\": [1280, 720], \"counts\": \"g`e<2mW17H6G8H7M3L2N3M3M4M1O1O100O1O00100O100O100000010OO100O20O00O2O0O20O01O100O1N1O2N2N3N1O2O0N2N3M5K5K5K6IU_]=\"}, {\"size\": [1280, 720], \"counts\": \"nhP=6eW1;E5M5K4M4M1O1O1O1O2N1O10O02O0O1000001O000000000001O0O1000010N101O1O10O01N2O1N2O2M3N1O2N2N1L6J5L7FUgT=\"}, {\"size\": [1280, 720], \"counts\": \"khP=8`W1;G8J5N2M3M3M2O1O2O1N100O10000O010O1000000000O11O000001O0010O01N2O001O1O1N2O1O1O1O2O001L4N2L5K5L4J6IX_S=\"}, {\"size\": [1280, 720], \"counts\": \"YXS=2lW16J5L8H7I:F3N2N1O000O110O00001N10001O00O101O0000001O00001O001O100O1O1N2O1N3N2L4M3M4L4K4G]_X=\"}, {\"size\": [1280, 720], \"counts\": \"b`T==]W1:H6K4L3O2N1O110O0001O000000001O0000001O00000000000000O1000000000O100O100O11O01O2N2M3N3M4G;H\\\\gT=\"}, {\"size\": [1280, 720], \"counts\": \"gPW=:_W18K5M4K3M3N3M2O2N100N3N101N102MO200O110O0000000001O00O2O00010N1O2N2O1O001O1O10000N2N3M2O2M3N2M4L6H]_n<\"}, {\"size\": [1280, 720], \"counts\": \"XQ\\\\=6iW13M2N2N2N2N3M2N2N100O001O2O0O10O02N1O2N1O100O100O100O01000O10O10O2O000001N2O1O1L5M3M3L5J8FR_n<\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"ohU=3hW19F7L3M4M3M2O1N2O1O1O2O1N2O1N101O000O10000000000001O0000001O0000001O001O1O1N2O1O1O1O1N2O1N4M2L4L4L8GV_n<\"}, {\"size\": [1280, 720], \"counts\": \"gPW=;aW16J6L3L4M3M2O0O2O3M2N1O100O2O1N10O01000O11O001O000001O001O001O001O001O001N2O1O1O1N2N3M2O2N2M4M2M4L3K[Wm<\"}, {\"size\": [1280, 720], \"counts\": \"^`Y=3hW1;C8L6J4M3N1N3N2N001O100O2OO10O011O000O10O11O01O00000000001N1000010O1O1N2O001O0O2O1O1N2O1N1N3O1O3J5HShj<\"}, {\"size\": [1280, 720], \"counts\": \"U`c=5jW13N3L4M2N1O100O2N1O1O2N001O001N2O0O10O01O0O2O001O100O1O01000O1O00100O2O00O10001O2M3L4M3L4M2N5J7FkWc<\"}, {\"size\": [1280, 720], \"counts\": \"Yhg>3lW14M001O0O2N101N3M101N101N1O2O0010O1O1O103L3Le__<\"}, {\"size\": [1280, 720], \"counts\": \"ghi=1XX12C6H5L4N3N102M001O0011N1]OihN;VW19MZOnhN4N7YW1FjhN:SW1FPiN:PW1FPiN:oV1GQiN9mV1NnhN2RW1>2[OlhN:UW1DlhN<TW191\\\\OmhN7RW1JnhN6QW1HohN:RW1EnhN<SW1DkhN=XW14N2N^OlhN<TW1CmhN<\\\\W11LCghN9`W1000001O001O1N4JgVc<\"}, {\"size\": [1280, 720], \"counts\": \"aPP>7eW18D9M201O1O1O5K1O01O0O0100000000010O000000000O11O01O00000O100001O00000002N1O3L4\\\\OlhN5dW1KT__<\"}, {\"size\": [1280, 720], \"counts\": \"Qam=5dW18K5J5K6L5K5L3M3M4M2N2M3N00010O1O100O10001N100O1O10000001O0000001O1O1O1O1N101O1O1N3N2M2N2M4L2N6K5K5J<@[_U<\"}, {\"size\": [1280, 720], \"counts\": \"lhi=7fW15J5K5J6L4L3M4M4L4M3L3N1O2N1O1O100O100O2O000O10000000O1O10001O0010O0001O0O2O001O1O1O001N2M2N2M3O3M3M3N2HTiNWOhVY<\"}, {\"size\": [1280, 720], \"counts\": \"o_c=3jW15M2M6K4K4M3L5L3M2N4L2M2O2N00101O0O101N10001N1O1O10000001O01OO10O100O010O2O000000O101K6M2N3K6L3K7K8G7HU`_<\"}, {\"size\": [1280, 720], \"counts\": \"Y`Y==aW18H3N3M2L3N3L4L4N1N101N3N1O2M3N100O1O1O100OO2000O2O01N1O100N200010O1O10O0O2N2002M10O1O1N2O1M5I5M4L5Id0\\\\Oh_i<\"}, {\"size\": [1280, 720], \"counts\": \"^PP>1nW12N2N2N2O0N3N2N2N2O0O2O0O2O0O1O2O1O1O0010O101N2O0O3M3M3M5I3M[Wm<\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"a`^=8eW14M2N2N2O1N2N3N100N200O100000000001O010O01O02OO101O102M3M1O5K001OO001O2N1O0N2O2N2L3M5L3M3L\\\\ok<\"}, {\"size\": [1280, 720], \"counts\": \"nX]=<aW15J6L4J6K5L4M2M3N1O2OO01O1O100O10000O100001O000000000000001O00001O00100O1O1O1O1O1O2N2M3K6I5M6G;FT_i<\"}, {\"size\": [1280, 720], \"counts\": \"e`Y=4dW1<H7J5L4K5L3M3M4M1O2N101N2O00N2N200000000001O1O00O1O11O1OO2O0O11O01N2N1010001N1N2N2O1O1O1O2M3M3K6I>_O`gj<\"}, {\"size\": [1280, 720], \"counts\": \"Z`T=6eW19I5K5K6K4M3L4M2O1N3M1O10001N1000O100000000000000000001O00000O1O2O11OO1N1O2O100O1N3M2O1N3M2O2O1M4H8Gjgo<\"}, {\"size\": [1280, 720], \"counts\": \"\\\\PR=4dW1<I5K5L4L4M3M2N3M2N2O0O101N10001N10O1000001O000000000O1000001O001O1N101O0O2O1O1O1O1O1O2N1N2N2N3M2O3K6FjoP=\"}, {\"size\": [1280, 720], \"counts\": \"Z`o<8bW1<G5K6J6L2M4M1O2O1N100O10000O10O11O1O00000000000001N1000001N2N110O1O0O2O1N2O1M301N1O1O2N1O1N3K9^OoWW=\"}, {\"size\": [1280, 720], \"counts\": \"ZhP=8aW1:J6J7J3N3M1O2N1O2O1O0O100O10000O01O11O00010O00000000N3O001O001O001O1O1O0O2O1O2N1O1O2N1O1N2N4J7GioU=\"}, {\"size\": [1280, 720], \"counts\": \"^XS=1fW1>F7K5L3N3L4M2O1N2N2N101N100O10O100000000O11O1O00O1O11O0001O00001O0O2N2O1O001O1O1O1O1O1N3N1N3N2N1O3L5Jfgo<\"}, {\"size\": [1280, 720], \"counts\": \"d`Y=6aW1<J5K5L3M4L4M2O0N2O2N100O2O00000O101O00O1O11O00O101O0000001O000010OO2O1O1O1O1O001N200O1O1O2N2M2O3K8Begj<\"}, {\"size\": [1280, 720], \"counts\": \"e`^=4eW1;H6J7J4L4L4M3N1O2O000O101O1N10O100000O10O1001O01O001O01O0001O0O20O01O0O2O100O1O1N2O2N001O2M4L3N3J7Fdge<\"}, {\"size\": [1280, 720], \"counts\": \"dhi=4_W1?G:L2N3M3M100O101N3M100O2OO1000O1000O0011N10000O02O0O2N1100O01O001N2O1O1O1O1O2N100O1O2M3M3K7GhW^<\"}, {\"size\": [1280, 720], \"counts\": \"ehi=4eW19J6K5K5L4L2N3M2O2N2N100O1O2O0O2O00000O10O101O000000O1001O01O01N10001O001O1O001O1O1O1O2N101M2O2N3L4G:EgWY<\"}, {\"size\": [1280, 720], \"counts\": \"^Pf=8dW1:H3M4L3N2M2N3N1N3N2N2O1N1O101O0O1000000O100001O0000000000000000001O001O1O010N2O002N1O1O100N3N1M5I<_OiW^<\"}, {\"size\": [1280, 720], \"counts\": \"Yh_=3iW1:I3N1N2M3N2N2N3M2N2N2N103LM4O10O100000001O000000O2O01N11O0000000O100O2O00001O1N2O100O1O0N4M2M4L6Glge<\"}, {\"size\": [1280, 720], \"counts\": \"U`T=8gW14M1N3M2N2N2O2M1O001O100O100O101N2O00000000000010O01O0000000001O0O1O1O101O00001N11000M201N2N2N3KoWR=\"}, {\"size\": [1280, 720], \"counts\": \"Vhf<9fW14M1N3N001N2O0O1O1O010O1O1O1O2O0O3N1N10001OO1001O001O0001O01O0000O1O2N1O10001O1O0O2000O0100O2L3M5Hmg^=\"}, {\"size\": [1280, 720], \"counts\": \"RP^<7gW16H6M2M3N1O2N1O2N1O2N101N1O1O2O000O10O100000O110O1O0000000000O101N11O01O001O0100O10N2O2N1O1M4M3L5C[`g=\"}, {\"size\": [1280, 720], \"counts\": \"^X_<2mW12M3M2M3O01N2O02HFghN;XW1HehN:XW17N4L5L3N1O2JO[iNQOeV1T1010001O001O10O000000001O00001O1O010O10000O1O1M5L3L4J8HUXk=\"}, {\"size\": [1280, 720], \"counts\": \"\\\\`Q<8eW15K4M5K5K2N3M3N3M1N2O1O01O02N100O010O2O1OO1O0101O1O00001OO100O10001O00010O010O1O1000000M4M2M6G7I8JnoS>\"}, {\"size\": [1280, 720], \"counts\": \"ahm;5hW16K4L5K3L3N3N2M3N1N3N2N2N2N2N1O2O000O01000000000000001O1OO10001O0000010O001O1O001O1O1N3N1N3M3K4L6I8F9JdWU>\"}, {\"size\": [1280, 720], \"counts\": \"bhR<2iW16L5L4L4L4L3M3M4M2M2O001O1O1000O010001N01O100001O0O10001O00O2N1O11O010O0001O001O1O1O101O1O0O2N2L4L4Jl_Q>\"}, {\"size\": [1280, 720], \"counts\": \"g`V<5iW14K5L4L4L3M3M3L3O1N3N1O2N1O1O101N101N4MN2O1N200O11O00002N3MO2O000001OO2N101O010O1O10000O1N2N2N3K5M3K8Gc_l=\"}, {\"size\": [1280, 720], \"counts\": \"o`[<6fW15M4K3M3N3M4L4M1O2N1O00002N3M2O0000000O10000000O100001O0000000001O000000100O1O10O01O10000O2L5K5H;EZoi=\"}, {\"size\": [1280, 720], \"counts\": \"o`e<8dW16L3L5K4M3L4N1N3N2N101N2O0O10N2O0100O2O0000001O01O0O1O110O000010O0001O0O2O100O1O1O1O2N1O2M3M5K7_O[Wa=\"}, {\"size\": [1280, 720], \"counts\": \"lhP=<aW15K5L4K4M4M1N2O101N2N1000000O10O1O1O11N1010O000000000O1O110O0010O01O1O001O1O2N1O100O101M2N3M5J=AlnU=\"}, {\"size\": [1280, 720], \"counts\": \"o`Y=8cW17L8H3M3M3M4M2N2N01O0011N2OO10O01000000O1O11O1O00010OO101N100O20O10O101OOO2O1O110O0O101M3N2L8\\\\OZ_n<\"}, {\"size\": [1280, 720], \"counts\": \"Pa^=<`W18I5L4L3M3N2N1O2N1O2O0O101O0O10000000000000O100000001O01O10O1N2O1O001O001O100O1O1O1N3N1O1N4L5Jo^i<\"}, {\"size\": [1280, 720], \"counts\": \"_Y]=4cW1=H6K5L4L3N2N2N1O2O001N10001O1ON110O1001O01N1000000000001O001O001O1O1O1O1O002N1O2N2N1O2M2O1K8Ejfj<\"}, {\"size\": [1280, 720], \"counts\": \"PYQ>1kW17M6I6RiN^OTV1Z1M100O01N1O2O0100O01O2N1O1O1O1N2O1O100O1N3M3JenP=\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}], \"areas\": [3383, 3636, 4334, 4749, 4844, 5553, 6228, 7190, 8082, 8964, 9198, 8841, 8469, 7503, 6517, 5215, 3922, 3157, 2472, 2214, 2531, 2491, 2739, 1781, 2840, 1353, 1838, 2117, 482, 2390, 1725, 2449, 2308, 2207, 2270, 2373, 936, 1912, 1743, 1649, 1724, 1112, 1983, 1742, 1881, 1894, 1777, 1868, 1824, 1880, 1954, 1917, 1615, 822, 1256, 297, 675, 1980, 2047, 1931, 1950, 2056, 2082, 2039, 2099, 1920, 1921, 2059, 2043, 1351, 1303, 1115, 393, 1641, 1841, 1795, 2097, 2215, 2178, 2236, 2045, 1451, 599, 2309, 2214, 2094, 1889, 1795, 240, 1827, 1524, 1930, 2045, 2060, 1813, 2019, 1681, 1632, 1914, 1307, 0, 1799, 1965, 1914, 1330, 349, 811, 1273, 2510, 2657, 2282, 2483, 540, 0, 1287, 2167, 2155, 2110, 2061, 2010, 1840, 1999, 2051, 2095, 1841, 2133, 2061, 1494, 1301, 1307, 1693, 1405, 1988, 2055, 1849, 2060, 1831, 1777, 1736, 1669, 1862, 1752, 904, 0, 0, 0]}, {\"video_id\": 0, \"category_id\": 3827, \"bboxes\": [[341, 325, 13, 60], [333, 327, 11, 59], [332, 312, 16, 78], [332, 291, 33, 95], [343, 268, 53, 106], [360, 246, 80, 112], [397, 220, 106, 118], [443, 196, 109, 131], [477, 185, 105, 127], [500, 182, 97, 127], [508, 172, 97, 129], [511, 162, 100, 135], [508, 170, 107, 147], [490, 176, 114, 145], [472, 179, 109, 147], [455, 196, 95, 140], [442, 213, 70, 128], [432, 213, 54, 126], [429, 221, 47, 116], [415, 234, 66, 119], [384, 217, 87, 125], [383, 202, 102, 128], [388, 213, 93, 126], [395, 207, 83, 120], [397, 200, 81, 97], [397, 208, 84, 121], [399, 213, 88, 121], [404, 206, 90, 130], [417, 214, 92, 122], [433, 251, 91, 120], [435, 217, 94, 121], [442, 188, 96, 122], [450, 210, 94, 125], [454, 213, 93, 119], [459, 217, 94, 120], [462, 250, 99, 123], [453, 248, 84, 115], [446, 230, 92, 115], [443, 236, 84, 116], [443, 237, 84, 124], [439, 230, 83, 117], [438, 231, 85, 122], [440, 251, 87, 122], [444, 219, 84, 120], [464, 201, 89, 121], [481, 228, 84, 103], [485, 231, 86, 96], [477, 193, 98, 122], [466, 199, 97, 123], [450, 231, 101, 123], [458, 212, 78, 119], [459, 206, 77, 122], [465, 232, 83, 113], [463, 234, 89, 114], [451, 212, 96, 135], [448, 196, 88, 148], [444, 234, 91, 128], [445, 201, 96, 126], [463, 199, 92, 125], [467, 210, 85, 128], [467, 196, 109, 135], [486, 203, 106, 135], [505, 243, 97, 121], [541, 241, 72, 123], [558, 221, 56, 99], [560, 240, 53, 100], [575, 276, 34, 79], [553, 235, 41, 92], [532, 228, 39, 100], [520, 242, 37, 100], [504, 224, 41, 97], [476, 217, 55, 116], [477, 238, 43, 98], [458, 185, 61, 137], [433, 154, 92, 149], [445, 161, 103, 137], [491, 166, 103, 137], [472, 157, 153, 121], [527, 226, 51, 35], [509, 218, 100, 148], [490, 205, 98, 135], [465, 187, 100, 132], [440, 191, 100, 136], [414, 190, 94, 129], [390, 184, 94, 149], [385, 198, 96, 152], [385, 215, 98, 130], [400, 206, 101, 134], [412, 211, 99, 123], [430, 256, 93, 122], [436, 268, 92, 119], [444, 278, 84, 78], [452, 292, 84, 115], [468, 289, 80, 135], [467, 262, 87, 121], [461, 254, 95, 121], [450, 257, 87, 117], [441, 223, 95, 114], [444, 226, 92, 113], [443, 242, 91, 127], [443, 251, 90, 120], [445, 250, 92, 119], [454, 270, 92, 117], [455, 273, 93, 110], [440, 241, 97, 113], [432, 235, 99, 112], [435, 247, 94, 121], [437, 251, 92, 117], [435, 240, 95, 121], [449, 258, 98, 122], [445, 236, 96, 120], [434, 222, 98, 123], [436, 231, 93, 122], [437, 248, 87, 125], [433, 235, 87, 127], [435, 206, 90, 143], [445, 201, 89, 145], [465, 214, 72, 120], [495, 210, 48, 89], [487, 190, 65, 138], [481, 193, 63, 134], [468, 198, 70, 106], [448, 195, 93, 100], [448, 196, 104, 121], [469, 202, 102, 128], [463, 186, 105, 142], [445, 168, 101, 130], [449, 161, 102, 146], [440, 148, 103, 144], [419, 131, 114, 133], [419, 135, 117, 155], [443, 138, 118, 144], [471, 109, 114, 169], [493, 92, 111, 165], [515, 105, 96, 144], [515, 105, 107, 144], [511, 94, 115, 180], [502, 97, 114, 186], [488, 108, 106, 85], [449, 83, 134, 189], [426, 99, 146, 192], [425, 129, 127, 172], [424, 102, 149, 197], [441, 89, 156, 195], [484, 81, 137, 205], [524, 79, 146, 116]], \"score\": 0.7142857313156128, \"segmentations\": [{\"size\": [1280, 720], \"counts\": \"obZ=e0VW1:G8J4I7L3L2O10O0M5I8B>I;BmmW>\"}, {\"size\": [1280, 720], \"counts\": \"PcP=f0TW1=C9J5K3M4M01N2M4J7I9Dn]d>\"}, {\"size\": [1280, 720], \"counts\": \"WZo<W1eV1:G5K8I5L4L5L1N2O01O0O3J6E=F9_Od0RO^^_>\"}, {\"size\": [1280, 720], \"counts\": \"oYo<>YW1`0F8G<F5L6I6L3M2M3N2N1N2O2N2M2O2N1N2N101O0O2O0O01O2OON3N3K7H9H9Ec0XN^Wj=\"}, {\"size\": [1280, 720], \"counts\": \"UQ]=j0SW1:G:G6J7J4K4K5L4L4L3N2M3N2N1N3N1O0O2O1N100O10000O1000000O100O1000O10O10000O100000O0O1O2M3L4K5L4K7J5J7G9B?[Ol_c<\"}, {\"size\": [1280, 720], \"counts\": \"fXR>h0TW1;G7I5L4K3L5M4L3M3M3M3M2O1O1N101O1O0O2N3M3N1N101O001O1O002O1N1O1N2O001O0O101O000O20O00O101N100000000000000O0100O1O1O1O1O0O2N1O2N2M2M4N2M3N2M3M3M3J7J5J7G:A?J6J7GU`l:\"}, {\"size\": [1280, 720], \"counts\": \"Q``?;`W1:G9H6J6K4M4K5L2M4M3M2N7I2N2N3M2N2O1N2N2N2O3M3L3N0O4M2N1N101O1O100O1O1O00010O0000001O001O0001O001O01O000001O0000010O000O10001O0000000000000O2O0N2O2M2O1O1N3L3L5M2M3O1O1O0N3N2N1N3O1N2N2N2L4N2L4N2M4H8K5M3N1O3M3N3^OnhNH^W12eh]8\"}, {\"size\": [1280, 720], \"counts\": \"_oYa08]W1?I<E9F7K5J5L7H4L5L3M3M3M3L3O1N4L3N1N2N102M2O2M2O1O1N101O1O000O1000O10000000O1O10010O00001O001O100O010O001O00O2003M0ON2O100NlkN\\\\LQT1b3PlN^LQT1b3610000O2NK6O0O2NgkNaLTT1^3kkNdLUT1Z3:N2O11OO1N2N2O100N2K5L3N200N3M2N2M3N2L5K4N2M3M3N3K4N4H7L4N4K3N5J9G5L4KY``6\"}, {\"size\": [1280, 720], \"counts\": \"l^db05bW1c0D:E9I2M7J4L3M3M5K7I4K5L4L4M1N3M3N2M4M2M3N2M2O1N10000O101O00001O0O101O1O010O1O1O100O1O1OO10000O2O00000001O01N101O1O0O2O1N101O1O1N1O2O1L4O1O2O0N101N2O1O1O1O2M2O1O1O001O2M2M2N3M3K4N2N3M3M2M5K4J7G9I7L5L4L3L4M2O3K^P[5\"}, {\"size\": [1280, 720], \"counts\": \"gVac0336WW1i0@<F7I7I5L5K6J4K3N4L3M2N2N2O1N2O2N2M2O1O3M1N2O1N2O1O1O0O2O1N2O1O1O001O00100O10O00000O110O000O2O000010OO1O2O1O1O0O2N2O1O002N1O1N1O3N1O100N2N2O2N1O2N1O2M3M2N1O3N2M3L3M4K4J6L4J6I7L4L5M2N3N3L7H7I4LWXh4\"}, {\"size\": [1280, 720], \"counts\": \"XWkc02YW1o0^O>C<F9I6L3M4L2M6K4L2N3M4L2O0O3N1N2N4M1N2O1O1N2O1O002N1O001N2O1O1O0000000001O0O11O00000001O001O001O00000O2O1O001O001N1O3N2O0O1N2N2O2O001L3N2O111OOK6M3L4L3N3J6M2N2O1M5J5J5I7J6L4O0O3L5L3M3M7J5I6J[X^4\"}, {\"size\": [1280, 720], \"counts\": \"knnc0<WW1m0\\\\O;@;I9J5J5L5L3K4N3M3M2N2N2N2N3M2O1N2O1N1O2O1N3N1N3N2N1N3N1OO010000000O100001O00O100000000O2O00100O000O2O0O2O1O001O001O0O2M300O1O2M2N2O101O0N1O3N1O1O1N3M2N3N3L5J4M4K4L3L5J5K4N2N2L5J5O3M2N2N3L3L6L:G6HfhV4\"}, {\"size\": [1280, 720], \"counts\": \"`Vkc0^1^V1?A9H7I5L5L3L4M3L4L4N2M2N2N3M2N3M2O1N3M2N2O1N101O1N2O2N1O2M2O2N1O1OO1O11O1O00N110001O1OO1O010O100000001O000O2N101O1O001O1O1O1O000O2O1O1O1O2N10100N1L4N2M3M3N2N2N2N2O2M2N2N2M3M4H7K6M2M3L4K4J6L5L3M3O1O1M3O101O1M3J8F;GViQ4\"}, {\"size\": [1280, 720], \"counts\": \"ZgTc0]1ZV1>G6J6G9I7L3M3N1N4K7J4M1O2N2M3N3M3M2N2O0O2N2O1N2O1N2O0O2O0O2O00000O101O000O101O00001O00000001O00001N2O00001O1O000O101O00001O1O1O0O3N1O1M3O2O0O1N101N2O1O1O1O0010O0001N1100O10M2N3N2M3N3L3L4I8L3K5H8L2M4I7G9N2N2N2M4L3M3N2N2N4L3N3L7GSa_4\"}, {\"size\": [1280, 720], \"counts\": \"SX^b0k0oV1b0XO?^O`0H9J7K7G5M1O1O3N1N3N2N2N2N1O2M3M2O1O2N1O100O100O100O2N1000000O101O0O10000O10000000001N1001O0000O110000O1O1O2N12ON2L3M3N2N2N2O1O001O1O1N1O2N1O2L301O0O2O0O1O2M2O12O0ON3N2O10000N2L4M2O2O0O1N2N2L4I7I7]Od0F9N3M3N2M4M3K:FnX\\\\5\"}, {\"size\": [1280, 720], \"counts\": \"`Pia07_W1h0ZO`0A`0^O<C<I7L4K4L4M3N8H2N3M2M2O2N1O2N101N1O1O100O100O1O1001O00O10O1000O100010O2N1O2N2N1O1O1O1O1O010O001O1N11O0001O00001O000O2O0O2O001O01O01O1O0O1N30O3M01N1N21O1O01M2N20OO2O2K4N3J5L5K6I7\\\\NQkNMXU1KXkNBTU16RRc6\"}, {\"size\": [1280, 720], \"counts\": \"lhXa0`0WW1b0[Oe0[Of0YOa0H5M3M4L2N3M2N3N2M2N3M201N10000O2O01O2N1O00O12N1O1O1O1O001O1O1O1O001O0O10001O0O101O001O00001O000O2O001O0O101O001O0O1O2N2L3L5M4H7J:C<fNjiNIUgS8\"}, {\"size\": [1280, 720], \"counts\": \"hXl`06]W1n0UOb0]Ob0D9G8I6L3M4L3M3N2N1O3M1O2N2N1O2M201N11O1O100O1O2N1O1O001O1N101O1O0O2O1O0O2N2M3M2N3M3M2N3N2N2N3K5J8E>Ae0aNjPS9\"}, {\"size\": [1280, 720], \"counts\": \"m_h`0n0eV1k0\\\\O;H6K7I6K4L3M3M2N2O1N2O1O1O100O101N100O100O10O01O01O000O2O00001N2O0O2N1O2N2N1N3N3L3L5I8I:B?@Z`_9\"}, {\"size\": [1280, 720], \"counts\": \"moV`0i1QV1g0]O4L4L5L4L3M5J4M2N2O1O001O001O00000010O01O01O001O010O0000010O000O100000001N1000001O00000O2O000000001O1N2O1O2M3N2N2N4J5H8J<D7H;G<B=AgVY9\"}, {\"size\": [1280, 720], \"counts\": \"nXP?5[W1l0]O?D9I5J5L5K5J5I9I6J5L3M3M3N101N1O1O2N1O1O1O1O1O100O1O1O11O00000000O10000001O001O0O2O00001O001O001O001O1O1O1O1N101O1O001O001O001N2N1O2N1O2N1O2M2N2O1M3L5L3L4L4J6J7@?M3H9L5L>AUhe9\"}, {\"size\": [1280, 720], \"counts\": \"bon>1b03dV1S1\\\\Od0_O:G5L5L5J6J4M3M2N2M3N2N2N101N3M101N1O100O2O0O101O1O0O101O0O101O00000010O000001O0001O0001O000001O0O2O1O0O102N3M001N101O100O1O0O2N3N1N2N1O2N2N2M2O2M2O2N2N1N3N2M5K3N2M3M2O2N2M2N2M3I7N2O1O1N3K4N3M3J6M4I7H`XT9\"}, {\"size\": [1280, 720], \"counts\": \"mXU?4RW1\\\\1VO>D;F7K4L5L4K4N1N2O2N1O2N1O1O2N2O1N2O1N3M3N1N2O0000O10O1000001N100001O00000001O00O2O00001O1N101O002N1N101O001N2O1O1N2O1O00101N1N3N1O1O2L3N2M3N3M2M5L4K5L4K4M2N2N2M3M2G:M3N2O2HSiNYOnV1k03N5I;E4LTWY9\"}, {\"size\": [1280, 720], \"counts\": \"ao]?Q1iV1?A;H6K5J8I6K4K5L3M5K5K3N1N2N3N2MO20O101O1OO1001O1N10O1O1002N00O2N1001O00O1O2O010O0O2O1O1O1O1N101N3M2O00100O1O1N200O1N3N2M20010M4L3M3M3L5L4K4L4J7H7L6J4L:G5L3L3M7I0O2LVo\\\\9\"}, {\"size\": [1280, 720], \"counts\": \"g_`?b0UW1b0C9F9H5L4M2L4N2N2N2O1O1O1O1O1O2N10O0100O10000O1000000000000000O2O000001OO10001O001O1O00001O001O1O1O1O1O1O100O1O1O2N1O2N1O2O0104MM3L4M4L2N2O3L101O100M5L4I8J5ZOjhN2OOe_2NXg[9\"}, {\"size\": [1280, 720], \"counts\": \"Y``?8<JdV1T1E9G9J4K4M5J4L5J5N2M3M3N2M3N2N2N1O2N1O2N100O2N10001N100O2O1O1O002M2OO1O10001O00000001O01N1O10010O1O001O101O00O11M2O1N2N2N2N3M2N2O1N3M3M3M3L3M4M3M4K6I7J:G4K5L2N1O2O3N3K]WY9\"}, {\"size\": [1280, 720], \"counts\": \"bPc?=XW1i0\\\\O<G7H6L4I7L4K4M3M3L3M4N2M3N2N2N2N2N1O2O1N1O1O10000O100O101O1N2O000001O0O10O1000000000O2O000000001O2O0O0O101O1O1O1N2O100010NO3M2O2M3N1L4N2N3L3M4L4L4L7H8I5L5I6L5M0O1N2N2O2J5H`0_OZgQ9\"}, {\"size\": [1280, 720], \"counts\": \"aXi?>YW1a0C;D:F8I6L5K4L5M3L3L4M4L3N2N2M3N3M1N3O0O1O1O2N101N2N10O0O20O3N0000000O100000O1001O0000O20O001OO1O2O010O001O010006I01N2M4N1N2M3N3L3O1N2N2N2N3M2M5K4L4K5L3M4K6J6L6K5K4K5K2O1O1O1M2HohN_OVW18loh8\"}, {\"size\": [1280, 720], \"counts\": \"g`Y`0<\\\\W1`0D8E:I7I7K4K5J5M4L3L5M2L4M3N2M3N2M2O2O1N3M2N2O1N2N10000O10000O1O100000000000000O10O10000000010O000O1010000O001N110O1O1O001O1O1O1N3M2N3O0N2N3M2O2N2N1L5K4I8J6L4N103L5J:[N`iNX1eV100O1O1N2N2M4L6HWWV8\"}, {\"size\": [1280, 720], \"counts\": \"Qbm`0;^W1<G9E9F:H7J6I7K5K4M3N3L3M3M3M3M3N3L4M2N2N2O0O100O1O100O011N10000000000O100O10000001O000000001O001O0000001O1O0O2O1O1N2O1O1N2O1M3N2O1N2M4M1O2N3N1O2M2N3M3L5L4K5L4FkiNcNZV14fiNo0gV1N2N1J6O2O1100OJ6O3EX^c7\"}, {\"size\": [1280, 720], \"counts\": \"UQPa09[W1a0D<F9G8G8K6I5M3M3K4N3M3M2N3L4N3M2N1N3N3L201N2N100O100O1O10000O1000O01000000000O100000001O0000001O000O2O010N101O001O001O1O1N3M2O1N3M2N2N2O1N3N1N2O1O200O0L6K3K6L5L3N2M8I8H1O1O0O2L3L5N2I8L6HWW]7\"}, {\"size\": [1280, 720], \"counts\": \"kgXa0d0XW1a0^O9I6I6J5K5L4M3M3L4M3N2M3L4N1N3N2N2N3M1N200O1O003M3NO010O0O20O1O1000000O10O1O1001N010000001O0O1O11O0000001N110O1O001N2O001O1N1O200O2N2N1O10O10N2N2N4K7H5L4M3M2K6M4L4K5K6L4J9I13NN0N3O1M3H8L6I6GSPR7\"}, {\"size\": [1280, 720], \"counts\": \"^hba0n0nV19G9G7L2L5L3K5K5M3M3M3M3M4M2M4M3L4M2M4M2N1ON11001O01O1O2N010O1O10O0100O1000000O2O00O100000000000000O101N1O1000001N101O010O001O1N2N2N2O1N2N3M2O0O4L3M4L4L3M2N3M3N2M3M5J5L6dNdiNg0bV1TObiNh0nV1M1O2N101N1M5K6H[_j6\"}, {\"size\": [1280, 720], \"counts\": \"chga0h0RW1<G7H7J5K4L5K5L2N3M2N3N1N3M4M1N3M3N2M2O3M3M3L2O1O10O0001O00100O100O100O100O100000000O2O0000O1000001O00000000001O0001O2OO1O0O2N2N2N2N2O0N3N2M3N2N4L3M3N3M2M2N4L3M3M4L3L5K6K;F7H3M2N3K4K5M6HVgf6\"}, {\"size\": [1280, 720], \"counts\": \"lPna0;[W1f0A9G7J4L3L5L3M5J4M3L4L4L4M3N1O2N2M3N2N1O1N2O1O2N1N2O100O100O2O0O1O10O0100O10000O10000O010001OO1000000000O2O00001O0O1O2O10O01O001N101N1O4K4N1N2N2N3M2M5K3N3M4L4M2N3M2N2N3K5Lb0]O3K6L3M3N2N1N3LUW_6\"}, {\"size\": [1280, 720], \"counts\": \"SjQb0>_W1;@<E:G8L4K4L4K6L3M4L3M2N2N3M2N3N1N3M2O1O2M2O1O2N1O2O1N2N2N1O100O100O10001N1O1000000000O1000000000000O100000001N10001O1N1O2O1O1O0O2O1O2M2O3M1O1N2L5K3N2O3M4L3M3L4M3L4L4GniN_NUV1_1kiN`NXV1_17M7H[iNkNdV1R1[iNoNfV1R14O1M3K4N3L3N3N2M3NZVU6\"}, {\"size\": [1280, 720], \"counts\": \"cafa0k0nV1=D>F7I3M3M3M2O2M2N3L3N3M2O2M2N2O1O1O1O1O1N3N1O1O1O1O100O1O100O101N1O101N101O000O10O1000000000000M301N1O2N11O10N3M101O010N2N2O0002ON1O1O11O02N1O1N3M2L4ROjjNQOVU1:_kNK\\\\T13fkNMZT1JPlN3RT1IWlNNmS1MWlNGbNK_U1;lkNK_T12dkNHaT15RYS7\"}, {\"size\": [1280, 720], \"counts\": \"Pi]a0g0UW1<E:G5K5K4L4L3M3M3N1O2M2O1O1N2O2M2N2M3N3M2O1O1O1N2O1O1O001O1O1O1O100O1O100O10000O101O00001O10O00002M1O2O1002NO1M2N3N2M3O1001N01M2O2001OM2N3O1M3N2N2O2M2L5L4I7J5L4J6K4K5L4O1M3O1N3L4N2M3MfoQ7\"}, {\"size\": [1280, 720], \"counts\": \"WQZa0j0SW1=B9G7I7J6J4M3L4M3L4M3M3M2M4M2N2N2O1N101N2O1O1O1O10O01O1O10000001OO100000000001O1O001O1O1O00010O1O2N2N1O1N2O1N200O1O1O0O3N1N1O2N2N3M2M2O3L3M3M2M4J5K6I6G9M3M3O1N2N3M3M7Fag_7\"}, {\"size\": [1280, 720], \"counts\": \"dQZa0:]W1`0B?D8G9H8H6J6K4M3L5L3M3M3N2M3M2M4N1N3M3N1N2O2O0O1O010O02O001O001O001O001N5L1O1O001O1O0000001N100000001O1N2O1O001O1O100O0O2N2N3M3N1N1O2O1N1O2N6J2M2M3M3M2M4E;L4M4K6J7J8GWg_7\"}, {\"size\": [1280, 720], \"counts\": \"WQUa0<[W1f0A6I8H5L7H5K5J7J5M1N2O1N2M3N2N2N2N2N2O1N2O1O1O1O1O1N2O1O010O100O100000000001O0001O0001O101N1O01O1O01O001O1O1N2O1O3L3N1N3N1O1M4M3L3M4M2L5L3L3M4FRjN\\\\NSV1_1:L3M4L4M2M3N2N2M3N7Bhoe7\"}, {\"size\": [1280, 720], \"counts\": \"WiSa0f0VW1?B8H7J5K5K4K5K5J4K6K5M2N2N2N2N2N2O1O1N2O1N2O1O1O1O1O1O100O001O1000000000000O100001O0000001O3N0O1O100O011O0N4L2O2O1M3M2N2O2O0O1N2N3L3N3M2L4M2N3N1M4L4L4I7F9K5N3N1N2N3N4J<_OZgd7\"}, {\"size\": [1280, 720], \"counts\": \"kYVa03dW1<jhNBYV1m0::E9G5L;D3M3M9G4L4K5L3M3L4L3O2N1O1O1O1O1O1O1O100O1O1O100O10000O1000000O10000000O10001O1O1O1O1O1O100O4L3M1O2N1O2N1O1O100O2N1N2O1O1O1O1O010O1N3M1O2N3M1N3M1O2L3N4K4J6F;E;L?WOlf_7\"}, {\"size\": [1280, 720], \"counts\": \"nX[a0264\\\\W1:M2M3E]OUiNe05_OSV1^1K4M4K5L4L4J6J6L2M4M2N3M2N2N3N1N2N2O2N1O1O1O1O1O1O100O100O1O1000000O10O101OO100001O002N3M1O001O2N1O1O1O2N010O1O0O2O1O001O001O00100N2N4Mi0VO4M5K5K4L2M7I4K3M7I4K9C^^^7\"}, {\"size\": [1280, 720], \"counts\": \"_XTb0=[W1i0[O9H6K4K5J6K5K4K5L4L3N3M2M4N1N2N2O1N3M2N2O1O1O10O1000O100O1O1O00100O100O100O2OO2O00001O1O1O1O01O05^LbkNX3cT1O2O0O100000010N101O2M2O1O0010100O0O1O2K4N3L4L4L3M4L4L2M4L4I6H:K4J6L4M5K6K5J4JlW_6\"}, {\"size\": [1280, 720], \"counts\": \"\\\\`ib0k0PW1`0^O?\\\\O`0H6J5M3N2N1O1O1O2O001N11O010O010O1O00000000000000001O0000001O100O1O1O1O00100O00100O1O1O002N1O1O001O0O101O001N2100OO1N3N1N2N1O2N2N3M2N1O2O1N1N3N1O2L4M3M3L5L3L4N3K7A`WP6\"}, {\"size\": [1280, 720], \"counts\": \"X`nb0g0dV1n0G5K4F7N3N2K6L3M3N1O1O00001O010O001O00000010O00000001O01O1O001O001O0001O001O0001N2O0O5L4M00OO101O0O1N22N1O002N10N1O2O100J\\\\jNnMdU1V21O4M3L5K2O1N1O1O2N1N2N3N1N2N3M3M3L5K4L5I6L3M4M7JWgh5\"}, {\"size\": [1280, 720], \"counts\": \"T`db0Q1fV1?H4L3L4K4L5M3N1N3N2M3N1N3M3M2M3N3L3N2O1O2N100O1O2M2O1O1O1N2N3O000O101O0O101O00O2OO1O0012M2O0O100O11O01O1O1O001O0O2O1O1O1O1O001O1O010N101N3M4M1M3N2N2M2N4L3M4K4L4M5J5K3N3M4K5L2N3M3N2N1O2N1O1O1O2K6Gehc5\"}, {\"size\": [1280, 720], \"counts\": \"^hVb0?]W1;C<H5K4K6J6K5K3L4L4M3M3N1N3M2N2N2N3M2N2M3N2N2O1O2M2O1O1N200O1O2N1000O0100O10000O2O0O2O001O0001N2O0O20O0000O1010O01N100O2000O1O0001O003K4M5K2N2M4M3M2M3M4M4I5L6L3L5I6K6J4M3K4N3M2N2O1O2N1O0O2O2L5LYhR6\"}, {\"size\": [1280, 720], \"counts\": \"biba04XW1l0F3_OoNiiN3Lo0SV1mNQjN4LY1PV1`0O2M2N4L6J9I2M3M2O2N2M3N1O1N1O1O2N100O2O0O2O0O1O1O2O0O1O100O2N10000O1O2O000000000001O1O100O10O0001O1O1O0O2O0000011M2O1O1O1O1N2N2O12NO1O1N2O1N2N1N3N2O1N2N2N2N3K4L4N2M3M2M3K5M4H7N2K6J5N2N3N2L4L5Hcga6\"}, {\"size\": [1280, 720], \"counts\": \"ihla0:ZW1f0^O;G<F7H8K5K6K4K3M3M2N2N2N3M2N3N1N2O2N1O1O1O1O1O100O1O2O0O1001N2O1O1O2N2N1O1O000O2O00001O10O01O001O001O001O1O1O1N2O1O1O00100O010O2M2N6XMmjNX2iU1D8H6J6I4M1O3L5I6J7Hm^T7\"}, {\"size\": [1280, 720], \"counts\": \"\\\\Pna0P1mV16J5K7J4K5K6J4M3M3L4L3M3M4L3N2N3M2O1O1N2O2M2O1O1O1N2O1O1O100O100O100001O2M5L3M3M2N1O1O2N1O2N3N2M2O2N3O1M0N2M4N200N2N1N3N3N1N1N2N2M5L5J4M2N6J3M3M3L4M3K6J6Gk^T7\"}, {\"size\": [1280, 720], \"counts\": \"V`Ub0n0lV1`0D9E:F7L4L3K5L5K5L4K4M1O2O000000001O00001O001O01O00010O1O1O01000O2O2N0O1O1O001N2O100O003M1O1O011N1O1O1O2N2N1O1N2N1O2N2M4M2N2O2M2O2M3M2N2N101N3L2O2N4L3M3L4M2M3M3M6H6Kc^e6\"}, {\"size\": [1280, 720], \"counts\": \"RPSb0d0ZW1:C>@?G5J6K5L4K3M5J6K3N3L3N100O10O0000O20O0001O000010O01O001O0010O1O1O1O010O00100O001O1O1O001O1O00001O01O00010O03M1O1N2O1O1O1N3N2M2N2N1O2M3N1O2N2N1O1N3N3M2O0N3N4K3M3L3N2O3K4L3K7C^_`6\"}, {\"size\": [1280, 720], \"counts\": \"boca076J[W1c0J5J5K9Gc0^O<D9H4K4L2N3N2M4K5L4K201N1N2N3O0O2O001O0O2O0000000O1000000O101O0O1000O100000001OO1000001N101O0001O1O00001O10lkNYLoS1l3O02N100O1O3L4L2O1N2O1M3M3N2N3M1N3N1O1O2N1O2M2N3M4K6K5J2N3L4M3K5I8F;I8J8GPhf6\"}, {\"size\": [1280, 720], \"counts\": \"cX`a0W1bV1<H6J7I5L3L5J5M3L4L3M4L4M2N2N3M2N2N3N1N2N2N2M3O1M4M2M3O100O1O1N2O0O200O1000000001O2N1O012M00O1001O1N01M4N2^lNfK[S1\\\\4311N3M2N2002L001O01O2O1M3O0O2M3N00O0102N2M2M2N3N4J5PM_kNZ2VU1J8gMcjNd1iU1TNhjN[1SV1K3M5J5K:Dl_T7\"}, {\"size\": [1280, 720], \"counts\": \"VY[a0;`W1b0\\\\Oc0A6J5L4M2M3M4L3L5M3L3M3M3M3N1O2M3M2O2M2O2N1O1O1O1O1O1O101O000O1O10O010000O11O001O00O100001O01O0100O001O1O000105JN3N1O2M3OfkN`LWT1^361OO1N3M10011L3N2O1N2M3N2N1O1N2N2N4I6M9D5L5K5I6K5L5L6I>@XgU7\"}, {\"size\": [1280, 720], \"counts\": \"m`\\\\a04gW1b0@5M2M2O1N3M3L5K4K5L4L4M4L3N1O2M2N2O2L3N3M2N2M3N2N2N2N2O1N2N2N2O1O1O1O1O1O1O1O1O100O1O1O100O100O100000000000000011OO01O010N1001O015LK5M7I2N2N\\\\kNlL_T1Y33M2O2N1N3L4N1M3M4L3L4L5K4L5K4M3K5I7K5K5K4N3L5J6JYXn6\"}, {\"size\": [1280, 720], \"counts\": \"dPSb0f0QW1>E:I6J4M2N3M2N3M3M2N2N3M2N2M3N2N2N2N2N2N2N2N2N2O1N2O1O2N1O1O1O100O1O1O1O10000O2O00000O101O00101N1O01O1N101O000000001O101M3N1N5M1O0O1001ON2N2M4L3N2M4L3M3M3M4J5M4K5G8L4M3N3K4L4L3N2O2L3K_h\\\\6\"}, {\"size\": [1280, 720], \"counts\": \"kPXb0c0TW1`0F6I7K5K4K4L4L4L4N2M3M2N2O2M2N3M2M3N3N1N2O2M2N2O2N1O1O1O1O2N1O1O100O2O0O101N2N01O11O1O1NO200001000N2O010000O0011M3M3N1fLikNi2jT1K;H<C3N0N2N2N6J2N2N3L6K2N2N2L4M2N2L5L3M3M3N2N2O4IR_`6\"}, {\"size\": [1280, 720], \"counts\": \"fPXb0>ZW1=H5K5K5J6J6K4L5K3N3M3M2O2N1N2N3N1M4N1N2N3N1O1O1N2N3M2N3N1O1O2O001N2N2N2O0O1O1O2N101N4M1N1ON33M1N2O001NO2O10O01N201KRlNXLlS1h3UlNXLjS1n31N22O1N000100O1N2G82OO0O00102L4M2N2N1O2M4K3N2N2RMSkNg2oT1XMSkNf2XU1J6K3L3M6J5K3N4K4M3M3L4M4L3M2O1N2N1O2O0O1N3N3K8Fm_b5\"}, {\"size\": [1280, 720], \"counts\": \"ghob0>ZW1>H7G6L5L4I7K6G6I8J8I5L3N2N3L4L3N2L3N3N1N2O2N1O1N101O100O1O1O2N100O101N100O100O100O1000O10O0100001O000O11O01O0000O101O000O101N100O1O1O2N100O2M2O2O1N101M2N2N3N1O3M1N5L4M5J4K8H4K6K4M2N4L4M2N3M2M3N1K5I8L3N1O2M3N3K5Ii_n4\"}, {\"size\": [1280, 720], \"counts\": \"oagc0>YW1a0D:G7J5J5K5L3M3M2M3O2M3L4M2M4M2O1N3M2O1N2N2O1N2N2O1O1O1O1O1O1O1O100O1O100O1O100O1000000O010000O10000O010N2O1N2O100O2O0O100N2O1N200O1N2O2O0N2N3M2O1O1O100O1O1O1O1O3M2N2N4M3M6H7J5J7I4H:H7J4M4J5L5GToa4\"}, {\"size\": [1280, 720], \"counts\": \"cbTe0f0XW12J6GVOXiNo0eV18M2N2M3K5K5M4K3O2N2J7K4M3L5L3M3M3N2N2O1O1O1N2O1N2N2N101O0O20O000O200N101O1O010O100O1N2N2O001O1O1O1N2N2O1N2N2O1N2O1O2N1N3L4M4K5K4J8G<C>C;EhWT4\"}, {\"size\": [1280, 720], \"counts\": \"jgie02hW1?D7J4M2M5K4L6K8H3M4L4L4M2M3N2M4M3L4M1N3N1O1N2O001O1O1O001O0O2O00000000O1O2M2N2O1N2O2N2M3M3N3K6K4L4L4M3L4M3K6J7jNXQS4\"}, {\"size\": [1280, 720], \"counts\": \"^Yle0b0nV1d0F9J5K5K4L5L3M4L2N3M2O1N3N1N2O1O2N2O0O1O10O0010O100O10O1O010O1O1O010O1O0N3O1O1N101N2M3N2O2M3M3M6I6J6J7Hc0[OfWT4\"}, {\"size\": [1280, 720], \"counts\": \"mQ_f0<^W1f0[O9I6J4L5L3N1N101N1O10O0100000O10001N1N3N101M2N3M3M3M3N2M4L4J6J7G:GZWY4\"}, {\"size\": [1280, 720], \"counts\": \"_ace0<cW15K4K7K0100O1O4LNGihNJPV1a0_jN]ONMaU1V1TjNnNXV1a1K4N1O2M2O1O1O2N1O1O1O1O1O1O0001O0O2M3M3M3L4M4L4L4L4I9G9I9]OdPl4\"}, {\"size\": [1280, 720], \"counts\": \"_Yid0?`W15K4J6I5MO3\\\\OViNOnV1H]iNOVf34c`Mi0B?B?C4L3M3N000000O2OO0001O001O1O1N3N1N3N2L5K5L5J6L3L6H7Djhh5\"}, {\"size\": [1280, 720], \"counts\": \"hYZd0g0UW19I4L3O01N10OO2N2M9[OohNJW_21ZgN3miNL_O4\\\\V1Z1I6M4M2N3M110N10O10O00O2O2N1O1O1N3L5J5M5K5I7H8F]XZ6\"}, {\"size\": [1280, 720], \"counts\": \"ZYfc05302MoV1l0M3OO2NL3M2F:D<K6O2L3N201N2O2M3N2N1N200O2M3N010O3L3N2O00O10000O1O3M2N101N3N0N4eMjjNN4U1`V1iN[Xi6\"}, {\"size\": [1280, 720], \"counts\": \"dYcb0261XW1a0G:H8I7H6M2O0O4H8L2N1N2M3M3M4N001O1O1O010O100O1O2O0O101O001OO1O11O1O1O00O2O1O0O2000OO2000000O2N3L3J6I7H:J9G8gN[iN4ON0Oco[7\"}, {\"size\": [1280, 720], \"counts\": \"_adb0i0PW1;E9G9H7L3L4N2L3O2M101O2N1O1000000000O10000000010O001O100O0M4N2O2N1K6N2N2HPjN\\\\NTV1`1:L2K5M4Hj0VOg_h7\"}, {\"size\": [1280, 720], \"counts\": \"Xgla0=X1FfT1Q2H<F8I6J5M4J5L5K5L3M2N2N1O2N2N1O1O100O2O000O0100O100O001O1O010O100O1O2O0O2N3M3M3M3N2M2M3O2M3L3O1N1N4N3M3L2K4L:H4K4La0XO=K:B^hi7\"}, {\"size\": [1280, 720], \"counts\": \"m]m`04hW1;Dm0fhNhNYV1f1I8H9H5J5L4L3M2O2M3M2N3M3L4M3N4K3N2M3M3N2N3M2M2O2N1N2O001O2M2O1O1O00O1001O0001O001O1O000O100010OO10001O010N10001O10OO2O1N201N1O2N2N1O2N1O1N2N3L3N3M3M3L4L3M4K5K4K5I7K5I6J8J5J8I;Ga0]ORYb7\"}, {\"size\": [1280, 720], \"counts\": \"^^\\\\a0m0iV1c0B>D7J5K5K5L2M4M2N2N3M3N1N2N3M3M3M2N1O2O0O2N1O101N100O2O001O1N2O1O0000001O001O001O001O0O2O10O01O01O0000000O101O000000001O0O2O1O0O2N100O2O001O1N2O1O1O100N2O1O1O1O1M3N2M3M4L2O2M4K4K6K6L3L3M3K5K6J6I6J6K6I7J7Gaae6\"}, {\"size\": [1280, 720], \"counts\": \"]oUc0<ZW1a0D:F=B<D9H8J5L4K4M3M3M3N3M2N2N2N2N2N2N3M2N2N2N2N10000O10O01O2O0O10000O100000000O100000000000000O1000010OO10001O00001O0O20O0001N2O0010OO2O001O001O1O1O1O0O3N2N1O1O1O2N1N3M3M2M3L5J6K5L4K6I7G8J7L3M3M3L4M3K7I;XOTQl4\"}, {\"size\": [1280, 720], \"counts\": \"oV^b07`W1=J7I4L4M1O2N2O001N2N1O2N2L3L5K5N1O101O1N2N101O1N2N2O00O02O0O2N2M3M3N1N30O001O0000O1N3M30O010O1N2OO2N2N1O2O1O002NO1O10O2N2N10001N3L3O000O2N100O100O100O101N1000000O101O1O0000000O100000000000000000\\\\OPlNXMPT1g2QlNXMPT1h2PlNWMQT1l2lkNTMUT1l2jkNSMWT1l2jkNSMWT1l2jkNSMWT1l2kkNSMVT1l2kkNSMUT1l2mkNSMST1l2a001N1N30O10O001N20000IljN`MUU1d21N3N2N2N3O1O1M2N2N201N1O3M1N3O1N3M2M3N2N3M4L3M2M3N1O1O3M2O3L3L5K5IhXe3\"}, {\"size\": [1280, 720], \"counts\": \"`obd08\\\\W1=M316I1M010000000100O0000O2O000O100001O1O01O01OO10000000O101O0O10000O1O100O1O1N3L4M102M201O1M2O101N2NdP`5\"}, {\"size\": [1280, 720], \"counts\": \"l`lc0o0jV1=F7I8K2M4K5J5K6L3M3L4M2O2M5K4N1N2L5M2N3L3N2N101N1O1O1O2N100O1O1O1O2O000O1O102M3N2N00001O001O0O2O1OO1O10000O2O01O0000O1O1N3M2N2N2O2N2M2O2M3M2O2M3M3M3M2O2N3L3L3N4L4M3L3M5M2M4L4M3M4L4L5K4L2M2O0M4M3M3M3L^WY4\"}, {\"size\": [1280, 720], \"counts\": \"^hTc0n0lV1?B8K5J5K8I3L5L3M3M2N3M2N3M2N3M3M2N2O1M3O1N2O2N1O1O2N100O2O0O2N101N101N2O1N2O00O10O2O1OO1O1000000000000001OO2O0O11O01O0O2O001O0O2O1O1N2N101O1O1N102M2N3M2L4N3L5K4M4K4M4K4L4K6K6K2O4K5K5L3L3N2M3N4Hc0^OQ_S5\"}, {\"size\": [1280, 720], \"counts\": \"n`Ub06nV1n0H9I7H6I7K5M2M4M2M5L4K4M3M2N2O2M3L3O2M3M2O2N1O1N3N2N101N1O1O10001N001O1000000O10001O00000000001OO2O000000000O101O010N2O001O0O2O1O001N101O0O2M3O101M3M2O2N1O2M2N3M3M2N3L5L3L5K5K6H9I5K5M3L3M4M7I8G2O6I3L_WP6\"}, {\"size\": [1280, 720], \"counts\": \"ZXVa0g0oV1`0D8I6L5K4L4K5K5L4L5L4K5L3M4L3M4L3N2N2M3N2N2N2N1OOokNXLmS1i3TlNWLkS1o3O1OKWlNTLiS1l3ZlNRLfS1n351O2OO010O11O1OO1O1001O0000O1001O1OO1O100001OO10001O010N1O2O10O10N1O2O1O011M2O2N00100N2O1O2M3M_kNgL^T1X38N1O1M3N2J6F:K6K3M4K8I6K5K4K5M4K6J6J4I8J4M^_o6\"}, {\"size\": [1280, 720], \"counts\": \"VhU`0<XW1l0WOd0_O9H7J5L3N4K4M2N3L5L3M4L4M2M2O2N1O1O1O001O100O100O1O2O000O1000O100000O100000000001O00000O100001O01O001N101N2O1O001O1O1O2M2M3O0O2O1O1N2N3M2O2M2N2N3L5K4N2N3M3L3N2M4L5L4K5K4M3L4M4K7Hd0]O1O1NU_W8\"}, {\"size\": [1280, 720], \"counts\": \"TgW?1oW11O0O100O2M2N2L4M3K6L3ejN_OTS1d0llN\\\\ORS1g0jlN\\\\OUS1e0llN[OQS1f0RmNXOlR1j0TmNXOiR1i0WmNYOfR1i0YmNXOeR1i0[mNXOcR1o0QmNYOlR1P1llNROQS1o0olNROoR1o0QmNSOmR1m0SmNTOkR1m0UmNUOhR1l0XmNUOfR1l0ZmNUOeR1k0\\\\mNUObR1m0^mNSOaR1n0bmNVNiN`0dS1\\\\1jmNeNTR1\\\\1lmNdNTR1[1^lNQNX1f0YR1Z1^lNPNo01SOh0_S1[1_mNoN_R1o0dlNmMh0T1cR1n0hmNQOWR1n0mmNoMhNk0ZS1U1UnNkNkQ1U1VnNkNiQ1T1XnNmNgQ1S1YnNmNgQ1S1YnNnNfQ1R1ZnNnNfQ1R1ZnNnNgQ1Q1YnNPOfQ1P1ZnNPOfQ1P1[nNPOdQ1P1\\\\nNPOdQ1P1[nNPOgQ1P1XnNoNiQ1Q1WnNoNiQ1Q1WnNoNiQ1Q1WnNoNjQ1P1VnNPOjQ1P1VnNoNlQ1P1TnNnNoQ1Q1RnNkNRR1T1nmNfNYR1Y1gmNdN\\\\R1\\\\1dmNcN^R1\\\\1bmNbNaR1]1_mNbNbR1^1^mNaNcR1_1\\\\mNaNfR1^1ZmNaNgR1_1ZmN_NhR1`1XmN`NiR1_1n1M3N100O2N1O1O2N3M1O1O2N2N2N3M2N2M4M4L4L2N2N3M2M10Z`U9\"}, {\"size\": [1280, 720], \"counts\": \"d`Q?a0RW1e0_O:H8J5L5L3M3L4L4N2N2N2N2N2O1N2O1O2N1O1O1O2N2N2O2M2N100O3N3M2N0O101O2M2O00000000001O1O:F5L6H01N3O0O101O010O12M100O2OO010O0O2O1O2N2N1N101O001O1N2O1N2M3O1N2M4G8M5K8G6L5I6J6L8D7K4K5J6K6M4L4J:H6J[WY9\"}, {\"size\": [1280, 720], \"counts\": \"m`Q?a0XW1<C<G9H8H7K5J4L4M3L5L3L4M3M3M4L3N2N2N2M3N2N2N3M2N01M21O2OO1O0011O1N10N110001OO1O10O2O10O02N2NN3N100M30okNWLlS1i353MO2N1O20O01N1O2N20O11N1N2O1O1O1O1N2N20000O1N3N001O3M3L3N2N1N3M5J6I8C<J4L4L4M1O2O0O2N2N8Ga0^OgfV9\"}, {\"size\": [1280, 720], \"counts\": \"cXd?=ZW1?Cc0@<D4L3M4M7I4M4K4M3M3M4L3N3M3L4M2N2N2N3M1O4L2ON11O100O01O0101O0O10O10000000O1O100001O0000001O0O10000010OO10001N2O000O101O1O1N3N1O1O2M2O1O1N101O1N2O1O3M1O2L4M3N1M4M4J6J6H7I:I6K5L1N3OO01N2N3M4L;F7G3N4KkV`8\"}, {\"size\": [1280, 720], \"counts\": \"UYS`0d0oV1`0H7G9I6K5K5L4L4L4L4L4N2M3M4L3M3M2N002N2O1O101N1O1O100O2N1O100O101O0O010O100O100000000000000000000000O11O0001N1000001O000O2O1N2O001O1O2N1O1N101O1O1O1N3N1O101N2L3N3J6I7L5L4L3N2M7J7I:F1N2N2O1N2O3Ke0\\\\O4KbfS8\"}, {\"size\": [1280, 720], \"counts\": \"Yji`0h0oV1<G9I9H5J7I6K5L4K3N2N2N2O1O1M3N2N3M2N2O1N1O3O0O1O0O201N3M1O1O100O10O0O2001O2N0O2O00O1O1O1O1O11O2NO1O2N11O00O10000010O10N101O1O1O1N2O1O010O2O2M001O2L3M4L3M4M3M3M4L3N1N5L4L4L4K6J8H4K4L4I6GRfd7\"}, {\"size\": [1280, 720], \"counts\": \"jZQa09_W19I6K7J<E5K4K4N3M3L3N3M2M3N2N2N3M2O1N2N2O1N2O2N1O1N2O2N10O01O1O1O3N5K2M2O000O101N100O2O001O0000001O00O100001O0000O100O2O00001O1O001O001N2L4L3M4M2O2N3M3L4M2N2N3M4L5I9K2O3L4J<E2L3FUiNZOoV1?=L5Hk]^7\"}, {\"size\": [1280, 720], \"counts\": \"aZ[a07eW16L4K4K4M3L4N1N3O2N1O3L4K111N3N3M1N2O1O1O1O2N1N200O100O1O1O1O1O1O100O1O10000O100O2O00O0101O001O00O1001O00O10000O2O0010O00O2O001O001O001O001O001O001O2N2N3M6J5K5K4L5J9G6Gh]^7\"}, {\"size\": [1280, 720], \"counts\": \"][ea0i0RW16L4I6M3L4M4K7H9I6L1N2N3L4M2N3M2O1N3M2N2O1O2M2O1O1O1O1O10O01O101N1O100O10000O1000000000000O010001O01O000000O101O000000001O0000001L6K3L4L4M3M3N3L4M3L4K7J5M3M6J3K6K4L4L4LY]T7\"}, {\"size\": [1280, 720], \"counts\": \"XZYb0g1SV1d0QjNfMVU1k2F6L3M3M4K5M2M3N2N1O1O1O2M3O1N2O1N1O011O0O10000000001O00000000000001O00000000000O1N3M2O2N1M4KRlN[LjS1d3;M2N2N2I^kNnLdT1U32M3O11OO1L5M2J7H8M3N2N2N3M2N3L2O1N3N2L5I7H9H<F6I4L4Ln\\\\e6\"}, {\"size\": [1280, 720], \"counts\": \"UQXb0h0TW1:G7H<F5J6L5J5L4L3M3M2N1O2O0O2N2O1N2N2O1N101O1O001N2O1O001O1O001O1O10O01O0010O02O0O1O1O1O000010000O01O2N2M2O001N2O1O1N2N3M2O100O2L4N1N3N2MgjN^MWU1c24N1L4M3N4J4L5K4H9I8I6L6J6L2M2N3N3Kjm]6\"}, {\"size\": [1280, 720], \"counts\": \"TaPb07`W1=H9H:H6I6J7J4K6J6K3M3L4M2N2N3N1N101N101O1N2O0O2O1O1N2O1O002N100O001O1O001O10O01O00010O0010O010000O10O010N1O2O2N001O001O1O1O0O3N1O1N2O1O2N1O2N2M2O2O0O2L3N3K4M3N2K6I7H8G9I7M4L3M3L4M2N2O2L4L5KP^[6\"}, {\"size\": [1280, 720], \"counts\": \"Riba0j0nV1>E<G8G6K5K5L7I4L3M3L3N3M2N3N1N2O1O1O002M2O1O001O001O00000001O01N11O0001O0001O0O101O00001O001O1O001O1O1O001O1N101O1O2N2M2O2N1O1O2N1O2M2N102N2N3M102L2N3M2J6L4H9D=J6L4L4L3M4M1LQVS7\"}, {\"size\": [1280, 720], \"counts\": \"d`Wa05[W1T1XO5J5J6M3O2M2N2N2N2O1O1O1O1O100O10001N1ajNeMYU1[253N0OOfjNaMWU1_2hjNcMWU1]253N4L;E`0@00000001O0001O00O1010O01O001O001N10001O00010O0O10001O2M3N2N001N2O1O100N2N3O0O101M2N20001N2M2N3N2N1N3K5L3L4K5L5I7I8I7J6L1O2N2N3L4LR_T7\"}, {\"size\": [1280, 720], \"counts\": \"\\\\X[a0336TW1f0F=B9G9J4L5K6J5L2N2N1O100O2O0O1O101O000O0100001N6K4K3N00010O1O1O01N11O01O0001O01O0000001O1O001O001O1O001O1O101O00O01O1O1N2O2N1O001O2N1O1N2O2M3N1O1M4N1J]jNnMdU1n1^jNRNdU1j1<L5K5L3L5K4L4K4J5M4M2O2N3K5JQ_T7\"}, {\"size\": [1280, 720], \"counts\": \"hPZa087ITW1k0A<G;G7J6J4M4K5L4L4L3M3M3N2M2O2M3M2O2N1N2O1O1O1O001O1N2O001O2OO01O10O00001O001O0010O1O010O10O10O01O001O1O1O1O1O2OO01O2N1O1O1O1O1N101N2O1O1O2N2N2N1O2N1O1N3N2M3N3L4J5I7H8H7[Oe0H7N3M2O3KcnV7\"}, {\"size\": [1280, 720], \"counts\": \"TQZa08YW1e0E?B9I5K6K5J6J3N3M3N2M3N2M4L3N2M2N2O1O1O1O1N3N1O1O002N1O1O10O0100O001O0001N101N10010O1N101O001O1O001N2O1O1O1N101O1O0O2O1N2O1O1O1O2M2O2N1O1O2N1O1O1O1N2O3M2N2L5L4K4K4M3H9A>I7L3N4M2M[VX7\"}, {\"size\": [1280, 720], \"counts\": \"f`\\\\a0l0PW1<D9H7J5J8I6K3M2N2N3N2M3M3N2M4L3N1O1O1N101O2N1O1N2O1O001O100O1O1O1O0001O001O001O00001O0O2O1N10001N1O101O001O1N2O1O0O2N200O0O2N2O2O0N2N2N3N1O1N3O1N1O1N2N200O2M4L3N101O1L4M2M3K5G9[Od0M5M3J`VS7\"}, {\"size\": [1280, 720], \"counts\": \"\\\\iga0e0UW1>E<D7J5K6J6J4L3N2N3N2M3N2M2O2M3M3N1N101O1O001O1O1N2O100O1O1O1O1O1O0100O10O0001O00000O2O00001N101O001O1N2O1O1N101O001N2N101O1O1N3N1O1O2M2N2O1O2N1N2O1O1O2M3M3N2M4M1N4M1O1L3L5F9I8K6L3M5Kimg6\"}, {\"size\": [1280, 720], \"counts\": \"[Qia0d0WW19I7J6J6J5K5L4M3L4L4L3M3N2M2N2N3N2N1O2M3N2N0O2O10O0001N2O100O2N1O1O1O1O000001O0O2O01O01O1O1O1O0O2O001O1O0010O0001O0O2N2O1O1O002N1N2O1N2N2O2N1O1N2O1O1N2O1N3N2N2M3N1O2N1N101N2M3K5D<G:K5L4Kk]e6\"}, {\"size\": [1280, 720], \"counts\": \"XXVa0c0XW17L6J=D3M4K6K5L3L4L2N2N3M2O1N3N2N1O2M3M3N2N1O1O1O001N3N1O10O01O001O001O1O010O0001N11O10O01O100O000001N101M2O1O2N2O0O2O1O1N2O0N3O100O001M2010O01M3O1N2N3M21OO002M3M3O0O2N2L4N1O2O0M2O2M2M3N3K4_Ob0H:JQWS7\"}, {\"size\": [1280, 720], \"counts\": \"SXl`0=`W1>B6J=D7J4L3M3L5L4L3M4L3N2M3N1N3M2O1N3N1O1O1O1O1O001O001O00001O001O1O001O01O0001O100O00010O01O0O2O01O01O00010O001N100O2O0O2O10OJUkNWMlT1n21O00100O0O2N2O1O2N001O1O1HljNbMVU1\\\\2kjNcMVU1\\\\282OM2M3O1N2MYjNnMeU1Q2520N3N0O2L3J6J7I7G8_Oa0G9LUgZ7\"}, {\"size\": [1280, 720], \"counts\": \"fPPa05dW1>E`0A9H8G8J3M3M3M4L3M3N2M3N2L3N3M3N1O2N1O2N1O1O1N3N1O010O001O001O1O1O010O1O10O01O010O100O00010O01O1O10O01O0O2N2N2N101N20O01N3N01O1O10N2O1O1N3M110O2N1N2O1O1N3N1O2M200O1O1O1N3N2N1K6L3K7F:Fi0QO]V]7\"}, {\"size\": [1280, 720], \"counts\": \"WaRa02[W1R1\\\\O;G5L4L4M4K5L5K4L2N2O1N3N1N3M2O1O2N1N3N1N2O1N2O1O10O01O1O1O100O00100O00010O10O010O010O001O001O1O001O1O00001O001N101N2O0O2O2O0O1N2O1N2O1O1N2O001O002M2M3O100O1N3N1N3N2N2M3N1N2N2O2M3J<A_V]7\"}, {\"size\": [1280, 720], \"counts\": \"gPPa04\\\\W1c0I;G7I7J5J7I5L4M3L4L4L5K3N2M3M2N10001N1000001O5K1N2O1O010O1O1O010O0010O01O100O01O010O01O10O0100O01N100100OK6O1N20O01O1N2O00001N2O1N101O1O1O1O2M2N101N2N2N2O1O2M2O1O1O1N2O1N3N2L3O100O0O3N3J;`NhiNd0QU]7\"}, {\"size\": [1280, 720], \"counts\": \"o`aa0d0UW1d0@7I:G6K4L4K5L4L4L3M4L3M3N2N3L2N10001N100O101O002N1O1O1O1O0000001O001O001O100O1O2N01O1O00001O001O0O2JhkNbLXT1]370O01O0O2O001O1O1O1N2O100N2O1N2O1O0O2N2N2O1N2O1N2O1N2O1O2M3N2N2M3N1O2N1N3M1O1O2L5N1N3ZOe0E;M6GYff6\"}, {\"size\": [1280, 720], \"counts\": \"V`\\\\a04hW1;YhNKSW1g0H:G8H4L7J8H4L3M2N3N3L3N2M5L3L2O1O1O1O1N3N0O2O0O2O1O1O100O1O100O1O0010O01O01O0000001O0O2O001O001O1N110O0O2O001O001O001O001N3N1O2N1N1O2O2M2N2O1O1N1O2N2N2N3N2N3M1N2N2O011N2O1N1L5K4M3E;A?K5J7LmVn6\"}, {\"size\": [1280, 720], \"counts\": \"Whn`02fW1?_Oh0_O8I9G7I4L5K6K4L5K2N2N2O2M3N2M2N101N1000001O1N2O00001O001O1O1O1O1O00001O1O010O101N0010O1N101O01O01O001O001O0IgkNfL[T1X3hkNfLXT1[37O1O0O2O1N2O1N2O1O1N2N2O100O1N3M100N3N3O0O2N2M3N1O1O2N1O1N3L5L2K6L4J5F:B=M4L4N3K\\\\_Y7\"}, {\"size\": [1280, 720], \"counts\": \"dXQa0j0fV1e0F;G9G9G>C3M3N1O100O1O1O2O0O100O2O1N2O1O0O102M2N2O1O1O1O100O1O1O0001O0O101O010O0O1O2O1O0O2N10001N1O2O001O1O001O1N2O001O2M1O2O101N1N1O3N2N1O1O1N2O1N2N3O0O1N3M2N3M3M3M3L5J5J6H7L4G:K4L6I6JmV]7\"}, {\"size\": [1280, 720], \"counts\": \"o`Ra0e0QW1d0B;D`0B8I7I8H5L3M1O2N2N100O2N2N2N2O1N2O1O1O1N2O1O001O1O001O01O00010O0000001O001O001N2O001N101O001O1N1O2O1O1O1O1N101O1O1N2O1O2O1O0M4N1O1N3M3N2O0N2N3M3L4L5J5L4M5G8G7K5L5K4J6L5LY^c7\"}, {\"size\": [1280, 720], \"counts\": \"d`m`0W1bV1<D;I7I6J6K5K4M2N3L5L2N3M2N3M2O2M2O1N2O1N2O1O2M101O0O10O1O100001O1OO101O00001O0O1010O01O1O001O001N2O001N2O2N1O001N2O1O1O1N2N3N1O101N2N2M3N2N1N3N3L2N2N3L4L4L5E:H8B`0J6J5L4L2K4Oa^h7\"}, {\"size\": [1280, 720], \"counts\": \"ePPa06kV1P1I7K4M4L`0@6J3N3N1O1N2O1O2N5K6J7I2N1O101N100O3N6J7H3N000O2O1O5K2N2N001O0000O1000001O0O2O0O2O0O2O1N1N3L35LO0KUlNULlS1i3WlNULjS1j3601N101O1O1O101N1O2N2N1N3N1N2O1M4M4K4M4K4L4K5J5M3G:I8J6K8G7I6K4L4L7G[Wb7\"}, {\"size\": [1280, 720], \"counts\": \"b`\\\\a0c0SW1e0^O<D<E;G6L4J8J4L4K6K5K4L4L3N3M2M2O1O1N2O101N2N001O1O010O10O1O00010N2O1O1000000000O100000O10001O0000001O0O2O00001O000O2O001N2O1O2M2O1N101O1N2N3M3M2N3H9L4K5H7K7F9K6K5K4M4J5M6I?B4IXoV7\"}, {\"size\": [1280, 720], \"counts\": \"WaUb0n0kV1=G4L3L5K4K6L3J6M3J7L3O1M2O2O1O1N2N101N3N1O001O1N2N2O1O1O1O2N1O1O00100O10O010000000O10O100000O10001N10000O101O0O1O2M2N3M3M3L4M3M3M4K6I6F;H;C9_On0_ORXS7\"}, {\"size\": [1280, 720], \"counts\": \"SP[c07ZW1e0ohNVO\\\\V1\\\\1J7J8H3L5L2O2M2N2O1N2N2N2N2O1O1O100O10000000O10000O1O11O01O0O2O0M4M3I60001O3L3N1O2M4M4L3L6J7Eahk6\"}, {\"size\": [1280, 720], \"counts\": \"nPQc02mW14L2RNNZkNd0bT1_1N3N2N2M3M4L3M3M4K4L5M4K4M2N3M3N1O1N2O1N2O1O1O0O2O2N1O1O1O00001O01O00001O1O1O1N2O1O1O1O2M2O2M2N3H8G:J6J8H8H8I7H8E:G<A;G9J6GR``6\"}, {\"size\": [1280, 720], \"counts\": \"^^ib02nW1001niNNST15jkNLVT15ikNLUT15kkNKST17mkNJRT16nkNKPT14RlNMlS11WlNOgS12YlNOfS12ZlNNfS11[lNOdS13[lNNdS12\\\\lN2_S14\\\\lNNbS12]lNNdS13[lNMdS14[lNNcS13nkN=kNVOoT1=UlNi0gS1XOXlNi0gS1WOYlNk0eS1UO[lNm0cS1SO]lNP1aS1oN_lNR1aS1mN^lNT1bS1mN]lNS1cS1nNmkN]OKi1UT1ROdkNd1YT1W1J100O2O00O2O0O21N1O1OO2N1O2M2N3O1N3M2O1N3L4M2L4K5M3L4L5I8H9E9G<E:El_j6\"}, {\"size\": [1280, 720], \"counts\": \"SWYb0c0VW1`0A<G8I4M5J8I5K4L4M2M2O1N2O2M2O3M2N1N101O001N110O0001N2O1O100N2N2N101O1O1O1N2O1N2O2M3L3O1O1O2O0O1N3N101N101N2N2O2M1O2M3M2N3M3N3K4M5L3L4K6J7]OihN2gVS7\"}, {\"size\": [1280, 720], \"counts\": \"PW`a06cW1g0[O:H6J:G6L2M5K5K6J4L3N9F3N0O010O100000O001IVkNWMkT1h271O1OO1000001O0O1O101O01O00O101O001O1O1N11O01O1O001O1O1N1K6N2O1O00100O1N2O1O0O3N1O1O101N1O001N2N2O1O1N2O2M2K\\\\iNmNdV1Q1_iNmNcV1R170O0001O1N3M1L4N2O1N2N1K5N2NXXn6\"}, {\"size\": [1280, 720], \"counts\": \"PW`a0j0TW19F9G6K4M4L4K7J5K3M4L2N3M2N3M2O1N3N2M2O0O2N1O2O001O0O2O002N1O000000010O00001O0000000010O1O0001O01O01O01N2mLmkNW2ST1iMmkNW2ST1jMmkNU2ST1kMmkNV2RT1jMbkNH4^2[T1hMbkNL1\\\\2^T1eMokN[2kT110O1O2NO1O110O02NN2000bMljNQ2aU1001O000000001O01O000O2O000O1001O01N1O2N=C3N001N1N200J^iNnNbV1W11O2N1O1L5Do_`6\"}, {\"size\": [1280, 720], \"counts\": \"]_Zb07Q1L[U1T1giNUOSV1`1L5K3M3M4M2N2N3M2O1N101N2N2N2N2O1O2M1O2N2O2M3N1O1O2M2O2N001O1O1O1O1O001O000100O000001O01O01O01O010O000010O01O00100O00001O1O001O010N101O2M2N2N2N2N2O1N2N2O1O1N2O0O2cNljNMUU1KQkN5PU1]11ZNijN`0YU1^OhjNa0[U1Q11`NdjN:^U1AnjN4UU1HRkN3QU1J\\\\kNGiT17XkNHiT17WkNHPU12PkNNRU1OojN0SU1OnjNNWU1LljN3aV101O001Nafh5\"}, {\"size\": [1280, 720], \"counts\": \"inRb05eW18K8I3N2`jNXOdS1k0ZlNXObS1m0jkNDUT1T2N2O2N101N2N101N100O2O0O2O1N2O2M2O1O1N101N3N1O1O001O1O2N0O3N1O2N1O00100O1O100O00000010O01O2OO00101N010O1O001N101O1O100GSlN\\\\LoS1a3TlN^LnS1`3TlN]LmS1c3SlN]LnS1`3;O1O1N1O2O1N1O1O2O001N3N2N1N2M3O011N2L4M3N3K3N3O1O1M3L4N2M4M2J5_Ob0@?M>Di_l5\"}, {\"size\": [1280, 720], \"counts\": \"^^\\\\a0g0SW1<E>C<E5L5L4K5L3M2M3N2N102M1O00101N1O010O100O2OO0010O101O00O10000000000O11O0000000001O0O1O2O0000001O001O001N1APkNfMXU1Y290O01O1O0O2000ON4M2N2J501011M10N2M3O1OQkNdN[S1Z1elNhN[S1U1glNkNYS1T1hlNlNYS1S1glNmNZS1U1clNlN]S1U1alNkN`S1Q1clNnN_S1h0YkNnN[19^S1g0ilNYOXS1e0ilN[OXS1c0hlN^OYS1a0hlN^OZS1`0flN@[S1?elNB\\\\S1=clND]S1;clNE^S19clNF_S19alNFaS18`lNGaS18`lNGaS10hlNOYS13elNK^S14blNL^S13dlNKkS10lkNMVT14jkNKXUi6\"}, {\"size\": [1280, 720], \"counts\": \"^^aa05]W1n0\\\\O8I8Ig0YO8H5K3M3N2N5K5K5K3M1O2N100O2N101N3M2O1O1N101O1O1O1N2O2N1O1O1O1O1O1O1O2N01M2O1004MM2N201O001O0O2O00010N101O001O001O1N10100N2O002N1O0O2N2O1O1N2O101N0O2O1N1O2N2O1N2O1O1O1O1N2O3[MakNb1aU1N000101002M2J9F6H7I;YObha6\"}, {\"size\": [1280, 720], \"counts\": \"^UVa0S1iV19I6J:G7H7J:F4L5K6J6I5M1N2N1O101N102M3N1O1N2O001O1O1N101O1O2M2O1O10O01O101N1O1O10O000O1010O000001O00001O0O2O010O1N2O1O1O001M3O1O001O1O1N2O1O1N2O1O1O0N3O2O000N2N2N3O001M2N3N1O101N1N2O3M3M1O3L2O1N2N3kMnjNW1[V1C1N2L4LXhk6\"}, {\"size\": [1280, 720], \"counts\": \"Vm[`0S1fV1`0D7I6J9I7H7I5L4L4L2N3M3M2O1N2N3M3N2M2O0O2N3N1O1N2O001OO10O01O100O10000O100O100O100O2O0O101O001O0O101N1N3N101O00001O001O1N101O1O001O0O2O1O1O1N2O1O1O1O1O1O1N2N2N2O1N4L3N1N3M3M2N2M3M4L3N3N2N2N1N3N2M2N3M3M3M3M3M2N3M3M6^OchN6eW1N00K51O2N3LaYX7\"}, {\"size\": [1280, 720], \"counts\": \"jm[`0P1dV1f0@9J7I6K6I6K6K5J5L5K7H3N2O0O1O1O1O2O0O2N101N2O1N2O1N2O2N1O1N2O1N2O1O1O2N2M2O1O00K500001OO2O001O0000001O0O101O001N2O001N1O101N2N10001N2O0O3N1O1O0O2O1N2N2N2N2N2N2M3N3N2N1N2O2M2O1O1O2N1N3N2N1N3N2N1O1N2O1O2N1N3N2L4_Oa0C<L6L4L5L2M2O1N3M3L4LXaT7\"}, {\"size\": [1280, 720], \"counts\": \"UnYa02dW1Q1ROe0\\\\O=F8H8H6K4K5M3M4K5L4L3L4M2N3N2M2N1O2O0O1O2N100O1O1O1O010O1000000000000O0100000001O001O1O1O1O00001O001O2N2M2O2N1O1N3N1O0O2O0O2O0O10O11M3M20001N101O00001N2O001N3N1N101O001N2O1O3L2O1N1O3M101N3N2N1N4M1N3N2M3M2N2IniN]NTV1`19M3M5J5L6I8H8I7G300000hXU6\"}, {\"size\": [1280, 720], \"counts\": \"Qm\\\\b0e0YW17I6I6J4M3L4L3Nb0^O3M2M4M2O1N3N1PkNYMeT1i280mjNZMlT1W3F4]kNfLYT1S4\\\\O4]lNgKZS1`4O3M2N4M2M2N101O0O101N101O1N2O00000O0100O1000000000000O100O1O1001O0001O0001O00O2N1010O01O1O001O1O001N2O100O1O1O0O101O1M3O1N2M3N22N01M2M4M3O0O2M3L4M2M4L5J5L5J6I8I6H9G8I8J4M4K8I5K5K3L5L4LfYW5\"}, {\"size\": [1280, 720], \"counts\": \"Z]Xc0l0iV1d0\\\\Ob0C;G7J6J6J7I5L3N4K3L5L4L3M4M2M3N2M2O2N2N2N2N2N2N2N100O1O100O101N10001N10O100O10O2O000000001O00O1000000000000000000O10001O001O001O001N1O1O2O0O2N11O0100O1O1N2N2N2O1O1O1O1O2O0N2M3N3L3M5J5K6K5M3N1N2K7J8G8I5L;F6E<H8J;D8H1M<D\\\\a_4\"}, {\"size\": [1280, 720], \"counts\": \"fnSd02iW1;L2L4L3M3M3M2N2N1O2O0O10O00O11O01O0000000001OSMoNknNQ1RQ1SOlnNn0RQ1UOmnNk0RQ1VOonNi0oP1YOQoNh0nP1YOQoNg0nP1ZORoNf0mP1[OSoNe0lP1\\\\OToNd0lP1\\\\OToNd0kP1_OSoN`0nP1ConN=PQ1EonN;QQ1FonN9QQ1GonN9QQ1HnnN8QQ1IonN7QQ1InnN8RQ1ImnN6TQ1JmnN5RQ1MmnN3SQ1NlnN2TQ1OknN1UQ1OknN1TQ11knNOUQ11knNOUQ12jnNNWQ11inNOWQ12hnNMYQ13gnNLZQ14fnNK[Q16enNI[Q17enNI\\\\Q17cnNH^Q18bnNF`Q1:anNE_Q1;anNDaQ1;`nND`Q1<`nNCaQ1=_nNAcQ1a0\\\\nN\\\\OfQ1g0WnNWOlQ1j0SnNSOoQ1n0PnNROQR1n0nmNRORR1n0omNQORR1n0omNPORR1P1nmNPOSR1P1mmNmNUR1T1kmNhNYR1X1emNiN\\\\R1V1bmNmN]R1T1amNnN_R1R1`mNmNbR1S1\\\\mNjNiR1V1WmNgNlR1X1P2N1O2N1O100O1O1O2N2N2N1O2N4K4M7IYkV4\"}, {\"size\": [1280, 720], \"counts\": \"bnSd02dW1>D:I7K4K4M3K3N2L8KOniN]NiU1i1WjNWNgU1Q2NO3O0011O1O2N1N2N1O2N2O0OM3O21O0O3N2N1O00N24K7J3M2N100O100O100O10001N2O0O100O100O2O2M7J6J0O020O0001O1M402M2M4M1N001O2N104K4M31M1OM2O1O102M1O1YMmjN^2\\\\U12M2O1N010O02N2N2N1O3M3N000N2O1O1O1N3N2M4M6K1M6TO^Ri3\"}, {\"size\": [1280, 720], \"counts\": \"lmnc0j0PW1;G:E8I7J7G7J5L5K5K5L3L5M2L3N3M4K4N2M2O2M4M2N1N3M3N2N2N2N2M4M2M4M2N3L3N3N1N1O10O000001O2N2O0O011N100O01O010001O0O011O1O00O2N1001N2OO1O20O1O1ON2O1O2M2O21N1O1O101NM3O2L3N2O2L4N2N1O2O1N2N2O1M3N2N2N3N101O0M3N3M3M3N2M3M5L2M4L5H7VOakNfMiT1P2n0VNQjN5`W1JiRd3\"}, {\"size\": [1280, 720], \"counts\": \"Ufcc0g0nV1c0B:G9F:F8H9I7J5K4L5K5K5K4M4K5M3L3N2N2N2M3N2M4M3M2M3N2N3M2N2M2O2N1O2O0O10000O1O100O1O1O100O1000000O101OO010000000O100O2O0000000O2O0000001O0O101N100O2O000O2O0O101N2O1N2O1N101N2O1N2N2M3O1N2N2N3L3N3N2M2M4M3M3L4L3N4L5K3N4K4M5K<Cm0SO8QObiN^ON6ZhQ4\"}, {\"size\": [1280, 720], \"counts\": \"lTRc03kW12N5K4L2N2M4L4M3M2N3N1N2N3M3N1N2O1N2O2N4L1O1O1O2N2O0O1O2O0O2O001O1N10001N10001O0O2O1O1O1O000000O11O1OO10000100O001O001O100O0001O01O1O01O001N2N2N101O001O100O1O100N2O1O100O1O1O1O2N1N2O1O1O1O2O3L5J4M1O2N4M2N3L3M4L9G\\\\Rl4\"}, {\"size\": [1280, 720], \"counts\": \"h\\\\aa0[1\\\\V1i0ZO;G:F8G9H6K5L5J6K6I6K4M2M3N3L4M3M3M3M2N4M0O2O0O1O2N100O2OO10O011O0O100O2O00001N2O000O1000000O1000010O0O10000001O1O3M;D8I001O00001O2N4L3WKolNN0X4iS1I0O01O01O00001OO1001O005LN1OYLokN`3ST1^LnkNb3WT10104L2N1O105KN1OiL`kNP3fT12OO2N8J1OO0O0N35K3L2O0O3M3K4M4N1O1O2K4M3K5M3N2O100K5N2O1O2N10101I8L6I8NNO1N2K5MdiY5\"}, {\"size\": [1280, 720], \"counts\": \"ced`03[W1`1iN?E9H8I6J5K6K5K5K6J4L6K5J5L3L4L4M3M2N2N1O2N2O2M2N3N2M2N2O1N2O1O0O100O2O1N2O0O2O001O0O2O2N0O100000O13M1O00O1N2O2O000O1O11O10O001O0000001O001O001O01OO2O00001O0O110O001O001O001N101N200O1O1O1N200O1N2O1N200O1O2M3N001O2M2O1O1O3M2N2N2N2M3M3K4L5J6L4L3M3N3K5L3M3L4K5L4K5F:J6K7K5K4L4N6J5K4G6@ehN3hW10N7KM4Jfag5\"}, {\"size\": [1280, 720], \"counts\": \"Y^c`0;]W1<G9F9I4K5M3M3N2N2N2M4N0O102M3M2O0O102[lNdMcQ1]2]nNcMaQ1`2^nN`MaQ1_2anNbM^Q1]2cnNdM[Q1W2knNkMRQ1U2nnNlMQQ1T2PoNmMoP1T2PoNmMnP1W2onNiMPQ1W2QoNjMmP1U2RmNeMl18oP1T2UmNeMk18nP1S2[oNmMeP1S2\\\\oNmMbP1l1foNUNYP1c1ooN]NQP1b1PPO^Noo0c1RPO]Nlo0d1WPOQNXM0aR1o1WPOQNXM1`R1n1WPORNXM2`R1m1WPOQNYM2_R1m1aPORN`o0n1`PORN`o0n1Y3001WjNnMbU1R264L4LH]jNTNbU1l1^jNTNbU1T21N21O3M2MO2O101O0O1O1J60UjNPNgU1U2O1OO1010OHZjNVNfU1j1[jNUNfU1j1[jNTNfU1n15L5O0O2O001O1O010N2O01O0001O1O1O001O1O0O3N1O001O1O1O2N002M3K4O010O3M1LPiNYOoV1e0RiNZOoV1e08N11O1O0001O1N3M3M2N3M1O1O1O1O1N2OPb`6\"}, {\"size\": [1280, 720], \"counts\": \"\\\\Ub`06ZW1j0A=C;E;F:G8I:F5L3L5K7I6K4K5L3M3M4L3M3M5K2N3M3N3L2O1N2O1N101N2O1N2O001N2O1N2O001O1N2O2O1M3N1O2N0O1001O01OO100010O000000001O000001O001O00001O0000001N200O1O000O11000O1N101N101N101O1O1O002M2O1O1N2O1O1O1O1O1O1N2M301N2O0N2N3N1O2M2L5M3M2N3M3M2M4M3L4L3M4L3N3M2N2M4L3L5J5H:K3J7H9H9J6K3M4L4K4M3L3M3N110N4K4L^Yf5\"}, {\"size\": [1280, 720], \"counts\": \"g\\\\Wa0<6LiV1S1A9G;Fg0ZO:E9H7J5K3M4M3L5L3L5K3N3N2M2N3M2M4M2N2O1N2N3M2O1O0O2O1O1O1N2O2M2O2N1O1O1N101N2O001O1O1N01O100001O00O1000000000000001OO101O000O2O1N2O0O101O1N101O0O200O000O2N101O1O001O1N100O2O0O2O1O001N2N2O2O0N2O0O2N2N2N20001N101N1O2N1N3M3M2O100O2N2M3M3M3M3M3L4M2N3L3N3M2M4M2M3M3M3M4K4G;H7E=J4M3M6K6H5L3M1001O000001N3L3L5JkYh4\"}, {\"size\": [1280, 720], \"counts\": \"PUmb0`0XW1;Ha0[Oo0SO`0D7G9I5J6L6K5J8H5K5L4K4M2M4M3L3O1N1O2N1O1O2O0O2N101N2N2O0O2O1N2O2M3N1O1N2O2M2O001N10O0101N2O00O1O11O1OO1O100001OO100O11O1ON3M20001O010O001N1O2O00001N2O001O001O0O2O1O1O1N1O2000000N1O2N2O0O2N1O2O11OO1N2M30O10]L`mNe1`R1YNdmNb1_R1VNUmNkN>l2_R1_NamNa1`R1^N_mNf1_R1mMVmNVO<m2^R1iM]mNTO;m2[R1mMTnNWOmNd2RS1RNYnNm1hQ1PNcnNk1ZQ1SNlnNh1VQ1TNWoNa1jP1\\\\NYoNc1iP1ZNXoNe1lP1RNZoNm1]S1O2N2N1N4N3L2M4M2N1O1O5J5L2M4La0_OZXj3\"}, {\"size\": [1280, 720], \"counts\": \"QU_d0f0RW1d0[OjNdiNZ1YV18G9M3K5N2M3M3O1M3N2N2O0O2O1N1O3N2N001O1N2O10O000101N2N1O00010O2OO0010O101N1O100O100000000O101O1OO1000O100000O11O000001OO2O01O0O1001O0001O0000001O001O00100O001O0O2O1O0O1O2000O01O001O1O1O2N1O0010O01O1O002N0O2O001O101N001O1O1O3L3N1O1O2O0O2N2N1O2N2N3M2N2O2M4L2N4L1N3N3M4L2N2N3M2N2N1O3L4M4L3LYRm1\"}], \"areas\": [565, 528, 1025, 2503, 4652, 6933, 9265, 10312, 9708, 9177, 9500, 10224, 11971, 12662, 12090, 11038, 7563, 5476, 4613, 6859, 8802, 9875, 8973, 7631, 5555, 7790, 8251, 8663, 8737, 8341, 8739, 8676, 8644, 8497, 8615, 9037, 7771, 7742, 7455, 7816, 7246, 7667, 8220, 7656, 7988, 6461, 6117, 8660, 8704, 9351, 7166, 6171, 6630, 7549, 9741, 10215, 8783, 8316, 8354, 7315, 9950, 10633, 8810, 6319, 4122, 4166, 1778, 2072, 1892, 1703, 2970, 4090, 2901, 6670, 10788, 11221, 11393, 12813, 1330, 10885, 10254, 10167, 10278, 9528, 5491, 10265, 9683, 9980, 9555, 9081, 8210, 5198, 7784, 8450, 7680, 8041, 7997, 7725, 7569, 8586, 8067, 8313, 8044, 7726, 8072, 8558, 8623, 8287, 8511, 9012, 8623, 8915, 8598, 8433, 8630, 9297, 10028, 6893, 3569, 6647, 4450, 5398, 6188, 8673, 9629, 10710, 7470, 11501, 11641, 10518, 13006, 12365, 14517, 14869, 4343, 9108, 16561, 17224, 6433, 16757, 21128, 7244, 21437, 22080, 21288, 13077]}, {\"video_id\": 0, \"category_id\": 49504, \"bboxes\": [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [513, 396, 22, 24], [527, 378, 22, 29], [551, 346, 18, 22], [575, 369, 9, 15], [0, 0, 0, 0], [547, 367, 9, 16], [543, 373, 7, 26], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [627, 388, 7, 27], [632, 394, 7, 17], [651, 368, 10, 16], [0, 0, 0, 0], [0, 0, 0, 0], [620, 326, 8, 21], [603, 319, 9, 17], [0, 0, 0, 0], [571, 368, 8, 17], [564, 350, 7, 21], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [583, 381, 10, 13], [592, 439, 10, 19], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [616, 389, 15, 27], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]], \"score\": 0.597484290599823, \"segmentations\": [{\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"odQd06hW12O1N1O2O1N101O1O001O100O1O1O2O0O101OAohNOQW1NTiN1^W1O3MnbU7\"}, {\"size\": [1280, 720], \"counts\": \"bTcd05iW13M1N2O1O1N201N1O2O1N2O1O10000007H7J2M10003LeSd6\"}, {\"size\": [1280, 720], \"counts\": \"\\\\Sae04hW14N3M1M3O2O0O1010O101N102M2O2N2N2N1MmTk5\"}, {\"size\": [1280, 720], \"counts\": \"bS_f0<cW14NO001O1O1O2M3N2LW\\\\X5\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"fS\\\\e03kW13O2N2N1O001K30N4If\\\\[6\"}, {\"size\": [1280, 720], \"counts\": \"nSWe0<aW14M3K60OO14K8_O_hN2^Sd6\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"\\\\T`h0;]W1=K101O02L7I4K`kY3\"}, {\"size\": [1280, 720], \"counts\": \"a\\\\fh07fW16J4N010003L8H]cS3\"}, {\"size\": [1280, 720], \"counts\": \"kS^i04iW15K3N2M300001O1N2N7HYTX2\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"]ZWh055MXW1b02NO04L4M1N3M2MY]a3\"}, {\"size\": [1280, 720], \"counts\": \"YRbg05hW15K4M2O1O02O3M2O4Jg]U4\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"kSZf02kW15K5J4O1004M2M8GRd^5\"}, {\"size\": [1280, 720], \"counts\": \"X[Qf03cW1<N110001M4N3Kbdh5\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PTif05iW14M11010N1O0000N5L2NmSm4\"}, {\"size\": [1280, 720], \"counts\": \"i]Tg08fW131O00001N2O1O01001Ljia4\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"U\\\\Rh01R`22kgN2WhNMaW1721ZhNK_W1;00101N1O1O2O2M9MJ1Tc]3\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}, {\"size\": [1280, 720], \"counts\": \"PPTl0\"}], \"areas\": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 341, 336, 241, 116, 0, 95, 149, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 159, 106, 136, 0, 0, 106, 126, 0, 98, 96, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 94, 87, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 152, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}]"
  },
  {
    "path": "examples/saco_gold_silver_eval_example.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"id\": \"417b89e9\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Copyright (c) Meta Platforms, Inc. and affiliates.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"id\": \"0e0d2e74\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import copy\\n\",\n    \"import json\\n\",\n    \"import os\\n\",\n    \"\\n\",\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"from pycocotools.coco import COCO\\n\",\n    \"from sam3.eval.cgf1_eval import CGF1Evaluator\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"1ceba210-cb61-4998-a153-c13c612e6182\",\n   \"metadata\": {},\n   \"source\": [\n    \"# SA-Co/Gold\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"id\": \"b31ab5d3\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Update to the directory where the GT annotation and PRED files exist\\n\",\n    \"GT_DIR = # PUT YOUR PATH HERE\\n\",\n    \"PRED_DIR = # PUT YOUR PATH HERE\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"id\": \"25613248\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Relative file names for GT files for 7 SA-Co/Gold subsets\\n\",\n    \"saco_gold_gts = {\\n\",\n    \"    # MetaCLIP Captioner\\n\",\n    \"    \\\"metaclip_nps\\\": [\\n\",\n    \"            \\\"gold_metaclip_merged_a_release_test.json\\\",\\n\",\n    \"            \\\"gold_metaclip_merged_b_release_test.json\\\",\\n\",\n    \"            \\\"gold_metaclip_merged_c_release_test.json\\\",\\n\",\n    \"    ],\\n\",\n    \"    # SA-1B captioner\\n\",\n    \"    \\\"sa1b_nps\\\": [\\n\",\n    \"            \\\"gold_sa1b_merged_a_release_test.json\\\",\\n\",\n    \"            \\\"gold_sa1b_merged_b_release_test.json\\\",\\n\",\n    \"            \\\"gold_sa1b_merged_c_release_test.json\\\",\\n\",\n    \"    ],\\n\",\n    \"    # Crowded\\n\",\n    \"    \\\"crowded\\\": [\\n\",\n    \"            \\\"gold_crowded_merged_a_release_test.json\\\",\\n\",\n    \"            \\\"gold_crowded_merged_b_release_test.json\\\",\\n\",\n    \"            \\\"gold_crowded_merged_c_release_test.json\\\",\\n\",\n    \"    ],\\n\",\n    \"    # FG Food\\n\",\n    \"    \\\"fg_food\\\": [\\n\",\n    \"            \\\"gold_fg_food_merged_a_release_test.json\\\",\\n\",\n    \"            \\\"gold_fg_food_merged_b_release_test.json\\\",\\n\",\n    \"            \\\"gold_fg_food_merged_c_release_test.json\\\",\\n\",\n    \"    ],\\n\",\n    \"    # FG Sports\\n\",\n    \"    \\\"fg_sports_equipment\\\": [\\n\",\n    \"            \\\"gold_fg_sports_equipment_merged_a_release_test.json\\\",\\n\",\n    \"            \\\"gold_fg_sports_equipment_merged_b_release_test.json\\\",\\n\",\n    \"            \\\"gold_fg_sports_equipment_merged_c_release_test.json\\\",\\n\",\n    \"    ],\\n\",\n    \"    # Attributes\\n\",\n    \"    \\\"attributes\\\": [\\n\",\n    \"            \\\"gold_attributes_merged_a_release_test.json\\\",\\n\",\n    \"            \\\"gold_attributes_merged_b_release_test.json\\\",\\n\",\n    \"            \\\"gold_attributes_merged_c_release_test.json\\\",\\n\",\n    \"    ],\\n\",\n    \"    # Wiki common\\n\",\n    \"    \\\"wiki_common\\\": [\\n\",\n    \"            \\\"gold_wiki_common_merged_a_release_test.json\\\",\\n\",\n    \"            \\\"gold_wiki_common_merged_b_release_test.json\\\",\\n\",\n    \"            \\\"gold_wiki_common_merged_c_release_test.json\\\",\\n\",\n    \"    ],\\n\",\n    \"}\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"2703e989\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Run offline evaluation for all 7 SA-Co/Gold subsets\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"0314ddca-46e7-47fd-9f66-346c4f8baf96\",\n   \"metadata\": {},\n   \"source\": [\n    \"We assume the inference has already been run for all 7 datasets. With the default configurations, the predictions are dumped in a predictable folder\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"id\": \"cc28d29f\",\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Processing subset:  metaclip_nps\\n\",\n      \"loading annotations into memory...\\n\",\n      \"Done (t=0.28s)\\n\",\n      \"creating index...\\n\",\n      \"index created!\\n\",\n      \"loading annotations into memory...\\n\",\n      \"Done (t=0.26s)\\n\",\n      \"creating index...\\n\",\n      \"index created!\\n\",\n      \"loading annotations into memory...\\n\",\n      \"Done (t=0.27s)\\n\",\n      \"creating index...\\n\",\n      \"index created!\\n\",\n      \"Loaded 26221 predictions\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 33057/33057 [00:10<00:00, 3171.54it/s]\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Accumulating results\\n\",\n      \"cgF1 metric, IoU type=segm\\n\",\n      \" Average cgF1       @[ IoU=0.50:0.95] = 0.473\\n\",\n      \" Average precision  @[ IoU=0.50:0.95] = 0.609\\n\",\n      \" Average recall     @[ IoU=0.50:0.95] = 0.532\\n\",\n      \" Average F1         @[ IoU=0.50:0.95] = 0.568\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.50:0.95] = 0.759\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.50:0.95] = 0.586\\n\",\n      \" Average positive_micro_precision @[ IoU=0.50:0.95] = 0.652\\n\",\n      \" Average IL_precision                = 0.916\\n\",\n      \" Average IL_recall                   = 0.760\\n\",\n      \" Average IL_F1                       = 0.830\\n\",\n      \" Average IL_FPR                      = 0.013\\n\",\n      \" Average IL_MCC                      = 0.807\\n\",\n      \" Average cgF1       @[ IoU=0.50     ] = 0.568\\n\",\n      \" Average precision  @[ IoU=0.50     ] = 0.732\\n\",\n      \" Average recall     @[ IoU=0.50     ] = 0.639\\n\",\n      \" Average F1         @[ IoU=0.50     ] = 0.682\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.50     ] = 0.872\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.50     ] = 0.704\\n\",\n      \" Average positive_micro_precision @[ IoU=0.50     ] = 0.783\\n\",\n      \" Average cgF1       @[ IoU=0.75     ] = 0.515\\n\",\n      \" Average precision  @[ IoU=0.75     ] = 0.664\\n\",\n      \" Average recall     @[ IoU=0.75     ] = 0.580\\n\",\n      \" Average F1         @[ IoU=0.75     ] = 0.619\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.75     ] = 0.815\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.75     ] = 0.638\\n\",\n      \" Average positive_micro_precision @[ IoU=0.75     ] = 0.710\\n\",\n      \"\\n\",\n      \"{'cgF1_eval_segm_cgF1': 0.47258639187690543, 'cgF1_eval_segm_precision': 0.6094191949336503, 'cgF1_eval_segm_recall': 0.5321857924434281, 'cgF1_eval_segm_F1': 0.5681401781600861, 'cgF1_eval_segm_positive_macro_F1': 0.7594764589657407, 'cgF1_eval_segm_positive_micro_F1': 0.5858321816359353, 'cgF1_eval_segm_positive_micro_precision': 0.6516289867940714, 'cgF1_eval_segm_IL_precision': 0.9157483928108933, 'cgF1_eval_segm_IL_recall': 0.7596648256028061, 'cgF1_eval_segm_IL_F1': 0.8304356444812764, 'cgF1_eval_segm_IL_FPR': 0.013198590231849291, 'cgF1_eval_segm_IL_MCC': 0.8066924397311339, 'cgF1_eval_segm_cgF1@0.5': 0.5675504156248331, 'cgF1_eval_segm_precision@0.5': 0.7318687877530184, 'cgF1_eval_segm_recall@0.5': 0.6391170051959989, 'cgF1_eval_segm_F1@0.5': 0.6823056445199827, 'cgF1_eval_segm_positive_macro_F1@0.5': 0.8723414712702943, 'cgF1_eval_segm_positive_micro_F1@0.5': 0.7035524168467409, 'cgF1_eval_segm_positive_micro_precision@0.5': 0.7825597234127607, 'cgF1_eval_segm_cgF1@0.75': 0.5149222822957974, 'cgF1_eval_segm_precision@0.75': 0.6640084211254257, 'cgF1_eval_segm_recall@0.75': 0.5798567730119127, 'cgF1_eval_segm_F1@0.75': 0.6190362594405769, 'cgF1_eval_segm_positive_macro_F1@0.75': 0.8153807596867352, 'cgF1_eval_segm_positive_micro_F1@0.75': 0.6383130136529085, 'cgF1_eval_segm_positive_micro_precision@0.75': 0.7099991898479673}\\n\",\n      \"loading annotations into memory...\\n\",\n      \"Done (t=0.22s)\\n\",\n      \"creating index...\\n\",\n      \"index created!\\n\",\n      \"loading annotations into memory...\\n\",\n      \"Done (t=0.23s)\\n\",\n      \"creating index...\\n\",\n      \"index created!\\n\",\n      \"loading annotations into memory...\\n\",\n      \"Done (t=0.21s)\\n\",\n      \"creating index...\\n\",\n      \"index created!\\n\",\n      \"Loaded 26221 predictions\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 33057/33057 [00:08<00:00, 3762.56it/s]\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Accumulating results\\n\",\n      \"cgF1 metric, IoU type=bbox\\n\",\n      \" Average cgF1       @[ IoU=0.50:0.95] = 0.500\\n\",\n      \" Average precision  @[ IoU=0.50:0.95] = 0.645\\n\",\n      \" Average recall     @[ IoU=0.50:0.95] = 0.563\\n\",\n      \" Average F1         @[ IoU=0.50:0.95] = 0.601\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.50:0.95] = 0.813\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.50:0.95] = 0.620\\n\",\n      \" Average positive_micro_precision @[ IoU=0.50:0.95] = 0.690\\n\",\n      \" Average IL_precision                = 0.916\\n\",\n      \" Average IL_recall                   = 0.760\\n\",\n      \" Average IL_F1                       = 0.831\\n\",\n      \" Average IL_FPR                      = 0.013\\n\",\n      \" Average IL_MCC                      = 0.807\\n\",\n      \" Average cgF1       @[ IoU=0.50     ] = 0.571\\n\",\n      \" Average precision  @[ IoU=0.50     ] = 0.736\\n\",\n      \" Average recall     @[ IoU=0.50     ] = 0.642\\n\",\n      \" Average F1         @[ IoU=0.50     ] = 0.686\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.50     ] = 0.878\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.50     ] = 0.707\\n\",\n      \" Average positive_micro_precision @[ IoU=0.50     ] = 0.787\\n\",\n      \" Average cgF1       @[ IoU=0.75     ] = 0.530\\n\",\n      \" Average precision  @[ IoU=0.75     ] = 0.683\\n\",\n      \" Average recall     @[ IoU=0.75     ] = 0.596\\n\",\n      \" Average F1         @[ IoU=0.75     ] = 0.636\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.75     ] = 0.842\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.75     ] = 0.656\\n\",\n      \" Average positive_micro_precision @[ IoU=0.75     ] = 0.731\\n\",\n      \"\\n\",\n      \"{'cgF1_eval_bbox_cgF1': 0.5003430734030954, 'cgF1_eval_bbox_precision': 0.6454559084753544, 'cgF1_eval_bbox_recall': 0.5625467145593948, 'cgF1_eval_bbox_F1': 0.6011063859782098, 'cgF1_eval_bbox_positive_macro_F1': 0.8129272614186818, 'cgF1_eval_bbox_positive_micro_F1': 0.6198941576942177, 'cgF1_eval_bbox_positive_micro_precision': 0.6903838360796344, 'cgF1_eval_bbox_IL_precision': 0.916437098044895, 'cgF1_eval_bbox_IL_recall': 0.7598020554320895, 'cgF1_eval_bbox_IL_F1': 0.830800752892585, 'cgF1_eval_bbox_IL_FPR': 0.013092112361504437, 'cgF1_eval_bbox_IL_MCC': 0.8071427471815361, 'cgF1_eval_bbox_cgF1@0.5': 0.5705998939047701, 'cgF1_eval_bbox_precision@0.5': 0.7360818482088336, 'cgF1_eval_bbox_recall@0.5': 0.6415316986326687, 'cgF1_eval_bbox_F1@0.5': 0.6855123686584659, 'cgF1_eval_bbox_positive_macro_F1@0.5': 0.8775997789207387, 'cgF1_eval_bbox_positive_micro_F1@0.5': 0.7069380179618158, 'cgF1_eval_bbox_positive_micro_precision@0.5': 0.7873179304150807, 'cgF1_eval_bbox_cgF1@0.75': 0.5297149268767383, 'cgF1_eval_bbox_precision@0.75': 0.6833433592887199, 'cgF1_eval_bbox_recall@0.75': 0.59556749986514, 'cgF1_eval_bbox_F1@0.75': 0.6363934960024782, 'cgF1_eval_bbox_positive_macro_F1@0.75': 0.8423507795177905, 'cgF1_eval_bbox_positive_micro_F1@0.75': 0.6562840696103028, 'cgF1_eval_bbox_positive_micro_precision@0.75': 0.7309084997915145}\\n\",\n      \"Processing subset:  sa1b_nps\\n\",\n      \"loading annotations into memory...\\n\",\n      \"Done (t=0.42s)\\n\",\n      \"creating index...\\n\",\n      \"index created!\\n\",\n      \"loading annotations into memory...\\n\",\n      \"Done (t=0.42s)\\n\",\n      \"creating index...\\n\",\n      \"index created!\\n\",\n      \"loading annotations into memory...\\n\",\n      \"Done (t=0.44s)\\n\",\n      \"creating index...\\n\",\n      \"index created!\\n\",\n      \"Loaded 50994 predictions\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 12893/12893 [00:12<00:00, 1019.95it/s]\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Accumulating results\\n\",\n      \"cgF1 metric, IoU type=segm\\n\",\n      \" Average cgF1       @[ IoU=0.50:0.95] = 0.537\\n\",\n      \" Average precision  @[ IoU=0.50:0.95] = 0.613\\n\",\n      \" Average recall     @[ IoU=0.50:0.95] = 0.624\\n\",\n      \" Average F1         @[ IoU=0.50:0.95] = 0.618\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.50:0.95] = 0.749\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.50:0.95] = 0.626\\n\",\n      \" Average positive_micro_precision @[ IoU=0.50:0.95] = 0.627\\n\",\n      \" Average IL_precision                = 0.957\\n\",\n      \" Average IL_recall                   = 0.918\\n\",\n      \" Average IL_F1                       = 0.937\\n\",\n      \" Average IL_FPR                      = 0.055\\n\",\n      \" Average IL_MCC                      = 0.858\\n\",\n      \" Average cgF1       @[ IoU=0.50     ] = 0.662\\n\",\n      \" Average precision  @[ IoU=0.50     ] = 0.755\\n\",\n      \" Average recall     @[ IoU=0.50     ] = 0.769\\n\",\n      \" Average F1         @[ IoU=0.50     ] = 0.762\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.50     ] = 0.868\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.50     ] = 0.771\\n\",\n      \" Average positive_micro_precision @[ IoU=0.50     ] = 0.773\\n\",\n      \" Average cgF1       @[ IoU=0.75     ] = 0.584\\n\",\n      \" Average precision  @[ IoU=0.75     ] = 0.666\\n\",\n      \" Average recall     @[ IoU=0.75     ] = 0.679\\n\",\n      \" Average F1         @[ IoU=0.75     ] = 0.672\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.75     ] = 0.803\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.75     ] = 0.680\\n\",\n      \" Average positive_micro_precision @[ IoU=0.75     ] = 0.682\\n\",\n      \"\\n\",\n      \"{'cgF1_eval_segm_cgF1': 0.5368885175297502, 'cgF1_eval_segm_precision': 0.6126733177941744, 'cgF1_eval_segm_recall': 0.623866410830199, 'cgF1_eval_segm_F1': 0.6181692134008732, 'cgF1_eval_segm_positive_macro_F1': 0.7490377907736454, 'cgF1_eval_segm_positive_micro_F1': 0.6255271013781429, 'cgF1_eval_segm_positive_micro_precision': 0.6272971894411186, 'cgF1_eval_segm_IL_precision': 0.9571307299155163, 'cgF1_eval_segm_IL_recall': 0.9180350114102273, 'cgF1_eval_segm_IL_F1': 0.9371748135185498, 'cgF1_eval_segm_IL_FPR': 0.054851556832937805, 'cgF1_eval_segm_IL_MCC': 0.8582977721459122, 'cgF1_eval_segm_cgF1@0.5': 0.6617782611463625, 'cgF1_eval_segm_precision@0.5': 0.7551805547951372, 'cgF1_eval_segm_recall@0.5': 0.7689771507351261, 'cgF1_eval_segm_F1@0.5': 0.7619664171091266, 'cgF1_eval_segm_positive_macro_F1@0.5': 0.8675783572433642, 'cgF1_eval_segm_positive_micro_F1@0.5': 0.7710357437976187, 'cgF1_eval_segm_positive_micro_precision@0.5': 0.7732059252215057, 'cgF1_eval_segm_cgF1@0.75': 0.584028878830561, 'cgF1_eval_segm_precision@0.75': 0.6664635050035673, 'cgF1_eval_segm_recall@0.75': 0.6786393053852088, 'cgF1_eval_segm_F1@0.75': 0.6724463056533416, 'cgF1_eval_segm_positive_macro_F1@0.75': 0.8029021282211246, 'cgF1_eval_segm_positive_micro_F1@0.75': 0.6804501861520328, 'cgF1_eval_segm_positive_micro_precision@0.75': 0.6823712921904396}\\n\",\n      \"loading annotations into memory...\\n\",\n      \"Done (t=0.35s)\\n\",\n      \"creating index...\\n\",\n      \"index created!\\n\",\n      \"loading annotations into memory...\\n\",\n      \"Done (t=0.35s)\\n\",\n      \"creating index...\\n\",\n      \"index created!\\n\",\n      \"loading annotations into memory...\\n\",\n      \"Done (t=0.38s)\\n\",\n      \"creating index...\\n\",\n      \"index created!\\n\",\n      \"Loaded 50994 predictions\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 12893/12893 [00:07<00:00, 1636.66it/s]\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Accumulating results\\n\",\n      \"cgF1 metric, IoU type=bbox\\n\",\n      \" Average cgF1       @[ IoU=0.50:0.95] = 0.554\\n\",\n      \" Average precision  @[ IoU=0.50:0.95] = 0.633\\n\",\n      \" Average recall     @[ IoU=0.50:0.95] = 0.642\\n\",\n      \" Average F1         @[ IoU=0.50:0.95] = 0.637\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.50:0.95] = 0.786\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.50:0.95] = 0.645\\n\",\n      \" Average positive_micro_precision @[ IoU=0.50:0.95] = 0.648\\n\",\n      \" Average IL_precision                = 0.957\\n\",\n      \" Average IL_recall                   = 0.918\\n\",\n      \" Average IL_F1                       = 0.937\\n\",\n      \" Average IL_FPR                      = 0.055\\n\",\n      \" Average IL_MCC                      = 0.858\\n\",\n      \" Average cgF1       @[ IoU=0.50     ] = 0.656\\n\",\n      \" Average precision  @[ IoU=0.50     ] = 0.749\\n\",\n      \" Average recall     @[ IoU=0.50     ] = 0.760\\n\",\n      \" Average F1         @[ IoU=0.50     ] = 0.755\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.50     ] = 0.863\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.50     ] = 0.764\\n\",\n      \" Average positive_micro_precision @[ IoU=0.50     ] = 0.768\\n\",\n      \" Average cgF1       @[ IoU=0.75     ] = 0.589\\n\",\n      \" Average precision  @[ IoU=0.75     ] = 0.673\\n\",\n      \" Average recall     @[ IoU=0.75     ] = 0.683\\n\",\n      \" Average F1         @[ IoU=0.75     ] = 0.678\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.75     ] = 0.817\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.75     ] = 0.686\\n\",\n      \" Average positive_micro_precision @[ IoU=0.75     ] = 0.690\\n\",\n      \"\\n\",\n      \"{'cgF1_eval_bbox_cgF1': 0.5536231468412395, 'cgF1_eval_bbox_precision': 0.6328165449001351, 'cgF1_eval_bbox_recall': 0.6417390943649743, 'cgF1_eval_bbox_F1': 0.6371965950434251, 'cgF1_eval_bbox_positive_macro_F1': 0.785505899426481, 'cgF1_eval_bbox_positive_micro_F1': 0.6449030271971777, 'cgF1_eval_bbox_positive_micro_precision': 0.6481993029451039, 'cgF1_eval_bbox_IL_precision': 0.957272212654602, 'cgF1_eval_bbox_IL_recall': 0.9180461328469408, 'cgF1_eval_bbox_IL_F1': 0.9372484265333024, 'cgF1_eval_bbox_IL_FPR': 0.05468042729410095, 'cgF1_eval_bbox_IL_MCC': 0.8584595256861315, 'cgF1_eval_bbox_cgF1@0.5': 0.655649783867308, 'cgF1_eval_bbox_precision@0.5': 0.7494286121079209, 'cgF1_eval_bbox_recall@0.5': 0.7599953615328335, 'cgF1_eval_bbox_F1@0.5': 0.7546250062240573, 'cgF1_eval_bbox_positive_macro_F1@0.5': 0.8630646236311263, 'cgF1_eval_bbox_positive_micro_F1@0.5': 0.7637515389479476, 'cgF1_eval_bbox_positive_micro_precision@0.5': 0.7676460229909632, 'cgF1_eval_bbox_cgF1@0.75': 0.5891939922914327, 'cgF1_eval_bbox_precision@0.75': 0.6734724947681224, 'cgF1_eval_bbox_recall@0.75': 0.6829682826014282, 'cgF1_eval_bbox_F1@0.75': 0.6781371571048335, 'cgF1_eval_bbox_positive_macro_F1@0.75': 0.8172625456348024, 'cgF1_eval_bbox_positive_micro_F1@0.75': 0.6863386970055625, 'cgF1_eval_bbox_positive_micro_precision@0.75': 0.6898435339270219}\\n\",\n      \"Processing subset:  crowded\\n\",\n      \"loading annotations into memory...\\n\",\n      \"Done (t=0.42s)\\n\",\n      \"creating index...\\n\",\n      \"index created!\\n\",\n      \"loading annotations into memory...\\n\",\n      \"Done (t=0.42s)\\n\",\n      \"creating index...\\n\",\n      \"index created!\\n\",\n      \"loading annotations into memory...\\n\",\n      \"Done (t=0.42s)\\n\",\n      \"creating index...\\n\",\n      \"index created!\\n\",\n      \"Loaded 82963 predictions\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 20241/20241 [00:10<00:00, 2003.85it/s]\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Accumulating results\\n\",\n      \"cgF1 metric, IoU type=segm\\n\",\n      \" Average cgF1       @[ IoU=0.50:0.95] = 0.611\\n\",\n      \" Average precision  @[ IoU=0.50:0.95] = 0.643\\n\",\n      \" Average recall     @[ IoU=0.50:0.95] = 0.686\\n\",\n      \" Average F1         @[ IoU=0.50:0.95] = 0.664\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.50:0.95] = 0.689\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.50:0.95] = 0.677\\n\",\n      \" Average positive_micro_precision @[ IoU=0.50:0.95] = 0.669\\n\",\n      \" Average IL_precision                = 0.933\\n\",\n      \" Average IL_recall                   = 0.913\\n\",\n      \" Average IL_F1                       = 0.923\\n\",\n      \" Average IL_FPR                      = 0.018\\n\",\n      \" Average IL_MCC                      = 0.902\\n\",\n      \" Average cgF1       @[ IoU=0.50     ] = 0.735\\n\",\n      \" Average precision  @[ IoU=0.50     ] = 0.773\\n\",\n      \" Average recall     @[ IoU=0.50     ] = 0.825\\n\",\n      \" Average F1         @[ IoU=0.50     ] = 0.798\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.50     ] = 0.830\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.50     ] = 0.815\\n\",\n      \" Average positive_micro_precision @[ IoU=0.50     ] = 0.805\\n\",\n      \" Average cgF1       @[ IoU=0.75     ] = 0.667\\n\",\n      \" Average precision  @[ IoU=0.75     ] = 0.702\\n\",\n      \" Average recall     @[ IoU=0.75     ] = 0.748\\n\",\n      \" Average F1         @[ IoU=0.75     ] = 0.724\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.75     ] = 0.748\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.75     ] = 0.739\\n\",\n      \" Average positive_micro_precision @[ IoU=0.75     ] = 0.730\\n\",\n      \"\\n\",\n      \"{'cgF1_eval_segm_cgF1': 0.6107822436866207, 'cgF1_eval_segm_precision': 0.6429349935778358, 'cgF1_eval_segm_recall': 0.6856112974213837, 'cgF1_eval_segm_F1': 0.663537765323902, 'cgF1_eval_segm_positive_macro_F1': 0.6891461338399572, 'cgF1_eval_segm_positive_micro_F1': 0.6772901260675144, 'cgF1_eval_segm_positive_micro_precision': 0.6692661193229303, 'cgF1_eval_segm_IL_precision': 0.9330264669889466, 'cgF1_eval_segm_IL_recall': 0.9132687540181867, 'cgF1_eval_segm_IL_F1': 0.9230413942007347, 'cgF1_eval_segm_IL_FPR': 0.018415390455738805, 'cgF1_eval_segm_IL_MCC': 0.901802964754481, 'cgF1_eval_segm_cgF1@0.5': 0.7346796584523991, 'cgF1_eval_segm_precision@0.5': 0.77334498201075, 'cgF1_eval_segm_recall@0.5': 0.8246775518006043, 'cgF1_eval_segm_F1@0.5': 0.7981368549267828, 'cgF1_eval_segm_positive_macro_F1@0.5': 0.829596777967627, 'cgF1_eval_segm_positive_micro_F1@0.5': 0.8146786905412517, 'cgF1_eval_segm_positive_micro_precision@0.5': 0.8050169926635622, 'cgF1_eval_segm_cgF1@0.75': 0.6666293625039935, 'cgF1_eval_segm_precision@0.75': 0.7017176741205416, 'cgF1_eval_segm_recall@0.75': 0.7482958149470458, 'cgF1_eval_segm_F1@0.75': 0.7242086950813854, 'cgF1_eval_segm_positive_macro_F1@0.75': 0.748186632903701, 'cgF1_eval_segm_positive_micro_F1@0.75': 0.7392184197193072, 'cgF1_eval_segm_positive_micro_precision@0.75': 0.7304562192291248}\\n\",\n      \"loading annotations into memory...\\n\",\n      \"Done (t=0.36s)\\n\",\n      \"creating index...\\n\",\n      \"index created!\\n\",\n      \"loading annotations into memory...\\n\",\n      \"Done (t=0.37s)\\n\",\n      \"creating index...\\n\",\n      \"index created!\\n\",\n      \"loading annotations into memory...\\n\",\n      \"Done (t=0.37s)\\n\",\n      \"creating index...\\n\",\n      \"index created!\\n\",\n      \"Loaded 82963 predictions\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 20241/20241 [00:07<00:00, 2785.45it/s]\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Accumulating results\\n\",\n      \"cgF1 metric, IoU type=bbox\\n\",\n      \" Average cgF1       @[ IoU=0.50:0.95] = 0.617\\n\",\n      \" Average precision  @[ IoU=0.50:0.95] = 0.650\\n\",\n      \" Average recall     @[ IoU=0.50:0.95] = 0.692\\n\",\n      \" Average F1         @[ IoU=0.50:0.95] = 0.670\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.50:0.95] = 0.731\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.50:0.95] = 0.684\\n\",\n      \" Average positive_micro_precision @[ IoU=0.50:0.95] = 0.677\\n\",\n      \" Average IL_precision                = 0.933\\n\",\n      \" Average IL_recall                   = 0.913\\n\",\n      \" Average IL_F1                       = 0.923\\n\",\n      \" Average IL_FPR                      = 0.018\\n\",\n      \" Average IL_MCC                      = 0.902\\n\",\n      \" Average cgF1       @[ IoU=0.50     ] = 0.730\\n\",\n      \" Average precision  @[ IoU=0.50     ] = 0.769\\n\",\n      \" Average recall     @[ IoU=0.50     ] = 0.818\\n\",\n      \" Average F1         @[ IoU=0.50     ] = 0.793\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.50     ] = 0.829\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.50     ] = 0.809\\n\",\n      \" Average positive_micro_precision @[ IoU=0.50     ] = 0.801\\n\",\n      \" Average cgF1       @[ IoU=0.75     ] = 0.668\\n\",\n      \" Average precision  @[ IoU=0.75     ] = 0.704\\n\",\n      \" Average recall     @[ IoU=0.75     ] = 0.749\\n\",\n      \" Average F1         @[ IoU=0.75     ] = 0.726\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.75     ] = 0.776\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.75     ] = 0.741\\n\",\n      \" Average positive_micro_precision @[ IoU=0.75     ] = 0.733\\n\",\n      \"\\n\",\n      \"{'cgF1_eval_bbox_cgF1': 0.6170650320984323, 'cgF1_eval_bbox_precision': 0.6504449386041414, 'cgF1_eval_bbox_recall': 0.691669313906474, 'cgF1_eval_bbox_F1': 0.6703740536459668, 'cgF1_eval_bbox_positive_macro_F1': 0.7305480221708001, 'cgF1_eval_bbox_positive_micro_F1': 0.6842570452919619, 'cgF1_eval_bbox_positive_micro_precision': 0.677099838003926, 'cgF1_eval_bbox_IL_precision': 0.9330264669889466, 'cgF1_eval_bbox_IL_recall': 0.9132687540181867, 'cgF1_eval_bbox_IL_F1': 0.9230413942007347, 'cgF1_eval_bbox_IL_FPR': 0.018415390455738805, 'cgF1_eval_bbox_IL_MCC': 0.901802964754481, 'cgF1_eval_bbox_cgF1@0.5': 0.7298487590689315, 'cgF1_eval_bbox_precision@0.5': 0.7693209759495023, 'cgF1_eval_bbox_recall@0.5': 0.818079563737977, 'cgF1_eval_bbox_F1@0.5': 0.7929014858831388, 'cgF1_eval_bbox_positive_macro_F1@0.5': 0.8289958931744248, 'cgF1_eval_bbox_positive_micro_F1@0.5': 0.8093217560752147, 'cgF1_eval_bbox_positive_micro_precision@0.5': 0.8008473542838228, 'cgF1_eval_bbox_cgF1@0.75': 0.6681534554056624, 'cgF1_eval_bbox_precision@0.75': 0.7042930379997401, 'cgF1_eval_bbox_recall@0.75': 0.7489302375506022, 'cgF1_eval_bbox_F1@0.75': 0.7258761503763029, 'cgF1_eval_bbox_positive_macro_F1@0.75': 0.7757641647406367, 'cgF1_eval_bbox_positive_micro_F1@0.75': 0.7409084706076228, 'cgF1_eval_bbox_positive_micro_precision@0.75': 0.7331546048467947}\\n\",\n      \"Processing subset:  fg_food\\n\",\n      \"loading annotations into memory...\\n\",\n      \"Done (t=0.12s)\\n\",\n      \"creating index...\\n\",\n      \"index created!\\n\",\n      \"loading annotations into memory...\\n\",\n      \"Done (t=0.12s)\\n\",\n      \"creating index...\\n\",\n      \"index created!\\n\",\n      \"loading annotations into memory...\\n\",\n      \"Done (t=0.10s)\\n\",\n      \"creating index...\\n\",\n      \"index created!\\n\",\n      \"Loaded 10846 predictions\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 13794/13794 [00:03<00:00, 3963.81it/s]\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Accumulating results\\n\",\n      \"cgF1 metric, IoU type=segm\\n\",\n      \" Average cgF1       @[ IoU=0.50:0.95] = 0.534\\n\",\n      \" Average precision  @[ IoU=0.50:0.95] = 0.734\\n\",\n      \" Average recall     @[ IoU=0.50:0.95] = 0.583\\n\",\n      \" Average F1         @[ IoU=0.50:0.95] = 0.650\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.50:0.95] = 0.825\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.50:0.95] = 0.673\\n\",\n      \" Average positive_micro_precision @[ IoU=0.50:0.95] = 0.795\\n\",\n      \" Average IL_precision                = 0.917\\n\",\n      \" Average IL_recall                   = 0.713\\n\",\n      \" Average IL_F1                       = 0.802\\n\",\n      \" Average IL_FPR                      = 0.006\\n\",\n      \" Average IL_MCC                      = 0.794\\n\",\n      \" Average cgF1       @[ IoU=0.50     ] = 0.582\\n\",\n      \" Average precision  @[ IoU=0.50     ] = 0.800\\n\",\n      \" Average recall     @[ IoU=0.50     ] = 0.635\\n\",\n      \" Average F1         @[ IoU=0.50     ] = 0.708\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.50     ] = 0.885\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.50     ] = 0.733\\n\",\n      \" Average positive_micro_precision @[ IoU=0.50     ] = 0.866\\n\",\n      \" Average cgF1       @[ IoU=0.75     ] = 0.561\\n\",\n      \" Average precision  @[ IoU=0.75     ] = 0.771\\n\",\n      \" Average recall     @[ IoU=0.75     ] = 0.612\\n\",\n      \" Average F1         @[ IoU=0.75     ] = 0.682\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.75     ] = 0.854\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.75     ] = 0.706\\n\",\n      \" Average positive_micro_precision @[ IoU=0.75     ] = 0.835\\n\",\n      \"\\n\",\n      \"{'cgF1_eval_segm_cgF1': 0.5340544897290387, 'cgF1_eval_segm_precision': 0.734205779536348, 'cgF1_eval_segm_recall': 0.5831005937305522, 'cgF1_eval_segm_F1': 0.6499373841120774, 'cgF1_eval_segm_positive_macro_F1': 0.8250979043741161, 'cgF1_eval_segm_positive_micro_F1': 0.6727883081524807, 'cgF1_eval_segm_positive_micro_precision': 0.7952174681839976, 'cgF1_eval_segm_IL_precision': 0.9171210458919072, 'cgF1_eval_segm_IL_recall': 0.7133163691999013, 'cgF1_eval_segm_IL_F1': 0.8024804230776101, 'cgF1_eval_segm_IL_FPR': 0.006024573919459011, 'cgF1_eval_segm_IL_MCC': 0.7937927625341562, 'cgF1_eval_segm_cgF1@0.5': 0.5816253704026515, 'cgF1_eval_segm_precision@0.5': 0.7996003836243679, 'cgF1_eval_segm_recall@0.5': 0.6350364862736196, 'cgF1_eval_segm_F1@0.5': 0.7078307231011086, 'cgF1_eval_segm_positive_macro_F1@0.5': 0.8854304579823623, 'cgF1_eval_segm_positive_micro_F1@0.5': 0.732716897727604, 'cgF1_eval_segm_positive_micro_precision@0.5': 0.8660462915809068, 'cgF1_eval_segm_cgF1@0.75': 0.5605505836352753, 'cgF1_eval_segm_precision@0.75': 0.7706293552321807, 'cgF1_eval_segm_recall@0.75': 0.6120279179303726, 'cgF1_eval_segm_F1@0.75': 0.6821828949054192, 'cgF1_eval_segm_positive_macro_F1@0.75': 0.8542637117087882, 'cgF1_eval_segm_positive_micro_F1@0.75': 0.7061674155931289, 'cgF1_eval_segm_positive_micro_precision@0.75': 0.8346678027555117}\\n\",\n      \"loading annotations into memory...\\n\",\n      \"Done (t=0.09s)\\n\",\n      \"creating index...\\n\",\n      \"index created!\\n\",\n      \"loading annotations into memory...\\n\",\n      \"Done (t=0.08s)\\n\",\n      \"creating index...\\n\",\n      \"index created!\\n\",\n      \"loading annotations into memory...\\n\",\n      \"Done (t=0.08s)\\n\",\n      \"creating index...\\n\",\n      \"index created!\\n\",\n      \"Loaded 10846 predictions\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 13794/13794 [00:03<00:00, 4490.07it/s]\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Accumulating results\\n\",\n      \"cgF1 metric, IoU type=bbox\\n\",\n      \" Average cgF1       @[ IoU=0.50:0.95] = 0.538\\n\",\n      \" Average precision  @[ IoU=0.50:0.95] = 0.737\\n\",\n      \" Average recall     @[ IoU=0.50:0.95] = 0.588\\n\",\n      \" Average F1         @[ IoU=0.50:0.95] = 0.654\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.50:0.95] = 0.859\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.50:0.95] = 0.676\\n\",\n      \" Average positive_micro_precision @[ IoU=0.50:0.95] = 0.797\\n\",\n      \" Average IL_precision                = 0.919\\n\",\n      \" Average IL_recall                   = 0.714\\n\",\n      \" Average IL_F1                       = 0.804\\n\",\n      \" Average IL_FPR                      = 0.006\\n\",\n      \" Average IL_MCC                      = 0.795\\n\",\n      \" Average cgF1       @[ IoU=0.50     ] = 0.583\\n\",\n      \" Average precision  @[ IoU=0.50     ] = 0.798\\n\",\n      \" Average recall     @[ IoU=0.50     ] = 0.637\\n\",\n      \" Average F1         @[ IoU=0.50     ] = 0.708\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.50     ] = 0.898\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.50     ] = 0.733\\n\",\n      \" Average positive_micro_precision @[ IoU=0.50     ] = 0.864\\n\",\n      \" Average cgF1       @[ IoU=0.75     ] = 0.560\\n\",\n      \" Average precision  @[ IoU=0.75     ] = 0.766\\n\",\n      \" Average recall     @[ IoU=0.75     ] = 0.612\\n\",\n      \" Average F1         @[ IoU=0.75     ] = 0.680\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.75     ] = 0.875\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.75     ] = 0.704\\n\",\n      \" Average positive_micro_precision @[ IoU=0.75     ] = 0.829\\n\",\n      \"\\n\",\n      \"{'cgF1_eval_bbox_cgF1': 0.5378561873516663, 'cgF1_eval_bbox_precision': 0.7365034817881422, 'cgF1_eval_bbox_recall': 0.5876295139864577, 'cgF1_eval_bbox_F1': 0.6536480883299698, 'cgF1_eval_bbox_positive_macro_F1': 0.8586355938093078, 'cgF1_eval_bbox_positive_micro_F1': 0.6764422338399003, 'cgF1_eval_bbox_positive_micro_precision': 0.7970161989834335, 'cgF1_eval_bbox_IL_precision': 0.9193020709713172, 'cgF1_eval_bbox_IL_recall': 0.7138018622237071, 'cgF1_eval_bbox_IL_F1': 0.8036220047681086, 'cgF1_eval_bbox_IL_FPR': 0.005866962657110365, 'cgF1_eval_bbox_IL_MCC': 0.7951250830369732, 'cgF1_eval_bbox_cgF1@0.5': 0.5827707578455898, 'cgF1_eval_bbox_precision@0.5': 0.7980019820579024, 'cgF1_eval_bbox_recall@0.5': 0.6366969450550463, 'cgF1_eval_bbox_F1@0.5': 0.7082322331345615, 'cgF1_eval_bbox_positive_macro_F1@0.5': 0.8975145206561829, 'cgF1_eval_bbox_positive_micro_F1@0.5': 0.7329296613555469, 'cgF1_eval_bbox_positive_micro_precision@0.5': 0.8635675488958368, 'cgF1_eval_bbox_cgF1@0.75': 0.5597151953142597, 'cgF1_eval_bbox_precision@0.75': 0.7664335511202087, 'cgF1_eval_bbox_recall@0.75': 0.6115096347599293, 'cgF1_eval_bbox_F1@0.75': 0.6802130814411795, 'cgF1_eval_bbox_positive_macro_F1@0.75': 0.8745268555395833, 'cgF1_eval_bbox_positive_micro_F1@0.75': 0.7039335159399481, 'cgF1_eval_bbox_positive_micro_precision@0.75': 0.829405387472316}\\n\",\n      \"Processing subset:  fg_sports_equipment\\n\",\n      \"loading annotations into memory...\\n\",\n      \"Done (t=0.08s)\\n\",\n      \"creating index...\\n\",\n      \"index created!\\n\",\n      \"loading annotations into memory...\\n\",\n      \"Done (t=0.08s)\\n\",\n      \"creating index...\\n\",\n      \"index created!\\n\",\n      \"loading annotations into memory...\\n\",\n      \"Done (t=0.08s)\\n\",\n      \"creating index...\\n\",\n      \"index created!\\n\",\n      \"Loaded 6562 predictions\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 12107/12107 [00:03<00:00, 3306.95it/s]\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Accumulating results\\n\",\n      \"cgF1 metric, IoU type=segm\\n\",\n      \" Average cgF1       @[ IoU=0.50:0.95] = 0.655\\n\",\n      \" Average precision  @[ IoU=0.50:0.95] = 0.733\\n\",\n      \" Average recall     @[ IoU=0.50:0.95] = 0.701\\n\",\n      \" Average F1         @[ IoU=0.50:0.95] = 0.717\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.50:0.95] = 0.840\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.50:0.95] = 0.738\\n\",\n      \" Average positive_micro_precision @[ IoU=0.50:0.95] = 0.778\\n\",\n      \" Average IL_precision                = 0.962\\n\",\n      \" Average IL_recall                   = 0.850\\n\",\n      \" Average IL_F1                       = 0.903\\n\",\n      \" Average IL_FPR                      = 0.006\\n\",\n      \" Average IL_MCC                      = 0.888\\n\",\n      \" Average cgF1       @[ IoU=0.50     ] = 0.737\\n\",\n      \" Average precision  @[ IoU=0.50     ] = 0.825\\n\",\n      \" Average recall     @[ IoU=0.50     ] = 0.789\\n\",\n      \" Average F1         @[ IoU=0.50     ] = 0.807\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.50     ] = 0.920\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.50     ] = 0.830\\n\",\n      \" Average positive_micro_precision @[ IoU=0.50     ] = 0.875\\n\",\n      \" Average cgF1       @[ IoU=0.75     ] = 0.697\\n\",\n      \" Average precision  @[ IoU=0.75     ] = 0.780\\n\",\n      \" Average recall     @[ IoU=0.75     ] = 0.746\\n\",\n      \" Average F1         @[ IoU=0.75     ] = 0.763\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.75     ] = 0.879\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.75     ] = 0.785\\n\",\n      \" Average positive_micro_precision @[ IoU=0.75     ] = 0.827\\n\",\n      \"\\n\",\n      \"{'cgF1_eval_segm_cgF1': 0.6552238992458554, 'cgF1_eval_segm_precision': 0.7333019582948739, 'cgF1_eval_segm_recall': 0.7013045276359755, 'cgF1_eval_segm_F1': 0.7168964364723446, 'cgF1_eval_segm_positive_macro_F1': 0.8401797467726777, 'cgF1_eval_segm_positive_micro_F1': 0.7375018641548449, 'cgF1_eval_segm_positive_micro_precision': 0.7777500429595898, 'cgF1_eval_segm_IL_precision': 0.9624470012341326, 'cgF1_eval_segm_IL_recall': 0.8497326198664531, 'cgF1_eval_segm_IL_F1': 0.9025839944636483, 'cgF1_eval_segm_IL_FPR': 0.0060564618534671814, 'cgF1_eval_segm_IL_MCC': 0.8884369397448539, 'cgF1_eval_segm_cgF1@0.5': 0.7373951194278512, 'cgF1_eval_segm_precision@0.5': 0.8252586823786763, 'cgF1_eval_segm_recall@0.5': 0.7892487451810898, 'cgF1_eval_segm_F1@0.5': 0.8068021593354898, 'cgF1_eval_segm_positive_macro_F1@0.5': 0.920431978172454, 'cgF1_eval_segm_positive_micro_F1@0.5': 0.8299915125541946, 'cgF1_eval_segm_positive_micro_precision@0.5': 0.8752805967752407, 'cgF1_eval_segm_cgF1@0.75': 0.6970450572765126, 'cgF1_eval_segm_precision@0.75': 0.7801034623682158, 'cgF1_eval_segm_recall@0.75': 0.7460638608622613, 'cgF1_eval_segm_F1@0.75': 0.7626540805113754, 'cgF1_eval_segm_positive_macro_F1@0.75': 0.8790410692537068, 'cgF1_eval_segm_positive_micro_F1@0.75': 0.7845746007327135, 'cgF1_eval_segm_positive_micro_precision@0.75': 0.8273883555153815}\\n\",\n      \"loading annotations into memory...\\n\",\n      \"Done (t=0.06s)\\n\",\n      \"creating index...\\n\",\n      \"index created!\\n\",\n      \"loading annotations into memory...\\n\",\n      \"Done (t=0.06s)\\n\",\n      \"creating index...\\n\",\n      \"index created!\\n\",\n      \"loading annotations into memory...\\n\",\n      \"Done (t=0.06s)\\n\",\n      \"creating index...\\n\",\n      \"index created!\\n\",\n      \"Loaded 6562 predictions\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 12107/12107 [00:03<00:00, 3661.16it/s]\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Accumulating results\\n\",\n      \"cgF1 metric, IoU type=bbox\\n\",\n      \" Average cgF1       @[ IoU=0.50:0.95] = 0.681\\n\",\n      \" Average precision  @[ IoU=0.50:0.95] = 0.760\\n\",\n      \" Average recall     @[ IoU=0.50:0.95] = 0.730\\n\",\n      \" Average F1         @[ IoU=0.50:0.95] = 0.745\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.50:0.95] = 0.879\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.50:0.95] = 0.766\\n\",\n      \" Average positive_micro_precision @[ IoU=0.50:0.95] = 0.806\\n\",\n      \" Average IL_precision                = 0.962\\n\",\n      \" Average IL_recall                   = 0.850\\n\",\n      \" Average IL_F1                       = 0.903\\n\",\n      \" Average IL_FPR                      = 0.006\\n\",\n      \" Average IL_MCC                      = 0.888\\n\",\n      \" Average cgF1       @[ IoU=0.50     ] = 0.735\\n\",\n      \" Average precision  @[ IoU=0.50     ] = 0.821\\n\",\n      \" Average recall     @[ IoU=0.50     ] = 0.788\\n\",\n      \" Average F1         @[ IoU=0.50     ] = 0.804\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.50     ] = 0.921\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.50     ] = 0.827\\n\",\n      \" Average positive_micro_precision @[ IoU=0.50     ] = 0.870\\n\",\n      \" Average cgF1       @[ IoU=0.75     ] = 0.711\\n\",\n      \" Average precision  @[ IoU=0.75     ] = 0.794\\n\",\n      \" Average recall     @[ IoU=0.75     ] = 0.762\\n\",\n      \" Average F1         @[ IoU=0.75     ] = 0.777\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.75     ] = 0.901\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.75     ] = 0.800\\n\",\n      \" Average positive_micro_precision @[ IoU=0.75     ] = 0.842\\n\",\n      \"\\n\",\n      \"{'cgF1_eval_bbox_cgF1': 0.6806904643208772, 'cgF1_eval_bbox_precision': 0.7600893518323295, 'cgF1_eval_bbox_recall': 0.7300429019642443, 'cgF1_eval_bbox_F1': 0.744713228137678, 'cgF1_eval_bbox_positive_macro_F1': 0.8793127218535274, 'cgF1_eval_bbox_positive_micro_F1': 0.766166324102149, 'cgF1_eval_bbox_positive_micro_precision': 0.806161117331975, 'cgF1_eval_bbox_IL_precision': 0.9624470012341326, 'cgF1_eval_bbox_IL_recall': 0.8497326198664531, 'cgF1_eval_bbox_IL_F1': 0.9025839944636483, 'cgF1_eval_bbox_IL_FPR': 0.0060564618534671814, 'cgF1_eval_bbox_IL_MCC': 0.8884369397448539, 'cgF1_eval_bbox_cgF1@0.5': 0.7348434113860801, 'cgF1_eval_bbox_precision@0.5': 0.8205550136275866, 'cgF1_eval_bbox_recall@0.5': 0.7881183467784425, 'cgF1_eval_bbox_F1@0.5': 0.8039596834419491, 'cgF1_eval_bbox_positive_macro_F1@0.5': 0.9208045886576268, 'cgF1_eval_bbox_positive_micro_F1@0.5': 0.8271193806924737, 'cgF1_eval_bbox_positive_micro_precision@0.5': 0.8702918216440054, 'cgF1_eval_bbox_cgF1@0.75': 0.7106209729640045, 'cgF1_eval_bbox_precision@0.75': 0.7935089183088213, 'cgF1_eval_bbox_recall@0.75': 0.7621413877989296, 'cgF1_eval_bbox_F1@0.75': 0.7774589352243091, 'cgF1_eval_bbox_positive_macro_F1@0.75': 0.9005662678327496, 'cgF1_eval_bbox_positive_micro_F1@0.75': 0.799855275229871, 'cgF1_eval_bbox_positive_micro_precision@0.75': 0.8416063646394022}\\n\",\n      \"Processing subset:  attributes\\n\",\n      \"loading annotations into memory...\\n\",\n      \"Done (t=0.07s)\\n\",\n      \"creating index...\\n\",\n      \"index created!\\n\",\n      \"loading annotations into memory...\\n\",\n      \"Done (t=0.06s)\\n\",\n      \"creating index...\\n\",\n      \"index created!\\n\",\n      \"loading annotations into memory...\\n\",\n      \"Done (t=0.06s)\\n\",\n      \"creating index...\\n\",\n      \"index created!\\n\",\n      \"Loaded 5834 predictions\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 9222/9222 [00:03<00:00, 2820.36it/s]\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Accumulating results\\n\",\n      \"cgF1 metric, IoU type=segm\\n\",\n      \" Average cgF1       @[ IoU=0.50:0.95] = 0.549\\n\",\n      \" Average precision  @[ IoU=0.50:0.95] = 0.643\\n\",\n      \" Average recall     @[ IoU=0.50:0.95] = 0.670\\n\",\n      \" Average F1         @[ IoU=0.50:0.95] = 0.656\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.50:0.95] = 0.872\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.50:0.95] = 0.720\\n\",\n      \" Average positive_micro_precision @[ IoU=0.50:0.95] = 0.778\\n\",\n      \" Average IL_precision                = 0.797\\n\",\n      \" Average IL_recall                   = 0.819\\n\",\n      \" Average IL_F1                       = 0.808\\n\",\n      \" Average IL_FPR                      = 0.048\\n\",\n      \" Average IL_MCC                      = 0.763\\n\",\n      \" Average cgF1       @[ IoU=0.50     ] = 0.600\\n\",\n      \" Average precision  @[ IoU=0.50     ] = 0.703\\n\",\n      \" Average recall     @[ IoU=0.50     ] = 0.733\\n\",\n      \" Average F1         @[ IoU=0.50     ] = 0.717\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.50     ] = 0.930\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.50     ] = 0.787\\n\",\n      \" Average positive_micro_precision @[ IoU=0.50     ] = 0.850\\n\",\n      \" Average cgF1       @[ IoU=0.75     ] = 0.569\\n\",\n      \" Average precision  @[ IoU=0.75     ] = 0.667\\n\",\n      \" Average recall     @[ IoU=0.75     ] = 0.695\\n\",\n      \" Average F1         @[ IoU=0.75     ] = 0.680\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.75     ] = 0.890\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.75     ] = 0.746\\n\",\n      \" Average positive_micro_precision @[ IoU=0.75     ] = 0.806\\n\",\n      \"\\n\",\n      \"{'cgF1_eval_segm_cgF1': 0.5492693860624807, 'cgF1_eval_segm_precision': 0.6432095082730505, 'cgF1_eval_segm_recall': 0.670390051111405, 'cgF1_eval_segm_F1': 0.6564685998234858, 'cgF1_eval_segm_positive_macro_F1': 0.8717482377036931, 'cgF1_eval_segm_positive_micro_F1': 0.7200264336735771, 'cgF1_eval_segm_positive_micro_precision': 0.7777168057004865, 'cgF1_eval_segm_IL_precision': 0.7970687706893637, 'cgF1_eval_segm_IL_recall': 0.8187608565033231, 'cgF1_eval_segm_IL_F1': 0.807768708426457, 'cgF1_eval_segm_IL_FPR': 0.0480320213411565, 'cgF1_eval_segm_IL_MCC': 0.7628461406064031, 'cgF1_eval_segm_cgF1@0.5': 0.6002145763651893, 'cgF1_eval_segm_precision@0.5': 0.7028636035479556, 'cgF1_eval_segm_recall@0.5': 0.7325649901724294, 'cgF1_eval_segm_F1@0.5': 0.7173570363948575, 'cgF1_eval_segm_positive_macro_F1@0.5': 0.9296877722553353, 'cgF1_eval_segm_positive_micro_F1@0.5': 0.786809481513619, 'cgF1_eval_segm_positive_micro_precision@0.5': 0.849845702782115, 'cgF1_eval_segm_cgF1@0.75': 0.5692213046663587, 'cgF1_eval_segm_precision@0.75': 0.6665721387419297, 'cgF1_eval_segm_recall@0.75': 0.6947399321885362, 'cgF1_eval_segm_F1@0.75': 0.6803146414763769, 'cgF1_eval_segm_positive_macro_F1@0.75': 0.8904105590365932, 'cgF1_eval_segm_positive_micro_F1@0.75': 0.7461810113031079, 'cgF1_eval_segm_positive_micro_precision@0.75': 0.8059650049377783}\\n\",\n      \"loading annotations into memory...\\n\",\n      \"Done (t=0.05s)\\n\",\n      \"creating index...\\n\",\n      \"index created!\\n\",\n      \"loading annotations into memory...\\n\",\n      \"Done (t=0.05s)\\n\",\n      \"creating index...\\n\",\n      \"index created!\\n\",\n      \"loading annotations into memory...\\n\",\n      \"Done (t=0.06s)\\n\",\n      \"creating index...\\n\",\n      \"index created!\\n\",\n      \"Loaded 5834 predictions\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 9222/9222 [00:02<00:00, 3370.79it/s]\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Accumulating results\\n\",\n      \"cgF1 metric, IoU type=bbox\\n\",\n      \" Average cgF1       @[ IoU=0.50:0.95] = 0.565\\n\",\n      \" Average precision  @[ IoU=0.50:0.95] = 0.660\\n\",\n      \" Average recall     @[ IoU=0.50:0.95] = 0.689\\n\",\n      \" Average F1         @[ IoU=0.50:0.95] = 0.674\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.50:0.95] = 0.901\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.50:0.95] = 0.739\\n\",\n      \" Average positive_micro_precision @[ IoU=0.50:0.95] = 0.798\\n\",\n      \" Average IL_precision                = 0.798\\n\",\n      \" Average IL_recall                   = 0.819\\n\",\n      \" Average IL_F1                       = 0.808\\n\",\n      \" Average IL_FPR                      = 0.048\\n\",\n      \" Average IL_MCC                      = 0.764\\n\",\n      \" Average cgF1       @[ IoU=0.50     ] = 0.602\\n\",\n      \" Average precision  @[ IoU=0.50     ] = 0.703\\n\",\n      \" Average recall     @[ IoU=0.50     ] = 0.734\\n\",\n      \" Average F1         @[ IoU=0.50     ] = 0.718\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.50     ] = 0.934\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.50     ] = 0.788\\n\",\n      \" Average positive_micro_precision @[ IoU=0.50     ] = 0.850\\n\",\n      \" Average cgF1       @[ IoU=0.75     ] = 0.580\\n\",\n      \" Average precision  @[ IoU=0.75     ] = 0.679\\n\",\n      \" Average recall     @[ IoU=0.75     ] = 0.708\\n\",\n      \" Average F1         @[ IoU=0.75     ] = 0.693\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.75     ] = 0.911\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.75     ] = 0.760\\n\",\n      \" Average positive_micro_precision @[ IoU=0.75     ] = 0.820\\n\",\n      \"\\n\",\n      \"{'cgF1_eval_bbox_cgF1': 0.5646399041045982, 'cgF1_eval_bbox_precision': 0.6603061905215142, 'cgF1_eval_bbox_recall': 0.6892275617275063, 'cgF1_eval_bbox_F1': 0.6744070010794116, 'cgF1_eval_bbox_positive_macro_F1': 0.9009593951299972, 'cgF1_eval_bbox_positive_micro_F1': 0.7394009518446127, 'cgF1_eval_bbox_positive_micro_precision': 0.7975684658366964, 'cgF1_eval_bbox_IL_precision': 0.7981961664046244, 'cgF1_eval_bbox_IL_recall': 0.8189705027073623, 'cgF1_eval_bbox_IL_F1': 0.8084493997068858, 'cgF1_eval_bbox_IL_FPR': 0.04777792605795036, 'cgF1_eval_bbox_IL_MCC': 0.7636450868719721, 'cgF1_eval_bbox_cgF1@0.5': 0.6015189050487545, 'cgF1_eval_bbox_precision@0.5': 0.7034306576855498, 'cgF1_eval_bbox_recall@0.5': 0.7342408779449281, 'cgF1_eval_bbox_F1@0.5': 0.7184556528947154, 'cgF1_eval_bbox_positive_macro_F1@0.5': 0.9340793389274347, 'cgF1_eval_bbox_positive_micro_F1@0.5': 0.7876943299834277, 'cgF1_eval_bbox_positive_micro_precision@0.5': 0.8496575051487155, 'cgF1_eval_bbox_cgF1@0.75': 0.5804244074542156, 'cgF1_eval_bbox_precision@0.75': 0.678763802700204, 'cgF1_eval_bbox_recall@0.75': 0.7084936162032075, 'cgF1_eval_bbox_F1@0.75': 0.693260171561296, 'cgF1_eval_bbox_positive_macro_F1@0.75': 0.9107485801358162, 'cgF1_eval_bbox_positive_micro_F1@0.75': 0.7600708986837572, 'cgF1_eval_bbox_positive_micro_precision@0.75': 0.8198629856211306}\\n\",\n      \"Processing subset:  wiki_common\\n\",\n      \"loading annotations into memory...\\n\",\n      \"Done (t=0.23s)\\n\",\n      \"creating index...\\n\",\n      \"index created!\\n\",\n      \"loading annotations into memory...\\n\",\n      \"Done (t=0.21s)\\n\",\n      \"creating index...\\n\",\n      \"index created!\\n\",\n      \"loading annotations into memory...\\n\",\n      \"Done (t=0.21s)\\n\",\n      \"creating index...\\n\",\n      \"index created!\\n\",\n      \"Loaded 8045 predictions\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 65452/65452 [00:11<00:00, 5775.55it/s]\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Accumulating results\\n\",\n      \"cgF1 metric, IoU type=segm\\n\",\n      \" Average cgF1       @[ IoU=0.50:0.95] = 0.425\\n\",\n      \" Average precision  @[ IoU=0.50:0.95] = 0.677\\n\",\n      \" Average recall     @[ IoU=0.50:0.95] = 0.509\\n\",\n      \" Average F1         @[ IoU=0.50:0.95] = 0.581\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.50:0.95] = 0.811\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.50:0.95] = 0.608\\n\",\n      \" Average positive_micro_precision @[ IoU=0.50:0.95] = 0.757\\n\",\n      \" Average IL_precision                = 0.822\\n\",\n      \" Average IL_recall                   = 0.607\\n\",\n      \" Average IL_F1                       = 0.698\\n\",\n      \" Average IL_FPR                      = 0.004\\n\",\n      \" Average IL_MCC                      = 0.699\\n\",\n      \" Average cgF1       @[ IoU=0.50     ] = 0.482\\n\",\n      \" Average precision  @[ IoU=0.50     ] = 0.767\\n\",\n      \" Average recall     @[ IoU=0.50     ] = 0.577\\n\",\n      \" Average F1         @[ IoU=0.50     ] = 0.658\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.50     ] = 0.905\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.50     ] = 0.689\\n\",\n      \" Average positive_micro_precision @[ IoU=0.50     ] = 0.857\\n\",\n      \" Average cgF1       @[ IoU=0.75     ] = 0.450\\n\",\n      \" Average precision  @[ IoU=0.75     ] = 0.717\\n\",\n      \" Average recall     @[ IoU=0.75     ] = 0.539\\n\",\n      \" Average F1         @[ IoU=0.75     ] = 0.615\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.75     ] = 0.844\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.75     ] = 0.644\\n\",\n      \" Average positive_micro_precision @[ IoU=0.75     ] = 0.801\\n\",\n      \"\\n\",\n      \"{'cgF1_eval_segm_cgF1': 0.42532135099113527, 'cgF1_eval_segm_precision': 0.6770954780038412, 'cgF1_eval_segm_recall': 0.5089505000184188, 'cgF1_eval_segm_F1': 0.5810550953744452, 'cgF1_eval_segm_positive_macro_F1': 0.8110173727833955, 'cgF1_eval_segm_positive_micro_F1': 0.6084924575531054, 'cgF1_eval_segm_positive_micro_precision': 0.7565875096115964, 'cgF1_eval_segm_IL_precision': 0.8222698066935975, 'cgF1_eval_segm_IL_recall': 0.6066350707705976, 'cgF1_eval_segm_IL_F1': 0.6981813291457964, 'cgF1_eval_segm_IL_FPR': 0.003917989709314776, 'cgF1_eval_segm_IL_MCC': 0.6989755513181787, 'cgF1_eval_segm_cgF1@0.5': 0.4817908882049053, 'cgF1_eval_segm_precision@0.5': 0.766985739738834, 'cgF1_eval_segm_recall@0.5': 0.5765180664001743, 'cgF1_eval_segm_F1@0.5': 0.6582016716689654, 'cgF1_eval_segm_positive_macro_F1@0.5': 0.9051793905267748, 'cgF1_eval_segm_positive_micro_F1@0.5': 0.6892814595536413, 'cgF1_eval_segm_positive_micro_precision@0.5': 0.8570310238186531, 'cgF1_eval_segm_cgF1@0.75': 0.4501092776435151, 'cgF1_eval_segm_precision@0.75': 0.7165538006875134, 'cgF1_eval_segm_recall@0.75': 0.5386100291574232, 'cgF1_eval_segm_F1@0.75': 0.6149194365440128, 'cgF1_eval_segm_positive_macro_F1@0.75': 0.8441369689666682, 'cgF1_eval_segm_positive_micro_F1@0.75': 0.643955681703983, 'cgF1_eval_segm_positive_micro_precision@0.75': 0.8006782989648239}\\n\",\n      \"loading annotations into memory...\\n\",\n      \"Done (t=0.18s)\\n\",\n      \"creating index...\\n\",\n      \"index created!\\n\",\n      \"loading annotations into memory...\\n\",\n      \"Done (t=0.19s)\\n\",\n      \"creating index...\\n\",\n      \"index created!\\n\",\n      \"loading annotations into memory...\\n\",\n      \"Done (t=0.18s)\\n\",\n      \"creating index...\\n\",\n      \"index created!\\n\",\n      \"Loaded 8045 predictions\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 65452/65452 [00:10<00:00, 6310.26it/s]\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Accumulating results\\n\",\n      \"cgF1 metric, IoU type=bbox\\n\",\n      \" Average cgF1       @[ IoU=0.50:0.95] = 0.443\\n\",\n      \" Average precision  @[ IoU=0.50:0.95] = 0.706\\n\",\n      \" Average recall     @[ IoU=0.50:0.95] = 0.529\\n\",\n      \" Average F1         @[ IoU=0.50:0.95] = 0.605\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.50:0.95] = 0.865\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.50:0.95] = 0.633\\n\",\n      \" Average positive_micro_precision @[ IoU=0.50:0.95] = 0.788\\n\",\n      \" Average IL_precision                = 0.822\\n\",\n      \" Average IL_recall                   = 0.607\\n\",\n      \" Average IL_F1                       = 0.698\\n\",\n      \" Average IL_FPR                      = 0.004\\n\",\n      \" Average IL_MCC                      = 0.699\\n\",\n      \" Average cgF1       @[ IoU=0.50     ] = 0.483\\n\",\n      \" Average precision  @[ IoU=0.50     ] = 0.770\\n\",\n      \" Average recall     @[ IoU=0.50     ] = 0.578\\n\",\n      \" Average F1         @[ IoU=0.50     ] = 0.660\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.50     ] = 0.909\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.50     ] = 0.691\\n\",\n      \" Average positive_micro_precision @[ IoU=0.50     ] = 0.860\\n\",\n      \" Average cgF1       @[ IoU=0.75     ] = 0.459\\n\",\n      \" Average precision  @[ IoU=0.75     ] = 0.732\\n\",\n      \" Average recall     @[ IoU=0.75     ] = 0.549\\n\",\n      \" Average F1         @[ IoU=0.75     ] = 0.628\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.75     ] = 0.875\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.75     ] = 0.657\\n\",\n      \" Average positive_micro_precision @[ IoU=0.75     ] = 0.818\\n\",\n      \"\\n\",\n      \"{'cgF1_eval_bbox_cgF1': 0.4427363803296873, 'cgF1_eval_bbox_precision': 0.7055568361999337, 'cgF1_eval_bbox_recall': 0.5294148470670139, 'cgF1_eval_bbox_F1': 0.6048754410558315, 'cgF1_eval_bbox_positive_macro_F1': 0.8646998403591537, 'cgF1_eval_bbox_positive_micro_F1': 0.6334075340614461, 'cgF1_eval_bbox_positive_micro_precision': 0.7883902742397528, 'cgF1_eval_bbox_IL_precision': 0.8222698066935975, 'cgF1_eval_bbox_IL_recall': 0.6066350707705976, 'cgF1_eval_bbox_IL_F1': 0.6981813291457964, 'cgF1_eval_bbox_IL_FPR': 0.003917989709314776, 'cgF1_eval_bbox_IL_MCC': 0.6989755513181787, 'cgF1_eval_bbox_cgF1@0.5': 0.4830441402948427, 'cgF1_eval_bbox_precision@0.5': 0.769787514130574, 'cgF1_eval_bbox_recall@0.5': 0.5776103612892368, 'cgF1_eval_bbox_F1@0.5': 0.6599450021609545, 'cgF1_eval_bbox_positive_macro_F1@0.5': 0.9093257078072422, 'cgF1_eval_bbox_positive_micro_F1@0.5': 0.6910744437110613, 'cgF1_eval_bbox_positive_micro_precision@0.5': 0.8601617307549769, 'cgF1_eval_bbox_cgF1@0.75': 0.4594543570790383, 'cgF1_eval_bbox_precision@0.75': 0.7321970410413953, 'cgF1_eval_bbox_recall@0.75': 0.5494043351540936, 'cgF1_eval_bbox_F1@0.75': 0.6277159968872291, 'cgF1_eval_bbox_positive_macro_F1@0.75': 0.8750107505715273, 'cgF1_eval_bbox_positive_micro_F1@0.75': 0.6573253616847772, 'cgF1_eval_bbox_positive_micro_precision@0.75': 0.8181580793592987}\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"results_gold = {}\\n\",\n    \"results_gold_bbox = {}\\n\",\n    \"\\n\",\n    \"for subset_name, gts in saco_gold_gts.items():\\n\",\n    \"    print(\\\"Processing subset: \\\", subset_name)\\n\",\n    \"    gt_paths = [os.path.join(GT_DIR, gt) for gt in gts]\\n\",\n    \"    pred_path = os.path.join(PRED_DIR, f\\\"gold_{subset_name}/dumps/gold_{subset_name}/coco_predictions_segm.json\\\")\\n\",\n    \"    \\n\",\n    \"    evaluator = CGF1Evaluator(gt_path=gt_paths, verbose=True, iou_type=\\\"segm\\\") \\n\",\n    \"    summary = evaluator.evaluate(pred_path)\\n\",\n    \"    print(summary)\\n\",\n    \"\\n\",\n    \"    cur_results = {}\\n\",\n    \"    cur_results[\\\"cgf1\\\"] = summary[\\\"cgF1_eval_segm_cgF1\\\"] * 100\\n\",\n    \"    cur_results[\\\"il_mcc\\\"] = summary[\\\"cgF1_eval_segm_IL_MCC\\\"]\\n\",\n    \"    cur_results[\\\"pmf1\\\"] = summary[\\\"cgF1_eval_segm_positive_micro_F1\\\"] * 100\\n\",\n    \"    results_gold[subset_name] = cur_results\\n\",\n    \"\\n\",\n    \"    # Also eval bbox    \\n\",\n    \"    evaluator = CGF1Evaluator(gt_path=gt_paths, verbose=True, iou_type=\\\"bbox\\\") \\n\",\n    \"    summary = evaluator.evaluate(pred_path)\\n\",\n    \"    print(summary)\\n\",\n    \"\\n\",\n    \"    cur_results = {}\\n\",\n    \"    cur_results[\\\"cgf1\\\"] = summary[\\\"cgF1_eval_bbox_cgF1\\\"] * 100\\n\",\n    \"    cur_results[\\\"il_mcc\\\"] = summary[\\\"cgF1_eval_bbox_IL_MCC\\\"]\\n\",\n    \"    cur_results[\\\"pmf1\\\"] = summary[\\\"cgF1_eval_bbox_positive_micro_F1\\\"] * 100\\n\",\n    \"    results_gold_bbox[subset_name] = cur_results\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"id\": \"c808a7cf-ecda-445a-a827-53a16dee4504\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Compute averages\\n\",\n    \"METRICS = [\\\"cgf1\\\", \\\"il_mcc\\\", \\\"pmf1\\\"]\\n\",\n    \"avg_stats, avg_stats_bbox = {}, {}\\n\",\n    \"for key in METRICS:\\n\",\n    \"    avg_stats[key] = sum(res[key] for res in results_gold.values()) / len(results_gold)\\n\",\n    \"    avg_stats_bbox[key] = sum(res[key] for res in results_gold_bbox.values()) / len(results_gold_bbox)\\n\",\n    \"results_gold[\\\"Average\\\"] = avg_stats\\n\",\n    \"results_gold_bbox[\\\"Average\\\"] = avg_stats_bbox\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"id\": \"c26b1fb2\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<table><thead><tr><th colspan=\\\"3\\\" style=\\\"text-align:center;border-left-style:solid;border-left-width:1px\\\">metaclip_nps</th><th colspan=\\\"3\\\" style=\\\"text-align:center;border-left-style:solid;border-left-width:1px\\\">sa1b_nps</th><th colspan=\\\"3\\\" style=\\\"text-align:center;border-left-style:solid;border-left-width:1px\\\">crowded</th><th colspan=\\\"3\\\" style=\\\"text-align:center;border-left-style:solid;border-left-width:1px\\\">fg_food</th><th colspan=\\\"3\\\" style=\\\"text-align:center;border-left-style:solid;border-left-width:1px\\\">fg_sports_equipment</th><th colspan=\\\"3\\\" style=\\\"text-align:center;border-left-style:solid;border-left-width:1px\\\">attributes</th><th colspan=\\\"3\\\" style=\\\"text-align:center;border-left-style:solid;border-left-width:1px\\\">wiki_common</th><th colspan=\\\"3\\\" style=\\\"text-align:center;border-left-style:solid;border-left-width:1px\\\">Average</th></tr><tr><th style='border-left-style:solid;border-left-width:1px'>cgf1</th><th>il_mcc</th><th>pmf1</th><th style='border-left-style:solid;border-left-width:1px'>cgf1</th><th>il_mcc</th><th>pmf1</th><th style='border-left-style:solid;border-left-width:1px'>cgf1</th><th>il_mcc</th><th>pmf1</th><th style='border-left-style:solid;border-left-width:1px'>cgf1</th><th>il_mcc</th><th>pmf1</th><th style='border-left-style:solid;border-left-width:1px'>cgf1</th><th>il_mcc</th><th>pmf1</th><th style='border-left-style:solid;border-left-width:1px'>cgf1</th><th>il_mcc</th><th>pmf1</th><th style='border-left-style:solid;border-left-width:1px'>cgf1</th><th>il_mcc</th><th>pmf1</th><th style='border-left-style:solid;border-left-width:1px'>cgf1</th><th>il_mcc</th><th>pmf1</th></tr></thead><tbody><tr><td style='border-left-style:solid;border-left-width:1px'>47.26</td><td>0.81</td><td>58.58</td><td style='border-left-style:solid;border-left-width:1px'>53.69</td><td>0.86</td><td>62.55</td><td style='border-left-style:solid;border-left-width:1px'>61.08</td><td>0.9</td><td>67.73</td><td style='border-left-style:solid;border-left-width:1px'>53.41</td><td>0.79</td><td>67.28</td><td style='border-left-style:solid;border-left-width:1px'>65.52</td><td>0.89</td><td>73.75</td><td style='border-left-style:solid;border-left-width:1px'>54.93</td><td>0.76</td><td>72.0</td><td style='border-left-style:solid;border-left-width:1px'>42.53</td><td>0.7</td><td>60.85</td><td style='border-left-style:solid;border-left-width:1px'>54.06</td><td>0.82</td><td>66.11</td></tr></tbody></table>\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# Pretty print segmentation results\\n\",\n    \"from IPython.display import HTML, display\\n\",\n    \"\\n\",\n    \"row1, row2, row3 = \\\"\\\", \\\"\\\", \\\"\\\"\\n\",\n    \"for subset in results_gold:\\n\",\n    \"    row1 += f'<th colspan=\\\"3\\\" style=\\\"text-align:center;border-left-style:solid;border-left-width:1px\\\">{subset}</th>'\\n\",\n    \"    row2 += \\\"<th style='border-left-style:solid;border-left-width:1px'>\\\" + \\\"</th><th>\\\".join(METRICS) + \\\"</th>\\\"\\n\",\n    \"    row3 += \\\"<td style='border-left-style:solid;border-left-width:1px'>\\\" + \\\"</td><td>\\\".join([str(round(results_gold[subset][k], 2)) for k in METRICS])  + \\\"</td>\\\"\\n\",\n    \"\\n\",\n    \"display(HTML(\\n\",\n    \"   f\\\"<table><thead><tr>{row1}</tr><tr>{row2}</tr></thead><tbody><tr>{row3}</tr></tbody></table>\\\"\\n\",\n    \"))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"id\": \"d3c85735-4aea-436d-a233-6f18cff29147\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<table><thead><tr><th colspan=\\\"3\\\" style=\\\"text-align:center;border-left-style:solid;border-left-width:1px\\\">metaclip_nps</th><th colspan=\\\"3\\\" style=\\\"text-align:center;border-left-style:solid;border-left-width:1px\\\">sa1b_nps</th><th colspan=\\\"3\\\" style=\\\"text-align:center;border-left-style:solid;border-left-width:1px\\\">crowded</th><th colspan=\\\"3\\\" style=\\\"text-align:center;border-left-style:solid;border-left-width:1px\\\">fg_food</th><th colspan=\\\"3\\\" style=\\\"text-align:center;border-left-style:solid;border-left-width:1px\\\">fg_sports_equipment</th><th colspan=\\\"3\\\" style=\\\"text-align:center;border-left-style:solid;border-left-width:1px\\\">attributes</th><th colspan=\\\"3\\\" style=\\\"text-align:center;border-left-style:solid;border-left-width:1px\\\">wiki_common</th><th colspan=\\\"3\\\" style=\\\"text-align:center;border-left-style:solid;border-left-width:1px\\\">Average</th></tr><tr><th style='border-left-style:solid;border-left-width:1px'>cgf1</th><th>il_mcc</th><th>pmf1</th><th style='border-left-style:solid;border-left-width:1px'>cgf1</th><th>il_mcc</th><th>pmf1</th><th style='border-left-style:solid;border-left-width:1px'>cgf1</th><th>il_mcc</th><th>pmf1</th><th style='border-left-style:solid;border-left-width:1px'>cgf1</th><th>il_mcc</th><th>pmf1</th><th style='border-left-style:solid;border-left-width:1px'>cgf1</th><th>il_mcc</th><th>pmf1</th><th style='border-left-style:solid;border-left-width:1px'>cgf1</th><th>il_mcc</th><th>pmf1</th><th style='border-left-style:solid;border-left-width:1px'>cgf1</th><th>il_mcc</th><th>pmf1</th><th style='border-left-style:solid;border-left-width:1px'>cgf1</th><th>il_mcc</th><th>pmf1</th></tr></thead><tbody><tr><td style='border-left-style:solid;border-left-width:1px'>50.03</td><td>0.81</td><td>61.99</td><td style='border-left-style:solid;border-left-width:1px'>55.36</td><td>0.86</td><td>64.49</td><td style='border-left-style:solid;border-left-width:1px'>61.71</td><td>0.9</td><td>68.43</td><td style='border-left-style:solid;border-left-width:1px'>53.79</td><td>0.8</td><td>67.64</td><td style='border-left-style:solid;border-left-width:1px'>68.07</td><td>0.89</td><td>76.62</td><td style='border-left-style:solid;border-left-width:1px'>56.46</td><td>0.76</td><td>73.94</td><td style='border-left-style:solid;border-left-width:1px'>44.27</td><td>0.7</td><td>63.34</td><td style='border-left-style:solid;border-left-width:1px'>55.67</td><td>0.82</td><td>68.06</td></tr></tbody></table>\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# Pretty print bbox detection results\\n\",\n    \"from IPython.display import HTML, display\\n\",\n    \"\\n\",\n    \"row1, row2, row3 = \\\"\\\", \\\"\\\", \\\"\\\"\\n\",\n    \"for subset in results_gold:\\n\",\n    \"    row1 += f'<th colspan=\\\"3\\\" style=\\\"text-align:center;border-left-style:solid;border-left-width:1px\\\">{subset}</th>'\\n\",\n    \"    row2 += \\\"<th style='border-left-style:solid;border-left-width:1px'>\\\" + \\\"</th><th>\\\".join(METRICS) + \\\"</th>\\\"\\n\",\n    \"    row3 += \\\"<td style='border-left-style:solid;border-left-width:1px'>\\\" + \\\"</td><td>\\\".join([str(round(results_gold_bbox[subset][k], 2)) for k in METRICS])  + \\\"</td>\\\"\\n\",\n    \"\\n\",\n    \"display(HTML(\\n\",\n    \"   f\\\"<table><thead><tr>{row1}</tr><tr>{row2}</tr></thead><tbody><tr>{row3}</tr></tbody></table>\\\"\\n\",\n    \"))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"ef79428e-9212-4e26-8122-3e82841447de\",\n   \"metadata\": {},\n   \"source\": [\n    \"# SA-Co/Silver\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"id\": \"55995b3b-1184-4d1f-b9bb-ad412eb734a3\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Update to the directory where the GT annotation and PRED files exist\\n\",\n    \"GT_DIR =  # PUT YOUR PATH HERE\\n\",\n    \"PRED_DIR =  # PUT YOUR PATH HERE\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"id\": \"cd254a73-7272-4df1-90d1-b818f5a7c6f7\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"saco_silver_gts = {\\n\",\n    \"    \\\"bdd100k\\\": \\\"silver_bdd100k_merged_test.json\\\",\\n\",\n    \"    \\\"droid\\\": \\\"silver_droid_merged_test.json\\\",\\n\",\n    \"    \\\"ego4d\\\": \\\"silver_ego4d_merged_test.json\\\",\\n\",\n    \"    \\\"food_rec\\\": \\\"silver_food_rec_merged_test.json\\\",\\n\",\n    \"    \\\"geode\\\": \\\"silver_geode_merged_test.json\\\",\\n\",\n    \"    \\\"inaturalist\\\": \\\"silver_inaturalist_merged_test.json\\\",\\n\",\n    \"    \\\"nga_art\\\": \\\"silver_nga_art_merged_test.json\\\",\\n\",\n    \"    \\\"sav\\\": \\\"silver_sav_merged_test.json\\\",\\n\",\n    \"    \\\"yt1b\\\": \\\"silver_yt1b_merged_test.json\\\",\\n\",\n    \"    \\\"fathomnet\\\": \\\"silver_fathomnet_test.json\\\",\\n\",\n    \"}\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"id\": \"c736eabf-6e52-4f52-b4ab-111eaa490584\",\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Processing subset:  bdd100k\\n\",\n      \"loading annotations into memory...\\n\",\n      \"Done (t=0.12s)\\n\",\n      \"creating index...\\n\",\n      \"index created!\\n\",\n      \"Loaded 31278 predictions\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5439/5439 [00:01<00:00, 3496.20it/s]\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Accumulating results\\n\",\n      \"cgF1 metric, IoU type=segm\\n\",\n      \" Average cgF1       @[ IoU=0.50:0.95] = 0.466\\n\",\n      \" Average precision  @[ IoU=0.50:0.95] = 0.514\\n\",\n      \" Average recall     @[ IoU=0.50:0.95] = 0.644\\n\",\n      \" Average F1         @[ IoU=0.50:0.95] = 0.572\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.50:0.95] = 0.669\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.50:0.95] = 0.601\\n\",\n      \" Average positive_micro_precision @[ IoU=0.50:0.95] = 0.564\\n\",\n      \" Average IL_precision                = 0.870\\n\",\n      \" Average IL_recall                   = 0.952\\n\",\n      \" Average IL_F1                       = 0.909\\n\",\n      \" Average IL_FPR                      = 0.196\\n\",\n      \" Average IL_MCC                      = 0.775\\n\",\n      \" Average cgF1       @[ IoU=0.50     ] = 0.563\\n\",\n      \" Average precision  @[ IoU=0.50     ] = 0.621\\n\",\n      \" Average recall     @[ IoU=0.50     ] = 0.779\\n\",\n      \" Average F1         @[ IoU=0.50     ] = 0.691\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.50     ] = 0.769\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.50     ] = 0.726\\n\",\n      \" Average positive_micro_precision @[ IoU=0.50     ] = 0.681\\n\",\n      \" Average cgF1       @[ IoU=0.75     ] = 0.507\\n\",\n      \" Average precision  @[ IoU=0.75     ] = 0.560\\n\",\n      \" Average recall     @[ IoU=0.75     ] = 0.701\\n\",\n      \" Average F1         @[ IoU=0.75     ] = 0.623\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.75     ] = 0.708\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.75     ] = 0.655\\n\",\n      \" Average positive_micro_precision @[ IoU=0.75     ] = 0.614\\n\",\n      \"\\n\",\n      \"{'cgF1_eval_segm_cgF1': 0.4660589364507352, 'cgF1_eval_segm_precision': 0.5141618280038143, 'cgF1_eval_segm_recall': 0.6443148170650035, 'cgF1_eval_segm_F1': 0.5718777188484898, 'cgF1_eval_segm_positive_macro_F1': 0.6686547809892744, 'cgF1_eval_segm_positive_micro_F1': 0.6012626762403565, 'cgF1_eval_segm_positive_micro_precision': 0.5636910131863614, 'cgF1_eval_segm_IL_precision': 0.8697795821143331, 'cgF1_eval_segm_IL_recall': 0.9523658301199219, 'cgF1_eval_segm_IL_F1': 0.9092006527905545, 'cgF1_eval_segm_IL_FPR': 0.19606986891001316, 'cgF1_eval_segm_IL_MCC': 0.7751336560003381, 'cgF1_eval_segm_cgF1@0.5': 0.5631327201709976, 'cgF1_eval_segm_precision@0.5': 0.6212459311276801, 'cgF1_eval_segm_recall@0.5': 0.7785057868277604, 'cgF1_eval_segm_F1@0.5': 0.6909925475999513, 'cgF1_eval_segm_positive_macro_F1@0.5': 0.7694734183454693, 'cgF1_eval_segm_positive_micro_F1@0.5': 0.7264975734336478, 'cgF1_eval_segm_positive_micro_precision@0.5': 0.6810905230262809, 'cgF1_eval_segm_cgF1@0.75': 0.5073531550281276, 'cgF1_eval_segm_precision@0.75': 0.559714336799628, 'cgF1_eval_segm_recall@0.75': 0.7013983164091225, 'cgF1_eval_segm_F1@0.75': 0.6225479779361666, 'cgF1_eval_segm_positive_macro_F1@0.75': 0.7080380721802793, 'cgF1_eval_segm_positive_micro_F1@0.75': 0.6545363513771957, 'cgF1_eval_segm_positive_micro_precision@0.75': 0.6136315930539561}\\n\",\n      \"loading annotations into memory...\\n\",\n      \"Done (t=0.09s)\\n\",\n      \"creating index...\\n\",\n      \"index created!\\n\",\n      \"Loaded 31278 predictions\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5439/5439 [00:01<00:00, 4945.36it/s]\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Accumulating results\\n\",\n      \"cgF1 metric, IoU type=bbox\\n\",\n      \" Average cgF1       @[ IoU=0.50:0.95] = 0.462\\n\",\n      \" Average precision  @[ IoU=0.50:0.95] = 0.510\\n\",\n      \" Average recall     @[ IoU=0.50:0.95] = 0.639\\n\",\n      \" Average F1         @[ IoU=0.50:0.95] = 0.567\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.50:0.95] = 0.673\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.50:0.95] = 0.596\\n\",\n      \" Average positive_micro_precision @[ IoU=0.50:0.95] = 0.559\\n\",\n      \" Average IL_precision                = 0.870\\n\",\n      \" Average IL_recall                   = 0.952\\n\",\n      \" Average IL_F1                       = 0.909\\n\",\n      \" Average IL_FPR                      = 0.196\\n\",\n      \" Average IL_MCC                      = 0.775\\n\",\n      \" Average cgF1       @[ IoU=0.50     ] = 0.562\\n\",\n      \" Average precision  @[ IoU=0.50     ] = 0.620\\n\",\n      \" Average recall     @[ IoU=0.50     ] = 0.777\\n\",\n      \" Average F1         @[ IoU=0.50     ] = 0.689\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.50     ] = 0.769\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.50     ] = 0.725\\n\",\n      \" Average positive_micro_precision @[ IoU=0.50     ] = 0.679\\n\",\n      \" Average cgF1       @[ IoU=0.75     ] = 0.507\\n\",\n      \" Average precision  @[ IoU=0.75     ] = 0.559\\n\",\n      \" Average recall     @[ IoU=0.75     ] = 0.700\\n\",\n      \" Average F1         @[ IoU=0.75     ] = 0.622\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.75     ] = 0.714\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.75     ] = 0.653\\n\",\n      \" Average positive_micro_precision @[ IoU=0.75     ] = 0.613\\n\",\n      \"\\n\",\n      \"{'cgF1_eval_bbox_cgF1': 0.4621052346722842, 'cgF1_eval_bbox_precision': 0.5098004175871937, 'cgF1_eval_bbox_recall': 0.6388493756384388, 'cgF1_eval_bbox_F1': 0.5670263110638136, 'cgF1_eval_bbox_positive_macro_F1': 0.673487715877281, 'cgF1_eval_bbox_positive_micro_F1': 0.5961620052169204, 'cgF1_eval_bbox_positive_micro_precision': 0.5589094683054214, 'cgF1_eval_bbox_IL_precision': 0.8697795821143331, 'cgF1_eval_bbox_IL_recall': 0.9523658301199219, 'cgF1_eval_bbox_IL_F1': 0.9092006527905545, 'cgF1_eval_bbox_IL_FPR': 0.19606986891001316, 'cgF1_eval_bbox_IL_MCC': 0.7751336560003381, 'cgF1_eval_bbox_cgF1@0.5': 0.5618032590322063, 'cgF1_eval_bbox_precision@0.5': 0.6197793749934364, 'cgF1_eval_bbox_recall@0.5': 0.7766679921960209, 'cgF1_eval_bbox_F1@0.5': 0.6893612262495565, 'cgF1_eval_bbox_positive_macro_F1@0.5': 0.7694834511677656, 'cgF1_eval_bbox_positive_micro_F1@0.5': 0.7247824354977579, 'cgF1_eval_bbox_positive_micro_precision@0.5': 0.6794826936072514, 'cgF1_eval_bbox_cgF1@0.75': 0.5065439178140905, 'cgF1_eval_bbox_precision@0.75': 0.5588216504570449, 'cgF1_eval_bbox_recall@0.75': 0.7002796588071941, 'cgF1_eval_bbox_F1@0.75': 0.621554999724056, 'cgF1_eval_bbox_positive_macro_F1@0.75': 0.7144152159611694, 'cgF1_eval_bbox_positive_micro_F1@0.75': 0.6534923543738753, 'cgF1_eval_bbox_positive_micro_precision@0.75': 0.6126529142771555}\\n\",\n      \"Processing subset:  droid\\n\",\n      \"loading annotations into memory...\\n\",\n      \"Done (t=0.15s)\\n\",\n      \"creating index...\\n\",\n      \"index created!\\n\",\n      \"Loaded 27006 predictions\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 9415/9415 [00:02<00:00, 4431.41it/s]\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Accumulating results\\n\",\n      \"cgF1 metric, IoU type=segm\\n\",\n      \" Average cgF1       @[ IoU=0.50:0.95] = 0.456\\n\",\n      \" Average precision  @[ IoU=0.50:0.95] = 0.501\\n\",\n      \" Average recall     @[ IoU=0.50:0.95] = 0.651\\n\",\n      \" Average F1         @[ IoU=0.50:0.95] = 0.566\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.50:0.95] = 0.717\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.50:0.95] = 0.603\\n\",\n      \" Average positive_micro_precision @[ IoU=0.50:0.95] = 0.562\\n\",\n      \" Average IL_precision                = 0.869\\n\",\n      \" Average IL_recall                   = 0.881\\n\",\n      \" Average IL_F1                       = 0.875\\n\",\n      \" Average IL_FPR                      = 0.125\\n\",\n      \" Average IL_MCC                      = 0.755\\n\",\n      \" Average cgF1       @[ IoU=0.50     ] = 0.517\\n\",\n      \" Average precision  @[ IoU=0.50     ] = 0.568\\n\",\n      \" Average recall     @[ IoU=0.50     ] = 0.739\\n\",\n      \" Average F1         @[ IoU=0.50     ] = 0.642\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.50     ] = 0.782\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.50     ] = 0.685\\n\",\n      \" Average positive_micro_precision @[ IoU=0.50     ] = 0.638\\n\",\n      \" Average cgF1       @[ IoU=0.75     ] = 0.481\\n\",\n      \" Average precision  @[ IoU=0.75     ] = 0.528\\n\",\n      \" Average recall     @[ IoU=0.75     ] = 0.687\\n\",\n      \" Average F1         @[ IoU=0.75     ] = 0.597\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.75     ] = 0.740\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.75     ] = 0.636\\n\",\n      \" Average positive_micro_precision @[ IoU=0.75     ] = 0.593\\n\",\n      \"\\n\",\n      \"{'cgF1_eval_segm_cgF1': 0.4557789681577404, 'cgF1_eval_segm_precision': 0.5005361319569939, 'cgF1_eval_segm_recall': 0.651409427375482, 'cgF1_eval_segm_F1': 0.5660435085175799, 'cgF1_eval_segm_positive_macro_F1': 0.7165954275000161, 'cgF1_eval_segm_positive_micro_F1': 0.6034929143710873, 'cgF1_eval_segm_positive_micro_precision': 0.5622289982156015, 'cgF1_eval_segm_IL_precision': 0.8689550948037661, 'cgF1_eval_segm_IL_recall': 0.8807439823018065, 'cgF1_eval_segm_IL_F1': 0.874809323784164, 'cgF1_eval_segm_IL_FPR': 0.12528379770375977, 'cgF1_eval_segm_IL_MCC': 0.7552349949836895, 'cgF1_eval_segm_cgF1@0.5': 0.516971131551212, 'cgF1_eval_segm_precision@0.5': 0.5677317852045766, 'cgF1_eval_segm_recall@0.5': 0.7388594219103226, 'cgF1_eval_segm_F1@0.5': 0.6420399499626908, 'cgF1_eval_segm_positive_macro_F1@0.5': 0.7817224981491459, 'cgF1_eval_segm_positive_micro_F1@0.5': 0.6845169185551006, 'cgF1_eval_segm_positive_micro_precision@0.5': 0.637706755759541, 'cgF1_eval_segm_cgF1@0.75': 0.4806464217139674, 'cgF1_eval_segm_precision@0.75': 0.5278433016810116, 'cgF1_eval_segm_recall@0.75': 0.686947616643896, 'cgF1_eval_segm_F1@0.75': 0.5969271686567055, 'cgF1_eval_segm_positive_macro_F1@0.75': 0.7403811012887065, 'cgF1_eval_segm_positive_micro_F1@0.75': 0.6364196904360181, 'cgF1_eval_segm_positive_micro_precision@0.75': 0.5929018741536706}\\n\",\n      \"loading annotations into memory...\\n\",\n      \"Done (t=0.12s)\\n\",\n      \"creating index...\\n\",\n      \"index created!\\n\",\n      \"Loaded 27006 predictions\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 9415/9415 [00:01<00:00, 5301.02it/s]\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Accumulating results\\n\",\n      \"cgF1 metric, IoU type=bbox\\n\",\n      \" Average cgF1       @[ IoU=0.50:0.95] = 0.461\\n\",\n      \" Average precision  @[ IoU=0.50:0.95] = 0.506\\n\",\n      \" Average recall     @[ IoU=0.50:0.95] = 0.659\\n\",\n      \" Average F1         @[ IoU=0.50:0.95] = 0.573\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.50:0.95] = 0.726\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.50:0.95] = 0.611\\n\",\n      \" Average positive_micro_precision @[ IoU=0.50:0.95] = 0.569\\n\",\n      \" Average IL_precision                = 0.869\\n\",\n      \" Average IL_recall                   = 0.881\\n\",\n      \" Average IL_F1                       = 0.875\\n\",\n      \" Average IL_FPR                      = 0.125\\n\",\n      \" Average IL_MCC                      = 0.755\\n\",\n      \" Average cgF1       @[ IoU=0.50     ] = 0.516\\n\",\n      \" Average precision  @[ IoU=0.50     ] = 0.566\\n\",\n      \" Average recall     @[ IoU=0.50     ] = 0.737\\n\",\n      \" Average F1         @[ IoU=0.50     ] = 0.641\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.50     ] = 0.778\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.50     ] = 0.683\\n\",\n      \" Average positive_micro_precision @[ IoU=0.50     ] = 0.636\\n\",\n      \" Average cgF1       @[ IoU=0.75     ] = 0.484\\n\",\n      \" Average precision  @[ IoU=0.75     ] = 0.532\\n\",\n      \" Average recall     @[ IoU=0.75     ] = 0.692\\n\",\n      \" Average F1         @[ IoU=0.75     ] = 0.601\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.75     ] = 0.743\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.75     ] = 0.641\\n\",\n      \" Average positive_micro_precision @[ IoU=0.75     ] = 0.597\\n\",\n      \"\\n\",\n      \"{'cgF1_eval_bbox_cgF1': 0.46120814518784153, 'cgF1_eval_bbox_precision': 0.5064979590642793, 'cgF1_eval_bbox_recall': 0.6591682885927223, 'cgF1_eval_bbox_F1': 0.5727861714864271, 'cgF1_eval_bbox_positive_macro_F1': 0.7257891003555039, 'cgF1_eval_bbox_positive_micro_F1': 0.6106816398223206, 'cgF1_eval_bbox_positive_micro_precision': 0.5689256418104573, 'cgF1_eval_bbox_IL_precision': 0.8689550948037661, 'cgF1_eval_bbox_IL_recall': 0.8807439823018065, 'cgF1_eval_bbox_IL_F1': 0.874809323784164, 'cgF1_eval_bbox_IL_FPR': 0.12528379770375977, 'cgF1_eval_bbox_IL_MCC': 0.7552349949836895, 'cgF1_eval_bbox_cgF1@0.5': 0.5157342686709716, 'cgF1_eval_bbox_precision@0.5': 0.5663735751921255, 'cgF1_eval_bbox_recall@0.5': 0.7370918156378099, 'cgF1_eval_bbox_F1@0.5': 0.6405038516738359, 'cgF1_eval_bbox_positive_macro_F1@0.5': 0.7784650302224031, 'cgF1_eval_bbox_positive_micro_F1@0.5': 0.6828791993174386, 'cgF1_eval_bbox_positive_micro_precision@0.5': 0.6361811415113126, 'cgF1_eval_bbox_cgF1@0.75': 0.48416171621299414, 'cgF1_eval_bbox_precision@0.75': 0.5317034775058727, 'cgF1_eval_bbox_recall@0.75': 0.6919713397341954, 'cgF1_eval_bbox_F1@0.75': 0.6012929216844536, 'cgF1_eval_bbox_positive_macro_F1@0.75': 0.743302410171559, 'cgF1_eval_bbox_positive_micro_F1@0.75': 0.6410742608973653, 'cgF1_eval_bbox_positive_micro_precision@0.75': 0.5972378304381096}\\n\",\n      \"Processing subset:  ego4d\\n\",\n      \"loading annotations into memory...\\n\",\n      \"Done (t=0.36s)\\n\",\n      \"creating index...\\n\",\n      \"index created!\\n\",\n      \"Loaded 54328 predictions\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 12428/12428 [00:04<00:00, 2599.59it/s]\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Accumulating results\\n\",\n      \"cgF1 metric, IoU type=segm\\n\",\n      \" Average cgF1       @[ IoU=0.50:0.95] = 0.386\\n\",\n      \" Average precision  @[ IoU=0.50:0.95] = 0.521\\n\",\n      \" Average recall     @[ IoU=0.50:0.95] = 0.689\\n\",\n      \" Average F1         @[ IoU=0.50:0.95] = 0.594\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.50:0.95] = 0.765\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.50:0.95] = 0.626\\n\",\n      \" Average positive_micro_precision @[ IoU=0.50:0.95] = 0.573\\n\",\n      \" Average IL_precision                = 0.901\\n\",\n      \" Average IL_recall                   = 0.912\\n\",\n      \" Average IL_F1                       = 0.907\\n\",\n      \" Average IL_FPR                      = 0.303\\n\",\n      \" Average IL_MCC                      = 0.618\\n\",\n      \" Average cgF1       @[ IoU=0.50     ] = 0.438\\n\",\n      \" Average precision  @[ IoU=0.50     ] = 0.591\\n\",\n      \" Average recall     @[ IoU=0.50     ] = 0.782\\n\",\n      \" Average F1         @[ IoU=0.50     ] = 0.673\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.50     ] = 0.842\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.50     ] = 0.709\\n\",\n      \" Average positive_micro_precision @[ IoU=0.50     ] = 0.649\\n\",\n      \" Average cgF1       @[ IoU=0.75     ] = 0.404\\n\",\n      \" Average precision  @[ IoU=0.75     ] = 0.545\\n\",\n      \" Average recall     @[ IoU=0.75     ] = 0.721\\n\",\n      \" Average F1         @[ IoU=0.75     ] = 0.621\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.75     ] = 0.795\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.75     ] = 0.655\\n\",\n      \" Average positive_micro_precision @[ IoU=0.75     ] = 0.599\\n\",\n      \"\\n\",\n      \"{'cgF1_eval_segm_cgF1': 0.38643115983316056, 'cgF1_eval_segm_precision': 0.5213111360174665, 'cgF1_eval_segm_recall': 0.6893477933583816, 'cgF1_eval_segm_F1': 0.5936188825546556, 'cgF1_eval_segm_positive_macro_F1': 0.7647892994501866, 'cgF1_eval_segm_positive_micro_F1': 0.6256311694898511, 'cgF1_eval_segm_positive_micro_precision': 0.5727797240533252, 'cgF1_eval_segm_IL_precision': 0.9011314369354836, 'cgF1_eval_segm_IL_recall': 0.9124197001164432, 'cgF1_eval_segm_IL_F1': 0.9067399372269952, 'cgF1_eval_segm_IL_FPR': 0.30278497399521215, 'cgF1_eval_segm_IL_MCC': 0.6176660925450091, 'cgF1_eval_segm_cgF1@0.5': 0.4381751660817137, 'cgF1_eval_segm_precision@0.5': 0.5911103463493724, 'cgF1_eval_segm_recall@0.5': 0.7816457097006957, 'cgF1_eval_segm_F1@0.5': 0.6731060611161825, 'cgF1_eval_segm_positive_macro_F1@0.5': 0.8419527726712451, 'cgF1_eval_segm_positive_micro_F1@0.5': 0.7094045980025753, 'cgF1_eval_segm_positive_micro_precision@0.5': 0.6494701487744837, 'cgF1_eval_segm_cgF1@0.75': 0.4042830454832554, 'cgF1_eval_segm_precision@0.75': 0.545392138165807, 'cgF1_eval_segm_recall@0.75': 0.7211909375882043, 'cgF1_eval_segm_F1@0.75': 0.6210422717504098, 'cgF1_eval_segm_positive_macro_F1@0.75': 0.7947833780726534, 'cgF1_eval_segm_positive_micro_F1@0.75': 0.6545333317836213, 'cgF1_eval_segm_positive_micro_precision@0.75': 0.5992382222753772}\\n\",\n      \"loading annotations into memory...\\n\",\n      \"Done (t=0.31s)\\n\",\n      \"creating index...\\n\",\n      \"index created!\\n\",\n      \"Loaded 54328 predictions\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 12428/12428 [00:03<00:00, 4068.74it/s]\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Accumulating results\\n\",\n      \"cgF1 metric, IoU type=bbox\\n\",\n      \" Average cgF1       @[ IoU=0.50:0.95] = 0.388\\n\",\n      \" Average precision  @[ IoU=0.50:0.95] = 0.523\\n\",\n      \" Average recall     @[ IoU=0.50:0.95] = 0.692\\n\",\n      \" Average F1         @[ IoU=0.50:0.95] = 0.596\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.50:0.95] = 0.778\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.50:0.95] = 0.628\\n\",\n      \" Average positive_micro_precision @[ IoU=0.50:0.95] = 0.575\\n\",\n      \" Average IL_precision                = 0.901\\n\",\n      \" Average IL_recall                   = 0.912\\n\",\n      \" Average IL_F1                       = 0.907\\n\",\n      \" Average IL_FPR                      = 0.303\\n\",\n      \" Average IL_MCC                      = 0.618\\n\",\n      \" Average cgF1       @[ IoU=0.50     ] = 0.437\\n\",\n      \" Average precision  @[ IoU=0.50     ] = 0.589\\n\",\n      \" Average recall     @[ IoU=0.50     ] = 0.779\\n\",\n      \" Average F1         @[ IoU=0.50     ] = 0.671\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.50     ] = 0.840\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.50     ] = 0.707\\n\",\n      \" Average positive_micro_precision @[ IoU=0.50     ] = 0.647\\n\",\n      \" Average cgF1       @[ IoU=0.75     ] = 0.404\\n\",\n      \" Average precision  @[ IoU=0.75     ] = 0.545\\n\",\n      \" Average recall     @[ IoU=0.75     ] = 0.721\\n\",\n      \" Average F1         @[ IoU=0.75     ] = 0.621\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.75     ] = 0.796\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.75     ] = 0.654\\n\",\n      \" Average positive_micro_precision @[ IoU=0.75     ] = 0.599\\n\",\n      \"\\n\",\n      \"{'cgF1_eval_bbox_cgF1': 0.38789930948498, 'cgF1_eval_bbox_precision': 0.5232915719125053, 'cgF1_eval_bbox_recall': 0.6919665924206118, 'cgF1_eval_bbox_F1': 0.5958741983272995, 'cgF1_eval_bbox_positive_macro_F1': 0.7777412549940348, 'cgF1_eval_bbox_positive_micro_F1': 0.6280081004393389, 'cgF1_eval_bbox_positive_micro_precision': 0.5749556866351561, 'cgF1_eval_bbox_IL_precision': 0.9011314369354836, 'cgF1_eval_bbox_IL_recall': 0.9124197001164432, 'cgF1_eval_bbox_IL_F1': 0.9067399372269952, 'cgF1_eval_bbox_IL_FPR': 0.30278497399521215, 'cgF1_eval_bbox_IL_MCC': 0.6176660925450091, 'cgF1_eval_bbox_cgF1@0.5': 0.43664849398208544, 'cgF1_eval_bbox_precision@0.5': 0.589050967602365, 'cgF1_eval_bbox_recall@0.5': 0.7789225217677006, 'cgF1_eval_bbox_F1@0.5': 0.6707608453779862, 'cgF1_eval_bbox_positive_macro_F1@0.5': 0.8401608683011329, 'cgF1_eval_bbox_positive_micro_F1@0.5': 0.7069329193430948, 'cgF1_eval_bbox_positive_micro_precision@0.5': 0.6472074493826321, 'cgF1_eval_bbox_cgF1@0.75': 0.4039522665286139, 'cgF1_eval_bbox_precision@0.75': 0.5449459394372886, 'cgF1_eval_bbox_recall@0.75': 0.7206009135360554, 'cgF1_eval_bbox_F1@0.75': 0.6205341416742646, 'cgF1_eval_bbox_positive_macro_F1@0.75': 0.7957011848300353, 'cgF1_eval_bbox_positive_micro_F1@0.75': 0.6539978014078505, 'cgF1_eval_bbox_positive_micro_precision@0.75': 0.598747970740476}\\n\",\n      \"Processing subset:  food_rec\\n\",\n      \"loading annotations into memory...\\n\",\n      \"Done (t=0.31s)\\n\",\n      \"creating index...\\n\",\n      \"index created!\\n\",\n      \"Loaded 54984 predictions\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 20888/20888 [00:04<00:00, 4826.96it/s]\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Accumulating results\\n\",\n      \"cgF1 metric, IoU type=segm\\n\",\n      \" Average cgF1       @[ IoU=0.50:0.95] = 0.530\\n\",\n      \" Average precision  @[ IoU=0.50:0.95] = 0.598\\n\",\n      \" Average recall     @[ IoU=0.50:0.95] = 0.674\\n\",\n      \" Average F1         @[ IoU=0.50:0.95] = 0.634\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.50:0.95] = 0.839\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.50:0.95] = 0.672\\n\",\n      \" Average positive_micro_precision @[ IoU=0.50:0.95] = 0.670\\n\",\n      \" Average IL_precision                = 0.863\\n\",\n      \" Average IL_recall                   = 0.903\\n\",\n      \" Average IL_F1                       = 0.883\\n\",\n      \" Average IL_FPR                      = 0.112\\n\",\n      \" Average IL_MCC                      = 0.788\\n\",\n      \" Average cgF1       @[ IoU=0.50     ] = 0.576\\n\",\n      \" Average precision  @[ IoU=0.50     ] = 0.650\\n\",\n      \" Average recall     @[ IoU=0.50     ] = 0.733\\n\",\n      \" Average F1         @[ IoU=0.50     ] = 0.689\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.50     ] = 0.883\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.50     ] = 0.731\\n\",\n      \" Average positive_micro_precision @[ IoU=0.50     ] = 0.729\\n\",\n      \" Average cgF1       @[ IoU=0.75     ] = 0.549\\n\",\n      \" Average precision  @[ IoU=0.75     ] = 0.620\\n\",\n      \" Average recall     @[ IoU=0.75     ] = 0.699\\n\",\n      \" Average F1         @[ IoU=0.75     ] = 0.657\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.75     ] = 0.855\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.75     ] = 0.697\\n\",\n      \" Average positive_micro_precision @[ IoU=0.75     ] = 0.695\\n\",\n      \"\\n\",\n      \"{'cgF1_eval_segm_cgF1': 0.5296031530873976, 'cgF1_eval_segm_precision': 0.5979719064128862, 'cgF1_eval_segm_recall': 0.67394967253306, 'cgF1_eval_segm_F1': 0.6336417199495971, 'cgF1_eval_segm_positive_macro_F1': 0.8386411473003422, 'cgF1_eval_segm_positive_micro_F1': 0.6721473109964069, 'cgF1_eval_segm_positive_micro_precision': 0.6704540304049686, 'cgF1_eval_segm_IL_precision': 0.8631095331666377, 'cgF1_eval_segm_IL_recall': 0.9029932269059434, 'cgF1_eval_segm_IL_F1': 0.882600535877229, 'cgF1_eval_segm_IL_FPR': 0.11172660643329413, 'cgF1_eval_segm_IL_MCC': 0.7879272064665432, 'cgF1_eval_segm_cgF1@0.5': 0.5756816178619664, 'cgF1_eval_segm_precision@0.5': 0.6499949694948377, 'cgF1_eval_segm_recall@0.5': 0.7325827386558004, 'cgF1_eval_segm_F1@0.5': 0.6887723611338412, 'cgF1_eval_segm_positive_macro_F1@0.5': 0.8833570881072849, 'cgF1_eval_segm_positive_micro_F1@0.5': 0.7306279223985785, 'cgF1_eval_segm_positive_micro_precision@0.5': 0.7287829785432497, 'cgF1_eval_segm_cgF1@0.75': 0.5489294451647743, 'cgF1_eval_segm_precision@0.75': 0.6197914899943298, 'cgF1_eval_segm_recall@0.75': 0.6985416325429136, 'cgF1_eval_segm_F1@0.75': 0.6567646881074378, 'cgF1_eval_segm_positive_macro_F1@0.75': 0.854577862666958, 'cgF1_eval_segm_positive_micro_F1@0.75': 0.6966753282025206, 'cgF1_eval_segm_positive_micro_precision@0.75': 0.6949184368378619}\\n\",\n      \"loading annotations into memory...\\n\",\n      \"Done (t=0.27s)\\n\",\n      \"creating index...\\n\",\n      \"index created!\\n\",\n      \"Loaded 54984 predictions\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 20888/20888 [00:03<00:00, 6545.85it/s]\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Accumulating results\\n\",\n      \"cgF1 metric, IoU type=bbox\\n\",\n      \" Average cgF1       @[ IoU=0.50:0.95] = 0.534\\n\",\n      \" Average precision  @[ IoU=0.50:0.95] = 0.602\\n\",\n      \" Average recall     @[ IoU=0.50:0.95] = 0.679\\n\",\n      \" Average F1         @[ IoU=0.50:0.95] = 0.638\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.50:0.95] = 0.869\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.50:0.95] = 0.677\\n\",\n      \" Average positive_micro_precision @[ IoU=0.50:0.95] = 0.675\\n\",\n      \" Average IL_precision                = 0.863\\n\",\n      \" Average IL_recall                   = 0.903\\n\",\n      \" Average IL_F1                       = 0.883\\n\",\n      \" Average IL_FPR                      = 0.112\\n\",\n      \" Average IL_MCC                      = 0.788\\n\",\n      \" Average cgF1       @[ IoU=0.50     ] = 0.577\\n\",\n      \" Average precision  @[ IoU=0.50     ] = 0.652\\n\",\n      \" Average recall     @[ IoU=0.50     ] = 0.735\\n\",\n      \" Average F1         @[ IoU=0.50     ] = 0.691\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.50     ] = 0.897\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.50     ] = 0.733\\n\",\n      \" Average positive_micro_precision @[ IoU=0.50     ] = 0.731\\n\",\n      \" Average cgF1       @[ IoU=0.75     ] = 0.554\\n\",\n      \" Average precision  @[ IoU=0.75     ] = 0.626\\n\",\n      \" Average recall     @[ IoU=0.75     ] = 0.705\\n\",\n      \" Average F1         @[ IoU=0.75     ] = 0.663\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.75     ] = 0.881\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.75     ] = 0.703\\n\",\n      \" Average positive_micro_precision @[ IoU=0.75     ] = 0.701\\n\",\n      \"\\n\",\n      \"{'cgF1_eval_bbox_cgF1': 0.5335343269652235, 'cgF1_eval_bbox_precision': 0.602410242357245, 'cgF1_eval_bbox_recall': 0.6789519394024788, 'cgF1_eval_bbox_F1': 0.6383451782260778, 'cgF1_eval_bbox_positive_macro_F1': 0.8687478456564411, 'cgF1_eval_bbox_positive_micro_F1': 0.6771365712295384, 'cgF1_eval_bbox_positive_micro_precision': 0.675430351516837, 'cgF1_eval_bbox_IL_precision': 0.8631095331666377, 'cgF1_eval_bbox_IL_recall': 0.9029932269059434, 'cgF1_eval_bbox_IL_F1': 0.882600535877229, 'cgF1_eval_bbox_IL_FPR': 0.11172660643329413, 'cgF1_eval_bbox_IL_MCC': 0.7879272064665432, 'cgF1_eval_bbox_cgF1@0.5': 0.5771958917885865, 'cgF1_eval_bbox_precision@0.5': 0.6517046004099607, 'cgF1_eval_bbox_recall@0.5': 0.734509593718794, 'cgF1_eval_bbox_F1@0.5': 0.6905841162112941, 'cgF1_eval_bbox_positive_macro_F1@0.5': 0.8970825996312395, 'cgF1_eval_bbox_positive_micro_F1@0.5': 0.7325497673535344, 'cgF1_eval_bbox_positive_micro_precision@0.5': 0.730699839394498, 'cgF1_eval_bbox_cgF1@0.75': 0.5540364082092076, 'cgF1_eval_bbox_precision@0.75': 0.6255573040610194, 'cgF1_eval_bbox_recall@0.75': 0.705040045696539, 'cgF1_eval_bbox_F1@0.75': 0.6628749209147204, 'cgF1_eval_bbox_positive_macro_F1@0.75': 0.8806948276411976, 'cgF1_eval_bbox_positive_micro_F1@0.75': 0.703156844518394, 'cgF1_eval_bbox_positive_micro_precision@0.75': 0.7013831440224643}\\n\",\n      \"Processing subset:  geode\\n\",\n      \"loading annotations into memory...\\n\",\n      \"Done (t=0.16s)\\n\",\n      \"creating index...\\n\",\n      \"index created!\\n\",\n      \"Loaded 14206 predictions\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 14797/14797 [00:02<00:00, 5611.66it/s]\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Accumulating results\\n\",\n      \"cgF1 metric, IoU type=segm\\n\",\n      \" Average cgF1       @[ IoU=0.50:0.95] = 0.701\\n\",\n      \" Average precision  @[ IoU=0.50:0.95] = 0.672\\n\",\n      \" Average recall     @[ IoU=0.50:0.95] = 0.840\\n\",\n      \" Average F1         @[ IoU=0.50:0.95] = 0.747\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.50:0.95] = 0.857\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.50:0.95] = 0.787\\n\",\n      \" Average positive_micro_precision @[ IoU=0.50:0.95] = 0.741\\n\",\n      \" Average IL_precision                = 0.881\\n\",\n      \" Average IL_recall                   = 0.975\\n\",\n      \" Average IL_F1                       = 0.925\\n\",\n      \" Average IL_FPR                      = 0.062\\n\",\n      \" Average IL_MCC                      = 0.890\\n\",\n      \" Average cgF1       @[ IoU=0.50     ] = 0.745\\n\",\n      \" Average precision  @[ IoU=0.50     ] = 0.714\\n\",\n      \" Average recall     @[ IoU=0.50     ] = 0.893\\n\",\n      \" Average F1         @[ IoU=0.50     ] = 0.793\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.50     ] = 0.899\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.50     ] = 0.837\\n\",\n      \" Average positive_micro_precision @[ IoU=0.50     ] = 0.787\\n\",\n      \" Average cgF1       @[ IoU=0.75     ] = 0.716\\n\",\n      \" Average precision  @[ IoU=0.75     ] = 0.687\\n\",\n      \" Average recall     @[ IoU=0.75     ] = 0.859\\n\",\n      \" Average F1         @[ IoU=0.75     ] = 0.763\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.75     ] = 0.869\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.75     ] = 0.805\\n\",\n      \" Average positive_micro_precision @[ IoU=0.75     ] = 0.758\\n\",\n      \"\\n\",\n      \"{'cgF1_eval_segm_cgF1': 0.7006886839682105, 'cgF1_eval_segm_precision': 0.6718811161345404, 'cgF1_eval_segm_recall': 0.8400571547562657, 'cgF1_eval_segm_F1': 0.7465664720739594, 'cgF1_eval_segm_positive_macro_F1': 0.8565758633142841, 'cgF1_eval_segm_positive_micro_F1': 0.7872827580159756, 'cgF1_eval_segm_positive_micro_precision': 0.740835425028983, 'cgF1_eval_segm_IL_precision': 0.8808211364986084, 'cgF1_eval_segm_IL_recall': 0.9747580982383764, 'cgF1_eval_segm_IL_F1': 0.9254113832735964, 'cgF1_eval_segm_IL_FPR': 0.06243154435303878, 'cgF1_eval_segm_IL_MCC': 0.8900089286014722, 'cgF1_eval_segm_cgF1@0.5': 0.7445249438888863, 'cgF1_eval_segm_precision@0.5': 0.7139124677344605, 'cgF1_eval_segm_recall@0.5': 0.8926092161071292, 'cgF1_eval_segm_F1@0.5': 0.7932730581226168, 'cgF1_eval_segm_positive_macro_F1@0.5': 0.8987084032239174, 'cgF1_eval_segm_positive_micro_F1@0.5': 0.8365364885258015, 'cgF1_eval_segm_positive_micro_precision@0.5': 0.7871804010661337, 'cgF1_eval_segm_cgF1@0.75': 0.7164815452728956, 'cgF1_eval_segm_precision@0.75': 0.6870237242867002, 'cgF1_eval_segm_recall@0.75': 0.8589900522799775, 'cgF1_eval_segm_F1@0.75': 0.7633934239343049, 'cgF1_eval_segm_positive_macro_F1@0.75': 0.8685617156358091, 'cgF1_eval_segm_positive_micro_F1@0.75': 0.8050273679824188, 'cgF1_eval_segm_positive_micro_precision@0.75': 0.7575320998975862}\\n\",\n      \"loading annotations into memory...\\n\",\n      \"Done (t=0.11s)\\n\",\n      \"creating index...\\n\",\n      \"index created!\\n\",\n      \"Loaded 14206 predictions\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 14797/14797 [00:01<00:00, 7792.81it/s]\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Accumulating results\\n\",\n      \"cgF1 metric, IoU type=bbox\\n\",\n      \" Average cgF1       @[ IoU=0.50:0.95] = 0.708\\n\",\n      \" Average precision  @[ IoU=0.50:0.95] = 0.679\\n\",\n      \" Average recall     @[ IoU=0.50:0.95] = 0.848\\n\",\n      \" Average F1         @[ IoU=0.50:0.95] = 0.754\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.50:0.95] = 0.872\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.50:0.95] = 0.795\\n\",\n      \" Average positive_micro_precision @[ IoU=0.50:0.95] = 0.748\\n\",\n      \" Average IL_precision                = 0.881\\n\",\n      \" Average IL_recall                   = 0.975\\n\",\n      \" Average IL_F1                       = 0.925\\n\",\n      \" Average IL_FPR                      = 0.062\\n\",\n      \" Average IL_MCC                      = 0.890\\n\",\n      \" Average cgF1       @[ IoU=0.50     ] = 0.744\\n\",\n      \" Average precision  @[ IoU=0.50     ] = 0.714\\n\",\n      \" Average recall     @[ IoU=0.50     ] = 0.892\\n\",\n      \" Average F1         @[ IoU=0.50     ] = 0.793\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.50     ] = 0.901\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.50     ] = 0.836\\n\",\n      \" Average positive_micro_precision @[ IoU=0.50     ] = 0.787\\n\",\n      \" Average cgF1       @[ IoU=0.75     ] = 0.723\\n\",\n      \" Average precision  @[ IoU=0.75     ] = 0.693\\n\",\n      \" Average recall     @[ IoU=0.75     ] = 0.867\\n\",\n      \" Average F1         @[ IoU=0.75     ] = 0.770\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.75     ] = 0.882\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.75     ] = 0.812\\n\",\n      \" Average positive_micro_precision @[ IoU=0.75     ] = 0.764\\n\",\n      \"\\n\",\n      \"{'cgF1_eval_bbox_cgF1': 0.7076143815954025, 'cgF1_eval_bbox_precision': 0.6785216560143518, 'cgF1_eval_bbox_recall': 0.8483598632317971, 'cgF1_eval_bbox_F1': 0.7539456529790385, 'cgF1_eval_bbox_positive_macro_F1': 0.8719976344244793, 'cgF1_eval_bbox_positive_micro_F1': 0.7950643626770375, 'cgF1_eval_bbox_positive_micro_precision': 0.7481574751151425, 'cgF1_eval_bbox_IL_precision': 0.8808211364986084, 'cgF1_eval_bbox_IL_recall': 0.9747580982383764, 'cgF1_eval_bbox_IL_F1': 0.9254113832735964, 'cgF1_eval_bbox_IL_FPR': 0.06243154435303878, 'cgF1_eval_bbox_IL_MCC': 0.8900089286014722, 'cgF1_eval_bbox_cgF1@0.5': 0.7442978718352845, 'cgF1_eval_bbox_precision@0.5': 0.7136947451154503, 'cgF1_eval_bbox_recall@0.5': 0.8923369961571118, 'cgF1_eval_bbox_F1@0.5': 0.7930311177647804, 'cgF1_eval_bbox_positive_macro_F1@0.5': 0.9007468930624848, 'cgF1_eval_bbox_positive_micro_F1@0.5': 0.836281353946468, 'cgF1_eval_bbox_positive_micro_precision@0.5': 0.7869403338501941, 'cgF1_eval_bbox_cgF1@0.75': 0.7230666348257286, 'cgF1_eval_bbox_precision@0.75': 0.6933376802379961, 'cgF1_eval_bbox_recall@0.75': 0.8668844308304827, 'cgF1_eval_bbox_F1@0.75': 0.7704096943096695, 'cgF1_eval_bbox_positive_macro_F1@0.75': 0.8818653539703468, 'cgF1_eval_bbox_positive_micro_F1@0.75': 0.8124262707812712, 'cgF1_eval_bbox_positive_micro_precision@0.75': 0.7644940491598362}\\n\",\n      \"Processing subset:  inaturalist\\n\",\n      \"loading annotations into memory...\\n\",\n      \"Done (t=3.87s)\\n\",\n      \"creating index...\\n\",\n      \"index created!\\n\",\n      \"Loaded 53887 predictions\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1439027/1439027 [01:22<00:00, 17398.82it/s]\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Accumulating results\\n\",\n      \"cgF1 metric, IoU type=segm\\n\",\n      \" Average cgF1       @[ IoU=0.50:0.95] = 0.658\\n\",\n      \" Average precision  @[ IoU=0.50:0.95] = 0.776\\n\",\n      \" Average recall     @[ IoU=0.50:0.95] = 0.722\\n\",\n      \" Average F1         @[ IoU=0.50:0.95] = 0.748\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.50:0.95] = 0.935\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.50:0.95] = 0.807\\n\",\n      \" Average positive_micro_precision @[ IoU=0.50:0.95] = 0.915\\n\",\n      \" Average IL_precision                = 0.848\\n\",\n      \" Average IL_recall                   = 0.796\\n\",\n      \" Average IL_F1                       = 0.821\\n\",\n      \" Average IL_FPR                      = 0.005\\n\",\n      \" Average IL_MCC                      = 0.816\\n\",\n      \" Average cgF1       @[ IoU=0.50     ] = 0.692\\n\",\n      \" Average precision  @[ IoU=0.50     ] = 0.816\\n\",\n      \" Average recall     @[ IoU=0.50     ] = 0.759\\n\",\n      \" Average F1         @[ IoU=0.50     ] = 0.786\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.50     ] = 0.981\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.50     ] = 0.848\\n\",\n      \" Average positive_micro_precision @[ IoU=0.50     ] = 0.962\\n\",\n      \" Average cgF1       @[ IoU=0.75     ] = 0.680\\n\",\n      \" Average precision  @[ IoU=0.75     ] = 0.801\\n\",\n      \" Average recall     @[ IoU=0.75     ] = 0.745\\n\",\n      \" Average F1         @[ IoU=0.75     ] = 0.772\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.75     ] = 0.965\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.75     ] = 0.833\\n\",\n      \" Average positive_micro_precision @[ IoU=0.75     ] = 0.945\\n\",\n      \"\\n\",\n      \"{'cgF1_eval_segm_cgF1': 0.6580192923425129, 'cgF1_eval_segm_precision': 0.7756659354606497, 'cgF1_eval_segm_recall': 0.721546369069409, 'cgF1_eval_segm_F1': 0.7475780939907699, 'cgF1_eval_segm_positive_macro_F1': 0.9345464754990648, 'cgF1_eval_segm_positive_micro_F1': 0.8066917804453706, 'cgF1_eval_segm_positive_micro_precision': 0.914747514495301, 'cgF1_eval_segm_IL_precision': 0.8477058323766221, 'cgF1_eval_segm_IL_recall': 0.7959997413454318, 'cgF1_eval_segm_IL_F1': 0.8210390273987034, 'cgF1_eval_segm_IL_FPR': 0.004764366701848471, 'cgF1_eval_segm_IL_MCC': 0.815701000423264, 'cgF1_eval_segm_cgF1@0.5': 0.6920242595075669, 'cgF1_eval_segm_precision@0.5': 0.81574816894191, 'cgF1_eval_segm_recall@0.5': 0.7588319951494304, 'cgF1_eval_segm_F1@0.5': 0.7862114792496716, 'cgF1_eval_segm_positive_macro_F1@0.5': 0.9812705268789929, 'cgF1_eval_segm_positive_micro_F1@0.5': 0.8483798096955603, 'cgF1_eval_segm_positive_micro_precision@0.5': 0.9620167341118989, 'cgF1_eval_segm_cgF1@0.75': 0.6797028038245808, 'cgF1_eval_segm_precision@0.75': 0.8012246594574692, 'cgF1_eval_segm_recall@0.75': 0.7453218162752996, 'cgF1_eval_segm_F1@0.75': 0.7722129495022094, 'cgF1_eval_segm_positive_macro_F1@0.75': 0.9650080958461289, 'cgF1_eval_segm_positive_micro_F1@0.75': 0.8332744516334855, 'cgF1_eval_segm_positive_micro_precision@0.75': 0.9448890718087305}\\n\",\n      \"loading annotations into memory...\\n\",\n      \"Done (t=3.67s)\\n\",\n      \"creating index...\\n\",\n      \"index created!\\n\",\n      \"Loaded 53887 predictions\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1439027/1439027 [01:18<00:00, 18312.98it/s]\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Accumulating results\\n\",\n      \"cgF1 metric, IoU type=bbox\\n\",\n      \" Average cgF1       @[ IoU=0.50:0.95] = 0.652\\n\",\n      \" Average precision  @[ IoU=0.50:0.95] = 0.769\\n\",\n      \" Average recall     @[ IoU=0.50:0.95] = 0.715\\n\",\n      \" Average F1         @[ IoU=0.50:0.95] = 0.741\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.50:0.95] = 0.926\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.50:0.95] = 0.800\\n\",\n      \" Average positive_micro_precision @[ IoU=0.50:0.95] = 0.907\\n\",\n      \" Average IL_precision                = 0.848\\n\",\n      \" Average IL_recall                   = 0.796\\n\",\n      \" Average IL_F1                       = 0.821\\n\",\n      \" Average IL_FPR                      = 0.005\\n\",\n      \" Average IL_MCC                      = 0.816\\n\",\n      \" Average cgF1       @[ IoU=0.50     ] = 0.691\\n\",\n      \" Average precision  @[ IoU=0.50     ] = 0.815\\n\",\n      \" Average recall     @[ IoU=0.50     ] = 0.758\\n\",\n      \" Average F1         @[ IoU=0.50     ] = 0.786\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.50     ] = 0.981\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.50     ] = 0.848\\n\",\n      \" Average positive_micro_precision @[ IoU=0.50     ] = 0.961\\n\",\n      \" Average cgF1       @[ IoU=0.75     ] = 0.665\\n\",\n      \" Average precision  @[ IoU=0.75     ] = 0.784\\n\",\n      \" Average recall     @[ IoU=0.75     ] = 0.729\\n\",\n      \" Average F1         @[ IoU=0.75     ] = 0.756\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.75     ] = 0.944\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.75     ] = 0.816\\n\",\n      \" Average positive_micro_precision @[ IoU=0.75     ] = 0.925\\n\",\n      \"\\n\",\n      \"{'cgF1_eval_bbox_cgF1': 0.6524418175713689, 'cgF1_eval_bbox_precision': 0.7690916710456228, 'cgF1_eval_bbox_recall': 0.7154308025592622, 'cgF1_eval_bbox_F1': 0.741241468795898, 'cgF1_eval_bbox_positive_macro_F1': 0.9264232147142696, 'cgF1_eval_bbox_positive_micro_F1': 0.7998541343369929, 'cgF1_eval_bbox_positive_micro_precision': 0.906994444831736, 'cgF1_eval_bbox_IL_precision': 0.8477058323766221, 'cgF1_eval_bbox_IL_recall': 0.7959997413454318, 'cgF1_eval_bbox_IL_F1': 0.8210390273987034, 'cgF1_eval_bbox_IL_FPR': 0.004764366701848471, 'cgF1_eval_bbox_IL_MCC': 0.815701000423264, 'cgF1_eval_bbox_cgF1@0.5': 0.6914428818421255, 'cgF1_eval_bbox_precision@0.5': 0.8150628891945468, 'cgF1_eval_bbox_recall@0.5': 0.7581945285663284, 'cgF1_eval_bbox_F1@0.5': 0.7855509702356662, 'cgF1_eval_bbox_positive_macro_F1@0.5': 0.9805502412911423, 'cgF1_eval_bbox_positive_micro_F1@0.5': 0.8476670759056795, 'cgF1_eval_bbox_positive_micro_precision@0.5': 0.9612085795740021, 'cgF1_eval_bbox_cgF1@0.75': 0.6652433786643561, 'cgF1_eval_bbox_precision@0.75': 0.7841810889665958, 'cgF1_eval_bbox_recall@0.75': 0.7294674054504063, 'cgF1_eval_bbox_F1@0.75': 0.7557854511254823, 'cgF1_eval_bbox_positive_macro_F1@0.75': 0.944243447752961, 'cgF1_eval_bbox_positive_micro_F1@0.75': 0.8155480725402617, 'cgF1_eval_bbox_positive_micro_precision@0.75': 0.9247894863661997}\\n\",\n      \"Processing subset:  nga_art\\n\",\n      \"loading annotations into memory...\\n\",\n      \"Done (t=0.45s)\\n\",\n      \"creating index...\\n\",\n      \"index created!\\n\",\n      \"Loaded 34558 predictions\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 22221/22221 [00:04<00:00, 5095.16it/s]\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Accumulating results\\n\",\n      \"cgF1 metric, IoU type=segm\\n\",\n      \" Average cgF1       @[ IoU=0.50:0.95] = 0.381\\n\",\n      \" Average precision  @[ IoU=0.50:0.95] = 0.523\\n\",\n      \" Average recall     @[ IoU=0.50:0.95] = 0.512\\n\",\n      \" Average F1         @[ IoU=0.50:0.95] = 0.517\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.50:0.95] = 0.754\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.50:0.95] = 0.576\\n\",\n      \" Average positive_micro_precision @[ IoU=0.50:0.95] = 0.659\\n\",\n      \" Average IL_precision                = 0.700\\n\",\n      \" Average IL_recall                   = 0.809\\n\",\n      \" Average IL_F1                       = 0.750\\n\",\n      \" Average IL_FPR                      = 0.118\\n\",\n      \" Average IL_MCC                      = 0.661\\n\",\n      \" Average cgF1       @[ IoU=0.50     ] = 0.435\\n\",\n      \" Average precision  @[ IoU=0.50     ] = 0.597\\n\",\n      \" Average recall     @[ IoU=0.50     ] = 0.585\\n\",\n      \" Average F1         @[ IoU=0.50     ] = 0.591\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.50     ] = 0.838\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.50     ] = 0.658\\n\",\n      \" Average positive_micro_precision @[ IoU=0.50     ] = 0.753\\n\",\n      \" Average cgF1       @[ IoU=0.75     ] = 0.399\\n\",\n      \" Average precision  @[ IoU=0.75     ] = 0.547\\n\",\n      \" Average recall     @[ IoU=0.75     ] = 0.536\\n\",\n      \" Average F1         @[ IoU=0.75     ] = 0.542\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.75     ] = 0.781\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.75     ] = 0.603\\n\",\n      \" Average positive_micro_precision @[ IoU=0.75     ] = 0.690\\n\",\n      \"\\n\",\n      \"{'cgF1_eval_segm_cgF1': 0.38061980191685274, 'cgF1_eval_segm_precision': 0.5226448021036465, 'cgF1_eval_segm_recall': 0.5118585116882325, 'cgF1_eval_segm_F1': 0.5171454354933199, 'cgF1_eval_segm_positive_macro_F1': 0.7539468395539315, 'cgF1_eval_segm_positive_micro_F1': 0.576201025016698, 'cgF1_eval_segm_positive_micro_precision': 0.6591742420468004, 'cgF1_eval_segm_IL_precision': 0.6997999691183566, 'cgF1_eval_segm_IL_recall': 0.808677098006992, 'cgF1_eval_segm_IL_F1': 0.7503088318552136, 'cgF1_eval_segm_IL_FPR': 0.11755136469738198, 'cgF1_eval_segm_IL_MCC': 0.6605677279137476, 'cgF1_eval_segm_cgF1@0.5': 0.434669236088304, 'cgF1_eval_segm_precision@0.5': 0.5968559804098716, 'cgF1_eval_segm_recall@0.5': 0.5845381272236059, 'cgF1_eval_segm_F1@0.5': 0.5905828471879718, 'cgF1_eval_segm_positive_macro_F1@0.5': 0.8377112359789534, 'cgF1_eval_segm_positive_micro_F1@0.5': 0.6580237237158217, 'cgF1_eval_segm_positive_micro_precision@0.5': 0.7527714557079914, 'cgF1_eval_segm_cgF1@0.75': 0.3986390156764444, 'cgF1_eval_segm_precision@0.75': 0.5473856178308225, 'cgF1_eval_segm_recall@0.75': 0.5360887289698226, 'cgF1_eval_segm_F1@0.75': 0.5416282897167458, 'cgF1_eval_segm_positive_macro_F1@0.75': 0.78142131171764, 'cgF1_eval_segm_positive_micro_F1@0.75': 0.6034793993576627, 'cgF1_eval_segm_positive_micro_precision@0.75': 0.6903780508074329}\\n\",\n      \"loading annotations into memory...\\n\",\n      \"Done (t=0.32s)\\n\",\n      \"creating index...\\n\",\n      \"index created!\\n\",\n      \"Loaded 34558 predictions\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 22221/22221 [00:02<00:00, 7605.50it/s]\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Accumulating results\\n\",\n      \"cgF1 metric, IoU type=bbox\\n\",\n      \" Average cgF1       @[ IoU=0.50:0.95] = 0.385\\n\",\n      \" Average precision  @[ IoU=0.50:0.95] = 0.528\\n\",\n      \" Average recall     @[ IoU=0.50:0.95] = 0.517\\n\",\n      \" Average F1         @[ IoU=0.50:0.95] = 0.523\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.50:0.95] = 0.775\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.50:0.95] = 0.582\\n\",\n      \" Average positive_micro_precision @[ IoU=0.50:0.95] = 0.666\\n\",\n      \" Average IL_precision                = 0.700\\n\",\n      \" Average IL_recall                   = 0.809\\n\",\n      \" Average IL_F1                       = 0.750\\n\",\n      \" Average IL_FPR                      = 0.118\\n\",\n      \" Average IL_MCC                      = 0.661\\n\",\n      \" Average cgF1       @[ IoU=0.50     ] = 0.432\\n\",\n      \" Average precision  @[ IoU=0.50     ] = 0.594\\n\",\n      \" Average recall     @[ IoU=0.50     ] = 0.582\\n\",\n      \" Average F1         @[ IoU=0.50     ] = 0.588\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.50     ] = 0.838\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.50     ] = 0.655\\n\",\n      \" Average positive_micro_precision @[ IoU=0.50     ] = 0.749\\n\",\n      \" Average cgF1       @[ IoU=0.75     ] = 0.404\\n\",\n      \" Average precision  @[ IoU=0.75     ] = 0.555\\n\",\n      \" Average recall     @[ IoU=0.75     ] = 0.543\\n\",\n      \" Average F1         @[ IoU=0.75     ] = 0.549\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.75     ] = 0.799\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.75     ] = 0.611\\n\",\n      \" Average positive_micro_precision @[ IoU=0.75     ] = 0.699\\n\",\n      \"\\n\",\n      \"{'cgF1_eval_bbox_cgF1': 0.3846578129287702, 'cgF1_eval_bbox_precision': 0.528189088752597, 'cgF1_eval_bbox_recall': 0.5172883759116633, 'cgF1_eval_bbox_F1': 0.5226319143318613, 'cgF1_eval_bbox_positive_macro_F1': 0.7754605892309756, 'cgF1_eval_bbox_positive_micro_F1': 0.5823139652062992, 'cgF1_eval_bbox_positive_micro_precision': 0.6661668514342891, 'cgF1_eval_bbox_IL_precision': 0.6997999691183566, 'cgF1_eval_bbox_IL_recall': 0.808677098006992, 'cgF1_eval_bbox_IL_F1': 0.7503088318552136, 'cgF1_eval_bbox_IL_FPR': 0.11755136469738198, 'cgF1_eval_bbox_IL_MCC': 0.6605677279137476, 'cgF1_eval_bbox_cgF1@0.5': 0.4324122177242999, 'cgF1_eval_bbox_precision@0.5': 0.5937570397016169, 'cgF1_eval_bbox_recall@0.5': 0.5815031421393713, 'cgF1_eval_bbox_F1@0.5': 0.5875162177317533, 'cgF1_eval_bbox_positive_macro_F1@0.5': 0.8378319170832961, 'cgF1_eval_bbox_positive_micro_F1@0.5': 0.6546069380197775, 'cgF1_eval_bbox_positive_micro_precision@0.5': 0.748862985013765, 'cgF1_eval_bbox_cgF1@0.75': 0.40385067625751253, 'cgF1_eval_bbox_precision@0.75': 0.5545413536480653, 'cgF1_eval_bbox_recall@0.75': 0.5430967854370555, 'cgF1_eval_bbox_F1@0.75': 0.5487094159071108, 'cgF1_eval_bbox_positive_macro_F1@0.75': 0.798977400359963, 'cgF1_eval_bbox_positive_micro_F1@0.75': 0.6113690681392848, 'cgF1_eval_bbox_positive_micro_precision@0.75': 0.6994030649559191}\\n\",\n      \"Processing subset:  sav\\n\",\n      \"loading annotations into memory...\\n\",\n      \"Done (t=0.46s)\\n\",\n      \"creating index...\\n\",\n      \"index created!\\n\",\n      \"Loaded 74229 predictions\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 18079/18079 [00:05<00:00, 3149.81it/s]\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Accumulating results\\n\",\n      \"cgF1 metric, IoU type=segm\\n\",\n      \" Average cgF1       @[ IoU=0.50:0.95] = 0.444\\n\",\n      \" Average precision  @[ IoU=0.50:0.95] = 0.587\\n\",\n      \" Average recall     @[ IoU=0.50:0.95] = 0.684\\n\",\n      \" Average F1         @[ IoU=0.50:0.95] = 0.632\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.50:0.95] = 0.768\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.50:0.95] = 0.660\\n\",\n      \" Average positive_micro_precision @[ IoU=0.50:0.95] = 0.639\\n\",\n      \" Average IL_precision                = 0.900\\n\",\n      \" Average IL_recall                   = 0.908\\n\",\n      \" Average IL_F1                       = 0.904\\n\",\n      \" Average IL_FPR                      = 0.241\\n\",\n      \" Average IL_MCC                      = 0.672\\n\",\n      \" Average cgF1       @[ IoU=0.50     ] = 0.503\\n\",\n      \" Average precision  @[ IoU=0.50     ] = 0.666\\n\",\n      \" Average recall     @[ IoU=0.50     ] = 0.776\\n\",\n      \" Average F1         @[ IoU=0.50     ] = 0.717\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.50     ] = 0.846\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.50     ] = 0.750\\n\",\n      \" Average positive_micro_precision @[ IoU=0.50     ] = 0.725\\n\",\n      \" Average cgF1       @[ IoU=0.75     ] = 0.470\\n\",\n      \" Average precision  @[ IoU=0.75     ] = 0.622\\n\",\n      \" Average recall     @[ IoU=0.75     ] = 0.725\\n\",\n      \" Average F1         @[ IoU=0.75     ] = 0.669\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.75     ] = 0.806\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.75     ] = 0.700\\n\",\n      \" Average positive_micro_precision @[ IoU=0.75     ] = 0.677\\n\",\n      \"\\n\",\n      \"{'cgF1_eval_segm_cgF1': 0.44362320439319136, 'cgF1_eval_segm_precision': 0.5869346360044498, 'cgF1_eval_segm_recall': 0.6838706968595512, 'cgF1_eval_segm_F1': 0.6316558570754611, 'cgF1_eval_segm_positive_macro_F1': 0.7677480052550809, 'cgF1_eval_segm_positive_micro_F1': 0.66049478026234, 'cgF1_eval_segm_positive_micro_precision': 0.6387574946422351, 'cgF1_eval_segm_IL_precision': 0.8998911013612405, 'cgF1_eval_segm_IL_recall': 0.9082273511612319, 'cgF1_eval_segm_IL_F1': 0.9040395093174578, 'cgF1_eval_segm_IL_FPR': 0.24096611117001196, 'cgF1_eval_segm_IL_MCC': 0.671652854269325, 'cgF1_eval_segm_cgF1@0.5': 0.5034829560314773, 'cgF1_eval_segm_precision@0.5': 0.6661259526094324, 'cgF1_eval_segm_recall@0.5': 0.776140972883037, 'cgF1_eval_segm_F1@0.5': 0.7168878262296565, 'cgF1_eval_segm_positive_macro_F1@0.5': 0.8464809541363024, 'cgF1_eval_segm_positive_micro_F1@0.5': 0.7496178313411686, 'cgF1_eval_segm_positive_micro_precision@0.5': 0.7249409363562374, 'cgF1_eval_segm_cgF1@0.75': 0.47003419875527414, 'cgF1_eval_segm_precision@0.75': 0.6218749985165196, 'cgF1_eval_segm_recall@0.75': 0.7245816867958706, 'cgF1_eval_segm_F1@0.75': 0.669261443378069, 'cgF1_eval_segm_positive_macro_F1@0.75': 0.8063026840071241, 'cgF1_eval_segm_positive_micro_F1@0.75': 0.6998171685976278, 'cgF1_eval_segm_positive_micro_precision@0.75': 0.6767828846107561}\\n\",\n      \"loading annotations into memory...\\n\",\n      \"Done (t=0.40s)\\n\",\n      \"creating index...\\n\",\n      \"index created!\\n\",\n      \"Loaded 74229 predictions\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 18079/18079 [00:03<00:00, 4783.75it/s]\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Accumulating results\\n\",\n      \"cgF1 metric, IoU type=bbox\\n\",\n      \" Average cgF1       @[ IoU=0.50:0.95] = 0.444\\n\",\n      \" Average precision  @[ IoU=0.50:0.95] = 0.588\\n\",\n      \" Average recall     @[ IoU=0.50:0.95] = 0.685\\n\",\n      \" Average F1         @[ IoU=0.50:0.95] = 0.632\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.50:0.95] = 0.776\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.50:0.95] = 0.661\\n\",\n      \" Average positive_micro_precision @[ IoU=0.50:0.95] = 0.639\\n\",\n      \" Average IL_precision                = 0.900\\n\",\n      \" Average IL_recall                   = 0.908\\n\",\n      \" Average IL_F1                       = 0.904\\n\",\n      \" Average IL_FPR                      = 0.241\\n\",\n      \" Average IL_MCC                      = 0.672\\n\",\n      \" Average cgF1       @[ IoU=0.50     ] = 0.503\\n\",\n      \" Average precision  @[ IoU=0.50     ] = 0.665\\n\",\n      \" Average recall     @[ IoU=0.50     ] = 0.775\\n\",\n      \" Average F1         @[ IoU=0.50     ] = 0.716\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.50     ] = 0.846\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.50     ] = 0.748\\n\",\n      \" Average positive_micro_precision @[ IoU=0.50     ] = 0.724\\n\",\n      \" Average cgF1       @[ IoU=0.75     ] = 0.470\\n\",\n      \" Average precision  @[ IoU=0.75     ] = 0.622\\n\",\n      \" Average recall     @[ IoU=0.75     ] = 0.724\\n\",\n      \" Average F1         @[ IoU=0.75     ] = 0.669\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.75     ] = 0.806\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.75     ] = 0.700\\n\",\n      \" Average positive_micro_precision @[ IoU=0.75     ] = 0.677\\n\",\n      \"\\n\",\n      \"{'cgF1_eval_bbox_cgF1': 0.44413891029397917, 'cgF1_eval_bbox_precision': 0.5876168879112194, 'cgF1_eval_bbox_recall': 0.6846656270924854, 'cgF1_eval_bbox_F1': 0.6323901506230807, 'cgF1_eval_bbox_positive_macro_F1': 0.7758898702585046, 'cgF1_eval_bbox_positive_micro_F1': 0.6612625964005575, 'cgF1_eval_bbox_positive_micro_precision': 0.6394999853591734, 'cgF1_eval_bbox_IL_precision': 0.8998911013612405, 'cgF1_eval_bbox_IL_recall': 0.9082273511612319, 'cgF1_eval_bbox_IL_F1': 0.9040395093174578, 'cgF1_eval_bbox_IL_FPR': 0.24096611117001196, 'cgF1_eval_bbox_IL_MCC': 0.671652854269325, 'cgF1_eval_bbox_cgF1@0.5': 0.502671530652559, 'cgF1_eval_bbox_precision@0.5': 0.6650524793295505, 'cgF1_eval_bbox_recall@0.5': 0.7748902085305182, 'cgF1_eval_bbox_F1@0.5': 0.7157324692328706, 'cgF1_eval_bbox_positive_macro_F1@0.5': 0.8464436229967108, 'cgF1_eval_bbox_positive_micro_F1@0.5': 0.74840972901009, 'cgF1_eval_bbox_positive_micro_precision@0.5': 0.723772681731684, 'cgF1_eval_bbox_cgF1@0.75': 0.4699260087048218, 'cgF1_eval_bbox_precision@0.75': 0.6217318687458687, 'cgF1_eval_bbox_recall@0.75': 0.7244149182155347, 'cgF1_eval_bbox_F1@0.75': 0.6691073957786058, 'cgF1_eval_bbox_positive_macro_F1@0.75': 0.806477309183606, 'cgF1_eval_bbox_positive_micro_F1@0.75': 0.6996560882869217, 'cgF1_eval_bbox_positive_micro_precision@0.75': 0.6766271173274823}\\n\",\n      \"Processing subset:  yt1b\\n\",\n      \"loading annotations into memory...\\n\",\n      \"Done (t=0.15s)\\n\",\n      \"creating index...\\n\",\n      \"index created!\\n\",\n      \"Loaded 23120 predictions\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 7778/7778 [00:01<00:00, 4510.67it/s]\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Accumulating results\\n\",\n      \"cgF1 metric, IoU type=segm\\n\",\n      \" Average cgF1       @[ IoU=0.50:0.95] = 0.421\\n\",\n      \" Average precision  @[ IoU=0.50:0.95] = 0.545\\n\",\n      \" Average recall     @[ IoU=0.50:0.95] = 0.567\\n\",\n      \" Average F1         @[ IoU=0.50:0.95] = 0.556\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.50:0.95] = 0.720\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.50:0.95] = 0.584\\n\",\n      \" Average positive_micro_precision @[ IoU=0.50:0.95] = 0.601\\n\",\n      \" Average IL_precision                = 0.853\\n\",\n      \" Average IL_recall                   = 0.841\\n\",\n      \" Average IL_F1                       = 0.847\\n\",\n      \" Average IL_FPR                      = 0.121\\n\",\n      \" Average IL_MCC                      = 0.721\\n\",\n      \" Average cgF1       @[ IoU=0.50     ] = 0.505\\n\",\n      \" Average precision  @[ IoU=0.50     ] = 0.655\\n\",\n      \" Average recall     @[ IoU=0.50     ] = 0.681\\n\",\n      \" Average F1         @[ IoU=0.50     ] = 0.668\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.50     ] = 0.820\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.50     ] = 0.701\\n\",\n      \" Average positive_micro_precision @[ IoU=0.50     ] = 0.722\\n\",\n      \" Average cgF1       @[ IoU=0.75     ] = 0.454\\n\",\n      \" Average precision  @[ IoU=0.75     ] = 0.588\\n\",\n      \" Average recall     @[ IoU=0.75     ] = 0.611\\n\",\n      \" Average F1         @[ IoU=0.75     ] = 0.599\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.75     ] = 0.759\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.75     ] = 0.629\\n\",\n      \" Average positive_micro_precision @[ IoU=0.75     ] = 0.648\\n\",\n      \"\\n\",\n      \"{'cgF1_eval_segm_cgF1': 0.42073151404696596, 'cgF1_eval_segm_precision': 0.5453179724143051, 'cgF1_eval_segm_recall': 0.5669906233339933, 'cgF1_eval_segm_F1': 0.5558931826749454, 'cgF1_eval_segm_positive_macro_F1': 0.719514022577698, 'cgF1_eval_segm_positive_micro_F1': 0.5836034594081336, 'cgF1_eval_segm_positive_micro_precision': 0.6013252291429667, 'cgF1_eval_segm_IL_precision': 0.8525862066515557, 'cgF1_eval_segm_IL_recall': 0.8407480870386093, 'cgF1_eval_segm_IL_F1': 0.8466252666543157, 'cgF1_eval_segm_IL_FPR': 0.12073429039286084, 'cgF1_eval_segm_IL_MCC': 0.7209201852121546, 'cgF1_eval_segm_cgF1@0.5': 0.5052842773564472, 'cgF1_eval_segm_precision@0.5': 0.6548990097237606, 'cgF1_eval_segm_recall@0.5': 0.6809267556323607, 'cgF1_eval_segm_F1@0.5': 0.6676093380670313, 'cgF1_eval_segm_positive_macro_F1@0.5': 0.8203674808429026, 'cgF1_eval_segm_positive_micro_F1@0.5': 0.7008879591958582, 'cgF1_eval_segm_positive_micro_precision@0.5': 0.7221608621188951, 'cgF1_eval_segm_cgF1@0.75': 0.4535093938858592, 'cgF1_eval_segm_precision@0.75': 0.587798359307308, 'cgF1_eval_segm_recall@0.75': 0.6111593143773055, 'cgF1_eval_segm_F1@0.75': 0.5992012730504395, 'cgF1_eval_segm_positive_macro_F1@0.75': 0.7591976776888013, 'cgF1_eval_segm_positive_micro_F1@0.75': 0.629070184451, 'cgF1_eval_segm_positive_micro_precision@0.75': 0.6481685933354631}\\n\",\n      \"loading annotations into memory...\\n\",\n      \"Done (t=0.11s)\\n\",\n      \"creating index...\\n\",\n      \"index created!\\n\",\n      \"Loaded 23120 predictions\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 7778/7778 [00:01<00:00, 6379.32it/s]\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Accumulating results\\n\",\n      \"cgF1 metric, IoU type=bbox\\n\",\n      \" Average cgF1       @[ IoU=0.50:0.95] = 0.424\\n\",\n      \" Average precision  @[ IoU=0.50:0.95] = 0.549\\n\",\n      \" Average recall     @[ IoU=0.50:0.95] = 0.571\\n\",\n      \" Average F1         @[ IoU=0.50:0.95] = 0.560\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.50:0.95] = 0.732\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.50:0.95] = 0.588\\n\",\n      \" Average positive_micro_precision @[ IoU=0.50:0.95] = 0.605\\n\",\n      \" Average IL_precision                = 0.853\\n\",\n      \" Average IL_recall                   = 0.841\\n\",\n      \" Average IL_F1                       = 0.847\\n\",\n      \" Average IL_FPR                      = 0.121\\n\",\n      \" Average IL_MCC                      = 0.721\\n\",\n      \" Average cgF1       @[ IoU=0.50     ] = 0.502\\n\",\n      \" Average precision  @[ IoU=0.50     ] = 0.651\\n\",\n      \" Average recall     @[ IoU=0.50     ] = 0.677\\n\",\n      \" Average F1         @[ IoU=0.50     ] = 0.664\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.50     ] = 0.818\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.50     ] = 0.697\\n\",\n      \" Average positive_micro_precision @[ IoU=0.50     ] = 0.718\\n\",\n      \" Average cgF1       @[ IoU=0.75     ] = 0.454\\n\",\n      \" Average precision  @[ IoU=0.75     ] = 0.588\\n\",\n      \" Average recall     @[ IoU=0.75     ] = 0.611\\n\",\n      \" Average F1         @[ IoU=0.75     ] = 0.599\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.75     ] = 0.761\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.75     ] = 0.629\\n\",\n      \" Average positive_micro_precision @[ IoU=0.75     ] = 0.648\\n\",\n      \"\\n\",\n      \"{'cgF1_eval_bbox_cgF1': 0.4236486808813284, 'cgF1_eval_bbox_precision': 0.549098643389262, 'cgF1_eval_bbox_recall': 0.5709215500614235, 'cgF1_eval_bbox_F1': 0.5597475175248535, 'cgF1_eval_bbox_positive_macro_F1': 0.7315482621439834, 'cgF1_eval_bbox_positive_micro_F1': 0.5876499085077717, 'cgF1_eval_bbox_positive_micro_precision': 0.605494196525914, 'cgF1_eval_bbox_IL_precision': 0.8525862066515557, 'cgF1_eval_bbox_IL_recall': 0.8407480870386093, 'cgF1_eval_bbox_IL_F1': 0.8466252666543157, 'cgF1_eval_bbox_IL_FPR': 0.12073429039286084, 'cgF1_eval_bbox_IL_MCC': 0.7209201852121546, 'cgF1_eval_bbox_cgF1@0.5': 0.5021932395356122, 'cgF1_eval_bbox_precision@0.5': 0.6508930007436738, 'cgF1_eval_bbox_recall@0.5': 0.6767615352589246, 'cgF1_eval_bbox_F1@0.5': 0.6635252744814799, 'cgF1_eval_bbox_positive_macro_F1@0.5': 0.8180919678307201, 'cgF1_eval_bbox_positive_micro_F1@0.5': 0.69660033085053, 'cgF1_eval_bbox_positive_micro_precision@0.5': 0.7177434132363022, 'cgF1_eval_bbox_cgF1@0.75': 0.4535737905070497, 'cgF1_eval_bbox_precision@0.75': 0.5878818178277265, 'cgF1_eval_bbox_recall@0.75': 0.6112460898017521, 'cgF1_eval_bbox_F1@0.75': 0.5992863577083599, 'cgF1_eval_bbox_positive_macro_F1@0.75': 0.7609919097954506, 'cgF1_eval_bbox_positive_micro_F1@0.75': 0.629159510041421, 'cgF1_eval_bbox_positive_micro_precision@0.75': 0.648260623520517}\\n\",\n      \"Processing subset:  fathomnet\\n\",\n      \"loading annotations into memory...\\n\",\n      \"Done (t=0.82s)\\n\",\n      \"creating index...\\n\",\n      \"index created!\\n\",\n      \"Loaded 25749 predictions\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 281205/281205 [00:14<00:00, 19028.75it/s]\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Accumulating results\\n\",\n      \"cgF1 metric, IoU type=segm\\n\",\n      \" Average cgF1       @[ IoU=0.50:0.95] = 0.515\\n\",\n      \" Average precision  @[ IoU=0.50:0.95] = 0.472\\n\",\n      \" Average recall     @[ IoU=0.50:0.95] = 0.711\\n\",\n      \" Average F1         @[ IoU=0.50:0.95] = 0.567\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.50:0.95] = 0.690\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.50:0.95] = 0.600\\n\",\n      \" Average positive_micro_precision @[ IoU=0.50:0.95] = 0.519\\n\",\n      \" Average IL_precision                = 0.839\\n\",\n      \" Average IL_recall                   = 0.885\\n\",\n      \" Average IL_F1                       = 0.861\\n\",\n      \" Average IL_FPR                      = 0.003\\n\",\n      \" Average IL_MCC                      = 0.859\\n\",\n      \" Average cgF1       @[ IoU=0.50     ] = 0.615\\n\",\n      \" Average precision  @[ IoU=0.50     ] = 0.564\\n\",\n      \" Average recall     @[ IoU=0.50     ] = 0.848\\n\",\n      \" Average F1         @[ IoU=0.50     ] = 0.677\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.50     ] = 0.828\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.50     ] = 0.716\\n\",\n      \" Average positive_micro_precision @[ IoU=0.50     ] = 0.619\\n\",\n      \" Average cgF1       @[ IoU=0.75     ] = 0.560\\n\",\n      \" Average precision  @[ IoU=0.75     ] = 0.513\\n\",\n      \" Average recall     @[ IoU=0.75     ] = 0.772\\n\",\n      \" Average F1         @[ IoU=0.75     ] = 0.617\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.75     ] = 0.750\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.75     ] = 0.652\\n\",\n      \" Average positive_micro_precision @[ IoU=0.75     ] = 0.564\\n\",\n      \"\\n\",\n      \"{'cgF1_eval_segm_cgF1': 0.5152972059816302, 'cgF1_eval_segm_precision': 0.47227706231315125, 'cgF1_eval_segm_recall': 0.7107059458476863, 'cgF1_eval_segm_F1': 0.5674160042153165, 'cgF1_eval_segm_positive_macro_F1': 0.6904954435936812, 'cgF1_eval_segm_positive_micro_F1': 0.5997854205638585, 'cgF1_eval_segm_positive_micro_precision': 0.5188866930899365, 'cgF1_eval_segm_IL_precision': 0.8390254059060754, 'cgF1_eval_segm_IL_recall': 0.8845225025500498, 'cgF1_eval_segm_IL_F1': 0.8611729531944466, 'cgF1_eval_segm_IL_FPR': 0.0027941442255456565, 'cgF1_eval_segm_IL_MCC': 0.8591359314756251, 'cgF1_eval_segm_cgF1@0.5': 0.6149429060698316, 'cgF1_eval_segm_precision@0.5': 0.5635963077131737, 'cgF1_eval_segm_recall@0.5': 0.8481276752838837, 'cgF1_eval_segm_F1@0.5': 0.6771405387867684, 'cgF1_eval_segm_positive_macro_F1@0.5': 0.8277623237104728, 'cgF1_eval_segm_positive_micro_F1@0.5': 0.7157690460153667, 'cgF1_eval_segm_positive_micro_precision@0.5': 0.6192183522838083, 'cgF1_eval_segm_cgF1@0.75': 0.5599254460996396, 'cgF1_eval_segm_precision@0.75': 0.5131761400573049, 'cgF1_eval_segm_recall@0.75': 0.7722529064180121, 'cgF1_eval_segm_F1@0.75': 0.6165582438611041, 'cgF1_eval_segm_positive_macro_F1@0.75': 0.7496616653080356, 'cgF1_eval_segm_positive_micro_F1@0.75': 0.6517309142663014, 'cgF1_eval_segm_positive_micro_precision@0.75': 0.5638221534257606}\\n\",\n      \"loading annotations into memory...\\n\",\n      \"Done (t=0.79s)\\n\",\n      \"creating index...\\n\",\n      \"index created!\\n\",\n      \"Loaded 25749 predictions\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 281205/281205 [00:14<00:00, 19965.61it/s]\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Accumulating results\\n\",\n      \"cgF1 metric, IoU type=bbox\\n\",\n      \" Average cgF1       @[ IoU=0.50:0.95] = 0.540\\n\",\n      \" Average precision  @[ IoU=0.50:0.95] = 0.495\\n\",\n      \" Average recall     @[ IoU=0.50:0.95] = 0.745\\n\",\n      \" Average F1         @[ IoU=0.50:0.95] = 0.595\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.50:0.95] = 0.747\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.50:0.95] = 0.629\\n\",\n      \" Average positive_micro_precision @[ IoU=0.50:0.95] = 0.544\\n\",\n      \" Average IL_precision                = 0.839\\n\",\n      \" Average IL_recall                   = 0.885\\n\",\n      \" Average IL_F1                       = 0.861\\n\",\n      \" Average IL_FPR                      = 0.003\\n\",\n      \" Average IL_MCC                      = 0.859\\n\",\n      \" Average cgF1       @[ IoU=0.50     ] = 0.620\\n\",\n      \" Average precision  @[ IoU=0.50     ] = 0.568\\n\",\n      \" Average recall     @[ IoU=0.50     ] = 0.855\\n\",\n      \" Average F1         @[ IoU=0.50     ] = 0.683\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.50     ] = 0.842\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.50     ] = 0.722\\n\",\n      \" Average positive_micro_precision @[ IoU=0.50     ] = 0.625\\n\",\n      \" Average cgF1       @[ IoU=0.75     ] = 0.577\\n\",\n      \" Average precision  @[ IoU=0.75     ] = 0.528\\n\",\n      \" Average recall     @[ IoU=0.75     ] = 0.795\\n\",\n      \" Average F1         @[ IoU=0.75     ] = 0.635\\n\",\n      \" Average positive_macro_F1 @[ IoU=0.75     ] = 0.786\\n\",\n      \" Average positive_micro_F1 @[ IoU=0.75     ] = 0.671\\n\",\n      \" Average positive_micro_precision @[ IoU=0.75     ] = 0.581\\n\",\n      \"\\n\",\n      \"{'cgF1_eval_bbox_cgF1': 0.5404467727790648, 'cgF1_eval_bbox_precision': 0.4953251162982368, 'cgF1_eval_bbox_recall': 0.7453898005477004, 'cgF1_eval_bbox_F1': 0.5951093668775386, 'cgF1_eval_bbox_positive_macro_F1': 0.7472057384312792, 'cgF1_eval_bbox_positive_micro_F1': 0.6290585144667509, 'cgF1_eval_bbox_positive_micro_precision': 0.5442093891698694, 'cgF1_eval_bbox_IL_precision': 0.8390254059060754, 'cgF1_eval_bbox_IL_recall': 0.8845225025500498, 'cgF1_eval_bbox_IL_F1': 0.8611729531944466, 'cgF1_eval_bbox_IL_FPR': 0.0027941442255456565, 'cgF1_eval_bbox_IL_MCC': 0.8591359314756251, 'cgF1_eval_bbox_cgF1@0.5': 0.6201953820242813, 'cgF1_eval_bbox_precision@0.5': 0.5684098835897048, 'cgF1_eval_bbox_recall@0.5': 0.8553713829911432, 'cgF1_eval_bbox_F1@0.5': 0.6829242853928004, 'cgF1_eval_bbox_positive_macro_F1@0.5': 0.8423905574972759, 'cgF1_eval_bbox_positive_micro_F1@0.5': 0.7218827187905563, 'cgF1_eval_bbox_positive_micro_precision@0.5': 0.6245069861553695, 'cgF1_eval_bbox_cgF1@0.75': 0.5765731241108606, 'cgF1_eval_bbox_precision@0.75': 0.5284327280049546, 'cgF1_eval_bbox_recall@0.75': 0.7952117766088179, 'cgF1_eval_bbox_F1@0.75': 0.6348897796975862, 'cgF1_eval_bbox_positive_macro_F1@0.75': 0.786060071093738, 'cgF1_eval_bbox_positive_micro_F1@0.75': 0.6711081483003005, 'cgF1_eval_bbox_positive_micro_precision@0.75': 0.5805844336627427}\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"results_silver = {}\\n\",\n    \"results_silver_bbox = {}\\n\",\n    \"\\n\",\n    \"for subset_name, gt in saco_silver_gts.items():\\n\",\n    \"    print(\\\"Processing subset: \\\", subset_name)\\n\",\n    \"    gt_path = os.path.join(GT_DIR, gt)\\n\",\n    \"    pred_path = os.path.join(PRED_DIR, f\\\"silver_{subset_name}/dumps/silver_{subset_name}/coco_predictions_segm.json\\\")\\n\",\n    \"    \\n\",\n    \"    evaluator = CGF1Evaluator(gt_path=gt_path, verbose=True, iou_type=\\\"segm\\\") \\n\",\n    \"    summary = evaluator.evaluate(pred_path)\\n\",\n    \"    print(summary)\\n\",\n    \"\\n\",\n    \"    cur_results = {}\\n\",\n    \"    cur_results[\\\"cgf1\\\"] = summary[\\\"cgF1_eval_segm_cgF1\\\"] * 100\\n\",\n    \"    cur_results[\\\"il_mcc\\\"] = summary[\\\"cgF1_eval_segm_IL_MCC\\\"]\\n\",\n    \"    cur_results[\\\"pmf1\\\"] = summary[\\\"cgF1_eval_segm_positive_micro_F1\\\"] * 100\\n\",\n    \"    results_silver[subset_name] = cur_results\\n\",\n    \"\\n\",\n    \"    # Also eval bbox    \\n\",\n    \"    evaluator = CGF1Evaluator(gt_path=gt_path, verbose=True, iou_type=\\\"bbox\\\") \\n\",\n    \"    summary = evaluator.evaluate(pred_path)\\n\",\n    \"    print(summary)\\n\",\n    \"\\n\",\n    \"    cur_results = {}\\n\",\n    \"    cur_results[\\\"cgf1\\\"] = summary[\\\"cgF1_eval_bbox_cgF1\\\"] * 100\\n\",\n    \"    cur_results[\\\"il_mcc\\\"] = summary[\\\"cgF1_eval_bbox_IL_MCC\\\"]\\n\",\n    \"    cur_results[\\\"pmf1\\\"] = summary[\\\"cgF1_eval_bbox_positive_micro_F1\\\"] * 100\\n\",\n    \"    results_silver_bbox[subset_name] = cur_results\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"id\": \"f90e949a-05ad-46e4-9b97-78b36a5f8c65\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Compute averages\\n\",\n    \"METRICS = [\\\"cgf1\\\", \\\"il_mcc\\\", \\\"pmf1\\\"]\\n\",\n    \"avg_stats, avg_stats_bbox = {}, {}\\n\",\n    \"for key in METRICS:\\n\",\n    \"    avg_stats[key] = sum(res[key] for res in results_silver.values()) / len(results_silver)\\n\",\n    \"    avg_stats_bbox[key] = sum(res[key] for res in results_silver_bbox.values()) / len(results_silver_bbox)\\n\",\n    \"results_silver[\\\"Average\\\"] = avg_stats\\n\",\n    \"results_silver_bbox[\\\"Average\\\"] = avg_stats_bbox\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"id\": \"791eefb4-5e36-4dc0-9f26-2870355a7997\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<table><thead><tr><th colspan=\\\"3\\\" style=\\\"text-align:center;border-left-style:solid;border-left-width:1px\\\">bdd100k</th><th colspan=\\\"3\\\" style=\\\"text-align:center;border-left-style:solid;border-left-width:1px\\\">droid</th><th colspan=\\\"3\\\" style=\\\"text-align:center;border-left-style:solid;border-left-width:1px\\\">ego4d</th><th colspan=\\\"3\\\" style=\\\"text-align:center;border-left-style:solid;border-left-width:1px\\\">food_rec</th><th colspan=\\\"3\\\" style=\\\"text-align:center;border-left-style:solid;border-left-width:1px\\\">geode</th><th colspan=\\\"3\\\" style=\\\"text-align:center;border-left-style:solid;border-left-width:1px\\\">inaturalist</th><th colspan=\\\"3\\\" style=\\\"text-align:center;border-left-style:solid;border-left-width:1px\\\">nga_art</th><th colspan=\\\"3\\\" style=\\\"text-align:center;border-left-style:solid;border-left-width:1px\\\">sav</th><th colspan=\\\"3\\\" style=\\\"text-align:center;border-left-style:solid;border-left-width:1px\\\">yt1b</th><th colspan=\\\"3\\\" style=\\\"text-align:center;border-left-style:solid;border-left-width:1px\\\">fathomnet</th><th colspan=\\\"3\\\" style=\\\"text-align:center;border-left-style:solid;border-left-width:1px\\\">Average</th></tr><tr><th style='border-left-style:solid;border-left-width:1px'>cgf1</th><th>il_mcc</th><th>pmf1</th><th style='border-left-style:solid;border-left-width:1px'>cgf1</th><th>il_mcc</th><th>pmf1</th><th style='border-left-style:solid;border-left-width:1px'>cgf1</th><th>il_mcc</th><th>pmf1</th><th style='border-left-style:solid;border-left-width:1px'>cgf1</th><th>il_mcc</th><th>pmf1</th><th style='border-left-style:solid;border-left-width:1px'>cgf1</th><th>il_mcc</th><th>pmf1</th><th style='border-left-style:solid;border-left-width:1px'>cgf1</th><th>il_mcc</th><th>pmf1</th><th style='border-left-style:solid;border-left-width:1px'>cgf1</th><th>il_mcc</th><th>pmf1</th><th style='border-left-style:solid;border-left-width:1px'>cgf1</th><th>il_mcc</th><th>pmf1</th><th style='border-left-style:solid;border-left-width:1px'>cgf1</th><th>il_mcc</th><th>pmf1</th><th style='border-left-style:solid;border-left-width:1px'>cgf1</th><th>il_mcc</th><th>pmf1</th><th style='border-left-style:solid;border-left-width:1px'>cgf1</th><th>il_mcc</th><th>pmf1</th></tr></thead><tbody><tr><td style='border-left-style:solid;border-left-width:1px'>46.61</td><td>0.78</td><td>60.13</td><td style='border-left-style:solid;border-left-width:1px'>45.58</td><td>0.76</td><td>60.35</td><td style='border-left-style:solid;border-left-width:1px'>38.64</td><td>0.62</td><td>62.56</td><td style='border-left-style:solid;border-left-width:1px'>52.96</td><td>0.79</td><td>67.21</td><td style='border-left-style:solid;border-left-width:1px'>70.07</td><td>0.89</td><td>78.73</td><td style='border-left-style:solid;border-left-width:1px'>65.8</td><td>0.82</td><td>80.67</td><td style='border-left-style:solid;border-left-width:1px'>38.06</td><td>0.66</td><td>57.62</td><td style='border-left-style:solid;border-left-width:1px'>44.36</td><td>0.67</td><td>66.05</td><td style='border-left-style:solid;border-left-width:1px'>42.07</td><td>0.72</td><td>58.36</td><td style='border-left-style:solid;border-left-width:1px'>51.53</td><td>0.86</td><td>59.98</td><td style='border-left-style:solid;border-left-width:1px'>49.57</td><td>0.76</td><td>65.17</td></tr></tbody></table>\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# Pretty print segmentation results\\n\",\n    \"from IPython.display import HTML, display\\n\",\n    \"\\n\",\n    \"row1, row2, row3 = \\\"\\\", \\\"\\\", \\\"\\\"\\n\",\n    \"for subset in results_silver:\\n\",\n    \"    row1 += f'<th colspan=\\\"3\\\" style=\\\"text-align:center;border-left-style:solid;border-left-width:1px\\\">{subset}</th>'\\n\",\n    \"    row2 += \\\"<th style='border-left-style:solid;border-left-width:1px'>\\\" + \\\"</th><th>\\\".join(METRICS) + \\\"</th>\\\"\\n\",\n    \"    row3 += \\\"<td style='border-left-style:solid;border-left-width:1px'>\\\" + \\\"</td><td>\\\".join([str(round(results_silver[subset][k], 2)) for k in METRICS])  + \\\"</td>\\\"\\n\",\n    \"\\n\",\n    \"display(HTML(\\n\",\n    \"   f\\\"<table><thead><tr>{row1}</tr><tr>{row2}</tr></thead><tbody><tr>{row3}</tr></tbody></table>\\\"\\n\",\n    \"))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"id\": \"9250c2da-fe9a-4c13-be32-edb54748440e\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<table><thead><tr><th colspan=\\\"3\\\" style=\\\"text-align:center;border-left-style:solid;border-left-width:1px\\\">bdd100k</th><th colspan=\\\"3\\\" style=\\\"text-align:center;border-left-style:solid;border-left-width:1px\\\">droid</th><th colspan=\\\"3\\\" style=\\\"text-align:center;border-left-style:solid;border-left-width:1px\\\">ego4d</th><th colspan=\\\"3\\\" style=\\\"text-align:center;border-left-style:solid;border-left-width:1px\\\">food_rec</th><th colspan=\\\"3\\\" style=\\\"text-align:center;border-left-style:solid;border-left-width:1px\\\">geode</th><th colspan=\\\"3\\\" style=\\\"text-align:center;border-left-style:solid;border-left-width:1px\\\">inaturalist</th><th colspan=\\\"3\\\" style=\\\"text-align:center;border-left-style:solid;border-left-width:1px\\\">nga_art</th><th colspan=\\\"3\\\" style=\\\"text-align:center;border-left-style:solid;border-left-width:1px\\\">sav</th><th colspan=\\\"3\\\" style=\\\"text-align:center;border-left-style:solid;border-left-width:1px\\\">yt1b</th><th colspan=\\\"3\\\" style=\\\"text-align:center;border-left-style:solid;border-left-width:1px\\\">fathomnet</th><th colspan=\\\"3\\\" style=\\\"text-align:center;border-left-style:solid;border-left-width:1px\\\">Average</th></tr><tr><th style='border-left-style:solid;border-left-width:1px'>cgf1</th><th>il_mcc</th><th>pmf1</th><th style='border-left-style:solid;border-left-width:1px'>cgf1</th><th>il_mcc</th><th>pmf1</th><th style='border-left-style:solid;border-left-width:1px'>cgf1</th><th>il_mcc</th><th>pmf1</th><th style='border-left-style:solid;border-left-width:1px'>cgf1</th><th>il_mcc</th><th>pmf1</th><th style='border-left-style:solid;border-left-width:1px'>cgf1</th><th>il_mcc</th><th>pmf1</th><th style='border-left-style:solid;border-left-width:1px'>cgf1</th><th>il_mcc</th><th>pmf1</th><th style='border-left-style:solid;border-left-width:1px'>cgf1</th><th>il_mcc</th><th>pmf1</th><th style='border-left-style:solid;border-left-width:1px'>cgf1</th><th>il_mcc</th><th>pmf1</th><th style='border-left-style:solid;border-left-width:1px'>cgf1</th><th>il_mcc</th><th>pmf1</th><th style='border-left-style:solid;border-left-width:1px'>cgf1</th><th>il_mcc</th><th>pmf1</th><th style='border-left-style:solid;border-left-width:1px'>cgf1</th><th>il_mcc</th><th>pmf1</th></tr></thead><tbody><tr><td style='border-left-style:solid;border-left-width:1px'>46.21</td><td>0.78</td><td>59.62</td><td style='border-left-style:solid;border-left-width:1px'>46.12</td><td>0.76</td><td>61.07</td><td style='border-left-style:solid;border-left-width:1px'>38.79</td><td>0.62</td><td>62.8</td><td style='border-left-style:solid;border-left-width:1px'>53.35</td><td>0.79</td><td>67.71</td><td style='border-left-style:solid;border-left-width:1px'>70.76</td><td>0.89</td><td>79.51</td><td style='border-left-style:solid;border-left-width:1px'>65.24</td><td>0.82</td><td>79.99</td><td style='border-left-style:solid;border-left-width:1px'>38.47</td><td>0.66</td><td>58.23</td><td style='border-left-style:solid;border-left-width:1px'>44.41</td><td>0.67</td><td>66.13</td><td style='border-left-style:solid;border-left-width:1px'>42.36</td><td>0.72</td><td>58.76</td><td style='border-left-style:solid;border-left-width:1px'>54.04</td><td>0.86</td><td>62.91</td><td style='border-left-style:solid;border-left-width:1px'>49.98</td><td>0.76</td><td>65.67</td></tr></tbody></table>\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# Pretty print bbox detection results\\n\",\n    \"from IPython.display import HTML, display\\n\",\n    \"\\n\",\n    \"row1, row2, row3 = \\\"\\\", \\\"\\\", \\\"\\\"\\n\",\n    \"for subset in results_silver_bbox:\\n\",\n    \"    row1 += f'<th colspan=\\\"3\\\" style=\\\"text-align:center;border-left-style:solid;border-left-width:1px\\\">{subset}</th>'\\n\",\n    \"    row2 += \\\"<th style='border-left-style:solid;border-left-width:1px'>\\\" + \\\"</th><th>\\\".join(METRICS) + \\\"</th>\\\"\\n\",\n    \"    row3 += \\\"<td style='border-left-style:solid;border-left-width:1px'>\\\" + \\\"</td><td>\\\".join([str(round(results_silver_bbox[subset][k], 2)) for k in METRICS])  + \\\"</td>\\\"\\n\",\n    \"\\n\",\n    \"display(HTML(\\n\",\n    \"   f\\\"<table><thead><tr>{row1}</tr><tr>{row2}</tr></thead><tbody><tr>{row3}</tr></tbody></table>\\\"\\n\",\n    \"))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"9059d9e2-ce61-42f9-9b12-3c61b3bf75bb\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (ipykernel)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.12.11\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "examples/saco_gold_silver_vis_example.ipynb",
    "content": "{\n  \"cells\": [\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": null,\n      \"id\": \"37048f21\",\n      \"metadata\": {},\n      \"outputs\": [],\n      \"source\": [\n        \"# Copyright (c) Meta Platforms, Inc. and affiliates.\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": null,\n      \"id\": \"154d8663\",\n      \"metadata\": {},\n      \"outputs\": [],\n      \"source\": [\n        \"using_colab = False\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": null,\n      \"id\": \"b85d99d9\",\n      \"metadata\": {},\n      \"outputs\": [],\n      \"source\": [\n        \"if using_colab:\\n\",\n        \"    import torch\\n\",\n        \"    import torchvision\\n\",\n        \"    print(\\\"PyTorch version:\\\", torch.__version__)\\n\",\n        \"    print(\\\"Torchvision version:\\\", torchvision.__version__)\\n\",\n        \"    print(\\\"CUDA is available:\\\", torch.cuda.is_available())\\n\",\n        \"    import sys\\n\",\n        \"    !{sys.executable} -m pip install opencv-python matplotlib scikit-learn\\n\",\n        \"    !{sys.executable} -m pip install 'git+https://github.com/facebookresearch/sam3.git'\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": null,\n      \"id\": \"da21a3bc\",\n      \"metadata\": {},\n      \"outputs\": [],\n      \"source\": [\n        \"import os\\n\",\n        \"from glob import glob\\n\",\n        \"\\n\",\n        \"import numpy as np\\n\",\n        \"import sam3.visualization_utils as utils\\n\",\n        \"\\n\",\n        \"from matplotlib import pyplot as plt\\n\",\n        \"\\n\",\n        \"COLORS = utils.pascal_color_map()[1:]\"\n      ]\n    },\n    {\n      \"cell_type\": \"markdown\",\n      \"id\": \"57e85e7e\",\n      \"metadata\": {},\n      \"source\": [\n        \"1. Load the data\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": null,\n      \"id\": \"a796734e\",\n      \"metadata\": {},\n      \"outputs\": [],\n      \"source\": [\n        \"# Preapre the data path\\n\",\n        \"ANNOT_DIR = None # PUT YOUR ANNOTATION PATH HERE\\n\",\n        \"IMG_DIR = None # PUT YOUR IMAGE PATH HERE\\n\",\n        \"\\n\",\n        \"# Load the SA-CO/Gold annotation files\\n\",\n        \"annot_file_list = glob(os.path.join(ANNOT_DIR, \\\"*gold*.json\\\"))\\n\",\n        \"annot_dfs = utils.get_annot_dfs(file_list=annot_file_list)\"\n      ]\n    },\n    {\n      \"cell_type\": \"markdown\",\n      \"id\": \"74bf92b1\",\n      \"metadata\": {},\n      \"source\": [\n        \"Show the annotation files being loaded\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": null,\n      \"id\": \"a95620ec\",\n      \"metadata\": {},\n      \"outputs\": [],\n      \"source\": [\n        \"annot_dfs.keys()\"\n      ]\n    },\n    {\n      \"cell_type\": \"markdown\",\n      \"id\": \"5ce211d3\",\n      \"metadata\": {},\n      \"source\": [\n        \"2. Examples of the data format\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": null,\n      \"id\": \"6ba749db\",\n      \"metadata\": {},\n      \"outputs\": [],\n      \"source\": [\n        \"annot_dfs[\\\"gold_fg_sports_equipment_merged_a_release_test\\\"].keys()\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": null,\n      \"id\": \"4b6dc186\",\n      \"metadata\": {},\n      \"outputs\": [],\n      \"source\": [\n        \"annot_dfs[\\\"gold_fg_sports_equipment_merged_a_release_test\\\"][\\\"info\\\"]\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": null,\n      \"id\": \"c41091b3\",\n      \"metadata\": {},\n      \"outputs\": [],\n      \"source\": [\n        \"annot_dfs[\\\"gold_fg_sports_equipment_merged_a_release_test\\\"][\\\"images\\\"].head(3)\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": null,\n      \"id\": \"a7df5771\",\n      \"metadata\": {},\n      \"outputs\": [],\n      \"source\": [\n        \"annot_dfs[\\\"gold_fg_sports_equipment_merged_a_release_test\\\"][\\\"annotations\\\"].head(3)\"\n      ]\n    },\n    {\n      \"cell_type\": \"markdown\",\n      \"id\": \"5673a63f\",\n      \"metadata\": {},\n      \"source\": [\n        \"3. Visualize the data\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": null,\n      \"id\": \"b1fc2a24\",\n      \"metadata\": {},\n      \"outputs\": [],\n      \"source\": [\n        \"# Select a target dataset\\n\",\n        \"target_dataset_name = \\\"gold_fg_food_merged_a_release_test\\\"\\n\",\n        \"\\n\",\n        \"import cv2\\n\",\n        \"from pycocotools import mask as mask_util\\n\",\n        \"from collections import defaultdict\\n\",\n        \"\\n\",\n        \"# Group GT annotations by image_id\\n\",\n        \"gt_image_np_pairs = annot_dfs[target_dataset_name][\\\"images\\\"]\\n\",\n        \"gt_annotations = annot_dfs[target_dataset_name][\\\"annotations\\\"]\\n\",\n        \"\\n\",\n        \"gt_image_np_map = {img[\\\"id\\\"]: img for _, img in gt_image_np_pairs.iterrows()}\\n\",\n        \"gt_image_np_ann_map = defaultdict(list)\\n\",\n        \"for _, ann in gt_annotations.iterrows():\\n\",\n        \"    image_id = ann[\\\"image_id\\\"]\\n\",\n        \"    if image_id not in gt_image_np_ann_map:\\n\",\n        \"        gt_image_np_ann_map[image_id] = []\\n\",\n        \"    gt_image_np_ann_map[image_id].append(ann)\\n\",\n        \"\\n\",\n        \"positiveNPs = common_image_ids = [img_id for img_id in gt_image_np_map.keys() if img_id in gt_image_np_ann_map and gt_image_np_ann_map[img_id]]\\n\",\n        \"negativeNPs = [img_id for img_id in gt_image_np_map.keys() if img_id not in gt_image_np_ann_map or not gt_image_np_ann_map[img_id]]\\n\",\n        \"\\n\",\n        \"num_image_nps_to_show = 10\\n\",\n        \"fig, axes = plt.subplots(num_image_nps_to_show, 3, figsize=(15, 5 * num_image_nps_to_show))\\n\",\n        \"for idx in range(num_image_nps_to_show):\\n\",\n        \"    rand_idx = np.random.randint(len(positiveNPs))\\n\",\n        \"    image_id = positiveNPs[rand_idx]\\n\",\n        \"    noun_phrase = gt_image_np_map[image_id][\\\"text_input\\\"]\\n\",\n        \"    img_rel_path = gt_image_np_map[image_id][\\\"file_name\\\"]\\n\",\n        \"    full_path = os.path.join(IMG_DIR, f\\\"{img_rel_path}\\\")\\n\",\n        \"    img = cv2.imread(full_path)\\n\",\n        \"    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\\n\",\n        \"    gt_annotation = gt_image_np_ann_map[image_id]\\n\",\n        \"\\n\",\n        \"    def display_image_in_subplot(img, axes, row, col, title=\\\"\\\"):\\n\",\n        \"        axes[row, col].imshow(img)\\n\",\n        \"        axes[row, col].set_title(title)\\n\",\n        \"        axes[row, col].axis('off')\\n\",\n        \"\\n\",\n        \"\\n\",\n        \"    noun_phrases = [noun_phrase]\\n\",\n        \"    annot_masks = [mask_util.decode(ann[\\\"segmentation\\\"]) for ann in gt_annotation]\\n\",\n        \"\\n\",\n        \"    # Show the image\\n\",\n        \"    display_image_in_subplot(img, axes, idx, 0, f\\\"{noun_phrase}\\\")\\n\",\n        \"\\n\",\n        \"    # Show all masks over a white background\\n\",\n        \"    all_masks = utils.draw_masks_to_frame(\\n\",\n        \"        frame=np.ones_like(img)*255, masks=annot_masks, colors=COLORS[: len(annot_masks)]\\n\",\n        \"    )\\n\",\n        \"    display_image_in_subplot(all_masks, axes, idx, 1, f\\\"{noun_phrase} - Masks only\\\")\\n\",\n        \"\\n\",\n        \"    # Show masks overlaid on the image\\n\",\n        \"    masked_frame = utils.draw_masks_to_frame(\\n\",\n        \"        frame=img, masks=annot_masks, colors=COLORS[: len(annot_masks)]\\n\",\n        \"    )\\n\",\n        \"    display_image_in_subplot(masked_frame, axes, idx, 2, f\\\"{noun_phrase} - Masks overlaid\\\")\\n\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": null,\n      \"id\": \"84a20e0e\",\n      \"metadata\": {},\n      \"outputs\": [],\n      \"source\": []\n    }\n  ],\n  \"metadata\": {\n    \"fileHeader\": \"\",\n    \"fileUid\": \"a2cedcd3-26e1-430d-b718-764d51077f86\",\n    \"isAdHoc\": false,\n    \"kernelspec\": {\n      \"display_name\": \"Python 3 (ipykernel)\",\n      \"language\": \"python\",\n      \"name\": \"python3\"\n    },\n    \"language_info\": {\n      \"codemirror_mode\": {\n        \"name\": \"ipython\",\n        \"version\": 3\n      },\n      \"file_extension\": \".py\",\n      \"mimetype\": \"text/x-python\",\n      \"name\": \"python\",\n      \"nbconvert_exporter\": \"python\",\n      \"pygments_lexer\": \"ipython3\",\n      \"version\": \"3.10.13\"\n    }\n  },\n  \"nbformat\": 4,\n  \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "examples/saco_veval_eval_example.ipynb",
    "content": "{\n  \"cells\": [\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": null,\n      \"id\": \"0e0d2e74\",\n      \"metadata\": {},\n      \"outputs\": [],\n      \"source\": [\n        \"import json\\n\",\n        \"import os\\n\",\n        \"\\n\",\n        \"from sam3.eval.saco_veval_eval import VEvalEvaluator\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": null,\n      \"id\": \"b31ab5d3\",\n      \"metadata\": {},\n      \"outputs\": [],\n      \"source\": [\n        \"DATASETS_TO_EVAL = [\\n\",\n        \"    \\\"saco_veval_sav_test\\\",\\n\",\n        \"    \\\"saco_veval_yt1b_test\\\",\\n\",\n        \"    \\\"saco_veval_smartglasses_test\\\",\\n\",\n        \"]\\n\",\n        \"# Update to the directory where the GT annotation and PRED files exist\\n\",\n        \"GT_DIR = None # PUT YOUR ANNOTATION PATH HERE\\n\",\n        \"PRED_DIR = None # PUT YOUR IMAGE PATH HERE\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": null,\n      \"id\": \"3a602fef\",\n      \"metadata\": {},\n      \"outputs\": [],\n      \"source\": [\n        \"all_eval_res = {}\\n\",\n        \"for dataset_name in DATASETS_TO_EVAL:\\n\",\n        \"    gt_annot_file = os.path.join(GT_DIR, dataset_name + \\\".json\\\")\\n\",\n        \"    pred_file = os.path.join(PRED_DIR, dataset_name + \\\"_preds.json\\\")\\n\",\n        \"    eval_res_file = os.path.join(PRED_DIR, dataset_name + \\\"_eval_res.json\\\")\\n\",\n        \"\\n\",\n        \"    if os.path.exists(eval_res_file):\\n\",\n        \"        with open(eval_res_file, \\\"r\\\") as f:\\n\",\n        \"            eval_res = json.load(f)\\n\",\n        \"    else:\\n\",\n        \"        # Alternatively, we can run the evaluator offline first\\n\",\n        \"        # by leveraging sam3/eval/saco_veval_eval.py\\n\",\n        \"        print(f\\\"=== Running evaluation for Pred {pred_file} vs GT {gt_annot_file} ===\\\")\\n\",\n        \"        veval_evaluator = VEvalEvaluator(\\n\",\n        \"            gt_annot_file=gt_annot_file, eval_res_file=eval_res_file\\n\",\n        \"        )\\n\",\n        \"        eval_res = veval_evaluator.run_eval(pred_file=pred_file)\\n\",\n        \"        print(f\\\"=== Results saved to {eval_res_file} ===\\\")\\n\",\n        \"\\n\",\n        \"    all_eval_res[dataset_name] = eval_res\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": null,\n      \"id\": \"a6dbec47\",\n      \"metadata\": {},\n      \"outputs\": [],\n      \"source\": [\n        \"REPORT_METRICS = {\\n\",\n        \"    \\\"video_mask_demo_cgf1_micro_50_95\\\": \\\"cgf1\\\",\\n\",\n        \"    \\\"video_mask_all_phrase_HOTA\\\": \\\"pHOTA\\\",\\n\",\n        \"}\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": null,\n      \"id\": \"cc28d29f\",\n      \"metadata\": {},\n      \"outputs\": [],\n      \"source\": [\n        \"res_to_print = []\\n\",\n        \"for dataset_name in DATASETS_TO_EVAL:\\n\",\n        \"    eval_res = all_eval_res[dataset_name]\\n\",\n        \"    row = [dataset_name]\\n\",\n        \"    for metric_k, metric_v in REPORT_METRICS.items():\\n\",\n        \"        row.append(eval_res[\\\"dataset_results\\\"][metric_k])\\n\",\n        \"    res_to_print.append(row)\\n\",\n        \"\\n\",\n        \"# Print dataset header (each dataset spans 2 metrics: 13 + 3 + 13 = 29 chars)\\n\",\n        \"print(\\\"| \\\" + \\\" | \\\".join(f\\\"{ds:^29}\\\" for ds in DATASETS_TO_EVAL) + \\\" |\\\")\\n\",\n        \"\\n\",\n        \"# Print metric header\\n\",\n        \"metrics = list(REPORT_METRICS.values())\\n\",\n        \"print(\\\"| \\\" + \\\" | \\\".join(f\\\"{m:^13}\\\" for _ in DATASETS_TO_EVAL for m in metrics) + \\\" |\\\")\\n\",\n        \"\\n\",\n        \"# Print eval results\\n\",\n        \"values = []\\n\",\n        \"for row in res_to_print:\\n\",\n        \"    values.extend([f\\\"{v * 100:^13.1f}\\\" for v in row[1:]])\\n\",\n        \"print(\\\"| \\\" + \\\" | \\\".join(values) + \\\" |\\\")\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": null,\n      \"id\": \"9976908b\",\n      \"metadata\": {},\n      \"outputs\": [],\n      \"source\": []\n    }\n  ],\n  \"metadata\": {\n    \"fileHeader\": \"\",\n    \"fileUid\": \"bdaa3851-85de-435f-9582-efb46951a1d0\",\n    \"isAdHoc\": false,\n    \"kernelspec\": {\n      \"display_name\": \"Python 3 (ipykernel)\",\n      \"language\": \"python\",\n      \"name\": \"python3\"\n    },\n    \"language_info\": {\n      \"codemirror_mode\": {\n        \"name\": \"ipython\",\n        \"version\": 3\n      },\n      \"file_extension\": \".py\",\n      \"mimetype\": \"text/x-python\",\n      \"name\": \"python\",\n      \"nbconvert_exporter\": \"python\",\n      \"pygments_lexer\": \"ipython3\",\n      \"version\": \"3.10.13\"\n    }\n  },\n  \"nbformat\": 4,\n  \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "examples/saco_veval_vis_example.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"37048f21\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Copyright (c) Meta Platforms, Inc. and affiliates.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"154d8663\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"using_colab = False\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"b85d99d9\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"if using_colab:\\n\",\n    \"    import torch\\n\",\n    \"    import torchvision\\n\",\n    \"    print(\\\"PyTorch version:\\\", torch.__version__)\\n\",\n    \"    print(\\\"Torchvision version:\\\", torchvision.__version__)\\n\",\n    \"    print(\\\"CUDA is available:\\\", torch.cuda.is_available())\\n\",\n    \"    import sys\\n\",\n    \"    !{sys.executable} -m pip install opencv-python matplotlib scikit-learn\\n\",\n    \"    !{sys.executable} -m pip install 'git+https://github.com/facebookresearch/sam3.git'\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"da21a3bc\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"from glob import glob\\n\",\n    \"\\n\",\n    \"import numpy as np\\n\",\n    \"import utils\\n\",\n    \"\\n\",\n    \"from matplotlib import pyplot as plt\\n\",\n    \"\\n\",\n    \"COLORS = utils.pascal_color_map()[1:]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"57e85e7e\",\n   \"metadata\": {},\n   \"source\": [\n    \"1. Load the data\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"a796734e\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Preapre the data path\\n\",\n    \"DATA_DIR = \\\"./sam3_saco_veval_data\\\" # PUT YOUR DATA PATH HERE\\n\",\n    \"ANNOT_DIR = os.path.join(DATA_DIR, \\\"annotation\\\")\\n\",\n    \"\\n\",\n    \"# Load the SACO/Veval annotation files\\n\",\n    \"annot_file_list = glob(os.path.join(ANNOT_DIR, \\\"*veval*.json\\\"))\\n\",\n    \"annot_dfs = utils.get_annot_dfs(file_list=annot_file_list)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"74bf92b1\",\n   \"metadata\": {},\n   \"source\": [\n    \"Show the annotation files being loaded\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"a95620ec\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"annot_dfs.keys()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"5ce211d3\",\n   \"metadata\": {},\n   \"source\": [\n    \"2. Examples of the data format\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"6ba749db\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"annot_dfs[\\\"saco_veval_yt1b_val\\\"].keys()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"4b6dc186\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"annot_dfs[\\\"saco_veval_yt1b_val\\\"][\\\"info\\\"]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"c41091b3\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"annot_dfs[\\\"saco_veval_yt1b_val\\\"][\\\"videos\\\"].head(3)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"a7df5771\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"annot_dfs[\\\"saco_veval_yt1b_val\\\"][\\\"annotations\\\"].head(3)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"24d2861c\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"annot_dfs[\\\"saco_veval_yt1b_val\\\"][\\\"categories\\\"].head(3)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"f9f98f27\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"annot_dfs[\\\"saco_veval_yt1b_val\\\"][\\\"video_np_pairs\\\"].head(3)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"5673a63f\",\n   \"metadata\": {},\n   \"source\": [\n    \"3. Visualize the data\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"da827d09\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Select a target dataset\\n\",\n    \"target_dataset_name = \\\"saco_veval_yt1b_val\\\"\\n\",\n    \"\\n\",\n    \"# visualize a random positive video-np pair\\n\",\n    \"df_pairs = annot_dfs[target_dataset_name][\\\"video_np_pairs\\\"]\\n\",\n    \"df_positive_pairs = df_pairs[df_pairs.num_masklets > 0]\\n\",\n    \"rand_idx = np.random.randint(len(df_positive_pairs))\\n\",\n    \"pair_row = df_positive_pairs.iloc[rand_idx]\\n\",\n    \"video_id = pair_row.video_id\\n\",\n    \"noun_phrase = pair_row.noun_phrase\\n\",\n    \"print(f\\\"Randomly selected video-np pair: video_id={video_id}, noun_phrase={noun_phrase}\\\")\\n\",\n    \"\\n\",\n    \"def display_image_in_subplot(img, axes, row, col, title=\\\"\\\"):\\n\",\n    \"    axes[row, col].imshow(img)\\n\",\n    \"    axes[row, col].set_title(title)\\n\",\n    \"    axes[row, col].axis('off')\\n\",\n    \"\\n\",\n    \"num_frames_to_show = 5  # Number of frames to show per dataset\\n\",\n    \"every_n_frames = 4  # Interval between frames to show\\n\",\n    \"\\n\",\n    \"fig, axes = plt.subplots(num_frames_to_show, 3, figsize=(15, 5 * num_frames_to_show))\\n\",\n    \"\\n\",\n    \"for idx in range(0, num_frames_to_show):\\n\",\n    \"    sampled_frame_idx = idx * every_n_frames\\n\",\n    \"    print(f\\\"Reading annotations for frame {sampled_frame_idx}\\\")\\n\",\n    \"    # Get the frame and the corresponding masks and noun phrases\\n\",\n    \"    frame, annot_masks, annot_noun_phrases = utils.get_all_annotations_for_frame(\\n\",\n    \"        annot_dfs[target_dataset_name], video_id=video_id, frame_idx=sampled_frame_idx, data_dir=DATA_DIR, dataset=target_dataset_name\\n\",\n    \"    )\\n\",\n    \"    # Filter masks and noun phrases by the selected noun phrase\\n\",\n    \"    annot_masks = [m for m, np in zip(annot_masks, annot_noun_phrases) if np == noun_phrase]\\n\",\n    \"\\n\",\n    \"    # Show the frame\\n\",\n    \"    display_image_in_subplot(frame, axes, idx, 0, f\\\"{target_dataset_name} - {noun_phrase} - Frame {sampled_frame_idx}\\\")\\n\",\n    \"\\n\",\n    \"    # Show the annotated masks\\n\",\n    \"    if annot_masks is None:\\n\",\n    \"        print(f\\\"No masks found for video_id {video_id} at frame {sampled_frame_idx}\\\")\\n\",\n    \"    else:\\n\",\n    \"        # Show all masks over a white background\\n\",\n    \"        all_masks = utils.draw_masks_to_frame(\\n\",\n    \"            frame=np.ones_like(frame)*255, masks=annot_masks, colors=COLORS[: len(annot_masks)]\\n\",\n    \"        )\\n\",\n    \"        display_image_in_subplot(all_masks, axes, idx, 1, f\\\"{target_dataset_name} - {noun_phrase} - Frame {sampled_frame_idx} - Masks\\\")\\n\",\n    \"        \\n\",\n    \"        # Show masks overlaid on the frame\\n\",\n    \"        masked_frame = utils.draw_masks_to_frame(\\n\",\n    \"            frame=frame, masks=annot_masks, colors=COLORS[: len(annot_masks)]\\n\",\n    \"        )\\n\",\n    \"        display_image_in_subplot(masked_frame, axes, idx, 2, f\\\"Dataset: {target_dataset_name} - {noun_phrase} - Frame {sampled_frame_idx} - Masks overlaid\\\")\\n\",\n    \"\\n\",\n    \"plt.tight_layout()\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"a2a23152\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (ipykernel)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.10.13\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "examples/sam3_agent.ipynb",
    "content": "{\n  \"cells\": [\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": 1,\n      \"metadata\": {},\n      \"outputs\": [],\n      \"source\": [\n        \"# Copyright (c) Meta Platforms, Inc. and affiliates.\"\n      ]\n    },\n    {\n      \"cell_type\": \"markdown\",\n      \"metadata\": {},\n      \"source\": [\n        \"# SAM 3 Agent\"\n      ]\n    },\n    {\n      \"cell_type\": \"markdown\",\n      \"metadata\": {},\n      \"source\": [\n        \"This notebook shows an example of how an MLLM can use SAM 3 as a tool, i.e., \\\"SAM 3 Agent\\\", to segment more complex text queries such as \\\"the leftmost child wearing blue vest\\\".\"\n      ]\n    },\n    {\n      \"cell_type\": \"markdown\",\n      \"metadata\": {},\n      \"source\": [\n        \"## Env Setup\"\n      ]\n    },\n    {\n      \"cell_type\": \"markdown\",\n      \"metadata\": {},\n      \"source\": [\n        \"First install `sam3` in your environment using the [installation instructions](https://github.com/facebookresearch/sam3?tab=readme-ov-file#installation) in the repository.\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": null,\n      \"metadata\": {},\n      \"outputs\": [],\n      \"source\": [\n        \"import torch\\n\",\n        \"# turn on tfloat32 for Ampere GPUs\\n\",\n        \"# https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\\n\",\n        \"torch.backends.cuda.matmul.allow_tf32 = True\\n\",\n        \"torch.backends.cudnn.allow_tf32 = True\\n\",\n        \"\\n\",\n        \"# use bfloat16 for the entire notebook. If your card doesn't support it, try float16 instead\\n\",\n        \"torch.autocast(\\\"cuda\\\", dtype=torch.bfloat16).__enter__()\\n\",\n        \"\\n\",\n        \"# inference mode for the whole notebook. Disable if you need gradients\\n\",\n        \"torch.inference_mode().__enter__()\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": null,\n      \"metadata\": {},\n      \"outputs\": [],\n      \"source\": [\n        \"import os\\n\",\n        \"\\n\",\n        \"SAM3_ROOT = os.path.dirname(os.getcwd())\\n\",\n        \"os.chdir(SAM3_ROOT)\\n\",\n        \"\\n\",\n        \"# setup GPU to use -  A single GPU is good with the purpose of this demo\\n\",\n        \"os.environ[\\\"CUDA_VISIBLE_DEVICES\\\"] = \\\"0\\\"\\n\",\n        \"_ = os.system(\\\"nvidia-smi\\\")\"\n      ]\n    },\n    {\n      \"cell_type\": \"markdown\",\n      \"metadata\": {},\n      \"source\": [\n        \"## Build SAM3 Model\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": null,\n      \"metadata\": {},\n      \"outputs\": [],\n      \"source\": [\n        \"import sam3\\n\",\n        \"from sam3 import build_sam3_image_model\\n\",\n        \"from sam3.model.sam3_image_processor import Sam3Processor\\n\",\n        \"\\n\",\n        \"sam3_root = os.path.dirname(sam3.__file__)\\n\",\n        \"bpe_path = f\\\"{sam3_root}/assets/bpe_simple_vocab_16e6.txt.gz\\\"\\n\",\n        \"model = build_sam3_image_model(bpe_path=bpe_path)\\n\",\n        \"processor = Sam3Processor(model, confidence_threshold=0.5)\"\n      ]\n    },\n    {\n      \"cell_type\": \"markdown\",\n      \"metadata\": {},\n      \"source\": [\n        \"## LLM Setup\\n\",\n        \"\\n\",\n        \"Config which MLLM to use, it can either be a model served by vLLM that you launch from your own machine or a model is served via external API. If you want to using a vLLM model, we also provided insturctions below.\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": null,\n      \"metadata\": {},\n      \"outputs\": [],\n      \"source\": [\n        \"LLM_CONFIGS = {\\n\",\n        \"    # vLLM-served models\\n\",\n        \"    \\\"qwen3_vl_8b_thinking\\\": {\\n\",\n        \"        \\\"provider\\\": \\\"vllm\\\",\\n\",\n        \"        \\\"model\\\": \\\"Qwen/Qwen3-VL-8B-Thinking\\\",\\n\",\n        \"    },\\n\",\n        \"    # models served via external APIs\\n\",\n        \"    # add your own\\n\",\n        \"}\\n\",\n        \"\\n\",\n        \"model = \\\"qwen3_vl_8b_thinking\\\"\\n\",\n        \"LLM_API_KEY = \\\"DUMMY_API_KEY\\\"\\n\",\n        \"\\n\",\n        \"llm_config = LLM_CONFIGS[model]\\n\",\n        \"llm_config[\\\"api_key\\\"] = LLM_API_KEY\\n\",\n        \"llm_config[\\\"name\\\"] = model\\n\",\n        \"\\n\",\n        \"# setup API endpoint\\n\",\n        \"if llm_config[\\\"provider\\\"] == \\\"vllm\\\":\\n\",\n        \"    LLM_SERVER_URL = \\\"http://0.0.0.0:8001/v1\\\"  # replace this with your vLLM server address as needed\\n\",\n        \"else:\\n\",\n        \"    LLM_SERVER_URL = llm_config[\\\"base_url\\\"]\"\n      ]\n    },\n    {\n      \"cell_type\": \"markdown\",\n      \"metadata\": {},\n      \"source\": [\n        \"### Setup vLLM server \\n\",\n        \"This step is only required if you are using a model served by vLLM, skip this step if you are calling LLM using an API like Gemini and GPT.\\n\",\n        \"\\n\",\n        \"* Install vLLM (in a separate conda env from SAM 3 to avoid dependency conflicts).\\n\",\n        \"  ```bash\\n\",\n        \"    conda create -n vllm python=3.12\\n\",\n        \"    pip install vllm --extra-index-url https://download.pytorch.org/whl/cu128\\n\",\n        \"  ```\\n\",\n        \"* Start vLLM server on the same machine of this notebook\\n\",\n        \"  ```bash\\n\",\n        \"    # qwen 3 VL 8B thinking\\n\",\n        \"    vllm serve Qwen/Qwen3-VL-8B-Thinking --tensor-parallel-size 4 --allowed-local-media-path / --enforce-eager --port 8001\\n\",\n        \"  ```\"\n      ]\n    },\n    {\n      \"cell_type\": \"markdown\",\n      \"metadata\": {},\n      \"source\": [\n        \"## Run SAM3 Agent Inference\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": null,\n      \"metadata\": {},\n      \"outputs\": [],\n      \"source\": [\n        \"from functools import partial\\n\",\n        \"from IPython.display import display, Image\\n\",\n        \"from sam3.agent.client_llm import send_generate_request as send_generate_request_orig\\n\",\n        \"from sam3.agent.client_sam3 import call_sam_service as call_sam_service_orig\\n\",\n        \"from sam3.agent.inference import run_single_image_inference\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": null,\n      \"metadata\": {\n        \"output\": {\n          \"id\": 689664053567678,\n          \"loadingStatus\": \"loaded\"\n        }\n      },\n      \"outputs\": [],\n      \"source\": [\n        \"# prepare input args and run single image inference\\n\",\n        \"image = \\\"assets/images/test_image.jpg\\\"\\n\",\n        \"prompt = \\\"the leftmost child wearing blue vest\\\"\\n\",\n        \"image = os.path.abspath(image)\\n\",\n        \"send_generate_request = partial(send_generate_request_orig, server_url=LLM_SERVER_URL, model=llm_config[\\\"model\\\"], api_key=llm_config[\\\"api_key\\\"])\\n\",\n        \"call_sam_service = partial(call_sam_service_orig, sam3_processor=processor)\\n\",\n        \"output_image_path = run_single_image_inference(\\n\",\n        \"    image, prompt, llm_config, send_generate_request, call_sam_service,\\n\",\n        \"    debug=True, output_dir=\\\"agent_output\\\"\\n\",\n        \")\\n\",\n        \"\\n\",\n        \"# display output\\n\",\n        \"if output_image_path is not None:\\n\",\n        \"    display(Image(filename=output_image_path))\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": null,\n      \"metadata\": {},\n      \"outputs\": [],\n      \"source\": []\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": null,\n      \"metadata\": {},\n      \"outputs\": [],\n      \"source\": []\n    }\n  ],\n  \"metadata\": {\n    \"fileHeader\": \"\",\n    \"fileUid\": \"be59e249-6c09-4634-a9e7-1f06fd233c42\",\n    \"isAdHoc\": false,\n    \"kernelspec\": {\n      \"display_name\": \"Python 3 (ipykernel)\",\n      \"language\": \"python\",\n      \"name\": \"python3\"\n    },\n    \"language_info\": {\n      \"codemirror_mode\": {\n        \"name\": \"ipython\",\n        \"version\": 3\n      },\n      \"file_extension\": \".py\",\n      \"mimetype\": \"text/x-python\",\n      \"name\": \"python\",\n      \"nbconvert_exporter\": \"python\",\n      \"pygments_lexer\": \"ipython3\",\n      \"version\": \"3.12.11\"\n    }\n  },\n  \"nbformat\": 4,\n  \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "examples/sam3_for_sam1_task_example.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"f400486b\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Copyright (c) Meta Platforms, Inc. and affiliates.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"a1ae39ff\",\n   \"metadata\": {\n    \"jp-MarkdownHeadingCollapsed\": true\n   },\n   \"source\": [\n    \"# Interactive Instance Segmentation using SAM 3\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"b4a4b25c\",\n   \"metadata\": {},\n   \"source\": [\n    \"Segment Anything Model 3 (SAM 3) predicts instance masks that indicate the desired object given geometric prompts (SAM 1 task).\\n\",\n    \"The `SAM3Image` and `Sam3Processor` classes provide an easy interface to prompt the model. The user first sets an image using the `Sam3Processor.set_image` method, which computes the necessary image embeddings. Then, prompts can be provided via the `predict` method to efficiently predict masks from those prompts. The model can take as input both point and box prompts, as well as masks from the previous iteration of prediction.\\n\",\n    \"\\n\",\n    \"This notebook follows the SAM 2 API for interactive image segmentation.\\n\",\n    \"\\n\",\n    \"# <a target=\\\"_blank\\\" href=\\\"https://colab.research.google.com/github/facebookresearch/sam3/blob/main/notebooks/sam3_for_sam1_task_example.ipynb\\\">\\n\",\n    \"#   <img src=\\\"https://colab.research.google.com/assets/colab-badge.svg\\\" alt=\\\"Open In Colab\\\"/>\\n\",\n    \"# </a>\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"644532a8\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Environment Set-up\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"07fabfee\",\n   \"metadata\": {},\n   \"source\": [\n    \"First install `sam3` in your environment using the [installation instructions](https://github.com/facebookresearch/sam3?tab=readme-ov-file#installation) in the repository.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"0be845da\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Set-up\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"33681dd1\",\n   \"metadata\": {},\n   \"source\": [\n    \"Necessary imports and helper functions for displaying points, boxes, and masks.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"fe773ede\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"using_colab = False\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"79250a4e\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"if using_colab:\\n\",\n    \"    import torch\\n\",\n    \"    import torchvision\\n\",\n    \"    print(\\\"PyTorch version:\\\", torch.__version__)\\n\",\n    \"    print(\\\"Torchvision version:\\\", torchvision.__version__)\\n\",\n    \"    print(\\\"CUDA is available:\\\", torch.cuda.is_available())\\n\",\n    \"    import sys\\n\",\n    \"    !{sys.executable} -m pip install opencv-python matplotlib scikit-learn\\n\",\n    \"    !{sys.executable} -m pip install 'git+https://github.com/facebookresearch/sam3.git'\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"69b28288\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"# if using Apple MPS, fall back to CPU for unsupported ops\\n\",\n    \"os.environ[\\\"PYTORCH_ENABLE_MPS_FALLBACK\\\"] = \\\"1\\\"\\n\",\n    \"import numpy as np\\n\",\n    \"import torch\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"from PIL import Image\\n\",\n    \"import sam3\\n\",\n    \"sam3_root = os.path.join(os.path.dirname(sam3.__file__), \\\"..\\\")\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"33a15e2f-c7e1-4e5d-862f-fcb751a60b89\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# select the device for computation\\n\",\n    \"if torch.cuda.is_available():\\n\",\n    \"    device = torch.device(\\\"cuda\\\")\\n\",\n    \"elif torch.backends.mps.is_available():\\n\",\n    \"    device = torch.device(\\\"mps\\\")\\n\",\n    \"else:\\n\",\n    \"    device = torch.device(\\\"cpu\\\")\\n\",\n    \"print(f\\\"using device: {device}\\\")\\n\",\n    \"\\n\",\n    \"if device.type == \\\"cuda\\\":\\n\",\n    \"    # use bfloat16 for the entire notebook\\n\",\n    \"    torch.autocast(\\\"cuda\\\", dtype=torch.bfloat16).__enter__()\\n\",\n    \"    # turn on tfloat32 for Ampere GPUs (https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices)\\n\",\n    \"    if torch.cuda.get_device_properties(0).major >= 8:\\n\",\n    \"        torch.backends.cuda.matmul.allow_tf32 = True\\n\",\n    \"        torch.backends.cudnn.allow_tf32 = True\\n\",\n    \"elif device.type == \\\"mps\\\":\\n\",\n    \"    print(\\n\",\n    \"        \\\"\\\\nSupport for MPS devices is preliminary. SAM 3 is trained with CUDA and might \\\"\\n\",\n    \"        \\\"give numerically different outputs and sometimes degraded performance on MPS. \\\"\\n\",\n    \"        \\\"See e.g. https://github.com/pytorch/pytorch/issues/84936 for a discussion.\\\"\\n\",\n    \"    )\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"29bc90d5\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"np.random.seed(3)\\n\",\n    \"\\n\",\n    \"def show_mask(mask, ax, random_color=False, borders = True):\\n\",\n    \"    if random_color:\\n\",\n    \"        color = np.concatenate([np.random.random(3), np.array([0.6])], axis=0)\\n\",\n    \"    else:\\n\",\n    \"        color = np.array([30/255, 144/255, 255/255, 0.6])\\n\",\n    \"    h, w = mask.shape[-2:]\\n\",\n    \"    mask = mask.astype(np.uint8)\\n\",\n    \"    mask_image =  mask.reshape(h, w, 1) * color.reshape(1, 1, -1)\\n\",\n    \"    if borders:\\n\",\n    \"        import cv2\\n\",\n    \"        contours, _ = cv2.findContours(mask,cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE) \\n\",\n    \"        # Try to smooth contours\\n\",\n    \"        contours = [cv2.approxPolyDP(contour, epsilon=0.01, closed=True) for contour in contours]\\n\",\n    \"        mask_image = cv2.drawContours(mask_image, contours, -1, (1, 1, 1, 0.5), thickness=2) \\n\",\n    \"    ax.imshow(mask_image)\\n\",\n    \"\\n\",\n    \"def show_points(coords, labels, ax, marker_size=375):\\n\",\n    \"    pos_points = coords[labels==1]\\n\",\n    \"    neg_points = coords[labels==0]\\n\",\n    \"    ax.scatter(pos_points[:, 0], pos_points[:, 1], color='green', marker='*', s=marker_size, edgecolor='white', linewidth=1.25)\\n\",\n    \"    ax.scatter(neg_points[:, 0], neg_points[:, 1], color='red', marker='*', s=marker_size, edgecolor='white', linewidth=1.25)   \\n\",\n    \"\\n\",\n    \"def show_box(box, ax):\\n\",\n    \"    x0, y0 = box[0], box[1]\\n\",\n    \"    w, h = box[2] - box[0], box[3] - box[1]\\n\",\n    \"    ax.add_patch(plt.Rectangle((x0, y0), w, h, edgecolor='green', facecolor=(0, 0, 0, 0), lw=2))    \\n\",\n    \"\\n\",\n    \"def show_masks(image, masks, scores, point_coords=None, box_coords=None, input_labels=None, borders=True):\\n\",\n    \"    for i, (mask, score) in enumerate(zip(masks, scores)):\\n\",\n    \"        plt.figure(figsize=(10, 10))\\n\",\n    \"        plt.imshow(image)\\n\",\n    \"        show_mask(mask, plt.gca(), borders=borders)\\n\",\n    \"        if point_coords is not None:\\n\",\n    \"            assert input_labels is not None\\n\",\n    \"            show_points(point_coords, input_labels, plt.gca())\\n\",\n    \"        if box_coords is not None:\\n\",\n    \"            # boxes\\n\",\n    \"            show_box(box_coords, plt.gca())\\n\",\n    \"        if len(scores) > 1:\\n\",\n    \"            plt.title(f\\\"Mask {i+1}, Score: {score:.3f}\\\", fontsize=18)\\n\",\n    \"        plt.axis('off')\\n\",\n    \"        plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"23842fb2\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Example image\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"3c2e4f6b\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"image = Image.open(f\\\"{sam3_root}/assets/images/truck.jpg\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"e30125fd\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"plt.figure(figsize=(10, 10))\\n\",\n    \"plt.imshow(image)\\n\",\n    \"plt.axis('on')\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"98b228b8\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Selecting objects with SAM 3\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"0bb1927b\",\n   \"metadata\": {},\n   \"source\": [\n    \"First, load the SAM 3 model. Running on CUDA and using the default model are recommended for best results.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"7e28150b\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from sam3 import build_sam3_image_model\\n\",\n    \"from sam3.model.sam3_image_processor import Sam3Processor\\n\",\n    \"\\n\",\n    \"bpe_path = f\\\"{sam3_root}/assets/bpe_simple_vocab_16e6.txt.gz\\\"\\n\",\n    \"model = build_sam3_image_model(bpe_path=bpe_path, enable_inst_interactivity=True)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"c925e829\",\n   \"metadata\": {},\n   \"source\": [\n    \"Process the image to produce an image embedding by calling `Sam3Processor.set_image`.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"d95d48dd\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"processor = Sam3Processor(model)\\n\",\n    \"inference_state = processor.set_image(image)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"d8fc7a46\",\n   \"metadata\": {},\n   \"source\": [\n    \"To select the truck, choose a point on it. Points are input to the model in (x,y) format and come with labels 1 (foreground point) or 0 (background point). Multiple points can be input; here we use only one. The chosen point will be shown as a star on the image.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"5c69570c\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"input_point = np.array([[520, 375]])\\n\",\n    \"input_label = np.array([1])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"a91ba973\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"plt.figure(figsize=(10, 10))\\n\",\n    \"plt.imshow(image)\\n\",\n    \"show_points(input_point, input_label, plt.gca())\\n\",\n    \"plt.axis('on')\\n\",\n    \"plt.show()  \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"c765e952\",\n   \"metadata\": {},\n   \"source\": [\n    \"Predict with `SAM3Image.predict_inst`. The model returns masks, quality predictions for those masks, and low resolution mask logits that can be passed to the next iteration of prediction.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"5373fd68\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"masks, scores, logits = model.predict_inst(\\n\",\n    \"    inference_state,\\n\",\n    \"    point_coords=input_point,\\n\",\n    \"    point_labels=input_label,\\n\",\n    \"    multimask_output=True,\\n\",\n    \")\\n\",\n    \"sorted_ind = np.argsort(scores)[::-1]\\n\",\n    \"masks = masks[sorted_ind]\\n\",\n    \"scores = scores[sorted_ind]\\n\",\n    \"logits = logits[sorted_ind]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"c7f0e938\",\n   \"metadata\": {},\n   \"source\": [\n    \"With `multimask_output=True` (the default setting), SAM 3 outputs 3 masks, where `scores` gives the model's own estimation of the quality of these masks. This setting is intended for ambiguous input prompts, and helps the model disambiguate different objects consistent with the prompt. When `False`, it will return a single mask. For ambiguous prompts such as a single point, it is recommended to use `multimask_output=True` even if only a single mask is desired; the best single mask can be chosen by picking the one with the highest score returned in `scores`. This will often result in a better mask.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"47821187\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"masks.shape  # (number_of_masks) x H x W\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"e9c227a6\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"show_masks(image, masks, scores, point_coords=input_point, input_labels=input_label, borders=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"3fa31f7c\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Specifying a specific object with additional points\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"88d6d29a\",\n   \"metadata\": {},\n   \"source\": [\n    \"The single input point is ambiguous, and the model has returned multiple objects consistent with it. To obtain a single object, multiple points can be provided. If available, a mask from a previous iteration can also be supplied to the model to aid in prediction. When specifying a single object with multiple prompts, a single mask can be requested by setting `multimask_output=False`.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"f6923b94\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"input_point = np.array([[500, 375], [1125, 625]])\\n\",\n    \"input_label = np.array([1, 1])\\n\",\n    \"\\n\",\n    \"mask_input = logits[np.argmax(scores), :, :]  # Choose the model's best mask\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"d98f96a1\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"masks, scores, _ = model.predict_inst(\\n\",\n    \"    inference_state,\\n\",\n    \"    point_coords=input_point,\\n\",\n    \"    point_labels=input_label,\\n\",\n    \"    mask_input=mask_input[None, :, :],\\n\",\n    \"    multimask_output=False,\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"0ce8b82f\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"masks.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"e06d5c8d\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"show_masks(image, masks, scores, point_coords=input_point, input_labels=input_label)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"c93e2087\",\n   \"metadata\": {},\n   \"source\": [\n    \"To exclude the car and specify just the window, a background point (with label 0, here shown in red) can be supplied.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"9a196f68\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"input_point = np.array([[500, 375], [1125, 625]])\\n\",\n    \"input_label = np.array([1, 0])\\n\",\n    \"\\n\",\n    \"mask_input = logits[np.argmax(scores), :, :]  # Choose the model's best mask\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"81a52282\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"masks, scores, _ = model.predict_inst(\\n\",\n    \"    inference_state,\\n\",\n    \"    point_coords=input_point,\\n\",\n    \"    point_labels=input_label,\\n\",\n    \"    mask_input=mask_input[None, :, :],\\n\",\n    \"    multimask_output=False,\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"bfca709f\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"show_masks(image, masks, scores, point_coords=input_point, input_labels=input_label)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"41e2d5a9\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Specifying a specific object with a box\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"d61ca7ac\",\n   \"metadata\": {},\n   \"source\": [\n    \"The model can also take a box as input, provided in xyxy format.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"8ea92a7b\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"input_box = np.array([425, 600, 700, 875])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"b35a8814\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"masks, scores, _ = model.predict_inst(\\n\",\n    \"    inference_state,\\n\",\n    \"    point_coords=None,\\n\",\n    \"    point_labels=None,\\n\",\n    \"    box=input_box[None, :],\\n\",\n    \"    multimask_output=False,\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"3ffb4906\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"show_masks(image, masks, scores, box_coords=input_box)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"c1ed9f0a\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Combining points and boxes\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"8455d1c5\",\n   \"metadata\": {},\n   \"source\": [\n    \"Points and boxes may be combined, just by including both types of prompts to the predictor. Here this can be used to select just the trucks's tire, instead of the entire wheel.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"90e2e547\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"input_box = np.array([425, 600, 700, 875])\\n\",\n    \"input_point = np.array([[575, 750]])\\n\",\n    \"input_label = np.array([0])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"6956d8c4\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"masks, scores, logits = model.predict_inst(\\n\",\n    \"    inference_state,\\n\",\n    \"    point_coords=input_point,\\n\",\n    \"    point_labels=input_label,\\n\",\n    \"    box=input_box,\\n\",\n    \"    multimask_output=False,\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"eb519a31\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"show_masks(image, masks, scores, box_coords=input_box, point_coords=input_point, input_labels=input_label)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"45ddbca3\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Batched prompt inputs\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"df6f18a0\",\n   \"metadata\": {},\n   \"source\": [\n    \"`SAM3Image` can take multiple input prompts for the same image, using `predict_inst` method. For example, imagine we have several box outputs from an object detector.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"0a06681b\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"input_boxes = np.array([\\n\",\n    \"    [75, 275, 1725, 850],\\n\",\n    \"    [425, 600, 700, 875],\\n\",\n    \"    [1375, 550, 1650, 800],\\n\",\n    \"    [1240, 675, 1400, 750],\\n\",\n    \"])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"117521a3\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"masks, scores, _ = model.predict_inst(\\n\",\n    \"    inference_state,\\n\",\n    \"    point_coords=None,\\n\",\n    \"    point_labels=None,\\n\",\n    \"    box=input_boxes,\\n\",\n    \"    multimask_output=False,\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"6a8f5d49\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"masks.shape  # (batch_size) x (num_predicted_masks_per_input) x H x W\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"c00c3681\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"plt.figure(figsize=(10, 10))\\n\",\n    \"plt.imshow(image)\\n\",\n    \"for mask in masks:\\n\",\n    \"    show_mask(mask.squeeze(0), plt.gca(), random_color=True)\\n\",\n    \"for box in input_boxes:\\n\",\n    \"    show_box(box, plt.gca())\\n\",\n    \"plt.axis('off')\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"b9a27b5d\",\n   \"metadata\": {},\n   \"source\": [\n    \"## End-to-end batched inference\\n\",\n    \"If all prompts are available in advance, it is possible to run SAM 3 directly in an end-to-end fashion. This also allows batching over images.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"d485f75b\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"image1 = image  # truck.jpg from above\\n\",\n    \"image1_boxes = np.array([\\n\",\n    \"    [75, 275, 1725, 850],\\n\",\n    \"    [425, 600, 700, 875],\\n\",\n    \"    [1375, 550, 1650, 800],\\n\",\n    \"    [1240, 675, 1400, 750],\\n\",\n    \"])\\n\",\n    \"\\n\",\n    \"image2 = Image.open(f\\\"{sam3_root}/assets/images/groceries.jpg\\\")\\n\",\n    \"image2_boxes = np.array([\\n\",\n    \"    [450, 170, 520, 350],\\n\",\n    \"    [350, 190, 450, 350],\\n\",\n    \"    [500, 170, 580, 350],\\n\",\n    \"    [580, 170, 640, 350],\\n\",\n    \"])\\n\",\n    \"\\n\",\n    \"img_batch = [image1, image2]\\n\",\n    \"boxes_batch = [image1_boxes, image2_boxes]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"47932c99\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"inference_state = processor.set_image_batch(img_batch)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"97af3c54\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"masks_batch, scores_batch, _ = model.predict_inst_batch(\\n\",\n    \"    inference_state,\\n\",\n    \"    None,\\n\",\n    \"    None, \\n\",\n    \"    box_batch=boxes_batch, \\n\",\n    \"    multimask_output=False\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"226df881\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"for image, boxes, masks in zip(img_batch, boxes_batch, masks_batch):\\n\",\n    \"    plt.figure(figsize=(10, 10))\\n\",\n    \"    plt.imshow(image)   \\n\",\n    \"    for mask in masks:\\n\",\n    \"        show_mask(mask.squeeze(0), plt.gca(), random_color=True)\\n\",\n    \"    for box in boxes:\\n\",\n    \"        show_box(box, plt.gca())\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"46f30085\",\n   \"metadata\": {},\n   \"source\": [\n    \"Similarly, we can have a batch of point prompts defined over a batch of images\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"1ab929fc\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"image1 = image  # truck.jpg from above\\n\",\n    \"image1_pts = np.array([\\n\",\n    \"    [[500, 375]],\\n\",\n    \"    [[650, 750]]\\n\",\n    \"    ]) # Bx1x2 where B corresponds to number of objects \\n\",\n    \"image1_labels = np.array([[1], [1]])\\n\",\n    \"\\n\",\n    \"image2_pts = np.array([\\n\",\n    \"    [[400, 300]],\\n\",\n    \"    [[630, 300]],\\n\",\n    \"])\\n\",\n    \"image2_labels = np.array([[1], [1]])\\n\",\n    \"\\n\",\n    \"pts_batch = [image1_pts, image2_pts]\\n\",\n    \"labels_batch = [image1_labels, image2_labels]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"848f8287\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"masks_batch, scores_batch, _ = model.predict_inst_batch(inference_state, pts_batch, labels_batch, box_batch=None, multimask_output=True)\\n\",\n    \"\\n\",\n    \"# Select the best single mask per object\\n\",\n    \"best_masks = []\\n\",\n    \"for masks, scores in zip(masks_batch,scores_batch):\\n\",\n    \"    best_masks.append(masks[range(len(masks)), np.argmax(scores, axis=-1)])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"99b15c6c\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"for image, points, labels, masks in zip(img_batch, pts_batch, labels_batch, best_masks):\\n\",\n    \"    plt.figure(figsize=(10, 10))\\n\",\n    \"    plt.imshow(image)   \\n\",\n    \"    for mask in masks:\\n\",\n    \"        show_mask(mask, plt.gca(), random_color=True)\\n\",\n    \"    show_points(points, labels, plt.gca())\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"4c1594a5-a0de-4477-91d4-db4504a78a83\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"74e3d07e-b0de-48a5-9d29-d639a0dbcdfc\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"d8b1de3a-a253-48ff-8a1c-d80742acbe86\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (ipykernel)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.12.11\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "examples/sam3_for_sam2_video_task_example.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"3c3b1c46-9f5c-41c1-9101-85db8709ec0d\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Copyright (c) Meta Platforms, Inc. and affiliates.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"6e7a0db5-7f04-4845-8b11-684fe6e9f7f2\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Video object segmentation with SAM 3\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"162d0b3c-4207-442d-969c-aa1cbb8fd4ad\",\n   \"metadata\": {},\n   \"source\": [\n    \"This notebook shows how to use SAM 3 for video object segmentation in videos, illustrating the use of the  `Sam3TrackerPredictor` class.\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"This notebook follows the SAM 2 API for interactive video segmentation.\\n\",\n    \"\\n\",\n    \"<a target=\\\"_blank\\\" href=\\\"https://colab.research.google.com/github/facebookresearch/sam3/blob/main/notebooks/sam3_for_sam2_video_task_example.ipynb\\\">\\n\",\n    \"  <img src=\\\"https://colab.research.google.com/assets/colab-badge.svg\\\" alt=\\\"Open In Colab\\\"/>\\n\",\n    \"</a>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"26616201-06df-435b-98fd-ad17c373bb4a\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Environment Set-up\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"8491a127-4c01-48f5-9dc5-f148a9417fdf\",\n   \"metadata\": {},\n   \"source\": [\n    \"First install `sam3` in your environment using the [installation instructions](https://github.com/facebookresearch/sam3?tab=readme-ov-file#installation) in the repository.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"f74c53be-aab1-46b9-8c0b-068b52ef5948\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"using_colab = False\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"d824a4b2-71f3-4da3-bfc7-3249625e6730\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"if using_colab:\\n\",\n    \"    import torch\\n\",\n    \"    import torchvision\\n\",\n    \"    print(\\\"PyTorch version:\\\", torch.__version__)\\n\",\n    \"    print(\\\"Torchvision version:\\\", torchvision.__version__)\\n\",\n    \"    print(\\\"CUDA is available:\\\", torch.cuda.is_available())\\n\",\n    \"    import sys\\n\",\n    \"    !{sys.executable} -m pip install opencv-python matplotlib scikit-learn\\n\",\n    \"    !{sys.executable} -m pip install 'git+https://github.com/facebookresearch/sam3.git'\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"22e6aa9d-487f-4207-b657-8cff0902343e\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Set-up\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"d3cae821\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import torch\\n\",\n    \"\\n\",\n    \"# select the device for computation\\n\",\n    \"if torch.cuda.is_available():\\n\",\n    \"    device = torch.device(\\\"cuda\\\")\\n\",\n    \"elif torch.backends.mps.is_available():\\n\",\n    \"    device = torch.device(\\\"mps\\\")\\n\",\n    \"else:\\n\",\n    \"    device = torch.device(\\\"cpu\\\")\\n\",\n    \"print(f\\\"using device: {device}\\\")\\n\",\n    \"\\n\",\n    \"if device.type == \\\"cuda\\\":\\n\",\n    \"    # use bfloat16 for the entire notebook\\n\",\n    \"    torch.autocast(\\\"cuda\\\", dtype=torch.bfloat16).__enter__()\\n\",\n    \"    # turn on tfloat32 for Ampere GPUs (https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices)\\n\",\n    \"    if torch.cuda.get_device_properties(0).major >= 8:\\n\",\n    \"        torch.backends.cuda.matmul.allow_tf32 = True\\n\",\n    \"        torch.backends.cudnn.allow_tf32 = True\\n\",\n    \"\\n\",\n    \"elif device.type == \\\"mps\\\":\\n\",\n    \"    print(\\n\",\n    \"        \\\"\\\\nSupport for MPS devices is preliminary. SAM 3 is trained with CUDA and might \\\"\\n\",\n    \"        \\\"give numerically different outputs and sometimes degraded performance on MPS. \\\"\\n\",\n    \"        \\\"See e.g. https://github.com/pytorch/pytorch/issues/84936 for a discussion.\\\"\\n\",\n    \"    )\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"e5318a85-5bf7-4880-b2b3-15e4db24d796\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import glob\\n\",\n    \"import os\\n\",\n    \"\\n\",\n    \"import cv2\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"import sam3\\n\",\n    \"import torch\\n\",\n    \"from PIL import Image\\n\",\n    \"from sam3.visualization_utils import show_box, show_mask, show_points\\n\",\n    \"\\n\",\n    \"# font size for axes titles\\n\",\n    \"plt.rcParams[\\\"axes.titlesize\\\"] = 12\\n\",\n    \"plt.rcParams[\\\"figure.titlesize\\\"] = 12\\n\",\n    \"\\n\",\n    \"sam3_root = os.path.join(os.path.dirname(sam3.__file__), \\\"..\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"ae8e0779-751f-4224-9b04-ed0f0b406500\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Loading the SAM 3 tracking predictor\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"f5f3245e-b4d6-418b-a42a-a67e0b3b5aec\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from sam3.model_builder import build_sam3_video_model\\n\",\n    \"\\n\",\n    \"sam3_model = build_sam3_video_model()\\n\",\n    \"predictor = sam3_model.tracker\\n\",\n    \"predictor.backbone = sam3_model.detector.backbone\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"dff46b10-c17a-4a26-8004-8c6d80806b0a\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Initialize the inference state\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"f594ac71-a6b9-461d-af27-500fa1d1a420\",\n   \"metadata\": {},\n   \"source\": [\n    \"Just like SAM 2, SAM 3 requires stateful inference for interactive video segmentation, so we need to initialize an **inference state** on this video.\\n\",\n    \"\\n\",\n    \"During initialization, it loads all the JPEG frames in `video_path` and stores their pixels in `inference_state` (as shown in the progress bar below).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"9baa05c9\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"video_path = f\\\"{sam3_root}/assets/videos/bedroom.mp4\\\"\\n\",\n    \"inference_state = predictor.init_state(video_path=video_path)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"edb1f3f6-d74d-4016-934c-8d2a14d1a543\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Example 1: Segment & track one object\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"aa2d3127-67b2-45d2-9f32-8fe3e10dc5eb\",\n   \"metadata\": {},\n   \"source\": [\n    \"Note: if you have run any previous tracking using this `inference_state`, please reset it first via `clear_all_points_in_video`.\\n\",\n    \"\\n\",\n    \"(The cell below is just for illustration; it's not needed to call `clear_all_points_in_video` here as this `inference_state` is just freshly initialized above.)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"d2646a1d-3401-438c-a653-55e0e56b7d9d\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"predictor.clear_all_points_in_video(inference_state)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"26aeb04d-8cba-4f57-95da-6e5a1796003e\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Step 1: Add a first click on a frame\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"695c7749-b523-4691-aad0-7558c5d1d68c\",\n   \"metadata\": {},\n   \"source\": [\n    \"To get started, let's try to segment the child on the left.\\n\",\n    \"\\n\",\n    \"Here we make a **positive click** at (x, y) = (210, 350) with label `1`, by sending their coordinates and labels into the `add_new_points` API.\\n\",\n    \"\\n\",\n    \"Note: label `1` indicates a *positive click (to add a region)* while label `0` indicates a *negative click (to remove a region)*.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"dd6778a1\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# load the frames for visualization\\n\",\n    \"cap = cv2.VideoCapture(video_path)\\n\",\n    \"video_frames_for_vis = []\\n\",\n    \"while True:\\n\",\n    \"    ret, frame = cap.read()\\n\",\n    \"    if not ret:\\n\",\n    \"        break\\n\",\n    \"    video_frames_for_vis.append(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))\\n\",\n    \"cap.release()\\n\",\n    \"frame0 = video_frames_for_vis[0]\\n\",\n    \"\\n\",\n    \"width, height = frame0.shape[1], frame0.shape[0]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"3e749bab-0f36-4173-bf8d-0c20cd5214b3\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"ann_frame_idx = 0  # the frame index we interact with\\n\",\n    \"ann_obj_id = 1  # give a unique id to each object we interact with (it can be any integers)\\n\",\n    \"\\n\",\n    \"# Let's add a positive click at (x, y) = (210, 350) to get started\\n\",\n    \"points = np.array([[210, 350]], dtype=np.float32)\\n\",\n    \"# for labels, `1` means positive click and `0` means negative click\\n\",\n    \"labels = np.array([1], np.int32)\\n\",\n    \"\\n\",\n    \"rel_points = [[x / width, y / height] for x, y in points]\\n\",\n    \"\\n\",\n    \"points_tensor = torch.tensor(rel_points, dtype=torch.float32)\\n\",\n    \"points_labels_tensor = torch.tensor(labels, dtype=torch.int32)\\n\",\n    \"\\n\",\n    \"_, out_obj_ids, low_res_masks, video_res_masks = predictor.add_new_points(\\n\",\n    \"    inference_state=inference_state,\\n\",\n    \"    frame_idx=ann_frame_idx,\\n\",\n    \"    obj_id=ann_obj_id,\\n\",\n    \"    points=points_tensor,\\n\",\n    \"    labels=points_labels_tensor,\\n\",\n    \"    clear_old_points=False,\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"# show the results on the current (interacted) frame\\n\",\n    \"plt.figure(figsize=(9, 6))\\n\",\n    \"plt.title(f\\\"frame {ann_frame_idx}\\\")\\n\",\n    \"plt.imshow(frame0)\\n\",\n    \"show_points(points, labels, plt.gca())\\n\",\n    \"show_mask((video_res_masks[0] > 0.0).cpu().numpy(), plt.gca(), obj_id=out_obj_ids[0])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"89457875-93fa-40ed-b6dc-4e1c971a27f9\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Step 2: Add a second click to refine the prediction\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"a75eb21b-1413-452c-827b-a04093c30c78\",\n   \"metadata\": {},\n   \"source\": [\n    \"Hmm, it seems that although we wanted to segment the child on the left, the model predicts the mask for only the shorts -- this can happen since there is ambiguity from a single click about what the target object should be. We can refine the mask on this frame via another positive click on the child's shirt.\\n\",\n    \"\\n\",\n    \"Here we make a **second positive click** at (x, y) = (250, 220) with label `1` to expand the mask.\\n\",\n    \"\\n\",\n    \"Note: we need to send **all the clicks and their labels** (i.e. not just the last click) when calling `add_new_points`.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"e1ab3ec7-2537-4158-bf98-3d0977d8908d\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"ann_frame_idx = 0  # the frame index we interact with\\n\",\n    \"ann_obj_id = 1  # give a unique id to each object we interact with (it can be any integers)\\n\",\n    \"\\n\",\n    \"# Let's add a 2nd positive click at (x, y) = (250, 220) to refine the mask\\n\",\n    \"# sending all clicks (and their labels) to `add_new_points_or_box`\\n\",\n    \"points = np.array([[210, 350], [250, 220]], dtype=np.float32)\\n\",\n    \"# for labels, `1` means positive click and `0` means negative click\\n\",\n    \"labels = np.array([1, 1], np.int32)\\n\",\n    \"\\n\",\n    \"rel_points = [[x / width, y / height] for x, y in points]\\n\",\n    \"\\n\",\n    \"points_tensor = torch.tensor(rel_points, dtype=torch.float32)\\n\",\n    \"points_labels_tensor = torch.tensor(labels, dtype=torch.int32)\\n\",\n    \"\\n\",\n    \"_, out_obj_ids, low_res_masks, video_res_masks  = predictor.add_new_points(\\n\",\n    \"    inference_state=inference_state,\\n\",\n    \"    frame_idx=ann_frame_idx,\\n\",\n    \"    obj_id=ann_obj_id,\\n\",\n    \"    points=points_tensor,\\n\",\n    \"    labels=points_labels_tensor,\\n\",\n    \"    clear_old_points=False,\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"# show the results on the current (interacted) frame\\n\",\n    \"plt.figure(figsize=(9, 6))\\n\",\n    \"plt.title(f\\\"frame {ann_frame_idx}\\\")\\n\",\n    \"plt.imshow(frame0)\\n\",\n    \"show_points(points, labels, plt.gca())\\n\",\n    \"show_mask((video_res_masks[0] > 0.0).cpu().numpy(), plt.gca(), obj_id=out_obj_ids[0])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"df4ab457-d91d-4ac8-b350-fbcd549fd3fd\",\n   \"metadata\": {},\n   \"source\": [\n    \"With this 2nd refinement click, now we get a segmentation mask of the entire child on frame 0.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"f52015ac-1b7b-4c59-bca3-c2b28484cf46\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Step 3: Propagate the prompts to get the masklet across the video\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"30b025bd-cd58-4bfb-9572-c8d2fd0a02ef\",\n   \"metadata\": {},\n   \"source\": [\n    \"To get the masklet throughout the entire video, we propagate the prompts using the `propagate_in_video` API.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"ab45e932-b0d5-4983-9718-6ee77d1ac31b\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# run propagation throughout the video and collect the results in a dict\\n\",\n    \"video_segments = {}  # video_segments contains the per-frame segmentation results\\n\",\n    \"for frame_idx, obj_ids, low_res_masks, video_res_masks, obj_scores in predictor.propagate_in_video(inference_state, start_frame_idx=0, max_frame_num_to_track=240, reverse=False, propagate_preflight=True):\\n\",\n    \"    video_segments[frame_idx] = {\\n\",\n    \"        out_obj_id: (video_res_masks[i] > 0.0).cpu().numpy()\\n\",\n    \"        for i, out_obj_id in enumerate(out_obj_ids)\\n\",\n    \"    }\\n\",\n    \"\\n\",\n    \"# render the segmentation results every few frames\\n\",\n    \"vis_frame_stride = 30\\n\",\n    \"plt.close(\\\"all\\\")\\n\",\n    \"for out_frame_idx in range(0, len(video_frames_for_vis), vis_frame_stride):\\n\",\n    \"    plt.figure(figsize=(6, 4))\\n\",\n    \"    plt.title(f\\\"frame {out_frame_idx}\\\")\\n\",\n    \"    plt.imshow(video_frames_for_vis[out_frame_idx])\\n\",\n    \"    for out_obj_id, out_mask in video_segments[out_frame_idx].items():\\n\",\n    \"        show_mask(out_mask, plt.gca(), obj_id=out_obj_id)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"3e801b70-72df-4a72-b3fe-84f145e5e3f6\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Step 4: Add new prompts to further refine the masklet\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"478958ab-29b4-4a75-bba4-adb1b03d0a2b\",\n   \"metadata\": {},\n   \"source\": [\n    \"It appears that in the output masklet above, there are some small imperfections in boundary details on frame 150.\\n\",\n    \"\\n\",\n    \"With SAM 3 we can fix the model predictions interactively. We can add a **negative click** at (x, y) = (82, 415) on this frame with label `0` to refine the masklet. Here we call the `add_new_points_or_box` API with a different `frame_idx` argument to indicate the frame index we want to refine.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"1a572ea9-5b7e-479c-b30c-93c38b121131\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"ann_frame_idx = 150  # further refine some details on this frame\\n\",\n    \"ann_obj_id = 1  # give a unique id to the object we interact with (it can be any integers)\\n\",\n    \"\\n\",\n    \"# show the segment before further refinement\\n\",\n    \"plt.figure(figsize=(9, 6))\\n\",\n    \"plt.title(f\\\"frame {ann_frame_idx} -- before refinement\\\")\\n\",\n    \"plt.imshow(video_frames_for_vis[ann_frame_idx])\\n\",\n    \"show_mask(video_segments[ann_frame_idx][ann_obj_id], plt.gca(), obj_id=ann_obj_id)\\n\",\n    \"\\n\",\n    \"# Let's add a negative click on this frame at (x, y) = (82, 415) to refine the segment\\n\",\n    \"points = np.array([[82, 410]], dtype=np.float32)\\n\",\n    \"# for labels, `1` means positive click and `0` means negative click\\n\",\n    \"labels = np.array([0], np.int32)\\n\",\n    \"\\n\",\n    \"rel_points = [[x / width, y / height] for x, y in points]\\n\",\n    \"\\n\",\n    \"points_tensor = torch.tensor(rel_points, dtype=torch.float32)\\n\",\n    \"points_labels_tensor = torch.tensor(labels, dtype=torch.int32)\\n\",\n    \"\\n\",\n    \"_, out_obj_ids, low_res_masks, video_res_masks  = predictor.add_new_points(\\n\",\n    \"    inference_state=inference_state,\\n\",\n    \"    frame_idx=ann_frame_idx,\\n\",\n    \"    obj_id=ann_obj_id,\\n\",\n    \"    points=points_tensor,\\n\",\n    \"    labels=points_labels_tensor,\\n\",\n    \"    clear_old_points=False,\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# show the segment after the further refinement\\n\",\n    \"plt.figure(figsize=(9, 6))\\n\",\n    \"plt.title(f\\\"frame {ann_frame_idx} -- after refinement\\\")\\n\",\n    \"plt.imshow(video_frames_for_vis[ann_frame_idx])\\n\",\n    \"show_points(points, labels, plt.gca())\\n\",\n    \"show_mask((video_res_masks > 0.0).cpu().numpy(), plt.gca(), obj_id=ann_obj_id)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"50a3950a-acf1-435c-bd64-94297267b5e9\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Step 5: Propagate the prompts (again) to get the masklet across the video\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"b1954ecf-c2ec-4f9c-8d10-c4f527a10cd2\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's get an updated masklet for the entire video. Here we call `propagate_in_video` again to propagate all the prompts after adding the new refinement click above.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"baa96690-4a38-4a24-aa17-fd2f4db0e232\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# run propagation throughout the video and collect the results in a dict\\n\",\n    \"video_segments = {}  # video_segments contains the per-frame segmentation results\\n\",\n    \"for frame_idx, obj_ids, low_res_masks, video_res_masks, obj_scores in predictor.propagate_in_video(inference_state, start_frame_idx=0, max_frame_num_to_track=300, reverse=False, propagate_preflight=True):\\n\",\n    \"    video_segments[frame_idx] = {\\n\",\n    \"        out_obj_id: (video_res_masks[i] > 0.0).cpu().numpy()\\n\",\n    \"        for i, out_obj_id in enumerate(out_obj_ids)\\n\",\n    \"    }\\n\",\n    \"\\n\",\n    \"# render the segmentation results every few frames\\n\",\n    \"vis_frame_stride = 30\\n\",\n    \"plt.close(\\\"all\\\")\\n\",\n    \"for out_frame_idx in range(0, len(video_frames_for_vis), vis_frame_stride):\\n\",\n    \"    plt.figure(figsize=(6, 4))\\n\",\n    \"    plt.title(f\\\"frame {out_frame_idx}\\\")\\n\",\n    \"    plt.imshow(video_frames_for_vis[out_frame_idx])\\n\",\n    \"    for out_obj_id, out_mask in video_segments[out_frame_idx].items():\\n\",\n    \"        show_mask(out_mask, plt.gca(), obj_id=out_obj_id)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"607507e3-6a2b-4fd7-944c-2371bdab9d01\",\n   \"metadata\": {},\n   \"source\": [\n    \"The segments now look good on all frames.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"2502bb5a-3e1f-43d0-9f58-33f8676fff0d\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Example 2: Segment an object using box prompt\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"8e2d26c8-0432-48c6-997e-4a3b77bb5f6d\",\n   \"metadata\": {},\n   \"source\": [\n    \"Note: if you have run any previous tracking using this `inference_state`, please reset it first via `clear_all_points_in_video`.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"6dbe9183-abbb-4283-b0cb-d24f3d7beb34\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"predictor.clear_all_points_in_video(inference_state)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"ceb6eae9-0f4c-434f-8089-a46c9ca59da5\",\n   \"metadata\": {},\n   \"source\": [\n    \"In addition to using clicks as inputs, SAM 3 also supports segmenting and tracking objects in a video via **bounding boxes**.\\n\",\n    \"\\n\",\n    \"In the example below, we segment the child on the right using a **box prompt** of (x_min, y_min, x_max, y_max) = (300, 0, 500, 400) on frame 0 as input into the `add_new_points_or_box` API.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"1cbfb273-4e14-495b-bd89-87a8baf52ae7\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"ann_frame_idx = 0  # the frame index we interact with\\n\",\n    \"ann_obj_id = 4  # give a unique id to each object we interact with (it can be any integers)\\n\",\n    \"\\n\",\n    \"# Let's add a box at (x_min, y_min, x_max, y_max) = (300, 0, 500, 400) to get started\\n\",\n    \"box = np.array([[300, 0, 500, 400]], dtype=np.float32)\\n\",\n    \"\\n\",\n    \"rel_box = [[xmin / width, ymin / height, xmax / width, ymax / height] for xmin, ymin, xmax, ymax in box]\\n\",\n    \"rel_box = np.array(rel_box, dtype=np.float32)\\n\",\n    \"\\n\",\n    \"_, out_obj_ids, low_res_masks, video_res_masks  = predictor.add_new_points_or_box(\\n\",\n    \"    inference_state=inference_state,\\n\",\n    \"    frame_idx=ann_frame_idx,\\n\",\n    \"    obj_id=ann_obj_id,\\n\",\n    \"    box=rel_box,\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"# show the results on the current (interacted) frame\\n\",\n    \"plt.figure(figsize=(9, 6))\\n\",\n    \"plt.title(f\\\"frame {ann_frame_idx}\\\")\\n\",\n    \"plt.imshow(video_frames_for_vis[ann_frame_idx])\\n\",\n    \"show_box(box[0], plt.gca())\\n\",\n    \"show_mask((video_res_masks[0] > 0.0).cpu().numpy(), plt.gca(), obj_id=ann_obj_id)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"bd3f9ba7-bf4d-47e5-9b02-8a424cab42cc\",\n   \"metadata\": {},\n   \"source\": [\n    \"Here, SAM 3 gets a pretty good segmentation mask of the entire child, even though the input bounding box is not perfectly tight around the object.\\n\",\n    \"\\n\",\n    \"Similar to the previous example, if the returned mask from is not perfect when using a box prompt, we can also further **refine** the output using positive or negative clicks. To illustrate this, here we make a **positive click** at (x, y) = (460, 60) with label `1` to expand the segment around the child's hair.\\n\",\n    \"\\n\",\n    \"Note: to refine the segmentation mask from a box prompt, we need to send **both the original box input and all subsequent refinement clicks and their labels** when calling `add_new_points_or_box`.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"54906315-ab4c-4088-b866-4c22134d5b66\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"ann_frame_idx = 0  # the frame index we interact with\\n\",\n    \"ann_obj_id = 4  # give a unique id to each object we interact with (it can be any integers)\\n\",\n    \"\\n\",\n    \"# Let's add a positive click at (x, y) = (460, 60) to refine the mask\\n\",\n    \"points = np.array([[460, 60]], dtype=np.float32)\\n\",\n    \"# for labels, `1` means positive click and `0` means negative click\\n\",\n    \"labels = np.array([1], np.int32)\\n\",\n    \"# note that we also need to send the original box input along with\\n\",\n    \"# the new refinement click together into `add_new_points_or_box`\\n\",\n    \"box = np.array([[300, 0, 500, 400]], dtype=np.float32)\\n\",\n    \"\\n\",\n    \"rel_box = [[xmin / width, ymin / height, xmax / width, ymax / height] for xmin, ymin, xmax, ymax in box]\\n\",\n    \"rel_box = np.array(rel_box, dtype=np.float32)\\n\",\n    \"\\n\",\n    \"rel_points = [[x / width, y / height] for x, y in points]\\n\",\n    \"\\n\",\n    \"points_tensor = torch.tensor(rel_points, dtype=torch.float32)\\n\",\n    \"points_labels_tensor = torch.tensor(labels, dtype=torch.int32)\\n\",\n    \"\\n\",\n    \"_, out_obj_ids, low_res_masks, video_res_masks  = predictor.add_new_points_or_box(\\n\",\n    \"    inference_state=inference_state,\\n\",\n    \"    frame_idx=ann_frame_idx,\\n\",\n    \"    obj_id=ann_obj_id,\\n\",\n    \"    points=points_tensor,\\n\",\n    \"    labels=points_labels_tensor,\\n\",\n    \"    box=rel_box,\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"# show the results on the current (interacted) frame\\n\",\n    \"plt.figure(figsize=(9, 6))\\n\",\n    \"plt.title(f\\\"frame {ann_frame_idx}\\\")\\n\",\n    \"plt.imshow(video_frames_for_vis[ann_frame_idx])\\n\",\n    \"show_box(box[0], plt.gca())\\n\",\n    \"show_points(points, labels, plt.gca())\\n\",\n    \"show_mask((video_res_masks[0][0] > 0.0).cpu().numpy(), plt.gca(), obj_id=out_obj_ids[0])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"73128cd6-dbfa-49f7-8d79-1a8e19835f7f\",\n   \"metadata\": {},\n   \"source\": [\n    \"Then, to get the masklet throughout the entire video, we propagate the prompts using the `propagate_in_video` API.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"9cd90557-a0dc-442e-b091-9c74c831bef8\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# run propagation throughout the video and collect the results in a dict\\n\",\n    \"video_segments = {}  # video_segments contains the per-frame segmentation results\\n\",\n    \"for frame_idx, obj_ids, low_res_masks, video_res_masks, obj_scores in predictor.propagate_in_video(inference_state, start_frame_idx=0, max_frame_num_to_track=300, reverse=False, propagate_preflight=True):\\n\",\n    \"    video_segments[frame_idx] = {\\n\",\n    \"        out_obj_id: (video_res_masks[i] > 0.0).cpu().numpy()\\n\",\n    \"        for i, out_obj_id in enumerate(out_obj_ids)\\n\",\n    \"    }\\n\",\n    \"\\n\",\n    \"# render the segmentation results every few frames\\n\",\n    \"vis_frame_stride = 30\\n\",\n    \"plt.close(\\\"all\\\")\\n\",\n    \"for out_frame_idx in range(0, len(video_frames_for_vis), vis_frame_stride):\\n\",\n    \"    plt.figure(figsize=(6, 4))\\n\",\n    \"    plt.title(f\\\"frame {out_frame_idx}\\\")\\n\",\n    \"    plt.imshow(video_frames_for_vis[out_frame_idx])\\n\",\n    \"    for out_obj_id, out_mask in video_segments[out_frame_idx].items():\\n\",\n    \"        show_mask(out_mask, plt.gca(), obj_id=out_obj_id)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"e023f91f-0cc5-4980-ae8e-a13c5749112b\",\n   \"metadata\": {},\n   \"source\": [\n    \"Note that in addition to clicks or boxes, SAM 3 also supports directly using a **mask prompt** as input via the `add_new_mask` method in the `Sam3TrackerPredictor` class. This can be helpful in e.g. semi-supervised VOS evaluations (see [tools/vos_inference.py](https://github.com/facebookresearch/sam2/blob/main/tools/vos_inference.py) for an example).\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"da018be8-a4ae-4943-b1ff-702c2b89cb68\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Example 3: Segment multiple objects simultaneously\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"dea6c04c-3072-4876-b394-879321a48c4a\",\n   \"metadata\": {},\n   \"source\": [\n    \"Note: if you have run any previous tracking using this `inference_state`, please reset it first via `clear_all_points_in_video`.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"29b874c8-9f39-42d3-a667-54a0bd696410\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"predictor.clear_all_points_in_video(inference_state)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"48f3f7e6-4821-468c-84e4-f3a0435c9149\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Step 1: Add two objects on a frame\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"95158714-86d7-48a9-8365-b213f97cc9ca\",\n   \"metadata\": {},\n   \"source\": [\n    \"SAM 3 can also segment and track two or more objects at the same time. One way, of course, is to do them one by one. However, it would be more efficient to batch them together (e.g. so that we can share the image features between objects to reduce computation costs).\\n\",\n    \"\\n\",\n    \"This time, let's focus on object parts and segment **the shirts of both childen** in this video. Here we add prompts for these two objects and assign each of them a unique object id.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"e22d896d-3cd5-4fa0-9230-f33e217035dc\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"prompts = {}  # hold all the clicks we add for visualization\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"59d9ac57-b14a-4237-828d-927e422c518b\",\n   \"metadata\": {},\n   \"source\": [\n    \"Add the first object (the left child's shirt) with a **positive click** at (x, y) = (200, 300) on frame 0.\\n\",\n    \"\\n\",\n    \"We assign it to object id `2` (it can be arbitrary integers, and only needs to be unique for each object to track), which is passed to the `add_new_points_or_box` API to distinguish the object we are clicking upon.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"d13432fc-f467-44d8-adfe-3e0c488046b7\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"ann_frame_idx = 0  # the frame index we interact with\\n\",\n    \"ann_obj_id = 2  # give a unique id to each object we interact with (it can be any integers)\\n\",\n    \"\\n\",\n    \"# Let's add a positive click at (x, y) = (200, 300) to get started on the first object\\n\",\n    \"points = np.array([[200, 300]], dtype=np.float32)\\n\",\n    \"# for labels, `1` means positive click and `0` means negative click\\n\",\n    \"labels = np.array([1], np.int32)\\n\",\n    \"prompts[ann_obj_id] = points, labels\\n\",\n    \"\\n\",\n    \"rel_points = [[x / width, y / height] for x, y in points]\\n\",\n    \"points_tensor = torch.tensor(rel_points, dtype=torch.float32)\\n\",\n    \"points_labels_tensor = torch.tensor(labels, dtype=torch.int32)\\n\",\n    \"\\n\",\n    \"_, out_obj_ids, low_res_masks, video_res_masks = predictor.add_new_points_or_box(\\n\",\n    \"    inference_state=inference_state,\\n\",\n    \"    frame_idx=ann_frame_idx,\\n\",\n    \"    obj_id=ann_obj_id,\\n\",\n    \"    points=points_tensor,\\n\",\n    \"    labels=points_labels_tensor,\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# show the results on the current (interacted) frame\\n\",\n    \"plt.figure(figsize=(9, 6))\\n\",\n    \"plt.title(f\\\"frame {ann_frame_idx}\\\")\\n\",\n    \"plt.imshow(video_frames_for_vis[ann_frame_idx])\\n\",\n    \"for i, out_obj_id in enumerate(out_obj_ids):\\n\",\n    \"    show_points(points, labels, plt.gca())\\n\",\n    \"    show_points(*prompts[out_obj_id], plt.gca())\\n\",\n    \"    show_mask((video_res_masks[i][0] > 0.0).cpu().numpy(), plt.gca(), obj_id=out_obj_id)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"1bbbd51b-e1e2-4c36-99ec-1d9a1b49b0cd\",\n   \"metadata\": {},\n   \"source\": [\n    \"Hmm, this time we just want to select the child's shirt, but the model predicts the mask for the entire child. Let's refine the prediction with a **negative click** at (x, y) = (275, 175).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"95ecf61d-662b-4f98-ae62-46557b219842\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# add the first object\\n\",\n    \"ann_frame_idx = 0  # the frame index we interact with\\n\",\n    \"ann_obj_id = 2  # give a unique id to each object we interact with (it can be any integers)\\n\",\n    \"\\n\",\n    \"# Let's add a 2nd negative click at (x, y) = (275, 175) to refine the first object\\n\",\n    \"# sending all clicks (and their labels) to `add_new_points_or_box`\\n\",\n    \"points = np.array([[200, 300], [275, 175]], dtype=np.float32)\\n\",\n    \"# for labels, `1` means positive click and `0` means negative click\\n\",\n    \"labels = np.array([1, 0], np.int32)\\n\",\n    \"prompts[ann_obj_id] = points, labels\\n\",\n    \"\\n\",\n    \"rel_points = [[x / width, y / height] for x, y in points]\\n\",\n    \"points_tensor = torch.tensor(rel_points, dtype=torch.float32)\\n\",\n    \"points_labels_tensor = torch.tensor(labels, dtype=torch.int32)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"_, out_obj_ids, low_res_masks, video_res_masks  = predictor.add_new_points_or_box(\\n\",\n    \"    inference_state=inference_state,\\n\",\n    \"    frame_idx=ann_frame_idx,\\n\",\n    \"    obj_id=ann_obj_id,\\n\",\n    \"    points=rel_points,\\n\",\n    \"    labels=points_labels_tensor,\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"# show the results on the current (interacted) frame\\n\",\n    \"plt.figure(figsize=(9, 6))\\n\",\n    \"plt.title(f\\\"frame {ann_frame_idx}\\\")\\n\",\n    \"plt.imshow(video_frames_for_vis[ann_frame_idx])\\n\",\n    \"for i, out_obj_id in enumerate(out_obj_ids):\\n\",\n    \"    show_points(points, labels, plt.gca())\\n\",\n    \"    show_points(*prompts[out_obj_id], plt.gca())\\n\",\n    \"    show_mask((video_res_masks[i][0] > 0.0).cpu().numpy(), plt.gca(), obj_id=out_obj_id)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"194718c1-734d-446c-a3ef-361057de2f31\",\n   \"metadata\": {},\n   \"source\": [\n    \"After the 2nd negative click, now we get the left child's shirt as our first object.\\n\",\n    \"\\n\",\n    \"Let's move on to the second object (the right child's shirt) with a positive click at (x, y) = (400, 150) on frame 0. Here we assign object id `3` to this second object (it can be arbitrary integers, and only needs to be unique for each object to track).\\n\",\n    \"\\n\",\n    \"Note: when there are multiple objects, the `add_new_points_or_box` API will return a list of masks for each object.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"86ca1bde-62a4-40e6-98e4-15606441e52f\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"ann_frame_idx = 0  # the frame index we interact with\\n\",\n    \"ann_obj_id = 3  # give a unique id to each object we interact with (it can be any integers)\\n\",\n    \"\\n\",\n    \"# Let's now move on to the second object we want to track (giving it object id `3`)\\n\",\n    \"# with a positive click at (x, y) = (400, 150)\\n\",\n    \"points = np.array([[400, 150]], dtype=np.float32)\\n\",\n    \"# for labels, `1` means positive click and `0` means negative click\\n\",\n    \"labels = np.array([1], np.int32)\\n\",\n    \"prompts[ann_obj_id] = points, labels\\n\",\n    \"\\n\",\n    \"rel_points = [[x / width, y / height] for x, y in points]\\n\",\n    \"points_tensor = torch.tensor(rel_points, dtype=torch.float32)\\n\",\n    \"points_labels_tensor = torch.tensor(labels, dtype=torch.int32)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# `add_new_points_or_box` returns masks for all objects added so far on this interacted frame\\n\",\n    \"_, out_obj_ids, low_res_masks, video_res_masks = predictor.add_new_points_or_box(\\n\",\n    \"    inference_state=inference_state,\\n\",\n    \"    frame_idx=ann_frame_idx,\\n\",\n    \"    obj_id=ann_obj_id,\\n\",\n    \"    points=points_tensor,\\n\",\n    \"    labels=points_labels_tensor,\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"# show the results on the current (interacted) frame on all objects\\n\",\n    \"plt.figure(figsize=(9, 6))\\n\",\n    \"plt.title(f\\\"frame {ann_frame_idx}\\\")\\n\",\n    \"plt.imshow(video_frames_for_vis[ann_frame_idx])\\n\",\n    \"for i, out_obj_id in enumerate(out_obj_ids):\\n\",\n    \"    show_points(points, labels, plt.gca())\\n\",\n    \"    show_points(*prompts[out_obj_id], plt.gca())\\n\",\n    \"    show_mask((video_res_masks[i][0] > 0.0).cpu().numpy(), plt.gca(), obj_id=out_obj_id)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"a1f7add8-d577-4597-ae2f-654b8c7b05e0\",\n   \"metadata\": {},\n   \"source\": [\n    \"This time the model predicts the mask of the shirt we want to track in just one click. Nice!\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"448733b8-ea8b-4078-995f-b676c3b558ba\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Step 2: Propagate the prompts to get masklets across the video\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"60bd73de-d669-41c8-b6ba-943883f0caa2\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now, we propagate the prompts for both objects to get their masklets throughout the video.\\n\",\n    \"\\n\",\n    \"Note: when there are multiple objects, the `propagate_in_video` API will return a list of masks for each object.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"17737191-d62b-4611-b2c6-6d0418a9ab74\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# run propagation throughout the video and collect the results in a dict\\n\",\n    \"video_segments = {}  # video_segments contains the per-frame segmentation results\\n\",\n    \"for frame_idx, obj_ids, low_res_masks, video_res_masks, obj_scores in predictor.propagate_in_video(inference_state, start_frame_idx=0, max_frame_num_to_track=300, reverse=False, propagate_preflight=True):\\n\",\n    \"    video_segments[frame_idx] = {\\n\",\n    \"        out_obj_id: (video_res_masks[i] > 0.0).cpu().numpy()\\n\",\n    \"        for i, out_obj_id in enumerate(out_obj_ids)\\n\",\n    \"    }\\n\",\n    \"\\n\",\n    \"# render the segmentation results every few frames\\n\",\n    \"vis_frame_stride = 30\\n\",\n    \"plt.close(\\\"all\\\")\\n\",\n    \"for out_frame_idx in range(0, len(video_frames_for_vis), vis_frame_stride):\\n\",\n    \"    plt.figure(figsize=(6, 4))\\n\",\n    \"    plt.title(f\\\"frame {out_frame_idx}\\\")\\n\",\n    \"    plt.imshow(video_frames_for_vis[out_frame_idx])\\n\",\n    \"    for out_obj_id, out_mask in video_segments[out_frame_idx].items():\\n\",\n    \"        show_mask(out_mask, plt.gca(), obj_id=out_obj_id)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"18a0b9d7-c78f-432b-afb0-11f2ea5b652a\",\n   \"metadata\": {},\n   \"source\": [\n    \"Looks like both children's shirts are well segmented in this video.\\n\",\n    \"\\n\",\n    \"Now you can try SAM 3 on your own videos and use cases! \"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (ipykernel)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.12.11\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "examples/sam3_image_batched_inference.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Copyright (c) Meta Platforms, Inc. and affiliates.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# <a target=\\\"_blank\\\" href=\\\"https://colab.research.google.com/github/facebookresearch/sam3/blob/main/notebooks/sam3_image_batched_inference.ipynb\\\">\\n\",\n    \"#   <img src=\\\"https://colab.research.google.com/assets/colab-badge.svg\\\" alt=\\\"Open In Colab\\\"/>\\n\",\n    \"# </a>\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"using_colab = False\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"if using_colab:\\n\",\n    \"    import torch\\n\",\n    \"    import torchvision\\n\",\n    \"    print(\\\"PyTorch version:\\\", torch.__version__)\\n\",\n    \"    print(\\\"Torchvision version:\\\", torchvision.__version__)\\n\",\n    \"    print(\\\"CUDA is available:\\\", torch.cuda.is_available())\\n\",\n    \"    import sys\\n\",\n    \"    !{sys.executable} -m pip install opencv-python matplotlib scikit-learn\\n\",\n    \"    !{sys.executable} -m pip install 'git+https://github.com/facebookresearch/sam3.git'\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"output\": {\n     \"id\": 1373952277654085,\n     \"loadingStatus\": \"loaded\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from PIL import Image\\n\",\n    \"import requests\\n\",\n    \"from io import BytesIO\\n\",\n    \"import sam3\\n\",\n    \"from sam3.train.data.collator import collate_fn_api as collate\\n\",\n    \"from sam3.model.utils.misc import copy_data_to_device\\n\",\n    \"import os\\n\",\n    \"sam3_root = os.path.join(os.path.dirname(sam3.__file__), \\\"..\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import torch\\n\",\n    \"# turn on tfloat32 for Ampere GPUs\\n\",\n    \"# https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\\n\",\n    \"torch.backends.cuda.matmul.allow_tf32 = True\\n\",\n    \"torch.backends.cudnn.allow_tf32 = True\\n\",\n    \"\\n\",\n    \"# use bfloat16 for the entire notebook. If your card doesn't support it, try float16 instead\\n\",\n    \"torch.autocast(\\\"cuda\\\", dtype=torch.bfloat16).__enter__()\\n\",\n    \"\\n\",\n    \"# inference mode for the whole notebook. Disable if you need gradients\\n\",\n    \"torch.inference_mode().__enter__()\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Utilities\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Plotting\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"This section contains simple utilities to plot masks and bounding masks on top of an image\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"output\": {\n     \"id\": 819341201047004,\n     \"loadingStatus\": \"loaded\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import sys\\n\",\n    \"\\n\",\n    \"sys.path.append(f\\\"{sam3_root}/examples\\\")\\n\",\n    \"\\n\",\n    \"from sam3.visualization_utils import plot_results\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Batching\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"This section contains some utility functions to create datapoints. They are optional, but give some good indication on how they should be created\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from sam3.train.data.sam3_image_dataset import InferenceMetadata, FindQueryLoaded, Image as SAMImage, Datapoint\\n\",\n    \"from typing import List\\n\",\n    \"\\n\",\n    \"GLOBAL_COUNTER = 1\\n\",\n    \"def create_empty_datapoint():\\n\",\n    \"    \\\"\\\"\\\" A datapoint is a single image on which we can apply several queries at once. \\\"\\\"\\\"\\n\",\n    \"    return Datapoint(find_queries=[], images=[])\\n\",\n    \"\\n\",\n    \"def set_image(datapoint, pil_image):\\n\",\n    \"    \\\"\\\"\\\" Add the image to be processed to the datapoint \\\"\\\"\\\"\\n\",\n    \"    w,h = pil_image.size\\n\",\n    \"    datapoint.images = [SAMImage(data=pil_image, objects=[], size=[h,w])]\\n\",\n    \"\\n\",\n    \"def add_text_prompt(datapoint, text_query):\\n\",\n    \"    \\\"\\\"\\\" Add a text query to the datapoint \\\"\\\"\\\"\\n\",\n    \"\\n\",\n    \"    global GLOBAL_COUNTER\\n\",\n    \"    # in this function, we require that the image is already set.\\n\",\n    \"    # that's because we'll get its size to figure out what dimension to resize masks and boxes\\n\",\n    \"    # In practice you're free to set any size you want, just edit the rest of the function\\n\",\n    \"    assert len(datapoint.images) == 1, \\\"please set the image first\\\"\\n\",\n    \"\\n\",\n    \"    w, h = datapoint.images[0].size\\n\",\n    \"    datapoint.find_queries.append(\\n\",\n    \"        FindQueryLoaded(\\n\",\n    \"            query_text=text_query,\\n\",\n    \"            image_id=0,\\n\",\n    \"            object_ids_output=[], # unused for inference\\n\",\n    \"            is_exhaustive=True, # unused for inference\\n\",\n    \"            query_processing_order=0,\\n\",\n    \"            inference_metadata=InferenceMetadata(\\n\",\n    \"                coco_image_id=GLOBAL_COUNTER,\\n\",\n    \"                original_image_id=GLOBAL_COUNTER,\\n\",\n    \"                original_category_id=1,\\n\",\n    \"                original_size=[w, h],\\n\",\n    \"                object_id=0,\\n\",\n    \"                frame_index=0,\\n\",\n    \"            )\\n\",\n    \"        )\\n\",\n    \"    )\\n\",\n    \"    GLOBAL_COUNTER += 1\\n\",\n    \"    return GLOBAL_COUNTER - 1\\n\",\n    \"\\n\",\n    \"def add_visual_prompt(datapoint, boxes:List[List[float]], labels:List[bool], text_prompt=\\\"visual\\\"):\\n\",\n    \"    \\\"\\\"\\\" Add a visual query to the datapoint.\\n\",\n    \"    The bboxes are expected in XYXY format (top left and bottom right corners)\\n\",\n    \"    For each bbox, we expect a label (true or false). The model tries to find boxes that ressemble the positive ones while avoiding the negative ones\\n\",\n    \"    We can also give a text_prompt as an additional hint. It's not mandatory, leave it to \\\"visual\\\" if you want the model to solely rely on the boxes.\\n\",\n    \"\\n\",\n    \"    Note that the model expects the prompt to be consistent. If the text reads \\\"elephant\\\" but the provided boxe points to a dog, the results will be undefined.\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"\\n\",\n    \"    global GLOBAL_COUNTER\\n\",\n    \"    # in this function, we require that the image is already set.\\n\",\n    \"    # that's because we'll get its size to figure out what dimension to resize masks and boxes\\n\",\n    \"    # In practice you're free to set any size you want, just edit the rest of the function\\n\",\n    \"    assert len(datapoint.images) == 1, \\\"please set the image first\\\"\\n\",\n    \"    assert len(boxes) > 0, \\\"please provide at least one box\\\"\\n\",\n    \"    assert len(boxes) == len(labels), f\\\"Expecting one label per box. Found {len(boxes)} boxes but {len(labels)} labels\\\"\\n\",\n    \"    for b in boxes:\\n\",\n    \"        assert len(b) == 4, f\\\"Boxes must have 4 coordinates, found {len(b)}\\\"\\n\",\n    \"\\n\",\n    \"    labels = torch.tensor(labels, dtype=torch.bool).view(-1)\\n\",\n    \"    if not labels.any().item() and text_prompt==\\\"visual\\\":\\n\",\n    \"        print(\\\"Warning: you provided no positive box, nor any text prompt. The prompt is ambiguous and the results will be undefined\\\")\\n\",\n    \"    w, h = datapoint.images[0].size\\n\",\n    \"    datapoint.find_queries.append(\\n\",\n    \"        FindQueryLoaded(\\n\",\n    \"            query_text=text_prompt,\\n\",\n    \"            image_id=0,\\n\",\n    \"            object_ids_output=[], # unused for inference\\n\",\n    \"            is_exhaustive=True, # unused for inference\\n\",\n    \"            query_processing_order=0,\\n\",\n    \"            input_bbox=torch.tensor(boxes, dtype=torch.float).view(-1,4),\\n\",\n    \"            input_bbox_label=labels,\\n\",\n    \"            inference_metadata=InferenceMetadata(\\n\",\n    \"                coco_image_id=GLOBAL_COUNTER,\\n\",\n    \"                original_image_id=GLOBAL_COUNTER,\\n\",\n    \"                original_category_id=1,\\n\",\n    \"                original_size=[w, h],\\n\",\n    \"                object_id=0,\\n\",\n    \"                frame_index=0,\\n\",\n    \"            )\\n\",\n    \"        )\\n\",\n    \"    )\\n\",\n    \"    GLOBAL_COUNTER += 1\\n\",\n    \"    return GLOBAL_COUNTER - 1\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Loading\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"First we load our model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from sam3 import build_sam3_image_model\\n\",\n    \"\\n\",\n    \"bpe_path = f\\\"{sam3_root}/assets/bpe_simple_vocab_16e6.txt.gz\\\"\\n\",\n    \"model = build_sam3_image_model(bpe_path=bpe_path)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Then our validation transforms\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from sam3.train.transforms.basic_for_api import ComposeAPI, RandomResizeAPI, ToTensorAPI, NormalizeAPI\\n\",\n    \"\\n\",\n    \"from sam3.model.position_encoding import PositionEmbeddingSine\\n\",\n    \"transform = ComposeAPI(\\n\",\n    \"    transforms=[\\n\",\n    \"        RandomResizeAPI(sizes=1008, max_size=1008, square=True, consistent_transform=False),\\n\",\n    \"        ToTensorAPI(),\\n\",\n    \"        NormalizeAPI(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),\\n\",\n    \"    ]\\n\",\n    \")\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"And finally our postprocessor\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from sam3.eval.postprocessors import PostProcessImage\\n\",\n    \"postprocessor = PostProcessImage(\\n\",\n    \"    max_dets_per_img=-1,       # if this number is positive, the processor will return topk. For this demo we instead limit by confidence, see below\\n\",\n    \"    iou_type=\\\"segm\\\",           # we want masks\\n\",\n    \"    use_original_sizes_box=True,   # our boxes should be resized to the image size\\n\",\n    \"    use_original_sizes_mask=True,   # our masks should be resized to the image size\\n\",\n    \"    convert_mask_to_rle=False, # the postprocessor supports efficient conversion to RLE format. In this demo we prefer the binary format for easy plotting\\n\",\n    \"    detection_threshold=0.5,   # Only return confident detections\\n\",\n    \"    to_cpu=False,\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Inference\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"For inference, we proceed as follows:\\n\",\n    \"- Create each datapoint one by one, using the functions above. Each query that we make will give us a unique id, which is then used after post-processing to retrieve the results\\n\",\n    \"- Each datapoint must be transformed according to are pre-processing transforms (basically resize to 1008x1008, normalize)\\n\",\n    \"- We then collate all datapoints into a batch and forward it to the model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Image 1, we'll use two text prompts\\n\",\n    \"\\n\",\n    \"img1 = Image.open(BytesIO(requests.get(\\\"http://images.cocodataset.org/val2017/000000077595.jpg\\\").content))\\n\",\n    \"datapoint1 = create_empty_datapoint()\\n\",\n    \"set_image(datapoint1, img1)\\n\",\n    \"id1 = add_text_prompt(datapoint1, \\\"cat\\\")\\n\",\n    \"id2 = add_text_prompt(datapoint1, \\\"laptop\\\")\\n\",\n    \"\\n\",\n    \"datapoint1 = transform(datapoint1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Image 2, one text prompt, some visual prompt\\n\",\n    \"img2 = Image.open(BytesIO(requests.get(\\\"http://images.cocodataset.org/val2017/000000136466.jpg\\\").content))\\n\",\n    \"\\n\",\n    \"# img2 = Image.open(f\\\"{sam3_root}/assets/images/test_image.jpg\\\")\\n\",\n    \"datapoint2 = create_empty_datapoint()\\n\",\n    \"set_image(datapoint2, img2)\\n\",\n    \"id3 = add_text_prompt(datapoint2, \\\"pot\\\")\\n\",\n    \"# we trying to find the dials on the oven. Let's give a positive box\\n\",\n    \"id4 = add_visual_prompt(datapoint2, boxes=[[ 59, 144,  76, 163]], labels=[True])\\n\",\n    \"# Let's also get the oven start/stop button\\n\",\n    \"id5 = add_visual_prompt(datapoint2, boxes=[[ 59, 144,  76, 163],[ 87, 148, 104, 159]], labels=[True, True])\\n\",\n    \"# Next, let's try to find the pot handles. With the text prompt \\\"handle\\\" (vague on purpose), the model also finds the oven's handles\\n\",\n    \"# We could make the text query more precise (try it!) but for this example, we instead want to leverage a negative prompt\\n\",\n    \"# First, let's see what happens with just the text prompt\\n\",\n    \"id6 = add_text_prompt(datapoint2, \\\"handle\\\")\\n\",\n    \"# now the same but adding the negative prompt\\n\",\n    \"id7 = add_visual_prompt(datapoint2, boxes=[[ 40, 183, 318, 204]], labels=[False], text_prompt=\\\"handle\\\")\\n\",\n    \"\\n\",\n    \"datapoint2 = transform(datapoint2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Collate then move to cuda\\n\",\n    \"batch = collate([datapoint1, datapoint2], dict_key=\\\"dummy\\\")[\\\"dummy\\\"]\\n\",\n    \"batch = copy_data_to_device(batch, torch.device(\\\"cuda\\\"), non_blocking=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/haithamkhedr/.conda/envs/sam3/lib/python3.12/site-packages/torch/_inductor/lowering.py:1917: UserWarning: Torchinductor does not support code generation for complex operators. Performance may be worse than eager.\\n\",\n      \"  warnings.warn(\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Forward. Note that the first forward will be very slow due to compilation\\n\",\n    \"output = model(batch)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"processed_results = postprocessor.process_results(output, batch.find_metadatas)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Plotting\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {\n    \"output\": {\n     \"id\": 3372746802877191,\n     \"loadingStatus\": \"loaded\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"found 1 object(s)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\",\n      \"text/plain\": [\n       \"<Figure size 1200x800 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plot_results(img1, processed_results[id1])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {\n    \"output\": {\n     \"id\": 1534672901285632,\n     \"loadingStatus\": \"loaded\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"found 1 object(s)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\",\n      \"text/plain\": [\n       \"<Figure size 1200x800 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plot_results(img1, processed_results[id2])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {\n    \"output\": {\n     \"id\": 4200415163576837,\n     \"loadingStatus\": \"loaded\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"found 2 object(s)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAdEAAAKYCAYAAAAsfMKiAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjYsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvq6yFwwAAAAlwSFlzAAAPYQAAD2EBqD+naQABAABJREFUeJzs/Xe8J9ld3wm/v+dU1S/c1LknJ2kUZpQjkmyCJJPB4AA2AsvgxBrMPg+Lwawfw3r3sUlO6xWsl/V68WMWLLwkg7EIIpiknDUajSb2dM90vn3TL1Sdc777xzmnqn6/e7unR/CynrH7K/XceyucqjrhGz7fcERVlRt0g27QDbpBN+gGPWsyn+sXuEE36AbdoBt0g56rdEOI3qAbdINu0A26QZ8l3RCiN+gG3aAbdINu0GdJN4ToDbpBN+gG3aAb9FnSDSF6g27QDbpBN+gGfZZ0Q4jeoBt0g27QDbpBnyXdEKI36AbdoBt0g27QZ0k3hOgNukE36AbdoBv0WdINIXqDbtANukE36AZ9lnRDiN6gG3SDbtANukGfJX1OheiP/uiPctdddzEcDnn961/P+973vs/l69ygG3SDbtANukHPij5nQvSd73wn3/md38n3f//386EPfYiXv/zlfMmXfAnnz5//XL3SDbpBN+gG3aAb9KxIPlcF6F//+tfz2te+lne84x0AhBC4/fbb+Vt/62/xd/7O37nmvSEEnnrqKdbW1hCR/xyve4Nu0A26QTfovxJSVXZ2drjlllsw5tq2ZvGf6Z0WqK5rPvjBD/K93/u97TFjDG9961v5wz/8w33Xz+dz5vN5+/eZM2e47777/rO86w26QTfoBt2g/zrpySef5LbbbrvmNZ8TIXrx4kW895w8eXLh+MmTJ3nwwQf3Xf8DP/AD/P2///f3Hf+hf/BXGQ4rIGoOIkIIUUh7FxAx2NIgprNWlYD6AAqq4EMghIB3LrXj8T7gQ0CDcvTocT796c/wqU89yOaVLQYrYxSwpkRVURVUIWggBIcVEAxBBVc3NDPlrV/8Oo4cPoQRiw+KMZagAc3voIp4x5GRYWVjlUmjbO56Dm8cZb51mt3tc6hYbDkkGIepBWMdhQTO7Wzw6w/fx6NbBYwGoMR/BigMDAyHB4Z7j67ymjuO8OqXneSuu4+xVgw5+/QWH/7UU/zcex/hYzsz1AMB8AIugDaIWo5WntffssOfuf8CWE/jB8howNzNmE62KNWztnYLjZ9hLBhr0QCiSkBQFAEkCC44duo9FEAkIglp7DAGEUE1jpeIYMTE64j9TEIexJA+FATide1fEv8vfQ1Srwu1uNo1+WjAp3kCwcd5V1VV+wwAs9SGiLAM98TnKGjYf20PHMrvo6qQ57HGbxQBRCHk6/Px/nw/gDQsnVG2tifMpnV+KHEC7X+//C4hBKbzOePhEGNMWguLfXzV/s7HVYkTLh3L3y324Pv61/feJX+DhoCmBWDo+kST1yr3rbbz5urzQXv9k58Rvy+25Z3Hq6ewFcYYuk9VBCHk+w8ay17rGvL395+e58bCCx3QF9379eeMoojEL68qyy3HjjCoqjhvNL6HmNjHIYQ4nnnu9l6k/e4QaOYTxAilLRC1PPToR7nn+S+jKCtCCKCKprbT8kVSD4f0jf1hDxrnaZ7DGiLTMqZA8aj6q31u790yX9DEz9P8k8X+8hoWu1IPHtv+selszvd8/0+xtrbGM9HnRIg+W/re7/1evvM7v7P9e3t7m9tvv53hsGI0GgB9IapYC84FRISisEgyx5XIPNR3Ey8khqChxHuPJqbhfaBpHKhy7NhRVldX2d7bJYjiHFy5soPzgeBjO4U1kbGH+Hs1KBkPVplu74EayqKgLAu805bpBI2LLYinwrC+OkAKEwWYGHYnDSdP3MOgUmbzPbwKYgaUtkBpMKahbArMoMSUK6gxSdAQGW5hwVoaK8wpmWmByphDozVWBitw04C7JspND2/y0PQKdeEJLi2CwmG8Rb1h2xvOTCse37N83vMuc3F7AMMKG8CYGX4+Y3VjlbkzIAFrDEIBwdMkxmYAq0ITDI1xcaGbbslKFqgLjNckBtU/lgSFib9Kb3Xk+yUJgWUmviBcllalxAuuOQ8lzyGB4JUQ4twZDKrF67Kg7/2tB5xPb3LAMw5+rqR50wlRbW846L5+C+0jNQuIzIRge3uXf/szfwBquwce/Hq9w5HxGSO9G/Qqv/c+ZKHdhcG76ttf9Z5nvviABy8KyKsJUt3XxlJbSRjHsbwel9If0Wv2LG7PSiuAhoa//vY/yZFDo9ZwaBXOnlLRqRydMpN5pPeOwWAFk5RadbC2MmY8rLBlFe9tBaWhL7KMSBKQvbfTvB72C1Fry2joqG+/WclKQrorv3eQxb+1O9+ng4ToQYK0/V11Ye0+E31OhOixY8ew1nLu3LmF4+fOneOmm27ad/1gMGAwGOw7vqClt5Q7kshkUqccuBxEkCR8jbU458iavDGCMYHJdEpZVoxGIwpbsrc3xzvY3trD+0BIE6QqDUVR4mqPMTAYOSQM8C5EoegDYgNWhNBaAnESFUYpC4OxltncM68Dzgu7exOObhxntHICzEVmsx2MSHxO0GT1Faj4aG0HTfxVsjGGAg6Y+sDOvGY684RZoLFKObAcPjLg+MaAlacMEKgRfGLOEgwBQx3g9LbhPU8MedXd4KnBW9R71DkmkwnOOYLzqAQwcRJGqz5NfukWWLtsVRNKkDV0OquUq/HV/nLvhENnCZEs1wPGujdfludOu1iSRXTQfMlHD9JeD35HOIjBLlpsy4KmZ5H1W5OOYSw0rZBMgIMfp9EiaXtEUl91TTKZzEEt3/N3vpvbbrt1Sdb1rdyFjyCEEP1FIktMfr9wPGgor0eYHdyqLh5oJcYfUVA90/N73ZyFj2CuMk+v1ka2tvOBfObaatCzes/UzpnTZ/jBH/xhptMawaRn6uLCMtIqYR3JwjpUVWxRxjEKikr8O6g+c2Sq9NdoaC3I/delFS2CJByhnR+qIGHh4jzlriXk2ulxgPLcR3wWfs/tP9N39ehzIkSrquLVr3417373u/mar/kaIJr87373u/n2b//2627nICaWBWAWbnnQ+8uVpEH2mWGGNbI1qyFgxBJUKMuCY8eOsbm5w9NnHkcIlEBhFbVgDFQVrIzHbF2ZMG8adndn7F45yy3HjrFSFRQaEOcoqhJHwIWAwWKMYAuDtdB4ZTJzzGslqMUWBWfOXODmmw4xGIIER93sEaRArcFIgcUiGvB4RE18GSNgooIQATBhrsre3LG717CzO6GxluFQ2ViDO05WHHpUcY3FF4YgQJAEExaYYpfN2vOBp4Z85vyA249O8WKpvcU6xfiA8QGrgBiMRjNRFUoExIBEq3+BZ0ggwoZCyBqgSO/fVUeeHmZ0lStCUlNMuySupqkuTaprXLvI6Poa8P42n8Equ+rjdf/f0i3ujuPpPstsnyxLt1wbtiTiBAK333Yr99xz1wHXHCBIVfE+YGyy+PuX9L/hAIXm6u9y/UJV2+/vvdZ/hhjJ7hELHxx/uw5puihE+wP22b/7fj6oiz+X5rAuKK+LwrIPjwvSIkFGbJZa2MJgiqr3iL4V2PVIp5gufl7UExfXrySXTUTpQvue+RzaiesWypX+3O8Jyn5PCIhmJV0XXmVZkObmtK90Xgd9zuDc7/zO7+Ttb387r3nNa3jd617HP/tn/4y9vT2++Zu/+Vm0YqDVBKOmsqhdhHhcW6/cvonet35GoxHee1zj8SLY0hJqh7UF99//Eu66427uvOkzPPjQwzz55FMMRhVlZRAbqKqC48ePEFyD3ROsGXDTyRFv/cIv4OSJNbxz1E1DIPqdrLUU3lMYSzkcEIBLV/aoXUHtFecarClwzZwz53Y4emid9fVVppsfZ17vUNghngBBGZbrYDqGi+nNZI0Ce14HJlPLzu6UiVdWRsKho2MOHxnx8hfu8TufPMfOtjB1c9Q3ECxeADxaFOh8wmSn4d/8rvA33zRn159nUjuausZNawaXd5k3M9QEjBgMFsVFZSZZhwZo1LM3m6WFzALnb4Vnb5wW/ZodLUK3tGMvRtp7oohebK+9t3cP6erM//s66UJkXoKUNPl/NN1fWLsoLPoYc6s1L35HH/K9FvtcFubSNxt6DLh1WRwAUy8zhNCDIsfjRSi6Tz/27s/wnocv8Y63v4q1Yclf+NE/AOCd3/ZGNCk5ueUzmxP+u5/+CMfWBrzjL736Gb6qo1/80Gl+64HznN2aocDf+5r7uP/WjWe8TxCQJcTgACv++gy8rkM/eWaL/+kXP8WLb1nj+7/m/qtcrwf+3lemzlye8q9+9zEeOrvDsLS86d5jfOMb78C28RmLY/KRJ67wM+8/zZnNKasDy1vvP8nXvOoWRIQL23O+4//6yL63+Jff8mpWBgX/9r1P8h8+epYf/rqXcPOh0ULbnZskvmpncS3xwZ4AXfABq2LLgqDR6ja2oLQFzjUYY6LBot23d/B2T7HsPTeuJxMN4L5RvKBn6r55fDVaRpcyD1y+O3nFk1FxgPF1XU87mD5nQvTrv/7ruXDhAt/3fd/H2bNnecUrXsG73vWufcFG1yJZYKK29WdmCiHCp5HvdA7vhfuJA+m9wzmHkQJjCzBR07bWxiCK6R5lBS979fN53Rtfxb//9+/i4UefIKhwZOMoqHJlc5e6bhgPxtx12518xVe9iXMXnuLKdIeyGGAHFVWhuGaGiIl+VIR66tira+YuO9wthSVGrhihbmZc3LTM6hWO3/wKLpz+IBKa6CMDGjejYA2fp4cCRjACQeKEdAoz79mezlg9NGKy0/C7j17k1MVtfCi49ehR/HybiYVtX3B22nC0hKd3PGE+Ze2xn2OlhEuX4B2nAoMi+gZ9UJpmzrA6u2hJiCxaC4kUTXA2HGhvHKDN75MZ7TF5FrP/Whdea8EuC7t9H9S+87NaiH+UVfvHSp6v/PJX7Tv61OaU//TpC7zlvpOsDUsAvuOLX/CMrXUs9PpAsdopr7rrMO979DIXduapjevvnAXDNy/mPgPu/5qQkC7w66CZ9QzPa62oLpAmHsuCJyopP/IfP82l3Tlf97rbefziHu/6+FlGleXrXn/7wpMRePLyhH/0rofYGBX8pTfdyR985hI/877THF6p+MIXHW+f/bp7DvP6e462fw+KKBq+5CUn+aWPPM3PfuApvv2tzzvgrU36xIjSpV646ndnvqiAdw5bFgvdqsSYEe8DRRkV1byi+0FFB7Tc/3La4Lpeh6j6ZyVA9ynEprdee9Zx/1OjSy27NDoh3L3Hs1ucn9PAom//9m9/VvDtMuVO1MVVtKABBQ2I2Oir0xip1++iHOSTf3Y+tS5wI8O9aBIA8ymf94bXY+2Qxx47zZlTmwwGlkFZYXXA+uoaR46s4XB4LLNGccFjJGBNw6gyFEaS4FZqJ7gQA1V80GTwRN9pUI8C82aO3yspd1ZYWb2Vevc8IXiCDwQN+OBTXEjHwrJVqgJBDXMPl3ZnfPDhp3nlHbcz9AV+e8a57T2895SDERuDwKo41sZzxHsuNsJ8b8JKJfx3f/t7uOnmW1mrlCNjT2kDTj2zyYTxeJ0MDi6y0kVSFB98O1yL01UOFKLd2F7r6LKG9Mz35jdavmL5rWXprC5fdKAsX4SrrvZyBz332scO6tP+O+5/+4OPwenTp/nBH/phZrNmX5vvfuAcqvDGe49FBRT457/2EBCPocovfPAMv/qJcxgjvPnFJ9qnRwsddg5oF6IQWx1G1vPnXxeFyqfP7nBhZ57ebMEsuQpFq/F//IUHuOf4CjcdGvLRU1c4ujLgr33R3dx7co3ffvA8/+I3H+Vlt2+gGp/xj/7CywD4yT84xYNPb+O8cs+JFd72hju5+/hK23rtAu/49Yd5/2OXObk+bNsMquzOXItGmF7kvxFhZVDw0VNXOLs143X3HOGrXnkLs8bznkcu866Pn22FaJ8+/uQWPiivu+cob7nvBEdXKx54aptf+/jZBSF62+Exr7zzEKNqMXr58ErFC29a432PXmZ3dmfbt/0BaZWHLCQ0W/PJbJMcGJTNuITdFVGBynxQk7isquwjjYhA3w2zMEpLwupatHzJgoCT/DH0lJar8IQkE/pyIN/Tfl9vzbZWbDqh6D6D61r0nIjOvRotaiH9ju0LUu2OJ7VVl+6PC8FEQbkM+0V3HhBdhNoE6vkex4+f4MUvvpeqrDh16mkGpWV9ZZWiEDYOb3Di5qPMmhqxQ0LwOI1C2amnEMWUgcZ7ah9wzsapuQB/CD5Ez178NkfdzLm8WXHz4ZOUgwlhto0GT1kM0EKJvseuQzS3KRGsrD1c3p1y6tQl7jtxko1xye3HVrFSw1zZnjRMnEdRRoVhEnyMoE2M4uQtt3Lb7XdTGTi55lgZBATP3mTCeLzW+hxS73E1hu+96xtwvev3Hdwvbg6Y3AtW6UGS6ICWFt9M912+BMgunLk+IXqtd95vue63shfPdSJyfx8/swfnYCF6LQvsY09ewQjce3J1/zspfPRUhB+PrFT82dfexu89dHHh/os7c/7Wv/nQgW0fbyHfZ3rna31Vd+7RC3u8/I5DfPFLbuLnP3iGf/quz/DP3vaK9vzHn9ziq155C6+75wijyvL3f/4BTm9O+YqX38z6qORn3vckP/DLn+If/8XunkfO7/Gy2w/x5S+/eaHNK5Oa7/jJjxz4RsfWKt7xTa/i7NYMgKOrMRhyWFrWhgVXJg1bk5pDSxD6oXGZnrnLhe05H39yC4Cz2/OF637+g2f4uQ+eYVxZ3nzfCf7i593eWtUvuGmVB5/e4VNPb/Pau4/s66NkG5CFRF6ALSyerWpoBWsWftoKWI2GiMY0PWMsOfL8mgUJpFtDBwm/qwUSxgS5HBuxDJsvHGqF9YHUE+RtjEHPT3rgLVf/mn30nBaiKRYlwisqZL/oVbWe3Jnxot61BpGU/tLT640xaFDUpCmgBpUBgTnbW5e4556buf2242xe2sS7wHg4YDioMMMSXxjm8xllNcC7CTnmLKhldzJDR0KjigtCIApMsYIVQxBD0AiVqhE0hPR+jq0ru6yURzi6fpLG14jAeLSGaWaEJvcDtH6FBON4YO4D25MZsjtnd/cKJ286xEuO38qL5kd58JNPcXH3KS5uTrhSN0gw7NQOHwxSZAEV4zznQbH/+Acxf/jbmJ/+KcrBAPun3gyA+/XfWNAAAfTUKYq/8s3oyZP4n/wpWua/pBHuE6CnnqB4xzuQBx6A4ZDw5jfj//rfgLK8+qS4Kt/tRM1BAmtZ9h5ov/3qu7A/8sOEL/5i/N/+nqu/wzPRJz+O/bEfg0cegfUNwld+JXzTX+qshCUyv/aryL/9aXj6aTh6FP2L34B+xVe0byoAu7uYv/ZXkPPn0Re/mPCOH4tnf+nfY/7ZP8X/o38Mr3zlVV5oP8s4tzVjbVhSFgfnbH7sySsAfPFLbuKt99/E7UdW+L6f+3h7fm1UXhX+HRZXZ7h9LON6AzzuODrmL3zeHQB88PFNTl2a8OTlSXv+vlvX+YY33AEIpy9POL055aaNId/0pjsB+PTTO3zoiU0+9dQ2K4P4vXccGfP1yWr84GObnLoc27z18Ijv+FPPb6HN/pQdlAf1VW+uX4Ve/7yjvObhS3zgsU2+4//6CONkaWaEbVBa/syrb+WuY2PmLvCzHzjNL3/kaW7eGPLm+yICcHQlCuZzW3NosQMWJzRJaPSQu26e92d891OT4zLmbUbjJOd9FtZ2Vp8x9B/ZKZqLgll6c1y1G98o5HtGRJoJCyK37cMDVmdnYO47pf07Ei59sIq/0Nx103NbiHoh+BTUIYqhRI0nEDptKg1uuxg1+vLEFAQPIibBED71b/wpYukSUXJeFWCUwlQ457h05QplWbJ+7BDGGOq6Zq92MPdYHzW1cuARMWhIOahiCDpgdxqwRQrOEI814H1MtwoaI21LMdRNjZgKVYtXwRt48skLjF9yD+XGCkWzh7u4h7UlAQPOd4vEAOoAi9dAA2wMVjl60woOYeqUYsWwvrLGC+6/mU9c2GG7rvHOsRMcF4KyZgRjQxsWY4xSnDnF+Ld/jdmf+krKlUPQ7BK+9+9BBsujwpq0xX4WWupG6a3jbJIuz1rvKb7v+5ALF/Df/M3Iww9jf+EXYDTGf8u3dG3t++X6aeEW78Haq5xfZi60qzIz+vzfZ3yZ3V3sf//fgwjhW/8b5D1/iP3XP4E/ehT9iq/Y38YH3o/5oR9E774b/Y7/Fvm5n8X8k3+Mv+XWBaEo/+Qfwc7Ovsfpl3wp+i//d8z/+a8Ir3zHtbrjQDrwS5YthgO40fa0aeHfZTq+NuDVrbV0rScvKrWtBfKMLHCRjq5WXZMHIRnPYu5sTxv++a8/fOC5Y2sVr77rMDdtDAG4uDMDlFnj2Z07RpXl0HiAV8X5gBEorcEa4bu+7IU8tTllZ9Ywqz0/8B8+zd3HVgBhfVzy51/XVc25vFfz0+95klNJUYjFEzoFUTpQvbU0sgCRhQAinxRZk1T86DrKwi1CnyFCtuRxVkQ8RipUhJD+ZzIKtiC7U4GU9md3JlOK1Us54x2fjZZitx7b2aCL66MfKHW11evN4mwJPcncyYXkTs3Q97NgKM9tIUrs7BCgEaUswajHBEVUEFsBLs2lPHFSSoI2BI2RvWJyqoLEAi0Jxl1Oys33xzSaFAEaAnVdR6EbtI1YsxKDhlRMLIIg0WLWFodQNCUB52hSVZ+KyWTsv0iaHjjX0AQP5QiM5eMPPc5tJ1dpzHEuTQJuHjAiBDFgNL1/cveH0MLDu/WMc5u7nDh0jEHZYFUYHxuDEY6MVji5MqeplWZvxtHgmBjPMKcxSAx8OvJb/x7RwNYb3ko1K1ixAfMD/xMA/s3RIuWnfhL5uZ8HazBf9uWLfRgCsr3dg3R76rI1sLaG+cAHME89hf8Tf4Lw5/48Op1ifud3ML/4CwtC9Gokv/ouih/5EcLrXhf//sQn4e678H/7u+G22zH/+iew/+b/R/iCL0QunIeHHsK961fhkUcwP/7jyKcfBGvRl7yE8De+FW65pWt8axv7338v8tGPos97Pv67/zZy2x3gHOztscwoEKKAXl1FfuM3kN3d+E1f8zXoq1+N+ctvx/zCz+Nb67J363vfG/vsy76c8GVfjjiH/Wf/FPnFn0eTEJV3/UfkPe9B/8a3Iv/8f15soKrQN7wR8+u/Bo8/Bnfdtf/9DhBIJ9aHnNmcULtAdYDl+LLbD/EfPvo0v/aJs6wOC37/M4tw7qFxxd/96oNLc/bbe+CpLZ6+MmV7Gv2nH358k3NbM95830nOb8/4jn/zYTZGJT/+La9dmisdnbo04Z3vPdX+fmSl4vYjKwvWaPxM5ZZDI247MuL05Sn/1x88wdqo5KOnrrA+Krjv1nWeuLgX27k84Z3vfbL9PbY5BuDvftWLYUG8979LePkdhzm5PuAjp67wSx9+iicuTvBB+ZKXnASUB5+KEcDPP7nK//fPvAQEfuJ3H+eOo2OmtedXPvY0IvC1r7kVgN/45DkeenqHF9y0igvKr3485te/8Kaums7l3Rj1f3J90Cqt2QK0hSUKsigcrbULQUJRzqYKailKPZ9rmpqiHLb9rkSe570nhEBRlFhkH59sW0gI1rWo70vtqhtdrc0/Gi1E8S6/62f5uOe0EK2DUpoGUaVwBqXAmSJVQVOMcbGqhfTKQyHkpN8YzQWili5aLTnJD8DigV4AkrRQcjcJl3H7aCF3Yd8SS9uFPKlMLPZgYolCI6ASC8upxuIHhmhcAiCBppljdMxuLTx2rmantuw2K+jcIUahTImrAipZGHewzTwEmrkwm89ofEUIBfNJLGF2aKNi/YplsAdMAhuVgHM0GisMJcyFlY9/ADWW7XvuZzg3lIMhuRSGosj734/9V/8KPX4c/YZvRH7rNxcH7vx5Bm//SweOqZ48Sf1vfhI5cyb+fTzCVYxGsLGBXL4Mm5tw+PDSjcvYcOqyD3+Y8Je/GT1xEvvLv4T5h/8A/2P/ojv/B79P+KZvgi/6omgl/p3vga0twtv/MjKbYn76p5FTp/D/+7+k1a8/9MGFNvmH/5DwY/8C+cQnsN/13x38XS9/Of6f/FPkzOn494n4XXIyFRfJ38vSJxyJFpt87GPwJ/4k8pEPx7/PnIkz88wZ5B3/C/qt/w16x50HPpuXvAR+/deQD3wAveuug56yj152+wZPXp7w8Pkd7rtlY9/5l99xiK973W286+Nn+YUPnuYN9x7jU09tt8beoDC87PZD13yGovzWp87znx680B775Y88DcBb7rupFVDWXPtdn39ilfPbcz78xCZ3HB3z17/wnlagLZMR4bu//EX85B88wW8/eAEflBfdssbb3nAnq4POTfC8Eytc2J7z4VOb3HFkzF/7ortb4f/S29cXrG/pzTsh6oHf9eUv5P/83cf5mfc9yaCwfPFLTvJnX7tcg1Xb+x6/tMdvP3ge55U7j435K59/Ny+9bQMEbjk05Pc+fZEPPb5J7QMn1od8y6vu4g3P7yJ1Hzq7gzXCfbeup6Y7CNlItKs0aAq0lJhHfpBp3grXxQpA2QqFmJqWa2wIfaRpPxIj/Uf0kOR+29Hw6PjqQXQ1gXo91x+EWxxcqOezo+e0EDViIiQLFAUE7wnBoGIjrNssVgbKCbUhZIGaoI4eJBCh4RhMI7qY0Eu6qp9KkwXkcvGGHOi0sNh6UWM54CwjmYqS3QrBK6hHNbfrk983ttn4QINha9tzcdIwmUXIRdXHFZz1gVwaD21ha48yrR3TpqZxjuAV5zymEqqhZTgoGZaWwgaCVdYUtlLwQHxfZXDuNH51HVcNqD3sNUP6FSblgx8AIHzVV8NXfiV6z93Y//Y7Uu8BhzZo/s73ZlU53ZWeMByysAj7P6854a+ymF79GsLXfz0ExfzWb2Ieegi/tdWdf8tbCN/wttjCe9+LbG4SXv1qwtveFr/3D9+DPP4YPPZ4d89rcpuhbTNsbaF33on/u3/34Hc7fOiAlzvoGxb9QPqnvwb9gz9Afu93KX7vd9GVFEGa+sL8438Ed92Nvvo1yKceiOeaJvpPb745Xno8RXg+9dSBfXTQ+77l/pv4lY89zXsevtQK0Xd+25val1aEr3nVbfyZ19xOVl6+8Y13XWf7+SnC33zL8/m2t9x74PknL0VL8ktf1q9i1u+d+LMqDH/rT72gPZ+nyRe+6ARf+KITsMTcT6wP+c4vfWGvva7t+2/d4N/+zc97xjdfaHNh6sXjtx8Z8/f+9H29p3Z0/60b/HR6RhZA3/8193XtLrySct8t6/wPX5ut+v0i4cqk5sGnd3j9847EdCQN5PCzrqekFYwhFZLpr7/OH5nmYOKPywFD/SjX/Hu/iMFVVmH0pR7AD58pf3tf3vNnY6FmI2JJSC9ULmvfVNt7rpee00K0siWWAq+eUATUO9RXsf6sRPjSSNaGsualiCghREc5ABrr2EKCVtNNosuDlgSsRMuzFcy9fyGEJe2oS0BeTEjuhCjJ+2qsoiIYzRWUPCIFgei7kFTbdO4dAcvWDC7ueeYzj9gS1QYJAYLpyv+ZbJEqueDsxDVMnWPmPLWL6THVsKAoLKOyYrWqGFfgGmVcwNSaVuvMPjtN0t8r7DZV72ufgUSQK9uUP/gDB57Wkydp3vBG9NYIZcn5VBpyOoXtbXQ8bq3QfaL2oIe3jEL3HyZbuskHdJX3vT5S5IknsP/gHxx89uUvx7/yVeit0RqRc+fiG+XSl7fE78UH8A41BikKGI8I//yfwxOnYDpBHn8c+Uc/gt6bhMbZp5Fz57B/6Ru7V374Yczf+GuEf//L6UBihNdIC+h9MAC3Hh7z+S88zu88eJ6ve93trA5LFvrw+nnMMzzt6g098NQ2dx4d8xWvuGXpzNWZaGcxpdYXFLX9eth+ptxX6p6Z9keU9ltK/AZJJ/LaOfgGkWs99er99B8/dhYR4c+95rb22oiA9dvuhOVBgqn93XT+S9VYFGb5upBT1PIn6OL6yX3eqVvL33lwKsxyyuLV3nPZirxa5br2dxJC1lNSW6SZzCJaayba289ifj+nhagpSw4fWWc63WM2rQlSEJhjg0SBIyUh1DFeRDOu0NpURMHWCThyfckEOyzXVoz/ohVqjGlLBWbqBi4WTADBmJRj2oNOcgGHDAFnAYUqNvk01QR8MqRtAahFneBmHrUlYTJl3lTMQolorM2L7ZSCNvs5+UZDUIKHoBbvSnYbz2YzpXSWgTOsujFHigHTUcneuOTcZAiqXNmdYrxvFQoD1CduYXDmCaSewWBA7btFZQyE17wWfvb/xvzSv0fXNpDfevfiwB05Qv0Pf3DJEo2/SqqRrK95LXrLLcj73of5d/8OeeQRxHv8n/7T8dKPfoTyu76L8KIX4f+Xdyy20yP5wAcw7/y38PTTyN4e4QUvgI31xYvSlND770cPH0Y+8hHMT/0UMpshjz2K3n473H03PPpIr813IrnNe1+AbmzA856H/6EfPugt0PVkq7/lLei/+j+QX/tV5JZbMO+JW/+Fr/3aeOWv/1qMAP7CL0L/3t+D3V3kJ34Cnn8vXLqI/Lt/hw4G6Nd/fbzvv/1/wyymVMgTj2P+9U+gt95G+NZvbeeyXEgb3d+chdHVWPViH/7Nt7yAv/mWHCd5wFXtdH92ZdKuhxTlG990J9CHqBefcf+tG7zz2964cNf+dpb/WLymQ3sOvn/xmXGiLNquPQVNO9SrRbr2vYks/LksELLFuPwG10jg4C9+3h38xRSd3N3Q8bj8nAydRjdWgqlaxCzvgFIgbcGD+HUhdAIuBM9sNsM5F5E6JaYALgxNDkpanBdGJEkcc/UpeA3q8+Hroa5PUyH8PH6t8G17J1roz/6VgOe4EP2f3/GjvPktb+DzXv1qDo8P89TlTUwlJKci1pgkiLqor/42W1Fo5smUF0K3SKKPO07E7B9ddkznQbUp3PsgSKJbpB2JSNqFQIjVlpr0ij4GJlnB+jiZbWEIThD1VNbQ+Bkj2zCQQIFBdEQQCzYlQ2vn7xAJWAGLErzHe8/cbVHvGNyooh7AVqGMR3Pufd6Yo0c9rAQ+fcUxMhMm4iiKWLDBpveevOy1DJ98lNEjDzK97xWsV/26liCvfQ3+m/8K5ud/FvnpnyR84RdiP/bR7uOrCn11rpKzpJHmP62l+R/+R4offQf2J/5PGA7xX/3V+Ld9Iwtc3R6cgtG296pXIx/9KPKJjxPuuw//3d+9sLCl919WV/E/+EOYH//fMD/zTjCG8MY3RYFUFL02X7XY5t/+7nhibQ199as5yPJtzYK1Nfw/+IfY//XHMP/rj8H6OuEvvR398v1BRSBgLPKxj8J/+OXYxoteTPirfw3uujte8vrXd+/1kQi7sr4Gb3xT18wnPxnPv+Y1V+ml+G6nT585qAd7/+1fTYe6yLUtys+G+ukt1/KV7btrmb8uyMCDhWQn9PINz2CZaucHjLEM0vVDNieFZFpGdGmB8Stx8wYlZhK0qzXdmtwn9ITAs5c5yunTGb5fKj7QxohwsFCS/D7Rl+oVwCESBWrTNNGVYaT73n39tZ+6Szupu98qjbEg0PHZ6xt7DuS9z4YM2XiKsZnXS89pIfonXvlKHvzgo3zsg4/yxi98JW989as5d3aLmUyjA92Dd4ot04RpIY4cbdrT1tIuKJEWIQ3IJn6HrQMHDm6GbpfqNrSNxdqny1WWkhVMSH5bJWWPxtB1L5hQUBjQQph7T1EIhyvYq4VNC7k+dDQ9cm6rx6phaC2VCFYUpwFXK7PaMXdzDHMOV6s4r7z3zBaT2ZwtH7h5DJ+ZKkfHA2Z7wlxymbHA1lu/msO/8u9Y/8PfZHrfK5l5z6Pv/H1GpWfDeVYHgfk3vA152zeQfSzur/2N9mvb7kj/bQXZQn8peteduB/5kaV+Sn89/hiQLLhrLbKNdcJ3fw/LFkR4+18mvP3tC++ksGBNdhZXYuZf8qW4L/mS5SHd/+rXIAV46UvxP/a/HnhWv/RLcV/6pd3zx2PCj//L62v8Fa/Av/s3F1+orpE/+H30xS+Ge+458LbRcIDg+cEf+qH9b3QNZvJsGFym1vX2rHjckhX3rO7546H+t/YhYqUTRCGE1u/n1aALpfWisLYSN64QEWyv3rEm7t3lMh8k1K9HnC5do45yOGAe0tuq9pqT3j19ftZZy1YMTVDixhIRyQqmBDvAY0EFr7RCOTfbubB67UlUPgpZ9Nh2z8390fuaawnGNnCyvaKniGhrZfef0zeC/rgigJ/TQvRF99/HeO0Qjz5xive/50P4ueNFL3gRK9WI+WzGbD6LZfFM3LKpnSKtRpZ3G+mNvi4OWqxiBB0gu9+3Ga8Da5N1S080SNa2WnmSGl9kCXlhxjYV1Eer1FpKtQme9ViU0ihaWFassGoENT3FN+3gEgV1wCCUYqgMGFE8gZlY5t4zrRs2d6fAJhtHV3nokU0mM4evHY2AFIaRUQ4NKi6azAQEd+udbH/+l3DoP/1HLn39X6VZWWOlFHywNE5xNlDQ00l6C3tROUnHRJbk4AHmhLAwNuZjHyO8/vWEL/jCfRxZ+k20PKNjHgcvm/3aeHtE91c0ujb9cTDwP4Jl19MK5Fffhezs4L/lr1z18rW1Ed/yzW9hZ6/m504dHORzg56bZAdjfvrCOlx45mufNT362d32rc/7OGUvjqQTtFdZmQf4PqMRdPD10dCBaM2mI0vweW7vIMG6fN0z0XNaiB45cZwjx49y6Mg6v/uf3svv/f4HGQ6H3HziJqqqwg8Crp7gQ0ClS6C1Egu355JZ0Q40uOB6sFQ3QEGTZqgJaNCDNKSs8WhPgOafJv5DUXw6lv9D1ND0IEESMBgqG30P8+ARCQyswUlgYAKV0SSgs08jCZxuXx+sKFYEawSvgbkG5kHZmTbo5oSdyZznjQe4rRrquBOKKQsGpcU6z7AoYr+otgb7+W/7/3D+b/5dOggt7uYwd0JphNVBDOzKFHtGFiCdZfvioGXUWqvpGRnmc9/3/Qv3dp2aaMGiy/281PjBnr72zDMtI03zQjTC3Qdef41G9lvl13HTM9FywMZXfTX+q776mreowvramPHqKqPd/fv53qAb9MdJuZDDwXIq89KlowsClKRXG/oe+9YWSs/QfK0e4GfuGUBttkQHPV27jOESPaeF6ObmRQ4fOsz999/PS+9/Bf/ix36cf/N//Cxf9JY38drXv5LjNx3n7GyXuXMYkz5VA0Mzijupi7YQr5GC+WyOLbqRFYmJyWT/RVDcQqcvwR900IwRmwYiVikSsfF5wUOCdDuVqRs072OFI2vLuNF1aCiL+H6IwQVlUIMJcybWoIWnsHGLMZWCWGEkmbkVKLGAgzUFZSp6r7MJtV9la89T1zvctjHiiIx4/V1HGQ4MdQg8dPYKzaThsgvMgido4PSZJ2lC6CZkzpEl+k2sRCt5VCpHxhqVl6432z71wUVfUJ70CmLskuxIYmwR2yHf1EYDZo22Pb+4GLrFQgulL5qqvcf1xnD/sfgz70CTQ/vb5/Qg1EXB3l3RT17pIVBJUV4W24sidpHfXEXIHnT4GkbxwT7QSG+76wEKk1PEFuHMjNC4pkFMQZH8xbLwCnEOinT9shiP0EEF+1A68hgsbm/Y3Zu5aDe2/Q/ukKXkPrkmMNB9X0AppAEt8Qo+FR4YD8doZXng4af49Kkpl6cjZru7NDUYtViE0pSMqjGHNw5xdO0wpiyYu4atvS2u7F6hYY5zNSE41Ac0gKsVrxYZCSsbni94zQavu/0Wzl7YpfZzEEehIGENinmsZhYcQecUZkCMUmhSf9jI09o+aoeAw2triNh2jgbtagEppGBJbdGePjAXF06B0sR+9YrMaj5z9jHufcFLMBp3ehFjUfyCcdjNGUMTDP/7Yy9qB9i0L9CN5/5grG7MFy3D7DM1kd0FTQUkUqBmFoAa8++V/dDtcuWjbLF2CNl/JZaoNRV1XTObXQCF7/qe/xcf+MAHeNev/hYf/sjHeeOfeA2vfN0rGOuU7cu7aC0My1UKY5gxJ2AxqqmmbaCqKpz3PTy/27Kr6//kWAeKomgDilS1reIhIohRYr3JjvblRZmcs9pt/7Mcmp3DsgsDprR452nEU5XKWA3jxkbLFJNgUWl3blmckNGaswDlgDDb4+jaOnfedJjjR1eZu8DN9x5mZa2iaWrCeMaZ7ZpLe5exZaB2yj/54R/sQbSZ+fRC2RNvtAKjQWBcKD4sLQsBTVHNC1NVpEVr86u3gQttIER3hybB0+Xkdm2icQPwlkEriCqjxrNX2iWXUBZq0qIDRrq84iyI2+jGrEHnxdYuuoNhp/5S7QvaPunSdfnnMuJxPfRsQvPjc/yBe4oWJlBIu8EVeacO6ASO2Bh9bsQnRaYXXpTSqrrYAxaZ84IFsaxqsXB9XoeLLpSwr91WgAq0dbSXG91HHS4iAlZXUPXM/ZyqKhmMxsx2G37tl/4jj9UnCWEV42rEwmBQMh5ucPc9L+LOO57P4aPH2JpP8aXg5hN2t64wf7pATu+hkxkmCSq14IJHrGIKT5gLV56Enzl1gQt/8gpfcN89+JkynQYKa0AmQAkW1Fo0lPFvAkEdWYlTk8eo689CDIMi22WpMxa0jq5yWuew7uZd8B7FYUTBRCWy8TNGlWGQfDYmBDC5SA0EH9pxiCky+7MYur8DQkTqFsdy+bo+9RRj6QyQZevyatakLn1/16a2/5a31bwWPaeFaEwYluSGVB557EHuvPNW3vYNf45PPfggv/4bv8Fv/s57ePs3/gWOHj5GU83Y3dlmtRqxYpW5m+Mp8FJgJQ50HHSJ25J5j7UlIoqqSwIkbiobhaaC+uSCTJMuC9FsxZIXf7RKQxDQlGulGgUKPYZDZ81mgaCaNu+2ARVlzzlEA03wDAtlUMBE02LIFl3a8Dq1iBCwOZLQ1wQ/YFZ7tiZT7Ooq5XDG3nbJbNJgjHBidIKbDu9x7ukryOoGa6/9av6n9/8f1x4Q7f2c/nGN8h8zHbw7138x9Pt/+UvwRUH24rd8sSfsITMoGI8r1tfHz9DqImO+5mW9x+xL31jYYH1/mwsKw4KfyvTuWWaCLH3jIh2cgrb/Gk2KtIQa5/cYrx3CKTx+5jy/+8kpF3ZuI8gAYwUtBZESi8EhPLV1mctPPIg5bQjTCcHVBN8QXIOrG4KfYcTjgk81tKO1NBgOcPMabwNmKBTe8O7fnbNRnuf+O46wsbLCznSKSACdIlrF2rcoIjOUAqHoKbFdn2ry48sBnZP5Sv/6zCfIvEdzSoygWIJ3FGWBKYSpc1gxMR1PlGDMwtgvIxbGmAOh25z6FxXYQC5so4SohLVtXS2Xd5GW+efyeF/dGr36NddDz2khGmVaLJ0XQgzBntczxuMBL3rhCxgNB3zq45/hP/zS7/CCF9/F3XfdFK2u2R62tNjCIIEEO0YBGjTu6wldonHoA+ba5YiGEGEZEYlWaXdJ2gme3kTOVYMMufDzcoRuF+UX/7UQInFiiXoGhcVVBvUNReOxBIq000uEuLStpJx/jdp4LDJRSLQevTpc8DQeaueZNYE9cayuDllbGbE+Kjl5YYP18RAXZtiVde6prrF7yg36/wt65MQhfLU/Qb6v+UfhanrHln921N2fc6oXoa4DI9VbZpiOL/mzlmG7ReEJ+wWsLP2emeTi+bzW+lbE9bNDQSgIIVANKkTgsTO7fPSRCZc2HWpXIG0gIbaksANEHdrMme9cQV1DUZYxl9nPUecIztO4hsY1eBeFZ1DizlBJYEWeA0KgKg1rdsTHH5kysHu8+PYNRKKtZk0BahP64nt8xRArFAVstHUX0Y8F5KSHcrUAQVfgIEPvGfHJ9ymGxsdMAQugAediTfLM4xCJMPUBY7os0IxYRHyyFAVUejyyQ2EOEsx9OhC92zffl0b5Kuc7OHcxaPR66DktREPuaYnWobFC3dSAsLo65qUvvY/1wZBf/+0PcuqxU7h6D3/PrRw+tILYsksSFu3BlB0ZY3Au58KlydpOiDjoITkFJYQE10n7biYJ3zQlgaiNq4S2jRbUTPv0hYTx+1TAIGdsBVVUAoW1VFUsvGDruEVaBGtCKyjJfl6SPM0VizTuHCFBCOpwGmiC4l0Tn2cNamDiPdO9WDWnGo2x80AoOxPu7a/+62z7gDoFNdTE2utOhUKESqA0wvNP7vGW588oyoZ5CGgQSmvZq6fsTfdoQo0VoZLIwOygogqC357wwMcf4JbPnEFnLkd2AbSVmJKhHTXqtrZql68XuzUHHtCOizlwcSiEbgHHQx0j0fZvuk3T0yFVErQsEd60BlMU2GrAp49sUB5ZZX1lzMrqkPWVMRvjERxZpxgNU5nHGb4pyKHbIvGNG4kQY4FiIf2XA5iUUjQNb/7RX+y+Zd/X9VhrZoz7mMxivuB+RiJLP3XfdctQYuyjZejw+sTatQC37nhSHPuWbxKu2j92TUr9jqAYVDzVcMyV7Qmnn97i3JZlPmkoyxKwkW+Ykri6LCF4/HxKox5vYwBe8A3qHT7lZTeuwbm4fjTNHw2KtZ5u0ililOHAcmUinL+8y903jbFFRXCzxEdCK+BU4trOxWIko1lkNSfP3+ziyZ2X4wd6wor94933q8afMZ4jEK3TpmmeuW9b94f06oV3vU7PXdKH5PdfdxDKsJzfu/zo/e/WtzL76+eg517bBbBIz2kh2k4qMYiUOF9TWotzNfN5xBPvfckd3H7HTfyHX3o3n/rIA5x6/BRf/pVvYXVlhK8brARMoTSOtgJRnlDRIgwUVUXQXCZQ8b5XDihN3qZxBGuwNh1Tesw2BjCR/WomwqpBAxoCIfikEKSvCqF9hrGSFlDUQA2WoiwobcGkNrEQdN55BhATF6MIFCbmhkq2conbDVmNu7rU3jN3DvwcdXOKsmTmZpw+e4VPn9miUmiKATJwVK5XlWgwhqbB4wnB0kgsmIQJ1Co0GEal8MjWYe6vNzk5FgrrmM2FsqySJe4IXgh4rAJiGa2sII8/zfATj3LL409RhQJnFJ8Ei0/vrcYgGjo/T9qBIisxmTmoekLyyarqvnVC121RETJ50Zukm8WdccSkfyIYa9vndvaPRj9QCGjT4JuG+e6EWy9uxnzdwqJVyWQ0pji0ztMvuJvizts5urHOsY0BWoGfz+MYF+AlIiPGRUtDxRJM+5rpvbtADDnowzjYOrzadQcxo2efQ5cVjIOtzGU/08F8aulrehKxNU4WhHVfxPZv22+9du+x/F3JGgLUesQOePyJJzhzZovZLFa3Chooh6sYOwCEejaJMKUxNL6hns1jLeoAxkPQhnZTaQUXKxYQNBZx0ZSyZqpsFRp8E5CBJ9Se3d3Azt6UY4eO0dR7BOOJ+d+AGpSoCCMmbT/Yic/86QIJSs1FEVq7kX68Rh/+XBQ0XQ9FvhZ5ZGGLnu85WpMxOLCX95rXSOJ1CwFHIcQAvfxeArEUayoWQ6yLnr9ieU71xy0L0mV/6MJVBwjPflpLp2SaJYXw+ug5LURzx4NQGIstBghKUUZIZ17XnHrsNCsb6/zZb/gzPP3kOf7wP/0hP/5Pfpw/+7av4qWveCmosr21jdhoDRgRvO/8o2VZMq9rjLEdvBvCggaXB9k53x7ra8PRP9GPJo1wsBXbq2biF3YzcM4B4FURD2IhGHDegxVWxgN2XYUpDEGVgTXU2TCXmIoyECgNrXWTJ9LMKis6QGslzCbQDDi7ucMTF3a49cRhNBjOP7qH1x0oh5S2ohp1i24wNFQ2Qks0ykjBVdBoAC0Ag9eGWRjy/lMj3lQpdxyBZu5R57FOoXaYELClodHAsZtPUv7OB2kePcOl85exCpNmnjuQbIt5Daj3izwzG9o9q1PzT+kKX2eIXJPVT5armrdMzIspRmVmazem3grWGmzZxL+Jc8VIHDNrDGqz5SuMsFFZyByznhOmU86dvYg+9BhNWbI5XmF0282sfMtXUQ0c8+ke05mnKMcRosejaZP2ro5jN6fiG/eipZdoH0PR/dZc270HkLT90WO+vf9GH39IDFbaY380is88iJEvvlw2qfvvm4/bnpCQ3r9rvFtCcWwBW9t7PHB+yKZWEKZUJcyd5/DaTRgzYjKZsDu5QFnkurBpfiVUQlzvSzRuLGFEwBg0RCU8aCznab1SSIERS2MCuDneG86543zqUsVXnvBM9iwaJoiOMFSIUTw7aCgRBsQiLS6NY0a1FgVBDmrTVlh0EdVkQdKLPA8hrYG0cYUtizgujadWZTwed+iOMQsKUhSm2lmR0gnY3DOtYJdufHIgYbdOD1bG+nTdet6S4dkJ1mWL9NkJUHiOC1EgVf0w+BCwEqPVmpCWv1hWN27CNZ7zFy+xdnSNr/4LX80LX3wvv/3bv8Kpxx7ixS9/Gffc+2JmkxmzMItWCLFGZHaKG+mYSA4YCoEUjZsXcvR1ZvjXmLhoRaIwtraDViJkG1I9x17EYXpKtlZbRhI3IyVklhmU0ghFKoAgBAqxZFvZAIVAiVISmWHLPMVgZIIPhsatMG8GbM093glro0DFnI2NFb7sTXdQhTmnL0957Nxlrlzu4NydeYNXpRShskLwDofDUmEIoB4NBc5Mefj8gCMrgdIqx8awNZ3BQCgaiwYHHlaPHuPJsxfQhx9nfXM31ud1ATFFLI8W4tIPoinft7dJOqRtnWj7MCitZR96W9RFA1MSSpDLniVGl9GrLDcgwbS5/KNC8JgmbQNlNM2LgLExB9eaxAgM0RxBelvtCVLAykii5Rw8frbFIw/ssvLDP8axt30xN7/4Htanjs0LO+wC5cBSoBh1KCmquKfSZ+G5yEcMXem0Hi0Im4Ns10XG0dfQpVUw+iK4Q236IQPPRPtZ1jIdVLovR0vG2S1L7yq9NdTR9ef5AW2Mw7FDxzn30Gn2dqfUtcV7Q0jnn376EcRaxIAtQ9slWUBISJvXl0IOkglJiKiCaMwE6BWQhABBPEEUMZbCDBBx1JMpW5cd0/IQFR7LGj4UoEVEk6zHE9oZEMfLE7SMY0K3BhbTsVgY6kWr3CT0Jq8ZTQhMtB6ttahR6rrGllF5lxC/LceJQBfwGS3csO+Zi8+OSmsU7Eq/7N/10rL/NX9Tjs7Nufl9ZXA/Pbv50qfntBDtcj89ZWGSvyFu9xUZqUGpoYipJHvTOdNZzU133cZX/dm/wCc//mEe+PAneerUGV73ps9nvDJmsjdBBKwt8D5qjUVR4EMuJN9FPWrQ9My0ZVCrGMfIXVUH5CChCPXmSN080fuQi0/WZ/Cx0hC2IASPTRNLic9RBAkRPhSiImFDwBobN8EWsIRkLcX3yVNVgVAXjFcLjh4q2VixTKc1t5zYYG2olGWJGMuR9YrV4QhXlFzZ28XtdP2+UhnUxz1IfRDUGgbGoi7gg+IQGgoGJiBGeOJyyahy3Hx3jVTC3HkCMS3CFAVXdnbZ+8Xf5OiuQ70wdwHvFOcb2ihlid/pvdIERQOtEMyRfK17rG+VtlZqpsxYliaTLnCWtNwSNGa69tt456AdCwtRwLu8z6KRZH0YbGJChlQwIslWEUNhDWulErZm7LzrQ+yc2WT0yudz5OQqq1qxvXmFQECs6b3ftZlLx1D6VsjyNSQ0pN/WIoOJQTCR+XQpBAc8py3h9sxMbxlgXaReoFC2FkzP4EwDK8mclqUC6/mdrhZxGb/pKu+VlFYRg0U4+/SjTCYDGreK90ppDMGBU09VBspCCFRxH2DTWTMWwYrg1ZN3zrHpu/IUVRPf3aSD+acSMCEGb1lRqtIyqCp801CbEUO3i/PnCASsHqXgCMgOaIOqRbVIqvISJJn6RExyCRGV7q7EXt8C68OoSRC1wUpRgSXNZ+99T9GPrbZZCNJvN47R8riEEPPhTauEmEWhex3UR1r6u2f1odtsVce/9ytg8Q3lqnPneui5LURF0kbWEbKM9dxNu1ra7kqogfeeJnjGoxEnb7mFej7n9JOPcuXKZT7xwY9y9wufTzUc4pzDNS5W5xFFxBLUJ4g2ruq2CpH0ku9TUENmTsYYfFBEYmpLtEp7Por2HfPkMwlSSVpg0rpVYlUcPIgNQIGPIhVS2AkG1MQaHgYQDRhjs8ciWTQeNNZtct4zdw1zH60kK47CFC10WVjL+tqIE1pw4fIm8ytdZO64LHFiqL2COIKJidzBJLAoCEJAJAr1zanh9JWCy3sl49WGXR9wOQgKKDVw4fI2Q1cwJipBzgc0xFQA0wolEK9IYTAq7eQ3KcQ+w+htPmYPrenySTVtTtz9DCEKf+9TZHQ7AqmJLMASXGcWoLIuJYCgeImJ5CLEtCmbUAkTRzND+yToqiw9jTPYc5eZasPm9iaPnjzE/S98CdW4IjQxstNIIKSx7SgqE/sZQ57/rfTZf7Y9fDWYrA+nth6uhWtEJOYPHmQ9LtEzG6rLvrjuqyLv7qW5aHe8cwD2fi6/jOQiGVkTysy2g8IVYsEAH9jb2cbVh1JBEInqq0AhHdrQhwW7BgTE9ARrWsfpXPRLRoUq7yrSNaWt8mcMjJhwdAjq5qDg3Wmon4jXFA2Ux+PuTQiqFjThM9Jl60rEUcFkt1Fsf1GZ6dX3TX7OiKLl70rRzmm9aeYokuuP94VwFpgsHVskYwyiprde03tJdrksCsHICztVbTlNpa80tNZnzwo3Yrux73/7Ab7T/r3XS89pISpkGC75vdICifpY1GpEilgFCKWwoMYynU5pmoZ7X/QCjt98nM88+Gk+8vvvoxoOuPPee2K0Z1vcOAlEiQEtBsV7Fxl7EojZh6loqhqSYbAoeGIwgWLM4oRr2bDGSWltQS68oN5nAC8KcjVROAWHmoqAELCoJis1h8qrQgq6MSZBu8TQdEtAcRRW2JvVnN+GOignDo+Zz6e4ahTFsoADghQMhspoXDEYdlOlKAoGWMQFxDi8CTQ5ijnhojY0eC0RA84ZLu0WPHRhxBs3op9TTfwuQmB9NOTKyeOERy9wk8JKEUsGVqmYRVFEOBwNFEOLDgcU1lIaoSyLmL+WmFv2K/cmSauN5shI13ic9wlp8NTzmnnTMJ076iYGea1o9EeHLHC1CzRDlosukMY5ByrEcyHlBRsbrfXSxKoqmAyvC0GgsJbQzBmfq3EXLvHu6Rb6VuHlb76fsqgI21NUfLo3Kj0t01WistLOpcx0aOfh1ZlCp4Ev+oeigCEzOelZ9mQYPO3VS04pkba9g+igN1hUJGnbpV0bOSCli5Jehh9TQwvP0N4xSb9qip2Q/FwB9V1/KflYyJMzrnkBlYAthdIYTNr03mho22wfl1CiGHgT+zCoRjQhf2X+NiGZpVE0CYJYSxCPGMtascOtq5YQGqx6fDiNcZcxoQImBKOIiztPGCkIarB4Qm5fI+8SJFYDy+PUR2xaCDULzP1FXxQFSXMBS8Zvi6KMfAuSIrUogPJYtGtxyb9pc8kiAV2yjJfhWNLezX0B3bccWyQwt23tQjs2BUIdlD8a3Wth3/FnQ89pIdrvxMxUDiLnaqy1icF2A3Hu/HnW1lZ4/Rs/j/tf/lJ+9Z0/x5lTp3jxy1/KrXfeyfb2NnUzZ1imqrvOUwdPWZY0jY8RttJpQ9YU1PUciIPaVT+KgtQHEJ8hINNpxxLzTENKAyHBGlHvM3hiIE23J1781OCjBdUmM+fo1WSVZXEeTLbYJS50Ywlq8A242qNeKKsh5coqjEfMq4IJjsfOPcWlc5e5ePYi9W6H587nO+hggIwCxihS+/iOQCE2KiGpLJy1JaUxTGrLBx4PvPT2VQ4bz+VQM2uiz7geB77u276K//t/+w0+/vEneFmjrI8HDMoRVWUoSostYtnE+158C1uvfwH1xjqhqJIyA3kHnOW5IabEmFhVyntPYYWmmVFUhqI0uEZBS/zeWdx0htub4y7tcduHn2R3b8Lu3ozdacPezLM7rdmdGAIR5m8gauhG2oVuktUj0XmL94oNUdrZIkVTZ2XHAFrivKMqY+TycVPwtTPPxX/3mzy+PuTulz2flbU1trZ2KIoi1uklC0+hKIQwnR8476Xl1kvCJ1skSxDW4u9JqGhuQ9t7+qRKG7l89eCPjjEvWI/d6/TeecmyXDgflc1O8IX9DSw3rIo0GgO1krIRX8djVCjVEHW5gBeHOVyxduwIxUWDOgheGRRCWZQREg1Z0IMX2vQqY6ISXGCwEksG+uCTW6dnWZGQqhStH4ylMCZG0htL0wSqouLQmmF9YwNfxwh9GTwPW94BMiaYozQ6pTYlpRGMFklQTlGqtO59SnHzFEnJ6WI7BC829WAuoZfGRwsIOV89EIJLlmPcWjLDw8Qpn1Aas9Tp14E7ZJi5904L49/73ftemmFPUetbnVeD8ftC8sApco37rpee00J0AfvXaJlGzSkNiYkQx2AwoGmaNlAIYucNqorZdMZkssegLPnav/pNfOT33ssn3v8RPvmxT/KGt76Z46vrXDh3kYGxjKoBlVRMJpOkGafUB+1ghKocpPqWgeA9RVmimiHeWMeW0sZFrT0Gs/RdxtioGS7EiMSgpFagionWD0pwgaGtKCDu2mJjYbDSRMDGKhQKxgcKGqpiwKAsKKwwbWoef+ocnzl9mplrmLmG6XyO7u3Ggg2mYNTjVPPZLPoDNWA1RMFcRB+hV0U057lGi9JIgVfDFW/4lQ8N+cpX1QzMDnPmeA1Mrmzx5GzOn3vbW5levszpD3+K0fufxOHwWhLmE2ZWGXzpi/n0yZu5Um/Cuctxzz9N4fJ5G7keJGNMrDVc2CFVuUZhVgguKkE+BII22NKxN72EhjllOaCshphbjvL4Hccx1AxK4ZB41puG2c4es8kuJz+5RXlxzmS3Znt7xsWtGdt7iguSEILsx1VsgqFDo3ipMSYGhiAm5txJwFrBe2U2r7HGU5WW0sIf/Nb7MRvrvPLlL+TSpU2qqkpwdC8SsqdYReq0+qtRm1/Z/uuszMX11WdqS+c6+7G1VGCZoS4/WPcJ0Oujq92jZLNuGdLO6yog+CInd8UqYTmsKgg4I6mYSlwjrp5x+5GjrFW7OLGEgaEYSLw7wb8aYl3oponM2ftY3tKHBpfjyVSTNzF9QS84Kqsk8RdPYQxlIVRltIJXTgwYrQ4oC6HZ3qQZWIrZEaYa0EIoSkejSmlKNFgUh4ijMRYJ0Tcbsk9BCrwRJO2xnP8ZmoiIpMDqgKA+Y18RfrA2CyvwLsQYBe9pfI0d2GiVB5JwOyB9aWGsDx6fpPHvH/EFYZrg2OuTz38kWjTMro+e00IUFjUa0V5YNaQJHjWtbOk55yjLCu8dztctDBjUc+XCJZ730hdz+OQJzpw6zW/90n/kFW94HbecvInp7oTZfApGGAwGSO2pnUt7CMaUl+xoz1bCAW9LUMW5QFEVCUpZFKJKF2gUgiS/R2w/EJJPNoDEVBJNO9KgARt83K1F4kIyIVCYAkvAGiglwrpSCcE6pl5ppjV7zS6lBCRoG1k30hIZrGKsBWOwTRedK6ZEncFgKNTS0MQIRk0+RgWxgpVo8cY01kBJ4NTEcmHLY0UpSs+83kOcJ6hw6uk9BpXh2Ctuxd+6yplHtnnqzFOM1wpO3nGE46sbbO9OKIu+RZF9o7btt3gy+W2YM59eYnfrScqqYlQdRmQdY2Kpx9DU0DQURYS7cTU+NFyZzWncjPGwwhpD8J565lgZ38SlF69j0o46NIGV7T3M9pxDF2asXZqxfWWXS9s1W1PBhWixqkS4zQSP2gThBQjUGJuYYd48PgRWV4UXPn2Z5pOPMXnBnYxGozZAIs77NFuCx/bk1j5rsjf3+r/1rmqP7rcCuoCi/pzOgrurvtVVu2l9VwcyoYBqtyXhPuZ5wJpZbGXJco7cn+VgFLPwHfF7AzkXO0Pd6b/BYtWgBLxx7M7mrB4+SjnyqDfUjadx4Gex0LtFKYxiLVRFSVUOqIyltJbKlkkRK7ClxdgCa2JltKLIazseBzAmwsgaAsE1BDfBmhHDjQGHDo25WFQ0axHZokzpTgEKN+eFg7NMduZMpMHlNDYBaepocceHobaKhURM8mkqqFe0sqg3cY6EZFYSfe9969AQN62IJVB9WnSx3m0IISIqC8pCHBldknbXUIP6/0ltdHOoD9VeS6ztR0gW2+uf76+j/vll/+r10nNeiPZp8cO1FUhwAPSbrsi+IB+iBm9FWD+8gRiDLQo+87FPMr17l+MnTjBcGVPPZqhG68EG00LDusSWNMGs/SCjPJWcc4TSdv6ZfYEQ0kG3AWLaTcL0RdoCDV6TH05ioemMrEhKsxCIEYSSCjCYWDAcF/VGH2P3MQjW5gpLYCWG8QdsitY1mNDliRZFiTdF9AlpYFAatK6pnYvCQVNuZe4VjQzDmLgp+JU9w+oo4QYh4DVgZIprKoIqvvSUx1c5OhgSjinDYcna4bXuO7TIRaporanWT5nsjBS9WdgBlBEFmM220FCj1Ayq1Vi6DSgLS2EsBpsU3rQhespDFiCoxRRDagQZj6OFaQBtGB4dUdaewS01XJlTTecMtyZML82pzm4h23P2ZoqqRSVHSEYLLqe/tHxEAQyD0rK+59k5f4lz5y9w28ljXL58OW14kDV9JQRPsVRIoWUgrV9QFuRTVuiXIzNZEMBZ848i1yTnaE73ycFWLUS7ZMzuE8ht23ntdceuzbA6qG/5ePTR5Ta7QLX8bXmtGyRGuEvXxREpcqAuWWcxNcvVlvFwyPEjG4RCcI1nNBpRmgFlNaAoLEVhsKVQDCpsUVBYgzU25ZIXiIUu4CZFyCefdlZO4vgFQip5Z60wtBWegss7e5yfNPi9CU49VtP6F49YoaoGrJXPZ3iTopMZTT1Habh9raShxjuHrxsInmFZUhDdPrHPTat8kAOGOnSZHEiU+ykrTyaVEg3BE3xMuSKhInlHrP545/mZ+etBOlW7VpeOsTB+vfmhV8uIvj46KAK3/7yDfr8e+i9KiB5EOezapwT9bC22C7vtOLClZTaZYGzB4WNHOHH8OL/yc7/IIw99BkU5fuIkRSrw3mhKMhfBeyFIaKFWsgMbSRByyhNMg+i9x/lAYXtWQdaSsylNFJ4qOQfLx8kvyScXAj5EIQqxOpGTKECxEoUNRP+rhMSL4r6q4pPpYnN5wFisQvBYo5gMzyX/rAWs6SZVaQRXxNxcglAWRVviLCQYK++vGSsL5TJ9ShGUrWkRA4LEImoJFowJlGJxbs5evUdlLRuHB6xt3NoufK81RVHktd+DG7tIv9YWSsEORgcUg4qqrGjqOcE7vJ8RwgBsGYVoWcaqT8mqNSbu6FNgUp1PiaH+amhCg/GG0Ej0+/qaYhSDzsLJVdydRxmNKk5qzbFLU7b+8FHqhzcpLs6Yzz1lJbQl/kwAqha9yFG+8XkWkUB9aZOLn3mMFz//Ti5cOB+RE2ntPWLVrv1pAddCvvKce6awfoVoacjS0d4tqlGgZsEuvePQY4C9eX01+PjgN73KNUnZiPpTJ0SlLY0X/+UcSUOvIIRqjF7PSiepv7Vg7seEaszJWzao1kG8sra+wmC0GhGowiLWYq3GQCA0KkYS2gBCH2rUNYSURx5U8b6O6y+NtQGCerwDO6gYjzdYP3o7ezPH2dMX2dm+QtAaUxhsEMpyRFkaTCloY5jVFYeOjNkwh1DbgN+jCXNm1Qg1Hk+DVUdRaYwU9AnmF5sEpwIOSWl4Uaxn91AKitOYIWCNocwVi9I6jxkApDS/gNjFcYrurgxZX30cW/7XmyufbarJ1ajLZ+7m5EGxAIvP/a8IzoVrM4PoIPdpsktcBMQoSR88xuSgHh+3ODNCaBpm85pmUPEVX/e1/Pa7fp0Pvf/9rB8+zOe99vUcO3aM8xcvpQizEHdzgbigcy6Gp9XiczpMnjI+kAKTTBJOOZdpKfw8TSqbVrsPSt9JGgCfJr3RaPlZAgUSfTLiIaXWYCLMalSQyrbwGxoZRRvQqsRUGWujj2hWY6uKqhcBWoiCr2OovknBSSEFQqWIRKOxGITJWq2Cb5TKF2xODBurMXBIxeErh1GwVrF2QElFgbK3PYtVqApQowQpCU6oSp/2ETTRb9NC2qnPFrTIGE1opeTk0bsjZKohWgahjudNyVwdMU/QYtViTYlFcaHBa0BMwGlNJRYn87hyRGCu+PkMCQ31VJltWTYpEbtKOS459mUvZe3pbYrfOcVvvv9xitJS2BQ1qUUM9pKQLPjIbNSAOmFYCbdu72A/8hnMV3xRGvFULSvHAUjcn7WdM715s7RI8oK4qvZ9wOrp9atCWjf9vl3W2q/NAKVrsyfms7UiYhZ51zVkbGtzJngxTtzQQ5fInYEXxfkadTUSPAahtAWD0Qaj0TpFMaIYDAnliPc8OSB4z+g2YUUMcZfMObN5zU69Qz2raeoamhk6m9LUM0KoUXWgHrxDXR0fr/ElssqTo5kFiS6C4AmNh+Ea7uSdrB97AbMrE5746ENs1bsMViuObazhZlNO3Hwrx1c3aGrHY4+f5WMXP8H6yVv4/Ne9hkPra2xfnvOrDzxENd6gHIywtmJtMOCF1RSrJcPhkNIWUXi7BpwHnaMSN+/wqUBKTrUzCLEud4yMV7VJLQmp8IJJEHJWWDpkoj+AWXc6aE62EeG9U8u8vA2QzLmerasr7FfUFtpln6A8CKq92rx/NvScFqLXMrkjBAEilrhriULwOOcS9BIjJZGMkSdh6lIwj0Co51yuZ3zBV38ZFx4/zSc+8lH+9U/8BF/6ZV/GS176MnZ2d9lLQUYhBGwSxibXXk1WkoqkgJtYqcZa2xYyj7l2huBjMYEFGIN+TlXOKUtBEilBObLfqCiUtqQiopelEEshkvJpU3tiBNfULeTViKC+aC12DUkLJaYEhcZB46h6jHoaAo1TJChqhGAFTNzdArVRy0/MUm3UVg2GQkownku1cHQCwxFYKzgPhYnWfQAwSTGoxi2iaI1QiiBBsU7wRlAjdLv7pqnfWmnxd6fTqCiZChcUMZ6YwAOIwZoIIRfJGo4QbqrkkPTzAhOtrbS4TBi0eW7ILDISU2JUqcRQ2QIXPLvzbc48NebQ4cPc+W238QU/WvCHH3oUOwRTJTjbxN0wTIo69SFQlAXeOcoyMJl7Jhd2eOrs04zHY+ra0cGX6bsXGMH1WaUHKZ79v002NRZuCoumQ9v6teze5Wuvh/ptXe2eaEkByScsxILw0dXhfNqGS2BtpaRaWWe8epTBaJ2yGhEwvPvTNc3eDO9qmmZKMz+Pm83RZg9CA74muBnezShNgXc+uj8CWBGMWEbFAJGUamIGGErKgY1R4bbAFgWIpRiUOPV4bfDaoBLwGoPuQj3HbV/kyQ/9Midf+Hq+7Cv+FGc+fZozZ57i0uVtyvEqZ85uce7KHoWAn8yZXZhyyyFLZQYEN8dtXWA836KZXWCedIo9DGdJQUbFCKlGHB4GXnh4znhjjdH4KIPxcQpTELShnm/h3RzjwarFaIVaF9P3nKMoSwZVRVWUqHPMZ3sUkpT0XpGHZ5wFmss+HLxFWrdhxtWhVTHK0nbN3bnrsGiXfaCdX7+ngF0nPaeFKCwyg2UNQzVWxogn4/nCFgQf2tJ9aEg7ExiC80QxFUO/gwGcZ/PJpxivjHjtm97Avffdx+/+3u/z1FNnuf+lL2FtfQOZzXFuj6ZponCztH4oY5IGl4Wjatz827n2HUEJziNFrEG57NPKWpcSlV1s8iX6CCHnmr753T3xfCD6T2PZsVSaoTCUhhhZJzHHqwkB8UplUtUVwKriXWSOXqHuZCgXdx2zqH0golSltNWdIpIWlYc670RjosXtpcZ4mDWhrejk7QRCRY1ixVAERRyoFSjqZHEZCAY1Ed4ONsLERknvkDRMTMYgo59JLEYqskZcpFqvRar7GzTCU0bSV6um0mzgUw66sR2aURob6wPb0BUQamJlH2uixd2o4rHUBlbYQIuGeneTxx6dcMdffg23bG5y+elt5nNlvFLhXI1ISY4s1wDBlxjrIRSIBuYzx5NPnuFFL3oR9eXNWISCLrjH9FCCSP3o22499KHW64HMWsg4W4rZ2iBaptEY9qgmZONAztM9p6scE9GCZYujbVy7p8f1vVzCMPrdxcY2m6YLbiqKitW1NUbDMWU5wBjD3NXUs13e+/AO09kmoZnh6ynTpgHfIGlrMYOlLCyGhuBiLVpjBpTDQ1i7TjVcpRquUw5XsaVFipKqGiG2SP8qnCnRSvChIYQmug/qGktAptv4ySbNZBP8PNbpVkcoBSkKSjfj8qf+kMHRo9z0ojE3Pf829q5Muby1izYOtQXGWIoja7zo3ts4dPIEZTFnvnsZdTOK4QBrBqkUaU6n8YkHTgnzCduzwEeueES2wV5EixEbleOODdg4ehvrh45jtMHVU5r5hOCSck9MqavnDZO9PcrBalRYTG/YDphTMR1mf8R2htz7QT99wdbP6cw5+st5nsvxLst0rfP7jgn0FdBn4RJ9rgvRPjPIlTf62rS0gT2aFh50KQG5LEPewSEzjXhUosSS6O+s6zm2KDl89Cive/0beejBT/LpT32Sw0ePcuLkLWysrrK9u4ODlI8ZrU+TIhLzpLEiqFcKsW0UoapHcoLwwufFgA4vBahHCARjEDxGQ/RJpCIAThyOCvUKLkbaVdZSCgwKYWChlEBlHKYwGEubN1sYKMRR2QIrMcBIg8eHpt1jtdAuOnckU2I+qoIIxpeRQXrf+mkDhiakSF2JgUVWA5URimbMzM2Zh5JBEeEcafl+VxxeQso/NAmi7VleMTY4jpqnxkhJ8jyjCKoGL3kRxrYDIdVajgqnAcTYiFIYIRfyE2LwjncNIhYh9lPs8Kx5hzwDQWPJyeyjFWMoNcG0mjcU8MxMwa23H2K6NWF3t4k7fiAJhuyAzjjWJs3hwJr3mPc/SvWK++Pili6ASrN12K6IPJOXhdoi0+iHdFzd1osQKdDGELRGX25HLHIAk2zvIelByViN+f4xhcuI4JwjI/CRecY5nb0fimCkTC4Zh2pIUfiCuAZbjjDlAFOUFEVJKSWPXAJ/cUrwm9DMod7Fu5orzSi6BjWWyywRsEVMO5IY4S2mRKxQrq9QmDGFHVMMNwhlicFiihIxBU4d3tdM6r3coQimjQAGF7cOc47gGixKCA3q5thgQWPUrGkhegtaEBrP7qUr2GIvBimpYXUU8CVoQkqsGEwxo9l9mnpP0WZGCA1mMCYExdo8olEBCSEGUGlIdai1iH7a4An1lM3ZnPnWHvL0NqO1o9x2yLC+vkI5GFMYS5jvQfAJcauZTLY5vD7A+JpAzBIwotgUday5JqfQW9i9WdYCF2n3JSWtv2Shao62D63gXESZoq+2RfOvMfcOnNNplex3gfYX0vVL0ee4EO2oH5DT12ZaBbftsC7Ap69dx45NtUMk/R1zMzCFoa4bXAgMTcXdd9/JZG+b8+fOsnnpEsE5Tt58C6PxKtN5jVMXNbc0QUKaVBlOjWHbvfclMl5tI2ZYGOFcXjBH58bJpElQGWYuUFuPjzXgIrCVGG1hYGijEK2MUBlFYtZK+89KCts3AYumMPfAoADVWL+2sn0hWhPEJjFikOCI2ZG+3TFFUgBD/qQYHBT9tnVj2ZuXzMclI1PEIhRIt9SkXWOpkIEszO88lvGwxqja3I95wEO8TtoVG/NqY26tknHiwkrcFaatKpMUq+C7cn8JuoubIQvR4Z3Rj5iBqKnYQrQMU/RlKv9mYsFUti/vceJ5xykev4SdupjqlJhHn89o8KmeaExNGqmnfOICKrleaR7nrDAsrAS6zurN7SU/8YJ4TW6FfYxnGe7t1dptU7navNCOcS+T9tZeO5Sqaby6cn7xuljWMgcLRR9nd0+EB5LrIJRYW7DbWFwtCDXG7/H05QGunuLdHPU1hQ8oBcg0uUIkCk47TPtySqvA2VCCHVKNjlMNNiiLMaZaxVeCOgchr+XYX17zthBEv51rwPv0vnEs1QecEE3nYoy1I4KPeeQqAUJ080TEKtDUc+ZNXINlaRBbYUTbteFVaeqa6WwaN3FI32DNMMYmSC8aWxSMI9YM9Yh4NESXRow5VJxUbGuBmyvi5vjZNuu7Y8Zrhzi5WsVCNRKtYGOiC8n5FFNg0pimbQezQpJRt25O9fhZb512qVEpYjpEPyy9a/qzcGGOHiBBrxaB2+Wlpj6RnPLTh3QX7trX9tXovxghmrv62qZ7b+CW9G+F6PnPgSctg0q2SVESAkwmM5zb42Uvfzk72/fymQc/yQMPfJjNnR1e98Y3Y7YvM/GOmYLRGFyvWrcMNs4Yg9caozYyWGgL2Of31QXHuUfUIcFH+MQoakvmwbLbKFveRYYUGoalYVQYVsuSQ6VQoliTwv+NEArLIDNMDahP8KjEIKkI/QTKomRtZZWdnW1iIYPO2og5pzbCxaoI8yhENZZXjMFOBUZj3RQxMa81YNHgqZspOxPPdGw4tlbQaCxGnTlPXmSLC7CXDyySKiQFCpSKVbzO8YDHxgovwVN42+5FiggUA1y7l2FUIBRPkEAZPEJFjGBs8K7BmgEqHkzeUSZgW3Qj5bClQIzFhR6ZobVlD4Ky7O3tMHvlnYSPnqbanjOdNJSjoguakA5+Su50rBUa57l48TK3T2cURUkTHEE9xiqiJaGHEuxfE3ldLL9ftyb2r4bectFFxtUP2tjvU4VFHpfnbxKQarHJ4g/BAYaiGKBBUsqWJ6Yl5+jRiKh4DZRSJYWsQooSrYY4s47uXeDMBcfmNODrCer2kOBi/ecUIGcGAzAxRCiFzCThYpP7hLRptMEEBVMSQoVTAa1hbxNbVzHuISgFhrKqsKNVfFEQrMWbOCedixtya5BUBjsVZNCQqhgFGu/wLka5aqhTpbJUKxrFjECSC2GeomHLNJ4x+jdauN779P7pX/AxlUyyZa3E3X1ThLn6aJFqAzisGSLGU1qlMBC8MJ9PODsd8fQEBhcu4tbOc+LuVzK2Q0xZUHplsHaUSQg4bSiICmJAqes5g2qcalnHdWJSLpr63sRICFieG334Nv6LFSvaDb2XfJwL8+4AV97VAk37aU953i/7RvttXC89x4Xo9X8odAs8whJpQMjd2b8uBdmotpG3sWyggvEYHXPu6aeohhX33v8ibn/e83nvH/wev//uX+Ylr3gtq+tHKGY1e5NtBIdonEgiYAslhDlWYsm6BS2o5WZJU0oTIahDEKwYfLrGB6VxSu2j77OQirWhZ1gIQwsj66kkwkUx7kfabY1alinZGjUk5wdZvwjBMRgPWFk5ycWLF3Hz2UK3G0ta+ClJm2hlmlSf06tikVZz16SfWCxlCVNXsVeXEGIOnwYDNrTPX6bliV6YNlGUWdhDtMTmtEFnUJlSq4Da1JeBURlTa0zq76CBxjmcGtQZPA2BGqdzRtWo7X80EMIsMvADtNVoHUZmIslqNUXnpzQmQsKNTimKgmPH19k5v8fuzgyRanFMyL7DmEdsrGCC0Ew8Dz/4OC98wYsgzJnO9kA8VgZYO9zfYSy22dH+wKODrce+AL5eJnM1YDiei7ZngZESNSUhNDg/SwqDwVJEnx4zauexViisocDiWIFinULnyPwyk3OP85ErR/GuRrTB4GORkaICW6A4EhBPEKVQT1CLrVax1RhjKkwQhtUKporWpozXGK4eptY5vplHd0dRMlGY4miahvl8Tl3XuGZCffEi89kc71ysUuYafF0Tmgbn65iWpgF8sjRzvIPG0U3Gb4uKtLVikwtDUoAdIjH6Pu00I8Q5FhKCg2TVs4wbe6fNHUQMhaxgraG0IDamqxixaKiZzXZAPaIeVYcP82h56wqo0gTHJ7eHfOxTe7zixHnuvuUk1aCg8oFCxogMcETBXMqIojA4F6tyxZrXRYz98AEorzIvuvlUiE0FJTKMG3mJkhApMYsw6wECtN/eVXNCsyLVn53XESNwNXqOC9FexzxDJyxbNdGnlJiEdIPR97FaawghavlpDw5KlBrHYLxCM6+p6xmmLPn8N7+ZT33iY3zgfe/j6NET3HHn3YyHK2zv7vR2IRGaOlBVw2g9aBnhMOnyBNPbxvdMP5Qi+kEkWnsuKDjHyDo2qsBoXrJRwbgkWp1GCRKYCJQS9x61EjDBY4MnFGUUyqbAmvhdcXF3tV19cJw+fTrWBBVh0MsDMyjB13gXCD7vTFMk/6knuFhZKajgxUStHE8IDilWsIVnTsVeM2I+Nxjjku8wBYiEvDVVB//JgvRK+ZRq0BA3S9dCiXawA5mxOlxldWU1ViUiMHeOi1c22dzZBWIFq0FVsbIyYjQas1YOqIoIc/rGsLm9zWS+R2EGGFOmXECLDXk3nzhfjBE0SAq9T/CugLUljWs6q4cYULa7u8fK2gBdHXRFuFmy4tJep3mdi8Rxff97HuAF9z4/Bjalb8Avs4MuHzIH5HTIS4TY9sNWyv5tojK8uihAr66h92HkRcrz2hoI2hCCEMVIlZ4ToUuMR5kQGhgUA5BYxUkJ6OQsnzp3mbqZoWGWlJU9jPWpz4soXKxgbAlyCGSE2BFlOULLIYPBagwYFEXLMa6q2JmDczNcXdNMnqJ56mF8PaOZN9TzhnndEOZzTJjRNNNoLSpoaDDBRThVsy8vdrERoR8g2HEebWVALwSgdUXleaWhfy7yqmB7KJqykHsuyS9jJEK/uZwHYqnFxrlp47aD1hbpp2WwchyjMVdUUMZ2jeDnNPUOwTUR6i4dbrbDex41PHXpCe49YRmtQDUSRmWB0wKf0tvKwuJFaJxLW51VlGXB3M/idn4t2WQN92ZP6w9f5MXtFmch8YC+5ZrmXFcTuAvU7OYkESFbsmiXkZSr5Y5eDz3nhegyXT2pto+vC4sFy+PRvLFsn3m0g5vaE2Mx3tH4OGHFNzT1BOcMd979AkbDFba2d3js8UdYGa9x8623MJ1OYmCEGIqixLmYZ6XSeexyYFSsZBOPxhJ6OYAla6WKCSUwZ23oObqijCdCxZyc1CxpYrV79UlevNGSFc2ltHIagKYC1tknoQluWWTymbxvQIq4yE2McC0qgw0QDHhJnCA7/yVrkxB8Q6NznF1h5iy1GzAa1FgbU1ziuHX3mY7L9Cw2QSUk/3ABEhA3p5SCsioxwwE7c3js1AUmO7uMzm1zYnPOxniD49WI4GJuXOO22Z2eZs/X7BjBDgpmh1fYufcEtx9e484TJ5hPG/Ymjr1ZwI6SL1LyLosQS2DEJPas/UcDPKTI7NDNORHmc4e75wiXzm1jHr+U9mXsGEh/zkneVktgWAi3P3QOP5mkSjkG70C0wR4o5BYZwqIS0qeetKbfTspt1sWaxP02+z+fiSLcaCH54eN9EdI1yc2hIfoYi2KAiuCaKbvTmqd3LTqfMXGO4GOwlsFgjFKUh5FiFWyFGosXg5cBaoZo8ttr8MxmDtm7RL13ETfZQaSElVXmtRDcFFxNcB4X5oh34H0qLOBifWj1yWrTlJaqKRGsU0oy/+5XY1roYekL1NYAbQ+moNrYFwvtCuJ7o6OQNwjIkc6kOWOMaWFKkahUxCZjx+c1q6aishVxy8IYFFQOZkgRkRszGCFiCcFjyxWkrrnQTNl8ao5lnRfaCxwiGgQuKKGpcXWNWEORonmdi/EURVnSLAAgvYj6hbklaN4JKc27LvNgP3UR4T00L7VD7ltSXy3Mxb4A1Xbf2uCvzyBbpv8ChGgWmOkvvZp12sUjxgksvSMpz9PaBcHbJgNnDT5bRlkqmGShaiwPtrq2wpFjx5Gi5PLmFTavXGQ0HjAar5BL98V3VXKd1KsHgS1pRil7M66migAMq8D6yDOMG20CJTnSOJfdy/ZFtIhy0YfQazdF0LbPTNMyF6HwfoEhAK2vwuSgihDL1xlikYfIAnxrTHdfJATfYAtwGpg7w6QZsjrew7SGt3SM4RlJQR0iASkspqjYqz1Xzl9GHz/H9uUZ9V7NYOoog2VwuGI0KmIVGe+o5zV+Z4rUE8TXOLHslZd58rEL1EdXcTcfZnjXMYrVMWPjqf0UzUFDkns1CvNYm7Rb2JFB9oNu0j6uDopjhwiHV+IuMs5RDSoWtqRC2r1UIWCMUJXC0b05s50ZZVXE7dUaRWkiZN3vkwP76Zl7s78eJCkw/fIfVw/YSH1xIOTbZ1iy8CoiGpGDZD2btqKOJ8x22NmdcnkCW/UK4uPuQLZYwdghxozQssSbFbyWeASn0LiAcx7VCRpcLFHnHfPQQDPF7W3iZ3txnQzHOA8SaghxvqqJ6V6iMXgsChlScFgyEW36FroNq3Of5TOSS/vRQ1PyyXaUkv8//a1tBNVipGoXxNVR8DnONEGeud6El9zNUTA03XrusGMFU+I1FVSwgrEFZl4jRYUpLNbGPFcjNkLfxZBaK2bqETynpp6988Lh4ZT1gTCqbJs2p0mBlg6LhYV51Clmy7y69Z/35lDf8tw/B9tWudok72TDAYpfUszjoYOU0Wem57gQ3d9xBw1Ce7X2J2sSJprzSQNF0St2fGDgRIRJEUthTazyYaCoRlhxTCe7YCxHjp1gZWWdTz/0AKeeeJx7nv8CqmrQOs2NyaHwOcAnwhQoqdTW4rur+gQFhQgrWmhUqArH6qBmaJWpCAMpov+TuJdIriCUF3VOCunKj+UE4/S9KbI0B0OY9G5ROHT9UFiLTT7juH+pxuA/iX/nfxBLAOagTiHmrY6HA3bnMGtgZz7iZluiviFvQt6vM7yfEsPOan+oEfHY4QoTLTh9/gqnPvAwr33qPKNZ+uqioBmOmEynzHMlDe/x9TzmwtZKPW+o51O0Dtz0hNIUsHlijeY193D8FXdy7OgYf3Ea43J7mnOcRz5ZA5l5mnRNTiZXInxZEDxU1YiV8QgZFGxP51QDaaO2SUpgCJoqVcU5UhYwnXo2N3c5vDHEWkPTxHH2vYLai3BW/D0z867/DmI4OQE+kslWzj4tf2k0FtbX/qowHaoTu2ixyEjc+AFCZNaxNBWT6RWaK2e5MBly2W8g2iCmQooVTHUEKQ+jxQbzomJWT5hNd2nqOc28ST83EbcDdY24vB1hQGnihg6p5rPseQwuxjpIEmkm+d00VeNJ+YlWLAWS5H0c27xReIyWjkFnWVqavGk1tItHcodIHoGEYkDM6879l+HhpATHTeMXxy8GFZE2sU7thTzacU3GMoRJ3JtuBqgK1oe4z7KJlrVzNTqvo5JoiPWyTcyDNeWIshxSlgOKsiI4z5nmFp6+5DkmF7h5peamE4c4sp5SXJoa0GiUYOImIKa65tzp/73Mwxf7sC9Iu5/Xsh4XBHUP7cljl6OCc0DWQe92LfovQIgefCz3aQ4SyhGV6WjyP2TrMCzg74s+oHg9QsotNDQDQ9N4ihDXVCOeRgQk7Q5TTxHgla94PadOneKRRz7D+sYaR48cZWVlPZYXpEkTvliyupKvtqeZ5aDhoCZd3zD3hso61ssZGwPH3A+SiOznuaYcy/SN5D6waYeYxCiNAadQqMYi9SKxLmwda9VaI9jlCFCNwVbWFjTGtUkTURBHUWIRnMaqpV5jkNagtIyHK8z9lMbDlckQxRLCHkgJYnqMV3pCoUuHEJHWb2QrULE0OD724SeZffQ0L7u4FasuasBb8FrTTGumu1diDeXQsxwSdwnOglXKMZReqKfK5uk9wpmPcfbDp7jjm97IvcePc2nzCppKpUToNkESyculknIgsdQ+tMFjooJhQB3mGBmwsrqKXR9z+eIubWpVNt2zArcwhxXXeB579GnGNx/m0MYK9XQ35eb1xyYGJe1fE1djCgef09ZJm/t8vyW6fP1+xrMorBWX9tIFDYamKRAZUlUVIo7G7bGzc4lPXjpEPb8dY1ewg3XM8ChNuUEtJY2vmU932ds+zd6lp3CTy+h8ggSHNYotoDQx8CxD6yKB6O9Pbo3so7YmbdUX87cNJm5uX8SNnI1UiJQEagojiJQLcHm2ITW4hBKB11hDN1cCRWNEraY9ZekJgIASy092QsCYHPHebfwePKlASN5YPrT5wUl3i2MEpCLQQFdjXiTe3yIBqvigSGnTloDRmo0V1VyqjSB4nTOZTbscUGsRW1KVQ8zoELYccznczKXthk/s1Lx07Smed9dtVOUoIj11Q1XG94auvFDeFi7rfp2l2Rk40uaNhlbwZdjuINeCdI3vt1YXZmRebT0KsdiNKYtnDeXCc16IXp+2kPMss16dUw5Ig6QaYp5mXxdP2mJrhkVkBReg8MSC44lX2RAFcCPgQg4/Uq5sb3L8+FGOHTvC02ef5szpM6xtXOHOO+9kPpe0wHxr/YoxMWdsycGdAt+j0A8BNVNER4xLQavAsVHD5d0hjZ1js8BRG0tyqQHxMWfRAiZAiJFyAXA+UIrB2LhptQmCLQvGKysURmmahno+W9AMjTGURYn3IW48PrR4F7XjYGLKDBohp7zbTC7CUI0H7O7s4rzDYNirh8xmNaUB7yNrEfHtJuoHWTqqoDYQMAS3wmioXDk/59ZPnGF8aQuSnycA6mK6g7E2FpawZdevqc5vSOkH4iXqGkGoVRkNokX49NMX+ZWfejd/67v+PBjb7njR+m4zHKTZJxqdLFHYa2uduAAaYlTlaDikWl9JwcvLVmKSXynalwDqlcFQuOVjj1LccozitffGdCRzrajHgyJxr07XZiBZoCYfvYZUDScQE+MPWovLFm/aeF7j+IxXDVVVsLu9ze7OLps7gSfqu6k5hKsscx+Y7dbMLj1Js/dxmO1BM0VCE5GWRimAooCiiqUjcw5oFDBdlGr0rxW0Lg0hRasPYoEFSbm+hLSxSyzsoDpD1TOZepQdMnTqfHKDmDTGGW4N0QoNbWCRtC6bzq6PdrFqp+7kIDq/kIsbBUmMnp/1JGZnKIgkQDgrXKaFfcj+T2PAEZENI5LWQYHXWKZS03tHge1xIe36ojGQ0SuEMEecIGaOn07QnU2KcoAdrlAMVijKVT68fTsf+WjDS49PuffkiPFoyNbWFqPRgOB7czFE55SIxgCpbLXrYsBPTo9JvUVf4duX9rLUv4tYTHtXe6TPS/rtfTb0nBai/e9e9n9mCsFH6MaY1pcXgieF2EV/h7HRUpUIY+X28ia6JlmiIRpxiNhYLo5oNQWi5qpOCeJj2T2JG4BP51OMGA4fPszKygo7O1s88LFPcvsdtzFeXQUTd5gpjUT4JWmoSufP9MQ0h1IVZ8GEIQ1NzAArHMerKU+wQoFPFUTi5rxYx1xMZC4I4hVVS5EssJCECBhwnuGgYnVlgLUwne1iBisAGGu7nV/aju3BvM6BVwopYuSjJIiOZEVLtq6hNjAslBVb0njDduOZuQ3GKx7nFA3RkhMhuscIUXvUvD1cHqMSUDy7DIsNdDjHJ0XDqI92oU8KSNLutWfltYxPJBamSsEiCgTRlEOqFIVwlyk4sevZvVJjbJV21InXenWgkqDIbqkHUrSClCA25pkKiBpqJwxNwXA4iEEeCdJcnL2ZMQuaIpdHg5KdzW3C1hbr6ilMFVEW05/7CyskIixKYlidj3bB/tS8T20/kEx71iiEvKH1AcwmR/UGYnR1UQwgpL5IioD3ASue0aDA2BIPXN6b8eGHDY1bZTYdMJ36mEc5P01d78YI0eCjn907hFg0QJIALIuoKNqiwBZxmz81JQMDYgqCpB1uklVvjG2VJg0e37hkucyThRiSYks7XyXNt64SVBzLQWFai6nz7/XWSKrk1CFbndkoRITIh1RuMF3X9nlfmEjcOjGnZcUALQXRdpcYDfk7Ifi6tfICCi4JkVRABUn2s3VYm4IPE9pmjUVsQWELLLlLcqSxJ6To+cw7XTPFhznNbAtjKqrxIapqzKcuwcXJhJfessfhlSHTeobIqDdjHNYIQhHXp/cEjZHskadm2DWtBmPpfJYmheplhW4xxzqOZ4JnTUpVDK5VauN6kt76sjHtx3RFM9Ccq3p99JwWorBfI+kfX851SyeSYak9GEBbBa+FrdKty5pNzy5KTFPaAbaGVFZO42bUIaQcwiiIBsNhjDYLgcuXLuGcZ2PjEOPxmOl0jwxhWkxqN/m7EqRrNOeMFqiGWFAdz5FxwF5OEGPO2GwLKScTOn2c0S69R1J/5PussclSj9VUGlenSiqLFoVJWj6pBFtcsTG1xkAL52Y3jtBVhZnXU7wpKIsYkNU4Za8ecGQlCWtjY7Sp9CdxDJFSckEKIQbTxKN4Q1WUFKZIWnkcuJCAJNEkJNtSR3n4NFrLxN1wMmW9F6ImP7YwSvWQO80h/lpYSz33FNWgXciavtVA8jFZRIoYmWpjZRcxhrIs0m4eofVZad7jDdIYSQZ6sVaYTueErR0me9OuBvOC+6Ev5LLWvnhcl35mdXy5Rm1/3AXprY3M5JI7RD0GGy2pWPyXnGetSZgOqgoNwqR2XJoI53YLLl1Rzu95NDhcU+Pmc4LfQt0c5yaI+ji+El0CxijGGoqioqrGFIMR4mdpbmlKkbGI1iiuLUYft+sCEddChvlbYzSr6XqrSDWXjW3HISp1MbI49qNdQI+ykJRsEKq2KU/ZWmz5PjktJe1E0kKL0vKb1p2T7m2aBmMTUhYy3+p+76fTqHdJSYjoWPCJh6RrMhwcQqwu1osdJ6T0uFwsQSQqoFGwWSQ/K6mJqj4KQO9R8TQJpXG2YHM25OHzNa+6HYpsuCRyzlGYgsQqUwUxjdWPUs3ffmUrScaMZFM/5GC0OEuzCpeNHtCeTz+WLtPEW7u531nsfbM180Q5EFk5mJ7zQnQ/9Y361JFpR/jc4SZZYdCFkWchuui0Tj6Jficn/xStLZImYSrG2k32kBZa3LHFe48RYX1jneGw5MlTj7OztUNhCoaDAVIUsUqO0m6J5QW8+lT7NU6AEAKFRObqnFJaz6GVWFy9SZVJ8vshaReXrEhItqQhR8jFSkmaBJihcXmXE0PT1EkDXhSieTJL0gQFSREMccd7S9zqyWu2ViOEJCizZkZtVhBRvEZYZ3dW4hVECiQJwtaFLQkLELswtnFLs5h0XjuhKiqKBNnmTVhiqk0eV1phIUnbjeV+Nfmb2w9L/Cv1GTEE3paGwbDETetoYZCUCQrm6jFmgNgilhb0ipUm5YZnSEpaf5eqolWF21jFFhlG7HyPMeo7c+AuIiQz3npzm8nZyxy7/Xa2rtRxw+k8a3twWDv/ez8XJ3x/WPePcZ4nqqQkfROr5aRuFSOo0zQHTCvs8UreL1vSBwWFi7uGS9sNT20bzk8qpntFjJT1czTMCH5OCA0WQykBU+R5I9EvbwVrS6pyhdFoAxmOaKabiA8EjVWcAgHvY+GDWAkof5VJikCG2w1lWWJMmYphJFixBGsrjCnjmpPo+hEpoIjCq+260KU0QS6SEcmkNZjjLOJGEDndJN5giw721RAi4zZmYQs1EYnrvSg64ZqHr2VznSFhIH6zhra0oPeuLYwSchnCxuGDb63YWFgmxL5MNXojTCdgq7g2xSZLLmBMrDoVi0jEzAVXTyHAYLjCXAac3SvY25uysVrSuG5uNc5TWkn76XaKZ7YSg0CQbPAqJrm8OuTEQTsSWbhmhDFZ/onfRHdYFqCJZ0unoMZnZ2Uk8Xd6lu110HNeiF7Nj9Nh5TlvCiJHTl0XMowSr2q1wKhKdhok3WBIb+bm8H3aocg8L8KDefE459PG2hYUptMZg6rivvtfyunHT/H440/w+OlTfMGf/Hy2r2wyr2doqr1Z2QJXxOoiPm18bKWgNAYJBVmPXRvOWR8qF+cVmGidBgGvlkpBSHU5k9qVNeM2fBwQa1AVXBMhM1tEezjv0dhPfyBBe5oUkRxLm/MRjRC3bJIIjWYYJfsl53OoQ4NYpRDYmo3woUrFEtLSMDYxt2z9ZUEaNVdNkauqsItSDuO+o1ggGCSk6kcpAElDhHNDes8QNO12AUYlFd3vRTSKwbnItEprqIqK4WDIdJ6txAQkqaEYWFSKdi4YK1ipUt92yk+eO845Zsc32Hv5vQz/08fwrukWumpUopK/xrRacWbWys3ntlh54BSr97+AS5e2qJY2RO5TK0TT36GF47SVmQf7g5QiMTWfYMNUMaS9PvjounAJITAaotKmI5p5gx3CYFRCMeKBBx/lo5t3UM9X8XVDqHeh3sHWE4JPRegNmAJEA2UhlMZQ2ILSVhRlFCRiRogZ4sVQ715gNrmCn0ffbJAoNK0UiHoKW1AUFXZQYosKa0uKssQUMXralmXMj24jaSOMZ8wgpYw4CDXONxSGBFEnF0iIiqd3Ndn3XTc1hS0wRvCtfSmITWiVDwkhSn2YdiPKheHFJGsv5BFI8yx4TJPz1+PczDpf6I1jCAEp8zYMab0VBYUMO8QuzQWbLNzgklXqPc28pqmncfeZkCoNJReVFBHGNqbE2BJVk2rpKopPezMbwDGfbuPdgFIGXNre49DqCUS6imdFYdDQoMEkP7XQeE/tGqpyEF03GbJWh/OBoiiQHBxlC4IaNM8bSWUN7aLgC8koUrELFmgb4WvymId2vWW+r/81CVFYFqTLsFXEDESyhq5ogjTbRIoD5LBIrLjTe8pC28bknVki44w1LeNuHd7HfUsVjVpVmwERLbad3T2MFe550b3ccsdtPPGZR/jZn3knb/qTf4LVlTHBe6bzGZNQY10gpAUeJLWddhIxJjKEYTHlxGrgcm1QHLE6EBhbxnSTBAdn60a15x/LVqgILkT4zEqro8Xrw2IHCaEtQWZEqRMc2WoiInGXmJAYY1AKK4yGAyiF7S2LI0JuoFyaFjRhjbJskmYftyprq6CRg0GkhRFtcLFIhC1RhJuPHKc4cgh3YZutvUlklAgU0QI3yTouxWTXFzlK0TmXIN0YAOVD1IyzP7HSgrJawco6jTsXd+cxBUV6trEGDansnBDLCYY4ZrGak7Q9Z5IPXFUZjoaMxyOubE6jH3EBW5LO6siIAXHdz2cNdm/eRv72YzYO2nZqHx3g+uj/zOR9TA8pqjL1T35Q2nPVOUoRrCgEHy0fPN5vcfjoYfbmcz7x6IRPnjWIPg9DjZntEaa7aL0LpoEiFugwGhWvykBZDaiGY5C4DV9AmYWGencHV2/hXUrr9GAHUBZD7GBAVVYMyhJbFZhSMcUg5ZTmuZrnfyrH1+4I0ysvFzwu7MTSdSnNYzAYx0CY7K9s/XCCqaqWLQx9hZFYF7aFSVu3Uuy3zjJKBTkSVNrnLssFYpxLCkwPOetJBbLr2oqNNSH6aEMSsjkKN5+zRQx+pDBYU1AZy3Bd4s4z3uGdw7uGUMffXTPHNw7PFFPmwvOGIDa6qkwZA7KspbRRcZzOttmbXiFwDG+6eWmxUbF3sUauiCAuQwaSilukqFyUuW9wCRuyRYGVAU41+mtRgmZ+1Z+/0haM0W6/iLZfQnDtBhOaFHWDtvEO4q9Wj3o//RcgRDuIDxYZgSbfhPqG3Iuq2QpLSd0J0rC5rmwPImlZX1L7lFhVpYN6BbQLVqrrBpc4mrUFTeMQbKrQQpKkFnV1LGa/s0tZWu69/4Wsro558tFHWTm8xvrhQ6ytrOCnM5oYkIsxcWNqDXHrIYLDqY+lAI1j3e5gdBCDYqD1kykZTMxJy6lf6CwKIQoQCYEy54gBaEhbRy31eIpwzL62QmOB+i5nLQUVGaVQwRP9eeNBwfZ0igabkKIY9OS1YGc64JBViiIuithfGZ7uKS+2oCyruCGAsTjA1XOe2plii5JyUFHaZBV6j3pNtcajEBcxqM2Rf8TdKIrkO00YZtBYusyn7eDGI8vqyoCV9SEbszEguBConWPW1ChCWRR4idm51lisRugVzcqKJCDEdv44a6iGRSc2jSQjMSoKeR/YaE0bvK+pBpZ6Psdt7eF8THUwC8zj2hTnap4Zi0y+L1tVFZ80e0mMv1XVUz1YSZbnsIxWnliQwlGtrHH23ISPPGa5sLuOc4HZ7GlkOoesPJmY51iKYViZTtBZgw81k/kerlGcCzQuQZVFhHOLYUlVjrClpRqNYj6iif8KYjoVyeUhwUX3uki0ZBJCKTYqWZKVE1IAkUnCBU35g7GmrMVgi7STiaTo9uDbwCIAyv+HvP96tiTL0vyw31buftRVoTIiMyszK6u6RHdP6y4AAwyUgWMGGsxAGGlGwxufaPwz+GfQ+MBHkgCfaMQQIECOADmN6cFMN6Z7urRIGerKI/y4+1Z8WNv9nBuZVV0zbznjaZER99wj/LjvvcS3vvWtw8xLrQ73cmTgj0jWVB7R0jI3HqmgJ1OvJ+PbmslJyC0YGdKH/SzrSRHtmIeWc8rlxmY9BcQpl3m4R6WnFOOUNGg0ztW4qoZ5JscsI92iOFY/9PjQQxmCIJmesPqTtlS2llFrQ2C/vaVuDL4/ssvJo00GPCEUZ5UVVhty6MiFHJnJGG1wuiCDUaYoaRfRMZFHhZZxLR/SIiTgkBr4oe98TAhKyUuNChV5NNBfCGB+neMr7kRHr3ffyo8Y/3Gh+Vi+7JBYldcrJniEEbLN4++ZbpTK4mDG2sfU24TAKjGK0xnhy1RqWiMNfoQKtDaEEDFKSZYKLM5WmNrgh55usyXs95yslqXZGmIMxBTQhdHGONMyG7IyPJgHGhvoi8EYF45s9IM+U1YlWBiDVa2mczdWMDWtxOmlWBh9x5vy3nU/zpJGaESyN60VZlqfslG7vocMxqRDdpkTISbWfc3JQuawyPfToOxE3NDFqPWdp911eB8w7Z6L6zW2C2RnOWkD87OHPF09pGnmNE50b5UW46xHSEgfMvKcIjF4+jjgw0DwHu8HvPf44AkpoIzC2poX/4//Hz576rqGh6eoJxcs6louZaZAxImoTWF0Mv0uwzSmqlw4pO1CT2tuqilOpLDyc9nYKWWssQQfGTYtd5sdVVNB2x8t/uN1e0y2uB9ojrZiYioePuZ4J02fL8ut1NFNha7EyO2HjruYiEPPrsu83hmM2fD8+TV3bU3INYpEHjpy7lAqFWZthcZQWQ05EmIk+EDKiiGUWrsyaFNRN3IPrbNFTUf+OGNExyslNKmwtouykTKTgzNleR4IMyVgKpDeVH/OolSks4YIWlucbdDGYmxVNMPSlPqN5Yx7GfwY+EyG+TizPPw8Tio5vvbH5JuxP3J81chhkNcekI3p/Qu6pDmUDabsuowhZAzQSuY7nmtW0tuqxUCUxEDeImVAK3Tl0DhMajD1jCZnUhjwQ1+E9uUcjFIYa8BHQky8/eSdIk95VLdnZJyPvBQJXvUocoEnlRGMoh4jRClpk4tEkkyIKTZkInkhKNA9LDIjPbXjHswwSWBOG3QMXOVP2axvboZfenzFnegXj4m4MTlSgdlimQ9pzJihFRWVUclgbJSafvslR1nzX8bRKGWSg3GCyQgeVU2BsaVGmuJTSoQgE1TOHjyg22xotxu6tsVag6usOJtEUf4oRB51OA+fNWfzyNwlQlIyfqxMAZHB4EyZRNYHgosq5yaOL5FLl2lGTYszpXJ9jgzFVEsdd6KSKHicviBQCpg0tpfIot/3HldVaFNo+rn0v6rMpneEZKlG26YtKCNTdHJCe898t0N1A0MvOp1N2/PodosKmWwdtauZn56yqGfU8yWLZomxldSjC8SW9QgLj7WoRPQDrd/hh64o3nTkFGXCS5S2i5gz4WcvmDm5fpu7Fr8fqGpHeHKOs1ZaBsoeTFEAqLHeMprLse93RDKmST5fCHyLsZ8Wmpy/M4phiOzbjsvLWx4+PMO3+zfWP2+UL/K9jGdCUQ6vOlrgR8+anIFAhdqWlgSl6ILisoXrtQymHjpP1xnW6QEpDgQ/lxmUORN8BzGStWIcVOBswdnJ+BjwQyKUwQVZVWijcM5hq0rqmtagxt7hIrFokP5vY8ZcPBe5SmGaj/rR2LG94bB2xwRGSh3yaoFsE7VdYHWFMw5XVTg3Q7mKzreE0AkUqNRETjzsi/K/DDKzM097dWpdKc9PBSrPucgNFihxvB/TJJPyHjGF6U7lNA6P55AjZLFnOh+0v6f9ebQuQB1I6uX9c8ma1diyd2zERu9rhCCnjMK5SvRxQ2DoO4ZehoKnrHBKoXKiMp5lE3n/3a+TSRxL4Lp6gdOlVxSQUYK65JES+I0CENpI/+yEcmXAGLRBBlVMTlTJ+Y2CORrI4t6SSgf+w8g10IfgRjGiBWrq01f2X5MWly9GzvcPhSw44zR9J5CubCKZPCCN2bow2vLoDzgsvDc/EAo15QhmLLTsXLD5EUbNCa2twIXj4h0373H2phQYQ+w9++2W+WJBM5ux26y5fP2KxemSxWyJ0aKmEaNHF0fqyuZsA5w4z6LOtEERAiXiTiQ92qqRGi/9lcdCBqlE1glhzMZR13eMftWbxuL4e461MkWcMvMxuigKSFnYmT5BbS2EXqCrrMnWYXRmPViGaJjlXDIIi+oDfdfh/cB8u+PtT15NBJ2MwWhDtTjBolB1Q9IQSNzklqHfw3CHSUcpoQLvvTiZ8i4pCsPQDy1KlZaZlGQiRQgF+hQCxKyZM1Ow3m4w6xfMf/ECKsMPfu+bPH3nLU4WM2rnQGXaTV+WzOgwwWgjikkl4tIhl3txWEsjDFlWzJSVaq3IsURwOeP7nhefPOeddx8xfEFUoRjM0ZFmcRDHq/u+Ez3U96d3yFnIHQVNsa7C1A271nO72fLies8Pb1esN5nYJVKQLM6qNd2+49333mM2W7Dvd7x8eSOZg3ZoJ85YG8XQt+zbnughK42xFa5xLFcnWOsmta2YE0rJ+s+lBUMCUcPy9IzF/ITg9+zbaxlAUPaWyiJ9mbQY2rHtYVK1VaoIEBSkwhhyVpxefI3l6UO0tsQ4MDGk92vi0EIUJxpCQGuKwS+lo+KYZeHko31yYIkqJKiXzTdqU4/tI8VJlv0qAyHk+Wmsa6Y0iVYcDNNYSlGH35WFrsr7TDVRlWXyEIwKh9MwDAlExu2isNkQg5QlUukXRUHUmmpWY5uGOnj63YauG1BoYuh4OO/53m+e8/CtR9zdbUj9oca4XD3EGkUiiH3QBrIhh0BKhf2uhU2dxnr1GJxmWQ9aG3zayzVS4/qVjHvsDx17o1P0lObfaf+gVRGAGI2DJWcjaEiK+F/ae/3F4yvtRMfjfvGeySCNfUJaQ9+108Kt6qUY/5hKDXFsrj8i1By/P0fOYWrCPUAqB3F5yS5l5Rqhlh/DZShGIqVzFozUC0mJSjuUcfT9ACTqecMH3/iQX/zkp2xu71itzlkuV/SDTDzJ2RTHmAjKMdeR1Vxz10kzf46gbJl0UmyAKpssxISzo6PMGO2w1gBBVIhSZCAWmn76kixJvlJKolg0RIGmYiEHiOO9f39kFJJls98hVReB1QcfqVXmahcIWdi7MSU22w2/9xc/k9pH2fxDDELYSmJoxAAFkXijKiPbDMuq4cOzR6yac8zpCbqu0c6Cc+jqqAZJhhjZbdZsrz+j61o637EferZdSxx8aVVSoBWbuyuuNDhXgdL4PnL76o7qL/8bNjNHWs1Ib10Q/uO/ybffecrQD3T7vbAgjwYcoBSrV7c8+ekLXsdwFEmP2YW6twQFQjUor0lBzuetkDj90+/T/Ad/xG0Iv3RvHC/VETEZ2z6OJrF94Ugpgc00ixnVYsFd2/Gnf/Y/8lfPZ7R9RegDOt2hMiSkh9IaB01DaBI/ffmc2s1ROdLtPbEfSFkIZpXTNJXFWoWtZizPFriqwboKVaT4fAjoXMZ7kamcxVkrzk5rjFUC6+mEma1wswXKJHLyGCIYg9G1MHULUmJ0GQtW/oyQutJjY5YlGdiHDd3Nlozoy5qcUUl6VJUurR4Zxq6rUWJzglSNNJaNOz94P7VqCxEmHxBXMwoJTKE7OaYpyE3FmVaIOMNUT02JmALjQHe5Z2KbpO4ZJ2c8eqHxvXJK+OyLox2JdnkSnBHEpDhqpMyTSQXbkvPNEXwcZPqLczQPH2I2G3If+Fp1y0k98ONPb/nhRz8ia/C7DNXXAfiTf/L3BcGJg8gPKo01FXHYc331Ajs/oZqtsMbS73eEvpMSkwatEtH3QMbqRib/BDlX56qJ8JlSpO97YvLUVTVZdq1FshWKXnA+TLQiDWy3W1Fp828Gpr/8+Eo70alfc1yER1mg/J3xPlI5x3y2Es3aVOj4ktvf95cZxgrg+P73IMqCr03ZkFLEmPE+ECMybDiD1DMiSuUymHaEdiTLaqpaZkGWSScoS9CRrAJambIhA8PQ8f6H3+Ll1QvWt2tur6958HiFMg7CILtYVThv6dwdb7s7XpsT7rA47fGY0roBqUTThoDGMEK02si4oRgHKg3vPn3CcuZ4/fI5nY9Tn6g+ClK6wdNrgY1SUhBNud6lczSVYeZKiZhBCSNdSuydgxRK35cYnjYqDCd8frNm6Pcshw3f+uFP2HR3YlCwpQE+MqsMdrYgY6mqhvPzE6zTDEPEWkdd1zTzBm1hhyKbHm08WkmUOaz76Z6Lr8ooq+CixgRN4xus76n6Ja9fX5O8kBnquubddz7EZc/mbk3XD3Q6omJiMa/Y7lp2V3va5x8z/OVnLD98xO5/9kc8fusxi9mMvR9o/UBNhalqQh5ouz277VDGPSWkHav07RaG8hiBj9N5chYIOIXA3dVaoviqnu6NVpCyoAq6OJDybSWLYGQ9Fy2okMnakzFEDiLhs2aBqQ2vd2t++sOP+Mc/c/SbWnRhU1+gNYutMyrL/d51HZe3LbXTkCJdXheLnKibTMyScQYPbYysLk55570P2K03pOiFyBPBYpjVs5JxCoRorIyfs+NjWpAYazWh/RytDU1tUcoVotGYfZahBsYwCgVIqaKsWQoJaFQXyodsHzSuQMWjOpmgMqnU84vjLI4dLShQFxJDGIgF9l3N5lgy3g/kHAtCNmGKjEzUcvNK54UY+jILRTKysf8bSlnGTRkugMmOqBVVDKSYRWbSIDOEU4GLy/NNrPChF1sp1ISSSJQhF1nLno+ZULLfUeQBrdFO2s9UzpLRKctqdcYmXNNUige1RquaFMWphZWGUrp/NpszM4psTCEpeTSZejbn7WfvobIm+J5295LVvOLz53vMomJ1MePB+RKjVhg1w8402hpQpc83iy3LZGIYaHcbdps1JxdPcFXF2NoXJ430YzlBsGhiEBGKXTfwf/47/8df7YDK8ZV2onIc8or7PzORXiQLEN3OGLOoxIzKzMAxbnucdB36KEcDdvxZkv2GEPE+kJK0OoSjbOy4t/JQp8jFEOoJchkp1hPLTmvIhhgTfT+wXMyptGG/aXn96hWL5SmPH5zLfM7i4LOuOJkPzEzEqkp2RHRgS+HfaLSxjJllPrpuiYyrZugcWW9bhl6TscRQJAwTEA5XxoeEL2yNTAIVGckIY00QDhnPyDQNyqP8TPq8lGjtCXNS+voaDWc3r3jw/CP6m2uMUzhTldaFirqSQekhO2ISduXgBzKGlBNDTPg+0IaORCKrIBBoOaEYhb5PGh279I6asZF7NGjluszmM5wPJULPrPdrHpw/ZFk3LFIUaPil5aPPPyflhK01J07jnKZ/dcnw//xTfvCNdzj59nt88M4TVFb0fc/JrIGU2bYdwzBI5KZHqB/I6uj+lFoVMq5qZHrmnGl3e168uJ6CPoB+6Ii5OkwEubdu0zRcAEBbR58TswBKRXI6EJSC0fzJX73go883XL7c0g4LYlI4l6R301rQGt8HQhBST2MNFxcLbCUB4rbd0/eBGC3RJ4yN0hdZSgeb2y03qzWzRYPJjhQCja3QrhCCSs1Y6wK76sKQNeIcrT04w8lp6gPZ5IBIFRGOI3Kh+EpdpsgcDKmo2xzY2+WJpce4gJwj8q5ksH2MgX3f0w8DfT/Q7vuj+5e51Tc8fvSQxhpIShCq+4bmyEYcQerjXctSvhnRMKnpqQPqQ1njI+JkHFqn0ipysGPKxNLCEjHOgBHC4qhnKupGhRtcEBMUohBkrGS4MZd6b5FZUboQtzLbzYbZ9oesTpeszk6wRhKWGCMpG/hcvs/Jw0dUJkOKhDAQY5Bs1NViCxG28Ek852Q5Z99/hJobFqdLZqtTorfSa6oSUaupjzojyYJWoKqGWWOpTpcoXZe+81IPLeQjVZSMciy9sq4Ciuqa+xUwzRvHV96JHpqkD4SK8fGcZRxPGtVAVBm9dFzTO36v6f3kyPlQwzhuAxiD1fE5MRZ911H2bfrfuFnHJ48QqBS6R9QuM4rjH1hxo6OOUTQuZ02DzZp9t2a7bZnXlmY+w7hKIN1oWdSehU04FH3WaCzkQdjtHG1SXTKw0XkrqTFmrWi7wNAlrEYamrM0sOsjdCMlSejHq5cOp8uYx8ukucI4VWpyDFpJA0wec36lIVlmNqHzFtfecrrZkY0W51nVVJVM+nDWlPfSEIU0ttvv0B2k5A91pCyReyYUxYdDb+BIXBA5uvFGKoQtXMg8o4h5VpLFZ8nawq5HVxZra2Fcq4ybVVhjGeIwiX3Pa0tGs2o79j/5iBe7HZXWvPfWI/Zty3y9JV/eslnvSFEc23Tbv6QUk8ulNcYQskzZyBH8EHn14pLHy2Z6boxJUI18EG/Ik0NNRRFn7FOUjEYlQ0z+3of/6fef88OP91xvoNtX6BSp6gprZT3HpMkpUxvHauZoGsdiXnOynDGEhI8e5zTr7Z7tVkgnduzLU7JX4+C5fHXJk3eeMps1VFWNzgnr9CSULg5US8+mlZ7EwzABwVMnduZxRjlB5KJqhcriGFVpACl2Q48arEru58QvPAqsx2JM2S7Tg9pohmFg33Xs93vafUfOmX4YpM5arn8fBpyzXJyeUlkj5LaUpvLKuN1VPg5umRwgY71ycrK5ILR58nOZ8fWlpFScySFWVpisDhKRWpfvqiYdaRnpd7Cl48eZQvnJI88iyN6JaWT0g8qJ9fqWh/mWZXOKq9zhfqRMPJp5a+sarSI5KaxRmFyGQmg7BS/Sr75gvjrFzZ6TZwbTNOhqLtwLF8Xu5jwNrslaY0jTSEdtakzV4EOUwG20+UkdXUrp5Q9ZmNejDcj/Osr+yfU5zvzE9BhTCstlQ40C1OVJUw1KkY8c2MFpvulEpR+yQC1TxAtju4vIzuVJ9WbE3EecPufM4AOVCxgr5zz2beoiWj1GfBKdaYZ+QCuoZxVvPfkanz3/lJevXnLx4AEnZ+coFekGOJl5VjZRG9jGRI00jKAlkwkBnJu8nVy0Ern7ENGVlR7JBD6E0qlVYKTjNXW0wDKKmMY5fGrSvDRFeSAWFaCYFTpqsIEYS9+bsiRTkaNlZXu67op+aNGmYnVyhlUK7bQwM7ViCLFkZBGlRUB833q878lxL2NgCuMWBSHESRieLNo6qsBx471EqYk4MemdogpJxKKMndi9KNjtr8EsycqgNNTOsTyZk+4Gdr30Ccfa8eDhBTOrUNdrfvajj/lLIh/+p3+byliaj1/iP3rO7dVdCTgOGaOaRqsx4s1ylZUIPIxB4ugcb66ueHr67HBrjJ7Ql2MRBUE35N/OGrTSDClTG5GTS95j7MEc/On3N+zXmRgyymqqueF8uSLmyH7wAp8Dj5+sePjgjOXZkvmyZl5nbl5tudttMXqOxhC6DfswkKLC2IOBtk6zubmhni2onjacnC0Y+i22OEgh+2isNhhrUSUDHWvUo8EdHWcJQ6d6J+NKPw5Mx6St1N71qAZ15L7MOAhbURSa5B7ocj/ydIsyt3d37HYt+66n7waWq7nUbouKUyKhs+bq5RVWG1YnC1zJ6kr4X1CQYyQtjxBOuffqKHIfnWieAvHyNCEGIl0IkkmWoEGXkDmXNpJJr9kITK1GmyhomsglFiH+FEpbnxCnRtZziDJJhxHijR6/veLJO2fMm4ocpW1J9o8lxINtTmHAJ49oLI/BnkgRjqx8CfYTRMfQ9yjnBFWKBqNFbUOnkpkXfSijdBG2V8VRqoJQjDdtXAWH6GIMUAFyiJPwzL8Ar+ir7USPHd2XPZ6LCIDCkJU0RhujixKLmSK5wySDQwYrrz+836ROVH4fYxSCSeVIKQtslULJaMb3OZzTaMhyEm3PYfDU2hzm/CVkYoUpSiBaQfZAwrmaGCO9H4gp8q1vfpvPX/yMq8tL1ndrvvHN3wI1QOxZ2IGF87yOjsSWnCEEYTcaI1MvpgwyZQiJbDTWGulx1Ub0XJNCKY8p4iH3FlXWRXQCctaYI3KCVgfYTWmNjxEfo6jMYPE5kpLBpIqMJuoMTY+KcPWi4mt6wWIJuqpQyuBzYr/fE4YtqdvS+Y7oowhopAgJKqtRhUAmEJvGGUfIXk5ejY5qDFhKBpBBZS1Za+EW5JiJRd96CIe2pTHusk6R8qXo/mYFytI0J6xqQ6UqOh+IObJrO1ZPHvP+eyc8brd89IvP+OjFc779G19j8/2PaK83bNctcAQdHgWBKafSByfXOaYymQbJMCJynqmPnJ6fTK+rqxm2skfZpxjlMSvKOYtijFJolTBkWmC1uCAcRUrbW+m9XMwty2WNm53SDi2Xl1saq3n68ITvfus9mgtHSoYQI1pveFJ/xm9+413+6n/6mJ/kBe7hMz546xv8/f/xHxLQqKQxJoteiErMG8vNp58RhgFVv8e3v/E+m+cv8MbgXBHOyJlElKHXRpzDdG1GqFIJmoBCMs/j5cpoJ0YDOpJwCplmvLJ6NAamTAwqge+IGh3IF1hjub655eZ2DYDRFmMizjh5TUiEEBlCoGpqck5cX9+gyDx68ICoErFwIkZ94gmZmpDgdER8KfuV4r+VBBpTTbRk95pjBwqZiInlRQWBUclh1EDOMhQhqwQpoY3GRy+ZfhE6GfqeHAaUkZp2yhljKgy5rFFxPjkN/Pvv7Pjwgw+IMRJDlPtUNIitPYzsK7H7IVsu381KFw3WSVtaJuJsQ0qB2syxpimowiAlBMp9UiLCYq1DGStJTAkyUs6YsfQ8+osSpOgs5yDTPVIZnlCu3a/PK/pqO9E3nef0OOWCjc5PywZzzk1ajWO0PoWWb7zVGB0dX3zgyDjJy4wx1HVFztD1A+QCzxW9zBwzBydcWkvycc3m8NnD4KlqqeFIBC3yWLHUxNAWrRS3u5c8fvQ2D87fYr255gff/xMeP/6Ai7cfcL5MrNqevHXQZFR2ZKJoQWrFEMBZWbTGiMZHyomqkg0Vkoh4V9ZArpF5q/EeC1DgIltCeoWi1ES19BGmrIgRwiDMBqUrjFUoHUnekWyPppPGbGXwKEJW/OcPB96OmeQNL59/CvsdIXeAQF86yUSIaqLqK1wlDpMC5euit1pVM5ypcJWo6Rht0bqCovk5ZheZRAg9fdiJcHmZSuG9Z4i+kMZi0TPOhKTACUrcB9jvPX28JiRRfcpASobWb+jO3kLbCjczvP+o4sEPnsNPr3jx/Z9z9fKSnANjn+5h3U0r7QC5j9ecWAI1hVaZpVU8+Ic/wP/216fXC4NTjW8xrb0RxlNK5B3Jiso6+nQHucZqw+Xrm+l9rFGsVjPqZk7OlleXn7PZas5PHL/x4Vt8/YOn+BToWlkvbzctT90OtTfkqmfxeM7FLz4j1xXq7Q/5bv8tvv9XP8FnQRMsCnwm6YBdGLbXV/zkH2/Z7Vr+4A9+C7Xd0/V7hujRlaU2tayBwoQPWUhG6g2KsS7B8fHFHIPacf8eeSlA2NbSshLZ9R0hi1ZrZRyVtRjFpKwzEuL2+47PP3tBApq6wToR2Oj7nhgS2kjrV/AeNMzmc+LQ4buebr/HOGFaj7wJBRO8OrJFcyn/RB8KQerwncaa/oSqjVlqllEUYIrAQSJkfSjHKNA2TaphkoDKKgsxYrSDnGjbHfv9ntWDcyo3Z+j2GOSa52jJJqGyh2ID5/UJ7z39Nt73mLrCKSFi+SgB3HHXZdaV2I8US3/6yGrSZK3w2UMaMMrQRU8btjT2RCYlmRofO3Rl0VQI8CvkpxhDGdahwYgNRulpqs09pHxEnMbk3oxZuQRmR9oQf+3xlXai43G8Jw5N5oeNM0E9WnqHRnmrsZ5wvxZ5Pxs9fgxG52MKPBugQAhVZach1SkfGMNaqdJaIxmQ1iIkYK2ZDKgMgNESFeoCK6cydYKAVU6IDWUmYA413bBHK1guFszr7/Dy6hOevx6oFk95dLZgdefoUnkfnUsPqKiITPUQrdFZFns/eKrS/pEyJB+xaHQqvW/Hq8pIfWo08iqLWHwuEGoIiRBKbKIp8IomJEMwCYIr19fgVMVce95+uKcZBvy2pd1uiX2Pjh6VBC4ygFOKxXyO0Q11VVFVNbZqcHWNqWqMdaKt6RymnlHVp5PikVIGoy2pZMtS38qgEn3fMYRWSBbFQafgUSlJr2gMpCAaokM/0Hdb+v1Atx/YqDu6oSd6JbVXldE2Qky8evEzXLOiaWYs5xXzquHj56/Y3a0JQ4/OMPY/HsN1+giDHGvHlICj9bHonWqM1ezu1kzwL0cI8BvH8V5QRu5xG5KQXaKRIOdIL3S2mFHPGnJW7Pctw14cUNsP+AS6csRWjPV780tOrRiwaB37HFi5mouzFdcxsH79inpuqSthhMpILcn8goqYHLGVJmXPxz/4OT70fPtb3+BstWJFxvcdQ8xkU2Z0AlYdbfoR+ry3iY+/d0GWStAs2V5xoFVNu9/Tti3tfi/ITYooramcYzabsVoucEr6hkH6FG9ubiT7VIoYvPRvl/Ufg0gxKq1oZrXYaDIYMejd0DF3c4wWB3BsW5gshQgGGGMwOYszLmlqPnwjRmLRQZhB4GO0BWOlJFPIQAcuqkJEdo9eq0ApSQg2tzfcvL7i5vqazWbNex9+Q4Q2SitbtgqVLTY50liXLAMhVBFI0TqLJm+Jce7pb2fhRkz3IR/QABmtKMFtzAO+25J8D1FjsDij8GQ0riAOEgmkoqOLLuzzcl1IsWSaR2sjMTmNMYRRuoygLBdX3WN+/erjK+1EpwZ18lRTOHag94zJuPLeeN74q1+W0yrF1OZxzM5VqrS6ZKlVGa1wrrQlhIKB5szY9EvJYKXfSRdh8vLJSmDUe8QWY1Ep0LdbjLYoVWO0I+WIUql0bQnTuG4cZxcX9GEgdtfo4Dhxp+yCg9KHlVOS1yJRqVFaMqdxnFPMEA7fT9j2AteoNxZV1pY0TkbIYLIoG01TUpTCOIGEpBanivKRYoieShuskiG8KsKpG/g9PsPte3a7ge3dluw9ThuaZk7dNDRNQ9001POVZHdVjbWu9O5ptHXTODdlNGiDsqIzPG6UqOWeRDVCaMWA1QZrGswIjZHL0PURzRD2XgyRWUz4fk8YAmEInPc79vsd3XZgv+/Y9y3t0JNyJAwtIUZIgcXigtZ7Ll+/xvcdI33iaDFPq3GsrzOuSwU5R6lZZoghSduHVuzbjv3+SPZvukl5es9jcQx5oEQ3uczJJBFiYLGYw5U8JSQJuHKCoffEAEZlvM9s2oFNO7Co5+x2HSsTqZXAzRhN8gE3qzk5OyNsIQ6voTqlritiHErgmKVNY2yUV+IA1NBx9cln/FQpHj1+wPnZGWfLBfMsmZK0nMl6zCSJBtQhXz+uBR8uxbjPRvlNgfiNsbRty83NHW27J6SALRlUBoLx+METvOfh+TlGG1KK7LuO3a4tddV4QLSsmUaDUboqxxaaFAuhsRhzq4WMFmLABxlY4UvGmcqaU0phnWU2azBKC2SbjpOEsgFHtGxkmypLHjoIe0xWUC2JrmJS7c/HwgqqoB2FuaoMYfDEoSf5jv1d4vbultV8IQIXIRCTRymDsxU+D5IRiyYf07vlMT3JU6JwuCGxBKsF5RoFbMbAsUDcKckwDbG9pkTkuXQlCCSviqyqsaU39yiYGomT4x7Io8HiCJkoHziWAg4P/mviROVQ975uKlTRyQwVZ3e0bBiHII8Q6xirjvJf9xcpR48dsRzLu41/awXWChN4+jPBxIfaq+jSmikbGhVljsklmeJIk6LftxgVsPUFtpoxpA6UB+VIORVoLnBx8Rbrm9f0+w1pMCzdE+KwhNyjkiFkBUScsZiUpU00jU3koroUvbCYR7m0NELTbzhRnwweK0s/gY0C12StCvNWhMIzMl5tbIMjZ4KP1FljSsChk+fBrOUbd58Rusi6S/gusqwrzpYLTk7OWKxOma1W1MsTlK3JTk/C4TlJpihXOR/GHw099C3TvMcpUpJ7mGIqBrc44YOgjxgxZB6pxGkKrANjsEpjFzNZdaWfN3R7+t2OdrNhvV5zfbOmDy2DT9IDl3uM1Vxe33F3c00OEYMZRc/urTUxIGWoNSXA4UAisdZMsm9GK4bes77bHK3T8X/j+x4c6aT5XGLPylhyEsiv8wOz1XJ6XbfvCklDF/Z5xqAIEW7WLS9er/nN3/gau+2+aLEGkpI6lQoRasviZInWA9Xtmo/tQ1aLhm6/L2iI3CerNZHCnM6ZeeMI7Z5PfvgTrq+vefTOM95//z0ezhdTLTAlyVbkuiQRUVB6gjd1qflO+6kUtUeCX85pymKur2+5vVnjQ6Cq7dFcS5nu4wfP0A8sZjOW8zmhH2jbPd4PWONkIPWYiY3QOeUcskC6Ci1QaVm3U4vSfk8/DNJz3PcEL5leKCUFpcBVjvPzM1bzBqNLnTMz4rCMQRclZspZi4JPe4teP5ca3+oJ+eyJBFMZyLFknoeSUspM55ZSQqtMU8nwjqtXL1m8/yGVk+/bdzuMcczrucDNBaVRaewyKGuxrFNy5r5TGqX9Dg60DA8t+1RP2bh1dlJUy8h85ay0tONkEai3ymKNEJLkUhyg2+Ph2uLQi60v90CGK4xdBHl8Fv8CiehX3IkeCTFPURlHNuQo/BmzDsHny0iqIJs3q4zMf1D3nn/vowpEq/Qo93VgBEIuvU5GBgcnRYqaRCw3hlLrKJT9cTpGqbsO3kvPFpBCQIWMyomZ0jhr2G+vydmhzIyhHyAbgt5BClgMjVlyd/mKZbVivgTdwev9NSHOMSqQYskclfDYVA54Ba60DsScCDlirZX8Vktvl9EKA0IiOhLFafeWTldlbFgmaUvICeNkKHWICj9EtustOoDVGoemjgmrBpK2dG6GbRQPVjs+eLIj3A6YyjGfz6kSvPP2GY/eeYZ2cxG1INKnhPUdcRiKtxHt3SPBnwM0bkpt2RyGCccohtc6RzaREEXiSyuIJjItoeKwRmF+hWTuOmVC7Augkac1mHSDPms4OTvnNAae7QYur19y9eIlbd9RzefM5wt+9IOfQFDEKAbfKiTouBel58MXKcZ8jIxDGKibCqtl2/oyDejl88vp5SlEVMHQjltc4rGyTQnQax/YaoVzir4fGI7MQQqJvuul3cQl7CBohlVwe3nHT8m89+FTmrljnxPoSOUUJpWuoqGncga90gxkcjvw+MkJm/WWjQ+CpGQPKWMLQzSTBKloDI2xDDe3fHR5zS++/xPe/+63ee/dd7g4WTA3iug7hl7LXjTl/pYsMOWIUZbJwZQQOo9ZrNIYY7hbr7m+vCGhCqNWoa2lH7z8XAgy9czy4vVrnjx7QgoBXwQ4IGNtYa4qYa76aRi7oEQmG4bBC9qhBb3pvWd7+Zrb11cSUGRpBdHG0nU93vspCNivW7p1S3uxZLVaMpvNqCpX+AZ5Wi+a0s+NxQ873OaS+vYFulLkm8/J8TeJZ4+JVQNDh0JjjaHrO/adBAWL1SmuqgsbHpSBpq5Yr9fsux6zrMjA5tULuqR49xvfQlkn4+Oyx+OpjZS1UpE9VUbQk2NKhVK2cDE84/ByNWat5T+lNNqumFUrUuxRBEiJOCQIFmM6IZdppISXMqiEOgqgUs6iWXEUR7+pbDeBuiVYHevnY0L16xxfcSdakKkp6kyoo25/uV4FWuUAeRhjyVEamFUhmYw9WqPakVKHrECOsS1GLnCeYBx5Xx9iWfwyfFurjLOSiVlXomfGWqkqMF9ZQEVH0h21MKAinohdPWMxfySGMGdc1ZBSwmJQrips2MhKrSBXuGbOWZ14O+/4h5cNJ01HlyK+zBlUpscYh8/Q6MzMRLLJpFjhgkK7gDYZnQ1DdhA7qmRYpYOazV1Y8XFydEOCoKiqJY/Y8zefrem2ibuXmndSxR9+7xF/9OpPqfMGW2kqO8e6gW3n+cHF1/hRtuy2L/jdf/5jKjuj73uWq4bzd95itVrSe08eNkKY1MUQjrqoJSh5c6kfBOaF8m9SRP5VoHmtCcMelJCqdBHeMKWhJ5f7kVMSMseIEADimyuEbHVUh0oDKSRCFlxE1Y7VxQV1s2IIPdu25fNPP6dvB4wCO7ZBITRghS41cAQ5IZPHn6VwKkYnSABgrfTv+iCKUp99sp2+f8wJy8jMLMeRQdBa+vYymc4OQgBJito5huFA//jf/Ecr/m9/9zVdWrGc1SSzp0+KpAUSXb/e8P/5O3/Cf/Kf/i1edjVP0g3V/obBRLQRh5JixqqaR6fn/HywLC7O+Wzxml2/L9mCXPOR+q2URmcjPblJyG6zgt199k//jJ//2Z+xfHDO+ZNHPHr2Fs8ePaCeaaxakKMm+Z403ELUqGrsFRVHM04rEsTJoNC021YYv9PkCMW+95D81N6inaZtPWHouXM3wohF5lqqnFFJxCfQRtRuQs98cTKxwSHTGKnVoxTb9YZ2t6dppN6szaj1C92+l/p7Emh9Np+VfkvYbVo+Xd/QLHY8frJk5Rp8lPUWh444dJjFkvnyhPjyI4aiSXyyEuRCX39MrmZk00gmbhu2m2tefPYR7XqNyhmvPe//wfdkAH01Y9eu8SajU0ds9wTjZBRaSvjdhu3tNYvlKbaekfodg++pbSVtMGSyThgl81I1R/M51QB6BRhi6lA5YsyssIO35KwBA0WkPqYKbS3GZXLekwrpaBrMkTNax1IKG3taDxCyGssmU0Al7TvThhtbpcpOv58+/fXHV9uJvnFMQXxxgGMtaGqyzQi8eATDTjCZ0b/k4o0s3fG59yHf8TnkPDlVChSRUgCkz1CZ49dkqqqCyRiL4ks6gn7Hz1BZoNVRy3JiGuaEsFHl47QuA7ljLZMonOfZrOeVb0gEMklqgX7Otc84F4ko9knRD4q5ycy11L6CMqAcCy2yhXdJc+cP9bsf3zl+4y3Nk8eKxiRO8ic84pp/v3vNw5li/jdOWJ4+oVlEzOKbhOGWfrhh129l4TcVH/pL3s3QxoGtm7PebpjP5zRNgzYGX4aamzHjL9nTJC4+PnBU+zu+H6qo3Cgs2histTjrUErTD73M/Cw9Yd57un57qG1BgbsO9+H4Vh+PijoW8s8jlAw4W2HmhtQlwjax3WyAUBytzL7MSRUYs4hPqMN3koHldkobpRygBT4uqIAxFmN6vvXRJ4fTKzWhcdLI8XodM9Ox93lcr2PrVT7CsL73vT/k40/+a/7RX37CTXjGvNEEBCUxxlBVBpsS//i//584ebpi9+CE88V7mMHzTfcZA4qUNbfB8llbk0Nkdn7GYrVgs2llsLjRZQTXKBpwQJSEcSljr7TWzJYzXEzEbctl+zE3H3/OR7MF9bLmvffe5fzshNqJyIWtmqIfG0BRhjccbuGoNHZ7uxZhfGNRgB8CPkbmdS2lBhTGWCIJaw273RYxyUIkTD6CKXM0Y2K/u6Gen2CMYiisbqMVdSUtakMnGsp1XWGMZjFb0XadZJ+Dl31sDE4pYkxsty0xRuaLGXVt2W3uuNms6fZ7vvPNdzBJmPtKW5S23HzyE9ZPex49eIvYtnR3N2xaz0kFKXXE2BNK/5ar4O7qBt+FMqPXYoeKyx/+mLpZEWLE6gryQNM4ot/TtVLbdcYStWNze0ddN1TzOVEZ/uLmCb+ntjRO2kyij2Tbo7GSOY/XXzsinpRblPZoFDEOmGpOYoZOgsRBxgdf6s5jLbvAujEKr0SP4hsCAcfop6xXa421luMRc1OZ9Ghvj8HwmH0e///XOf6VcKKjkRvZbXKh9JS+K6QXEA7R+TQCLaeSzTLVG+6rFskvxgwSKPJRanq3MSuSI02vEfYnBywBcYYhBDGKpjBHOX6vL36/8bHD38fnd/jZIg3GOWUWqeMPHrzm7352wY5EUOCzYcjSkF2nxJAkA/IZdBPIwdIG6FPG2YBtFJfdgpw0J0eR5H/09JbVwlOpllls+T1/zdeeNJy3gYaMHdaEbWa/BeMuqKo51gDDRpiPWcO+x6bMPCTSrGHo96yaGfOmwVo71S0ONY0xgDlA+GPpQx+TAsbfTQuhOJWcGYKIbsRSCx0JY+nXjT2PCCzHvlspVZSxDvWppA3VzBJyQGfFbtORgaoqI7RS6TlWihyitGqoo7IBClQUU54PtTDpGyyONUuN1LR7pAXmTad+WJ/H1+aekIhSX/q83a7lj3/nfW7Wjj//TIYWoMC5grx4YYqb5owzfcbmpufm7pbTkznz+UOJQpKmzRrvDJqEVwpTVRhryXkAIKQs47NGo6akNgqlLSdnIjL82yiFSpHoE74fSEPk5hYhGb33lGdPHzOSUo6PqS6nmG5aKoLyIiiQJ8Jm8oHo3DHfGWMUVVUTohe2doF/MxFb1RATYejIKeCcI/gBP8gwh1iCrlENx5hRZzqx37fSQhUimYyzjpEcE2MiBC/j+ELi4cMTZk1Dyoq+HdgMgZN6ASEURqpmsTzj9Uc/Rz3zNLXDnD+hvf6MqskkO5YNMjmLHGYIYQqklFY0TU0YOlrvGXVoYwbnZoSwJ7WDcCCsjIiLoS81YUH0dkFh6hnzeS3kn+TL3FdL23sosV7yUVA6o1DKopK0PPl2Q1JjF8Vhr5uCFk5wrJqSSgm3RidZarwyoKN81hGOfD/HPKz1Meka98Cbpby/7vhXwonCcbZQrvZ41ScHpb5oKtUoAZana/oms08uqtQshDE3Gh+OPDJTdD9i7JJdlraVIxIASlhlImxgy/vCCP18aWL1K7/3YQGM0BUpYXPkWxe3/Ph2wc9bzRAtYOligTezQiUlaLdW9DHRe2j7jI+RRRN5eKqZAbVWPFAHBugfXlwS9rfYuOPUeL7RtTxsT4jWErMihkDeb9DBY2cZs3iMM3Nqu6Lt7khH5RylpO5ytlqymM1FwMIYYBzXdlj6o4M48BTUUdl7NMT3AwxULpFsnITw5XdlCkapoR0HSdNdvHdt760KOa+jjSfnV5COXDjBVlPVNcvFnIvVKa9v74SlrDjA9iiSilLfmUx3cW6MLRl5fJQxMNAKMBnrNPt2gPlsukZjwPFm/WdSlSpOajTo47it47V/eXnJO28/4btfj3z04gVX3mDMOKxdyhdDrwhYnD1ns3nNTXtLu+/p+oXU04voQciicLQLd1JPLPtOG03wkaRFTUfJVOhyBzLjfOTRkWqlS0aO1KRzwO8DL59fslwtefb0qVzPIvN5TNZCloK8f9m/xhi8FwnOslSIfiDFUdBfFbSnEFxKD3UaUScl8Ho/bPB9i7UVWguDWXS6I6HsfWOFTDcSvEKI9O1+ghJFCUgCf2UMsczPTDHTx4GuG9DW4CpH6Hu6tudMW7TvSdqgXM387AnVy1fcvPyMxWLJYrEgrhfshx5Vn6HNSIhL5CDTg1UeezUzymScsfi2I6Mw1qGyxdiaFD0xdIDG6loIbqOE5mhvUbzcRK7arqzaUkbRipk9kCp0mZyVS8sZSP+371qwlmwdGLGNYzY5SqoebUH5XJGGI5X1NELjYgZzIZEeXntvT3Bwml+w+/z6x78yThS+JIpQlEWaSp/jwfBJhpqPrtZhWsubl/B+v+nY2nKY56dQEzSokLqUiN6XmgwZ8qhkdJQxHznPowemzz92Cm8a7OPzHB/qs2emFElp9rriZBn53odrdj86J28tUcPtHoiawVlIGZMT1czRdpp+P5CHQJVhjuHfOYOvnf4cbT34Af6pfM51f8O3rl/wNTwXZzX1wzl9kJNUSmGUxpKFAj9sCW5GVV9wuvqArvsLUIGsHCkrQg5YA+cPHpE5rncKaWAMMIRJWBpDRthzzDQpriYfHpYHFSl6Rv7d+DwhGfWTU9Va5oXezzK/PCI9+Nr72R7qcC9Gpp+PCdvUvPO1t3nr4gH/r//uT9juI/OZRpssjM2kidYQYsAmi1GWpDRJhSIsoIoMZBIavxKGJCZTVZreKXa7AHM5DWsMfQyTZvT9NazLljg401ERZ8yAx6Pb7+nTku9+45Tt5Wv+r/9cc9pIi5TRYEzG7+GHP/gr4vKclVmghy0//vOf0qwaqrrGNRXWalSK7IdBvocXyBQD1lp8iKSYi6qiBlUgu1KnVGU9iH5xJJXsI5LQcWBeW3bbPbu7PSFkjNHkeMSqPuoJH9tPRL5OIFjvvRBgtDA+h66jWc7FXhREQGXwUUhQRgmRLcRA5ZzUEvuWEHoWp49JocdVjfRKZz/dA+ecMPYRBqqwwykiDVKD996jtGS9Srkp2Eg5c3u7LvdfnIrerWk2v8APgWF+Sjh7izg/593v/j6/+LM/5e72jv3ZnCfvvoV/dYd++CG2WRa8wqBUpnYWQ5r0xPf7LScPLmjQ9H1PCB5rGqQWNfa6F9xGSb0Tcmmdy9jK8T98FFlvNvReWp58kA6C0/gcvv57ADSLGbQ9KEuKEvRXWgY3RF2YCQXiN8ZgnS1iExJUT2W6o3IE2txrBT0gV7/keCNbOXakv/J1X3J85Z3or/OFv8DIyqMTZcosR+WPsffoGPZ648041oQaHa+1dsqUBI6IU6Qkn5Sm91RKyQQKeYfJHaYpKhqd7cGRTtJmxxBcPnbAoLwlzwPg0UPPizV8+6nh1PV8/8WCf/rZgue9w956htoxazKPVpEPH1j+8vWOK5WYrxRLY6ii4v/0fcVvPXvIk/PEiQr8L8rnPL045TcWW+abliEbhi5Sa3A5i+yWVuSQSSZhjcPHPV3aMl885vTiPW6uf0HOIkpOVmhbkXFkLZtbkSYYVyQZ78O1Y41Urvwh3Si5fCG2SuY/NrSPzd4FZJfARx369o4r5W/c8C9dQ+M9+TJ9sLEualUm9j1JGerTFf/L//V/zD/4b/+/fPzpFSFnFqsZqtK4oSsC2UmCCiVZSYiJQxaqCzNcsuqqMlR1w0pptjfr6bObpqJtd1hj5LsXRymoS0IcqXxbqSsZQgglCDx8h7ppuLm9ZbWY81vf+xb/95/8CB01xmasNaIKdeK4ud3w4z/5B7jKMasNzmX6uy27sJ5aaVSBnqumwtZFaEPLeLKmbui6jlAa9Ec+znEfX8kTSeSpuR9ERhItQZhTgr4MJdMkSsqaFdIfOX032TdaSV3z1asti8WS2jUkJNOLQ0+zPCHnzH6/I88akh+oTCXZuxI2+2x5wfWLn5GCZzZb0sxmEDRd7+n2LSg4PTshJ+i6Dm2KLnZKMravkWBRpr/0pCzZa4yRqqqpa4ezBu8DwctEJx8LTyK9JKgN5Ezd3lHvNuze+wO2uuWD3/9jXv3sEz79+Ge8en3FN//Gd5kZTx+uycEwa5bopubsyXv0fUfb3qGyIqTEbnPHfPkIYxv22ztC7CEaKutQpS1IK8egFTkFZLCD3B+tRZz+7nJHu++QtuZI8oaPuxMuirDWOkVWKmCSxTgtWr4xgF2KWD8GjZoyyTwiBUdls9EGHGxjeS6HPlqt7ZEy3f3juJ/0eI/8yxxfeSf6piTfceY2sm2PayTH2Z1AQ6IMchjb9ddcyGJAzZGeZT7KnqSPW2Ft6b3MqkwcGfFLgCx9VcZxrKgyfodURIW+zIAfPbF8rwJVZ6iNRUcIWZMbqGPm+jZxUm353rtbfvtxw49fXvBnH894vU3cBMNVbzhtFf/zbz/hddC86jJtyMws9KrmR1Hz05uGKsL/nv8LAB/+5EcEm2iNCIobY8hKS18lIgemU8biUBlC2uH9jnZ7RV2fYeyS4HuUSlgnWYBWpsxyRBh2BV9TBcIbr4NSJRaekvk8bYhDJpmnCCSGo0Dk6NoxZowl001HDmv8a8zexsb3qV1i2m9fjHbHe2WMI0cw1pESIorgFH/rb//bPP/5p/zkxx/zo5+/wFaak0VFKOIBCZlOYbJGG1WygNJeYh1tu2Pwgf1e0Q8D5xcNdXM4h7quyLv1RCy6t14ApVKBCnPJwM0Xzh0ArYhDIjWa2bLhvccLfv7ZlkUlk3RizgQdOTmdU/eDrHMySRlc06BDICIOP5dso2+9tNoURZneD+hsJuasDzI9yFhVcmamOth0bpOiB2AUPoA2Uqu1CvZDQltFSKIRPCqGaVuE1fNBLeftt9/h6vKm6ONG6qYipxWXLz9nvlxS1zU5eK5vbnl0cSZrTYM1IkM4DHvW11ecnD1gcXJGJuFDJkTPcjUv6zjT970Y85gmB66VIqC4W6/puo4YgyhwOYv3nhhk5J3Rmq7vqWc1M+toECKNS5GcNgTryENCbdbYzeek1WO61PPgvWecvfWQz3/6Y/7Jf/dj3v1wxfnZAqs1ty8+olosOX/4Hs3pGUPo2e/WnDx5zO3L5yi2WOuoqoZut5a2lTxQ2Qpra2IEcFS1FQhbORFtyZbaOepaSGXWORlurTOr+WEtPv/8BW+9+4zN1ktvui3KbgFqVaMY7YgEFSACNQLLHmz5KD6SswSEWmeUttNQgePM8svq/urNNf8vefwr40TfhKPkwfvQ1fEvxsxDssU3jeEBKn3z4o/EjilyKT4wZ4l8QhKSwNj7JXDUfVhNa+k7PARD6lAcHzOPUcml/BsKGUaPxfJDzWf8O7iMy4iAAhlDIipFH6TQ72aJ7zy75eLEs955Xu80Vx0k7fjkTtPRoLVlZhVeRVYu89j0hLxlabrpO2jfEhIIW8iWIKRQ6ZWaalchJZSKAmVnBSrRdZuiQypDhZUSo5wnpzRe/xG2OXJ/iiOFm6P7/8aNn/q/jgIUpTjMZVR5ysYm1upxHvpGNjSNuJvW2JdvvPtOS8apjW1VWitygE3bsXp8wbdnMx4+fMiPfvwTbve99H8qK61Tuoxlwkp3Vs5oLfNl/SgrSaYfIrt2uLe2ZbjCl5GH0gEOPxodNhqpLzuSMvQxUdvIv/le4iefJEIqfblZyWSOmFGqqFMZx8zV+BDYxYEcRGBdG0s1n5FCL3XoVK5HzCgjkF0M0pNryp4Zb4MIqkMojjTnUbUto1UkBdBWWkySogjTZ/KoXV1swNjLOSIPg/dUtuL84pztZkcIAecss8WCyjVs12tYLpkt5vSlPGMrmWWqjIzKfvX5L5g1CxarE1xdsW9bfIyHSTNIsKFKXU9G8Env6b4diKnozi7mU2nHuYphGKaaaoyBnJO0UPmAMpq6qYm2obtTOBXFJuhE9DtCCqgYiYCqDBcfvM03kmWzu2FfN8wXoiXdr1/z2WZPill607Wm95GqXsmMXpVxzuFsQ9/vaWq5v2hL364xTtrxdBGRDyojMHGBXTEoLKgWYxRtpxgl6D949oxd24EqE5KyBLE6iXiCiDCUgDnI+lRT0nI0E7bYzJIHT2vmV+3NN53m2BP6Zdnqr3t85Z0o/BIHenS8qV4y1lmmSfEF/rufE95//cEwidGIKUy/G2+MtW4iHeQ8SlMdSB4jc9NYiZAPrpr7RvzLz0LqOvl4IRwyakCmkRRtUZ0TPgWcUWQvTEJM4MR53n0A/qTjWQ+bQdHFGZ/f7fj5i2uWsxlN49j6loeLE96eObrcstDtvfMLKUBMUl9KEFVExUP4oZQmkNA5YLJFZ5Hg874XZ1uIFCOxQqv7gQHHznP8qve+tppKk/ciyvG6jvf8CBakGOSDI1QT5PhFR3y49/fr0WPQ9sV7dey4xhgrU5yo0qiY6TtPNXMsz0+YNw2owF/86OMy81CMiIzTC0xeAGEx+uAJQbIhyd7AD2OTvRzDMEzkouN1IzV6c1Q3VgX1SId6/hvLTpsyQDrAH//2W/yX//jnpCToqCmOiZjLPFqFdTMePHrK9e1rtu26aNVKpKirmpgDOQXKJRFCiy01WW1kaEEE46S9ZLzeUxKamXSpM0zQr9Q0DWWYEGZqQRqB4HEtUEg9AT8EnOmp6gqz7/De473HZstiecJmc4tWivlywWI+gyhtLuIMM/2+ZX97xfnXPqRqGmIIBF/askyZJYoEATLBRKQkQ/CEIMxdbVxp05CAm1R62G0SwYLSiy73rQhFxEju92zo8B2sTJJ7YcHnhO/WEEQnV2kZfXh6XtG+RAhJGlKMDPuBdXsJyuIM1Bb6fUulHXFowct9qaqGTbtmMTtBa0sMmb7rqWeOGCJ9t5fBGMbQDV1ZV6ro54qAyeATebeb1tXcWtp94Q+C6HhnXTLQ+8lMLAxiIQBmKEHiuCBKp7agWKMKXUGx+ML7vZGJHv1bBgvct+e/7vGVdqLHJJtflrKnL7kgCmlTmYxiYdCNW+7N4z5kLO8wwmHj52mtcZUuYvNiYGIspImiMJMLLVVRpqyX9z4kX8WpH2XO0++/eFZH37XEYgmZXFBgpG2/p9FQmUjEM6SBdaewrsfanvPTzFNnObWe1+sd/8UP/xtOwpKHakG/u2HBO7x9+nVa3WHzgV2X6po0eHIUebCsM0kFkf5TmaQyTlUkk0RasBi+iIGY0MoWclBx+BQ2qx71eEeHWu7JkUNNBQkoxTL0OOfoC2viQCeagp83pBXHbPcL13iCPr+4oSa5sGMkYzL0h88WxneUz9GqaBBnau3o255eK6q64Xf/6Hfo+siPf/acvR+wTlEbA1mgXWE3jmOpPDFlrBEHp9DkZO6N/uq7Dm2P90Ni1OgdGeapwN3iiA6Q1xGJEQBnlMyJ7Aaevv8WD5e/4HIbiUlja3kPoxxRR1JMWOc4f/tdtkOHunxV/H+GHIne0/kC8ytVanABlQyVsWDlfL0P2CTfeYwNEplSPhcHWjRaNaL7a7TDGBm5FXOUIc8pyWBldQi2hsHTdT193zMMPSkknLUYq4lJ+ofbXcvFxQV3N1e02y37sxOWs7ko9hiZaxp9YL2+wZJZnp0RY2S/2xFiQOniGMcdmmR/9Puevt8LCpMzrtQ8+34QuT+lqF1FTgljLFplUtLEKPN0Q/I0sxkmRnS3Ye3v2PSOTkesTmRp6YT+JSkMkqXlJIF8VtS1IvZb1u0dISoijn7IpNgRncItLKnfEM1SFIiGSE5wsjrn5u4K52pAMQwDfkgYF4lhwN9dU/vA6vwxu90VVtXEnPBhICuPsY62HXhntmNf7uf11TWLxYqcg2SaGZyT3tyAyISOMqPDMMgUpZREiCLGaQTcIVAsKMVRljqJ56hcer7VhEpOe3TcsvkQbo174V+EXPSVdqLHkJ/UrvIUtRyuQZ7+GhN+bcRoT2xbJY3ZIQSsKRDjhBIeQ4LqyHGayXbLQ/JeQscW2ry1VhZAzjIvGoUem/zLCB9xEke1uULJ1lrfg9oOVGyNUoU6ziHiLs8iZslQArC9e8W7T98nqEROGhdrXCW1wiGc432my5ErlTh/8JTHDx7Rh8CemtXDr/P582vO318yDBY7HDLRn37zKW///CWrXYvPAzbX2BBRNoEqxgyZAqNM0dlNGmKgAoYSfRsyKgWMrkRTwFAspRZVHyhQr/Ty2tTgEbatJsvQbwPb7Q1zu6R2Dp0VyScwnqQ1OknmkNXYS1bISzmJGFACrxJZOca7qFAFNj8mTZT+vWKcVB6vv2TT0gYlIHFKnqQyOdsS3cr9S4UBXtcNOSVCN/DJfs8f/Bu/wwdff8YPvv8zfvDjjxi04uJ0IfNjKdlXkqHf1kClxqADNrsd71ysJllGHyLKVWVdHvaEUiIbqMqcSI4MxVhTSkeTeiamOVLf6obMv/dex3/5TzOhttQkfJR7YxBy0m674bPPPyNnhXMVkUhMERUD/X5D7CNZZ0xj0bUh771oohsrM0aRHsZhD1SlNqo0igJhxzxxECQIFQNpnMYYadnSaGIW/q5OIvRua83Ll1e8enlVWiYEMqwqEZR3VoK6QWXabiCozJNnz7i9vuSTn/6Uv/GHf8B2t2GzyZycyOCD7fYVi0dvs/eB2A8yUNpoTFVRGUcIHX3fsm939N2e0As72VUN9WxBTLDfdSQyg/fSDlNJ1iWiIMK7aJpaslFVcfHwnOXcUacL+vBNcuy5fv0Zdzd37K487auP0SaxONHYygCG5A3WwkBPGBJDn+n6QMw9TktXgdZG9gq12BitGPpA6Abmc0NSFlxF33Xs2zVVIzCrMwsGv2G/uUHrJcY5VJKhFaYkKfu9Z7Fo+E/+vT/gvyjqlFW1gCjTa3UhDUoNX2MUIqA/zpyJSabR5ICLnhAjUYvIlUC+Gq2tkM5SOhA2iz0POWOrWjSR84HVm6LA7BOBcerfHQmKv/7xlXaiSufSWwaQIEkfm1xeYXVJQ700io/ZpyRvZY4dhyxHq/v6ufeP+5f2yzLfKcY5IjOlNGZwo8JQKZBnDTlOmRioezzPL4MU3iQhvXloKENqZaHsdjsG74t+bIEXE2Q1FD1JGcyMSqQ8cHp6yvXNLTkmVsszTs53bG+vWDVLZno2fY7VFZWx6AxpCHgCdrZExP0lskxWk11NylbqgypjbCYnzUyXTE4pVFVhMihdMQZFKMrQ74iJIpahlCaYgJVEn2Z+xjAkXnz2KVXUqAcz6tUFVS3EhCEbKqULcUyTlRbDGlUZqB1JRHSZM9illuB7UpBh3ykNoDTGyn3xKZJzpDEV2liCDwx+EEWlDBlbYEaR87PKiQMpBh+lCmFMxO9B4ZxDZ8vLq2ua5ZLf+cPf4hsfvMOf/Pf/hM8ud5ycNliD1ND2A+P4sIgiZxlT13ew+Xe/C//vH07r7JevnWNo+5evdHmSEl3kJJnkZr3nN3//2/xXf/VjtinjY8aYgvSkTIwZ4yoWq6ecPltxd32JCj2KSE4KWym0F4UtlQXaVChC8FSukjKENtTNnKH3dENAh4A1o9b06FCFAahLdBKCZNi6sItD6KltQ84GoxQpDVzedFzd3OCDF7hUSdZSOZEmNFqeq8nMlgbtPamIsXR3PT//4Q/54FvfYbe+ZXP7At/twSsYEjeXV9TVnMotQCVi6Hn+8jOy3+P7jv1+wNVzHr71jBALTyJnNtdXdL7j7OyCpm5IKWKNkOxub27IWWqSs9mM+WLOk3ffptq9xl59jLl7Sddr1Dd/n0ff/F0eqQRhD53n8rMf8mp9x+tLT98GTmykXsEQDedV5nSh4XTOn79s0dpBkizP4XCVCIYYN0PpRNd5Xr58QVIVn31yQ1UpKucwOmP0HNSADoYQYLfdUs8rFq7COoOxwnUYho7//HunvP2gguJEQxywJZg79HXKqpThHQEQYpV11dRSmJJMu6mbRjJYdbC9uugvlx0AHJBCjmqeYzDsnC3PGklr43P5Qmnnrzu+0k5Uq4TRYxqey0UU+SfJDMah2vJ8cSQjkDdVycZ3mzLK4+cfILHMfUea7l3rnNMEz755HDe6H8gqBU4oGeibEMJk8o7w/+OM+E2Ci1IF4ixZkzS6jyxX+ZOLo1ZKpNYkczXSAZBqZrNTzO0dQ7dls71huN5QXRiG3BHTQWwh5iIhqDUpG3xKGD9QZ4t1FcZWmKoRdRNBbcfSp0BP2pKLKLzKieRE/UShOIx7Ulit0Y2MbItJdExN0vjQ8+L152y3LaGPsDjjZrPnevNTZouG8wePIMJgKmLsGeIeHwZImUVzgVKZFHtyCsIKNhYdPTl4UgzEFAgpUM/mbNs9t3cb7m7XoBRVXXF2Ome5qKlcEQIno0w4QMxZ49MgLQ05EUKpzB3R7UcEQROxrhKFG61wZ6f8W//h9/iLP/8RHz2/IlqFc+Kodc5krcooqEQMibNlzTvffHdyolrrLzTdjCzj+8dB2OHL2nTGX42j7DabLU/efsKjh8/prwL7LrCaFyJdUmhliDnw2etPacMjjKuEaBdCqUUG6tpJxliCQecc+07aO4w1pZaoqZoGEwIhDNJHmtI0wWQMBrWSeq1CYY2ekANFLsOZCyISPXd3G3a7lspWU31YK8UwBKw1UKBs1xhszuSQSGSWpwucTrz69Jonb+9YnK64er7l+uo1pprTZY32iZx3dP0dQ9hicqK73ZGwzJdnPHl2zr4fWG+2BcKtsdayOjthEefUzYy+G+i6PZthw/nFBc45EYFXmn3f4YPHnZ/z0PdU0aNILFxi//nPCG9rsnOkbsPgd6z7nlfXCWc07zxb8fY775JdRfvx9zlRHXOX6XOi1ooUhVBkTIV1M1ylCSlijWPRGOI88vzqGt8bYuypqoaqbjAqEWM+DPTWUs802jEpagGh7zAv/5zvfP1/VSbUMK3RUc9azNqh/DDCrlJuTzRG0xSZRG2EoNV1HbWrGE2eELaUjEAcbewRcjtxYYoN1sqUzPVAFFQ6T6S8f636RMcNdahJpVI/Y6pZJd6Edt90cuWZWSLaUUtX3l9NuovHmeeUgcKUfRycZ6m/lfrNxDAt7zkusFww+Ty95vDce/XOPMVLR+f8q+ukoyMVGbEiuJzHvlgFWSZWpEJ+GJ2vsTUpQfABlGG2PKGezQiqI/rh8ClxzLRlQUq9IqKrWhyorVBa5uKMsGPWpfivhESi9xvM/g4d9qSL98muIsfDsAClDTHBdr2WWpL3aA0PHr1DTInd7SWbmysx3uEOlyuGYUcaTjk5WRITdJvX9N2Owe8JviP7xLa55OTsHGs0pEjUiuiRUWglYFJasqtuv+fuds3d7ZrNuiWEiGsquv2Ohw9OeXB+NtUY1VhCKExkQUJkg4/iGho7qQONAddYr4lFUckYw/z8lG9+++tkrXl9c8d23xMyWCOL2yhpCdmHRP+1B8zOzqZ7o7UWCDx/scZzTEj7Ihv9y44RvsjEMKCM5euPNNe3HW1vSulESeVWQ44du5tPSfs7kt+SCUX3WTLWMeDVWpdJIZrBF8g3KawxhYRzgM+D8qSio6zePNss/7bOYLQqo7fEAI+cA+8ju3U7Bavj/la26LvGVBi3BqelzqpqjU8RPW9YLha0d3tef/45Z48v2G23dN3AvDohpsjQd3gVScnT7loqYzhdnpC1DINQrsZpg+mlFuq9JwQZFO1MJQQ7rYuTMKQYqWqBVUMMIgahFHc3t9QuQpIebpsSKe5JscOngX57y83NDes7z9xVnJ0vefTknNnpkmga7LymHgZUiugUqI1A8dZajEbaalJCaZEtlHo7KOyE5HkvrGJpEYplcoy07NiJK1AGdCBDNt69MJwsKq5u7s+8FZt7JIKSj0pWU5lE+u/jSCriYBPFWUsgngvByIxDujmsdyEkpem1I3P/kCgdvO1UTx2d8K95fKWdqBz3nRc53rsIU7ytDmzE0aQdWF7yP2kBuE/5F/GFA1w7NfIqVVD7jAydFhbdeDojnKv14TUHJ3k4Dj+nqQ51z7zdM35Hr5leeOTYJwcstde6riXyzqMTlsWZU1GGkSSjQOKxRIDiFGzVcPH2GbPlCh/BD/vDCcTEYA2xsjifGELAVIaqnmPrmUh2wVR7StMXUkTtSEOPvfsMd/sputvQuxN4+C7Z91Dqd8Za7nZrLl98Rt/ekPweFRPOOLSrCe2GvL0i6MSw86yaMxmG3hjysKcPkfXVRww7ccA5BlLvuU6fUX34LRbLU7Qy9EPHvu8l09WKymrqyuGc4fZmw+b2lr7tsUozhIHYB663W0xSLJsZzmlyFGVmYVlKFj3OY83peOLLEVIxPqYNmoCUI2Q82yYmHr33TAhCP/yY7cev6GNg5hSjdAdZMwBnf+tbRHvYxiP0PY5SG5noX3SkByjsELIfr7zC8BUmD1or2v3A7//Gkk+e33K9N/iQ0EYJLA6QPGnbsru7RDsJFeRUCmOzzNg11lJXjTgS1xFCLNOIDK4w3Md2CZRiyLno247yhJOpJOeMNeKEDhwGydZjygw+0u97rJMWIWmNUaRRYjBFdFLorFBZ6mu2dqgYhLA0tzz5WsfPfvwjum4nkH9hKOd+z25zi1bSOhU7jXeW0/ffImvNMHjWmw11UzObzRn6gaEfSrZnqZwlJWlRqqoa4xwhRpq6pus7hn4gxYRzFfvbW24WNVHVLPQcO7TsF4bY7el8ZHOz5urVHTbPefp4xfnjC5qTE7rgIWuq5oToB+KwJxlDpTOm1J2VSgxdS0wB5xYMIeBjIiWN0TWZAa0MwyAMZKs1xvVopTFKkJKqqiTwqCopn2Rp1f72d94T7sEv80oTfHokvzeVYRTWVvS9l3tX+ptHYpEoXMk601pg+cgh4xR7n0ssqEs7jha7XZKmg9M8qM4dB2y/zvHVdqL/Atj14anqKPo5GJEDzKqOXlP6kPLYS/jGUcbtHPctTvbri2cAjJj8/ZacL2fg/urv9ib9fzwkK5Iod7U6KZnJIQDIWaGwxNSLzTSqNPUHrAvYOtKHgb3f8uTRU1aLc4Y8p1MN8JdyZs7w6fuP8VctTz+9pu8jb7/1mNnqpMBlARUTKSiSGaFIhKW6OKP9/t+j2V9h456sNb7dkFMi6ozOYlCj1Xz08Q/xrz7FqoyrHEYpfvHn/4Dl4/dQCqrlUmBZu8SYGaYBVTvu2g23r5+j+g2EKFMilEFXC9bPf8H66iVVXWOaJc9f3HB1tSWkAaOhqSxnpys+/PADhtfX9H4gk5jNG+bzmugz121Ht+1oNzvOz+fEdFxSEOMRyUUW7XDE6Et5YYzCQYVAVIaspC0h5UTqBj57seXx02f83vyEh6cf8/f+9C/Ze3ANdFGQk8XM8kd/8B22lwfFIqk2H4ViasTTj9fNm6HaIQo/eqAYKGmGV9lx/fqad99/m3d+0vPR3YZdH5g3ClQkZ3F6xtiSvYqkY8rSulNhGELEmNLIUJR6tDY0tcVYiynwb93MSFFGC6YkvZPiAEXNZ1z0Vsu0n1i0X2U7FzJVjkUdSLLN8eporYkk+q6TySVWMfQtMUW5Z6aRmp8x1PWMplnQPH5M/clHhPaOmBGH0q1FQGMntbnTkwVf/+BtuqGn9RGVPL7vhS2dhPflo/AFLJqu3TFYgZQrV1M3DUoJ+9WVLHmsRVXOMXQtd5tMWK3QT5/gX/0V123i7uUvaDeJ2GXOTyp+47vvU5+ekRT0QXpybQ7Es3dJQ0LlDXb1ALf5FKUSVfEAPisq2wjsWvpZfQwonAwWzw5jNbPGcrpakPDs2w0aRV3XLJZz1u2hjSVnab354z/+TXz/JvIxIoLHNvfwHK2tIFZkUBbvY+FIqJIVl0BICB5H7yHWfUQxtNJMk6+0BJcZsc/G3B/WMM3bzRnKbNtf9/hKO9Exsv8ijj3q5B7DrJKFjZnh1Fun7jvOA3nny3tPD9h6yRLVCAWrSWFE/N9Y/4q8mUnKa5nOccKljj7jjWfzZv1Tkuexd3TMdCcgGG1ETSSljLYi6J5iLJ8VoGjTkhUpGLqt5/zknM+rJc9f33H78xf8kx9fsd0oNrstab3hf/foHIBV5WhdxXa/Y70buFjNWZzPubm7BTJVZagrQ/ZeCBjaMITApr2B3ZrHZ+8S3bvsNleY/lM4e4Lfb4BE0pqAwt+9JqyfU1Wq9Axq7HLF2XzB9ctf4GYrXDMTYggRaVaNdDcv2d08p6pWDCGK4DuJrAKoyEwbalux2w9s7q55/sklVmlyCOhK2NSv95cMvSdnjbEVIfTc3q4Z+p7zR+fUS4ObKdAR7QwhSHahU8YZQ9PMSvR8MPhjChVjkHtQHo45AUPRah3F9RM1kVef/JxmseTZN97iPzuFH//8J1zedGw2gevVipP/7N8gAPtwmPV6/9DjBzOqLv2LHDlKrUgZSINh6FqMe8IffHPJ9Sc/5x+1Z7JTkiAxmiywXvTCIijKRForQqQEa5oQIm3X09QV3nsJaEobT06wXm/Y7nYiSmLkvZ2TySE25zLAQbRZYxLyiSpi9hKcJIjy7Ssrg7NRkpnmFIvknxP2REzk4CFETMrEdEfbeXwnRDJMwmPQWrJYo0CrSAgdRsP5g5qT8yesTh9QzR1537Ld7fExYI1mcbIihF7sgkkMMRBDYj4/IaZM193S9R0hxokIE0KkqRtmTSNs1BCo7IwhDuzWl7Tr57iup90ElvMFT9855fTBGc35jKxq4iDD2qV4jEyNMhnz1lOUfoekHb9xcsEnH/2IGAaMc6xOZwze48MdtTOcnMww1nG37RnWmpxlskuKgV07cPHwMX23p65r6mYhQUyKWKvxIcL2Je9W1zx5+Fu8/rzHq2OE774TvbdidSGRaYOxmlcvXsoYw8oJx8B7tBJSkNFlzORxqS1GcYj3HPMxIlMC23xgy39x16gvBMC/6vhKO9FfbhQOpJ/DBR5T+GMYdJyuUuj8U2H58E6j/igcbvmb6keFdD/97v5t0dPzfrUNUxIBFX/55mfc+85TXKBKNj1i/mVhKogxMQyDRLamkpYSpaQRXUeSUiI8nRPGVsTkefzoCX/5g4/45JPI5eUN2XYQNDFHXIxQnChWEYJCDdImcv5oxScvX3F7tYUIi1nN47fOaFxFzpZsDNrVnBjLJz/6R6h3f49VY6jsBT4vyNUKqxJaj0CKZjY7Y141pGGAnEkp0G5e8+TJ1+m8J8aB6PegZBiymlv6oZWWBV2T0x6jtTSOo9CmAgLoyHyxoI2R7d2GGDzNYkHMMIRIYyxnJ2fELtC2W5xz1NpAZSEN4KEyNeDY7CJ7f8t6vaZtBzLgKsvqZMHXnj4mxuHe2sllXmnOI3kqSWtPqSmDBGWJQwvNfrel1Ro7O+HRu19neXpDGCLtw3P41oesOwtHalKj4zyeeiHBZJpQkC9moiNR7V4kJzXsEnBmDY1tuL665uR0xm/+9tf5Z3e3DH3ClLcLKtNLqoZJGZVUUelLpU9W0J0QJFPx3rOcL6TsoEc1pcLsJE4Bh7NG5u+qslOzJiVTlK8Szmms1iXjlOwkpkhlHXVds1o03Nzd4armgAIoxXzeoJXC6JW0ZFiNVVJf9V1L1+7o9lvC0HHbDvgoMGfOiUpDY2F15pivBmzVMoSa3X6LR9HMZyLMoArVMY2chETWiYTU+E9WS2IScYUYhX06BhQiFRjxKTGbz4m7RBwC2Tje/eZ3Sv+5tKQoY4hBE7PHZWEx5xxRWeqcZGnrIAaS8hijePb+h/i+Zbe54259Q0qJ+XxBSjB4iF2g2wVqV1PVhugjvhdW883tFYvViqZZYG0t+lrWkTN4n2hqx3e/9S5khTb5njh8jiWb1GqS+EwpEqPYN1M0lHPWeD/Qtlv6oSNlEWkZhy9n+WtazSNpb+SoqFIiEL3lqcJR0BIm8z+2E8UofkLUog598X/d8ZV2ouMxOslDj6ienOGhYA33slM1mowDwSelWAzNWCRnYlS+GbEck4zG+udUyE4yoUEIJW/WMw+Z8dGjY+BUzmjS4fjS7ytk/DhBgzkLkSKOg7pVkSZTalJPQQl8LdF+nuqWClFgCtHjnOLi4TmnDy74xSefslwZtJVWBusP5zKrlsSffspss8ZWmm3fc3e1I/pIColtjNgbxdvPnsm0ht2d9GMt3+Ld9/+QbQ83g0flRFVpXHspPYymmoxHRWJ58YT1y8+l9uIM+2FD225pZgtCn/G+ZwiKed1IpIwovjT1AqUV23YrfYRGmJ9Du2exOqeazek7T46ekKRuWehXhBDo+o7Vasl2txN4XEnf26ypaeqGfbdh6AMxBpyraduedidjonwfiH1g1VQsFwtQhV4/kSNGUe00rUNBpWQd5cIYHMkSKSVyjASVUdbhmgZjZebm/i9+SPvb32H2xjKRNX8fNZl+d7Ty3qyPfqH2PrHVJeu2akbXbZnPG955dsG71S/4RThDm6M1W7Sjsyr/LtcvlzLHiBJrrairClfXhYgm8J33npQjzhkJqibd4iJuXXaFVopKWx4/rDhdzqicQ2a0IsElWWagonhwcUrfdQxBarLaaIyzaGuoq2qSrlMatKtxSljY1WJOM6yIQ0ez63h9eUXqOkLJgperJaEfaO/WsOnoe0uIPcFr4nxG3VSMAvMpZZbLFXVjUd4LOacod8kkkywzSkvduOuFiGOM2J99txNIG+mJtbOmqD5lstIkpTEodBZYOhdIXSVQJpDLVBoxgyKM0tQzfIz4LDrGlXVY17Dfd3S99PCen57Rdju5vkbL/ug6nKqpmqUEn11Hzgpn3GQrm6bma+8+Fv3lPKCUvbfWOOJwTHnpVLcPQnLCFVWjRCmPlyDo0N86ZiZpIhup6d/jajamtC6OpKKDIZ0OYXbr6XlfliX/suOr7UTL1cjFeaQSJRoDSo0X+GAYRkf6JpQwkm7e1B2daqFHxKDjx+U4XPCDE5W+uXH+6DGkfPz8+1mx/H76DA5qMr+eDNUoNVfYh9pMzD+lx3quWLGcNBmNaJ7KZYoEcopcnC958uQU1C+oXIM2CW1Bq8NQbmcrFte3LNotuq65u9vS7QacFbGBED27jcwGpL/EXH+KIhPdkkdf+13Sxz/n5e6K3vc0MbKiY7O5A1OVPlrIm1uUciTkO2hbY1Ngu92wWp1Lb2GK7PuEmVly9Fhjsa7BzeYyysknjHFlhJTc99OHzzD1nCrvmdcOUiZ4j2LURE7sdltOTpcYZ0vdpThSo6f3SsFLMBI1ORTptZRIPtPHyNXVHYv5fFqf0/pTh8CHLE4mHSI6eVJibDWX+5akJSeqTKI0xu8D1c8+5e7b77P8EojjPlv8AGt9+fHFYE+Qk3KeSaGMZJghJmKWGuC/9Z2HvPiLjLEjAxxSgFTEQFAH+b6oMybL9RN4rqKZzUUqk5IhpUjX7VHJY7Uwv0WDWBjPWutCQCpzJrVmvqpYrRZYI6bssNalz1WhWK7mnJ2dcHe3wwcR5ndWhqCPbRHjEXOALFNqTGWpXUVo5lS15/Zug973kBSBjLIVLliSD/RDx93as1zVqJCI+5YhDhhj2e/39DHRzMTZG4X0CpeANmchTDk7rjfwQerBVS0qRt1uj85WRoNZK1D2MICxQBIuQfao6IkohDVU5AZz4JiyKKiIwJ7tvqftxMkbY0kJfJB7YW3Fgwfn+Nf7oshmqZLi5naDjTVg6fo9fd+hleVktcIPPSpsWdaJJw8v6Pv4hfriVH7LEhQU6zVB/zLoO0E20yhDfUSMG9fCcTB4LCCiCoqSZbOJqhkHVu5xu8uYWI3Q5YhLHs8g/euOr7QTlYtSYFkFOUpKbwtsJYQPkT07OMA3xMZhIgJ574vi0FFUPt64oyhGaiPyfloplBkvY5GWU3kSWZiIJFAgNjNlyNM80inCouhkZnSJisZWhdFO5pwFsUtqXBbldwmDgxzIOZBURU6QQkTXdlwv5NK0mZLASVLAH1A64wdFhWFhxXiloSM5R41DH12yUApOGYjel15ARd/1pUHaYZW0DaT2FdXuGhMjbf1TLh98jZN336GPLbev1nR3idVszv7uczI3aAMxZl6/3lDXNcvlHJUSdC2uqtgMHZvNhtOTGc18xXp3x+B7nDHUiyW2WRCM4cXzz1mkhqpZklQg+D3NasXp0/cYjGKxXPE0al59esPQC3uzqWYoBX3fcvn6NbPZkn3Xk0JA64QfelA7jHZUsxqtNN2+Q6fMct6wbVu6fmCxOmG3i4R+wFjBjVTWoCzkQUTVtZFNHoYJZ5JbVNpjKJlryUhRYDIkbRHaUoQYCZcv4eGzw1pW+g0DkO9DuKX9Rh4/Dv6SZCvjq44yZzLY7IimJ+PYd5nGKX7vj/+Yv//Jn9EFg9Wi+NL2ntb3GJXKGtUoZXEWZrahqitsVWFcRSqf3+93DP2e5HsgUhtHVYmohdEG6wSWdc5hrZU/Tli0ptTLROWmpLpJ1GgoCIwHHr/1hKa5YbfZiW6uBkUi+A6jDMpatHHEYYeqlgwhSN9w8HiliNkRxlpszERveH655Y9+9zchKa6vb+n8S569/Z58p26P9wMxJpzWDF1Hv70hOk1MAR8V82ZFCEJ2srXo41orLGJrzRRokTNhP2BdFiasMZBE9UyZGus7zOaSvHuFSz3BLIinT0jLB6TSNmOIZa8LC9kYQ/Ce25tLdutbnLEoZdnsbokhspzPOT87p5rXOLsi6oytHRbNbgfJ96zXm6IDHLDOoIyi6z3L/Sd87bRmtThlfXuNMRXhKBGYAmUS5HgIKEuPuGi7GXTWbG+v8Xspb2iliICTyJKcAyP13xgJLqcAteSVOUaSKbKuRc3NFBx3oopmyEVC8ugs+XWPr7QT1UaylDF6q+sK7w/QbM56qgWNxwjTjvDB8WNN0xxGQ03sRBm5M6pmKJ3JXxh8IUZq7En6ZZmjvOVo0Er95gsZwP33PHr1r3iuHCmnErHJYgpxIMQBFcXwGKuJPgIDztaCI6YkBiQvCaFnNl9wcvIQyMTkqfWcTFc0geUIUVoCcspTD5czGo+j6zx0O+ZVQzdosjLYymAwDPUZDReE3RblEmYRicM1/8M/+wHxeqCqNFVT46qakBbEtmVuIVshG4XkmZnM88s1WT9msZjjjGPfr3n87EN8NFxf3XB385J5fULUme32UuA/bci556f/7L8lqhmnDx5z8egtvvfv/D5/7+/+I1xlQUmP3unpW6y3l1zfXDFrTkApdu2G1eKE7CzkRECIJ9GJUdrt9igU83pG3gd2cUNQp6jsxOBZx9DfcfL8+6iQ6OcPaB+8i9NS+xzXzbQuCwQ8Rm6pCJAfr+U6Rr7+pz/l5t89+RWr4ovrRpVhyn/9MU7kOSi+OCP7bfAdlXX8b//2h/wf/qvPaLPGVFBXCn/TMWtOpTneaGrnmDUrKLNEYwiEoWe3W5NCT9M4HpwtmM8e41yNbZwQSbQu0K+eAtsxsNBaY42VfV7ONo/YOCPbPZNjJqZApOfk9JSTM+nvjBGZGBIGXF0Tomd9d8V203H2cElOiX4YaHdbstGQHMF7rNLUs5qmafj5J1f8+U8/4Tvf/gZf/83f4MHbD9ntOlI2GKcIgyX3nrcevcXV1Qty8syaBavFirvbK1Ic6IMnKoWOI3tUQWLK4LU2GFdRzSqCD9TWsJpXkDt01aB2l6i7S/TuGu0yAcjrLXQJ9gPq6bvYMFDQXbkqCYyp2N1cEro9KiWMc/iwxWrL6fkDjK3waELOBN8yWy3QRgYE1LMVfRtodzuMsxhnsVYGSrTtjmdvP+GbH1xg9cgKT9M83191SLIiZZWEQLTPnn2Nf/bTv5K6cQooLeheClnqykoQN+8DOfOFJOjIjBeelYJRa1odlwhyqV/cRx1/neMr7UTvwaw5Mw6IHfVMhe04PjaKHtwXHD/u4XNVdShOH0G6MMpRjQzHoj0nH/6FcxJo8PDe5TdHcPIvcZ7qkI/ce/geJDw+doCGD+SVopOqDdZquq5lu7vhYjkTfUkfQINBJAfl0xQxJFA9Kde4WjFbZFydZU6jUWgq0AexhbvNjqXPNFmRtUDXMQ44V6Erae24WM745Pt/ikt7qp0n9QPrq5/SrBvSfMX+8hOG3Q0qRX6nssz+xrtctoHbzYbtZoOKoiBTz2fUlZvqw87A228taLvA68s7FIZ9V/H81a1Aukrx8OwxIewIecDopoiZZ0LvRVCi2/L6F1tuXlzyte9+m3/7e7/NR5+9oPeeRGLf76ibGdttz/X1JYpEVSn6bs+imtEVApPKmRwlnk0xlibzTDcMPH1wxrxeytSQHPFE9H4ghB69C6i4o5rtiI8u8HdbqjKkIOYkLRshYrQuurlv0O3LOtZaalS+P0Dt6WjNTepcE6gyRuSHZXu8Rr9Y908Cp48Mx4KyqBQJYWC9u+Hp46f8h9/+mP/6z17y/LqhqioenZ8xb5bi8DUoo8hKSCdduyOEAUPi4mzJcvlYFHqctLnko8/K5fuEEEptVZXvrqfEWvRPpXUhpojW4yxSCUAUmcoYfFR0vi1cAUfWlr6/RQO7mzXr9S13t685f/ABfhgmacZ6NmcYBvquhZxZnZ2zPHtAQvH1quaTT6/5qf4F3XuPeXR6Ttq07Ps9qYwxSzGyXl9T1TN2u0zrMzVw8fQZ+13LPEOMYZrsksrIuhhi+dlzd3OFdSL/t1jMWa5qfOrRbonudqihFbxNOdnjFoZuj8+3VI8eF5EPfxiSXsyQ0Q5yWWM6YXQtvd3RE5NHRU1Vz+i6PVVTASI633d7ZvUJ0YogSo4ZU6kilhJ5/Pichw/OaNsWyFhnCPl4XY1rTU3nI8FjaU3JGl0Ylj72nJ6fyNrIGqOd9LPHYouV2H0YB3ZLcqJUnurJUbQhy/sb+f5KUEkoJYA09iD/+s5zPL7aTjSJPBflpuhy8dR4ldSBpXiAReUifRlZaHwcDrj9WCcdHzs44FxuwpFFYnRL4w2TDTLCt+Wsp7/H8zxg+qPqzV9/I9XR/yajqZF+quKs68Zx7PBzZoJLdS79qlrE3dGJkDLKFNWWQdMG0b6sVE2V3fTZ7b5lVaL9nDIxiBEfvCfHSNVYTh4/QF9DHgxqcYLSc1buIS+2Fndzy6PzJ+jVknT5c95jj3lwir6oiC8NfbvB1Q3ORRKJPka0MlTG0PmAqxuqlPFBhBKydqxv9sxnhqq2IkeoDTrPZHBvgfiyBkMA19D3Pd2+5e7lFU++/W1WmwVsdvSDx4dIv21p6poUE37o6LuBPu1FEFtliYxzxrgGrQVuFOGNxFxbVqeObthLXU8LcYXThwx3pyxna4z1tPs1L398y9n5OaaWdTo5QaNL3+iBZDaVIfLR+gYGf8wkPKxpIZId/zyiG28+dm9VHf047h0moW7K+tcaUgzc3F3zzd95n+Bq/vkPb/h4o1FU6LqWSTPF6cUQ6bcbjIFmXlHXjrquqOsaypzPPNa1Ckij9KgFDWM717iHFYXZOQUY4+OStZMOAbYk9aLtm5Ui+IF294pFNWfftdxcX7LbrYllNqr3nlRISCAjDtf7K4xx2HqGcjVx6JitTnj6VmK/2/Py49csv7WkqWuiHxiiRiPTWLq+o6oX2DJr9er2hrfqx+jKEIcoQ7a1RheGrrGOLnWEwZNzomkayRDnDct5LZB9MhAQfkPWUsvM0maVRHteCGntlqo5gWgmaDiT6eNAPatZLBpS6AhdxDYi3ZhNYDY/YTY/Y+h6fAjM5qeQPe3QY61lvjpl23Vy/ZOGnPGD51l1xVvn59S1JQRfmK6gvuBqDvXZyaszOjFxdsYY2v0d1ilmszl1PSeU+bnWyAtHUzkGlYd2x0MNNU1dF0W72egJBj6kqW/snn9dMlGlSs0oizFXWh02O0zFZhihAviCoRgfym8alMMxObl8kJK613969LZHS4NfCZkd2bLD/VL3jeWXEj44vKg48eM2nsMn5wJ3BUZG27RQS4SmGElIChVlPxjtaFzF4wvJ+uqZZ1YZ5kdF0W7fTw5f5ATFoA0hUhUWq6kbTnUgq0RcPSTNnhDSCWbwnKo9qwcP0Kkh7D+n2g7kek5TzZht76grqBuD1alExRmlEjkmBu+xScgH5IyPA846YcVahTeh9F4aaXhPvlwNuQYKpPanwKiMyXC3a6csxlqJVL3SWOuwuicgI7q8D1RGszpZgdb4mAip1OGdxftBBhqnwDDs6a8GrKtxxmG0IddLklugQkuMgc12Teg05vxikiYbkYxMloz0CDV5M0oee9988Pce/aULbVpTx0HdL3/dOEw+5yN5tjSuM4U2mrZrOT15yO989y0ezSv+0Q82tAS0atlnR8JilCLHHqMDTV1Tz0RDVtsyB/SNsxj3sCoZqchFHrHVC6qklSIeB7DqCAHP6cipjrCeIfiefr/Ftxu6lNis12zXN3JvZ6cyADwEcigse6BytTBSrUM7V5CHgFYVq9OlOKtdx/XL1zx4eHGY3pREWi+lhPc9SolU4rAfWK83LBYNkyzkEQdDKfneIryvqZs5tbHMFxXWSdatKcOrlSVlM40ctEc66lIVSIU8ZY5ueiaScJXh5Owcow1du8e5MpWnqqmbFdY13NxcEzFUzQyyI6ZAVTU0ixmbTuaHjlrOKUW+8bjhwUmF0pkUpYwiEdHx+jpKib/UVI61dE0/tGgtKkVGWxQBcpyY46gi3iGALOPg9ZyKFX2z33NKskZrebCh/7LHV9qJTlBAHunNFOIQB8jqCB8f4dT7zMWjN/sSJ3qfZHR4/JjwI2+bUNNcx+Lcf4XqxTQKLedxLRy+0/FnfuF8yml+ye9zecIoGJGS1DVTOnLLOWMrR9/3kBUGg9EQfYak0bHibOn4/d9JvDuH06ea+XzPQiv4O+VN2g6dk8wRDVJfUUZhY+Z00XBxumLfB2aXn9Kg6BYP2MTMy9eXnDbnXDx9TGo0cVCoszNub1+hckNAJMQWc4OxUdpSfISifLMLkYgmbXcoU5OkcgQktD7MHlRGM6tPiaEl+TKXUydq49CuYhh6dE7MZnMuHp7z8Sefk4YO52rqqkLViflsxt3thpQ85CRTIlIiW8XsZMVsNifGzM31LfveA5ahH1hv1gy+Q6WOrAOVtVTOSa9u3WCc4dobwr4nqYHHb71LXTnCqMHLWD6Ik9oK6pgwwVS+SOV+h6NMVN2zTIdH76/bo/c5+t19tS4Q3vCI3DCtq7HbRJxg5ub5a+aLmne/9ZCv/+bbfPaL53z+0RXfv664Y0m0FaSBk9M5rqrQxkGBqvUYyCFEEKM0uQiUj0HCcXZwCCjunej0PWOK074ff01x+H4Y2G2uGNoNVmmuLj9jt+0F5jcNrjqVwCUKkSWmRIgRoxzee5rZQpCFGEBB3+5wTc3p+Qm+bXnxi4+Zn52AdYi2h8glGuMYhn1pyTBYXXFzdc1y/kw+J3hi0dMFYTg7a3B2htaK+aKhmdXEHGVeaiqr3gSydSTlyBEqIFlF7LMk6EahbSM2o/RWiqfJKCI+JhZnD5ivzun3O3zfo5ylcjNyzuz3G65ub9B2CVpRuznW1dKmqSMJUTsztghiqMQ7z96iqVKBww2VE9nAkI/XWelXPrbPR4nM2N4lxHgRiAkhiuZwual5ahAdmb5SCtHalfUQiTnLGp36QZlYvmos9/HlydW/NjXRUW9WF5x76FtkXJQr9Ztjo3F/8olEqXkyTuP2NOOkjfK6L5vrebjAIsB90HBM9//kQ5tKuvfacYOX2ugbKNu4kI5bYA7Q78Exv5kVK47EmLUuaj1HoZ4SZ9r7HuPAKAvZ4kOPs0JF3+xeoc3n/Jt/E95/Bhuf0EkzP0IMf+ezayDiQxYDnhM+BM6XC84fLDg9X7LXc9Jyzq6LfPrJZ1y3n9D0PW//zb9FpwJpl0AZePwbxNNvAAPOOB4+/hqn8xOuPv4hdragwhKGjtDv0HgWywV1fcGu89xt9uR9wlpFiAPOVsxmS2aLCkXkdP4BftjT9mt2/R1d35P3HZWGt97+OouLZ7zabtGhZzZfMQwyrLmuF2x312w3dyg0WiuG1FG7GTEYfvhXP2F1csJbbz/lrXeecnN5zdAHmqomzVZs+sh2q1DWgRpIqmdjLJXbAIEqV6xOn3D29C30sqa7XuMoM1QVhJwwRZd0rJHK/T667xSR9ajurU9Z4mPG9uWqW/eee1jZ5CNDlxmj+SKYr7LUqUZ7lzJh8JJpW8N+6Nld70R842LFex884VuXW378SccPbmqSriQpmOqa0lKkj+DmAx9gbJofzwQo5RHFgakevJesIydSnKJZEXJP495IMjtWLbl+9SOG9haVPcFqXj6/YV6vUNQYu6BuVgxDh9VWMu4k19L7gK0FXYk5EjqP1jW6HkhDR706oZk3DKnn85/9mGff/C46JNQwyPdEhB9izoVdXNG2CbAsljXV4Bj6fhpOoQyFiWzQSlrGlAIdFSkposoQI9pq1OkZfvB061viPhGMgpAZsiZqTRMD15eviH7PxO1JGosmalBW46qKqp7hTk6o6iVDt2G3vuH2+oq7deCDD86wWhGzJ+SIxlJpR+OMiE9gmFnF756+YjV/Sk6D2NAiu+dDPMLIxjXwJYFeeShRWnK0Q2VR/wpxIMYeH/dUtgLqgwMu5QYR4hAnbcZrqUqZoNg/SS4CRtflI8c1N6anb/z9axxfaScqg4STMAi1ous6jHESjXwZbPvGcY9U8ctS+jzqNP4ygzRuegVIJqoVEwEsIzUZaTT/Fec0JbtH/VBTNJ3uP+mXHOLMJQuOMXJ+9gSlPTKdJpT6mEUbIFpCzmRkJiYkXGNp+x1X12vuXhi615GTtxypT2yHo8+2EIeeFIURF9GQDJd3e2xVUS0Cpgk8r57x8uO/osqap4+e8d53/hb6wTfprn+CpkXFSO47lMmiHdt7yS4Xp3zwR/8Bv/jRD9i+/hSVA1XT0Jw+4ObqmnDbURvD3CjcyQzIrC4ecbe94fbultttw6PHZ3x29VPms3PquuGiUgw6sHj2mNXDp0Rt2O5a1jct1jli26ONZD3rzS3bzYZMZrWcEYIhhA5nG1JKPHr0BIXi+sUVt69e/f/Z+/dY27b8rhP7/MYY87HW2o+z93ne962yC7v8NraodtskBqwYE2jAELBCCwQRlojsFiIKEhKEYCGRAFLARAIJtQIoOLSUBrrpEIMbN01Dm8I2fpftcr1v3XvOPc/9Wo8553jkj98Yc861z6nyrQRCX8G0d92z155rPsbj9/z+vj8ObxxhBNqmpqpqFqsljx+/Q+hh+dLL3D055Fh29O9+ioMbt+nvvMZQL4jdjnCxoa6qzP8KE/pDG5uPfB05AlGUQ1kreYnO1tEUoiqrYn/tvMhTffHqEhzFNCRBFKXUM0Y7rgyDJ4YBQXBo/0jiwOXlBZe7qJ5DC9VSG3unWNCPisLMAbiJw1TIbE12DEeqAs2dQdI0GKWKVlAEvhoAGgIMIWQGKHL5SMv28ozN5VMk7hCByzWaY4s72vYmi9UJrjJsr7QeFzQSUlUVQ++pmwWuqiEDDIN07LY9h0eHBKsdRY7vvMSzB2/x+LOfYbE6xNiKmJsThKCNyoeh4+ryjOXygLNnT3n5pVvUBzXt0o5dSUAyZqMYT5Gh73FOy3wSaC409gwSGZYN/c1TjQg0hh5hkETo1shnPw5GcJJynl2IXgFAdb0CoBOldhSjFH4xRDbrDeurjtrV3H3pDmlsHQhCIHg4Wi252vR0PhBMYnWwYIgRg81GjxLIA/RxXtJwPQIy/Vr+KVYwxhFCZNkuM7GEx2bPN3kZAW8aFMztH00hTNAynsIEZjIAyYdBZWttx8hh0QBfiuKcH+9rJapUYqo0DIKrDM7OQqozhQTMlNKc3Wi63nUPcP69yYtUQaCF6Gm6k84bYrRVlW5uva4PkZHOcSaq5sCiPbn3gmMvDHft1ImgYW5xCSIOUlAaMWNIwesC14LasVOC5ngEqPB+SfQNda18pSkFbCVYY8f7Kbl3VLBPXSm83AQYhM2658njZxxUjwkXz3j13ms0yyXt8SmhPlSgw9kV0UWCE6JNSAgEO+VBSImL86c8efwAE4W6akli2G3OIA2s2iXHRyuatsb7wNv3H9CuKur2Fuv1msvLSzbnZ7Ttim67wR6ccHz6Ksd3K2LwYIWhH9hse9bbnoUfELGZrD/i/YAPgaauiUlRlsZamrbh4uqMOrWsVgfUVcV6c04Inu2uI5G7gFSOul2xverY9Ds2HZzWHXVtcP4KP2wRYykda1IO2ZZwFiWouxf1YCSDKIC2sl7f/PzT5xfMcyGqyTKfLb4vkjbIofLydwtjh6SUSMkrbZui2XTlJSHYikCgyRRAzhitv/SelD2MiVHMjtEijTJKJgovkaLZo+f7Fo9DEAXBUHJf5DHJj5sJzEUgiuHy2ROS73OuWdh1ioLd9oGDo4p2UYF4zVum3LQ9ad/Mvu+UyGVknorgNaQoGOVr9QEBDo9vc3X5GLHgmjZ7S2qkKm2do26WeO/pROgGT4XWhsdcM6k54Wl+XF0pqjwERQoPWzaXHQwXufQjEP2AbzRyFHIrYaKOjgM2EXyXo25R65C7fkPuxYcYy+A7nFvjnMP7REyGu/eOsLXF72xeezrAwYBrHHUICtizQt3mRtgxG0mijk5Ks3U0rsF5lG22TqPmiE0eB+3OE8aIY0qKZNZ+LS6Tymj9J0a06XrSVFbM7E9GXE71QfLqYNgYxpyzjnNW/uWZXrgrXny8r5Wo7ptcKC6CsS1iHCllsMJzAmJy2/cncn/I5oLl+Suokiq1dsU4VkBgmGL9oGVIaVpQoKxKxpa+d/uKcX6/fa+1dH5J08mzMPXe2+X3UsstEuLAIhsZMT+zpEzLZrKRERMxiVqnVY1xBzy5rEiLxOahFuE7D7+n3CfOYrspN+FNAlbzc9tN4vhmy+HRTZbtIbapkGZJGDxut8VeXTHIlrhokNUxiYGEsqvkHtCcPXnCsLmkXazG/FlMPau2pq4d9aKmXiyRCCG+raGvpsnhPWW+IVm67Y7IGtfuOHz5lM36QscxRtIQ2G22mLZW7zwpV2sSaKuKxaIleG1c3DYrmnpB3XZIDglFLGIr9YcK0MgPWCJN3RIaUVq0YcDbAH2kM2pQzSZaa0CRL7DuioGUZn9JUxZAhIP1Zn+NpkkJTQpmdr1RwU6GJky9bmeLbN84l8mTLGxcChqZPGNtJ6bPKQILlzipdjwKNSkXs5ew7PRchVd2uvd8/Zdc7EivlZ8ttyYY33UEjCT9awEWhqFnc/mUGHqiKO/z0EfqJuF7bc1WtQqaIeUac5SBKRG0nCk3Ro1RPycmnKsmD7lgJGyF2ErnPGqjainrY+gpnW5C9Axe+4WWRuUjdV2QyYASQYxju1vTbTf03Za+37HbdES/zSjinECKQK9gWTMiUQW11wyNJGpJNFbo2xVucQN8n0uohCGXg5R+oSHCbtdzdXXBom4RXAb0FR0tiLW4JNROyf5TjCNH7d5KNvuqptSy6zq+HtLP3mSMXK2fkZJBjHYJkmQyl3Ocxj3OFvkYrSiOi5nWBZkSlsyAtPdAupZehH/51Y73tRJNaQrxRAQxi2vjuRfnmn02JZev69nrgmwv5JtDAoUmUPa6xURiKq2epu8qT+pUiyop8/MWbUEpa5me4UWe8P5zFkTuNeVPRMRloRKIEun9QIoBoVLvJwUyK3b2gCRb6xD9jtWypl0d8/bTFZ86P+edt5U+zfSJ//Npvo9XFii11HsQq7ydtSH6QBwMi+NTXH2D0G/VyPEJ01/QxGN83yHrh7BaIMtjShSa5EEqEGF3dYX4gODVQCFhnWVVtySTVDgmywBKaI52ZTDOsFwuCXHAhw1919N1z0hJuHP3lvZyjAYrKJJx6OmNw1UJSV69HVtxsGxplgs2my0xgnMGY2tWywPiMKgw8x2uagkR6mZB2PX0u45+GGirFldrj0NMRahqGM7pl0uc1ablmDCWYpRSjVFV5vRBGj+bhzNH9afC91ohe0opMySN0mPaE/ObzIxIkbKeZ8eoHPU7Kff5085FgtK2TZyjKYEp6Y+oLFbLOnKv2fGku00yYXqH0WpM2lEor0MNYapXPgd8TAZi/igpEYQpuTCK8cBomBhR3q9ht2G3vSSJcsj6QUtuYi1En7TjUe2gIHJjnI1XyghTcjN6VR4xaqjfWZOJXVBaxKHHViu63Ya+67AmUNUVIobBe+X8tdrXsveD1jBS5XKdHINIE24UEfq+4+njB2zWl/hOlV4MiRAhRu2Q44MawmrDKyairgy1U0L/hY8cuYEjm1iJZXvjEHvrg6TdGXF3pdUN7QHrXUfoN3Tbjg0Dj5+tce0DXn3lFayt8jJVr7sbCmkFHNUxd/CJecxkVGBqDMxUzTjX4+ZTqE0AAQAASURBVMJinHDRCJrBQvQ8e/YQd3SKMU4lbomQiCpKlQ3qSUtMRJkM/JSSInpFDWu1wyxSKCVnkRb1/nXsp1323o73tRI11o4MQwkUQXkNbDM/nv985sUVA2Q/vrsn1OYjWwp8R0s//837OIWfyFRTMY6y0hSrc98dGf99XYHOj7nKTNfOHdk+Ul6Uouwdg59ImsdSBSmhQRUatasQEbbbLTdv3eTu3XvcOLrJP/yRZ1TVgnohNClCVqIhRGYRJ3a7HW3VAgZPZBciw64nhCsNWdYr8D3dk1/h+PBr8Ae3GC7uM1ydIZsnmMNjzOAJRFIYsBju3rrB0899kuHyEtqKqqlxGK7CMxp7yKJVns+z83N2gxIhxLhmGHpiMNTGsPYGAtR1xapt1YAwsPU7Kmc5Oml45ZUT3nn7GYv6kLaxCJ7dsMHaIzANgY4ggjWw3j0Fb1gerRi6nm675fDoiPvvPubG4QmCI6VBQUYusVmvud2sODq6SXPvFTY0yMERyQopBuU9jUNZeFlnpVHnvWgjF+Ns5tJ9Uat55s+NFv+LUhwF4ViO0vFF9i9SVt7ozZb6v8lfnnkVYjBWf4rQLKHY6Z5FcGk1rJKyZ2939Din9Vv4c1X+TRynKaVck53vObIcRYau03sHjSKRsgIKgk8K5jLOat4wBZxYReZmNqR20bC5uiKFDlAFPQwdN2++RPCe9dWa3g9YWzF4r/XSaO3mrrukZUkMUFWt7peuww8DVV1lh6vkwy1WahBFGMcY2HVb3nrrU4Sdz6holTm7XpnDRCzBJ/ygXLN1E2iscLBacnxyxMnNQ1x9CA8+TuUHJCXWBNz2jGF3iTm7T7p6QsRRf+CrWBzdxPkbaG45cfLgIb/0yc/ireOl27e5sVix2+0gRNa7jhQTp9WOrzrcMfhDFk2rkapsCEzUkdcdmkJHOTcLVZGOaQKEuq45Os7PE6M2zs49aTVUq2BQTUsp8YvJEbY6d5WRlFm3yF6pLQQ8yjMQQkBCwrmWAjiVL7aprh3vayX6omO0JNmnSPtSj+sKV3LSU6IFci4m57SMMdlCy6CGNInBlJIqVlRxjQXBBUjyHt8JnpeV+wq3eDI5D4B2L9AOBkZDUznfK/mammcw9H2Pcw5jDLvdDmctX/8NX88//qefpqoDTeNoZtU6lbOYnDsSiSyNZdh5peKKAbE1latzDXYg0SNEjNTE80+DgdqAaw5J7THRoWxJxhCTEBCOXjrm67/9hM3Dtxl2O4jKG9ocHJMO79DvLrk8e8TV00cQOl56/cP0uyvWF4/pNs8Y+o6b4qg/+DqLo1PaxSGb9RZjcvjZB5xJfNmX3eXe3WNMCDRNQ+cj7zw6V48lKHjKIFjXwNATQo8RoV20GCNcnp9zuDpkt91yfNBw+/CI87Nzhhh59dYht1+6xeHxId3VBfbum8iwzlZxgjDRRI5Lbd9W21uP+R+qqIoVvxfaV1yFGCXVGNdJ9jCvF1ztKdAXHlP49kXHFxYzE2GJEaHK9JzOTJGfsf41s8WoEZyVokwEE9cGQaM6pkSSGL1wETLRuHJggyJcSYmu2+HqBYhj2/VsvccnDXFKApuExjicq3hAYDcMOFtTORWPaUi0zZKQIrvNhuQHbt66QVNbpHXEFPDBs9lsuFqvqSrtfdkuFxzYQ8Q4rq4uMt2gZ9dt8d5T1coLLGKzLBnowxZBSeZBWbYun3hipWMXfGLo9dmJEUmeygrHy4pXXzllebJisVgqzaWxJNeQLh8iw8AQVNkYE4hDIFw+g36HM4nKBPp33iHcfZOI1jojhtNX7/Gtd0752Z/6JR5uI+m1OxwftDx7tmV31WnNb9tycKjPrMCzTDvqFUuQUpzIDV6wTq4fatxriVdda20suetNjIpw1j2QWdf22v4puYMxWroH2RzLsi7hUNSn9lOWGCj56rEW5ks83tdKNMU4wftNIXZPM3o++GKO+TyHMl6zhG6fS4TPThz/VAa9hGUNxmje04c4yrfSgqx0nyiLbO7pzu//hcAeX/h9ihudpvAgU4NbPUWyh5pyBG0an1Icvp87gGgSQxxosci8/CFCHzoVfFbfuXKWzg+0leNwoew9MUDCYlJEaku6+RIXD8+QzbtsZKCPlvDkCUE22bExaN4scm4qqvaA6viUxbECjlzjsAhPH7/F5eUV52eXPHt8ySu372HF4BYrVrVjGW5q6CZByjXmve+x1kMaSEG72MRk6FLE1TViHB6hHwZiarDG4IdeLXwsVbSEAZKrSWSqOVvhd57aGLbdluXS0B4uuXXrdbYebGXGEgxMRHYbPB2FkjKOoay5nzh5ofN1pzXFqMdapjMrouvrZdKJ+4po79+zEoAvdEi5EYW0fno6/ZYZ1ws5xKZ9LPOvec1bW8K1M85e0ZpOm/lVlWDgutG7DzoZUzd5f5ecPvmq1yk7S6Pw2lp26w4qSwgOI+CsepplrGMOK9+9c5vPfOZtQjIE0SbqYgx11bBdX1E7y+GNE27dOmHwPcYa6tqxXLZsdzuWy4Wid6OOQ4qWtrU0zZK+3yKSaJsGb4XT0xMFzpC7lsTM8SzaScWK4fjoBm1tuX+eFYhAY4XT45rVqmV10NIua+q2zjWcFm0bGkAiPjnMbsCagMNTOAoQh18eMlyd47cJVxvS0RESfQ6pa5RgvbliWbe8+YFXOb+65PLJM06O32DozlROGIN1Ql0LbVUTwpBTVRNI01nLcE1slbRaeZy57hKR3L7QajemszNuHJ3kXLI2u7eVI4WUIxAKEjLOjp9p43bNn2qU2IxrppROaHgYrI26zmPS1NKXqEjf10q0KLAxzVJSJnuCoSjF57+9T8Lw4oHbD6upIrp+5nwii7BQYTKJRZ1EM1pN+4QKX1xx7t/verh631MYcxCz88fP52+TyjfVi7VWwzchBhDBOuWblZQKp/d4KImDAkokEzm4qmLTb7FNy2LR6pqMCetqIBFCxCehdy0pQJcsvfcM508Y0o4Qes3L5v6ZlQi2WeGaBdYYrLFUqwVODJurc64urtitdyxdw+sf+CB9HDJttVGvMSuQEAe0s42CvhIVGEep7dWtHCEZxAricrnU4HPPT8nvHKhcRTIQUiKFODZxGoYB6yr6JGx94uR0Qd/1+xD8JCRtoFVispluURSAltM82mN9z6p7gdm0vyrnf9e1p0p6Dp4bQ6fTItnLOUJivtJGNLqUv5nZOTk8l63EyRNIRZfqM2VPtABnnlvJ195tCjG/yDDYX9Nlz48GKeX1JgavGCMYoV4eKiI2eg4XC2KdeHf9jM4HbctWGZDI4BOL5YLT0xvsdkNup9frY4fIoqk4OFxyeHiAsVq6FoMSfdRNxdHBiqurDUNMIDaHpwO73RrQ8hWDgIFm2XCwWuiIxJJm0fRPQcEmIkkMVW1YuEjTNqyW+nN4sKBqNZdrrcNYM9XGigAK8DEpElNFHKB2ghNh2yfiooJmBc2SOPQMxmGWK5TCXqaIfYp03tPeWNGFnt1uR7fpNfJmDIduy52lw4yqREay+ZJqE2NyiH6cTUZgUVk3e9Od10pZD5gRV2Iyb3IxKIkazbE2f2smV1NpdJ88UczY2SdlYpxMYIkRB6IEESoXYGzt9R6O97kSVYGRZkpovqnK8cWU5VzRwXXltn/e3Hqaf2fyfkvYVbK3sc/DO+fx/eLe5v5RlN8L/NZrua1CgHXt/fJzjyjHVCwOlUQhRlxVK91ZjIjV0hWxKcP+Jo8JyE2M1WvUzaYerzXQtBXtaqEgB6PhlaEf6PseP3hsdcI2WAavjcB92BAI+H6DMxquixFs0zDszkj2MluMgm1c7rwR2F5tiUPgxuEJN+7e5nJ7RhwGiIkgwkS2nmnCJAEWRL1OVaCF/3eAjJp21tA0hmcXa6wRQhFqfU9lDe2ipveegj0xRuh7j7WOYYCrrTY8DrmEyhhHqR9OJih0cvTkIBVlVoZ4Hu2YK5msBKdlU8BA+6vCjMbaixCus2td+3tK18+343oeE5rMQHBSwHIxA2/KWlSFRPZCkjE4Kxw4z7mX59f+TBmPu+sacb7e1xZLcPzsC+3Z8rv+CK5dcHzzFl234XC5IoXE5UXHxXbLjZNj2kWNiLLi1JXj5q1TLs6vsEZyaYSWgx3fOGB5sKRu6pHUnByKds5weLjScq84aM41JUIYGLpO86FJQVeushwctNSVzYZanp+8JgtnbyQxhIGDwwOsGzg4XLI6WLJYLrBVRRTlwU4xg+tiyFUKs/WQItE1DN4Qcj197yHUBzTGIquD7IRZcI7kOwW9ISARE3Wf2npBvWjUKNhsCEnLSU6bNS+vEkaq7Hlq3a6gLFGjMbNnoGXGopkDMPKQ54mfy7WmafW62UCSXNJi8tofjafiTJUVErWMKUlm/8Lk8LKu74IRKaWAKfXZGfvS0oDvayU6hoBy4+QS5ik9OfeQtl/E29S/72/C+Wf7dv90/vW6UxiyhYMGwFIhPp7Oe5ECnf/6wvCaZI/xOcW774XufZoV5US0n8agXDEKi3eQUmQYhiz0rTZMDpHFUhiuNO85r35YHLRsN/o95yzGOTbnV9w8PuDG6THtckGMiXohPH30LpcXa/ptjw2Go9du8e6mo+8CJkYaiUidOIiOl5yhrgTftMRXPki8OIe4pY+G3RDZXjyg6zuGtKDfBXAWOah5cvmEk4MbdOmKftgRGSCZ3E6qJkmu7cMhxhFzyJiEgg6SJUoghYAFTg9bPvOZ+0o0nrRsJ/UDN49qXnrtLvcfPaHrNWdkjcM4WK+3CmRoWw27pQqYygfAEDJQx+ZwVwxfIC9elOreR6UUZmZMlbU1XxfWIjaXA1zTm5OCNvmK83BoGtm1AH0vU5Do12r5MjhHBWFBAUtGl6LtAss6Fw31fejwgp/uj9iFsk9zD8jsXZT1bQpQ8DmlGLOXzbXPJ++V/Ipx7IZS3i1w67VXQcCJEHuPaxe8+847vPKBNzG1Gzmgez9QtS2325ukcEoMXj2UjGYP0eP9oPsm0+ilHBZ2zqoCfrZms93R9Z0qtgiCx4dA29Yc3zjg+PiIwfc5TSJa704gmUTJYWvtKLzyxgdJ0qG5CWWo2u2yshNNYxkBcrlVSp7cjgPxW6grgqu52Hg1jFYr3I2XiN2atGiRZYuLkHpN0Wj/zZx/RrT9XR+wdU1lEn23o+9h1VQcNTXLKjsvFJliRtliTGK73RLN1MDCzDzTqV5/mu8QIzZFXFVhxLJYLDQ3ai2emKk9XVG/mBy5CCEUHzavaMlyWMsPS3cflY/FUMgAtKz8VXLDi8TwFzre10p0LBv5IgCJUgryohDR9PfrHt3zh1rIpR5Ni37nSlkRYUApyRhrRJ/3bN+7D1puHvcE5aSUr6n3NPe6J5NszL9mU805xxCUhxMR2rYl+gAEfLawG1exbBc8ebYjeIFpD2iXixgURBUDSwMnxwfcOj3AVFVO/je89blPsLk4Y3u1w5oDbt35KnaLW/hHVzSV5+TWIfdeehmzPGDxzk9hg8emAVMH+hunVK4n3P8Mtj3G3HuD/uRrYLNlGAKb9TmXz57y+J1H/MSnPsvhS3d57c3XuHnrhLo65OpSyedFFLgBaM1nCuqZIgiqbCKRkAwSNa5RmcRrt445e3KO9wlbVRzcPea1N0+pmwOOh8DjZ+ecX22RZNlsBtbrDYvjQ1aNQwUsYK2y36SADRFxai2HrAyVCSirxTyVYxxh7nQxAcH2pvsFhqERx0iUMK6XaW0XhOt1a1tEsLPPVNAVoTNPTeSohpG9c1NKymhU9mJZdwjWCFXlx/sUdHrxRvRedtp7e5GS+TvOvO7RadFrpDgZqcVhHVG9EXbb7ejtJsAeLXn91ofptlsYBkTUcyLAsN3oXjfaTSS5TC04AhYVmKIgFmXWEVHhD4mTW0csuppu1zEMmiowLtI0VS7wV7ChNVYNoxi0hMs6oo/ZkxRImrvfDT0F+a015xYxbY7AhTFKEIMniMFFo5LdCCYapIHmpVcIj59CDJiX7iBpIOKVNCKvRSeVRp2iUnJqgVDIeWSPsUJtW6xUcPaYr38JXr/VUFdu5P1NabYeYlQQUMFjvODQKd93LOqqUc7dCNvtlhtjJE3zpIKmmkpZWImwzZXxVA42I4mJEfAaiUoBpWnS8HdKktvkzRbXezze10q0sHUUAMPkEU7HF/PyponLhNe/isc4dj3J1lZxGdSqdojk+8+sKmOEEPYBD9f//eJn2n+HF3uw+58ZynNBEU77XrX+bLYbmkWLiCPESNd1OKMIyhKGbNqaps1GAIn5sMZMCm1EqJ3jpbunRDHaiilb+yH2PHn4mPVVx/aqp7awah7xwQ99My9/63+EcR3SOGK1IG4uMcGS6FRg+4h/dMZ2/YjDGHDnjwg76OwdQm2wVcXiZsvq1j1e/oCw21zy6POf5vO/+Cu83Sy5+eqbvPH6q3i/od/1xDCACMYmteap1N6UREhKi+iSIaDlDlItufvBI+6+ttP6ORGwDh8Ev9txfLxg0TqOztc8ePcpp8sFr50ecnxzyfJoSddHyB6FwWh9sfWYaDEGAp6U9K+REha9pjR5fg0mFGFY/NFpYc4UmqswriikyMjzbWaGIoXSZlKsqtjmitEh2SMxyewp0rnRqiFbICblwU2CNY45u40xSp83BwIVg694LCUErfXWMdfxfeFDgWsTSb+C+sxY3lA8nBSUUD6GgeBMzhNC8tD1g3aZtuoJ+8Fng1vZiZJXRWZE8MmPnrAaCLnmVRJh3FoJMZFdvwWgbRvaRZ7NpDyy3iudn3WJFHIvz+J5er2Q7zNYUtSbKpSjpS4dfJZFJnvQBURosSmAzWC2IYeU+w5xDnv3jq45n0jioXRXEUMEfPRjyqEkOyIGY72mZ4LKGGvhW1/e8uqNU8QJWxJtzKhcND1kkEwnCsvlkm7O+jeC1LIBpP7vuJYVc6Fz23WdVhikgsTWJzPGEQmzmk9yBFK7yaQYc5QyT1hUr5QUcbkcpmQIEjk4M1v/X0q67X2tRKumpq6racNkFpWQG+paa4kx5BKUaVNPQiONgsBYk+sfzSSg8pjq5zKGrcbcIpMQSCmNrBqgOThrC0nCdStsPww7j8iNUbe8KZlZRs+FmbMyHzk2I+q15m8YHN5rI+jKWYzL4AMpzbsnTtJiwafci9GJwboKMd30yOXpk9Zy+RjxIRc3G0GS5hsSkbauSUMkbDqO2prbt27y5puvszo0XHVN3hCGFBIeyxC2LBuHeBiGREqe7uQN5LPn1H6nvRdzM14l79XwtCDIcsXpax+gvbFmfbnh4v67fOzpU17+8jdoq5rkA/3QM4I/s8cwGhYSkTThTwMJMTm8mWkRU4zETO4hKWErx9GNQy1RSELlBFdn/thx3vLG1QGaheULwnYKeZpxXlUwz62WMQo7LpLikWYLfR4RyUhqUzgoZXwURMK4VuahW803kQEp+TMjY64zq+88Z9fWIpmkwWSPNwEUdpkso0UF6UvNGfd9zUYAyRRxWQGWRTat5ThDEJfQ31SiNe45kfGnHEr5lqYIDICxI5Y4ls/SVC9NeQ+Y6jF14lVJoimBMZWXxyGVZy6fxRL5Ke+jz1+mbgT/DPt5wvJ1EXKeNF9/L9w+nV3uXiJkk4c+AXskK2CA6CMpeR2nmLBu8hxLNCTmdy9XC6Dh31Q+EaKo0rp5Y0l7UGlqpKBa58YVk/NZcqXTy5Y9kmUvGk6F4paASIV1lsoZYpxoOWP0CDbX29f5BWS8lhpBBko9cVICkJgbKRhjtQ+wyQA88rPE0rEoYwrMe1eN72sl2u22OFssYs1P6VyETHBQ6LvUdp9FhYAc7hlXb95w5ZxZOE2KFGLuzZYNMt/EhXpHhbROKtMFn/sX4xzOPyjdZX71o3jCZaNlgSNZ0VnL4HuC77VsI3OVmgJlQyHee0yRSZWTRaiqBuFKx2Wf/w3IhBERfAga+mKukA2Hh0dUyXJ8fMCduy9x8tI9BuvxSctfCAEjkWRrtnZB5QP0ni5CcI50eIducZvod5jFER5tlmySz/WVGZRROerlAYfNElddMPTv8vTZM+Sthpu3TqkqR4wpt1KK4/zPc95T6JvRwxjDp6PuEvU8ei3NsNZyeLQcIyAxRRWOJTR7LfxKxg9PgiufN5vLEoafr7lRaBbJ9NzSmCtEi9KamfLBeLpWmOznQvWY6CDH6xgza8WYPUqJvGhZjuHjspZL2c5MgVe15dYy8mQX2QYzLnpTegCLoKUeOZOVMmJ6lg7RockCM69BY6aNGsu4Fi94bnQUwU6ajW1ipFtMpRyoKLz5Pk/T+Xk9jOmS7DmPhnmIWRGWSMJkDBUFQVJDTT8rMmIaWDW0ppEvSilKnK6R/1qalsv43DMwWJ4QdRgCKSitpTGGFOyeLFPZJyNCfDL9otakSklX6adVXWMri0s6lyYTvocQx/Et6/r6PjC5emF6zj0JlAk6tG7dWTMaCuSojmCzJ25Hc0JmUbc5U5JyfAMh36Ww1I0yO49oLCBAyWjiF9ewvuh4XyvRB+++w8mNE5aLA9p2iXMuw+kN3vfaCNc1WvRbBjlbnzFGrDGj+x9SGcS5AtP/WqMJ/ZQtthK2nSOBCyBiTsE27eG5BzlZ8r+6oizCY37NdO3v0+8yExQppUwk7TVXkxeOMRZbmYnKLD/T9RCdGEO7WABCCGmvVlpEqQDLZWNIaIOLOFrCfe956bVXqIylrhtse8iVWMJ2p3R+kWzgBOyy5fLgDfyDz2C6QFwuSHWLJcArH9QcbAr47oqUDBJ2FFRqignxA5srD0aoWse9L3+T5eMzfv5f/zyXL9/h1p1bHB4dKJF49KNA0DB1yGjjwnw1KdZrsi0rNxlJNgqoZ0/Qly/HWTh9FmpIo8CYKZ389+KlKCAqAzXyE0maUgMxC32bOZj3ULU2F5HbKRxqZsJlBLvNKf4k18vNXtYYsycECxvWKOD3LMH5EJUShJkCy/9t2xaTw8qlmN4UJGiawtcjAXtKCswa0cYmC0v1IEyuO43ZG5yIVtL4TibPtUan8jo3NvcLDZkwJO/tPRax2aTD6G2qjVCelfzcRcFPjS1Kkntco3ndlDUUSvMBMoAnlXBofH6Pi2CSzfefraMsz+T6XOTFW67iMtAyZuJ5iZHkhwwes+UN1cDO867XTPiUMClhjb6HkUjrEk4sBktbaRqrDwFnK4LvKem1si6dOIZ+iueaqsJKDru+IJpg0NrimFRuu1qpQCOgYD5LjD0yWxfGZuasmRwzxhClhHEV0Zui7o3RsJydX1fas1XX/F78+Yse72slenp8m+A7Hj96m5gGnj17xs3TOxwcnHCwPGK1OqVuFM227TqtgQR6r4wmQwpjpwCbVMg8nw9VJaMk7RmpaszI8HMdwl2QZ/OWVdN13uuRKGHZX+3YR02Wx0jjZo1BC69j1FB16RQxEVLIHvF4WcylDRRGEMtzuIAQEsEr+0+ShPYO1LZYKcK22+Bcg1jL2nv8+WPS2UOsXaD2Y5Y8KTJcbqjuvsxlSJihp2kbfOixD98l5tZPMUZC32MFkmeKLGTGG5sF0ZAiayPUTc03fOQbeOvzb/HW5z5N2za89NJLlJxNUQ6K2o0IYZyj0ilk7hEUTwtmDFIikzHFRKk42uDmWp49C7G966VsRTPenpAVQkGklnsW3TUS/pcrPQcI2l8X+3+HqV3a9G6SoxLzoyi4yUiYvDuZrz2ZxuaaXTeOE0DbtljTQwrZGBiysPaj0Nb1M0ctz8YJKKU4Ygwp9cQQCHECDKkSzx4sECJIKXXIpOTTPBeCllzzbBnJW9KMuCGlRMiNz0MqJSh6jRgiZE80xjgqrJLpG2VA2pcH8zVUhnHwwzQr83WTIlI8o+cMmL2Ryh/te4EhU5GWPVdmcCArIn1hlYU5p2yMVbpGawgENeIQlmbga48vaVcntIsWm7vbVFWjBrqNowItjHHGGOrZu4sYnDOkZF/oSMQYCb6nW3fsdjuGLtA0Cxbtgr7f5FmuGcMcs5KuOUtdgpHPV5zF5r+bqsF7XRPTGldUey0NIsIwfAEr8QXH+1qJ3jg55HB1F7CEkNjtdlycnXF2/pT7b3+OXb9jdXyT4+NjVqslbdPS1jVHqwMG79lttmoJRWFIEe87jNFaRO3oE7JCsQxDIPmBkDzO2D12lJJPBLXWRq9j3DgyColf7SjhWIn7/KbvLbybw0cyGQR1XY/PUh4y8ynsC9u5dZ2t/GW7UIRejMisWLqE4FLSEGnvB/CVCqBSbpRgQL2+lGlSTILBX9HbGhNVESgqdoDdM5qjG4QEOz9g1h3BBFLvc54JogQNiGaLMpHGhruFmSSS8CGw63oMwu07txj6ns1mw9OnT1gdHM6CR9laTWkKL6H2y3XrXoprKpCYteuahc1GzzV/lsIcLaj3K6G7ImSNkeyJkHVUyhZ6QbOGrNBlvM5oRef/FuMQUOYWKWFODYGVM8UkSCU09/yamteXG1Np0+eZMI9x6tAx9x7Kuimh1ucBeTomB4dHfMXNh7hhzeeGWsnAxeBsoc4bRsRqCUcrOCgTmWQEfEwKPospjuNafGsZB2nW+EH2ve4ykCEoDV8s0agYCT5merk45S9TIoXyTjEr41kZyExBxpA3V9K0iN4y17IXStB8rnWWFGXUi3bmTZX1IJlHNqX9RVlyuSM+I6XpPoV8P6OrY4p7c1YsnRj3170xiq3R58lAKmdoXAWN9upMlcUuW5au1o4qjXqJZlDjvK7bvAWyUhN1YtKeIeeU2EQnQscwFOSz9km11rA8OmZ1eMDy8Ahjte1Z3bT0nSKFJ8asMrWaCx3nI4eoXa0I4RIgMMZRVYVBTj/fdjskkKN0ZiSKeC/H+1qJBh8ZBk+IHbttx+HhAe7mDY6PVUn2fU/fD0Di8skZD7sdfhio65rbd+9A0oXsqgrnHHXT4myVlYNn2GmH9qrS+g71PI1Cx83EK1omrUDdy7EP2Jk+n3sJ00YYP3mhrr0u9FKag2PyOdfHJwTqulZYuJmstapyU/ixCMB53j8/98GqzZ7PnCYQ6sqxvdpm2jb1dvt+N+ZIQb2rFHtdtZJLBVBuTZLPVroqhygCBJLfMWFaLZ0BE0ueOWDFEKNF2Yf0fWP2XorvkopwzxtzSALiWCwPs8C8Nv5kg6J0dSg2xbV5Ku9UviUUROk0jqMXOvcC8/mqO2dCOZUIRxFa043FWG1dFyMx+hw1yIabZMQhmr+vKjOuz/yQua/tVL8puTGDAoBKvaUwuZ4lBDl72Rmx+zw8t7feSigMMxuraUz2D8FZw/HRCQeXQtVD5TTioKHOUueXFYjVdxkBfbnWt3jzKaE5/lkEoDxvSWeUfpIKpNPyrRiVmSYGRfaH/FmKWq41PW02hMbZyesKrSsuxfqVdXuRC2ssh9vP0y9vY8TQ7h7h/cDV6vUMbcnrNoTZmGXjNvfAtC6XSc2Uc1nvzq9p+2dctC8p0Cm76WnsP6bgsZCmGkxr7aiYU/ba9XpmNNzUMJicAUSQFKGHwXqWItxabHipjlSppW1WGGNzjl/bycUYlMxgZuZZo2U6drZurKtzfj6vO0mIzcaoCK6qsEZTLE3bcnh4hBilYMxLvGTx94XeLCQMZW+Lhu9BW0CSWaEo0lsbFjhb5TIrg179vRMuvK+VaAlLxhC0iW6M2hC5rmmT5kH6fqcNc43BOkvf92w3Wx6++5CD1YqqrrDDgKtrUurpe68Kta5YLJf0fZcXWQmHzMFCZAFTcqFpOne2Aa4ryhIGhOt1pNcs+JkX8IK3f+GYlDxLeZaqqnDWqWWf1FozZUMVhT06qZOVmki0tSbwr+kFtvdO6a/WpLXHx0wa4GceVwLGxt1mFDCJMOahS0TRpEQQQ8Jq2C1/XUOcuRBfwqQcS3E6szFLkWQm7p6CoExAiEmBAtbg9vhidQOWvJqImb1kFpxmz7LQc/bcQBnDhqNDD/uDNZ6e8vCmTIuX8jPu1wArXV82JUrrMIm5Ddl4h3Gs+37AXQ/dqgUw85L3FV554MkXKAHdLx7tmDzP5/4wPldRPi86EkK7WHF8sOPG5RV9dUg/bOmHAS0xM3lfKBLYmDSyU6esDGduh66j/LfI1MYsxID36mWWWuYQQmbkynt09P51XgwydvsY869Zc6UEC3+eoxEGIadyRKgFnNNeuH3f0VYLGmMwuTuPDRHfD1TVFcWZFWDoe53bEtGC7BkHJEePoISXU4bSoGVb3Tm+KVGn/TXrV6f7uIwYsS57ruN5+rXSuHya9rLXi3er49P7AdvULA/g9RsrTpar8XmKsWlyvrpEdqacuIZuw1yJ2irnxkPeD2o1jXs/r81hUAOnrpsxdaZdfspaS9M6LsYWMBGNjE/IPJ8eU5ZHRZaI4jZAMnXrhBl5L8f7WomWQbG2YtFahsErgXMaxuFzy5YaYXV8pAI8Rh6++5DPfObTLOoak8D3A33wdLuBh+8+oqoqTk5Pee21l2kXjbb+ibNwSJoma7RnTPaL9qzHfY9zXxkWXtZpUeuJz6vHF4beZB7OGW+y9ze1Qkv+VhObwhxc8vwzTsIQFLeShdUsKXr+wXvw4DHm8hI/DMoj68pS0pxRjFr9Jjm3klAQEZJZVUSrFR3qkWJsLoJOOjYm4XJtl3aD0fCNEYi5rlftyOyFiuhmHMsbNDRVSkdizI14TVHCczBDmpTOOP5TuKgIlz3lUM4bNy5f5JgpmWI8zDynArAooz+CvbJHKkbLaxCXlUz2xqJ2KKlmG74IoPnzFMUns3sWIMv0fPvvN4X9pu/s53yncwvorKji+Xfm948J6rrh9mHL5uwZn/Hgh0AIPVVlcdbhB69e54hBkDGSU5SbCs6kfMmR3PBBFWdpdD14VaDBe+LgCXmMS49REz3W6D2d0w4uztTj3hk9b1FFdNyvIWn31JiVhXUO8ZZKakIIbM7PiIslj9Zrhv4JPvSE/EwxPtCwZkZx97udKsjBTwAbo3vTuqndG5l5SWIGU+Uo2NB/So2tMcKUx+e1byoTqHMQooItTaGDzPMkMXteOnOaAlBTt6whBMQKvtsSa6hlxemNE24fH7PrNoQcDVHHQsb6ZLMXbREld5l5+cbYrEQZ16UaqBmwmbv4DL5XUKhoPrPsEZP3sMq/snezgiwrUabISSl7MdYqeDSkYs8CZsS4hBBG4o9/b8K53g8MURWEqdwIvhjr2URIfcSnzMSDhm/vvvISp3duKXpXNIkeozD0gX438Ilf+QS/8PM/z63bN/mmb/pG6rqmaZqxbVEIgasrP+ZOUm60XZiRrgvUuVLbP/Y90C+U93zxd198TCHkSTCOAiF/MgzDGOKZJ+L3NqMIVe1GMNIQpvtfXl3y2skRTYz0fQcGjK11LIJ2gRCgqltV2lnoGxFSDFmpopuvhALFE0YHUQvYjYnEpAAERAtEjIEkHhkp19IsJDU5dYLkzkYJY0tuRvNkLtOjlc02zsE8wsC+EtGxneXqnlM4e2f+Kr+XejYd78kAK8+flIM1lfBa5irOqHJNcalBUFUNcZgJqMx5u+d5XlOqLzp0LUwnWVPhbHnva88vpbi/hHx9Njkm5fp89IXcmkpYrQ44uXWTn//EBjGWqm7G/F1VO7bbK2UPKpspz62iMgN+CAxDz3oYCD7g+wE/eEWi+6BAEink946mOWbROPUyEFy3ZnX5jkZogiHuIomBJB3DoMTzQ9ROLD5E+r7nft+rl2gUXLjdbkEsu6HPIVJdP91WcRYYrZkOQQ3rQquZkoatBx+IEY6WLSklfNC2YWIMzlV5XahwRxIm6P6xWYGXo6rc6GUaZwkf/5cIjEQUVkl5tE1ZjoB575U5aRaFss5QVRZjVOlZ53DWUtvAqXEMT+ChvMbP36n4yJe/wZs3vox4ecmzp0+5Wm8UcBXjKFdkVrkwr9EHcs4RCpvQHJnrvYLMrLU01YKmaXK/YxADla0IKO1iiQAq37caCIpEn/ij9foFjau/u6qaVSdMyjQl3XNjHfF7PN7XStTYKk8aWrqgn2ZvVIdhyAQMVHa0PtbrNZvNBnLvO2ssKahA+rqv+Vq++Ru/ib7vePfhfR4+fDAtvhAZIty4ccTBasFqtQLUG9B8S7HiXuyNwuRl5N+uff7i43kWpi+gVNMEIiiWVGmHVjzPmK3s69fe+ywvpKZRZKz2JZ3e4/TmCfb+OWKFqnF0MeKHLZIVmuQ5cNZNnRFAfUaxmJgVhyRC0nZVITqMlDwvSDIjybRkJRmIiqH1GSGZZaytKipRFF4IAT8oF7AYvY4A6Rqllw5LIiU/WZ1pHk7an5+iqIOEa14diDVjGcNEYjAPJZV1IHufT0fx1PR+1laYqNRwMZQcXwDRfrXOWKrKUbmKYfD8vw8Cv/5z0xoAxoL7VFrEMK3FUsYwrsLRc7i+oKbnn6/n/ehFUZ4vjpTsHQa6fsdi0fKhN1/j/uf+JZ/oT2iaNqNaI13fE1PC90OOXmgHoN1ux9B7hj4ryxAgZA8bLXFwrmXRKkq0RBZM6Gku7iMEQvL0/Y513/HYdwxDT9/v6LodXa+pHN8PjPnUOIGMwuDVEIuKSk+i4cZS+lYGryh6KPM2G4+Zl2eto20bbp0e85Vf8SGWywP+q3/wD9lsE8JO9ZtoqFHTH3FcMoJQ15auD6yWbZZ/kbZtCXHH0cGh3j8GlgttNO0HP61BMdqmMElOfSRiNyBmwDlLSv2oCBVJ67AifO6zP8v/8CM/wf/t4Id448Nfxq//1o/w677+G3jtg6/gL57x9OlTIMsYEWWpElGl7WbAIrGY0VjXR0qU0LPLctRRV8qbm1KidjZ7qmoQxKiyISWvqOYQaBerDFJTI1eKQjVTXXJZ/9ZORk6MQXvAGhnBkTF53uvxvlaic0Rpyafohp/qvSxJiZhLOHIMg1qMUQsnIWCV9urZ+dNRULTLJa+89vpoSfV9x+bqksvzp9x/64zVasFytaRZLKhsw3J1ipGKrt8R4xVd12Gs21OCRYnaGcWePpadCJSzhxiClgCINcyprLRRNKMXpT0PUQ8hh2GIEawHLD56TOgUESdGQTemhHWyxRjUoNDckArhZXMDK9B7z9JN4Y2f+vlP8lUfeI0bi4b2M29rbsLPCb81zBNR70np57SYXyhzVDyl3CQmhVEpKmUYSKaPU+UEFIaSNIG1Bh959/EzfIgcHixZLBra2rLprjAiNO0S56pRuWkua1IGJm+2MKufzBEu9ZpngCz1yuP43Cq90ki9lkZXuOTyCttRaR035ROL5zKN2Sw8GjzWiLZ+LyQgCERPCgPWNVRuCYsln/3KV3hluIKf+SwAPg2Ic7m1bMxF8BNT1XT37EWKzglSclNlmzhkr8zquvIsxgC56wezv+W/5/E0uVfjkHqMESXniMKv/5Zvofm5X+Lt3rPz6o2dX17g+x19PzB0A34YNL/pQwYVybhuC4tNibL02wue/MLPIDiOj2/QLGqMeC7Pn/Hg4QPIvTb73nO13qiBMkaTigcyiwrNQxsllDhNO0LCimRgERp6NcpUlisy1YCMagBKLoVC4OCg4ep8S6hf5/abH+a1l+/xr3/6Y3zy45+mahypNIrGI4imNAKYpBzHySdta7brxmhM33mSwHZ9Vt4Ca9cklN2tcY7aGWonJLQSoURkQDBBSH1PSXWYvO+kAH1SoJKEW3ve+clP8F/80mf40Vf+e77u134tv+27/he8euc2D59cEWOv7JpWgV82GqXcLIdzYHM+N6m8jjmUqpazgHWQKh5dPODNYUtdHeX150lBsK4mpYhrKqpmSQggVnv9GpsVZ2Y4SyGHro0dHawU1RhOmYQi9CDLA+pdRyKx22vd9sWP97USnYqji6W9j5iFXGzNBDopR2mOXayhkowPswJg59wYYgH1yGpXUzdLlssDQuhJKbDbbtmmLReXVzTNIitntcRsITwInpi8thxK1fMxX2ZlMujejVERjcxIpnXjTqGYMeYPmh/IXkFKyj+plOrk8cnXN4nZ3gFKlq0EXvVf1mj2URJ75Q///F98jOpgyVdKpM0MILGEVIrnxVTewZjAL95j8dhktHhhCsmqZ6bhTS3DK0w/CZ/DbJI0b9b5xCeOb/HsyTO6J1fcq7d8WWW4uNyx63a0TZfDY6p1VZAxPkcSw6eOVpkWLAvn/A5vXFxQpcmosTmHJEbXlbF25Ac1mWZsVPjlKGG5ys2KTbKhMYJcwEruT5kRpSOasjxsYlIi1jIcHHBx7yXCzVNedqf7azsbVzaPseo7RcDOcz2lJELR0/ucz5NxynPHHDEKmR4uqcqwIrlkJIJR7z+SSCZhcDmknxhiT7V0fPC1m2w++5jPPN7yZBM0lNrv8IOGZmPIRtMYMM41iCkRnn4WUNIEpfWLuDRwdHiApB39+grvO3abHZeXF0oQkg2vbtfnK47TRJUBaCPb02zfjDlCU8ZAv2QLMjdNylgz9rm4OgkmBYak6NcAiBGcU3afi6tL3rn/NpaBurG0C0X7ahw2gwGTgFNjTJICgkJKONRQImr5mCGXqMQwRiKImhrptzu8MfTO0NQGIWCrcSNkhVnkSJYiWa6EFPDWYY3B4XI5yoC/6Pj87rNstzsGEn/4u7+TBohloceCnjd7yGct3gVfQHVZtqUYSvIhryePcw0mqWFZZLnujQzMLPtTgBjm2qBciZLfT7k+uUQFdGup4RNSjx00Oll6v77X40tWov/sn/0z/sJf+Av85E/+JPfv3+fv/b2/x+/4Hb9j/HtKiT/9p/80f/2v/3XOzs741m/9Vv7qX/2rfOhDHxrPefr0Kd///d/PP/gH/wBjDL/rd/0u/vJf/sscHBx8Sc9S6L1KPmWsp5o9C9ZMfMezo6C9CqPJHApePMeSR51g/o5meUS7OsYZuLw8Y7O+ZLu7YtetuTh/qDH8ekXdHGRAz1wQZWCDmXrZJWZCKU9cAeakmEjWQAyZSFyQXIAfc/gvJfUmVEhOJQIT80m2hovGTKIECpBJEnIhdja8Y/5JKWLNoEog7XcF+bmf+yxvfPAuL716h+ODJeZik8OueTyLJ1JAEVmIa/nFbHHmxVxo03wunA9RS4z8ELhwll1IDCEw+EgfPINXSke8lrC8+9Uf4rPnPT/7sU9xL/V8x+1TQow8fvIUay+orOZ6YsxsOBkjLwgD8Hf7HussI+drFia/2UQWOQRnMw1ZlZt2Kyze5pxU+XdF5ZwCQ0isuo5KoKoqlm2LM9qgeuyQItkQlKK841jyMq6HwjMaFTxjbIVxjt1qyaN7t2hrw8mNo2lI0ZICRkGY6wGtrhlbqCmLUTZ6w3Jt7wQKenG+n57P/05eW1llpoSS54aZJGyqMkdxxKeB880Zx6eHvHZ1xluff8T5uxuGXYd3CzUa+i34YSzRKus3IfgwsH33EwzBs9v1NPWC4xu3ODm9y8nJEY+fPOTq6pLdbkcc1PNOcVDWHRR5Xpyl0fhBxlxgea/JANWccSQp0jSp8VGMkxhzKiEEjUIUpC+CNRFJhq5LBQ83zsn68hmf/PgvcPbwLfpug61sNgoymjQpg49xs3pgY7BEKlEv3+jKxSJ4idhkR+L9hMUnw2bw9N7ThaTGuVhMLOT9ml6x1ighgZ074IYQB1JIRFvhRXugLhtY1A1D73n7k2/xuYcP+T2/9TdyYoWAVf85GaKJVHWDDXNgke6XFLx68eNeGDR1lDJq319xdHCbul5grBrmhgpjwSfRNFw2QkUmY0DLuzRnb6xBXDbEc9rCWDfKQGsttlKmOht6kqswtqFKs7KxX+X4kpXoer3m67/+6/lDf+gP8d3f/d3P/f3P//k/zw/+4A/yN//m3+QDH/gAf+pP/Sm+8zu/k4997GO0bQvA7/t9v4/79+/zIz/yIwzDwB/8g3+Q7/3e7+WHfuiHvsSnyaGwcXPPWUAkC5DZ2TMBYIyh73tIEcdEi/XCu+TrxRTY9lcMnaKAK1txdHzKjRs3CGHH+eEZm8sLLtZnPHn6DiIO65YcHpzQtksWixXL9pCqchrqlTjWhinsujDp5AbfEgjRa1eMaDJVWsibXhVbRDsHphhHBariN/cZHL28IjejEijIlIeT8f+UWkyC4PvIqq0IGCKeOAtviBi2jzou3lhx+RWvcPwvfxLjKg0hZgtPYmQIgcraLGuE0i04pETvPX0/sO16rtZrun6gHwa23cB6NygiOiT+rh/4/Laj33QkH7FYwqCto+racXp6wv/s3huIRA6PWj716Iy/8ukz7t68x7vPtkgYcAacNTnfA66pcZW2DPPeM1xcMlhD3TRUVa2cw8PAf508IgljTaZ+TPhQ6PeC1iiHiLUVgqGuK+q6pm5qBM93p8hhXj/GWaq2olrUtG3LYtGyaBdUlbB0FZK5ek1dZa9Chf3Qd/k+gb4fOLpxjLgan4Su89w+vbnnQYros1lrMzOTej4RBZ4MMWANGFHwUtMsAPA+MHcWhlCiOzynOIuAtbnpsk85/QAMQ09lBFvVDH4gDAMENXSHBH2u6HVYiInNbsdXf9U3UqeG8M6P8N999F/w8rd9NxHozx4Qzt4BNAQ8BB3v4BWf0GGJqafbeQ4PW1oajhaHfOKtz/Ls6UM2mzVhCNikhk3dtrgRmKPGcuUsTV2TUmKz6zViIKXNmYaRFRwERFGGpKAelJbWkCkwNUoSo+6rykFtDc4JtnLUtiKQCIO+fwAObhzgHFxdXtBtNmw3O9bbhKncmPdPERpnGIZECGp2O6s9aWtrMMYpUK+ypKrCmkhrHfiB0A8En1hULXUIbH1PN3i6zGBk4r48NHkeTZrIRIZqgfgeGfxIutKHgT56uFrTVg3eC7uHieHMEtolkhRc2EVlQarbY0IQ4AyAg5PbLFwihoG6cri6wsfEtusxVY0Ez+7qCY8ev81qdZfl6ib1YsisZQ4xQT1UWxOCNsEYKzVMhbZn0brvqqkhzEgZRMkWOj9A0h6wdVNp9KHbkKoG1xxSdf8WuXO/67u+i+/6ru964d9SSvylv/SX+JN/8k/y23/7bwfgb/2tv8Xdu3f5+3//7/M93/M9/OIv/iI//MM/zI//+I/zzd/8zQD8lb/yV/gtv+W38Bf/4l/k5Zdffs/Pcj1HM6Gxpv+GGap0DnSwOV5vRK0iX6zntC+Q5t9NBBBP1Rpt1xUTg09aED8MNPWK45dOOO0HNrsdIXr6vmO9XnP/6bvaSNc63nj9yzg+voG1NUYMVaMGwK7bMIRhDH0UBORY12QSEkrD8RwyS7PQ7LWxKY+vaLxMcaZuJmTGkJKRAxA8RjRfa0WoTK11Xz4gfpKwKQWsrTk4OuXGnbsYWzH0HmdVkSwWS46Pb7LtFN7vw8Cu77na7ui6LneU0PrSoVfvIEXBBsvnpeFfNQZ7YHl69hTTeF5pB9INFXg3Do/ou132uAQfPD/3cz+OwTNsLzmoI62zPHr6NnJQc/fwBm++/Aov3bvH2dkZbdPw8NEjnjx5Qt/3vPrqHb7sA/8R/+Inf5pd1+fRCNSNQ1Gx0zg6Z9hdDVTOIU2lflZMNHVDQpHfVeWoG0ddHfMvndOQvjGYBBfn51TJMVz1dM8u6YYesRX/qQiVgKssTV2xWC44PFxRKvFMVVNVCZ/g/v13+OTdO9jXXubXnByQGLhab8e5GXYbUlMzDHHk1g1DGplaBBTxbA3WupHmTks2pkX0V378vVri1z3Tevbvir1GtF/o+KlnwEvw4d/PBz/8+8ePl1/zne/xGaZjAE7zz/vx+PJ/1w/w/8Pxf/inAzoD88MA53uf/OTPfxSTAhdnFzx89yHnz86onHDv7m0kRTbrJ2zWj+k2T9hdCW8/eJubdxcYETZXHjEeWx9wenSCNRlLksPsGuWTMR9dVQ2mFnxQwysE7VfatgcY0ZKqzXrDkydPsAuhS7AeEhcX6/f83v9Gc6Kf/vSnefDgAd/xHd8xfnZ8fMxHPvIRfuzHfozv+Z7v4cd+7Me4cePGqEABvuM7vgNjDB/96Ef5nb/zdz533a7r6Lpu/P3i4uK5c+ZowH2SA/Pc3wtvrFrJYQznlPOuX2M8kkGSwqOHpExIWkfhCQQMlu3QZ7TfFhFD26xYLW9w8zQyDH3udr/jc5/9OK5SAggxFUcHx7TtisPlMQktxo4x0PU7woxKUMZYS0bZjmHTHI4tJObG5JyiWtQpZTCTZuDHECqioUn1chVogREinsrViEScVLhZnkjEsd1t2Gyv6JtXefaRj/Duo89x55c/yfJih9t5nnSGwUeurq60yD0lTIS+15IA7zXPa4xATPzjZslgLV3Kdrokjg5bRe56raVz1nB0uIKjA06Wh9Su4nx9yS/8yq8oEMcbvLcQhZPlEQ8ePqUzCq46ODjGGMuN4xtcbXc8ePSUy83AjSFxfrVm8AND0DmNMdG2jRoZCSRp7dpBXXNy+5C+3+KDV+RoSvh+q558NPhoiYOhly0xRpqm4e6du3zlV/wafK/r2NWWIXiePH3Gv/6JX+S/WDRjGDAZYYiPc0eYAazhjeWC33q0pPuO/znP3n2br/76r+Mrv/qrOb11m6tNh5uFYV9+9XViUyu4JRMXFPBYyPW+l1dXbDcbNZ3yutC184JN/R+O/3D8Gz5uysChg00bee3eAd0Nx9V6w8v3bnLj5IRNPxBi4HDRYlPN2VWHcR4xiRS0EsNWB5pakZRTV5GEn2EJco/pmB0Ql0ZZmZKnMjsMkT5tIZzTyzO+8t49PvGLn2Tz5IqqOnzP7/NvVIk+ePAAgLt37+59fvfu3fFvDx484M6dO/sP4Rynp6fjOdePP/fn/hx/5s/8mec+n8PtTVYQU00mXLeSr4elQggjKkxc9dw5z5epSIaSgjWOELW1kLIhGQYfFHxizMiDGZPPdFMRaw2HB4cslwuWqyNC7DOTSuDZ2VOMOSMlBSS17YLKNThXU2WSiBBUmfjgR37XEaiSVGDOj8LjG2OY2sJFbZytJXs5vyeGICnnDkqeCC3dILEbAvXs0gugTsLKWE4WDcPhIQfHH6IzFZ/9zOd5650nXLz1jCHArt+BJNq65uTgQPNsIgy5LZgVQazh3G8xtcOK4QBH29SQGrVTMoCjqhzOaUF8XSsAY9HUHFa1FthHwYhDnIXguXe45PXbt3np+JgDa2kWC+6c3mBzdYezx4/ZXV2Q4sA7n3+LtOtYVRWuqojBU7kqF+YnnIHaWZaLlqODlu020Q9eC/yHwBAiYtTj1HwMDLmnres9Kwxv3LmHcYarqyt2mw3Be45sy8Wbl3SD5+HjJ1xerbXEI+Q6UZ/ACJt6gznuqH/8Y7zy+h3evtqy/uSn8J/6DOcXG06qmt+b5+YffvSn8a6iris267XmKq0q7aqquHnrNh+4d4fT09ucPX2i4KMcDq5s4j/7dTvNI41NFSZSj1iI3JPm603Os47h5Jw+uXHzJp+//4CP/fIv85m33uLs4pIYBBOS1l+mRPAalgVwSdhdXXL17BFnZ08ZeqEfwsS8k5HslPrYnPBVH1pzdMYp6DPGiB8SNrfJikk7ELmRSzYyctqiHYhsgdyOnxaswhSqTTGNm21mVl/7ZXbkc00yLBrHcllhK0dIHh86jBEW7QF2JGOH84tLnj7d5RIMgxhtPG5RCzkKJMPIFx1D7vgz7mVHDD1jn1YSLhPKDz5kWKIocUfSWtGJ+7cMgYzAGxK5v3LINb7TyxWU8cnpMbdvHnH7dEVv1SAzQa+7XNb8tt/9nXzb63d5/OgZHqFaLHDpNlZgcXyK5KhaN/TUdcNyucBFBUs2toIknB6JgiFTzKApD2YJyTOh/YMqUaZSMWOMliPtTVIieo8zWubmmhZXt6yOjqmOVxwdPOaUhmZx8gUm9vnjfYHO/RN/4k/wx/7YHxt/v7i44LXXXhsX/r5y/OKoquu1awXaXpmpcfd1r3R+ZSEDVNDcWMwIPGUUUc+3QKkVbJA91aAtdpqmYbFc0SxXDEOvwn/oVRj7nvV6DWjbqKZqtfNFrmEigwCcs9qdPk0MSWb26uNzF+GXlCs0ZRIKaws1nMnlCapVjRGikOHhhmgsB6sVkira2Ur5hQ/dg4tn8F/9iP78akcCuq3+/Ns6qkp/rh++h09+Sn+uHzfvgNcx4eZ7CABuBv0BdACd/hTAa0l2ARm5pMfnPw9/5+988Ws3DprjL37O5z+vP//jv/6Cp/yjH/nnbJPQLmrWl9oL1jpHImUleodv++av59d++Ms5ODhic3muCOOcA6uzwTKCjcZ3VS9ZRAgRYmk6T4nyqJdrastq2fCxX/hZfuzHPsqnP/1Zrq42RJ+oSLmcKZecRS0tIkFTW+raQFIawySBWJpZR9EISZh0lsmKRHILO1OCQlEjHopMUrVhLIQh4DOAxhTsQEbRRTKS3BRQXjmKV5OyQpcxr6bH3smzb00JkoRhM+zodkKzaDILWMA1jqqAv0LAx0AKgTQMJGPRKHs2gMnLKmPiFJCXZVkGqUlSuUToSLGghYDC9kPIY29yGZxXLmriCGQkz+VYJZ2ULcyYhKKMBYk5veECPsHTx0949ugZnw6GcOBpoqGqVgw+EoiY27f4X/7v/gDiluw6JWYpcqgqxAwiLMj19jbjcxMMUb1Ot1iSjM2MZJ4QesQ0xKBgPKUxDWjqIBP/J8UCJBNB5mQPim4vGATnKoxraFIgNg63WLE0NWk1gfV+tePfqBK9d+8eAO+++y4vvfTS+Pm7777LN3zDN4znPHz4cO973nuePn06fv/60TQNTdO84C9T0UAJ0Y7AGikR0OdDvJCtEKc0c9aWrunPkxrALBSc1GodfA6P5iJk77UAu64b7fYQ8wIMCcXNqUWVkjKtbLsrlNWnwtmaumo5PjplGAYuLs949uwpZ8+echGfKrBFHE27ZHWw4uBwRdsu8VaBJgVBXJb+5ImrYLBOG/LGECBGLZxeVGrpJw3hhaSoc2OE4HXThAq2Q8Xrr72GHyJtDNDt5zb+w/E/vePj/8NHOe8iy5Uleq2Fs9ZycLii7zp+/mrg8S9/nO1v+Y38vt/7u/nY44csXTsC0ELxbnID9+s6QtlfdO2I0VKAWjSqI86wXK149+37/PMf/ac8ePtd3BC5VS1xtUFczB4uJCtgBYaAWMN26Nn1A6u2UUKJCoKfwH5VITVQ81XLRPDUlRt9y4SWfGhZVCYaT8re4yW3KMiozRjUWBSj6QIfJxo6SHuyA0AodIOzI4Xxr/veTsbdi14jJh3X/nKHGGgaw8HqgLZuIQlDGKaa5NydJXotOSqGepQ4IolJ6kEbUePDiJaA9J3XspesbSUZAhZJQdmICqk0Canr7BCE0XlI+brl33pmwhmDYJUNC5Vjtkq4FHFVRSU1Nghd1bEKNWIi3iQ2feC/+aEf5o/+/u/iKFqImTwkKGBPBnKULEHGpXivEQRl09Zn6XZXpFRlmsaE7zusdaXFPYV9WAFh6qwwyl+taIghM4CRUda5DCjlJg9+GDhcnDLEnj5sCf2L9M2Lj3+jSvQDH/gA9+7d45/8k38yKs2Liws++tGP8kf+yB8B4Fu+5Vs4OzvjJ3/yJ/mmb1Kexx/90R8lxshHPvKRL+l+aRZyUrNSUXUTOwyMNqG8mGs2hIB4j3FfHABRCrp9CnkzqbUoIjhbQZ3w/Y7KWpJL+JAIEnHOIgSlq0vKFiu0OokABIYho1ElsVqtODo85INvfAARYb1ec/bsEZeXFzy8/zYf/+VzlgeH3L79EgeHRywWK6qq0lyj72ZdLkIeAcnQ+wGRxHK54hcfrPnc2w94dnbJZtsT0tRQfL1ZE8LAwXJJZSteef11knguzs/5T+57qkY4PLzJ/fuf5eHDx5yf7ygsi+NYGaFuHFUrRJ/ou8hXfeA1/rPf+1v4xGc+w9sPn3K56dkOiW0IPLu4YOuV9sxZUWGRnz54j1iDq2uadkFVVxruXqw4WC5pFg0/9RM/gd92RB/Ybjt2XcftV1/le7//+9l1W0iJpqqIQfs9vn3/Pv/0v/vv+dmf+VluHN+EmHj48CG3bp3QLhrOLy5GI3C73SAElq1juWywlRpbfT/Q7QaU/jTho7LZpBQJGYTlh4EYPHVlOblxzBtvvkbsNxi0rd768oof+6l38qgVZDmIBds4mrbCOq3pS/0OqPFYnANnwRAJPSyWy9ywWFjcvcmyrKxBgVJFQR4ujzi9U/G5tz7LP/rH/4Rv//X/Mac3b7LbXSoAL7NMmbw3Yiq9cQSReTxPZj/Qk/ApUGNZLQ/5kX/8T3j08CE+edyiBuPohmHsIAIgIWICY4OEOipPbdcN2o4qMxil/F7WqPL0qZSTJJ6eO0hKXm+BRoTDxnKzTRwta9IQWG8G3t2oUO0he7iBIT9I3QANmAhKepay4SFjqVNB7l8LYj0vH1Ia9xyjpwyuVi8xBEuKMPTCMEQ+9OUf5N13H3C18UQCm21HTJGq0vrQGMmARzXMQyCj8U2uRU5Uu8DKGpbGsVpY2uPA4cGKO6c3uXvzFodtyztvP+BT9x9y/2zNk7Xnsk9syDSoBurK0DYVzgqePoMYddyVAC0hBMQMI/YiJUeVHKaPRNPja0djhHUKWGdpbc1J3fLo/DE/88lf4Dd+6NdRW8PTq2c4p2QSIUaca6hyKq52GqP3IiSJSNxRV0uoLIM3Wv4Voa0WJCzY3MwiKiuaoZBg6JwVlLoaV6VhgYzvlUiIczipibLDYlk2LUcBLMsvPNnXji9ZiV5dXfGJT3xi/P3Tn/40P/3TP83p6Smvv/46f/SP/lH+7J/9s3zoQx8aS1xefvnlsZb0wx/+ML/5N/9m/vAf/sP8tb/21xiGge/7vu/je77ne74kZC7kYvIwEFPEujqvXaX80gRyUXhAbp6jIU4gQN20BO+fiwBf90AVmVuEhoYQkDSGbZQc3OKjhhYIgk0VAcHHTDFnEikE+j4QWWloKRUGFBlDDkPXMRSgSGaMObp5yvGtO5AMvus4O3vA9nLNWw/eYr25BHG8+YEvB9uwXFW0lcElS4oVl7FHSCyrikW74LMXV/zff+i/xHeBuqqpq5ro1aLvdrvMP5y4MIKtKz77iV/IxfNqeMcg3LxxxcnRIa+/cUDf7VAEcWQXBtbbLVebHdtdz8V5T0rZej46ZPUN38B//G3fyvrygtB3+G7H5uqSd965T7TCpu9I/YD1A33f0206Ha+gtaPdrmcIXoFm23P6pxFTNayvLkmuwTvDlSS2w8Cblecf/53/nItdzxATxhnqqiYNkd124OGjxzQp0a2vOD69hRfhfL1miEoX+PbnP41Nhn6neZa6doTBc7yc6okNCl5YNQ3LxYp2sdIwfNuwqIWjgxXLxYKDgwXHNw6p24bU1LhFy+r4mNXBIb/tp36Jz3/qM6wyB+7V1YZHZxf8+Md+mfvnazYhar5aYLkAayNhQEttrBAIDLsNkNQIcVVON1itG87oYmsNKXh8SoQ+0G86Hjz6HG++/iqbjcVich8NDdNpmL/0ZlTFrnRsuYVYCIBGNtRrSSQT8HQsDhZUtiJZh5VCKi/Y4EcPJ6bE1nes11dUldWISEikaPD9DiuOMES8V6pHWzf4wTMMWjN9WDu+48tWfO2v/QZee/N1Tu/c5Pj0mNNbN1keHOBMRSFZCQgxDETv8V5rEet6wZ/8Uz/Az3/sU/TZ6DG1Rpc8lsurgX4ovo6o0AbERIxJiFGlpjInjVRyoHiCkJGgpbG6SGLRSg6dKqBhcXRC9fgJDkdlWhYVVPVTBh/5X//O38p3/+Zv5ytfv81b93+FZnmYyWG0UfaibTHOjbWj1hqauma3fYgRx+nN29w4uaXuxRAJ3tNvt2wvr7g8O+Pdzz3kx37iZ/j5n/g4H3vrXX5psyEKHFoh2GzSRfXWh5ionJBipSMafTZGohq7KZG6xKLSGk9nIyYOWjIlFjEnOJQ9TtByPe0mlbTCIRT2onntviXYJdFYTCCPuUrclAzg839V9kalwwfJRBNikNpps4okiEOjIFm2WuvUSslpDJEKcY5oFsjigGq1j+v5YseXrER/4id+gt/wG37D+HvJVf6BP/AH+Bt/42/wx//4H2e9XvO93/u9nJ2d8W3f9m388A//8FgjCvC3//bf5vu+7/v4Tb/pN41kCz/4gz/4pT7KpOCSlpmIrbLHqEvfGsl5gJRj4JPDNNKyJV3oY6udPWTv1LSZWXhnomorVk/JJWSrVSAYIZbCfnIeINOMhW6gqnI3kxwIGv8tmkchlcVC5gjVxHkksTo8YlEfcnR0g8uLZ3z+nU9hiYTdhqtuYC0JZyzLw1NMldlCRBCjXki/2VBVDc6pUBCnlGxSCZWtcjsgocIxDLlGNib6IRCsIM7SmYSPA5uww3cBkyD5gdYZTk5W2LikdcKNoyWrRcXJ7QU//a9+lKHfAYHlos7E0gZj4fa9lxkCpJyTs8bSuHokOSit3EylkHYj4Kw+3//l//rXuVwPxM2OOEQW7YLf8bt/B6/dvcXTy3O2fUcQcHUNPuFSxdB5EpbDm7f4z//W38K+Jdy7fZtf+41fy7d85NdRuUj0PTH3MDRWO880lWitoUxsS9ZaqkprT63T3rTRVFirHpQSLEAfPHW70DRs7gLyzd9xl6/rPkJVWaU6TMob+3sen/HOWw945+37fOatd/jpX/ocH//Ep4gWhuytamNiRQALGplJuSen9x3WWWL0dIMfwSt13ZBiwIrQ1hVRIkP0RONIIsToSdk4UBsu5+LHfVRaRRVyEz0nBSAkbBKWVYMJiTT0yp2cIk0lDMYgUYghISly8/gGKQW2V2tS0j6STirG5kYL9F7OEX0gxMDLr7zCV/yaD/F1X/c13Hv5lOXRAc2yRZyC4DqB3tqRCg8MSSJ1u9D+mwkaW5Fczd3XX+fBu89yeZalbmuMNQQM7eKQbddzdn6m49YslJvZe0WLB+W4cjnfXHqSIuSxLt2EIsu20bIoYwhBuX/rtqW7eMZuc0nvdwQi2Mirt27y5OyCZ9s198PA1/6aL6ddJJyrJlmXEpvcvSSGzOWM7pmrzuOM4Lc9XbXRfRMzfV9dY05PODo5hldu87/6pq/lN/2eS57df8aDX3mbv/uP/lt+8nOfw7pKQ69+yLzQMaeKyE3CG0xuwyZAEkO0lspoPW6KAxZtWeZM5NbJLZrjA1qBm20mhp9xdotkwoqkym273bHbdfgYkKjOhrXaei4UUBtTdLDISjVsIUlhI5qQu8iUOy7liillspGxH6zu57ZZUh3+W8yJfvu3f/tz4Jz5ISL8wA/8AD/wAz/wBc85PT39/4JY4UU3mwYyxoC1TiHPQZWoEVQppSneOOZKBTUZSSNx/PW86pgU2suPTHWD10m7Tb64du3SsoESlixVmXpLpaWZMjn7zCiMOdj8offZs1bVb5zTfMhiBcawPH8bMYmqaehDwvsePwzYak3ykWF7ya6yXF6d8/jpE+1k4pSGLyaf2z2VXHFm/chgJZcJEsRCLUat9hTZbtYkIo0V7hw7Tm4cY4ks2pqj5YIbBwdUBk4OlyzaGpxhHXtS2FHVjoUT2sbhqoYu6CaxdYVYDdNoA2lHFINPmRKPxNArmlkNnEjwidXJKdvdI/oYcaJNqt95dEmsl+y2HYMvZOYdVgy1UeRtVTuG5Dk4PuTezZvcunHCarEiCsS6oj5a6nNlRelcRdM6mloVpbO2pF8UnJO7aYSU8NEiRutY+6BW8pAMmz5lGkhda8vlClMvWCct/jbGYFvH6rVD7q2OWNy5w9HLr9Ke3sNWju1urewyxmgXixQ5WC3Z7bQJAqJCZtf1OZWQeUJJeD8gGHoRbEr43uv8ioZSFf0parmzH84sSGX9i3oR2qBaDUUrxZe1WGMzYtmyOljRLhaITWy2W0Qqotcw/cFhQ9M29F1g0R5wdHAj8xxraVZpMmCMIXlPCAO37tzkpTdf5+DebS7rhseXHelyi4+RwQ/sOg2JUgzizBBkrCUk9Rgr66jF4Ntjbr/xAULXq3e6aPNYCAcHRwzec3J5zjAMtIultk0clKReIzYWVzmd8+DxOXyuTe/D6PU0tTYLSEnnwHuPGEuSRLVawLAjdAOr1YJXX3qVxYPHrPuen/rlT+CPVlw8e0RlXEbSqvc0NqiGzJikMixsnilQZ/Eubbsgoc3LrTVKOGItdVWxOKxYVku2Yog3Djl+4yW+/Cu+nPsxUBsF6Pg4jFSeIXiN4pmM2A0zgnZjwFpMNCQnJK9K1CK84o75xCc+B+s1tbMMGZEtSZ0c6yzOWqwYmrpluVrSWoNrGsR7CDFzXuU8LznNYAouOY1RQSm57JSrC8gMU4XBq/SjjXHMl8+jkMY4un7Ai8+Ume/teF+gc7/YIUYRYwUxm3LgXEijFyk5f1BGrHiZYgxpRhU40eTplIhItjjzxGdrZdRuRdFJVrljXkSw2UUVUdJoxBBtfgJjcm5DmWnm3vG+ls8CLG9II0JE6IOSGFgRkjUcnByQBBaHR1RpxdDtCNsNvu/w2zVhd8nWJJ5ax3Z3rmQHIeGTV0VvLAYVVAWlFwdPagJYQ/AB4yxNuyCst4jvCdueo4MDXrt3l69+ueK1D76OqayWEzQN9+69wnbbY0UVfkpaLlA3FXWjDXDFWMQ6Hj4943yzwdaSeSxhu+04v9yy2XasdwObXc9m1/Pk/IohePo+ZLagxI26RVLAmMTBwZK6WfL//Pv/Leb4Bq1E2srijGGzvqJuHCIq6GwGpbz66it81VcoMcK7j5/wS//N/4veJk7v3mG5XFHXDZWpqI3j4LhhsViwaBvqWvPoPgRKOyofA7u+R3yiamp6r42Fq0r5krv1RhGkoorQJ/A+st6s6YZBuYERmuWKum5ZHBxweHjCh75hhV1VPH7wgMoYKmMVwTgMLJdLnj57xmZ7RfA9IUS63Q6SInKrytH3A33fqVGx8aQhcvHsAvdBhzUuCxdtC6h2XYRkJ4BJKjsjqUEXNaWRrCrSgkoPEbb9wM4HmmbB6uSUGycn9H4grc9omyNSsMq3GjYcHt/ieHmDW7de4uatO0rBlpQCcvCDGkDBUxnR9me7NT/3+ft89JOf5upiTchEIiXErvnoiKtUtKWYMEHY9gN9ptE0ojnFw2XLwe2XSUENGOsa3b/G0Ilga+Fodchuu6XOlI4pBrRjVAZfGX33mCJ+0Hyic47ke1UUtgAWtRuNDYEqe2G9EdrTE8zVmuHsgpOTE+584FVi27LZdvzkv/op/tmPfRSsYWmXo9yyzsw6/ij5ig8K9jqorNYDi8qTPgScc1RZUVkxNFXF17x8l089e8rji0tSZTg+Peblw0PuvfIKVTIkC8EkQPut9kOnEa2soLS1Wza6jcEaoe8gStD3ThGboDqp+Pv/9Y9yeFBh65YgDYmINYbKmtyCzbFsGk5vnHB8fMxXvHGbN++ecnOxIm13yvQkKv8sGhJPuTynHOrAeMWCJI1gOKP8xBTif7QESyRhsEpewxR5NK5is9nRDcLiaPeeddD7Wokq16qqPOdUqATvcc7SNg1t2/D02YWSYpdhvOZESy6aDGHI5AU5hCrFq4gzfl2YI2jSNVNGwxI5TIHgxE26W7QeMpLr1pyQfCrGahZijMQICmTIPSZtLsT3GrLzoSfhsQZsGmhMRWsW2OGcqqpYrGrMcoWPBtcIZ9UBi6pmsTxgFT7DwgvV1qsisQ5nam2ca8J4f6kczaJh2S4oZPqLpqU7HBiGgcPjQ776q7+Kb/zGryPKOZuNKrxuNxAu4Nl2jY9DLqdRyrZtr9arsw1dr7HbqlrgqpreC95vR37LofNsNxvOn51pOEeEtF1z1zpunNzBUjHsPGcXT/nFtz7Fo+0FRwcr3njlDb7iQ19F5RaYTSLUQnu4ZLFcsuk21NZpKUHUcKSta9rViqcffkZdOSRELp8843Of+ARmHXG+BxvZseZp3+Pvm5E8IuXowDAM+DR1Aokh0FYVPpPyIzrvXdfRuAqTEn7IXpOrqIwl+EFJHVLCh0FzzRLph54YAnXVcrBa0XtPqX/sfWS33XJ5dUVVVYSorcRWqwOsFbrdlhgGjIWDwxWHByu8GM7PoE5BDaurKwVv5fUWUySKYcBPgZg0a1KdMq+x92A0zxwHHc/WN7jG8Muf+Lgq7S5w9uyKs7Mrbb1VH/Lmm18B0fPowTts1w9pql/C2RbvA/3QYQwMYcyCjJEgQwnpablHTJEGNYYykXT21PRrTWMVlGUguUTXBfpe88bG2szgJHjf57A4aliGRNM00z4Qoa7r3AmkeOBxbMml1H+TIWyMYRg6jV7kaEXInWK817CozUxWKSUWdUO/3bDdbrn4lU/zi7/yacSjfM8ihMFTu4p13eC9khBEUr5mQqxGEVKRP33U0LFoxcIwlLApiNMa9qauefPXfyPrT32eT3/2XZ5ebDEJegvLhVEkc9SuLi4Z6qqiz71bS5uzEBPGQF3XVFYwDGy2WkpDVIS3RhSEXiqCDNnBKcJyJkrzf0vUztyBb/32b+T/+Ee+n+NokeC1k48MGIxmpwetTyr8zIEMpHQOEUsUy4AlGXDZCSnRPREtcSGn+CRHZmISqmZBdPWenP/Vjve1EjW2wNW1DrKqEnWtA7Ber3n06CFNq+hV2A+/zsFDpQVPyfFEkoaDCvtP5ghN7CvN64dY3Rj9MNAPQVsN5Z6EIiVUagi+Z+j7aRJnXmciqqVkSxI9EbFao4W2CLN1S8IRiGxSoveHvPXgit7UxNRBCurhSsUghugNh6sV95qX+A3f9b/h1Q/+EpIStXG0rqKutOegWzRs+o4hBarFgscPH2FtRds0SILdbsd2u8U1jijwoO/5L//pR9l2kZgGYtKNEmOgGwZM1dAPXlP+UQEt6/U5dVONvKt9PzB4iEHZRYwNGKuQ86ZZYEStycF7ul7D1MkIyQxjc267GXh2/xmPzDmf+sx9fvSf/5jmPgToFZ+VshWk0LApvB5ltEVznEHn3kgau7HoGsnzU2lJkyRFNroqo3IlZf5Ova/rcn7MOq1LzILUGS1JKF1f2HQsW/WAXEbHNkQWUQvltW+oxdqB7uFj2rYdGywnryCnxeFN6rrCOIuthHaxyCT3NucvDU0mqVgulmxfD7SHB3woLbkXGh4nT3IqFC2GxjUEvxsbHaccZh4R0yGMxCZ1XeMtuv+M43hxyHd/5Nv4vb/uN/Ls0RnrsyvSEGmrBmktIi2Vq6md0PdnWCdY25LINaOmp3KtNqHWwLGWVtRuaqacckmbyZ13SqoEo9zFtQOJuEq757j85HHwORRtsLbCVlZbbhmD1qV5DfdWlZLMi85R8DH3O01gLCmjnYuhq+Os6aCQtOWZRO0tWlcVfa9UniZ3AYpJy2qqqiYMgSBCMhmBvOuo2kZTCmg+2cRIn5x2L0J5oK1V7uHCc1GwGb0tBoUqDpsbuYeoSJGYwIfIrod6ZbDeQwIvlqoHuh3RwECijx6/G7KxEMcG4yKGvteKAhFBbCKYnqGvIenng/faaKDvaNwJXhTUFf2AEdj5jhgHjJWMwUgMvtc9FgJHt05ohwZz84Bl0gqK6Ad88NRNi+/6HDnROfK+V0IZEXzo8b6HuKWuhUSTc6QlVaX7PQXNnxpTwEXqddM02PrfIgH9/5SOUvY0AoDQ8Ks1grGOqqrx3mclWpTfviepbXR0u8ZRiRUC+xyxnQGOmF1pTGznoIDJJAVNhu0PvVcAk1WmkiEow1HTNCyaiigms49MwI2UJkaVlHINV+5JSNJNNSThYmN4+9EzHj49Z7MTYnSE0DP0GyT1OANYYZeJ4FO8j/uVj3N0dMT22RViDTs/0PkBU1f4fmDY9sS+J/mYc0lBSx+yqTsqgpzAJyvM5Eoyvxgm2j3BilGgi0QcQhUFM/QZzah5Vxcji6piYWsVmNZinLaQa1LAWqiqGrtckA40t6WC2yHWsXAaHg4po2Yz3VKKCiCp0Jozk1S4OCvgjCocm+scFWGl5OM5z2md1sU1VaOt54DauQwAcjhnqZxV5Yp6F7bSDiuIAdfkEKfJCRr1orCKGsRq7tuYRaae1HNNhn0qUCZ7ZJkgIARPZZ2qjaSZdBGrDdMlNx1X9AfOQGkgVtazoJ5LjIJYx+HRgnrR8kbQjVRa6Wn2UxXO5BLOdtB8L0hJcWQvyzruvvmNmGTxQ0bxFoyBVROm9Kn0YZeVUyb8kASitJQSZ8aOyEgyIAUdmHIZRiZLH72ZbMggpUOJ5nBJc+SBXitmFGjM7m5GSJCIGCm5F83NWrHaE7Q8U8ZPFLRiyvk3I/MEjV5tkRX9XlqoyJ6Yxi5MEvW5egKVlM62EU+kZmqtUmSWtkGbPkspUedoWGlmYW2m+IwRm+Vciok+5TKeFCh5RRIkL9rwIkHMxvjUzCPlMXYQYfBDVmIgEklBKyBCClqvS8SmHhOXJCmld9MaiikbQaWLS8Z8EDXaEirR7j9RAWFqPGvdrAmRZErIPiIxYQVS9ES/w/dX7NaPOH/2AOOyokhzN0hnUuWaEAO0zYquG+gjtP46B/AXPt7XShSmqIAYy9hoOP/FWEf0E0yd2YLYZybKgJ5U3HvGZsh2RKfl1PW1eLAwKVIx2j3AGJCkBPVkLyXl+5gszKL39F7DgMWaVjq1MHq9MSstADJhQoqedrnk6eNL3vrsfR48OgNpMCzovcfvrjC+o5FIk+uoauv0+QTi2ZaVq7F1xUos0Vmsc5hqSWiCtiFCBU9ttGuKlfKjCsdZtdattWN3krrVxtfWWqyrsFWttbeiKNISRrOiHo22NNWRaytLZbTMQowFoxahzeGn6d8aEnfOkcSBcdTWKupWW5OoIsqcwg6LkZhDcKp4jJVcZF/6yWpeyaClA8WbLF0hKusyMjjhrJuARia3WcqaRfvO6k8yQrKVVi7n6EOSzGBkNI+NyZ5yRk+Ov4Oy6AijQFYUYTbkRsBDKv+fvZCS9VFhp3nozB0tkovoc+426U206baG7FSOaVOAJAYxAYkZ4JUfqiz94qGXyI7J3o4y3AhupRGYzOc0Pkcie5BkLApNjtK4fB9FTUZR2kHJazZns0YhK1KIEfKeG8dBtI40BaVgHO8thNx/knGMlAbPYsf8oiA5fZOVAhOQR5Kf9nFuP1bc9FExFCAWmnfTsHv2VEuEq8xY/pIpde1iSCFhnKPLfTWzbmGIWgdrZnOv2Z/y4jKtC20oSqlZNXHiybZ5bceYSDYQQumbqVfzEhHrcktDXQ9p5uqW9SnitcTHaMmT5LGwlQIAkSKLI0kCKeqeTxQCjIwJmMnRhKJ9NfdeqTMRvBruGeyVjCiGBUhWSDblxuVgbQVGCUaM025KWLhYX5HCmjQL56qtImP9tBqZ2o2JpOQcwc+AU7/K8f5XolJUUIYoSykbUSvT2GwpF2sYKGHSUkNXthCQASJFgWkuypeej+Pin6pG9/3TbLlnKD3FepOQ24Fpjemw3WKdos8sMZdx5B6GGXSUIKdndcGRyxeiDxwdnPC0ecTto2MaabVgPR7Sp4HQbTF+oImBA2doBJp6SVUrD6+1lvrGIVVV0bQtTdNoDnmxwNbqAZYyDRd72qqldTWVcThjWDQNrrZUTYupamxV0bQNy9ViRP/ZqoKqxtQt0Sl4KDlLrFXBkhsWa0lPHr5YjJrEGDgfBUQWpDksru3bZgIk99ws9BUF1BVLdbZMZAECkzTIHxTKjjSTcaQiI5MK0WnlMFqwpPEPvrxP0nnTiiZFKJryDvNrZrpBHzY41xCDWu56kiUGjzVVVhYBkwnqI7rMyI8aY1Sau5KPVVE1Ck7N/1hC7sGqykWLfpN4wOWwWH47rzlRrFclmszkoRWDLgvs8Xcjym2bEs6a3FxAEcEp5ypL/rAwbFlrNG/bD9S2ZuTkzbXdRbGNE5Pzz1N5mfbFtNbqHksTbiHEAWfsuEfJSjTltZDQvLWCu8riUgEdQ6CqnIYGMyahhGqNMRQoihuft8xENlJjkSspz/kEcByP+UKLfuxxG8I0TimvcRFD8JriKdGE4uGm0ciYZJ/uo3yLmMZ1G0m5J6uC36wLyGDUgBFVvINRhR9D1Bp2UaMkMSOpGV+jGDJ5D2cDOngQCeWmJNtDqnF5zyhLWikfzPtdH5bC1ohRtHsjmhceKEWLZV/mmTXKCU5MWOMQ4whBAXvOQnJLpD4gbNaj0VJki/YdtWOp2pAR/0TNsMZ/X5SoboJszxSrEJk2lXOYWCyQHIgRcp/FUvOmlpF2N0kYY+n6HdvdFu89N2+2lBIZ3ZBFiMaJd1slc34OUCRuIjpDTNoiTATC4FmfnVMnyxsf+jWcnr7McnlM07Q0i5XC+63TH2exlQJ+rKmonP6IVDy7fMLZ9il3X3qDW7fukNIWG090Nk1FMpaY9Xmp5xqPmENJmVx+WpRMeeKitUzM9cizsp6YEJNXetmiMn0XyXmXqAK1WMxFN/kYMKKglZCFgQ8RlzSEF/B4BkKMWHEZTacWqh88TVUrO1PSrZUyaCeFgE05PJYCIUZMZXMXFlTJ9j3iZAzbI7pZlR6vGGCQQsj8qklJMihehHJ57oUUszFWhOtIKxkVFTkEDW1JNo4ADZcWrtMxRKk5LJVHMnme+QhlqLMHX2bUlxpnyICOIvQDpSlBUVDOWbZ+0MbcgE8esXU2aFRQW7H4AXBFiExrw2QPdp7eUAWRQ7/IGD3Za2wdJT9DJGYAiBiTjR7DdvZeIDhE14ZMNo1NkjlgS9WK5kS7OPPvRGt4Y4x0PuJyaUdIPt+/jFyeaxH6MIxh2RIeHbzJ5W5k40sZdZzTGtaYIruUNIRocw/eMu5ZrszNa1XsOVdadk7uSZuMQTxI9ASBzve40WPSusjKVISc6xxvVoBOecxDUOILYsYMiCL5Y05zpBgxYSpZwjsiA1F6QDBR0xeddCixe17XcYq+FYVT5t9ZZQFLRDCRYQgkqdW7DiDRIo2w870Gwo0WfUo2upInx7xKuDjmPrgJDwwJJCjSOGS9Kei4S4okXP4wIoS8XuzMivWEQaNIxjDOS8y5XfWrsmzL0ZSr9RUbAW70vNfjfa1EI4aYrfwy6aBWFEVByjy2nUaLLEXlb1Rajoh1AT8kjCzo+jPOLx6y23Xcuf0aKfXE2OdchAI0/MgqdO2hsuVuUkQspNBjzAm73Rqz7fnw8iW+53//f8IcH4O1Mw+HHC4qtqaMHlKVYLc7Zxi2rFY3+dwTxy//g39FXR1y+7UPENIJIe00t5Qg4bMHQEaNlpRqaQjca9f7lMZOL4Q4ghEkW6ExDnsCc3zdgmAuzy4gY7gtW3xJkNTmIelJ4gkY3QDEMVSVpPCjlpAWkC1vUxm8z4rAaFP03mXhk/NcMHmsAfaeNfVlYDNAzCh7SxqjEqOvAjGj+0RXyWi5zt5fHWUNA5K0bTaZ65SY6+iKJyiOfrfL66uEjArIg/xOBiSNZB4ys8r2xz2NuSl9x/L8aiFE1CMlqYBW4pDSCm1SOx6jXuneOHnI9abTZxEZ9hUO43dmYwak5Bk5ZPP9U0r0dIxpifHsWX5V9jfOlFoRBdpcS5vMn3n8rkl7BrIRYZDir0d8GhgipJTrNq/dZ+8eaeL1jbN5V+8nI8bJSNi8do0x+lkqSO3peiYnRZ4zUGfvHZjK76a/W6UnzJEFgE7USy5ry+T1lBdSniNDGMzIdlaiOjrvs3FG0wQhTbOi81ZMkeePZKa1kJ12IJL67fjp9Fr9eE1JkDZqKA5M61auKePyvFqi1GdDQ9c619ZBeUpVmLvRY69d3o9pGKsefBK220e0NpFSmXedl4JzSFn8OVsz9BsOT1tWzQmLk9deOBYvOt7XSlQ3LKPwNjnRrI5iygi+ubc0bf+YSh5Hwwu7XUddtXnytaVW5XTHWFNl7kX1WIvzO8rfbH2X8l9lzRCEhuAGurQh7Ta8fPtVfuv3/G85P1kQzh4Ro/akJCN4bTI5D0ZOzKciufH9mth3XNmnHLYN3/KbvpVgDJ99+9MIDS4FguxTGJY8awl3Fs+zQsNbheIPkVG+lZBgShGXpuVR5HYJYRXLX0DpvdD8SHHPBQjpMgOQchgsGiRpX0B9q5InUWWa7aGsRDxpKH5JziPHnkEEoRgfOWdVHjDnoTQEq5+H2cYtc2VLOCilXO84KdQcYJo8zpknpvcbb5Y9mDI4Zcyz8vXDJCBJWjCeipdS7O/isaTZtOk9NLRr9oQ+pHHM1VqarIgJDZAKGUz+3uTJK3K6RGAY30WiaD46CzKts5NxPrKjNoUYmdSheuuZpGD+TNOqGRU9Ude35pw1V1wWi8zGU6QAcPLNma3l2flG3ExxyThGJTxYxk1m54mMV95XosIY5S+flxzauKYoHjNTBOKakC9lbj7HWeT6vfKzl2uaEKe9iQr5yXSa7qNaI6/vTC4QYsyYP52glGCVtULau2b24LOcigScsaPcHNMnMrvv7Ltlw0vZ+GkfUBnR1EWZr7ljkCd0fmG9et4bFJks0z2jQJjJkTJm8yOlhKUex3Loe6IxOA0VaDRMhEV9AOF8dCBU1OU5GeWFgqh26w3LxYLqxg2q439H/UT/3RyTYBsX3AsMqn0rOk0CIyPWQhgwjWXwA9ZWHBwcEUJi6LWoeLTsTNlc6j2VjaaTPQnARMKJoQmGbb9l1Tac3nmZ5Z03+NyTd6l3PZEwJu5LOY2k2YKLGlb0UsJtlvXQURGgEu0+0Q3ALpNrT0Wno/gvgi+VRZoYYha5UpT9fDSn8QqpzyHI4h3nvJsOyDiGpEzlljNyku3wSIcmRhzaCDF7LKPwSeMzpfJAEhEJpGhHgEwR5kZMDg3ON1XKApy82SbBD+BGIZBzU5hM9cXY8i0RMZNaAAymarTGdbbGTKY8K+Orz1ZAM5IFSB6XMYqesnLPqjeXQ4xKW9w4BjJqrFEFUFSIgjXU6BtZsGRSWlLGcFQSs2cXNdJ02kp3liKytD5utL7yAohRATpQML7agLxkkEeEqT7V/upJ01opIJdIIsaZot5TWPMNuy8s55/EeZ0h5PDp/nHdK1QDIIz7dH7B+d7NumF8i30Fnmc8pfGzuWJldq/y7vqBGa+99zbjeWAqN/6SyOjesm5mDys5YjQar9ctnGJsx0lJl8NKWXNgUgn5l5SW3mM+6uO/s2LOYm80+yQbvZNEYVz7M5e0WAGTN861uZ6/YhkyYe+scjkp41a+mvJnKStUA7ukACyHKtLdrufR5RabJiNNwUQ5fWfGhxwNCgVvTmxT7+V4XyvRgkArCnRulX6BbzCFfkq4xTB1qFDGE2Msi3YJSem1FNBQUIhlz8zuNU6SCpmQrUBDoonC2vc0h0csDk/YRcP27AnG1FA8NxHCDJhCUjRmaYwclVAWMvcm/Y7dEDOvZCJECDbX8ZX4xMxWlNmyTERiMKOwTsX6H+8ro5D2dFOyfxzpjFOeK1EMEnvVTCjS0ohSdpcQ+xiqFJufKI1GqpgsRBIkNPxupCVEgzWelJt0VnZBSkXpZWFRNl0RBlnBSpLRsNH1kWt0U/YPDLPNnXsSjrRqiiYWydZ7ppHDWJCMdCaDNfLQxEIjlufQiozEEZOEgBSUm3Y8VwqAJYdeZZqtsYtJAqKWSBSWltGEiNN3S7hMl8BkbUMuRUqCyc2qJ/9DydLH3wtjVSzEG8XTTxM4J5Xny8UtcdCVkdeDyfNb3j8mTRsUAOCkr3Of2zSpYQHCHPhVlNyoHBmVWfGOyx4cPZmCg8jqUMRekw/l2jPFOld+iUlJlXVWZiXLijkZw+h9zzzYuY67ftvp9klTPsL4fHP09OySe4pXkAxGyvuvlIiQkGSzopjmwDBDnY/KLmg0oHzONJZ2VNLKPa4JlaKrZVY/ncaohbKelQe9NsbjpylXPeR3T9Ppo0EjGTw1jlma2S7X5brmN4mJygrbjKx2ApIS/eBhOOdGs0CYoi8iGZyYsiGajQljCn2jx4V/T5QooAOfGDesTn5Rquwt7PKFFFWRqqGsSsA5p0w0lO+ogrVWGIY0LhAVKrN2azlBoiGTMC5ESQJhg3UGYyLOViSpudx12uEgWkzOVaVcH1Y5mxd+1Ca9KRLFYlMgDBCQXLowEII2ydX+iZ5ElYv69wVk8cxVDqsQarJACimDN0QVPkn7SxdFGelJmeRBqdIszig3qlqmebOmBK2WT2ieTxDxOBpMEozpVZCZUzBRN5IxGOP0xwkiA1qXqrykdXMKUoMMhKiF1GJbECW0KDjJGBOBUsaUc79Ja0QkJrZ0I4DFB8EPXs2bvJGDz+hIa3LLuFxKlJHQ/dBrkXgIiDHsQsYAR9Em0TEpVV/w+OjxSQvro5A5cr0iPWMihcgw9IqUzQo7ef1e9F7LmzJnrPa4VIAUMWL9kDvpTN5YykZfyJ2MtEJf8EFbhek6JdfNRmVQInvJMesDSfihR1JAch2kM+pfmlDqELPyjDMrPUVQVlKS1/1gjcU6qwA4k3tj5v2UkrCwVvEKWWFpeqSEM4v3V4B/+SgfzkOUqTCCmdlJ2WPbU1ploOrnFGgxAOZhWjPPRc+eMWWvPKEE+yZdU6JMSrBczxf5Izx37/kzhFiMqJIDnYBbWt+ZDR3n9tZ4CaGGsttFz3V1g7EVCQgx0HmfDUcodctiDINUCraxJqe8MiI6N14v8xdRIHk0Cu6JIrnO1+pYoDLDlRD8LPw+tx4ENfgp75XS/lqIcRxPk69bwEYvMoBEBOMSaQiEbiCGAdu0VBLpkqdzCXu04D/9T34jN18+wMTE4AflEXB2Whv56WxmsdJxpZjH7+l4XyvRFCMhhBGJGN1kJZYf5/ZfcbLG7Pi71oKq2iAGQvSKqBQLqAD3vRIlaN61WLxZnhRLSSZmlySRwSR2ISLdOdVwmyUVx3XLBQZhA7SIaajqzEBDD8lnq8hipEIwWFNDUpPVVEGVb3K6mE0i2UhFQzQltKYGgDHKi1oUfkoKZkhGH9iaAjP3eN/TDfquxijZgKCdZmIyxCTa2g3DkHSD9lHpx0KKxO4mu/XnCd07BL+l63SULvtnbPsBzwra1/AD9F3ADx1Dv8V3a/rtmmG3hQAh9vRph+97fBdhsMQQiKEj+C2hh+gXeAFP1L+loEXjUdldJGi/TgkRR68grwSNc8pD7DtMCpjkMUk3YL1QJGexzE2uT20qLfcp/KyxfZUQe1LoiHFHYsg1pw3WNhjXYmxDhrIqR6hYKmtHCW/JMHxjaLPXqh1fcomQsZhGa2XFGGW+aRYY46jrButqrKvG2tTyfMkYghGMcWMdr3os+VmsxbkanwKBoJ1n6haMzeQC2b8X5cyV8syjl2/HcobiqewBhMbYWzbr9zYr0LRcP4pslPcgs66fq3WSJTLyBb8FhC/0x9mRaxS/4IUmxZ5m/3s9V6d/SRS08hc7Rm+sGPspgcT5CVmhmxyNyV4ZeUr3ohL67yF0uU5a15oyS00phBJx8hlQUtJIyn6UQX5xMh7wWUFLvkeMpOCJ2y1p15EGVUxhiON1Ypa9MUbC4Kca4mwEFdms91DlX84HBUMWzEaYXSvOlCpoxMGHwDB4wm7HjkRTWUyKrLdrPvXwbS4//Rh/81Yu5TM515rQeNRkTMcYWdY1jx8/4dAdcVz/O2rK/f/vw/se54oVKFAGe1SuEe+VyqtMUBGQ1s7yVyIM3lM5hw+9elyuxsfIbndFVbVMNWOMzB0FICFJlE8085SmjNhMVFSmYnt1gV9d4l2POXAsX3qd9ZOPsdv8Mil11NxEqpeIHBPNiphZVIahpx92RNaEbcfQDfQt9FdrgtQkn/DRszUDdqNNwQuvafCDto/KHV2CH8ZODP2wJvZem1J6DQsHH9n6ni4G+qQsKclXY1hZggc/kPoOhh0SBv1cwLgFjXHUIVJJwInW+jXtCbE6gMoglUPqn8dIosrKohKhTsIyJRqbcBYWzrCs0O8fHFJXS1xVYSuDc4nF4oimvUFaLIlVTcwcpaaqqSpVMMY6jSJgEMkMVNZgrOYfpbIaRiMRCJkQoc1Gkx4p5frca4oimgnUNBEgMAqphBAF7EyAjn7SnjGtfw+Zg0FD3zOhfD2EZUogz+R2eVn25pB2CZOO8ZZMe1h0YLH++6SZ60gpm1GhV5o2oHfQ6M54H60r1Z+k/83IX5PAGzta7nuo1uvH2k8e5xgihf2waoIZEKh4nuXa88Pk5ymfqmdZSBL2PY3R43yh2iuRmhK2nMLtLzyPAvAq+zwjAbIBpj0+JwW3/ySzayWYymGKTJm82wJCI3tnOnjTGhg9eEkjsZTJTEppXAsml7MVXa1j0/j950o2h8dL2Dg/qBUhJUOpOigYEjE3xn1gsnc6B7+p0TU3MvLMzxzUsi/KUJfcu5al7c3qnpVUDImEYJLJYxMYRHAZdBVTYhgG7v/0/8jjzScIQdtPGmOz8TERZhTWOO8H7ty+Q3N8498jT7RsDDEzITYpynnebsxdlPCUiWPZ1cQMJDirBMaIxUrEtQYjzejRilHC60KeYPJC7/uezS6ClEbICuDYBY9dHLEOno99+uf4xf/HX+FnPv4ZzPo+dvsQ6TaEwRL7htY0tHWr9HLZI7GuZmgCFZYKy9BYxFSE9pAkTnuW1pYDX1MlZfiQFKmIyr3qCpmP8u4aa2hW9zQkFgUrFmdrrGugqUlVTaocyapi0mB3ARRofZbEOHLLijFIlb3eYvGKCmRnWzA1SMpE/0qMXbhjJYenhUwlmJ/RSQIM1jrsjIVIjMkemBvrYVX4GU2EGNVIKgjiaI0XOSyi4e8EUy6VRJA0VgvMZaemCeJYDoQIye8mOrrZGpy+q0jcghwvNy+CtigQSmhSZh5J2bqzEGGRPYXPp+Q6i/u1JwjTVP+c0iyPR0l1mCm8X6z5vXxsubeGjCWZ8TmkCNf8pv8f8v401pY1ve/Dfu9QVWva05nuOecOfZvd7CZ7ZJMUSZEULZmWTMmyZUuwhkiB7FhCgiQIFH9IrCD5YsAfYiAGbNhBYAcWEBuBkxixbNnWaIKiKMkaKJGtHtm3xzuce8+wzx7WUFXvlA/P+1bV2mefe09TMuAbVuP0PWfvtWrVeuutZ/g//+f/lFqpRhMHmYvy2ilrdWKMJj9SU+OYyjmffbZhzA6n8KlcfNz7/nLq0YmPMYieWO6JWd97c8pw3uic9o7i1EtWns9dyifTgDwNrPerJ3n2+4wvSrJnp8tVHGb5vKKehBh+FadrBCqVPlXGe5WKgMz0m0I7cUxq8os0uA+1d2mlnCWnTrntpZSNROLz6v29et40XKi6uviTZU5DQLh3bZNrzy+UUwzPQ5QB3jGgs05AUIlwXFOFBqInhSJDmWViBy6AmA5Ri5tyFF7s+FA70XIMBWP5l2yEIZqb7sps0ApcMal9gMo1uoRSIliWUkJXkRQtokYEKMtAK8lCAEoJ9Kez8ojMWdQS2aaIXd7BG83pk3c4/963WcZbLI/v0cxvoroO33lCF5kZzbyxNFmXtTI1VT3HVSK315iKaDW6npPqJclYlDHoyrCkoi6RIFkc3Wpqa7BWZgmKVJ1iNj8cAgCtREXIVjPMbIaqKrBW5gNaNdZRcgtEMaIlSEaNPZyU/kTyg156L8VqMxA0hjXPEaUiZ3X5tUL/y8SDAsVpFIYymlxOWyj2uYUk7k9Xgf17rVQZYjw+7cP1pNxNlx1MIjEIx09qMlKPnRrCqfct7405Kxpcy2AIR9tRAsDiRPedRpq8v0TsTN6vBoc3rm/JwLQ22dmOfbtkY1ecaPm8CBRWeUlby3dSuSmyBCDTY7w2DcmPn7N3XZOPBwpbdXpMCTxTwzp1L3vW/Ipxf/aQoKG8RFZ9dPJ7BvqKsxwGZl1z3nG/7gdPQzBQApO9dXrWGct+vxKADU5m33CnUiMa1Jjyd5us1zSvVklQkPEEcn69F9TImofxH6Mg+2S/jd9gsv+Gy93/n8oZ4TN1y+Ey9tdhjJvGMO/qNaerFzJZp/EFKbPIE1pHgipOFEjSm0rs8hrlWaRZiU7rKkfJY03e1Jp2uyXNNtTut4jYwtSwyZE3dP7ZM2QCJOuRvT6GOjoVR1jlZ7SSHjQSmEDwJYOROoExAn86J3MIlYQxQIY2kBprJFApSPM7+LCjcWt+qFL8y7//j7P61I+immaIlBOABWWSNJErcm1KMaHIgkoEFMbn51UnIeoEB3oqtH/F0uzZW4k4Rt5uHF4Qcy1Cao0uG/TiqqS/NERhLA/KUMni3QYIpAgxSh2373egw/AAquTlGZnUNSSCNxIpxjQ2s5fsSEYtZCdexMLDmHFm5zB81Qm7mHzV5ZeSIRVN1mKgcv1KjXslZUJHyv2UhXktUbLUxEdfGLKTkUh8XP1iHkqAJj/3kyK69P/ldqQrTmrQc51At0PAMXyjHJyo8RUFAixktavtJJI5MZxb3m+KTRKyWrbEU4M8JE3DNhwNuymt+mry2rLm+xZyvBslq0xpaDUoWYhSNp9k3LsjTDyui1Llubv6CWNWO2SE0+y0HFczooGQ+GwKWZALjZLaYR6qXlSIpuzh0us5xlDT4EBRmOojZJw/t2jzjl4elaRuJ+McS3iQhpp7mnibOM3UJ9+3dIOW9RT2uhmc8uAMi3OfXltCBrWX+5BdqkqTNjogqCkqMM0m1fD1dcrtdmrskC5qZsO3UFAgwuuCGTX9ixJkzMRElaCLfoTjlULFiL/c4vuAyS1tWiV8DELsNAplpDwXYqCpKzbrS3p7zrzrnv3w5xwfaicKWYoKlSMS2dgFLiyPV6mLii6kwLdlnJTO9PcQIrt+l+GYkIvYPmtzSmYj0NcY+4Ug8ZzWGtf32R8aopYpBC5aTBRH7C7PuXnzLn/oj//rzD7ycTZn7+JiS9CGQloIvdQfvQ+4mDBNhW07vE4yy8+HYWwbLohogJLalvMeFVLOpNUoeJ6c/EeLCLp2vTwIJqCihaiJpoUwB3ph5+WoTU/gvPLf64gXUT0LfZRId4+9OPmZmvystLXIC9nLygoCMLSZlHOw7ywnv2Aa2woqEOWcStFZS91FgaNsbvXpI8kAMdd9jMBVdFrEmXQSyTJRDCBGEbUQub28NirlACRhjBVSxmB7pHYYowwjLuzblJLUc5UaiBMpr09Ko85smXoSYxyUsspryj0R+bkEMcm0jgIt5rU2xmJNedyLc1JDlqJSzAOW7UD6KIQlpRSb3W7QdUWBNoaqqnDOyTBr2CeBFMcwuUYoPwfSKBE4Gs00ZCkhjSSSofu58Bry9/dF5ETv71PytYz9lPGZ3w/7sPxhjKPKOYc1LChGjJk5OpKGSg1TxpxNzKkW9xSDSCBel8lbayFJsFpGqlVqntdY2t8UiuA1hoDJayTqVGrvXBJzJpKTemxMIxGn9HZPn8E4yU5TzsbKgO8BclXTJ2ncOkUIf9omVq5jCuVPy2gp77e8wFRaoSN4A16BDok6anqTn5erH3sFDRo+D7HhTiW0Kk1nQqwyybEFtKkJoRMtACUs9OA9MeQ2IEJuNbJURljfKV69gucfH2onWjcNVV0PNy4E2TQh7ddKrt5YkUWTLCpk+v6eg5g0sRdyUswFe5l9J68rDy9AlxKu7Vgul5CEvaq1TDlJCaqmYb5cgan53ne+hXJrtAjTEo0IINc+oWYNtrLUKHYXHa3z8oBbI20YKaF8hMqIGk+QB2deVUQTyZA/2gZMikRfYRIkZKgt2oiAQ/RlcfAuYeqIDWaIiC1mMITwrAEqxzXVo/3fX/O+551rOONzfz/mEiWA+aDzpVyEVUoRnUP1mmDAENA+kHwiVgtE5jpAMtjQ4EKkN604vlBRJ5mpugtb6a9DSVtK1q71bTcYDe/cQGYrot8uiASHMGStvCf3oO66jtJfabSW0W4+UNX1sMd8CFgrbSLWWrTJ6lYpDXW6APRJEJKUkvQVx4Dznr6Xz7CVTKpwzrPZblFIkFkcZNd1w35v2y0phUF7dblcstts6XYtfdcRYhDyVughZ2Gj0yTrzDL8O0ZhCKMk6O26LgcdpYdPSE4lsY7EwgGZTDCR76oUJB/GICQHJuU7ytg+yQ4LijDdL7Jk2RkAKvMPCmMUpYbXFOddNJensGvJiAeHkQ9JrgVuNLmFZPoVyvfWgHMdvZee7Ko5zFqxAXTCVBW2mqO0BDQ6TxeyeZSfznwBrQ1aG/l5vhatNVUuA1VZ5EPlr13NxPSXOS4ksMYQXZEMFcdqc/CWyNwRIjEkYhwH0RNBeTX49cFxk4ZkP5Hy/UhYpdgpj64r6ggmQLIaFUUzeex7npTdYPiu5XvXdZ05LFMLke389H2px9bSynX+9AlaaWazuQR8qbTcKfr5DbRVmEqjzG+RmmjITNQBwnrGmOYxZuVhmeA53o+i2UM2kdIzf0qrQzlKRFqO8jpjDLGyFOq0RuN9j8dKy0xMJAxKN7h+g0nClE0pERKsbE29ajjbrDk7e4Lfddx46Q43bhxzvlmLwXBRWktIqLzZy+bv+laCAaMki/JQJ82u32CNodYGi6E1CtsJI9WqbLTtTAxv1PJ+leiiz01A+2t6nbvaQ8R4Njq9euwTK579+fsfBba87n5fc3hFMpGgAykkDA3OOzojEbeKGhM9qcvsVO2FBq917g2UnsmQPCoFbGXl/mcjRRaun81m4yxLAFNqRGrIpJVSss4ZFjNoYkhoU+OiJ/iA9x3u8lLIKcWxxER00mPadR193wutP/gcTKjs2MEQB6RFMh1BMbquhyROrNZQZWs6m83ouy63w8jzIoPtYUYuT+QAcwW4psJbTVzNAaiqKhNcSmYmcKYxuV84f3+ltChQTUhiOYbEWDs+o4DOjOoC36biFNQ+QSbG0bCWDK1k5YMwA5NafN4+0/rm9OdZ92SPfQ8w1NdTgZOfNfCk8fdjHT6Oe6CMOEyM16oVMXhh0HsZuqBVNdg1mSSTIEnAlUIQ6HHaDlJaSTIK4DEjZK7K2ouDm2allRqRuuJZq6ZGWyuDG7TJCl0Kay11bpkyxkgrltIyc7eMFWw0Wos9yT4JGd074akXgZsQOfCetesI0eNilC6BzCNRKo9K0zJf2AyBwuhARyi+gMrjPb16mGqOUqJQVM+OqKwViVdVylcedKLrA3VzwGJ1zGy2fB+jsn98qJ3oFI4Z7KkSCK/Uf+RHeS6jSvl3Ae8DOnvV6QP4PEM+zcjKBn7G2Va5hQKZau9TJzU5JQ9riImYNEYDIRGVqInUSnOB5+n3HnBycCwEIu2ICv7G3/27fOoHf5D5rCF4T6MrZsagKpkTGEOEEKnnczBKej1DRGOIWrNqalKSGlZMCqcC1bwhhh6fGWwJUFoJhJwSJioGYkpZjuf4t72Mf/Ln/Y7nwcLvd+zdY9i7V1Nj9owDz2CBRWaVpqSR2cEZRowGr6QumC1NhnxCLkUL3BMIGKUxZoZMiQm5thKIGaFwLmTjl/Chx2SY3zkRa6irCtf1hBiw1tA0Dc6H7Fxk5ULwXFxe5tmneTB3gspIv6z2ARsE2sNoyEZG6j0MWUhdVYNzSjHKFBGkhmcVuUdY09Q1LmfOKrObq6oe1zI7QqU1dVWJEMKkDaOo44x7IBvuCUqjspOPXgr5opRj8mvGHaPy+VIKE4c5QtxXA6dBVHwC55a2pDK3VNKUcib2NyyTH0wz6LTPEN2DKQtKfwUeHSDL/PNYrj075jLoYfhO2QGIY8vKUNlhphCHf0t2Poo9lNJTEScIGdJOSSa5hOLAC/QrXnt43QChhzKYIg7OtbTCSBE1koIgKEH1tPkelhKAyRnxkNpmn6YoQcd+maGUFpISsYpqXnN0eESla5LzMsO0qQl5DxulBqd5XbfFaHvf394IIqJFvEYZmvmSykq5RWuDImbaRSQFJ9dna+rqt0ifqDYGkwdOl/qJ0lkVJaahVifZY56+kW98inGY1zlVxZhCtDC2v0yN9NR5FocqN1znBy7PcMg1BZX76EKUrLMyBh8FHqq0oTEzfu3RNzn7+jf5bZ/7cRaLJb0P2Lrhr/zSL3NrecCrH3kV1dSEXU+jDW3vwOZoMMJlu8PWNVZbKqUIPvFou+b+0SFRKTZtx+V6izaKvmmodeJyveZyvaXSgdfvv0pMPSaIXF8ypriR9z3UNX9/ESf5/cK8++fcP/f7fVbUAZIgA9bUuCgsYpOSZFDa0CXQlSFGjajp5z/JSKdMTlMimtBLxtC7nq5vcd5LX6g17HYd27YlxoDvttS2pt3taNsdwQcWTQNBSFt1XbNcLeV6gFnTUNtKBlk7j0JRoXKdUjNv6gEVMVraf+rKZmjYDE44KTE8prKDk1JKHLLO48kiaoDYiovJiPAQzyeKLc3PhRJomAyFlqHfqdQ4Ycjy5OdXmNgZVo9ZVjFmoQ+ZNhOG7M8oTfAiSZffzphNsg97DOiTynY8G/MJP6KQyT7wUCO5ZrrXymcKeTAJoeoq8Wjy2QVbLAzcKWwsf93PlUSSr/xclZNJ9pWvPwRBSco9Hk853r0hk48us47HjFgkeDJEmwdzOC+kyCKMIMpXXnrKc008xMiu6/He45zDhyClspwVhyjqYiHGrJJVRG7cAAGrycB2TyIoZFzdoua1117j1uqI1XyOiQnV1CRN7hq4PqGZ2tzhZ8P/jUsy2OsUpZQVZLxb3dQYpei6LuuJF9nJhFIZooYMwb/Y8aF2opRIJEMWxhgRdE5FPzQRc+uCc/JarYQQUWpqstiJMhFsGq2Vh2ma8ZSZd1d/L+eLo0pMgspWwnQskRoJH+R6QojoEDALS1jO+Av/8X/D/+Zz/xSqrvjWk4ecvv0Of+THfowffOUH+Kt/7Rf56Z/7Gb7wEz/O3/nq32NbaX79H/xDbp4c8/HXP8qr91/m3/+P/0OOwpw/+Au/n1c/9hpvnL7JX/tLf4VP/cBnuf3x13nw3tt890tf4nd87DP8yoPv8E//+I/yq7/+Rf76r/xNXlnO+D/+n/4tghVZNxeRzIsrTvI5m/q67fZCUOv7vO86v1hOqTI09bxrmv6sQmqFHvC6p1YJgyU1S1LdoIyi2qwJriNWBqhRIVJp0NnBVVWNqWc8utjy9OE3aGyF6xzb9Qbf9zS2YlY31CgaErU1NId3qOsac9vmQecCk1VNPWidohS2jNbLn6WNppnPB1KIznJkvc9swbgf9JUaXo4biYArv/ciN0ieMzomVwWWlPUu9dCSkZCStE7BAEkaY7LRyWucxM0qpQhxdNZX0QKuGvuU+wljIuRnU6lxLo/yCZsUKqh95wQDs7iwq0MMwySY8hwXbWxhiuTMUZv33Y8qp1Hl7OV95bwJlblJpTF/AgPDGChMWPSJwuQd4eNi7afCFJo01o4pNJfymrJuYwBfbNNIiFJj4JISKmUltom27uDQy/3QCl03ozqXUjLIfgK1y5QdcUK29MMrslLS2Ds9Zt/ZWcYsmZllM4Pz9G2L9w7nHcFFTIJv/uoX+aUv/WV+6Cd/jJ/6iZ/gJNVcXl6y0BVegx+y9ecjTeP6J8Y5z1MkSp6p4HsM0ttO8AQUlRIoXQG11uhKE7oeVVUkZXD+RVSu5PhQO1FR5+nzwmbJv9KArTJ/MyeWhXkmAXGR0SrPixqmbpT6p9yzCVwYi1MOgyMdnWi5YRmzV1rgU20wBcYNgRh6CB0pb9ZFU9Omnm99+w1e32p++Pf9LA8uznn0tS/ytS/+Orvf9XP84X/tT/Dn/x//Cb7tOXt4ym+88S3eO3vC/+Rnfg9vvv0W3/zVL/PWV77JH/3Jn+fV1z/C3//yV/lv/t7f5olr+bxf8Q//0l/hp3/3z2Fdj9l5Vq+8xuo3vsHrdsXq8z/O3Vv3uBV3zI4O2Ty8xGpp9enjpPfvf6THi2S8MSbQGmUhxA5vlkSrWTx+h8Xjdwj0nH32ZzHaUDknmaix9ClREUnG4BQ8vHjKX/oLf4E/9i/8AXGOxmCsBSUoRMjOh7wfusIIRlCP4Pzg4CiGKCYCLaagHylBl0ibc8qg4kJqMcO+3L8nI+QKJDBJDJDOsKmIHMtwZ5SwXqU9xWTyk8/TWUAZIwYhB4WF+Tg4CKOHejBGVGy01igfKGpH5XaMt0Wywvc7tBYt05hbnEpWWM5Tcreo4+AnlVJ4GMYSDgHWsDD5/1JCT2DXkh3vL2IRwpBPml7t8NIhM5323r7fkSHd7AeVVsP3GLLJBH3J5NUo/F4pJf1F2SkrrXBBAgZrJ0hYzja1UuhMeAwh7gegime/e5RRbSZMsr2JoxozPYXFDjXYoc1Ha4oKfWnFksxNBjkMM55rhZ436KMZM6VpciTXRPjUz/8MP/63/hF/+W//Mv/fr/9n/Il/7U/CVrM2eZ7xlSW+/jmPe/eGIfAowYcclZ1hjSAJwXkSHlNXpBAy90DaqnaXTzk8us3R8W1q+6xE5fOOD7UT3T8m0V6ugyRCZuxNpjgkiDqJBF55Z2ZYXs1CxSiUSLe0CozOc8wIGOj+KSUkbFUEjFDTjfQhhuCIvkPNZxBLnSjRdz3MK2gd0XnqWcPRrRO2/Q6qGQeHK2xj6V3HPCQqFMvbJ9yykdO049Hjx/za4+/i7t7kxskBP9gekd695OvtOb/w+/5ZPvnDn+Jbj97lq+++w3/x//y/c3D3FR7uLuhQGJW4uFwDHpMkuvfIw1Gk6MZ12nda00jx6vGiNc/3o8hfeeUz73uRzwiqJupAwmHbCn0Hbv6Vv8zi7UfYy550usP/vb/O7o/9b/G2QrWOKkfoLkWWywOePnzI17746/zcD36cw4MDtrsdznu0L9ppajKbMMOfpYaZYTmSwHEyVUMBBpTA+UNNSim0yahGNlK6OEgZjCrKKtMvOEnAUrY+hSBTfqkQqcXBQ6hiUMNgcoYbnQNEXWpYA0Qac8aS24CyRU0+sxvL55XMS5VZLhlKBFD7Fz8l7iQYxgLG3LY7dX0DVFucNIhqFpnPMAQwKpMJ9bhl1GhUh8S4OK/hE6T/uvx9gEtzYprR3GGe6LXH5BdFTKhYorIa8n3l/iakLWOAcVMi4Qi5L3poLlY+a3arjJalIYNME2+TUiKpkt2PFyT360rwlef0Fs1clLTOKTXJspUi5Gkvdm92a0Jl5ERaedSkHzsLzWfSY/JS241qXNsN8NWvf41XP/kan7n4DPU3v8GXvv0NfvzOR1n7LkdE2UlPED+5pOlzX/aP/H0car//jETiUDZRJhGjwiepAZPhZozGaJnJ7EOP9b9FxBYKnKGGzT8uspiP8aYz/G2cDD8y6PbhMchQi84PX1KDvFeJ2MbaaXmPpm1bmqaRdhSpYsg4M6NJmQUWfIvSSxRCRKm05aXDm7z36AneaObzFdYr3OMLFs2cJyEQu55KwXI+42SxIKhI6DuaynL3xg1WaL707W/w3j/6R/zwRz/Gq5/5LC+/9jJ/52//HRZ1TTCJRVPxgzfucHT3VWa3blIt5tTW8lpd4w4WbNeXgB4a3yV4eDaD+M3AtFPH+CLvVxOjl39yxdmOMN/V8151wkqJKD1RY2dLwtMnzL75DtWyRn36I6h1YPXn/zO63/Ue3LkLlaX3HqUNwRhhtl5esjs7595nP0PvndjWUmtLuT0jjmPCtFbgZSyTimJ5xbincW+mIs8mO1Xr0cgIoWiqRpMVosb8bBJrF6ueob1EzjTH86UMtw2tw4zXUFiig5ShGhmwYtrTQNAjQ/cjf6B4ND364EmJZYAkp4oLEyc4GuvpfwQCHZyOXOge83m6fmXI93RlimLWnsN+xvWNWXPZL0U5OZX/y1lwIk2g7Ol51XM9qtXjp8YYR0GKIZhXwz5WeSYSKZKKLm75WbkH5DVNWVxjiJT28kxUZsDufYnpWu/fhhLmyP1Q4reFnCSfW0iZcdBjyfZW70PaEpekaz4hSxFOyj4pRVLv6FJgeXzIyckJDx89wd77QZTrBh96HdI0fd5VUdp6zk0oAYdRcQwW894iRplgkxOlylS4vsf7QCyvecHjQ+1EgcluyGZHXdk2qZiN/QfwuhNNnejIOhwNwRRe2ycrpNxPGqiqlCPF8mkRyToUKQWCa1Fa/u28p5k1vHR8E1s3fPmNN7j36ke4cXSDm4c3SLbijW99C51gbmvmTc3L9+5x7DticMwqy72bt1CLQ7brS+YxcnJwwEuvvsqd9DLu9JLZfE6PZzWf8dmPfpyXP/oDdHhCcBhjOToydEcrut0WpWyO4FOuXRW4ZPJN9xyU2vvv8xzkb64+Oj7mz5yjiKs/57OmD55SPisHGtLcEp840sOnpI++BPdvoYJi8V7L0/UGbgWSrQh4bIooY3Cux3cdFYrFnTt06+1As4/ZkCutUHGsUVlt8TEKgSKPnCPl5LLUtSCbz6yHesWg55eOta4yZzIbx30HWuzpYCrKGYZssKAwxbElAK2Z2Yqk0mg4FUSVhKXI5PNVOV/Rv0kkXYZhTu5aztzSxJHvoRmT/x//Mxp7pdQw0zYNELkesqbyGslqi8CiHp77cqbikPOV5pKZpLIq24mYwp5zLY5gMPeTYGAMHvIaqrL8+8Hd8B3KCRMySSmN91aQifImk++Syj2xJhtwuVZ5maUEtGpvuZ91jAozcW7qym/336HLFxzMXGbPGjvuswQq5HY6pSg6E6k0wGUWsVL7H5fKxarJfht+o5gZS9d2mKZitlqwfvpgQF/gWZsxfaanRM/84mxfYTIWJ39aRJPRn3wPtFLCMp/cL600zjuUliEW30/3wIfaiU6L7OUOFqWS8UXlIc0PYEqENBaNS4+p0omUVWREfSTXf8oQaC2GobAI5XOE5l0invl8kaNig0wEacEIWYSsZxr6jrmytEr6wCoCq7nmd/9zv8D/7T/4j/gT/9q/yic/9UO88gOv842nj/mL/9X/h1/43E9y4/gGuMgP/chncSHQ7XbUVTWo0PyR3/YFeh84OzvnyW6LtZbf8y/9i1xeXrLdbqluVMx+4GNcXFyggxmYmiklqkbo3ElJXVeVsXCM2ej1jjK7geR5v+MqHPO+JIErh5oQR66SjqaXMghoPPPhiKYvhr5bw827bLvH6K9f0rQKkoeXFpg7t3DGQu+lxYiETYmeCNZyuDgQhaiSmaU8Ps8I6y8ZhTIyOaaJFVRQJYWL0g4TQxhITiXPGKz+ZI0KSW26NlPGuNZjsDdkfJN1Gd837nlITJdal5ECxnD78IDO9Vy2PZ0XNRcfe2priAlCSISUMEpGxeGiNOhrjSv3cOjJlH8bU+7x9cFTyZQGB1SyPEVu0ldZRFzqr6kytK4ntDJfT1uDbWpc3xNJVMZSaYtR0qyvR/V/0FJetC4QVB4YjwxCDPTkXCQ7sf2MeXTt2YYULDdnxsUGZExzCKy1NvTR4UKABPN6hgseH0QBrbbiFBWgks0wZCAmT1IVNoHJM1s9gG4GNyTLqsa/M3k2h6B9DK6GI5UgSF4XECa09FyKMII28l3mVYNVhhACF90Wm4LIQeb+zEFnWlmR50y5RQwYhfzScGPHPCf/RoGxmhATxigqk9ieXuKmkcf7HHvtTlHWidylUSRBJTCV4QgYO7QBgTCfiyZ2qb93oQUcy8OXmS0O2VNR+4Dj/y+c6F6TO0yMTIKs0jhIkU3ePzW6BdqNsdQ6yRuvkJD2+0jH84/n2IOcSsZaXpFSbnTOaihK0SSN2+x4b9vy+37nzxO95z/9D/8jNl3Lax/7KAc7z5/8ud/FK5/+BLHSrLsdMUsTGmPovafre2KMnK0vKWQRgK7r+OY3v0lVVQBst1vOzs6YNc1YsCmGYXqkqem4fq321uwF2mDKa7/f3tDrPveD2LjPHqJ1q2PPMgT8rZd59If+Odb/+f+Lgz/3X9BozXv/l38XdbCC6b5RMJ/PuTw7JYTAycmJRKwx4W2H1gajK4JThAp2u5bOBXrvCX5HkxRP3JabqyNuLg+ZV7XI+IkmZfl2e99hhEnHgGA/E9yXsyt1+attWR+4pjbSupa3vvOAf/Mf/D1+8OXX+Py9l7g9r9lsNhAqFsfH3FweMTMVHke0CWLAWzuMy6q8x9u4n1m9yOdfY+OHdSDDqgMcqtAh8Pjtt+T757F+3nuCrVG9B605vnmTu3fvYoo2cCWmTYdEFTTBWuoYicrjCexUoFYaF8hzexVMDOcIKeY86spFj+teHKjco9lsxptvvsnp4yd0rkc3lQhS9FGmHGmNnTe8+pGXSTHQK4fVhipZdJR6YrSKnVIQZRbtHi36g1f3hV6lAN3UPHr8iPPLCzrfYX1ifnTEN5+8i/OBk/mST9y+z+HNE2IW60CBitlhKi9tgun5++9q0KxgIJp556hnM5arA/yuZdt1+wj19Ful5/Al3sfnCvt5/yj3TWsZxiGvG1WZpC9VjyWMFzg+1E50lLzKdZEBQsoQwrDwITMD9mGAZ+t0hWmbYRcUkTicexRZKBnp2CYzNYLT6FVlKCklcazeueE9vVHoytAkzbe+/QY/81O/nd/+mR9hfXrG9uKCm3fv0C0Nm7YjuYSyBqOLrqpAUUobtBXpq+jD0JxcGMRlh1WVbHwfHGav+nPVuRW3/4KG+TkW8TcH4V7//quw8fT4ICg5KI9Bo5WljwH17lvYz/9O2o9+jvX/7BGh71i9+sP41KOzAysKMPrggNPTp1xcXHA8E4UetCVRI+PePJUyGN2w3Z2hnKMmopXHYzlONToodiEQZmC9MEorrdExEnwAY6697qvfb5ARvGqUCozFPmz6vmvbRZRLNNbi/tbfxX3ygur4Fs3xCbt1olpZnNuw6w2pmWMqK+0yCXQsQxcUqrJYSlXt2WDn+XXwkkVPoNTih1PCpEivRLBEJYXbOurlMW9tLzGLhpPZivvzA54+eUfmZ1qLsYbL9SW3jm/Q5rm6pAQ+4R2klcWohI6aKonijlKeYDPqEq+BpeEZI12GLpRnK4QRqTHGcHFxgQ+Oo9UC0pKkLcf37vHUbejbHY2xHNQzLs8uuXH7NtrvKAmk9ppe9yinaLDopPEpkEyAQgTi+ufg+zkUioW2nF1eEr2j8oG4ccSZ5e7qmL/2n/znvPHWd7nx+iv8wL/yr7A7b6kbg1JSmkhGREFj6El5MLwKZRPKeu3f27Gft7yElHC+Zz5rWK2W6NbzdLfmdrMQYvIL9PYOtn7IkK+3AWXCnFJqyLwL0lZsttUa74K0WqXvz359qJ3oVYamGuCADF0oNSgGCXHxWaHqadY6QPjZL5Q5fDpLlI3THdS+03ymNjhCO0mprHSjRRPT+6GgrclBZvAEBe35Bclq9MmC+UFN2xhU7wkxjdBxFDjR2mo/41Yq07jj4NdKQDAAOWW9yoa+7lmcGI1rt7GakFrS9+Vun7/Jr3GYL3p84GZPhTmYSKZGE1Bdi5nNMffui1GMHYmYM/miwxzQRrPdrGl3O+qD4/xZBhOylIBOEoQHqFdLlpWlrht0EMjIAru+w0dPFQLJClFDDI4G0wD+Woi7/Kz8u+jbjuWL8Xge+WL6++nPuq6l7ztWyxV/6n/+p0ko5ssDXNczn8/QNtF2np6I0QmjNVUyOALNzIrGakpc9i0zKiL+2mTg+Uzra14nV573V67sxYiLiZ3b8aVf+4f8+f/2rxFCz6d++JP8vl/4BZaLCpQQQlwrdTu3cqJ4E8DaCjuvMLVlu9kINK8UJrNNu5goo2rUFcOfKLZgH9QdyFeT8oIE8RBjYNdu6fuOup5JqSVB2K35xf/mv+ZLX3uD+WLB5z7zKf7Zn/95+u0a24ydA7N5w3G94nK3wUfBeCpl6FR8BjD6oPWEYtsYnue9KpeC7WaD6x22aZjN57S+Z9O3/Ozv/Wf48e0OYy39doevEiezQyok+HM6YdFYVdHFMprwg5/b4kilHo2orWmNaWoWleV8s+HObMmUhPecM2V8ceyBlbp4EaCX1wxzcin2b1yMVNTecqSkUWCszCvWIvjxoseH24l+wO8TE39xzQ25GtGL98vwTYHMpr+ffOpUcOGqoZjKXpXPLgxC5/q8m3MlpkB4xrBttwSr0ZXB1IbWd1S9bDRyn1nRy5V4YWKkUhomfZTvNHzHYpgBcg0Xysa6bk1K5WT8xnvfcRpZ/uMFxdes3T9eBvvs+fN/EdWZlICuy7qcmeLuW5S241cp90tBu2sJITBfLvBZoMMkEefwgFYO37e0MeAVeDQ6GZL22ASd76X2rSKdjqxUMzy4RkstbmDJKsXzarvTgG2q2HLVAT/vvdO/++hwvqfWDR/95KdYryVQCL4X2r9PNLrGmEpIcJA1daURvouBpBL1bEbcub3n67rPv1pqKYZ0f4/mf2e/VsqPIQX66FjUFXeqGpciqxRJroWgiSYHvErm5QZkio214uy2rud0c8m9xULUaKLUXJWCiAjop3RVVmR/3YanZ0Cc9m1H/pao3DpnjEhyhhgEzeocS69YOqj6gO59zsQ8qpI6utGKqBNPdluaqkZ7GSJN3iO6SBx+0D2+1vE82w4WUiT0TiT3jCZkgZht23Lz1fvYBLHtudxtSSkS0lLqzloTjcIoi61mBOdI3o0P2gcd+V4n9DDPVRvDQTPnfH2JuvXSEFQNK3v1O5UIfvrzwVip0fCnCeEp37PSYVH+pPwsygjjzBVR6oUy4XJ8qJ3ogCKpsrnU8GNytW6AV/N6v5+RVom9cUvlPc/ITKU0gZBHyGp/g2dYYPAzItTQtW05iRh2JYQUpRKh0ZgE2keUC1QpEWzpVVUDxp9Qw1T2weCn/YaeZyn900WbfLnnvCZv7wF6ubIAk/+qa9f2RTLK9zMGV+vP0+P7yValR0/nFgMvrDyszF/ykWQCytSi2JKdoyBEWuT9ug6N5vDwSObH5vmoCU2IGhUv6NonnG0N263In6m54mnXEzrHbLVkcXhAs1rSBsenqoZKJXQK6BTlMrwIyU91Qq9KTVprhnrO1KGOBJcXXxNlNMpA2G35+hvfpmkalo2hqhSdd5jecLRcoquFONEQcKHn5uKAr779Fu+dPaWylt/5hR/jwe4trDLjSLUPgNdVgXquQGapGEaEfGUR45qQZ+13/o7fwe/+iZ/O2WlgEzrOzy7QRlE3c5no0dRoY/ExUM0azi8veOPNt/jrX/wG/+s//PuxVuNdnvaUAootmoqkKsltkufZqHL/ryWontauSwAkIhyWg4MDdus1offoqqGyDX/sD/3L+N1OMiCjWe9aODrARZhZQ4XiwdlTfunrX+Lnf+ynODE1pERrAvihN2lYw+vXd2IQy3oz/nP6vpAilTYYNP2uZ+M6qmaG1ZEHb71N1/doo7l5dIKKvZAuKwvWUteGi/WGTjccVJZGa/qu/aBtNwTtYpMNWslkI7TmeHXA2flFARGzs312TxdbKn4yTb7qaJNUUiKOkxm4YsXyWL2UW8+mKB7yWhkiVxjdv0WcqCoai7mnaqhnTobbKj2Biq49R85Ai87kNCpWzzqjaRRaCB1XM4PJC0WVJhvnGALdbiuvS7n/SosDVVmEXOd9EZVC+UBtZ0RktFqIwoysqgqf4nht2bnGpCCz5EIxshkCDJOmZf0+XVADnJjXrzjnZ15fMornnOc3SyR60ePZTGD/5+WwxsqaRskMUpphjSHpgEdGIRm/IKlNvu9kUqhi13b0bceialisllx0azSRoOdEVaGSI24vMb7jleNX6JewdS2XtqO73HEWWh6pSPSe+abj1eaQpbEEHfAm4U0idZHdbifXmgXjTa6T7hOGRJy+qqoB1i1/yr/FsI810uvWB2A1P+BgcUCo4K62pAg+21+LpteJVCVsr0TrFxnld3h8xH/5f/63+Qv/9V9mvjjgr//iX2TWzInePfNZV+/PCyMMOSZLSjR1rdK8dHyLbQrYkxUpRExSHGnD/PYtamXwMeKy+kyFot91HN++w7e//hv81f/3n+fP/af/JT//Y5/k85/4BCbJJJ2qrnDujEiLoQYl4ufPI6nIl1CDYb9axkkpsdvtuPvSPbqu48a9+4J0eEf0PaGZYasT6RboAzeSx0WH7zzN4Qq3bflb/9l/xf/u3/93+Tf+3X+LP/iz/xQfOTrh4tFDjmYHdMnvBavPXdc0wZASTIstY7Yqp7p95zbOSntHg0b3nl2KvPLKK2IhQiR5z3Ye0NHifcIoxclyyZ/93/9Z/uI3v8K/+b/6M/zR3/17cX0ZYj06ymdvbaFvBbSyGLIOrtLcPDnme6dP8SldGXt35fsCgwzd+BNGixUH211+Ooj5U/ajIpEn4RR0J3lcTPg0iva/6PGhdqKSnZUbNjqyQfx6qNjtRx3TIw0Qbm6TyGdg8oCUzxojvWc3yTQyBbnPKcgN06oUtGXWpDI6O/e8MaIoBdmkhPiupYUg2CaPbBMB8LqSiH/bbrFVnUWx5bMNiqqp6LqOlBJVVRFjpO/9MFx5iJpfGIMddTn31+LFjn8SjvQfF971TiaCWJWJYjqQYk/SooxDUhi2uCQ1UfI9rLThdH1Jiom6EoZlasFS0aVAImIVNIev4qMlacVMJxbG8FJzwA8fnbDzHbvtJandsXCRSkFHxESNiQqPQql+2DtXWebTTCelxGKx4MGDB7Rty3w+5+7du4MDhv02lvc7Yh7XZ0ND17cCa5qZDDeOAWPBtwBBpsOYhhAil5sdf/Sf/wP8jk9/ThyX70m9ZLZXn4sXrYc+cyQGtaJkFFFBn3oMirg+B51wClrAMsOHHmWMjP/L6jPL5ZLLiws+9rGP82f+F3+KP/ijn+Uzr3+CbZvLLE1iFxzW3he1nuTlu6qrJK0xa756KKWGoelTMt9ut5MgqG1ljIFSGFXh1h1ey75LJFSAhalxCnZtj64rfu8f+UM83F7yL33uJ7jZLFh3HVUuI5SpNu+/ph8UBeRXJaQuX0HlE155LjU0WlP7PJs1JQgxjz6rqFC41AvRqa750eom7/YNR1Hhrmr0vd/NTUruVVJDHTkZxcnNY9q3vodPUWQPr77z/WxJgXCveY/Y/oDIskq1vVyL9DxHVBSS1KyuqbJa1/djtj7UTlQi8ZSVWEb4r7SSxBixph5ePyVr7Nc7QDJZtfeTmFUtpvDZ+zmFMTqU2gshkaze+10pWBepMukZH6fKp5TQCXSIeaNp0BIddb2j73tCimwvzgkZBow+cPvkFrqzNE2DtXaYxGCvaK6OmeaLbfwy2Bh4xsj/D31cDXw+KKu57ucm67WFFIlaNnyggiRKRskYYrRoLZ8VQ4AMza2fvIdWiqZu0Fk3NhkwqpZ2hODplCJoh00GFTTt5RlPv/nfs3z0Hrqe40xNOjkmffRVjg5uopygDVFD0olaVzS5T7eqKuq6foY8VPadc47VajXAvwIB6z0kZP8+P6d+loNPFXuUMlgtbUCJgLaa6BK11SiqHERFtIHdbsO9j/8At15/lRACbdeScouLYr+08SL3a3oUO5g7NuW+yMViMJioQOXhEuRxWYDTEmCanJ1EI4PQg5Pa5OreHT7+z/8Crk+YJGz7RKKyAu+rkLJ0nhLZuxwPpMEhjY7pemb09cG5SiKpLwhRkmlTOZFVShCyPiZUbcFH8LC6eYM//Kf/JIcHS1ICHxNoix4EO57NQPcJaBL879VHr3MuQKqkfqwBkxQmKLwCatH9RkGsDUErql6RVBQ5TBSXmw1/4H/5r/KTu3+Jk9u36He7/Uzxebc7kfeSBpcRtwzbLldLYtvjY8Aag6gRZWGPdL3dle89/Xu2antQ7X7iMz2N1pmomTyul55hmXQTJ22AH3x8qJ3oAG+QNxOCxSnILGZ5QKYbXpU3UJqs8yIrRVFBURkiLvdguBlJIOTysymcUzZxcTiifVkK2wJZpKRwvs3iylM3poZ/lwh4hKDld4UlenJyglEat2kxyWCbGbOTBbP5gu9991u8fPcei9WKXfDS1KykHSdBjgDJ9qJ8T4aeqHSFAqhfQGx7KNS/32u+j0x2fMn0nCNkMxiyEmWi9+5F+TytC8Mv5eHW8n7GM1Ga/oXTV7KOJL3blWV9uUYrRVVXAq9DFkuYSpglIhG0GYIaXVmSC9haobQlKCPzY7PBSEaMlM4IQ2UrEhl6VqJyVKDcgYiDjCKbz2acJejajq5tqZs6G4x8NwqyIoWf6cru/V2pQu4psnkRVF63UPa0QaVIbvQi5daOymp0kkkeOo+uinGs8V+990OBQLC1iYPav8WDQSRf18RxCHKjByF32bsiyq61yhCgfGeB8/KzrQx2uSL1HUoHSu+3XKcXPzNxNldDa9mTeT5p+QrlG12Fr7ODRBViTBn3ljC5pq2UygMHFJEg1ZeYJBvSmoPjI3m/96hcvgmaUav4GuM+OvdydRP07HmBTH4+0vBPmdqSVFFbyplilhdUSqFzxu1d5PD+TRp9Rya2OC/ogdUic5l7SmQkcZL2HaVJKj8DMctaKlOqQtTzOSZFfAgkLSpOewHBNd9hsOXlL4NNUKPrTOX5v2Kn8vizgrQpBV2/Y55FGVJ4fwGZ6fGhd6KFuKNycp5/LNMEpptpcIjPnmb4kdbEILLxKuuBjufIj1cqY6X2HySlcgaaxgxSmZRvsECFCUXvtuJEs9Mv+P1o5McNUOq5KsF6veHy4oL79+4xMxX9bEeKMFuuOL5zm/fOTjl9/Jjbx8fog1V+IIoRS+ShhbnlZ7oORbNTHpg986GeNYrDr/YMyPN///1AeuNr97PP6+9ZrmekMYiaZgZK6SGLKkZA5XYXrUq/L7nV1yODsSVACjGireXy4kLaO+pKHFsREEt5okXWnNVRoawipECyluOPfhJ1dI/5ck5QCq81WEsKOdfKLU4mO1Vjq+zwDUUztWSY++UElSeeSH297x110yBOUb6PGr5/CRD370lKRTuaQbWKDLPpwVjKs2OUGD+SRQWFtg1+u6FzO7xKHKxOhr2VEPRFsR9gXuMrefbKJhmMApQuLIcBwkxZf1ppPTxrKJ2vsVgANarwaENKMj3H7zoaMyco2S86t6spFYaZpuMYbSbJp6K4/PJj9vb9NcFhGXNXwnNdZgurscad19kqTVKCdmmt6H3H0ydnHNy5jVUao4R85rT0tqYYR/9+TVa6/wWeDQSurnmpLMacMhs1BqQlGFB4yHVTYg43k+XJk3fYqTkH8xlNZUBFolakVO4aw7CAQQFYRJFzHUV+kzIXxM5m1BqZz2srjDK5NU0P2fsUESTf8YH/sjenLwdGClSWYB3rnGPrihCNxJE2tqLt25wUxL0BJR90fKidqNigSRaYN3xZZJmeMm60D4KZRpZrdn45KiuR9vA6OzbIl/OWU8U4yF+TclaitQzPVgG876/ADM/PzgYCU1PTLBesNxu++vWvc+POLToN754+4sm3T+l3PV/90tf4vT/907yrHJftBS8xozaGTidqnzApSDalBNo0Sh7KFBOzqpE+1eQgRREdmKzJbwbCvRpBvvg59u/TdbVn+Z30Nlyd71reF0Iafv5+1zb93JIOKRTWWrabNau6ZjabEaKEU1Uo2rIS81tdoWPEJoNHpMz64FD3D+mMRUUwKBpTkVDoyDgdR0l9SFsreyXXRW1l87adqGQlhF0aPLfv3B7WtNTlBrZ4QRfYfz6uwpBlHUIIVFWV668KH9LwbxAHZXUFKJwyHBytWMQ5fYgYU5O0hhSEaakU02EOQwY51Trdj0n3DzU+m9IXW+3dw/Ha5RtqW+WSSybThYA1ldRxc6SYDJhKkZJDJUMVDQoZKt3rSuZWei/BsbYC5ZEG+D6EkIeeX996dNWBln9rM5ZRyrxWZcwQcVprhe1NhVE2Dz1X3Dy5RaoqTO4l7r3DmIoYndRFjUgbKje20JF7x68zJc9ccXlNTEMtt9wvm+1aIftoYzCZeFaq3gmFtpIANHWDsVZQj8oQe0dlDMlUhBQhBumRr6APo6RqsaUpBWHRaoOpZOh82/UsqwY7QQc+6EjqCuhSfp6EZavzPNk4mV+7f/80WhkW9QG1maFVCXhf7PhQO1F4VoxYotDra3dXjfLUmJSHXtoIgKyqOV3KmNIe5BmDKGsMwgqpGK79z5QalkdH6Psuj7N68ZvUdx03j0546fgmFxfnnHUbvnf5lF//7m/w1qOHLA4O+OHf81N898aK4C+4edGzWNzmpjEoBUGLk9colFHUoSEQaeYVs1mDthqL4unDJwLbaYNLier76fD+gOM3QzLaf8/+e0uWNh2wPkT7StG7MCqVvNCnjSS18pmX5xfceOk+81nuhwOcHSFKjSImB1rj8CijmDUVyQfsRjFfSHASlBhEY23W7QyQBMpVpsa5HYO+t1bEPAnNWCtzaFMCbfC+l/7WSZsLjGpGV3uEp/u7MHiHbzvJjIJP2NpQ1XZYW2vtUFKIgFGJebOgW7+H1pamWdJ3nTgdxeAoSi3+alDzHOmO5x7TwEia3xN1LdC10mCrmqQsOmufVrZitWowRrNerzPZCRnhRqLSCu8MqjHoWotQ1HlP37ckU6EsWYc1B8UhB03GXhuITbbNsz+asEeH/asMxtgcXEeCT9y4cZOYDN4FYpARiCr0bPFU2tI0c4w1rPuObivtICklbEioRmrnoe+l6GI1JnxAY4badyD7PI+p0TI5489rqARtsHn/Ympu3LxNl6wQ9lSSsY5anrsKqJTGKnBEOqZBUEbtGDNBnTS2rrC2ps0ypspWkN5f8GBMRvZBbPl3RqHKXiyvy3tyuKVJuiNSSgTfEfxOYOn0W2QUmr5iUKZR9iCEEPc1b+H5Gej4+7LBnu/sUhHpSLrgaECWKsuflW37OKM0JLzL0RDPwivPO0yE6DxeKQ4WK5r5nJOTm3zs/qtsfE8bPLoyzL1mYS3zqmY2byRbSKCslVqFj0QFuttxcOsG72zP+Fu//Cv8uX/7/8r/4V//M/zo7/oZdNfjNhvqWUXy163Ns2v3P/RRIN3r6tDFYJvJfQepNw4xwAtcota5dhxl4kMIMou2quxA1FKC72PRw+iwlBLRSOlHG0tlKuwslxKUkqw+OywdC1xaD3vXJ1itVsN+U4o8BNlndSuDSYmu72nbluViIRc8cVDOOckqcs9oilEyhMlr+r4fps+M+1MyN8mg5JQ2i6N7LwQrWYeeymgOZic8efyAul5wcFRzefmU5CPGSoO6MYamaZjP57RtK3s+Z3MjnJiuQKYjArmfMY8wnlKKuq6G7yIwq3zX1XKJMZq23fHwvffYbTeQ4ODwgMV8gSJx+t4DFrducHF6zm63xQWHqiwffe0HOFmesNlc0nadQIBaQ0hoLRFxTFFGbQYk42K87lIluprZlEAOQGmNsZWsJ1BXNfVCvsvT06est1uqqmExX1LX8PT0AWZ1wHbX02+29L7l8MYdjo6OiM7jnKPXDoI4CIwRImKS1pHiSvaeUjVe29XnpzhKWVVD0mAnNkwnTb2cY40iZTKbS56kLfNMlio56nG1wO0cnesIucarQqJG40x2arEEfhKGpuglSDSa2liBc9PzmObCcbg2Rb2CaE9roKXMQWLiTDPvQAGx9P0qlI4oFZjCvh90fKidKDwbdUNeqMHIvth5riNFwNRw79Oeh1up8v+lfGPK7/NrZdJ8frCil7aAYfr8izkgk3v/QorZ4GnmSrGY1SSgC46QIrXJ8lVaLKLWWqa3kz8rM42DFjmyCk0TEvHxKfW2E7YqIKSqrGc3Wdvvp7553fGiDvgq/Hj1HEqNpKAU4zCVgTSGPEZr9sfVvdAVAgJdhRgGWNcYQ5koUqA9uZYMb5U9mHIWog2ovO5JZfUdRVXVRAU+CimnGMIxEBRUQzJBg83DA0qmba0VCLjcp8neTxkCMXb/kS6rd12wKchLoqqs7BnSwPodoLcQiT4Oovl9v82jvRK+29J3HYuDY0jicJ1z1HVN04jgvo+jMElI4/ip6woZ1wVr8p+E0mkgtpQ3rpYLfN9zsV3Tdi3OOZrMbk4TY937DUf6PqTHVCbK2CsXees732Z1dMzhwZL5vGHb9jmrz5rYk+xFqTz6DlDXPLfjvRgKIQIraykFNXVN0zSkENluN+x2W9abDdoojLH4EJjbGufWxL5CIQLpfbvh9MkTdtsts6ahmTUsZnM2281k6kyS4HjI8K7cfPafo6vZp/A3BL4NhfxkDEYbZvWcTbdjs94N5Z8uOJazGbuLc4wV5a/oIk99z7yeMasbrII2OHzwQ3loIJTlmy+3cty71pjRiXJ98L5vQ/bvwyQum/wml+eStLpc3XkKsdUxBqREJPNc0/Wm6drjQ+1En1fjTIKeCQnDx73fPfcccqJrM9Z0ZVcOQU7eBGRjVCbOjxqNihQS2haiCITgiHmE0osa+ZSL8iGBj7lnLIh+pUGxQIsItFX0RKmZxIhWhpDIWsuKpGVIeKo0fd+zUBU/dP9V/sAv/B5euXsP1fZSyteGGBlUaKbHdU7tg47nOcvr3zt9FPZXYVz/YsxGg7an3jk8hIWI8cFPxMCCzffU5+zWWivKOSnD+d4TjZ5kxnqQQ0vkKT0pV5BywFPGZUUlo9FESEW+o9X7rSEhBPq+J8aKyorDlh7hKv9dEXLaWMoPU+hTIdlPDGMkrQdjPpJnhkw+ZuhUFcQkt03FiDEZyUGgZ3mzR+skMnWho28vWawOUUoMcAyBvuuw9gRhp+qRsjPa/Gdv8WBYnzWMJOld1JkRNQyeUHB+9pTdbkNKkaquWS0XtH2H0tkRGEjaM5sdUKmEqZT0vcaaJw/fYb3dAnc4ODigqswgKH/VgOspIv2c7bQX0OcsT2fxk1kzIyXpI724OKftdkBiNlsMwYytGhSB4DzzekW10Oz8Ge3O4bqOdtawCkusPhQymnfC+i7XyJjtv/iRnb2xoDTCgZNatM5IxOX6kr7dDcSzzjuOD485f/guWKiqGhMMT85POVitSIsVtq5QGrQ1pN6JEx2cU9n9qlwBKd8v7x2FDDgN+PZt8NQLp2fuybO3J0r9NUWpkapR5rF0Z8QUhUic9tXfXuT4UDtRuF4tSCIPRZm2MK0fwbNKQ/t10mfVfK4SXYZgOP8lITBfilJ/LLqfKhe0yyBggJAL7CPN/oMPF4NQ+zNJQylxiH2OsAQYUULLNmJgTJJRQ7oS0oJCDKZKGmxk2/fMmzmf+fTn+Kl/73dw9uSUzdkZySp0YwnBkbJCy16gcWVNrjte1PFe//3Tta/Nweq1n+FjoK6bTJ8fih0UBmdxOB94aNEoVRF8EGF4yUQl0+37nu7yfDyfkod/dXDEbDYT8QsFKIdRMj+2ntVYWxEDPDl9ikpwdHDAohGB8lSPMn8hBFzfc5kuOD8/l2Hs8znL5YKD1Up+pqTVIKqRVCTXaej7nu2uHWqaJYsuzrYoIqFG1mvdzMBoYm7PKcsfghOyE7LuRtcSyec2GGMUMXr6/hLXtdhqRmUM89WKR48ecfr4McfHx9R1zXa3vfKspT3UQL7E8H+Uths1ZP5meG0IAWstq9WK73zzWzx+8ohX7t9juVwSQqDrO9rtjtl8iTGWutKY6KhWByTv2F6eoedLbnzkHvfvHPHG177DG9/4Boc3jvjMD32G7XaHz9eoGOHZGIMY3xI4P+cYiUVy3UoplrM5xhi+/e3vsNluqOuak5NjqqrCOYfzYrwrO6cyFR6N1TXKVMxnK27feIkQWh6++y4P3j7jdPmUH/r4J9luN3R48AEVhcGsCmlnCFrGzH2KQBQnpK0drtP7wGKxkL2sRKD+O298AzW33Dg6ZlbPUErR+o4bN++we/KAi90FSiuOb9zk2HVs11u+9fAhs+Wcl+7f42ixYNdf4lUhbCpQkdzEMATDUSF1a+/epwat9v47LRFMYMFn3qFUzkdTGn1vsQc5dRdhmk7qopV0LLzo8SF3oiXKTyNkikCYMUr/XpFQez9m3fC7YqiHLOPZvKg4Uq3HlpriqG1eThlxm+fWZZi3GC3n3FhvTVfP/pxvac0wSNhkpnAEklaTNhZAW6k5JPCV9FqJcw3oBFZp+rnFOlHm6YLnnafvsf7W17l9dIJqKulHi6CpgAJ/DMvzggD0P9njKhKw9zulMNWM8+029zCK0+u7jqaqRO7wRT9Ijb26kn0IxKW09G62XcsiBEwCo6QO2IZAv1lTzSRzLKzCqCpWByu26y0XZ08JLrBarVgdHpJIuJTok0O1mRAUx9rpzeMTbhwds16vWa/XPHr8GGs19+/fZ71ej/2GxgxZY5kbu9msOTs7G4g+L730EnfuvCRkm2lApPXgZNus56yUtOz44LC5lWQYHq3Kc5R70aNC60reYxRKi+M9O+u4e/cub731Fk+ePGF1sOLg8JD1en3lXl69kXJ7U35etNbEIDBlVZv8u8TBwQFd1/G1r30N3/V86lM/zPryksvLSwkeq4rZfIm2FcrWoKDdtLSXlyxv3KTXkc55dqdnbPqWVz/9wxy99y7vPXibX/7rv8TP/uzPEqPH9X4QIS/OdCRCqL3rHlARvd/KopWiripspfnSl/4RB6sV9156CWstl5sL+r4VWc/gcbRSQkk1wYuKlMLS9RHiOVZF7t6/S9t2PHr4iC9/9Ut87GMfG1i6qqmJvdtfz+sfGcbeYCYv0iyXc1arFU+fPuX09JTtZsPL91+CRUW7aemdgwiXmwsuT9Ys5xUX257OBbRVBNdx884xt9UJF0/P+e6XvsLJnTvcf/01/Nkl0eVkYLjGHKyoRCRQWYsLcWIfnw1+x0TmGltAsdcFKiyEJo3WdkBTpHQntlppSSyC81ijsBqIMkD9RY8XnWL1P9qjwGUDeSenLAXyue4oZJR9Zu8EioEMtY6wQnmfkJVEyacYsCFy0vK5040iAZIabqBzLmcQ+Xan/T/XHToGyLWqnkSvEsFAEHuNRmGTaOKScX0dErOo8QZ2FbQWooqoEIgqYY2hUZaVnXP3zl28UbgU8USUSjRIPWmPlJWzmOk6Tdfl6hp/0Pe6/hjP/37Zo1LSzuFjxDYNq8MTvvilr/KNb36Hl19+jXq2wBVmKy8COytiqQci2sNC1lG5xTbXxO7eo379darXX6d67TUOXn2dNloqvcSmObgaFRpWzZLN2Ybt5RalDbfuvkQ9n9G7Ftd3xL4j9V1uVJeHvkC5u11H2/ZYW3Hjxg1unNxgu97w3W9/Z8gwyxpIO4ogFNF7jIbXP/IqRweHtG3Ler3Be09dS41Sq5IhScA57kdQyIzH4TZrlRvkPSFEuq4DZUlJ43wQAX4tPaIQUVpR1YbNZsPt27cJIXD65JTtdjte83VQbjlyEFsCX2NMZgkLi7Oua9q25ezsjO12y0c++lEuztfEmDDW5qxKYPOEpneBXd/jleHcrbGpoqHGKkOwiVk15/Sd96hXc+585GVu3TjhjTd+g7qqpCVtIqwi8HHaEy6fIiqlTq8mtqeyFZWt+OpXv8rx4RGL+Vwg3XaD0WBz8JFwhLSTLFtb+n5LSiGzYWvQPahEt91itOL+y/dwfc+DBw9QWovEp/fXouSqrPmVZyoh7O/hWrNa1unpKY8ePUIrxct37+Fdz/n5GdF7gY5jGqchZdKZT55YGeqk2J5fsttuOVjO+cRrH+Hh22/zzsNHmKqhbpocAOZJKYxiBzGWmvf1z/oLH9l/JhgcaQxIjVlbjLHEkNWg0ChlUNriU8Iww+gGRU4mXvD4cGeiE2koKFFKGEkmSYOp9t8yiWSmD0GMIm0mcnxxgDhSuCLBVpDiFDFGYlChUsdcnNY56xTFlUhEJ8kjFRavHBZPpwxSbv9gFliaPAQFThr7wvJDYXRuk1A5Mo6DGECtLKiIU1EyJSJRN5jkUanHR0Xneyo0kYQLkdBHmuU819smElxlvXLdUR5MucCr8nOFzFLgo+LQlDZ7EeMA0qhMBEsC+YD0Y8bce6hVFiRIIniglAI94+zyjK/9/V+HN76KX634O1bDas5Hbt8ieiFDDM9EcaoZ51EKCEnaovJVeZWy7KKQUIqYwMxWbC8vCV0n9fYMBVWVxrku7wlL3VSs2zWb7U7Y2iguzp6SkijkWGOpqkr6LKMa1yKrBonyFhKhh0RVGV559TUePX7I+fk58/kCayq8c8SY2KzXUjNVisPFgdQGF46P3HsZO2/oosPSMGInEoFrk7i82DFvZoAjRYcxoLVA45WuCFHh8EQcKQZC1+OtIxCkToiIFagscqC1JriINYbFYsFut+P8/Jxbt27hgiOoIvrNUOpAqaywlMlHSO03aenJtUrqqsYYTh8+YrvbcfPWTbxvsxPK9xKDNRXOiROKQTSOYwS6LVErafdSkVrJMGmrwfUOoyvu3L7HN7/9NW7dvEFl5xiUTEpVue0ITdKRiMf4mlQFUsysayO1aqsqlEpYW+Nj5OL0Kaaqmc3n+Zo8KkWMtmibA14tQizeO5KuSDEQUyCR0MqQkidpIzqzAZTR3Lt/l/feesB8vmA2mws7VytsDAREDERq8wqtHKiKotaUlwtyPVREXRJ93/Hk8UPmTSNZoW8JsZdeTyv9twSZYhR9JNUrQSb6Hh80yqo8CSvRR0+wmldf+wiP3nyH2SuJejZHGZsnUMXBLpATAI3Gh5B5BVP7d30gLOBAbpNRKjtkCQY1EU3EJSEeqvzshxCltq5HDWk5V0XQieg7SIref/BUmnJ8qDPRElNN03th5kkRmdLmck2W9Pwjyz4VTDxNSRUjpg5pkMoqsEQMIU8KKYYwZaOY8s1S+BQgeq5oA117bcO1Z7WhAgkP8FHxo1oIK+hRxlBmK+YsFS3wY1bkscYOKYGcR1FVEqWREj54nPe5GfxKNnqVvJDUAC9ejRin4+LS5F0pZZLONDgoZ89rKzCZzo5aZzhxlAlLeV01CV0Zzt5+m/qNr6O/9iUefuNrkKbZ+biez6x6GhvJC2wXFaQg7FyhwWtCSszqGhWC9Ob1Hcn3RO9YzGc439H2O1x0mMpyuV4TYgAiwbds1mdsN+dsNxfsdhthk3onRj5lZu6QSwjEpbVBG0tdzzk+vsFqdcDl5QVtu8v1SRny7nonE36MZTabgzbUTcXxyQmL5QJSkLYNtGjH5gAixIBzHmu0uIvUo3UuQ2ghlhitMCUTDwG32+BcS0gSbIIe0ZeSheWgdLlY0MwaLi8vcr01Dc+K7L1SdxxuxVCe0WUfkDAJKmNou5btdgMxcnR0SN+1IvlnFEobtLb5/Z4iOp5iEOfT90RUFhyR0oYyCluJUVdoVqtDKms4e3pKzOIX+4gKOeiLY6aiElGVsYRkpRzpZXW9ZHEHqxXajERCrbPzNBkOV0Iec8GRlCH6QIiekHI/sTbD+hZN8KPjIypl2G63dF2H0Sar8jD0sktlIasuqVL+UcPzoLJdUZmZvVmvSdEzqyu0RnposzCLNnYYnKGUIrhAMrV8nvMEn1BWSXCgDQHoUuDkxi2aBBfnp+zaLaaqBksw2jCFyUpcIYZBjvD9ugL27HpGC0S6UmyUKnUHVWoEk2REj3D2sOeSwgMxelKK31dX84faiUpNcSyU72/2K7WL4T3PRjXTbKlkTAPbcbLp0uhBR+cwcQblXGXTsgefyP9iCHjvh1l313+vqxHXuJmmDegj+ULLNI+YcH2P871It0UAqScZo6mrmuhlmLJOOzSByiyYzw44XB4yWxxRNyuaesZi3uCcy5T3MmEkG9O8N1EJ5wObjUCG077dgWBRDFGUOX5GCdU8RonKxz7esvoBrX3OVipUshgqKl1jVSPOIAWSFphR92tevv8yH335FczTc/TDd7k/1/zQyz9A2AWCSkSjc4vGeO90ftB0zOpMUTJrnetFAn2qAWIqdcejo2MWiyXzxZLl6oDVwQEHqwMSir53dF2HD4n15Y6mrvC+pW0viKkl+I623bHbbdluNuw2l3haEk6QhVxB0Ihijq3nzBYn1LND+t5zeHCEcx3b7Tm7bo3SkRActqqobC3KR0aRlIGmostIySxpUmil+V1FgTuDYrdpBTpVgYQ4fIUMtRbZtQ50S1UpTFqQek3bntP3GxEMCL3AiVYGYg/33FpCjMzmc5qmke+7XYsWbErolIQIU+rAJeCMkdoYLFqM6/An0RjL4yePQMNitcBqLe1jKLS2WDOSqEL0+TmJyMirVohSUaOpUKpCnL9si7q22Eq0Xe/ff4Xz83Nc8JKBJnKfK0OvtwoZwYgadMQbj9NeEAUVSSriXUvXbnD9joPVcuiVNdZiqpqIoGSlH1aY2QKr964Xgk0uL9RVLTU9bTE6t1zFxO2X7rDbbdltt3nebKDoxqKK3GfCJ8nEkk5Ekwa7ZAb0Rdb44uyMmyc32Odu5EXKgezQ3+z7zL/IvdK+y20ymsrWWNOgkqXve+5/5COsL9dcXpxjjB6cWDaMsueMrD8xDAH3Bx0lKBl4L1eC+RBzf3KUGbKCmFgShhiL79C5lSsQkgJl0abBVrMXugb4sMO5lCBDLLDOcObgYPKUcnndszdmnzotztDaCp8hiUGeKqqcSYrSiJwvg2NK4EmSGJMpPTqprKMxXNPICjbGEFDD7Mr3O0qmNxUln36fGCOnp6ecn224XJ9S1YZ79+5zfHRHJkhYgUJJCjtbYLSn7x5wuHqZo8O7XFyc8fCdN7h3/xMc37xLCD1nT9+lYc6uawnK5vYCEfA22qJUT9d1nJ9vePDgbe7du8fNmzf3ss8yaHpU1JFNbXOmMwYhk4AlCavV2Igxjr6LQ8QMDqMhBSP9r0nh0ZgnPf1rr7D9V/4olkh9+xUe95c8MWvOu56lrrhrFhyrih2JFi9ErRxoeS2Oq6S5Smm63Y7drpWpOylTtIzBMLK7y71ou56Dg0Pm8yUhRr73ve9ijcH3W2LsQAnFX2UiTwwR1+3wbkdQgUUzy+tUY+2M+XyBMhbnHbtOBg/0uy1KJ46Pjzm/OMN5x71792nmM6rcfxii1LuV11hl8Fp6UmMIYNUgWWqyIP7lxYaToxO0NlhbQxoRizLdSgQoIqiOvr+g7U5p/ILEFnRLUg7Xt1htsVUNyoDRWX/YyPxTo7k4f8pLt16iF7l+lJbh24EkA7iR6Tap83S5Zq+sQWnpN9S64ezyklvHRxwsFoTNltrWct1K6mxGV2gVc3Yj0n4xOUIU1mWqkjzLUfaTqWRiidRcpaf7+MZtHrzzLpvNWkhrxuB9QBvBk3zSBJWw0dF4Tet6jJ1jTYWhxbpj6pnm8aMHtNsNqyyOIXtbD/W6ejZDZzWk4CPe51YijQRirkcvIrPG4lxgsVjBOJCKtu05OT7hvSePaXdbur7DGktICuWkhpqM9IKTbOYHBIwyWFWRKkOIXnpUe0e7a4khUNc1m+0210wrmZCTxy6KcEbuDkgRZWR/uK6n212ibY0PPShLbStqI6jg7PCQo4MVzjuePnnMYrEYk5mMOsUYRdnNawlor7GJ+/Y6w+tRnkFtFWDFZk8SoN45qtrKsxGFNZ2UjFTQgxONaBWGoepi83+LsHOn2WBxmFpNZnoO0OP+a59ztgydVBidSIQBCRBYVqGUQal8g7R65lx50MIIL2SIQQc1YK8StXnUvMrKzy/2PWGaMTP8G6QJf7lccvF0zWI2Z75opEfOWqJzpBh5fPqYp49P+eQnfoTTJ1/Buy0739Nvz3nrrd/g21/+Vb7+pS/z6msf4+XXXuf45h3OT8/QSlRttLFEwHsHUdE70QBeLuecnJwM47yu1pmnAUCJ5kOIpFSIS3pgmaoEygixSamKtm0lQk2eGMCYGkjYKhCTJiULKB65h/zgqqa5+3OEqmbtW0J/wT988iaBxN16xWquOGwqkgatDDZBhULHRFc093Kd2Vhhrg7qPnnJy/fROYJPKQvCG8lyt9stu7ala1tee/k2FxdnKAWVbqQFCkXvPUon2Ucx0l3ssIcV89UcM1tgqwaXFJdPn7DbnNH3a2LwaN1Qas/W1IQQeXp6yWq1yhlIHGqMkUSf++GsrUjKcHl5yc0bM0yE5Htc72i7FlM1aNXI61Juv1KI7FpsIYB3CW8vMVbx6mtfoJnfQIUlvq2plMFocWbW1viQUNrQVPVwruVyydOnpxyf3JQ1TGmQ5YMCnSkwQnB5/M5DXrt7P+s7B7S2dJstm4tzbhweYCoLLmBUFrBIntKTqZCgTOlIwuNcx8X5hsOTHfO6p7IKbSqMrsqnit6wBpPHqjXzhq7foXeW2WyFMYbdbk2lF1hViZBAtcGmJRdrODKBuqpw6gh0Sx8Cu3ZDjJFFVqMqbGkY973WipRn1cbo6LodSUUuLi6ZLTcs5oeSuRkr3y1D3UOmraCqa3rnOL+84ODkhNB7yPB7SorW9zTzJXoXCDERakU4rFA+o0M64vqO7XrNyckJfe+Fua0lS660JZlCWDSomPAonO+YVxXWVsPg+4gW8QWtsoqSQtc1XdLcOLnF06ePOTt7zOHJD+B2fbYToFQiRSFS1fm5m9q95x3TclAKggDkLz/8XimF8w6tjCAVRgIKpUWUw4dc2dI1x4c3s02W2vaLHh9qOFchtQRVMrRrXjPI/03+lOMqw04yz+Iwx/miafLa0bCOtcLiMIZzZ7s79GoN4HuGbXp/7TU88/3U2Gj8PKZr+f1iseDe3bvcu3uXg8MD2q7j7OwcpRSnp0948uQxvXPM53Oc7zk8eAWlF5xdPOFy8x42aYg7Hr37Hb71ja/w3oN3WMzmVBZidHjnCC6w3VxAilhbU1c183nNSy+9xHK5fCZQ2atppJQnlUyHT4tzreua1WrF0fEJJ8d3uHHjZW7cuMtLd+9ycuOI5WpGVcn0eQlUdJ4ZG9A4lA9w2BCVQ7kd89Qz0xU/cnCfnzh4mR9a3uakXsjzERNVriV7hOms9P5jkDJ0W9cNZebgICd45bVFz1RrCQSEwarwvs11QKlKg5BHqlpIRVVV08wWHBwfcnB4QtMsiDFyeXnO48dvc3n+Hq5dgw85K8yDhTOJjRQHuE8MJhDBBIVNDpOUJC4x8PjsjL/8K3+Tkxu30FlZKGZCW0yBzgm5DN1gbENSFpTF6kbmdCaHDwl0w+HJ68xmN/GuJ6ae4C9yGUoMrRjVihTB9VKvjTHRbnd432e4GExMVFFR5x5mjEZVljYF/ou/+pdx1qDrGp/FD9brNconmcSjNDHLMY4Se4L0SG00IyHeY7ThI699ksODI0Jo8b6VoEMpEgala7SxmKxE5Zwbs6IsWJFSwnth4ns8TgVqvaRWkb/0S3+N7z45oz5Y0Z59jXfe/CV2lxfEIPU56TiTbE5EDPQAURcVQZmHqoheGNk3b95mPpsTozg4Sa4kW0YJXDqbzQhKRh0qhMegUiSkSNKgosI4gZ0PD5Zcnp3x9e+9yXcfPebOwQ2c63NvZE/f96QUhjYpo80oX5rbvGTP5jXPmagPgd55nBPCUyChjCr+Fm2E3a20obYNOml81+GDGzobUkyEKChKH11Wy9rv33+ejZx2UihGWHcI3oG6rsEoXHK46EAldFWhq4pmuWCxOmC2WNKHiIuBatZQzWqUeebjnnt8qDNRyI5G/iKLvZeplf9eyRifm42OvIe0l00yQLEvcj0MkK5CiEqQeUUkZMzOs5IOzz9KBnrVqZa/l3pd0zRgIj46khLlj+1mzfrygr7rqGyDqSx1c8RycZeLXc/28hF9u6NqaiIdIfRs12c8efg2y9lS2Ja57xZUFkQvjeeSUdW1ZIjC4h3h8VLrUbnYXwIJrXVWkzHDdcu/LVrZzJRN1MoS/IyqWtA0PW3X0u6E1FLAIBGXtmCFuk4KGRaruNccYJXI8iUFLomYgM79ubEERsPNk0uMeeQWPBsQlL8PjOPJpimSeVVlc6ZtUcoilH5xgMYoydqqmqqq0bYiqUjbbun7jr7f4fodKTh0puCTEr53bHdrYgx0XU9Rl5mFBqjyXtci/5Y8MRrqVcPT8wu++Z1v8Zd+8a/yp/74/xRjK6JvcU6chA+91AtpqFWdpw5ZrJ2B2+HDlrY9xS6OCClSmUpgydARYg8xSmZnKrl/WuN9FLJTcLi+z+SnHu96sJlMk5ngVlsIkbqqUUrz5vfe5Bf/6i/xz//CP8fHXnmFqm5wvmez20qdPCZSiPgYqOpmgnLkgFoJcSfkFjSrDcfHd1B1RbdZE4NDmSqvnzjPqLJSk3f03U5YyLkWmjLrvrD1YwygNavZIU++8w3+5i//CvOTu9x7+Tb28k226zWLo5YQPEpLe47zPsPaRtrjUtizW+S/xyiM+hs3b2PsLMvsBVDCvLfl2dEidOF8L8SpEHCFZzEYL0VKmpCEZf/Wgzf5lX/0FY7vvMTv+smfQCsI3hFiEoKbYiiraJMnJJEQj1yCxykSFgkxSnaqJWiKRJRJ+Tql1h0yr6/sjRgDveuz+rQ8w+W8IUmNu/DVr7ODV35AKcNp82zZLqRIu93iu4tMFDPU9YyIkcDAVnhrUQm2lxds/RaVhFl+uVl/sGHOx4faiQ7OhWz8Ytb4TGncoFxP1HmuIx0ISSlL+UXh5ijxglNG2N45KQ5dFQqROPaQZFSRKbBgEubni/vQYeNOnejUqBfIVGY6ilFbreb4PvL40QO6VlotZrM5MUWa2U20WRHcI/r1huQUuqmE2GEXWGNZXzzk9PQGh8dzUnKk6FG6zmOZBPIhJSI+Owz9jMMptZOCh5bWmCJUXtc1dV3LpPuCqCLQbUpRDGyqqesFTZOoqktidOzaTogOaOn30hXG59xfhTx1xGDKdIlSc0kxR9hyn3Uq5BZp6i/c3ZjJLyX7SMV4JJEHk4hXWnuGMXkpChnEe2ZNg1KWyuY9qLRMHgFMZZjPlsxmS6qqYdPu2GzO2W3WBN8jY8XEySSliVERfKDbrnn4+AHe98ToqeoZxsJ8voBa2IWSX0ZqIj5CNV+wefdd3vjal/m1v/nLvPvoCTePl4TQ07c9fe8J0YljKMQiPcdWDdbO8EHjQsuuO+NgAT62IhOoK0Ci/crOsXWDrercC5pwrqVtt2Iw+47gnPSj9t1gpKVFSEFUBO+ptUFF+M5Xvs7bX/02v/G1r3P/5k1un5zw+PGaXd9K60OMJOfoXU9TVegszKCyspFGoY0Qm1KU8WzGNIQUCEEEFKyWrEVbI+Wf3GPbtTv6biuENyVMzZh8DlIbcfzeYxIcrhb82je/xZu/+iW+8rHP8IlPvsrnAKVv4UIvghXZXnRdJ1yAOo+Yy+WhHPcMf08p4VNidXiEdwnXu7x9pCVs7H3PdcggBCTZdyL9WGr7SWmSltq0qhu+9+1v8Pd++Re58crrXPzpP01tKi7bNT4rZaWURJgfLaPZTBknJ1wQsXHZ/pCEGBi1CFsom+uaQq7CpsxF0sP6jvZV0JqlFeQqMCJSMSkqU+WS3AfbxcJGVkog+VjGralyvsR6vSN1W9Hv1YbYeXyQvuJOm1wCgND1dP2WsyC61W33W2SKCyDQaWJg1GqtxzroB7x1ms0VqHZ0SBCiKIBoZUdoN439j2EKseaaWsxQW0opR3LZicaRLh+933Pi19U5p0cZYzVc2+QayuurqiI6n3umNKayeFqppymDbRqa+YJdt+PJe+/gDxXbzQXa9yzrJed+g1EzrF1Q2QqlWzbrx1T1S/iwFadmwJqZMEIzPT9GNcChV3tvpz/L+TzWGhaLuYgEGEMgstlsSSEOvV4hRWLQGJUgOfq+IyVYro44Ob6Fe/RgIHhZW+Nih1YKp5E5FlGhVEClxC6TNaqomWmNV+Ayh1HLvDGiVsSQsj2TzNVYK7Wa3FIgmsdyT4tRNMbSzOcopG0oOkff7ljMjvBO0IGYs2Zraw4OVlRVTVXP6J3jydNTLs5P8X4LoccaadDXqiJETdt2rHcbNpsLtk8fs9lckJTHWMMcj+sW9P2WyjZ0ztHutjjXcfPmLREFQNNdXqDefYef8/C9777NveNPkoxBpUToXW5fiPje4VrHzmyYL5eEeWQ+O+Hw6GOYtGDnLNoGtOpJPuK7gLEVi4MbVHUjbVbB0263nF88JTqPMYrgO5xvs2CDHzIvU9WAwivAZKTAOc7Pz/lTv/9f4PLJYy4vzrl344S42aFiwHuBAr13hL5js1E080Td1Fg1SmsWFAHEGLf9lqjyZJoo8LoxGmsh9C3bdke73dG3G7R2MnLOaIIL9F2LrSpS1AMxS6Ogqtg9OOVnfOSlzTkmNbz6mT/Ck299heg3xNCTlCEFT+8FHmaxYDZrqJqG3rkh4M7uKdsvS9f1xJCEDJPHgdlKMv1EpO12rNcXRA3RZ5QoJw7JeaIBr6Qfcmlr+i4y/+47fOLdc5YHHe9cXHLXGPq2I+ncjZBSHowQMKZA5dJW5miBOJS4Ukz46Kmt5ebN2xAjvUtstx3WLjFKqGKBRFXN8JdrtrsNu3YjcK73aKuJIRJCAp3tqILFfJ4V4CZkw+ccWpF7XGXvJaT/Vkh2UNmaj3/849TNDbHdmXTksmDEdHRgTJGqkbm5PjjOLy6A/+B9/cdgn1/oVf8jPaYp/H5NMmUnF2XA6jWO6XmZ6LNzEEuWdQ1jq7wOMbxRje/V2Rhn+tje+WRzjON+ps58em3l9VONVIHL/DOM3b7vMQqB/9D0vef09DFawayuqeqG2WzGen3J4/d+DVvP6V2Hc2uSClizYEZFXVfYZoa2x8Ca2Ctpkk8d0SqWs3ts28d412VFH8vVlSzXVo5pD29VzbDW0vse3/mh/jTUlaWbD5UiPol+r9YK73u+/vVv8pGPvE5dVwIR+kBfKyHLkJgFR9QKZ2oRUAAWUZOVDHFlbeOkX1QlKJKxWZXIh4CpLFWeX7nbbbncbjheHuB9T1XVbLc72rbl7v2XhyxG6mnSm2ibCud75vMF8/mSWbMALT2TT97+NtvdJSn2LCqFiQprlmgje/X88oJHpw/ZbC/Z7XZ07Zawu5Re3qqitnMq2+B6T9u2pHTGfDHn6GgBYUZKBuV71l1POpxx+2O3+cxJwzdv1djT73C/mlEZWF+es9tsMinMkIgiGrFpubx8Sr1cYSPQNygb8yishFEVCsV6u+Fi13PZBqwVkQFCEIcXHLNZjfcd5+dnaK3YdS11jDQJlLIYU+FVxFvDW7sLLroNN37bDxFvHBNfv8UbXNA//C4vzQxmnfDRset3NH4uzN62pe0DVW2pmzmzZsli3jCfz4ihxbuIdz3eXYKuUMhe9T6KfGLa4nYtzgURrnAdzSyx3W6ZzQzNTHqOZJiz9MkapdjEnv/227/G2U9/jBtf/Tx85jbfqy/4m9/5Mrc9nKPpeydby/doLN57zi8v2ex21HXNYrFAVzXL2qA8uF2HdwGlhABUHGtKARc8ldNs2y1t19JtN7h2h13O2W63+BSZLRr6vs/Zr+x5nxx92zILcPTqCZ/4qY9Tf/TjtHXN5cN36dsWKiOD1VH4GAg+ErdbvPc0zYy6NjR1g8YSvNi3wrhv2x4dY+4+FjTD2garZTZosprNtqc/P8XT07ZbduvN3jDwMihAMmH/gs0t+3ZmHJuXa2aFexICXedp42W2lQqrhJlb/MPUPrmNR+QAI9FvX/g6PtROVHq9LSF2kg3YSup0SHQihJYyKkuUMATq04OjLfBtGXNkrSX4RIgarSpE8aQwg8jTQsbIr2D6kgmX1wjpJSRDHQ29kcZ9FRWRgO/cHpnlfeHlyWuAIQstf99bD3Rm/o11HN00BO/RtqayDaTEfHEfHT0pBEDgk9pKDc/UNVVlhdKvVvjQkqIiRUOfHHXT44JG6Zj3q7CWp47/OjaxBBqWECTj6F2LD9IfmRIDw7TURKQWJPVJITJoDg+PeOett7l18wYxQoiKKjUC5StNT4ZZJxl6UKV6Kv9XYPf9xYWioaSBmXQT0nknDtUalgdLlkcHGH/AbDZDbzb4p0958vQMqy31bEFMAec7fOip9YLDG7cw9YykLBsf2azPefD213j44A12m6fU1vLRj3+apl6y3Um9d7fdijDD7oyu3QoBxAcqrWmqGjsTB6qSBFN9u8YoCHUjY9CU1KHsTHH59CHLxQE/+s/+i/zQj/4Ub3drlJqRdEOKkXW74XK9FkJJirnPDjrnMaoibHeE4Aje0ahmyCQTIvqfYk+3fYpNMJsvsJX0L6YEwQdSVdFudzx87yGvvPoK3W6L1pqqbggqIxgpYEKijpFDKg5u3efTr36C99YPWegZM6/YBsfCKHrnab0jOEflYGciRvf0oSb2Hb67ZHs5x8ULUrTopCFFQg92AW3s6NfnxODYtlsqY3BuKxlsTMTg8L0WWFIFUnI419PoOUmHPMXEUiW42ToOjm/xyX/jz7LZdcQYqa1iExwmSLtMGz1922Fqab8jiKxi323p2zXaaOarY/q2p4sOoxLRZziWiAs9m00L2++xrWaUVhCZ0BNQrmfXbUErlikJc14ZlBaVNDAkO+Ot732V49/9z/CJn/lZgrI8futrLEONTwEdyS0hYNJINHOho/M76n7GYrmkrrVM+KtEhS1lNj2quE9pF4qxp6eW4GXbc7k+p+82mOSJ3ongg0HkJck9skRikL7UylbyJKaIUeS+UTKsWEpt+TAql4ADVimJm9Uo4CFclKbQUURBLoE20mOsUyY4ll5FrYbPKNq6L3J8qJ1ozKyuEmUM6hwoytrEmDDA0ITMKIYw+KBi+AejP94sRX6hyv424/AFoixMspg3YirWmrHOEbMEVYqRED2+l9rDixZG3w/unZKOUoaESj3XWoEnRINUGsxTSixXx/mjBaJJKWAzscfaKhNfDCEpUVrpZJi4j47ZrMf5gHcd1giztm7M9Y5z8rMy8Lj3O+K2zapSSWpiweWIKCIaRIoYO1LOAJpqBii26x3b9QXWeJRuqKo5Vd3IuRPErCKjJ5nmeI+fhfenKznsjnxvq6amDx4XPKF3hLbl4nyLMhXOaHrv0D4wX61QRpRyUg4obNVwcOMuAUXrPNvtGednjzl79CYXT99B+Z6DZkHdNLjO026fsl6vadstXdvStVt2m0sJfrSiqSoaa9FV0c1VeXh4IkVH3/c0LhC1MD5VVjOyMVEZQ2xW9Ddf4hP1HNsHqqjpgfms4rtvv8nNGzczsaaXmnHUBOMh+Tw0OWBCL+zoLKFYRqZ539F3G6zVGDUTNmpMQnxxLdvdhs36kuViyXp9TjOby/OSMjEFqWFVysg5qopvfOe7vHL/ttx3D955ohFVLe8cXd+jbUVMLfQGtM0sT4/2gfP2berZTWb1Eguk6PBe7nG7u6TdXjJXiVQtpMZNXtOUaNuOrutYLORZ7fuOup6LAk4Odg2KW/MVWMNbp5ccz5YcLxckIpt+I7YnRHz0rDcbDquK5GPmEAiLVdZaE6IheC/MZaMnGzYTqJyj69bMZivJuLJtijHQdzt611HVDdZo+r5DNTMsJgf5EZsMK22wi2PqSpCq46YmXO7wMWBzm10Mkb7rcmkqEYOnjx7vAj4ECRyNxnsJLExWORrROY3vezbxgr4PKDTB9Wy350gNV4hzKCRgQZKb4clLovBkKztOq8prVfLy645YmAwZ9JP1K1rOYLUQ2MrM35jiMFdjSgLVw/NbrufFc+IPtROFkaWqSyQiP568IkvMAaMZzQ6zRB0Itk6cQqlMXjt56/TcV+DXq8uu8u9iCqgk8yZTTATXURz181pX5BpezMlOj1hkB5OoFIHJaidCX/dBxoaVAEBpUHmmZJV7/aqqxlaVzCTN7QLOOXovhlYrhfMekpZm9yvXOvbD7YtDKB3p3Y5d26ESWe7MQhQIt9QPExCjEGi0tngUfed58PZbrJY1Z2c7muaAxbLKQc7k5lxdxnLv0vjP69Y4DgwFMQy2qgRdCJHkA7HtuTg9ZzarZCao1tSLJYeLBT7vEaUMlZ1RVTPq5RGPHr/L09OHnD15l7PHb7E5e4fazDg5vsN8sSBpxfZyy3pzxmZ7gXed1MF8ILge0NSmZtY0VJUharmnkokoFJW0YSiXm8kTaNBJan+11uikOb9Y82t/91f5g3/gX8BdtrSuIwA3T475+1/8Ip/+5A8LWzMGVJSaX4/LHZxizGL0JEKehSvRf8ozGEMUgosQjoywm1Vis12zXp+TkD7KrusGXWs5T8DkYQ1GKZSxNE3D3/jvfok/9kf+EIvFDJ+83P8YmTc1fdex3m2pTo5IvSM6hbKBgJBobO9Ynz9kpRbUtiFpTVJCHCJB127Zbi+oDlZ4LczpkMbHe71e07UtIZZhBALRT9sntFIsZguWxvJf/3d/kS98/vN89LOfpWtbLruOZIW85r3n6eU5x8dHdN5JWSgTAIP3KGPpypi4FIjKQC4/GcWADriuxdomO9HcX5qg3W3xwVMpYSn3fU/VNMQk0jA6RVLU3Dg65vHbb/Pg8TnNcsWPfuFzfOP8DYEzo1B7Uoh0bYupqmwLJcjxRLq+p+87rBFpwbbrWM6bgRksz5ii73a47QZjs7Z0CDjXUtWWdrfJTlTOHXVJbiTtISl877CVHWxOyrXa8VmFaSi8x0eZPvI5k8+WJPvDDPPGQAi5NDaoJCXIBCgJZCayry9wfKidqNWG2lb0vhNCjbGE6HP9MlOu97KiIkQMe5Z1cozOTFFYacVZXxcPTWFMnWVeYpRoXOWorvzeGI2xhhD6QeJt/zPH40Ug3quvKyOklEp4L9dbVRVKSdtCArrdjtB3KAwhBnRmKsaYqOuGumqwtkFbS0XKlH9pAfBeSAHHh4fENBugXB9Gyb9p9ln67IYJGLEn9YF2vabv10CibhYcrl7Ch0igh+jRMWHMTPQ0deL06SMePPgu3/3ON/jCZz9Ht/Fo5jCX2a1jHTq3pTBGx+P9vm7xxr8GeY6G59EAVVKoKMOe67sLzMEclTy10aClHuxy9hxiwFhLPW/wMfDw8QPefOPLPHjzS1w8/R4xbDg6fpl7L/8gdT2j7dacnr7H7jLQu0tC2hGDE/ayrjhYHUhWa6SlQWnJ1KqqYpc1e0OoCa1nsWwmZLhElQJB1eADs9WM7knHv/Pv/Hv807/9p1ktj9gFxy46Xr5zn7/55Bd5+8G3uHXrPrUVwosxAdVGei2N+5UWYl3yMSv8RGJ0Mp8UkXMMQQhgVTXHB89sVvPue+/w9OkTjo5WdK2wXofeVh0QATZNCpFIoraW49Uhf/e/+xv85E//dj49W7KoGjpr2W53LOqad88uaH3g5MYRwdXUBmLcyjOvFS5Iv3eVFPiA10GEKrRis73E9bLOKom0ozIeYjeIlD98+FCchw/ZqIJzvcyrjTFD2Yl2k7jz6n3++1/660SrufX6a9zWNTvf4r1mVjd473n36SM+dv9VYtZ6Lm0rYo1yO1WO9KQX1I/kyJxNGqWlZSa32SkPs1nDw/MzSkuX7zsRZQgRR6RRsod3OsCi4eztd/kLv/RLmBsn/NzP/HaBSWPCp170tZWV+1JbQt+jYsKiiEoIlF2MdJm30O62zCotVfTQi0Z0UvS7DTvnMcYJqqUV1igIjt3mEud7zGyBzqQpZQXa1QqpN6OYZfWu59nF59nslICkJhrDZAGLHmUatMp2CESsArJd1BlhFIzAOyfI3vdRnf1QO9H15gLT2EwDz8LVWphdaHIt9P3hUNi/LTGOUyZiHI3qdW0yQ3YzyUhDnnJgUPgc0ahMzS8N077fMYo5XH98EDPt+e8bHbbAvIm6akAZnHcoLXJ5kgXLe7QwkkReVpe/F+agECu879iuL+lWa9JyhbDnhNgkiitxcJpXmbkgyWLUhqqpuXhvQwotfXfBev2YykZm85s09QHoRnr0+kvOz9/l/PQxKhmWy2O+8OnPE2Ni129ZHN6iaebfD+ryvkeTIPg01M0DoKxhvduy9T3HN48IlcEqPfb4xiSygESSj9gKdm3Hl7/ydVJ/wcWj75D8Bavlgrsf/QJ6do9H6zO2D8/oNy2xDVjr2O02mbBhqasZs5nQ/4tTlwBc2oAGsQclczbPzk6p6znr9QWzZWS+WBClJ4tt27Ksj7nz8it84ce/wK997av81E/+DE2zoN1teLo55Sd/6if4h1/8Ij/2hYaX773C2cWOxXzGzl1SZUJJDCJFp7XJ5RFN8op2vSH1a1J9SFSRkDqMlf5K5xRf++o32O3W/ORP/Chvv/UGy4Mb+BBwzmErUbjyKWYiUkRby/LokM/9yOd58PCUj77muHF8zIOLp4Rt4MbtQ84fn7F59IQH84p7y9usOScpqX+aIM/8bHlE0hLE2mjZOkWlNd517LZPaXcXkF7HOSeKPLGIV0S++71v8/kf+RGCFx3kZiayfX3fyxi6fEN2vefo6BYf+9hHuIyO7z59wusf/SS8k+g2F6xeusM8Ntg3W771+G1uLY5IRmBWnZWxOu+oZktEbSogM3wF9QlKhgt453Fdi9I2jyArKM+Mb33zG7z62keorGa7XbNaHdH5gMUQjSYYhVVwuVnzA5/8BAd/62/z6PQp565jYWtOXUdIgcpWmMpwcXbKob0lZi0jIlRCVAw+YawR/WtjMjIhYhDeS73YagXRi+pYCkQXpRWpadhutySjWSyXhLaHxggBDtnXbtvivaeqs2Mr07OesYPXPfAFjRLeRZnnHKKn6xyL1SxrAcfhefLO4f2oP973nvl8Pugkh98q80RTkijReU9wnhCm7NqUyTX7X/E61mipcw4/UyPUWlhfRRMpSUWDhLRQqCvnEidMJsdIJmqrWhqYc4bcdWtUJsE8jzn8vD/XvXb6faZCDCA9T0qLQLg1hna3xrtEcEUAP7HbSR9pXdfSv9lUaAumNrTdjr5r6dsdXbuhthUu0/OVyaPTssrLlFR09Zqih9iLWs/BsoEUsXrGnRuvM69v4/sdp6ff4fHDr/L04de5PHuIVnPuvfJJXvnoD3Pzpft0wdFuN9w4OOTo4IC6rvfY1NM1SrlA/UzwwwTUT/nexxLJyj4wCWwlog9d3xN6zzwZmo2jdgbaQGw90ed+vCw1abWmthYVA2enD7l99zVe/dhPcPLS5zhfWx49fpeLx49pz7b4bYvvL1hvTzHW0MxWNLNDqloGSmtrRN5OKUJBQZTKAwykDaP0CFqjCc6xvrjg9PQJ2AqD7P0YElYpfuQTn+DLv/4VKqVZVTW4yOnFGXdevotet/z6r/8DvvQbX+bGjWPOLi/QlRj85D06RYxNhOQFNiaSose1G7rNYxFrSJJdbDYbDlZLfuVv/DIkzysv3wWSiNUbaf4PKRIRUZAYPWg11MH6tuXu3Tt88R99kfeePMbOZhACi7rh0aNH3Lp1k5OjQ37ja19lNwflHKrrib2jdyFDxpqAwoVA129o3Xt4v6FSFSoafB/pWmkz2252GF2x3ez4yle/zN27dwVuzuILwTsIgsSklAOmGNEx4QO8fvclzh4+5o2vfYPjowOqeiEs+M0abRQ/+IMf440vfoVkZVxe3+3wwaF7j0OEGPre0fVO6sQp0XuHj0HUsLSi9w4XRGyEJNOEvvTFX8OUTI+Yg7kcfGupW4cEJnm6y0uOb9/lpcUBzXrHt773JoeHhwMKQIoYq/DJs91tpBeZRNSQhr3WST9qjLkeCn3f4YKTWjmJQKB3HV23o+9F9L+qKk6fPuV8fYkPUibp2laY3OSJQRi6TcfFdjMMI4gxayxf4VdMj2mLisqkoJi8oJExYbT0o6eYBoEXrZQIgGRd77qu0drQ91I+slaSMe/cM5/3vOND7URnjUjPaaWyDmShOpdqpzjTD3JEwH4hWZEtbNx/fTayw3QJJUzS0twrN1qMiQ+5fSN4UfLJDDBlLCF0Jb147nd73nW+73fIF5+S1Ea9d0LrzmQUlxVxfC/GQSFC3zpHfALJAlpacEI20pvtms1mTfABkzcjVxz2+212cs3JAKHTLOc3WMwPMNbSe4epFlTVDVbLlzlYvcxq9RKr1W1W82MaM8NEDV4ccT07YrG6RV0vxKlMxKpTKupSqnzs3ppN/6jJtQEErUQjVOdd4wPL2YzYO1zbopTCp0TneyE7aIG6YpIZpykpgdCT4vjoJrY+Zra6gYuKy82O7W7L5fk53a7FdR3RSyO9tQ1VNcNogd3RRsa8KUSOrqoxVYPShuVqxenpU2IMWGvodjuKoEVVWRbzOYv5nL7rc3O5FnZvCPzo5z/H177+dda7tcwCNZqFmfPeuw/47Bc+S9i2fOPLX+Xbj9/h5GiJax1d20qd1jvOzx6zXj9lt7vg8vKUi8snWCNs89610qivNPPZkn/wq/+A87Mn3Lp1zI2bx5yePsFaeU5VjiRjkX3LNS2Vm+MvLi74bZ//Ed55800ePn5IIDJXBjur6boebxJ2XjHTFX//13+Npl4KrOhbgtvRdxfEriX04GKiz/q5gpR4tI4onWjbFucc89mcp09Peeedt9luN9y6dYPtVpABCULiYKxTklk3KQekZ9s1n/70p6gUvPfO23LP6gZtDV3bCgw8b5ij+dLXv0IMgZmt8F3PNgcQKY2tajF6YnACBlFKKAIvOueyjnTk7Owp7777Dnfv3SWlJIQgCuqFyOpl9Z9EEoh+teTmzWNmVvP1b77B4mCVA8hI8KKj3DQVT588FlGM3KYUoiflrEwIY8LKKW2FitwCiKxpZvdk4pjHe887D97BVpa6aYS1nWHhAbkC+rbFNDW1NUPgKM0Ok3GKz/zJtiUJ6qDU5He5xm7rmYhVqDwAJM9GrZtZFpuXtV8ul+jJ67R+cd2/D7UTBYaepaHRupjHvLBlc13NVqaOqGSuw5HTFSG45OHQw+tTrlVkF5jSMDmeTNYJMUjRPiVc9IQgUG5CalvedxOc7p/cMcAVuV/KOYleKyvN5V3X0vXb7Ew7vJMWE2tsrm+KDnEJCLpWXrvdrul7qRttN5shIiUX56/WQq87FAmlAiFEIQUtbtA0B6SseWpNQ1MdMKsPaZoDIejYajC6JLC6ppkdUDWHKJOl7tSzTlM+b2y+f/81K+SEJEOgtSIoyRCODw6JPuvh6iLmUBSrZX/pUvZOSoT1USwODtFVw+nTDdtNi+tb2t0FLrMflRKNUWtrmmaJsXUuyCLEoMm4KK1l0HRV1bjg2bXiOK0xMk9TqVzbVtRVJcLv3kNmJHZ9h/OOT3zykzx4+C7vnT6m8z11XVHbiovzSw5vnHD75k1S3/Prv/r3effJI9nmSC+d847zs1POnj7i6dP3ePr0Xc4vnhBSQldzdI7e27blnbcf8J1vf5sbJ0ccHCxQKrFrdxhTZ0RnzDSYPos5EL68uOQzn/00qu149PAhT85OWTQVyYqG66bdEgjcuX1HyDIPn7ALPg+F9fS7M3y/wfWy97fdDu8UXevYbS/p+ktC2pLwGKPZbjc8evgel5fnLJeL7LB6lGJgdO4H4iBj1Cxn52f8wCc/ydF8zub0CafbDfOZiAUQAm3bsul3vPrqy5y+/S4PHz1k2+6orCGkgPNu4kgSfSfM7Oh7XC9/79pdnrYiNJyLy3PefPN7NE3DcrkYgmUhf8VMLIxjQAlCGkqBuy/d4dbxEd/+1rdolosh+/fO0/U9VWUJXUe73tB3rdz/kBWtUklIGBxoSTxKEO36Xure0UugFD1Pnjxmu9vRzGfUVU1wUguNec5rqeXvtltWJ4eC5KCyAx2f0T3bkkYbzIAgFSLRaAfEltnBeRZRX20qKSdkkX7QNDNxtsVGa/NbRIB+t90RfBgcgPfSrCuDeAWuiSEP531uQTqr6aixXaXAAwkG3dgC9xUiS9lI5b9lfuEUXjRaJOEkkBVmHUrRdluYGPnrHPtvlpmrlGSe1sosvydPHkk2ajSu29FuL+n7Hbvtmu3mkq7dUhlpfFdK5/aWhqQ0F5dn8prtjhQTs9mchw8fcnl2LuzCiRMt32NcizRm7EBSCa8d1B6sYrY4YbW6y2p5S6TK4pp+d0q3e0rfXrLbXdDHlmQSurZUsxmz5UomymAhZkatHtdJ5cBE1nIEbq+rZz+z3j5CkL0QlIzfOrl9i4CMOkta0xNlgHVKqBDQIVABhOwQEPlAZQ0p9Xzpi19hfb6hqSqc22BMoqoszWJGM59TNXOa5kACgowAiJKOTMdIAZlfqRRV0/DOO+8wm82oa6md+eAxWu57CGKUvetprEiooRLOdbjQc++VV7GV4v9H3r/Gapal933Yb1323u/l3KqqbzPT09M9dw6HIilKJke0JZoWOHIYB4YUIAgCWQ4UIFFIArogMGwIiB3HZqxvQQJ9smEBigUFRuxIoS6QRMsSJFIkRYUih0POkHPr6Znuqu6qOnXOe9t7r7WefHjWWnu/p0719DDJh4Y3UHXOed/97nfvdXmu/+f/fOUbX2Oz3bFeLglmZLE+5Svf+CYf/8wn+eynP85v/Xf/lL/x8z+vgJfWEa2hD4H9fs/Vo4c8fPAGjx+/xXb/hO0Q8d1zdN0aEeHbb36bn//5v8fJcsFnv/czSIw8fuchXbtAjMWhuXbFcSSKpJyH3nf7HS989GU+9sKLXD54wJd+58usT5YMElifnrK7uqTfbvjAyx/ktbNz/skv/RJvPrkmOvV0D7tLQujpDxuur97h8vHbxMHw5PETnlw+5Pr6AYfhkqazNI3jS1/6bR4+fMCia/nQhz7I48ePM8k6gKZzrDOVGs/mcL+xluuH7/D8h1/hgxd3aPZ7vvTNb3J3fcbCt7TGMux2vPXwAR/7/s/wcnfCF77wG3zpG1+l6TpagUNORaUYCOPA9voJ26vH7DZP2Fxfsrm+ZL/fMo49i65js9nw+uuv89WvfoVPfepTXF9fKzjKKmJXo2ADHqU/TCKYpGxdl9dPeOXll/nkR17lzW98HWlbbaEnyp2rRAfCndNTrh4/5vrJE4BMxD8SMxFCMZiVtH7a79Yo7Z4a573W3I4jX/3qVzg/P1UqTBRAhDN6vaieLCmxvdpw9wPP0zhXmcNu1pwfHZIlan0/I8lLNg6glMsJqMfpatNwk/mIo2QYYu6XKqJK1DX/A6kTbbXwqXJqWu9BAkV4xjTirAIYiqxNt6Fehao0deOodWKs0bxXdnlKmBSeFsQaCm1o2kx0gMXbFhph2Z7gXMvhsOfy6h2ur0vni9sV5XdC5j5Lwa7Wa/p+4FA8Hmc47Hre+Na38LZhGAeePHnMuulqQl+7YETOz9fqGS2WxBS5f/8+V1eX3H/wJqE/sFp0nK7WXFx8mD4kxjGw2T/iarPFG8vFxQXr9bqyj1TO2ZL3Fa2vM3hlRpEe0wS6k44ga2xoaZeTQWGthwAYUdSzBWMjzQJoAsk1GQNltAwFprxISkcGynsZO0ARiSZHM4aAd47tdstytcIZSxtBrGQcg8m1r1MzZTEJUqDfXPODn/6DPHrwNt9465vs4kv84Pf9Ee6//nXisFVF4i04yxgN3mju2jn1OhGritkklquWlISvf/3rHPoDr732ErvNhl1/oOta7TbTtBmRKIrMlEBKHjFJuWVDJPYjf/KP/wl++1/+Oi+en/HC930v4U3DenXC7uqSb3zta9y9e5f/3f/xL/J//8//Kn/p//x/5SOf+Aif/MSn+MRHPsGHPvgqsX9ETIdsLDjGseGb33qL3/jir/Hmm9/ESOL3ff9n+L7v/Qxf+Z2vIJBLMwzeaxNop7T66pFqfQyg4VxB6Revttf82Od/nN/46u/y1a9+lX/9X/kD8I2E8y3rkxM2V1d8+be+zPf9qz+I+Sf/nN/6R7/I75ye8MqnP87LL7/Gafc8m8t3uLr/ZZ5cvo47Ocfbuzx/+gIvPv8qm/0THrx1zVe//musVp6PvvIhlosljx49YrFYMGfBEdGwo2v8FM7NYaooA1E8P/i934sMB/7O3/37/MWf+Wm+8eZXoHH44Bl2O/7Fv/gX/NDnf4z08/+YL/76F/nNL36BP/Jv/Dgffe4DJNOo8gk5Xzr0APjG0baek+fvcthd88u/8ss8evSYe3fv8mM/9ke4vr7OzQ7crJON0BhDiEJjhIZEdOAWCy4fvs1Lr3yQV4Y9j3/lF/jdb36dk/Mz9rsN/aZnOGwxwEvndzlstjx69JDH14/5xGuvcjhoSmeJYbn0JDE8ubykax0xjJmzNjEcdrRNQ+Mbnjy+5I03Xufs7IQ7FxcKjhLLol2x73vOF00OvKiB8uTykuWrd5Gg5Vvz6NazjqMUWi1JmSIGkjSJpCBL9LoOTR8Yg581FUmxNPhWlWi+C//yfa1ED7srFqsTjF8RTWJphTSOtd5TUICAc46QxkoxVXJXeuiExaTFvyEE5aOUQHYhSWlarMpxrwMcYySkRIiJse9zc93IMOwY+i3jMPLo6hEyWlqr6NiHm8d8+OwOThLuBorVSA4N5AmPN8IlzzrK+7/6L34FULRs13Z89LXXMDgevvMWMYw4Z3juzocZ9tdaPpLNtjE6Hl1d0afI5eYh/dDz9tv3ubx/H99Y2sWSrl2Q8EQzcn7vDikltruB/fU1cRgJw8ji5Q6bwT7lSAgmP5TM7tdZ9b6SSSy6NQM7YtSwVMn94PQTKfeKdH5B49d4t8CZFvDaZLkMTWaWMjXOE/UZ5dlh5nLMc6lJtGvKnYsLIsK+P1QGJTHazq3kk4xok/EQAiaJKlvf8fr9b/LDP/yjfOV3fodvfOPr/J2//Tf5Iz/2r+HsOX2/57DfcdhvAfXwY1REt7cW4xpc5+jHPd96822ur5/QLRo+/tFXePz4EaCGxW6/Zbk+wfsWg2Al0ohnGCLtalRkLcIQBr59/y3+2P/43+Tt/9t/xdWTJzx6fMXp6SmXj99mtT5hv9/y+NEjHr3zNp//n/wx/nB/4F/++m/wW//sF/iHf/P/SbNcsD5Z4VqnyM1RmYOcsbz64Q/zr/7wH+Dec/c47Ht+8wtfVFo7l6nWvKVxRvPNGW9g0IbZVjISWiRHQuDNr7/Ov/ZHfox3nrzNV772W/zuV36T05M7XG8u6boFrCK7OPAvf+U3eeVjH+cDH3mNb735Jr/7G7/Ov/ylDa+++hqn6xUxwiHeJV7uMLzDm99+wGF/YOgDmK/yqU98jPXJkt1uw+MrZW4SEc3f5jCg4vOShu0TEKIif1tHF1d8+/63+fAnP8Z2f83f/r/8ZX7zf/qTnJ4+h00GGSPBDmx3W/75r/8LPv5Dn+XV7/kEv/vbX+Jv/ZW/yvmL93j1k5/m+edf4OTkBLdY4NoWCZHHV5c8fP1t3nrz27z99tt88rVX+YM/9Pvp2pbLy0tS0m4j9Z9xuRRNaIxG56I4EO22JH1POhXu3rnghz/+cf4ff/vn+F/80X+Ts+UJu801m+2OFnhLRs5eusBfOu5/601+8Zd+mY9//JOcnV2wXnQ0TtjuN7z1jd9lv7uibbRsLxnL+dkZT55c8eabX8V7z4defpnFesX2+lqpJb0FG1kYrVwwJuEQQt/zxuMHfO/3fxxtXJ6JV27ZsvN9nMwM2Ckqz6oMTZEYDqSYMKbFWMM49mw216zXJ3TdUt9D0ycpDtmQ8zmX/D+QOlFJNrfdCUQ5cAgRZ5ZqbaQBCQPisvBOIGI0zJVUpCdTQkkgJELSMK41FifahLptWkRgzMi//X6T69xcpllTiq9xCDjv6RYL4iFxvdmSxshyuWZ5tqJrWowR7obnuLhzp/bTm2jxpmMipn6P41DCwbmHoRGqQWCNkr6HcWC/P4BE7ReYmwSU5dicAAEAAElEQVTHlLRjwTCy3Wh4aBh69vsdh8OB03atdZJdp1aaIeczhLZtuXv3Dm2uRS15myMEtN7h9JfRes4pmK2F5pJS7ogSFMwhmUjfeZz3ON/SNgvabqkED7nkow7cM0LhRo5zpcelQ7NcuLFHUYVxHFmuVpWruD8cNFdptL6wAE9KaKvk5seUeHL1BOccT55cce/ePa3pe3Cf3/zCF+kWLWenK9arBXcu7oLTvE1pIxdTYt8feOedt/P6gJPTE07XC66urggh4L3LuVKta+wWJ5ydthMS3VpNcxh9Ft+0hDAitHzP93ya3X7P17/2VV599SM0TUNwnsY3SIyEIfH1N15n0bR87LVX+ciHPsRmu+Xy8gn73R6sEvc7Z+i6htPzM+0fKsLbD94hxkjXdTkyk2ujc6qgTtZs2AXBpJTr+3Q/jEG7rbz6ykfohw2/8Ru/wY/9a/8W15vHGstoPO1CCCx48PY7LNqW87MzPvvZz7Ldbthstzx4+21i7nijxB072nbJ6dkdlos1bdNgMey3WxDRlEb2P5wxpdJrtnhK1Ek95xASnfNKILBY8+LLL/Ov/ujn+Be/+Iv86B/4nDYwsBbXNvjQY/YH3vjG12kWC5579UP8j577t7n/ta/z8P593nnzTQBiAUcK1aA8Wa547Qd+kOWqU8KTXkFSXdcpQ5MrwBsdu0iiyXItiWDJRqa1bHY7fNvwIz/8w/y/fuGf8uj6CTjDarkkjD27/R4XlPO3bT0fePkD+Lce8Pa33uCtb32L1ekp65O1AsQah28bxqFns9mw2e/ZbjaMQ+Du3bssl9pkYjjsFbxIhZooQ5WzGpGKStjQx5Hzs1NNxd2iwJ4K69alpBvc5OiAzMCgJSxc8C3WOZbrFb5tlLZSynUF66dqie9G9sL7XImGXHTuGp/5UiGSck/RDAiKGhOXHLJFTCWHdzUMopORYklQa8PZ680Vw3DAWk8MSn9V2C688zjfZEGfsLbBZhq8sFiyPjnDJmhWLY3taH2DtbCSgGCPEvNAtqJuJNB5evHcVE/l3gFeevElYoZzt22bQQtC4z3BO4Yh8eTyktOTFUMmsY8x5rZR2skjRgUFmIzq7fuBxWKpvS+dCulxVO9+sViwWCzpOiVDB6VZnLBdU7eZuTAib2xrlSXJ5qa6MTWkEIhhMjB8o/lB5xvth+oVqTr3bI+PuTlyM5R783U5Ol3S5PkXZeC9ZxxHdrsdTdPUMZ2E6hRJOBz27Lbb3EWnYb/taZzl5GSNyPM8evSIGAJXVxuGQ89yscA2CpBKuW9liEEbJfcH7fjReAWihKSF4yUU5T1N0yGmZ7/bsV6dZWEhlN6XGo40IBZjEofDgVde+Qjf/vabHA5autQ2SmOoys5B49hsNsQwcnpywnK9ZLFcaOeRYaBwHFsDy4UyW4Uxsd8rBZ22uVOFrs2dS69Pp/uvCr/5us7KSYrAN2y2O1548SWGsOfLv/PbXD5+RNe0pOCJwSoZSOPZ7zYQA03bsmhbnD3LrdYMMQwTyEa0Xnq5WNF1C5yzCp6RUYV8BtQpi1Yp2p/baJKvpWssifJvW4R+DCxOz/jcj/4ov/bbv8X1doM3lq7tGOOoAnoY2W02uHFgdXrC+Z1z3PBBmieP2O729P2gpWN5kdrG07YNpycn3LtzhzGO9Ic9KSYlubAGZ9wEnssGkxgyYnzmsaWEtY5+HGmd4wMf+hAffeGD3H/7bS5OlH6y88ooNfYHtiERlguaruX83jlcb9hsdlw9fsTV44dK0hAOxHHQMqBKBGFYLBas12vdK0n3sfdqYDtrs7eXiWickkgMfc8haGs76cOxcXxzd98akZtSbjWcCxmcZ7U9Jop27zL5fYwpB6nmpZDmSCS/1+P9rUTTyBB6bFxgUov12tPRuFyo7hoIrcpKo0pUe9w5RMi95HJoVrRpchhGYho5HLY8fPgOT64esVqtc03RktXqLotFp9a2c3WuE1K9y9OTU05Wa+1W74ThECvBucTEZruriETIC2P2NxyHF6cXi+gxFKJlgWwkCK++8qrWlGVPaRxHjEjmzlU499XVFW3r6fu+giVIiXEcc8G0U/BL1xFax6N3HnJ2eq5k9rMwdttqPs45h8FB83QbtMkYMHXj6EvTSrXWY9slzpdcatRWRjlHUVoyGZdbMxlXG68fee9H3q+ZCecbqWfz7F1yE7HdNvqM+/2eq6srXnzxRXa73RGQyhhFxfbDwHa7Y7/bc356Sj/sSV2nfULjyGq14t69ezx8+JB33n7Aw4eXiu71Xmkpc/E6RnDW8NzdO5ycnpBi5NAfuN4dtHm5dfrPebpuwcWdO1xd7bRXZtIQeKN1SlgrpAghCm1nOBwO3Lm4SwyRR48fcv3kipO1jr0qUW00cHK6ZhgG3nn4DiFGxFqMd6xOT7W11+FAHAbikNi98zaNWyhbU7Og7RokN53WRuvaAalxTfXcJa9lNaYmYEipcXRuyWa74fT0jFc+/BG+/e1v8bu/82U+/vGPEkLLMPRYq2O1WHTK2bq5JsSIcZ6Lu3c4Pz/ViElM2YjQMpE4joRhoI8B502uHbQVpFI86GJom6r0EyJWAVcJjGtJorXBu0OPbxo+9ZnP8ODBfd56600+cOcOq8WSw3AAryjQzjbEYeTJm/d5iPDiB17iEy/cVd7aYdRUUpw8qSSRcRjYXG+IRhuMt62vnmfTNNVdtpmpx1hlzzLW4kxG7Ioa0iLaU9e3Hf/K930/v/a1L9H4FzhtG81lxkZ5m8cd2+GAXTRcnJ/x4vmLnF5teXj/IQ/ffofN1ZXWCluD9Y7lsuO5s9OqPMdxpB96QGpUQtdCTomJaFOF3Bxjt9syxEBu+/2ue/TZx0zzFmc157Yl4xyKAZpS0nWZcjliCLStA5qZrHj3fOz8eF8r0dc+9gmCRDb7HYtujRXDqus0n+EsWEgSaHyLJMMwRg79wND39Icd292WzeaK3W6DlX3uG6pcs03TsTo559XXPk3XrTDGgwhJBvrcE2+MQ7VQSx7QFkRuTIwxIi6RosnhQgXkJKOJeEzxYo4tXqDmRfVIs9egrJLim5aPXF5eap/OfA/EVBdwiCP7w5Z79+6wWCi1llKQaQ3r48uHXFzcyYjiSEyRs7MzJCaGYWB/2LM6OQGg65Qvt2x6cmG/sTaDe6ZC6PnxLKSdNlReANBkHyXVYbDZUJgbGbPw8OwoSm26vjYUPgaMHZ8/3ZvWDyJU4yJJYr1eM44jl5eXvPzyhwFtkdQ0TUZAjyR03u899xySadGSBPo9NG2DGZXQYrPb0jUtr33sozhrCGPgsN2QJGCM5BwikCJjCDx654Eic5uW9VoRsN5ouZLFEMLIeXuOu6PKte97ZBxpmxWPHz3m9Kyj605pXcc4XuG9Z7Pdsmhb7l1c8OjRI3pvaRqPyAJENLeL0kU2Fy0pRsZhhH4kvP1Im2uPIxHBn61YX5zTuoV68TmK07RZrGRhWFpfaSTjRmRljq7MBPemXSIibLdbvGv4wz/6r/PPf/Vf8uDBA5brhq7rGA4jZV/4Rr2cEBNDiFw+ekwIwwwQaKtn5q3SKDZNQ+NLeRaqOGfC3tsZM1W+32RLeFTDvThHGpWqsZeByzjy/Z/5LP/gV38Zk0aev7jgdLEi9lsOJjEcBowzrLslQwq88dabEAImr8227UA00oMpRC+Toi9KwFqbQ/oW32r5k3NKGLBou5w1EUzmona1y5KCjq63O+4+d5c791dcX14i6yUXF2cc7u+xbcd+VCq/cTfy8HoLwMn5Gc9/8Dle/PCLuu+HBIMibKMEejRao20NdatOfUk1MigpYVKu5wzKpzxue7a7LevzM83rugxKnBEpPGvPzt+fzpuUZV1bKYIplRLaONw7nw12rYMNY6w84PqZ967I39dK9PTiOdrmhNVmzzAe6HeP2PcD4yD0/ch2f8W3v/7buiCtWpMpaaF541pW61PWiyV3792jWZ2wXq0wxmlxrrbDIMbIZvukCvNYXH9TuFoBXK3RKnVfADGMGvqiVUVgwLXari2EgyI0KQ2Ep6P4UKb+9u5HWT9t7n9ZPGJrLRI0vLfoFpyenGjOYRhY+IYxBIIErM8LMT+XyTWHKcHFxR2MgabVJsrWWsIYcD4LR2fUm7dFGFI3rLXTAjflweCGoivPqxZ1ZOKtLGM+rxOrIUG5md9kOufY9axfPH/vNqCRNU7nKSOw+77nueeeIyXh+npT7/3Ro0dcPr5kt9uyPjnh5Oyc680VY69C7MMvv8x2c8Wi6zikSIqBtm252lzn9bTNnJKJZJQBJiUNi8Wg3VG88/jFWlGPIZKGLRfnd6oQdc7pnDjPk0eXrNaqUraHPQ/feUx/SMi3ej74oQ/z8odeJUTN6xtU+HZdx8XFBfvDFpfh/d43LBdLfc05FfAiGO8wjaOVZSXwcFY5kxGtxbZec7vOCM6Zyn/qnBqkUyH7cfVuWSs5iJmnS/Ct1w4/KRJk5BOf+ARvvvUGxna0jWex7MBE9ruNtv3K6zah5U1d11II3MUYiGoklTRB13XEceJ8LvnbMr43BfH0e06wMdBLy8I1NCkQSYwiLBcnfPaTn+Lthw95+9EjXrx7l2W3YrzuaZwnWmGUBMFwr1kxSm68IIAYQoz4tsFZm5vUp6xIwWIz2Ykaq9baavQZE1iulrmDkWTCDq0VtijgppiT1jmu48AnXv0ob7z1BrvDnicO1us1+00iesElTTkFCaQxcPX2I65EEfdqUBiwIHlOnQjLttOOP87V8VKvz6nR6Xw1ViQqfeS+7xlD4IWXXsRbyz5z+s6divdW7lcyrvWDVc7EoIZOkT/Oe8axJwZNXVljtLzNfScim9uP97US/eJv/Rr77SPC2LNcrxhjwtolDx9c0u8Hzk5WvPKxT2Oto22032Kht1NPNVURK6LsN9Y67QUatGjZeXX7rc+Q6mSOvKySb1HFpcI35e4Ghcmjpi2MErHvtlutYMoLTWP4ynN7nDucvkM///RikpmHpS3FdDGVBVxYa5xznJycsNtuMM5l+i4ty0kiPPfcc+rhFEQimstIKWm7s1wWkyK0ndLRmWJyGsPTXqA+bwE8YEwm275pOZLDLTl/VwwHU7OnZRAqCCBf/b1vrtm+ONojR3tuUq5FkA7DwHq9puue8PjxZbauVSA0jadrO158/gX+37/+L7l3757SoA0DYxiUTzik7F1aQozs93vu3bunwKkYtB0chphGzfOZlqZbkHIEwZCVlRl5snsHUKVkrakeU0qJs4szSj38YrHgyeU1cTSs1wsaa5F0AJNwrkHEaHPlPK/DeKgUZ857kkTa1GojeO9yX8pSyC94n7uZ5PBr0zSqZE1h79L85lQmMjVfLvNuctSiTL6mGW1VaBEyn21+P1kWqwXndy447Hfsdge8V0q3MPZIAXoZS2taxjFk8wvEop6P0fXo3BTdsd4oAKiEbp2p94CdDMO6XDTRhhiDzcX9MQaIATEJsY4+Ru6cn2OicL3d8ODRQy7O1yzCijAMhJgIEhmtAhtdVI9Xt63S+Gl6p2AdLI1rSFaJ6EtdZDFS1Cv1+FyCQypoeGbpHz0samTEFMEkvDPcubhDu2vZ9zsGRSTSiMOKEIxoGsU3auDFcBQpmwMzvVUUsHd+5i37eo9QSsKU5i+KtukbhoFdf+D0zhlhGGdYkfe6v+sOzp/RcdN0WMaepJCDIspEZAr2AfSejFWgJanK0+/mq9/XShRJLBcNbtnSdScMonlQQwMRzk/OWN+5UB5F5yrgwnmXm2iXRtC6aVO2CvUw2atiqiE9Esblj+xHlcS+zCahKJCZIDdGW/5ISnWj14R4zgcU8MKUMJ99780hmF+7hHFFai5gflijhfwhF1Y7q81pr68ucw514OzslPV6BWJmVro7EooFOZszDbNneHrxlRxoIcTI0uFW7MCkI6dnEjONL6acNP/Msz3LG1d/+iU5vosC0CrGTYyR5VIZaK6vr3KuKbeBA+3sIcKdiwusMbS+0dplyS3kjPbATEmJENbrVVaOgsEhuf2eNYbD9oDx6l2KSVWQau2oJZ6eZYDGXIn6eh+StHDci7B8sWPsYbVasVi0hLDP81QQzcX40Ub2IokUpRqB3ntFiObcWxkdRLDO6TiJ5m6d98p9awrVpsnKylfFCmSQx8yjm2yjI2PMGA1dasmX3rPkPda2rSKIx1E9eQtN0xLJRfwyNaw3UggwcihRyne7qthdBlRNYVJztM6roZjXz3ytO2OJGcBSwq1jUjFsrWW1XiEIV7srhnHENg02ClYCXox2MCljm/eDyp4cwanL2SpSeA56MlpBgCkdkqxW35bnMlrukQAjQuOK4Z+vbZyC2HKHomWKxBSIccA6TyMamk9axpABSxbJYWGTx9QVoWgMxk3zO/fu9Tsn2aupq2ywALv9ns1+x4c/8rIa/TUO9Xs7Sk06MpMNdd0VPEzCOq/0g2XMXVuxJZMafm/H+1qJnq7X3L14kdYv6A+GIQXECffuaLh20a44xJFx1AJmyUoyjAft2AHT5skbtWw29SJTbjVXkFuF1aicOw205iGnvCiitafW2DKLgC6glBIpRGzb3BraZDr9qWPm51EkUYV0OwuFnWmuu/PFSqPuOPZZH2n46Pr6WikC9wfaxnN2slZL3ChHaEUMWlOf086RgVA30E3Ear1pU8YoViHM7N70OfITyTzELdQ6Fcme+3w8jjzfp0O7zxrIQtxoylDe2DM3EbqbzYZxGNXkEm0b56wjhMCrH3mVy8vHGKN8zkaK4leDzSeHC5bz8zP18EzunJO9PGe0R6hYRYxriEyv4azBOs95d6FzkYWldX4yCp2WUiTR2uI7FyeEweB9B2ZgDFu8W6NY0pSNQkuKQYn2EUYE4pjn2WJcDm1LAYVNILoyOwZw1lXrveQQS15+CsFMe2g+HSKTh4jRe9L8ZTZosuUkWGLQLjLL5ZLgfO6lqRzCRhwRLZUBA43XPFgWqElKCUjJgSpAyPtJKSkatyBzj7lW58ZAeW6Dq91YbKbNjHEEK4RR8G3DyqzAJHa7DaZxiHMKJks6ZzGOdakLFISg7iVr6lwXoa9NliaPXdNUto5TWbcmG3CSvUUMOcpWlJqBUQnwMeAaz7JbcOiFlFs4JhIpKDk9CFiDxWYOcMGIzSUk+p9kLx6YGSbk77MVM0G2n6z3SExst1t2+z1np6fvKXR7/L7c2LiFlrCcq1zgMemcFsxLSrp/xNrMBWDAemyK6lyJ3IrpeNbxvlai47jjZP0B2mbN2w/eQEwmNwaMs7SLDYZ2UooiGfVpMjgDdLtq6xzjdJEkmWDSVKWgVnyS2xmL5j9TEmLUUIyRUv/ltQls7nkXxxGXuvqZKszf5Zhg3OXE4wUUgrI16ULTHK2xtnqm3jcMgwprYzRMnRDatqn8m2Vd6nhRcxrFozZGyysKWYNkS66Ej8uYlHCTelS6so+8xYLAu/HgxVG6+YzFp9B3btF6t45VGa6bAytPf9xMOe5YKRyFk5MT2rZVHtTtFusbnn/+hVzrmoE4xnBxcUYKWsd3OOzxzjPmnKE1CsaqyEvnsvcXSDkPY1uLbSymKYq91KHqrTrjcL6pnqu12kHH598xhta3eN/Q93sW3SkGq1SAcYftOpwxmivLc6xzNePo9epFD4eEsZZWIGVEanA67zEqMMMZ7QDiMCSnvLY2G5qLxZL+MIAxWG+wJX3gb1ngGTGvDFqGIBDHfW1zJ3ktjkk0PBgNxgupa7WlVvZsCvIzRjU8MFTgnM0NE8oFjbW0vmXMHWSkeJ3adDJ71k7/zZSUMVYbDwAxgiUiFsYEBqU1TATEOg3lW8fJySnWaDuy/DAatUIN0TGjZ4sX7ayv0TBjLY332lDcOm1cPjNM1JCdvH/QKELI9cXWqczpx4HGNYBRZp+g7GHOOvo4Iuj6tAg7iUSj8sojOAmkqN5tMtrEOsaIaTzOTPvMZfmq2Ik03ZedmIestXinfUvb5ZKxH9lt9+z7nuViSRznrRSnPf5M5VrlSZ5vUbmXF4XuE2exsRgkmYDeKTuWYDFOI20xCY01pKSYhDS+91Zo72slmlLk9de/SuvP6RYnHOIObz2QsM7hzZIxaR+50rDa51KK6vYDOgmFtX8uXYunlxAJNexzW45yXqhvszXZezXknHKygFha0/GYRAg9jTkD6yEe8lW8MuLkUFbxiuWGsnyWArHkZ0iqdKz1SBxzbiLVsHbrtPZRgGXT8OILz2l+KYRqMRal5ZzLrblajPWAVTqtTEpRvAgdj6e9wTKMKrgnxJ3I9LliQR6NaZ0aW6/9ezluzyfXKsXZKwXraab7zZ9br9fcu3ePr33ta/zg9/8A19fX2kS4eOXWctj1+R5z94+gQlJcg4PcJHig8YXUwWOaFmeV6NqKem9dFkIhlP6HWbi77DVYBZgUHlCAkALOeqIYJFqa9kRrgNOTnPtW2slEQKJGXaxRFh5V0BZyGZFJkeQjJCFIqCFHTwmxTsZMMoYA3Dlb8NaTA1/+2rf5rS/8Bn/hT/0hHvevEMyA5LVvm4bKAmQVhWxt5tMt5PvkZtW2m+bNKvLdYxSrIJFkFMi0WpwSh56IKveYAkYCYEnOkMyU1wxEiIHnLu4y7Ad+4Z/9Mm+FHf/Ln/zjbA8bNv02U91YjG2xvsE4D0b3gnrNKowtTr0Y2rxidL+LMZAshoiRggZNWNdyuj5jHAclgAghe9raYKHJiNXeRVyEDk9vwXcLTtsV//l/9Vf5/B/9Ce7dPccbCENPMhp5cjR422i9sdWaeJfzjjZpmNmV/DPKiqWdipS72FtNbYWQEKOAszEYTFDDIgSDtUoCYkQwThQQmT1bSVpqs1qf8Ev/7FdId075zCuvct4u6LcbaME4h8XhxGPFEK2wXC7ZfuMBI5HFCxfcaZdshg2hqN933e9FRmgeFiPEEiZGCV8kjQwEvDnBtetpQ2dHpGI4mCoJEmB9o4bOEG/53tuP9zUB/fnZHVarJc4rCEaT/qMu+gTaqcFRCoGL8px7kOWYdOo8RHn77/PjJnqvemwFaXjzEFXKpYyiRODrmyUU+3s5Zvo138GUC7BauqM1owqL985jc0+96mHOkHXWqWXsnL5uncM2fuqcM3vmOZLxGTf01HjNRvHp18zxZ2p+o4zXd/ju+nrdcOgEJ8nG6vH82+zySdJzCmin5EVfeOEFvvGNb+AXS3AN0TiS84j3JOdw7QJ8SzKeZHxG0Pq69rxzSq5trIIwRNhtNvzWl3+DBw+/zeXVOzy5eod3Hr3F11//Ku3C0nYO5wVnEy2Ohg5oSMaSjCoxaHDiaUzCmRGDEpBIAm8WtH5N45eARYIqGcmlD7bRUrAIFfRhvKftWmVS8iajsDM61CWcdmvDWsE6wXlYnF4gl3uuvvA73P/lX+Hh4RJJb2L9QSn/kq+AGFOZcx2IISWDRDDJYmnwpgHRhs2SLClZEIc1LdY0+s82eKc9ek3+R25jp7Xg1DC3depVLm0D1rFpDG/3O+7/xu/wpX/+6zyUnsGrEeFcrhHVhQBEjM2k5lYQifXfBJ7SNVblSv5dx0Z0rBwYF/GN0LQG3xmcNxgrGlL2algs8dVrbqwauxIi3/o7v8Rvf+N3CRJZLRZYgUUex9Ro3bQNiQVOmx84NSKig2gSo4ERCAgBIea8pkNzu1nlqgOQ1ZD+tLTG0VhPYxyN1TXcOk/rPd5q9GHhWy6WJ/z6P/gnfO1Xfh0/Js7WJ2ANvRWICfEW8Wp0GxwnqzVff+ObjOPIc3fuasngOOqc3rKXgZnsnlI/9UxTUlfylJw3lL3NUbpu8ngz+XxS7967tkbS3svxvvZENUwKYBiTkixIZj0Ro4vF2WMBe6xEp7j9dEyey7xgfzpuC6XeCC/m8JDN+Z3yN4AYTdjHMNYk+9FX10tli+k7hC3LM5ULTHkUXTwljC1SgCgCpY3Y7HbhBl2fMdrxwBUl6mcdEMwU5nqmyTh5+cbM7/F2D74q1zwARynTW+yKad6mc49PKsozg5rKa7PPHRV1i5k+ZUz12sdRGwu/8MILfOELXwBJMyGaG1eXFnSo42ytUf545zBJBUdpseRy+NahJUmLdsHl4yeslkv2ew0FL5cL+sNI17Z4p0kzm1QZCEZJ8HOJgSkEFJIFVM5HJQwYW0wpRDRsqeH1LDyyQCHnYFXha2jW2IAlA9VIiGhtpMxAL84owvvt7Y7HboSXljz/mQ/zzx9teK095bxd0RmLOPCSezTmXLvGG1XhYROSa6fHONL4CyADbbJhq+helyWhhhWx2TVX35yU/2mUqdRPC9ZGnHfEEPj29ds8Gq9oPvkC93YLfu3h1/jg4pQLO+PLtTbnRUtIt/yz1ZPGaGrH1DWVV511WCeQotptScPHRlyGSAk2p3zEBJxo7lJyvlBEaLzlSRp563DJVex58Qc+xrebgd+6fsArcoe7bQOioC6btXQqY1uiBYJGxACvzY+P13iRfygwSaxGh8Q6jPU4p5oqJo0OSOmzW7ePqdd4NOz5yuUbcLFmu7T85tV9dkvDuXc03mr+tCC1TU4RWceT3RZ/suLu6YWC9YzVWtIbBvczQYO3Oikz5WmzDJ/v+bK/zTE7UblUUa7fTT/R97USHcZM+WcsIWYlmoo0u1WyUjzS+d8w6YKbOc5nHzcV740Jzx6gOX63/gthgAx2KCdU3Xnj5xw1+q53ZOYfLOeXhL8KfGPIuax8L1ngxDQeXb94UuqB+vzPZfq276RAb47P08e7ju9MaR6Bl2a/y+z/I3P0qevL0efe7TutKcJRx8UarYn13nP37l222y3bzYbGOwwCKQBJOYutmYcz9Br528WYnIMxFTFdhq/wCIvkhgah0CeWXLPem35uIugowA11dC1Cg0gGexk7W2maGtCvLewtNmO11HNypRQrCztBKfsUQ6Jh1GK0SIYVCeRQoePJbo8sPXc/9kFOnl+zDSeErkHJvL32TgXEzEgDKCjY/FwkEiMx9rSNUgBm8HINvZUxO45AOKxVAIzNaRtEI1FF1iMJsQrmisMBHHzgez/GWRjY77cE32G6LhudPv/LQJScSzPGAVMpj85ZBFMAV2rwWmMUxVrvVbCmQYyQjJbSlEyGIYdERUtPUsYuKPBpYB8HnsQ9n/7xP8jDiyV9GNgNB+6tO0wGy7jcI9Pkspxyb3PsvM1G1PFyz0rRTDnhaVGq0je2hDml6sy6a4tispZd7Hl9c8Xzn/04/brlUdiz3l5xtrrASkbnljtzjlXTst8f2IeRu13HxfqUECPW+SoL54b3M8GDVW7WDXIkF2qESmbyffb8ZR9Ol5qvq/cepH1fK9GUbLbuPWEkx7d14To7gWNuHpMbf/t7x+Ur+bdZcrt6euamQs7nGqPgnWQmo43JFhIgjr1aXbgcYigTaygk109feLYannqe/Hs+r8T7izcKgAeTrLZ2mtekppRZc6bns9bhGuWstb6EzY4bcH/n8Tu6q6PxfNZnZ7byU893dF0ptWDm6LynvvsWI+QYNZznNQM7MEbLJfJdhBDofMfp6Smnp6e8/vrrfPzjHwcST55cZt2ZZjkw3dCK7lV0N8ZgG08YAimqp5pS4tAfePLkEZ/85Ce5f/8+i9ZzfvY8Dx8+5GS9hJSQmBslWIthUEWXMnpUwNgI1iKmI2GQqOAym/vjGgqYKEdpUqGwACEDz5JVNh5xYJVysbEOwRLFaq41r705F62uL8sZhsX6jA+eXSi/7cHS2IixDeBprMXagBim4n8MRiwSbfbgDT6HY8O4V9J6TB1fNaykIkLFOcaYcDZh8VpukoRMt6DPnI2EZCxjFJbLNa+mNUYgnBmihfagIBkdT4OT3HPSZMFfNm/ekiICNlWUb5ExlbaQzAsr+nzOOK0sKl51dgi9yaFnl4FFGRXbWkcYA6euZe08r6Q1/IGXidcDC6chVYJg28ztZQzeGLxYzW37WRPq7L3Xes6yA0XXoHqV2RIzDjExo3mz4ZcVc5KYIxkFlFaMV6WDXMeW1+SM9Lk/QBoSbgysjScKhGGk6xqNyonBe43qfO3rr5O8ZX1ywnq5IqSkwLbciendnZj5s5R5BlP6iWZlOBE76D4wuQojMXuGmTyY5vLo7e94vK+V6Nn583QuaCd66xXE4yKSHClZFRIcu+W/F0YKeNoLfJYCnb4HbvakE3ITWSu1wL3UxJX09G3hW3Pj5zPvcf5LDmNK9mamXGei8cog44wjRaUEVKGYOKI/axc1h+qyha7hwndXpM+8v3fJddS/j9zxZx/vdRarAcEtc29mAAaD8sTO78ugYcAY8d7z7/67/y7/+//D/4nPf/7z/L7v/z6ef+GDHA47JaUIUgWrwUDYEcZRN22KWmYlCSOJw35PSonmg5bf/33fx37f8+mPfap6oovFgsvLR5mJxtQohRAhinp3Ga7feK8hTTOi0tpWjgCHEvWnlJDCpGUsY4rZaFL5OcZBkf4pKx2BQCKkkRhHLUuqpV26RguCVFIk5ryZESHtDqyToV90DAy4FPDSEXHYFDJwrniOgqHJdZZKqG4dHIYRmzRKc5x20BKemERLWFCkbGlLGkUIURDitKYE2mQ5WIMEw2BVwbpRW9t51zKIGg6aB1PQjBghuYzhFA0taz2mrqMYdP+aWAxW1bQxpozuznSgxAyImurQkyQCQdM6+x6XoyCDgRFl7Em5W87aNMg2Ik1HiWtFEhFoxNGIV2CR9/QISyngJyWGsVajE8ZPBnBB0leJI1qdIDHovcagc54SJmmJ4E2CmfmceGNZmNzbOaP5RxLbpKBGMdD6jqbpiCJ8+Xe+ws/93M/xb/ybf4wPvvgBSKWiIWaveYq4lO/Qn6VsKt9LBlA+SzBOXY3MsRFx4yjXzy1LEBGCpFvOvP14XytRg89tuByHcYQcvNAaT8M4RNoWJh9Qj6fzcDM3v743M7iO3PvvTA11893j/Ksecei1H6W5Rbjf+qzHP5953m2x/pnnjDG1cBtcbtwcsd4hKSvQ7N0XVpw5Avfmdz37HuTGa8/+7FOAovegIW/mouD2sE+h+5qf+nRIKJMIpMmEMcYQMsWjElGMLBYL/jf/6/8Vv/CLv8i33vgGz7/wHCC0TttexRAIIRJjQlzMXTkyUUQShrHHSMqv6XqIidx1JDPPZJYXY0wm71bP0UsGo3irxo1fYI1lES02bhGuaJoly+VL2l/UeJxtc0mFoW0czqlHZLMhpLld9S6KF51iZBwPpBQgRO2qk5sukwqohinvZAyMDcYJeFX1SSKONbYVzdvise0prZnQxqUW02fGIC0tGMEId/3z2SGZVn3Zf4XXOYaAJCVtlyz0YwzaWSSNCtxLWu8YG+Hu6IiGTCAgNMbSO3AjGN/gvMVl6kLrsmIvpTvGK9uTMTjXYI3XKIHcBLWlp2RLkkSKKbdlK6VToVI8DkapFV0UTIjaFaUtKHuBGBkaoZFWnUvAJohdo/110VBu8palKXW2OaKV88IhatmG4jCykhh7tBwvK/4YGPsDh36nzFtj0HsZ9uwPO0IY8/3HCrgLmdVNQ2vo8zSGZlDSBBpHwCAx0boGI9pCrh8H/si//uNcnN/VnL4tjS+a/B3He1l/nWNUdJwrwUSJ72XZcVv662aY9zZ5Ma/znxvf3+l4XyvREAMxad7CuiwIouCs1HwKyJFCvHlMk/Cs49lq6zaUr34jlcqu/F1sqKJgwhhydHZWwiHv/n3f8ZC5As0XvKEv6lfkxEwJ95bi+GIwTAvwtvvJSzJ7jbflD55WsLcbMrcu1pvK0Tz9xpEefBcjpHhy5fcaUs8xG8mhupFY61xhIqGf/9vvD7Te8Ad+8Pep1WwNpEQIIRPAH+hzv0cxwjDkVlz5nAYVNOPYa6E9iSETgUxjYRjHgWEYa39WkuCTVUPHabmIMR5JnsY2pPiEFC/Bt9jF8ywWC5Qjt1WDyVraxgORMQw1pJpEmWq6Vs+XpHXUKY1oZ0qIST3plOtaSzlHNf6Mwbu2rjvN8fVc+DuMrSU5S+M8tmtwuQ2h9of1iLF412Iy2USSRNuqIRFCZpLJQJ8UZ0obEBIpjoQ4qnJPSrsZQyDFoCTpSfuSJit0QdMmYkQJDiz4IAxW+50657HeY1AQUWnbpSAiq/lRWwLhuewnM0ZJkkyDGJEQtS5WJHfn0Y4l4zgQ4pi5kVVBjSEQohosKUYlYDFaw5lyuFysxSZhEC3zcTlMGwBsi8uepnpPkda1FQBYSoeUsjFN9e8imBzlqHlAQLIXquVCSl1qJOgaFYGZh2gtmbEoGxGNZekcgzcYD41vaNsFJHBNg2lbbVhvHYvVisVqyTBoJylnHV3T1aYOJRpWDZFC7FT3eXF+JpmiaaxJJmuk5L2Dg47kxXd5vK+VqCTY7/eZrqrNmwyyOVJ/f1Ziuv6ecxWzK9+S1H5P8JSjwwiaGy2LbXYFJT9mpg1mCvDIbWLiJXhPE2ye+r1ESedWcrVWzfS3sZNlPd3rTY02f+lYeb37OE8fvL12c37bz3r95mnmSLDedh/zv4ulWSzwKTcqsxCcVKFTrNNynlregTt3zzEYbXwtwpD7QC7HBeM4ZIGqHXskab/WMAZS7r3YD30OmQVi6CsKVSD3amwJi6BkAcWTlizQM4ORNj2A1ltSXJKSUWRqAqKQ0giOCjiJuV/pMGRAm/poHPo969UpBqdt08YAJoAEomhxfoqREEfGcciWk+6zJLnWsGmI2RhprGO0A70fCE5ZehrncS3Y1EAOkTvntROMU8KRGDTsuVgs8I3lsO/z3KqSmytRRXmqcg9BIzqFr1pSRJL2pI2p8FknyD/E5n8OltEwGK2P1chLg0HZmpROL1sL2RO2mXIRo3RxrW/y+in/CXFUgv44N7CTknLEqB5yjKroY4ykMKjSEr1fIdFU9WDBOVyCYDJMy1qM80RrEBNoXKsemQiRSPRGazldQgqhvm7+GpXCmKp8jS0t35TXuzTSqCF8CbnHcA77ZmpI60xeB3mMnMVmmz0Zi/ON8pXj8V2r5AzW0VpHt16xG3riGAhhapQxDAO+adAGB2bWwvHp9NGU+ilTkNdl8WKr93q7kX2bjPi9KFB4nytRMFxfX9Efrrjz3MsAOK91ZyLk2q65YH/6Ck97k8/wamTuEd0MN9xUuJNwLnnRutgAxGotH4lnqebKxT678Wf5Wybrp5tR/6cWHiW8a7nJKlIIz9X4uKlAb/MqZ372uzjyxabBlFIXfcFU4TSVIjzzGnNjZxY3eJayvOlBzpVj6XBz87NFaULpimFyr9RJ0IjAISSeXF/jjMWXsJ/3mlNrW3zXIknbQ3UzL05EPZJC/JGycjJpzItVGyCIaBOEtm0JY5+NG8soQuFOd7bJuS5huV5gTQvSIDEhcQRriDHkziqFczUryaCephARm9hsNiwXKyRZYkiMwwAmMoYDxBKKDAzjgcNhr6HdlLInqOFea712JgFWznHlFnjrWaB5W2MNHoM1rUZoEpicd/JpUPE3DpWUn7YhbreEUUtIfNMyUUGamrcSEcbQ631k5URWmikWZZCAMRtAQrJq2FproGkhAK4lOQdGgYrWOygsR8WgNKrQfLvENx3DELGR7Im73HTaMIYp5Fg6rWhKoNX5JSOIS15vzGshs6btx4PWa+f6TUUhWbqmzRU9Du8a7GJBwrD0HY0rWAWBXPbiKjXgJDsMVEpAa/y0k4p3KpI90WKABA1DR82hj4MaUiEMOsapZBH1+kESyxGiUzIKazzOd7jG47PMEGC73WEar8o9lzylMDIGYdcfMLmzSulr7H1bUx6lYTrMUPeldEcqbGiSJrc4IE9XZ9yIcjwjwvis432tRGPbMl4N9Nsr+vORpnHEoCgz48CZxY2BKZ8sAzd5H1XZSYav63KfKd6nBXYh7Na/Z5Mj6lEMKdKigIREqdWzODllFx4TZdAQkdFcjzelkwAZUj5ZVHMLYJ5zLFvEIDkAN/nMGm4js8KQFW2m5Jb8rKKsO7XnYPZM6wfqt9jK2KICbcrxTmy2poasj/RuCbNUz/iYhWhCS980RrKXPLc4s1WtAI1JMd5UluXnfL5uuzaoEWLcjNUlU9hVg0Impp5V06nHkOPAkrJ3lH+XMj8RjUOlqP8kkYJ6chpqLIp1nN2r1LDDsDu+x5gND2s81vYqVJx27vBePeHshKpn4hRgVJnsrME2ypgFy3rt04sXdQZzvlMVkeZAQ37OybqPypaT62St6C6JRtcHphDjN5OiN7aSc3jXzerwjoEuE2hE+V6n+mapBs3N+ZNQ7lmVpSpSZXqKYaydlVIK2Vu9gUD3bW72bjOxv0fIjeCN1ksWr1Tn39S8zNwQU6DVMTJbZERSzM/lpmcrYecczpecQ9cQdM6bRg1DSw2daylPoSG0VjurzD20EmLVvzI6NxP46/fquBBVHkkcytacKXbyuanuL0UnG0gqF53NCOAUiWSuXRQguTAeFtlbNi4TWKiHX/sN1zy4m/kISg0pRvcyJZITR8ZtX9eQ8x7vG9pW+ay9Qe87JWCRnZYCoEtgDmCccv7O1k7VCfWRM+tzHvPv9nhfK1Fr0NZSy4Yh55OstYSk1jLu3ePiz7I4Mkj+Vm9zWrTzTxyDhooiCSHgvULMMyuE8oxYk/MNgs+AkjiOJAacz/VxSSAZvIFkZsqA7D3OPMB8RzgmQTRZmTUCpzIaFbZSFaVuOCksPmZWlE9gMh6084heZFYwT87PHA2HcGN4mLbzdz6KAC1HqRWEuRDVcgZrRHua1rxs8Xhv+uXzsZr9XRVpKezPn7NlHKeNpcpxElzz9XPztRAFkZjLAxTkEmcKs4BMQjxgJD51HX3w+WuFdcrNCNInzlj1YDOhQQGXFACPnYrdyzOV7hXVTHITuXgyoiUJzlcjhRgUpGUdJikxfSSSspBVu0mFLaZ4XNlryGT6AwMmmmqoGXP73iyEEXPDaG446k+DSZNilUKCkSSHSxOx5CkZq+dfD2uwEjFRFaUJDpcJIZqmQapSn9ZW4TI+jvZMWIK6Royp1J1IqQ2eGZrG4Y2iiJNVES/W412TjZUpfwkos9GM5ERrYxWQM0V01LtPOcdLZWnT97U3aRkn8DedAopDl47GXcZASLpup363I4imKVI2uEoCyJhSUz6xpNnSHq3ev1FZkvPNVYJlueMw1XDT+llmezOviySMxoDxOXw9YpNHohqixjgsa6w0KPWkGr1HEqGW+d2Qmd/l8b5WojFExqAKs120REkY67FECoH3uwQJ3+WdyZO6PWR4nFO7GSY++tMazR/kDSgSEQO7w756JCkkRakppbVaVJkkfowFdTwL/N4WapCpWmoWuJj9Tw0RF8Fewox605NGtoU2DYsVpQKzkmvm8gWrn2rAGwvPGOk6luYZ9310h5NAfGpMj5zxzL0r5qn5KdcovLClEfEslnwkJKbrF4Gcr5GkRgOkzInRJtE2P2/1epkIuGs/xDgpzBLxSLnvZX0NLT2pa6ksO5meoxg2OJvBIEWBTmNBZtCxaF1i8YaZ/ZuEnMmglzJ/s4Gu41LWRf475VxYXjNJooL4RMfIZlJ8YxLG5LIyO1ZFXzxNmSmaYhjcvhqO17GUiTHz1yQLR+rCLkQLBUVaxk9Kwna2mIrY1K5hCWPGejfFIy3jq4xDRUEer/QpilKH8Hitipp7ZQ+UlEnxekRCvU8jJRIxj5qhsgz1aM3xRrgxfTo/TQaeKa5bjWZXnYk8cnJjTJjti2I0YjDZKNEtkEP4OXc79VCeGWQ2VGWPnZFr5DVQmIAMhRWqCgeKEzRxbOs75ep13Uyn5+8CYwKOjn7YMxy2WCN43yKjzPyosk5uyozEDbP/uzre10rUAMMYSMPI6syz6Xtc2Zw573SzTvTpKwCzsK2UGaqy96YFrGc9rVyfvrItjYiZCWME33iGoEg9XccOZ6fcAiaziRRv4UYotwj0SQFOXpBqODMJkHp/k0Kd32v5W6RQBaoXbLKlbCnhGtFWU8bkzTEDJ1VXl+MLz4cY9YCeecw9RzlO8tdLzRUh6ehBjuYoP5RasulojKqiePriR/dRvfL6PRrOU+59k5dIUXJKnS5lMA2YNGbvO9V5NTYLSFuEpEFck4FF+r12Woj1h4EZhdnUp7Ecxs4Upcis8wjZKxSQ4glwFB4vfxSDXMSg3LVS14OekDC5vKWE+rRRkcVJ3j9VIOY9ZKrqrsK/7Ijy9/E8zNWnMFdQ8+budexLCqIopjwfR8ahAEmTHUf7CFMZgnTCROdHUIU/u09b6PTy/dQRmz3PXJZMv8+sIjNLdQBHAIuiY8UoS1CZSzO9WZpKH61Tk+8mG6iCGjrWaTi6xJ7l5vchxFQ0UfksFchWjZb8M8SoRqmkKTKRu7lUkolMxCDM1tN8jVZFWmY85Xuf0mflg9P+nO3ZMrKm7Lvj960IgwlcXT3C2cRq2SFkKsH5Zjq+rWmpwg3j5L0r1fe1EnXGMoTA0A8Y1DK2RdgBMcWjDgY3D5NDHGXibjueDvneft5MvucXOMrjpOwXWQxt2zIETdJPVp6GR4QJOJFCoLVKwl3uofTzNJWkQervFag022dHHkURVBl0Ub0MeXoBG0V/kBCsJJKZSBYkJ/6rYKi54Sms/dQo5Y1Wb65Y5fn3afDyq7MaoZkpUp9FV/4s7JTmzznLj5b+qrP7eOrW8v9FAU8i0FSlW8W9Jfs/eSMbpXqLcfIetCF31LmsCl0RpTXbPqOlNGlu5MwMoxt7v+QiS31iGRMj6iml/AwWW9MHkqZ519yehg+nqmf9TjGTcpBkatkGIrkAPmLSpIgiGm2WZNVMKfYU056ZUN4mD93Mm5iPfbVbdX1Ya258fgb8KusrachOPfrZ9SRjFWQaNwT1pGf2sTEQknZbqWLfqlC3tnIjaUTGqD9USpDmEYIaJp95R9aY3Ic1h1nNBIKpijE3qLBo6NWY7JfLFAmq3lfVosf7q9Yg1+tmRHFu11Z1cA5Hz+fFp+Jc6I1PeyavV/T3VKJCAiQLRkOjalhP1y2h0gKOM0XxFQNrtn/1M/qagYy1IIewp/ksVIUlOnJ8nwkKT3JKuGTZjYHN7prT9QLvLVEOWLeoaYr58x4dhqdee5Y+uO14XytRbfeUGMaR/e6adnGiMPZkaknAbbmmMjxSJ3f2Wl48t+VLy2LQ/XMbB+/0t8XgnacUp+tFDcYKTePZH/Zsto853T6iP1wTUw9EQiIjC9XyV1+oMLdkAyExKc68SCPQ5VZSR/kZEWZR2Gy1TcAeY6bi4rm3YAw4748saGsVAGAo4Rr1brpmUYVjyXvUEoHZ4JV8yBxQcpNGULlGZ7SF87KbmccdURBNSlpfF2dozJhBJiJCkH5CyKYpBFu8lfIv5nGVwoJSmGtKDWDKYbDUK7NRBjQoQbfJ9aFS535kjitKGfFYQFAllJ4mNGQBxxSATMqkDaKzLsYSU+m3mLlrqtc1GUvGkEuAM7x/ViOo02vyHilrXBs5lGdMor1wJSX6cUtMo6KNx54YRm2lZWxu7aZkEUl8Xf9JUh2HaX5t9c+KYph7A2W9YVRJhKBjMdueOTpC9VpSEkx0KmjLQjY63jGFaiQiou3dcpg9zQzPFCOYOdpX0a+2VeBL3TTG4BCc1XRLyDluBcTYqZWbgLHar7KCifJ6np7V5O/U3Kg1sRogx7LmeN2XHqlFkeo4TqQdum8s1i2rUWMziKft2tz/V6oyxhS0a0UjZYOhmRwL9N5j8UTTtHfqHs6GZsqIdm8KHWImbvE5bJtbJ7rM6GVzDa61pdOOrV6qNxmukSa5Mz1nWVMQQ6JxlkVjaOPItx5f8uqrr3J+sqJ1hpAS2AGFVpYJeraz9Hs93tdKNIzKENP3Bx49fMDZBxaYqIIeMYQw0DTtu16jCOu5J/Qsb/Pm5ybhf6y06jnWEqMW4rsaGNXTd4cD3/jWlxlNz/nJGSnsWHSe8/UZrVvgjKex4AyENpNWkwnLjTtqR1Z43sY4maVSF94N6kFR0EvKea6Uu7kXhGs5R0ToxWQhrEI1ppR/CmnUlnNRhH3a5Oulqghi0tKIECfFFsgk65neLowjwzjS93vGYcjKr5RVRPqgpAMhjIQxMIaRYRiRGJExIDmnHIIW18cQMthBX5OUCNGieJPMzDI7RzKbjIImhmxFl+J07WARYspw/8yS0qfsEWblG4MS1QcVrDngoHNdQo2gBpRRKsr8QjbM8/zMwGOTZz55Cwo2e1Y4fPIrKzrxXY6bq/tGpG92GwX0kfPU9fXsSVH6zsajyEKlBDy6Py2reuZhphCAkexhzK5ZPKhykzUaMb/EzMiaPQRPh4luOerb9pnnT7r/trza9Pmi6CeJcstzG5flRQG93HLvN677rkcWA5rrnkU15iJKpl+RmVcsNy40+eYwVRZlR2Dy5G+7B/1K8/TwTVOnf2b5OY8EWqs1xCJWDdCUQKboRzW0rBr+IYJvDS88v+KP/ujH6N2Sj3/P/5bV6oS47zG0ONO/26r7/8nxvlaiH3jhRXanljdly7jb4JwuCImKtLytJ9x8cZejhBTK7/XcuXV/Q8mWcMR8Bc43gKDhohCCCiOTAyzZS2u6lgdvfpOrx484PbnA2Ig0wtU2su+FIAbjHUMckYMyMaWYMg3bzLNJMSumgiNVBZCSEIJ+f1EGhRXm0I/5tZjPGbP1XxRBVhCHPnvE6rqUKBax5E30v6cZRaaxKwaKhjansTsOMwsKBFEBUEI7lWElX694klXHlO8pkQOmTV5OOL4P6jnM14GRSQHOJlCQbLjmQKNAlLk0mMJjNRc7aYKnBfG72mdyy++xfig9+4PT2po99/HXllG9GTm5KQll9g8McfYXVXKmeZ3j/KPPfD6BufHw1H0c33fNncvRj7w+ps/d3Mm3KqBpob7rYdAwagkFH71nKHifp+7+6SMeLYO63G7eloSZ0imnvsuV7ey+br2gjrCd1PxNE2OKBGCU0/fWL5rO0vuc8vVmdu2nx39mMN4qYcvrcvvLqAEQcpnQNG9y+1UNgEOisHl0zRtf+TrrD3yY4bAldF5JLHA4IkJhZzteIyanTCQbEqYCBr7zepkf72slaqxh0S1Yr9fsQs8YRpw4DB6MVIDMu/EgPg0QOrZyb4KKnk4437AgZztIa6hKHrEI8ISI1qWFOLK5vmZ3PeAbYTfs+NLvfps3719ytRkQp4wxjFmBJKmeU0GFigghxfxdx7mNOd1XLauAqfBcisKNNZeRNRIIGu6abaJpE5v6d1aL73XGeC8L9Kmzjl64TfA/7UqZm1vPPHuL1yvn53FGfTBfwnhG81zOGnCp1uNp/X0zCzmpx+WcRaxkJyiDedByFIEpHEtZTzlnViN2UqMIpVNIkyMPzpcyl6lRgDIY2cx7bKuhZnPXHZdblk0huPystV7XTK8bwVq9H2c1bB1TAmtwTXuUGihjYHCT7VCez5RwpZbkKJkHtQuLNaWm1NXcWjHe5uFCMDnl4Gb3aHJ9tb5f899pjpqfNM60xUWFqSlC02QPztS0gc5f7h3KLI0gOTecV7u15sjjupluEJPyGJkpgjrbLTEpwYKpxpcajjEbssUoLICdmCZilsKAZkupWV44WjK1z9GWmPOZOQIiVEOxYEYqT22+51rrypTrVaaqMZe3xErEoPeYsoGskaqpx2gZ7ht7M9ttpb/upLALAEoN8oSZOTVS762su9ION0YFFK0a4SPPOdxz91h4i5ERIyORQ+6iVCfpaPPPQWki2Uaczed7Pd7XSnS729J4oVusiIdrLb62PsfWUy4g10ma2R+ziZbJ4zATtLoM7LRlbh/Qm+Fb/X0q9yhotiIIVEgoM421BiueMUT2hy0isN085vXf/Qpf+dpbvP1wD5iJqEWOniALIc0raCjVwLxeC8lF9oWkeTIQYpw1vkWVhq/5nSw4rME1S0A3os25CBU4OZ8y57ic5SzKxcXq2JsscJRRZLL6i3/kG59BJyU8DUc1ZfN86wzIYfKGnOr3JiBGRQ87M6FX82cLV9OxN6bCzhlwWJxBuUpJSkFmFWBiXcA5k3NQ4L3LebCp96R1rhCLzvI5ZkYu7yYvJKNJ8u1XJVpzy8bhcu2idY7GN7lptipr70odnpZmeNeAM/l1g7UB5wLeeJw9x7oBg8OKB1PakBWFoWPqbK5ndgEFvAliwTdtrZ8ti8cZQ64Tqc+gfLeZGMK4zDbjsDLVSVurZO7eO4zJ9XsiIJ4ksRI1VEXn3LRm8/osodcSpSg555slLlpOkg875fHUuJa6zsiK3ze+Kmub11dMSUkE8uqx1pNE0wmlw5HkPW/wJBOr7LBlfVXPRwgpqTdajN6UKlWgzHluMzo8hmwAVM91Wh/M9vcQrgi5TrYYDdbamgOvBAzGTuDCIhdE8udMZTALcWQYBkIIjON4lDYh5RpqEU2xAKQJhFeidXXFZKVvc5s9fV/TU9pDVfPx0WQlmvfDVCame0UbhOcxEaExkVOXCCvPvbsv0FmIvicOSlphi0SsDs5NM7oo6JnX+53C/7Pjfa1EH7z9gPOzE6xbYo3QJkdwHcEeIA1qOcaIcbUFdRXwNdpWTLFMhWeNirRJ0AYVejMvTy9j6s/jXFWafopyk3aLRVbkmvzXQnRUwFjBmIHrXpP8yyWcnHj64RRPw2gDpsLRtX2RNYbOe9rWKs2hGFJoGeKAN4YG7WbftYZu4Sl98mLm8NxtD1inSkGFr8U3jsVCSaBdFnqr5RolKDE0jTKFNE1L4z3doqHtPE3r8X6led9GFYhxyk4izmFd7keKx41e8x5WaLz+dAYuzk5w4ui6Bc47jFeF07SNKg3vcc7SeId3WSg7h/MO3zQ0Xou7vfOVkMDnLjQsmno/xjUY14LxaP9Ep15UZpICpv1VnW+ZFAQ552TAUAT1WGt8pzyhGghzwyeJ5LKD6Sum7VyIHhIlnDQTPflqGQV7o7myrQXkeR0no6lUMXktPgBzH8Rhxk8hzQNgDZznGygkGrPw3C0N7StA7igkV+7THp2r79ySmzU+G6e6N4SGZCCZLUiPFXDpDHEREZ/HUt3+ZBKRwFGoN6aZNw++lGCkoPW3IorKTWMWkJO3WLwwoKZAC0gvGCEEbR7gnIJzhjDo3jCCA6xZcBguCWGgazu8XzCmSEgDXXOm0R0SU155brBlgza3PStYAlC2JJcVWGkPBqKAndnI13TFbI04oONFVt7jS8SienSF/GDqUKIlV7PpyUp6fjyrleCRkslczDfTWWqwUGWuqpsm18NOxuvR+pnzndbhmq3xLAfVSWqzMZ2jcimwubqCEAnjnubxt7h89EBrrCkOjxwtVVOmZZ72+S6P97USPVufsFh1XF4+5vWv/i6vffYFWrNmwBCx+GSJREKa1ZhRLMxiwZbaqAKhZmax6DFZQdPiuc1SmTzTHMixntV6jZkJKA2tJFzjeLS7pt9suIiWn/xj/zbuzguQAkvg1DZ03jI4h4n5u6zFtY7FumOxXtEsVtimyx4XRNMqAk5sxqlkgToXfCkxjgesm28IHR9X+ELzMVqn3mxd6DlUnm3rHJjiNuBEWZL5qbMIiRg8Q4gTMg/oxwPX19ecnZ7SNmsM3S3XywZMStPfHIdd5lY1IiRjMOMAEjOSNCLjQJBdpdkzOfqQzEQNpu8V9O60FowxGb2rHUSmXptZFeY4k8nKuYQWbyK9C82aCFDAYBrMolCzHeduZmGtNMt/1jkNWXlcEtNDGvcCQ3pEwwcw4hG5JvBVWvs7hHQA22Jsp+FAQVt75RCbRuW0cXuKPZLC0Zq/+Tw1JDZTUPNjbmxiIYYdjkTjDNYlxhjp3HN4DCk8YrP9NcQ/z6I7xzcnWLsgJadhRqNGmbMtxjaTQqXs0SnVkdLk5cUwpXTK/ZRQ+PR5XePOGVrnKYQfGhZ3RElKV2dVsTsanE8YGznsrxHZ6Yp02it0YvQq6o3Js56v69IcO99bGEYCE4hsUnoVeFA/a8viyGvBkLd7HBnD+LRCyPtibgbdCsYqf8tR8dBT590mC5+9Ror6E2W/uvl8xlFKwpTtac7SVjxmReAXWko1hPUM59vMOeIzrWFgCL2GVf7/fLyvlWg/jDTB4Z2lXbWkw57eHpQmSxRa7YwCjCSHUYrnKQbSmHslAm2zuGE7a/G1UosFtYhzfd5tBObA0cIpJOJ932c+DI1NJAlgtCTitE/8/k//ED/xb/3P6c5exFjN5WLUstNyhBGM1y0iE/8kOWSdCITYgxlJmc6tdpAQi6SmsMdp+NQYXHM2e9bpnkNGp1bwjdEidUq+tDAd5XIP8gKWYsnNdqfc+N9KwkvQDg+21XCSjKQ44hrwWPabDXvZIjF7dTWkU+Kcc+BEylaoGi6lE0o5yiYtaZByJ9OGzPlycjQhqiCemGTmQoDs6BlsHPQ6KWJizESIuVaX7JsZwxg1VFiinwaUjlK0bKDegzMVvYxIDVWWdZZyrsniSJLZoUp43Zi8Djyq3Z/HcgexhiQnOLtUBSkniJwph3Nak0xAm3hPZPuap3WA5liTQIp7JI3VsJxqFqc8FjCreVWPZzJYpxwmxmCbFos233ZWBeAQB7zvtPRBBobDR2m7NRiPoaGGimvZ05RPM9WYmx2mLsvJo7YclXLUNZrHuxC1JzN1PZk8vtKWTLDWT58XwCQl0yicyU6/Ywwh+9C5tKiO2cwYzOOUYrXajw5xZloPZT3PlGBd3AhKqDE3AGe3OYuczdW3FKxGWd8cKz3JC3+ug25VtOX8p54hp7CYalkxicYIkttW6mcyWUOeT+tybCXfmJTnMZLTLQZxiuBVZTyQUiCmlj4ENbBwxBjY7zeEGGmcu+X+bn+m38vxvlaiGMn95xyb7Q5vDOKslm6ECClhG5st0vKZ+eeZXPlZQbTUNwvyMmXKO1O9n3cf/GPAgUjOu6CbwTnHZrvnIxcv8fLLn6S790GeXF/SiKg3YwRjA1GgTUp0IDmcQw49GSZmmpSJBoxESj44paTMNq6pDY7LZoa+3mcVOvNnknk2Jx0NGXmjSgG/YKhdFGYhKzPz7k2eq5LrMIZcl5i0o4RYrFXvUww53zWbrCNvM//MQrGAPkqIqAhtW0+8Zd6zd1nu3xlLlDAZDzfDOvW1LMwhAykkc5JmRVzGLTezLaTeRajH3LHFZAaclLTTB5ItdClTUuoOp4x8YXe5ecy/w6BkComgXpslg4RAWYimsSz+SCEhmIJopYDeYJhKecogmlzndzwnVatkwTyF8Y5/gjGNEkNIJBFZmCbvPRWSq/UHwQZS0S2FdMMoS45kY0afNRsj+ebLWB1Pt2jbtJlCAt2HBXhVjWLRXioxTWQiyuSVck/TISt0gEgcCwAs77mgtYvWaW23PoNkr3YyyKRGLKbw6bEimhRQUbplnc8V41RIJDMZl8pqOToXkw3QvJa5sZSkrMH8R1Gi8cbGKQq3/H7DLpkU8vQks0+rc6C53VnUINcoF+7nmKMiRsgkKvm5qtLO4d1SRy1qjIxDIJiEy5GEcRzfFVR623Fb5Og7He9rJeqcIabEdnfg8tEVzjuSKb38crhsxgYDk4AqFr8VW62iLFvzUaU1IkHfkMmavRlGfBpkBDHOUYZFQqpQGMeRbnlCtzpl1/c8uXyDhgimUzCKjYxYFiGQrObwFLAhhDiAAedarGkyGUPCJ4OxAUH/Wddg3BoRn5WoUOoRpyjcBAi6ef+KLT6uzzOzZy7GhsmF58VrqBazRGr5hTGk7D3FFI48xVKXaTOd4LQFJavw4wU93Y5UNHwVoLOw/XTuDSVfXytbvFDp3TiqFznL7fjyhUVqT+jNiqi06m1Nz5gFQhYcJTKSqhdomNZdLgXKEmrywmeMPTJ5NGrFl/VYcrIZYFaK6w0gbf7cAW1M5vOolXKVLBhRY2AilX9q9KexmHvqZTXM5DQ3FL96FVb3Uxz0bdfUCIBJBmcXjHKoYTqZlRxMq0K/0xbCjHznMxvoWHTP52YWvtzv90Du2jLjbNVuL8e5QGtdJsbQvHXXWFIme1B2L42ExDGBFyi5dlQWYWYsSjl0XO8lD2IV3mXeqxJNT91PHRPJQJ6sRE3eb0dKsc5cXjuS0OYSZja/x6Sckyzws9fq6Ne5L9ctcKIK5qKaVXUyJq+Y7O2nasBoJUXE2liQH0eypnR2qSFdBG20IDXNkkof3cYAKdfovztPwM3nrT2Hn1mT/fTxvlai3lr2h553Hj/hyTvX4FqV2WWybCEEL1RS05KpiE+rodHKsVrNcl0MBkuSAZs03KWXnyub2WIq1msuR4kx0rbNUfmIzUpLRAhE9sOezeOH9E++xeATTXuGdx5jhdG3mH6P8Q3GdohpsBZCvEaMEGOHNW2uDz0gqcOYHkyPMGLTGhMbSIU9JQAHnO+OLa0sLW8aX1FShZqXDe5QpKSGUMsmzkxJdmr5JNXLmXtKgG1ruKaM/5i77vhG2xGn2X3c8IOn8Gvx0o5yOmYus+snykauIfXiXUklGMPhq0ot2J3iqVZksPpLWehPAsM6N4XbiuPUrhQFnfO+YgzWNpnb1uf1VcLzplrMWV5QQC5lTaVq9pvpO5iMBGuLwI5qWKUBYzwFBZkErIMQBxwOa1tdj8WwkqkkQ714W8f4+ChdNaR6wZaSXijKvcxHOlpUMTlS6kHGnGsFCVtsIShIQoh7BskUhRm5PPGz6rOqgjC4XFushlTRUHC0gNDQofeeMI4qwHME5+rxY8Zx5PziguViQUlRxCgYox2MClOVtZZhGOj3B1KMuIsTnF0RB23UbmzC2QaiNinQtWWy95woaYSyrhBtjxhzWqKO7bzUDKpxJ1Kevbx302PScXd5Hkqddf2+fBEFMUWQYWZgSh3HaZrVoLJ2MZt5pvm8xVOr91anoprZ+pfJQDFJuexnepYyTzEe16VqZCNVqtIYRwWOpYRxi4z81jURRSsyYgxAIowjbfM0vuJZx2153PdyvK+V6Dj2LFYXnJ8/x2J5RhyFaFXxWQsht0bSrgMqsMqKndB5c2tsOuoCEwtpxEjDxF95W2lLsZCLAjfV46iCPm/Ikjcaw54x7ZHWEdOCBSDBZFBAJPmG5E6BXu8Bg4sewwKTAkYGRK5BDlizQxYfBDqQjiSGJB7jesQcsnA0IIY0Dsf3fWPBmOKd5oJsyXD2JJExhdxqtJRuWELyddCqIVE3ZMl59Nj0EOhomjOgI4klJJNDuJY07rU+05aGyGrh3pycXJgw84un+y7PIxJVWKapfKPmjvJsqYegvyeZ5vYmFeFcUNmDr85dWQfOTV5nOY+9li0V4ETpNVnyYfkOEJeqh1DqQus16oOrypzP13R/8xyYerHOJUyyGiYzVj1P48EelIikPpfa/HPhYbDqCTtl26rh4lk0YvKCMw1hSRXkWanTdaNwPdEBudG4aSG1xKQYBmsMlswLjSeljMQVYU5aXvKTIjAWblVzc6SOvhZjDEOunRQSVnSOVycdY7AkBg591PZvZvJ8dF0kxQg5h0kBTCAQuD5c0/iINhVTNLCEHokGbMjdj4phFGdK1FDR0CIECdkALeceK8cpsDKliKb1OOVJyxGqwZHlXJ62bI9M164febbSEEkMaXfL69lAmn1O0s11csthLMkY5YosJtvR+TkSkNnT5qHY0oc1paC4EkBSgADGCJ6OElEJmYwmhHjDMniXWzPH92K+izDw+1qJbvcH7n7ghFc+csLDj32M3eVjlh+6QMZIioJ4C2MgRq1F0/q8G4NjTKbNE0jH1GRa6OKzp5VufOyGImUujHRCvPf0/QHvm3peimDa/H50uNFCEhp3ihXPfhxAtjRsiIee3fouLlmU/mqLtR7LErD5S5W8wZpzZChF7Q5JCnRJNgITeMSKI8i+hhgNTy+yargaRfmWz4IQkzCOfbbOFSDibW7xlTd79aCOBieQMtn2kBLWlRxXsY4NpVR3DCpoZHaduYLMopujyEKG08+FkFgLhZKu6oDJ46ufNYY4KgeutVY/V5CbM05ZSUnLdWp4Xj8fojm6pt6jIoJdJkYQEaJom6oi5Jz1hIPOjTtS2lNnjLISLfYpQVcFJSVvpq+HFGlsoyxUkhRY47ySkdigCjVvfW/d8fVMykX5Bl8JJOZCdkZpV8gaZqMh1UvVwvm5wLZcQ80VDoi1ei/iGIkIPWITPpaif6lRgBQzMDsbXNbZ3OJM4XXVzBGpudKy+CSTN5T8ZMw8syKCTYJEzaVpfp56nswUkoYZFVy06BySImN/oISJQbKRahljr+bEM+R3EdgigolPy5XyHPP1dHPupybax15pitl4pESXJgKKeU5Y0rEz8KzvMPZYWc5/n49xDYW/63lQVot+9/z5ssHgbF03tyvk8rohhT5XV0CympZQcgqjrG65Cfuzr/X0UdbJez0f3udKdNWt2Gy2PHrymHfeus8HP/Bytlwt1jUKKCBoCC0PfEnlKJBjFjawpoaFyoKyGUSiRPAlXHfL4JpjVVSE7gQmmMoSrFXi68Y3HNLAdtwwhh09AyEeMkKwI4ijH/aY+A6+WWWBJZkhps/3kYkcck4v9uBswJpSliAwa+sEBpMGktEci80hzqMw0zy8YgWT+3aq56XehwIjnOaEkiWUGjE7AxsZtAlAzW04EktKiY8xI5jMTmMMxjQT8lYSYgpJRQnRThEE3UjHwt3NGjkXoVEYgqZPZ8FQ5t5M4eGSQ08ikJG3M+dLFQMJCZKVYkbOZm/LmWkrGWOwSS0CySTnxgAxYMSw3+8JYcQ6pzyfMTJWdLFUUJYS0WvuqmlX2TmfCUIzU9zMXgQSB4xRo0UEYupxpsOKtl4LEpEY6UUIYVQLPylDUQgaDks5LVGNiGJ0ZeOmGOuFramUSVnrcK7JddBaSG+tIWbyBTE5Dx4HjPQTQ48IYizjjGPP1B1a6mT1fgjg0uRpHQee0wywo6FfK3k+itKr4yaTEZD3zIzGt0YtYihepMFkxK61x2tAkoZwk0yvzcTMZLjJdN2a+6wWSLk1qT8pym6msCcPMtXnmaco5jGAo1B3PieZOCmyMvZFUpRrzXbO/BqS1+d8DOvvc+VfrzDt69pIwORIUjFayrlZrpk6aEUuz7zE8t/EREPAYGyu3Ya8XiO5xL/et34HTx0303L/g1GizjqGYWC7uSLGHu8l558KmrXA4qUi8Y4OU9aUTA2ni4cjuVsBLk9CRNsAubrZ5stMbvxWXzFaqjBNTG7Z5ixDGtj2ew77njCORBsxosw3EUuUBhsDobCfiOSehxnVWQvzAUaSBJxV5SiV33SepyxKU4vIj5F3kzKpvxckqkABmmjOrWzYnEezuSWVmW0QAe1RX5SRjpezhhBnaMUMI3DWVF5aQTJCd1ZfW+erCPP5cAspZQ9RcmhWShi/zFHxVE397mKGTLMFBY4xKe/pBCHV1+WG4El2QncbYzSNIKU1nX57iANWhPFwYAwB5z2xC5UsX9ehPjUop3GMIUdL1LswhrpWS2lJLao3E4PPkF23mMkHYjiQApCEKFFbxM2I+KfQdp5bgyrznKcq7fdqqD/vKyNSlbmRwpClXqMxmQXLWVUwvsW5FpdJMZwzygJUqessiMWIckIXcnYF6LqZsC0KZIZqLus7rwHNb4oK4Zx/LGjxIuxvznyZzyrAq/CfhfvrRxS+JfnB9ZxYP2Nm31OMjxKGnr9eOh1VmXLj5zQfORIhqPGZZUoF2ZRnSBPIsXTuEZnv47Juczu+oryLMgZIhUoxZfdQr1v2W/XQj2eD0r1nuve5KWJqq7PJ6TCTwSC6761xeYynvSVM41SuY2wpyUoYk3AmVpajlOtIvXVgnfZBno3FfL7Ja2a+1Wuzm/d4vK+VaFnwrbOszlpaF8E4khEdUB19ra+zhSJP6mYrU1QW51T2rMo2SUByc1vd0AHDonbxgPnyzfeUEXllzk0uXFcsg4ptQenExjCy7w/0+554GJEuYUNUS9eYrF48ccxCLl9XUlJquSJAjMk5CUN0KpCKZ1WBTPlmrdVcmXMubwgVwgXGL5lfd+rDmnPH2QuKKeLt5BWmJPhmhRCzkgHQon3tYj+NlcEgviXEuZJSvtBkZwrSTEwpMtvcxyCi6W8AE8NT78/nZ75R5iHiogzn/K9H59R5JY+/1Ben14zyDM8+m3JNaMlzipQ+l1kA5muPw0CImuM56lTF1GEGhBjHOte2steogildU8o9xagdcmIYCWEgjANx6Amhr6CbOeLYOaUtLPR8xjhlgMqlONqJJ1YQRzlSihC1JCQkVcpGIMaR/f6gjD3K9MY4DGAanPM439I0HU3b0TQdNitW5zxYhXipB66KvSB0U4xH81mFbNJ9V+ZCxa+uG4PJaZpsWBqhNpCeryO5EQLOi+fIi5tozvLeMUdr6CZ46LZwZlUa+fUp+KOKrnqx+RxDbkVmfTUiDFN9b6rt8fLc51KRiXK03G+Rc0WBKwm93lDCpNnzy6RExaiyrko0fzdMmf3yfykNKu3m5kZIltZ1KG/u4ZLWsVadCL2rSdE7U0p0DIGE9V4NLiLGJLADEaXA1FKySGPXedxKSR+TEXSkOPW+bhA4vefjfa1EkyROFqeYe4bDi3dpGsuQnO6qLKVKX0CJxaLLKMs8YHMItp6YXzcm02/la8h8yKfFUD9W1p9Mi6p8ooQa63lR3w1hJI4HrBnpD9d6/oiSGxjNvzduQhYXrzbEEe8zQjaT3HvvKa285vfhqvcwfZ6C5MxWq/eeGBNt21DCoWXQhOJdFOUsjEzfYYwhBUUyVn6EfG4yNpcFaA2mazzDmEssMvrSGEgxEM2ANlmexrKO9yy8clQ2Mleizwq/3LAyb362KlET5h+ps/e04n73nVbOj1EjAepAKgS/lCm5tsORsEbo91t9v94Xdc1Z30HMXkBQheqty+wsSr7Qjz27/Ybt7orD9gnj9gnjOCKxx1pL13YsVyeszu5w795LdIsVi8WKtu0qa08JwQom88/m/JZR0TeVINnZ2Agl9eGrm1OITYS+30/lCyL0Q8/h4bcYDjsOhz27zdu8s73iMCh5hbVOKe+ajvX5Bzg9ucOyO9W2gCgmIMpASAMxaReiUldojfLWRklICApUYkbCkddkUVjzkH+1Q8tT3Vh8R3+XsK1QFc3NdURWOvPXCnK7zPH8SEZyi8GilGYNK6TsJUfqRwrYSddXCcdOXmtMgcnMUKMthoB3Vo2w8p0pEYn5nmYkDk8Zp0YDcCXqwPEePDIw8uvFOSkGTgiBrutqNytrPClpDaf3Zf4UF4IIiYFkhGRUgVprtd6TQu+Rc83RYaOWxBkHUQLGhLwWFLvg1i1NEoZopjpho+C7gvzV+VQcQNSHzxGqZ8iTW473vRLFKFjn8aMtX958iVc+8xquUaUXS5PkTDRdbPD6v8xCBXkxzEOA2SVATIPIcdikhGfM0avHFmj5+zh/p+953xD3B/abHdsnTwBLvz/QlDpicrhoVNKFOMvzGSsMQ583J9VKtdZX4fB0s2tdNDFGXONqg+f6fhJ2u0nZGiv4mIvSvaJlUwKTv2sifjfKaWqmDVg8SudKz8SMdDQRMcqCkkK5zxxuN2pt3+x/WkZbisc9s27NDYE3jfH0zMdzkhVwlZpP5z6O/74R0r1x/Wcdmi80xCgKmDJgMgvVvt+DOP2HweVyTW0KMBk6EgbaVgna8ZZRLMTE1fUlm6tH7LeXjP0GCCzbJct2xZ31BScvfpCmW7FcX9As1pkeDyUSH/dIHNn0e+L2iiTKETuRtlMNMees1ghXz+V2sIVR5yCH0OfVfWR+46YC69p7H2HlPY1zGmoTIQw9/WHDod8y9HvC2LO9/xZvvvlVDqEnGvDdktXpc5ye3mO1PKVtF7TOIWOgH7ZI2lKaUoMnRUMUm3d8Qkys9Iw6NwrKK6VEearr+njWXMtsLPQjM9rA8l0iGsrPoWPLDSXLzBuV45RQyqHI4+822rMWmShKJXPqzpivVA+mms8s3xtDwHivSqwYolLGZB7BKIbFNH8a6dD7mfcbLvt+MrYV+Fb2mXVTWqFpGvp+pGm025ERoW09zukaq2trhmnoMqJdTK5wWIgC76QYAIL1rnaxETKi1woXqzMeP9lwudtxaDe8Yp7jkKKaAHX9TrW0R5GD+tzHMv07He9rJarciZGu87z88ic5cQFjBkJS681bS7SjdtjITCtqZcwuUf+fK4F52CWo1XnDMykBMZ2D+ZDPVTVVoR1tQONwTlGqh8OB/XaDbzz9pmeMmWnIWpIYJPZ1oYgIMYRqDpQfkSEbA+MUainh61lIkBzuSoenPTnvPCEMGDJQxMJQlE+cvFANI9ucoyhxTVW6BRUoQmUgKZ6JiMAhI4StVyvbFiCKVS+CUMOlxVKsqFsMxXW1N+79yMvnpkVdZwIzqx802eUzGFIOnx4HcKnvzw8xT6Nkb94HgMmgExW0VrvsoAaPsSAmKLPWISuiFDFWSfS7piU4w+6wY3u5Yb99Qtg8JKaA8y3L5Ql3L+6yPv04p+f38G2nnTtqODVwtXlMfPK2MgNJUrS6TGbf0VNJqgJOo2eS80gTKM1gqnSZG4Nq2IRs0OgcVWE9BtIQGNiDCPHyMYWMqaw/a30O565Yrs+xtuHeBz/Oh6MQhgP9fstmc8n1k7d59MaXeRADxlqadsHqzgc4WZ3SuFa9uBQxjITQE5JoF5nMe2tg8j7nE5zXBlIU4fFenu/vuq5qtGTy7ko+9SYXt77HLFw6fYdAjuJMUYgQdV2A1Nsbw4AxzeTZFglmZsp2dtvTa4oINwVFPFPkLvmjfGqhmnRO8j4r3rvHZVa4mZSs6OaKQwhBAXfAGENmGsodo7DsdgeCCEH0mUVKW8cJWR2jIuSHUUktxBjGYSTk/tDG5EIKm2tHg74XRFnHQuqxQ+Drr99n9A1/7H/2kyyXK67HJ9Pcq0VLIXooRlUsQMK6JKbI1Hc63t9K1Jayi8hh0L5yJreRSkJ24Qsg41m1f+QdkZ14k8sJKDH8iDH+iB9UD3PrAgbqwqrfU8MlGXCRg+/GWfrxwDuP3uIDL3+ExnuSDESJ2sYtE2KnHF6z1fsrlu38EbQPZCHVrgoke3oFOOCsI8Vs6eWegJoTjTTZO1CFYxQxCkimxFNi59y7dGbBSw6Z15ZSeWyctRhncxRKeWQLCEZH3BBMqd8toVJD8WfmrdDyhFfFOvcin4Wme8rLvCE/ayhftditn3n6tcKW+/RxpHBN9uKtrsHSAk9SQRTmnJ0xWNMgThlxDsOW/vEDxmEHUUNurXWc3vkAzje07YKmXeCbFtc0hDhy2A05JBghas/HFGbCWCYloQ59tuBna7Q8YlUa1lCQjvOjhPCOQRlzZZPquM5RtzDL3VbjxGDNiDWDhu1KSU3f5LpRg/UNJ2f3WCxOuHO+Zxh7xrEnjj3h+pJHlw+1MUPT0nYdbbfMXowwSoQxHIFt6maVKf8+PZccz2zxFGc/88nTnpJpTAwTuCbVXH5+T4qinYV2KZ5nHtM8ZqW8p4ySRjo1J56Q6dpV8U/rrzaunxmgYPB+Vn9sDA5LEjs9W/6emIQYtGlHjJEhRmJMhFFJDEKIjKN6pYf+kJHcStv5+PEjXNPgrOPQH9hut9y9c5e3Hz5ktVzRDwP7w56L83MuLy85OTmpUZDVeg0i9H2Pa7JhHRLb3RbnGrLFAEZyJyfDcNghWLANEhNBek6d5xuv3yct16wvnqNtl8A1zlKdCmugbTrGWNJVJnNMxzpEzkxRhu90vK+VqOTi+RAil1cbmnZk8VLpnxe1aSvzfFMBFk0LT7ICLZtpkpcaOxcEYxu1zHlafE6hHepPufFbkWMlhFN+Wu8Y4sC333qd6yGwNBZnU1USUbRVmMTJM5u82SLAyF6oQUETRcnqvbnah1HvxdqEw9c8F2hj2xQjLhNol5xv6Y+ZckG4s8pXmlv6Zv1jCSZB5jKtz5wSUmsCy+AmRFy1AI/GuOQDjUVDnZmtpYSX9YGV2yZbk2UMjrzTWzxU/TEBaebzVa4hxfC/qXif+sx7oAMzhtLFBWtyrZ2WrGAKVZzJYzciIRBiYBwO9Icdw2GDJbFsFiy7Jcv1KYuz5/G+wWXQSIgj/XCg7w8MYazPpy2pTJm9bBTmI3tkSjqRiQsKAUj15rMRVRmQ5gPB0wrFaPnJpJsm5SolvCnTHKhe0VWr95A0CiGJ0sUmXSnIyzcNTdPRdiu61Rkn6zukFAnjQH/Ysr96m8snj+mHAenh0De4ZknbLir7la5vM2tQMPMEc7lFBfIIHJHfzRTkPPpTvU6JE/k8zELf5el0nCbjLeffZOLyhVFfK0Q9OW1Sx6qO7yxnX5W9mRqa5xWQFPyhf+dHTjFpg4XsfSVJDHGsDEFFMQ5jZOgD/TAy9AP9MLIbRsZxYL8fGIaBcRwZBlX819stQ99jbMNydc7rr3+T5XLBarViu9vx8OEjPvrRV3n9G2/wwgvPM/QHttdXvPzyyzx48DbPPfdcxkxETk5O8d6x2W5YnyxYLFpSSFxfb1gslqis0DXbeIf3jsNhj3Ue3ywYhxExI/7klN1uBBtpmpbhoAC70jNX0e6WYCBk7l3rbAkLFHuGOE5Rhu90fFdK9Gd/9mf5b/6b/4bf/u3fZrlc8of+0B/iP/vP/jM+9alP1XMOhwN/4S/8Bf76X//r9H3P5z//ef7yX/7LvPjii/Wc119/nT/zZ/4M//Af/kNOTk74U3/qT/GzP/uztWnxez20hMAh0XK939Iernku2kqeYJ0gQfNNzquATbcKQYFJ1FAAE/qbeqLWhQxsvM0HmQ5TYONMymF+XfV2lZnF5QL417/5Ov/dX/+bfO9rn+KlDzzH+cUZ3WLJZrchhYD3LcY6QgxcPn7Mbr9nvVrX5sEpJVarFcMQNHRtFa0bo76uRORaahDGkbPTlnHUPEXTNAzDwOn6BCHRdW3OX8CqXeOchuicJbdKg6b1WEPNbR2SNlFW/IQKwpDBRgrqU2UZg1reGjqxlSawAFmUucZiTAm9THnCGv6jlHPoGOuYHyvWaS6O2YeqAp7Pu5GsnO3R9Z6e12yEvevsz77XaLhMU8+aFhDRMUc8pS52u3nCkyf32W0uSeNI4xc8//wrfOjVT9Es1yRjCCGw317z+PIRMe41nJFAxGpja+tzLj/X1xpIWAJSPROSGpSFI1ZqlGHmUZqpzlZHd1KOxkzkAcUmq5EYe5xvkuJpSlGiuidc8pkYIf/LY6/twBwYjxGLk4Qkw9gH+l2PpEda12gNvuloF2uW6zNe+uDv58UwsH3yDk8evcWjt9/g0f3XScD69A6rk7ssV2e4zHsbxlCRo8ZSn1eyBrNMZCvzSNXN31PO8c+97PLelD/WkSp/F6BTvQYF1T2qMi7GhZ1ARfp5Pd/bDFC0Fot2p7JGe+aafO2YEtINDH0gRmEcE/t9z3a7Z78/sN8fOBx69ocDbz5+zDAM7Hf6/na7ZbcdGIaRYRjp+8DQJ8YITWsJIVEdZGC57Dj0mkbq2obTk7d5551rTk5aTk7WDENge9Xz6O1HxHFkv91pKirC7npP6zuG/YCxhhiE+28+YL1esN8fGA57ll1L6xvifmQYdcxLgDw4RwyBZDOtpjnkULRhxxXjOGDDyMM3v8m39wf2mSjDoNTXvmm4/+ARYPBNg28bDocDjfX1Abe7w3vY6Xp8V1rrH/2jf8RP/dRP8Qf/4B8khMB/8B/8B/zET/wEX/ziF1mv1wD8uT/35/hbf+tv8V//1/815+fn/PRP/zR//I//cf7pP/2ngMa9f/Inf5KXXnqJX/iFX+DNN9/k3/l3/h2apuE//U//0+/mdlQpGTg9PePjH/9evvIrf5uPVZh1cf8tYRxqs+mbPqMpxjEWGJHZwlVDUCjoXFPdlfdyGAo7Tkwpe1kTIgyUnsr5jrt3F2wPwld+91u88a37GOcISej7LTFG9iFoftH6TLKsNHwhqJU4jiNd17HZ9TWcW7rRL5cL9ntlKGqahsePH7NerNhtD7SdKtHrqw0Xd87Y7XbEFHHOsVouORx6lqsFi8Uik3TDcrnk7HStTbO9Z9l5Lk4TZ2cXLJcrlssly2XHYtFxdnZGu+joFkuWiyUn646UhKbpICOK23aRxzwSJBJHiGP2+ZLWUMZKBpDzTTFviRvh3AJksrMwcOUPZT7+x4pyDgarwJObs3lLyPhZyvYIxGQy4YQ42vaEMAxcX73Fk6t32Fw/YtUannvho7zy6R9mdXoP33T0Yc/jq8fs33qDMGrNm2sS1rSUnq76PNrXtA9lw1tVRimBxNw8AHLbUEa0/MQiCuevglqece+3GYxm+kzeDo45KCNnA/M1rTHVExtNrjsUmArlgaQssmTFGsRpvs6ixFwZcJ+iEMOOw/aaJw/e4PVxT7c8Ybm6YHn2Ih9/8WO0vuHq8k3eeetrXD5+nftvXIM9486de6xPT/BWIwRx1NBdAUVJkmpGTwb004r0WWCjm6+VEqdiwBSsQl1fmcWqc155jHPCzzVez3EOrMn0dYFDHzgENSp2uz273YHHj59wdbXh6vqa6+trdrs9D55cc73ZcDj09IfAfj8y9ErAVbxdFWN26qlc5xtWa0/jDM5B18Eqwfn5mqFXRDRG0fyr1YrHj58AsFgtOblzh2EYuHv3LierJf3hgDeJl55/jn635d7FOZIiu92Ge/fOePToMaenS0xOe9y5WNN2nhhPtA+wdbTWsVyf6FiVZSOCdw3eWkIac1mVKOAxRVYLh/OP6VrHJ1/7EPfsgod7QZLWojtnaZqGh29vWa/XrFZrmrZjWI5H/WXb7mm6w2cd35US/bt/9+8e/f1X/spf4YUXXuBXf/VX+cN/+A/z5MkT/ov/4r/gr/21v8aP//iPA/Bf/pf/Jd/zPd/DP/tn/4wf+ZEf4e/9vb/HF7/4Rf7BP/gHvPjii/zAD/wA//F//B/z7/17/x7/4X/4H9K27411H8gJ4T1tu+Yz3/MDfOQk8jjuNPzpPDEavLMzFJphrgVr6rKEa4x6pMYajGSotiQkashYEG4LlR9ZowhTnVVReMce6dR+Seu0nG24uHsOm0S76JSncwh4v0aAMywFiQ7QNW19GkTzwK33bA9DBv5ACMqPu1qtePToIdYalsslDx484PnnX+T+W/e1zME5vvKVr/B93/MpvvbVr3IYB7qu4/z8nG9+/XW8UVLrMOzZXF+DMTxaLBjGkTAqXZ8grBYLYih5Pr27xbLJm65h0TpOl8J6veKFF54nJQUzLVdrlusT7t67w2q9ZLVcslwuWXQLVquOrm1oGk/rW7rG431T0ZUpphyO0nCoMvxo94ZSPzdHIJYQ6lz3lbDy3Mh5GrT0NEr3O6F6EYdvNJkQY8+h37J7cMXV1SWr5ZrT01M++NIrnNz7ODAy9Fs2D95gHA6EMWAMOD+F4mW0mhczY/bkVNUpxN/OhHRZjzk8CxCkhhWd0bBezOHAKZc51Z6qSTkLjc8UyBTKNRTXNJpYHxubCe/TVFJgxJJCUEIKcxz6nI1YDbfGLPBrP7RyjzUdoh/wriMNI9eH+1y+8yYi0LQN65MzTu++zPruhxkOOy4ffI3rzSOePHlI03Z03ZJuscBgGQtqlxw7mj+rHP/NdBv6+KUWMxsTCgxTQ91ah/UG73wOwcvR2hpj4DAOHMZEP6iHuNnu2PcDT662XF9v2Wy2XG+2HPoDjy437PZKzHI4aJ53jEkR4CGzS4mw7lqGvifkFnetczRdZH2yVOMhhtrjtGtaCso2hACS6LoGY5ShaUiJ5MAtWiQFxjGDmrwnNYbRKp0ijcFI4rDtMXciKY25rjnm9EIA0VK3MQaNIDpDSCFTOiaGYcA3K2KMRNFa9lF6vPM1LZJEHY/WNSzaJcE4nPWK1DYWa4RhHBmTY+mXnK7OiYeIdT3WqxMUU8CmRJKBpj2laS3GBNruGDBo3LtHHOfH/1c50SdP1BK5e/cuAL/6q7/KOI780T/6R+s5n/70p3nllVf4xV/8RX7kR36EX/zFX+T7vu/7jsK7n//85/kzf+bP8Ju/+Zv84A/+4FPf0/c9fT8VzF5dXQHQmAYJ0MuBtusxqxPcRvNDMMHttS9dVACC0TJN3Zc575N3sAFMMkgGFmEFI+qBeWcz0lGqEpwfRfikpF6SmQmhUoRc+mQWBF9KBmvAhYjvFoxX1wgNznisEU18i3b2qS2ARIgmIdZT8iZGIBAIKeBnYapYcx4ph8o945gyMYxgvVEwkxG6tqPpOkKKeGdZLhe41rFYdqxWK2IIxIwkPDlZE0LQBTuMGCxnZ2sO+0MOmSWQROMMh75n6PfsNsKlTZyfDnzr248Zx0SSLGSahrPzcwrHaeMdznt84zg9XdE06vWenKw4OV2zWi5Zr/TnYtlxenqiXrWzeO9wrafzC+2GUyjgxFQyiUJwraE5zWtF0TlNs3Ce0rhpyLFEEDQ6MSnMgj6eh/mNMTS2Y7e54nDYMo57UgosFwuee/5F1usLmmZJwvHk8bc0xB0GUiF8Lx5izcMzhQhrNEQleQRsJpsvirII7OqDC0fKqBBYWGMzcUgOuef1670nxClKQ77eBJYp+VOqN1P1LQWwM61/qTWAM4MGFNMgk64u92lybWxhIyrnlMevudo65FJ7UsbDyBhGlDDCY42lW58jfkF/OBDDyGbzhKurxywWHaendxhCJERl4iolVjYbXKWrurPqIVY8gFFFGVOq9yGgxmWIDCHov2HksD+w3/Vcb7fqIfYjm82W7W7Lbjdw6ANDHwhjZBhH+jEwjmP+F0gSOeynaExKQte1StIhOo+qqCONB8RiYwESgjiDd4bItKaT5AhFJnavQLcio/KAa6tUlY2FxN5mt7CkalKCMZSqAKlGrRhhDMpONgStHEgxMfYDY0w0o9aOxphIMjLsRm0aUkpomBDVJqmZo+kcS0yRaH1Nw4jNaTtpsM7TtIa28YRND0lljaY5El4sjy/vs764w8p5TBwJYpnHnyZu4u98/J6VaEqJP/tn/yw/+qM/ymc/+1kA3nrrLdq25eLi4ujcF198kbfeequeM1eg5f3y3m3Hz/7sz/If/Uf/0VOvW+OyRTaw2Tzh8vFjWrvCZSFkZ45n6URiKgKVSciUxZOo1nVhgjGi9UiJKXfyTE6oWchBBW/+boqk0OS4eg2m5hgs4BpP8j4XwjcMzmJ6BebYpO2WCiNKmjGaSP7PJmUTUo5QCEnLGsYQlcVE1EwIUUENMVuq1ho9NykZgtkDSbRO0KtX3jRa2zd0HUkSi0WHpIZmcPRO4eVnp0ucTYyDqUbCyckSY4Rx1A0UotbxbXYbhiHWsosEbLYHDoc9YwiZq1W9rfPz0zqOi0XLYrFkvV6yXq9ZrlaslgvOL85YLjq6rmGxaFktF6wWS7pFR9eod+CdxzcKzPHOVmJ4YzL4igJkmtPrlc0kdYIrIESKop0UcjHErHPsdldst08Yxz1kI8X7JcvlKda1hJjoh57hcFUdLWMzwYe1HO/hvFByJmBSOnkFz71gmQGFinCXecg2G3Fk1HAtp9CLF7BZjIVfZvoOKexVVYlmAFIqa32WT81CWPIYGpONguLFlf1SrH9TT555xGZa4/PREFUCBSd9M8099Aeth7aepm3xTcOiszTOM449YewZx4HDfseiW9H6BeIbYhpRvMIMsZqxBSKqwGKhSkzqBfZDT8hKeAyB682Wvu85DIFDP7Lb7dluNR95lcOs4xDYbHf0h55+GDTfGHNDgmyoFspNDe1CHBTpjVEF37UNdtQol7W5LjmBb3I5ntE6ZVtpeAqoKRNpJK1iCFIaY2ewk1P2sjKoFrUjLLofLJmqEQ3TqwJ3FeDkXG49mA0p5zQaNSd2l0wQY4zVDkDGgHFaKJsZvrTMTlNYJnv2ZMPQZePIOos3OSxtA82iwY4aCneNo2uaauRUcKPTblb9YavPbixWChis7LXv7vg9K9Gf+qmf4gtf+AL/5J/8k9/rJd7z8e//+/8+f/7P//n699XVFR/+8IcpSLgYI7vdjq997eu89vLHWHTrKfNpbSW81gT/dGgY53gTqkjP5Aw55CrWMI4jIQbcck5eNR3Z560CbULt5hydnXomTkJBN4VxlqZxNKslZ+dndF3HcOjo+gMxQRgHUtAejJEI1mFz55AMcaC1hj3aN1FECSgKGKhpGtq2pWk83jvatsEYXaDKdKSK3TtlfgnZA7YYhkOPrE9w1uKMJYZe2z6Jjk8KSvIcguYvU4pTaQOKHiy5SueEbtFybk+OzotRWJ2sOfS+5nLHcSQmaFtPCJHD/sD19YaxT/hm6pEpSYEsJME3jsWiZb1aqZe6WLI+a1itFxoiXiw4OTnhzsU5q5WG9Lqu4fTklNNON51vfAZcObxziFgKotJ7TxpT5fFMoswr+vW6jqxVn+7R5X3CEFgt15ysT1mvz4lB2O8GhrABE3HOIFHBDCIomtIKcFwIb4zRMKzIlMcq/KVHseksfGcGWy2FEskgjGzh1/Ck1M+WJRnDqNeeafIiBKe9A1M4mMmoyNGEGDIzc75fa612LBEmgJKZmLOmvwuGYEIwFzTrPLw6YRnS7H40BO5tqWvUMRgOO6yztG3HYtHizBnGwu98+Su89e0HfOhDr7BYr7jaX6tRPo41yjKMI/vDge12p8CcPrEfYNjvNR+53XA47Nkfevb7A5vNhn1/IA1CGoVxDMSktllKZIpFl+fS4Rromga3aDIxwYDJPUgxmfTCWHp3mI2x0DiLRKflcDlMWQB6KhMls0xlj1qm8HsZK5UVOXoWI5KgaRqNOhGywkYJP+o9aU5Re7LqNbquY9F1iEDbthphy2xSy6U2nVgsFloyM4xY72naSNN0uBymHZPQ+EYNI2tqDXnXLbBiKmBSaRIyQ1vpYyyJQwicnJ/RP1QGMNe0LLolG6ONu22OKiSrMti5hqkUUWoy4/dy/J6U6E//9E/zcz/3c/zjf/yPefnll+vrL730EsMwcHl5eeSN3r9/n5deeqme88u//MtH17t//35977aj6zq67unmqkrUpO24mq7jox/9KK1RdhRJQoqCd1NIVRGxN6+R5UdlCol1A+s7ingUMydcoFrZ0zFZX8d5FEWblutVoqBMaYUxhBg5Wy345tcfcPdej7OeYQg5FGJwvsO5FlIkZQYRyfVvNj+bx9A2i6oUU1Sh771nsVjQti1t2+Kc4+L0gm+ab+Gcer5N07Ber2medDjnabzLkPMWBBrXKEWXExhL1w6XOT3V4CjflWIkSpoJQpdDRjpuwzCQQlCmnEwUHaKwTB3OJN1U1mOtHHUJWSxa1uslILRtR+OAFOn7npASw9BnCjEh9hsebh4TQ2LXG2KaaYh52NVC26rlfXKyoFu0LBYd67XmLFerFcvFgtVyyenZKWdnZ1ycNZysT2hbNUgUnLDCek+3aBnHga997Ws8f3LKD/3IDxHGyG67J4ylq0wCG4nSM8aB1i1r6Jgcsi+eQfEQRSCMVGWU8lpOSQvES4/Q6jtWhZPrb7OykwhDzFyjM4apOjYaY819RFWZT9aoIaGCvyDZi3FErWOmhuIkv19AWikJ68UyR3Qkz2sxae1RDbSWBM0NhKnOW+skpSRW61wmEUKK2ZBLGh4NgaGPPDkE9vtem1Vs1VNMSdhsNvzyL/0qXbtStLtzbDYb+r5nd+g5HHolQ9nvEdF1d+gj+/1U/lCYd1zmrLa5h/Dp+gR/4nOHnFSfVRG2qPFlDOQif1U60Kc+4y7UNTKZ3cpmA6mEr6wD5+eeegHNtBwOvebU3WTArFYrttst46ggxZjbI5bDWgshTikDEaUJaTw4S0iJkJK2Pmxya8cEjVfln1JisfB07YLtdk8SoW0b9vstq8WSOEaiRKzXEi/nLcMYaVuPtZbQR2gVpd20Hc6roG7aBYfdniiFKzgbtG0Doo0bLELTevZDz/b6mgD41RKDY4yAsxT6V0wihIG2a2myRxurWDA1nD9no/pOx3elREWEn/mZn+G//W//W/77//6/57XXXjt6/4d+6Idomoaf//mf50/8iT8BwJe+9CVef/11Pve5zwHwuc99jv/kP/lPePDgAS+88AIAf//v/33Ozs74zGc+893cjjJelDZM1nCyPmd7dYVvItb5GShEiQViSvh58rias6pKjdXYejnDOU/M0O6mW+PaOHEqmmMY+3RTpcC5xge0h+jMS81frnRexmFdw8IbhjAS4phRtyMhZE84Kr27iBJFO0uuDQQjQhpGRu8YYlBBnRL9oWe5XHI4HDgcDpQ6vN1+w+EwaF3YMNL3PTEErq6u6iIdBk30qy+aMnl+hu6TwzrOZGWqz1FRsXYyGpxtsHbMBojF26bmmI2JGgWwFhNj9hwkh7tD3fyNV0EUxqC5w3HASCR5RxJhCEFDPbbBtUoxZ7G0WdGcoXlXn6nP9Lp1CjBWc+5JOobDyH6z4/LhHmPf0dqzwuZjLClFYsylOTl8aRAWyyXWWZaLBc5ZrjfXDGnglQ9/kG7haRvPcrXEOUfbdrRty2KxUG/YZrTjYknXNvimoWtbvHfYRjltvbO0jVEBlgvNjdXaWN802EYBSMbkcJubwmsYXXswq4eTTJZxAxx1E0TjUMU+vW6yEqSubSX8jpSysBLVKP1RYzYQYkq8NVznyIPOcUxaoxhzQX8MahT10daaxHEsNYuKi9BIRSSExHbTM/RjrnPUOsaUhEP53DAQxhHnNNoh0SjCNwkxaf6uaVqG4W1Sinin0YW27Zi33HNeCRzapqFr4eJEmbtSjIo1yJGuGEac94oY9dqGLyETZZ4R9XkSDMMh4xQMpURFYu7EZBpKaF0/F7VW25hCJqRKL6eoSpTBGMMwDDN5VKj5Yg0TzxHrmtrUHG+RZ1qKozlSgHGIjEMEsdphxarR76wCpkKIHA493qp3fegnOtKUhHEYGHOON6GOTZLEGCIeSwwGceoljgFsowo7Djr+TdNknl1fDZEYIy663PkJhqjPKJ2QYmC5WnB+cc7CNZrrdjZD8bR+O8YRMZrmcdYw3swXfJfHd6VEf+qnfoq/9tf+Gn/jb/wNTk9Paw7z/Pyc5XLJ+fk5f/pP/2n+/J//89y9e5ezszN+5md+hs997nP8yI/8CAA/8RM/wWc+8xn+5J/8k/ylv/SXeOutt/iLf/Ev8lM/9VO3epvvdqi3olb2MAykMdE2XomzsxBGyoLLHQuyC68KNOUwBRTIvXqSJocKodR0GtMouXGNgpWw7C1sOTVvVLZRKYC3U66IWd7IGE7XSxrfsF6tOTk51SLiMWAlaqcP0Qo7J0ah2ikqubwIYRgRb2nGQXOcIQOSZgvQe493miMigyIK8bh12lLOe19r6lJKNI1jOAxZSToK6UGM2tFj/twTt6bJ+eBsNRqq656SkmI7N3kYOgCJFCBm8hbvpzFVxaUsSRatvzTGZdEzp1aLxGQxUWkgNLQlOq8aUGDIoCebFb7Nda4igf8PeX+zK8mSpAeCn+iPmbn7iYj7k1mV9dNVLE43q5to9vQsZgACs+CCi9nzAcgtl7PoF+ALcFXgki9BEOjdLIZAYzBoDDCzIJs/YBWrK7My8+a9EXHOcTdTVZFZiIiq+omTlfcWVwFa5rkR4cfd3ExNVUXkk08+iZGwIEFS7BFWW2qHpYJD+lVQ3IExByIGgFvF0+OHDmnmmPGLn/4KrR4QbogxGbym50o5ae7b0IYlae5HMCQbY6LRvEAEOSWQQWgujBGDX69Guos1/QbQxeW9HhjAnbhCtHIPz/V5vmxQ/e01QW9Np46Iug+1FJ1L1qnI540w97nnjgtRwK2o6k5rSqAptSqL19aQQFD2gtbQc7NqdH0OOIStylfHbYeIkmxElPGaYsLT8zNiyj1dEJhx2S7YbwWUdX6yaN5fSMl13jqQiLBtG47j6IznmJIhQmqEKwsisxL/zJlh1tfF2OIJltsWqw7niagVyIySNk4nIo0+o5EayUr0QkBMWk9cUcZ687xxh7aHVpbOjdj3KC/9yjkP/VxxpSzfj0gF8E3xLNj6AqkDum0bnp+v0C5WAeuyoVbNCeeUsS0K1d5uDZfzW3x3fKvPZVmVoBgT1nXrasIpJ7w7n8GNcdrOyMuKvew45xPikhW2FRWAeXN5QACNaJ+A4yhYlgWJtK1iY8a2ZNC7B3z8Dz8FZcL5ckJ2j1+0jIdsr1PVNQ+gFGHUpmqfOpLf5/hBRvSf/bN/BgD4e3/v7929/s//+T/HP/pH/wgA8E//6T9FCAH/4B/8gzuxBT9ijPgX/+Jf4B//43+Mv/t3/y4ulwv+4T/8h/gn/+Sf/JBLAWBi8QZ3cGOUoyA7jZ7IOl2oFm2bWPg9l3QHyZJ12rCNnUVzGMHjDTeyc26GPrkmh3PJ4TGLWBwu6ykk8a4I2jnj4bQazdyhHW1PlaA5ObAaxj65SXs1+hGCssuUZKA3e9o20PWm1PusnVO0FZU/diWyeG7EhaP9PnLO2K/Pd4vRNyAKo8XXa/WSfo/uZOiwsjKeezcQj9Y/rbVT2HKEPARVTHKbqDouBgNCSQ7E0pt5i7UX6108Alm7LiUTkDCCoWOVgUgWedgQxBSRc+rC2J7LIxw9yiMiwPKnx6GG3p2yGKM6bo0hFnnVpo2cIcARDlwJ2KveX07Renc2M1INFDwXqezHJWu3nZS0VdncfBxQmcWU89BPdvJFVINdTUbQJYS9E9BLA6V5LVMPMiPq31mO4hMdx2E1rDErjN+0qTcBuFwuPZpkZsSUUPeqvUVFOhlHRJ+rOy7HMUOlug5Y2Jpiz0Yh6eeZbXO1dIpFZDE4szYgRTXqrXk8oj/babWcJUPY+4RAa0mNgCgGbeocDBBoGZXY3OJpLwENhrNg8C2Cy+/ZPAzklC3rwhTQS4pYLGoWd7I9EJj4Fn4PNF7zfScl70J9j7j5PJrXWTCnmGQgZSGE/mmC5iIdbXOHQMRK/kQ0Io0RpRwGIecxL1NG44pl2UAULb+q+dRlOaGWmzl6CUnbandI3+epjj87wqxC94ZYMRQ690qohIDb7Yq4nXG+nIwwxABFd7VBCMYcdnGVuRr4r3f8YDj3Nx3btuFP/uRP8Cd/8ie/9j1/+Id/iH/5L//lD/nqVw9VhGmGY0W0yghZoaQQYoeiennCi+v3ieGSrW5w+++bgCIZXEP9Qbw0np/WDI5H039HConOnw9E/TGeVlXNOI7dSEy2KQdRw+GBFYAqrCQN0o29gJHauJYQI5Z1xXY6gZvKCFIIFoHopO0Ligg5WfQyGX0ihRmFXDYxIBjxSDDyXYLRMWbo+o5uEy5srfev42tCRXfTV0g3EzSF+7wnoupy2k/UC9RcuOY5ArQdUnCmn7P93KNkhoiWtpiKsI45C2onZQwDovNKxywti7Wk8zwlo5RbN6IiamwDAQRGiiblVxtAjBSD5pVBXdHJx9pznysrNL7kDLF6Pf197WgFi2DfK1JHFtTgVPEyKB3rSgGlskUcY44DQF4W7LebKcSozNy66vJ3qA82H0+nFaVWtNoMhVGCVzKFLd/sGjNKqYjR7smYzIEI67p9EkFyZYOjYxc9cIcjWbSXQgDZruRRFTOjlvGMNOJe0LiMa4ca+pQT1m2dml0HrUk0JEebfauRO4UNYiUZaECClqzUqMxxsK5OLg0Uo+6W4qSusU4oWIojeIpArFzGoFczsGpQ3U8Vq9lkeHGFiDHbvUyuv28Ipfd1RcPo9TUkQyPYpTXneeBMY3dO9Bf6M85FHZ3wsXaInkUlQl0AAoAhgTqGy6IRd3NN2phwvV2x5FXHMwSEmKHlZql/jqiApWGvOzjoCmX7jhSj8h0oIBpKVE2UPiV1jKpCF4i14na74Rzf4OF80rrQxsgOYYuOcWutowsCgB0ieD0W+I3HZ62dG1NQVho3bKcHnNaTWhuyHEU5kBaFNkZ+wReXERRceBz+Od2gdePTRaT1VKQL0jd+ealqMxlNUuvmkzpMnrZGOmMxpJhx2jZU+YhAKmdVa7EGtRHcPJ8Jc8UCuFRQs+4yJOBa0FhhIM1BMaTpBLzuqmYToV76um2oVSMCFkZpBSknfHx6wruv3ukma5M7JDKHQHMPLIMYJeQsT8CL1Acz2TZ/4jvDejRGqtryjA0SJCLAtGUpOLzu0C9MaUrl2LxvqQhrmQ8ILWjbuEqKGiAoTNkq9aieGOpg2TOtE0y3Lguw35CmZ0cAAmuXCJBCPTlGrKcTfnkchmBYaUBjLOsCBMZRCgBn/i1oYJSya74tJQABjNijPF3XFVQBRDJYtXSPH/B6RUI2Y42oyAHFgNgicsp93GPSyFP1TINJNuoYnE4nfPz40RjGDWDCum0IFHC73frcVQQi4na7aT21QWE5Z6wp9j6WDhE+PT1BJIz1BYXWT8uCTAHVCGgigrKqMUfQOj9YJNm0GFFhYXDvndrHmEdelpupdYFQq3UnaXqPMRGoNST2htTuqCZ1nxjam9QMTdsrSAgJGUxqZBG0L2mV0h3EEHUO13YoSYgEKiSgf2pZNGvDAArIQeMlboJaBtOaghPwNFocgiuqMy0IyGkWbCCkQDhQrc7E2P2Eaa3ds5Zrrd3pdJRi5ErZ0gPBHMFB1IkhoPq6tZK3QIJSDq29zhFUCygI8hKVG0AAppSKPh/9ziTJ0iYRO7ehxmRw/rJmXJ8EMdq+wUo20r123E+MEXnZ+t9TSmis951yVEGSoqjMm+0MEiDFiG1ZEEFW3zvLOeqf2/mMFFyFzslE8518/+OzNqLMjBSTsTIJt9LwfH2Py5cnLNsJYK3/i4bbvoQe58iUwIiRUGqFMmcjIgWIHLZxrOq5GOu/s3B9IvmpiHv9VLMcSzTv2z9351VCMf7NVIjWdTVd3AZGREbGXgtKU4AoU0bdb+DQEM3IX44CJjLCt0VfjbWlVlHD5NDv6XQCRTWmTmxIy9KZssG81GLRjJCRQJpuhgiEyrBODkDOydi2dWxcFn1q2QtbnSiDkABJqBzBHBRCCwGAyfuxNdcNh8FCTclEjVGrANZ9plUVv6BACK63XQTSRGd0IqApOFdYdZNzIHVQWhvPg4Cj7GjSUJsv0tjh7cJASkFr54jw8XpDiAH7vvfnKqIwsd733PJuR04LgIRagFbUcG4PmwrcsbK+uwEMlm+2OYKetzKD3cXaG4gtI2wF9WyRO7WkhfBN4UGy72lgSGSgaAeMQoIiFSg7Ukp43p87fJ9SQkNAldajP7iRk4yuak6MS17xJBWMhGKayD4Ot9uzPtNmRQkUQNIgXA0xsAYGJsWlrdcIaAdCXqZIarSqcudWSB1GCQpRU1Inr7Lgkhfsz9fBHAaQU8CyZCWUdMKQjnPqnYmsED8lY5uaYICoIU1J0ZbaCCyOkghAUTdhYjTx9ERAChHJDZLUyXFq5nAoz2DJZB1rAOLWS6Q0og9IIUAS8HytuOu1ayjUHJkGS810eUyLGlNKOJ1OVmJydIRniQmHEaR8P+3lKzpK2LYMsmhdRFMJp9OG5+dn1KbErO204OPTR5Si/JhffvNTiAjevfsS+/ERX3/9Yz1vjJBAaKXh7Zszym3HkhO204bGGxIa0rJ2TkMpmvv0ewnRmMHGUD8/aF417geWJeHN+YLn5xu+TBlfvP0CwlrqU61NG4WIYKIZ59NF6//5r2M274/P2ohSYwgaJFgyfM0404MOTmsAVPBY4oIYbwjkTNmGRgsSokZylufU3yWQdFwTkGg1RIN23/VY3ZBCC9HnQ9N5Ci86AS5EGvAIhgKIIOLN6YyaBU+3K5anJ1MAEWBNGv01jZoLKo5y1eS7bdqqA0rI6zpIOAFo7YAWaI88zLadABZs24Z1WbAuK969eYvb8zMu2wVLXrQdEQhbviCF7+xKtQ8oSKHIYDlRNY7ROmIEeOu55ptJDqhNBR8IjNoqttOKG5mQBHTMU9TG58QRqvumOc9oXmSAbxrcIWuPGtS4AxIEDU1LPoL03JVHNLWNqMDLc5ZlAUQQk0K+Wp+qURFBayUDFJri2tAqIxreqA5VQyABizpfHnVDlNjCVQCh3oBY82UmFi/ShQrY8mBewkIsVkyuxp7JtV4dGtecuEce+ks1djFqowXnAYQQULl1x4YgWKISnOpRsCbTbmWg7AVoAFeFvzXHHpToAVLGOFdEItxSwrIs2EvryjcxJiWd5KDdj0RTLmLIgaMMzdIMaED0jRECCbmvrQ4xsipLOc9Ai/91PXZagAFAXOudAQWZok6x0jBLQVhgCuEKR0E8FcBVa3+jqxdBkYyYohlJqysMnqJxhis0b9rhVOizlmbPf6Qmeq6RGZQ0b61Hs7p2PUdkRq0BMczV6eSX0aMsEZ0bYvsT23NXZyz08ymypvvbwRVVLDlCQEhReQOaR7D1nLV1IrngAoFE67k9imxVUOuOuBCEmxKsoG0Qr48Vp4vmKtOyIC0Lnp6egPI1uAl2qWi4+SNEODQ3yqwM3tCaXmdtCKQMaU/DRGgK4agH9j3haf2Ap9aA0wnLwwVUC2pqiJQQRRCkIUjB3g4thTSnZqqn+Gsdn7UR5dY6SUVzbRFHY0TL2TCCtTCzcF26IJ9ukDZ4Lu0GGWQfwCNVS3joC0oXmHIP9jKkG1o/hoG1D0M9chosMMPi3YNPOaLUilpUCL+1il1UssohFiI1BkFMLosFrWj+rFlOjbkhWI2Y108SERpr8ffHjx8B0Vo1bowlL3hsH41cYESWUhFT7hueCPpCH8xkh3900UavEZzgSDeqevvS4RSFcWn09BAYc9UKw+HKJj58Bucx924yzuh0ko0rtwSHC0mfyX0u3K/hvp53ZluPr9Qm6BABJ8vTzp7rizzKp8y+e3h7fPf0fnfCBPAyEQBdkAAWoSoXWWxzGz+/7hBI10j1v/v4+ThBYM6mRa2iUS0H6wkKuz6XTITNVRaF0UKAiuiMMqAQgvW/HbKDOrY0/d3u0xmrlpcTH9JpbZkXZINoozomxSdjIBjR/UvUSTuw+YYpZkylnydYOkdIjU9vryfKsZ/Ef+6enxtN/0XPS05wtB7K0lcBD72PJozARta7m1qDtYyeH51SJewiFb4QBxTr+VdM9zvKbMa5WKSPA9v72BxPmLOmdcOeevCL0/eaje7drVLOcNZvM11hdbyVPBltnJulolywwR8sMyvvod+WiUWIrWuyfdsnjaVe9H5EkUMiLKcNp9OGY9+1brkDtfpTWUB5LlOjsd7+GsdnbUQBhU2pq2dk3HjIZXX3FOgbO2nVMoZ0wpj0+ucrozmB5Z+Qq3wSvNhbgbHop8sYCxzoD5cN2oqdWUdDNMFyIG7bdYIbM5Y1/+lqLl5XJ7YwAYWKUzLiicFWV1OkKaWhirZP04Ui3egdpeI0kWCEucNJ6tUOBqGzFJNRW30jZGatFzQD4YZwHkOXG6vc+oKz7U0FMrpBdmbg2IRnpZt54/QfLdc5xlwJZCSNezKGw14ELXj355KM9aqt3Q7E5M0Rvr/Pqvus3P10kEN0U+jD4TbLnLJpi8Rs9IHpfruDgnH/QdnHg8h2P+76HeN8bK3shjHoPuX0Od/ofHOf2J5CY+w8euy3dH8Xw9CMv2vkrpv2TJrp3z3Nm+GkfTqXxjnNMemGyC+f1CGeL8cjextNlgbXNetQso8J3NEee8ZLR2wYO0ca/E8vLeKuiaznQDe2dHdZ+hxD0CiQxy+mPW04iH3Pk3Ev3b5OiJnPbep/H8/FBS3sNiGYUhQ85gN1uFej1xg1Qk8p6x5Git0FY7bnnNGOG2KIWJbF7o2sxpbsM0A9irZ9tPXIpLwF7caoalsx+Jo0dTZoA3ZHeygA523Fw2nDfhRtrk3U0UXlcQxVrz5U0x7/Q4/P2ogueUNMCUwEaYzL5QGyXwCDrWqryCnr5sQMBO1qLiBoF3NnxU2j9+sGcw4o+2svXxxuNftiJvQm23cbPIyJB43A1iVDhJGTliMgaD53WxczNNy92hBUsICZEZqrgBhpoRSj/Y9NMruG5L7rJOWGdr0imYdXvAOI6cs6WYWhmr4ArDvKaB9VG1vHBTGYkNVY27goS3VAjX0zs8MZfg5zKpPTNH2tHAVBa/8AqGrKYYQoQs/ZAoNd6puFw7SJgsF4MhkU9M2OaEQynvvTa9GOMG+//hIxBjCrkHhhJVbc59bvjbcfo/H4zObE3Ubr80OE9byMabNSGNIDaofHdekHOLGtO30CrVkMARJIRT+m8ZjJJs2633xirKbDP+e3OFIYCnsfpeLjxyfklNCsxsBK8npOXGbSCY0lND8LGy2dITKu0ccvzNZ8ujZu/CISG85pb+0lZnAodEe0Q3fuQPi77a1BFIYkz8kGLW0L8FTFYJ17neU8duLNsyWBREx7doyjZXVVLxsEpgE9u/MJKLs30n0/XADTNeGT7waAnBIg9j1EqCaEopKCSsQiqJqRO/Ijn5o7S7oxQ0LE+XLRf5tjRzFiWTVPyYDpV58sf3nGuq6oVaPSr778Cj//i1/g4eEBx/UZy5Lx8PCA9+t3SEvGsmR1vi3X+fEoyDl18lQ0R89TYGTBBazWO6esJW0BCA0o5YoGxmXNeHfacEgzPoyFTKKcltIq1hD7mM9zcDi039+iftZG1Iv6nEV7O3Y0YasR1IbFKvcok2zfgDBUsWN4rgDg8Nprh0zR5V81xv1XHW4YtHNMZw8x9qgOAN68ecAvv/kF9uuO0+UNrter9oSMDnWq5Fozcox6v976q4JS1o7sooQfhae4v5dZJ+TT0zOWvKAc2iXidDlDhLCuK3LMCCFiWTe8ffsWf/6/MyhknC8PCIHw/vEb1OPA119taJxxvT2jWrSXUsa+q9JSjAQVAEgg0ugYNhbruuL5+bmzlvWzCSktOA6glL1DVrNw9byZeOPwfd/v3uOlN+0oQIyWS6PJqA00QMfPoHTSvGGVZnKHDbfbMx4uZ5xOG87nC37xq2/65/w8rxmf+Zgdpx6tSIAYRD+TkSB4sSEPYzIb7ruo9sV7i5GcusMjzgIdPS39nD72+rpaawYUpwtJx0YsUg4DHSkmlFCOgvP5bMYlqAfA7jgKgqgaDYtCwYHUwDqTN8bYEQBfN9oLeIrwgC7YMZ6+QCU1RlTZwRpBh13VUSJ4J5bfdMzzTDueMOLkVM8MZI8N78pOMN7r0eY8D9CNruZQRdwo446B3Ik0wclIMjkx6OU7s5MhZES6UmyMx3tERJXJWuvRvu8dfs2KRBXcbgdqGSz4x8dHACpvGEJGoIDHxycAWvZ0vWqzbY0qI95/+KDXjYpfffstWmv47rvv8Pj8hNIqjtrw/v17/OVf/mXvzBWTGtGnj494fnwyB0yJXnVZtLm8aB76dr1q5VFteMxPQIASt6pAbnot25LxsK0gURW4JQcLnmDzWYmjEoZMJvqfw6H5vsdnbURDTFaqAdTjwO/8wR/i3/38T5HXgLxsEFNSiRQN8hXrWgGLREeObxy/acGNVTtDdeOzn26iAKa8yCC1uGET0cWRYkRkLR3YNqN1U0DjQw1OyBCYbogRUFRHNKAyrE+eRgTcGNV7bdaK223H09MTiAKO44aIrG2WmLEFwtVk1TzafX56whdffY1AAceh4trruiLGPETQ2dRsbBONMd1FEmpA7z1pj0AdTmmmtXs7dpxj6tBWNNnGe3ambzxuCMbm40ZBpby0v+C2bepYTRsUmYevq1SUhGRkCAoahYg0FSqPI8caU8Dbt2/xzTff/qZpCWCUNbkD0/NV0zRSFHMYub9q4c6R4WwY76Pi8d7ZcHeI1YsXIUYEs/eYkVJfcjgIEG9/NpSR1FgEpLiAiS3yDAhhGBICjAhG2mDZr+8FGtNHixw+9vn0qfPBEI0qoPNNEQzvRjMZlTDVQNq1vDZG9OJauiarMbP90G4wM7NeHQRggnMt4nyR1RmGqj+Hl9PkN2/VHpkSxf59n+RaP3HU7PsNpqToestilQP3z4D8HDAlsDDmb1oWrf+9qVMcYkQ29jREZfmWvKjDG9Ej507ag/Y0fvz4XiPJELGuqzlf1PfCbVlRFu1lLI68kZaWBSiap/OK1PiFirisytIHcJOKc14Rlqj7fya0W0VaUoewYXMSGFCzz5I7h/Q3PpX747M2oqpzKwqbWKT2/PyMEyLicgYlly+zGJQFrRZ9vWKa9N/PU73/8tde88fsiiV6zBvo3QK3RSnm9S1rRtkLuLWxeQcCUVKlIYtcSVSRRZP3JlgVCJTUgBCAHFOvB13XE5iBlHespw1yHMjbihtuOKx4X6RhXRdTlBl5Dy1wTshZaypbNd1MM3I5L1q8L7rQ/Tl4JGk3apubbwgvNjQMIoa+3et0Q4+k/e+usqObky50N8oi0vNXyTZ9ZUEWBNud3Y7I9OS1lRJ6X1lAN4do39lahUBzzrCIcSAS00SwsOkeIpVhmF6WFk+Lt791GhsxWcp5vryMVvx1P5+rPPXI2zy07nDJgIAbAzHBRNMH0YaN/TjnN2dDI9MAqB7IcFDuxsKebbC8FKAbI6YN3JswEGmOrkq72+T1R+e4Px8hrav8BOZ8xVC+NNyvGdT++Wksfb7ZTaihJGW/tvlz03/HjcudP+3PKprjOL6DVMQrzI7iuIZPnaPxFS8RD/tAJ93p+iETV9B1QMHkRih0B3Q0O0Avg9PIWFMgMUW0Y5QuxTia2seUQFAxktYYl7enrloFhDsHt7UGZJWuTCndObciArH642bG2VLkmqpJqkjncp0iagwJ3sZNa3Sf6jMagNOy4JwzrsehdaA+aPYcNYJ39MMdoj6EP/j4rI2ohv0CASOI06ILUinYmja6bi7ICiVQ1HKA8gV9G7XF++qk/LXfC7iYAoA7TwdwOMryPK8t7P6nfc7gnHVbsX93Q61atK9Q7f1nFSI1GJTtWiggGMTrDXQdesuUtd+fGZVlXbGTwr3HoWUcOUUzUJYTCQAMbusyajFpuyO2HIQZRTem4/pcTmt6zcYFetqpBGE2ZCPfF8gE1D3JNkc4hM469V6GHPh+nCdjE4OyIbsSlVsrDPajbmA2TyyPpoXp2qbMGY+wKB8iliZ4GRWOjU4NetNITpwxOfLC+r00jDFLny++ec+kHJ8uHjTCfnP3OtCjRyeq6H4aeukT9Q1jJi6ZM+cGer5f0pO7as1LCNlZu3Ok7/fnpDDqG5b/csCgISijcswbXU9+7Q5ZquSe9FFh2xR9k57Hwv/uf3Yjintm6nyQ2Dx1WAifGjEvsfFrauJs/mGI3PHxchmZXr9vxNhHsc9Bbm04KHb9PbLsl+uGeIwXMBPWfEIMq+BqZf3z9OLe7J57WgRjTUbrL6oRv5e16TxIMeru2zRf6Q6tRufcO9h01Sr/EdF6VeZOjiykPBYGTAABttgVtmdzYkABEk2B7NDyxCqCzIQP5QpJCduy4pQyHvmKINJ5DGp8p9SQTPc/OcY/1I5+3kY0ah6mcQNbsX+w4mkxo6DqP1r7xE2N6BLe3HnTwIgf/bjHyXE3wLYHwfkRnxhggzq7Is90zEQB3YCHp3raNnzH33bhgtYqSm1jo7LPxGCTye8dhL02bEHlsghAo6DlGTY5vTE2EeF6fcZy2lCrdrpIIfbvdAOtSiVJa+ysUXbOC4jinYfpC+/lvQG2eQVXiGFI1VrDuSPDMHZqECUoozeGgKNW1fUUACxIIRpspvJwgQhkxl7Yi+LV0y233dpN6XjNCIBf2+zksGiDAh3riFIPXOLJ2JHKCtyWFR/kozGVlR4yK+r4Qu0QpAlY3M2pMKJHlU3UDYhlIBc9v9aZOmRR7LyZkTZaF33djSsRmVydG2Zozaz1i3zZAo3bFDH3wQECPBITuz7u86dv2vbMwZ7ju2c+jx6W9l7TT3XHt0dkHhnYmHjEFpyF7ujFdO2at9T6Ux8TBlmt5L0B7WvPb87+HPDnZLxfoAE9EhdlqXtEGswZIaA7zRqlc/fFMO0L3oRizmd/iiaMf3P/TkVXJpe7jyXQfZ3+utaXWzRpxlFAXcN4dnJm+Fv8uYlYPWZCysnKWIaTCCjxrtaq2tKmFx29S5I58SKK+rXWes/dVptJmx642LhxrV1lbd93LCCQs44t6uXCJuavYiwha8tFZkYjoMWAlTZ8vF0RThvW0wlLSqMW356A8w5Gac2IRMP9CvhBhvSzNqIwaIkA5KyTats2EAK4VWvnBahKiJJvahMsHhX1T+v0VEOqv9HPvfBWiaaJ75HHpxGsGzud4Pzp70U3teDMPMt3OFyifQE1KtTGgd5xxWARUriDIQrpihtV6ZJZHMamdxzaJqoZ7Hm7PeML+hKA5nxKPRAgOPZbj4KO4zBlHn3Pvu+4XlUJhhuUKRsGPOZGGhhdQOacXYoJAVpWw6WqoxkURnYoq5MoGCDiu2jFc1UUAC5WC2uvt31X89Lu82Mefc3P7x7WA8QaYDt858xmj+S9QweZDmwIsKj2HiZ0rV+Swah8Pf+nu14nMZhHxi/YsvYNnc2Ju7mnUYKQ1rF6rB9ovu4w8v/T5vzyuXhE7Ma/ayLzIJ9oNKP3VU16kYL2mW2tIUcC8IqTMvf7hElP9v6yI1eYUu7Gd8D+FsFORtihXXf02gzTwBnYuJsD85p9ba1qHtCFEozAZyIW/lw9wHO1ozg5CvfnFiu5s+/l6Vpecag9NldFP+rvIbLnPpWc6AWQW9eJqT9Kwhxtag5x2hogCkhpxXEU5X0xdW6COuGK2KjEpqdOxhy+XC4ox7XPSe9LDLjgvUpNfvXjE9Z17evy8nAx/WhlDKeUcDqfcTsOIx4GCGmXmmVZdK8jIAZNpVAMkFJAKXQnM1JAWFSQI9kUawyUGLA8Zbw5v0HeVnBU4QZe7qNOsjlGc0Ten8vrc+Q3HZ+1EaWUQVzBUC/rvGX8xe0Z5+cDP2oR5x/9DvbrrxAlgZFQsSPiiohntKZPQFsQAWi6WN28um2933wAFzNXSOoF69YWbAza31FLOJLJn1Vj0WqXikAa1QGMxge4Ct4sAX/RBIKI87ZBuKC2AJJqzEaVE1NmqrYkYlFZq7UULBLRomp+UiSQdUwBrIMJgDfnN1jOD1hS1uR6zjhvDxrppYS0rIhpQTsK0rogxdw3znVdkNOKp/0J3gB4KxVXPEJZw4AK9esmU+sxNjKLPgBB9c4qNHomaumEbhRzHgxQI95Y82mlCZoISlPZPI/kc4wmkK350Kd9x9GqGQfdjGNnOHccx56dF9erP0qiyEROo4sLQ8tQGqtYNkBAVSSEa4E0VRnqE4WSqhEZWYO5QbqL5hGe5rJFRCUCZWLQwqIMi+QBdRpKKSrSjhGhIwUlxmLAh8dx9M1CNx8CQ0ky2rXDiCM5YN+PPibMupkKoMxaNnhNgDVncKmo5Ea9aauulCClWkRqBj0kBGkqOWjRm5NISOVxOiTr0oD+TIoZiCVFJSzxyA2L6NeEQIiIdzCl58jV8VIHlptgWU5gbmhkKmA2GxO5gda9QAJQpCIbz0AdMBd/1+bU3Bq4VTg25ACHADYX+26h0SfUiLHAOssY8Q5A7dCxCVOEALRBDApw1SGvXzVizxIRorJXRdxYR8Sw2t4Se9QFBDycziqwopsUam14+OJLdTJLgfZDAS4PZ3VOU4ARs/Hu4R1++tOfApywbhsulwfVVj4qtvNFST6tIhLjzSWD67POC1OE26Xg3eWCX62ryjWKNtTe8oajVuztwFErKjMkELa8IIgzrxk7BFteQaUiQBs4AAEVjL0diEzItGCNZ/zHx/+A07s3eDhvWEGQ1tCwIImPphIJJSQQzUHQnDj54cdnbUSD975s6nkf+6FebTggUGFnEvSopZUDHz9+h8vlDYg2i0SsxEQmb8T+BCa4pMMZ98QR/5nhSX2/xhHqcXunkxXrcsLz8zOQiubL2OFGLXEBab/KUqqKeyMjRSMhtNYhIbIGubp4Gdwablbf6DmZ/ShweT6FeTWBf73d7iNbW8ssXhunEDAsQnNH+C7HA6jHHYMxPaE1phOMruNn2qBEPYebrR+mR3+tNeQckLJ23iFTNhretqtSOVwc7HowNrEJDu1/Tp7/cIYGLOXvjNE6r0wzgFmZzUGsLOGVSMLnBo3potfhOck5erT9WtqAMfsVGQyo5/FCrDG/AIuCgpdIeFTUL/ju/T2CehkNy1TUT27MQ5/X88GWB3PvPJg377nFuQyFmQ0R0frG1u6F4+exsFtUg2BphJCm+8coaxo/A4acnyfbQ+hR4BQR9nUqLrV4ryY2P8PgknpWetJ759K8vu9mx7QnmObWNBYvUwfunBPu19A9l+L+eb/8fJeD7JNtOJ+gYbyLNUHo6IK0jhJ1kQcjHI2yOXVSb8eOQITK+jQYhNIqSqsqayxi/27dkS+loNUCAdBqxdP1sPRRxdPTM5gZ1+szlATUcL1eFRkzJ7eUqogJqeNWSlEjSurE7aWA4gFpDcbTNQeEwVxADFQSbDHh2A9czhds6wrYfhYUb5/MpG52L/cGeTH/f8jxeRtRIjCNzbLWhtWkp5SCrQaCRWE4bg23p0fwsSMsC9C9EcbclUX3lPsJPh+vGVHfvPzz2jrMfqfFd502Tn3Ssy5OEtxuVwhXeFcHwli8feO071GIRusNyRivPT9qZQIiQKmMnAhkouEEQjn2u02tNe8Jqb0ie9RgbeYcpvRWSG5kPTrseU+MTaLnusIo8tfcluaJWUZeb958+rCPpzAZoU8NwmtQuZNMXj67+/zTHDHov+drAc3toCZFppSm7x+QoV/vgPk9T2f31dWD9IvvjbFvqOM6AnTDemnYpEfLwynw5+/vnY3o/bi4AfSDx7XKp/dxp4ZD9+cmwp3CZV8DcHMhvQQC3agBIQ4Ck7DmuWB51E78Itw/5+k7XsLi4gXycAUeWJSt4gg930VWviroE0z8vtwo9TU8jVd3uGAG1hyJuwhmzoePOTuzmu/HHXevy4t7fPWgl+Uxrqz2qTPQ2rimu7Ga5z7Q119fh35eUI/2OViELSquktzY2Ne6c+snDjFawwt/di6U0OBpAyJ1WrUpesJCBOGIHBMOVnEWEuUAUIqgxqDofYOhyIzdue5/yi5Pi1Y2vNs2LFkFdhgYjtWY3mMO/xXHD4F1P2sjOhZbsIgTSBRRmFFbUSPKRngIBAJDSgWxiYCbEYUMUKiP9jyIMv3mbgN2D8eiuKn9mW8GXAGWOCIWqtrySxJCUOHy49jx3fMT9v1qUTOQcsKyrAiUJhaomN6oEnW8FMAnp3EzwAw01gkfo8J3HDXKK2W3jjJTtGDdK45j16LtqFAdc+tlHp4/UWmtgNIKYtMcRkgRrQ6Wao9eJiOqIua6KjXqc4bh6FnI7gTY4XD5eA7SpdCEGVyd6OKbqm3YzojthsU/PudM58d7b1Hn7/VIqtaKGEb03IPZu0nicK0bS9+ITYv2hbMFuHKSbvSuJ+q5WI2uZ6KO9KjeNz7fhGe0YDZAPYcaaHLguH+/PrN+668e93lkNVfanvqFmSfd2D0q9pfIrFtKCSGaGg5YYXG6N/ov0yOv3Yv/zolM8z7gpB+3jURAioRaJzZtH3Mz6jw5DHZHqrPt80kJbF5W4SxdwTCe47o/dX76NU8/mP/8Nfc3os7JaDtqcGdElYGq6lqhSwzCDKJL7fXnI0oSVCYtACsXWhatZpidlWxBiTbhRicFKrEoK/QvDCHC6XzGc3tWRGrJWJalf7fOv6BkpKT79XraDEVj5BDQdjGFNp04cUkQqytVhbfhUMdAkKJPeskbzpcL9usNl23FljMAARMQWKYnooad6N4B+c89PmsjuuQEaYRDGkSUnPL0/iPq0xNyiKjlQGMo5icNOSecT2dsywlXZiCMTaGDSX0i30/S7lFbFxN9VSNF7tGHf0yLhaVFpLBBeNe2V8HrzwTrGgAw3v/qA/7sz/4UX719i69+/DvaTYQU+88543Y9sGyrbch6jpiTioB3wpFuQPu+d3H7oDpxWJYV3Kyt2aJtvkJS6CTGiGVZbSEI8pIQrWykttZh17sIII4NHETa8LiqoDRBWbVjs5ZObIjeKaTtcLqMExD80PwoA6RGigIgaGAJIAmmdM3KJHhRdKkbWVPvmWIvo+ktqyZj5JG9Pi577iZQ6rVnDcNAuxE9n89ThOchyVQ/6QbLQjV9wvP13YsgCLtnZb8D9B6mKFREyRQ5DLJWdyQCQWrRc4ahZDPucTZkpP1F4bm7ETdOE/cOJhaLJGX69x0TeXY9+6bvajN2YuuCQ7ivOZ1Vpl5es8thjvPSi/uZIwkn4pCxeodR8veFKAheDmbXEmFRK6v3HTD2ArSRSpjHz+He0ZCij9T08+sPj0gJNhtfRDvz/bl0or9O5rzrPuA5jLm+kRAiIGUIUDiK5E4qMJipKs+nxfKORKSUURsDFBFiQkwZMeY+xko8SjgOZfI/PDxgXbXrU2Pgi6++xq9+8Q1CiFjyCm9I4CpKIOCoBZUZUQihKt+i1Yq0nbBtGwJGa8FkAi4RVsqGpq0qRUAhInpjb9br+fj+CZd1w2bEI55AWofO58qCeczlxbN7+e+/6visjWheEvbd5eEIzdrlNBE0VoWTggSECkhEyhvS+Q2W9YLncuuLHhR/0/zvx6fw44ge+nsAaPV+w358UIGAxiCJOG0XAMB//NN/i+cn1bL9oz/6b/DlF29Q82r9ORvKrv01Y8xKATdJNSL1yGopd9GSiJaACMSgKwbMwB5H01IfUcm2lBJyWqzGU+/lfD5rV/g3b3F5uMDrvE4nzeGKaEcYhTTFzrch58UiQ6CUw6BahWw8N+YGo9YGgokgcMQMOSqDMIFJvd1RbxbgJJh54rsB7s8hiBKTzOOORKNWMsQesXDT9/aNkdyrt0geNicw5PGO48D1er0zoiLu6d/nsYaxhLGDtRXWuE8xprhtyAij9rSfZOz4FMKdv/AS3gaGMACLEkTmaE4/43WetklblBUEd8ZHRIVLwPq7JvffO/9Jmh+5M4JeT6gGViNNtnkbY9RyKnOafByVdOet9QYxj1mJWkTePYXGmAiA5jwD78t7n4f9RCQCbTxzzI5PG/vAfK8vninI0Y3uUgAelb44XqZ7Psm/vfh5/RhRo4hHmfra/Ny1BnI8c+8f6kewvKVfMQNgobuaX3dSvMyNjSAWKOJ6vd4prPV1CVgJmUepgg/vH1GrXs9+HPj48UNn9tfaAFLkQIiABtyu154K2q830JrBAtXyJuvXWysKCyh7a0GVLgwQlKos75gzQgCeasO6RmTjaXDQ9zWBNR4ZxL3Xxl1+zd9/0/FZG1GCNmeOSXF0AXB9vkFASOuiossNqE0ftlAEI4Ep9nNoDoAt0r/3Jr1+7OWA3+e0xmtjUQTzewoAxrY9IIaCjx8/4P/7//tfdbMLCV//6MdY15PV5EE9rhQh6idDKIKodAarL2xtVK1G0RV9SimQqlFM44baXEOVUK1eS4RRzYh+eHzEbT8gAnz37XvknPH8+AiEiFpVCPp2u/XFplGlEyoAMMC1ocowRh3ONaOgn1UDGWIACgDyyFUXW62Mdc1Kv7fcsTY61kiPMOdVR1Ti0WF/HhQhVO8k0ZotRnK4u0Nar2x8LAaH67NVJ2BsHvOG+Nrm6PNEN/Sm85FH2c5rkB3Iaj37nJo2SNJ8v7NZ/TkDvhlaT0ug91yFGQU3ZP59wRjaRqqFSwT42Hk+UkSL132EIlF/r59nzvFFIm0/ZdAu/OpmsX+ojxIxHM9gSEAIAU2UTe39bmfZvp4GYL4bP7LrVgk7gzgnp+J+rAVE8zbnrzlhabp3HupYFEJnW9+v/2mP6AHoi/lAw7S+hGjv5snLjdycBXoRheqfc3Tt3zPgaVU3S59E7CGq/B7FqI51UyRtaG+PqFVTOod+3u49TxyTELT0zq8nkjb23kOEIODh4QGwiHbbTjidLliWR+S8ooqmInJeUEUj/9PprOTIxggsyOcT5KiIiRFixLZu2pACisz1uUzAkjOu1xtYGMu24nQ+gQEsp03Lu4pqMhumpWijYHTkmcbpP/f4rI0oen4DAATPtxv+xh/8DbTyhCUr7EAhgIoAUTdzhAghV/sRkxCT12UsX7ZNwlgIs8gAgLtFIUZoymlDToTHjx9xu11xvT6j1oqUMi6Xt1i3DSkmkASUWrCdvNGyLuh1W1Cu1kzYdiO3A77Q/EfhPXSZK2dPsm3kRKSNrxkIVb04EVi9Hfc6TN2kqJckzJCmLiSN6vzfvrF77ZqPjV+Tep5W0hLIoDqjgpAL0CvANTJOrkBjJRrzxoF7h8Wfw4BAhwff84bTc1LVk/sIHu4IYHJWzDFZ1xUpZyzMSDmBeu2x1yXO1+UwkJJeaHbWJqPW72WKPl9d0GL/mSMX+w69zvv8n7Fqxh5/R7yiCe6l6Wd82bAB0z3h/rU7owD17vucnBycT/KZpFGRlmyM+1Ehi6C595ffPZ3jpTEixzfFN0l//f4ccnfOPvPsPGPsZ2TJB/DeMfZnAZsv08i9Ygv7N76I4F++9vJa+2D1/qvjM6qmZzn76UJUHSkY03YcnidOSSM8sXmubcjiFFmrQ+EdlRS9gUlker21O0Aa2QqzlZt4e0GNHG/XG5ZtQ4wJpVQsy4qjFGtlpqjSuiw4np41LSakCJtYIwQxYRxvIi6WTkExR17LlCQpigYG0BpqKdpV5uGEnFUm0LeEATTIXf7704HH/ZL4nsdnbURjiDbIDQGE6+2KP/rDP8L19h7PT0+43Xbk8xkB2nQ2UADSAgqqcKM5Ud+2X5nMEHsIvoF8OvH19UEsGCUhBCCilYb3779DKTcQCF+8/QIprdhOJ7DVokUi1NYQU0DMEcet4Xa9Ir1ZsYuXtdj1iEBoaJ3e5al6lCDWtNsjIS0BSiFBoiBQ7QLkIegUULhUjZpDcOP1cb8z7EZQsoCPlcJOpt9L1DdIcQ+7R1mhe88eJQXT3fX3+rloEkp/efjCdcOn159MMNw3X+7PaFyDb0L+tD3Pe29U3YjmnMwJuM8P+3zoj5umzdPnwosc42tRqT/D2Tnp18PciePMqh/bbYdFQd0A2P140+25bvFeKSv08bAzvzq2bhD93/N9+GvqSGmWbhabn507hZoH0Uybdo/o1p0tTJ1+Xnverx1EI6f3mhG9G3tTh9IpNjGFXzrD9r97acAJjQDsPC+M64uvfekgvdw77p0D6ij+/Hz9Od09c7j1ED/REG65sw9GARNMvXcDgpW3ufPjRtT/TnCH596Aav5fhVZIYCVKDaVUmwuC/TiwnE4QEdyslO6270g5a+QsDctpw7UxjqOYE6CXXXcgCBv8qkISEG/YMWryfZ9tTcmI9RDs1xsaqRFNOfU51p+DjR+/8rz7m4a1/UFR6mdtRJUpZgLIAlBU7dzHxyc8Pj0hYMUWlOIh0E05bycE60lHlpMKdJ/TtJhkfBEJZmh3bgbte9GcsM75DGHCL7/5KX72s/+EL774Au/efoV13fqkarUiZN3sW2vaDihFbKcNH7/7gJ///Gc48VvcHj8iUO6QGqC5z1KqEUx08ZRWETqco5KBIDEjpkSnKhXH9TY2tl7HN9pnBfNoa63IUfOzXrJPgBYrN62/TTFiSUu/75RcJJ9Ra+v3FWJCaxW3m5b0hBSRKGndmDx3r16L/CPSkczj1JyPlwZ5tOwwq0v8+ca5riuWRXO0t+erGqPeuWSKimBeOdwQp85U7MbfdEBVck/H8Hq9vrrBiwhGGYL+vnHVFnZMJkIx10yqp12thZS+rq2vfBdUY94grP1TRRSmpxCU5i/D6Otz0yck4kxcKy6fjbb9Z3ZUlIVtZQn+Lkc2PJoX6VHODG8Po6z/Uc3TTx0E/3tfI33tyDDUsxMzfe5ljvMusnstisNLIzryYPNrWhL2aYQrIuAmo6QL5nJN9/Fp9Hg/HzyWFaA7tndz5ZNr8XP73+8jYAEQEO8j58l5G06+lr6QSYIyqwTofuwArKYUAgjjer328XcnpqtRYSgouUqZNsCWnl/NOeM4tCb0KIrO7PuBZVnRasNxHMghmgIlab2nSXembdG9gUUrASDY9wMERg5O8tTAgkRzr4MoKF1K0ElGIUVwqxAA67apfKjl611Mpefq+d5hVABH0ClIP8B4+vFZG9EQgLxuWE8PKLeKDMZeKrbLO1BecX1+wnHcoO1v1dpVPtCOK/J2wWH+ZJwG7l4/tzvHA+ry/4guEH2ogpgWXM4bnh+f8NO/+DO8//Aeb96+xX/3t/8OiAj7vmu+oRSd9OZ4hyAIiXHcGIsE5PWCtBTkSDhvG2LLXSZNF5ZuwiEC2ppIxeT51pBy0ntsFaU0pBhRBdir5kDCEnGYS6HlBSr3dexX5OWMxw/f4rTfEGNA2hbs148oCDjaARTG7faIvC5G4ikaXSfPW1UcdUdC1nxdTABVlHIFIfXoTphx3Hasm9bptlaQUoCggOWYon2t8fI2a7o5RIjUTgyCjYM6FsB+fULZr9i2bfSqFICgRdcxZuyk4RkZnEzWYGsQTNQpEygLupTSn935fIYbnw6hwwhIMRhpw4lOWjPHxsCNyQ25GOFGiRYDlvYoxDfThgYBuOnzIo82GZHEfnxu0nCoQoIIo9Z5s2DNo/fJDHUuYgBiQsjDWMJ6TjKAyrqhhxCtAD+gQUsHXM+ZTPaNG8BSgSZ9w3sZxXYjJYwG6eQvXWv6upZCfQoH++H3ihjARZ0R6BWAiVBFC/HnqDHxMEo+t0Ze0dAaqNyk5hZNbMHCPxbv+Us9QiUiVR+yzYHC6IwSXfaPAkji/bUTdUdKf7zWUh0xAFZmogz6JurOh2j5PJcCZDK5RNKWh61pXSURGgsoRos4A/KaseRopUXq2J3PD3h+foT6JgFAwrY+4OPHj6rOhQBQwGlZzeGuxubP+Hg84VYOrOe3kOsjKADrkvHll19CuOJy+QqnbUPdd+RlxbYm5GVBMW7G119+Ca4Ny7qqARTBNaqy0kKxz7EYIxYz1i612MXvU8LG1fK7AdtyBhHw7nxBzBk3ZhAxAkNVs+JijneEYLWI11XRRjncX+f4rI3osmTsxQrg4eF+6yUnDq0KRi6scsPT4wcslzdK55YJgnPP06IXETFj56QZU9IAAFJmYQgB65LRKuNnf/FTfPjwAcuy4Pd/7/dxupxVlKAJVHYuobZiUXGCwCE73ZQaM5YckSKh1YqcEg7owonWHZhIIy8v/A9B6zqZGeuyKsmoVhAE67aqJqsAS16wbSccTzfkNaPVAwgVjRlbJNDBeH78FsuSLTITLMuCHFfkvEBawbIsqM03NhWdXtb1LhcaDL4V4S78oO3UnHSDKbKM2gAb6LlWssVTijkaLzxDIpXv646M9UyaczbKAHYizN0DBgCLMCxysiCFRaN2NZINecmIrDVqx3F0hnLOeWJjD2PusLkbWN1QqyEabNJ2+twcIiPrVOPR4Mg/GVtVpDNziay3JwNMjBDux4WZgUDIQZ0FkWZsSzcew4iJwY9ztOfz36FXjQ7VUQ1By2LIS8LMADl5y9eWE7PcEfJz+vXPz9Bz3eM98/tfh2MBQBz762iQR7jaiPrl/YAILPUuqvbf3ZO+fKewa/zklXHtL6/rJXnMx2bsGeGTzzlq4/PCneQYo8khMkQapKkh92h6TqfMzpxrZ7fWuvEMpD/X6xXNmKwURgqkNWfiRqSYez9hQAUMYjDColjzCaBHn5eLNrBQlr4+s2+++aa/v9aK59sTLsuK7777Dg9v34JrRS0FHz5+xHO54WBFu4gIUgqe+Qkl544CUggoR0Iph6aAfH6yafYaOSxShNT3CFHw5nRCAqEVU2tLEcHGtpSCp+dHrJcvR/rO4Zm/vg39vI2o4/oOS4kpWnrfOe8zWsnbXKk3d31+RL48TGeivhgB3K2cDqvQqDnyxZGj5vD22w3vv/uAlAK+/PIL5GVBXrxxrRbSV6g6CQXtZGHfClgiTUkqjBy1C/vRKoL3naAB97xcQJ5L0WSZbiYELRMICGjcEAyyhgClVJwuZ5RjB5lx3o8r1vXSk/ZestIaY9l0sddWbfIuELEIQDz/FlB98ycTklYsqzd1HrCgiwkEeEcYbfKrNWsOJzsBQglgo9uE5wWDdzIhdU78Gfpm0uFCL6PR4R3PdXpRcC+wEGzzcCepb26NEdbwYvMfG66DeOrQab3dyGv53IGKb/cqUrmfv1PE1K+0b9TeCYThzOy7ecrS5+YM1wPom50Tcrw7DsmAYTUi99fg2cPpOj8lZaij+cprvrImwzUf9HLTkvvfzRBn/+7+Vvsdq+qROkQwWSIY0uDnlDuDBrvHl0b15fW+NP7z6y8N5rgBO5/I3Z48Ox4voVu/nvnf86nlxbkwOT7dgArg7e4CEYLcOxOmj2jXpyVyx14AQ2P8umfnwwUbPP/oe4//xOiiDhpNuwCDPh2GkKg291T+56hNM2SNicGBtSOOJfCr1bQDABkaw612WNyuCszQNIejNrWACHhzWpFjQNuVaMR2XmWEM0o5sFrHoSH1+p93vBR5/KwOEZhHi6mfn5g6kaCWQ8WYYYNtG1It+9ThYsDg1B/U/B1GSLCd8p4g0VCOHfvthtYqtm3D23fvtGs7SHUhBXB9S2BEXH3rnSYHAVhzQIgqsxWto4R+3+tMxT4OUx5ExOThROsig7FchRtqK4gxdUgphoB9vyHnhNqccGSqREYY8Ci/+YIydovDXCEmi6jVUXGDHShYT0lfbLpo1Vi7IbGFLA6d0xDQxtjA7jxwjOcFKNxIdj6aPuPXbo/dnvOLSII+3dCJtAzHO9+IRalhuo55fvTPQQ2P59CcvXrnlLGW7fR7ljE7e+5gPregG1HMdZgTZDlHlOyNDkwr1fP3d4ahX5fB2n3kqOfC3CkY3/OpvN0YZ33vJ99D93P27sfvb46O+zOdjacbUFHBdczn0TGhaZzcQZj/jX4v99f/yTVNr9/f368nOvkcvPv9Cyd8Ps/8vXPnonkGvXw/5rF5EdHfPVP4nIfqxdoaCvN+Yz8OT6sB1fONfLcKSvRaW7uGGGMvcWGr/fWZEoNzFizdFHQupWyIWUyaV9eYAYlCb1Ho7RJjSoOtbSIvBLkjQanmNilCE4LWgAfrvxUI27oghoBm857NuSIAJNYtCRi8Fh87nzN/jeOzjkRdvH1JCTfctLhftMqytYqn50dcHr7o79eHGZHkplFFCCa0LICMDhD8YhG9PHLO4Frx7fsPKEdBThk/+clvdyjVIQelpE95MhrEBc2n2YbhcBgRti0j54irFCx5w5IbAI3yhlc4rs3PmVJSqAtOa3eBgmDKRl60b7WPEEQnVVkpC1s3jRgTcs5GZTdjadHnyCVZrWZpCEGbaHdjCUCEEdNQ2QF8kZrurrdNM3YwUTSqfJju8b7mbT465Ij7zcQ/p2Pj+ajJwaIAoslY0Wjx5d87nzfGBAJQQHdlQH6+u3IOTNsgj2hosjHwdl1+3LGHCQqJ8r2R9giTpteciNSZvCIAN3DVzQ0sSgKDAKbgpEiFG+fXDcVr4+2vDWh4bNyfjH23WeM8I6K8h1OtWKE7kB4lu6GQ6Xo9Dh3PBugMBvv7bITm6/FbeS26HPeIX3u8ZkgHlKrRHU/PrEdMf0XkOiI/6o51oAjNgEw59+nCPA2Sc9a111hL1aC8iGSt6fS0Ac5Wn8lgIQTElLGuK/Bh6mIE9PMq8qISfUCANAaCQtA5L4bSNKzriufrR3BryHHD5XRBrW2CqivO5w0fvlO1I11IChMvSduuxZSAoIjVuq29Xtuj1jEvbZv05+CoXohIROBbAdaEnEZjjkjuQIw509MIfQ7bfnXvr/wgg/pZG9FAhNPphMvDA56fbiiHoDEQWkM5DuzXq24mZBBvysjrhi1o5HU0z5nqCA7yjrtm+rrXS67LimXZ8L/923+NP/+zP8P/8e/8Hfzkx7+logWkLLImboh0su/W69IXRSkFMBii+3iiG1+rjPO6IqeI63GAhPBa+cHIPc6bmnpTISTz7hkxZsSo0ocUVQCcTSeXLAqOMfb2atJYC6hzNoUUVikuh1G9XVpK2v5L0NVRGjfUUoFVYRxAm/CWUpDzZiUX3iFj1LRpRI8+DmTX5P+eYUndiJwMdL8pjVyce9cCgKHlHIBHIn6OLkwnYqVmIy/pzkoMEUteEKyrTa0VFO9r8XzezAo5/j3uKPg5AXR4y5/ZMCq6mb2EintEO77NPsfGquZu3DRKjnfXAAHI1LuaQfDOUNScZkFrZXIgGkTSuEYCBuxnRx9nQFjTFcFQIZB6/F2Fib0fyKf7kj8jhkKQCi+zbaAjMmqTE2TbK0RG31pmcxYE8DrDMTHGxvuaAzobqpcR6MvnOT+b1wLTl47cSyP4ajTrBmH6cWf1NeM9/1uJbDDHMA5nMKYOf3rk6Neiggdu5BS1il1XeTCxCao4trfdgg/lbHhT7aGpq2me7bLhduwANKKkoATHEJL9GZBt72itddlBR81ytt60FMwpYbgSnaq/jQABHm0LsMSESIRvPv4CD19/hbxow+7jOIA4ghTP39+lhvyZWd76rxmIft5G1I2FtIrgroQoiSKEgNNptbeZqaQAooTr9QmXGEGVu6fqHq8PMjNrzdOy4O3btziOAz/96c/w7/7tv8ff/KM/xOl0xvPthrRcrRyCwaIJeYgu7FIKEFTZw6MvP3+z1mTBFlETFY1fsuZErzdtil3KVR+wjEXae07axNDrVkICS7H3E0o7FKKFqvtU03HzBeTqLH6fMSaLOpVIcLvdrDBbp8lxHBMpoaGZ3Ne6rth3XVRkDDg9lxJhAkUg6nUvS+5Qst9LSp9ubqOv6HjcPvHVCx2LYUSdc8utOWIxOD2QOSbuiAoc6OpNv2ncL9eGUnRstm3rMme/Dt4jorvyp1kIwntLs3DPdePOkfIInjqxqF9/EEySQNbsYIyZfkczuDfYM6zwCMPn4nHs9vz0u93pmHOn+neFGdENPwCKCKI899bh6unCLTelBr4BiPDcrV8jwmh8MN7r8K45sFAdbHEHCCNfp4pLYv1egTpF4XfPhFTsRCPc13OZv+611yLGe0fn3oC6kzfyj/781amYpQndmbqDeKMaBY0+fSzHfPJodYbEAWCvmtP082urQVX6CSn4Zej3hWGoBSrZWQ/t9es9dmeVKG9mfxcJQrqmcYwZo02cX6xqd7fWwI3RQkRzFKE2SKu6BIU64/ioFRmk64EFWIDnUlSGUny/WPRaeqgDeKkONYa0hkqE2+2Kh6++xOm0IkRFGHMMCBzAsD0yBORVWbrV1orAo9Cp/vcFUvObjs/biGJEKmwi9NwXR0Wrhz64EOBdPQQBt6fnqYMBwfOJblh88jw8PICI8Gd/9uf45pffgAD8d//t38bv//7vgkjhj6ZJR/W+2KODccRO2fcefyPSUY+eDVMggIE1JaSc0DzXkLTZ9Ws5lXlBAnIXTUUKKE01c0MIaHAmZQZz1ddjRGyMYy+dJbjvO5bbFTEQyn6gHEWFGhzqnuAeEdFIu3GXbWut4SDt+BLsPa1WdOUUZjRpvRG0yhx68XRFrS7uYM6M135gwGTmE91tgjO8pn7JBAu/kvLw77ATfcJQXZcVDL3OmVYfX+RE/VxqQG0+2Xf6huTwnMOOZEm7nmsHoHWfej13aAjQhYjIb2ZCMIx51Z1HfcYJgbSkYYam1Tn0Djp+DtP2pXD33cMw+z0KRCK8VZ86BITASpibDcn9vLyvD5Wp3ISsmYMLQ3guuSNCbqwF8F5mvtFBRr3wfPiY90wJoAQY5o5KDeROVC1xxttfO9jzrp8Kf7yMGBV2pGkf0hzjHNneOWI0WpGxO3WfRL3js9pIPU/OInqk2DVz/fyGNgHQZuIWiToZMJISDz2VRNNzDDFgXVc1UrUix4ycFywWMaYUVGReAG6MmJJGlyxYrB6eGXh4eOiNMgAty0kxd9GQJWcsSevR87YC+44UVQay1qoETU9LYLCTW6tY1k2rA1oDl4Y3X3+FTJrK6HNRLBVgyyaEoOVo5YCE+9pbzcD+8Hj0szaiFLT1WTL4wGFSIWUjPj0+9uhIKgGICEHrjpT0o597mefyaOvp6QkfPnxUKatlweV8wW/96MfIWXMKITi0MMNyvgjGpg/4a/feb4e4RKNkrmydU2InMMUUO+QxF5w7lDUTXVwX0ls8qbbooMArvHOyBP9gwqrBUohXJiN5dAYg9b6VPZJmJ5vc5+pk7LoAjXGl7qyMjdKCcIOvBuvZ2bl+9M2UPDrzDX/q+cmt/97PAbjx/HRpdPKMeEQ2nhMEfcMEvBZRTMFo6/tth9HdStsX9r/K8G8xzQOLuywOHUaxf7+dO9D4nE4slU3rX2R3NndvGfdyz+KWiWFrYbk+P++76REjuD/X3k2FYZCYCsqz1dGqIQWIQ8/Z93nw4gewjTya9y8eRbrAhMm0YXoe0m/I3QaY7uHLzOcnhkdfMwNDAc3bFDr0BNMn9jkwnefOgbGr69C4nfglTDsitteP1yBhAL19oQi6kfPZqfO59c/6d8UXjgOgSY7OfLf7EeCO0e7fOdfxarP3ea6z7WGazjhMXCHE0QRC18EZIUSLWKU3g/BUDZGyd93o+wMh229yiGgmSE8hqFw2BSW4+cbQHQ3fP0Z3m9ag5TpJjebt6Rlv3/whMkzz2MaA3cmcAq4ZrdJaXurIxfy8vu/xWRvRGDMCBSxLRooRB42NqVbGh48fNcohDe8DERC0PrPVZrWavmjVgLlwe60FP/2Lv8Avfv4N/pu/9cf48W/9CEvMuD5d8e23v8JRCjavxwoBpYz8lx/avLp2L2xs9r6gdQtlEdPMVcEEZZe1fo4QZvabdCPaF00YHTG6NJ1MbDrbfCMJlrxqfaapkOScEWJETrlDpDGo5+jRZJgaf7tXWQ6G622+VLGJUc/PxqrUPpzmDFgEPp8TmDZvHcQpQpuNkX5WRRLYd7R7gysjRykiKohh5/Hcoo+7XoGOzbx5BoPf3WFQD1/w+PiIy+WhG3Xd6N2JwtTvUL+vGQPyZZ7LIfP7rRpTtNrvxjaDMV/G781L8n/16GOkDuaNW8d2yh3CpPjmHrhwR8jyrH6fdj9sUatDr4CXOqX7Dao7OPfdZLwG1Z97F4YXgZdm9fsG0Otw3CT0jW5EJXcPvo+pjCdNY/PtjFR3MoOSzMaYDIM2z5M5380ycsrz0ccP4zyDePXJ27sjmWJ48TosNB660Xq+0B2L4YR6GDUcFzeCPHEz7hugozvprVW/2r43OYGNoGSfUgooaB0pCDa3ak/X9Lxq1I4vMaXuYAPa2WmofwUI6fduKQGWjiq+VveCZmUyIFgLNSU+os/jYPsj46gHclM08P37D/j9N2+RiRB4PExt9qHtJ2ut2A3G7ujD9HDcJf6hx2dtRBMW3G7v8fz0ASLqMVEtyOsbnE5vcXr3FrfjPWL4LYjsADVEAMu7H2OJEYiMowlKY8QkWPMJ337zM3zzq1/i+fkR27bh7//9v4+jNDw9f8Rj+YgQIk6nFeu2orWGelSkLd8TfTCinxCSET88xTRDky7DJZB2oKUz1pyxLhEUDnz34TvEBBxHRaAyJiM0h3eUglLNUwwB+z4IImqw5tHSyVvbDe9/RTj2HSlEHMx4Lk84np5AKaAcBw6LvK/lhsePH424QbgemqdFIFWqIYJUVYpRQ8W97nAYKoskWBCt1IZRARowI2AkERKV+YtqIIi9XlGVeJRsG0abLgqglIB9NPYGBCEkpLSA6OhNxQWCNa+mGnC/CcYYQdWXjxJwUl5AAajcwMXh6obKdTJMqhgFstiShlevCIgb2hGFutRf0BfsO5XJ7P8MUZ06ITLB8YBQhwPVnzGP67BHfIeo+O/YDJY7BV5ahPExNDeKPiaJwGUgKgpXE2CN1dWuDsfxHuZki3zRrwUAxLSqdUjUUCRKVoLla8PPMzkqNi/6fAk0lfj5e5R8EsgISma85ibvCG6mjTeBwfoFzRG9oyzSSzCa9AIbhYTl9ehXAISkMpfS85nokd9AYFwQWQBSTd8co5Hs/BnqM44hD0cPEXtVBm7s5W8RoIhrOYYMJpT1G0MACSO4IITp3x6lADEqVA1ds60cAJuUHlTxLCwZDQxmAjcBQkUIACFpJymrCS1FqxJiCOC2AxaRlmNHOYqSi6yVu0DrNeFaxmJs/eA10PrsmsmXBvebSIlvcQmoIuAGnJqAWsPHcuBH786IoYIbrNwuIkoFKCByQdt3fPd8xe8hYCf0Zh1ARpTqWM2dU/59js/aiAqa9cbcUPijNpUtmtd88/Ydfvu3fwfPTw0/+tEKKVeUuqPWA+eHgJwFt1tFzAuWTYUG/t//679CCAG/+5O/gb/93/4PWJYFv/r2Z1qzGc+I8YRWb51g0vNARHce9901ThuoQ7BDy9MhnZGPijEhxQRvT9WqoNVmXWhG1OALej4CyArnO+7Uv5tZrL8nUFuDBIWymqjRaRCEGFFaxe04wOLMvgF9+UL2Q2n2o9NLaw2lFnMcjPxk3iwH27AaW6TDHYp+Df5jZpD1O1UGajM9YFVYeZmb8vvUn9DHyLfNl+99Cdd4FAcATErMITNSSj8lsGhOZ2YyvnbulzmzoXA04Hd6MWc6BKulj30xe25r/j6HvQWDDKJF5fq73s7Logs/Vyc6sUDYHADca9L63/X5mBtkn0EcEY9b0pwzXJhcXxtR3QxaA+goRi+VmkpmXtu47nPDnz6/u2t+8Vx7aoEnlAT36ZXXvu/lM/xknk3T5lPnwe66P9cBx78GDzKzaT3HkUcO1pAhGeJTClSQJNo4oSNI3ndX5fgWXE5nPOYMRC1LCUHzmlyO/kwjVHUrZdtzCN1Ry9azEzEiLivSsqA9vu8lcKpVrvfq8K3Wiya8efcWYNWxXRcVZBECztsJ2/mEJWkelFlUc3vbgENbJEYn8nHDYecIFJSDHyMQCImnHO+ScYKmd5YcUaWiErC+uUCaNt/o+X+F+kBWk0vBYWtBIJU3FGpm3Md8/wFo7udtRFNMENZ+i5fLBdfnCgS2CG3H8/MNp3RG5SuYK8AE4YD377/DEt/g4c2P8XS74i9/+VM8Pt3w4x/9Pn784x8jxoijPqPIDYQVORGEBMKlb2YpJbQyDMjdIvkNjsz4DPe3EzRnRBaVaJ1mVV1ZGQtTRd0Hk3RW1Knkah8GEzdGMwjWverT6WQMUpuQgVQjth5KmSdASAk/ZNl/j1xebnYecWcrkiYRJKOyx5i0Q71obW5MWq/mXehnAzQbE69tg0fp8EhEo6O+uQmsqTSgmrqe8/NPBdiuf/d8ZsPth5eG9PdRg/RQRzC66BAeHx+7tNn8PNXjN93dxmAh1VANzla9f/6fGi3dP/X+NErrzkSgTz7rEOW4zvFItSGKOVI0IMBZiF1zcZbnChYJigB3xtbzYwNeH1G4jo1GH9F5SZ/cp/ft1MvTaJFE9ZsVfnWW7v1nX47P3U1Oh44BetOAOefV0ZB+8b4yfv3xmiH95J4wjPhr0LlCxwNGn695PN/p36TkLHVobc2582aHkx6TNaroz4cBikPNq1bTWraab8ggYM0OnPYN9bpbQloymjDyto6+wU4IMwPqa9VLVAAn+uk5n54+YjttVp8MpGXBcb31Bg7BUlKlFuSah3PWah9LYVaYKap+LlpDlogKfZ6RAnITNBDqcaAtuhfeSsWXv/UVIKq0JqQBApnONMuQVZyetq4P29u6xuYPPD5rIypitOW0IKeCJz4Aw/Iba8nJ48dv8NXXv2M7q4pD53TB4+MTPj5eUVhrS988vMG7N19hO51Qyo5SCyISIApbkm/mE/yoEYlOpZkV2OGkV6539qDHIrWNnj0a1Ul7u91wPp8gMOa86J9h8nZnj1k3tXsCgxdov8xZad7T85mEFGOHF0MMCFnbHMWYJiJCQ4oZo2UZLOqJ/TaCbcwqHajuKBkk9tIA+3XP0KNuQl4HqouArEPK3JZtji7mSE9gdvMuQpBpPCKI6ifODxm4N1sCr2114hORqjgpoVpLkRyunfNX45gg0ztD8Ep08uIc5J81p2AeH4WKR6eZl4a1f3D+ximKfRk9EeZx8Huf9YEHWnBXs+vnceP9Av3o99cfg0z9HH0D+/Qa5/UxQ+fj9y8iQB+nFwbwtQh3Pvenxvl7hh/2zH29/HpU4v567teqzie5M6jTJj7de7RoDNZH1FXJfOwdxTqOYzjRhsY4Q3uUrUg3+t0JU1uLUhvcpRcR1MYQE3h3iFvz4jMiMpyJY9812mxGPCP0SLq2hkRKcKu1oWWDcLl1xIfNIVGJPuVkiAgo+XDbuLUGrlp5UI5D1ecIuDxcALbSPzCiOXqEUYoUp56rNpum+T+e6Q85PmvZPxZriZUX9MS7PWydhAnf/OKnyEZA0pC94Hx6h1Iqfv7zn+Hp6Qnn81v85Ce/q5qy9TDVIY0GNCpxqn/ocMYMmc2bNI2EkW7wk/j0vAkBry0sfT2GgJSDtSvqZ+2EFGCox8yHn3+e3J2di6mmzZLtgcixQzWoRL5jKzReKwJFhZdBpvJkBlJ8M1PVowGB3G9wTvUf7LrXp9xLCNTvxzcJYBCoBPeb4ZwHFnc0Jq/bvXB9buHu+ubv9Ch2/ox76zqWES7Zdyfd6DvG9Nl+D3jNaep3Mb322uIdObx5flBw3ejXF/xfNc/6uE5/n88yG67Xxmm85/689wbM7+/+M59CmvcO38s58Juu4eU9v2aE7+75k++fv+eTX31yXrL1MctbznPs7rt+7ffQ3dy8f88gMNlfuhPsjPrG3HkR9m0QjPZg9sFuhOe54OvDUal+TWY0W/VGCbr3kaWHfK6xjPRVjxxl1M3Wqu0XZSykronLXmZk3+/rojWGN/X2dETvmiNuEN0TNUTFcx4QSGtopSKGgDeXs0aizMrKhYCazm8v/7vff8T+r//T/dVTJd/TocJnHolSn2SqhLGsKx4fbwP6FMJf/uwXyJmRmfB0Lbg9f8SbC+HNmwsul6+xXd5gPZ9x2ytYbggRPbJqTbBsnpfQlk8wT8uhjA5J2PU49CY9JNJN5bVHMud7eoQbAlJW+apaD4VaO0nB2XEOWwEOqc2TembrShETWtBFdjqdFLoVxrHvIBFlHT8/IsAgoaMgBiU5HIf2CiTLMRxTp4dmyiCX07nfz1wv6HCWG2OPlO4BrU83u1mMgWiISriK0uxN3qk3KUUCBDOWcGblnHu7/85P4DshANH+PYnXW/TjfRTH+agbVN+gfFPu3i3GJvrS+M8e9oxU3G+wZrQCoN091IF5ee2fRmxyN0aexxpRa+iRTpB7p2x2TPz83sFGI4RmG2B7wc6eLvvFvb/mKL3886XKz191BPuK+d5fM7hz3Smm77sfp9ed0vnvL41zd39ecRDm988GS16MMwWfO5+iRzFMMC0PcZQQ4h26pLk/NUSlFETShhAQwbosuD5+NH6D5k+3bcNxHH1dgeheQShErOuC8/mE99/q9WynE2LQ1oAxBSzbiloLAMayJFwuF3wXAra8GZtdz0lEOF8uiETIeUHOCdf9hrxoVYCmsIAlZRQZ80xTOsBRtXuUcjLsWUetH49J1dUKC6gBv/3lO4gICldNSYFAFZBse2CpoBz7/qS2nqdmCxaJE+E1pbhfd3zWRjTGgGXRspTn6xXCjJQCGhekvOK/+v0/xNPP/h1+8Yu/xC+/+xUoEL7+6kd492bD9eNHhHgGU8Dt2BFiBMlipQ0Cr1veb8Zs9BUr1PsJahmAdSTom+E9XOReGdFYHO5JOp08kEeKAgqEnBKWZQFxwOVymZinujhzznfG0jeJbMLyfvgG6QSFGwgpRGBZkPOKSECrBTlnnE8nnLaTQrYp4M3DAxiCvGR4FFhrw7IknM9nPD8/AgZjp8UISO5p2/dKmTbxGBDIJNwk6M9EVBrQnH9Go1gIWYH+aOHkIgV3Ea+GBmY4gRQI4dfAhX7MpQv+Z69ldS1UE0Hw95RSulfbITGDnAdABAtQW/fy748RyY2NWRTenDZTL+/wtnIvEQ2HZl8avLvxxHBCXqvj1BKCgVBqZOpOkHb58ffWdlikMjsQLw0D9d+/Zgs/MUQdBsYL5+ueaDadATBI07/rzkgbczf0sRoIjM+P167H7/e149Moe7pnRocdX479a07DJ+NgYg++Vl8S7johjcZr2oLsQIy5y3ACwPX6aBrJhi4QYT+Ouz0HAhx7xe35+c4BrbXi+fqInFXD+np97qVepRRABLU1HM4HYMH1eEatBSlF3G43PD094fTugo+PHwAKEAJ++c03oDWDawNvDYIVT09PPT8vTdeRk6QoEPbrDSACB20JeUoLJCnsTKyauAcY3BqeEXBcrxAC3lDAUYpCuQGg5nsko9WiexstQBtpG486o8+7V2fAX3181kbUYT7fAXzBt6b6kUQR33zzDb7+7W/xOz/5feTlhFYL3n/4VpsHC4EyQJEBCVZ+ECGWV+XWEIIRYlo12FMjUpoWqh/KsreN/5WNczawgHmooE64EKnWD5CwbSvev1exiFmwncj1KgcLs+clHIYQJRZ4FBlTMJYmY10y9scrQEpvZ25YlxMeHz9AO6wc5m1qNHYYEzmE0R1eFU8GpKNjMpckUN+c/Zr7grUoU+HRgCUt2K83xKBQFUQ0/RMjpFY4ZEqIVraiELk3qRYxw+ybqMCo626A0N/n1+7XNEdjTlCZH5uSjWr/PKDlJ9EdBjO0zA2StOOOAhDDCM3P+5PZK/pZV1fya3i5l38SvUzOw2xYycZJfE3ob9Sgy5TPfOUcr0GgrZqzYsSjEWGPz8ybvn/e6zG9nGUevzla9HPNkaDIEDsZnxuIzt01M6P3nHW050UU/uvg4k+PYXTneXDvYE1KRBDLFQIRofMZlBE8KSthROciBhmOJwNg9B7SKMv5BdLhS38v8/jRgzW6Elan8c4E6BXoMxqvem1nSgnP+/Md7J9SwvXKSCFiTUkZtQblHrVgM1awozGAcimir3WyXr/2mSiwpu0AhNGqQq96PzpHxHKSMQRIjKhcASvNWSlgjwJJATnoHskk4EgITR1rIUKjANoyvn7zAHp6glTVlG5cUCQi23OhoF1l7iopHDF0px1jrXzf47M2oqpMoyUHgUbHeJ+eMWj7nS+++G1sp4tOIKmIcdGHqO4HBNHUezzKdGh2GAcyKFfXK9l3D/UYsm4BPR/w0ucVjShewnU6geduBWoct9OKn91+oYl8jOJqn/BzkbOf5y7SmKIF3xXcuJRSELOpzLSKmBJK2ZFzxu32rAuElBDkjoX2BiQtj5kicWbuzL3+3S/ITZ9ujhYxkkbdsPOJGXWPuuRuY5Me1XmfUR25l5u/5V/EjTX672ej6a+N0gJoetienRrrBKIybTSMmcU7jLI5DQDA/Ml3zs9lmg7Te+6vf3a25p+XZLKXz707EjwM3av37T8vns/8nXdzlOfN+f74q8t9BqztUe999H+fE+7rbrrG0OeBzxvqzaZBVl7mtaA0EaemHPVLKPnlceeI9Hv3a3r5eUvZTMx6wf0zjDFqM220yRB2b+LOMI5SJNbyjBDVQJserr/XKk+GQ+vGnIcRan7uyZFNKWGfnjFBSYu7NbrWBu/UnXUdc9w5yo5+xRhxlL2zc8tR4KTKJWeU1rAui0XBAUtQRbjTtuGpNcQUcdo2VFGN30gBTKSlMSEgiFiT7ogYlNC4VEJOGcmMaOUGxIAWBDlkiDA4EGhbkIIFEmKlbUJoRMgTHyPF1McN5nSLQB0yl4/6gcdnTSzSuaLEF/33KHMA1Ctaz29w2S6ay5EGFmecZssxEUiGhF73kcjb6PiG7JGOw2r0YvFNRJPp6JsSRlL/7h4weUBmmFIM2LYFeyldeJnshgkW+bgsmzEo8cJLJ9uw1MEwkeoQQSGaKhNbMh2IMaE1GZqcbE2zgyssGZEFgsYFcw7wDlIkr0cd0cmA5lQ1RRc9o+ccKFpan/wmgTu4Fv0ZSM94ok92f1b9vu332jfQN7dBxHHIdjyfoKIG/ow8qu0s1z74FiHSbOf6PJmBQn9Wgaw/ZzdenrUdt9rRjB48ayQTaPT4JItW/PnrZ2n6cfPi1zRd4GR4IkWvCu3AM7kRIr81HtdI48Ic4fCfEdEOrGBcxTDWcFdo2JC+PnX+CPDis/xijfRDZuNPL3xVevFW6Xc5NsZ7cs0cJd87OC/WqHz6Hr9e/x3PvydXpfIIchDeRuWyOQnWuHqcE1paZmvMoeLR/9jKgXTw9KVAXad6Plyv2vcYAayhhKkYkSNEESG4EdX1QQbHusZ4ToteT9Wew9t2grfWC0HrVGutyHnRSwJhWVaISJdIjTEiLQvSslh1QEIISXuC2p6l2rpaORBzHP9OUetHg2r+hhCQl6VrRMdTRgroRjSAbF3rmARTaHMt8z6GEEBGJ6Tet1e+fyT6WRtRSLJoYdRKglRdQ1jzcNuXv4Xn998oNBtXgDKqHCAkiKjXTg1wcYChv+q5N6VlD6Fu7aAR4FHqRPmXCM31zVHmdNAgebzMlzAzGum6iIGwrRGVBRHRNmMtbUmBEIQRSRBIhpFIVp9JOsm8X1/O2fruJeRlxbKcABEk0prOEBfEuGq9bUxIKSKniEhZOwmhal8+KBTL7UCKCyChb6Ri7DqtzWJUYaRl6RGcbyhAgNZDMoQqGA1NVAqMRQkDiN66TI2BN+AVUwWKFKwGLgCm3RljBBqDzPjEEKc+p56v9KjNHoR6Hap4FCI6UYcEFAUIgqMdkGAlP1FLb6SRQfxxMm72wxgG0LxdYTOKEBDz+DEDGqACC2y1eYFU+zOHYNidgASIJrSUKFq9MxCQAAkgJgQVSwKRIEZ1ksCw6L4CQggSTUidIfCuQhrBgIBA0ud50mJTXQPGBnYj5qgGAeBW9H0WSYHjtCGpWL1/xjv/cDOokgmtiinM6O222lBbQxWxHy1XkMksNoH+jl1a0A2YOVCsSjeNK5rULj+pD1+dTu0MYu+tFa1U7Zvp7PauMqb5V02p+H3qjwXHKqHHDZUZlRmlVlVCY4ARUAtAEiESwHAd62gypNGmj6Y3hBsaGliAyoKj7Lax1x6P11ptXqhaEGzdMDs7dzgblJSYyD7f+35WgGZ13EbS0UUSQZQhJh951L0bdK4VrVakmLEsC452gxCQ8qKktVJAFMFFx2ZZN3ApgO13TQSFGU0IaATd0ag7F0zm1OrOqusjBq1dDxq16jNT1q23ZztuFetlwWlZUFqDNEKUhBYiIh9AY4S4IOUNCQTmqoaPpSs2MYuV29hE/AFG9LOGc0dukXq7qttx2EYp3dtKiSBNrCRGEER7RGokJmhSR8naK+H8XXNn8sgTFt1h/A7oUm/cxsY9ItdXDCtGXKDcJU2yn8+nDtXpglKoMzm8Y4vbdSKDdUMhte0QNCyrPt4Q1XipY1Agwjifz7jebqi3HW/evMGHb254ePMAEUFjRlp0oVyvV5y2B6S8dILCaVOyU6kHWo9UdQPXfJyX0JjXzw3izOEIpGTt4poamBgiiBuu1w84yoG9BhwN2K+la27mpaFhxYdd+1OCG2JUeDkE4Gm/aaQagRgJvJyxy47jaNrzMkQEYqxrwK1UiwcEt33H6XJGAKFWZzibCkze0A7dVGsQLCkiKqqETAoNtdYQKUJqAwkjEiDSQGlFbaOrj7Ob2RQtVOQgorYdTITKrPIQMYAj4Wq5aOEGMGtNagoowcYYwCEFTAKJAjKiTWC9s1IZMZoMXlCVKm1BRYiUQAhgCag7kENGaxXFjVJrkEVz/wEaGYnYZo0p3WGRTOWCClXBQiAIC/ZaOiTqzoCEZq0hq6pwQfdxJCBSsNAMoKZrEqwqMiTcnQzmhBjYWogdYInWGk6dmtbKWGceJbKySGekQyQATbkNFB2ebepwWhmYwRx2r4IQ9JwUjARF7nizzX/9SYkQLcohAEiq0RsoqGgAATlH3G7FhCf0u9XZLNhiQuAdAmBbIqIZlBw0NxiIcBCQYoBURhJCtPvPyWoxWUU+uJYhskEBggDV/lARlDn3rM22a+ceEHmnF3UGWRqYNTL1ulPfwSoXHMehJTjcQKyOqRLxlJSEcmC/3VBuN6SYwWrJQARwrUCARrp2PR4Fd+ja9iYXiWytoh4H0Cp+9MU75JT12kylqzVdsxDGvl/BzNoqLTgaontzjBGEBKK178nr1J7xNx2ftRGd84vruk5qFBoh1lbw85//DH/z6ze40I+RYjKpyoSyH6DFIkImDEqzTH+EDqHJ/J3QyFDzCFrXlPIKERNPnmCi13Iss9c8nRmBFGolEawp4ig3PD9+xK01lKNo/jKQkjjt3OTfE0bfPGEVm3A4rRtjFhzHjlornp6veH6+4vl6Q142PO9X5H3vXv3j4xNOpxNutxuen5WFR0QqMh3Odr26YT4/PwPQ8ai1aTu1ZRnPwyIDR1EJsTsBIAK3Aw/bCT9+8zUW05CtOYDOF2zbBlcf2rYNzycjFtlCiTEhxITrw8MU8RLW9YRrviEEr1NThSsKAfW0IkdCjOptb6cTbof2XmUnN4GwrSfU0tBqRS0HbrcrOBAqA6Vp6U4yw7KlCGFVS1GI+ob1fELOJ7Awrs9PiIuypJdFN7j9OLCJSr+t22bEOMZ+08iRKGqDZQDHrgozMar3HmNSp/H6bHJsuQOpOSdcn6/QfJU6EIgRrek5cwJSiCBaQAw8PX2LGASX0wU/+vLHeDhf8Muf/wV++XTF0RzhIWzriqOU3lZLRFtgXZbNILFm0eSCn/zkdyFCOn6t4XRasKaMJS9qV5vm0gXS0zE9x74mHCZE3ue4rbNAKhMXU8axP/r0UicpqX40EdCK9b4MEeu24ii3aedwuBTKaG9N5xOJ9o09iuX+dU9wRKeU6rtvJ9Q1Zz7be0MIyCnjuVzBwsgxozVBPq34+HzFT3/+S/zsF79EOkVIqdiMtFUICJkQ4gIJwOV8Urh4/4icEq7XK7YAZBPS59sOxgJiQkTAEhMKEq77B2O/ZeSka0ZEa0BFAGQgL4tq5wZ37meCXUDMASEFgFTAIaUF22lDOfrOO6VQAkJQmLRwweV8xv70CIB0fu6HwrlR027buqJJAyqQghp1BM2FFjTt7qIPFMuywDs1RUMbg+0zYMayJtyiOumndEIrO0rdtWxGtKFGKQV5Wa2frmoLe91pjqkjJIBG/b6P3a47vu/xWRvRftAof+gvQSPUh4cHfPz4K2xf/AFiyhoRcUDMi0pLmUflrE0LNfWvd0mXYexG2zXunlmXSpPBonUGGtwj9397XqMnefofENFefqfthGVZkEMAu3A7R21c5XkzW4CuSuJjwO4Sm6Ft1lneo+YQsulnFsRi/QmDbdAiRjvXvKAn4D3ymOXHSgG4C7KbEhD8O0KHed0RoRAQHbpujJSAbIYsxYDf+/EXeNgyWmm4Xa/IMeHLL78ChQwRzbE8Pz8DPFRZQQqv3m6HNjZvbKU4G46jAHLTSBiEvGifw7LfNLIgdZ6WdQNzRWvVNmoAIljSoga4NZRjx7ffVISUgAAcJeIoB+KyYL/d8OZhMVhTa3zLwch5NckzRl0T1iXhOA4EUgZ4awxPs6aUECMBxDhWwppX1GpkBwQ0KXh8fMSSTXsWQA4N61cP4FbRWtHSnpQgUlEYyElbUiEEpLyiCeH59ow1R6w5oTXtiHEl/e5ty3i3Brw5LyinALCmFELQPr3rCuxgtODkKUJeAnL0uR+s48eG33r7gHIwSqoAGNspQ0JCTurtOyFNc9XWd9Yi921dsccCj3pFBMQWhYSIYO0CI5a+VDXfr3qrIQS0NIxayhlh7mrejahgyQtaKWiKJeK0LIgQ1Aqwsa9TWnrTADEnmY2USN66lHURx6BN6c/Q6F7nUUCiAFkyvrycUG4nFC7gCJBo+UUrh+YTuSCGjGRyidQaQmyIwkgBSFGhbAdDSzM4uhYc+6HQf9BnHqA17bXWnn4ADXLgkNa0UREvl3OYuymiBILUhnoUrSE3Mpn2BFaikpMJ1cmTLszQZHSFqa1hv91MqQhIYu9r2rhilwp4oGORKEG1w9nSCb00hhlJMko5UPYdP374EYgbquWQkzBqEQgxKMQeZHnqAkDvlAUYumT5XCLo2vuex2dtRF/mFp1dBvOSAxHevH2D/XgPYf0dS7N8ZgCmhTXnQl8azplN9/JgS0r70eFb+uQsBk0Aw6D2LzdsXidviOpZxhSVwm7atG4YBzPBvsSg2mBY7hAOZ2Mt679d5g/kqjsG2VirL9/UlAAXe/ukGAIwldM4U8+HfjTAdu9U85Jida/uInhut48T0Jt9CzMua8bDacGRGJCGCMK2ZISYIYhY15M1V+ZO2lBCQrIOEToHaq3KEjwtIFlQm4pNMJQAcZiRJDOiOa8I3pXC7tevjUQgXHFQxTUJzish5oCjRuxHBEXBlQVfbKb4ZMarpoSYdVyZCVgz1iWDt6iwmlhJD8QWMyGlgBgDSlqw5gW1NovCtbPMCStyCoDlTnNMiEmRlGoOS0wZ3AiFKtacjSCiql6NBRsWhCjIESo0njIyraCg1x0sd5gi4ZzDVBMNbDkgS0TjUTqRk6s3KRwbYsD5dMEaEyg0hKiNDdaUULzu1GY/BZpINWrXvCwkmINJlnOMnpMN1OUWA1xvNtyToMQbn0OhfwwilJ5U7Ll49s1zqo7aGHHPokuNsAcLWLMzZFGSRbGipDcWfZ0EIHbiXQBaxUKEhy1jv2z48NyArHOmieXLRT+kTYzYCH5qLEMAYrDZYMzzABhPQX9aqyDRTL15+b0+OKXUx7axl235j/Q1qOmHgV5FK0sTQ45msQ4lEA5kiIhMr1vTC2xOaWPLaTPjqAUsjCBA5TrBtGrUiu2PZN8RgC5t6ftGJ3mJdAfi4XxGCtEgamX/JnOURYBtO1l5kRtHmw0ex/j+aoEOy6yx+1cfn7URHYxZ9IeuEnQKOYkITpcH8LffqQfpCysGtMpKRgF6vVePZMUXIHq+0vMbAmebouc6XzK5/KHM6c/XcqF6D/Yfi0yZBYFVbYMheHp8RD6f0cwzU8IIA1DyCshVjJrmRWWwAZ0s44a7Ly6Lglzw2YUU8qKEGUbraimt8dAINig6G4vXn0FvxwTXAiWD6JxJOBwdHTgrCLe8DohQawEfN7SVUBDQiLCXA+npGSGoIW0h4bkcBg/reaPlKY/jsOCCEUTbjW1LRgpn1NrwfLvh2w9PIIqozYvRbSiTEndqVc84RYU7JegGWsuB/bhiP654WNXQBBIQGupxgKSC0JSIRQ1Si5UXyJgf0kBo2Jao848FpTa0eoAiodnCzymBmCG4muB+Uoi0HHhzXpCjGtqcMtZlwceP75GXCMFizzaDkHAjxpK1XEBI86/UGt5czqj1BmGtGQ5pAUtGlQYGUBvjdhQ064RBvVyJkXJS+J3FuvMoHJ9jNqalGsjzcgbbVPVm8p5fC0RdWs2Z4LMhdQ1qMWdSoTuxWlVjO8PK2RBB0uDenOZNTUbOpxsFHU8zcmKCFmLQv2/IrOEPaq3d8HSClDnbrdf0kjlCrhikULMVbulm3QaJzY1NDAFrijitGbedECgihwikCGm6+cdops7SCikpguQ5V5gx8s1jZgSDBuPZa3QHe15Rri5jinuD5MpHbgyd6x1CRDEjpix/zUXOpVmCodnNtSq6ZyQpQMC19S1u8MLRO0A5CuiIh//br91FafQcYy/x2vUmjNP5ZG0kFzRqWioTIsAVLKPRgnaSmcugjLgXIyqPfbrUac/6DcdnbUQH7XzkRWttOErrGpN5OeH9U+ld1mPMgAApZxys+UeN3+49HTVutjBcDxJqisakUdZrq6NWzyXphD0CA0ZE+8r1s3vLplwUNM+0rhHLtuI4CvKmDDLYpOxKNmYYlTylbOOunctKjDgmzUgmpZ57noRb76aIVtmYzrqxlXJonmI/OsTj0TqPgkr7fFCIUkZXC2+vNNiAajyVRR0MJlJIXEJUakXZITUhxgUpEhItuJxPAKKyd8XgTxAQdGPNWa+57KPkIsSIBUAURmg7lhARThv244BIVcKTbZK1CXLLOKwRAbPV4GZg2zbUwii14PH6hNt+xYfTBa1U3I4brvtN/VYh4MoIJOBWIK3oI4ralzVF3VC4PqPWisXyvPtRgKrqWuu6ojFwvd5wfb6icQGgkpYCwn69Ys0Ry5IRSJWntmXFsiZc96vedwhGegH2WnFwBVFCoIyQBKWpeIVYpFWuH/FweavCHLcbIAfiOWO9vIEcykZlhy9ZEPZDxwbT3CZgu5y0lEJU0alwA1FFIxX4CBSgrPUFxM6EBLIR1SJZVG7ztIlYp0lT6BHWCGu2JqL3IwJQU2dyQJTDMAtgWtgeg1jEQcqKLaYX26DQOUN6r1oEM1ikEGoTdC6Efp+uqYahaQ1RJ7iZkSYi3PYbLm++Qs4ZDEZ6/KiIAguWdYGAUYquXRVuCND+r27EvU5Z9x4lgKE3vAgpIhsH4XZlkFSkNd2VuVUzZARgyaMZ+ghExj6qbHRtZH3dD1wub5DWBbw/aaeXbAI0tWpaKEZkQ7m2bQWgTsPD+QIhYMkRq+XC13VVBv/tGDlYdx4hyNbcwvfbSB6V9sFXNjeA87qBCLi2A9vDBfV2QGqzOSNox4EcCSSEUrmPI5GW9TnpL4SgEqi5+2Ng6Qng33h81kbUD69TevfuHY6jIMYD3NRjVk97gftBupiNFAGNsILMwO6oNXUDOh/zv3yyuWpKL8S3jafDPpNY/ICdrF+jQWVggEmbPqeckeOCxeurkjJd8Umhv0FpVme1LIvClubheU7CvX0n+lwt2b8fB0oVbHlVbUubyIDgdDrdyZAFM9TaZUEhYIGg1B3Lkixq0kUaAiHnhGKt4yCEgKxet3j+1DYik0uQkBBT0r6JIUFuBcfzgXpqPaqXqAXWTRoqH+BawKgqli8VkaIJzAcgJFQGqhwAVTQKQFK1J2gPYwQGIjEEqjdK1mMVIpo7bwfABQRGDgGnLQNQkmqOAWwiE61WZNL+hBIIMS3g2oz1B0TRHoqFGcsSEUOGEGlzYaporPNVrPdlyhGXvKGWAjGIMK4n5EUNJ4lCzeu6oJYDrbIpsQi4Hli3M5ZVc3IEhdZDjLjtj1jyCUIZwipGAgClCpgSQkqQHLCjIp4vwGMBuCqsHRKWGHHAIUKNFgkBT4/faQlVXnE+PZhISQUTI+aAZV1RS0XICU2Mx5cimAAOFpkSIJrUBY4D0WBMYUEQQBrrdTBUZlFES68sitdA0uInHiiQwBqMewrFtoEQrQ5cFEYdNblhLHnBUBNyx4HVqIVAo/yox22ESKT5zxTBZHncnHA0ZZ1quiaiNcF+FFweAogEzLU7FafTCUSqUcykvAOVZrRrEBlVAeT3zNj3XfekJRsqJXh6elJjFxUJIIEiCH3v9G4v0w7IjP1QUYWcM5gZz8/P2PfdkCklGJZSsSwqlfr+/XvkNeHp46OlVSref/crLEvCh+++w+24AiDUUpGWBdKalqi0pvM8Jhxg0Lqhd5ppjJQiWjl0vINWMJeqRU8PTxnPzzfsLLh8+Q7MALHttwGqBYAGygFiusAxGalRqvlTmtKh/DLy/C8kEgUMxpWhv+j5CZDS0pdlw4f3jziOHWdA4S0UNaZEtuBU+WQIco1zu2syiwYAng+cBRfQc0f+/h4avTg0BepQn3qsiawO0Wq9wgQ9wKIMl+JrdWh1BvLiaL1+Zp0cKUe0KkhxMRh4EIO2beldW2JQOT2HXNUpZXAreHh4wC9+8Utd+Cn1SMNLg/y6qpUzuMIJW62hv2ZJWwSKmiuVIUUWUwRCxK0U3I4dpWTQuuLh4Yyn2tCTS2TyYplQjOYekBBY83SVtIF3BJACsCR1gFpJKJVx1AaphBxP2K83kHvdHBAkgMFGCtMIVyB4ut1Q9meU46YlKDFDxIhVMaKGgFYriDVnFymCwZpbhSmtWL65toK8KduZAC2lIIIcjEDqrCxZ2autKkEkBWUYChF0Vqi35Xqgx34DoPA6iz7jGCKOXdmlruBFIGQELHHBmldspweAtK9urRWX8xlPz09KhKkH5PaMcOyaG6YMitGgUEc+oPlMHUXcbjes6xnbuiDnszJBIcgpI6QEgvaHDQio3PTZRY04oqUSuqpT89rUoPJxICST1aMY4aQVJdCpLCc785Q0XwwjK3mKRCMswIXxGFrP3PF8GyPXsLWkq9lc6a+JGWnvjETGwfCMhQjA0MhZ57sa/Ll1mTbUXpDSgv3xqqQ/svQBM7KRdGptYLQOmzofoVWtte1tDLlqTtScalUtUqSBPcUVdLXIVM88p5u0nGQ0r3A0qtaKxoKEETx4Cmvfd3gdN4DOfFX1MttDzMlwmBiidbOH7KBSwRhGvNaKIs1EqDQK5lYVni9FneMQIIFQ2djhprtLMeLhi3cdgdOtOaD5RU9db5wYeSfrZyjGkEyZpVN+8/HZG1HH6pN1ctH0nxk2CFJcsddHhQobQ1yzhQOAgs6ghfQ/xsntT5kmkeVVBjSg5QbefFcVYOxD80OxHEo/DD5xiEZseZNA5eci45wJZa/2Npv0/sBdIcgUNkKICGIann764C3LPGVCva6RjYiScgKg3Q2C6JYlFHAcBSltukhFAFM8KuVAZdVdce+9VtMYJlOFMum+6EokotttjA4DTjqulJCIzAj0wdGnF8Z4kcHYRAkkN2PWWpRr3xNDQIoBKeomDTQUEGrTTQK2aZOhA/pN1Meq57ma1iiGHLVovxWFvpuAkmhdKDGSht1gNNsMLDIhgVBCCKKkLIJGdIGQoqg4dtDIKULHVUQbVcdpErI0RFpAMYKTfX+PqETnMvE0x8xRKlVnFAMUuDsh2hGkgNsOigkUgVob3pwu4MpK8SeFNYm0nhQBQCBEZuSwoqWCwA2Boa12cUKKDet2wrKs5iCyMmKT5qFYrI5SmuUwjSlujpgvWX2iavijzW9fn2QNzu/TCEPL1ufILD7f12t3hu19Zhx7xxjfAnqO0ca35zlpGFQ7axeEt3JSCwfBEvp6dsc6RI0iPZeZUjbSWbS+x17KM6BVQbM5ocHAaM4lgBghB+iEQxZRoQZj3BGgTk+IZuQnA9nFMrVeVoXexpglk9yrRWX9VKwFCqkKEEICyI2YEr9KK6isDgq5mESA7R3UiX9iqGyDdAfG72FoXo/9KgTt2CKdDKkOpB7K8Ygx4Kt3J6BqWqCBQQaJ6315UDXlkOEOwXzIeL/gex+fvxGFypnlmJFSsQbQOrkbM1LcEHKBlB18VIhNfBLdgHSwgi5eEBifDjLbItLXpBMKABdziBBUBMoQUdZvN80i08OaHqLYpISBuyK2yZgDQIx354zHJ+4TAGa0QGHAEqwlOjkQbMcDHN6iYF3qh5QXMyNR0giKgglR7KAlgUTLRRASSqkIcQFZEbhQQAzJ8ojefUYj71obAiUzQm3AzRThu6Q7+CwO01W9NgREUSp6iAEpJDAIR91RbbPwMhCd3Hn4NvZaiKFHxSlFgx8151S5ojIbXOX5MhhMqptuA6vBIG8FtwNccFovCqMzg2tRL1dtOQIIMSg5pPJhkZlGHylHhLCh4YYYFsSQUFtEEEYO6kyQwZjImyEYRVWOWgO4mFPSjKqlVtejXYKWEsWYUEV7394z1dVQg3UDJjOiRAGt3PDcDoSYEMNi5RJa6kEhgCMDcQGFw1YFIIEQakOmjCMD1IDYBBWCSBu209brgsXWRUzaPUj5Aw1KFZDJGdIrVUIOesQ1oFV95p5+78QQNn4AYI5pnxxm7szYSF+wPTUjk8VzzVc1AjaPSJ0ZErEIVGxvgDnAJsJpxvWl5LsjBUQwYQuowxA1z+n1iClFZRtDJSfZDJGWe8mUGnFOBqOxlq5FUpKXRnliSloBLFrKpuOl693bRLrB1cyPjlCz+/F9yZ0wEa3bjSEopE5Wi235YWFBTGuvsQa0rKnWA6UyCjcAcTjQRjZLMSv6Fmi05CN1TECa704UEVPS8WYochUjRHJ/TwgB0dTJNL2lJVY/eruhFSiiBBdMSSpUP7bvfo8vDegcew5T/v2Oz96I9uJ4y/kl6yjSWkNAwnbK+Pqrr8FSUMoz0rahsKqKNIkK4U3eySArAd1NFod2YV6eGUqL8pwB697nHNxOJhleVtMhI3MZFeiiAZMZtHs5X/AcduS4IOWESBUUQ6d8z707U9R8ozKNwyQKz4gRcOHvSNqvlNuo3xLx36t+bs4ZrRWsp7NCJ1YepJO/muiDRja1aId5rTEdHSFqVYWcXjeGUUPr3r/D48629Ag7pqwqUg6hg3rkSWRMOlENT40+gSUG5EQahUJLJI5ae8cboqDKTw79+VMh3VCZC7geqGVHLVdkQEsJRNBqwb5ftZwDrB46CXLyno6WowsBMWectxPO62/jVx//A9ZFPftrEdR6AwlhiasydIXx8OU77PuOUgFukwh4UKIcc0AtBbXs2JKOQ4pZRTcoA62o949mBkr5AdzYGixYPpsC4rLh+qz3SFyR1xWJVvzymz/Htm5Y1zeI+UuFO/lAazd7cqRF7HtG3k4grGgt4GhPyPkJ797+GDFm1FZRSkXeVnjJgA91CsmgRY2OOvsTZHwE3+mkK2ORRfEighQiUojYTX4PhN61ySqFNJfJ8wIcxlXPPKFDZrQ7HkE65sO4Dla7R7HEg1ikBDl1TKgbozh9m503EKQq6zcG/Q7vV3y7FaS0oCtZpYB9v+J8eYOUE0rVa2jVIkBjBhOAnANKrYgxI6Zo2hGCnNVo55Q6gUgRCIujjUlfKyNnZdErV8KdXV+L3FugOUu/Vm3GsCyLIQ7Aw8MbvHv3Dt/98hddJD7ZuLTGOJ8fsB871qQpJCFgW9YOqc7kIu9Nqxeia8rHufXyIsKStZZUEUFCDgkP5zOeblcseUWjil5Db/v3iHBHIDOXzfg86QTTH4DnftZG1B+A61wqKSMjhogiVmRcD/zN/8MfIyCgyRXBwvUmpdd9fW+/w2BV/btFNTT/cro2X8e4s9F3197bJxlM4oot3BpiZFweTvhPx3uEFIx9Ozw5nwz3TbADQkzm6dsmFex1I1GECJxOZzw/VcSckUwhZNtOCJGQl4R0izhuV7z54suuk7nk3HOeS4rYU7QCdwYCtCuMDJ1cFmiBf9M+fqGN/I5vsCGGrgZz3CrKoVH1lhL2olJ63BpSQhd44N28fZi2pm18py2ZA+UlPwFHUx1TFt24UUdz8/uHAZxOKx7LDa3siCKIpIuDqypF9ZIeYaiEm0btDQ0cIhIpYCWt4vq4Y3/+j3jz9it89eVvAUL4+f6/gyTjOB5RjkdVpkHEIVdEIpTWEERTA94kgVk3XqSAzAFLSgYJw6IynWLKbaMxNyniedduRSAleey3G9aF8ObygMaqpRpzxK0dSJSxP1fU+ozz24DT+iVubLlPEMAZzwxs5Yr9+ZcIcUVIJ0TsoAY8fnjsDR2CbdwqBO7509SfS4hW8zxBufNz9V58qAABAABJREFUcBa9l1Epq7b1/J+0MspibA0ESJ9bzNaqrtejajSpY+WxHaG3dwvSZQ2Zm6IlPCI0dbLG390xaE64C0FJjDI+45GOl8ON9c4GyzJSDnjzdsWHDx+sP2hD2Q9spwzmhlKAavMup4zn29EDhlarGRZjynOz+l4grwmlFnAp4NpQ5Jg2IOrXp8+DkbPnJTV69u5QyvTXZxFCQCnF/q2Ix3EcIAhutx0fPz7idruZwtmu5XLRug6R1S4jqGgCey9lZfGnnJGXbPXdTVGTaS91YwgWCDU0e9aBlBvCpSAK8OXDW+yPzepPrQlGvIfxf9NhWfIffHz2RjSGoBRz9ohjQIdEuqBujUBSEMOOWBdoLkkANkiXBhZO7j3/mtFUeMXhp2AbBU3XpO96WRLz8hBxaMuo+65vaXARwLhcHrDXm+rD1oqjHOpxx/umvSOKQzdaGrFldSSsvgvmXdZacRwVVRoQGqQoKevp6VG1K0Xw8eMHfP2T3wVCwHG79VrAGCOuz08q9Kw3guv1GTFOzcONAKHF8KbiEkd5izhWZ5u0lkdcuxPRmuYvvXehRtfU6/cCEcSgOIVbK5aknShUUzpgLxWtjSwWAFRWD97rBkWk5yZv12fUY4e0CuGiotdszEEWLEtWApWwfRfAcA1egzCD5mG4NggLjtsV3/ziFwA0sgMqfvu3/wCEhP244vn2LWoRPJzfonFBoBU5nfB8PbCkjFJHXaLpeCs8a567Em0UBoO4TLuO92ldQaQ1fqUWtHjgtL3B11+9Q2sVHz4+4Rffvu9jm/KCaMQ8FW2I+ObnBU+3Hdsl42/98e/gdq1YwtfYj4Kn6xNutxt+5yd/hPN66SLvKWc0QNXBLKWiKIYSnkL0WlGN/jwf6B1vnAzSnRa7HwdO1yUji7JeC1cr+5r61ZoBkOCwK0AhglEB1hIWGPQfAqmuMnn+MNge4A6EXYvnTl04hABtTqFzrm8h8E0/AEZSA/S5aQ60qFB9KYBouUfOG67MOISwLBl7qfj6fNJ1UA9T99FmEdu66noT7ZkMtnKuNeN0WhEjEAmopNKP67qqA2nOiKY7MnJSpKCZAL3vebVVNEiPBkUE+77j7ZcZp/MJT4+PKIWtJGVFrYTz+QFv377F0/tvcDmflJti+0QIAWveEIWwpMWiWS/H0+e1rAvWZQVAKFQMmuYuYpOzpW88C0be01mrKgSApIhTTmA5wKTsf+h2B6ZR+vZ9jk4k+wHHZ21EmVUjdoZgtfZSegJbRJCWE9rxrBBmrQh5tc04QahBUHuuBPS62Rvs3Km2yqMqgyPj3Wg6VPDrr7/7hxa1dnagffB82nDUYlBQsGJyZYLeKXdM19fPzaw1HDL8cW0npkdtDCbNP+370Vm2YhGey2dpDrRaXksXV7MG5cP7byZuwT2JpbCj6rV64sk9U4J6vdxYJcgMVwvetq3fk0J2yRqh9/pdKLQl7iiFe0cGIaCWijqNvQD3z87Or9JoFUBD44oQgMt2Bh876rGDYOMNdTQisUJmBDQmxJwgErvaDxu2qPniaoXpujmXcgNEVPs1BoAa1pQBaTZeSowKZFKN5iSKkJGylLna2oDw/X4s3gKIkEJAgVizZoCyFo+2WvDlF+9wux14fLrhKDe82b4GbldVseGINWQICpgr/s//p7+Ft+++RIgZ9eOf4lcL4fnKSCHjlN/hV40R42KOUNJ6xS3j+bZr1KWDruvJn6kxbtHniiU6HFUyla3aOsirJCO4OIeWVt2Oo6MKZPP/vu0cei4sEHr+3GPR0N831o8aUW1oMM6inXaYLB/u0ahIh3btjno6YqAWoha0g9c+d/W55hj7HsWNccAN/7gXEQbbvSXrNQo4OQfIKZkOMizPir5OgunsCk8pJzFlN3ED72t6YufG0WTc31OLQrmKEizGcjVInBnSGtbzoucwJ7+1pl1kjoJtPRkPQNuaESXIcWAIwAymbqAIcICgqaPiO5jdU6+Lv+5oiUCnDUtKOMoTmmHu6jh4oGLAB93nQ+f90o95n/++x2dvRGUq/O8TmMbC4tawnR9wkEIV3BhpCahiUaAtqz5kZhTHInr9oA4T+ANrwx2djlfM8ad/dwfAXzKjs20rjmadFSxnwEb1B4YR9UJsOKmHB/mJG2uXCkC7MfhnmK2jhjJXg53fIevGqp3pzFHtjIDpeqfyHlcA8VUK22BgRso2Cc8r+/cr7Fo7NJcMsg3BNi5pcLIOkZZYIBKqidQQVInFexlq3lXvUyW+yAf0VW/Gf6uwGZs0pBpNh8lSJLQYUFtTcXywFnDbOQVACoQcNRqUpjJ32eaibjQmm7duqHU3qKwhhYwlJqvnNPQESgpqrarmquW4c04gqQANYY8U1468+Lj7eEUzrEpGShAOKMdNxRBKQa2qTRvSonm75sYkYt+f8XDe8D/+7d/F7/3u72PfE/79/+cvsF0S/uz6LRgJ63bGm9/6XXwwObwcMnI25wBirHHq85lZCV3d13E0d3Js/F6cUdth/+AO24Alx/pzQ4buoA38wXMqcvd6N1LzKrTv7gHnPE8IQx7OPn9HyXFDasZrjmJBVs7mexSRcTcSUqxdZELXoapl6dhM/6NuRvQcwZvAa/4xgMCldUdE3yuAOErl6LmmkDwS1nSY7Q1WBuKyoF457+Vzx6EqcN7ou5QC7Uuga7tVVuNqqk5EumYiFhTrSERWi+1ISiguPzg5/yL9O7U0zpEAkzoMRo5MCUf5CIlAfHvCmjKeawVHmcqvVEiHfXt0mP3XGMj5d//FGNEuNyeCIA0pKYSxGMYeoDmt87t3WHPG9fkJt/3on5VajbEW0TCK+vvGi/HHfLhWrj5sNq/LcpNiwgD+Znp5Do+CqO/t4spI9kb37LRnX0E5ClJIKMXVgO4b8Pqm2mrr8CKLtgArpSItrnWpeYdaj27I2D4fcsZxfe6XeDtu2G83nE4XEAjNPNFg5AELMtSJacCaMnKMkKbydwSXKcTdInbSiNf3et5FSwHUiOYckWqyXNGBHFbErHWDMRDa7mITGgGeTic8Pj6itobSGKVwJxONYaU+Vh5F2OihlgMpaQRcjh2/ev4I1Ip3v/c7YFHZP5UD1H6tyQxD42pdSmrXtI1QQ7KlhNKa5nEhaDXgD/7wD/HzX/wnPD5+RKAFp+0LcH3UfofwZtgK3SmznLBmFTpoWFCOipwSWi1qdAMQTRygPxdhkDDOpw2lNXV+CFi3Ex4/fod/82/+jTlJgnXdwO0AIFiWFTkvEK4otx3//f/wxwj7d/iLf/sNvn0EKG34r754g6dfPuI/ffMe+1rwf/u7/1f8z//Lvwdn1SQtraLcihK7khp/LZzXOemGUgsVFQlqqAa/MVzaoNfs0TCW3jS6VK2Y9WYGvUTJJlo3oz2dYnsFXLwPHUGaDZ1/5jUojwJATGYwDf2wetlgEaBI6KSwweyFPRMvd1EjmkNENpUe3hsIrI5bP78ZDSMIgggCRzXQ14uQkW4ao+0FrQFLSqBDO/4wjzUYKPZSsGoNKchuiK0mVA3w4JgA1D8HI9C5TOh+HACpwEvKGaUp4ej2fEOI2uOZRfkTvtdQjxBNSjRq1IvwKdkHsD2aGTFnnT9BKyGOQ43o034AgXB6e8FmXAIBQZqydKP3hcTrBvTls37tGr7P8Vkb0R6FycgL5pwV3rNNLsaEvRRwJUAyUhR4LnExMoyLd/+wwzxc8/56sfhkQEc+7v7B9e+yHIuutwlmME8+5xUUCcu64Hw6I6eMvRzIOcNl9QB0BiqyLWgr+M9pRSkNKWv9bGsFaV2wmEZuyhlpCeCjIW8bbnUHWYcRIuByueD5tuO8rtjWjI02/OXP/hwQVTI5agJu6Co0hRlVVCVHgmA5LTgOoLDqsjpFnWkEKdpfNBmk1XpEfz6fsbxPgOmsurZplwmDlgos66LnyAF7VeZsrQJQQA4RTAFM+myCzZloZDQWUQPJrGSUWhVi4gqI/ki9geoNCYxL1hKOc1bVobZEPF2voL1oU2wiNIOjz/FLfCh/BpKGJW6IecX7774BmmBLJ80vh4qDCWte0EQjQ24HCKpeA+i8DtJArQKNsawBLWlrshwjlrio4SKAYkQICwIWNN4RtJATFDVaX9cTnp8fNZcbE1L8UhVsjhvWJUAqgQLwP/3f/yf8b//6/4H/5V/9v/Cnf/5zfKzAxwP4v/zx7+N//K//azzxT/Gv/+wXEFmQLtKL7GNUIfIGoB3aoJlImykc7Ribv7i8HIBqGrgYc19Y+876MvEcpYj0NlnZUgufrlut1+2bpRttWEkW0NdsX48WhjqK1Rmi9vsUVRCk9TVvaBChiwsAjFqlz7Ge+oF+V87JSkQqGjfNgYeAbQFUM0OjytutKrlu1a5Aj48fVFc5ZaSctOQkEY5S0BhYY1DnhxLKwVhtH4oxGBFvt3ErILpYxxXXyzYExCBcH3AvJeptyGJQwQ2DEpZlwX694jiMVQygHIflfVXNTBs3NFwuF3z89jtoY4fUSZE+Jj53WrNOLY5CMJscq463l7F5HS9FQj0K1pjwo7dvkZhRSXsTO8IYhCBSdR/+wfv79z8+ayOqhy6WGCOIa09oOx3cJ1wk1SCth0YdKaygXjc2F/B2ZAwUNNPkC3hg6ePbaYI19Wp88YbZdupLE95Oot6S2PeLQZFeCA5RIxFixH4cWNPRjX+tWkwfJ5YuAEhtaCQqi1cOYNX+iqXp+2troKC9C499R0QCKOL6vCOeVlyvz1jXs+acbrt9D6xLRNPWcV5P4B4eYIQWq4cr3uqsdOdi5GJGecmIRJWU0xmRomzLZVu1eTeNXIjnjHXzidiWhBAjyr53KF9/rwYyRULxejzjrEd3eqBQdynasFn7hhrzEwxCQ0DF5bIhJ+DYb9iywnCL97+kgHg+Y0mi1xIIYO2PyvyEt5cvrQNNQIs79tsV4IJEEUtKWLcNXzy8xbfvv0Mg6wlqNbqlKISfU0KMGSEwEjEupzNKPayuUMsYrtV6I5KAEXDaHrAfQQk4Um2eifUsVXiewWh0A+GEh/MbgFXPeF0J/+r/+S/w9P6XuEpAWc+41h0Pl4x/9+0zfuubv8Tf+p0H/Pd/8NuINYCOhuWUez6TSGsZQ46oTedKjITQAJnzhTTIQCE4aUrfE5Pl7ew9KaUu6r/lDQLg8Ii0ny+AEBGCVvwLcY84mTxLYRm43nUIgBi5JgRlERODWeudHTbuDcOjyQS67KUIWlSYUcAIRa+xwQyT6D7gQvcQa5IeI7Z1gxDhdr1iXTMecFI1n+NJ2/q1inJo2zCGKDqzZHDVvpjLsuAoe3cqWQKqtqHVdRIjQlb93JgTpLTuxLiueClFhWpy7rXUa96w5IzdUKuUMnJe8Pz42GFZ7a1asC4q08ksuO0HHi5v8M3Pv8XD2zcIRHj++EEh55SQ7FqkWf4/Z3WYZDCCnSSZU0TKsRtxQUNMVtPLFWmJSCng6fqIeFrwo/MbRAh2PnBqQdWwoCxmuOIr7iHa10pdxv7/X1BOtJPYPefWGhaj0Du2HkIGQycb8oLUMiJZM11DzonYJM1gE12NGkxR49MB9VwL/v/k/dmvbVmW5gn9ZrfW2nufc25j16413oWHR1eekUlkVGVWJahUEhRJV0hIvNCIR154RQKJv4FnJJCQKCRAQhQSRQlRiapAKiFRyqjMrEoiMyIywiM8PNzcrb3nnmbvtdZsBg9jzLX3vW7m7pH5ZMRyv2Z2z9ntWmvOMcY3vvF95wwWEOlI/CWG++bftuDjLl7GaSnnpdFEt/AmnjF6QtRMTL0mA6VVy/jOPeDuI5r1pOCbJxjrJkQdmhbABdP1BFxUaCTGBF6Y4shrpxVLqI0mhWEIDKlDbTAMk23Iuol0k2tcn+9SkoC6xwREMtpb6YPdAj7hJONF+57eQ/AjrQpLzqylMjXPkBJT8Np3CaJat9avHUJgZ9UxIpQmrPN8QW5SOJCokJFulDqaIl5oXl02pFW8VERWqOBpxCAk8USXCH5HclrRBRnYB6dmx2nQpKd6BhzRNaZoM4FEqnioqukbQlRiR9URlT7j6Jy6unhx7HcDuWW8iziJ5Oz55suXSMsIiSaBGj3jOJHihFjPNiZVZ3JOJf+8V4MFHcHBDJB15AEHT66fkuLIq/svuHu8JXqHlMqUdkqqCnpP3H5xR10XDgN84/me51dXPByF+8/vePdfe5ff+PBdyPCPfvBThuna2NXtjb4b3hkJiG0+tLUuhdc3KoX+o9emY7eh03EdTYJV47YLxHeZOR1B6iLzrVcrvid3XZZSmbfRRYo/WxZ6b8LmKHKABVzowh66hluHIWPnXZj1l9eetgseFSGoBBpj9Fr5OCPLuKAkF5OtaFIQG6RwMZLWytIKzlViEqiCVOFU4FRnqDO7SY2p15op87IhKRjsn8XhSoG2EpK2r1QURedQS17w1qutTQ3maTpK4qPugzlnWim2RtTIWqFjg+lLpladjRan4y0lz7jgWeYTNWfwcJpPGuTzinMwryvZ/pxOp413UVH7NzqXws5nKEJulZI8PuiNIlUIsarykgVc7xxDTNwfF3b7kZvDRM0VJx4nJv3qTN5v23LfDJg/24c9B9Kf1zf9suNrHUQB6ytaltdhCJO+6012h1ZETSo+KNEib4pDZwjm8tC29pusunPm0jcB6GrVfZ7psrvZZf6c9UN67Xa+YPbIroyiL6jdMecYoidFTykrpRaT6JOL97c+kvWanHdbSu+refjRK0HdXHwPojZvGn3QgGmMO2W/BarNzOlgs7Kgg0FX1Sj3PnQ1FyM1uN7T0vxjGAbmma3SAMEH1eDdxMJFlCVtAbJf0+i1WsPk3nSv0014nAamcSSmoJuEd5t+qjivAvN99Wx9OH2uD10lRqtrL+pYE9yopy44gkSir0QfCH7AixCqkny66XJDqM4RncPJoEIM3tHwRKLKAxp7sYn1PEPcCA96DStlbQwpILkajRSkFj58fk3OK/dz41gguR2lFkL01KZzj2P0TMPAnMCJJjKqNKOECu1HOQK6+R2mgRgSx6NnALxPqjkcmu73XojB06rKZQ5hxkXYEzhI4be+97f5l37n+xzqwkd/8hGf3j0w7A9633UhAmHrZ256r63ibTSnU/iC75Q+FQABtvETh1anqpeq5wnrJ3blvwCIM2JMs36jt86qczgxNre9V20oDOu0rx5tnRhh3AKnJawG69Zm2mMdHpaGuLPptBeUTIRsakDqzX1RyYgl+94hVRnJzgcCnooyjGur5FqILRKD3kOtCq5WUgxMaaKeFoN9tF/UWlU2rnl5Sl31/rWA0JoKi7RWCA7yRaW37UvepPia2qD1XamPkgEqaCDKNFfhDW1p1JLBK3GziKjyl43XNUP4at+LxcQm0CShNaHafG5nIRepjFXJhK00necXRyua0Oh9oGu2ipiTUmbyjt1uVG1lOiSt+x5OPY03cPDnQLr/vP1Q+LoHUYNKmggueFJIrKuSQEJQA+DaFryf1JWgPODawhhvcG5FWAEPEulSff04cxW+LCPp2UxnsPV+itgm+VamwxZr33y8EXQuMyXcVuMqQWUYmU8nTnFgx46c1X+yQytq7xZ08+rqP6bt20QoJVMtiDaEMPgNue7ejMui/aplmS2QBiMgaXAty2ywp76fsnYVMhdB/QJxxqxVgkUtlZubJ9zfv9LHBo1sMQ6bvRT2+dRNx21m3+qsAWEYQC58KKmMwXF1GElR3SWyVBpqAycExHo7Pmcka3YNWNWrSi6lqlB1qwUvjbKsxOka7xbwVQNqOBF9I4WJJpEYjrjoGT34CFX03AXnkJRw5rXpRb1JD4drapupNZOlErzQCIgoRK1jAFDyiTHtrX8DzVWqO/LyJiFl4IftgbuSmdJEXk/ECEl0xGjykaeHa06nW3K1cZx+T3in1VoIWi0JQOV4vCeUwnWYEJeY48Bc7/GoqHn0nmHwDMMT5lWQ9cg4CL/63hX/1v/4f8q7Tz7hH/yH/wH/8Pf/GTPfYHziqatV194bo1mDX7fs6wIGMQ4b/yCGpJtrWzkzULUnRu9XmguOWn45q8C2RWQtjYQYXdt5pwQgp6SbnkzjjXDle7B153nlwpaoia1DBXG6FnKz9zKZhKYD/94pMZHOpwiBaiIZfm3oo+Q8ix48rL4vbOJSWZ3tOc1BgVVg2gWijyCRKoHTktlfXzOKuTTlbNwBVYFKQZO3knuVHGjlLL4COvMr67y1nrYCAJvB9apYdN7H3NZu6evy2LQd46NyKRRuhxDPyF+yhDuEsAmhhKAz4ur72/XD5Yz89YTDOXWOaMbOt+vb14r355YM5niTW0WCY5gGeleuj8I4THrRrukvGmu5PP4yVSh8zYNohwLUoeA85/Tk+poxDTyeTqzLwrQ74BDW9UReXnO1ex9oiGScS8CZ0v3Lv7fBtzbk2TH9bi+F9Jr0XLlefm5vWaC7kMoDzkG4CcF59ruJ5II24VPCZ5Vt2waOnfX+8kroVYApSAbvSSnhsblQqRC0Kk8pAcrUGwbd0LZ+sg/EMJiiyECe543t2vVQ2fR43dYL64ux/6lFx2dqbThXWddVTXOpVv1rArTfDTjnOJ5WHk8nUgoEaYRpgKIVYHIg0fHO1Y4wet1IpOHdwOMpk0NSgfYOS4UBRL0qFQZS0YmSV7WKqwXfGmXNBA/NryRJRAchVHY3kTQNqmfsZ8KQuRmvOYrN60mjuap75KBSe14aUjLUwtOnkXkdyCWQ2kBqlfk045swjjuCD9QmLDtA9oxEqqucaubYPB9++7sMNfN6/iGf3p4Ynw/MdVB5h6QG3DVFppsbpsfn+HWlkFlDJY6BSmR2jv3uwBQHPv7kM9558oxSVpbRU8LM6BLDeov4xNoi1UH1gh9G7o53fOtXvsHf+O3f4nf/zr8K3/+3WMM18R/+r1i+WHgoT3HXN0xhxzKcQNQaLa8rRIUrtWUiEJwyy8tKsETJObXFajSqVGJUnddadVNNPljQbCoK0teRqmkQvUd8RE2LgkF/bqs8ep/Nec/SGtX8fRtacRN0za2FDekQaUjNtKoJahOhNiE0Z3POVhGjyZUPkdvlAT8o9JyykPKJFB2LaywSqWEiFEc93RtSkpAa8HJkGBIhzfzOy2e8u4/8v//wE/78i8Juv5qgeyV4z6tXD1wdJh0fksBuN1Jl5fhY2MVEQyhl5eZm0hEvU/6ybejcf44q0FHEgnrrgfbM4u17gDhVnXJ0eFO1mtMwbdMPsatPofPcV/vDWd7QaZXqgZii+Y52clXbUJduaB47ilZMQzuoV3NLjXEc6azekjOByGG3R2Ik7id2Tw7MeaVKI7dMtHGY7p98WRC9HUzfnh09kzZ++VjwtQ6i0PuCkLM60h8OXTlHeH13j2eglKMqeAw7an6k1KNRiaIFg/IL3+fyOAfQ81xap4iHISm0+gtwdc34TBmEPlPnLth9BWhc7fcs95V1zQyjBqs+IgIqSt3l+GoTdZY32AOrSnSZmBRfCFTnSFGz0K5fOY7TFhS9U8gS+yxdMDyYYW5rRenyGytZTIy6Z3XnpEJwBoEFpJkUmKWKzc6bVrndIFn7NWvNlNyITWctp6iEk3dfPOG0nsyFpnK/LjyuwtMpcJTG3Bq1elhmZL8Dycoc9WGj5QfbSNTZZ4Um7FUsl1Y9K5FXf/AJ+d09Q0o0B6fq+KPlc96/fkYIar5dy4pvugDnXWNMgTF4dn7gi1c/4Fvf+TXmtXH3MJNPlcmPHOcHiDMuRJL3QGBdH0neqbD7Grh+eJf/zf/y/857L0dqgKV6/uTzjzdYDXN8GcIj05LItwuPdw+E4Lm+3iM0yuPK3I7k6agkrbzy2Z/9gCVn8roieMrVEx4ftC+6LhkC1GcTt7vX3H3+wO//fz/h3/8Pf593/g9/j9/61f859faOP/zpA49ck/bv8DTf8oO/+AyfdBzHATWvVkxG7UvqHcC6zjg/wZoNPVELtIe7O4a0MO72xDSwrJkxJGuPKBSc88yQBmI9m763Vql5gaDGCL2CCjGyrouZUGhPM3qIzmywnGMIjutDUG9LJ6RBe7dK6Gk4UV1sZ16moekGG63t0prnoR7xTjhET10XioBPe378+oF5hefXA1PyuJJpBCbvWG3sYnADq3ektGccHhlC4xs3A//Df/3X+Z/8n/9T/DASfdbzJ+bsI0oSLHmhVu1BaqGvc81jCniBU642b9378cJai/WNtY2xrobA0cztyJtbUbX+bQ80WJ9dE+put+aILMvC4cmED8ESWlVgAhjTiA/BZEUjMQ10Zq/zYYNeQx9/oQuQBGIp5oes6krKDjbJ04vpXIcGyWk38vT6wHKatW0UxKRdoUhjkKBf9eL40n1ZzghgLwN+2eNrHURV9MQ0SFwgJV0oy7Jwmk8W7QK5zIxpzzjscaKyZ/iEd0oj3+DFn1Py96Cl8EN442edkatBQ6u0fnN0/QUx6PYN8QXpVHidCeu/3V6jNa72O5a7B8QsvzY2Xmvb+59OM2DKPfZeTaCsqwmDmzk3ilwv84w0Ry3VZsYC87zgnc6cibGcTycVdD6JsOZMzoVxnJQ9ajq03kMpQs5lq05FCuuaz4mEoTX9s+EFmmmQVvUejSkqqarqqMtaq1mkVaAyDZGXz2/Y7Qc1sSZwEuH1svDhk5FIYs0z3q04X6lxUkKVE5U5xhiYwUGpNLM4043E4daF4oVFCuTGf/9/8F/nN//a9wiHiVYWyv0rHh4y73zvu/g162sI+N2IC5FQxXR1BTeos9A07agESnPk5lQvt1VaW7WH6YA2UuotLibEjzQGG4ifmVhw651aXO1ekru2b/Coy0si7XYQA2tZ8QSmMFFrI5dFUdGWIS84yeS86KZZdMDfJc+SK85PSAXXCtHNuPyadhrJDj65/Zwf/vjPeXz1mpt3XvI7H3yHFHbshx2HJze048fsY2Q3DIzDwBAVupO+Hvq6aYKvjVYFvHpDtlI3HVjnIz4kfBxMOcrUeprONcd0wUSXLpqgLNOQEqXWrS3RjaTBZqGzjjkYLgQu4G2DLiXzcH8PHq6uXyCtKkyL9PYjIUZdq7VASLigLNkhqYGFGJbYXOBuFf7Tf/YD/snH9zw8ws0w8uAaLl0jyz3IjAsC4il4JOz5+O6e1F7xu997xt/9/gf80+NI8wuDS+RZq8HaGtMw4J1weoB3nj/nVh4ZUtKq0XtOj/dUziMk3ghotdhe5M+QeDMT7rD16fVQHom74FEot0Gt3M5Vba11S150D9NALwYJe+dVeUhMgQi1R1PVoo7eGYtEtA3S90SjHOJNpaxkRQCUDKgQ7XJaaKWyS4mnux1lLWbjoa8NzhSrHI560Z9jK176Hr4Jd/THWKvtlz2+3kG0ByE6tGsGr9YYH4aBxzmrD60+yIg1ndKgJ7z1gPfWa//M+9k/3cV79xRmm4FrFymNsF1Se9G3Xu88T9Z/d3YP0GCymyZau9dZ0KoqQs2wfsdZssuhA9UGUiDOUasyFptlkXhnItCdbGWZejS1IrpotC6m3AewN1KC9oJqKR0n4lL15Lx4Ln1M3RZAW5M3HtsJEsrCDOQq6lpfK7kUxjRyXB8JDg7TwLPrPUSl5D8sC/NaGBx848nAX3w2I+KJIeFd5ugTySt8nbsTzZYkNTDXE+fAJc84DdTSiM7x7vOB//x/82/z7hiJw4SrGXncs9TI9P67+Nx0OFYHMHHRQ1bSSENogycyKrjgA+ID4gKgsoatrkjN0Ir6HmahtQBxjxv2uCGof+KaccdXuLIiV+9S60yrKtHofMCJogphGihSQDzRjYhdMx+g5QWZj7iqxBCH4ErD14pIptQTbrjCkXCt4tYH6l3E1QEZ4PVp5MN3Rr747J7d4Ybh6sAYEgOBMEyE1TG2QHLe+ms2g2kXfRu0t0BaiyIRag/WLobwIy4k4jCx5nlrE2zjUjaD2vt2DqCZcECI5FI4zQvOwTiMhtbo8/O6auA2T1znAuLUWLrWRCw68/n0oEk1Te24mq127dNDqypgrAQxbR802saSb9J44QLP0zeZ1z/lDz8+cmqeNLgLrkOjqYmc9htd5H5p/HlZeP/ukd/9znPu/+zEj14XHrJAbVQnJKezxc5M4XUN9dlXTcRViCVs+1Rns4LbpAY7o7mjabocz0FDTeTdz8j+QZcS7EFYLLno61orTO/Mn9Rj86Y6JXEZoCoQWkMLfBM+aefRP+itIhWz6ZroW0LuvbanzKziehwVQWioPCPGiHY2FnfRF5W39+G+J2x/582//xLH1zuI0hmqShboJ8R5T0wDafDcvnpgiAMiUEsml5Vp2NvgtGqR6ovJBlf0k/zVkOy5gtTH9ZuPnwnGbx8OzuMtcq5Q9cn9ImsFVUpmtxtpohqzOt/VB6CDQTF6E2vACvbuKo6tPpLu/L4ozd3ht7EYMSWeHuz65+lC0HTpb+mKHwop9g0Jd/ay3HrUvhMYmt3O9p264hEdkOn9CP1Jro1iPbE1Z272I8e7O6YUeHq9ZzcETs7hY+Ruuee0LHywmzgMnrv5kSojQ5rw4jnWpnCpJQi59bk/ZVcaF0QDf/BMN1ccv5g5DCO/8xsf8q2//i3m/+wPcKejsUc9cZqQ2zv9Ri4iviFLphtja0HicNkjTSFb3Ll334q5WjRopVDzQpMTab7HLQJhxE173G7EDTuQAPOClBXy5wTXCH0cQwSpCus67wlNNYglDPjdzrJtwZeC5BUpmRATZV3wpSJFKy7f7nDxAVxSL9uc8ceGa/esp8J1TDz/8FvIN7V/rS4tOkLBnJHdM1hQkYt1ZcmLwoDmJwpGDJJK815No9FzgIOWiwqHBIcnkpeiBuoXa6y2Blls9viiXVC6e4o+Zs3atzzNioy0pmtmbQshJCXjRFXYwTnuHzRYL02DaL1fcTS8ZqiaCOIIwf5eG0IBt+LjwO2rO3bjjhA9+EaTzOgdv/WtD/mLT2+5PRb+4DW8v0s4WYz1G2he94DgPaNzPDTh02XlTz75lH/zN77Bv/T0kS9eFz45ZgYaLTh21koRVMx/nmfKmrd9pBYl+ihhpyNmbZOXpAclW8ta6Z+Tys7GVfccZZR3jkl/HdflBlujNUgxWaDWPURdes4TElqxFvBsM+HeepveRFQ0sDkj+imEfllIOId67GpDnE40qiZSn7xnnyJ1rluy70xwo5uhXe7JdvfovrlFTC0I5OJBf2Ws0HoV2KXCpGlIvLq6IqXE6TTTJOP9DgGWZeH48MD07IXdcBdeokYaOB+/6Cy+GVy76k4PDhqQ2vbIsxqRvbI7/3mjWgUjK+mw8G5S0+YQAuM4UqsSdC5VVUJKiKDzeha8oJwF7Q2OalYgNINh++ENlvFbNdEZezCO0/bfG5S8Zg2iBs+si5I4SimUbGbb8azx2q+NGqibAIMZdktTxSUVdhgJcVDmbc5U0RnBZzd7Xr7zhJpPxJg4rYXH+cgQGr/9wXv8P//oTykBRhGCn6jxgHv9Q2R4l+CFIUYkCctamJISm7JX2n8y6DE7x2N+5MW71/yX/o3vw59+wu6zR+oYKLs9Ml2TiuDWE008EhISR8RDdMVE7B34QEgTuALO0QxJUruyVXVSs+AqxOZYS0XqFW70FCotz6Q64/w91ezAvGu446fqiqLNaRSpWIkuUJeKN5cd8StyOtJi1O6WqPqLy5m23hLM/1IQMokhfACvfkA93gEDYXqXdW34ttBawJVHSvmMHCbSk+fU+cRaCktrOLdwWiKqvKnoj5ihgOS6jbiINILzFGZiHADtTzeptLoAHnzG+QwuMQasT95bKGxVRMlV5R1LpktL9mSuS9Q2sd8bShJaIPTRjFxVhlA0KI7TxG6Y1C5uPZ6TaouiglBb2Ea0FNVyyLzw+pOPOXz7W4ToaUBdhYe58k9/9DHfe++Gu6Xyj/7+T5inG65CtfsjQQiUfMvYJoZQSAGOi/AXn3zOnz6N/Oa33ufTxxOv7h/4IkeuyDSB43xCqvZtKcKyVFKaKC3beXBq3B2DzSy37fx5p0IHtQk1r/jREnBRiL0TsXpy3f1HUwpU0+/uifS6rrQmpMGKk1qtF72qiII0RFSgvuWMSCMXHXOKHhzVRuPFklpMQMRvQdRLwYkSl1rJW2CvrZGCZy36HoOHKQZWm7NuvlhyoPBjlUq0fuzb86AeOQfQi0r5rxQ796uO3U6D5qvXr1Vdo67EkCzDcOS6EtJAaWxdSF0wvZf51QH0cnJUKwzeIBL50JU12le+BpyhiYsXNsi5GTSrG9A4DvjgGaaBw/WeKoVxUsUQ54P1IAXwjHFQcQFEPTBXIyEErGJUA1tpjuEC8oox6vjHMJCS2hJ1stL19RWfoD2ZGAdijJyKmnKLa9vmFofEUEaWdSbnlVozNzc3fPb5TxVG9OcxE2mNKoJPjmGY1MQ5BkprLCVTpfHicM0nrz7i3ScHXjx7wtObK5rfcf9Y+dFHP+HlFHgyHfjDz19zz8SL/YSsC3OeeTgJRUa8U71fmo3MeE/NhVbKVom1BtdPb9itA985POX733nJr/z2t+DjO9i/hDjjW8Y/viJfv0f4g38IT5/in72D2wVYK0jGRXSDlACrg1ihPuq1BCgOWVR4wtVVe5V1ZViU7ESYSHFQ9nRdcAzEVUUbqFUrxVaJVFizpcqmnFSyitO3qI+LUW+m4PT5eYVlxpuofnM6jjO0O5ZP/4jh6jkh3dDyynz3ET6OnNojvni8CNk1ZhHGu1fgBpYmrGVlrDOL3yPrahtn095nDBrQKttsoor0R+asYv+K+DRKhmHo1a1qBp/uH7QdM47EISFSN4H0JjoDWatWGc4H1D5Jx43Aa5JpIzS11W12s7oLYRSn7Y21Vk1spNFC0OHQrYZx5hLVnV26S4tHUuRX/9pvczwdyU1Jd8MwUWLltBaePpn4zfef8G/+ymv+/b+45ddffody+gJpK4MkrqJjcYFjiDAeGKvjdn7kD14tHF7s+eDlC76fV37/x7cmGOCUx1C1V7sbBnxcWdYZnPYtj8cTa1lg3NHlDFUjXGe8+77TJf+iTwqlGopXigVPUSs/cJukqELFZwNt0Op3ZVW7QBFOpxO1Fk6no4rUF/18xalYxVpW6slxqpnJR2pQIlgwJaPm/BnWBYr3BCsmugpRaQ3nhbpmSI40JWJwPLSKE1sPKD8h4t7of/6czViLnl9YOH358fUPoqJ9CWczmpHAnBfm04l5nhERBqdKLirBFoCCbyM07cX46LbBZ3tRFA6xXof93NmWeB5W1sd2OnnNleD0lG4WpXacTcgsbCv1Ddccvim5xjkUUnMqOrfWwNWUWHMjF1GIlolCw0enJBURXEiQVxoZynnQPcWEx6txdKeWA+sqTFMy7dKVhqiEVpzIVTeNcbcjzw+kd95V38qakfrImCZeSaPV2ZKIgdZO5GUxVmDEea1z1nXFu4HmNDBmUfmu4JXqXlrDLSvReabQOEklOx2EL/lEunrGb7285rf/5r/MN3/nb7F89kP+r//O32PJcPX8wC45Pvv4Y/Jj5acPj8QtAxcikZYG6mozaaEiLEieyBJp3uOSo3nHYykcb3/Mv/yb7/E7v/ESVqE93iPjHb4N4AMtOOLxFXzzm7g4Ii7Qjo+anMQ+JqWySsKMK4CJwSsO13BEhTWNyexjUhhXlo2J6TRCgPNIy9a9F6TMVK+jP1J0vtD5hmsaYMU8RVnvWe4LYRxgmjQ/zBnXRF+3FnxbUW28ShxHWj5ZE0Btt+Z5ITdPcmqDt+RK2u14bIL3TauZCkfGbQ12Fk5tAlV0jILeE3fKCq6a7HVRgFoKpWZWAe/U9goXaFFVejLK9m21kpLChbXzDGLQvjSec74q1LraenPqOesCklfWIlatiiVxJkFp10YDhbZBcl5tzMPRbD22psHWeW0D+CycSrbec1C5x9b0LnDwWD37/RV/53sf8h/9yR+S60J1FSeVVqBywLlFmcBmop2AT1+d+OKLWw5J+OaziZ/eCp89RqZxp/39LDw+Vg6HxtOnz7TyK5kB4dGBCwOFTlZ0hBqRUGyuO+CcIL4R8KQ0ITgVSRCtJmOMrGsGV4kBQhxxZEVTnI4dNVnJJqbiREfnUkx48UzDlQY8L7gQac4xDnvy/KgEv5RIVXuaQxrNFs1BSOSqwbK3d5xzDDjWYMx/EYYg+P0IDyttHHHTjuhVAnGIAq0r7jZWEUY30N2d+uE6etn//lY8+SunWLTxWW0h9yquM1mdZaH95MSk1lNJzkENOS926FXlz3nPS1JRf2995htwgeMCZ7/os3D5iA3bvahypfcvhXFI2hM1YpFzquIxpEBz6pDRSoVaqVWzVYV/2R7fP5dD+yq16WxeqatuGCGos0tr5lhS8c6zLiudglVbs/k5b441WgV4H7TgqcWYfhgcZM71MWk1YBVJ7QQf7w0qM3UUI+VIU6jTe3j3ZseLd94hSebuoz/jT//0D/nhZ/dce7jajVzvBr797MQH37jmrjo+/umnzA9HolNjYhp4Ct5XI2F4fIxwPOGqKhIN6cDt8kA9nXj3w/f47q98C3c/AxmpTpGBFrQdk49abTbReUJQ6LahRt8dCmyorZoPdouISa1pstTl1ZyLyOiRAs08ITVN87Au1r+yKkkcvjXAjNkJqvpUFryvlNs7ve67A8GbRmwxa7yqM5zeROydKAKBKJRfin6mUhV2y0UoAqVl9XwVR1sL4qN9Bw04a6mUtjKcR6P1bvEeV/va7GvL63ew+ehWFZrTgJwJQUziUeWtXH8e2t7oustnljubbFxneYqtkxACFaE1Noi3VK1+m61X7c12dZ9uy6dtjtbE2jt29zvt5zu7jkUUltR9g20faEaSEoHjnNmnwDvP9vz6iz0f52qcU0WJOlO75pVWGk5d4DlVx6ePM+9fBXYp8eLqwBenhZsx8ZiLJh4ilOL41jsT96vwUAprEwKeLOf9RJol+EHPg64zPX99olt9X41wZslCq1nvX+dIaSSvC6DtouA9j48Pikz7QFmzMfwdp/mIjuCtOFGW7zzPxDRqEKuCOO21NhFyKXRTdiVlgnRhEIPSgwuqLudEE0F0FrycZtKQGKbxYj69gQ4EIf1/TZDwdoX5liCOVe3ANjb0RtT9BcfXPIhe5BJyhk+D88SLIKrCCtVIHt5GG3oJ34Og2xb7zz99bw7snqF0JY/YL/oPtwvaH8/F47fXuAjCl4FZpDEMSbPHWmhZ9StdM7ECjMFYsmX2XeBdX1uNczuBSMlXqj0pSCsqJ1hWBj9S8gLSDC7Sflaey0Zm6AQC/dydJGCbnXebWPW5V6/fKZg0IEY26J6Efa6Ui+8vouzcWiovribGw0BLIx9/9GNOP/wz/skP/5zPjvCrzzVwzxUO48juxRM+fPKUQOaTH6/Mp0VZsVW0/2K6cLV41rxQ5gda1rGg5EbWekcKgefvv8s7L18gr14pTE8y2L3iawBmcJP1JK2lHUVHIhzb4nMVmqyKEBhERTPzd6xv7lQ60HmQMMA2K6zscVcW8EGdZWqhoQo1LiizUzCGcJ4hJdrxNc5HwnRFTFo9OyMfYZC21KriHgaZtWbJWtXkppTKkgulGtnY/GWb86xrxsdg30HvreO8kEuBw7gpzGjiaEOMdt9pq9gM2rd7yUTHq80Tq2q69n+D39ZiXxfVZgd7EOis3y3QaVTVTbmpIXi1YOnEaeXZtBLd4FGasl9rNeKeENoZWRITUgEjVfW1XCuS+jylQYzGNQhJK+OyrAQiV7vI7/7KO/xf/uieKSnZpbVGCwXpvcgm+tlqo5H47JRJrjA5eLI/kEw9a/DCGGEYYF4az/eqEPXYMnMp2vvTOt2CZrUA6bZz7nqG7s6Bv3t1Ouks/GqMag2U67puvAxAdXAt4SutUlrFec88PwJQMgQJuBBZl0UTcLEec61bgOxQ8ZmYaK0p68lWVCO5qqLG5lccamNeZqZpZByGjc/Rt97LwqTv7ZfH5X5zFlk4/979zDN+/vG1DqLnyk22k9BnfnRI23piQTf6Vhuned4EEbz59p1jXrcB65ntZYC7eN+tarXn06Ghs0SZfr7zJ/2yQNoDph5n2TO9uGoSPU4HpingpFDmGZcGQhDqWsjSyFVwRYhjVF1Jy8513yzEGNDbUTeWXFZSUqUlsTm+GAPNtHlrrdBgN46c7u6209uaLjAfg9oSbd8MpFR1xzGhC7ggIfXZ2d4z9hfM4FY3ndUYBtYlc3ecWeqBf+3Dp/zBXPjhx19wd/8X3D3MrNkTDld8+OLAR68f+MEXj9w+HNn/8Uf83X/9X+WvffgBe4R//M/+lCATzXlK9ohEBGE5rbz+/CcM4QQsOBy5zVxPe37jReC95xMuCuuyMkwO70aWdsJJY8Qj9YiEgPMHE54QRFYdjQkTqn/cEFY8Beq6MRWRbr8VwFWaaLbv6wrjNS6frL83UouKAGA9TNYTYbmD3TvIbqdKPa3CcsTnFT8cCE8+sApLIUUnCmc66fulutaIbZBtqySrMr4tScqlkhustdmm7KnNsZSqDGC77rU1jsuK4AhrU6k/p1Wjr723Jtum5sxZp6yFsxC824JXwyOu4Vsl4MyZpFFLYVlUaFz3376WtALtM9Bdu7lhs8eA2JhDKZXSbNxL+rN1/VZRK0R9HQwdEUsKVChdPXCrqtI1EwA0e70mgqP0JU9ySfeQVpnXFQnwd37zm/zffv/vk5tW2hFHEZWcDKZ+tZYVKR5fA5+fKuW0cO0r0zSwc8Lnp1vee/KEm/HA2B758f2J+8cjTw8D90vlx7czwzjoa6MKXU505Kivv3Nl76hVg7QWGGcCUvSB8iUJvhNLHmozUwDbq+ypIQZayzrv64L9iRBWIyqq7nfwzoiFwiWrvxcxKubgtCUkleh1frzvlw6YpgP3TTjsd+zGEddEE0YBXNsQAoVsKwa6XMSHix34IpD+ZYNnP77WQVSwk+LAlMHo9kopJa6urqi5cHo8soujkVqEx+M911cvqajyj8f/pc/eGwHWKjIfVV1HxJkR8c8+5xe95uXRamWMiRg99VSYTyeNtVKIKeBc0g3LqoPuwehMucM1rZRcrUr4cEaX8FHZhlY57acdrz7/gqvra47HI601dvs9p9OJ/f6K3W5PzSu5VK6vr7ZkIQ0j+/2e412w4WtPdF61UNrZ4zUYVKkzoaaKhEM4W1INw8Dd3R3vvTzwd77zkqXe8edfJH762U+JYSANV9w3x3dC4Viu+Oxh5vZhpfmJmcT/8e/9JxDUb3O/v2Y9LkwMtMnjSuPZzYH/6n/nv8a3/5W/we/9e/828yefs087nr17zY9uM3/nu+9zkxz+J58ziuqczvvGQMNXoGaYM+w9NFWTwjtcC8QGtPmMQHgPbgcUcFVdaAjQzjN6HpNmWxpEj497ehXhhyuQQi2ZFgPOj4ThRs3b50d8iPg4wtVTkKeQGyGqcAQCLasutHeWWJUVTERjXWeWZWXNxTg0qrtarE9YW4MwUoBPPv8cJ5791Q34QT0sLVFTST2VLnycV+11B2Vfr3VRyNvYod4ZPObS9txe8dSmkB6lEYpw2B+Yl1WJc6ABZl3JZnrfGb+lFCUkeV0HmFVeKwJoMlCaBmix0bFsFSoC+LZBsbnaz03MvTVNwnE6V7mu+lly0WrYOY9kfZ+tktJhR3zQHTwvJ5oTZhd5Z2z8V37nW/y//uhjHk6FmymRgGMtULwS6nwhTSNlzUg98Mlx5uP1gSf7xHfffYcfzDs+uT9xnYRfef9dnj4r/IM//4y/+SsveXd3Tbl55I9PC5GobRVv8pwG0oUQwXdlIoWdm82Cd73sEFQWcS2N0gRcIFi/P5fCMGCyppoI7XZ7jsdHaoXD9YFGZr/bQRWGYcewP/DF549MB+29emBMmliXVm2c5qzZu8mRWjKoRLqAc1HN5Y3lfbXf84Nl4Wp6h900WOWKWVeKVa1YdWpTAhftui/bd7/qd7/M8bUOotshgOnnOuc294DoA91UdllWxnHHd779PW6/+DOcg1qa9pVCF6D+8iCnwgAXMNJlhubcluZ0m55t0PcSvhXowwU/96uICQA4t0Geu/3I7eMDr25vmYKjnI6MKbGI2oANqAdlbN25RTfqnDNxTRfu9o510R7aahZFrQl3d/e0ph6i87zQamMcNBP84ovPAYf3kYf7BwiREBzLMtum07YFsCzLBvPOWe2PQgjkslCKCtiv66oQo5HBWmusq9HSfeCDZxO/9cGO/+gnX/D67prjQyONFSETauBXr3bcL695NR+ZS+V69NQws3+5Q5aKL426NGYcEoSpKiz3yeMr/u3/7b/D+L/7d/ms3HF8uOd0d+L+2Hg+Vn73f/TfQK5GG51oRBcZ5xMyBiSOEEY4vKQlHQB3CFRHq9qD0zTWan4RYmt093GHKSXJCqUom1QcoXnYXev90zzUrDKEUdsP8niLH3Tsh3WF3QFItLVAORFaRk4PtKpBojVVoFLkwGZ9DTLszY55mbUfWRtrVuZq2+5t1EFEoLTG6/sHlqVwtVaevvPu1sfs91gtmVptThUV3ndOK8lubbYpyIjgQiOv2Wb5wAfHac1aRQbd7ed1VfM8sYVtwXpeFqqcSR+1Vk7rqgnF1rpxlGatjabfqwk6kmRoUOsB0zvdL9B+fqmVXCvRe9blpMHDRrtCjMqw1lke6387aoevOe8BoWngd3Gktcr94z1/sj7way+f8ff/+FPuaqW6RhAhpIm8VEUWCByXI9f75xqwBRYByfCuFH7t+cQ/++ln/NmnD3yE5ze+/SGHw0h2K2OsPDsE0m1jLgvTs2uGYVRrQBp41a5VCzeFaBuN3e6wKZPVpl2KcZyY5xnQpOTqcEAN6h3jMLLb73h4uMU7x36/5/PPtTqfxivuX3/CzZOJXBac80zTbkuGotnvdDTKtfrGHtpVpvR3Ggi9090yBptVxeFjYD/teDweeXeM7EfVke5qSr2FBtoh1V6v/7lBVO+0s7qc3Xm/9PG1D6I9oF2eHiUKaIal7u0OHyK1quRdzivsvTlJYPDrGXLkrWDaO5WXP78kIZ3fU91MgodLavX2/C89Lp0V3nxUJy/FpCVmrpVBRF1EWgUXNlUQrbLP76cJmcKIzqm0l/YORPuqmwyXLqra6qbMUpsY/KKvpgWrZo/dNaZDdR1+3v44tEK7CLDd0QH8ZvS9Kdn0G9s7Qoi8esz8kx+/4ievC4XMaXmgSmSXHB8cnrPb7bh9mCnLgmuF4gZyqew7LI0jL41QG6OrtADLXJhPCzVXHr54TV4fcVJZcuPzuyN/97/4bdKLK4W/moAzAg4N17yNwjzgXMaR6KQeEVQIISWtTrwSxbwoa7OVuiEkIWilouzbBZoRv8zsVVxQokwruDJr9v14T1gXJI3U5YQrGZHCWprO0AZw+USWQHIKj68lg3i1TQsRMfZqlUou6g2pgcQ8N9Fqsom5/gg4O49Pn72jhJw4mLF1J+boDeZD1FaGQZ/N7jvdsFBJzt7ncGh16iMiVQlm2pKkSMVVwQehir6WKou1MzkG9SPd7JCcsjBz94U18/acM7WYzJ/34LwFX7+Rm8T6gs0IYLXqyFVzXklUztt7agVTcjXx97bZd3np1BWM8KXBE1es0nXUplX+FwXGofGNZzuOeeXutPJkd6CVlbmoRm7yCmfj2Zj0pTQea2G9Fl5MO77xZI9rC58/nPji/jPevbk2gf0ZFzPff3HFf/KTO5AzWaq1RjPUo0P5oPPLndncofcuy1dqN43Q1ssyz9s11DGTqrOc/tzDjDFRq4pZZBa6a05KiVo0mDvOVZ/aKp6N2X+mGrT7SE9lh6IV5k8xsZgQzTgo07/pZoyFQ9twu+zEee++JJC+sd8KW6J5/gC/3PG1DqJnybw3m8W6aYfzCXP6s1wby7ywLCe9OBYMepXZn395MX9Rqf9lge/LJk1dJ150lZBfdJH65iMqyxdigBaIzjOXleQno4P7LaN27iwHpjJk0H1Oe37WQjN4sW8UnhCTVk9R5we1x6E9DG8kj/7CXdmofydvM4eCqHVVJ8Y4vz1GzXFV5UZhQJAiW5B1myRb5NPHld//8Wse/ETYO6qsDM7zJEVe7FWBaC6O1jxOPKU5Y65aoPaCc5Ukwiiemc4AdZAiq2SVvbNRhV2Ef+Nv/wbEwP3Dilsr0xgZgtBywVex4NbA5Y1MogowaNZsPR5NWKx/1BrSe/IiuOI2gkdPIJThrBt5c2mrcpx3uKA9QKhIW1jmk86ZeljtezsRPJVFCpPZdhXzrFzySkyC8xGcpzRRONP6fLWpN6bvgXGbN7bOkwvsr64Q0QH+YtUcHco1s4JOPxe73xRLU9SlooGq8zxqq/ign6fSoEG2jT4Ec8IC28A1WKtajlg7gPP9aOujyDmoA6xZCVKKzgbjddl16cQ/TSnVL7RqL7ih/coqIE576NL6YjLUClGFKGdG95aIOFsb0tTZ53wehdYiq3M8rCsvn458cUy8+viITBE4kVs5s2ObrgU1vnfQPOJhbkIIiWf7kVpHxFdyrow7HUlaqkCrfPPJM/6zT4/KVrUkTxnJ5z3FWYNQBLMms71G1EtW3Z30s3epwNKJc15H9YopEwWzqgMhRt1PYko6NSAVnKiBfZNzX75/GH/RAzUky3UctsnWp720TNv2UhGWUtjvJ8ahB+n+iLN04Plr/2L0j4s97c13+8XH1zyIti+tQlMa2O0ax/nEMAzkZaZJwRGIKVLqogozSVmEUs/kIn3dc0DWE/xmLdkv+iUc0YNFhxQuX2d7HmeC0cW7felFlg5AiTAknd9yJTDGkU/vH3j+ZCK6oNmzazZCYYHLfPyqKDt2M3sWrTpj6vJ94HzbyEDqB+rAqwJTbUVNnk3Creve9sDgvUkPeq0ewhbU2YbSe9aJE1ZTsemv08+tC143V+94PVd+eDtzuBlIEhlS5OXVFd+6fkpM6khRnKf5CUE1a1PwhJCQlhHJhJDx4ollwnMiuagVacuMbcbvJ+blRF0XPrxO/Nq3v8kP/+wTbm/vAMe7z9/h2+/uYYVUnJlr6zmiOJ3HjR4XEmEcVa+0rdYrBUeEFaiY1V2jVLg0LtCOoeCqsqubE62SDFbzPtPinupAamWunjWr8bLCf55WG941TnmleiUCCYparDbeES2hES+E6Ah6w6rghAVIHVVySugRqASq86pz65yyfrs5AeeNpjbUYsyG6c+ojAbRUpXA1BfXUoSUeq9KCW+nNRO9ztr6lAhpILd8Zm/Tyc16fw/W+O+kwV4B65SLkJsmBLUnMhUayvpVz2ZNKb1XxKXUPu5ixCSxAIbpP6OKYCUvau6NIJg5QtNkRK+5HhVFwbrTURp2OCL3y4kn14l3ryd+8umiRKUxmAKSajtLcbiYqN7RxBOI7EbPsWYW2yKeTInD7imvbwc+PX5MGq+p1ZEXcFeJ3TgQ0gBBCUspJVo1i7OgaJWlQ5snr1Z6quy1302kGNTA3tljqrq9qCewtstSSronWVIdop2XFKlSMMObbQzO+YqO2laD+cM27uT6ntL0HvKi+584nV2WpgmK2OPykllz4erqwDgMpm6lQdz7y2jQ5/O/Yj/+kmLpcu//ZY+vdRDlImvQc6cOIiHq3NCyLIBYJaSBZRxHnjy50YVvx9un62fL/TN7Tff9s4bk24/vlOo3DITfON6uXPVnb1+zzmAEmMaJJncsp4UhJt5//z3qqiMPPg7k6PFVk4ONAZucQWTWrrP3EpTEY3FQIWjVpCOkaAa4jpi0MolJvUwFmOeZ/dXhnIE7hWDVDaMYK1I2eKbaOMtpWVnW5WxZhWXIXgP8JaO54sgSWNbC1fFjvvvuxM3kqRwp+ZrDDGW5p8pRJb6aZwyR0TdWceQWWQpcXe9YWWAp0DzNJ+ryBac6M4yRx1vhql3x3/7b3+N//+/9Hv/rf/f3OJVMiI5dCvwv/mf/Pf7aX/8e/qOf0B5vkRSJqAerzqXpPdacqhB5U4LSkZST9oFzozXdbHPONCrOWghi1ZAzWB4p+DTihoE8H5F2z+pGaAuuZQoDJUbcOqt8oHM015gfH2guceeU6TnFgI+eOO4IMWrV2Xvi3tvn0KBlCOtWcTXRylBJRipy380E5mXdBNnXdSUvWUd4AKFsFQXYOIj19PsoS60NnCoZddPm1hRy9kGVih4fjyxLJg0T4zhSqvZ4deMWYnSsOZtUncKDS1UZP7GkTdm/jWK9VIXRNdi1PpdohLYmZYNOQUXS13UlBZ2XFJyJzcOSC9E+Z5NG6gIrtSeGvaKCeSlQMj40VhdxNTMzc3018ux6z3deFP7o03v2+2uSExaH3gOuICVSvFANnqYkmsvcr6+RcoJS8S6wG488CQd8njmuhbsa+dP7n3I8rtzceJyPOJe3ijOlhHeNajCsF3A0pOoIlWtCip4UvP6sFIi9Jxw2omCTRq6FOCRyyZRczcRCP28pK7UsOH+GkZUlbNKjfb/hzDHBqeF46xaPJtzRA7TCuEqGjEY8ijFwc33FOCaqzame92hDSGxMu+/Fb4+2vD1y+M9LMPp6B1EMfZRm590RBx3uV/usQJ4mvri9x9MIoeCjME4fQBvUlsnZOMlFVfk2cUihAr1I53PcnQXOFyeZbmS3Qjs/8qvHZS6Dam+sa580UIuAeA67PdMUKevKYZoQblhQ/7wQhRT3BBFOJ71Z1Cor0AKq1OTPVaTzmd0QmJdM9J7gHCEkHIndcGBI97TSGGPkcUEVbtyAc5HWTiQ34MJOM3DR0QbMskgZfgmMTdekMA0T/hQsy+69mEil6PhAFX0Pc31YW+aU7/jO9Y7v/9rf5v72L5iro3phdBnxCfEJiEQH4zSB97QQWJeVVjNjhOTg9HhiPByYAsjdiWWpxDXRDlec4i27XeE7vzby5//0Y/67/+W/ydXzG957fsP3nj/lvSfP+eiPf8RNiMQ2kD+/5+rZE9paaTIrEzsF8IFQdZPPUsmiue9yOqGKV8YmrQrFhs4Y1SaOVohOe3eYHGGtDe8CpSw2tgRVZpBmyjM2o+gS49ULcllUk9RBEVShyDmS9QHBM6+LkoCcY80zy7pSqsenkVLVwF2FwoWUdtQi5CzUlilWcfmYzEXI0UIiV00AGgFXdZWICCUXQypUFlMwyJ1ILgKl2GrS4fh5UbZzcFoZr0vGF91I+7yj94FTzgqoOqdVfVuo9hhVL2rkJjrnWivOC96IPjGqx3ATJUZFHMtaEUzSzwmYHuuSbfzLeVxrm3nBao4o4FmziTd0Ji/1DE96jwx7CpW83LMjkP3I/eNK9I4P3r3iDz9/pGS106N5fPX4IMTdwM4FsnvgKAVP4YZnDP6aLJm1nhA5EdyeF8+uiekp8sUr7j7/jM/bxOo90QsihZaLJnF1JRg5SJrH+4SXhTQM5KoGACkNrNVze39HbisuOEIaVTb1uOKDZxwCg1f5yiEOtJxVLS5GQhgopTAGzzDstPY7qm2f5JmK3kvSCkgEF9VarnnEeUppzGvWGeFWVMbSN4oMPPi4kY1qrrxeFzJwc5gYY2Rd9fwLXjkITvTai66HKvXnBsd/EWau7mZf46NT5eFMrOkVXIyR/X7PPOvgvfZyAPFbteRChwvOiPqXv4/9x0Uvpv99gyLs8+gGaPNZbwXOL4MLuvBAa2fx5/67GD3zPJNzZlkWlTFswrpmy6g7gaox+PNz9flKqtLHmRCE0/fKTu2hOhSVs/ow9sqhtko1lpKK3WsmmNe6iUw3C5I+vEmMOs8HCssyk+Kg2SgQvNsEwztRwLlzNl9qJUbhxTjy69/8BrIu+Na0osASA+8p4m1DMKFyI/Z0SDGlpKSy1ng6DjxQeNVmqgNSZb5/4HnwfP+bL/nVv/Fb/Nnrwj/6449gGPHyyJ/9+V+Q5wd+9OqRv/W9D/ntl0/5cJ/48atbUgy4BkMMDLVR1zsaMIwHRBy1aKbeepVPh6F0t6/+TJRxLiC1nq+ntQK6mLhWa9oz1L5x2PrgveflQiCQWMtiVd95/nkt83b/VlHGbc5ZBcVrU81X10wNyFSdggOv88drqQp3GvTvcRvTWzCZWWejLmJm2SbcoEIP+nuF9x1NsvXROgxayeuiht4O7aHmor1MZTJtJMEYVDCBC4KTFzT5uGhX6NiUEIgXPXy/reHOnD8f1o+zCs1+BEaIc75XrWIMUIUode26rcd3KUbQmiYIzqnijoin1lXHwrx6zX7z6Y4ffHGn19I7sqgCVnQw5wUnhRScyTnaLLrXFoKzfmktM96f2A3wzs2OV6tY1V5p/RzrFyMbIrAWTV5FdB8pOev4iDWuVVxe77cQPPN82rgLtVY1dBcxVu+RnFdKKZyOeq+djkdTOoPT8aQs6vmotCLRFpU6sCzmheptD63kpkQ+Rc6aojPF4aLOLavZQ9DqF2FMiRSCuSPp5+/16C8CY/9Fx1ouj691EAXO8ayfQAsawUeGNFqfLygzDc1udeHucenLu5TwZsD7mbcUOPOh3cVj9RL+otfbGMUbmePNnivYvtBU1HkcB3zUnlj3SlWBB7X86RXyxpaD7cbH+m+6oZ0NfnXuCvrg9TgONAuc2+xmUsgmhKCzqlXFG0LojFVNYELQqvlyM+qLrosrOINbWqvaS1n6d9YbOcVElUYAbtLALkVORWUHEdnkux6XVRVx6BW7Kb3YJtGrgWVZ8D6Qa9H5xuZwcSKkxPJQeO/ZxK+9vOYJwjefXvP/cQuLCMfi+OnDkdPxls8Xz08eZj68WfhwP5DXogSaptqwrVZ8FWrQ3myT7kiTwQdy6WZLPclQQpOed93sRUyl6aLKLLXimmNI0RCQs6tORz70j/Y2W6usRS3EpDkTFTACCTqqIKI2ZLlCqVBKY62N5NRwuRq+H1BN2tIwazrtERZxhO3vet1qE/Nr7GzQtpkL0Brdp1cMwm0IzUZfSsk4D8uaGZOOjrSmFfE4RqL1dnuRKcYodl7RI0ErUO+8in9YwA1B1Zy08NIA2lsO4JALzkKwntx5ndv940yu0ylZrSc3Yv1Uh3ENwEQZztVMNIERJTN6EoPC53XWCh5HcPCNpxN/8slnYCiRC1CLQ2ohZ9XUDQ6K6PrMJRu8jTGEwTWhlZVA4WqMuCHxg9tMsXOv5DE2I2ytxC2B8+rFet5PtPrOJibvDcVYu7kAjVKL7kGtEP1IzosmX8gmQq8Jod73a9aAnLP6sPb9MjgNpNU3urJVb5lp4qmoiAOFikUZBH016SxzZBgGLUKMiwCYEbds9/5lq+3tiYrzfi5f+rhf9vhaB9HeNgAsy4H+EycXeoj0RYH1d2bSsBAlnR9/EeQu8fGvhGB7DL34vWam52D+5lN+9nUux0R6j9X1hQusOQNwc3PNNI3kmlnzalVAwzkb/laJmi2Inqvc/j1szspkADvlHNsUaq0Mw2DSam6riKdppJaM9xGVDCwMQ9oYf62pRJ/3niJlI4NsBCu4ICXZZ6MRoreiXjPn1hpDGui5ScSxzI/UYVRmofVcfRROxxlh2ja4FBO1FCMXnBOJeZ7ZHw48HE/Mc2NoEfwOSXuqW3nv+RW/8fKA+8lPuEmOdO1hivhx5OrJE6abyLNwTUie19Vzu1YVM6+eRtXBeypjGPEkShZyy6xNKz3fBnKuWzWk16Xim7MqxygxoubE0iupi77ixnIObDOKa9YRFf2uUa3nzAqrNu3BdhUaHfGw7amJwl0+qbxfFea16mCjj0aYUfakF6h9HESUuNPHV2rfiEXHYTD5wU6WwmmPVUW/9f5Wxq3ggo3b1EYtjRCcidKrEEQtRe+/eMBFuagMlVGspvCaMIp9p27b57BRCPq6tGTFe2KIalruxBIwt1VTev9fCEN4T/Sdacy5mrURrf5z71RyM/izUIvCxgO1FgiOiCdKYPUQWREbNQvSeO86cT14brMmVikGTgsgxUhqKo9XUWGJNRfyqp6tIIzRawKkkAfJNd65OSA/UmRER5baxXfwQDW0BrpR9hk9YvP67OvSu15ZVwukGvxyyRxioEnVaju4rXcuTVTYYUMJsbVq7lYCeIeXN/uU3ns8SvLU2WogRoWfgyd6EOeJLtLygptGC6KOteXtgktfa3qVeVvG9cvGW75s7//LHF/vILpdnHN2rH9Xttu66sD/mhc8lbht5tBkxbvRqrX6M3rDP/9k2lLdgmD/qUm7yTkw99dpb0Ct/fXP4yL9CCafVtbM3d0d3/zmNylZF3gplbuHB7JkSl3VG9ItrLUSEGIY3oBWq7EcU1LBhZyzkYrMLd4YiGvRLLLVxjzPtFa5u7tjt9sxH080FywjFW5vb1VEGx3RKLXQ57dyJ33YILVCuicVAEAQb8SNlGw8QxfLPK+kvQbjta68Pj7Q0nPW5JgfM65Cagk/COID+bSCCDFEgtf+aimFaMF/XjOYXFhuBgNSaKdMiE+4cZ/zn3v/Xf7Wd77NfPs5P5hfE6JnzkeoQpsXihOux5nXD/APbgt/+FHmu8+e8p3nL3knOIYxUgb4yfzIrkXiOFKdsLbKkmcSbuttOu+BZoL85pEoAqVSatZeeoczRbNsESE3G10SZxZQcP94VEg9Jq6vbiitcDzOOm9q5CDnbBQhL6bQ4/BhMENtj/hIRUlHq+n6OoPDqkCIA06wSkYTgYAFtKZawDqB0JEEvWe98+bgo+8pzqqfajq9zeB/wPlIKVkJQ0HFzcdhAHSDK6XgjA2MdP6B3qPbqKh05MkCaFD2sYhXlyOc2QUCoc+ZKqNXRKHThv5OTDDdBdXe9ra2xcgtOAfBq0mac1vC4Ls29MVeFGPEBSGIw2dn/x2JYQQaUhdGqXzvg+f83g9+TFlXgtPJgasn17iaeZihVMGPQgw7VYwqOrrkvY6JOTmQXKLJwrouLK8+ATwxRs6ewc2YzWeRilYzFXNqqhm/KFv65uaGcUjcNg1mwXue3tzw0Z//kBASu92BcZyoDXWQ8RFEA+ButyOlpIQwFG3oLi+DD0SzdNT2TdMpgRA3HWfnlHGdbH246GAK+FlHnwg6YEQV1uOJ6WrPblTN5tLqG/t135IbEGXbjrd76+3jly+evvz42gfR85c9n6kOUw7DwNOnT7k/rkgNRuBopLhnXl/jV3UAaK0qfftf8BDO7NxfFhB4+6LlZWFZFtZ15eXLl9RamcaJaRqVMWubgGbl6MYRAvGtG0WryoAzFnLXsfXeE3zaoNY+rlMs8+0Sfb0qPZ2OHG6eMYyjsi69J6TIcqrmRyrEFFmWk8I4Nl4jUnF+2OZUgS0rrK3ZaIUmHLVWWqkG+w5kBnITlqxVUimZVjKpNdaaKcUkDL2NOuSCT5H9bkcuhcfTwjDuOJ5mWvFkMmXIDOL5ox/9iP/W3/otvvH+U/6zj2/5f/z+D/koHUlr4rR+xutaoSUkNH7y40e++977jFfXvGqV//jHn/APbu/ZT47vHPb85vU1L/d7bjnilyNOIo5Iajcc8wOtQQhtOwdNGpVsxtiatavRcL6A+E2iDcirCtZjwaDWyjjt1BWj6QiXOE+aRoQzlKzjSfLGTF6v6mqteDzTMBJDIgvbc7qQhveeZZ43tvW53dB7jKJaBwaPehfoYueqEyxs88ROVBHMODmur1fXrJLT195EOZyoug2KpNCTTVExBmXzqpG6qyuNBkYc0ddWOzQnEEMiRK2kY+gjOk77qOLIWfVyvdmd4QNSKjYXo1BwXyMWGFR3WMfBYtTE0TlHLYXb+3veeeeFVsjY3KOx3WsLeFd1dMlnlub4/jfe49PPvuBPv2i8XiuxLRxPC0tWwpb3kbVUqjjGGLirhXXNDENC8Li45/F4RNYCBOZVZ5u1x9i29dlaYwgRalNdXB9UErEW8qpOTq1V5nm2wKvnMZfC3f09PiaKGKGsCWsuSIPHxxPgSSm8Ue11n2JJA3FIzMeZXYqKijRlU8fItlcCVplaGwjrP5fG1FQNK3tt5zjnmZeFJzfX7IcBb/tJ6qpSnKvNt/fgnwfV/pWtRKF/abg8ZVqhBoY0MI4TDoVZqy3y/Xjg7vQJ0ipcjF1cHpcn8itPaoeP3oIGnMFwnd7fD48z6E62173844FX9/eaAT59ui2E3W6nbHNp27iOtESwzVU/h2xi7j2IjuNoxBQN6zEOOrPZMPKK3zbNYVMN0UqjVWW+ruvMnjO0ppJgSlmXHgBbQ4V6+4C8bISpvhFv0Lr1M53N4vXZPR80yZHWWLOjnAqS1INTNVYruSpFX7kYbvseuSw8m55YH1avcScW6QYeYElkmbl+Ernnkf/4pwvHZeWjx5lTm0k2QB5DIg0DjIEJj0uJwUdexMSraY9rmXYa+fNc+Px4y7cOr/nNF0/wJBqRXOFkRslNGlJ17g3vNOuuxa6T3rMOqDlr8uK1Npfae0X9XtGxjLWUM0zamiEZncikjis9o6/S1LoqatBBvJoe2+buXWAYRwiBZVl1BEXfSueDo64Zmt6zm5uUN+CzWbugi304IRjyUqtKvWkBaIo0nYBkM5TSdC5RvCP6rqJliWSeGYaR4KMluWrDh5jYgW2+0zhSbM7RexVAaBWtgsURozcDBmUJl03P2nxCJeCqoVDO6TVz5551JxVpr9r9DDQISmDsm/OzZ89QSFjXurcRriEKxISXbI48heYmno4D33yy4+P7Ix8/Zp4GCC0qUa54gnh2YUAQUhL20wjtCrzn7vGeuK86lypVCW2SiNERQ9SxplatB6+pUFdw6/KofiNZmf9wShRRcZI+9z1ME6UU0jhohY2nFXj27DkPX7wihKSWZFaFhhCYponWKiFEdocr7rMiMDEoErJxNVyf69S/j+OAtKICISEgXgX9x0GQkk2gITIvM1fP9sQ+SkdjRDF+S5Ms8blM2r4cxn37OBc1X77lf9nxtQ6iHVLBmZtAx/rRXojz6p+p6VUPaJ4UI84IDpdEHHgzE3k7eL7xc3eRn28XwxrZ8rPP1b5sb6S+3cxWuOp0PDKNo+lcevV5RFmtY4pMo4rouwYxxG0RdNJKiFHp9UCo1YQl6rZQnNM+mi70c9UBMI4jy7KYDqzCVTFGlpOgQtyabYYQbeM4fwetPM+9jV5J1XpBLTdYsZ8vlYrTM9MHvoNzlCo85kLOBQlKnhGnow+5qUJNGkdaVh/Katq14zAwLzPruiicKTpEH5PYsHYgBcf77zzhWCr3r1eO88xxORFjIIWIH3Y6EtQa3g+4vWcJCSeNWCHEpP3iAjUX7gv8RY0kOfHBk0CKugHlXAkJG/o3BmfveXb40a5HdxFyXr8/zm/wk/Num5mjC59f3FH9tDqrBh2o6k7V+b/upNEh5W222QhN4hw+Rpu5PJPKFLn0SDCCm1X9oqtIX9/pRovvylf2qZxq0TYRulKYoCgwzuHM67OKVtkbGUxL8+01gG3MBDHkIZwH9JXIZAHC69whVv3GoOpYwTR1sUREi2ebBWpCDA7EbwScbaN13vaS81n2vhPuNjVgk8p8895HjNS3nZFGkKBjJ7WqsII0iPq4b7/3Dn9+d+KHX9zTQtgUgrrij+ZG+jyHVtQuRB5rd99R84BSKtUPW39Yek/UdVUgI1E5PU8pJEXO+mSCJRLK6GfrBTcapRWGuNP3tmAVYyJbpdslPjvDvpOuinEw9DOdNYbFksCeQDvvqK1sULkTwbVKc4HmMBJfIYgjeOHx+MjVt56T7Nr2Hv3GU+Fiy+kJ0pccl9Vn//vbI46/zPE1D6JsVZi4ntf3HchZgqMQUHVZs2dnbhMbfPuLT9aXl/i6wf/Sn/UrKltnkbhUJYfc3Nxs/cUOSwdgHBK73WTEh4B4h7eM26NqHzFpEPVBs+iU4qYStFW7pl16qTgCME2TCtYnnclqrZLSoGolXp0hRGAYRqIpnYjBvzFG+14KG6cU39hYnAVlTWVUxxjbtp0F0RiiBlERHquyTZ3BhdWphFwVFdwf04gr68YGHYeB4P0GTTnnFXVwbGw95+H6MLGbBua8sK6VvApI0w3CR+K0x7kAxxO+6Ka9SqPmhVCrDo2HCUcmlEZt8OpRyMsRQuDJbiQ6TysN8aLwoKCKWIBihFiwdBur9Zy4mBRa0E3emTNJnymNMdKNv7tCj/aP4ta3c3hCE0JNOLz1BIO9rt33zYT/qwoIaCAw+NIUqPSaeFoPLha8vGjF2SzABR/1XMuZH5C8qmVVWt/b+rff1p2IbOSZS99QDPUQzOsSSyRN/cr7YH3eohKHF+vIu/O8MoiuBedB6pYkdx3WZt/DWdBR6UMjIMr50/TP6lxPerXJ1iuojcRiAbyPv2yBjIKXiJOq4gNoxR8cLLXx8p2nfPPz1/zJTz7n9tR4ET0lKxPXCXhpykEwoYPtmjuU04DbmLg6D6nCKF0bWcfTgibBlrw6p9WqBh0LGCJ4m7VWeF0TidqK6TCbZrbB8l24Q+OeBt8Y1UrRxQBOUbROVHTW0hDMks8iXBdJqEYaUzTK7guvClLzuiK5EqKqMD2eHnj/sCNa0iL9Glmv+3zhLv71Fb3Ot0UWvqqA+nnH1zqI9pu6bwCYuHSnc4PCLdO0J88nJZiIAymM47RZNyGOQvmK1//ZjEV/povkbeS9WU+oP/4ys1GCUdsu+PbfHm4/fcV777233ZC1KhxXjFwSXCDaBnR9fc06z7TcrDr0SM2k4WwttNspDLYseXNIyDkTfKSJMAzDNhsKGkQfHh5IaSAinE4zNzc3fPrTn6qepmvn+TEfKE1l6kJI23nZBPitEu0LKHTpL6d9pGmaeHx8NJkuzYynYdDxDu+Yo+NYMnuvg9hLq+TcmJzOzTURpfyXjAf2+z3LMls2rJFzWRWubiXgKewPjQ+/9Q7Hzz7jVAsOJT6E3YSvjmNe2KUjo3fUuiJ3R8TBdAhUKq/XwnV6Ai5AGgm7gKuZu89+CtfX/ONP7/ngGt47TOz8ynFWpublaEFwNhoRRPuUnO/R3rsCddJwVsm1ahs8jnGcyGU1JvJ5JhbogjsgnpQ8oBq1YpWRJpMeHwJ5Wam5UFthejLaDKG+SN/wMgVqN3+3/ruxKiuCuKAzqj5Q0aQvZ4VpHUIKGtirjVtoIeovKmdN9lLU8Sla2cTRlf2t6MqZZW+ShSjygfNInTcm+Na3RN2bdLRDq07vdBxH6TOGyqBVnQSPK5p0NedpJb8F5emuXIrN4G7jW6JCBNYTPe8ZchFglX3tm6OVlepV49U7VZZamkM8fPe9J7x6eI//0+/9iF9PgUBTPVwH++RV0zaM2nuWSisnoHHYH3h4zBRj6Jey4OKIs6TMPpEiGv5cVPTAuY3vGKC33+9Z82L7U2c+63dPSXvmtWk7QG3MFMEax4FSMofDwWRGPb4pfyOEQBqSKSZZYhGE4BNLN/v2UWU0uziJ0xnbwQVKhcflpJKWDrI0TsvMs6uDBVELerwp/3reg8/V6Fchi2+zc/+yx9c6iF42pqVDQZwdQ0QCYRyIXqERHQxXuCJO7+sGI+dq8OcdbwZQsX7UZZC9vCBK2VdZtGDQFVYtC1IVIo1OOM1Hbm9vuXlyzTgllnnVebjgtwy/1kwYPH6KfPzJT/ggBvLxpCMGPjAWz9JWNYHeIFYNonVd8L1SKIVVhLWsxBi34NqJTKUU09ps3N+94urqQ+235AVqw7uR13dfkNKE91HVWkrFMeKc0OqKyM5GYjzSCiXrGE5MWkXldWVdZhwqWu3Q6nLJq80Xgis6/rHUhbIulGNVLVzvWYpQF1P98cq+e3bY8+r+RBwOtFA4Hh+JPtHE49vMk5sdz5/f0Grmk7t7Ykwa4Folz/cQGjHA4AeCiyxkKrNuFOMNUgrL/Bk5f8yuXDO5yOHqmt3VgXdevODHn3zEbrrm1jsGKm53IC8P5KYC/j44pei3qopO4oBoEmYrIpAu9j1vsG5eVfFlmIIZCzeGIZJSpFXZNvVlWUghKRxXG4iGvh74XEy6gdJIMZD8xDSOtoEFbqaJ2alnZ6e+OvE0J+aRG84Zep/73ZJDW1reEQdLC8SQBGHrk7aq95H3wZiv2isvrRDToGNazRG9pxo83VsNYotUx6rUdDw48wd1fR06gmjPtCMu3quvZms2/hSjoeP6vfTFYdP+RbZRr4fjI93Oy7tg3qtJ+9pOXUsUEWrnvr9XrsC6zpu3b2CEFEiM1EX7wdUJuUF0mU9u73g6Jn73O+/yez/4Ka9fN4g7Ql6pZWVxjmPeqauQQbroFK0KrciKOJXSq9lRCbiQlCnd5IwmNPVN9s7TmlaIuSy0Viy4enKuONfss+s5zSdjbldPXnVO1AUhzwtLOeF9oJbI6XEGDzMLIQUTiJl5uHvF/d0jk/VLbcPEuYVSCiFFXGf2l8oQI8Vr4J9OhbkV1nwEH9iVRmqe14/C9bsHJRo1NZpQbygN8J1/0VqjdC3diyC53Vdvwbb/PAFUr8bX/Piyqvs8+lK3Br97I1Jq8H1brPhyGPftE/xV1Og3TvyGJNsAuHOIqLaqpkR9Pk3h29vXr2m18vT5c6WDoxWbb10qsL+sYxh0BOD+fuZwOHAsBSdiM3iVEBxNdO5Nhd8jIcIw6Yanm4/+XsSqBumEgpGURh4fT9zcKDwrTdnNOZtikU8bzLzbX73B/lMiwVl84Tyf2q+DmNKNQuvjODKfjqx2PTwqtpBSJGdHXgvLWgg58/TJDUfneHw48Xh65FQ9RFVSqWXl6nrPw3LicT6Rph25ZB5OR0JIvH595BvvP2Pajywl8/r2MxU2qI0hqci2jWrqRt6UtNWaYxgmAI6P6o14vX9GridimvBU1jzT7oVpGPjmu9/m89e3zLnwWBptLhyAsi4M40hMg323RasE0U08DoEpXRmsqYxGheEHM0EetVrwmoUn25x88PjBo8SuRgjJxorOouKdnX45s7uWxSqncLEmVEu5NNnGWRqebt1mURGVuVRxj9qqEYtURSmErtBlWqshsZbKmvVPq4VWCsEnQExwXUehcGdGb4cl8UHJSSnhXLggyakQukMF6S/XpWzQpLrzXK5RoUPlVldLo1uFXb6Gw7HmYudk2s5PKWVLSnUt9w1ZTQQ2Wc9agW40b6b0zQHaqtG/C3hNjBuwzJlHaaQU+C/85nf4D/7knp0rjCmyOvVflUnMfMAr+cZFjo/a89VrpMmnik+A1Gq9TmXDYuSszv6+NJIIoY/EyJZM9yoVhyXWAZFKzgslr8ToOR4fWJeFNOh1WYsqMoUYGJqN0VWx8Ty9QLVV9W1dswbLWomAb43T6YRHeQE6twvitH8u0qA4qlMWsgAvnjxFDKFCFAZu1nM1aHLbe7Ye+5f0Oi/RwvO9ILZv/3LH1zqInscCzrAERtS4/F2Mb6q9WEm4pf6XBKOfl438fLz8EtLRjK+2ssm5qSiy9jG8Fz7/9GOcBKbdgXHc6RhH6BR8fay33lSrOgM6DhMfPXzGfJp16N4gvLpmhQjbAtZXq9bQb+YhuM2p2kagQVAJEE0y8/JIrQu1jTjXqG3d5Ah1BjMyDNGYl46U4uYc391bLhfqJcHKeyVzdbeKzlruNA1pWpH0NKeKkIuQ55nDBx8wxoTwBa/vb5lboEhmXRc84GPg1d1rlrWRgXlduDs9gsB6WgnxCbjC8bRwf39vcG+jqwCNadI5xqpZuX7P82hQKav20WPAN0dIQllnpEVccxwfV54+e8ppVAWXLx4qV9eBJ/uJKLKRt7z3ypa1IOqCwqHeq+G5M3k3UBs6wW3wlzRRyzmUYe06XNiEEKPNAYbtGus9r9er9+20D5YUWrbrpFrPchbJ8B4XI6UKyYGjbtezK8g5W0itnXuEW6tiG9RHqzSraGIz5qzoBtiaV0eUphJ13nuNS63DoNoWqMXINQLrmrdA5hwmARfP8LDX0ZtaK641vDv3+np/sJoGrhLNMMKX9uN0W9DEsI8EdZbmmXxoaBYqOiFr3jboy834DC2/sS2omlOtZkqvCYlz3Zg8872X1/zeT26ZHzWQaw9c3zt3shbgMe/cWvGI+hcHRXW8cxdiHGeSZW1FA3ctqEBCsD2g2ribJkNd/Us1t3XutRRTgpJGKSsx6PmprbELgRAjedUpgjWv231VSlWxi6gtJCfKhwihEVNCvCcmZZC3JsQQGGIicRbqSMGrUEf1DEnVn5pUrsaJ4MO2t3kR86CVzfHF9WvY7Pr0i3Gpba63nF3/82iiJj+/3PG1DqI/c2xZhC2gpjdVvLDx2u5skzvqxIAvC4xv90S//C0vf9c3MbHebKdlat/VGfHj/uEV67pwfXjGbtorXFRWWjRs2bk3DKuV5BEYhkG1LWu1cR2rEYxwsP1xyqoLlhyUWunVd7U+TbANujaz6spZYa1N67XaObBK13tiCCzLYtms9iNqLaQ0btXnOas/kxtUNjCSTeC7lN7TQwf3TQauJzbq/ym0nMmlbtdozpnHPOOijo2kmGgID8dHcJGyOo7rwpwXaskc0kgavCYFZVVYPIYtG9eZ2ahEG1nBoKzB1IFqrRTJiGnWSoGcT9AKOIUl6zJT6xWHacfjY+Y4L9TxhFzvmDbmpsL3XbfV2/m41GpVco/b1GOCj+qFWbVa806ruA4TavFm1aHX5MxJs42ejTSmwc1YkqbW83bg2zxPbRM59/QuzZLPSaZWpGc91bMMZa+0jOBhiZKP6oRSzKRbmqN6aM2x5M7gtd0M/V5+Swoa3qfte7jOQnayGdL3Hmfve7Xzt9QkwTZaasMU8U3Y3n5f2wbzii4ohbL750FnaR3aW3QoVFqlqmWY7y4utokDZ4bShSyg6+cVY943Ap4lr0gtPNkHvvNs4J/ca6IQg6cG9T0tRixSVqsSrLrkXvBO/fNMzal2uzHbP4IRrc58DDZ0ShPaBhhiJWe7O+/8dn3VlFzMMeWc4HuvgienMjNOI0teTbEIvFe3oa0d4NTdqoUIPpj5RbC+6UCKUR1nnJKliu172VeCdwxpMI/aymEcwF3sfXrjbIFy24vc+ddboWXJYEcvtn28iyPzV4idux16NjD9EfuR3s7BqqDLDcvZmd30NIU3nveLsPG3+6P2Xxta3E129bGeVlXz03mPVOGjj37Me++95PrqKSF0Pzxjzl1c1D4ioixYz5gSznv10CsLuVRoatiLBxeCZpK2aVcjFYgvuo+b9JfUZcsWcyt41xSW82kjCnWo9yzXpxXl6XQCdENrVclKMZ6Vks7Xws6KVSy6ERXLDOuWudd23oz7RlRboxgk9dOPP6asM/O84OOesjxQm7Afd4wpsuTMUgopatVdmsroNSm899577MaRGJzOsaW0iZn3SiPXFQRSGqyHpsxF7xIPDw9KBAmeNA7My8L93T37/YHgB6qoo8j9wwPPnryACuV4x+vTI8d8zX44UPJqZBuvqG3QvpsSLZyOm7RKJwHV1iBX0pD6zaoC/DEB7TwKAApTtotZO+cJWz9Ltg1QhchVRL3WS8KIVjyd0NREE5cYowqDO4/3ZxnHzYx5axk4VbgyCF+rfNnmdPNalHyWIt6JqhVdAEHiPMvaNoSkSUPrKV0rymIO+KDrN5e83ZNNtQWNgapVjhP7Lt6rAL7tCTnX7XtaHDNB/jMJsdlspO9qUbngvfY+cZXTPDMOA94Mu50ZADQaXiA6m/HdNm+nfWVhs/oLMYFTJTEl+hVcDJQqrEtB6mv++svn/NMfCbVpXJRWKYjOhhsUTrUk0OZ+vQOxQAmmVNbOcGS8mHd13nR3t6q5w50d4u7So5f61+fHnxEIq+Q1TaJJM2OKZlB/IPjzDO2ZgW7n3/aAJqJG9BbUAUNo9Bo6lJkftl68BtHr3YgYqrUVO7Vt3xNniUfTatjpxbIkwm8McJBNpjJwQU6S/HNjwOXx/xdBtFm27zhT2Puwv4hsaiwbvBE8lHPAZSMnfDmc+1VY+ldlK120+fr6huOjEkeCg3U+8smnP+XFixdcXz8B8VtgiUMyAYRejJ5fv7ZCSpHdbuK0zNzf35NNREEa5hyyMiZvcO35e3gfN7F453QQ3/nIYmSLWis5Vx4eHshZHUV6lTafTnjvWVf1ZY0xsuas8lwpUb3CvbU1NQJuKunVqhjdXXs5CFZ92vlsbhO47ous2EiL890MWQfVS/WsBZaiFWyKo5oVT5PC4p9/RgMeTwthVJH1UhpTGnjvxVPKXGjOMXg1LH44ZkqFtQquFe6P9zx7ds04DVq1owa/tQrH+VGDXdJFuTvsmMbBbM0ScTzQvHBcZp5L49n1Fbtd4McPt1SXePLkGaf7W3LOjNPI0+unBF/A20ImkAZPLSvOMu1xUuYpDlqpSgwyp49a22YIsPU0nY1l9BTbB918rWrQhMpmNa2PqZC5Bqh+/UM0w+VSWEthGCadTeyJYa+ELE/qVVwpReeT7X6rHR7hIqiaw4sPydaSqgyVqhZo2n86C3p4bPZ4s4/W9+rjN6qjqzVeQ/+7CjhL6gAlp3nbqG1e0epAdZoxNAcx4QVDCOZ5Vk3WEDWZW9XhaBwnq2qtogqOblgpFijfaAltwJAhEX3PQOeGS2nkvFCrJ8WBYZgoLvP9ly/58I/+jB+9OvFw0lnWdNhT5BxIEFX1OVztOM3oeWywZGHaTyq24M/kr+YA8dvnEZSNf7lPxBi5urri8y8+Jq+V/T5yOBzUlWWFq6sbclmRhztCCDx79pySK7vpipvrJ9w93PLBBx/wxRdfMI4jKel6evLkCR4VadHxu8C8LADGxUjmPasJ0jYC1Rohq4CISiuqqbiz5GA/Dee9+ILb0nkZzgHmXeouzn/f27SSlg25Cd4xuPNrDuGvWCXaexdg2Tl90Z9P3sYobLLZd50FFkB1zN664S8XBV8N+V7i69qP8tvCcg52uz13d7c8PN5xdXXFs2fPqQXrl+pzyrqy5sw49gvZtiDTbJQmeEccIi+ePeWYM6VU8+e03il5C5beK7u3+/xt32WecaJuLFqpwPXNE7MsSux2I/1M7nY7RHQ2dDftOJ0e4XRiHMc3KuWrqyt+8nHBo/2RwTRQ+8C7ILimVbjCbZezdWGDFftRW+O4rOyuPcO416x6mXFE5vmOl+88x0XHsqysa8EPA0te2Q0oTb44rvcTY/TEVpmXE4/LkcfjAzk30nCFDztiiDx5MvDs+Q3eDdZDFNZ1xUmkNWVMT9Ok4wxzZp+ueJwfCWlk2l1DzZR8ZD+OvHPY4xzM8jGFwGF3xZOdBiOfJshKHDK7ZzwRZ5WdumA4DWbS5+YCiKo1lWa9SUzs2wdwStzwuE1s3IkmTjqs7yAk1CqzMedVM/utd22bj/es9VwFBNFzgLUlzo492vcuVSuW1rQn7WLQz5hX1lk3yGGYFMLeKpqyBdNedfbZwo3TYAhISIFaGs7rfXE6HtkfrrYEYBtxkgL4LdFQprd+bS9Ox3ME1MvUbRwAoWNWmpxg86zaqowmu4cJthjEbo/dwJam85vOpAl7C03sH6LSSZsKVRP1OV1z03tWMQ58SNs5CmHkocG/8q1nHOeVP71deDYeoFVOy8rOC3GrIAPNOXAVlcQK5nvahSjOs7ExKsLifVDhDev3vykwYxUzYvrdep8sy8I4au+55EotmiA/Pj6yriZBirpNPTzcW6JRCOLxAU6PR0ve175hkkvRnqisOofuHLVlxnE0RSttC7hirSUp1OhY1oV8mmF0HPYH/GouNFU1nr2P2tKoFnyjtjD6eZHWqEYc6/u/oPPBIQTm8mD7fDNJw1/u+FoHUcebFWTPsppVMjqoXkxgwSAhy37Ph2Xw9oo/9/0uAuZXkYv08/gtMIxT4uOPP6I2dUo5HA7UIjgXca688Xq9CpUmBoPqJ2q1EWJiGAcQ4XR8VBq40xmy1hQuphpks/1PhRnUPkrPVwtlmxXVxwqtOpBAjAppKkQYFdpF57iUFJM2x5eekGilnyzjl62JH0NiWRZwjmEYcd4zz0dSGm1ou2oPUJRQEHwkJgh5RSrkNdMejxz2jcfTzOv7e4YpMB9fk/wHPD7ecXd/1JGgpfIwL+ATJVfy40zYJ/JysuptpZTKMOzZHfYM6UCMIzGoksxuN+I9xKiZ6jjuaUX7VofDjmkaCd5xLDOlNYZxwLlAzRlaYSkLYpBlCNo++Pz+kSUXhtHjgdyqCQYopI5Voq2JwpUiW6YuAKXo5qx0ESVfSGXc7fV+Md1dHyPrspgUogl8t6ZQv2YqgArTr6VssLn3eg260owzuLcU/VmM0UZKVEDe7lJbW/qZsFGPHgQAmCx59N4eZyMnxlYPmFBI87SkxCicM03aCuK3kZFWq8GLtjFHNWDXnDLi6O+D9b50Taj7iPZ0NUCbTV0tukYN/lZZOMy/pf+xCsUpWSX4PiMqW6DX76GQZPRhC0Y+JWsT9T3FRO/tHOj1tXXlBC+dHVxprgGJVw8nvvnimm9/cc+rOTPnwoubgTWvjFGZwf2adcMH25xoOFKv5Byb8tUloaz3mXvFp+IoaasKey6bYmIaJ+54hXPaglhmnSEdx8nmRiFEFXUZ4sCYBnbTRIqR6D3Fq+yitkqUpOQw1aUYdX58Q8wSMSi0rUFc59CzVCaXSOMErXEqBbdXdj8nnXcW73DSEFnVMq/qKFCKiY8/+YSrpzdaVJRCWRWp6Mx1b3OtAMGlrd/u48/qBnzV8bUOohtm8gYEa3NAdqMvi5pyd3JRzz7ePs4B8s3G8pdBuZfM3/PP9OQDNkiuN+fd3S3H0z273YHdbk+MA3lturA36MHszIxko69xQUxoavSdUsIH9firg9oCOSNzNKnmjqDfvZtxq25Y70F6g86CwWztnHjUdsEGViJRLRUfIs1GAoIPZynCELTvUIp+d7z1l5r1TXoP2pmikQXtrTetWb7ol9XNxnbDvpHVXJgXdeI5zo/gRw77gSHC64eZh9MRJ4GyqkxgKWqxVZaCt2pACMS0Yx92NAJx2KsmqyUgQwo6z+jdRphwOOalAEqaCCY9F0JEmopdQ1AUwDV8H1molRg8MXlODyrL5ietNktTshCiTEgzygKzRAtBkyaxqmBDUbzptvpgvcjLe9igXtfPmzKb9f61ygrte/XNvxNftJpTUknQy4GKi9tMpQXzy8/Sr6/2DlXQvVdrDo8Xve87NCpN+QFVpcIMrsd6hW1TunJOlaqaDfJXcwHqFVVtWjuWmolhp3q44pA8W9p7RqKcCbf3nu75e1+c04b1F89rvNnvVaReqKUZYqWBtRokbG+najrGaem95ZTSlgBbDL1IPs7Jiq53NZsXM3B3BPDCMi9c7xPffHHDpw+Ff/yTRz5MidNxUUu8nrzaK18GxybOjDTOZDzsWnV1J6832dbu6vfQ5chT/5nea0oC9LY/6nkYlFUdoLWySW/WWg2aLWBkxdUStk5g8naepWRctMSlKWifEXLNWy+UBqtUBhFqqLYfzExPrhnTQD6tNCN2iVUcHiMOoiNed3ev2d9cqw/yoPKeiq4lXYfemyavI7rOQxCGdllo/fzj6x1ELYbqRmEUc3RGMxiNcVnZnE42chF64jdKPj8fsv2q3uebgbb3grSp7k2385NPPmYYRg6HA9O43xr3fdRDF6RWQJdVQr/JVdRAN7oUAzGFrR/VpEBz22hDaWqp1KX0fAg4Gx/xNtJRW8ZJBQ9dxzP6QC7Kui1lVZmvqOblg/Vqc8lKZc/q+6i9DE/OK5tykwXbS/JB70+EEEhR1XW0D8xGAGgimlVjELdAwyO1Mq8rSynkUliWI7/2q+8zJLO7E2EIkbxm3Ricp1v3JquenUyMcUeME/NSUOJS1mzTNfvONsNo/dhSKsfjiVZVTKKEfl0dfvAKwzYLFs6TholQ1H1kjJFxSjSrhnyIhKTenAlNQtR/MRJRdrG00iEIu67qsxmcMA5JZSuL4KNm0wovYkmRM0nGPsrRLLN2288AQnAKoenbUGojWz++0fC2GRdz06ntIsmy+7pa/1uFEM4qQV50Y+8VmPe6BpvTgOXqeZYyXLReQvCsa8ZhTOhSrfI7V1zV/jgH87JwNexUpALHvKLJoK5g/acPynnA23yto1Wbn7THdNGGauMsPVmo0tecGRnYvKz4gBjRZMOteoUPJnBxJih1Q3TegKnlDaTMiyaLYmpR3nmT+aucWuP9Z9d897HxD370GueiXjSb08Xgex/OZualVRUXcZgQh2wf1nmPlLq1eRBts8x13tjgWKLQ90YRIa8rpax0glHpUKvA6fSIc8K6nphPuq6VV7FyOum9WXIlZ0WBclaVMG9GAK23i5omu8EpsbI0E863tbMgtLUQmpCXlePpxNNnT3n65An3ubIuqyZ0TgVqgvNKtnRetcad9n8PhyuGlFRW8I3k6aKkEr/VY7n+FZkTbeYE4i2T0pugUteVbFBVDOBqwW2MWaFWrCJSdluvKr8skPYbb1sEF1Xom73Qi6BK4/j4yI9+/Bc8efKUFy9eAM58O+3xQS3JLj05z5/B/Drt8zbx7JwwOiV9+DbgQ8JXJWiEAHHcw3zcKhW111J6fDOINQSVQNS+TrWsFFyI6priPAUDG2PAsyq0Kar5OUwHoOFrsZksKK0wTipKrw72QkjOiAWV+XTPNCTAs642yJ4GYgpUGwHpyYNCQI7sKvctIscHrp8LKV0R/UxZ7vn2uy+4fT0zjXuurwYeXs/46Nhlzxg8cZcoz664evo+h6cvWE9QiiixSQrOReMTalKRosJSJc94Pyrpall5eLxnmkY0K494D0s5Efye5VE3hJgiIY68f3PNIRa+9+wFh+HA5z9u/NDfM6URYqKGQBSFq0hB1Wfs3qLBMO1NK1lVhsYYOc2ZNat+8RAnxDiajbYZd9emgce5YBWe9i+XVdnYVpNSa2UpboPhS51VMjFo1dbveec9026gNMhNiU2KYqgWriZKyp5WEXqgNMI2CmHRCh2Y75u5Gi6DeG+C9t0wWljKipQzORAqTTLjdK3BA1XCWtcV8QNZGlS9lsqQbtta1FGMaNV4BauaSxNoQtf7kSasRUlx9LaJD3ifyFJppdFZp7Up6c0HtQNTreqAF+1Ptyq6Fi2B13aRnXnncUHt8VrJuGCLPyRK01aOhKjs0+bZ10S7Hvn09ce8f33Fd9/b82vPHa+K50CmtkRxjhAVZmhzYESlA1WqIBFa01nupjZzDW1pSctaNTYdy3HNmwCEthYcnuPxkd7qqqWwrjPOOYZBrQ5LWW3f2vH4eI8jUHNhmR9Zy8xxfiSXFbf2PizkshJqINdMV02qtn/WXLbEr12OYHkdxStlJUXPSRxXIeJcRorwcndDxJGBFSFINchEpRtFIPiGW4UYxJCgiix6r7bWlJfg3Bt/4Bw4O7rwyxxf6yCqCbdWZLWcg5s3uK7WQm2rZkhlxTm9uMiZ7dfaeSayH5eMurePy6Hq7fFdHUTapgt7e3dHCJFvfOMbzKcFsYa9Mj/rlvU5d25e9I2gVxjb7/W3hOi5utrz009/Slr2DEEXc6Hiw0wp6/aZulZuh2n6n9Yah2FgXbNBPB4/OvLxka5Z2qSS3MDr16/pSibee8ZJSSB39/d4Z3NbpfD4+Li9fimFZVnsJlSW67JmIzScz90Z+m2WABW8V/1cVz3HCuV4Yumm6rHy7pOntAxfvDpBmkjRM3NHWmbSOLG/umY3Hnjv5n12NxO+VUpuNLFRHxHiqHZMOWfmNeODMkFdGhGnwT6mCQEOV9ekccBFVTfysdKaBt9lXnicjww3ez758S3f3yX8d36F/a98gw/ywj/54Y/ZH3bs95P6WK9KYEkpmTCGQnIuCKXqjGyfZV5LZr/fb/DhWspWcTU59xlbv28wjdrW/T+hY4mtYYlUNb/RYo9tlFpIpmiF6MZZSsHHQcUgnD4/t7r10C8RF5xXBSa03/5VnIKNNBY8uWi7QNVmMq0KwzQqfF+bQcGRXOvW4qhGSvFBK/FaKlIqwbEhO1vbxZ9hYGebckM0+JeLvqapAuuhqrodwBPpYuZnIYpt5rv3TC+IO53M19Zs79trXjGkwDRnrU+MV/OBXBQRUss0z0Mt7Gdh8AN3x4XaGn/7V7/F3/vjTxmud8SmEp4xRgiex+WB3BrORwaXyHVmLV2N7CzErwzsrHCqP8/Sqk2ika06ZN/aNtftnCMvC9Gp00/JmWps7CZOR4liIAyDzYpHhqQiCDGosMg8zzqCh7YldGbV4WPYCIp9L+5TDQ5H9wte15mb6x03N9fctcYpL3zr+TXOmNW9+Djvx+6Mkth9EL15SYt9T4eORLkvv1//ssfXOojq8WbVCOdF0D09nXOMw0i1DWVdLq2KHLif9Qn8UiYubJvJ21CwiIoxPzw8cDrNTOOOD97/kGU+V58d9r2UEdPfBZwNmHe7sMvPUWq1RejY7/e4xbM/7HAVpFbGGHA+kYzZqV+usa4q/9cXiQDeBeKgi8g3ZcfG6PFRJcVKzkhVyzPHwm6XyFkr3NH0VvvcZ5dPa0XJRiVnq24FH8Nmzdb318vZs2AM6Q6zq3yd9ky8cxwF+16RJjM+eL7x/gfc3j0iIRkMu7DMJ55MTzk8e8r+6opApJWVPJ9wsse5vAWgilBaVVhHxHRlI8M4IFRiSNofyoWrJ884XN0QU7DNvJGc4CSxlEy68ozxQBoGlhZ498VzDuOOtiw0qRymUb9z9Nv3cy4g5uTS7DwpIneeLQR9r2CMyt7/3ARU/KY0vxljq4OHBjdn4xq5lg1ibVZF5qLqMzpL7PAxKVGo9FlN1B8UE3mw4OkuVouihHqH1lIwoucG/fYENcZAQ6giRkCr+M7EFlMJQsd5cqkXcPBZxlLZ53V7j7IsjKLqWF1XuPf1+qLRVr1Qda5j2xNaq9S+NFAWcDPxB5zgpW0wcJNqXJOz+g2inAdn/efWjJDUEwRxmyCC3u4CeBNEMUeV1sxFRSvULufgTIhDnMe3uhHOUor8yoc7fu0u8Mef3+LCShoCuEgKI/hGFYeToMYAVpUb0mzjP41xHK3qLLTW+7wXqNrGrWlb77dzA1oVhiGQ4oBDRWPOcqDBCkAx0wlFoJT/EBiGUZnILlqyJUjfB3xAb2BFCM+G8EnPNSDeEVplGBL7ceTBO+Yys7veqThN62NFX57AlY1Ip+YXcpEIsbny9HtH1825K+x/5vW+6vhaB1HZeHVniau3g9s2mO3OpJFuGNsfoxIH58DaT+4bgVTOOf7bxKIOWa1rZp5Vem+aJoWWaqYzH/vrXQogAxfv2y2pzj/V9zGYQxoxBtbHrLNg1dxeUidNof0XzhWed28GfJyOO+gIhJKDcqsaVGOglUJtukF3iK9n3WfBCmM4OuhEqHDxnVSm7gyB9+f039daEM6jDSohVoBk51lhmSpKmBjHPbshQQu8un9gISJVq8kpJA7Xz9kNO1IfU/JwFXXB+z5i4zXJ8SGoILyLtKbD8C5EazlpllyaMIyTeZ4azzl6ooOWHZWixLExkkRVj1waaGtlfvXAJ7d3HMaRYUwbe1TJQfB4PCkLOkSFX1tVkfRaz+vbn91XWtONR7aq0138AYw0pPHLmRCXBqDa+kaJVSBNg9rFa2Bm3Odr5c5JRzVExqo3RT9l24BzbUhpdE3aPrAvAuK01yetjy8JNatZ98YStbZGl3jr4hEb6eetFougn8G8YNjwIunBXU+HNO3fOuc2F5oevPt2KwoGWP9eH+Pd5Rq3a/HGjuO2f/Xg0RN2/f+5v7p9GNcnXb3BzPYrZ5ZuFmS9VwZzbmqwrUtGkAC/+cE7/MFHP2UdGjl5XG0M4w5cb8n0MZyzHrgiPdWC8VlARczGTe0K2waBD+OgfWxLCpTDECmlcXU1GglMk56rqyvu7j9Xxyebwe8eyMM4UkshxsQ07ViWrIIV/Zs7sSrXm5FC3Sp5Z+uktyG6E0/wqnQkIqytcnN9sBGMNymiHUE8w/vVkvi+N9m18R0BvLjW/b44f9Jf+vhaB1EuTpr+u2cPYsFImaFdBgvABzWAjW7aXsbJeUFeLpqt3L+4WH0TePsxIsLjwxFpqn4zDAPLohJYKqt21pUNIbw5u/klsEL/Tucgqu+RhshjVji1uQ5VvRnYxUQPQvCbdNdl0K+1E0QcOJspE1StBXVTiR7q2vBOz1+7UE9SKb94pq1fwLQ9M+83fVeF6lT21lpP+t4gem2Qupmlq8JOYy2F/XRg9BOnx5XXj0cIEcKII/D86gn7m6eWwVudER3v7HcgTQe0gwb9MUWKaEUfY9De2LqoGhzdMECh0WizjxStqkKIylRez9lvcOBqIzVh9Sr4sD4UfvLFPc92I+M0KSRatQrDi/XXDLb20HLVLL0nOuLAB6SqX6n0LBkNgnbHb/er4LbHiCV+3mDOXM9atoGLJMc2VBX61ipALhKtTrzZEj9/ltLbYHiDOlV7wMYqUPciVdczclPr1Zy6aXpjOl++zvYVt8TgLJXnQtgqDmVhombiPlgRIdvjRWTjALRm8LhjG+vQc6ECCW1bM9uWYUnbBurSxT96RFW27zmRrPU82O+cep4KxoC3a+k8m49nlU4u0kBWjJVaq8o5TgFWgVY0oK80Vif86nvvs3OV2uBUGs1ldv4p4gq+i+BLRS6UeM5VF+e1efE9QuxIWLOg2m0Uz3tUjJFSVYkI7y0J8ex2O25fN1JMRB/PakJgYvYqEhLNs9gbw1yLGWFIOnITvIOGkeuaztZ6I6nB+Vyb3KEiLsLTJ9cbM7qjCv27XibutSM69FAhG1B3RiTO1/8yJH8VmfTLjq93EHW8kXVA7//1zFgXXh/LGMcB5wOv7x7PAUcuA9WW237V222B1oeL+TDvefXqFcuy8OzZM6N0W5/Erlr3YuxQMFwE4O0fbBtpd4LoP2tGFHh2c8NnP/qC5ANx6nBp3SDV/vo+66hFd185zwMqhBLDsn0GEcdyHBjDxOpWmsscdgOv5yO7qz3H460q6gyj2patmTQN5nTPtlB6v8nbfFgPrsFEqu0bspk/8+ZNH6JHWsAFRytACDwus07peEcYK/dzJgXh5sk7XF89ZR88ZUgwo3ZgWjfyzs01n5V7huGAuEhzNpNo5z6mhMCGHtwcdpswxDie1aMUAtO5XoUXF5J3JOeIDapvDGTcFPgsL7y6P3Isgd98ec04DGZCoIzPkAJPnzyjVIuV3uHHnsydWdYdDlXYsFd3XeCdt/5cjmb1ERdPp9H1JKvvFl0/tTWtJH0I6gZ0EWiCc5TWV4PNa4purF0mT6v7oIhI0ec20QqwCzL0kq0Z09cPie6E0qgc5yOg1Qvem1lz3RABrWTPxD/Z1ra3KhKK2NiG67J4vVrWNg3OUcpi9xr4vouijF+N/2LJW4c3rUr3eu9mg1iRPjpiiNZF2eoQXFTlpF6BO6csXCdGgmwKbzfA+6qOMXbpQgsMoZKDQp1NOhzveHIz8uvvHvjT1zMPq95PN0sk7Ru5FWgZ/ICIh6Bev4Jer2CEy5zfEmKp+XwOnELoq6kz9X5obarqVERHn2pTsmOrhWWeiXglcSJIrazLgozCvMzU1hinkbUs9j7uHLRt3Q152PqywXuQSit6jvCO4AZyUzOMvC7kZaUJPHv6lLyei5BzJHRgkLOFYXa7nSXHHTXoj3tzp3fI+Xr2ffiXPL7WQdSZD4hmVz9bzfWNZrfb2Yxl4LRk/Mefv6FL+2YQ/bI3OlekgA06q/7smhfuHx6oRXjvvfe2Dar3+tq2QZ0zpf4ab8CsFz/rGeHlzzv8MIyRx3U2YfJeCYJQ39hsW2usSwMzGxexKsh71QxtJjphzf5ctYLIJofmY+DheGR//VQHmkMgRb9BjD2laLWo2bcFxd7MV2s3jETSdBYzBFrrpAcbcA5nebKeRVdphLbnNL8mRM80eVzLfHL3GQ/zwHc/+BYvX36LNAy8/vwTQtPRldM8s0ued2/2PH/nBr8mHgrMRWilMZ9W9S+0yxmCZxwTtQqn+ZGOaih01XCmhgOY3+GKv5oIjwviHNmDlIV3X97QvPD5vPD5cSE2+M6771oFHBhCYAoDq2SGMZBcoNZi7MOdZvj+vBSL2Up1w+1+jyhZS89/nw8838eVJqo2BBDTiA9WuTr091YRttK23j7Oc1rWs++nCLUU+mzkBqOibNtWzoQ41wfVg/Q41EM57UKJqpniUTXR9b5qQ0ibm03/PmUbb1BxhFrKRvlZ88rktR++lrqNRimUGLZ2SLWkwhvkca6wDVh1JpVoECxo5eyCwotdi9g51Jr1slqyn3vnEJ2boTVwDULr7i1ug8ArJglohAxBn6e8gmCVpIrzn3JFVkeWjCPjHSQmfvTZp/zGN97hdX3Fj18/MOfK3NQoQRrk2lhFW1S9n9nvm9C/uevbmBIch2HY9j61WBs5Ho9W1blNGAKnVWqMUTWkU+TJk6e0Hzau9geGIRFTYNyreMFumjbOR4eN04UspLPXW9d1W2/eIFvFS5w5zjScywSU4bucZnKuxDTyzs01zRx/tlYYl7u3o6tN7P9/7P1LrG3plt8F/r7XnGutvfd5RMSNiLx5MxPbmXZm4kyQUir5qqpRQoBlWZYlskHLdsMty9DAErIs0UAgA6Jj0TBuWYiOhQQSjcIggylBUbaz7HI5VSlANjbgfPjGfUWcc/bea605v8eoxhjfN+c+ceLeuEXRCGetq3PjnP1Yjzm/7xtj/Md//P+n2y1k2j3ffk7Pv07sfPocX/7xlQ6iHcrq1c4+a+8BT+EHdVyvTenyzvHkpuqTWd69C3jjdfrXRAkS65o5Ho88PDywrGpw/d7LZzoL1aElg1mySZt1rcb+XJ1k1GEj3DaftT00m2+iM23ew+l0wEWzFmujw0NwfowreO/QoqvRWhmQVD8ECKpi4ugbyzNNCRdFB7qbV0hRdmpENkR+Op1Y88rRHcdYUTbR8p4EtNbUKs07Qwg2ecM+ctEp5L1arbu+awiBXIVcHXmptIOnece3X7/hZ37il7g5vaDVynK90FzkII5rrRxOkfdfnPiJ9+94eHy0OTqdTUtJZx9jVBH6kguuYK+nTj/92vngcUVnxqLf/Dhdmlil4JLe4+Iat2nm2fvPWAp8+uqex/tHfvLFc37sg/e4ro9gYySlCZnGjBEslHZDadozH6ghYn6uusF7glFK2SpDS9Ra1ey51KrJyW79FvNuxOBSkYY4GTOXzuBIEQ3OxdwwnPWB03ygrkrewHkTboAmXePYQRXEawLarGXS91+tdfRDm6jY+3Q8aGVadEDfeVVFKjbnC87aH9U0cFWYoFQd/yHOSp4RPeRd25Cafm621ihZR6ZUBcj62s5jUkcWKIY8GHvYU2dLZYP/umNSrUgrOgrDU45D///amglwyLiXTrSSl6IVeIcoPbrHqhR7C45FArRliBd04Yr7RUhx4sde3HJeC9/67J6WEm/uzzQCi3jOq4mlVNQlpxrT32sFDG6cOZpg51HAiSj8X3IXgNE1cb1etZcPnC+PiKh5w5s3ryhlJRcVuverWprpyJXw2Jn+rbGuK1fnR9IsIsONqiOEyhC23nFrFDPrmFeFhbNciTUomck5buaJUldkrO3e4XcG7+s9KU21c0VvyS5uyNNo2fsJO2Tzabf1Bz++0kF010zZVZRuZBSaZQWC18Z3ECElpct3KvfIUlsb1Z9zvYp9C2ffvcbj4yMijWmaiClab8nIPbsb0AkdeiBs/dQnfUS2oN/64el7daabQIzSfTweWdeFdV1wzhRSzMlBk4DtfauqUO/xmOi7t2GEJparK3v2Zp4ICMl7fEzMQTPV4/EAdAsk9QZcV50xjCnhvKPUQkxG4Wg6L9hZgvvPOxw4LNDuZ3P7+21VhQl81LGAOR5AEpd15Vocz47PRyWJ83b2ZqbDzOE4czwcOMTEm8cL820kGrQ5gkDtFlEbFO/MDUPNpfUaqYKQG9qnIEh1pGpJkteK9fYw47zj8XLhshbmw8zP/Y6vQ83a//MRFyK+OSINV0wdJQS8i1rvdhm6UeVrtaCVpo2kwOauhZF7nLNeq/VDd70kFdrYxApaq6QpElOkZZ0XrLXiox5uDw+PtNa4ub0hpkSpbaAoOkvdB/VtxKZBEyVDOY9J9FnFWRs+yMjwezLQmrPel9D9dTtzuIkMx59aK+KVJSutYY1XY7pua6ajGF2ZyO16XIN5aUFzI9Zs+3MjEHYI1kZWbL82xOTk+v631ooIUsvmlCJaW/fRIdDZR31bmkSMdo1VtFRNYqqrBAkqkuIS2Z1JLpjWtGP1lTkcEHfm5WnixWHiH65wLhl3XqE2avM0AkIxhSdb2xa8lHFfNNBYMMtrHmfZkzMOtKJvQslFnXSkj7do0MwmyNKFKnpCUYuKt+yfc//f/hARXZ+mRez6/LBVxcHpONPkE61UnSG1kStH4+Y4G6qxFSCIjHO/P/p10A+1nbEb+jsOegSve2dkFnzpx1c/iL4V6HpfzrEPJlFVXWrZVYDV+oNqYttaHfNF7DaaBqYeCPsmVxujw+FASHoJlcgTcFKtOtbf7kaxsAcf2AWQHkh3n+Htz2kwm2Zhs/YkzGFeBB0O9xuBCBifpc+7DUIQIKUfft20tup4TFNlJO91U4NasGGkgFoL06Qi0WCC1j6o0a8N8qt6n4xbI2yBdLwn1w+u7Rrr9fEdhNu58ShcdV4qpQTmOJFF58m8qF2WSxDTxM08cZoiPmhQ8d4x2chGT3JqLeYp6ugC6xokeqKzMYa3nvWW4PiqEoEhRebjTDrOXC8Lb+7PuAbvvbjlxz96yXq+4pNX6A5n13FnvI1XLWIJ2zUY165X7gqXl1pxJp24LXsZ/U1NTGywfh9E7GDqyj/RvtdnMts4QJT0ooG1oRareiAKvarq+Oe2nmrT/qFvjNfopga4Nt6Lzh+j+6/W0ZeWupknd7UklY2TkYDVVvFisigdyXEdhjSMcjAwt7lBO3E3XVzrQDxdcX2NbmiUyEai2l9roSMpmNcvW1ujz4X2AGtnjCI9PRF5eoBXGz1q9p69Jc6ISWNWhln4wQVWEW4OkReniUN0nK8rU14praG+rx7IGqDttfGaBNbanrg7OR9sD+t9HSRG9nwFhVW9ExPT6MIMmhyUWochPAgxJa7LQvBKwtM9FFA5YRkMXQ2gEUJTswUYyROiQT4YKhe9zqNKlDE36lLgeJyorx/1DDMFJzHFJ9lfa4PPkX5DLYnqZekIpv3sZWC6v216osoP7wtZdTf2AVCzX83AnYN1WXh489r8LO2gEesntYxznQq+g1qdR22EKs5r5vPw8MjzFy84nW5pUrlcL8QxiN4G0aEPMjtUZ3OXF7H1YUFkc43o7zs4T+nP4aJ6g+J5niIHk5GrYmzJLJTQSFUJFF2Jpg/WY5BfLY2UPMuyEGMkOodHxSq2fqpez1JVGm+9ngeT7/x4r24uDw8mVA5YctLKjCPivUnpVZUhw6mUV64qbNCaMn57RaMbWPV8S4HWzHuwCcd0YKn3LJK5Lpm26BpXoXQlGM3BwekGKTPPo+f51PCxkk5HfIOTV//E5hqHQ+DxYdH7afJgTSrNN6agRAoMwhUZZZLdI7ONa8Lz44Gbu5l0k1hb5rPv3vPw5syPP3/OP/HeS0KCNSa8b0aMKYh4YhRqrFD1XopPhOb1+ldLfpxad/UAqUe8jWFIG7CpoJJqOWtrQSUXNQDoCJElMm5LAkpR8kiH2AmBa1a4/+75M2rR/na+LkzTNDRsveudNXaMYXveVpGwBS3nde2GsK2/akiGa9oPFWPl9v6hs+CrmqsAgQiWgBjhCLTSMi1XQGdhfRhBtN8tHxx4qD2EOqGisDS7qn9UoD0Wo7PEWwjtZ6ooVIwlxKIeol3wvnUOlXcqftCfQWSwSulok/dj7El8xIn2TRvCRCG2mewa+KaqWi5R3cq5VV5OE+/fzfzYy8QnD5lDylyqtlpCUzKaJhxaJIidEaoRC1L0wwY/Wa+/V/J6f2vNhKBymT44ayE0punIZXnAuUqMkcNR1ckO05Hz5QFBON2c+OzVKzV2KLpetVetDktp1jlTvQSOUhdS2uZMOzrkQ39fKkwRpplGRNaMAOH2htNh4tWnbzaESARxE0EH5xFjYLtSkBDHGfyWyB97gZD+nXcWMT/k8dUOolqy/OAfcY7kIufrhfv7ex4fH3nx4gWvXr2h5EwIapBcy6IQkwuIU+kxaSuRO5o8Ms+3XC8L/+iT3+Tjj7/O3bNnnM9n0121LNgyX1rTofXW3wMW52U7gEbGuqt0ZWMMd6WU3u/pHzfNkZQS1+VC0phNZ2dWMVKIsezUCi2weUhWpCw2eG3Vd9hIPSoWrq9basYx0aqquYARRKQiZCCDywgruVUmNHssvd9p82mSy/Bv7H2RTnDS4JxZlsJQwhGHNId4JeFcL5k4qRoKvlHaqqQZn1T1JE1cK5wO8Ow0McfIWoXnx5k1ZyQ4bp3jVB2fsHJ8fuL8cEGqI8TIMR04L2fWIjinEoo6F96JWgpNxxjxMfC1F3ecXt7iWiM/XLi8vrDkhZ/++gt+7y/8LN/4sa9x/vv/KzMVbp4b+rHQgh6T0c20GSKOqXnuW0G9Em3sBPAxUnOmihCmCS9wXfOOfKTJYpwmfJy0cgqJQDULrqfrq/+79517C6HmMiosdv+FXuEGukbxBnjJqFLVyH1v16cHtlYIViGyX+c6RqLzzTZ25RVi1kpy337o1bQbsE6TbuIsmj+Pb3WGvlVcbldJtg3ma9JUy5cNHRnaQgKFNhJdx/bSvZrVJGsjYI39yTZio8+pX1Md3zAq7/5Cstvnzc6EnkhQCpmmXpgORVrIBA48XrUP+GMfPOf+O403OVPKFVcDRWYIE3i3jQ3t3tP+mnrXldNWtB+vrYV1PavYglfRkVaVme5jop4F5wKH+cTp+IzrJXM63lq/unJ3dzcInLoOHMfjYbRLUpzNxciukFdWvwbR7aK7gK1zZ31oON4cuF4uIMLzuzsdJTIUZN8HdduC2JA3b65Io0LdJ8a7v++u0VOU7Ic/vrwsA/AX/sJf4Bd/8Rd59uwZz54945vf/Cb/xX/xX4zvX69X/uSf/JO8//773N7e8su//Mt8+9vffvIcv/7rv84f/IN/kNPpxIcffsi/+q/+qz+STuGTx4Db3v2nl+m5aPZ6Op14//33hw1Od5EoJSNB2WJaFAa8mxE5UsjcHN/n1aff4vWr3+Cjr33Iy5cvOT8+7qC0tlUuA/raHu/4kn59d6OcQYnDhQUZPR+dk9JAo2MsbvQAD0cdNWFAqSqbFqf0RHS/B4J5ngnR27icmG1W2UQBgsIxgqdJpskKThV1m6iaiR68HucjjjAYolsCoLB58H6T27JqIgSH20mztVHtqacgDmKcWEolzQfWktVHsKi59mVZEeeYDgcOpxMu6nv48Oi5mRIhJhyevFxUrk8aETgdDvyuD75GaJXjcWJOAWrl9fWRQzpwuDkwn2bSHEiTYzpGbp7NPH/vlg8+fM5HP/aCr//4B9S08Ob++3zne9/mk+9/n6UIMxPLkrkGB9/4kNs/8H/i9PGHuDgTDycOt8843LzgcHoB04kQT7Rw4BwS8+moLQGvQcebNViaZ6b5iA8JfCDNB9I8EdNECGpLF+PEdJi1qrYDuxOzButxKMH40dOOUWf7tnGwEWZGXxgUSm0mnDGIIchwKIoxWttTK0bvghL40IquE516D1wlC3XdqI9m1FOTDkf2P8rQVlEQ2fb0ri1h4KTBt9t+qlZvoCATzVoag47LVknvzxFxbx2cu/36tsNJD9plvB5PrMew6r/Dz/1F2/hZHdcZ/qRs38ujKle/VssXuF4eWFrBpcB7tyd+70//DhYJJuAORRwutCcjHc6pNKdyP5Qc5VFBdg2e+lmCTxyPJ52hr5lpmpnSZPfPcXNzB0QQ0wFukDOkaaK1xrJcyVklD9es8qNrXrler+RlZb1m8jVzva6cH89cHh45ny+cz1culyvX65Xr5cp1WVgXlQ29Lheuy4XLcqHYGJqIcHdzJOI+FwDlrSgoRsbUEUCx5EzeOod1zQ++y5Z6/O8H537jG9/g3/l3/h1+5md+BhHhP/wP/0P+8B/+w/ydv/N3+Cf/yX+Sf+Vf+Vf4y3/5L/Mf/8f/Mc+fP+df+pf+Jf6Ff+Ff4K/9tb8GKBz2B//gH+Tjjz/mr//1v863vvUt/ugf/aOklPi3/q1/60d5K+9+vL0RwA7zSHEaANI0cfne90eF55w2sZskJn8CuYArVBP6Ppxe8J3v/Drr+aKi53dagXZm6tjYrWP8reehu1uyHQBf/N41DfPe8DKDm3tfs7VKqxpI53lmWRbN7mLCB5UpU0aiZntq9RXMsmrTBq61DhcMN3pmYupKbch3jZ6TQa49QKaUjJwTCU71SUut5stnn7MHRukfzZSDuiWX3autLyw4ZyMX0sBrhbKslUAhzQ7xkcdzwcWJaVYylwtq3lzKlQ+OJ6akh10kUGNXWFK4rzmhrlc+PN3wellYg1BwpNy4rgveEqIYAz5EpeanQB8+z3nler1w/3hBlkpwiTTdKPHCB75fF37t//33+PST7/M7f+Yn+fD5BxzktW1O7ZlPEbyNiTSHitFntY3Chs9dCORlGWMavfLBrO70cG0jCdnrLnfyi9+dFFoQOoPJrB3UsEO00Zm1PRH0YZ9Xmzi46dgqGcQbrGxPZMlfc33Z7nvw7OY2secWTM1+rIWhrISKdLRW6Eb0BrXoZ+72bPbexJ5jv6tGm6ZfO1tsY+8Z5Nw6sckqI+tMbhWsPJ3lHn93xuBtQgd/9fe3z+CdiUw4/U6lG4RvLPVR/Xs/iFMdkq69PhZ93lLVOrE0I3CVzOnOEVtjMWQeJ8R3jM1V00zeTwbsE1hn0HbOeSBnypxWNrEfBLfNBSav2fgFXSBFPY27QwwGedeymr+p9Yi90xnwKorUjVGcjnR7mwV2vYWJ8yZOY5rEd8dp8EzGx+znvpOx3/rnTH63G94uZHruOGaEezbGj/T4kYLoH/pDf+jJv//sn/2z/IW/8Bf4lV/5Fb7xjW/wF//iX+Qv/aW/xD/zz/wzAPwH/8F/wM/93M/xK7/yK/y+3/f7+C//y/+S/+F/+B/4q3/1r/LRRx/xT//T/zT/5r/5b/Kn//Sf5l//1/91pml65+suy6IGz/Z48+bNl37PvVrsfxwMxwwxCrjetEl7ZyJ0hZToZ5brmZwvzPOJ0/EWsd8PVv3Jk0W7ZbsGGiG7Q+VttloPTP2xJ0V1dq59il1wEhWNcCoYME0zkisESLYh6AHQe9I0kW2koJM/RnB2m9pNSjOlNNO01Xc/TQcNlkFZwK0K8zTreIiDENXUtrY6yDrCNhzf37t9OKXgj7GeLbj2Q855x4izLrDmlSkYTBg0e0/TTIrJDmSHiGf2jbs54oIeqNE5WtS11NC5QWesxURQ8lRwo1JWFmjBGZrQqlAoBg3K2OWlFNoqlEWQIIQIbvaU3Fhr4Hvff2T57MLl1ZnX/8RPEi6veXF3x83NLSHNNDLRgzQdVwqGOFhRMg4P71VqsWfI/Xp10kjHWjr64XsbQfTw2hDQvs62YDDy7X0C2XaznxaUXO84icK3TlQWciOFGWPXaYIyzATovcn9AWlJobeAbcF5IEn9ELP90n+3E582MQiT7nMCYhXoLiA9STx3KFUHiMRte6z/fIdeocOcb+1lYXy2cTE7fGsfsJt89+ur37cr2Ct9O7D390KfzmuV5NyYSaY3aE3Utt/x1tRGrLbCsxleHBzfLZ61at9/8okO1ff7uSFRdq1wY85dE+nOoq8jEDpTDevm6s5h8qWKZpVciFErPNdHwpwjBjX3UOk+TbK8V/RJK0H9nYaMdWaX2ICC7d5sCIYmVmvJNFc5HhIBt6GzbgC6u3NVn1jXih/36YvOXf22vr8NOPjykfRHgnP3j1or/9F/9B/x+PjIN7/5Tf723/7b5Jz5Z//Zf3b8zM/+7M/ykz/5k/yNv/E3APgbf+Nv8Au/8At89NFH42d+/+///bx584b//r//77/wtf7tf/vf5vnz5+PPT/zETwDvru72C7T3YfaHettlg91sGhyJRGOhNk+tTmHdJrz6/m9yd/ucl+9/yHy65Xy5DAhXX88upMGh4yByWy9l/3jXzXkSYGEcBm8H3/7c8+FAioF5ShwmhXNT0F7plBIpRf1aShzmmeM8czwcOR6OTNNs8J7CtipReGCaZrwPA+5z3nM4PMO5iRgPeJ8opXE4HCnrikPZrjrWUzboGehgW88QOxkGLHt1nYXsiD5aILWjxyBGQY2a9dDQr59uZ9I8mfGwG/OSH9xMTMmPm9Grw9Yqa21cc+WSC6XB/eVKbkX7w63iXOP2dOQwHYghgTjKWjk/LNy/fuT+1YXH+5XrpVGy55hm0mGi+cY1X6kO1pyZiMTplgdm/l9/77f47/5vf4v/+9/+Nf7n3/gW5zUz3Z0oaIXtvMPTSFX9Mz16UGjQUpeOFKMe1VLNnKAjvio3GLwjBvfke44dIW6sJnmyBgdp7i2048keYhv1wSlTt5OM+qru+6ond3tXHi2iN5h4H7hVfF5bAwAu2IiPExpNExo7TAV73sESFsYsqn1K142V3RbY3Hjvtuf7WqRXh3Ytdldmn/KJXQM9VJ1V/8aytQ8y4vCoehlIwOAxPKmqNtbw29eb/dcNkfHBq4mDjYLUBtLsPTkhHRs//mJinhLiPeIhuLR9CHtvXdPWOZXe88FbS0aDRvcuds5RCzgXxrhazsVmzoNVp12yNA9VI+/72oNos/qboL2Aa+j2Vg7G3sGqr52ugd6vlSbsdROQd57LdSGXzPGgVoa9dTUq+/6x3Q45a8169G8HzO1GjyRPGMnyPhH5Mo8fmVj0a7/2a3zzm9/ker1ye3vLf/qf/qf8/M//PL/6q7/KNE28ePHiyc9/9NFHfPLJJwB88sknTwJo/37/3hc9/syf+TP8qT/1p8a/37x5MwLpeHwuWvVFNJGzWpH1i90ztG1GEJxcWAvE6ciBGy73n/I//y9/jZ/6iZ/jw6/9DK+v3+O8PJBCGptApbM23Umt5MwDsbXx3E/e1v6GOvfkBveA3t/fpq9rox9OSTl3tyd+6x9+i3WtPH/2guv5jJtmllpGVtQX0jjY7GvKWZAxy9kzVRVNWNUWzPR250Mi24ZpJbKuKy+O7yPiadXhYyTGmXV9GM+1nxFLKbAs6gtYSuKQbsZnVQJDHZq6nfiSYiTFA/fn16ToWM6Zmxu4fX7ix378azQKyu/Qw6TIys98+AElbDq92cH5fCZNgVWcwoVFEN94TI4mXlmMTVippDXjQrAq3I/quN+T7Tp5ci6E6NGk27FeGxIch3JFfKSlmfmjbyDtyieffMLp00duP3jgvQ9fEI0VHI8nEB33cE5Y85XD4QSC2dc5lPih/22tUXMmOA2ytamIQjew7ofNXrFqHAaGuOwPhd5llAa55a1nWVUsBB9t/KbPTKJBrpVtqwVNNNUEXI8RdQ1RMkhr6iwzepqCiTts87ljRMX1arSNMZ0nspA79mu1997XubfRIRlkJ/3Z1lGlHtDGOSkDMt22obJ7x8/a3tySwk4k6hUmwCbQMT5DRwxGxSn9quDpFf62PzsRTyu+TarOodVc6PwEQVWdinqFrrXw5v6eH39xw2+9zpxzptLUvSjnHcpkXrLL8tZeL8oDaQ4i1Ja5Lhd0vMVxuVztLutZcLlc7NpVcrnw5v4V85w4nx+5XhdaUxlAlQ5cbBpC18LlcqYbf3vMM7RU8CqAr0FYg2EAZT07hcybdzQ7Q9dSmWbP85uTUsT9F9R/sv2lz4lulSjj3jEg/l2S9//l40cOor/n9/wefvVXf5XXr1/zn/wn/wl/7I/9Mf7b//a//d/wFn74Y55n5nn+0j8/ssqmBIV95ehD4HA4kPNZh4mjBlp3vOU2zVzOV37jN3+VV5/9Q57f3JHikTePn5LF4TiqeH3YMt6ebdWKqeHUJ5Xk597b7uuaObknX9uCkLLX9qQraQJVeHZ7x2+17yiUB9AqNS86FtMZimyBCWdybqYg45HR46iY8IRvrMuFaUoQPMtVN875cgbfcF5Yr4vCvGkmr40okegPtPbGWMpuuLn0bNXbuIgIO/1cdP4sqFjAxpQ0INEqi5wL0XmeP3vB1z74gN/89mtyXphunxstvvLeaeJm8lwQ7bmKo1aQVsAlpsNMLcK6LLyRQm4OKdWqYM9M4ipXgmz3wBu0g10bhAGvy5JZayYjNByH4rhcLny/ZaZ5JkwzNb+mcuF/+fv/Ey9vHXl9n/rwGpaKe/4cqYI0j3hPwHG5fh/BK0xt+r6dOanL4amcY+9RiYjOzgk7SzIZ7OuhGNTaGILva0IDrjMJSBvFaQrrhRRVnN7mSWtVcfMQzN6sWWVWRZWv3upDabu07uaWbXjE0AfvVD0s1/bU2MAqi1IENzlz8lErbUVMt0MP33BOtXjFdUlDBgSN/bs/NhCctyqTLZF+F59iH3ye/Fu6xFQPnSiA60fMHr+jn81QLydPE117z72CrTVTXadXCcSgc+zSaJKBQgyB2gJLrnz07MC1NP7Rm4Xbm2f6/tz2uiHoYVVrpbSmLYv+yUOv7J+KFniv8pytVfMJVcOM0+mgxCVsJt4FpnSgtRWhKp/Ae7VNc45StMiIMWhfl16g72ZTLcMSEbxU1mL6vFKRlmlOmerrmpnvTjx7fkPOhU2cHktgtIe7J7MVSyie3tONC9KXgljrTUUz+uMttOAHPH7kIDpNEz/90z8NwC/90i/xt/7W3+Lf+/f+Pf7Ff/FfZF1XXr169aQa/fa3v83HH38MwMcff8zf/Jt/88nzdfZu/5kf5eFwtq/2WWXvWciTf/vgCc1uckp0+ACx3ks6UPLK//hr/x1eFj548QEv3/86a2jM7opvgQQ078llM7vuxBxv8OG4ubup0HfBshsjjFEx7h+6AeLu331IvnB7c2OQSCOlwDxNrE0UJqUhrkOnDVfK8I2sNnxc60InOYioL+RyEcq6ELzQKpzP90ynZyxLIU3eHE9WluWKD8KyPiBuwgftl5U1A4yEZT/SgvVql6uae/sOzbWG2M90Y+7aKxQ/EYPwtZfPOR4nclnxXs19q81RHqbAh88OKj2Hpzo9cINPhAj4qMy+Vrk6DZxhhaEoKrCgkK8vu01JI+dlVFMW2rUCXKuSmpzOF7+uBS8ewom1glsWJBRCnvnZ3/t7+Jmf/V18+OM/jn/xEpkuMCUck5J7WoO88N4HX7OKXFVv8vWqYyveDmrniWniulx1hMGpfV3OWZNLt401dIcgvabKhm3SxkD7OFTsgB39KfvTA0qzUZEQNKGpVTZ5QLunYKowUozJ22ci34KHnVqzBY+OIezIUM77sXd6L+4pWDNwU32f9B6tIybz0KxmRm5BSQOqfhbnLaliS1xHoLWTYpSO/T25Pp/txtrsX9ub3Ot1stDcf9Z1vFis9tzmfcWg6Z7A9qDaeRkdlcHkHhxqkaZF8EQIAqJi7EUKzlWez41nB893zonmgin5uFHJxhjBhGCaoXMNkOYIoRHCRDBRg1oXa+0cyPlKk4Z3E4fDDQiD3d2F6qdpIpdELYoApCninc2Zem/ojiVf11W1gJNHciVNyv7to7ta4wfEN9udandYXeDm5ga8J6bIs7tnmmyIEA0B0Bup7qzd0LL3z33fQ/2eC+Pve85Jax0YlN19/XKP/81zokpxXvilX/olUkr81//1f80v//IvA/B3/+7f5dd//df55je/CcA3v/lN/uyf/bN85zvf4cMPPwTgv/qv/iuePXvGz//8z//Ir92hGbBrOfodG3Nv9DB2MKlClowMCHFMaebv/0+/xutX3+Ojjz7i5XvfIE63XMsjIsZYpVcG+4cbwVBs83yZJMb1CPaO4NoD8zZ+YJ8XlRWck8cFFYj2IUJKTBUq3QFDH60t+Jh0DtYyc5UBSxrwRSEnlxJj9MAntH8hTGnisd3TJNBwiCuI17GGKoWG+gLqLCU2lN9nA3WOcLPJEtPh7GxKnYtssjvcrefWNV5vD4n5eCBEp5UlnlwsqElj9p4XpxvWtmym1dgt95FSCw/XwqU2VgPiylo3iNKrY8Z5KQSpfZXooW8zlM1YhcF7PIFMJQLRe5PcC+rD2oTj7Li7jbx878DLm69x97UjH3/wgufP7xA/4WePJI8sF9SpZwKficEjRUcE1IS6aEBB6DZQWomoXZQTJdvUUmCedj24HhDGVdhg9maEI2tj9DUXvCVb3umYjFWStWbW6wUQUprHOu2QsVYUfc7PiD9ssnq1dZhMySGbOpTNRu4IW7uTTZ/PqVBAB1M1KZwsOKqmruCZ0g5etUpOdonbeGoPVA1agY4gPZ2n3W/ZwZx13fGk0o0ets7r/hAe9eSosHqFq9XmhrB4qxRbnzkdfT1PIGpQwQ9BCo/q6yrpztnzQCCwCkwBZu/UZcYFastDxELE4UNQIX9rL3kfrMRo1KYtF4XOFWpXy7+AZGX2pjngvDkZoeIJUi+EMGk7SxzOaRgJQZm7rc0jeHUonqb3vPV2vEHweqk0GJbSNGEMDqRxLSqQ4ERFFGIM3NycdCQPdkejTTBYTemMgOdiUJLTLljKqH7d9nd2e22c5U+Lmh/0+JGC6J/5M3+GP/AH/gA/+ZM/yf39PX/pL/0l/pv/5r/hr/yVv8Lz58/543/8j/On/tSf4r333uPZs2f8y//yv8w3v/lNft/v+30A/PP//D/Pz//8z/NH/sgf4d/9d/9dPvnkE/61f+1f40/+yT/5I8G147GtXfbRTWT39b5X7Ye9VzKM7/0VEZw4phD45Fu/zvF0y4v3fozT3Yecr4sqsjT1TRTbEFu/hv3+Hy+or/dUl/KL3v6AEHfZ7l6MXYOUGETsqQJzVG3ZbjjbfODoA6tkcJ7OVyulMc1qqOutmZ+iJ8dAWbMe2KjgeRWIVYjTrAeGTxznA6+cOcP7gPjKNN/qtfS6QcXYgiH2WUILkl43iiPSpOAdzNO8HXbOWRDb7ltn5jbnaC0zhwOEgAveBBBUWL05x+wbN9FxczhwOV/woeFEO0/VVcQncl54falcstCcjv3knBEzSvcxEqLjeqlEb1Cd8ySvSir9ngy/RY3exKA/E0IkuciaM0jh4w+f8098431+4uMX/NjXPsLdJlyrSHHULMQw0ZKjXV7jK7g0IcEjrdDyQi2rEjiC9o6qBVBapRixRwwWC94RdoiGrTwl7LhxLOCcuvQ0Yzl3NuWA+7zT6mWQT7z1zQvrciE4z5wOeG/+qqgARfeUpec+rleYDp0p7rOPxsS1fFBJQga7uS0gbdAeiDNJQtOMVpEEP4hHXZWmYa/pHF30vdHhwmCEp4YLXiFnk8ccXU3nLFi7XbB7GkS3Q6UfqqZ8sztkB+PXgcODIVLO6yEejL3rxZnHr16H7vKk9ZMmac4LARVKAfAuIm3V/qro8/uQiC5x6XZrTlkTwQfWejW9ZGWgO+fItdBsPevrO5BKLYI/eBt/6r1v81u1NkCaAlkuqqglOitaS1YuxHKxCk73S4yR8+N1JFpiimAi4JoSClsRaM6UuTpu7QwxKIRosp0VmgQcK7UsNCPcHY5H1pzNoUd0RronbqiICabsRYjDv/Zz0xE7D+mtQu2Etc6v+HKPHymIfuc73+GP/tE/yre+9S2eP3/OL/7iL/JX/spf4Z/75/45AP7cn/tzeO/55V/+ZZZl4ff//t/Pv//v//vj90MI/Gf/2X/Gn/gTf4JvfvOb3Nzc8Mf+2B/j3/g3/o0f5W2Mx7uYhf3rgFWI+wvXGaFq0+MsnWnSWPLKz//8L3CcPEJkWRbbABO0nlHaNttVwP319vqbPPnuVmE+6YfugufoCfas2LnPfbb+7zUvHA4HbeIvq/kECng9WPdkksMUSUEDSxPthcboiS6RvVVbTRdnsGA9TerxFw0KEhGOxxMhOF59Wri7vVWxh5Q4TLMSd0obwhXDH9CkB/t7DyHakPcGlejcbrLf08MyeGWmelROzokQQiKlA8HD7FXu7YPjxHu3M85X3T61g2eO6iCkyLpeOV+uPK6q1xljNFgqKrvVe6J3yBzxpu3pUHPlUsp23/wGtR3SESemfQocjoHvvfpHfPzhc372F36KX/z534W/nuH7F9b77+NjIqQb0nTQ7FgC8XintliamRGPR4gBn1VObbVgSVXotk/5eu/x00QtOp/rQzIyTx5V4vCvDR7fOhQIzkp1Z3tgn+D19eJMZk8JVoF5PtKkUQyu9DESarNDU78mwJ4AqVJzbaAO+r+wrf93buT+l/4kmkTV2mhVofrSVKpSqsLTpeUnT9H7v/01VInQ2Uyhzoi3rsG6barBRsdhLQN9H953RKUioiNcrfbO6lPG7TAg7xdhVKPCJmqg16YHMtW7jZTa74c3VmuvYKMhTWjS0qpNvagvcb9efb+BI8bEsuq91LNLA8hyvXI8Himl2h6PSG0ENBHzlnQH79UMvlSkbiIbyzXbHLru78v5kcPNc0pd8SESvHrKHm4OHI9NNcVNML6rooWo1ylOiVYd05xU9MVrANXrbmQqGrjIzc0B4aoM46hw7iFNLMui6ISNJDVplCrEYMYVXb4ylzEu9q4F14udAeGK8K548sMeP1IQ/Yt/8S/+wO8fDgf+/J//8/z5P//nv/Bnfuqnfor//D//z3+Ul/3/yWMr/9sIiDjtH4hUzo8PTNOJJoVSFFKcDgfqOdCqIyQVE6jlBzO52g/87vbYV6f7nujbYzt7QolzThdoVAGFUjMlL7pg/EyjEM2CzAG1wrpcBnwnrdGKzvXldVWJwLZRzos5PdSSTYEkU0olL1l/Vhx5uRBC4PJ4ZgqJKSqcuCzqrxpTZL0snM8X3n95Q4yRXCqlZK7XqwbsoJJivbeiQVaz4VYb3gkxOR7zwl1TNSCI3M5HlnzmWXvBx+/d8OPvHbmWK8EnKJXqBPGCBIwoEzimiRBAjY6V2Kcpj5EPSlHTZLPe8t7T/NMqI3hvYzVQlkapF4UVvXB5I5xON3zv26/4q/+Xv8rf/ZX/B//H/8Mv8o3f8buZVoUUXQxwmsBlpArOTRAaEio0h0sJvFUs3lNX7TGGAERDaaseMHXNo4Lpq6S1pgbpTsdtgNG76tB1DEEDj+vaONv6Eq/6qxgyoesF0uFo4ygGm3qPEzVi1/loy9wFqvnEOqfX2VtLQlsIcTcis98ECu4Wc3fp/cUmGji86bu21rhc7zm4g7UINNECNqUggzu3/dP3dqPUVQlQTgsthbd1pKhK0YCx4zio8McObYZR3SuA4tgfy64X4JrFqYi+KCQ4BvjtA4/3Z6Mfgd6LVmEP7zqCYGNMTkeaSDpPKtXpmqExBU+QiLhKtn3cSmEKyUzR4XBQWdPb21seHx8ppSrbdlwna0fVAqKCDcl7VoAmzNMtyEyrMkbici587flz1qXpzLp3LMvCnQ8Dpet7R9cTxKh2euuilWnKlvAYga0BZS2stSCi8obeTUhovHl1T5bGdDxwd7qh3t8zh20PeB9IXls+1RKFFBPH45Fa9zdxq3HeDqD9nN163f87VaJfhceTjbpLDJ3TTTbIAtHBigH0CtFezoLvmLgTcl5xLmqQ8Vn7Dm89voiFu38vb/93/3tvB83PzexZttuDTamV4zwzTROlFJZ1ZZomLg9n5hv1W2yWTcaoUGg3HPfWItY5NO2jCMFUaZQ05h1Ic1Y1OiMQRJp4amscDzMpao/MOcc0pUEgSikRQzQ546qkFHu9FJMadTtn/qOeEP3Q2mylQlALsJgipakFVBNIMXI8TJSr8LCs/MKzAx89O3F7TKwPaihNiBpE7bUVOgu8d3dQL88G19JY1qcD/ThPRQ+Pvk5C9Lim1U2pmVIFX/R31vU8hBG8gmiU0pinG6ITPruHv/4rf5//83zDez/9O/TadN/XOOMPUTXTcsZlVcXSw1dw4glMTF4Pf3GBGvVgbVnhLpWGKxq0MJ9R0YDhnFb3vR3QT/Xe92FUaxCiRp9SILpoTh/WS1NcUqvKijK+yUSXdueRG/e2inEjpY3qxdssYhcI6Q4++zlN3LY3m7PeIJ7g9Sf65+ijNWMPGYzX9Ws14KnwxrbPOjTLQEn2+W2IqrLDED/X/VhKUcNq0Mpp1z/WGUsjK1lV5tgLK2gkbVS8j1aleevnK5FNK0RwBBVuUtUNVIdYmaytYdZ9+lljVDhCgvIyggk0+OgUgbFqeJpmLme9dwrxKhlQRPj2t7+N915nxZOymqdpGvq1OlqlJKBNOtFRysqynkmz4EOhyQKucD6/Zl2giVoBXi95F6itAmyVmldS0jNUnCgxrjGQBIXFdd30GWO9jA5ahkPk9cOZ5mA+TBxj5NOcTbaytyU2TWOQJ6iEFii7M1b6WpHPBVGsj6z//vIV6T92QXT/6M1i77ed2/sXOiyc6T6GeIXInHjEqZZWvzkhaMN/m+PavwZP+rFfVIi+HSzf9dj3YgaBY2TH+jOtFtIUmedkUmKqd5pNV1cEshSESqmdGbjlzaUUnFcFI+w11MJKCKID/iWv6j5hLhylZIVlvOf+9StiTDRZKTmTY7GDuw0xgGizsh0S7RBba3UkA9uYRmcObqMboTUVJdD9pT0mKiEl7u8LH5wSp0k3jBMdLSqtWi/K5M9yw6dAdA0pxuYLQksdeu8wUlBvUO/GPKVWECoIUM0QWsQPxqHrMJgxS6vNV4qD4hz3a+PX/5ff4O4bP8F8e6si5BKU9BKjSe/p2uxEIWOU4KIjiBB8Qf1PPF4c3pvRtavg2lgPnd1YirlcBK309drv2dImg/gkx7Qe6kgoGEmlc1sp5sDYt968QDXB6L1I74IZZOt4gvfW97MqUT1mMz4d9QuOsaadCX8AJiZg/ErRv4vo4ZumA878fx3WhxuDKzZX0mFU93RmGTtIa4fr7MuqoNVnVjdTe4VTt99XizK9R2OcxW9J+YB0RZOF1kwUw/gXtVpvXfrMcWdBi5bGbtv7IQZ887hWCW/1VK0bjgTTz05mFUamic2UC+xVrWj97NikBFttdk3aUArSJdnJPzLY2dM006quK++DjUF50nRguaxEPxOSZ100MU4p2Xyr6SGLWEKlHApvLaJpmkBknAHee0Jfnk73tRNPPB1ZL58xHw8cT0dSiDQRNmNAOxcNwvbBU0uhmEJbP3v2wVIGC3H3NRE2y8vfRpXovof4ru/Z3zAsBof1JGzMxfurQU5i+H0b6hv9wgfbUJ3D8AVvZNzQfYX5w977249+4O37VfsZtY7/h+CZ5kRZZOcLKIPar+eIN5NcGVUjdtj60BisT2Pt5qIJA9LUxswUQ0TMGcaITo8PD8SkPeNijNIQInnNxJhG9aozhtWgR9X+7dkuOFMl2Y3CmBh3N5DWINoUymyV1griArktfHCTSB5yKUqaoVeRarUV+qGAJQhNr4PzXtVYjIyiQVShtw7v9rEDZeqpUbtWeGbRJgHfhOAhBqP8L3b/vUOCo6XAt771GT/z6p75cAvHA4Q4CCfiEy4ppKcnLnZIVxt9UcUacTqjKg614bO+VBcOr7USg4fqyK0gzhG8wvkbHKnrZvTYR5zp67QpAcYO/311iNsMw2tXnbKkU6wP73rfz6lu89Yb7eu1GntXK+f+cDgjG21BrRN2GpXgZn0z9hrBTVhGZT3qSBcM6c/YezaqnbD1sbtKmXcbt2EgUsFYs0HZ1ikl1lzGc/XeL7gxY9mr1GDIVH8tcdC8IhRdr3iwoy2R1zVnVZfJ4PWr0lo1hCDimyZoIXi8tSMKgrhANGuzMEXWqklhbdvscH9G16FaaTrDadrAtVbrtVvzyY0JS5uHbkMlKqXEsm66ujoi15jTgXvJuAHhagIRox9jLfqZugG3sn61/yykFJXA5hVtcLvEQiw5Ds5zur3hu9/9PtNh4nicic5Y127cCTu3nsaCnuzZYhxrctsTPc3Y0ECRLXgOBv+XeHylg2h/fGHAEstS6Ymqw3uTx5tUPs4ZXTrFxGfXM6cp0lFbcUrbXpdV4RkXhoanPukW7Bg39vPBfVRjb73Pd8G4bxM+Nkhiq0xzzkzTxHJ55HI5M9/cINJ49eZ+QDIhhJ2rRCc/KCEmpoB3WkEJKjmnIiCaCvbnAEjmAtFMciyFwDTPXBf1EQwhkNLE5XLl7i6Z/qh6V/aAWoq63udcxmJ1WNDs1XI3I3bQnCCogbV+/oa0zHUt3B0CX3t2xCFcsw6DXy6LVp2i5A+pAiFwvlxZSzXVHAc+EEZDRFV/nHhcvVKaM4/UOuTNgkHO/XrEmFgLtLIQg3BIgSkmNZr2AR8dzjcymddL5PKd73B6/j7++deQmxm3PMIqkJIq/viCdxM0c3HJK2SheSA6pEDzYnZe7sl9KUV798kHJDh8sErCqugpRR7XZfRI53keJJZe+YwDPniryuTJfFyIDlwwjd+G88YY7ntgf0hZ1QuOJplGo9rA/gioffTAaQBuovJu1RK2krMe8hRcMkKXbLZjpRRl1pvnZS5Xk7CLW5KABl3Doegygc4F4mCQYwFMK9RO6JnMmaY83O/2oCl7WR8fC9C6p+IIsppnOwKKZinzXF9D+7BaRY4KuScp1QzvLVHE9/2nlV+IAV/UX7OJVo4+qMRoTIm66P5umM1c703b66uXqyJRveLWqnMHd9rsqg+ew+EAdJERAVco5cKyXqlNZ1NLWTSolyvrGmkSWJaFdVnUGo2ONPgxZaAWbZVlVd1d5+sgNOnR6SwWKlPZ+UAMgVOauF6vpJvAPCdcrxTtLsJWHLUB/+tnGzDyLkhqHviDg2jvj37Zxz8WQfQLHyNDsX/axU4p4V0cB4pSrxvRedZ1YT4eVLjdelBhSkosMfhqw9dl/58NMt7+at//wZXp2/3Rt3++MwVbq6pS5LUyOh4OHOYDwXtevnyJmxPrmukmtymlsSC2IK1s1+A8LWg2b90T67OqbVwwUelaq5ICRDfkfJhwD5rROut9edGgM88z66q2SIfDgdPpxKtXrwBlDs7zgVK7kYDeC4XVA4LpapZKTNajRKXmaqumHTrxT33jgItQUPm+1hou6gZ1VXANMo6HXDivRQ/L0T+x+TzRSlua9iRzKRpgoyN57RN1Kccnm6+Bd2eOpwNY0C11wUUQWRWOFI9zE6+WM5+9/jbH8DPc3B1wc1LT4EMAD7J45L7gQgUWKzGtZ2iWc61mhfJCgNooVft7fYPHOLFczqSUNEiWSq6ZVjNTOgAKF4tda8woYP8Y/UsR65nqkdZouGAEtdaIaZsBVpKW7QcjcQU73BHPmhdFBbzOEDrnaZbMdmcTRSPKMHSpTd08lbbUoVxjG6vGliVlyaTwNtH8bt/mW09AO0lIr5P23TeLOP2sW9DVnqka0ddWDIoUW6NuXCfB4FznjOBTrSryI0lVchBWxXc96TiSnx64Slk1gIHBwl7nJHdnwsbUdfTZWLwjxomclTSWqwoYTlPi5uaGh9cWUATwjru7O16//uzJ/Z6nieDUOCKatGAnH87zjDNTbPAc5jteffaG4+GWm9MzUjyyLPDeyw/53ne+x/PnzzmeTkgTTsdbYph28LjXcRSrTL3Tva6Smdovr8UqUdfRJEah0tB65v7hNbfvfcjd6WgnhxYITvooTiWYlm+HiEstdCThSRDdISWNfU8U9qf2/z+IsqvsfGe+Wi/GAoxmZ302UDf18Xjkci7UJgRzgG9VG/NVGIPqb19g1yGG7SsWvzcYYfu5H/z3t98/MPpbIQRqVqz/dDpxeXzF5XrldPeMV69eEeeJapmf9hy0P6ANBn0feV1hChRzEulkpSWvxCmxlkxZNzJAjBFpghfHNB14fHzkeHPLq1ffNYusrtq0zSHue579MCu1jKrT2WzXnnncr1uvbL3D3GHUiSZOB6IXfvqDA5dlwSWFtYQGoVHXlbYKa4WLOM4IIc1gsnEAayms63XoAxMjLkSm2dPlNLRPt1PtER376E4VvjiW8wpOhbabjWFouyATveM4JS41Q9F+cUhJ8+04a0AvF608D7fw8IaSK65UfG04aaSOaDTA8vWCwXBVq/YQEs4VJaHUau9RD50+mnR3d8fppqqLT9EDsgfhXiF0OF1ACSp9nQPON+1ySsVHQ2JWGbBolYYLOk7S+2gVYV2v3JyOWqkBDk+ujTTN1JJtHlYTu7jzQK2l4BFKc0xTQMRbsDaAsgWmdCSEGRGI4kgxkFIcVc+WLAK7Q7I1TMxd720YX2/jeq1l5XI58+z5cxMnsP1RlLmqsKz296rB/T0JcSZvmbOeK8GrDWHfCyGELzyYO3JUWwMvlPpUI1gcRINi98E1+ohDhSlqUzuz3ArSIAYN3Ou66rynseL1PFmhqc52tJaGE6hrJi8LiKflosIUaFIEmjTmvJKi49WrV6TJIawsi3C5LDw+npVU5LZrnfOizPPowUU8UUdsRBFAsY5G7xWreK5JhdaMb3C5XPhgmjkcZr0XVSUn9f6ofeG++u33tRtXvF2/jKDK02KllC5/KbT85T2uv9JB9IfN9Li3asK9Ik3XdXXOyveycrq7M487bTJrMDQ5P2vS43ew0Qh+PRjaRrZMW0/Wt7D6tyDcp7BtGEzEnhkGr/NjNEUgixeoldNh5k1MSPNq0ZYZm4S+uOaJ1NSUvA+2e6kEp3CRCjwLU9BSK4ZAQCvUeDhRrwvH+Ub7jr4ypwPL5cLhxQukMYhLMR0obVUBBlM9siGS3d0Qmz9NVpnsejcu0FwbOyqIEL3jWhqv7x9wwfFSPF9//oLDDEsVoqv4IOoQUVXTeqmwNmEFVfRZr8PkuomOFKQ0wqV6rsY4DvQBNTc0o209CShb0FkzpWRlEYc+rpNHktKA5j2zF+b3XhKOL8DfoCMxZ5o/4MOKGFvRhYiPk0nXae/Jh4jUlYImH6X2XrceltshAGmex7Wl6hyk9/aZqx4koL0/bCxFoWw9MHoSWa1C97Yf1F92E59o1UgoXfSh2QygeBQtFHREqZLXBX9j0FotWPeP1rL2SBFTwNrGShBdMRsPwIIWijKIMzH5oL1GaW4T6zDxYOd077Uq+Bisqte10JM5Z03Awbh1IGYFF6Pqaktrdn+whM9OkiqbLnWTwYTu77/rEfcEWlq1Fk817oIf1aleYwd2r5xoNVmajBmAEBwhQsgaZGMWg8ihhMZEYF2vqgHuZ2ave0u0+bojV7lNnSipybueLZoYOXH2XgPzfGJdr4hT+F6vR2GaTszTgRAiBeEwH8gr1OJIMXKYZ5X1MwPwfr75EE2lqXvBanKyrhnHxtpWprS9X4P6a1u5LGeuWbi5OXB7mGi50FwhuoR9UppAQBn4zbgCtVZCnBWJeUvCr0O2+/YbbBUwooYPX/bxlQ6iP/zxVnUH5hjgiGFbZK3p4nTzREiTklhELBvf1F3eerqnz/25eP5WAN8FTHgaTEcQfet3lInotkxcFD5rtXKcEw6bCxRdPL5uM34N6/lZ9S3OFizOQCk3II3oYLY5w+CguUCIidKuRIM1nfNMcWLNC7dhO/wEIaaJJZ9xjpGVazKgmsLsko0+0tKl7AAd1q7ZPv1GkMq18t7dC17c3fL8dubrL4+IyxQD1jRJUdPjIo6VxqLhynon3dVDD7Zoh65qEBthyFlfptPgnR660toYrhdTQHEx0pqiAh2VcB2ZkK7GoxDd6Ri5+fBj0uklYEHUNeCI+AYxIKnAnEEi1Aglgs8Dc3JYZag4NNF7quuG1UqCi1GZzUgzWTloRpyqrY3qxbkucmFjQDbm5Jooe9iuZ/DBZAAjzje8jZtIsaCiL6CfVdRqStgSEB+EaT6YATsWpcTCpsoZKqNYk4KBDnnptZau1W6k05EAG0ObpokYJqR5WnCEFIiTQnmtVqIP5FVF9MXuJb1H6Dp5BWMTwwjWzuElEQ+qqhVsbbMLusI4d/HVrl8pCkV6rzOdLRmka1CuawSpVvEnQlTeQPOOlIJOszoVPKg5k8tK7gbYRsRJop99lkKpK81HRTzShPvOPSILzupGW+6bxGZT8Pxt1KsHV5XcNlUoUyZrluLinCUNTRMXaaaohREPGzkXUlTZz1oyw5Dc1pm0RnVuVOoNsWrPxAkNw+2jV86QB2XYKTGwCByPM4d50grUy/isMj5bVxtiMP1jUAP5PYQ7zlaETfrP4GMbWaq1sqzXtw/0L3x8pYPoD2XAwsgc+6LpcEjwW7+j9z/WdR2ZpevFJJuot17sPTwgu//f4JYOr+t/t6D5g4hF7/qZDnfuil0lC9TKfND+5f2bR84Pj8qalU0wXBCKsQzzqvZaIUS4ZloUKoWCkngQIYh5WorgUVHvnFfoMBPK8MuXs0HLXYlFN7gsXaZMv76sOhqSUmRZ3JgXjMms4qwS0ozcI6tu9gasIhQcNRf+qZ/+XXzw/i0uCS/SxLU+cvKVEkCczq4W5yjeURwUhxJznGM6HVVOxxInFzzBZmh91U3sgye4rkazrY/awA9nF0eIkTQlnOviEEo80nhkpC3rs+Idh9tb4u37lOLIby467gE0t6CB2SMNQsuUdYG84FtR7drlilsyNVd0ckcF8bXt0LYkTzMkrSJM29UYRlppR+u/Vj38fOyOOl6h2+BQPNY0gC3YtFLARxW4c0C1teMdxAJFe8o+eNLxSHNBnTVqJQXH85sb1lV7UtH6XMt6IUVFAJo46pjp7OvcG9nFU+tKmrd5yRQmnPfMcWZKJ1URq9BcwgXHdDho77ZW5piIueCCBtF+3btSkAbEtrkbGZzrnTeIUDd9h3hL1ZlHdXwyKy9p5LIiUkfA8D4wTbOS2KQS4vQExaq14H0XZ3e4xwMhqJtNjIHoPcv1ypozoZQxfuWcx6cTOMHPiVgrIpHgHC9f3PHtT+5xMdNqYSmLcibAlIiEZvAqhg2JmTdoz93G0Ni3uYrxKhSlOF8eUR3nyuV6xnlhmuD+8Q2gqMPFNXJeOSNMxsPAOVzzG3KyOzJb7KNEKOInmvjFzlzW75owSwIc83RgSrMhFUHP4d35q0WO29aUtfDe5pj0RFkaT5AyFV5RK8ZlXXhz/8CXfXylg+gPfexgVBjLCNjPhunmDaaMsjmPPA1y1bByH7ro+o5Cbf3WNrLX/pqf753+QFiXskENu/fQvTOlNcq6Imnm5njkcJjx4aILRuDCgmu9RykkycTOkGuOIJVLXliqeUj2gF8ryXtSU6eQUhs1BJa8kNyBWtcx+K7asx02q+RlHUGl5IVq86QhBFNG6bNaumFj8hbQ1Wy4ScGbigxtYxHO88x9drhypi1QM1wPjTWthFVIpk5UUULL6gI16Yzq8XjAe88pTmTZzS7uSDUDzutIQx+t491tAuc6M3q1tWR+haZV6+xwFmmqN0vib/5f/yqh/mWSc8xx5ibAAmQJeJyqrEwnZaa2OtR3rqUgwbOakot3MDkUCt35hoqI9vEvF67XK8F7TsdZKxm1sRlrQYhEP1FTopQVaUUPqHBQtZgYNtmzaUJCQvKEFyV5rWYrFZuiAEkvih5IQGiV2go5K4fg9r3TcJM5HCZuxdx+StU/reJcAOcJBqnn0pm8Z7yPBgIblOl133kJ5ksZ8MHmC6MmufnaKNGRDje25hR1GHZvaHBUBOfMzelmjGiB4ELG+8D9/RtO8aRB0whBh8ORPiLURIhVK8nHx0cO80xKE62prGYpGecSPiSSCzra5diqeuCQnlFLg/VqEplBEzUgDMMITV7m6cC1FZij+eAmKJk2ncg+UXEglYuhQQan0KSxruu23jVfprVq1oXJqtRGbRnMcaZb6YWgcn7rasiX0/EWJOGdEpJag1Y9MR3BwZSOVAtUPgZm56lVTBtdE74QAk4mDWJOz+XkFBbW1kKvLNUvN7fKzenEcT6wrkqEHPt47GdGVdnlL/dTDaPaRHv6TZ6es6VoOyvnzOs3b/j+Z59+7gz4osc/3kEUNvzFqjntWQRbuL3/qSdohxT6L+37ld77J3OiW+Bj/Oz236dB8F1//zIjL62ofqz3fsAf0SelfMdbUvSkGDgej4QYWdbzWEBdCi7FtMHCQCuVMCXziHSI1yoxWWBrIoSmi/5BhMM08/C40BrM04FXeR1+gf26zfPMp599YgQX43BW1RwdZA+BXBZmMY1ar5u3z462pl6HJWfWy1kP3OZId8/4zsMbvvPZK37xZ383MR14LDNzSuqe4qzX47SS8imRjgeb90xEeu/DjYpkf29VG9UcUfbLxm2kEEOcbBlpoK9NZReDU0HvPjSOiNqKRbj3R9J0ByGwIiQRnEy4cISSkeUNBJVqbHXrdxISMWng6vcuCzTx2utK1jsS4fVaOS+Nab4heM/SGuIi3qXRP0N0aL/gcSkR40F7m+jok6AHgRLomt5BF4hzF5xoEL1WV63prB6wLosKhvfD2A5J1XN1hGgHmBOOp1vO5zNuSkwHx2Swd7P+c/NVlaecYwoaBB8eL+RSubk5UhFO81EPYLbKSee9PXGG6XgzSEClqX/tFCMhV4OIVRGo1kqukThFfNOArJXfTGvC8xfTmHnufpugiFRDoXXvdUyJUGlMVIlUqQSxGV9BSWAIKRyobaHlghiKIM0TpgnmA3hHESE7xxXVqlb3EU3OrouSc1yv4oH7y2vm08RjXlhbJsTGYQ6c3TampNtuc6txtsc1kDYOc2KaZ0pZ6bZh83ygi4iklDgcA9frlR/7+MA0Ba7LyjRFYhLS5IgJ/eMCrVamQ6TkrKQzHGlShS7vrSLvkRzspOhiFc6ETCxhbbZjq5oOnG4OzIeZvNoID2Z/hxtnm14f2dyQehC1xKcHzn2S3IVIWhMmH/j+p694uL//7Tcn+oWPjv/3v8MGxRkrEHp/rozB8rcD3Eb86U/7VgA0cP6HwbM/+K1uWZP+MtshP/6nFki1VebgcN56TaK9yBBm9dHEKUHFK/ml9x40k044yWS8QinOQRSiWRBJVIbvwdRbpmnCnfWzp6SanB0aR3SwvEh5Kq3mNqg8xIjLHqQAzYg9CokpyUBHdlorRB9NLCDhpXJzmvhr/89f43Q78eKD50zHxDV72lx1eL8J1SmTLvZjXFRVT+H0agzJPpvHSDJ6H6U/0vQUXnTmCuKHhRfblrX7r9n4NrvZsyznHBIOTDee2QdiSIiPIJXoPDVAqw65zlzrQvQKTbsqhoY4puCJaK9SRgKQxmiDt8Qul8x8CEayaFTvmQ9HreKaQbTYnKL3+JhYlysld9urpBVwrVRRnVyt8iYqGZGA93Ywx66SpQdhPFg/2PdjRDmlIUYiwdjYep/XKqTDrQYGW4udGewMIu1s19qU8ZqOd8qUN7P7082NMqJLoZbCFBNznMZ9VTJJI6VpKDQ57ylTNtTD5oBd4NnLl/r5XSeaKapQmzD5bRwGq3A6SqWC9tCqwqSneaZbFooIpemYjPfOSDsq4tBapJRKrV2GDg5zIpuNIIB3MweZCDEZRAngidGrQX0NiFOz8vPZc3d7QslmqtTl6zbrqMIH2zjUdu75gab0Bd/5AH20SyzYeB90jlk8KR3xLkBbSOGWWiDG2a5fxOOpRbV9pW1SiTR1a6lOk6zaCgRvZ7If1aNIU5MEkaESFmPSvwdhip4UGFyLkQQ7G7Wiw7TbGdr3advNhgJPZkdb637QnvPDI599//s8Xs77OuiHPr7SQfTLBig7159cmL4h+obuva196S+WTX1RVrK9/ufhP3h3tfmD3vfbgbn343ryJto8AOdIJgSgh4POSrpsBxQOCWCaPf0TG8TicVVnO7WQdNCHx0fwM1XYEJgmIxZ5z5Rm7eGEaEpB2gNNKWm1gjIn/RgrMvjMO6R0+NSNINwzyJ7MaKAzWR4a0cOrhysteF6IsWEbON9wZrLtnN0fZ848KFHC+whNbOTGVoH3uw3GeA9uB7XpYYhJlaGHjJ6kgB6ezspt5y0HScHGhfR64BzBRebkSFWJW2HyNFG7K5IdXCEQy6QyiQ5q7XZjwhQnkDjeQzPEJOqQnfaRWtNxpS6igGbzN3cnQLVLOzKibhoJnCdGR2vJxEO0ClBVp6Z9QhF06Mf3TgXzNI3Rn62VIdTaiEFN7mUkRp2+ptCh9gNBXLLboOup5mwpAlrB1or3sK6VFL3J2oGIR6LvU5oKP+aVeDyaWqJJMAbwfrLr0FeXkLxe31LaqPZjmmyWcjt4LTcwdyNGEg4MaUdn+7HWQvTa0lBfXDUz6AZw3saopDkzVdBedjd1cM5Tspqr6/MKq/EIGmyohMCyLBzmW3Jeua6Zx5xZljf81m9+i9f3Z2p1eg+q7ufO9A/+6VhN8NpK6S0WHW2SoWrW9+FWyTpDFXofUtni3gJx7JaQreFT3AVfB11wwvU9b2vG1N86GQtG7mlVshYOqhmsI31pTkxTInhHac3W5LYvZZyVm0jE1jrraMJ2HTauibU67Ly/v7/ncrlQu1/pl3x8pYMoYOnaWxFy99gHxv2jB4zQWZZs1HOFWxSqdM6Z17HdpBFQd9Dr7q10NHifcTu3MdZ+9IfrrSddDHboqeRcwAWd84zzRI+2YhmDElA0GHjnVBe4s9icDtRrJuqQIHjxwxKy9zZ7JQuOKU06oL2T+tI+SLI+0Ebg6Copsc/wvVX5eedx1tvYHDQYFbfzDtca6eYW7xN1aeSl0WShlIkUVaGoQ1ytX3sEqToa1GqjuTY2mQtxY9yiG9HbAZal2LrwRsf3diD0YNAG7Kz3WkZFpobFfhxSustt5m8ttNoIsoJLSI1EHE4KrWr13SHbPqTfD1CHaveJlhe0mhEpalvXdDY2es9aupg3uJJBbq2naP6frZGXhTQprOWDZ4oK69U1azUhza6HjQAo+Iu3Q9kHNQUQ3QR0HbgIOKlDFtDZ+++QWa3VDAcCRZqtRU00al61MmlKTMnrgkOouTAfDpYgQmtqYC4VvA/Ukrmcz4hTS4jglFkuTlQ9azFBj2bBwcUB85WsetmXsFIFCw4yqkPnYFlWbYPgRotCtYllIEStqeTkuiwDRVquV1W4il7HSFBIN+d1GBwoqUkVilJMlGLBzTnWvJLXlbXkTRmqNR4eHvjww69zPl94/XjmzXXh5rTwvz58wre/+5pcdYa7WYKtbGYVS+hBYkCmxk5HbJbTKkRd+84SLyMJeqdsYNF9orPeCyEEcllsblMh/5ACEgSGyIYR+kw/WJMdHRcKNrrUv69jLU4FYFrDxYhzyhRfliuH48w8JeuXNiSo9IQIw9x9a61ZAmCvoaNYT2HcZuzgJts+zjnz6vVrFWlwjAT/yzy+0kG0e/pae+iHPvbB1Dm9mcGcGUqRIS+m2qA6TA8MaKdan8/vKprtyXf16LihG5EI5zCq4w+sRMf7Y/u15CO5Cc0yzvN6AbTHI+ig8/NnR1qY2GTblFCheq+WaFTdWHV9RHqftTZybUS8QowWYKR0bdzMui6m16A9zFLUBqzlwrou3EwzMU6UoupGwat8msoTnvT+GCmpCzD0LL8H227sW3LBx5Xp5ohQWWQlFsf9w8rf+/Xvc3/+DX7yo5/i4/e+xs2NJztHbg5Xmx0IeiSkuJCvWXuvBtkoy7rawbJlzWKHuM4P6yxdDKr+pBWUro1pmhBTXOrjEiF4UtQs3NOTHc3cV1mhCOW6cjnfc3dzwJVKddCI4CbSpNWnGLHDWdU4J7MPqzprWdeVZ8+f8d3vfnskf4Kyn9dSuJwvLMuVUjJ3d8853p6U2GWWWHnNHE83FOkHlwquO6dOP2teKVUDTIoTp9s78rKouIc0zssjp+NpsDdz1qQpxEjOjcvlouvEOXIuzPOB6/VqyZXn4fGe4837lFJYcyGb9ZZHiEkh5vP5TKuVdDqRzDcy50wIkRiOvHj2Eie9+muk029wMydSiJRaua4LeGHNleN80IqxFOqqsn9pSmOEZ7mu4z56m20sZbWELhCDio2s64oPgevlwjRHnPVFS8lg9mOHw9GIdGd12pHCdLwBF8jrMlou+jmUbfrm4bV697rA8aikmbxeefPmFefrRUd3YsSJ8OrVa/7+P/iEN+dHqgvcPnvBs9PKP/r2Kx4eCtPpGXe3k8mTKsu4w7q1bAmNnm0d7uwOSnp4dk3fPoXQYXYVbvC6v2tGxHE63ul9igdaXXHOE9OBGBfEBRsnsppcGOMtWmRoQaGKQnZ49/c3CGBVz2YvvPr+Z6R5Mla/I1czWbf2XEdE6MhI0/aXd3qWtdqeJL7jvwLSnYta4/WrV3zvs09RRbs4dJK/zOMrHUR/6MN9PnDqcPkOxu03UBgi66DNbGfSYKPfh2Za0H+/V8FPHz0jEst0em9Fv2zKCT/koQvESE3B46tlb02GbdA0q/7vb/3mb7G4leXhDDAO866Usv/83nv18UwJj5rwri0jk81Adrm/ognD5fyo7goiPD7ekw6JT199n+AdNcC6XLl79mKDZPvr96peX3hc63meSXGirotV9QYJicLExxA5Ok/xHjcJi3gOcaLNE99ZPb/5m3C5fALrxMs7z5vrwpvLQisaxJo0citM00xoXmXpbFbscDiRSxuHMHTRDXAWWLVHovnO9XrVfq2oKL/S9606cbrZUtSqvKzrUDvp0Njr+wdqWcl5YV2vnJ7fcrleFcYyCL2zpy/nR0rNo/dwOBx5PF+HkbK2JLUCKDlTW0ZoTGkipsR1udJqw4lnWX6TZy+e873vf89E6nWOcjqeFHrMF6iZGCfu7u4oTbhczpzPZ87XK++/9z4f//g3+OSTb3P/8ECthTePZ168uCOvW0W2rAspJp7f3lFqYVmXrac7HbRHJt3ZpPH8RvuWGkSVCYsTbm5vKLlwua6INOZXV16+PLCs6j+ZYiD5zGRTNp5ADBO0RKmJWqBWoRS9prUKJWqSWAr4yevsK6K6zAGY+vwy+MmIUUWJSjlX4uFAFIfPmVIzp0PSLoMzda5VnWWkiVkI6lrz0bGuV6bpZHBqIucL0UVimFVIwzlwFRcaIW7Snctl5fmzG2ISDseZlKJJaMJShGuE6hLz7TNa/h4tTkjyFtSvNF9sL3U5PJvDDr3abjubPN1zy7Iog9fu07ouZlyhDN7Xbz5jPkTe3H/Gup5Z88US5pX2atV56xDIVK7nM8uie6bDxN30vBpE7vDMMVmB0noPRM8mMYH8lpEQSIcDrTROz2+HjWJ/DiUC+l3bSxsDA7Z1aqjQRxz756XJuEZdQa2Uwve+9z18UnZwSBH/2yWIDqi0f2CDwwa4axetQ6y9MvRuG1PxTgjeofow2PhG0EzfyA6toXN0zqnTNTtN1a7OYYfi5h6wBdexaPv/v2XU2+EgHSzfBoB9VK/KFLDZOof3pswijRAcaU74w4Hnx+es8UC3sKql4r2NLeCG5VFMEYpmgV3ZY7LREpyKWoPjer5wPExMhxPp/EhrEOcbQgMfJmKYKZQhiu2cBpLoUMcRo7bXsqgUnQ/Uav6GVJvB14VeqxjzsbGYpmZYFxyR6BrUrD0wrhxPwu18pAGXstKoHE8z65JHl3V2B9PxzfjdtU7J4KuoK0TYEgul9Ss7tXkVhZila4oqvH2YD6iSVU9K7P55ENcoYoo8TSuPtT2w5DOlFsIUmeaJkBIhKqxeisK5teq97KMFwBAC7xu9taxSj0vQIFT02ocYOR4OeK8HrgDpMJOmxLMXz0dQc9J4+fI9LpcLuahmstTKPEcOcSKLQGlEH/FTQuoK0tTxKM7EFLm9fWaHTqaUlcMhcZhvuDsdWfPKssaRON3e3HE5X6g5E4On1Mzx7qi9xDVTsyVdMZFCwEnDTepF67wye1P3CBX9vM6XoXilaEahNCEZ2iLWR2uivdWKDuYnN7FSRlul9WQHGeIRAqoIRjV2eb/u1RLs7nIk47q00tXOFDanCqWo0XrzGQmFVhq1wFozU3LEZAIoNmOKwd3N2gZVlIW9rl3AX7heCs5FylLIkmnTxM3LDyjf+59xaIXr3ITkRnNmEOd0FrhIoYoDUaH+5jzNNb0uaUIcLFnncYM/WMXXiDFxPN7oOVGE58/e481943p95NndC97cf8rL5x/xeFEm67ObO5ILTCkNCzfE0D0HLRhXAXMNStpH1/NXZQqXJev8co3EODEdD3x2feT5197n2ByhiAo3tC7M0smYjeAcDh0szkWFIG4dLDYP3IkM4jRodph+XVdev37F4+M9Pk6kmEyJ6reJ7N8XPWT3X9+D7K6vqRXe5s4Rgm6GeT7YwW8SaKE7HmzZy7t6qxsebxXkD3hn0nHf3WHc39NgceyeoLNI3Vbf4sTrCEHQLC/XjLoNdtk2cM6UdawqMt4JHihiPTTMm9LIFbWqMonDq+KRg5xXW1DaoPfB0S2otL9Sd33fPVTtxwLviQJP4FvYJxqDSYeqp0xuMvg6MM8TMXouD6/xQSu9koXVNdaivqHSdgiDpkF4r5qyNLvyI7kxuAuFcgLdL7OrqFiCHDc2phenrhm2ATscrMzNMoQ6WisWzBziHIfT3fBRnI8ndbJBVA6vqf0xIjb/WQf0LiImnGFVRdXXqjaPWVuhmHaxHvJ63YPZmsWoiaBNldMtxD3OepKCGOvWNb3mfQSgz2vOx5lCVf1Tqfig+qlCBa+KMM47lnUhl9VYqZrEKclHqK5Zj76P0AOmAKWzwQXvlIfc1Xv6fCtOBQeUwabvrdqB6cM2q62i8Iq/qGCCWbJZg3/oZAOYtd0gnLlNnrJzD1TauQ4yl6BBx1m/2zcPUSFy2JSqquyY684patFER5NQrkKzPmdtjeA1mWlS8KKQ8p7DqBWlIOK4Xh7JeeV4d8vHH3/Ip59+VwUuJJjASdC5U0MyuotLr0C16jcW8a5HONo3OGOyixHLusjMOpKW/XOpnncY55j3W8/Vd7ccwWaYsHaQt75/IE4HcGpcEOy6h+RoeBV0SBPzNHFdF75+cyQFFYLpNWfnnfRF1b/eLHAPwtRAI/XE69V4r0AvlzP392/IOTOnaYjwtLadTT/s8ZUOotvgh62d/fcscG7lPmy9Shu+tz/BiC2qUlKprm5ViutU/T00rD2FvrA6Pj9e9wc8nNtFSdlg234gf+4z2ufYWgf6M036HGjgdXkYDeIei4VOgpIRm3tlPkykd9cFh+lM6kbCtDRzyUNJRqThoqqwOO/HoTCIQfaee/XWkxXeuj7joBkV3Z7FrD2RGJM6t4gK0B9OKl4egqcZpBN9pLiGt797eqGgiZDFjwFtjb8bW691GEjcgE17Zdo/x37daI/syppXI9KYylKrLMsykobaKjEdSOk0Znhrq8o6dpVaCutayUuliM7JLtfFtHrRBK9YtdmqGgSsK+Id5+uFdV1VbLwURE5MaSLnYoYBGkhSmtQ03UQSgvMsy0pZsxJEROdysy8gOvOZ14XSGssSqRWTgVO3j7VoMpXzagEzW8Cu1KyBtrZKShoYSsmm+6t7KZdKyUqmKjlrr9HWTYr6O7UUCDY/WgXpPutoQMqlKhoXIblELRsi0KrQKipKYstXCz2hNO1312CzgyP5UfKMjXNaYqSns6IkbbClmzSwsTFnyl7VZOKK9flLU8MGSxtBzB9XOkt8S8p70GpSUSlEG6tCl4k3rkar2pKJU+Au3PLi/fd5/733+Af/4H9S+J44AmaXn/S7lpVlEwqDG0rX1z0j8VU+QRfJl6YjVDF41rWPITFaHd2covvxys6cvMOz9OROujRCnz9WY3sN9GYuIEJplUY/5PRaBA/LcuX29kSI3swQ2kiIxYhqwJg/rSMLMc1luxfbSMsGcy/Lyvl85nK52G+IFhXOsZ1YP/zxFQ+idgy73T96/6//zC6o9cXfyUFjvgvdHMuyjOymu5OAG0Si9pYrd++n7oPfkyb+F1St+o+tV7rNh7Ynz9FvOuYQr1+rIIVar0Sbn1pLYZ5uaGE2Bhy4aIvFWIQ7AJMpFdsw5uMoVTdenJhSAhqLW5kOR5BCnA94VB7Pp1kVY4IGuFIa8zyPAzGEaBm73o6UJta8kTg6Xd65bVbrqQCC/qZWto7LdaVK4+b2xIvnd/zGP/oW3kWePT/y/vNbzteF5hx5bQYXZ5YlqxOGkRRqUdm7WiPN2JG9T6rEoA4bsvWgvVp/qa2SBoDLZeVyvaeWOoJotDnRx8tFD7wYOB1PfPTe13S4yDmWvPD4cG/uMVbdr5m6Fh6WR6Z50mTFIMMYIo3Gw/nBROQbl+uVJWfWsrAsVw2IpTCZZV/t97Jo4AohcTlfFEoOWpUui/7uarCxd57oJpyrXJYrl+vFZCULOQvnh4VlySCBloW8FFoV6tpYc0GkML+4U2/PtVBzYfKOsmTqsVKWRim65i6Xws2tsmNz0fe7LAtpStqHq0V7ws3Yxre3SINaep+6cbleOMwzMah7TykN6abfVYNwt/NLkze5xAZdcGC3l0stlCJMUyLGBCJGiPK0Ui0oiZHl1A3FZRWJTynRTeVxDmfoSg+mtanoAg5DdOxI9oILyladiMSoyWZK0SzJ9OeCeKZJWwm1VFJKfOMnPsTf3NF84PHNI69fvcGHqDjpjkvq7P+VQqjnoUcIzg2GcCfN+JDUOk8K3gupExNFSDFxmA48vHmN917V0bwGwWfPnsFvwc3tEUF7/nNM1JiIIVoP3qvEYNsY3841Y7BDWc+E6Mitqk8rVrQ6dfNBJubkOF8fefbsFt/F/lFN61GRss0cO1QTW0VE2I14PT1P+3l7uVx4PF9Y18LxqERGEU3SW29xfYnHVzqIvv14uxp98rCDvavL9IqjiyiE6LhesvUXtFeX80pKycgG+a0Lq6SYL5oFhXcH1Ld/9l3jN/uH3vCiaitJ2aWQqBVSjMwpsJYLQqatF2qvjBULskDa7d604lxzZfYzugD1s0gTastKFEC0J+YSdTmTokJQy+UNx2Niub4mnU6E0GjlqpfCAqpmnpoV69ycbvF+TUUY3qzVqsMufFFrGdmuJ3B7e0uuV777ne+SL4/87p/5ndzd3LK8euTh4ZFTmlUPN3qW9YoLjptnifcPdxxuDiyL+nTW2sZgfxdGGHNiVWf6gp9Gpd9Zw4/nM2sWSvG0ptZSL+JhsIzVsUch1A/D+xwOBw4H1fiMMrPWM6Vlrmsg3TZjwzrErKhwDeLXefPmDaVmJSqlxPl8wUWPvzG41fmh39xElZJyXlnXldPhhnk+gjy3RK9Sa+Z4PHL3tWnAzDEmnbsrasvmg2OKMzFMVCnEZ8J1Odndmnjx3i3TrVDKBBQez1EZw7V7ej4z66jAfDzwvjvgrHR8PAeePTuS10kDS/DMhw+IB+2xmSDfWCPH45FaMiVnjocD+eE103Rgmg4453h8vPDmzRt+7ud+isN8oFUNwPN8HIQTgFYrr199xsv3P1Z1JJdI6RnRFLZub295eHjg4eGRVoX33ntvjC4BdOu/63I2oQKhlsbz58/NZ9Tx+Piax/M903TidPqIFJPN2TaWdRnrrJONtOdfeXh4IKWkAvopkvPC+Xym5kgMkxLybE+cr8v4XHlRcYLvf/8Vn/7mJ+qRm7uoehca6BBn64fKO86iLVHVL0SEVc+05vESQVakeGLQvqZIpdSVOSazVKuWBGbAsy6KiNRcKNdFz5tSqM4BRmBqTatwARGH+KAzyFJwEgcoF0LYaWlHUjLCn3e89+I5HjcsIXUMbafL25rNGNto3UCIutqUmCrRVoVer1fuH+55PD+apZ+SmlrVoL6uvx1dXJwhmj/ksQUsZ1nrARFYcyMEJQ0BQ/HDvRV0a90zazcG7tsv/XZw/aJgu6863/3Q8QHnPdZaoYMNU/RMyZNr5tWrV1yv69CMDMFvPcodJOy914pmyeO9a8WVwao0HWWp+BWujyvVhNbbWjjMd9THzHxMBDfjSHqgTZOOYlQ1yJ2mSec3a1PbsJiodR2H1p4Z3a9ta87g34DzyjgUr+QZJ55Pv/OZiuefL3z6+hYnMy440jxx6SL7a+GyZI4lawVZ1rFxpmkeQXQ4d3izQ7PqrFaFUX3wOsOIDFZmJ2MchzSaCSt49TZVpq9Xlx0qgUoJjRQ8yc84I17EcMKjffd5PvHZSQ+4mNRa7Xy5cDwe+ez1awt4m+FwsYq1tkItWnFOMam7mQiNSpXG8XhUPVmDdAFOpxMglKzjSzqzl8it8PGLZFKGlbrCPEXq4UhrERUaT8zzkcfHR5R44tWAOUym/6HWXsHB6zfC82d3rGtRQ+8pMU8z8XhDXldaNSs2afgYiSmxXM5czpXoC7x4RqmNadLXmNOkVU2pFJ8JQS3LFEpU7VsRdWIKHWq0taXKRW3AdqWoJ6v3fozUjJ6+rcXadH9U658p61h7wXmt5KVR2yPOnQnRm+iAirdrW6CR0qzV8+VKR1bmedbksRRyWRV6NVNsJ8LlfMGFyOV6tXsTKFmrp9f3b7ivDZ8mjocTJX8boRG96u52chy78+SJBZhj9OAbAl6oxVtwKUAhrxCT4H2j1pXz49XWObx585p1WXE43ty/plV4/foVuSy0Vvj04VO9hq7h167qpCNaKSWtNl3Eh0nNK3DDDtGh0G6rGxLnEEKrXHPj+bNbhd2rwtWtQHNlQ7CAgLYsrAek52Vr1JLBxZFI957o4+Mj16vem8NhUvlIsCS0visP+cLHVzqIPh1R+QGf+h3lqcMRY68yy/ixDSLQg7+rXQyx+rYFwx9UQe5/5nPBtC/ut97XuwIvqFUYQXsCWVaV2KoaNObkVdEjHaliBs0WGLrdmNQ6ro/3nmSVSLM50c5QDEFVX6zlC9JoFBzJ+g0ZJ57SFvogf22FXLJBT230VzrRqCue6GurbFevyPqjB7luheXQzDV4tUNrStkk+gQJlrhSWuOaM14C1ffxiIHpkx4vLOuqhAan6kuH2Wv13PomUeJITJHVepq1VnLJerAv6riCUyGEeZ4JZxnBUu+vmaWXDZ6WpizEJV+5LBeWvFBq5Xg8UHM2U+RmTFStZM7ns5FttBo53dxwuV62ns9aSXPifDnr4L9pxx7mmeN84nq56pynUwb06eZGq4eq0PO6rrx8+RIQyrpYVu+HO8vj45ll1RnYaTry8rnj/v4Na74CjZIztzfPWNeFUtXBBByn4zPrea6mTKRks+9+qolYE2W/4xynm+es69USNiVXzccjznnWZWFdrqpFHOB4PFkVp2SbdS04PtORnhA18JiutO5Lhdydd7x5/cp8Yq1PHLSSmg/zgGG1jXDW3npUOzcdtNf1o5UYljTrencepMoY3SmljLWg8HIeLYtp0mpR2egdStS1WIr2hqU1fEx4rwf/uhZCdMPoXeFJG3FqrtPCaBZ8pZ9/RtRyzhkz1w397H6O+P61fsI5DY765hzOqx6uIm4YgzbQHk0e0shL3ge6qb2aSMyIJFKatCjZkd2aETanaWK42CS1iusCNk36iRuQVAlek9wpBoITxMHdzY1V9apk5Oh96bHdd1Z3Wz+4r42SFeHC+r3LoiiAyh/KJttplERdrptZxQ97fKWDKL3K6v/8wh/8fBQVUTp8dxPZz1I+4dvsAzVvB7oBVj7pi/5QctHud+UdP//2v3vF0xU2vPOUWohOe10henN70EUMbgjPCyC2YQUjF5jmq25OhXh7lariAxXnFb7sYwPSICNEHzgbc9l5ZTZqtbRJ9w1pv11/WiG8rvzz5MNaP+Np30INo7f7gCh87ZJndZ4qYj0oaEUH/Lu9WhN1CVmLmvcG75AKKxXvzaXCkqNatbK5LpeRvddWmQ9qJdc/j3hPCYJIoWYbWWoKE3WIq5jwtVYEaoZ+uS4sRrq5uTkiNbMuq1W+gvjI6XTi/s0brQ6dJnKnG60gm92jumbSPHH/cK+ydGgwP80Hbm6Eh/sHcsm44LhcrhyPCvd2dvSyLJSqWqrrslBLISbVzZ0PB169euB8vuK943gUQrzj01dnrssFZ2MB16zkLg2YmrTc3KjTyfV6ZV0XvHekNDGvConVWvT6i/AszyzLhVwWS7Aa80FHsXLOlK7sQ0Wainf0Q13EMU9XUlLpwxiTurgZRF9rM3EPhbAPh8OoxL3PtqfCkyAqqCxlKfr3nLPNLwrTpD3ynLvhgDoQIRpIRbb1oS0IS75MpKEf5AYCWcBVCc1mQ/6tic60iM0oV8H5Ng6y3lYYibfXQFjyqup5wQ9UCumjM8YiHqzULXlml8B7p0L5dgIpBD1PXK4LVRT9mKaJnDM3p9PmNBMiIerZoGINSkYKIalRd87Yi9OMUR17EDZ/VO8EsVlrZdPqKdrEkYJDfNQRw7KAg3lSlERKVnQN9NwxoYTWxxjZ9qHeM0uKczVymlDqhkp0reYhdyjbtd+f+T/s8RUPonwucg6toB2s8fYF6YHQ+zD6cx1y6YSCXvrH2I27+7jG/4b3iuhMmkWFd92od1W5IjZioikXnpm1KBmgM8pKvZJ8Gm2RSNDg5j3Nhbeq32qatn1YubL4tbdMdc4uCGk60QikpJJij+czd9ORe3lFmo6ktA6yRkyBdVEYfE8U6lmwt+pSg6MbbOGeRASnQzo9sOnMJEqecDpeE2wGrpRKqY3cGuRGBKro3JcaIkOaAmmOJK8C2posaM+wSR9RqXSZs9b1RKPH4zkdjrjj8Snc7gSPV9Zsh4Za1X6ewcGlFK7LVRMPcXiv5K9aG65pNXo6HFjXlct1gXggTonTzWnco7VoTww6WUUIBwd4ZcwGVWSJKeE7mSMGcOCj2lPp+NO2F2KcmQ9Hm5ETBE86zFzXK9PhyHzUeUKHjUJ0w+tmzxs8ISnhyRMhmKJXFNIUaSRwDRdUVevu2TMeHx/J62KCFJ7b21umFLhePWtetEcdI9OkVmJ5TcSokJwPiSkkYlJDhHXJem+CEr9C9IjTBEadXJ6KC3T0oR8SY685HV0CDXDTPI3ELU3ReoeVeVaSzUhES9GRIaBZi8dURUwO1pleru5zsfEakc1qr+vu9rEV2DxDdbub7J3T73Wpwhgjuay0FBFhzKWGYOIDTteFEnNs8sB1HdzeZ9zLmqKjbM60mdHEdkonqrxBXCOmQEozy7Lw/nuzZcL6OgAxTjtyZkHIBDdTpGzJs/Ugq68WLAMiheRV8jHYHunkH5FCaQ43nagCZV30jJLKslbaqgiUE4/4NpCKVhVtK7WS19WIdm37vG0TzFnXlfv716zrlZTSaNl5b/3nfv++hCBOf3y1g+iT+u/zj8/1Ifd/FRUz6FmVQkIZDKbqF7ZLxOm/gxFvGP2TUeX+0P6n7F7/3TfoXVAuQGDSjeYL3mWkrqzrhcN8wzxPpDRTMsSjIK6Pbqw0UdcXZb/04Aw+VHJZNGP1mg2KUxsrwAKKbqy1ZA5VvS6rsRyv2eBcL1SnPQQ1+dXKYSr16WdynVhkjjNuuxn9c6aUdMNJRSgqdjDdcH58jQvgT3A6zjyUzOW6cgiPzFMkpkn7TDWj+rWBGGaSP9JEWJd1EMJ6cL9er9RaxvWe5sjpeDtIZaB6wB3eVVhQma/VlFz6rGjP/u/v74d/ZoyR4/EOXKPVhVoctQamdOTxrOpFQtVDUCpekkJ30m2ytKpalsWCs5GJSj84tDrRQ91RMYWZqjq0HWbs72ckNVVF2PuYwZKzVeyrirNX8K4Rp8hlueDEEVzSRKNBdDqc36i0qpVNigdCi0SpNl7ikAL5WqlLoxXdP3UtrNOVWlacVFWEqRXxKmbeTFJTxIE/cC1QazaIG4ooezcEjxBorajjZHQKbxtqtAWhsZPASChpmkai0x95NX9Lq6jUcixScg+gyowPIVlVqYhQqQXnJ0RUVEQEpHkVA/DJ4EXd6yL6vD4ay77IGOZPbquEcIpu9OrIBnGIKRBjINtxE4JDnJDzQozHEcQ1FjmV3oz+ieb3Vm0Z0uLUAk3HndTFqcgKNFWDwrMWJUpN86ztAoOr11UJl6VUgtdRoiyBVQJrDRbEnYqRuYYLEedUUCGliegFHzZ+hOpcwxw9NS/EwwHvHHnxnJ7d0Wrmsq6U5arEyeb1PHMqDCN9FMnB8faG00iY9JpUQxPWVUdaXr9WxvHpdDLzDEW0grG+fxiS+PbjKx5E33o4Biz4NjN2fN0yPucaQbaKqNU8YEbBFkZRQ2KGCbeyMXFu0MQ7rv+ux5NqEgv5DjC8ve76Fh16cU9/waplQbzHSyLVR66yMMUTkQnXMsGr7ifugDgoTZ0iolMnjFKyDR47cs3MTmG8PrbSpFGk4K2PWqXiItRyJQTPPB21l9kKp9Mtra1MPlDTgeRnJF+ZphuafA/vYZ4nDscbzuc3TMEjwalKztUjzpOmGXe5Wt7hQHTIujSQphBdrpVsTQ+ttgUJgbqsZBfIzZGLfj0lNxiYel0L1+t5KI9s98Eb21o4HE5DgDtZtTPo761RnMq3LctVR1KaQbvoPKASMjRp2IybD0zzTEyJmAKPb87UUkawdV6s76QqMDSIE6PqrhVyrVwvV258YF27jJvnci3G2NRkTN1zZgRY10auOu/oXNVAYKbWtRVVA3JuCNVX6XOflRADl8uV2jLO61oupeBapLbrRrIQp2uMpv3CkHT8Zs3EZGyCDoe5gBibFVQhp5VMbkKpUJtHnMKeqQVcsArLhNl9XJmOJ67XdejZAuTSCF6ZvaBzlK5FpMOCu7lg5zWwqd9Asaouj33c81gJUVND2YQEGqi3ad+ITken4jwZWUXXVKt1OJnoOEzZyIjSTAtW+2sep4iIKaB5F8jrSk02AiNCLpo8Ygm7ojwzN6fnfPez7+LjRMMrZNqguYDDGN90y0QUWWmq4OWd6joH04h2tTG5RAyOOQYeROdrm5uI4UApC84lgp8ITu37CAnPmZQi4hKSL0x3E60ueD8rl6IszL7h0gaVi4PoHZCZQlIhemmUXICqbSPR60IVso0FhVrJa+a7n77h6197zu/86CMu55Xz9QxemErg6pvN8CubNhq5rHMwuoRnzgXfJta68ni+8P3PvseSr7x4/hJx1ssvhRAmassbLyL/dmPnvgO+ficTlg1V1S+4MfYwT5PqklYVFvDOE30PxtuAL7vA59j6dV/0PvRlvmAM5l2Vp2WP0nSmKjch12JVkmpG+phoKu9CnBLH05H7+3uO86zGv5ZZifMUVpuvAnFOB+MjLOuqOpFdVxMdsG475Z5lWcboSS15fPbgdW6uFtVlXdYr02E2BaM2iFpdpzempAQW2IymrdLXnqgx7dymNCK4YZzepLGuhfv7x+G8kaaJedaRktPxaJqum+1RtJ6NihBsBAv1S9xEFfq97BVKMa9K7XOuptikVaePnmafoZYyhCZOt3c22qCEtD5Del0WWin2ul1KrBM0rOJcM/Go16YLnlc7zHuWXEwco3+GrecM3bmjw+JYRdY9cxWq7nPROmbUTMTA+6h9uOTps5gxqqD7PM+s1zPFiBra/nBm1PD0erJb330OU0UXukdooFSTH+yKMLIRP3rXvG/NDj92qFNbAn0MSvuNMUbdA75XhzpP29/D/t4qrb0Tv7aTYM936DuxIyeaFIi5L23CGk9+7gftc3nrZ2UT+uiEVfsAAQAASURBVBC2ZLn/zt4lKHh1nelJe16yrmMXwSvzVck7xmYHuihHGNfLs8mL9s8p1vJQGFjH5UwEQnTELURv/VodoxKU+XptunaCF2qLSM7kFvFB51BzzlyvlydqXX1Na3VcCCFrxV8z0spo7Sjfo4s1CNIqZcmcz2eOL4+a1NZMrgUvUJqKK3SFLQyBUISpjrVTqwbR6CKXywNv3rzmer1ymGdj724kq9H77trCP0I1+o9HEN09nkCifD447R/9+/2i66ONjStGX+uVpp5bTyvcvvlH3vqjIQFGKtigTemb1wgCgsF3teKCbkxvfdtmm+x4mLl/VFHyUhdKMacZ7/FFbEPbdJ4pg5SmgaLL7HnnaI5RuXnvTEbMbfTwXQIxfs68BQ83eq26zVM/HFttiBmF7w+ZcYC5XYXeM2m7jp0MgVmdrWsmTDomEEJkSioOkcIEcTuMGtsYTX+tz1/29mR+dFmvlKxjI6VkCxhl9DoFVdAJUQ8yF6JKloXANM8DIhRR0kQpxUgPSuwRUbWdsc5Exj0EI6fYeupkFT0g24ARRbA+X9eHFi3dd+QAPSDbJrzttGrtLQsdMTHZN9m0nvssb0dsqhHUlCDjxyG/TxTFkoh9Eum8Ul22a64BssObTgRkO8z7GtAf7wfq/k7pIe/wlqA0XK3MRhQZ62a3j8b7s73rLWg8yXd7K2YEvafnRv/9PdLU+bGjWeE2Us+49rJ99u1M0O8PM3g7i7ZrJJ/7N+Oe9INdGD6F+tI4lIPQe61O3LhOIyl5CzEbakXdJg0xrWLtH7dW1IibptC702GUUou+vtMRuRghZ2P0e8eaM6slrB31cvYazmNjTeoy21pBWofUlRzlu/Y4MpK2Ja+8uH35RJJS0QWFxzeRml2nrH/Wts2pr+XKw+NrHh5ek/PK6WRs37rfM9vv7Z/3yzy+0kFURln5rlLU/us6nLv/xtaTVDPfMJiv2+ZWyFe1TRlP1iuYbXHuMlDZVbtP3ueXgHvf+nvXdXVeez6UrN6JMdKcOsj0Gz5PkQdf8MnRilBdAR+tZ7ll8zjbaLbwdReiDig+bnAQWHb6lmqH6Ov2as85vX5qV6XXsDYTkA8beUN2WXmXENM/elf2fax+uHXIDFAygw8qNh7D9lwj4+49ITugmimfivYW9/fIOWcJwDamAML1crYZwkp3v+mbSYO8vi8fI9E5SDx5z/0Q73/URs0j3lmFWSm10/3bkJ7r16ePcnSLsmbsTSW59UCng/DNWZ/N7gVmBI1B9J35it1H5zZpymKfT6QTXPSaeR+IppsoIuR1teu2Gx2wmcEu9dbXaj+49TWsopI6doK+hLfntixfZLRE+j1/m1DT75d78r02pPSwiuddu6sH0Ea3xVLimkWwsSZ25aJdiy5W0t+mBq+RXuwQjLE+5Knps/3YuEa7jT0SIoeMc6Rv/X6d+2v1f+tzG0MfhqBKD7Idwek98i4vKOOzyLimfVa025OpJKGqJjmnfcOb40xwgNRxTrSy4qNCsnm9Mh9uWK4rTRIOTxWHOK9kq6bJtzM0pX8O9U1VGJrADmnwuKgEz1Yr8zxTcmEthbu7O0s+GJ+5r7lmn7F/TvUu7j3rNua9Hx4eeHOvIvPizLnJheGBi52r9ck9/W0SRD8frnYb43PrV9714+MGdzZXh8oUBttrzHaoaAcBSVf8+eJA+eT1f8DX9wdxf0+lqGTbcl7JpTIdDhyPR87L1T69bpgpBOY08ezFjxGmR5blaqa0EMM0ej0hRlIuOKlIjdv1ampC7BwUp70VZUk+kJv2eZANYhxFjteB8Jo7SWALigpbeoVgWrSMRwe7u2CAEkmEnKG1OCoLEXh4eKB7I7pJmcTXJZMOutDXZWW5XEkhsoqOc3TCz4B0Yti0Y0Ow198g6w6fllJMkaU9ORxVTDsSfbdM082+d1uppQw7qf57WsWpSIT34LIGydJ7XuiYkbYSnAV6GWM3znUhEBV+WNcVqY1wCKaks/dINDcQ+yMWMGNISBJKNelCkwN0weNjJFQ3EqAUJ5VGxIzYY2ROiZozbZ8osKk+9T1QSiPFMFjZndVYW90ZN2jlA+9Md7cq2P5EH0wNyWaHhSfXxnk9QBVJkeHMsk/KtkBrDNV9kScbR0Lfn577rZmdHlb92VpyVlXV0t6dr8vTw3wTAXj7MDb8YBdwR0Iz5kE3+8LWmsGzov3yNasry1X7xJFgCU/usNqoihU16mYPdl2kmoCIA+9YaxljHbqmNKuOIRFCAoGyVo43L6nf/hYxTRwOz5nkNTfPP4LyKXd3zwkxAW+4uXlvkNA0GYHD4birsg2RKTpK5HezmM4FxDsymZRmnL+wlszdndrk1ZpVjMEHkIKUHXKwg+QbzkRDlGnbWuP16894eHig1MrpdGsksWbXtVJzIfhd9SqauH7Zx1c8iH65x+cr0e3RD9h5nk2PVE2QexY5dHNbt0baHwzw7mPh80HzC/uiPA2gT9+XU2JLyRxPieM8U9b1SZUUouf581v+3v/49/j6138MlzOuqBGwT571/HpsTGdSYsoWXel+qCJqCK2vW61AjXboTyj0WUalEWOiNdG5rzSxXO41ywxhwLM9W+/9LRf8GKmJxogGrA8Ht7cnXt+/oa5aOczTxPF04rpeaFWsFwdxSlpRlTyE2LX3kZ8Et2BapF0cQ6TPHOqwtbqh6KaZ55k+rK6zwxNxSla1Vrp8WK0FL43FpPQQQWrD45BiWX7Tw9fHyHK9GuQWOBxPlFLwKYJruOqevNdm1W4wjdB1XYkxkszcvNY6KPk96Opq3pxx+mMMj+8CU6ndvYJx0I+RHOsl9b3QPU73fU7YbNn6o3tFehco5u3onaf7rToT8w7BtGedEWaCftbeq37SwnB9VMSNmND33KY4owmd3rc2Ksi+b/Z7aMDe79if70D56XwErWh0f2xBaAvYfe1uqFQPvx0WtIKsgxho0kntOb5qQysTVi2+ektAR3b8k/VRayUeT8QQuJbMcq1UCre3z0nTRK4ZHs/WMthGzPrzpJRM7F4LhePxSE9qtQ+bmNJsvfIJ56L2SJOjyYL3GVym1gsPD58xv3zJw+NnEIq6/dQz52sd7ZE+w1navJ0/PgKeZrPZGP/EZoVsbS+ssZHzgojwtfc/0N+xW+hx4D1ZYD9y2JPXUupYp07g/v6e82VBCKR0VIlMu3eKT1SarFwu6m+rjPlNAObLPL76QbRDLn2h7rIx+/YP+NUtc1QHl75B/DiAOrGmZ1LQA+Tnd2BTKt47N+14b/213xFQ9/0LlTNzXC9nbm/vmKZJKw4XcHQISZmYhymxrFckC9FFqtdG/OxnFgfBK0kpoGSTOhQ69PViDCrTV5uJ1Zs58ZoJySpWGiJ3OOc4HE56zYL+3ptaVYnEeqCtqamwc9pDCiHiaKYeYxR4nKnTPE1WQA+bVjPeWX+ERmmV67qYjN3CpcI5JNKU8M4INN0j0jmqFFxhNybSdFxnPpBLfhIgpmkihbgd5mwHZQ9gHXaLEml5m4XTQLSymiF1v8+1VXWWaIIXGYmDvjd5kmhs4v16X4ZIQFDo2ntPsoDV4b1eeXV4EOufdoF/PNSlspSFXFS44Xg8ovJwMqr1m5ubJwd2KYUpTeNz9D6oe6v3uO/hteGm4e36h20Luu1AahW1BjMYuSEEefqcoMnKPKfP7bVeOau36Jb8op/8c4G0Xye/r6Z32qB6bhgE6jVxbK2aSLoiOfgtWfHOjSCu92E7amzlGcIQ6FVnE2Wo9l5fc08P/j7K0ooM5KkZkbGJrvtg98qFMFTJqkCRNngKfe9Ky6ToNTzUqmHdO5sL3SD4Q5ooNauil3cmeB/IuVjn16mDjpu5O96yZkeavQZaf+T2eMPreCLEAzHMTLMjhBlz70O8JjYxTCbx64bvrsMRokLxHbruutkiWnWqylDj7vak7jwmEynVPFNbL7z3xYfH+94CqZQ1c39/z7Kspves67LYnK0mEHVck7Iseh9bG0SjL/P4SgfR0bOAke6NgxhGX+Dt33GOcQDpr24QTH+4TnRgy3j2fbWeHY8DZrRXvjhs7w+e/c1/shAsq1ZGmt7seZ5xzhvU0HuAGPkEDrOKi5emmpFNHLU5Vpv9U0cF1KqpQaPYbF0nvehnbEZg8F5HI9Z1VcmG1sxJXivjTi33IRB8oNP7VRlom8MLQRMQaTqE78NW1XuvFlgCOhy90zDFMv6OULWmSiNrVqLPer1ydpVDSByOR5Ixf3vF6Bw4o8uved2RftQPNUiwA9QNeFAhrWp9kW0D9ep13NoYnsCpOjsn4+ecwyqpSaHXohVrbxfo/dfr7q0vpetCe9A94KeUWNbtvaugudvWx1g7vW/ph0dms+cpbavMR/XZM3hpOPPt3N5Xn5UT65vvGa4Vh45qDDDU7lMPNNia9D4Ykaf3TvWehBgoZfPUFBE7SBk/49yW3PZ/a4JlIgreI7VQatkg3CYjme7Ih21+ez/OpNx0o+5xKd3A+l/X/27XQywhltbZAl/Qfx2FsKEeu+q1f/+LkuvOaO9iH08Cqygnoln/23vGc28CDW5UYVpdYRJ/ek2FSrcxHB64VmmVXAZ6IGBs/ABeR3SqVFJUlx11P8qUvOj89Xo11vyi40Tmw2zdbf2s/Z4ghpHbGdq8kTZVJ9vuvjJ0fdTPZOvjdJgH1A3ouvK9lt8TMTU49oSilqouLY+P2rKYZkLoXINCiJuHsJ45niZlawm1d93pdz++4kF0F7b2UOgXQKz0n3b2M7sgqj0v63FqWvnkBj0NvHoQ9q9Lj6Q/4GX3AbP/9+1AOqpq+3O9XIixC0GYxJwdvA5d6F7csGcqo6ejWf51XYc3oIgerrGqPVjpUoC77H0IQFsmvixXErsNKnC9XnDOs67ZDm49iHNWf8lqtlQ9UNammaU3GcGay5jBCiGMsYTORu2Hg9tuztggVxaTh8usTtl7pe1HSzY2rENlzfQA6gQyb7OWJmpgt1rJUJuIQq0q5K0ZcrUelhvBq/dTBbXU8kErT7XGUlWjFHXTIkIr+hwS/JPDdb8OoMNR+t5iVCFyEa2A1KKO8b73a+nJgY3KIfpStApx2xiXD34cdDpdpElHV0fq1bCIQvUafHYqN1ZJeaeV76jyRqXag7rayA1KrAWA6XAwj9GsfqEiFvh3IxhO119/P52I1mFV1bnV3+1rxzbO2Bd9hz6pmnHYiO04L7brNo79LalyugK7pJ+z0ZItsd5K7S2Ifj451vfl6L6ZY5+z/30ZwbON/Wpog9c1LM569KYb673HtY1E1L2Dn35uG/eKuje664lzW48eGK93vV5MoU2FP0pdbR1ecGjwWfOCNG0BiDSVIHRiYvlqebiHk0Pz9NlNb//T++zHfe6kLwdKIrQgjwgHY77LgMp3134UHJq4DCZ9U61nlfc7qyiH3ddaCt1BqFnfXl9Zk+ONn8CXfnylg6g+9p/27c7nbrZrZKrogO/u95xT+FLhNiMkGEQl3TqFfQDd+jTsyEWfe2fy9p3YoKn9Yfjk+/oGgcbjwwPvf/DSnCH6yIcOg2twUKatM4iqtQXnI95XQmxcr4vaXzWGpFxFkKa9ib58bVcPckifPQvhyuFw4Hq9WIbvyXXRKum64EPgcDwgDXXGCAHGYdAJPjY3Wd3oM63rCk0oObMsKz4lfU2Dx1S/ulmvxlNElEJ/KeQP3tNq0psqS/AjgVFIzmbn7HDWKn4LDiqSvZGHWlOXnODcCGJN1EA72ThK09RdK8TWyFYJ9cy+gYp+d0XsfkCLro+xHndVoQySlfZkV4ON96pKDqzn6Eihs6V3iMlbC0/JNIWSK3E6jMSwlDzm9cAQzKpkFhfSCLJ7mLhXO/vH5+QcexvEYQeys3ng3ts1F5ygz3V7e6eHedFZZl1TnQnfq8AuCP40QRhVpqEXT8aPdixjdhCfHcv9Yln1/LlNt3sttbVTIh2IlAFVO3svGxiwG2UZSUCglHWME/X33Wdbn7C9LZD2yklJTUZytARNf16TM2cztkhPL8Pbb18rfRMBqW1jOHvnBiu738ehziXdkNtR26rByqlYQ84L4NW+zIH3CR9mIm9oolrdMSlhEbYEc+M7FES0v989hjfGccERRrWvbHWFpHOxUawmHOaJsiwmXoEmpfY66Nunj+qMBLdUk/e7p6wr0+lg2t59JEvG2lHoOpJNp7kjYu23SyWKlx9Y/enjLfDGd8llXZDam1P7Iw1Sw+fAMur9weKBQIyOh4cHnBP1CAwq/N6KQZKD4buvPjuRYTfPaBRD7x3UTG0rU4qczxdevXrFfDwiBKso+sfJQEBiU9p5EdL0HJkaKR6YDkfECbVkbmaVThs9OOdIeGqQsXHHRm36mYedV2s47jjYAR/wSj2/X5n8geIXQvJaXUjjePQ8PqJ6tlJNL3VmqcLJGdtP7wApHamlQoUQmuqC2jX2CNRG9p5LLgZpK1t4Oh6Zb058GL5OEjhNsxqCu0Cc4qjinHNqED3NxgTevCurKaZU07+tRWHphkq51XG4B1ZUDaWb+4qokW8XQ/A+jR66FsCe4LR/2UolhaBqOnShb5BS7QD1VCC3yqv7N0/IQWPo3nmiwZtrLVA9h+ME66qzvSEiTcgts5SFtajrTEjhCcTqgkcKXB6vBu2rks0wDbDrq8eBYwoR1/QaSOyiCar2tK7r9rx24KcpjaChh1TF+Tocc2ptxBi4f/Oamhe828hU3usRFIyTAE330ZRsnAY6aSeEgJQMreBdBcl4VP7PcCPt5XVxd7wyQC03fUKM8htZZZPH09nZuqxWhW4iFaWUEZKda2O/dOuzvqe1J5rG5+6JrncmFr9DD6roPqQ5nPSxIJBmr6RSQhoYmieVipMMslqvFXDe+qSi2tPiaQTViG4QQsI7x+lwoJXMYq2pGD3OV5oEvJvMum0FCUYmLIgrzPOJFJupM0VOhyNLnEhRYAIfEj7OT7gN/Zr1HC+kg1X5Ee9UXD7uJDY7WP5wfuR0uoV1QeIjbfLcJOEhN9yacE2oFFbJJHcYIypNVOBFWyeV6+XMq1dveHi8QJiYp1mTgrKg/N2gUqrd09Z6o60WHh8fjFnOl358tYMobC2N/Rf6Q0tJ+1qfqRp15egR+RCYp6NWXcsyNghsPZ49XAUYLNhn5vSL+77q0yp0cyQZ79I5tHATqsGbMQSWZSHnwuFw5MMPP+Tx4TKyVpGGx4bt33rCOU3cv3nDLQ6flA05m5OLCixoxVRLxQ32n0LEwemYivfeRBj0otaayVl1aXE6jnG5PPDyxcecr59RSx59VBG1lrteryz/H/b+pEeWZFkTxD7Rwcw94gyZee97r6pZ7KomQK4aJNggCG4Jbrgi+HMJguSiAS6J4qKGLk71anx1pzxDRLi7maqKcCEiqmoecTLzFnqT97UlIk+EDzboIOMnn2x7B2q1UkECpBCRY8KlXBGElRmlBeys7+eUsMeIWjRXESX1GjMQIcWIh4dzL/1Aqb0EQQiQTVDaYENqtSLHhM0Iqat99nQ6YS+7lgtUBQhl0vGoImgOkhEjnICAp1B+CAHJrGExowlN+zrqGAVFUbdRxwu4d20hSqPjK61ia1r2wkGNNoJ6oefzWTlFRcCtopbaQTQeLVDP2ej6LAwYLBy65qXz7nLVvo4iSvQvNEpR3IMjJgQjA2fyDjfuvfQgaPda5zXOhlDO3tAeQI5JyzNAKNsNEQHVGjh7iYcqJAt3iqMlNSqUY9ICfYJFe0bZh9cN7vveQ9Zu7CIYpzBpPXDjCoSs/LIhdA8YEAgPkJemNgbDEsh5Zq1kBwK0CnHAWCAt1g9Bc3DiYCBBlKZzYREZaQyk0EONWopkihRa01wtIiQAmCxvSQSkCEFFNkrJUjZLlaSeOxcRbKRyxoFyXrZVa50qDHS+HORWLV+/LAseHx/x6dOlg5sU3FaxPiZtYJEWtBBRRJsgbiyoTIjESEbc0qx3MKJGQAQj4qZe765PmDK40AHMyRAs0hCrdgSqtWB9d0KKAVysqxPUnIoSdLz72qRuhG5bwcvLBZfLCxpXnM4nk7cjxy+sJU/NvFYlkqm4XG9vRA9//vj1K1EMxWZ/3SmYEdIh6v4APPwAkR42y1kLh2EWobZIg4Wb/DpqhWpXgEkQgbpCfTNQ22/yCFLyhaACJeJ2KyCK+PjhPWrjIX3705m1J8GKx/XF05rx9PIJYSEsvGIrO5qVSZTa+ve4ABENKSlLBxgIORyEYwehJG0Urf0/A3JSYuqcF2jKwhqXhwgWMaUXelhSIesVZDmxGCPI8h0aPjfvxaxW46VBA/qmUMVSe76iltJbkBVYHZ+xnXgutofERHOW+773v3vJypQrdUXtQhCAsUGZweIeCwiU4silkD5LXDLI5rIx43rblJpRjDSDALFwFoIiRJuokiKogaGNA5rlhulYDyro4ejWChpXMNeedkgxotgzOMkFRDtbCCuIKAZgyRGRopYLGcgjhqDALPGaRenpjVqr5XGNoQoaMoR5x601xJwQxMAp5v2SjLUdbG8BQCRAeZAEMej68T6Xnt9yI4OU6R7SBESM0e1DDNUdsO8V0qTrePf2PGQYyDOkrKxNjmC22QxBjTMxxdAMmBYnw9g3mBMFNDcmLFw9pVXNoFLjnCY54AjsWRb5OEfP22Owk8Hywx6irBbe7cCoHkoeIUyPlqjBPxDcGtZ2Kj8H3nmvZPPebX+ysClY6xrDwOm0olVBoAXRWpSlpNG3fCJLuRBOy9oVdohKOeiMXZ7VUHtNuxnVpsAlbkpXKGaYhhhwu17RWsXD49nKZdRwCnYeCHVl6HSVIgKwUpVeb1ds+4YYDQXsMtbWiYIBFXuhjRsKbpcXBVKmiIiI2vPtP3/86pXoWOt097ot9ru8jmukwyYxT8fzORjpFfu/h3f1eh73Z3b4/vy+DOf37TtG9yR7jkw3974pJdu6rlhPJ1yuFn6bHnRg/FRoQtRDXdYFz5eb9tszKHiFoXXZvSsXftVQf0p4wOI5OtcWBBH1lkSi9SI1wJBZ2UohNuoAvckzejiNDRSlC9U/64rHw9sqF0gbh0N63s7LIAbnq7Y5atYhhplRLUQXNR4HMcpBV6Js4TLxHwyQD0GVdwxhiLbJqIJIR6KKw7VkytHZGqrcsGDR3COjIwMXi16QgTZEyMZcv+NkBIkClqRlM2ygLC//mfsbhjAAJN5Vxuc/Ru0rC1HYRm2tC01xJRyCKQIDCxE6d3JMqeeZfFxS1M8L65wZVXNfu/6fepsAOb8sjVSGr7voPTY7SEi6NwrBoCkUhkzrx4V+CGNDiZGLAyoEmz2f+NqhMTe6lM1AszSLh6N8HkNwYJABiez53fd2deZjx3bPavCNPW3LWMnrQ7Ba83FP3jTa963YOuuK3picZiAgQUPNXJ2IYYCTXKmKDA5ZT9l0BcpsRkUwQwUYihj9GiLcoxbqHcJatwEpAnVXMnsSBkSBdNKqGhWi3yNa0YFkPIXPmTWSY6VSQhENih5mFs3d8qh4kBCwW77y8eGEEIaRJWScIiag9RptGMyl4HK9qCfbGpYlqbE2RVMIgLc/DJYT5qYNK5gZS1h0n5bR6efnjl+3Ev2GAj18ZFKW9yHQ+du9NoxmbzIchKYLfg1bxL6o52vNyLF+Nc+P3jmoBHShA9Z2Wuu6YlkXlOq5JE/ae0UVxiay8zBXnNaMLRGW5RHrotaj6jQx0oEG5jqQek1UiUKrwqvlJfVaet5SG0CMbbuaAhHs5QqWCkZVrl4DUmzb1j19Zja4vIqgUnbsm1rIIQXrdmEo42AGxL5rmM7CjQr9b2N2TSHq36ZU4eTsSmPITRWMkCqzaqEzYrNCWRSdJ+Nv59BdlkVT7LahqQkUbR8MMGShOfMqvASJawWbBU5iSjAEgLX1FhxMwtoHsVLVULJt0kQBa8rYcdPicyvtAavHBRIDDLGByBQkUmtFzAs6Ftk8KuGGfa94fOeKwD2NUZbktH0DQDQheLsStP6tNu6BgUxmLDXRsaKg/4YjzZx+3wyQ4Sj2vaU9INWny8xdELKR1NfKiMnpDkVLH8xALbWilM3KpbTbj+41wyyI5hERqBNYkOZdhkdkukwjBTzKmpgRROt63TMiF8Ck4X0SWGsYCz83BrH9hADihiA4yoagQDcdCx8b9T4VWGSIcF8nbMYcQ6kRjRKyVV2rbAZtziobujaElafZeAJANpCTdPyFGjLbtvX5YmYLoeo6rqWhVq+F3VBuFwhX1Lph37Vl5PPLZ5xywl43BfRZj9AQotVto0eFELVbFIUEilnJ6WM0zF1ADEYiUgvODx/w5dMTWBgfT0qM0CBItmcbBIWANJU1OeJc6f2ecNs2EAE5R0AaRKIZaAMQt+8VIKXIlKYlfyllxJRAgIXXf9nxq1aiZJDptwKoXm84H90yxHGzkwxQhJYC0ASfn9grdG92xTqj3ea86Jx/sK9N4AXzb+1vbqNiO+cV799/RAgBt107qCigIXY7wUNa+sBe38h4eFjx+/oVtV2xUkCIarWty4JaGppoITJR0AJoErUsQVhyhLRb31TuRawrQQEdKwIFLPmMQCtiOiHQAq6EsrXOSHM6ndSzI+plLEQErhW1FDBp/WHhhiQmDGyxbrXAPYQQgjbYLVU9SS/Q54ar5S22siMAuGw35FIBOmOrO/aiCMOyN8SUFdlnlq2W6ai1PZeT5JzBe8G+F2UligasSrF/1+d7gQJv9sYojcF7RTzrPLRSUfeCdV01D+ZUfO5VtYaEoCE/W0zXfUfcNxRhIGiHF83tBQO8sbZHqzcwM96/fw/EAMpGhE9KTdg2QmlqEIWUwEKIaQEooNYdpRU8rCuWtGj/TggCWd2lhSmrMEike6oaIjUF7Z5szmgh6I+RZyBF6yeKkatbF0RO2PcN1+tF90VMQEwgK/0Ry2FSjGZsMBiEdV0QcoLUgFoAYq1RpRi6EhQRxCqI1SIEAgSyvO8aES3kL7Vq7SFrBx3UAtSqxltlcNDca6kFxfAABdyVi0dQ0CooRRRuuNWC2CNDjGqkCGA9Tyw7aiu9llWYVO8akXvZi5vDYAq9d6wbCpddUaKlaC/cjTU/GVJBs448zQgztv0GR/iSBLRSNFdvhqOvXc+PsnmnzvDlIWYvQ4m29jWSA4TlOzw/7QjLR6TlhJAWpFMGJOLx428Qb89gqTg/vDdjcuSuVZ6obE0paUP1gC7XvMmBeuQCJkGOgPY1FpwfzuCqACmPxmm5D8CVUUTrx1kUjPblyxe8vLxoOiQrsxe3HUt+MG/d5XUCUVPmslpQqxKRpGU1g6PhVQDzJ45ftRKdDw+6/vzhn5q+QcaeQYSODRLPZVDfUBBADhUGdFA8M+x9XOv+rlSR9pyIeWQ//vFP+M1vfqObxxh1vLHynS3QLVp9Bq1xPJ9XtBYRcEKkMwiM00kpxfIiSEnDuLVtyDlaOEdjNnlNuO2191DUjd+wnB9weX6GyI4QM2IGQA0hMlq7IcYFeQlYcgYBOJ9O2LetE9I7Uk8TomrFcwMkKSGBEMO5biha8bUJP++lGWMAWPkyIdoQ+Hw+44UbtusV+7ah7g2Pj494eX5CbRUpZ7XcIXi+vmgIWDQUGkJQw6I1lFohUGBFpIht29FatVKBivN+HYxF0LDQcvnaeXLViW+4BQ077rs2OU71BrDSI3p5iYeYz48PyqJkBd2XywXvbleQhaOK5Xw/NkUV11qwl4JSd53tEHCz76eU0GpFqU2p+6qXlGgLPfVaC2rZUWpFWk9oove53W4oVpx+Op3g3T3IvKMW1DvZq95PCBGnMjiCAcsnX1+QowqsaqUce9nxoXzAvt3QWBVtrRU3/j3Kvini2WgY37WGas/ZiuZ6l8tNaTj3zYr5NcdWSXC9vGAv6v3sjfGSBbVoznNJCSyMaIA0LYOxulib99bUMItJPfPH+gHFkNrBcr0dOGU5bohof96UlHDAlAUFXfs6x5ozrpXxvlVtoddJPgg5KV9rqTofFAJiAG4i2MuO7XZVb44batJeo5s1lN/2G777zd/oPW8+/lqKsy5n1FqwbVfdyzEru5nNsxrDa1+DIQTEFHE6nbpRn/OC0+kRnz79iJQWLbnab+DGeDgveP7yCSknsNzQSsV6OmO7voDbBSTaZL3xpgaxtTKbu8UIFKwGDkAjpGUBmubLyaJVkIYlBu0/bPf6/uM7VcxV1yPMLwqNUKShVDZPsuHl6ROenpTi9HQ+I+eE2/VZI0ak+9zL7gaNpZe1VJwfTiAE7Jvu4VL/voRz7SBLylvK5vDOKyXm+ZN+jPKDEd7yz2ruZXRbOObF7mvYDlf2EO7doa+1HuZyntSHh4cR2rVjsJA4q4fYvR7JvEUYp3VB4w17vaDWBJGGrWxY0mK5sqC5L+zT93x4rKXRxIJizgFCiFjyCYEiuCqlGxAV2h4SiKLWj5a9j3Wv6ZtqDyFAinmErGBttsRFt5YjNHhJiyIIubGiZpuWzuz7jrxkI2ff++ylJYGSoq0bqXVamwZlmpX6aspGUES9h2JgDN53LOuKXSpKK0hGURi5YW+1e6zMDEkJu2ju1cNoX7ereuNS0aRhQYSQCo6Nq7aGixGVK8AVV6PhY9bSlZOoEqml9JDaf/O//l/hX/yLf4H/9Lvf43q9YVk0HC6kwKutVlz3XZt7U8KtVANMAG0nhHxCbYr0blXzWD9++YLHRwa3Yp6JldXUaIQTSqjBwljbikvdNUIgAt6BHQrUYrP+AWBdVqys4fhi995aw7XsuO0bAOD9aYWAcdl3a8WmNba1VKRV88O1em6LbaIqdq7YbZxbbVjKjq1VJRWhgK01XHYdtxACKhawMLIt8Gq5Y4JSXgoHtFo0TQJBk4bEOuet1t5PdzGO6Z6XBRQVFYDSgCIWdpcASRGFlUAihKhoW0N6V8vPiwTkpM0UmAiNrEY7RDQSFG7YmhqxrTKaCIroOhUIKsRalTkTkbNP+X5Dl0tRW/H09er0jopEv5NDIr1mNIaEfVfyd+UCVHKHNa0otxveffwOMSiOI4DUo7dqAa+1VKuSwG2qqSdNgyBGDamTYQdAXW4DZuDGiMbK80yR8P7Du96FyYWVCMAt9HZ+rTVstys+f/4Rt+0FMa8QUgkfYwLFAKcDdbmkHrlGJUNSikyX7+74/Dko3b8IJdof2HXm0IJ3/96rVAvpYoAMjop0gFu8jG8mVgeOivRwL3fHrBz7d2EAmVrx7vHd4bwQnphbPOI7AAUkhvO1yc9LhqApgXPZVDlXXXSRDQgRlPTbw14uCOteB6JSxqb0MKoXw3u+r7ZquY8AaYycEy6XS1+Evlid69XPG528oKMGbVyYQKzeqfOcOlOOE0AAGCFYqOHAzGjQcYpLApLSGwpJZ22iQIbmrRBDVXvZjzbWVpTgsq49X+e5Kl8v3geSRZlZUlSwVTHlej6fkXJS79Y6YwRnZCELqZExIwmsZ6kR2NtC0Oi8hk6JgOvt1p+hSYNQ0npWIhRm7FVBYY0Bilq07q2+Gjw8CzVAAGQiFFbBzsxG66ZhY4a212Ie1HAOHBO/Zyf1tjyng0bW9WSKXc8doACQKloKIiK47ju0qXmGUOjAEr1PoLJYeZGuk0TKC1vBaCS9162YEeHrfjYGFaSia2JZHH5nwpCm9A3Q3wOorzef59oYGc7wZec0ow7mqbKvXUtF+Oe8ubXnY5s4wboxD3Gz57D1S6mbxSyCiDBwAfZMsPSRyqBRn9oFQ8dJ6EuO9vW97dGgI9BqgA0dDOhr31G7bCQMKSnqNqakNdwiiClrXShZWRYRQkgQriY7rByMyCJ6AIRAohSGc+/dWV4EZly3G/ZSEHLAu4eHvt9dznHT9SI2FmXfcb1c8Pz8jNYq8umszgmzGe1uBI9wrn5P74GCkpk0A+OBJsT+Lzx+9UrUvUrTMZbyHL7mwNnpX6o4x/ep01BRJwgnmhSXKDDDJ3tWmm/97tbS4R6nF+YQsAutHsPf9m4hAs4gNJ+I4NxlipBEtzhzygCpkqyl9VyaCJBMiYKgSgdiG00XTbPuDtwGrJ4bg1PFXtTboyCoTRXdvt26kVHKbl1dBiOKPrMqV+rlAgNp64JacyFBlQU3pJgnha2KL1LsoeGdR2sqAAOVSJYnMcElLB00AlIQxr5vKgAIWnvXKlpTVhSwRQBEQ3hs9z/30vSNvsSkwJNSUZoq4sfTCSlncG2opQCsLD0pRASHFEpT4FBTqeIAJkfLxhBUyUS1rv+f//SfIiRF3eacuqAtrRmHcENMuj7SkhH2BOJBTMG6gBSMBEVILjkrrRobCUAIoBQQc+osWCKGkiSNFAQrjwAIS1JyCWIo2pkEa0wIKYKq9YMEEJKSV1CMqKXg68szliXj47uPCnqxmlAJ2m/VlXgT9YQ9R9tE4TcxBkRkkJVTgaaa3RDBZMhuFpCI1ewKYA3oCYRE6jM1M5qDla1FUrIVnoSCd01xInNmRo7Z5hG95Aky8A4wDmH35gcwyT5sitXXbG0NucsLV3ARQFNDhQfHNEEN1iqtG6BqTHtf4LlWfXR4Ct0I1z2nCjn0aIEfzn0NAMuy9vw9QCAnFAla2gVhhJw17w71rMnSSkoNaApYTBgLAK+BdSTsto12b6KmIhEBRZth7HXHaV3x7nRSMgsLI6nhDOwsiHaPt9sNzy9PuNyuCDGoHBQHXmVDvVtTcQiCyS2tUAjmOOk1uSpY6++VEhWfo/sQ7qSIZmsNrz4LOL1c35TGVUs0Qq7VitrdM+qnusuFen/MoTQnD3hSpFr+kfD8/AJmxof378G1QKSpAg9Kgac97cJ0Hs0bNjRFmBIpMKEx3p0XnHLC+XTGejph2ze8WxNQTViRFqBnYTAVowGz2jI26kBCb4YdnfjAAEClavgTCKj1AoDRGChtByGN+kQIRAr2/QqiRyhV3ShbiNE2vz0L0QKhDaUVhJi7p82yIUVHFUZQVCPhcrvgu/oD0Aw5KTu2veLl5cU8WrKOKsBeTCk00Vo3APtesJxPYAIqD29fwD688C4azdqaiYO/WLBzwV41rMhEWM4rmAR7LR2MJhDc6g7KURHEpON8KzfQrh4zIaLVhhwXxKCNtpt9rkHwh08/4ocffov1/AhKC55fnrBdrnhsaoXHoIjHOlnaAIOEIbUhhkcVhA5uEk17aIRiR2XN/eacsJ60TVZjQkRCIEFj9WyZAIkExAjKEW1vkETa/qwKwpptjkIncYhEkwCHtZeLqsylgaDI49IY1BjJgUz+IwoZhBjimrR2mtybkAEKZBCqCWLdPEARVURNjNRczEuWaJ4erP5aUHnmlKXenNobt3tutUegAMNPKNrXldFM1u8t8bROhEBWJwvz1BEJ294Ql6wlO00NOWYgJm31x+bxtipQkDn1cpBmVKQhEihUgAoYShYvZK32AhRc2DwSIlZqRhBSo0m4KFqWBWXbkYJeR0oDjNVq316MYL5ojXLdkffPuD7fwPETzHrFy0ZY80mjvCYnUwzQioBFqf+iQEJDXATgoikkEASaFgoScF7PkESQ3PD+IaLtAKEacItAHFDLTQkg6gu2yxd8+vIZz7eCv/mbvxkpIxFlKyNCyAlcdpRdGYu0hDCbZ6/7JkDLdVobuJBfevyqlehPHd/KSd4fc5jjAIaBK8iBzHwbPPTT570vgQEUqXa5XPrvM5oNFLp3/dZzzD71/fPGpNRhIRAeHx9Q2g4JrE1ooZbwGiNqfezhUPeGPfRaa+k5wBgTsFc8rErb9XR5wZITwAk5PahwLoqmvP3h1r8HANu2Y13eI0W1ZEvZEWOy8waD2HvINlgXmHpA0W3bFevyoNZiUI9EpOG2XbSKIS8KSCpVC+9F8yAg5Va9Xi9Y15Nap1nQuGq+iwmJEta0otl87pvmKFNMnecUrJGKYGHoGJWsIIaIHDMiRazrgtakKwqnTay3GyIIyXptMjNyyDgvJ312MChG7K0inTOQCe12RbkVCAEfvvuImAOen58VcRgJ63nBuw+P1g7O0M87EBKBIkAWKyQh5CXq6wnYd2WBQlDyDEEGID3yktKCEAqAYuAWUeAJqUfqvUxBhMUIwVHMaOiRG621XvIJRMD7h0e8iDLbaFsx9eITGUlIICzLivP5rCjJ1hCChhBzzojJPHBp3R62wBAAC1Oy1uO2oIT3S9R614WUw1k5sgMk6t7OE+DICRByzuZttr67XBb4/lgMkDT4cKfUj0Umot+jVDhhRsdOmIHuJWpE2lCg7gUhCSgIUlZ8gPO+enmI7pWKzVh8SIAlJhRu4CYdrX5aVpSiue4QXDmoB+p9d91RAIBSFf0fLCIRYhhEJDbaFAhCjFqAnB8BrhAUPOTfoqwr3p3Wfo6UyQIcoaOPs7VRXNeTzoN5ntyqRgXMQPeQtZN+RCKsecGSz9hfCkplK6NSBjT1NCs+f/qEP/7pj3h+/oqHh7MCMUuxCJfHIBW5m1IEt4CXlwv2fcfj43vDJSi/d4wRt5tg2y5WofHLVeNfphLVmK4lrufQ7reVH90pXEfqCsJhU/U86hvKcfq2/XAXQL3EwoSSJ/ydzUXP8RpX/ercs5c9va8C4YxAC4QjJCSFaoO0YD4EZYxpDRAFfAQDEFSxriVBAUUEFaQUNAzTrKUWt4LTacV2vYBZgRgIgrxkNBakvCCV3QTWcvfcAQ/nRzw/f0atBY8P70AUsJW9l8ecTtrrcy8bLO6CvCTUtls9KmE9LVjXxcixNTK5phXvHx/RqirrvdzAKeF0fo8UUhc+IZ4QhPB4fkRJRUN0gbBvOz68+4BiHWbcsFjX1QSoN4NWgf5wfgdetQRBhHFaVsQUlYyiNsSYcH6/WulMRaMECYxlSXhYTogSUINGHVgKFpBS3YaElA0VzgC2itQE5xARU0SKCacQOvMKQcN3pxCBnMBBIR61Mk4hQkLESoQaFYhxWlesy4KdgBpDN2pyiHg4nZBIS4soa53rQhExZTTSa//w4Tu8XF6wM7AkwkNcEKsgpARZFiQvETND1CnpfD8BGi6MMQEs2GhDQMCSst53TArCEiCHCA4REiO8cXMMEUtelLEoaINvF+IakYrIKSIt2UjuSVHhNBqVe37R70dJ8nUcCaK8ykXz5yROyDFaqwUK3VBK9iOkrb2cyMCFskgCKKBYTXBMWq/YjC7yfDqBgJ6iIILug2XV1AAU+5BixMcPH7DvG55fXlD2HWiC9bwihITi7f5I96LUfepoFLCuZ2z7tT9/ztnQ1BVrNvINS8ec1gVclJ4vhgTIisZkhCsFjBvEfkplpLiAoSUzc+lfCAHUijKWBeUU1iiCsWiRRQUAdRyEEGLG7XoDQRmQ1pRx2V8U48Bi6RIlSry8XPH0/ITb7QqI4LSeLO3GlvrQe97KDYEWk3eECMKnz5/xcrkZS51GHrTjk1h9e9DysF94/MUpUQ8HQjyBPykhsv/dvwTNzWVDhMYY0JlMDMU1LEuyXJmeZDAWHYWFhjXpVWLfoefRhIPTT4lM94fx2be8aTpcZzzjsihd1227YQ0PWrMGC1VClWQOCgo53KeFNJg1HBaiA2KAdV2RY8S+q6e2LCu2y0VJnQ3oczqfNSQGIERt7ltr7TB67Q06hLYY6jAEbW2179PGM3BGjLbJBD1HE0gV/Gk9oeyqWJPRIq55QRElPnAhm0LEkpKSIISCGJJyb1IAk5GCmwWcQoIERdTqNBOSMe34xnTi7OSF4qI1kSlqI/NgubYYRjeTYB7QjFjOS+4dbwAtiAczWuVeNxvJAEw5I0C7l4QQkENCCxUSuSuQJSUAq3HYAjUy1mXVmedkP4ycF1VCgVBbMmpBwZKU3JuyNnAXgXrkMK7SwH0dp5DQqMG7rXheN8cEa2OtSqdpyFa9Q/s+lGIwiO3TvCKFqAasGW+Rg82Hkt+T51RAyCGiWh7VlWGw2loRzX4mxzWEoP1u4QxMGmEQEiAMpiKP7HraZ/BfK1lJNMM2W8QoBoHIoMn0nLbtMGU+M9BdsBRKjKr8Wy0QapBgOIioTcDJ8s+qgGMfV5cnippXqsyckhnGFnWRAdAh0jVfaWA1/D6bGYEhJlOiu0abQwAF9PvIS8ZWbtBIRdAmAqhIOYI5oFa972VZbL2TGd2wkrzRF5jIjQmYAh1ychhXI6UGybhtFwSjEQxEGsYNXrtqzUG44np5xu12AwuQ8mIsUSZETb4wq8ESkjegGJ1aZB/ddrTciRFTQlqWV/L1546/OCXqh/T/fzuk60hD+8NAHLlzvqrXNqxXEXRFCswhW73WceD197klEjAQpkomPui3HGsod8q4s374c71SqtJ/liXi5WnDTgHr+Wx1kHqw3VFc82T52r2x5l00ZGUbOgRNzqdsCEYNB3kT7nVdsPOGfbsZly53Fp4QYrfogtHXOVBieAANIajB0kNsHd0LBamIIvCcTUzBWKpQ8rJgqYs1DRcDmGjnk2gF34FM+DGjULC/M5IBRwhQZqCDd2cFN0GtVubBIhODsgsF0jKTCBUoZB5KDAEpKFDFQ1fukRGsE0jQvFsMVoNo5RqBIkiqpdGUgjJbD1EyZazKKqNFXZchqcGh/MVGECGadx5KlMGcIBDlPM0LYg1IUee2MWPN2Z6fEIKGCXNaOmhEQ53asST4+FqYNie7R9JyYGElkiAxBRSikttDwT2am1eu4JSDKQUbe9F847oojSLMY2UzwpJxtzZTXBoVNv5bpl5sEaCsVzlGpSW0daDGtW64sUaOYVZXOqpUAIoBrXIP+yrIRazOM9jcORWi5vtj0FyfLVrknLTxwX5FJY+IEWJIlh5ywN3RS3b54TJjNhogxUBETStSLPoWKaDMxjc56ljBhhlOCKPRIQ0zw/AYZCkT3QxqaGwgar1lnRMe5rwAqF3hBESkkNHMB/DPals5K50T82xguObg4EgtGWM03LYrUgg4LQtIjBIQ6MhkAZTe7+UFe6mgmLBa44NWJ0zKLDtEjXAntXCqy5SSGqdR8QkxJpxOZ0DkALz6ueMvUon+dC50aM0e6LWcJIBuCTplnSsOZFiuQd5Qajz9fnwvxtBzMGz1dN06bI4uxUCxzl7ylFe5f67xu1peXCvWNeJ2LUg54OHhhOfrV3BtCCn1GtCyc2dP8VPXJqDSRigjanf7ANbG0MxopSJAsJcNL8/PSDmhNa3V9IW5bZsJElU+X758RUwZpTbsZcftdu0dJrzwWTfzoCtjUyKlbB2tG0JU8uuoVHDbZi3PoLWAYFj5SgRoRSwmFIMCZ4QbNhO267rgw7t31vB6WKchAClqjdu0MiyyofnYnLNyykYNy1XYfdeG5byo5xqqNijelfSBGVqjGyOiAAtFUCA0NBQuKBzQiqAVQFoASVKACDSXzSzgiN4VZFlOBjizkiU05JC1LZatvlorUsgqGMNAoqaYseYTAgJqK0gxAdywphUpZqS443rdEHPEmjKqKPKZWVGL0kaXkBg0fH2yzjL7XhAagZKmK5a8YLMCfDe8tDE74LR3RJbLI1U6RYBSWMO7Nq7EFdHGjyhgSeoBsYG+FopgKBo5CBCa4BQXVKloRFozbMT4TuqvoBNPp2Qlv5iUVEparoQ6lV+4kWJpHlfE+h0zIOxzwUqbTJYjpzh5kGLdQxjL6QS+CggFCrLD256oGWKXi+bz1nXBtu3dEA42fv5svfuOechb2XukByAlgmiqNDQUrnNMiCjFe+WqsfD16xfEGHC9XIzlqODpy1c0EQBK+6kGoO5fIaNPsQYHOa2goIaipjtYO9rIQOtDgFYJtRFerjvymvBwXrsihLReTkYMvHz9iqenL1qOtCydXMLbHnopFgHdWHLaU2b9zuPjO+Rl0SiGGYROEvM/KNFffJgXaajUCLXGl5Q6dZ0frWorLS0J0SG7J1x46xhlLNKZToi0UbRMsSS1xkaBdL9DGWxI33oGRdWq4Ho4n/D8+aYISgbOp/eIwsp80lSpEIC8lBFWFQaomMW6ARJAojY9EBDJUKsRKK2BiAGqoFiBWCGiTDPn9axCNumGd8CDGxYeYvPcYgjKmrIsC56fvyAa+pZINM9k3uJeChprvngrGz7/6TMCncB1w749q7JNCa2pV1RLwfX2Ypsz4uP336GWgufnZ6QUUUrBy29/q0Xa2wYI4XK5KPq4Fmz71jfj4+MjgFGWEIJ6YefzA1qruN6uParAENxuN1wu2rouh9hzT9frTcuHRLDkE5Rb+IbL9aLz9Kh5630vvV8nBUJLjLJV7NuOaN05trzjdtl6SqCUittt7+stUtDQe72qQLN5DgTEUAEO2nu0bD0/H1LuueBlWXC5XIwNqaFZjlD3RgY3sbZpOpfbdcNyOkFE0eREQLPISmNBKUeGmOv12vcBAOS14OVynXLRgr021KrzoKxKghAaECIu1wu2sumeaUDIV7y8PCmgxPZtXBZs29a791AKWJYVOR/bg/ke2zali/R2ee/fvz90/hHR0iJV7EphKaKpDSJVSqUWAIxsdZXXq9Y7eliRmXGxfCYz43LZ8eHjM56fvuB2u1q3I0KpSibx5esTLpeLeYaM7y83/PjpE55fniEC5LSASAxhDwRKaKKpBw+WiQzjK1nEIBCUi1eGohXRUrUQLBVTql1jBe8ZKeneBjFCZjCuuG0FSySEvGo5WWU1LoL2hiUKYDAu2xPePTxiLxc1PGIAyKJbwRwFz3cvK1pjPCwrltXywhM1oAjjul3xxz/8J1wuX3B6eFQv1SJg3Br2Wkao3ZIsjcuYI+t7GnPu/Xi1V2pGSsb81arVi/+y41euRNWaPeYOZ8YhyzDK8A/vka/jT7c4qSPvQiCIwe7UYiPLnQRowYSFfGiuJeypLoQAIzrmroRzXszqaaY05rDw0dOcn+v+d79/J0MvpeB0ihAUaAPlANoaUgBiWtGY1XKjgJjPfhGlQKNdQ46i/TBDjBqJEgAhY0najLnxV3z88Bv88Xd/wrq8QysvqO2K77//Hv/yv7viu48/GHuRbsrH9+/wcvsM7Kroc8qmNLUw+nQ64fvvv8PvfvcfelhJZ4LBXBFD7nMhgFKSPZwVux8iQlqxpIyYIrbGKKIC7rYXLMsKMPD56QnMSlRfhbGXgj/8+CO8Vo/ZkLxthJzVwBHEomAN95rJQtxfLi+DEg7Ay+2GWymoVTvxtNaQzis+lw3bbUMtzajrBE+/+zstm3HhXBrWfQcZh2opBWlLIBIs10WFqoE2mIF35ab54FYRSK3/Z64KJGncrffz+WxE/Mraw6xr+Hx6RGs7ai2aR2LGj19VCWkJktLZna/PShzBGksvtSF8+hF5WVBL7W2jnG6v2n0CmmfT3pelU9+FEPBkClRsjLdtx9eX58EgIzqmTxelsHMWIyL1KC6u7GqD1e+jpoDdKAelNi1R+PTj4ToBgmXZzWjgTmPIUrHtO5o10OYmnZj9fr/VojWbLE4YrxEdNR6KoZ8JTQqw7ahVaxp1PQdcbzuutw3C6pmnnPCnT58BqQBF7a/LjFIY14uWaIWUOo/z16cX7LuSnDjq1SM5zVi1AqWu+F0Oat6U7PkYiBgkKIyeFxSb+9PpDIE+6+m0IsSKGAU5nZDDIyo3JCT88P0D9usXPD4+IucF223r6QXNsyoRvdIOJsSQEUkUX9EayEgdHLBFYKyLoLRnhHhCSgGlbqhtR6QMkPL/Pj094Xq7aG10JDTesVeNxjQzIA719Y1RLYzrhtV3P7xTdHSz0H5M4Da80VqLhZ1/2fErV6LfyhHS4U86vPf6u7O3lHPq6Lvx3lCkHUYkd8rY/h2bb7zvdXyu9JycXtfQ0QOdPdvulcq3vVFSLW7hmmjlGSpwRYDGAmHrSUBkIUwLRVk4yEBtBpIIcP7UFCOCiFlmDSFqGDAQdUJpb6PkPL8prerFsIaIPLTFVlLTw2W14XK5YFmWwRGMkVsLVsfnmWKQWrjr6YyUFg03B0LlAooBMa8qGEGgUkAxIcaMuERFF9v8JgpIq3qDXBWFmXNWujMZDFJEgpAzlAtU0cshKPQ9Wp5VEZHqzcXlBAQGU0AGsJ5OqKWC1gjOPLyCqaE1M2M5SQ/h5arUfYokNc83J/C6ggKhlqrE7in20KKHp9jGm51gY13MGAmI7JEFUeHTAihpGGuJEXlJoJ4H1DQGxYAg2mgc0LIKNxZDiliCIpcBzQ0rccMIjcWcgBgQWuprN+YFYoaISMMSV4ScsWQV/mw1oxSVizlmR3Zrji0lrbFNprAAwrJkGw9R3mXWtY2IbgATBIgBIKe2FJuPDEoRqCpMAwWs4YSUl55qcStcvSStzw4xAZWRltVCjAlRNGITg9Y0xwQry6AODFvXk5EYAGtIOD88opVNjTTL/a7nkypKEVAcxvxyWrVjUbUwPjNijqq8p2NQlFrIn6jTO/q5uIcrNejpMgRQRGvHK4SIsut6FK7Gdd3QuCJKAlBR6k3XGRh135V4g2Nv/g4IrtcXfPjwDpkIbS/Y9l2JRTAoChUOEVE3lWMpJziBCxGjtYLL5YLPX76ooXm29oPNwrSk3qqmdLQmFkJK88iMGHWteGhcPVwrZWSYQVt79I+6Fv7541etRF2vHPULHf62QIB9ELZYpL/n1pCYoFZBeWTWscy9eZmjbs2gBHcepP7PmzaLKO+nAANE00MFqmzvle441y+rSe3CNGrepdWK/bZBasMudaDWiHq/TUemAuj5htIGWboCKDIqV3DhTlO33W7GNWl9SyHY9g3LsqLUioWzebIKNNL2VFo0z9x6X0bhhm274fn52YBIBYDWpnleqXUBOkYrBrXaSRKEGuruzCrKDSyiJRdwZGTKQAtoUeeQoATY3LR3pTMHLasq0daahdjtPIqSUEIaC4croTdDizQJKBUpLwBVJKhSPC0nbKJ5XZ8jNzbUkNI8D2zDphhRU0Ctlv+DgUqsu0aIATVZ6NiFi62RzjLDzmgDa2zABupKSMG7aSxaRtLUi9XoQEJMExuOsI6bAasAGLtLmHJrI38HAQIzojhmwLp2MCuzjBmDy7qACqEGgiAgAUbMLghT2VeIWsc600MCyuoVCvVaaMBKzwz1DRkIaO/J6agl1RERCNJ7p0ZD8BIryxAhICZbM1QPQLi8LJB97wo4QonuVYkaOpjE0MFRyzpUoCiln+Vfm+3VSEossElDqYBo3zNtnwcgcuz5+JQiliVj3xOaODE6K+/rTh1T4XMyy45ge1XtiNCN51YrBKM2XstNpBu8gTS0u9UrUlqg9a8EgXZ3abyhcUWpZCh6UrQ80kBLk7aXK+WC01+9R5CA7WJRDFKjhq0tHlgjE7VowwIFdzoNKuN2veL561e8PH9Rwg7ykqUBqlIKwtABiRpBKZrOiBHLuiJlqIfcWKkL+xqXDqwkSr5wftHxq1aillTErGdehTwNCde/Ieh/U1eooouDRx0odaBAjzho6IRHoTZZvzGfBAKUgxYj/Kotf5xQPRkXji164yb1Ta7XOJa1DIV2BDnoM46+kE0ED0sGpOF223F9+gJuDRsVGPudJszBiJSUn9I84yZs+bWiApGAmAKuBoOHKG9nbYx9L6BAuN2unfh72zfk5Wx5hwUpam/NZt0uAIXQwzp6ePgG0jQU1oYhpOUh3o9Qy20cdLVvxUJ0GmbVfJOiQVstel3jSWUIatsQqzKygAEJGiKs1RmIoPkhAC1o/ko5NK2kpVtpMBa5BhFCSla3xgzDYaLVosCJVkEQDYVaPjUlRWd+/vwZTozk3Xp63SI0B1lLNXCWKTwLR4VmBPTG+qP9J3W9rGmxnpNOB6driwBdn009nUARiNzvUzlDC5hHob2jz6Uvet0TblwNVh7u9b1EXsYwpzQMRFO5KzbdpzxFGPSYr60hZY/AOMUdHM+qTF3N5i76NQbJR78WfH8ZQUqMfc1ADEEMJXyH9er16T40v5YBKtKfqc2hcwtb2Ee9cG+y4KJl7OXSSvf2uRYta+KqnUjYGLJ4NJ13hHBr0P+1BmkCCWRGYgCTFmCJhUp17gOUjUSbagdyhrNgqG9t/yZW402BUM3TzCl2RqeYIprsiOGk+VBKgGQADSEktJaQcgaFhFYKohaiWz68KCOSMAgV1DaUCjxfL9j2ArSqTRmsFVkgQnxpYC5YktI5Xm4GEJKGl6ev+PrlR+z7V8REIEkWPSALTwdQSDY/k/xl3SfB0lQ+niGQMkXBeYJ1lTl3cOMxbz93/MqV6NvHAdX6M4OhoQyty9tvW2c2cY9BBYKi/xy4MRTD6Jkn0hyqBM/VEhFutxtOpxNyzj3m7qUlR2tHrUZfAPcAo36v09/62lC+YVnAMUAyYXlc0PYNp/MjuAioEZIEFDAqgHV6fhcUzoKjT2FITEMvllJxoqS9GZGR0xk1NcR4GXkVUQXgISU/nwuhUgqShC4YmwniWivO57Pm5UxhenhaE/2jE4X3Lk3W1WIGX81d7gc5hliMeFjqXeDSqP8lqzed6dveAnbN9bXzZ+7D8A8PD7jdbtpOzAEu0/y5UPb8lJ/bEZQeoeghOMtZdd5h0vz1QfkRwE16xGO+XzXeBMk+Nx/fTk2QsQYOxTl/zu+dprzxMPLcM/aerYpG10bbswE79usYd4Ij3qc40vG4ez7/3cepmhtKDFU6wUnc2yHC42xZut58Ll6nVuZx0ib3Cs5xo1uA3iZR6QF1xL18RlMEOF63jZ67IcAYlmZDeuA7Shm0koG0LpOZrQbb5ou0VeBsUGhOm/p97fuOl5cXpJSw1x2Xl2uPvD08PODr1y+olZGzpp32reH8uOJ6vWBZE/JyQtleAFFCAgoZRNGiNmysUFFBjCGCSwGw41lLT7GeHvH47nvsk+HjY/BwOiHGf4OUck8dQQjPLy94ev6Mfb8gIhoDW4X2Mdf1lJICsGbWNGZV/Iyqzgac2L+ZDEndc+fWNIzvlQP896gV2lu5wr5J3njv7c+poOiC11IhKWl7IIhanyqkK1KycK8J6RAItY46SAIgzLhuG3LOVhOK6X0TGLZBVMDGLrDf+gHuc65+DFATidU8IqHWiNPDb1GTAJEhTTtlVOzIbKhMQzM6nNuJt8XCqKpUCJACQkUIi5XHjJZpIoKXl2csacGlab1XxgIiDUUmI1HXko5mTdTJIuvqdX78+BFfv37t+VENBxlpNoaQvR8ffw9QIeW0bj7WAIy8wDplTCUA7uFXYWTScg3QaH91H7qcD/dIhrIfgthf+/z5c1fYrtz9fmOkvoFn5To/oytRV0oa3iqAROtqIkMhRC0HER5Kf9J30zOICf7XaYK3xrjn18Cv1uBQ+MdzzHtrPq+O03HdquGYDniAsbadM3ru1qLlYBLQPYfjmB3lgQAHfIg/1zyvRzIUhufJ5uO43sazdj7d6bwKeCLzer1bbjDj2J+JQBGWwx5gxHntzs8RQuiGBEFLeRo0OuIN3/s9kJL3M9m8B6UIbCanEK3hQ1M6vrws4FZR9or8cOopCyIy0pUGChUUlPJPW5VdwHKCQNd8a0ZVygwJhj8I0QhHNLLw8nKB1j+foCU9mrfUtaShZqGG634BEZBDQN0U4f+HP/7OmM42hJiQ4hm1XQCJfQ2wKLqeG17JAELsTSmcAMKrBBS34dGgAApmlPzylOivW4m+5Zn56yOc+8sOtZbV64pBm+0uy4LbbdNQgedVMKxdvf5Ijvu1mZWwvuwb3r9/byGEWTj3i9rmExDxgZpsCMO3odbjmadroyEnALLh+ekPWPIPuN4uINZ+ioEiGpfuvTrYKBhylIvVeQLQPGZUZKdbxSBwq1gWRcuFQJrDEUWDfvmqnULO57NtDkE2wI/IbiCYDArBgBvAup7x/v1HrZ+FMq641zgLO59rVf5kZS3tYAzc/wwP7egpat5jnJchCG94lEdF7UjqEX6f19+9cTPnP49gD3TlNx/uAY2OQXglSD1XqkXk473GFgUJ1CMvIiNKMY/fLz08WiBmTN17N8Pbr92Dux+P+bqzx/76Xu7mB9TD0q/tYE+/jL0975eZHWy+l/v78OtqOiT0cZ/n1T/f79KYg8bf8/OO7irwEKu4zADIQSw2oBQCjp1d0O9/pgEELHpi52RIZ91KFCb5pt62c3ED6MaXe6chJaSUkdKCUgryekKKCVWUDSjnFcFC8zFGrMuK263gu+9PuJWbknUsZ9TthhxXhEUp82JUxioRTddo9IIAJgX3haYsVyEAAdYxhkCGdBYAKSwodUNKSlwfKKAycLtc8fLyVWkLCZ2LW1ozTafj2aYuUgLNwXHTvLTzP2uplCjbUgidtrMTopADubyhxi87ftVK9C0v9FshmG99xq3++bWUlNYqbR7edFIv88z0LLbI5bBoAWclUjJvRw6+dR9ujbs4uA9v3IcSf/7gTnu23W6Qxqi3giBKHqG9NRta8O4oKmjVcxZ4WxwVJEFbUVXW7gnGVMK8YVmUbABERijQOnMN7LtO/TcLyFkpuvEAwMjnlfpOQ6tjAc9ejyMb78fuXkAf5reP7vyKKnj/GIsM1hkTXsdlRNNZXgvb14aOe9Hjs+NZAM8d6ni8Xq/3oVi/xv2zjet/e43fK6K3lMk3FezP6N05JHtvRLx1zvGaz4ocog1jHkeKws/9VkTA3/Nb7WHVb+yZozLVb3k4UP+WV58//u7rbXh+Lj/m5+uf7eP7VmstpRX0b8vdOebrhhBGxypXtr4W+uUHIM+P2Zg8RAbCKEUJMYKMZkhZmTRCpqCxhFa5U1gS6eddeaUQLdIT4KCevt9F84rBm2JTRjcixJ9NFa3qfwUXLuuKnJXHmGvDy/MTbrdnTelY+qBZGLx77zTtQTN0uRljmPbN6Xvg6JiM37sBZPcffmbtz8evWon68ZZg+XOUj4cWPemfUtLei7OQElWjwdhX3ESfQ1+AKgSPy6+nEwQeRtANqNFiixcbBdZM9XUfBuv3eOftjLDb9NwCxLgghBW1JgifQAb1JiSrVWRAtKm0FqcHa5ekiN3GGs5VdGfRHFYLXaC2VrCsGlrUmreEfb8hxRUA91rHdV2x7zfU+gCYV9qZVKbcxO12w/V6BU+Nev2po4VJHW05h1nnkiGdo0GIMHslEB3vMX5j03grOGAI4dGYfSi9WSDez8dxrc2f8fM7leGoYfOQIR+8xjHfR4WiYzXCwfFgiMzPe792VOC8Nszuj59Spm8ZJ/O/hJGG6IC7O690/D0bL/N83Ckvkmnc7pXLbNAc7zOADCRCuN8nr1/j7rWO9fRt+XEfldBwIL9xfzh8Tu/4aCzbGXFvvM/Xut/bTtbud9U9bjHZFEaTjLeMGT+fA+KItIctpnUTQsC+b9O6G+h4Fq11j02BULUWRHGPm7ErZN++q/dRmQEKEK4GKFTxo7SbFWCCWK9Uoozb9YLTclYuaGnYbhu+fv0R237RqgkDDrVWjHvax05z8N0ZcUS4CFgD2V3ZQjw/7V7nbLDPrRF/uRb9i1Ci83FY/MPgxVtizj8CDPBEB8XUisYFKUVjhJktLbvORPSgC71i2zcN91rtITetP+vBV+G+GfyaHhqerSI/5s3/U4aBCkwgpIi0BOxoyI8aagkSIRRQKID5BNlftKOEBYgAgNG02ssQh3vZFDUbycpPgGUVXK9XhHTCy+VZ2WtAuLx8QU7v0KxsJcW1h4xac8CVPsf1eu0gJqUw0/ZkX283RAISEohGeLBMBfxuadfakPMI95HVox3zjtGILjyC4GG+2VIPIGJfAAD5eAiOtbzHtfJ6LtwS95zaQJr6Rp1Duco7SpNFfFQ641/q55vX2ZxrdVDXfQiw31kwuqk3vKx741PuBGq1HrdvhXL996Qw9lfnma8/wtmvQ6H3yvneqw9ExsM7Cb0pvED2PmHkKO+fq5dM2foYhq9/xnzBu+31eoxE+3LLMITm3O08xkPZCqwa426M0NM6R0V3HON+L40tV0eQCSTVwXMWK7rvpuIKY6YSnCnt9n3Htu1ojXG9XrsXWYrWZVJgbPsVtW6QjdCkorYNl8sToo9/DIhxh1TFdsSo5TlNRDtKSUPICwQBVVSZ1bpDSSA0LbBCsF1fQGgKnCoVnz7/iD99+h227Yp3H35AyouSalhOFwRNP/G9MXMcTxjC30satcQoYnZeDvMOWArrlx1/cUr09UHorejdgbw7zInvNYqAToCDP0SAEHTTsfTdC3giWjRX0qxWI6fcycP10GbFDOl5q7cU4r138+eFcoGGG5YTIWXg+emmuaXrbt1LClgY54VQOpcpHYTL6XTGvhfr/RmRsnUSgYKtliUjxoTz6QMulx0xAOuS8fL8GY+Pj6BAWE9nPLx7jz99+iNyguaVrwOAcT4/4suXL+gcrDFiuxW16oMgcUM0+jKR4dXM3r5a4BZemjxRnZYhKIjQy1UUDGTebnOLU4E2RErIIBS71+rXYft+Vy53wl/XxhDwChYCgJED9+/2rhExgWggCVUhjsbQQ5geldr8bPfRDy250nC9WI7NDYaBBv+25+TPO7+u1wSIlH3HvQxYiJpZtOExhrEw3/+9Z/1WlMU91IFoHz9KgE69ljcMx+tgEvgpXkVquvn2OqIzH0PJu1I8Hq+iQhjRqzlCNM47og+6PgIESjAxWwAiMtqBBTqgc/2+7hHR/T6CNiNvZvYZTGoaUz2c01oElg9Nw9ic1mbOCTlH+5yms/KyoBTGu8cPuF2ecD69x7v332HPL4jrglNaNMoWtINUK6wN440spPJQ4ClmsGjpF4iRFgKEEDgghoxlzWi84d27M8CC569P+PLp9/j8+Y949+4dhJVhjAKhVWjpjPMwT1meETUBOrXhHFr31cZs860zqoxk0Wdm6IxfcPxFKtG+iNxj1F+69SrTFiTyLUaWx9TSgWVZkIs3x5W+4O1Lh9dFrOdma8iGLh35UUbl0R6oVodOHyfpWKdnnxC3nF/nRt/6m2rEghWZVnC9IcVFSdzNi45EkBxxjoJaR44tkZJiL2kxXlTbrCGCwMhZ78kRbDmfQEiWpM9gafjw4T3EnoNAOJ1OuLx80eS+sQu5t6ReotLkOUBGBdmgw3MA0XhPDp7dnP9xQTWPnYbbjgqjtWakA1NOydeAmE9xp1w8T9I9SWaEMJcf6WacvZw+H3dC1pWdR+Hm+f5WGM7HC3Dmq9SF66hXpqFJ+n3Z3du1owkSb9f2FgDn9bqaADOiRo3vJ4IZI4uN41SbN5/zuKbnMK3nFl97yUPnHYNqs5em7RHY+gDrue5Lk2DXoE7YHw5racyHPdO0JjTC8Tr8TXBQmnul90bJKF/SaIaB4PbSfVbj04Qa12IyXnOVbmiNMPRxPXQDZQbT2Sk1R+lzpT08l2XB09NTj1qkmHDdb4pmT1rOV6uWYGkt81cIvJE6ADAoWAkJGvZyQ60bkAiSYldKO1j7fgqDlK4JrbLWbuekIVUh5JBQ24YcFzRmJZLIK5bljH0X/PaHj2hN8PT0FS+Xz6agz+oBl2YGqKLpvUmIRpuCyTTHHXj/XwaFNAwpos7qNRt+rVWt8aYBOvylx1+kEr3PgQBv5TqOG9e9CrHwYAwR6N0j5o1EfXFpjzwG0FDLDm6MvCjCbUgCOoa7YMjDceGf8Drf9haOv1sYigJYYBtFr18boUmA6hK1yELUVl1b2bXAOXoetGHfm5ZKtGohF3tO1gLqWm9W6L8bFd6w4GsrCNCcRa3aaeLpybtD6MIULoB18wCUCnHuS+kIZhU8RpIwjQ2z1qGJgR8ozMJ5EnyIwwCxUaSOyNVxGj6KzacLMkudeB7bGz6Tr5cJkORzdJw/VxTU7+etudTPqA/hRgDz8G7nz4so6GHkdu3eLb0Quis2woDBqBPFwoYsSjDwRnpX7+5egYiHK8f9D6/RBFcgCI1ayPFc4940NMbWW3NEEsjG24iH4GZJiCps0ZWs9D0jEIsk6XME31tigt66kjTPQZsJxBBEmu7SHsHrvceLPn8D3ORjM+bYCADIcvYT7d5xrn28RqlWMLCesCAm46l2xO+0/cXc7UAEFkKkiMpH9LjOraNR9Yg9BO/GZjK0ra41gkdS2FiUEpwiT0SjRq2qgktB9x9Zs3KEDEJAEIBitOiAGSDMQKtoVQ2TvOo8wFiOWmVtkm3PJaJeKjNDKKCCsRPQ9h0pANvtGWV/wvV2Q85KwyitmpwSgHT/6+86Ap6z7QbIZMToRPLYW+J7msawE/kWAsLr0P5PHX+xSvReKb0OX92PkvPGDqswkPfNu8IFrgtHWw066sKdYzIGRa8dQnGTQnc6Mp5CCff37v/6IiUaz/TaAzWhFyKaVMQEpKzMNntpqGah6z4lJFLr+7bfrBejFzU3tJrMRdJwWrEOERRU8dTS7FlvxngTtc6Ngev1GSksEBbsBodXXiKtv1ThZhy8NDcThoa+hQ7W+7Ks2K83pedzo8jmhrm5SW9z6ywzs6cwhLBe35o3Q9OfzYTUDHt08EEXhWRNCFx40rjeUGQEkdGVROdDrV8WqPA25eNCsDd8B3diereKvWH6IaQslqslC/+xmmFkbZ78/DKFEKkrSxX4LNwfVaDzO3vY916Xd/Gx4Fd/1sN4RFdT7hX5OPrfRrpgIT8/p6/pAM1uWJqsGzzBPLTu6RFUWdOYHP1+BEiNWS1TSEgp4nJzEnnbprrxdF78nKLk7K0NGk9ggJ7ePrjPeyBvczjedaU5SmY8yuCNKgLIGJvUK1ZwT2vHfc1s+U9RiRNjQmn7dI2hRPs6ISDECN63LjtCSPDaVB06XQBBNbQCdaxBQa+fbBVrfkBEAteqPMECiHVrDQiglDW0bntSGKCq3Me1VpMfUPYiHumH4SGnA6iv1QKpDcI7ar3i5VJxuz1j2yvevXuvfNVWIMysAWylKnQiCbGGABWgrKxxzEbVaWBKCtOe7clwM1D1fSINjZP7Rr/w+ItUot86jjmL4+vOHlPqDhHlaF3XFSldTEgr12o0XEOkCKcA80L7h4cHPTtz35jH8OzRM34r9Dd/bv73bQVqr9vftVouMza09gzBM4guSClAEblBrbfGWIzWUFpVqzIksAxy/BgicoAtTkO+UkRKBG4BzEH/bY5UbMg5a9FzKUjWaYJ5eBBCAdVqbEGK9K214Lvv3uPTp9eGDhHZRhEE42ktpSDHZAbmXD5iXuzEeARAc6I08qr315BJjzbzMpyd6lXuzqzf4Q2aPTt9xst73jqGZzJd/Bcc/nH1sk2Rmnfqb0brqcneXbyvj6Pn58qaedRI+mcOUY4gavBP35mfY44OuFHhz+d3HQIhGl5AGoOS38dhVCDdm7dbD+6lWrgT89rQMVAd5ehgnW9lhtIIkud/+RBm9vmCIYA1Jz4rzfvw7NG4sAbWfXzHd+dn8nXWPTWgRzDIBkCNwdf5zsGtrPlOAMaRO8LEej8zgcZQpDonooDCMLAInqccaYCRXmIWo5j05xGUuqM2gKJ62wxBZcbeKnZpahA5w1kTUCGEfAZAaE374JYaccrq0YrNOwHYa1PWIet3SxS7ofP16TNSZHDTvsvreuosVz26EwjNvHeBdAdBSS0Ge5enQUBTlBFjnfbUgN2b71vfD7/0+HulRPX49uA4iQJb+UdKOjxKwqC8sjEEcNOOIzFFcCvY91FYb/beNyfBN/TMWOTHTylS32Rvnbdb8RyQ04o1PeCUHrGED/jwcEapey/LiQnISVuhSd+U0i3IOVfYUHG73ZCj8knue4EgIC/RPN6A5aQdWE6nk5LUkxPtD/Qot6bF02YJiwiWnAFSsM/1ejXvYCxu50ON0/2o9Slowii1IgaH7bdJwITDeHne0HNf3zJqjgLqOFeet/LX2EJZfhzzi5OwfnMFTILZ51OGIadCcVov/X8qwJ1SD9bcWSBgjl1gvza0nBxiGAVqSKn6EvLV00cCQJi8h6NSmYkjxjlHNKB7XDSMBfc6x7NrEwKyKkMXcH4bVVjdgQAECYd7iMRmRLi37c9EqIXBDQh50BGqAZBtLQimy/TflFZx4sSd516cwMGMDpEeJh7jC4xd6Ir5bn1ZqkKm7yqv9iAJcCNxpiYUkf65e6Na0/Bjf2gbr4qUdD14/1b/fCk7IKMqQESs25Jeq5SGWp1BS1Mxa0jYX67IiAiVIbLj4XEF7xUpWNSAGpg3gID1FBGTkrunrGVvQznpWlywgqx8bckZp3VFJMJtu+Dx8UEVPBLWVeWp0x2yaO1ptojf3O4OISKY0d/Xu8kbkkFAIZgR7NTHYRieGqmq/wN37k9Z+G8PzlBQ1pqqNfDU4ktRnFO4i7TWqTYNra3rGTMM4l7hzQLp8Ps37v0tgfit5+xCJhJUOjZQAC6XK86PK5iV11aguanadME78wcFoNRdSaE7YIdBabXWncF4TzWP2doORb5pDkaLtJvVj1a02vDw+K5btjFp+zCUoOTTpITXhIYQVnz8+AF//MOPB48rpoRt3w04Mp415Yzdej66oVPKPDeiYc8JGeue1Fue6FuHiDPDKDn/cZ5mj2wQHYy5GiFf/3k9jWo1E0J/vvGM4147cIonUBs8b3usrzx4PdP5XoWGD7WxNAFUAAXK+dPe12/OygF3e+F+TNVTDFYupfNpCsgaBigrUYCG8X2MPByqv9/vgUiEZspxKCxT5MHdYYHTMAIjLzzvlXnOunFAFjrt+Vf3bkjZvtxjdsXrwWlx5i8fi6MC9vNXS/uwa/+7wyMYPg76t3tQKtnDsM/6M94b2XNuPUaNpj29ODvYipwyrteX4XnZ8GqUKel+XhJSVvDfcl5A3oNUjAVpVZYjIwYChLSTjSsimxdhv0/tpRyCIJB2S0nLCgEraxBrByo1JlgbREDZzhofgXsxKu6ilE3XCKChWFvRnSf4YC15KkDnuInjIAJ6IwtxdiNbF980gV8fv2ol+i1Fc9zUR9vzp44QAqopCkAXjoZ0o9UneWcF3bT7Xg1hlnsbLreq53DXW4fn+N66t7ee6V4hvzofAGECMiFEQQgNX57+BEofsJcNtWp+YiMCkbYGmguvma1pr1n3IQSEuml3iVZRJ4q9Whkx5l4e4+TwivSrqK1iycudx2LAp5SAVs0K1s71MYaD4PGxJzK6LhHE+dnNygxWYF7r69pE+3T3Vu7Dkf5vf90Fcdd8JkRNaLkH59EGP2ZlPRDCnquc5aUYtyhh9gyPivCYlxwCErgn9+h2tSFtu/c/hYjn842wK3WBo88nh7HX57iPekzSu9+X/j3meJT62IdeCXjTjVPFGc1P3z+bUjzUMqpQu1v3BAhZ+RHdectkuFd7vTQBJlKIOa/oHYTmc5ML4zsDzjd3Xz9wwXxvCB0PXxd899rxGPtkHmciQs4ZN1wP97csy4T0R//c5XLpERmCGxE6V3O9LBGQcoTcDOgUc+/zm4xesrWGlM+orP1p0XRtLMsZvDNSWlTxsEYvqghSXLSVna1RzUkqjkGacftWB2dFC/tWcFC0cKu73isFU57cvWp9zmRede3sSQ52boyBrhf1fGmq0fcUgL09FC9g9ziiBG8ZOt86/jws76/uuFc8v0Rhjc0/OoGMEABPTCetaVx+EJ+P7fRWaBbTe69yc6/CcK+V5r2wOxgRImAJEAQFRSXCvt+g3TWqQbgFpWprrVaatcTSGkAgQCiiMqE0oHCwNlZa1uEADKfzU0RtmBRr7eHvVrXNVgzxGJoyJeS9TIVFOYZLxXo6AaShmzaFYD3MOsaU0beB4JVgdMXo3tVbx5sePkEFYhf4AUPdOAL2OH/3c6jXHqjMsU1lumfua9EV/C+5x37KwxoeYJ9Oov3GPb06Z1fub6+lOVR7fy4f1x6G5qOy9LIkme/TvbTgQU/xyP1hvuwuDwrGvQKfi2FAwBSpjB901Q4iGTPwjQjE/XU0bE/9PMoB6+f1/2wGXBjTt0302TsEjub8W/czvyTTOgkhjG/aZ+77hg4vlLsTAELPn8/zqcA3Reo7+UuIUw02lCFJ6TgDaqndKOKm+W0xo5vZWwcyWm296bz3bPX97yVnXAmQhFYBsCOxBbUURCJU47P1yEptTfuD+o+Fn/380n+f52oea5r+mPAIPuciVmZnSpSP6ZRfcvyqPdG3Nvp86GD8MpiVez0eBgF0Uj59+oRaS/d+IBru3a4aTogpa989AyT1nSACTKG8WaiMvMhrwNDPhXBdeOlNH71xbY+k1lpeziA84v27HxBvz8pcIt6nU+9Nw3mq7JgbKhshtAgoEBJpx3qPQLVWEWPA5bL3ja1dEEKvsSUi69lZsS5GdJ1NoZGXvCj1FzdlWLleb/irv/4r3P7dFbVWZV3BsauM3xM3zeEya8cY4OgNzh7hfVjwW4ZNz1WJ26eKolQBPHkdchSCalTQnYA6FnLbMphgIGOJzPf3luf3ylDqB2kZFZHd3pS7vVtH/q8L8hgj2sRC5Ir458Lc9948DOkaJ8veTqef6xUyAisy0WsoPLZ/fuSTxXpwtsNzqzkTtFTDcqi9/Mi/7ww/mMdveLCxc9cSPExNNDqAkBlQLKLANB6KGUBX+Jax1nOG0IE/8zj7fN6P3Vvrbhj20u/lmIsWiLjnKErs0kxRyH0NLrr88mvq+QpEtKQG8L1veIGZthDKXkSkDQ2IDVUsSuO5b+g9iZ++7qDGKHztfVkB4LZX5DVDDGwo3FD2asZ2RUBAThkpr8ZkpiV2AQm1NNRSQKFiWU6dFIInwwZAT6/010weNhp19nS3JqkrT5neMyNwMiy9Zp1C6GbvLzl+1Ur0v6/Dt3kIAY1HuQIB+If/8B/i6ekJP376EVfrNwroAkvLop+dasX8i7beDr977F2v+bbivM+l3h/HAODxyAuBZYPghjUHbDfG+fwIloLdBEKtFWE9qVIFIaeoZS57hUhBTjoazBUIC8CCnBOIBKVY30NS9J+OUxhhIAtpt9Z6LpmC1f5V3dCnZcW7d++w/90Vt9sVMWpB+LbX3i/TPU8HeoEcIKQcvxpYd090HrcZHOQAIIA5HgTOW8rJUX761aPnr0CZCG1ABXih/OCB9bpQ/dHbtw3Zw6d9gqdfR42o0gbCnrH233XdzBy4Xj4xjMPWmukm90Le8nJcuEZQEGMCVAGYcwZzgPctdaDJyKnqeM60ejEomCeEoAqH1VMgCRZqD0DwfKd0Qm9VpMd7Gl652LOaIWf31z83EbkfvKteu+Pnc4WpY6ZrdTZMhgHjnx954eHVeQ54rLHhpfjaODJleWkVucaZ0NzH8PSsBMkiA0Sh56zn110RjhMAbS89xOlRm23bjKg9GPE743K5dKNy3/c+ngTqICYirbPdtms3lLRkLCI+/IDL/gXvHn4A14paC9LpDFRGzAkefwtSQbHhfH4ELEJVizYer223lBchRoBbhTq+Aq1vJzWwRdneBNovFRQRAHViMGSjI7FzzvCyLwf6+dr1o0pFQD7KVTM8XRYDCpDqjS/uqCx/7vjVK9Gf9tz+TL+cHOas3ubDwwPSxPjBjYGo11yWVbucNDZWngSgQZrxz/6SSSB6c7KG8DoK8x7KmkNa/qQiQCNQIiw54/Fxwd/9h/+IP/7xHUq5mYeWFYQTNrCxuJQQjJwB2PYNIC2AFwCRBKAEkQRCQIqCdVlBuCLFk6FolenkdrvhN7/5Tc+Pam4rabPbGEHrghQDPn/5pOMaonoxwvj48SP+9t/8O9SqvJ8xJywp43q5wHNpPUzooTnRnKqHmeaNM3JLRvknR6DRNN2dSu4+ROufe6t7yjzmM/pXhZVRtYlRQgYHD5kQnaxfZzua5/fQVeKwNlxRe92w15oKZFKoLnidNet+HY7wuK+vgWwerEkGAHJNDlXs/b6Yj1a8j7m9xlKtM4h5pcZk07gZoljn1J9XLCQ47l/rJ9lC+90ACiMc5ylLN1qcIlLPCSvfpi4w/X492jDnipnlMAd+X2MuRu3urGjHMcca7qTOK68U/fp6rdfhfTXk2LxwYEkJL9O8zd7mvP48rdRqRYxKW5pSwm17Qgix12MLi/HdjhKYZdHWjyDCupyM/rNgiQJpBUsmJW2pjESCEBiC2rmNBUpQE9OqXLyVESiCYlCWMEJv1E2AMrxxQcoZBODHL18gJEiL9l4+cv6GbiD7j9OqOmYiJVXnM6uZRsWqIXfVSJz5zn0Mdc4GmEhD1X9PlOjPK6rZ0nwbhGTvgiDdnQcN72Hfd9SqvLOOiBAeqMNA1MsECF7YbEXmHmaamGT61hMP7U0bip25RD83Q9GBecLdZXJBYkqVBIIAUEaMK7ZWkeKKvWohstZTaVmOH8pnqTWeTQAIgz1MHBrADczVSksqyg6wVCA4R2tDTCsutz8hxL9WWi4Q9r1pr8Ki11XmFO3hp62MgilotfBzCio4RZS0f9ENx9plFzFG5Jiwb8XGGV1wPD0/mfXvr6lH654VkYazqNjoWSmJy77uTOrUd5DO4BfW95QWcBTPe0eWo4CFWdsj4qBOkpfZeA3xUKS+IFyIMt8pbAtVUv+8e1lj47PG57tFflzrI2z4uiQDPa9FtsbUsndtpXPsAlv3UlSKN4vAOKuTQAVqIDWQRr7QfiHnYWYE8yBiAMA01pyFbfxeQtAm0kIO4NF5C7b3NKzs4VzL13qcJ3gUiHvkx8O6aohUU5AyvTaMMvfKjnnl8e8xHGsheYtrOXWmGlFuqKHPvUYZdDw1BSP2+4g4wFqPsWCsWUyecuuw5q5weFZoUalMHVULUpnSBMghIVLuVJc5ZmUhEy3do1rAbEqSBFEKBBWCBmkNFQziACW70HKbmDKUtGVXjES0ciynEZWmnLcW3lW8BoGkodabAgw5dOpCZWDa7R6G0QQENDZDjBUF3MgjDo5X8QYDw0jtBEcwj3UyykaTEJMjNAzTnzt+1Ur0p47hyb32IN487mSO/6n1nHcgECv09dCUChj/hinUV57LUNB6fn7tTc61SZOgBe48k3sZ61emBhYyrzGB0RBC1MUdrBNGVBouPw2Z5E85IRWzTJ08XXd396wDBNUIztXoqKit4Ly8Q21GIm//1aLt1NSDb5hRh6UWNPfC7FopGZCotp7cd2NkCNXQPQp2XkzKcAQeTWN2mC843ZmP1CT8CGM8uwB142aQOdiFQf08Q3jN+qobazRyta5dTT/cec3zv7M3fJzk8cr8DJgAT/f/jvMSvf7eDETpz+NhRGCUjGAolU7m3e9EbP1jfA/qRYYQQIYC9XDbqB91o1O9XZ7myg1NPg6qUTyKyzgAHiK2+erPZv0jaR6LEcYdXqArseMu/MlUyiRTBivT0ch1EJkW8MyAqZGXG9e6j1h5KYcpYyI4naeKFRp7FjDvfWCcX+dJFVnfDS1hNCM/CWQ5Ueae1yz7bikZTefomOn65WpeoAhaaWiREK2phth5Q9RWaqXcrM5bGxjAjGjvWKYIYeUab0FAwmpYi1FU2noMJh9cCaujITY+rlR1RD1143MM+F6bOghZqsFmcPqcGml977+SzD99/MUq0ftjXquvc5DArPjEZsatO+/mEsiQZsIIIQFEaN/YdEfvcZ4wdMP8/l7k/kZ/4rz+UfGVhMniJkLOEaAGkYqUgSgJSidPaGEQEzgEfskRvCwoTdsigXWhBQu3gHVpV+OgjQjYmbGXgvfvIriOovA55LJtm3Fa6iYLIRizTFXOTpbe7mx+Rvcg/f5qVeHiwlwbn2ufwrmVXJtKcd4at7dAH/qvC7U7g6VPifQ5imF4e0OwDiOqNZ7Kf7LyE9u4aFjdIxZzpCQcFOx9eDrckUgAMJrK15//qWMYAeNcIcRjFxkMwE8QQWCe1isfxmwe5vt7eOtvIvTU5UDlDu9wfj62ukq5e8Z5LvsziJ/XVMKBMhO434P3yu/1nL+tXO/D/v6ahgtnnu1xz0dzeSgE3SfzWI0Qsl/T0egUjW3srtPL8KyUKYylIXZvrI3GCGzsY1a7TQKUfe/zyczYy01LZ8qOZuFUbiofy64erHLhCppo6z/xKAk1tFaw3S6o+6aRLyGkmCDcrOxFjQNu1KN5IloOVmpFE+1BKmYYMMhq1JV/m0Ws5CUCkczohRqoMho8dPS/TbaH+APESszGPN5zKvQ0zp/B+/erVqJz6OWt9+5eefP1owC821CioIuUMlKMKMHBK0Be1oO1fMy1dfO/v3LfieWQ6PZnmTbQvbciMteczljP+fsW+gwBy5KQY0QtOxBJG+kKIy8ngDKYi1lwtrmiIIYECCEiAxEoreEcM5o0FBIwRawsuMaIeD4hiWCJGx7OD9j3DWvOWNJoZr6uK263i3XFWZFSwnW74fvwvRJaREX1fvnyxRTpeJ45lOYlNs4K1YWWC3OoYlN6P60vY6+2noyMeWznEhp/7Ri2c/PfBXwAogB3AJHjd/Xa2jg7IudxDRFBztlYZZxLlboQ5cNu/mlDisS9bqiQeLX85f6RXz0/ID2EKwLAFIAbYaW10TGTPGVx5zHKyAkfri5T3umw5n1OGKC3Q6VHheYP6grGPBGBheKOBiiZ5+MoUrsqgEFtON/j7LXPCvze4Hqdn9YremmYRxGcAjR4CBeCKow41byOaxAGSGw2KGx96rvafYcV9GZBMWsB5krUQ7cJQFDd0nPbep1WBXSyORdNz4ByByDp9f2ZWLm0SXOqkApB0RRGII0YRUJaFwTsfYwJiqjNeUGKqyrnoIy7IoKUct+bEMLGG9Ip4XTK2OsV21YQcwJL6ewVjLGnnKVKQUds5Bfcr+5bxhzO6RjGmRsuAKznqc9HeDX3dLemf+r4VSvR/z6P12CBccxF5ENxHz0Uf+/+nAeBcnf8fE7XP4cuiP288/f9OjGsKFxAQlisJduXT18QT4sGWRsjxIJAucPhvcYzpL0vsi78mLGjGtSdsMaEShUxJSQQIgWEnLAsamWu6wmn8xk5L8pIFJKRVKAz40BUuZZScLtF1KpUZeu6GrRc4fzn84ovX2aLnY1asA5OTCgpw+ADDYgpIS8LcNENiCmsNzPZzGM3oxyP6+D1epgFHZEiHO+bqvvvbh37Z/3a7pHNTQo8DDcbdW8ZiMG9qOl9ZQeC8ZPO5RGOLo3TvQcPeHZqs9HQewiO1qzEY/J43ANn5h5y05Doa4HT8QDTa/M+uFdQ3/pbRb9e47AH5lCcJV8FIzIwjIxZYYXuebkH/K29+VPHawT0CA93YeyGzBvnn9ce2+d8Dc9IXN+f7lGqkRGRQgRR7J8LFJBjhCOtlfgkaROHvUJCwGk9IUUCuGFJEe/fv0dKhFYblmXFdx8+4t//3WesyxkPDw+4XF4gYJwfEpbnE/ICNaSachXHXCCbgtooBE0ZwQ3+ChZGMkUXU4Q2lmI0c0I4FCBGUASoajQqkK47zauPMen1+hQQUkLdSleggY4Oi0cW3SgNScdrxqX0aJBN4708/Tkj9v74C1eis5dJ+Nbg3CvIV6Ebs/a0BEZbB43PT1c7TMLR65zrGA/K6ngjgIwQVbeupu/dh5GO153CYnYPMSacTsqVq10ZIpoIQhrnjgQIKXqSjaqNWUAMtF20iJoIi2Rc9xuYGdtt01Btrdg2rQW7bVdwU67Mfb8h5xUCzYHGmrvFd7s5WtiVT0OMq3bAEQ0lOWGDjIc1Jav33JgRTWnN3l4y7yoE23h9DAkzMfxcCA5MZY1Egz95yuP1scbIdd4X6/scKGF2w/ms4z6EoIak1SiDAYhmhene2v2yoPmPV4q139/ddw+W9eE7w/tpRh5yDDWrcSI8moZDpMdu1VMiSAgHg+ate/YnvI+8+DPf399BaPqdyrG8pju4rvTvQm9j/4x96ErztaGsofVXUSEZYzRYfgi9rdkU0vdx816gg11nLpd5vXdFnEhQ+r25Aeaf9FppNYS4k2r4WPm/wow6GW2ArjuN8OhY1E7diY6iF8spN9HPrlZ1UB21XAUK5AnDECY1YihoIwiKqkS5aTuyvTabkwDhhgRCqRscSV6blkLdtgsgFbVs+rwiWuxuGAfvzBQCel449nwo9/zmTAXghpuNKAADK9qiUcPF0l4iACsALoYwtVakN/fht46/cCV6f/ggfdsrvN/UnjOKKWmBtag34ewZb4eNxV2FP//u3hCSbwEg5u90i9eur7lEYFmV5D2nBIFgrwUpeagpGjqu9cXTn8UEshBAzk4SAqow2q5lKIUbmi1kBRxAlSoriKi1qjVjGCG4GCMganWqInJLu5qXJgApWrSU/fCcs7ASADQZI3OoxhVWF1LdMnXSiGPopod2BcayFIx1ZeTQjvNBfaPqd4dycCG673rvOed+37WWSTk4cIq6YD5O+dtryqnc5rU5bmuCSvjvdK+kTUjrHdhau/e+j6tLfD3InP/VsJoKOzqs8254dsU+kO7wXCW9/vz89/yvc/UczjnO9vqO33iOmXyDrYb3rTG8lwH3x3065p7Ss9P7Mb8ah3vvMlgRsDtSRENhOxOPG3SuGEXGmuzNze2/ZoblfK/M96mHEaVQovYBfNv33da11Y8aqPB2u0KkKio+RdPHAr4xEmz+2UKuVhbijSgEuq6pOVDMIh9VyVbESp64MXr7NAwZ5MPdnDuaBYGmFMRdNFBEzLOM6JEVwSDNmP7PcmS06m/N9/ALj1+1Ev2pEOw3vjH9OxdJ6/GtzRNCQDZwEWQHUbTaNjkIJz/1UGzQ/71x3oNAetNbGIdbv6/CuH5qUSWliX5VIOua8f79gxLLkyMem3lp7obCOGCUEYbM0gWUAUSIQQzEoOagIzbzsqDZc+eUNH9AhGKE2Np/tBl5gg5ECAEp5lchOeUvbaZYBz2e14z683pop4+bvTeHSGdFGibBDxzrI2MMXeANIAJAHZRCrzbnEIY+rWRCTvtBEqGD0DQCMDw0F5IpRZTiIVBTEoEmwd5n/O7fEbEyUXhYRy4Nutfmwvsba0qADojzUPQ81qp7B0jtre8fLf7jGn6lSMfdmiFgiNKDDTArm0E2wEKgb+StuxDtXidejyFNYzcJ8uMxjOvX0R28+fdcR/rW5+Y1cz8Hns8nQe+l6kr0lTyi47VHJKt1AdD/s7n3+xv3cPfMAiswsCbW0A4uzAwKxhPO2vpw224ANexlR5CkEazGqMKgxbrbsBHnW79PH1FmXfOwOe9r3iI80craGqCdnqIRJwSxtIWugWYAMxJGw/Dsh0ExpQOmsXQw3uwbuCxl5rHvycbZnZ+7yMTPHb9qJfqt42gxHt8b1vkvU74pKCNPXhbNv9nCboZY1EmYrOM+YUPqCfBqw82hm196uJAR8ZrB++fVjgnLacHHH77D97/5Af/x3/4J70rBkhfEkFGKspNoTkApt2KKSDHp30EXVmuMVneUIEiIyFFzC9tZWYdaKSBmVFMcOS2mND0EtSPH3D1yAqn1KYJ1XZWovmwgyNRGbgh2/5m5N+9zZ86zez+mfgwlPoTaWx5BAODkBcwCaRPSL4yaVF86rriGBzrg8X7dEAJeXl566NZBVN2jJOrK9yjoZoPK16k/k5o8JLFLi8O37p5VAUE/ZZyZVc5sdYs67wEDpHa//uyLnZOVmQ/t6vy8MhZnP89rhfqGYTgpUkxPPqc0/PkMf6L1qBYRUK/DFEw3Apx96JgSma9972W+ZeSO+zrm0ecUzVvjMBNojDFs0O7PY5jmtmjN6l8rMwqX6bktKMpOrs4WUdD1kqydmvRrOo2nhladZP18PuP55WuvyV7XBVst+Jv37yG0o5SqlKaR8LCc0WpAXjJyjgitAiljyQFiCjFbTnTfqdfAsjTkJVkWQMPRkAaOAhBjtRZo+87Y6gYgKmgoqjmrTosOObfBDqaG39izfYl15C36OB2NpnsDZUrHTWT1f65z9hepRMfx57nl3zpGmUuAcAXF0bHFrSU/3Cg+elzBuhIIBHM+9bhJD3d+t6n139Hnkd6wwloFYlzw+PgB33//W+zb/wutBtSiLES1qoChcEWIgwVEK0B3lFowmkoTEAihKH/lLoIaGrBX7CDUVhWOzoLn52es66osJKZM9u2G0+mEnBNaU9J7Nxr2fcdpyeCU8FIrIhEeHx7w/HzB7XZFawUpfQfg2j0eooBlOfX58BDX+XzW0horr5lDajPgyMnxlTyDTdnP9Y+hz2kwBevf8c17mF+rjfMcnojgdrt2BTnmcAg45jm3J9odqM7F/kfPRRW4IjhnZqM7TMuba6h7IqIoT7n7nAt/H59m7UzclIkx6vzeKS9nAvLQotMVOqBpVsw8lcbM61jvAyAZpRgeqterT4pJ1FBKSId71nHPr4yj1z78yGEPJXj0OPXHKRPHjz+X/4Tg4eDhNc6Kz59hvh+PjGgov6nyZKAUC/tax5R5zQ5k+lCCPn6BwjBabC12RU0MskK2GLQLUq3Kh3s6ZVyvWpuZQkSMuedLQ0gdQ7AsJ7xcrwC0vaPWpj4iUEUMJ8S4AG1DXjKCZF1/QRTDgACpDXFZVEaSdG+wVkZAQKQEbtrzNLDWsiuPrqCwlrpxawiTbJxz5L6XPI/sn6E4cZ5bPlcDZDOL0T1AVFdK99gnEv4/x7n5VSvRewvy9eFW/Ly1xuD1T33D8vCBzDHi4XTGtt7U8oJAuKhHgKgChRJEKporSbeMfIPOQTjxO5vIt6UZ1yhPMTK3XuN031YbSASRpmAgbkgp4rY947c//Bf4cP6ILz/+EedckRIh56DouOCCwXNEen0FAeWpP6Gm7D1f0QwspNe44BwftJbMmmFv+wU5r7g+XRBiMkJ7xnXXvwkMbgVNgLQkDQ1RRBMtnbjedpzOsEa+1kZJAG374WFZQK3QbPfopSzKbMLkvL4q+LycxAWRkuenLqDgOT0COAjyZDi8FVp0sFSMGUQwQRTQ6jCK3Ht2Ev2Hh3NfR8p9a15t9HCewHlS7r1e39gxBrAw9tIQg7afc7dRGEg2l4HU02hyDCUrkbqH9qzaHa4YAkppWlOsM97DghRCL8IXbhOoyNaRpSlignPM973kIBsAQAfZTJzAMfQQMLuXJNIbdKdDeFVLAnUcbf3T2L8qJBXIohk66y1J2rwdDCRaoWVHDGYHEnld7kyNGOAK3J8+QDuNtObsVW48HBWoc0DP6G83xGahHEJCTBkUAxqz3i9p6chiFKNK36d5w/PpZPWcPi+E9bRaDhTd6I0xou0NFaK13VGR7iAgSAQjQkiJLdjXDIL1CyaAmrUUY4Cbpnai1m8uiQFqENJcfyNBEkHDBuEAafq8KRJupSlfbVByhZwCNsMIkBQQVzAicAPyEkGouN2u0F6rABOpfGyMJloHzqX2MWcW3PaCZVl0TQn33r9a06puqW4Rqy13cSoTMtfXssk6AYMs+kImZX/p8atWoj913Fu99tthA47370NJR4sdUBq3VptZOE6LRj2kRiY41Pqezu+ui11/bFjq9/PKEOi3HqCcq1PMR8YzeOhB82rNvJsMboKvX79gWZIS6gfqPJpqYY9i/xCSCioD07TahkKAAhBSc7o30n6BIaJZ4XfOCwSbKuF96/ke7zDvVHcsjJRWBeLwUEwhhN6rtBf8wwgLppKLEc4awzq/rq+F/rqHVH3s7sd4zhkKRnF1EF8n96HHUUw/55vcC/XfvfuE9zp1YTt7PGpfjfmnwxoZx6zEOzISHk5WwTeiHeih22+FKWdjUmSUbukcORqyGZrRx8bWt18nkPLizh7xZJTeG6P9nvvfdw9pzxYmb5esHEUPR0kPY3h46lMYnTTXNhiczIOHGhJ3ge+78fHz+rk1vN7vGcd1+NZxLzfux6F7zEDvjzsaog+u17nFmVnRCniLAaUYWno+33Q/6o0CEI+uiCljpddsbLnFMK7ha7/WoupEuHdOoihoXKB9ahtiA1pQr5L2pmkMcyIE5qlLA4upICE0rnD0dIhATNo0nBlY1wW1qKEuMGzA1CgbzBAL6fpwGkYRTYZx1bEK3dAARhclNqfjMPVjD0Fvb8z16+jizx1/sUrUj5/2VI+feUsAOHLViY69OBkwjwFjIYcYLfzybTQt4MGIWQi9/dmxOHzBCxyNIBibBKRlJA8PDwiBsO03XC4XrOuCVjWEA3h/VFX+ukmCgWxif56CAoh6cmIKwD1ibox1PWHJGdfrDSEGzW+2gpAiStkQrT611qZlQWY9ip1TH5e7t+KE7WMI9IE8THqII8i90fFWJIJMQbxm+HFBPNdD9uvBqBx7xxIHF6ld6mEvv+ZbSjncCcUeJjpcv8OXzAajw/PN0RP1YHSdzOU0Ljx9fQwBYN89jOVxbPw1X+oDEXpvWE4GBrvAGt+34e//zEKsv+bP5M/9hmS6zz96hMSNNpEAhNf1skQOyjJlR8cIAvl4kJub42esA0zr6d5DmYfxDcAP7g31Y772rXSMH6rg4mG9eFj7UDYF9OiQg2Bg+1CEdW/Zc5LNu0yGuY7NMcypBrKHLfXb3jvU1y1bbXcMEZU3lXl1KG/NyTblye6PpuHkGAkpEmIkmxNBTlbHnrwpBcAccD6fcOGiDTxciYYAooFgN09B15ftS4jK4xDn1In/z+cC6JqTfG8cDzfYgdfv/xK94cdfrBL9cwbhHtzgr2nNpFi4JWFdV5wfHnC7aK2kpcwMbDSEbT8HfkqVAmNj/9T9ujc63Stpol0TVKpYSyn4J//kH+N2YTy/vICZsS4ZIozb7dpzcbU2BAoqGG3D6XtGqh5VT8dESvJMZPlTFUzruiAtGTmrslxyxtfWsKRF855nZSfqjbsBVFbPJqVooU02s183so6lnotb7W2/OnJ02hz3fK+ujEspk8GhQJ7GbB1qhlAGPIqg3L3UeWHYAwt27dDvQz3FoOF3tEM94Mh3cRd4PpcOTuq5R8Fh43fFNQmCEWYfeba+VHxRYRLMU3jRnwceJCb3xGcP/thKzQW45wRH/ugozAVBHYrk9zuseGBkEv0LAeo1MHCo950Nijm/2RmmpsMNhPtcsRsYs3J6JSLFXBZQH/P5ue6NrHF+o8EzCa6e7Lhf/c5RTnxLcc7G1NEIwpQH1tdaa700yucJQgfqvhwDorIQaN7QDJNIOuspBLRSkdYVKalBT92QG4ZFsDSJh/hnSsnT6dT3z+l0wn7bsC5n1Kp7ackLUgBCFG30AGP+gZXKLYIlr9qdhYZHrPWpHuVKALScTJhRm9e2Ws26rV9rF67ry4wGXeuaN2Ua4EBFGx+jDP6vhnblMOf9c92ZoVff+6XHX6wS/c85XlmYog1qQwg4n88orSJ9ScZSkzvwQ1GC1Ns8QVTw/9Q0/DmTNAviwyRb+K1x7d7Ey8uLokJTQE4RIgUxROS0oHHVjRp2CBi1ElrzYnLj1TTI4/V6BaKzp5DSjpk3TjfCtt1ARChlw75tyPGEvVTspSDlBcuy4HbbEUIGUUHj1ht3swxeT32mapyYHjYdgJ0ujGzDeO2lCxZAIwDecszfc1CQ8ADVONDAx28OrXpujPl4bl8ILBUs1cAlKvRdmXq+y0Pmc7G9h3sduOPdXXzzOyewM+oM4Zu6gSMCCLfBLXDv5ZB1QaERohPgVZh0/o6Hu/x8Xtiu1x9t1LpyCdrHNSGaQla12YR702v1IIN5Qq5o6XCetzz1OSfZlTozGjw8+XpPzMr4XoH1z2B4mr9kvx2UMo06Rf07QqxGXPf9MEI6yvsOVDQTNTjSXI2+1BWKAom0Tdm6rti2bQj7QFazrEfO2q5Mc6fPUHTvWM/7voNbQ1wJibR1mT97lx2k50VQeZXXFSmn/rnFsBFEyaj6FIREQY3IQBFpiRqZIKBp+x1dow2AJIgECGuYvhY1pmsBBMqlS2mBcMHttmHbbmhth0iBsKBRNRKHI05llP9oyFkjJAxHzUufsyEfR2j+p9G2x0gRfvbz98ffOyU6b6ZvhVxmZFYwQEKz2inn7NQQiSE0GyMtC1gM4EF8CPPeb/J5ot/6zP2GvxcC6hXKgfDhdDrh6flFcxcxoLUd54czlqSgJGAIeeXhtKS7eTLa/stJobXzCiRYCzNNvPt91Op1jhYGZOmJfpc8BELZ9g5Y8nzyDLSgEJDXBXW3OtHGh+c9AFT8uQOg9W1kStx6kIZwoKWDKbBwCGHGV5tjBhp5CGgoAKfDc499fG9WCLOSuGe4mV/XOt1RixpCAGMQ68/n7uvCf6aN/dY6EbGyk0AHnfOWMOj5NhyFlPLTvi69eiuUqUrQoy+YxsnukcbP7OEfUOt9bGL3ykc4eNobb9AKzuHT+3vz+1N6wvGU31KkPy1g57FXjT4U02vPc4yNHNbH/NxaGz1/DkgpdApLX0MxKgipMiMAXcGez2dLNwRwU1m0riuen5/7eu59WEMAolcGeD2z3merFctJf6+1IqXUgVEa5m1g2VHrFdw2VAE2FKSQwIlQuYLF+XBF5UUI/Xyu2E9LQq0KWquBQJtSfQpnbNsNtRREIrD139XItN1jc2PVPFBW2kBKx/1CQYF2o7RlnsN4mL835/1uCfy9VKLfFhb/eec4WM88UJXrusIJCZRZhKZNxd38FeBgQb8VMr6/9itFSXRgchOPVXZSArfAInJeUeuUs2harrCsCdvthpRWnB9OvVRn3/auaAAHaWQzFhok2QJOsS/eltSi3rYNpWjOkwLw8vwVp9MJIEXXarmLfjbnjMvlCoEgLxri0Q0FRAPftDDYi2bF49+fxzeQ5ydDF0IzuOc4h4AEmMHh0+Ge0ggVafgOwKzcvAixz48LP55AS2PuvJTBf58V3fACbN1YDotx9HbmQ5/Zc56vlezxc3IYO9DEXXv34wAyb0f2eh1Oaw0/o3julMtxvO7CpOR0bUOBDu/iGMp1Fh3CyIXN4TY/B7OSwPvc9iey6+k5jvdy/wxvvTaPxTHMi/kq099jzGZvZr7X43p4HY5ujd/sZlSrcuZ6NxNHegeKKE1Rq/q5qYm1GVJtoih00eHRSyWIV0BQaw3SzGNspT+RG7UsbEa27wMvuzKilkB9vjRi5BdhpEQaFQtiBpVGXFIOWJeEyzOUVjQkJLIUgN1oDCOXO/M/2xJ/NVcI4xlfz+/0r8wv+fpUSleyNXPf3eWnjl+3EpW3E/k/dRys/F/g4uvvunBiGG23PIdEFBFiOFju/fbw5yvyw5fdGvdFAUwWuzt92omBglql3AWmLpYlR5SyY993rKfFBC4Bdywdyi0bukdDBFtU2txXEckBOSezIjXURFNeNQS9l5iGRZq8MBpAtg4vfUxcqch4QH1Jd4KXFHhHBQ3xJvWO3WqFAaZE4H7RCC3SGLh5Mt8abB9mL6ux8Rlfe1vgehh99sL8oiFG9B2PEWol6Olj95Dnf6dzCNAbGuP1WvolSuGnvueet19MxBWBv/d67co0V8d71jHvz9LDaJNhNH1mLunR88w3aC9Min48wrg/OX7B1g8dFemdIfuWsXUcmyPobB778RV/Np3Nt4zfb0W5/Bnu9wAwUhh+nmGYmVERIgRWWqO2tNauA0ZSYEahRclEtGtxH2cTGj68zsKmRqQg5wQ25erGGCxEH2MGQfmuU1yA0LRxNUVQiAjK6Kl52hi6ARQCIaYIBUMCQobKByzdJL3tWqRgWA8rzaEpvN9ltt3S1Phg0CzO6+Ro1NwbLTbkh/V7yKm+XhrfPH7dSvQXHvME/JRSuw/BzK/Nng+gNW8hJKv5iyhtABJ+Ohv6C0IFLr9neWUC70DCDYBC0LIWhtEQDmSw5jgSah0ADueordUZl0a5xpIzmKuWxUDA7KUqbhFqJ/rWlLiaWUnriRQ4EGKAI/e844rnK8HoLCREx5yj9gZt/XkV4KIoZ5k8ABFlO9r3HbWN2jEPEYsIxKxxL59xEMgh/Dp5afqeeztmfMBKXkQwfH7daDNXrlv+rQ0vICXNmYM0fwsCagVCG4T3917sMVzoBsAAjswaxj234WxNhsI3lt1boeLhAc4Kdf786zV6UObmIbrlPocyASOsILL2dCNsG4KXA6U+loOAwsbeHmi+3n3oVkN4A4DVFTgNnOV4bp/b+wGSHrmxK7753D0MqydVuMskC+5P+5aynv89Kmo9p99/jBHw5vZT9KCDF0kxGKVVMAQ5Ra0vhwBBuxSdwyAOYfeu1P08zFVeFqzLCgoErjuWZZlCyVHz8iGDkBTQRGy10BmCiBQs7EpBUx3izzh6ugbzDonieCYrZRFRudFaAQUvq3L79Zguca5tf03TUaP+VlnPXuf6gZE77ePvw35I9dDdz6tl8M3jL0KJvqUY37Lej9bk29+736zzwaJdEDR3MW1sU84haE/LbnX189xtfulOhhWYHz1jcmaUaRF0ATOxr7ill3PGVoom8+1ztSra77vvv4OgYVlXPD4+GhPNjlM6DaSsWPjPLskNphiC9r9s+gzCjFIEe9mQ84rGjG27IYWAbbtBCNj2Hel6w+PDIy7XZzw+nhBDQGkNl8sFp/WEVtXL9FzOy8sVt9sVeYmj1tas6F7bNj17CAHE1EFAbymlGKOiC/er1YAGKGAmoVYFOvl8hxAQ0KycRKH6gIetggl/BZaIFAP9eOnOKB3yedSwmBxCcELAnN8disU3d+wCAx6uptBDm/N3xtqVTnXm6+0+pKj3yW+8PsYsRhfiA1wUJgX1rb3Qo2IHZWFek3Mxq7n/SiF6c+V5DOb3u3eJsYdmL3g4mtKjKfMeaVamAQKioZQ7sKwrfmBgij2OYV4gXMjqZ0TdrDfkyqj1nYX+vA6d3N0Bbzln29OaJxxKSxl/iAhLykg5I6SIy+XS1/2yLDifz/j69evBcCEibNs25g5AsH6hOeeDctQ994LL5YLl9AgIsG03nM/nw1pJKSJnjUJ5F6LWGBwjhCtYCpgDuEVIVQMzGDDQ601TUCPT76kxQ3iHMOO0EkrdQUF0jKHetOfb3VHxXLGekxHjDLZUwzQlB+LhsBd1myRDWL+1kochGVzj/5nHX4QS/anjGFqZLc1vC4hvhc+WvODjx+/w8nLB09Mzlph76CPHiL1W8KRAh3K0yWYtJPdzM15b2ADG5k8J+/UKB/BoaYi+v1tXkBiT5mWrANhR0cBgLEtExIofP1/wb/71f8DD8gB5+Uf4Wq54yGcIXBAKUkxgBLSgIZ0gAZfLDsmEiITzEhGFwduORCtQAhoYuwg2IYQWkBDRrhWIDRGEfWu43S5YUsL1+UW9s48fQQyU6wV7IiQ64zGvaOczHk/JvMyCGzPWdcGyZLQtIFkY6bQmnFNESxHrEo1dCHj/4RG17ej9kqC1ar/54TvE9B1u1yuu1xvevzuDwMDDSZUnBZzWBSUvuLVdx8OUal4WBCKUvSKQCi9iwbUaJWCMyrrU2EpZsm1k9Yz/0W//Cu/+6jf48ekJXz5/xn69IkYFuug0KtsVS7HwN1k4TnC9XHA6PaCV6u0BkPLajQgvHSJqVv4jWGKGRGCn0g0LmTyD3tQcEYgCilru1AqDedU1xG7UNSABwfpWEg90tK1eRFYBGCMAGR46Q+nfKFqOTFL3zkMUJGdtIhXceVkNRTr2ZgSBUurgGIZgPZ+BoK33Rr3jMacc3FAJhBxHS7I1n1DrDpCueSJCyqu+H7R0gyzK0yDawk9MngYCrQtYBClYmNTKnHgrCjyEA9siOJrXY3nEnCP2nbCsJ9RWFZ1KhDVlBAFaIKRsvNyNQUtSozYCEgUhkf5AUzZCmuuMOY11apiAnDNOOeHdecFedpSNkGPGuiqndcq5K1EwIYaMU1blXfKC07Ii1Io1rwhCaNcNa3rEfrtZjbfC9FrTXG1pjIfHE1KIuL1cbXwqshmZElXOptMZ0QhaWhWESlh/AK5fnvEQE/Yl43m/gnBCIuXNal0+S0c3i5CVzikDm1JmUkfizxgHwBWs0nvORlgzLzfQXCusnq1G/ejPSor+qpXoveX9rWN+/z87Rwm1VJacOyWU0uEJUtZGtdSam8eH63bL+idCbnP4+D5spa+Zh0TUQTuOWvPOLeyhS3Mnn1+e8T/+r/+n+O4f/5dYKOPDw3vcgqBcrlqgYNZxY8a271BquwIWxvvf/hWu+1er/wwoiEDO+MobXl4aYgVQKkgE7X3EUxTUxxOuOQJBIB/eQZYA+v4j1kRItSF8/IinP/4RX1ml9fWy4fnpCbeXG9YvN60vLTtu1wtyjsinEy4vF61hDYQUIs7nM66XGxQtrMv+N7+54E9/+jyITRHw+HjG7bYhRO/nKTg9PuF62dw8BQEm6BKk7ODKSDnj44d3eN52bNumHk4gxKC0bvvtNln1ASkvuLxccDpp6UHOCWuK+Pzjn9AAXK43vHx9wdPTk9berVk9ocggNLQG5BxR9qJ5oGgUg1sBISCSRjdaJw9XhDgBaFyRLL8tQX0ndeqcSm4YkOuqykyXYECQCCgzG9ZVvc7GWpqgnr/SqGnpk4YN2YRPa0WBZXGBcNIymmZAHzAoEqQJuCqVG+zeKWe0thlAzKkkE8CMhsG+FKOyX0XSNb63ilYYrTBiiEgpAxCkuKIWA6CI5vKZgbJXeBkEgVB4A4yVR8wg8/6YQQjUACEBExBFELaKEgAmgCqQbwXUCiREXXdgSEool6t2JiIChwDJGbfLCyoreXxKumau1w1ilJ4pROSUUEuFcMPGghQSlpSwpAxwBQcCV31eyQzZNHXRpKIURiwCvuworaHGDU7Ivt9u2AOBG1BqxfV6xV4KwCqxXl6u6m9zAAvhy+dnDXsR8PXpCd99J/jTl2e8XK7IOeNlL6i8g7CoYcFa1rQsKu+W9RG3rSEQUITx46cnDc2+EscWYRLdizkKTjfCj3+84vblCVF2/PBXCT9+3UAxWzDIIk1c4SVX7tmLpaP8Oi4zR6rLnZfBYOalMPqZo3PVa3ldRv+Zx69aib513HuRx/AtDu9p3uVtrfYWGEOtF7N2gB7u0mJf7vF2AQ7KUsy1nIsPRlh3KM/7a/dckOc6LVeq5AQzKEAXRuAACXpxloZyueB//3/8PwCUQULIlMA5YLs+o5iVpgq0GIAn4nZTIomHhwc8f/o7rA/vsJUApgXxdMYuN9xuO1KNQGmo+4Zm1vfz8wuIDKovjNvlBR8+fsS2bWi14rSe8eXrV7y8POP8cEatDZ8+f0arjBASBITaWofZp5yxbXsPwxOAJWdt6t1a5/X9/vvv8OXL1w6rjyHi48cPuFyvAHkoPCDmhH3b4VyrHg56eDiBX24ou173dF5x23ZsZdfwk68layTghNnazili2W8IMCCUWcGVN8j7E0KOyCnifF7BpeDy8mS5HWvQzYKXy6YeRdB8877thsAMKFHz7jsrk5Sjgwlat6vdYZz/NqjiYkaIRl4hDtzQexUeERC2nNTN8uRshPrCylaTctYQfi3YC3oZEfc5IqRSrOuL9MiGh+QG0b+Gu7kW1OK1x5o/33abC0stlFqwrgW3bYfz1JbGeP+ecCsbuBk4i4C8FwPeoDeEXrIKxJS11pCFUaghpIBmhhNDtDZxF2iPSoIEUko7FoQUwUZ/SURoS8R+UeWGmIAY0UDYckMxMg+l1owoDw+Idm2kBFky6vMFIcae326kDSECAasA52XFKWWU64b/9PvfgVICokYnamtoIFyD5vuXFMApYD8taK1phMT6Bd+4glJEWjOwJIQ14wQgPpwRRIkUIAA9nsFc8S79Fu8+flAcwznj4w8/oJSGk3l+y5JBoSHFFefzA2IgEGnHn9NpRauCnE7Iy4KYIp6ensyrM2MPjsAORn7S0Lgg54glE/7LfcXf/b//Fn/6938L4RvWRRt2iwzmKDFh6uxKtr1U6cVBGuFK9K2UiXjuzL7sOf9XOe3Z2fqWt/PG8atXoveJe+DtXOS3TYzB6vJLDg/3UQhdWwYi5RTqWppsCdHdZLyBNJsAFH7+ew+bpXnSqy+gZMLVcy1EhNi0dqxBICGg3m743/3P/xusjw8aMjNFz6jYygYPfd7KjneP74BApogUVXv90++wvHuHggQJGSmviFDwDYUFlQW3skOM2N5ZgwBV9LfbFSll3TxViRoQVGCFGLHXgq+XF1BMaAJsewWDwBRNsY6QHEAQbogE7PvNPJ2GUioeHx9x226ae2IVoB8/frSSAa/XVJi/AhnUU2FuaHXHup5QnpU0Yt93zS9rahvbvqNa3uV62fDw/j1ut00VKQFNGgDG7XJBqztqKSjbhuVE+PDwHba9YStKzrA9fcXf/f/+tYaRIkECsO6Cp5dn/Pa3v0VKmj97enrq9bcOXhMD5NyutyEcmubEX15u2mzdXqu19sJ8EQ29NWOFaq2hSVM+WUrYyj71XbS6YxacTgnfffhgnK2bYgGCAlycgu563bCuJ6vPC+YhG8gFAi1F1gL7JoLArRNbAMB1u0EBWAsEQC0FLy8XrKeAlc895wwJ+OGvv8O+7bi8XFFKRYgaHch5AZGgVQWnraeE8+mEh8czQIQqDXtoihDlhgjNp7etYr9uSFBUKEJADQEoDeH7R+Cyg5qAckT9eEb78QlpyYjrA5AWNGYlhnfZEggUCMtpwePjO0iMCDkhpoTPn79iXVdtBQiBSMOaF6Qc8C4GfDw9YAkJP/7u96B/9s8QH06IS1blXipob3hOAooR67LgdDqjNiUnOJ3PVvvN+PTpR+S84N3jo6YbLIeaYrQmAZbzBrDXCx4fH/H+3XsIN1xenvHdh482l9Ql5rKqMn14PGNdMmIgcGt4OD/i048/IqWE0/kBp/MDyl5sLqXLQm+zqDiPXQlL4hmBgL/57X+B/8f/5b/Ff/t/esa//sO/xQ8/RHz+otgLp1r0MhxlVBsNALolhSFv5tw8MHLFrdpzi3ug7mj537p+BbMS/eUu6a9bifqA/czzdhBGRz/+sgF6hQa0n2VZlGLLKc9E67R4AkSoAj1S9r0+/7eBTPevz0izFKIKBXjxsSkcFN10AQhLwEu74f/zz/85Hr7/DjUFbK1i//QCpKV7K16ewzJIJByMkhOB/9MzEATMFVwLTvlRwSJWYqIAkkH4rnk0ZSU6EQHYkMnpyTTkFwMB0JzuP374CGJD1D4maEBQvZxAqSM3mRvI21URI6dVu2EQoZnS6GMF6TnjIIoMdqNkMMu4EWNWbAxYVs0vsbAqetLfKep4b9uGuJ5QrR5Ym48LIA3cqnoXUA/xFFcNpZF6LrCQ2+XrV9S2g0kNHYJa8O/evVMvr1vVAFcDJZn1HFPo4BG10rVn45cvn+DGnBszOaXODtFaw+12Q8radUZgZUMx4XrbzfoXC7tWoFWcTgv+wV//A5zPD5pvS9oP9Q9/+D3ePb7Hx+++x9evT/jw/jtciwqhYGugasGyhn9DGCxOlkvWfLJg264o+6aF9q1h3ze8vLyAAtCgHUSEgWVZVBnnhMvzC8peEAwJnaJ6zGTzvuQFHz68t3rJoFSLVVDF+lSmBAoamr5tBRSjIkihBuiCiMeQcOUKJiCHiEdE7E2xBhptGDK8Vk3heK5y1L42kwTqkUd7bgGjtV1p+rLmTqU2cGVIY7zcrsCyQOycBCAZAtG9+JgS1tPS974fDmDKRrkHaFlZs++lqOH8fd9B0DSCN35wJ8KZlWDo2lq0WfZtewGgUZ5ECy6Xm3l9ahSwgYcCac/ljoCHrqnaCtaUcFrOuJUTnuoT/hf/9f8MHz/9Eb//5/8M//d/8a/xX/2TM56ebmhi3KOWk25GCRmJAIodNMKND9zmfu+e6nK57XX9ItCIlwxAl8vQuffwEUfz88evWokqpkuP+K3PHAZjILH+vMNKKQzx+eHDB3z58sVCfc1oq2ySg3Fe8rByxmlGUtRh2nNhvr8+33eMEVwrZsagdV27ohuIY8aeVgTKEAgKBMtvP+L/+n/7P+N/9N33+O6H7xA/POJ3f/oj/pf/m/9t33xEAWSKVD0/Dc2FEFDpglVWhJAh0BwwQ3sACm+IFJBCRhNCFSgpBWuS3hVVCFOI0YU6vIVU0WsT+vMEYSRTbIVF+TV1QJACYduvhpA+I8QM0Cg0p64c1f/n1oAA60qiLdPmUDmgQrK1hjT1avUNWErRWlnrghNCQNhYvWmLL5N5yylq7IlrQysVcclApH7vEO/God5TpABpjEaMmBL+9OOfFEVMhCWv2LdNr2nKV3N6mkcMpPWC3BhLXvF9/M6sf4ZQAy0DlUshICyEFt4h52w0fcqbxNwgZ0HMwVCTgkjROFgZl5cNL1+/Wl5Y5+hj+g5yEXx5+oQQEl6enxCwGS3iEOjSyw0IjQBpQCLgWqsxf0WknNR4ixryW0PAmh8B0chFyGddk4bKlBvw3fk7xMdgKQ71TpyQAIFQ94KvP/6uR2xEGDlomBhehkEBIWmKA94H1RCdIUb8x31D8LwZAbe6KxinsraFAwAKGibHiISFoCw+EMHot6ohx7pX4MB8pR1S9ibYa4MI4eHhAef1pOMjAE21lcn3iIhGRmCRpaDo42DzzRMRDFsHk1EL7uVF2ivWRZJHs5xdLFq/5NYYhAQH7CCoUqx1syoEgNlCpCigoDXkgQYZiYA12nNacPv6FdfPn/Gbv/4HWE4L/u5fr/j3//bf4veffgRowdNLw7tTwssu2KwEL8YMtj6jsEhSDBEgTUWRDNS7y8cZ7Q285kl2julge3iO+vln47cUyhvHr1yJjth7c+UkQ02qweKKy8IAmBUV+gKbiiQOVxivDO7WdV0t7KVhOpaJ3ejegpEprHtn6fyUtXMoeyG/C81JaPhmfF/LLxqKXLGAEJlAVfA+n/C3n/6Af/lv/r/47fc/4L/6J/8ThJRQquWlDFQgVrazl+KZDIAEjIwtQgE7jSHlhi9//Hco2wWn8wMQo/Yv7BFXFVpiYWcFt2QVgAxthRYtT6KrVz1PtwRhDFDQ/F0IeRqHEXgQaWjbrcPfIZoDDtYf9AAYgNWnkiWhxAM1XsumYcq9aFF7sho3QIvBAVWi3cNakypPCmolV+5h00BAShmyaI63QAV3IDXyRACmgGKtpQIRJCbsTUktxGrpbo2BlLAJDLylsY3WBDGrl+PjXEpFCEaYbrnGfq+YBAcz4F6sEXGIedwxRXCrEGatTQ1KZE4xYiaxFxFs+w4Sbw6tHj8CoM29XHD2zQcvuhfDGbO56yEoJoClIrRpf7AK7hACYMqRpzndShkXMAWo1JSwtEa00LKRbZBgl6p5yBC6EC1sROhcDQ+gY7JJw2l5QJGqMSQinGIGE5CyMfy4gZtXXQfmebE0BDO2GkOFtYUl8zJKcVyZ1tsNJ6pYgqCZclMvriGQor6qMJiA2rgblOwRGfMiqjb09C042+oebpkMdsZu+8g74GhvW+B6vWqefSLN4FZQeBA+BGMRS+uqyid6NYLxSIfUUyfOC7w3wcfH7/Dy+z/gb//VP8e//9t/hX/5T/8V/vhSsN02fPrygsAFf/e7jH/01ydU2VDbjsaEFBS0xeLMXypskssGISV+CQGtKiJaA0XS8/I5D7pPNd5nZapDNJcO3oeFf+74VSvRtw7b7rrHAKv/HQoUeCtnOiu0twaQxtenkKf/jR7GRc+BuifQv3uvW0W6l/qtnOiwkFyBopdR8MR3po8YsDCZ9apovNAaKoA9ABsp8ADXglItR0Q2BkFbKknfPEqDl5E7Ty6pyQuWDZV3ILwDxUXvIWlOlFvVsBjEui4kVAjEaMEapG8uvW+dLW6OPGULZxICR4RgTcLJlYEbIAxIBSFg230Mj2PrhhNhKBInwiCRrigdpFXJSjK6tSpWXwh4naAqZbPqXRCZAebXdPBYohN23k0Be3SKICF1hRuJwDLWh9hkUg8/iZVFwZpuawh9NHgOtvTc2KM+Vq223vwattbEyju8jV6gaPWQFsIG69gEfWaSwTbErK3zRARkhiFItHuGGWOe32dR5ec5UhAgrAp+FlJiEQLqe9TWhFfd+7aZvQUZe45MiYJMuVgERVjDjvqsAhFFwnq0QqDoV7KOKJ5ma7AmEiFj46rKkoAMza1GIaPdn4xXW8suQ4hGGY3AhbPt10nGmBsHsuuwWp0aOWDNOwu0AYbzcM/MXWLzREBXMCSw/PYsz7TW2veIQFBbQ4Qa4jAwHIGUJnDyykQEjW9obIC9tOB0eofzw28REI0L2gSQurSHHsCBtPTHy6MYwI21P/Hf/f5P+O/+wx9AMSLFgHcn4Mtzw2++K4iJcFoSrls1RLDWMGtjCrL96F72kJmOfdChtf14F5q9d1xccoutKd9P8velxGW2GPrEw5WTgxIOful0yOG1n7I7vO7Ixz9YnkdZf3zBTguXCCowXudDj17o3YTeKdBx7fGMB3aersD1TElMQFgYihnIOeF0PmM5n0EpYrso7P00GQLa09OVDvVQUaAEagBFWII/IJ0eEdcHnB6/B8UzsgQEcoSkNuAd3mU2IeieJ7rBIaIKx70kzZ+ooNfFHyzO4F5oeLUhdLN4eG2aZ9H79w3S7R1YyQVUAEMUGNSaIOSsZBrslql04aJE3A05L4CFPf1+tZxDN7VY/jIQIZGxOQUdVZUFytYk4I7OVILtpKFViHlpFq50gwydU8FCWtrgIMWkaNU5F0dmmLDxncIUmK8Z9aWAIIghW0TFUI7QECmCtY+qMhQjK7ClI9BZrBieIDK6bgSiLnRD35+DA1iVnkY7PHztkRb37lF3Q9LSYWMGsu9IJ3zr/xd38kzhR7/Pvpcs1Nw1D/qe9q6czRQBBw13NhutKGTGjPTrAID3Mx2bW09duSqSFZhIPar1qu2LETElW1sewdG3oykCnzMC0Py+/VlFQGEoCWY1DhEUVwCRQ1cjxQIMw1Hs+QkB7HIgBDV47WARMAJgKY8QI8KyIC4ntIqhROFyd9QL+751p4SbYkZCXrA8vMcGYEMAt4qVCI/ngE9fGr48Cb7/uOB8yii1KRYCI/rj4WfFRrjC1LmdnZs5GjX/7b/PIdz+nvT/AVNq4ueOX7USBV57cf5vV0KzzHVrAwAwOp57CcRBqd5pVQJ1YZ5S0prBvdoC1u3WGgPO/hJY41CAWu7k+dHpHt94nvnex0QPherJ8wPBOMTaCKn3IMzYmIEcQCzgoj/SIqJklOsNS1LkIBGB2O9VDkZICRVMGYQGMWq+8/kf4PsffoBQgIQMhEULwYXNKrWAcFRgTilFPdxACNG4QDF5vCzdw9FifgKxhteacWi6tQmEnq91ZeHHaItkuaFqBdpwZiFGTAEsSlno4qyxhT0xjBP3LA/9Prs3hA5UAixkZGhfNwBCIHBgRMNrsABVSHO4pSJAc5cNDFRvim0fVqcdtSqdYjfOfD2H0OceIEQ2ppdJWIg0LztXz9IFiuWPXdloGG8SFuRE/EBlIC4DcBIQtPwjxH5fxM0UYoKCnJSZCSGrMrqzX7VbiyoLNVTIesx6qFPDyRIVbdqddDHjMyjSEiKgOESX2HVNByKfVkhrZtQQRCJCntsJAsF4WzX8p/s4hQC0hhKAhKSRAggKGKuE0WFEADZDcc1ZOzmZ0SDUsJIigIkCTpE0Rx6DPb+D65RhRwx9SoyRjrA8PkG7K4Umut9s3AZbD1uLMp8+zz8bsxcpcIlE87COdA8xKoZhijIoYHFEOZSpi/D/J+/fYm3L0rNA8PvHmHOutW/nnIjIuGQ608nFBuMbtlUFmV2tpoTduLrd3SrZ3Q8tBBbNS7sMsjAPliUeuBshteAB00II0U+01bRa6hZQGENBWW0b4TZQ+EL5ns50RsSJiHPZl3WZc44x/n74/3+MMeeaa+99IhKokx6hE3utucYc9/HfLylKNhnLD9w0Dpwi9sM1iMROAQCMX0hsqf1E3hFH4cpjZMR+BO1HnDxc48nVB1ivWjB7gS8tQC7i/Q8SLk49Th94dPuA/QBNPl6YGIbaWrDd05glSqjWAkA2aqsR7VR3yoia1tC7GujPQjPeUl5qJFouxSE6cq6IsgATdRWXE8sKULWmf9ME2U2ryOHwvhErP0+QjCoRDg5DSgB7dY8gpZSSilMKIswHNYdtYAzjmJFI43ymogURQEzofYkxStVcAEU4ylEI4iFE6tFyJ1xPihi3G/zy4y/i//g7vwunZ+dIDPR9wPVmh9OTM3mvOkicCGOKqnMlEQP7Bo8ePSpAmBwGRfblAkobsYrjaZy/1eEc2SlNKB1OJkphuDQNih9ThPOGxaZbVDh85WQ9kBBAHOBNURSdphAuO97mCFI1Rysr6zVp4pRiDXCNg1wyifLTNp3o11AuNSYXsvrcSc9FRzM3hCgEVL2Wht0m4lDl1jKxpf+yWLHaDwOsuYeKq7ld/1PWKyrnOCdc6yhcNYU/qUMEEcEb8JZBJJgEoiJgUxXIftbXdF0OJTnzogLlAwlPbqMi0mRIh5aZ8zWytZ2Ipeu1ZiXTiCai/rrfnKC6WmIhpqax1GztIqWj4jJCuUL1qtSSG4N3ph5pMXUJmYpEadIus3GxETEN2KmbVdt0AIs1ili1RzhXrOSNA+2aBq+++gi/tBnw73/+1/G71w5P348YVoSudegcIcDj9bMRT66B95/tEBNhfcLY7UcQRXRECAD27JC4QRuVEyUjPBmJB9WPSqStRA5DDPAEOC+EgWWGsTlLYpEWMUpQCgtcsXCUjpaXHIkuX5y60OzkvYjCmKv/2aU1ANA0Xq3cguo7IsI4ounU8lHlPpyMQ5kWp7oZA/w1hWRcxqRu4oxya8qqjsoBAsYYASas4THuA9g7OE/Y9gO+8P5zvL0J+LN/+s/jE5/8JE7OT7E+P8NbX/EVeP78KZASOt+g8x4ODHJiqedVfG3+aF3b5EwNAOB8k52qnQV/915cVFpxKXDqcE3kq0ARqktTJ36Q6VGa4jRNPhv2OCcZZYx7JW9uRy5HGyn7KyKt6FvkbNY2XueLv5lhYzeVFBglb8IqMIQ7hssiYWYU8/kKqxOEyzIRnVhuliD+Uk/GG7gYNEzOHscCDGEXvojKTKLiyGLcynzsTJkeN4NDLqI/4T6rd2zelKWc+rub3p+MIIw7VoSo62PizKJawRQa+Ta/D7Y7MJ23INtijVmMcSjvAwD1024mXLpdhCkQZLEq5fKujFPn5ewRaUAwylmFsugahfvLHHeFcCaIy+onwJlESXa+IDQguzLRRPxJ1RSndhFdtHPBWbcve1mey2Erka1qJkPaprxnbP7XqbQJNn1rlDkmBjk5wzEmtF2HjgjjLmDY3SBYOL0EgJ0aP0vAELPUTszYh4DxyXP82q/+Kv7HL3wO/MihT8DmesDpaQt/4uGQ0LUOjU+4uWa0HnjlNYfdjRjeRbAkl0hA4xr0wzXISQYZkSbEzC3buUYMoK5FypKnYp1slvhGVHjzpZ3s4v3KS41EaQI0p5QXMKW4bWFm9628e6A7nZIjpV3ZCAvqnH+z4PTclmZ4yplZ3WQAy4wNSMK6ZZ2RjlYcuoOYYnsDpmkyHqOeSQ8vk9DeFIXLSp6xv9pg/coFvvrrvgl/8Pd+C77w67+KYbdH6Hv0MeD9MWEXSdwz9hKfNMYAcgFQy1lHghRCDOKDSBINJkVxSSmFbaVlTZ1AVwJJeDgQ5hSwvacamMzheufkopBThGvIpiBVR5aSTYkciF7GUrAlaiQotlekyKV/iyBDpNa7up6kvqRmRVyOEKH1zYQoErG1UzFW8UVt1YJaCAMlLqioDMTIRUK0ZwMce06E7CeXAb6bIX4qKavsvUq2kQk/BfIiarc11wNKk9kJkiS7MoZkC2lgfWTEBiUiJviNSx8ZOShySjWhYmOfc4jC8VhTGVlUXJ+OBpIQunq3RlpGbICBKqQg2PSaNk4dIzOgdzAmq1/ZLhBpVCZoUnv139VuTaViYeWM05yOh/OccsrCSoJSxNqGzNTQLhWJA3NC5EqHyjxZJ0aYzNV8NjOHrUg0ptnYKgKyJpSYGASxb2gbcXeLISGmHkwJBDXqQtGPWl5l89cMwwDHwPnHHuB//Z3fifce/wb6wAiRse0DmBhnavW+boC+B3Y7wuUzjwcPGU+fkUIGwBPDu4TBOc1HfJiTthBx5lqHCcA39UkmUiBxqzlJEBKA9e/9ykuORA8Ni4Apd3rwicq3TJm/SKe8jEQJZmVqYjPOFHF9+ac6z1QPJosxrViWFe+Kxd2B2IgZMJ9UA3BgkTx7JyHOxhGvv/YGfv9n/uf4qm/6z/Frn/40Hn/xbVw9fYLdzRUwDlitGqQwYBh7ydwSApiSOFF7B2KN9hMiRlLLQ72gMY3l4pnrjwqlooaUA4Q6TxYDlqeUckpKvaYEmKEMRN8oF9/DN22mmg38Fz2oro2+2zTi3J5iytaBYsR0eHYA5NyFZqHL5ahkTpABrHyTgbUgQuN8jCASC8Wu7QrAJ5EYOOcqxC0tO9+gNqARJF4hK0CRo8/v5DYVGJDOTQXMAlQ5qYUqcn1DRlkjTAUxSiUjcuxQVgjRRkI0u3eyNxkk1wA9A2c9nxOLR+nHrCitnlPn+kJkSbvFPcWuTN0+T+raGPLeIeqa8ASJ1vcJdvZIQgjKfquwueLiE1sydS7zURhuvpmir7NUfqg45Arpk+1vsTC1JNigAicIkj8UmjdUiPApjCunAsoBTsWyNewxgiDy9PxbI66KPAUCIjMakrPd+g5ds4J3DZwzwz3JkJIoiF2EwTrSZNzcInEApYSPf+qr8M1f+dX4/K/8e/y//p8/AQ4DxpBAA9B6j9YT2sZhHBl9z3j2jPHaa4T2GtiL+60YbHEsYRTzGSzns54PpyiyXD0llgN6fv/tTJRb91sEic51HPas/gvg4DAtH65pKXwJT56ZuKrTWKecRTuiE4gxAArwCvW8PF4DvimJArzf7xXgS0LsEAK6di2htqrADMLtiLLeLgYRqfVAkLi5msB3jCNa1+C3f+Vvw+/7lt+Hx+89wW8bGa+dnuHZ2OPaM1zLEkaMjRMUzjwa8FAkQM5hP0SMDDB5kG8EgHOPqCbniTUDAwHkfI7kZAHkY4xiiGOxVvVv0pBzMQTElMTxewy4urrKDuAXFw/gnEM/DIg8wvSfKbGE2wsjkqbA8k7M61Po1UxeDL9SjDkoA2DB6SOShn4D1BAoBPGnNV9gRfyRRwE0eRdT5hoAcTtKKQF9IbCStiFbZM8BgNFmXaoYCGFyVlQcDKAxYDoro/qDWiAJTiMswpN36sPIRWedkSbJaaZsQMEZYcjZ5AxOwEX8KDokTQ1nVtjUwpBiAedl3mY0la17MwdauBYR94saYD+UYB3Wrs/SBiMCGIAG2cj9stExRUql59fWdDLCmitVqmnK7UIJjSq6DYmvayawnElMikg+SyGMy9VYwl6DljCKvt25piIES6o/W2cvwADtqs37xnB53hZu0ZAfWNQi3ln0MCN65nCSVPVSE1HC2UlIUWmfo0PXMJ5/8BiOxZCq9R6NF/9MqGV2oiSewqa2YCGkvfdoVKr04NW38OZbH8fv/8avw//lL/5fsXsaMGoQkevtiEcXDdouoRkjdn3Cs+sBw3iC01OH4bpHZMD7FcIwwjdF/Jo0/2oRuasfMoxRMT2oEUamhilnMUWICUtFMN23vNRItFzQ44iyfrZUJiIXeRM0eUBKvaCI1pjRdV02qY4pgZwBK3m5Dsc1oXhR9CsSaUUoo77v4Z2HxS9NUSwVL87Psd9vNN6r9WFImlGBOiBIaLOEJIpyODC1CGPC8w+e4jd+8Zfw7uMvYMN7IDFiGCUEWZQDuB/UktZ7CR0YonJOLuuOLq97/Ge//7N47fW34JoWQ4g58DugYjFG8Q0b1YIZkqmjXpP5uhgFC1cOsekugGJZJ5tjOuXCYZSF0MuiiMjajjECmjzd0kqZZSKLR+vkbMQYBSlr/8457IYeFtqQDAmUkwLLoxhCP5nnRIdWnVXvOzU0nhJX9RkSId00xJu1E9SFonDSKRMGAhQqK+6YMtCYU9q1eEsnI3OEWqQCysWXObDaA1BTu0uV+RUpg97Rpq04Ss4EVI04nHPCncwAfzG+UqKUxX/T+qz/1msECBIre1TSuiGvhOrKFKEVjq20lbOIgCQgR9XXvF85p5UthPbvXaPj5zwmX0f3qc8IKNsCgCHn1tZ3IjujfGdqgT45h7pYGjt7m/K6+zwmo9hqAiS5Fg/OT/G5X/tF/OK//7d4/O7ncf7oBEgMp9mrhKv3iCGAUyjqH0lqJ3cvRDx78hT9nnHab/G7PvlxPL7ZYtz1SImwZ4cxNXj0yhqu3YOv9rjZRfza5/b4uq85A3ODq5sRu36HVXMBxA2aplVOPWUuXfZOY4n7kqO3vnNCDBoRoqAjAgXZvpBs8uVGoh+mLF20A24WBqYNyVL1ixTJciDZC4ZxhFOqq+tWSCmi74cM9Al02E+Kkl6KPUKIcBCqcAwDQhgxjgH7XY8QXkNIUY1Zili3FpcxNGnuGKSeYzSJEQbG+vwE1yliu9uh3+/QvdLB7zUFWnRgXsFCyZ1BongwOOtdapEpM2F9eoFHF2fgcYcw7OAScOY6mCFMThWlpD15A/wETTCoF7RwZLIu5mdpwEfXzELnASAI1y7LaFybcBpTW2vRv5nLkVDXJWdkFvXpOMh58ZVEwWUZkMyLzYeFmi97qheYy/mow+A5k/uCJ+cgsvjYil+o9a3CXA3vRoCKriSCkrneODWGyhwkqUCXVQ+W2ysSF5kzMtARg5PiMpSJIecmp36SNDyLvpLOrVgmL0mA7LMlnD7o3/rR91sTZ6tesazMFDFw02Yjn7xuM2DJzHCZZdGxSOXSthE5nLKh2LSw6t2ztgE1oNVdLcQWOYADUO2/1awJKmjkJDYfbVIO1dQdQQIMOEeIbOS9/R+VCzwDUdpsVGplc7W+ap0/iMCOgQiEwMXvOf/TNIOcwCGAL8/xSneKT73+SbSJNJi8+AP7RqRMMckNbJQQMhhq3LMnifDUNR3ef+ddbJ4/QbeOOGFCGh32THj2dIM+7PDKxSkePDzFTX+NZ5eMp097nJ+fwvkWT55v0YcebVdQl3OSWQiECZEWQsBqtcoJOpZgf35eBWew3+5bPhIS/St/5a/gB3/wB/F93/d9+Ot//a8DAPb7Pf70n/7T+JEf+RH0fY9v//Zvx9/8m38Tb775Zn7v85//PL7ne74H//yf/3Ocn5/ju7/7u/FDP/RDEz+f+5SlhZn/ftfzYyJdoemLWMeoU1bA7TUAtnMlIDKpOMSo/8w5GPWo1LeFKwMKN00kKa3EgT6AY8CDi3PEUDKjiK+26iNTKsYJKo5w3kkIsAQ0JIG1MQ5ICBjjiL4fwQ9XCDcbFS968RX1DcBCYVtneqQma+acA1LEk/fek0tpwKshuGyRIkZBpAEWSrB3RVasvoYqAiNSsRKZQNGBXJuj7RSBQDncSZFtuaYFOWWkAuSYuQZ1EjOK86LotjRcBjwPWaRn+1LEPcrbcoJPDib9gzLOBpDt3BBBs9YU7oG9AM2iFzSOg/P4y4pTBnbGdqUU4bK0w84sGV7W/4lFLcAarY9zuyjbA/XGrZjfgkhyMQsaQI3VShSeuj9KSWkjGXkt6M6h6RTlgmL27ctLXVmXy3cxOMkxbW0NnZfdMgaRCAhD9nPNXJpxYNoHM4sBzPR6V8SbnSFde6aKiLO6Itmw+5Y5Np2ryQvsCXMUFzfncx1bFUfljBCRBHJAOUdmUCbcYj4qWJWFlxM7D6ChU4lxyDpdMBcjKU5ZMgBmxApG2b3J7ed5JjgMuIwO/RCw3UlWpm61ArsWMUSISTOrCkiRtu2PK8wDx0JAhSHganMF74CuIwROwMBgdtjtJNbxet3g9Y+t8P67I955L+Ir1wHdqsHZ+hw3+x4OQIgxn+EUY47ZK/umhEAq0q9jajxnPrEqKSvSmvuVD41Ef/qnfxp/62/9LXzjN37j5Pmf+lN/Cv/wH/5D/P2///fx8OFD/Ik/8Sfwnd/5nfiJn/gJACIi+47v+A689dZb+Mmf/Em88847+KN/9I+ibVv85b/8lz/scHI5JsZdQqg15TzlFCvuxy68WUuiIJRaXGC/TUzKUV1UE1+ZtRgqM3eo2IxDNswBGCcna6GwaoBgQFfhq5mmg4DkCZRk7BGQwN48IiFKns6R4Z2krpIMKWIiDjKzcGRgS9UXM0QhAOQ9NpsNXM4Ez2DHcDmkm2VJcYBGMLHxksaudRpfU6xLxXK2cD4kyF0zRJhVbiFS1K1B44+CqOLyyv6Yzsi5pqLcJQqTpCss1HIC4HwUoEWqQ+OpcNc4DUrVJbR1orI3VpuYJINMWdn6jelYjeuukKedO8OSKRVjKq7nau25eizmn1ijCDMq4myAlCxoAhxyMJ0KcFvbuT9SXlDni2SGZ5poPM+xnm61JlTC3xGQAd78nkoAguJLDdLckVyal/MVq3s965+Q1ypVNgp5N+s+3fTsHOiO9T4nrgideiszDJFNsPczgaDRszhJQBJnDTDUz7wQfjlTi+rdzVgpLygXy16YXUHFRYptgSFOyw8rpKIZ9U3gCdfrX5B04c7FpmG332OMQgBLrlPovfS6PjGHphQigyZhthJUr+yBoR+xHUZQA7StnNWTyOiDQwzAdjsCYFycNTg9Ba43AzabiOaiwcmJx36U+5ERdLWXNTwHxFaCmjbDhzkCLWeGkKkZBgqbf3f5UEj05uYGf/gP/2H87b/9t/EX/+JfzM8vLy/xd/7O38Hf+3t/D3/wD/5BAMDf/bt/F7/n9/we/Mt/+S/xmc98Bv/kn/wT/MIv/AL+6T/9p3jzzTfxTd/0TfgLf+Ev4Ad+4AfwZ//sn5W0R/cst4mPlpHj8fcPkCzXpvxWp1CRZlyRNxLlYDuiHCnDkKgZp+QLZlgQagCj/vnjOCCmgLYVv8z9vvgLlomicClszWjwMueAxAjMgCOsFEiFKNlFVnQBkEfTdLCwWfYvc9sKO5AScmBRAJE1eggncBqVlzPuugAPA44p1Qdd6gpwKhGLDFFkRT+EcCFvEWUaCcatqLBpxIXIu4KsvfPiN0p1qjMSvzLfZlcYQaokycuJkU1rHQG+Mo6hKshDPluy8CnFbEWdLyQhSx8EYCR4FFcnlRXI10oUKOuRD1yFv1QOUp3b5DSequ5rOZUGsxyIxLhJgDDl8dhZrxEB2HwAhVDh+kjqfXBGfpBxXMVXmZGQwqh5SpsJEZNnVN0tUsLAIgaBoFbnlo1IkaZzQOMn61nrBk2ULQRBmzlQW4hM7BgWBYFzDGCZugS+T/kuyxmQGYKQpQXyTskilDjbNWfCOSMcGwcDElSCRWufGJGDWO4HSUAeUUSoIYx57pwY++1W9s8ylcQoRn/OZYRnluA13DH9shABJqa2U6JxZjOyleAIx4oYuVm7gPOMhJijBiWOQJLY1YJCxMiRGsrcprnmmKMUEwGNZIza73vsIgMN0HQObUuSG2oLhAj0wwhwwsqt8OhRh/efBFxeMVqX8PDCSVzd3YDGOySnYTC52Jpk+ApJp9ep7hQQGOV9FRykYpgAIfwV+B1dn3n5UEj0e7/3e/Ed3/Ed+LZv+7YJEv2Zn/kZjOOIb/u2b8vPvuZrvgZf+ZVfiZ/6qZ/CZz7zGfzUT/0UvuEbvmEi3v32b/92fM/3fA9+/ud/Ht/8zd980F/f9+j7Pn+/uroCcIj4jlMkMwOQWy68LKQ+K8aSML1CQ4Qhjmg083trWexJLs3Z6Rogh5vtBtvtFqvVCjkJLBIiJ7RO5PSJxShJDmQCKKEfdlitOjx8+Aj7fURCSUxtyvIcbIFEbJeihDkhjf3KxPCekfoAXrVI1CCOPcbdEzA9UrcZ1Q0S4NHAdGL10og+yeK4lihQw9jDa10mhogVZ4H5lfI07g7kc0g5RkGw9sByBqrgDGkcdM4+W9MyAPQyLg+zK52aWRiDYBaLzlXcHorOpCB7UpeYtViIOvsn7jReEysbweRW6xKRQIkKkIPzIoKGclKWCzVzCDMDOCspH7aKw7B6BpNTAoeFS10Th0QAIhJLlpMMzyeEprpimbQEHpmUMZqMFYGSy1IOWy8/GbsHfIu2O5kNqjIcYhO5G471WQoDlPPkbC4lcgCAtrp7SlA1NXAjgOdZfjiLFG0NiShL8M2CmhnwEKtRE9VSSqBIlXizRiTFAjslCWZve2pGXPX3xAmwsIPMOfby3NIbIPRhFLs9c8PQXJfTbTbic0LT53oHqqhFZqIY+1k/8zrVA+W2E4gjxp4xDGK+6hszT2rKvkE51Fqvr/+vVcxO/+s3O9yMQNNGNK5F03ZYdQzXjbjZROwHRj9GPLns8ds/fQH35kN88P4NOI148OAUr75yjt+8eRfURDSNB7kmr4UnAKzEddOgdz2gqc8wOxtWLHhKHTnsP6hO9Ed+5Efwr//1v8ZP//RPH/z27rvvous6PHr0aPL8zTffxLvvvpvr1AjUfrfflsoP/dAP4c/9uT937zHOdaV36U6B+iAJ7VTOVTmoiRkWadVRcSbOlA0B/TCAmTCOEkPSUlI5J7o+M6M2s39PANRdJYYRJ+tTeO8x9KNmUPFqQEKF28WUwzaAKZS1imqS5IY06njf73FzfY03uxWeVUshYecOrddklkZ8FEKEwLi6ukIKvQSV1jRJU6RURK/mG+nITRBYjchy8PR89YplXQHIQk8DosNIAAIY4BJoHmT2iUp9c8rBb+ZrdlieCvBgHYFy3AQviFHXbMWqQzHOxs4OUQ7SAEeSDFoDLGTRsvc5u4gAfQfvV9OzqVIO713R74FAZi9AlXjcvhuRQA4ESaeWSQsuwDY/8wXByh6gWv+yL7VltJXC4ZSzIjpas5SGcrY8i9aVcmzYzLmxndWSgEAGyoAFZk9qppaS+kcW380UDseXUtIA+FUwglCiDBkHl5K4oxmByiS607me0BCqZE1SsajeqTLmKfFuBIpxQwDEen8yUt1b9bdMC+dyMjdj3/P/q/2cIU2bt8EsVuRezy2EYULgpZTEHS1VVuDMitQdmq7DyckZ2racV0vSsFSKDKSMy6QbmxtJvN40LcZhxH4nDFLrWtWTSnL0znn82uef47d96mN49eEK/b7Hr//GE/zer/04Ts4Ifc+A5HqH0+hJMQgx5VTDSfCIMWBUIj+7KqU0QZqyjAUGvkh5IST6hS98Ad/3fd+HH/uxH8N6vX6hjj5K+cEf/EF8//d/f/5+dXWFT33qUxXrflhehJIoYi87lLIRelcn9ZKR+ZAsGm3bomkaEccq4InJMq3b8qbsDwcgZ1WA6jtiFE6073u8+uqr8N5j3w/wvkE/hgyImYvLx1y2X3RycnHFf080YM55DEOPy6tn4t9K4o/HgACGBepMG508Z4gIzpCR+XZaqqfi6lKPr7708v8a0dbIF5Dn5kgtBEqTuTLKvoTi3mLDddomOOW+LINIdJz3kAtMm6ydLENlqGRiPxOfVu/0lEBQ/SkXPRxrSDW5uQSEOIlEVJ2uyXobB5ILCXCUIPtlvC7DS0N05bvtjyB3swjXvpSdrevV0oIirS0AxHS0lFEETS5C/Ym5nGvOBIitW6XmqF7kip2anF99zs6Mt1LmBFlfZBUpinSkBBbII2KAOUz2No+BMfEZTbEkrlbWa7JPeZT6kDJina5CoWhsHVUlkutU4t48Tmm00eAd5f7qHJQAsecSiKHSfWogluwSp5KcEMRWwVRHuQ0LIGGww83PwvRc2p0zo6Fi/yH9Olc8jGviNK870XQhdR1SitjtdxkBO9cASErQjGgahzhIYoCLizVu3me888VLfOpTFzg7a/H0gy2ePbvE2RpIgZCSBRrRWMS+zUfVewdKPvvYZwlLVAM/b64uUwmc6VPvW14Iif7Mz/wM3nvvPXzLt3xLfhZjxI//+I/jb/yNv4Ef/dEfxTAMeP78+YQbffz4Md566y0AwFtvvYV/9a/+1aTdx48f59+Wymq1kszyL1DqTZ1zokuUBtklmvw0c7xVAGfH3XuX48COw5D1NjGEnCxZ+tOLVV2AHOOR5bKFMODs7EQQMoseJ1UxTo1zOPCrhG16tCHmBCcSNaWB8x5jGHF9fQkHpzoTuyxTH7VyBwS6Zh0amTJfxJ3Je4BYEwVTAQQZwdefYaxPfi72Urw4l4wUjLNDGZMglyk320w4PuWC1fCLnRPr0gnHRlm8ZRtrMYitjqR1csrBQMcj6bIyB8IsUQ1BOYqMPBdrUIo8ASRkdWpAU1kP1vfWxlgdvervMoFolp+H4jnpyyIxZQAn7OCsPSMkqpM+IyZBFfGAAvzzWlYB6ad3qtix2kM2mtR6Z4Zafmn7JnrWOwQq41kyua3mYSRAQfK2P4WxywQ0l3lNrdJteSiry5Yiwk0kOVBNm3GCKtaOdieSxaotvrY1l5h9lyuL2vm9nxPS5XM6eGbSKAIJYceULWfrsdvn8hyAhspsmlb0qIpgasnRwXGb76f+dRAYcnN9hRgBEbvK8pt1vndA64CUAjb7PVKM2IeA6+sdVqsW69MWj9/f4xMfJ6xaxjAmJBYON1IAUZPVcATWgCPRqCs5rcqFGuEhoPj+TNe8vBAS/dZv/Vb87M/+7OTZH/tjfwxf8zVfgx/4gR/Apz71KbRti3/2z/4Zvuu7vgsA8Iu/+Iv4/Oc/j89+9rMAgM9+9rP4S3/pL+G9997DG2+8AQD4sR/7MTx48ABf+7Vf+0KDP8ZtHoigbvluVIcBOLnVIvfX2nWP+f+JRQ9gIkujdBg1h9ZU73FGNNVAdAwSMei1j72GEBhhFFeAxKp3rGl/PhS9UgUUlU8RbiBJhB6nkYNurq9ALA76rSLyQyqyhuTV12rc3nukJG4p5IGQ1A3Bqk+QaPV+prhRRHj1P5R4wnlM83b0ey0mbV2jFKXL1sDeCcdKTVNcabJDv3G/rgIanClVQW4AvAe4cKiAs9uej8YUyRVjNEPW9qsh6AzcdOy1L2lp1Bgj4wazA0bFkeUveThKahUMYb/aGHXsnKdwwHPJUzIxuukF87BknzMbXANxwzXGfRvhWLVfWerWz41wMaRGUYP0EwSJZtGbIWhX2lJKY4rj7RbYmU5lzEZAKgtOGZFSRpD5DBN0sWpCoZ7DIdFt+k5Sq9iU/0VEjplDtHqjGhbNkehUrFwOmZ2zmmAElT1RA2Glk2pJj8fEkIqtPaqIonr9bM8cYgK8JtxwlaW8UmRar8AkG+u8SDYpxvXNNaKdqWzNK3U8EeAZQ0i42fVyPhzw7NkeZ+cRZ+cdnj5J+PjHW3QrkST0g/qKMkNFQQCLXWTjqYhxlICxYnGOodIbRoGt/8F0ohcXF/j6r//6ybOzszO89tpr+fkf/+N/HN///d+PV199FQ8ePMCf/JN/Ep/97Gfxmc98BgDwh/7QH8LXfu3X4o/8kT+Cv/pX/yreffdd/Jk/82fwvd/7vR+K2zwm0l0SUR6VdTMALqbrlU1ZVanmBeS5uLmUfJDOecQUQE7CAhLNxa/SxyTyjopHLKzd0yfPMQ4jmEmNR2lyWedUo/01ji1GdR8gB45iPemcRBO5ubkSkVTlXF0Qskx+aY1qbt5itVo9EetU3OzCmguQKAeZAEFQ1TwKx2oUo3xOszHNEW9KCUOKwDilymtXIxtTLTo2wyPr33tzz/E5VFmz6myA2Ro4KTXuQFlkjKr9RpGzuSlVkwQqxGSIxtdhIki5Fs6QXOaEtACUCuCZQHIy5EC52nydJcfofJ+qelByIANJ6Nhtj6ohVNKL+tm8TQFshTgtyD2VdsyNx5sURaUWbBbRpj8vZEOec34yt4cQUbtw6Cr+1Vezbg8AMaPhKoLV7IxZkI7EUaxtNUftnHvM/wLAUZFnKrF7mYueMotVZ5IVO6e1oZ7zVE231Km/y5pP4d6iqBXGKMAY8MmZqdsqScdLn079vW3fjHlgIHOhNOlPnzGAmLDbbgGvIm+lrRIE6XkPtJ0DWsI4AogjfEMIo8f1dcQwbtCdtri6djg/JzQpoB8SHE7gySPECCGyHMYxoPNR8xXrzFNJVp+qPZ4epznsv718ySMW/bW/9tfgnMN3fdd3TYItWPHe4x/8g3+A7/me78FnP/tZnJ2d4bu/+7vx5//8n/9SD+XepVBV+r38kAGxrXNKqQROIZlP23awUHBC3YjlboxB9YRqvFD1aUY6MUUMw4CPf/zj2G332Pc9GIyuazEECd2XFhyGs+P0rLC6EDgiBAgAct4jDgGbm2s4OKzXq4mCvZ75BKEtFGYVkej8SQMw5N/1X60vEYvN8n79t54XG0VKJBfOiXnGErFkcYcmyNKAAJCjvtiIiqN54W4Dhzzo/Vh862x8RhwlRSbMjM4ABxYQcw0EGz/5vQaK2ZjKOYwL7TjM2oSK4DLnABSNFGYIoQDRKXFZQtWlzF3PIgBVe2IJZ245ClUpEapqWZ4B0gzYxZwzc4rSqdP9SKa50PjN+hwVl8QAYgnYYO48NvZDcahMwCdxxg+piDoTl4QR8j0hIB4g0XnbtrZ5joaRZyUn8DYuTS1wHVEJLmfnCPO9Klbkub0Fj5Ql5uGYiJbZ3Mjurj/93UmAhW4lYfbITdfBpBG2DEfOS02EP7t8Du+BxntNYBFBXk50ShGBRfJy2jS47oBXL17HxcUpbrY7vPv4A7y2CnjviUfTEdq2wckpYXuzR9O1AIJK8YCGCOO4h2u7ykVKkKwzR+o6l7FxsSh+yvcpHxmJ/ot/8S8m39frNX74h38YP/zDP3z0nU9/+tP4R//oH33UrnNZOtzH6tRletDUIMMUiiQ0qyFQIU6m+h+nB2y16tC0JCmCotWRTQhhzNa7wllZZhMgcgCIcXJ6jo+9/hZ+9Vd+VRN4ixsJNGDBnPs1K7r5XAoQMUW5Ih/fIMQeNzeXGIYBTdeBKwACCNXsGn9w+eZrSBlgKwXtHLw3ZGxhCWXYrgLoNSJckh6UZ4WULQi9WIBqbXg16uFKZ2oiGQNcRZ/KGu0n16x0mPKwZSOUSr1iWSpzTcxwXCFj5ZRjGQCyf2MYtSccYKIJh2zNZ+SYVx9ZVAe51jXHMi8m2jagsFQvA8ya8M6IOj8pbd2zpIr4mHOatTg52d3IW1mkE0bYACK9kRNfiNsJQrN11qhGkpdZFqrmIG047CzKldxjE5IXIsvW1euYCbDgH6SW+NWa1cJPl3WLswVzrJLgijCx/+cjTvO3JoWqunNL0oO6up4ONFnjHCDiyLmRd48/T+ywPjkVq1zJbD1py3KbzhmRRUKcgTRGbHc9xgQNl1pKZMaKWjmgGjDkpAM218/ASWDl+bnD82cB1A54+ozw8MKjawneBXBs1M7BYLjDMPRozSIegvQTB4nxrbuZVXH3oxgPypdV7NzJgb0FChwgCgWexsZnmKCioJrTcc4jqiUYuRL+j4jUwizBNx3aVnzY9vuaopU+zbAohQjfeDy4eITtdo9ojvgkIqeYEogaiBN9Ge+hKLYAGAOAmaLWDBRjStjutuh3O0kErMxIoRB58Uozl+AQwp35LPIESJ22KVOozPU+TF2Z63Vc4kTnezjdqwJyUkolDVIWeealk14Nz6tOi4CcI1C4vQJEZasLQLUGB431qhI3aTfVLAFnYDwft7j+kCKIPMKMfPP8JvIvZPG1vVVz8AXZ1fCq1kdOQfl0TQ3wpfyO1al1mJk+mrEVzJPVqebPVS7eMrLJauT5ljMy5UhTNcapnpjZSAjWnLZl35wvYkTZHZeJHQP2IH23SocFmBEWqjUlMBVil3LtuXjYzkK9fgvFxKVU3pvD6Ml+zqQpNWNwtI9payAIGZtq5JuRyuzs5HEdElrln0ODBl23Rg5UX91fE+MunYylNsESz3az2cB5Qtt6cEwIYwI7cUFJ0dpiMAecoMXQRWz3N3DUoHMdwgrYDxFXl0DjItpXgNPzBteXEa6x9ZL436Y6y1IusPj6MsM3YhldDD3r8d654Ll82SDROQIthxGor/QSB3TAKej/C0cilC9BHM5J2yUIEm0ViZrBQAO15IxTUbD1L9yKXPiu7XB+fo7Ly0v1BxWjiXw4JxzZ4fwO5p/hoRmEEDw5JCTsxh79zRa+6zI3nIMFuMrM+wA5l1Uh0iTY5AFyaFyLRAV5ZkSq1LBF3hH9T/E/Q4XA6gWqgcp8zyb8UrUPtms15V4xtOX/Bv2otMEZyVkIOX0xSdQfMp2V9T4TiQkSTQWoauMpISfhngPPem4T3K1jMZ/Ieu48M0DKcQlQraMNqFoRNrEA1WsLFGalAC2Qbb1xLxWhwUsSBK3HlTSgEoPNQX8dIAsZ8df3j5ULLISMUQf1npuBzPReVOJ8NuMXecepHp9may8EkupLwUis86/Pe/5f7qb+s0CsFw7H6uU9nJIWWdJgLHJG6jNCmZwDzdQu+U9FDEknXI23vjFTdYdlMar7lPXwGeaI4V2Dtl3BIk1NZpbXi/Q8mb7fEFKCuApKmE8wEMcR2+1W7D2cAzRDloMHea/ufraSCY1rESlKmsPEAFo4Lxqk3Y5x3TK6Dnj0oAPxAGaxxmfd08Y3Weo0xwkTySNqeFee3ae81Eh0iQM4rFQBRjqsX+vN8kE8qJeyiImcOOwKBS5ZC7q2zQDHkOnQ9xiHCBMTA3bQNcsCGF3T4vT0DKtVi5ubS/VxEiuzzMkgZirW5lvSNdVz0N8zEExgeCA5tJ7BnrFJI/aX11h94jXs9xsQIsQkXMPcIWUECqASC9tjE314WAA433RoLYsHlDepx8PFOCNw0KGlnLQYzOCcJ5MLMOGCYAuIFZibfSiJShqxvBwF0AKQ+LALVOWE0gfkotcVPNA5P0dRRswWIAdgSWFFvoT9o9mFTDRtr760BADeqy7YkD9AcHee9fmvy/U1KLpF0VLImMkUWfx7tKPjrxDZXSqTeXuFM57e5UkQexnWYrG7sDCq6dfW56cTXXJ1UvRmLo732Nhv00kuqZjqOWYJkFITbkZy1Igts5j5t2r8PCVWElUqCBjyNot0IXzJOTTOIRmyhCBMr8SxcY0E4c59u0LodwCrmwoleNdI4PgcNwwa3iDpXSIQBQARMUiSC2IgjQN2ux0sZZ8joG09XGrA3oEbyb5NTEjJI3mAh4STVhJ79GGHfidE3QiH51cSYOHi1KFtgCEFJDSAc3AuAewq41yW77qWJUypxOk2WJNDU96zvNRI9FipOSjLmkKEiXkzYAe8WNmVZ9PfZaEdatZfEsQ28Cy6RKP0mrYFMyGEiKDhweQyqIN0GnFycoL9boOLB6/h5OQUz59fAZhGiKEJMjtUci+KRFUjUixaGZwC2EWkJOnVNteXOOveEi4smT5LjXNcacnEVnM4UYudyNKWzerMAYxxe51bZSRU/OBi5e4SkUOnmasLak4l5Uw2BY6Qfq/WotrnY2Li+e9a6fA3rjg1XZsJwXWkmG7KKPW6f09TtFoo4vk4KwKwCrb+YXU3pdWp7y0jHRBLL1LqfJhL5/eg/xkHaes5ES1zxQ0vvG/93dUXaLpzQqdR9aRGQfO7vzz2Y4TBHHYs7VNtUQtAAy9WxJyOMc04yrkRWOnIW4cCf6hV7lUMHa0/p8S/bbSezrI2JH3M74Pz9r5amkFEsgQGNdY3wCyhMgkMjhZhrQPzAPYB1DSIzOj3PcYQ4RtCt16BOGEceoxhQEtrNE2DMI4AAatVh92u1/nIeLwntC0jBs75iccReHq5xSsXDvG6EzeZJmEIETFIQIfMW0w3TTwVXEUM6LLPccVt5csSiQKHiMc4OaC+hCkDDaIigjgsak0JyYARIW1JnjqH9foE6/Ua280+G9hICLiyETEKhdX6BoCEJRMOlnB1dTW9dFQuYW0pODe9B6bcnoUGzMHzUULiEYAUAp4/fR+f7L5BKUfTa+D4HZ0Agyxfy5x3StFyDh8FwBkZTWIRO5hLSd4XhugtHdQ9YOp0ntjilCZYqi6jtQuyYXDlF0Y6sGPi79x3mcDh+Kv/m9GSccVYeM841CwCrs6eNZVfZSBlDognbZlrRpna4cVeEnsvS1uWZmXPD8Of3cZBGTCt2+IJcloeY41sy19LO1aQCRJNHPaPBQM/FC/fo9DSOM0UuRrDsdfviUDrvZB5VinfjEYA4Miy1dQ3FZl4KNKx4p9JGpmKvH52JnpVq26QcJxzpFiPOfdr/5+dYUASOMBNOTONTOadZCkigkrOEiS0MQEp5fsd4QEEONdgHAZcXl3hg+fX8C2BNZ6wibP3+z0suGdKCeOwA7PkWDWmoHEAOg9OEf0ARCbsg8PldcIn3moRh4T9EBEGRrtqEGIhJLKtNxfpkvjSpwzDTLC1wLccLV+2SDQrvxeT7AI1ECBCBuYiQqtX0OVDz8J7id6zaYSLYgEObSsJgiVWrINkjkgYhkEOgQaYJ3LY7XYSH3ccMIaYEb65VNQWjcAUiB0r0oYZT1Tcm8Z/dfDgRLh89kwzodQkvhp05GUxYW1BTmb4AXAOVlDCjc1XdDr2uwrV0AUazUVdQJo8KE37VDuiK7cq+1DpXs1CtuCfA6RwAFyOiN/qMfIMWaMG/LoChassYq7DCddzZ0nJeJQAsd8OxblTPc+h0dbdnNoRgqf6a/OpRctzoJvzmh4p83FMx7Pwonezp3Iu5vt2X458UWozGYON73YL2GPt3YXIp/OenhXWuycnSzlOJ58lsZEZNJqIVHSkjiy2cuHgmQR5yrmXvlQedSAuTou39bA4jVhkMTOJSGPQ1mdN42+z1iXJMyooKyGOW6yaE1CSPLu7McKvGoQxmBwCjRoZwUT0DgicMpGQCTenGW7UD7sfJXnFzZZwfcM4OZGYy6F3ICY0rczf3NTMzce2jCEJPJJP0q7tzgtIe76skOhtgHL5oB9bqDlnUb9BqC1WCZyRqJmfCDDXnISkJvsKDFOK6Pc9Xn/9daG2xhIhPQPBNKf0by8H+lGeGpoIR9MAifDs+bOcZqm4IdRXW0HWjNOdF+eciINrkq1uaFYMAHA9tiOl5vyKpEANWCzAvq4xM+ccrBYpyjhXqrJ2lAGWtg0osFLCBY/Vi1KAj3GFx87WImDl0s9kmYxuQO02NC+3/bawbotI6sXKdF75EwqiWWr7xcZ397vzZ1PCNhPIL7gux57dRyz8QsTgkWf1J0GclOmRjCCpDmtpOXepSlxQIm3JthRkWRNiXP1fJ5AR9hLnuTTXfPbsOigi8q5RI7FKilPDEseQJOaS9SqNWzSrU6QYMA4jxiiIJ4SgyJ0kJ7Fz4CicqHMeftViHAPE8j/qGAQWiJ+ppGdMgTAG4OnTiE+8kdC2hDYAIUQRD7MxFUKkUKFMFT4KN+os3NN0s+4sLzUSvdPQoroch5euRDBhXcj5O0ulcGBTbscyugCkuk+JgNM0Hn0v4g8iOThEhFdeeQWXlzfo+36SMaNGXnfpZXiK3RVZmB5RxSwMpEgAexB7PHv6NFOuVrtQxVxEkNUaLnEOzjlEKr6p5afpmLPhB1e/VFUmUk4FJlzREFnsYpyQjk8MQptSMWnKKUOiUTw42bjUKkpTpmo1k7VJKMuKEMTNiEBz3citl6tCt/mSFo71kBvSfivx3/T3ghSXuLC7iMT6jBwTZy/1V0pRd9xeT+rcR9R52P8xLvZ2JPcihMJ9OcVjz5bmcNv3Wpx/vD8lyGqkabGiFzIe+RzbuUR9Mo84VuQmbS8TvYJCijSpJoyOF4UzClzs7ggS1xCHQJ6rwEPO1vqJZU48buHiM4SB0e/2CAAamBRLuL+UAtp2JZxhAnzjsF6fYhyvKhgjd8kMMxsvwt/EhGEEnjwJePgAOFkRVmugvwlofDuFZxnx29yQ5xZzjtkKyd6jvNRIFFjgwmafawB0eLiMIptzEDUnOAWAloHFqzl2ShEpJmy3WzRNIzDTAaRc2jiOak2bcnq0b/jGb8b19RWGccwWYnX/Ey5wnn2hmt/0uVBrkYMSBCXQPDPDuQa+afH02XsgdvCuQaIGJnZkBpgSRI/iYBMp7RfO0ETYpEi00L3y46LIjSuulZABx+FeTvdkLtgzTnC6h9D0YwRniko92ZYWSy7iYUQaIzwMmx/okquQYdLb/SUE8gJlzn4yX5LeppyKUMp2/rj6/U5x9EKp692n/mEdy3E5dQWwulPkNu3nNmJ0qmOf9r1EF8tvHnUOzBfjti1h+fFyG0F+/77MaAgAOLuQaCswpFR0lw7kLR+xiGwPQvllApUw3YH5+GoYslBHJlkkQfedEgHI6ecq/91YbCoAAidb4wgxpPMAWs1HPGIYHmO3ZWxuNhhBeNh6RGY4iJg66v5455Ak65xwqt4hhpjXTmAbKcOS0DAQHRBcwD4Cj98DPv4W4+yUsbkBht2I9qQDnLq9VHA0RxBrxIJ3Cl/vuT74MkCi8/Iisuw7WtK/02vgySHCUhGFHMT4ZLXCeDLg+voGMUGs45yk9IkhYhzFwff09BSnp2d48uQpYpxyv8CUy7R/xzI4iIcIKVuWskWrcFWSc5NInNedB9oOuLx6X11XimO5AW6LB1t1hiK6nq6vhDDziDQiU7YF187qK01+VD89Bax3idUycqgO/mQNq/rekgB4qZM0TZaJg6MZMFFSIJEUVixzbibiOsbJHXKNnLnNqjWUVZkhovrYfcSjfGycH66d/K365cO3bW0awrlfcdN1La3dMib7rRAnx8fzYeYztXSu22KQJj8onGXJn6tcp/qLl6AMC2d/4V4dVjg+9mJ0X6Qjd/KgOlbvPIZxlHE5p0FJtF0i4RjgcixqxoCkNhhwEnYU7gIjrrDdb3Bzcy0cKhFY4wo7eKB14CB+naCIUV1hVisP5sPcnwDgGoJnRhMCOg/sGXh6BaxPCF0DPHrA+OBpQEoOzjcAATEh23GkFERs7NQdLYk4yhI13Ld82SHRY2XOyS1xQfIsTQC15JozAMc5Wo6E55PLEmPEw4cPsd2KNZmJcwwpBT0EFt3o7bffRdt28N5hHMVQZxwDmqY94DBN9MoVsF0ykijPLeiAebZLrFGxXknY7p5jDCEDRUZ9twy4cxZDZk6t6qusjZiy5wgzahE4LZT/ZBGytrmIAI0jOdTqlHB1zn6p3yvdkYroMzdtMi8CiJoyQmY0TXFvEoIlqqFSlT5pgZi5jTmpubHj58yGW3FwbHzuVMR5Hy50ifM7Joa8S1VwvL1pHbFoX4bw83NatzsFiFOfPGG+liUzU656WVS9yMnmMziXMh2OubRzHFZIWUaepJb5yCn7qqxBGlEp69bhcuQkBiYWyRVvDyIgFIZ0NjerV6ivO2kBe+lIvUyganR4SVyect7eLD0jpzR7BBOjIacDTSBKSGCEBHTr17E+fQXP0+dwc3UF8g5IAveSSuw4Rnh18wMxfCMESOII79XFBQROldeCl2VeNULQ7+OABODqMmFNhE98hcfpnrGPQY2xGvGPdRJdzgrD4PqHQ4dfvkh0BgzmZVFvcB/xl4peUpLgC04ptqYRSgcW2BiF2ouR0bSSwDvGiBACXnn0CrbbDfp+n5sW5MwVtNJx6oGfiHkrACKCv6RiY8mBmTjorfRgRIiDNGE7DuCh10vuUNx8GDlrNaGijPPqKF6126eIugbKhhwB5XSna5wRN5cgDvel/qnaE15CnlUzbAs2F5/mJNoKeHTM3ilQhlj+ZStgcA5knzjBJRaAMSN0pMfCqdpcbU2o4pgNQGUO1HIq5kHVQ6ZMfJQ522HQNm7hYD6MLvC235bVDsv3ZRlRczlrpaXqTGXabfbOEYSf0Yz9/whCt/6qhb4bSdbjK70oJQYil/en/p7T6zmzni1EbplrhewpC4+qHu0flf3VtVgqd90gnnw2Mg357mapiCE2VlH0RJ0DDUxUxMGU22MNjsJAneEJwvUl79GdPEDE27je3kAMAjtxszHTdLXqNSYEUNEuG1zSMauqjBwhqmiXHNAQw48ayWgALjeEV/oGZ+cD+mcklr+UQE7utMAnTQySvMxL+03VnO9TXnokepvuxZ4fQ5j2+12XqObaKtIPhFk6LEciCkiAJBMmcWeEJLX13mOv7i2rrkPTeJHVJ0U6FddX/BGTAuEpwpnrlSTCkervzDo1OQmEzQFEItrch4jU70HeA96DxwR2CS7pxLj0XQOwY9yvBYlomsqhmaHqNEKxqpy3s4D9Zu3XP0/hKsHiC5f10L3Ke1QB/CPdTHWwyNZ5xCy+qsxgJxfK5QtW538UkbAQVGU/7PMBoJ5xQtJ9xZVxFVxAKBEU+F0BEmbhEqrzMlmzyfyW5vzipUZ09+lv+belvgn1HkidZf3lwT2tMG42miHULLPSTofjmrZFisiK6sJcRKyNfN5NfKnEc0n4XvtxEu6CK5PjvUA8zd80RD7fv2pmWPo4qVuLYtmQ4rSxgqxN5FoazSecygskVxFgUs5VQow6AI4FfgUX0bRrRCbc9FtICvsGjEGQGpxa3o+AU5NbIK9rNrswqSAJsucoLcExHCI6B/QJCIlw0xOeXgKfeBNoHMTjIQXxIecEwionMLAcRJlQ5aSB++9XXnokuiSumn+eI9I5x3kbYLG6NedholmeWdSen5/j2fPLrCcl8gghoeskFU9SLnQYBlzdXGMMYr6d9HCMKmJISprONRdzIHXAETEjhphDBvqGMIaImBiOPJwD+iFhHAawBYJQhGORgMT/kicBmY+tD5GKq8MgnHhFNhfR5zR+cCEI7g6xlsc3634C0O991u9GAvPnBXBVHakIsxbvphSyVbBZeacQJhyoFUPcB7SEdsj1QtV/ud4vLgIPHD/Hc1Hoku79cGxl/rUeuLy6zO3dtRFTbq7uc35/l+tZH5OxZbG+SR9KI9O5TZMGyL+52sGemaVsSWVnCNOAekGotfj9ULx7Wzm4UxUNUGZVrcQCPJhUrAvd8nvdcLW/DgC8GE5C/VDBvNznfA5cdzlH8gLLDO6Nw4C29fCNwxjVlcV5jKPYInhP8I3csRAt4pvPEoyUBEF775HAkPC7DmGM6FrReQZ2GEfg8eMer77S4ux8xH4PxOTV+NCDEQB0Emc3q7xYciOnOXVxe3npkeh/zGIXqRbjAbKhjx49wq7fo20btcL1gCrQz8/PEcKI/a7HMAy4ublB27ZwTnKRDsMAEGXL3wzwmLMo7xiArHWndaxbAeY5c6FQiglI0eH66RWa1y8QySHEBOcbleTeLsYobRdiwnmnMYI1Aon2lfXIMEKkbulYH/dAql/S9+4qBXHZ+CV1qjww52wxTEgTgBOjpkLjgnDL5wi1ClvkWuuyFI6yWIHOODYlVmoL4jlReUyycHepOck5Iqp/OyRip9KHut/D8dyLsJrUW9YBTomRUneOYLOY1TnNSDQNzWfSlvyZORug4MhYb9M5z4vRAQrDgRn8NjrpKCE77/uWOia8MMlMqvZN46xJPXLZUjilcNjgsVLRefWae0dACOh3O9xsbiDqJdacnuJPKnBObU38TMJXSbSs1RCCRjISF5emaQAXMYzi3paYsB+Bd99jfPorgDgmDINwyuRbhV+yt3JnAhw1eZ9/y4hzeQb0lyjr42LI6bO7Fq1c9pipZ3JiKNQ5uXh932drT2ZGiuo/6ggRktnl9HQN7z3Oz8+xH3pETuj7HnAL4h/GgaVoltvPknIbd0bewyEhhBHjGOG9y8EbIiK2+z3ef/wYn3rzIZJvMFieRWaEFGW8mlx8uUz79N6jzwEO9JDmeUy5//uW5boTdggZ8hxBoEvnYgmBlLGmMuJaalGLXnN3hwI38S21OE8JvgpAb4ZNluFE+lfO1fxXjVCyABJgFBE+l6wtkzUoSKnGGfX6c+YS6pyJ0/FrdtWD81cbAMlPJQF6zcnVHOtSKZKIY1zsfG+O+Y5O3ijcJxW9JelnmiBMyvOhed3K+Z6r32xl5keRQKL3s7R6eLGzfXw2MwR4y/yzrl3HaKVGkMfat78MTSnoytlJnNA0LYLooNC4BkPsq9bvHr+E7pwOvvENODGGvsd+v0PTAAxBgilKX23bIGr2IrFBqI0XU2YQnHNwIIzjmE04mB3apgUxsFrJ3RmDPH/3/YBPvN7h5CyCfMRuk+DbBEKrfSVkn3ANLXyrMGShvNxIdP79Dh3QhzG+mPfIzPC+kSDpzKAkF2273yGlhLZpMDYR42gBwxnDIByocJoeIQSMoyTrlvCBh6LN2ogIChgte8vESnSyGAasFWnGAd6vwFzGsh8HPHv6FJ8GwTtDsOViFQva+1HTpHrEYzqE2ziM5f1a7udQrFSDkCLMY8Y0CUnV7bHzQUTT9qn+OCWwnJq/m5hqilxLK1zyTMnPVBCXvKtp0pQVmex3/m6ItYxVqhVXJkO4su/adoaUlX8nmyWlzqrmxmqExPMzZZX44L2iHFtY1wMoVLiJ6QLPCVptjio0b5/zX3msrvHKbNrnWtxakCmA4r5FRc+pm3RkHgvI3DjXjPTvqZaoyp1I9xbO094/1Jq+CNyf3iY5uvIsMkuwAxai8Fjyi9s6JiWYJLx1gidCHEf0/YBhCGg7CZJi6ggiEtuQWFluG5zLezXtV/KEmv+o5V5mNA2jiWLQFCIwRocnTxivfgxYrQn7LSOFANdKLlHxEExoqIMlbj9KiRwpLzUSBW6jbY+XFz30ua85I5Q4m4EPwyAIttFsCUb5J4mfG8KgF04O5n6/x8npifhFYRoQwrjMclHLuA2omqBDfi+Q3LgXszBjlnB4xiORgwS8h4kjp0QG45D6nq7BFLk6RyIO5g+/rpNFvfXXQv3Pay+j2Hv2mhd41sZR1mpG2S+wAEKVU2lTge+0L8lPOxlD9f40gXbZ24JEC5I1TpYp5XrmqpQ5hDr59aQfSa5QPbCFKV9nwRaKyP5YoclnURnO05AdW+Np4PQD7lJHwjPuE6gNfFD9xaStxXFmTnkyusODxAdVy0+zu3EfCdeH5WF5gUy5j850ylQoCcFK9JB6B5Cv8q9WwVRmBHH+XhGH1UlSAk/cw+MwYthLrHDfajzybDBUxq42g7kkZnhXhVwRQKNrS/lMFG8JMSSKDgiRQfB4+izi5Jxwfu6xWifs+wTyoXC6lDIhYUv2IsKFlx6J3lWWDI3uY7W4zLXK4o7jCE6UqSMiQtd1OQIGNGBz0VtKmCoJ2izcwTAMODk9ReNbePIIYcjjmHPPMcXpOKCBoWfXpliNShCBpmnEVSNGJI0WdnaxwubmJotGChBhodQrUcp8fepnxj0TOXTtKov4DHHntajm9CJI9lZKfOHZRIQ8qXC//Z++caxe4dczgDaEOqvrDWkem0FGpr48m4/aRPQqdiwI2Jx4JtBGnigyn58jx0B2z0mle86se0UAVFzx8hqQDaOMfIa0ap2kcX13GXLlsc789fLvNRsKTMSG8vFQxDxtp57jtC0C57RkkzJrptYjTqpNJAm3i6MP7tbS3h8p9bwiDs2j0qxOzWfX/XG1T6wUCTUOY4w4WZ+g7TqAoCH2pmM+vMuaNMIuQ3UWxFVGYOY4jAghgTxE/1z5appBU+IS9IVI0nvbXbCxxmy4aUkeBK6mJMhQHQ9AQaQPVzvG2aVD13k8esXj8ds9YhjgWobEFHcYwl7zrVqUrls2YVa+7JFoXWpub+m3eVlCJBLtgtC2YkwSk4pmG9GPXlxcYBwZu90wadfEqeMoB0cQcYLX4PU1Eq27dc6BLN+ncr9z5FQoQgWkCqO9axDHIEBTBoHVyQk+ePpUDpylGJpwt5MVwBy4Ti+THOSuWwsX7j1ijDkO8EczZLl/uQ0437/XpQweS/ztcRBnIlj51yzWdIYIDAPfNsCaA0VlYJQ53OplZ+0VIH4odWsmCG26JwU53LlXzKh4A+WAD6fyooSTjVK7mLeGjG4Ofru7D7t/5XtFx5ionaeiy8V1qLo6zmm+6Jw/fLkrY9cSyuf67CjBLyHxCN36FGcXFwAD/W6X35nPdfI5t5a9RnPTZjexH0f0fcAwituYdw5N0yJp5hYxNPJIcVQOtlJfoZIMgGDugzkcIYC2bcEhwDGjJSe3OSXs+hGRgPeeirHnw9+xxvlZxLYPcCQ2JOA1UroBUZPX6kV28KVGopn6w0x4dA8xSl0MqBzmIJ1T9KzEFmlkjZS50CEMwn0pAHOessl2CBIMHSpOTYExjgPCGNB4j6ZyJyEN3kwExKiRjjLXOXW3sTiwhStE5lrbtkW/C+DIAInulQm4vLzGu++9h/04YEXipmMJpJkBcoJ4IhjZC2aJQK/WN6lLzOnpKUII6PseIYRqXC9m8faiCHcCrIkyMVFzjXU5htgPOVA3e3WWsab60VXtShsaW9S4UTb9ZFWBowQgn3EuVl8HpcSAiTS1xpElKmOY+h3P2y4GU7zwbn5Spjhpi5Co1llO/sAIudLcFHkd1J6Ns94WmhALivmoJgTqPqcoY7IWC8VEvrkHCy5gbaihmLRTuWyJkDL3kc8fK1eU79Ns7Wfi0DsLT5f/NneT+xbT5YNkBk6TZkQW95CHDx5ivVpjt9thN/S3jre+d6aDFiGLRqGqYGeMEX3fY7/vQatGoyBlYYvAnCi2G3ojJv3Iipu+W5J9JA5im5IS9sMAL84vIDg4B7QNMAwR0QFjYFxfR7z3bsCbHweG94AUG4mYRHvYfgosnBJcd5WXGokCupHABNLfpgxfoqZuE+VOAGO1uIljdcHEAs1pBCCoiCwhwZFH03rEuEeKAwCCcy2YgRBG2ey2wRiDBrAnPXw6D6fEIiOLb2vErpIYCb2X1H8QotSXAycIPAEYAnC9HXH17jtAjPCN+K+6BAQATJIvlRg5uEBZV5l7eayuOHroYxR/1JPTM5yeX+Bmc4P9boeonC4BcGrEVOd4zeRJJQJyPI2UMr/Dc87HKcdJXECNveMSIZOyJsKyfg1AE+BiBaAnYyvfqpTsBxctE3KGXPLPh7qr0rkH5X2aAg3onAyAUJXg3bCpQxFxlQ7N6IQru5+yl2RtpimXSIDocPP78tTO43wCnLgkP1/gd6b6/CWgVJO+t/2G6Wdr9+C1Sg9a0byAAXtAdK3zsU4rW+7UfJKoIC85Qm76tokxK4zHszekVC4atTQp1ykW2Ba8wO4NAMAlHK72/GQZgVFt2RFc4JI4tQycgKZFd3KCVx6+gq5tMey2GHY7YAwgfzfCN8IBKYlERAkoBiFyQOsbjP0eu7FHIMJZs0YKg06b89yJfM5zmpQIaciDLd61CdOTEKTCVGjUuIpXNZzgKWHVAmkUI7TNkPDFJz1efbPD+WmD7TZiZEnOAfJgKBxld3jkbykvPRK9J033QmXJQEB/yCCRjENQYC/BmpWa8Q6N9whxhGQyEGQjMEooqZgiQhjRNh7dqsv94gAAJL3UxfoyI1C9f2UcKQPVOtgBIFztvg8YA+Ppkw8Qxh5Ep3CNpiKymWm/DlRob67/TqlpAudYwsMwoGs7rLoOZ2fn6FYrtWIWE/UUUgmpp8HyM/JjM4RZWPcJl2b9u+oOGgAr9SfczYwjyvej1pFZlEJChkJWe3LGUk1YTcXFJam5iW0PEYshyNxXJoZqJDmfLSuCk7FZXscpcWG/T0HsZMbzyzJbXuZDhK9HdjIVrl/KHwsyQPW9XB0hLOt5ZqR30GeNfBaK7S3x4s+H+rsyymXkY70Kv2NqALPZpsmc6m3j6pxZtCmGoZUyWP1nuFbPBhthRofzJbDADJsqSWi6jKsrovNwLEfmSMjJtAHJ6tS4Bm23xsnJKc7PzrG/vsb+5hpjv4Mr2eLzui5JLmzGNXys179xDmPfYxhHRCK0vkEfhsyE5BE7J3Ah04OyWHNOOCEhZj5paUcFljgnOUd9kFpjYlxvIy4vCa+etxiHiDRGCZjvnbj2fIjyUiNRUsXPbYj0gMJf+P3ehkYViWfyekNeFj/Xe4+ubbBatdjvd5XesRx+AjBqmrTVaoXVyVpNto2TQHV4lavg6T/7xWZPJCEGE0um9xxUPYlFcBwj9tsBnhzev7nEfrvHxasMtA0SJJdeA43jqpRd5iInh1jBsl5YykZUDmM/4IavEWPA2cUFHjx4AIARU8IYE3YRCGHA0A9IYQRiFKqcxfgJKqI2Qtwp4VGL7HSy8CyZ65PFuCXkWKVSU9fA8WQtHS9IKQDEgwwz6QCUZw4BNAFk1p+MlXPdJYBjujmBEawpmhhz7VYlnZb+UmZzdMyF4CAY0OWaBpmuWfVZvJJcvbAFuRlyr8aglI58nGFQylyAnIMJkpxh2oiIisKRGjQlRAAR201nULdZzeRI7sySoksmwdXUCsSeIy39P7v8vRAglQ+x3k+xaK0Q5KwInrS9TnrvCWx5Ibi6Q4ZZDSnZueKqT1aXjLxPJNIJosJdsxENlQpF50okQQ+cl5i+3Kxwsj7B6foE66ZDA8Kw2WD75AP0/Q2YGK71SNWBOq5mYZVgcRGHTyZJGIcBYRy1vtPsNtauEXCiJjBirriXTS2wzbd6vuCTO6cEg/eadzTZP8Y77wQ8+poW7cohMmNICb6xG+8OzvFd5aVGomYUw5giy4/qarEk+p2kI9PfRIYPeO/Ucdnh9PRUuL696AUdif9SiBExSCD4tunQtuKAzCmha1u0batuMtBTZR3Prqj5dapILnMymQoUoCFOzAz2Do4YYT+g3/U4v7jA5ukW476XthqH3WaDZtXBpQQmEVeFO+UZnEXMPTOcd3BE6IcB+/0em+sbnK7WeHjxAKvVCmfdGm+cngHegbxDIglAMSbGfgwYwogQI4Zhh2HYIIWIOPbgIQiyjckS0WjEmAjkZBnFNcj2j7wkRE/5wtnl8kIYzKh4l2JGIJm+nh0jJiC2esGYgVS0bQRGw+aLSAj6tFqtAnRtjwE0egVZcrVV564S/xHAFPPZmxYZgYfiuTTV4yrTU97Lv4nY0opHGZMQRUBCRFJZeHW78hoLVHOICPkeHpRqTZ2uW7lXVJZ5gtDjDMm5CYFSOGhX7WFxxZDm6vYO7/Nka23sDFhQcuOSKCNREzQ6iQxLANEc3ZdiQRyKjpWtUUGbOoCGo0pirOfc+2ycgyJbS0TNSMkBzoOcuNWR92jbNZqmRdM08q9r0bUtfNPAewffOAkS4xy6REi7Afurazx/9hTPrp4gUAK3DuCIGEaQXx2s3WLJa3h4bxyA7XaH/b5HSsiGh5jtCyOCOEnIQThExEp3auqNKrZSJbfP9KxK7uRoEprGo0kRSdPghAi897zHq+8zXn+tw9pH7J71cI2HV9jAYMTfKkgUKITzh7ME/PClIFXRGzFHtG2LLgThbGLQ3IFA07SIo8Oosvwx9GBIPMgxSkzJ1jcYaQQjHc0iMNfnMrNeJhHjBg0A4dRZKgZJa9CPEZvtDpvNBq1b4XocsXl6A3wqYbVa4frqBifOI2l+VHZuIpC6rRARVtk0XUTZiYDIEc+vL3G1uc55Tlsv+t+2WaFtV3rZO6y7U7x2eoq2W8OdPwQ3q8ylGfhKzBhSwDCOGGLAOFwjhB1ijBj7EWkYgBjBUcJ+cYrgkEBxFABOJBS4rWHBfmBOaLhFpv4z08f2SKoyY1V+BBNn0GpERczAwbQ0U8RJrChJmwlVAAJHrgIGnNdXxmBUPJYwovB4NPmx2qTp19rFxUrKIkXtlwlw6vW30KQRsEQsxI2NdTJEIwr0mSHeCXtfsSP6RuIZ4qZZ3XrSuY6vkGtdTyQdpSlSRFTX0L9OXInqZ1K9gSFpwiznrq3F7LPp8TKnTkKOWMe2XrEeiHKrOdeoKPykjfNX0DQNWt+iazo0bYeOOjS+gXcNvMagZWgKv5SAFMEpIfYDxm2PsR9EpBoG7MIG290OY5Sk9aqUlDviPBJ7SW+GD1+MG3bOYb/dYr/fS1haApi8+A4r11kbb5pbjfMkho5cuE9bU+Ncbc1zkoFMMFsQB2DdETwxRnIY4NDHgM/9xoDz0xUuzltcnEfcbEf4zhs7CzqiKlgqLz0SBaoDP4MM90GstcXcosh0IoYr9S2yhnBAwpGuVmvc3NwgpYjGi/9kSgkhBMSUVMRPcE4s4WKMGMcRMUas12tstltpD9Mx2SRJATzbX6POGeCYJIVosLCDmhPTIVNzq24F364RIrC5vEHoA7rzFRrXAElcYmQ+KMY/RxB6vSYpRJCfGgN57zUtUsriuQhJ0NCPAzD2KhYTR3zvBXE3rkPj13BEaIyCVvch3zRYNw1OvQOtH4Dca4CKyFIS4iNyQkgRIQgVG7kXIKH7MPRDtuiDGmNxjEipZGcpEg7hfqdnqxYZ2zooAMpCIEbGLPqVUYXuM3Ej1/hSAEFxXDcRoj5hnzssiLb0mBHzHGEeQapUcY7lihSEyWAgVYQA22gKspvyATSRCLEe0rr/mg+sr2XhSlRsV6e+mt/fwjIKAidDztZWGVGZP2cEZYvnYMRSrW9mAFHhaJlTtdMotgfz8U9hRfJtRoCkfTuIf7fzYl3qvYdrTjMn6ZxH2zRoGvsuYk8HEkLA5s4iweJhRApbjEGsVDklhDggxIgQR4QYEGOQUKRgtUcQgtQlkTKwGi6KCseB4UAR6jNLgpTvKKxr54hgWoe5dGG322EYhmwOYKWQbchrx0pEWRKMGFLmDA0pxxjz7ynJOA/hvSBD3zowW2xxmU8fCB88HeGbFhcPT9CPASAuNht897ytvNRI9NBpuYJbC4B/ye9zqcwpy9v8EKWOfK8DVzt1d0mqKHfewzctOIUszbM4u0SE9XqtCMsax0Rca+IS1t8qCJYRHaeYkSoYGkA6Ya/htnzjcX5xCjBwdXmJsR9w8ugUZoyUgUAtxuapqcDSUiSqQpGrmEsyLBA4OXDeGclSnxQgCJeTQIkRRkFihD3IbeFIDLWcZpTwVVJz7z2cX4F8K783jXAHFeW+ck700VhDZW8AGPGUs+UfoFxuSkixLzGPs+uQcLZIRTogyFatU9VgKkd1qRAwagInI7pq23K9CvkyDnSatt4WCYbqPaByXkjD8s3jlh5YBQFZLFgTaMU0tKpXcd3125M6wnSjoHvOz5kK4VA3bWOVtzjPXwSdxfrVOOsZaVz+71SFQdM6hRep0V+Nf41bLHQHU3FJykFUQTkRNdT2gnP6s5IOTZCdSBJyfF7fgMirBIRy8A3jZK1Nb2EKUQgHjgkcIqIiPWKWiGcpIXFETAkxjkhBkGRKQqQnjuDI+ewmu2u6mKyTZgCRNFMLCiEivydVU8gY4+08SFnxGh7RjGhkidAWwgDnq33l6h5oEVuThS50Aw1GifhaFMwOnHW38+HKHhOcY3jH4huqlZ5fBqxWDqdnLc4uOuz3AzgEWHrD+5aXGokuGW7oD4uIrxYF6JP6leW2Zn1l7quKdyuRMwQAe9+g7Vq0baP1GN7LczBj6IMebJ+BNaDOwiqKsHajhrLK/c+4ZJtDEe3GPCeCAznGMIy42eyw241oWtHFEoAnz59hv+/xsGlz3NvCAWOKtCskcHBMCaLjNA5dgUNKCb5gDDnsBKhmDFmyR5QNAhInJDIklxBiBEZFUomri0mZm3Peo3UezoueRxBti65r0fkGRGvJSNF4tF0H1zSg1udLaMBjtPnqHqSUBDDF4ovLzBjjTpBrSuCYNLiEIFpLhcbMoCoRQTa0qILAs/7mFJlNdXhAzmkuOEKRZ8aYBQXNIM7UKrRoCs0oAxAiyZBKMQLRdiev6+7TdP+5dhOZ4NT5WOq7xNnVqEa2pivOCLZWNNuzyeUsH7OLTVGMovbfzNU1AtKUGJSOPJGkAAC860lEQVRvXhEaExBIjJosg4kgPCdZSMylyztAfxdizsN7l5FpRqJcEDARZZ9r23eThlDYAymI9bpKpsIwIoQRMQRJB5YShjiqjloIuAiN3U1QQgKAY1BqhEPXOZITRO4MYSknODphQUk5L5D4sGc3EmnwKKMxL9kmYwJaWUXWikTjiKYhzZ6igWuyWsApPHASmL6CjTl/qPXDnI0IGTUhqeeqQrbZqJMY5BMcAa0mHr/ZBHTPHS4eMF599VQNj5Jyq/ebN/CSI9GlUpvpT7lIpXXzoagMhRYQ6Nyqd36YjGMxatQuxmq1whmfYgwjQNcqVjWI6MSCNgQJDA9GiJLFpWuEqxIm7RCo5mLh3YyC4zI+7zyg4swYE5qWcLPZ4fpmK0i0EZ0rALz77H3cDBt8smuyqGQ0XYQq5tkV45tMR1dEiCABhxYk8wVAjYT7S7bCZFZvCY6K83ZmmNllcZK5NnhK5WUPEJWAbIWaLrFkAzMQA4ZQCI2UElKIYI0bRkRolPpuyOUgF40TsVq7fgjnVbekRhmrroVrPKjVFEnegZpX84U3ZB5YxGWCJ0RcNo6jjqNwBtE43CifOUYg7JTyTYicEJOJC03XLZQ8xYjaGtXcaTKnAajuL+a9gS5hOf22ik7OpO4tW4Lz+syDAQqY3BMAov8v8wSAzrDeDO4QGZ8nA4kVskR1lib9EyFUDdXjmvq1Epxrch+ZIKNpHlBxxO7kd0Vy3v55iR9NTt5zaIQzdI2cFaf6SVILWCK4GEGhZONJKereRoxDRAyDEINhBMdRnitiFF/qiBQTokqNQgoT4rfsG+c5AaqzJiOOHRp4uZeszzQEXnTTWLfMokJJXNYNAJrAogZRqQQYYqvnHJJjBDASIho+ZjpVSmYw7NzMzkCMEfv9HjEEtA2hcUByhBi1NjnV5yY4RzDcaSLbIrYVojWEoKKDmO+Ht7XiKbHkVKUESvAOWDUOnhibXnzjL29GvP3OHm+88SrOzs4Np0M0YPfjRr/skCgwRXpTJHp/6mJe6naYkTe21pEyl4Td3psekNGoAsa5Bqenp9j320yJDsOAy8tLnK4lGH3iASGK+NAbx8FTxGoUmQHoDFhVvOicR+tbJIx4553HQnF1Jzg7O0M/7PCx188x0IBeL3w0TsN5SMLMCGICU1OWjMiEXRNEmlLCAKBp2/wdIaLRKEsIjNY5gLyGfJjsFGowqwsKnrqnIXI1DACAXDhQyhGWCt/hQHDwBDRUdEmGcJkZA4A+RnAVuzNdPUENfIRzcBNugsiho0a4D+NE8n6LqLnxXqQO3VnmjCXvagPfuKwrznLZ7mRyjvQwFQSjiDVU4i9m5YSNmrc5sAHnMtclAjDGARzHbGg1D05iiJ77p0DcQZCU03yLaimLijtrH4ihiJ4T4JAIBaDuVuUMUc25Vf9yXkdtrxioFDEqkQQxnL8LnlrMEgBfSRKytCCJyDSpVCDGEWG8yTqxnVrTpzBKFK4wIHFQpDkT+/PcGNCIH70rlA7GafpZ8g08O0hwFjvHgh0rcgkODeo0efKs4r4U4JswnMtlgVVmaLalBHjVDXqND5QIGFlsABw5eBA884LL1HKZ6PK5BIUkIsQQ0I8jQhxV5ZTyvstLcm9DFFuGWm1jSNgYoqZtESqjTtGLz4y9ZmfeOwAs6jW/IvA2wDsgscMwAk8vBzx5+hwfe+Ut9P2IMCZETwD295r7y41Ea98+UgMfYV3yohYxVgmtN0Wwh4h1zm3l54CIFYlymE0xvVaRQmK0zgOrNfbrNRL3YI7w/lSAOQGOVthsNoCLYrzDyIZFu/0ewziA2A53UmQhCb7lAlnuTtF5ZCMVmOlCAFwCPDDutgjtGt15BCVgt9/i/GQNcgP6yyuMu70ggRgxYESbxFye2SOElCMRZdHWbD2sNABqk08hCLWeF0Ki+BkWlFc2JP+v3oXq04ELt353S9snv2ubxkVbi2xA3NMU0Btiw9ToxXzVWMW9AzPAo2D2WIZfj8/ocgOWBuyykYgrQNTNOCcDLhnI6HPvW6mvOuID5AFpq04sXOvyLCsHEYn1c9OVUVdSgMI9EpAegtMAjgEhjUp0sCI/D+86kGvAfn2wQ7WZFXTtLKgGV/td3zNOVm9fEQJFTB4NOJpIT8VvSbk6U2mkKIDaCM+YRe3CtSQuRjZZfM+sltXIYy4fdVwLCEWMmjxMojoVp9fSpOkpmfxi2bLrX/K5pYM36pqCLMs7S5xgXmeT0lFprTIvg6eZf+7ShJcKAZESMDigdUoIMxAJaQWgH7DZbLHtBySIRwIxKkmLjs2J5CClJGeFSETNUJiiBHHrvah6YCoYQgRh3a0x9D3GIQAgnJ62MHsFUradHUAt0DBEHZOAcSD8+1+8wX/xmR7nZw/g2COGq/vNHS85Es3iRRNW0dQ1oyDJKVU8EQup8ulAt1RR1LW4Ag7wjrLY1Y5yjAmEJP6SEEDXdR02u20eLaDGRNDoWEk0EFF90NuuBe0c7IFdcAMiOqBFxCFBByLIMSglYAgY9wMerCUO5mrd4uz0FPshYHd9je3NDmFM8CoSc86DA8BJ+naqtLeLZYeaqMQYnuuhyyynH7KxU17/pd08XPvSwtIL97zgSy0tvFoHaqiLAKn6vJAC6Pxr/nTA9SkgzEKhKKJyA7gZ2eYxFQRedHoCml3FwUHPOcgQch6ctF2d3QlHV/0tsy1IM7epiDQRlIWPiGmEWCxqFk8VexIV/9bDc8nVTwVZmB7WAPgBx8yaoq/mpvUeTLgMC7bBUbk+4YQE8E4RGM/6K8PNvN+twdwFzlSfs9h1ejaOvj+DQfUa5WEsEKhzJHpbH3MEethXXff2+2Pn+bb+5nWlELKPsyP4pkHc7tEPg2RfUX2k5WEGJCiCce025gwnqnWxc+kcg6IYXokO1YNjwm474OLiHO5cuN/9fqNt2znSpghwXi2QCQiJcbMZ8cH7G3zstQ7duoXf3x81vvxI1A4Da6QTIVky8Fl8bw6kLb17feDoCOg26ONsf4vYDSQHhyLBkcd6fQJ/0+cDYeKrGFPOgFUoaEbbtiUprUwwW4GaUQDUVDtf/gxUxC/MOwc4QhxGXF3v0BIwpIRV4/DgbIVdH7Ddjri83qDfjyCoszZ5MEIZK4l40GkoLuN4l8R0B+u5cPHmCGZCIS+8s3T573Ohby1zBng2HkNsXJ8FQ16Tl2fc6mRNSl8ZUdb9sPUh30ssnIrLyo1ZA5j4OoLLT9M1sWgU82lPJ+5QWVPztGbhRoFEjSJshrg6FDcLVkQKECiOeaBL+5aR1kR1Wi2UiSiN1uKucFdWlWR+NbAm5xVhVkE2lMgReEtTGGHcsRmx2GpkLnJp7PVYbY3me740d5699x+vLN2zw3ILMj6CeI/WM3UQVcaJ+v/GN+j3ewzDoLlBxVUrpWlkpTq4SGZNKiRezyMlyQJj0dgcOUTWsKPdI5ycrDGOA25urtB1TQV7pHUijWZkjE0SacU772xwfn6GrnNYrU7utQbAS45E6+CuzAyXQ+fp8U1CmR47SLZJ5fIdUqqHb7rpRSZSasgOlEYE8S1OVmusVisVzQFdu8LF+Rl+8923wU6MWpz2G2NE13XZCi2T1qgQTmK1CLWE36LPSSmBXASPCb6RCCN77PEb7z9D54SLHPZ7XD1L2O8dvGvx/Pk1NjdbCUjgPOos8gCyiIwaIUxqQ6oJJX5kXW9b76V697v4d/fxwojWmpkgrxqA2z7MeE+eP1GijZVgIAMKBq11DzOrOfmjn2tCriICSczzZTgV8TSbinGq86UhLgQCmDVIhE3j2D4ScnQARUjiolRmbZISSd5o45qs0qT4BQUbVWtjnGuihMObZ/xzIbpI/Zo1XHmuyanUAwg863eC3rgSL/MhrDiUULnJeI+XF0NExwjUl6Jw0c9mKQADzAktgOubG+z7PVKK8J0vaRyqOSY1mBSLZ5f9683iudQT8X3rW3hfjI0khzGw223RNK4cWapDtBoMErMEg2tGUD1+vMWj167w5hsPcHZxdu/pv9RIlBzUXNpPALyVlDTCSBUYYV4mXIe1a23o82K6LkYrZhRULkAVfF6t2+AYIQ5Ydw5nFxdYr09wcnKCk7NTfPLqEr/2q5/DumvRNR4hRWy3Wzx69AhU6SVMjyb9cQ5h56iBd0BU3ag5RKcoobqYxGUDXYd+7NGEgAevPsSbb7yOX/mZX8J//b/73+DZO9dwjnD9/BJN14KDpWorhIEhTtPP1VGapmt1vwv/IgjuLkT9H6vcPmaZO6vko4x3JgmZUWNFHHlUVjL5A2aE7LfmDjjL+r15NCKT/peRASUf6SGXOm0uKnI6DDAxreerL4ekZ+ZsuR7JLQ3SrMICYk4pgWO/2M9hB7dwXXe8P1X9GIEyCzO0WO6DaP9TFVr4fJsw+wVKLYaFSN+G/YiQGGwIkcv9LlyirFNS7lLsADTlmcJagqqX1MXK4mz7hkDUoGkEid7c3ABEWK0kP6iopkRPSyA4lkhMY5RgDI0DPAF9YHzhi5doVy0+8fHfIpxoLdIiC5OFKpoQYt4QoByXqSjRdry4q1ghAFNuSaP/EMN5qVFzGhmpeoJrHHxL8J7w1sdfR9edgDW48dd97Tfgq37nV+PXf+VX8P57jxGT5Nrz3qPtWjR9gxAkt17KB62OpmOAUvzY2DmkGNGtW5ysW+x2O4TNFv+H/+V/iaeXGwxX7wFxwD4MODn1+M0vfg6/+avv4Wt/z+9BQ0B70iFcbRGQ4NUyj5nRNB7jOGZEasEbTD97q35mAQHahVjUN8/Eu8d+f+HCnL1lDuDZhOFc5ghum4+8cKhLFWMiKmIpHccSMqDZ3xm2rRnlatCz8ZnRFkzfvEAsVv+XGLvzOXIlllOkl99Sgz0AlLlB4dAlqlMJSbi4S0Yz3HcPM3c4BfYTETosgAGyKNj6mnwnLFuY3oNAm+/58lmsCYO5XXC9VyZJWO730Fbj/kTkMXuO2+qUMc7He3sf97rzpj8moHEOw36PYRRf2MZE6ih3zdqU6EoxB9ZnEnPQbIOhRnXGxDRtmznRtmswDAN8Q2jaRuCyMgFpciYqkS6p5a54BmJ0EdfXCe+/v8F61R6d57y81EjUeXEdyBurf5wBNtKYVnSozwMBxK6+a1NRMMqBMG4DAHxjDs5ACYQpoi8LPUfOoVud4OErr+HkdMRrr70KZocxMBx5iSEZovgsNg04ie8TM0teUu/A4wikaaJwUfeab5og/my0wwznG4xjwG7XY9f32G+u8OTZFc4I2G73ePz0EqfrNT7/hc/h2eUOu90GYRgk9FdKoMZlS1wTfQDVIa5EubXvVhnfMhA4ptt4EX3oh+VKFwL2TMoSv3B/0TJPP6kOTpBAnODMiYdkLS3JOqSaOzzEt4Sa26tHjgpWFwOKIyOUTzbOSXN0WJNT1hNW6Ly8wjLuRNOMMPP2SlnmdirNRf5eNQTAgiJNCZZY64knYmEqzPYCUTE5EpkgvBtx3U3ITTnUw6ZuP8P3VZHcJfa9+67YgZnux33aunWMZDluk0odBOltb7bY9wEhMlo2lyrK0dAs4EVhfnTtWHIy228u++zKPhnMBIpPaQluz9lDI5mYnoQQI6Wqm4ZAkYEUEVhEvDEyLi93WHXz+3a8vNRItF2t0XQrFKtCvZD6uydAEqzGLKYESrQhZ64jhhLVDL6OkGKhscxCMEeNUV0MYJyvkywoLOIB163xsBdH7NOTM4xR9JaeGmyur/Hsgw8w9H0eUxiDHgQRDafE2YPnkNIsOktmzlFQyHV4/vwpri6vMIaAp8+e4fEHz3DROew3W1xe7bA6ewVXm2fY9wG7bY9ht4d/2CIyJhaqIr7lCWfOLJFDLOLIsWKHfA6U5sZF9fOPbDRUtXU4oLvfu0tvfkuPB9+U7Kr6voc4sc5tOvtLqJDMhK1aWsvl59VgDps5VptuqZjvGR1t7/47qq5IWVy6wElV/7ePqUboGWnV6zg1Lzw8GzUXeYhAP8qZPDyGd1BzL9T2f9xkG7eViXERCbETjbAnQuMkS9SoyTHkHbkfWa+fkqrDSKVVrNGYSvQpQaQaQcp7iUeuMcHrcKudcqc5IphaA8vvOmglsJwZzUXxlfcK83a7Ec+f7+69Bi81El2tz7E6OdUTK24ZEqZNRKGNF+dwEREYkhUKRn5vRWyQGBEJTQX+5PJRhUQBRso5NlMsOkTvxDEYJK4IrmkQUkIYHcAjmqYDKIEQwQkY9j0ev/MOxhCyYVAKEtnEsjGYSMs4hoKAkopZi3jXxNKEFu+9f4nr6+d49ZULbHZ73Nxc4zpK+0CDzrc4XZ/habrGZrPH5maLs7fexMjPq34UoAFZN1EjQCJImiSlENPccOMFqOb6t7sAw11Abqn9o23Oqt4mrlriqOvk19NGGTwxPrFx5MZQSxuLk7hZqNZtltBm5bmbtkxFJ6s0/IS9PuTBAJR4UreWkjB6Zn6U+7MSj+XHnrWno6gJixmRdTjW+t0aQc6cNFhj8NZtkC031Y8mrR47n7cRVfcrWTzwP9FybN1foIX8Hmd6hIlhGWEcERrnsdlskDhKVhYlrLNhm+o1M+PjJb62cadACSjDyiE1TZMRKMj8qEXVtF6vwYkxDINEgmtbjGl+1hOIPIw/cJ7gIqNhQiJxV9xsh3uvw0uNRE9Oz7Fer3Ow4KZpJMTWGODIYb0+gfOtWnlB0+pISDbvPVarE9EpsuSPs0gZpPLaTC3lDdS4mpmqNGdfzsEXsnUZJ7TNGuPuBuM4YOUIKUZcXV3h8btvo99uAC8hx5xzSAqI2qatXF2kj6IziEpdTfW8zkmYuu1ug2EY8ej8Ar/3q34ntvsR74QvYBMlXVLXeVADnHUP0DZbXF9e4sl7T/DWN381LuMXYAEp1IEBREAIY14TsYwTAuL6+gpt2+D8/CJTjMeQ2DF95/zZi4qmXkRfaoYJyKs2FeHf1d8BIs0IamkMCfNWJvxj5RxvHimswRHruiKCAsCkCa0X2mZ9phwcoYoyJQMGlkZznzXLdmQLoj9w5jgot3fbrJHHeLDCFQdZ/n87QqultFTdxekayj7l3JAVwZJ7+ojc5/1F/0DRmR4SMC8qRn4RMett5aMa75koPgfXYM7ZcSyi09XlFRIH+MYpjLZIYXwAH6I5zcMIekOgESkBIwDiYngk3vEJiNLWOAa0TYPVaoVRk4Ab7KoZAfM7JgCNI1DrsO8TGgLggH4fcN/yUiPR8/NXcfHgPMvPHRXCVkTgDkMI6IAsDpANK6LfonM5BBKSbUTzAToBFF6pq2EQUSw5gvMkZvkJQEpADHAcsG48eH2Ctmmw73foxz2GYYvn772H6PXgBSBFAvkGzy6f4+LiHN6L18AwBOH6YtL5EcIYsB0G4aBVlBvDgH5zA/gRZ2ceT57v8P/5yf8BCANWjcM4CuUVfILbP8fZowuExNhud7i+uoInhyGNaHEqSZg5IqWgIe2abH6e184BZ+cPQUSISVK73YV87vv8fmUKsG/jYnM/RWr3JSz3E9EddFkjf1oAiPmv6dxNXLlMpHDWFZo/50cvEy7jWB37S4crsbTMpNle7qezu38NPZ0HI7hD6XBYn8o63mcML1pMV2dHlTmC6G7d210I8z+daNeQHUBICBAJoEcj8JQYiYCbzQZ9HyWiqCJdr+85Z7HHI2IMUGcZHJ4gcyX0CIqEGQbXdd8SS/o3iwHeeAQNi2n+7jEm1au6kjFGkbT3DhEMjvxCu/9SI1GRNukNVkG3hKbTr0RYoVFCl7WKis3SNLKR8xqpHCgUC7ssLjDxZoyqf2xb9fFMGFV87FvxW4vDiDT26NoGO9WLEhG22y1+7ud/Drv9Hq714Igcuo9ZrMyIoMirhHZzzrI+BBCEE62DmTOAk5M1Pv+bT3BzEwBusWpaXG0jdpsRr3xshWblEBPg+EQtfgm7/YDNdouTpgE3knnEzjDFqCmfljmMOjzd0qW+i+s8VkqV4xyqIfP5uO5l0XjA9dyvHAKq2xF5zeXWHNKBjvjWUWR291ZAWea9UOcF4etcHHrvOrPtWFrhY4TAlE1/AWnEgUT49j2iI8+XG74Psl8uR/fioA4wPR0frtRSkoNzeMvd+7DIdy52twhWqvgSw6GkBpgpIsSIbuURR5fVVxYXFzAblZK60ftW4k8DChOLO4x4R0wODACLZW6ZtKTt3W43iS0NAL6RGLrjGIunAAlj0DQOQ4hwECYm3pMefamRaL/doPdFNGWxVWrAwxafEazyqWLBKLjX5US5k4tFJqZCjunJzGpsQ2iaBglilh00kHmmdsIITgFu9MIdE6FthOK8vr5W0YLl2eSsq5XcgLWxko1VnIkBiPsMW1YGBjSR8zCOeL4dkSIkD2SKOD1dYe2A3/Ya0J15XHGHt9+NCMOArgWGcYPLy6fCaXsv8SyV/2ldk7ndpXLMFQT4qFym6WTvrjm3AP5SlPuJ55a5tPuInuu2X2TMSwDyo4viXqx/4KNxPcf26kXXYt7ebeUuUWhBMl/q9Syij8P+6t/qure19+K/vaiNwYepQzkElwjrQqU2IXJIKWKz2yKlIADXlWTb03GaJKCcETsXNaFuSJEzkKhUM6jcG9lExdPz5TREoMtx14vRKHNC40kTxNz/LLzUSHR7+QwujRUxTJOg0gBJYltAvjMj65TyBrqcTLdYe5Vku6x+SEluWU4T1jTCdQonKfFELaMDkdMcm8D67EzbEf9S0s2TXKG2ybLpIYwTizNDnDFKolg7ICkEmM+xJU6+ut5gH4GWCClF9Psd3vj4GX73myt8otujWbd44k/x7Pk1xpsebcsYhw0un32AcQwyA9LDRAA7Dzqiu7nrYr3o74eXfUqdHwJZu0gfpu0PP87SqJ2vD19eFGAvIaD76uO+lPqyDwOg70KSH/U83fbe7cZnJWLNwtsfeUx316k6PqjK+Q8RsoUpgKOE1P2kFXeXFyWuROqCCeWb/cpDwNXmRsL+qeTQDIPqADmG8EpQmThBnhO3MIvS5n1RxWtGK8vLnFLSftIUiarnQ9OUfM7GVDnn0LRACozxBbQiLzUSvbx+jpCGCWBPYRpNyFh7yT6PTIEUwxwR60oOzqi/AVCxhPkiOeck9yQI19fXGIYBzjmsT1Y4OztF27b5/bZZocUKzjvsd1s0jnB1c4X3P3gfN9sbUOsx9L34hAIZWY5jQAgBTdOgbYuz79wUwzkHjowhSF7Ktulwtd1i7VYI/Qbb7R6bfcAnXo34r3//78Tn3t3i6mbA60T4X3zjq/j//tvfwObpFW62I7Y3PS6vn6PfXeHi4hQr8ohpQArXQHN6KxBaOuDHAO+LAvljYqnb3r2N68gX/dj7lP+H6aeqrpHcR+bzYRD4fUTdXwrDkg8rXr9Pe1+qul/qNu+HOF6cC75/fSO2ZtlR6r1gPkCg2dXH6iki5aX3PwS3eZt06UXKlLCjrMLQryBK4JCw6XfZvqRwiCUiUZ2PuQRjcTnIi8FfqzsMQ7HOBdRuJGYvglSJjM3YFMBEBeUcIQRDnmIv8+jBA7z7+BpIwLoj3Ozut88vNRIVS54SUJiIIMk7S4nBrKw0NJ5ZQ/LMPcQ18peKSMA5sXyVv6IfTQm4eO01dF1X8gmmhL3pRZsGAQlh2MDDY4Rwh8wRXdei6zr021450HKBnHMYxzFbDtdIVPSwSccnHO84DgAzGj1kV9c7DCA4l/DKww6vf2yNf/zfb/E//uzP4H/1rV+Htz72AP3mCq/iBl/1qa/A+8/2iOM1dtvneOXsNVxcfAwDRO/KTCB3gRYRBdgf34UvlShV2pK5Evk7ke+LtXuIGG/ljnBMU/Wlm+tHLR9Fp/WlEMt+KcbyW7kYF3e/ukbESbmvRXpd/kPtkTCCCQ1Lnl2oNDAmxrjv0Y8D4CSSkPdeuFJFlpZk2zjX7B2BEmjGEGOde7bEw5U5GhNTB38ZhgHeNxm52hr4pqQfPDk5wWq9RkwRn/zExzGGL+D6ZochANiNuE95qZFoHEfEoYf5jGUKW38nAHCW9WQqgiPlPPLBiiEL1hNraD9yaAiIIWJQysf7Bo4IY4qap1CTwnqAU8IYxty3J4fAEpKQPEPOV0QcRzReLH3zWJX6GscRbdtmvcE4jnrYkiasbdG2XhG9PL+6uUEfAvqUsPLAG288wu/6HW/gl97+d3j/0uEf/viv4uu/+lV88+96HQNt8Quf+3UMYcR+y/iN956hv97Cnzi4jaIOB5AbkPjQ33Fe7HDfVe4DuOWyqOk5MY5ZLh7jpGo9yocqRU2S/xzVit5TnHcfLvLYb/N51oBg3u5S7Ojb+rpNovBhuNaltb+v6Pc/XFnmBA9qKYFFwJ1BKD7c2bKA+iWYhEmf2PqcNHtocFa72wHLZ+Cu9b5NevHhxL1FFAowOJFEuDHRLolP/HbokdRAaKQxc5bGEFibMUZRJbFI34goMxX2OShTVBsnmTqsrisIuc1MjvURQoCDqONqJijGiF/8xV9FYlZp5f1jCb/cSBTiI1SQpoK9+qCleZDj+uCYv6Vkn7fgyOabFCGIMIE1FyJLdnsQOISia02STscOk2ysxZklRFbtonKOEqjBI6mj/HSTI2JMaBqxUOv7AVAu1PsGbdMhpYAU5Z3ECeM4wrkGcdwhRgeKDq+dnuB/+19+Df7Jf/8reL4Z8Eu/cYntPqLxhN1+RNc4jE3EEAdsLjdoV2uMm61ZLIEtT2XWGU2BaW0xt1Tu+3xZH2p1EwwA3teg5rgec9L0YZtLYzuUtJW2bitcKtVNFqRcxw+9vyh2jpRfVKR7rN3bnn0pRKz3JaBetJ/lNqd3/IWKilaz6JRebGy3ryct7nUW0R6aNk/O2TxM4xyJfimkC0tn6z7njZT6kHB/BLCENPUk8GuIIcPaup+pNFCsVXzTSEJvTZU2z+JSi4O7rkPTCJe53+8z41HWqGR5qX3rgQJ3xzCCe2iQHvFOaBqPNTyA+/mKvtRI1Ki4em9rapLBcDypLZ+qZ8wiVmUq1B/nRWYk0hiM+i+hIFnSmKGJEwhmnKRuKCii4xjsPUbbNjIOTsgxIoEs4zckakhTuE0zNmrgfINh3+d+Y1S9aNfB9z1SBG42A5483eDrP/0qfuKBx7BNeHa1x9OrLTp3gvUrHU7WgBsHMEdcPr9G98k19rQBcYJj4aBbLHMYSxTwfXWeS9Tv5N0K2Zlu+m6sdbzPeyPfhS5qWFbrThdB97zthQaZuZKLTwHJ4pjuQHL/KTm9jyrS/bDSgmNnaVarHhWoDqm4QEgRVYS3vX4LAr0Pp34oPZg/mxKj9yk21mNrP+dUj435Rcq93yMAKUkWoUr0OvY9hjGCvAOZX2Ye7zTudoEFGjhD9aaWWLt0VN6pxbp1JCPjcmukO0fegBgyjaN4Ray6VhOHq1ga9wv991IjUcc1koRQkRBqktX9I9P+eRHnZuW6wAwgGDcpMYk8JBKRvKVIVB1RiZXbVIR6enKC1ntcXl8jpgC4lAFpiAnkHBrn0HWdeaGpLYv0H5NEzzAdgYnnLCQgM5A8ABKRRuM94hhFyR4iurbF2q8Q3B5vXz/HB//mBl/zuRZnboWTswExjhgjY3ARZw9fxdhH9FvGMAS8//QJfvtXfhKJGJ1zaMkhUoS54Qjsn15KO7zTlHB10G0y7JMvSK2vyLUyUDAeDeqUf4hs6nfyli8954KL8+8TYqoaY2noADuy7XvV1/3xRJlTDTTna8m1H9tMnIwK8EoavvL7XNyX3QP0bM/QCDCrj2xSVzo8BJjztVuaYz3wpd8MMfHs6az2CwF5nt1h6WVxayaxrl35TPpGjZQNsRUMmn+pKuW/RFaHZtUO13M6v3qDD7uYjl/xU43ktT7neczO+rH28poZJbh8F5YIAPmO/L1cq8M+M6wlYL/bYxgYqxNJbBFZAsS0TQPnfPah994jRRHpUrXu3rtJpDbvReVl3Od+X5ga06laMZGv6UvtnwEIQdAecKTGRyTfCbhXHEstLzUSnRzL2SU1hFnLtg8vnny3PJ01xUKzi5FgQREYnk4BvgZFD2pOEZ3HF379p+E//wV86n/2v0dar7C5+QDQUG4DAlbcYuVanJydY0cJD5oV+jEicvGbEqlFQoojwtjDOWCMA0IKADMaJJAjNI7RtC2ePr/Cu+89weXlDR49eoDVyQouApQGtBzwU7+2x4ocLs4eInGL9qzBH//jfxjj6Yh//P/4Ubz79jM83w3YPX6M9ek3waUvIsABboUmEEbHACIkapMDkQcrwndQ/1ZH8DlReQ5lIXsAzdRQUYBzMVRxhrY1N8RtF6PmVO/QvXKJ6cpQcay9uyBKq6TUC+dCxFLkmtLmvQE9LXytEVydE7RG5Ifvl+HxtO7Ce7KC2YZ4QtxUkQYFRlTHu0bkTAXJ11lEC36Qz5aEGUCOkTRXmYj7VS1l4Bn3cd9MGaXN/G41vmPoOS973rc02+4aMTEsQlTBNXSw4jxHfKn6LdMkhmFvO681wXcLEaJ/XPW5JuSM9lsik+QBVe8I8cGWaH1pPLndei+N+TBDTnG/Y6NWAUgqKIaPjBQcoveInrDdbEBBBhpTwBgjvPMIUeOEK+wLIZR55IWUv7VfaW2V6xxlcW0EY921WHcrhBDElgQkSTxSEn99R5r9SoLrSOjKCGj8hjEGMUJKCcNwP6Mi4CVHosfKi4q3lvQLmVPEAU0NjD1iFxHCgDUanPkL/LI/wT/4p/8Y33N2gk//7m9Gc/4Gnm838B4g5Sob3+Hi5ByUAtifwDGBo7rSjD3QiNFSSklEtG2LGEdIwGQHZocUCQzC8+trPH7/KZ49v8FqdYLtfgCCQ9N4NH4FTy1eeQQ0cHj2dIOPvfEqvv5bfhfIM15dvYE3v+LTePxuD+4JN5srwDPgOlBkJDj0XYsV77G93mG9XsO1clSIFYAyI4aAGAP86kREObJ4GYiQc3AcM0ZjSjCxuESvoQogVJeDJHqUcK5VXFitm1JxpK7Bh1z5QkWbIOIucbTUXwKYTgNtFGTEOp6DNiZ91Ahvnq5LfmcyScQSF7j0zmy88lL5rNKTKXcpv1gybM5EhqQINI6Cs2WLvW3IkXL/xyIr1UTLFOgu3Bvomuq4zd9d1sHlusxLTnqHYkye9XOIF4pef14O50PAPN3cwpQTEhIlCOkIsKuIhqzuEeKdJisw51b161HF+7weTQyQLPscETS1nU5zko3tUFWQ24iz9TLfZ20rk1FZX+bgIIkxkBjOkiOklDF5ckB0ottsvUMDj/3za8ARBk5wSU5po5GJDAESie+oWNNaJCPWMIDIKc5sje0+m9Fl27Y4Pz3BarVCHEZFupQjJ4Uw5gxVRsgXS1+Z9n63g1Mu1jjj3xI6UTk4CxdkIl6cAs8lYFo/nxhxzOoBDGIG+waU1micxnsMG/zer/i9+Nlv/U783//5z+HrfmOL//y/+P04e/0rsN9vRU/ACaCErk2gFBBSg8QBEo1oaq0mPqiE9ekJCCIObpxH07YACRfTD1skDGg6RrcGkBgRAYkTuvYUjx4+ws3jd2CS0a7rcL4+x37T4+J0hyYkuJQQkbB1Ht6vEPsePjKoZaSGwezhfQNmS4tG4GRITqyX4STrQlKQxorYDvWZlhEHypQdio/Kd8O1RjmiQpj2fsr1ZrtfRE0EEFJFwU85vDq6VabnFbMk7bSWcDiiLN43YnlCaFXNlb6WgSdYx8c12rJyN4cmsLduVFpISqCkSiZQxLks0JdK/7XRnE5Zh6eBRZhyKweFjWGz95ewAVdc8KylCRFgZ+UwCm7mpu5Yk8OuWZHPjNfjmkSuHteP7MzMRkHwcNlqvXD+4HLuCABxib4DIAPrgz6rTpYlZVXv1U/5jDOyAVxigDihoEGqKuqdgq5wzTTUf/Mc9NSQuZdAz0wdOUskC4mTGk4mODglzBI4MLabDfqYsGIS2KnqHwuGYGoh8+WUtVJu0WGi1zRdZoIErSEuSDWOAQMDKcYsWQwhoGtbULRdEoRrHgXZYIkZq9UaQUXLAHAfjwMrLzUSPWaUcR9l/6E5PmDUen42/TGLwSISKLWSrSUNGOMOr61fx3/2+/4A/m8/+Qs4efIUX7+5xoO3vBoJWd/qSMwSH5JTBHPURN7Wo1Bg40g49+fyHosIhUjSrXFibLdbEDFWqwbMI4glCD6HgGEcsO8DOAEJA0AifnUxIvYjEkdwHIEUEUfGBx88xUV7gvXqBDyOcI1D6xkuNWjaTkIiOidRjZwJDROcU1cbKmBuwl1VnIxuTMUU1OtcIzdN51XrgPJ+RuQg4Qo9eAb5DtV6nC9QabAC3Fzqocoda0DH6kqaLcpUdxavUvX+ZGpcrcMRwMg15/jCKAIz1DDhTmdkSkZkhTOz0JcME9Fm63YlFm037hzZUQQ6GVnpnw6fT7/fkzvLc6UFYqoe22GhjDEKNzsliOZovCDyKUKuhzzPxEOH9SbjhqgbquW7BYXqGTz8LT9KGrSTyihrQiqfQ1rMI4PMpub7ZnWLzFpgbtJ7mGAJuBMDDhGUJLF1Ez0ceWz3e0SC5klmBIs2tGD4xFwMPesi6RaFGDaDT6fcojFSYRwRQ5y0JxHkODMnoGLLMZ+6ZYSxvKb31tzgZUeit9w0k+kTHSLT8rt9Li0u96OVHIG9wxhGNCAJ2E5AahMeP/8CvvHRG/iWP/BNeM0RutUJxmGPlPZwjXIJiQBqkcjhdO2xS0AYkibYTigqJKXUvFeqyRJhCwILYcTmZovGNehOVri+uoJDi4EZlBIuL6/wwdNrnJyfIbGIg2PcI+6v0dEbCBEYOAKUELZ7/PK/+Xc42Q148OA19P0GObtESJoHUDijyAmNbyXovVKBwp1WeksDvhnZoFxKYApXCgzMhYjgmHM2+hroppiEkSIoqp1zs0siv9I/QDN7gSoJOQcY8KYsJys+sHYpSbP5VDOqe8rjlW+pMAOLhgqEdJeed7EcQQyJ4ViAqNBkFea0jxwrYkb9qyvJTd1FyrqzuxH8bTX4mJHGIkJgmOV6aftI63l+hgCWO9F80dXm1Ei9nNOsGl4g4ApRYqqekuuVuBDJ9gqTKwTewdBqjGr1qrnUU8vvxsnZXrTSNQ+DSYL3g4ZUImtjnVy+isOE+IuDkHXZeq4TWM6RzZsBzxLEnRKQYgDQofUem90ebgUJtKDcbIwxG/sYHPFeIgvJs8IEOefQti2GMCoipHxjWg29alHlgKmqxTmHEIIGgCgh/oqBkW69wq/Ge81BPd+v28tLj0RrkdwSB3o/YxA9MXZNOB0sJANZxt6B4ChgBwaCQ5daeEe4efIe/k/f+ocQY8DmZoN+uEG3atCHUbKiMKHrTrBaX4CHPVIMQsUli5Fbkhsb8G6aBvt9gCOgbQiOBmxu3sfrr57i/PwcJydrpPgK+m2P33i8RwwbpLDHvk+4vrnBG689xPV2g3GMGIYRb735Kn7l3/8PuPzib4KxxW7d40f/fz+JP/Pf/Al823/1B9B1K8QY0DTAsD7DiCCGTSSZ4B05rFfrbOXmvQOaKkQhFd+uHKoLpFxe0TmU+lMAThKbcRLEIetBfEHWASKKrvfYEVVJrmELOT0HynXxHOg62XezwHXOSbQrez8lIMWsC8rnYgkBQRCTnTxl1utfy5lCSetUuJEFtmSJ+JgVclBNXQUI7R1D8qxcSe02YEQKpvVFnHsEYL9IKUsv3d0CpARhehGd52f2PitnbHxeTsaq7R42zGCR0FhbNF3W3K+rF3jyZ/Z4at0/l4ZRRjTIqJYyMk2lkhWXcvaTeg1qPbxwW22ZJw6bWXqw5PIi55vhZ7MTYqqeKwtyAcFTo+oNXScPICpB6RLgCG7VgH1Evw+4eHCOi24N2uxx+e778H0Eughq3STqUNu2GIYB4xgy3ABK4BD7bnrTOvPLyckJ9ptt/k3mJnfY5it3mAFPWJ+cZLFxdp2B/m0llGAmkl7wqL/kSBSTi1Zk9aV8OABg70zf80xo2CFQh+QDViRcHiMiOo/EwDvvvCdGI+p749kD3MB7B3YM9gx2hP1uJ9E3lONq2xbDvpcUZzGBY8I4Rjx88ArAT+HJwSHg5uYpzs9WuHw24N3ffIa+7+EbxmuPXsdX//bXgHghot20ws//yvvo4w1ck3C9vcG/+dlfwr/6d7+I3XYL5wivPXyEr/r4p+C6Bv/tr/w4/t9/6SdwptlbnsWEb/rqt/B//hP/Da6ub3C9uULTelACwiAi4+1uh2Ec8fprrwtVqpRibaiV158ZDdwioLN6cpMFwEvEommsTfuXUsKKOjTUTN630I/ajLyP4tpkzySXoNZ1TpFKW9wIiDSsYyjtgeC8w4B6/JUwVkXtVI1HP+jfGoMIIpAxW0v2v8mHg35mj+sRIIGQNKtPdn+oGia7H1TxVUdUHgCQSLmaDMgNjdB0iGw6xukoJ+5nSEgW3KQmMFh+q/tn5wvGNakA9HwBuqcSHOc+pTkYyyGC4Qq5mmGTIZYJksyW1cU/capbT7M51gQ9YQ5TKDQqAJmpkurxOQA8HJwBu1dF/Mo5kbqFz5wQSuyUgEwAqhjjmbCUzxaJTd6XQAnREnuoyHUMPRryYrQTgDNewa2EA90R8OvPnuJXf/mX8Au/9stYdx4pis4SisDGccTp6anegRExBpyfn2dutA6SAAAck74rboKt86D1Ws8zqXrLotPpTqQE7xsMQw8A2d1lHAekFMWdRbFI47xkRE3jURh1rLzUSLQu+QLMKLpbJL5SX6nKIo3kyfO6JAJGElN4jgnRi67SUYOBCR0TGq/bQpYvJoGQEKNcwNPVKVatF4MkJ64jSZPIsshZ5OKq3lOicjTgmBDCHjHc4OxshRsP7PsRu/2Ii0cd3n36Ht5+r8Vbbz7CowdrgPcYUw8khxQIbetxerbGJz51gn/9766xbs8Q0oDnV0+wak7hE6M7d2DnwORwwg7Xz6/gnUPXtWiGBiBZp7Z1cM0K5IFmbMBekUaqREYqkjajFgYwslnMHl9jAotxhBkSKE0PCCec9HLfTNxEVCxXyy2VZ1kyU5n0pxvvSS0RUaxYnTcdqbTsnDfZR0YkmSuqCAdAQjrWfUzvZUHsXL1T8O7hJZ4+W6pryNkIF/vdfiuEReGmqjYU8NdzOfQTLD7DUylg4QSkqARhQuYiW0iWtqbmPcb9ON9UZ2NqVFL3ycmsvDBpdy6RyiJXa3KC2Aqqqy27cz8zYlq0jtKYEdp1u7n/gs2qNosRUm5fVwnV/BjIBjiwPTOXknq9JmtIqgLxud9yf+R3oZ7UOMcpoqzHCCUakvalCNU5B7Zx6vMUI05Wa+z3W7z9m+/jZ//Nr+HklOGpRQKjDwOu9jf4IG5BLQCvXCgKp7nf7zNX2HVdPneRk/YrM6t1qCkl9H2PcQxwauQUNVHIarXKfqe19a1LbhLcPiVJzG3jiSFIhCXXZKngiyDSLw8kesd8D1QHNL26hfOs6xxSjuUaiGjWBXHMZQI8JxHhOTHKcHBI1AIpgFQpTg7oGkG0PTnApSzqmQOJmBi73Q7daoW2aTCEPTiNeOXROfbDNcZRQmwxNRgj4fJmj3En8W73/YiLE8Ju3+OVh+eIQxLRTIz47Z98FT/3C18EMdCPI7Zjjw6M1rfgRIiUQC5hTQ5XzzcIY4R3DRoNNwiIURRxAncA+QYEB++c6PfsksaUKUFHrjLPL6CgAPg6BqcQG2ZAkMVsjKoNJynuuApJOBHXTuQTk0MwBRoVB5GtGoGURPR9enpS7c3hxaoR1Ly4CnHJ37LPhZObI/npO9NiDxWFV1xrrY82JFpNc8LgHiI8e1YIm8zFp/p3A65zgkHHq9wU2YXIY1VEYBVtOypiJQ+SBRF410wkQfLaDIlCHPcnkiaemJBVnCBJUHRrLy+GcV9K9OXWkw3b+PXqF0Oiyn0bl271uD4RZU2n3CpPN2XCtRZCLu8FKCO1w5NdZswsrjfWdq2nPcgkUxu9LdKYRigoQUu2GqofTQnrdYMx7PH08il+6fOfw8VDrxFhGEwR0TGCa7Dmcmbq8zlNhQaMwwAG52Qfec2rIC2F6Ihw6l6Uw6vGqEi0iIUlk0tbcdbF1qLYKCbAuQx3bP3vW156JHrMEveQeq0g08ECLdTBlNqWtbWLHcHk0QUHJkZoIjpmJCQEiL7BJwb7lSjws3ukKOQbLwYlmbbTC1OMY0Txv9vtJC2adxg5IPGIR48e4d/93BdxfdMiwoM8YbMN2O4AJOCDZxuEGOFfP1WRSQceA/Y3O+y3WzTsQSzilEDASEAfepycthh2PRIHNA7wvsWz53vsb3rQiSj+OSYRlzRek9Z6NN7L/JLLBkEcWTLPRMCTODaTg+ZeNeIk4xGZu5nOswORAyMKcvYu87IEaExfQkxRjJ6ywRUydzDVAy3ZIRbAYWBp1EwTRAREQhxHuLadnCOuRGcHbU44DjMiq60QM7ae9r/A7d1V7JzUJXMrlQVzLWrM/KqrgPQMsJczLu2J2wJl3MeayCFzqtZmBXRoHocTFVKeEYqZayPKwJHNlYptzQwDp2qs0qjmNcrzOOTU5FWnXGtZNb3bOjG2c5n3KmUEOtFPZpSq/TmX1xpW3/Y1c/yz/dSDmuFWWfJCaMwKUTkXta60/DUYWDmP5k2fx56u7958rwqRUeCq3B9HZe4piZV+u+pAGwmOQitCc9phDA7eYl5zREctuiFko62sq5ylLgshYBwGtG0D73z2l+dUoqIRzOahBFkA5EwTlUhGJlI39UzTdDJypsypiuubEnhkSyZWv4mh8c3vV15qJGobABxyCbVoBLNPy5TXXVH7SYG/HFDigNHuTxRRJUBwSTYvAXBpJwfMOSTd1BCAs4vX8OzyN4XSZ1bkpAHlE5TCdei3O6xaCcPXuIjr3RV+6Vc2+Bf/ao8HDxln5w7eRewue+yjQ8MJX/mpV/Gx18/RtBEPzz1ubiSXqIPHqm3w3/34z2M/WMJbsQvwDTCOEV3rwCxi20DABsDu8jlOTs/g2gTeB1DTICCCSAwiIgnwjYjZ/cU5h7YyNkoKlCJqaFFtA7kM1CVijloi+vJuqa+XyQnHmrmdDHQLByNbcpyypAqImI7UOQdHhPbi4qBuHTkli9JmXFmec0oAfKZ66zOXuScD5DZYnlXM856jB32eiijvEFlPCYrayhhU1aqh+Ly4ZQJ1XrJP4QIhOxnvkWaMo6+Jn3Kvj7d71/Mlo68lG4ljors50j/2/m3lQAS9MKaludzeFoNqxpKBSXypGvZVx2JxlkYMZYq2fkW/JFVlsAh1nSekkdHAI/YJYx/gHeDQovMMIon6Q5FwftJi9A4cI4BiLDSOo4pfFYGOEb7pwKDiV6+SFTDBk+SDZmju0MYjRtPTTuPwGneaQOjHEf1eMmM1jdd45ADHiJS9gcSe5bzxWJ2cYt8PuLre3LkfVl5qJFqXex/s+5//qu0P93KMUcUGTlP4MLpVh/Pzc6XCIqDxHvu+16wCEszeq4XqMAwYqYM/eYSGCT/3b34JDx+s8RWvt1ivJbN7PGkxxBFf99Wvgfwa7z/Z4Jd++T2cXzzCq48e4a03HZ4+vcTbb3+AZt3BOYeTk1MQOcQoIa76fkDbCpLwzmO1WgM84r0PvoCvfOt3Yr06xXAlBlMx9SBqFEHwjOOaAojs/kqQtHILS2jIKPOc1W2n6QYAQA7sUF93A8Q1x0NEiGk8CrjqUjtfLxmnlbGm6h3jBoygK5zaQTtF/lZNHDPI9gLna4GDmJclBF84SL67u1s47w9TDhifW0rx5zv06T5sd2GOMwLnGDK7e8wf0TJ5NsZjZ/A+c5zWub3uXN98+9hKa5VkXxNvuJwnS9zLlMP2DuwIm+0W19fXCEGySUkkomJNu7nZgZkz1xlTVPhzgmEYsnuK90UfmVRsbXYFKSUkhQ82tzqC0XzdChktpes6BI2u1rQ+p5vsNCk4kUPf97gaLvEVr57CrzrsQ3/P1fsyQqJWjlGP00rz53fJwOcbNaX2b+vbRBF2QOp8oTEVLtQOXRhLgjcixmazwcNXThFTxG6/x5gY5+cRX/fVbyHGiMvrLa5pkEhDCBh219hvdwgh4ebmBtvtHp/45GsgR3CNIMd10+HBxQPcbDZ4vr0Ew2F1ssKw36PxHl3TYdW2CCniydMneKv/FNbtGokjfF4LdTZgZEA70ftlDkv/pxyQJzO4KuIuIvPfVKBdif8k4tKhKBDG4SmpbCH+DhAmi1O47ZkB5xqo1YYL9d+Dy1m1vQQLa0Ref88LkXHS3Qhw0m4Wac6f387RzFNITRCLrtXc+rTMhRSYVeLbI3eEDu7TtJ16DMdxco3kCv6u9Vvz9miq5sOSr+gxguhO5EhlrBJ9aJmzvassIc+JyqGag0oUl9uZqZqyu4w0fjB3KoNfaI1n/VRrv3DWnKoATNoQWYLE+MYjpIB+HCTIIXkJ5MJCbKaU4J1DsNB+bSMxaxUJOufhfSFMmTVymzSQR1XnDS1jckf3I+mU7PdeEbUjSMQ1FScH54TwNWmNP8c77zyXZOLxt0g+0fse5kOgXp5PN6JCiPM2Kn3B0uU7Ki5UQOacg/MOvnVYr9eCMIdxojS3qBqcJAE3mLHf9zi5uUHkAXE/wrFYiJ6vV7je7nGzC3j8rMeqA37989dYdR1CcFi1K4wpIqSA996/hHMOZydn8Cz5TDebG/R9DyJCt+qwVbeXFBP6foAj4GOvX2C3C0hBkBm5AOIWgBp+UALNIFmt46NKV2U6TVtDoxcnwEGBu0GTLAo9UsqeWUyyQ8tMsyw0joZQXUZFaPMQX8cA723cjdWzf7U4MndW+YsK9T81FAHNxX1FtjZZZ1UdGLFRHk9dvI4iNVtnYHF97T1Z1QrpHA2aMHs++3obcp1yiPVvhU03MXv9Lh/wG9VkeFnEfmwsh2Um+6TS5mK/i00ck2aU+1HRn3VHy+OuCE5roKwhqrmXs3G8lDuQ9eu1qHhyDks9E/s6SHhJBmMYR/TDiMTmgwmQRhIivVs+SzQIFulICFeR/qRUiCTz+6z3ybjYg1kc22Mu8FrWTKMrgcUYDYV4Tqprb9sGicWg0yId3be81Eh0rvuw8iLItfytDvkM395DgjIRIR2MgUUv6r0Y4nhfshekqlGxHGOkJOnQJNBCj/c2H4ARsRvEJJwDY9V0uMGA7S7i/WcjXn2wxvXliEcXDVovcXbDwHDe4fnVFuuuw4PTUyAw9sMeu/0WDEv07TEOA85OBPGO44CucXj48ATXNzvEIal6LALcgixhbb4bc4BYracFtq7WeAo3GBrkVt6ssEzO0jFDDtP9tjY5I6EaQNe6wGVgztmoaQlJ1vt5TGxY73eNSOt6eag1Mz0fzKS/g4WqO1ZubSqivE10OR2P7RAtwttJ3bpPVKHj6mHP3z/aXmZZl07CQd/HnpXvt5JYZXz3IC5Ksft/MIvy+70LTT5m0sDan52dY2e0NFEdoAmcqeCYEobzNbqNi76r34NxOAfHACdGCBEMxunpqca6DYI4nYMjtZBtGkCRExRJSizuqRRv6T7N12cyjqr+bThP0qmp1wAzGjcndIuKg7wS3gtpG4+VlxqJWiYA4P6IsxZlTDfBHiKf04nQzTb3lpaXAD0zi5LdOSBGhDji5uYmR/OJMYIJ+bs4GwekFNG2J9jud/i1X7/BGCPaNeDXjKYDHj06A1PAw1OPEyKc+xOkC4f9GHCzGwCO8L6FWyX4pkUKwn0+OnsNm7AVsYkTy7r9bouu9Wi8xOPxrsXZ2SneffIBvvDFE+y2O7zCryByhIdYsJF3SJGRQoTE8wIMn1K9eotAvOJu8sWfXpK5n+UcOR5QplT5SBoSZzNMuA3JOEVeKXNamROb6Uid6qlR6WaW2r0VKCnyMySyXI0V6FIFa+38LQOUmnCpCYepCHrBqEbX7rYyRao04aDnvxeR9byR8rxmmu6DNO3ZZN9uG3e+w4dnr27r8L4WucDklZrGvp3Fy21RpoIqwjLfjePjuqXVfHbmZWk+9fPb2r4NQdXFk+oq9b4KEpL78+DBBc5eeR3b7RZvv/22LQKYSIMaaN7lkEAaQKFOpl0TsBYgvh53rQ6rE3VIPlCnXK2cp1SN/9jcmq7DqDYoRJSTea/XLfb7vVhWu/ujxpcaiS6VQ92liPvujWQnF+ZYmbEUB/1qLd1IC4IcQsD15goPHz7EG2+8gbe/+DZ2ux1ijBhHCf8mCnCxTr2+vsbjxx/g7KGDbxq0XYfVao0vfuESP/rf/Vt0a4d9jOjWDO56nK0uMDYO+wHYDiOGkfHgpEVDHpEi4ID1x87QvXeDfhzFjcM5nJ2dqVGTZD1om1YU/onwO776K9G1HqEP6FYXQIgSqk4DKTjnSoDqY6ula58WLnpdlj3ajrc3ARAZ/lUIVG9WjrpWI9oDoOExhZsMy4VSF7vEH7XcfsyMVaz6nsh9Zy1RXenQSnPSMhVukg6P8UHdW0b4QkW6OmCvF8ttusoph7VUb9rH0l4fa7cm6BZGhfvOesp584sxr/ds+z5z+ijtWx/MDK/3HBoEQZCPz/GtAWAYezx89ADvv/++rqOsZ5a4pYTT0zM8ePAAIQS89957kwTahlT7vldLeSUESfyBE3N2s5lLe2Tv5DxPc+fYPEwkLL6kXdPkfLiGoGOM2O0SwN1CC7eXLxskOucoyyETEWmd6LWOB3p4GIvQqlCNk2uJifik6n/+PWlOUAayn9rpySlee/01bDYbAFKn61bY73tAqStADJCePn0K7zyGqDL6xPDMOD1/gMdPn6NbOTStw6rx6Pcj0v4STEAkoO0kt+gwJJx0HVzrMaYR773/Nm6ud7g4v0DXEUKIuLm6yYmZRwhCjzHid/yO34Z3H7+Hy2eXOH/wMAN1Io29mqZAhVnEtoxDAJx1KnraabZXx9DwXDRZP5v0QYR5K1KvuMFMdvWAe5wxNsrJ1I+W5rT0vOK18u+LevQKwC5rfw8DHixUKX9NgnJPAHtftDABrOZydCzg+zHOGrX+e7o+05oMC75hHD/NfB1rQmDS8eK0WXNv1pKk6kLPEOcxLjrXXfr5iIj04P1b3rkvore69yHkigtUuQHHxjD/PJFgoHw3ItL7Fv1+wPPnl3jn7ce43Aa89fGPoWslkLzBuxASfNcA3iGkiL7v0XVdNlaySEJN04iPqHdZJxkTT5BdVBjrqIhjyzgtIcChRMi4YQLQegkRKNxnEASrvjMphZzy8UXIxJcaiXIlzq3LFEBWVLdyKHed1yI+MzRa7k85iMfFKva5iP+mAPf6+joDCeYqrBU5xBgqbhQ4O7uAGwcwSxScsO/hvMfpw1NwEkU8Uo8ItXxDkpyX7NA0DiEAIQU4qA9rDDi/eIAYE0IQXyxPDfpxD9d4hFFEI6uTUzx8dIrP//IXcXW5xVspwq+AOADkJP1aFjnWMCmv4RSB1KuWs6QAk99srev3F5HPAvIqeu1DAFOyQgBqjjoF6WQxN03kWe+/YSd1mYkB89nadEykVCPP+mzYOJfE/sulYMYDcebiPT8uHrxLrLfY+wHXw6h1ex+m1Ouz2CeqIA55g4zYmI0nb8McOU/r5KBXFYG3hMbvXBqqIcLt5UXX+64zcfBMktFOfhMr16nY3iJnLd3L3HdVQWJlTJkB4yozPGWG80AKEbvtgH4v+Y+vry4l9SNzEdkSIUSJR5tixG63Rd/vJwk2gKKaM7hpxE0t9pXxHRLSk991j2sda9O0OXk4Wcxs0nPAEskoxSgwQbNFLOGVY+XD5GH6n3xZ4i7zb/ZvzkUe/FfxL1yeTU5cBrJFvDD/Jxyp/DNub3OzySbeZm7dOAlywDDxgsTbdd6ja1q0voEDYRxGII1oWgfn5VY3TSsHz3lAk+IiCoUlYuSIMQSkyCAmfOzV18QoIIYcecTWxKIKifGTw83NFpv9DiGOGkXEVq1kCyFdk2wRV39mcy+p9B9AzkHIJnoFBHwqkWP/qo2QdxNPv9eXG6YDrbgOrgGfIEiLAyrMPWfxOWcCi8uLVPoCJB3bVH9WgO8EgRpnVB9Fluf1f6WTJe54CdnMG637WahP0+c1/rmNy64rT27PAhE63YfD5/ctWdyOuZtQTYJxmdNkKWh5HPUZwtL6TvfgWMlnI7dDthyTfkv/digW/uWzyLnO5MwfGcuxdS5zmZ0bZr1ny+E6pmd+cSD598QlzTuz+JDGcUTfjxhDQtMQxnGokJu4xnTt/7+9Pw+2LLvre8HPGvbe55w7ZWZl1qwqlQYkCw2NZCNXu8EEUgjxeIQM7giM6WiwHRBgEeEBEwZHGGx3dMPDHURgB437hSMQ3c8BNjzLBBhsMELCYCGhooQkEIVKKqmmnCqHO51h773Wr//4rbX2PjdvlVLVQqWsuqsiK2/ec84+e6291m/8/r4/Xx6ZiLYtWy6XwEAIX9V1uSdVviZx2xqcHZoWjFdmfQ3M+hIfOT9VVd2gjGNUzrhxfjaOund9Ifv2lvZEx+N40IhBxB6zMbOgTtbKjVdb+9cRP2rtQI7zH0cP0BoLUXotl5EYA75ucL4hLg6prGfVH2KdwzohLDuWi542tHjnMGjRc9v3eERrn9Barcl0hlnmfnv69YEAtkakUwo+EQwBbx07O9tcvnwJJ4am1ppVXyspvnMGY4SuXbI87Kkay1IWLNolO5OpWotxiZFp6mi/AkzJj2SjA7RjUraOdcV0rbPXV7waUb5h/XTybI1lHH7Np3fw5MblIcMBO+rZ5mdU8rLFClAO1BC0CLtyFWMo/KBqR4aUCCJW6wbNjZ5LNhjGodmcrx2MijSjpJxzlGMcqoyxTxyyrlwnynDf5fuiGb5r5JUM9yWDLTAqkUlipCgFY+ya4MlrnC31YfkHr8YM1klZpTX07vh8pfNyXL4qZgNqfI7IC1SmUD65nhJ9bh9gMGJH18sGX1E0ZrSfbjQCRl89mqVZO+c3zCo/YDlGthxx40XyBNcV+WDGHz80rH5ETo24vteiB1Dee5yBNpaBptzP+uwCKrMMliBartcul6y6nk60S45zPoElOzL9pHeGKgEotS+ySzIpUNc1zvuyt3PVQkwdY6yAsyZ9Z9qXa/MdzpQcZwSm91SVcueGGIZmHyHivFZLGGtwwRG7fm1f3Ox40SjRo2GyPI4Pp5jxfj26r8vnnj1sCPl0D//OCnPcNUHHYAUpm0ddN2xsbFDXDuuikjWvAiZ66lqYH6545vI19vcPwVmkNogE+j7g64qmqtna2WCxOGSxXLKYt0lB+0LeHLqOxWKBMa5wShoMy27Jw3/4h0xnE+q61ut2PZNJjXPTBHLq6PuOC5ef4szZO7AGVstDZHML70Pa5B02SeFOvBoreX1UKxDMCMGKgPSMfI31Rc/GrxEw2l4u/34wDGS0rGpY5Gcn+nDWnrFBMNKpQMoCZuS56r1G7NH6x3IvOWSWm6KD86Z8NsZ1dG42mNPV84Y5krsdTTsb2GYodRIZMrt9DCNllvfsmv84En6mfO+glEdKryjDQUiU+0jPq6xbNjgkf3ptafLHb7TWj3Vmh2sHkbX3HLVtB6/IrL9n7Xrj15/bWziqMJ7rHGdr40ZvmjWBao68Z12ZHn9bJfdt8sbixj0hR2qmM8XoiFThuUKYx837uDU4boxl59HZWKPdknKItewvoG3bVNep7cmGvp+eGEPp3TmuRBh7q8vlMvFqmzVgUfZiQTu9VFVVFOHQgBvG+7ss5THyfrmclzOf+xQbpx2hVul+vPeYmJG+Q2/pmxkvGiUKN1pXR3937Gee3zcd88FnRwFmb0xEvdK6slSVx1qQ0NMve0InNNOa+WrFwcGca1cP8RUs226tl9+kmYI1HB4uWK1aQhSa2qfmtkqeYJ1jMpmkULEUsudJ6r9njBRkbu7+fuedd3B4eJhCLcljblc8+rknuOPUBi+74yzuHBwu9tmYbrKKASTijVaNZhEdUWOhWLPZGzHKs3vDyidFpitYdOj6ylpTDnfMQlkEBY3Z8j3jo2XGebT8t9E7zerGmBQBN5EQE52gWgI3eCHrim89dKf5ndHe4/nuKx1HiQWMSQAjicec7SxIhKFt3Dor0/j+j/793OPZqeNuJtxVUNFHvjEbPJIo5MeGhSn/f24hdmNI/VnAW0cUz40FOqNK7dGvE3VreWGQJ5E11DSGY5fCjO5PRor0Weej175B0Wfle8ycjoZ2j67BcT/fFCCJ4QnkIG5WnPlsWmtZLBZ0nZLLBwRXV9qLNIayeG3batentI9z+qrch2iKyaSwrQKXJClji3OT4oBk4zCkSN6NQMN8btZMawV4eq2fD86tAZJUJkuak14bPj9uZjxuaSU6Dj0d99rnRTQeef9x137O7z1qccqw+MYMls96jlqtqXa5QoIqAbEO64V6UvO5p/e4eGmfxVKYecOpM7fhnGO5XNKuWurJBGdhtdIwqndVmZD3vtR/glp6zvnkcQpt2yaUmibSsxK11nL+/IUU+tCQTN9rrkOwdCsKcYP1FaFHO1hgNLdqOkonjGStmhT4khL2TGjekfDLPmne9OVcpf9Lel1EkBQWLM5Mzh/fUIYi5fwoCttSlApjtTA+aCmMO/aUzLrgK3vBwtDBOR/i4atLJG9N+aYw75H9OA6bjvebNYNWKT5XlmwjK1yNFZskWzIMBjd6zGGR5pIWRYYwcF4BM9rrWcmViOqznYP0vsGLGV1jzePMz3tYz5ha2eVGCKODw6jn3WisBfJuvJfPZyxnYy6t443vz2QSJCPqWa4/8ibzp57lC5/le46/t/QTIsd0XZF1j3T9M+u/ey6v9Ll+f6y3TuqEEiOS2o6ZtO+NMapE+x4xst7zM53lqvDhjozWBLYc7/3xyAaptaa0MWvbrnig2uHHjp6nDMb12hjVicZI33elA0xW4joVi/NOOctVMyOsk+B8vnFLK9E8jtsoz2aZfrFGCeE95z0dYxUaTcAr0wfJO7IYGzEerl1bcO16SwiJ+NlYvK9pmuSVWJPIGNSKz5Bz9UiH1kICCTCjBcykf1tjCIllZPAYjSLsnBkVMAvRCmItB/Mlh4dLsILgtFbMGGUtycKCI6JtFC7MgqsU6qeNr/8bCxAGmcH6tYt3KFnp6cgNkgcE5nCtIceacuAImFHmLiuVG57b2m/GDoW2fUtPfuxhrT3uZ4mCFGUy3pvpviQOAlDsSAuXuUOpdxl9LyPgFtnLygJxdEvD/STvT6zeZv7Oo7MWKUrl6DzGXsRx8+TYb8+KLN1p/p+6F+Opst6RI6/0cUr16PoOrxz93rV3SclQH3nfs33m6Lfk9V0vhjt27zzntY77Ls11Zu9oWOsjZT0yWsc01syMZ5GJn28U5TT6Ellbs2Esl0tC6ItX2HVdQtsOoLkcxh0sMm70RtP3jvlwh9/faDzka45D0EU+rM84zVsIfVCZlbrRUAwqrUkNMeCcKb9/ydD+wXMry8+3eY8rQRh/9mY23XHvWA+3oOHI1PYsbzZ9jg6sQ1Aw0bJfsLvXcngYaSoHFlZ9lxL2nqbRvpd9u1oLXRgz9M7rU91VFky5kFjDIw7nPaFPm1rQMGYMVFWdyLA1pBElra2N7O7tsbu/Dy4mRRyIYhUpGwWM10OTezaKKZu6yGkjqoTz740UQbC2ZskxteaoAE/fJZI4aG2Sh2od53BQFsKGvDeOogoTKIiRIE3yuFjjuiGSRB8/2BzgulERkj1mwMQbaQXGucq1vVXQxoPyG1OODRb3QKKfv0+vkQ2hGwX4sP2PfF6/JH3l8LvnSoEcDYuOR/Hpx97MEQU7BhiVeedPH/m6tXpfgeOLCOTIT0fC+awL6BvnlZShGZSxxJsoP0qGXf6R5GGtv1evWYxFjry0dk/p3tPHtaZ9nTBjvOY3eIyjZ5iXt3zn2i09S9j5mLH2HeVsjIzRZHgul0tC7HFOo1ntaqWoWmOKIeCcKXlIiccry/F5yM5HNiI0r7q+Hs6pN1qwJjEMJu2zzLHsdSPKZ772nGOR1XnhvhAH7JZWoseFA76Q8Vzh4OMEisjQgmf45chrYn0DjjFGgkKordhEsFBTTxqqqsZ7x2Tq+ORnHmHvYEUXBOt6Js0md91zD/vX9wm9Kt7VasWkrnAu50E7uoR0W+QkfArfajjXrVFkOedwldJcxSS4JSnZSaX5h9AHiIYGQ82Krl0RQmA6mdB31zBO6NBDaQPQatugoxSMa4cDSD1ZFGc8enbjMJQeVlv6e+b1XSNgT1FMXVMGZZwVc1GGZvCShZGwG1ScSIQwuucsSEfPc9gfQwjy2fZPmcvRxO7oWushs9HcSeCSG6xxSap7UPIjJxIz0thKgJHnODJA8vcf8ZqeSznmiMaRm7lBMbgjz/vomRn/vT7syLk+7iwPzy/P+0aFOyiQsTKxZl2R3vjRtGjFAMlr9uxj3WjSH45TcOM7XvvOkqcfXTMhhwcvXYqBAxy7t54zylYiQEfec+Tt4z69xdssd23yl2OdT2+I5MiVQT3B+XxeOrU4hM3ZlOWyRRKdqBrweqm+H0CF1rq1kG65p1JTP+xtjab1JVUVoxRe8TyywZ0/O14vUM/X1x5BCeYzkFBDw6qwvfeIhNH3v0SU6OcbNxvSPY5/9zhB8IVes4RWY94MUhRxCD2xD+nBBTBw5eICQuSB++/ggVfcyVNPPcHB7gGL+Zy+ayGGQuocoza1nUynqjxrz6Su6fqeLpE1AIWPt+97jDEcHh7SNA3eK7NI27V473HesmoXpdvMtGowMXJqp2J/f87nPneJC5+7yhtf9RqWyyXRGWLOmeRO8jGWORZe45RniAIxSUH9a/192WrvBfo4IJpD6m5fhGViMzExYIi4lO/MgiBblOr4R3w0g2dMFhYJqFC8OPUAYhYSR55x/nff9xgrJYSe7z8jYXMOVVhXtMeNAdFrjt1/x3yCQdjrv3UdFYhRwtJHBOLwWcoaZ+/rqOc93OtIoR2Ttjh69RxmL59/lmbextjhOslQyRGUseFy4xh5WukHk27quT5xs4JwXKv8hY4vRNgizzHFcq28P2FQrjcXJRsbo5/3Xo1RQ3Z8c8OL5f1K5gJquer+Nk6V4mKx0Fxl+uxsOiX2AwmonpEwdGEpXmZPjD1jOtZ8vgquwwyo3gKYTCjeto1rRDbjcdw2CkHUCLcGkT69LqV/skn9kPs+G8m5a1A4evljxy2tRLMQG48bEVvHhJ+OEZLjv8fXPzpuvD7PepIz8XL+OW/KGCOLxRIh4J1eI4SOSxeUCtC6jtVyl9Viwe7uIU1V450Bb7WBdt8iYkpv0slkgq8cJoL1Hh9C4oJc3DDfuq4xhlQvFVNdqBYkr2Kk7XsySnC2fRobDS4aPvv0ef4/P/u/8+r778R1KNGDtWAc1ptyUMaeVjQD2MkZQ2XBWaUjdNbinMVbl+pUm9Q3UOtZjfdav2VMqp1V/uAsoKPV77fOKQnEoA0LLZiIIM5ytF3bEPJKROMmIjIchTHQp9CTJcoyC/hUvI3JxhFrEUf1HI+Ge/OP2RsZFEMMAUYC7eie1svkutkMG03GAUXkqfWflKMdzTN/nSTlI2YgEz8aSsue+iCMZO3esiBdOz/p9zmcXUJlMiiEsifG55J1A7YYFOWent2LLd95JDy6vnBpPWW9Zd+6sTwYcMeBhIbIxZGLfoHj2ZVbvvZ6BCPr02KXHOmfeVQOfUHKnAE4U/LNa/p15F0bEnpWCtLeQCmFCyGok9D37O/v412NyWkdZxgryhgTM1Dxpoe5ATeUr2T5nvuJ6r8D1mpaS/doQu4f2QfDcghiPSFFnPLI9ILlDNj1BhPKvtrd1Fre0ko0s3Gs56fWrTSTQhGl+60koWBMAomYtTOyFh664RtTrJ+IEy3ZyOFEghBdjh2mLSqWEDN3r7LdhK5FYqSuaiZ+QlM1OG+4dPUKy7bHWcvhwZyLsScGoxBxC4JN4R4wxrGxsTE0q7WaH1yu5umAJW9yuqHIthBBAt6nMCkquCUIWQq1rYaL9bAqecKkqVjMlxirfQOfunyJq9f3sNGUrvcYi3WU/MmYbQajjEvGaKG9t+qNuOTt5vyJ9x7rfKLkUnVhc2cYa0sdmXXZcjTK3WtMCiWl7zJJ6RqtInXWgiNdV5U2UHI4xXiy4FJeN69nnoJ1ab0kEnoFZlVVhXW2hIcz4GuYuyo7O1oLW9YkvW6OCMY09yGURhFYKuEH4gXIJf/j9+rvbdq1dnwG0kU0n+SIyevOzkGuBcyKWPKi5BAeWdCZ9Lv8HYkiLZ+nsacJZW/ltZGRZ6VKdBQeTx9cU6qjMziI1mGMm7vDyLAdc+Vmz9xkJOd62DiWXHG+v7xmgyG1rnTHd7PuFY5/znvjRgN+bEjBGGyXrZyxN1XuJ31t/uwNkY68z9Cza9OeFJGUorBDpAZtUB1ixGYll689vleM5j8lHXWVQljruX71Gqu2A9HfZRIDZU7Lzy4bfMN5y/iNsRLNc8gAyLznhn6jY0axAVgkI/CVkLxN1tfcpCeb31v2U3mPpG2dyxDHZCU3N25pJZptqRz6iHKExSMpnRhjOs8mWVM3ep02eWcCxaJWhWWKYtaqFUMwgotRhRH6OxuE3hmsxAKwEbGJnSho49mgIYsYtE1Z7Wtq74km8tjjlwgIJkbmBytW85at7Q3qpsY4tGN8OgzTyYS77rorebQLFosFsY+sVnN8VWseQyxNM6VtO6yxehhS2Y23FW3f5v2jHVy6PkHKnR7kGKkQDtsFJgbtQN84nr5+qHOJWZQPmx7WBfa6N5FfPxqiGiz9sRX6XBa/peBuMegmNoC3BmfAiuAEal8p6jkpLO89guArN9CJoYreY4vgydRjxmZjYVBm1irLibXZizWpd2IKQSUvzpk+0S4OvwewXktSjDHJUMjfp8oGTLmPvE+NMRhnKYjatNDWWF37pPRNZi1CcNYNa25V4UURKu/Tvh2eS1aixRNO4AsjMQmXHMJ2pWTBpHWzFgIGY1yia9N2evnLrRmo28S4EuWwzkDKy1nvBs9UVMCbtO554bOQHFRmUt4cBYkIzg8KM2sfa1KPSGMKu43VkEyStOnKIojo+qdDnIzUjMOVNYWTm1nkxbTOJl2ohlQmgc/XgHHuc/Cosydfnnc+D2uemZ61GCNiXRL+4zIpNbBD6BCb+2JKqqt0RAnqkSF4Zwi9MKyoPo/hHKa1tdq5JUdkLOCN58JTF1islD/bJ5YzjYSEtC8TJziqDAcDxyIptTQeuVmHpnc0SqZ7ypbUVLEzjKYwYgzl0Sm4cCxT8qoBEsi54oF7Nxs96bGHiJjBMHnJ5EStWMwojuaMLRa4hg4i1BUdEQm5uN6qIu0j+ITuSsW7uZtAiJHQ9xg0pxj71AUgWToh9rQxJAYdq/RsViD0GIlEE7WDumjz2hACXdQuBHhL6HoOdg8IYUmIS/au7/PJP9rVWtGp4J3H2YpAz21nTxFDT9u2WudpHLfddhv33Xcfly5dYm9vL4UlIpPJFIMjCvQh0nX7WAu33XaGdtWyf7CL+Mh01hClJ4qGQduVEkgry4gk+i3P1avX6LouVzlirWNru2K1Eto2hX0xGlK5Yc+Z4hXDsDE1ZJeFib6u9z8IowEQYAorUP59FkAaxtHvWImifZfCwMwjQNeRBYQxEZFliQqUPWRVuD4ry9fRc2m9Ct6xkn/W8zYW+WOrOT7Xh57zpeO+4ejbh8iMKe8ZLi1Ajcafl4P+EbP2DuPARY7DwhR1m5X2VIbvUCFrsDIycpKQtzZ5GFZb0GaQpEm1yiYZL9rJKCmyYkjYBI4b1tRiEziEksdSkIgagypsAVJjaChK15BZcVJkgogNnUZ3GMKH2fMZR7sGO8YMfyeFVqILJs0/fc46S9cqe5a1Fp9qKHP4fXw+sreIDGehNU6VVMw4ixs3bIwBYzx96AvhOqKKo+97uk7PrKscvQQqV2lP4KDGUjYMBfVSQ4hgvZ7ZvgeUI7dpPHvdofYtrh2+8ngMq64tiltBi4a6adbIDXS9fVJifVk/k+5RDXkFM8XYIzJEYMYhfzU0TDGADCAh6NlKStWaAaeRP+u8W1tvlS/5WQ3K9SWjRMVZxI0Er2SOR4PxqmC7ZYeva1ppNXQTIBVkENq+lOq75BUsFoo0VQvaYY3DNzUxBO1rZwzW1fhKPZ/QB7pVN7SxNBoOlRgIqzm2tnSmUWBN6Fl0c9peuOfuV/DM5c/yuc/9Kb/x6x9PuXujpSNWwK9oO89TF84zqSqmkwmz2QzpA5cvXy7Nb5umZnt7m93d3eRN6sZo2xW+8Yg4br/9Lg4P5ly/vkesImJjyquqgKibisrXHBzMcU4pvLz3HOyuwLoUDon0sQMczgbqeqhS6Duh70h5BN3sdV0VD7cMAxjBV6pYMym/HW1sZOg8ASQ4u36R9/oFgvJwItB3AesMm9tbWO9o25bDg0NMpa/bgEYGZM1hSUI3M5Ro0+Gyr57l/AggscelPPYIN4WE4ecQs8UbVTmNNLS1mRB8dNHR8qz/QLlvM7qv8p3ZMmc0N8mh07GPP3yNAaxpy32WKED2Lm26/+DpCes3uHZvw+/bEgtItBVrtaukThkCcST4gMLfS2QNzfxsnLhr3osk2sl871m5jtM7OaJ0M302cqQmf5UbpmnMIPDH/08PIX/qWXO3+WGlyMw4KsPIK32u4WS8ol/4MImHOeeAB61B2Z9qYA7GXjYMMOC8TV61MG3gzKlGWy4iECO9JFKTmIxWkmeaFN5xuJQxOA+0FZn3A+VeNpqzPFCjagDiZYpTY4ffeSzjgzQOzzpn8c7RjxRr3ieKIbAFK5DD0zczbmkl6qwdBG4KG6kVF4fNMqmIAlXiQxRraNMCGROxAjYmqyeCdzax/msyWgRWQRPMzhgmGIjC1f2r2ijbaxNrSaGvvu+IYsFU1JtTbA11jEyso7EOI6doJhUf+6NP8Kv/5Xd4+I8+xaoXvAdfQeU91kZEOoztuOfcy5kvrtH3Ws6SW0dnhC3A7u4ufd9jTQ0SmM4qbju7zWOfO481nosXzwOwsTmlrr32+HNOiekjJf84mUwUlt4HKudVgVqwRjS/EiL9ssVYS+UsfS90vTCdNjjX0XUDSXrXdcVLcC6j3XJoSRFzOaGvOQ/9pOKFjFrIAt6n4ueoyjPLIm25BN7rtV5x//2cOXuGy5ev8LGPfQLX68EIKMBJyCF7wCpNWYz6/cbo9x0dxzmn1miIPu+5mISMJdesZU84C8YRE5NIgfxnmZ9D0/mXKphVKxclqPonhXRzCkGvP3hMWWBnnEB5y5on75xl0gjOCodzQxAPJqQaXgiSKAxdr7otRwhkRItYnrJGf8SEQUiOXWOjXufwIAZFVYyD0SIbk95yTHlQ/r68HkWhmFFN8VpzAd1HzpqS/83fccN182ui3i5JsMckYBGTnhNrm+I4xZD/HgN+DBr6Xgdtpc/GpCSeJRJiRgpkrBDyXDM7GeQce+a2Tp93pPrMQKGELWXk45SL7g1jDH3pXwxYZR5yRsPklYXKgzcWX9fahCJo7aY1GXMwNOLO0b3xGqnH7FL4NpQ60oLiHZsKcWjsTb7TFHnC2vI9R0GN2cscvnOol5eoBT7rYXCz1o34aLj5ucYtrURzmq+45lZDGGpNJA9DIm2vZOlZQDlUqQYGy8t6XxYu56hy0XPt6xJy7NDQRrRe25dhtcWY0ZybcR6CSr0I9Ltz+sPrXL52nevX9tm9tiCI4+E/+yR/8qknuL6/wlZCHyQBdDR07GxFkI7773uAJ58K7O5do2s7rDPUdaOhW6NF9HkjSLRUtWNzc4NTp3e4dHmX6XSrdCpxTq2xVdvhXI1NoU3vLc55rBW8U37Z1WKJS7VTxihYxxtLZyNVVWOdo+8DZtVhiNSVxWQKWZM3caSpbVHSMUTIoA8ZQigueUXjvo/GahgwK6Hs/RWUc1Icggrqp54+z9Xr11ksl6qIVTvjrFEmkpTv6uJQxqLe5LoHMh5ZaGeBJEkpDl5tsmajEE0ukTimBErK/27Yv+u/z2Gmsb+T3lsE7zhvNi6PGX1AjnwYKcrNWjh3bpNp4/izT13HiFXP3qigtlhCL6l+MZTrrs9JyldYZ8GpcZQNijWuhKSIkeH5ljnl9+T1ZfQonkWvmNFrg8ocKfDRiooI2d6RYQmOePVmdB/KoTyEUaV4a8UYSZ7jsBxj73WkPEnRLO8UB1EUst7oUFCzVkw+ul5egGwgrachcnDDpjOSw7zGCtaZxIA1moLos1jLMMRhwdVQlRL1yOyW6gmGsnecV8XcSZdC4woUrKoqkcePwt5mIJZfA3umex5C8zn/bxFiUsYmPb8e5+ryb0mdWLIsGIbmULNCzM9z+L5BJqmBpMpeSLlba0dP8iWkREPfF1i0SExCfFR7hsHRQx+ICTFGEBzQdatUka1AgygK4a6qSksARnk6axQ4Eg2JOqpiy22r5xtRSqkoWONVoksHUZPZj/7ZZ3jmySe5eOEily5f5ZlnDlmu4Km9yyx7wBpsBbV31JV2CXHO4J1DeqHv1AvOwBVjnaJUs/WNwfkK10fEmmT1Odoucvr0aWazLXb3dpMy9MT0t1UIawnm5c0+mTQgkcODfTCKliOFtp0x9BX4qlFDw2jeWGIkSKRqGgUyJCh7iNBU6jvHKPQClXeEIFqiAskTBG2cFsthd07BJ12XUH9ZyFtF5uXwq0T9s7t7navXrgBqFFSVJ/a9Xj+VucRcjsJgsWZlOFZAau0OXovJYSHy20buljGjMgt9r/eOEHtiYTsb1SCOPa9j9rTenqy/nuYecl7fkpDNlrZbV6LZqDjq4ekiJoUjejJUUA5AoWzUZKGri64GT6GzHcl6SdLWKFaq3P/IuSm/NBjFDYw9QjP+QPrHyKAZqZPxpdJ8DMQbiQdyjj0DlRCNWIUcey/Pd/wZikEmGWiU193cqKTX7m58k3ldjjGY1ujr0iTWogVHP1VAYvnzRxaghJLT80l/EnOd7ktRY2jQNWMri2xXlgevMpDCXAaKlC8WiNGIiHXaatGJ7kGXytTGnqCuq0k83EPuExhRAQ6KNv8sKQNiUCUZYp/WKA6GQj6XeR8UA2N4GJoPP57wJEeJpABAB53xfMYtrUSXXYvxtliXDqPF+WlBnFVEWo2lqxJLxqrDWMf561fZcDWTzQ3stOFwfoj0kdvOnEFENJcXE9o3hSSstXhjaKY1vprSRaELka4PtMuWzgoStKYQE2i859f++wd5+A8fYblokyWrVlCVLDpjDM5U7JyeoHAKSQX9EWNmPPyHH2a62WCMJGBFYgtZLNLGTWhA53EWorFc311w+Zld7n/gXgAmbaNKWSIHh7tsbp6jbdtkdUrZ5CEE+tT3zxhLm0Ky3jt8VeFdhUTNE/ciWOtoGou3hsPlgs3NDUIfWC5XdAgb01prxTJdH1URcqVOEd3E0+lULfYkALX1UUylNwPCUa3J3GjX0LV9CvvWLBZL+hiUAaqp6VplZslpkhDBVr4g/iTVoEkW3kmBR1m3+nUkoSIKgMnhIZL3HkUInYaldnY22ds7oO2CPs8kgNZLJaTIQ8mXH3k86qHrL1XG2cGgsCQPO6Mgy+0hDDVwaworjRiFJ5/c07rd2hJDV5SeiNBLT+oPPyivpEtNmoOM1kckYuKQn9TnNCDmU9AhGaGBXG4yvo5zOQqknsdRxRizpilfMBhAOtfhfpz1xHSNrJqrypLcrkGIlkuNHnRSENYoFzXpdcld+WTw1LKRNb4ta1IpYvH+pAAUdZ6mPAMQvLOadR49p7LeI8MMUsg6GTOqeExqEJ/yg07D10aEGEqGW68ZYykp0/VUQoRMR2pMTulEQp/KAVPZnLWaF0+AfyVMST1CnRgqLD6VyQwlKuuh1aGUS9K9u0I+MwYL5RpNiZo+ymHckBHJkms4HUcNJ+dYU8z5DIkM95P3ZE5lrQ+LJB5gzDHRpOcYt7QS3bn9dqbTSYmJO+c0hBrUQ+1Dz3w552w9oY89sY9Y65meO0MzMXzyIx/nzrPneO3r/gJ3nLuzuCCHBwe0q0DsNdbfh8jV3avsX7vC3qWnVcEeLJjNNphsbnHm3B18zdvexmKmqLfFqmW5WMJyj8m0x1rh1KRio3G0EpiLJcyFatpjXYDYM3EVUZaEELGmZjLZYjKtWW12OGfoemUdapqG+XyOQXCuQgQWiyX33HMv13evYaiUyWjiuPLMAYvlPqDAoBgCfR/Z3JwR+pqDgwMWi3lCNSoX5nK5xFpLVVf4OhkOlUsQfZPAPYYag/VaLyldS1Nv0YfAol3Q9S11U3PHudu4du0aW9tb3H33Pdz7svt473vfy6lTp0ooWkSVUe0NlfNroZ8QhO3NjfLvPr+3rnEmYiQgjUOiMh1tzjY1RCtKSba94cFNCVHo2p7Fomdne2uNJcml8HXl1lF7R0NQWUv13ZwQ+kT+0AAatXDG0XU9k0nDq151H5/+zOOp004eg2s2ztnnfFEsCieFr7PSTa55jOD9RO+hdLQQ3OYkAbg6BTpZP1wzNRoYFIUCJrxvdA1ruHJtn8orSky9sIREtw0hdmv1f44MThnWKE/NJEslf1MXBo9BDMymDbUzzJcr+iTsQtBwq/exRDS01d0RL2/8T6OKzRrDpKpZLFq8dwUktli01B7qOgtQqGvBW8h0etkp1fCk5rVDKtna2dH668ViQdt2hCBQqaDOz3p8DcjXMoUFTBW4nqkQAr4aGw3JwE/emMs11gze8dg7yyHI3Oov54xdktwWNcinE8/GdMqqC1y/vtQ96Un3pPstG0G6A2EdSCV4bxFRRiARRetWk4rKOggRl8hZmlmN9UaBjpKqG6LQJCTu+NyE2FHVrmBX8hr4TM6S5isjxVVAQj53lBo5Ri6jsOMN6xVCSIEfKSQ3g8GXXa1ByQ5zz/cWS2QgG1E3M4x8ISr3y2Ts7e2xs7PDL/yr/ztbmxvKzjNfgrNsbW1SN01J5Hf9kk9fPM/ZndNsbW/jJg2L+YK6Nyw8VHVF4ypWy479vT3m8zmucvhKFbJ10MVI13cYNB85p+fiY0+wsb3D9Su7fOpjn+SxD3+c+++6hwfO3M49D9xNdduM3/34H/GfP/D7zLYaJpuVKvmVQDRsnK1Y7C2Z+Ip77znHxmybRx/9DMt2Dlh8NWW6cYrDxRUIhqZu8JVnuZyzvb1Nu2pT2FSt3Y2NDXxVs5i3dH3E2pqN6RTrexbL6yyXh/RtZDbdQeg4ODgABOe8Mgg5hxyp8/DVJOVSHcZA3wdWq65A0DPyxAjqJRklh+hDoKlrvLf0qbPDbLbJqZ0zPP7441hnyNy4WWn0fZfuQTezclmqcMseeIzKxGSt1r2WWlHRTjcK3NH8b20Np3a22Jsfsly1NHXDG97wRh566A/XlIC1Oq9xeqXkJG8IF6mgNVaKJT2bzYrhASiwSCS1cFqRW83p9Qyg+RdDAgClkGSQMUuSxflEIpF+p56DLeuFSd+FGero9E5TTWyVvG1VpC51/3FuwD7rCnqMOLS6UxDpwRnaviXXjgopVcE6GUIWZpnMInt/1kLs+gIWaZqKO++4g6quuXjhEnv7+9pCS6CPo9pEA5Wv6LoxU0yuh82RCynzzJy9GYgmAmfOnMEYwzNXnilGSggtkIArKd2Q95j36rmGEDTV4zQWPrBIWe2ZGdXzN07D9zl6kwV0nmvbqmcfI2xsbHLmzGmeeOIJqqoqqYO8hs65BIWWsgb5+ec9E2OkDxp2tM6R8+DZOKqco64cldfwpXOe/YOlhv4TQUmIAZPOqTEGZz3OGdrVCufycw04r9G6vo9sbe9w6tQOV64+Tb+KQ17SGGzlMRYa5/F2IIFZ78yixlCMSjoymU7Vww/K/KWGrKQoQi59S/lKFMQmMZZaa2tTeZMRuqA5WmMtRrSuumkqVukcxpBSSjLKFY+UZiFrSIaJRkYcRH2mUYQ+RC4+E9nd3WV7e5vnGre0J/re3/ivTOoqHYaKxWrOubPnmM/nhK5nNpngvCfWNXvnlDO27TtWyyU7G1tIVWGdx1lH6Fu6NtFYoeEE7z3T2TRt5EAMgXnYw1QWD3SrJStpWW1azm9F2mbF+e4yp86v2NyfsbdZ88Cr70fokT4Q+sjSRzZmNY2z+FlFDIGLl6+y2cy5/dw5rh3MWS1aTDDQ9VRVUxCkkgqSvffM5/MisEUiy+WCSRCchZaevcNDurCg9oGq6rjj7Babs032d5c8c3XJ1tYWi+WcVdcy29gBcYneK9fnCd0qFis/JqvRWZeQgXmraxLDWq05reqKSlIuFUNVN5q7W7VcvHCBuvE4TwpFaQJGBKq6IfQJBe2GGr9C15asV2tzEbch55VCF+i6Fd5VhezgvpfdA3TsHR6UgvT5/BDnbOmr6r2GhIUOt6ZEkweRwGZZeYN6iAKKlAUkBlwiGTDWYJwh9ppnzOEtvWauNxxCly6BmowxhdQPyXnPRNCQvVPRSIJhMCqMN/T90LXHpX0SYsQaUkccDcv1NtAgGOPIzEEaOtaWezblwaIoEKw2NdY4SGthUiiuaurkiSUgkSUJuvTEjcEZC7UrqOvQG65d24doOFxEDudweNgx2aqZeq27Nc5gnMNUDgkaQlRloopDFcGNZREl9GtyUWtgNttg2tTEoGHMTlxRFtbnKEfAGC15wDnERzIbk4hiHzK6s2kqMml6rqXsjSrTrlPjOhNzTJq6KGpLJPQtG9MJfehV6RsDibTCAsa7YihBrl0dDDdVIAYhKcJs2KXE4cBcpZGi1UqVeOUt1nqMtXiXEX85hG20dM9XeqaQhENwROdoTUffLti73mONS82xk4GLUCUWr75vNY3k/BB1SCAdU7olJaXVR0XJSxzARlbPT4hR86reE7supeasPpc4jg5pxGM2aVgczhOkReXfahmKIpa8jgl9z2i/i6AWjgqSct5FAj7fF0NThZsZt7QSDZs1YVJTT6fUkwnz64HVBHqx9M5waHscjp2NDWJdM5fI4WJB2y7xswnSCfQBjMURECMEm8onAjTeIYkf0ntPQFgcHjKd1GzMNlhJYGNzxitf+2o2bj/LbHObvotMfU3tLM3+FdoruxwsD1nOV1jvOHXmDN4H5lev07ct1js2z5xl59wp9p5+hhiUXcVVFucNjauJXVDKwD4mazERQ4u+13sVWO1qrp+zPdauWCxalvScPtUgRNpuxWq1oKo80+lUY/+pBsE5l2pddeerFRfwxtP3XUFfOutSrlTUW3KedtUnRaDlBJqk19yHHljdxH1oaaYNIiqUAQ1VClhTEVEYvveaO82k+aT8nLXqL3VJaeS8R0aHiqjycJXec9seFuUlIly8eBEVzinP6ytA8DELpyzI1mkB9dAPIWCtT8t0aVIEhTHKamWdJTDqdzgO45mEAsx5sORxWHOUxI4hIQaJxWgoo4kELMobnOvlbOYaxkKXhbZGVAhBFYn4FMqL6d4NSCxKylo37KuULtANoYhIn0LuipkSiCOBYwalNs5BxQgHBwtijMyXgdN33M1r7r2bT/3JR5GwUmKE/J9AzGsyegZu5J1lYEgexhgt5RBR78oqYjQrDC1BG0o4RAAraT/nsrgUjtYXUVCKGiiZAED3YyrD8k69pZDp7FTZWV+pAu17kMBqsdDaRxn2sdIgDiTrqjCH0KSk85MVZoxBSWLyqSk51NTQPClT57RTSUzt9bIxBkM7Mr1+SLlzJaVIkW4w+iwro/JkterV+7UJGs+gYEpo2KhizRpqMARTvbIMRmm2IYbnpp+NCYFnM8YjDucw5J0mQgjJ2BM15skNJIpBeKSZg+R1SM88R3XSmZW8l4Y7IlcOfCHjllai/8d3fB07O1tMplOcc1y98gwGmDQNMUT2ru9CXzFLoc5V11If7NEtl+yc2iGKS/00ResRo6TckoJr6qam7zpi11FvbiBRWCHYpub09in2Dw84NWn4ynO3c9vtt9NUU1bzJfWkZtUueN9v/DqXgwrK4C0bW6d4/VvezMXzn+Pw6j57uwecuu02XvvGN3PnV76K/+3/+a/ZtIbt7Q0mG1OWnVLztb0yIMUYseLo2kxgoEKvqjzL5ZKuXWGslpU0k4qrVxbM5yvajYpnruwxP5hjpeGOu+7AOMdkMsW5irZt8U7rQaOoQupSPq3vO1arlZazGMtsNoNOqLyjmSgpg8R5yuVq+HbVtVS+wbocFnNaXrPh8M6zXPR4a0G0rhYxhKhemkuAgqqqR+ADCg3eOK9h0zUq7zFGIwXGO6q65qmnngLpsfW00IldunSphPCqqsI5W1hSVJANYbaMKhw8BDsYLs7dAImvKq/KrQ+DYBt5TkA58Pkw53BjPrhZ6GcpOVa+OVyYCSxCH7Eu6DysxVivn49C7Sxtu8SYCnGqfH1nEF+DcZjEaZqVnTWpG88otzReYwNoB4y4dk/GKIo8E41k2rUhHGtSWUsK25nAql3wFa97Fd/8bf9nfuqffZrLV+ZaMhVF6eDaUAyS8RqMASgZpJKXVfP0GlrOeX0Nn6ri8l7bBg6k45nPWrmQ+1SrmAX8UV7aAcmtBocAvvKp60hiV0pr5WuPDSF5d5EuhfT9EUSq96Z8Lu/pUtuODOFxY4ix13I6MyiBbMzl+zYWfGVpJhscHBzQp4jaOLefjcOQ0E/GUML7wShHrTW6niKaYw4hp1aUxD+DfawzNNWU0MdkwKZ9cYwHlylFM7BqLZIglBB1jg7AUHc8hOQH/tzFYkFT1XR9WzzbtR6lksGBx5A8HDlX41EI6W945bnHLa1EX3H7K7T9TtCQwNZtW4gITd2wmM9pr/acOndGww3OEULP5mSGN3qwfD0BktAIPVEis9lsFMaLTCYTjNFcX5CIvMoR247F/iHT2RbWOZw4Ln7uKSpXU1uPdXDp8nke+sB/Z+o38N2KM7Md7rnnfr7yL7yBj33iYWbNFFNXhD4yudLyqisNV8/v83V/7et585tfR7SRn3vPv8PUM6y1NE2jG2i5pO976mpS0HaScpIWqBvHmTNTzpza5spW4PEnnuHwYEGIHURD4ytMUpyL5YK+79iYbSK9pD6Aurkrb4CK/f3d4s05p8CJ228/y+3nbmO5WHDlyjPs7Oxw9epVIOKcpSF1iukDTVOnguoOa3t2tjbYmE5pphOWqxWXL1+mwtP3c6yb0XU9Xd9x9uw5qqpid3cXGLzBjCpWRrhI37cEyaAyoesjUQJV0+DMhFUfU12ZKQjgfCj1WgNXaT6I45zc+MDVda2MPHHoRuGcYzaZUHlHu9KoBWZce6jKsbIDKAcGBZtHZvIZ09ol/wgYF5Qro1R2EPOh985CtIS+I8Q9Jnfs8PTFPeyiZ7ueUG9MkLBCjJKOW7KhENje3mY2m9G2LZcvX6aua/3+VO5hyXyzQ/41zwEghIyGNUlB+GScaL1f13calq5hVjmqJ5+hf/8fcW62ye6l80rD6R04Q7da4QpHbwoPj0Bf60PK9w0KZVB4eXVjQmxn4S2iYd66rulDl569Gb0eitGSn1n2pKzLHYgcy+UyLwTZi4ldS991ulesL+sVo6LZx3vAOV9y/KC9grN3qN2epBh0IfHEWmupvCs5SJGAREvfR9p2yWy6rWQo6Zpdpy0TVys1Tuq6AYTlYl/pSHtZuyc18vS7Km+pK4d1FfP5kp2dHTa3ttjf36ftlmxsbNBKp/2HSeHgkfLTeamhW0pXjJQ+yPm51bUt9bwDG5FJClQ7O5Vab6DvR8QJMqByu67DpnSJNQZcyimXa446yozOVB5x9CzV+nsJtEIjUvJdPgnRra0tLl9+hv29fTa3tqmrqb41RipXM20mxPTQTdSHVVUNJMEa+8iqG1CV7TLnzzwYQ0yIy8rVNH6CoIX/04kCnKZbMy5cfpo/fvSTXDy4ztnbt9i/vuTezVN8xd338+aveB1/+lVfxft/5deZzRp6Wn7pv/8aP/sff4l7X3Uvn/vMYzzx6Kc4bOdsVDNk5ZiHlpah9GM61Tm1rR5YYwxbW1vs7R6wOd1iZzbDC8jygEntOVzpZmmaCp9SAU1TUdUaunPOc7C7ZLVSJLBxGnpVPt4JbbvSEKyxeONZLeacf3rFarVUpLA9oKrrlGs0+EROUdeevmsJfc9sVvOye25nYgXE0QWPLKAOE2zscRY6WeG8xxjPE088kQSoJeclx9azTfm/SdNgDCwWCw1XplyWNVVi7lEllkELVVWNaosHb5AoSBYGaZ1Xq5WWB5jM7dnB+CDGWJRyn3q4xhi1jjflXkQErMGkMOhxSlT/dsXT0D9JiaACwSaBMwj6UUgzRhYHh7i6xm9MuHgp8pXnbuf/8b/8L/zBRz7Ev/+5/5VXuIqDegvXq7GYvRTQlnp33nmnKmcRrl+/TtM09F0gSF/WOufqxkOBKrnlnyq01WqVSpRyCYuWoVSVga0pv/u5P+E/P/Iwd09r7Ezp42If8AG23ZSljWtrNPaOs0INIdD3LU0zVUMqoYGrqtKyjWyIGMEylERkz7/veyaNHZizUAWp/45r8yuk+yOvMSt3O/Ka8z167xM7UMS6VDI+CqeSrpk68q4hxfN1jTGp73BgMpkocjkZAJkxy6JNs8f7cbFaJoNBeWQnU4Up17UnxsByudA8d9czmU21Zjtqjb11hm61VF5j58r9OgtNo3t8Pj/EVxZBz5F1hqqpNDqWQ6jkEG7a684mBiQ1Xs6cOo2gHuWq7ej7xJmJJec4QaM7OZW2TM6DMYa6rul6lRWkSI9SaWbrOtH/ByXPL+oi/yy5IUfGF6gnu1y2JRJwXK/SZxu3tBI1VqhqVw5p3TQcLuYsVwusM2xva8i2Dx1GlFkgCPi6UevU5S4bAH4IRZiUuzAKstCHEQhRqegsCoKIUX0Fa1N+SJQcYbaxxf0vfyWvfeUTXNw9wFc13arj4hNP8vD7f4fHHv4YEgJNXWk+SWC+teTrH/zLnL/8NBcvnuewDWxuekJtsaHCxCEUlAX8ZDJB6oa9/X0m0waxh3z2icsc7m/y2le+jK947V1sXH6Gw0eWLBZKbB9Dx/7eNQTY3NhkY7ZB33csVwcYow2xpSMpjSWTybTw4CIQpKfrhK7T3LGi5uyAmpXc888SupbDw0O16KNH2pbdZUsbI4tlYP9wxd78kKbyypxjJK2tHQnuHN7KOVClC8uNxi0wm0xVOOpSpnlon8Mc1vLes7Ozw97eXrGSx6HWOAq16Q+DssjKRhINGgxWbFM36qlF3SOCgiS05C2F0XJLJwklL1ZIDCQL1hyiPKJo0wqUfE4cShWyZyoirNoVL79tyl9+/TmevlLzGx/+HH/8n3+bV7z2fv4v3/29/H9/6n/lvruFkJC0JI+7adT4+eQnP1kUlEtAFE15DWHTcTi9HJR0nwqsWkciDyFZBW7EXutJt51ne+KwFXijOW6JCtaZo92KsrLLc76xNjDXEcqaYtUzGNbCuYKkcpKBPs6lsq7hmWuessrfmxrBm5R/y4X72nRaUfEmgXmsuzHKIGFAEee8XH4/UMK7ztvSvSeXWIw97KzwFeDjyQ0Uchgz52mrqmJ7ezshc4MqcGvKuvRdwNpqbR0Nlrp2hGQMWuOYTjfouxUambVUlUvPtCbEQLtaMZk2CdWs7qXWmg40enkP5DBvnkcMfYnyBImsVqv0Hkr7Qf38ulEyn8/Z3Ngoxupisbgh5K7rPjTqHqcBRNbvL6dwQzHGUiQlgxiHo3tT45ZWor6qFYQCqei94uBgjjGeZtLgXUUXFAovWRArbQvO+iQkVH7phhjg5SZZM7nofMhn6bVy+LCs9ehhqsA0XLtyndmpM7Rdz6oLPHXpPIvVkotPP8XmxhRnLKEPeNtz25kd9i5f5mB3n9BHKqeWnvZ/l7XQ0mw2S/VotnCeimjOqQuRvYMlT1+8xuZ8we78ECMG7ypMYt7tEjx/tVwqiUzOEaHdG7J3X1UJ0WgzF2aq78uhRTHJ+9P1m81m5JpFSaG1uq5LLu/qlWvKE9xUVA1s2QhWCBjazlLbCmNdUXpZqRzNh6mQ0I0e+sDh4aGiZbMw98p0ZGSdByZ7ivlaa96gHRSW5FytWa8VtdbR910J99sEyhHRpJR2ndE65SGnlQ/kwHs69hwyCcA4D6rv0esV/BU5fDfyaNK9OuuoK8+q7Xnq0jVEKpxp+R8f+RDvetW9/M//0//Enz3+GR596MP4PlInrzaTWwCFsq2uK1XYKZSb2ZqOHzeCMrKyGufiys/JGLHOKOWkFZCIiRo9iBLoTMSTG0KM7FozCmmnfxtjRmHBXB6VSl5SCB+TfzeUjBgGhi4RSX1RE7lAQpXG5CFSnt8gQyB3F0rAnnR947wabckrz//lpuBZoEvK14GijrVHp03eZ0/l6zQfKUjy2I+ofBj2yOAEDAosRjXWtHxkqPPNZWUGgzjtVmVGHWpEFF0feu32kutcQxCiGEQMxgih15ZquWQoy4u10p20F3JnGX3+qnBXbatOSc6lotgJYwZkflGCSe44Z5lOJ4jA4eEhvqoZogPDucqI8LFCzxGR8flS+XVjeuBopOVmhv38b/nyHd6qYhBRyPZy0bKYr/C+ZtLMCEGVoGaAlFjOGVNCdESQIJpT7VO/TlJPSAwmChKiVjjHVGeVBIw+cLWastcURXCVJ0Rh7/oun33sc5w+c4aNjQ2iFZ6Z7/LoU59l1a2YTadUvsakhrVnN7d59FOf4uozV4hdoPYVoeuI/ZLclHYMEACtb1TAj0lhD8NkVhMMPHnxMh//5CM88eTTiAh1VWPEYWyFMYrIC92KrpvTNK4gTq1V79s5zcMOIayMajQE0TCJQIHRZ4WZlaYAzntmGxtahC1wbfeQvaU28j2zWXHfHTMeuHeDnVMVxhl81eCc1tPl0hqj+gkggQ9iqttTZaLMRU5DXMJIkKRelWYA9yyXy+HQycCjaUhhneQBYzKLkxRhndd9zLRirSmKSFIj5PyeHH5WX0RLgdaBKqkgXDQHNyaAGJ5xUgJkjyyMrpEAGkG/dzqp2Z23/MEjF/nc+RW3bdf86VOf4ekL57l3Z5vv+p5v4fb779apxwGpGhJDVV1XCo7KXlgcGYh5HiMBlENuJfycXtfGA6O6zvTezKYjQG8iSxMIKGBOY2/5HPZoSVFqtZfC98Zo7lWiKqmM7s6lNopSr0rphqBKGRSBO95PwzNgELCy/vu8j7IxLUnx2tTQPXcBIgGI1AB3owbnybs16n3mfPHY+LHOFhKU/Ow1HWDS/VpymDeTkihKPqYlk1KjC0qWolEWU+4l52K1nMXirObPXfIk83ebdE8DOMgmoGVPl1IVagCYJHd6lY0jRTTOY5coisRyFqyzGKue7/DeISIUyjoM19NUnU3136E8k7Guy5/PZVr5GRejYWTIjvcsST4MyG/hKHjuZsYtrURD6IhdiwmB2loe/9xncAiVMZgQ6duOhFFLuPRkYRpYdj1dH+iCbhJJHKvRCL0E+tgTjGirNac1kCH0mBgwoqg7Y5TxxNjUKkiEZjpDIsz350gfObWxxTRC4y2TWU29UXPHudtZHRzStUtcZdnY2mRqPPa2bfzmFKkcLQHnPUpopCCYzIDz+OOPF7ah1WpVNtbp7R2m0ymu9tpHVSxBHL6aUNUTcJ5eDE09I/Q9TeO59947+It/6auK5VnXU3Z2TnHm7G3AkDPIYaUc/liuVqy6HjGaU/a+5vr1Pfb3D+m6kOptzSAgjMVubLM526BqA80qcMdkgzfd/wpedmqLxqTeisVjUyJtlQ+qiDSEp15Hu9L8xcte9jL+8lv/CjEaRcaWPKUKOl9puD+HcLOSr+uaad3gjU2dIoZax/GcYRw6HLwfpfwbwk7L5ZK203vK9aFZKOgfVaxZSeTfodO7wQDO+bC+7+n7oU5VWVxGYW6UqMIYg3EVPZ5r3YqJ8ew0NQ/99vv4+X/5k3zLqbv4v/1fv4tTzSbtcqWeNhFr9Rz1ff7TF2q54eYGQZUNBlVkERIIBxk8xUxE0Cdu64IK7Xv6vqOLkV4Bten6WuNpLVR2MILGeUy9lfV8sjGOup6QC/ZrXyVkt7J25ZZ+TVNRVw7vLE1dUVXVUA9uDCH29KFbE+Tj72FkAKXgMMZKYccLsafrW0LoUFCSKeH3soeScaafE0gMQXVVK5lEiBjR1ISEyGq5omtbbd3VdVS1x1UO5y1VXeO8U7CgEVylRkLfa720MQqi29zcwllPXTVp+VLkwwRi8iT7viP0rWIhiCwWC93v3mlaxakCbhpteKH7w5RIBlDC2eNIUTY2M8p94CLXsjhfNZDKvTR0XZf35j2fPz+ZTJjP51y8eJELFy4wmUyGaNjI2FHsSjZwdL5dt9JzYqQo02wYjz1wksK2KaxsvwDNeEuHc5eho6KCELl+fY/dxSH3nD6tYZUoOrugocrQdXr4sQgG4ytsKnSOQehbRbHZcfgqWdhd19G2bTmo1lu60GuHASxG9KBX3rMxmbIKPed3r7Loljz12ce5fvUarQngDK6NmNjDbIolYKOGtGLj2btylWliEAreEwWCb5hNG/Uce80pbGxsABQBNZtprm95uCTSIwT6KNjJjMY1CAExEV9borFEWdHUNdev7XP92h4H+3M2NifsHx7S98JyGem6XGISGLOKzOdKEygRoknlFqEvFF157epaAUk+CUXB0kXDxMLuKvDUZ68TP7vLpL7EdmM4NZsw7wJ9yiX2fY/zA+JSvU5t42aMo5lMMMDFC5dp/IxpM6UP6uWSDol3nlNnTrFYLrh27ZpGJjJBecrDZGFwcDAviExr7Qi5q7rSOcd0OqVuKpbLRfHExnlE9cwtVVUnr7lfO+j5d9k79jbn6NT6H3squv3Ui8hKvOvapIBHHq0VujZQ15aZr5m5ir32kENr2DxneHp+if/Xr/waP/kLv8J9d03ZnE2ZTmu6tHfGaEgYQvZtq55StiwGDy3fnZbO5MSi8w7HgKI9OpcQOqII3hk1cqMQpdOetMYpYYUoOtykCENODfR9X0hG8vUVMGK1PaDVBhFZATrnwAxAnBC6dL3heTinHmafPLvcxiuv+9jjHwvcbOBl/ltbpK0KapFc4mSUMjrR4VXe03UrNEdoy5rkaIqufYqMGM3B5nvY2NggyNBsQ2t8B7RpjoC4RJIxHtbqGilHsimeHwh97FLOO9D1Ld5X1HWVjHVT6rWNcek8qAK3xlI3NV2raQs7OkfWgcRMS6mI9uVyqc8Vk4J642iL7qUcgl1HYg85XwW9aZ51friknjSE1KJybOCNR9kvqZ5USoibtTM0jkoM17j5pOgtrUSlCzhj6WLg0qVnuO3UaSZ1Td9HCmbaBM2XRQMyotMKferIosweIupl5XBUfjAHBwdMJw11XZXQZmGMGcXdrbVYb2gmNfPDfS4+9SSy6qkry9bOFvPkNfahR6yhETCmQmxEA1uwtbFB7AO9GKS31LUwwdP12hBcoiTEm27eTBgvi0MCAbGG0Bu8b9jenGGM5eqVZ6gqj8NhAniBxeEBk8mEndtuw/uKq3sLMB5jJtofta4TiUIssPsSXmw7BJhuzLQZrmjoWSQkb84Wa9SYxK9rrYbyQmDeRVw9YaOZEmKg7zoOxREXvYZyc0G8M7AKTPyEXpQ3N0b0uVZg8BgxLNuOT/zpJ5nNJvoc02kRUaRif/ESWC2OX3WBurJUdUPbdhwezum6jq1TO9iqp/IVBkO37KjqCd1qRR+1pMYFR9sti5LJnmobcq1mZjbqCKGn9h5vUle8GAlR60tzeC0LzsPDgwEdbFRwGKt8xVloDl6rlDC25uUATKKoDLRRMGKplBKcsOrZqD07d02JEbp2QRtXpZm6iMG53NMxC7EqedkUxT4WLtmbyIJHw4L6wjh3nUsSVOhrvquqqtK6K4O1vK+IqS65S4C0WQo1atpF0yYxdIpBMKmjrqjx6YzFmhGwyAQkJA/JOKzxdKGDbig10VxblgOKrdAuP1rW0bYL3cvOgTXEYFNqp0891hVFqtfLqHFbjLA+5S8NGvKVqIxi6vmbpCQHJWJMFvAhRVpyHlmV9Hx+yGxjxmRjgpKqLLWExukzjEFz+F23UrrTCMtlJvtIXr2hGEV9DGB8epiJ2Sg/u7Qnhn0HMXZJKYqWqhAhOipvRorfU9cZHyHapB7DtJkR+xyeJ2FSgqZgJIP5EgJfwloOXtdlnE5K3qeDGHsll0hhbn2WocjivB/Uyxz2qzG5DGpQ5NkAcMaU+u8bCeqffdzSSrSpldprsVTC5a2trWHhUKVjU61QaXVTDrugrZnNmgWkbxmsabVqB2tPWG/dM3DAJuFmhPn8kOu7u1pOI0HJ47t+6Bri0qEkEoh0oSeGwJ3nzrF3sMd8uSLESNsHalNRJYEaEjGA5gYGoSUMQi4L2L5TC25c80rKdTVNUzYPxtB2PZPJlCopvJjCiBmxOgaybGxslPWpnGcymdEulaBCC89TvsdmRqAhPOpTqDWvl7MGWzvtJtH3eqCNA+sUXUvKRTmDE0VJ13VNSMpGBZXgK79GOJ2VwXy+JFiLrTy+qjm1OaOdLzk4OORlD7yc0+fO8md/9gjLy9d5y4NvYG9/n8uXrtCGFa5xBKt50oGtiJI/TVupCBtrU62iZMCO5qRyEFA7vwi+0nxd1ys9WybtznmpnANV0o9Ycm85tzRGJcbcCiz3YDRClJ4oAWs8ziTQSLmOpY+x5OyGe3fF48/POyM78z7P52qt1m6kWLPlnsvB+jS/7MnpubPFCFEgl+ayYzIwGu8RAes9fUpdGLSjSlPXtF2bvtMU2jY7vqYZkLIh6Nmy+UyY3PVkkB95ffNnjTdqjDpDNIKVgIlWsRNG6J0q3+I/FWGsEYtBoWS2pmT4lnBwAqExMFxlQ2k9XJ5DwRkBr6FwklwrgCpRMFXf9ylSxgCOS+ji7FDFxHQnRktPDOt8stlBCCEmxqBByazfXyglbOqJp8+l/KxGA5JRkJwQrXvtKP1K1wwpUONiADgNoyQz03zQVpCSc6pD7j3PYRwFGfZrcgCQtA5DPntIDSiNYz7TX8i4pZWo945Vu2I+n6dcV5XAI2kjpZqEcYF4TryT6rhENLOUC7zHDzLnzpyz5WGsP6BBoWbrO8bAfDHncD5nMp0mqr2lhh6M1mRGa5AQCITS5qfveg2JeWX2yDD6FYHNRjkuxUW8r1it1CNaj+urQMgNxJerJS7R28WEhENy8bZ+pm1bMH0RJrkuqw89IcRSVD545jBr6hJim0wmbG1tcSCHJXSZPYxMENF1fWIH8gkcoOumns+AdhSrpQTea5F67JbakivFUxMvjD6njH40atW7BDyAcb5MicOn21v4Sg2JaTMhLFu6tmU6abjz3FmuXrrEZ568wPbGJu2yxZF6JBqbagCVBD+GgLGu8NPmvVQYh3JINgvs1NFYQOtErSOGHld5jYJEJVJomobYqkASTOmbqjmhCOT8rCmkBmO04RjsBKT6NzXQbEJPdpkMwaZ7TZJ2LCwGwaZCRtmchpDicWCLLLCGhK6ui5Ygtem6tlDKpaqe9GH9XLb4fVVR1Q0I9P0qrUYSbFHAOERG4Wdni5KQxCLkjLbpU2GbDI0Ro00oCotkcITSAiyHE0PolZ0p/y7Pn1gQ3NoPLCuWDGQZe2Uu3Zfui1iI2bP3OpTsjIFq+UzkMwSqRHMON4PAjGGtbWAmZsiGkdo0CRyUFQx5z+ZnIEf20mCEZmWdxzjsP977eb9kEFGI6hXGIFjraZoag9D37do+GrzCsdIc7ue4/TmeV3YKNNo1AgnZVI965DsGo2YY+Znl8LxJxqsc2cs3M25pJdr1LfP5nPl8zunTt7GYq0dqcionLXAYufmQrSvI9FLPZngYYwo9XLHwynuH7gD5us4a+k6ZgFbtip1JzWJxqMltoyhBYwbBoUTlkcY6sEsuXT6PxeHRJtg9noN2TtM5KueYTicJzq+1UznklSvSrNUkfB96FstDfDMZJflVmNSTmsVikdalJSbPdC2HJVrHlovvIZNxC02lOY6d7W1F3tYV/pQq7itXLhcaveyRiBjqeoJzjvk80LYds9mk1JllcALo+5paqQgxFu9SV5bYE2JUJb8KWqubiPKttSwWhwmlO3hJGdx015nTeGvZ3dtjef0qYmE6q7jy2c8il65watIgG5aH//BhLC55PRN9ptbQJ+R2iKL1rAxhwQzgmM/n5ZALqTF6tMpLnIRpn8O5IkSTEINoFECQBOYB5d22kFDn+ZDnGrnsrWgNXwYwpdyRNVSVwzWZUEKVmLUZpWkL9V323grIpghO7dgxDs2O9/m61U9hf8ph0mz95xINlb25yD3ibOJ3lVjOgbUuAWBqmqbh/NPXsZXX8K2ovbtcLIlGmZMMJuW0NSRonCllaiGo4tV71drdrguFoGRoQaeAIUQNjxiVaMWEgG9qYvaC0l/WGCYYejH0I2WY927+dwhC4gAoBmKX+vKaI3K5tE0rnn0oueC8vhmoVdc+3f/grSoiv1LaRJsJCQTSubAltZKIHDTModEeskJTL01JF/oUubJkhHSMkel0mmgTXVGoAyFEjxBLGiDf72zWcPvt59ja3OThhx+irquyD7OyzDJg2DeK9u/6Aa2cowhB8r/HXmzpep8MJin7WM/O+v49qsjHEUdV3oMxnCMUNzNuaSX6zJUrWGuZTKZszDa1jVnmQU0HN3sK64AN/duY1F5HtN5wYFm5MSQwCBpTBFBuERZix2I5L4q6qips5ekWSxbLpaL4kuAMoaMPEW8bbF3hgC0Dp3Y8V/Y79oOwCgp48Fi2LISupW+Hhz+ZTKiqShk/UluhelrTrlpFraIKUzd+/l6lzOv7ns3NYa0mExWYbdsSY588yNQpZpHzw4NBEYIwm0w5PDjkYH8/CYeK2Wyyltj33nOwuwdAv2rVu0ohdxFFG3vv2dzc5Nq1a4DV/o29rvGqa1MrtRW+ctR1hbOOTiy1T3m8kEoYnCuGQLZQ67rmrrvu4spyiXcWW9e0iwVEw/ap2wjW8HToWF7Z5d6d2+ljC2Zg3+mTgvDeIVWFBDU2QuwSIjo39w2JTrBLwCKjDDWiId71o5hCaJLASsbSBVUs3g8RhQHqn8KUaCRBvy9TyrlSZ6dGnHrkla9Tj8yUP0oefghtWfNx3mjMuKTC0RY6O5/C+zDk73Ku2yUvfZwvzfvk6tWr5Xmo4lYwljGGNil3awfig6qq6NqWg/19pb2bbbD3zDUkBuqmYra1peAiMckIEaKzeFKJTvLecYauV5atvm/p+p5IpJlssFp1mi5wGt7O9ZdA4jtWhRK8I8xX1N4j3rGyQrdc8RX3v4yD5SF7+3PatqNpqjTnYc9lsIrKpAkgLBbzZIyup0aWy+UIKEVaE5sUqzIO6VmcqCfPOBQ6KI42ASInkwnL5UotDjGJX7unrhucM2n/DOknGNq5GZOVM8UINia3FwwsFovyfVmB5rWrqpooqVWkMSV91IeOZ555hjNnziTq1BzqtwXdnvdH/rvrOmXTMqghwOBN5jIfLXsya7K6KEdCagiva+pMVtK6l3O9+pjDWN8bigc6+BE3H9K9pZVou2qZzjaoqkoL7rNVZ0gMHxk5OeSw1F1Qa8Yal0JGAmbIEQwKN4Vj1lBfUqxs0A4k2dLp+xZjhNnGjO2dbeZXD6gqT5Sw9vDx4KOjNxrS89YymzoOe8fBoYZ9vfeYXpTiKmh/O2OMtnNLSOG8aWOMhDYUr0KFtKVtV1SVtnKr65rpbMpqtWSxXBRvIZd9iGjDbu89dV1TVRXLVSg5u5DyLhri9mxsbNB1XUIcJsIDo2vS9R0iiTtVLCF0GLFMppNE0aavaRPggPeWppkiInS9KvPtrW0Wyw7nJ9TeUxlL17b0ocfXFRbN3WbgU1YIeV0yIrNbLUEMtfM0zrMgIqHHd1CJsDGZsYhdilYAWEIUqkaNg9QVjtirwJrO6rImOW+p6NGaXBEqTnASqWr1JnNfQ+/rNfADgOlHvJ4IuR1X17XMZptsbm4qx2sfWK2WHBzslXC1tYqanM1mJcTXth20AkkEOeu07lDWUaSa2TPFmBpyYsN5yTmssaDLSkMRnHFgriELpcDp06eLcdf3PauVrq2QPa3MltTo+kRtuefcjBACu5d3ecVrXoNzhoP9PS6fP8/m5iaTLfWIQtdhJdKHHIaj5PFUUKbyiOyNRUn11LoCmteuSijUJuKVGAN13SAbm1y+eoANgZ3NhumG4+mrVyAKXZ/TKK6ga3VtXDGu+75nsVxk/oYkkBPJQVGYXsO+CLkeNlNSZs7c4umJIFhyyib3/10uh76cbdvnGDbOeTY2NJqyu7s7SsdE+l7lWUY7Z0IGERJWgrKvjTVYZ6mc1+5RQWvps3eXDTCVCW7NMAHN93784x/HV4k/WyKkNJTu6b4ocl9VhCQjnWFN2etzNWg4P+ey1wlHwohkIe/F8XuyblBvONdw5/B5ORlH5P/NjVtaifZxYBgae5t5owrKtlAQX6LCJ7c/AqUbiyl30vadtqqSHB5dp4bTaw/fMzDnaFgvAfBoJg2bW5vsxV5p1WKf+72qZQ5IjPhUT7bfrWBR0aFsLj5EQmjpI3ROka8EtaAWi0UC59iSBxl7Cfk7rDNU1FqQ7YYCZe8rurZLnsBQ5LxcLtjYmJU6yhwCb9uWra0tAPb39spa59ySHiK/FnLq+o7Dw0PqqoaU8619zWQyYbVasFotCAH60OmB35yxmCsFGCZq7W3ax84aYtfRtRGxDGHNOCBVJ5NJud+j1nLltfg9GmF3dxdXVRwaJd3wWJwx9FbLORS+7+j6nrpWakG16AVntfNLTGGiEJRerg9dUSTGJOJrazExcv99L2N3d4/ru3ulqL4wsqSGxHXd0PcduRzDGqWka5pJeg6Rtu0SRVpfDLgcFXGJO1VEDaWNja3iKSu4R71RFXxajuRspZZ5aRE2hO77XvfswcHhWs4/r20mp9c9o9GX2WxWCvDH4cnsQWQGIUHZnLKw03BkvSYsjQExhsZ5zt1+lsXWJueffgocLA4PtdzbqGVjEjo5RlEDxACxZ7Fa0Exq+qAo2XN33sZ0MuGxxz6jYcRKW+Dl0iVVUnrep9WEV73udbz6tX+Bz3z6MX7nt97HxswTncEnQgfncgcVC+QSjyE/mHOxJmruttRTjkBhmXO3D7lFmFUsgmi7Q1XqWkIGml5RA0YJEESihrZT5CQmD1INyb6cgewZ5u+FUd0tGTcCVZUJHUQ9x/SM+9SYg0DicFbE8pqSMolYIQohdloaYxUlrGV4jrZrQRQ8l1MLGfCp/YtHsjWlOjTHmSOA2fu1JUV11NlZTzcARK3fT558jBlncCTXKkl9GlteG/Mnf75xSytRYxzWeUhCKSsEk0iI1ZJOSjRmurCBfi1zbeq1EoSaIW+Sc1HZi4QR9ZjRmidSOM17n2rNlBR9c1MJ6c3oP8mfT2AIE0hlHj1mpe2/jA0YkywlB6Ctq7JSyAey6/qykTNDiYbMKDGJwhUqo3s1iUyf9X6ZGrpzyQvqCkCg7Tr1ApoGs72tDc9Tzdlg+SWPPeUsipERA7nddIiRttOQpwKaZO3+l4s5oIfUllyJhjUra2m8JxBZIfR9VCWYWprlcPsYAKFr1JVG5ib1Qa3QcpdoLb3V5gEu0aflEgVjTAodtyoMU2JMibQDkrpIKJAiUlUDGYMwBjclRWIt4hJs3thSKyeiXLJrqQYoQkQbbi9TlKMf5dV07WOMySvOOe9K0ctJMGuIWVKaJ4cBBwFYJ37ZXG+ch69qmslwNvIeyRR7mgfttaSiRDLagiQduqqM0h5W61xD8uRUkY1rYw0ZaDadNBwc7NM0Spqe85npXbr3SKUvMW13iUCgqh0bm1usVq2+3zokhlEdc0xKXf1Dn/PUCFhLj9Dtz9nxFafqBg9I6LGmQiqtCTdx1FCeQRBnOTJ6kqOQo6Jji0JDQ6EZWJhzcePcs7EwmTTJM0zkIyiNYN9pRKZEAVhXbH2i7jP5O4ccFhgSW5aU/QTriqNulB93sVgwX2gZlkkGkSv3Zwa6zOS5aJRJS6wwgwyyVtmZVE4PKOW8XONzO3iNhhQFJ0V409k4uublo8WY1T2ihhKJAF/yc8tPqHxeU1bDHSSj7CbHLa1EfVWrNY0hJnRmjBFXZetc3ydA5rxFstsPMShri3MO7zREpw8IMsJRiCpIxSYPV+hDX8pByKiyOCx60zRsbmxA1Pq3GNRrALXMOol4TAG/6H17DC4BHQx4T2UFL4ZepAivzOZxfXdPN7TRgmgtJ1mQ6yRDGGD0XaqRwxqcNdSuLoXgOSScw8RdtyIX9Isot+/8cE5dVZw+fZrFYpH6A+rGU1YQDddpiE83dwacOKuGRdcH9vf3tUdrVgiJNm8xXyBRVFE7Rx8UWdt2K6TvOHVqhzNbW+zN99k/XIJoz9CmUUaaw8PDsvaDEIosFnO1RWzFpKo0rCTCzDfgNJweJDK1FV3oCzGHT42pJQa8VQVgrFPPJg4sRBqjNCUSkeuM1Zp2PPHE0+Ri9aryCB1gUuhJhVDXrXAusxCl8gwJhQZvCN3aVBeX0ZJSQB/z+SGK3g2p92uXQq6KVHfeFu3sfYVL5TgGqFI7Lt2HBoylD5GNzS26dlVavo3DYSqo/QiQNiDXMyq7bZe0bS6698w2phwcKODDpnZuA3LeFCMxxsjOpufKlctcvnKRynt2NrdYrRY0Mw33xj5gRRTR7TSiEkNHiIHZRs0DDzzAn37yU8Te4VzNlStXWC4X1HVdDA5j1IiuqioZMhCtYd6u+NiHH+LgqYuIg53NhmW7xEUhSDJ+R+VAkJocZOxEDjXabFyS7i8STWLGSgCfupnQx5BINGICMQ4t2zKp/OHhIQf7S8W9WIN2IVGPMu83Z1WUZ2Oh61vaLgOFElo75QuVrcqk91cgphihoOe4aRq2traoKs/+wS7ODarCOpc86MTGbUzhyhZRKkol7B/APXVVEYKha1VGqCFjShnYOCWQ9LwqQKsI6AGbMRgFGQyUDdexgoTk2WKw3iAhItmBikMpjN5jijimcDUJ0Haz45ZWonXdJIDFAPcuIKDs7luDSyzy1ozqgxBCXCUvr8b7GcZo+YGJA7orH44S4nUAEWOlKE6TwhnKKtPpwd/e1k2+aol9xDunzY9jaiRb1TTiME4wlX7PlUuXqauaytU4a+nDnNi3tK2SNDjvOXXqDNOpY9V1ieNUqboWiwV1M5TzZOQmjFl1lHVH82quUORpTsqPSCQszoG1NZUPhD6ye31PQ8kpn5m9l7qumE5rTp8+zbVrVzmcH6TcsMXZzDCkNGLeq9LbOX2KSdMQQs/e7h6EiKuVqq2azLDVlG6+YD6fM2ssVSVY27M83CcGQ9Mo0f1isSjhqjHSMcu3ruuoBA73dzlc7DNrKizKVqOdxwRpO5Z0YIyCSfqe+WJJXXtqn5ivUoeSxXyOBM37bGxs0PiKp598KoWfxyTigb7tuHLlOtPZlOl0RlU11I0qWmKgSIQgCb2trZ8UMKH5JbD4qtL89lKFj+aGBiv64OAA5b5VhPRifgiop9x3HSCEPnt7yvq0tbmF954rV65gjSfm/WIMy9WKqxcvcvr0aWrvy1qKiDLnhKDsWMslW1uKR6iqunjGcQTa0TC3plRi1NBqXdXEKKxWmlLIAJEM+PDeIdawtTkt4b42tPi6pl0si9LGWm1A7yfKOIaA6cEETu1ssjGbEvsViMU7Q5uaY2tUwoCYQroPYBF8EGocy3Pb/On1pyFGpt7hKkdPwC6VmclWvjyDYiw6x+bmJvv72qdT0IYMO9s7XL++V74rR7IylV2UUABGOcdYsBPA3t71dDYHxLOIcOrUKT3zicknprZjBY1qh+YP1oLBE5IBqKxEHZNJBgNqmD57sMYYdveus7+/y5kzZ9mYbRFiv/acstxrO33Wk7pJ4WNtL4cM3XW6rkte9AD01Avo/3IQNctPn1IeUVIPV6tRirzHMtGFta44OTmUPc6Dguajs8IsVRlm3WPNIeQcYckUqDc7bmklamIkdp0SV4e8QRLAI202X+tm1DwFSh2ZaiedrZFSkq2gCC17CeQuDeMktoiiZuvKEToVctblPIfCsLuuY1JX7Jza5LAWpTdD+XgtghLxREKXrN8Y6Q97rA346RSXIO6xb9nc3OTChQtlo3d9y/kLh0wn00RKoO18rBtCRfkQ5nyIAjz0kIVe0a9dJ8S4pKor6qYBMfRB80JNXWGMogqr2rLqF7oOwRJWCqQ43DssaNzVasm9d9/DqVPb9O0C51BBfPWqEuwbkqDVkGrfgaGhbbVwfTbboq+V29NYZfShW+EbgzsA6yZcPQhcnx/SM6WuDEibhIlhtVQe08yPq6cO+r6lnjRYggKDxGg413sES+MbPMJqfohYTQnMV8si5KbNTCMWicTcEtloKiChAmOk7VacOXeGGCMH83lKFxhcVdHHyHRT6RnbrkUQKirlaS5CRD24LDBXqxUH+ws2NzeprKeLHaHtiOV5qnLNwK9s3GVC/ryG06nmETe3NjU32nUF9NZ1kd3dXYxx9KjSqHxAYs+yE64dtOzOBWs7JC5oppbNLU93GGnnHeevH/Dyr7yfN7z+Ph7/s89w7dMXmS9XiLR4hM3JFvtdSyOWynpc3VA1De1qqc2hl0usMVRNzd7eLsTZUJJhLa6uWM0X+YTjrNIo9qHH+KqgLZGIhEjPqvBHV9WUvd0V73vfh9nYnClhRuixwSSEak8fB89DoawuRVRE+bJjT10ZTs+UHSiX4UgQgusTB67FO2X80d6WLTEE2tWCc2fPsL9/wMHBIX0XcNZz25kzXLx4HiUYURO+D62i9iWXxzm803ZjyODVhSC07ZLXf+UbuHjxIvP5gsl0okYjDomqABQVnnqPZoSt8ywlYulx0dOGjkCgcTM2ZzV9DFS+BgYyjY3NmRocCbQ1P9wnhshdd97FarXS1oYme7Q9iGFztsnL7r2PJ5/6DIfzlZa0WQUteZdSI11MrSSTUhs1ETCoRxpSODmEgJjcyYmSF87pFsh1sgldbE3CKjhFWpNr+NPaYMiZUJPeD6TGAcrZDSbVBAs5XXKz45ZWost2pYX0DMCHPsYcqSCDaTL4J2eFcphF2U0GBn/NA+YOHCZBwnWJYowlh5Jj8sYoMClA6ZSuDYnVM7PeKnF9PvgpXxlCYNWviuLLKDznVJlkoM9yqbR02jFegSdVVTGdTZkfLsvGd1bJqMcWWFYGmXkopnWxGOqmIkZHZsLJhdhd1xESGCWEnlV7wMtffj+7e3uFmLqua80NxowoNVx55grXdq9xcLCfiBq0gfdkMk3kAygfqq/Y3t6h63oO9vfo+zZ1q6j1el4ZbDLh9ubmpq6/MfQJYFNVLoEg1IZ13idWqhalEtNuH9Y4lsslzkHtKyW4TofQ+xplwukxzrIx22C5ahVYlPLOGRAxzr3kQ51BQNnCHdCtlDXPROjJZQGG8oH8PhIwKH++8hWh7uj6FjEmeesheToDj/AYPDU28jI6+eUvv5+9vV26Tvl/bbrH/F5XwtNKG7m5NWU63eD6/pInL17TMoS+IxKwvaVfWGLoWNnAKx68l2/55q/la17/On7h//2/8csfe5ytqkZiZBV6nKzom4aJqwgxsjxc0e3tqzD1nlXbqpeSiin7GGgSQXqMkW6VSRrM2h9rLNHk/FcGtTXFG8xo0c3NTWKMzBcKjPKpVZ9NPV5zjazmMg3OSQJnZf7gRHyR8vEhDBEuXw+1yFECoQsJC6FCerFYkGt4vfcgsLu7uz4fWyaRvL7E3y1gEj1iCQKPQuiqQOdUVcXOzg67u7ssFouEcNY9V3ltb1b5BEISJfYPMSDBFDCTtjILWOcZI7B1vjk/7gov8nQ6KbXQ3mtu2zmT6t7VsHviiSfoQ8dk0mCNAuK6rk81s6rQNC+ZvchERxi1I5QxAjZVG/R57UMih3Hr8uyImyjqopbrDuuGJuSieqsxnbkx8G0oj1HDOP/+JVMnmsEdmY9Ti7NHIVarkG8VHkNWOkryPQuH5YjN3wwPQmns7NpCa2ujMbhIUuhY39N1Hb5y7GxtM5nOtHdn1No2kVg63BMSD6XJPJ4De0kGBWTiCGuVQSe309KDOhgOmZXlKAovbzxVYiZZfCbRcA2eq/ee5aIldz8gIeIUrxF1TUVKzi+H/jAJ2i/C/u5e6pgAGENV1eU9Q96MQg3ovEMkHxB9JjGo0Iox0LW6oRVhmiMFmZNXEhAq9Ui1iirUXJcqif39A+695z7my8xZHNmYbtCuFlhDEZCqhF0CurAWvu/74RBrScGyCLVMU5ZLEPJn8wEuynJ0uDNAbVxXGWOuc7TUtcc1NfMFKVRXrQmTHJIf9sCQ28k50xgjly9fxjlL18noHER88g5CiCmsrQjKELRut+u7Mg8lb3BI7wgd4OErHtjgvpef406xtI9fpTtwuNRGz7gq9abUEiFTGQikGkLlRQ5RO98YbKntzbiA8bqMc1vGUNI1w7qtA3nGqGwR0U5G3mqHkkIEMQb5SMnDhTCgj4eQfEx7Lnf/sIXYowhpkncahnIJ0GjCZDItz7ttV2U/qJEeU+ODkXEgUVu8ZQav0TnOn93f3yMTFczn80KuEWIGGGquXfuTaupIjCmsVZo3HPKPyss7KKcsO0IflDXNWuXrTkaFziOvSTonziZaTEl18r2muXJAyI76uo5AZPnZDWmyTIiQKi2sKsXxPh+HwsfnLMs39BuSw5SW0KTfybjqMwHt9AP5f2v7Ke+7mx23tBLNCiRKtioSm99IeOf3oaktICexk2V2BF6duzmYpCBUeMf0t15XOThjafoco2iLo+QNVE3D1uYWt99xO+2VAw1diIJYQtfjJjUGFaCZccinUFXXd0XReu8JUbtUKHWdNqTNXlKm7+sT72xJuLNehDy2aJ11ec+ppe69MpKscsuq1KGiUwv/+vVrqaQkpnZwYQSRN5qb8lVxuLS8xuGrhN4dfX+UwGI+L0IoK6IMklFFKKmuT9tKKQOMLzlb5eAMqf1aRQxCRoGqklGhc7B/yNf+n17H5b1rXH7mGQ5296gax3JxCKP10TVW5aSEE7HMLySu3slEGZbm83nynF1BoLYtKew34m/N3kqytLMgFDElYqHrn+gKEyeudxZXKWBHUdBaNjJuz1VKA9K/x56o5s0Mn/3sZwsrVL6nEKH2Gt7UcJlRzuGo9I+rtmO+0H3V91pq5F2jXpoFWztefueEs53hyY88yp/tPcJnn1ow3ZwUsJJDcOKRyQxCq9vDOapG7y2gHqh3jsopc1HbHayVKxwVnFpHeoTBhkHR5/RF/nzbttpcYWeHxWLO/v4+UUbtufIzMibtr74YKTmsrkpTUDsoSWObQ4+qxJ3xiM8KQV/LirJIdSiGnbVu4EMWwSRFNxjCApI70KzzFltr6VYtznm6vuPatWslT9ilnKS1ViMYoikkA1iv3NbeOObXukL4no2xcblaXncMeJPQ+2htpzIuOZQsRFMn2XgoCivV3vd9XzxU9Vp7Vexm6EaUGdSG51YS70UuCcNzHsuy8ZqsKdB0D9ndSQjN/BSKvswRwfKE8gsxK+byy5set6QSzQvati3mMG84FSRVXSfotVoheTmcJbd/LIe2qrQsJYc+THofCVlmbZuiBLFYMyJ6+GLQMCuSSmkSrVaIkUp6DIb/w5vfwsd+78PsXblSvBmMJbR9At2EFMZ1bM9OsXewz7JdaVmG8zTNhFW71ENN9uQqmmbCcpHAO85TVXXpNaoKVGsC1XMbCySt0Vqs5hgzAI6UOqxi1S7pFqtCPB/6lvl8kXoKOnwCkCyXit4zGLo2sNftM5lOU+hEaflqV6McokLoh3ZpFy6cR5mi1KoViQmckNouocJBw0EtYNAmAS4Vo2tHibbtkKhAgVW7YDqbJuGohx0s08kOD9xxF7ffM+fy+af5sz9+COljup4aDCaF+XIdpnrnpijFQmCAeteqxPVYOpu76iihRNmfKfSfLYuxkDfJCAvZ6yJiU73m3t4eGXGrStohkhiuQs4XrTcN1p/zfSswKbfK0wL4jsVSvSENnQmVcxrmTLkw7yxV1WAWcLDfcup0w6xuqDcivop48Zi+5r/+16fpeQIc2MpTTyZMrDaXD33Lqu+pnOMNb3wzn/jw7xHagHGOqrKKqPXq+fQhEiURY7jSWBQRaFP+K2U+ircRkkfjEwOXJKRsjLruaojBatVx+fIVNrc2kRg0F5cMra4fysIq77HWl1AfwWCM5kAzMEaN5tzLlIQtCCm60GNtalsYMobCEMUq8cEImbtadRiT83o+GU8pSuR1D2n5U4qmab5oTZSbZNBbYzHOsEphb5270ocKSvhinZYjVcC9d9zBy++9k9/+r79H13b4RuetXaBylxZJ6SJ1IUOn3mcIqT7BKDVpNmS811z+ctFifcQ7vWZG6gfb4Z12+JFkeHdBuZyttWjZeySjzHNBiUTKc86UheMIQx6a3kp7oGjE3JUnkotVTC7xY8yJS+pEUx5PihCG0sGF0buPIn6PG0Zu5l1fZuPJJ5/kZS972Qt9GyfjZJyMk3EyXsTjiSee4N57733O99ySSjTGyCOPPMLrXvc6nnjiCba3t1/oW/qSjL29PV72spe9pOYMJ/N+Kc37pThneGnO+8t5ziLC/v4+d999d0khPNu4JcO51lruueceALa3t7/sHsCf93gpzhlO5v1SGi/FOcNLc95frnPe2dm5qfc9t4o9GSfjZJyMk3EyTsazjhMlejJOxsk4GSfjZDzPccsq0aZp+NEf/VGapnmhb+VLNl6Kc4aTeb+U5v1SnDO8NOf9YpnzLQksOhkn42ScjJNxMr4cxi3riZ6Mk3EyTsbJOBkv9DhRoifjZJyMk3EyTsbzHCdK9GScjJNxMk7GyXie40SJnoyTcTJOxsk4Gc9znCjRk3EyTsbJOBkn43mOW1KJ/vRP/zQvf/nLmUwmvPWtb+XDH/7wC31LX9Txz/7ZPyuk5fnPa1/72vL6crnk3e9+N7fddhubm5v89b/+17l48eILeMdf+Pid3/kdvvmbv5m7774bYwz/6T/9p7XXRYQf+ZEf4a677mI6nfL2t7+dT33qU2vvuXr1Kt/xHd/B9vY2p06d4u/8nb/DwcHBl3AWX/j4fPP+ru/6rhue/Tvf+c6199xq8/6xH/sx/tJf+ktsbW1x++2389f+2l/jkUceWXvPzezpxx9/nG/6pm9iNptx++2384M/+IPawejLdNzMvL/u677uhuf9vd/7vWvvuZXm/TM/8zO88Y1vLCxEDz74IL/+679eXn8xPudbTon++3//7/mH//Af8qM/+qP84R/+IW9605v4hm/4Bi5duvRC39oXdXzlV34l58+fL39+93d/t7z2D/7BP+BXfuVX+MVf/EU+8IEP8PTTT/Ot3/qtL+DdfuHj8PCQN73pTfz0T//0sa//xE/8BP/qX/0r/s2/+Td86EMfYmNjg2/4hm9guVyW93zHd3wHf/zHf8xv/uZv8qu/+qv8zu/8Dt/zPd/zpZrC8xqfb94A73znO9ee/c///M+vvX6rzfsDH/gA7373u/n93/99fvM3f5Ou63jHO97B4eFhec/n29MhBL7pm76Jtm35H//jf/BzP/dzvOc97+FHfuRHXogp3dS4mXkDfPd3f/fa8/6Jn/iJ8tqtNu97772XH//xH+ehhx7iIx/5CF//9V/Pu971Lv74j/8YeHE+Z+QWG1/91V8t7373u8u/Qwhy9913y4/92I+9gHf1xR0/+qM/Km9605uOfe369etSVZX84i/+YvndJz/5SQHkgx/84JfoDr+4A5D3vve95d8xRrnzzjvlX/7Lf1l+d/36dWmaRn7+539eRET+5E/+RAD5gz/4g/KeX//1XxdjjDz11FNfsnv//2ccnbeIyHd+53fKu971rmf9zIth3pcuXRJAPvCBD4jIze3pX/u1XxNrrVy4cKG852d+5mdke3tbVqvVl3YCz3McnbeIyF/9q39V/t7f+3vP+pkXw7xPnz4t//bf/tsX7XO+pTzRtm156KGHePvb315+Z63l7W9/Ox/84AdfwDv74o9PfepT3H333bziFa/gO77jO3j88ccBeOihh+i6bm0NXvva13Lfffe9aNbgscce48KFC2tz3NnZ4a1vfWuZ4wc/+EFOnTrFX/yLf7G85+1vfzvWWj70oQ99ye/5izne//73c/vtt/Oa17yG7/u+7+PKlSvltRfDvHd3dwE4c+YMcHN7+oMf/CBveMMbuOOOO8p7vuEbvoG9vb3i5Xy5j6PzzuPf/bt/x9mzZ3n961/PD//wDzOfz8trt/K8Qwj8wi/8AoeHhzz44IMv2ud8S3VxeeaZZwghrC0wwB133MGf/umfvkB39cUfb33rW3nPe97Da17zGs6fP88//+f/nK/5mq/hE5/4BBcuXKCua06dOrX2mTvuuIMLFy68MDf8RR55Hsc95/zahQsXuP3229de995z5syZW3od3vnOd/Kt3/qtPPDAA3z605/mn/yTf8I3fuM38sEPfhDn3C0/7xgjf//v/33+yl/5K7z+9a8HuKk9feHChWP3Q37ty30cN2+Av/k3/yb3338/d999Nx/72Mf4x//4H/PII4/wH//jfwRuzXl//OMf58EHH2S5XLK5ucl73/teXve61/HRj370Rfmcbykl+lIZ3/iN31h+fuMb38hb3/pW7r//fv7Df/gPTKfTF/DOTsaf9/gbf+NvlJ/f8IY38MY3vpFXvvKVvP/97+dtb3vbC3hnX5zx7ne/m0984hNrOf6Xwni2eY9z2W94wxu46667eNvb3sanP/1pXvnKV36pb/OLMl7zmtfw0Y9+lN3dXX7pl36J7/zO7+QDH/jAC31bf27jlgrnnj17FufcDWiuixcvcuedd75Ad/XnP06dOsVXfMVX8Oijj3LnnXfSti3Xr19fe8+LaQ3yPJ7rOd955503gMn6vufq1asvmnUAeMUrXsHZs2d59NFHgVt73t///d/Pr/7qr/Lbv/3b3HvvveX3N7On77zzzmP3Q37ty3k827yPG29961sB1p73rTbvuq551atexVve8hZ+7Md+jDe96U381E/91Iv2Od9SSrSua97ylrfwW7/1W+V3MUZ+67d+iwcffPAFvLM/33FwcMCnP/1p7rrrLt7ylrdQVdXaGjzyyCM8/vjjL5o1eOCBB7jzzjvX5ri3t8eHPvShMscHH3yQ69ev89BDD5X3vO997yPGWATRi2E8+eSTXLlyhbvuugu4NectInz/938/733ve3nf+97HAw88sPb6zezpBx98kI9//ONrBsRv/uZvsr29zete97ovzUS+wPH55n3c+OhHPwqw9rxvtXkfHTFGVqvVi/Y533Lo3F/4hV+QpmnkPe95j/zJn/yJfM/3fI+cOnVqDc11q48f+IEfkPe///3y2GOPye/93u/J29/+djl79qxcunRJRES+93u/V+677z553/veJx/5yEfkwQcflAcffPAFvusvbOzv78vDDz8sDz/8sADykz/5k/Lwww/L5z73ORER+fEf/3E5deqU/PIv/7J87GMfk3e9613ywAMPyGKxKNd45zvfKV/1VV8lH/rQh+R3f/d35dWvfrV8+7d/+ws1pZsazzXv/f19+Uf/6B/JBz/4QXnsscfkv/23/yZvfvOb5dWvfrUsl8tyjVtt3t/3fd8nOzs78v73v1/Onz9f/szn8/Kez7en+76X17/+9fKOd7xDPvrRj8p/+S//Rc6dOyc//MM//EJM6abG55v3o48+Kv/iX/wL+chHPiKPPfaY/PIv/7K84hWvkK/92q8t17jV5v1DP/RD8oEPfEAee+wx+djHPiY/9EM/JMYY+Y3f+A0ReXE+51tOiYqI/Ot//a/lvvvuk7qu5au/+qvl93//91/oW/qijm/7tm+Tu+66S+q6lnvuuUe+7du+TR599NHy+mKxkL/7d/+unD59WmazmXzLt3yLnD9//gW84y98/PZv/7YAN/z5zu/8ThHRMpd/+k//qdxxxx3SNI287W1vk0ceeWTtGleuXJFv//Zvl83NTdne3pa/9bf+luzv778As7n58Vzzns/n8o53vEPOnTsnVVXJ/fffL9/93d99g4F4q837uPkC8rM/+7PlPTezpz/72c/KN37jN8p0OpWzZ8/KD/zAD0jXdV/i2dz8+Hzzfvzxx+Vrv/Zr5cyZM9I0jbzqVa+SH/zBH5Td3d2169xK8/7bf/tvy/333y91Xcu5c+fkbW97W1GgIi/O53zST/RknIyTcTJOxsl4nuOWyomejJNxMk7GyTgZX07jRImejJNxMk7GyTgZz3OcKNGTcTJOxsk4GSfjeY4TJXoyTsbJOBkn42Q8z3GiRE/GyTgZJ+NknIznOU6U6Mk4GSfjZJyMk/E8x4kSPRkn42ScjJNxMp7nOFGiJ+NknIyTcTJOxvMcJ0r0ZJyMk3EyTsbJeJ7jRImejJNxMk7GyTgZz3OcKNGTcTJOxsk4GSfjeY7/HwtriDWB/raKAAAAAElFTkSuQmCC\",\n      \"text/plain\": [\n       \"<Figure size 1200x800 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# this is the prompt \\\"pot\\\"\\n\",\n    \"plot_results(img2, processed_results[id3])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {\n    \"output\": {\n     \"id\": 1134247454956879,\n     \"loadingStatus\": \"loaded\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"found 6 object(s)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\",\n      \"text/plain\": [\n       \"<Figure size 1200x800 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# This is the result of the visual prompt. We prompted for the left-most dial, the model correctly found all of them.\\n\",\n    \"plot_results(img2, processed_results[id4])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {\n    \"output\": {\n     \"id\": 1447567612993533,\n     \"loadingStatus\": \"loaded\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"found 7 object(s)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAfoAAAKYCAYAAACbwTqFAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjYsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvq6yFwwAAAAlwSFlzAAAPYQAAD2EBqD+naQABAABJREFUeJzs/XmsbUt+34d9flW11trDGe747pvf6272xO5mN9lqNgeJkigm1GDJluRYghWDEGAgCEwhAWPAIhJI0B+BkPwRy4hkJIoDOIgjRLAjQNYQaqBJiaI5NrvZZE9v7jfe+Z5pD2utqvrlj6o17H3Oue+1pLh9r051n3fP2XutWrVq+A3f3ySqqly0i3bRLtpFu2gX7bFs5rs9gIt20S7aRbtoF+2i/f+vXTD6i3bRLtpFu2gX7TFuF4z+ol20i3bRLtpFe4zbBaO/aBftol20i3bRHuN2wegv2kW7aBftol20x7hdMPqLdtEu2kW7aBftMW4XjP6iXbSLdtEu2kV7jNsFo79oF+2iXbSLdtEe43bB6C/aRbtoF+2iXbTHuF0w+ot20S7aRbtoF+0xbt9VRv83/sbf4MUXX2QymfDFL36RX//1X/9uDueiXbSLdtEu2kV77Np3jdH/7b/9t/mZn/kZ/vJf/sv81m/9Fp/97Gf5yZ/8SW7fvv3dGtJFu2gX7aJdtIv22DX5bhW1+eIXv8gXvvAF/vpf/+sAxBh57rnn+At/4S/wF//iX3zovTFG3n33XXZ3dxGR/yGGe9Eu2kW7aBft35CmqhwfH/P0009jzKNv4XbfjYc2TcOXvvQlfvZnf7b/zBjDT/zET/Arv/Irp66v65q6rvu/33nnHb73e7/3f5CxXrSLdtEu2kX7N7O99dZbPPvss9/tYfwrt+8Ko7979y4hBG7cuLHx+Y0bN/jmN7956vq/+lf/Kn/lr/yVU5//H/73/yGTSQkkCUxEiDEJEsFHRAy2MIgZtH4loiGCgiqEGIkxErzP/QRCiIQY0ahcvXqdb33rZb7xjW/y4OCQaj5DAWsKVBVVQRWiRmL0WAHBEFXwTUu7Vn7if/qDXLl8CSOWEBVjLFEj2o1BFQmeK1PDfH+HZas8OAlc3r9Kffg2J0e3ULHYYkI0HtMIxnqcRG4d7/NPXvleXjt0MK1AST8GcAYqw+XK8NGrO/ye56/w+e+7wYsfusaum3DzvUO+/I13+Tu/9ipfPV6jAYhAEPARtEXUcrUMfPHpY/7Up+6ADbShQqYVtV+zWh5SaGB392nasMZYMNaiEUSViKAoAkgUfPQcNwsUQCQhMnntMAYRQTWtl4hgxKTrSPNMRnDEkF8UBNJ1/V+S/i9jSVw/EPpz3jXdp5GQ9wnEkPZdWZb9MwDMVh8iwjZslp6joPH0tSOQrRuPqkK3jzW9owggCrG7vvt8vN/PaBq3vlHGuF663+TPN8fXjSXGyHK1Yj6bYYzJZ2Fzjs+dy/E7EU+9t4g9877x9ZufpR+Nsf/bdO8ngmYLZfeM4Tnn74fxGnS/p/dLfXnvCTFQuApjDENXmQ5195+1lmg/+xrzWmyMJe2NDbBVhz141vi2/xYDghJDYHcyoyrLtG80jUNMmuOY5yx2e3f0lP69Y6Stl4gRCusQtbz02m/z4e/5PlxREmMETXtIRMjHl3Rq0zPS+w9TEjXt024Pa0xEyxiHElANW+vB1t/aP0FVMz3P+y8fra4FjZu7/Zy1HX+2Wtf8J3/5b7G7u8vj0L4rjP47bT/7sz/Lz/zMz/R/Hx0d8dxzzzGZlEynFTBm9Iq14H1ERHDOIhl6UdKh0jAcjpiJlsaCEEJP2EKItK0HVa5du8rOzg5HixOiKN7DwcExPkRiSP04axLzien3siqYVTusjhaghsI5isIRvPaEMaoQUaIESgx7OxXiTGKyYjhZttx44sNUpbKuFwQVxFQU1qG0GNNStA5TFZhijhqTmSGJKTgL1tJaoaZgrQ6VGZemu8yrOTxZ8eJSefKVB7y0OqBxgejzQXUeEywaDEfB8M6q5I2F5Yc+cp+7RxVMSmwEY9aEes3O/g61NyARawyCgxhoY0QzqbUqtNHQGp+YvxnIinRMf4PgmUxEt4mgpLmWxOcY9dELD2zft0nYty1Wki546D6Ubg8JxKDEmPZOVZWb13XCyOhvPeP7PJIznnH2cyXvm4HRa3/DWfeNe+gfqWQ20xFKgEgc8c80PgHiaJ425yadlcB0Wp3J6FWToH0e499m2ttrPIxhmxifIXgQQfNYOybeMVPVTio8xejTZ4lJp4+6wzMWIMZMZmD0bWsIIVCW5Qja3VyBobfhu+452r1vN/G9EJC/3xZG4tmru/0+qcvYC8LqA7NZxaSc9MpNLxR38zESPIR+Mvq+Q/BU1RyTBW/1sDufMZuU2KJM9/bM3DBmq0YkM/FhHlS783Ca0VtbJGVMQz+d2s/HMCeqikbZ/PscAe4sRn8Ws+9/V904u49D+64w+mvXrmGt5datWxuf37p1iyeffPLU9VVVUVXVqc83NYOudYtNIoR54c46JiKCZOJkrMV7T0dkjBGMSVpLUZRMp1OcLVgsaoKHo8MFIURi3sRlYXCuwDcBY6CaeiRWBB8T4w4RsRErQuw1qrTRnVEKZzDWsq4DdRPxQThZLLm6f53p/Akwd1mvjzEi6TlRs/bsUAkJtYiaeYB0Si0KeGAVIsd1w2odiOtIa5Wisly+UnF9v2L+rgEiDULIDESiIWJoIrx9ZPjVb0/4gQ9BoIFg0RBQ71kul3jviT6gEsGkg5LQkXxAZSACPWlRzWjLiMiNmMXZZ2xMkgYGNjAaMgJwxlqP9sv23ukPdKb6Z+2X7tOztICzxwicGsnm8099P3Cdzd5kIGobXSuZmcnw2dbNgvYsPdHx9CZj5n/2Oyjv/y7joY87HISJU+M++zU3nn3+d4NIM6wdA+PsWaX0fZxHqjfXoDuPY2FkGOv47+7eQZPfEtYy4zn7uf0u6jvvxMIO+TqHp5/bzprb7QELJk/EFnpg5BRakDfJxhmxrkh7KCoq6e+o+v7e3DI+o53geMbMSD7RIkjGY/pTqAoSNy4WySv2MFSmH8JpAX8TQRr93vX/fu/1iLXvipdBWZZ8/vOf5+d//uf7z2KM/PzP/zw//MM//IH7OYvQdkxaJBOaqD0TSDz/NOFJUmvo+4sxwYBGLCAUhePatWvs7exzdHfB8uCYgkhllUkBswpmE7i8N6N0lhAiJydr3nv3Jjszx7x0OI2I9xQWrI2oBIwohYHKGayFNijLtaeuA1EV6xzvvHMHzyWqyTWm5RShIYpHrcGIw2IRjQTy+EXS4TVJiAGICLUqi9pzsmg5PllyvF6hpmV/F56/UXKpUipjsc4hrkCkgOiAEuMaHjQtv/nuhJdvV0yngbKwWCzWKyZETIhYBYfBqsGoYBQKhEIsVgxxzECFfHjzGDtkZXT4zj/DepoHnboiEgmMWfZY69z+GV20ce3W7jq117przxzjmb8/vG331WlcG8/PWhC9FtqN54wO5f2JoWb046w5Oc28t8e79bjMTB4G4Xfa+vazhnN6lpDR7QnDpkmGEYqTfu/vFDbMdkMfMurr/Lk5a07Ge8jaNI6YzX9n93Fu99CZmUbw8/aafpCOxvOWaMAGm9y6Nz9Lhs9PrcFIx+8QNSMWwSDisK7CuIxiZUFzQ6PuH3u28Jk+3vyuG0P3vO39IxgEi2BJJojTaFG/RqMZ1E6A6Fk4G/eMfzf5KXl3njvfj2L7rkH3P/MzP8NP/dRP8Xt+z+/hB3/wB/lrf+2vsVgs+PN//s9/B70YEkQLHaS3KaXF9Ln2VuJTB3usRU6nU0II+DYQRLCFJTYeax2f+tSnefH5D/HCky/zzZde4a233qWalhSlQWykLB3Xr18h+ha7EKypePLGlJ/4A7+fG0/sErynaVsiDQDWWlwIOGMpJhURuHewoPGOJijet1jj8G3NO7eOuXppj729HVYPfoe6OcbZCYEIUZkUe2AYDrAZqfMa8TFSN5HlynJ8smIZlPlUuHR1xuUrUz778QX/7Gu3OD4SVr5GQwvREgQgoM6BWtat5b/+yjX+d/+TuxyFllojvpjgyhWuKPGNRyWixmCwoEl/T1NvcCRtxZjE9DvtOykZ78dottsm89u2bXdXfNDjmrwJBm2XkV13uCj3mJ9jRDbND6PeRhjFqW/H7axxnzE4wAyK+0ZXkgh7b7/fYo5nP5Uzmcn7ts21McZw2iFZMiPuSe25o3j/1pHs0/qIZE14rB0bYzLDHQuLknxytu7dIvn552xmfWpUpxDEQZFI67k91k7BGGvYDxEwBsPDd9x6jVnSc8WY5DOSHz8wYzl13zZSpaqIKrZw6bxiMNZRWIf3bV5/IeqAUg1zOxL2Rs9NZg6TgIQxuLCxTc4Sss9u2yhdRwO3786nGQXiGXP7eLH10+27xuj/zJ/5M9y5c4e/9Jf+Ejdv3uRzn/scP/dzP3fKQe9hbVPys2w7DsWYoPLEawYnkY37SZstBI/3HiMOYx0YJYSItZYYI6vVgqKE7/v89/CDP/ID/Lf/7c/xymvfJqpwZf8qqHLw4ISmaZlVM1589gX+2B//UW7deZeD1TGFq7BVSekU364RMcmuj9CsPIumofadk4rFWZK3lxGads3dB5Z1M+f6U5/jzttfQmKbbLZA69c4dgndFlbACEYgSjo0XmEdAkerNTuXpiyPW37ptbu8efeIEB3PXL1KqI9YWjgKjpurlqsFvHcciOpQ9ax1xTduVfzma3t8/JkD9qdwL1bcunOXGzc8UTLBiKG3W8ZM9DvHPLJdW9OXiNlk6pta1iaz3zj8kqV16YiW2SIaCVY8i6BuEIXu7+FxmUxFEnPVgan0/Y8f1HfaE9fNPdbvRgaGcnofnnq/re82X2DopSPbqt27PhyFGMY0EOH0WXffmKm+v0BwytdhJG+cZqijF3hIv2fb6895lsbBBtxr3aNxd6r95p0bc9CbfM5Zm7Nsv6rJRyOZ+MZ7Ke/J8bX9gkkvl41NKeM+RyP8l+I+Y3OGkJV7TD8PxnRCxFgYO/3O3QwG77GFy8JK9z7JhymEiCuSA19HeceOeGf0vPHCaY50+ApQDd8Rkz8Fy5tBcx/PxfhVk/m0M19t7a1z9sGj3r6rzng//dM/zU//9E//S98/htPyJ2xo7Zq84UVssh1r8sAdL2PnSNT922mZyV6UpcMMK6Oa7Ov1ih/64S9i7YTXX3+bd958QFVZqqLEasXezi5Xruzi8QQs61bxMWAkYk3LtDQ4I1m4UBov+JgIR4jZPqnJlh81oEDd1oRFQXE8Z77zDM3JbWIMxBCJGgkxoLabg0GqxyQBJ6qhDnDvZM2XXnmP73/+OSbBEY7W3DpaEEKgqKbsV5Ed8ezOaiQE7rZCs/aoKFpZmgZ+5a0pT11peKKqqUyNb9tkxxPpkRMURGVjtoVNRi4jrpBIfxJORubL/HXnTTsijt2fXV8b+rQyJipnMctt7WVAfrNA2MPH3XQOT9gmQ2l82puFuv00blnHOXc8G9duE6+Nz7aeroMypChndX0a/O/e5eEEtSPuYy21d2LdgqsHWPv0O5z++2xG3xN+0Z5jDGjd9ouN+9gSWvpLzpq7TSb/fm3bn2ObMXR9nb4m25HHNunuvTb2+Daio8PXp5CBh6M/4/El9F57k0CS/TvhQXrlp0PUOhu4dH93J9kV+eF57TNLL8vOZp8klzPNYHT3vP+eH+Zy8327PtK4hzk/19Y/mqcxH+ju6d9vJEf1gn/+QtFTSuGj3h4Jr/vz2iZBHC/+mNnr8Hkm4Lp1vzECZNhv1KeIgKH37I4K2kaaesH160/wyU9+lLIoefPN96gKy958B+eE/cv7PPHUVdZtg9gJMQa8JsHBa8CJYopIGwJNiHhv0/HZgLqEEFPYS3o3T9PW3H9Q8tTlGxTVkrg+QnOIjzolx9QM7931KclO3wS4f7LizTfv8b1P3GB/VvDctR2sNFArR8uWpU927akzLGNI0LTJ7lzGEgx8/S78wP2S3Vlgt7JMp7M8P9mx0ZgsqHTvlAksIGIwGGKGytHOA1lOnfTugAsjr3XZ1AQ6vwRhTAgHm1x3zbY9bpOBjpGCLd+ADboq2x9stYH4jFGDQbDZvPoshn7W9xtewZBnQ/qxq3ZCjbKtSW2z1H6Ep7Te/ovRO/Qf9kx/U0DK4tkGA/rg2tj7OzPKqbUb9TBC8MbC5GkG0JkSdLyJtsbSP3nD9PdBUAc547PtZwzv08syIyF2u51iYqM9+oHRnxEt7EbTCSXaS4f5nMhgdpDuef0lkpQkSIqPShZkLcZYOpv4Q5PKyEi0PINBn3c+EgbYncfRGegUstEU6Iiun2ojYaOnKZ1Qfs5dH2wXPzrtkWb0ifAkppgI10gqPeeGfpttaJUGyY5h3RVC1vajoiZvUzWoVERqjg7v8eEPP8Vzz17nwb0HBB+ZTSomVYmZFARnqOs1RVkR/JLOlzSq5WS5RqdCq4qPQiQxdbGSnNbEEJWk3RtBY8zj8xwenDAvrnB17wZtaBCB2XQX066JbTcPpHfJ0jwIAahD5Gi5Rk5qTk4OuPHkJT59/Rk+UV/lm197l7sn73L3wZKDpkWi4bjxhGgQZzBRUR8JYnh3Gfny25YrOwWfeX7C1WtXEDH4NmAdOU8ACVIVHfSxXuPqCEmieAZSCKQxbDMqoIfox+0sXws54/PxFZ0T1wZE2hHcqBtM/pSddKPbsxj++c6DQz8RNJsyzmQkg27eMbluLJvXbD932MvnxJf02u3Z6uKZosAp7XzQpOj/7ojw4KQ2MNUxSnJqPJzWjjeeOTqJgwyxrbKzwUROj30swcThed1/t97vPMZ+FvOX7K2+3TbQg5EAdqpP2BS2Nr5ggzltm2QertUPDFtHe34APXSYx42ex88Y/u0E6RTXnvZah+Q4awdh1phhPvv3G57RCQ9jNCWZzMaCyEjRyULkhliwIahtzYGMhditORnfkSXW9xPZz6Mij2p7tBl9EGKwmUArhgI1gUgcpNK8AXtioCkOWowjhiTtJ8gp5D0QMgGzDEFwo8NiFGdKvPfcOzigKAr2rl3CGEPTNCwaD3XAhiTxFlVAxKAxx+iLIWrFySpineRnBayBEFI4atSAqKEQQ9M2iClRtQQVgoG33rrD7NMfptif49oF/u4CawsiBnwYDrIB1AOWoJEW2K92uPrkHI+w8oqbG/bmu3zsU0/xu3eOOWoagvccR8+dqOwaoTCB1keOg6EqAq0TfvvdOVdmBU/ueSSeEKOlshUGD0GJVrDOgG9pxRDEJsBBweR37YmmtSPFf8QhIK/tWZpt4jK25yaxE/MYwnNAiBvEZdw2+I8ZiEx3dWcn3Yw07rpK18cYe0cfsqYx1m42iInqpsY5JthdSOMGBRKkjznuyLHdMlWdTZS0o3z9e57xzI33Of1HZ85Kfcipf41xCXkK2++d2YyehnK70Z1ynBz/3Ul2OhK8+7MpmfnrlgPithlvzOx9fqHRWHT45bQZZ7zntudKksnbd2MbMdPRMyR2343HZPvX67zb7WhOvfc4MRgxOaGWYgqbdZixMLM9pLRb03MiKYg1ohrR2D0rjV10/L4hT53JZyaZCTsGnGDu2D+7m0+RgJESFSHm/5kOTdyQxTohbeyguCWg54OanFoHOps07iFxUie4bO/9sTC8mWZpeE4wm7MWR0dw4Av0ZkOFLQPvo98ebUZP2hAxQitKUYDRgImKqCC2BHxavNFhTt6kLVGTA5eYzr4oKWlYd5j1bILUw9MkQt80TRIMovaeqFaSdqpiUiIbIfXfU3JNBxFGmmbIScs6guPo7KPet7QxQDEFY/mdl97g2Rs7tOY695YRX0eMSHJ+M5rHn6HNGHtTwEmz5taDE564dI2qaLEqzK7NwAhXpnNuzGvaRmkXa65Gz9IEJsYm7hwjdWugjBzVDd+8Z7n02i6f2l+jcg/sDqoFqpGlBmahRGLA5qRFGruscoOU3yeBGesWZ0Cpm5pkFwJzup0OoxHOhAQ2dtF2H1t/bylv22aA88Yy7qvTzs41/o05vAD9HA2a5zaEzzasnYl2D82e+X4bOtIZ15z1/fu17Xu6ddwkyufefS583bXkGDm2Aw8x1mf3vb2HpLM9PARdsNYSQhiN97TeJwompufGmDRDY9L+ilmL30QlxvenJDA5VggB/Ehzt4UDTSawhCKmcYQQ0vXn7uHhnVVTtk9BU5Stczjn6AQBIb1nN8bE2BOtSABg3Nizbdvgikk/J0qieSEEYow4V2A5C53KV+e9/H7j7t5tyKL3AaJR/iXatiPuxlj/9T/ufzTtkWb0TVQK0yKqOG9QHN64nHBCMcan7EljLQChS9yQtAIQtQxeqJ2X9WnbEDBy2pPebDAclLE20TmoDM45IAme7qVzkxL2mJRFzAiopCSrqimBjSEp6QBIpG1rjM44aYTXbzUcN5aTdo7WHjEKRWbKAiojDSW/SR0jbS2s6zVtKInRUS8DQQOX9kv2DizVAlhG9ksB7/Ex0kbBOEPEgvHUVnlnYfjtd6dctS/y7LOG4zZlwhOjFDZpCDFKzmUQMkHr3sVsCEwDDR4z5kj/ZUeRJDHzU6SjgwH7bHE6EJhe8dtkaBtEOD+j1yG3tbqO+Rq2GOnQNunwWHPvmHzXzzAW0z+j25v0Wf/6Z3Tvc0bbYKXdmPN7nDW2Qcv9zjWW086vm5+n389+7pljP6Of86/ZPFtZRT2j12SG23ac23DEIt8rbCAssXOEHX122kQUiZJ+kv6cBPZuT3Ze7QHFp4VOGFMnyGVt3uedZnPkTOzww8He0QNHxpiNcac1OB0KKIA1KWOfZoZpXI5NBzRqdk6WlGfjLBG1FwBGcfEio3lJ6XVNyky9JcycFuxk/AgdtvG476QcmY01227nMf0Pcv1pcY2NvfFvQnukGb0Rk+B3wDmIIRCjQcUmCL/dzEAnIik1eOyIVdp5G7ZQyZ7hJsGm24SrExT6v/JOHhPAzvt47IXcXTsWEoTusKSD1Z3nGBQ09NCn5t87BtGGSIvh8Chwd9myXCd4TTWANYPM0qWJzfCiiBBQVo1n1Ta03hOD4n3AlEI5sUyqgklhcTYSrbKrcBKFRhWjATGkbO/WslThvRPD1+++wO+zhyy8IUqCDkUNUdsB/lU2Quk6dj2gGZkAiem/G3Ho/rBmkY0NYtKtXf9pR3o+IEPTEQXq13gsJA0Q5KbWfbqbJCiy9eVY4OgYetePbF5Dtm93AsfoUafeZMvE8Z20TsE945sNIngWIR1r1pufn080z/KLOOUdPep//Hwdrf/m9bLRX37SFvEeCSHIGayoGx+cp9KdGvtYgOwOcN9hZnwiGE2raEnJo1BJ0SskNDwCMqYlPdvsdu92xrbTmu723BkkQ+qyeV/Xt6aICSO2kw43+8l+Ax19PBU9ImMlJtM49Pw92r2J5JC2LXq4jY71d5yB5J31/h+odYrO1l7bEAb7kW6fy8ejPdKMvrQFFkfQQHQRDR4NZcoXLwmqNp1yKINTi4gS4wii05R3HkiMJ98kur2xshAgHWPaJHqdo8q29+3Yhrah/dDtpySQGJs80I12WeICIi5leFMy8gB18EQsh2u4uwjU64DYAtUWiRGiGVLhmk6zH1TFpW9Zec/aBxqfQvPKicM5y7Qo2SlLZiX4Vpk5aIOh1YhXjxAT2mAdGiMnIfKVO9dZrNaYIuKIOeGd0AYwNsU1x6yqSidIadiiCpnpb2Q9S6jGhhPUmDAwrFE/odApTv1/zmZW/aWgI0etUzTEbHDFU4Sn/03hFKMYaYajGx5OqNKzVDZTqI7twBtsTDaztW1/vq2zPRz+FU5lnePs8Z7u5/2187O+P0+zerjD2em9MF6fTVv75txtL9E24xgQJj3zGunDcDKDE5Oh+wy5d59rpjcKjszVSedbTTZGGCG2PtMBwRpJUP4WipH08W2Ge75GmsakG+86nu+HISnJjDlMqbX21HUxbhec0V40T9elmRrEFU6N5RRSMqKND2PyZ737++3PTpEaXBTpaUR/QgeNiyyTPVbtkWb0pii4fGWP1WrBetUQxRGpsVESU5SCGBushU0PWOl/utCktFkGh5JOI4dNYtpp810WrrOla0Fy+lxjcgz+iOR2SXg6uL+DwFDFZhu7mkjIgIR1gFrUC34dUFsQlyvqtmQdi6QVhAh2EFz6DBbZVh+jEgNEtQRfcNIGHrQrCm+pvGHHz7jiKlbTgsWs4NZyAqocnKywIVApxMJS+IhRpfYRcREK5b3jyN/9uuUnP9tSlcJyVVCEwJQ9vF+ibDJ1yQx8DGF3wz6Tf8g2cZB8by70MmKo3YHeZnDnMugtJtkRqa3HD393dlnpNKfxPuHhrXu/h1zYj7O/No3nPLa3bS4aN9ki9uOBDJduCrEb94/2/bj/bYI7mKc2x/V+zP48ZnqWb8z4edv3y9bfG9cwEHCylroJ63fvrWfOZcf4u/FFpP9Bkr4eNKIx5ARdCYYOwQ95BowdjVWQzOlT1rkiVd3LeTo6jbxrRtNeC/3cnP+uaI5wEUGMTft0NP4OJk8mywz36TDfCVFzSJ+0JufejwMTjjGwXq/x3idhRrPcc0poTwLr2MhmRDLHMefsy4e37f34fm28P4yM4uRHc9mJ4arKaWPI49MeaUb/n/31v8GP/6Ef5oc+/3kuzy7z7v0HmFLIRu5kr4qQQprIytKYSHUapIxO0EBU0zlN1LaDY7edObqNZ3OoyVnw02DjGpqI5OpLQsrq1+YhhuTMZwWb8+9bZ4heEA2U1tCGNVPbUknEYRCdEsWCVVIlr4HViUSsgCWVrAwhUPtDmmODn5Y0FRw6ZTat+ehHZly9GmAe+daBZ2qWLMXTukgVI6UX6sIwVaW0JY0GGg188ekZv/yq4zNP3+O5qyuK0lMHRykNjYPGJCJW+ewI1GkNcgYz3s5lcMa6jwl1ZDsF0vZNpzUZIzJ+REIottOsnsOjHj6ybl91nAUGTd8Of/ZQ5/tJBu+vVmxraBtCy8Y4xm0bfdgWix7+jG1G/5DRnbpncwwf7Dm6tSe22zafETGbzGAk1IzP7fZZ1S651hmIQe8kpgEbYyrgpOAmJWVZ4FyqYmdtgUaDOMn7PEWgJCc/kr9K62kWK9RHjldLjLGU1vaVMK0zvZkiajxV9vg8LjmauQTUa+fls2UikcEcdSbjlI56pJMVFMAjkph+27YQY64pIlvDedg+6i4dIV2ntPusBHA6D8X7tbNo73fSDJ2ClwSsx6k90oz+937/9/PNL73GV7/0Gj/yB76fH/n857l185C1rJLTSYDgFVvkTS2d/TRnZ+uJsiS4flTXewxfQQfnDLYeOJvQdYT+TNqkObxqg6B1xHiAqUU16wy5Kl0QTHQ4A+qEOgScEy6XsGiEBxbEjg6RdF67AauGibWUIlhRvEZ8o6wbT+1rDDWXyx18UH7tnUOW65rDEHlqBi+vlKuzCq0bVBz7xZyX79/DWQsh4BDUON48XjCpKn7x1Sk/ZgOffnbNewdgi5ZJEKyaHLizSRNUUzEhsSYVzTgDiqWnTVuQY9by6ddxPP/jG0fpTePZ69XDtWeuZVrItC7jymKbDOlh9ulx2yRAOtpfD2O2DxMqxnPT/cgZm+9h7eHXjonx2XDqaY14qCm/uR6DwHz+s8bPSaejc549jzmcfp9hPfSUINC1TSRnEOgHZCkzy6ytG2NxhcW5gkoMURyByHFbszw65mixYLFsWa8jvl4TfJs0cZNQvNlsynxnxs58xnw64fL1y1xSxdc17arG+4bSFQRVQgxASkzjQ6Azez1MFU7fWzqfHkF7ASUOMGUvbD50j0iaHzOuW5DRBWstzrl0brqzsS1cMVZwesxl9DN8Pqzl4Ji7+U7Dem0+YBtJG/kK5GgV1WH1x4LDtsL2uLdHmtF/4lPfy2z3Eq99+01+41d/i1B7PvGxTzAvp9TrNet6nVLEmth7nsJo88gg7eYvGBtnOscVZJyp/LStPV0H1nZEb9DWRDrCxGZ+DN0kPh0z6g+zhqTdW0uhNkPxAYtSGEWdZW6FHZNsftINP1euS8JExCAUYigNGFECkbVY6hBYNS0PTlbAA/av7vDSqw9Yrj2h8bQC4gxTo1yRCgVKI8ys0KogRrCk9Lt328hnrhWc1DvcPmw53K/Zs5Z1bDHikAhmFKPbMYXYm0GkdxxMF408kPsf6AWwET/roPeedMhpj/x+vXXQPjpCPl5r3VoQ6frr+zxPs9oW/E6JDJzVHsqqRgLLByFF/V7sN5aeHsaZ/Z8/mrME2sGEddrpLV9xxmedEHOGtn6GZv2wzz+IUHIm8R7ti1P+AHT29rPeIf1uraUsS9QIqxBYLpfcv3/I4eEhxyfHnBwvODlesThuWS4UQoMhYgyUzoJEyklBMSkppxWT3QnXn7zB1euXubK/z+50Cq6gXi7Bdtkokze+sSbHw78fuDzSgE2O6BnRN5Gx3XzQ7rebjhDObl4032/E4IztEcx0DKU3ZZ1nV9fxmc1jHWzysL0/NsYz2hMb/Z4jXEv3PEZCvo7uG/V7FvPfvu5xaI80o7/yxHWuXL/KpSt7/NI//zX+xS9/iclkwlNPPElZloQq4pslIUZUhiQIVhI01oc2AQaDj35kUxo2UdRMQDWDSnqWpNlznhGT7/5NpTW7wJv0GcMhky65yPjt0jE3GEqbtMk6BkQilTV4iVQmUhrNQkRnY8tMsTsEClZSHnprhKCRWiN1VI5XLfpgyfGy5iOzCn/YQKOIEUzhqAqL9YFZUeBDoA0NM2dYtIraRAxiiCxIMGXdOt47KHhr5vjMU4aTtWJsNxxBcwnKxJg1axmkvAd2fNQ3g8NUNdsBhc4BKn+BID1R7BiyZGiyEw7GRH6jIM3I4S2OiF8SQjRB/OQ+8pT2HtGqORwrZqKSNR8ZXc94nwyIRm9W6cYkY8Ik/ZiHvbXVRs8Yf9TNwfu3vE83uv6goU2bRPc0Ax4xkUE1zppyd0aG55w33OFkbn0u21eN5rVnPPTaf3fPxjrKaUEi9aF9aefxuxljcGVBG+HoaMn9g0Pu3bnLO2/c5ODufZZHJzSrmtgYXJxgw4SiTJX9QmxYqcfrmoAnSCQakMqye/kyTz3/DM+/+CzPPvcUVy/vU7SWVrttqqlctqlSaeqeDsV8BsbMcXOfdcrFMIV5z0ny/JcuomfjXXXURWKkUclZAIUORXMmpeTpfQk6jT6nrO3nr+fFHV0coQSyTe+GVThr3TeZfB4enX9Otxc33jTviTSQ0+Gmm4LmaKsCcira4FFvjzSjf/DgLpcvXeZTn/oUn/nU5/i//Od/k//n//3/wx/8Qz/KF774/Vx/8jo31yfU3mNMflWNTMy0T8/awflGHPW6xroxgUgwFSFkCE/xGxtjrLHk7rOmY8TmzZKy4YnY9LwMyfXUJxO/bmOFkDLpWVvgvSfGlsKl8SEGH5WqARNrltagLuAstBpQcUkbjpnhlKCkJDzWOIpcSEfXS5qww+Ei0DTHPLs/5YpM+eKLV5lUhiZGXrp5QLtsue8jy+BZtzW1bxFRnG3T+wRLiAVXS/jdOwsuOU9UoXJzPv70ilINXmvIaX01CzxRlSDkanZAq1hrkiuFgGIT4xVBQ+xjgl1RoBIwjlzz3iTEoyhSbK8IYgzWuEGrMUW/LkgOYew03jFRiR3rTVULU+q+tD5Rtbef1rFBYySMkoaAw9guXDCvqYwJ09jfNxHNgRmSrpUcBdLZiMeJb1Qx4/66SICNnddpOSOieK6SdFrD3m4DYU1zNKBY3XdJW93QwBH6/GRdBsqR0LKhcXbj3XjWaK66szO6Lv2+9b4dytO/Ta6MmNMwyhayMYbox3eRnybSYikImio/CjCdzNDK8q2X3+LVl17j3dfe4PDWTZaHUGhBqYaZm3F17wYfeeFDfOyZD1PMpxytF7x58y1eu/kqCz1iXR/j2zWx9YSTwMGtO9z++gFfvfpNnvjEdX7ij/04X3zuWd67fYwPNUigECAUGBdT1szoibQUUgEGpU3vgyGOTY8kYcdA1r5NL/xuMrUOKcg0Q4e16qOUcmnZqEnIKMWyahvU2mTLjonFbyjsMNChTsCISXAwjqG8ce8kLZvCxqidNtV0NvyEdMSoOQlQdm7uOs8CUY9KnCHEjYW/YS+depNHvj3SjN6akqZpWK/vgMJ//J/8r/nN3/xNfu4f/QJf/srv8CO/9/fw/T/4OWa64uj+CdoIk2IHZwxraiIWo5pz0EfKssSHMCIgQ9GMYY9kZxTAOTdAWNne3IXXiUlJYsZte8OK6WL6h9KMG98zMB9nwBSW4AOtBMpCmalh1tqk4WOSD0GSWkbMbBi3aHYJKyriesHV3T1eePIy16/uUPvIUx+9zHy3pG0b4mzNO0cN9xb3QSMOwWBzPpoOtg2EXFv+qosUReS4drx82/Irb7f8+Astd04KWk0ClWogWMWqYNrYH8QALGObNISsjdu2ZWc6wc2muKrC2QL1CXvxQGtIzoBNQ902rOoVjfe0+WfV1LRtS2hShb+OyMQYCZn2GzG5OIdJ0KxzFK6gqgomE0dVOIqioCgKqknJpKrYK/coTPKj0BCJrU/1zlXxoaH1DW3boqQwTc12UVEoXYEVUPHJ3CIGiZYAtDnXvhUwRAqUtvFYM8FYR9BmvJPOPRPa22D/5dtZUOnpliJLznWY2lQnz3vSGb12twvaOdH2KFjnIX8eUxibE+J3Ng+Sypca3UUJhFjjyoJqOmN90vKP/+4/4NXXX6W9t8KtwFawf3XG1Usv8uM//u/w+37fT/Khj32Ct4/u00yF1fE9bn77De5/+cvor94l3DvBGIcrSoIxtKHGTMFNWvzC8+1feJu/9s/+K/7M/+bf5sc++WHcWlit1og1IGuIRbLIWUmWLSrIaW7TrNl+jsbvZHIUwJiq2NHESCfIdTeMUDFIgoVqyNbAFGXStA1FUSSTW+zuD70gGMMQjZQUmGTaFGMojM3jHtZJMAz5NEbDP88M0yNv6ZpxZEH6fDN76XY7C6EaBN9OsH28fPAfaUafkj5INosrr77+TV544Rn+3L//7/KNb36Tf/JP/yn/3T/7VX7qf/5nuXr5Gm255uT4iJ1yytwqta8JOII4bM6mleJGU3rLEALWFqQSpD47YiXpNjF2hdEhAAZG36EBjAlUTJniNMeiaoLmOvhpvFF7u7MmBumMpbARFWXhPaKRNgYmTqkcLDXDyB1WbugPbYJTc+iPCBIaYqhYN4HD5Qq7s0MxWbM4KlgvW4wRnpg+wZOXF9x67wCx0MZIrS1iBYwniBKigASqCIVYlsGCOk5aw5dfr/n80zWiFpGImojRASWRFjQEMEoxqVh7j0aLQ6mssHvlKneOjjg8PuR4teJoseDB4SFhsaY+WbJcN6ybFt/WtHVL9D6lEIyJqca2c2zs5jDznij4oKlYUbc2wLrTPrKwYW3KKiZWUhrRsqAsKia7c6Y7FbPZlJ35jN3dOVevXObK/i47OzPmO/vsVo6ZQKhbog/EEPAhsG4b2iw0qk/rZW3AiKWyLu0REUIbaZtANZnSamAdVxQyxDOP9Y0NMtUxP4XeGNEBBzq+eswQut4epsF8EKZ9elCnHOs2wvdO97ntjDkIEeMc+qcFkM133GxnefGfdY1mYV9igw8LZruX8Arvvvcuv/rPf4WXvvImvo5YazAzwdg5BY41ht946xVe/udril/5+7QP7hHWx/hmRbtesl4s8M0hVlq8bwi+SWF4hWN+aY/18YKm9Lh9YdIa/vZ//nNc+V/9O3z8uSfYn885Xq0QiaArRMskYKKIrFEcguuNF2NnvYSwjN55rM2O5re7vqMT9P4XXTieJIQteFzhME5YeY8Vk0KBRYk5E2fXxabvUvaP6oGdcVRTElISEhbpHCg1xR6O+tpm+Gev4zb93F7v87X68695XNojzegT301OJzGm8I+6WTObVXzi4x9jOqn4xu+8zD/4e/+Mj33yRT704pNJe10vsIXFOoPElBtaSUw+ZtsrDMki4hjr0iGGPsZUNEJEknY/QmNjd9CgP1Cdrb4rJrHted9t0Bg7+2/6NgFVEdFA5Sy+NGhocW3AEnG5wh3ZrtZVZ+h+TecrJQpykiC9oB4fA22AxgfWbWQhnp2dCbvzKXvTght39tmbTfBxjQ8gziIhaQQaAkKK+/cmz5maDDFHjhYVX7+55uNXYvL2j5rDV5QopAQ8RBwQfIvTlGyonExwZcFLb73Nr//W77A8WuPXLb5uqduGGDyx9XnuU+KjDmHo7I6S8H9MFno0Q8i9x7AZ5jr7JjMJgwaoqXQg0krP/D0QxHB8+y7WWaxzOJs0tHJSMZlNmcynTOcTJvMpu/v7XNrfZW9vzu58wnxSMptMKACCJ3hPCIEm1KjYnpkJEauG1hpqIkjASqQviAKnONpggdTRT/f3tkgwzumgW9/BNhEdiGeXc2IT1jwzAkWG0YjIxngH6P8cBqzdGM4b03BWxjbV7r+JUA/a2Acn2YLg0h6sSkTg9s27fPUrX+fVb75NbNM+wRRIOaMq9zCxJiwPOX73DWK9YDKbYeqGtjki1jXtuma1XrFcLWjqmhhiKlwVcgEmVYqyxAdF1DOfW3ShfO23vsaus7zwzI2kISNY40AtShL8B7piQFMRGzsq5jSej4Gfj/T6EePdQGX69RnuUwxtCNkBF9CI96mGSC8idma2M9Z07Lyc/AIsSswadzqgA43shNbRcHVrj4z63X7WWeagjVU+5/sBut90tH5c2iPN6GO3GyRp2cYKTdsAws7OjM985nvZqyb8k1/8Em++/ia+WRA+/AyXL80RWwyJHrLTyXYzxuD9KDVrn+BmIJQxQ6US44Y9MqpisoCQjw2QtBqV2Pcxttt23vsdmkB/X04fKRFnLWWZkufYJpWv7ZhVx8y7kDJD5vldZjyNifFHIarHa6SNmsKAQkqfqwaWIbBaeNQYyukMW0dMbClycpBKBImdE1rytvUKTlImQkUJ6njp1oQX91e4ostGKKkARr4nCgRRbPQYHEXpwFrurRp+9+sv8/q33qA5XEMbe0e4KAkCtNLJM4rYLQ95M9goUy7yvE0ixHESGRm0C5O1l/5462BzT30lASbWSlx7gq5Za642KBDFYAqLKx3FtKLc3efylctcurrHpf0d9vfmXNnf4crOjHlZUrkS64TSCb51KYVxppya/Qh8DDjoCfjZbZuxn75y7LDU8dDThHAQLM8mcrL170BkN23um/1uw6QfVFvSM37f7r0vgDwedxYAHv4u45YZDIJiUAmUkxkHR0vefvNdXn3p2xzdWzGbzrCuxJQzTDEHCpRIbFvqo3tIrGmrKaJCaFeEZk3bNNR1zapesl6vkkISIiEkn4+iaBIUbwBRrAtc3pvw7hvf5s6LT3HjiStYVxL9Ou/V2DNhzfu/S/jVQfCDKNYx686c101e3uNjhgqnmNuABHSTnfyLIumctG37/nMrknNWjPfHMOtkGjh43o8fOL7uLLRm7C9y1qNPj22srW87yZ46N48Xn3+0GX2/8cUgUuBDQ2Et3jfU9QqAj376eZ57/kn+wd/7eb7xla/z5htv8kf/rT/EznxKaFqsRIxTWk+f6a7b9EmzjriyJGqXMjc7awHjQjht64nWYG3+TIfNqdlGjXRwWILQoyZHsxhDFlryW8XYP8NYyYc8SfIGiyschXUsm+TZK13FPUCMph8BZ1LsfKfxptQyBqupml0TArX3EGrU17iiYO3XvH3zgG+9c0ip0LoKqTwlltIq6teIc0ho8RKIIj2S4WwkqhAwYOGt+zvcWbTcuBSoisi6BmssBoO1KQZZCQmOFKhmM24vlnztlW/zra++xKVQsXaR1kQCShs9REO0DjTkNQFiThFMLg5ESn+sscEHCJqh+tFZ7tnW6LzbvH7GpJK6xkasFYw1GJdSnU6LksQ6OwppQBXfZPPBskZWC07eu8eDoKgzSFXidubMrlzhmRee5cXnnuHJ69e5dmmP65cqClpCXadeLQRJrk3WJyZvxBK38/l8AIjxLC37vOsGLVvp0YPvGMLs5vhsbX3b7nk2Ld0KJxxx7V7J2xAoxmLA+LbTKMAwju33yloloDYgtuLNb7/O66+8zcGdA0TBq2e2d4Oi2gM1LA7u4lyBOEtsVywPj1ms1oSg2Bq8rlA8RgUTYd2kHRMUvIfoAWrcXDAGjHE0C4/Zb/EnJxwd3Ge5XHJ5/zptsyCaQMqPAahBScI6eb8zQlu6VxfIsHlmtr3jGxv+Q2Ooe5MZDjOU6Fqikc66THehi4sVpXeC60whkD4zuc8hJDPbwLtxCVkRyAm/IMfv5/2wtafG69Yx+237/PZe2Gbw45C6QRA2nCWUPA7tkWb03eYASbGdrkJQXJHgu7ppePP1t5nv7/Gn//0/xXtv3eJX/vmv8Df/T3+TP/3n/jif+dxnQJWjwyPEOtAkgYYw2OuLoqBuGkyOHYVOIOjGMGxE78dOQ5uMvvezlmSvT+ky7Sj2NGxUcfI+1dAOqkgAsRANKXmGFeazihNfpopyqlTW0HSaa9asK4HCpL8HrRTWVplrhTZKXC+hrbj54Jhv3znmmScuo9Fw+7UFQY+hmFDYkmJqEY0UhVD7Nkf0K7TKVMGXCY5HHWAI2rKOE37jzSk/WirPX4G2DqgPWK/QeEyM2CLl0b/21A1eefNtvvW73+KNr71KGeHe8jiNOaako4WAxkCILR140tEAjYn9itE+NS1GCGoIQQka8T5mL90kxKXY5PTjDSRYOyLqk7cyqUaQM+CsUJYF5XSVPhODy85F1haUrkBLR8ww0eWdkhhaNLRo9LA4wt+/z+/89kt8yRlkOmP32nW+8EPfz5/+w1+grDz1asFqHXDFLJljCGiOWBhyGg97Kr169kw/g22eInp6WitO143/GqqISU8ARwxi9N/kcxIzE5D+s3+11jGC08xmow248vBR/7kdMTIZ/TxkbBkNsw4Ojxa8/to73HznDho88zkcrRs+/MznsO4q9+7e4ebdbzCbJLuyxITEtRndMUkBJ1mAFOfA2VQfIoZIU3vaCEUllG1gIhOcVKzKFlbHNI3lvbuHPPvgDi9ev8JyYdG4RHSKoUSMEjhGY4FkpzzJ4XcDOritQUsGPzqG5oY565hdFsQ627qQGK5ILqGLom2gUWU2m/WLk2jWqDhPdsCTYTik+hZpryUza8fkh/XpMu11ZssP5l9x/pJurm93w9DfEO45+uIhz3qU2yPN6CGVZQRDiBErkaieNmYSJZad/SfxbeD23XvsXt3lT/zZP8HHP/lRfvEX/yFvvv4Sn/zs9/Hhj36S9XLNOq7pvHqHbFgmx9sPkl7KVU/2su+ITbK9d1C/MYmwiCSBwdoBRkvwfMz5lwdoqyepMspJDUgqZk+yxCXttDCCM5JtWhEnyXtbSDK2kwQuFpDB8g65MxhZEqKh9XPqtuKwDgQv7E4jJTX7+3P+yI8+Txlr3r6/4vVb97l9tGbZBoIUHK0XECKFCKUVYvB4PJYSQwQNaHR4s+KV2xVX5pHCKtdmcLhaQyW41iYGGGDn6jXeunmHX//V3+Lg3ds4hUXdYlyVHNpCOvitTRqN6zTOvCxiLZZExCJCUAjZDyJEP/K5ALLXcop2yNB70Fx+thMY6PtvgTYAQZG2wS5SpUTrItYIIi2uaCkLQ+EMziVHPlzb1xQXUwIWO1FuXDEQPTG0NIdv8o//m1u88o1f5ad+6t/mI88+xd7K8+DOMSdAUSXnRKMe7XylR5q5brDdLn79HIa7wRDPSsKzSdzG2lfant3uHJj82THID2+nyep2247vJ2myvXd5V8l9831OE+fvLA6687m5duk6t156mwf3Djk5qWkaR1DB+5bf+q1/gi1LjIVy7tGQfWAKm7Cy4HMqd4M1KXeHF5IpRhWJAStKpclnHomIB28agomIKZm6XYxZc3z3PrfevsfqUwUlAcsuITrQHP9iQ0bEujlLnu9Ri2wHp0cJxxp2nrBhtjcmO9GwoYaH5oqToDHm9NVK0zTYIikYkoXlzm8JBifptIe6fP95nDFuPVvJJX6S2XGUAveDtm1/gE2Pf+hyl7BxPrbb4xU3v90eaUY/xMYHCmcyPJtKsUaFqAalAZcI+2JVs1o3PPnis/zxP/1n+drvfJmvf/lrvPvmO/zgj/4Ys/mM5WKJCAlaDgmmd84RYpdOMnuKCul5McfN5xqz3cELIXnqJyheUU2wcOeB3x3GMbwWshYfQ8poh3XEGLB58yvpOUq2kWep2xqDjRFrLNhU196S+jCZSHfHSYHYOGY7jquXCvbnltWq4ekn9tmdaI5Jt1zZK9mZTPGu4GBxwmq1QLxSt56d0tKQmGmIglpDZSzqIyEqHqHFUZmIGOHb9wumpeepDzVIKdQ+EHM4jnGOg+MT/uk//gWaw4bYWpa1p6mVdZ1SGUcUDJRiaZrI0menppjrEWQPXXrkYqTx5pfuSEr3R4+2dLfHgX2kjGIk5yNDSnydF6tLCRqDoikJeAp5rEOqT+CSt75zUNiCwhqsSQ6MJpIwWxGMOCal4+l55PjtA37p53+bm58+5vkXb3Dlxg47WnL04IBITCFW+v4sEs7SVE6TNskaVtxwTNkkljBUKRvClzafkydrQ9d/6NjO+T0/dcOMAskq0ivuGbqRDEvIKP56PKbzPKnTe50zrixYixgsws33XuPg8B6L1Yq2UabO4WuoY8182jKbWFqdY5zHuoiKQRVKhNIYGtuAsYhARa6gqWn0wSm2lKSgmIDLtjcVjwuGoIbSRHZnFfPphNC2NGbKxJ/gwy0iEatXcVwBOQZtUbWouizOb8HPeU6S30rnEZQYa5qTsSY7hswzs+wd/JJ5jcz4QwgjZST12kcXybjftEaqsad1SUFK+UKM0MfpbwgGH6CNEatx1dAxTN+hE+nv00JiGqGcu3cel/ZoM3pJjEw7G0+izv2J7pc0I0QhBNoYmE2n3Hj6aZq65u23XuPg4D6/+6Xf5kMf/x7KyQTvPb71Kf29KCKWqCHD8Yny9NnuZIC7Ok/gjoAaYwhREUlhdUm7H9nM+jF2B8QMnAlFsvbSZbIigNiUoCUktg9Zl8WAmpQrKvn2pNzc2YKWNcMAmmJqfQjUvqUOKZzQiscZR8qgKzhr2dud8oQ67tx/wMniBO8DUWEmBSZ6mqAgnmgsghBNBgajkOzlSfB4sDK8feC4vyiY7bSchIjvHAeBQiNH79yjrpMQ02pkXXtiiIi1lIXBWklmiDYynTicJqJsjMU626MoCf7rSgl320GzxpZ+Dz75QISQEt+0bUvrI00TaUMkkCK3Y2Yw0jFZJddHSk6FiQlFNKRQw+AjtGCL5DtRWU8oDEVhKKzBkYRRsSDWIFimswZWlndefQsf1ty/f5u92YRPffzTlLOS2LYEHzAScwGfcUvmB2NkULi7gZKQnLO4mw6XjG9Kf2XOOhDH/JxzrjVdydb3oZHvr/Bv24ZHghebNtteEBgu3vx3ezBCTrecJ6lnCJExKiLGoiGyOD6iWa0JbUCjwZO084kzVBm10ZjWPwneGai2FozFFnmlVAGfBMcstSSFIOJsYmzOCFE0R+kk51FnhZ35lN2dCeprUAj+bWi+nfaga6G4nmvZC6oW1KS9IUMyJlEhO5tkE2FayU2Bq/fSy5s976fuKs1RDNk5VTuK0uXe30oMlcbD1mcMcwy9gNCZONOOG2jnNqMe0KpMGUcPGDP7TmBjdI1qSl62dUBOjW389+PI8B9pRi9ZWobslZ4PcZJrk3Qo4lK2ORRnQY1ltVrRti0f/cTHuP7UdV7+5rf4yi//OuWk4oWPfjilfOwLJmSmLSmFq0EJwSemYoeUrt77JPXGTsJVRFJWtpSoRVON6G7sMhCyrlSmtY4ueY6G0Ce2iKKImsRAo0dNmUtlWlJSkYhamxJRqYImZy5jMoxPCotJ+eY8zgqLdcPtI2ii8sTlGXW9wpfTJDoIeCCKo5oo01nJZOJwq1SgxomjciXiI2I8wUTaLjohLQg2tgRNGeu8N9w7cbx0Z8qP7NfpnU16L2Jkbzrhwzee4re/9R6LtqUohSZE5tWEsiyZTEqq0kBomeyXXNnbZVY4ps4ymVRU0xJrBWdNDnvLYXWZ0UfNyYx8Ktdbr1uaxtO2gab1LE4WnKzWHC3WnKw86xBZh0gTI94HfM/Ildb7pNW4zqSTCEgyEyQzgMsaQsBTtEJRGialwxQlqu3AHFGCUSZVSVgecee1E+688W0Ojo5Z/NGSH/6xT1A4IR6tUAmJ4JokmI2RCekJ2QhuT7M8+uzsE9Q7JG3Z8zuovDOjDtCv9IhJCt0UBlj9/LCks0awKeyOkJj+bEifQrXzx9iGmnNHG8/Q0WeSf9XsyyPdcwU00M9Xoh3k5EcGgqRy1wJRPMVMmLsCV1pw4LQl+gw5k/YZ1qJiqYpJRpsCIQZCjCMmnFNvC4BNML9octqzJUEaTFEy39thd2dKjC1WAyG+jfH3MbEElkSjiE/VrIw4ohosyTk2y2aIdCmiLZ1W3fG7Tl+XcY3ZMxJ3pUiQ7DhHytmPKs4ViW5B3s+bTLJbi42CUZ2AJkOOkTSWTYRhG3pPibVgLESMNfAeUR31Pe7HZufBs/Z5MqXGU58/bu2RZvTjhe4I31nN+wZrbXZ2GzbLrdu32d2d88Uf+SE+9dnP8I/+9t/hnTff5JOf/QzPvPACR0dHNG3NpMiZpXygiYGiKGjbkDznZZAqrXE0TQ0ZYh+y7HWOOSAhpjrhmEHLkBSHn2LDE4fSjlljCLme+1BTOb1qDAkq7xJSkL3pE8IxuFJF0yEfAsagxhLVEFrwTUCDUJQTivkOzKbUpWOJ5/Vb73Lv1n3u3rzL4nhFUGjqFY1vEWeQacQYRZqQxgg4sUlQihHftlhbUBjDsrH85huRzzy3w2UTuB8b1m3yYWhmkX/vP/rjmP/rP+U3f+s17txb8dT1Xfanl5nvOCaziqIqcCbyp/7k57k8nxHLCdGVWeCCrvLf9t4QUyQvekmlQp0V2naNKw2uMPhWQQvC4iZ+tcYvaprDFevDBTdv3+PmnQNuP1hy+6Dh1v0F792zNBqp28BSSZqUTVzDmOS850NAMPgCmlYpQmLSpRQJ2o+atDFriXHGuqnZmRqm0xlFMeHoaM3/7f/837B/6T/gUx97nvnuLoeHxzjnUs0AOgYvOCfEDKNuw48dcRzPR7d5kgZ1dgKRxHCT/tY7eKH9PZtnkGzHfQiTHzGPDS18GM5ozFsa+sb3SSAemHM83cF2x6pImzRnlS6aACBgVCg0OYnFGAniMZdLdq9doZxPULcgtJHdiWE2mWKcHUw2RmhMRmZEcM5SlhMqLSis0mqg8Q1NM0TU2JxF0gdFTTIzelcxKSxTZylswWrlmVU7XLt8lb39fUKTIm+k+gi2eB5kRjRXaXVFYwoKIxh1mZmvUMp87kMOrw24LIgNvkZCEJtnsEsnm9dHHcQun0ckRp/2lqay350pACHlEEl4/takfwD8pjMpjMa0sf6j30MYhTiPhMmx9n6eyWbMyM/cIg+573FqjzSj37BFadLwu1hkATAJzqqqirZte+c6SAtclSXr1ZrlckFVFPzJ//A/4Cv/4tf43d/4Cl/76tf44Z/4ca7v7HHn1l0qY5mWFaWULJfLrGGkmG3RATIqiyrlo9ZIDCHlZ9cOzk955ylsIjw6IoJb72WMTRL2hsO10OXFSdWiDDEXbIg+MrElDlK1OgsFJEIAWAWnYELE0VK6iqpwOCus2oY33r3Fy2+/zdq3rH3Lqq7RxUlKumMc1jqsKWjagI8RVyQI0ibsGHXJUTBoKgyS8gCkMDcjjqCGg2D4h7814d/6gYbKHFNTEzSyPDjkrXXNv/vnfoI/9kcf8K3ffYlf+Bevs6ahiRZ/dJdrl+AP/qFP4U3DK4fHEDXl2daYBaa4cUA7Kd9HxdkJZbGLM3OiT4JaiJGoLbbwLFb30FhTFBVFOcE8cZny2eu88Kmn+VghOAnEtmF1vGC9PGF97Dm+V3P35oK33zngpTcPePvOmpU3BCwtIZHPFspkvSAEpTYnzO0EYxxqHAGDlZAQjDZydLSgLBp2pyWzcsEv/qP/np1qwvd/9uPcu/cgVU/TTYem06Fx72/j7OPP+59BW988X2PCu/XdoIf3Gl8GZh/yYD3F5D9YO+8ezV/JaDzdozIaRrKLp8DSlI2yc0WMAt5IToiVzohv1jx35SrXpxPW85LgHJNdg8HSxEBsE3NsvWexTI6eTRNpWqXxS9aNpGyPGrEjJmJt5/OQxho1Jr4VWyaFYzYR5jOPRMe1T+8x359QOKE9ekBbWdz6CiuNqBNc4WlVKUyBRoviEfG0xiIRLEI0OWuoOIKRVEpBtf8xtGC0450pV33oMMQE41jbMVQIPuJDi+biVrayGGuyy9LpqJDTAl1GSXsrQfeLnLknNhl+Rqw+mAzxr9Q2lcfHpz3SjB42JcPOiaqDplS7mHftNWbvPUVREoLHh6a360YNHNy5x0c+80ku33iCd958m1/4e/9fPvfDP8jTN55kdbJkXa/ACFVVIU2g8SlDW8wVzDrbU6dtnTHaJNH7iCtdhs02Gb0yOOfFKNkOl/qPxOwjEEFSGJvmSnxoxMaQqtRJOuwmRpxxWCLWQCEJwpdSiNazCkq7ali0JxQSkai9x+xUC6TawdgELwYFnytSSO1Rn9AGp5aWNgWmaS4AoyBWsJKQgxTmHymIvLm03DlM3seuCNTNAvGBqMKb7y2oSsPzn36SP/3CLq9+85C33nyTnf2K5164isz2OD5aULixZtYltrH9vKUvsx2Rmnp1j5PDtyjKkml5GZG9JLxIJLYNtG3ypCeCbwix5fCgpvVrZpMyaWIh0Kxb5rMbVFdaqquR6x+PfKwJ/NDBCcvDNeuF5/DBmre/fZuX3znhzXuGlfesBKJxFNriQk0wkgryRAic4IoZIThiTCGehMBTN4Q3X3uHt799i49/7AWm02lvN0/7Pu+WGJKXvw6a4yDsbBHa0W8jPb7/dMN3pGfc9Gbv8XdJu++yPA5Z1Xpb6pmEMqI6yru+fUbOODObvWwhEIlDse3AZTaYRHqLSDc/nVkj/zdarKbKa8F4TtY1O5evMtnbI9464HjZsFxDfeABQ4VS2UhRwRPVjJ3ZLjtFyayomJcz5rNdZrsTylmFKysKV1JWjsk0ucA5W1AUEwCcU6IYQusJ9RJf36V0l7n07D6Xnn6SanfOul0kc1xsCCY5CMfVGq1XLI9rltLiuxBaAWmbhFwkQoLaMqWFzhEgqqBB0dKiwaQ9ErN6TvIFGWvZhlQIK6UDD/nQpfz0McZc7W8bRdr2BMh7Tgf0c2N9x7g+4/07cnQ+tRc222mkabO/8ffjczT+ftve/zi1R57Rj9vm4mjPNOEMmD9f0Xkoh5jsRFaEvcv7Kfuac7z81a+x+tAJ1594gsl8RrNek3KUCzaa3gygW6RTM6Q+dszriIv3nljYwV54ynlIBpg+gmRPb9UUI94l2QlK0uglxXV3KJoYITu/IibH+pp02I1R8En+DhGIyTvf2i6TH1hJCWMiXWU4k23VLaIm2+eyP4JGqsKgTUPjPcKQercrIomCMclHwWvkYGHYmWb8JUaCRoys8G2Z7OlFYDqf8KGPXmFnv2UyKdi7vDu8h7ouGSK9VipDFq70zPS5sxUUCU1Zrw/R2KA0VOUOzlYIUDiLMxaDzYpDRAh0eRoEiGoxbkKDIFJgTdJ0zKTl8pOOvSsJrfGN8sInn+Cj9465/e6KV759hzfePeT2QSTGIkG2mfWIeNACJSMzsdsKlt1ZyZ07De+9d5tbt+/w7I1r3L9/PxdR6jSmlAI4QcK52p4MYZmDnVo2eGinGJ2G+bv5zASZTtvSbFfOiBLaOyj2cPwWKHAKgu2f1Z294bOHE9VB4Nj+PNmMuz4H587u3bqzntwezaAAQEbcPKjPHCiFhfrGMptMePGZG9hWWS8arly5zLTYZz7fTf4iE0c5t0x25lSTKVXpKIuCwlUUZYUtIIVYACRHUqRGNXmai7h8Nj0+CGINZWG5XM6pZcor797iraMlzcEtam0oY4bZRTGlYTIreerqCzz9jNIeL1itV6xDS1ns09Kk9MpNCzEwKQocycSX5tz0AhKdk50mZTstdbLl9xiRdghj2gcxBmJI4Z5kdKmrBDpe725/bkeAbKxgd1a3PmNj/Ub7Q/XU9d9JO8uzfvy8s35/XNpjxejPal3IRwjZzj3y+BxDiapgC8t6ucRYx+VrV3ji+nX+4d/5u7z60ssoyvUnbuBy/vRWc6IQEUIQosQeVqdz+kCyuSA5w3QbLYSADxE3rsHeaRsdJEFi8CpdjGpIB1RSLegQY3L+ygTXGcFLYvJYSQwRkj+AxEwvk/ewhAyv2i5Vbko4JASsUUwHxWZ/AQuIMQQxCeKzhpDHQBQKlxweQ0hak0G78tWIphS9hqR1uKgcrhxF4ZI9Xy3RgjGRQize1yyaBaW17F+u2N1/pidOQRuccx19GkHLXSRDmruOyIBgtMJVJWVR0jY1MXhCWBNjBbZIjL4oUnbBjA4YkyoZupyVTkRSmJEa2thigiG2kvwQQoObJkfNYuKYXam4Or3Gc9oQ7q144RtTvvr1W3z1lSOOjxp0B1JgdUSsR3SeHEmzX4ZGgxhLQYU1ntu3bvPWm2/zye95gTt3bicESnq9Gc1oj2ZUqa8C1u+iM85E9/0pwqcbBFYhaWwbHQ3wa9q2XY2BTZZ8ikiP9vV5poKzR3rONQpk5t0hY8n5bIgB79+HhD71fWnykTCdYEyqzGhxOAp2duZ88ns/xnM3nsY0gaeeucH+1SfZ3dvFTipM6ShKxRQFgUi0kSgRr9D6QOsXhPUS3zR4Hwkx0NQnyaEyR39YIMSaZg3l3g5Xn3qBZz7+I9x5sOK3f+WbvPPu6/i4oKgshbfM5peZTx12ZojzgsV8zosfucLzu9cJqwUPjh4QW4NUVdrjTQvB50Q9kqV6A2Izc1dSREAKAU44S2cKTOhhigQwWJOiRgBiPucpsoccYhxTsavxygm9GbVPeHGWPXy8G0aM9l9nG/I9DHvybN+UzbPwOLXHgtE/LAYyOZWk1BIqkgqzkGoXhxgwpnOEC6n8rBFi27KuG9qq5I/9e3+SX/y5f8Jv/cZvsHf5Mj/0hS9y7do1bt+9l+LWSbmr00ASQ8RIis/K2lAXitdt6xDJznwpxjpJytkpZuu9gHxNCtUbG+0jEPLBNJo0aEsuKauKkQA5rA+Ta0GrIKXtoVYUJOioPjQpTM/aZLNcN9iypHQFk1lFVDheLQm+TaF8Jjv0xew8qDHn+U8JfUynHSiEVimD48HSsL9TURVTVDyh9BgFaxVrKwpKHMriaJ2yHTpQo0QpiF4oi5CT3nTaUme+yHO2IY0nNdlKwY2rH0Izk0UCGpv0vSmo1ZPiqC1WLdYUWBQfW4JGxES8NpRi8VKnkyMCtRLqNRJbmpWyPrQ8oEDsDsWs4FM//HE++/0f5s2v3OZn/+YvMalLKHKOA1MRjUGkzUJR9jFxEFaG/R3D/Tu3ePlbr/GHf+L30mXuS0qS9O8bgk/fiTnDNtpvpu5AvI8WM05WoqN5VcjnZjy3p731HwqwDn2ORJFO65Mua9H48nNar7tnKDlt3DhC6fL9IgRJJYTVN0gMGITCOmazfabTPVwxxVUTiumUws5obcOVD1sKYykl0ugxD45PePfkPU4enLBaHMPykHBwn9XJA3w4IcY1Gluo18T6BNHsQ5ISPqc9aCTntjBUrsC3NX7VwqWnWX3mx3juk3+Mgzfu8Ev/1d/jzeOb7D65w8dfeJr64AGf/oEv8L1PPs/iZM0v/LOv8P/65v+b577v8/xv/6P/BS88+xTx5S/z1V//r9m59gKz3csU1S7WzVhhaSiYzCYU1qV0vm2bKj1qjUoqCBZykqsuzNcgpDoaKdmXqs2iU8zJc0w2F3RC1YDwjBdwkO9OC229tj/6+LRDaXY+7cI9e7NmPC1MbvTLKWZ+Fix/9n59vJg8POKM/mHwSoKbQMSSqrUpxID3PuVbNynWGelsNpnh++wAJxCbmvvNmt//J/4Id954m9/9ym/z//gv/0v+8B/5I3z6M9/H8ckJi+yYF2PEZoHBSKdpJAatItlJLaIxYK1NUFoHi4khhuSVuwFZke31mg5VVzAmMYmEXSf2moSZwhaUJKS6EFJaYHK+gdyfGMG3TQ9vtiJocD3yoTFL86RwxNh6aFOSF4xNzoYhEpqAREWNEK2AMYgtQFOq3A62V5vNGBicFGAC9xrh6hImU7BW8CGlmW019qlrFTDljI6nWSMUIkhUrBeCEdRIDjdLTdPCD3RDBK+rJMyZEh8VMYEUPAiIwZpkLnAZVUhwfYehJ8bpMElrzQTAxCqviYCsE7EzBUaVUgyldfgYOKmPeOfdGZcuX+LTf+oz/CU75T/9L/47ykuKm6R0uWpqVNNaaUjhfpNpRVg1zKaee4ct337tLu/efI/ZbEbTeAaoOr+3am+qSUxvmJNuXrZPylnC8ZgYGnMGEeyQoY2PNwn9w9sHJaDjvs67J2mkkJwd0z02e4qnkMiUfRJ25wXlfI/ZzlWq6R5FOcVaS9M0LBeHnKwWrI7eYnH8Ds3BMWF5G/FLtDnGrw9p1w+YFlOadY20ARugNEJhKq5Uu4itEFPi7D6OGbPdEuNmuGJCMUnfVXtzaq2p45I2rgjiaWPAmSl+ccT67W/wy//F/5Lv+xN/gf/sr/8f+fW//6v8+q99iZdffpvp9Sf5ta+8xVdfv0NloLl7xME37vP5F0vmbo+wOmb15te5evwWq8NvcNhqguCxKJbWVjC9jJ1fpdq5xnT3OrP9Xaazq1Sz6zjjiNrS1IcEX2MCWLUYLVHrU+iw97iioMpCv3pPvV7gJCsSo0Q95y+r9GindufrrFv6Ilznw+hilFG6/q3HvD8ysG2TH/xMRkLiY9QeaUYPmwRrW1JTHbJ7dYzX5XzTXRpbNOaKTIboA4mVJs/aaAAfePDWu8zmU77woz/MR7/3e/mlf/HLvPvuTT71mU+zu7ePrGu8X9C2bWLAlt4uakyWhLvNrUpZlunwaBe+pEQfEGcyBNtttky6s/SqkDa3zbbtkGHALiY1jz2Qvo8ke37KoJvT6zhDYUges5JiYNsYkaCUJjnxWcCqEnwi4EHBKwSNHK/rFB4UUlo6EaUspM8imFDTJOA0XQU+k5CLIA0mwLqNfebAYJcQSxpSyVsXFfGgVsA1mTgYiAY1yZQRbTIJGCWPIUvqmA5vRiQ5Axop6TQLl3OzO5ttnpqgSCP5rTVFDBgg5Dwixg6oUGFsyudv45CorlWihpTyFGhVCVgaA3P2UdfSnDzg299c8f0/+T38/pdf57e/+jaHR8q1J+bU9QlGplkwSj4Bvp3iygbiFKMty+OGt956h0984hM09x+kpDsMDnHJNDSYobrsjWNqlUxJ47NxPgLWn61uT3Uad6e1kTT8BCoEVDNCdCZ1HPobkvAk1GVbc+s71+Hp6Xxvp/NNfiBiU59tOzgEOleys7vLdDKjKCqMMdS+oVmfcHD7LcLqGL86wi8f0C7voO0Coy0WcFrgqhIrK3y9RoNi3S7zyy9QFs8y33+S3cvPMtt/imJeYCcz5jtXMEWFKSdItUvtpujc0oQlbbtMdekXC0ptsQ/eobnzOst7r0FzhLOGOqwIU8FUU2arA175O/8pex/9KJ/7E9f57E9+kduvf4xX37pJWK6I1RTnSqYffZo/+Ud+iBe+71PMJ0cc3XoVrQ+Y7u9Rul28z4xewfsWH2pU7+GP7uIPX2YhJStjodghVvuY6RXKnSfYv/ose5euY7RN466XRJ8VEMh5+luWiwVFtZOEKjNatjP2VArFOy0EdOaVbQfQMcze/d7lMNmOg9/2v9puD/v+tBAL48iBx8xE/6gz+jHB6jI8DQtojPTOcJqJAwyx6F1qna5yVUfY0qeSuKok+3vT1FhXcPnqVX7wiz/CS9/8Gt/6xte4fPUqT9x4mv2dHY5OjvGQ49WTFm+yp3G3sa0IGhQntvcOVg1Il+Rh4/WSE1SQVK1NiAnqJWA0l27V9I5ePJ4yxfj65EFbWkshUDmhslBIpDQ+VWKz9HkFnAEnntK6nLxD++Ix1trekz6qEFRYx0jA9xX5TCgSEQ+h9xuIGNqYPfAlOeNZjZRGcO2Mta+pY0HlEnQnPW/KDEBBYvYENxmOH2mwBtOlKiHQYKQge0KgCKqGIB2hSH1HYq6NkAR3A4ixCe0xkmBJQEgOb8G3iFiENE9pwjsNJnY7EDRnvMvCpBhDoemlJK+9bz3tOvIDX3iOt96+x83bC0KuTof6DD7nhCahRShQBGc9oW145ZXbfN/nPpUIkAxOh9pnZkyoQ/KN6Hbz+Zrx2A3qfJ05weFA79PSK89dP2J7v4DtNhDwDJAIuZZACh81Injv6awticCnPd1ZuhTBSJHNbx7VmKNrBPEttpgmRusKnCsopCC0KxYHtwntAm0WsHqAb9bE9gR8jUSPU8VhkXKCtZN0FmyJdTOktMymTzAtrlKV15heep52OsVJiavmSFFRxzV1fcKdk1v9u4rYbMYLKA3R14S6xq+WFBIJ7ZKwPqL0JYQdCAFHiahPhV60ol223HzpDcrpbcpigomOG5c97QyibRETKY3FTZXlrS+zuB0JywOCX2L3rkEbMKVmeUkoYiD4NRrXRN8kZSZ6ota0fkFsH+CP36W+U7F4d4ejK88w2b9BOd+nqGY4Y4n1AmLIyGXDcnnE5b0KExoiKfrHiGKty3sxI2LC6GAP0Q4Dzc5+JUo+f9l0pV0UTeyZ+yZal3wHzjH7b+y9M/d0Hstpk/zoHj37/ke1PeKMfmhjJ7axVNgrCv2iDk5xYy0lLX5XZCb/neLCMM7QNC0+Riam5EMfeoHl4ojbt27y4N49ovfceOppprMdVnWDV58k4LyJY974HXSeQkZG4yUxB+29zNjYhV2q3c7rPm14zczUsPaRxgaCJmhVAJuZgTMwsYnRl0YoTUrBaszwY0VxRrEmppzskurcVw5U25HNS/ARjKTSsYkFGCR6UvLOkB3LEgOg8yQXEjMk+RE0rWVRF9SzgqlxKZFQXonuvHUCtvQ5jkdrnacmfazJBtrNY7fgMV03VBRIeQdS7gGlswk4K2gIpJChjjmaRNi6wy6CkVQRLV0TupGmtSGtXVoakyp+QXKcIIXSIcrR/QUvfOg6T9zY5f6DFeu1Z1LkxEkyAOAaWsRZwFM4iKHm5ZfeQ6XLL96tcyfUpDuHRKHdZI329pbfwoYIoJuEsQ8/2ob2R7nx+zDSPm6+++40gdTR2euXUjWv15DaNl2XUjx3DnbJ5j7ck2CWbCaKRWYuioQWCTUxNDQn92lO7tGuj4jNCVV7kvdVjZPkeOpchZSXUCOpOJFJWmMZplBeYufqp9jdfZbp5Bpu5ynaHSGs16QiCwHRZP5rW58c8kTR4NH1CpomCZFBCb4ltm1Kz20LmFylKC8TWo9vmuSg51uCbwkCGj2rkyOOli3WeqZThyl3sDb2Tq6tKsuTE+4/uE8MNSKKs4bCXkJ922fsTJbDAK4GrRHTYE1DCA1Q4yJIGwl+hfoT6uUdDpb3KE+eYrJ/g+nedaaT3ZRsTFwSzkwyF/qQfVxMXlMNxJhybvT0dQN2H8w7MjqnQ1hmjoSIMWOKo5wYo720sY/P4PLnedYPcfvQhRgI2/D9xl2n+n6U22PD6Lvt8HCYZrS5tvQYheRE1zlrbRFR6wpihOVyjfcLvu+zn+X46KO8/M2v8fWvf5kHx8f84I/8OOboPsvgWSsYTYE9qk3PBNKuNgRtMGoTE4C+KE43Xt1wNgmIeiSGBJUZRW1BHS0nrXIYfCKasWVSGKbOsFMUXCqSXmhNDj0yQnSWqoPLNKIhQ+GSHAuTqSNSuILd+Q7Hx0d0UHCCsGO2idpkGlBFqBOj15RqODkIOoym/FxiUtx/xKIx0LQrjpeB1cxwbdfR5rAjOgGL7Uxr2j+/+zxl4os4lJIdgtY5R71NmcRiwAVLkEEbwFX4nPcAyXZoAlEiRQwIJckzORFeaypUApiIGIeGVLEu5jTHqpr9McwWMUpajbXFCG60LBbHPPXMVZ586jJvv3PEvXtLplcm0BWPyUwvhEBRWMQoZSUs1zUvvfQaq9Ua5wra6IkaMFYRLQix6Ut8bu7rAeEaH4s0vuFMnD4No+OyJQSMHZ1O2/g7gXvz2k5TRy02Iycxprh05yo0Sg4XTTkBjOm8whMyFTRSSJmFxhJxBVQT1O4hi9vo0W2ak7s0i7vE+hbG11gRJoXFFQVuZw8tKpQGm2IpQAK4ihDblI0yGAgW1yrYOb6ds1YLegK3X6M82cEHT+kjU7VMdnaort6gqSb4qqR1hlpb6vWS2K6IrSUG0JDWqo2eRlN9iVVbU69rmqYh+iWhbQk+1XawGrFXwMSI9y1HdU3btkyB5PgbaNqWdr2mbRtibFOCrhiIvsG5iDVVZsoRK0ssJVb2sbGFuCbGBSI1hdvHuJbKRaoCQiMcHd+lOXiV+v7rHLkdplef4caHP491U0zhKIJS7V5lGSNeWxxJiI0oTVNTlbNEyySdE5PjYDUkpDMJH5KjIOjPc6+5q5JKDA/hsts29419d4bZ9jzn7HHIZY97bdnqx308Tu0RZ/Tf2WL02r0wQPp0Sz6+bki12XnUpxS6CiZgdMat996lnJR89FOf4LmPfA+/9t//C3755/8+n/7cF9jZu4JbNyyWRwge0bTZRcA6JcYaK+WIOA/QZqK4WeLMmzWqRxCsGEK+JkSl9UoTki3eScnuJDBxwsTC1AZKSaaD5CsnfcnJnqxLp9UbsjGOjlfE6KlmFfP5De7evctqtcox3OleY7P9P+ZEGyRt3eR82kEVi5ArySQ/AQMWS1HAypcsmgJiinHWaMBGTvGq3LYPozN9ID3ruEC0wHZh1d6gsqJRAbV5LiPTIoX1mTzfUVOGM68G9YZAS6TBa820nPbzj8bkVS3lOFyYbv8lLTsRPMnav3GDQ5wxCf5vdYVzjudfvM5b7xzw3nuHGLPTx6Z3ezN50affi9LgvGFxr+GVb77Bxz/2CYg1q/UCJGClwrkJZFh7a9ZOT+SZmfPGCIDQedMzIqwfjBCeZwRI3yUd3mGkQE1BjC0+rDPxN1gc1lRE1jQ+9PULHBbPHNweTmukvo+/8yrNwTss6gUmLrHUlAaKagd2J6jWFAhOCrwo09jgY0m58yTl/BpFMce2hku7T1DMr2N3b2CvPs3eUx9iGY9olsdYaykmc+5E5R5rlssVx8dHnJwcs17c5fgbL3F8cEhTr5MHfb2kPjkmrJasmwXe10mgaTyxbQgxRaegSpFZnZEOyhFc4boNkwo12ZxsyhpcBGddTudscVIQfFYeJOFaIlOaVcBISKYy46jME1SlY1ZBNbWUrsCZAvUnHBy+B9okJGS9pm2PEoIRb2BDQP2a5tYbvHpyyLVnPsaNJ5+nrBxliDiZIVLhCaCeQqY4Z/C+yZk0Lc655IsUYtb0z98dIoITmxC3zicp0xIlI3tiNiH1M5j8uL9zY+Y5XcXuX3dI3//Y2iPO6EeL9z4Lta0dJhtnJmQybJixzd9aQ4xt+g6DoBQoDZ5qNqetG5pmjSkKfuzHf5xv/O5X+c1f/3WuXn2C51/4ELPJnKOT45x9Lx3qtomU5YSoLWiRbU2abZNj5EH7fxSHFYORpDX7qOA9U+vZLyPTumC/hFlB0t6NEiWyFCgk1a63EjExYGMguiIJDsZhTXqvRNhTJIJoquP+9ttvY0xKIpTmIpXSMQhtaAg+EkNXkc9le34qHqMqyaYvBo2SNOfoETfHukBNyaKdUtcGY3y2ZWenqtiVDR2gXtngsClPuKhBo01FLJyS8AQPsmZnssPOfCdlvyNSe8/dgwc8OD4BUqbEqiyZz6dMpzN2i4rSJUg7tIYHR0cs6wXOVBhToKKoWGzsqhim/WKMpEpmud5AVznP2oLWt4mgZBi6LEtOThZcvjrjxpN7lEXZ77tUZjP1q1HS+/VOR1BZ5Td+9et87KPfk5wB8zt0kt8QszzYy3VsX++0e+0cQPuTQYL2E9qUxTj6ML4t2P98TacTEk63bl9bA1FbYsKxM4Ii2ewRwQSUJbGFylUgqWiTEtHlTXjwJerVLTQcYUyLE4crM2ORCUYstjS4YgbmRcRewRRXqOZXiNNL7O09SZAWbxSdXiXu7PLusbJeHbI+PmZ19zdZfOkf0Rw/YHm04uR4yfHJAn98hG0PWC4foD5gIkS/wvoV6lMCrYTCKUQojCXkNNiDQUVziG+aE59ny3aZahGajBZpSCuQhHODMYZQSBJYRRAVrC0gO2Ia6xBnkyBQ7tJqKp2LFBybEuMKTGkoJhOKKqV5LqqK/eufxOoaE9dYVabVU/jmiOXJu/j1CjXgpitWh+/xzdtf4d6zd7j2xA2mcyinwrRweHWEHFpbOEsQofU+l6EtKQpHHdYYKxhTAC5nE/Wbu6f3z9ikxX352ZhpwBgByHtuyOE/ODcPe5KENG4hA9uI1Hmx9Y9Le+QZ/XY7PzHC2N6TtJZN5t8lptkkcP0GzP2JsZjgaYNBbImElrZZ4r3hhQ99jOlkzuHRMa+/8Srz2S5PPfM0q9UyQWuSssp5n+JQVQYLcudMKChdspfkBNc5fWUiLIqJBVCzOwlcnSuzpVBS0yWmkLz5+1rP0hGYhAiIdmkluxAkzUUxOhuZ5kxYo9C1zpnQCG3T5oOXhOwYAq402AjRQEiZNOgSmmintBiIoaXVGm/nrL2l8RXTqsHaFF6X1m24r8vIlj7v1kpQidlfwYFExNcU4ijKAjOpOK7h9TfvsDw+YXmyxjdw9dKTTCfXCW1kHVoOmgX3D2+y8kvmVijmFdXujEuX5zx3eZcXnniCetWyWHoW64idZtu4dFW6k9uV5EQkyRM9jb1LlNQ5+OUbqWvPfF6xvz+jKA1N0+Dc4B3cedHHCGJcymxoApOJ8NorN2l9i3OCdSZpdNpircPHnBhmzNRHa7cpKI3bsAPJVn+DECU7cOhmDYHN/fDBCWJKBmSh61aVxO5MhneTwBQ0JjhfBN+uCM0C1g+Iy1tIcwDtEhNTCWlrI5Pph7HTJ6HcJbiCxjgas0+0l4i2SOYc33BwuMbceYnFrW+yvv8uIjPkiac4XBjC6j66PibUDev2GGlW0Db4pqFtaorYIqFBYpMcb4JCjARCsitn+tBZGyJJYsu+m9k3pT8O/awrdP6+KLlbIZumlK7YnCJInc6CZKAlRpNCKru8HTk01rlcajrb022RGaFxiLXJCddCLHY4KHYxtFirlOWE2e4DpKoQa7G7V7CmpPUN1fQJzPKExdqzePsEK4FLtuESSWnxUYl5vsQaXPbS9z7597iiSO/XCckj4XFzbyUBt9PmRUZV7s5oQ6THCBXN/aTvu713eu8O+zaHpgrE8MGUxkexPQaMfoA8YdC00u8bllN0dEfnete1GGOGpmWLiHVqdU4/KyQnrXx6JcdY+zawszvnyrXriCu4/+CABwd3mc4qprM5XRrbNFZNUC+bSNRm287rnTRTJICURGBSRvamgYmR9DkFXQRBl4K2A6kk+wbIVgGKLsxM+mfmo9MlEgqhP0GdxmnyPKRkOAm+F7Upvl+TSKKEHpQY3kiIocU68BqpvWHZTtiZLTA9gJEP/geykSmoRyQizmJcyaIJ3L93wM0797l5a8n6eE1TB4yboh96kstXdghe8c2ak+Oam++uOTq+j7SLFN1QlUz2Jjz75D4ff/E6T17bZzIpmZlAE1boRlIaSb4Jks0iDMQnCYZjR7V0n/cwmU/Z2Z0xmVrW65rd3Xn2MO5MK4bgYyZyCVmazoT7d45ZLGpm8zKZTlpFabMvyKbQenqe3n82pdOIOhQl66N9L+c6OeWrz4T3x0RVNoYiogmByVkeTJ+5LRDXx8TVA3T1ANYPMO0xxjrs5AlccRlXXCFMZ7TuOgud0ahlHZTFKrCuGzTeIfia0KyJ7Yojv4TFA9Z3XqM5uAXBYC5dZ92C8ckbXyOoDZg2YGJLwogCVsCpBQloTiWdhM0Up55KZA8OtilxlRn2QhZYpX/njgalkFIhnasQY54KGTLTaUrPrWOHVQXfxJTsBp/L7SZXj9Cm58VsD/eSK8i7LklWCh7GzqijYG3AFAZbVhSHx5jpDq6qKKopRTHFmRLn5rjJZQgtXhu8Bg7WcPP+CWXZUhYF09L2IbuahXwZcPd+G8Yc/N4hdtu0uvfnGO2hsQZ/eg/2u5PzNvnAG84QTrPykD76IMjVo9kecUZ/enHP2ij91b3krfQVl7SLt484NyqgcKazUYLEEYuzJmWTMuDKKVY8q+UJGMuVa08wn+/xrZe+zpvffoMPf8/HKMuqdzQxuVxlTxw7LyYlp53cHLtqSOCspMOtFloVSufZqRomVlmJUIlL9ngSxN5lqssAVx+QhgxhK53Xa/Loz9pVhiJNHlu6Z5jDVO8939/ZckMiYqLDD6R0uJ2ztpDs+rNJxUkN6xaO6ylP2SJ5mmexZFwX4HTLTKWD6GKDSMBO5izV8ebdI77y5Ve4//ZrxCOLU4udTCj34c79+xzFxDy1bWhPjmnXAV1EFsdLTg5XnCw8J1755xV85NNP8Qd/76f5zEef4salinB3lfztRxpI2kdZLZMs+OUkSUNCECVB1Y4YoCyn7O7O2dub8PaDI3Z3d5NmllU4ESGEiLWOlIJUmE0M9+813L9/TFHtY62hbdM6h9CmcqmdoLZl5pB+3rp/zyKK4xCmITzrtLa0tRob5+t09rEBHUtTtJkoSjLhj8mvIaVAJNT3aB+8k5h8u0RiwLodZPIEbvcjmNmHCZPnOZrscHh8l4MHt1gcH7E6WaR/j19DVu8hJ8fIuknoGR7VZYoQCRGix9xucFLjbMLKAxbrXPIXMYmpk5NbzUxFJT6BXMYgYrHG5TPrEQkUTlIqWFGc7UrHdnPa8ZSE2OUch1hJtdJjCDlTZwrVjNmx1YrBe0/ruxwR6Uw2bUMMig/J2z1mRh811YxPeTbAt+mWwgY6G3eMhjK2yRnQKdJCXJ8QjxdEYxEnGOso3JSd3avY6WXms8tM57tMyh1803K89iwXJ1TumP2dCU89eYX5JJmitG0ATYoTJu1lVySlIviexp7n97FNw7s5TLt3zOyHfx+mhW8IE7oZu6+5+mVCNt2p/fu4tMeA0Z/9WbfunWNd5ymdP83npdOy44Y9aNMmma4nnV8MhrYytG3AZQG8lUArApKr4jUrBPj+z32RN998k1dffZm9/V2uXrnKfL6XUu3SZkLqtrTX7DswknC7YICoJl/fUgdDaT17xZr9ylOHKrPxcR6AHINOhynmObC5Ml6GCY1JCXGcJm3FiKQ87k3KLZ88zQN13VJVE+qmTd1Yi7WO1vg+YCsJC4ndWQSvyekoZMfGqrDMJnPqsKINcLCcJK0oLkCS3XFsOhlsbkMolohkDQdsCSqWFs9Xv/wW3/jau9x65yaVgI2eplA0nrB8cML929+mrRvUa292EOdAlLYukUqZT2DWCif3ldd/7Q4v/8Y/5XNfeIE/82d/iI9ev869Bwdo1koSTN8BszkyQXKMOJYmxN7hUlQwVDSxxkjF3t4+Tz17lZe/dYsUTpTXJsVj5oqICeYwJsUpr1cNr77yLpeu7HBpf06zOsFaR5d3YNAVt4nUWZ+d9Z2y6dAndA6PIg+HNM9y2NsWKBSfai8AGg1t6xCZUJYlIp7WL1ic3IWDN1icCLZ4kmrvOez+RzmeP8eJmbOoTzh6cIs7b/8qt176Teq7rxCP7mL8msIp1QSmDiYu266NpIgVdVgXiVZRm9jsvHS07RJnlUIMTiyxLAhToSgmODPHmBlBTqicxZpZjrJIjMXm8xbCmqAeDNTR46PS5FdXTTUhog8p37x26GDM+RuaVCMipvDOzhemqz9hDbStMi0LouZ6EtGjxiekIMncKZpdQMVvmgJC9vEoyrTPNNV3b0LETCsa3+JjRt9CC35NCocX6njE6uA+zihHVQVlhS1n7MwuYa+8yO7sGrFdc/fugpv33+DalT0+8uKzFMWUGDxN01IWyXwFKd14l+hJczhef55VN5Qw6ePqY8+cO/jzLDNS/4mcofVv7MhtnIp03nzAFO6xhO3hkWf0H0zq6uLQO6m6C3fKwHWyB5kuN9nQdY/m5JOjknLRuABqDDEL7DYmIaEV8LFz2VMOjh5w/fpVrl27wns33+Odt99hd/+AF154gbpOknnMUjyQtLpRWB15rAkMz57dMaJmheiUWSFoGbk2bbl/MqG1NbZjimpTekrNoURGszYSIRZAYk0+RAoxGCupWEUUbOGYzec4o7RtS1OvUVWm0wkhJNSjsElS9yEgE0vwqVhPNClcD4UYtK+y1yXSKWcVJ8cn+OAxGBbNhPW6oTApRFmJSUO3W2VnGZiJKqiNRAzRz5lOlIPbNbdeu8XRzZuU4jGieFXiusZaQ1EUOGeZlHMEeg0qaHqHVldILRAE74VFDFzeSxD6nVfe4h/+rQV/4T/+n4GxfaWv3pegg/60s9Eno18SSLQDIfARNCb7+3w+45lnbuDbrye7ZL+fE7NP8qVibLo5tp79S8IrL73Dxz/5Ateu7adQSFPQIQpJ2xnnBT3Lw/789nAi1zH9LkFPCsVMwrHNPh2nemRTGHfZByZlVJztGMrScXJ0xMnxCavFGg2GNX+A9bzksG45uHnC4Uu/wvLO30IPbsPyAcYvsXjsMjJRZTKFyQ5UBclLv5iAUYyJ6Uds3gMVYork+2BT5IiYfWwxQ4xFYyryEmvJues9MR4StebevZYg7yam0Ap1E2izsBtDquBmFTSAM65n3or0/pGDGQ08kaBQdnvJJCGx8bGfsS51t3MWwmG2xdPPaeKJBpNNiQJILlTV1Zo3FlwBaKRwBmcNpXNMtKLVyI7ZTfsvKFagbVvWraftBIrQ0qgSFg1mvUDsIfW9O+i7r3E426Pcf4Jq/wmms+u8d/eEm7d/mw8//yQ3rl1lNp1weHjIdFoRgk/7RGz2Y0h+I8ZY+sx5W05yXWheunpTiD0Vckd/zBhmiI3vNgXasSLx+Gnw2+2RZvTjtdm2x3ctxgCZkXdxmTEGcuklRMjOT1l7kqE/zfH0Jmv0MSnDiNiUOpWkfUZImpVXooTkSCMRYwyreoURw+XLl5nP5xwfH/L1r36N555/ltnODphUWa8wkuA3sncug309IJgoFKp4CyZOaGlpMVjnuV6u+DZzHCFnqrIp2Y/11GJwJhOZoKhanADGZEeirJH6wKQq2ZlXWAur9QmmmgNgrCWGlOa2T6aSy7eKkAzPQXGS87dLhmPJaIR0KAU0BiZOmduCNhiO2sDa7zObB7xXNEoOISKZaxNJSBqxJIaY1qgAlMAJE7ePTmoaLG20uNgQjGLalFQoihCiIib2BDiGCDERk9iA8dk0gxIkeUOrRqrKYK2yXK45OWgwtsyVBDVrTR5UMuw8kKNI9vCRAsSmOHwBUUPjBTEFly7tYh2E2JJK1mYhDdKqqwW1EJUQG67sznjz9be5f/8+z794FWdKRAxBfDbXbBOr7Lmc4fxOoOx0//5qjdlhaZN4jg9ZJAya1amnDNeoKs5VEPNc5JDDECJWAtPKYbKT3P3Fmve+fZe6sRwfNpwcNsQalke/yuLkPdr1Em1bbIzYdo3oCpE2lTy2ltnlkokVysmUohKKQol2ym4Bxk0IkrI7Skzv52yRBLvo8b6hWa4RY4nhKJ11bSEqPqTQyy4dcor9l2SSocS4it1JkZ3bbO8Zn/ZtNp9kgSJl3DPpTOYMj0YE4yxN2ySnwi4RTVQ0hB5d1Jhgq/V6hSM758aQ0AHRXB0vpBj8GBCFtjlOQqxPZkCNycHPGI+RkENsBS3XlIVgbXbaFUNpC0w5YVJWVJoEc/WCRk8IDT626fwbRUPDenmfuj1icfAmzu2wc+0Fdnev8+13D3lwsubZpy5xeT5h1awpbYUTAzkZV4r2cUkQCoGobZ5hM4LYMy03dmRDN9m9tRM6Rw68eb9qB8WbHCYdfS94J2E4C9QKgsUak3xkOsdZ7WL5H5/2SDN6OC3ZjT8fkiOMvs9OKtpJdenbXlDuIcoOSc39DaA/wye9g0j6xhpyilUlaobgTBIGjLVUk0lysIqR+/fu4X1gf/8Ss9mM1WpBB1dbTO43JgedDN+bzh6IQzWmIi0Erswi9n6Gk7uI9r44Q4Yi8ssZHUILJc9Hd581GYzUSAyB1jdoZmqdu6/IIGOTiWFnJBSjfWxw8twe5sxkG3bdrAjGUbjkxNh6ZdFUXJmTiabFiiQUom/JrVAxA8yiCTYwKARD6QoKWyZCkfP7t4k9YaLBhJD9CegtGSEobQh4hSa5O6MkbStmvmcduFKxRQ6TkvGcJX+Fpg64suqJjeZ3NSRThhiLiEse57bLIGaZzacUhSUEj3M6ECGTRLzkr5HIX1SlLC337x/z4P4By8VqqJlgOifKbUbcaT+bGotu/TsYRHXruuH8pCRG9ES4Ry9IaqzBopLqIaR0qTk3eWb4VVmiUVg2nmXtOVp6bt5ZcOfWIeo968UJq+MjQt0Q1oc067tIbLCS0DeL4IqAKwomkx12d64x2buMNAepxLINYBuClJi4IGqd8rz7huhjgrDFJtYQQx/eZZ3LmRUFkQkyFSpTYm2ZI1ZS/nxjimxls4gUqVyypvtTaKXps16qZvt0lwWzM0dZyeiiYKwhBJ/F2DS/GjP8rtrH2xtjWC6XFGWRBPOQzqZIgpu7Ij6dAB7rVUKqfMD7gG88rU+16VMt+XR90za0IdLmoFQRobUBW3RjNwnltBbnCkwsMfneSEDwBG2IbU1oa6KpWboSGwVTViyNcv/eCXvVJCGLucx2EEvEY11JJpU5U6WCkR7dGmdQlKxwpa2Z/Cs6k1JPuzMy26Fipt/zKUVm5+k/7PSOPo4PAj1NlDMRqke3PfKM/nQbAzh5scX0SUmSMpvTjnaQa3eHjgWGrF1lTX7c3zg9Z08Ic/J0Y7MJIDN5SM48IQSMCHv7e0wmBW+9+QbHh8c445hUFeJcysam5NSrQhAIGnKu9rRJY4w4SQzAe6WwgUvzVLClxW6MD8nV6zphRzpEArrwlZSRT3vNpPVddTdD2zY5uUzqb8PJS5MjWJ86NsYkrWNSYRxjUsW+PGfJPqes2zWNmSOiBE2xBCfrgqAg4hKsivTuBIm3mmxqGWudJmvSlsYLpSspC4ezhjZr561Jmf+IKXRGchxUQmTAe6VpY+/c2KVzDXFAVSKKsVBNLNWkwK+aRKzpkFRHrQFjKsS6lGY3KFba7PvUwY855j4zAmMMs9mMalKk/AI5vz50RCY5LopJ6wKCsYL3LffvH3J4cMJT129weNBkR7zt+PjR/h/9u7nhGRG5QSAe567v6txbkxw1Qwx9OJkYQb3mPZCdASEZhy392oMhKixWkbuHC+4eLLl7b8H92yc0Dw6R9ojYHuKbI7xfUohjYjzFRHAuPbewhrIqKIs5u/MnuHL5ecylKyzvv4a0LSGus0e4Twls6iWh9USv2X8kFS5KdnsH4pjO5hTFDGtLjBSIOMwMynIX52bJl8VEjCkxMoEqmRxMRl5iiIMWTzrjHf0obBKEvfcYk8xi2mmVmYkUziWmHAMhRqwzSIKS+kgKaw0TUeykGvw28o7q1rBHVGLEksxFIXh86/F1ChGMwecMfC2h9ayXa9q2TsJGSEJA2wZ822JcQk3ECOoMFDuImWBNicUQok81GPyaEJqEPoihXtxDgrK7/wTBGI4frFjse/Z3CvBJQIgmpc9FQsqYySAcd4JulBxmqKTKl9m8OSBQKQtBLyLlc5OQ2uxjlelNMn1qv0ZdvgllSBit0pkMMn2HjTPwOLRHntGfZ1ccbDdmpIQlLtcJhkZGXtFZe++cv7pFz7J2JoC5D7rvxlBrZzOTlFY2O/Z5H5LTjEmOMKvVmqos+d5PfYa333iTN974Nm+8/Sa///f9GEcHD6ibNRoCAqncqbNobAg59MqKozAGiS4zI2F3UrM3Ue7WZbLNEYgCQW2yARKySJLGLNnnoA9dAcQaVAXfeiBgXcIVhhrfyeNfNEvD2knQOXyHjkkkadpJyuUSM3QrpBr3xgh1DU1sEas4gcP1lBDLrFvk42tszijXadEds8+QZMcMFU7QVA60lFSfNzikTYzWtEnQiUA0KT1vjJHWK8GnsKVCDC4kuC+k2jxYsdTrhFRMK8dsMmNSTVjVHQaUQUM1uMqi4vq9YKxgpcxzOwho3d7x3lNVFft7++zv79CsF6h2GmGgK+oRVXFZUxRsLvSi3L19wP17R3z0Iy9y794hFV1R4NOtZ/T576gDOtMrNdv2ziwEukx4k7lDyVmf+utjSGYqn5EWozEJljqlrVvsBCazAnFTvv7NV3njVmRxVNMcL/AnR7B4j/LkDqGuExrkBDtJTpSzqTB1BZNiwqycM5kpdjJBissYd4nGOE5ufo2Du2/QHLdJexdP9FCYCUYbJsWEyWSHam9OOZlTVTOq2QxXFUQjVLMZ4sokXGZ0QmODK/aIXtGwxoYFdbOkKgUJgaiBJkaIkaIoaOo2RQ+EyGKxYFJVqQRuz4QEUxQp2iCE3pRhjEne7doSQpuqQboilbIOg9Dmo6Tc9Ms2KysptlwzTQsx9uvovWcyq7IAbCjKkmIyYSpdaF0vO1OKZbla4usW37b4pmF5fMzy5AG+XeTQRI+u/n/s/XesbdmW3of9Zlpr7XzSPTdX3boVXr1Q771+sXMSm2w2KYrBBAhDFiSZATZM+w8LsCjbsA1LAP+wLECwnAiJNmGAFG2RotjsZqubneOL/UK/ynVzOnnHFWbyH3Otffa9VY+SAANmFbgKVbfOvvvssNZcc4zxjW98X4NTFpkblOmhdA+d9Qleo3VGbgIxWmpfY5QGamYnD2iqGVpf4Xi2ZGu4jxCrRF1RqfCKwRKDTF4FQmC9p3GWzOSpTdcahYTocD6gddIB6GC2ECXRd4ZIao2SbB6hLdzSGGS7R9MhZ7HtAbYt2o770+6L8V8G+n/xju/Xnz/PfVMA6irTTnVsPcT1fSqh8NSQe3eLpEPKzpEube4hRHyIqWfmk+99JKbsdD19lSrf+WKJVIKbr77Mleeuceftd/kv/v5/zo/82I8yHPQJ3lPWFavQoFwgtElFEO1rtw5qUhqklhS6ZH8YOGnaMSJaeFiZNOrWQv8CQUd4Wfdru2peCFzwaQZYrHPd9PwW0hCx6+2GpKAnEhu8CaGdIY5raFuqxPsTEmSIaCXoFTkYwWyqcDSkGerIcamxYYQxNm24ItnIdh8xVfSygyIQQqJCItwJlVzeLu9c4MaVS5zeP+T+0REq1wkG7KXqQCHIdYaWuk3EIsFHGuep6xoH2ACNg9qzNr9xITIKBb3+LkqMse5JciWUGt2+t1SSGNK8tRKttG5I10ypTUJh2uC7yq/oFezubXH31glFkfE0jpiIl6nCT0JLUSR1ufl0xfR0uWb0+0Db7mgr6vdJ4b7vhnnfWj//q/O/8z4piunMJN70+nVbroZzGCFQIiZo2AcCHu+nbO9us6xrbt8+5N2354iwhWGOOX2CO3lMXDwGXRILcBpUgEJEhgZ6wzGjrd00TREijsCZL1k8fkC1vENTJl320ICZRPrFFtnoAoP+iHG/TzbKMb2ILsbobCuJyEidhFyCJwZL8BaiQ/mIjJ4okiGT8DWVe4g2OcYYjOkzGe3iWsfIGHw7EpjuIT3skJjI1mSEkUmp0ZMS406EC3rQjnKlCjMFZi00MeTrsdrEIerOs4AoqGQAo54iqq0vRTgPTqaXEyzr30+Kcp6IxzqX1PpaXwVb9PEEKDT5oMdQG7blVYKzuKaiqUuaaoWbV9R1TbWc0SxLLBV6sAQlCVITpUGbjLHuEysIeUbPSIKInM4e05/0COwRWy8PEZIMdsARXdK0F0IgXJewCFQM6QvGlCbX3uLaVFZpjRI5LkYIqTAIsduvNvdrsRb9iuceVOvTmtwCu10uJUGSuObfCG//+ffQh+z4CAT6D4Dq2yPGmPpM3nIOTXbVbCvM0epPq04HXpy/7np7btPnSFLvOof1RVt9JdioaSzOp5tMKY21CZJt0fE22iuia5JBznyBMYqXP/kxhsM+9957j8H2iPH2FqPBAF9W2Ha/l7IlBYVkC0lwuOiTLK50jNUcGXNCK5DRzXpHOuC4E55ozwvnlZkgoa0iBEyrtpXidqpwuxxHyrRcvHdrlEQIgY6BboQxFYotEU9GdBR4QClBP9fMypIY0uYoW6Kgj5p5mbOlIlqnGzedr64VsZFgKY0xWTIZkgoHuKbm4bzE5zn90Yi+yYlIpI3ERbLzNUayvT1GSk2QiQAFUNuG0EsMaJG8e/FRsKxqGldjXcnutuHC3ojBuGBS9QGBC4HGOSrbEBEJhhVJvUBJhWqRgzXlum06CqHoRtikkgwnBb5dG13CFWJiI/t4Tn6UQtM0C0ajjMVsweGTU5wPifQp9Pmt8MGF/VP3x9Pa+k8LRG0G+tBWSKKFP9clTwzgA6Kt4AuToZRBKBDakQ1GPH6y4s6dGccHK6rKcnr6ddTJlCgWhFgRTUPw0FeK7YFJCJLS6MzQuAWH0wPqMlCWnrJO50HnCpP16W0NGPS3yQY5w51tMpOnnq82FAjqkGBhISTCV4iGhERk2bkcdaYJSKRO504BRgi87q+bNj5arKto7JRMaLIi+cErmQMR52xK5NqEK486za/HkJCYmIKudxatE5oVkWvjKCFS0Eucm9gmVjFVrp0sN9DTWaej0xYUvs3BU5KX1kjEGEmdt86VrZBXW9YmVcJgCD6kVoEEYXRL8EsVvY9pP1NC0+uN6Q/GxN1AtBFXVbi6oqlWLBdTympOjA3ahFbfQ5L1C5zOGfTGeB9ZlAvKxRl5oaitSbbPvkIgW0JtQmK6+0NLlSSFW0JxJKKkwsgWYfXJPVIaj/Qh6RxsVGobpRupoEsuoue6HF3R0rY3Rbu5desasbH+N5OGD//xIQ/0XWR+endba09vkDM2pTzPe+7t73eQ1jrQi3V2uX4aCbYW6wy93Rjbdw8x4n0KjB1UnWDSFpLdFAqRCuc8SohU7QODrREqV9imppovcGXJeDRMpLaQgqsPDtkyVek80aMiCsVu31FoR+3TF+kWdwgeIc91ANcOUh1sK8X6syudYLUkL5rgSNH6SW8mBs8mVk/dSDGxvKUUadyog89CoKpriEm6dF2lx4DzgVmdMx4k/7n0/SSIjugkWuGdQF1ZVssKax2Nd0nXe9kQ8wzdSD5542N8/sqrTLb32OqNMbqHNAZlNLrIUXmWyuKWweytpalXzOo5ZbWkLJeslgvKcsWqKilticokvcGIr//hW9Shpt/v0S9yCmMY5HlbRbcoQQh4qRKpqjvNLUoeO91uzq9Rnpt1wzW1KdadbkTripfGtMB5T5HnVGXNyfGM6XxJVmTg/PmSfWrdbhKUnr5m3X62ZiDzLDKWXiy9ZmyXW8vrUBkySxtx2VRMfcA3Na6x2GqJNie8+85DpscVrg4oPHF5igtThHQoLcmLIYqMUc8QQ0Nd16zKCuctiyrpTCiVoc2AUTFAGUXeLzB5hsky8iyjl+W46HBuhcChVZ9IShqMytoRTYHppfOtdceQb/vkMa6FhogJlTDRooMmNmBUj15vGzMuyPIhVjh8dOvWhg8ORFxP83S3hg8BEX07eRDb+4j1/RFjK3L0TJJlbUP3MiH6dUKodRLUEbJVnvRt6t61UEIa79M62f90HJ/YJgPJR6IrkmMaB24/a4hpEkXKiIkiiQW1Ey4CsBHQAj3soQY9jJ9gJjtse4+tFpTLOc7WeB+IKmCUJM8z6rIhWMfVi9daqWaFwENMXAWiWLf+YkzrPo0JRiKW0NljKwEyfb80ouuTLXCISQBMyPPxZNppiY31S4Qowvk9GGHNNVrfoKzbjkKsb1Y+SseHPNC//+iqkvNgnyBV3/qLK9VVup1oimirrsD6Lvt+Hc92T/wgXlOM6+S5fQXWUOpGFx/oxvkCkP50LjnHbe3uUs3nrBZzqtUKrRUma/uHIQldIFvymzj/HDZKtvqevgmpp7eWq0lCNbL7Rm1vuyOFifazpeAciO0UfpKhlS3s2J6fDSJeR1iJXVARqS/duU4l2AxUEC0jPN2YZW0xWYZU6cYltvoAIjKvDS5osnbPlVKDUMk9sIXyYvSUZbL4rOoa51ISI8tAdJJRf8Le3jYXJ7uM9y5yYesSeT5EmRTcHYGoU9Uegyf65BVelwuOlwcsl1PK+ZTl/IzoLKuypLKW2jma4Llz75iiL1nMK3pFzmjQo8gNeZFhtE6jSu0+EXwCG7v+XxdOO12EbqPWOq1J0ULv5wuvIzDSQphJB76vFQtbM5suODo6Y29vi2q2IHakptglCZutqrQpbi7kp9n5m1XMhrrduoJPnAWp23EoIbABVnXD4ekZy9pSlw122WCXNb5ZUZUWLQpMCFSrM2LTELVAqRyTZfTzHqJFx8q6YrX0lKVLyZIcYnJFr9+nNxiS9wZkeY7KDUqbZJAiIMMgZZJglQJE9CiTY3JDpjO0EMk0Jm/H39rkSraJeOf5oNpA4WOSvh33LpKrIf28z2AwpDfYQvaGTFfHlNUU56rUggmWtVZBi26JtqUXnX26uGiRw25nsbZGSZX67+1+ZV3Tdqdim9j7NfrWuDolc+3fxa5L1t7LSdlNYKJrkbVOkTKtqVS5twEt0ibhbcAUAhUSByaENArZRUbh0+8Ko0nTD5J+b0ihDKGpWExPWc6nOLvCBUFfSER0mEyws7PNjes3iQS0Sj4ZURuE6HQ82qAs0kRNW5rQqdUR074SWyRjXXi3mv0iJC7CeaEhEZ3omQRiCm9BpO9JTNM/aX/ZuFfo9EzEWsdE6H85XvcvzPF9eHjrQ5CqKGUkdZXg+3TjJcelBOOlBdYJm2z2ej6gwCHd2Jsyiu1ISGx7RWtiU7JmdN6vA2IXIDcr7oTfKXxtKRcL+oMBRa/Hcj7j6PCAwWTIoDdEyQ7es8g22Jv2xl85GBvLII+snMC59B5CBNoEna43L9r5800xmoQ8iDRDHiO+0+FvN6ooWsHO9nt0Mp3dOfJt1ejXCEeXAbVKezGxrm2AXGtwdSIrRknUBiUjs0bTeEUvxhYK1QQfqaoa2zSppxpC298WaDWkV+QMB4YChRxPcBoqPO/GIxbTY8TiLsYphG9vagGrskyOe0IgCbi6Ivia5eIIJUJKdZylX2TYusZZQGiMydnd3mNbwP3HD7lfHWFFRA4ydvb3uHr1EpNhn0JrhHes5nW7ZLqgTruxd94B7dohpEpTnK+Hzo1LtNeQ2PY169hK5UaWyyUPHj7h2vULlLNWJ/8p2DKd/25UNHBOBkxtl81AL9ms5tfM7m5nFRJtMlSexI2m8wXHp2fcf/iQ+w8eUU/nuKpCekkht5ieTvmhH/txdnYvcDo94NvfuYWzEWX69LMcnefoTLGcHXFyNKUuIUqDKUb0t/tcvnyNotdvJ1gCNlik7CXJaRWQ2mB0jvcZl6/d4MLeVerylJOjt+kPB+3om0DFpFXvtMHoNLapWjdGQTrPRkuM0mhjUJkhBsn1l3+ES9c/jjY5db1EKJVy3eP79BfHxKZESkldVygt2sCbvAlSmy0RxaD1YxckUR3OlRKtbdLJdjZVua0AUWLvu7XWQxKWEjS2wXmbEoCWAxS87XC09v6MhKhwLnnUJyGwNNrmnEtyuTHpikQn1yimQGw4wIakXyESFF6EjKa2xNiJACXipFWGwdaIYjJhXK6YHT5iOl2ghKGqVmzvbnPjpZfZu3SB6XROsE3LKTIo1W9Ful3aH2TSi4jOEUI71dKNmNJW410CHSM+pmkHG8r0nUW3fmUL1XdjjC1Hot071l84RpAJsTwv6zUxKpxNPAYr/xuCy4fs+FAH+u7YhMC6n8+rlkQUq6tV6m0BWT5MAcqHtqediF8bAxdPvz4bAWwtpHAOY58b1qQqPW3Yqt0ANqBRBC2ggDEaVOpfEwKZNAhlqOsGCOT9ghdeepHb77zL/GzKaLTNcDiibppWzEWtKwEnDH3pGfUl0yoJskQPQrcOb7Ld3GP6DM4nlSxooTyZVOPAJbW74GnwyK49sP4K3U3RVgkhqco1PpFqfEuoScnB09dHKAlo5uWSJCScWiiN9eQicrx0uJigVh8C8+UCWzvoZneDxzY1ZVklMp214BqehIpcGyQDMiUZm5zLoy3++POf4Or2TfT1q+jxGN0vEP0ealCspWUkAWzDwcP7PHn3q5yeHTJdTjleTHlydoxdLgkuQc1oxcN7b/Omjgx6I6LUrOaWu9+9x3t3/hnD7QH7l7d5/sVrfP6Ln+XVa5dp6oaqLLE2CY10pkmsk8FI01RtoG+DcIsybSKHUgqUMchS4RqbRggby6337vETP/F5gnPp/D4VvJ9awOs/1hAuSTXtg5/cVogyGfBkgwHTVcVXvvk13vzeW5w8OaGe1SgbUVFgMQglyPMcsbVFNfH88re/zmSwhwiW6cmKerrAxUCRCUY9w3iYU/QUxWiXizf26Y+2KPoDVGZorGdVVeig0UohRc5gUNAvemitMUZhckWDwxlLvnuFfryAzBzeLcmxRJOR6YJc5Ugp0CapI0qlEypgdGpTSYnUEiUMMha4DE6qh5zdekyIHmstxgeUD2ijkDpHyBwZHTJLyIluJ1YIEWM0xhjArKvzsiyJMiVdvusTtwBLyLJUbbbXnhgJ1rYEwqQ+GUJgKGS7jpJYTnQ2jce5lFAIAc6mfrf3Ht8mBcG386UtByC0yXLpV4RgE+fHQ/QR5wKopJHhg0+/Hx1ZJgk4MqEAkzT1LaymS7Isp9/vs/Xqx3jy8BFhVtEbjyHPePv+fd6881aS716mhAYl0GaAzAqcb5KQlZBoleGbkpPjx+j+mKw3QitNXS5xddUKC4EUAW9rIKJlkRwPXTpHxmRrknSS7K7xwZJn2XpnlzLJlwM413BOTI4QGhaLRVIDtf8Suv8X5ljPs3cF5EY1nf6MWOvJjKHfGyWN+dCOAiUc5+mYHqHrSHev/xQcndLf9ahUFEktzlqH9yCFoSOjEROxRmuNc4n2KUjVapHlyUu8dXhDaJz0ROGSRGQUEB1NU3HjxY/x5Pgxs7MZZycn7O6PEMqAa0jspwxjNZWZctVMOVRjpmiMtFgUMnbqWCnaK1wSCIkpMEiVxnW8b8gkXL98kWHPcPjkEZX16zn6rs/eOWl1c6chCPCqPd/tZH1IuvYIkWRw23TchEBpDATXzsWm7HrlBYoxD09nNHWJpqY8m3GyekRsLJoemcxQumF3aCgG+wR6DIcTbr54nbyvWC4a8rzPeDJhe2+CygUHCIKZobMSrQzlsmLxYL6GwoVIla4qBPGlEVmp2Sq3yJdzRrPL/NH33sGtaqRQjEdjfvgHf4a+L3l07x5nswWnukHVjr29AQcHJzx555S3/vAxv/ZPv8uP/OTLfPFLn+XqzjaDXo/SNqxsQ06GynJsFExPjzl6vMTovK3qSCRRn0xRYgu3JoJfmxwFidYRV1fcf/dBqobyPKng0hYsIaEzyaRjfbcQ12zxtNgDEeEiUdrUthGpXSLKOb3xBYLRHC5n3HnrHX7/977F/NEJtm6w1uJ9xMQCOUpz5bbxzE5PefP2IeOeIbqGs3C/5Ts5RluRxkMUmrKMuMZy6aXL/NCP/TRPHj7ANUnopW4EBYad8U6qsrVCaUlWGJQIFFpjtEYqyUBBXmiqw6+jjWEyylGqh8nSiFvyaUiCOCY3+JDaKUIqtGoNWEhEXCV1yytJuG70EYSibzRJK6e9HsIThUVKQ/BJx90YhdIKoRVVFTmrHItqQeMapBRc2d3FxEBZLtZVeHBpnC7dnI4YPVIpovcYQYqkMU2LCMBFR5RJqyJGCFqidK9V/UtcAFNAoxQjW2FrT+UgZILMdQp6ls4gK68HrOpZqvw1RC9QpnXDE6Ht3WfQBKroCM6Bs6nPrg06M8mhL0ac9QjV48rl53lQv0svFwxyiRQ5wafAK8aJYNq1LbXURJU4SdZZJJG81+fqlecRUeJszWr5hFE/4+GjEjXIGO302N0eosQIJXronkRqldYsad9Mo4cR7xpWyznL+YzxzkVMltGNFfu1p8mmtC5oJN6l6Ytl1fB3f+Fv/f8wWv3/9/hQB/p0bPYXN39mTRRL1ZRus9mI6gRznm22b7xK9/vdLHRKtjffK6EIznmsdYSQxqzcRlUrNj5LbCviFCBb+LsdIenGO9aQqZQQFd4H6rphOOiTSUU5X3F4cMBgOGF/dzv5u7dJSJQZ435DT3m0yFITyhvQdYIElUQqTVehx43zFoiYrIeMntliRVNLIhrv/Lkvdowpew6dCEX63UhIjbx2zr/rUcN55SiETD4BwiJsL83BCg8iJAE8oRHRU0iIzYKz6UNOHj/E9CWDfMiwN2DcHzEaarJMUccBjVN4IZmv5gRyXHDYxlNOa46qMxyOKCt806r2RWiaBtfURN+1AJKCnjE6qS0En1TdiMSo2N3bpl8mLgDS8+DkAa/cfJWL4y0u+IZyuSR+5y1+66tfxwZLMdIUfUW/0Nx//W38wnH1xlWee/4yL1y7iIiCuq4Z9woa73lyNGWxWNJpK6/bQXFjrbUM6hgl2uQ436CNxvvI8dGUx49PUv/eN7jQQZFpNPD9qpFhbVgEILWhjoGeAyGSoIkPgvF4B6ck33njdd5+6zZ3337Ek9tTrKvp9SK9PKdX9BBGsZxWVLXDyMhWkfHii/vkowJiw8HRKdNZiW0KmpUjLywiJNEUS+TR7ce8e/Ueu/vbZL6Pq2smxRBTpErbyCSYo40i0xKjNabI0cZgVBIw0sqkBLaD37VGSrWuiAUKKUzyZQ+tf4KUrdSyxvmEoEVi6x2fiI/JeS21/pCSKMGF84RfCFA66ec3Tc10Pme2WDCbLTk+mbb7S7rnb+tbfOrjrzApcpCKpqkI8py30cKJrRCRXFM1YrsHuZjg+w66DjGp19FWo10i3o0MS9NDK08eXPucPKEDmcX7Gt80ZIMMMmiaKs3wt7B1Y9PnlqJVqFQCXAkmJ7iItylhEAIsAiUNUgMi8OjxI2aLKRf2xoy2RmiVvpf3vn0tDVKfk1KDx7mm1cFP6zu1upL639hvMx72Kes7iL5iMBnSG03wVqO1TMZAHbGyhe1DSyYWWUGv0GSTIULmrS5H25/vHD1bxbzYoYYmI10BkOYD4a4P7fGhD/SdcM25slFcP95JUYYQ1jrTIW6oJ/F0Qf9s77K7eTaD/LptKc6f432rx95JoK7/00qTrp/cwd2JHHJ+Q3eGO+KpCgxS/08pQa8o0FFSVjMWixX9XFP0eyiTJfjeawa5ZaADBkEdJRINsSG2yrFr8pxMgb4j9SUtekWUglXlaKqAliRRithV8JxvRqHr56VzEs4/Lh0eklyAUy90zbIlIkUavosddiIkBE1PB2Rc4JopTbmklxtG/QGj0YTRcMJwOKSX52keNyiwgRAaDo8POTmNeJ8qwhDS7Hvi5pZQS6ILhGAJwSJxrRRoJ+2b5IYVJvX2lEBpmfTavcT7pJRXR0/VzNDDnLxINrFeBXrbQ/I8R9kFSke0FuxOCkLUsFpw993bLBYLMil5/tIFytUKKaGclzy4/wRnXav7zflJ5rydyDpxEhiTU4WyNSCKlKuag8dH7O4ME1zrfDvCdb7xbwaBpBIlW22Ftm0pBSKoRCyTkSzLWXl4/Y03+cNvvM6tdx9w+niGdorheESvSONf1iczl0k24PJOj+3tAft7Y65d2mFRWlZ2RX9gePjohMdPljgrMV6CSqE0RIFdrnjru2/wqS9/jp2dLUZD0N6RDwxaSrRKgT43mkwbdJ6hswydpVZTnplEEFPdGGZKZqWUKRCKFACUNAgREzGvVfijDZBKnVvJxtY5UCKSimK3D9ByHGQ7fdAm+SZTLBZLTk/POD495fj4jBADs3lSvFRSpZ/LBf1ewUvPX2dQZESlwbt2bFGs74NExwjnK6DbO8SG6VE7qUKkVeZ7Zgw4OiIGIRPULSKgIlEqZFQoKWgQaYRPJ3QjuC5Rb0D4RMwNpDFXEukRJEEKvPS4OlX8jQ/IPAk5iWB5cO82k2GfPM8wmVlfE9ES/lCSKFSr15QKB60EKiZyXpS6NbKh1fMY0B9NML1HxF6ym5ZZP3GBjE/7bowJNQKilCjC2m5bqhyVFViXmP6h2/OD6Lbn1CoR4GJI5MA2iYv/UgL3X8xDbAQe6AJza/voLV0kShlxtzLO9d4FcSPIngf2ZwN9mhdPPZ6O8dn14joiSBqzaivZjaw7tCzaxjoy41o7yI4kl+RckwlDEnNJWa6kqZP5Rd7LuHTxOR48us+Tgyfs7O4y3tpGCE/VwLhnGelArmDhAzlpWA2ZgplzYMw6IqeT1up0W+fTbHFMjGrrXDvJGlgz7luCTmcdmT69wIfOx1msNapVqx7jQ2L/+iiQXoJ2eK/a864JKiN6zUjXVNUx1i9QZsBzV2+QS4HpG0xhEFqxqBsCAm0EUtU0TcXx4YpyNcM3J2AbcJbgLAhSpSnyxKQNDVHUSGFJEr+ilZdV1LVFBJPm2FUiVwptiDFDmgJlsrQxKjg8fgfySwSZITRM+gMuXt3D3V3SzFdUjcOO+rzy6kvsFILX37nP7TfewdmGF//VnyFTmjpYnhyfcOfte61pyjn5kTbtWOPw3VmWAa0LlE18iBg9wUdOj4/ZvzDo0qtEYGtn+Dflb2UnWCIERiukkDQhkiudrrK1aKXpD/t88627/JN//M84uVPRVGkzH13IeOHiZZrQcLosmS1qesAnPn2ZV1+5waXnL7G1P6EoJPfeOuL+8QFaSwyGcvqQ07jENRJTdKSxSNHXPHz3PYY7+4w+t8W15y+ynD2ipyRFnqEyjck0hTaYvIfMNEbpNLuuZfKDJ1XzUnQjZx2cK8/vX9kiT6h0bwnQWtHYGt1xVbpWRgSDTOQ5AULLNmEKSXCH1Jrq7unbd+9ycHDM6dmM2XTOxUsX6PcKdNQQBc47TFC8+d03yY3hytWL9PMc4UOLfp1zXto7aE2gFC1xTHXkV9GKwLR8AO8CsZ1A6M6ppUEGmQKVVCnAKw9CobxIuvPa4LxFk6NyTVAJ4RIk9MR5h3cR5yLB1UilcT4ksRqToZSgblYI5xExjRJG55idHvGxG5+hX2RE75OzpUpiRS5ECA5Eagc616JnIq7Pr7NuPW2TCpIA3tDUNcKY1E7xCiUdBI8M6fxFkpKoEjLpoSDaYC7accr4DGq7xlK6s5/+3/m1lsVHjIv34Q70m8H4gx6PrZCLQBFb5yalZCtMoehkbdtif/1am9V793prFbz27733GJNBZgghUtdpzl200NvmrHL3uomtHvCtV3MuFbqTdQwQg2/HSVL2TEzjO8bkeO+pbYMPno+9/CoPH7/H8dERs+mMl17+FIgGfM1ANwyM5dAbAgtiBOcCQujUtxTnizyECC4QlURrlTQAZOqJxiAQwqLWIlUC2nlcYgtpAjFKVJQt3yeuR5g6sw/rPbZz00JjoycEhQoZEYmXEYoa4eH4ccbueMyVKwV6NECJnFVwVKfHVPPHuOkTzpZT6lVNaFapZ+hhUCgUHqVSgpFrTS8bUIcVkQQzSp1GKNdIShQJcQiKAo9XJSJGQhlpakDBrOLcYjflDhQDgQ1vYn3EBsF9WbC1dZXLY8NAjZguK5rQcHB8xuVPf4qfvHKd46PHfPPNN7nz+BGvvvIcr7/7kLfffsDj+8cIaNfAxroljeep1k8dwLoGT5pGUFJADNja0dSere0Jmoq6snTaOedVfLpgcqO6V60ngmynDFbAaLCDkPDg+JD/19/5e8zvQqYE+7sF+5cm9HevczI/5o03HjMpNJ/7+DX+wp/+MbZeHuCcoWosJ3aOqyv2nrvOk9MTotG89LGP89Of/ZP87/8v/wcqoZBeYVphJCscu9s9bv/+V6kXC8TP/Th/7md/goff/DYrY+j1h/SzHkSPpyEzAmEkQmo0Os3At+stac4nISatso3WB621UZvXtgmAdTWR2E7FOIRI7nZoBTK1BkIMyRTHpxHd0BqzCCnJ85x33r3Drdv3IQqMzjGmpp/3E8mrclRlzbypGU7G+IXn3XduIWPgE6+8jJWWJroUoFulxE6OOqw3nsS871AH79sJjLbKz/M8sfNj6t2HGJJssRQgdYvcNehGgAkgHdFFpOuRiYAXMpHvhMdKh8o1Zb1Ck1MogxCSxXxGqJZI5Wl8Gtc12ZBAcgFUQKwbGmv5wmc/w80bL6zRpUR2NCBEu8YDnStcGjrqhHPSXqQlIEGbDCkVEY/RBSE4ctVHqyJxmESDcwGJSutaJCEtrQ1C6VRoxbj+s5XNOI8XLVoiY/oMyTEstIZMLWry0eLifbgD/bMBfv047UXtArRMfU5jzFpb+VyassPi3//anaHHs6NH573P1MvL84wYoaobiKkPmipDWt3q85lmKSUidkhAuxG37900liwXacMCEsnE41s3O6RGCsHZ8gn7F66yu32J2fyEN17/Pfb3X2Dn6i7bw8BoVRMXBoqIiIaIT9rNUtA4MDrdWEqlnl6IgSxLG4MLnkAg0wpinuqc6Imhhco6nD55yLbntoX+ZJqzDlHgPbgm9f6EzFBaIKQnWEPQNZIqjTsJhUXgouBLn7/E/k5N08z5ztf/gHh6QB2mxOiQMaIt5FIyjMkEREhBvzD0s17qvcmYerd5wWiwQ//CkP5wSJb3MLrAqCHEpNGdINGk011Wc2b1AU1TttrfNatVybIuWZUVTVMTmgYZI6WTiH6kFjCv4Pi0ZG7fpbTgWuTCh4zjVeDsxmfQF0b0dw1f+NSAizs7nKwCX/3qH/Hmd94ihOpcQW9j3bXNqI21JojCQHRYWyFJ1rl5Lnjje/f50g99IvXovUUKtZYO7nr9Mca1UmIndUwUZNpQhynEHC0VR8fH/PZv/gHLA0HPCK48v8N4a5fge/zR61/n0WPNzWt9/tQf/yx/7Kd+gKWvOTtqcFhUNGQhw3hLzGoG+31OV6cwMgy+9Ap/fvZn+C//3/+UVWjIo6RAIsqA0yXFhYwn77zFf/1/fczB0RF/9S//JeSTU85mJyztCj0sGGUTVADdyiPXwZMbjVLmqftWSZNGGNv+UdcO60RVYjgXokloGvT7Q6SUVHXD4eyM0nuKomCYF4zygkwKqmpFhNZkR3B6MuVrX/0WPsJkMqFX5GgJs9mMprJoI7DBUq+WCBnZvbBLPZ9Sns04Oz0l7xtcZdf9YgBC6v9rpYHYsv4DtrGp/bCx53Uyyj7YVJXKNkrGHpYVIhqkT/d+HRTa0e4hEZV7gjNoSRoiksk4q6wbMtMjBs/R4SEnpydcfeUmo16P+fSYjIAUhmBzgnEIXyIiZFnOYG+P4bCX9AHyDNM69lkf1yRmgTyv1jvZ5JjGDhNPISERNloIDUooKm9ZuQWFHieHSJVjfYXMNJIMWp8HF1MBJbu+oRJrXf3Uilj3wNKVF7TtVjoIMjlfdq2STdmJj8DxoQ703bGOw22P/tlKfz1uJ9NsZadh366/Z3rjT1f1m49BFyBVC8U7aOGiLNNpNM0n1mb361KIdqyvc85LYjBaJz5tJ2Ahkek+lW0LIbSbFA4tks55aL2co8upmhIpYDgY0M8/zpPjezw6bMgGl7mwNWA0NVShfR3Z+qxHnyR6ZcpYhZTItkqvG0uW6baigGA9GplgwC5gAB2mlQYWWvZ6TGzaKBIk51zAtfbqieyfFLdcUDgVwJn2/CqMyOhLy9W9kl7esJyecPT4MfVsiq5XSO+R0ZMBfSW5sHeBzIwZD0YMh2N6oy164zHZYERWDMh6BVm/Rz7aYTh+DtGSs9IIYU7wsVVGAyESkXC+mLIoD7G2JvrEgLblCuk8ti5xdYWtK5qyYjFfMjt9zPx0ztnJgofqPmeLGU0lsS4QZEDlDdE5/uibv0p/+xKTyQ6XL4zY2tnlt7/+XR7ff0C1nKKiQMp8PdlxXp2ej3l2ynZCKozWrFYWCk2mNVmuuXfvASDXCoLfJ/d96l4QyeKelQsUWiU4VAuEs7iDGaul4coLI0bb+4QoOT05ZHGaJlCOZgtWPqKHA+xRiSBDUreJqSRkhjI6RiZne2tM7R33X/8Ok92c0XBAWfs0CeIFSitqaclCQz4w+Fjyu//wV1mVM/7sn/mTvHDlMleip5zNmDeeaJIBjRJQSL0GXzsezKY6XcfDWWsRkFCS83nz9FhvMOb49IzDwyOOT07wMVJZi9KawaDPzvY2Vy/t01MGW1UgBE1wvPvuLTKdI6SkqUpsvUqQdlXT1D7NtmjJZGuMQpARibnGRsfZfMqF3i6mbZt0boXOtVau7X4klCJvOTjVsnw6UMVI0qno2oKhtbyt0F6D7kFWkBOITUgmNTGNE3spITQQz1uZqSo2jEdDHty+xa3X3+Tdd9/h4aN7/MTP/El03SM07e8UChl6FL6Hb9LMv1Sd1W8SuZIyoqQmU+sPnK5Vy9fpBpK7gmw97xRjqygY8LHBVguCrcFLFBqjEpFTYlJ1T0K3Qqt7j2ynSkT7c/Inhs2ee2AdNLoaX8jWHrxFLDsd/I/K8aEO9GKdjkW6we3NIP9UqO54Ts88r/ur74cNCMFTI2abj4e1Z3SCU41J1UZ0a7ybTriBbiMXqSebzE7adxa0nuXtDS5FstMMjnq1QEmNEDlKGkL0CBFoDSlRUpIXhq2dHWrX4KsTpDOMzYSlM4lxLlLLIMTUh5Mh9bOS6FxrS+pjKknb7ydFqvQltOSZNiUSqacf6eRRQcWkoBdFew8JgTKqrS6TEkdS2BM03pJJhRY2JQkeJqbhym6Fd46jgzmP7z4mrkoGOmOyvcdkMmGytcVka5vx3mV0b0R/MKbo9dCZRmqNLvook6OMTipeKkMWPeh80hGgWmGSFoYNsbUSHmYUZpusFSyJMeBdk8w1gicEh3eWuq7ZaRzl7IRqXlEtal6YPeH05IDTx0tOTk85mZ5wvJjhYkO9PKJuaqKr2N9/meNlyZvf+x7lbIpGQNTrDWiTId9BxFFsKqsF8qIgRmjqJHNqtOL4+IxyVbdVvuquCpuM0Wf9Alot54S2iASpOu8YDvt8+mOv8su//ZCyaaitI9jIYr6iqcDIwGoVeHi05MHxgouTXQ6eTJMGQ4wJIleSYB2mlzPe2mK5tDTzGcPRiMl4SHO0JHqLkIEoFVrqJNMsPCJa5Lzh7d/9Kr8sJR//5Md48YXnef7iPnsxUtXtmFhopzuCw0e/TnKg9WGQT5dj3odEeBTtuGdIjpJ5XnB0dMKtW3c5PDqmthW5TCOyIUbK2ZLVYkW1WvHqzRfIdIazltPplMPDQ7ROs+3WWYSIZHnSc/fBo2OW+u1tG881TUqsVWrtFSZn0Swpm5qyqqmqJAGspMKHTpZW0OsX7Oxsk7Use9/50dPqILSBMoTEpYlEouoRFmfE8oQ8CMLoErY3BG/BNxDcmvKXNASSMp2Pqe1RL1fUixl+NeXkjuP23Vtc3ruIUQZb1TRuhVKGfj6k9MvUjrQW8oxOtqmT4V07f3YsiBjp7KBjq3YZOxGyLr2N6e9CSAZdae9teRcittNGtOsmSYwrnQy5N3UqOrLxOh50G9bm/da+oejERtYP/stA/y/YIZ66JJ2V47k6WwrIXdiPiLUYSAend51MubHZwnnf/vyxDfZy+2rdn1Ikgk8I4fzfdUvgnAuQdOTVGs5aE2/k+XsnKE4gg6AuVyjh0PkOOuvRhAqEBWFSDzF4iI6dnUvMTg+pyzmhUQzNRXwzhFgjgsJFAfhEZgoxjdGHmBixLQHN2wSHS7ER6IU4D/Qhuc0nxnIb1ANon4R/ohQto16glCGSiGHnrP00c5tHiWqTIhksu70V28MVs1VkNvUsz2oujQfcuLzPtasvsH/1OjtXrjK6eBVVjAk9jdCtjKYv8U2dAiRxbQTjVzNYHLbkHtEmWoBIFb1tHM75VO1rjW7VBCNpzjYQiW11IJQE00NkGYXS9PZ3EDFBjcLXlNNj5k8OOXz0gAf37/POew+YlkcsV45VtUKHOVmheP2dO9y79R6hbDDCkDqc7TLdWHsuJAg+inNLUuc9PQlFnhFckkDVWrKYl5ydzdiZTNBa0zjLZoq7WcmvPRra/DhTOmmbE6hsw2A04os//AVe/pVvcvfoiKw4Q6GoG0eIkVxCaeHWvSO+9fp9/uLP/QhPHp0gYkbAE0TqmwrnIdcMxkOkbFiFhiLvc2l/i7PTUyprwaT1VQhNI9J4k4qR3e0e9fExv/eP/ynvvPsuH//yF/jJn/gxPr53ARNByIQYpZGwpBRpTJbGI2NC1DRZgm3bIBNCRESP1hofLN57tEm2wu++c4tbt+9TVhWDYYFpIVx8pK4ryuWK5WzOxd1dLu7uUJYrjo5OWC6X9PI+pa3b8d3UCA4xucUpksxs4y1Caaq6Juun8T+lNMEHjk5PmS4WnE3nTGdzyrJGIKmapFUgBQyGfW6+cIMrF7YwKpE0oyAJ4YhuKqYtKhSEqLFCEY9uo+59A7yEy6/hX/h0UgKNIF1DUK2ssUp7pffJxVIpg3MWowJbA41vVrz53W9z4Sf+OEU/aZHMj55g8gF74z2cczR1lQhz/R5d6QW0kr+s773Ynp+uZx42gnxrPt9WXW2rEoFujXfW92ZMbcjgAz4m0xstdGp3CJWSBM5herFRyYsW/Ymcu492kw9pOiiuY8dHrKD/kAf69dXoIMs2mK8L0/f31qVI0JiUSRyh81lXpBt/8/lPvVULxwspcM5t9NjThpJmQRVKCVQQBC8J+Hbx0MKy6V/VuYK1PIDGWlDgIamcuYiIgZ6QGK0oFyfEaBCqR1M3EBVOLiE4NIpCDZkeHTDMRvSHICs4LE9wvo8SjuA1QSiiSPxUER1WgGl1vn0MuHYjVKT+lPc+CYlAEmBxqSAQrYlOjDJ5t4dIkBoXA8poopA4L7CNZzFbIHxyczNIch/QokFJTWUKdA67oyU3Lq+o7ZJ8OGJ3b5eh/Rhf/vLzfPLLX0D3L+CFwGKZBUtRntEsloiQiDMBjTIC0Q7wiyBQaHQmECpDZQVSZggU1qbqoFf08MZSNxXO1SgJNmuSqUYAWnvWEDy1TTPMRmiUC9TNNF0n2UJEQeLUFuqFba698ALP1TVfOJjzxjvf4a1vfZuj6RmDvT0u7O3z8//ol4iloLYKESO58jhMcqtbr91OOz39vPaxj4qqWjKeDClMjiCyKpd4C48eHLI9GZJlhqqqkGZzfbWKbBs8gNAWOrl1LKTAGEFdN0Sp2L26w//mf/6X+Mv/3n/KbLqg6El031LM0xhWoeDWm3f5rwn82M/8ANt7A46nFSiP0QIVwAsITU1mFHIkCSLg44JPfeY6jx48ZrWscNGgQkl0gVyk0U4fPcumZDDJmGQ9Fu/d5rffeIff+Ie/yE/8hT/Lj//wD/Ly1X12c4FdTZlPPa6uMK0Oe6dAaIMlkzmdlrtodQW8cy1jXpFlGffuP+Ttt97DR0kv72EQ6KJgtljSzwuUhVBbxjs9/vCP/ohPf+Ez2LpitapTYkEgz3ULVSuMzkCskColiUophkKxWC0Ro5xcZwQBs3LFkzfe4PYbb1HVjhhlaueZgul0ympVrtXaTh8ccfrgkMMXL3Hl8iV2d3YYDgdUy2VqCfrU41ciElwEUVDOD+g/eIPx3W9iBgr/3teIzV+kvvEazXCCWE5RQVMUhrPpGSdnJyzLJRcvP8dgNMLkaaJEZpHJeMiDB/c5PZuTX0qKog//6FucOcEP/Yk/g+r1Ud5S+wqLJVephRlaCXChdLpNCESfyK9CaESUhGhb9c11qF3/k6YnRvSyEcHXCByEkHQxnEapxFVJLbhWbVAEBOcqkyHGloOwxn2f2tfPUd8W4WoLsi5OfJSOD3mgb1HIdTUU1ht++hlaOnv7c/oFpTTRJyHUBJm3CeW6J7ouAtcs0XUV3zo7xeDPK6QYsS7JZYqWpCZFxOhU0WojWpnLrncvcD65W0FEqKT7bKRm7awkPBaPHl1h0L+QNusYMVmRlKVQCJO1LHfPSIwgZpiiz1YeuBqX/O5RwbioqILHtnrvQtUoZbARChnpKU9UkeAzjBNI45AqIqOiiQZ8RRYUmVAYZXFknDUDDmykagI4QZYNuUDJj1yZUS0C0yeSayHjC1++wCuXLb2tinygGeV75IM5T05L3rxdc/9oxll9BMzJzR7z2YzLl7d44cuf5eqVy8xWS8LiQSIN6hQKo04JmZQapUWCwFODOOV9RhFla5lLBGeJSZUnmXdoTbk6RQiRyIBG44PHkKXNSESEEUTvUUKvYcYAYCRGDVGtvXEI6fnBLnCVo25HKuVkwOWXX2K0fZlFOePJ8RFf+4OvMz9aYAQUKs2tu1gDHiE0tCOHQpLMd4JObRMZExNZgK8CxaCgyDUhWqgsGrh3a8aLLwXyXjK+0eiUG3T3ycamJWWaa45EKt0k0lQQ5MYQG8fBnUe89vlP8e/9u/8af+fv/DqHj6dc3h3j8hPmTuKVJNjA/e894H/91/8j/m//2f+S4nTO/HhBuaxolEcqmWbIfUSLnP2tLRbVKfs3b/LVC9/jcHaSerDeEHUk6nQfqaiR0uBEpHKW4aBgZ6SJ3vO1//Q/49f/9t/m4is3eeHTn+ATn/8sX/j4S4x3FYXYJ1iDW81wi9vE2iQvA63SPRcCUktcSLalWhokhsNHR+Ra0rikZIkwnMyWCFcmQRslUIXi6GhFtZxyp/9eQlQiZL0e0gekc+i8AJ1hK4urpuxduJagZJH2nq18gClSP//RvYccH52yNZkQvETnvUQQjnB2OsU2Nc5WXNjfY3dvh96gh5Rw+PiYP3hwi8mFQ1577RJX+lssbeL22MUZzfIMc+ESe5evYr99i/nxlGYluHo5A+dQ7/42erhDzCdpoqOY8OjBO/zhV3+Lo/v3UCHwB6bkJ//y/5Rhb8j2YJcDdZ8yi2h7Rn10TJX1aKoVK+dYHjzkyZ132b90nXy0w2p2SmNrcp2hpEoUORlSWyAqBDUdwz2KBtQEUPhQJTMi1UMqifULYpSAAhkJBHzIkFqjTCTGktAS9dZmXzEipW/bnrLdC+K6zuvu4bT5tk2ENn9u+6bt81JQebrE+2gcH+5A/8zRJWtdkF73NzdY8jFuwPrE9biVVvL7XOCOfd899/09/haD32BPp0o/BAckMphQm78TybKMjhgUY0hQ+QbM372HiN2cbGhno9vEJgaSaUN6OyklEUf0OSIGcmO50qs5sEWStSTgBUTb58RGjPF4BGUQ1I2gryJ9afEOnFAgDAOZJHynQVIFhbcDyoVkHjSf2Xdc3BcUKjCO97jACZ+83OfipQnbFy+yd+VjjHd2yMU2VXWf2fI2B/PHKZvfHXFjWHCx7HE6HXPr/kPuP3rI3t4FJtvbmDxnWVUkedEkZNPFc6V0y0xPEr4puetu1PZGjsnbXWuFJMeYnKIoKIoCKTWLxQKtddJmF4KyLDmbPsF5u66AYxTrpOv9rHixRnQ2/0393/T7/XxIvmfwZ57qyRMePXwIsaaOSSpZypzgFT5apFcE/NrXnBAJVEDS8k4t9S5BTf1cAGNysgweP36Id5/EmHzdowzBPdWb3xTQ6Zj9a6/7duwzjUdKvvOd7/CDP/hlbt8+4Dd/49u8d+eU3S1DTYWoArkxDIc5hbX8n//m3+H6D1zlpZsvcOnKLiyTzXKDIESJJ+DsnFg27Lx4g/2r+zx8dMjstCTLFDZETGhbJB2ZMAaCEFShocGhtWL74g5967GPD3nr8Le49dtf4ze395lcHvNjP/ZDvHjjOqNejkfTG44JLibVOwmZyVOu3yKAIUSqsuHO3ftYF9CmQAjBalGxbBr2xuM0qiYURdbDWkdeZBwePAYSaWs07GHLGjJJ3uvhrOPJwbuMd6+TZ4rFqqSxFqMk4+GIpm5YrFY45xmPhmRGs7+zw9HZlLOzKcvlCiEEJstACJrG8fjxEY1t2NvbYTQqOHhwh1sP7zM9OeHP/9yXyW1Dg0aYHkJV3Pr9X+L+D8z55MufoT48ZHr3PR4erbg2BOfPsHZG2dQgFP0B3H37FquzEp31GPQLimXD6z//TxhvXaGyDYUeQlwy2RrQVCecHilcU9PLc2rT5+HtO4xGWwz29miUxNYeqzxapZFjbz1R18hWZTMKASqp6YVgCXGFkBZJUnZUWZ9ADxkSogkR23Ie0n0VziF87xPPSSqkUu3+J/DedsKPSCnRWq91T7qtuYvv3X3RSQh3Vfzmfz8qx0ci0HcBtDOHSBdTrqGa5FPWVt/t76ztaWNoUQG6eP0UcakjoXWVONBKKYr1q3WwfDrCOXGN2MX8DZQh2dIKQUvM6egr7+cFdMeaTLxBLDn/fOc/a5JIRAyRQaj4/O4hv/ZghyUBJ8BGRROTalweAk2QECQ2giwc0WlWDuoQMdqhC8FRNSAGSSYFkyzyA/sVQs7ZLVZkYoUWDde2JB+/MWS8O2QwyiiGlio7wc9PyAcv0h/t0csgLh7S+IAIadlJoxgMC3Z3tllMT7iytc3uZIu8KAhEtDbJUCZdyTa4yvY7JwGYZG+pnurFsaYEyVY0ROJCYFlVCCSNcwilWlvhgIubCdozCdzG+Y7t/5+rprWVaDfnLNPcc4yAjhSDnJGvUUFw9PCMAAyGrYNdSGvGowiuQakseSe0o0BSSKJIrmGSTo41fV4p1ZqQVPQyjo9OmS+WbLvRRktpk2vy9Ll5SgxKvP9n5wLL5ZLPf/Zl5kcrHr77dWpriQL6vdR7XZUWKR355AY31Auc3Z3x6MHbXL96ge3dHjoKhFf4GGm8JWpFKSTZYEBe9IhhiRCa2oaNpCkShEOGriebRqOsj2glMFIgnMPWNeVsgVs03L4TEUEQfcMXPvcpwgy86qSAU+LXXed0j6dz4X1qVVlHq7CW+D2uLLH9fmJPiBTWs0wwGIypm5K6rrFRMOgVBCy94ZhoHdXilOBq+v0e5WrJalnSWNdqHsiU0ElFnieUIQTPyckxy1VFUydzlV4vkUeFlDSNo6pWySq5tLz6sevsbG/jgmR6NOfhvObq+AKxrkEHjFZcuHiD13/rV5GfX7E97mNufoajd/6A4bbH5QYfEk8n+IrQROqqxHuXvqcWTLbGVItTjpcriIo8y6j9kv5gm6o6xdkl0Ud6RR87GNLUc5xtUutBaZxzmLxI8sgSQrAopTBK41xD3TTUtSXYBOtHlXQhREjInF3NCaKbjjrv9qsWdV1D72JdnCcUtgvkIv1ey1tMd+hGkh4/4N6G88Kwuweebdt+FI6PRKCHzWpZdDsy5+zJDrJ55miDrBBxfd2fnc1PF160Aea8ohabMaF7bP1QV6W3I3Oxw4nS+4TgW3EavQ5c3ca0QZb+b/m9NzgJtD+EgI6ej+2c8fbZgFsrSeM1oKlaN6sYRfIDb2VQax+oLazqiPWeQeHZm0h6QC4FQx3Yzi2f2J2Rx4aVXRApGfQC1y/ucvXGFk1RJFW9usafPMLUK4qtwOWLn6SXX2BcXOZ4eg/vIt5DCGmsZWs8hCuXubizR28wRGQGCGijEfJcg2Dt/pZOeArm3R3fChWdA3AkFTTpsb5OTlmtZbBA4KOlcRUxRKxv1o/Hzav41Lk9v8adW2Fs33+tPCc6fYb0CUShGY5HXL5wgZtXrvPG7Xs41woqyW6cSOJkgxAt8zfK9rtIIh7wxFaKOM1/pFG6NM0UKHqa05Ml88VqzR1JqND7HR3XM/kb5xM6sur5c4wxHB4ecu3qRT7ziRd5/et3eevgISaDPBPYJlKWDYu5pBI9+sULPHz0Ou8d3+L4bM4Lz18g14ZM5ECqzBfLhsPyLsuyWW/k2iiqVYPTIgXEduRRxITUpImrlGRb59Fta0up1pgkVKyOS777jTe4fPkSn//8DyCQeOeQuhuh7ARoupZeJIp0DbIsY1U6nE+kMRGhWS6x4zFdsqhEQOeSYlDgXYONyY7ZWpfOf7/P9OAhy/kxeTFA60Bd+pak1rTVpCDLNUWWnPS8d1RlxfTklE7hKDMmyXVHMCpLo5oBvA1Mp0vOpgtUntEfDqjmM06Pp9wwOX45w5oM+mP2brzG8Dvf5dZ3vsJy7yL7Fy5g71/kdDFFjG+gzTYKmVj3tceIiAiWGCxR5MgsMMgKVkdnhKjIix6SnCwf45olTThDREWuJ+RFhscRvEsOdO0c/LIsWdWWdkCO2KJfSkVyY8iKPI2uEolSr8WBtBDYagVaE7UBlfbGrirv5MXPF3R7byYJUgJJFXKtlxBpFUrD+rH0eNx8iadQrmde/iN1fGQCPXxANtY1PUne5+c98q7SjxtX9Nyl7tnL/PQ8fjdWF877+YhzvW2SP/uaidtVFrFTzNtAHjYC/MYD6/d/KnBtZJwbX27jeVBHS08IgpCUMmM89Hz5xRnLt7aJC42XcFYCXtIYDSGiYiDrGVaVpC4bYuPIIvRR/NgWPDe5hdQWHz21s9xxFlUNUcpzdb/g5ecnjC6MmFVA8Il4pxR94dP4zeox9XyHweQlrl/5KaZnf48oSrTpY4OkjpEiM7z4sU8kffh2REZrjVLiXG5YtipbkGQuxdPXKbSM3U242kcIzaol9zwNYdeLJLoTY2pP5LpIG0LywEGIwAdl9gFaEQHZzvu2m0enIS9J7RYZWTWW3taEH/zRL/LZl17h3/kb/xFPTmp2dzTGRCrbYIKmyQyVrchDTi4LnFJYUWFE4iOE4HGubffIAts4yAODoSbvSw4Okipe+i7Jj7zzeHh6Dcv2ljgP+JsSzbFdaM45nHPUYcxzL17iZ/7EZ/ja//2Q57YaJJpcQ2YC5Sn84//y/0Nz6SZX8gvo+WN+4Z/+EluXtxhMxgy2RuS5RvqG08WC4Dx+VVHWNSIT5L0ey6rB2SQva7RCiDSi551HhpCY4VLivMcJjxfgCVjhaeoFe5Ocg8cnPLl7QlVGTK6JdZqmoB3t7L5/8Od2wZnOcdayWq3QpkAqhXM1y7Mzti/voYJOWhOtVntZl0QfyZQmCKhtzaDfR8TAan5EXc64cP2TuHrGYLhFVTvKVYlzDoB+v49zSVo2tomCc5GiZzAyqXWuViukUgyHYwaDXlrzJKva27fvYYoMoQx5UWCe3Gfy4DfIViWL3eeonv8szYWb/PCf/8v82t/+P3H31h1Obuzx6R/6LKvv3UG98jP0ti5jIE0lyMCkV3CAxYUGhOTk5DHXXn6JLTTz2ZyyWlHk26AyCE2Su6VV41eOTCrA40MaL+z1B3z3e69z/+ER81WNQ7CsLI217O7u8LnPfJIvffbj9CQ0iwaEJvhUmGRSYkxSyjwXi0pomTZJ4jipjsbzVtlG6wmpnhqVh/cH76eOZyqqzWD/z/29D+nxoQ/0/20uyvuYlrEL9Kwr9E5Ao5vN3IQ4n3kxNvURu+Rg7Za1hp78OuPsHJm610yBTHbTpOuQHdbZZZcQnAf7Z/vB50ITG9/NamLfARbZ1DyewauXFRNT8/rjAd94MOBRbdBnliY39IrIhZHnxV3Ndw+XHItAfyQYKkXmBX/7dcGnruxxcTvQMwLvDe8ur/CvXXmDl2+C7mkW3rA4tYyNoBc9IuvhhCFWAW8seTZgZU9Q9hF7+5/i+ks/ynvv/mYaiwuC6CUmH+LpEUzWyrJ6pG5hO5Vcpzavs5RqA7hpb87g14Erna+U4GXarPkN0MF/rS72RgWwydx4/9qJ71tDkAiBHVnzvLef0JwA5CJSz2dYaZg8d4W/9w/+Y/79/8Xf5He+8jalj+xf2Ubliv5qilcSHx1lrOmJDC0MlbWtYVArSmQkVZXmtofCsD+acOWa4s6tByxnNa6O9Iqcs7MSrVTSSO/OiRAkE2FJx29OfU6Fc+4cLRGBPE82yqdnZ4wGfT7zI59g5+/9ErpWmCJSFIbeVkH/Wp9btx/yC//hv89g1Gd7nNHvR+b3HnPw9oMOnEgbdoDh1pBi3EumJCr1o7fG25ydnVE3Aa0FWgMtMx7SJIqPAYTEE3HWtYk2eApQFbmBgXQUoWEZkyBUtAKUICqSVLLsrn6AmDQHBoMeB999gwsXLzGZbOGEoTfsUS+mTC5eI4TA6ckT4s42rlwyNEPyzBCFpw41u5de4p1v/Qq2XLG9c4nt3R0oNafzFWcnRwgJzz1/He8i0+m07Ren1l2e9RhvJZe9+XzObDbDBU/dNDRNw2g0Zjzq08sNq1VFXVpm8yXLOqCiRLnH1OIBhMjo6C7jg0cc/Phf5ZE64qf/h/8Tvvurv8Mf/PY/4zuvv8XP/ff/IjtZybR6m1hn7GxdZjAZ8/xrP850fsbR4V1kkJTOcfDwLnuXPkGWrzh9fIeynkFtGPb6yBah0WrAUkmCrwmhWes+KO1wZeDuGwecHJ9S9AQhNtiV4e7xfX7/197kH712jX/3f/SnKYRDBY0yEqRMYkZ6SO0aWgPcdUUe24R6s0Wa1urm3tg+d+NelVJvKKA+fWzO22/eIx/V40Mf6J+Vp92sgDsW/bnk5dNVshBpdn7DEOq/OXFoN/nOiCSxu8Uavk3ulAKt29n0KMDH88AsANKoj1Cmfb+nv0PrjLl+/PslHJ0JCi2HIFca6cFFSSwg95GTs8A4W/Dl6wte2y94+8kO37zb43AROHWK41oxWQn+1KsXOXSSgyqycpGehlrkvOUl754WBHIqmyNDIFz9Fk+OJVk/khWBPMsIUmEzjcSl0TcXGNBvZWMPWK4OOD54h9HoOfL8ElU5RQhP0c9R2qBlhgghVcQ6oQ1CxrVSXDrXCYbz3cVKJ+IpYlz3vNB63lvnz9st7ZXvDF+EEK00ZkyM7Nj6U2+gJ8k/27d9vPNzLdrruJl4dQplQggy08dbyPIezsPJ0RnHA8H/6j/8G3zj136Pf/oLv8PP/9of0htqrl4YUgVFjcPR0DiLCQZtFMgMoiI4R170ODw+YLmsOTkVzJZLXrg5YTSRPLj/iKtX9/j4J54jRr8m4z21XkhIRWc1vHZ5Wz+lJUAFn85z4wmFpD8a8qOf2ufXv/qY/WFGkJLaB5S2XHv+AuPZAhsEEY8Xhv7WBFXV2FjjvCU0gegds+MltXNgQCjJbLlAR5MY8dGzqhw9k2BuJc7v4xDazwwQJNGRDE4ywaoGnUN/AIUSnJ55dC9SuUgWM3SUNK7G5BrnferNK0EeI1/+8pd568338M4SfMN4PMJfvcob3/oae/uXmYxHhHrF27du84kXn0/uZkrRywsmZsx8ccL9t9/k2o1X2L92g4hjWQXqpuTS5QsonRLB2XyG1hrnbGqrCYmWkjoK7t27z9nZKXVTMxyN6PV6rFYrmrrGaIPRmrPplMnumJ28z4QMk2f0bY13D6mKgrD0yEf3KR58FXvlNc78lI/92Bd54dMf56u//Av8rb/xT/ihP3GVm8/vU2jF7W/9JoO9y9x89cfZun6DZTXj+OAe1177FHe+/Q2keEQv7zMcbXN2cJ9xdhXrFwz6Q4pigrWRKHoMxwVFMUKJPsEFZCgYD3pMxhLvNcWgz3Llidpzedfw5MxycOeIhw8e8dq168wXNml36FZB1EEu8raBFhAxCQdBEhlLSfn5Xq60bruVsRUYigip2/3inKvyLA9lvZV/v731I3h8ZAJ9N+f+9F8+DVNu/kVXwaWq+1mY5xwWfnaBpA1SnGeAbZxO6K3GtepqtH39xAF4GkKVMlWU50mlOCeUrEl5cuO92/aDkEm9tp0N6T5y96czMYmKhDSXrQh4IahdjpBgeoGPXzljZ2yZLS2HS8lxBUEa7k0lFYmV3tMCKzwjE9lXNS4uMNqzNYKtXGH9glUJgQwoUDrivcE7hxICLQyqHZGSokk3X5QQHWdnjymrFQKJ0RlC5kQlCRKkaDkQCJKwjVyzsYF1YNp0ZuvkYzePLotPWXtKrkSrDSBEK4whUiLYJQ8x+fytL7/YQFE2E41N34LNY3NTSddOolWStxUxoLUkVJEHx2dcfu1l/uzuDh/7+Mf4+V/4Je6czplsDclljnESJy1OWFQsCD4ldEplNI2jrC0uekIQzOY1R0eLtsJN5izr7/g+wl1Y9znXKIYQ641081hLPAuVAnqM/MSPf55f/sovUvmIDgIRJHUTiI1HCoPWGcoM2O6PKeuKVb3ANw0EUKZguLeDL2fUviK61Nby1mMyQ5YZmjrQNI5Mnc9By/YaRCnaccaIDxEXU9/dSItfgenloDO8pDXsiWBaNKOVrHYuBWmjDRFYrEqGvSEvvnSTR4+eUFUVvV7B7t4FhoMJjx/ch0uX2L2wx9x7nPX0hgU6NyijkQT+6Gu/zvb2PvtXrjEYDTk+OqK0zbp9FRFYH1DaUBR5UvJzDU3dcHKypHGQ5Ya9/b11wOr3BywXC5z3NHVNU1fE4HGVo1pWyMww3hrRbG0zvSfoS4tR4JSjLg+oXImyliYK5NDw0k99kZ91OQ8ObnE63mLvwjZZ1mf+8I/4yuMTXB1wZYXUmvmqYTC5zGK1IIjIoN+jV0yYz0+YjDJMNkConNnxffJegRQRLTVaQS0DgixNlKCR5Eh6II7IcsnxkWNnuMXHP3mDF69eY7mqQKSJJGJMlrshtta+ft1uC641JFoXVptJul/vmN0I3fcr0z54WqrdjzfaAB/V40Mf6OH7BPmNY/MiJ8henAeBroITm2S69//++eYZEULhg1v/Xbd4tData1LL4m0JIZsVHxGUTsp4m13mp4HjD/wUbVW5uVif7tGH1otZADIGbHAYJYhWJ2KXcoyN5fou2HHFlRrmjaDyPR5Ol9x6fMKw16MoDAu7Ym8w5mrPUMUVWdZweSjZHQw4s4HK1VB7lNJkucBKi2xaupgAKQ21cOhQk3mBkpqoNOVqRvAOowxKGYTQyUBUuNYrvP3+4pns+31Zecurb22Cuxu1u1ZdpRrb8cNOmrf7//Uonmjn79+3AWy+fxcgu+t4PhbZravuzy6RCz4iFUlhL0a0UIgmMDtbMdrtc/HmNXa3tkHU/N1/8tusqgYvIdcZWgucr1mrnqFQqqAsS6o6VUBCC1wQrJaR4GE87tPvZzRNvSbkba6brje9BpXa89k93iUDsUVIvPet773DNfCx115hMv5VnLXYViPCOU9sIlFKPIrhYJtXPv553r39PZ4c3SdYB0iEEZjBCOtLQlkhfKrgXHCEPGKMIfhA4yNVE8l7rSxzi3bFdqmHmAJ9GrVKfusE0gy2zgmt6JSKHTEz9ZNlJ9ksBI11VFVNuSyZZXOGoyH56Smr1YrVakVRFFy8dI2HD+6gpeDCpX32d3eI1lH0cvK8IIbA7PSQ4ztv8sUf+eMMJls0dUVVlvgYWrOdVkNDSooifVBrG+pqRVVVNI1DZ31Mptv2lCL6SJ7lhFaTwzmLtQ1ZlhG8S0ifbYjzEx4xpZzC5cyRa4EtIivvWU0fQNUkaWANg4Hi+s0hR9+GcjbjTEVc0zA/WXD/8A2EzOlnglFPMDs9ZKD72MURpQgYoxkNt3l4eI/9netoXdA0gdnZnNF2n6ZumE9PEK2V89niLPErgqBpPMo0SBGZryxS9njl5nV+6gc/Qd9ozpY1azApgIiyreSfLriSyNG5tXEnZtXecLTyOkkjv1M7jaz3dvFM6H92T+kOKeVT5LyP2vGhDvSbxLTvB8+ED7hoKUB0UG9MuP26xn9/Tvh0eyC9Qgd9du8npcRksjWwSf0l731ropJO81qqktR37ohc627CulIUTwePD/z2m9+1zWkDSfoz6fuyqEsKCZnyeCxNaJhVAm1qtK7ZnkQuG81EWw5nS/7+m7/E2A3ZEwPq5SkDrnF1cpOVrEBF+mpAZhwZGZVbEq3A1IFe6bGdzasIeBHoCY3LA9JXiNYFsIkZ0TqMLNYzsCImFMLgQeetIZCjm1WPJFnednodHwJaqbUbldIqXb+NG1WsKUMtaacNylK13Avv1lwIqdJryo1z2sHc6TqENVS/bs9E0c6ds37tzbaREKBkANmx+SUSgQyRie4zPZoxVZLRZIt/83/8P2A6b/jFf/YNTpYL/EAyMhnCJhg/itaCVQrm8yWN9RSZQGcKiSb4DC0zrlzeY2dnlJTx5Ob90LH1O5SolWZu5UnXs8MtEuE3EBKjBHiLrRou37jEKze2ePv2IY0P9EbJXtXIHo2xuNqR9Qa8+MUf5vHiDPHGd9J5khFCQ1OuOFutUD5QSIGKgtpZaucY5AV5kdooq1VFMYhIk7VXJCZyq6T1KIhEn4YqDQaEIzN9TNYDJaijxYQ+IjiCEqBkKzwE88WS0+mM2WzGcj7H1pZB0UuIgtXMl3OOD4946eZL3H3vbY6ePOFkep1Lu7ucnM3IskSEq8uK+w/eo0fg4o0bNLXl5OCAuq5QpofJNJLOvTIlT9OzGfPpSXLBi5HBcMR4NGA2X1CWJUJIRoMR3nnyLMe0UrSNVJgio7YrJjs7ZNaiTx9yf3WXh7MBZ6qmMA4/gLAA5t/GVQuiqyE4XAAbJOOJoJ495v7RXcpG0MQ+82WNrc9o+pJe1sNNH5FlF5HOUtmG4OHalZu8e+ctev0xEcl8vmC5cGS9mqZeUN57m3FZcvnGaxwevkchRzTes6oWRFliij7Hh3M+8wNX+OEffpXPvvYC7929R9abEKNLFXtMmhCRiCNJZnemMk3TTsuEkJId79P9/xSq1nJjNqr9tQCaiGu/gQ7d7X7nfN/lqb1/sw34UTk+1IH+vEcKcO7i1G3i3XO6PzpwR6pkbriGf0UkxMQ01sogOs3krpTYCKrnwV2ti8D0UHqtNAqSpDe11ud2kp40AiJVqqDojGFiW12lD9lByV1V1R3nYyASIdogtobvz6s3H9PcsAMW0wOuX76BE4EYJMbnmCwx/Bq3jbWRKnqORWB79zL7uxeonaMkZ7R3k4ePTti+MaRpNNHWadPuRySePAyIBFZ+Qe4n9MoGV3iE6iGcpKGhEBqZGVbkeKuITcUQWBhFoRUZAeVKjBnihEAaEsTvDYWWWMALSZAeFQU9xixZpRl7AmhBk8Pjx++x17vMqOiho8StPGQrnNYYlxNiwIumXQQZSiRHPOUj0gpWyhFEL13fFvFJ1axNbn9rsqXA+iZVFTHp/guhQGlCkC1LOuDdCqsCxB7JeCjB4U5YQohM9BbeOarTOb93csxf+Z/9G/z0v/IF/uE//BX+0S/8JgujuHn9AovVkiaAix5vlxzMVhSZYCAihkBU8PDJAT/wymWuXJ7Q72WcnC3Q+hyi3+QNONeQPNtTz2gT6eog/5SUdqI6SS46REnVRH7iRz/Lew+/QlUuGAvLslb0lMOQUdclh48f8JVvfIUYBP3+CIul8Q3R1syOH9LMG5SO5JMCPcrxpyW2icQ8x5i0tnyoWJxGGEKeqySQhCX4QLAB55L7pDYCTyRIyHqKzCiElZioaXwgUqN8TiEK8pHmO99+i+9892201hRFhg81w36Gcw29XoFWgsUycHg6p1SB1z7/eW6/8ya/8yu/xL/+1/4q9cEjHj4IXLs2oZf3efT4u1z8+Jc4XVY08wXVqkJnCjMYMswGVNUZ09khx8cHzM9OqOYLvI0MRluMd/apXeTk8AwbA8vViqZpqKoa5xz9Xh8pBEZrJlvjRN4TI1589SaX9/qM7IvM65/DNzPeff0PuPvePQ7eWnL03d9CZZ7964pikCFEjl1lFEVkyYxy4VlOPWezGhvnFFqQmQxlMrxWGEZp/1GCxaymPFuwt5fjVYEohsxnZ5we32c0kdja0ssuslg+5KR+D60uYfp9RDu2l+k0BnpyuuLC/hb/+n/vx7hyaZe7D4/IsgF4lwquZG/ZckokSkAUuk3UI/gASqOiw3ibeBayNTWilcqVmtDunWuSc7ufuxjRWU4UAhHP2frBp5HATn9Ddghgm+R/1Gr6D3WgFzIRttIRWhivg/ta4RFakpE4r+JTEdz6INOBwCDF03r3Tx9PX/4PQhDWueIGATC0EH/qscd1EE9iPa0LVfvbm12iD4KPniXuPXsk2DzB0SEk0ZPGJlONjg8QA0mCUqbvLUUAEQixYTKZcHJ6RvSB0XCL8faSxdkxo2KIUb10E5MhJGTGY12FW1aseEKxezn1JG1D9KB7hqAmOF8gokTLgMkD0Rt2dERiiUohiiFZBCUHQGvZqQXeS6DBuJD6rlJTmopeI0AFJrvPs1g6vvnV32dUG+Qr24wvv8hwPEbEwDJkDJTGSIlQhqg0PjZoK7A+YmOTfnaeGARTd0RdznHViuhqvFuCVGT9DE+kdA0+WLbNAN1XlFXJYrlIeu5eEMlxThDwOFFTiB7W2iSzqxRCSZxL69NbD0LQ7/cxoeA7b7/D5PJF/o2/9pf42Z/6Mv/Hv/m3+NobB1y7vkVuSBXj2ZIYkpdCIyQhaAyK6RR+/Kc+y2hrQtWcayR88NoR65W8uV7ft8wBokjjiSFC9MxnJZ/83Kts/eK3eLiYUjYKYwTWC3CBpomY/ogLl3+A5z53hXvvvIGoZgjXEJykGCr0KpKUYT2uaZAkn/dhf4TUCqkyxpNdlvMVZ4saU1XkmSTPkjOZlhqLg5gkgYWSlCVIpTGZQUqoqimjYosYcowQOLfgzffOeOv2e6yqJXlWEEUgeMuwl+MaT6YzkD00gZ1LGWa1whkJQTO7M+fX/qt/zE/9mT/Hwf07PLzzh6ymx4iVIi487735FuPhHoPePkI5bDXjm9/5CqE8ZTU74+RkTn+8x8c+80WqJrWwZIg8uPcm09WU5194ka3xBOssRV6glOLWrVuEEOj3++zs7HDhwh6f/qEvMjx4neLN3yG7922mc4X8k3+Fj//sv8knlCdWJ3C64s2v/iO+c/8ur7+xYnZccS1vGF2BZZNzcxi4flHB83v8P799hFb9dH2so8eAwSjN72f9HaR2nJ6VfOvb38SLIV/5/XcZDiSDfo9MB4zZQ4glujJUNRw8ecx4d8h+f0jRy8mKjKgky+Wcf/uv/Cm2thN3w/kGGSVKmqcKp3PRs9DumYkQqk22HmdOY6YuCWo5/1R7L3nJt6/R7tXr+Xnx9CieEAJjdPusjnPTPZf3tfE+CseHOtBLEVCyg1xie6GTFGISuQkblXkHv7aQ+Dk4273aujLffP45/Bl5OtiHp9ZD6m9+/wC97iuvodLYEfA5RyY2+kec91LbN1i/dzci+Gzi0TUzpZQokTbHNSIlEljVapAgW6jZR0XwIEJOrzdBnU1pqgXzxSnNyZxsR9HEisonNb9RdKjUVMMrjZMZpbcslgvGIaffG5H3hpjBhOAjhYgoE1NfWUChHcEURJksLWV0+CKpbInOKiQEfJD0tEYPcpogqL3A9Aym0JTVjG+9/nUePz6inDWI/ed59+EJbz98j539bW6+/HGwsDADGjtj0ZyxqubgIxcnN5Ei4uyc4MrE9jcF2paEaoWzFY0rqWzNeHuXx0cn3Ln3kDu374OQDMcjXnh+l4sXJowGfcqFBQLSVGmZBDBRsXILjNH4ELC2vb5KtyhNK11MQGHJe0NWi5pGSwYvPMe/8x/8df7u3/55fuub79D0BL2eRgXQ3hNzg1DJjrUpHS9cHPHiy8+D1FSVfUoc5Km1wbOPdz8L3j8u2j7eJqZEwXy+4OLVi9x8fpfTkzOOz2qu7uVU1hN82ritr/ja63/Acf0JzGCInuZUVUkQAusqxuMeJkuOaYhEPDs9WzCbT8nznCzTaGUYTrbJipKqXFJWDc5GMpOSVSkEUifJ6qZJxiZ5lghh3iXhoaap0HIAUtA0JXfuPuLJkyOGveHalEpJyWJRkRcZCIdSgv44owiB0HhsjFx6bp+BcXz399/mtS8dsn/9Cm//4SPeefN1stEu06DRpcOHQ87m91hUj8iC4/T2AV702L30PK99/kVO5gvuP3pMfzhiPJqQ5zlXblxj3+4xmewync45PT7h4eIhL774Iv1+nyzPUEpzOj2jrFb0bt7k1eWMgVshsez3LMdf/2WqLypCr4+fPmS+POD+fMb33vX0Ms2Xv3CZL3/phwn9IUe/+w+4Js/Y7UVmwTHWEm9rhNZk2ZCiv01/qCmtpZf1uLCdcfmC5ZtvvsNqntHYOYPBhNF4C6Mcto6pKhYJoZQxYkwfHwJKp6qiaiJbWwNevnmh1RBwaN2FnNDu07TE5Q15ZplQSR8DhZIUeZZGYVUSz6mqitxkdFte4rKI5DK55l91G+gGN6vdg6VQLQIQ1/uokOeo1kcNtocPeaDvqtTUo4HE6uiucQrlgWdh/GcDcfvMmHqYnfZ9ev1zwZbNCn5dydOS49ZZ4fl7dMS5DkHoXrNjcLe6asT175w/96n+e1znnRuf+Z/ft++CvdFm/f1C7HQDBMTUQwxCcJ4vCJTOkwyodSAUveE46XiLCtvUqQcWHdF7aIUrlMjwzuGaGjMckxVDTDFE6pxCgI4RdCRqkQRmhCSoDH36AHNyF12e4F76SUJ/BNZBcEQiUmfULvLo4X1ms1Oq1RKt4GOf+CEa53hy5w0evvsmWmU01V36YcBieYBfPsf1qxdpHJw+/B6z6SHL5QlleUZcWR5PrnH9hRfJjQJvaZSkEaAtpIzHI1QA5zk7O+Xe7fvcvf2ARw8OKcuG/vaI6ekBr778HK+8eCOpHHqPCJ2lryKGxBwOPjH91yNCpkguh6TrmpjtrW1ubfE+eZpfuPk8P/fnfgavDa+/d48np1PKAEUmQUWMUrgQqRrPD/3AVfqTCYtVhY82WZmu1+jTPcfNIP7+KZMPOjoYKKYgqjQf+9hV7j885vith1jnCbG1fVbg7BkH7/4+7uQudvmESE1rVY/3gdy0vhLakBUpkC2WNdY1KCXJs4wi74PoWiWKslzi6ppI8oOIxE72p+3VC3q9jMyopKeOwAcwJiW1q1XD4cMjRKD9HjZVdEWBdQFpHSbTmEwnImTLHF/ZBr27xeX9CxzeOeF7X/sqL3zyJQ4eP+HsbMHu6BqNa5jPTlkqi3Mrjg4OGWUZz126TjBD+pNdZH/EQBmy2RJCZLVaUtU1QisG+QjdjtBlxmCMwVrLaDQixEBV16xWK5SS3H3vNuNejXAZAysprMfVJ7j6jNItmD66w3u33+P+nRU7vSEv3LzIJz79ItvPXcZmE4q9CePFAuUs2leMjCAoR5HnZEZQrlZpFFX3qFZLlKgJFgRFa4mbzuXx0RIjPYEGRSA4j5YZudHJGVBLtFKEKGhc4OZz+4z6GbNpiQ8RpeV63p34tAf9Otgj1mRMrXWyno5P74mJI6USH6cl5aXi5rygWo+8ro3K2sfZLP7OM4J1f79LFD5Cx4c60Kfj6QBL9E9dqHXdssEyju3fnLM303/S+NHT40Zphvocml+LMQjRdpEitJudUmqDz9VBRRuJwTqQnx/nP7dEEp5eYx/EAk1r85xUQve51kmCaHW1c4RoCWPt40LI5EEuQir4BW37w7eZdCJr6axg5+oWveEI6yGEhqasED55gQuZxoaUUCzLinyYMRzvUYy3ISuQQtArMqxPbPJI8sK22QC7nFLc+Sr927+Pnj5iNrgOr/wQoZwRrUUqSZYX3D24xxvf+gqzw1v41QnCWvr5ANMfUx89wD95G6cdiycl+dbzxMrBJCcsT5lWDQ/e/k3mB6fUqxW+qfCzFe+4rzLM/1UuXnwerTJm5SnHszmNlxgtGBSa8aBPv59z670HPLhzh9nRjFxqFtUCOyt559FjjBNc3N5h0NcE6wg2tuYaAR8cWoHz7inWsI/nCn60FqpRGTQ14IjOUtmSh43nEz/6BXQuUP/4d3j8299lZmt2+wpHaL3X00b4uS+9SBCKIBxCBoQw7SJprzcfILS0sbZYr+zvc8QIMSClYFU2vPLqc7zz3hPefOcRq8phMomjQQHCrnBPDjm49wa6l4iUSkfwEh8FjbW4EDBFwWg4QShJv39GXdcoJTHGUBQ9XIgIkyGkAilYhID3ARdSq0uphEhJFDF48lxjMtVWaApEMpGyLrBYNUyPp/T6ObaxhBiSNbM2QKS2NdpJlEnrM1c9euMesqkxpk+2l/PpHz3jV37x55lODyhXc5wHV0OIxxw8uoOWDhklzZlh1S+4/hOfJSrFfLni/sOHjCcTdnf2mM8WLGZLateQ5zmjfg9n0540HI3I+z3qumYymXA2PWMxn2Otpd8fcHL7Frf2JzRqzEV1gWJxxOl+Tj094WxleXTrHm995y552ONzn7rMzU++xPa1a0yrFTEqhltXqZdz6tUJ3hiGxpMNPXkhkcIxnx7T2Ir+YJ9FVVE2DusUuRoTWKKlYbGoqVc1hVFk/TlaKjIh6fc1w+GA0jbo/gClk5Sv85FXP/48MnIevJ891lD5hhStkK2+iUDrjLq2yY+g1X/oyHidsVVCyNJonue8ck/7fWzzVYls/w10UuMb+hviXN20K8I+SseHO9D/d+ilnD9VbGSR5xvd5hz0+e+cq551meBTR2uFeB5kaSVQP/ATAF2P6OlxwA9m1v/zv1vXr3/2FISYHO+MyhiNxomQJc6TlBgFAo0PdRorU6KFUh3aOHTuqV1DaRdcvHCZ0WCbJvbBDPFyRRkFeaaJwqOVhpgxmxd86Qc+yc7lawgl8K5CNhZbClymUEGgIggtURee5+gf/G/ZOn2boj4hKMXq6CHhpseqgPIWIwQ2V/zW7/xXLL/zFQrpGQz7ZEry6/+P/4BLn/pRlIDhpUv4eoYvLmGyHba2QU563Dl+wO3vfQM5e4SoGlSUeGlQwwvc//pvcP+tbzMaT8i2LvGNP3yPN998TO2WGB3ZGvV44bnL/Mwf+2mW33uH+XKOx7Kzu83e7oRm5Xn36Izp4zOOHzxh/OIFcB5iIr55GXEebBMxUj11bZqmXI9f0vJHZKhopMHLNB/vgsefLvmD08e89rnP829fuMar16/xv/tP/nNOltDbitS1RwTJxa2CL37+48ym00RiFHpji1rDUu2i3Fw3z6aT5+v+mZQSaMf5ouHk8ITrN67y8ksP+cZX3+BgumJvK9mOxphGJU1WgFSIIKlChQ0NUXoGZCyrBpNFhgikNiwWixTc8zS2lpkU6MaTHaxtWJUl1kV6/RHWWry3NLbGhRQYcq1ZVR7rfRotlQJBMg3yoUmeCjbN56fvF1BaYaNjdnbGoN8jzwXL2TGNb6jx5NkWRkXyLGM02mFre5+tT73G5Hd/i/LwHjZGlMxZnN3n9OiU08PEd7l+7SJ/7Ke/xNlyylFZI52nnM6Ynp2hnaAWkbJpIEKOYnp4wLJQLBYVg8GYyWSClJLFYkGv10voRIgQIsP+gMXZEXcfBaorV9Cfe43yu/8Fbx877n3nNzh6aGnOIjevDvnTf+EnGT3/PE4IZlVJ8J4i1Njnfhi7cKj4iPzKKwwe/R5SzhnmqY2yDIJRvkW1Smx972rKOiLzPjZYZOyR55qdrYLrV/bxccXJ8SM0gvFkzP7FPe4dH7Zwepp0MUbzpS99ElsLpJAolfhAz7JEnm1rSqmhHYlEaKxNPJoUvFPbUIpWzTQ+vW930zppPr7T06BVK01GZyK2yeKGAZTv5uhjBBHX/K2PyvGhDvRdT/z9fZVO134TUk8ZZVdhbxqWvX+2soXLPyDWPquJHEUH+7e9cdn2xemkF/336duz/ozpBZ5+j2eezbP9+ARCdLP1HWKwBv2RSqN11sJprWNW2x9GuDSOJtNrBKeoFpbt8TYPsyGPDqec3XrM198+ZjEXzJcLlrM52kX++r/1s1y4eoGanJOZZb5Y8tKVXfZvXuDdu7chBkbDnPHQEMoSSkFQOWVd8ujwPeLBA1678SM0/R/h4OGbZLPfRzz/GuXZQ2J0OK2ohGZ173uU977BaCRQUSOUorh4lRu7F3jnW7/BYPcK/ckOtglkNIjcQtNw9t63efLuNxkNrzCvGrwVyQVOlkRh2dE542LEk5MFD++8zTd+9w0KYfBVRW+U09ia7528yXxaEoIi640oqxm379xjMZvz4sdvMrqY099WRG3RPUNZNdRuhXaRXpaxNdlmNi+xjW3n4NtYKwRNXSVeSAQXIzY4oghIryG0kxzCMsbynd/9NbYuXuLzP/tZ/s5z8Iu/+ku88d4ZDx/V7F25zJ/68z+MA1YuQ+bt2nabiFQrIkAnmPPfrU6JPvUuhYLQKJpqhTIXeeXV5/mRH/4Ef/8ffY0YxbpFoYgUXhHrEisgaEAKlFbUTURlAqEUZdVwcjplPB6yWq0YjkZkJk8bsYcHDx7w+MkB2oAxEuccg/6A/nBAL/ZxrkkBH6i9Ie8PUJmhLGuISUI5NmAQDIpk/INIFb5vdfVHwwEqeqT1NOUS6obcBhp3l6OzktVZgw8NZI4Sg9aeGANagpY1de0wGl54Zcy1Fz7NledeYbg7IJ4e8ujghLKuyHPD/vUrVOUMozQ941k2FXXl2Nu7RuMDp2e3OZudUTU1RqXtuKpqJpMttre3qeuapqwY5TssmgVP7r3B4f1vMDibcvig4uLePp/7wVe4/vINtl/YwcsRzWKB94IoJGioygqRB7LPfB6lDV73+VNXXuL3fuvnqas5+aDP1ed2mZcrlvVdxgPDtWu7ZEWfe4+nLCtD9CUxSGxTc3C05OVXPsl0esJoNGG8vZ+QQNuQ54ZVWaO05uWbV7m4N+LwYQ3KImV8eoz1A8ob2fpuS6lQWnLw+Alat2RLJRPiJxKRTsnWAnyzreqThwVPJQ+byFaLKkSRksMP2JslAvVB6MOH+PhQB/rvv3GdE+U2hW66PlA6OpJdtxDYIGOcv1KnFw7ny/JZlb0E9ZyzR59eOnL9vH/+Ppsy68799tn3eOo7r++Tzi6360G1fVmReqJNq5ttVIaQIjHAo0BITxCCEH3aGHWGD5b9Cxf57ht3uHfPc3R0StQVOImPie2aFT1G4x5ogasErgroEHnhE1f43W99l1tvPYYmsr8z4VOffZ7twZAQCkKWY/ojrpkev/vz/zHyR/4trm5nDIqXKcNFwugqhbQo1YJmwrC79QJ7o23cYo7wAe9qjh69zmuv/SuclStss6Qpj4kix1Y14kKP2eIYZ2v6ekJwJ2RasVgcQ5SYbACiAt2wt3+B47rhyd2H1FXJZH9IEyKLsmHL9Lhx/QbNtOTo6IRef8DYZDAowC6hjAzzEZE+Dw8bTle3uX//AYdHcwLQHxZcubbPj37uNRbNvHWGi8jYoi2EdrwnyXt6CdI5rLWpEhIRh0NFgUJwcvCYY6XJd67xiR/5aS49f4tqXjPa3ufFV24yqzQoB1a1cb3jiDzt2JXsbeX5Gnm6QXR+b2xC+20zMyZ5eApdcHJ8wmjc4zOf+xi/8uvfZD73ZDK1f6wIzLxHaEHmItIlMaJGOLRII1HBWapyRfCO1WrF/oWLjMdjpErBWGuDyQ0+NjRNhRCBfi9nOBymygwBRuMyQ1WtEFgGfUOuNbb2RBwxSqyzZEWPyXjElf0tbt25R384WfuaCynZ293CSEGmrmCMQuWKXOVkWlCeHXF2fMDp8RPqxSm3jhasmorKOkrvGWjPdi9y5Uaf3StL8sERy6rhyekTyiiZ7O3S6xVEAToGlA8oEZHSErTFiRKtI9euXMI6T9PYdsSuwpgMKRVaK6y1rKxle2+P5tBTz2tC1ueHfvbPY3QPCAgjkVlGU2maWNKLSSDLxwbpLFL3icHim4BrKrxcYXLFF37yj7OaHnHw+C53772Hc4693X2cjyxXgtOzitODilFvzGiS06xqVjPHsNfnvbtvceHyFbYnFyl6Y1wU9Io+MUbK0jHo9/j4q9chCqSKoNJaShB80saQUrSud7Rcl7QGlYyJjxFlQnZWC+qmIrQWzUgBIa2tIM5X87mfxTn5Tkq1to7ulr5q11q3/af3lHif7p0QQrLv/QgdH+pA3x1dID+foZfrgH1O8oCnqvx1G/+cFBdaje9OflaQIJ8Pkll9Wno3bPx/bEdEOpe6Z/vrH5TNii4BbT/RWu/pA79vmsH37URBSmCkTF7mQiQlMKE6b+bU70r8rw19d6W7Wg8hFM5bjBHs7G0z2d3h9r37DEcKqZMtqGiSCFC9LOldvMzx6Qk+NuTDnCfTGXfffkKzavC15Ylt6L0n+eIXPo+YzxEHryO0QF36LD/8U3+NRzN4d75CRstwoOgfvk5VL5HZACkkRmuG+5ZLL36a+9/+Gsooer2c08Ujjo8es729TzV/wnI5Y1lL9ibbxLACPHnWY2t8AWkUjw8fE0XE5AaTaZbHJ+xfucFo5wKz0yW+SdrrwTmkCHg8dV1yNjvlyuVLPHpyiPEeKQWZUexsjZiMtzg5e8RifkzTlPQHE46OZhwdniLQlPOaZl5xZWvIpYv7ICPeWqKIuJbE2DGQYwggBd6TRr5i62fvIzamXqGzjhAbUAFR9OlPtjC5ReWGo+MDhruXyASAXcP2ac0/jT6t1+3Gyvv/svensbal6X0f9nuHNezxzHceaq7q7uqu6uqBTbZEUlRTNBULEsXYJqIksoRYiIEoCJQYsAAJij4EAiR90IBAARTDkRALEJJYEijLsmhRpCh2kya7mz3WPN66wznnnmmPa613yof3XWvvc+7tJhXog6viRVbfe/fZZ++193rWM/yf//N/LvbrH+GCdFMoAqRDix5VNaPfL7l6/TKvfPopfuPr75HlHo/HhYB0MUHx0kUFPOKEgVNEQae4wwStFaPRkMFoRF4OUDquLF0sFzjf0O8XaB1HqpSUBGdSOh3vlUwotC751PNDbl7dYtjrEVySK5YSHwKVsQgUzz99i8npKfPKYn1AZYqiX6LLnNFgSCY1SoHSAd0f0xOS4WjIcG+Pjdl1mtkpm4enfP/1N3h4chrbAgSuXLtCdTbjofsQslMmk5KqnlAvNc3uFqONITrLmE5nGOu4cuUao40ecjFHyUBwFp1lOFMRnKff6yXypufs7IxAlMi1xnJ8dkhTWwKKPM/pbW8hPDjvcFLjVEYWJMrWGAJeRB6HtAKRVRCytM8jgK9pQmBzvMOirll4yWQxY9Dr0etvcnRywtnZHNvA07ee4PDsgGGvT8j71FXF2dkpfTlitHmVxsP05IQQFL28jzEW5z2DYZ9bNy/jmji6q1I/PbBWrLSoZmuXHY8kvoYgS+p5vls5HJXvRKcm2FZPviPoie7vrTUrlcamWyLeypF2h/cuFWuCc874Y3J8tAP9GvFNiJAyNx8Dk2iNYOW8ViNp6+6uNTXxiE5415tfZ3xy0YGujGIV6AMuaZSvQ+ur517kA6y3F1a9Urn2fr/70VZlafudjDeCTKIQoX1VIQheRu6yiKsdhQCHJXjH9taQy5c3QLxHnpVI5ZEahDC4pef0eEL+XEFtahyGXlHwwZ0HnB7M6RUSHzzLesn+/UNErweT18jf/g0kjrp/hU98+T/G/Pov852DN5gspmzWDdc45e79O4hsiNYKrQX+3ntI2ceSxeUevRGFrbi/f5fr155i2B/ibMPx1JCpAt/MKbOScrDJYGuXyWRKvTRkxYC8zLq99rde+DzZeIehk+xuDHjNeur5AkEU+fDecnCwz/WbV8n7RSSAeY+UUOSavMgR0uGqJbXxYDSutuQ6arebRcW0aXjzzQ+4vLuLIOCDSxW9iHYaAjEHFEjrcKRqPIQoEGKj8r5rbc15zHKCkQ4r8tjeaBzzB/coxhv0pcIGlwL9Ouu+taQVhPn449GENNpnume8QKgAUmCdxwXoD4d86Ude5tU3HpDnHk/AuoCtwQmHEx4RQkQmpKTRgSJo8iKj6JX0hyM2t3coen1c8LimxpqGs9MjpF1QZpJM5oQQuSAiCLTSaBUr3UxrCqXZvT7i+tVLlEUPAkglEioEjY2b3q5c3ePJ2zd5/84+i6pCKUm/zBEyoPKWqR3vodpXQE6Z9chHJePBgGpzl9HGgvc+uIs6niCcoDYgihE9erhlxdnhCXc+XHD52gZyaamPHyKaOLd/fHzMpDZsbW8zHAyiNn0TBWK89XjnkECvLLDOEgIsqyXOeZBDvLOc7R+hQ0meF5RliQWa+ZSQlQjh0LVBuwWqWVAjCcUQkfUBhwsVMqRxWyLp0FlDYy2HJxMenk4xzpPnPYwNLKoKYxt6vRHPPvsUi1dP0JmmPypxVvHOu/cph2NCKDidHnN2dkamSq5fucZyfoaQsLEx4PLuNnXtUr97bfStMztPC+aL5JukFHgX2yQEhXdxt71cI5PGNdDyXMK6LgIlQkCEhA6GsFLETLfY+qhd1z5dQ23bPv/H6fhIB/q2R9+O1QUX4RudIErnW3hergXpRxeStkW2MSYp261VN61xrWWDIYRuu5YUcT66PSMhIwTfCuW0hhVfSgKqc6DdPvsuU10tFEnCnbRjUt2AQAjxnvGiNd30M48ig2AJweJFTvDgrUMWumvzByFBSLw3cZZbSAJNhF4bQY5ioFXsYTUVPssoyJAemmA4Pp5gsQQlCE2gWSyplhVeCiancRVnr9+nlBkiywhH32Ww/xa5MTwc/RKvP/NlbnzpR5g2D3n3O3c5/cBydXuH4w++TuBtVAaN8Xz/ew/YGI+4cmUHZQ3h5IjBcMDd+Rl3793j9o0dtnau8eH+B8yWU/pFzsbeFYqtPaqs4He++a+4ZDcZbl7GiYrl8oTNa9e4+cqPM88Ul670eKVRfPc33mE6ndDrFwx7W0ghmEwPef3732Nr+wonpxNMXaGVYzmbgDok0wOG2xtooTg9OUNbz5XdTfYfPuR0MmXv6k329xuq6ZysFIBDeo2QBbhZXMKpMrxSUE+JS48EcadqwInIDm/laAOeIAO5FxhVEIIh0OCMoTk5hMs3wEZZ4CgKdb7tcw6uDyI53sB64rqyxbXHOqMBHTKcqglkLKtAmQle+ZEv8uu//puczjyFyhHA0XTOw8WEQlhIJD6lSnolbJcbDMYjysGQvD/ApVHAs+MDFpMjzGKCCIZh0Wc0HKJ7PTKd0ev3GW2MGfQHlGVJUZb0eiXFqE9eFvSHfYIo8CJC8iEtsJFxIxIL4XnxpU+zuTFg//4Bi3lFX4HEsVyckqscWfSQeY96eoAaXWVWLfH1ElstmEuBcX2WzmBsg6kDzSLjd167z3/6H/9HYAVvvvUeZ4vv8Pkv/DgQmJ4ds1jMaRpDTylmyxPOHrxDPdA0tmZZS3a2rlJXcelOMcrx3lL2cpra0Csjv0b4gPCe6nhOOQj0e5phnoPNkEEjizHF/JTiwev4g+9QmAmquIS5+RnslecwFDhjyKkJMosJftDkeUa9mPP+O6+xf/d9+lmBlCX3Dt7HVDVX93Z58omnGe6N6b93lUYFylGPQmQc7Afs/IwP796nqpdx9LafoXLBwf0Fg57m0s6Y0WCDyekxSuWAS8F0tSQq0C5jItUgcQlR1BBVyCCZnR5jlj4GdyFwQBaivwvBRuye6Dd9t5aZ+D7Enr1X7YrwOJ+vEmbf0asDBBSt1E48/qdA/z+aQ6abuR1ZKoocY1YwfAiy6022RwvJdxDS2mNlWa7WdooWwonrEFt1JiED4ZGFX9GA25nNH1SBx5dsnW5SV3lMJbX+mmu//UOeGw8f0ix3aj1Y12Bdg3AarTUqVZ3QkOmCmAl4hNbIMMTaOvakx7tAwHlDIfsEKpy3+JBzWlcYF4XHnQs0wWO9p58rFgw4O5lzdnrI7nCDs5nGy5xylGHImW88wYZ4lurgPqJnKS7VHM3f4m/+P1/FvD1jMNQMN8f0hmNqt8fDoyN2S/ClphKSys7Zzj3ffO1DvC65vLfLIO9zcnaXFz//0yzrjLffeIf33/4Ouxs3qLVj//5rSAFaF8zcGb/0X/3nNGKLW8+9yDOffIn//Z//3/CX/9Lfoj/ogWgYjcfcuvVZPtx/lbffeZPtresIqTh4+AHXLt3A9wpksFTB4EJN0xNIpzk8OEai2B1tE04qDpu7LMVtpOuDyxBln+nZB9z8xj9CVJbJ3rMcPfej9LRCAsbZtNc7itRY64j9H6DdmxAgy7IO8hQhcHY2ZbxZoVRxYZnND7abmHD+YFtaR6BkaoO1/c9MxfutMRW5zvjT/8mf5P/+d3+Jk8WEbBgYjyTzt0/Z3rwZZ9Nzzag/YGfzGmHcw3uPqWuWswmH997D1VM2Nvo8c/syezsv0u+PKTb6DAYDtI7iOkqpbmuhTZLSOq2LtdYQRGzABSQhxApSo2NP2FqMrWmYcP32ba4/cQPjDHUDucpw9YzeeExVL/jwzps8uH/CE+Or+OA4m045OtzH5Qrh+tTzOYXQjLfHbG5u8K+++ib/5b/4dX7+j/57fOU//CM894UXODg4xfqcrK+QsxI3WfDyC5/lzbe+ibdLdjYvcXXvKnfefwNTz5guFwQpyUwW+8nENbwaiVQapTLy/pDB9oB6XjPq5VzdG4A/RY02kfuvoz58DX3wFrrnqYDw4T7h5JuEkxnq5R9DH8/igiUZF20FFyh6I+68+zrV6QnSWIpej2V1n1L3uPXUcxS9EcugqX1guXjI9rVLqBxqA+Oda0wOK44O98n6JXm/pChytMw4Ojrg9hM3ePaZJ9GynfZolzOtqugfZHciMd6j2QeuXbvFt9/+Ps7HsdU4QgreBjwGKSJyaUxEQi4WamtuHCEi0Q7R+vh23XUK+mv3xcdNNOcjHejPQeohxJs8tJuv4pD46rFWuEZ0gb19jbafk+X5itCxBt9DK83YMpfj4pr05o+ck0h9yfOs/5UK0w8M8C3Z6BHH/Ch8vz4mtSILJl1zqdBaUlULZvMTtoe9qAdtLEhivyzEzyAQOOtB1PhQkBWC3iCQFXH7WqYEkhyroMmiFOvZdEZdebwTBOUxjY/96v4QNepRyIxnru7w1f/6/0rfHjM8rDBnU+6+8d+xeWcTs3uV49e/ymz/HZRr+F+PSrb+lz/K6w8r3r17nwf37yMa6G/mjPa2GQ97EeEIgn4BX3zpEodnNd9//Q6SjOPTAd/4znt4M6cUkk888Smq+oClm5OXG0gVZWyryYKsGGHP9vnerz7g7d95ld//H/wc//mf/V/wq7/1TabzJRbL8eSAjc0dHjyY8OabryGFZTRUTM5OuDzc4SSR/mQIhEbgg8A2TZrj9ZxNZ3z2+dvsbVzBGsOyNhhqspMpVXWGOqxR5oDh9iHNJ59h/sEDRroAFI131N7hq4ZMa1zwGOc6JCnackxydYgqYU3dUPaKhNTYrqLvVCA7cKqtbFZme95GL95fHo9KLSTRoVXCO6xtmMxPuHrpKl/5qWf4p//it/jG77zPcDjgE089yd7mlbgvXIPMJU72CT5wcnRIvZySCcvTT1zh6pVP0xv0KHo9sqIgpL6rVFG9zHqHtyauXk59pigpHMlYQmukiqtnG2vQSqBlqs58QAbPIM9ZNIKzxSEiy5CqR9Alk8l7ZAEO3rnLh3ff5YN3v89Tz/1BFrMp1jgQktHWDrPFjOnpQ/Cea08+yZXbz2OF4KdGG/zGb7zJf5f9Cqe//4hP3HoKe/+Ik8k+xjQ0dYVraj689xaD0Q4Hh4c8nDtGe4KnX/48Jw+P2POBpq7jdTQG4xxKauq6oWkMzi146503Kfvb7F3a5fKlHa5c2WDupmT9K6izfeT0EAVo1cfMKygCs7MjFj5n9MKLaFHQ2GXcVSFWXaJc9cBHG/PakekxvSLHmgXGLZB1xnC0xdnpCcPNISI01NWMyekx2xs3MEVBY8HXnnyg8MYSvOHa1V12dzZZLBZAQGcKZ0nV+noLVbT/nwqvNBYXJDKxko2r2dgao7QmtNK5UuDdag11SL4sIgWxgBIioFTkVzlrE6wf25Ui+dp2vFrIuKRKdH7143d8tAO9j71BEjwu0wUW7ZVcG6dYQeCJvPEYgl37ONARQ9q+ffvYKkkIyVDWvCZt6GyNKkqVniczrwJ/e56rHlN67DFa5RcPsfY/XaCXxHnTdCMVZcZ6UhICSAXOBWRI8/zSx9aF9FGoRKVlLY1kYQVNU5GLAucznGv48N195ovY05YCnHU09QKA6WKJb2pGmyU3PvUcugz4aYa4dB2V7XG99zzf3O8xeOc9PvXkS6hrV7Cv/it+PycUz94me2ZI863vMD26T388pt9vsMEwbRRKFozyjLP5kt7GFkN7ynJ5wtH0jKD6fPjuMXtbGcONHl5rrC3IZE5elHGG1nqc7pFTQ2+Ds7MJZydHfPCt1/nMH/tjXLt/mXv3DpjOouzq/v5DNscb2NqymJ9ydnLGmTtBNBakp7aGxgeK3gZa9xmNxwQCwVu0Lrh+c8Dp9AilCoRSaCEQN19g9v5NrmzdJS+XPDy6w7f+m/d48qknKcbxVjQ+sZKzKI7jguuQms4sQrIxqRDC0RhDXoREXFrnpJyH8VdqeRcfW7eqNdsTK7tpl3+Q7D+u4rWcnB3z5EtP8+/nmqeu7PC9b72NYoAajdC5RKo4BlVXNZP9+xQZbO0OGY/7jDdGjEYjhNRILQlKEaRAWggElBJxP7mIn0Om7WNSxG2ASmtIqm1df1bE1bqt45ZCYK0gOE8+GOGFZLmYcXTwPfZGuxyfHvLO26+xv3+XxniEzFgslthWpAXoFQM+PHqTLO9TjreRgxFmesr2tRu8/JLheP+Eb/+b73Plj1xmazSiWcyYG4WjQEvP2eSEwegSZX+DebXkjffe4eXRp9D9nGbRoPOcvtZoY6gbQ1H0Mccn1M0C5yybm5v0dMmt3U2ubMcJhczmUENwGd5rrA3MXYN2HluAaALBNCyPHjDcuAFNlkaKHZ7AtJkx3hlz6dImpj5ledpQbuU0boorarZ3brC9e5v5ZMK8qtjZuUUIS6r5hF7ZY+/abR6cnhBqB1JD8Czmc/Yu7bC1PaYoNNaaxGBPQLoQXYuotbOVH1uvpGNAVkqxWJ6hM0Gv16co+thFkxCd5Pu6ems1P78+lx9CHCsWnQ0RpXL9hUKtNe8Ltv9xOT7SgV5EtZrY7/axP945JOgIGkBXCZ2Hw9sn0iULjzu6QBxWsorn5vPXXnbNfPmhUPuav13ZlEgg0jqR6ge8RjqfttfaTh6s3jmglcZ72zFVu5spZbqClrgnEAkpVjKjzHIubUOmoOgZerlCyUgik24W53JlvGms8wgESmdUy4aBlmxvjsk2Nrmlary0NNdewOx8hsrcJJ/NuSmPuPb8s2izSX382wzvzwjjXTaH22zd/4DxIDDeLCgyh22WNM6jpCPUhuliQc9KbNNA8CzrBb1en8WkoikFy7yicQYhcoRd4M2iY7QLr5Ai4IOKi3lCIPfwweERxjRoHXW1JbCQml7Ro8w0VfAslxXLecUol1y9cY2hHjCvLbUD6xxlr8diMWO+mOJtxXR2zOSNGWV/RK/ok+uCML6M6ePR2fkAAQAASURBVF9G1sfUzZJ7D+5SnSmyp5/Ghgg9+uAReIKIlbwPrrPbi7K1LWnIGJOUGS8Y1kVDW7PJR01drP3XmtgK1epEpnxrZwKpJItqwcZ4l+c/cYtLg5LNfsEk9FCDnMo2WBeitngzJVcVmxtjNrY2GI3H6KJAZXmEk1PCqpBRwImka68kKkiQPu2VT05diCi16n338aSMkyE+BJyLevZCyLSpTCBkTj2fMD1+wOLoLmfWcvfeHR58+C7zecVw+xZeQFVV+MbH6Qhg2BtzdnZCvzcg6w0IgDU1Sg64fvsKvmlYHp7y5ndf5fnnn6YsCoyROCtxymKtY7GYIlWOtZbZ8Zy79+5zaW8T52wUx0moYqfHoVWU5kWxsbHLKC/Y3RtS9nUMdCHDeIOTJSIUSBuQwVLG5YMR9REBaxwq1yCKeIOHgFCeGkt/UHD9iSfJspzTh8f0exKvLMVgxMbmNXq9Td59522akDHc2IFQYUzFcLDFxt42905P4thu2r1gjOH6tauMhj2EDHhHZMYT59pXQmX+vMmtzHLNtuO1q5sFUsZWjZIagYXguokQRLvFvmX0J/KxT1704jx8Vwi23nLlQz/Ox0c60HewTzdaQSLbsYInz7HoAc7D6ede7DGB/jwxb/X4OkkuvqxHtL0fUgLyQ9SVujW1IbT2uvpM6+/5yPmk03zMz0N6Qiv6433ss3u/ljqEgM4z6rqGIFAolARnAniJdDmbw4xXXvLc7MPGVUm/v6TI4/jqw8O4opJcEFTAWUGRDZG5omwcty5t8cyt65xMKrZf+w02EJxdfp57jefb33uNm1tP8eznPo3Z1DRTgXziCd5/7/sIv0GFpN9XXNotyHsNeaaYLRswFcYb9qsGg+bh/iEqG2HQsYccHFrBbDajbk5RuWR7dIumOsIuLYYGry3jrE82GDCbTdHesr29y9MvPMWvffW3sbNTBr0R4+EQMXbsbu/wwbv3sGaJCBZFhnMzfKHYvnGN7e1dTON55833OJ4uKYuS+XTK3bt3mS7PkGaC1xWDXsGw16dfDJDjTfJewduLnOXxGU7MePHlH2VjOGDpYrCWKWGz1mCsicFOtPKuK6SpBSmFEJjGdrDo4x3W6rGLI3XnA/vK8UXz9p1zjhok6WcptkqtETJwcv+Q/qBg99kr/OwnbnP3vfvce/+ID+4tmCxqTGbAzLh5e4feYESW9xBaY31cNyyliDwKJJnUBK0IwXX8gDiGGCvCdryu898Xkm5jbZwkSYqQPn1nOs9YTKcc3HuD+fFdSqF54/XfZP/BhHoypyi2GIxuYoLFGYPyEmMsVdOQiT7zxYKN7UsolRGaGkRg+vCA3uYGt5+6yeLokN/5V7/G3q3r0Bsg6jmysWityLMes9kxSucImVPqAe+8/haXtz+PDIJlVVPXdbeUxdQL+mVGv9xCa8Xu3iab22OaUFP7htzFkVmXLfFFDyd7+AZGgCsEZhpwJhByie5txJZ0kGBFBPikRxLn8y/dfp69q08xOT5gMZsieyWD/jbBO46O7/PGe2+jyyuQCUb9Hcr+OPoL3WCDQuWSrIyiRkopbl6/htZxAgoUeVYQaDplOyEUETQNHRLTKYuG9VZqsrPg0v3gIm+lvebdAH3ydyEuwZEyS47QxXFPrWm5rjH5WRPOScTsxxWAH7fA/9EO9EkfXqa+S1MvCEEgUx8nxu6VE1j1hpLDpK3Ou7oclebm20D6uL3wKyOIK0FXmsv+/H9hNSLnz/3uemYbunZVRwLterGrdHcF85/fNLaOLpxb8CAl3l9ImUUM+LWpURkooSFojK3JdCQxTecHSHWPH/0yPHENpsYjvaQU0NOCnUtD3rw3xPkaWzdUiwqcZV7VPHVlj6efv8TNJy9zku1gruxycNbwG1/9Td5++FU2J2d88T/7i5yKJe7AEmQOL/4R5K1/jxBm9PI+L3z693F77yav/5t/Qm/7MgMKqtkp1dkhWViwd2WP8cYzHJwsuHP/iKMTQ1FKqmbGoDdke3uHnUtDRKi5vfeTzGcnPJzc4eDsQ84mZ/jjE4YZfPaLf5BLz36B79x/QFadsb1zjdn8jNl8wmh0iQf7b/Pg/gfIoNBKMTMnjPvbNHXGP/1//wuu3LjGy194hZe+9Dneee0tZpOKjeEGdtvh3zc8eCCQZQ/kHKem3M9LBr37CCoGYci1m5/myc99FnV5zMlbd+gLjdYKJ6FyllwocilovMV4CyEuawmBLnHTSuGS8E679GPdlrkgtXzxuMD66FpGHS7kY7Iau0kh9k1bn+wDtjHorCDTimVTMz+eIwNsbY+4/eRlnn844+DeCfv3j7FqhMhixYUUCOHIM4luBX5C22ZbVejt8JUQIkmYkvTK4+22XMxjL985bGPjrwaPLjOsjZwa6w0yOHJ5hbe+/0+ZP3wP6RdUheLb33iX3Y2rSMbk5SVGm9eZzU8oVI/gHMEF8ILloqI3GlOMNml8Q3W6JFNj9HiGm54wun6Djd0NpnbCb/3L/5Yv/OGfRy8tcj5DCY/GMOz1aFxIEwNDHh5ahCi5fGnMYjZlOpugdcxepBb0+z16vRwlQ6pqQVUS6ySNDIi6QpWa/OaTzGcLTu68jzmyVLmA2jELmlppNpuat177Hs3iGBdSu9EpSjSNBlVqeoMho9E2vWs3GI6vMDu9y+Hdd3nv7Te4c7fmJ3/qCQotMW7J0jfoUDAq+2z0MxaNI5DTL/tsbg4py5Lgm+hDkwStsS6J4KRRxh+UjLZmR1r8JDNE6FHXBusanKsxbhnXClOskoTUWhIk+xQSlZDdqF4qOv8XCyCLkkV6y9bxtmX+hT8/JsdHOtBHBxchPSEFVVWhVBazusdB9BeO9SAZfhB8E1pd5R/kNAMrhb1Y0UtBR+wMiFgNhd8lS+xAg7V50a7t4M8/6QccMeGIaIJzjq3NywhpInzZkbQ0UgFOY0Mg0ETZXjxZqVnUc46OJ5w9UFSHjvGVDF97pPfkmaAyQ0QBZj7F1EucExg0wgVev3NMmYRG8s2ab4w+z3f+zf+HQVC88onP8/v/+F8ge/ZnOX37n6PFEaJp8JMTZBZwssZNllgUYu8mP/Wf/l/4lf/mH/Pge19DhZrh5iabt5/jnTfeonr/HTbynN1M0b++TcBz7ZlPcuf+O7z/wXu8f3+DT7z4JL/5+i+xt/0U4/Emzw4VU73k0udf5NonXsGonPsHR9x995CiN6A+OiPLJA748N777N+7hw+Oq1cuUVcFy+qUfm8Day2f+OSnkQje/tYbvPfd73HtietkSrA5HjDoD9i+tMNrr30dMxfsfvYVPvP0dW6JE2bf+WWuPPEC0xe/zGK4hZ2c0tx9yHAwwHiLCSTehwQlaVyDjcMeHZLjnE88khBVxWiDv0+741uHuUpwz9vO+Sr+MSa4Zk+6Qw4I4EVU9pMywubGWLwzxO0JKjpUb5hOJ0wrHyuwHvS2Jc4rvNPRloVHSY/EIUSW7lkBEiwG5XNUIs+2gV51uuURqosLpeKCH+8t3ktAo5SiaRqsMRACOtOUwxHH99/j6O6bCHeGFHBvH8bjHSp7yubmc2xdeopeT3O8P6WWHmjIszjat5wvGY63KftDsA5nF9RywunRnGs3rmKygM5ybr/4Cu/8ztd47dd+le1L19B5H9NYLIG6kQz6fRaLCfv332V35wrvvfsWn3/5eYZXR2yaAq2zLhhaa7vNmd4bFrMZZTki7w/wIuCLjGBnLIRhsbvB7Lmnefebr7IzypjhWSpPMznkg1/7RVCSUnnIMhwCU1uEbxgMLwGCM+k5zAJSjxltXsI2joeHD9m/P2FYDHnp5RfxzQRna7QMyGBoKrhxaZcHRzOmlcEYy2DYw3iPREFCY7TOkzUZ4tz8um5BsrM1k+waSEogpcY5T7/sIwL4ENcJKy0IVnQk0Yg+pdXcshW9kXFSwyWiXiLtWWeib81Vh8C2EeDjFtzXj490oI+KnzGwSQQ6iysS49FC648XvFmp6K1e72Ilvf57q2o8jmDExTRh9U4JThSS6KSQXWJgnaeTX15zp+tkvEdaqReOc5DrhaeuRHbWM1eBEBqCQ0kVoUxn400YBQe6DVEB8E4AGdb28bYgzz1ZlhGCi5u9hCJIhbUjrG9w1pCpnGw4YLGs8KEhzCUPD2a8/vo7XOm9TvPhO3zp5R9ltLfD5s1naQZXqI4eMHpvH9OzNKXA5R7VNDS5itWaVEjv+fDOW7z52jfRTjLsb+Kl5uzwXfBzLm/ucvP6ZTa2hiyrht/6+jfZutRntPk8BweH3L13n6MP3mVzc4/J8UPyq09x65kvcevTfYypIFcsZguOjqccHM3ZGixRIkdliiAcy2pO1dSMRyOsr6nNkiwv2NjY4M7++4z8Jpf2rjDo9zk4ukNjKo5Pp/ig0VlJOSgYbl7iZH/Cw9kZDyfw9GDCYKgolgcs58dIlSOsjeOhAoLSsYJ2gagnF8l2SsZqN4gQK35nViQjv9qVEC6OdT4CR4pztsIFG1+3pU4Zcr1FpKDbDBkCIdg0sy9j24qYpDiV4XAUET9Hywyd9zBVjQ81or3GUiFkHBX0wRG8jGxrpaN6nloJoiCIsHYICBU3kEGII4k4gtBdchOTHWILICF+Tmruv/M6vpoRhMM4yenEImXG8dRw+caAze0eQixRIsO5gA0N1luaxjOfnWGMw1tH8DFYsPQ4K5FC45oGXzcI4OqtT7B/7zVEHuiNNkEovNeAwzqL0iWD0S7LukLMJJNFRV/kOOcxwRC8WQm8BOJ2vsGAvDfCNob5fMZkfszDB2eExd3I1LcNtl6yHEGzsNR5utYhSl0XwfKwgtqFtInZ4AmUs6OoxyEUUufMqw8pywN6vR7LpcN6zac/e518WLA8zeP4p/f4AI2G3rjHyBiCEmR5QV7mccTTR/8YyciuIy2L1Hrxa2b1SH88JawyVelKxZHJFrkNwcY2AY6ATsJgcT4eKVBKd/eG93ETpxQ6tXUh2FgEKe9QStLOy4eQEpQ1T/pxOj7SgT7e28nJCYFUJUJqQlCpb34xcq4gmvP9yfOXdd35PfoKMZC2s8jJz8aR9OBSxZFe1UM7G9oGee88UrVZ7fngvf5+F5f0rCcWyas+cu5h7XPFDNjjvKGXEiGfzrkNHEKuJHF9EBAceZYj9ZCjaUboBRYHnhBiGyB4xe5mSblVd+8b0g2jvSQUkmpecXIYuP3cBtduPsfexlWyjQFqtItZNhRnR+QP7oM8wW6PEZdv4sOSEAp8cGgtCDLw3quvMz98wNb2LmiN9Q7r51zaHDIcloy2hwy3d5EWavtbFGXOcDxCKIlzNWdnJxByJsenWPYpNk+5+rlneHh4l+AdwVrcsuH04THaDVFZdIzg8RI2Bn22tzcxCSXa2NhlNNpmVE0QeKytcCJH5j0CEq01i9pSLxfkwTIabVAvJI0PTBcL6twQZo6JMlgbOpEmIQXOR2fYrhMOtElndDlBrO3QFq3B0fXsO8JlZ79tayrZ0znzbiv+FWS5mippH19Z47kip5szXqk+RqLVqnUkQlyu07ablFKpN+1AmHT/JAg/fa5Ip0r3SXKycu3+jMlonAgRCbcXgAwpORTt6Gw8C594OjIluM1iysP7b2PMHCcEy0qymFsGo0A1d+RlyWDcp7E1wRNh4uDTPd1grYkBx0d5WhsgWE+v10MJSfCe4B0Shcr7yHzAbL6kNoE86yOJao/z5Tytj+7RmCWLZc1iuSQvVLKDqJKnvEwTFQKkRGUFJ6eHnB0fMpucMJudcno0wS2PMXWU9bUhSSnPwVWglCdLo40qB2czRsIxUp6xksw29yi3bxOqOcEYAoqltTT1EiWjpLFxcHo64/7+h+wMNgiUNE2NTC1J6wVCZ5Q9QVnk5FrHaYc1JLxrlcokQ7u2ba6F3cM5P9gWUwK8ZzY/IYT2+5CIINPuhZTYhpAgpxZyT/cHbXEl18h3SR6dpLR37raQXeuTtZf7uBwf6UAfwopt7hEI2btwzdfDdFh7bEXIuJgLXAzy5+D9BP+0krni3JY8jw8Opc4r6Hm/2qMc/XTS05eSFWu+Oz3gPPLQ/vv8ebZM+wsJCh4hdHLyDi88jTUE7xBkcb93cPF927dPTtIL8LZi0M8pBxvcPR7wztkZ9+4KnA/UTcA1gp/6QsZzA4MQGuMtjZkRZEGBIhsoXGUwC8327Wcohk/QTI/xweErh5rfYcPcpppPqQ6+g5jsoHZvgo4ZPL6OyIOUnD7YR1U1khpv46a3rJdxabCJ1wGUoA4ZcyGo6th7NDh0L2Nvbw9jFyybY2aTGWeT9/BB8ulPv0BtGkob+989LQjLGbOspOg7pKsgeGTe5/ruFuOdLY4enmAtlOUGWT5gb/cKbrFgNj1hVk0o+ls0FoajbYybMjudMFvO2exvMh8Gcp0jVI+mNyIsPmC+u0uZDxE6J6gm9oJDIKTFGzEwRxnQyCCPhDJoRWsiaB3tQSYOiensurVHEVreCOv5bUqAO0vr/lzpjK8bYlg9K5BUFUPa2CgSFLvSCA8h7Ylvkw8hECrC/FpmBNV0n0GkZAAPQkemvZRERcmgIjDV2WdMglqlu3i/RElcpWKwj4lPqg6lwDkfx/YEVKcPOT2+hxNLfMiolop6aSiHFlcFdFZQDntQxT0E1jlI4jI+eJTOyAl4Y6NCsZNY67l6ZYeyyLCmiZMDSrCczcl6eyzPHjKfnpHpmv5ggBSK5XKJUgqdxXbFvJrT1BUh9NEqT7PhPmoGEFX+vBRMZ1Peev2bPNy/x3I6I1iLaQLGgrWSuoGq8VgnEF4ghafQin4/Y1hm5EVOgeNGseBG7tlTOce3r5K/8BX8yXu4s32cdcjNKxyeTqlnh0xPphwezHn97UOKzd/hS1/4InnWR9gAvkEBp/OGxoLSGaN+jpYy2jGhQ3tiAiZA5uka+ZVZhs6w6C64iEikRIG3nJwcoMfbSKmjx00BOdpmSsYS+iV8wIvVMpoQQmTqi6inQiDyuaRY8QVaHyvTOYSV9v7H6fhIB3qZFLMgXhiJ7ALz41jrjz6+Vg23idx5LL+74KHzwu2/Y++nq5hamN76VBS1/SORIKT2nJPj8hf3Mq+Cevd+F471sB4uPLdTlQrpxhFRJcrY1eKHbkxKxFGkNhHJdYYQguVyyc7uDpcvX2FzvMM/+6UTsqxH3ovnG0xAiYBp7KrwE4LT01O2BpuAYonjzFjmp3PqZh+sQQ4vQTVn+sY/4/bVX2B55XmWH36dxYP3EA/fRF+7iVosMRhcDYVQfOb527z9azWL+/cIGwP64z6l0Dxo3mMjv4LeKnFG8N4HH3A6l5ydHmPtIcvlDFsrBlpzWCtCA6Nhj73NTYJvCBpOqlP6ZcH1J0d88YtP8vXffJetwVW2tnIENaeLhxT5TYTeoGZKIwW5goPTtwmVYvfGZRbTKZPjY67duMnXv/0aT15/CiF6+LBkdlazWQYeHhzyyY09btx8jvHLX+ChGCGu3MRlgmBryEtYzmnFnCJT4gK7/lwATrbtEwm0XfRBqwvx6Cpasfa3sGZjF9tZj/2zBafOvWTKGtIPRHIhbfPoXHUmZByP0xKlBHUTUgUecdRYJUc4LLYhkm6FzlbtrPYtaR23SpwSCEGidQZEO67rOiIIWUoAUrtqcTbF+YA0IW7W8RLjwNSKpRc4JVBlRilLRDCUIqM2lspGiHtze8zDB/u4ZgpCYp1lsTzj+Wc/R10tOdg/YLZckhc9FnXFfDEnBI2xcDq5z0bYwRoY9LcIBM4mZyyXSwaDXlp2ZAhNgFCQiSGIBiECzhpOz4746m/89zSnNc7GoBYQnE0DVSMRMqOpPdXCInzOYNSwkUsuX9rj5lM3eOrZq5SD64hv/yK9ao70gcPQUJ68z/LkHur9bxD238RR0PvJ/4Ct61fo1U8ghaJpPE9/67v8k3/xr6myglc++Ume2L7E2ekpNJaDswneeTYGfXplibGGXlESvIUQkRm/Zreis731bZtdCUYb7LuWEII8zxlvbCKExHm/EksiJX8iEqhb1Me5NskQ5DqL91JI6o6k6l61ImpRh8U5h3ABrUtakvaFev8jf3ykA/3jjlbBDtrdxv+/HReTApGa8MIrwEaXlpTypJRpZESuMaBX1ZW17fmsiTqEdV3l3/0zwaNo0vmkoF3RmPpSxK1NcXOTjBVHqnhEes3Y95I0TYPWGiklVVWhleKll1/iX/zKu2S5oyg03oOThiyr6RU5QmUY55HKsqszFicVbhkV02QxZFAOMYq4lCLMERi0GGI++GWEFgy1oBhfI2zewvbAhDE9leG8oEZy47O3+F/9n5/m6Hv/A4uTE4LzDPqa8eXb+OsvMj25y/33vs+Dt74HZsorv+/nmZ3e5/Dua0wO32Exn/KcKhn89JfZuf4Mm9vXODo8JtNRWzxUNaUO/KGffonPfvo2umkYj8ecVZbf/v77uKCozARnligkeblBWMxpzIxMSra2t9BKcfeD97l26Sqnx0fcurzBJ6/d4M67H7Cwlh/5xFU+9coLXL15lcmDu+Sf+Unk/ADfspKti9vKvEtiJqKrTC/a4bpSo0/BniAQun1OrKSUiuRPqdbsJPUhLw57Xgzujx4rqP5xxw92hSvRKSkEWUrIiyxLGx1b+4sMc+ccIhBbQFqBDASxPq6avgvn8bg4cpjuo3YiRkoRORbAcrkEoNfrIYJnMj2lN9whiB4n0xnH1ZLKwdAVSCfIvWRDlxS9Ad8MDSeLOWUxol+W8VyXns2NXRpvOHv4EFctef7524yGGZubJc41VHXNw4dHPDg4oN/vkeWarf42V65dQ6oeD/bv0Niauq6YTE9YLpf0Bzllr4+UOc5alJwzt0cIcsqihOCppkvuvV7jBhrnoKk8y3nAGkWwDulr+rnk1u6AH/niM+w+dYmdrR20zvE6x5cb+HvfRcwXLI1GaInWDXbRUN9/h2J+RqE9fd0w/cZvY178CRwznKtBap750mf5P33qGf6r/8c/5rvHlvCjL3Lz6ibvvnPM6YMJ4+0xg+GQ4WgQ5WeJkuPR71mkUjGwt7pd54zm8ePHsQBxsQjJc3SWkKTUv9dax4SyVfc8t5I5CvRIGceGoYXsRdJh0ESmtIyJsk/tukQS/dhh9un4SAf64P1q9E22y2LCmlQt/DAQ5jyE1P49nPszPu/CE7sfnddvjkSj2IePQjLxV9r1sFLK7r+2v3Tx7H6YJOkP/jwtHNHhvECb6LTnnvpjaeSqXSwB8caJCoDtI7Gd4GXAeEOJQgSBM1Hr3FnBcjHFi8hq1soz6GVMlpbNfo9rWxt4HNaAJyMLBjXI8c+/wt3vvI94+G2OxIKZzWjeeINGHcapBKnxwuGD5YOsx2DzCv1bT7N9SyOlphiV5Cjeeu2r3Lt3nw/eu8e7rz3gi596iVxqejuX2Rv22H3qOYKHzAtcFr+SaTUny2rwC3yjCGisy5h6QzkcIPUOlZDMlgusH1NoTbWYsZw7IKNfZpgF+HKAExqhNKHoUZ0tGWrN0eSEnd2MZ67u8okXvszxErJBhtISHywohzh5SCWmIBQhyG7cqZ2WuNgelFJAiNWv9W6t0k+XM5HgViqPbZW+sov1yuTc3+XvFuTpIPKIBqwrmrVkJdnZCwlOFXjaPLcTf2khdqlZTwKMaciyjKhYJld7JlglL+s23N7XNpEYpVbdveRCINg4c91+H9ZatAgM8pyT/Qmin9E0BVpAL7dUtkESEyfjA4USfObFT/Krv/qbmJDRCIExFVJnDHpjTg4fMChzrt9+iudfeIplNUdniuGoZHd3g5PTE/Z2trDW0ViDc+BdzsZGxsZoj9nsGCk9G6MReS549pmnyAtNSCQ850DpgiBgWVVkUnPz+m02hznf+MDgbKCQsFFInr414tLlDS5d2WRjZ8RwY0hvMELJnDoImrohNI7al6izBbluKKnjOKMUCFGw3LnGYv8DqmNPOSwIN24gbJVWtkb9gv3D++yOtvjJP/CjvP/gLnfffIenbv0488n7OBGQmUbnOXmeU2Z5JIsm5ltbWGil0oKxlY2vWk2PokZCrPYb1HWNPT1lc7yFUhrnTSTTZZrg4ns4F3lCUqvuMe8dzstuYqVbXb42EhVbAZHP0IkuyPP3ycfl+EgH+jbItj5uRSi+CHKfD+bdT8L6zx5/cc+3z6OHvfjMdWNre//R4a2g+Whosss+z/dDf3hwv9iJP58MnK+4up7Y2vO7x9c/TWh/M6IBSkmcCzjvQAiUjlrjIkThCnycJiiyEmc9xkYpWCljW6LoD2imx+QbW2zvbOJEZNzm5RDw1I2jdp5ZbxN/AJOQMV1WLO68ztKdUTdTnCSqkRnDQEjyjT2K8Ta5yiiynN6lbXpS8/DBBzz48D5nh2fs9MZ8+Q98hbmbY4NFCIUuxiASAdEuwMapACUDjh5ClwgRM/s4MmYJXqNyhSoyZCY5mdcEZ/FBIgJYa+iXPbwmac8HVCIOLRYLirLH3AmOKsdTz2wzm8zRSkJIaI+QBOoIvXogeGSAoBRximrtOvru4tDqIrSXbq1r3lnnSkch2Vdq36wTTlu2/JqRdOS1lV2t2VxrL22DvqvuV73MNpNdVVShjfcd10AK0cHokXO9ssmLMtQdEqdWNt5W/uertvg4XqxadyEyrdukwaciQCjJaPcqo/EOlau4trONGXq+e/gOk8pQ9kr6/QwhLcvasbOzxTNPP8nJ6ZzJZEK1mIEQhMawNe5z5eou165dQWcS68CaOPI1Gve5fvUSDx4csXCBTBR4BM42nJ4eIsjiZJCQCC0YjTa4cmkrbVjzcZeDkHivcNjIDQiOIDz9oWa7Z9nYHLG3M+bS7pirV7fob/ToDUryokBlWeQluICXIIKLQlLeYUOP5RKGhaRUkpOpx2wPEONLhPEeZjHD6R56ew8v6mQbKkLrzjNZLtm8fYkzM+Xs5IzJ0QzrQChFUeb0enl3ZUF04k3ttRFSIlMCuN4iPefDzrm/ZLdrdt3ynKSQqx+JGJx9u5r8wuuGNFoXt3kmXlRqHQihUroaJycQPnIkSIt4zm2A/OgfH/FAH51HWHN2XbW8dvywgL4ejOGiAZ5/3noWuv47K4fUQuwR5mdt9WenxnTutX5vRxugH1P/X+i1tmKQFz5fOu/2Oak0pI0mznt0FuU5g48rPvM8izvI0/Y0gkBJQVHEDX9KqggQiKhHLpBkGsabfTb2trBBInWUxlxMl8znM6pFRd57iqMmZ1kVVGZO1TykCQ3V/IhSBbz1GBsoxmMWZ+/js3tI4thkNi4pdIapGo73TzALwxNXn+TJz3ySu8fvYRdLcB4vJUJaBBLvaggGKT2BHOQIkRUp8CZeg1ngjUPnirJQjEeadz88INeSxou46nI6pZdnbO0MmS2XWAcqxGplPq/IspLFEvZPamohqJ3BNR6tewiRE9B4XYPTsQcY4n9OxiUe3rcLZ1aIUmcnqZKPmxFXNhETxxX82Y4geeE7m1v/2do/kjNfPRZRJ849v1vj2SUJa8RR0RJMfZI5bW1RksZN4r0kJUpFpx/Jrym4t7BqCI8gcfE5olPjaxeUtCJY69/P+c+2+nebfAshKDZ2uPncC0wnR1zb28M3gft3J1THJ9x+8hab20OE8lRLw2DQ49nnn+HunQfkmSTLog1rJbl1+yp7V3YZjgfUy0gc9TZuVCsKzbWrl1jOa4Kd01iBdY7GLJmcTRgMNglphXbZ01y5usmgn8dFMCElMmkTp0sJjwueRb3g8vXLZOWCK9f2uHxll63drbjmV8SVzs6CrcDZhkwJVJ70NBBobzDFmGWlMcKhtGRaQTO8wjjLkXuX8SJgQ44oS3w9Qag8fe8eTUbVLClG24y2NvDGcHL4EOMEWV4y7BcMCh13MaSkLyJNUb64S8o4n6ytrldr7+tFyPlrWxRlfN0EwYs0TidpJ1Tia7UDKZ01eB9/T7gk+S1TK4EY6EPyl2kMOYQm3WsfrxW18BEP9K1iVtJK7BxFu9P94sjRD6ra488fhepXldKjgHnH8GWtwsCkTDGBnaFdprB63uOC/PlK6jHnKFLl/UhycL6aP/doCubrsG4LwMq1rDoGF48xkbktpSLLc0rn6fUFZhZJK5HYLMgKwdbeJv5YpP3ZBbosefjBfZ6/fZXbT99kc28Hax3DLcVb3/829+4eMjueUdSKG19+ge88nDCd1Gjn2BAOOfJcsT0+WyhGA0k13sB+8acxH34A5oiZyzhdWI7vfIuz2YRl2GJ+aqDIkVcGvH7/DZ6+cptJ2Gc2P8GwQHmFCgIhB3h6uGAhlKi8h8Wm2W2QXiFDEXetNw05gmeubfErv/JN1GBM4wTVosbPFzx3Y8BnX/g03/jeG0xmFaiMPCuoysDh/gnDso/f2MRZR8/3QczxzkYSUNAYGZ1RrjXCe0zjHtuIWekisArKIc4Tt2uQAzGXbBPb+JhAKLVClS7E9hVuGuFwwZptrgXJlQ361pVeSBRCCuRtYE8VWMtDlWt2LgRSi8gB0QpnGqyNi57yPItJZne/erI8T/eE71oV8TO6NGJFl4C0q2vbdkbs2XucdXGxjYrPrxvDJ37sSyAEhZS4+ZJyvMW3vv51fuQP/ARq1MO6KLw1Wy4YbG7wyc3n8OZpTF1jG4PUUfK5tjXLagEogkua6tYgRVyT/dzzz/Dhe/scHp8ymU1xtsHbgKDCNDUbm0NuP3GVmzevs6xmCC8RqPSZKkIWEGme26WJnS/+vq8Q1AR8TnBxt8Hp6RlKF1E9TkS9eS37eCEJrsKicUIhqxMY9mjKEXeOKpS0hEt7lE+8gpsc4Le2UDtblA7CbIJHYAsbPYUNGCHoFQo7bcgHQwbXPPPpKdM57I3HbPZK+lm6Rp3tys63SBlYLpeJA6Qe4U2t9ExWd4LzHhU8OosCQr1eL/bqVVr05FxsnSXLlWKFqq4wrHQ/0E6uuIQ6tMlBq9KXZJZTgtJa1w/paH0kj490oO8c3g8hFbVjaG3V8PifP646uPC8+MMED0XhhvXEITI9AWy75r1LBh4J7P+2H1T4c0F+lThcSEHCOnqxSm07PkBKebXWGOdwIcL0ZVnirQMcNq10LHRGv+xxdFLhrEBncWJgWRuW1Rxr4xpNaw07Cp66dYXnn7mK7vdw1lEWY7726/+chx++z9H9E3J9hRc+/T/nZPsFlq/eZ9yveOr567z8uc+hd66y89v/BZlpKNwcPWyY3n6GQTGl+cavkG3eQr/848yf+gU4PGK+aDg6+IC777zFa7/9Kv+3f/lvuPbKi/zYT/4Yzz3/FJf6Q+7fW7CwC6QcIGUMHnW9RLkGfINAokSGUhrrLMZrrJNkeAba82OfuMn7r99hWXnyfp8rT93iyz/5DIONq9xeNLz2zh3ev3+ECgUPHy44PHjI1q2rXBqXhCDxFshzGuEJviG3FqEjXN+EOrJ907XxhCTTSRewouNcXUufqpN1m4uXWLYIOiCQQhOEO9+PFyvbjsncqoW0blMXUYAWrg/n2lAJHZLrtp8qN7FKCERrd0QkKMts99yWRBdJqh6tNTpNfqw+mKDlwKxIeT6mq2v5bZxCcF2i3wX+9psJ4Gzg9OiYjsgoILu5x+974eeYnBzDYh7Z/LmGBhbHRyBlXIErM0KpcN5gTfwMWVop7JyjcZIsK5GS2M7C89RzN9meDJlMpizmluAVWWkZjQbkuQIRqGtLlhVxqY2rCSGQ5yVNZcjylID7SB47W86RocT7JUL6qCSoN3EEvGsAh8BjTU0jNIVTiCIQMo+2CjWG8We/QPP6WwRjyD/7ItLNsVSEOk5rWASF7BOsIxiHkBmKDEVNU/fQqkLngmG+QS76iHdf49oLT7C1NSDPdJQDTZeuswcfr20cgz6/Hnlld9Gm1n1knhVRI99HYuVmh0jGvr0gthVZ2zTavsb6IqZoJCveRyzGbJzpDw5EvBahjRFKdufzcTs+0oHeeY/zbi3rX8FE7fHDquWVccXBpt+t8u62vaWsta31hYgMdyHS+69lpzLN9J5/v8cnHT/o8RAeD/dffEzSnhdA6Jzq6nXjf4vlgqIX+9QujSXplG27lGEXZU5RpkSFgHcBqRTjcdy4RYJTh72Sz33mGYxSLHxDJqCQEuPmvPHd19i/P+Vkf84gg0vjV/npP/y/5fP/2f+BrJwgxj1sfxv78B6qKQhihhcWXznqV9/l+OBVrtuG3p1XaU4DZ/mLNENNPhiw/dwWl174LF/4g4LTh/f53td+md/8r/9b/ofxLs9/6Sf48S9/iWV9xPx0hm3mICVZHgg+Q4gekgDC0bglQmrKAA0SI0D2d/nMH7zOSz92irc+VpJZybIRVKen3Lq1w9Zmj+sfHPA7336LZ3Z2+LFnrnHr2T12buwynTmC9qigUKIgyIDPKrTL0TrQpB0JSihscKukcO3augThdju8gdCiVYkj0F37do+3lEidEXwSWyGKl6xX2dGOVWcnbfB/XLLcJoYyyHPBfv25EZ4HfECKtsWjWVfqk1KSZVmC78/fYxHqTepsUkYSlWvQIj8XFdoEJX4X8f9sE8VsWhRP69VYXZSQjQI0zhhss8SUCukFyoFYBqazBaKqIdO4EDDziuAFUtqo6FYJCBolJbWv4zmS1DN8mzR46lbqzXuUDpzO5oBgYzxic1MQsLH37ix1bXHeUZQe1yis94QQlf1cHcmE1byKnzIFIes9zrc7PQKEJYKIvrmkcBnbZxl5MJDF75FFnOhY3p8gi5L8xRfjcqbK42WNwMYWltQ4AZglThlwCuU8AoNFkWVLrIsImRKCslC89NInubw5QmjBkkDpfZecOh+lidsxyH6/z4pJb9cIlqG7mh0DX4i0jMutjUxqYlxvsdKAlBqPW5uJJyG5vlPwi2hveiufWlHBozsBptSVIoFc/ry9fZyOj3Sgz4qcPM86+KcVY4iBVSRoz3XknPVqVySDap2VVBLn2lWYXSMUID0uOoiy63V3kGZylkm9CVzqK7ZCNxdRgvOQe9cCpa2E2qe0Vfl5B3ve4baQU+xgINoxO5BorG2wtibTCqllnFdOCUm8ScSa042QKT6ghUTpDCHr7pTbETAXojSlMYaqrmJioyRStCtHHePBEL9wNA8n3Ngc8Ynnn+cn/8CX2buieHC2gXEBIRTeeGqRszAn7I4KZB1YLB3O1Uyf/HHu/9r7jJZn6GGOUQJbR4EWEyInQqEQO3s88+U/wNYTB+zfe8idb3yb/9dbb/GFn/kJNgZDfFUzm89QOtW5zqVA5IG4z1x4FytswOARmSeTCnSUow3OYn0U3pBOUvRLbt6+StnvobykVyjKYUaIqi+p4u0YJAQiuckTIER1riB8Z4ur6+AQwkNwK1eYzMD59Qo3jgVB6n2LqMsgUWm+OOkxt21tAUK4zlbkWhAVaf1r6J6Xqvs1WxXI5Ggv2GKqnSOCH9KPWxWzrvNAv9/n5HSClCbtX1jNREuVRJ6EjzPYxJ3yUrWKaPFzW+tXgilEVKCFg4VMssFEEqhzbiVWRVRw08Tr5xIHgBC6Nh8eJJF34hz4kFoiPorkBOLMtfctwBb70B4XA0t705rUdvAJRQutvkUMaXEJkccvDV2xEFbtNimIUrtp/DakL9AHB7Jdlx0Nog2okW+SdBJC5JQIQASR9lkIbGPxoUIKA86Tqfg9+kAncmNJq6gJuPR5hQwJWYhz6HGLYMZou6QYZPFZLVt9PQGE1X6Y1salgrDGoWp9LxE67/wfIEQWV/VqifdmLRG0CFTSI8mTsxTda4V2OZKMiomEKOLk03ImKRUuxKVfovv+Y5LWqvLFVbcf6dD4yPGR/jR1tUSr1jHJqIIkAVwSqYmVj08zxl287YJqSucItH3HTmxOrJ4uWk/JesUTuh7iCvJsSTBp6YhYV7Rbc6zrH2ItyK/O6vdK1msRhZVLbiV4QxBIpTC2iX3CUMSlDyIKDbXeX4rorLsjRJehEGRZgWAWvxfiR4vOPvZlnXPUzlLVBqHiytjg43ihUpprV2/Q8zm3b17hUy+9wtMvv8w8r6mDwzQgaoNWDp8NOM436VcNYl4xsdCUBe76p5hsfwpbnZJt36Biga0sNlQ4b3He4n1A9UoGu1e5Nt6lGNxlMf82b73zLvKrY55/4Rn6gxJrPVUdYU78GhrSVrNrTsknbx51u1etl4iQBBpjyLQmzzOuXd/tGN7WWZqlieIdboWorK5lssdOKXE1ghTHmWxKwFKAlKILlsmXdRBoa5cdfB97SgihELikBx8fa989Ktie783Ho2XU0/1OuzCHzrJACs/jzFJ0amVtkrrWhkhmmuWR+1HXdbLvlk8jkz0KfIiLXOISpjTumQhqMdFIO0ukjCx14nRIW5U5HxISEr/H9mQj21pFhy8ESEGIJVzsaSf0QaYP0K5UbTPuuFymJQkCfjXqF0LkA8TlWgLrbCoiHN75NKpG508CMWE2djUD3jLK2+e0PIyYAMQiRARwsu06h+66aiHxsh1rDMSGkEOIdM4+2qGzBt9UBNI4rchS4E1nFTw++bH1pZcquKi4J1LQJ6rLZXncDaHT+KdMS2Sca5Xv2kRxZWsdY3592iIVTeesUcWdB1JKdLvzoC1qROQwRHRVdUWZWEMv1xX5gvexq+DSu7RqqJ3PTp/fhzT+GW1fysfP+H9Uj490oH+wf4+tzS36vSFl2U/QXbx5rG0wpkboopu7FW2GJyNJTUnZQT0utBf64ugaqHRDtM65hejXGf4yjZK0giCwVqU/Ap9zIQD8oGOFQKxeM1z4+ToysMbPD/GGttbGSjAZt5QKlcnElF6d00U4VkhJ2esRK6cAKoqweGI2vLQBYwJegGkc/VEcjcNLvPVMZ0te+bEv0s9yBoMRxeZ17quM5vgEWTVIG8mKxtRku5vcu/wTVN/6FfSkwe1s40Zb5BjEF7/CcjFn6hqW0wcEp5Fm2SVDznrUcsnD/fugBP2NHi//zE+y9/q7/MP/4h9y/3Of4vkXX+D69avM5hOMrbqVpwGfdMw1Wmbnvt9YhZ2/RrEqSdWmlHHywLkLhDhiv9L5BCNHyU26n/uVgxF0zHLvo6qXdzHJUlInJ51sLoDWiqaJc8QhiYm0FX5c3KFinzEoxNpMulxzgB1BlLVALkRHaFq3gRbBaZnvrf13cOhjjnY86bwjj3+WZclyMcN7jzUGm9m03SztW0g9dq1XCZJ3UbI5bqhUCJXF+5FWFdOn4B75Jc5ZAo52VamWkRBmjKWdy5dZhggSYw2mZWaHODK63kJZ/U2m80hDKCLV9wHwHteOfskQRy/T6F9IxYB3MfGKLa/YYjE2IY2JXOa9Q2udkp11mdio5Kd93LPeJYiJD7FCTVICKFpkbuV/iqKI37k18XMai1suyLOSTOcpzYubASNpLib7CKi9R3tHphXBeoR35IVCC4VEUWaxZdk4h1YZzja0baTWp2gR/VDHD1EKEpTfJbpiLSFAolTUn5BSovMsJoIAIt533jd0s/EiJgat7a7uVRmTl4R0JBFGhFKr5Hft+XlWRN4SIiVKH5/jIx3otzf2cLbm4eFdfDCcnJyws32J4XCLYX/MYLBNXsTKc1nXcUYcaKwBwATXbUhSIUGYj/TnYyBse3IiVXqtktyKF5CMVCbRHB94NCj/Xo8A4veWUZ5nQ7enkaoDIfAOnI0Ih0yOPiJVq4Aj14hV7Q2nVOyrIgUiJf7ORacRiMG9qT1aSYKKuuLOLqMACXBydkTRGyPynMO6YnnnVcL73yXLtilEdM4Eh/CO47tH9F/6PPeNR82njLc2qJoZ+Xe/hev3cSEG12Y+IxPgq+iYkICOTjizDu8s8+DYV4LhaMSf/N/9Sb76G1/la//mX7GxMeZzn/ssLngaa2k1DeKNbZE06cxD10vuWhqINOccv1+T7KftBUfC0aMOq+0XQ/y+nTWrCqSDGkMkV6XLFgK44FKP2XYJmbUmUtNEDPjBt06ufY/4eVpBpnW7WCfeyVTVn0eY2iq/tZ0WwYjM5FVSuooeYt321hCRC7nnORstyxKtMggiBpvZgm51sogtHylU2ni2aosJkZIdT6y2lUIqTfBTjDEY63Bt9St01LiX0ZkbF5C1RaR/O5OCGiB1TquvH/CIXGGaJiJX7Q6ClHw0VU0IYLzHtsISgG3iLgnvHc5aiiyPCYtInee2em2TlvR9WJvIiaK9/wLLZX0u8LR2471B6vj8dpKhvc9Fm3itXVHhV6iNlJKmtlFgKSEpIRhAsMQQNeQB71BakGUq+bcsavJnikZ4pKebeigKTTkoKXslynmC92RZEYsI5Tv7a21fSkkuNda5yKiXsdcfgnpssRPvlYZ6XlNVFaZ2FEWPXtmjaRbEdDJfwUVdwF+9Z2uKrf6+0AqVfi6zAms97SbFNlkWSpGLAiEExvyATPYjenykA/3m1ojR4DKRARuoqorJ6SmnZ8fcv/sBVVMx2NhhY2ODwaBPWZSUec54MMRYS7VYRofiBSZ4rK2RUqOkig4gVT9KKYxxBGtwwaKl6kh/6/1toOvNrf+MdSf6uxwt9C7WIOPfW/UfXz/u+14lLXk3rtRiwJA0cWK7rXPWbYIDECuoftmLAc57hGo5Bw6tYlvA+0BlK6bVEqoqqnF5C8FjfWDOEiEUXgNCoLxgWT1gng1QToKTGK8QbgFnbzO6/gSNh7N6gTo4o1ENYVqBixWvFQ2WOBbnXUy8vIrlS8+lQhrHsracTudkQfDJFz/BfDbl6Oghb775JpevXo391wSbKp3gRkz6rsGK8/LJcftguzAFAnFuuNNtJ20wY1X1g8T6mBiubEElGDgljwSUFngbl3MEHwOLdQbnKiLhKsGpCar2PnSLm1qbMNbESlHJbuRMpi1vK9IoCBkgtIthHrWpNvx3NnGBzOn9ajPZelLT2o1I/YXHvS5CMByNqRYV1XLBcjlHySwqHuYF1jU0TU2uM6x3aNkiZDHQFblGZQohwXiLtybJB4MgkuRUQrSE9+BcJOK5gFMxmEUeTfz83gfqZklTxzaQtYbGGprG4q2LiIOLIirOe7xJSpc4fDBYF/Xcg/MdH0AIERMFISD4uEIb0ux2wJkmyR1HmyiKAu9ipSmEoMiLZG/x3iJxYYTU+NB096+AqLjofUI3fEIrVIT9fWydaR2VCJ1zEZGTknbHBSKiYYLUWiCgrOjGbGMQ1KgiY9zrwTAHrQhlhuqX9HUeRz6LWG1LEwuIPC+7+6DlnjgXGe6Rc5HuJbkSTwo+fo/R5CTBNygl6Y83GIyG9EfjtL5YkRclTW2RqrXtlVdte/PrbbkgBDpvmf8xx5VSk2Vi7fsgrg12JLTzvErjx+H4SAd6ZxMM5WuqZc1oNETvbLKxEQN508QRMAhMj045qCusMeR5zt7lSxBA6bhNSmtNXsSKI4Qon2mqqEmeZRHWjRW8BNlmjslhhxa6013Ah/NJwLrvO8dsDuHcv1nL0NePi445kqrOowYXc1DnHHmexwpVrrLeLNPd73VOeg3Jbc97OChj5SPb8cGooT8clBweHEfYXUtsE5hOz3DWgkuJjVJ4O0d4AUojZI6jTIlDnWZbJR4ZBW5Cg69OiXQyRRZyzjRkLoPgCJhYFdgcHwwq8r9wIa75sGmI24uItEhb4Yxj7g1C9djevUbTVFiXrp1YFZ9SCZQsuj5z+13KtZt9/d9BtctZFFKlQC7DWgCU536vDeod89glbe0Q94Onbyw+W0ZyUlQqNBhTY51DyiIxzFuhj4AXHp0rev3+ysZEFJZpAxoJj4i/I0HGSkak94rHGqN+ZXDn7PNRvsEa7Mk6giBZn4les2C0kgxHW1TLmqqq6fXG1M0sQqzG06QgGdeQpjZTOwooAzYFeB8iKS6TWQzxrWaASBU0sXK01mHdaod8VVVxO52xmMZgrKGuqoiWWIOzzdrZpsQqJeoyoSUKjfQ5SkQ4vMwLVLFCgPLNSA7WeYkUKr1fg870SjgGsKaJdpiuGSHer8ZaiqLXJSMtIqASoBTiRYlSsKkQIURbiFV8LEa8BWujreVFjkTiXcDZ5KtUJOsGH1nyPsRxR9FKwAof1zbPKhZ5xY5QbOicnijIQkZZDGJikWxEaR2Jz2rVN4fIvA/ImPSkCrz7ectFEHEFcbqBIpIg4/1VlCWj0Tiu8XUR1RCyZZWsIUnJZs/51tZGZdSwFCGuqwlpjXH8Ly5e0ipyFkRK3FfTSx+P4yMd6FsI2jsXV6F6j8501F0OkS3fNBXOOoSM1VvTNCwXcePUcDAgyzOUMeg8J4SGpolQbJ5n9Pp9mqbueq8dq/VC0I7w31rQZ72afzSYx57sOmFvvU1w/rXb33/Mp3/sdyJEeyPF98+yDJ2IcqT2hEz9vK7f2hX7rSuJganMVdera9FC09QMBwVKgbMV3gZcYwk6ffZ2KZ/3eGvRIpH08NhgyHWsaqOqXkAHTyMSOchaFMT1plqjCCih8NITRHT80qkE1cXzFMETgsMrmcbBQHoBPvb0GutRWqOzgiKREMUa1BcJij4mae34VhLOkCquxgwJspZKr3YJiAgZykQIagPNo0S39vH4XQvpY2unrTiCS2z60H39Mjk50sYuEQQhESlDCkAheFwIzGYLsqIgxC80Oioh15yeSNd3FZSTB2TFq2//fgEDfoxtnSeYdj/ofqlLWB5rsYKyN6A3GFGcTej3h8zmJ8wXC0KIbQprHEKobnImAErGWfggXIc40bWh0ncYPNY5jDE0tqFuGuqmxjQN1sS/13WNs47gIvs9oiixH66QqKC7hEapZA+p4tM6S+RIHXkCShGEoCxK8iKiZsvlknIwpKorglTEppCl9gLn1LlAXzfteuJInguBqOdgDIVZISXOxd62FivCpACapukQR9IdG40/tn1ae3PWUpQBpWV6RtuWCWRKr1W/EBv+qzHGNPfJvFqSjwbsbu+yO9piqz/oJH5aBEgmiVkpZRw77DgaEq3TlsLWxllNmBBCQj5UZ4EtOdWYyFfI82LFg2nRqtY6WjsWLYLV3t/tZ03Jbjq/yFNp+RPtzxR5XkBKsFeQ/sfn+EgH+vbCKZXRKxXGRGcRgukuse6X5AgGG+OYXXrPwf4B7733Lr08R4bYZ2ucpa4MB/uHZFnG1vY2N29eo+wVVFUVVb9a5xlWBtXlhVLQVkbnx9/icb7vT3ruyrl2T31MQf9YmFWcn39Ob3LuZ7H/KztVKtIClRUh69FzXDlsUCqeWCtJGjws5wuKXDEclGQ5VIsFrrGoIkMIIrnIO4z1aAQyz+ICChGwrkaoPj6xnTOg8OCCBFWgsjKSm4LD557SxoBuFYQQxUOUBEROO16mQoTIUQLhQ5TAtRHedwKUjNKxxkY+QYTk2h5iWnkafEoeIoM40Pa82xWskURHF8Ri8AyBVBWLcwIyj1y/NZhACgVSoNND3juENTFZCcSJACJ7WqqMLBdIFRd0KJV6uz6OX3nnODs7oygNm1u7CVESdCz8c7aysrFoG37l0LvA3Nr1KkFdt40W8bgYzH338VbJw/rvt+/vA+R5Qa83oCj7EARV1WBsoNfLKfIe1XIJaVTQeo9wbSJNGqVr0SnPzC1wJhLt6sZQVTWLqmaxmLOoqojo1TV2scSYBh8CWmqU1CiVkcmMIo9a8WVeUqhBl4SrloMjSLvtE/wuBE7KKO2rNbVUeHKcc5wtLTUNs/kC01RY1+BsrNLbyYzgI5rTVFWEtI1dkdJkvDeVTqNhgtgCEALhY4LXbsA0jVlB4EKkONf6J99dbG8dWlconb67DlJ3SaQo/p6WMu0Y8KuWnhDITLKsahazBVootje32NvYoKoXcYZEkIofgdTx9WVY5zmJbmnWCvls4XU6u2zvvcglineAsVFFUQqVevqJ9Z/uuej/QpfgCiHblUldMh/fpyXspcVKLtIso3nKjnPlErE2cg3+p0D/P5rDWoPxSVoxQWOxmFyVqaHx2ERuggjVX75+le1Lu5GVnxy+9yISzCrDW2++xfe++11293b43Oc+S57nFEWBzrJUbThmM9sRdUJomdftDXf+PNcD7/njfCX/g/rwj//dxx+rdsHKeXewa3rEGNMRzs73os87jCzXHYHPu4AxjtlswXQ24eq1S4w2BsznE9CQFYPIrG0qvIsjLYPBJlLoKDlLDLTOGnSmYmcwSLzQKCFBLqnTdj1J3CamtMV5RUjEGxsMmRZR7MNJhAPvfFpsQVrfmqBWBNIGhHIRClSSpjGxYsr7qSKM7PZuJHINvhatI1n7bp1zaKVSMraCDS/Kel68Vo9eO9Fdl5UGvEvQfnz+crlck3fVaKXwQlIbi7MhwfsZ/cGIurFYG5XUojM35yt4xCM2efFY77lfRCQeOX/RfuYWAbEIWsGm85D/6vVJa0MFg8GQnb093nztbaTO6fdHaC1BeAaDguOTfVSmEUqmRDOhU7rA2oa6qpnP5xwu5tS1oZotqBYV9bLCVDXOWJSIzO087zEe3aYcleR5lkboouPXaYQr+g1D5WuMMdR1HQOzj1soYwuwiRKrMvIulsslCEVlmjSrHzP0ehl5P8ioqe5cTP7b3nes0h3GRqGkcb8khIB1caWrSEQ4kb5jraN9SheSMI48Z29ZpmOfGaLEsI+plk+8ARXF3+IK2ZTEWGtRSp5D85SWiYgXA7PS0eZy5RjJPmeHM94Y3YedLX7kmds8sfk0fjrl5PiY2XyBS0G69StibSJppXMSLvgcdc72gE4eOa417lEUBXmbkEjIVIbDpPsn+vq4nyMmt3HCpG3ntIlry7KP/9ZZtjZ1tAr4IUT0xIfwSLL+UT8+0oFeqlZtKzJn06Opqo+XyiQRHTLVZXHz+ZzFYgFpd3Ick4pz45958dN8/rOfo2lq9g/uc3DwYHWDOI/xsLk5ZjjoMRgMgEBdV4kl3c5EP76qh9ZpPgrV/7BA/qja3w8I/CGkvcqrzVGtClVbwfsQzjmKbmPY+mPJ2Isii9Cp0ggpaCrH919/g6/8kZ9gOqtQRtCTJRNrqeYnSOsRSXhEZwVlXmCMQ9AucnEEkaEcka0vAiYYFIHGlWTCgfAJrtY4F8Uu8SCCx+BoQsBXsSoKMo78Ff0+A5nhjKWpG6rlgvlygVSgQpaCkIqjaqknqBQIGXB+GQmL6bKEVJ3Hy7b2vSdHZrxP0xergCYzjXNx7FB1/flW3GbFQF4hMyst8HjEuXGbpjeKoodxnmrp8DbOaLvQIERsPRVZTr/XY9Drs1hUvPX2OwiRkWVFt/muU9QTfg2BEt21XgPqfyAKtYI9z1fn51GgNsA/HnE6d0iom4per+Tm8Bbvv3cXXxvG4y2KIsd7y3Q6w3hHtWjwwWO9pzGGk5MzlrMli3lFXdVpDWuciRak1ky5wdbWGK3z+L0rhVAaoXqAY+ktTVNRNzWNbTCmif+uK+omtu1sY2iJh1FwJ/7pjI3EMp/63CJCy+3YbZskxtGvSLhsx+u674OVZoJSmrIs2N3e4IXnn6XfH/JPfvGfsVhGXfxAamGJqPYWhO/MRSDIc0XdOAb9Mvk/T1mWOF8xHo7i+3tHv1cihMAau7I5ISM3JIi0CCbg6yhkpLUihKYL1kLEZEMJwQfvf5tf+6Xf5r8c/gNuf+Jpfv+Xf4QvvvQyN5+6jp2ccHx83PkPKURaQRxRqNhzl10CvbIlWoAskgi1Tn5Uk2dR5z6EQK7bkbyYtHhPFKMKFmMNwjnK3gCtdLoFWjuV3drczgxTgt0mYt47siwjjs46CD6ulv4YHR/pQL/OFI8iGz45pbZRDIo4TCNaJZwO8lZIGTPFgAAF1ltOzo47Z1b2+1y/eavLSJumZjGbMj075v6dUwaDHv1Bn6LXI1MF/cE2UmTUTYX3M+q6Rip9LlC3gV7JldxsPC21WsqQAoNzJmayKgaCVtbROxvrtBRUnLO0MuiyrTS9B2UBhfUW6WpE0hgQPkTydQpEIUTIW0qJJN78wQf6xSZKQGMtRSkZDAWvvbfk6996i088/yQbwxFHxw1KBEQVlcxo4XsBlgyvJQSH9O1SCZu2T6WlJQhsgOAbvIzz3JmQ5BpkURC8Q0oR9RHkIH6udJ19gEXl+Parb1M1lmtX99jZHrM5ynk4eYBWgo3NXcpygEp9O6Vjb1VImQJzgkJdE5EhoeL3IkNEH7TuCGFaapy30DpsISLrOqlqtax5CLhAQkNWJCuEwrMmhxx86vFHVn2LudimJteSSni8iGJP3kuEq3HNkrw3pt/bJe+NCHLB008rLu9ux+UjwbQS3knEJc0kJ0XEZIWpPZGCScykSFSlBNGvdsenG+dCgF9r81zoZ8a41/ZPJTLt+jahQcq41U14wZe/9GN85/vfxTnHfFlRNRUf3L/Dcn7GfDpnMV2wXCxpljVNbXDWxuCERMqsY+2LBLEvqhmHR0cIMra2tin7OVIFFvMlDw4egFC4AE1jmc0XeBfWULm2kltD11IQbD9UTNHobE8QItNfpoZF8AgZp1PiVYvnio9Kc1G9L/aph8OC2dkSl99i74lPcPPaFb7xO9/n7TfeJSs0gUTYxCIQ+HgbIYNACUmwAS0Ey6pOkz50Y3TL+WnXj1dqTiCqiBZak2tJrgWBOGEU1tpR0glC09CN5ol4P4qWHBccmQjoueXe19/iH772Hr98/Vf5zCuf5o/87B/ixqU9Do5meN8QFKn3F1BeErKQrKtNclN7IdC1oaJAVYjtMKUhZBxOHvCEWZJn4/R7luAESueE4NFFRlb0icT+nJZc1/IrvPNxDbBUiNT6ib3+yIsI3hGcxTUg+kPyqiYQqNrNXx+T4yMd6FunuuotnmfCQ4RyOkLV2rE+bxwz4vhztybioLXu4DSIlW2uc/KiT78/xLmGEBzVcskyLJlMZxRFLyUQMaNVrWiNs1EMIygI2aP4PmsjekT/4r1IO81dcjhJtU6sYLeuB0XbWxXd53c+ysW2n7x7/cQQX2+1pnCS/ox/UzINdQUQId70PgR+9de+S79XsLc7BpXhHAQbl0XIRPaTIu6qJ4BwMpLpkpiH6L779KeOcHiEUGPvI0KPgcbaKBvaBJwPVDbOTYvgccaxmBuML3l4eMCdO68yGve4fGWLD++ecjY5ZbwxpSx7KKFixZDpGL5ktBfVaiHgaPe5S1pBHRuTDKm6a68UCBURiSzLyHS8hbTWkQMgo1BLd00TFFr2SvT6NQhxdCv+F5MCa9O0h/dJkjngfQq9HpwzBAQqL9BFGed+i4KtrVsMR0Ok1DjoVPGUaHuZ8Y2DD+d6j+3qz8hPSAmKdwnWbRPQx9x3F37mIK7dJY64+RCDJ0mYyBMIMiDTWGLwAeMbsr7m6rXL3Ln7IR/eu8eDg30mkzPm01PqRUVTGZxxqacqiPP2KnErstg/D1HzXkiF0pq87DEebSEk1KbGLmuqRcV0OsGlFo+xnrpqEtKTvgsgkzFIdKqCa/dNx0+Q7XcQf0nJ5F/CKmGImntJfCIIZHCY4KMoEiSbiITDyWzKvft3URjyQlH2InqWIKd4/wQBOibfIoASEhcCmpjM4QUyxM/iRQpeLWnUx6SzWVZYKWm0pMglAofK0ifvgnrrR5IXSX7FBYdVMeHV6DQKZ7CTmg+r91kuKwyB/+SP/wwF4NuJTt+uIUra+/jIo0lTL7ZtlyXfFrxrG03JnixaF8hALG6SL49oUSIzh1QYxX7FejRoXyklCIm06XyHrgQAEZMzFxqUiShvJAb+Lr2uj9jxbx3o//W//tf8tb/21/j617/O/fv3+Uf/6B/xx/7YH+t+HkLgL/2lv8Tf/bt/l9PTU7785S/zd/7O3+HZZ5/tnnN8fMyf/bN/ll/8xV9ESsnP//zP8zf/5t9kOBz+W51LJ12ZBCdahav1c0HJ84VJOlpCiBQraLLtL7UVeNvXX/WWNEV/TDnYQEuYTk9ZzKcsqxlVPWdydhB7SvmAvBgmEty6s0xjWXK1Czmw5jiTccXxoTRfqiR4l5aTCESI41FdTzrQ6WEH75LTgdUSklRVtFE9iCiCA7HCS5BgW8C0UiAheJQ0ESUISRfbB/p9wbe+9S7PP3mJMnuC3mjIZF6jcCi/qm7iRjET4boIS8QArxJbN52zSzd4u1ynsZ7aBaplRb1sqOqGqjZUjaVqLHPTUDUW7yzBODCBZ24+S1PD977zNo2ruXbzKsY63nv/fbLsHkUeWzzOeqSWaV5wVaE2TYPSUW2tY+QDcZ9vRDqyLEcrTVFEnW2dKfIsiyIwuv17QVkU5LlGEidBtJIMBj12N7cotSDLJJnWUbpVRrIUKZnwhJjTRWuO4jHtKkTnqGuDyvsxyGuF85ay0Gzv7MQqL1XlyZiSs462JFS0GdXKNLeJY2jh+hSsQoiTD0khjDWH9wj5c/0+C6uJfdkt4llLHkWIbZQQH7HBcLY4ZWN7xNlsSP3OgjvvfsByOmdZzQkuQFr8o3XWkSjbTWjWBhosxlmqqqHIe2xsjtgajdjaGvPw6IDZbEpVVXgTEYzgTVQbJE6UtEWnTOiMRHS96fZzrZLkSITzBFwbnNJ0Q6vqF2WQXWzahNaOBEp6RJDUdWjVsbtrMp+e8PYb3+P04A5NvUBlKgp7ycQSD1EpTmo6wS+kROHJRERLZLRcFAIr4jKlkCrZgMIGycJYGmupU/KohUImZCzyAZJwmBBItQ5kSJw3BBfwKsOmhLlfQC8vMI3l7tt3+ODggP/w3/8ptpTAoSIOESReerK8IGrQxlHYOP6pCM6mv0djDT6qVKoAMgScnTEe7pHnPaQi4QEZUoEN8Z5pJ3068mBC60hjclJJhCaRINvRQt35QKUUKouKqMo1BJ0hVRwh/Dgd/9aBfj6f89JLL/Gn//Sf5o//8T/+yM//6l/9q/ytv/W3+Ht/7+/x5JNP8hf/4l/kZ37mZ/j+979PWZYA/Ik/8Se4f/8+v/RLv4Qxhj/1p/4Uf+bP/Bn+wT/4B/+WZ7Pqk3ZwEKs+Yktu6p695qSklDRNA8GjUV1f7rHvkl7PB8eymWHqyO7PVMZ4Y5vNzU2cqzgbnbKYTpjMTzk6vocQGqX7jIZblGWfXm9AvxyRZTrC+sJ3s7Jx5KPt68aFLQiH8zbJocas2HuXHFMKvgQksZfYBvkY3FW6gVvCVOvbfVJ5a5nQ7XBV/J1YgQts4xmUGQ6Jx+JlgAwyYsW4PKxRc8Hu3pDJ6UOyoh+XaPhUHYfAvDH08wypopMUSY+g8Z7ZsmI6X3IymXJ//4DJfMFsvmAyWXByuuDs7IxgYpCrlzXNoibYuMTGGYcnkOea7e0tdvtbCOEZjUv2D0958/XXuLxzBZYeO5uCjBW41gofQBc5OtNEqWSLmUwxSpIXBVmWxx0BJs4jCxGQSuKlog6BM0fXyzMmog1KZQgkeZ6R5zl5kSOwCGdjAFQSXWT0Nvv0NwdsbG6wtbnJ1tYmg1IyHo2QMqCkQg/66AAKj8axmFkWi5q6qZnOF9zYvYnO+xgL3lr2tndABAwWESJZK5KtVBrbixWkJ5K1jHdRg0BEtnpR9IA4cx1CHCNbv7cu3jfrR5ssi7RXAQKVbciUQGU5xhqcNeBiMu4DLONaHzQKXGBqKl75zCsMRM7i/jH//Xf/NZeuX8YKn+5dgfVQmXqF7tjIl6lRuNBQV5bxSFKOFBtFn7fee4vjo30WiznWOFTQaC3JtIzsctmKD3kyrSiLHB8Ci2Wcd5epYHDOpd32PlV/IonwtAzy+L06H6WLrfftRBqZFuRakWmBlHGJjLFRGMY7j7UNg2GOlo7pyUOq6SnLecV0UiMzSTAukUuhyDKMb/etB7SWKAmFSkqISuIzhc8ylPKUShOMwTUGZwNlVpI5x9I2VMZSG49XlnV0OvatWxGZlVZ8kxUIWyNsg1JRSbRxhrqx4BxlVmCsoJqCmGp0f0AZLE3avqhlzajcxAuZEJUQBbfweGfIM43OM6wPLOsGmeUIZ6lmRxw+vMtgcJn+YIe8Z2Iy1WiEdLHSVznO+bhfoJ3AklmE3ERUvMuKHNyasI6Igjm1NRCiymReZBHFqReErEAXI7L6/8+17n/2Z3+Wn/3Zn33sz0II/I2/8Tf4C3/hL/BH/+gfBeDv//2/z+XLl/nH//gf8wu/8Au8+uqr/PN//s/5rd/6LT7/+c8D8Lf/9t/mD//hP8xf/+t/nWvXrv2ez+Viz3DFslz96Vw495z2UMkxSSHRSsURrQvPeWQ8DgfCkpWSYKP6m7EB7y3eGIp8wMbVLbYbw6KKiltNExnC94/3qaoaoTS3bz3NxsYmSuVIIcmKmKRU9QLjTIK5Vszmbu5TBoRLWWwLj4Y1GP7Cd9Oefuv4hSDNuYc4jtb9WosoWKQIXe86k3lkJVuHSGSeQEwOlMoZjrfZunKd7OiQxWxJWWQMB322tne4fft5jiY1i2VF1Sw4m895cHzGZDLBVLGyMrVlOVvgrCc4RdEUjCtBITKu721zfHqMDwZXGsJmdMqbozFNXaWWRFwi8p3v/BYSi1lOGeaeUisOj+8ihjmXR5s8ce06V69c4fT0lLIoODg85OjoiKZpuHHjEk8/+SV+/eu/k5beQMCRFxqQHeIDoLWkmsWFNqLIUu88UORRFlhpRZZp8kKTZxsR2u/6mzA5O0OcWI4e7nPPfEBtGoTKyMocoaDs5YxGA3Z2trh2/TJRtUyg+gN6/R6Vh29+4+tsjHe4deM2zz37DAHDbL4EFYlgOXFBjDE+2bjAmdApggmiUFFQEqXiSFFna0JgQ1RbUwI6+PMCse6iJoQOCfwQIrIjgboxZEoz6Pcp8xzpo5hRnrTRnY+rjqWUXL52hQ/37xK2+pRXNpguG5bLGNhtmqRpzzNJE+I9FAp6ecbGeEjTLHnr+9/ie7Uj78GgX9DLCoKGurIM+v3E0l6p2CEFp/M59mwSQRMf/UX3VukrEKIDQEgt9pVfWLuLBFn3Q+viBsssz9kY95nNJ91K3byIUxZF0UPg48ifiMllYHUeLTi5TJLLAhHbFd7jPHjhkSr27w0B56GQ0CujDTQmUC0dQtT0ioyyUPQzjQ4NXkefE9sNUefKxdi9TksgMKPUID1YGytpmWU0daDQjr3twHNPXOLLn3+Jy9s9pg9PqGVEcbANB8f3eaN+Fyc04JA0CaDyTE4nHOwfcHZySqYFVy7vIYJnMT9iMX9IvTiimgnuPrjLzuUeUggWM4uQFpUP2R5voWTiNqWWSkRLW6aJjNK8ucC6mBw6F/fdl+UwTgQZy2K+4OjoCNUT1AHmJjCZzPk4Hf9Oe/TvvvsuDx484Ctf+Ur32MbGBj/yIz/C1772NX7hF36Br33ta2xubnZBHuArX/kKUkp+8zd/k5/7uZ975HXrOopdtMdkMnnkOevO6LxQjXzk5+2N7owB5zrorn3exdfojiARIY5mmBAV9+LMr8XhkCiWpkks3iVCSMpiwKC/yc62xyThjuArPnj/DXQmE4SbMR5uUJYDRv0NAmCaBu8ddVPh1mR1RYerJfZ8InUlXgutxn6stpJTSBWIkrKTo+w2ZIkonRodYCKTSYHHkukcITxaZEmww6FlFDRZVgsWy7igZG/3NvcP3uXO0QPcaUZ5VvHGRLOoHQ8ePKCqKoLzKAvz2QxnHU0Vx5K0lmB9hOKT+A069vPGwwJJjrNx1lgryXg0gPGQrf6IXGeczad87803UVJgrcRaBV6w1R/z4OCYWkZC4nC4gZSKzY1NZsuKB4fHTBeGTRM4m83jghMXr6n3gbIsogsPIEKc7R3mOVt7I5pmiXUWH0LqES8jIuIl1iu8kTQijscVRcHlS5d54bnnsEm0RecK4yxHxyd847dfJesVNAnyDVJg/Nuxz90Y0JL+oMfNazv89I//PjCSVz79aT75wgts7+4xW9SoIqMxUSlNpv6jUm0LSnaEy3Y963Q2Y7lYxPQu2UW0HVLykrgVLcMzHeuLnNbnoT0J/g6xurXes7WzgwXOmpp7J8comWGSs62sYWFqGusopGLwzl1e/dZ3eOuNO9SnFYvKs6ws1kfoPI5Yh64X23JurBcsHVEuWAZUockUWOeZLSxCWIKHpvFYL8myuOY0clfiDLq1vuu9y0Ac68uiIl68dyKHggRvs7oV4/0Y6Pg/Adv9MAgwFubzil6eMR5tkWUNy3pJYxuUjnyito3T5gsOou68ijPsAo8MEh0EXgSckATZ6sEnMnLMmZACGquwNr62T6tuCTCvDY2PAdE6kCYQ3UdLTia1LdvPEv9HBU3d2MjNFHGhj7E25jwCLl+/zHMvP8WNT2/wy1//lwipqc9OyaVnZ2eTF594lndef53p5ARdDhls7GKbiiITLErPzStD6k3NbL7g2pUdNre2WDQG5x2jXokKOaezGqlt3J7n4oSVyoZkOupshCQBHEjFSIifK8rppiJJh85XhmDJZIXE04QluDMaccILV67w1qtvsziakWWjR2LMR/n4dxroHzx4AMDly5fPPX758uXuZw8ePODSpUvnT0Jrtre3u+dcPP7KX/kr/OW//JcfeXx91EemILaaWYeLZe5FCNIlPexICMseec6jI0eiG19S6wxsKVE6bqRSSdBDpW1KPtgkvehRSjIajuj3e/QHY5yPs7nWOk5Oj5HylBAiia8se2S6QOucLAn9OBcDnnU2ttbF6hNGbsKFee6UIHjvVit7vUOK9MwklyqFxImQellt3xKEimStyjicCGjhsbadVZcUecHmeEC9MGw+/Sw7m5u89uZ7vPb173M8+R61g/lyATIwKEsub2x0wnJNHQVMrI4LR61t0JmKVbHIKYscQq/bzqVklO6No1MCVWR4KVC+JMtyjDFxX7zQSK2wzrIx7HP10iV2trYo8hwph+xc2uXScs6DoyNOZhOWwfLO3Q9ZVDU6VeDeWbSOcJ4koCXkWtHv9xgPS5ZLaIyJSmyNwziPkFnSJY+VsXExOjhjyaTm+rUbqEwxnU6pFgusMfTyAYdPzqiNZf/wIbPZnLpu8C6uwg02eu9ptmB5UtHT3+bm7cvcWVTM7tzBfniX07MFo8EAaxqMsZi4PJ08z1jM56l1EBOLLMvY2d3jySuX2N7e4/T4KBL2Wja+iH3xKF6ymghpg2zb72//jMEkcj1apT/vYWt3lzv37vPdV1/l3Q8+4ORsgncC6UJcCuOjzrqzsWTVQVBNJ0xPDqmtwfvIuVnRSCP5LLiWNClWQZHYf5U68b9CDICIlLSEgJewrBtqG6vYlscggo/bAhOVXsQXi5yXECdYQsoAgm/tnu6sfuiROC/OwmS6xDmLyiK5VyfmeZ5nkVgrktSuVIDpdigIokSyIGBC3BQZZFJ0E4l4R6t9EXUBvG1InQm0iEI4Qoi4qyOAdwJC3JInEr8gtvDoyLwteY10bYOKtBZCnHxXIgZOL+DO/RPq33iLt968T61CbI24qITXHxT83H804sefe57Dg2MsgqzXjxsfgd7GNiKhk7VpyPOCfr+H9vEzFmkB0vZYRAJx8IloaEH2IVgE7aSVi4Eekmxxigl2NUnRXhhvLS1VRxclOi8ZjDfINgaMhw/ZpqDobf1ervJH5vhIsO7//J//8/y5P/fnun9PJhNu3rwJF6rwePxwtuTjxEx8CGStcMZjWgDrr9yS0gSkvmMkuEXlqoggtGMcPlUEYBP0KCiKgl5/QNEfYEwTt2+ZBmscxjbM53MgrvQsspKyLFFpxrOFEbVWeBs6MmLL9G0/enfe6bP4xPBuZShVK5ydzjWWK+nzCNJoSuxLDwcDRMjQGnQenWCmlzQeZsYyXUZYfrwxYDjYZtg7Qbi73H3nXQ4fzpBS0BsUuM0RpY9JUZbnsSdMIITYO1WK+KdQaJnRH/RjkJetsIzo1rcKIGgZ+QmdgploBbDjje8sn3r+aW7cuMrW9laSKs3oDwYMRiPysiRISeM8J8enVLVhnMddByakpSBKIbwnU4JekVPkOXmuEaJEGx2VGKmRLiBUnpTb0kVyLhGiIqGy6Pfp9fv4IHA2JIi0wSGSBoPDGYOp6sj6t1H6VriA94bjwwlf//r3WTq4fzYnZJJ50zCf1Qx7PYS3kW9gYr+47OXMp7NYsWtNIKRAf4nf9/mXeOUTzzAcjllMz6I6YILqWxtv/xbW7GnFVWmJdtEmZWJBByJJMctzvvXtb/O1r/0m7777PrPZAm8DWZsYhDTu6kXcHhigyBV5LhFKkGXxvdvOFEC7TKwb0hKpeSNEt0JZyBjGY1sq8QdE1HUP1sVKNu0SCIHEPI2z8SKVxLFF1/oI0UH1scIVXZ+3PZvHHQK6QiPQqv8Zil7RqU1mmSbPckQyWhci0iZoz51Uvcb3aZOaTtspnk4klbXMfxFX8opUBQiZ+CEyYiDxu5fpuqWxS+fTxGpb1LSfNyGg0idOh0AGETfjCUA7bICT0ymnJ3PefU3ihpbCS7JsgLE+jhReusIf+j/+STboU9V17LwkP5SplVJfj6RHohLvPkCk6Fh0r0+QKkrleotzDUIWeJcQDyESeTRrzzxeNykI0oPwa35fRH0F0U5WZUhdUASHLzS6N6Avc8Jg/Nhr+1E9/p0G+itXrgCwv7/P1atXu8f39/d5+eWXu+ccHByc+z1rLcfHx93vXzyKoqAoisf8ZAUttnB8R0Zr+2otrHYBipcyIgkktn2rkLV+rFf0rSiGkmBsgsKTkIS1UUQjz4vIFk7qZt4FIh82ZqYhOIxpWNYzQvAIMrTKybOSjfE2xhgm01NOTo45PTlm4o+joxKaouwzGA4YjgaUZR+rHE1jusmAzjV3iEZ0XkqLWNG76DW01qheRmOi5Kr3qT/nE8nMgpQBl8HSZNy6eRNrfKx+bI0MORzc4aSp+e57dziWNQ+PZgQkZZYx3hjyyS+8zJkxPPzaa2QhMJCavsqxEuaTCYPBII1JxeskHYz6g1ZNFqEgUyqy1vOoJW+sYVEt2R5vRIhVlkipUXnAmoYsFxgnCM4SbMOoLPmf/fRPInsDjHU0TZOcuMaY1If1CtcETO2xtSH0Hd4ZqsWCMsvRIq6xDQGQASkCy8qhVOy7WwEBQyvZGoLHE5OSUuRUwmCFZ+4M+6dHbDY109NTrHPMljPeee9dfuOr3yDPY2KH92gJ/H/Z++9A25Kyzhv/VNUKO5x8zs2xMw2dyDkL4ogYQMU0KBhHzKMjvuPooMyYZ3QY9adjYsQ0gyLISEZAgSZ2Q+e+fWPffO7JZ++9QlX9/niq1t773EDL8M5LOxacPvfsvWKtWk/8Pt8HJ3lc5xvvrHSwdmGVj73vdsAFwKZCKfHOdAKJBuMVZenpdI1QAXvBo0xMdimLgrs2Khbvf4D+v3gB3/KNr+CexXN0klYD2rRWWPdUw60/PoRlTNaO0qKYMiXRMZVoOt0uZ0+e5u/f/3ecOXmWpHIspB2STKMSJ53lAG9EIVNZlNH0q5JBWdFt5cJAmAq/fXwn00hMIya2lKhRk6VJ46N7gkfvkJBt6EvhrKVWEVAXcC9WDFrhSwhpAj+UGaOyQ94vPfT6GwHRqN8tXmOop5EQQeBS8JTrA5SGPNdMdCdoZS3wispWgS9C7st7j6tdwDCIM+GUayoE8OCsRysxkLSS8rOyqKXkLihy5TUWg/JWWO9UnCWPyrLgtNgR5jo5bvy3bCleumLIMeG9xaTSpyJJU1KVYayiSAu6NkNpR609vdLyN3/yTn74X34FU86AkwiKtwJyVVXATMgipXaeupb3XeMxyLUUgw28TwWsajx1WWBMEjgpfGSmCCDKkGdp5K8KkRthvVOE6olQgig8CjV1VTHZnqNyJaXtY8tL6ZtH7/iiKvqrrrqKnTt38r73va9R7Gtra9x+++183/d9HwBPf/rTWVlZ4VOf+hRPfOITAXj/+9+Pc46nPvWp/6jzeTfKPy6ha++HfdbDVsBQAY556T4gWesanVy5nCIC0WpvwwtvhKRBKemxnXnqckBqDD7x1NZjlSNJDAob8mEGSFC0ZKEBII04BoMBKE+322VqcpKrD1yFUorNzU1Wls+zvr7GudMneeD+VToTk2zbtouJySna7S5pmooiq4vQLlVqRiNESMp+hBu70+ly75lNjp88w/LKOr1+ifWxJahns7eJtRUTnQ6pSdmzfz9e1aytrbF4ehGTO26+5VZOnTrC+97796ys9ImMw81caUWeJ0zMZLjKsTzosae9jR/9mhfx4JEjPHz2Amu9kn7p6FnL0uoK5xeFAjRJFMaAOS1XX9c1ykgUIG+1WdssSNOUdrvLRLdLq9Mi67SpewPaZOA9/aog27mdyetuZDDoYbxnOssC73fCrtpzzwNH6A2OkLc8nc40G+sDnEpRJqemIOsKVqKoHaW1UDuwDqNrXOkoy4rBoMTWkoutnVRISOhYFEBdVThbs3humdMnTnPVVfux5SYGaXm8sbaOd5piAMWgH9aZGDpJbsgnMkwiNc952Yd2To2R6IoBjcOW0OlMhta1QuccQ9O2EnBhVOKTnSnmtqccP3GMd737fTzv2c9gbn6ewWBdQKvaCC1seDeaHC+qwbqEJzzyAyWe2lsyDN3OJO959/s4f+4cta9J2hnohKKqAlVsOIJ1aEvTdClzAjQrikpahZrACBfuy8TWvz6WsnmWVhPwFaFqnVwpJnPDfMsz1cnwlWWzV3G2J4K/BLyyOGWpwoVkOZAL2EzINX0wjsTTFO88dpH7PPLB++adI4S/lYIkkyigtQbvoCoFLHndtVdz9uwZNno1DkuvX+C8I02HgMM6vlxSYRmqbHRoPOhJB5au0XR0QrdtaE1bJie6bJ+bZ8f8ApOtFqdOnuHw6XOcXtnkwmbNeunpESjBNWSpppWnJEZRU4r8COkMIdr0KCxKVw0WyPuE1Cfo0uF0SZ0l5Fqx6S0mMbRMxmzW4vzqInc+dDcvuO4pZEaztLFMkgghkHWOJMlJQ9o1SyQfUyuFVw7lBmRpB1JDVevQhAhaaRsv6FNZI86hpJYoEBnJM7PWCgMeCKDZD3k6fMCjqCQhURlODTAYOnmLKQuGzuUf9qNw/KMV/cbGBocOHWr+PnLkCHfccQdzc3Ps37+fH/7hH+bnf/7nue6665ryut27dze19jfeeCMveclL+K7v+i5++7d/m6qqeO1rX8srX/nKfxTiHgIhiK1w3mGSLLxfhggv941ShiauG4AnWMjylrRW3RLt3+rJiycYBZuEi4RJjGYRoQy1kzASVmF8KmFZF/Jb2uOtpSwtjq70PPeRaUs14aWqKKgi3Db0Mp+an2N6YTt4TV0UrKycob++yYkzJ9jsrYNKOHjVtWByOt2UVqpJvMG7lHVXovB00pR2q82xtQ3++E/eQl1YsjQjSzNcLZ5RMRgE0hrPmlaYLOXYobsDAUoIJVrF/Mw6s1OTzByYoNw5QCoDHANbsdnvs9Eb0B+U9FYLvBcvJJ+aYOqmm3jO057K5voatiyoiwG9jXVOnTqNM4peWeDLClNXlGVJ0StkvqyQ5RSDksrWFEWfenOZjUVHL82x62uQ5LhEUynhFZ9PKt73pt9ibVBSOY9ONFma4SvHoF+xen6RGTxqc4OZuQWWtabc3CRxFR0NF04cxnhNOZC8X5ElLFY1050h34JBkWrNVJ7TaXdptbuScmnltDPF1ESXTrvNxITUeGetHJ9nJO0W3elpuhOTvOIz9/Hw4aN00xwXegmcX1njE/fcz+nVTTasE/yEgk4bjHHoCpRFmA6xlIMe4KVpR5KG1JIRXoVQNWCMxtua2ntsaSl7BWfOH+fg/r30eka6tyEvRh28dRg2H1Em1H4raVokqSiJEPkYyteWmoL2RJvUpHiTYAKyGaUwtm48Rec9/bpgc3ODNDUBie3xTlOXA4xKsJWjrh0WMFku7WUr4ZSYzBK+7JouNz/hNvYd3M/c9nmm56aZW5inMzFBolMiaM+icLbC1dJ33piELGvzb3/69dx1z2HKYJjpTKJ0NYb1jYqyij6jEsUCTfdBpUXxiszxDa0qCL7FBoS3D7wXSnnaLRU4MiS03p6aJV28QEJCqlu0U0izJara8c1f+1K+7iXP4zH7t3Hi9IPkncmGZEoZTbvVQidJU1tvjCbPMgb9c2iVMDe/jZnZBXGBKoeta8p+n/76BusrK5w9fo6PfvJO7vrkA9xz4iz39Xo4BZNGYU0oVHYS9aicJ00U3qUyo64OBpMLvQM8vvC0U6mBT4xDuyqAGA1Kz5IgLKUKKRWWLppeKpdsZMkb5TYxWNPBaYO2hDkXieu9BurwW2SvE0YOUIEsSGlUluDQklpNkGhSkK3GJGJJqZiSSlFJgtNtVHuCtDuOM3u0j3+0ov/kJz/J85///ObvmDt/1atexR/+4R/yEz/xE2xubvLd3/3drKys8KxnPYt3vvOdTQ09wJvf/GZe+9rX8sIXvrAhzPmN3/iNf/TFN0rYS4mbMmnwvOX1NKF2N4axozUKDRN5CNeNo4njsSMVrYvSaSQ6EP+K+bgx61+B1QqnFbHuwzsPgXLTFhVpmgYB4mMwTf6thBYCHxc0YFUDNnF4upNTtLNJpqZmWF9b5uFThzE47KDHRlGxqTyJNnQm59BpYKVSCqXFmyt7PdI0J0lEcKlE6ElVqkhNAJUpRUpCFVpmOucpK4s1CpUYCu2pXUXPDqgLi/bg64pWopmd7WJch1aimJnq0G2nzG5rc8fH309VDgBLp52FZhUabWDbzt1UVvKOJhDI5EmGCY1HYptdnUo5jVaQGLm+//TG32V9s8L1BrjK0W61+ZpXfA37diywtL5KvyywCpIsg9qT+JSqqPEYJucX+L03vQlzQrFz2zae8PibefpTn0KaOFxd4kIPbG0kB5qnodNXyC2qIODTVGrzTZIKF7dOBVcQ6Hu1htLWZK02tUdIT5KEJ33ZDm4pnkqaGuk57j11WfENiyucOnGGUydPc/TEKe647zgPHDqMM5KvVkauCQTZr5AIl7cCVqvrApMYnKspqlpahXrpHuedxShFK0txylG5GqcTvFI4V+MJ3Pyhjh1F4NgJGBSlQy5YD3PRFrAe4xWdNEdbj69KnJW0T54qKq1RTuGstBeen57Be0t/YxPvpQ95olKapo5t5FxJgqst1ll279nDDddfxy233MTO3XN0pibIOy1UIsDRQkFpTEMLCxqvHFmrjfMO5SE3KT7J2LF/P2fOLlOVtTRwaWXCSIim1Z6kX5SsrK7IvOVtoTuua6kCsQ6HCt5p8PgFORfmOgIXHZ1WLiWZWmOtYCmyVotibZlBb52yHmBxYBx7F+a5sLLGcn+T07bi5uuvpdX2JEk6lHXe0wsoVWclPSPEPIaNoibRirpfUqQ9eW9cqFbIMvTcLFOz07BnG1//xJt54Tess3x6mTMPnuQv3/VePnX8OCZJJcxeVwLIdS6kBQl9IHJ06JCnAK80zhhSnUg7XFdhkHayiXYszC6QT0/QUjDfCgRaIz02IijQeVHA/f6AwaCgdlZ6Z/hgqColoEznGwdJxLLISiE9QgCEACOIfJSWVKGPOIzAgBpy+SrELZTWtPIO6eT/5Tn65z3veRcB2kaHUorXv/71vP71r7/sNnNzc18AOc6lTjZ82M5Zab6ifKBolByWj/n1RkmHPFwAskBgDlPqojx/k6Qcy9cN66qH6H4ZOhxcQDKCGdZNME+OJ6cM5UvNwccZuGgwAeHDug4RCjFPdJJIfq7dBa3prJ5EaU+a55TWU9cldVVh0k187aj66wxSw/rGKotLF0JZm0Ipj/PCjx0RxVoHdqkA8EtCXbQykCkt3o939HubeBy5UWyfTpidmcbgaLcypjptZiYmSDXMTnZotzJINJuuxNsBaZbQThStXJqRFFZeZJOlKCMhOYUGneCUpvYCzHJ4qlKqFMQIE4R1d3aO/uA8pXMkSqhpT51fx2UdBv2Cqq5EGPsCozSZFkR9miVUvmZiepKd8/MszMzSbXdxClyWkk115LqCMk+SlLyVkGdpoLw1MR0ogLbQRcx6T+0MSkudf2nF26i8plf6QIksa63T6aKzNpteCDy01phWQnffJDu7U7S3b2dq915aczsxaUJ/sBloebXUhXvHRLfDYCCNlVAiCAdFGdJGwjzo8VLHjaZUCuM9dVnL81USNhdUtxIPiPHQdaxAiGAxrYTaVPkA7FIxJmAw2oRKBEN3okur3UYZT6/fR6kUV0uf9InJnLyVUxaWdmuCqYmZUO0gZaFONJgY4XWNtRUL2+fZdXA/Ezu3sZ7lLK4X+PU+tXNUdcWgkPA30WgPTHTaGKwXzzs1CZnS1K1pth24CluU4uW3W2EuFBMTU1R1zez6KlVV0Wp3pKV1JY1vJPJlSNJEnrmtqUOqJE0TiSYG7zHPEtKgqOtacDVKG7zypN02VANsUdHtttm7ay/tM4tsliWfuf8Q9VSXteXzpDoJCHnxQq2N2BwCM5/IMNtbFnBb+yytVhsPGGWkXbURzpAsTWlPpnTSDn2lcTOTTB/YxbU3XMtpZ8m0gNpqJ4peoeR8XiIZWkl0aCj4NBiDdhqfKHwtit6g2JNMc+jQcdjcJEsMVai0UF4cMZMYEmMwSpNnLTrdDi2jSfIcVdfSdpoYcg+VCN7htQ4y1TfRVRXSLniaMkyHx6KbyEtQFg1+YzSaq3VCUVbUqg700f90xqMCdX+lobQgQSMS3odEjsI33rgK+az4VKO3Ls0whrS5Q8pYWTZKqWC5h8UZrL5GA0dlrIJZ0OTplBCOhHPrIAxdKJdFB8Q4oEeUfMyzjq5A7z06CA2tFA5FGZqkGKXwRjMxO4FX0J6cIvVdqmKA7feoy4K6v4kdrNPXniWT0B+s4mpBg9de6mG1NmhEmEb0ratqfG7BaGxt0Ykhb7Wxm31UXWL7JVMTE+zbuYPH7U7Zd/V+dGpwCpI8Z+fOPfT7JUaJUeK91FhneUqW5zKbWrqLnVtaYbXXw2Qq8E5Dv1+wut6n1y/YHFT0BiW9QcmF1Q0qW1OWNrDSeWayFspbtPZMTHTI8g7/863vRU/P0FKOVmpItKa3uUGWJyglwtgEINfevXt47A0JaZpydvEC9/3NOyiNZ27HdjqdLlmWk+qUTCdMTOe0223arZwsE1xHbaXxjkdy9YOyRNWeNM8oQy/yNJX+BsVmD9yw5Wjtoa4dm71NiqqidlJAlne6ZFmL9sQEk5OzXHdbF9NNWTxzhlRrUm0EmVxVdDodlpaX6fU3sHWJtY5iMADvA2lPQllWlGUhhk+vxleOteU1kqulP7sIQMnxi+3pwJshKMvHN8OL0ekkfeWNKPtYbWId9MuKQS2EMN3ZOWZmZynrCr+5Qiufwlsj/Oi2x+T0AtOdGRYWdjG/sF3oSL2X0sW6EiPN1qRaUVUlm4NNPvfwaW5/6Agba5vYQAYV0ymCj3AkqYg27zzaKvplRRkopaV8DSY7LSa27cZbMbJMksv7qzWFUphMMdWdZNDvk4W+Bt5ZpFNmACxquXfnHXUl+e0kSfB1KcrMRJCvl/VqLWnwZkutaM3Nojc2qVbWmJ2dZftVe3GtFr1+wac+/hk+9NHbwWg6ptPILZPopoRMurDJfDkPE6nQJnsl8qS0gktJgzI1SpOnKTft3sHh5SUW19bxqWZ6bprdk5Ps3LOH1Gu8Aas9kJAkCWVVSGQwKFFpuxscg9DfoSzAKWHFNN5hPKSzKW992/uZnEgxWQurcjwOozWp0aE9bkInz5mbmWV6epobDmzj4I455ttdfH+ARfBOOqTLnJfn2PD5E52sWrBJXiJBiZZ+AsRmQkj5p1IejRECMoYRXJ2k9HoDikrRnhp8oSrpS3I8qhV9bW3TfzlJRPDZuiZJDK08p9XKWVpek0Yb8VFvCUaoUFRubRUIaEK4XEXvzI3w4cMo6sxvMQklBBVCUigSlQztC6WEbxwJ7SeJwtdD9isRtDTkNgL+EQCQN4FMpZbwbG1LPDVGg/EVuU5p6TamWhWgWjdDd7rUTpPkipV0gnaa0e5M0LVHadeKtF+LsjMJic5IkoRKD+k9VZqQt3M6rTaxQU87b1FMSk/3yelJHve4x/L4x9+CU6v0eqKUi0GFXYPl/ia1q0Ipn6WyNf1SvIDE5BSlxOnTtE2SZpS1oq77DR91VdT0ez1Wl1ckdKcUvr/JDpMwM7sdQ0o1qFlZW+LeE4c5319jaqLLgT0HuOG6x5ImbXTPYzNFa1JK23pFj8wkeCt5PKU0Jstodbss3bgsrG3WsX5hmeOHDqE3HUldgnEM2GSpLKlPC8VsZCf03kkNvx92QHPW0kpTaudCT3V57kVRkCcp2nvqKnifSUqqDbauhJjHe2pbURYDUI6yKnHWkqUtJrpdyrom1oeXtWPQ77O+sUGaptIvoCzpdicwRlEM+jhboQ1MTHaZnOhSK83qCmTeivG3sYGta1RYb847nNJU1MOAVvCEoyFb1xJ+RgvuwVUyn606J8k19x96QAyLwrKyvMHKyoa0Rc0mOXjwBnA158+cor95jjy9j8S0qGtLWRVoDZVtMl5NRE0Tw7dSP++8I0cMttD4oakJB8hzI0BGDT7xFIWlLAOOwZjAFKio6zKkQBDj13ryPB++B0qRZVnogBYjGYJZEXyCb2QLSPShqgqJAoWojw0d8upaQuAmEaPSe087yyn7Pfr9PmsPHuHeB4+gaqk60Up4GLIkZTPLqWshknH4cEyPMhKN8VH+lE7SBEoqkaoqhshBJcLxkWcZB5/9eDYPP8yRY2dZWuujPZQGOm0tFQpOutklXpOlgZCptk0LWhsqQrIsIzUKTUWvr/BYMWRD9NI6RalSrKqCExaF5YgoDb9j9FNvh2c+7/H87Pf9ANPOoGxNXVusqtBoQUtUjtiKS9gVAvg4SVDK4JShwuA1JMFRilFSpaS8jthkK0S4nFekeRuXZGNy/p/CeFQrem1iqYzUiaepl97ieDY3Nzl//hx5S1DpMB5qHwXcxfaIMefo8BL6iyxzJuTvGVfsW4cy8vKWVUVZWbROJG8amkfoSE5Tl1RlOVxoI967x4nFaSLwxOMwUsOKEuWetfAkWBw97ynrSU6c2aDUGc4X4K1EClRKpTSu1kx2u+zMd/H8r3gNe6++D+U9mU5oJSlZKj2rk3ZOryyovCVtt1k8dx5jUlp5jvIwGAzo9/skeYJTcKYsecvf3U6/cDhf4by8zM5ZiqpCpzllVYfGNQIC29xcJcsFB+C9pywr4W23wmKljUUbKXfJ87Y0drGOqq4pSklJeK3wusKrGuUUplexfHqZ83qVw0dP8/6//6jk4hRQCqbRB0tN4JTDVIpTjU0f4jXy7LXyxCZ0skbC80mlnFJ5QSwnqQg9r3zg25bzJkXI15pE6raDsE+0lEPpkOenV9BpiSeZBNR7jqPtPEkgCUoTgzEVxbnF0EQnkfPUAgxsT86TZSk6MZhU0Wq3A2e5IXYIzAPRUKfdob/f0pqc4DrfYafNWfQ1PhHBbdDkSY6tBxK2DQZubGEMYoxGcqosy6gN8v7phOn2JF/31GfxjU95AcvnV9hc2cBXjlaao1oGpVqkSUaWKMpyBZMojGnhCTX1uiRNWuhQ5icIc4PKEpI0bSIOzjnBl4xE4DRaeg1kCShHkmqSNBGCGiRKJWkHLSWSqZF2qFojNbF1aGCUSuOa0InR1o4keM5ogw9VDNEYl3mW1J/10o5WuUBJnKaUpdBay3YCRLR1TZpm2MpilcLrUFkwKEhbuaSPEHyDdo7SJ6EdsvRtMMaIzAryLGKFShONHlFuRofrcoJccl6YAwclZF2NqaUfQ60MaQkUg4ZSt3Q19aAKBo1wgkSchlSdDCQyajxWl1RlBl4+r+pamheVBXkyS60ECOnqCq1gUBc4V6GNCpggT1WHElhrmVqYpVXl6PkJOl4qo1xdUduaLG9RF2WIQMkzqutSSMGUorYldV2C65NlCk8ecvYxLSnvu7eSzxeOKylVTZIE8hyT/V/e1OZLacSy0AY0h4TajVZok5CmGXVdB0UfFfS4Ry60miJSXKNoI793iM6PgPQYOVIDBgkBIB2IZvJQMlSVtYD+TIL10mkLxGNo5ylOBaIXhmAn74fMXd6HGld0YOSTF7/yirWe5uT5Zc4trdIbKJxLsLakKnsoX0o9tlEMnKOyFu9Okzz4AFNTU/SXN1BGM6grirpCZyl1WVH1S1xZ4msXcptWyq6Cy9AoqwB6ISh1n0QATDSepNGGUVrAYcqRoEidQldlQCkLDiBxjnaa0jaZCPWAHDdJQu6tEOmkGabTxk9IrlWUS4IyCe1EUgHWBzR8oPXzTkBXKVKTq70IwMQoSLQoRRPqwAWVKA1NQt7dJFI3nKe5sJcBWZIE0FxCkhjSxIgBgHhpJk3R4VgkeQhn65AwFG8UI2hgjGAxtG4HGmbZVgc4t4DLgmcbSF6srUlNIqrNC7JDKYMxIX85REyRaIjNXeN6VogH6JxCmYTJqTZZu8UBKy9SZEaTbLwoxaFrPfIGjb4LKqazgrdqEnYcfDzaG+oqoPMj5sWImRX7nNd2EBRoIG1SnlBOICAsgkGmhPyFcB8xNOc14MN9R69Qx26JsTObYArwo0gYOZYL6G4XwgYBsYPHoVXMs4WOfsoIAUy8psiXH67Hh3ywVqPJODlaOxgjYynAKHucb7pPKifXVWJJlYrmCTVOehiMOichXRCpJmO0JQtRxdggy5hAd+0cJsg57zylDyWE3hLz3HjwtZBSyetiG1KiIRui5LJxUNVVULSglMPbIfmP80KYY3yJdh28imW/wzXkfDDUYve6gEHCSdTKpkq6HjoBUYqBL7wC2jq8jukZaX9rFHhX4+oBdbnBYPM8q8tn0ElQFH7UVZMnKXJN4Sy08i5FUVE6aIX+Av9UxqNa0cMwAqS0CVnv+M6Lsnf1sESGkUU7zoAXQHA+hnJEcRFybLEzHIw395CzDJW90tI1SWtQXqg+Cd6eD+fRQeC6uqasJeQbvRLvA3AqRA9cUKwABNIb72panQ5Li+ucOHaaM+dXQOVo2tIEZLCBrgty5chDnWlmhKhaKXArfbpJhslSusrgEunjrdMONrfSIhIRjpmWbnFGxR9RirEXuzEGnRjSVkrWSiWHZgwmSTFpJtwEStDhMWRqlHiG2tPMXCs1pFpKvJQ2oMWyNiHUOPy3pD+SJMGrBHRCZoyg6YW+q+l1j/ckGLRyIdwqylEbFYhShl26jBGMggoUthG/oZQAtwTx70lMMgTn6dACM2g/bYxgDrQRgWRSYXYIURyvAvucFlwFOkQcAiq6+RvAxY90EI4+UIV6mhPi4/+DNxezkCKQBRcRej0oFYhQApbAy0mkB72EZ0XWemFQVBqlLcoFUGS4qLj0Y6QjRsh08Bq99zitSLoSyUoQAROvwxM8cQJ+izxEuxIiQ5zH4pRGhXeu0esRPY2ASMUAD+9cMw9K6uy9xWjdzItSStr9NrEbH3gsFAbT5LsVKqTqguJiCH5Tvh6+x6E1bAx3NMorgheRPLCkWILHHyOF8YmFnXTk/VAab4XpsQh92YP+o3LCE6BHnr1k+uKNq+G6kIb0xJp+7YZ9LUxY2855vLFYG/uuy9Fq5VAmESBuWA9+JGQQ16dStZQXBhY+FebCpAKaRUVZ7PDK4p2888K2p4gldGZEjnoExS9YkFQcHluLcxEAkl4rwVQB3ii8kfkFMCYFLSRROpEukhhY29zA2038SOhe7CnV8EuIISxdKPFCsGTrEbDhP4Hx6Ff0KqrJUB6hYsmaWOvS/1wNvQoghsRjjXF8zYEAqopKVnKjtYulJPEFHVbVj/v5wQMKZTxEK1jZ0LpaavCrfh+TCKrU4EIJWeiBHYB6HgJcQF4KQumUqy1TE7Ms5efZNjVNrlpCOuImKX2FLfrouiJ3lolEkyvIsw5pJrz5xhiymUnSNCVvtcjzXDAN7TYmE086loglrqSVtmglGalOSLSmneckmSHNW+g0w6QpeSun0203qF6TppBm6KyFSwRw5xODy8QIAAOxI1+j3KLh5WmSJI0QC8I+pECkte6IkPPyXCIFUQRCusiwoYaELwqGEit8EGmX/Igcxkc57kXQD1cOjSeAb76o4/14eW5STSnIYx3vYfSYtZystj2SJMdZ8YBkI4OzNUanQaFZdGh645BlRrhU5xzaxfv1AZ/sG+Eu+UiDrd2IAhRSBK9qIAkh0HB3teToMbUoeq+Hnm40OoNSaf7WSlriek9iNHVt5fj4hiM+5rMjk6MxwiBYlBWZyYiNdyL3RVS+zYMJeIhhaav0VTfGyDvmhzga6yoSbZp3lKDofVgLHsFRCCAyLi5RIs5a0jSRMHDAyMSwvNaaCN9KmuuNTyIY0i7KFR+e+RAU3IzRhebq4NFqrB3Okw9rXCmNrSWdF6MyMVIQee5j0knK1nwzDzjfrFuHpJZAAKMmsahKi5GlxDiotBglzjrh+FBiOHlGiMaa24jGVniHg5Fva1DKxpPiTQk+IwnvjLBxxtLl8L7LxeLi8bVUseRKcAoVsWA6vpfhyWrp4YHzQo2tE6wVkGtiwCcdVDaB7W02hlWULdK33jRlslWo5CG02Xb/rOi/dIa8qMEujNY1avjiJwnaRUsuBN0UoU93rAkWC1O6unm0NhTlgP6gT13XzM+3iOV5IjSioHdNfkz0xohgUhpnPC7ROF+TBM/PVjWbK6tk3nDguuuZm9tNpzNNnrfI210pLTKJ/CQGkwpIzuiUNJEfpVKW1y+w0l9ix64DLCxsx/s+xs3K09QpXhtcsDlivWszXAgbhoY1wxeHIW4halbtAqfESEmh8ygd3sYoRtRwX1TIAzoR+tHziPqzdhatBOhlg8CqrSPxEq611NRUWOcwKgkoWbH066omT7NAZyuvvw9AN28txodQqLdY59CpCd3nEEOgLFGJalI0KBEoQhUbjURC040QvdEj3hPCvT0WPg4GY1QADcWyE7RzZSWMqYIBB0ho3Id12oSjJacqMlMNPfgwbJzqEAmJT7SOHBAQQFBRMVlio6OoRJPE0K8rTLAUal+jTBaMLlEmRhnqCkiioBuuDR0iAaOpLFFiIcyPaqJQSSCPkSiVCtfgcAE0pbQOhpmmP3JfoEhQsjbU0O4yXgkgk1gxJzn6wo34yUo4DpxzFLUjCWVl1tfh/HHmwrNW0ls9huBjKLyqdSi1JRiIwtyWJFLj77xj4L2Ei41EFOJzc0GujLoAYnyE3H18c7QwVnqtUTUoV2MVFHVJ0nieUjee6hQbcu/NySI4MMy5tUJehAsYFiUVOi6ktLxzaDssl6ROcFQ4VQIK7SRVVagCaRYT1rUbRjGjUozPPzGpRIBwoJ00zlGZRCksKGdQuWJQl5L00FIUr4Jh6GtC7DCmBqS1snWeGqg8KCsVBDbodoXMu/IOT2ippxwKG9aLGbG0a2wl0TitaZ6LC1gD8f2CbAtRqY3NDXoKmCn5pzQe1YreoXHBW4oLE1zojR2BUaO5Ft9Ytt4J37LQPzlMYqkrj1ZtinKF1bVzDAYF27ftw/sS58qQGxNQU92w1225qOABae9QBrwt0XqWwWAT3S+5sbOLV/74L6Cnp8GYEU+REBqMNrtqPM3Uw2CwSlX16XbnOX4h4f63f5wsnWTbvquwfhbrB5Lr9OCpgydFQIPHFH/o7U0pDVe8bzrcYV0D4FHBmneuGhPqze3GyoR47QpUE1oNlrNXKN8KU1LiVY1Fy0uKa8KSXkU+8xi+BIIHo9PQRjRwmztvKZMgIEPeFYaev4Wxa/VlnNgAqtTCEuab6E7j84ELqF0lq6TxAEbuXwIOEvLFa7mGwE2OC3XG0aNWCeVgENZXDA9GYBThnjQ0nd8I61EmdHzefZMrlXuM1y9WjIOmtawm1J372KZ2qBprtHj3Y/NUQ6jHH37mUNW4UqTZZ2TOAO9rGs73cH7vPSUFTQqq2Xok36/GX5xhGk0JOG1Limz0mpt9tR8z4rVSVCrGPRy1r6gceB/q2recZ+wcfsjD70aeu3iRoRKEgHAPa1drLZ/5WIExPJ4OCbCLjOiR+7YMS3+H3xuh6g0RGoBCSbQhri0d1lNYSOEZaWylG1bNGB2T5z4yz0hKyPrhU5HnFs2li4fXw7UQgh+Aw5f95tPhbZXNMZUH3xNjtmK4btUWgyFer5RHlsEYkrXOlnUQr1KU+qCJfGRJeB991VQz1V7R75+nZTzex+cuzyXibnwQf4nJqMoek3Mtuvks7dl9l5yLR+t4VCt6ESo0CkYHcIY43D4gc0e9zqGIcj7mFSWUNBgUZGkrLFCFVglpIm+10WngShbPPwYRGh0RvJhI4SDsTApFjk0qCt/DD3rs3raXl77yX7E628aunMc56WlOQOYbr0NelgBm8VG7UJebuLJgwywx2cp5+gufidWaYyePoMhJvMWqcTrfmPePoe3owadIKDPS3aJUI4Nj+Nd7R+KHyyPqlhiujB6UAqG6RPJ1McyhAOvXA2gvhDydRnnpKy13FfN2ovCDzRYUXY2von8XcA2upFIKRTSQQg41XmDIi0q4XT63I8IlPisTQ3/eh3rwodIPwcSh5z7i0cr5mpMFTzBOTpzzYCDU1VCI44X0w0dvL/ox0fPzI49NziFhfD2mmMA3cy4W3dDSGaJTfCQdC/sNIyJSEREjWTT3opwSfEQQtlKHrJrnERzeYTiZocqWqEcgmhm9puGqaYwRXGi/qlTTHjkuFjUyn0pF0Fo4OSNreWR7rZIR5aqaOYqh4DhvamQ7pZojjyt6RZPRiZ/HnG6zpoiRB4aRnC2KKJbY1iFepbaeK1x7PKa2bvhuIopoaN4NzyOaLazvQBBjnQs4WXlA3kM3aC4/dswQCQlyymFJtGnkZpMqUyPnHdk3vvAqvvixix/DCIv1w1z9qPMSHujogeXo4d0gymQ1PKdTYEfkSJyz0eG9x5A1c1mVJU5rEgm5SFRRKdrZBNjVxskRUReeSSMvBHg42OzRabdJZ2ZIp/+5H/2X2BgK3+aluIRhOu6N+KFQC0hUayt0bqjqCmNSJiamsNZTlUIM0VjIOgoA8UKjMJAFORTSHk+iNLnV9Ms+3VbO3PbddLYf4PiFs2SDEodtwC6xlE/5kZfCSQi5VjG0atisClIspEq6bhUVMAgNO4ZF+Y2KisLZxxfJU7mgFlQ0SEZnczhf1pch3ByjDCEPLBPSzCE+0JqGDLEK/oyjQBJ1CXhN4/k1AtI31+TjBSmHUhbvTAMqiwpHKx3CwKMvvg9KhiAQhsoJpCd3NNulMkMH2kuadrwehx6qLkCj01w4AEbWmA70n3F+5doi0EwFIRfmpcmY+GCABPMglGI1hoVKmjlQjVZt1BRRzQnASQzThm1RDRWrinPYKLKRa1diSMpji13poliV+uHGQgwLwDkBtUHE7nspOQtxpgY5Llc1vnr8cK1EYJjD49yIMTGmVEdf2HGBPvqJG63DhhAqHx9bvWsxUmzzno4ecPTdDfqruYtxIyM8ce+bz0aVPyPnivcuH+jm2GN302wHOk2aPzwBtR/XzcjFqhB5awzsrVZYdAjc0JCIw6i45kD7mN6J6Us5x+isN/8OxkMQe41pqoJhPpQoNGt/xLWPlsowqsGWZz16i3HKFGNbxcOpOG9xVx8+80Hpaxh4AS0miLIfDErOr/cxfmhICgAvpGp1c5GN0SOA5yGr4T+V8ahW9BFZGpX8qHV/mT0YhvliaE0z7MwlzFpaG9qtDnihmhQQUEQXx/d65FzNQhJBaIM1rfHkTrFZl+STU7QnZxk4TX/lAlpnED1gpbAjYC68oKxVCGk7IYCHwJVNOWBQucAD7bEOrAl1zjEWNWJzq5FXx+NwVjcKxUcvqjmvahRJTTEEyDQzHeoPRhU9GuVK0Z4IgloraQMS0ylNWFqZcEW+MfaVDoLOg0dSLVq1sE5jdE3sxp2aNtJP2zWlV5EsxEeBFYwA5VVjfMn6CBwGPvhZmhEBFHpaNxSjUiWgVPCCAqWqND0PFQwEgFOYGhcpNcMzNEo15D9DKYa00WVkWxVBXyHMroZPq+ne5gEn5VmRDawxc9xw3xgalSUw9FoglEF6hVYGP/I/kAYszd+RGdFF8qQYMfFDQJuP1xcK61wlKyOsBx2eb7x/5yVFFEGzQ5vChXzp0FRQgB0FS0ZF3ChwGoUbowzxHWw8wojLCSpbKbNFPsRjjyj/UQXtGSrSuM7iUwmyYpRQp4lijEQCRvXw1tMOT+8lvadorm+0KmLkkGPGgUIFAF94/2J5Gh7lTVBmw2egGakmaRSylahK/JzhXJrGkJBeIZI8i/aEGuGX8E30R9g144VumePmUx+qmcK9++HmjdGlAuCwmTM/Yl9tleuSb8d5UqPoh4qJRIHynrKqoVplJm+jGEaxlAqAXh+M5WDwaB2pjGsS+8+K/ktreJHlUajIAo2Kn7GXL+7gnSh7cThEUSVJIoxnxH3ECDBGUVW+WcQi+EZa4YaEnYTHbPOyKK/A9jCJRmtHYlK8ylgfFNLZyRl0yJ36UD+bJia8nI46gKmcMhhvsRVYVCibqrBWob0AiKyr8aSBmGVciMcIh+gKEZR5EJrWB8CTEqMED4ahMneU+EDUI7ShhkQLl7lY+EGgeA8tKd2SvLNCqZqEHO0VWpcibPUcaCcvu9ZonchPolCqQur2hUc8y+dAZaAqrBMyDGVaoISUKOKfnfNYYgllwCJ4qU9TztOnaEBftVXUVS0mWBA2tg6oZ6NDO99QxhgqHMqqFKIPa1FaM7AB2+8UWPDOC22traldTe2FHMUpAqd9LQhu5/HWUVWlIOCDUeFr2c/VtZRWBo536ZEuoEKcw9RV6CA49Gp9MExt6OAoLCuK2kobV1mnBF4BJ0x9hGiDCzpLeeqqRHmLCnXiiRY/XdtYpx0UvBvxdrwDYRHH1/I+GG0wiRHQqA691cP75L2ibYzgZ4JSlVRYDF1HLzqCZcOIH46Go31kntQjGwXPd0yxxonKLlLy0UgZDcnrUWzEyDX6EN3wSNMe7bcoeoaKOh6vjvJHcdG5R6/BumjoxZz8EOwo9e/BGEuSsTUew+U2vu1Ktk2yHG1SPGCdpajrYNxC5HVQWlOpVABqRof0Zqh00MEYCM/PIQUiTgsgzikVeBCMzAUiM5KYbhlJtYxaOApxSoj35f34WnCumU8djhsBepcy0pRS6MTjK4stKpytMHmLVDkKX1MkHjPV5ltf9gLmd0+gnaeqK+FZScxwbYSrM4EtUeaVaML/kxmPakXvncNa2yCMXTK0tuNPkozf4tCqNc3fUisvqo2RvuJKGUCUTF0K2Y3gAKLnEGRetDjVkEHMK0elPQPrUMUqabWNDinTWYs1NIoe0ELpnDQLTGeU4OtgXRq0SlFojM7Ai+mvUysGgk/khdMebxwpOU7HMKoYKVoLj3k0SrwXAJDXcsFGxxKXmrouKSq5V62FMEYhHfac1zivpO0umsqLECmdUHFa73DFPIPNh7HFKWzdpyhkltbLZfplRU0XWvuoKygLS10VVGWfutik7G9SDfpgwbqS0g+oy5K6cFAZnLU4W2DrPrYEV7epFdQ4+c5bIf5wwiKmrPR7V9aRUAow0kOeJNI3oC7Q3qJ9jfYiJLK2ILSjh6ND/X6eSqlh5FN3rb1YV+JtgXMDPFWoyc8xJkcnLbTJCRB14fRWhtSYRgsZQgmQ1rSC9y+d7kJ5ojboXLgElNbCsJa30Tohy3JMkmGStKndj9fntcZqhdZJw3Mgnl+4FmNIkozaWyxWOu5lLdAmEMSEOIkSjnsVr7mJlpimlCp6fGOguibOGtyjsZcVyFtsHVF+q0cgV7duK3XkMcJ02b0gRISuPEIN92UPNDQ+/Mh/t+aO5RtPrEK40mi82uiQeA/KjW4QjA4dolrBuyU80rHojvy7skXgkZC1JgyGw3RRjNzVAeAUU4bCsheAsW5o4FAHI0KFcziHtzWu38cPCnwlytNWrjmOC7LXOYet6iHHQjDUomyWc4iBErcHARBHDJEdOZYbUfwgkZvaWqqqxg4GDPDkqUF7x2Z/k8PnTrJ+ZJF6fiGUEeuQ+/dIXG9o8Dvn6GQZi4sXmEymmM7yKz67R9t4VCv6ui5JkmhNK4gLojEAHHUttJZxEUUhbsxIPlUpqromTRJqW4rnmmTUzjEYbJCmLYY1tTQMURFUpLwS/u/AK+4DEtuTkuqU/sYadXedOinREwmdXfvZvHAPg979eF+QMY9Kd+GYxukuLrB1VVVJWQ1wbGL7BVVRUbag3NjEqgxfe2pX09cVpuepy0HDQ27rSlp7hk52tq6aDlRltYkra6itINwd2NrRr0sKZym9sHH5Om1SCMrWUFf4soBqgLKVfK5AJ21ynZBZR6osiZJa6Lw1i0snINWoNEFld6GVJw0KLVWKzCs63pMbT2KgnWg6KbL/xCRZ2iFJU0yqSRJPuz1F3prBtzu4NMMFTnGdZqSpKEFtEonGoFEqMB0ajTaSD1epkZApHosNpDatYNjJ8D7wF2xRZk4PgYBDEhsaQepROAVmRMg3/uaYUyLf28CjI2mOEcWxNVypY9BWh1bGQT+E9EUMiTdxq0ABHPV09KJKL0gKRyzZE8EcG0EhZ5AoWXMeqbuXHy+/A6Jfe6i1aTygMbT61rFZDz33JhwO4yF0DyPguejBx2OPDh2uJ34qHnokuhn32BrP/ZKqOUa8Yoh6mFq55HZEUGR8zwMyJRiJ0iN+qITHr2TkWB6GpXhRpgyjBBG4SfByZfKGa6CJhCjfEBjqwNjnm7WgQylttCdkbvJ6/Lq8CamQmCIIF2qUwntNrCaKmCalZ5r3QAcvfxQwKobhqCEUnvyIox/fizjVEQsiJbFjT3XMkovGjkehvQ5zY6mUIglARec9VVVx+o6PsNg7hLXSGlxrEwykIelRZCet64rt27aTT8/8s0f/pTSGgBs9ImiHynw0j9zk0mIoUrumLHXIQKdIjDRFQBmMciQtjVZ5ExlQWppoRAIcHV7GsizpDRwoG84noKeBrTHtKTZtzT1HPse9f/pfuPOBo+jN05j+OVTRw1YGV+a0dE4rawnVavDsTJJR5ZYUQ4qhyg1Kp9jWJF4l0vM+M0zUGakXJinlHSlOuNKTSBonPPnaaPLuTgl/OoVRhsRkmCSHPMOnGT5N8EaUpyQ2IghH6leVcw0XvNIalYboQfQclCiNxLRAZ6B8aB4kzTYi17sKqQhFoNUN15goD2iMSTAjbHdK6+DJJg1fgAhoLYk5LVpThJVrvJqoK5SSVIeHYW4fj1W+qVQale+SEnJNKSJK4evBkJp1ZA0O9xWEfawIiSePyiAqOWIYWo14dlG8jISDo3yMvHEx9x7d2DFh7Yf8EN6P5JWJaS09TOVEr2gMHxDPLekB5XVzHSoqgHCnMXev0biGqihuO4pGHxGYIx+pUQHu4zEvfrdh6GWPhsrl4t3Y/cuhh4bG0E7SI9plRPWM7exD6HaoQMdGNDxidCMcO6bKRp0G31SzbD3Ixfcz3MjLmh2drqjU4/kiSx+inJQbnSNQPtbxM3xWPpKAjd4pDEaUpxr5wjcqTo1dWkxdyqF9KLmLKUKhu976fLce1zcXqrZO/sg0+8ZoHbu2kWsPG8ohmvfB4bRBO4sOPCpWeexMRmpzcDXeRkrmQJneYFNEdAgr6Shm5p/OeFQr+jgakIX8JYu1sYpH35wgdGNoaiQXByrkjD1KCXmn9x6dOrxLENY7QCU0UKxA5qKUhHl1YLiSPt1aPATvSLrbqY1m6cIpVo8foesW6M7sIm/Po4qCuqixhaNlNO08IQ886qnJSLM2VSrUs7lJcYlGZ2181sWbBGUMOjV0ScmiRU1ouJJossSQJNKLWmhbFa32VGOkaCVsdUnawrRaqDSFJJH+0oka5vVC+VUU9NHZQA1r3In12wRhFGvTRbPQgJqaOQ+WuSJ4x2FbgfUGsE4Mu2oUBounjmFUH8t7QvmaG+8qB+PPWikl+fcRJdRcjw/VxkEJejxNM5qRHKHgA0aF9aiFEPd1wbts1F8jrIfyLRqpUdGPKzY/sn/0fBjZXzVKeTi/0ZPV2gSDYMhrQBDIUdHH8zkgVotE9z/ekwpF49FIGh3Da9Pg6+F5xq5r5PRARKGPjlHQ26jwH1WBYxpniwK6eIhhEzeRWR8aImNKZItCb5qZXuK4w/U6buA1Bks0nsbm6WKDQdb7FiOxUYTjysXHfGDD+hfubWS+RuMTyks0aXgAOb4eM7xkzu3wj2GTl5H1NryDkfXXXO74/1TwrC/KozeXMT4PQ9tuaIpuvWa/9UJG5mm4gQ/VIR6tHVZFRQ94qd3HFWGOQi/7wHiqdRos+SFGxGSaQa+Hb22SVf9MmPMlM0aFr4zw0oXPLgLgIN6jvI9Dk1H7qKzTIEdSqdHFg7HYOnqCkrcyRkLdVSV9rJWYg0AIYyE5f4clVeDb26ltn7za4DGp4utf+i1MPPYJqDxvPA4PkIAyXohAFCFXqhiBvoPyWBSmDjJFewG32Qr0aPOeLdJwTCeIVTTE47tmAxdyY5L7roLSiepU6u+tk0qEhoHQJ9TVJmDxDpwTXEFZ9kHbRkgoX8t7PJJnE0/IiMXt/JCQJHqZ0mIqGBqxAYkdeu5BgTW3OlI1QLjq+KV4mpFDPQrRkE9Vw7XiAwjKh3rzWFEh3oZgNIb62gZFKB7NcPajCItGpHxej4A6pD46lEJuUaQN//pImL4xipo7CgaUGm4Rw70R4Lm1lE08UJpjy/4myk0BeAZtMao0GuezWYZD5WMi3Yoa2TbO+bgUHz6N6J1735Q5RW9OqSQcZLh2hymB4bwoFd+7rWcYRgcaz3rUy49jq2fZgHgvdsVjBEijJJdtQ0pohFN/mN7T4wbDmAGjiBUow/RAOG/k0h9aIigveWRptR1NGN9gQPyIRnSjEY+R+43V8nE+pSrFNIZDo7CjATJ6bR78KBdJUPvKj5TwAlaNRldGvXLV3L72odRXDRkkImtmcxcKYqj1UgaXGv2HkgijcZ7UQ+HqYepFKZRz1Os96tJiQjmtVp7aWQFDG4Uykoq1zpJnKZsb65TJKu2iuPjkj+LxqFb0EGgZUcGyk5cvhoajCIh5euFxllB9bPWpQ+mNtY5+2Q+hNxuAH3Xg0hYPUcKcQxvaWrGLtdZUZRl0tsFp6b5UuQTjxFio1leZn9/Jy7/lR2kduJbNlTNUboDVhgj0saXkw+vaUjmPyVOSQUGtvfSCrm3TUpfKCvGLklxrVdco60NEQg2bqPhKfmlprKKrUl5WY1EuAadxZgC2DZSCug3Wrx4J3cbflwIrOXVxmCt6DGOo5JHP1MhnsaRONmTMu42RlKbELR6DcYU+8gWjPoJEV5wcUymKJCErnIQek1BmWDq8AVzIQxoJTVJoIQHUXug7hfUF54SYSKhnw9woH4wkjzGJAJka+Si5bOecUO56QdV77wVfoFQDNvJhfrwf8sLHbm/OuYaRMW4Tn4lQsXpwXrqUxTBymGtjEhITX/eoQFXj7SnvBKugkwYoFUF+Sik2+/2Ghx0F2hjSNKWqKkwibUDHgFNReY1cI8TPAT+kyx0Kdt94e9YPgVcNO0TE2YT7ryNRlR5fp4RrGdabu4u+b9Zh/GFo68VjNnMYo0HOBUT4EGgXc+rSgnZEnGpRoc4KHfClIiJJkoAXgzq2u01VO8yxlN4qFLbWGCwmzJGwIKqxY4ld7PGV4AOcH4LXIvfF6DvoRrx8H7xaE1vxxvC6Gn2ThksnNtcZLVGN1zGathlNmfqw3sIEk2qFdlAbqBVo68mcpjThfdl62i1RteZ8iAyvlEerWPAqYETjK3qANhnWFsKVoqS6xNY1zoYSRGwoc0xIjVRzeLf1Ch7d41Gt6LM8J82yZnFZKwvb+vHc3dbFJxSh4o3aUDo0psRGiEgioM8FkIv0TpbtooABKLynGhR0u13wgkrXWrq7eQ9pntPuToDJOH70MKraQAuRPM5IU4Ws9qhWTpImZCj6awWDqhYhlBgpAfMeVTtIjbC+WXm522mKM46QgkInFuMdrk4xHjwWqypBWKOoXR0nh7rymMyRWNN4FgmmEdZwsZCM4xLZzPHvL7Hf5Y7VHPGy3w99smhkfb7j+QAKUErhqgpVaqwBg0XXFl97XNpBWmdY8IbE5lTWUZqBKGebkvkc5aFve1J/jJKSuMA1Xw+KRrDVVdUAQGMjkcoKjZIg3xPZJ9To94uCWH9utJa2u7UlzbJmjdXWkiRSopYkCdoEFkXvm7yxBUovkSbvvfAuOEtV15SlnCNJpUNXVdVs9nooxBCOSrwoima9DwY9vLcNV3q326W/2aPoDyiLAuusAB5tCcGbHSp2Ai88zd/OCfIfJYZ5URTBMIo1zgIMjAEKh4u4qZHObXKvSoGv7dBQCsZTvEdpqSxedozGjK4XmbKgsAAV8DARCY5SzTbRwIg9EkZD7DGy0Ci1MCRIIaFlE8rXRm8h3rcGqqqgrIWzIs2nAre7Be0xaUqStlFajC4duiomoc2yDvgVrQ1aG/k8XIvWmjSk/NJA1KTCbactEf2xfx0eEmNwVaTPFuWfBAPTE7BMOJz1OFcFem0PDlStGtujMS7wTdDE48Pz8CRK0Vc1OkvJHBgLPtEoJz0OhrwQIylWaO413neWZQFTNSohgpwf3c+XJJmUka4uX0ArTavVFqPUx3JfRdmeQycKk2qU+ecc/ZfMsAFh3oQrLxL4ocVsfKFHYnd1PWzE0Xhl3l/0E8us4oiWfRxxO2MMLk2IZRsaTV2X1CRSruc8HoPSOVW5ifGCgPfeYz1MJBnZRM7K5gYrKxeo+wVzO7YzNzfD6uaGCLXKSVkbHhVeyPiCFuVADBajxButIfOafrlJYgyZNiQYBkaRFII0T1RQLElLlIPTsr/yFK4OBYjjc3oplToW/eRiK3/rGAcjXfz5lUcMUV/qeV9i1ApvHFZbvPUYcqq6ojDiuSinMa7GFwF1rmspwdE61E5LTbn1NcpbkjSR5x8EKaEZTqvVGvZCBzAxZ6maiIRSSuY5hEANGmc92mRUrsbWlrouqNbXBdAVlZ/zuEpq8IuioCxLKSmydTB4VDA+wOCaiJV4jBINKooSvCjaTEMaJH6r1aIsilCKJ+9LlmUAtAipqGAETwBVnlInGjfRBiBN0wAKix6uhK6NCXwK4f6V0sJ0OAKsDHYuJkmG7yigQ6VEDNX7qLjUOKjMuaHwj55ujG405DqMYEPC8hnNt49+HrirxqpqABq8h4+pg4uVEH74/RAX4oZrILaf9gyvVSucraUyppZGTlqljVyTDnoevBiF3loJM4+WosUythBNqTHD9IiKcy9KeNS7T9Uw4hm1f5pn6CSRZlDaBCZIRZIkZKFc0xgjZaBKYxLTPEuda7QWeRL0JkbRdBGU8wSSMuuYrGs2qgLrairnpPon4JqUCm1stZbW1o0xM1Tyw7RLTCAMn+nWYdI2SgkTXtaaJk0SoTtXMVVZg/YUpSXLJ+lMzNBqda8gVB5941Gt6EdDb43MVxKujflI+Sj09VY+fGepa4sOmn9USFxO2Yx6tvElu8ggSEP5FmC0ofaF5IiVCBTrPM5rjAasxylhrcqUZo2a5eOnmZ2cEdCdrnAKPvzxj/PY666j3cqxdU2uU1rGoFLpM+2sA+vI2m0wSmrhrUNjcFozkWd4LzlV5xWVsqTtHGdL6oBM9YDSStIF3mOcogFzxem4jA4ei5yM/FxpXC4FcKUx9oxh7FmNCtyLjIwQdEmQXvfea6wGARN4cIZaSZ46SMMQ3rMBGiGhPYvFKI0xLaQ7ng25PosLkZ6qskFAe2pbYkJKp6qEcCdLU6qixDpLkhjyPKeqbVCAMnPW1qytr4uwDGVRykNqhE9A15bEShgXoyEIQsk/0nhzWZo2CtQ7J93TkJxyoggcCpo8y6hCBEKFqoU0zYZzGZS10posTYXMZqQELLKwDddAUC4j0S4VDBFXC7BEGNlM2Ga4YlQ4nvd2RKkP0xlbjbumUclI6D6WRMa+9+LuxSMxvmAZ+WA0EuHHkd9jIemYkdkSCm/C0+FzF689GA+xeVRzT0FJifINDIRBqXvrmr8lyjEk7IlpxkgwY0P6wnvpYGejkRHD/GJZNNs16RIbm125xgCIZXiS1Hd4K5Eoq0oG4RnGdI8JkYUmRBD0riIaRuMppZhG8koIh9J2xvTUNKnO8FWN8h6dZ9iwho1SjWK/VBXVUPZeWd5IZEkLAZky5O0uaSKpNa0NChdgQA5vK7m+JCNL/7mO/ktmaGMwRihJYz5P6cC+5XyTOxYvPHQdC4vTO9f0ex9lXxoNx8Ow9G5UkYwq+Kj0ZVHqIBRC76qQ41Khztg68d5TY6idhAJTbchNizvOP8TK/Q/x5FueRKfTpawtSZbz7r/7EAvdSfYd2IfKM2y/JNeGQVlBEqxqB+uDPkmWkeiEVCls7Tnf22D39BROKTYHBesbPbRRlHlOpj3rGxusb/RIteXg7n04X2KsUNd6Y6Kqu+JQl/j3I1Hk/9iQ/vgxx499pXM5bcFLhCUxGZWT6gDjvXii2lB40KnBOY106Ak/3kiVXnD3HBpbiudVViVFOaCqa6mbTwz9fkFvMMA5S130yJKMQb/PYNDH1pZOnoMVoGOWZXQnunI9QCvPyZKUBEiqGoUiRYW8uaadZ010yWgpPczSJKQBTGMoeCXC0aRJo0iVEqNBG8P5xQusrm1IKBbPAPDZeBfFom781uF7oTR2cyBCGwJDY1Qow51HoF3xAyLQy9eBwU8NSyaly55tvGhjNLYWetawO0OvnPHwUeORh5A0UeGE8G8wUrai2S8/olb2TVifeM5wPdLU5uL1NmoYAA2yfmuEq0kJNGdUQSSNmMlKiJakNh2sHdbpoxSxO2scGpiemmJhYR7lqlBNMIwsCNVbCMeHZl9VLUDiSG4jDIu1cG4EjIZ1jn5RUtc1VVVRWytp0RBdsE5YLK1zgY0xEpVVTbhf6aFsrvFYhbQS7mTs37+fhYlpJtptjPOoPMNrQjXQpZ2uUZk78tSGBlxcdlFeeydpSyutd7M8wyhFURSh/0ekYPYoFdIRENIt/3TGo1rREy26EJ4yxkiTCB/5vj0ulE1VlQ/vq4CIYo5XFoQndmsdtXqjchn1HGPP5K3fy/HckI3MQ5qkgmCOFi+e2sr1WOvQ1mI6Cbbb4u2//w5+8JbnorKUwxfOsXTyFN/4xCdy3d6rec97388znvNMHv+UJ3H7vZ+gl2ru/PRnmJ+d4dqDV7Fv9x7e+Pu/w7Rt83UveSn7rtnPoaUTvPed7+axV9/MtmsPcvrsSY7ddRfPvuYm/v70UV7wpCfwqTs/ywf//h/Y223xb3/6DdhEKE4rh3iwbFHkl3nxLvVKPKKw+hX2u5TujodUIQx5uWsa/SxFctc1UOuSTHkMCT7v4rMcZRTp5ga2KnCpATKUdaQadFDCaZphshbn13osn3uQPEmpiorexiZ1WZInKa0sJ0OR48kSQz61nSzLMNsSTJIEUh9DmmcNNzlKkcS2x+Fc2mjydrsBUulAzVnWAQXsxg3TmFMOti0OqOL3tVDvEvrUX1ha4nU/9e+p6yGhy9hkj3ioo+jtSz6Q5nPgEij14bEv8xCvuKEf/3vUChn7buu/L3Wc5iIvf42f9/vPP8qqwtaPhIHv841R1MslrstfNDsAmETxQz/wXcxMz8qeMRqjhoQwTSmjCtUDatRLBmJHwSwJ3QXBdNtkwXFB0TDrNW2uCX8T+AOcEzCzDcZAZanKoiEUc4FO/Phd9/HWv7+dq2+9idtuuYVZn7J57jxtk1Cr8bbE0eCM9zAe1QlTpGIpqQIEsOztgEFvBeo1DFKdEkmhVMDHyDFBp2LEn13us6lWWLMPA3D+/Hmmpqa+4Kf5pTIe1YpeWODKIJQC/W0k0VABlx0c9IgoVYrQCjYq+mBVu2Ash3y8rKWR0LCLhoNtlP1Q0YP0uw45JKUlVK4NJobsrcXZEmyBD95MJ88Y+JLDRw5xsKe58V88i9Nrq5y/77Pc99k76T//OXzDa76Vt77pv1MPSlbOLfHAocOcXbnANz/zxZw4+TAPfepuHr7nIV751Bey7+ABPnn3vbzjEx/lQjXg1nqCz7zz3TzjRc8hqUpMv2Zi734mHniQg8kEE7c+iZ0Lu1hwfVrTU2yeWyfRUmZYupHa6C/R8UgiB8550BqVgHUFteniEk1n8RSdxVNYSlZufhZGG9KqEo/eJJTek+LwxlApOLe2zDvf/na+6WVfLQrcGEySiNDUApKMVqdSUESkPxI9slXdKGHRyoH6kwEmRpG8h8LjN1el7iF6YUgeO97z6BiG1wEPxkONhLCFpAjQCus9670NKmv5qZ96Hfv37+NKCu6SIdG4+UXK++L9tzrEVxpbbYo4LiwtsbGxMRIpGr+WUcf7Umr+Stf3hY+LD7ayusKv//obx65v61aXW6UXH21rvECNbbfVrAFpxLJ4YYVf/pXfxCTpZa/80me+xBHHLmpL6oPxe7nos7GLH0Y5RldaYgydboe6V3D3HYf5K/9WpmamJZ0QNrzSW+0v8a/xoRoLwLvYgTHuEQyGGA3y8TIVzlZSk2+OofRH2blzJ9/7vd/Lm9/8ZrZv336FK/rSH49qRT8+/PBXyMt5bEDijnSv8uC0FzrYuGdATm/15iHWJUMsqxtV8EPPiqbUyHsfrEuFxUhZjJE6bWsrXF2g2i1wMW/pKYsS2ikMKlxVk7Vyphdm6ZV9SFtMTk2Q5AllVdC2nhRFd9ssC4ljyfc5v7jIHYvHqHbOMzc7yXWDafyZde4frPKSf/Hl3HDjYzl8/gz3njnFX/3Jf2Ny517O9dcoUBjlWVvfAGqMF/rOGkkrRFrW4TyNK9YrIfIfaQ7+SuU5W7a8aL9Hcg6rMpy2eCqSQYreDvPvfhedk+dJ1kv8Up/6Ex+k/00/Qp2kqEFFGvKBlXd0u5MsnzvHfZ+9k+dcdy1Tk5P0+n2qukbXkUdUjfS2DqHumFMPIVi85OGlm5gCDChJ3TQ5UqXQJkSHQuhQRyVuZU603iLegkcj/AMetG9AZfFLRaAdDinVA/v3ce211za7j83iiPa8lLK6eHuaVstbRyPcH6ljHQ+p4Py58/zYv/4JyvJLn7ikKkuWlpZYWFggTS8hUi9lHP1jxxXmzXlPnreakuHPN9wImO+LNa5oaG0ZSmle/R2vZtvCPEVvwKAsMO2cTpKFVsiXitR8IRdlpQkUduy6tj4OURkKV5fU3mN0hjYpH739k3zgAx/gzjvv5ODBg1/4dfwfGtPT05c1SB7Vil5FoNCI1duANRgNgo3agLHHecy1D8vrRkFd3keAlEjRSHUZATLDXH7cRzMYDMjzXErhkGpQ5VxAYAu609YDlO6iEPBWqhN2TM1z9vwFaqNptydIakW1uEYnb3PBWlxRkirotlvMdjpY5bBlQZ4m7JybYwLNXUce5OznPseNV13DvptuZs/+Pdz+0dvpZBnWeDp5ynVz25neuY/Wwjxpp02WJOzPMqrJDr2NdUA35CVi4FwsDL6QkPyo8n4k+yu1VSOoLQbBeD529LhbDQWlpNENTpO0utjlC7QeOkXazVCPO4DasEy89c8onn8Wtu+ENKGsa5Q2WGMEsb6+Tn9llV0330RZV+IBqNDJ0IfSMDds4aq1glpaZirnieA17/xwbfpIVSorVeu4hn0A4Y2yngUmwjF/ZMTPUvGz0JjFj6SbGuNDMVZ7PTKZ47Suw20uJWqbS46bju4brzBI0saPuowhcNGJRv5cW1ujLEt+6qd+igMH9l95//+Px2Aw4Nix4xw8eJB2++LGPReNL0Txx30uehgSxRTZkwVK7suPqq44fPjIP/LkX7xx+vRpfumXfon/+l9/k06nM6xQUGAC0dClb/YLHI9UXoV3WP4t0eD1jQ1WVlb42Z/92aYS5Ut55HnOm970pksq+0e1ogdGpFEQjU20KOaPZMGMGgOX8xhHFb0eQwRLcmo0lDrOhexDvb0lTaUcauhROcR7U3hvsdUApeXvqq7JWzk7ZuZJspy7Dx1i174DzE3PMT81h09SDh0+jPbQTjLaecaeXbuYqQucrWilCbvmF1CdKXob67SdY3Zykh379rHd76FaWqfVblNSM9FucfNV17LnqqspqLG2wpiE6WlDMT1B0e+hVBKQtMLI1bCObfHqmxkbzf1ddl6/MONgq5oZO0Zs2HKZc40aBUrVgUXX4NsJ7kKFP7eMv2oH7F5AWUXn7IDljU1YsPgkxVKTeIcyhqoqqYuCFEVn+3aKjV5T4uO8Z2lpifXNzbAuBNlutME25DguNhlrFKN3sW2oGhKDXFarhnsS9NsWubXlWQRsCp5gGITIVjjGydOn6Pf7HD12vNlTK8Xvvf8uPvHQWX7pW57FZDvjNb/9HgB+73tfFOaUJgp7enmTf/vnH2FuosUvf+uzm6sYu/xLKLL/9Zmj/MN9Jzm72gPgx7/qidywe2783kfG8WPHqcqSHTu2s3fv3os3+N8cvaLm+IV1OlnC/oXJi743JiHLPn8YfFBZjp25wI6yZs/+g0x0O2SJuSTGBKCsBbjmvA9rRZGlBq2gtl74Flx0SJRQWBsFHgrrqGpLJ0vRUTQFRd/v92m18s+r6Pv9PnVtOXDgAHn+fx5ZfujQIXbu3MlP//RPc9VVB4nlox5HYpIvUFZcbkRU/tYVenFsSkGobJCIg0KxvLLK+fPnueqqq2i321/E6/rij2PHjvGGN7yB1dXVf3qKPobZ1dC1aBixhhtFlR98Ie+xfgiaiTX4Snt8YCsTlqvQ8c5rPE5Cpl416GA5j5SYCIjP0253gsdmkE5oAzACsCLwj9uyoK0SBkrqZFMsE23Ni77yJfz2f/1dvvU138ENj30Me68+yIPLi/zt2/4HL7nlqczNzEHleMxtN1NZS9Hvk6Vpw3b2jU9+PGVtWVlZ5UK/R5IkvPhrv4b19XV6vR7pXErr6mtYW1tDW4M2oUree9LwwnslOAMVW/Yy9OovrczF8o2Ax8uNUeW7lbzo841h6mQUkBOPO9yuIUG66OQIBz+GstiA+Z30ikX0/evkAwW+hh0dzPYFKpNAWUt5I57Ee0ocJAlTnUlhIiSUbXrN4vklfvL/+RmqOpIPBbOwMURiLbhcyBfJR/mCR1VVrKys8h//4y+QpRkE5XJ6eYNuK+V1d75NrrGQqMUP/OA7xuZYKahqx+LKJita89rb33JJJxMuvs+1foHz0C8qauf51Tv+mvxSYe4w+v0+i4uLHDt2fOwa/nLuNHd31vmxU9fQcYaf3n8/AD93/IaLjnE+KfiN3UeZqRN+7NQ1Y985J0p1XSkeXDZj33k8H5la5o7ZHou+R1dlvDA7yKvaN49v5z1Hz60yKGuphNCKKlDjZolmY+U0ZX+NmR3XopRh6fR9ALTmryVPDdZ6aueghlRZVs8dRumEyYWrA/mlZVDVbPiS71p7B9t1h19vfTmltbQSMzbJ9WCZ1bUNAKYWDmLMxR7oBdfnt6pPcufecyT+IZ7i9vC9ncfT1ZfyVhXvLQ7zl4P7OWs3SZXmWjPLa9q3cVUyA8Dt5Une1P8cp9wGc7rNK1qP4SX51dTVgD8u7uWvq4d449RL2GOGhlSr1SLLMq46eIDrrruucZ5q58jSdBihczW9tXPUZQ9QpHmX9tT2Rh5sHWV/jaK3jLMVKIVJWrQnFtBJigKqYpPB5hLWlmidkHdmyNvTgKe/sUTRW2Fy9gA6yRt9snhhiXa7zbXXXkun07nkeR8t45+Eoh8jKmGoFGJIPXrco7zKcbvxfRzOxdw7IRoQgXvjdfbD4w+PMXodjecft/A+kFWEsh+lyL2m2uxztjfgXzzvhbi65o9/53fZLAbsv+YqJvs1r3rO89n7uOtxqWaj6OMCTa8xhrKuKcoS5xwrG+soZZqUQlEUPPTQQ6SpeCW9Xo+VlRVaed70sRbXb4tIju4bWz++WEWJEntkub4vpHb+Uuf9fCj7i4dw02tX0rWWemEP51/+lWz8zz9n8g/+ilxrzv7qr6MmJ5q0jA9ItHa7zfrKEtZaZmdnxQN3njop0Nqw2V+jqmpe91OvY+/evQHY6fHeolFUzjVdCJvGItFIide+9X4Z9ze2bnepvz/frMZ9iqLk5MlTHDx4kFarhXOOs2t91jZL9s536eQiaB88vQrADXtmAbiwPmB5cwAopjs5S+t9kkRzzY5piVnZy6wBpS4qUzp2fo1Badm3MEknv7z4OXr0KD/3cz/HwYMHufqaaxhUlrNscoe7n+eyj8ccuJ7MaH7Qd3HOs/fgzmCICX1uO0vouHUoj2KSlH0Hr0YrRZrohklvUNmGXrh20YPW/ElxN+/kPNcwzVfnt1FQs+Erajuk4DVGsz6oKGvHRCultykNpBxiDGlfUfRWyTszxPbHneldYggg5XPeC62yG+lap8I1esRTjwQ3cRitqa3DJ8OSyKrYxJYbV8C3yPi13se525/nuevztOem+NvyCB7Pj3WfdtG2p+06v775CdokvKZzKw/US7yvPMpv9j7FL0+9kFN2nf+4+RHmdYfv7tzGewr5bqfuchPTfGV6NW+tHuLPBndf8vgQU3AMW+6OXPrm6hnqsk+rO4f3jqK3EuZw50XHcbait3YWpRStyQVsVVD21+ivn6M7uwdnKzZXz6BNQntigXKwTn99EWNSkqxD3pmm6K0w6K3Qmdpx2fl7NI9HtaIf0j+GzmpNblJ+huAuG/sQXqTcR39DRNCH8D0Kh2uOPSTKiZ79sERvtL5+NF+p8A2jrneOuqqafUqj0Kkh95rDRw7xzKc9naffdBsbSyv01taY37mdomvYHBT4yqMSg9GRB91KVlYbdCI0kK62DcFErAyIYj5NRdjUtsJgmiuErQo4miaPkOt5RHmNf/y/57teKUUwOj5f2sCqGoNGq4TSWdSZh0lufR6Dq25h49XnsWXBxL4bqX2JDtiLyDSmJydZWlpmbW2NmVYI3ekETwbKoZSAfA4eOMDBA/uDQJZnLuVGoEygJzUaFZr2qEawXWz8XDqwCMR9Rj/i0t70RXMZfg8GA4xJuPbaa2m32kL/eXqFmdrx2L2zGC1A06J1AYBr98+zMajon1tjertm+0yHlc2CiUFFmmiu2zNHWVvuO7l8yfPGbcZuY3KFXlFz9Y5pJlpXDo1nWUa73ZKwqan5cPkg3sHzOleRIZ7Xb2x+GoA/7XwtAG+zD/C/ykOYQvP85AAg6YnJiS6DqmZdVXRSg/OeUsv7nBkxxDJrKLG8i2NkTvGKlRvITE4W3pd7WGru68Y9cxRVKL80w3flvO/xg/13MUfO45MJPuHuo7Oa8V2d27hmdY179Qa/WB/mqv4M21zKZ/0FfsRew9WtHbwpOcHnzAbF0p3sN1O8wtzATWY7MarmvOePijt4X3WUmUGL7+48nieaHfTWztDL2/i6j7M1+EJ6bIQxpXOO21Xusue5Sk3xwtUFrt9xPbe703y4PM6r0xuxx1fpL6+z8+aDmCzn5J33ow5CWyfcpGZBO94HTKiUsl/wF6dux855XpZcxYvMPra3Ovzs5t/zN8UhbsqfyJxucaOZ4x/Kh3lNusZ02uXUnYc4eehBLhx+mN/8su9jPu3yit/8Sa7/sqdISZ6rJe1Vl9RlH5PmtCbmAagGG5SDddqT2yT1eYmhlCHNOigUZX+t2a7oi+EqXvwU2qRsrpzm03/xTt7z7/87t3zt83n+615OVW7ivWWsZ8EjGCc+dQ/vev3vcObew3Rmp3niN38Fz/nBb7qkvHK15b2/9Ifc/TcfYnNxmdbUBNc85wl8xc9+H62pLqsnz/HWH/s1Tt31IOVGnwNPvZlv//NfbPZ/yw/8Ioc++Cl+8EO/R3vm4pTT5cajWtFvRV6rAAyKP0qphplOAMkXN78Y9f5DpRIR3xT7OOtA1znsajXOznRxrnqYw/dKBUY1LRzWdY0yoRc74lB7W2MVDFbX8IlGz3ZoT2YMcoMqa6zzwzSBk9BxkqTjkQulSIzmwuIi6+sbI/cnHkQsw/JNQwxFJBW/aEF6uccmJrHl60bBBANGfz6w1WXGpZT0lT77QkbT5sLrkIywuDQVpLizOK9JDj9IjSNRpukjX9cV0xvrHHrgQTbX1tk9tx3z0BHKzSqwiDlOnjpFWVcMBgVFJZ3+tNK862fu4oH3nuW73/Ec8umEX3ni3wLwo596UQOmA5nD5SMb/NErPsLUrhav+Ztnj137qLLXxpBlWVOy9+Zv/wjn7l+j7NV053Ouf+FOXvDjjyXJDFufxkUpgxB5UEpCzcbIGpW1ML7v+kAM0/nJNvMTLdppwqEzK833idbsX5jknr8+znv+3We48WX7ePHPPUGu+QsgHTnx6UXe9YY7uPuOe7mgNlg708dfLd99zp1Ho3hsuoCvh1S1AIt3rnDfxBJ/uutu5lWbb2w9lg8Ux5rvT39miUXV42ev/tglz7tdd/gv7Zdwol6jxNHxmv/f7J2sqII5WnyLuYkn6V1yX49gPS5RYOnyta0bePPgbn5546P8Atc33x9xK+yr5/gm9rKjtZ3fqD7H55JVnuXmubGzjzf37+KX6o/x+vI6EjTksOj7bPaX+Wp28pbkNL+08TF+1d/KtEn5V+p2SJGfzXvGruWvp1/BKScyYYFhrnmb7rBkBzw8WGH6wjrdbTPCjmcrbtx/Da/xGX/o7+P7N98HwAE9xQ91n4LrlZzLpBpiezIB3rHgxWg7ZdcBWV+PMfPcbS9wl13k6Uq+T/Oc7sI0z/rGl7Fv5066u1NWzx9urml2x/VYK2tO66F60ibBuRpnK8wWRa9NSmdqB731s6wtyjNXGNpTkqt2ocIqHi/+9iP9MpKsRV0NqKsBWT7JI02yDVY3+JPv+FlQ8OJ/+1088N6P83f/6Y+Z3DHHE175kou2v/Mv38dHf+ctbLv+AM/9oW/mjv/xHj77l+9naucCL/yJb6cuK7oLM9z45c/kzre896L9n/aar+Gut3+Qj/zuX/LCH3/VI7pGeLQr+s/zvWcESHSJl3M0Vxy9LCJ4Q410tRozKOT3KGnOVm9ylAIynlty956qKhtUlnj7w3Ks3qCHTTQ6NZjMMKgL0tJJNEIHVLXzQ907GqrznuWlJf7NT/5MIET553H5IYtiNPISn+vQiPHoxNDv9Rv+d5MkIaUjD6Df77OyssrDD59svIeNUyV3v+MUB79smpPLp/BL8MQf3oVS8NDR4xet2fWTQoRT1TVHjh6/7BUrpbjmmquEvhPY/pgpHvuVe1AKPv5Hh/nUnxxl/qoJnvDNV13ybsf/3pIOaf5z0VmB0dD81o2kg9zxxXUurA8A2BxUHF8UYZ8mmqktHv2VxmCt5E++88OgFM/63sfw93+gWTvTZ/P8ADOTcNZtMKkycmUYYBkslxBA7hN7O9xvHwLgBYP9fPn0NexwXX6m+BCukuufsBnfuXozUzs7KAVFYdl4uEc+mbJ95yT4oRLvGceTe9uYZ4K3te/nt+pP8ePrz6DjU7mvzhx5iJJVo6mL8D52vObb3X7m2tdzr73Ax6tTPKg3mQgidwctXl3vJWnPYbMp7qpXybziNfYAbXWQY8kq766PcN+E5ql6OxT30SHhezpPwjnFQzg+WZ/m3nqR5009jtcWN+PKdZxztLqzmCTj80Xl4iror/SYwtNdmEUrg/OWY0eO8lf7j9DKEr47v4VDG4u8LT3Gr576O37E3twcWmnhxbflkJfEBfkzrySvfc73R96phLzb4TEvfjbXXndtI3ettWO0yVvHPX/zMd7z+j/m6mffhtKGhz91L9uu389Lf/EHmTuwkw/+5zfz8d97Jzd8+VNZO73I2XuO8oMfeyPrp/q85z/8LmfvPopOEvY96Uae/6+/hWRieOze8hp/+drf4MQn7mX7jQd42S/9CHNX7cbWNcXaJr3lNRhUY9ejk4TWVJfP/vUHGKxt8LTv/Fqe8i+/imue9Xje+ILv5uN/+PZLKvoor2f2bOfqZ97Gw5++lxOfvKfxzuev2sMr3viTHPq7T15S0e+57Qam92znM3/+Ll7wY992xTkbHY9qRd9EmVUEeQ09JUL2uAmlNwCuy1tqyg9z66P7XES5GEK8W8vGxnNkSjwnNfTfnLcUg0E8SONVKaNRymNzjfGga4eqLKn32CTW8suxg98lzFMjBon3nrW1Dera8rrXRUKU4fzE24me+PAm/zfG5RLI/zvH/mJd28jhYowHGjk8PLwa346R7UxiWFtbA++Z6E4IZbIfzuPRY8f5hf/wCxw4eIBrrr0WvOfv3nYPOHjKN9zIvmsl7PjXr3gHAM979RPRKD76uw/yqT8+jE40t3ydlI6lacr111+Pd57+6njteFmUnHj4BBtLfWa3i0D4sn/zOPqrFcV6xX3vPs2Fwxtc5I5fZtz11w/ztz99B9c+ZyebRcX5u1b46A0zfPUvPJn5qya5948Oc9+bjvLAv9jH0slNzty9zNe/94UsH1rng7/6ORbvWUUbxWefsp0X/ptbuHrnNL3pFQCSgeeOn72bE59YZPtjptn+y09l/qpJ1tYL1lcGbK4NKK3jvNtgNdHMdlvUynN0bYOjb3uYwVrF0159PVd/TZeJt4sW3zjXZzoIQoXk091Iigwgm0xQ6yL0hEt/qLS9h8ldbRbPXOC/TX8O+iM7ipPOts0Ov9H+cnbobvN8v6lzA5N5l0+Wpzju15iYTziop6QCAphspQK6Gwxw1lHWdkyiemCzEMrkrWOOIeJ9LEIQ5EL0WcveKpsOyMWB6K+dxuiUoEPx3rOx/DBvbH1ueO7qOKOdn5+T7WO3Fs12nj4wA8Ci66GBmTVZ1KorzZUM8EBnkwtZxZPNDp7Sn2PP0Q3edh18rrPO4HSP7S0B8J1zm9T1LPcfPgJ7YXbdc/r+IwCsTi/DjnGxUGz2WDp2mj/42p/kCc95Es/7iVfQnh1q3VZnGmPE+3ejXCeBOfLYx+7iuT/6LUzv3san//SdvPVHfpV/+eevl/QscOgDn+bZr/0GrnnBTWwsLvJnr/5VestrPOU1LwZruP333sHioRN8wx/+cGOcH/mHO3nWa7+OzkKXu9/6Ud76o7/Gq//qVzh75yHe9f2/xLsueno0IfWlI6cAmN69TX7vkRz/haOnLrEX3Pp1L+TkHffz6T99J7/+rO8A4KaXPZenf+fXXnL7S419T7yRu972Qc7cc4RdN13z+XfgUa7oVeREFvqvYX49NmpBSGmi4r/0MYInH3mhg7fWeONbNI5sEnL4wZoaNQTGqEO9hNmtF4pdZy1FX8qLlJdaaadFyavQ2EQHReKUQtWWLGnhkLa31jkMijRNqb0bXlswAGIkc/8IIcrwukdC4aOa/0rze9lZYxgqGT3OViPqCwm5N5r4i6TpGbVDPLHuXf4OtJk+ITbwGcuPa83K8jJGaaZnprEuRkpi10NFmqW0WzndUH5z4uNLKKO46ik7SXIzxg7bbrU5/OGzfPg37mNqZ5vnvPaxfO5tx8PtKlqtNqsnN3nj8959yfuY3HWSH3jfixsA0+985fvpr4hEf9xL93Dry/dfMW+/dUaPfPQcT/q+62ltb3Hk7Sf5yx+9ne9+64ub7+9770me+9rHcvWLdjFYL3n3932MYrXixlddhRtY7vuTY5x/aI3ve8eX0wre7bGPnuf5P3oTc/sm+NSfPMRbfuRjfPdbX8R9HznN337/xy95X/ufvMATf+EWNh4WDTy9W7SYTkKL3oHM+3bd5aRbZ7OqyLVhekcHJP3K0j1rXNeZ5j174f3ZMbYNJvjA4Gg4jsJZmLIZP7b2JGb2CICvqC3FhZLBSsnMzi66o5hP2zxrsJcPu4d5q3mI3WqKk36dOdXi+s4cmTJ89cr/hD78j+mv5eC2KY6drVlz0lAmCfn6nnL8kT7OnrLiTnuOFgnXuy6ndKAyDuhxW6zRyTJu9tN8Vq3y++Y411SWD9UPk6J5YvtaWsZAcR995XhzZ4UdqsNn6rO0MDx+6lq6pPzkQOGqDbx3ZO0Z0qyN0oaN5YdZPvsA+3Zcy+PMAvfYRd43nfKRqmTJD3h2sodW37OcO35g7a+YUTl/0H0x28oM5eEue4G/KQ5xdJvgMA4k08zs3c4zHlrjA7NL/HX/AXRiec/uFQC+qnMDcwfFKBsk8jy3qxYKmNg+x1zdY3LHHDt3XsvhD91Fa2qGr/ql7xf2yADIM0lOkrWpyz6DjQsBJC0y9upnP56nvvqrWD13mLve/necueshBmtDy+3Gr3w6T/i251H0Vjlx+yE2F1c4+IxbeNKrvgxtEh764J0sHnqYpcNnSLJ2OOZtPOlVL6a/vsSh993Jmbseore0xuxVu3nij34ju3bvIs/GSxG7C9OXXMsXycAt4+Sd9/O5t36A3bdcx3N+4Jv4zJ+/m7ve9kH2PuFGnvrtL7vivnFM7VoAYPn46f87FL14uVGJjbSOJTa7iKCycTKc0RHZ8iQnP0LYcBEALIrQoQe/9TijJV5Kg7fiS2o1bEhRV5V48FoRfRLvhJEu8QqHkjC91tgkD+10pamI1NsqeoMeSZpJ2CbcvyH0GieuNbnez559A2c3P8BzD/wlmZninQ89E4CXXP0PF83FRnmMvz/xzbSSnTzvwFuG93wF5VvZNT53/g2sFQ9QuhUyPcPuyS/nutnvoq7dWN/4RzS+CB79cnEHdy7+GNPZrdy28Gsh/SIH9yhJl4wcX1E2OIdG0SvFWnGEB5b/Mxv2fpILLba3X8C1098LId9YFgX4wDHvoXJrXDi2RmtaM9APkLkFcjNE8Xpvue+DDwLwuG/scs3X9Jm55gDHvuk83juWB58mmdzD1/7aU8fupypLTp85zY7dC6ytrwvxjlZ81S/fxsbigE/98THu+dtTHHzOPNe/cMdlFf2gKJue4ABXP3sHz/2eG3ng1AonP3CW03cts7HYY/tUm/uAm1+2n2d+72MAxYN/d5rBUslVz9jO1/74bSgFv/PJFc49sMq5B1abc1zz7J0887seg3eeu95+nNN3LdNbKnjck3bS+c+XRl/TFbR6K7uyOLrV7OCEW+NkssZN6TYG60O3dfsN07TPZbx0cY0PzZ/kLwb3cNvqAvdMi4efoki94cbBPPNukiSXvPfaep+l3gazdbepAnh1ehPrF9b40MTDYOHmZBvf0b6FLDBsxnlVXtFKDfsXJnlwOaGdJdSJyKF5clI0b68fZJvu8J2d25gerHEKUfTGJHQmtzHYXKK/cYEfaD2ONxX38jmzxierVfabab6tcxM3pDs4azehgAXVIdUZb6+PNMfckS5gneNWl1BVp3GuZqq9D2MyPJ4VF5+N4kc7T+E3Nz7FP0yeI7GrPDfbz3emj2WVE2MLRSc5e8sW33x2Fx/avcFftI6RZHCrneN7pp+C6SVsrzK+6/wB/tfOC/xedRdTNuHrz+9g92qPJcSZuWfvOUxbcZPZhk4yZvZs53xvlazb4amv/ho++CNvZPHBEyRZR14gO+zB0JneSX/tHEVvGVAk6eWJiIzJSXOJCrRnW5SDddKsQ96dkTtXiu70TgabFxrPP+/M0EuG66cuBxcdd/nIKT71a39+yXMeeOrNXPWM25i7ajcAqyfPye9T8nvuoISKnLW4yqKMxqQJd73tg1T9gpte9jxueNHT0Inh/vd+jPvf87FHrOhHS44f6XhUK3opr/NN96eolGMZm3OOZKSeNIa4x2q5m29jaZwaO74KHeniflcChg2BZMJjjvX44JXE72LjC+eEC13poKjNkKZXe9BWQHNKadASqizKirIssd7RW1vF1tKP3NWWbbMLbGxsjJzLs1Ec49TGO9k39VVkRhoz3Lr937Plxq88RpDeyvuLlH3lNtmojrF36mVkZobDK/+dwyv/nUTNUp1/wv82+v6iy4mVAlfIP/bVOUiGjGVf2HkcJ5PXUbPEjP06Kn2ck5tvZWOtZta9HICTD5+irCqKoqQ3WKVUErJUSoNLGPhT2Gp4/+vFIawX78P4CbSboq6jgFEocpbOP8xf/ehDl7ym9rZF0qsrojBUc4psDvZ/eZezd6/xyT9/iPSa4rL3ZK0L/SFiaNThbc1kK2lsuUFRNBiPzkLGYBAwBJWkE5x3lFUlDHD/CGPs/INrvOWHLw2E2/WEOZ73609k7/UzfA5YPdljni6uFqWZ5BIteEFykP9VHeLDxQluSrexuVjwX84+n9kDE3R25aDgy+87yFfb69l54zRH7zvPy86Kx7PMJgD9lZKz96+y55ZZmcNL3ENHpbx8aRfXzV97EVHKMbcGwEuyq0mvkB/VSvMt9W5ekz+O7nQw9nbv5lnAs3hSs117cr7590+wrwEFj44dpsvb576BohbCnO+beCKXOvXk/EFxKMKzdLU8s7wzjUKxoDv8ePokHjx2iOuvv552WwBom3nK7MDztpmvb9ISAE9bm+GVj302/ZUNzj54hCRLmXY1yxekKuO2/gxfOf1U8J7B+ibsRn6AFV/wkLuPp6ldzGVTlL0By8dPsLG0TLG+yUd+638AsPdJjwUFxz7+Of74W3+a3bdez3e85ZfROqE7s7u5lqxzFIDDH/4Mt//+21k5cZZyY8DOm66hMzeFScTjbnXnmN4mz3z/k6boLsxw7Pa7+Pgf/C1Vr+DCQ6eZv3oPe26+keUj58Mx7+Djf/i3bJxdo1jvNcecv24fT//Z72Dv3n20WuMefWtaDIubv/p5vP+X38Qd//O9zB7YxQPvlajVU14lSvuzf/l+/vrH/xOPe+lzeMUbf5L5q/YA8Jk/f5fk+P/q/QBsv+EgAOVmn7ve/kHOPSBya+P8Ep/+s3ey66Zr2XWTRGnXTst1z+6/uNTwcuNRrejH8qsqeMgRZe6RcLweKugmXx883qaxQfTgResGEJ4OwKtoOQXPXw296FG0fVSuEW2vEAUttoMDJS05q3ogbXRRI56Xav6OlQPDdIN819vcYNDvMzs7i1GaanOA8YYkb9Ga7dBqd1i68FkxFIJb+vD62wHHzokva85z57mfAWDXpHz20PIfcWz1f6Aw7J36qrHpdd5S2dVxJzves1ekZppWso1n7/3jplbY+ZL7LvwGa8UDtPzj2b//wEUvyehY6n+aT575Aaayx9BJ97HY/xgts53HLvwbZlqP4+T6O7h78T8w336KAA6Lz/LMPW/G43lg6b+yPLgDT81U9hiun/t+pvLrWepvcPYM5G1DkfwpZ3sfopPsaY7pvaMKAvviJaVJzRSL/Y9w7Mw5trWfw63bfwCvSt5/9MVsJh/gqQdfB0gEKMsyWq0WSVZSVp6ZvW0uHO6T+e0M1FGcHnq7jh5XP2uOO960wmf/4izT2+a56+0nmzWUJ3N0Fk7yyj94EonqNvsVZcHDDz/Mnv27uPqGnRz+8Fnu+V8n2fv4OTxw+K8F/Hb1E3Zz/fXXc/wTi/zZqz/Krltm+LY3D5H8jeETIj9HP3Kez/zxMZZPbFJt1uy6aZb53TMkgcgmTRM67TYeuOZpu+kutDjxiQt87L89iK8U5+5fZf7qSbZfP8PZ+1ZkPX34DP/wu/exfGKTYr1i102zdOZydtw4w7f90XMvOeet6ZTdu2bpv6zL+3/tLu74y6NsZF02FsUImtrZYaKVsut8mydt7OB9U0f41s5NpO0AgDzXRxvFZtg+C59vu26qkRGD1Yr1s33yyZSZvcO5rctgTLQuXbK1ddxdn2dOtfiXW8hztg6lVKjPXqU9uYC+TEnYxftd/rs80eTJIyx7Beqqh9ZSO36lkU92qPoFxUaP1lT3ou/bMxPM7tvJ2ulFVk+epzs/zWAtOhXyn/bUxBhW5y/6h1ED+LbJWwDQqfDwr59bYuP8ChtuhWd/+1fxvB/7tmYfAJ1ceZ6uetZtHLv9Lk584h72PP4GvuqXfuiy27amJnjl7/873v+Lf8THfvetKKO5/suewpf95LdjRsia9j/tsZz6zEOc/uwxOeYv/oDMy1SXbbdey8EbbrgsYU57epJv/v2f5V0/9zu86+d+l87MJM/9oW/mCa/88ktu/+Rveymrpxe55x0f4h3/9o20Z6Z4/Cu/nBeEeegtrfH2n/yNZvsLh0/y9p/8DZ77Q9/cKPoTn76Pzvw0Ox979RXnanQ86hV9BLspFKPYYK1CGLzZkLEo9OhoPtIaZ6UVjZSzMXKM4EWFfPjW0L6k+iPfeWgBaUJOAIdS0pO+rHqi6INhErEAw3B75OmnwRcoDxsbm6yvrbF71y5aJqVs9fEOWt0JZrZv4+zKEivLyxJJCIda7H8ChWEmfxyXcr/O927nwaXfoWW2c83st3Nq4z1j3/frs3zo+CsuOfUxvK9V0kyq947zvY8CMJs/kT7QauVXpI/MnRgBa+V97Jx6FlPtAzyw+N/47Pl/y5dd+9dkhURkLvQ/wbXzr2LPzIuY6Mzx98e+k/XiMNfMfRt5Msu9536TT5/9UV54zVuGxyzuYefkM5hsf+vYMYt6mb879NJLXk873cWLr3sHZf8sABP5HtrtNsZMkCWzFPUiKu3RSuZptVporTBGY7Scc/8zuiw+2OPhO8+w8HhwjHvY+5/R4Wk/OM+df7zCh37zs9z00gMc+/h5FJAlElLe9/SciWw3cc31en3qB1a5+rqdtNttZnZMcuGhTQ594Cyu9kzuaPPM73kMz//hmzCpJkvlWpI0uWjutdbUgcnv4NO3cfijZ3n40xfYfessX/mGJ1AURRN1qmvLoJDrVzl8w28/jff/6l18/A8OoYzi2ufv5Pk//ljKuqAsJQx64OkLHP7IGR7+9BK7b53lX/z8bfT7fchg1xMu3+4zbvPyNz6Z9//S3Xz4t+6l1o6JHS06CxnWSvTu287eyPdUt9KZSWAhoRpYeksFF46soxNFd1vO1J42zjraM2nzbtuAvDepIpsw2ICUH6wJC2DWTZrPIhHXYHBxOPcF7OEF2R4Y1PSReRwMBs0+C7R56/TwnWlP7sB7mmP/vzHGwMIj8i1vz5K3Zy+7n1KKNGszvWsbm+dW6F1YbRT9waeNGzLTe7YxvWdb3JOZ6E1uRbGG8ar2Lbyqc0tzPUmasP2GA6zokvmr9/Btv/UGrr/uuka+nX9QPNgn/ctLv5dxdGanLqncn/ND38RzfuibLvp8541X8c1/+DOXPNatL38Bt37dC+It4ZyVKoKQEn6kY/+TH8d3ve3XL/ndbV//Im77+hc1f+vE8OKfeg0v/qnXXHL7mX07+Jmj/+uy5zp55wOsnDjDM7/36xt200cyHtWKXiLJI950yHpH5Std42I4fbyncfxsdDS18TFpH3K2Wumxml01YnUOa9nlb3npQq7XWnSaoLXGJAZloa7LYYTgcm/JyLEBdJ6RdztsbG5y7/33M7d9gULDmaXzXDiyRNkvufeu+7j1qquxeIrQCrVfPUxqpkhU3oAMR8eFnoSZ9k9/Hfunv5aJ/BpuP/k9zfeZmRmG+rcMo8dzZs6XfPbcG7jQ/wQHpl7BjvYLOMrFYXN39Ah+eQlz862QJNj774UWTOXXcuP2fwXAmfW/Y604xNr63bgjhyGHhc6TeNyOHwRgrTjMenGYbraPm3b+SLiXz3B248Ms9j5NWouim3R7uP7U01Bz85zJwzGLh5jMr+KJe/7jJe8r2XJfY4DMK7z9mZklM7Pc+HUln/nvcPffPsxzH7+AB372/pfTL07T4xzWlzz/tU/kOf+qT786TaK7vOR13wR4aieKxblLh999r4c7eoSdtsd3vWE7auFx6N17Ltru3IMSRXjK1+3A3v05KApIM8y2bSiluBBCr6Xu84TX7uaWsgN4zq8f4dz9GQtfpvia518HdcF9995LEydWcOuPznNjNdn0a3h4/QQP3wNcD1/x59c113ADAlY62XuYkyNl3ZnRV/RamYCnvX4nu4+XfPIXU86snuBznxv5vgUrPTg1+pmGBsS+Difvvsyxt+zrLawcGZBOaFbuHaKky7Lk2LFjHD16VKprrnC5IAx21lp6vd7/p81Psiz7vNdalCWnTsm9tlrDtb66scTRh4+zrb860rDriz+OHz+O957BoKDfHzR2yeGPfpaDz76Nq17wRPr9/ki0VUYVuhjWdS1G4SMZjZz9vBsCwjGidATa+i/Jzokf+29/RWtqgmd8z8v/Ufs9qhU9MNJSVobWWvqPX0KaXNzZbNwIiIxyss51WGwj5/J+rCWns5KzbMhxfDQ+xs9Z1zXW1mgHZVmEVqOP3GQsi4L56Vl2zMyztrbKSrHJ8fVl7jz2AA+fP0dncpIbX/w0Tm5sUOAoXBXaPQKoBng2PiNDvIE2iiTVqJH3x3so7UoT6t86WslOtnefBUBl1/n02dexPLiDa2a+g+vmXtPkdkeHHwzwFxZRC9sg9FfXu/bA8uefg3Z6qXyUGvnXUBm7Uw+L8E9TVHcaf+4sfmIIvCnrZT518nWXOc8udk4+l4lMyt761WlQUNk+pV0l0RO0knm8d1hXjhiPMNW6hu7j9nLzVzvue9tpnvr9s0zOCQLZ+BQUJLpDK5nH+Zp+dRrrL/Yax+/PN/fljx3Ba43eux+/uow/dRKfZTKfI+PY7ee57lnbuPGaTaCD3n8Af+4s5uET3HD1tfR3ZMBpupNTXN3K0LZAOY9r5VQHrm7Olx26D58aqv3DEGE5GHDs2FHMxDw6faS9z4fj4HwbrS5jM40s0Ilum263w6/8yq9cfmou+ii8z/gh2VMY5hI79JZKBqsVU3vamHSo3Mqy5OzZs8zOTEsa40qvqpK+F0tLK3JG/48En34Rh1bm8+ImnBM68O3btzf02P+nhzGGxcXFsUjEY77vpdja8sD9D4wQnw1H+tg9vPQtPwd4HnjgwfDpI5Chj8g1D4reO0a59AdFSbfbJUm+dNTky//Lv/mC9vvSuYMvYET620uB5RoyGzfOUQ+X9+SH38cc/OUVshfHHbwO2l22M9oMgXfhnW963FtPXUm4b6sRcaVhHLiqplaKyc4EebvN7Ow81+zex2ZdMrA1OjVcOHyK96qEjsowRtNOd7NZHsP6kkRlY++FMZrZzpM5svKnPHjuz6gHGUv1h0bmFnIzx5N2/ectNx7vU6IEte9z+6l/xUZ1hIX2U5nIDnB6471gJ4B5+tVp3n3kFeRmnhd3f18wEHNDAJI7fRJasFYc4t5zv4nfWGetOETLzzKxMRurp4bntpZJvYfJ7GrWy8PcffrXyMwM5zY/SmZmmbfXslKeghas26Pc33knfu006+4YrWQ7U7kAdZ6+/7cuOdd6bYD95MeZn56nww7Orf89h+77Fda6F/DUHEy/AvvJj3Nh+gwfOPw6ekVQKdaxOTiK8Rlf8e/38/zXT+Koaae7wUFKF01G7TbpbRzDKTE8Uj1FxH84Jx6EVtmINxLWrbVgLWrbdtT27aipKezqZ/Hnzl6k6L/hvz4Dd+I4/uwZ9MKCfO89/thRkuUlnvJN13PTy6/BLF0gOXUcve8A7vhRVGLQ0xPNOrFzc3BhEW8UvtUGFH2tSBLDwnQXExRFoiX9lBrNZDttDOa4VqzzHDonSOxWK28icBeXBwwNm3379vF7/+13WVlb5XhyJWPoiz9OHTvBG/7dv+dffd+rObA7oKcdl3QeIo/Fysoq6+trPPy5t/4fvdbRsevGrxqyvo1+EQ19BefOnudP/+yv+PZv/5fs3LGTcWOZiwwFBSgdojA+xE29p9gYNJvqxJC0UpGX4bFv6SoygjmCiYlJpqcmsHVJLIROtGZtdZWs0yJNDBp9Gdl7Oak5nmKV98mCqxvzb+tRRm9VAWVVkKQ5SmucdRw+eorbbruNY8eOXeacXzrj813jo1rRw2iOfESRB3CdKPBHdpxIXnPR5w3oTo156k1QKL4dXnLUTZf6Rj6LoaG1BldT1SXeuktarZcbwm8P1jspvVOatlJ0WhkeKGyF9Q6ddTBKNWC8hfZT2CgPszq4h7n241FbqGoXOk9lX/dVnFz/S05s/il7pl/EUv8z4f4URucsdJ68dULG/iztKhvVEQAW+7ez2L8dgOnsVmb54eZlVcrA2qo8l+7FgJ+Z1k1sbh7lXP8jTLKX22b/NWZ9XMD7ssR97k4AnqxezT3pWzi+9FYcljmu43EH/x+ytXzsmL36FGeTDzLp9nDbrtc3ufTtE+MlbM05Bos4DqPWN3nStp/m7s3f5j77PzAbLa6a/Qaud68EzuE318N9KY4dPYbf7LHOKawqAYfxGS01x3L7DL6uoSywVPT1ErU/AWgyOrRbXc64+6AY0GeZQi3T9QVp4FVHawYoeZF9Tat2qH6B9x53/ARojUkvBju68+fxZ06jVlfRF5ZwZ07jV1ZQ58+jnWewOSA9dgi9cxfq1Cns8ROobgedZE0TFb90AU6dhLLGzy2glKI/6HPi+HE2+iUmMWLjBqR2bAqjlKKTiQ8d25AeX5RwkV5vD9+jixT9l8ZQSpG1W+zes4uDB/aHElzd0CmP8VAEOaOUwtmK+2tJhz3muT+ACUoDFGmSkOc5g/Ul1lfOUBQFaxeOoRTSXCVJSTPD5uoFdJaDU7i6pqj7KJXRnd7FxOxuupOzpFlGr98T77wueegjvwPAtc94Msqkw1d0S2QRYGlpmXe++wP8yZv/hPWTi/9H5nPrmNg9F9ouD5dAojXr6xuYPAuNoBTuItl9ZWGuRrfw4J1FuOsZkVuXS5d66rrGpJnoDes4fvIc119//f+rqYwv5sjznOnp6Ut+96hW9JfLuXsPaATpWrux7y57DDnQJT1/v+XNiSH6Jn+rVAiJRdrboTfmrUcnwnXvHFhb4UKL2/Ge9le4TxReeayH2knHLW89CRqDooNGacOSSRqAInj2Tb2Mo6t/wZmN9zHXfjxeKV5yzUfkmE5q76+b+3YWqq9kYWGBtNPihvnvb2h2Lzm2GEOdZCcvufrvL9psMCg4unmcjUp4rK+eeyX+eCEh+0u8OEbnPMG8Ft9/BWrPXvSu3fiNDfbd9wz22edj9twK1qKvFo98gmt4CiPKWhtUewa/doYFdwNfNflW9D4Jv9vFz0BVYVo3PaL5BlBT00zsPMgz9FPxn70TbI2+/gmSBgC2zTyDl+/+AG+f/A7e8PM/D2Up95WGHG1ZyCJJc3Fz6mrL9wOZ40jE4Rw9dwaPo6N3D0WRUpR1zZlTJ9nuLen0PL47Bd6jzxyTNXvgqosekXcOf+YMFMFY0lrc0ixD7d6LPX1KgKXbt0NR4M+fgyxHbd+BDSAf1e+jzp3BT07B3DygKMuCs2fPMjkThbUf1vWOXEMnTzBq2CTq9KqkcnZO5VfO0W8ZHjhnAq+6k32jonUhbF5iseG+R417Fc5vrcWYBNfUaYf9vSNzisQYtNLokJs9p0uqTDExKQZpU8lyOSbgZt6HX5gkw6Sthpe92+lQ9NdYWznL8tkj/P/Z+/NgT7LsrhP83MX9t70t9twrqzIra1WVlpJKUjVIiEVgEhqW6TabmZ4BYaJ72KwBtdnQDd3Q2ADd09YN02bQPTYDbdOAMTa0DdMgBBIIBKiE0FollWqvyso9MiIj4sV7v83d771n/jj3uvvvvReZKYFAUYNXRcaL3/v9/Od+/d57zvme7/mekPXcK1+BcWAcvpr07bArX4PzBDpCgNN7rxKy3sbB/iHVZEEIXd/7QC/LcMbcnTuuXLnMf/ln/zOO7xzz937fX3qbT+Ff7fEbv/97cVMV5GlTxFrH0d4e//jv/wiLdz7Kk9ducDCd0XTtTsUUjDuG5nstwYuY3FRbodYUIu3mlLR9A+cEiUHJ0iRsVhq1ub+DwhCJW7ducvjoO5nUNZvlkt/1B/4v/OAP/iB7e3sX38ivsuPw8PDCXvTwkBt6uFiVThXQ1OKN69PLcVbRbjdvf558M3Yo+uY3+kIO5o32vU8QrXarU8grC2yI6TenGAOlfe7bbdbSpYixWeUvpyLEWtpcRmD0bDRRN8Sy3BfVO3hs/zfzyunf592X/4O+lh6j6ICzlsViwcG7DwldIIaga8e+ebvLPChvcdXqLNzbfIKDyXM8c+XfR1785Nu63weeMQTSly+uMWcywR0dQSEYtZkjECOEAM71ZWVv/zC7jk3v4QFVxbVr1/gf/8e/wv2XXya98BWY75EefRoA+9IXod0iTzwL7QZz6xVYHJAeUefDfuXTEBPp6fdh2i33bv4wn5z8Vd7b/nYejd8wfOdswfPB8qf+T9/P92/e4JGv/yjdd30v5vYrzP/8HyLeeAd7/9e/AAjSdSQDOD/MrZdfgu2G9OILdH/pL+C+/ddR/6E/yun3/S783Vvw+vP5fgAJyOnrtP/3v6HG7ZM/R/1f/ynCv/PNpO/9fWAsLzz/Rf7Pf/pP8x2//X9LvZjQNmsm0wPtomgMznu2G22l/A3vuso7rh1wf7nmP//bnwfgT3z3M9TODNUho6EeomObh94QjOHP7X0RgD++fS8+ga9r7KzmB+5+js12y95iTlVVqq6WVCvA+YrpdIqzhpN7Nzm49g6Wb7xK2zUYVzE9OGIiieN7J6y7LdfNlN/4yHtYbbb8mfkX8QdzrpjLGExuahX7HPhZtcwLZ45zfSfJxXSGQfjyp/85m+UdnHNMZ1OctdqKNsPdznqsdSSMfpd1VL5mMVuQJLA6eYXlnRe4PZ3zoW/6LrYibNzAPRE7qEtIHy6XzYreaF69eoWjvQOuVOrI/Ht/609SzSbEIMzmMyXpSeLOSzf5yb/2Q1BZZpNpTksq+XD/4JDl3Vs0XaPtX6d7rNZLuraj6TqO3nWd537TRziYzOnWG7ZNy//y7/95AN7x1JPYSZ35DQHrHIeHhzxx4yru+hUef/xRriz22bbNGUN/dq8elUcnk7uNRsAQY6BZ3Uc2FucTKWiAhVHNktxSLK/pSIqB1N3n6o1DZrMZ21N1wJ955hkODh5cLfKwHA+5obc5mpABHsciMWqETWnVenFEf459Lwy5qJFBlzOfURW8oZyvOBOekh/LnqO12AzpS/6eruuG/L+8uffd36V3WAwOcLkCIKELW3LEpI7F8Dgl38iHr/8JuP7HKYUEYnMnP5RcGLuOuNlQeQ873cbe3rW91fGeK3+wL/GKkwlsNxpZZmfranoP373+H3Dv/ybk5D7y+k3k9i3Ee4WOx1dUVdjn3vuAQcqch4NDmEyR+/eRmzeRzUqdoesqWiKnp6TPfQaz2MO+7/0PvG45uY+5/Tp0HcSg6YYzpBwjcP3ada5fvkxyVt93/QBSRLprmOkM+4GvIb3xBjK1YCxcPVAn5PHHYbaAd79PnZHnnuGj/L7zF+IcvPoqi+tXeOp+x7s+9+PU3/4R4s/8Y2Jdsf2tv5vLzz5L+rv/X7b/xR/H/vrfBH/yz8Bqhfzl/wH3vg/g7t6h+Zt/Dfb3mf1HfwT7rmd44T/+k5jthscvz1h99nO4v/Lfk554ivD7/yjpnYqauE9/kqqu6D7wQdK7nsVYQwwNk+mUy9cfZXYwY7s5ZbY4wlrfw/SPzGacnJywf7DH9euXefwdTzH7MdUteMc7nmLiLzD0+RkWq2StRRK0BhZXVOzmHaunuLJ3yJebe/zo8gUOZ9d4+tIRbdtmlMxgncsiWo7ZbI414F6LHF1/hINrc7bbFSElJvUCiYErzz5Ns1yyWp7yD9av8nve+40sKv2+dBsKFceUBDTDNepEGb1yxv5bY6irCl9ZfvIf/b+oK8ve3gJrLW3bEI3BWa8OCkH3A3GIhGzALCEKSIM1wt7+HiFE1qsVn/j4/4f3fuS3YMbjmISdDtQXXBNoMGNHpDM/nVDNZuxNJuzt7XHv3j2++OOf5OV/8RkOLx1A5TK3SNGSLgaiJCbTii41JBtV1GgDi6MFexiaW6f89P/t7zLbW/CxP/TbCHfuD9/fj50aaWOERKTyni6m0f54Pp06Fj07e5Q4XygOeRru1/r+njVNq2vZWDBiiV3AO4O3QAqEkc7+V8PxkBt6ekOfsg55acfay85ecOy0dx3ORHEYkCJ8Y8DYIYouXmXSc0Q5Q/SzxascoG91qhUmikDXdbmOOU/JC4iBZw+bIoghAtFYkpGdGnuLwYt+d38v+fVkhs3K5esq9fsFlXB1TRqlERQhUGfhgZF9HqcHHRf9xhwcIJs1slph9s/3UjYHh5jHn0BuvU567TXM5ctwejoaCIu50LvW+w4xYIzBP/ce4leez+x7i7l+A/toUdnqcd0HXnu5VpZLZLXE7O1hn35X/yz7z+fSK8GTnnw35tbL8Ppr+utLl+CRJ4lBhiYE+wewOtU/iz14QiF34xyyd3HUkLMwiFjkD/4J7A//v2n//H+FOTyk/Z7fS/i1v213LzdKeAttS/uJnyH87b9FMAb3wQ9R/8E/jH/mOZII2498KwDu0X3SZE/tw8Eh6Vu/fTjVpxSBSd/4LTpyJV+F0UhUhtJWXTvgnKHtOuaLBZ+5ueLO6ZaPvm8oAeyzWhcdohuvzUiYc06Jfvmo65rtdsvp8pQ2dFy5fIWmaQDpJaY1ytOoO8ZEMkLCso0tFRaHyu0mK1Ti2ZyuqCYVi8N9Ogxf+tIX4b3lEVtF6sr5UzxHNCvrX/eN4VqttVS+wkrip//J/8yk9njnQEQhd0P/R0h5z1Gp6zZsEZJG+MaByVFq12GtZX9/n3vHx9y8eZNLl4bugBcNbZk/BSEZUM/hsNZSVRV1XXP37l1+4Qd+jM3rxxzs7xNDS9d02BxqCPRjjOi+m4xFnDbk6rYt1lkmdcXMH3L77l3+xf/0D3j3xz40GjMtzUySenJfStrB82K+49tAGEeD0I9DNvYpWr3+TCps245kBZ+J08ZYggiOKc5OMDiMvPn+8LAdD7ehN8Ugj6H1mHuuCyIW3C5ce6EMrsllda5I0yZsngSSyXSjr8xRtPbxVi9XcfuUcu4d3ZiNKKpgRReJwRNMhyfQGIdK77x1Oc4451igQ0myE0aIs6RMGnzxxRfICQW9Vhgij3wrwsD7F/J9j78zCbboEJzd3FA04lwUM1qL26bh1Vdewzrb1+tKsyW99DJmtcE+lg3v4SX9+wtfGD48zzmxzfbi358dH3Ir1RR1s7caPdv9Q31D08KXFPKXO2+QXn0V+9Q7MBecU+7dI738Ema5Qh55FDvf11z0Sy8Nm83BJVit4fOf768gbHQ3rRYHCm92gozld+dHUIKEibZE5aWXHnhP4+PFF/U88sz7Mf/dX2e+53HecvpJVSeLKeK++3/F4ru/J+vgW/z+Hvav/s8Yp1GuweaIdPc42bSkr/1Gtv/kE32ElXdD3Md/lPT+r0He9e5hrojqTBRims5/nXc9zJpEeSTWc2cb+ckvvN5/XzSJxJC7V8UrTUthjBJOsfnzkEYEUuccn7r3Mi/Kkvl8TkqhTE/9fjRSNXSImJFDD8ROHVeDwrfY7ICnjAY4pnv7/NzJLUpRvtIJkxqliDLBbSIRcKFGqogki00G4wzdaJ+ofM16dY+7r30JiHg/AaTfm6yxeZ4W42s0WDFDFVEJPISk154EkxLWGfb39zh+9bNU7j39dyZr8CQiSe9TSi66A1NlRyRLco/mgAYNQts2fPqHfpzuzgk2JqJJSmbL6QvdM3O6MwlkeXFFUPUZmmy4oySSNRwcHrJ69Q1Wp8fDdeY9tpccF4PN/wsxatAx2ksugu0hb025k6HNML4IGARLwpLocopSHR4hxoS1Bqzm80uyw5iKaIUUGhBDG/71Vnr8Sh8PtaEvvukYykkp9S0NSSD2vCf4IOinfEgV7opFFM2blbqREgkjPRQUky7KFCPafs7qNigCNl9fzgkF0ZKP3SabF1/b4IiMyE7jay+pBmvoUmT/cB/vLX/qz/5Hb2v8/mWO2jwy5CqlXAi9V9J2HXfu3OWRG49QjURE5I3bsF5hnngS3qYs6Ns5UoyYpJGPON1Ebf+khkNuv56h/AfoRC9P9Rr39pHLl0cbXH+G/Pfu84u5zM5N9P2pSyPa1+417DpIZ+eBXPAa1NWEw4ND2nXA1Qbrh/dIlnq22ckRdPP2JW01ytf2YlD52HYJb+1whSYjOn//b2NOT+i+7w/uXlq/CQt97JxJb4UMV9ZkVVW0nfDCGyNUZoc8JsMXljvPCJ0qW+6u3Rfb+7yQTjn2kb3pnNi1DHXPuYulycYZJbuq4UkQI+KGboI2OxcWS5KINRY3mXB7f5iT1moasFzcsP4SRrLrY7Izf6b3QteccHrvde7feYnJpMZYbS0NA+pYmlIVgxdTQjKCKJK0+kH0WRaSb4mAJ9Mpm9M7LO+91n9nXy2RoxF1gvSZW0NO5xnOVt8Yqwz/m59/ieMvv8bEa6lciEEJm3bklKR83ihgXIkSVFDMgUn6Pn12idl8TrNa88aobavkZ28wmc9ketJiTFHncxknzhv4c6+VQKSQo0QbV+WayH7O9QFTvpfCObbGghgCqPNoHedd4of7eKgN/RBJgEYWgwdf2PDnP3PeO9w1/EZ7Xac0vJ7RyV6y9gwisDPnRCdcv5h7qCxHQjESQsie/YPua9cR6RX7Crw30g8o3ITlcsmVS5f5L/70H+NTp9//yxzRt388W/1hLDUxQNOsmc6meKd52pgir7zyGn/xL/1l/pP/9I/x1FNKQLvI6On9nT37EOkNq1P/0/cryBGhEXCTCau790h3bmMM+MuXmR5cIsVYOJkDhHnBYca/Ghmd0/snLDLRK4rQm8Nd+wQYbn9Wy8cuv1vH4eT2RvuM5wi4v7PeJ1KC5tBEZHd0yvwt82B/b59rV67SBc1Hp7g7PwqBrT+szesArBQxEMOAf42/tbxSnAxD+p5/l+Z7/tcUZKg4CQVBk5TTZcbg7BCVgm6kxdDHlAjdoDDm0KZNiBoml/PyKRMd66pivGePb+mnNq/RVpGqqrDG0CXBOXUONBWV+1f0KF8xjIGUIs56RdZMVq/MX+Cs7aPa/b19+ob1xRikiLdeDbUUvQ4gWXCJYLTywKbhak9uP8/y+BYpBup6QehC/j691piyocwi26lPF6qxSyliJWllgHX9hDNkMrEI88WC5f3BgKYUEeezkZTcpVEIYpmg6Qoxgi1rq3wuRLanWz7/wz/JbDrp13C/zxUHYTQnU4qMen0iSTkSWKOBkVFhnhgj+0eH3P7UgF6ZsaBZjsCds+qgpLizXt7sKOlV51wO8DISm68p5mvSVuFlnXhdAxkR1fy9I8VIFAPGY12Nr/5tjv5X1SHZAGNyMG0GmMnoLpDfd37y7OZ+1EHwviJIhoRyblVSjhZImnNFnYwEOlmsA7FUPsNu5YxG+sWh1zSw/Z1zRAzyICGO0ZHSqBY/38v4flJK3L17l/vHK05X93jsHQo9fnj/rwATnBesEbq45Pb2R3Be2G7uUtf7TCeHNE3DcvUGi/2rVL5CJLLdLAHLfvo2vN1T8qFZ8dnmjwDwrsmTdA3cv7/itdfuc7B/lStXrmSGsm5Ys+mUwgXIe3ufPtgxrox+mSNPkyFWOfOm3hwKkKMqExMyqUnXrmIQYlXTSSQQ1UCLoTIWjz6zEmsXQ5IucARKVDUKdIe/zzyuHXlkLCe312RP5NymNTzq/Ls09DsYjH95zjlKk8Tx8T3u37+n+UxJuMqwel3H+XQ2xdjR5WdnYgeEMKX/g37Pi7eUcHYyrxTK3H1Cow+WszhEIq+8/FLeYAOYACYSY6AyVlM9+kXqLOc+ADrvs448mqwyNnNOEDzKJ7EC0gQaI3jnMN6RRoHytt1Sz6ZMqgppO5x1uVmMZGPvKCkGI5IJf5EkIddUD/MG0LSGteVJaKQ8W1AM/bbdMnMVIUSsU4HtIJZoBJ86JsGy7Vqcn+FdhWFALtabDW3TqONS5oyUvQic9+oQRelLc/U+tMNlShGD9rdPSZ0mRgBYCJHZdMZyvexf884TrcN0ra4dBxUWxNPFSJKIMw5vKtKIv3Tni6/y4o9/Csn7Utcpk9+UHHZJY0q5TnWKih+eQiSGRquMoqYfnLU4k5GdyYTJZEihJmKfAy9oU8rVEjFYSHLhnng2V2+xPV9KES6v1zcK0tquo6p9dp7AOocYk1EYkw19wppIXdc4lwnV6asrpn+oDf040i1G3ZpRT/h+2e++9wFnQ2H6CmcFIQ4BZYHsjUYDkjexs+ca58P7CJyEjaYPUUSEFAJmVqmFeZv3Cecj/fL93nsWiwUn95bMJ7NeTa6q9kjBcdx8nOPll9ms11y5epVmexvJZKpNSJyc3uferdvcuXWPw8NL7B8dMV8csd1sOEk/xiX7TUyqpxh1XKVpWoxULBYzLl26xGQy2bmmg/19nHf8uT/7Z3chj90bKyM/ipTHud80/i29ARptmhRIM6Zef13JkIl17BDAG0ttHN5o1CD9mRTiE85+jT7b9SojFd4/cN6UBhj3X9Zyvr1HKtpV6BGXc4ec/WFE5jr7bC9wFPIvMQ62x/q7wyfqkacyNtLFaEvvXEoe6FLXvj/xI9Rq9F2mwFhlvMwQubmK6fyQFEQzq0YNrrXKeMdYvB3SHfWkphAUYkoKm7tiaFLvfOCUFPbGq7d46pHHcMZCGsrHll3DpelEPxsVjnbWIZQIPV9lTpmBRpRN0zGZdXgXM/rglOQ2utfigI45WM12y2RS4Zxjs1lS2TneVHgD22qFlwUnSzh0kbqqaOJAqOzaliTCtFZFynH7WJO/zBjAWZJTJyqGAAjbpsHXHZWP6oQZu4NQampREMNOYxPncqrBaqQqYtiGlslsgd1EYhJibYgHFXI6SFSHrqVrW2azmTbfMbm8zxicsRrRG6tbmKizllJQJcWMLpoccGmagR6SN84SxDCbDd3f/GRoi6zTXhQ18Z7a+z6geasAaDiHcqnEaMpmGCf9XRc6rMnljk6dHmNrdS604g5szdHBlbwnG7z5NyMP/Ct1PNyGPpNvCmFnvM2Vo0TDY+h7bDjHhDyd5MWoD7lZ3Rv1fSVi7eHg/rNx2NhNWRCyGxRlQx/bgM1Nxc6K/uzc35ugEeP3iAjz+ZzFZA/jtr2hv398nzj9HMfLr7De3CelROUrtk1kMjkgmYpts6Ztl5rPlo7V8pgQO/YPLzGbzpG05DR+mi4tmZon+u/1rsaZCmrhxo0bfc1wGdMrVy7z5/7Mf8by7l2Ovv9XLpWw/G/+G9LEE0ygkgmIJRAQErebDQ6YupqFr6iNp5M4lBEWJ84qcdJIzht6R0T4qY//BO97//u4dOUK27alGm2qPR5gDaGFv/0fKLHvfb/2Mqs7Gxbzirbt1ACWcemf2XD9ztXaCMUqIzumRAwtMXbaZrkQ6HIe3WAIXUd92fDZv6GEoe/5T9+FrRIkybniiBiD954ocPf+KT/5iU/wv/sdv5179+6x7QJ/Mte1f/t7rlPnDVsdDiU3GWOxBFJoVfvBTLIlTMzm+8z3Dtis7yKpA2bFeo04DfleYtoppTOICunkazXOaF8GZzHes5XI3/oHP8Tv/97vo/IVYT3qmZDKGQy4zHoxIzIg2SD10ac6FUeHV6nqaYbwAyWoN5SUh/TXFkdlVTHn50WEECLOJ8QFxBhqu6BOib//o/+Qb/7ot/J173uW+y/8XP/Zbnuq6z0/NZufbyn96tPJ+cqtMUiKWGuZzxe9cxljxHq7Y9SsdfgswT9W/TCSCGKwFmw02GhIFg72F7z02uu8cHzC7PCAX/v4h3nx3gv9517/xecplQWF+T+arcplyJBRGqUtk+jzjTFpZI+iRsUnMcVxNxY3Kv0NsQOxA6xuEjFF2tQRo2PoHXKeND0+hv1GlUatc8QoveS4QSs1UmroYodNSed6VWGsw3tH7TwOOL5/SieRvekEaw2meZuNcx6S46E29EAPDZcoTHaiovL3mcj7TQynZMMsO1E5jJsdvNX10G/qBiX3Qa/PQFKRiHMuyYOPMuHH4hHjRZBSoqoqJpMJ0dKnGEWE5fYlmvaEGALWeoyzODehrvZpukjXrIghaLSHIBLp2i2b1Sm1VznIIPfo0h4T81h/TYX4ZEzKHbsU1hunQq5evcyVxZwr9a+cd3z85GPIdEH04AJ57CPGep5OCW/yJmpKHrQIJkn+t5b1kA09GMQpfP7iF77Ik088zrUbN9h2bSatMUTgWBIQtsKVSmHbIw45vJqYTz1dF7DG9XMioYzfEv1a53K+UuHZGLX5UYqdlnQycEKSJLpOm+iEKjLbhyuVjvVTTz2hBOhcCWGlIYpj/3Cfe/dPeP3eHX76F36OP/aH/whXrlxhuVkzu6x17VceuY53Fuc83jqs08yrtR7ihtAtaZsNtrqEGJNbPRli7BDJeVyrDncx8noviSSRFCPdqEVrjIFkVcs8IXjrISbqqsYYy0svvsQ/+gc/ym/9zd/FM088sUPkhPyMRJ+dyyhenzumGJnsoOcmJdPpApwltq0SdXPOW515nQuSq2bOGvryeqnCSSmCtexND7jzlS/w8X/6Y8wuPcKjj1/DLl/uP5tig7EVktEU60rkm/eDPn01RAxl/5rN5r0uQandF9Q/Ncb0xLqYImN1TYWbc+8NMYhYrXdfzHj5tZf4sV/4NEfXb/DrPvpNO9TBzb3TAcEZzW8NcEz/mv4wPIskkh0rJeUJmQNgyjnGwNuoaVDXUftJfx5NWWjFReFZjKCv4f7O7tt5sxbRxlxn3xMlsV2vCc0JKeozrOspCV17la8I3mME1qcnrMMaIwnvHKerJV9Nx0Nt6HsDiE6LVIRYRHrjX943Pt60LlPGcKcuUlyZqKPPnfm4oTgdmbSUy3IkikZmrixoQWLciere6iiLbGzox95tj1qQdpQCXbVlc7okhIAxto8SnJ9jTE1KK2LbKU/AWxCHs5XW8jYrNpsZk6kHSSTZEhgmv+QcaCL0xvOs563XNAzU/b/8V0hdy/zHfgyfnQvrhiZA5TkWBEWjiML4FWJoadoNq1/zMY7+4B8BIIiAqXBBPy0mkgBnHC7rDWjaLhEk9epeiMHme7BGSCZvuqhDYHMUl1LK+cjB4KoIk0a/9oyUsaSEn7ghijH5zozmFK0zeF/hfY2zjjYEurZRI54iFG2GjBgoGUwIbctqfaqEK0nIxgKqbJZixCZHzKVVNYmQoJrNWd28yRc/+4t84uP/lJu373DlaIHdjuaOqJpcGXlvPNZ5rPWkZIgpEOKWukJz3UIvHarE0Px+53rINcaOELockYa+vz1A17V4W+UI10AyxBCorcMk+MqnP8crn3mez3/2czx25Qr7lwY4vE9nRI0AnbW4shayw2RQx4NYnHTBGNdHoIWBXpjkJqfsUkrEEEhhSBWkFEgSsiM9UeckBJzAwd6cT3zpy7z0M5/i0898kOfe8yRjgeUkCZc5HiGGPK5uSJPkaL+fHzqiJKCeTHXv6Vn6Zx1MIYVIkLiz3guCgYiy97PynqknvPj8F/ipf/qPuPzE05z83t9LNSo7TilhXH4vpifLDXvoGJHJ6ywHQt5X6vRnx0oyY9XYsg+MAx89mqZhWs3UuDMgQUkMlavymuItj74qITtcl5fdAAEAAElEQVQ+KauOFg2VJMJyuUGaNVZy9VQTCFFw3tNYTeEYIDYtTbvmOCa8d2ybX30tav9ljofa0AMDTF6gOmv7SfVWtnQcFRc2+2A0Ieb8oDW+n7TlfVorOqQDMAaS5DI8zaGlAg0kQdJQH5tCYOxoXJR3Hx/e+wGOMrvXUN5fVRWpC32JDcDd+A9JqVVo0Dt8VRFix2Z5SppC1zaYFKldzTZ1WHzmKFgwgbZdY92ClDoCL9Pau6NrzuqAaSCPXQSzje8tICx+9B9T1RPcbEphirchZAJOQdMFrdMPQCJ2AQSqesps74DtqI+29zUdjbKwLURKCVDEiLBBVGI+WabWEgx0aNGRtQacJ1lDitJDwUXK1ZgS6aXco0CfadOoNKdznslstjPPYghMpl7P5wbnx1pHPVHjbp0npsR6u6HZbkipg6T5WHVEdNMLIdB2HW23pdus6bqGorjog6EYelKiaTu2+T1XrlzN3ANLc3qCufkqvzbAiy+8wqNH76EepSAsRh2dGNV4mBZf11Re8H7GdGqwVHTJYmzCGC1/SpkTUdWzPrJWhn3HttkqA9ooE9wRdsYnVQ5X1YAhGMAp4tJ1Hffv3+f7vvt7OL3zBqcn97l0eTD0KYW8DlSytOsgeXDe7Ra39XMur+PYISYTyXItuULgIDHQhS5LQLeIGZyS2EXaZouvKiTZnhhoMVBVbF67y8dC4sbqPk4mPP7+38lnPvtTAEr+Mx5tZ5wDhqrCe4/1VptdsesIS06ZxBiRRO+Y6PwZ0IcuBtq2UU7QWAIc7XKZHASj6NHC17RNYvbCqzx38z6L/YZXT065MpoDkg1mIX4aO6Azilopd6A4KWrkI2Its/kip5wE6QJQ90FRAu0x0LSEMBjOGII6TTERo/QlyGJgPptlLsmo6ukBh9IXNJXTRU3XOeuzI6haBs8++yz15LLOhUzU67LWRHGSjFHxnmpS6ZyMHfdPToC/+MDvftiOh9rQj+Ganb70ZhBtsOZiItWDIvrxecoks7lU6RwTs7wv59iSGT5rs8Ho+zaOzqcTWPr96Gwu6uz3q5Jeyc9ZQgjnmPht2+LMrmHdbNYYo/XUznm897Rtw3p1E+s9MUZSatV4mApvtPmOdR5jp0CLRIUrExGRtmf+htBgnMNYfw5kK9c2HiOA6d/7QWw9yRrfkZS6/tnR33+G8kQAZUrrQozceeMuh0eXmP7IP+7P2RrBZfWyaexI1tC5GqJ+7zxZDRyBroxtGqkYGIFYoiuNAkKMuMpT1TUiwmaz5nS94mixTwgtVVWzXm/Ybrc88tjjpDhsYpKft/UKrVa+wlc13mnzkrZtWJ8e03UNSKRyyja3ts418EKzbVhvVrRdQwhBo+OuyfCvw1lHuDtA2svVffYvzzk8nEOcIuIwoWXZtMjBlGvPXOODlyZ86WqNv/sVrjB8tgsdzijvXWHmiLRr2maDq2vltEWPsYp+iOS0DdB2LU0XaYOWNWWPG5PLwox3pBTYbreAOmdd25Ccljk5VxFMInjHy5sTTpoVl7/xvaTLR6Snr/JFTljeehFyn44kiS52uKRzLgSNzmzIqQdfU1WOynskhRwV6xxXQpvNyJ/Qti1CR+qCqvzFhKSA8WPDqzUaSQJgSTHijGGVWn7w+U9w/K3PcPkzH4YPXuPF+oSfeOEz/We1a2Wn5EXUqDRNS9cpklVVFcY5amfoUmbaR637L5SGUioYJWGTkspCDCpbHTpsVfUM+bIH1LZGROd8kI52u2Ua4fDJSzz3zc9Sv/NZtnXN8uRkWK8iWDLikUR7JqSEdx7nbK8NX5pdaSrMEULMvJaCRUjmpOg8xRraLhK3G8IIKRnzebQyQxEXNdZv/yj7jKZgBtS150LFSNMEtuk075UGb5RxX+wDDHtmtwqoNG4ihfUv4Up+9R8PtaFXES1PTA0GLY3DFM030+e4nLVIUsUlhXVt7wwUqD5luN97TwxCTBZrqgyH94AyfcaspAeyYVJEobxHSBKJ4qiTo3WqLmWSNl0ITddHwfAWqYTRe4A+mi8/74wHdlSXrRPfuCzha53mXQV8tZ91AjSnZ4zBZaaudS6XRIGYWslLuRg9ShwMfdKSFM0jux3nZMehGd9D9nlizoWm7LVT+BCqmaYjLKlfsIpGGiaTKacnJ8zrigI8VtIhOJKxtGRIfYR0xBFRExlSLLuDW1TQNKqfYqkwNKFTo+8di/0Fi8N9XNhnOp1iVyvCvXvcuXcM7fAsUyplTBWT2Rzj1Ih2SZXHlidvsFreoWu3OGu5dPk6zld0XSBsA13X0bUburAl5EYcRWnOW4f1I9g7H6HdEMMU571G1hb81HB67xaL+T5f/52/jfd+/TfzSrPEmCky4kx0bcvEO9Tb0eelEK7T6DCp0qTzrpdJVvBKQCKx29Cha886Qw9ipYRYS+gCp8slxdCHFLTrnCmCMREXhTolDqjYv/oYH3jyOV5f3mJup0xGegEpJUKpl44QrGBMJCaHxECKLV3jSdIgYgeYOYKtoJNAbBskxezgGGLqevheUkJGQpVCR9e1TOwMsdo10hhPJXBl27F/dJX3/LH/hNWmIaVEPRYxclNCaIkhYJzP0r8pz/2OGFoVNaqnxBCJoiC29GkUXSddF6C7T+f8sE6yToBJkRC7vuouhA7rHcaqGic4xE95+cXPcPQbfwPPfezfIRrPGy9/lslYkDMlJKr7FkUIqSOmQEwdLqrD65zBWMAVbYKSHlXMIVGefSSidempi6rpH1tkZOjFQRdaDHmfISkvRYTKVzoOknCGXFfPKMUxWrtu+E5vjPr2uWonZzDATgo9SpVKBaxTRMVmQmBfJ933WjAULfyvluOhvpuUUi+KYDOEo9WuSgTRMhAhV9pStvxxDSiQSWX6Qp9XouSN8xtN3lt3DNPAilYkoJxTKLChQvgqx6jdtQKh7RhUvN76eDNof0zUkwJP5cNajWLIUBx5fOp62vMFSmbc9oSqUiqlRDNjDPRM1kAJBtfrDcEJdV1TT9yucb8Ati/jHFOHdKGPfBGN1snRvPROWqAQkbS21dC1HV3bsI0ds3JK0fycQo8ZgRlF7MMzPp/KGY/kIPGq91BNatoYtIFH2xG3W07urzGuonOWNnTYEJnt7RF3gGMl29WzPY06Y6LrtjTbNdvVCc32BFJk4rVsK8ZE6La0bUMIXa4l7nLOXtnEzlm8VaetPMcxUtJ1LbGLJJuJUBLBgE9C5Rxpskd75QbP1TN8G3Gj4vTj0xP2FnMtt0yxVwkTk/keubWnkdgbmJKPLTB6DK2SDI3vx09/H+hCS9cOzPmUmexjboA1hso4vLe4quILX3mBJx67xqSa0o6IfB5Fl0KMVNbqHEkmIzEgEjApsQ2nuba9VpEj0RpqA4SuIXQNlRGwNUOxpa6DEBPFm41JDVVdzxRhyg65w3B1tgfe8fLdU46mC44Wc2JsKMktEUOKkbbtmExdXpelXp881oYkZjT2hh0hatEUSYgt3tfZccmzNUf6Mcbe0Hddi/M1nkwuJeHFsWcdfn5EXYExjqNJTXtyf/Q1CTKqIP06SMSQewWI4L2mL1OKxNIqG9lZYClGOmmIUffgFCNdp5UhMYwEaHKK0/V7kk6rrm3xlR+6dKI6C/qELt4reynmDJ7q9SgSY6z2uU95jpV5R+5WPCZO26xmOpAQfynYwq/+4+1RyX/VHgP7vDS1yC/3u7iQ2dbAsKBNju6y9wa9zOQQmZbvMLsWYXzuM1D72alh8u9S3iQFhT9j1/TXMS77G+fc9RrMeWP5Fkdp7gNkhrfCmjZLTKYkfUc/crRsyKkGO9RD2/wZk+uhY0x9CgGUPa0phN1rPVsVsHv9QkwdTbOhaxtC12npVlQnKMWUVd+CKlVFJRulGOnaltPTE1IKNM0Aq5neA8tPoPe2ZPj36PUHjXVCcgMgVQXzVaUoTUxIiKRty8nN2zR3brO8+Srbu3dwoeNgPmc6mfTncZmc5qoJbdexWp1wcnyb+3dfY3n/JqlrmVRT5rM96smcrmlZr0/ZbE5ptiu6bqM55aj5BGcclat6gRV9HgnsYABDCDkVknW/JUI01NYxEUt7suQTH/8ZHj+4zOXJPsqY0uP05LgvDZUc1SoCHzMZLY4c2pSFhGLZVSn52pTiyNlVx7jtWtp2y3hlFK13hdRjvyKdMVS+YjaZ8M9+5EehTcyrKdWoLGtqHTHzFpIFISJRer3zGJX/0m6XxBAGx4ScHhLJTlQz3JvIMGVQ+LufEzHSdZ1eZzEUObU3n8y5Vi/4xI/8GPdfvsml6YIFA1JijL53024Vjk9D+W1h4ute0Om1piK9LRkiH+ZrCiGPbxljffZd6IijZ9m2bUYTdc+yKWGicGX/iMnxmvsvvMbm9jHvuvLIThqyVJ+EMXdIirJdoGkammZL2zS5KVfs15RkhIVMZmybLc12069xrWLYVUckpV7euFeKEENoO3zlNWgr976zVs8HOSW3L2df7zUg0zAPjFYVxRCzIyE5Uihqj8WRHcb4q+V4qCN6bx21r2hDoxuc88SkzGB1UtOZ6LI0N4DiwZ89hs3fUNS8ikNxkV85hqxt1tXXQCjnmc3we+cszjtibIvTeeY7h+PtwPln31fKiUa/xTqbITLNT8ZO4WBDyjWx9MbcOZ/VxtSoOEQ98T5CG67n8qUjnJlijCPEMAhnjCL7GKO2YC1XY0GCMshjbAHB+YpJvaeleWTWuSibu3ghm+2K5el9jo/v8OiNG8R2cDiMDOPXs8H75zZ+3hcN3vBjFDJTWP/tgEoMJiWquqZ+ZI7bn2EkUDsLVvkJXS/4kj9XeZIIq/WSk7u3OL1/i2Z7H0kt0+kBewdXcM4TQstmsyQ0iZhaknT9Rm+No66V1KRwcU7DWIOzli5F/MEuL2AgkAqVRKKpIUSme1OaOw3/7X/73/Ed3/Kt7C0O2cQRs3yz5vT0HrP5Ps46he1NxAStb1ZEU2HwHi0S3TBLmaJSUSIRqwTNXKK0XJ7QbDZMpxWlYKM4cMZGPNpcRqJu/LX3HO0d8JM/8s/46Ld+Cx+YLgZlOaD2nuO2ISRhNpsg0eUim67vIBskz2VQg2LAGI8x0MU2lwUmRZJSzNtA6CO+9WoFGS+SlDBOI+W6nuQKDAhJ2K6E608+xk/86D8hecvVp5/iyiidUrmaZC2nmzWX9w97X7QgWQOGMHKCZHiWJoe5hszfSakvHTZJU4zb7ZZgF31fLJGIjYmOxMToGGxshPmE41du8nd+9Edxly/xaz/2LTAKCCRF5YekpM2xQhhaWRu9plBQzaTd97ybKKtDYr8vxNASYsJYRYZsJjyStSH6ZReFGCLGG0RT+cp/wDCdTN+0tfiD9mz1A0wfsCnvrlNhLzfpKy0sEFPIaKWK8yhSq5qNoet0/B+4aTycx0Nt6JerE9zEZ2OVm2HYLLhgybn5N4e+YXfqFM9fPfIRSHDmc6ZQxEe/K8bNoDnfkD1DI+rxasSlOdUiyPOg460Ypw/+3PBz2aBdzhPHlJvuRJCRlRucIXrjOny1Gs6UAl0bRpB46qscqqqi6Frvnm93uay++7cw+4EfZLlqIQVibGhP11irDG/vJmC0oYTElu12SbNROdm6mvLo9Uc08hgZqn9VCNtEIAbpeRwRMN6x3KxZh5ajK4fEStX1eg2EJH10UI4kgdu37iCxoVkfQ1IZ1L2jRzF+n3W7oeu2xDYgQbBW88WIMoGdrbRkqTyH4SENcCPsOCmha1kuT5gutIlI0npQ1tsti/qI648/wdd95Ov4xGc/wzd/9GNMJoNK2e/8yOP83V98lWuPPMn+3gHbpqOqPF1sM2lK11BASzRFBItBkkZgxBZxkzxLAsbmlFo0vPHGXer2Ht/5DU/yiU/ma42JruvwlaIgQVLONSes9ywOD/jQ136Y127d5Z1PdVw7GtoZTyYT/HZLt15zWln2qwWtNIixGDHYvNZ9PVFBHNGSxq7A4jEQui0hNMARMSZclq1OGW07PjkGrgJaBra3r2PVti3ee3xxPtvA4eFVnnnmHZymjhfu3eHJJ9/ZX+vs6pOYkwWr+5/h3vqEeTXN3SXJRFMIRJxXDQo18Bl1iyrIVSptUi6PLflGQY3Uvbt3WFxe9N9pRGhCxONIzhKdwRs4XS1513ueY//H/zm3797jftcw94MDJbnipdlumNh5Ti9koaPSoTOlXvWulJQmJMv1ZmJwJh/npGVOVcYdWV2A1LSkSUCkdFWEbr0lhEBVZ+Nbuoae2wcvWvAF1cvpI8U1iCnQNB3zvSld11GEdTSF0xFCEYkytG3IyoBa+jzWU/hqOB5q6F5Eve0uBGIXiHHMmpcsdLF7i+NJs8MMl9FrZoDVSzRV+scJsf9jZDCXY2ivpAW0cYLgqzqLsqjj2DRLTCaOPagi4EF/Lnrv+H5237Pbp9taQ+haUkTr+/NbQ+i0pMw5nNc/xqLylVGhxRjCTolMkbdMSIY3U79oHjTeiTnhe76Lam+u12Y8i9kRlZuTYmCzOWa9eoPt6g7NdoUxnr2DKxwcXWO2t09I6nFvv/Frh3OOqiTGYyQj6HPnehhspBKkJCvQDfPACfjKg7U0bUtsAzNxTFYddedgG0nbQAq5XnmEojz50QVIYrtZsdg75ODS40z3brBtLav1Kc16Tdgq2zvFhrbbaFmdr/F+gnOq2mVsSUcNUZ/uewOkWg7vLLHrWJ6ccPfuHfAVDp37KQreGL72uef4xU9+mspYFqNN/vrjNzBNx82br3Hrzi1msynbpsGUipYMcWrknKHlPJFjaAndGpHYG4eubZnUNS+++BWQxJUrR4w355hzy4lESJ3yPqzp87Ltdssjj1zn53/h53n9zhv4yVBK+eRX7vOk1VTJnTfeoKuAFDEhIjFmVcFA6YEQRfPbIS5JSWVQES2ljEFh2q7tsMbStYF7r93kg68NfIIYlN1OTJmUGinpC5uEEOHpR25wfOsNvvjZL3B4ODgly9USYw1Xrlzizs3biM357ZDXSswOZXaQYxx6s8dMHM5BKiHnxUvqzDvH66/f5LSd4PyQNirz3liFs6OAk0BzesrRtUe4Md9nstzw5RdfYv9gKFu89N7rWKsIaBc0VVG+u/AHSmlj2RN1fEpqJ0PfpP46Ywz9/rvZbtmOUiLNtkE76KUcZTuaVcPJepUVOjMniN2g4ewxLo8zfXOkoKhuEpx1TCYTJbNarR6wxtC1rXIbnCJn1jpNe6SA9xowhpFj8tVwPNSGfjqpqas6y0emDB3nBw+DZ/kWxhLYDQ0N2Qqk3fdnQ1A0n4uwSRFoKGIWMUVC1IWhf4siQxiM88TY0DNIHnA86Drf9B76i9cjiXqnxdlR2LTr86PqCBRxlpJn13svcGxKSXOtXUuKsvM9F+Xi3wyFcMYQ6kPk678Jc+UqJpfZGVvh3Iy63s9/FtT1nNpP8dZrt60EgmX5wQ9QPfqO4R5HDTCkj3byv8+M2fjPCLAAIFqDOAs2z5oQWUynpLaj224xxhBEaEKrBCFreqKSGemmu5llOp1j3RRfz0gCbdvRhY62aTLZLqMARhXorPVZe93m/EG+D2uz0fcYY6nqms1mg0jCuWHpiiSqyjOfzZjPZrRNmwVCLF2n3e6+/sMf4rOf+xzLzZLEANveev0m3/L+R5iENXdvv8Hx+pTZtCYF5WQo6z+x3a5p2w1d19A0G5p2nQlZqd/UweB9zWuvvUqz2fDEIbzrWs29eyP9hewkpBgHJ80oAz+JcHJywjd++Gt59aWXuPXGLeI4MbJuub4RHmks3lhevXkT72qlmCZVFIyxQUIgRUiCigjl/LiSG3XxqjFKeF+x2W44PT2hbVsePTjsv04dpUGESkSUx2Ut3jmO10s+8IH3Uxl4/dVXdpZzu1V1wvnR41QYbr2h/SV87pTWlTxwuX9jsiOVsiEdNByM0VLNkrvebrcsl6dcu/HImdy08itC7HqVOUFouw63t+DKlSOm3vK5L32R+d6ABOCU5+GcY7NeK2FS0sAtKojVeO8Z/VyQu5DTfMOeq9d/enrK9PHh+8SYXQQQaLdb3KSm9lmTQUqZ4SBpfP4PeT/O8tVm9Duje62vp7kpT9YhMBbnK+rJtCf5WmtZLBZZ/VDfVzhNXy3HQ23oQWsybVG4YpRFzw9fGEgu+vJ5g3+O6JHDPqF4jeP3y06OTb+nPxNSSmjyIu6SbiiagzYY6xQ6LBjSv8Jj2CT0iFEdDmc1d6qbW5cjiIE8NW4iMTbaIShZrutaYmsw7RCxJM2JDNKeI2P7oMMYjVzcs++Dd74bLl+lMF2tcTg7wbsJzk1w1vc9qrvFlO3VyzTXrxOfeY5q79LopOcNu748ODBvPmb52RpBLCRriEboQuBo/4AUIk3TgC2CPKULhs4vO9AwALC14egdc4xzbDZaNx1jIHRNjjZRY2NVRMeVcrkyHwzsOk428x+0FWcXAn4/Mj0cRcnZCNRVxaSqkdwcxRpD0zZ0oeO597yH127d5PW7b9CMSp1OT0559vEjnjqaMIkbbr72Ksv1Kg+mRu8xRa0a2K7Ybpf6p1kjCMZWfUlnCIHT01OO7x1zZQ5PHBmOJoHtdtANH0dsjNdidtZPT0754Nd8ALNtuH3rFnfv3xs+6xz+ZM2ldeAxM2N9esLpak3IpZgqrrQlZWc2hECXjX4Mka7LpV7SIVmOuOta1qsVcbnmUpN25oztF9MZ44LFWs/x/WPe9Z73cDibsbp7h7vr1TARUlSNATvl8PIN1idLluuVlvVZ00e/pWSRzIcJQWvvUww9opAk9fyTpt1yfP8+iYrFYrFDvi1VEAPyknkAMdFI5JEb17l6dMjzX/4y9XxI3+At/vIsN8UJmUMT8v6XSYSj7W93P6RfaUUyuG9lLInNZk1YwOLJwYEy3vTkwuIkbNZr9i4dUHuPwTCiA+2idPpDvwfTI3FDWWL5qAr/+N7AYxwYh3WVpo5y4x+wTKbqEJQ92rqvrqY2D7Wh36w3SupARRNCyA0rMtQkouxekYt7HPeLN4fphWlZoCCBbBAHaLeQvwqEWv4ubNgxlOysapirQ5xL3Yxh22jeuWwqD2Lc/1KPMTsYIEXHer3u82cpdIROCUnasapR4Q1jc0RZmPdKhGuaLV3bErqOtJ1RN0MOMoag5+1LvjgzFtIjH+UItoM6gDfYD30EPvgNVHtHSF2RKqEzgdZEOptoTKKtDGni2T71GKcf+TDbb/oGvKuwY2rJjm6AGksdywGkv4hfcW68Q1LZVIRoYBs7Ll27SkTYNi1iLS0J73IDnBixMSrPelQCVh06Hv3YHFMFXr91m3bb4Z0jpRZrtYbXVR7vKxV5cROwrldeNiMt/LKJGQzOO05PT6mmloN3RuZPjdjhSR23Lj/fiVdlPYzQdQ1dbHn0iSfxleFLLzzPcj1ULUzm+3zphZf4lq99mm9612XufPHLfPbLX9Y14azWvKdE1wWazYb16oTNZknXNbRRsH6O81qmdro85fkvf5FZZfiOD15n37bce+OOlnOWx2VzdXtKlN18nGZZb9Zcf9cTPHP9Bse3bvH5L32x/+xif5/1yTH1vVM+Wl3lqqt58eWXOd02iHEYDKHbkpKmmZpmxXa7QqJhu9USx6ZdEeIW5zSVdefOG2xPTzlaB75eFhwfD44FKLJl3dC0yubUjrGW0ztvcO3Jp3js6BLVZsMXXh607itjaddrXr93h3d/47dz4Kbcev0Wb9y/p1UZAqE429lAd+2WLrPV22ZL22w1LRYD3juatuX4+D63bt/hsac/yOlyuVMJo2hii8dhydogSVUhj0/v89QTT/DcO57mtRe+gox6CJiDmv0PPIKdOKaTCc12S7Nt8tzSKouSSijlxzuVGjIQBgu6U9DAe6fHXP7aGywu7Q2X6UxfbaP+WWJ1suTyo9eonOsVKs9qcuwcUlQdy+9zSd0Ov0hL5nSamSw25fvIPYnqBijYqpr9ImroXfVQ09fOHQ/13dRaGKpecVIyDxIoG3xMHc5qDqvYg3Quj41GWXaos+8Z+tZoHlbUNxYZytfOGguNuiqqOtfEYvG2hkqY1Xs4V7Pdbjg+eYPT05OM3F9szN+Kcf8gJ2C+WLDZDj2x99K3cTf+CCcnJ1ijMPl2u6HOkBUCIRggMZlWKpbjK0QSy+WKptmyXJ3qRmMte/NFr3bfdYH15i4nyxXeWI6OjlgsFr3KVa8RP7o+v2kheUJqtSvaI0dsv/PbWZ2eQOz6HLQ+A58ZcapFP5GECRtqbzFp1yEqbOg+T5fSjhP1dsYOwCTBm4wKtQHvHKvVitl8jjOWOoJY0aEzRlnQVmhHUDgp0HVLft3veR8/+Bd/kuOT+yxkj0euP83q/jEptrqBOQPWEBPYLCdcGsOAUSNowFc6nsfHx4QQeOd3TmjTiuXJkEvWhjC6sXckJhJIySMmYSVhQyQ2Hf/73/E7+ewnf55LewMys5gvCKtjXnj+eW5cvsx//gd+K3/hb/xzPv4T/4LDy0dcuXKFK0dXONg/IsUNIiHH+ZaUHPdPTrl1+yanp/cxIjz6yFX+vY9c5Utf+BICeD8hjsZc29locxzdaZUur6qAmj8/WZ3y7d/5HfzCl7/IV55/Hr7+XQBYX7HY22N5csJXPv0F/t2vf5a/efcr3H7hZe7WNYdXL3NwcETtF7TbNc3qDs32PqaeYs2MxWTB3vyINmxZLRvuHd+kqiwf2sKjnePu6V2mi1l/rTofc6165QfoPq+dKC1RPF/3gQ8g7ZYf/gf/iI/dKJPd4StPu17zsz/7s3zdr//tfOIf/x3u3LzJ7Vu3eMe73snl+b5yPFJSka6c2wayIp1lbzEjdJZXXnmFzWaD8Qve86GPcXp6SoyRyo+3cKEyhhCFyggViejATacc37nNI089xlPthns/9eN8+eUXhimbItEIl3/NOzn9Jy8QmpbNZs223XD56IgQtDzOA9bXCNrC1zkVIpO81mPoeoS12W452Z5y9NHrXL5ymfZ0QDs2TcNsqtLRNjsN94+PmT19GQmaAhqjhA86dtKlI2SkIC+SDDAIgIHJ/YzUgfAjbk3pK1D60ZuHOwY+dzzUhn67PmE638P4OdEkZlZIXaf53Bypp6S5p5C6Xm4xBGGovdZJFZMKOIQQSCGq7GUqYjiRqsoiMmmAumOMhJSUSdw0xJAIIdK2a9pmRdd23D25i3SW2jq60HFneY8nDy7hJOHG7PQcAVu9JCC372QXyr3oKL//mZ/9KTAB+6y+fvnwKub+d3Lv9ONEcwdjDPPZIbFrFArsv8eyaRqiCNtmTYyR1XrJdrnCOoNZPkUVb5D2h+niK8dmvWVzekpsO0LbMX1igq3rPqcJ9PWyAJe/7z/8l3jaFx+SRmOTipMAGg7HvFu/eUqhvLsnVIoQ2o5LR0dEhE2z1YYzBiS33cxeH0YMfoQqOGOp/YQXX3+Jj/0fnuNTP/Qyt76w4gtf+BxPP/0U1kwIMWTWr0bl3nkK+qxtPh3WGUIKnJ6uaNoGPzG8/7dW3F/eQfsMDPejYipaP1+Jp20j9Vx7FySENrS8+vpNfvN3/xZu/7W/zunJCaARVowd88Uem82Ke3fvcveN23zvb3g326bhhz75Mi+9/BJf+dxnsd5T15X2gs95W23OZDg6OOSbn7nEex5bsN00/OKnPs18PsfmqNmNCLFSOCBEknSlCCTnWC3ew2tfeZFf823fzhv3b/OFFz4HqKE3WCaTKcwj69jyyZ/6Rb7tycf5/KOXeCksuXvrdW6+0nJ0dMSkqrTCRWbItgPWLE9XmSORMPEO33psWSwmbJen3AuBqqrOoGrZQKSkKZoEhIhYwdSOSZzz6uuv8uRzz7DanPLDf+m/52P/m68BYD7dg7gk2JbVesVP//zP8q4PfzPX773OrZc+xxc+8UkmizlHV66yWCyUFOa18Q0psWm2rNdrlstT1qsVdnqNJ558lkldc3x8jErUOvxYsz5X9lRZsTKKA1FpYmka0r5w+dIRH332Wf6XH/57vdCOxbBcramB+Tdfh0977Mt3WZ2e8vIrr3D58hUmk2nuFS+ktmV5fJeuazIyok7aZDJh2zQsT+/hrtQ88W3PMj/aY3V6SjVSmpsarUgyJuEQQtPw8r1bfODDz4LYPjq/aMmOn08yIzK05E6GZQ9NkRi2Kitsaow1dF3DcnnKYrHHZDLLksOKsqXYZmfTZ27Dv62j/1VzSLJ4A9YGomzZhogzM/XaUouEFnE5Uk8gokpUJCUFpRJBCgiJkEzfe9mJ105SVY0IdJ3majebZa4DdllyVMVkujbgvGcynRK3idPlitRFZrMFs4M5k6rGGOFyuMrRpUt9jrzPxY7u62yzi7cchwKhxaTlc/kIIeDNBGs9IemmTt5QSzliEu3ShlFRmkIgDG1k+co1FntzanNIXc97bxcU0qvrmsuXL1FXFapdnjUIL6ps+JU+HpD2MKP0or5tfH0jboaxO+hM13XM5vO+t0Cz3aomgTG9sEiJKHZEVlLi/sl9nHMsN6c89qEF+49b7ty+w+0v3MZ5x2RSUVee2XSWo4vcS4FBuGS9XuXcNcwuWa68L7DarogZxh2ThVbrNfW8HipMrNWUViZi+qrW3C8173vfe7m/GnLmYisSHmyF2EgIwguvvMy0qvjQU0e858Y+6+2W05NTtk0DRpvVOGeo6gmLvT2crZhYLSuMMWLrKRHNd2IsYsatZneHXRBMX7ql66ELKo/89FPvYBXXfDp/rLOiZMmJxyTBuRm379zhoJnwnDO06YBtt+YrxnC63Yzy1RGRgHMVl9eJS1tD7TyWxGa90lK8ukKsJY1IjjlY7C9a54ciECEkJk57RzBdcOOJJ/jWb/kooGmRInDk6gofGsxmyysvvURVe+aPPcszh4+xvv1lNqsV6+Vyd24KiES6AJ3MuHz9EerZgi5E2nZFSklbUqeA3bleQyRR5X1NNewzwdZalus1vq745o9+lL/zT/8ZBdeZT2dIE1lvNrjKM31qn/nj+7SnW179qS+zOjlhySnVpKaqa92rnMUmR4qBtm1pO/07xcTBuy6z9/ghk/mEdrvBOzfWd1IlRGe1miBqK+Mmdhwe7Cvkf4GRPQfhFyqWbt6KwuV9vF/H5XOZb2WdY7aY4+uKkIY+GiBYP1RB/VL23ofleKgNfQgtKXS4ymd9c2XZqrBEJoTE3Oozw/OI6RvOuJGaniDKKs/eZIiR0+UJbbvFWk8MKr0Zo04O77wSqYzFuYS1FdZZ6romTGcs9g6wCap5TWUn1L7CWphLyLBn2jFO6o2eIZ1wfoKPTFj/U5mYj9x4hJAaSpYxZmb9hOtIdDTtkk16hUldEQu/oBB3RFXg4naKtHuAxbRHbO8bJocTqqrumwiVYzqdMp3OmEwmdFnEJmXIuVyznU5546/+P/OGaXoIDbRuOaVACg1tuyWmjpQjruIE+ZLP9hWVn2B9nY2cOnBmsltetOsynYXtz74uO28vgjDGKJw4mUzw3tN1Hev1mqqqMlNYLdWYdVyO9Xqtc6Kq2KwaJoeW+mCKPzrg5qohpZbuNBI3FVU2BsKI9yGl3CowvRGoJpZ6X/CHDdtt26M3bgTZbjZrDuJB3tCE0jtdoWcDYjEmsd1ueeqpd/CVl17rP/tff7xBt4GD/OdNjjGaGYFN/vNLOfoNejyvswGVYpQ0wrx+4xFWafiCv/iBDpjkP7+847W3fstwqWaYHoVFLnkeJ9HqCIvQdIHp/gHf9M3fzObmP9LPSqL2js5nuDhG1qcnuEnNfH/B3qWruOaUtFnSNI2SNkPsv9Aa7TY5mx5ydHSJLgaaZkOK2mzGGMHZYc8AevEXrQQZRb5Jmw41XUftHI8+/jjvvPYob6DwfT2ZEGKj19FsMa5msjdlerDPpePrrDcblq/cp9lsaDabnJoLfR6+1Ni7axNqa9l/8hKzS/Os1pjw3mt/kTKWZDExZ4kx0DYN29AyqSqkCfQswgufyUW/GNKrPXQPud2uSoADWOeZ5IY62nQo7/t9Gba5EEX4ajgebkOfOtrQYOMUk2qsT4TQYXIDBuMqCLXu55noZKwSz0TIvYgzDJ9lPUPbEVPHdrvizp03uH9yl/l8kWsuZ8znl5lOJ0wmE+0vna8lIX2Uvr+3z958gTOG5IR2G/umKRITy9W6ZxpDnryjf8MulDy8WLZHQ2neIJAdGeHpp55m051yT+WlibJFJFLzGBIvsd3eYhtewJm674Cn5025bSak0z1cd5XJpMYdtty7c4cD5hinKmHlcM4xqSa5s5WD6nyLWlFKP0xnfWri7DqygJUZNrV5A4naZjLnzLzP5Jki42vcoBA2Os9OFM+4E94ZKoR58Eo+W4lRVzVVVbHZbDg5OeHGjRtqyEfkQ2PMjnrb6fGGS1f2adsNngnr0yUxdsymc97/7Ve4e/cOr/7Mhs0bLZtWlOmbWcOFFGqNYbGYceM9YKfa9vX+3YZJrvk1OIyM+omH3CQoJXBCZS3KDBdShBCFeqKEtEtHl9lsA3D7gePwK3nkgDU7uWOyVSZwWo3Ul6sl+/sHPOmeAo7/jVxrUSWEEila7cqXwLhaNeCtZb1t8FXFu9/3Pn4+G/q7X/jb/XkW478b/dOiynWXK+CBBO8GWML6FeryeTf6FeiJ8qEk4lKyanEmM/FFtKOfJIIkfD3hGz7wNfwQPwHo/HHJ4JLm1lep4XS5wk4rDr/mBteqmlc//iVObt5jtVzTNltK3Guspao9i70pVz/wGFXuqLdZrgFhMplg4u6ySyKkEKkmE2JGr9oYyDqLb7pGH3yMvIMS9Be0LPNuipOcUsr8Aunr/uvaAdVor/jXhET+azoeakP/zmfeTZDIcrNmOllgxTCfKFRtnAWrSmWVr5GkLRO3TUvbNDTbNav1iuXyhPV6iZVN7juvrPOqmjDfO+Tpd76XyWSu5DARkrQ0uR66i23v6RejaQvTPia6GBGXSDFDmKLs5WSyDrcp+fndyAHo8+d6pNFrUGZyifHLR46Pj3F+gL2+Yv5jVfOc0Qdrh/l3b+fBe+DR/PPqgt9rnbXqyFlrey12YCdPX44HMWi1s54ys6sc6w2tOnNr0R1HqHx+dzH27N/+/JaBJXBeaXAXOUkYq55IYRQnSSwWC7qu4/j4mCeeeJICzVa5t3jbdjtQ30//schgmCzno+QbvJ1jC9z50bf1Vo6OjnDW0TQN0nXU1Zx7d++xfzBhMtmndhO67gTvPcvViv1ZzZ/5jZe5d+8u88UURGHX7WbDZrMa5rPJjUnaDtpOeQWddtiLCNODGa6uqd0067MnnBV8pdbIWpsllR3TekK12KOy1c7Yjw09okInplbHcLVaMXMVf/bV6/zcz/0CV69eYTr3pBRZ3l+yaVaarhLVvI8x0YaY0x/tQKJVLT9Am5y4LJNaeduLEg19ITSSruua2ta9YRURbRpEKQcFnCN1HUmERlpS+29WTa1tO2ZThSEM2i5Y0H4JZSxCFE5Xay5dudR/7uN/9G/+kr7nLE0tAUtOWP6TW2/r88YIEiJ1VdGtGlbrFYvDA7xxiJMeJdD3PnjNjn8/vG8w6P3cShFMqYBSdU/vfA4qgpYUdhHvhxTTuDnYV8PxUBv6/aOr1NUe8+WGttvSrO+yaVq6VmiajtXmhFe/8lmNfq165SlFrLNUrma+2GcxnXH5yhWq+R6L+RxjXK9MhtEofbm63xucWGAeU7TVAVxfw1pEIECVtYSERZmqyYCrtZVuCFuM18jrbJxbYlHT//TmR5njdV0T0/ZfelzfzhFjxDpR9ATblzVIT3xTIpZeX+bAjzztneiZAuUKkUFnuoz5uI5WnwsUgt1FG8Hua0Oidfy7i8h51jh9Trmyomkarl69SkrC6emyv/a7d+9yfO+Y9XrFYm+P2eQtIO9f4eP+yZLkdMNdbTfceeMezTYhrzQ89viTPPH404SoPBODwznHwWJKZS+x2a5w1mG8w04qKjNjs12BsxACySTq2mImE0SmqqJoDc5qjwNEEBsxXmv9nRGcM71eufZen+CsdqdTzYvhKHNFX8sEURF87SFpvbWJkfe96928dvNlEhPqyrM/mVBJZLNeqqCS0fa81iQEi5uqQmGvER/VkdOUEEwmNbELOJOrHTC4LKXrMVQ5xTc2JKW0TKdTSyM1U1dRpUAkEYzjmW/4Pbz2xqu8cfcuzlmuX7pEu15yurxHkFzOJQkJicpYOkk5fQaIIWTSp8ta8ZL7dTincr7OKhtfyzAN1nkVhnE19WSaOzcKYvSPNVrhYGRwea1zLEe8kn/dh5L3tM/BpmnoQuD6Izfw1rJJ2pZ5HPi8PZ5PfjblGDn9qkUwwInOe9VUCNoh0xqjpbXurcTIHt7joTb0n/7MJ9is7hK6htliThcT1s64c+uYZtNysDfnqWfei7WOuprgvIqw1HWdI/7UmwERQ0qa30ylw5FkLewkWJ/LOZLZiVZLbjX2anNqKFKM/YQu80+blIiyaBm1l5UsprODbe/CyPr58xNeRpGqNuxwPMdfzP+OhNjSda22QO22rNcrUibdqYjPcP1ahlJKUVKvYT+pVYHQ+xrrJ0znc4yZ0MsFl6qBMwbcZBSjvC/JUPY2vhcptlv6p9HvpzsCSDvsqLfb2c+Mh3I3B7ezLwwOgDEa3bVty2KxYDK5z717xxhTDFdFVXkm9YQb167zs5/8JB/+c1focjXDM+96J8vVEgkN282GtmloQ8ftN25z5fLlvkufpIigrXu7rsskN1VjKyJQzjpC6Dg5ucO1a9ezwI7pr2Mym5PcAUVkbTqdcv/4lNgZFouptnNNWzAJ5ypEDDGoAazrmrbb9nKfznuSROqkqR28y33N6eeL91mPPUPtVVVhfOYEYLBZUbLApn1Fydjps4UMqg9f095FGMhqCVuWkkYMkizT+ZTDS0dsN2vW6y3eq7xp6FQJT0SwxlKbmq4L2UUEsTmCNDofXc8zSVivXIeiM2Eyg9zanNe1u/PLKFaNGIPNAi0xBogBMQkqR4fl8qUrWHGcrpbcPr7P0eGCadontNr0JUikk0AyBhetNofJCJ7PY6nEMgEslatINvXGX59VRk1cha9qfJWj0aRBTb9mRtdvMX13RqkN3/0//QHW6yXr9ZpNs8YbIbZbQk4FhdwR0Rqte49x1N3OsENm9sai6XDbrx+feSTOWS3ddRVVPcXXNTFqC+W2bVk3W/YvHRDabsRd+qV27iyfYUgrmMyFSiFnA9QZLeW5BtQJNBbni0Z+2Zd/CV/9EBwPtaFHErNphZvVTCZ7tKJ5eUMFEQ73DlhcOlLdY+d6kpLzTvNESXWaDbqxpOxd62FydMpQY79jMEaELqRnOBdDoTapsOoHY2OMNgORlPrNqCeR5PxUIfwMJJPR954dgvG5rc1d87TzWTKhr1nWiAUqJwRrQGIWAkkcnxwjInRdy8HBPovFHERwxuMcOFNhqbBmgjM15PPnzNfoHs4vEJFylSZ/Rsr/z2XBBjs+3JOYYXz1K85svm8SoZ85+/mXZPcqCgRfHLAYI7PZDOccp6cnOfeprHBAO5qJlix5b3BVRV1X+AnUyRI7y9R5TBVJm479oxl+qs8oRWXakyBJRdttMM4xmVVZqlXHwDlHjUH8HvXc5XmcDX3tcTVUboIkjRa9CLMbE7oG5vM502lNCJv8nGwe+OKggc+6CSlK76h67zEC0eSWuHl0EME6p+MkSgZz3qtWvSmy0yYbVN8bfyATo0aR8eC/7TiMxrjcNEUwWbVM8hqr6xqJ2u9A61yhqmoiZNlWGVQes+eTE0H99xjjeuej1KqXa7LlPkrDKTM2Nrtz3RlLzKQv1VC3dElNhbWW+WKOIJysT2i7DltV2ChYCXgx2rmtjG1eDym34LN27JxarCFzi2yPMpTyWFteH0nFOqOlZgkwIlSuBCf53EaDGakdM7fAVhbWEGOLdeBCRxfUkqc8piYaTMzPJY9pjpB0bNzwfIuzpDLNuYeGy62vvccaS3KKZa43G5abNU++4wklDfZ43i/vKOJLyGhv6Odd4WclrPMqxVvG3NU912lwFb56jofa0O8vFlw+ukHtpzRbQ5sC4oQrlxSan9ZztlH7T4PmAZMkQrfVTmUwLPC8mZQNQaPxpLolPSOzqOeV9w6TQXPzQ54e0dp8a2yZaYBuF9qRKmLr6kIYm+Ht545RvEzZLftyEmc1X1lQhLJf5JNpRFYRuybbTKPVBaendF3LdrOlrjwHewuNaIxqepcFU/KYKaVMZBwusizyMfrQ31e2p/rZ2BsKRtem95HvSMbpDKFn8UlGQMbjsYMgnIfxHzSQRcTYlKE8s67PMu+XyyVd26lbKEJdT3K0HXj6HU9zfHwPY7T/gpHinKhT6ZPDBcvh4YFGykbV1kpfeGesRvi55lyMVXQHNabWeQ4nR/os8oZunR8cV6dlXElUe+HS0R6hNXg/AdPShRXeLVCOeMqOq1UhpMoDQodA7gporcW4nMYQLfU0MhBPy9MxgLOuj4IKKF9V1SD8k8e3rKHx4xAZIm2MXpOI7aOy4t0JlhgarHXMZjOC82w3a0KMGgGLI6JlemCg8pqXzZt+EkNp4WwzEztJ0lRCyefaYgjMOW30scNS7tvgsLnCxDqtvY6xAyuETvB1xdzMwSTW6yWmcohzKpuc9JnF2PVTXaCwanUtWdM/62KYLPSiSpo+1I6HgxdMfy8q6KRRN4aMVuo/rDXQBX3mRlsrzyZTto1o7wgREokUEr20rDVYbO7ZIRixuXxN/yMZDQFGzhP5+2zP4SH7eNZ7JCZWqxXrzYaD/f23BdPv/l7OLFzpgzN9r8VaiCnL2mYOVkq6fsTarJViwHpsihoAilzIMXqYj4fa0Hfdmr3Fo9TVgtu3XkZMVC8NMM5ST5cY6sFwi2Q2ty60oSOdtjU0TidykqFEg95waTSU5GJlvPHfKQkxKuxmRCVErfVYp9rKxhpi1+HSpP9Mb3De5BhKSMobdye5ymGWKEQ5A0bbjqFiJBVtqwZF24/qndd1pdUK5ZRS0AkyQ7UY+6wolVMg9BHfwGYtY1JytH1FgZxBJwqz9syNl4Dz7D2OVfbMRZb5wrEqw3V2YOX8x83AuYi9nLGwt7dHXddst1tWqxXWV1y7dj23ANYxN8ZwdHRAyo1rttsN3nm6nMO2RnPCKeYaX+dyFB1IOS9oa4utLKYqzkep09dLdUZVC0n0TZQS2pmudCisfY33FU2zYTrZx2BVFjeusZMJzhjN3eZnrM+qQK0O7yuQRLtNGGupBVJmmgenzz1GJTM5o61EHYaUoVmbneHpdEazbcEYrDdYpxwW5y+Y4LkSRtuVGs1jdxsmk1m/aVur0bL3HqLBeCFNauK2VT8wR43WWmJU5wijKJ0kRSEYzUNjLbWv6XLnPCnRuzYtzwiF0z8jQ2qM1WZGQIxgiYiFLoHB6lgQEOtIQUva9vb2sUZbxeabUfQPdZa7zIovaISzvkcVjbVU3pNCAOtwI4dD56vbQVFA0ZiQeQjW6Z7TdC2VqwCjCnJBcGhaqIkdgs5Pi7CWSDS6X3kEJ4EUFSVIxiKZt2QqjzPDOnN5f63qGjJKasjPtg8ULN55YozUsxld07FebXqlvNiN21wPa/yBDkC/n+TnLRlpyA6MNVmiOxanKTe1cSrwI1iMUzg/JqGyhpSCpmG7r642tQ+1oU8p8uKLX6b2h0yme2zjGm89kLQBhpnRJe1DLJJy3sgNBnpnopS6lbEFKBFzQiT0EN9FOfOx2IrNXnnj1SF2qv0FYqnNhHskQmiozAFYD7EQ6Lwqr2XYsqALcsagP8jI2UIRTmoYrfVI7HKuLPUpjNppbbgAs6rixvWrmu8Mofe8i2F1zmF9hfU1xmpfe0lGPfw+6inRxPmougyjGpeBSSsyfK544jtj2j+a0nDnwlt+y+NifkNfxT16pdQ2mOF68+cWiwVXrlzh+eef5+s+/LWcnp6SYsy9DHRebddNvkbdsFPQjVxchUODtZRaKl+EeTymqnGZhW5Fo+BJ3ihDKP2zswFyOfqySsoqut0AIQWc9UQxSLRU9Z52B0v31cBalWBOBCQqemWNqr2pE2EhlzCaFEleW78FCT287Clw+uBwJWMIwKWDKTfvb/n886/ymU/9At//u76Ve81TBNMiee7bqqI0GFEiWWblM8DQEDXas5PhuVmtAPAY5c5IJJmE9Zb5dJ/YNkTUAYkpYCQAluQMyQx59kCEGLh6dJl20/LjP/GT3Axrvve7fger7ZJls8p0couxNdZXGOfB6FpQ9EENhsVpNEidZ0zu9mgMJIshYqSwvBPW1ewvDrQ5VGhJIWTEosIgVEnr3hsXcREmeBoLfjJlv57z//jrf5Xv/A2/iSuXD/EGQtuQTO6bQIW3Fbaq1AkRNboWh02aUhgrEzq1gIjmjLSbngghJMRY1cQIBhPU+QnBYK0QU9Jn70RJxBkhkKRcnvlij3/xEz9FurTP+596msN6SrNaQg3GOSwOJ9qJMlphNpuxeuEWHZHp9SMu1TOW7ZJQXIQ3Xe9lj1DVP4wQs8On7b8FSR0tAW/2cPViWNA5WOo5RQwVQgmVWcZaTBsv+N6H93ioBX0PDy4xn89wXkUZlCij5U6SQAg5Gh1D7+cbyEDv7DOOLndqsx8w886ycvvItzCIzx6ijkPfFAJGhjwb8V+mURv7APkKhtxUblbjnEZuVVXjnUpu1nU9ROrO9fdhnUYYzvmcY3PYyg8dA0f3PC5necAFnRuv0Sief83sfqbPt5Xxeovv7l/vNwX0ASfJTv/u87c5dJak73E5Ci15+uvXr/PCCy/gpzNwFdE4kvOI9yTncPUUfE0ynmR8zlH6fu5557Rhh7F460CE9XLJZz7/C9y68yrHJ29w/+QN3rh7k6+8+GXqqaWeOJwXnE3UOComQEUy2mwmGQNUOPFUJuFMh0FFpCSBN1Nqv6DyM8AiQQ2h5LIrW2kZaiQbKWsx3lNPalU+8wbnySx6sC7hPFgH1grWCc7DdP8IOd5w8qkv8PpP/hR3tsdIeg3rt3hvqZLvSWQjxgiIISWDRDDJYqnwpgJxiDgkWVKyIA5raqyp9I+t8K5WHYv8h9xiWLUy6FMa1ml0PrMVWMeyMtxu1rz+C1/gcz/989yRhtaro+Ocdnm0OhGAiLG5UYoVVGFP/wyEQ51j/b6Sf9axER0rB8ZFfCVUtcFPDM4bjBVNH3h1fmb4Hn2orDrkEiKv/L1/wWdf+CJBIvPpFCswzeOYKtWVsCExRasnxKmjEx1Ek+gMdEBACAgx59kdyjXIboEGKdlU6t+W2jgq66mMo7I6h2vnqb3HW0Vxpr7maLbHz//DH+P5n/p5fJc4WOyBNTRWICbEW8Rnsi+OvfmCr7z8El3XcfXSZS1X7jp9phesZWC0dw9pvv6dpqQp5dw+byhrm53U7IAc5IY2KQsVubpHJL9ajoc6oldIHMDQJRXKkayuJUYndKmZLceuoR/ySMMxRIBj0ZXhuAg2PwMlZyjQ5nxj+TeAmEQiaemdMBiicsr+VNnzfAuIutxTOcGQ19MJXlIWIi5L2Aq4asfOlUj0rGPjrNdOW+WPHfLCPaT5QNd7QEuMGV/jxUhI7wDkAdhJ4V/g+wzPbXjv7puKgc9EwPLa6HM7whxihk8Z06MfXddRVRXXr1/nU5/6FGRJVd3oXU5JZGMAmXmszWqMc5ikm1tpf+kyVO/QcshpPeX43n3msxmbjcL+s9mUZtsxqWu80ySuTWqwBKONdazRarQiIiR5E8350YQBY4u7h4hC1JpKyRtc3vTInAB1ShSGNzZg0ahUyWxaOy4jopgz2h/i9mrNPdfBIzOuvf9JfvruknfW+xzWcybGIg685B7fmfuh2LIaZWxCsrZEFzsqfwRkclp2vpW17/JurRCylinka1YwnJSRB6ToSwjWRpx3xBB49fQ2d7sTqueuc2U95RN3nuex6T5Hth4hDjbn6Qt8X/7YHpHAaBrP9HMqzzrrsE4gRfUtk6YKjLhMKxRsTu+JCTjRXLrk/LWIUHnL/dRxc3vMSWy48bXP8GrV8pnTWzwll7hcVyBKhLTZk0hlbAvqIiiyCHijjvHOHC/7H0rmE6som1iHsR7n1JrGpCiL2FGxr8nrJZ/jbrvhS8cvw9GC1czyiyevs54ZDr2j8lbz+aUCw+R0oHXcX6/we3Mu7x8pwTU3+Cnbw1sSbS8MpEYG3uY9fLzmy/o2uyp45VRD1cC/NfS/ao62y/K3xhJiNvSp7LgX7v6UyH78bxjs1dmc+4OPs87BmUmZI2mz+9v+TwgtZIJQeUNv38/8PWaDv+kVmfEHy/sLSUaNkjHk3Gq+lrwpxtTtnL9EpD1bNkf/dpy3fNPrefNrfdPxHRn2HcLf6GcZ/XfHrT93ftn53Jt9pzVlA9dxscYSuoD3nsuXL7NarVgtl1TeYRBIAUi5x4AZw0J6jvztYkzOCZq+l1IZPs3166YTcw91KHBiqd4oe/QgslTITgoYWIQKkUyQNHY00zQNpF9bVMJs5jdqBOpKGWjekAUVulHelULmxbGSTMUTyLCw4/56g8w8l595jL1rC1ZhjzCp0AYhHuMMHpDMqC9R8LiUExKJjhgb6krlcK0p4MtQyWJ27j+z9K2SxmzphS7SNwnSis2EWCVAxnYLDh79wDMchJbNZkXwE8xkkh1jn/9k8hal5NQBQxmhPrMIppAU1Sm3xiDG9fxbYwRrKsQIyWgZX8laGTL8LVr2ljKXRsmCLZvYcj9ueO93fCN3jmY0oWXdbrmymGAywczlHusmlwSWaxvXxNjs6O1O92y4zcBRGCalOibGFkhberver9piPK1lHRteXJ5w7YPP0ixq7oYNi9UJB/MjrOS2y+XKnGNe1Ww2Wzah4/JkwtFinxAj1vl+LxwHBw8k3Pb7Zr9AdvaFHumT0f4+uv+yDodTjefVQw12nzseakOfks1Rkid05HyLLi5nB0LZ2WOAbC7+3W7pXP5pRAjpI2Zz1mnI7zVGCW/JDM4vg08pQOwa9V5xGU4qk89QGmecP/Foxp67n/xzfl/JP5WoHgAPJlmtmR/X7KdEyhyEcj5rHa5SjXnrC0RqR5DXxcfFY1Le/+bOk26i4/fv3t/OeaXUypqd95377gscpd1qgNJIRHpSW8qiRwZV/5v4Cfv7++zv7/Piiy/y7LPPAon794+zfU+jnKxuOsra16oNjMFWntAGUtSIP6Wk0rb37/Lcc8/x+uuvM609hwfXuHPnDnuLGaSExNx8yVoMrRrjlFnhAsZGsBYxExIGiUrItCYjDRQCXka7UpEhAiGTNZNV1TdxYFV+uLIOwRLFau4/z70iVVvSTgbLAYbp4oDHDo6YVDVpa6lsxNgK8FTWYm1ADIOACwYjFok2IyHaBdBYQ+g2OFfp3M3jq86f9ExvcY4uJpxNWLyWuiXB5FpoMdI7MslYuijMZgueTguMQDhQgZ16q8Qyk8VynOSe5SYbp7J485IUEbCpZ++XPaaX8CXruIvenzNaR1bugRxYe5PTDC6T8TLbvbaO0AX2Xc3CeZ5KC/jIE8TTlqlT+Jwg2DprSBqDNwYvVrkWPqeLen6CG+rdywoUnYManWdv0TjExMzSz85pdh6SxIwIFSJncbAtxjsWseadckD6lo+Q2oTrAgvjiQKh7ZhMKkU3xeC9omPPf+VFkrcs9vZYzOaElJQMmjtQvnmgNb6X8pzBlH702WCXKiHte5/Hm9yXbnT+Mb+q+OpvGec9ZMdDbegPDq8xcYGuazFWW306F5HkSMnqRsYuBPPLVT66KG/0ZudSxGjXKxTUo9cSnFzKZAo5a6gBOPfdZ/5+4DWOf8iQteSocMi9J20Ok6OBFB0hdnnjToylQF097XP6Lkc6phfVeRtR8tnre5PcW//vHVjjwcfbfYq9k8MFz96MSD8GQow7zoMYFSaJMeK953f/7t/Nn/zT/yXf+Z3fyYc+/DVcu/4Y2+0a7z0pSL/5GwyENaHrdGNJUUs8JWEksd1sSClRPWb5+q/5Gjabhvc+854+op9Opxwf31V2c0YZdPuLEEWj5FwqVHmv8LXpUItie50Xhzb/SbmlrN6npUsxO3a6x3ex1SqjlA2jQCARUkeMXd8FbthgpWeGS4rEnMc1IqT1lkUyNNMJLS0uBbxMiDhsCplsWiJwwVDlOnSDMw7rYNt22KRo126KScsHYxItn0MZ8KWtfRQhREGIw5wS1TTYWoMEQ2vVCXCdKtB5V9PmXg+al1WimRghuczNFk0jaL26zqMYdP2aWJxq9QZiTH3DKJFEImYS4aDTkSQRCJrC2zS4jCa1BjpUGS5ZjfYXpkJWEakmFHwwkohAJY5KvJLxvKdBmEkhDKq4l/ZGMFm5cODVaM16fpqiVUcSg15rDPrMU8IkLU8+KxI2fibeWKamUic5V+l0JFZJicBioPYTqmpCFOHzX/gSP/ADP8Cv/y2/mcduPAqpVCrFjD4MyFX5Dv27lGzma8mk4wdtjEM3R7Pr6Jw5yvlzGzREhCDpgnc+vMdDbegNPrdIdWy7DjJQVfojd22krmGIpfU4nxceQTr970aO6w6U89YyiWd/u8sH0CO2jfYzNxcYoAvvdffvB77votzTCIHAmF58AxxiElYi1jskZSOfUZIidDFm1p/9rgdfg5x57cGfPUfCextW/GxuFC6G+Ir05fit5+G/LASTBjfLGJUjLRyHruuYTqf8H//D7+PH//k/55WXX+Da9auAUDvtQhdDyKpiCXGRruvQ3DeQhLZrMJLyazofYkJTIllJrKiJGaP9vQs5zEsmcHmrDpifYo1lGi02rhBOqKoZs9kj2p/eeJytczmXoa4czmlkabOzplwDjdIKGpFipOu2pBQgRO0mGDP7PhUiGkMe1BjoKowT8OqOJIk4FthalEeAx9b71GaoIii16j4r02lZUwdGuOyv5cBumPVl/aUUiSkSQ0BSpy2Ds2GKMRC7lpg6JbsmrQePlXC5c0SjCQKMUBlL48B1YHyF8xbnbS6Dzc5HKRs0XlUFjcG5Cmu8oi1ylgiazu0tSZJ2hQxtLj9N+V4DMQRaEzAiuCiYEImhI9alekYgRtpKqKTWIB2wCeKkwvkpFQrbJ2+ZmaJDkJHBzFMIUUvGlBeUDVnXoKXA2TmJga7Zsm3WtG3pptcR2w2b7ZoQunz9sSephqweqhAlej+VoWpV+IbKETBITNSuwoi29226lm/7dd/B0eFl5ZhYg7euV+LUiHq8X+scGDhTOs69SFDBSfPecVGq8yykf9F+MdZBGQcIXw3HQ23oQwzEpHk06/JmFQVnpc/vgewY7bPHMFEedDzYtF7E3tdvpJd1Lf8uvmgxgqELGYkflY/Jm3/fWx4yNvL5hGdsWv8VOVFYoP0icFKcmmGRXHQ9ednk6PuifNZ5J+BiZ+vCBXXWgJvzv9ix1W/iKJWIuPzcp08yPicZlu2IvQ4ADI1txn82my21N3zk6z6k0Yc1kBIhhNxUZkvTaPQjRmjbVqOl/J4K3Qy7rlGxFBJt1/TXWp5B17W0bZcjTIV1fbLqjDktVTPGI8lT2YoU75PiMfgaO73GdDrF4LC2VqfOWurKA5EutD18niQSU2BS6/slqc5ESh3a2RxiUkQi5br/UkrWO6jG4F3dzzvNOTcc+Ut0tfZ3r5zHTipc1o933qvkqLF4l1Uco35HXauzE0JWLMvkuBRHjgUgJFLsCLFTBySpBHUMgRRD7s6ofe2TFSZBU2RiREVqLPggtNbiXIXLqm0GJd45O2rSZKzm621JeuSSQ6fbp6TS7jkiIapugGjOXZJ2auu6lhA7YihGvqMLgRDVqUoxqoiW0Rr3lFMjYi02Ca1oiaHLkHwAsLX2KchGMEikdnVPmi1liypfnAZ9EBFMRov6vDQgOZrXUkWV8TYSdI6KwCjStlYrPsueQWWZOUfrDcZD5SvqegoJXFVh6hpnLLV1TOdzpvMZbdtoGtFqJ8zSKKqgir2zVAQE+3VeArRhT9GU5bAnK+L0SyfU/XKQyofheKgNvSTYbDZZurHOGwFkt67/+UFkjv7nnDsbnfkCIsjbonTtHEbQXH1ZEKMzaEMFRhZrZKR3wk8GbZm3NQnNuZ8LIj6ONnqv3wz/NnaIUIZrPWt1xy/tGtg3H+fhgxfXto8v+0Gvn32b2dn8L7qO8b+Lx14imSFXLyO4VfqNsXj55X0awQQuXT7EYIghYkRoOyUyzropXdfmTV87FUqKxJQIXSCFjhgDTdtkeDQQQ9OzywU1GiHUhGlQwZeCSEg2OlkpTxspQe0tKc5IySjjPAFRSKkDR0/SiqICSm2bSaAa67JtNizm+xictrTtApgAEoiiAispRkLs6Lo2e3e6zpLkWuyqImaHqbKOzrY0viU4VYOrnEr12lRBToc454kI3qloVAwKcU+nU3xl2W6a/GzVEI8NvbK31QEJQZGx0l9CUkSS9naPqfSfSJD/Epv/OJhFQ2tUP0ARrAqDqgKqtGz2aDKioGqE+lqSRO1zf9k+3STETpv+xHEQkFRYKUZFGmJUZyTGSAqtGlbR6xUSVW/CLDiHSxBMpjZai3GeaA1iApWrNbIVIRKJ3mitu0tIrsCwuvh7dA9jegfB2NK8R/twlOZcfbpG1PCTUxGSZZKtM3ke5DFyFpvjimQszlfaXwSPn9QqsGMdtXVMFnPWbUPsAiEo30GCOsa+yr0cssyzIlznU4VDmq88gjwvCxrQowAXBwIX7RFfrUYeHnJDD4bT0xOa7QmXrj4BgPNalytCrn0dG5/zZzgflT8gOpRxZHkWWjrrFAwGpOTp+wUBIFZrnUk8yH3o+7uMLvxBcavJNvRsFurc4qBA+Zaz6lWliYo6SGeN/EXR+QiveBNApPhdmFJmpy+YfgMdyqAeeI6xQzbCXx5k0M9G4mMDrs2H0rnPFsMO9Llx53Y3QxHYhsT901OcsfgC8XqvOd66xk9qJCWiBCajaFhEI7si3pSyATWpy5NVmyqJaGOluq4JXZMdMEsnQunH4myVc6/CbDHFmhqkQmJCYgdWm5DYnJe1WeM7RQhBI3YhIjaxXC6ZTedIssSQ6NoWTKQLW4gFdg603ZbtdqMwfko5olZo31pPl3Oac+c4cVO89UxRHoGx2hHOmlqRrgQm50F9anWL7lpSVMeIuiKuVoROy9d8VTPIIps+jyoidKHR68gGlGzYUywGKwFddtKEZNX5ttZAVUMAXE1yDoySe613UNT0itNr1Oj6eoavJrRtxEYyoqEpLmcNXRjgZWMHtEykzk2kcmVAyTN3eS5kdc5Nt1U9i1zfrsw9y6SqczWhw7sKO52SMMz8hMoV7oxALrlzvUzusHcY6OVxrfHDSipRvkiO6IuTFDTlEJXT0bVdbpDV6hinktXW8wdJzDqITgWFrPE4P8FVHp/3DAFWqzWm8uqA5HLLFDq6IKybLSZ3lKsqnyWo6z69ZU0ptRxV05SyQempdsNuckGQdL7q6gxa9ACk9mE+HmpDH+ua7qSlWZ3QHHZUlSMGZY8aB85Mzzy88snycIcorjfIkktndEmOnIPzRqU0AdF/jyaQaGTWpkiNkngSpZbZ4mSfdbhHlFbhQKO5R29KByVyOcvgmY69lHEOvCxjg2SwdcAeFFolq4+RnYHc5kPyvYqqu/U9q3OE33+g/xbbK4PppjtwDgb1edOnJ3Z8gwKp9QjDrtrdUAVx1mHKaMPYc8/RiZKaBuN91qCXv8fP66JzgzpKxo3Uw7Kca+/0yKAIN68mGnllzF9SjjLzz1KeT0QxxxT1jyRS0IhYYeVi/LvRtUoP37Tr3WuM2TmyxmNtoxuf84gI3iuikIN5jfCckvJ6VVdrsJUqM8KsP/f+0Q19gjn/rsZSc/Ih3+cQJUVVZcs6AlZ0lUSj8wNTGphUgzNibC+w5N1kVKe8Sw4biFaqzz7oP0jvdJ19fhLKNatBV2OvioIxdH1HyZRCjvrPVJb4GuMGcSNrPYLKyGoZqe+je33+ps/BjZ1FJSfuVlyIdEiK+b7ccG8lxZBTN5I5HZpuyHn8qCkH6dMkWkZYJHmt9argNop0C5yu/8qs+9wUSL9Xx4Wo+5HEtizNkfNBfm/q15dWHRhIui86m5n9KRLJ2vgoqXhqPEwz6mBcFiFSpKQobg68DDeKY1QmWYyuZQoiFju6VdPPIec93lfUtfaf8Aa97pSAaQ6sCuk0gdmCcarRP5o7vU3obzl3achj/tV4PNSG3hq4cuUKaVbR5vymtZaQNOrAvXme5kGeWy7QuTBqHxbW+BO7RLti7EIIeK/lLVnZR/WsrMn5L8FnElbsOhItzuf64SSQDN5AMiODRY7CR5F0viIcw2Y5eOs92qp2BDUI0htz3RSkqMWZkbAKgcHBEYq8JKV8Jxvoc6rQRjgzPAxbzlsfZZMvR6mlhvFGr6VU1gjGjcmSBTk4i2+Mx2r0797YF3GW/DlbxnFY/GrAh811PH/OvhaiIBJzaZISw+LIqBdiVohbjMRz59EbH79W1A3dqOnKoPGuSEAWpSmErEJ6s4NgSbmn0rWrd+Xc0LAkGdFyKOd7R4oYlNhoHSZps5tIJGVDoL6dGgRMiVxz9JUb9LS0mGh6Z9KYi9dmEf0ZO29j51b/Npg0GH8pQkZJMjSeiCVvTtcjKP1hDVYiJqoxN8HhsqhPVVVI73gMc6v0HthFzQZuSz9HjOllrJGinTByho3DG60OSDa3qbUe76rsUA35dEAV9EZCVaodoCS2ARlTlCRlzgG9Gqj+Xnvbl3ECfzZwoQTGaWfcpQuEpPO2EAhj7EA0JZWyU1iSfcYUzY1BjdO6USvg4tyh18/IwStOn8P0zqXqCzBam3leJKEzBozPqYoOmzwS1Vk2xmFZYKVCZZjVMd/ZEfoS4zN75lfh8VAb+hgiXVCjXk9roiSM9VgipSnImwDCb/KbISK9GB7ezfGeTQns/NMazWflTUIkIgbW200f2aWQlH2qbTLUM82NZ7pYqglGIP9FsJIM1aQjkGr0X/p0QDE+BVLWix68BlskRLFYUVlMK7mmOJ+wj/eN9qJ+EPTej6V5wHXvXOGwaZ8b0x1QI2vlizn3fMo5io57GrC7/vvHG9lw/mI08jmS9KiKlGdiDDHFXLEwQg8Ymnr0/bTjYNQLcpRy3/T+NbTsrZ9LZdrJcB/F+cLZTKAqRn4YC7JSm0XrtguqwOjPsBGbTBQrz2800P24lHmR/51ybjbPmSTapzyJjpHNjXaMSRiTS1pt1zsjJWKXkTEszsvFs2F3Hkt5MGb8muQNnH5iF7Gcwg4v4yeFQDCaTGVr146uCWO6/mpKZF/GV5XtihHfnekDGtUP4e5cFXVJyxoo6bESPYqE/jqNFERnjD6iexmKDJjdhXDm8enzqTJZU+s11LF3fcCTR07OjAmjdVEcWwwmO066BHK6JnMJCmJSRAYMgA29Q4IdCSTlOVAU5wxFfbDfHCiB2tATQ39Tzt7Pm+Ht+bvAmIBjQtNuaLcrrBG8r5FORrFemSdn94zEmdDkq+54qA29AdoukNqO+YFn2TS4soHkPOjZOvrzZwBGEL2UWdTbh7ORhL7rvANw/szWmv7nYQEJvvK0QRm4utYczg65LkxWrSpR1xnYvhidwUgP0aRaYTNscv31DUZ/fK3l3yJFNlfRBJMjDkuB5kTbgBqTF/CI0NdDBuyeeDzEaCT5wGMcgcsuMaY/1dhYk3ZuZOcZ5ZvSiCDtjFFvzM6ffOc6enSj/x6FbrWfj8lTpBhibcciZTANmNRlFCP1z9XYvInbspEbxFWZjKffa4eJ2P9lYCTnabE7aRVy1JyNucio4xo5uhaQElGxkwop/yiBjYhBtealnw/6hoTJpXUF1tUGjRYnef30m3ZeQ6Z3L3oDVVZE+ffucxibeGFsREuDH8ZjX9JNxXjm57HjwAqQNLG1s44wvRKdPjDR5yOoUzK6TlukZfP19CM2up/xXjL8PPLczCitBewQfoofIEbV6MqzNMMv9TGNnmvvGErvRAvqjFmnqYeSZ5Cz34cQU7GW5bP05M/escp/hxjVcZY0IDy5i10vFJTFdITRfBrP0d7Ylyee8rUPqdLywWF9jtZsGVlT1t3u760IrQmcnNzF2cR8NkHIsrrjxbR7WcNUhTMO1FeX4X+oDb0zljYE2qbFoBGGLRsyEFPc6dx09jAZziqT66LjPLx/8ftGNii/wE5eMeX40mKo65o2KLFl8JYVChMGslEKgdpqY49yDaUfvOmFdqT/uSf3jfaCncisbKaZqNRHa3J+kRllTJEQrCSSGYRyJJNl+s2r5yoMKYxzo5Q3g/7iSnSTfx4GL786qk8cuUv9vejqHEGMaXyfo3y9xGGhw5mHNL5W6Z2EYZs2vWPQmyRLjiPzZmNU9jTGIQqz1mjL5JHxL8piPftjJNFs0tgRGzlvZ/ankhsv9dtlTIxoxJnyPVhsnyqSNDx3zTUrVDyoQuh3ihkMmCTTl4whkkVMIiYNxjKimQVJVl2p4vMxrJmhesPkoRtFZeOx731rnR/WmjOfH5Ely/xKCs8qMjI6n2TujAzjhqCIxMiHNwZC0i5zvWmyanhUNW6A5DV4SH354xhp6VMioyjTGkOSQazKmIE41hvv3PTKojC7MRnfkAFR66PY3tLvrq9eo6E/b64UyK10ez8hpx7Gz8WnEgDphQ9rJs9X9OdU0DUBkgWjMLg6/8N5CyxeCKWmGOfiBI7Wr35GXzOQuT/kdMXwPItsb0GZdq8zQelrkBIuWdZdYLk+ZX8xxXtLlC3WTfuU1Ph+dw7DudceZA8e1uOhNvTaijPRdh2b9Sn1dE9LaJLpy5Euyn2WRyj9BBy9lif4Rfn7MmF1jV+kmT/822LwzlMERvSkBmOFqvJsthuWq3vsr+7SbE+JqQEiIZEZwxpBaUxZFMKyE5MYjHteSBGY5DafO/lCEUaIe/Z+BzKcMYNAxDjqMgac9zuRiLVKmjEUaE6jxEk17Tfwkofry5NGg1fyc2MS1llJXdUGH0n4jkv+RshFRIlnKWn9cRyxrGMmZokIQZqB+Z4GuL1EfeVPzOMqRW2rKKSVGumUIc/UqIJeJgFp0w+T6+elf/YdYy5eykzmQhwsaZM0sJwLoayQylIW3hF96mIsMZV+3VkjrY9eB4fOGLJEQi4tGtVQ6+M1eY2UOa7Noco9JhF1WlKi6VbE1GkVQdcQQ6dtTo3NbXdV8CeJ7+d/ktSPw/B8bR/nFuM1jqrKfMOoIQtBx2K0PDPKRB/9pSSY6NQYlIlsdLxjCr0ji4i23s0plTRyjlOMYMYsfmW121rJYv2iMQaH4Kym1kLmXCiJzA5tdgWM1X7nPQEvz+fhXk3+Ts3VWxN7J2l3r9md986WMRv4EGPhJV03FutmveNlM/Gtnmi9fWnpqtdVWOw9gy87NdUQ/KDXHktEn4a106/h7AynXKniTZEGzuJbPkP0ua21y8qRNmsUWFs6DNo+2vcm04fSsO8M91nmFMSQqJxlWhnq2PHKvWOefvppDvfm1M4QUgLbonTk8oAeHNB9NR8PtaEPnSqRNc2Wu3ducfDoFBPVGCGGEFqqqn7TcxSDMo4oHxS1n/3cYKB2DWv/HmuJUcVUXA+C69vX2y0vvPJ5OtNwuHdACmumE8/h4oDaTXHGU1lwBkKdG2GQm6AYt9MqtmiednFw76VfHGdkeEWJYinnXdUISs9cL+8RERox2VDoxh9Tyn8LqdN2wFGETVrm86XeWMWkZVkhDsY3kBu3ZKnX0HW0XUfTbOjaNhvoUtIVaYIKx4TQEbpAFzratkNiRLqAZI5DCCqQEkPIBCF9TVIiRItytLIC2Og9klXLlGjU5mikCIxo564QUy41ympcTcqRdXYQYtDmN0E3/wzc6LMusDKok2dUljm/kAOc/HxGhMsB4RiiLiVoPij1McTnPev4TY6zs/sMqju6jEKUyryJ/vUckeZ+bIa4g9D08rg716clnQ88zAClGMmR2uicJRItF9mjOuNTjBzB0U1wHm674Oh/bR/4/sE/uSjPO3y+OCPDjnLBfRuX94tCFLvg2s+c902PvA0o92KEDo23KBl+REbogpw50YBxwFDVmIOVARG56Br0K8354Rsenf4z759jRNVa1VgQseokpwQyoEi9M2g1OAkRfG24fm3Ob/jYMzRuxrPv+/3M53vETYOhxpnmzWbd/98cD7Whf/T6Ddb7ltdkRbde4pxOWonKoL6op/B4AZajwEfl5/694yjpjCNQoKfxKhkvUkGhwRCCbpgmg2k52q0mNbdee4mTe3fZ3zvC2IhUwskqsmmEIAbjHW3skK0q/qWYsiTpKEJMMRvPwg9XI5WSEIJ+fzFYRX1s23T5tZjf0+UoqhirbMS2TUYWNAQsiCWx5PH0P+eVq4axK06UwtjD2O2mFAQlT+kmVWC8Xskrn69E5L0dLN9TEBiGjai8Yfc66N/DeB4YGYz06AEKkgOADCoLRBnvWAMU2nMDBmt13li8qQ8pF/wc+w+lB39wmFuj+9792jKqZxGos7u1jP6AIY7+Rb+7p3Ed+PijD7w/gbGDc+46dq+753LIzl95fgyfO7uSLzSSw0R908OgkHmB/Xd+ZygcuXNXf/6IO9Ogn25nL0vCyDCWt77Jme3oui48oY6wHVyRs27QgKhgVIP/wi8a3qXXOfBHzOjc58d/5NReuMOW1+Xil1EnJeQSxeG5ycVnNQAOicLy7ikvf+krLB59kna7Iky8ChHhcESEogK6O0dMTo9JdnZMT2B56/nysB0PtaE31jCdTFksFqxDQxc6nDgMHoz0pLI30y0+T6rbjRbOEvHOkzTOeOKjVa41piWvXYxMQkTrdkPsWJ6esj5t8ZWwbtd87ouv8trrx5wsW8SpMhldNnJJ+gi0sL1FhJBi/q7dXNtY+rIv6YJBPESKUxD73Fq2miAotDla6MNGY/p/Z9P9dp8Yb2cRnXvXzgsXGafzIak5uz2YB29D/Znz/TijsawvkK3RvKuzBlzq65VVQ6UawYsauTpnESs5mMwEOLQUTmCA3inzKedwe3RWejSmdEirMoLjfCmxG5oPqVKezX0KbO9M2txt0OV2sgPcmu+11zMww+tGsFavx1lNUcSUwBpcVe+kgcoYGNzg35T7MwWa1nJAFWSi7z5nTam5d32utziYY2gYTE4vudE1mqw/ob/v+RhpXA0zWMVhiYtu+KZs7CZHwqZPEenzy73nGaWMJHMV8my31uxErmdTS2JSHiMzoOWj1RKTiuSY3kFU5zZmZ7s4roXkFtMgrlWUNm0pc80TR8s1Nxm1ijm/npEkoXdmC4ep15XP19xrATBwD1QRsculdbEX09FrTNmJV8Rv6FFfhvvM2sy+pSJbpn+hIEeaksk0vT7wkv7ayrzLFYPEqCS8eSW846rDXb3C1FuMdBjpiGxz98j+Ie0s/jGRUyT7saPn+dV0PNSGfrVeUXlhMp0Tt6cqoGF9zvWkLAKiE2nkx40mowyRmxnKOsrDH5b1xQ/9LFSvPw+lZoWlWjYr3chUAc1agxVPFyKb7QoRWC3v8eIXv8SXnr/J7TsbwAyCYLJzB3mj1DyXwuYGxvWsSBZKKY0fBicmxqF8rRg23+cb8+ZmDa6aAbpZ2Jwb000x5/fGmtSjHFo5uVgde5M3RVWuGqKnEmf6ymeiVklFwE7N7Tj/PyI/mbxpDPXNA3mprwpwZmCl588WTcDdqFY3ZGfAYXEG1RYnqRynVVKWdQHnTM6Jgvcu52WH3uXWuSIEPsovmlHDGjdEc5mBlS+/N/Q918E4XK7tts5Ref275IK9K3XKWhbmXQXO5NcN1gacC3jjcfYQ61oMDiseTGkRW4yajqmzWe/BBZQkKogFX9W9vkCZPM4Yco1afw+qT5/FfYzLqmYOK4OOhLXaIMZ7hzG5vlkExJMk9mI7vTF2bpizeX4WmL2gPYUDcba8TkvZ8mGHvLIGANLPM7Jz4ivfOxQ2z6+YkgrB5NljrSeJpo5KZ0fJa97gSSb2e4ct86uPIIWQkkb1xTFPqZfNlbEufa76iCE7KT0CMMwPRuu7DSeErCNQHBtrbc/J6EV0jB0IuWVfEMmfM71SZogdbdsSQqDrup0UGSlrTIhoOg0gDcTVgnr2MyY7Jja3QNbfayrSGNvzQ6LJhj6vh6FEVdeKWN2ZY9Dvrkxk3yXC3HPl8nUmFqJviK0KD9myI/ZB2FlXvzgRI/TgrVI9D9nxUBv6W7dvcXiwh3UzrBHq5AhuQrBbSK164DFiciSlz3nwiqUYUIEiC2uNbruDMQi6MY+iZT2N6f/ezZ2m4W9RLfHJdJqdDSXMqJgIuglawZiW00aJMbMZ7O15mnYfT0VnA6YvhdHWktYYJt5T11Ylf8WQQk0bW7wxVIA3wqQ2TKae0mc5Zs3t9WqLdWq41EBYfOWYTrWxhMsb83y2QIWwDFWlilRVVVN5z2RaUU88Ve3xfq48hEqNnHGqgiXOYV3uZ4/HdV7zcFaovP7tDBwd7OHEMZlMcd5hvBrFqq7UsHmPc5bKO7zLhsM5nHf4qqLyKtDhne9FZXzuvse06q/HuArjajAe7b/tNBrNioXAsAf0IIYMRoycAzVgKMak6zUQhry1OjFj5yyJ5JKn4SuGLaeI9SQKdDjaHvPZMrt9J6FOfrUgMahgjSvvS8AtMK+DOEz3HqS6BSyAw3wBRQhpBMWa8RXmcZCLoPdynXbnvfqbC7gCxmcHWteGUJEMJLMCabACLh0gLiLi81gqfJJMIhLYgfVjGqEi4Ev5VwqqTyCibPvU5U18iLpLNAv0KflCbA1GCEEbEjmnhLY2tLo2jOAAa6Zs22NCaJnUE7yf0qVISC2T6kBRMhIDz2HsVGanO7ekLdwWUFU+l41sad0KoiS30cj3qanRHHHAhBvMvccX5KePjIuAzdCZTcs9R48nOxLj40FtnncMYe6dcDZ1qU4V/Z6r5qbKegGDg70zf8ba3/1wjeZ43gc1kKuzw5/RzRRYnpxAiIRuQ3XvFY7v3lINCkpQJjtT1ZTHMk7xfRUeD7WhP1jsMZ1POD6+x4tf/iLv/OB1arOgxRCx+GSJREIa1eBSPPUSCZTa0VK+wcjz02PwJocJfpHHN0T4GbSznvligRltogqjJVzluLs+pVkuOYqW7/rNvw136TqkwAzYtxUTb2mdw8T8Xdbiasd0MWG6mFNN59hqkiNXiKZWZqvYzO3Km/54c06Jrtti3XjR6vi4ou+dj846RQX6xZjTIjlGySAkF5GNyrLJd523uYjB04Y4MG6Bpttyenr6/2PvX2Jt6bKzUPAb8xERa6299zn/K19OZ5LGFz8uroJroXKChBCy7JJQdXCjVA0wEi0rbQlMA4Fo8BBY0KFlaCHTspCQQJQMQjyKhwBzqfJVSQUWLz/4nc7/kfn/57H32mtFxJxzVGOMMeeMtfefdlLVuOfcG5nnP2evvVasiDlnzDHGN77xDdxcX2OIBxDGR86nTlYp7WdsIbY+OgEzChFoXQDOyhDP4HVB4vsqOUuK4hRqMpnyO2Plt7VARMrKl85prVe7mmvFFEkdCIORLys4THKUGYARKAW4hMmUbnOJHYRZunx8ndOkBu45cvkI0X8KS/kYEZ8FcQDzLRJ+GYP7L0jlDLgB5EaBfhnSdlXhVEFgnUTGeQaXtFnzl/dT4c/OiPZH7xDDATndw6MgeoLzBWvOGP3bCCCU9DHujv9vcHgH0/gEIV7BuQmleIGUSRxH7waQi83ow57RltYqpUXLObX0nV2PpT3a52WNe08YfICJNkkKxCNzEelWJ86HR4QPBeQyzqdbMN/LivTSa74pR5oJRkMo+nXtOgFpIqRlRUIjXjbDXIkw9bPOFoeuBYI+7nnFmtaHRkufi95Ve5TAaD/zpnDxwfse2ws/eY2YiWZRWby8P/KwclRRFezVQA15kMoak2gWZ13e4cOgulFBJX4TljQLPPW/H6+2oZ+XFTF5BO8w7AeU8wmzO4tkJEtZhych5bFCZhbBMwFl1V7bAIY4XcQgIqAhMptJIgutX36sKQqAzeK2xiTzPKvukuBQhRNAUo51PRf8T9/9/fih/8v/DePNp0FOuAUg8ZClFGoFKMhjzE0vGpqeKEhIeQZoRVFp09o5ix24RFNSFaicCD7edPfarjkp67wS1kiERmD5e1PU01Iz6EPG5hF3Owhf/NdxQeAkna3cINAhryh5hY9AgMPp7g4nPoKzRscVvjNMuycbFfXmxbmyDnB22EZiaTm7krZpKH8DispkMRZNsazfqKABM8HlRc5TMihnFQVWLQNojEuENQssbEg3ASLNzFKyVK/BU61KAHOFpW2dFc19OngUVhVCS6UQ6ToIEA/kHTi8AXaEwlfwbidGnK/AfCM9F8oBhRJAK4DWwEd4Ax6A5PwLAyWfwGWtzm+r6W55VQCdJoBEjs2pbjl1EMHFAQ4Er+kIJggKFUYpu+IFy/k7MIwHgAIIETUtUEsuW36XqsPZHVSXZUMmHDZlZHWN6nhb85dCrdtbi5ytZSzDudA+zwCoiCCS9Tjw8h1rSopFaFljHbPOYdVxKrlGFpuDPbX1YOu5M9R1cYMhoki9k9pdZodA9i4GG3fI1je2hpl14fd28lFnwN7/4B40XYlW6w8qiMRgbSkun1HBHZ1P5xWj0gtjux9iTa0R2AszXxyGBaUk5DJgTkmcQHjknHA63SHljOj9I9f3+D29rscrbehBrP2LPe6O9whEYO+kbCxloBS46NSzt8/0n0eDbTpRC66/NEZ1UflXqlHkN18gW5IOs+YBIQ+s9x53xxO++PQz+PznfxvGtz6HF7fPEZklKiQGuYTMwFBErIYVuoPCjISmgFZULIY4w/gJpRRRUPNq6Llt0sBcr7NujP09cZ9dLJshg24mbIQxEGr3qA6epA4lIZ0ry70RQeu2i3TSYgfnJIpnguZfu8naRO36t27cRpQyONAMi6tvfGTeNUq36/fkkDk1B+cSwquvqcEBlHzEqiGuzoKNmyNB403KU8+RtVMdqdJaKdLhDKyRDtuUWF12Y4iYitjl0X8HQQRxCpJEvw5KrANE7a6NpcV1JiTTAFMTQSEQWhmhDSJpHfR2TqrlU+PRINvt3wBRFHEfzijImCjqsycb+f7wOcAlFLN/JpxEosbG6nDJvarDpBdvY7WdbpaWtp3RBOQ5NLJiddxZesjl0gShRDGygJxDyYs6HQCQkVcjTeozl6S223nRvpB7YEUHmtPIFflpUPnWWDYjaY6BrfPeeLciRu72uGKrZfNekDrJupZxsZTY1qD+YIY+Xzw45hTYvy98p+Y0tDvpPi0BjHANOvRFNRysV0NWdIkYKoSl91UdC4XyTWeCxWFal4REBV4RmXVdvykR+7HjMQTudTheaUPvPSGXguP9Gc8/fgkfPApZL2iFRjvVMaBtohY5OXbVu9T9X49qUcCc5BfcooJLyPghMQ/IuWcP2y4uG9e6rhh3Vxj317ifZ7x4/lVEZIBGIXC5jBUOU0ooTnLKQnJipLwABHg/wFFUQZ2CUAjkEhjyx/kI8gcwBzX0DKvXbohrI9FdXr/UDGzrl6m7Z3OISMVDLPqqkQdn1NIvIhSNQnNJm4jb6tadSuu2bYLVzdg+dO1yuFbi1E2+S9G09144IvU124ZMVvbiqNF4l2sM9oVmWRoruzKlnUSt7R5109LNzRCmUqNpQlt3Woaou2hDMzplOG6RoURDth6NI6CkTBNIIQA86OfOkKaxQUfNSuV084Y4LK1RzYPRb2PRIx62GjpbggvnRKIzJ89TXuTXPlYkhQrBuwkrnysky125U1sV8p3ORI/0yjs/bWte+rnpoOrT6QRAu9V1GuvS5W6bm3bOq7iR8CjG6FBUsEdUJAVRymsBAgPG/YDsRaBOrU/TBPVadBCrgbF5r4a+PLieOias5Dc19KTP28Zw15nTtcMF0rCKuvndClS3vSB0r9XRr3Nv5zUKXiVAorp+dTIaugBFTUp1sqRCKsO5bEykzV5jHe0qfA+GNG/imlIrmVHKCkQCUFTD5JvrqFzerz2zXMo3e/srd7zShj44h9N5xjeevcCLb9wCfhC7YgvKWZMRk1Vsy7oyuZ3A4FUTvYY3smAJDoUXuCLQppy+N4jdgrcoQEvhcs4YhrgpXXNqWJkZCRmn5YS7Zx9hfvHrWEJBHG4QfAA5xhoG0HwChQhyI5ginANSvgUTI+cRjgatnz+DywiiGaAZjBWuHEA5AsVUuhKAM3wYtx6r7uiXTmzmUstcbBPyEAa0wOW20agin2vtOLlGi33ECcANFZqz8V+122CIEQSqqQbgAZ7QoHaLdjc5RurtSv2EbTY1fWJRKlexTXiEavaN72YRf2X8S9yphqltas77Bq1aADrspbpBeQhMBOeiatEHXV+WiqEaeeieBiOG2ZoqNXyi9h1ojoxzZlSyOH9lAVGAsZsLA84DKS/w8HBukPVozh+3cjBBQ1wd4+1h3cS4ogkOlkoyB8Tmo2wWVS4epcwAr5r7Bzgd4UxkpjBSPmFhlevVioSmpy73KkaM4FV7QZw9s6LAZgFBYOIQAtK6ipFRJOzls2dY1xVPnj7Fbppg6aicGUTSudEUEZ1zWJYF8+mMkjP80yt4t0deMpgTyBV4F4EsjY9kbZGiEAWWMrJ1BZbW1VlTUHVs+zJXoDqgzHbv9rvLyFPG3es8mA5F/T49iRD/MsBL5wRzHcc2zeL0OTd1M482n49EvPXa6lTUUEB+IiVXctGSw3YvNk85b+v2BSEqVbY751XIlqWA/KQVHbImMkulVc4JQEFaVwzxId/nk47HeAWvy/FKG/p1nTHtn+LJk7cx7W6QV0Z2YpydA5K2rZRuS7Kp2lPVWLe9V9uO+hCwA8oK4oimN/1YWZ1FGuZkUI3cqjHSTcPymGs6YS0n8OCRy4QJACdSIk1GCRHFXwOY5RpA8DmAMIFKAvEC5luAz3B0D54+B2AEeERhQuEA8jOYzrqBE8CEsi7b675Y1GRRvopqsJbSFM5YS9JW9VY25pBKqINWnZ26aVgOboYrHwEYEeMNgBGFHVIhhesdynqS+nXnm/MBejA5WhTV4Qvtuu1+mLNs6KWVjtVcps6WRFry78Jtbi9lefvN1J1DDZJtHXjfond7H05SMmlkI+tVbvlZvQKwLzXSsrr5eo5642LW+/lq19fnZAUN8L6AihNIlJxE8BQAdxYxqXpfEjv1GxzBCaLgRdWxpgY6VKehCSrJa2khnZU6XRfiIwUjgFnXzQCUAbkIp8YRwUH7OCCgFGXYM6NvhGL5cmZgNS10uhypzdeCiLBobTmjwLHM8f5qxJocChac5yytealFkLIuivDqvAeVBFBCQsLt+RYxZEjDV2H5c5rBmQCXtOujOW+5M/SEWuXAjMRJnWR779aAN4CqpQPbemx5eztSdYp0n9NpU5+pnbt+5JMNG3PBUu4feV2duO5zXC7XySMHORQi0U02t3LzfkVUVKWzh92LqXiWJDwnAFwSkAAiRsAIQ6aSCoqllC+8l29yabS9FvoWIf//tR+vtKE/ns5487NX+MIXr/DRb/2tuH/+DLtvewpeM0pmcHDAmpCz1OpK/fLFBBKphCwDZSvTKUV2QSPWcvGxC2OPfsOURRNCwDyfEUKs7ysZoEF/nz386oDCiP4ajgNO6wLwERF3yOcZ94c34YuDSEEe4VyAww6A0y8VAR5HT8CLCZN4cBFyWHEZQCNcOfZIfKpwMuHhg1ADABL2vn0WYOTCWNdZoxwhVQWn7Vd1Q6qR6GZwEoo28FhKgfOWc7Uog2BSBmuSzZC78/RGXM0LNgiNlvL0GyU7B5g8a7VTLXKunyVCXkWz3jknnzNGdqcBz6VIqWBNxcjnU6bNOeUahenvVdyGmZFZWojaRuxdQDrL3PiNY9E6gtlKdHAPNuO6mcPyuPJ6KhnRRVE75CJkNB9EUMolMfr66Afnt+ejosIqhFBFgHpD0Mm7muBONxpco30RP+mNisMtUHPXC9g5uRb2WJHBmMGuIGQTbuGKppSsBRfqFDrvtP2sUFKrK8Zcc/e2+FgFeCxfnlUXnpnhCoOz5HaFL4L6Pu6MpkDKQsibRg8uGet8hqUEAFZH2mHNs7g8n2BjzKgwMyg/3FfsPvr1dDn35oBdRvclq4MLQ+maiFDPUeCyDVg+6TvIbQ16/+9+jGva45u+D7DVIt/d3586Nd7VdfO402CvE0qatWoKKE5SUCIwRKIeWlIb52/mgHSHrZPf7PtfleOVNvT7cY+7uyM+fvEM33j/A3zus5/XCMDB+SgkHCSBS3VxWGpRyE8dROSoQoC26J0Sr6S5jEGzjywA2ppLMwyNgNNKopyTZhoxRJzLguN6hzXdY8aClM/K/B2R2GNeTqD8DYS4102VVYls1utQMR7NMecZ8C7BkZVEMdC13AQIVBYUkpyfUzh7Ayn2UJpjkPZ9lwhWojghE3nJURaHZDW0riPoEaSxUM21eRTsYOWFRCtAqoJGBKLYGPVcwGRCQwbHNyRGHvatAfLe1YfaNjZTomuf1s3L5p5aKsA4HYUZUEZ9F8SK8UIBJ1bDrYx4jVo9tUeJiOCKeC2sjVOIAOQEYsLpdEJKK5z3osudM9ZaNcCVyCjNbSSXGoe9ghzdZk2dc4HuRQAFZxCJY8UM5DLD0wjH0hY3cQbnjJkZKa0SKRVRwktJoM+iKajq6JhjqA6YBT2mCmglms55eB9VJ0LEUJwjZBXQYVJeRl5APDclOGYwOayd3izVJ9R0BOR6kABfWsS6TTKUjuQmML9jnQ8zzHXcuDkq+sx0svsV/cnJonECKRPfue0a4CJwfeH2WrfNNOeS23lrLr56SXZpXP+GGeTOqWiReKn306ejeixlk9bQ9xTKzdja2NtOYefqnpz+HKzrsx/D+u/eQalnaM91bU5EisiZY2Xv1X2N6qDZvtxF2/afpiaGBAI51bYAdL1mqARKvW75Djw4LlOw/7uh/1/R4Z3Hsiw43r1EzjNCYM2HGkvdSnK4Mmw3B9m6Z2XV64sgWD97B68LJUNaNPq6IfSPAl/8q75CUibVFo+20/UOS1lwnE84n2akdUV2GcSisJbhkDnC5YRkKlvM2jNb2dpVXAUAVhRO8E4MOFc98j5vboZdhEC2jNpm8Oq/jWHOgJGzJAdsm4rmdZ22C6XuIWaA4AUNUSyVAXhHSLljISv1xjuqOvIMVuZ9pz9Q58sMTj/cjFI00maF4dlSNjZHFvFT/W5zldpsAUZhag5GewOj1Nf5YnMsrlVtEJGkjNjaBsu3p7zAMWM9n7GmBB8C8phqAx5Zh3LXgPQgyDkp6iRRGhHqWrWytiqMQk0pbtEQOKuATE5nlASgMDJnad/bNfdpaQydW4I4HJo3tdbINa2jzxUxV4eD2JQYJfomUrVF78QIhgHeD/AqbOQ9idpclXF1ADsQSw8Ha/gixHvfGQQzcl21gq1vXQOSb2cxFJoPtyoQM0iXM2/zWY1MNVBdaqd+RCiPrDcu78n1M9R9jzlIlnLoX7cOj3VPufi7zYciOgxxkHVPqcQ0u4fSiMHWsZC5f45t3WqrZHMwzGEAgGKywkXDbDmvPW8V6djOBqxrYbv23l2i2oa2BUbUnBqW596R1zFuzxajjZOdh5yVgxYQFXjKVU2vaJ19cB5wHlRLV7cGvQ9w+ke9Nvl7jY5X2tDbQzl4h/3NgMFngDwKsUy6rBCpP3YmF8t1Q7BlZA9Qk64Qh6BwApOonsmmk0CYavcyoH/E9JqUaWvrklR8RPg/YloYIq25phWn+Yz5NCOfV/BY4FKWiIFITWBAXnUj1vNyKSKzapsckebICNnLpmkRaiX/6cU6J7lb770+tGIorISIVQ/fV6dIuQwaTeaSEVyLrkthhLgHI6shBAARXnHOA2hjRSBwGJByb0hF37u4zohTU+TibgPaEu/azwBAOT34fT8//cPcpwPMYPd67Zv31HmFjj/XF9trJH0Bus8WrZm3vDuz9UnXTVrPvS4LUpac46aLKFpnPYCR81rn2lWVNDGC1i3Oriln6QyY04qUFqR1QV5mpDRXolpfSeC9SPiaVC2RF6VBLQOUDoS5Ep/sKCUDWcrRUhHHgRjIecXpdBZlOFE9xbosAEV4H+DDgBhHxGFEjCOcGn/vA+CEFilIhjgfxrwvOW/msxqCIs+dzYWYCFk3BNKUnDq/xDD9+M064gu4XxfPJhpucpr67NBmDV0S7h6Drqth09cbiCbGuKIB+h6Ctol1oTo6hKZ/UGrrYp17LVNr8tt2vbbPmZMhjW3kggqodPfPzdAziUNRDb1+N9CYJvZfK0u0VsC9o6S7dR3Ky2fYUnjOSaAjV9WcEU9WHkhIKHAhiFOIDKICuAUZIgctZawZ0R103KycGM1R2xh3ua4LocDX6nilDX3hgqvpGvQW4fzpNxGjw1K8PPm6k1pfac7mGSt7Wie1L/+QN+rrRCpFqefgflm0BVs/Zs8It4VvnzBYub4vy29TWpHXMxytmM+38v4VIlBDwlmJvlUMGDqQ8ooQlPmujXNCCLA2q/11+BqFtc/DGNrq/YcQkHPBMEQY9G2DxrAozRwIxor2HUSEkoShXDVu9L2FnJYkSY26jwHLquVdyqomAkpOyLSASIWNLga3h9I2JWu9of8kqO3CW7/8bDX0lPqP1Nl76Fx8893A3p+zICoSiEv5j5VI+mGER4Ejxnw6yu/rdaGuORdGIGs0lcToB+dVBUwEdOZ1xv3pDsf7lzgfX2A9vsC6ruA8wzmHcRix219hf/MG3nrrMxinPaZpj2EYqzqcwe0MUr14zbeSbM+t/NF1Y8OwNFeo4aKJUzHm+dRKp5gxLzPOH/06lvM9zucT7u++jm8cX+K8iACRc17kX+OIw5PP4vrqDezGa2nZDOGoZF6QyoJcpPui1V07Ep35zAWckpD70Akp6Zo0o9qnd6qvbHd1sfg2PxtEz6jG8HIdQQ1j/5pVZNgc90ch1vbPZji7Jlhsz5JHmVcYQVDWl0HvLfrPJaG5QuJY5pQQvBNH0b6zFGRkvaZOiOeBA00CZBp6g+0zuHGC9HULoMwJSylhHMfaxdNRQClS4x6CzZ/wlMCMggWFGIXEyDvnpB4eJtGk3Ifs4bKU45IHMicQJV0LwqXxhwGxMJZMTUeBhLBqjH6ZT+GlZLl5Rfo+YT95RY9X3tCDhOD27OMj/vPdf8IXvvdL8FEMc9boxZpXWCxT/8sdLKQLtod7NbQCUwTzFiIzKI42r249eft5m0+W34UQkU9nnO7ucXzxAoDDfDojmhYEFBpcRTgnd3lncoxlmXUDQfX2nQt1A7tkjtsizznDRy8lQP11F8b9fXMIyDFCVmGRICz4UgDS72rNZEg0yKltEhaZe289t5XBTBlMorZVkl2nplZIopa+Xri7OkUVGtNeYO6HRvdy7Ldzok5C3dkf5uK2P1/A9xfn/6RD8teEnFlIhgSQqh2e5hPAXv6A4LWcXRoNNWeM04JhkKYvCA4rOyAXvLx9jruXH+N0fI51vgOQsBt22A17vHF4iqtPfw5x3GN3eIo4HVQqFtKcZD2B84q7+YR8fInCouneGsGgOoveO9FQqBHg4wQlkiBL0yV99TO0H0GsZNThrS9iHwKi9wKrMiMtM+bzHc7zEct8QlpnHD94H++998s4pxmZgDDusL9+G9fXb2G/u8YwTBi8B68J83IElyNICXZAQMmEzE6f+AKmXKWKZW6EyGpljDrVdX180lxzNxbykU5C176LWdI2miZwuHAE0EX1vE3/FYWdt99NyFmU9qpcN6sGfqewKLa61Py6fW9OCRSCGFpzltnGpEeCzPlp8yeIkVxPzg2tsOe+BQRCFrXnzPmWQooxYp5XxChdHokZwxDgvayxurY6js2olSpMWrk0sZBV2ZwUhgu+du9jKFPfMZ7ub/DsxR2e39/jPNzhC/Q2ziWLm1LXb9Ma2CAw9b63e/rrcLzShl60jjPGMeDzn/9tuPIJRAtSES84OIfsVukspope4q11p6j/7Q1VD7El8d4vIjwDP2Wd9MuidydQje5mkyAP74V9fj6fcTreIcSA+W7GmlXRzjkUJnCe62JmZuSUqstif2Us6rCsDVazVEUH/0KhzXJ+GBEHH5DSAoKSqxywmIHMLZqXlIHTnJlh2OIYGNuXGVXpyiI8ZgbOyvx3QaIVZ+QtJ9EYUoXGzeOubHoQDAJwF9e+QUtwGZnUmQB19dWkoTOBUBQq34L1qL/vD6aH7PfL6wAAUqKWGAMn3QUhThk5gCmJguNZjWXJICeNecY4IHnC/fkex+d3OB1fIN19hFwSfBiw213hzadv4nD9nbh+8hbCMErHsgqdJ7y8e4b84uuiQMdFqlC4uaabu+JSN2FBSlnzmo3ISaC6A/YOqzhfSZ0umaNqUNaEsiQsOAHMyM+fwUT/bP05FxS632N3eALnIt763Hfi2zMjLWfMpyPu7p7j9sXX8fFX/zM+zAnkHOIwYf/GZ3G1v0b0g0TDJYOwIqUZqbB0z1OdegJaFN9PsK4NsBnr7bPcP991XVXUqUXJlt+/7J0hv0MHjbfvYEDRsIbmpCzrAuB6eWtaQBQbQmA7GHUOQXfZ7TWp9CCrDuicDV/CJr9vssvesz5nhoIEeFUf7XbJWrVQeTEpCUkVwJqTKtppp0w43N+fkZiRWO6Z2Vput4qJnKXyZVlFmIiJsC4rUpaAhkgLpJzW1if5XWJRt0xlhlsSfvXdD7CGiP/z//UPYLfb43Z90eZevG6YWI85ftnIt3VJNITvdThebUPvrOQr47xIX2LSFp+FoXCNkZg+qTYa+tQqYENaygTLKWUQhY2etxz06EMGoC7++j0VGlOSkiaDyDvM6xnf+Ph9fPbzX0QMAYUXZM7SYlebbBSFUl2Noi1C6G9B+ohbo45q5DRiNrKNdx4lq8esPaUlR58RNcoSo0jCBAfAKg8rzSJE675G0wTlMaC1+9Sx8c6BvFPEUXTfjTgmI05IZPoGBosTLC7s29TqhFfj30fjn8SSfRCtX+zxNW0jlvbRzzx8zdTtHx4bp4AUDXGyBq09MRdjCmsOmQiOItiL8tp5OWJ+9iHW5R7IAq8OzuP6jc/Ch4hhmBCHCSEO8DEi5RXn+0Xh3wxk6RleUmcwuBkyAUY0EurWqN1iNWyOYAzm/jC4dktk6g1iqePas+mBjktQHSiCoxWOFoForZxvjlpXT3Ah4urmLUzTFd54csKyzljXGXmdkW6f4+PnH0mzpzhgGEcM406jQcbKGVjThqBWH1ZufJB2X7ydWYu4u7/1ze2Z4jYmhEZIK5Vbor9jcwY6GB8WweuY6phZaaGNkqDawtEo4Hbu6py09Wdj3jvJACGETp+BCB4OhV27N/2eXBg5SSOwnDOWnJFzQVpFiCaljHWV6P48n7VCQySsnz37GD5GeOdxns84Ho9484038fWPPsJ+t8e8LDidT3j65AmeP3+Oq6uriibtDweAGfM8w0d1/lPB8f4I7yPUqwGItYMlYTnfg+EAF8G5IPGMax/w3979AGV3wOHp2xiGHYBbeIca+DgChjhizZaaJO0JkesQeWpozetwvNKGnlUAJaWM5y/vEIcV02es/3JGzpqrQzMGtSGLnUONvD3wbU+XXA6DQS5KhIOHW3yD8VD/5ot/2V5rcJ397YLHkhd87f13cbsk7MjBu1INWWZp48q5RbgNFbBNFhrNE4RoZI6AXJuvfbzlWpwr8Ag17wowHAvRyWtTDuMgWH/1oqIe3om+OKlsqthIh0QFUO3xes+lgGvNtA1uAbOvnvRmjC0/TQ4Ca6sqmKUS5IZFQ029chuDTZT/SKQvfzXyWT9fdg62AOrSOXjwmd+ENCYRrHsdHGktspTLgUw2lXTsVnBKSDlhXc6Yz/dYzndwKNjFCbtxh93hGtPNOwghwivRKuUV83LGPJ+xpLXen7QLJZs9dVz10MhWhINUfMZEnCoqoo5eVdrrBwIPjR5J6Vuzn80BYIOyuc2B2D5ZtXINRdAcLrDufeWlECNDjIhxxDDuMe5vcHV4A6VkpHXBfD7i9PLreP7iGeZlAc/AeY7wcYdhmKrKoqxv6poedRG1lnpV8hsDGyHYzoj3KFqN3jm3hjZAl+awu5Nxag6m5oO5ae8Dq7xmgnCaIqtjVce345BUh4Q0XWFTREIUtjnQWy65SNMmjWILFyx5rUp0ZryXNWOZE+ZlxTIvmJcV98uKdV1wOi1YlgXrumJZxDm5PR6xzDPIRez2T/Duu7+G3W7Cfr/H8f4eH330Mb7jO34L3v1vX8WnPvUOlvmM4+1LfP7zn8eHH34db7/9tnJ4Mq6urhGCx93xDoerCdM0oKSC29s7TNMOslfImo3BIwSP8/kE5wNCnLAuK5hWhKtr3N+vgMuIccByFlIqaetfqWJxSAQk1cp33hm8Yj4X8trQmtfh+JYM/U/91E/h7/ydv4P/+B//I3a7HX737/7d+Mt/+S/ju77ru+p7zucz/sSf+BP4W3/rb2GeZ/zwD/8w/tpf+2v49Kc/Xd/z7rvv4sd+7Mfwz/7ZP8PV1RV+9Ed/FD/1Uz+FEL41v0PKlzw4O9yejhjOt3g7uyqA4zyDk+Q/fRAjUB7dqBlo2yGMZCT/koje+aSE5cdiuXaQlaygGbD+vIIaiAKYVxGTd3/tXfw//tb/Hf/jl74Ln/ns23jy9AbjtMPd/R1KSghhADmPlBOeP3uG+9MJh/0BIYYKSe73eyxLkjSFExZ+zvK6NDeRMqe0rri5HrCukjeLMWJZFlwfrsAoGMdB82nAfjjAe4FjvYO2sQXiEOAINdd6LhnOeRW6kM06KUFPyLpi0HOSCEZgMlclc438JQppDkQGs7W8dYV6YaVkMsYy5lvj3+Ziq3JXnYR+3onVgXCb8z2cV3UUv+nsd99LAo0KFUJSQMwy5uAA0w043r3Aixcf4P7uOcq6IoYJ77zzBXzbb/kuxN0BhQgpJZyOt3j2/GPkfBJYqADMDt5HBBeUW6L6AwQUOCRwjfBQxOk1TXeuaE0XmVPTIZDRbQacqAnAmN9YES23zX+yRexshl6eCV+CitvoHx17adXqAQogdvBcwIWwzgnz/QwuH0vdtyOEOGKYDtgdbvCZz/1P+HRacHzxDbz4+H18/PWv4uMP3kUBcLh+A/urN7Hb38CrTn1aU2WEk0O9X1Yr69AEs3rE7/LfRTknPVphv2t8Bhkp+9nIgfUcsGqNVRwGc4BcI+LJ5+X9wSmp1zk4SFdOR0EceT13LgU8LljmhJwZ61pwOs04Hk84nc44nc44n2eczme89+wZlmXB6V5+fzwecX9csCwrlmXFPCcsc8GagTg4pFRQgQYAu92I8ywpw3GIuL76Or7xjVtcXQ24ujpgWRKOL2d8/PWPkdcVp+O9pB0zcH97whBGLKcF5Ag5MT5470McDhNOpzOW8wm7ccAQIvJpxbLKmFsyJHmPnBKKU4lpOmvagXCPl1jXBS6t+Oi9X8PXTmecVOyIIK0qQoz44MOPARBCjAhDxPl8RnSh3uDx/vybeNJfneNbsqz/4l/8C3zlK1/B7/pdvwspJfzpP/2n8UM/9EP4xV/8RRwOBwDAH//jfxx//+//ffztv/238eTJE/z4j/84/uAf/IP41//6XwOQPMwf+AN/AJ/5zGfwb/7Nv8F7772HP/yH/zBijPhLf+kvfUsXz4KX4fr6Bt/5nf8jfun/+Q/wW2uJh0E9DmldEGNUWHsbe5MFGXAAVnD3cIlDzTDWPdWw7zdzEEyFLZei0WpjegIi1ejDiDffnHA8M37pv/46vvrrH4C8RyqMeT4i54xTSpLvdkEbN4gkbUriba/rinEccXc/V+h+WRaklLDbTTidRAkvxohnz57hMO1xfzxjGMXQ3768w9M3bnB/f49cMrz32O92OJ9n7PYTpmnSxh/AbrfDzfUBIQTEELAbA55eF9zcPMVut8dut8NuN2KaRtzc3GCYRozTDrtph6vDiFIYMY6AVgoMw6RjnpE4I69AXjV2LlJjnqugi+Y/sz62F9C9kf9cB/lXvW/047815j2BspK1LmfzkfTAJzkEG+IfqWgQewzDFdKy4Pbl+3jx8hu4u/0Y+4Hw9qe+A1/47v8T9tdvIcQRczrh2ctnOL3/VaRVaoJ9LHA0APAQtUaAqKDkFXOyTcmJwSwF4KwNiQBtO48VUvrmwFJKVI0Jf8K1P+bUUvuMPg4ePZFJs9N6TkdUI9qVtC6bgSZ2AqCI6jvU+Cf2kj92EAFILaQpmZHTPc7HW7z48Kt4dz1h3F1ht3+K3c2n8Z2f/q0YQsTL5+/hG+//Cp4/excffPUWcDd44423cLi+QnCCtORVYFojEnLh6uo3J/+hsf8kgt7la1ZeaU6WcWfq+lK1xNEH6TugCWgfg7zHe8CRSrkmnOeEcxLH5/7+hPv7M549e4GXL+/w8vYWt7e3uL8/4cMXt7i9u8P5PGM+J5xOK5ZZhB4NNZBtTHg2jU8gl7A/BERP8B4YR2BfgCdPDlhmqXQASZXOfr/Hs2cvAADTfoerN97Asix48803cbXfYT6fEajgM++8jfn+iLeePgGXjPv7O7z11g0+/vgZrq93IE1xvfH0gGEMyPkKhQjkPAbnsTtcyVjZsmFG8BHBOaSyakknC0m4ZOwnDx+eYRw8ftuXvg1vuQkfnRhcRKvDe4cYIz76+hGHwwH7/QFxGLHs1rp3AsAwPpT+fZWPb8nQ/8N/+A83P//Nv/k38alPfQq/8Au/gN/7e38vXrx4gb/xN/4GfvZnfxa///f/fgDAz/zMz+B7vud78G//7b/FD/zAD+Af/aN/hF/8xV/EP/kn/wSf/vSn8Tt+x+/AX/gLfwF/8k/+SfzZP/tnMQy/uW5DAJREccIwHPC93/M78MWrjGf5XqBuH5AzIXjXsUsJvaWuqXSD5kgie3IEYi0T4QLOkh5gMB5L3Wy8ejBaHaoZ5W1k31pjSh2rdxFP33wC3BUM0yi62ktCCAcwgBs4WBUMAIxxqHcDFl7CEAKO50XJckBKome/3+/x8ccfwTnCbrfDhx9+iHfe+TQ+eP8DKbHyHr/0S7+E7/ue78Kv/PIv47wuGMcRT548wa/96rsIJI0y0nLC3e0tQISPpwnLuiKtIl3LYOynCTlZ3lmubtpF3RgipsHjesc4HPb41KfeQSlCANztD9gdrvDmW29gf9hhv9tht9thGifs9yPGISLGgCEMGGNACLGypksuCj0K9C1KctK1yuqLe2axweW9fbYUQu+IPST6PWTf/0ZsfbBHiJI4ynnGeT7i/sOXePnyOfa7A66vr/G5z3wBV299J4AVy3zE3YdfxbqckdYEIsCHlnbh1UmellaNiMUcS3mR6wyJrUeF4gEgcYWQPQmEmxX6bbn1Vpsvbm+XBumMXIPtCRbiZ8r1tuG0iU5p5UzEDiUlERWiLczdjViF1rMapdqr1q6xpr7kA8GPKMuK2/MHeP6N98AMxCHicHWD6zc/j8Ob347lfI/nH/4Kbu8+xosXHyEOI8Zxh3GaQHBYjY0PxeD6e+Xtz2iXIbdvterq8AiZUoIJ5zxcIAQfNN3Cm7W15oTzuuC8FsyLRNp3x3uc5gUvXh5xe3vE3d0Rt3dHnOczPn5+h/uTiGudz8I7WHORyo6kKobMOIwDlnlG0vbDg/eIY8bhaicOThZUg0AY4wBjz6eUAC4YxwgiUQJcSkHxgJ8GcElYVyUChoASCasTaWFEAnHB+TiD3sgoZVXdh6yppASwlNmuOQkS6wmpJJU3LliWBSHukXNGZtH6WHlG8KGmwApLcDT4iGnYIZGHd0EqMMjBEWNZV6zFYxd2uN4/QT5nOD/DBQnUcklwpaDwgjhcIw4ORAnDuCXZkv/myO2rdvz/lKN/8UI8ujfffBMA8Au/8AtY1xU/+IM/WN/z3d/93fjCF76An//5n8cP/MAP4Od//ufxfd/3fRso/4d/+IfxYz/2Y/gP/+E/4Hf+zt/54HvmecY8N9GDly9fAgAiRXACZj5jGGfQ/gr+TvKVQCv1kb7GWUg7JGXssndoHlJ3GQJAhcBKxoNjEEskG7xTBjNXQ90ftkGWItEmdRulCUlYn3Vj5pZCcAT4lBHGCevLWzAiPAU4YiGLsHRdrO0ZmZGpgF2A5fGIgYSEVBJCB0nmmoMrmhYJWNeiAmQMF0gIgMQYhxFxHJFKRvAOu90EP3hMuxH7/R45JWRlCF9dHZBSkodqWUFwuLk54Hw6KzxaAC6InnCeZyzzCfd3jOeu4Mn1gl//2jOsa0Fh3QhjxM2TJzBN8hg8fAgI0eP6eo8YBT24utrj6vqA/W6Hw17+nnYjrq+vBJ3wDiF4+CFgDJN0ATQ5VKYqCGRNMwSGlTxrZpnT0kG3Imkq8LIhMYLyNKNuVQV9SoeIEN2I+7uXOJ+PWNcTSknYTRPefufTOByeIsYdCjxePPt1SWekBcWayFikXXkhaHBwRZXE2mQAThvYmDE3o1KxDMbGYJoIkSOn4k+aXtH1G0JAyg3tgp6vEcwsn48aFVafAEZya+ufa41053QBwrHh5k/YdZJqB5jqnb3Hbr9yB+qQc+1pns8r1rRCRH8CHDmMhyfgMGE+n5HTiru7F3j58hmmacT19RtYUkbKovho5Z1OnULoM+udRNqVn0JizHMp9ToYEAc4ZSwpyZ9lxfl0xul+xu3xKJH2vOLu7ojj/RH39wvOc8IyJ6Q1Y1lXzGvCuq76J6FwxvnUUK1SGOM4iNASyzyKM5ERAwB2cNnItwB7QvCEjLamCyvSo81iKjnU9igdcGJIoILWGMdpeG1puVKANVm1D1fHm4mxJlHBXJJUBJVcsM4L1lwQV6mtz7mg8IrlfpVGZFa+h1YpQUVcMUndOeSSkV2oKTd2mqLlCOcD4kAYYkC6m4Eie42ktAoCOzx7/gEOT9/A3gdQXpHYocfxWi+B1+P47zb0pRT8sT/2x/B7fs/vwW//7b8dAPD+++9jGAY8ffp0895Pf/rTeP/99+t7eiNvv7ffPXb81E/9FP7cn/tzD1535NWzXXB39wLPnz3D4PbwulG6LoC3DmxUmeVoG6Et8IIapZjiGLHUaxa0XN4n6iN28JIYB/1u2G4mhBKJvqjmvBwAHwNKCCpmErF4B5qFzOaKtMI05a3SKWex/scVUa0TTW8gFSmpWlMWtSwWVyZlIQJl9fidI3lvEUEbOgEoLHXUQdCNGKX2eRlHFC6YphFcIuLiMXspbbm53sG7gnWh6shcXe1AxFhXechTljrnu/s7LEuuJV8FwN3xjPP5hDUl1VaXqPXJk+s6jtM0YJp2OBx2OBwO2O332O8mPHl6g900YhwjpmnAfjdhP+0wTiPGKFFW8AEhCpkteFebzRApYRFG/uulZu2B5zrBlUTF5gw0p8GcRec97u9f4nh8gXU9AepIhbDDbncN5wekXDAvM5bzyxqwklORJuew3Wd0oWjWpxlGXcE9msAduc4MEPfwvDqa0GqAWsolJzeCZs6mY9a+g00lsRp6Je0VW+tdfl8NBesYEqnjYtGwPS8WRVF9c4csUFvj/WiwGCqrf7ikXSzzWfQiXEAcBoQYMY0O0Qes64y0zljXBefTPaZxjyFM4BCRywrhz3RMdOW6MIuRzSYbXCSanpcZSR2FNSXc3h0xzzPOS8J5XnF/f8LxKPnxlwqpr0vC3fEe83nGvCyS/87a5EidaZOfFhgfyItUcIDECRmHCLcKWuic6jYUIEQtBSbRcXBV7s2IgCqGVKQ6KSnqaG1tyYtKpg2qg/g6DvI8OKhsMSQlI06Gr6RA77UttDp73guq1zeLYRX5InLS+ZAIIC9CAqokKSW+kq4kRUigzqtXB855h0CagnAJcYpwq6Q9fPQYY6yOWCUEe+niOZ+Pcu/k4NgIlPasvX7Hf7eh/8pXvoJ//+//Pf7Vv/pX//+8nkePP/Wn/hR+8id/sv788uVLfPu3fzuM4Zpzxv39PX7lV34VX/r8b8U0Hlom3rnaRENIMe0QyG67UYjZUYEdhdfZEdZ1RcoJftcLObZDsYO66TY2vuaMXeu53TYueXDJO8ToEfc73Dy5wTiOWM4jxvmMXIC0LihJenhnZMB5OO2YprQgDI5wgvTdZhYRISPQxRgxDANiDAjBYxgiiOQhEkU9cT6CF4WxpEiCA2E5z+DDFbxz8OSQ0ywtOVnGpyRpHJGS5NNLya2sCsIKtty594xxGvDEXW3elzNjf3XAeQ6VW7CuK3IBhiEgpYzz6Yzb2zusc0GIrcc6FyF/oTBC9JimAYf9XqL9aYfDTcT+MEk6YJpwdXWFN54+wX4v8O04RlxfXeN6lI0hxKAkRY/gPZgdjCkdQkBZS9XdLiwKX/L1so6ck9j44+cfIC0J+90BV4drHA5PkBPjdL9gSXcAZXhP4CwEIGYIS9oxgK2YCREJ5M7c8qqmN77JQ6iB6JzKWobJrMQljZQqFM31s7Ykc1rl3J23YRt1e3aABv2jOT6KyuSknRT0ep1z0qmN0Uh91BQa28/GaWmVCcZS76H0xq0p3fVIuiM4q/uWMVjO93DeYRhGTNMATzcgB/yX//xLeP9rH+Lbvu0LmA57vDzdSuCwrhWtWtYVp/MZx+O9kNnmgtMCLKeT5MePdzifTzidZ5xOZ9zd3eE0n1EWRlkZ65qQi/iPpUDlhr3OpYePwBgj/BRVXGYBaQ97kAoXkcPsz90YM6J34OylFFchaSO1yp7IqmaoyAS3VIuNlewVikLmDC5AjFHQOyR1KiCiTfWaJMftnFTohBAwjiOmcQQzMAyDIJWqWrjbSSOraZqkXG9Z4UJAHDJiHOEVkl8LI4YozpujqrExjhMcUyUZi9SNKoFqtw7ignNKuHpyg/kjUZr0ccA07nBHDlVMyRGKkz3Y+4hWBs01cfW6Hv9dhv7Hf/zH8XM/93P4l//yX+Lzn/98ff0zn/kMlmXB8+fPN1H9Bx98gM985jP1Pf/u3/27zfk++OCD+rvHjnEcMY7jg9dFtFBapcZxxHd8x3dgIFHh4sIomRF8g8+F6X55Dt3jqiJVrpuM/EaYzEy9aA5qtNKO5sVu83rCIrfzVUE6lXcEEVLOuNlP+LVf/RBvvjXDu4BlSQp7EXwY4f0AlIyiSlWs9cFO7y2AMMSpGu6SxTCFEDBNE4ZhwDAM8N7j6fVT/Br9OrwXBCHGiMPhgPhihPcBMXgtdxkABqKPIlfpGVitW5lXDW5xiuy7Ss7IXLrN2is8KOO2LAtKSqLIps0nUmbsyghPRR58F+Acb7qjTdOAw2EHgDEMI6IHUDLmeUYqBcsyq5wmI893+OjuGXIquJ8JuXRWrIfYHTAMEsFcXU0YpwHTNOJwkBz6fr/Hbpqw3+1wfXONm5sbPL2JuDpcYRjEaRJCzx4uBIzTgHVd8Cu/8it45+oa3/8D34+0ZtwfT0irddMrgMvIPGPNCwa/q2kCaHrGIiyLtJmBtKIazKJruRQR+bAe8zUGr0ZR9QnUIHMGlqza4J2SYR0bwdO1D704HM1jJhSIcbIKFXPgUHUeUGFX1t8bsbEUxmHaKTLGOq/mdruNRoSUI/ZOTNPBkDpytkR/ncvCjFSyOptFoPCUsMwZL84Jp9MsDbCOEnGXwri7u8O/+59/AeOwlyoW73F3d4d5nnF/nnE+zyJodTqBWdbdec44nVrplSm8ee0x4bzIOF8frhCugnYGLPVehTkPcRCJABVqEcMIzGVWHpCEmKQqik6dOIMBnQd86BEPI5oNOJ9n4Xj45mTt93scj0esqxB7s7autsM5B6Tc0kPMIvUUA+AdUilIpUhb6qhttwsQgzgopRRMU8A4TDgeTyjMGIaI0+mI/bRDXjMyZ7gg5aU+OCxrxjAEOOeQ5gwMUn0RhxE+yEYdhwnn+xMym7a/Ot1DBFiaQTkw4hBwWmYcb2+RAIT9DgSPNQPwDiaFDipIacEwDoiKDOS6LVBN3fSqh6/D8S0ZembGT/zET+Dv/t2/i3/+z/85vvSlL21+//3f//2IMeKf/tN/ih/5kR8BAPyn//Sf8O677+LLX/4yAODLX/4y/uJf/Iv48MMP8alPfQoA8I//8T/Gzc0Nvvd7v/dbuviSi3h/JBDz1eEJji9fIsQM50NHpBJxmFwKQk+4qGGBmHtykuuxd3gfkLWsJI4H+CE3DWTaltC0izKRiooFSQ/6LtrXLxdpS/JwPmIKhCWtSHlVNv2KlBRRyNIyhlmaT3gHrZ0GiBllWbEGjyUnMSalYD7P2O12OJ/POJ/PsDrl+9MdzudF6maXFfM8I6eEly9f1gdpWYQcIzF90YY8WjYEhfA8qcGX+6hsd9ccG+8inFvVSXIILlbOA1EWNMU5UM4agbGmNlLdoGKQzTKtSXLZ6wLijBI8CjOWlATWcxF+ELlVB4dBjeENhAcQVAZUzlunAOSEA1J4xHJecbq7x/OPTiD3DanNNdU4ciglI2ctC1SomsCYdjs477CbJnjvcHt3i6Us+MK3fw7jFDDEgN1+B+89hmHEMAyYpklQBacs5mmHcYgIMWIcBoTg4aJo0AfvMESSTVbFQsiJdkCIES4KaY9IoVXfoFSQrD2gqxdmFTy6IBReEs88xPlor5MaatS1LU1EMqwk1dAhb/wUdWJyKXh/uVUER+Y4F6nhzirKkpM4bnN2tWZ7Xa2mW3g6gvhkpFRwvJuxzKvWgUuddymMs31uWZDWFd4LasSZhLlfGLlIPjnGAcvydZSSEbygNMMwom+H7IOI8AwxYhyAp1eiEFlyFu6LIoY5rfAhCBM8SIvkAm7yscQSOxZgWc7KmyFYeRxn7UBJEZZGkc9l0bIgMtE6McyajjS0hoiwLEu3H5lMba4pgb4SRVLtwjmw/UzKACVnDwDrkrEuGWAnneWcBCbeCckwpYzzeUZwglKc5ybNXQpjXRasyjkokOCrcMGaMgIcciKwl2h7TYCL4lTkRcY/xqi6+KE6Szln+Oy14yWwZLlHHhklJ+z2E548fYLJR+FeeKf0VdG3yHkFk6T0vCOsl7mh1/D4lgz9V77yFfzsz/4s/t7f+3u4vr6uOfUnT55gt9vhyZMn+KN/9I/iJ3/yJ/Hmm2/i5uYGP/ETP4Evf/nL+IEf+AEAwA/90A/he7/3e/GH/tAfwl/5K38F77//Pv7Mn/kz+MpXvvJo1P7NDon6JFpZlgVlLRhikGYcaijA9lBopyaFa8TIF4WkACv3kYicFBYGrOadKErDhIp4GgT/iCpbzWPao24iJq7lLtHlMYlwfdghhojD/oCrq2sRglgTHGfpcMZSgeyZpEykZGlYw4y0rODgENdFcu5JSXzdQxJCQPCSs4QSiayZifPS7jeEUGuOSymI0WM5L2rIPUy4JmfpZNbfd9PCJuUnqPdNqBBIKdJow/sWqckAFJQEZBUJC6GNqRhXUeNzkPp0Iq/bYy8zmpGLA2WR8hEYk2VeBZjBokRBp06JUx0A5gTvCQMCOPgaqeYhVQjSWfomMVZzstTJ8Q4oOeF497LC19FHfP29j5HTAi4Z3geFUuVcIQbhYihqMwTJRTKafLEP1BoiMSOGAFK41MSNvLPrFcRgiLEZam1YY3oJADYCOV5LzSz3bPnbVmakrzFq22BxlsTFSesqa0k7NNq64VLq2jPnisjhvIq6W85COltTEna+PkMMxjqvyBmVKyCOga0BS1eIwuJynsEsxDRmYbIHH3C8v4cPsaaGXCk4TAfM5xUUZX0WFh4KkxBSra0zEWGaJizLUisZfAiKrImjkArDlyJkWXW4SpHXWatAApRrwaqeUTpyoyM1nBEpKYLoAXglApOWBzsHH0RvIWFtz5vxGGoao2kyytrwdY+ystMYY9O7Z1NktP2IpKmOKms6fb5A4iRP04T7+xOke6fDOExISTgKMURMg8Dy53PGYX+D58szmZdhFFKvDxjHqar/hxjwZL9HyQW7aY84jJjXGfu4gx+iQPQsIl7Xhys4UENNCFiWFcMwIJC0vM6lYBoi6MkVbn/5PVAk7A87RItKWEoISfc6Ufe0IE+QWml4+9DZfV2Ob8nQ//W//tcBAL/v9/2+zes/8zM/gz/yR/4IAOCv/tW/CuccfuRHfmQjmGOH9x4/93M/hx/7sR/Dl7/8ZRwOB/zoj/4o/vyf//Pf8sVLowqBtkouWJcV0Up4iLTDl2jH564CqOY2N/A7aYcxNT6FJafmLG4zR6DPFdKDazLongwK1cjPoNGa0mTrBiUdw652o5a4GIwnrUMDJEeMIsa7PoAkvb7tcE5Yo0LMkZvdTRPodJaynygd46RNqE27kL8sV2fNKOw+YoyYT/ebDcM2SXKt/epj9eR2j+YIybAWqWSoXdAM9XhYiywQdQsdCaLMZ3Zb9MIU8oUQg6iwCMaAwNr6tXYvc6StVIWAQ1zgFAlNBfCkEZwOgQ8eMYbabMNyy4SlRstEBGg+f1nEGTHH0XsvzmUuYI1gU2Y4J4ZzcQtOBMxJ7i8Gr73fsxrSDHKWGxdW8xCly2AI0kbWDJ/XlsXeOYQYW78DIyx5cSqSSuqa5L91QLw0opJnVZU6NfT2neuy2kLHsmiNv4+SsslZEBAAh8OhRuWlFPgQkOYkvemZK4GNWebVnKtl6WFxeQ4KF+TEMEdcDFeQz5eiBkBTZxrZemeMeYfgxfHI2eI6+TPtRs2hF3Cx/miQWnsl7bLC2LIGHRhSwsm6tkq3l4Ba5QKj8X+cSdHqOnRkNEftPulQyxkLK/rAFghYsNLxf+weqL1m+04Ivu039pQT1XXUP2dOHXfihjg65+qnCZIbN9TSnBZmLTdmlsjee6zroumC2NZliMglYRgmEHnN90t+fxh2SOtZndGAgITKZ9L0QbHrNalhhjTPUeSvQNIkVoUZ4HA+n+CnPfaHnZLsCkDewgEQnFYEmEBWr5bw+h7fMnT/Gx3TNOGnf/qn8dM//dOf+J4vfvGL+Af/4B98K1/96CHKY1kxS4+cClwU2NA5X2HHWhp1cf22eE1i3ZyC+vvMIE8KzVFdLJcG/mFNdVs+9Xck8Hf/eUdUl9puFHWmZZmV+KeGw7EYNwtQASQuQmwiMT4rCkJu1+K8xzCOmHY7lCySuuScRnLyYNWHnggxaBTYOSZEAikzmYSwg1OyHqPlXxmtU17T4W9dtqxZhty/jK8K4m0eMSbZ8JAF2rWe2qKjrX+8XKBwMyTv5iCtKp0xeI3Fa555KWCWsjpV/ZcxL4xUiUzNyMm6kjELw6Dtgi1vXrCu52romcUhcAQQCoJXWduUASoI3gnPAVSVA22sLRc/FkmDDDGCtZ5Zfp8q6lOYMc8JoSI0YhQTWwmmjHUihzUVjdzaGgeAOAyYz2dVIhPJ1XGUx99gXeh63O1GrCkhp6xolpAigyo52oacS8G6Jniv96QVCo4I4zg9iMRLKpp68FW4xpyioFFzcA6ku5JFp6UUpLXNkSAXA3JZ27VDnJEQA8ZprIRJZic124qI5SLOCIOxcxNYy8GQgQApl0teKkJQ5OksawZ5L7slGxGyPSfkNJ3lLB3EWqqnMLs6AWL0zZdmrWkvsMIuZq1YsRLd+r7WfKU+V9QMc32GuGn6m8x0vw6sgsAcKPmF/Gnnoory2FhbOqawyGWbiA8ARVRlDIdBkItsGvI+4HQ+YYijjKdzcD5CSl1D/RzRisIZc5pRnDyhRb8jeC/8G3LwirYlbXQTgjhvSSAg+JRwPp+x99e42u+kbj4XREtXsIxxzrmiNAygGNTyeLzyWhyvtNa9D07YpiVj2l1hN+7EIpLmzNYFYRAYq+W7bANQUo81M4F9ToyIbM7yoEu9KcmmYcaJL9XTOsNOYoHtwXNdxCIRY3tgg4/YTRMS38KRSDumJIpPznmUbPl1qEvrUNYEytpVjxglrchFID/JiRZwlofkNItqmodEO+M0ISWJrAoXrHlFiAG3xyOevPlEDIE+gC6QOi2SCyvcyIRMxt4GTGikVRyogaKyMf5LLghJ2tEWhX+JCFAteHKWSjGYH6poKNKk1veeuUiJIQjZSUvfRIK+wAkknRNVdIQKxAnUOU0dJDsOAzCfEbq5IwCuSHcskMB60XuMux2+sSyKBGlZUi4YxgFwBcu6AjBG74CMgnWdJf8bAgCHAl+jZdl7EigB8KQQ+lojJ8DquQlRHQp4QWDIO/jsEUOs4+6DRPCiP+5UvljGYLfb4fb2VisHMlAI4zTBkcP5fK5rV5Acj/P5LHoTCnvGGDEGX/ugGxx8PB7B7NrzBUmj7IYBkRySkjaZGesoDgec1EFDI/IsxdqSAkABUnMUzejYuUtWVUgQUtKubFnu0QcC5Yygn6njhyAuXoH0tldjmOcEYkJARCFxBOCkr33itTqxzssaTnkRYh0xRAxG/hbZiCJNiMghOok7S2aktVVQkDPSqkTdTTRL+kIwHGLoRXcIwREWJK1x06odQvesbasRUkrVMTa0p+Xui6aCnDqrjdzmnUOy51bLbR0x1nURbYroQWkFOUYcvHBVCECXPpP5ke8MHDRF5jGX3FT/NHUzjBGnI8N73TeKEPRkr233471HHKb67xACcpH7DtGLqNQq6Nb1tAcxELzHNAzwINU/6KWN5e9pv0dwpnZqBLz+Tl6v45U29KUUBB+UbU04rxn3pxc4vLHDMO2AIvXRXjH6S5i5j/AJBd4T1pQgjHgPTw7Mi25uo3iAWnFU2fW22O1UVGp9adacn9coxj638c4hOadJ1e7GcVQd+4wCj4iIOa1Ys4CBkSLSfEZxGV4dkcOyohBpsYlGsblIu9NVjKfB/LvdDuTF4BsZKAxDZcA79fZXjQqZlDiVZcOGI6QC7WAFxBiURZ/a5qpRvJTcFa2jLyAEgANS8SjFCVzqHACVui1A5gRyi0KAWQh4uSAlBrTrXk4iYESO4KyHx8rgzLKiAwFZgNi1SJ+D6EicqJzbfBCwrDMyZ6RsG4mvqYy1ACE4qS0mwu3pDOcd5nmu88osKQG5774d8YwYBgABaQXyKsZ9uppE7LVINUc10k75D7pGUPOo6lTUBjAZVJShoKIoRREQykHETLJAwaTfk1HAvgCrdP5aibFyAtYZIQTcz/c1VRNCQIZD4lyjaJgh5ojaKYUKDnHEkRMKAlbtYWDjcD7fy5xmLYgiB+IMLkmRF22KpJKP0haXgLzAxaGLSFsbUXPAmcSpZSfpCAriiKbCOMQB8/2pVQQAiMFhGKKQsCrJTsY51I6MKqYSgrLIVfSFxdiHIKhVyoTChjYxQF4MBRVktlSUQ3AewYwmp865y+oUCe9liKSd+gAquZZnCjLiEJwDB+D+lGCMcN2whEzX7WtO03BVKlqj7xACdrudlrctFSkbfMCipELbT2vpnIwSpimCFPVglrTRbjfh/v4eKQuZcdoNuD3eYl2Fr/WNj94DM+PJkzcwL7d466135Lzegx0hrxk313us5xlDDJh2E3KZEJARhrFybNZVcvF2L84r418rT/ZXkuf384JhCLjeH3B/f8YbIeLpzVNwkTLDpC10yXk4FT7a7w6ij1JeV9O+PV5pQ0+5gJHBTgkkY8SermQCcwYgTRTYD/D+DEfGgM/INCDAS0SseXf5XQBxxbAB9lpj2Up+qn66GXuImEh/SHpZoGQjtjpPDQpDU5pieFzv9kiRcTyfMByPqjTFwBgkis6CPqxIWNaTEFbUsIhuNyGOYyOuOSDnBSKy0fKC07QDCmOaJozDgHEY8eT6Buf7exymA4Y4SKtIEKZ4QHDP9UqljzxIYGenOXox4F47gTlYW+BsG150SFlEewgFKSdMuxFnUjEgyJgHH8GcQcVDNFAlB+/VG3ewja3U9IRFX+KAAOwYGVnKzRzXXKpFhim36MpKA4dhAJjhg8D7Ur8v0SVBaskdBIYsKSOnAq/Ysjh9GY4YhcVBNPQCLGSwkhhgIRPZdUgNhW7GKjZTNC9r5XNUWAVBxCEpZNrslgYRjoZFcPJLMcjeS/Mm46U455BKrs4XgTF4IQWmZcUYVGu9AOu8AhkoSVIdwvlwQo4CSSVISfBEOIeAYRgwr7kqrHkfhKgVnXR9ZEmvsSIwhtZkTSkhA942bzDYxfpsVTi5iIKh8V5EwEWex0pTUSCtpLQx8iBVblu1LFXTTRrgg0uCoUmW9ilJtBG8qeRBECEfvBpyrbt2lo4z5jokj1+hc8hcc9b5b2momvsuBRSERyFHVt0POYcvBSk5eNerd5BdRo1WmWVtsO5PReddHEZXzycIpexvS0lIrIkwAlzwwmORnJE+z1HaWpOJ5hCIRe/CovGcGCnN8AOBSxZSIqRF9ekuYXeQ3HkYBoRhwPF4BNa3UDJj5oSMs00h3CK5+lKEme9ylutMGY6k8sFSbh6SLlrSgnkOOI4vccwZ2O0wXB1AaUUKGZ4CPDMcZzheMedFyrDV8erqpF7b45U29CXnSuyS3K/Hkgu85hALnLaXVWiGqzitbOI6wSZzCm4EOcAifk3AyQtCselyYfoyuDoDdjQnQD8MiWyosTs1N2SRUIgea0pIqzTXyTlhZpFvNDiNSAyWY5WOLIy8Sj43a463lAynNbRWX05EyEUEPG5vbwGWWt6SC4Y44C7fKiFHyV9rgg+xbsrMqJtRqzgwqE82Fm811B30bIZfbp8rdCaQPbVeZgxlpKu4B0xBy4ZPodtSahc9Y2obMc0UwpxBwyRzsuVm2DVs9Q76Kor2lSyyv8woQXkDfQRwkdd7yNjdpjLad3fvN0eRAStRA1BFZaCRvtQYsG7A7c8nHQyumub2bxs/Gycw1CHW6J8FHShOe8pDr8/kg6FrtbBAps5BxNpaCaJzTuD1muQyIhd1/9b7NCa65onZhrR7ttRT00HUUW2L4sEYMBpKconeSXdc29RZDT7X8zhN3TGJgaytj1lqZzqRuc38mWG3X9Q8eZd6kEOqb0SESe4jc4ErSnDdLK1WjYCar+/SYsWEhuxBbLC78QHQ3W8r8WvnKsx1HIq+r6hzDHUoRVfB0kx2cfJe9SNqV88QI4zNn7UPgAQHQjj2Os5Z044mumMTW0oRHk69LRX8YX2uSfdtWzSaZpP7YUFgiTDsJux2E5Z5Fl2HCsrLn1QYFPsSWWrP22t6vNKGHhCInKpKU8S5NOnI6uYD1fiQKE+gyd+0B1P+fmTGu+TNA0KiLdSL/R9oG1N3GW0TAuoCLApj+sqYpSZ8ozk58z/kIVTGe5F8vKmGWd0x6+YBSFogBCVrKUR5UuWzdc1ILK1t5WHmapiXNWHXEce4lAodSnTQmMHGPg5KWbfNupQi9dRqxMxY92No0pup5Lop6BYsIkfVaTDGbzMUvaJav7nbHykVXNpacaTEpi2BySBOgoiW2LwEZbNL290FPljDpd+87y+2gDd/KljEsnHV4TC7qo5jt42jd0yA7n6rE4V2/06qChr5czvu8h3tfEXbDDeDVf3e7nO2GZsB6ljcTG3sLAqvt7S9i2YM278FARHD0hPN6nd366Y5kg/XUjunOk/VWNrlkzjt/eUYQqKjWTjD9DNr2sDGBBYMtD3j0llsBtkQG/vbyhpL7WEg50B1CGhzWTKPzkk0Xdovuj2tObF1z+N2L9UH6JBHW9tU/93mxUSJ9DbB6NJRpa0HqtC+oADeC9IRQpQ9jAQDdVqxEmNEXs7wzmMYBr03Ug0C0s8AaVmlJbc+j4WERyOdskXV0Tt7JlUFFE7QIkXNyAH7acTVbsK8rPAklUKG0gqvqKlH1qHq9vjX8XilDf0QJ/gQUIjAueBwuALPB0AhypQTYoiygZYCuCI5TxDAFTzfGu9PmvA+MK+vXb7YwpNiGw6pc3ERiXkowxYSyY5DBHNBDFIKBSf8gmkc1BiWGh04J6IzpRS4bGpTSvRZVy05aht5NM3neZYHqWTk0wlBPeXVOp+pHrwRvApEgx+AdoVrrT1TLtppihUSLuJQ6LgI+7zBynXD1cOYuwZpC0NbNfi1FA5OaqMBiDrXoiRCQuUQAI01bhuaQfKBnEK23Bk91A2ZqEWElouWa5FOeDdvvQHvHUqR5iRrETLSluuxdTDssByzOQq90ewjUlkyRc5b0G2oAjkbMGGpENmeHIwMWh1ThtR0Owd2JMJN3Xj0BK2sXf8eGNTusM/ZLbZ0laQ4ljXh9vaIGAKy1jdpyXLlaHBP1KL2CPVzoaMlK4TbNdr4ud7j6K6t5HIR0TYHurZdZTWK5KqzXGFac3Ls3fpWxwI5k3EEnJTVOlhaqlWTWB16P3bMul45gJhVK76No7IMpL8FCIVamsEcZEBY+55ay2U72jXhwXcDQAwBYP0eIiQVsxJ5XSEvEkQ1z4KNlt+PtfohlwJ2HvvDQX5W55O8xzBK3rwA2m9ip/n0PcZxREoS3b/5xpv48Gtfx9XVFZbTPYYh4urqCi/G5whDxDBECRA09367rIgxVMKhV2fU0p2kARBUCyOGKOW0DnAZWNcTMgoOY8ST3YSFs/KzNKxj4VitOWF0vo55vwab0/16Wf1X2tBb0bOx48/LjMxFa6idlP0UyCZKFsM3uEqUoVoEAAAGpT52cBelf7N1UH9VoaVW8oLu7M77Gh0DwPX1Fb7x0dcxn2bsDtc4nU7SU9wbrC3yo1kJZRJFWFvWBAoRWUVJYgwKRZb63lLkoTke7zHEAesi3bF2hz2YCeM4IvoI5zyGccLNzQ2++usF5CL2hys4R3hx9xHSsuCtNyfkEnE63yNp1BxCxDyLop/3BBFxCSASlAE6FuM44v7+vlYjyGcDQhiwLMC6zhWe7Jth9BtejBFEomrXv8fK/vKyAt5rbpc6w9tQFRk/TZuQ5LETZ5X+zTif73F12GO3m7DfH/D1jz+qn7PzPGYg+6N37mrUxw6s6ZiewAfGhdFoBq93LjbowMV7VyUGVqeMjd3deqLbOW3s5XXxKAogmKwLMjasiINrKNOqYjfrsmK/36sBdOKlFHNuGY5F9aywwP6OxAkwhr73viIp9tw4oopQ2WsmutRmnyGySC06r6AXo0Ls4swRrAPdb3T060w6vRX4zvHvKwssxt6UvKG916L2fh2gOgaS02c2xwGbyoJKPnNG4OPO0UItHewdISYln66rjnF7DzOLAmbOFTWxvcOuWRC9FefzgrS26pa7uzsAIvXrXIQjh7u7IwApuTyd7pFT0ujc48XLl3LdSPj42TPknPH8+XPc3R+x5oQlZbx48QIffPBB7Ujqgxj64+0d7u+O6iQKOTINA5I6Vw6E8+kkVY8p4y4eAQchOyYGn+VapiHiahpBLGqjQ3Qa4EHXs5Ct2TXJaNS/m9P1Oh2vtKF3PmiZGJCWBZ/9whfxXz/8b4ijQxwmsCp2efIK77N264JG9C3n3I7faFNoO0sPy7bPPtzoAXR5ukYEM+PLLA9w8B6+SNnSNGlJCTnksohRdBEM1adS0pbofjukAu2zLJFVyQXJerWnhPN5xvF4BJHDspzhEaUFZimYHOGkEqOGGtwfj3j65ltw5LAs0rBjHEd4H1tjlaKqabrRex82EZkY+W1EYpG8QWdZtfHPy4y9DxXG9CphvGVd2+ZoxqptkGa4RNZS+lNP0yTOX7eJkkZKspOwEPeUQEROojnmLM1PfMv5++Bwc3ODjz569hstSwCtpNKcrJo/7ZaRINbNEH+zzaWPsHvjvUUX2nt756LC6VbcDVbypL5HDan4u82JAVtr2qbAJwbNIfgBhYpG8A7ONWNHgJInCaV30C5QrTpaZKkCW08PHaQClugMst4ECbIufJ3hc12NuF7LY2NEF9dSNdS14sIO6YLXV8yIEwN00L1G7hcZvGZM6zxcLpPf2JxYhE/k6/c9yP0/cCb1+xWSJm/9EVgrgrZzQHYOqOKka+s3DIPoI5zFcXfeI2pVBFgkaoc4iFPuURGISnQFsN/vcXf7QiJy5zGOozqIVPfCaRixDgvGcdR7LCgkZa0OgorKuiIx0C7BD6NU3wA4c8I+jnCDl/0/EvI5IQyhpiugaxJoaQVbJRun+TeclVfveKUNvejSs0BkGvHe399jBw8/7EHBpDw1li+MnFZ5PaF7MH9zHv/2yx97zZaiKWPJ0W/ym01INw5W73kYI9Z5Rcm5GRhHIAqiaKcIALEofwnhRcUbHYGCGDkCEH2o9fLjuEMpQIgzxt0EXhbEacQZZywqwMKcMY6DKpe1PJyIVATEKDXnOanOtRriGAcRYGHZjGweLCLXG9UN2Dati00XjbwkbzcdA1cRCfu3qbnJBiqbkTkOzFzzqUENk7CbVzi1IGbruJt5aXOp06cIT9SWtiIWksAQDgQ08m7ITrcQNPzcwuHcjOel9EK3wdS3dmPDKtHcr5fLqM9et/OZmmBFMNSLrE4hN7g/F8AHaCOWRk4rymru8+29MeRuAETTqTlRm7HQuXWaJwVk80ZnZKyxE5HkjBPnjSGSP7LGbX6YpO78AaT9iDG/dC4eM/r1891Y2nrTmxBjTsJqz/3nuv+2G+eNz29z5dW5bd9BIhbpeme2XcNDB659xSVypB+oRFV5fkgFcuQ5IKeSUeSqk9waKKGW4ArCIOkuHzzy0somvXd1nn0IIIigVM4Fh5tdVUcE3MYJzzkDUWScQwgbB5yZwarPkNWBUMqGpOWCKJ+adDWzGGyCtdgVDYNjukcGsBsG7GPEaVmkTt4GTedRkBBDkcxpq0P4Wh6vtKEXiIfBKHBsJRkrwrpiyhlhnIThqU9cKRlpXUDxgLrV6wbz6IPzid8LmCAOgI3HCBj0qHnHxzaf+rd+TqG7cRoxPz8jJRFeEVh++1mBwxXyLnot5OAUzoeSEQ1mjRSlX7QavmEcMZNA+8siJWQxeDWimqNzABRarZKiPkgryqI5MTXcZvDb9Zm0ZPeajgvktF35U29sW/7ZkTZlsaRvHykSKpvcemEXV7bj3BlE74TlXBUPzaKisZplk9V1onldEReRFrLGZIaiJWDWlNBldN02Y3E6skTEbEzoxlOQ76XmMBSu68UMTE9ks+ViwTf0N5vXgRqFG7lL9nxXyy6pbmo92U8dTnMi+vslObmpo12mC4yN3yMmdn9GpKS6qdovG+TtnDCl27qR58mu3eBpkZ/lOipFN24zJP1Y2L/t72rosWWc9wexrlOD1/DQ0Fp5n11TZqvSacbSnDMr1ePu9W2T7DqKdQ2WnJsTpddfI/R6ueYstPECepKnLYhmuUwVs36eLu5N77mmwNCeSa/96QU5sZJaWQfBe9l9s+TPzekWlKPUzn1VHdH+MEs9fymVULyS8KoKoCI20IddUjRFHS2QA3tVulykNDoxIxbCy/UEDgHTMGIXIu7KCY658mrEQejSgNzdf+e8v462/tU29F7ygrlkFBVscSqAwWq4RGVOakNLFkM/uOtNVAK0ONyObd4Gm0Wg+ySMU/TASVBYuyq/dUdPrhEj0Tz+3TTheXlWxWdyTlhTbpupfsY7XfB27yDMKWNyIh1JADI5KQ3TB0hEa8RrPp3uMewmpCQdvoLz9TvNiRBFrCA1yEla4sY4gMhvPHXbHC7vDdAN1pkSWQEnqcXuO1E1gyxGm50w9b1zWFISHW4GUBjBeYVIRSrVEYHUIeFiwiYSMaznWVuBynj1SIpdW++IFZamRzLWHmtacPA7ZT0L23caRrzkW61AEEpVr9xmm0mFm1WEaLOmXIvCRUJYNsnCDQGq+d7KbiNFA/oNl+CqY1GqA0BEKt1qzgNEU0D7jV+2py25Qx7q4AAOFtGyXl+p66caFp1zFMs5bysaWg90fa/qnZtzXiNbi7B0TCzydVZdYihQd+2SR5f6fBuTAtJa8q2Rr8+e3Zz+3aDuzsG4QFUqosFSfWKRvVOHiYDq2AvaUaq/iG5fsMZWPb/iISrTfi71OwWl6sKCOpZA9cfq66K/oVG5GnAG1Z4DvSPWpzrY5o1Z69UDQgxaQtccWUDIqikl6QWh/R28dYfUQINZ0NOcszyHAHLKKvO94KDjVlKqap7zPGMAgayaQNGDshZtECSCWi5KO+xSCjIB2TuMNOH2fILbTRh3OwwhNK0SnQHjwbSyvhbRu+0T8NoZ+1fa0ENhRAIQoyz8aZpAcCg5aatVQNSohLCWMmOw6LJ+Wh4hMfbyG/nchddP1D2cFsE9RALMIMtDWB7+nmXjdca41fybQWPSV1qia2k8bZ3mFAIjgbYKWOB7NsPPVT6yuLYxL4u08MwKcZ/P93hKbwCQHOSaFjgwlvlco8llWVQBTt4zzzNOJ1EcKxnCgHcNCjVHAmjdz/occvABDlLSV9YkDruTlIHBlpV4VACison6LHdKDiiragXo63mexQTmbb7Woth+/rYQLsDIlRRnUYf3jXhmnclIddudg6IDW0jYtPmJG1P68Xy07MyV+KNeY7lgwes3VJY2NmtPoi0mqfM3zMRRf92u8VE6A3I5L4YsmINSexiURtiSqFDuK6kMMTkgemmgEz0BeMSR6vvFQ2WYyau4TstdhxCrg9BSPIoEdI6CwfjmjOYe7oJVVmCzBvpn9rFnVfLSJnajpFcVIrJ5tUDZVPV858xsz81a7qvfW7precTpN4xD1G2pvodI570rd5MLIPMAugqcVo5qqF02OFufASKHEEYsyypcyUKVKyOBgiBfIjdtabK2hg+HA9blVNfkMAy1kZLI+Yrs8pvv7DCOY30uD1cH7fcglQAhBOz2e5yXRcm6DkzSnW8YBtnrCPBO0mbkHXhdQcFVR9iTgxtEVCnoEssFWL3DcIy43l8jTiOKF/GdMmyjd9I1Rj2yUefl8TXyOhyvtKGnEEEloUC81f0U8bXzPfb3C97OHvu3P4v59DE8BxQEJMzwOMHjHjnLKpH2kACybCjmApj9326QgDVIEfjxgk2vm4p30h9cyseCSoEmZcdLdy5HEh0DBbksKIlxPTh8LTMYHvtpApcVKTsQJ2Usi7SmMM6lXWRhkXgc1xUDe2QvGt3kCaSd4gDt3Abgen+NYX+FIUQhpMSI/XQlEXMICMMIHwbkZUUYBwQf6+Y+jgNiGHGcj+qMDJjWhBPuINUAgDT/kY0wpaVtthrFAYxkHeWo9dyWsi3ZzPq8LCCORi6S310zIzNjzSIha4hI9F6bbkh+/jjPWHJSAyYGw9fKhYrZ6dyZQIr49cSC8MTQutcVSAlcLtKAAyAgCaJU0grOomZXFwoFUb1TglMpGVzdSIuUhVvBzCKXyx0zHhqtKSICiGOzrqs0fkFDOhCcEN7RoOJlWeqGJhskoUCIZdKtTMlW0WGelzompciGz4Aw5otCqQyMMaKsCYnM8cjSRjUE8Jo0slenwwU4ziK/q1GwEa9IZNgq/G4yuTYnqxqxIXgh+ZXGVWCWr3GO4OE3kLRxNsQ5FCe7ZMYw7FBKRiZVm9TVGMicCNkL2AErJ0TlvYiTaA1lAuAFXi85wTA2A4oY0LVYdwuJ4iGGtjC0o56SVQGkmiZQcSHngNzIdA6mbmf1/UqGGzycF1Y6szkUHt6Nurf4Gr0CDle7vYhkySaFlDKunr4hjvC6QvrAAYervTjQwUELLvDk6gnee+89oASM04TD4Up6ISwJ0/4gxLic4Kng+hBR0r2sC1UenXnFk8MBH4+jSBczIwwRU5ywpIQ5L1hSQioF7AhTHODYKioKZjCmOILWBAdpCgU4JBTMeYEvhEgDRr/Hr979MnZPrnG1nzCCwDkjY0BgG00h37ILIOoDtT5J9noer7Shd9Y7PUsEs8yLRAduAUOaRRCjRn95XXB7+xyHwzWIJo3otLyNO69O/wY6aKxCV1uylf3poWh5v8RjErlYh7cR47DD/f09EFbJ3xaDlqW8DiT9ztc1ScMQRASvxJ2cK/xH5JVco6SanHHW+m/LEc7LCpOqFUhfSC+n83mLEOh+U9hqhwXuh0a6FlBsco6ARC7eKYMbUoPfpUxk/FTLm6hyCqL2U7coOueMGB1ClI6DpAp6LWox9UNLDTi9HrSNtoO+699dBNUctgZB2ju9145z3QooRSoWHGtJ1CMRma0NastFrsNy5H0UrjaFc4Os6xUp5CvnsSLQtr4AjSadlWdZdFkvePP+GoleogrcCbOQORyuruv+KJqXtSjHaVRkue6+BK6UosiS1H/nvG1G04+F3qIYLU0ZudDdP1pJZfvTIOd+PotOQo2mu8i6PqdsssNb1cp+Dp3Jy2rZGynS1rekbpkfNeB1T1Btx24sLtNEFkAQts/Qltuzne/Lz1dp5LrYmoMMag7Gqo2VKkrDuaJtVahHSXqtZFcc6fMywxEhFZmNAsKaE9acpA0Bs/6ca7CxrityWsEAcko4nhZNFSYcj/copeB0uocQ5zJOp5MgjOqIr2sS5InEuVzXVQw9iaM5ryvIL+Ccofx7dZIKSllBBUjEmHzAMi847A+YxhHQ/cxJbqUz5bLZXe4NfLH+X7fj1Tb0RCjUNvSUMkaVYZTyDzFihQVyLTnjfLxDWWa4YQCqV1fQd6OTfW/7EPbHY4beNlj7vLR11d9JcXItWaH6YBbZQIhxPp/AJcG6WRHaBlM3d/0egeOkHpuUyV7z9VqixAysqSAGAmkjEgJhXebNxpuz9RSXXuM1+tIWwAZJW5tKcwQsyq55eLSNrOZeXRNqkVyr8BYKtzxzv0HWYW+z0BnKh0brsbSIEbMu526bD+0jL/m5vxZQ36qzU/4Lofv+Bg/b9baUjuWN9b6qSp188dZhsE2/XYeDbKqXxpcr6tAcF5t/e29v6LfjYkbajtKulR/ex0Z1jbbnJsJG7bk+AzCTxrX8CtXwAs430h8XybtC8/qVLEnYznP3HZcpEDaRE5jSGxStEIGbmn8lLe9n1AXGdl9mOOsz3I1XdQqhToA6O5tIsOdntDXbVytsxx2b1/niHh896LI0zxQ8HzosObdr2oxVv/aB+vzV59DOC6qoSXGKVLAIZAUziPq15oDbiZ332kTL5s7EbjIsRUQkjnVh0dgYiMDFI/qApYjAFrFwUih4UC4gL932HCAIl9657H9SNRIGqVh6Mk0YooikFaA5f215tzX8TY7XDcJ/pQ192xCcRu5AII+1FKS8iqEvShJyBEIBrwlUtLGIGnpwAwDriugnmrvfbIyEeYoaDXetaW3DKgko7FvkR0nasXKAc9IMZVlmPL8/Yp5Pij4AIQYMwwhHoWN3s+qDC7nNypDsAVI+E0oBcpGH0nuBaouXaHldZ+2k10Vd2rVrWWYR3vACy5aSa4mZ5fNEZtJhzSt8lpyaCx45NfZ5jQI7Qy+NUWTnkOjZmMOt53UxR0UPS420eeAqC8qloCQjh9nGr0bFmO7V+NnH+xx+P71bq99/r0WkKSV411CICgpsFolB82bQzViodvyFQwiYQp8YI9P/Nm6AoBQ9uY0rOmKbsxmKHnXpjWTN6TvqnMxSv1/mrN76o8eW1yAm1QlmtjVgJMbH0AV7idQChxDgvKquoUgKhLaOyWUq7LF7sd8Z+a/fB4woZ/abCAiekFLHkq9jro5H6ZwavSPpi2HrSUifVtJl7HtGM/Dtuh86aPWauz/o//6E+2vRe+dYGPqyMfTCLBcVR1fldqFG22Rn6/ywEGuFIQ9ASxWHQaqUeocqauCUkhCajUgrZLwoaR4uYCLs9nvc53tB9oaIYRjqd8v6c0LgC7Jfj7tJ0ciC6BzyzKoEKgvHDwGsdfeiJNqcfu8IvMpMD3HC/nDAfDrjMI2YYgTAKAS4wt2MiPNBtHWS/rdwvNKGfogBnAkLZzALoev44hbpeER0HmldkAsE3+WMGAP2uz2mYYdTKYBrG1cFDuvDtn2QamSi3dvkVYm4S43i7GMi+MDZI7gJXGZpSeqsPpcxjg5AwYuPX+Ldd/8b3ry5wZvvfFa6qJHkomKMOJ8WDNOoRkPO4WOQxiKVpCeb5DzPtWGOE81UDMOIkrXl7CAtWF0QmMx7j2EY9WFlxCHAa8layrlC7JtIyjcjAyKQV5ifZRz9BrXgSgby1iEtzzCKmZF27JB8fQFIDCk5gJFR2IHYafeMIuybi6J02WyzRCHkawlfbSfaGUxDSGS6dN5VUNxqczOaE2GGfr/fd5GyhXZdfbkZVQ15ZYb769sK2XAx709/B8g9dNE8sxCQomsEx+rsOAKnVc7pmmJau8fe2JL0p4flklv83S3cTUqANSLn7udNhUHvHlfDZKpmemLt/kfY1uT3aoaX12zS0O28dHE/fURm5DVStn4znPY+5xnOSlH1Wjw0+i8SITi0vQC5pY368TNovzW5qiPV/fnkwyJ7gq7Gi6ixvz+TEbbXSQMM2QcsX9XXfxOcB3htIkKGxpkjDTTGuUjVipiIITohRKRcAPJwPsCHCO9jHWMh6wUsi1ToXF1dYRyl22UuwNM338LHX/8IznkMcYQ1OTK1PhCwpBWpFHgmuCT8n5wSwrTDNE1waG2fg4pweWgZLbK0EWcGOQ/vhKCbilzP7YsjDuOEScl6pQPkLU3SVwz1Y84Xc3f586t+vNKGPg4B82xSqYSsrQwzM3IRJa0VAXAJYI8QJ4T9NYbxgPv1XDcmkP+NntF6PISaWxRW3wNAFFgy5uWliLzkAmKP3XQAAPzqf/svuD+K9vyXvvQ/4I2n10hx1P7uGess/dm9j1J+ovKiROLZpnXdRJ3MUn7GYIUpC6BOwLJkKTNkkS8NISCGQWvg5V72+z3O5zP21zc4XB1gdbC7nXAKmKUTnsDXrOebEOOgETawrovC8gLPWa7WjFpKGQQVsikePbwszOCAQhI1tHpcByOO9Q+nOQl1HhwLmU8jF0/Uasmdr5FfyfLeunmTRUeKiEDXBJpU7LIsOJ1OG0PPbBHTNq/aDDqU9S9tStt9slaAqNGAa7X59STNKpFzG5/mMpUBNHGXwkKq6qNi+YzVwash0WjVMTYGklnEp1Dkd5m339v/TZIL2xhqq7cWJ0Ai9qLr1nsvpZzq2Nk4ClHV2h43MmspQm4ksq5x1MaEAWTjvbjqdPS8gAdCP8htztE7Z7ntA/29XswpyFCi6vYAFt1fHJepvQf54Is/jx8t+ma2aF1e6+ddasTbnFv/eTuc5tHtiguAwrTRRDBHykpsi5IqHXmcTqeNkmd9LgEtX7Von/HyxR1SkuuZlwW3ty9rxU5KGSBBYJgIyMD5dKppv/l0Bo0RhSG9N0gQKE4Ja2FQtLbPIuPrwFiTVG/4GOEccEwZ4+gRlTdUnLwvM7SZWSO7Pjbu/An/fh2OV9rQE4DgRZ6UWB650/0ZDEIYB2nkkIGUZUEyeRQEFPL1HJKTKorqbL1yq6+9XBTbHGt7rT24Tv3HFUDBNF3BuxW3ty/x//n3/4tsyC7grbffwTjutGYZ4rkGD5Z4A0weRGtlptvmU0pCzmK4TTluXVdwkmgwl4yUTfOckLSelbkgqaF/eXeH87yAGXj+7AVijLi/uwOcR0rSXOJ8PtcNQaJzIyEBKEBJGYmbwazQvRou+awYcecdsAIgQwBkQ0ipYByjlP4olwHkYQX0hD7P36I7i7LrfJAHU9rIg2bdMMhSGxW+fGRzLqypD5lbcVTaBtdv2o9t4LZOxOhkWY+llQw+Bs+CtBa+rqluEyfhnxhL3eYZsA1be6LD+qaropiysx/mi0lrxGXN97Cx5ceZRYDERsgT1ffaefqcsyeS1qAK48Ourm8gBPGjPJpz7BRRcc4hs1RJOEWJegnbmvIpZTN+pNctcq4KZ3eOz3asGUT9NmevGcmvu/fSVBjJuVpFsX3+uz2iBvIX64Ga+b+E4zfr5NLYqENDF9G8/N2jFPY9LRUhKprhAfLhvEjRkvfi/GdBJFuvjBb9S/pukc/rvceO8+SclP3a9XhyGHzA7DwYDldXV4AiA9O0w253wDDcIcYRiSXtFOOAxIKg7HZ7IRTnAlcYcb8DLwk+FDjvMY2TNLmCIJx1LRMwxIjT6YzCBcM0YrffoQAYdpOUlq7SQ0GxQUFtGa0TYTdO/1s4XmlDj5pvAwDG/fmM3/KF34K8HjFEgZjIOdDKgBeDA+fBZKpyrHKa/Ljs9GVLS7SHtReKAbB5cFlJgDFMiIFwd3uL8/mE0+keKSWEEHE43GCcJgQfQOywphXTjrSGVzadcRqwnpKeEwKPW9DqtuQ0gXJRJR+NFV3U2BARgjb5cUm8YWZoPXKpdeqykVIth+rha3nYJTq2n834WG2vjY1dk3jwWk7nSGFZpU+RNbURMLNlQE3pTMvD+s0NW6fK5qHB3S0Sqnnsbp5EXWuLhMCcFXQOlTpP4zgixIihFIQYQFWbweq2++syyE+IYtQ7lJ3hrffSRfGPbjqs/+kjQP0Ouc5tPlqZaM0ObciK1EH71P1pX9bsVHdP2L62MVyQKKmuyc4Je5BfJ4kupVys3Y+IETnhglx+d3eOS4NJhmWzbeT2+vYcvDlnXXl6njb2PUJnA7h13m0uoOulG7lH7HX9xgsk5PK1y2utg8W2jttnRFlWOSTdhYgKn1MGfTuMtxCCRMqs61xaxPoOoRCnxzpJCgoGlYs2PQpz0gQh4FK01M1aP0sEfj6dMUwTvA9Y14RhGLGsq7aZFXRuHAYsx3tJgTIJUsnaXIlV3KwYoVNTZ1g12JASSQ6CRqIAyBlpXaWb3tUOMYpkrm0JDbDhDR/j4cBj+0i8Rscrbei987oQMhwIp/MJX/ril3A6v8D98YjzeUbc7+HAKKzqW2EAOVFSkxy9mZZHHjiwLhTb5B4+nPJ6I+O0cjQC4JHXjBcvnmNdzyAQnt48RQgjpt0ORWt1PRFSzvDBwUeP5ZxxPp0QrkfMbCV1ej3MYGra5Ju8aY22GI57ERopPwwugD3DUapNTZyTJSDQuBheg1vb6+1+e4iVIAQbGyuBGFVvn6hu4myRSo1WXY1CLNp0qpNv77VzUdd85fKwzcWMs1x/0CYkZiBKnaN2DbZR2mwb72Br+M3QxxjUUdnyFWw91OmmboO3tXCR834surc57B2oej2l1IKQUkTvvdo3jSarkdL7cWoM+rrurSKjq+OhZ350bM1o28/9fdhr4uxJ1rhvYNM7oJJWaOTMktuc9FUZ6DocPjbfjx1ELcf8mKHfjL2qEMoS6yoALh12/d9WJrdDdQA9z4UDcPG1l07c5d6xdWCoZmz6+bV52sw5zMKxnaiJb21smNImGVUlUdA13wi8ZgBrCaOJ+LhaflvTQCxrKi2iWCnlkRnrmnQtMOZlwbDbgZlx1jLe8zwjxCgIBGcMuwmnXLAsqzoqctlpBhwXhdpFDAhsTcCaZontszkLgTctjPl0RiYx9CGGusbqPOj4lUfmu76peQSvXbT/Sht6YYBqUwUGyIvW/d3dEXfHIxxGTE5oUQwxHHHawWlPY9IcqaNtjl1ju/ZFxOhh/PbQUPUae5JHjHtwIXzjo/fw/vu/hqdPn+LJzZsYx6ku/JwSXBSDlHOWVo3BY9pNuH3+Eh9++D525Qbnu1s4ihU+BSQXv65JSVnygK85wVXoTuRzQayGVsiBiROW07ltvrXOubU2dRoZpJQQvfAFTHaFABGcyKJPELzHEIZ63yFY452ClHK9L+cDck44n6Wc0AWPQEHqavm+Rkci1OIRlqCeu+QgrSzRUAeD1E3u1jb3cRwxDMIZON+fxGDWjm1ddAmNbmDOQqgM5OqgqG63yM/KGJ5Op0eNEDOjlUDJ73NJ0l64kAoJ9TXlErEkbe8pr0tbUtupxeHI4OJQ1CDlonLITkRUzDGReZMZYjaGvQqE9I6F/qd3pqS6Qkui7F2GEBkqwlyjxT6V0RwH+Y9olD90Yuzf9Rmpzw43Z6J3tLrPXebcNxHyY9EwLg19y8v2r0k56kOkgJlRMrdyUqhb2N3Hwyh8ux4ME2CgOt+btfLgWuzc9u8tksAAHPwWgegczBaISNkdqTx2KSKHPS8zAK25BwNccDqd6vibo1VVD9GU+kwNM4QABtd8f4wRyyI188sqKNc8LxiGETllLMuC6LyqMZPUw6uMdZgG2RsKS4UPGPO8gFAQnRGjJfghFi5AI9dyldU1Yp4LHiUnMIBxmkRKW/kjJohVuSNl69QKEMaotL3XzMDb8UobeueAOE4Yd1dYzwkRBfOaMB2egOKI0/0Ry3IGpDgDACGVBXk5IU4HLOqX+25yt3r3NchosKb9h+UhloXH8GHAYT/h/u6I9772Ll68fIHrmxt8z/d+H4gI8zxL/mtd5cHUAMY5hgsFy7lgYIc4HhCGFdET9tMEn2OVDJWHXwyF84C0jZQGNeWcEWKQe8wJ65oRvEdiYE6Sk3ODx6Juj5Q2ifTlMp8Qhz3uXj7Dbj7De4cwDZhPt1jhsOQFWAvO5zvEcVDi2yooRbA8asKSZgREyR/7AFDCup5ACDVK5lKwnGeMk+gY5LwiBAfGisJLh5pIDay1wJUNzIM5VTIddBzE+QHm0xHrfMI0Ta3XOQMEEc7wPmImCXNJUwekzU8bKUscR4ZUN6zrWuduv9/DDGRNl0BJe94p0cnIgVJTXJRZ74M5G6wkNSEntRSERXO24WdkMFCyzBdZ1F7gifWPrU1qTp8LYC5Iqd/QivA66mKGOEDeAT7AxWbQoT3LC4BUxOg451VExSFDypas/wKpBGrJQOEEZK6b8iUaUA0pF2RwJUzKsyavSxnmQ+jfDrtXeIeyisMEuQIUIiQWMZU++g6lGU5bWy3PragXRHpZct0qmKNhdFFHzJAg05OAjgH0mkgdfm8SuORA7LfXTlSdPfljtejiLALQEjepjMksIYfzml82WdxCKh1M0o46Z6k7J0IuDPJeI3eHOEYM0WtZozif+/0V7u/vIP6TAxAwjVe4vb0VFUg4gBx2w6hBQdIqnYjb5YjzumDc34BPdyAHjEPEG2+8AS4Jh8Ob2E0T0jwjDiOmMSAOA1blCr31xhsoKWMYRzHSzDh5UfAbyNc15r3HoA6FyQ7XhjohYCpJ+QYO07AHEfBkf4CPEedSQFTgCkSd0Q8aHHgwRkUOTH2zleK+rscrbeiHIWJeVcQEBu3kWu5mMDqj5WZTyTjevcRwuJZSEu7gVvPgNQpkZjXIRjRTxSYAIGEMO+cwDhE5Fbz/tffw8uVLDMOAz3/b57E77EVYJjNEgjUg5VXRhQCGwbOyceZSMESP4Ak5JcQQsEAebimNEmIREap4i3NS915KwTiMQsxLCQTGOI2ioc7AEAdM0w7L8Yw4RuS0AC4hl4LJE2gpuL97hmGIGuEyhmFA9CNiHMB5xTAMSNk2X2lkMYzjJjfvFKpnLlW8R1rdGlENXYTuEcKgn/V10xNyoTpDFx42kUjZVmdL+1n2OURh9ht5bDPBAKCRmkagGuwVFvRDDHlGHCJ8kRreZVlq5UGMsauyaA6HpUjMCZBNPykyVFTmVebN4FDSDn0WVbd8qLLQmSvjnkh7wxegUIFz23EppQCOEJ04NMxZWdRm4JqhZYWa+6jZ1r/B7BJlizPtnJTkkZWjqpE0wqM9W0ZmNGfNzmnX38+hcS/ae/r3Pw69AwAbzltRNUMKCKXQg/sBEQqnDTphv9sSJW2n0Gt88Eq79svruiRc2ti0PcM9+JyhX7YuzJH33qs0cAFzBmdxNgyV6FNnvcNpvS5yztXAO5I/p9MJWRnq5Fq6K2dj2HsEH6U8154jTcGtWt0To3QIsSj+cJCmWFJ9I3P20Ucf1fenlHB/PuIwjHj+/Dmubm5QUkJaV7y8vcX9esZSBDUkIvC64r4cscZY0VRyDusSsK6LpPtsfRbV2FdCpScPTi/gPON6t0MAIa+qCho8nI7tuq443t9hPLzRUrUGc73edv7VNvSWZzIIklWB2voWW5/6RNaCVLzi0/0d4uGqOxPVDQPA5umuEBq1mkx7gKOXnPJ8PuPF85cIweGNN54iDgPiMCiZRMRQEkQFi5x08NJvBTSxK8SugugdHAFLTnDWb4satHf5kFtuT5K3suERpETJwSGXDKfpCTCwrgm7wx7rMoPUgZiXE8bxUIkuVi6Xc8EwyYaUctIHbACzRlJs+WCHZAaKtDmF4JYqpdqMTBOEcbBOeDEOKEVqei11YKQhIU22LluWp3bWwY3EgbI5tA2vQsNWwifD2+a1e5GxFclxusGZI1c34FzgRndhoJpRMMBWnE6pR255Vls7kIYetcqet+u3izzrlVZjYh3QCqziYrNOC9e12admANQN2Uhs1hWQuEHugmzYa7BsdnedD4lM4gw/8po9WZ1x7Q+63Fh5+7sezq7fXd+qvyuiridOG1T+DorY2Dl5Y3Sh93hp+C+v99JB6V+/NOrtBvR8zBu70TtHlzC9XU//c39qvjgXOuesGnkGrBWxI4LjrcOjWsF6fVKeu8wroKiWXXfvIJnojuXDbe+xP96bMI+gEiaiI7NTwMTSS6MrPTb0KytCWaiguCKdAJVQklTzAwBIUa2SU02B6FWhFEhKy9CvtIIIuN6NiN4hz0LOK3peqfQoWNcFo3ZabLLnr/9xKcr8Sh3M0MgAXT9oVhU8RloXafAAXRC6aaZ17jp7tbQM1cXUf4eSeHQ335KKMtZlxnw+I+eEaZpw8+QJ9vs9CCQ6zgyYHjXQItdqHroFTADG6OC8SE567aQl3/c4A7mOQ5eXY1apVJa6cafsdS4ZKa/wPlT40DuHeT4jxoCUjaSn6ndKsjG0JNtDr4wwgzSdD4pMiDNlToUjpz3JbUOQjUUcCjN2utmwpUmoNeVA22Q3kQzafAECLZOej7rP2LXrtOs8X0Rk9NDoEEkJoHX8Y432XXcd/fqon4MYR8vpGit94zgWKRms98xtddY8UX9uRjX06OvUO3i6j8yLNU9SbXPjk2yMV70uTWHUkaOamzXHpX3PQ6nXNs7y3gffQ9s1u/lj99ejDHVOewNvRp6liQv688iYUDdO5sT0P6Pey/b6H1xT9/r2/j6ZHGhrcPP7i0ChP0//vX3Hxn4FXb4f/dhcICObOYWteYi+uz5Drt9v9I+lIsTIy/ka/0JEgaoWgV6D976W1xXVRrCV4p1xaDS16GQthajIow/C85C4BoFcbR9trax9CK0KQ4W6CLwhDkqPDBKkyznRyHDad9QRpnGAdw5Z131RB5AAEGuXSKDxrGzsbM28pscrHdFbQ5ghBJxxFoEWlir0nBOO93c4XD2t75cF5xH4LNGZc9q8gQFuna/KxYN+ecQYUVLCsxcvsS4rYoj4zGc+XWFzg5ekHKbL21Ij+0h+Vzc1gz6JME0RMXqceMUQJwwxA5BouXnX7drsnCEEgTVhJTUmMuNUQc+EV7Q2HAxvREQtoyvaRcz7gBijltGoQdcovuU2tZZ9zXDOS+2qGXQAzAU+NDU3wDYS1cm3lrbK+ifyWqbjunvc1gT3R4WXsd3w7HMyNpYf7ZxAciDqDCq19qv2vf15vQ8gACtoU4Jo59uUkqHbqkuLKjs7CGulasemKoAg8HfZOhIWqVP3mpH3KkOfGSgZJckGjMJCnAQDqhQoiI85EI8bs8fG215raYBmXB6MfbWr7TwtMt9C51ooVZ1cQxvMmHF3vRbPt7kBKqNG/90byv567FYei9LbPeITj8eMfYPNJUou3ZzVyPObIAAtgqbq/DvykGxXxwHpLsxSXjFGefZykTJZCE8naNtgOa2DVaH0BErnHHyIGMcReNl1bwTqeQXBErlawIFzAZykG2IcFO3KGMcR96dblJwR/YTD7oCUcpeWSNjvJ7x8Lqp68iBJSmAI0hLXhwA4Qf7Gaax6Fhb9t3Wp26TNg6GjziMQoZxXYAyIoTX78mROTlszNWVU17DuV1uf6rUz+q+0oXdE2O12OFxd4f54xrowcgFczliXBfPpJBseKZwfIuI4YXISwS7Zcvgyy43wZi6uvG715OMwYhgm/Of/8h/x1Xffxf/x+74Pn3nnUyI8Q8IOzWzGUh7IWXul24O7riugkFP1lVk255wK9uOIGDxOywJiwmOlTy0X3m+84pU6FzRKKvA+wnuRASYvTUWK6tqTogne+9r6lnMREYwYVYmriCylQebWyjYEac3KqCpcuWSkNQGjQHYAkJOwY2OctNzLOoO1ml9BRlDHgfSa7OcegpbN0gh0242z5YYtSmEABVJKBlhEZ+eoIq3MWorb8uTmUHnnMcQBTrv5pZRAflurbOumV2Kz7zFnxs4JoEKZNmfN8MmGe5kWqMhA+zb9XNFqiVINsKANfnMNYIBUJTJrusWYx5JjX5Hz2jk5GcyhXSMBDeLVo44zwEVSU07RNZBETlXtr1gftId7p81RgcDNkkoousm3CDN3jpqaADCjrpNS1KFhwOqw28JoxuExJ7k3ppeR/OV89nPzWIB/6WxeGupHUQEzWt0fc6gfczD6n4X8CXVefXNYfahQt0Xgdi0iWmOGWNA/X/sgtAoLgihbznnWAEk4ROfzWRj1VQNfUnrTYcJ5mQFIZE5OSMHOBf3bIerekXOuEryGPsYoTkokp45TgSmeispoC2JgqAUDgw/wRPjo9uu4eutNxCGgZGH9w7dAyvgkmzSgzZnyKF4z2745XmlDbwaNc4Izl4yFeOScw2436tvUnJMDUcDpdMTBe1Aq1eO3yMEWQilFakKHATc3N1iWBe+99z7+63/5JXzHl76I3W6P+/MZYThpKVZBYSGxgGXzWdcVcKIgZVGsnT9r21inD3pmaUQzRMnRn84L5nkW1jq5en0AWs9yXbxy3ULiKbzq+wlrXgSOh6jIJdU0tYfcVMDsPr0PGr0L+eZ8Pqu4hiyTZVk6Ik9GVunLcRwxz/LgkzJb5VxCHnPkAS/XPQyxpg3sXkJ4uAG3vvRtuu3hFG++PbAteu/bofaRn6ZOHKnzZA49w0BNi9aJ2v2WlLGuMjbTNFXJz0+CcoloU3rZi/mw7jmFS+VeYOPsGRJClYxXr98xOuk5baDUxky+Iyu073QOEyxSs7W4LLPOn3y3OUZ9Ll/+LZAyqnMCgDwcS/1KrqmJ7sI1VypOSAbgYVwCu0YpkeD6HeboCHSvTjakbwWbk4aWPxZlP4HmHYDUoRmbOSERrBKk4PHc+ie99ljkvXXGtkbeHNGWD7f5F8enl+k1h28D53sxXBLF21i29WRRf5/+AIA5SY7dzi9toEVRzgVnlyHf55ozwRD56rQs4lRrVNyrEYrTu0WrJDUnTpj3Ea2Fr12s9NrIOaPkguw8sqExKYNzkkeQqVYSLCkhguR5KAwMwP26iiQz234xyLXUcAywMkHKBZwzEhHO5xOu3nwDu90I5wWpjd7BFYcC3SOdQxyFfZ/0WWFYNN/pI1wgXq/D8WoberSIr2hjm1If4IScFllczsG6mTEczsf7rnMTwfLbZvxsgV9dXYGI8O67X8VH3/gIBOB7vvt78fnPfw5EAnVlSYKLF1ssymqHr+VC1iO6RYwSGRXFjwgowBgCQgzIlvsKTiqeLiIK2/xaxMCbqNSTw5pF4945hwxjSEeUkuR17+FzwTKvlf07zzOG8wneEdZ5wbqsIrZjaY0O2mNmQSxyqRKmOWcsJJ3unL4np4Sq0FUKMmfM8wKAVfLXBDASUjKBHnW4rO4MDRJVv22zUfdQqvhOXQrgkRScfYee6AHzfBxGFMh19iU9/iJHb+cSI6/rSb/TNk2DYg1iJk0iV+4HAKmLl+vZoEpAFbwju5kOCVK2YnVwZY4DHEk5VZ+GEAfWOgfaOVSLn9zmu5vzYPfIYPawNsritBBcEZJpb+y263JbP89dqRtpgygT9zFuQ0XWzKFgwPrM2mYMbnoK/WFjXrNigJDGSqnoXkNpWZSD+9zKY0cxHsBD8abLyFsgZur2Icl59wjBxlmk1ia2mOP5AD1on/XeI2g03TsMzncNouz8itoBQFFdAKfPiHNOApDSOghSN4/OO4zjKIY0JUQfEeOAQSPvEJw0rmGg5AIfgkTphTGoXkgpwNXVVW2+BUhJYPCxCj8NMWIIotcRpxGYZwQvksgpJSE1WwoKreog54RhnKTqJ2eUNeP6rTcRSdJWdS2ypn30sXHOSSnsuoDdVptAGAGvl4G345U29OSkLW1QqMggcSZhGR/v7mqUyYkAeDgndZlClJPPXeZdLWo9Ho94+fJWZB2HAYf9AZ96+x3EKDku5wxG6iFYe1CbYQLstW0UUeFMFrShpKId43wl/fngK7zVi4YYbNmTw0zH2dpvihZ4K78RKG+npJjGcBejKnA+d4Z8qcxeqn3PKyJRjKC1zR1zswwAtXGl6lC1zVzBDIUqWzWDse7tqBs+WZRrRqnrGV9y/b2dAzAD//DxrYQztsi2zRMYdVMHrFabVSlvqjahpkzMk9AvrP/kFiegWwcav2o83wx3/X49t6P2OVlYIiFav0jvrO9a1+5lW53BHXNe4Q2ZP+vbbpE3Sp3X2kWuQOFPaVJTVGdAjD1AxVUOSV0HF38ANTZeoyi2aNxEglSyFN18cL0hc22gGsCXmfgHxlFeUyNIDtlaSBuEB+0nYGugO8/GydKrq2kQPfElJN8i38ePx+B/ALW1NDOqIbbVKes518/ad/kL5waQhFataNH7YWBTqWLf2esccCmwLp6APYPyjHnnsahAjvOtsZQ8B3s45zXy59pgytJyRMLKN8fEJoR0v4nOI2uTG3JO2luQE1KobQzVGbL9o3X1yxlSKhjEsJ+P97i5/iIitEeBjkExR7gLCnvUT7QOqCJA/Xy9Tscrbei9j3DkMAwRwXss1DbPlApe3t5KtEgC5TgiwEn9ek5Za9ltYxEja81gUlrx3te+hq9/+BH+h9/2XXjnU29j8BGn4wnPnn2MZV0xWb2qc1jXlo+1g5x4nubNNoNkm45s84VZNe5F9EZYo7mew7me1crV0NcH27VOYFWmlTuWrBoIT4whjlK/rmpXMUY47xFDrHC4d+KBW1TunEWapXrn61Jg+tiXamney/mLsqWlj7s6LIpk9OcEOgMjg9hFur3BlM+K0E2xXXfrFHDLmTOziBrpeSzXbeMuVyBj02/wTlMt5tRIpMS4u7vD4XBVHQ8xRubooeuXLd+Xldl8mXe19MjWnKCL+uvd6IbV1kv7vXpy9lON4lqaqDcuMrZdLhsqS8u0MSQGo5fSIuqWdpDo32B2wMosw3YTrU7Ytoue1ejbvNdmM8ywstB63wBqDaCZrboZt+huM/F1TLnNNDUDUZnm5gg7IWa2MWlGt18nPf+icOM49EcdP7TzNLLig7dXZzd4d/E6FGJofR7kfK46P81RtnC0OVdmqEvHFWItQ4Qy4i2QyDnZ1da9yUifBCHIresKclJnD4KurVRTczXP76XTnQ+hBgGAdLRsKpMOTPK9UwiAph5Xe1bnFVlL9EDQ9rZCFkZdx073x4IlLYhZUNUXL17i89c3iERwpU2mNBCT1uApJcyasqgoTjc55ra/jscrbegDBpzPL3B/fAlm8TwprYjjNXa7G+ye3OC8vIB3nwLzDFCGBzA8eQeD94AvWDJjzQU+MMa4w7OP3sdHH38D9/d3mKYJP/iDP4hlzTje3+JuvYVzHrvdiHEakXNGWhLCFLfkOLQo0rmgZClLefYwtElSMjgvyGGPMUaMgwe5Bc9fPocPwLIkOFrbAwPJKS/rijWpx+0c5rmRqsSo9qMlD1jKZ7z4mLDMM4LzWErB/XrEcjyCgsO6LFgUwTitZ9zd3irZiXBahDcAR6KIRgROokgmxrTUuuxmTDUiKwyvZX4FCaAGKQNKrCIWyVsvRoyK1XOL4puQ6F1roUoOFAIwo6ZcAIZzASEMIFr0IZfNfIyjKr9sN2rvPSjZIy6ktRAHkANSySirpSYyUkmd8RRlQpDG6NSiI0GSzBlo0bzJ3jp5Qb9TKhTsR+fF8WQibWLi4FJz8uocl3YdOsUbZMp+V9SomuNiZY1oH0M2w21jEghlbciUpCYIQowyX6Y5t1tIuyiCgHotAMDaW0KGRIxZoKDln/Zs2Hk6Z0rXRV0vjroSaHuPELYcKalPDaxVvMjAmiuhPB40Nj+oR0YMreJa/pW5FvcJ/M+PowgMwAWRfOaaX0eNoBuSZQ0MGCDR4I/eKzHV5lDm2LvYnFF4zEmY9b6W3nqAPE7r0iShIWx+7xyIC5yJ+qhe/bKugPeSloA8s3ldgKKyshBlTTdEZBSUQiiZAZfgHEAI0kFTa+bXVaqNvHMoeQY0sl+XGeuyCiFPmsaCIfXssN4DrFU4zjQiZO6ySnk78+1IyKJ+cEjMKBnYZQbljNt1wdtP9vAuoWRoqa+H5wSQgy8r8jzj+f0J3waHmVAbgAERnpNhXpvA4XU5XmlDz8jaW33CWm4RQkRaJc9+ffMEn/70Z3F/zHj77RG8nrCmGSkt2F85xMg4nxN8HDBMIhbz//pf/jWcc/jcZ34Lvve7/w8YhgEfP3tfatr9Ht7vkNO5krJqXpJoE7lsrrHb5A1ub9rbBt+1/Kj3AcEHWOvQnBg5Ze2+16Iv23T6w4FU/KRijPW7S2HtDw+knMFOYMvMYhgzGM57rDnhvCwobIzdBnPaZmOHlPi0Dnc5Z6xpVedGCYMaFRSnm2ouGjGWmnZ4DOotpYC8dqiCwMWi3y9KXpe5UrtP+ePqGNnWfvneS2jOomEAKCRkNlJDKrRyQmHJMfYM5cfOfZnDbUp6LdVCF2umwu1SGl43HMu19t9nKQ5GI1CJMIj8rrZa1SjNzlXJgYXBRZ0UbDXk7d8yP+qq6WfgW+Ro1j7GCGt2Iq+16LhPUACoaFAt0+zK9R7bXLdchYfzt7nmi3mtaaTSoU3YptIe+77LOXywzrpl89DB0buu89pSL49BwaUU7c3gG6/BaZOnoMjZukJEpbyOEyoSF4L0aBBp2gGH3R53MQJeSuKckzx7WZc6px6i7hii7jmE6kxG7fkO7+GHEWEYkO9e1PJb6S0i92pQvdTTB1w/uQGK6M6Pg4hqMQH7aYdpv8MQJC9fCkuPjGkCFmlf7Y38WjIWPYcjJ7U13gOOEErHORgidpBU3hA9EickAsbrAzhLQ6/KRxHIFKSaBeQsRcFwJFK/TFkdkLbeXzPk/tU29MEHcJF+3YfDAaf7BLiike6M+/szdmGPVE4oJQGFwMXhxYvnGPw1rq7fwfF8wgffeA93xzPeefvzeOedd+C9x5LusfIZhBExEJgYXNa64YYQkNdm5DYP8m/gELbPlPp2guQwSaM7qWNPogPPbfOQRjGNId4rtyUyVSlNCeSCrHC7RSe73U6Z4frQOBJN97RIuQ4BTEKSI2XMWAR4uSEbchFV6IKYEbSMxvuApG0ondbLOueQSnrAPO4NntX+wtAOWEQnUWbdgFnI6EJeai03Fd1UI+hrNF1hebeF6dtrXc0/ZXANGRmteyDh7u6uynz28ymRk+rk54LCJJrnzljo2/l/aFhlj5f7k2i3OjyOHnzW4Oh2nW1KpRGcOnvU4N6+uYvkhjXv6jSiZgY2DoHla1sqpaEZMjYSxXnj8j24T+v7LpcnUTex9FsQqN3Y99vPXo7P5ia7Q8YAtRFRn4OtqFK9eHsyPvl4zNg/uCc0R+OxNImkCVrKpL/mNr/dzySERnG69ZkzB1MPIwoHbX5V56cA5JtqZEraG0E1McCNtNg7mdJ33nQJCGGIyFwQpxFZW7waX8eMvD2rVh4HGDlWznk83mLaTarfAIRhwHI616ZQTtOPa1oRU2wOZE51LLkUgeu86N0jZ0T2SJD59OQQMyODkJYFeZC98LwmvPGpNwEWRU8mCWJI+0IUbhLD3WzL86F7W9Wbfg2PV9rQM2vJRBgQw4pjWQDNLeUi5W53tx/hzbc+q7u/NJyI4YC7uyNu705Yi9TeX19d48n1m5h2O6zrjDWt8AgAC0RNZnA6qFkiO1nuPdu3QoePXG8fibSNRI1RsaheHqzz+Yz9fgeGVu2w/O26qKGPPGTj3ZJ+TGTjMocqeXjLrxOC9xVKdt7BRWlB6X3oyDsZwUe0drLQ6NHX23BqPERGV9x6Uvjz0kmw6+5hZtkorU5eHlTSznB9y9w+SusjZoba9k2kxd14eBClBw4aKZDbWyur/TeyIJGoBUqhhJRBGjTf51Pb0cHjG2P1SJR3cQ6yz6rj0o+PpAVah71L418/2H9jhwZcRqGEfhzs3ns9/4a6bDQN7DzmYFygSPX+6jRw1w/cNtmH19g/H32apP3+IpK2cbow0o8hBf25HzoQv8kwTufcnpdPRne217N9VmU98cbod4amu3evUS20D72pX9rYGxq4LEtz9BXVssqLVjLH1TGpjqL4A1hThoUdzIyUC1ibxlg6Q3gaPbLUHJ5lniVqz0rWJFREIuWMQEIKTSkjR4XrS67IWVGnSeRqhSPEzKBgw63jljNKkoqidVlE5ZSAw9UBKFp2jAKvziihlUF61wy9rqZu/bc5fd2OV9qFKaztSuOASlbRBSkPSsBHX38PUUl7As+s2O+eYF0TPvzwfRyPR+z3N/jMZz4nGvBpUXU7iaokurMyI1ehqx4e7Q0JtQSmGKGuoUW/UQKPPfzyuncOITptJVnPWklcQFMp6w87f/8AVtY9uppfJag4IsOJxegTmVWRNEhKcOQllQBSNUE14mwbrqjrNbhruwlbmVFjzT6+5C7hbrsf28iARjpkbDfsnpfA5gx10YtFMzJvbnN9/XcaGtB/xqIeGUsPk6/dyBjbrtZ9tt4DHnPs6l10rz22wbSccr8+yFmfh8c3pW+2zuq4dv/uz9Ib18fGqb1ne96tkbX7237mIXy9dUov18BvdA2X9/yYo7C55wff33/Pg189OC/p89FLPfdrbPNdn/g9tFmb2/c00p/+ozrqVimTS6k8Hf02MFrrVv1gdRT6tWDPh6F79ZrUsOdkzZdk7yNNBdpaK9xSlTUC56YrkJK0xub2IFUN+2Iljvr99lzkXFQRU8bWCJtFx1iMtnnLikxZfgsMzhl5TfDO4fqwl4i+FGHbg0FZ1reVHm/3H9b/y/9kf7W02G/S6XtFjlc6oqf6IIji0jCOuLs7N5ibCR+8/3XEWBAL4Xhacb6/xfWBcH19wOHwFqbDNcb9Huc5ofAZzqNGqDkzhsnyZNKOE+qxGmxV4Se9HoNZuYaWsvE9tmz6/GNFCpxDiCLlmNIisHol9hjr1SBKwODT/sHrWfi8sorlyEaw2+0EpueCZZ5BzFJNcH8HB4X/lhXeCTFoWaTXNGnOa+k6XGVVoDrs9vV++npqgy7NYbCIcwtePtyQe0EdoiYMZGp9vVe+UQkUWhEIatBhjOk+F7z9zgdQLRMArz93DXE0irQ+3O18VI2+baJmOGqUgLbRXzoofaTSIz5bI6CG1QHS1UycrMtrfxj58maMLK/aon9XI0bHW8exd57s/Na5TyKtrJt0vqi66C774t4fc+Yu/75Uk/tmh9Ov6O/9/8ven/3atm1pftCv9WKMOedaa+9T3/reqDIiHBlORaZx2omwLIExBswTLxTikRfeEDJI/A08I4GEhJEACclIGAuRKWEsmZRSuEobpx0RGdWN4han3MVac44xetF4aK2POfc+JyKcb3mOcoRW3LPXmuUYo/fW2te+9n1flRTczuVz835vnqevTpxv//vtBGJP0b4iibl9/G1Q1bfOs4Rx73wZhYvhBpLvV4GrEOIbKJ31oi1YllKIYiZTqDJPE5fH1863sX7+4XBg27Z9XSHyplJdiMzzxOl05OUX9nkOxyMxmG1zTIHpMFNrATrTlLi7u+NFCBzywadU7DVFhNPdHVGEnCdyTlzWhTzZtI+1K2FKmaLX+8zad7BVc800jpBf62j6GjGZimfpijT41rvPUVVKr9Z+RJAKmn0PLBXJcd+fLB/pNwZOjmiI8FWKpF/n42sd6GMMTJONxJ0vF7R3Ugq0Xkh55gff/xFPP/s9Pvnk53z64nMkCO+/9wHPHw5cXr8mxBNdAsu2EmJEdPKxKmVoT6yLM5bHrqKy+1HbCJI7Me0b9pvQ4MhuRa4LeGTkY5QlyKi4FQlCTolpmpAeuLu7u2GU2waSc34joI+NLLtZzTjGJj5IPQtCChGmiZxnokCrhZwzp+OR4+Fo8HwKPNzf01HylBnVdK2NaUqcTifO50fwlkWanLQ3KhZ/Xy03gSYGgricqQb7uSH3XWHY8RxDA1BxkZWrveYQmnkDObASy4M7pCCEPwcaHsft2NT4333Wf2iXu5DNeEwpZa8OdvjT2wtXMBAv9NteLb15XCvia/BQg7JvNvwxWjYsf99GhgYM/3ZQfuN8ck2UvmrO3caXrmi0VfgjUTN3w/HY2jav+G6TnLeDl+x//6p4/aVguUP+vJUgvknOvHkFcPh6vNcbiYQz8sN+rq5I1rg/vurzjO/7VceX0Yqb79zZIea3z/1XJTZfOg8u2DPW6tsk1Z3EKdffmT3sRox5l6QGuFwe3dPAURoR1m17Y89BYVsry/n8RpJca+V8eSRn85y4XM77mGkpBVSprbENfkpXLtuZWgspRZZl4enpiePzO14/vgIJqMCnn32GzJleG/3QUGaenp52vog2W0eDWChBWC8LiNCD2XUf04QmazFINw37jU5vjTOB7XJBBR4ksJVisH0AaWOP7LRabG+TCdq1RTeq9zjuu6+8A77+x9c60A9Id+xSY1NqzfSeRSKfffYZ73/rC77z7e+TpyOtFl6++gKaElWQDBI7aPDRp4h6n7+3RghOImvVIW6r7OVmMxmHTfh4cPqKzf02CQDP9JGdpKRa3U9aOBxmXr40wZ9bExiRoS99ZVfvfbIBOamRcUY1HlNw9nVnnjLr4wXERmt6b8zTkcfHV5iz3OZZu1W1m08YhBB2BMOUta7wnZ2T23Eo2QPI+Mz7puLVukHhgSlNrJeFGAyWRNXakTGitTLgcSH6yJy1Q7pcg1bzTGrAf7JX0tdgcw1st0H9WtUOUtftZTOCXt2fDzb6FkdS48lA7w1N5jRoQM41UN5e7y/dvWrPHSp+4zO8HW++VAXeJDi3wV/8POlYE/YXSzr0pr/+Fa/xVXB3q55QOVnvilRcn3MbmMbzx7z6GKW7PX+3Vfd4rduKWvUqWHV93hUZe+Mz927SCurfcZzUm/f781oDXz6uicHtffBmEnijeId67xoiYefXGNP/RsGPK8qh6vDw9coAV89Fq1YH30V3qHo8tvfrjx3dqlTtlti+EabsE9g1uv52zL6nlDiv5zdaPCklLpdOCpE5JWPKO2y/1cLB2f4D1QLj9sSx1sUkdfHnRMXGcAG006rB7PZ97B5R75HHENAYqb2CjwXOElijoimQg+2RXZQehdAs+VcRmgTkkHn/4R55ekKreUC0XigayX5dJJib3hsTUgN5HYUF17XyTTq+1oHeFNBs3CnIME2BsYRiMGvEd975Fofjnd3kWolxshvN0jiU6Cpxo1ofMPw1gInD9raniL/3VaVM3CVp70+9XTuoVWZvQ7O2yG5dmiyAH44zP1s+MfILV4GMsShvhSrG67xRsd1UpIiSlAABAABJREFUXWPnGgGwlELMrmbWKjElSlnJObMsZ1vEYiS6kfyYt7TYaN4NotF73xm5+3u/RQj88gbulbcYeoG/nnriMapXfWPz1b06Hj71dubeDlDeD9SRULD//Tawj99dx5owuoJfO0soEiLlZjPs3LLzr4mDJzYAvX/pPW+vy83tcPOYNz//bUJ4+/M2AfPt674nO/0ajL/ye4+ft67P7Xu+cY/22wDy5vEXjxpeWxgDPXgTRXmTo7Cvu5vPGPb7YNw3ruhoNn9W+Y1ZebkhG95wJt5uG7x9vJEs7d99fKa3n+/tuZuJGeXNaxhjRJtDwTeJjL/JG8H7OgbZbTQsREsiXL9+PNan3q5J90g4+jVQtvHaN8l2Son15hoLRvRdy7YnzFe1uebnnDeS+YEixhjZyrqz7stWGETkKWdKa8zT5GhCYAqmPHo8HHhqjZgix8OBqqbJHyXQRWwsLwSCGiopEonBSMBTFXLKJA/0tTeIgRaUHDKqnR4EOUyk4MWO+litCk2EfMMPSjHt5w0vDFSxpHHIFH4Dj681Gc/uZyOL2b+vI1Zg2eV8euDucGe9RW10HUzy7D1PQfQqJ7vnmjIsDkfQGBXjgFDlrQ3ihpx1c+wbJ1cizBvfgZtM0oNnioHDYWItZTdzEP/CgleQQ6LUmdG8Ve2Ib6qWBLnxRYhIiK7+152AAjEmWtOrhnY3NUEJQ8nPyV8orRdue9JvwMcy5vWvVd4VhjV1LtuYOnsPTKJTYWR8SXgDmme/Brp34NkX5LhW+/f2v5vv9NiAr+S1Ac9fr08wYZpxjQY6sLPX95PvlbbcxuL9PrkFhce1CuL+7nuAHSyC61fdUaEdhLCKMMjVI1686hvX354rNz8jBI7PdPMBb4JjlDim5vcmg4xAKeOr9etnlOsHG0jR+LkiA1fM5foprgkFI127xrl9fdr9o/DWc/tba2Q/9DZBkbfyaXnrobp/y+vm/SYh7RZteDMJe2uN6pcfMz7v+Fu//bsM9cNRiV9JoldlB09kovux76+JjbX6GhttAd09NHwU0U6e/SrI7itxewx/ibHHKLhJlavlyUDaIiGMQG/rQxx6H54gOU32eWpFu3I4HBm2xyHYHH+tlZwn+0gI0zSjqrtceIyRNE2kafKpn0QIyTzlfc8yLXybCIo5Xv+dos3XB9PoDyGQp2n3dIjHTArsgT4gvq7tnARXAh3eI/s5REGvDpDqRL+Byn1Tjq91oEeTV13XWXLEVJy0W1/48O5HnF9+ZjB8nEEyVTeEhKpVP9JgCLxc9dJHL9hGQq7mH+YcFhjV/s24kUas93xbrd8cciVGvd2/673TxNZuDMJhjtSuRKIHDBurS0EI2omiBNFrIEs+vy62EIbfc87ZfZsTeZqZpiOoksRm3kOciHE2PYKYSCmSUyRKNpdHqvk6Y7B7bxspTqBh3+zVWbM2u9qp2knTtFfCY9ODgM2Ld1QqnUZTk8XsaiQb4rCVtYBlOvuYjgFmo2szwgFcazvGCK0jHiBjiNTSbAPb++ej+vULYZmRKeuFyE5uE0WiQlC2tqHBxw2jjf1pE2/nxJsA7D+da5D2qkG7B24U6f3640E+YCI53WeXg5hWdw7BcVpFFKIL+iWJrgcBgQQakC4EE+VDRInREjk6jpJUUCFodHOWjjLcFK0SRCCI7vd5smF8WwPO8h+BdqBDAvRW7HFekdLjzaZpBjjjOcPxsDeHpbvQqrqSmX3dVhu1Naqq/9iolN6E7qbY3/qQ2R1B1pO8bopqrVea1l2K2S6+JcbmiOaPrZVWqvmuj6mVXc3S+ADWPhvf034cZDA52d6ovVN7p9RqipsdOoFaQDSiGugM34noktzRbx9rZWlvNBpdoXZlK6sHn7rjGrVWvy9MlQ5fN70P1v01IZJkZN4+7vd9PyvQXOfCiW22SCIiGXUp5a2ue9LRa6XVSoqZaZrY2oIKpDwZ0bMURCK92LmZ5gO9FPD9rqlSeqepQBNsR5M9AeriibftrLY+YjBtj2DVv10zY9MP69xtqcx3E8dporSGNiFqooVI7Bu0TogTKR9ICL1XC3xdd2XA3tVH/fxG/IYF+q81dH/tdctuJbpsm2/mumetKQna1MfxlKDmMW4VrdK0Xkd6vwK6GXDt2OkGtGdVMte/wS572ts1uFwRgK8I/lzrK+P7GTHldDrusKwteoO104DyfAMaus7BXeDE8g+UxjTb5Q3RAqwlLwXVzul04rIs1GXl4eGBV58t3D/co6q03kmTLebL5cLxcE/K007qOR6MIFjqRtsrfgsy1h8e43tePfWGjomACCm5lW+zIBhDRHrjcnnFVjbWGtgarJeya2TnqdGYebWavzm9EaO1EkKAp3Wxij9CjEKfTqy6sm3NPNNDJEhnngNLqV5XKcu6crw7ERBqHZMLrjaWD7TNNv4alClFoiGIZDEYsLVGlIjWhmgnCqg2JM3UdnUzHFML3VWJTKgmUttKF6H2bhI/MdCjcHFuhPYGvdvMfgqU4OcY2LTQRdGoiJPTQrdvVmonRpeEDaaGaPagQpSEEOgaqCvkkGmtUkbgbA2djIsSsApT1QMKN60trwhrL1RMbZEgaFfWWnb4eyQsGppbi1dTe8RiDQmiBC9xQZqtSbqplYn2PRHqPRFDd3vXja7RbXst8WqtXNfZqLa7scNvESPVAM24NhIHFN8sKfYRVIeL/LsqIdhrSnDioIzioPv9bz8pCdGrRQFIpqkfJJjwi0DOkWUpLh5k720JceEQE6GvKHCYItGDXg7Wqw4ibAIpBrR2kgrRv39OPqveTaip13IVSpKAEjD9JhOyuuVClFK8VTftyJc53FnC2rXRu1X4Yy5/7GC1F7Zts/G/3pBuybORV43IR9lYl4WyLKSY6RZtEYFeKwQMMfDPM9CEvU3he9MQTG6tUrcNWuWDd56TU7bP5mqQrdmaRTvreqH3bja2YaBKtjfHGBESIvO+J8831tnfhONrHehv+93zPN+oHlmlXVvh449/xi+9/8CdfEiKyaWlE2XdkMkr6y5cxyn05n/CDpfq7XtiFbb1tWzuM+UZVTdkuIEEv6rnd1t93LwyQQxWF1XmFNnKwvnxNUtrlK1YPz2IkbP9tWW8T7j6Lms3waABne4JQ1e2baXWytP5wvl84XxZyNOB83ohr+teHT0+PnE8HlmWhfPZ2LUiYsYV4eSf1zb18/kM2PmotZnV7TRdr4dXWAMxF+KeqCBCbxv3hyMfPrzP5JrvNQfkdMfhcGCo3B0OB85HJ+P5Yo4xEWLicn9/gxwI83zkkhdCGHO8pqQoIVCPMzkKMVrVcjgeWbaN2ozkiEN/h/lILY1WK7VsLMuFHoTaoTQbG0we/A4pot1UuawdsTCfjuR8pGvncn4iTjb9ME22Ca/bxkFNBnU+HJxM2lkXq8BFIiEmFNhWUzKL0aqgGJMltpezS5PmHTTPOXE5X7D+qSU5xEhr9po5QQoRkQnp8PT0BTEod8c7Pnj3Q+5Pd3z68U/49OnC1gZSJhzmma2U3fJU1exJ76aDw5/Nq/KJb3/7u6iKnb/WOB4n5pSZ8mSxvxm3Q9G99bZzPubE5uYm+z3u6yyISabGlNnWx3F7WSKXzO9BBFpx7/QQmQ8zW1ludo4BjWOTKq3Z/STK4XBwbfa2w7cDGSuljgixk1DbmGjwx4YQyClzLhe6dnLMtKbk48zr84WffvwpP/vkU9IxoqVycKJjEQhZCHFCA9ydjtYaWF+TU+JyuXAIkN2cpy8rnQnpQiQwxUQhcVlfOWM0k5OtGVWbkVcFMuRpMq37MAqQW1JqIOZASAHERHhSmjgcD5Rt33lv2mWBEAwSL71wdzqxPj0CYvfnuhl0H63FephnmjaokIIlHgTrzReaudrZBWWaJoZDZXTUNvg+Q+9Mc2KJVkgc05FWVkpdbWRPzaSrlEKeZkop/l3SPpefY9qRJjD0ZOxjy2Xlm3R8rQP9fsh19Gr/FVbp39/f8/r15xze+SExZasseyDmyWQWPTMdbGwv2e0/32gCXgPy1RK37xnuLhuqV3b8YJYyKpvx79Fn25uO+/+gal7Qx8ORaZrIIdCHGUyPZio6+ri+SQz1q3EO+igtPBlorXrLwL5BCNn1rguxuL918CCi6iMv1qcepJVRwd1KcZYCfTd5ccU5xnuEHdIfyZKEQBxtitZJCbIH2xQD3/vwHe4PmVYay+VCjol3330PCRlV6/mdz2foVxV1xKD0ZdloaoHWxgAPbFsBXQxRQMiT+WSXdbEKTSzBm+YDvVdaqx5MAFWmNFmS0BplW/nis0pICQJsJbKVjThNrMvCw/3kELZpIJStk/Ps8p+dOifmKbFtG0FssqO1zmj7p5SIUUA62yzMeaZWJwgRaFp4fHxkyq4VD+TQmN+7p7dKa8XGClNCtVI65GR2oYRAyjNNhfNyZs6ROSdaMyewi9h7Hw6Z53Pg4TRRjgG6tY9CCHRV5hlWOi0MwqGQp0CO494P7nR24KNn95StU1IFOodjRkMiJ6uaBonTuBP2/OYIyGGeWWNhoAeqinSv5kIkuJVzZNqXqvFPTB89hEBL18CbciZwK0M8Ar0y5YlWCs1wY47TRESpFbpPVaQ07UZE6ol8dyKvuJy6dlvEMQRSipwwlMTuo0CSgE6Zd++OlOVI6YUeQdRGv1rZrL/dCzFkkksHS2uE2IjaSQFStLbFAL5L89ZDLWzrZm2eYNc8YJoftda91YRcCbVXmWk/KzpGdUdLoxkyh6C1UbdiGhtOwFxXZ96ntBNwLRHVXVyn6dUNr7bGuiyuiAdJ/XHNzLBWrTCKMa/oBfP66N462sfyeidpppSNsq58eP8B0hvVOQ1JO7UoKh0JcS8ER5sK2B1CwVE65xeIYGvvG3R8rQP9273uwRrFq40gwsOzB9btJdrtb12b99cD3Cz+297828H9liX79tGdyDGOHaqXL72Kw1BwDfr7m3uvyBZYiJahxxRtfMa15EfwvrJ5/E0clg+O21/NSLpPI9i/h+QtMtTdHJ5zG9ax8RqxNe7WljEEuBnlGwzccerHeNj4xuJ9cnVdgJHGDK7Bfp6ANGbje+duztwfJ7bUQRsR4TBlQswokXk+GvnQ2xnq7yUhuTOW3QO1VmP/HidEJ2ozwaCOkYY2D+TigT7nmTDcuPz7js8mqmivbFK5JOU0CzEHthpZt4hE5dKVdw6uLOgBtqZEzHZeexeYM/OU6YdoEKr6OCHqG46QUiDGQEkTc56otTmaYY56R2ZyCuC9/BwTMRkiVT2piinTm1CkMufspCpTj2xdOTARopIjZl6SMllmJNjnDt7LTlE45XCjGQGHHMgaaf06tpXTUAk06D3EwOl4xxwTEhohmlnSnBJlzOX73S9BbohoFnvHSFrwJFi8Bx4HRyDILj0cGPrw4U3ioPadlyHDk0zHDIcw9Al6H93g0eMf6JeTXb1KN6Tiyu63Tpx4telogBpRtKv9XhSkD7JqgFaZRLg/ZNa7A6/ODbLdM02dv6H2JDNv7E6KtYAeAsTgd4NPlARw3oz9tFYRNeaIVyK7fkJKaT+3rY+R0fGj+xq0VtMVBYw+EquOwN0KLhnp9oqwiYj7a1grqXvi3LpzLHpnq4WunaBQe72B5C3wFt8fxd8jwC7zPPaNnRipuic596cTKURvRxirP3kyrwqHw9FHG0cA97th1Fpjf/VirOutJv7X//haB/orE579xjQ5VoMXVZXj3T39ixeWiY/FHwOtdiNwwT4PuyMCOjYJ9v756Lcpg0XO3nt/m6E5bpzbdvxX9ebtO/j/8wq/dyV0U3XqKE+Pj+TTieYZrpGsOmCEL2So5TXr0+uV5TsIZiO52DcAryaHicQQw8mTkcw6bVflaq1fNf297ZCdnT+uwW6VydDuFodjB0P4mozZiXNRD+8zIkKthb4ttFkoBJoIa9lIT2dCsGDfQuJcNm8F2OtG75tv2+ZFWieoWcEepkwKJ2ptnJeFL149IRKpbQiK+KlMRnar1SqMFA3a1mCbfC0b63Zh3S7czxYMgyhCo24bohWhGXlRGlqLjzbp9f7QhtA4TNHuv66U2mh1Q6LQfHPKKSG9o1zcxCcZHF42Hk4TOVoykFNmniZev35JniLK5Nc2IyQW6UzZRpVUjA8grfFwd6LWBe2mqRDSRNdM1UYHaussW6G5A5jso5KdlJO1Wrq6K6G1XnLMzqC2IH6aTnS/VYMM8mnYR8aGzOiY8LgN9sMzQj3hNZhWfZbfpxjwUVoioo2RcVof3yVVx+0mwc6nB2J1USL1Ns8IGt3KSGqte3DcSYVeELRd80A8WRvKdNZW8KFRCyjtSvwcATGGwJwixzmzrEKQSA4RUkSbBagYPRx7CyklQ+IGBwAPmGPzuGX6I9dJhqFhcJ2KMbRwl/TmzaA5FPZGwB4zHCFEigdam96x3vjtWKhy9djotRpK6sRCUHpt+xZ3nfdgd74caOpAjsa/x2cfwmL2Gte9ZGh7NO0cT0e3+J5o0mxML0Tola5X8yZz0LsdwXSya4zUft2nS73Zs74Bx9c60F9HXq59+lobW2m7JnSejrx8Kqizs2PMoJByZuvWD7c6+M2M0QKwL96h34yFy+uNbWz2Vq+zzEOeVfuoZOGKDHzF5++j6nCFvGB9z3mOTIeZbSvkgzFD8YWzK6Z58DbCoU0R7Fr33chE243Gcxcbexl9u952N25a7T7BYJtvKZv1zdZth/MG6tGvA+f+/GBwtF7dvIb15ZXlawHepiOCQ4LW/tAQjY5UVrQmYpxIUUgycXc6AtFY+epQNwLBNv+c7TOX9TruFWJkAqJ2QluZQiQcD6zbhmo1kqBv5LUpuWU2Nzfq3TUKMhwOB2rplFp4vDyxrBdeHe9opbJsC5d1sfxfBS6dIEpvBW3FLlFcmKbJSFO90euZWiuT8w7WrUA1Fcd5nmkdLpeFy/lC6wUweWdFWC8X5hyZpkwQUzg8TDPTnLisF/veIThRDNZa2XpFJBEkE5JSmgkQqVes5fKa+7tnJq60LKAb8ZSZ7x7QzVjmfUDVXQnrZueGm3tb4HB3tDEuNeXA0hsilSYm0hQkYNMoE9IHwxmykzujOLrh92lTdadyV4LTbpXqbcRT+z6qIM0S3iscfU0eFNy7YtRyXrmJsd2L67s3rE3SUZrBDRA8qIrB5U3ZuTn2framGlcPCtQS9eaJhIiwrAt3D++Rc6bTSY+vDZnpyjRPKJ1SbO2a+E5AJHmw1uv68b3HSJPsJlohRbJzYpZLR7SS5vTGiG31YCvAlNP+Wa/F0nUftSmTQO/KZd24u3sgzRN9fTKHu+wiYrVaCzBGsqOFh8MMWGJzf7pDBaYcmZ2bMc+zTeYs25UTMBJclOyGWWO/jTKq+/3k25QGcJoPiMClbRzu76jLhtbm94zSto0cBVGh1L6fRxEbKR5E2RCCyYHnPWek605I+EYcX+tAP44xx/n8+XO2rRDjRm9WeVjFMjHySdtwnEiEVapBb0H86yz+CPK3x+2/xoIY6ly7mIpvjjvEd2NAc4UY3e/bYVE6dGnUXkk5k+PENOZPkzHY+ZJYi8OmPoc6TZNB1J4pjx7ZqJoGOe7iBJl12yhVOeTZtKh9sYFyPB7fkOQMnkyYu5TB/YpS6so0Ja8+bSMJQcg5UdzWFxUC2aoXHf183yxd8kZDIqZkvtshoUthO2/UY9vREY0mktG0UftGr4VONQMerUSJbloTICRqh6obSKVJgGSqgkQ7laFDlI5i+uASPOVSNS5H26AXhE4OgeMhA0Y+zzHQXSio1UoW87fWIMQ00WtzNi9ENQ/u0jvTFIkhoyLEKVCl0rrdr+re6SlH7vKBWgrqcHCcj+TJgruotRXmeaKWjVa7K34pvW7MhxPTbD1iwdooIUaW9ZEpH1HJaDdBKYBSlS6JkBKaAyuVeLqDxwK9WgsjJKYY2RhwsFXdQuDp8YWNb+aZ0/HehaYqXToxB6Z5ppZKyImmzn1NkS7Qg1f4AmokA9g2okPW2pWgoK3b5+iY5LCqjX06GmIFudeh/YqmKWZv3Ee7zLeBEF0nQw0yv2oWhOuSV66qdSO56RZ4Q5Dr6ONe/wpRxPrxKdLFeQU5sTVjk1trLtKasm6Fu/uAiNJ73ROf4/GIiHkKdDEejMkU+2dQvU77yPjOnXVdbU+asqN7ytPTkwXkaIiKKIbE7HvncLm72QF7Z91MGCfnTO+d8/nMuq6O8Bkpt5TKNJls+MuXL8lz4un1o7fQKi9ffM40JV69eMGyXQChlkqaJrQ1G49rze7zmNjoyHxgd9hrnZQirWx2voMpPJRqA5f3T5nzeWHtyt27z+kdpPt+GzCtFBqSA+o6/jE5EVir53zWvpP8dgX/Tyr6f6wOg+CuesmjX4bYSMw0HXj18pFtWzmBQZkUC/givimYwtZVnPL62iPFuxV+gdGfvhXNYe9ljsfvJeZbh7XkB6xrmX8Sn9P2WdhwAzPh1dqQpW31qq0dZAhc2Ofv3W7glCOtKilODvlfyXSHw7S71cVg0rIDXrfkvtNb4f7+nk8++dQ2p5T2im2MJY7PVX2UaihpdZ/FHr9zEgFBovXu9SrLGVOEEFlKYdlWSsnIPHN/f+KpNvZmp7jUZhaKj9gEEqFb37hKskwdSAGmZElaK4lSO1ttaBVyPLJeFmRULz0QNNDpTqQ0pEBRnpaFsp4p22LjbzGj6mTEGKkh0GpFuvWQo0Q63Xr9uKKX8x9qK+SDTTEI2BiXCLp1glhCNWVjpbdqpKoUjDmsIthdYRnh0O/e1gWwVkpXu8YxRLbVWONDKVIQMoEpTsx55nC8Bwm8fPmCWit3pxNP5ycjj9UNXc6EbTWugmQkRoe9B4KE9dftLLIsC/N84jBP5HwyhjdKTpmQEkLCRvUCtTe7dtEqt+hto109sI3Z/WBSqgjJJWYlRgbRy0inJlHdB6NcjL+AE/xGO8wqVRgisR3Te9h7N36Ohua8kwA8L9D9d+qJxHCEFOcEje6UKnQMgbD73ZKSW1vZlBI5T6Q0sT5ejCgr3irqnezEtlobnbZD5IMf06ppEewW071aj94Tf1PHM8Smj3ZmsNWiN3oPt61FG2W7GmINVK/WSutK4lrgjHbluq4MnQtgZ7SbSqbvIZ4IjZYAaroCm65IqXSuiUatlaLNxQ4NTeitWiumFEvgQ0CDULtPfbhOvsTI/TvPdyTTtuZAGx/6xu1vkInfkLh1NOgqe3Urf/XNOL72gX70jpI72Fk72oMvSooza300WLh1dGiD9QAUdmY8uv/P9cX9f/XmRvc+3xUGslEn9Y3DlMb8Sbc3jvf09sOhsgHHqW9BopgUa+ycslDW6g/zhTluyqFE50pOIUSCuub2ePkw7GRHC0/2ue/u5K2UE2CuTkFtW1UJbFshpYNtJKrgynqlbNRu+l6jCqrVPQHE1QddxjYOxSu1kBDjgHxvdNclkUQ8UO0nx65euJ4v8ZaFSEJ0cca8owX+PjEEUgykaIEEGgWhNtvI8MAijrLYO8l+rva+a7MZ7pCjCa+0Ym2OpkhSm5uXTjL4gk7zDcsrPFFUEiGoERkFq4yDkKKa4UawCjRi51W1EzBuxrjbujaiTEiM9OTvv1emavey9Jt7zJO5Uu2O6iCh74mSOaEVeluRmJAItTYejnf02m28SAzCFrF5ewIQhNg7Ocy0VAi9ETpoBOFIio35cGSaZk9iuzHdk/VFu/qcuTbvqfsEiCeLY8naFbXkJPr9PdanEfcGxDzO0FV7ftwjt4Y2+3rdE3Z/nAfw3SlvbAF7z9vP7953l2vQ91fdTWZ83N7LarqGfT2P5D9Eq8ZHbz2l7ERNGyUbhEcZhYPzAuyesILlapyqoE5ig52k21VNbMdZqgKWmIXoichNEN+Fo01PwARFr+csufxsLSZxa4JbGHyuEEICGYHWyJKlFWq3JEqGIFDA9w7ZybLqCHxD9yRrfIerR8V1vwrBnOp0JxBbkmuHcY5iDLz3/AjVWkCNjnj7w77XKPxuOA2MpOX20OvjlW/U8fUP9Ji0Z46ZlIqRdoItwNY7KR4IuaBlpW8V9cUpapukXdBgGwxC58s3QveFbr/TnYQDQ5AnolSCZFSNzb+nD6o3N9TNjaa+cHAgX9U3Qk9SpPP8lHl86vtNigdWJFwhqG7jgTkIvivDgDLFyHTDN94y5U6SZJWoBBcTWpEpIWqjaoREKZUQJ8SFPFQCMSTvaw/XPUMwam0ESR4o27W1IJGxk49CqeuAZKt9NgJRbQwmxEAKiY6w1ZXqG9oYQbMFmK/5l/8uxLCjCylFh5qtB1p7pfbu0OTo3+KQuAWGRregJsOmd4VeOM531jLpnV6LVQuWbxAQYjBCVe2bV7hWxaUcCeFAYyGGiRgStUWCdnKwhEccsiYfHAkqpqbXGvTiiVNzeqNlBgM1EGyMMcZE1Urvb2rZgyUTdAsS4oFeJNDKwrlthJiIYfJRLRszkxDosUOckLD5qgANQqiNLJktgzSITakoUQ4cjoddN0F9XcRkronGZ2kYdUVvEjb7pEZiY69crzC6XfNBB9nJVN35KuDJ835zeEj2gKj7gt3bcHoTlYdGuwUqv4/EEi5R9UpefW/Ak3QXpPYE4G0bmYG4iODiRFhSE63vPua1U4o2RYDJL3cPljZqqjdtsMER6rRuY7NRjBhp1bK6YmOgq43R2vmy9T4svEdSYF0+O0PNv8/Yl0aiqGq6BjEEa5+Ia1U4X0G7EtO8a1CAjVTWulFqp/QGxGuS7wTNFLOhmEGudsliyRNi/IskkZiSne+OIYAxopr3x4QQiK6Caa1MG+/84NmBVjBkjiF6lcz85rp979/x7SB/W8Nf041vzvG1D/S7wIn3oJM7qbXWCCQOx8z7771P10IpZ9LhQOmmXtU0Glx7k+VdCX6wlxs6YHw8W/Zg7tXyYLaPLP4WJLhJGxgjfTs86Km3gZpyhUQdxr873XEOKzlOpJyIUpEY9nGTW+/3FK3/bRME4cZophMjDDORKOZ339t1vlV1/N307nPOtFaYjyeDyXw00RZodeEeqxBradRafAb/6oRVqymx7XO1XDUGRhU1WiGDRT2QipiyqRWOdgmyV/AizpBV09y2Kh6mGMhJrJrHxrO2WnenP5FgCoMD5h1XRWzT773Q60YtK7VcyGBjTKq0WljXi42S0a3SESWn4QnuPeMQiDlzOhw5zd/i89d/wDxZhXQpSq0LosIUZ2Pea+f+3ees60qp0NuNsUgwcmnvgVoKtawckp2HFLMJJ0mGVqyKonkQNb5Kb91Nm5xfIYE4Hbic7TtKr+R5JsnMp5/9KYf5wDw/EPO7Bm33jdYWv3JiQiRrJh+OCDOtBbb2RM5PPH/2ITFmaquUUsmHmTGuNE51CslhZKsyd1Y34vyYsRvrrsAojoaoKilEUoisLkWLsLtV+pSi9db77QK8JgD2yjcomycWO64jds6vCcB1WmWgAdKvZDwjlVryJHvAjDfv5q8bBK3G5o/B3iM4YXdZCilN7IqJKbCuF053D6ScKNU+Q6teSTvjX4CcA6VWYszEFF3/R8nZEouc0k66MyTH8QifkKm1k7NNxxh3ZyTkYy323Z52TN/UagZP0zQ5cgP39w88f/6cF59+shvPJD8vrXVOp3vWbWVO1i5UgcM07/D5LSGvtbbvUzgPY5znto82ClO2WXtDVoUcEvenE0/LhSnPNKnsGiO+f1+RgmuxdTuyN+6TnZT9DcPuv9aBftwkQ5faiEyZGCJFXSiibvzSL/8agUDTC8GhmaZln4v9L5y/OYRu/+3Vodz+8eazjb2GN/KINz77bm3pkNhQBuutEWPn7v7In2wvCSk4q/6aEY8b9jbYiwRCTF4x+UYa/PdOPAoRjscT56dKzJnkSlSHw5EQhTwl0hLZlgsP77y761pPOe89+ClF1hRdpKRDwNzw9Kpr3xUTaWnmAx3atd84gkCIYVcd25ZK2QydOKTEWkxWtrdGSuwiPX31qgnXwvbN+XhInuSNccPA1kx3vKsFF2q9jia9cTHgeJx5LAutrERVotji6NUUCfdxQu2YnKmhH41GD5EkBk5qq1weV9bzH/Hw7D3ee/cjUOHj9c8QzWzbI2V7NAU0IpteiCKU1ghqbaBhvNS7BQdSIPfAlJLD/3h1a7eY8UHlem9K5LyaSyNixKh1WZgn4eHuntZN+zzmyNI2kmTWc6XWM6dngeP8Lkv3XjwCPXPucCgX1vOnhDgT0pHIijR4fPW4m0QFDy5mLjL6+Wm/LiG6JsQNbH97HcZ0zBjhNLZ82/vR2sp1JM/XQED3e6t3txHe5/WtKrdzNWpkYbfeDbpL/PbeDHXq10rXEsHrf4/kpQ2SaghG/NXrc0bFOEZxr+u9OwTfSTnw8Gzm1atX7i/fKOvG4ZjpvVEKVL/vcsqcl20valqtHvx8AqY31z+APCdKLfRS6LVRdLvZgGT/fHY9OjmPPrmhEMMV0yZ47FqEECil+L8NOdq2DUFZlpXXrx9ZlsWVNFcb1Y3utiiu7UAw4Ruf4R/+ASln8pRd/6IZ+nSzl46ATVdUGs2vdRDjKvVSiArv3j9jfWw+n+/GWvHNls1fdjhr4xt5fO0DfQzBxlv6qNyuMLGILfqlCaKFGFZinbDepkJ3+F6uvRkZVcifc8UNShtQY/DNTG4+kz3q7XG8tw/VAWP62NDQo3ZoEDp3d/esdTE991rZymaViy+220Bv1TB7YLXKN1uy4/OveJZea2XbKlUbhIYWIzI+PT2a1rQqr1+/4v1vfxdCYFuWfVY6xsjl/GTmEfZFuFzOxHh3Jbk4acgETVwtLF5H63Tgsh5IbDTrsic6rVk/fXhfG0oh+3xzEEEddjVovTIlc+Ayn4rAWiqtXbuqALVbJTTmqlV175UvlzN1W9FW0V7MSKM7I7gr05SNdKjd3ws6QzPf4epgfcFeG9qVbbnw2SefAFYhQ+Vb3/ohQmLdLpyXL6hFuT89o/VCkJmcjpwvG1PKlHqd23ZvEIPivQIycppBnuiwfrHzfZxnRGwGutRCixvHwwPvv/ec1iqvXj/xyRcv93Ob8kR0MqsJ70Q++7jwtKwc7jK/+mvfYblUpvA+61Z4ujyxLAvf+fYvcprvduOYlDMNTIXS22eGBhlJMMQxS29V9OhPD6e/QaDaEyv/PgMkn6dMVmOzl1595FT2oDuClIYBsYOESKdCt/E5vM0TgpgPgox+dvA9YCQ5/llGL3+IPwmY4ZXdc/sWwghMAZzYCXbdrCdfzPymFFAbNcv5wKV3NhWmKbOWyvuno62DurmKnBlQHebZ1puqESe7j5LOmeNxJkaIAlVMBnmeZ0tyPWGy1lYmJ0NcmpvajD2vtkpzRGPsUeu68uzdzPF05OnxkVK6j8PN1CqcTvc8e/aMp5efcXc6GlfK94kQAnM+EFWY0uSowBgFtus1zRPzNANCkeJtiL4LkeXsrbrR8XRED2xaSgFNkWNOdN3oYlM92HZHl+vY7X+RYydffsOOr3Wg79003W/hdptN1530oaqk6UjbzgZX10rIsweMhEpDqXvvDvnq0Hxl3d/Mno7q1KHn+MbZHLDQn//59zzbq/+d9etPPB0PbLU47BdcEMQY3m8oRN18vv21e7f5Mb3WNWb1akdtnS7WD13XbWfPq1fKQ0rSevLV+6y2AZi050h2nFU7tMF3xrGQXF99NEJHhi9Y9dBbNzlOx1DDsNTdv5PBs1apu1uen54gwc/XlWC3HyFQS6XenHuFN6+dv77JhFag0XolBLg7nOjbSt1WBD/fWDIUpRs8KtC6EHNCNe6qct1xZOMvVBcXsQBSygKqptUeA0hjThm0+fkyMmEQly32RFZVnMhojPTWru2a8X28bgURUggUFHHZVMk2XN9q4d13nrMsG49PC1tZeDi8D8vF1NJ6ZA4ZpdB75Z/967/Ks+fvEmKmvv4xn0/C+dJJIXPMz/m8dWKcPFlLNs99yJyX1apXO+m2nsY1dSY9+73iTa2BzrmaY207oG/EPIbAko11Ltu2ozPi9/+blsDsvdkg7HyOUdOH/XHX9WOB3kySrq9iDoNdnJ8xqnrVHcb3b7S3nq7oj1qU3xsV496165pj3Peo3jobIzm5fhfVTvfvltyrHgahDXJK7luA9/3Z10lwXXztN+1FdQVRHUnIWNM3rPsYuB0JthadwfaGtkzOXvf2R+9oa8ynyV7DC5HWmrnnbYXDfHReilnOiiR027iKeF0Z+EEi9IDSLJkaO5h/p1035LLSkiDHA1NKbOWJ5v0VS25GMeUAkrzZn7/dL8dxu89/k46vfaDXG/GWfZHJdfH31jic7tnEYKneOmkKVPVq2pf+flk9cF8X+lcfskNC46Zq17T+5viKlOHL/z2SlPErD4yHw8zW3FHKe1jdx4zgGuiHmAaDCNevhMHeurlzgblQjef07k5ixkgP/vqjPdG6aV0PRrg5QnHzeW9GC4fS1NhJ8E0QD6S+kQ2ew3h/g9jrDsMmh+dD8M1VG4PgJmLjXUShuhiaYIpfwwvbeAD2PU3uUsYJ/cqMa/zVINLuMskW2AckmqLQYqC2ZoY7dBPh8NdUIAUhR6uqtZnka/Z70TZDl5CdD9S6OizaSCEzxeTz7o5CYUS61qpppDvnIueEaAW5ijOlOO8I1jjv43xFD/5G4EtoD5RtMUGbUqjVtORDmqyP3EbAi6zrmfvTgd/6je/yve9+n3VN/P7f/wmHu8QfX76gk5gPJx4++i6vXBo2h0zOnsCgPg0i+/3cu5Eg93xsIPc3ydf4LoMpv7d4wkgqrxD0df2NYMueRF5xnNE/0zd+vwfS21Xo770X7rf3iXCVSvXnv0FjG8HeA+wtGoD4KO3Yo0ScS5RIse5CQbYOTZXRzs3N/8ke6uw1XIyqY/3wgNBL25Mle6yCDrRvdEqsXTgQBWt9+t7gI2hDInsoi4zR3W0ztVFDBRKlFMzryNZ2q90SAFcPFLE1E5ko7sQorlUxEKlQhhTvTYGiur+njeUORMVlf4MTilNiK6/RCPHZkTllzrXSo96MfpoYWh/b42ip/DlB/PZv/yTQ/2N07NKrqgRtpGRw1eQ9n4D1WE/PnzPnzOX8xLJu+3O1VmeiRhpXYZY9OHD9n9tjaNvbDdk9e/Veubq4y3iwvP0ao5qUPf7oUODzB44M2TyfC2UrpJAoZajOhTduxLHxt9p2KLmr2bOWUknT0Ka2Plit2x5suz8/5Mx2Oe8fcdkW1mXheLxDEJpn9MEJN16sWaLVYE6ZHCPaTApWGJK9vLHRDKLV0D8YfUAbQ7JAn3Mk1eS9y40cZmK2ueoYhLYOwSCrpI/HI4+Pj9TWKK1TSt8JeNfTKvu5GtWYnz1q2UjJkISyrXx+fg218vx736GrSeCaNG4yMqMHr9aru7PVXYM+YsHukBKlNeMVoLQa+OGPfsTHn/wJj4+vCTJxPLxDr4/ml+28A8FgWpsYEeZsYjWNibJVckq0WiwxCBBd4GW/LtoR7ZyOB0prlqAJzIcjj69f8Du/8zueyCnzfKC3DVCmaSbnCe2Vsqz85l/7NcL6gp/8w8/44hEkHfjBOw88ffrIn3z2knUu/Ct/67/C3/57v0/PpiFeWqUsxciQyRIUEz+xe3IEcxvkNkStUR1q7Qx5mn2mWa4BPQS7f0s1RYFhkLSPR/qNtof6vXXmewVDyJYdibsNxuM5XwXbSgDp4kHdUSTXEwheSauGnUh5Zezj12SM2lmgzyGSXQ2urw2hW3K5v74HNifVIoIy0CH29aLiRLXWaWuhNZhSQjZzOuz9ugaDxH0MtbrJlfgX6j4zb0nClfMEsj8PJ50Oyex120BMpCvlTGlG0lvOCyEmjPxqfJ6x18heabusdjT0gPBlghz4Ht07MWe7f4JNOG2bBfqndYMgHJ/dcXBuiyJoM/Z9HJ7dfHWQf/taf9Vn+KYcX+tAv1ezeu1T55wNyvWNOMbEWgq9CmgmRWX0ticnkA1DkH+0wysFz6J3wY+bIH/tD795c+3v5T0/2xNuICWviHKekShM88TpeCKnzFo2cs4MiVlgZ5aTfdNx0ZacZkpppGz6Aq0V0jwxuaZ9ypk0BfrWyIcDS10Rd1YTgbu7O87LymmeOcyZgxz4+c/+FNQUs7aaYGFXOyu9U9XU2DQo03Fi26B001Ef4zFdrsWe+dMnhy/bjoycTiemlwlcF31oke+SmdiY0jRP9ho5sFZjxNeqIIEcIl0CXezaBL9nohM4u6oF8d6NwFWrwYm9gtqP1gWpC4nOXbbxsVM2dbs2RZ4uF2QthG6bePPWwym+y6vyx4g2pngg5pmXLz6DphzS0fgOobJ1Yc4TTa3C7m1DMJU0sPs6aENahdaZ5kBLZhubY2SKkwVXAYmRECYCE62vBBt0R6KhHvN85Hx+NG5BTKT4rimlbQvzFNAqSIB/7X/6r/G7v/3v8Pf+7v+XH//px7yu8HqDv/lr3+e3fuVXeOo/5bf/+BNUJ9Kd7kIpMZq5SQPaVhAf6ZtiYmvbNUDpkFoFqmvWc733tSshXqv/0TNX1d3CNHsb6cvr1vQM9g19JBb4OCjsa3Zfj17ODzRwZ37731M0Uae2r3lH1YRdIAY6tep+j+1tPuy9ck4+nlZpvRknIwQOE5jukVXny1KNkDqbG+Lj4yvzQUiZlJONuyVhK4XWYY7BEjRJlK0z+z4UY3Dy6urnrSBy505zw9/CkSSH68cJH2OMu0VsDCaa5JDMNE2slwvb5tMCQNk25yGYaqaZQTXu7u54/cULzCwq7UTicU7GvdOaO9QNNKd3lya38z1GaIfOgUShboU5Jj549ozUO1XUJMj9mgYVVKvtw9/A4P2PcnytA70dtqBjjEivOwlkjKKMRRHFNMPrZtVbCjOyz9XeijDsKCgSrPM5Nplrb+f67nIDYdunGRtMuI3v9qub/o+oZZ3q768OOw8xD9QCWYiRdduY07YnKLWaIEq8Yd8DaG00UZOILRvM5s9dmj2+toYE877e1pVIAolczivxOHO5nJnnk/VAl9XfB3fHambrO2aZRqYMTgLzeeEybGjLngBde4PX0bZrRW9Etp3prMaing4zKcUdMw3hqlxmG2TkMCVCjJR13ds29ncL4ikKZcwr+7xMHIkZ1tYopWBVfaONWXk6QiNQubs7kBNs68IhG+Q6Df90CcTTiSmpfZYg0IXeNnp/4tndu+68F2hxZV0u0AtJIlNKzIcD79w/44uXLwjinvKuYVCKtWtySsSYCaGTpHN3PFHq5nPXNkJ1qe6tLUoncDzcs27BSGta/T5T97y3Vkyn02RBOHJ/eoBu/gPzLPzd/8+/xdPLT7looMwnLnXl/i7ze1+c+eizn/Or37nnN3/4LWINyNaYjnnvr4vYrHfIkdrsXolRCA30tn8tVwJdCINoaI+JyfvI/piU0m4UdMgHFNhGZb+/XkCIhGCqLSp9r9y7jI6Ud4R3t0VAnZAWgk0HSKd304MYLYI4kKHokrlDAlqVFg1SVjqh2GdsePBU2weGeQ5q3A2JkcN8QEVYLhfmOXPP0VTjtiezXG6Vspmla0cN5ZoyvZqv+jRNbGXdE9+ugVotgdaR9GXTu485oaXtidbwASmlmNhYzrvWxJwPTDmzOvqXUibnifPj4w7B55wppTBPJlndu7KsG/d3D3z28RfcP3sgiHB+/craCymR/LNocz5KzpbU6ZXpP4jFOUVSjnuioTRics2DXklTJKXA0+WReJz44PRARFn7xrEFU13EphMY6ue8Ccd/1Zjddf//Jz36f6yOfYBm9IBbY/LxndHrCSHTsQVBnkgtEyW6gpJ36KW7vCe+GC3w4spNX77oo/fHtRIAVEdn6Bavf/Nfe4CUm5cRK4mDdrpamOkamFMgJstozas8UnvzzPnKSRg+9MVOCqEHojPVYjLhCwUkug43IMlgsJQyBOWQZl6KVX6xdbpWpiky5QGrwjQdPGjYRpdi8v76mH81Yo255kVUC9brG+IcCiEjWghqffgQIIaZ3pS1FLbaOPTAlDOHGKwPGNW06Z0/MMXI0VEGVKld2ZblhhBo0C/J4EHbzG0sToPSg7mLaW8Ebahu0CDQSVHJGkiSieFIFquMo06cokC0jU9RegtMCEk6h+Qz0ySaBmimwR9jMjJUs/G4MQMuYm52QYXTcaL0QpCEaKKUwPc/+gjtBSXTNdJSYJ4P5HRAnUOQsqkAipj8bQhm2mTjf3CYZ5rauBUCzx/eIaeZL15/zqunF6QgaG0c8tGIiNHuiRefv6JtK3cTfO+9E+/d3/N4Vl5/9ooP//kP+dXvfggF/v4f/Izp8OBTE/2NPjBBnDjHPj/f+5CFHZuptXlSsCb4sAi2UUFL1E2TfpjODMlVG38cxjV9VH1hJKBDotkY9UkSNVztpENwsxQMgcGTAhjiTLaG+4Cc0+ABuS1rMI6FxIAJyTQinTkFqyDFCWYSjRjm0kNdK+pDXJISeWusvSLSSFmhKdqUS4VLW6AtHA8Tqt24Osu6I1J4i6eoILVC34jZWpUmbGVz+rWsBOcOtN4s+HUbYwvJ9sFSCr1WXyNCKcXbBN6SqYXWTDtCxUbralmQGFiXC60UCHBZLpaIlA0RWLaN4j+Xy2XnATXMmpfB7fHzGatSeqPmQIh2o2hTYmqm8OdJQRBhSpnX55XjaebZ3YFWGqIBUZdBF5e63bfcN4P6l3kB12D/F/Xxv67H1zrQA97n9mx5QE4uAzuIKYJVll0bIRo5qezKdle47fYwKsibbNlrBjg2KhgOGGPe87bbPiRvxftzowa+3lT+yKHAZS9o3VoRphTIKVDrRm3V5Wr15v29r+m9Twmyl0ahuQc0o6K2DTCMQO/z+ClEC+rOpDVWa6T5TLGJU9h0Q3SYsvm4T4hDNcyJQDJ6rJYjTdPEsrBXbKCEaJr5uwGJqk0/eBAf1zQFq3px6VPbjy1QzIeJwzyTcrSNLMiud64SzLRmrPC9L2zPDXGokRlKEdSc+qLMduqiEDWRQiOFSAwTQZXYjBgXQ7j23UVIIohOJqYThE4gkUwq11nJXb0HH9NOErJr2KhbZ8oRLc3p4aCt8t33Hihl4/XSOVfIcqS2SkyB1m0ufE6BwzSxZBC1ZMsUzYyEZP1RIWIb9N1hIsXM+RyYgBCyeQTEbjEpKCkGejPp6CkuSIITkTut/Pov/03+qd/6De7ayk9+/yd88uqR6XRn990Qk1H2/vquz94bwccCB+01hkGDNREnYB99E6zKN31zO094f3uo4EZAxclk3fvfwTv9Ioj6lIa/V+sY5C7G80i+TnwQxIO7J9UO4bfuGpejFaAdlYG+mae6qDP5XXWuixUg+36iXpAEQZtNGkiIRAINmxxovVFaJfVEinYP9aZIa+QUOeQD7bI6fGa9wd6bsezdC17bZvevB63eTRyq90oUKDcV874vBZel7WZRO3alMcYKmCiN2gSJiSdZ+6rVAsHIzlXVFCZ9tLc7UtrGXqwuGIQlMr0rzfULxnRB1cbcjIDbaze9ExV6taTL7gNbs03VHSQLhyAcj7N5ITDaD7bvIdGSzgGy/gXw/Te5Pw9f90DvsFhXRWIgx8y2GXEqxkCMYr3KcDA3pvqI9JU5PUNkQ9mAAJoYsrXjuPJ7viqzG1nhYKaO/p76Rv5WxsieD7z5eCe13WacyI4VGKlrmlkuFy5p4siRUsy/fMBoZr0bbYMdKnOuxd9VqbXQPNB3lDiFvUsxvL3X1fqn67p4sI9O2rMEoK6LQ9z2fsbGt/aIKuY3jThj3khJrTaePXvO69df2GOjRd+Upt36E/985iIoPjMfbTNUiNMEeuNjTmOOwv3dTE7mqlW00TGLXiWi3msMpaDFqhTA0QNTDKvNzC96qwTt1HUjHR4IskJoFvTjhRQ6OR7omkjxjKTAHCAkaGrnLoqgOSPu1R7UvO3v7h5ofaG1QtFGDEonomrtCBtBglouzPnk/UTo0mhy5qNnGa0TP+6PvKqFQz5QtgspQVYbbzyExDt3D1wuLyjNRwHHPRHEqt4YrepUgMb5/JpYKw/xgEpmSRNLe03AjFJSCExTYJqes2yKbmfmSfmlb93zr/7P/hd8+Pxj/sN/+//Ff/QP/iEL32N+HmiboxQh+KSCBehhpzxEaFKadj5MitkCQN+4MsutR8von7v7n9mxiley+yLy9lVGfQxDghhpToyoNhJ+gpMUw0gI5KrnUNmTSfV1aGDY8C7o/l4uddNNtCWIkXkZ/J4YaS50FLaOPUqvWh0xwBbGwiatjU18z+kCFTaFwzGSQgJNNI1c1sLp4YFZ3Z2yFOeymNpgjpZg1jLQhkivVwEtME0E3Za9zbgXKbhGQTBlvOs+JntrbazLc7fWW0jG7bHWCsR0RVCzFwUxxl3MKkbT0Igp+qho93N8w8oYTMUUoPvUjV/fsVZCuLbfcKe/0hsahekwMTqwYwxPcBliv6Z/2Ujd7fFNq+bhax7oB+xjzkzXOdDnDw/MeeLpcmFbVw7HOwRl2y6U9SX3x28DHdWCSAau4yT/xd/boXofgh89pmH9iY7a/ooA3H7u4Nm03MjGAtdEoStRAqfjgSzRiCs5E4pJmO6iEeK96LIRRzXlis8xBHLOBHxuXhtEQzdyzoAxcKfJNt2d3xAiKU6uXDVRlmVnsQ/9cnb9fNl7s2PDGD+t2uheax2RxrZtzNNEozmKYkna6TghIpwvG0+XCzlHonbiYYJqlXQW0CS8f38kzsE2O+0EmXi6FErMZvoyIMg4gZrXuUF+JhxUy2Y2vq0SeqduhRigh42smSQQY+P4LJEPk/kPhIU4FZ7ND5zV55m106XZPj6Z7GzQjtYCrfLOO4llmyg1kvtE7o3lshC6Ms9HYoi0rqxHQE/MJJo0Lq1w7oHv/vAXmVrh5fJjPnlxYX5vYmmTSfTkyc5vThyePePw9B5h26gUtthIc6SRWEQ4He84pImff/wp7z9/l1o31jlQ48IsmWl7gYbM1hNNoAUlTDOvzq/4wS98j7/2m7/O3/hb/xz8xr/KFh9I/9H/lvXzlcf6DvLwjEM8sk4XULOtLdsGyaBpa48pRLGJkboRPZkTMcvSTqdpIyXTZW/NNv4cogf2bsJOYx2ZIhIpBDQkzKwxOswrewU3+r4SAmvvtG73ZMeQC6Ktua2yI0aqHW2F3iyJ7qq0rsQurgPhyAKWAIaYeLE+EiZrM+Si5HIhJ2GVzqqJFg/EKrTLa0ecMtoiQc9MUybmhd/66F0+PCX+7u98zB9/XjmeNjeJacQQ+OKLR+7vDja6qJHjcabpxvmpckyZjlLrxrNnBxsvdYVJ34aufIhkIktVPfHoIxm4svPHHqBi6obCgLLNWyFPh32qKQ2VQ0zv4v50d5X6Fav2A5Byct/6QUjsO3plSKta+yYEqO55ESMhJHruzPPMYOvXUogk7o4nNCXS6cDx+R1L2WjaKb2QfBSvi+2Ht0Xb2wH/7dn6K4noHykc/GN/fK0DPYw+NZRSac1YnqbQprx89ZrARK1nU4qajrTyRG1np98lD1j1L32f2+Ma5K9zu2M8JU7ZYPS/pM9jmbMrUDFmjuWGtVuBzv3pxPq6sW2FabaAOsbTwIwuhjRt60qvdYel8erOlrLL0sZIEyEny+aH3vQ8H/bAHcTgafyzDBMSs5g1QozJ345pA3WDi5EdXxMfRRzujGh3WUxPubufN0MLoNTulYL3JEsndZtFPyQjaX34wXMu28Xd9xqvt5WnTXnnEDlrZ+md1gKsC3o6ghZjhIe4jwRF3+zM0XCDrpxM3J7eAhuJL377Y8qHJ6ac6QKXJvzu+hnffniXGJXeCq1uhG6bxHLszDkyx8AxTHz+xR/wgx/9CsvWefW4UC6NQ5g5L4+QFiQmcghAZNueyEHMLGaLPDx+yP/+f/P/5FsfzbQIawv8/mc/3yFU3Oluik8c1kx5sfL06pEYAw8PJ5ROfdpY+plyOBuxsWx8+kd/wFoKZdtQAvX+OU+P1qff1gIR2rsHXhxf8uqzR/7Bf/oxf/vf/ge8/3/+O/z6L/2vaC9e8Ts/e+SJB/Lpfd4pL/iDP/2UkG0UUIBWNi/Kk/XJ7Q5g2xYkHGArjkKZPe3jq1dMeWU+nkh5Yt0Kc8zeCjPYv5SFKU+kpq69YNMZrawQzWxpVKIxJbZtdWMr67GnAEncolSEKQoPd9G80UXJk3EJjATXETUfC+nqCI0FgeQttt4Dj+1MEOUuBdq2UhVCPvFnLx9ZNnjvYeKQA1ILncghCJuPfE0ysQUh5xPz9MQUO997NvE//hf+Cv/z/+t/TJhmUih2/tQdDdWItbWstGY9cQNMTPdhzpGgcCnN9SgGP0TZWnUeg7Wsts2RTLq7PAZ3aWzOJxjBEOd9WNI/rHCFxLqu3D0/EGL0pNuU/gDmPBNidIntRMoTg7EvIe4wexyjdwwRqUiq1RJxMRU/Y/27/PeNeoFggfxwnHnn4Y71sliLMKrLnEPVzqTRvurN8ZX7sl6R1FGqfJOOr3WgN3Et17qSSM62mNd15bJcPCJHSl2Y84l5OiFqEqCETBAbYdmh5L8A3hmB1aCm+MbvBtPeAptVu+MGHho66jD9GwI6OsZwbGZ2/HV/jd65Px1ZXz2ibse6s2x739//clkAV4jz9+oKddvcbKSb9jjWpViXBe1Cq81naiPLshLEZnLVpxcuFzOJuKiylUIplXk+GCvcdeNDgFqVUupe5atWtq1ckx1H5sZnIyh01wxv5l2fcjIiYrMxu601t69tQOMwJT567xnH00TrG53IRZWX68p3n88kMltZCLIhodHSwUiIomZLgDOro0BtdLeftc1OkG2lBmXVCqXzP/wf/bf4tb/6y8S7A72u1Ndf8PhYeP+Xf5GwFXsNhXCckZiITV0HX5HJHBUPhyONSO1C6WL69r3R+2Y9dQH6TG0vkJTRMNOZXNRk4cCKbK/MfvT4EWVo8ceAudtl8vEIKbLVjUDkEA+01il1NQS8FygrooVSVtvYq4m0SA6spSHhgDaQXkmyIOUl/TJTBD5+8Rk//rM/5umLlzx7/yN+6zs/Iscjp+nI3fNn9PPPOaXEcZqYp4kpGUyrYz2MddOV0Dq9KQTzFu+17brtEhIhZkKaXKHQVeG66T6kfDNhokP4xtjjMWdqa3sLKue8J6DaO73YiJXjayCR4EGk1sLj69cQ4P7hA7Q3g+TR0Q4npmRrtVWIGYnGfp+ymWKp48ZdIq825T/+h3/Af/bz1zw+wbNp5lE6kh/Q9TXogkQFDVQCGk/8/NVrcv+Cv/HL7/Iv/8Z3+M/PMz2sTJIpi1XVrXcO00QQ5fII77/3Hi/0iSlnq75D4PL0msZ1fC04abNV34vCtf3RuyXmceeN2GG8Jrnh9RjXxmx2r+hAa21PsGwPs2REHf4PEkzhTl3pDrOuNXW8gYI6q0mt5TX2RKfpElwNsxZDUoxAa3D8elnptXHMmXeOR+pW3RrMXhvElREFod30YtkLrLGH7+JL4zHeVv0mHV/vQD8CJQPGt4q0OplkmiaeloLKUEcSJ6MNGpDdFH0E5bde+0vv5/9fbt57pIL7jHC/SQ2V/bbzF33r9a7ztuNvV9ckC3jHw4HeX9usfDO1uu69J+EqXymYKIYDUqgIrRkTuXs2ThA3lhgERa94kqviMYwobMGXIaKxE3msN9lqHZggt+pa1wUeGF9pJwpiAh63jx2kImNXR0pTihvZlFqZ88x5eyIK3B0m3n04QbJxoMd1Zdkqk8D3nk/86acLqoEUM0EK55DJwVoVZTjw7YlcB3d7EwHJgfkw0WonifDhexP/5f/O3+TDOZGmA9IK+nRibYnDtz8klG7iATagjqQAxYhWHaVPgcRsIE2IaIioRMAkfnvb0FagV/PNLkrvEdIJmU7IFM1/eyvI+Qukbuj9h7S20JvJFUuIiBo6Ew8TVStoIMmM+jULEXpZ0eWMNCNTCYrUTmgN1UJtF2S6R8hIb8j2SHuVkDahE7y8zHz3/ZnPP33N8e4Z0/0dc8xMROJ0IG7C3CNZgvd7fUbdL/ouluLBvlVDdMy6td8IqSQkZtJ0YCvL3hLaRzV9Rn/0kQWgu/hLTJRauSwrIjBPs6Ne9vyybZZciLWlRCIqwrqutJZJ1Wbi37mzxJ9uVqndV7vxRqA3MxwwUqW1ijp9n37p2vlAIu/l77Nsf8jv/PzMpQfyJDfcm043g1/rf0vi9dr547ry7VdP/I0fvcfrP7rwJy8rj0WhdZooWUx7QbTtbTuRoQ1gxYKJacV9nxosdZBddndMKgxU0pbjNbCllGz/eEsCF4as7kgU1BOgsa6tUg/i/vYBn8e36afbINqA2DsGlLh4Vb+OHcNoC5og2fAw2YuGEKwV6QZYD/NsSEzHpIrxSQfxkdybPr2+vQ+PPWH/N2/++xtyfL0DPYN5bgSbcdEkBFKeyFPgxRePTGlCFVotlLpxmE4ufmHa4fZiukNT40b48+H3ayVujxsLhC8lDG8fAtfROr1W+vbkcSNaJVpr4Xic6Wqa8Db/OkQsosNuttAsqEZ/dzPcMB9yub4vNmIjhH0kT13xbQTk8XmGuQTDTkSHspTBx2PTRK5e6DtnIgzST/cl599pKOsxwLfRH7PflNap3qPdSuHZaeb86hWHHHnn4cRxilxECCnxan3NZV35zvHA3RR4tTzRdGbKB4IGzq0bNO5JTOljLtpY086fsuQkBg7P7jl/vnA3zfzWr36XH/zTP2D5T34buZydFR5IhwP64pV9I0lo6OhaQPp+fgmClIB2g+eRK5ekV3fz6tBrpZWVrhfy8hpZFeKMHE7IcUamI2iEZUXrBuUzonTiGAVTRZtB+BICsZtngMaJcDx61aKEWtGyobUQU6ZuK6E2tFrlGvorJD2CZEQFSiGcO9Jfs10qDynz3nd/gH7f+BTmTmfjWywFPb4LKyZUtG2sZTXI1/3owcl02ugh0FyPNHoHp5dq4k9RCCTKWqm1vbHGWu9Q1LUZblpDdbjG2WO2Yn30y2IIU++2Zra+EmM2AlsyJTdEeP1oCcXaLdC31xtCJ1gWbckqQoz+79ZRKshGSBMvvnjFcT4SU4DQ6VqYg/DrP/guf/rJC16cK7/9Er59zIiuzuaP9GB7QAyBWYTHrnyybvz+x5/wL/3q9/in3nni85eVj8+FiU6PwtHbZooZBC3LQt3Kvo+0auQ4I7kN5LHvUsuMwOlr2RCTa+I7WPbmGmiTIoPzNF5HhvRu7/QOOWVPJmwPMXfC6+STVf4VArtmRvBee3AhLAu+4uRYa5fcFjsioN6GNEjByHnNjW9yCJxyoi1tL0jERZOGUe3tnux3j+2be1S3okVvHvRPbGr/sTrUM2WHj7qF7fv7e3LOXC4LXQshHFFgXVfOj48c3v3AF8WNF70Tba7HX3al30wAhrrbCGAWNPv+yKvqnb+yXH/eqPrBCX4m+HA8HBjs+nmeac1IbbfqXTFnVLF5Zg+wUK8mOQ49di+0ukPu4wgOwYW9KhtMXJjnw/7fe9tgKxboHYrbViM+1VqppRr8n66a7OPaCMag7717hWtwYHXfeGUmpskY9aXQ1Gao33124qP3n9PKhZQyl63ytJyZYuc3v/Mt/t+/+4fUCLMqMRxo6Q55+WN0+pAYlCklNCvrVjlkIwOWYCNH2WHmIsJTeeKDDx/4r/2LvwF/+DHHT59oc6QeT+jhgVwV2S50DWjMaJrRAEmqG+MIhEjMB5AKInRHDc1KdjNd86JIg9SFrTa03SNzoNLoZSG3BQmvaW7VGqQj50/MDc7IEhjis5Ek0tZGcHdBDRt6OdNTsm6rmsqYlELfXhDdP11RCpkpfge++APa+RUwEQ8fsm2d0Fd6j0h9otZPKfFAfv4ebbmw1craOyIrlzVhKtSGoqmbFGlp+3idaidKoLKQ0gQYX6Jro7cVCBAKEgpIZo44b2O0y9irsVqaSR3XwpBZHgnnkJTv6n93tCn2SBxjYaWZJK9a4J4PB47Twax8t/M18fdIryitx3081NBBQZeVlx//nLsf/oCYAh1om/K4NP7zP/k5v/ytZ7xaG3//3/spy+EZ97H5/ZEhRmp5wdwPTLGSI5xX5U8//ow/fCfxaz/4Np88Xfji9SOfl8Q9ha5wXi5oMx4BVVnXRs4Hai9+HoQu4v704t/Fq3IxsZrWlVY2wuxFglo7ZZAXRwEw/OtzjjT32xjJ/rZt9K7kyQuo1pwbsZkQjnZUzfSml4Jqp1QbsUwBhObSIeqJNy4CFfZAH7QiamS/XsuefLTeyTGwVXuPKcAhRTbXoeihegJjMG7TRnJ+wNvz8gG9BvkbxOGfsO6/JsfxaIH9i5cvTcWpbaSYPVMTStuIeaJ29q64LerRW//zg/ztZL1VarxBvgtxKDj1P/c14ApD3bywtxe6w/C2Sc7zRIiB6TBx93CiaWU+mDKVhOg9cQUCc5pMIAY1D/XNiTsRr7yFHBLahekG3kwp2ejZNJGzWUYOgt/Dwz0fYz3ClCZSSlxqNWKe9H0DTlNmqjPrtlDKRmuFZ8+e8elnPzPIOFxH3LR3miohC9N0IB9mJEVq76y10LTzwd0DH3/xEz58fscH7z7nnWf39HDk9VPjT37yUz46RJ4f7vidz17ymgMfnA7otrKUhceLUnUmiOnz031cLwRaqfRa94q2d3h45xnHbeJHd+/wGz/6iF/4zR/Az1/B6SNIC6EXwtMXlIdvEX/7P4J33iG8+z5yjLA10IIkbBPXCJtAatCe7FoCVEFXEw+StlnvvG1MqxEEiQdymmwqoq0IE2kz4R1as4q7NxINtuIlhyv01WKGNz3Z41KymymKPb9ssC4EN+rpYqOAU3/F+snvMt2/R8zP6GVjefUTQpq59CdCDQRVinQWVeZXX4BMrF3Z6sbcFtZwQrfNN/duvfgULeg29tltM/5JLMUMhAw569QC0zRQAtP4v7x+tNbbPJOmjGrbTVe62ox4a1atSYiYbaSNOkKwRNjH91pv+2x7kxtxK7FW1taaJV/a6THa8PxeC4q7Yw5Hu+FOF9Cc+KW/+pucL2dKN6LqNB2oqXHZKu88P/Br337Ov/QLL/nbf/qCv/LRj6iXz9G+MWnmPgmrRM4xwXzH3IQXyxO//cXK3QcnvvPRB/xG2fgHf/bCRV/EeDXNuAPHaSKkjXVbQKyPfj5f2OoK85Eh7WueHqaBMfadIX+bQjbY3NHQWj3Aq9ksg+zy2tYWkD3ZB0MRNjazclblcrnQWuVyOZvxTbXPV8UEh7a60S7CpRUOIdGikSejK+Z1CVcIH6ghEL3gGWp3tXckKG0rkIV8yKQoPPaGqK8HjC+TkDf68X/BZmyF2V9a3H19j69/oFfrk4nPsCciS1lZLheWZUFVmcQUw0yONAKV0Gfo1hsMSXbxCn9RDPry3pv/XnzbvgpO2GPHKEsrjSh2SneLez+uBrGeWhilFelC6EZIE8HgUzEB1q1F7g+ZrXRKVYPjOVDphCRG7FJFYoay0SlQr2IlOWUCgYZXW/49tk05HLJrjW901OQk04HSbGObj0fK8kh+/0PzPW8FbU/M+cAX2ult8URnovcLZV2d7ZuQYPXitm0EmehiwbuoSVnGYGM2tXdk3UgSOMTORRtFTMyklgv5/l1+/aMHfvOv/zN8/7f+WdZPf8z//d/4O6wF7t+745iFT3/+c8pT42ePT6S9klESiZ4n2uYzu7GhrGg5UDTRQ0Cy0IPwVCvnF3/GP/Nr3+K3fvUj2JT+9BqdXxH6BCHSo5DOX8D3v4+kGZVIPz9ZApXGiKbJ9ykLUgE3mDHMtSMkg7B9QiGkbJC9rjvDWiyKgQS0F2eTKFoXWrCxQ602fy2hI92SAHVPerbXrK8rcZ7gcLActhSkq71uq4S+YTqxjTTP9HLxho9Zoi7LSumBLGZRvJZGPh556koI3arCBmfmfQ0O5lrrCk1thIvB0RBj+zdLSIewS6uV2gqbQhCzJEUiPZkaXMFY/L01cjZouA3eS4rGkyBwzamV1jZfb4KGgEpEy8ZW1at+9UTT5Zj92lgws5ZXKZuPmAnd12PvlhBIsJZPKMqlFudCRJM+7t3uAoGnFjid7vlbv/xd/t3f/x1KW2nSEG30Co07RFZj+NdCL4UMfPLFhc8/f8FdVr7/7oGfvVA+fUoc5qPxTYry9NS4u+u88867VkHXwoTyJCBxojIIvkJsCY3VdS8iIoqGTiSQ8wFFTOhGrSpPKbFtBaSRIsQ0IxRDpcRGHrtuFBfEErWx3ZwyQQOH6d6CclAkJroI83SiLE9Gis2Z3KzHPuXZLWsFYqY0C+ijlSciTAhb9IkeVaaohNMMjxt9npHDkRRMDnhKCn0o5Hc2VWaZGK6W45CBAo9/vxVP/oky3j+Gx85T981mVMODoS6ezY8LmLLZgma9Bl70uiHBqM7/gve8JeKN97ZnvgENCTd9n5u+H7eP2HH8G7RARz9dmadsPXon44mYWtSUI13MGazXBq3RmmX9BvWzP358LsH6fK3b7HJtm21qMZqjXe/u1NYIEtjWjUFbbL37fHFwpz6rpkKIVji26gxeHPqzMaiUslVVXtm1QYoLwWFRV+FyIpt2g7VDgA+fHfng/ffJWnj1kz/iD//wd/jxp695CHB/nHk4Tvzw3Qvf+d4Dr5rw8599wvJ4JglUi1wEKiE0Jy4FQkpwviDNlO+mfMeL9ZF2ufDhd7/FL/7CD5DXC1DQJoaw9GjtwXK2qr2rzVuDwfQdpN/wPTpmeRui3yLqsqOW0A2pUZGEzgGt0N1T3FLJANvq/VSvNlUIvQObd6yiqQvWlRAa9cUru+7HO2JwTffqtsXNZtyDG+OIGpKDWtumVvtMtRnEWqpSFWov9NapKvStoiH5d7CguNVG7RvTVTrC7pYQkDbW5lhbwb6D60f0ZjCsJQ2FGNXljk1GUcbzsFbW8Em4Tq+wS6gO9rb6Ookx0lB6Z4fzazMUoft6Na7AUJEblsnW0updvZXnd78Yv0T8OlY1CNr2DfZ9oDuxUBXOS+GUI++/e+KvfHDi56U5l9zQtjGB0cpGrx3p1oO+NOGTp4Vv30eOOfPB/R2fX1aezZmnUi05UqVW4QfvH3i9KY+1snUlEih63U+0exES7TzYOrPzNxQvtFuvW/Zz0eit2P0rQs4zZVsBaw3GEHh6erQuRIjUrfjkjnBZztj474aosfeXZSHl2QJtU1Ss999VKbVaoeJ7r3bQIe7kbZMo0VRMRS1ZxbQy6mUhT5npMN/od3SwYUR0/F9XNL5dqb8lauboB7CPLL6RGXwDjq95oL/JyfQKlUcJpJtAb+I4zYlRwceqBlwzArXsG9JffInfFF24tnaMcOV/GL/cb7rxeG4ev7/GTaJwmzyodqYpWxbeKr2Y3rR0F5zBmcm1eIU0TGPstVtrN6Q7IyyaVrSivZq0bt2YwkwtK2h3aND6q2WpOwFokG7scw9ijW/IQXYDjCu/xb5TdJlcnKAzPK3H3D0331/VWPetNj64PzDfTfQ88/Of/BmXH/8R/9mP/5hPz/BL71lysTS4m2eOHzznu8/fIVL4+M82lstqbPem1g90jdRWA1tZqcsjvdhIYpaZrb0ix8h73/6Q9z/6AP3iC2vJkL3F0ggtAgvIwXvkTrFIauNYwr5BSIOumyEtDkfSG0r1qyZWuWLSqRon2LUUbCpE6gohmqNeq3RMCU2iMbYVZ/6XBXKmn18iIREP96RsKIQ4YQ9vX2hrJtDk8GjvnlA2S8BqbaylUpsPETQjGHQJbFshpOjfwe6t87JSaoW7eVcys+TWh7z9vjPqQnS4fdxLbmTSXG/BnFiMjxDDvhbHumg+Wz0C1WDz78HYIr8Fjt5NOMYDuqhYBd+tot+hcLqx2ltzsqsS+xWhUxfDAicijrXcGprHvLnDyc59idkQhrpuRBL3x8Tf+IX3+b/97msO2QhivXd6rOjojXe1z9Y6ncynl0KWykHg+emO7CqNU1DmBNMEy9p572RKhE+9sNRqvWjDOzywNw/isp9zGVWEXJOT4fUuOqZrmk9KWDDftm3nCQGmW+9Jae2N2hsSAsvyBEAtEDUiMbGtqxUJ6pyH1vYgPtoCVzKvtyGdI9AwT4NmqkiWaKkSW2dZFw6HmXmadn7R2Hpvi6ext98et/vNVSjn+nf50jO+/sfXOtBfK2DdL9SYiTShDe/RRgtGvXUuy7KL2gT3fb7G5WHROiqE2yB887579e/PZ8CAV7lO+3zXT/pVwX4EdTuuEqB2A3a0N+bDHYdDRLRSlwXJEzEqbasU7ZSmSFXSnEwH2qsc29srKUVsydjmV+pGzqbopz7nnFKku5Z+aw06HOeZy6tX++nt3TaBkKJZRu7fDLQ2cwV0sSK4Ie4NbYHBYQg3jP/edl30FCe2tfDqvLC2O/75777Dby+VH//8c169/lNePS5sJRDv7vnuB3f85OUjf/D5Ey8ez5x+7yf8y//CP8df/e53OKH8//7hHxL1QJdALQHVhKKsl42Xn/2UKV6AFUEofeHhcOJXP4h8670DkpRt3ZgOQpCZtV8Q7cwEtJ3RGJFw5+JBiupmY3nxgPkVdJSNQIW27QxkdFijRpBGV6uaQttgfkDKxfvNM62akAveU2e7ENdXcHwfPR5NEa43WM+EshGmO+Lz73ilavCxqEHXomNPN7c+9U287xV5s0kOT+RKbZQOW+seOAKtC2ttxuz3695657xuKELcusneilXfoY1er+4br7ijYN0qV3MZ2QNsJ6DSCb0REXdk67RaWVczL7EYMdaSVfJDI2J4LXRcmwFQH7GqtVG7j5rqeLat36ZmU22vg6NM6omLma/EaJLQyav9jlWmYAmAUMeSJ0u2PaQ3lm1DI/ytX/s+/49/8O9RuiEWCaGqyS9HV1nc6obWQGiRzy6Nell5CI3DYeIoymeXF3zr+XOezXfM/Yk/e33h9dOZd+4mXq+NP3uxMM2TvTamBClq445j/V0REqE1SySsCLqS9lKI1K8oQkQ9wWndjYZ8r/KnxhTpvZgegkT/SRA3J/eaT0cM4mRc5XZaZxRaJsgj1v7TRgqmrzH2SwEOhzted+XudOQ4z0hXS2oVkL4jLQbPNxy8uokPNzvwTbD/Jgb4cXytA73iF07AVTIZ1pc5Z+7v72mlcnk6c0yzE8GUp/NrHu4/omEKc4Hwj3yF30gCvLINyVTcVI0081XP+cte8/borTGnTEqBdqksl4vlA1pJOSKSbVP1Kmt4eIsrREm3ilNaM5KUOMUoJGMRewV6Ohz54rPPuX944Hw+03vneDpxuVw4ne45Hk+0slFq4+Hhfk9o8jRzOp04v4ouoBFIEkxzqw8DDfOItoqn7YvbpC2udqHTNPHq1Su+9dEdf+tHH7G2V/zx55mfffozUpzI0z2vu/CjWDnXez59XHjxuNHDgYXM/+Xv/AcQza/9dHpgO68cmOiHgNTOu8/u+Ff+e/9Nfvhf+mv8+//Wv87y8Wec8pF3P3zgT14U/tYvfptnWQg//YxZTZd8OXUmOqEBrcBS4BSgm2ohQZAeSR3oyxXJCQHkCFSQZu57ROjXGeaAy5SuHVIgpBOjGgvTPWil1UJPEQkzcXqGiiLLEyEmQprh/h3Qd6B0YjLxHxR6MR+HIJ781Q1cCGnbFtZ1YyvVeWemk169b916hzhTgY8/+wzRwOn+GYTJPNA9mTR5WZPxfVo2415Em6rY2mrtDWd9B3EoVPL+3FE5tm7wLbUTq3J3umNZNyObggXBbaO0vld/Y8JjmpIhF5LNNEagVwUsYandkgj1sdXilT4KhL7D7qX5790gpncrFBCbO982+yylGqogEtBi77NXpDYMTogWZcp6oYuySOL9ufPf+K0f8O/87s95vFSeHTIZOLcKNRgJNVTyYaZuBW13fHxe+Pn2yPNT5hc/fJ8/WI58/PrCQ1Z+4dsf8s67lf/wjz/lr//CR3x4fKA+e+L3LiuJZC204FLVDnbGmCAMBTxrMXTXyhj+FjGaRPBWO7UrSCQ6/6TUyjThEt+WrB2PJ87nJ1qDu4c7OoXT8QhNmaYj0+mOzz974nBnXIAAzNmS/9qbj/JdNfZ3aW5PWI18GhFJtG4toyiB+9OJP1hX7g/vczxMjgDgtuLq1T9e5fv0z01r9qv23T/vb9+U42sd6PdDAde7F5HdNSmF6DPngXXdmOcjP/rhL/Pi8z9CBFrt1ueMw9TiqwOxibvcQIa3ma7Ini4OC8VdrOEWqlcYg01/4VdRF3ER2eHt42nmxdMjX7x4wSEK9XJmzplVzaJ1wjzMUx+OdRZMSimkLe861kGEbbWe7ub2kb0rr169pnfzoF+Wld4682QZ9eeffwYIISQeXz9CTMQorOviG2PfF+m6rjukvxSzpowxUupKrWaKs22bwclOoOy9s20+EhMi33n3wK9/58i/+9PPefnqgfNjJ88NpRBb5Jfuj7xeX/LFcmapjYc50OLC6aMjujZC7bS1syBoVA7NINiPn77gX/8//BvM/8d/k0/rK86Pr7m8uvD63HlvbvyN/8l/G72ffWyrkyQxLxd0jmiaIc5w9xE9m4iHoNCE3qwnbOWAYyeqpN4xI3S/H6KAblCrscRViD3A8cHunx6gFZPkTdZq0qcXhMlGDtk2ON4Bmb5VqBdiL+jlkd4skPVuSoeGwLgWgsPDo7G1rIv1x1tnK8ZI7/u9jTmnKdTeefn6kXWt3G+Nd97/cO+rj3us1UJrPsePmfmIWEU+bGd3pTJVJHbKVnzWGUIULluxajxaRFq2zYyN1Re2JxTLutL0SpRqrXHZNkt69jadULu3sbp9r67YOKSjan0E9SC2X2D8ktoapTVSCGzrxQKcj5XGlGxywuYInY8htNGq4LoHxG7JiaSZ3huvn17z+9sjv/LRu/x7v/cJr1qjSSeqEvOBsjZDaIic1zMPp/csqVBYFbTAh1r5lfcO/MOffcofffLITwj86g+/y93dTJGNOTXevYvkF52lrhzefWCaZrNtpkMwrXmz1zU4vtM5Hu92BczWrSM1zweWZQEscbq/uwM11ct5mjmejjw+viCIcDqd+OwzQzkO8z2vX37Ms+cHSl0RCRwOxz1hS247OFA96e2NPXSoGdrfLFgHsd0yRZ/lRwgpcjoceTqf+XBOnGbzfRiqfaNdCtaxN+5B+AsDvd1pVxVTv/O+UcfXPtCPoHt7CY1cY5lqrRVBCDHRmsm/lrLBKbiDFg61X+Fl3gr4o3N++/tb4t71Pc3FLQa4HevYn/+Vx62j1JuPGoS/lK1UL60xqZp7Wm8gcVefMrTi+n6W2BpkLGIyl9bLUuvz75KUtvBbb7sCWOvqUJu9mhX+loUPt7wBy45Ww/4jWKV7kwQMJysIPi9/7bHuiy8IMSa+eCr8Z3/2BT99WakULusjTRPHLHzn7j2OxyMvHhfquiK9UmWi1MZptCAQytqJrTNLo0dYl8pyWWml8fj5S8r2hGhjLZ3PXp35l/+rPyR/cG9QZ1cQJ63RkR58DO8RkYKQGUQ4VUzMJmer8oKRK4MaG7vXtiNNMVrFZ6z6FbqTJaMFCZVo5LJekbpYFfP0mritaJ5p6wWpBdXKVrtpDESQcqFoJIu1QrZaQINZ2saEOiu9aaNU8xa3YOee7VhV3tXdDhXEz+M7775vJLY0Wc7CILPZDRZisraVw9zd7zvbVDF56tHTEqzKDwnVZqRMa5FTtSFNCVFpaq9lCpb9SijD/Ox3G0gxdnVp3gISe49SCq265G0IIMEThLATAtX71N1Jk63ZuGeXYMRDCf6eVgnW0txQpu/WqkEH3QsnSVqAR6ojBkLrhpZ8XmGeOt9798i5bLy6bDw/3tHrxlJN0z4Ha10Q2Cdkau08tcr2oHxwOPK95yekr3z2eOHz15/y4bMHN+1ZkFT4jQ/u+Q9++gr0SjDsvdMdPRptGzB9hzGxMNosQ6K2tmFEZW22dVn2a2gjbs1m3cO1p55SpjUTJCqsDLfAnDOtWsIhXKtns7yWHYH9UlXt95GdytF2sJZOTpnVxcTmySZ4um3GeMj2DXdIB1337lvS9Rv7rbInw9cP8M05vtaB/iof+ybBwgJLvF5Usd+V1lmXlXW92A3kAWtU6+P5tzfcXwbrfFVw/qpJfBlkpaFG9ZfdSGODVJOojSlCjyQJLHUjh4OPooS9MhG5SmOaJCfe87ryGXrsDiWPzSwQU7YqNNl8tfXcrKcWnBg1Xngo6I3vFHwmW1GzFR1kMgn7Y2KMdFdTM8gXtOqeCMguT5r45GnjH/zZSx7DgXgSmm5MEnieEx+cTOluqULvAdFA7eKMdE8mgiLSyKrMGlgYzG6BnNi0mASsj0kdE/yLf/NXIUVeP27I1jjMiSkqvVRCUw/AHaTsBCxTGsOqD+85WlLl/cze0cERUUWq7KSokeTY5IIFmy55rxYlCBKtJw0N7SvrcrE5/ACbf29RJdBYtXJwS9Xqnudr2UhZkZBAArWrQdfed27dvNXDCN67HoN3QiVyur9H1URYqlfFDNjeDZDGWIn6/Wa4qaFXDQumgxvVeiNE+zyNDh2KB6MY3aUUPMhYQmGqbOqtH673o6+PqtfEA2ArRio0JD46F9KvyyDLWtprfvPNuAkd6583BRXjdGgfi8nRP9SUCCWYj4InS+JrQ7s5Gl7Po9J7YhPhcdv46J2Zz8+ZL35+Rg8JuFB6vbLeu62FjpMTe0ADLF2JMfPuaaa1GQ2NUhrz0cYh16bQG99//i7/ySdnY6F7ImqTBtc9RbxhrYrbxvpeo2oukTnvn33I5tZBNg02JlxdAS+6jTAoKdl+knK2aSBtIErO5t2w80TGhwk3PXlHBGVg7l133sCtne2+l6qy1srpdGCeRiIxHnGV0b1+7b8cReVmT3vz3b4Zx9c80PevrOZznjgeO+flwjRNlHWha0WIpJyobTUls2zsYG1XQp697jVpsJvgzZp83Ji30NMIaAM+un2d/XlcSXk37/aVN6IOsFGVKdt8q9TInGY+ef3Ie88PJIlWhUj38S0Pru4D3dRY78MS0hZ+JOUhZQsS+k6gMz95gWBKf61XYjCXKbOvvKIXqOlbhxgNBmV8/7BvMCMYxJRAlM3V0sbrjHMrMVgACMLLpfHjFwt3zyayJqac+Oj+nh88vEPK5sRVJdDDAcU05nMMxJjRXlAtxFgIGkj1QOBClmSVfS/MfSGcDizrhbatfPch8ys//D4//qOPefHiFSB8+N77/PDDE2yQq/EuRhJFFdMrSAGJmTjPpi/eN+/dg5BgAxpuQ9ypDW7NkKyDrUizqYkuatWmQ6ghFHo60QS0NZYW2MpGCDjUG+itE6RzKRstGHlOMfRn89Gy5EmXBiUmIdoNa6JBHsRtTFKMBKfQiDQJpksvYmz+YXjEdTNsHbN/dUGUK7plgb42I/2NxbVWJefROzWS6GUrpGBaBCFnYp4ovVynMhhDC3Z/T05EGUTbgSTYhJ1SuiUtbSRbDTrG5ieMzy6EYMhVbWPUzsl86kEW92vAlCdrWYlBXFDVDZe6JUx2ze1oGJo4HB7zdERIvF4vPH/IfPhw4KefrEbum6Mr7ZkXg1ZBUqYFoWsgkjjOgXMrrL5FPD9k7o7v8PLFxCfnn5PnB1oTygpynznOEzFPEI3kl3OmN7efjYb6ecpmCpS9e8VsCpKn44GcIkFs28gp2302JLe9NZpztj3JE/+Y/LzkRNOKG/3tI7gSGiZF0LylE/dRSxl7Srd7KKjtfyqm7aDdkij1x5W1sJXK/f0d8zS5iqIlGiHcRoOhX/Ln7MdfUdDd7v3fpONrHei5yb7s+ppzWkw2V7muK6BeUVrwm+eZ58+f2ebkx9uX9MvQzpWVarHpqvn89uPHOMdwifry8TYCYL97+74azGSAw3yg6yvWy8qUMt/+9rdom41bhTRRUiA0S2B2ZnsWh0O9fezvpRjxzWO1tRtMn5WYEyFaIE7ZKryUIzkPG8+F0/3dtZIRg9vNBaw621l3KK75KN1l3Vi39WonilcawZKQ20mFhlA0sm6V+/PP+cUPDzw7BBpnanngboG6vqbp2eQue2COiTl0NhVKT6wV7h+ObKywVuiBHjJt/ZxLW5jmxNML5b7f89/9m7/M/+nf+vf53/2b/z6XWohJOObI//p/+T/gr/7Tv0z4yU/pTy/QnEhkLEiYQQ0IXUztLrjioI3DXYyXUDq9W0AopdBpiLeL1KtK8RYMWgl5RqaJspzR/ppNZugr0guViZoSsi0mpStCl87y9EiXzCsxBvchRUIKpPlITMmq98HRCME/hwVWR9P3yrWrVdhGzDPjnGFQtKzbbvKybRtlLTY+CCh1r8zAR9GcYzLG6FrrIKaYF2N0zXNrL4RoinhPT2fWtZCnA/M8U5txDiy4KCkJWyku22pQ8NpM0lY9sTRWf6d6b99aJhaQ+5jbdhJo17rD5GDGK9u2kaPNkyviBjawlkryz9m1k4dIVhvJ66hMYVkr1EKInU0S0goLCw/3M+8+nPjRB5Xf/eQ1p9MDWZRVsHtAKloTNSjNWxHUTJfC6+0lWi9QG0Eix/nM83hHKAvnrfKqJf7w9c84nzeePQtISIiUvXLPOROk0xxyDwpCR5uNb0pXcgrkGOx3tUIaHIW4k2u7dkqrpClTaqGW5sZY9nlr3Wh1RcK1ZWDsf5fhHvsNV84TIrYnDfttF18aSYRB9kYgTk7WSyny7OGeec40n+O/7tGONLmMxdiL3x6re3vc+ZtMyvt6B3ocadbu94aQJhNoMWvTSDkc+PzFawKdGCshKfPhO9Ans8wUH2W7qc7fJtsZLGQ30vU+GI5K1xsou87zsKm9PvLPH9W7DfyDjGJ9+0irChq4O544HBJ127g7HFCesWL+yzEpOZ2IqlwudkObjWmkR0wRMFyrcQmF4xRZ1kIKgShCjBkhc5zumPJreu3MKfG0YkpqMiGS6P1ClgmJR6tk1MaqcDtJY+5mcJZs18phOhAu0auV0RtMNKqNLjW193C3q60XLuUVP3o48hu/8jd5/eJPWZrQgjJLQUNGQwYSSWA+HCAEeoxs60ZvhTlBFrg8XZjv7jhE0FcX1rWRtky/u+eSXnA8Vn70KzN//J//nP/+f/2vc//eM7713jN++b13+Nbz9/jJ7/0Jz2Ii9Yny2Wvu331O3xpdF5uwyBFCJDYLREUbRa2GWC8XTFnRWeLNYPc4mODWVLRKW6yXjEvzttYJEql19ZFJaLqAdlc48xluycz3H1DqahriAlUxJTwRsvelIbBsqxHnRNjKwrpt1BYIeaa2bpthsCCd85FWlVKU1gvVK9eQsrsnCj1mSrMkpRORZqtEVamlOuJjEtGKt1dIlKpQq68mEzhZVptiiGIIw7YWQrXNfsyDhxC5lGLguYihI32l+WNMJa9TupoOQGtIUIKT41KKNhqnRiZMCOvWUFzeVhRcP30tPnoqAel9N0Ta3AkOAltxAZ7B0KddoegQ0OlEpVHW1xyJlDDz+mkjBeE7H97zO589UYtZHdMDoQVCVNJx4iiRIo+ctRKoPONdpvBA0cLWLqheiHLig3cfSPkd9PMvePXZp3zWD2whkIKiWumlWqLZNqIT6rQHQsgEXcnTRGlmKpTzxNYCL16/ovQNiULMs0mInzdCDMxTZAom5TyliV6KqZKmRIwTtVbmGJimo9XQZ7NU1rLQsHtJewVNIMlsf3tAJVBrZ9mKaSj0apLOoVN14jGknaDXSuPltlKAZ3cH5pTYNjv/SjBOjKhde7X10LT9hQH8mxrcb4+vdaAfYzpwJaONSjilxOl0YllMPMV6i4CGveqUOKCha4fnq9/H/+OmNzj+vcNO/nlsk/b51beC+1dBQ0M8pverocT4W0qBZVkopbCuq0n6dmXbilcmg3TYmcL1ufZ8IyLa41zMR+y9iph154AdSzEf71GBtd5ozuwzAx3LqMvWduOK7oE8xDfJhNf5aWVdF3KaLKsHYpDdhGSQa0SuVVFtjZSUD+aZv/L976HbSujdKjM8eQmBqsE3LTc/cTLcgI9zzkbE7J135olHKl/0hSZAbiyvH3kvBn7j+x/xS3/t1/mjl5W//3s/gWkm6BN/9Md/Slke+ZMvnvhnf/m7/OZH7/DdU+bPvnhBThHpMKXI1Dpte0UHpvkOVaFVq3j6QEsYkKNFpBau5DKRiLZ2vZ7e9hkGJVb1Wg/beAxx52WMHqzESCSz1dWr56s+xFaX/f5takz6UoqZlLRuGu3SXXXO1QOjQDB9hq02g7a9zROQfYJDcVl48TE7tXunu/iOifXY362VI3Qt3tcdkHejbCsp+n0A9FKtt27sv51Ym6KJ3nBDCgyKJUg3rSkb2VQi6YZTEvY1PCZirof3h73S9V+Bk0gljOpfndltcLStXdl7zreCMr1bEiNiym6qgdY2G0kNgSiR779z5A8+f2XXMghFTWkxCSxlRbSSo7i0sWt1BGsXiffvW10I4cJxgvefHfliU0c/Gn2cY/tiFEdWtmoJtqrtI7UUG11zIoUZ1tj9FmNgWS47l6a1Rtk2VNXZ+mdK2ai1cjnbvXY5n11REy7ni01HLGej4qm1I815bmXK2ZIpLJkr3civhkB2Q7mqIMl0HcxAKhqKgDLnTI7RXSHt84+6/i8D3r/J1ftXHV/rQA9cY+64yB7YYkhMefa+czTGKVYl2OZyQvJXd83hzaD8pbdUuM5iyM1j7Tb7y15vnxTYCVBvcgDA965uRhHzPBGS9Wirk5dMpMfsGAfSsLNgYV+ceD/YNl2DO016tO1tiNYq8zzRPbjvs+3Z4LkYo83yNxPgiXEw0S3JitHQh9sNc2wMQyBHHFrrvVlvbx3f2RZbTpmmnQg8yxPHnLhUk+BFdZeyfFo3U15jIB+uKOYb2aiq1nUlhEhp1ea/uyDpQMyZ9bHyrXcP/MpHDzxH+f47D/w9WVlVOVfhZ49nLucXfLYGfvq48N1nK989TZStGumsm5Z7b43QlBaNK9B1OPEVCJFShxHmSISMBGjn3QKSqqsB3lTrtTWkC1NOjiRd3QQHgmQ/1mvvvbFVs3fVLi4M46QrbExK1SxiS4PaoNbO1jpZJktQvJcTMQ352nHbYOtZVxXi/m+7bq2r+30PlnffDYvo3cf2LNBb+0zpPnZXa0ECrFthzja21rshC/OcSM41GMW6+qSABEPhFKvkgwQTcPKkIEZTDbQC1oL8aC+BoDccmug94us69/tHXLpajOA5EjD1/r7g3BdwYZ1rVZhcJMoIwIHMZK2SthgSghAFvvfOgd//+FNwtE0itCpoq5RiGvhRoKqtz1KLtzJw5j9IV3rdiFTu54RMmT94Uah+7o1waZyd7kl492tn7Pp6s58YilHcoCY4GrQNwyI6tVXbg3olhZlSVksQ0d3YxpJWu++3YklDKcW0JXy/jGLBvoXOUFAc7VFLjg1dErC2gBqjZawm03pITNNkhZJzY8AQ+7HPX8XP3gzsf5Fozjc1AfhaB/rRxgI8W4TxG9Eb/WLGwsX7jQt5Wkmar4+/CcS3/Zo/F24fcf7m75bhXxOON5/y5de5HVEbPX8ZmwuwlQLAs2cPHA4zpRW2snk11RFxAQ+TQtsD/RUtGN/D51BdEneMu+AbV2uNaZpcZlR2ZOFwmGm1EELC5HMr05R3Jm/vJlcbQqBq3QlUOykRboh8/tnoxBQcHLEKpPfOlCdG/pQQ1uWJNs3GGHYOQEjK5bygHPZNOKdMq9UJOddkZ1kWTnd3PJ4vLEtn6gnCEc0nmmx86717fvWjO+SnP+VZFvJDgEMizDP3z59zeJZ4Nz4Qc+BlC7zYmhmktECnmXgKjTnOBDK1KKUXtm4Vc+gTpbS9qrTr0ghdvFp0Gpl2G10bFelNn3ufXojsM9xbsfE4+67JbIHdprR14wQMtTMbL/MttKtBmyGb1G1Tlq3Z4HdITjIzVnRQaGMUTY3sNkbn2ggWaqN4uBTvIBgi1vM3IxG7v41Jr0j0Ub/WabUTo7jRjYn5tFrt/kt3SNKbCtsmBXpXHw/1qs3HSkMw9neQa0U3KnwJgRQTVaslCLZY9qrU7v8bcZ8QSGFMEHBFBXw8dPw+iMlPx3AV27IWwURrFaKQCCSNbAESG+pjrlE733rIPEyBF8WSv5wilxXQ6sROk4ptmDjQViplK9RtA5Q5BUvSDDoiS+f9Z3fonxjCZOOS/eY7BKA56oWjQgMVddRFbkjGIgQZCEXzYG8ButTCXYp0bYZaRNm5HNrVxHl2tBVfq+7qqUAQgr7ZNw8hEDBitGlPAClZqyEGUgCVQJJELytymD3QC1sv+wXXsdbsKvO2pPlXjdZ91d7/TTu+3oF+v4GuVYb921is22aiLVtZCTTSHnCg60aQ2ave9iUPg7/4gvt2sgfq8VuXOdVr8jBep78Bq4/Xv46qjSO6lGjdCq9eveL73/8+tdgmVGvj1eMjRQu1beYtLitba0SUFKc3YPTm7OWcTTSnlOJEvO7jLHYutmrZeG+dZVnovfHq1SuOxyPL+UKX6Jm98uLFCzPmwMbDaquM+dYyiFIuhmHw/cVEXFA0ONkpZx8NswW9LBv5ZAnD1jZenh/p+T22LCxPBWn8/9n7l1jb0uyuF/yN7zHnXGvtxznxjnza2MYmwQaV616RutUoIbgIIUQJN2hhGrSQoYElhJBoIBAP0UE0jFsWooOQQKJRPMSrBHULMG/rWnALri++dpKZEZEZEeecvfdaa87vVY0xvjnXPhmRdl6qJEeUl30yztmP9Zjz+74xxn/8x/9PrBE3NJrzpNMCrRF8wDvt9+ecCZagnJcEJp2ZqkG+ZOop4cMtN/I+v/Gt1/nvvvgFzs/e57+cn+OD45yOUBr1PJOlcT2eeX4P/+5Z5j99LfHdT5/wxVfe4FUvDGMgD/D18wO7GgjjSJHGUgtzOhORtdcuzgHVTH7MY7s1yIVcknI7OnTdtFpprZGqjU02MXtOuHs4avskRK6vbsg1czyedR7fCHUiNgaVZlOCE5wfcDEAjuYCBSXqLabDLwZ9lgY+DEjDKkJNVjwWdKtq9+v0U0dkdM06ceZcqK/ZxKrIYrr61Vo9gLhAzklJdl4NU8ZhAPQQzjkjxvKndT6MrtF1lL51BM+CvNepgtacujsiZuUM+D6Hr0z91hQmr+j3mpmwiFevDGd7uxkhDBHwTg1sRdakxnUvh4uzKISA+IZvgktifw8EPwKVVmbGVviet1/h3/yXr5KXBS86EXR1e42UxP0Zcmm4sRH8TpUJs45NOqcjqtIORInUNrMsM/OH7wGOEILxfQCqTSxsQkO1JArmUFkSbtYpiJubG8Yh8qxqwPXO8eTmhq/94i/gfWS3OzCOE6WiznkuQNMgvdvtiDEqiRJFbbq73eA8wey2tVVXdfrHh9V3QUQnKaLtDwkCk8eddewSr8ONlMZyPDFd7dmN6rGQa3l0XvcjuQKhrcfxurZefvzyC7xP7uMTH+i3G7LdzQ5JD8PAkydPuDsutOKN9FSJYc95eY5b1Pmo1qKjI/+Nj8bGuv/lgj8vL6w0z8zzzLIsvPHGG5RSmMaJaRqVCW8HlVY36OHmPeGlxazVuUdsuqDrzjvn8C6usHofFcxWQXS52l7dn05HDjdPGcZR2dTO4WNgPhXzs2+EGJjnk0J2NtrXWkHcsM7xA2t2XWq1sS5Nikop1FwM4h9IDKTamJNWmzknak7EWllKImeT83U2ZpUyLgb2ux0pZx5OM8O443g6U7MjkchDYmiO//yVr/B/++9+gM++9YT/+d1n/MP/8At8LR6JS+S0fJPnpUCNNF/5+lcf+O4332K8uubDWviXX32Pf/fsjv0kfPGw5/uvr3ljv+cZR9x8RFpACMR6wzHdUyt4X9drUFulkDRBQ6uf2qqSlNZ2jsmVAmlRExwsYJVSGKeduoFVHR9t4ojTSGNrG+hoZHs0s9yr41IKDsc0jAQfSY31d7oYknOO+Xxepyi21lLveTfVqzEo3ImnG6iorn9j1VuQpsqTxmOTvl+lWkWsz70KK0lTFTUUkaInxE0FdZSl7/XdlIVKBSNb6XOrVa00CD7igyISwffxQNG+fhNSUn17Z1a0OE/LBZvJU9i/7xELXuoToKOoIWhyKyKUnHl2d8err76mSAM2F25TLKV6nBQdm3SJuQpf+uybfOObH/DzH1SeL4VQZ46nmTkpydG5wJILpQlj8LwomWVJDEOk4ZCw5+F4pC0Z8JwX1X7Qnndd92etlcEHKFV17J1XeeCSSYs6WNZaOJ/PlhzodUw58+LuDhciuRkJszaWlGkVHh5OgCNG/6hq1tettDgQhsj5eGYXg6JLVackQmA9KwGr8K3lh/EhcmWqqrqYnLbuRBzneeb25pr9MODsPIldvZCtan/5DP52sPyvVvS/wh96Y+Dytmql7xniwDhOCAqpFzuI9uOBF6f3aLXAxcjX5ePyZn/sje9Q4UswkBjk2keL+sMhBtO29Xkv/zjgw7s7zaSfPFk3626300mXVtdRwVYj3gKAvo+2GsT0QD+Oo5G5NPUIYdCZ9ooRvtx6sA+rOpVWbLUoo31ZzuzZYFSVx9RxmdaDdK2osH4XOWkrybAHi7WNYv11sVnlPtvsvCZirVaWJORTpkX1cFdN9EIqOh6k/CVZP0fKM0+nW+MF6D3uZDwNMh7mSGpnrm8DdzzwL9+ZOc4LX3s4c6pnoomABB+JwwCjZ8IhMTK4wGsh8uG0R2qinkZ+MWXePz7j84fnfP9rtzgilUAqcFrOeqi1Sis6F4wTrV5Ktvuka1aAkpImWE4xjlZ677KvFR0JW3LeIPFaDRHq5D91muuVUWlVbUWDBkaao4oiC4IG52EcwXvmedHxN30p1U8IumeoumZXp09nIHe11lAXbJKGNwSrFJU91ULalM86ac9mzFvVue3mhOC6WqMlu+nMMIx4FywRV4tkmgnWWICYxpFsc+DOqYhNLSia0IQQnJk6Kfs/r/4T5jPfPFIMzRPReyYbh6IT8ZQ7Id8CA4OSfnsAefr0KQr/6153Nj46hAYh4loyJ8JMlYkn48Dnbne8e3fk3YfEEw++BiWXZodvjp0faDRibOynEeoVOMeLhzvCvujcfitKAm2REITgg45U1mKcEE3XulJolwp3KzFR32uMkdxUYKrrYgzTRM6ZOA6KVOCoGZ4+fYX7Dz7E+6h2sVbNe++ZpolaC94Hdocr7pIiWcErorRyh6TPveu/x3Gg1awiT97TnJoEjUOj5WQiO4HzfObq6Z7Qx3ipjGg/x1I5S84uE8uPhuxffmyF10cf+Z/Uxyc60Hf4DDEXpd57Qntz4tR/XdPUHnQdMQTESEGX5DV4nNG9HOAffV0u6px1wRj5o33r7ypPoDf2XyaAKDR5Oh6ZxtF0qZ36hKNs9TEGplGNeaRC8GHdqJ3o5UPQ0R7Al2LiQGXdzCLa19XDaKveAMZxZJ5n021XaDKEwHxqqLmHZu3eBzvcts+gFfzWa+sVaSkXYy0GIffrpbKpemW6aIcXIZfGQ8qklGleCWdNdOwqVVVCi+NITepjXkxrfhwGzvOZZZkVum4qhBJiM8ENT/TCW6/ecsyFu+cLx/OZ43wiBE/0ATfsdByxVpwbkL1j9hFplVDAh6j8hQwlZe4y/NcSiO3E27eeGPSQTKngIybcYszs3oPvULPdj+6eKE4/P+JWqFGcrDPFdDOVixXVL6tYVS2g6m5F56O7g1hvH6zaD0YCbCK4EGwmfSNiKkrtaN5IoYaeNN1F+vyiwQDXFRbtXYlqx9fW6IqUDUX8EUHMK740RStWAqVCHOtzAOuIG80QHL+JrCj5z4KY07lsDEUIXlUYvWngY8mSghA2h1gbwQs0t5LW1mAgzs6S7So710mqq3q/yUY/Xvs0I8KuV6Tim9eRt1JUHKdVCPpzX3jzVX7xxYlf+OCO6v2qRNeV5TR/098TFJkQH3go3XVQDYlyLhQ3rHyF1nv00tXnjHgoep2ij4pA9okjS3Z0UoeVm1Cp5JoZwk5f2wJqCJFkiEGXu+6TM52omI0TpO9p8wRolqj2JF+cUGpe2yLSGlILVTxVMOJrxjfBu8bD8YGrz79CtHvbOSMrb4qLI6cncR/xuKzi+79fHq/+tDw+4YGetZpt0uujfkqKJYoK9xVJWoWIuWytUP0vfUM/Gs7RIPTLfq8fgxCIZQu5KKHq5uZm7Xf3FoQHxiGy201GFvI0JzirXByqKhWiBnrntRqJMaxqdCtqYFrjl8pWANM0qQlO1JnVWgsxDqqK5dQRqzUYhpFgilrNoP4Qgn0ubRHEGB4dfmKJg6Zb6juAhRaxQB980EDfGg9FWeRi0HARlVMtTU18xjgieVlZ3uMw4J1bYUgRp+iNsLJwxcH1YWI3DZzTzLIU0tKgVT3EXCBMe0Q8HE+4rIFlaZWSZnwpKvzhJ4SEz5VS4cOHRpqP4D23u5EgjporzTWFghuqvAgoHowFdFnZ6ltyZbKgXgORmCNbn7kPIRhpjFUJTvuZYe0jCw5fG75EBGc9am/Pa+u+mplQUREYDVYGVZvSod4TR+0B0AKsa1q5VwvC3gW91m3jq0SnqoyF2s/f/unXfddaWwlnl77zGHrUMK90LNk1lUXnvPEOssr9XuwjJ5ueAzTdC+KglTWR77rp1T6HWGBUGWAj7bbt3fT3KtITc2369kp0JX5ZktFH79ZgS8a1gLSiAjIocuIF5lJ549UnfO795/xvX3+fZ6fKa8GRkzLspYFrVTkxJlaz3nNBOTbIyrDXeXEVt+peBjoa6zVRtwRbRKt+DYwW1FrDmRaFtlI02Sk1m2+CeVxYC6aLL2ls1gQhBLW5luBBFI3s5F6x9lXD7JItCnehm2JES0X1bF04VSo8LwstFXxQtb+H0z1vHXYES6xav0fGvdhu3MV/Pqb3/rJQzscVeZ/0xyc60PeN1w8pzLCij5KAQmvTtCedT0rKagItM47TaqtJEzL5Y57/WzM//Zpu5Jc7QdV6lP3nLzNEJeXVdVGuf3fw7Bsf8uabb66bphSFXrMRsrx4gh2S19fXLOczNVWrsh2tJOKw2T7udgp5znNanaFSSngXqK0xDMM6Ow8a6O/v74lxINA4nc7c3NzwjXfeUf1rqdt8rfPkqpKt3sf1uqymPlbR903uuwymaF9zmiYeHh5MslIrjGkYdLTMCecgHHNi71RMY66FlCqT6FxxbU3HjXLCAfv9nnk+W1Wh0X1etDVRs8eR2R8qn/n8qxy/+U1OJSMoWcjvJlwRjmlmF4+MTihlob040gSmg6dQeL5kruMtiIc44nceKYkX33wHrq/52W/c8fY1vHmY2LmF41kZ2JdjTV5sLMs37ZuzrdHeSwV1EBOriGuxIIQwjhMpLzZhsGkGAF3YDZojRgeopnyzClMTXofznjQvlJQpNTPdjjZjrU/SD+VEhlLoGmYqw6Bs6UKjidcZfucpaGKakkLyQiN6TT6KjXppQe8uEAhNSGPQ0U1qXg1XdKpDUaptesbke1EECXG0cl4nPNY+OupaqWNlWr070VFApZwZuoVWx807JGtiWMVRc3oJttXIkbNpFKyjo03FZKxHv50Z7SIJ0KkKV4WaF4pTTXYnqmA4V6E5+O43b/nw/k3+5r/5Ct8XPZ6q+vUC++hUg96PyoVohZpPQOWwP3D/kMg2eZPzjIQRscTR3pEiQ24rfHpwX0cHDRjd7/csabbzqU806GePUTkcpWrrRy1mFQkcx4GcE4fDwSS3Ha4qn8h7TxyiKfNZ8uMb3kXmZdEzwgWVlO4CU6IaBIN4coGH+aTyzgKpVU7zmadXBwv0Fph5LIW+ncFbVf9xCO3LrPtP4+MTHegvyRytw35sTmmtefw4EJzCYCruodBUmN7SQ7BtVfW3ezwO8s36o5eJwOWi0XEhlQj1BlNiqEOjFYXDgzRO5yPPnj3j5vaacYrM50Xnhb1bK6VSEn5wuCnw7ntf5+3gSceTjjc5z5gdc12gLBdwugb6ssy4XnHlzNIaS14IIawJQCf/5ZxNG7ty9+JDrq4+o/2/NEOpOBl5/uIDYpxwLqgqWC4IIyKNWhZa29k4nqPVTE46AhiiVqNpWVjmM4IaYQhapc9psflrkKyjZ3OZyctMPhbVrneOOTfKbOpyTlm1Tw97Prw7EYYD1WeOxweCi9TmcPXM7c2OV165oZbEey/uCCFqEK6FdL4DXwkeBjfgJTCTKJz1MBtvaDkzn79JSu+yy9dMEjhcXbO7OvDqa6/x1fe+xm665pkTBgqyO5Dme1JVUyDnRceDalHlwCZAMDnPhdYgXpzNziD8tKiy2DB5momwDEMgxkAtbQ088zwTfVTotVRoGp57cJYQ9ZCnEoMnuolpHO2Q9dxME2dRz/dOaZfmqNJUAbCbPwGt6yKsCaxtLSeEwVKXZohMY+3b16LryDlvjHblbuSaCXHQEdEqBOco1orobaVmm1RHOitGwlaTHun7UPBNe/gduXJOfdlrtdHLEKwTop9LnxxWrX7aOmZ6f3ygW6068SoO5KLyLETd2hRZqxsPxSl3ZVnOGHUGzwjRExkps/ITijRShSCJ95694MkY+T998XX+zX95h+fPK4QdPi2UvDCLcEw7dVM0+B5VGVCxrLbQRGVlSxIKHvFRJyBq21CZ2nAowlGrVtopz9SaLQFwpFQQqfbe9Zqmk01kFEdadI5efCOdZ+Z8wjlPyYHTwxkcnJnx0ZvI15n7Fx9y9+KByfr3dmAiMpNzxseA9ImdXBhCIDtNTqZT5lwzSzqC8+xyJVbH84fG9esHJedVNa9ST0xNQjofqNZK7tr3F4F8XVcvQfSf1iAPn/BAD+v9e/TYxu7KSoqRR9FcE4SXDRAuBRVeXgQfN5bxaHGsXQMT8RChNdVC19Syz+8qVP/s+XNqKTx55RUdRUErX1e7bG5/WmEYdPzo7u7M4XDgmDPSms0oF7wXatO5YDWTCfgAw6SHsh6Q+v3WrPpqnYQzEuPIw8OJmxuF4lvVqYWUTBnPxbWlsNtfPWL1KvlmE9DZ5vf7fWimqKZtlHEcOZ+OLHY/HCqYE2MgJSEtmXnJ+JR4cnvDUYSH+xMPpwdOxUFQxa6SF66u99zPJx7OJ+K0I+XE/emI95Hnz4989q2nTPuROSeeP/umitOUyhDVuMNG2TXYVCU61ioMwwTA8UG9ta/3T0nlRIgTjsKSztS7xjQMfO71L/D+82ecU+YhV+o5cwDyMjOMIyEO9tlmrbaaBpoweKZ4ZRC2MpW15TKQcmYYRq26nFYz0Q5Q5x1ucCgZsuJ9tJHGzaikT51cahosebYK1F/sCfU+yLWto3QVR7fVtciNSj6rQFOpxch4qtbnfVeCNG10H1lyYUn6p5ZMzRnvItDMxEXHMJGNqd8haJxXQl+MiPgLYqmaqwhqcnO5L9sKQ6sr4eUebfS2iOETrdJtXC+fQxCWlO2aTOv1yTmvibPu5R401JholbguBUxHH/Q1WhVA23L67wZOk/cKzOfEQ6vE6Pkfvv+L/OP/7Y6dZMYYWKSSlkSbmhkaOSWsSeD4oBwEvUeaIKuAELRSrPeuLHeM0NinOi7Nqbzv43htTfh7tY9gyb+ntUJKMzkthOA4Hu9Z5pk46H1Zsir/+eAZqo3wlmajwXqDSi3kUkhL0oBeCgFwtXI6nXAoT0V1DaCJ8jlaq5CFIjpd0IDXbp/QDOmjKeRfjQNgEO969qycj4/ovV+irttaaHZuf3oen+hAv40kbRAURm66/F4Ij1XFrLReS6hLUt63y+q+ff/mEr7TzLnUvEqbqtGC9tWca7z/jXeR5pl2B8ZxpyNkvo//6M8665XWojPy4zDxtftvcj6dVTjF4NqyJIWD6wzW5y1GgqnmQb3O8dthpYFaSUO1Jc7zA6XMlDoiUil1WaV5dUY9MAzBGNVCjMFcyNrqWnd5mFySEp1TAmR36erTCJ3a1KpWdj0VK62RciOdzxzefpsxRBof8PzuGefqyS2xLDMOcMHz4YvnzEslAedl5sXpARospwUfbkEyx9PM3d2dQfuVrjY3xknnvItWN/o5t7HEnBfldQSPq4KPjbycaTUgVTg+LDx5+oTTqEphH9wXrq49t/uJ0NpKeHTOKQveAr14hb6dE608TeoU1CK4ISvU2WpTO2B0ckI6NFwbPgSbk/brPdY1r/er95G1Lxu1jWD3Sb0Z2iZ05BwSArk0ooBQ1vvZ1VTFNlKtW896bUutYitotWuVYajGiG96SNfq1Amuqlyrc05jZ+2Qt7aASjZCWoNlSWuwFcHkUMPWCnA69ldKQWrFydZ77v3qYpr1Ss7ESJLaH9ZjQZPXPo7Y2dcbYddQQVQ4qC1pDSKXAWNrIzw6FlQ1sBSdqkCTJhHhtCScJL7njWv+zdefcX7QZEM5GfraqRMcAYeautRScDS8A7yiY07kQlBpIyaXmjW5KBkVufF2BhQbtdWEratMqkeG6gLkbIqDrZLzQvB6fUqt7LzHh0BadDpoScu6rnIuKlgUtF0oTfk53ldCjDTnCFEnQ2ptBO8ZQiSyiS1F71RsqTiGqCqDtRWuxgnv/Hq2uaZqfkowNM+6fg+r3Z9+My69SHTJ2f3fxqI1Qfv0PD7Rgf5bHms2Zpu86sIPFxar6+4zWb1Opvmo4P1yj/6jX/Lye/2gbcYV6HRr5QGIkaXu7j9kWWauD0/ZTXuFBvNCDdZHEKF1CVswYpRnGAbVoi7FRgWt1jKSzvpHlC3rLYHJpdBRjGJ9Q29BpFSzUU1JIcxVm73YNTDEwDmC98zzbFWB9sdKycQ4rlX8Vh1thCCV0A0kMw3JufeYUfEVk0TtyZf6xzdqSqRc1nt0TomHdEaCjqzFEKk07o8PIIG8CMdl5pxmSk4c4kgcnCYuedEWSPBrVaOaAkHJaW0Bgy0HU6ErpZBbopnGfMuQ0glqBlEIusxnSrniMO14eEgczzNlPNGud0wrI1tbNV1n3dn1uNRWV0KcrCpl3gX1Ui9a9TrRarhDwloEW5XtNIGUVi0YsRItNQAb+9lU4V4Ozt12WOyg23rMvSLa0DANaHpNtVd9KcncK1YjRVky54I6wOVqa6QKxUGtwpw6M99OXPRzuTVxqTgX188hfbpAFCpv3TfAFmszlMs+pSYyFgwoFXPZMbMc+36pK6TfdENp26K/H1RrQNBet6CweGlF7Vxdd6+zQANsrL4LiVzp1xWbqKl4HHNaaCVzu/d88enAf7zTZCZ4R/FCqZisrTntOSUldvlZ70S9jU01sHQrWDs/vJETN34QK8qnSXcFDPlrmxWxE7feX++3/a4TCIYSOhWtOuUz4zQyp8WU8cA5dVlcWz+irp7VB3DeDLW89fEHYgjqtCdKMMx27iVX8E4Y4qCiUrVwGAeQi7NPF84azNezSLZvr8WgJawdBVrP8W5mwK+y7n9lPvSOYTpX9iXdct6qyctDVezur/rXjUe/90v1al7u19vf1s6Aaj33g9FRi2p0i3O00vja177Km2++wfXVE7zvfsrGiL1YeH08TdntjjFGxDn1YM4zKReoTfu+DsR7zcgtsBQj4jSXNdaYDGYr85p1p5pxUhWCdXEl13VYf5Ou1cr8dDoBeujWogS/EDZFvu1e2FWxyk8Py2wZdlkroFK3gNEPy1Ir2eDHd959l7ycOZ9nXNiT53tKbezHHWMMzCkx50wMil7kqpKytWXefPNNduNI8KJzvjGuBim9YktlgQYxDtbTVUayk8j9/b2Sp7wjjgPneebuxR37/QHvBkpTJ7W7+3ue3r4GBfLxBc9PDxzTNfvhQE6LEdScIvRe+8BKThIddauFTpwrtUIqxCH2xaqmPiECdRtDAoWk68Ussjj82l9t6yGt5iZqzFLKJclKK8dOAqxNk6sQgpqNiMO5TdI4m9aAX9tDokqK1q5RtKStOgZpyUrYjAEnTVXxLgC1Jo55qSvSVFtF61LdKzqd4HFe92/KaV2TVXV2jVmu1aI0+yzOqamOnQkplfVzWqw1k5+NuFttdtx1VcKUcU578UjhdD4zDgNOtMoWMxWqVFyDIKaBsAYYUZ5DY7Vh9iGCqGKlkmMzEjy5NJY508pzfvCNV/hfvtIoVWN3q4VMU+0Ma3tQLFE1XQQn0CyYgyli1g16Dhd6AOJMJ39FHzq03dsZXYb70q9i+/kNyTFERFM5aqtmdlWtrePxbtMY2CZL7PrbGVBbQ+wMWEecDQUTC9wFRZt0HWqgv96NNEMH14Ks1PVzIpYcVUUVRG+WJTpuneyAtko2ey4IfS192xjwSXt8KgJ9tapJ2MZnumBLa21V/VqhLO8gb0kBK6Hno6H7j+vtfFzW140grq9vOD4o2coLLOcj733jHV577TWur2+huTX4hSGaiE0v6rfnLzUTY2C3mzjNZ+7u7kgmhNMq5pi2MEZn0Pz2OZwLqwGNiIqpiAvMRlAqpZBS4f7+npTUSa1Xu+fTCeccyzIDeh2XlFSqMkaKU2i/1IqPg8qeF2s1hEAp2lukYVW8Xc8qq2lGPwiyjdOJM81sVGwkF8eSYc6KBMQw4gnspklbIO9/kwo8nGb8qMYtOVemOPDma0/I50wVYXCR3Thwf0zkAktpSM3cHe94+vSacRoU/SABChcezw8akKMeHLvDjmkczHI2EsYD1TWO85lXWuXp9RW7neer988oErm9fcrp7hkpJcZp5Mn1E7zL4OywwRMHR8kLYhXLOCmjHIGai5LpzOGslLqaDK09drGRsF6qOK8BwqovTfpslt366toe0SDa778PehCXnFlyZhgmnd3uyWuvKC2X69Vwzln1G2y9lQ4zcRH4zdnO+Wh7SdXsclF7Wu2HbqJMDtNmsPGxXmH10T/VvddauaJ/Lw3EEk9ACZ3OgonNc1s9rQ57horRTDzHkJbz+awa6j5owrmos+M4ToYOWGXqhW543iyYP2r/rQCbITr9zEB1FXKupDRTiiOGgWGYyJL40htv8Jn//L/zlQ9P3J901j8e9uS2BTuaqscdrnaczuh1rDCnxrSfVDDHbYTJKkBz6/tp6JTN5TkRQuDq6or3P3iXtBT2+8DhcFA3ugWurm5IeaHdv8B7z9Onr5BTYTddcXN9y4v7Z7z99tt88MEHjONIjLqfbm9vcajQlo7+es7zDGDcIE14S9Ekbh2/rBWfVARKZYa9/tcSmP00bGfxBdeq84REgKIkRbm4/v1sU0SirQiYd8Ig23MO/lcr+l9xj95LA6ty6AfTdoNXpnBtq7XqJpIDqun50qa83Lh8PLx/2e/R/qhbN78I7HZ7Xrx4xv3DC66urnj69BVKxvr3+jt5WVhSYhz7YqtrIKw2xuedEIbAa0+fcEyJnIv5u1svn7QGdOeUtd99otfPcj4jTV3otOKD65tbs5OM7HYj/Urudjta09n53bTjdHqA04lxHB8hDldXV3z93YxD+3WDaZZ30ZJGQ6qiGQqtXs4e+xVC7o9SK8d5YXftGMa9VifzGSFwPr/gjVdfQYIwzwvLknHDwJwWdgM6opOF6/3EGByhFs7ziYf5yMPxnpQqcbjC+R3BB25vB56+coOTwXrajWVZkBaoVSchpmnSUapzYh+veDg/4OPItLuGksjpyH4cefWwRwTO7V0ynsPuitudBkwXJ0hKtitopeIIiFXI6v4lGnBbnyv20FQVMFfrlWMGIs6DKNnJIauBiTRN7lRwRcBH1Gq9ck6LVkgrl8IOSOdYylZN+abXAGtBbU6FysPIRSu/WpUjIcHre0wLy1kP8WGYtF2xVoZ5Dfi9eu+z1yvHxpAkHz0lV8Tpujgdj+wPV2uSso5Xtgy4NRnSCQ792K6JjgY2qE3MYreuPBDrYtMZmR3ZxwWToMVEt6ydYj+7glZV59vFZHp7S7fZ/zSV6FvVDmtrmmCmqmtWsSKcj+s18n7kvsL/+fNPOZ4Xfv7ZzNPxALVwmhd2rhHWStxTRUAKKr3oSXVDLlbFSqyNY3C/u5DFfiwSZsgDzfw2dJ3M88w4Khcip0LJmsQ/PDywLCbHjbps3t/fWTKU8c3hPJwejlZgLP3AJOWsPfq2qE6HCKUmxnE05URtAUm2NmLLlCDMy0w6nWEUDvsDbjH3vaKeDM4FbV8VSxCCtqv6dWm1Uoxs2c//huoneO8553s756vJ+356Hp/oQC88rsR7tlqtIlSxkWwiOQb/WRWxPawSsmf8tq93EdQ/jpCn78etwWucIu+++zVKVYe4w+FAyQ2RgEh+9Hy9mm+1GeSt76iWig+RYRygNU7HBx1BEZ2xrVVbAxSD59b/U3EdtfbU61V9Xmfp9WcbtQg0TwgKXyscHBTGR+dclUgWV6e7njQpYhKtcmor8SX4yDzPIMIwjIhznM9HYhxNeKNoT7opCce7QIjg00IrkJZEfThy2FceTmee390xTJ7z8TnRvc3Dwwte3B11HHEu3J9ncJGcCunhjN9H0nyyKngh58Iw7Nkd9gzxQAgjwati2W434hyEoBn/OO6pWfuoh8OOaRrxTjjmM7lWhnFAxFNSgpqZ80wzeNp7bRW9f/fAnDLD6HBAqsVEX7R9glX0tTaFpltbK54GkLMGEKVYKWGpFcbdXteL6eS7EFjm2WSBzTSkVm3raDYFqNnNkvPaInFO70FXNBOD9nPWr4UQbJxNTWlsldre0veEjZn1QAXAZAmuc/ZzNu5mUygeE3uqjhqVTIiIacgXaG4dV6ulGJRswSNMKtxTAQkI/XWwXqzuCXVdU46BJhFmIVyy7lFrdahEKuZb1/9YpSdK8PKuz9C3NRnRz6Hwc3B+DZguRmsJ9jPFjHTsGuj9tX0lDdc6679QpQKRD+9PfO61a77wwR0fnhPnlHntZmBJC2NQxn+/Z91Eyg4nKkLsFbGwKixekjA776FXzipwFdfquufbMUSmceIFHyKi7ab5rDP24zjZXD34oMJcQxgY48BumoghEJwjO5Ug1raYEvsEU/cLQfU1VuQxEry2MTTRUJ2O1AqTROI4Qa2cckb2OrXDSfUgmhOkVVpb1M646BhiDJF333uPqyc3WvjkTF4U8ekTKc7m/gG8xJX/4cK36qp8kh+f6EC/4mOP4Habk7TNOM8z4vxKyOtZ3MuPLYg/JmN8FGx/yejfvqYLBDAxEN1AL14843i6Y7c7sNvtCWEgLVUPnxVmMqtZI6bpc1yQeWpVb+sYcV49osuglo1iBKjairlC6WevnZjS1KSjk6gUJvUGqdYtOSr1guWv5LuSC84Hqo0jeec3WV7vtQ+Ws352nPU7q/XxOidCTDnPEouVK6HVUtMPqweindj9sC0pc57VgfB4fgA3ctgPDAGe35+5Px2R5smLSubmrPanec44q6oanhB37P2OiicMe9VQtyRpiF7nvZ2sJCNBOM8ZUKKRNxlW7wOtqoEGeEVTpOL6uFQpBO8I0XG6V4lSN2nVnqsS7GjKcDYTUzC7Wu81sWtWXa1olDOddeetN365hg3Wl37ddGJB169VqGgftgeoThbTqliJWF5vB2pYYjPnlnBcvpd+f7WXrSYxveoVHK7puu8weKvKVykqSWmtGax3XVdFRRFVRKwmxlLM/bBXpqVqDZ5LIvid6tc3oaWzpeYboidmBtM5BtvnvrimFet3b3u82vfV+KZRcjXkT4N/MfjfXk5V24wH1rkOMcY1Sbc4f5EgbQmV7veqanVFdT0ED64xn2eu95HPvXbDN+4zP/v1Bz4TI6fjrHbFPcG2Z74M4LWJmXNtBFbsXnUVQaeLbG1t9jV0OW7Zv6ZrTYmzzs5HvQ6DTkt4qDWvMtSlFIPhMxjBd7GkspP+nF3nlhMSLLmq2qBJNFJJa2+eCksrDK1RfLHz4Mx0e80YB9JpoRoZsllV5DCyLTpe+uLFc/Y314Tg8YNKXStKGXUfOmca+kKQzotpDPWyGPzkPz7Zgd7ivB5mNt6CzrB7oyfPC6vD20rIQxfHOg7Et4fnP64X/zgZ6L1JJaI409l+7713GYaRw+HANO5XsksfM9NDQyvJy2qrb0QVptHDOAZPiH7tj9aWoco6VpWr2l12WVnnPWKja87GyUpNSCvgoOtuB+dJWdn0OS8qeRkgLTODcQdSTjpGk9Q3XHtrjpQWVoVASwguCTu9X+a9JwZVcVNeAitppram1QnWzmhQcbRSOC8Lc86knJnnI9/7a95iiGZF3BqDD6Ql6eEljqrTtERDIaRNjGFHCBPnOaNkv6RZu1T7zDbjbfyAnAvH44laVBAo+35fBTc4hdyrBTRxxGHCZ3VdG0NgnCLVqkrnAz6qt3tEEyX17w4EdGqg1dyhHLuv6tPupTEOUSWcc8MFrUoUSsYSNzF54j5GVq1CkfVrAN6LwqX6MuRSScYPqVScBYxsLoKlXiSCtq6L8TFUzGZTo3NNg0+vZJ3TPVhFg6qUbdbcX7TZvHcsS0KwCYdcrILeKtdif0TgPM9cDTsVGkI4L2jCqjtY/9d55eDgTH9AqMXmy+1nuvBOsVG6ntCU1vecmSOZnkBznmbkrBX/60gJmEjRRuqr/Sx41JJojxBH1zShbaZK6MSZ5G3hVCtvPb3mux8q/+4rzxEJetNMxwBr1Tgb1ezuk7U5nGBiSm19s+IcLZe1pUfTltq5nNcpDyyZ6Wdja420LOS80El5ucPqDU6nB0Qay3LifNJ9rTyfhdNJ12ZOhZQUTUtJ1SidmQvV3hqsmpB7UTJyrmbGY3tnplGXjK+NNC8cTyeePH3Ck9tb7lJhmRdNOkVFxrw4JSiLU28QUT7C4XDFEKNK7D5K8C7KvubWmjGVX52j/xXzqOaA5iwj1YVaKMtCMlgyeJCSkZUJ3ygFqyyVtdqr848K9n1zrBv1opp/3Ju/CPxUjg8PfOWr/5Xb2ye89tprgJjvu/28V7vYS0/37T2Y37u939ocO2mMokQpVwecj7iipCbvIYx7OB/Xik+tT3U0pxqc7r3KAWufsVh2D+KDusWJI2PAcvA4FoWxm2p0D9MBqLiSbWYVcs2MkxrdpJTANXwUI+MUzqc7piECjmUxMZI4EKKn2PhZT3AU7hOSFO5qoB3vuX6lEeMVwZ3J8x1feP01nj0/M417rq8G7p+fcUHYJcfoHWEXyU+vuHryFocnr7GcIOemZMCWEQnGE9bEJwaFIHM649yoRMV54f7hjmka0eom4BzM+YR3e+YHPbRCDPgw8tbNNYeQ+Z6nr3EYDrz/1covuDumOEKIFO8JTaFJoleVM1tbVBimvXkbqJrdGAKnc2JJ6jcwhIlm3OtK1eoFhY5ztpEzhzLNs4oN0ToUrdd3zrK2XHI5q3yw1+q3r3lxjmk3kCukqmRARYNUu16TOZ2KUGMbIFf8OoZlERUVPekBxzltXzTnzCSnrqOnc15oeSPUQqG2xDhda4BDFReXZaG5gdQqFL2XOvlQ172oY2DBUI0Chj7k2qA2uq5cq40lK5GU3iJzHuciqRVqrnQ2ealKFHVerVrVW8LjmvIlamm6F63I0NagXXlxiFfr4poT4m3z+0iu2rZrPiirvDr2JVKvR77x/F3eur7iu9/c872vCB9mx4FEqZEsgg8K19SzZ0RldFVuJuJrVa2LqhbAFW1ftpq0+q46EijVmYiPtpEEx/H4QG9rlpxZljMiwjCoDXXOi51bOx4e7hA8JWXm8wNLPnM8P5DygiydFwApL/jiSSXR1fmKnZ8l5TU5rZfjn07HgHNeiMFxasKVD4gkWm68sbshICRgoeFbMehJZYxbA+8qsjSCb4aoFdqsa7XWqjwZkUd/YAvuHaX5tDw+0YFeCxetbEveArAzaLaUTKmLZpp5QUQXIG1j8da6zYz3xyVT9uXHpTDG+vNdharVVcf92YsXeB/47Gc/y/k004zkoozusmbPIlszrR9WvVJbv6/fxQfH1dWed77xDnHeM3g9cDIF58/kvKzvqWvbd0iu/6m1chgGliUZnOdwo5COD3SN8doKUQaeP39OV8xyzjFOSpx6cXeHE5trzZmHh4f1+XPOzPNsG0XZ6/OSjAS0XbsN5q+WpGWcU717KY5jgXw8MZ9OLGnGhcLrt0+oCT748ARxIgbHmRfE+UwcJ/ZX1+zGA2/evMXuZsLVQk6V2mzMsDXCqFaZKSXOS8J5ZXhLHGmiCUmIEw04XF0TxwEJqqLnQqFWTRDm88zD+chws+e9rz7jS7uI++J3sf+uz/J2mvmPv/BV9ocd+/1EdVAXJX3FGE3cSOFX8Y1cVEOgaz0sObHf71eoeMl5rVxr2/reta8bTFO+dv946LhxrViyV8yvPtvPVnLJRFNOpOnhnnPGhUEFfUR/P9WycjoukSvEqdIfyv/4OI7LSrT0jpS1NaSqZolaGsM0aqumVIP9A6mUtZ1VjMjlvCIaJRdaLnhhRcjWFpvbIH+xwFFpmqDkiz67qfjrQ1XwO1jbWjdI2cSEVk2M3sO/ILt1Amxdkr1uxw6aIS6mEW+8BZwaGqWsyJra2TruS2Z/bgxu4MVxptTKf/9rPs8/+LlvMFzvCFXlrEMI4B0P8z2pVsQFBomkcmbJXfVyM/fRyYqk0LnbtAbUwtoIir09U+uqeyEipHkmiDoc5pQoNmVRm+gYY/D4YTAtjcAQVcgmeBWHOp/POv6LtqB0pl9wwa+k3n4W92klQWz0VFiWMzfXO25urnlRK6c08/lXrhGbmOgF0nYey4Y22ToIzlv7zT6noOOY8tHr9dP4+EQHen08rr5h26jdE15EGIeRYofeMl/aSArIt/pMfyTDHtYD72XYvzU1eLi/v+d0OjONO95+6zPM562K7xD/paSmfs8jJhLSrVwv30cuxQ4KYb/fI7Njf9ghBVopjMEjLhKNsa0frrIsKoXbN3IDnHjCoBvdVWW9h+BwQeU1c0q0ona0wsxuF0lJkYLR9NH7XHyXEq1ZCXo5JUMJGi741Ta3x4DL2Vxvkw+9paJSrtrDcyIcG/a5ArWdcd7x2bfe5tmLB5qPBrnPzOcTt9MTDk+fsL+6whOoeSGdT0jbI5LWIFlo5FoUwmvNdOADwzjQKAQftV+ZMle3Tzlc3RCit4BTidKQFplzIl45xnAgDgNz9bz+2iscxh11nqmtcJhG/czBrZ9PxNPMwa7adVL0dZu9Bn0tb0zp3o9fhbrc6l6jgyKIOZdpABYbFUslr3B6tWo8ZVU5U60FwYWo5LrcZ9lRf3lMqMcCvFzsFkWEdYWWnDEC9wrz9yQ6BE+lUVoz0mbB9QmLZmp06ChhyuUC+t8knXWqpKyvkeeZsakKY/cB6H3mvmmUOtIoOlO2ngm1FkrfGii7v5qAD9Jwra6Qf23F+FmbyhpNOThifIhajcTXk5gmq6iNLvcGOBO1Mie5Ws09Tiv9LskjJqbUxOFqWUmaMQa+6zM7vveF5+fef4b4hTh4kED0I7hKaYI0r2ZDhm5YV8FGDyvjOFr1nqm18w4u0MmVj1ZXLkLnqtTSGAZPDAOCCn9t0tjeCulmRlaK5CkfxzMMo04YSLCEsNH6OeA8uoAVae3COc5FvdZAc4KvhWGI7MeReyec85nd9U4FxmofafzoJDOv5FM11GoXyRqrG2FfO7pvNpaC+5bn+yQ/PtGBvq182U3u8eUAvIpryEa0UqWwjQikMjVb8O8L4FGwb1ut9DIZr8OTy5I4n1WGdpomhRFLojOa+/NdmioAF6/b7UK3r+rrGKTVKiF4loeks7LFXO5iJxqi/UC2StnJ46QE0VErHb9SQl2qRQN/8NScKVWDSIdze/WyiQ4Zc1mgkwf9xWdSydat3dF/p3+/lExjG6tSOc0MRLvOCsGVpiSjcdyzGyJUz4d398wEWtGqfPKRw/Ur7IYdsY9IOrgKeii5Pt7nNBFz3qvJjARqVUET8cFaoFpt5NoYxkmJcdj8QnAEgZqEQlay5RiITdX1JA7UpXD+8J73nr3gMI4MY1xZ4Uqog4fjSacbfFCovRY1XillO4Pc5jpXqx6Oba3e5eIPYEQ7jbFigo8aJEvthzlWyVUNvBfPQalrpbMemj0xKoZsWRWsSHdbg0QqlZYrXUO+i660Bk2099xqH51slJQ0We7sb2thdbnTLgC0EuVeaqc19D2YBx4r7tZ6AqKXo1XlE4jI6r7XE4weEpqCKsYn0Z9xcrnH7V48OnFk/U8PcL2o0P/f+v3rm5GuBOCspWDfErPbtUTAOZ1MSLVQch97azQP3//2q/y/v/YOy1BJ0SGlMow7kN5+6yOAm3+HImbFEoZNBKuZxa5aSde13TGMg/IqLHFRTk0g58rV1WjESU3Mrq6ueHH3vjpdmkZJjCqZO4wjJWdCiEzTjnlOKjrUP7k0QwucmTOVFRER2ye95dQdCL1TRb3WGkst3FwfbLTqMa26I7FbK6dYodHPJrs3riOpF/e6r4vtnX6qHp/oQM/FjdX/9iysWcBUxneXhARwXig1E2Ran0badmhcbuwV2rlYUP2gevlnWms83B9pVVXWhmFgnlUOUiVGNx147/3j2faPgJD6Z9oCvb5GHAIPSaHzKh2WfJx8NBOu8d6tMpaXiUkpnVQlIDZz21BVMNRFLjgoS8WJXr96odKnsrZhG5m5gOR7hdM3Zlcf7GM0tdaePD8iR67tEzFio2iCtuTMfjowuonTw8LzhyP4AH5E8Lxydcv+5olVQlavBeHV/Q5aVZENr4nJGAO5KTISgtde7TKrMirdhEhh8GCz4WStTr0POoGwbFWEF5BSibWxOBXtWe4zX//gjqe7kXGaFP4uWs3imvV7rUXhoKai1U5PxpqA87SifvetVxtooLYVv67Xhqw/0yw5dQZpp7Jpz3suEjE79NU8RKupdpEMdrLampy6TVZ2bbkYrK36MTbShbo2qtKsEQJrr4rVjd3ZBMPl86wfcU1eNtlY8X6t3JRdDU00gcMCdP/51trKSanVWiHCOlKm10JFbuq6Z9YjwxLLFcCnCzj1qK8s/i3ZLWUTZxFBiV7YZIvdS3GsPvCldUKeBttsbPNSVNp48rA0qFmTjoXKIo1f8+Zb7KRQKpxypUpi557QJOO6sU4rtAvFt616ZdubF5/Dh44oVgv83eJ6O6NCCOSiinc4Z4mSY7fb8ex5JYZIcGFTrQMzyFGhpxDjKvXsxLQepDFEHffzTqBihNSq2gPOiJ2wXWuT/lXkqvHk9nqdeOjoTP+sl8VF6cgYPVS0FfDckJ3t/l+mDR9HwP6kPj7ZgV54lL1B70f3CkMPhz4SNo4D4jzPXzxsQbFdBtO1Rvi4l1uTAecv5med48MPP2SeZ54+fWrjJNa3s5XVvbw77A8XScL6P6yHfXfA6l+rRq55enPDN7/yAdF5wtSh8bLC5/35XdIxr+46t81LK1wW/Ly+h9aE+Tgw+olFFqokDruB5+cju6s9x+MzVW4bRrWUXRJxGnQeFtbN3PufzuZnewLgzfjCPqEeNH3e92Jj+uBo1SNeqBnwnof5rBOCTvBj4e6ciL5xc/sq11dP2HtHHiKcUatWrb959eaab+Y7huFAk0AVm9m2ax9ipMGKwtwcdqu4zzhuKoUKd6rugULJM9EJUYRQobjKQEImzzfTzId3R47Z8/1vXDMOgxkbKZPbR8+T26fkYvHcCW7sCec2PdGhb4WIe5XcTWN46c/lWGgfr3N06mlPBPuJ1vXOa9WK3HmvLogXwdCLkGvfDTbP3vTw75KxipJ4RZay/m5tWkl3UZ1e+lZj8Lsh0h3gKoXj+QhoFYhTQ5XWyoqsKCKwkWXburedVeOQm42MSZeI7aiDtuQQIefZ1hq4ftKjTH7NUZolmB3KNrTD6dpNBqfT+tiaIYMX5b/QkKAKfR3JEFF2vTQjDldtZVTAuaJOeXbrfPUMvpC8wtq19daLcHsz8n2vH/j552fuF11PN3Mg7iupZqgJ3EBrDrxXJ0L0fnkjKaf0kphWSds1EG2XLKYC2Pvzpap6YG46dlmqEoRrycznMwGnxGcarRSWeaaNjfN8ptTKOI0sebbXkS2xsH03pGHlCXjnoBVq1muEE7wMpKoGW2mZSfNCbfD0yRPSshVKW7QWsPaCpQrsdjtL4Dv60n/u8UkvtO1+9nP4U/T4RAd6Mf8zzVK/tSruh+Fut7MZdM9pTrh333+kI/840H/UC22VPWBiFaoXv6SZu/t7Sm68+eab6yHae891PUS3jLM/xyNI/eJrPbO+/HqHmoYx8LCczeykV9TQKI8CQq2VZa5AXl9Xg5dTje9qwkFGkElFK7Fk0qAueO6PR/bXT1SUwnticCuc3NOeWjIppTVwdwKM2u5ixKuqs+reU2snCplIhd+kOns1UlrF1z2n83N8cEyTQ2rivRff5P488N1vf5433vg8cRh4/v57+Kpjc6fzmV10vH6z55VXb3BL5D7DOTdqrpxPi/pf2+303jGOkVIap/MDHR1SmLIiproGmF/2grua8A8zTYTkoOWZ19+4obrG++eZ948zocIXX3/dkATP4D2TH1haYhg9UTylZGMV77RScttWzGb5GWN8tGaU4KjXv89Pb+u4UJuq2gGEOOK8IQCCft8q65rryjVBHKd52XzjW6PkTJ8dXyFzlEVf80YilS424luPlT3doF4oHlZT1itm5NJ3rfdxdfHrnyevo1UqcFNyXmlyS1qYnPIzllzWsUyFjf3a+iqW+DiDjjakwkB0Mdlgg9tBEQjxCiV37wARaJZvr1Wnfd2J0HRmj1pBKvjaXetkbXcUTB7XCEIN/T3luXiryNXw55QKbRFSSwgJJxCZ+Mo3v8Gv/eyrPC8f8tXn95xT4VzVfKlVSKWyNG1H9v56Xze+f3Lpx5iSgodhWM8+tb8dOR6PVh3LKu6DaLUfQlDPhxi4vX1C/YXK1f7AMERC9Ix7FaDZTdPKQeotgnghkSz2fMuyrPvNGTyvuJOY015FJOFR5v58OpNSIcSRV2+uqeZ0uLY9uTy9ha4YtN9fbWHd7vn2c3r+dTL04+f4dD0+0YG+w5a9arysfnpQVqjpoMSdqqM6IjxaePpkVr9cBOX1dfrXmpKKliWx2+24v79nXhZCCLzy9EZnRTuMaJBaMpnPrq3cn6sT8zpEiGzzq9tDq6LadObXOdjvJySY7WtdO454ceuolHOCFq+VWvMKP/aDCq9qWULf/I5hiEhoKspRncLH7UL1zoRA9vs9S1rYyW4daUxmhNITlVqr2tg6MaRlk/rt4159fKVX/eWCB+C9J5VGKkKaC3VyVCe8+/wF3/f5H+awf0Ithfl8okpgasK5FKZ94NUnez7/6jX3Dw82Z6yzuzHqbHgIamyTU0Yy9nrqcNivnfMOyTpTG9zm5y5xYGkZiXqPs1Su4sjNqzfMGT54dsfD3QNfeHLL26+9wnl5ABthy7WRqIwYKUmpauSqHI4VIUZ7mE1nItckKOe8VdiWTNaiVUguRROoi/Wbzfsbg8ZbqzRp60y6GPTcmiYQ2VzAxHgJcZwoixKeEGfiO1Bb9yQQKI3mNEmu1h7r+6+Usvbna1MDmWE3aYWfVWRFnKrvZdNBALFWVzHNehWXyUVHDwmjEs6aBiKpG+LVz/ZaKznpuKaqzRnPQhwmqWfBbJWh5BLi1tn7tkG93SmyFFrNOobHY85N/99Sq4kotfVeSlNEpGVFMjoc7dA9Vlq2tyDMzUOdVwGaLj50NzdiGHj7yRXHJfP1D++oMfLi7kjFMzfHcTHBq4K6Axab4HGKJICsZ44WAWkthFvTVk9OXcRL18T5fFZuCXA8PdCaGkK9ePGMnBdSVvMct6jdrI57Nh76BE+tLMvC2caf+73qLpwdaVXmv3EZaiWbAdi4aAsgtTOheCUAinAYB3JZaOva7owTsVaO3pNcVeu+6S25iBvtcUTvvaMLhPhx9/+T//hEB/qL5t5FZS5rZqbZqsc7JYv41ohRR3X6GMma7de6VtEiHQ14qe9z8RoPDw+0VhmGgRCD9TqNEHexSDoJSg+trb//qK/NlpjUfsC7XuXqRm02TrLb7ViWmWWZETElLnOw0kRle9+qXtd7jmYk42wQqjareZQVfxgHPI3oHC5ERq8Z/243Ad2eUr2ll0VnsEOMiBNyyYRotKeq89Sd/Xv5eVfnMUsGLrUL+vutRcVlXNCRpDFM0CKnZeGchZvd7VqRI87iQ2KYRqbdyG6amELkxcOJ8SoQDMZeA1Xp9p1b20XMBcz7zeFLlepk1SqHRitCLJbIOa38r6YRccLD6cRpyYzTyK/77s9ASdqPdgHxAVeFQEWyqXB5j5OguEGXZF3REq26tGK3cTjYnE8xQpyI9f6tP3/R21SxpE1wptZCHAIhBmrSeepSCi7oAXx//0CtlcPVgRAjudQVjVKtiS62YuN9FWpTAqE4TK7WKvdScb6tlVJPWGoV68U2DbaNdSKgtrY6HZZSaE7Z761WjAhgDPZtzXQ0qCvgyUXPdWVUW2DfyGjb/txItx1ut3E526+VZtKqff9bG601WsmbQ1xTjKKPLYLOhuvb0kRnbc0ZMkDRRKtIwTevQlcSSXIkijdvCGFxhdFPNDnydD/wZBr4hQWOOSHHBUqlVEfF08imJGhr2wKsTtJkDYYWcNOS1rPs0RkHiozURk5ZHQRbH63TwJ5MVKuLDfWkp2QV4Lp8zsv/9kdrTdeneQdI11cwdMGLjlIOLlJz0Rl7G/cUKofdaOjQViTR2nru90e/DvqhtjN2Q/rXg56G072zZj98qh6f/ED/UjDufWLhMuAFVQ8r+aKSLtavFh03qmWdv+TiMNDg2YN1P4jUYnKaJnzUS6jkN4+0YiiD/rYKy3R0YQtqW5Drwf7iM7z8OQ1S1Wx21B5ZWlbznJoruI10B6yfpc8DryQ6oOV+QGtLoNWio3lVFfic04MH1B4XI9KUkhkGNZ4AM8lwnloyYmIsqmTb1lvT2IL9+p6kH67bNdbr4zrgeuFCqNDkcS7k7BnDQGo6b+uaWplKhBAHDuPAfgg4r4HPOWGwcbGeiJWSzZNe6KYtGsh6MrZNAmwcii0Jc0Xlcn0MjLuRuBs5n2Ze3B2RCq88ueKzbz5lOZ5x0SlMi9h19FZBKkjpfaQ0v12D9dp1BERbI7kUxGSEt2Xf1n67Jk8mjnIZ6Ozw7Apzwb7XZ9bresgpUUyDf8UFDeLqTd6r0451b+upVO1nu8r6Gt0oCanre1F9BnT/lbLyJFox73NL/nLuEqptTRJLLbhm0lYdEZMOORsevTKrt7lqiwqbjr11mx6vuL5GN1SvtY14eHmtGx2R0oRA3589aZ+b70mAnTGKmPVk6XGQKTb2WO09O0vuaSYTXbCpCZjEs7TGYQo82Q9MQTieF4a0kGulNUdrDkiaRNhr4zRRLaU+crUU520P631dib9c8mcUQnfSTBCpi+toApNL0Xl6M8IJMXKeZ7xT4qruIY/K/7eVea9BPoCvauAEa4JH00TEG7oZnM7rt9DWuXqJnt1uoDx/0DPMlAKbKQu2y2ttrRJav6GW6PXyfg34/exlxe9/tUf/K+pRtx0squ90GaS1itBKRgSWeeb+xXPzQ7fDsFl/syZE+hjKBawuDrV4LIjTDPL+/oHbJ0/Y76+orXA6nwirmEhdyUFdjEJQXeyL/JKNFwCtbW5Z/X17ceT+HBLUWx7HbQxMJqlamrGgUyP7SixKOuqKZ10cBYN3S67E6JjnmRACQQSHCg5t/X29nrmoTOxyPq4M3ePDnbrY3d+b+QlgCVTNI0LAOZOVLSrJiaisZSoqTlOrMvl7ZaiHjOrv5wy1mnd1bezixFzumFviPCfqrPtQzVeUlDd6gf2Blkdug+N2qLhQiPsdrsLeqf92lco0eR7uZ72fJpVZW6G6yuCVfITB9a2t5abdI7P0rY3b3cTheiQeIktNfPiNO+5fHPns7S3f9cpTfIQlRJyrRibLtOYIoVFCgaL3srmIr06vf7EETdRWtQdxDUM2AtbqCpE3VF40JW0jqfywBikdX7RkS7ZEJWclXPV2Ct5zTtraub69oWTlW6TzzDAMq+a8k97p5WISwJ63FprfAqs4Xbveb+uvGCIkVfvzzdj2vZ8tliCoRjqAJ4AlSUbSA61YTXsdUK0A59dA3++W8wIOSg/z0ihoC4IL9GSt5Hu+gGotbGG+n/tN2wJY0t7Ug76b6NTOO3SiAjb9GVpb2eJ01M65deSyuYA07eNXGgOZUEeSVHBV1RslUmThWAtPh4FXr0fefhp55z4xxcSpaFvNVyVwalKkhUyzM0I13aFl/bDeDcY96YiI3t9SEt6rdLTzYu2iyjDsOM33iBRCCEw7VcGchh3H0z2Nxv6w58Nnz9QsKut6Ve6EOkvGUefw9RIIuczEuM3hd5TN+f6+VFzIDyOVQFsSDfBXB/bTwLMPXmxIW2s0GfAqLEKzyQrJmebDega/JHjLpchT/85HFlqfgscnO9Br6fftf0SEKIHj+cTd3R0PDw88efKEZ89ekFPC+0G91fOscKJ4mqgMZ6sLgWtqe2AcrzifZr72zn/lrbc+w/XNDcfj0XTSrZqwCoJaVXik9veA5SJtOyTXzP8CMWjbJEBX5Or9x/5x4xiIMXKeT0TNK+is69KMSGXsWbWp9Wwe5IWWZxPPMBTDb0Q4NSDR180lIQzUoqphYKSqVmgkIIEkGgupFgY0C8+9/27zuy3l1f+79+k6KVATiMQ8Z1bFtSa0KjSnxLXzKREGVd3CVXJdlGjmoqprxYFzgf0EN/uBMQSW0rjdjSwp0bxwJcK+CO+wsLvdc7w/0YrgQ2AXJ47zkSU3RFROWLU9OrlR2xAhBFzwvP7kmv3TK6RW0v2J0/MTc5r53s884Tf84A/wubdf5/hz/zsjBQ63hiLNVK9HeZCROkJAGKrjrmbUa9tG3gAXAiUlSmv4YcA1OC/pgrCnCW0YBlwYtAL1EU8xe9TH66v/u/MgeruopLxWqlz8FzpS4OmeAhu42dZqX8RvY5R9fNXZIdsrbS7XuY6wqf6DjXw6bSdoRX7ZauqohKzwWDW3Q9FSeA3QHRWr1SpXuajI6wbp1lZVe58NZVo17Bpk6pqMC9tLd1RAE8GNtLjuT7bxPn1O/Zrq7vsVwegv1C72ebUzoSc75Eyiqpe6oIgVCc/Ew1n70m+/dsvde5UXKZHzGSme3EbwAzjZRhYv3tPlNXXSFToXlB+ibaRlOapgjlPhqFp04sSFSDk2RDzTuGe/u+F8Sux3V8afKFxfX6+kZ10Hwm43ra2xGEZzb7Qr5HRaRwP9dtHFY+tcjBcBu8PE+XSC1ri9vtYxRkOTLvvysi2IDcF05ga5VvqXyfvF3y+u0WO08dPx+I7kf37yJ3+SH/qhH+Lm5oabmxu+/OUv8/f+3t9bv38+n/mxH/sxXn31Va6urviRH/kR3n333UfP8Yu/+Iv8zt/5O9nv97zxxhv80T/6R/+P6wqv0OpH/+mQTMpaBez3e1599dXVorC7Z+WcaF5ZoFpce5yMtLYjkzjsXuXZB1/n+bOv8Obrb/D06VOODw8XsGndKsAV5tweH/El/frFYhKDjVf3Odrag9Q5Ug2GOkIna0962umYGytsrhKiYYiPjHx6sBrHER+cjRM3szTNm7CLV+it4agtUdsCogr4talqlgYHh7iA4Ffm95akaIvEO7dJT1pV5r0gFzKlda2a1ZMagRAG5lyI48SSk/pQ50wV4TQvNBGGaWLa75Gg7+GNneMwRHyICI40n1S6tlUCsJ8mvue11/G1sNsNjNFDKTw/PzDFiekwMe5H4uiJgzDsAoebkdtXrnjtjVvefPsJn/nsa5Q48+Lufd775ru88/77zLkxMjDPibMX+NwbXP2O/wv7t95AwkiY9kxXN0yHJ0z7JzDs8WFP9RNHHxn3O23/OA2Mzmxb4zgyjDucj+A8cZyI40CIA96rZXAIA8M0KjphQaWTGVc286o45laORQg6+7yNoq6hcOUpgMLm1cSPVjIVbXVmDCFYG14rbydeSa9oZdzJgZ2TofK9um7Uhz3oyU6HnvsfnbxQYae27emLFpQB0QbVb/upWN2GgnVUa1+tNHs2ROLyHGny0uF+sV9fdnbriUVeX49HtrAYitJbDf1F6/qzOiq4+tuzfS+t6Eal0xMqcD7dM9eMRM8rV3t+w/d+N3PzZgoDuQni66NxMhGVqVYukhIKHWryogFeP4t3kd1urxojJTEMI0Mc7P4Jh8M1EKCZbn+FlCAOA7VW5vlMSir/uySV4l7Swvl8Js0LyzmRzonzeeH4cOR0/8DxeOJ4PHM6nTmfz5xPZ87zzDKrhPZ5PnGeT5zmE9lGYFtrXB92BORbgnR7KVI3IzDr+HGzBLK9dA7rml/5V1t69KkL9N9RRf+5z32OP//n/zzf933fR2uNv/pX/yq/+3f/bv79v//3/Ppf/+v5I3/kj/B3/s7f4W/8jb/B7e0tf+gP/SF+z+/5Pfyzf/bPAIU+f+fv/J289dZb/PN//s/5+te/zo/+6I8SY+TP/tk/+9/+aV7erGABJ5BFg1QcBk7ffH+tlEWU+FFbZHB7aCeQTDHzkGn/hPfe+0WW40mNVK61ku+M8/Xwqb3nVHs+f7FstkPq49+7prPOGTZqrYXeZ6+1UIsG+3EcmedZs+QQcV4lO5VprFmz2rB6sxPdtPxLKav7l6w93GYqfnWVslx7oAav9yAeYzRCW8CL6onnUszX2T5nD96tfzRTqOt2qXavNp5CQ8TGvVoFp5XevBQ8mTgKzQUejhkJA8OoBEjxXm1p85nXdnuGqAdywFNCV/JTaLdKoyxn3tgfeD7PLL6REWKqnJcZZ0lbCB7ng44FRU8XEElp4Xw+cfdwos0FL5E4HJSs5Dzvl5mf/Z//Mx+88z6/5vu+wBu3rzG153aAKIdjCOBsRK0KanCT1NITExAR70nzvI6I9QoSsyHWAFDXROnSJ6ETxtzFaaaFtRgkau3Jih30lc6Y78mq85e5vxmOmO68EqictRDsiSxBrdKX7SUnhIu5duy5G+aQs66FVcEPFVqqNRtCIB2y0s/crXPtvTV7jstdtbbk+rWzxbbuPWsv1E4GtArTOuUbEtAea12sfxdj5tdGB/r197fP4MSEgkS/UzCFvrZNn6woinMr2bC3H0rHGZo+by5qa52rkR5zYn8thFqZrQuDNMJHjOwW8zi4nPi5TLLF2hgppRWB1IkInRJwKyl0c79LSzK+Sxe5cpoQOmc3R9sbJS/ELn1rQlXiVVa3ifJX7GMapO9MK0F6Sx1xJjBmHgLXu2HlPa0fs5/70tb91j9ndBe74eViq+e3q4ZCzxj51D2+o0D/u37X73r07z/zZ/4MP/mTP8lP//RP87nPfY6f+qmf4q/9tb/Gb/ktvwWAv/JX/gq/7tf9On76p3+a3/ybfzP/4B/8A/7jf/yP/KN/9I948803+U2/6Tfxp//0n+aP/bE/xp/8k3+SYRg+8nXneWae5/XfL168+GW/51519z8Cq1NYs/ETXViD9nJboytxBTcyn4+kdGIc9+x3VzT7fW9VdHu0sbaqwQBC2sXB9zILtQfP/rgkEnbWvX2KiwDaVPhHVPRlGEZaKuAh2qalB2nniMNAsnGmTphaEwjZVNViHMm5mga9vvthmDSge2X319IYh1FH0wR88Go0UstKcGtsAif9vduH0/GfdaRwSwD6QSxOWHMB8SxpYfAGCXutguIwEkO0oCG05hhd5XoMiNdDP4hQg66lis5Vi7GRI14Jh15WxEHZ3RkxVKaWRiYbDNzWkyjnTF0aeW403/ABZHTkVFmK55vvPzB/eOL07Mjz7/oC/vScJ9fXHA5X+DhSSQQHreqopDfkxoq79YBzTmWHe6XRr1cnWnXMqqNIrreMmh6wG9rd19kWsNa65TLJrRez8RY4pXdAm0L10lQieSNSGhNfNIlaDYrovfLLQ9wSV2dJhSUQKyLXD1rbL/13O1lwE/QxGVtp0KySvwiaj5LjC7SvA21Ntj3Wf77D7NAh7Zf2cmP9bOvF7FC9fUBxF2iBPV9PHTpJsAeVy3uhT+e02hRZNRvohAEToe93vFa1eC01czPCk0n4RnYsRXkog4v0tky/nxuiZ9cKWXVANNnv0zFlDdZi6pSlFCPsYlLeigrmlAlBK2Xp46giBK+GYSpjq4mgc4riaUWtv1Np6zqzS2yAy3ZvNiRIk78lJ6oUdlPEIxsSLyt4f3Gu6hPrWnHrffq4c1e/re9vA2A+XdH+O4LuLx+lFP76X//rPDw88OUvf5l/+2//LSklfutv/a3rz/zAD/wAX/jCF/gX/+JfAPAv/sW/4Ad/8Ad5880315/57b/9t/PixQv+w3/4Dx/7Wn/uz/05bm9v1z+f//zngY+uki83Ue8LXgaeepFV19Y3sBCJVGZKdZQiCuHXxrP3/yvXV7c8ffUNxv0Vx9Nphev19exCGvS9Hpay9fYuHx+1gB4lAbAeWC8nCP25x2kiBs84RKZBofvotXc/xEiMQb8WI9M4shtHdtOO3bRjGEaDchWiV7neiWEYcc6v0K44xzTdIDIQwoRzkZwr07QjLwuCsth1pDBvbQagA6s90+4EMrAqQPp0gRBcsGBvx6PByQ0ht2YHm359fzUSx0Hni20j19p47TAwRLfejF5l11pYSuWcCqeUyRXuTmdSzcpXqAWRytV+xzRMBB+hCXkpHO9n7p4/cPfsxMPdwvlUycmxiyNxGqiuck5nisCSEgOBMFxxz8i/+89f5X/6f/5r/l//9mf5L1/5OsclMVzvyShSIU5wVGJR/3WHHmYaWNWdLIag4aQVMzzq6L5K73onBC+PvidckEjX1dQercGVaPoSavRoD7GNGSLKwO/EvL6q+77qCeilG6GCEVtL4DK5UEMbbQMBiLfxQmlUqiZdduA37HlX9n9jndW3TynOGzy7BV9Z37vt+b4W6VW2XYuLK3OZlja7Bnrwi6Eoxp63D7LmCit6wIqorLyaR9XpNg3w8vXm8uuGbDnv1BjKxtBKhVbtPUkj7iqffTIwDpHmHM2Bl7h9CHtvXYNeRGVonXfWftPAppMzZjaVQcSvo7IpZdPk8Fbld/nutKrnOdfXHgTTMtlMchpIRbe3coIunTv72umeJf1aaVFRNlMacZzOMykndpPaTPc25YqQ9I8tFwhkrcYZeTmobzd6TUQba0J/mSx9Wh7fMRnvZ3/2Z/nyl7/M+Xzm6uqKv/W3/hZf+tKX+Jmf+RmGYeDJkyePfv7NN9/knXfeAeCdd955FOT79/v3Pu7xx//4H+fHf/zH13+/ePFiDfbr41sial/oAympTWxfED3T3WaoQdqJJUMYdkwcON19wH/5+X/GFz//63jj9e/j+fmbHOd7oo/rRlUZyU0nWiti89CudX3uR2/rctGJPFqEPeno72/Tw7exM1Ei2/XVnq/+wtdZlsLtzRPOxyMyjMwlr5lbX+zr4WtfU55PW2fde8avwjeLWraaPv44RZJt6poDy7LwZPcqrTlqEVwIhDCyLPfrc13O0MbomWf1lc45MsXD+lmV9FNWDfxOFoshEMPE3fE5MQjzMXE4wNXtnrc/+zqVjHKi9MDLbeH73niN7Ddd/SRwPB6Jg2dpotBwbjRXeYhCbU7ZybWxUIhLQrw3NMOtKEO/J9t1cqSU8cGhxYuwnCvNC1M+01ygxpHxzc/R6pl33nmH/QcPXL12zytvPCEY2z/s9tB01EyksaQz07SHhlkLC0qW0v/WWikp4UUTgVJVCMc7XXP9QLxURlwPLEOuLg+u3vVuFVJNWw+9qOATLtjoX58pRwNxzdtW85oM11bBSILqlqYEqlrVUW/tsTdMoGfTL1jH46RX9XUdEXwkkXzBai/23vs6dza22FaCoP5s7ehcD7rrWd5WeHzbhsraX3/W9uaWuHbyXa/UATaRpfUzdORlrdxbvyo4OlKy7c9OXtXKeZNtFbQq9p0v01D1wKxe80vJvLi747NPDnz1eeKYEoWqro0pXaB1uqfmeX5pr2flJVWBAKUmzvMJHa0TTqez3WU9C06nk127QsonXtw9Yxwjx+MD5/NMrSqJqzK6s0056Vo4nY7WNgCHec7nAk5NdTRR0IDtQacZRNsj1QnVztAlF4bRcXvY6+iH+5gatW1/6XP0W0XPeu9Y2zkXiein+PEdB/rv//7v52d+5md4/vw5f/Nv/k1+/+///fzTf/pP/3/x3tbHOI6M4/jL/vk1O69K6rmswJ33TNNESkcVhAiaDMjuiqs4cjqe+cp//RmeffgL3B6uiWHHi4cPSE0QdmqI47fKoWetpWCqa+VRRf4t7+3i65qByqOvbYFSWamXRMVWG5TGzdU1X63vKWwLUAslzTqS15nHbMETMWlTUypztLXnVjDxIFdZ5hPDEME75rNu7uPpCK4irrGcZ4X040haKqEFgpuo9YVNH8jqYtezfmejaq1xoXePzud6FXzZGNAGGluFllImiOP25gmvv/Ya//Xd56Q0M1zd2khO4ZX9wGFwnGjKAWhCKdBqBokM00jJjWWeedEyqQotF0MTHCORczvj23YPnMF42LWhsbZS2pxYSiLRqAhTFk6nE+/XxDCO+GGkpOcUTvz8z/2vPL0S0vIq5f45zAW5vaWVRquO5hwe4XR+n4bTloTp8XdGtC6Hx9LGvWfaWtPZ4saFXWxbpypWZbpaVyGTviY0KRCTQ7YxwKoQro9BDW9s3r4UNUzx3qxnq1W4panC4kt9UW3flwtdBxtcMxTHiapUplIfmyVZhZZzQwYxB8Oi7QwBJRjYsewqIqqd36TL+7K2G7B/98fW8OClCm9L9j+K33MZIB/9u3Upwx7eUbDerXnF+jv62Qw9lPY4Gbf33JGAUhJFOiWxQfCq89EqtSUgE7ynVM+cCm/eTJxz5WsvZq4ON/r+ZHtd7/WwKqWQa9X2VP/kviMkj4VnnFOp6lqL+cyrCdd+PynZD9MMEc8QJ2pdaBTltzinlrYi5KyFUAheeQZ0oONidt+ywNYarhWWbHr6rdBqoopOoCxLYrzec3N7IKXMZniDJVnKKbgkgGZLeh7f042b1JdCszarCh/1x0uoyyf88R0H+mEY+N7v/V4AfviHf5h//a//NX/pL/0lfu/v/b0sy8KzZ88eVfXvvvsub731FgBvvfUW/+pf/atHz9dZ+f1nvpOHILb3L7Pz3kNrj/7tvMNXW4gx0qEimvUC40ROC//Lz/5PuDbz2pPXePrqZ1h8ZZQzrnoiUJ0j5bRWr53M5gwqXhfgxdT8R0HwG9OTtfK+fOgmDRf/7kInmavDweCvSoyecRhYalNInEqTDpNXJOfVd7yYgEQpM50Y1Jr6is+nRl5mvGvUAsfjHcP+hnnOxMGZ09vCPJ9xvjEv9zQZcF77t3lJAGtSdTlOh3EH5vOslVyHYWul2c8ouTFTeqXnBoJvvP70lt1uIOUF5yJa6Wr1Ng2eN24mlWHFUUSDgncRHwAXlLFbC2fR4O4XWBXAG8wovO/yxcFBJaV5rUot/dBKeilKBBTVX3heMq458HuWAjLPNJ/xaeQHfsP3830/8D288dnP4p48pQ0nGCLCoIS4WiHNvPLa64ZsqLpaOp91ZM5ZMBFHiAPn+azjU6LWwiklTYBlG6nqzoh6TZXlXltdRUnWg8+CwNovtT896FUbU/Nek65S2iaVa/cUTH2sZWPo95nxl1oBora53qEjUBcEQnFu3Tu9N/wY9Foxcn2fdM6AEKJ5sBfl+GOBU4O+fhZxlvixJddrMmAnxVqC9/ckXb9C1rXZv9b/vrYCu+xd/1npvYFmNfymh9CsDdGT7B74O0+oo1uYZI+g9rUKJgx436CpwUtuGZHC7Vi5mRzvHSNVvCnGyYoIhBDAxLyqoZwVaFXwvuL9gDdhmlJma+NNpHSmtoqTgWk6QGOd2ujmN8MwkHKkZEVS4hBwYnP4zhlKZgnieVHt/uhoqRAHZfV3aQPFSjzNVdudakVdxHM4HMA5QgzcXN9oQtQawZAUvZENh9DNxjufw/U91O95Y/37JQeq1g6wtov7+ul5/DfP0et4xcwP//APE2PkH//jf8yP/MiPAPCf/tN/4hd/8Rf58pe/DMCXv/xl/syf+TO89957vPHGGwD8w3/4D7m5ueFLX/rSd/zaHYYDu99r/21j5K49tQtIXOFp1kySJgxx5Of+15/l+bNv8uabb/L0lc8RhivO+YHWjIlOr7AuH7IG7GYb/JeTDEqPsh+RAPTkYRt9ss+LSuyO0SFeTSecDxAjQ4FCd/7SR60zLkTVCbAKRyUxoyYlTeFFiZF17MlFtJ/WGOLAQ72jNk9FaJJpTkeqSstU1FdaZ80xYZU+O61z1puFaTPd7M6S1rnx2i4CkPWAuyb71RQZdxM+iFboOFK2wNsqo3M82R9Y6mzs6X6d0CBfMvfnzKlUFgNd81I2ONqpU9hxzvhW+irRwGQz5tXYwt45HJ5EIQDBOZOf9bigpiy7Ubi+Cjx9ZeLp4XWuX9/x1mtPuL29prkBNzpadLT5hDoUDuASwTta1vGkWgpSswY9Gt2iUys6tfKUpgS1kjOMw0VPuAet9SpsLZVqJD1rWfU1550lhE50RM8q8lISy/kENGIc13Xa2wNamfU5aCPLsUnMltohUSVUbSqENjt+QXK8OH31+UTFXjpwronrYAFcNfAbjiFeQOlWEbeL5HJ9agcUDayejsQ91hu43LIrI16601uhm0dtTIDLQLHW5Wul2pECrdo3pMpZxV37TP7aZ3Z4ggY+3Coq5FA9fCWqij0PeDxLg8HD6ETd9cRTalqFiFoTnPdqDmStROe8lUGVUrW9pm0SbauoHbOnJWXsx9EjzhwcUQGcVk54P2jrsgkiGka8V0Z+reMaYHvbhar3vHZ6iLVb9FJpwM65alLrBVrlnFXkRpoK4YTgORz2Og4MF0ejTSZZbS5GWpXglRh4EdDbiiLI9ncu9tp6lj8uvD7pj+8o0P/xP/7H+R2/43fwhS98gbu7O/7aX/tr/JN/8k/4+3//73N7e8sf+AN/gB//8R/nlVde4ebmhj/8h/8wX/7yl/nNv/k3A/A//o//I1/60pf4fb/v9/EX/sJf4J133uFP/Ik/wY/92I99R9D8+tj2F5cRuLWLr/fzxH7YOSWQud7vaw1pwuA973z9F9ntr3jyytvsr9/geJ5V+auq73azTbv1D7k8o9YX1Nd7rCP9cW9/hYsvqoZLgxcNpM3aAY7SYAyqBS+mCFadZ+c8S0sgjs5DzbkyjKNC6EaAicGRgicvSYMKaqJSGoTSCMOoh5qL7MaJZ1L0cHCe5grDeKXX0ukh0owF7EOftbZA7nQzC4HaMk5gHMbtQBaxQLvdt864ryLUmhj9BN4j3pmIjZq1VBFGVzkE4TBNnI4nnK9I005okUJzkZRmnp8Kp9SooiOHKSVaMSOZEPBBOJ8KwRksK47oVLGr35PVr1szDILXn/E+ECWwpAQt89Ybt3zX517l82894e3X30SuIlILLQslNYIfqFGop+e4AhIHmne0mqlppuRFSU9ee5nFgjy1kI0M1wwC9U7wF8iQrTwlucl6dCGi7oTVphc6S3qFdp1oFbgStpzxODLLfMKLY4wTznnTdlcRodoRp56fSa/UBdVc6LPhxrC3nFWJdQaxyhY0NxgXmpg8r3k8qNCNW8l6Xf2sYq8pQjeSqXRo2BtJsCLeaXvBpKLXLruIJRRyEZAfB/rtUOkHvymsXQSClckvIDgwZE+cBhpvrHzXRHUK7Dp0d0utQzWRFNfwqNgVgJNAq4v2+5s+v/ORIJFTt8IVZfF451nK2fwNdLJEREglU2096+sLtELJDTc5G73sXAyF+ldzqsGT2kmVG5vO0peclJszn6wS1v0SQuD4cF6TwWbKk62BVCXh1tygiilA9h6FGPKS8cEkrAvU5hEWSp6pRlKddjuWlMyZsKmGRE8uUSEqTEESH3Q9v4So9uJuXXlrpd9Jnp3v8+l5fEeB/r333uNHf/RH+frXv87t7S0/9EM/xN//+3+f3/bbfhsAf/Ev/kWcc/zIj/wI8zzz23/7b+cv/+W/vP6+956//bf/Nn/wD/5BvvzlL3M4HPj9v//386f+1J/6P/TmP4ox3L8OWKV9eXM701stFMXSwtoqc1r40pd+kN3gaATmebZNOkDtmbkdBRdIQn+9S71sHn13q9Qf9ecvAvzao+7Vhci3fLb+7yXNTNOkxJd5MZ/pBk4P/0sC1jQEotfgV5v25kNwBIkkZ1Vr1Q3kLaEYBvWIDgb7tdbY7fZ4Lzz7IHN9daWCPTEyDaOS3XJdxYdWf2mT4e3v3ftgQh0bLKa6BtF+Tw9075Rx7lBpVWkN7yMxTngHo1Pp09d2A69cjYgrusVLB0qFIuBjYFnOHE9nHhbV1w4hGAQZlLXuHMEJbQw40+IWUK5Dztt9cxusOsUd0kyrHJh2nm8++xpvvXHLD/zgF/mhL30P7nyE908sd+/jQsTHA3GYtMponrC7VstSzR4Jux0Ej0sqLbpYQKcoTN9VEJxzuGGgZNUvcD4aAS6t1XYnlIl3uNphXxCDPMT2wGUS2teLmOSskhI947ijtko2aNqFgC/VDnb9WgMuic0qu1pX9Eb/z2/r/yM3cv9LfxJN9Eqp1KJtmVxVtrkVbUXkmh49Recj9NdQVV6xmWvV0KhdM33bVOuUCYK1h/R9ONeRqUJrOj5aS+/0P2bSS09w+0VYq/rGJkyj16YHW9WnD+TS74cztnpHAoIhdmhiVYtN3Im2iux69f0GQgiRedF7qWeXBrn5fGa325FzsT0eaKXi0WTRWWHgnaMVtRNuZRNKms/JdDp0f5+OD0yHW3JZcD7gXaDkxnSY2O2qeoCYCU1X3/RBr1MYIrUIwxhVuMtpkNfrbgREKkjgcJhonHVyICh0P8WBeZ4V5bFxyNoquTSCNzOsLuWc8jqq+lELrhdkK1zfGh8VTz4Nj+8o0P/UT/3Ut/3+NE38xE/8BD/xEz/xsT/zxS9+kb/7d//ud/Ky/195bFBPXYM2ov2s1grHh3uGYU9tmZwVPh6miXL01CL4qIIwJX97hmb9tt/dHpdV/mWP/uWRwUsSlojoJgoqgpNLIqdZF7UbqWSC2cMKUAos82mFalut1Kxzz2lZVC63buMu2RyuSk6mdJXIuZDmpD/bhDSf8N5zejgy+MgQFDqe51nV0mJgOc0cjydefXoghEDKhZwT5/NZkwqv8pq916eJgFYVtVScNEIUHtLMdVXVOQhcjTvmdOSmPuGtVw589pUd53zGuwi5UKTRXKN5jFzm2cUB70Gc9ihL7f1AI+zkjDSoZovqnKO6x9Wad85G+iDPlVxOCiG7xulFY78/8M13n/GP/u//iP/00/+S/+G//yE+992/lmFR+FiCh/0AkmilITKArzRfoAoSIzir/JyjLNrz9h4IhsgXPQTLktZKsK+SWivBDtYlaQDsvdTepgjea3CUrsG2ra/mVC8dQ3h0vUCcdjYKZxC5c0jzikRVrWq1IoJSswVzvc7O2k/aLgoX43mXm0CB/Gyudr3fXZsGN2d67LVWTuc7JpmsHaTJILAp0hm0ve2fvrcruSxKGhQtWLWVoeOMpWUNahecGxVvuugswIqSKBAlXIYO6UCGZppqzNMU/l1FWOwDr+/Pxs48nRuh4kxOOhJjI5Si45REnbdvRXTNUBm8w7dAk0KyfVxzZvDqPggwTSrxfXV1xcPDAzkXZdGv18lajyVDU9Gd6BwLQG2MwxW0kVraOo6bUub121uWuaqmhxPmeeba+RXt7HtH1xOEoFbHy6wVfkyWlBnpswJ5ySwl05pK/ToZaL7y4tkdqVWG3cT1/kC5u2P02x5wzhOdtveKJTMxRHa7HaVc3sStDns5yPdzduNe/P9xRf9JeDw6TC4SbBE9CFaCTRBYsIaRwvGnY8P1Ho00UloQCRoIXdI+2EuPj2PXX76Xl/97+XsvB/ZvmWm2qqEHxFwKu3FkGAZyzszLwjAMnO6PjAf1666WlYegsLdY5uuMsqBzutrXa3hTP1OipRNoVaz6FiPdBGpzlFrZTSMxaM9WRBiGuJLuYowEH8x+oCiRy14vhshgDnfqX+/wwa3a2DUX8GrPGmIgV7XnrA1iCOymgXxu3M8LP3gz8ebNnqtdZLlfdG7eBw309toKk3peuZ7UC77COVfm5bEoC+Io6AHX14kPDqlaJeaSyKXhsv7OshxXcRungCk5V8bhQJDGh3fwz3/65/i/jgde+d7v1mtT7fXCiJuC6oemhCRVX9QA0ZDm8AwMTgNUE08JevjXpNCmyqRmDayYT33ToCaiKElv/fTI0/uQrFUv+KARMmcIEszhzHq7ikFrdV7QSQ4SQeLFmSnrvS3NOM+trlWgs1ntLvLUnQsv59iRbW9WsV41Du/0J/rn6GN96x4yyLbrzWtQVvGkbZ91GJ4VbbrMwX1QNTdWQxXdjzlnhmHQYOucmVXZ63jj9zihWHUrXIrjaLSvFJwLVu0645co+VMrbRC8CgSqchLqG6AM9VoxW2X9rCEorNO88oS8iey4IIpkGaowDCOno947hfOVQNta491338U5p1oaUacVhmFY9eZ1rFOJc5uMsJDzwrwciWPD+UxtM0jmeHzOMkNtatN8PqWLZMIq6VooaSFGPUObNCWTVlZERlsgum66BoNeRoGaYAo8vz9SBcZpYBcCH6RkEs69BbV5EEB7hO5oEXVxxra+Vtq3BHqM16D//nRV9p+6QH/56AQL57bTpffTVPAh0X2wcQqHSnM0UV3JvoC8V5LMNud6+Ro84gd8XEH/ckD/qMdlb3AlPa1Vhv5MLZk4BMYxmqym6pMn08FvDVLLNAq5dMbvVn/knBGnSnnYa6i9aMM3FWnJaVHXLXMfyzkpBOccd8+fEUKktoWcEilkCy51FXQJpiXQ4e8Op9Za1oRlGxHrjOBtbMzXqsIyegZoz5OCj5G7u8xr+8h+0E0tTccacy3WGzUp0FRx0ROk0rKxdH2jxt5m6ZChV295J+u8uVZiKupSiqkoNrcyiaVDnsYYLzZ/3gSyCHdL5Rd//itcf+7zjFdXamzSvBLFQjAZWl2bnVxnLCwkCL41vMuo75vDNcG5ZodlAanreuis5ZzN3csrYqLX/nIKwiSBH+XB1tNfkx7WxFdkK2kFjFXvzEtek6DeG3eiJD7Q0SjnrA9t1Xar6g7p4k6/IKxrWky8CTBBGONNN/17axog4jAh3loAWF94HZqzmbYOmctjTQfssC8dmrUvq1Jjn+l3F62mjUin11jsuri1RdfJcWubzSAAZ4HMGUKjUrHG9Whdk6FPNzSFGGTb+z54XHVILfiXevzGzqB587uIZuNKojbT3GhwqZ5I7WfHJqtbS7VrUldFOl2SnTDX1qmLYRipRdeVc95GMB1xmJhPC8GN+OhYZk3eY4w2/2/+Ba1Z0qecHmftwGEYoLX1DHDO4fvyFN3X0hxhv2M5fci4m9jtd0QfqK2xmTbbuWjtCucdJWeyKYH2s+cyoLeVuXvxtdbY7Mh/taL/FfW47Gl/1PfsbxjuhmA9Mhuxc+5s8GKzflJdVZ764vC26Tvv52PeyLroLiv1X+q9v/zoh/Jl//Ryhrf3o7x3DGMkz+3CV7qtY0V61jlKLpuMqB1wOSecr6xsbmPjp6xJDa2qxawpU7VmjnhGDny4vydE5TBkY4p7H0hLIoS4ogA6g10MZlat/l41gJj61cUYnhl86Ix2sUBfFbauhVozTTypzrx2iEQHKWclmtGrcbVB9f3gwpKYqtdBnFPVLyNwaaBXmLVD+X3kSRm4la51ryqAGrBdbXgHwdu40Wz33wnNCzV6vv71D/m+Z3eM0xXsJvBhJWk1F5Go8K1GBSyQFBu7U2W0JjrD3wS1SLY+aTcjKaUQvIMipJppIninrZsNetZ1s3I+1ljY12lV0pgFqMsqGwtKIAa9mxBP7cmxtUGMxFflZQlRTSSUla8IRH8IYgS9LfB2klul4GXUN2Ov4WXAsj7jTAS66FN/xt6fU/2bjVfR1TCdbFybFdnzxob3OkURY2RJeX2uzkUAWWfQe7XvDeHrr9UEqlOkp/sLrFMPVmzomrPq1SRh+1WptRjSEnBVk0jvHc5aT5lGE08w21k/BJaiiWu50Fbozygdlm9VZ9xNy7+UYtwPazTKOoFuehF1VSOMMTIvmw6+judWxjhx1xKywvWa5ITg1pE6/UzNPBKUza98iEaMQUmfTlEbuUh+miXwXhz7qwPf+Mb7DNPAbjcSxKYpZL0Tdm49jgU9IbXFuK7JbU/0VGhDVVvbAvw6mfMpeXyiA31/fGxQbZbt0xN+wTmTih1USlVsVCOGyIfnI/sh0BH6JjoyssyLQnHiV81tfdItILMuvm9NQNaq9qX3+VGQ/cskqQ1+2ir8lBLDMDCfHjidjoyHA61Vnr24W+E37/2Fm1YnDCmJLESPE61EGyq/qmJTmlL35wCI5n5VTX4zes8wjpxn9aH23hPjwOl05vo6ml64ep/3oJ9zo+RCSnndUIIF9o46NGdTBFCl0fDQOvxWaTVxXjLXk+f1mx1C45xU0ON0mrV6b0qYaqWB9xxPZ5ZcTJ1NwHn82qBTdTlpDilnctVZb00yFAL31l7o1yOEyJKh5pngG1P0DCHSSrExO0FcJZF4PgdO773H/vZV3O3rtMOIzA+wNIhRleVcxskA1dzr0gKpUR0QhJahumZWq/LovuSsXJLoPM0LzltFZmjEEAMPy7z27MdxXIlfvYJcg5B3Vt22R/PDPgiIN03+ijibBOh74PIgNfQAhNoSlUox0ZU16PexJ9EkoTaVOi2WVOaUNBCRkWgkyLZZwuacdWLGPNNTPpuca9gSGTQxMDyPLpkr4gnrZAgWZLXS7yS4wRz58v3dxR40BUnjlWBJhO6psCYCWgsIHkUFdaJEX0N5AVqNr0hDT6SKkoGLJbO4vv+0gvbB47L6s9emFbjzKrcdYqTMur8rZgHcuRL2+oXN8rojF1q9X0DbNtvvvGOaJqALRTWQTM4n5uVMqTq7n/OsiUc+syyB2jzzPLPMs9rW0hEbt04PqX1uYV5UJ19cWUmAenSKxWudQBDnCd6zjwPn85l48IxjRHrFbXcRtgKurq0e/Wxry+AikGuu+u0Dfe/Xf5oen4pA/7GPNdOzf9qCiDHiJKyHno59VII4lmVm3E1qBmM9UT9EJWMZVLn1e9rlf7b2wPZX+/63r/Bf7te//POdAVxrUTU8pxXmbpqYxgnvHE+fPkXGyLIkm8HXkbCXSSYiymL34qheqyLr5lnfXy19vRlVlFKUSNP00BinAbnXykCsF+uaBsZxHFkWtaycpon9fs+zZ88AZQSP40Qu3ZxI74W2UDwN08HOhRCtZ47KrpZaTOt74Dd+bkICZFTKttaKBD1EpDSkQkK4T5njkvVAX/t5Nr/cFLFoVXvkKWdNAoIQnfYtu6zxowOigpMju/0ElhjkMiMBWlsUem4OkYFn85EPn7/Lzn8fh+sJGSNeCkweHLTZ0e4y4gswW6luPWyzA64lKWzrPZRKLtpv7odQCAPz6UiMUQN5LqSSqCUxxAnQ1kCza42ZD10+1n56a9bD12O3UhFvpM5aCXHTSFBio+0HIz56C0A0x5JmRVeczliLOKol3N3RTVGdvBrZlapu8Er167C9TRGolqMljtFkYTcjnm6t62pPkjuxTq+T8kA2+179rFtioD18IZeko6K+9+U7ooEFSIPuRYwUV6y6dGsirYQ6DA3p/g9hTdB6cM150SAL1gJwOkd+cSZsDHyhawfghBAGUlKiZSoq5jsMkcPhwP1zC3oNcML19TXPn3/46H6Pw4AXNaMKJrPbCbvjOCJ4q64d03jNsw9fsJuuOOxviGHHPMMrT9/gm+99k9vbW3b7Pa029rsrgh8uWiFOR+Gswneie13lo5W/UbJV9NJROdZiqqI11939c65eeYPr/c5ODi1ipPUxwII37f3eDsgl0xGZR4H+AnGqXPbo4fLU/tVA/wl5XPbX6opJsiqEaZbbZ6f14NntdpyOmVIbvuloXS1KZimNVWzk5UUgHU7avmI5xgYZbT/37f/+8vsH1n6r956StPe03+85PTzjdD6zv77h2bNnhHGgWAatPTDtV2nDS99HWhYYPNkc1DrBb04LYYgsOZGXjUATQqDVhmvCMEw8PDywO1zx7Nk3zL60qwNuc9qXPfh+4OaS1+pdbPb1cqKgX7eOEDjBXPHUgS8ME8E1vve1idM8I1EhzEYFXynLQl0aS4FTE440fBzBJFQBlpxZlvOq508IiA8Mo6NLImnf+EIdrunIWXfoclmYjwuImndUGwHT1lAiOGE3RE4lQVb+go9R65YwatKRT1rBT1dw/4KcCpILrlSkVWJHhipgdU/GINei6If3EZGsxK1S7D3qwdjHIq+vr9kfiroXZj3Ee6LQK63eOmmgpK6+zgFxVbvureCCIVpLWyHw0iridZSt93ULjWU5c9jvtOIFBEcqlTiMlJxML0CTz+D9RgLLGUcjV2EYPK05SygMjK6eIe7wfqQ1CE2IwRNjWKvHLaEFLg7yWjGDGL23fv16Xa/XkhdOpyM3t7cmMGP7IysjXSF47TcXa+30RElM6jklPVe8U4vovhe89x8bPDoCV2oF18jlsaZ/EwgGu18mAMEFBBUXKlWtZlPNtArBa3KxLIvOw9u0i54nC1T1xQjWvpIGZUmkeYbmqCmruBCauIEmtiktxCA8e/aMOAiNhXlunE4zDw9HJeLJdq1TmnWiJDiQgCPoeF9TJLVZ96pzF1Ts3mSzS8JVOJ1OvDaMTNOo96Ko/LLeH7WWvkQR+n3tZlgv11hr4OdxQZVzl4Ju1JT5ND0+0YH+l5p5lJdq60vls67DLmJQTV7YX1+bR7ISMzRgm7StEVtwFxDhGqB7wLbDxioWPf1f6h29BNc/huj9yjDuGbZ3Ol9LVbQ5uwalsJ9GXoRIq07tcxPrRqZvgHEg1kbKaRUnca3gRaFBNY1oDF5L1uA9Hq30w7SnnGd240H74K4wxon5dGJ68oRWWcl+IU7kuqiIjqnr2QDbxd1oNp8frcK76CWKp0pdd71vjeCEc648v7tHvPC0OT5z+4RphLk0ghScb+qMVdQnYy6w1MYCqhy3nFU73uBJ59HeoIZ0nCii0IPO2laoaGVQe6KSt8C4JHJOOh3g+6hgWhOpClTnGF1jfOUpfvcE3AEdxztS3YTzC81YyOIDLgwm46q9UOcDrSxkNEHKpXMv9EDfDiqI47heW4rOiTtnn7noYQfai8ZG4rRtoYdaT3SLIR3O9oP3gVI2AaFajLjVhXuqzUg3hyLDDR2PLKRlxh0MRi0Z60ZTa9KePc2UFreRNpqumI2XYoEVRWuamEGN1953q7IJLpnYv4juvVoaLnhDR3Qt9IRTrCm9MukFmtn0hqA+GK1Wuz9YUmonSWmbj0Rt64RDf//dP6An+a0Wa+cV49K4tcrXayxg90qaVuW5tnW2x3vBB/BJE4GQmrVDIPvKgGdZzurZ4UZGp3urKRnggpAomwpejGBJibd2pDSx9+oZxz3LcqaJtmr0emSGYc84THgfyDSmcSItULIQQ2AaR5W49X5FUrTlEUwN0IIxmkAtS0LYpjF0AsLer7V1Sl04zUfOqXE4TFxNAzVlqmSCROyTUht4dLKmGnellIIPoyJaL8nZdnj+stUKG5JAUxOpT9PjEx3of+nHS1UymFOSEPy2EWrVDSTjgI+DEr9as6pmUxF76ekeP/e35BwvJRkXQR0eB/w10L/0O8owlq2iaQqV1lLYjRHB5qabLnBXthnoivWgDcVoYpsKMQBSVvgqCIw2h+0Fqnh8iOR6JhiELeIYwsCSZq78dkA3GiEOzOmICGt1owmLegBwkRD1cbou6wqo4EZJ9uk3UmEqhVeun/Dk+orbq5HPPN3RJJENRNVESigIuQkLlVlDqvXyupuZHr7BAoN6BhjJTqxP2EdwRANDq3UVSGmmtCUhUKuiKx3dkY7wtK76pnDsfhc4vPEWcf8UsEAvFdjRXIXgaTHDmKAFKAFyAJdWfFGwClt7DgTnKJLtuilxNASdWKBVk1iFamTDUutaBYp0oSIbQbQRS6lNpwLsenrnTRI3IK7ibNStZQt8+gL6WZvagDa2JMn5xjBOmljqhtP2GRVBpX11UkATlxVlc63XrLpWu4FgR1RsBHYYBoIfaNVRveCjJwwK29ZSCM6TFjXmaXYv6T1r6YQvbEoA1oRCBNciYVL1Rm9rm4vEoLHGBlyx65ezws7O6cx7jQbfG2wvFd+KIScRH5THUp0Qo9dpf1HRmpISKS+k0nkBSl6LTT/72DK5LFQXFDmKA/LeHa3NiNXfttw3uemqjZKX0cOeAKhFhqkPmgJmtTQcEUtsqiZXrZpyI0bWraSUiUElsEtOSkrs8sei+7yIrIhHpVnVbEK9htf3sU8xBEdZqUqmzQ12u5FpHLSSd239rG39bF3VjnWCJ3jHpXfCozOXxiaDa60CG5cspTAv55cP9E/04xMd6H9JZjusGXhf2B368m7rv/V+3LIsa4YuvShnMwrRBXEJBbWL/92gtd7u0f9ugf3bkfE+6mc6tH0BGijBphTGSfvpdy8eON4/KBu+bSYkjUY29nBa1PrU+wDnRA2NQiajxDdawzfzRG8NhxqFpLRAhxRR5m46Ha2N0BW/9BBqc5fs1K/Pi46lxRiYZ1nnqUM0G1+rKLWycbRFD6QKLK2REUrK/Mbv/R5ee/UKiY0nceBcHti7QvbQRGf7swjZCVkgC0pmE2HY71S2zZI78Q5vGgOu6EHjvMNLVz3b1kep4FZHO8GHQBwiIl3gR8l6GjON6Gh9f5wwXV0Rrl4lZyG9OOmoGVBlRpMHR6vgayIvM6QZV7Nqzc9nZE6UVNCpQTXZ0RZT3RJRzeK0GjMtdmPlKWIRjA9Q9IB2oTsJOoXpvaDYu2n2W0CsOYMLKvYqQLG14wRChqwcB+cdcbejildHsVKIXrg9HFgW7ZEG67vOy4kYFEmpTSjrzHtf584IYo5SFuK4zZNHPyDOMYaRIe5VrbJAlYh4YZgm5RKUwhgiIWXEa6Dv170r0mnQrpuro0H3TpzBwbrpO5yfi86Eq9Ol2ay2SsoLrZU1qDnnGYZRiZ+t4MPwCA0sJeNcN3wR5GHCe3XxC8ETnGM+n1lSwue8jn6KOFzcgzTcGAml0FrAi/D0yTXvvnOHhEQtmTnPyuEBU7xrVIPSMYytmSGUckBsBJbLlmY2no+iPcfTA+q7UDidj4hrDAPcPbwAFL05SSWlhSONwXhBiCDVbQjUxZFZQx9jRJHTpslp6BMJ+l0T14qAMA4TQxwN8fF6Dl+cv1qIybamrF37MuepJ/Ot8ghxVPEstcmel5kXd/d8mh6f6ED/Sz4uIHNYlzpwOTurB4w3Ba7Nce1xIC7Wu3G+G7lcjG9Y/7+uVUB/zW/t5X9bCJ+8wUoX76F7r7dayctCiyOH3Y5pGnH+pIu6wYkZqb1n3ogtETrztQq+FU5pZi7mQd6TklKIzhGrOqTlUineM6eZKBOlLKt4iWrFd4i0kOZlDXw5zRSbt/femwJXn2XVQyVEZ0mH1+q6ZZyplVE3dvA4jtwlQfKROkNJcJ4qS1zwSyOaCl5BSWCLeErUGf7dbsI5xz4MpHYx231BRFuh247Y9NFjProlpGN2oskPWhGoc5vZElsAaa2qPjyRf/X/+Ef48neIIoxh5OBhBlLzOETVvIa9Ms5rWVXezjnTvGMxxTAnMAgKe1/4zrfWlFdyOnE+n/HOsd+NWhGqfd+6FhqB4AZKjOS80GrWQ9RPqkoW/CYBOgw0H2lpwDUlRi5m+RmqoilRL4oemoCvhVIzKSmn5eqV/eqiN00DV81cDnPRP7Ug4kEc3tonKXeG/hHnggH+Bls73XeuefM19zhv89dBE/F0ruQgxOlga07Rm9WKFw3gioQdOewP63goNMQnnPPc3b1gH/Ya2I1EN007+nhibY1QtCJ/eHhgGkdiHKhVJaZzTohEnI9E8TpWKmzoCDDFG0qusJxNLtprMgn41YRKE6xxmDjXDGPANXA1Qk7UYU9ykYJAK5wMVTNYitoqy7Js611zemotZisdrdqvlJrAnPa6zbH3Km27LIYgio7W0SJOlMRXK9TiCHEHAkPcUSyYuuAZxVFKMy8TTUq990gbNNCKnstRtAWgbaReoYuiBrVw2O/ZjRPLouThdR+v+5m1Ou9S0JfTSmvVjnJMant8zuasrcuUEs9fvOD9Dz/4ljPgk/z4dAd62LA2q4q1h+Ztc/V+vJ7yHT7qv3TZP3fOPZqj34Iz689u/30cqD/q77+ccbuaVe/dObdCXcFFHTcJV8TgiMGz2+3wITAvx3WRd1nUGOLWAgBqLvghmse40JxW29GCb20NX3Vj3rfGNIzcP8zUCuMw8Swtq990v27jOPLBh+8YKcy42UU1wleCVIOUZ8ZmmvJOD5g+W1+remXnlFhORw0KVYjXN7x3/4L3PnzGD/3AryXEiYc8MsaornFivUfRitTFSNxNNg8fCfRenKyV3eW9VS1zc4K7XDayEakMXbRlpMlIqSpB7EVNQrrwB62p5WuAO7cjDtfgPQuN2BrSBsTvICfa/AK8yhbXsvXf8ZEQNbj2e5ca1Oa09xqtl9kaz5fCca4M4wHvHHOtNAk4iWs/l6bCKxmHxEgIk/ba0bHLhh4ESjqtegfFE8YuGlQhOK1Sa9VZZmCZZzUh6QHDDnLVXxd8sENWGrv9FcfjERkiwyQM1uKoxoeorqjCoQiD10B9/3Ai5cLhsKPQ2I87DRJsFajqYTjCCMPusBLncgUfAkMI+FSsHaDKc6UUUgmEIeCqJg1aQY/U2rh9MqyaEN2vHRTZq2gbxTkdkcQXKgOlBUor+GYaCA0lTtKIfqLUmZoyzdCYVh1+GGCcwAm5NZIIZ9RbQl3XNIE8z0pok46GAHen54z7gYc0s9SED5Vp9BxlG5HUbbe59IntcQ32lWmMDONIzgvd0nUcJ7oQVIyRaec5n8+8/dbEMHjO88IwBEJsxEEIEf0jnloKwxTIKSlREyEOqgTpnCEbPdsAOym64JCYGJUl1dV2bFEjo/1hYpxG0mLjg5g1MbKebXp92uYC2QO9JWc9uF8m8l1MqtbG4Dzvf/CM+7u7X52j/0Q9ej+q/x022NXYvtD7xXkVB3k5CG9kuf60LwVpaxb9UlD8t3+rW/apv8wWiNb/U3vKUgujF8RZ77Npb9z7UX3YESV1OSWM9V6YViQRaYmEU9hMBEIjmD1kC8rcn0wlbBgG5KifPUbV0O5tEJqKg+SWH8uMytYW8SEgyUHLQDUynMKfSszRccFaM8EFE3yJuFY47Af+2b/5WfZXA09eu2XYRc7JUceiAiy1UUQZsqGHmqYKs9o6KcZ87rPLrIlQ7+v1RxweQ8libmhutVdlO1bs/mtVs82290xQRGh+Yjg4RucJPtJcgFYI4igeahHaeeRcZoLTNoSUZqiSMHhHQHvnbU1S4jpW5Sz5TDkxTt6ISZXiHOO002q4GhyPzXE7hwuRZT6TU7ckjYoklEJpqmuv1fJAIdGaxzkLHqGrMephHSbjJ7h+jChX3IdAwNuUhd7npTTidKXBy9ZiZ/yLweGdxV6qMtnj7lonYIImpvvDQScdcqbkzBAiYxjW+6oErEqMw6oEKM6Rh2TokekkiOfm6VP9/NLJmYrOlNoY3DaKh1WKHe1TkxyoRSHx/TjS7aRba+SqI3rOiRHdVIin1kDOhVK6JCtMYySZxTOAk5H/D3v/tixJkiQJYix6MXM/l4jMrOqu3sYCS7REC6J9BYHw/3+weJoFDTAgANOz09VVlRkR5xx3N72I4IFF1cxPRFb14Kkz0FYUlRHn4m5upqYiwsLCfLIFMWWHowEgIKWAWhtSjzARqARcLgHPTw8gQZOKkKHvs+AUr9lHMfd9L0xUaiz4wU8ZY6XmATGESJ0HC8j5jCAR0A05PqE3IKXVr19CQEBv1OI33WWDoXSp68JEsGsDYvA9Ocwq3ExpvGQ21ShTyvx7NCwpIEdM7s9M1MXHPDEg+X0PHc+pHmbnAdzN1quPlIYQcHl9wy9/+QverpdjrfZdHL/pQP+vDaIee+5u3nhox6Yzeq1HmMc8K/217G5//6+hXuDbVftfO+/3ycPoD48k2NjMAkSQXcyFGxhnyaX6JgqBRcC14cYndjgtQDpn31mQCzAEQGaAdhX3GLEsTsYLAUte2VOMyRXp2JPPObPqAxnRYY40OlQaBNYGVC4zURiZ+Ei4GIxd/g2KFIBPrzdoDPjBnOWugASFSJ9BV00BcUdCkFwUQgLUfNzPV0EIh00A8xzkAKtyw4bLdoIbIXd7ANzgxWELCZ4n5eijirweEEGUhDULcifZMS4BarQiRfbNNUaktlAyWIDehxWsYUkLYGmegzrylDiEzL6mKkclhxAOWBU9Pj8AoNb4QJjoIpYBCUhJoJpdAIrVFNUDlX1rM3DgMIyuFNZlmWOHe9vK0LsixYyBjNk0jfKAqN3704BJ9tvA9dRr9TQGRAJ6RwhAKR05BZd4BcwCLIUxxU6ouRak89mVg12OOAIhLH4dxuoy5MDr25pO1CTlxWfN9+Dg+Yu7OmIWCgCmzLH489h7QwpsX5lxDbbWMMx5g49wmoobNZFbMYyiRAJaBapPdggMxXktCuzojgHbtuG0PqHWglupeKsV2/YF/+Wf/is+v1zQu/AedD7PY4InhvuRvhjYNhvtNI5V2lTPHM/hjgiIozOjL84pkODJQhp23aoIOR0SBAGGaJCMZ97XjKuMDgIjMPNjRxtY3FDjn+PEec1YlowYBE3V1+T+XNrcK3ehn71NOlCZ/Trs3Cdva/l+//Lyguv1ij787r+j4zcd6AF42vsuih+OY/A+HiOoxcGexj72QmiNsLQImc9zIc2gf4DZD6cykP9j5SKyM1H/2w8ZrVAuWN+YKb8aIZFz8GldMDIC86yGpC0GrCBCHf/BThWKojCjF1g0BAvTUnz02gciAAiWvFBk4yB7yb5c9r7kTnoaalxpzDi/q6CDBIj32nbnMEzkQoJAVJEfnxBCRt8UdVOobWhtQU5Uwhtwpo5rD4N1jiVqV6jo3Agkpp1JD24WwTfZas3XRfBRoOCb1ghYOlsMvNc2K1tVA81YBkFRAPGZ6NKgXRGtAJJhPSFBINagnSjGgOeH0MrY5AXUsTWWadBeYdZoKazUDkghoLRhEAJIq4A9eY/b/eNVUbcNeSGEGWLAkgjh9lJZlZn69fDxIwL9CB44QqTRkPEhwNBETQDE+pTIFT//AY/23t3EKKKZ+lpkMtRrYYWnJHPVskFg6LVhPZ08iQVUBSEssA6EENFbxfVygQltpqJwYsTEqNK4uSiTegCTNCHdVulvcY0F3eABzGaVLQJsW2HLCzLbUfQSsIm0qVJ+uWzbROO2241KiilwhA2E72st0zSJREAq4eWU0ZoHYEkbaXEAAQAASURBVBGUWlBLQWl1VyBUxevrK/7+7/8Rl8sVn98u+HLb8Piw4f/1+s/4458+o3ZqXKgXAZxSoODNCGQTHvepE5jPunulzbUvnhw6sTYIWf7G54RaGBtijKht87l2tndijrBowBRKchKs6/0zIeOoYvSxyfF9jtQJRbxUISlBhBMg23bD6bxiXbL37xUWKR9kxns+npEddwOnLBzZPKqKwp9lorj7c1xrxafPnym0I5hFyPdy/KYDvY+G+wP0t3/+GPBFuOCiO1K1ZlNqk1reFEQBMGG87n3ncKgM9xc/1PVz0e3kO4jAKcx/taKf54f913JIqGpQz9wv5QqAPUcDxSo+fjhD44JdwpQkJOqzezLU+fD38gYbff+uqF2REAgnexC0NrTsK0rZXHOHPfXWaNGqtaGUDY/LipQWtEYVvRgoJUqp3gfeHyfyDRGdUS2NhIAe56ymQipYHs8wdGxWkJrg5bXgP/5//oKXy3/G/+EP/wP+4ae/w+NjQBVBVYF09U2L21ZOG+qtkgvg8BynJ7pvfnv1YR5oqK/AWeMUqTLISpRrY1kWmCv7jVGtGANyYjUTMBIyVkDFCtAM7VZwvbzg+fEEaR1dAEUCZEFeWMWbk6HEq+81u7Vr5yx6LwUfPn7An/70x5mgGjjVUFrD9XLFtt3QWsXz80ecnx5IhnS70loqzg+PaDY2V5q4iNDhsNSC1hkEc1rw8PSMum0UaDLFZXvDw/lhsrJrZWIXU0Ktiuv1ynUiglob1vWE2+3mCWDA69sLzo+/Q2sNpTZUt0UNMKTMdsLlcoH2jvzwgOy+47VWxJiQ4hk/fPgRYqOKVuSH/4zHNSPHhNY7bmUDgqHUjvN6YuXdGnqhBG5e8hwf3G5l3sfgs9+tFU86I1KkYFQpBSFG3K5XLGuCeJ++tQq4NezpdHby6YUOg9awnB8Biahlm+01fg6yyL+8fsayLFCJOJ9JNKvlhi9fPuFyu3JsMCWIGT59+oz/x3/6Z3y5vKFLxNOHH/DhoeB/++MnvL42LA8f8Py0uFQ3pwcGhN/bnnRxbxvQ9nCO5OY5NPjHdNFoqVB8J/D57hVmgofzM+9TOkF7gUhAyiektMEk+iijYxuGOVrHQohFD5XrfPMe5zdJk517czB8+ssvyOvi0zqC2tUdOeXQ3hiIG7UvzBN4A/00jsn5/K8BNhwbVfH50yf8+ZefQeXUNH0NvpfjNx3o/+YhXwd3CoQcIPuxyAzTuAUgAURcJnP2n8GMFRi/P9CE+2NkluYZ4+j18cuufvM3Di5iJwLGgNA9C1ablo7LSr3+//JP/wWbFGyvFwCYAWcoch0/fwiBPvA5IyDAuqJohS0+Iz6kbxuTmuvlja5SZnh7e0E+Zfz86S+IQdAjULYbnj/8sMPv4/0HOsI3ntd6XVfktKCXzdERh/+MLYFzTDhLQAsBshg2CzilBbou+JcS8E//BFyv/wyUBT8+B3y5bfhy3aCNgVZNUbVhWVZEDZRo9Vna0+kBtekMFMAQTgLEgz97dszJbrcb+QNGox+ODnmVJ9wQciK60UqZqloDBv388oreCmrdUMoNDx+fcL3dCFl6u2RMRVwvb2i9zj7T6XTG2+WG5kx0tshZSbVa0bXCoFjygpQzbtsN2hViAdv2T/jww0f8+S9/duMbzpkv5wfCzPUK9IqUFjw/P6Op4Xq94HK54HK74Xc//Q7/8L/77/HP//xHvLy+oveGL28X/PDDM2rZK9utbMgp4+PTM1pv2Mq2cwyWE3u2NhzdFB8f2UdnoCfDHWJ4fHpEqw3XW4GZYv10w48/nrAV+pfnFJFDxeITfgERKS6AZrSe0RvQu6E1XtPeDS0xkW0NCEugNgCMPgoRwDL0HYCwOJmwkdxXa0c6nZBMEGpF6xUPp8yOkrgKZKGjnqm5vTPXWkiCUm5YlgeHzjNqvSJJQoorxZBEAOmQqIhpl7HergUfPzwiZcPpvCLn5HLSwNYMtwR0yVifPkDrn6FpgeXgiccNGpo/S0Ma1nUq4kAt9GBhzGdu2zYy8/0+lbK5GRaZ+Z+//IL1lPDl5ReUckGpV0/qC/RToR5FjKjouF0u2DY+M6MlkFKiKJW3QwQBa8peROnod3FvMjfd0QqLEfl0gjbFw8enaXE9XoPk2XBocbIJNCF6oUnTGK8enxdq8xoNpc7WGv785z8jZLL+Y04I/x7o/+0cExYfN8Whzwnk+40dcPqosIPsI3JBDDEIqEMGHx2LrJicIKQKzhmLgDJgBw30oQLlG/fumrQnAPPBGv9/Z4O3j4FQHGQXcQiJXuc5wmePBSG4ApgpYhTkNSOcTvh4/oiSThj2or11hOAjU5BpR5lyAhqz6aEgtfhYG4RGGYDgdrnifFqwnB6QL29QBdL6iKhAiAtSXNHQptGGCINdEtBpzcdqetsoyxoiend/bHTXUeHD2Ls5o1mxuQZ2LBsECUkU6JU9WdxwfjA8rWcogGsrUHScH1aUrc6u/yon192vCIdrnbNDlYkrxLAnPxwpIutcA4V9Vhsa4GxlnNYTqJg4Eie/fwEwUTRz5TdlBVf0FVu9oPWGuCQs64KYM2JiC6U1Qve9816OsSYA01xkbEaqlbLHW2SgbLz2MSWcTyeEwKBgAPJpRV4yPvzwcQZeMcWPP/6E6/WK2uhxYL1jXRNOaUE1A5oihYSwZFgvgCmdHtOKlBOenj74xljRWsHplHFaH/H8cEapBVtJM7l7enzG9XJFrxUpBrRecX4+s7ddKnr1xDBl5BghppAlMf8JZOzn4TFv/LwS2lRWJCrU0NSQHbUy7+uqsdffQXGVLAsK2myh6UjIYFMAyAAqT6L71Mi47t2LgOHuaPO6aBuqmmyRoBta6ySghQqLDdoUvQGlVyxZkLKLWPkMPry1od4i6sbpilKGKZDhdm0QSWhbQ7UKXRY8/vh7tD//PyEgUiCywKpCxc17hVoJzRq6CWA0/1EJUFFel7zABNgq9QpiOHnlrEgp43x+5D7RDB8//IQvL4rb7Q0fnn/Al5ef8ePHP+DtSob6h8dnZIlYcp72ujBHSQXQ6NwZuFtiJq+D+y8le7etUt+hJ6S0YDmf8MvtDR//7nc4qyA2o/iODnGtQWBWRBEIKLxQG8V8ngTYXC9hEGtMGNhHS6aUgs+fP+Ht7QUhLcgpu+Lhv0vg/ps/7PDfMBKBQ5+dlfLuShYjH9h1PXlwcjnQOJye9izwW73+vT/klfhfOTMbGP8hYIxzmsynwwsMdrjsOAHEAseXIrPl2ivoVj0kTAERV3Dz6tK5WggAmnlPF+5t7oSk3qmAJQhU1hOg1uKLnqSWEAXDHpT9vn7gIRzbEmE+hCOZwR1UDxyTocmQBVW6Flm8VRGxrgtSCri+fkaIrJhbNRRRlEbfedMDUsNUDSFQAx7qV34mYA5tgrBdxPBbH2pdXmiknWUdTOgW5pvEgP7JyG5TbEm1ecAVmAhOD8/Th3s9P9DBD0ZpWO1sNJj5fHyfbRYzc/Ejr84636v7vHrXhuZeAwxEvO7RLWdTYrLqyiDs24IKbOyRG8zZ9KK85mP8aMyzr+cVDZ165dYRIvXODR0IVB6TINjKhtqKs82ZaJIYZ+iizhkZMigAXGmQ2gkNQThfMFTixvw/hKIxZH3y3Lpv6iHuWhY0miGORdEbt8t1wsn0tQAAtx2eJE3ZpZoHF4ZWDH0SIA0MjOL8i6ABSGyHALsiYrfDRIoI0R81jkWC3Bn1vntXRQxMuNQagrF9cOT9sjI3mAlu1zfUWnB+fsI//MPf4+ef/0SRIosuUhU5l++I0HCvG5U80ROfDjj0rGerDuITKuZkzCEUVmZidXwt+m/EuY+FsHMAwnAJNPj8JLz1F5yHEpGWEyA0Q4p+3WMWKAJFefKCdVlwKxv+8fGMHCnmNWr3wYMai2p8XT25mCTDiepyxxuoxqjkr9cLXl6+oNaKNS9TSE1135u+h+M3Hej3oTNf38fveXDfoR1g7527gIr/iU4GoxpWR5e+V3syxoSObQD2uMbiH/2i+b5/5RA5RHLbIfoRNL76jP459lYWf0ZtzMlHfG6vk7Aw8gXDIA7azB8GwjEYxMfrAoHrQvNhh2tf11anYpmZQhLVviSEuXFNMp2f86iCR0KFd9dnboazMj5OJ7BHl1KmY53R1Ob0QEOUGAPU4bsUEpoogv89YBRcTNY8xk0Yc/7dWbg6ID+TCZGPCn98juO6Yc/2hlKLk89czU87tm2biU3XjpRPyPlhahx07ZwmkI7eGkrpqFtHM+oIbLfNtfXBJLR51a6dpkOlwILgcruilEIDk9Zg9oAlL6i1uQkRg13OC1odvXQSnbatoJVKUpVRt6CGBhhn4mvZ0FSxbQm9wyVR6XJWGhO+WosH9epJRUevTAa6duTM4NVadZ1+Pku1dbRKAmKrlb1vXzc58Xd6a0D0+fpusO7LEAyatXUirwnIktHbjqxoN2gHhaV8+bJgNjQl/6JHn62eCRoJZz7u7skbIwjRJp1TEGoK+MiquIJkd8nU5ryTpjSB8tQWMLLn1cb0x144jMCq1kFZYB/pBJdJcO6Qdrbf0hLxHJ/ww+9+h9/99BP+03/6v7NVgzSD+pBiDof2pGc8bHk42jnWPWZyTn7LMN4x5fhmigGljBFIzLbWMLwSD4pme2tuQPEYCagNeZuhzxAouSwC8daNmaFph2JscrwWMQDbdsPT0wNiCm6wpDNpNyd3Apjz+X1mSu6R4PdiH6fbWxrbVnC5XHC9Xv03jIWPCPYd6/s4fuOB3kOFHP4x+tHjZw6Bdzygg1A351/BB3jbtpklDlc2QCb5Tu0+yxv9/WOAviO+/Er1z3/svft9fl7vXmMsTFiYn8usA9bQ+w3J50tLa1iXR2hcndkKSPIF7ezgA1iNJTd/qN0H3Do3h7RgyRmAYpOC5XQGrCGtJwRQKjbklcpkkUG4NcW6rnPTjjF55cPbkfOCUnfi0xjVEdlnWe9FbPibRAgE11tBN8Xj0wN++PiM//y//VcESfjw8YzffXzC5bZBRVCLemugYtsqHcCc2NMbJWB7T1BnPY++Pcl0AyLGzokItGWl5SWD1PVacL29oLc+A33yOfq365Wbcop4OD/gDz/9HQcbRbDVDW+vL+6a5yhJqeil4XV7w7IuTKgcHk4xQaF4vby6MY3ierthqxWlbdi2G4N2a1jcTrmPe9kYXGPMuF6ubBtEVvfbxt8t3iIIEpBkgUjHdbvheru6xHJDrYbL64Ztq4BFaDXUrUG7oRdFqQ1mDesPz/SGLw29NixB0LaKfu5om6I1rrnrteHxiaz32ni+27YhL5l94d7IUVCfInh6ginQ2+BNKK63K07rihTpWtiagqJMRES6X4/WKvISXDpYgSEac3iWW29ozbAsGSllwMxJhAHaugdOc4IpXeCk0ngm58wWQGePWRylGgG/K4VzIHBkzMNGMEgkC31BQkpMiHNObhfLn4sWsCxsG/XWkXPGf/+//3uEx2doiHj78obPn74gxERM/MARF/9/0m65HwYYoshk/g+iWYiZtsbWEIIhDzKvGXLKOC0nvH75jBACVTgDA/WHDx+A/wI8Pp1hIAdlTRk9ZaSYnBMSKLer+ySHiPpkCtDKBTEJqnaow+SqAIQuhrAFaxZcbm/48OEJYRgIgR4Us7LHrskgoIcFhaBwGC+930/Hfnu9XvF2uaKUhvOZ5F8zFhI62pnfyfGbDvTvj/dV/d3hwWeomI3KbQjhxCS4Xav3u9g7rrUg5+wEnfru5pNI9muz8sC3g/77n/3W6N/x4KJsVPXKZI0DGb0DOSWsOaK0KwwVWq7oA2Eg7ufBfljxsnIvtWMNK/iQ8LOYGrpWkmtg7NFKRt8uyIlw43b9gvM5Y7t9Rn54QIwKbTdeCg/6zOBZXXCumNvQuKZmnFGHjECMKV7Ue5tVQ0DE09MTar/hT//yJ9TrG/6P/9P/iOfHJ2yf3vD6+oaHvFK/PgVs5QaJgscPGb87PeP0eMK20ee9d53iLEPcZs7Rds48x7BMxGRMA7xdLijV0FqAKm0/f0inOT1Ap0LC5X8ff4fT6YTTiZrcyVaUfkHTiluJyE/qLHeBuU0oRIH0j/jy5QtaryT35YzL5QpJAeHRoXUJ029BjYp8tRaUUvBwesS6ngH76MloR+8V5/MZz3+3zJZCSplzyY2WuSEKlrQixQXdGtIHw2178Lu14IefnrA8GVpbADS8XRInAfrwhP/gtp4R6/mE38kJ4iX42yXiw4czalkY/GLAevo90ok9XxennWvkfD6jt4pWK86nE+rrZyzLCctygojg7e2KL1++4H/+n/8HnNYTtDNJWNfzJGkBgPaOz59+wY+/+weq8ElGzh+QXMnx6ekJr6+veH19g3bDTz/9NMcmAWDYMt+2i4vNGHpTfPz40X3qBW9vn/F2ecGyPODh4Q/IKbsOgWIr21xng6BHDkrH6+srcs405ckJtW64XC7oNSHFhSRWfyYut21+rrpRYOYvf/mEn//pn3EpDVaHUcsQixlwto5N5Rt70Z5M8wsJhsI9TQOCJcAKrAWkyD67WUfrBWvKbnfbPVGtAALKRmSp14Z227jftIYuAsBJf6pEMwwwE1iI1GiwBrE0wc0Y48H7IiFnJ8kGwU8/fESATLtujsAedPRVXYPBx3on0jZUDc3V7/Zq/na74eX1BW+XN7dbJhFQOxOPUv7dve7f5iGOXv+NYw+q4tn/CWZAqYoYSbQDMJWl5F1i0PuRMb8z69+/9fsE4NcSgmP1/u2Do0sSArzVhwEsLSlgyQG1V3z69Am3W5kazzGGvWd+gP9DCKwMtzrPnZVrBbza5RhdRyjA7a2gu3mLlobT+oz+VrGeM6KsEGRuusvCMbDeKT26LJxv70pL15TRe5kb63HiYVxbVXGoP0ICmcQWSDgTC/j5X36hIc/lip8/P0FshURBXhdch3FPabhuFedWWYm3Mh/uZVlnoJ+OZcGtar3K7Z2QeYiBM96wybYeBKbzlAl1cZwQkUJ0Bn+guyA6IjpaVOQYkMMKcbJSig8IIA9kXR/wywM34ZRpe3u5XnE+n/HL588elNOEYZtX/l0bemPlvqRM51kzKDq6Kc7nM/XfHb4HgIeHBwCGVjk6yZnmjKoN//BDdlnfjl6AdUnopzNUE2hekrGuZ7y9vYFkrYAUF4S4uIYTbVejAJ+/GD5+eEYpDV0VaclYlxXp/IhaCrS7Ta4pQkpIOWO7XnC9dKTQgB8+oHXFsvA91rywOmwdLVTESDtZwsbUqjejA2UcsLKvLSrk6YRoW+u0sQ1hjvNNjomvxa58Prr3czlNQG5CLR11U3R9g8gFMQUXjqEhDFtAipxXohDXGwZCta4rE9zWUFshzB6pGS9muF6ukJhwvd383kS0yir088sXvHRFyAvOpwe0+kcYFClQJ38QSnHYT+7sWQWTE6IwIBh6Cx4AG4CGWoCUDSEoei+4vN18nQNfvnxG2QoEgi8vn6Ed+Pz5E2rboNrw8+vPvIaiCGWoB3I8NOfMql0SQlxoiAWZVtUCwvjad0RTYIjacauKjx+e2GLpbE1oA1TajgQCiGB7yvt93C9V0VsFJM1kf/To397ecLvx3pxOC6WUAU+U+7dypd/08ZsO9PfjcX/lznyjzBcIUhrVeps/tsNBDE5DVWka4OgesP9aJX78ma8C/ngA353Xt5IDgDauiOxRVSuUm+wMbGsOVI7KZ3SD984YvIYVrPU+r08IAdkrOvU5+sE8jpHqYk5BAEyhaBBk739ViAU03TDEWLo21FYdZtTZ7xvkvKGsxfemhOWobMcxAvGwKRWwAoiBVrVKKjZSyEAGtlTQVHGrFcEiehijWbN/g/x2xVYKSUBClb/TGohC6HiQSbZKOaF4j733jtoqg89GpzkIxWzWdUW82AzovL+sRnrbWxGmZBdv9YbrdsVWN7TecT6f0CsZ61B1hjkrwsvl4gQ1VnUPj4+43q57D7J05DXjcr1QvMW13k/rivP6gNv1xjl44WTDw+Mjq7DONkMpBT/++CMAQyubV0dhutK9vV2wFWoELMsZP34UvLx8Qak3AIpWK54eP6CUDa3TuQ0QPJw/eA++uAIeCZp/+pnJohqnWiCCh8ePKOXmSSUJiev5DJGAsm0o243eARE4nx+8GiZBrZQGwS8cJ4yJwdF9IPhcsr0iQfDl8ycmb554UhOhYD2tE3Jny+hCrkei1S7FUrh+WNHCE3uudwmAdZtjg621uRbYSqizPbUsrLo5ZTJgY67F1shVMFWElBECg1MpDTExOVZH1gZxTFUGlRLqCYKN/c/JjSLijHuZfhdjHwnja2OHEwZwnpxAAvXriVzCmfER+uZSyU74CyF6C8plgsMKs4ycFxZOB4KoOsl5WRZM975MG98hQqY2dtwIyx0xMBFfUkQUgwnw/Pjo6AgV8wSDJzEf94MN8c5PGGujVSKFcP7BthFNoRSw7RLWTuPlct0NsL6H4zcd6DGq1fHPX/3BryO9GUdxhovacdb8jqN2TCbwPhhPYPquT/83CXmH37Vv/Pz7f4/KcSg5BQlovSEJe68xBXe54oMGyDSzMQDmm4rBCTmu0c4NhHD+qPYpINMhgVD1GFkyBSoMKURcfCJBAhnLrDp3Gdspc3vgSxCuHQpzdx/W+2v3fTTTYTA0+UTIKUFyQJGAbuY9UUAbRVqG9a0a3dFKa0hC6UzrQEFHCO7O5Qlc76wQb9t1VkFdO9YTbX7H57EQ0KLBrKFXH5dUQoIDzmxupsHKylBbxfW2YXOi2uPjGdYrylYcQTBYSHh4eMDLly+ssoXJ5sMjK3H1e9RLRV4XvLy+UKIVTDge1hMeHw2vL6+orUKi4Hq94XwmtD+mHrZtQ+vUPi/bht4aUqbO/Xo64dOnV1wuN4QgOJ8NMT3j508X3LYrxEeSbpWESAZ1JlaPj3R4u91uKGVDCIKcF6yF8GfvjdffDB/qim27orbNk0DFeuIYaK0VbSjIocOUAkwj8JgJ1uWGnCkDnFKmw663Y3pXF2hiu+J0Ok1EI4Tqz1S8C/QGSjS3xr/XWn2+27As5GzUOkyM6LwIY7A329cH202eILrQzgg2DqZ5UkA5aXWhFlXjPJ25hkM3SNC5kY0W0iwOAoN1q4VKsjFMdA82xvZ8OmCyzfcEH4ciIwjNd3wHYrthXXC9behGFGlZFtRa8fjwsDvsxYSYuDdQcIcEvhgzTuvJ0SM+++qTEmkkCtFRLzGYa1GQJc9dVE2Qo8BC4nhz2wAB1oVok7VKlBLgvuNiNzpGqLE/h7xnnrjX7oROQ+s7ujO8Fab0r+3X/rjnfw/HbzzQ46voPjXpDhDW+5s2gnUIcfaLB7w2SDgD5kkpYMy5myn+fz68goTuzP1vLaZvoQVmPt7G1BUBK0ojgWYwRVu/IYc823QJkQE4BKjEdyhCdw36ITjRsYUyWvicQ46GvDxAEZEz5TXfLhc8L2e82Cfk5YycyyQ4pRxRNrY8juS6UU0Er9IZwGVOAYxEJwoHBEfw5Uw5SDgSjvZFnxFuraN1RVUFqiIB6Ma5WBGSf/ISkdeEHGjKwYSGPWy1MR7XMSQ/deh/p4CAgIfTGXI+37dWxBAQyIYfMKB29pcd+m+t4bbdmByZIAQSJntXiLKqfzidUErB9bYB6YS0ZDw8Psx7VBp7tMAgeBniSQAEMuEjlb9SzgiDAJUiIEBItA7l6OX+LKS0Yj2dfYbYYAjIpxW3csNyOmM9c95a4GNYUajOpv66MSBmkgQDEhBdOTIZ8pKgyIAoJFK98fnDB7y9vaGWzUWFAp6enrDkiNstoNSNnImUsCy0ea0lIyXCryFmLDEjZZosla3y3kSSJWMKMGGSRQe7e4GYgeKMTWI+a8KxSYBBeFmXmVzmJXkvu2NdSUybyXJrHFcEoN7Oc2Uol28X17fnc24+2me22yAPnfwxMgfsnvN83F0CVvi9IdubUkJtBZoTzDDn9mN0ARnhuiCZzSeKZOjWj773UeIbHKMV91IAk+8lP6DbF5goUo7IecW2bfjdT6tn63wfAEhpORCaGwwVUVY0a3uC7z3xHroH9Aizhhwofxz9GRmEObOGpgJZHtANaGXjHmUdW+nQQiRPLMCCTsRHO1HL1jtqKU5O1f3z6i56VkrBy8tnlHJDznm2Z0NwPsS4f/8KUbPf0vHbDvR3dfTXx1d98eNfjYI0Izsl/FcBhyTHzR9yqfx3dLIaZj9vogV/sx9vh/f/9iL6FmwPABELN4PQEKTCekEpV5zWR6zrgpxXtAqks8FkjI0VqNHtjoyxkUAAIXbUtjHzD8yqTWgxCsCDHh/+0ipOnV7p3dnLt+rQfTB0YU8rRtpU1lqxtH7/mWSQ8dxpT/abMT5nzpmbgnUYGgVrlkdc3j5DIhAegIfzitdWcb0VnOIb1iUh5YV9z15BvfmIFFfkcIaaoWxlkihHAnK73dB7m9d7WRMezk+TiAlQv39A+YSAyWjvrhg2ZulHFfXy8jL911NKOJ+fAVFo39CboPeIJZ/xdqFKnqFzo7aOYJkwrQ0LU1an27Z5AuEEvDY2N1Z5DDyCDlcy69SNH5DyOJ+ZeHUau4wRp61WRz4KDV86EESRloTrdoWYIEpmMqRAEgqsKDq0s0LM6YSoCcm6j7YJrAH11tE3hTY+P700lOWG3grEOpXHeocFGqSoy0ubCRBOuDWg9+rtDKAZWfkxBhgiVBsdy5OwleHo2x4o55MEOHErL8tMxsZRi/uje2VKO9iEVkeQ58RLjNmrcyJrrTdIWGBGYSgzwDRQ0CVkh5L5rJvxdUPy6ZlmU5Aly15RQogSjSrThwCRckRKEdW3mxgFJoZaN6R0nokG46VQhjqFO4+OvWp1xEpoT8tRS7pXNisAlKqDCCiN5MJlXdka8tZEKSQpt9YRA8cYq0UUiyg9eqIhFL0UhcQEEYri5LwgBUOIO1+HvhTAmgJ63ZBOJwQR1C3g4cMztFdcS0HbbiQba+B+JhT3sjEGKcD56REPM6njNemOypTCcbrPnzlJ8PDw4IZcRAajT3P8LUT2t3j8xgP9u0MwIeD3jPf5dc+cRRTR9spSe52QssEXbzMkHykxh9eELz5HVEaf6VvHXVUOT0sEgPd/+qGPNmA2uf8FRx0MFgKCZeT+hpttWNIDEhaIVsRAnW7ICSZAUzpkJaEDWGvVxSMEtVesQsh2jMypKZo1BO/rd+uQBPR2Q4wB63Jmb10bHh6eoFqwhIieT8hhhdUbluURan9GCMC6LjidH3G5fMESAywK1dhuASYBeVkh15vnRgIYhTKaAqaEY2vvqN6EI2phsBjRt4IqEVUFtfHrOctkVvO6Ntxul6lwtd+H4FMUhtPpYZp6ZK8a5+iNKppQynTbbhyHU4fxwXlpkpiY2ATv8Z1OJyzripQzUo54+3JBb20mBBLM+6BUG4MCacFEL3oHau+4XW94DBGlDEnTgOutORObCSNdA1cYgFIUtXMeXKQzWIUEAwmF4onqML/pNubiO2KKuF5v6FohgWu5tQbRhK63nZhkwjUGZf86Zo7+lYqUnd0yoE+JMGepA1Ri01ZR1dA60DXAhBB31giJXqm62UtIBcv5AbdbmfrzAFCbIgYy9gHOmYsm2ICAD7oJEhh86WHUvDqu8zkeubbFxPTVdjEYBRDn/sE1VkpFWhcneHFNae/TwY2jeG0n8Jq6djv7vQFCZMmVNoNE1FLQs4/fmaE2JrjwooJo2YrHh4/40y9/QkgLFIHwuAIqEQKf5MCwswYRKqVSZBD6MET3dJCuWCQjRcGaIl6N+gMqC1I8obUNIhkxLIhCa2XEjIALck4wybB6xfK8QPuGEFZye9qGNSgk720REyAFAVCxxExzG1O02gB0tgiN1wXdUH0kMfaOWir+9PMX/OPffcT/+Ic/4HopuNwuQDAsLeIW1DVOyJJPTsgcnKAhZ11rQ9AFpRe8Xa74yy9/xlZv+OHjjzBxbklriHFB17rzdOq/s+7/7R3faKd8k+GOHUHnF2SOXK3LQh3xTnGYIAEpjIRhF2nAITgL9v7xr50H3+ZXRvC+VcF7Fm7KmdOqhtqbV5vUeA4pQykjhrRknB/OeHl5wXldobV5/wswCWgoPn8KmAjFTRKwlUJd56GDDYpk6EEhbtu2OfbWW52fPQbOFfdGHfWt3LCcVlfK00luHLr6KWeSvgCUWndInzq4TtRxtTYMgo440kIL0FIaXl7epuNYXhasK8fZHs5n12DfLSmT9xApJLOTkui3vQvjjHs5Kr3mXufsuxdXBmT1HlKA+mforU2xoIenZx+rIolzzNjftg3amr/vkNUcpCav3EtFOvPaDBOV7gFnVBvNBY7GZ9g5EMBwLBstEHhlS8XH6G2JoRvBEUd1IZoQEvvCOWDMqqdEk5h1XVFuFzQnN7HVJW7+dH89cVjfY06dwjnDYz6idZfiHcpjtpOlBotjPJoDah6wNts/YwST/e+UEp+BMKps6g2MczjeW46rDLLkvhMc+TfjSRwIFBMXc9fJXRzp7uf+2nNu737WdrEmw57Qj985uiPGQLe9UVjUrXIdSwICGe0kvPmUCoAhrBTn9QrYpbbH5zRvbxHy56iuC/kYx2tjCs4f4AingYz2m3LtxGDommC1ompCiJzTr7XidrveqUKONU2UoSHGSuSkV5i22cYj/2gI7hhMO9pWcblccP7xzMS7V9TeEAxoSoGcoeQIR3KI1PW5dnpnoE+ScL2+4suXz7jdbjitq7Pyd2Li5GIML4DvrKr/PgL94biDv/F1AD0e4/tjYfDQubmY01JHxc699R4pGBvUzP//W9fHgTDDzNz26j5wA++9Q3qHRG4ewXkE6hvB+bTi5Y1GJ61vaM0d9kJAaOabjk8vuwJVUwazITkbRKCCWQGHIC6pKftoyiHJmT/n3tSnR16rYcE5NnDtCku8QseNcG6yckA6RkXi13EQiOA2tKVUxIUjSjEmLJkCPzkuQNo3TMU+wjfe6+vLrnfz9Vu5oVWOrLVWPai12Xs3UKktJm62EhPlO2PEsq4TDjYj0ai15kQhkuHMqOo215nZvIeAE7p8PQ2CFzdxnZCxGbzvPPwcjBDIgazCTVx3Mw9h9T/aUxxvcwlU270ZhtbBQL66kzpJKgszEB2TWfNE55joSiA9bL/mDOIDyhYzwPaAM9YAf3xs+sc7xUAkCJ5EKaR3rE6umuvm8BzN8/NnN3hgu8vJR9ttBub7fWP8/hGxG7z32ZiSnQg3r73tn33fE/h9Hefoe9F+jeyrf2PekxF8DNNDmm8NATkxo/cvJvM6zcTpHfI4VfGGhS3MvQXIZ1BtCLIAULZZhINwrTe+v3A8NyWgVp/UCYJSK4on1QM9FH8PCfCRSkHw9zAd7RMSCsPwCoHNxHKrBT88/Xgnz0yUhq2QXWjs0BUdn1V3HY/Sbnh9+4zX18+oteDhwVn8/fjM7L93fN3v5fhNB3qb5fm3Snr/rwzo/viNvUceQnApyv3nuAER3qcWOeaLjUpwf4AOmbwdUIO78/xXQPvv/j502CWwB4lW6b2dElTonDcW5bokvIaGkAXaDF0aEJL30PeqCOKbgT+c3ClA57eQdugP8Cz/nTqU8X1H1SzC60crUV7Drm5KE3fCkx2qmyGnyT+8K8e+6tiABzwKgASgEGlgkuL+WrNyGT1K30TVlcqNve7jPRIRT1L2ESnAcLtefMa6Y7j+jQeeiQjPK6SEJAJk3J3zCDTjDy1uAyyIV+odrY9RI50yrOP6jDGyYR+rzsomMXQEY4qZqHjf1+8FESdlUEeYjHb4fRTZZZqbfz6zQQrjNQshIrmGsJmhluLX7TC25DPVQ/Z0rNURXPgeXplan08C3yL4a3u1ZDbbX+Oevyehjfsld9/TKSsLrxy/9XSNIK8YlqUke3qUnWviUHb7tRiCU+M0GWBnCnRAgub6sJ0IOA4RzGt0eLBn0iawuY+MR39c5/Fe4998bZ+8AaYo1kgEBhI2OBtDatfmZ7F5Tccs/bCOpTwv1flE2Md+PK+IAsD63Ce0FYRE+L2WG9bTI7ZbgVqGIKCbwCSQoKgsEMRRqfE5YqDWvUqk099EbAIkkRStvWNdV7TaUFrD8/OzJ0iYn3msOfXPOD5nyi52ZiBZ1/UwXl9f8eWFxjUm7lgpbg/t6BRlqo/39N8D/b+h49vw2bdi/9jQvvXzIjJZmgMWJeR51IQfsOAB7rOhLPfrwfzu/f/K14/BYpxTa5Qv3S4FtXUspxPO5zMu280/PR/qJUasecGHH/47xOUN20YrVAGQ4jJ7jzEl5Nog1mE97ddLCxW+BGjCXh/Zz6+oyr4jbIeTZ7EYKOrR6yDW7IGbEHUg3KbJszKKcwzRF5KvDLUCqmlWaGbA6+srhre2LJwQuG0V+cSHsWwF2/WGHBOKcZRskOQmfJfirvUeo7//3p4YUHlrzZW/9G4Dp0FHQgrDzpYb0tFlrrc2rT7H77EaptBPCIBUBvI2erDgiCPbRuLJiM2RP5Eh5kTxnlIKrCviKbpi29Fj213Q/I95UE8xw7KhdZfxdWlciQEhJcQuM0nLaaFMMIhupJSw5oxeK/SYzGBXFxzPQGuKnOKcthhs5a79YAbFChL4Zkq+own+J4XoqnuurWC4uzYSuMkTkbLpSHdMHPdkwJnnx2LZds4Oz4+xSdWtjuFVtK8l8eq0N/12TWH3AWcXcnkfMByHOSQFM+ma8/K7tbSqOhRv5G+USje6G3kLCdGTsjrgyYkuEH0bBlJ+Xay7CJQAQVB6myNlXFPM/FPMiDEDBrTScX78Ef2P/xUpLzidPmKxz3j8+Aeg/Yzn54+IKQP4gsfHnyZxkwkTcDqdD2iFI1uNY4zhMKsuEmFBUFGR8woJV5RW8fxMC+PeKwV1QgSswdoBgTm0XxTiwk9k0KsqPn/+Ba+vr2i94+HhyYmV6te1o9eGGA4ogDG5/p6O33ig/9cdX1f0+zGCwLqurh+uFLPwnWHq3OuwrTxuXsC3t66vA/uv9ulxH+Tvz0tIBmsV54eM87qilXJXbcYU8PHjE/7j//of8Y//+N9BaoW0jZVVDiiXz3PzEJfVJAu8QMSrIqvYdfa7F/rJA9MCwtxtVmwpZaga52Lzgu36wmw9xgnFj6pn9FslhjnOl3zSAYD3hYGnpwd8fvmCXliBrcuC88MDbuUK7ea9YSAtmZVpq9Pchb24eheAo2uHD4EjszGTTcEMusDxwV7XFUNwhNoKC9KSvfrvGFKavTcEU2wuKwszWFcECKx5taQMECElbLebw6sRp/MDWmsIOQGikC5356qOGkTX9C6lIKWEnNKU3B3jQCMx4GreHQHHMQVADsGz9eHahRmM5jig9zbHs5Bkv3fjGQB2y9xxDK/xIBHNvcGDBH8tImLjNUsp/r4RFvlZB3firl0lY0xNZtwaz9yubMakk/dNZyU+npvjMzRbHN94Pr/R0cHgx7Ay5POxB8o9qRhrd0f3RoowIGAvbAcYBCbG6KMOoZcDGe60Xx3tH44Lhrv10XtHOj8gxYhbq9huHR0NT08fkZcFtVfg7eLtoX28dbxOztkNdFjMnM9njMSbvICMJa/O3VggktizzwK1DSFUQCp6v+L19ResP/6I17dfgNjoctgvuNz6bIWNGfem677/hAQgQF27As6H8jlFX9sbSlLUusHM8He/+z1/x29hgAAhoBpwHHceCXZrfa5TMeDl5QWX6wZDRM5nykX7vSPO06FWcL1WFkNRfM39+3jdv61jwGvjYTpktf7tv/KrewZO57rxEIe5SQ4y2shIgRHEv94llBTbb24s89zGe38j6B/7aZT2FNyuFzw9PWNZFlZuEiEYcCEZ1qclYys3WDUkSeiB5JU1rNgEiIHEvggStPpUguL7pRQpWdvVDXBIsqqlImav/KEwe4aI4HR64DWL/L0vvVPxynvyqobTeeX1gxvdQF2lzMdvIK6Cdp9QAdwQtVcE8X4dFE07bmVzSdcN1w5cYkZeMoI46Wx4jIugW4M0HEbUlKOC6wm11bsgtiwLckx7wMG+mY8gOyDWZAla91lhBsuCUrYDXM6qorMURTCbyQ3Pze6Sod0QiPdlCr1EtilCCMgeVAeUOyrYAQXD+/nDNAgB6FvH1jbURvGd8/kMSqXaRD0eHx/vgkprDUte5ucYfXl51ws/9pR1uogFv/5xfwRl3zS1g7at3jJQGKLdvybAhGpd81fP2kAg6E2/J+jgJ/8q2I/rFI6oxEEnm/uGw92Bya1qd+MVImIIe0IVRGaiwfuwbzW+8hypiRjVuxqZ56P3rHIfnMYYnTabCJ46+VeN6z76vZIYp/plN6CZTt7MeHZNK3IKDGG9M/UI4nPze7vllBe0XqkcGcRNdCJqbc5EEDoHyorn8xNKFeQ1MBkIZzydH/E5PSCmE1JcsayCGFe4szIsMPlKcXFJfl5jE4VAEBPbLqNNMXwuzFi9U81O8fz0QFdCl0y2rlAZ0ww4rBuuvxBGu6ujlYqXlxdsW3F/Bq7L5joETHL6vCZt23gfVSc573s5ftOBfvbQgJk2z2ABzD7V+98Rwdwk+as73DYOGeQg7Jnjsc87qoy5Cc5236+nFsfN8bhA7xarVydkmnJBrusKkeCw0uhJwwlbwGmlYUlTajyrCboKis9G00kKtNFUQNF89ngQxfgZ1Uk/IXAsq5RC2R3lSBFAhGGMtYQYEUPEGC2iAt0+pxwjkyRTCqmEuKMjIdCe1AAKXBw0x+GV00AjValoVSrJceV2w0U6TjHjdD4jO6N/VN4igPioTqnlQJRThCiIFn2TlwkFE77s3qfbH/KBAsxbm+IddM7ZYps/JwKvSBfC7I2V/2gN8f7zugfvk3JdkBMxkpKcM7aynztNUmRfH3PtjD56mB7r6q/TdEc4ZhU/KiFTiPu+7+c1ZonNeRxH5nqHgGNiE/j2+zSCIXxNhhCd/DZ6+bwnMUW0tnuym5lv9pg/I7In4OPfTAJdCCcEWG9ove1wvdpM+AeC5A+/n4+4rCkf1CO+xweY/5Xxd78e5km76WCv/AofYAIKjh4dUIDx/V8rAMakyhBsugv+Ro6OOh8jBMzX3kV2ZFazrFLhcre8poaOYTE9eCmjYm21TRTGAJ+yiUDgeGC3jpzoLkjXx4pWN+pTlJtPw2wcZey0hna2BT/ruCcw74f4HqrBic70tfC7T+Z9SPxMvj4eTutsawDgugoDEzmSlxnAR9LTW6c73dsb21PLihgH96UhJm8vjTVkAWptb//pt+70b/f4jQf6Q2g9wt6/Aqdj/LT4zxwCPXuw3nNnen63iO6TA27W4+s2ov1fedtjUB//fR/sJzrhf27XK1IaYj4ut+rBQcCHMZhM68w2e4yslm6lTG9pMwaA1Gnd2oYs7qEKmqYSXtFs2w0Zh03EgNvtCpGAUqoHFwaLWulP3t0ydATzrszQg0vq9trmjGqMcY5EDZb52MBkvznzIb5hc6nUiiJk5TY9jrXtLHcBJT65SQ7SZfBZdBem8VtNAuEuhNM7zUFYaXTvqcoMsKO/b6DdaYis4GlbSvW8nLixwAza+BoWw10AOK4DYECPPLeUaG5ixkqS9sGY531cS3dBBZQGDq2xmpN9hDTEMDdjTjYyMRoqfANVMGNbhgHyoKbmFWkQIgizWp4V/0g8aPE7qe4epJbTyT3qK/3mzTw5OYx/CdffOJ9B3hwQOnXp+btj7fiDM5+L8YTeoQ8QuATB3C/26zZD0574CVfgkLcVH2vbk/8dstgD/dcJPM9LMHzX53OO4+/bDPA6n1dHbQLXsIlzRlznPYQA0Z14pzbW/fFz+6hp4rMx3N5Eds4IgPl+t9vVlUAp3tR68XV4hYABstQNpmz3mCnleMXcgId21MfWQdSAMdse/H+8z2He50GUFIDEW09EYIaTT7TYbIscrv0siphczQkZpTcDpW4vFFby+9pbw3BOVOeR8J2ZwO98GXxXx2860PM43pH3nfjD7OvM+EGRhsPviRCqJrTqJB6HI21YxuEY5Pe+IQ6EvK/OzN6vlh2GPG7Yd9/nCQJQvL2+4ne//9Edsca4GQU9GMDIoBeHI1U3SEgIoSMmxe220ZpUMeVVOwym7JWNR8x3nkmoGrO5Md5wOp1wu129UgqofWO1edsQYsTpfIIp6AgWIzA3rEGK87nyLrPvWUoB1NBqxbYVhJz5ng6F0hNDvXcY0Mw4vnNtqL//iVV5cPWvGGaSRfjVZ4s9gBAN2QMYjTd2wp0q3QGjyAy0ah0IAdlH4ZQlECttVVSvKEeFpACNRIbLxggixvUx1+OhurZJTCRHoHiL4KjeJ4D3wAU5jimIA/L0buGRgNbQakdaTjN5ba3OeWbA0epOApjEPBOBY0tgVI3H4ytp49HyEnjQENdLGFwDd/+LfK2np2cGnEatB66pMeEyqulhMnKfxMxq3VGgu9HHw/QADnCuh45xsRyF+OqhO7wXLYdJPgXM2mxLiJ/LDqocxuhmohLRWpmjjOO8x+z/3RSHB/tRgZII6MRgTyL580wgxTUIYCMFju9Pn4iJCzl13ScXgsicthj3capAuphTjIKuhQFVKLhT6wYg0FpWgBAyQlyR8AVq9NZImSRfYE+Cd/5Ngxn5JhLur5VqgyBO1IRTKGw/1OZjoGo4rQvatrkAEZg4+/uAp48xJjiT8NZd6vYFrRQsDyf34hjjoDbXDtsUCdV9FQayqN9ZpP9tB/pgf7WK5vEOqAvDxoEPDXvFtKZkIJ3+Tl6ZHDe/ACAiJcHr6ytEjB7TkWYy2hx+nsz9YxU/yD+HeW+nDocgQK/oWrDkhMvlik+fPmE9n2GIXpmNj1MBRFhSjrw0Q14+whZFTicspzNMDL1VPK6UEZ09YRFkBPRoc3OZm4nyM0+rVVUInnHyIBQROPbyUrCEE1rYEHNglWaK8zng7Q3Un7fu+uYrtm54EGfx8g4g5zN660AHYlTqePs1DjCgK2oIuNbm7QtOASznM9bHB/x9/EdkAx6WFeu6IkhEWtKshkUEvTYsy+oM/937vLsyV3e9+t7YglBQ1rTPABRRQNUt1d2Z7nq9TkGbEPLkdBBICIjCfrq2jhwjVdswzEMAa903+YAOoGrHp5cvd4S6KZwiAcmh7NIb0ANO5wUohdoHMcHUULViaxtKo9tezPEOTpcYYA24vt28jUPFtGlE5NeX24FgiQmivAaWhvANVQVLKfvrelDKS56BjRtph4Q+nQJ7V6QU8fLlM3rdEGQnIIbALSg6RwZQPkdL9lE+YBDdYoywVgFtCNIBqwigFK7jb+wtD8MYBDK7PX++IxOGneC1S8VSW6Bvxav5XWiotTbTBhGdz8uwpR3PNHv0eX7ukYwHcQOaAwrTjc8hVCA2RhIBU38nStYxeGlAbh1iFbDivX8AErxvb/SKsABFpKeDAjFmBBE8nE7QVrF5GzKlAAkdahFBFrfVLYBFJ+A2mDSs6wNyUlcBTHg4nbGlBTkZsAAhZoS03nFtxjUbeWjMJ0dLEoLQsCYd5KZHY+T18oaHhyegbLD0Bl0CHrPhtSqkZIgaOhqKVWQ5zfE4NYp0sU3Wcbte8OnTF7y+XYG4YF1WJi5tA3n5kbLivrc379Vrb3h7e/WJEXxXx2870AN7i+34hXGwJPevjZnTWZ/PnmWIEetyZvW6bfMhBvae4xGaBOAQ8Jgp5hePff77an53YptnKQIWwIbuUHaKEdu2odaG0+mMv//7v8fb63Vm/2aKABdMefeCa17w8uULniAImSzn1R3sKJLDyrO3DpmsXrYDonBELoTgQjq8qL1X1EodeQhHwa7XV/z4wz/gcvsFvdXZ1zej7e/tdsO2lUlu7LVBDEghIseES70imFKBqwcU5fdzSigxolX2zqKlOYMLEaQY8fBwnmNnqG2OP5kAthlq31X3emvIMWFzk4vmP3s6nVBq4ahSI6kuC69HM0MfxDJz0SAY9NC2CSEgeVVhntih0xec1yhwOqLvOgfAQCkcjnZp2tobts6ROw1MLAWs5s/nMzXAzaC9odU2iWcDdSEC4dK1DvkGh77XvEydfG30BTejeZDJPgY3KmFRQXCDEZXh7DeqwAl4z+r/uMbVJw+yb94KIMfE0TAI6nZDRECrFb22OV7GoOnQtg0WNNG1HBNFVgSOmu0jZ2OuupQy2xMjIUdwDwChXkLXBoRMPfgQJpIAGEx3YiTbWLuSH2Towvu4IAzoDTZIlkEouBICe8I2CHSGaJ33wpEt6wqkMGFljkF6sAc1H5ojawZAxfvoIkCKMDRkl1eudfO2WJpcDjPDJtxnBrl0jIy21g6TQ7xfgxjanD+yLAseHx/xyy+XSQgkIbRhfUw0xUoLeoioRoPqTQ1NBVEUycW3elfqZ0QiSYYduSR6UPgJU4ZWuSNAKwyLdcRGJ8TWKtanE1IM0OpulmDKFy3wes+1KTNR3raKt7cLLpc3dG04nU++3+6cE1OOW3av/ikG1nC53r6Bwn4fx28/0GMPvv6vd0Fwh+9EZl2FATXBbEKkOVP8AZ5Z074WDi2O92E2Tzekw2YJmUH/m6D8PMl7Yt9YrNz0Im63CpGIjx+e0bruEWJ+Os+aLbgACL94WjNe3n5BWASLrthqQfcRrdr6/D2tQERHSlSDggIhh7sNfBK3EpByAv3jA3Ki2UXOC9hCE3fBi1AzD8xhQtAcl2kQ79HGGCHef2OrxKtAz/5d/wwdmA8ug1+b/bNW67SHrfA5Z1fVGtyACX8ae+illPnvOS536N2PZGJs1ABcddCTqlH5QSAp7r094WeJS4b4veyquN42yhSbCx8JYA5dIpD53Y2BVMAkiGZE3bkKcj8vb5ith94rujaottliSjGi+mcYQkUwOnqZkngXA7DkiCiRo4pOjIohkMxoY6bbZiurtea8AldCBOFhOMrQe0fMCcGc0OUogti+toM/WwAQBaDeniEGrp/hkz76rSMRErrnwLpBRLG7nJlPawSU0mDdZh4yquYBDwcZHXulOuCYTPC7GQITSPPg1Z3MGQ/J+3jAhthLHwmPtyYObX5P+lhAyGEfGJMVx71oXOc4eCTYVTDhfIUBRzeH8ieZcLYNdrh6oE4sSvbJDLYwhqztIKsGpGljbfP5VFNPAtwtT4HTaUVvhiALotvHpkQUM5/E22uC07LOpCJEyu8OZcjRwWJOSRfH1kn2007pXvPkOcSA2/WK3hseHs8+qsfkLvjrwGQG7CHdbGaAUrb7ertiKxtidHb/2GN9nZBASy4QzaAqbpc3ko9TREREm/yP7+P4zQf6/XmUd1/3B/Jdn3FEzbsH2SvG0V/E3u7z/x9QPt9v9KFUx+jQ8fu2gwjfPmPMinz2bLkBlY3ypOu6Yj2dcLk61Hr4oDt3lxs7jJX+si54vdzo1+xjKA3OwtdRpY4Nujmbl6I1aqNnPCKawIxVp1l0L3sn2Xm1QjnNfU66tYYhvbnrpkffQPr82REcRyuDe5fAHKIfO+cYwdo12mlB2d0ZT1XRHI6NxF5hLr87Ar06NGrjD3ZinIAJRvQxKhnX2BM/mE2GuQ2Kox16xr6GmnYsWNgLV0zG7+IokDjRyUz8mvN3hqBMkoAlcWRPncg4Rg+P/tgh7KSr4aY37n+MkVWskerUep8bu41EIQQPVk6wE0yvg5jS7HuO65Iif96U98ytFebaHf9j1Q7I0IOXvW011l0cHu2TWGezqodhl+w1hR3WzwhMIewPlLlhCcCNuvvns7F2ZL83XMqeRHpLbcB64z6GMMh0Tr7zzz8wjBFyx7VTP2cmpfsz7cuYhjghuBbHfk4yEkp/bs3X2UxGXDHwSJ4VsK2gbYjp7IS+EfjNds330Z6bQV7VE5/gyRSwJwuY72GmE/1hlQ231QVSBFqhQY6YAkbyqfXGxMf4eyIrJvlSD60SVSJiPqZpEtHBqQBVI5dA90kmCwHF++ePDyeEsCeCJq4L5Rs036PvSX2tuFwvRAR6x7IkJpQHVEoADGvq4BwF7TTBUlUsYeFzWneHw+/h+G0H+l8J8nc/cgjo7+Hu42/P2Vk5VuXhbmMfwYkQVZwP3vG9jozQ+W6jX/+u0BdgboxQWp2u64plXVDb6G0OosuYOMX+oPvrqDac1owtCZblEevCLJxx11w4pkO17Qzcbgz0oLJH8z4534uvW1sHRLFtVw9yhlKvUGtQNGrrO/lo27aJmKiqj+pwm6y1oGysNEIK7vLl0wPBk5xSCMk6tMyxo77fXQ/a/LcHfgzDF0r6amcQNGHAbQ6Tino2r0bWre3/Hpr3y7KQ8uGbjnQDJ32Ck+wchvXqbIw/amtQr2TEPFCHAChtUTEIWEof7SaNbQPfSJIErCmj4EYBER8rhLJyhZiT7NSJlyRWtdYQ84I5Y+CVqWlHKQ2PTyNYjYptH4kcErY76e7AzJ+BmgnUSJaCAlk8oevGayWB/w33kqv8fU+S9oJ7Plv0EGdtnFXnZq1ufNOaIqYh/Wscu/IkuraGWjcf1aTLIZ8159AY+9oIMkWIhD22vbL0eEvERfeRSlUEo+7BqDBlBAlhK0cMbonnrYauEPU/IUC0Ixju94ZAciivxbg2rOJJxvNJj7FO1BNOBWWCXR65N65V9aQ7Z+4NM2LDR2P9egJAdmKgTT4Qk61t2+b9UlWHy7mOW+1obWgFbKi3C0wbWttQCu28X98+4ZQTSttIgnWP+RCi61pgomuIdMmUkCAx0/AmRuepBsTgQlCt4vzwAZ9/eYGa4uOJ4jYdhuTPbIehCpAOI5VjkoRSty+4bRtEgJwjYB1m0ZPInURaSgOEctHWOW6cUkZMCQJ4K+X7OX7TgV58XONbYPmYxz4eM8PG/YYkthOJOIYkh9Gdg0oS948Z/I8s1mOf/tgP8187EH4cJ/B/a99VN3Je8fz8ESEE3Aqd40gCijOXGfAlP/CY/1Y8PKz4l/YFrV+xSkCIzH7XZUGrHd0oJiESKGIhxgwdgiVHWL/NB39UY+sqIAlqRZCAJZ8RZEVMJwRZoE1Qtz6Vz06nEytkkTlCJyLQ1tBqhQrns6t2JPMNyx+orVWMSiuEgNY7CTZdvTplELt6H22rBQHAZbsh1wbIGVsrKJXM4Vo6Yspk7HqFwBFBVi3HUbacM7RUlFKpfhedjJji/N1xvxeQrFa6onaFloZ45n3otaGVinVd2ZcdsrSjOu0dCYHwri+maymIZUM1BQKd7dhrDk4SVVrXthtUFc/Pz0AMkOzmOkKZ3r4JamfSFlKCmiCmBZCA1gpqr3hYVyxpof87DEF8Lt0h6WYKMZsVP+FwTyIGIpAzegj84wJISNH96LH3jtcFURNK2XC9XvhcxATEBPGxQ/OeusToCZFCIVjXBSEnWAtoFRDlDL/EMAO1mSE2Q2yOtBgQxHkIa0T09o61xtlspXMgWgVaY4LZFBrIBaitojo/pUJnABxIFHqDpIiqHbdWESfCpmgubAPl68Ra0Hqds/6mwtzAzWFqqSNlhwpFgDYPTmaGSyH7u9YC1Y5N2S8PqaK7E2F30aOt3DCY+2IBvVZyRzy5HWt39OvVq/yhJDnaCWMELvraJyIGhOUHvL4UhOUj0nJCSAvSKQMW8fjxd4i3V6g1nB+ePeHduRTcT7i3ppQQojjBMsxRVnj5EsSgYsgRMCW6d344QxtJhQPV5KghoE1RjfoaaiRwfv78GW9vb2x9ZSpIai9Y8oOjHmO/ThDpVMhsFa1RTCotqydFHV8Bwb/x4zcd6I/HANj/9jF+6vAb4ipNIph8Ohu9NZkPPQywu+kmuQuOx5Gb/b3enxWD/ezReWX785//gt/97nd8wF25LaXBJH/3CjbBQgCcAT+fV/QeEXBClDMEitOJ8pp5MaREyL71DTlHh+6Iz+U14Vba9ODm5tSxnB9weX2FWUGIGTEDkI4QFb3fEOOCvAQsOUMAnE8nlG2bJjeDgcsGPash7YAlisqYKIammkQX0PANenixxxgApb41TFBKwfl8xpt2bNcryrahlY7Hx0e8vb6g9YaUMysgGF6vb4T7jbB3CIHJT++orcFAMlKUiG0r6L35mFLDuVx3ZTwQAlwuX6auPcGQjlsgxFxK4fu3G6CUCh6jbaOdcH58oFqfi3JcLhc83a4Qhx6rcxA+dk4LtFZRakVthXc7BNz891NK6K2htk4Z2zbG2WhvzOq/otWC2hrSekI3nud2u6G6wMjpdMJwNROvMntglVcazyeEiFPdNf0B5zdc35AjN9XmY2SlFnyoH1C2G7oyGWit4ab/glo2TjK4JPFT72j+OXsl92C53ChJXTYXZGHPt4nhenlDqawiS1e8ZUOr7MEvKUFNEZ3EyRE81w3w+947k8eYiHA8tg+oPoERnHswyYbOuYAZWhsJYvXKl0nSksc9JoehNcVzb7Q3nkJNgpyor14b74eEgBiAmxlKLdhuV1bF2tESveq3zQN9ueGH3/2B57yN688xwHU5o7WKbbvyWY6ZKpp+n5mwr3MNhhAQU8TpdJqFR84LTqdH/PLLz0hp4bhnuUG74uG84PXzL0g5Qe2GXhvW0xnb9Q3aLxArEOvoujFpd5vZo0uegQRPaAC6IC0L0MnfEEf9YB1LDFhicJttwfPHJyYPjesRXruFLqjWUZt6Rd7x9vILXl4o9306n5Fzwu36SuRN+JyPkd9d0nmM1DWcH04QBJSNz3Bt/w7d/5s7xIks3kK8+85XgXb08+axjz7tUOb4WfYCd5ep+z7t+xnfu3cecP27g1/rE9IcuuYPDw87jO/HrnY11KPMz/XeIMRMcVoXdN1Q2gWtJZh1bHXDkhbv3Qb2YlEOvzcuj9tNHtS2vMhCCBFLPiFIhDbKmwKRYzUhQSRyvr6Wea3nzPNhNhsGpJh3eBJugWojvHAUqmOM05EZrF3Jhu8c2yulIC/ZDV/KvHtpSZDEKYouzPJbJwDXXQqBLURDNVZh1QlMWgqWdUWxhtorksv1Ru0ovc3KX1VhKaEYuQADMv2yXYlqWEO3jgURJtzcNm207Y0RTRugDVeXpFXl2NzJGOharRM+/T/9X/7P+A//4T/gn//4L7heb1gWtj5MSFbcWsO1FPRmgCTcanOSEdCLIOQTWucER2/sq/78+TMeHxXaq1d4PtLXoosGURRJTbH2FZdWiLSYQQtQQHKjehUFAOuyYlW2Xqqfe+8d11pwKxsA4Pm0wqC4lOI2udQgaLUhreQrtDZ6reo3qqFoQ/Hr3FvHUgu23igMJQFb77gUXrcQAhoWqCmyL/DmXAYB5Z9NA3qrbInB0K0jKe95b837290navZetwJkEgagdqCat1gswFJEVYoAhRDJovcJjuZ8EbOAnGjQpCLo4hoWIaKLoWrH1plo96boZqjGdWowNJjbyA7Fu6FyOJ43zH0p0oJwrtchdcwJk3f7kNmcqY8hoRQaylAXlwI9a1pRbzc8ffwBMZBXFCBERnwKaMyiM/MVaD9ojghbXoiR7RNxLgtk7tuAJ+Exoit9GSQKnj88TffJsVmZAdrDtFruvWO7XfHp08+4bW+IeYUJd/gYEyQGDGnssS8R2SC6GxLlosf+Poqz7419/10E+nlTRlzfI/W7/74P+w7fYyfm3Af7nRA2xpyPZi3AfbC/O5d3xzGAz9+Fk8paw9Pj093rwvSgEDbQ/Z2EI+b8fV+geckwdJpC1I0JROODEdXJQ4FGIgPiHJt1K21nStu+cQzIfAiajP5z6817cQHWFTknXC6X+aCMB2pos4/XjUOAZrKB/bqoQJRV/tAlH4psQ8QHwA63g8mNqqKD1ykuCUiU+jWxqQ4oQZyl32A+LTFGDnujIEq3jmVdZ/949E7Hehk+4mpUAEuRBMXqCcD5fEbKiSiBO4KFofwlDp+KK/AZ3PPeTXF8IbATQ5hcBLjebvMzdOswSZz3F0FVRWkkUnYFJFJ4ZNiwdgwoHkySAGQRVGXwUVWXOGWLQEHrU9VdJnWQLW2c8zAK8b77IFqt68mTD752AElTzTiGZma4lgIzRUgZJmGSsXieQFPz0UaukyTUcW9QdHGCmcD5GzLX/TFhJbGLa2JZBmXVN2w5tOqA+T1A5nob97l1RcZQkvTX9MQTXvHrWLvedho/J+6jPvgB3YZpiyvcaffP4etX0kzd1QwRYeep+GeCtwq5B+3z+3NjmLwdfmmw+MezPVC1e3LiTtAdBNqx9gcbX11IJyWy6WNK1LgwQ0yZc/PiI6EiCCHBtPne4aOoIo6MAjCBGOV8d0nq+wAcVHHdbii1IuSAp4eH+byPfU4714v5tail4Hq54PX1Fb035NOZBZSqFxYjUd+he/4ez0ECBam6E1ghh0mc7+j4zQf6UZ17HPQW/F6z7/xZ/ovBff99mZKMMk1HRA7B1UhmGgvyGNi/9feRdd6d4+ELR7h/bKyzp7SVmWkDQ6nu+EKCoeNJ5jNm5p5TBoSBvNU+e7tmQPJADwEDI8w3Ay7s7q5W2veRHu0KTQ2lsmqWYGidwbhst5kI1VrczW5X3uJnZgIgc1RpZ9CPYMLeXGBA044U8yGpYHCOEmcboOhuGwpgZxuL9+18czW1SbSCkLhUysZNSsDZ5N7QO9W3oI6kGOFa9fM/erGPzWiJiWSt2lA7k4XH0wkpZ2jraLUCSjW4FCLCoApbJ9muc+cbpL/Bgo8hMBBGVin/1//lf0FIZNPnnGYwqL275n9HTFwfackIJUF0FxdSLiAS+EDm85IzJUbVhVxCgKSAmNNUWzRz9rMQcQk+mgUIlkSBIFFwikEMa0wIKUKa+4kDCIkCRBIjWq348vaKZcn4+PSRRDGfmbcQYGH3WO9GRGFwBrqRshZjQESG+Cgn5KBpECJUfGJDDWLmmgYGaJ+VYxLWnt0T++Ajs1EomKWHTWG4xQ1zFFVFjtnvI+a4JWzn38A1/wcqspP5/Ic9+I8123pHnvvFCMIRQGcypbsnhIBJdbM+k2Qm/CSh4oAeDo7HKFD2PUc9aQgTdRnH8KoAgGVZJ58EEMgQhQocK4UpQs7kgYAIhXgLkTK5niSYb8YGYGgEDIb7tu1WvMZ0VkSASoOt0gpO64qn04mCRA7HMbkHihqin+PtdsPr2wsutytCDNwHbZAVs0+zEN0DDMH3LU4eBS/u+J7aSHD890D/b+ywsY7ew/WHYHnMevHVzwJDanVuHK4tL7LD682FSUaFOV/qXW9++Kvvgf2AJByCPUfPEl5f36Cq+PD8DG0VZp1JRqAcLD2Rw+F12Mfu6GSOi5DM0xVP5wWnnHA+nbGeTtjKhqc1Ac03VKGISDaFSnVJTJ+9VZfRFfqrD9i9VaA7aa42Qt1AQGsXAIquQO0FgrTPb8NgVlHKFSKPoGzrPjIVo29Q/llEFphsqL0ixDwRC7UNKQ62cIREJjKX2wU/tJ+A7oxoK9hKw9vbmyMD4k5yQKkeuLpxFhhAKRXL+QQVoOmOmhh0XF4M97DulrM2CJNqKFpRGiFkFcFyXqFiKK1OAqfBcGsFkiMnA4TX+VZvkELkQRDRW0eOC2LIUFF0/7kOw59++Rk//fR7rOdHSFrw+vaC7XLFY2c1EwOZzO1QsQAKMYW1jhgeuVkPQqCxxUWkp6ApuQg5J6wnWph2FUQkBDF0JUKgAlgUIEZIjuilw5LQmrYZwpr9HoUpxBNFDkEGbv0bmXBYh4ATBbUrpCvSIP+NP0aaLcwnKYTaEjKqMtuJtApB82DBhweoxmDZzY1SzNEGi14xw/UpDE2PGvDC5FRcN8CT8NFO2WfCd435ETCPBkDDrpgzagJxHQE44oEo2EpHXDLHBTuTTVUgJtowqyMHvRk4PCJzFK27LHeIAgkNkAoFDWhM3AY5gITcPhAl8zFXgQkTO9NKFrwa6laQAt/HagdcPbFsb25aU6nh0Apy+YTr6w0af4Fn2HjbBGs+EdH3fTLFAE76LJTBjQYLHXExQCvbhRAY2AIMFnBez7AksNzx/BDRCyBoTnYUiAa0eqOIT3vDdvmMXz5/wuut4g9/+MPeHjSjKqYIQk7QWlALlfE4vpwdIeFzE8BRwd53ntL3dPymA/1fO36tR/7+OEJadwQyjCC+M66/Tbj766/7fvwOIAP1crnMvx9ZqpAwUYpvfY4jNvH+88ZEGc0QBI+PD6i9wIIixgyq2hvWGNHa44S+B6owYPbW6uxJx5iA0vCwUsLy5fKGJSdAE3J6YACpZEnf/nSbvwcA21awLs9IkRVBrQUxJn/d4OM9A54P7n7X7tix23bFujww6w6s7Mw6btuFE1R5IYmvNoqnGPtyEGqhX68XrOuJWX42dG3sv6ogScKaVnS/n2VjzzzFNHXJoUR8grccYqTgTAwROWZEiVjXBb3bDGZDQrjdbogQJPdqV1XkkHFeTvzsUEiMKL0hnTOQBf12Rb1VmAAffviImANeX1/JJI6C9bzg6cOjW/X6VEMBQhJIBMRxYTFBXiK/noBSqDaIQAEkQwZgE8FKaUEIFUB1QpiRrCWs7LtXRxDB4iYjqJ7YTASMWhRLPkEEeH54xJtRQY2Wr0RDkrjQUxAsy4rz+Uz2c+8IgXBxzhkxOZJhfebsDrABcEhaqVfQA010lkg9gEXouUBPiwCLfLbzgaQ3RGxyzl619/l0jb1gPB+Lk/h2/fpDm88RnjjO0RqG6NHk8ngRMcZjRWhS1EpFSAYJhpTJVxk67WM0jc9Kw+ZqcWLAEhOqdmi3OYVyWlbUSu5FCCOAsZIvpcxe9/gMtXGqJziyE2LYxaT8aksQmChaBXJ+BLTBUPGQf4+6rng6rfM1UhYHisKcKshucb2uJ94Hr+C1N6IrXkSM9sQQbooiWPOCJZ9R3ipqUx/hpNImK/aGT7/8gj//5c94ff2Ch4czycu1OlI4sFwy8lOK0B7w9nZBKQWPj8/Ok6EfR4wRt5th2y4+efV9hcbv69OMg/i9kz2OMP6vB2h5lxQMBr4h3D34s6//jQB++G3/o3OTnONdvnEOksxQDeNrfD3T8dVrH9GKw/e5aZ0RZIFphIXEMREIRU9CoDJZ74CRJBWcdNPM3doCSXgCbvYSCLl1tzvVXnE6rdiuF6iSvIRgyEtGV0PKC1Itvqku7z53wMP5Ea+vn9BaxePDE0QCtlrmaN7pRK/4Ujc4xoa8JLRefF5fsJ4WrOvihhtEode04vnxEb0xoSj1Bk0Jp/MzUkhzgwzxhGCCx/MjaqqEY4OgbAUfnj6gurPeSH7WdfVNvk9ikxnwcH6Crhx/MlOclhUxRQoKtY4YE87Pq4/tNXRJsKBYloSH5YRoAS0QvVGrWCCUpg8JKfu0hwLYGlI3nENETBEpJpxCmApfAkK1pxCBnKCBtKjWFKcQYSFiFUGLJC+d1hXrsqAI0GKYiVcOEQ+nE5JwrFEydQAWiYgpowvf+6cPP+Dt8oaiwJIED3FBbIaQEmxZkMZ4qifLQ551PE8AoeEYE6CGTTYEBCwp87xjInHRgBwiNERYjN5HDoghYskLlfGCkCfigYbIXkROEWnJbpwjnPYQfKX6OM6Hxju8jgKjD0Iln0NsiCrttrdBwkzmkv8xoe3qEKMZgcMsARJQXTMhJs5zd5dOPp9OEGC2o0TA52BZ2QYCuTgpRnz88AGlbHh9e0MtBeiG9bwihIQ6rJiFz6K1cnByDFjXM7ZynZ8/5+xTEg1rdgElb72d1gVaKVUbQwJsRVdx0awKxQ3mf2pTpLhAwXG949hxCAHSK5UxAz0AiMa4WqM4ugKwuDFBiBm36w0CKu2tKeNS3si5UfPWGEWDL29XvLy+4Ha7AmY4rSdvsaq3uXjOW70hyOL7nSBC8MunT3i73FwNlQgOnS7N9T8CR1O/o+O7C/QD+oUN0sshUIr/3/svgb3i7EzvGAOmYpazM/cMXbx3yxfZlfHuNzRC2PIVGWaMvUTfwIYUo9nh/LD/7LdQCbl7n/0zLgulK2/bDWt44EwvHJYGA3kOJFLdnafDV6qEPkMcJDJgXVfkGFEKK95lWbFdLjSKcHLc6Xwm/AkgxIQQyPIdIzz0lt8DizmbOATajpZy2Byc0BSjbwSG2TMMwiTktJ5QC4N/congNS+oRvGaEQhSiFhSopBNqIghUStbAlTcaMQriRQSLJApz9ssSK7oNjaPYcaRhtiHcWY8xQjODbD3G8Pu4ha8kjxOIuQlT6c/gKImUEVvOnUFojjpL2cE0LUthIAcEnposKgzyC0pAVhdcx5oUbEuK++8Jv+jyHlhoAyC1pPL7BqWRMMQyUAMbGOlmEYnAzHoXMcpJHTpGC5zg2eQYyIPBE667ITnWWX774Nyu8H8Oc0rUohMsj3BjBr8ftBQR0b/DIIcIpr39UfADq49YMZufBo8mxAQYkTEUPojUmNiQNgV8QaKP1p8u18FBaeiJ9/ZkbcYDGa7ZPTgWPgTRoVNJ6oGb5fFyASltwqTDgvOy4kJ6sz84KQ47j9xRwP8e+S8ZOSUPHl39Mp2UpsI13yTnTs0zrN7ohpi8kBf2FkIARIwzyMvGVu9gYhPoDERGlKOUA1ojee9LIuvd/HCAD4O3OdeKTISHniQ3/fJPQHc26ewjNt2QXBJ3SBCyD6M2X43HNOG6+UVt9sNakDKi6sR+ibq+4sqk6qQhqnV7lBnZXcZ5KilIqaEtCxf7a/fw/HdBfpx2Pz/X4fvB4PY/+HEpzw12ln97lWAGWawB47wPN/rfnHw70e7SmBnjtOgZJeiHBxie5cwTHWp8bm+Cvw2/yxLxNvLhiIB6/nsc+I81M8orvlQQfi5KfuAhCd90wmBhJaUnZlM6C8GKgKu64KiG8p2c+17nWpvIcSZGQeXch3kor2S6giBSdWEUydrHyR2GZm1Q1mTBEYGvbwsWNqCCCYSJGXR8S26aEcQ36BVUSX4vzOSk60EoALdXZXsw36B2b/qrlYWA1XsgnDELYKbnnilF0NACiR3DZhyVLYCd0AL7APH4DPaPioWJEKseVuXcszZPejFEwYG1IweuS5DYlJEvwEX+THyIPZAr1BNMBg1yvOC2AJS5L3tqlhz9s8vCIGQcE7LJFoR1qZTWxjX1yH5nPwchXIJphQDEvMgGSINc0BCHLki1PZPOXjg8mtv7H+vCyWF4ZW/eqKYXGu9e3BlB8D16lXmoFcA1RVzjJTo9XXAAoAP3L5G7iH1ERgZ+ACJAb3phPhJDDOfgw9+74YsMPknMbD37IsWOSeaKZUrmgxkURBD8lbgIKneow1j/xh7xjGxgVUn3nVOwzmKGSWgHgsEGdMEJOhmDFEvomxsKcD5QeLtMT4MTIY2iPRpJzzEf3NeALQZFAMiUsjoXqeMn6Xlr4/tmldf8HmFMAjFHFdVdNy2K1IIOC0LxFweF5gTBwZQ6vbtDaU2SExY3UyptwNH6rh3GAuFIUw0ZJ9TSkygI/kyMSacTmfA7I6s+D0c32Wg/+u9+T2yT1Dfe+QAZkY95FtHcEOG977sG4FXD3+//16MYfYE1eeNZ5bdB2scOzv9iDYc+nzvP9f+d2aw2hrWNeJ2rUg54OHhhNfrF2jrCCnNGfladKp0jZdu3SC177BVjBz1guJ6u1E4pDYEGErd8Pb6ipQTeucs+3h4tm3zzY4B8vPnL4gpo7aOUgtut+t01hriFdxwdulO9UBX6zZZ+CFEGmpEyqJum9vRgrPSUPjoXARkRay+cQeSzUw7Ng8I67rgw9MTP5ftWX4IQIqcAT6sDEeIyA/IOVMDPhKCbfDzbh3LeSECEBpiiGiFwj2qoIZBjIgGLBIhQdDRUbWiakCvhl4B6wFiiaQqkFuhatCI6Ya2LCcnafq4JDpyyLQs9dXXWkMKmZt32BnmKWas+YSAgNYrUkyAdqxpRYoZKRZcrxtijlhTRjNONKiSjWx9d0eLga2KkzvqlVIRukASW1NLXrC5iMpIDkNI7thICVgR7y0LA2M1oFYllO/XVbQh+vUTCVgSK0l1ouQiEQpOGQQDQjec4oJmDV2EmgputjOMgkjUGq2zTAGjQyBNiaOSaIfRr5FIeUtvJAv8HU9y/OeCj1V6vEFO8VCJm7umKZbTCXo1CCpITMW3K3pPFi8X9pfXdcG2lZmsB79+47NN10FHGrZaJmIGCMV8OgMb2x68x4KIWhva0Ok3w5cvnxFjwPVycTW9ipfPX9DNAFACm0kqn18Tl8By06ScVkhgMsvWltLJz/YpHBjQm6B1wdu1IK8JD+d1BmtYn6OsosDbly94efnMUchlmQJBw5J6jIEKMBO6IQGuyt95fHxCXhaiQZ60DqGvfw/039Xh1bizzSNY1SwpTRnXcfRGm1OOo/GSvRfN+daxj9DZVNQSEUqkHnBDZrW7yMU8Q9tV937tM5Atz8314XzC66cbmdEKnE/PiKZU2OoMfAIgL3WH0E0BqZ75b4AFiLE2AgKiOBs9ArV3iCggDRIbEBvMqGh2Xs8MBImb0iAJjeRnwKmj1x0C1bmWZcHr62dEZ9WLGPueXnWXWtGV/IWtbvj0l08IcoK2DWV7ZUKQEnpnddlqxfX25htIxMcff0CrFa+vr0gpotaKt9//nkIb2waY4HK5cKqgVWxlmxvG4+MjgH0kKgRWs+fzA3pvuN6uE51RGG63Gy4X2grnEGcv9Hq9cXTRDEs+gV4AN1yuF96nR/IoSqnT712CoCdF3RrKVhDdlWzLBbfLNts/tTbcbmWutyiBbZZ25abr9zkIEEMDNNC7vm6TLxJSntyEZVlwuVxcda+je8+az0aGdnNLW97L7bphOZ1gxikREaA7QtXVUOu9Etn1ep3PAQDkteLtcj1wIwyldbTG+0D1PkMIHQgRl+sFW934zHQg5Cve3l5IwvLnNi4Ltm2broWSApZlRc731q3jGds2SicPK+Pn5+c7x0MzjjUy+aCcsxnbWCIMnLVVAIrsc+fXK+fBB4Ssqrh4f11VcbkUfPj4iteXz7jdru7yKKiNgkCfv7zgcrl4ha348XLDz7/8gte3V5gBOS0QMZ+cAYIkdGObaYCOZnuCmBx5CQJq59ueDJhxTDYEb7vV5u+xQktGSny2IYqQFYorblvFEgUhrxxlbcoEKIB68RKgUFy2Fzw9PKLUC5OjGABxlDB4MTP4F8uK3hUPy4pldZ7CQSbXTHHdrvjzn/4Zl8tnnB4eWe07kqi9o7S6t1W8oda17vcoenspZ4SYiDR2jvam5AqTvbmexvdz/MYDPauC+172UdnOO96219nvGe37P0fmLpNRG4LAnE7LzFe8lxfAYS2H9+Q4az1brwgBbp6gM1HIefHssXtgO7YA7iv24+d6//dx/sNgpdaK0ynCUGEWkHOAbB0pADGt6KrMgCUg5vN4E8qBSiG8bPRTDzESdTQAIWNJmRuffsHHD7/Dn//4F6zLE3p9Q+tX/Pjjj/hf/29X/PDxJ1fJ48bx+PyEt9snoDAZySl7YKe4xel0wo8//oA//vG/TAiRd0Kh2hBDnvfCAMpzPpw5NxQiQlqxpIyYIrauqMZN+FYqlmUFFPj08gJVmt80U5Ra8aeff8aYZVZ1hn7f2wtMwgyxkuA00Afxdsbny9sujwrg7XbDrVa0RgfC3jvSecWnumG7bWi1u4yr4eWP/5UjeyOA1I61FIhrntdakbYEEcNyXbjxO9FJFXiqN/ITekMQVlGv2ki+6jqroPP57OY+VIdT5Ro+nx7Re0FrlX1NVfz8hYGS44+Udj1fXyn+o+yb1NYRfvkZeVnQapuWnkN6tvl5Auz70ju9ThnYEAJePMibX+NtK/jy9rorlRmv6cuFcq5DLU+EldllBOTW4RosaCmguPyutc7xqF9+vnufAMOyFE9sdEr6qjVspaArDaS02zR7ef+8tcqZdrVhQkNkjAlO9akGQbcKbAWtceab6zngeiu43jaYEuFIOeEvv3wCrAESEdN4jhXXC8dDQ0rTd+HLyxtKoVDVYLMPRKy7emOQNJOTsQ+yjy/++RSI2IWsFLNPbX7vT6czDPysp9OKEBtiNOR0Qg6PaNqRkPDTjw8o1894fHxEzgu22zZbSez709yGErwJMWREMfJ9eoe4MM8gOQoU62Ko/RUhnpBSQG0bWi+IkgGhXv/Lywuutwu1I6Kga0FpRLW6Jzl3+iNd0RyyH8nfDz89ceqhexsnJmjfq/rWqrcYvp/jNx7of61nLXf/lLvvff27x6oz5zRZtfv39mA/qXf2LmHw/+4bxP79Mec8AvMwvOE6v6/kjwjBrO7t16t6Yabh0Fz00TAGBTOgq8HUvZhEHK522NGhPyerOrEoYOidpxgRzDzD7QiRkG8QmSYVw+Jy6PKntLIaVMKBA8ZUH+eb0GjruFwuWJZl1/TH3usNPuc8mAsQVgrr6YyUFrYWgqBphcSAmFdu3hBIrZCYEGNGXCKnBvz+JglIK6tqbWRX55wp/Wm7UqGIIeQMandzKiEEjt1E7/uT6cyqOC4nIChUAjKA9XRCqw2yRmjWvboaOup+z5eTTbg2N8rYkiHuCEJO0HWFBEGrjWYxKU4YeUCR6tdbh0jSunjCFBB1IDTGDbIHSCJkucSIvCTI7EuzZSUxIFjEcPdYvA8qQtnQJXAiASBXgeI7OwwacwJiQOhprt2YF5gnS2YdS1wRcsaSGaDUZ+ol0jsh5jGxwZ5vStQgSB5UAcGyZL8eRp8E5dpGxEzSBQbEAMiQeTa/HxmSItC44QcJWMMJKS+zrTYqBVab1K8IMQFNkZbV4eSEaES+YqDmQ0zwkTCZZMp1PbkQDbCGhPPDI3rdmEg6F2E9nxjMzSBxLziW00qnxuYtG1XEHJlgHI5drtvbOyJT6ni8lk5omgD32EMAMtUnfyZE1ML1aNrcm6Kja0O0BKChthvXGRStFIonaSRPxaV+r9c3fPjwhCyCXiq2UigOhV2ul/SciLZxH0s5YYhwiSh6r7hcLvj0+TOT4bNbQ3eH5IVVP9t31AyACSWPVREj18pogxAp8DFqhSfdbaKoMjOF7+P4TQf6EfvuY6Dc/dtBH/9B+IK2+b2RVZoHE27m9wpuznbxan2f63X6zbtKnP+nZrNa742mKJN4NmEhJgTvE4P9tf51M/tzw4/sA/bWUG4brHUUazsbVWT6tQ/GOYDZ/6p9N2Ah6SijaYNWnZKt2+3m2tDuew/DVjYsy4raGhbNjgiQnEfrUAqfqPbp623asW03vL6+OnmvAuDs7uhz9rnJ71crBlY/YgkmHa0MBS9q+Ztx3AuD8Zwy0AN65D0U0FRDO73Ph0LdsjLQ9969neKvQ2YRhc+89UGTEAWH2AWoDSkvgDQkMHCflhM2I89g3KOREDHZY98RvqmkGNFSQGvej4YTsdxVLMSAlrxNMDZAXyNTzUyHchrcLEmdCJmQwnARWzjC1okGEGVJiOmgumbK6+ZkRACuIhYOvd69nwwDgiqiDQ6Lu5WpUsHME9ZlXSBV0ILAEJAAN3sxhMPIaYic8z9KJQNUjwxVplYE4GOvPs0B2ycbhqf7YPoxjkUguGqiUnQoxEiLWQMh5ORrRtodeTQvC6yUmSRE0DyHgd5Z/2LO+o8cKeOGQnlb5wN0f1ajUBxms47aAKMnLa2NAUSNkx+SUsSyZJSS0G2YrSh12otMjs+4J8e9I/izylwnzAS/twbDrh3CUTebSXkQwvhbuyKlBdQHEBjoatd1Q9eG2sSnY4RTMEj7FITQ+rfWC05/94xgAdvF0SBh4qVuWQwlwtMqTZBIiB6S4Irb9YrXL1/w9vqZoksyxiV3IiLleMMk8RKJqmxdxYhlXZEyiDR0pYzvXOM2ycgiaSyc7+b4TQd6b3LjGAu/gred4Tp/wzD/LTPoGxew7nPyMsk1E10iTKa72Ia4F+xYKAJQMx471E47xmHSklxzzR9M1xIfGxHf436kbg+698QgfsbdV7yb4WHJgHXcbgXXl8/Q3rFJhSvBkmQCRZREPWlHGLqp93srN20BYgq4+ggOjDrbrStKqZAguN2u00xkKxvycvY+2IIU6c3e3eUL4PgO3MlsQHWwTtiz78kaR9OGnzVH/QZRsWzV4VhC6ux/kuXdW+X7uq65wtD6htio/AUFLBAObm0o3YH9SgA9sJ9KzWsfp5uZJFxRtcNMkJLP9arC+dXorZJs1BsERtjb+/spkXX96dMnDAG+4VI457rBnnirzQmNHpQdegzdTW1cXY7+5Vwva1rcs3xIo3JtCcD12VkxBolA1Hme1PiuUN3FUsZUic1Fz2diJIC7+ptO/QORMUJ1bF858azpDL58TvWA1PA4vjfbBwPJGnKvGDx1KkJ2v3dxvMcu1DTfC+P5cpGrGOeagflkAGgiAxMcJ3CGl/x7Ih7/HCyohxeAw2dEM4Zx09ha9me59jpRE22VI5Xa6MCmrsToFfpI3ESA3sH/6x3WDRbEE9kAFQ5/msPivPcBVJQCUgSCDCXN4NMctOY118CQIGhesecUp3JgTBHdCmI4sT8vCbAMoCOEhN4TUs6QkNBrRaRQh/MzKpX3TCFokL6hNuD1esFWKtAbjZ7cJjaIIL51qFYsidLGl5uT6qzj7eULvnz+GaV8QUwCseQojHgrIkBC8vtz2H+Vz0nwluS4niEIFQkxdP25yobWf9f9vn0Px2880H/7uGOr/40bRtiKc8vltk0FrVF5cdMiq3eQnfbgtXsum/VB78PgDogIbrcbTqcTcs6zBzTG2u6zRmbfY5G+J+XNcz38m1/bE4SwLNAYYFmwPC7oZcPp/AitBumCZAEVigZgPXz+sZkNtTV+CmdYOyu51oaTJHp7IyOnM1rqiPGy9/mMQWrAh+P1xkZZa0WyMDfv7sGitYbz+cw+sQf10YogOWZ34IoxopRCiA73NsFDiGhs9lPjQGQK8AyNbQDTmpiJHfkaRynTb5Ehj/oDx59533J5eHjA7Xaj1esghR3u3wgco186XnswowfSM+FW76FOnwAhn+IuQAug3SZydDxfJpiG5D93PH69DSWuoLsH9+PPjXOXA49hT0QHwsAgnDOnTHp3Od4jsubXb7/ugjHJcsDj7o93n2/8fVyn5uW8KBgYwzCG6XdI2VBl5Hob9+LrNtrxOsU4SLaOJokn92G//+ZXfIzusR2E+/ft9XDf4Up+x2R/5xvVukssB+Hcuqq6RoXfL6GN8zHpIcdC5nmVUvD29oaUEkoruLxdJ4L58PCAL18+ozVFzmwxlq3j/Ljier1gWRPyckLd3gCjqIyEDJHo6Je6+mAk8TdEaK0ACl45mo/19IjHpx9RDsnZuAYPpxNi/H8jpTzbhDDB69sbXl4/oZQLIqIrfTb0Nq4VkBJJi0d1TlUmJ4rGggjDLKj7HpImAqK9s2UzJoL0321q/00d3+pdzwf5G9/79s9xM5vBwVtzKdG6EcYsnoGkISWH9j2QhCBobZ8TFwCmiuu2IefsM/M4fN83NX+IGQTiDCrf+gO85wCMYycCivlMOBJaizg9/B4tGRAV1ukQ1lCQ1dnWzlIeoyTDzMMcMmfgE8AqBA0hLD6at9vZmhne3l6xpAWXznnYjAUihJ2TG7NwnKzTJIVdUyZOMeLjx4/48uXL7NcT+nMjDuyB4P31Gd8DuJEOidNxrQG4AI07hB3GjwZS0kyRhaNikN2a9D1MfTxGZbcnJHuwGF/79OnTTCpGAjLON0aZm8wxATh+xhHoR+AklFkBi+7mZnvQihxFM90Tk0NMPnwG8+D0dUvoW9d49nuhX63BPSm5f43js3V8XV6n+3XL5Dbd8VP2tT08Ho4udRxFtYBZgd1fs/v9wIA7TtX4XMf7ei9opRh92+Nxv972zzr17w+vS5KgOHqgg93jCfz4TAKJcE7FTuA9rt3j5wghzGRHwDHCDqJMpnp/DkJDIBW/74Fyud33KUQ3keqUps3LAu0NtTTkh9NsT4mIC2d1SGiQQPlb2sheoHaCgWu+d5ftVoUF58OE6KJRRGje3i6gPsQJHCdkH51riW0Fk45ruUAEyCGgbZzc+dOf/+iKmhtCTEjxjNYvgMW5BtQ4NaMdX+0BgjiNroaIz5j+IY9ooGoBEjxx+r5a9L/tQP+tCnd8fYfu/3UHqw5WrzEELJkM8dttIyw0+nzYqwa+/04oGe+tShOcWjY8Pz87XHQMIPNNfYMwiOidTOe+YX97zGP/zIf3RkdOAGzD68ufsOSfcL1dIEo/7iARXetEAQZBLzgjXKvPwQNgXz2SsT2qCwi0NywLWbAhCHuKRpb35y90SDufz/4AG7KT5MyKE8cyJAQnOwHresbz80fqC4DKXqP6Pm7I414zQREfqet3Ccv7P3ule19xsw+3v67CEL5Rmd8nE2NCYm+1HNff+wTs2I+/J0hhBujjMSrJ3SkRX232o3dPIZD9e10dTQoyESyzHe05Xr9/7TFQF/OE732VuKMmbVbC76/H8X2PyMfX5/Lu/kBmC+LrXH202vZn+/i8HFUoj+fy/jzG+7L1FeZ1P97X8fPzLF2hbv/38fPurnIYcLqNPQOQQfzyCyoh4N7RDvP8j5K4gKNQ/poKm+qOScJhfyNqMbwzAMwEcVT5ISWklJHSglor8npCignNqDqX84rgbZgYI9Zlxe1W8cOPJ9zqjYJLyxltuyHHFWGhfGyMVEY0Y2uOKJAAKiTEhk41xRCAAHfKE4hPMBiAFBbUtiElmuEECWgK3C5XvL19oYSvYHpnWO8ejXk9+8E908B+q3byJIZfA8c0jap+IUwJ6ylqJYP8OEy6vp/jNx3ov1XN/xrc9ms/M6qn49dSosRj2gaUPQQuvcLlq/iDaHcPFjDU72gQMhjB3zqPUdWMLes9lPUeNv7bh04J0O12g3VFu1UEowAQvdk7ehiucAwGRCAMww6Qm12gTWhTuka5IpbqhmWhYAxEXBSmT4U0+O8OGdzjJn4M3CPBAeCGNpSBJYy+P2TH6nEwlt9fu/dB5O7+zqt7/AqTkPFjararm/kGe7+M5PAqXweEr5OxgUbsP7t/FmD0snk9vl6v72H38R7vP9v+/r++xt8Hy28FvF9NAv5GbnCE398nOt96zf1r467YHWqz38e9HTVe+1vIyvjeONUJof/KM3Mf8PlbA/rlv+2rn7//+1hvewU99o/j55s/O6/vt2xPKbE7ftvevcbxfUMIu1PnSAjGWphvv5NYx3FMeO8QlrCPwYUYIS5nR/U/Io0kWib0plPOWYQ/PwJsCtERs4BBhJvPu7HPzfFAAyRjJjo2PhuTAeYoJOQu64qc6TugrePt9QW32yvbd94q6t7ymCiIHJ5BT8a1uzIl/QLnM3BfPO1/n0man3/4G2v/t3b8pgP9OL61+f23BMgBIw+iTEqJ3t3HjdQY6oOrfI1S5whzAgxao0+0nk4wDMiImwQ7A94bcDnIo+zle8hznuO7qnGHWA+f24AYF4SworUE0xPEx0wEyWe5FbBIeD5QW55WlmTidyV0T9Z2ZU+1h7np916xrISROROcUMoNKa4AdM6Cr+uKUm5o7QHw6n4qdh16ZbfbDdfrFerz1mY7VBsdEh8s6iOkfhxX5D3aRW2O1R2M13u/fvuDPWx6gT1QTFcy2QPzcdN+fz/u19rxZ8brD1nffcZ3wMN6V33v9/s+6PFa7dB/vEuWjp/3/drhpvh18vj++GsB/1sJ1PG/gr3lNEmq76r7/d/HBOt4P94FWLHDdXsfAI9J1/15BogTqwTvn5Ovv6az+t/X06/vH+/RHUK/+o3zw93P8YzvE3p/RbwvMI7v9f7ZHgYw46wmcmG+N4XdeOtbCdd4vUEiFRFER/KOhUop22Hd7VMvatQCiZ3kwdYqog3kQlE4iuO/y/NoqoAEmDYn4XL7oQR1A1RgiD4BlHG7XnBazvRusI7ttuHLl5+xlQunoZxs13t1r4hx7cgJmQXTmPQwg7JpMRMC2OBLjOr9WFQcbau/r0j/XQT643H3gO6FA761FY8fAXbC0SSStYauFSlFVx47Zqz+PgexHj6MDVvZCO37bLZ2zudOoN10PrDjPUcb4JhdjuO4Qf215IWbOhBSRFoCCjryI2G1YBEmAVUCVE+w8kYnLQcDAUDROQ3rTOJSN7Lho/joG7Cshuv1ipBOeLu8UiUNgsvbZ+T0hO4jcymuEx7sfZAU+Tmu1+sk/lHOk9axX243RAESEkR2KLgeRFhGxdJaR847tCs+r3vfB48uVjSQmAHpHiueABEdCwCQcT0M91oH92vl63sxKprR490Z5GMzOcL21AmXQ2VxHxj3/8p8veM6O/b+BxHyPdw7zyy4rOE3qtX3CbK92/RbqzDrd+f0viedOJ7y1esc339vXXwNe79PIN6jI0HEdfMPG/MBphH/vmDvmb//XHNc09fHnpyPn/Ga+t3j9fU1Mqh5JTp7+TuX4HiN94TA4JNg764RZgvvPhjfX+N5Ll29dyywA7FwEk4dc3vvIjeC2lFW9yjvWkrBthX0rrher7Mar5Vz6xIUW7mitQ22Cbo1tL7hcnlBHNc/BsRYYI1coxg5GtjN6KRpHSEvMAQ0Y8BtrYBCPmwBrTBs1zcIOsmGteGXTz/jL7/8Edt2xdOHn5DyQmEk5xhAwFajvk+47q8nfHJnjFNzvDHeFVh39x3wduX3c3x3gf7rQ+AzdQz430jUHLCZM9wAF8kgTJkBIXBjUJs7DDDIG8beXfc5sZzyNCThEUBRCZt91G8F7fdV4n8bbA903LCcBCkDry839jqvxV3bKtQU50VQp/a43G2Ap9MZpVT3jo9I2R3UQILismTEmHA+fcDlUhADsC4Zb6+f8Pj4CAmC9XTGw9Mz/vLLn5ETyHO47qSl8/kRnz9/xtRMjxHbrbI6CoakHdGlPM326vCImrCScSjxUNHztuybmQjmqBwJdI4a9JG5k5wmQlEdkzir//E+6r8/A+C7AMW1sQchEuwAYOdkjN+dblkxQWRnCDNoDza9HDb8+8B7/GzvUSSOe7I1Y97zHUnNPuXx6xXo+LzHr/M9ARGqvI1qDd6OUDWEnKDYE5rj+b9HKL6FVo1Kf59U2f/QVEWm1kHYC9i7tGW8xFeI10wxv0bGjseeiIzAfX98ha5hRwGPSNv+ujuKw/URYKBI0DFLMbPdqjXIHet+nNf7SYd5HkHYW/fU1KmFh2vKY3hQmMH782lPiA9rM+eEnKP/HFuXeVlQq+Lp8QNulxecT894ev4BJb8hrgtOaSFaGeic2avC+uAacbRtJBkpZqhx7BSiSIsAJggaEEPGsmZ03fD0dAbU8PrlBZ9/+Rd8+vRnPD09wZRKlhIEvYFje8M34dDR29EnYMr8HtsoY7Wp+v3mHaXyZRx3Zo8Z38nxXQb6udBH5c2/zCrADtuEyNgGxPvqHFtalgXZzSCGzO7M8ma1tj/gXZu7vSUMn2yAJLymu3Vja2Ns434h3c8x+0/YqEC+7tV/69/SIhasyLJC2w0pLjSGcTQiisByxDkaWtt7vklotLGkxXXMfUMJEQJFzjynwUzN+QRBcmJLhlrHhw/PMP8cAsHpdMLl7TMJMa5iN6pOVtuUjB2kMm62uzTsIN3t37O7CvnYjxyb6fHaEVq9D2q9dxeOOfQ4xxowr83eBcDRt5sVuSpCOI4+csM4VovzfrwLBCMgD8T1eL9/DXId1wsYCotpBoBdz0H2aDfPy8/e3zv6ZjesdL9FWvt6XR1IZsbEazxPAk+YFr+Oh9nl42ver+kjJD963V+jDXtcvgdQj9UuLZeUNrbeN38/Fgl/D5kmQOFuLe33wz/TYU0QKfq61SEYRM5R3b9PnPbRSaJCThwtddb+ri0NFgDmcYi985EM7i2H+/Uwk6gjAdVfkj3zca/oAb8sC15eXib6k2LCtdw4pZI4Stwaxz+p9fAFBm8JAQAUEnx8DR2l3tDaBiSBpTgDZ4HSN94UQllA9KbUtsiJ8LkJckhofUOOC7oqxYDyimU5oxTD73/6iN4NLy9f8Hb55EnEmUhC7Z4kc0pmGI8RtQu+pw0ejE6JYAlpT/ZEpnrkMTntvVEDQ3ai7vd0fJeB/n1PDvhW7+1+cxnVmTkUHEMEpmvW8WGX+QDQY1kBdLRaoF2RFzJX991K7qFNOKN4f+O/Ur1/u+q6/7tDjhKgBn+Y+f6tC7oFMN4xsw2RNqpbLRSpiKMv31FK55hWbw6v+edUimC0dnOxluKysHsl1HpFAHtordFh6+VluGLx4TGtgLuYAZQFPvqaj8kEbo4udHO4Nqqc0zUnDEk4BpDD5oy4J0l+FWUy7Xmd9lrP7+fYbL2VN3gV9DggRMhf2TffcY/u798IZjLP51v3kj/DWmwkKqo7SnD8eTMShXaugZ+7t5LCLGl3yDe4jLA5RKxGkZhv0A14du+DnA1oej//vfr2zTUITPZZ8f1z7edGGFTdm31HZMSvtwvcYaROITIgYCYCNp8Zgzkix88RxrNlHozcja0PToSnaQpDlMNZ+kcYehj7F8f92wmB49rs99hFXMQ5JAcJ2vt7Pa7XPiYanOBqaojJfSUGk//w+JvDFkEEaoIoEU3vp0J4bwfLnEec7ZaRECdn0XOtCQYipa7WlzDkYs2IvvXGIJwCnz9WuQaEDEFAMEBidJTFkyRVoDf0xuQpr7wPcDW93hQ6ihVPjpoLj5kENCiKAL0UpABst1fU8oLr7YacKUlsvfk+ZYDw+effeQUGh2AmSYdEizdS92fLxjMt+2UXGY8QEL5u4/zWj+820L8PnF9Dle/v5NB537PrIMN3+YoRFMYG7iuWK8N0akLHQFbqHex6SDqGNKceYKP35z7+Ox4kkf0zfV3J+8YcIro1xASkTAW1UjuaVzrcSwRJWMXcys29vIcwRUdvyUtNQqfVnbEkMDi22v2z3lxZLXIOWIHr9RUpLDA1FB/Fof4d59O5AbtmvqT5wANgm8PkrgpalhXleqNU7Ujc/N6o9lEa+b0dambHimsPFHx/TgIw8NBhLXjVB7+fg7Azt2txY6Oxwcv+fnuwFZjtbmy8H6wi1MAA4wFybNQjABt0mt2M6qL3ezIZkSTnDohDvcpUUdyCc7y+HeBimQGdQUlN50c18P4ekYr31etwL3Sgc37Wu+sRRygd1eW4juPfLpzj8O54zbGmA9jJ8rbtTMqCV7qzYhYwoZD95vD3IyBMuDkilZBSxOU2jGn8MeWDx/syXtNo+NL7LmkN7ETBbx8673uQYUG9f3cE9n1cb6A1w/wqQFwZkOgCCXG93z/Xqt6PN+44MSbUXg7vsQf6uU4ECDFCyzb3jhASxuw+Lx0XQGAWQXKbmx7N+fLesOYHRCRoa9T1N4CYUKASZMpso/gzaQpIo1dBa833D1AlT/dW0440pDsibG8V1jpMC1q74u3ScLu9YisNT0/P9JdwAQVVNiso2zvEgMxNhhogmeqkqi5b7QRkCYdndpIzPInm90XYBpFRv31Hx3cZ6H/tuO+h3X99qJTVVmBGTfV1XZHSxQMJtdGjc4GiRAw5zCGW8vDwwFdXnZvHPRR/jzB8C+Y9/tzxv98O8v51/3dr3luPHb2/wvAKkQtSCiDTPjAL7orFJX6tN2bnIUFtN9yJISIH+APkjHaJSEmgPUA18L99MJA7cs4UrqgVyR22VPdKzCSguQYBhAz+1ip++OEZv/zydTImIv4wG4LrqtdakWPyRP04uuZowEFZDwB79LL3+d+/hx1iffdqbaggftVL9ipir6q9Ljj8zBgt/NaxV3iHN/9XHOPHiVZ4sPcqf3wzuie7OrFsXx/3FfRIKFT3GfLxM3doUTAWToffOX6OI8oyEp/x+cZZhyCIzl+xrpA0zuPuqsAmKuKnHka179A2jmuD14BxdLD+eb+pQEgkbvAR9K6lMO4XnNlPjsYxsL+H4u8ToICRUvFn9989fqaxzmbFC0wkSPwCMGH9uv++eyGw/w7ANe33lgDP5yiCtAd73hMjCTfs3JjRN99bPnsrUdVcbnl8HkNtBa0DEolaKAxNFaU3FOtM2oaSZjdIFYR8BiDoPUPVUFvEKRMZML/vAqC0TnU76460xZmMfXn5hBQV2gtCCFjX01RTnChZEHRHQQw2ixgKE+0qkaPlBTmgtdjX6WwD+bmN53Y8D9/T8f9XgZ7Hr9/AIYSjPnqWEi8PhXSoAx9DgHY6rcUUob2ilF0cxfPmX10oY9M5KuON468F+7ERfOt1ZzWkATmtWNMDTukRS/iADw9n1FbmSGBMQE60qbW5cdjMxI+9646G2+2GHKn/XEqFISAv0ZGDgOVE57nT6UTjGxnmPTsrXHunAIZXFGaGJWdASJC7Xq9eZe0P4NAvj4fzYRZv6KaorSGGMTLUD5tguLteo489erG/lnjdb6L392r0UcfX1GHLcdz3uw8B5Zsr4BA8xv20Pdnkxn1YL/P/GGSGvCyd2iizqhpnUPk6GRwCP3viwmSPIdZkrJ55JQCEQxV2H/iO4j/7a+6oyqxcZU9oRvW+f3YaG4lPYY9NeJxGM2VZFYBg4e4coqgnOgO1GJ9J0KpCOxDyLs3LJCX7WjAc3mb+jRLDBw374723IcLjiZHZbAns1xfYn8KRPLxbX96WssPv0gdjF3oZiexRptfM5s+9T/xJC9mfD1qsNqTE9dB7n8+WGa2jYfu0j5m5yyTfq9aO1oZSI9tua0gob1dkRISmMCt4eFyhpSEFR1+kQ3UDBFhPETHRMCZljtzuAZRrccEK8dHZJWec1hVRBLftgsfHByYhSFhX7qdD+leNs/nZkdOjFTFCRPDCZK5332/EdhEhw3EyReZ12JNjIn7t37Xu/+0fX8P0x+PbN3APom4b2jv0YL9KdvYB2hTOgrZOGHVdzzhSh94H5eOmeff3Xzn3b23av/Y550YYBdzBOyQAl8sV58cVqtShN7BX2jofyqEwJQGordBoYpLcFJJWt34PrlPOvnrvBWS0sidIoY3u8/UNvXU8PD7NCiEmWruiBhpaCE00BB0hrPj48QP+/Kef7yrXmBK2UpxstX/WlDOKe4aPZKzW470xQtwHxvuoSL9V0X/rMBsKZDT8ub9Px8p2F6vZ79UO748/X99GVh+CMD/f/hn3c51kQz0QQTF4BPfz53fV4+H1vmoD3GkHyIHUBZBcOj7t+/n2YwDDu2fh/TVlxR18VJP304OkmxBR/S6ALZtxjQb0zb+/fwaiCLoH8D2oerIRBqxgGJLEwM5TOD4rx3s2ExhxmHzyAUaVKFSVHMjDSA5GI8KGwuS4FvdJwnj95i0+HRnKu2MgQeM68N+jEmX0CXsOOT/j+0LgyPWIkajky9tQoVyRU8b1+rZXsH55idYlPs9LQsokzC7nBTI87M3V9laq6bkAHWBCB78RLP2+mI7z7OSIBEMQusSlZYVBqU6ndN5kwqM0nQJVNbvek11jJA+o1o1rBCDs7it66vrfZXSj7cN73G3wcgKmOZYNFT1fF7+apv82j990oP+1YHi/8dzn8H/tCCGgeTADuLgJ30ef3xyOUtxYSmnOHM3TInVUJ0do81vH6Dl/69y+9ZneJw1fvR4AUwGyIERDCB2fX/4CSR9Q6obW2C/bRCBC28ajeIaqQgfJzTeM0Da6avWGdpCbbU0RY56jecNwhgzehtYblry8q/ycLJgS0JtXE50iHDHcbY7j2ou4dKUZ4vGze7YeXCSkta9nt/2nZ9X3Hnoe/51fH8FiRmff6H1jHZXwQG3GcUwodub/6J0f93RzLXDBscK+D9b3ffJ9EwfeCzTN+sQZ9BNFObQDjq+3Q+wyN0V+Pru79vwc79GjQ4SZ58V/7/d4HzP0H/oqCHn8Pky7yvHTz59NKd7NenPjfbfuBTDx0Ud5hzqI89n967UbcBD2Ofa5h3Pi8bVlBIx3SeZ4uOf6wQge75O1+2OsC333tftjf06O11lEkHPGDde781uW5TDBg/lzl8tlIluCkejwXh31BESAlCPs5uTAmD3QU8ZW4FMq+YymQFoXoHNtLMsZWhQpLQyOShSomSHFhTbDvkbZIyevxrpr8bdBaIwO8Tdo4BRAb4XnKsEDvE50gp8zOTrRpkrfGGLoin1qxoggyEHDZLR7/Nt7cgD4Oe5oy7eSsd/y8f3NEdwd74Pj/5e9f4u1ZOnOQsFvjIjMOddaddt7/1fj3zanD6eNAYOt7oN/qV8a0PFRW2q18FMLgYV4sgyyMA+WJR64GyG14AHTQgjxhizR6idADYYGIYERlumWwHDMwbff2P++/LtqV63LnJkZEaMfxhgRkTnnqqr9241chWOr9lorZ87MuI77+MbrMNVGoFoFtGbuKR2iVs7qJ2rFVNqRP2eGR/fZia/4xOR6yti3BHkl6IigCEPAGkgYCfN8hFYVS5Y+IliSlj3NS7ZypZojDTCEAlIhLBlYCluJUU0p86Alh7bVSHnumH+qro6ctARq4LA2QxqjZDftFtGaAEvCbr8HSM10uTO3u0m9zWlBPaqCE+LtzNu11HPtrKWEoES7MiVGY4ke2b5ev+0a6rtbtHUjJdL1udS96ELI6/SxPnK1h1uAXC3McaZPJ8+sAsj5vdSb5bfP8nmtLoeyZuieEil9P13bZTdwi3tpVutlvVwxQdeufC2akANj9tL+oYofIJK2AvdYcrbvURcN1ecoZrs/1/+zFXCGQferEb2WDaxVjnP96S9Jt0+YuX3T7tnWnW/afKmKCgg1nqNfTw0W1QwcB/Di0GFUQJH4FJqakZZUBbeSNd5CTDEoxcs6F+SUlflC31sq3fDfBSURIBE5ASieYSFIy4JAhGT4826hSjlrfXn/Z64Gf77U3/u16ueauj+6+BhfcxFL8TVGX9aus7elvdEa/Tli1DddsNcLn3Tt0U1egG6cZ8+eIaWlapEQNe1PBzUdhTho3WYL4qunVQTozLY94Wt+utMgu1eZ653AaqfXVg0tXalS7zBegHCFhw/eRTjeKEKWeJ137ZuabpUhl5KRihWZEAExIVJRmB+lf8g5IQTG3d1ciY9Wf+KKQUBEVvM9YTda8YzBmC55up3CYJasSF6HwxGf/dxncfzlA1JKiu6FdTU971PJGlNQilbKA9Zada9Zb03A9wlf1XcqLudrdLQyiU57kzWhVsGHNkR0DcZh26ALnWpbpO/fOQ36RJirjTSFk8i618USbPaR/3RmE0JA7tDuXFh4lUtjaxWBRbCHTkOyx+l9NTtPYAlu+g4Ne6/3t/gGsRrueTVuFblY08TMp19TH/37jiSHfv6aJSBUrHmCuySIWuUzMiGviGgwZ2nCA4AqlFgEhT6TuQbL9fPs67mdu3P7rikfUvuyjo0QiLgGLgrOlY2ZyRajAJV++Tv1eQtENJ0P8LNv8Ss9hC8UJY9IiyRRsWwBUUjrebKlFMH1ixmUC5ZyMHFY33mcE4bdALEAXSkZy5xMIUhgMIY4IA47Q8zU9F5GRFoy0rKAOGEc9xXYp3TCF4DqSqvXjB5majgktNmTVBm8dJ+ZoNoJv47pQcxVNH9b2hvN6H+9mpMiZkYuLVWKAHzxi1/E9fU1nj57ioPVqwf0EMRx1Hu7XFr/op2J1e/uC9J3nmfuW9/+tq2Nves2jIQiEwRH7AbGdCy4uLhCkQWzEa2UEni3V8YPwhCDptjNCSILhqizUUoCeASKYBgiiATLYnWzSaN6dZ64mfzMfZFzrrENxJYbnZTo7McdHjx4gPmrBxyPB4SgoB7TnGq9ddfgPTgS5EF1ismvThTX6Pt56wPqPGgOKCWsiOI5BurRu/rVtQVFg8sCtDgo4GAnDbfd8+b1n3bfiEY1ldcF7n5tOfQKoQsbY6q/677pMes9dasJsDln45+uzZ3TFp0BBBCLoeIqkR6GAaUwvO69B2c1H7/OZw8xG1gD4JhZmWJRjYuEza3CALv/XWqREGX26z4164bYWE3YtP7V+7riMCstteYN+vOcqeuc6V7thacmZPn9LU6haccek9D2WNP2fG+sERk9rZOcK3ZZGmtXRM+oySwsRFxjKPrrzqzbA4A8L9Wc7davaZqs+AtbMZmCu7u7KvjO81znk0A18I9IcQim6VCFOU1XDQiX7+Jufo4Hl++ipISUFsT9BZAKwhDhdkyWBAoZFxdXgFn60rIApSDl2dybhBCAkhPUgCBQ/A9SJUAUVVRAWg6bAhhQRQuNNnqGxTAM8JRTD471vestSQJjWNNVE46dFgMaVFiLaW1gnd+G9sYz+pdrwJ9yschTLFRrv7y8ROyQpUouQNB3juNOq7vlYuhvEUCGZMOLf52NQnR2QzUCu2Y41WzZmy99pCJAJlAkjMOAq6sRX/2VX8XXvvYAy3I0TXfQwDWeUAwtbGE2gB1gmieAFMREAAQSgCJEIgiMGAS7cQfCATHsLTpeEbWOxyPee++96q9XX2tEyebz242IgfHJ82c6rxxUG5SCx48f4xd/6ZeRkuJ0hyFijAMOd3dw3241CbsZVtTH7ybF/nA3X6fB38o6OK9b7gqrujXH+33nqsb1c95H9StBNdhSMXhk9oA7I/SdFuGoev36rqpprfaGCxOOq+C5+ALpmL4zB0dn3O7D5grx/dUyFho6nwXNubQBFT5qv0pZa0M+53atSLKKaKbdG2JaLtkyBXRNfbxi5t/Wf80vL+bGqUIaN9Oru9BdsHK4ZH0mDN6CKlH3/rrVpo9dKEVWa+D9amvRsA16YaC13mazoTon2j3q+/Vdp64cFTaLWTOAMUbcduvWa+39/nMXYk4JISiEd4wRx+kazKHiVUgRw6dv6XfjqGW5QYTduDco7AVjEEheMA6kwFupIJKAuUCQai0CgYKMhbhT7PxUwBRAgRWNkqDgOwYzm0tCKQviMIAAPH3+HEKCOO4AtMIz7iIUKat96hDjHsMTo4ocPXqmWheTReSrINvXJ/E51DVrAXjqlvhNRv8bpr2amfYS+/nAPfsUBKmmG1DTwuZ5RkqKE+9RRFJaNDET1RQlgoNTGFCImxQ7xLJKHsTNuN2hL46Qpff1aTBAvyld9XRiZ4yfBAIGaEAIO0w5IYYd5qRgEppvqimB3hR/WnPgswCQguIuAc5AySglWVpbwjIDRRLAjqmeEeIOd8ePweFzClEJwjxnrXW96HsVoUtrQGuZSTYhQjWlIbISdxEtBDQqUShFAXdCCBhCxDwtNs+oxO365tq0KL+mlgHXUInUdEmLzZ6lsTl9rkq5Ln0NbGv1APQzhchtACheiW7NBGBaS7PcqLLpKX6OsdCYvW8IJ/SlbIQKM0tTvd+11Uacivpiqmaz3uvNRHyaDobqZyXbY6ohOUfVNXamomcpKNypWbIcPVCgRJ9Jhbjmv7ZfyOsmFLBpYoEBFGp7zsxf3hdmVlhX8qA3XTe2s6cuBDfdW/yA28vYrWmlWtDchK/CUjImLt21Jji6druOc2g/16Z3c7+YfdBhpFXQc2ESde3VWqPzqe42sd+b5QZWFrYI2p5FZ3HINV2hMsXSM92gsN4eLQ9SmpIFGDgi0FBhn4cwKNqlaNowpQWlGCMnQZAFggRBhuSMhAIqDAUs0lS/EAco8NasMTvBUkEdUluyYtSbKV/jhwgkGSkdNSi3cIXxVaS/2frQBDuAkYsJi0Wj+zO55cbjp7xoUROkK5AeTPPvBMdWeMzoCDXh+W1obzSjf1lrGvGpJna2beii/6n57pvAKQNrcDOkEkH/hjH9Ew2wCRH6/HKqlfe5mx0zADYa3pYP+JspowiZ9h1RkMEc9ACyVQALCknpjyHjTnGIiItJ+F6QRSlQtVAwBMmKpqhglJDygovxAVK2wjT2X1q01K1aQjL6aOIlLciuzdq7YrTgu5RrQIwLTI3wc9XMiuNY0wCPrKVuzlbrBYf+9JnqCDShzWcl8i6ANUAeezGoPqcR2J6nVoGSWuyASwDGwzbWh/5nb1VYL3K70o8BXZDg9md7LtHp9/rgrToeNxkDLV0NjfHVAiG1J2L7H+17UG2cmUEW3e2m1ZZf74KxWg1Kt1YuDJf1pBrcsTgdBuDuAFuvOjarP079XDSTfdOmndGuT+FL3WYdTWnof2tB3AMvNXmwDzJsfuL2rq3lz9PITGAggkNbK1mhdmYBs4K03IVTv71mzFRhUAqyAVgxmY++lOpnX+bZ3G/qutM50/1bkmnTIshLRg6EYIW6xJ7LQcvcLsvRcDC0KBJM0Pdqshr5r7VBMgtIigr/YnDNth/Z6IMLCqoMic2PM36dUXfT+RoDfta6yonmVrIV7O5TQbKe/RPK/Oa3t5bRb1t/nk594kDPnMV2j0vJXsWOySJIpYA5AkTI9xCGtRbebypUBWfbF9l29CXP9VvFdzs6zYUIwxAAyhBJiAMQJEJL1BAyN3AZT78Zh4AyjliylqxE0cPAZlpD0eOXDDM+gDGXgnlZ8PBBQEkN2KM3r03TZBjUSgiY2RDMkmJsF6mlaPsxuibu/UtJCaAznJyzaQ1hVeY3d2mA5+btXKCU/nTCuxGq6pJIXaPATWtuxL8JejmXLvVw0HoCNi/qQnHLT29x4pUQsHVF8AYICIBBNp/e/7LWBJX2LOawrp6HFiTHIuBSuv1aVnPWT/O2D+f+JkJ1pbdo+6Zl9+MrlncumzH2a1nHIP5cY1sr+Ghgewa3DPp0zc8LAFsXj19T03BfF6P1eS3SN6al56Sfq+Yu8Hd6lgkFQ7XcVLhrGqoiUhbJCFWrza3YUjGUS8O2IAGWea7rWUrBvBw1bW+Zkc10XrLSx2VWS4Bi1wuyaFlmcWsTZeS8YDreIc2TWhCFEEOElGwpdyrAlEzVKiqiqahLSsiiNezFhJcCMgwPrZdRRCzdLgCBTDCHCtHSikbVrB5bbHfnMMTSW9s6bnFxqsvuLcPAfaMZfW9mO/fZ5srZ62sivTn0ooFKMQ6IIWBhD/gChnG30jrWvt+qRtUr2wp0q+AQH0t3yLdan0ifk9/HcPffNzM3M8YxYggBaZmBQJCiuPfDuAdoQCmLScJGAIIgcASEEDAAAVhyxkUYkCVjIUGhgF0RHEJAuNgjimAMEy4vLjHPE3bDgDEOte+73Q7H451VA9whxojDdMQ7/I6CEgWN1n/+/Lkx+zae3mzq6X2OPlgJqzMcKPNVqFvNvy2OmNEJQv3c9ul7fm1tonU1ypkQA0GATVDV+rv6bqJgPtH2DhHBMAyGXubY51QJfVlRnJcLeyRuvYASspPtL9shn4wfkGquFwFgTMoFxSXnVnGd3D210bylxSis3i6dH3S1531NCkDnzeJrpusDdSZoGp3AzK5rIZlMg/TocHsrgAbz2/ext370QsZWKDyNl9A3elqqW2McDpvdXA9BkoLQYQK0dxBaYGUv9Nj+1E+16mDRQFEzLlp5Vmf0bqaPAFj5X4210PfkJKC9rbmoKw401KA9fb+PqWjtC1IfPyRBsKi7ikktb4EQdyMYc51jgkbKD8OIGHYqQLAi5IsIYhzq2YQQpjIh7iP2+wFzOmCaFoQhoshSEYgK2plyNEQN1CsGYFTq2/3ImOLetSZAunAFaPaJ1PXgk7WnzZ5+09sbzeh/PdtpgE1rPRBIEy7Wmp5/tn3miuht2qtjDPw+VGbhz+2/7+8JvMNSFpAQRiuX+/zZc4T9qAb1XMBhAdNQU3E8B57jXA9CJdClYEayNBvCLkQkSggxIoIQiMFDxDiqtL7b7bG/uMAwjIp8x9GAhlAR2CAqACzLguMxICWF7dztdpbWoqlEFxc7PH/eaz7FYHZTw7CGAus0/G5GiBHDOAJ3SiTQmXB7xLR+7vro5fU+ON0PPTEm0sjl3qLgn/k8elARUStR7JptX/jITa694HlOiGXXRrvPFYUOhifep2Z51Hjo+s5u3K4wn74XeljfnC29rNMc3ZJRSqnmVTV/nxLFGp/SXevPwZaJ3ve3sid9x+oM9GZXCwYQNAtLE4R6pspVg3VLwn1n82XtNLOhuQIqw3Bh68zz+71X7D7fw32EvZ9P18xVEAqIHEAU6n1MjCEEeAaFgldFLQw1Jwgz9rs9YiCgZIwx4OHDh4iRkFPGOO7w5NFj/JevfoLdeIHLy0vc3d1CUHBxGTHe7DGMUGEva22BMCyQSQNBiVndg3ClJKFIQTRmHGKAFtQsyKYoFV6AEEABoKRWPSbddxrn0eak4pkQg2NEmpbK5JnWSpVbaF1w5qjz1cdJVauaLeOWnr5K0H4T21vO6HttnXDfAm6Z+ImZzqRmTb/Tso7t/u5tq42y1t77PO8VQ113BJBmjqxSave9rclw/d7OBGp9CCFiv1dse61GFZBFwLE9OxAgpFHRxWBLSxFQAfIsCoRBhFEGHOYjSimYjpOa5VPCNGmu7HE6oGTFtp7nI4ZhB4H65EMaquR8PHoWgDPIjBB2WvlP1GzooDvSBmuCgPY5l4JgjLXXmqNpqcxGHOocEvpiMz2YB9ClfRO1egedX7nONZrvfQu44mugRTgyLi503huhVveDCo6woLueqbvWu90W1P9xwvxr/zbfXWkoq+80LTIbANTaraAClFQ/rTH8zsrCIAjzSug612cf4daC5WPe9m9F2L2nsk7tq4YCF0w2ZtZ2fto5dMZ+KsyrG+XEuiZtjhqaHKGWnO3cNz5vXku+obj1qXqnZ1fEQXWl9s2FRL/TsSRUWCsVGMnnyn9KKUidYAnovlNLmc5FqjDWqNkxYjEOWfTenWUTJc9GSAINfuMmrJMKWsRaXIqCMvqStVTsnLKtCUNKRgRhSRM8QyRlTcM8TneAJKRl0vGKKBiIxdx4RUpm1DiFUP3zpfrbe6gUFy5tRgFYgK9tGhWuzMUpAhQNGg3MXdlrOnsO3+T2ljP6bfOFvF+73hIe92GGGBUkQ1Qrc5Sm8y4CcZXr0/fuDCE/FzTUf6dqDvZ+9W0D404LxwwxQiCY04IY3awYLOo11w1ex2JMQwggR8FiRpKCPGsK3FIysh02DdKBMv6igXc5J82pRTO3hhAAUeldmaVrLMm0XQFIo8CXZV6NsyeoAoA6gak3yzlTrYS0SvgO/LM201UzvsDQ/NjQvZpPd70eVImJfrcxMCf086x9H4ah9julpWNgHmxIlXmsl/z8nnJY035vtm514UX+O20FCWMk2gPba1srxnp3ie8H6eMR1ISqBJlW+7wKx1X4aBkscN85nd7f/93/dEy41TPb0057fGYcPYBSMYyDc3O4pQHbtnW9beGtK9RtKSfzsNXS2UASXCElakKFI7650OnMW6TtSacJ/l824bfvaylbN1Oz9mjxlxYsOs+z7WvLr7dA3OPxAJGk2S4xmMwgKMeCCFv/YuZ1S0nz4lYC3deUPbjSLEhJAbPE0i1LLqilbdFokE939loPRcDUuZs2VlURMQ09oFqoBA34qPt/kTVyYv2o78Nb1N5oRv8yc/s93+h+9kAX2u474MyMwQLyIDOIguX+yoqA+qMb84X+78xzV0TzrNbVmmsRJyZ7f7QoI9XgGGVyu92Ahw8vtVgNeSRzNm3X1XkY1pgij5FpDIAiTQkVUAECq1jtkdjDOCLbuIcY1Z9FhMWKbGj9+mwAODoRzIwYhhPzq+KNZ2P+DSrWc+p9vG7Gq/Nmn/Xm8J7Zc8ecgHX+eAhciXIL3gGoBnLRCQFpBNuXlYwQaz1xItTATbWkNE3XCXmMAcvi5m5jZEwd86krvvnZrJNGrlf7yClW1X6dwdyzpwSoQaTudujnWuWDFth57vtrzWm9h0+YfeutCSsWKb6SU3qG2ABjihDonjiKSuir9o7TOaRu7jpms25NATi1kuHs332e/bn7+j2zXQOPLyEBxJeP1n76+ixav7tZBHMlAPU/W3vvX+vDZswCSxzSoFWCVq4rpYDY6noULUs9TUeAMuZlBktUS2AuSFJAo1X1K1aMx+rF+4yWonsetuZ1z5ulLFhKbQa0wmUw8BsWc1HpHsgWlElSkNEsJE3o6Vw/3Vx6AGuvvzgtLaW0c082z66gbSw8b0N7oxn9fW0tea8/a1rO6wkIkRX5bRhH9Qfb4csWiawbpdMy6qZqlFmAE6LQm+letzkhFPGc6u14tVLUuB/x+N0neOe9d/GrX/kYD5YF4zAi8IBlURQs9VEp/GSIATFE/Zt18+dckNOMhQURAUNQX9d0oeh2eVlApSAZcxviaIzdzY0zhjBUywaBVIoXwW630+I3ywSCdCV+G/Pxfz1W9taX67j42zn11gSNRnjPaVYMwAFoShFI7iJ4ueXs+9Zx5to0+Zaa4+9lZtze3lYzvQceVs2cqAoIa2LcC32+T31MKpaRhErRVt/ajFWD6F4mQJp2U4rldeu6M1pg53b/2RcrhnopZVVK2J8rbXPW55wy/TPCa8fs0Y28d1/5+CxmS/P1zbKi2psxwSqoOMrd2v3Vv3urrZ8TxFu/1nEdvTvu3Dz0IEhtDjNAvWsJq5K12fABUilYytKN2wzgxQu2FLPM6H6JVupW6jsd0lrN6F645eLiAje3LypmxW43YkoLPv/wIYRmLEtSeO9AuBwvkBNjGAcMQwDnBMQB48AQY9qD+ejnmSpGQJGMYYzm8VHXAySjBAGoYGflaee5YEoTgKCBdkFFblWsdMpLbiiUKpy2M1u3WI2oR52ntWC3FaI612tXAOfTK5C/8dtbyehb+/WRylqKHUNKAoVWqc6lTm+uXKw1V7ZqTAJB799fE5JVzzeER3+2OuF0RprNCQhhxNXVI7zzzmcwT/8JOTHSomh3KSkRJD6AQ0Ob0gz5GUta4OUyAQKYwIviTc8iSJyBOWEGIeWkqTBFcHNzg91up2hXxvDm6Yj9fo9hiMhZC+m4YDPPM/bjgBIjblNCIMLV5SVubu5wPB6Q84IYnwA4VM2RiDGO+7oebs68uLjQtD5L7evNp32QnhfcUQCkYgJJnx/OdU3ZhAD/jhOY1fpa7rD7lEUEx+OhMvG2ho0Il9L7mkWrIqYesGWtAaqQoZHZPYLeJg7s7B6qGp1o9LZs7nMG5fOTrYybi1shBF3fDYN1xDk3Izt0rwcB9sJD6dLy+n2s/QBIWhqYu2X07R3zFBXmIuKqzzrvw4kAd2oLaTEVjVGvNXf95/DB7Z+Py/8xu+m/ad89c/Yx9P1xC5O6bbIy+AIsi5n4rVJcv2dbxklj1D5/TNwEK9uLVZigArIk2sBa/TElxa/f7wccDpq7HjkghKH675ljjWkZxz1uDwcAWnpbc/evwJQQeI8QRiBPGMYBLIPuPxaNqQFDUkYYR6WRJFWrTqmAwQgUUXJCjAFcFOtDce8FS9E025IzuKONfcyGnyWPa/B7KHQ1Siy+QA2NPVreNqhad0q1fHSFfT6NAvYmtDea0W8l8dPm2lB//NsC17vukeB8sYcQcLm/wLQ7qgQLgZRFNSsEJXoUIZKQnZG7hOlEpDe4ivesK+gh2bDBS2cPdS0gdP223GkiiGQNoCsZMQYcpxt85t1vwKOLx3j+9Gu4GBJiJAwDa9QrO/Fyn6W+XwPnhq6+tYa5uP8sW4CdvuMOF+FSc22LErlpvsMw7HC4vgOHaEVyCg6z/k0oKHlBFiCOUc2AFJBF07YOxxn7C2jazmAlLgXQcmduggdUmh+sj55GpwhahRyHX4mzp7I5sdSCPLESUbiPmYDCgqETbs6ZkT3AMIQBRDBiycipCW5uhfDCPJeXF3UfKVa9WQeCm24Fjse1tR448QmBUaRgXjICa2lgV7+lANHWkkk1tixrt4EWZ3EzriGWwJkXY1myYi7oilcTMDFXIBUpuQvEs31kLqkQ4XVr6lnywDQAQA1M6zD8A1dzf3FtUzSOWgSIK1O6pkzrPNr+p3Z+lZBr8Jd6jK02OYkWQClApB005bGgFA++c9yCHiaY4UKGj56hFdZydpREF3DWTN5rNvRZHS4s9oyDOSLEARQYuRTtL2na2mhw2wplq37si/3e8t19XQi7/c588qiCeQgBec5IEMW+CJrBAgJYAgoChBScqPieAYODnjtQtnKvBShZ3XhB89vHWADKENLYk0yCKIKMCVIYknW8MRCOS1Z8eVaAnCEyJotZIVlAJaEgAEdgGAMICcfjARCN8ylESh9zQRbFyShLqnNeiuA4LxjHUfeUaGgdgy3nX9V7PSKGveHkVLqIe9/LRusEBWRWLDIq+za1N5rRv6xttQf7bUUk2udbs+Fa8wEU0jSnbJKiQ4RSNZ+SETfVYrrnuwpo729EhWp/ToSV2nWGYqR39j1pY3Azk/p5s2mJA0oWvHjxHOMYtUgPU8W9Vk2lAbYwRyWmFoCWU25MCxq0E7NDn5LWm+aAbOAdwzBCMKmgME/V/6jBPhY8ZyUmY9xp8FppzJOZa637CtoCA53p0r2a6bJNa39dr3G97uZzn7vtHPc+bEEDyGDxfbI1MzdAlN7/6dq8/+5Vt5gdp3tdkcwBUBypr5qv77XqmLaxybPWL3baHlDN9PeZpHuBV6SljeoaeZRztihlnxvb3/4eJsWx7y0LneC8FZhrn+vfm0Ha2LizGpClwmnz7IcmsDeLR+cyIfX9NqRAs4RAhZ2Nk2MzP/5cf7a6Umqfsd6H59qWbmznoVoeoIFgAahr6mvge6g9R887m8C3LJYF0T+v649q9QDErVRiAoNCTedivm5u7/C9n9KiLE9KrRhJQZDLAiJBloyQgcyqndOc1WVlio7ALB6SUcTYpBBySfCsCA5AiIxSNC5ltxuRFlUmBBarQlobQSAK0mXme59Oi+tFliYA1tiZKgwBrXpkMcVotfTtDEG719b61Er7NrS3ltF7e7nGv77nHJHyiHQvnuAAE4BpXmiHjUMwU9v9UfKAG556Qnn+3raB/VAKPIJH0A4ySFPYLi8vwUyY5iPu7u6w243ISc11AEwQUQFFDzJbYFqo41mwAKIasRiTcstCyQW73R7jMOBwOIIDq789L+AYsCwTguXvp5Q1JdGkcLFn6nBL1fq8CEybAh2Qm8RX9hjZCkbnLDpkTOwUSc6ZRZ8vXt8HgzWuldo8IE/lezdx+jvPCQ68IdzVJLh6fw35MzmRVuPrrVCqCeo+6VP5nMD7/mhEyr67msv13Pg13+ot0nsr/HZCUHGi2r5v019/9IS2XvMx+bjPUM+tP9wtTS5YijDAp3gCRB7IaAyZ1pYY8vkgF4nbv7YP0O2nrabXT+OZIDlslYl1/MA515s3ZcJhtV/chbFK2QSqlc0Dx2DnUKTo2bJxkq27dMqDzs3apK1CvJuo9dtee973bTHsi8ABqUxK81ITMDRGIGtdizo0dR2EQIiBEALZmgiGaDgf0QtdAaUwLi72uCuLFgVzRs8MopaZYtqM7i87lxClxxx6N5n/z9cCqNyd/GysmysVwOnnr8M33qT21jL6T7NQ24Agv6Y55WKmtYjdboeLy0sc7zSX3Fy4FqDXGEJ9Bl7G7oFGfF7WX9fqu76SBqeow1SZ/7Is+JZv+WYc7wpubm9RSsFuHCBScDweqm84pQwmVuJtREE/s0ItQWWJEEkLRxCZP1+J5243Io4DhkEZ+jgMeJEzxjiqH/5CUfAc2IYApKIaYozBzNjF1CclNjqX+qySUy3JWiPCuwO8xWd3gWFZlk4o0uC3XIpV5muMA3BrjGLtU8UfK26gsXdz7Ydq3KyuFuRVvnTzv5ZKlH0tPaCv+sIFK+JUmWtHrJpLpfl961bxTYWOeXSmZB8P3CFAbtHoLSHrMrfOZNxH3fyZa4YjYFXMove3aUNA82z7FxiqfRVghYfQCz29v70iGXbNhZht7IILQT0DPSHjYqofqM55P66tINieb5CwxmXUItD6q99Z04n7mHsv8K0FNXRxCXot51zTMn2dILSCsR0CIyiSjPqxTXgKpKsemZGXhLjbIUZVOqgKm034YXOJuTunh1fe7/f1/Oz3e8zHCbvxAinpWRqHEZEBDqLFo2AIc7A03VEwDjutSkfNsqD5+24tjAA0lVVKQcqe+2+YHrZ/NcLDLKom2OheVz9+oRZQq1kEa2uN/1QzvqzWvN5XFS46+d7b1N5aRv/1tBNJXYBcVMq+uLjAkhPi82hoaEMNltLoX6olOCHKnF62VT7NRuqZxWojmqk1l1S1stvbW432jowhBogsCBwwxBG5JCUmPENQkBIhZwcEMRxsC2U+HA5AcJQuUghOs2rQkTBNRxARlmXCPE0Ywh7zkjAvC+IwYhxHHI8zmAcQLcglI6VF+ykNh1vHlAzD2k3kLcitEkw71J6b7sQPUEuKl4P1zzyQTkoLRPPgHJ+/3ozuvtpS1s/2jVAkoUiygCxlTM7w3f/q7pEeMMVN+x7s5lXtnEA5hr8jtzUGEasQJgJIyQ0fZqstklV/o2aOFeDEJN5/x02b/jwHJ9H3txK3lQEyIUMzMHQKlbVnKQhdzIf70FvON62ec87i0fvIq+BRCjLcFH16JnqBYctk6z1oGvvrnLeV4EAtj1v/DhDD0NBz3wSlmr2xCcTrwXY8g0QF01iZngbfaQnZ3W6HaZoaQ2IyTAdtw6ClZNWXfwON2m/7eZ5nlJwRdoRIWlbWx15pB+lzwUqvht0OcYj1vtFidYiiwdZq4B6xCrpMAXEMauEhIGvZQd2jGYBEiDCkqEsmLSrwpwUQKPY9xRFSFhyPE6bpiJxniCyQIsiUDIhnHTfVUg/VvaCWpgLPhpG6Zo0+NjfMy6Po1xY3vPL+N7H9N8fo+wN/n3mtj7hkC+LJllvqGNtqDrPI61wQxxFFLCiKysqkvyVE/WY8d8+WKG0JlWrXsgLt2e/3uL65VV9aYOQ84+LyAmPUQD6gMSLFzbZAFdMItTSrF5rQinMQtvKyGqzi/UjJ88DN5FukBsc4dSQQlmmuQX4e39AHJxEzht2INFsefS6r8a6CunzcDGj+L5mgYTXsmVcQrTAmyytzdTg5wH1wnpv7GpNyaFi3fLTv9UyrZ2RbJLX+uuIYtFx9ZkZBK9bTP7vuC//XEZ9z+0TEUt6YVnzxHMGq/l+sCaniyZ+mfZ4zWyujdisWunmyPlL711tKVtkodW5CtW400393Ns5A7Pam8m3fvH8K1dtGeR+zfzkT6OdepY7GPE81+DY3stof/bgVO6K/D4iRK5yz76EQNHAvlQIGqhBwcXFhriVGyUqLdrsdbm5u6n7OjjvBDATP+HG8B+1nTgnjXn9PKSHGWIMJ1aSfUWRGSgeUPCEJMGFB5IgSCakkFHH8elF6wVyf58LHfoxISQM9ExNoUthrKQOm6Yi0LAhEKCyGyaFCAaCQuCpQmyZfFEKX4vq8EGtwakur69cwrNbv7LpvtsBvMvrfoO1+gvb1PWOlhZQWLb3b7eCgMopgRd3BL1WNEGCliZxzD2zffcLMiVaopuJ26Qos45JswDDskFLnQ8uaKjXuIqbjETHucHG5r2mC8zRXZgh4YNNgAk2GRDtkMdQDlqNqJtM0YVnUB08M3N68wH6/B0ij5jXVTu8dhgF3dwcIBMOo5jw99ECwgLXMDSWvZ47+/X5+mdxfzpVQ9gFx6zUEhGFCkS+Ha5zNLKimWgA9A/Yk7bo+TqBLF+jX1s7TqPz3nhk3bcr2jflUC9ZaY990zO6DPxUE1vfJau5AHdb85p8HXXqp2NN92O01vII5bhjger42JnFy6NLG5JuWtjbbO1oboflme9OqP6MULSzja1tHZO/TZ6z7sh3DuWv9XKxN+ujf0v3d5qzXCvu+rvfDqesh53K2imNKinHvVdw8g4MpYMkaja73pXpmyIS93MH1OulwS7UWndEgupwzJJvmnZc6Ihe8ixRTBPwceMqngW0x1fVSy5u/pCBGUusiiwl9armKA2M3RtzdQCG2OSKSuXuso4FbbEFfr8G2+MlagdsYT9e3+yn9Jd+fCm9Otme2Ve3e9PZmM3o5H/zysrbSll7DnKO/6+YO3Eqiuk+TKIADrzSg2j38Gnw9Jikoc7d+AJ3m48qzVqAiVum+VKKuG3ocApZlxjzP2O1HYwoEbNCgFAueq2ZIBNv4ASGQZRowhiGaNK5mRer8/MzalxCbZB8d3ALAYJXt6pw445M2QL2kp9XTmbySlJrzo1oZXPqHBRmKwPXLZkamNnH9Yp6bbJ9mT+mz+WlfO88U3GXSa7P+Ug4BlSqhmdUJ+vhQLQ39z+4ZAjgCn39/+/7t368j8LbnoXufmV/Fg7m8A/c8y6e29lnnvI6lmkw74a27p08n1Of0HbQLnTDShtD6J+sv2P6hNbPfCNvnBML13KwDNfu5b1/xselqnhPQ77MW+hi2ZwBo7ip/ThMeTfDhAIGl9am8r9gegAHNmOBq1kYR0dQzn2cjGj69jvapgq5gGCKKCQAuMMLcMSEMIGh9ihhGgDOYCkABxAGs6NYaNxC4CmnMhBADNIAYELJsG8Bci1JL4gZiiz2ytEDqXDmVZluXumJKDXK43ydrwWsrWNmUr/bvysd/ujXe6PZmM/rXbP0meRnj3Zrb+mu9BgloTjBztJzogCW3IJ6Xe+dfwyzkPKanqUaUV4U9ABCzptQVGCRvi/hXn1tESi3oyTHlU3Jkv5YqNg4DSkmakgdBKZ4m55J1Rs6kgTVGmLKl9eWcNGgPGpHrlebcf46CinZFtPaBa235XMerQWGavSCdJiWiqHrzPCPlllvr7gARgZhW46l7Hji1MrV32q5+5lqjCUiwdDsRNNuJEoMe2941qJybNhWjxnCANJ4ABKQEcG5FdLbWgLVp2IWUFmzVc0HXgJvS2gkz92y7c26Bpkn3TL+//3SPrgQO07RdA+rN1oCBDhFZ6eBmomf2VMRY57KBCNnc24D6923N9GqubUGLVcigFj/dxu1ru50gqRYwe+PZcVeTuz5UQ8Q6WrB97DmBov+5Fib0md7/EAKQS3VduBWmBvySxgQtOaFAMMSg+BsQgLU64wU38KfiWqqq8au1GsYRu3EHYkJJM8Zx7NwGQeNEeAAhahAgFcOKGCAIiGwmdmJ1a4mP0d1TbIFwAFFoY7I0OhGlGzkvIPaUTpex164xr43h19T12PAJFF3zNPYEaL78Ov8+7Su3Hm3+nWyDN7q9FYz+HPM+pwWtpfLz39sSlL4V0epP6kvriI8JEMxaE71Kr/U5GwIlVVkzkJC1hYEcgavbqJUIdihfLjEPw4BpWTQAxu5LSaN4n7zzBIKMcbfD1dWVIZ7N2Md9i4AXM/XaK0uGMS/W+ulZxyClYFkE8zJhGHbIpWCajojMmKYjhIBpnhEPR1xdXuHucIOrqz0CM5accXd3h/1uj5xUW3ff4u3tAcfjAcMYGhaBaSM197cbOzODCtXAuXOMM4SgUcPzwXLkGRpkFpGSBgf6ejMzGNlS2TRNCHATJRuD0mAskcUC5TxtsKUt+jqqCVRW5lYhoI83aMzPCVCoRA3umiCuZuz+O23vSoX99P22NR9rP8uZ623OQnBG0wLyuGOi952FagFdMTTTPr12gqpNJ0xbgy/Dag76z6uWjnaGemtCU9ilWqX6M5ItRQwEBMs+qMGYVTgBWq6A24NMm4YzAr1HVF09Q1caFkLPmPp96AVjPEh0GAY70+q3boxVkeWICGMcEIcBHAPu7u7qvh/HERcXF3jx4sVKuCIiTNPU1g4AW735YRhWDFzP3C3u7u4w7q8AAabpiIuLi9VeiTFgGNSa59UXcy4oIUBKQpEFpTBKDpCkQjBbMK3n40dWQdj7lEuBlBlSCvY7wpJmEIvOMdQq4fEfrkx57II+syCEPkBZhecYPXgVq7OoxyRa5sS5ndyEXXap5C1sbwWjf1lbm9F6if1+InafqXQcRjx+/AS3t3e4vr7BGIZq5hpCwJwSSsfkGwO3DVkUDMSfXXCqqQBoBCpGzIcDPOhN09L089mqoYUQNU4gCYAZCRkFBeMYELDD00/u8Eu/8Cu4HC8ht9+IF8sBl8MFBE6sBTFEFDAyq/mOhXF3N0MGQkDExRgQpKBMMyLtgIWRUTCLYBICZ0ZEQD4kIGQEEOYp43i8wxgjDje3quU+fgwqwHK4wxwJkS5wNeyQLy5wtY+mrS84loLdbsQ4DsgTI5rJcL+LuIgBOQbsxmAodsDDR1dIeUatZQnN5X3v3ScI8QmOhwMOhyMePrgAoQCXe2XwxNjvRizDiGOedT6M8Q/jCCbCMicwKYGlIjgkg8cNQdH9crE0usGIjVoYvvEzn8WDz76Hp9fXeP7JJ5gPB4SgwWG6jIqqWGQxVweZ6VVwuLvDfn+JvCQvOYQ47Kqg42mLRNlSDwVjGCABmGmpwo90GpYzGUYAgoCCplrmpaCUne6h4oJnBiLAVvecSst6sN2LUJRIhwBAmqWjQKFQKZjPVmK1cnAQREcHJGUuw7iz6PB2NgMIFGMNKCsQ7C4uANayyC0ffB3jwC5MMWEIrVzsbtgjpRkg3fNEhDjs9HPWtDEya1mGaHllMZrPBNqNKCKIbCZxS7Es06LBuvBg0IASTHs0v/YwBMwzYdztkXLSqHMi7OIAFiAzIQ5WRyMX0BhV8A6ABAFH0n9Q95yQ+t7DENs+tRiVYRiwHyIeXIyYlxnLRBjCgN1Oa1DEYaiMHoUQeMB+UAFjGUbsxx04JeyGHVgI+TBhF68wH4+GgaGhrTlr7MCSCy6v9ogccLw92PwkDCYIS1A6G/cXCAaylZOAE2H3LnB4foPLEDGPA27mAwh7RFJ8xlzps9SsBRGytF1F+lT4aKoZNn3MDeBCgEJd94JiNmsBU4+loBYCtZ7SW+ekf6MZ/VaDua/1n3/dPnOoxDcOQ4VHVGhYQRwCiASUs6sZq/dWDeUl5tXeVbA1Ueo10zSJaqCbR6N6xbriZmpTy29ub/Cl3/nb8OSbvwkjDXh0+RBHFix3B02OMi0jl4JpnqEwrwuKFDz8zGdxmF9YfjxjQQCGAS/KhNvbjJAALAkkgvww4DoI0tUehyEALJBHDyAjg955jF0kxJTBjx/j+mtfw4uiHOVwN+Hm+hrH2yN2z4+af7/MOB7uMAwBw36Pu9s7zfFnQuSAi4sLHO6O0CwAPZrvvXeHjz/+pAGRg3F1dYHjcQIHrwcv2F9d43A3uZgPAowYR8gyo6SCOAx4/OgBbqYZ0zSppsiEwApxOh+PnXbEiMOIu9s77Pea9jQMEbsY8MnTj5EB3B2OuH1xi+vra81N3g2qUYYCQkbOwDAELPOifslgcLvTAgIjkFqJci1IopkfBCCXhGjxFsKqg6py7LCqTcjd7ZTh6hZksARAUUqx26n2noumRakFRSFFNe1STcTFCGTOiwZjhhFSoqbwZQuOQwEFgmRBSQprCus7DQNyniyo0mGVI1AKMhrKXwiKshhI9/icE/JSkJeCwAExDgAEMeyQFgvaEo0tKQVY5gRPwSIQljIBhv4mJjR6fXUWAmVASFAICCLgKWFhoBBACRiOCygvEA6671AgMWK5O2hFRiIUZsgw4Hh3i1S0IE2MumcOhwli8NaRA4YYkZYEKRlTEUSOGGPEGAegJBQmlKTjlaFAJnVTZUlYloKwCMrdjCVnpDDBi7zMxyNmJpQMLCnhcDhgXhagKMW6vT2o3aIwihCef3Kj5kMCXlxf48kTwcfPb3B7d8AwDLidF6QygzCq8FM0pXIcld6NuyscpwwmYJGCp8+u1Qx/Qo7NUid6Focg2B8JT792wPH5NYLMePezEU9fTKAwmFHNLHYlwdM93UIi5nr09zjNbG5NV7AaUqan4ek9awWwYh04jX4L2xvN6M+1rTa+NtVj9Zn6Ac9z3nMBTCoFmtQIVNOmAjaU6v8RYMXQxVT0PvGpmfAbg9++u/om3fduvnsFmOkDaXTzcmEI68uLZCx3d/if/y//Z4AGkBAGiigDYzrcYDFpV5n8YkFvAcejggFdXl7i5tlXsbt8gGlhFBoR9heY5YjjcUZMAVgy0jwhmxZzc3MLIksTkoLj3S0ePX6MaZqQU8J+d4HnL17g9vYGF5cXSCnj2SefIKcC5ggBIeVcU3ziMGCa5upyIQDjMOB4PEJyrjj877zzBM+fv6gpPYEDHj9+hLvDASB3ezDCEDFPMxwb3U1/l5d7lNsjllnfu7/Y4TjNmJZZTY2+l6w4kRfh0FKbAeN8BMOCB02bSGWCPNyDh4AhBlxc7FCWBXe31+Zr1LQ6LoLbu0k1M9b4h3maLbKasQSNA5mLIhZ61D9BcQ20Kp7j1bMy11LAwQCIxIOdtK9SmiWpmI/0aHEbxYr0SFFUtDgM6q5JC+YFNYWx1DUixGWxandSLURufm3Fg9S1UdKCtDg2g8ZzTLOthbmRlrRgt1twnGY4rvySCx4+JByXCSVbQCMBw7xYsJpGiKectaKaCOKgudhFChbK4MjIJtwViOZuzwKtcU4QJoV3LQKOAcWgoIkIeQyY75QBI0QgBGQQpiFjMUAmhZkOWC4vEezdiBEyDkg3d+AQarxFJi0yxQTsBLgYd9jHActhwvsffgCKEQhq5Uk5I4NwYI0/GSOjRMa8H5FzVktTjBAIjiWBYkDcDcAYwbsBewDh8gIsCoYDAejqAqUkPIifwYPHjzSu5mLA43ffxbJk7E2DHscBxBkx7HBxcYnABCKtdLjf75CTYIh7DOOIEAOur69NOzaBFJ5ZwQZglZHLgmEIGAfCN807fPV//UV8/F9+EVKO2I0FKReINIRCMWLqKH52vJQxhwb844z+nHtM3E9qX/YYlJMYi14hvE8je0PbG8/ot8EuwHnf+P2iWkMPe53mpl1irhydiRS7rkoSZNucNhvmTARpF3Tkz99aKopkd8LWTR6NAbjvj4gQsubWZgiEGel4xO//9u/E7upSzaMmjBQkTMsEN3MflxkPrh4ATMYsNVr+8PEHGB88wIII4QFx2CFAA9aIR6QiOC4zxIrlODodoMLI8XhAjIMe8KRgO2AlqhwC5rTgxd0tKERkAaY5oYBQKBjzb+ZXgCAlIxAwz0fTGDOWJeHq6grH6ai+0KJE/vHjx5au5PnsmmKkwT+q8ZWSkdOM3W6P5UaBf+Z51ngHDbXANM9I5gc83E24fPgQx+OkzJ6ALBlAwfHuDjnNSMuCZZow7gmPLp9gmjOmRQF2pusX+OrP/4KaDANBGNjNguvbG3zmM59BjOrPvb6+rvgEHvApFsR2PBwbAcsao3F7e0SMXK+llCq4ioiaWbOhD+ackSUr/jtFTMvc1e02XIYi2O8jnjx6ZBjrk8amsAaFORzr4TBht9tb/jKbpcECwyBQqAYFScki4JIrOBEAHKYjNGhxhABIy4Lb2zvs9oxduagxEBDGu597gnmacXd7wLIkcFAryzCMIBLkpAGdu33ExX6Py6sLgAhJMmbOGvldMgI0viNPCfNhQoRGe4MZiRlYMvidK+BuBmUBDQHp8QXy02vEcUDYXQJxRC5Fi804bWECMWHcj7i6egAJATxEhBjxyScvsNvttEwzBCIZu2FEHBgPAuPx/hIjRzz94EPQv//3CJd7hHFQAWRJoDnjJgooBOzGEfv9BVJWgJn9xYVhYxQ8e/YUwzDiwdWVupbMpx9DsMJDFoMBYE53uLq6wsMHDyEl4+72Bk8ePba1pEoxx50y/MurC+zGAYEJJWdcXlzh2dOniDFif3GJ/cUllnmxtZRKC70EtsYdzQo6FS7ABHz+M9+An/4n/wL/4v91g1/46Ct4992AT55rLJDDDnsKoCJ3tqJCVdpDozd9rAjQYhdysnGLa/KuDPrfun8FPaN/u1T7N5vR+6K+Yk1q4FKNan69RTyJ8rV/4zgq3KTDf4rmsZYuiEiZ/Bq+9vT59wf/ba/3EaSRgxIuOICEMUUsShgY4JFxm4/4zz/zM7h85wlSZEw5YX52C8Sxan2eGlikAQF5ANcQCeX9G4AFpSSUtGA/XGmAlaW3adBVKyKjfl1Fv9sTAZgwkEN1qnk3MAHQGINvvnwMKhYpfxWhxl/VFplijcguJYO8lCgVDHGnVcCIkI2x1bmC1BgGFo34d8GpIZi5oGXaQGCMO/V3FikqjJD+TkHne5omhN0eyfASiHWckIySk2ppUE17H3ZqNiXVAGHm1bsXL5DyjEIqjBFUE3rw4IFqy1U7AUqyQD7TQkLkGnCl2o7W/H7+/Blc4HSBa4ixIvzknHE8HhEHrbYnsJTFEHE4zqZFiZnYE5AT9vsRX/jcF3Bxcan+38hIacFHH32IB1cP8fjJO3jx4hqPHj7BYVFCybYHkgI6qKmfuaEFWmyDxjcIpumAZZ4ULCVnzPOE29tbEAMZWjlNCjCOowoMQ8TdzS2WeQFbhkMMankgW/dxGPHo0UPLJ2eFHU6CJFbnPEYQqxviOC2gEDQyHCokjwi44ohDSSgEDBxwhYA5a+yLWm0an0lJ3XXuO2/YANkogVo2go1bUJDzrJC1g/ryJWWUVCC54PZ4AMYRYs8kANGidt0aEmLEbj/Ws+/Ng/4Gg58FNKU12/diUNfNPM8gqMvIi0m5ouMIfrCo+bRkcBAcp1sAai2LNOLu7mjaswouxQLumKLuYc9sge6plBfsYsR+vMBx2eM6XeN3/87/AY+ffQ0f/sy/x7/8D7+A3/otF7i+PiKL4XBbjEQ2eORABFCoQUwll1UtEu+7uzWdbjvuiQjUcigtCNJpqGNx+Hd+LS7e34jtjWb0GqupLdx3z2rBWoTlp2uWxmWR3I8ePcLz58/NrJsNwtE2IhtGdWnSYntMc9J7ikgPruLX+36HEFBSQo9Mt9vtKjNumQQFc9yBaYBAsEAwfuYx/un/+x/jtzx5B0/efYLw6AoffPw1fMd3/R8rgSBikDF71aDVDMvMSHSHnezAPECgMQkFWkNayoRAjMgDshCSQIGFiga2ODNl7szJznjg5T0XfTehjoelIBrzXYooHrZOCCITpvlgmQ8X4DAA1MBCqDJwtaOUnAGGVWPTcra9WwRQQp5zRnSTvLQyr8uyKJaAVf9jZvBU1CphvgQyq0MMamcsKSMvCWEcgEC17xCvQqZaaCCG5IJMBSFGfPz0Y80OIMI47DBPk77TBAT1Matfm0nzqUsuGIcd3glPTIsqEMqgsUXbEzN4JGR+gGEYDLJW8flKyZALQRjYoqEFgYJhphfc3U64ffHC4hR0jR7HJ5A7wfPrZ2COuL25BmMyiODGdKSmOhEyAZKBSMAhJUOYDIhDVAEzqHl3x4zdcAWIWoB4uNA9adHWcgSeXDxBuGJzZ6mW56AyYEKaF7x4+kG1fIkUDKwuAXgKGDE4qjsL1LvHBBwCfnWewO7HJeCYZg1gS0VL9gIAsbpE0CyKzIoWB5E6ds9CSHMCVgiLWhluzoI5ZYgQLi8vcbHb6/wIQF3uefQzIqIWJpiFjjWrgG29SwfmVaxyW8PK8NTGoMIq3DyuVkFHsVSIXsuMQIQHuYGVcac0WXYRUIqZw7GAWDE2mBqglKCo1Ww/4vjiBQ6ffIL3PvcFjPsRX/2FHf7LV76CD589BWjE9W3Gg33E7SyYLP03hAHF6tTDLHKBA0DqdiRp2SxOH/ssDuC0roHXhGA7w7311O8N9zGUN7S94Yy++YKyM1BprFwFP2euZvJBz0xRD0GXoLV6Q7vSsNZ3u52ZONUkW6RD0dtKgtKZ8DcS48ukxlXKHXkv1Eemprr2fU39yljkgBGEUAiUBA+HPX7x2Uf4j7/0c/jMO+/it37LfweOEUsyP6kF4oilDM7L4p41gAQFA6YADXLLBbIc8fxrv4xlusP+4hIIQetfV+u6ElYxF4MGhA1KpAu0TG0wv52eMNXgXaKGIQ1C/cnMQzcPzYAjkpGnY029gWhMAlt9+VWQDSx/n8wpKm6U81xfNUnPiwKTRMsBBhTQA1BGXzXVXVQGT6zaRirVRM4ExDhARo05WKDMhUkFURGgEGOxsp9MBAkRc1ZgIrFc42MuQIyYBBbwqDainAVhUG3R53lZEpitCIv5vmtf0RG3UgC3BhiYkpjlIsSAkhOkFM3dZy2OQiGgL4wjIpjmGWRQz4HUcgIGtPCqE/d6+ODAKWL5A8XMHswao1IkgXN3PooyF2YGjIGXbk2nZWkvMCatMM0wF1YwN4IBJpFglqR+ceZK6JdixVVKsvgUnZNJMvbjJRZJaosjwj4MKATEwZDkXAgfdroPTIMtksEmEOYCZShmgh7GlgboDD8dj9hTwsiCbAxYteEMJo2UTFJQCEi5VKG3uGXLNJ2kBeH9CPb6hJutOqWiYLZz5JX/mHQtDoeDxn10wEclL1hKA+1hQ6uMu50yyOBZRlb3gWN1kzmO/5wFj6+e4PbDj/CLP/sz+C+/+LP4j/+fn8XXbhdMxwnPnt+Cy4KvfjDgGz+3R5IJKc/IhRBZAx2LOMKkEpvotEFIwbuYkZNmOqjBTWqcyDA06GtVMHqGr1PUpy1vXQBvQ3ujGf25ZiRJ6QBgGA6NyQPnfPg90z23yNS+3pm3/W9Ukz2qT941qvrdLf8Xqdr+fT76Jmk6k0dN4Sod9qcOkTEWMi1Ao2w5ZyQAMwMTabAODguWZD5LsjlgLXcp9YArJOyAoeLak6oOKDIhlRngB6Awah+i+uhLTmoChVi1qYgEgRhEZoZUAqD91tUq2SPKi5muCVwCmJMJJM6wXEgqgCQQGNPsc7ieWxfuCI3ZOZgRiVRm7oGNiSwdrEr9YvnXgOdRq+Bg2pETSxMS/Z0ecBlpj7nMJiS4JZIgHKtQEIhQpO0PscWkamoUS8lUlDDVhBsKo0ObOk4y2V5Td4aV8bQ9KsbYiQhe4pgpWL64uStQdG5Yx0zSUO1K0bLGIgIy4RUkWjXMBEaPNymiDNp99iBAigohPSEVs7RQPaO2Jxw5xY9Nr3VJO3NkjB5kDNAsUVLUxKxjFYhohLtbfQQa1U5WCc7dvhlWmIoHTCUpQydggPr6g5CV8ukEbNvLTkOIWgqfwBmIndeOxpg6DLL3FJWM1QJTNA5CoEW1vG5GjxAptk4EVCZIAou36OmZYlH4GREIUs4IUGUBFkBKIIXM7bRbEUEuR+RiQa5xxH7/ABeXnwEjWO0GI0BqGqjxHoAKEIG5pmYWAMdSEHPBVz/8GP/Lr3wECgExMB7sgec3Ge89WRAiYT9GHKZkkf6K8aDFrsjOo1srGs30WBydWjuPGzP8Vrlyyi22p/w8yW+m1/3Gab3kVTcnnIF6IM9Kv++arK69TH7zvEzfI2x+R0WX80PVHS4iKFE79c+vtfnNptsw+fbuNsYVClwVMvRJUYyImcmxFGAYIvYXFxgvLkAxYLrTlJt9J6xoTXhnjFTNgkwRlAEKsKAYRtxfIewusb96BxQuMAiDySOfE2AMQ7X0wQi1a/CoQpGIMkXXNtWfp8xIDyibvca1eT45tHqg3ZTarbNo//0QV5kMlu4FZRIQDabLWcDDoIBIxSV8qQRQi3tkDMMImInb+6upZEp4xPzpTIRIhhrIOqtKrxQVUFBq1LUW7YhqRoeYtmumaRcaUXFxzHypRZNiiBqF3vuGyYSnYvjkMCbre0Z1UoAFgQezTFn0MtQcDrbSnkka8y4aDFYzS4oYoAlBpFUbY6LKGLiez4bZr4xZrUbuqnCLlVtJkGaLkKfVwWSy70gFP63/F1eWTSgJ3s96lsytULkj6pn2qu7ZmFVhNW1nm60gZAKX1PcAMAbe7UfrbipJI9SBDpgpqSUFdTMixGh7yy1h+nEwZuVrRgCy99vHKgLixshKUQEWrHEuEFlVc9TYlCbcio2fwChOB5hVKLdWRFDAgLm3OATwOCKMe+SExujhdLfhKfi5dcWpZI1h4mHEePkQE4AJjJITdkS4umA8e57x/FrwzuMRF/sBS8oam4NmRXNXg8bqOFPXte0VsN6q1//tv/fm+vqZ1P8BnRvqbWhvNKMHTrVh/1kZZc8XXGoDACst4hpQi773527eA6oMJ8aoOdVzskOmJCHnAjjKGBe1OQKqAZH767s+nhlP3/e2GRvT94CTVdESiJV4VC1MSsFUCjAwqAjKov8kBwQZsByOGKNGBBMRqHhfZSUoLZxQaAAhQwym9uLiC3jn3XchxBAeAB4VzEOKSfdm/A8azLYsi1oKmMDBsLvRWQ6KVE1RAVkIVNSUmg3z2qV2gGv8gDM0b61kpfkqk4FswBHsCkJkFFH4Xie5uZiJG02Acg19VS++apWowX2AmQctit+FFGZC4YJgMU5FgCSkMQVLAkN96RkFSFwDDt08Q6RBXkSdAOn72e4V4wqhGKJYR9BEskOHqIbuRM/iGZwhqsm2I2jkxX2AVIAwtiAtBmvqGYfaLyrZmHaEBgYqAiB4UIa5kbG1Sp0yNBWmNCXN8QEg6jqQoFHk1dghJiCzRlBDBBQa6RJ7r/FpDPsdJGcTvAgiATz0pZ4BNpx1NfXqOY7MQM5YGIiIanGBYEHBTrhVVhOgmDC7GwatYGmCjVDGjjSyn4ixD6QxG4Ft/B6QqkhuYlHlVNBcTxZXQtCqkpxFz5vNW0OFK1Y+1pfP4yEMQZI02I9E4wI8g4VD0JiazlqjQb7NWqSIkISStYqe+u4DYmRIyTjO1yDSuBkAcJ2miJddVrtRXtS6kbMgTwvouODi8R4fv/ga9rsBIkHpywAQZ3z0tYKHlwGXjwLGY8JxVstA6RQtgcX+iJ/TXC1z6OYCQA0E7YWBtS9fkK3kdOCe6G9git/w9kYz+nZwT1kmczNbAm7WbOluXg2pe5r9LCuGvL5FN3AIUaN3A0EryWUwGHMpgARLzSKTOIuZzhqzroepQu8I5mWpjC5yqNqIMito+k5omODUjQUwpmiamTJHQqYJg4yqPZaM5e4W/+sHv4L/6//me3F59QBFgGlKuL494PLiSr/XbXYphKVkiwEgNfmHiCdPnjRGQYzZBJJGJPQZucPddguK3yMVQbCspDEpbjYTcFkX2sklg4Nz2vUSNUuJWQQCUJBAkhDccZkZofuSABgqUmFvGdCZDVZ0ey35J3BkKCFQNLkhjurvRSM8WBGN7vdR39x8htvgoSbk9XPpHHhl+jattwqE9q+akLv1cOJf39Bphy/3R7b5yqaBb4XrHu2x15RW9xBB3S3OYLQTBW7J6YTs0hXH2bxrPS+nFrFtM+fBiaWsPqMTJLVLpxHX2znyuV25IPq5FhMliVZunf69Xo8+dFOsAt8as9PnLlO51+xIaEeon5XeAub0zl1hA9bpaGvzN62eK+LWgIxcZhwsxXOIIyAaHaXZKhnMLfvFNfkxRrz77hP8p9sZ//FnfgH/2z3j6UcZ844wDoyRCQkBn71a8PE18NGzA3Ih7C8Eh+MCooyRCAnAURhFIoZsGj25cCwoMpu/XhEdCzHmnBAI4KDCi1fE8zFrsbIBOSuwkIMPndlKb3R7wxn9+cPdN9qcjk8TZCHd/5ywOJGKMVj0ajL/W0ZaFsTRIprNxifFNb11Y/MVOnPqJU3X1lb3FqliQS+h9uhPIGDJGRDCHgHLMUECgwPhbprxyx99gl+9Tfgzf+rP4Ru+8Rtx8eAS+wdX+MJv+S345JOnQCkYQ8QYAhgCYo3ADeaq8HzdcYi1QhUAcIgVGIO9oEwImh43aDoTG2gGUejAfsy3a0AsIPfrxQZ8QaEGwzFrJT23AlDwlEeuqFZtfdV8mcMAVL+h9ZdDy8d1iYHXFhfXiNwwCYFaGcDV/C+ClrrTSR4E1VbdHKsR2a0wkN6n/U3SgoBWe09yI9hwotTMom6ZYnJMeh2P7ymPK6gkW5qZV7X47js+bqoWbfuc1+enMjG3MhjTtvlx03Vzo2FNMcNQvw/xM7AetwoELcq6BbBRXQcAhmMRV9YOPwhrQi0aLS7tu9pPGxf7JTLgSarVFKubAk2LrpaLjimumKvfXwB2y5yufGO6QE2jpJWpm7ohruN0xuz7Qmqsia5lu66brSEo9oqQPpvqmonjU5T2TIj7/7OOsQiIdQ/nXDCMI0YiLIeE+XCD5NCyBYCwJTUo6JNnYBQRHFPC8vEn+Pmf+zn8L7/8i5AnjKkAt9czLi8HhIsARsE4MGIouLkWDAF45z3G4UaDVTNEC1YVIHLENF+DWCvnqVUmV6uD72vkBBoHlGrBa1kHnmHjgk9wrIHVKr497Y1m9LQi7GsJFlhrLr54G5rQvnviy1+Lde25ulm8UET9zAveyNAeI2sN1+8tTlQ9QIcU4rT6MK23CsqRNA0kOMEvq/64FkJ2wIRUh6Gs2moJguOLW+zfeYjf9jt+D37f7/5O/PIv/BzmwxFpmjDlhI+WgkMmTQ07Kp54zgnECbCIeCZlXCknzdEmRR0rWdPhWhOfaZ1TVg5AIIVKBWGrSfj3zCNYLQWBWQ8zsQkFzhAb42fycrkmiEH9hF4et1DUQhvBGLe09ztSGZFF5dt8kuXae3ZA20KEIcSV4KYuCjaTZcvVHywzQoUXE4CouYc0MEzLvtSgNb9OhJpHXJkSb4QTauVE/XudjagKp8aI1K3ic24blFajU0ZOfmRcEGjii7+jMl+YoLPiwdLeURmYMdDSC1Pe962mrZqjP6oytE57tt6gCK+/2zNWF4ggQAevC3E/u/fT+igC2BnMxe/vYmmIDP1Pj3XFN7DXuvvMIVZdY1/3R+qYajnpzhLVXBjOcC04tTTLjUhBls6nL7KaJ0FajdVz2qulwhh9Lpu+dUJuL8wJCQgabzNETbXNqSCXCUIFBAuERPPXF8OZ8Hz2NM9gAR585hH+T3/wD+LDD34JUxKkLLibEoQEV5bNso/ANAGHA+H5s4BHjwVPn5FRBiCQIHDBzIzAZMpDJ7CjFzQ9rRcrgu+usipIQetMSFEgKUDs59vT3nBGfxqMB6y1/JPfqP1VNZxP81I5z+gJHj3uJlKpmkVPoNY++NJ3ppqsvXl1ucAtkvbERCgCeM6+E2GIehkCK9znsuCz730Ov/e7/g/473/P/x4//83fjA9+5Vfx4unHONy8AJYZu11ESTPmZdKKdSlBqCgQRmCQGKpcyljIIoqNiOSyNOLgaYdmgMwGrwqollMcs13WGkcppgWUAnhwGdT/rcQpIMShah/Ooppf3ubGvhujApSUXGrUrwb+ne4dALX2tUfeS9sqVaMWALsQK0NRZu0apAttGnk8DmNjSqSWF2buhAt9MoeIPuhMBY2OoQLGwEP9Tn2mESyysZkzQQm/FIs8R73fGWaNUKDGvPUmF8R8U3ZM23tCtDl3ujaVbfRMpzIQ25+rSGZ9j0dH+31sAClNENTnttQ4PzL982V1r/ehrh2yzYmsGH1/nuB7jxROV9fbHAudNaQIoTjTrTEcqMy52P7TzAOLz69z1Qkm5OvbIseLI3BRoxMErT8PqzuvisKaxrVdAdOk1yb4nva40JJlvf/9IdwhHIKALIJIureHMGKMOwSOYPZgV60MVyhpnI7TOiJwZAwyoEgClYIvfum/x3d802/DV/7zf8T/8//xLyFpxpIKaAaGEDAEwhAZyyKYJsGzZ4L33iMM18BR4Qk0yFFygxSue7Dtz348UrLa7W2XaOZJODn/vifaqftNRv8bpm19bn6t/wngZMOfPwDr1vQ7WV1z0+Ro2ORSzXjqo8o5AUaUmxZyvr/OIErRoJHpeDSmBOSigVPjsFfYyQ5cR7VGDXDxw0tEFnGTFOeeNRBtyQsGjvit3/Qt+B+/83/EBx9+jG9ZBO9dXuHZMuE6CHgQhdQU16jVwpGdwBmjImYc54xFAKEAClGZjEzIlu5SxCpPEUAcKmKgF6XJOWvwmmOj289i8Ks5JeRSFLxjSXjx4kUF8Xj48BGYGdM8I8sC98eXIgo9mxYUK08aWFN7SposRUeDJUvOFVgH8II3GcVgUAELnktJ8QYcK8GEkyyLEsO6iqVqX4CmPJZSgKkJgcWeoUvk1wFAMFTfvgbVYbVXzPQPIDrB37TF8uUdDEjKAkcSDGw53tJiKCpjJ93NVIOOpDI13ZtSSR6kmZrVp2llez27ggY4424sp43bAw1r1H7V5Jv2p64ddfkc5wa45M8N1WrjgooAMKCk+l5xWatZ+2z/+pyuethr9ybZra0GMGGoQ1EjxQKoQiC75am5X6o1x60Fhv0fDHhK0OI/mGMnrLYyzD7PQYkBht1Q103AddwOPewMGqIusMCOUumC2ZZOkrnZekFPNWSF19bnS2aMUfDJ1z4AiwYfDiEgBs1fh2VcFCqKpOAuKlFhP4SAaNa5R+9+AZ//whfxe7/9d+D/9hf+7zg8TVgMCOr6bsGThxHDWBCXjMNU8Ox6xrxc4PKSMV9PyAKEsEOaF4TYTO0lW7ZLda8YTgNcmXK/vAtv7nJre7FkaEhVJ9S9Te2NZvSNiNzPzPtr59rKvKbfBK0ukEmBaGZUEYzjWNM5cikgdoKqX+6hKVeaA5q/TxG9VMKcpgmBAxxvvGSNQH744AGOx1vDZ/d3uCAh6MgxkBTms6BocAkYQgPSUvDJ157il372P+H9D34Zt3IEiiCnReE4sx6S42wR8iEojG7KpoFy9WU+v57wv/u9X8Z7n/0COA6YU67FZAAzgQpa7uximQnQCmX9nGznxTUBcDto7ksDWsSsLo7HODRNrU2EHWhjlv7snDOQjdkxWwqiMWrN+F/tjZyzCg72fmbGYZ7gML/kjKrtFHgd7pSm1ThXPt1ur4YwWgLBWgDs95AaZNdwp/6cZOlbzSJRqvCihKvLzsilEratxtKbMm0wOkZYpDlg1pA2BrH4FIp9qmYbX7PW2BmNQ6eZSxXyeubGzKrlbZhTC1g0wVk0v93f2f/s5whQRtvWqJXcRZ0J890a022ab3tWrZ4GUlCl7l3b9+o+7WJz7P2Bo/Vfap9CjyLX7xFQjU2BQPetz+/KBkn1zPTOG2JG37zEsH+b6ryH2ieXKnshqfCARw8u8Ys//7P42f/4/8UH738FD55cAEXAVrVTrSMBOSVISc3VpwWH9eyljGcfP8V0FFxOd/gfvvGL+ODmDsthQimEozCWEvHknT14OEJeHHFzyPj5Xzzid3zrFUQiXtwsOEwH7OJDIN8ixsEsHqVaO3TtrPaH0WefV3T3aGEpV7ZgWQOhzelb1t5oRv/1tHPE4MQqAGclLghQ94k2re6kVZvmZQGb9DqOO5SSMU1zZUwEOn1PyVr6UwJSymCodL2kGSktWJaE42FCSu8hlWwBYM2E35tGBcrEZEl6HwtiEaRZsH9wgeuScXc4YDoeML4zIhytPG1miOzgsKpXULQogVQ/YG8eFyHsLx/iycMryHJAmg/gAlzxCA8eq2U8TUWi4MyJYAWqjYg0zVbnxfPQnUDanDmMLACCWj90Gl37VY1tnUOh/mBPd1QtpdUcr2Zd6wdx0FxyNH5bid22+XhEtaK2pkZkpO2PHhKW3cYPWe2DLIpBoHnz/m4z3BvUKQFmplSkPk/7YwsgrJo4mfFezC9bn9csVzpmVMKoQVotXbEKbMyrXe/mTh+jmjmLja1lHJyzpPnvkvL59/t77PuDuy7Mz91mZs28JA41MK7O24agiwi4qn7WF725PdsFMSk1uHLdxOJAqmcJPTOwVW0CITEgCejW3+/shT4YQp84hgWZpu+uraQgMcyELK6C+P/RQYQIkPWZ0ax/PlZ/Vx+DAiIIC5CBlKThQtR/VgJaCiQlyPMHeGe8xJc++40YClmBGsVLCFGtdbnoCYwmrDkNdStEIEUSHOOIj776Pm4/+RjjPuNCCGVhHIXw7OktpnTAOw8v8ejxJW6mazx7Lnj6dMKDB5fgMODjT+4wpQnD2FgXs1ZUBGElSKaUsNvtatGvc7S/Xu8Advyzt6n9mhj9X/7Lfxk/8iM/gh/8wR/EX/trfw0AcDwe8af+1J/Cj//4j2OaJnz3d383/sbf+Bv4/Oc/X7/3la98Bd///d+Pf/bP/hkePHiA7/u+78OP/uiPrvIgX6edW7zt56+6fp/5XnWjZsJzKV+MuQQrqsHciiyQmb5ci6oamEvhpsU4dCfQrBJEWm5UQVASJCc8evgAObWKcIq3Yf7xUlpAj5meOLDCYRYgkhbrwDKjIGHJC6ZpgTzeId3cmik5aC59iICopuIvs22/mjNmBkrGxx9+qITDCWwkcI3i0kA6MpCcVkDGGKpYLraZO4nMhEhuPGYQDxXVrRlW2gEsJhA0UtIYaGV8QMW4d8pYRNCSu9XXapBHCDJX862vSzPtmY1ACkJhuKUXZoBwpuH7hghWra9pYRKUsDc/tWtuUvvfZpwqQXb1tZQMrlYj37PksoP9TyPlATHkWqnPRVseGFpBZ0RozK42jzoDLMCzob3176NSTH7TnvdOjQrTamIBKNfc5zrVXdaI/q1BWhWD3ueQg66WK9pEQJorDkDVdl2TtXeIiAaNrY93J2D6HrK5F+oETb9XLUR+3qrma2N1u4tfEcmaXsuh3uOzwtT2CBEpGA/aPvIgTNW661bBrk287tgtCJINJee5xhhApAUWSqkWFoggdzTKz019fh1nAWPG88yY5oS7g1ajHHc7CA/IKUNTFcTcfSZY+PpwU3AkNyEvzQkvbl8gMDCOhCQFmAUijMNBaxPs9xGf/cwOH72/4KsfZnzTPmHcRVztH+DmOIEBpJzrHi45V4x9XTcTVkqzIt7nsmXHDDCLY7N6vT3t62b0P/VTP4W/+Tf/Jr792799df1P/sk/iX/wD/4B/t7f+3t4/Pgx/vgf/+P4g3/wD+Jf/st/CUDNod/zPd+DL3zhC/hX/+pf4atf/Sr+yB/5IxiGAX/pL/2lX9tocJ6Jb6/3n/faQLu/0yKdKHkUNBrT601D/tkqnQUdMXFTpUeBokuxgZlIJdVgNkBwcbFXSbUnWs4YjAd4WgwIKIFARfueAS0WIgsKstZ5XwSBtayoVobT9BSQp6SgMgTq/vDgLQJAIeD29hbsUeYQCAu4wpt6dTgGDCnL+0uGNc+Gh61R4xoR3zRIUgHEKmN5tH0TpCylyvDCQdRpy2193IfJHDsNSNH+tNx10zoKAA5ZCSuZT1fWhnzX2Kh0hMLnidra+N0kpJXz2sz231j31a0XHYP3feecvJQWgCj9WP153PfF87d7NuaBeFKD9ooD34BRQds65uLPru8j06ltvCgerDmYMIvuAT7cbk6oQcESUIny9pwqiEzDmgBZ7XFpj9f9lbtzvXk/oc5V6WJm6mr27+T13jmJZbDzXKQTxvqlrDREF8G/X4UYQ2mUoqBS7A8QGA5HE05rhTqLA/EAvzqh0iL24XEunTausS7O3I2RmxXLA2FX9ET6+W+CRLNyaIzN4XjEklVIR2CLZGcAweYnV5hmFYRoBedYYHEOAZinBXfzAorAMOhevciCKTFyAu7uFgCCh1cRl5fA9e2M29uM+DDi4iLguOj5qEJEt5Y9PQc0dofiUOnDlsm3PUOoEpcAzVzydrSvi9Hf3NzgD/2hP4S/9bf+Fv7CX/gL9frz58/xt//238bf/bt/F7/v9/0+AMDf+Tt/B7/9t/92/Ot//a/xXd/1XfjH//gf4z/8h/+Af/JP/gk+//nP4/f8nt+DP//n/zx++Id/GH/mz/wZLUn5mu1lpsLzDPz+758IAtKnEfk9TRr3gKS62dAOHxNVRCZn9B7QVYmAc2pY0JhhrCzLjFwShkHz1o/Hlk/dBoqm7Yk/xoA8mYEiSCIAE3ZGSFPWqmo7eghQQIwjHELS/1WrhdE3lIIKBA4gi6FUSYGUxXRit1I0AucEvJT+MOq9SkAbMp4zsxocAxWuKDhyWdQCH8auY9T0xcBNoAgcNK+e+jK0pHm3YahpeMr4CVxY02dcQGACQhdQRh1QT91bOvGl5JodUYkGoVpxlKgVBLQ0S7O56J+d2Vfno264jseaPanbt4UN/9zWte1Kp6sMIg0IVEZBtT++13tmBfEcaRWmpN+Sdh7YRSRyzbVhOQgKSlqszn1cCVp1RN3ZIhNeHJkOBMsm8SqMxtiZgRhW89n7qt1toULLUDV5n4gqkDmnB0EqZr8OXYvplHqWdQ/oCEGoVhf9TqueWKTmK1ThvjJF74cACgwkGkVSBFmSZuSkDIhB65q5PKWljl2K4Hh3p+vnFdpy1kBZ5sqUPcOjpzse76CCirskfJcYLnwVCBTg5r6mgaH+XICDoCBXdLoiGShaa0JZiAYGU6SqtXtaoCdpChEQtVLm8TjhkAWIQBwZw0BaE/MOSBmY5gWQgh3v8OTJiI8+Tnj+QjBwweOHrDj4hxkxMAobJLS02KdKX6Gljkfz5QNKo0LoAJ46pQ5Q5cSI373z8ya2r4vR/8AP/AC+53u+B3/gD/yBFaP/6Z/+aSzLgj/wB/5Avfat3/qt+KZv+ib85E/+JL7ru74LP/mTP4nf9bt+18qU/93f/d34/u//fvzMz/wMvuM7vuPkfdM0YZqm+veLFy8AnDLn+yW7TdDUS4iSLrZda0HQcD9XJMKcF8QQMI4DhsGIHOnBvrrcA8S4ubvF3d0ddrsdRBpxzFIwsPqNimggnx6aAlDBNB+w2414/PgJjseMAq7R1h5gUgFzSE20JSucFhlWu5AgBEGZEmQ3oFBEXiYsh48h9MRS9sxXTUBAhPto+6lR/6bjrje0wXmZEOxeIYGakDfFfkyCdy0ZFCq8qqAJAX7Ba06bkRRlmW3MoUbJCwBM2q8Ajxdfhya5ouWRyMyd1ozmw2sCCVk63l4jv9n/aSpfGBQq2IU63u0bqowJPiAGB3U3wDRStZi0dKpt0Ki3Ujdbp6n5fc43SoGkM4SnF2CJAGQU0epuleeshGFLA3WrEwKquOVyoxiTJ67WIp+vsOp7AMKAYbzYdKoLthN3r7gcEKo1C2j7iX0sDf0FwNCdPRP6Yk+ACZBtdUOp5mOfQyKq3hrPjBABAjQa3M3yVAooU2fK7pldy6woRQvk+Jp64GP/d5ECOASvSK2VsM3gAAhTWjTW1VPArFb6epldQF7pHfW+E7fjWYWnBcj6e7b3dBfMalFAkrFMgnnWsPQQPaQvtnWDafp9nIn9vw95YPtvuj3gZgHikBF5QBxG7EYBjwtubjOOs2BaMj5+PuG3fvND8Ocf42sf3UDKgkePLvHuOw/wX27eB8WMGAOIY52LQADEFIAYMfEEWFlabPaGNwfA6hEq/5v30f/4j/84/u2//bf4qZ/6qZPP3n//fYzjiCdPnqyuf/7zn8f7779f7+mZvH/un51rP/qjP4o/+2f/7Gv3ceu7f5UvH+g3u8qgbe+3w1RE4MjoTA0QokqIBEzzDBHCsijms5cLZVbfs6dweMpRIACWKpfTgov9JUIImKfFKscFC7qiZjXA2lLhRF01FDPLFa0t7lrGcTri5voanx93eNZNhUKwnkal6ihdQGrCEkHw4sULlDRpoQorYblmnM3M7rnjTLxisj2zrQVZKnloEbONaaheAqhPrQBIEEBa8RqQxx2bFiOlgqxt5+y0PVUCJ9YDs1wQgjJvm7OdmE/PNUTfO0QVaAdMYNOaajQ86d+hpjypFh7Cbr03zVoUAjd/Mwjk8SvUuUL8bxdkiEHQUrdV/JHGEOq10IQAXQN089/Wpc948NY0xbZXNGbAMyBgFgLZoEKWiuVeNWDxvdqKGmlHBfBiL8VCO0ux/PGW217Saf9KKVZUpwOUSQ3NzjXhUjQV1oVoIfXlb/3WzvS1WqSZwO1MtT6vFQwXolyrBKBZOaue2tpaPno5sy9XY3MzSP1/t54bxu7jdpolJoD0Y0tpXgmhpRRNhS1ddoeICR6MOI64uLjCMLT96oWfzrVmS2r9civR7c0tiLWk8zIvOB5UiRt4ML89Y0TAyAE//5VP8C1f+gzefbzDdJzwC7/0MX73t30RF1eEaRIga1YsG0pfTirwsXncCQE5JyymiNQ0yVJWjF2nsdHAt619Kkb/y7/8y/jBH/xB/MRP/AT2+/3/v/p00n7kR34EP/RDP1T/fvHiBb70pS91ZprT9mkksmbi9IOjm8Xoyeq+4uoStHrYMAyIMarp3YhjLmpyC7XwRqn5wgBqNSmY/y1n1einacK7776LEAKO04wQIqYlVWYh0tLNtr6m5iNW4qL5zeqRZQ6Y5wnPXzzT/H/SfGUBlHidkXLtoavrAjW3OsP03Hcvw9nS7Pr+9YRJ/98LA72AAOh1B8NQISpW7ZZqrrWm1nl32Z4JKfVdXjkts9Q1lEZ3V3On09AF97mJ103l3XcmKiCYP1+aX1gMXlSpCwEprxDvut21mm/X5GojJeBauKf1lytNd2bc/vb1UQHEMz3sXWYW6O/rrS7NMt+InMcMUGVjtDoI/W8ibV9LFZJ83jqXVvdF6dTS1f6168Ie8FiqRi32RTHzsVqZGjhM7ZEAImm1trUPglVOfcmlmvBNhV2tU+2lXaTK/Nez0KQun0dzf9V7OtN+7ac+NBoAUzu/NgYTkvy6gul0vngD06rpuGYRS0ljZ9xNWJ/hIEBOO3i7F9b70s+cB9q1eCR9L3NDYOgF6DrvROuJtHkoJeNwPFQhgTkCKCZ0LYiRkWctNvTw4R43Hwm++ivP8aUvPcTV1YCnX7vDs2fPcbUHSiKU4mBRVjsgDHWrhsCgEioGSbVUZQuKDZ5mt7Zkun//bWqfitH/9E//ND788EN853d+Z72Wc8a/+Bf/An/9r/91/KN/9I8wzzM++eSTlVb/wQcf4Atf+AIA4Atf+AL+zb/5N6vnfvDBB/Wzc22322G3232arq423lajPyexkR/01Ucb8AQjwn4kQ+CK277Mc/Uj5pSwLKmm6ohxmP6QVkxmUYKQ0oyrqwsVGkT9iqXDJHcN7CTvHL4xs3exFnZTdK4IDgFLWnB9/RwMNh+eH+h1Dm87p8oBqk+XPABGTdslBIBEgVqMEawICfrf4Spkva4xhnJ2LJVxuYaM1idlgGurQFxpzmZNsGBJYdao8ZXmS9WU6QvrNQP8Hi25yaYJwvqjpUyrJieiCL/qeOmua5Q3ZVkRO/J7emLYRQX3tMX72G297ud5IdYjuk9NsfouR/yrRFjV6s3zXNjpdvpG4AV1Ag4ag6pz2RW5WZ+pFp/uF8XlZn+7CCxa0p7vbgY7Q6DWn3Oh9N04XExpgoivT1OQq5AvbVzrbBOfHqru23PoqCuLGMzz6xq1uTCyn4ni2PINi6DXtiu2Qxcpvz33W2G//V5OrrlVj0AqfArViPi+7/57uw7AYKNjHNSvb0ywt8CdbLftetpPhtKQm+sXyBlQE7tOv2fdBAYGBkpJuD0eUXLGMSVcXx+w2w3YXw744KMjvuGLhN0gmJeCImopyJRAFKvLlSAGGpVdAtTdatq8C0dKil9fMXwT26di9L//9/9+/Lt/9+9W1/7oH/2j+NZv/Vb88A//ML70pS9hGAb803/6T/G93/u9AICf/dmfxVe+8hV8+ctfBgB8+ctfxl/8i38RH374IT73uc8BAH7iJ34Cjx49wrd927d9qs7fp7WfmBtf8rdLb06ElfKoH8ru7t9Y/19E/VJunnaJUdBrurH7nlRm2HXE+qDIdO995j2kJEiLpiEVMT94r0PJqZmdOsJt+p5qVUWR4NgQ6m6uX4BEQVYGEzZOpfGe23R/dv0OIaAUTYmjAKRiKVB++4rRd9+vmguaubb/h4b/X/u0fY793ZvEB44mmXON8g+smj/F2NL4KiiLWxG4I2xSJX5lwABCAKRp+gA7RapbY82IWwCnCxT+qQsRlQBb3/tc+/ZQVzBdq67JX51mW/+o3TFxsHEx/9T7aH2XOoQT3VWvkrtM3E9du6XrXM0JPaNxfuhWDBduu+d3Efj9dReunPFStsI/BGX01czqQgS3Z5k0tJZD/BT4ni6tzy7kmimDKrOnysTrHibYZPXCTD+GU8XA/e9k0e6l/svIkqum7fctFoy3ZfRrF0LbZL7PeqEW1NbEAv9NlustZgGr4EPx51EnuPXz52vGyAUIVsSLuwwYkxrtvkaTvK/bplU0Bdc318i+p2qUvt4TiIAgmFPBzWHS/cHAs2dHXD3IuHow4unHBV/84oBxpxaZabZcehGYSQ0QjSWOgZo5zIQsb16XAGYFEzTa+t+0j/7hw4f4nb/zd66uXV1d4b333qvX/9gf+2P4oR/6Ibz77rt49OgR/sSf+BP48pe/jO/6ru8CAPxP/9P/hG/7tm/DH/7Dfxh/5a/8Fbz//vv403/6T+MHfuAHvi6t/T7z/Tlz9L2+FwEgLW2mixXtbup1Kr2uKXatnjhzQC4JxAqRS7Q1tes7VghvZgpziNenH3+CZV4gQhYUTiuCspW+/adrvjlb6hIxJGtUNLOiVt3cvFDzYweQ0YQGHfy5OeqtIo6t7vepCa+zCpyZcyVk7bARoEy0G0fT/F3y1t/Lpk9b4aCUgrlkYFlrN32ao/epdxN4sJ6/PwRPDQwVtjPuRu9gjfIvptUwqLoH0D0/mgDhKZLdIIGOeTozDD3UD5n2J5Xb6JhQzhDORhxX3IacgVG9bTvPWqN+u07dfTCRpRJyWN99jboudFag/tr2mUp8mwDdBJDSnuMphMGtUWb9Ec908HiOJtrUMdcr2/gcdauopcNM/fbV6msGQCKI0iElbvaYAy0VyRpFX+RE4179S4BkY/ClYe2LNL95NaFvLFS+T/vgVg7UDbfd0/+tc76me2fN6nBlBm7IWO2Z/lmltLluwrLRL+mEQhOF/JjT6n12TQDkgsPdHRDMvWHyX4Ey5hCAYWRgICwLgLwgREJaAq6vM+blFuPlgBfXjAcPCLEkTHMB4wKBAlLOUEGQsSwJY8igwHUvikXdu7K2FaTa+O/hFW9o+3VHxvurf/Wvgpnxvd/7vSvAHG8hBPz9v//38f3f//348pe/jKurK3zf930f/tyf+3O/3l157dakU/u7fVCZhe+FUkoD6CIdzzCMcFhUlRI1Ij/nZH5rC/jp3umBbblkzPOML37xizjcHXGcJggE4zhgTgpjW86APlTwi00TS19iIiQokeQQkOeE25trMBj7/W4VlNKPfMV0zzQRM4fZ+MlAdOrn9q/332kkdvt+/7Mfl7hkT6REgTWk6ZxA5/h2K4buhAqo6GLeowYW0qwESVLt9HFpucfePxfgijE8EcHoxA1nhIeeUMew+rwn3DUAkRnLmecwNs+EmVurBgY0Dyk2TKsR+rUA3GBbS7VSbJDmujXxQnsv2Qpda0iIvd3WiX1lPhqmXTVufSnbehT3Ulm9BbuOTtsUALmB7ngqoff91PStAwhFAVVSaWbtIq0Ilf5dkJBPGP322T63dYwuNWwaWYyFuLZrkfVM1IBWfR9hu1YtO6Q+70w23DkF5z5zvIinsL76/vXnrCA5404hZ4nX8+BWHZ+Ge/ZLryg8e/4JQgBiCFYUK4OC7uhSMpKoBesyRlyPwLsPP4uHDy9xc3fA+x98De/tEj78OCCOhGGIuLgk3N0cEccBQDJrKBCJsCxH8DB26ZkqCLADTXR+GHFrABqOw9vSfs2M/p//83+++nu/3+PHfuzH8GM/9mP3fuebv/mb8Q//4T/8tb66tnMH8L57+rY+DBbE5A5uUtnfmbwKeWt/JNsh2O1GxIG0fGP2e3SjpLTUqHzVUL2iG5AlASS4uHyAz3z2C/i5//xzmm4HTWGDgc5srQgeHbsdSyN0HlxiDDJEpDzh5uY55nlGHEdIR+QA1T44hhMCsZ1DqkzFNBFmhOACg0P0are5Yzo9sz5nhWnXmkrQhI4W2W13I1ggnHQ+fDe/OXFt/n0xVLl6Z+dT14uDuDDX7msR4zrWIgKWTmAwi0NuHUDN/06LvQkn3HJlafDHVwZeZx/VLAslPb3mt23uxnDCde6+StR7BaYKE/VKe9ZrttIJSFuNvXcdFD8bdSmblceFL0CtYLrjmwC+Yro+z4aeR9Lmr9fEvTvCjqao59gdIk0Q9HkN1mcCHMCJLMOmm7Pe0M3V172ZMBaz+nfCk/+/bnHafmvVqLt3GyF+cq/NJ4NWc1xBfu7ZN/rd+68XYewvLjXansPJHizSmby7bX5WWRCgLBl3hwlLgUGHt5ZFsKNBN6iBPl2MwO31M0hRWvngAeOTZwk0zHj6jPD4YcA4EAInSI4Wd+M0nDHPEwbPdIEKJkWS1uSw1axu19eTat/I9lZh3a8O1Uso1QkzMwLvJptKt8zs12uMzAHZIjyJGxQuEVnkaEGII4ZBc3yPx14z0Hd6MF5JGSEGPHr4BHd3R2QHUyE1L+ZSQBShQCitv6dm90YEnUhXzcQqby2l4O5wh+lwADFV13OTtOUs2RFpAD+q5YZq3gbIgDeoSvoi/Tqs4Sj6eTyn0W/XcL1WjSyWUlqJymrerlOnb3VZxHysBNQa06o1N0KvS92Ivj9wNmx2s67qc0uvWkllGNt+a9ohGROrPawCQh3fytaJ6qrwb/WWkMaQe5ra+8fX7GY9p06cS/2O39P71KsMt1HPRFaz041f2hi6nq1mo4637ZG1Zl+6Pq7jFkRczNEo7ba/BByayVhXh6tA5gwJZN/tSpUCHriIbk4JQk0gp3r31hXge6GfvzPNTePUvrflI6v13FileuXl3nesnwaCitqlFxAq49vsndqvU2Gw/WNERIzjHrX4TXd+3WR/bmeceyZE8edvb2/BgTAMAZIL0lIgrOlvJfuzBCIJFxgwjxl3xxswRYw8Iu2A45zx4jkQOWN4B7h8EHH9PIOjz5fW63A3abUWQhQLQQQhasZDC47u+/vKCX+j2lvD6LdMvh0YoCc75zTJE43L/t80O9UgCAoaQvZcgjL6wRi9B9lEWIR2Xpv9/f2q9SlRGocRDx48wPPnzy1fXgON6gFaaban4zsZf6XZHkRFCMQoKDgsE6abO4RxrFaFCvjCXYrJiQDRZoVImTtRAIgReUChxuArszetwhHe1B/Z8nPRMdl+gnrCt12zld7ZrYOvWq8BdYaB9n+n0NSeIZURO5yqfbEouhy5D9XfvjF/KqMvjfDbw0ux4ibdPjpZK9RpaGsG1JzxfuyyCdqr2DLo5tE71M2IuHmF+rkFmtLXCCvIl961wE4YknOWGLtPOqtKZ/LcsqceiBFVOOnPn5g23YQtl2D6NfegsvW56Fw34gFj+h22uBLazL0Kcea/h6CIjb/f7/V/9TX9jzMKRdMU/b66hmvxp1ps3NRQBY+NME/MoI2Lrf7oBDZ9iXT97U/M2rXl1Rv7d+p8hEpzNFg1Yhh2cETD1cjqfJHtJ48/caZZoGnKCnkNAfKy4O7uTuOPmAGrDMoIoBAs1dhnsiDygExZS1AXATCAg3oLDwfB9SAYR+DJoxEkM0Q0y0ZsTWOI1Xq35QkrCy56eteuvS3tjWb05zSp05s64k2n9/d+3HpYTu4r1ZxIrKALqslotaZxGCpRdIY/TxOWOcNdAoAfRqsuBcEYB1xeXmG3G3Bz89xyQDV6tGqEyFUb8PG2Upr9GOzzSqgLBAEojCEIJAhuy4Lj82vsvuE9HI+3IGRoOopBvqJUJg+gcwH4ZTdzBTgYaogjBq9eBtPx+v5IC2hKkqxrxYKtLMK+1lmXRvCkCQGNDShfqDnmRK3Ea52OxgwAKJ77Gel8pTEBSoz6GwIwctiyUVcKGiEGcM6BSqFB4NKGaBRaP68nLAQAIVhsggsoAIFfude3n56/3wqtOFqjUe8qSunkv8ZzrP8ds32Ve2z7vGZhWJ/lVWEc7dbZ5mfhTK/Wfw6hXl3FNnQ7xU7m2f7e1/eX+cjPuRP7MVZLmkk8vBGLeuZbVfX6Wdd/WQtUhTp3E1zA8EwTFc6JGZEZxRk6lKkHE+Bd+yaolSMMO6TpAIilyFFB4KjFaCo+JQyipthZIhAlABk5aeEsEqAsMw6HA7ycMhMwDAFcIiQwJCZ9txBKCSgBkLngYtBiYVM6YDqo4LmA8ckLBcl5eMkYIjCXhIIIMIO5AMJd0L3o3zaXDbJb62o4rakwzW9Re6MZ/X2t10S9WhwRVqkVgB/CFj3brq0/183A6M08WYAQIoKob9sl5jgMECGklJEMKlMPrIFclAUXFxc4Hm7x8NF7uLi4xCefvACwRiKjFcM9DQw5a/42D12LVBdISRDOKEVL395eP8fV+AXVZov7Vy2gjduT3ES5pWW9iZG8pOzmni0RdK155F1llC1POHepdhkVRtTT7NBrfKVW8Gu0juzvbi66db7PJbD93G46/Uw6jdfmZiUU3tPcV+oaT//+QGvW3zSLbT87IbUr4PJr9SX2zE7Nr+VEoPs0ra+nfm7/nrx/o4n7fK7cCNJZFc5839/3qneB1iunsiR1V3o2uT375/t+n/CypR3n1qmPlAdgIMSdwGl9LBvNfBs42V4U/IVKf2gwK4AGB/v72BQUX2jbnW1uSN+xPQ8c/PsWnQk1vxMEFP3dgIjCRhMEkh3Jc4TIDAkJFCOyCKbjhCVlhEgY9zuQFCzzhCXNGGiPGCPSsgAE7HYjDofJxqP9CYEwDIKcBNmiY5YFePr8Du88ZOTrUVP0YsGcMnJSUJ6q/6wXTTOQuBNYbNq3vOJNb28lowdOmaNrxEBPKEolbETN3HTaLEoaWvkrQ5+ldY4Z+/0F9vs97m6PNShN4VDbZslZJdUhRAAK0amWAMKLFy/WhIEaoegjgLdpP8Baa3aY3FqQBw0elgCUlPDJ04/wjePvMgnc/Wy4n46sCFa1pVYLRikZHmt4H5OoDHNVO4Dh6Wx1XQTqR2dYatIaOKSI44oXeBlV11kaQxRIlzdL1rH7XB313W0Ap/3v/u+Bfm5dwJnvuaZfzf3d3vNH1a8KUKomKatneVpYG9op8Tnn4jhvtTo3Kr9+CgX6Mk3UCX7/LFkx0PN97AWC9tNLwjaGh0Ir0JX7CoycuhJeo9G5fnqKQdeH+77+mky+XwsdZ1eO1+UYAExepa8/qagCTrMytvx1MgRECvY7u5ndsjVAqrlvGXff5/pe//9mDwNaFAq81nANATOwVmckglkgC7QUAQGl1POdEQAkMEcs84znL17ga59cIwwEMfx/d10cj0c40HUpBct8gAgQQgvKjQxgDJCSMc1AFsIxMZ5fF3zDFwbkueA4Z6RZMOwiUm7CTs3hkGalU6yRUmmYGwjP6FZvdHtrGX0NGDmTUqKtJ5CoDEfNpf0qcz2Yojqs+uFjVG1UlIANw4AixbDdGVoxq2CeZ92oVrSGiHE4HBTPfpmxpFyFEk/n6iOVgTWhva/pMzzgqNOCDa+dESCF8PzZM6sA16tKFgRVp8UN842BerAUIBVwpkFvbmd03fdXNeopIAw1zNLPYu2UleTswURM69d16GIBPPK98cgTxnVCAO8xtfZ9lI1AgZ452Qw07byZNE8H3I9dtKT3vUKSf3Zqul/7HU8DHV+t8d4jlHU/fTy9G2HLGLSo0T2vONOPdX/OfDHw5qrui+26va5l46z1a9UH79/LI9vve96rhI31uNd7Rezs6c4yzZ31dy3o6EHAbg5Xnz2T10JolhAhZfC67/VdZtc7cQ2Us6f1tLEh4zl+NBEZZny/16xehti9pHXqla0W5OUOu3gBKkBOGYclI+wi0pLcnoNogXlwdwwDSUoVZKpwyVbZz3AqpkULYt3cEa5vBBcXWiMhTQwSQhx0/J4i6ymGvmQCLQpWQtHn+uq8Zbb7t4rRv4yYnz+M9y3mVkPrv0HoI9EJUhm9h2wpw7Ga1mTpQkawS8mYjhM++9mlpFUiAADjk0lEQVTPqtS6tKorlVCXrcb08nbir5d1cJZqhhEohGefPKslMFsKVE9+jKxuLAbbxsxq+u9F3/5Bm+ZESvq+3dN6DbpZXCzoy4v22ByLiDL/UioioVsAqKtW1jrYnu2ES0yjaLy2n5RGIF27vm9vnSX+0t6zmiaXbdCnLG7byz47M29nGemna+tx1d/QmOG5Z3+6/r36u9tra+G7CvGfcl7uu/Y6LoBPJbDec63/TZk7VZmpMnHqIZ5ZIWuJumJIDdFRl6Ux9F5YlO7/NoAqVJzT4M+Nte49Pw7GLANHC6zsrGE9LWGBQKFpIxHKcoe4u0TJCcu8YMnKeFJKJoAQhC32KKtGzxwQdgOWJUEzerL1QWmB5uFr6eySCEsCnj7N+IbPFQwDYUhASlldAeKKjwpS1KRno4+q1bPDCq4X661obzSjf2VwUneATwlDQ8oSW+ztd861psmutUavZAeQ+eIVaS3GgGlSUxeRbm4iwjvvvIPnz28wTdOqUljPYF/lJ5S1BGIMzf3aZlIToGQCJIAk4NnTp1UD8LubdiHN3NzN4TkNjJmRqeXut4/Wfa7BUtJ90t2ysmgbwZNOzqkmNtcorX8a6B3bjcXKgTqjz5rhLq7td2iAVTuAaT/U988iv8nmYuureykB6ESCSkia5n+qVdp7O1Pv+vPGuM9ps68SZPs9cp/r4tz7WmuurZffp/e8jln79P33WQNezog/jTDzuhr3fdfOjeFlf/eum/vfZ0Jjz9i9tsOZSo+h1mJo6IKejSvGgPXZ5wVzZXPNKtcLb/c3ozNGXPzsqKBhcL9AHavSQ6lZOEV0TLLcgfMzpFkwHY5IACLcGqhadCkJw7BTDbsAITL2+0ssy4uOxuhZ8mDmGNTQX4QwL8DHHyc8fgRc7Ai7PTDdJMQwrOlZFU58bKhjy5ZZtRIE3pL2RjN64Iw2u/m9J5KnB8Al260m1mvUayLtleeCpYKUklFywd3dHWKMStcZINN2l2WxKPlSS9f+rm//Dlxfv8C8LDXys3//SpveVp3qxre+rlJvlmRCSyteIyJgjghxwNNnH4KEETiiUISbmEUAoQL16zF8IO35TcN2dwUZo2/6g3541rwqnfZPqMTtdC3Xa7I14rpGvV5DWGlYArvj3Ha2lyxVYnGKfObCkUscJ7ENHXymvu1TmvTMUnDCLEjfttb4VOPw/Sfd5690PZxp/X2vc//pPV4jfZ2G5PeuGfD6PS8TmNcxH+t3n5Pd9bOAvob6p7NaqBvtZe1lSsPrv8sD7QBAavqaPQXOOJsvnUEhuuoM5ngKa1uFaMJ6Bbb962nImXt0kM2i9rpDIgC1NHCHb5BbjA9AkOJznKHBpwHAoKb/smCeP8DhTnB7c4sFhMdDQBYBQ10S2dYnMKNoRWDV+AMjp1znTmkbmVJVEAXIDCROOGbggw+BL35BcHUpuL0B5sOC4WIE2FLuOjpakSqjRuav6etrzs8b0t54Rr9tv36+la2Z1zR3YmR4mchUCyNc7HZYLmZcX98gF2jUK2u5xZwylkVBGi4vL3F5eYWPP36KnNdWBGCtrfu/+ypXaXYamXpbaqS6aqdas51IAUg4AMMIPH/xkaXNNXAQZy6O3969DM1NsZ5fhfMMyLSgaghNHtjcb7rNvfESa+L/KhNqZWDd4VzNYXd/8MJCQe8pVsLUTf/Zg/6oGCErRs/Oa8BuzrxPIz7VvqVq7d3T0GZlwyz7bfdr3Mr39fPre079q/vk63+2P9OZ4us1Xs9re9pL+uSfNQHq/v58PeNZZzD0zxKQFVRqGnqFP/aIeMPTaMA6Z/b+mXN1esP9fW/JNM3K9Epd3voaOGBeFu0XswFL2XOJVKsB19oRghnFYoLACsENfogFL3B3vMXNzbVq+kQQqwPACMDAkKR576CMxdLwdrsAkdPa8QDAkRBEEFPCGICjAE9fAPsLwhiBJ48EX3uaUAqDQwQIyAU1rqiUpC4CtlTYomY9L/70NrW3jtHf17Ya8TltUq+VFTPRWsVOhKWisilUrR7onDMeP36MuzuNEnXTnTPOZBvVUfR+9VffxzCMCIGxLBrctiwJMQ4nmrqb2aVjCOcCi9p1B45xdBLFBteIr4K7wydYUqqEW9Cff2dAUk3OVePt3tXmRtNoKpKZRfquG9Uf1V1gzzzLpF2zO/UyNuhW9k/677XXkbljqlXC7ZsEEMXWQxHE2FIrVajKFtzXlbY8I3C9TMnrtdr795l3t9OExe0Fa3P262jz5zTo+0zOr3IL3f+89T2aqXKeC233af/cNdFe5yyrEnvewrW2Tpx3S5y1CNQ9uLXWnfa5Ped+WqHtPIMny7hBLafcVUs05L4a6wGuCH0CrDINOhsJiIDUFPvN2Py+JiG+Ul7xL91zXxWireJMzpofz6ZVVysksekVGUKCSGwdLSAqKBCkAoz7z2J/+Q4+Kb+ImxcvQIGBonSvmOVTckawFGOQIEQVkopkhGDpdSBI6bKRgk7zLqrSccwzCoAXzwv2RPiG3xJweRQcc7IAxqj4Aawopt4ETtffXnb49o5sQ7C27awf63VMnWZmK0UBdNgk3xhVYoQXS0CTmnMWxCFiGAbknJFSwjtP3sHd3S2m6VgfrQKEdBTV+mmHcmXS74icGnmLuQi0hnqRZJQjQJChIBeEu2WGzJMRIkZLMRR4ZK0e8tU06niqZmrE3fyLdd6cgQNmMVjPcRUupAHxvK4WRd2ayDkG3z1GfMK2pnLpA+vsywIENsYBjeit0f2QWhynSAEXUaK2Ecb0jU3j97H6nFBneXAiWjV5r8ldO9V3maqA1Mbsm8Ge8RJN8OvxTb/ss/MupvPn5bwwIW2vtSd1e6rKl5vv3COUVFbo/79H6PD3dRP9akbe96+9xaRFEHFdn/7vWvqYPSq+CeJtrJ1AQtUI173R/1FbX5uLc+1VJ0hWv7soiXp2q3XJma+Y22HluoMB4DXTP9XniQFcCdBXtoRqzyUEjBePkPGruL67gQbRjpri5yknFq3vihJgZnxxumR9NrcoMSGbGZ8YiCQIiyHmzcDzW8I7U8TVgxnTM9KIfiog1jOt9MmKjZWg47L3lm7Mb0t74xn9y3yBfv0+pu6fv+qg99pvJ0KDsClVyqRmnwIIimm7ygZiHBBCwNFS63bjiBiD+o6KMcZOe2752sUYxZopbv2ciqRn/mSPOi+sxTUkgUjN2MeUUaYjKAQgBMhSIFzAxQYm7d09kb3PiuBAPzF2oBQCc+8SWrT09jlnOPTm+f3Ha9pP8HoAbT5sreoadUzpntesYwJQo25JRHP5RSCsh54rEejrh6v5X4W+th7++wkz2WiU+vpOu5UOIEalJTQe0xE7EdW2uv2ymrPV+M6N+dO3nhm/zvvOf3bu3YR+DfSe8/70k3PaSQU10IzQmx5Mvjvt1/pZZMy2uak8Pc2fUfe7m6pNwHfsdw+SI1r/u6+ttvcZAW/7TRc2tuvXjQznfl3d25vdxRn3+mFNoHDzento3eHUvkB6FAEhswAo3DYDYFH6lTgjDntkIdxMdyAUABGCWRkv2DJqFoAtlB6o81rDgNy6SiqQSNYngQWMjJGBqQCpEG4mwtPnwDd8HogMzWQqSTE2pICwq0WRvPZiFaalWDGgt6e98Yz+nGly+/uW2W8195cRP7+31+DcDC+bSPkHDx7g2SfPq9+eKCClgnHUMonFtPl5nvHi5hpL0tSRYht4MXNSMRF/60nbEtITzVIEOeUKnxsiYUkZuQiYApiBaS5Y5hniYD7GFB1xTvPTZVXk4b75ITLXRJrVotGpH83Mvcb7b0LLq+FGa/82r18xndc+j69mVNvrjbh2LzJzdW/KLyXVaH/P3igprTR5by5cnMg79kLpJ6r/Kf16STMc4f59vDV7n4sFOe1bG38fl9C+el5rftVCrLXi/p3b83v+Pn/Hqm/VheNWnPaQ9djWhYj039bF5Nc8Ar6VGXam7oynMf3e1XJqyn9ZOzlTnZzSRtXNxBl6sLqxb/SSz/sHd+vLABA02BiWpw+R8+/cjkH6V24FEaVlTveWecYwBITIWLKl0XHAsmhsTAiEEPWMpezIoqFagkpRISKEgAKBwuUz0pIxDuqDT8JYFuCDDya8+86AqwcLjkcgl2ABuwGCBGBUXPzq3hQwWEF/3i4+/+Yz+v+azQ97b7IFdNM9efIEh+mIYYgWXR8ACzp58OABUlpwPEyY5xk3NzcYhgHMWst+nmeAqEb0V6IsUs229xHx3pffY9Mrw6mVr1XiLkDJjOunLxA/+xCZGCkXcIhmtX+5yao9uwk8HNgw/Q3pyt5V4xrgwlL/pPve8RqM/9f1e69qjbl6/0tuFxxgQ4N5yooo5mxlaqUJBe33DIukPKv99+0cNHOL7t5oviZQ9ZkBW8H3PgvNq1uvkW+ZZf/ZqaC9tuL07z3tz2sJf6v7zvuk1wJTu3crBFSTOrNVYlzD1LrVqv4uUoO2cE9fXxYDsW0uqxifATY8xmW5e4Xt7btfco8bgdzCVbp1MzxPvY+4ZgCUkk4feF/rZNF+zgMTkBKmwwE3tzdQV6JYTXjNt1c6Z7FPYWMp7SyD/tSUkiHmaXpdjBHgjHnR1NoihOMCvP+h4Jt/C5CXgnlWiwOFweiXrq2emQSmWNf5N033v4GabBjTOQ3lfpPz+tqrFrYRpFy1EGINrhtZicM0TTWKW0RQsuXXMyFDK9pdXu4RQsCDBw9wnCdkKZimCeAzpj7BSQR49SP1+eu1fwIKAYyClBYsS0YIXAF4MjLujkd89MEH+NLnH6OEiNnrdIsglaz9JT6Lr1871b0zhICpgtTYQarjWFtRXredv3elVqJSx3uY/Ll9cY7Jtb6W1uPe+tOb2evrTo2rmnvveIIFoStq48GAXtlN328WAM/vd2HOQYAgaO4aadXqVnPQGGfP1/r5l6pt9TW31/0XK1273X990Jx+VDom1lWs6zTDc61ZdO6zBmzX5r7c+tU3mhZPzY9O9jutmDrV8dD23g5ARbrPfGa2W5FA6of2ksf4dHv7/tFsmPRLxl9jP6yP3nomft/z/afAyj1z2ztFCmIckNTfiMgRc566p7+6/wpjve58DBFSBPM04Xg8IEZAoIy6ZH3XMERkq9qoMTF9wG+pSgwzg0FYlqWGFIkwhjiABNjt9OwsSa+//1HCN3x2xMVVBoWMw21BGAoIg72roGJmWCmAlxqV3tD2ZjP67d+v8El+PQFL2zeKCEKIWnhFBFSUGNwdDyilYIgRS8xYFi9CIphn1eRVYw9IKWFZFoTABqV7asbuA+9gxNur1q2iv1eT4QzFGHueEcIOIq0vx2XGs6dP8c0gBHYhoB3+Fhn/eloJmV/7Pp/WyzS18+t1/j2nJsSezDXDrQjWxde61963P4ho/Xzqf10LgWypN26SXAsA7SnSaoDqx9SYq37XStiaSrda7/q3M//WV72tpVG6UKDrbs+u1LzLfxePkLZR9VptzzRlu6f8Jjn5XnPWnpnXE0rZtLL1BG+FbnscdaKI/15/6mWDNzGl3X/vTeuN4QNoqaPU/O62SPeM44zA4RaAKpi8pguqa68UDF6iwfv3T734n4Y3rU+Tbl29lkUUsEZUcL2voNbLXkwm1Gk5ioJAhLwsmKYZ85wwjAp05a4nItJYpdxlZDidq2u1fq/Wmff8eiAXLYkboyBmDQJMGVgy4+OPBe9+BtjtCcc7QUkJPGgtes1OLog0Gvx4nZDXns03ob3RjB54mY5wf/u0B7O+a6tQFqkpKPM8qxAQrUqUa1BF8e5Tmo0o6OE5Ho+4uLzQvFGsQX1cW2/EpPXbCb8btfTzxm1cC/TIURGFhnVdkxhaRAduel4LQoJTLWY9B2sBgJnU9C9f/7yuJvWlnzYtanv3eTHgNd9aJ3jzjHtV1I2GdEaVUu2G2jONQazfRVppC6eEXf9qyI392jZG3wQBtwgIlXqfp0lWTQtuBaLNHGrBpu6CT0z7cwOY09wz9zVa/a4u7G2J2PvmeF2M5URLt57IRosH+qA4dD+xetbZflaLw6p3pxtJTm5tH23OxutYCr9ediJnRKnX8eGvFR8Tc8QEM7KsHwqaNQDTeKvVaC201787AbbbSSaEKnxGnhfMR63tEQarH1KD7FrfLc62tiKCwB1slhIam1uqe6JlQWnwXWYgZQEh4OmzjIsHhAcPAnb7guNUQCE1iwGVKuz4lL1llvs3n9G/qp0LznudaOTz2r9ugGVZIIWqlElEGMexIi3BikA0P7pCNmohCNWy5nnGxeUlYhgQKCClufZja4XIJa/7ASs2sTnaLRpcgWBijJomljOKIWdePdzh9uammsEaoRPVeDqz2XZ++mtuhSBijMOumnNduKhz0Y3p0wgCL9VozlxbuQtWN7ze+q+/cd99ze5RmYgz/c29wRn7fSOoDD+0a9teuzvGTMxNSPAEwhVF1CsmcGz3EQtQUwNLe71UE0gnpHTWhfNzQN6N1vMNY+195K49vyr4sfZ1k89cP+/VeWBlItZfT90J6+f0Y1w/iyC1ZOyqbR7T+7VXt60sMi93PZycrXNrf0/rx5VxGlJYNvf09or+fdKtk5jURJGx5IyL/QWGcQQIBje77vPpWbZCVH4Yur2gaXpKM5d5QUoFFKDxEF0uuwcBFmnAXUSEgHYWvK+5Bjt74Silq6Uow7aEIlBSK86Lg+DqOWMcA568E/DBr07IaQYPAq0BwpjTUevSk6NBvmQR3sD21jP6vvVa87nPtu0cs1NUJcIwaABWLmaGj+qvf/jwIZZFcDjMq+e66XxZdHOrsFAQrCBOz+j71zIzyOvFmxVhy0CbZG3E3vhI4Ii8JCXs2gnsLi7wtadP9VB4+ceVlWA1A9gygPWB18M2jnu1ZoSAnHPF7f+1BX+9fnsZA3n9t56rXHbOTnA/GXZzu/6LZ+9kZ1YuJbysg70mjy4or1oKui+zP68xmlMLa1wx3fWaNAb2yrUSQadjmSXhdCifVrjzXtortk9DZYknn736HX7+2t+drOVuFVmbqc/OQ/eq+zX2Tzvmr7+9qprqObFE+r1jSonCwxLG/SWuHj4EBJgOh/qd7VhXv9en1az6+miP4zkuC6YpYV40ZTUwI8YBxSrWaXBeQMmLWQI6VyU6CwsInrpcoXkBDMMASQksgoFYT3MpOEwLMgEfPtUA6cf/3R4PrjLupgQmjWmC7FHKDYhinav/eiv4X6e90Yy+StHYGApfw2TWNyd8pzXst5qRmNBKhuBUqjY/p1m1WCOyHKimi6SkBVZgpvOSBMsyIy0JMQTELpWNrCAEEZCzIepV7X2d6ue47U27RtX+h2HAdEiQLABpLIAQ8Pz5Nd7/8EMclxk70hRBNbfZe1mZY4agZuCdU3S6+S2Wjnd5eYmUEqZpQkqp69eni2T9tELBiqEQVYGn1777dp/wcarJ8+arm0p93YfcPVefYVjgrtWL+8u7GyRrUZONBuj3W6dMYHHztd1xzxS1PqxxGbbPbkGGcua79Uob4upZhEK9D331Ay5stsetGezJ3Zt+9stCK4HGuDP1wkr/zjVbW83Fmebm/foGB4jxZ1hwpT6nSxdVg3R9R91/YtplPU+bud+Yvl/ZZD39L0t1e93msSUgHQFbIa4smpr2+NFj7Hd7HA4HHObppf3tz53HRKixytAOO9qZc8Y0TTgeJ9AuGtpeNVopzckaS2QnYvUenXGPv9ACYkWSxkqVguM8I2jiHQgMZmCIwDxnZAaWJLi+zvjw/YTPfxGYPwRKjorMR0f4eiotXAuFb0N7oxk9YJsNWHGjlwWQnJNKX2a2XxHvbgMUyR0R0MhSNqQ5mDm0oIApIA4BOR9R8gyAwDxABEhp0Q05RCw5WVEcsgNi42ATugXVVN8LH2Z1UxjaYvnV0EAYPRQqZBQAcwKu7xa8eP+rQM4IUfP7uQAJgBApcxJUgJg2rzr2dtnSAO1g5qz5+heXV7h88BA3tzc4Hg7IZjEgAGyBf8T9nNrPztzHskbk2tKZrQbJprmTNHLo3+FCqCqBmyv9vc5ECODcMZFV39pfOrvZft9YO/xOZ4D141Nfant5ANV1WhM22JicyBG3dznHZzRzZnuhB2pJFyvX1pL8mWWtbROgMQX1+3rV9+N2AFJEwUfWM9p1sdfMzxHOXjx/2WdY/+7PPfla55fv5HLAGRKgvv9tX9c3q6BdbWNmdGk7gcDrb7vJuuPKsvmGti49rLfK1XtaZoUD0Pi5AQBwwelsb3eWC0Hdkt3Dr7hoQt0sBYgDxosLvPP4HYzDgPlwh/lwAJYECq8WSly4QSlqWTIhT0DIkjCEiGU64rBMSES4inuUNNuwpY6dKJhrxJJVRRApQLw+hTtOigrNqvgYOmmn8ztPCFSwG4CyaODm7VzwKx9PePfzIx5cRtzdZSyiBb9AAQKjo8KnW/4Nb288o39N2fhTtXNBNfZBJdvkmpYxJC0AYVJhYMQQkPICreCkDFHpqEqkuWSktGCIAeNurO/FCZEqRnhaVHVl8kYjWj9KJfw9YA2g1oHjlLAkwdOPv4a0TCC6BEcrE+kjs/cyqOkw0v9cayUEqdj/8zxjHEbsxhFXVw8w7naWnaDpMSWVBi9rBXgqgxYPHjsz7ytt19/PHZ1wItvuX2mJG82ynuHeZ+uIvYRKKf3u1R4rvfC3dg2Ufv5WmmgbjzPx+q4qsPWMfDtaMSasffO64GsByD9fs4HViLeHZTO9IqdCiW3Z1VCk/1L9tTEsdH+3o6PCbz/OyphP3tkzyDPN15bk7Men/uTWy/MM0t+qeqO7fDwXg1Zj6pdNun3mqIYCZ32ts/bP5QHbG+LCI52OlyBKM3yopDCtVZ7oBOPTvtwzRoLFCREArWYZOWIY97i4uMSDqwc4Xl/jeHONZTqAHZ62m9dzFiAfcU8f+/mPzFimCfOyIBNhCBFTmquiVHvMrHShyqw6WVuLQkFBrrrcuRVVWsKsNetD0ruWIri+y3j+nPDugwHLnFGWrEV4Amta4Vva3mhGT+aIfBmzP9GUznz+2sF5najs/iNnsI53H0LAOETsdgOOx0PnB28HlAAsVsJ2t9thd7G3dBHXyNAdMNPOZP3PP/HREyncbhEgl5aCJ0Uj/fOScbybEYjx0c1zHO+OePiuAENEgdZijjDcdZOQqza+OmjGOoyoUA08ZCzTjBu5Rs4JVw8f4tGjRwAEuRQsueCQgZRmzNOMkhYgZ9VuRAMGYe4IV2jYhKPePGuDRRAGiSH5uSbT5X0LbA5YVnPJcsbaAyCfVNYrJ+ymalqgFbH192lfpd57jii6r1jpmFj5TMHW29p5IvR9paqL1ucmFBGcMUgvJ63nrPtdMyK5n9jGgF0A6fpg0pj+uuHyVLUp3QcrRr6RBjIyOilM76C1sASoiXY9gv6Z3Ujuqb3eyqfqIKQbWuMqW8Zq/xeufzchqcNYsPOpkeodE9805eW+1sXOPUG81pR0Z8i5vzNO31fSvVMsHayuE6mVh6hZKcQFm85dZmMlUuAaDorBL3GHi/0FLvcX2McREYT59hZ3H38N03QDIQEPAaXbUPe71MQsgdJcH6tBEpZ5RloWu5+tqp8/14VMdQm5wNlSW9eZFY49sZ3w1ZkzoSYEq1tf/J/gq19NePKtA4YdI4tgLgUh+onnk338NrQ3mtF7IJlgzdB/rWle58z8q1Kx9pn6lIAQ2MAnGJeXl6o9H9VPzaT5nSln5KTFZYY4YhgUREJKwTgMGIbBUvRgO99fvCEjnvdu5teqEVZpWgmbAlEIJDCYBOk4YzpMePDwIW6f3mE5TvqsyDjc3iLuRnApEFLTZHql7UqqO2ESAQcGE2GaZxyPR9xe3+Byt8fjh4+w2+1wNe7xucsrIDAoMAopiNBSBMclYU4LUs6Y5wPm+RYlZeRlgsxJBYJcvACfIZNloBYJa2mJvn4UGDEGw6PviDEFFV422hCXXJlc1VM220gIyIMRARGgNO8vQRDFc7UJya52s9UYg68xgGhHULSObrfvOlMvAUK57r110x4EGC8u67gCUx7b9+pnaqL2FtD6pIIbUJBRzO/Rna46x0p5GRmpnsOT1s0p27y1c0VtmldCR94wYl4JUc0Swd0atjQwfVz/vNPzvFpa77sAXujEtU2qjN6NyqxI7gQQbUWS1hyIp/n8xR+qrN06ECWbRcvfXN++6edsAoEypCKCUhjgAGJN6aUQMAx7xDggxqj/xgHjMCDEiBAYIbICfTFjLIRymHF8cY1Pnj3FsxcfI1GBDAxIRk4LKOxO5u5sq3N4em4YwN3dAcfjhFJQg3WxWRdBBklR+F0wMnLny3dXVofh1/loqsxtFlDdmoQYA2LJKFb+L2Xgw08mvPuR4LPvjdiHjMOzCRwDgtEGgSD/JqP/jdV8u3x9Eb5ff2uMX/2YIhnDMGBMSTXEnKz2NBDjgLwwFvMtLWmCQPGbl6wY0EOIWGiBoNxbPWkbXyAiduDVZJ8MxIctmTQnLec0LRm3dwfc3t5i4B2ulwW3T2+ALxXsdjtcv7jBBQeUbJjtzCvj48saEWFX02LUbVEIyJLxyfVzvLi91rkSwRA0HmGIOwzDzgjSiP14ifcuLzGMe/CDx5C4q9quk9gigrkkzMuCOScs8zVSOiDnjGVaUOYZyBmSFQJTSoakAsqLMhki1WR8DhuHhkhBlAFVi6rKs/glvVUEu/YhhKSSfxd8ciVg7jVcM3cSY5v2mNSByDBxR7Ckzq/2wbUhnOPaqivT6sNukdZ/9ul13ko1H9t7hQC2rOgzj3Qhm0hUAPO+rrrogotdc+FgZSbp1Dr7RpGNcEGbe/tB13tCJwD096nFqD2KjFn2d9hP1jTG/preHuGCBIEa8E4/F5vf3a9cLR6kIpO/2Ocr9x0xrb/WqlcHtD7jwTuIMWIIA8Y4Ig4jRhoRQ0TgiGCY8QIrr1wKUDKkFORpxnI3YZlmNZ+nGYd0i7vDAUtOcHQ4+BnhgCJBS8/i629uVWBmHO/ucDweFUaeAKGg2AqmvfcBz57Sx4E0OFiaFu9z6hYAn/NauKgK9Q7EA+xHQiDBQowZjCkn/OIvzXhwucPDBwMePsi4uVsQxuBmAdA9bqE3tb3xjB7oDuWGer0O8+8jYc+ax1cm13a/IzipJqma/W63x83NDUrJiEHzy0spSCkhl2IuJwKzRrjmnLEsC3LO2O/3uL270+dh3ScfJBkTEv/pWo4AkouWoE8OwWs11RlVKt6NO4Rhj5SB2+c3SFPC+GCHyBEomo6n40ELmLtH6OjnpKQMCusAuhCClaws1RSboYWppmUGlslMoAqmEoIKF5FHxLAHEyG6JmKpiyFG7GPEZWDQ/hGI3wPMHFqKCkhZClLJSEm1gSyTEjJbh3maa6QuLIBRckYprSpdsxSpFWG9t3r3gM+DEclq8BNU7md/CjoYWzctS8/TlVg18BE3F9sVCfWFTRhob6zCw5ap38P4qdPA2xFpTF0gQOmEFfHeNIa81qdoZVkT26T9+3t9uj+WTbszE21flnR7fpvqrUIGuQDhz2o9auOXykR98hgu0PXxDwIgG61vY+pWGi0WZtv/Na0oYahMmuzdDMW/4KBR4yEEcLysGjlzwBAjYvS/1cTNIBVWfOyilkCZF5R0hyVp9LmUgpRnpJyR8oKUE3JOCssNsfgYFZq5qLVGLNhX3XUMAYMyDFOAVHB4RRObOyaCe5i2VprD4YB5nmt4ircmWqLOnZig54W1cipVw3bBIedcPy9F+3lK75Vhh4Eh4rVAdDxTInzt6YIQBzx8fIFpSQBJiyGSV4/7TWpvNKM/BZ7oaOsZ5nQuL/5c20roL8vT1nv0774YBluqXbHgEg4BIQ6Qkqrl1nHxiQj7/d6Yqj8cK9O8m8bEPuuobGXGUnJl/BBYUYqCo0FPhv8fe3/2a9mS5/dhn19ErLX3PkNm3rxjzeyRTTYHsSVZlARbhGgLtiDZkiHDDzYMwYINWdCf4AcDhgwINgTIBvygF0keINmQTdgSRxgUSVGURbJZ6oHd7Krq7qq6dYe8eW9mnmFPa8Xgh19ErFh773Myb1WxWXndcZH3nLP3WrFixfCbf9+fs1xcnkGC66srxv3A6tEZJYCvEqrWZZHm4TWnpiJKU94kmzS1spSQoiHVlbEUpD6lvQlSRGLCj8pohR1iNhjR4EaTK2nZXDGwxEEYu0Bsp987p1pWowEtjNH4CJZkOyuQCGepRvRCthbESAz7qUZBTVtUCwFxsrKoQJCjznOQYUUPa4QEWiGsMuNm2ep1jYCQOPKxl/kuiGPSroFM+0UyRO0hzvhRJB1UE3ArRE4h3811jfWivXt2jRovmESSVD9PMgk3bddlrHpXqu+vRu0pqr1YKA7E9+n/JrurZH7NpNO1LLqVEYrWPclGSaZ0yAp6jmrjbSxQqqVpp1K1ypDVIlPx9K1DxGZLklQApWIRKH3aAtnLJNykEEk+EDJjlpQUWTNGYgqEGAlhJHpl5DGqIhFTIIVU924sZy1PZsovnYAguUIdk7Ck38fsktIxhvv1pGnGW3okB4JtUiRQ7weMbdY1NecgN419OvGIvICFRqmrQgMeDKnGEhwOV9dYMCZhTdLc+XzRiyvPYmE4O+84v+zZ7QaS95TS01+k9loz+lPBTvmLk8y5NfvkT9pbTvd18KyqxTb49IrQpEzCWkfXd3Sdy9clrNXPSYlh7/Phs5WhQAZ8yGan0m/IsI71+QfWhvIOkxk/1HcSDGISwzByu96y3Y64TmMDBPjsxXN2uz0PXVdx6idLAnPBomFUR0dJUJ97sXRkAhZjxE5cTQ+kQPbUUq24IjWIJqZIlMKIIz4EGDMjjakhHlK1YmMtnbEYq35HFQY6+r6jtw6RpVbicpau7zHOIZ2thKIQuLG8b16DGKMSzzBhFaSUGMNWBYAYSSFmgCAVBkqZ2pQS0hQ3qsFJTWGZlL8zmeHOfcpks2ae38KQKjOe9P6joNHWjMDkuS6BTKCCXGF8U+BU7nd2e159ma9/alPUZnz/cCztWUo1zbEVCErsQhUC2sCH8tnscE6/1vS+yVFPm99eL89Ie3OBVf+ymekmAS8aCFgqtylTNlp9raSTWgP5exU4LdaayvAro0+TkCAiFZOirHuxKonfQfSalZItfH4Y8X4keK+lWmNkCGOOmVAhM5BrbQhZ2AFMQqJTS0d+RzEqbJjCVLNGPRpV5SVrsIhifNQUNu3wTmXosNUYoRlpTdk9kRl9GHFOctW4DD5WXUAm0wOjxW4a2ljrz5fnpFQDbxOtsJv3VSMQ1EBoSYiNGIHOaIzD7drTvzBcPkg8fnyWg/Vi1vpf7b1fl/ZaM/pTrU0RmmvjWWeoG7cJrjvB5A+j9Q83fNH8ilRfDu9iseA8nTH6EeQmm9AL1TYaGe+9Fpsh4YNWr+udaqeq7B4T/toK1GmRhNM0PmssZNN1CBHXCbfrLTe3G2X0TmMAAD5+/pTbYc1Xe1fNYmPxjeVglmSmgLWqjzSCkjIqQ4fo+wLiFPo2lhmWEs0aMTIBcFTDQzLVdFjSqqzE6WYLIhM46aSVTNjvPiUInsFPwlCMkegDKWNoigguazFOTAUqckZNqN3yIcZmX2cOZFr0HcZZpMvlK61B3ONKlIrA4ZOaRpWXqWl0HMc8jknDCsVSEPT3FAL4bdYgIiFFQiym4RJ7oRqRhEAbZV5S+arGBtkXHerakKdw2v1lFo3uyby2SY6xKDQ+wTM7J4DGo0zvCdAXznxAG0WKvqwDCQ1Dp9lLs+eL4JuO2nHN8/4FY1x9RhUaZV5HXoEqev0+M2Jb/lmt9yBG7zM41bCN071isr9ccmS7CCYExE9VCGMMeW0D4xAIflCB1Y+kMOrnmXkr1kQghkjI1jcf/UxAn9Yt1XeCHEMhRYA3OKyey5Q/y3Cwwcyx6VNSd1lM07wBOJ/U5ZWtOyQ0vtUYokl4EpGAS3eFG06tKkFl3xzsgRACu92O4D2dE5yBaIQQ8tVicnxBxBih8Pdinp9M9CpYe++zCSbU82HLXKW5QGey+xCJWAMLZ7CSWO8VO+TqduTDj3a8885jzs8vityBeju/OFr9F47Rw5wxzxn9Dy+ltf2kRN18rc8+pVQlfWuLXzrhskPQGMfZ2Rm7/aZK9MMwcHV1xdlSC9zENOCDmopt0dzSnPkXybYwkUr8synZGEtnOyIjH330RCXXfsX5+Tn7Yctbb18wyMA+E6VQNDZj0YLrAUlCEjdNmUgxbM6YfYyRAXBdV//GB1xG88MnOmNAbIbtma0ULSvIE0qap+8SUjMMAJQoILEi+U36m0EwWAEnk2+zCAUpJQZgHwKpwdqO15/REkjVwMxMKxMx9OJUiysaXV1vdSs4a9V6059XC4MxRoUIZ2rsQrXB96vZPsqbaWKCmfn7xtSZUrYoFK2ovEMqDGR611NCaggDKYw1OPEQYKoII2n/DMIWZaQm1+vOEfA0Wm73QIOr8j6BY0EZyKme0x6SVgNu/tW64Lm/KahrMpmLKKDv4b2keSS8ALaxyFSrS1TzeMzWlRBG/HhbfbTbnCUT/ahoj34gJp8Z+4GLJx0G0BYBLZ8ViUfjLPECYh02GRRgq+xj5eCNSIfB0ZYw1s8aLTYzpeL4SNNhoVycyFUmI9jsq7YZhy4KjEljUowYLIJN6US65uk2iy1JE0CyiBC8Zz+O+DBm92Ks66436bn1QWNrWhddERSK0ua6Dt8EQmucxkGA5MGetwZI6kq1CyFtPNZATIZhhGdXA589e8Fbb7zHfj/ix0iwAuxe7eVfg/Z6M/o291lyUJyqgHXhJ5PlBDM7FwKOmf+h1lo/BzUhi1RYbE37yOajmOiMhcWS3XJJTHtSClh7pgxHwMiC9XoNJmjAW6IG4213O4ZxQFI5gDEzNAuppOqU2u/qg6uBXZRwHw8mgoVxu8F3S/qLgETY7jZcrJaIGdhfXTNud8qoQmBgpIuaqpOSxftYEe+qGfNgPkpzQBvKrYJ1vs6qsDPlYU9seVqQ+r92FZrfjmA48t/m1PLp97nPYo0oPabCaKzMmVFhvswDxUoub8qm/SElSKNKH2Eafju+ot8Ugl4Icg2sMhOhNwcaaCGAlRDmz63t9Pocs3DE4NC+KpMsn5f3NROca3ILkuunUTfWlEkLF4gPSXEgBY+PYxaMUmbQFmt6xDiSXR6tUBuaSJ67AoyUmvVuz1mK5bpdI6xMLpFQCHgx32ZTa8zacXFfxaDMpAjHobpVVPuLaQpMq66alHLGBHXM0695XCeYngYCWor1fO46aa1y810y+0YaSbbOWSu8zu9or1SGPt1zSqOu81ysnTL11oRkYuUAv+DUC59qAkEiDAY6k4X1BEGIC2A/sF5v2OwHIpppJInGYpXHZtQCE2PUvSKibgUyTclCe2etuvUo7jYhICz7JcN+zzh4QDg76yjxM5LNH8mAdOAS6nqLMA7Cb/7WLf/kn9xzcf4AkyzBX7/au78m7bVm9NWUXAyTMk8Lmxj5XLuYmQCzM/TI19loJq1pCgPWSDWxl+MWQkSImk+OEuO+71lvN3W0kAPwyEiRUT1iIeOIdH2HbA3lg0KECqHLAzrJ3BQ4JiAmITHC4Bl3Aw+Wilu9WHacn52xGzzbmxs2t1v8GLHZ/GmMJXlIUZ9tcqBLOfzl4IlMNQEO4yKmt5z/UgME6/yfWs3juZ96OHXDKxKhUz2duLUF22mbEtJ2v0hmIvXb+tuR9pyJdTUABnWLFKZQBYI6pknImHzMyj5MowmT9zlShIY6OO272bszzbj5Ob3txNhrn5nZRyGbQgIhjmgkcq4Cn03cIlP+//G+TM1XE0MrcQGFyRxZHlIun9xaJfI5mGlrBTAphaw9q0apzGHOZNPB86bhVh363gIxSmea36uJfb437rz/gAa1c1SHcUKIPmT09z3jkMkfP6u99v7zU/bzfc87vFabUDEgjGCdI2x27IdBq85l/7jEaW8YI9X6UcZc6UQzL2VfGpOQoMGK6tO3pBDZbgYuLy8wF2pF2O3Wue+yj3JXAsbmzAIBHxO365FPn655682eftlhd681azxqr/XbSMvWU0bUUtGvEsiT9x0yEpNRwtpDIXewl0IhTdmDk4kV0c0tQTBiWS5X2Nt93bTFVBlCrNVJJ00k0XVdjdzPL1iju0sgDTlNpBKoSvg0b9YaA0YIw8j1zZZOYIiRhTM8OF+w3Xs2m5GrmzX73YiQATfEkvDTWEVNwSbDUhbLwSmT7NF8niAOh0xwpmmcuOcUgXoVonNvOzQkHIynMN/U7oXCYGc3H2j9szmZnlWZefucVJ6hf0+Ya422WjsrHTDLBSdNX83npCAKHb72/MUNTZZEml85afUQxWWhIqFpVlOKV8rMHgQJYx3oqXWrjHXmym8mqpijizyY+klLLZeKvl/LUMTYzNQboKQsiClPkDmNKFaGEvhVZqNq46fG3o61zNHhmp9693Rw3+9dO3XOjts9AsMdwsGd1xXXnzQBvfn/zjr2ux3DMOTa8pomGuMcwa8FiKrqUyNotO8Ro1a/K6ifRgwhZQju/hGr1ZJxHLi9vabvXUN7tHeRjJpXlK+oVp+PPlpzcXFO3xsWi9UrzcHr0l5rRt+CsaeUMBVGNh+xqBL+XZu9bKSJQBxL/Md3mjmxEclSZdn0GXnKdqwWSxaLRTbDQt8tuLw45wcff0gyGghm8nNDCPR9X6NLq4pCwxRjypHeOXo7I+vFGBETSGPEOkWy2rHje0+f0xvVxofdjuvnkd3OYE3Hixc3rG83CipjbE4DnEhiMYeKU+GpDT6caTR3zOt9833qulcjTi9/xucWBko3MwbbMpmyDgc6fDr8JAuWKQs1UghX4Sh5DavKPvuRf2+FzUZQFU0N0uE0At7BqxSN/3BqJE1CDClloJ/yGneto1ARXjLT1PTI6a2LxUmLf5dxzWZp1uwJh680c1MsAFEixyev2CEmwVAy7kMugVKvTHG6DoR08NwZC06NKyEd04pjS5+Zjffu9vmY5V1C9GvR0hQvUK0pCVKKdMDN7S27/Y4YA7a3U2mo5h1jDjLWTAZT8UdKJsN0nbpqOtth7RSgl1LEWthuNzhnpi0rLVx5oUEaJlPoWhH6njzZ8OjNa9595wHnl+e/d/P3e9Bea0YvhpyqYWdMqLQYM5JVA25z2GbaW+m39JE/n9JmNNCrBNJNh7QpaJOjVjEJHwaWveH88pLlcsVqtWJ1fsZXr6/4nd/+Lsu+o3cWHwObzYZHjx4hjZ+s+HX1eanCuRpxWAMh++oLqEUMCluZRNPF6Hv24x7nPQ8eP+Tdd97mO7/8Lf6F/+4/x/OPbjBGuHlxhes7ki9ldCfhpTD34i9u0QDnc/VqROnzMOGXCRO/V+3+Meu7p2xBmsZ7YFE6kBgn0/OdNqfZD1LC17xec6Sht/cdot4VT880Mpjq2R9r+/PuQmagxyBB8+ts88exeFwtBKkdyT0dysEFJ4SHGCMp7E8+5/gB92ivL7l/7uYrQtQBnN3J9irCwD+oJid+v89x8Tlaa3JHrZjDbsTHRCpMO03ne9K2dZ5i1tI1LiWXo820VsiuxJzeWepiWCeIOJxTRn97ewsiLBZaX17dkBo3IAgmKeLfGBRQxxmwAnufeP+DK7pFx5e/9Psa/U9Ma82XUiAjaVDrCHXTwLSl52bjsiunVLnSBJhrnRllThLG6hWtxlYZvxWMM9hOsFZ470tv0/crUi6Y8It/+I/ysz/zc/zud77D00+eEKLWarbW0vUdbu/wXmszx3oYWtS2Qsw1zzcZQwyBftmxWnZst1v8esP/4L/1p3h2tWa4/gTCwM4PrM4sP/jgu/zgtz/hD/+hP4QT6FY9/nqDJ2JzxG1KCecs4zhWZl8AeEq8wL3+whNMuhzak/EPB6b8u77/3C2lmql3RHNnivtpzeq+99Ebjn37GoAnkwkyj+MUw5KDnwcSQWtwaAZ9ML4S6EiJfzgh0Db/V0z8w3dMjQk2M+Z6Vw5yBaRq1WrpUPTACZ735CoVueZV17Bq2XOGNHOXUEBoqGb/8qzZ38LpyPFXECIP1/z0XmyFl8N4/3atikXm9HOPY4deXdC9K77ovmumMR6O9/5nvNKZL/EMAs4Yht2OYVSsAFfcJ0xnrfSpKH6hFutJoiHUNSYoB6IWRct1XdXou94xDAPWCa5zSpezohJne6Ix30uOyNesZEYTuLmJPH26Zrno7nzP17G91ozeWE1bqpsv/zCF+ErGd5Rj/zICkkxLD+Zmf6ZNW7Q2AOsKSAVMwNVq5iwwrGIM/WLFwzfeZHU28uabj0nJMPqEEauYzz5oTrdzpKi5oSklrWtvDWkcIbZadNbOUsndVeGkBrqlhLGOcfRst3u2+z279TWfPb/mXGCz2fHk2RVnyyXff/+7PL/ast2u8cOgMJgxIs7UCPti5oLmoDVm+za3dRrfaUJ1l6/t8/jnf1jt/gQw3Kyd0rte3Y2Q5r9ln7AyqjDj67MM8tbqVH2arZZ9LBMIrdbcjpyGn0xBR3eMUH8r45x1J8dXplj91o3IMd2SdNxR5pXwDvub2mmtsfFS1b+bjoACvjcXqkIbtzBzAchktDgh+My2RBVaX85cXy5szjX9467u38Ov6g57mYn/5WelbJj5erxKX/eOUTKCY85sIAclb2437PYeHxJdKumcUlE3C2jRpKDluUuJ2ChgpmIa6DoVmglTzv1UMCfVzKtYXDKiwqJkyd85QUKCGPBJzfkhJK6utiz6w/P2erfXmtF3iyWuXzBFC2eikb+3AiRDSqGapGFCtTMlba2w7ZyC0yJxFZjIEvlb0cmybxCKBcFo9bekpiDTL3m4VzCNs9U5Y1A/uhXH+uaG559+yrDf1zH50efNqm6AGFPNHjyW2Ccfekqpom2J6Xnx4hnXV9eM3vPs+XOefPqcy96wW2+4ut6yOH+D6/VzdnvPdrNn2O6wDztCYhZ5rqb6NLNwpKQIVQXZ6q5WDuIh4TwMyGs//5ED7Zq+jgf08vteFsdxzxOP/sqiYfPsVzAdN8FnhyxVaBjhTD09NZenP28Gc9zNXVfLPRfWcyZ39vfqK5rTIKtp/IRG2vy//BpboaMy1nYe5yG5x3uj1caPmfyPsiePt+FLJM7P1ffvbQGv+9osIE9UIAtF+RDBGa2OOeaCW3qPno8aZxJjdn1KtvqljPo3oRwqs89IhdZq/ZBcw6OFHu+zll+RJ3OUv36fB52FQFMCTYNiidhM87bbkRcvtr8X0/d71l5rRr9YXrBYneVTpSlhClmqZm9nFeBDzUFFEFBJUL/v1EQUE4GIa0i0EghpGD0kYq3RHsPk07ZGwR0QTYMyzuFjxI8G0ohzPUhECKQIw27Pk48+YvS+BtNFrwhapQpVMV8WzWtikjGb1CdTfnFBCB2fPL3i5uYFj9+4ZL3dcXt7w03Q/sHR246z5TnP4g3r9Y717Ybz995lTC+a52SiC9VX1jJpEbSEZZa042Gw0+fQPtrvXka8XkaIT/V/Z58Hl95nmjxlmSh+wuNOE2kWsFXGUTujtSxPQB8l8rztc4L5nD43855lihHIutDMTHGsywITbuG9LZUslkafL+8wtwiEWZD83f3lUbTCz4EgeDzW9t6WiR8kiKWMmd/2IWW6pf1o1utd+/M+we/VWjWz/IS2u+b9c/RQ70tVZkqSKJXwjAjOWNbrNTEFrUaXhf8aDJr97FU5s1oPo2j5MIGCpazFOecqk0cKzoS6FZfLJSkmhmFQxNGuY4yHez0iYik6jLGCCQmXhCiaKr3eDD/UnPykttea0a/OLlgul7UAgXNO4SZHjxHDcrnC2C5Hb5JLHio8qbWWxWKlPu6k9YcLIpNk23yVOusmyzjYVTovgA2pAujUqNEU6dyScXvLOA4sjBBD4Pr6micff8h+swar8JvGGGImlp3rmjQ7fcbkwwpZSp3HHRijkK2b7ZphGHl0cckf/9mfYbMb+ci/zzpoKcu+t4iD8/4Bndtwc3XFZ598xnt/4ue4Cu9TQIVy8hQi4P1Y50QjXlXIubm5puscFxeXVfK+i9He5X8//OzzmiE/j/++BPNQZ23urnnZ846YfWWip8YQOexlpoc3ACclGy5loOD2WjU3AkkINRv/oO+UP8uasNCgGeqA4dRoXmXOauzlCTMvqWpuUvu7762pYzya4UYTn/5/P9NtLfLSnMX5HOo61drijVBVn/QjavGv7uaByYd/LGR9XpfB5zGp39d+1IDX4napAEkp1aqABTnw+uqamDzWmUyjCyJlOqIPoYCKUJSOwuQDMcIISJqC9RQ9JELQvsbR0znHYrFgHDMst8hcWKDQdj1zzgjSGXb7iBPAwH7n+SK115rRX1w85vLBRfXnGJkUBHXJGAbv6aGafnRTTWb+yQd4TMi0ylquJ22UmNkspQ6Dmt3FCMaKpgRFIEYIHpM8S2dJyxWdc+z2W/bjjmHY8OKTTwg2Hw4PMQhiHc+vXnB5eYG1mrE0DF615xDz+wl+9GyGQS0R2Wwf/MB+fQt25Pzc8tmLLf/vv/Er4AcWzjCOKsF6GzG7F5w/usTHxGaz5eb6GiuGIY50nBGJkAIx+gzv6mrqS507A+cXDxERQtSyuy9jkK/6+au1OVO5zxpQnzNZaH+M7dXMsUePbAUUOUG0688SA1JM06cFqVR91yXf/UdvM23trmvKTzmeiVPTLLnK3av5kF/9irw7j0bwEgfT8fUyzeOrjOHztuI7Lls1pYDIy33BL2Pq/+DM+IUhgxDxqCXV4pSeSiIK3K7X7PdB0bWzYGDzfcaUWiGBEDw5UY/jHVTSmC0+CwqJQtfzusWkpXlLzQ5n8RkiuuCBhBCzn99MlfKyIGGtIZBIIf19WP1/sO21ZvRqWcxUJjteFKY1/ynCApcVhpQvySbSOEfQMzZXP4FJ8kummoaKKTuE7A/vupwDHxmzq8B2mtcbhpE47uk7xzb76UWEzWbDr//dX2e722E6SwpUGNuUNHpUhMxgJ5hTY0q1K4+gGn1bICUBq9WS7//gM25vPaSOheu43gS265E33lrgFoYQwaRVjuQXtruB9WbDyjmS04pr5ZxJCLkc52lNrYVqPUV4Xqa939WmS+7W9IvAcTiuV4pUPtIeX60dE9P7hY3WWtBqmkcxC/eOopoN7iXm03ufuOZz8oBD0/crX3OwHKdm+C5hZW7u+BxWnSPr//1rJHd8frrjVxFITrc71+LoGpjvjh+utdamo314z9n7YQWEQxdLQUrMTk4Ntos5aDkGfAj0C0sYTXVVFhx7KDFTU1ltazutFwGZJk6peJr1NNswQKk9UiqIat/b7XZWCwLAOsW8H8cwZQCJKi/OGQYfMKiiFb44NW1eb0a/36zZ28kMWTC8WuKYCp4yKdsip8hklQ9yNKfNsJulSTFJUjG4U0o5QE1wzhHRlBCfi6NUqdGPpOgxo1UrgwidU8n95uYmm5FKnfZUYwe0tnQb4FfGqoAQgKbupVKNKoGomX0YR15sRmJA64jHwNnZgqWBP/Am9OeW69Tz4ccBPwz0HQzjmqurZ2qxsFbxp7Me2RlXrQan2l1paPCjauslRuDlVx5G9v842quZYk9ru6/iZmj7/jxjPkXEf3Sz6+d7Pvxo2uNda/V55+Kwv/vay8zeEyP8cc/nZEI6fl77XXvtff19/u8+b8zLD3ONVKhHNXr6xkUmYogxsN5uiNErwTVyVBBssrJOQEdlrxdXZbm2MO5UiUTjhqNJrU7FLTDfXybD5ZpaJ2UKtE4p4qzkwnhfLJ3+tWb0m6vnmDg2SoXMClWAEFpc8pSoPs66yTKjz8A0MJmTJnNPjhhNqZZwdU61d9XIFf+7VLISMblGOyzPz3M/mn8veYNprfmyEXVjej/OIkkLcw/BUyo+gZa5LbgRkgPhrm/W7AJ0IsQY2O+2vPOlc/7guwu+3O9wy47P7BnPX9ww3u7pusQ4rLl6/inj6PUNJG94gWQscocv8WWH//N+f0yQ5lrOMSMoh/2H6fuHH+fUadlfP3z7vEzlFJN8Vf/wj9N/+8MwkZcx8h91P9133/0BmxMy2om7f+Qxvfya5sFHl6b6Q4QaOQ7cKey9mtXn5e3zCoBqvWImnVfcDe+5Xt8qBG62wJZguhbkrDDlCRgszBj8LCW1oIFaO4WG5EqeXddlhSzm58Q5o88ZTc7Z2k9R/IwxuA6iT4xfIG0eXnNGf3XzAh+HGfOJfo5aV8w4xpgGSW/aOCWjWGu4h/wdkE1QJVfTGKO1yxFubm4YhgFjDMvVgvPzM7quq/d3bkHHAmMNu+0GZ4Tr22uefvqU280t0lmG/V5z5qEy9HH0eO9xztF1E2DDYfiSMYYUEoPXuuad67nebFiaBX6/ZrPZsd55vvw48C/8Yz/Ddz/ecH078LYI/40/9pi//l99j/Wza243I5vbPVc3L9hvr7m8PGMhlhAHor8Bd3YvoTx1CO9iDp+XEd1lgrzv3vu0t0qM7rpf6v+Y/9ZcW1SXO97nhxEyXsWt8eMIxvphXSmv0t+P69ofd5+vxtw+vzXh1a8vAuFBVbh2LVI6YvI1zbBcl5l9OnX/D6G132el+zxtLnxKdVflPxGJJB9Z77c13mnStCfku8LQW+YPpgJ1Ffpbrh2GYYq6hxzHFGp2UGzcAyVAG5i5G40RvC8MXuO3Hj14wMdPbiDCshdut18crf61ZvQa/TYVKRARtPj71IIv0ZMZJrZEOaeD1DTj9KdM5h9jNKJdf6q/Pka4fPNN+r6f6lHHyK746Z3DE/HDGotlRLXslAJ939H3PfvNPmvy0yE3xjCOY80IaBm9xgXEPD61HIzjACnh8kG4vtkyIBgTeeNhz9tvLfkLf3XD3/u1X+a/86d/kffeesB+fc1jbvnZr32Fp893hPGG7eYFb5y/yeXlWwxoHEBKgphLOgITQ7p7FX5cZnPtS99VxL5UQPh8/R4z73u1TO7ynP7kHP4fxcf64zDB/zjG8v/PrWjDr3ZtETS1vWqmSdv+fq2RKtQRl6zmw2eraoiJcbdnPw5gFLHOWqvafWboIYQZpn3NemICCyvMuzDqNjaovGNRtFoAr2EYsNZVAaDMgXVTaejVasViuSTEwFe//CVG/z43t1sGD2xHvijttWb0YRwJw56SU1s1lfy9AJhS7W1ubpWswdXNH3x19MSUYW7F4ASCDwxZgrTWYUQYY8h1rpVoGgspRkY/1mdbMfik8LxiE3oGAmEccVYj+OtYsxQ7jiNd11U/1jiO+UBEvA9Y29F1Ngsj+vn17S1779nHyMLCO+884ud/+h2+9eGv8vTK8Gf/2m/zR37uMX/i599mkA2/8d3fZfAju03ie588Z3+zwa4MZp3ZmwExAzEd54MftnIAX9Zehbnogc5pL5K4KyL5Lo209ev9UG1y29Ufd3rpX9F0+yra+F3fHb5nS6wO+z1V6+G+Z91nmflhtP9Tc/+qZv6/f+20Rn10VRYCBV4KJPTD7a1SpGcCBCpWvFSeOev2OEizTfWF03vgZfN9nxXohzPtT2ZvSKQoilJWzPiimCGbYU/MQXWjjFVDL0pL6TOEoG7DpFZMEamKT/ndZ8WtDegrrs/2WhUauqqIlWd47zGo67VV1EII/NZv/TYxpWz1/TFh//+EtNeb0aM5lBNjz6S5PQzxsHBCu7lLPrqQJNaCCyV3M6DMOpJyLe1ETOrPTt5Pvv+opQ7LhtfNV3DhhZCytztr4Aq2Y4kZ7GS+EQMhRJzTyNP9foCszVvr6FxPjJ4Y9J6YIuM4YowjjFtCMEgwvHm24p//U7/AX/qr3+HFeuBb37tisws4K2x3I70zjC4whIH11ZpusWRcb0qUH6nUOa8+zDnBbyNhT7VX/fy0f75cGylE+lWD0O72q8+6Pu7z1NiOrapTX/e1NF3UdjkJDi3e96ub3Q8Fh89rvr+r3/s++3GY019VyPu8zznd5/yMf66WzejVTC6fb2z3z6ecXOtqjj9OWZjts0PI4kNG/+Ow0pzaW6+y3yRLSAp9K5AU3tuK0q8h+Epr2+fMraoaPWWdI8WUo/anWKnDcYoIfd/jnGrru92uKkfTHE3V7VrsEZjo7uhH0p4MtKZZR85Zlljgi5NL/1oz+iINt/uvlcoTCZNmV+tvzWcpqQk9ySRFp7oRElEyZnL+F5kEAckY3zFFhBLQl1PgmNwEwZf7El3ndBwpUjGdofqcCqMvjF219hKg5zDWMez29bkhZD9932P3e2KA2/XAZ8/W/JFvPOY/f2AZNpHn1zueXW/ozYrlGz2rJZhxIKXA1Ysb+q8u2ckaSRGT1BLRcVpTO6VJvKoP/pQWMbu3YcglVuLlnPXuZ76ygHDiES29bX35J9nLYd8nOkwpNT6QObE7OaaXMOJ/kBrzj2q+/2GtLnftpYOr2lEhLbzwCWFPpFEOyu33MPlXsXgcW2EOP5sLzK/SyljvmvtDjf+uMX+e9sr3CRCjVk9szOzjfs8wBsQapOSt1/HO62RMtCCDH2U/vjHtO8vsntaE3yLmFWtBKxgcChigwX/jqNlOi74jRC3brZC8XxwY3Nea0ZvUMnJUGkel8pRTz6oOVRf6MKUlb4IE+KKVK/adRRHv9K7M6HOivqSstWemf7Za0VnL1c0NIXowsRJ7HyJiDM4Y+r4vWbo5/kufH6KiNBWfVTHFFnjclCBaQNR85awljEEDU3yg7zqWdoE3Oz68ecGn37zlF77bcW4WrM4HQhgZQ2IwgfOHjxn3gf0mMQyep88+46e+/lWiJHpj6MQQJFBSAJU/zQlHOWDzcr1tIQ8pHLIe4tZ/Vq+qhKvoumRglWOG2N5Tl/zU52mSF+r3M4GvGePU0REHT2Xdm2e9Oi+b3qkl7Idzmdo83wPXAQ1z0BLJ0/eHpt2ampT39gGrg4PrqWGo0wOPifrh3J16x3bgp74rzDMdfHpw9ediROngDOtTTi7NrDaFmX6XfEcrOBTmO3H5+k1zUf0pUq6Rg8uO53P+fu0CHz9iPv7MQ1tBJF+f6nsc7PW7+qtzVqTV02fhlJCif1P/no7V8TMrrRXYbXcMQ2Kx0mJZISnIV+ccxtiKMWKtJQY130sz79aaGSKotereLFr8bjcpXsXHX1ox7xf/fflXCIQKERaM5IA90b8FXgnT+TVqrzWjnx2dA0JSmHrrazkmDvp3qfPeSn5ycHgjBdgmYeUM0g0SLOLOCMby/u/+Lez33+dr/8S/RFwuWN9+ChnWdMCzSB0L07E6v2ArkQduwX4MhDTllaqFKhLDiB/3GANjGPDRQ0o4ImIEZxKu63j24pqPP/mMq6tbHj16wGK1wASQONAlz3/xOzsWYrg8f0hMHd2541/5V/5HjGcjf+H//hf5+MPnvNgObJ88YXn2D2HiB3gMmAXOC6NJQEDRAQ0ilpSFEkPO/zeCpaTBVDgiXQNyhapGkj40OU6AFmXOi3BRDm+r8b8kFiBNGOyJbHov954wmzYeiRP7Qk2QYtzU5yszIznxZ8uE25ryrbBxfP80vDS/9sR9OoM1N2AmgDWou0rHmu3dChtJJkGkrUI/8TD93TBdF+pX9cn63FTOYBlyOtDiXrVC2NRnvbcZ310iRJ32um7xYLlb5pkoSIQTP5SjGU+HzDk231W5qUgB9+3XVii9R1DKP0zzeytsFvn0lCinH0hzjwpIiXhCNjtlASj/ioJUgp819TcViRrQEpgJGxLRG4K1BCts1mvE60BD9IwhYI3Fh1zXI9M+7/30HnUi9Webd99G2xsj1TQfSCz7jmW/wHuvsU2IFgaLUfFMjOSqnwqQpjDOATIGzxi8Bu7FyDB8cQLx4DVn9He1z2vKPOXvqho3R7oJjHtCH/B+YInj3F7ybbviP/n//AX+F+crvvEH/wTu4h1ebNZYC5K1c2d7LlcXSPQku8IkIYWcxjfuwWmgX4xRzfFdRwgjWoTBkJIhBiEhvLi54cnTZzx/cctisWKzG8AbnLM4u8BKxxuPwGF4/mzNW+885o/80s8jNvF48Q7vfuUbPPl4T9oLt+trsAlMj4RExLDvOxZpx+Zmy3K5xHS6VSRlIp8SwXtC8NjFSs12OnmV0IkxmBQq100SKS4QRUmThmg1B1gUpVAtAA2Oe742xgkMoyVxSpYmbaQYdF7metDrTxF1k8GSJoaZ8niO+pg9o2XKh6VU9fskxaJzSps+dc/BePWm6fdshZpr6fpNzEQ7VUFIyzcXzSzVaLByd2HgUp9/F4JfK1jNGcOJc0Oe0zzuglmi82DqtSmdSmI+Nlmng+cc864pzuSwHb+PwGEp4BOvHIlEiah4C8k0gk117amCIbMZONT68593BoIcXiezoL1SGViEXHY4v+asUu6xW6j2EQ7mq2BD5L6qqFd9owaDFtsiJkwpuBRjlTaigWDU195Zg8Oye3EDRhhSxETdpS4j4BUmLaK59RolXxDzUobEpZafLXNcznMJVO66jouzFYvFgjCMWTCQitDn/VgrcxZlY4rg19febbeYbA0oFobf99H/hDTd3CcO8cyUPCfwpwh++/ks8OngOkhISiTrkLjEmYzP7Nf88a/8cX7tT//3+Q/+01/nF7+34R/9J/8xzt/+CrvdRv1WKYJE+i4i0eOjIyaPot7No1A1R19Ynq0Q1PTvjMV1HYhqg/thQ2TA9Yl+CcREwBNTpO/OePTwEbdPPqJYwfu+52J5wW695/Jsi/MREyOByMZYrF0Q9ntsSEiXiC6RksVaR0qlZK2QYmHEmpWA0WpTMZPdlJnvsX+9VAIkK7fHpsLp7yIPFAmchqmX+2O97mD1J7OigBAbTWiuKbcoilUvytwv5oe2liIjUl05RemYCYNNd9OzThN4Uh5fallraS/XdJU/tJ1qDzELUbGxrUym+6QcQqbnt4Gm+ZXz8DI4VJLay1FLRfEt95/iWKmxJhz0NBNUyl45Rq2vWulL5uT40SkzyAOdObVifPNx+1HZMwejECymZqNMFhTStO8EkDShvAGVoRw9s3nIaYtj8/Tmq7rHEzVoNCaQFJlYtTQX5jNFnuFWsWl/1nfIu0ZKaht5z7QIjWqhiSnmYOOIwWThMZJ8YrNesw+RRRKlndnVVwBtiguw5LrrXGWt2zDzsxffekSBxyRNjD+MniFBDKFaaL339F2HhLJKKhSUTKEa5JcSi8USn90IAK+SSfQ6tdea0d8VyPQqATLHqUBQtJ762fzLavIMRCR2WqUuDoxhy5vLt/lH/mv/FP/e3/gNVp8944+sb3jwns2BdeXZGQwiKZ5zioGUFHVPqm1OJdlxFC7shd6X1FwmoqVwU0xsNhtEEouFI6URSVpYJ3nPMA7s9p4UITKAqKndhEDYj8QUSGGEGAhj4tNPn3HZrVguVqRxxDhDZxMmOlzXKzywMYqeZ4qBOGJMTvOTiRTPtNRGI8wL0yhX7Ty3DDiXWm19knU9A7XwSKZw6YA6H7uZUz3kU4cNc0nTddSSxKkSxnKtlkCVqr1UU7o0989eLTXzcAfxTq0G/rnZGAfsa6blH4hSldlOGm6BgU4Uc3zNWskCbVmNl47sTiY/G9n0fDn+fP73K2q59V3lhMDXju24SeVqk1VgLrQdihqTsDEXGtohH1YglOPrZuNGXUvN9N3D5vMePP6ufhQzgLVMo2yFvboP5WT9PKq6X89buXbyTyjNjfkcxszoEzGBISARQki4YDFi2ex2BAFrDdYkfEG1OxEsmNIUHN02LYWtAnsJkjZZ6y7Knh9Hgg+z/hSpNFUFCpliiw5fvVTCszlt+ZW9dK9Je70Z/T3UoPiYRI4Z/vR9+X3q8fRz8kVGSNYw+hGHaBEYgdhFnrx4nz/26B1+6Z/6h3jTCP1ixTjsiHGHcVnbigLSEcVwtrRsI/ghEqNi2U8uzSzxWpulT8m5ncpkvR9Z325wxtGvFtxcX2PoGFJCYuTq6ppPn92wujgnJjX9h7Aj7G7o5R18gCEFkIjf7Pj2N3+V1XbgwYM32e/X1KpaPuY60qphhhRxttNCOlmaVi2/8aMXBlEZIhPhgDntm+h0bSKCSTmToWhi+fsYoiqkQhYHDq0Cp8y70/NBDmJsJqldkqcwGKk20QkjoBAOyVUMmzdqn1THq3/FSak6GdwjxJfFHZxsdzCvmDBJCb3KjQ13L7+m0AhcGX+isYC1j4jVl/tyIeS+K9JdgU0nmVaiZKRMfd/Re32/wqROP0RS+xrSHngVbvL81FCFE0LmJDgVt56pf0maBPlySxIzCaFHQ2u5frmueZf21eq9Yba3T0bfl8yhNsuAo46y9b2MdXb4Gk0dxdNAqLEVeV9Hku6j8t4JbNLCMBIhBg/0dNay3u4wCxQsJ1sFQgg1QK7QEWsVwU4/mxQ1Ywxd1zH4MTNrqSemyzDkBb0U5m41Ywze+wziM8HdTkF5eekz/XLW4qy7W2h8jdtrz+hb8+spTf7VAqjyri5HOcWjxU5QfT49ghHPlgTe0McOa4Tbzz7hf/qn/xlC8Kxv1+yHW/qFY+9HrQaXhL5fsVhekoYdMXiVhmPBtA/1jBYG45xjt/MYgc4JRgbWt095+/EZFxcXrFZLYniD/WbP957sCH5N9Dt2+8jN7S3vvPmQm82acQwMw8h77z7mO7/5K1x98AMSG7bLPX/xb/8N/pf/2r/Of/O//U/R9wtC8DgHw/KcEa/BgILWBBDDcrGs0avWGnANXK9Mua8VthLJ2vLkA5uunzMZUZziGRBP9cvZSaDwqNuhXWMjoqmQbWsYdf5FFahDxmB03UtkvTFGURXL/TFCDNU3WffFKSaJMs+y87LRo/122lNMJTcnre6EendKQDpoYsie44ZYl3uKIJKydtemLBVBivn1arq/g6l8njZNvT7uHkKqTN2qm6R+Vu5P2cJQ9OUCc1n6Pe44kdTSVfqS+bTW55p2gmc/Dj6eZ+0cWhWlMkOqOCCV4cfpotJMrFXf2jlo40JUa+2m9+S4m1MfnEq30/2dsAdvpwJf+65JGSCCFZddWXmeLBCy0GsiGMEsHMkG9jvP5YMLLvslst5x9fFT7D5AH5DOzNDtuq5jGAbG0Ve6ARP4U/m7+PHbiner1YrdelO/03fTM1zeV89wAissV6vqIqhpe+SfncLqVkHuC6bNw2vP6JkRg8l3NLUfjkiVe+b32SS4ZPDSE61nIaotJwLBWGKCjz76RAOtct6fTRaSw1pDMolkE8kIu+1WUZ6y5tp1HcNur+VnQySFyDgGHj54A9IzrBgMntvbZ1ycL7h6PvDxD56z3++xLvHmo7f5uZ96E8KlmvHjgr/7nafswy3GRW42t3zz177F3/zV32K72WCM8ObDR/zsl76G6R1//jt/jf/Xv/Gfc56r1j0PkX/o597jX/3X/zWub265WV/jOotE8IO6BzbbLcM48vabb6t0nyXuNrixzn9KOMxJYlyuU2qjTEiR8ebY2OVfjJGF9Dhxs/sLDHLuRu9nSqssn2kt6nytMZnxdVMKk0iGOPZTfwjGGgba8TeG9+xWkWY8+Zf8s+Vyyqx0zKWn8r/ZL0fPOfi4HQERIeZqhjX1qulYyvmQRj+9w70FECVrh5XZFFYn8yGm4vOej3KW+kokFoCqVghK+l37/GTsJBUU6wp5f0FeUwVhe5XmjsZyzARTIwCUYMDC/GaMvGZMTPnb81iPePCOrdIhHNIU8S4bkg7chu34DJCGoz1QztVkatd0YP09VsG1vkwyWciNQFMTpAq/+ntB/NT7FewmlGJh2bw++j1OrAa6eThPC8xCNfmtwO8+f8Zvf/tb/MbvfJtlb4lBfehkJjuOI2dnZ/kMjITgubi4qFp9C3QDkELM92qKcmcsslzm/SzZlVlQUPNKxIi1jmHYA9RUu3EciDFoKl3mIs5YIp4Qxztp1OvcXmtG37Z6SA8k43us+3p9ls4ny3Oafd62KDCKpuGkEAlWfedGHEMS+iQ4m7eOlDp5ESESghKJs8UZi85qEJ/RtLUYw/TsmE2B2Q+v6E+OFCLe7wj+lvPzBbcWdvuR7W7k8lHPx88+4cNPOt579xGPHiwh7RjjHqIheqHrLGfnS778tRV/51dvWHbn+Djw4vozFu4MGxP9hSEZQxLDKhluXlxjjaHvO9zgQHSeus5g3AKx4EZHspmxxcY8mN0PJRAsAWMqkfB3z7GQNKCoBN9k3QjUohAzAbqdpahlE2xro86636nQrtnz8sJbyRHGTNHpxhafvfZsjC02pMrsqnbZCDeg8MbtM+a0YxI+UnPPJBscE5r5Z6euLQJEEa7K9+W7SfiZtNKmj8yc2nc5zqOeMBXmFt9Jo9KWLTEzUZwa+Tz1NQ+JK1qksa7ZG/NArPaZKZbISGb9Hlr2qnm9dDljvhM7bjM26nMOBH71gmtnRRlo+63Pnzhu0+cUuFf7z7NE834JatAaZc1KOls7X7M5lOzusvW50/nR71XCywFtJjPzdoxkwSbmZ2Wmb4whlXHmz2MIrBZLdrsNH/7gKb/2zd9hdZaw0hFJ7P3A9e6WT8MG6QCbtXkmjX2321Xtuu/7uu9Civm5+matTz/GyH6/Zxw9JgcGhlx8bLFY1Lz8NqreRDMrmBNjVOtfHk/wXpH8jKvW1S8as/9iMPqXrMmRK0vm5GXS4NtrjiXw6aiqGd54BVdIAjZFNdcaDWQyGKJ0ED2SA0nEQO9UGNiLAROrWe+QkIWY2G639IsFnXMMfkeKI288umA33DCOCjeZxDEG4ep2x7hVfPrdfuRyJWx3e954eEEYoprhQuCnvvqYX/+ND5AE+3FkM+7pSXS2I0UhSERMZCmG6xdr/BiwxuEy9C5oIKGkSOpBrEMwWGPU31wISYhVojZimtSgiVxNTKjFzFaBqATdVJNqounDaPnh1MDzzkzzMzvPbBPMCVujidVoZYhR3RxnZ6tmbY4Pf8tED5tpmKv+nNZ50ogPBZH5PfNWPsxiRqP9t/ERhdE3rzkzFBwz5fLZJHxVa0hsvy8M4FCoyePNWqmUA1HHmplVubAsRyNQ1UEmZVbWuJlFTW87YPQo+MrMYpdmYZeNRi1aaKX0VyejaLFZMK29xzLsYvdovimMPlsxirWjXJfaHTHN6VzrT/NFmWn/k7BZ1wKpjPd4Z09vnJKm/ZW+27iBowp6baDoSTm4CDNZ6JYyG9lfHyPLpWP0O55dPeNb3/8ulw9tRvVKJAkEk/DGsUzTnmn357xMLYzDQCLVAmJ1zhugrUkwCpic2lihxkPIjH5yAWgFu66xUEyxP1NcbwRjKt0p8/9Faq89o78rwv5YC2io59EinriGudai61+ITyCJpfeGJAnvAn1KRCIe9X/ZmEh2oUEvNX1cg1ic1SCsKiPnQz0FlGmwzHa71ZK11jAmT0wjjx494ld//QNubjsCFrHCeuPZbIEInz5f40PAvn2WzWM9afTsbrfsNhtcskhS05kXGAX2fs/qrGPY7onJ4wxY2/H8xY7d7R5ZabBMClFNY05RrMDirNX3i6YG0aWQtOJeACsKTiFG30mnUabZzjy6pu0kg4ghEVSAsKbaBAQyBr8QYtBAwRqkSNWy5n7JU/HFE3ErpHPMFbZEBIIQxhHTdbN9lBoz6VGfM82tBF620cVVopg//4TW/LJW9knbqtbXZCa0ZuWq95uGkRwwn2mPa3+aMiWVP6dcHKpq/KXPhjDKISY1jeBwIMxW7VekEvBU0jhTmbMiJcRmrNpprudY3+NY49VbTdb+p1nLZzu/WCr7sq5VrEx+5i+vbD8/z5g615Try7pWy8nBeuaNWunWNOWTMHTQRKZ90frup5+FBjbJ9XXRD2tFtGfvcK0mQWiiq3p+jEzvHqNm33SLHlkrwJUsBHfWM3qDLTUqUqCXjn7wNdCx+s4Pysp67xmHga5zWGMrnkiKE/qmUGJwJqAc0D0tMiHmFfdJccU51+vIk1SNX9NusxAqZco0mj8mcj2SL057rRl92SRwrG21ZjAOfjstwb6sWpFkBqWHSJJnLGc8qFkaBBN1g0XAxK0eAmOIeeN5D+eXb/L86geqMaWUGWguUhPJmoJhv9my6BSS1pnAzfaab31nzV/5mzsePEycXxisCWyv9uyCwaXI17/2mLfevsB1gYcXlttbrUVvsCw6x1/+a3+X3RBymonG0lgH4xjoO0NKaqL3Amtge/WC1dk5pouknUecwxMQ0SCiIMogAqGm3hlj6JoAvZgJZ6ClaM0yiKmMR5HZcoSxne6drs8H3qjmX7XGyhgmTVCX5G4JXRpCV3z2xhiMCN3l5dG1LUJXNZseaLf1nWMEbNUe2j1XtdDCbMpg08GF9b0PWVj+PE5m22OBYi70tNkDSHNVy2kOmzktRB+2mnN9QtiejfeOboplpBXQpnN9d78v+/xUoOSpmJ27zLSHgsld99/XjtwNJ8Z06l3u7yshrYKeYIZj2NK+ZlucfMsisFWpu70l/xGz2yqpAd9YIY4JhyXsI+PeYw0YOnqbEFF0OQnCxapjtIYUAjAF2I3jmE3tmcmPAet6EjLhjmQLFUmwYhQkiFx73llCKHEDc9z8ouVHhP04st9pRVDnbK4fAikEYs1E1PiqC2dZrM7Y7Qeub9YvXY/Xqb3WjL5tr3z4Xv2MNn3/cDeHELKJyOTyiol+0XNxcZGl2QAZn3m/3+dqSlogx+bI82EYGKXHrh7hkvDr3/wWDx8s+crbHculRQTCqmMII7/4c28idsnTz9Z869ufcHH5iMePHvHeu4Znz6748MNPccseYwyr1RkihhAU7nG/H+g6ZWTWWBaLJaSRTz59n6+/9zMsF2cM1xpkGOIeEZeZWDrQXOdErMIDCFry98QUFoZZdfeGIsl8AQAqOE9LkgqzaDVHESHE8U7i2rYWQONUQOc01tjcU7SqInROGu9RP5OttXlxDqjv59hfJzSxw3ZKCJk08fTyx91jwfhh2pECeU+b8p2PMS+O+z3xjgdC2F0M9+Vj/hEzDg7GeNcefJV3nF9z/7WH8Q/3j23qrfHi5GJeptYH1dTWbKmwhmSE9WbDzc0N3msVTUW8m6Lk17dbUkpVew8xZPqzYhiGmhpn7eQfj9lFUeJcYozETB/Ku7VIeYfzNon62vq+x2cUT9fZWgq8tzZH8hv2+z3XwxVfeXyGXfTs/P4VZ+/1aF8YRl/aXVL4/KLDz1/mkzncTHOt6b5nF7NT2cRtvfkQJ22+HAw/TsV3RRLr9ZqHb5wRYmC72zHGxMVF4Bd/7j1CCFzdbLiRQRHt8AzbG3abLd5Hbm9v2Wx2fPmrbyJGME4Z+NL1PLh8wO16zYvNFQnDYrVg2O1w1tK7nkXX4WPgs2ef8d7+ayy7JTEFbJ2LnOiUqMxg5oeummr+X9YkrZQgxcm0KVLy2zNjaUy9iux3bPalaMpZ5Shwt0dMPSmwR1mzwkBawtsG+7Q/jwhI0/cpet0KG+3fdSIq33w5k571W83Xh5/frxkelvecMb88V4dR5dO7SCa4jan+jjMiR+dp3k87hrvlhpYRTzJG62897E/mbmdO5dLfJbS9lIHLNFZFuTttIXhZO8XgZ+6l5h2y9fh0PwduxZqqp50fvbtMgz/RWzp4TjP3J/aaye6eYrUJSYG+rLP46NmPgwL+ilUwrqQCcYwRawy+wNx2TjHmM6M2xmLtJDynlBFCtYM6qrbu/DQmc+d6xPxK5ft9FiaMoMie2XXgjVHhvFi97AUfffSCEBNj+P169D8x7VUP3DHjmT6fb5aGaR/20fivThGIO03DmdgaYzDWYDvDcrlUpj6Ms0CTgt6UYsT7ACmx2+1Z3d4S0kDYjZikkd8XywU3mx23W8+T53sWPfzu929Y9D3eGxbdgjEGfPR88vQKYwznq3NsMsQQWK9v2e/3iAj9omeTU+5iiOz3A0bgrbcv2W490SvDFeOR1AE5WEoickBtW5+zNL7T4mMvc1jk7hkBywyoULxq9r6jTWtW8DmPI65LxHDRDIWGYGSmewh3eRdzuE9LLNeVf63puT6syadXLWoeXIUcmnYnO+psnrObqAhE08fz9NI7GW+ZZzg5v+U+ndWGMd4JfHPw+cGf9wkAc027/W4ydxSXSntvOtLbmpdJp90pd43luB3YuWXq8+RzT3Zxl1VoOh+NjNw+6PS4G6G4dDDNIc27T3vj7jadgRrv0boFZvtwuq6Y+A0KtZxIDOPIfhiJqeSog2TEOslny1bLkFAQ9VS4VitajJMgV/Li23Uq1oCjt7hrjdNEr3XOMoofSQM4mQT8mGM/us4RkwZBF0S9L1J7rRn9oS+utM8jAEw/m4N4IBO8grVsZi48GkNSP721Grxm7VS1KTadakRoIkYtVatgOXs+WX9KIrAdNB0l+cTC9dwysNkGnj4fefxgyc3VyKNLR2cVF98PCWMNL643LPueB2dn4BO7Ycd2tyGhQXads4zDwPlKhYNxHOid4eHDFTe3W8IQs7s2QOoQcsGHen4PiXYzn6VYRjPHc9qWyKD0emfDCWt1sgMGNl/v0meqjLJlIq1v+jTDSTUQ8BQjb9fzLhNxu94ts2+vq0NtjRKHg5k972ii2gdnrXdujr7PTD0fT1khOckTZte2z6SBUW2HfXj/nf1V1f/UTjh69l2fTX/fKwZO43sFAWhq5fwfvcX0/Ss3mf1axZfS/8HeuWuPTl00G2hGZxo6loXXwzm6zxrxsucejcMYTIIUE94HEomzs7OMTe+VuRuDkRz57hxkBkpm5Fo7Y24NPXWeDudnNo7m+vv4spa6zdlAKeHMoTA+ubPEZuXgREnt17m91oy+VECCV2furdlqvlHKh9SzNDOwlg14T8+nmFFKSQNTjIEQ8GHk9va2osaFEEhC/VsBIzwxBrpuxWa35Xd+95YxBLol2GXC9fDo0TlJPA/PLCsRLuyKeGnYjZ7b7QApYG2HWUSs64hetfhH52+y9hs1kRmNmN1tN/SdxVnFfbOm4/z8jI8/+5T3P1ix3Wx5I71BSAGLRqaKNcSQiD6g2JZQeL60s3eS0TRaYiVO84N8mId+yMCPJHxpcsiLoJFKMM99jNBkBhurxlo12gOfvclxEzS+wlP93ks4M4MujO70ZSkzBmn4Qdl/p4leK1y1ws3c3XAiEC3P3X1tzvhlZok4/H5yTxx2Mn3eKp+vwtjLZ7N1u2/c9Qwf7722r+PzOtlXZre0esD9qnLtS6qk1gi/9WzcPa57eq1757Cdep/28/v6vo+Jts1K9p3n86qMUs/PgweXnL/xNpvNhg8//LBMAkkkA9MoVn3yEckgOMX03v4TmYrOtONuXZ9t8S+tJ2+ydUD3U2zGf9e7ub5nzDFRIlo5L4TActmx2+00Y8K81qzxqH2x3obDRS2H925EtuMOeIWzfKCaHT03X5U3Wyms4L3nZn3Nw4cPeeedd/jwgw/ZbreEEBhHhULVoBGNOr+5ueHJk085f2iwztH1PYvFkg/ev+Iv/uX/in5p2IVAv0ykfs/54pLRGXYDbIaRYUw8WHU4sQQJYGD51jn9J7fsx1FTyIzh/Pw8BwJqtafOdRokE4Wf/rmv03cWv/f0i0vwQWFbMxiOMWYqenHXbOW5jyeIUdtOZ/ze3d+MiFUa3TD5fPorAmkrDBwRNsuctidKDbi2FULzo7b7t1lRuZtnz2z8Bz1Je9Fx9PWsZ5m0cjnexkfX3jPCz9X0UUdmipPtPt/5XFM9dd38GafW+q5+W6HzxKh41beeWzDS5zMCvGLfr/JOP0r/5RkpJWw+52QgG2WQttajABjGPQ8fPeDp06d5HnU+q+UyRs7Oznnw4AHeez755BOcm9hPYfz7/T5nwGRhVRQvIaZUU/wOrWa6drqf5zUDy3sU87/m2vfOqSuyESJCCGy3EVJ/oofXv31hGP2hZj4dBDWHT9jmMsPvPj4wk4Fykr5npIOZqax5/uHfMdeUT1DzeM9WZ7z59pus12tAr+n7BbvdHrKUChq09+zZM6yxDCH7jGLCpsTZxQOePHtBvzC4zrBwlv1uJO6uSAJBoOu1Nv0wRFZ9j+ksYxz55OmH3N5suby4pO8F7wO317c5RxpGVOgIIfDTP/0H+PjJJ1w9v+LiwcPKeEQyVnqcE76U1ESfOGYS1ceXT6QcrNVdosKhGbr9bPYMEQ570eumFLzZqh5p4QcKYtYI249OvdOpzxudtX5/Mq6jYQKnoxGOQWtOXDL9LJaoV2QCr8q6ZsS/pDveVUTmLgsFbTzGfH7mVyYKgFKxnMhBLngrrMwefPK1U67d3lrkmgN9wNzvskbUa099fYc5/Oj+e+55VWGkXPsqwuaUfjmdgLvGcPj7zBLE9HcRdK3t2O8GXry44qMPn3C18bz3pbfoOy1OU+id9xHbO7AGHwP7/Z6+72uAX0Gsc85pDr011UceYpox5JBprJHJ9D6NsxQZOrasFauCAJ1VuFzV4r0KATlvL0Zfy3F/flH2J7u91ow+Nab7ts2JeKO9ZE3vZWdqMpUWVj+d8emw3G1CK79Ppt45U7i5uamELKUG4lEMIfhGq4fz80vMOJCSoq353R5jLWcPz0hRg1eIewI5opWoNdOTwTmD9+Cjx5Bz/IPn4vIBIUS811xVK479uMM4ix/VDLZYnfHw0Rnf//YHXF9teC8G7ALCAGK0NG41L7d0s87hnMm1s1arw8HsuzLX7f0nGeQJBjvFWRwTwakaFuQw8znbkYKRXczb7foXDprT9YLn8G3L6xTzYcvg271RxnnKxXO6Tdz7yHR9khbdbQp+mQn35NOPtMdE62v+YVo7PyefSQPEUxeoCEQH46nLcChAzK+p4IqNEHpK1Hjp1EhLEe5vn3e+X7Ynjj5LUgXv6azM3UowITSeOpf12c0Finc0V1iKdl7paUoYC9EHtpuB/W7AGOHm+krLcqc0medF8EHx42MIbLcb9vvdrGgXTG7YQjeLANaa+HV8x8L+7Pu8xq3P37lOfe4pZXAdyedVFM00Ra3IiaiLVTjJV17n9sPUyPyJb6e09Ppd+XeojR/91+iBafpsdioqI5hMSYf/VLPXf0VrXt+ua3pJSfVwRoFqEsWUpPj4xlp619FZh0EYhxHiiOsMxirlca7Tw2EsWmwDCCqpqssgMHpPDAlJwluP39RAmuArwlWZk4JepwGDhtvbDevdFh/GjFZVZm2qkiZ5Tmqka/t7KqltjT8Oag3rVMzsoCQ+C2LlX7MQem9M879bAkTxyTfaW2qJszLxgtutRpJUXSWpCoFpulGmZ4GWyp37cycGMWPyRcNst2LSz9v/poecsjKcYoiHnbbPOXG9zD9veeR91or24tnpOSEoz9fh+PNXbdW1wmGKYismpumdZlMhp8fR7iFOze98De5qdW/UfqRMx+y50/PLpjjxr+7FVK+Z7fk7xnLXPE/vcrBvUsrn7DTk0nzPnxxI/T5mrP1ybowoeuR+PzL6iHPCOA4NA9a0vL5zdclS0pKyu90OmIrMdH1fx6QCgmQsesGaqRBSOzPzOZD5FB+cn67rjgSGGBWbtI0XiE3V0r9frpF/UO211ujbdjrQSkjJnDg8hZlkqe+4t9lfB/rojGi0/rjDQz5Du8vflRQ2EXD9AusWxO2azjj2fo2xFmMTYTey23qGMOCsRVDgisF7HElzQ9Fc1uXqDNmVes36+EAA05PSqHC0KSEEnLE8fPiAp08/wSZh0WtOv+u10I61gkhiHHbs1p5uYdilLdthx8PlSqXuuEPSipgikT0g1V9XBCPQapZFy9AZ07ku2nPVDpPWB9C7s4VADK2pvVCYSSNuU9MmInBoIShrVOMEqqSimOUhKJBGZzvaNJxJHGiEvZRIyWhetRxrgEWoac3wJX5gEnzyG2UBoliLWrN0jD5jvtvaT0zTuOvzokzParS7aVxpklea9LxM6irjEjEz4ljmuGg80/RP2qFMElSdpVlUfnu+8nk55T+NRchrzxFlguor1DvnLvr79ZRJ0G76K0JpZYbS7KdjQaV5dPOWMjvnR29VFjidoC0H5pCUygvOhY1J1Tjd1IVyQKea2hwzKwzUa08JkS0NlDqe+dsFlGYJhpA0VXjY7diPnjFpdUBrXQ4wHilQzM4KXQ46NkYywp5WqOv7Hutc3dslGynmSnkmgTWSn5n35ex9pzOVTgmq+ZquU6z7EMNUQCxErNMsKDGCDZY4+tm++CK1LwyjPzSJlnbadCbtmTo8e/W+u03EUCjQ9Hdh6m21KG2TNKmoUX2/4Pz8nL63GBu1AMQ+INHR94nNes+nT59zc7MGa0i9kFLA+4DrOxZdz+XDc7bbNdvdju1myEKEqwUhwjiy3W4RsRUDWhB2445v/p2/w+psSd/32u/oWS57rF3lwMAR70c+fvoBj996FyOw361JF5c4F/JBHDGZU4zJqUBV5kc5F0GayHQSJE+js80nvSgRkkC09G/5fBJeUjOtKvyUtUu6OLM1FhKSRiWahQg2FgAda8Qc5ofXsRTzqFQUPOuk3hvjPOq+KB6597JhDmIJmtcuiopMaZYpTZEGPoaG4ZY9O9PDGwIt9bmT4NAw5sqwJ0JWx5HXq85bEYpSuXs2NeX2Y63npFFg6jukNLvmUP6etEuZXzPrr/3+fq3rkKndd46LRHRslWBG9OXgmjnDPz2sGoshZWNxvCfSAaZEgdtugHHuM1efeu9Tc3CqtbTz8G2MaJXIYk6v+wsYhiHnvWvp2KluvCPGUGu/txlGrda/2+1yHQyZBeMVawBohbuu6yqzLkh6deIOpvsUvd/tNvXMx1yNT6xWwtzn8TjnkFgi+A0nN/Nr3L4wjB6OpdTDz07e88M96cSNd0f3Fq02JdXu+87QdQ5jIAWP33nCmFisejb7Pbe3G54/W+M62A3jrBb0crECI6zXW/b7gRATi94pVO6oADjGWpbLZXYLpFpAYpnrN4ukGnFvMjzve++9y3q9zma1bHkY9nzne+/z7qNzvvbuW9i3Yb294Xx1wT4GSBEnmlVf2EhEBZqqFRStThQX/2jmM7PVGax8fj6zRioBioVxpIQGWpr6nPb4S+vXLT9FR1pYokj2dkgkxAytq9LKkTY3Z85zM636G5u9xw+7r7QdgsOI5KC8FE/Qn0LsElNJ3zn6Xzv+w5/3t7thVF/FtFmzHQ6eWISylMvStMKP1P/fT2iP3Sd3BDweMMfj5MAGyaL5OEOt1y8mehKZZUMgnJwKacaXGmZ/5/to30fCSBEQTrzToRn/cA5O/f5KQXxMK1AM9oW5l7NpjGG73TKOWrAmkLB9p7XsY6iTNwyDVrvM+7i4Kus4kroTJZvoNdgvZYHBYO2yKklFgA3ZInocnFvOzUz816Bop/giwdpZEJ/S5JTfSfuGl8dxvW7ttWb0rZnx1HcvjVQ+uP5U3/c+91ByT9MGEZkkyHlch0qlw25PCsqokrEYl+iXPd/78Jonn9yw3SXOnPDo8ZtYa9ntdgz7gX65xBrY79Vk7mxXX8g5V/PjQSVma13W3BPDMOToUw0+KYzeGMNHH32czVxqfvNefW8Jw7ingu8Y1xE8WrkLUV+/jNQKYFnql2zkTNXEnaP0GwJddPtyMOvZz/9P+fuUEimbgKtSWOIZjlLgUj3jml1hqIyPlnW1xCCb7FuNU+bEue4FQ1ZnSx8NQ6hjmz7LpIaWWbT7pY3lKM8wMnG+qrsW6ttoMypQmUx9s/AymSNaHKL8LnlS0mTyLzMgzV4vjLhaz+86B/m6SRts+php7mW9p/mMucxwKa7UHByaesRNmxltj8fyMoG+CJx5Ho+vL4BAZEHvjv4brbzcdccD73jO6bHl30jpRLW5NNfs5/fMP7tPu7/v85NWD3IFuBhJuSSs5H0vIsrovSdJmteMz2e5q/j1jWCdA5Tbvd+2IjQbI7XE7DCMVZPXyoamWc80KQCz1uTRx4j3Y618VwQNfRWDdVZrjKj0QGIOZPZFaK81oy/t1Ga+S8L/cbVqrr13TCeka9GgFUWUImuZBjERcfD8+ZbnLwZCyMUkxOBcz2KRtTsjGVBHtaGS7qKa/VT2MUEOMlMQCvLfRoSQ0awmzVs0ctZKA0KRiCaRjOF2s2O93oFJJKzm0oooOlYhaByQ38Y0XIhrBVvJh1P/1xI5JrrGvO+qZafCmLXFzDymyOqpr8nnn2MySCCNJ7kwvqN1m33SKmZakjevfKupzpb7DmtSZXjt3szjSnEi0sk0kkJ9d6i5ds1zaYIdKdpqIdrNkKbxZC06GR1meebhW6dUGd/he7Ta2Kn35OTTC7PNIy3/UzWtfVXmlcjKTJ9i/IfzO31z+NzZValGTBxcd9c9h08p8ztPxD25d+7t69Sz1PdetMxprg9SClMzj7nNRKE7aOLLWmWgzUPSbM6mttvtCMFX7XocxxxFPwWaFpP9JDVyrNXn57b49dPnxwJO6bN1N1T6MH/j/N6J4IPSrFyFjyr0ac5+iAFrpX7++xC4P2HtPob+sgN2Kv2pvfdVDsapK+amNdT0nEvSlgOhe82CsSQ0AG/nt1xdD6zXkUVnwcDejznIxbFYaN10P+xnZiqRqfayz3mphXgWMAg1hVmscwSfD15CTdYx0HV9LrCh5quY8tyayNX1NVc3N2BjFhYCMRmNgI8JxOnBLjW/k9SDV3mJJBUUyueSKrGazVlW8I0cMpn8rJQyZrzJNFu1jGL6K4xCKHvjMFo4B9LREPvMM6pWoxsic512YYsx85hZUywPgMRjaJjWdz7bWzWLYGLQLfzmpLlMhXnK87SPIqwdM5lp+x/crw/Jj5w+u8/ddWgCb1u1jbRa4YEQ0Abl1fcudx88boaHkOB0clA6+O3AdcOciRy/V2bYMgkMKb5C6mMWPsuvZE11fq32WQVaDr6ajSmPPd+umB9z0KN2zo8072YNy/TWZ86GdIeL4USbPaOejUZgzsLxbrcjRI+1ahUc9nuNlhepwoq1Uv3iKZ5m6O15KApSEXTUzz+fD2tVq6+xTzFMYvcd71j3uiStPzJb51hpdZm4v59K4j+I9loz+lOmn8/T7jP9nyJ6KU3lEacPG+2T+SFp4/ISmr5hkskgOT39ckHX9ThnWa4sv/k7v8X17Z4xJIz1LBcXfOkrX+HmxQ3Bq3Cw3+9Z9h3WFr/8yJgjWLclcCWb6tV0b2dwkdZabKeQjzEzl5QFgWWn/rDgA0RhgdCzZxz2hBBYLZf48TliEyNKOEwABi3peAhHPDvAQK5Fp/kDzdq1JkclKKbWhy/zOyvqki3WOqdMAkMRHirDlsnakGgI8sSGU4oQmjEXYt+s57Q/JnPzXfunvsthoEHT19w82rw7OSDrSKtJWbyYBJFGGUcaqUJBjMo7NkJSef6B9nkfAy+WoYPBHDEve7Deh2em/TlvpjFSnDrL0/qV9z4WCiYm1zI8I3Nmf3xrnrQqJJU5u7vNBTv95RQTbkc8e2aNG2n6zBkBk7UjVSEMOLm37rVWVkvawTUHl8cCfJS/a/X1KZNDMNblCyLFAiioRr3ZbGqFOkvi4mzFbjeQMrS2KhnalfdTIK4xdma+r2OqmCPT3larpK9uyRhTrQNSWlEKyr3tfIFaEFzvSGjRmhJ8q24AFSqcc6QUmuf/PqN/bdqrmu9P4eWfIlaft89qRo9lw6YqLITgiT7kzRVA4LMnWwiRn/rGu/zUT7/HBx+8z+3VLdvNBj8OEEMtFBHjyGKxYLlaKYPvHcu+Z/SeMQPuABU/33uPiLBer1ksFjinCFbDOOCcwzrDftjWKnurboHEyKOHHTc3G773vU/4+HvP+GM/+wfZ7XZEK8TiwyNmwSbWd6x1CLLfKyaImVLrj/l1RfvxCXycMhU0uGcKoEoZNUtiQIjY7H8vxKpI5mpAibgok4WBQtBycE/VhlWTioWQHaxx+dt7j5hU3SVl/CXCvfj0E3Nh4FSbIvXl5P47cQcTQ9K/dR41eKm6IA6I9nQvdY6LFntowZjG2jDdEy6qw96LS6Xeb05bBkTM1E8WpoolqhWujlujseZfJA/qvjtelVi3WA6ft30uhpDuecXaV9mfMAkAr2ZtbAXml45VRIXtdnDTl/V6BeQCla51f4tVxr3dbtV3nu89W62IfgLE1jMSpupzVVv3xOhpocnL+apxRjJF69cg4xydPwxxBkbWtlPbKISkioIRUvL5+0TX2XyPnmHviyBfqiWGw+5f2/ZaM/pCaNt2HIl5wtR4gpC3P9v+D9tx/9xJbUoxh/J7OTgxRrbbHYmAs9pHCCOffKywuMaO7HdX7Ldbrq7WLLoeZwWcYRhG9n4gJam17ZfLJa6zSATjHC6EjN28PXrfvu8RIeeTxpw3r6AS+xgZvKdE/549eAMTBRuF7374Ef+nf/f/wc994z3siIL1GANiMU7qYW411ihTgKAVoTNgjULzWmOw1uCMzXn8i1x3WvP9xTnNbxXJ2AKK91+YSDT6fGOtAvlMHLtCZKaUSNZwWEp3Mm/m4iUSSWk6Cm1wXIXqzPCdBnAZgAMpAhwz67Jq4Iem/fJr0eom5hVDgIboHu5p7abgCpRw8CzAUMmyalGZgZvmPcvjUmaQSaYCJYdm02LxmAhmmo2tEPvZ+cmfF9dFNYumiWnVPdGeS+ZCdhV66pjutgbUZx6YwucTl+czzcspzwX6Scg8FVg3WYAOOv2c7W4GXPqeW4IKz6+y00H99UM69Hk10HJeavzDTAZorBRCjopPNYNGoKbhhhBUkfGem5sbnO2R4sKzQsvMY8wIdNUqMb0bcJQ6V+h7qUevfweMURem7tGckXOwD6bpSCTjCNlyV1qB2q1nwMyLVikS+fi55vQnub3WjL6gPs39pXNpV7LZiXLQUyZcIjmwSmbneGYKPHpi9j0RsUnTxYrpmJCIttiJ8zFKhhAL1r6iqoVxIMVI3/Us3ZJFt8A64ZNnn7EbPNYY1rcbnkRPDKLpKQYSJpv2QMRyfn5eD4AY9Vfv9ptMBLJWvjrXiNUQIQWcyyZxlLmkkCiUchjUNaAERQFwlouO7WaHGK07/cHTT3j24hoTlZkiBsRgLNWf16KaIYrsJ6JgKc6oVmez1aD485xzGOsyPKWyNFMq4hlT82yNLRK4KNa+SDYb5mdJFgxEs+ytMWDJ/apgAVSfYhXwDNgcZ1Dms7yCsXm+UiR4DWbsug5jTXUFlCDJ6d2VIZtmLkydk/y9HBDv/O6T2ZRKVJULTeA5UGBb2mv1c5N3rWnPQO5E/ZuWmK0XRckqudJFWEhlUoq5lkKMJX9WnpHhQst5ajV2qHurzE1qNFRl9I0rJN84Y/zNGZzI/9RiNbcfCN8ttn2xcEiJ0J67CGKNXSjjK3M2CXtzwaAdzVy7bn8ve+NYyWiFPWgDVIsk1mqldTz5seXeI4tR2Wfo2TV5T6aUsjvKTBYvEkYgxIgpjLj03Y4VUX98ykddqRDGOF48e85+GCHpZwWIRhE6y9oVoXQ6byWeqGX05R1K0HDZc1O9+ha5cgrGS03AYiJr7cznXPLKlmvrfqrXpLytSwp0Czj1xWmvNaMvMmkxc8V0gBaVGWOMMdMcyVLpsfZuspaboGomylSlCg+aMScESdgYlWCin5mQ8FYwKdagtJRMRsELxAgxqHkqBi0h27ue3jmiRH73+58QSEiMbG737DcDlw/O6Rc9YiGmUA/sarnkS1/6UrYMbNlut0Qf2e83uK5Xv1oyLBYrhmHEiNEDm1P+nOkY/FD2uFauG31OZ7FKbGKkI7EetkgMWAtpYfnwxVrfJRZ2Mx1MmDOVuVZWvj80R04aUyvN36c5GWo8PYJuYgGcEayASQmboHedZjNkpuqcI5FwnZ2gNVFhxGEqcSwwnGKKQDMxXGMUTcuYYg2QXHs7mxuzNmzFZwji6XMA4zQdTkSyMFOepwwRpI6j7FMRQayhRsrniTZidO6zYCIFHY+ENXaac6NMOaZE51zet9O6FEZfLQo5YElSzASwuCtsTZeSPG/GQEAQsRm6VEsdl4cbmWBMk9hqLTJWIPuJjbOThp+UCUme9zLxhZBPbD0LGBwGViWsm5h64ZBGco1xkYqiZtS0lblB7jklUtL5z4c4C9Ilvj7NmGIpkFUm01iT+bUKe6WwTOkDWl/8ZJkoFpG63uU8zDRcPWsxRpKxmUG1KZqqBIQwkkypq55y3rklpqCaLQlnheAT04zqekznMM+t0Yp1xbJlACeOjz/4mO1e6124jKapFqWQ92Wu4YEy7EkIM6TsRmxbKQCmrjy1NuqeMtUNWWUhUXdVjKEunQbktjSlzBqQAiV2YcLKL4JZXvYQSTIJT7/vo/8JaiYZpLGZWjFVk1EzUYS+YySSQgFIMcrsfQSXozYzAEOpohRiJHiPoD7u6HP1oywxhugZYshIbUahSk2C4JEUiRIJKSHJ6MchMEatvoQzhNFze3VLCDtC3HH94obf/JUrzaVfJZx1WNMR8Lz51iNi8AzDoHnwYnnzzTf5+te/zieffML19XU2QUWWyxWCJSbwITKONxgDb775mGE/cHN7RXKR1dmCmDwxqcl72GtRCkWzShmK0vHs2XPGcSxZ4BhjuXzQsd8nhiGb+BE1nx2dC6nWBZgOj5pnC8HT73X8E8Gcgmikos+VzwuRVJOdPmOfNIp/l5gQ4BIwjhQiJhJJaVetK3UPGWUAdyJeHtIO45Q5tILInTShZUut9hHvu+ner0494fDyycIl9Zqp6wT0qK9hN/HIJLMrxIKNnIofqyJBESxWaXqGMgLBpEYQy4zImKypGbBmCn6WjOUgWcDSCo6Z2VZhx+SA0mlODSYHVFH9qhpYpQKrMgSAqG4fqIKBUNDXsoWHiAmjWsmYTMVFg2ythpOsJdPPzHSrlUby++f7jDWMg6I0GmNwOce8uFra81G0btJ0Fgaxykhjifs53rAxBkQcPvhaxIWkzM17zzjqmbWdxadAZztiSFq/ITPzYjUMud4GxumZ9R5QTPvFwnE9rrndDtBbXOdwCPtxqMKFBvoK/WIxA6jR+XaZ0fo6f5LHqMqGBgDG6ElpsmS17h0VhqQKaQKkEPRsZcZvZIobKvdaZ2fzrfSlrNUkAPw+o/8Jaskakm2YQyqYzII4FQLG3Yjre4Y0qJkuQE4GIwy+wq3YrF1ttxpBrpqIxYjFLXpiCFoXWQRje1ynGmTwgXE/TmXQRU3fKQbCfoPpDaMsNBgteLbjhsEnvvLln+bTp9/le9/7e/ylP/9rOd5FNG3NJHB7htHxwccfsew6VsslZ2dnJB94+vQpH374IQCLRc+DBw+4urrKWrlu3mHY4xaOlCzvvPMl1rcbXry4JnaRZGL28ysR6xcdneu5vd1grcJZOue4vdqDsdn0FfFxBCzWBPp+ypDyY8KPZL+WHsi+76qloDYBJOE6Zf6l0I9pDh9pqrgF5FQafZBz+oCE4maTwI8BY4WLB5cYZxmGgfXtGun0exNQC0uaKX6ZMRQkLLDNd3ed8QSk6LE5rqKJNSSF6fcQi+YQlYE2UoQxpchI02kzPfNfqOOWZlz1mUXDoXm3VMzkra1keowARoY6zmpNKVq6yeMPDk+YD3A2tunzodpUMvTQLLefXCEsQWyIM1Dx9onMshTuwrCfaYEpQzCXsRcBoHXlFcvcq9TuKhav8ig7vabIxJTa/+dFKHfdGUtQFitbuFrrFo12f1+zqZ3Rz98k100oMQkTZ6PuTxWCJ4G0CC8IWGeydSKxWsDjRwsth02CGPEpA1PFLFiTNfzMlE/FSbUBraBlYp2b4GeLYF/ogQp+U/BqgfsWM33mMLQHqTXFW2tw1uIb5l/2ica0mBq7UlwRX5T2WjN6a8zEFLKJUKXhOG3oZUdM0GX84mSEIS+iSMQkMDFLjxGcNbnakQZwpAT7oEEZVoQlAjHx7OYZ1jk619G5Tp+XEZhiMiAd/cUK00MfI0tjWRiLpEcslh2/+iu/zn/yF/4a3/yVb7P3CefAddA5hzGRlEbEjHzl7T/AZvsc7zWVzub3LZHzAFdXV3jvMdJDCqzOOt586wG/+72PMOJ48uQjAM4vVvS90xrR1mqxm0j1hy+XS02J8YHOOmXyBowk9feFiN8NiDF01uB9YvSJ1WqBtSPjOBVeGcexalvWlijWYkbUSNgSBKM+OL1TY+xENY0EzmUAi6gMvtBLLYcJzmlfP/2Nb/D4rcc8ffoZv/qrv471engDGhSYKO4ZwChkZ4z6fBF93mE7peQbUXdM2XMxE0JDyektFoVCvBvEv5RqulHhS8UNUT5U5qGSQ2XUyiOz+b64i7T/SfMsTKXErdRLZhYRaw3LRcKaxHojhORAQsY4gJAynK/1yn+LpSU1EMF1ldWKliRMhLw1MYhq79NCTMy0CjDNJIvkS06kJpbnlfmoTE8azIVZwSLdR9ZIjUcozzjqt3yX1GpAZj4xMwGS5HVitilOMa/ysw2SE9TNMQ90zPfGzMjusChJw+RaplXetaBgQon5KLUo8v2WnL8eqBDuFWajda/p3hARfMjvns9KZx1W1CXSGegcODG4vtfCVkFz242UGBgVKkpGTZsKpxp5QMRmU32oefY1Or8VZ2Kq/VFGmi14GFOfcxgIXLT16ZkTnkiKmlw4d3lIi95w5Fp43dtrzeiL27maYYyaq1Qqy5paigxeC7AUImpRxh+YJFjjXF3c4jMtwBW966t5eUTNWNE4LS2L0fKvoj5gsQ6CUuYI+KsNfv2Cp89f8OL5DVfPt4Rk+ea3fpPf+Pb7vLjZY7qEDykHtambwJqOkEa+8fWf4gcfBK6unzMOI8YKfb9QM70oEErZrCkaut5ycXHOozce8snTK1ary1qhzVqVavfDiLU9JpuxnTNY6zAm4aziwe+3O2zOLRXRADcnhtFEuq7HWIv3AdmPCJG+M0iBfJdy0CKL3lRBIoYIJVAqTeYym7XLtm64GDX5FkZZtOiavZCZW0KZyQcffsSzFy/Y7nYqLKgEgTWiiFfZ/zrGKYVOtfK5Jte2wlgK0UyZcU/WgawVxESUkp51Iv0y1f8d7d/558Wk2OqN+drKHFo/bpua19yQDm4mVQZsDLz99gWrheVb336BJKMWElFmYjAEn3J+d6j9zt8p1UcYa8CqAFeEnhneTRYWSNP61ncq15T5pVmKO3ifNN9NbL0RMpoZTSlRZLI0TcGBdUSacWjNg8lknqrWWwWmrIFP09FaARoGT7YKOqtxOVVo0IFOyXwzsI2mvzIBRYibu5yKkcjkM1JM+mISxkpGWmxeIelazLxJcZpwFaZTtR4VpGfVqEPdO9ap8DCmMbtBNLi267pckKZxcchUrGYWIJ3HPLlhSjyKIRGzwCB5/TzW9vXvlCvQFVowNfXpF6Zd1nN63kSTVIhTgSSRYwmMaVby9xn9T1QL3teUjJRiZjRNbi6CxYMPxBwJSkhYYBz3GVVDg3Ni0vSRrus0/ajxGxvRYKsoZBjFjkv7QC0IEYVXjAkjTrlOGiFqAMh3vvU7fPqDH/Dk4yd88vQZn366ZreHD66fsvOAEUwHvbP0nVZHs1Zw1pJ8wo9qTSjBXmKsRp8XLQbBug7rI8lIlp4twxh54403ODu75Or6KjNsR8w/jYamV8NtOZDL5QJSZH17A6JRsGQ3hhXBd+C6hQpDonEMKUZCinSLhQb/5DSaEGHRqQ0ixoRP0DlLCEnT4yBr1KBFbWMlSNZqwNY45mjewoiMRtwWU3uK+u/q6gXPnn8GqODSdY7ovfafU+xiSYVjkvwLw26ZpGoNk/YnxQRIuaxRW0WaFC+91jlLiJ5YkT+bHO1Wgz2xp3V4af59fvdQ4kwMOWPBMIxzRl8En0NNWScxM8WkJ0OJ+RRcVwSvwhh00lUoq/DzDT9KmSOIxhfW8TdKYv1QEI1jaTVraW/IfzRCV8Py2q7y+wjEY/CYEvNRgvtIavkLxc9S17e9hyo0phKcV+ZdjgWJ2ejaQZZ5OSHUzaBc80vMrC6Hd9XAynL/wQRUt0Fen/wvo7jqvkwqsE38sJUEKbJvXXilgVSETNAMmColiVqWjNUy2DbpHrQ5RbbVqHVeJdfNmHzxQAOLOwkD5feUvV2CMvIQfZ6jOAkz5VyWfVCFoGkxND7jNGhVsbalGjQ98YwvanutGf1uHBBnqpRuEQVYyYtmjUaa9hjGLqMx7UfEWD568Yxz27O8OMesFqw3a5KPvPn4MSkl9S3HHMWfzU/GGJwIi1WP61aMMTGGyOgDw25gNIkUNOcaCSyc48/9Z/8F3/w7v8VuO2SNQKXJLkvGIoKVjodvLNEQpJRBWSIiZ3zz7/xNVhcLRFIORsqoVNttPlw5ytc6rIEohhdXW55+esU3fuqrACyHhQoOKXK7vuLi4m2GYcjSe6oHMYSAz3WjRQxDNr87Z3Fdh7MdKWrcgk8JYyyLhcEZYb3bcnFxTvCB3W7PSOJ81WsubYGupauEuOZxowdttVqp5pOJtJaljDntb4pcVqmc7F4RxsFnE3/PdrvDx6BIg4uecVAEsOK2CxFM52okb8o5uqkwmCxkxDTXnrRlwpc0aKyYAslWkJgSYVQT5MOHF1xf3zKMQdczE8l5mlaqNDuV7hvNUS0d+qHSYTMJPYZsqSjRzXV4JKYc4RlTzS3GxA9+cK24Br0hhrEy5pQSPnmM0+moDDbze8nvkJr5SSkicfKX6zpNmTDZeJMF5UBJdWv7sbZY01SDO2TesXDD+oBJSNN3ncZjjSPmPor40HWGrL5OhL521Sx0ZmJGtHYE+ftUKianSeMtgmA7LCM5Vbtq0akG9ep7Sl0DSDhrNAqiWac6343wCNk9kQUuZY6iQXQl/dGqq0JSIoYacaF9xljTWXU+FdSmQHOLFPddJPicipxTdo3ROI2cyKOgV7nGvE1Ch8HlFL0pPW5uRp/SSFMeu60AYm2AXclhT1FdhcVkH0qmQSo57pZD4c5aZsJDOUMpTeMpe7K4LefNkDJuP3LCKveat9ea0T985x1Wq2X10Vhr1VweVNP3wbPZbXirX+KjJ/qIMY7V249ZLIXf/Nu/xntvvc0v/OE/xLtvv1dVufXtLcM+EL36nnyIPLt6xs3zz7j+5EMVAm63nJ2ds7y45PHb7/Jf/9N/mu2ZRrNu9wO77Q521yxXHmMSj5Yd5wvLkAKbZAibRLfyGBsgepa2I6YdIUSM9CyXlyxXPfuLEWuF0Su63WKxYLPZICSs7UgJttsdX/nKV3lx9RyhU8S8peWzT2/Z7m4ADaaLIeB95OLijOB7bm9v2W43OVpZsat3ux3GGLq+w/VZuOlsTg+SHBAn9AjGaT55GgcW/SU+BLbDltEP9Iued99+k+fPn3P54JIvf/krfPVrX+fP/Jk/w6NHj6rbISVlmL0TOutmZr4QEg8uzuvfvlzb91iJSAqkhSVFRdS7OLtQc3xSeM4H5w7sihAT4+DZbj0PH1zO0PhsdlV0dh6Ne2huLJzUjxtC8BnAZwGo9ceKZRw9y+WCn/3Zr/Pbv/P9XGGwtEnFbWNIiv8yVqaYXRVFMMgmjhjBuaWOoVbyStiLZQ56HDU40Lipz1y8aGJmGmTk3ELnsIfPnt/QOY2sVG02Z5iYBSGOs/xoSwnomuaovJpkaao8aQyT5pUEzlYLeitsdnt8JsghqGnduVgtQ1qG+EBbbv8UZb5GhGXXs90OOGdrYOV2O9A76PtC5KHvE85AgZYtyr2aojXOIuR00YcPFZ9iu90yDCMhJOiUmZS1bvuA0pdUtEkVMvRMhRBwXSvYZCUka7W2YFAwWRlaLbeYm0sZ5hLDYDPlNqjSsFo6zlcr9mPgxYud7klHHpPutyKo6Q6EefBhwjlDSoo8l5JG4XfLjs5YCBGbAbYWZz3GiQYHp5y1FBOLHGHfnpsQR7re1liqMgeuAGzl900Nc62Bda5U0myUN1uyK+LRfIUQsgEtVaCySSgt6uAkCEzvXsYWq4WlCHpflCbpNRRdrq+vefjwIf/h//7f4PLiXFHgNjuwhsvLC/rFoga/jH7Hbz/5iLcevsHlgwfY5YLtZkvvha2Dru9Y2I79buTm+prNZoPtLK5TocFYGGNk9COC+sc3eJ787vucP3jIi8+u+Pav/ia/+zd/jW986Sv81ON3+MpPfZnuzTP++q/9Cn/2r/5/ObtcsLzoVBDZJ4jC+Vsd2+sdS9fx1a+8zfnZA77znd9hN2wAg+tWrM4fsd5+BkFY9Atc59jtNjx48IBhP2QTuWoN5+fnuK5nuxkYfcSYnvPVCuM8290Ldrs1foicrR6SGLm9vQUS1jpFqrOWdJBj5rpl9u1bRMD7wH4/1vSXEq0lCdU2RQF+fAgs+h7nDD5XtDo7u+DRw8d8//vfx1ihYNkXxub9mMegB06xp5UAF0tGjIr4Z4ziAtRc+qQV/jTYTeMReiM8enjJ9WbNbj+w6Bf80T/6x/jlX/47M0ZljL5X6+6rPvIj06AyAzGpaiRnZ2dVOAI0GC+lXF5zTykDrP0JoP5AIQfNZfNzSC0an8G6DASUP1MNzNT5QvKzkCnPWEeaMQO6bLVQZm9z1UNrp5wGnUGHJItmvydS8mCFwQ+U3PpEdksxB7QpBLcAEhUt2hiIo68BVotFx3vvvkvX9zz5+BOub260vGkCH5vcbYHOdYxji0hW8AKKBSjV9ywY+yV4MyV4/PgxIsKnn31aBakQBiAHe2XXUtljzqkFIISgbj2rfo8JrdBozfWoFhSx6qopVrDCRMq7DoNaSGKE8/MLHj9+g/fff5+u66qbqMyhtTanOKQ6B2X9y56JMeKDmpiNtZS4jCLAddbSd5bOqanaWsfN7U7dPBlkKsSA5HMqIljjsFYY9nusLesasE6tnt5HLh885NGjh3z27EP8Pk5+chFM5xADC+twZgLymlekU4EtRgWOWq5WaikJijCpwnbK1piSdpv952jgZ4qxYlEYk1MrJTEGjRkQY5CkuBOLRcc+n8MYsvswNbELDWOvgDtZeFILk4WoaxpTwofIk08jV1dXPHjwgNe9vdYa/Z/5S3+RZd/lA9ux3W94+6232Ww2hNFztlxinSP2PddvK8b74Ef2ux0Pzy9JXYexDmsswQ+MQ4Z0RE1HzjlWZ6t82AIxBDbhGukMDhj3O/ZpYH9h+OgyMiz2fDQ+5dFHey5uzri+6Pmpn/sGCU/ygeAjOxc5P+tZWIM764gh8OTpMy4WG955+22e327YbwckCIyerlvUyPCUQSWcc2w2m8pUUorsdluWIWENDHiu12vGsKV3ga4befetSy7OLri52vHpsx2Xl5dsdxv248DZ+UNINkNdlvzlxLiPVVuKWfq2xuaI33Ic1almjObkd31Hl7JvH6HrF+pL3g88+fhj+oXDOrLZUR2CKUHXLwg+ZzfYKQe6QpdmLcCYAsQhFD9nGAPjuMfZrgLWfP1rXwFGrte3FVRks1ljrVE8gswMjRESI3bG6LMmlgM0i4ABqmkn0Ah4IMWAzUAxYgSxQvTq9y6mTO2z5GNPZmqbAwFFpALckoofPoPsFC0/qUVGmAQfcYL3U7VCm/dJiBEj5EqAaoL1JrAgIWIpCHXqJtByyCb7ZWPS4MleeoxYyHMh2ezaLfqs0ebgO0MmxnnFRbBioLc1myJ44fnzG4jCehtZb2C9Hlle9qyc4hKIFcRapLOkoOZiZXjK3JRZHadkVTO/lKT/wNnZOatFTwxqsh6TrQzNuGItCohouhXWklykoP6lpLE4JWp7segohVhKrrkXZfjjqApAAVdaLvoqTBgiwQ+cr5b44FUwEYEMPGQAcbYKc1By+yfhUpmckMjMugif2ZE9ISSqxW2/V0GjcwZjHGIMzpYo2eKuEE0bdp2eKVKOi7FEaxlkxA9brl94jFisNZMQTqLLaJHeD+oytG6y3uTANqlVIjNj9VGzX1KcAvSMnp8Qo/r5nSOOY3bDGl2X2FrZ1HJ0tlywXW9yiJXSv/0uVGEhlXnMWTU0+z0lVApTQlLPe0oBV8bFVKjpi9Jea0YfLnrCsqdfreiXSzYvAvsl+GTwVlgbj8Xy8Pyc2PdsUmS93TIMO9zZkjQm8AHEYAkkSQSTU7cCLJwlZTxn5xyBxHa9ZrXsOT87Z58C5xdn/Mwv/Bzn77zF2cUD/BhZuZ7eGhY3nzF8dsXtbs1us8c4y6PHj3EusHn2Aj8MGGe5ePwWD99+xPWHnxKDonjZzmCdsLA9cQwKn+tjlrpzsYmk1zqnRHXYb/Q+4zFmz3Y7sMPzxqMFicgw7tnvt3SdY7VaqS8q5z9ZazMWgJ5OlYYDThzejzWq2hqbffdJtU7rGPY+MytNZdLAFvXFKVHRg+bDwGK1ICVlHICapRMY6YhoCpBz6ssvhXjI/mJjVO8cM2MrfrgS9Z2SMjjb6ZiHYV0ZbEqJJ0+eoAwkxx24Dki4WAhoIbZziFwlTJO5X/N3C3RoqsRMRNETjTUEmnrZrclWcnRv8ctmzc3IIaArk4MWMlrelMIXCRgU57/kE5tSGwADY2EsapkiBGV2yWWzbcxjF0ixMlJj7LSvsmtIN4RGOrvsXtE4wwSxIYoyMd7WJxoj3N5uiTGy2QXeePfL/MGvfplv/8Z/RQp7Bbcp/yWIZU6aNbCNlluCqUoTEU0jS0m1VKOR4IWpafrrlD6WEmBS3s8lJTe7HvRLNJBLhagC4qL7MaeAOqtaZyjQrsqQjeuUyXsPKbDfbjU3PE37WCGBp8ItytQnM3TK56cw9RiDAn2VU1N9+iokF4ZvrVZoi7n0cREYYSoVq/2HHMuhwELZqwGia9mJ0pP93qsVweSUFyYmWN0Aosy/cNFJWM14DmkSnIucM62b3htz1KopMUdxOoeh7LSUCCELpEkVDkpRqiq0HhSISmUe8poX61g+s6nspWlElIygL1p7rRn9P/HP/CkePrxkuVphreXZZ58iwHKxIIbI9Ysr8B1n2ay9Hwf622vG3Y6Hjx4Sk8312JPma8eUfZ0akNYvevw4EseR/uKcFBN7EmbR88aDR9ysb3m0XPCLb7/Dm++8w6Jbsd/s6Jc9+2HLX/5Lf56nQYl5cIbzy0f8kX/4l3jy0fdYP7vh+uqWR2++yS/8sV/ivV/8Wf4v/7v/AxdGePDgnOX5it2oMLWDV6S9GCMmWcahgNAoYe46x263Yxz2iNGUtsWy49lnWzabPcN5x6efXbO53WDSgne/9C5iLcvlCms7hmHAWc2Xj0mZ5pj9u96P7Pd7TaUTw9nZGYyJzlkWSwXWSXGTYwvUVL8fBzq3wNhiArWa2nducdax23qcMZAUd4AkhKjars1BOF3XNwE7VEjY1s9mch+dc4ioxUWcpet7PvjgA0ge068qtOYnn3xSzbVd12GtqWhcSmwnk2qJFp40LTMJV9YepeN0nVMG7MNEfBsNFKhEqRCcYlouxKUwpkLJWwGhmIYLCFHwEWODvocxiHF6f0z01jAMO0Q6klUBwY1Ccj2IRTIGeWHIRnIVwsbX2c6xAFr5K87GJKLZIQUsqkCQTqZ3ySl12UQrgf2w5ef/8M/yz/8P/yX+7f/Vb/P0s42ma8ak0KhDqEJTOwdt0FYJ7CrTqnEj6kYocSZqKlfm6pyWdJ4KmZT6E1q7wOdc7sKEDnHkpwwNFYoS4DqXq61lFL88V653mBCylhwZs/vGHUSaOyf1vrKnK/YHaXKFiBCj11RemRhVETjLuMWA6wyL5Tm3t7f4bJlsY02KABtyxKAI1ZUTRDHljeh8pqQxDyEUN5oWBioBcsYKi25F8DEL2XlfnNCEC7x2CUacWWQS1R1RrCww4TJM7pcJ73673bLoekY/VAvBrMZ9KgG1J4B6Ds5V22qRm6NvXv/2WjP6n37np7U0YlDzz+Wbl6SUWPQLtpsNwzPPo7cfq2nJWkLwXCzPcKKH3/VLIBO24IkpcnZ21phsI8vlEhH1PYcUST9ricPI9mbN6uwSYy02WZ587wM629Mbh7HwydOP+OW/+p+xcue4cc/js4d85Svf4Bf/0B/lV3/9m5wtVkjfEXxk+dnAz3624NlHN/ypf+Gf5pd+6Q8TTeTf//f+r0h/hjGGxWKhm3y3w3tP3y1rFG3KPnID9AvL48crHj96wGeXge+//ynr2y0hjhCFheuQzNy3uy3ej5yfXZB8ynWk9QB2ToCOm5urqhVbq8FG77zzFu+8/Sa77ZbPPvuUhw8f8uzZMyBirWFBrpDnA4tFn0ExRozxPLw853y1YrFastvvefr0KR0O7zcYe8Y4ekY/8tZbb9N1HVdXV8CkVZdsAUVHjXg/EFIJxEyMPhJToFsssLJk72POu5Ua2V8Ih/Y1YYsXYtH6iFui0Pe9Ir/FqQqXtZaz5ZLOWYa9Wn+QNjdbGXhnpkA2mISA0gpiXAvxmvVMoAUFUeTComgXwuSsgWgIfiTEa5bvPuTDJ9eYredBv6Q/X5LCniRayMRQhJnAgwcPODs7YxgGnj59St/3+vycamYo+PBTPEB5B4AQSpS7ZCbmsgCl+dCjH9UF0cNZZ+l+8Cn+r/wKb59dcPXJRwpJ7SxYYdzvsRVTP7sCmkDJeUv1eRPTm5hymd2YMzEKg0lJTfp93+PDmNdemu9DFazKmhWN1NhSedGy2+3KRFC0wTgO+HHUvWJcna8YNUul3QPWuhpzArDf76uWrVUuUxU6Q8Z1N8bQOVt94ikFUjR4HxmGHWerBwpolfscRy1nvd+rANX3CyCx294oNLdPszGpIKrP6pyh7yzGdmw2Ox4+fMjF5SU3NzcM447z83OGNBL8VBK7pKlVyxcqjNe0OUkMg4J/lXXre1PxDibUO8lMXitaViwMwPsG/CZN0fbjOGKya8yIgM0xDrXPppJec6ZKi81aqoT6xUHHe60ZPZHqf3WZ0F9eXvL06afcXN9wcfmAvlvppTHS2Z7VYknMG1OibqiuW0Am/tFH9uMULT3sij/XgQgxR1J3tmfhliQUvGW11KDA1eUZHz/9kL/7nd/kye0L3nrnkpsXO7568Yif//I3+KWf/8P8vT/xJ/gr//Gf5+xsgWfgP/rP/hz/7v/zP+KrP/tVvvc7v8v73/k262HDeXdG2ls2YWBgSjtbrfSdhkGJiohweXnJ9dUtF6tLHp6d4RKk3S3L3rHe64ZeLDpcdk0tFh1dr2Zaax23Vzv2e43wF6tmdsXPXzIMezW3i8GJY7/d8NGHe/b7nWYAmFu6vs++b8FlgKG+d/hxIHjP2VnP177yDkuTIFnG4Ehb6MMSEz3WwJj2WOcQcbz//vuZyBuKn7zVQkz2Ry8XC0Rgu92qaTr7Vo10GSFOGW0J9Om6rsFemLRqYiIVgpXneb/fa2qSFCzuEVpiEWMVHLz3tQSxserbphASI0g2eZ9i9PrTVo1N/2VGhxItk4nixIwa83WMbG/X2L7HnS958knkF99+h//Nv/lv8rf+9n/J/+3f/3f4adtx219ivQq0RdsDLXf83nvvqQCREi9evGCxWODHQEi+znXxHbdNg7tKOWZluvv9PqdHlvQ5TYHrOoHLFX/9e7/Bn/2tb/LlVY85UyjV6AMuwAO7YmfibI5aK0Nh+iEEvB9YLFYq7OUo/67rNGWsCEuSMEzpWMWC4r1nuTATQiPKxPXvOHu/Wsin0b6LAGIa60MZo3Muo9BFjM2QGo3pnNynoP/aDJDSr4gKVSEElsulZiRkIaUgMxpEqz42+3G732WhRnHflytNP+h7R4yB3W6rcRejZ3m2UkyLqBgkxgrjfqd1CKyt47UGFgvd45vNGtcZEnqOjBW6RadWxmIup5jr8163JiPtqYD1+NEbJFQz3w8j3mf8aAzF5w5qJStu011WcESEvu8ZvdIKssVMYaWLBpBLCgUtyFPZRfk9lSJfJd5FLQK73VAtKqdq3b/O7bVm9GISXW8rIekXC9bbDbv9FmOFBw/UPO/DiCRFhwkJXL9QKd+W6mIAbjI7SfaliQYm6YYJhKiwrAYNHIpRdS5jsr8yKcDN2fkl3/gDP8Mv/Mz7PLm6xXU9437kyfs/4Jt/5a/xu9/8VVIILPpO/ZsJNpc7/ul//E/y0dMPefLkI9ZD4OLCEXqDCR0SJ7NfYULL5ZLUL7i+uWG5WpDMmu++/5T1zQW/8DNf4+d/4UucP/2U9W/t2G61WE4MIzfXz0nAxfkF52fneD+y298iAj5CGsmMbcdyuaq49SQIyTOOiXHUWAaNhjVTNHwqNaMNYRxYr9eqGUVHGgaudgNDjGx3gZv1nuvNmkXnFKFNUp5b0zCXYsosPnmFzhxHTTc0wNlypQRcpzK/h9bJLiZM5xwPHz7k+vq6ahutWT02ZlX9ZWJohSGmDAkKkzaw6Beq8UbdIwkNLNKU4GwyLeU2U6h+2gpEkwrxL+boA2Egz0D1L8YpTapo+Ckl9sOeP/Dmij/5R97mw896/tLf/B5/98/+p/z0L3yD//H/7F/l//xv/zt8/cuJkCPkyZaLxUIFtN/8zd+sTNTm4C11wU4m8tZ1Ug9KHqcGI84zDCbzuwY7Ra/59g+s48HSYjpwojEXKWqA2wat0lgYcnnn49zpkmedZsxfz2CYme4TKaeyTVCqNqeUTmuufvOuPDfmrI7sDy7gK9FHIprtIjkAzthja00KU3ZA8ROX64FqyrfO1KqFJb2rtVQUoUSD4hylKFMxWZe4ga7rePDgQY64DypkGKnz4seAMd1sHgVD31tCFliNWFarc/y4R63whq6zeU17QgwM+z3L1SJnK6iarrn4E6Rs2QPFpF/eIwZfrWUhRfb7fb6GWhpa758LTpvNhovz8ypQb7fbI/eKzrupDLp1+aQ0H18JKQhVYMwWqRL4Ox3dL0x7rRm963oN3IIMXNJxe7tBxLFYLnC2YwyahpMKs1B4MKxxmZApjdVNO6W2SJYKC3DI5F/VvoqpuO6HZsMpUReef/aCs0ePGUbPfgx88MlHbPc7nnz4ARfnK6wYgg8443nz8UOunz7l9uqG4COdVYnZo+hUrRnx7Ows5+uailGekvpAxxC5vt3x4ZPnXGy2XG3WSBKc7ZCMlD/m1KD9bqdgZcVniVatKlaSrsuRyqZgV+f852JGTpK1aJ2/s7MzSk53ymbUvu+rb/nZZ88V13/R0S3g0kQwiYAwjIbedIixlTEXxnfon1VCpocx+MB6vdYo+MJwnCLqSZrjjRWNu/Q106rNxFRTiR2QeS69MRbvx+raMTmQLSV1kmq1PcVxmHyshWhMOOWtBlaAXFq/vF6j/dWYRYqpttEM81itsfSdYz94PvjkOSl1WBn4G3/7v+S/97Nf5Z/7Z/9ZvvX93+E7v/w3cT7SZ+tAASgCKnxp33cqVGSzfUEFPN2OA5kKQ219w/X3LDAZKwq/bBKkiES1wsQUGCXiKEWmGtlbGvdF/ltEGhNwSc3M6XbZXYOUz6Z0NWFCgkxJAw9LdoLJ0eIxa9rU9ZtoCJSqijkYLvcv1qlgma0b5b+Y/duF6aTsPwbNJtAa7yZr8Z7O9fl9Us0Qib6BjGPaI5OiMjHZGFWg1NS1CQehpLQKQrJapVOaynwpadZM8FrlruAAhJCISUhJEEkEr+VuS7pioReztMG8F0pFPV1/FQr2w6CKU/Hto7E8IlPGTWXUme5Ya1itlqQE6/Ua1/VMVpbpXJVMj1boKJal9nwp/Tp2BR1arL4ozbz8kp/c5owyr5Q0XWS3Hdhu9jjXs1ycEYIyavVIKsiqFanmWCKkkNTH73O9d3JNcQSJiRSiolTEnIeaiaBuSpU+i/YZU8J2jhAT1y+u+O7vfo83Hj/m/PycaBKfbq74zgffZT/uOVut6FyPoILFWxcP+M63v82zTz8jjoHedYRxJPpd3rzzoBrQ/G8NkpNs4hKWZz1B4AdPnvJrv/lbvP+DD0kp0Xc9kixiOkQ00jaMe8Zxw2JhayS5MWrFsFbjAiZzZYlWFkJSk1iCmsJTmHph7AmwznF2fq5AGgmeX6253o2QEo8vOr7+7hk/9dVzHj7qECu4boG1mm9c0vpEeShADtiJOa9ZGZ4i5Fk1ZyYaYpdrncsUELfb7SbCkCbcayGb8LIlASlogakylDLvLaKXMVKZpUb8T2bm4mpQnU7TEOfBXRnUI6lPuAXxmdY4MyqKZhuaPnJQU9DnrpY9V5uBv/VbT/jeR3vefNDz9z74HT78+CO++vAB//L//F/knW98WV89ThHoISMh9n2nAYVFm42NEFveoyGSxbxaXQ35ey1m1OS952sLalsCvER2EghokKnaWcs59Gg6Yy6DnF01IhoLkKIy0pK1UdL8NPukq2ljCRUcQCPr2/00rQETE0jzz8s+KgJ/ysKB7pEcYZ9rtKeqJFgVNJnOjFpPpviFVkAz1lQgq7L26vqRPF5DMekXYCnNfol5ylLFMAAFvFJrldSxlNgATaUzWKPxHDZr5OXZksc0BdSZHJzsGbNbSoUUyXTHK21smGUbV1GtUSnWs2CsQYxaEKZrJ8taqPMw9aduWZPxMUJdk5Yfl/tLimhZ4yrYNMJ2u2fJ9GHK6EgcBpx+UdprzehDGInjgIRAbwzf/97vYEl0IkiI+GEkx57mnJgsqQvsRs/oA2PQjZwyJnqUhE8BHz1BkpbBtZojHoJHYkCSRtOKKLKWmFzGMSUWqzNShM3NhuQjj84vWUVYOMPyrKc/73n37XfY364Zhx22M5xfXrASh3nzAe5iReosAwHrHAqcp4FjBWnt+9//fkW12+/3dfO/8eAhq9UK2zvoO8ZkCMniuiVdvwTr8ElY9GcE71ksHF/96rv8I//on6gSfN+vePjwEY/fehOYfFjFhFhMXbv9nv3oSaIxDs71vHhxzc3NmnEMGY9AJiImBnP+gIuzc7ohsNgH3l2e88e/8dN87dElC8m1uavmq8U5lIYps1RzrWpvw179aV/72tf4k//YP0mMohHv1W+uxNh16top5voiiPR9z6pf4MTkCllTLnj7ztCaiSctUuFvJxPjbrdjGHVMJX++EC79p8y/MLLyGfp6R4pE8c967/F+yuNXtLDGpYGCDYkIYjs8jufjnqU4Hi56fvk//cv8B//bf4t/8dGX+F//T/5lHi0uGHZ7tVgQMUbPkffln68wq9PgJmJahBplthFy4Bpp0rgLmIzPtShqtLf3eD8yxojXQPncv+bAGwOdmQS11q+uQ5nHN4hY+n5JAV3pXZczNhQdspRbXiw6+s7irGHRd3RdN+FliBCix4dxxmza59AIadkRgJhUkWJD9Ix+IIQRDeST6mqpeygLkHpfgoxE13e9AgKFiCR1Q6UQ2e/2jMOgZVXHka532M5inaHre6yzGmArCdupIOO94kmIaODpxcUl1jj6bpGnL1uQJBCzRu79SPCDxuYQ2W63ut+dVReaVSFhsdAiWro/pFqEgOq6aC1uRSAu2StT7RBNyXXdAnKqqbop+npt2fPl/uVyyWaz4cmTJ3z88ccsl8vJqtgIZBpLVYQwfd9x3Os5kVQZfhHeW0sGWagw2YVgXmvOeNxea9P9Lox0dBAiL15cc7Vd85U33lATWkz6dkHN0mEclUBhSAjiOkwGq4gh4QeNTjWtqTJrKuM4MgxDJSbGGcbgtbISBklKjDrnOF+u2AfPR1fP2I47Pvju93nx7DmDBLCCHSISPZytMARMVPNlXDiuP3vGKiPVBeeICYJbcLZaqAbu1cd1fn4OUIno2Zn6nnfrHRFPIuBjwizPWNgFiUCSiOsNUQwx7Vn0PS+e3/Di+TW3NxvOL5bcrNd4n9jtIuNY0tsCLXrVZqOQuSlClJzqFXyFqyxz1/caxOcy4U4YxigsDVztAx989wXxu1cs+094sBAenS3ZjAGffdvee6ybIqlVe9cSuyKWxXKJAE8+fsrCnbFarPBBrQXkg+ys49HjR2x3W54/f64WnlL0JPsFC8G6vd3USGtjTBORr/zcWstqtaJfdOx226rRtn5ttXAYuq7P1gc/I0bls2JlcKb4jFWLajU+3X6qjRVBYxyHLCQ0lgGTGIdA3xvOXM+Z7bge1qyNcPG28OHmE/6P//Gf49/6D/9jvv6lFRdnK1arnjHvnTbKGSb3zDCoxlmkn0nTLaPTtL3i6LbOYpmi4w/fJYSRmBLOigriMRHTiIL7WQUdSpr1IdlSU9xA3vsKFFX61yAro6WbjRadKkzaWgsyBa+FMOb+pvWwVjV1nzXkUmK1zHtrOWmZQhFCC169qRxBmUlKJb1StMRDhobtnGMc96jP2tQ5KVYpnftsYRKNCShjOD8/J6SpgJdiIExR5MWSZDPQUduM0TnSmgZSNWhI+DjmGIzA6Aec6+j7LisUUvEsRGw+DypkGDH0i55xUBeVac6RsZBigWjWTJXdbqfrimTjaGu10r1UzO3zDIspBkEDRdXvv1nv6JcLQi4f3gqhbav7Jefbp+rOYHaGWuvO1McXy0n/WjP6NAasGMYY+OSTT3nz0Rss+x7vIzVfQ4L6b6NAaqAlg8+V6BRBKiXVVovpsWye29tbVssFfd9VM3ZFJmv8QMYYjBMWy57N+oYnH/yAtPf0neHy4SWbrH374ElGWCQQ6UgmokZMuDw/J/qAT0Lyhr5PLHGMPmhZ1phyJKsesFKEJm3XBALJCMELzi14cHGGiOHZZ5/SdQ6LRQK4BNv1LcvlkodvvolzHc+utyAOkSWd61RjsI5hH2vKTzUlDyMJWJ2fIUa0aJDrSClkrdhUqV4k4+Ebo2bbENiMEdsvOV+sCDHgx5F1ssStV7N9ATWxAvvA0i3xSXHuY0TXtQPBIUnYDSO//vd+k7Ozpa5jPtEpaQSyf/IJGAU42Y+BvjN0/YJhGFmvN4zjyOWjh5jO07kOQRh3I12/ZNzv8VHT+WywDOOuMsKi8Q+h5LIXBL2REDy9czjJFYtjJETNvy+m1ELc1+vbKepflLiJ0foChbBP2n+qLgv1EwNIhmsODDEhydBpmRHC3nPeOx5+aUWMMA5bhrinwHinJFhbaoIXQttlawVV+GgJYNHKCnFUE7B+0cZSlHQoZUzqf+26rpZVLQGOznXEjNsw5iDOs2xWVhebushiGDUmRmx2U6uAbMVgpAnGk0AKWdMUixHHGEYYpzQ39f0WOqCxPlrdUFPKhmGre9laMEIMJrvx1NqRRKPDtb+SDWKqoOizP11Q836KilypFhTJjHxidCKFCYVssSpxDSpIbDZrzs7PWJ4vUWCsnabvWV3DGDSmZBz3Cv0dYbcrgE3ZOiJUwc3HAOLyYmYEvbJ2eU9M+w5iHDPjTpomR4Ro6Zw0womj70u8TiIFjVJYLc6IvrhiyDFSQd1tqQTA5syaFGYxITovreswa/EWYvQKEJRdGrqWodLish9UW5/2q0hJwZyEjSKkWJGKj3Fc9Ob1bq81o1/0CnO53WkRh8vLy2lxUcZoci5lLUNYCVIC1LzcSpJ6yaSVqHYwSc2JeVnFCbM9E2BJbDZrXlxdaSpfClqQZvRTtTSbCQeRQGQMnhgC7739Nte312x2e0KMDD7QS0eXiX7I4C7qq5oIa2IixIUJ+FEl4RYTgOx7XSwWdYMjwjB6lssVXWbKMZuMSyR6G/x1fn5e56ezjuXyjGGnIEMKHpL9j6Ygz02mcJfN6mW+rBFMb7WKlvdKdMSCsRo1T/aNWsEmzX7o+56QGaIS04Tr3KyIRWFYm82OYAymc7iu59HFGcNmx+3tmq/91B/gjbff4lvf+i12T1/wD//jf5TrmxuefvIZQ9hjF5Zg1G8/oeJR/fl5K1WCaEzO5U4lyE19pMXgqxXvEq5T//HoFaq0FAIpftLik1fgplh9wcXX2UYbx1KmtdTwlkRMnpgCRhxWcqBV7cfgY6w+5GnstlpOynqXiO2yz8u5muUiN8y/aEAlFdXn9ysasZ47UwUlDX7U2IqYhaCFc6QExjl8dlMJWklu0fcM45CfKRXC1LR9yhQBH4KeLVPOhJRqbxP9KPNb7hUnKjBbIUrCpIBEo7E8kvBWBYSqh1aGoZafiekVVMAsnFfTfw7cZEJSLMLc3DVSzP4ls0XdHmS6VoMQkwYgeu+zxZEpoDRnDRTFNGbU1ySa9ibM8d+LEhNCzMh0EyOcjy/U9Fm1aOT7cryAWlWy4JIVJcUFGKn17mfCHqgANAUFTq061/P7oGW6U/HxT7Eg5R1aa9K0X7OSQsrzMMVXTG4ghTQuZ/qL1l5rRu+cZT/s2Ww22ffa5YCrvNlzPlQL8lGCVch5rimpp7OAdLSbrfhyrTV1w8w30cT0ixYTY2Cz3bDebFiuVhl2dqdmJtGc9WiEFAKBUEsw+tGr+dMpglRJ4dkTuFgoJnWyEec69nvVLOd+JiVafT6Eu/0Om6FeY45wJRUADr1nGAYQXwleyVv1wRNCrMAgk4UDzhZ9Nacul0suLy+5Tetqpi6aWgH5GUefUehcDqjReVMNcopiTkbTmJxToJE47rRcaradZ/wxXacS1SyqHdkcrAOt/1aLkawe/P/a+9IYO47r3K+W7r73zp2FFHdRpChLlh6t5cVKTBB5VoKI0ALDUOz8cBz9UBbYsCMDWRwjdoBYSf7IcIAASWA4DwgQBXiBlTiILNiIkyhaKNihpYimoMU2I8pUqIWbhpyZO/f27e6qOu/HqaruS8kRlcgazbCPQVOc2zPTVV191u98Zxo6YWenm3VgxyWqskS3k2HLxg04c+oUfvjSCcxM9VGOSyj4GdtC+h5pHqzjrIWQKvLJh7MUme1C+j0YFS50s7qUAkIqOGugEs3ZJMdkOFmWwZWsNAnMJCek8DVKByDgBUQkpmmiiJsAQQC+P5idSOlR0VUgtJH+Xr01aCq0WvmyImTWwDp9/HoApaBUa4AB7wu3P5b+58pIr+o7Cv038/eFyEknCZI0AwgwpvC74ZWvI0AoEDVKDUpGQ0aerU4JHqHMBsE7Qw3mNBuNKrxTZON41pA6ttYwC2D4Wlg/XOzM4FmtwfgF8FczulX+vvhcuDjsJWQB6nbBJrgzvBPhHQLY0AdMQQBOCoGJkc6BXCc4b+x3eUBdMIIIZzY8AzrnLNWOcnAogjRLPM2zH85LAN5Zx9G1swQpNbIshQDBmHLiHNXRddOw1/fzeuezua4QuHDWsAGsk75f/5zfUTtetYRnFkoxwjvYdM5ZXiuyqg19ZUqMRiOMRiOsW3cR8hFH9iKUFv0hsI2UDhC8VCBQLf4oB04IEalSo6ccr62nIoWfq6SAqZhxrigLzHZS5PmQASGC0b9C1MqNh584ZFIBcoxTp49DQkFDQEkFA43lcoSsUkiUQrfb8a1E3Fsa0puhY1dKBq4Ya5CPh9BZpwGMYYWXdlLkee73pYTzEf5ETZW4zzcQqABhwAchS7jmNjszw4j6NIGeY+difv50pJQNkR2RQJp2oJTCaGRRlhV6vU7sww2AHoCvy1Km5YWQ0MpPo3MG1jl2RArLXAZ++I6UEnk+9Oj7OtoMgMCt69dBS4nFpSWMF86AJNDtJZh/4QXQqXnMdTLQlMSh7x6ChPLRY4efqRQwviPDOuJ+f9Qp4AB6Go1GURERhE/FS54j4BW+Cal7IjjhkcDgbAqBPAAO4FkeEvDdJEERhR7iEPVxj3MA/flaphRIEgWVBVIgNrRSBvS1jDSwIQqOwLSo3HlSWTMN3zznk9ETIstgSImHKCq0h7F9CEQlDkp6PnZy8T2QUnnQWIosy3D8lQXIRHOqntgnH+djOMEMfQLCYyw4/SuUiC2y1rJzwPfK3AZVZSPJVD0emEF2IHaOnGOyLGEtdJbChWjS/yWFQAcChgRMw2CHsxv+bS3B87hEJ5bLgTUaPOqvMNI2ZkhsxCaE/Q3gxjTV/v7rqJ87bRKmEJaBVIYA/17IWEbzZDycLuKsGYLR5WiXiXOMzwBKhM4H5xy63a6nEFbR6NekPgYEF0s+4X57vQybNm3EdL+PQ4cOIk2TeA6DQQ86oD433MVTmboLIWRjLIV/N7MBNu49O3UUzzG/O5Pn91xno5m5ZQejdthDpmetyKo29K/Oz0NKiU6ni6len0fMBt5yr1xCxDUJcuK/hfCjD4n7sWs2r9emf2plKKKSDONbrauQj0fRmUiSBDLRqPIx8vGY0bleuVtbwVgHLTPINIECMC2AuVmN+UGFgSUUlkFCGhLTErBVCVPWB7TT6SBJEmaW8iMf026KsigZjQ426vxyht/L9LHGGPT79V51OqzUy7KEc8ZH4n5CXh7wCrXTYy2h1+liuDzE8mDgFViCXq8zAYbRWmN5cQkAYIqSo1RfXiHiLgKtNfr9Ps6ePQtA8vxvw3tcVKUfc1tAJwppmkBJhYokUu3ryta3TykVnZXg6adpiq1bt2J+PIZWEjJNUeY54ARm5i6ClQKv2Arj+UVsn90E40pA1CxvxhsxrRUoSUCWHSLrKt/pIGIZhal1Kw/GE8yERpzOn1QXPl1KHuAnJCrLxk/rOjNTtxn5lDQ4I8O/L9CrqtiHzI4mZzYSnfoZ676e6TMl1pZxz5t1zCazHytwGaldtS/lAHU9OWAvlM92NOv34ZycOXMmPg92LhjAKIRA6R0QKWvymiRJUJUllgcDpoDtTWHp1bMgZ5FmCXrT0wzII+EdJYJTEhq+PdBnQaAEKsNsjsaUqIyBg0PWmUJRVFwaUlzKCP3pAPx8AjZ6VivYUYFUa5BWKCShGhd4985LsDweYmkwQllWyLLEr7k+cwHgxTqpA4CQ5yPvME+WwcbjcQNcCL8n0ht/Zrbjd7HDGRE00961cSs9iLjT6WA8LtgrIuHnYRikaQalhD8/dakRqEftChEcCERHXYgw+tkiz/P4+4KRD3uXJCkc+THeQsRSobEVXn31Vaxfv97TiIeyjoxdK+F8hL+rqmLWRgF2VlBH5aHFkFsuxYSujgYclsuAfk+VCI4En+XA59GcOcDX2hjJ17HO2krfr2pDXxYlur0pJEnCpCnBOxbwTFIBEV3XVDnsYq9QCuXTgwSIumZVOwU+9TaB5qQYrQA8eS14jMaUEILQm+phZnYGozPLSBINR3bigEID2ikYwelbLSV6XYWhUVgecopfaw1hiOkeLc9HFkLwqF3fARBeLOccbGljdMaGRKIsCyQJj9lN0xTdXhdFMUY+zmPUFVrOiCyM4QEWaZoiSRKMCxtryNbXAbmcoTE1NYWqqjyS2JPWCN6TylQg8lznJGFtBUESnW7H05XyZ1pLEFloLZFlXRARKsMOx8z0DPJxBaU7SLVGIiSqsoSxBjpNIMFYggAWDEYr7EtAWlfFGCCBVGlkSiOHA1kDXQEJEaY6PeSu8lkfAJCwjpBk7MD4ib1whpVqt5fGPQl1dEaFpwgd86QIihySlKPyMBdb63QCMAQAwjR4uEEIo1KrqkSv10e/32dOdmNRFGMsLy/F0oSUjIbu9XoxnVuWFVAS4NWkkor7smkSHc6VZhEdvrpGW78voabaVMbBsDEy29UMaQiK02LdunXRATXGoCh4bwkhYg2sfBnvj+NxyEr1YK3F4ulFXHbllVBKYHmwhNPHj6Pf76MzzZGlrSpIcjA2pFwR68qszH1rVohqHXm+Cd4BxlkkMe0tPXmWcxZpmoGm+jh9ZhnSWsz2M3SnFF45Mw84QmVCyUxF1DzvjYoBgDEG+TgPHDzeaHiimmjUNaf4QQh8AYGeOXDcx4iZCASJUJ7jvVIYj+u57mVpQr0CSmlMTXFWanFxsVF6czCG9VnoYgikOkTw2B3Ecy2kgFQSidI8NdMy10iIkoOTyDpBTThPAOMPnn76aejEz7sgB/iSI59pE50NnSSwXkcqgQmHhJ+rAJduArZikjTKNohywllsXhNsA2cVAsdFKJXEN+Mc/b92ZFUbeuNqJrtm1B5eJgIz5kQkJ7GCDKMpAabedL6WV5qKx4hSSIVP0qTyz65/T83QxilcD6xF1snQn+5jyRmmGHUGzmeopWTyHnIO2vfbDqoCyBNUYNYwbR2sLWEcUClGtMOyJ5rnuQe0yViXa0Zb4XdIJZAgZVINVZNMaJ2gKisfUdVEFeNxjqmpXuwzD+WOsiwxPT0NABgsLcW9DrVOftH1RHqxMhWGwyHSJAU8BiHVKTqdDooiR1HksBYwtmKl1O8hHzEdJoRjbgL/rikp4KoKVelAEnUK29UI9E6nE+/33Kgj0Uxg4gRhcXERKkkwFEycpCGhhICR3ErGrUMKlTFIU6bZ5ciIoCRPvHM+JWgtU60aW0VjJ4QfpiElhHPYueMSLC4uYWFxKRKjROYvx8Y/TTMYUyG0gknB9KxZ1vHPwaEsK08XaqKTGbJLynOdE7EzNzU1HTMODIjjqJ6VM7dCKplwhBPHt9ZlGmP4zC4vDycwKGFvw8AbPjOcxer1epFEpZmKDpFYYKojMGtgUMicek4nFLoQAAmBTGls3LQB+XQfx195GVBAPhwyHYZg70v4rgPniJ0kAcAZ5EWOrJPCWEa/b9xyEbqdDo4e/SGnjBMeTxzaJtmQ8vveTTq4fPduXHHV/8IPnz+KRx98CFM9DacEtCflUSpMjpMAQntZXa8O2ADhGEsQ+80bQMrAkW9sGN8qGRtDPIqaHQ9uXwW4lMZOFpPYEDkuY/gMlPORODu7Jr4DIcIOvxdo8BIg4JiAJAmkPMQRuH/Gxg/7goWfucCdCBOGVHhyHEewruK2PMnof24BViirEiAGnIYyUgBJC55QVetWX9bimnvIpIYsgozlyHMDssnSEgA45jfxGRHnAu7lnNo/eRMvZPysOe9gLciqNvRCKEilAa84g9ESfrABRyTe0LtAnVlTkQZubP5Zvn0DdR0v1EZDNA40aDgF94TCp0611r4Xlwet9Ps85EY0/kfh+z2ASFj4FjMDUfBoViEthPAepwIAHisaDFdQGlVl4ssWmLA4PYqYf4rc3tS4V+EH9GBy3jqnaZWPJqsIqimriqOpLIOYmcFoNOIIXdS9rzHz4Wto0RFyFoIHdnIXQcXpbQYB0sT9j/MRAFYkMtbuOIWdSIlMa1g4FCAY49hQ+3GzobTSBA3xHjEVb5Kw4imrCgm41c5JCSN5IJHyVKKhPUoI4csEJStsX6jl4RwW5KdnMfjIIUlqQh1CExDojZ2UIOVbdoSMvcREzP0+UVYCoqIzxsK5sc8WmUadl/feOeezCwGDkXBXgjceXE4gX3YMKd9aSaeeDz7wMQTRSYqsU78b4YwEulmuyxtu54oZoTIixOtpco0Sl2QeAOsjYja2Te4AgQDO7HYyLC8PkGU8iCXU1/1VfPbg2+6cP+7kAFgkqcJUfxpFUfL1UoGcbfA8OO94cJytA24CBEgJA0I1GGFWJ5hLM2gAZA2kSEAJc2YIFxjkpNcXk8jvxpNspJcZ9R6NLjjtHcC4oTbcxEIICXQ6mY+wPYEUmFLXVJzZitkUTBpf42lsRfiddb0SEPCsjBTPEzBp3NKM+ezzPMco5xZQ4Z02Fe9P1NTRPrribB23d0LUOkhKZgFkPV13H4Ttar63dfQt4Cse8Nl8/26cu+fxW6PDzWeEnTn4oToUnlt4QvH7uTxZ34F3HNeQrGpDr5OUoxIIOI+6ds5BJSHK4esIQOCoB4UUD+Ass4MppaAVp2P5EAEBuUxwrOxJ+kwBwVgTW9EQ0KKuPhhZlqE/NQU47g92lqMvgD3cihw0RASM8X1rCCgPDhKA1kgkQZOAIYoKNrBGLSwu8UsnmNSCW9lyhD5ya+sWnsr3EEMKKCmQqjSSeYT0fygJVFWBQMpCxFz8o+EIaZJg3bp1yPPcz5fml4PZpzg1y+lcfgEDSEtJdn4qYzEYDJBmaW20PIVsPspBjtiZUArGMmK+rAqQqTA3N4v109NYGg0wGI4B4pnzWcbMZ8PhMO59rSgd8nzE/pJM0EkSTiESoaczQHHpxJJDVyaorInkSpoblNlASDZSQiqOEF3Ndsf5aBEzOoGHgaMShRdffAWBcCRJNAgVAOHTjKwoq6qAUoHtzreGkY2UsHWaXvq+4YCCpgiUGo2GYFQ+lzJChqAouANFaRk9CK0TKN8KKAAkflQqn0MBCAljHab606jKIo7jbaY+2ZjoBoiz7kgJ3RZlOUZZBuIUjd5UF8vLDJKSftRu3REjoiPrnMNsX2N+/jROz59EojVm+9MoihxZj1P7zlhIIu7UUJyZcraCdRa9qRS7du3CD77/HJxRUCrF/Pw8xuMcaZpGp0gIdvSTJPHOFuCkwKgs8NTjB7H88kmQAmb7GcblGMoRLHkHvdGKCPjBSQHLE9LKMjjA8Pfn4IRnYPSguDTrwDjriZCcB/7W43TDoJrhcIjlwZixYlKAp69xZB7Om5KsyoNDU5kSZRXAdb4Lw9evmRVR+OsTgER0lAF+j7Msw/T0NJJEY7C8CKVqUyGV8pkIPz1DiDjbgohpmXkIUA2IS5ME1gpUJesIdrZEbEFtln+8L8JGWnJnQ40Vqh2XAKALznXTiAM+QwABqQXIOlAI8lzdhsf36DO3vjQBDwJdS7KqDX2aZh6UVLeaROBcSO1IAeUn00jR6J8EwbrCR8sptO5BCG59Eq5GbYYXOKbzFQA4CEnRuAufumL2soqV08wMv4hFCWcctFKQQsA5Hv8qkxQZKQhFEAn/nvlTp5EmKRKVQkkJY0dwpkRZMtGO0hpzc+vR7SoUVeU5yZm2Ms9zpFndShgQ2UCTvY3Z3bjOqyJdLNdIdYMISEIpQMoUibawxmFxYYnLBr6+HqLANE3Q7aZYt24dzp49g+Fo2WMVJJQMTHZMqak1G+bZdXPoZBmsNVhaXAKsg0qZtjTp9CCTLqpRjtFohF4mkSQEKQ3GwwGcFcgyHp6T53lMTTYRzEEHV1WFhIDhYBHDfIBelkCCWdF4KiyBygpjVIAQDMAyBqN8jDTVSLVnWPST2fLRCGS5Djk1NYVMJ3jlpZd9qaE5mMTClBXm5xfQ7XXR7faQJBnSjJ0BOIuotSz5rgwey8kgI653AhI6SRhvMWYFybXKOhpZXl4Gc9Vz50M+GgLgjIOpKgAEa0LUzOyC0/1paK0xPz8PKTRcOC9CYFwUOHPyJNatW4dU67iXRMQMbdYyC+N4jOlpxsckSRozDK4BdOOSBpfPnOM0epqkcI5QFFw+CqCqAJLSWoGkwHS/G1O7pS2h0xRlPo6OBaSEkA6Z7jCzJQgQBhAWc7N9TPW6cKYASEIrgbLk7ANndwRAIg7yAQAJgraEFArjjTP4wcIrgHPoagWVKBhYyDEzAMpEx2cQHVql0O/3MRjwnHcCD3manZnFwsJS/F0hIxhoXR3ZCMoLNe+I5QGwtLTg3826k4GIMDc3x++8Z4xzfiRsRJnLeqCUlICAhvVOKrPfVeh0AoCWSzIhEyCEwOLSAgaDRaxfvwFTvWlYZyaeU9B7ZcXPupNmvlTAo39B9VTBqqp8NqIGR/MP4P8LCfOgP7UvbznizBRnsiiesUBWJKWKgVgoWzTr8gDjI4JRj91WYjLyD+WCkKkKdOBrSVa1oRfOwVUVD8Ow4RB7UJR/IXTKLwzXzcBUz763XMkUFGk1GEjELXcWYTpVE/hBxGj4NFGwFStiqULdjVtAqqpCJ00wO9fHMCWm+gTz50sQmPDNwVY+inAOZmggpYXudqF8e40zJfr9Pk6cOBFfxsqUOH5iiG6n64lleNSiVHVaMCiKUJ9jUBQrAmsY1V5VBOfGSNIEaZYBJGAs1ymzNIEQjBZOUonC5LwPVsIWDD4aLg0jyr4oxti+7WLMzc3AlDmUAhuLM2d4aI+ANwacPjcVIJChLJl8pNebhkmZi1tIZo5DVUBnAmoZkKqDM8sWC6MhDLpIEwFQ6RWeQDFm3vHAZ8+aATCmRNrJIGEZTEeCU/dagyCR6QwahGI0BEku/4yKcVTE3azHmR8/GEXCYSpLAHi0r3MoqwLrN66Hcw7Lo5EvDQmoJIFxDt0+UxWXVQkCIUHCcxWiouNIOCj1oiiwPMjR7/eRSI3KVbBlBRefJzsAASwZHNAw5CfsYbfLde3+dJ9r9VUVgaJV5bC4uAghFAzYsCXagpzBuCKcXS6xOCJIWYFcjqwr0Z/WqIYO5ajC8YVlXPqenbjm6h049h8/xNnnT2I0LkBUQoPQ70xjUJXISCKRGirNkGQZymKMLMt4YqIQSLIUS0uLgOvV7WBSQqUJilEe3nAoyZTCxhoInUQUNciBrINBEec9JEkXS4sFHnrocUz1e0x6ZA2kFR55bmBcHcExRF35zBTxfAtnkCYC63rMQhdaAMkSrDKes15CK2aW49noJZy1KIscGzesx2CwjOXlIUxloaTGRevX4+TJ42CSKA4zjC25G4dCa66CVjwKFlRHx9YSynKMq99zDU6ePInRKEen22HHFgrk2Ehxt4efXR+Q80pjTA4SBspplLaChUWmeuj3UhhnkegUQE2INNXvsVPkgY6j4QDOOmzdshVFUfDYaREyAwYggX6vj0u278BLL/8Qw1HB7bSSgX5a+TJY5fyYb294G4OJBDiyt750YK0FiTDBEhGnEEprQOAR8F0DUnjsjOIOCgSOE783EAiVeeGvB+CHEfGMDUB4zgRCKI2tJVnVhn5cFkyGghosZJwLWSkEAFoAzIUqZUipMYtWPbmI69Jh8pjw7Si8Rc65WNMLNSIhGMxnwfWdgEZOEo5wpZY8DCcoJ18/t9aiMEU0zgFdqxQbvACOG4+ZolUpJokh4nRet9fFaDiOL6eSPOCi6ckGgxUY7pzfFwmBNEvgnEJgXAtkGlVVwXoAl7UGRbmMSy/dicWlpTjsIk1TrlW7gBQXmH91HmcXz2J5eeDJdiyE4LZHJpAB85frBDMzs6gqg+XBEowp/ZSulH+eZqa0MMSj3+/z/gsB40FpSaI8cIhjAaW1Zz8swbSaPOVMCoXxeAylgFQnPDTDKwqtUzDjmoFQElO9KYyLksF4HgcRQETNWmBQPAE4FyKFGrWOuOdhuIoP/QDUrUvhOngwXfj+RCewaYXKlCAhfNbD+oix5v1vAg6bjmjoOrj00p1YWlpEVTFfv/T3GK5VsRTBFMr96S663SksDMZ46eRZboEyFRwspJEwuYSzFQppcdne7fjQB2/A+6/ejXv/7//D/U8dw3SSgpxDYQ0UFTBZho5KYJ3DeFigWhqwwtcaRVlytOebzY2zyPzQFeccqiIQ7YiJP1JIOBHqsQEImsWoOqDA+/0+nHMY5Qwm1H6MMqPiQ8TrWRdJQCnygMbA9+/Jizw+xNo6U6jTmqvBkYWtrMfmsCHJ8xyB40BrDRCwuLg4uR4ZF+GjZz9vgwDhqYJjwr9RLmEjP0KSJJidncXi4iLyPPedC3zmEs2jZxPtgXvEw4KssyArIgCQx8xaSKXR7Kzg9Qa8hopzDLrdTuSK0JqxFkoJzwvCzueLL74IYyt0OhmkYBBpVRnPKcBGl+vkIRr31LyOJ2EKQYD0XUQm7L31BF9qUp+dE24Th/rx59b7Bi6+Oo76nX/nmmDRujWPnffw9baP/h0kARAV+LOZYKORTpfcbsIKrkZyOPIxfOScbkwxEvVhYUpXOXEYeOxkE5BHvkzA11RVBZ0ozE7PoNPt8ex3x72/RJ6/W0rAet5oEXi3a5asAKQJ5D9SMlNbGHXKyqR2bgL717no2vBysKEV3nMWnpKyzgBorTHOS4SpT/BIV8Y4Od5ToliDDmleCN9WRITB4pKfFAVACCRJGq+p67iINLlKKxCFl5ifibOsWJ2zqEp+6Rg5HjIugUOfPHiQxxQLyWhhrr2yIRsMlrH94h0YjcOMAYep7hTKIocUiEqcHQXlwWGYKNUYUysabmcaR8UbKDtD+1P43qBkokFvKKAA6mz2nTsX+sAl0lRDZSlGOXxaNplQeKH8Up+ButYYavjOOZw+fRpKSVQVNd4DB+2jLGudL2EwMtpa5jWoTBXXwQQ8CmQUbAVAA+/eNYUdl27EFpIoj51Btayg/IhjoRI/25zbE0UiAAvfY81zDKzjiX8CMnIfBJxKc1+atVYhEEtz9b5Ngt+a3RZExBMcteTJbJHMpwmMo1gXtrbuKqjLL86fuTD1TEZypmhI4KN8W7dqAZyV6XS68XmXZRHPAwcSzg9Tajgw5Hj8bmCKbLzH4XsHgyUEspnRaBQJkqwLoFzGfvB8ey4TkhCRHZHr2HU9nHn0awMadIc1ltk5peT5Gt7x4XWEPfHviZKeIpo8j4jhkmZIrPlui7oWXpdD63eC06zBQZJSgiQb7uY5b5Y9mu9Z0G/g3+CDOr+Fwn+Nml3xHpzK3xD+b+I8hXO3lmRVG/pg5BwF78wz2zYMTLgOXGoFEIAf3sM9p7UjTLES3oixgXH+b/65zJntICjwzBOPn/RRVZJlmO5PY9PmTSjnlzlNRQz8spWB6qQQYCUfmO20T0tWporOgNYa1vF0LqZxBYbDYYw2A5Wt8TzxEaSCSSKJZmSgpArvBUc8WjPzVRHGifrJXBVHSgsLZ307m/Ojem2jPUdwrVQnMXDl1j4FnXhUfuP3O7LIR6OoKIOxDMAyNtbk+5555CczjemIIWDObOtH4yZwlhDQ3WwIWTEuD4a44f/sxumlszj96qtYXlxCkimM8yHQ2B/eYzagTBrk4vqs59bvdJjJbzQa+QyEisjysoRP8Tb41kPU5yOWoKyJRMz88P576l7PYa+VhEoY5MbdDdyy1hydGtuS/L+bET3XcQVeeOGFyD4Y7sk6INWcyubUqOAZAY6pkIuywijnc2UMtzlqlXG0KwGZKly6pYMNlcBLTxzBfywdxgsv5+j2OxHgp0BQpEGdHmBLPh5KIcn43iw4ktdKIVHMkFdWyxOtUucqd+6zP4cpDbUzEkpV4fvLsuSBTbOzyPMRBoMBHDVGp4ZnJIQ/XyY6UqGEwoadwL6atxgypJnZ0VBCg3QwWvxZMObR8gDR+ZRS1fMLiCC8Ma6ddQIoTN6bnDMgpURVlFBKozIVzp49G+vWla+RSyk5E0RcLhQApOZZFFoojM5WcYhMcBibrbJh3yEALXxXDrj3nZn9FJjwictkwcGJRtVzkxhjYqTP0b9h50PUUxgDU2f93CIQJOolQv2cm7qsuScTRt7fQwjJPKo5PIVo00NmNT6h8IELzkP84pqSVWnow0MvyxJiGF4KVnZJmvq2D/bmwiNTEmF8eFQsScItcSHNJfx18IhRKUufEXLRKyRiBeEsp9RBvo3PU0xa55CQgYDA/37v9Xjq249jaX4+RoUQErY0HqhmfcpeYaY3h6XlAcZlwS1hSiPLOijKMSsehIg4QZZ1MM494E1pJEkaZ9WzkeeeaY6Am0qTe1jzYgQhapAe02gmKMoxqryIw2ysKTEa5X4mtYL2oKvxmFG5AgJVabFUDdDpdn2ajClqU5WCOb8J1tSjbE+cOA5mJOTogMh5QI8fiQlWYJz6KwEI8OAh5QlFeJJWWVYgx+CaoszR7XW9AmeFBEh0O7PYtXkrNl08wunjr+A/nj0IMs7/PHZqhE/phj51znKIaLgjCQ04S8GOBqsOJcM0QSYFiufTl3mC99M0RMI7ijZEr3CQvp99aWkJAUnPjoQCkWdStKF+6SYUH/93uG8G84UxxkxiUiEfc1TJaVJCohSntH1tViuJJMkgcmB5UGJuXYZemiGdctCJgyYNYVL88z+/AoMXAQXIRCPtdNCRXSQ6hTUlCmOQKIVrrn0vnnn827ClhVAKSSIZKa85gjTWwZEnN1JxMD2IgNLXY32VK0Zt1keG2jM9kkfAO8f7zs4iUBQVTp+eR3+6D3KWa8PeGaxM3ZKaaA0pdUzrwgoIwTX5ACZjx96rSJ8dZtIYB8BASj9S2gZMj4AjyeQ1DcR9UVQQItSZtXfwfLZN8xni1kufleTa4IS5ET7okEJCKIHClzh47UylTWDSLqm4FTIBsH3zZly6fQse/udvoyor6IzXzdMvw3Q68qVBDsVtxVG8tb7vSDBNd3C2tGZsyTgvIbWDVvwzQweOlRW04smG5IODyvLsBSklmBbEIXSPhGY2cojPOdD3NjM1QbiU6c9AtNphGqFDaJQTob0YTQ57+Al88fH4TKuNk+vQuPpcJP9qFUGrcCUvvfQSLrnkkpW+jVZaaaWVVtawvPjii9i+fftK38b/WFaloXfO4fDhw9i9ezdefPFFzMzMrPQtvS2ytLSESy655IJaM9Cu+0Ja94W4ZuDCXPc7ec1EhMFggG3btsVy0WqWVZm6l1Li4osvBgDMzMy84w7Jj1suxDUD7bovJLkQ1wxcmOt+p655dnZ2pW/hLZPV76q00korrbTSSis/UlpD30orrbTSSitrWFatoc+yDHfddReyLFvpW3nb5EJcM9Cu+0Ja94W4ZuDCXPeFuOaVklUJxmullVZaaaWVVs5PVm1E30orrbTSSiutvLG0hr6VVlpppZVW1rC0hr6VVlpppZVW1rC0hr6VVlpppZVW1rC0hr6VVlpppZVW1rCsSkP/pS99CZdeeik6nQ727NmDxx9/fKVv6S2VP/iDP4iDUMKfq666Kn4+Ho9x55134qKLLkK/38cv/MIv4OTJkyt4x29eHn30UXzwgx/Etm3bIITA1772tYnPiQif//znsXXrVnS7Xezbtw/PPffcxDVnzpzB7bffjpmZGczNzeHXfu3XsLy8/Dau4s3LG637l3/5l1/z7G+55ZaJa1bbuu+++2781E/9FKanp7Fp0yb8/M//PA4fPjxxzfmc6WPHjuEDH/gAer0eNm3ahM985jM8ufEdKuez7p/92Z99zfP+xCc+MXHNalr3l7/8ZVx77bWR7W7v3r345je/GT9fi895NciqM/R/+7d/i9/+7d/GXXfdhe9+97u47rrrcPPNN+PUqVMrfWtvqbznPe/B8ePH459vfetb8bPf+q3fwte//nV89atfxf79+/HKK6/gwx/+8Are7ZuX4XCI6667Dl/60pde9/MvfvGL+LM/+zP8xV/8BR577DFMTU3h5ptvxng8jtfcfvvtePbZZ/HAAw/gG9/4Bh599FF8/OMff7uW8N+SN1o3ANxyyy0Tz/4rX/nKxOerbd379+/HnXfeie985zt44IEHUFUVbrrpJgyHw3jNG51pay0+8IEPoCxL/Nu//Rv++q//Gvfccw8+//nPr8SSzkvOZ90A8LGPfWzieX/xi1+Mn622dW/fvh1f+MIXcPDgQTzxxBP4uZ/7Odx222149tlnAazN57wqhFaZvO9976M777wz/ttaS9u2baO77757Be/qrZW77rqLrrvuutf9bGFhgZIkoa9+9avxa9///vcJAB04cOBtusO3VgDQfffdF//tnKMtW7bQH//xH8evLSwsUJZl9JWvfIWIiL73ve8RAPr3f//3eM03v/lNEkLQyy+//Lbd+/9Ezl03EdEdd9xBt91224/8nrWw7lOnThEA2r9/PxGd35n+x3/8R5JS0okTJ+I1X/7yl2lmZoaKonh7F/DflHPXTUT0Mz/zM/Qbv/EbP/J71sK6161bR3/5l395wTznd6Ksqoi+LEscPHgQ+/bti1+TUmLfvn04cODACt7ZWy/PPfcctm3bhssuuwy33347jh07BgA4ePAgqqqa2IOrrroKO3bsWDN7cPToUZw4cWJijbOzs9izZ09c44EDBzA3N4ef/MmfjNfs27cPUko89thjb/s9v5XyyCOPYNOmTbjyyivxyU9+EvPz8/GztbDuxcVFAMD69esBnN+ZPnDgAK655hps3rw5XnPzzTdjaWkpRovvdDl33UH+5m/+Bhs2bMDVV1+Nz33ucxiNRvGz1bxuay3uvfdeDIdD7N2794J5zu9EWVXT61599VVYaycOAQBs3rwZP/jBD1bort562bNnD+655x5ceeWVOH78OP7wD/8Q73//+/HMM8/gxIkTSNMUc3NzE9+zefNmnDhxYmVu+C2WsI7Xe87hsxMnTmDTpk0Tn2utsX79+lW9D7fccgs+/OEPY9euXXj++efxe7/3e7j11ltx4MABKKVW/bqdc/jN3/xN/PRP/zSuvvpqADivM33ixInXPQ/hs3e6vN66AeCXfumXsHPnTmzbtg1PPfUUfvd3fxeHDx/GP/zDPwBYnet++umnsXfvXozHY/T7fdx3333YvXs3nnzyyTX/nN+psqoM/YUit956a/zva6+9Fnv27MHOnTvxd3/3d+h2uyt4Z638uOUXf/EX439fc801uPbaa/Gud70LjzzyCG688cYVvLO3Ru68804888wzE5iTC0F+1Lqb2IprrrkGW7duxY033ojnn38e73rXu97u23xL5Morr8STTz6JxcVF/P3f/z3uuOMO7N+/f6Vv64KWVZW637BhA5RSr0Fpnjx5Elu2bFmhu/rxy9zcHN797nfjyJEj2LJlC8qyxMLCwsQ1a2kPwjr+q+e8ZcuW1wAwjTE4c+bMmtkHALjsssuwYcMGHDlyBMDqXvenPvUpfOMb38DDDz+M7du3x6+fz5nesmXL656H8Nk7WX7Uul9P9uzZAwATz3u1rTtNU1x++eW4/vrrcffdd+O6667Dn/7pn6755/xOllVl6NM0xfXXX48HH3wwfs05hwcffBB79+5dwTv78cry8jKef/55bN26Fddffz2SJJnYg8OHD+PYsWNrZg927dqFLVu2TKxxaWkJjz32WFzj3r17sbCwgIMHD8ZrHnroITjnorJcC/LSSy9hfn4eW7duBbA6101E+NSnPoX77rsPDz30EHbt2jXx+fmc6b179+Lpp5+ecHIeeOABzMzMYPfu3W/PQt6kvNG6X0+efPJJAJh43qtt3eeKcw5FUazZ57wqZKXRgG9W7r33XsqyjO655x763ve+Rx//+Mdpbm5uAqW52uXTn/40PfLII3T06FH69re/Tfv27aMNGzbQqVOniIjoE5/4BO3YsYMeeugheuKJJ2jv3r20d+/eFb7rNyeDwYAOHTpEhw4dIgD0J3/yJ3To0CH6z//8TyIi+sIXvkBzc3N0//3301NPPUW33XYb7dq1i/I8jz/jlltuoZ/4iZ+gxx57jL71rW/RFVdcQR/96EdXaknnJf/VugeDAf3O7/wOHThwgI4ePUr/+q//Su9973vpiiuuoPF4HH/Galv3Jz/5SZqdnaVHHnmEjh8/Hv+MRqN4zRudaWMMXX311XTTTTfRk08+Sf/0T/9EGzdupM997nMrsaTzkjda95EjR+iP/uiP6IknnqCjR4/S/fffT5dddhndcMMN8WestnV/9rOfpf3799PRo0fpqaeeos9+9rMkhKB/+Zd/IaK1+ZxXg6w6Q09E9Od//ue0Y8cOStOU3ve+99F3vvOdlb6lt1Q+8pGP0NatWylNU7r44ovpIx/5CB05ciR+nuc5/fqv/zqtW7eOer0efehDH6Ljx4+v4B2/eXn44YcJwGv+3HHHHUTELXa///u/T5s3b6Ysy+jGG2+kw4cPT/yM+fl5+uhHP0r9fp9mZmboV37lV2gwGKzAas5f/qt1j0Yjuummm2jjxo2UJAnt3LmTPvaxj73GiV1t63699QKgv/qrv4rXnM+ZfuGFF+jWW2+lbrdLGzZsoE9/+tNUVdXbvJrzlzda97Fjx+iGG26g9evXU5ZldPnll9NnPvMZWlxcnPg5q2ndv/qrv0o7d+6kNE1p48aNdOONN0YjT7Q2n/NqkHYefSuttNJKK62sYVlVNfpWWmmllVZaaeXNSWvoW2mllVZaaWUNS2voW2mllVZaaWUNS2voW2mllVZaaWUNS2voW2mllVZaaWUNS2voW2mllVZaaWUNS2voW2mllVZaaWUNS2voW2mllVZaaWUNS2voW2mllVZaaWUNS2voW2mllVZaaWUNS2voW2mllVZaaWUNy/8HEKn0JRugQxsAAAAASUVORK5CYII=\",\n      \"text/plain\": [\n       \"<Figure size 1200x800 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# This is the same as above, but we also added a prompt for the on/off switch\\n\",\n    \"plot_results(img2, processed_results[id5])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"metadata\": {\n    \"output\": {\n     \"id\": 730073820047625,\n     \"loadingStatus\": \"loaded\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"found 5 object(s)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\",\n      \"text/plain\": [\n       \"<Figure size 1200x800 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# this is the prompt \\\"handle\\\". Notice the oven handles that we want to remove\\n\",\n    \"plot_results(img2, processed_results[id6])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"metadata\": {\n    \"output\": {\n     \"id\": 1456993692192335,\n     \"loadingStatus\": \"loaded\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"found 3 object(s)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\",\n      \"text/plain\": [\n       \"<Figure size 1200x800 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# This time we added the negative prompt for the oven handle and the unwanted boxes are gone\\n\",\n    \"plot_results(img2, processed_results[id7])\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"fileHeader\": \"\",\n  \"fileUid\": \"76928cb6-3532-4024-bafd-4d3a609dfe2a\",\n  \"isAdHoc\": false,\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (ipykernel)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.12.11\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "examples/sam3_image_interactive.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"id\": \"5d0e0b69\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Copyright (c) Meta Platforms, Inc. and affiliates.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"11912666\",\n   \"metadata\": {},\n   \"source\": [\n    \"# <a target=\\\"_blank\\\" href=\\\"https://colab.research.google.com/github/facebookresearch/sam3/blob/main/notebooks/sam3_image_interactive.ipynb\\\">\\n\",\n    \"#   <img src=\\\"https://colab.research.google.com/assets/colab-badge.svg\\\" alt=\\\"Open In Colab\\\"/>\\n\",\n    \"# </a>\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"id\": \"8517f5f6\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"using_colab = False\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"id\": \"2540e376\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"if using_colab:\\n\",\n    \"    import torch\\n\",\n    \"    import torchvision\\n\",\n    \"    print(\\\"PyTorch version:\\\", torch.__version__)\\n\",\n    \"    print(\\\"Torchvision version:\\\", torchvision.__version__)\\n\",\n    \"    print(\\\"CUDA is available:\\\", torch.cuda.is_available())\\n\",\n    \"    import sys\\n\",\n    \"    !{sys.executable} -m pip install opencv-python matplotlib scikit-learn\\n\",\n    \"    !{sys.executable} -m pip install 'git+https://github.com/facebookresearch/sam3.git'\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"id\": \"90073483-58f6-404e-90ac-c22efcd76216\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"%matplotlib widget\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"id\": \"13325376-658b-48d6-8528-2a006f223d44\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import torch\\n\",\n    \"# turn on tfloat32 for Ampere GPUs\\n\",\n    \"# https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\\n\",\n    \"torch.backends.cuda.matmul.allow_tf32 = True\\n\",\n    \"torch.backends.cudnn.allow_tf32 = True\\n\",\n    \"\\n\",\n    \"# use bfloat16 for the entire notebook. If your card doesn't support it, try float16 instead\\n\",\n    \"torch.autocast(\\\"cuda\\\", dtype=torch.bfloat16).__enter__()\\n\",\n    \"\\n\",\n    \"# inference mode for the whole notebook. Disable if you need gradients\\n\",\n    \"torch.inference_mode().__enter__()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"fb863772-56a9-4ee2-be52-5d8933066519\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Load the model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"id\": \"f84b4ccc-9db2-4d88-ac8f-4c272694d25a\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import sam3\\n\",\n    \"from sam3 import build_sam3_image_model\\n\",\n    \"import os\\n\",\n    \"sam3_root = os.path.join(os.path.dirname(sam3.__file__), \\\"..\\\")\\n\",\n    \"bpe_path = f\\\"{sam3_root}/assets/bpe_simple_vocab_16e6.txt.gz\\\"\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"id\": \"de01a36e-1221-4497-a5ab-e6c796689480\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"model = build_sam3_image_model(bpe_path=bpe_path)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"id\": \"b01ec8a9-d9f6-4baf-96ac-1e5d21fd90b8\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from sam3.model.sam3_image_processor import Sam3Processor\\n\",\n    \"processor = Sam3Processor(model)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"e6172a69-35ca-487c-bd67-6f1f1ecb20d5\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Jupyter widget\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"id\": \"2a4ac22f-5d5c-4272-a5a1-dfe0c04253a7\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import io\\n\",\n    \"\\n\",\n    \"import ipywidgets as widgets\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import numpy as np\\n\",\n    \"import PIL.Image\\n\",\n    \"import requests\\n\",\n    \"from IPython.display import clear_output, display, HTML\\n\",\n    \"from matplotlib.patches import Rectangle\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class Sam3SegmentationWidget:\\n\",\n    \"    \\\"\\\"\\\"Interactive Jupyter widget for SAM3 segmentation with text and box prompts.\\\"\\\"\\\"\\n\",\n    \"\\n\",\n    \"    def __init__(self, processor):\\n\",\n    \"        \\\"\\\"\\\"\\n\",\n    \"        Initialize the segmentation widget.\\n\",\n    \"\\n\",\n    \"        Args:\\n\",\n    \"            processor: Sam3Processor instance\\n\",\n    \"        \\\"\\\"\\\"\\n\",\n    \"        self.processor = processor\\n\",\n    \"        self.state = None\\n\",\n    \"        self.current_image = None\\n\",\n    \"        self.current_image_array = None\\n\",\n    \"        self.box_mode = \\\"positive\\\"\\n\",\n    \"        self.drawing_box = False\\n\",\n    \"        self.box_start = None\\n\",\n    \"        self.current_rect = None\\n\",\n    \"\\n\",\n    \"        self._setup_ui()\\n\",\n    \"        self._setup_plot()\\n\",\n    \"\\n\",\n    \"    def _setup_ui(self):\\n\",\n    \"        \\\"\\\"\\\"Set up the UI components.\\\"\\\"\\\"\\n\",\n    \"        self.upload_widget = widgets.FileUpload(\\n\",\n    \"            accept=\\\"image/*\\\", multiple=False, description=\\\"Upload Image\\\"\\n\",\n    \"        )\\n\",\n    \"        self.upload_widget.observe(self._on_image_upload, names=\\\"value\\\")\\n\",\n    \"\\n\",\n    \"        self.url_input = widgets.Text(\\n\",\n    \"            placeholder=\\\"Or enter image URL\\\",\\n\",\n    \"        )\\n\",\n    \"        self.url_button = widgets.Button(description=\\\"Load URL\\\", button_style=\\\"info\\\")\\n\",\n    \"        self.url_button.on_click(self._on_load_url)\\n\",\n    \"        url_box = widgets.HBox(\\n\",\n    \"            [self.url_input, self.url_button],\\n\",\n    \"            layout=widgets.Layout(width=\\\"100%\\\", justify_content=\\\"space-between\\\"),\\n\",\n    \"        )\\n\",\n    \"\\n\",\n    \"        self.text_input = widgets.Text(\\n\",\n    \"            placeholder='Enter segmentation prompt (e.g., \\\"person\\\", \\\"dog\\\")',\\n\",\n    \"            continuous_update=False,\\n\",\n    \"        )\\n\",\n    \"        self.text_input.observe(self._on_text_submit, names=\\\"value\\\")\\n\",\n    \"        self.text_button = widgets.Button(description=\\\"Segment\\\", button_style=\\\"success\\\")\\n\",\n    \"        self.text_button.on_click(self._on_text_prompt)\\n\",\n    \"        text_box = widgets.HBox(\\n\",\n    \"            [self.text_input, self.text_button],\\n\",\n    \"            layout=widgets.Layout(width=\\\"100%\\\", justify_content=\\\"space-between\\\"),\\n\",\n    \"        )\\n\",\n    \"\\n\",\n    \"        self.box_mode_buttons = widgets.ToggleButtons(\\n\",\n    \"            options=[\\\"Positive Boxes\\\", \\\"Negative Boxes\\\"],\\n\",\n    \"            description=\\\"Box Mode:\\\",\\n\",\n    \"            button_style=\\\"\\\",\\n\",\n    \"            tooltips=[\\n\",\n    \"                \\\"Draw boxes around objects to include\\\",\\n\",\n    \"                \\\"Draw boxes around objects to exclude\\\",\\n\",\n    \"            ],\\n\",\n    \"        )\\n\",\n    \"        self.box_mode_buttons.observe(self._on_box_mode_change, names=\\\"value\\\")\\n\",\n    \"\\n\",\n    \"        self.clear_button = widgets.Button(\\n\",\n    \"            description=\\\"Clear All Prompts\\\", button_style=\\\"warning\\\"\\n\",\n    \"        )\\n\",\n    \"        self.clear_button.on_click(self._on_clear_prompts)\\n\",\n    \"\\n\",\n    \"        self.confidence_slider = widgets.FloatSlider(\\n\",\n    \"            value=0.5,\\n\",\n    \"            min=0.0,\\n\",\n    \"            max=1.0,\\n\",\n    \"            step=0.01,\\n\",\n    \"            description=\\\"Confidence:\\\",\\n\",\n    \"            continuous_update=False,\\n\",\n    \"            style={\\\"description_width\\\": \\\"initial\\\"},\\n\",\n    \"        )\\n\",\n    \"        self.confidence_slider.observe(self._on_confidence_change, names=\\\"value\\\")\\n\",\n    \"\\n\",\n    \"        self.size_slider = widgets.IntSlider(\\n\",\n    \"            value=960,\\n\",\n    \"            min=300,\\n\",\n    \"            max=2000,\\n\",\n    \"            step=10,\\n\",\n    \"            description=\\\"Image Size:\\\",\\n\",\n    \"            continuous_update=False,\\n\",\n    \"            style={\\\"description_width\\\": \\\"initial\\\"},\\n\",\n    \"        )\\n\",\n    \"        self.size_slider.observe(self._on_size_change, names=\\\"value\\\")\\n\",\n    \"\\n\",\n    \"        slider_box = widgets.HBox(\\n\",\n    \"            [self.confidence_slider, self.size_slider],\\n\",\n    \"            layout=widgets.Layout(justify_content=\\\"space-between\\\"),\\n\",\n    \"        )\\n\",\n    \"\\n\",\n    \"        self.output = widgets.Output()\\n\",\n    \"        self.status_label = widgets.Label(value=\\\"Upload an image to begin\\\")\\n\",\n    \"\\n\",\n    \"        # This box will hold our matplotlib output and we can target it with CSS.\\n\",\n    \"        self.plot_container = widgets.Box([self.output])\\n\",\n    \"        self.plot_container.add_class(\\\"no-drag\\\")\\n\",\n    \"\\n\",\n    \"        # CSS to make the cursor a crosshair over the matplotlib canvas\\n\",\n    \"        css_style = widgets.HTML(\\n\",\n    \"            \\\"\\\"\\\"\\n\",\n    \"        <style>\\n\",\n    \"            .jupyter-matplotlib-canvas, canvas {\\n\",\n    \"                cursor: crosshair !important;\\n\",\n    \"            }\\n\",\n    \"        </style>\\n\",\n    \"        \\\"\\\"\\\"\\n\",\n    \"        )\\n\",\n    \"        # Create VBoxes for each accordion pane\\n\",\n    \"        source_pane = widgets.VBox([self.upload_widget, url_box])\\n\",\n    \"        prompt_pane = widgets.VBox(\\n\",\n    \"            [\\n\",\n    \"                widgets.Label(\\\"Text Prompt:\\\"),\\n\",\n    \"                text_box,\\n\",\n    \"                self.box_mode_buttons,\\n\",\n    \"                self.confidence_slider,\\n\",\n    \"                self.clear_button,\\n\",\n    \"            ]\\n\",\n    \"        )\\n\",\n    \"        display_pane = widgets.VBox([self.size_slider])\\n\",\n    \"\\n\",\n    \"        # Create the Accordion to hold the control panes\\n\",\n    \"        self.accordion = widgets.Accordion(\\n\",\n    \"            children=[source_pane, prompt_pane, display_pane]\\n\",\n    \"        )\\n\",\n    \"        self.accordion.set_title(0, \\\"Image Source\\\")\\n\",\n    \"        self.accordion.set_title(1, \\\"Segmentation Prompts\\\")\\n\",\n    \"        self.accordion.set_title(2, \\\"Display Settings\\\")\\n\",\n    \"        self.accordion.selected_index = 0  # Start with the first pane open\\n\",\n    \"\\n\",\n    \"        # Create the left sidebar for controls\\n\",\n    \"        sidebar = widgets.VBox(\\n\",\n    \"            [self.status_label, widgets.HTML(\\\"<h4>Controls</h4>\\\"), self.accordion]\\n\",\n    \"        )\\n\",\n    \"        sidebar.layout = widgets.Layout(\\n\",\n    \"            width=\\\"380px\\\",\\n\",\n    \"            min_width=\\\"380px\\\",\\n\",\n    \"            max_width=\\\"380px\\\",\\n\",\n    \"            border=\\\"1px solid #e0e0e0\\\",\\n\",\n    \"            padding=\\\"10px\\\",\\n\",\n    \"            margin=\\\"0 15px 0 0\\\",\\n\",\n    \"            flex=\\\"0 0 auto\\\",\\n\",\n    \"        )\\n\",\n    \"\\n\",\n    \"        # Create the main area for the image display\\n\",\n    \"        main_area = widgets.VBox([self.plot_container])\\n\",\n    \"        main_area.layout = widgets.Layout(flex=\\\"1\\\", min_width=\\\"500px\\\", overflow=\\\"auto\\\")\\n\",\n    \"\\n\",\n    \"        # Combine sidebar and main area into the final app layout\\n\",\n    \"        app_layout = widgets.HBox([sidebar, main_area])\\n\",\n    \"        app_layout.layout = widgets.Layout(\\n\",\n    \"            width=\\\"100%\\\",\\n\",\n    \"            display=\\\"flex\\\",\\n\",\n    \"            flex_flow=\\\"row\\\",\\n\",\n    \"            align_items=\\\"stretch\\\",\\n\",\n    \"        )\\n\",\n    \"\\n\",\n    \"        # Set the main container\\n\",\n    \"        self.container = widgets.VBox(\\n\",\n    \"            [\\n\",\n    \"                css_style,\\n\",\n    \"                widgets.HTML(\\\"<h3>🖼️ SAM3 Interactive Segmentation</h3>\\\"),\\n\",\n    \"                app_layout,\\n\",\n    \"            ]\\n\",\n    \"        )\\n\",\n    \"\\n\",\n    \"    def _setup_plot(self):\\n\",\n    \"        \\\"\\\"\\\"Set up the matplotlib figure.\\\"\\\"\\\"\\n\",\n    \"        # plt.ioff()\\n\",\n    \"        self.fig, self.ax = plt.subplots(figsize=(12, 8))\\n\",\n    \"        # plt.ion()\\n\",\n    \"        self.ax.axis(\\\"off\\\")\\n\",\n    \"        self.fig.subplots_adjust(left=0, right=1, top=1, bottom=0)\\n\",\n    \"        self.fig.canvas.toolbar_visible = False\\n\",\n    \"        self.fig.canvas.header_visible = False\\n\",\n    \"        self.fig.canvas.footer_visible = False\\n\",\n    \"        self.fig.canvas.resizable = False\\n\",\n    \"\\n\",\n    \"        # plt.close(self.fig)\\n\",\n    \"\\n\",\n    \"    def _set_loading(self, is_loading, message=\\\"Processing...\\\"):\\n\",\n    \"        \\\"\\\"\\\"Show/hide loading state and disable/enable controls.\\\"\\\"\\\"\\n\",\n    \"        if is_loading:\\n\",\n    \"            self.status_label.value = f\\\"⏳ {message}\\\"\\n\",\n    \"            self.upload_widget.disabled = True\\n\",\n    \"            self.url_button.disabled = True\\n\",\n    \"            self.text_button.disabled = True\\n\",\n    \"            self.clear_button.disabled = True\\n\",\n    \"            self.box_mode_buttons.disabled = True\\n\",\n    \"            self.confidence_slider.disabled = True\\n\",\n    \"        else:\\n\",\n    \"            self.upload_widget.disabled = False\\n\",\n    \"            self.url_button.disabled = False\\n\",\n    \"            self.text_button.disabled = False\\n\",\n    \"            self.clear_button.disabled = False\\n\",\n    \"            self.box_mode_buttons.disabled = False\\n\",\n    \"            self.confidence_slider.disabled = False\\n\",\n    \"\\n\",\n    \"    def _on_image_upload(self, change):\\n\",\n    \"        \\\"\\\"\\\"Handle image upload.\\\"\\\"\\\"\\n\",\n    \"        if change[\\\"new\\\"]:\\n\",\n    \"            uploaded_file = change[\\\"new\\\"][0]\\n\",\n    \"            image = PIL.Image.open(io.BytesIO(uploaded_file[\\\"content\\\"])).convert(\\\"RGB\\\")\\n\",\n    \"            self._set_image(image)\\n\",\n    \"\\n\",\n    \"    def _on_load_url(self, button):\\n\",\n    \"        \\\"\\\"\\\"Handle loading image from URL.\\\"\\\"\\\"\\n\",\n    \"        url = self.url_input.value.strip()\\n\",\n    \"        if not url:\\n\",\n    \"            self.status_label.value = \\\"Please enter a URL\\\"\\n\",\n    \"            return\\n\",\n    \"\\n\",\n    \"        self._set_loading(True, \\\"Downloading image from URL...\\\")\\n\",\n    \"\\n\",\n    \"        try:\\n\",\n    \"            response = requests.get(url, timeout=10)\\n\",\n    \"            response.raise_for_status()\\n\",\n    \"            image = PIL.Image.open(io.BytesIO(response.content)).convert(\\\"RGB\\\")\\n\",\n    \"            self._set_image(image)\\n\",\n    \"        except Exception as e:\\n\",\n    \"            self._set_loading(False)\\n\",\n    \"            self.status_label.value = f\\\"Error loading image: {str(e)}\\\"\\n\",\n    \"\\n\",\n    \"    def _set_image(self, image):\\n\",\n    \"        \\\"\\\"\\\"Set the current image, adjust figure size, and initialize state.\\\"\\\"\\\"\\n\",\n    \"        self._set_loading(True, \\\"Processing image through model...\\\")\\n\",\n    \"\\n\",\n    \"        try:\\n\",\n    \"\\n\",\n    \"            self.current_image = image\\n\",\n    \"            self.current_image_array = np.array(image)\\n\",\n    \"            self.state = self.processor.set_image(image)\\n\",\n    \"            self._set_loading(False)\\n\",\n    \"            self.status_label.value = (\\n\",\n    \"                f\\\"Image loaded: {image.size[0]}x{image.size[1]} pixels\\\"\\n\",\n    \"            )\\n\",\n    \"            self._resize_figure()\\n\",\n    \"            self._update_display()\\n\",\n    \"            self._connect_plot_events()\\n\",\n    \"            self.accordion.selected_index = 1\\n\",\n    \"        except Exception as e:\\n\",\n    \"            self._set_loading(False)\\n\",\n    \"            self.status_label.value = f\\\"Error processing image: {str(e)}\\\"\\n\",\n    \"\\n\",\n    \"    def _on_text_submit(self, change):\\n\",\n    \"        \\\"\\\"\\\"Handle text prompt submission via Enter key.\\\"\\\"\\\"\\n\",\n    \"        # Call the same handler as the button click\\n\",\n    \"        self._on_text_prompt(None)\\n\",\n    \"\\n\",\n    \"    def _on_text_prompt(self, button):\\n\",\n    \"        \\\"\\\"\\\"Handle text prompt submission.\\\"\\\"\\\"\\n\",\n    \"        if self.state is None:\\n\",\n    \"            self.status_label.value = \\\"Please load an image first\\\"\\n\",\n    \"            return\\n\",\n    \"\\n\",\n    \"        prompt = self.text_input.value.strip()\\n\",\n    \"        if not prompt:\\n\",\n    \"            self.status_label.value = \\\"Please enter a prompt\\\"\\n\",\n    \"            return\\n\",\n    \"\\n\",\n    \"        self._set_loading(True, f'Segmenting with prompt: \\\"{prompt}\\\"...')\\n\",\n    \"\\n\",\n    \"        try:\\n\",\n    \"            self.state = self.processor.set_text_prompt(prompt, self.state)\\n\",\n    \"            self._set_loading(False)\\n\",\n    \"            self.status_label.value = f'Segmented with prompt: \\\"{prompt}\\\"'\\n\",\n    \"            self._update_display()\\n\",\n    \"        except Exception as e:\\n\",\n    \"            self._set_loading(False)\\n\",\n    \"            self.status_label.value = f\\\"Error: {str(e)}\\\"\\n\",\n    \"\\n\",\n    \"    def _on_box_mode_change(self, change):\\n\",\n    \"        \\\"\\\"\\\"Handle box mode toggle.\\\"\\\"\\\"\\n\",\n    \"        self.box_mode = \\\"positive\\\" if change[\\\"new\\\"] == \\\"Positive Boxes\\\" else \\\"negative\\\"\\n\",\n    \"\\n\",\n    \"    def _on_clear_prompts(self, button):\\n\",\n    \"        \\\"\\\"\\\"Clear all prompts and reset to image only.\\\"\\\"\\\"\\n\",\n    \"        if self.current_image is not None:\\n\",\n    \"            try:\\n\",\n    \"                self._set_loading(True, \\\"Clearing prompts and resetting...\\\")\\n\",\n    \"                self.state = self.processor.reset_all_prompts(self.state)\\n\",\n    \"                if \\\"prompted_boxes\\\" in self.state:\\n\",\n    \"                    del self.state[\\\"prompted_boxes\\\"]\\n\",\n    \"                self.text_input.value = \\\"\\\"\\n\",\n    \"                self._set_loading(False)\\n\",\n    \"                self.status_label.value = \\\"Cleared all prompts\\\"\\n\",\n    \"                self._update_display()\\n\",\n    \"            except Exception as e:\\n\",\n    \"                self._set_loading(False)\\n\",\n    \"                import traceback\\n\",\n    \"\\n\",\n    \"                self.status_label.value = f\\\"Error: {str(e)} {traceback.format_exc()}\\\"\\n\",\n    \"\\n\",\n    \"    def _on_confidence_change(self, change):\\n\",\n    \"        \\\"\\\"\\\"Handle confidence threshold change.\\\"\\\"\\\"\\n\",\n    \"        if self.state is not None:\\n\",\n    \"            self.state = self.processor.set_confidence_threshold(\\n\",\n    \"                change[\\\"new\\\"], self.state\\n\",\n    \"            )\\n\",\n    \"            self._update_display()\\n\",\n    \"\\n\",\n    \"    def _connect_plot_events(self):\\n\",\n    \"        \\\"\\\"\\\"Connect matplotlib event handlers for box drawing.\\\"\\\"\\\"\\n\",\n    \"        # Disable matplotlib's toolbar navigation to allow custom box drawing\\n\",\n    \"        if hasattr(self.fig.canvas, \\\"toolbar\\\") and self.fig.canvas.toolbar is not None:\\n\",\n    \"            self.fig.canvas.toolbar.pan()\\n\",\n    \"            self.fig.canvas.toolbar.pan()\\n\",\n    \"\\n\",\n    \"        self.fig.canvas.mpl_connect(\\\"button_press_event\\\", self._on_press)\\n\",\n    \"        self.fig.canvas.mpl_connect(\\\"button_release_event\\\", self._on_release)\\n\",\n    \"        self.fig.canvas.mpl_connect(\\\"motion_notify_event\\\", self._on_motion)\\n\",\n    \"\\n\",\n    \"    def _on_press(self, event):\\n\",\n    \"        \\\"\\\"\\\"Handle mouse press for box drawing.\\\"\\\"\\\"\\n\",\n    \"        if event.inaxes != self.ax:\\n\",\n    \"            return\\n\",\n    \"        self.drawing_box = True\\n\",\n    \"        self.box_start = (event.xdata, event.ydata)\\n\",\n    \"\\n\",\n    \"    def _on_motion(self, event):\\n\",\n    \"        \\\"\\\"\\\"Handle mouse motion for box preview.\\\"\\\"\\\"\\n\",\n    \"        if not self.drawing_box or event.inaxes != self.ax or self.box_start is None:\\n\",\n    \"            return\\n\",\n    \"\\n\",\n    \"        if self.current_rect is not None:\\n\",\n    \"            self.current_rect.remove()\\n\",\n    \"\\n\",\n    \"        x0, y0 = self.box_start\\n\",\n    \"        x1, y1 = event.xdata, event.ydata\\n\",\n    \"        width = x1 - x0\\n\",\n    \"        height = y1 - y0\\n\",\n    \"\\n\",\n    \"        color = \\\"green\\\" if self.box_mode == \\\"positive\\\" else \\\"red\\\"\\n\",\n    \"        self.current_rect = Rectangle(\\n\",\n    \"            (x0, y0),\\n\",\n    \"            width,\\n\",\n    \"            height,\\n\",\n    \"            fill=False,\\n\",\n    \"            edgecolor=color,\\n\",\n    \"            linewidth=2,\\n\",\n    \"            linestyle=\\\"--\\\",\\n\",\n    \"        )\\n\",\n    \"        self.ax.add_patch(self.current_rect)\\n\",\n    \"        self.fig.canvas.draw_idle()\\n\",\n    \"\\n\",\n    \"    def _on_release(self, event):\\n\",\n    \"        \\\"\\\"\\\"Handle mouse release to finalize box.\\\"\\\"\\\"\\n\",\n    \"        if not self.drawing_box or event.inaxes != self.ax or self.box_start is None:\\n\",\n    \"            self.drawing_box = False\\n\",\n    \"            return\\n\",\n    \"\\n\",\n    \"        self.drawing_box = False\\n\",\n    \"\\n\",\n    \"        if self.current_rect is not None:\\n\",\n    \"            self.current_rect.remove()\\n\",\n    \"            self.current_rect = None\\n\",\n    \"\\n\",\n    \"        if self.state is None:\\n\",\n    \"            return\\n\",\n    \"\\n\",\n    \"        x0, y0 = self.box_start\\n\",\n    \"        x1, y1 = event.xdata, event.ydata\\n\",\n    \"\\n\",\n    \"        x_min = min(x0, x1)\\n\",\n    \"        x_max = max(x0, x1)\\n\",\n    \"        y_min = min(y0, y1)\\n\",\n    \"        y_max = max(y0, y1)\\n\",\n    \"\\n\",\n    \"        if abs(x_max - x_min) < 5 or abs(y_max - y_min) < 5:\\n\",\n    \"            return\\n\",\n    \"\\n\",\n    \"        # Get image dimensions\\n\",\n    \"        img_h = self.state[\\\"original_height\\\"]\\n\",\n    \"        img_w = self.state[\\\"original_width\\\"]\\n\",\n    \"\\n\",\n    \"        # Convert from xyxy pixel coordinates to cxcywh normalized format\\n\",\n    \"        center_x = (x_min + x_max) / 2.0 / img_w\\n\",\n    \"        center_y = (y_min + y_max) / 2.0 / img_h\\n\",\n    \"        width = (x_max - x_min) / img_w\\n\",\n    \"        height = (y_max - y_min) / img_h\\n\",\n    \"\\n\",\n    \"        box = [center_x, center_y, width, height]\\n\",\n    \"        label = self.box_mode == \\\"positive\\\"\\n\",\n    \"        mode_str = \\\"positive\\\" if label else \\\"negative\\\"\\n\",\n    \"\\n\",\n    \"        # Store the prompted box in pixel coordinates for display\\n\",\n    \"        if \\\"prompted_boxes\\\" not in self.state:\\n\",\n    \"            self.state[\\\"prompted_boxes\\\"] = []\\n\",\n    \"        self.state[\\\"prompted_boxes\\\"].append(\\n\",\n    \"            {\\\"box\\\": [x_min, y_min, x_max, y_max], \\\"label\\\": label}\\n\",\n    \"        )\\n\",\n    \"\\n\",\n    \"        self._set_loading(True, f\\\"Adding {mode_str} box and re-segmenting...\\\")\\n\",\n    \"\\n\",\n    \"        try:\\n\",\n    \"            self.state = self.processor.add_geometric_prompt(box, label, self.state)\\n\",\n    \"            self._set_loading(False)\\n\",\n    \"            self.status_label.value = f\\\"Added {mode_str} box\\\"\\n\",\n    \"            self._update_display()\\n\",\n    \"        except Exception as e:\\n\",\n    \"            self._set_loading(False)\\n\",\n    \"            self.status_label.value = f\\\"Error adding box: {str(e)}\\\"\\n\",\n    \"\\n\",\n    \"    def _resize_figure(self):\\n\",\n    \"        \\\"\\\"\\\"Calculate and apply new figure size based on image and slider value.\\\"\\\"\\\"\\n\",\n    \"        if self.current_image is None:\\n\",\n    \"            return\\n\",\n    \"\\n\",\n    \"        # 1. Get original image dimensions\\n\",\n    \"        img_w, img_h = self.current_image.size\\n\",\n    \"\\n\",\n    \"        # 2. The slider's value is now the direct target width for the display\\n\",\n    \"        display_w = float(self.size_slider.value)\\n\",\n    \"\\n\",\n    \"        # 3. Calculate the corresponding height to maintain the original aspect ratio\\n\",\n    \"        aspect_ratio = img_h / img_w\\n\",\n    \"        display_h = int(display_w * aspect_ratio)\\n\",\n    \"\\n\",\n    \"        # 4. Convert pixel dimensions to inches for Matplotlib and apply\\n\",\n    \"        dpi = self.fig.dpi\\n\",\n    \"        new_figsize = (display_w / dpi, display_h / dpi)\\n\",\n    \"        self.fig.set_size_inches(new_figsize, forward=True)\\n\",\n    \"\\n\",\n    \"    def _on_size_change(self, change):\\n\",\n    \"        \\\"\\\"\\\"Handle a change from the image size slider.\\\"\\\"\\\"\\n\",\n    \"        if self.current_image is not None:\\n\",\n    \"            self._resize_figure()\\n\",\n    \"            # After resizing the canvas, we must redraw the content\\n\",\n    \"            self._update_display()\\n\",\n    \"\\n\",\n    \"    def _update_display(self):\\n\",\n    \"        \\\"\\\"\\\"Update the display with current results.\\\"\\\"\\\"\\n\",\n    \"        if self.current_image_array is None:\\n\",\n    \"            return\\n\",\n    \"\\n\",\n    \"        with self.output:\\n\",\n    \"            clear_output(wait=True)\\n\",\n    \"\\n\",\n    \"            self.ax.clear()\\n\",\n    \"            self.ax.axis(\\\"off\\\")\\n\",\n    \"            self.ax.imshow(self.current_image_array)\\n\",\n    \"\\n\",\n    \"            if self.state is not None and \\\"masks\\\" in self.state:\\n\",\n    \"                masks = self.state.get(\\\"masks\\\", [])\\n\",\n    \"                boxes = self.state.get(\\\"boxes\\\", [])\\n\",\n    \"                scores = self.state.get(\\\"scores\\\", [])\\n\",\n    \"\\n\",\n    \"                if len(masks) > 0:\\n\",\n    \"                    mask_overlay = np.zeros((*self.current_image_array.shape[:2], 4))\\n\",\n    \"\\n\",\n    \"                    for i, (mask, box, score) in enumerate(zip(masks, boxes, scores)):\\n\",\n    \"                        mask_np = mask[0].cpu().numpy()\\n\",\n    \"\\n\",\n    \"                        color = plt.cm.tab10(i % 10)[:3]\\n\",\n    \"                        mask_overlay[mask_np > 0.5] = (*color, 0.5)\\n\",\n    \"\\n\",\n    \"                        x0, y0, x1, y1 = box.cpu().numpy()\\n\",\n    \"                        rect = Rectangle(\\n\",\n    \"                            (x0, y0),\\n\",\n    \"                            x1 - x0,\\n\",\n    \"                            y1 - y0,\\n\",\n    \"                            fill=False,\\n\",\n    \"                            edgecolor=color,\\n\",\n    \"                            linewidth=2,\\n\",\n    \"                        )\\n\",\n    \"                        self.ax.add_patch(rect)\\n\",\n    \"\\n\",\n    \"                        self.ax.text(\\n\",\n    \"                            x0,\\n\",\n    \"                            y0 - 5,\\n\",\n    \"                            f\\\"{score:.2f}\\\",\\n\",\n    \"                            color=\\\"white\\\",\\n\",\n    \"                            fontsize=10,\\n\",\n    \"                            bbox=dict(\\n\",\n    \"                                facecolor=color, alpha=0.7, edgecolor=\\\"none\\\", pad=2\\n\",\n    \"                            ),\\n\",\n    \"                        )\\n\",\n    \"\\n\",\n    \"                    self.ax.imshow(mask_overlay)\\n\",\n    \"                    self.status_label.value = f\\\"Found {len(masks)} object(s)\\\"\\n\",\n    \"                else:\\n\",\n    \"                    self.status_label.value = (\\n\",\n    \"                        \\\"No objects found above confidence threshold\\\"\\n\",\n    \"                    )\\n\",\n    \"\\n\",\n    \"            # Display prompted boxes with dashed lines\\n\",\n    \"            if self.state is not None and \\\"prompted_boxes\\\" in self.state:\\n\",\n    \"                for prompted_box in self.state[\\\"prompted_boxes\\\"]:\\n\",\n    \"                    box_coords = prompted_box[\\\"box\\\"]\\n\",\n    \"                    is_positive = prompted_box[\\\"label\\\"]\\n\",\n    \"\\n\",\n    \"                    x0, y0, x1, y1 = box_coords\\n\",\n    \"                    color = \\\"green\\\" if is_positive else \\\"red\\\"\\n\",\n    \"\\n\",\n    \"                    rect = Rectangle(\\n\",\n    \"                        (x0, y0),\\n\",\n    \"                        x1 - x0,\\n\",\n    \"                        y1 - y0,\\n\",\n    \"                        fill=False,\\n\",\n    \"                        edgecolor=color,\\n\",\n    \"                        linewidth=2,\\n\",\n    \"                        linestyle=\\\"--\\\",\\n\",\n    \"                    )\\n\",\n    \"                    self.ax.add_patch(rect)\\n\",\n    \"\\n\",\n    \"            # display(self.fig.canvas)\\n\",\n    \"\\n\",\n    \"    def display(self):\\n\",\n    \"        display(self.container)\\n\",\n    \"\\n\",\n    \"    # Add this for more convenient display in notebooks\\n\",\n    \"    def _ipython_display_(self):\\n\",\n    \"        self.display()\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"1b9bda74-b455-4957-9767-2a46a041b50f\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Run!\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"id\": \"ebfb9b85-2318-4328-bb0e-e93e4a57fefe\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"ea0e04a1bfd7486b93baae650d87e0b2\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"VBox(children=(HTML(value='\\\\n        <style>\\\\n            .jupyter-matplotlib-canvas, canvas {\\\\n              …\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"bbdcb3374c29461bb379d4bf9c319a49\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"image/png\": \"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\",\n      \"text/html\": [\n       \"\\n\",\n       \"            <div style=\\\"display: inline-block;\\\">\\n\",\n       \"                <div class=\\\"jupyter-widgets widget-label\\\" style=\\\"text-align: center;\\\">\\n\",\n       \"                    Figure\\n\",\n       \"                </div>\\n\",\n       \"                <img src='data:image/png;base64,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' width=1200.0/>\\n\",\n       \"            </div>\\n\",\n       \"        \"\n      ],\n      \"text/plain\": [\n       \"Canvas(footer_visible=False, header_visible=False, resizable=False, toolbar=Toolbar(toolitems=[('Home', 'Reset…\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"widget = Sam3SegmentationWidget(processor)\\n\",\n    \"widget.display()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"50a14560-573a-4784-9f55-689fda9147be\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (ipykernel)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.12.11\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "examples/sam3_image_predictor_example.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Copyright (c) Meta Platforms, Inc. and affiliates.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Image segmentation with SAM 3\\n\",\n    \"\\n\",\n    \"This notebook demonstrates how to use SAM 3 for image segmentation with text or visual prompts. It covers the following capabilities:\\n\",\n    \"\\n\",\n    \"- **Text prompts**: Using natural language descriptions to segment objects (e.g., \\\"person\\\", \\\"face\\\")\\n\",\n    \"- **Box prompts**: Using bounding boxes as exemplar visual prompts\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# <a target=\\\"_blank\\\" href=\\\"https://colab.research.google.com/github/facebookresearch/sam3/blob/main/notebooks/sam3_image_predictor_example.ipynb\\\">\\n\",\n    \"#   <img src=\\\"https://colab.research.google.com/assets/colab-badge.svg\\\" alt=\\\"Open In Colab\\\"/>\\n\",\n    \"# </a>\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"using_colab = False\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"if using_colab:\\n\",\n    \"    import torch\\n\",\n    \"    import torchvision\\n\",\n    \"    print(\\\"PyTorch version:\\\", torch.__version__)\\n\",\n    \"    print(\\\"Torchvision version:\\\", torchvision.__version__)\\n\",\n    \"    print(\\\"CUDA is available:\\\", torch.cuda.is_available())\\n\",\n    \"    import sys\\n\",\n    \"    !{sys.executable} -m pip install opencv-python matplotlib scikit-learn\\n\",\n    \"    !{sys.executable} -m pip install 'git+https://github.com/facebookresearch/sam3.git'\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"output\": {\n     \"id\": 1389103906143178,\n     \"loadingStatus\": \"loaded\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"import sam3\\n\",\n    \"from PIL import Image\\n\",\n    \"from sam3 import build_sam3_image_model\\n\",\n    \"from sam3.model.box_ops import box_xywh_to_cxcywh\\n\",\n    \"from sam3.model.sam3_image_processor import Sam3Processor\\n\",\n    \"from sam3.visualization_utils import draw_box_on_image, normalize_bbox, plot_results\\n\",\n    \"\\n\",\n    \"sam3_root = os.path.join(os.path.dirname(sam3.__file__), \\\"..\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import torch\\n\",\n    \"\\n\",\n    \"# turn on tfloat32 for Ampere GPUs\\n\",\n    \"# https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\\n\",\n    \"torch.backends.cuda.matmul.allow_tf32 = True\\n\",\n    \"torch.backends.cudnn.allow_tf32 = True\\n\",\n    \"\\n\",\n    \"# use bfloat16 for the entire notebook\\n\",\n    \"torch.autocast(\\\"cuda\\\", dtype=torch.bfloat16).__enter__()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Build Model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"output\": {\n     \"id\": 785101161160169,\n     \"loadingStatus\": \"loaded\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"bpe_path = f\\\"{sam3_root}/assets/bpe_simple_vocab_16e6.txt.gz\\\"\\n\",\n    \"model = build_sam3_image_model(bpe_path=bpe_path)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"image_path = f\\\"{sam3_root}/assets/images/test_image.jpg\\\"\\n\",\n    \"image = Image.open(image_path)\\n\",\n    \"width, height = image.size\\n\",\n    \"processor = Sam3Processor(model, confidence_threshold=0.5)\\n\",\n    \"inference_state = processor.set_image(image)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Text prompt\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"found 12 object(s)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\",\n      \"text/plain\": [\n       \"<Figure size 1200x800 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"processor.reset_all_prompts(inference_state)\\n\",\n    \"inference_state = processor.set_text_prompt(state=inference_state, prompt=\\\"shoe\\\")\\n\",\n    \"\\n\",\n    \"img0 = Image.open(image_path)\\n\",\n    \"plot_results(img0, inference_state)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Visual prompt: a single bounding box\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"output\": {\n     \"id\": 4344515869113686,\n     \"loadingStatus\": \"loaded\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Normalized box input: [0.41796875, 0.6527777910232544, 0.0859375, 0.5]\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\",\n      \"text/plain\": [\n       \"<Figure size 640x480 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# Here the box is in  (x,y,w,h) format, where (x,y) is the top left corner.\\n\",\n    \"box_input_xywh = torch.tensor([480.0, 290.0, 110.0, 360.0]).view(-1, 4)\\n\",\n    \"box_input_cxcywh = box_xywh_to_cxcywh(box_input_xywh)\\n\",\n    \"\\n\",\n    \"norm_box_cxcywh = normalize_bbox(box_input_cxcywh, width, height).flatten().tolist()\\n\",\n    \"print(\\\"Normalized box input:\\\", norm_box_cxcywh)\\n\",\n    \"\\n\",\n    \"processor.reset_all_prompts(inference_state)\\n\",\n    \"inference_state = processor.add_geometric_prompt(\\n\",\n    \"    state=inference_state, box=norm_box_cxcywh, label=True\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"img0 = Image.open(image_path)\\n\",\n    \"image_with_box = draw_box_on_image(img0, box_input_xywh.flatten().tolist())\\n\",\n    \"plt.imshow(image_with_box)\\n\",\n    \"plt.axis(\\\"off\\\")  # Hide the axis\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"output\": {\n     \"id\": 1972454793596108,\n     \"loadingStatus\": \"loaded\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"found 6 object(s)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\",\n      \"text/plain\": [\n       \"<Figure size 1200x800 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plot_results(img0, inference_state)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Visual prompt: multi-box prompting (with positive and negative boxes)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"output\": {\n     \"id\": 1157754466299886,\n     \"loadingStatus\": \"loaded\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\",\n      \"text/plain\": [\n       \"<Figure size 640x480 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"box_input_xywh = [[480.0, 290.0, 110.0, 360.0], [370.0, 280.0, 115.0, 375.0]]\\n\",\n    \"box_input_cxcywh = box_xywh_to_cxcywh(torch.tensor(box_input_xywh).view(-1,4))\\n\",\n    \"norm_boxes_cxcywh = normalize_bbox(box_input_cxcywh, width, height).tolist()\\n\",\n    \"\\n\",\n    \"box_labels = [True, False]\\n\",\n    \"\\n\",\n    \"processor.reset_all_prompts(inference_state)\\n\",\n    \"\\n\",\n    \"for box, label in zip(norm_boxes_cxcywh, box_labels):\\n\",\n    \"    inference_state = processor.add_geometric_prompt(\\n\",\n    \"        state=inference_state, box=box, label=label\\n\",\n    \"    )\\n\",\n    \"\\n\",\n    \"img0 = Image.open(image_path)\\n\",\n    \"image_with_box = img0\\n\",\n    \"for i in range(len(box_input_xywh)):\\n\",\n    \"    if box_labels[i] == 1:\\n\",\n    \"        color = (0, 255, 0)\\n\",\n    \"    else:\\n\",\n    \"        color = (255, 0, 0)\\n\",\n    \"    image_with_box = draw_box_on_image(image_with_box, box_input_xywh[i], color)\\n\",\n    \"plt.imshow(image_with_box)\\n\",\n    \"plt.axis(\\\"off\\\")  # Hide the axis\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {\n    \"output\": {\n     \"id\": 1352849976506927,\n     \"loadingStatus\": \"loaded\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"found 5 object(s)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\",\n      \"text/plain\": [\n       \"<Figure size 1200x800 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plot_results(img0, inference_state)\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"fileHeader\": \"\",\n  \"fileUid\": \"ff722971-ca5d-431f-8450-ccfea4ff0708\",\n  \"isAdHoc\": false,\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (ipykernel)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.12.11\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "examples/sam3_video_predictor_example.ipynb",
    "content": "{\n  \"cells\": [\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": null,\n      \"metadata\": {\n        \"bentoAICellStatus\": \"none\",\n        \"bentoCellName\": {\n          \"name\": \"License Information\",\n          \"origin\": \"ai\"\n        },\n        \"collapsed\": false,\n        \"customOutput\": null,\n        \"executionStartTime\": 1762496606600,\n        \"executionStopTime\": 1762496607864,\n        \"isCommentPanelOpen\": false,\n        \"jupyter\": {\n          \"outputs_hidden\": false\n        },\n        \"language\": \"python\",\n        \"metadata\": {\n          \"bentoAICellStatus\": \"none\",\n          \"bentoCellName\": {\n            \"name\": \"Copyright Notice\",\n            \"origin\": \"ai\"\n          },\n          \"collapsed\": false,\n          \"customInput\": null,\n          \"customOutput\": null,\n          \"executionStartTime\": 1761950741027,\n          \"executionStopTime\": 1761950741468,\n          \"isCommentPanelOpen\": false,\n          \"language\": \"python\",\n          \"originalKey\": \"49a54601-c2fe-47f2-a436-9a1f91503520\",\n          \"outputsInitialized\": true,\n          \"requestMsgId\": \"da31cd52-f746-4a69-bfae-b2037e84d00c\",\n          \"serverExecutionDuration\": 2.3454379988834,\n          \"showInput\": true\n        },\n        \"originalKey\": \"913d6f63-449f-4836-ae81-7d55a42ccf8c\",\n        \"output\": {\n          \"id\": 863592039347424,\n          \"loadingStatus\": \"loaded\"\n        },\n        \"outputsInitialized\": false,\n        \"requestMsgId\": \"913d6f63-449f-4836-ae81-7d55a42ccf8c\",\n        \"serverExecutionDuration\": 2.4641010004416\n      },\n      \"outputs\": [],\n      \"source\": [\n        \"# Copyright (c) Meta Platforms, Inc. and affiliates.\"\n      ]\n    },\n    {\n      \"cell_type\": \"markdown\",\n      \"metadata\": {\n        \"attachments\": [],\n        \"bentoAICellStatus\": \"none\",\n        \"isCommentPanelOpen\": false,\n        \"language\": \"markdown\",\n        \"metadata\": {\n          \"bentoAICellStatus\": \"none\",\n          \"customInput\": null,\n          \"isCommentPanelOpen\": false,\n          \"language\": \"markdown\",\n          \"originalKey\": \"50280cd0-12eb-4f23-a78d-c37f5bda1fe6\",\n          \"outputsInitialized\": false,\n          \"showInput\": false\n        },\n        \"originalKey\": \"9e88cae8-b006-498d-9a02-c1c369a95f57\",\n        \"outputsInitialized\": false,\n        \"showInput\": true\n      },\n      \"source\": [\n        \"## Video segmentation and tracking with SAM 3\\n\",\n        \"\\n\",\n        \"This notebook demonstrates how to use SAM 3 for interactive video segmentation and dense tracking. It covers the following capabilities:\\n\",\n        \"\\n\",\n        \"- **Text prompts**: Using natural language descriptions to segment objects (e.g., \\\"person\\\", \\\"shoe\\\")\\n\",\n        \"- **Point prompts**: Adding positive/negative clicks to segment and refine objects\\n\",\n        \"\\n\",\n        \"We use the terms _segment_ or _mask_ to refer to the model prediction for an object on a single frame, and _masklet_ to refer to the spatio-temporal masks across the entire video. \"\n      ]\n    },\n    {\n      \"cell_type\": \"markdown\",\n      \"metadata\": {},\n      \"source\": [\n        \"# <a target=\\\"_blank\\\" href=\\\"https://colab.research.google.com/github/facebookresearch/sam3/blob/main/notebooks/sam3_video_predictor_example.ipynb\\\">\\n\",\n        \"#   <img src=\\\"https://colab.research.google.com/assets/colab-badge.svg\\\" alt=\\\"Open In Colab\\\"/>\\n\",\n        \"# </a>\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": null,\n      \"metadata\": {},\n      \"outputs\": [],\n      \"source\": [\n        \"using_colab = False\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": null,\n      \"metadata\": {},\n      \"outputs\": [],\n      \"source\": [\n        \"if using_colab:\\n\",\n        \"    import torch\\n\",\n        \"    import torchvision\\n\",\n        \"    print(\\\"PyTorch version:\\\", torch.__version__)\\n\",\n        \"    print(\\\"Torchvision version:\\\", torchvision.__version__)\\n\",\n        \"    print(\\\"CUDA is available:\\\", torch.cuda.is_available())\\n\",\n        \"    import sys\\n\",\n        \"    !{sys.executable} -m pip install opencv-python matplotlib scikit-learn\\n\",\n        \"    !{sys.executable} -m pip install 'git+https://github.com/facebookresearch/sam3.git'\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": null,\n      \"metadata\": {\n        \"bentoAICellStatus\": \"none\",\n        \"bentoCellName\": {\n          \"name\": \"Display GPU Status\",\n          \"origin\": \"ai\"\n        },\n        \"customOutput\": null,\n        \"executionStartTime\": 1762496607874,\n        \"executionStopTime\": 1762496609713,\n        \"isCommentPanelOpen\": false,\n        \"language\": \"python\",\n        \"metadata\": {\n          \"bentoAICellStatus\": \"none\",\n          \"bentoCellName\": {\n            \"name\": \"Check GPU Status\",\n            \"origin\": \"ai\"\n          },\n          \"collapsed\": false,\n          \"customInput\": null,\n          \"customOutput\": null,\n          \"executionStartTime\": 1761950741471,\n          \"executionStopTime\": 1761950742878,\n          \"isCommentPanelOpen\": false,\n          \"language\": \"python\",\n          \"originalKey\": \"c3f5a31b-c8df-45db-ae6d-0d219ee60382\",\n          \"outputsInitialized\": true,\n          \"requestMsgId\": \"cea571e0-345b-461d-b2ee-8f95e0ea4b4e\",\n          \"serverExecutionDuration\": 1091.9901590096,\n          \"showInput\": true\n        },\n        \"originalKey\": \"fb0eb6a0-acd2-4c80-bacd-bafd09669e7e\",\n        \"output\": {\n          \"id\": \"794918370206651\",\n          \"loadingStatus\": \"before loading\"\n        },\n        \"outputsInitialized\": true,\n        \"requestMsgId\": \"fb0eb6a0-acd2-4c80-bacd-bafd09669e7e\",\n        \"serverExecutionDuration\": 1123.5525669999\n      },\n      \"outputs\": [],\n      \"source\": [\n        \"!nvidia-smi\"\n      ]\n    },\n    {\n      \"cell_type\": \"markdown\",\n      \"metadata\": {\n        \"attachments\": [],\n        \"bentoAICellStatus\": \"none\",\n        \"isCommentPanelOpen\": false,\n        \"language\": \"markdown\",\n        \"metadata\": {\n          \"bentoAICellStatus\": \"none\",\n          \"collapsed\": false,\n          \"customInput\": null,\n          \"executionStartTime\": 1761927188199,\n          \"executionStopTime\": 1761927188659,\n          \"isCommentPanelOpen\": false,\n          \"language\": \"markdown\",\n          \"originalKey\": \"8304fc58-e145-4f5f-8bdc-a6d2dfba8a04\",\n          \"outputsInitialized\": false,\n          \"requestMsgId\": \"8304fc58-e145-4f5f-8bdc-a6d2dfba8a04\",\n          \"serverExecutionDuration\": 3.739742009202,\n          \"showInput\": false\n        },\n        \"originalKey\": \"6702b9f4-54e9-46be-aca8-82ad9f96e9cc\",\n        \"outputsInitialized\": false,\n        \"showInput\": false\n      },\n      \"source\": [\n        \"## Set-up\\n\",\n        \"\\n\",\n        \"In this example, we allow running inference either on a single GPU or multiple GPUs.\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": null,\n      \"metadata\": {\n        \"bentoAICellStatus\": \"none\",\n        \"bentoCellName\": {\n          \"name\": \"Configure GPU Usage\",\n          \"origin\": \"ai\"\n        },\n        \"collapsed\": false,\n        \"customOutput\": null,\n        \"executionStartTime\": 1762496609726,\n        \"executionStopTime\": 1762496617098,\n        \"isCommentPanelOpen\": false,\n        \"jupyter\": {\n          \"outputs_hidden\": false\n        },\n        \"language\": \"python\",\n        \"metadata\": {\n          \"bentoAICellStatus\": \"none\",\n          \"bentoCellName\": {\n            \"name\": \"Import iopath library\",\n            \"origin\": \"ai\"\n          },\n          \"collapsed\": false,\n          \"customOutput\": null,\n          \"executionStartTime\": 1761950742885,\n          \"executionStopTime\": 1761950745386,\n          \"isCommentPanelOpen\": false,\n          \"language\": \"python\",\n          \"originalKey\": \"5faf15c7-5bbb-4b86-a9a8-70abbc76cba2\",\n          \"outputsInitialized\": true,\n          \"requestMsgId\": \"e63f2826-d537-4849-82ac-fe7280cc9de0\",\n          \"serverExecutionDuration\": 2117.1291570063\n        },\n        \"originalKey\": \"5d0ad6b6-0225-4371-9455-e6291e92604c\",\n        \"output\": {\n          \"id\": \"1459804151757142\",\n          \"loadingStatus\": \"before loading\"\n        },\n        \"outputsInitialized\": true,\n        \"requestMsgId\": \"5d0ad6b6-0225-4371-9455-e6291e92604c\",\n        \"serverExecutionDuration\": 6628.1851309996\n      },\n      \"outputs\": [],\n      \"source\": [\n        \"import os\\n\",\n        \"import sam3\\n\",\n        \"import torch\\n\",\n        \"\\n\",\n        \"sam3_root = os.path.join(os.path.dirname(sam3.__file__), \\\"..\\\")\\n\",\n        \"\\n\",\n        \"# use all available GPUs on the machine\\n\",\n        \"gpus_to_use = range(torch.cuda.device_count())\\n\",\n        \"# # use only a single GPU\\n\",\n        \"# gpus_to_use = [torch.cuda.current_device()]\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": null,\n      \"metadata\": {\n        \"bentoAICellStatus\": \"none\",\n        \"bentoCellName\": {\n          \"name\": \"Initialize Video Predictor\",\n          \"origin\": \"ai\"\n        },\n        \"collapsed\": false,\n        \"customOutput\": null,\n        \"executionStartTime\": 1762496617103,\n        \"executionStopTime\": 1762496677439,\n        \"isCommentPanelOpen\": false,\n        \"jupyter\": {\n          \"outputs_hidden\": false\n        },\n        \"language\": \"python\",\n        \"metadata\": {\n          \"bentoAICellStatus\": \"none\",\n          \"bentoCellName\": {\n            \"name\": \"Import Video Predictor\",\n            \"origin\": \"ai\"\n          },\n          \"collapsed\": false,\n          \"customInput\": null,\n          \"customOutput\": null,\n          \"executionStartTime\": 1761950750102,\n          \"executionStopTime\": 1761950806603,\n          \"isCommentPanelOpen\": false,\n          \"language\": \"python\",\n          \"originalKey\": \"01683eda-e85f-4af6-9d91-86b3f1822170\",\n          \"outputsInitialized\": true,\n          \"requestMsgId\": \"822fb211-d78e-4d1c-92fa-848e0e755100\",\n          \"serverExecutionDuration\": 55998.664824001,\n          \"showInput\": true\n        },\n        \"originalKey\": \"aea5a4b9-de9f-46ed-9fd1-20928ab60d2e\",\n        \"output\": {\n          \"id\": \"1581259049706846\",\n          \"loadingStatus\": \"before loading\"\n        },\n        \"outputsInitialized\": true,\n        \"requestMsgId\": \"aea5a4b9-de9f-46ed-9fd1-20928ab60d2e\",\n        \"serverExecutionDuration\": 59514.871851999\n      },\n      \"outputs\": [],\n      \"source\": [\n        \"from sam3.model_builder import build_sam3_video_predictor\\n\",\n        \"\\n\",\n        \"predictor = build_sam3_video_predictor(gpus_to_use=gpus_to_use)\"\n      ]\n    },\n    {\n      \"attachments\": {},\n      \"cell_type\": \"markdown\",\n      \"metadata\": {\n        \"bentoAICellStatus\": \"none\",\n        \"collapsed\": false,\n        \"customInput\": null,\n        \"executionStartTime\": 1762140878760,\n        \"executionStopTime\": 1762140879318,\n        \"isCommentPanelOpen\": false,\n        \"jupyter\": {\n          \"outputs_hidden\": false\n        },\n        \"language\": \"markdown\",\n        \"originalKey\": \"2cb37dda-a58c-46ae-85ff-118bb3ff4c02\",\n        \"outputsInitialized\": false,\n        \"requestMsgId\": \"2cb37dda-a58c-46ae-85ff-118bb3ff4c02\",\n        \"serverExecutionDuration\": 3.7622059462592,\n        \"showInput\": false\n      },\n      \"source\": [\n        \"#### Inference and visualization utils\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": null,\n      \"metadata\": {\n        \"bentoAICellStatus\": \"none\",\n        \"bentoCellName\": {\n          \"name\": \"Set Up Video Processing\",\n          \"origin\": \"ai\"\n        },\n        \"collapsed\": false,\n        \"customInput\": null,\n        \"customOutput\": null,\n        \"executionStartTime\": 1762496677450,\n        \"executionStopTime\": 1762496679879,\n        \"isCommentPanelOpen\": false,\n        \"jupyter\": {\n          \"outputs_hidden\": false\n        },\n        \"language\": \"python\",\n        \"originalKey\": \"10d98ae4-dd65-4824-8469-960a9801ec72\",\n        \"output\": {\n          \"id\": \"1183417547004803\",\n          \"loadingStatus\": \"before loading\"\n        },\n        \"outputsInitialized\": true,\n        \"requestMsgId\": \"10d98ae4-dd65-4824-8469-960a9801ec72\",\n        \"serverExecutionDuration\": 1535.9860829994,\n        \"showInput\": true\n      },\n      \"outputs\": [],\n      \"source\": [\n        \"import glob\\n\",\n        \"import os\\n\",\n        \"\\n\",\n        \"import cv2\\n\",\n        \"import matplotlib.pyplot as plt\\n\",\n        \"import numpy as np\\n\",\n        \"from PIL import Image\\n\",\n        \"from sam3.visualization_utils import (\\n\",\n        \"    load_frame,\\n\",\n        \"    prepare_masks_for_visualization,\\n\",\n        \"    visualize_formatted_frame_output,\\n\",\n        \")\\n\",\n        \"\\n\",\n        \"# font size for axes titles\\n\",\n        \"plt.rcParams[\\\"axes.titlesize\\\"] = 12\\n\",\n        \"plt.rcParams[\\\"figure.titlesize\\\"] = 12\\n\",\n        \"\\n\",\n        \"\\n\",\n        \"def propagate_in_video(predictor, session_id):\\n\",\n        \"    # we will just propagate from frame 0 to the end of the video\\n\",\n        \"    outputs_per_frame = {}\\n\",\n        \"    for response in predictor.handle_stream_request(\\n\",\n        \"        request=dict(\\n\",\n        \"            type=\\\"propagate_in_video\\\",\\n\",\n        \"            session_id=session_id,\\n\",\n        \"        )\\n\",\n        \"    ):\\n\",\n        \"        outputs_per_frame[response[\\\"frame_index\\\"]] = response[\\\"outputs\\\"]\\n\",\n        \"\\n\",\n        \"    return outputs_per_frame\\n\",\n        \"\\n\",\n        \"\\n\",\n        \"def abs_to_rel_coords(coords, IMG_WIDTH, IMG_HEIGHT, coord_type=\\\"point\\\"):\\n\",\n        \"    \\\"\\\"\\\"Convert absolute coordinates to relative coordinates (0-1 range)\\n\",\n        \"\\n\",\n        \"    Args:\\n\",\n        \"        coords: List of coordinates\\n\",\n        \"        coord_type: 'point' for [x, y] or 'box' for [x, y, w, h]\\n\",\n        \"    \\\"\\\"\\\"\\n\",\n        \"    if coord_type == \\\"point\\\":\\n\",\n        \"        return [[x / IMG_WIDTH, y / IMG_HEIGHT] for x, y in coords]\\n\",\n        \"    elif coord_type == \\\"box\\\":\\n\",\n        \"        return [\\n\",\n        \"            [x / IMG_WIDTH, y / IMG_HEIGHT, w / IMG_WIDTH, h / IMG_HEIGHT]\\n\",\n        \"            for x, y, w, h in coords\\n\",\n        \"        ]\\n\",\n        \"    else:\\n\",\n        \"        raise ValueError(f\\\"Unknown coord_type: {coord_type}\\\")\"\n      ]\n    },\n    {\n      \"cell_type\": \"markdown\",\n      \"metadata\": {\n        \"attachments\": [],\n        \"bentoAICellStatus\": \"none\",\n        \"isCommentPanelOpen\": false,\n        \"language\": \"markdown\",\n        \"metadata\": {\n          \"bentoAICellStatus\": \"none\",\n          \"customInput\": null,\n          \"isCommentPanelOpen\": false,\n          \"language\": \"markdown\",\n          \"originalKey\": \"2e38c5fe-1aa7-4000-9778-25e240daf5e5\",\n          \"outputsInitialized\": false,\n          \"showInput\": false\n        },\n        \"originalKey\": \"7f803ec4-a343-43c9-9be3-5d3b9b66ae9a\",\n        \"outputsInitialized\": false,\n        \"showInput\": false\n      },\n      \"source\": [\n        \"### Loading an example video\\n\",\n        \"\\n\",\n        \"We assume that the video is stored as either **a list of JPEG frames with filenames like `<frame_index>.jpg`** or **an MP4 video**.\\n\",\n        \"\\n\",\n        \"Note that you can extract their JPEG frames using ffmpeg (https://ffmpeg.org/) as follows:\\n\",\n        \"```\\n\",\n        \"ffmpeg -i <your_video>.mp4 -q:v 2 -start_number 0 <output_dir>/'%05d.jpg'\\n\",\n        \"```\\n\",\n        \"where `-q:v` generates high-quality JPEG frames and `-start_number 0` asks ffmpeg to start the JPEG file from `00000.jpg`.\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": null,\n      \"metadata\": {\n        \"bentoAICellStatus\": \"none\",\n        \"bentoCellName\": {\n          \"name\": \"Set video path\",\n          \"origin\": \"ai\"\n        },\n        \"collapsed\": false,\n        \"customOutput\": null,\n        \"executionStartTime\": 1762496679887,\n        \"executionStopTime\": 1762496680687,\n        \"isCommentPanelOpen\": false,\n        \"jupyter\": {\n          \"outputs_hidden\": false\n        },\n        \"language\": \"python\",\n        \"metadata\": {\n          \"bentoAICellStatus\": \"none\",\n          \"bentoCellName\": {\n            \"name\": \"Print SAM3 Directory Path\",\n            \"origin\": \"ai\"\n          },\n          \"collapsed\": false,\n          \"customInput\": null,\n          \"customOutput\": null,\n          \"executionStartTime\": 1761950806610,\n          \"executionStopTime\": 1761950807097,\n          \"isCommentPanelOpen\": false,\n          \"language\": \"python\",\n          \"originalKey\": \"59540fba-3749-4498-bd14-c28fb7d61dbc\",\n          \"outputsInitialized\": true,\n          \"requestMsgId\": \"2807b3b8-a4fb-41e9-9976-67e2cd1f2ca2\",\n          \"serverExecutionDuration\": 5.3407719824463,\n          \"showInput\": true\n        },\n        \"originalKey\": \"92d7b964-a3e4-4efb-98d4-202344994413\",\n        \"outputsInitialized\": false,\n        \"requestMsgId\": \"92d7b964-a3e4-4efb-98d4-202344994413\",\n        \"serverExecutionDuration\": 3.5114740003337\n      },\n      \"outputs\": [],\n      \"source\": [\n        \"# \\\"video_path\\\" needs to be either a JPEG folder or a MP4 video file\\n\",\n        \"video_path = f\\\"{sam3_root}/assets/videos/0001\\\"\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": null,\n      \"metadata\": {\n        \"bentoAICellStatus\": \"none\",\n        \"bentoCellName\": {\n          \"name\": \"Load Video Frames for Visualization\",\n          \"origin\": \"ai\"\n        },\n        \"collapsed\": false,\n        \"customOutput\": null,\n        \"executionStartTime\": 1762496680695,\n        \"executionStopTime\": 1762496681428,\n        \"isCommentPanelOpen\": false,\n        \"jupyter\": {\n          \"outputs_hidden\": false\n        },\n        \"language\": \"python\",\n        \"metadata\": {\n          \"bentoAICellStatus\": \"none\",\n          \"bentoCellName\": {\n            \"name\": \"Load Video Frames for Visualization\",\n            \"origin\": \"ai\"\n          },\n          \"collapsed\": false,\n          \"customInput\": null,\n          \"customOutput\": null,\n          \"executionStartTime\": 1761950807101,\n          \"executionStopTime\": 1761950807480,\n          \"isCommentPanelOpen\": false,\n          \"language\": \"python\",\n          \"originalKey\": \"86f88968-93c4-4ca1-81fc-7fad55ccd566\",\n          \"outputsInitialized\": true,\n          \"requestMsgId\": \"96b17a72-b889-4a1f-af98-b40c773780f7\",\n          \"serverExecutionDuration\": 7.6176410075277,\n          \"showInput\": true\n        },\n        \"originalKey\": \"10fe83fd-eb12-4400-a8ac-b6e137819136\",\n        \"outputsInitialized\": false,\n        \"requestMsgId\": \"10fe83fd-eb12-4400-a8ac-b6e137819136\",\n        \"serverExecutionDuration\": 8.1744140006776\n      },\n      \"outputs\": [],\n      \"source\": [\n        \"# load \\\"video_frames_for_vis\\\" for visualization purposes (they are not used by the model)\\n\",\n        \"if isinstance(video_path, str) and video_path.endswith(\\\".mp4\\\"):\\n\",\n        \"    cap = cv2.VideoCapture(video_path)\\n\",\n        \"    video_frames_for_vis = []\\n\",\n        \"    while True:\\n\",\n        \"        ret, frame = cap.read()\\n\",\n        \"        if not ret:\\n\",\n        \"            break\\n\",\n        \"        video_frames_for_vis.append(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))\\n\",\n        \"    cap.release()\\n\",\n        \"else:\\n\",\n        \"    video_frames_for_vis = glob.glob(os.path.join(video_path, \\\"*.jpg\\\"))\\n\",\n        \"    try:\\n\",\n        \"        # integer sort instead of string sort (so that e.g. \\\"2.jpg\\\" is before \\\"11.jpg\\\")\\n\",\n        \"        video_frames_for_vis.sort(\\n\",\n        \"            key=lambda p: int(os.path.splitext(os.path.basename(p))[0])\\n\",\n        \"        )\\n\",\n        \"    except ValueError:\\n\",\n        \"        # fallback to lexicographic sort if the format is not \\\"<frame_index>.jpg\\\"\\n\",\n        \"        print(\\n\",\n        \"            f'frame names are not in \\\"<frame_index>.jpg\\\" format: {video_frames_for_vis[:5]=}, '\\n\",\n        \"            f\\\"falling back to lexicographic sort.\\\"\\n\",\n        \"        )\\n\",\n        \"        video_frames_for_vis.sort()\"\n      ]\n    },\n    {\n      \"cell_type\": \"markdown\",\n      \"metadata\": {\n        \"attachments\": [],\n        \"bentoAICellStatus\": \"none\",\n        \"isCommentPanelOpen\": false,\n        \"language\": \"markdown\",\n        \"metadata\": {\n          \"bentoAICellStatus\": \"none\",\n          \"customInput\": null,\n          \"isCommentPanelOpen\": false,\n          \"language\": \"markdown\",\n          \"originalKey\": \"2f67df56-df50-470d-bb6d-f736a592dd47\",\n          \"outputsInitialized\": false,\n          \"showInput\": false\n        },\n        \"originalKey\": \"552fbb00-7387-4014-9161-7f9c32418701\",\n        \"outputsInitialized\": false,\n        \"showInput\": false\n      },\n      \"source\": [\n        \"### Opening an inference session on this video\\n\",\n        \"\\n\",\n        \"SAM 3 requires stateful inference for interactive video segmentation, so we need to initialize an **inference session** on this video.\\n\",\n        \"\\n\",\n        \"During initialization, it loads all the video frames and stores their pixels in the session state.\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": null,\n      \"metadata\": {\n        \"bentoAICellStatus\": \"none\",\n        \"bentoCellName\": {\n          \"name\": \"Start Video Session\",\n          \"origin\": \"ai\"\n        },\n        \"collapsed\": false,\n        \"customOutput\": null,\n        \"executionStartTime\": 1762496681434,\n        \"executionStopTime\": 1762496694273,\n        \"isCommentPanelOpen\": false,\n        \"jupyter\": {\n          \"outputs_hidden\": false\n        },\n        \"language\": \"python\",\n        \"metadata\": {\n          \"bentoAICellStatus\": \"none\",\n          \"bentoCellName\": {\n            \"name\": \"Start Video Session\",\n            \"origin\": \"ai\"\n          },\n          \"collapsed\": false,\n          \"customInput\": null,\n          \"customOutput\": null,\n          \"executionStartTime\": 1761950807485,\n          \"executionStopTime\": 1761950821459,\n          \"isCommentPanelOpen\": false,\n          \"language\": \"python\",\n          \"originalKey\": \"eec3ee85-e95a-4fa7-8401-921fba35d6ee\",\n          \"outputsInitialized\": true,\n          \"requestMsgId\": \"262d936d-486c-4a75-8f25-ad2a2832c3f8\",\n          \"serverExecutionDuration\": 13503.750298987,\n          \"showInput\": true\n        },\n        \"originalKey\": \"2b5917e0-95da-409a-b2c6-4868fe5ff88e\",\n        \"output\": {\n          \"id\": \"816533627765185\",\n          \"loadingStatus\": \"before loading\"\n        },\n        \"outputsInitialized\": true,\n        \"requestMsgId\": \"2b5917e0-95da-409a-b2c6-4868fe5ff88e\",\n        \"serverExecutionDuration\": 11971.027362999\n      },\n      \"outputs\": [],\n      \"source\": [\n        \"response = predictor.handle_request(\\n\",\n        \"    request=dict(\\n\",\n        \"        type=\\\"start_session\\\",\\n\",\n        \"        resource_path=video_path,\\n\",\n        \"    )\\n\",\n        \")\\n\",\n        \"session_id = response[\\\"session_id\\\"]\"\n      ]\n    },\n    {\n      \"cell_type\": \"markdown\",\n      \"metadata\": {\n        \"attachments\": [],\n        \"bentoAICellStatus\": \"none\",\n        \"isCommentPanelOpen\": false,\n        \"language\": \"markdown\",\n        \"metadata\": {\n          \"bentoAICellStatus\": \"none\",\n          \"customInput\": null,\n          \"isCommentPanelOpen\": false,\n          \"language\": \"markdown\",\n          \"originalKey\": \"4d26ec1b-d78f-4054-879a-cbf06b84a79f\",\n          \"outputsInitialized\": false,\n          \"showInput\": false\n        },\n        \"originalKey\": \"1da36b65-5759-4803-be41-ec6d2ec8c5d9\",\n        \"outputsInitialized\": false,\n        \"showInput\": false\n      },\n      \"source\": [\n        \"### Video promptable concept segmentation with text\\n\",\n        \"\\n\",\n        \"Using SAM 3 you can describe objects using natural language, and the model will automatically detect and track all instances of that object throughout the video.\\n\",\n        \"\\n\",\n        \"In the example below, we add a text prompt on frame 0 and propagation throughout the video. Here we use the text prompt \\\"person\\\" to detect all people in the video. SAM 3 will automatically identify multiple person instances and assign each a unique object ID.\\n\",\n        \"\\n\",\n        \"Note that the first call might be slower due to setting up buffers. **You can rerun all the cells below when measuring speed.**\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": null,\n      \"metadata\": {\n        \"bentoAICellStatus\": \"none\",\n        \"bentoCellName\": {\n          \"name\": \"Reset Session\",\n          \"origin\": \"ai\"\n        },\n        \"collapsed\": false,\n        \"customOutput\": null,\n        \"executionStartTime\": 1762496694278,\n        \"executionStopTime\": 1762496694675,\n        \"isCommentPanelOpen\": false,\n        \"jupyter\": {\n          \"outputs_hidden\": false\n        },\n        \"language\": \"python\",\n        \"metadata\": {\n          \"bentoAICellStatus\": \"none\",\n          \"bentoCellName\": {\n            \"name\": \"Reset Prediction Session\",\n            \"origin\": \"ai\"\n          },\n          \"collapsed\": false,\n          \"customInput\": null,\n          \"customOutput\": null,\n          \"executionStartTime\": 1761950821467,\n          \"executionStopTime\": 1761950821992,\n          \"isCommentPanelOpen\": false,\n          \"language\": \"python\",\n          \"originalKey\": \"be47ff32-7721-43c2-a1ad-b9d1689cf1c1\",\n          \"outputsInitialized\": true,\n          \"requestMsgId\": \"c5eb2442-0530-4408-9ace-ba74e7441cf6\",\n          \"serverExecutionDuration\": 10.527236998314,\n          \"showInput\": true\n        },\n        \"originalKey\": \"f61b9bc5-5ec7-4c72-9071-e50bedb89f0a\",\n        \"output\": {\n          \"id\": \"4255598051433217\",\n          \"loadingStatus\": \"before loading\"\n        },\n        \"outputsInitialized\": true,\n        \"requestMsgId\": \"f61b9bc5-5ec7-4c72-9071-e50bedb89f0a\",\n        \"serverExecutionDuration\": 9.9740919995384\n      },\n      \"outputs\": [],\n      \"source\": [\n        \"# note: in case you already ran one text prompt and now want to switch to another text prompt\\n\",\n        \"# it's required to reset the session first (otherwise the results would be wrong)\\n\",\n        \"_ = predictor.handle_request(\\n\",\n        \"    request=dict(\\n\",\n        \"        type=\\\"reset_session\\\",\\n\",\n        \"        session_id=session_id,\\n\",\n        \"    )\\n\",\n        \")\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": null,\n      \"metadata\": {\n        \"bentoAICellStatus\": \"none\",\n        \"bentoCellName\": {\n          \"name\": \"Add Text Prompt\",\n          \"origin\": \"ai\"\n        },\n        \"collapsed\": false,\n        \"customOutput\": null,\n        \"executionStartTime\": 1762496694678,\n        \"executionStopTime\": 1762496699791,\n        \"isCommentPanelOpen\": false,\n        \"jupyter\": {\n          \"outputs_hidden\": false\n        },\n        \"language\": \"python\",\n        \"metadata\": {\n          \"bentoAICellStatus\": \"none\",\n          \"bentoCellName\": {\n            \"name\": \"Add Prompt to Frame\",\n            \"origin\": \"ai\"\n          },\n          \"collapsed\": false,\n          \"customInput\": null,\n          \"customOutput\": null,\n          \"executionStartTime\": 1761950821995,\n          \"executionStopTime\": 1761950825751,\n          \"isCommentPanelOpen\": false,\n          \"language\": \"python\",\n          \"originalKey\": \"ca780545-a450-4ed8-a192-f6033e0288ba\",\n          \"outputsInitialized\": true,\n          \"requestMsgId\": \"d784487d-5758-40a4-8a66-1ea26ad3bcfd\",\n          \"serverExecutionDuration\": 3358.8370049838,\n          \"showInput\": true\n        },\n        \"originalKey\": \"55538638-8336-4b1c-9daf-517c0dc31806\",\n        \"output\": {\n          \"id\": \"2083947459075035\",\n          \"loadingStatus\": \"before loading\"\n        },\n        \"outputsInitialized\": true,\n        \"requestMsgId\": \"55538638-8336-4b1c-9daf-517c0dc31806\",\n        \"serverExecutionDuration\": 3957.4137909985\n      },\n      \"outputs\": [],\n      \"source\": [\n        \"prompt_text_str = \\\"person\\\"\\n\",\n        \"frame_idx = 0  # add a text prompt on frame 0\\n\",\n        \"response = predictor.handle_request(\\n\",\n        \"    request=dict(\\n\",\n        \"        type=\\\"add_prompt\\\",\\n\",\n        \"        session_id=session_id,\\n\",\n        \"        frame_index=frame_idx,\\n\",\n        \"        text=prompt_text_str,\\n\",\n        \"    )\\n\",\n        \")\\n\",\n        \"out = response[\\\"outputs\\\"]\\n\",\n        \"\\n\",\n        \"plt.close(\\\"all\\\")\\n\",\n        \"visualize_formatted_frame_output(\\n\",\n        \"    frame_idx,\\n\",\n        \"    video_frames_for_vis,\\n\",\n        \"    outputs_list=[prepare_masks_for_visualization({frame_idx: out})],\\n\",\n        \"    titles=[\\\"SAM 3 Dense Tracking outputs\\\"],\\n\",\n        \"    figsize=(6, 4),\\n\",\n        \")\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": null,\n      \"metadata\": {\n        \"bentoAICellStatus\": \"none\",\n        \"bentoCellName\": {\n          \"name\": \"Visualize Video Outputs\",\n          \"origin\": \"ai\"\n        },\n        \"collapsed\": false,\n        \"customOutput\": null,\n        \"executionStartTime\": 1762496699796,\n        \"executionStopTime\": 1762496734325,\n        \"isCommentPanelOpen\": false,\n        \"jupyter\": {\n          \"outputs_hidden\": false\n        },\n        \"language\": \"python\",\n        \"metadata\": {\n          \"bentoAICellStatus\": \"none\",\n          \"bentoCellName\": {\n            \"name\": \"Visualize Video Outputs\",\n            \"origin\": \"ai\"\n          },\n          \"collapsed\": false,\n          \"customInput\": null,\n          \"customOutput\": null,\n          \"executionStartTime\": 1761950827402,\n          \"executionStopTime\": 1761950861260,\n          \"isCommentPanelOpen\": false,\n          \"language\": \"python\",\n          \"originalKey\": \"06edc7da-9dd1-42e8-b502-0afdbfbaddc2\",\n          \"outputsInitialized\": true,\n          \"requestMsgId\": \"33659e9c-a817-40fe-b3b7-727b7a344d74\",\n          \"serverExecutionDuration\": 33374.819626013,\n          \"showInput\": true\n        },\n        \"originalKey\": \"41e1b095-80a4-4997-9b3e-2baa28dba05f\",\n        \"output\": {\n          \"id\": \"2605449593181328\",\n          \"loadingStatus\": \"before loading\"\n        },\n        \"outputsInitialized\": true,\n        \"requestMsgId\": \"41e1b095-80a4-4997-9b3e-2baa28dba05f\",\n        \"serverExecutionDuration\": 33588.144188001\n      },\n      \"outputs\": [],\n      \"source\": [\n        \"# now we propagate the outputs from frame 0 to the end of the video and collect all outputs\\n\",\n        \"outputs_per_frame = propagate_in_video(predictor, session_id)\\n\",\n        \"\\n\",\n        \"# finally, we reformat the outputs for visualization and plot the outputs every 60 frames\\n\",\n        \"outputs_per_frame = prepare_masks_for_visualization(outputs_per_frame)\\n\",\n        \"\\n\",\n        \"vis_frame_stride = 60\\n\",\n        \"plt.close(\\\"all\\\")\\n\",\n        \"for frame_idx in range(0, len(outputs_per_frame), vis_frame_stride):\\n\",\n        \"    visualize_formatted_frame_output(\\n\",\n        \"        frame_idx,\\n\",\n        \"        video_frames_for_vis,\\n\",\n        \"        outputs_list=[outputs_per_frame],\\n\",\n        \"        titles=[\\\"SAM 3 Dense Tracking outputs\\\"],\\n\",\n        \"        figsize=(6, 4),\\n\",\n        \"    )\"\n      ]\n    },\n    {\n      \"attachments\": {},\n      \"cell_type\": \"markdown\",\n      \"metadata\": {\n        \"bentoAICellStatus\": \"none\",\n        \"customInput\": null,\n        \"isCommentPanelOpen\": false,\n        \"language\": \"markdown\",\n        \"originalKey\": \"e341c66f-6083-4731-b8e1-ebc7b4177a43\",\n        \"outputsInitialized\": false,\n        \"showInput\": false\n      },\n      \"source\": [\n        \"###  Removing objects\\n\",\n        \"\\n\",\n        \"We can remove individual objects using their id.\\n\",\n        \"\\n\",\n        \"As an example, let's remove object 2 (which is the dancer in the front).\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": null,\n      \"metadata\": {\n        \"bentoAICellStatus\": \"none\",\n        \"bentoCellName\": {\n          \"name\": \"Remove Front Dancer\",\n          \"origin\": \"ai\"\n        },\n        \"collapsed\": false,\n        \"customInput\": null,\n        \"customOutput\": null,\n        \"executionStartTime\": 1762496734333,\n        \"executionStopTime\": 1762496735487,\n        \"isCommentPanelOpen\": false,\n        \"jupyter\": {\n          \"outputs_hidden\": false\n        },\n        \"language\": \"python\",\n        \"originalKey\": \"cf97330e-68f5-4f4e-931f-bc5b6faa77ff\",\n        \"output\": {\n          \"id\": \"1345936250272478\",\n          \"loadingStatus\": \"before loading\"\n        },\n        \"outputsInitialized\": true,\n        \"requestMsgId\": \"cf97330e-68f5-4f4e-931f-bc5b6faa77ff\",\n        \"serverExecutionDuration\": 127.66883199947,\n        \"showInput\": true\n      },\n      \"outputs\": [],\n      \"source\": [\n        \"# we pick id 2, which is the dancer in the front\\n\",\n        \"obj_id = 2\\n\",\n        \"response = predictor.handle_request(\\n\",\n        \"    request=dict(\\n\",\n        \"        type=\\\"remove_object\\\",\\n\",\n        \"        session_id=session_id,\\n\",\n        \"        obj_id=obj_id,\\n\",\n        \"    )\\n\",\n        \")\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": null,\n      \"metadata\": {\n        \"bentoAICellStatus\": \"none\",\n        \"bentoCellName\": {\n          \"name\": \"Visualize Video Outputs\",\n          \"origin\": \"ai\"\n        },\n        \"collapsed\": false,\n        \"customInput\": null,\n        \"customOutput\": null,\n        \"executionStartTime\": 1762496735493,\n        \"executionStopTime\": 1762496742056,\n        \"isCommentPanelOpen\": false,\n        \"jupyter\": {\n          \"outputs_hidden\": false\n        },\n        \"language\": \"python\",\n        \"originalKey\": \"a4552ab0-1b08-42b6-b90d-bfd1814c9398\",\n        \"output\": {\n          \"id\": \"1747332999309403\",\n          \"loadingStatus\": \"before loading\"\n        },\n        \"outputsInitialized\": true,\n        \"requestMsgId\": \"a4552ab0-1b08-42b6-b90d-bfd1814c9398\",\n        \"serverExecutionDuration\": 5491.8713629995,\n        \"showInput\": true\n      },\n      \"outputs\": [],\n      \"source\": [\n        \"# now we propagate the outputs from frame 0 to the end of the video and collect all outputs\\n\",\n        \"outputs_per_frame = propagate_in_video(predictor, session_id)\\n\",\n        \"\\n\",\n        \"# finally, we reformat the outputs for visualization and plot the outputs every 60 frames\\n\",\n        \"outputs_per_frame = prepare_masks_for_visualization(outputs_per_frame)\\n\",\n        \"\\n\",\n        \"vis_frame_stride = 60\\n\",\n        \"plt.close(\\\"all\\\")\\n\",\n        \"for frame_idx in range(0, len(outputs_per_frame), vis_frame_stride):\\n\",\n        \"    visualize_formatted_frame_output(\\n\",\n        \"        frame_idx,\\n\",\n        \"        video_frames_for_vis,\\n\",\n        \"        outputs_list=[outputs_per_frame],\\n\",\n        \"        titles=[\\\"SAM 3 Dense Tracking outputs\\\"],\\n\",\n        \"        figsize=(6, 4),\\n\",\n        \"    )\"\n      ]\n    },\n    {\n      \"attachments\": {},\n      \"cell_type\": \"markdown\",\n      \"metadata\": {\n        \"bentoAICellStatus\": \"none\",\n        \"customInput\": null,\n        \"isCommentPanelOpen\": false,\n        \"language\": \"markdown\",\n        \"originalKey\": \"4606cb85-5007-4b11-baa6-41ce9017de8e\",\n        \"outputsInitialized\": false,\n        \"showInput\": false\n      },\n      \"source\": [\n        \"### Adding new objects with point prompts\\n\",\n        \"\\n\",\n        \"We can add new objects through point prompts.\\n\",\n        \"\\n\",\n        \"Assuming that we've changed our mind, and now that we want to add back the dancer in the front (whom we just removed in the step above). We can use interactive clicks to add her back.\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": null,\n      \"metadata\": {\n        \"bentoAICellStatus\": \"none\",\n        \"bentoCellName\": {\n          \"name\": \"Get image dimensions\",\n          \"origin\": \"ai\"\n        },\n        \"collapsed\": false,\n        \"customInput\": null,\n        \"customOutput\": null,\n        \"executionStartTime\": 1762496742064,\n        \"executionStopTime\": 1762496743435,\n        \"isCommentPanelOpen\": false,\n        \"jupyter\": {\n          \"outputs_hidden\": false\n        },\n        \"language\": \"python\",\n        \"originalKey\": \"2ad8f8e9-49d8-4003-ad2f-f289ab5befc8\",\n        \"outputsInitialized\": false,\n        \"requestMsgId\": \"2ad8f8e9-49d8-4003-ad2f-f289ab5befc8\",\n        \"serverExecutionDuration\": 15.222899999571,\n        \"showInput\": true\n      },\n      \"outputs\": [],\n      \"source\": [\n        \"sample_img = Image.fromarray(load_frame(video_frames_for_vis[0]))\\n\",\n        \"\\n\",\n        \"IMG_WIDTH, IMG_HEIGHT = sample_img.size\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": null,\n      \"metadata\": {\n        \"bentoAICellStatus\": \"none\",\n        \"bentoCellName\": {\n          \"name\": \"Convert Absolute to Relative Coordinates\",\n          \"origin\": \"ai\"\n        },\n        \"collapsed\": false,\n        \"customInput\": null,\n        \"customOutput\": null,\n        \"executionStartTime\": 1762496743442,\n        \"executionStopTime\": 1762496744333,\n        \"isCommentPanelOpen\": false,\n        \"jupyter\": {\n          \"outputs_hidden\": false\n        },\n        \"language\": \"python\",\n        \"originalKey\": \"3baca8f9-8e19-46da-a320-29524ddbf950\",\n        \"outputsInitialized\": false,\n        \"requestMsgId\": \"3baca8f9-8e19-46da-a320-29524ddbf950\",\n        \"serverExecutionDuration\": 3.9295850001508,\n        \"showInput\": true\n      },\n      \"outputs\": [],\n      \"source\": [\n        \"# let's add back the dancer via point prompts.\\n\",\n        \"# we will use a single positive click to add the dancer back.\\n\",\n        \"\\n\",\n        \"frame_idx = 0\\n\",\n        \"obj_id = 2\\n\",\n        \"points_abs = np.array(\\n\",\n        \"    [\\n\",\n        \"        [760, 550],  # positive click\\n\",\n        \"    ]\\n\",\n        \")\\n\",\n        \"# positive clicks have label 1, while negative clicks have label 0\\n\",\n        \"labels = np.array([1])\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": null,\n      \"metadata\": {\n        \"bentoAICellStatus\": \"none\",\n        \"bentoCellName\": {\n          \"name\": \"Display Data Points\",\n          \"origin\": \"ai\"\n        },\n        \"collapsed\": false,\n        \"customInput\": null,\n        \"customOutput\": null,\n        \"executionStartTime\": 1762496744337,\n        \"executionStopTime\": 1762496748117,\n        \"isCommentPanelOpen\": false,\n        \"jupyter\": {\n          \"outputs_hidden\": false\n        },\n        \"language\": \"python\",\n        \"originalKey\": \"865e951f-8d0a-41bf-8d51-72092703e3cf\",\n        \"output\": {\n          \"id\": \"1240547311311464\",\n          \"loadingStatus\": \"before loading\"\n        },\n        \"outputsInitialized\": true,\n        \"requestMsgId\": \"865e951f-8d0a-41bf-8d51-72092703e3cf\",\n        \"serverExecutionDuration\": 1224.9363859992,\n        \"showInput\": true\n      },\n      \"outputs\": [],\n      \"source\": [\n        \"# convert points and labels to tensors; also convert to relative coordinates\\n\",\n        \"points_tensor = torch.tensor(\\n\",\n        \"    abs_to_rel_coords(points_abs, IMG_WIDTH, IMG_HEIGHT, coord_type=\\\"point\\\"),\\n\",\n        \"    dtype=torch.float32,\\n\",\n        \")\\n\",\n        \"points_labels_tensor = torch.tensor(labels, dtype=torch.int32)\\n\",\n        \"\\n\",\n        \"response = predictor.handle_request(\\n\",\n        \"    request=dict(\\n\",\n        \"        type=\\\"add_prompt\\\",\\n\",\n        \"        session_id=session_id,\\n\",\n        \"        frame_index=frame_idx,\\n\",\n        \"        points=points_tensor,\\n\",\n        \"        point_labels=points_labels_tensor,\\n\",\n        \"        obj_id=obj_id,\\n\",\n        \"    )\\n\",\n        \")\\n\",\n        \"out = response[\\\"outputs\\\"]\\n\",\n        \"\\n\",\n        \"plt.close(\\\"all\\\")\\n\",\n        \"visualize_formatted_frame_output(\\n\",\n        \"    frame_idx,\\n\",\n        \"    video_frames_for_vis,\\n\",\n        \"    outputs_list=[prepare_masks_for_visualization({frame_idx: out})],\\n\",\n        \"    titles=[\\\"SAM 3 Dense Tracking outputs\\\"],\\n\",\n        \"    figsize=(6, 4),\\n\",\n        \"    points_list=[points_abs],\\n\",\n        \"    points_labels_list=[labels],\\n\",\n        \")\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": null,\n      \"metadata\": {\n        \"bentoAICellStatus\": \"none\",\n        \"bentoCellName\": {\n          \"name\": \"Process Video Frames\",\n          \"origin\": \"ai\"\n        },\n        \"collapsed\": false,\n        \"customInput\": null,\n        \"customOutput\": null,\n        \"executionStartTime\": 1762496748120,\n        \"executionStopTime\": 1762496774486,\n        \"isCommentPanelOpen\": false,\n        \"jupyter\": {\n          \"outputs_hidden\": false\n        },\n        \"language\": \"python\",\n        \"originalKey\": \"cf1d2dda-464b-4a6a-aff3-d729aa486ec3\",\n        \"output\": {\n          \"id\": \"824678093489712\",\n          \"loadingStatus\": \"before loading\"\n        },\n        \"outputsInitialized\": true,\n        \"requestMsgId\": \"cf1d2dda-464b-4a6a-aff3-d729aa486ec3\",\n        \"serverExecutionDuration\": 25528.605932001,\n        \"showInput\": true\n      },\n      \"outputs\": [],\n      \"source\": [\n        \"# now we propagate the outputs from frame 0 to the end of the video and collect all outputs\\n\",\n        \"outputs_per_frame = propagate_in_video(predictor, session_id)\\n\",\n        \"\\n\",\n        \"# finally, we reformat the outputs for visualization and plot the outputs every 60 frames\\n\",\n        \"outputs_per_frame = prepare_masks_for_visualization(outputs_per_frame)\\n\",\n        \"\\n\",\n        \"vis_frame_stride = 60\\n\",\n        \"plt.close(\\\"all\\\")\\n\",\n        \"for frame_idx in range(0, len(outputs_per_frame), vis_frame_stride):\\n\",\n        \"    visualize_formatted_frame_output(\\n\",\n        \"        frame_idx,\\n\",\n        \"        video_frames_for_vis,\\n\",\n        \"        outputs_list=[outputs_per_frame],\\n\",\n        \"        titles=[\\\"SAM 3 Dense Tracking outputs\\\"],\\n\",\n        \"        figsize=(6, 4),\\n\",\n        \"    )\"\n      ]\n    },\n    {\n      \"attachments\": {},\n      \"cell_type\": \"markdown\",\n      \"metadata\": {\n        \"bentoAICellStatus\": \"none\",\n        \"customInput\": null,\n        \"isCommentPanelOpen\": false,\n        \"language\": \"markdown\",\n        \"originalKey\": \"79b12d4e-cdf0-41b8-9939-bcc1d5422115\",\n        \"outputsInitialized\": false,\n        \"showInput\": false\n      },\n      \"source\": [\n        \"### Refining an existing object with point prompts\\n\",\n        \"\\n\",\n        \"We can also refine the segmentation mask of an existing object through point prompts.\\n\",\n        \"\\n\",\n        \"Assuming that we've changed our mind (again) -- for Object ID 2 (the dancer in the front whom we just added back in the step above), now we only want to segment her T-shirt instead of her whole body. We can adjust the segmentation mask with a few more positive and negative clicks.\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": null,\n      \"metadata\": {\n        \"bentoAICellStatus\": \"none\",\n        \"bentoCellName\": {\n          \"name\": \"Segment T-shirt with Clicks\",\n          \"origin\": \"ai\"\n        },\n        \"collapsed\": false,\n        \"customInput\": null,\n        \"customOutput\": null,\n        \"executionStartTime\": 1762496774494,\n        \"executionStopTime\": 1762496775380,\n        \"isCommentPanelOpen\": false,\n        \"jupyter\": {\n          \"outputs_hidden\": false\n        },\n        \"language\": \"python\",\n        \"originalKey\": \"8114fb12-386d-45e0-b875-f74eee630d96\",\n        \"outputsInitialized\": false,\n        \"requestMsgId\": \"8114fb12-386d-45e0-b875-f74eee630d96\",\n        \"serverExecutionDuration\": 4.0469909999956,\n        \"showInput\": true\n      },\n      \"outputs\": [],\n      \"source\": [\n        \"# For the dancer in the front, suppose now we only want to segment her T-shirt instead of her whole body\\n\",\n        \"# we will use 2 positive clicks and 2 negative clicks to select her shirt.\\n\",\n        \"\\n\",\n        \"frame_idx = 0\\n\",\n        \"obj_id = 2\\n\",\n        \"points_abs = np.array(\\n\",\n        \"    [\\n\",\n        \"        [740, 450],  # positive click\\n\",\n        \"        [760, 630],  # negative click\\n\",\n        \"        [840, 640],  # negative click\\n\",\n        \"        [760, 550],  # positive click\\n\",\n        \"    ]\\n\",\n        \")\\n\",\n        \"# positive clicks have label 1, while negative clicks have label 0\\n\",\n        \"labels = np.array([1, 0, 0, 1])\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": null,\n      \"metadata\": {\n        \"bentoAICellStatus\": \"none\",\n        \"bentoCellName\": {\n          \"name\": \"Process and Visualize Frame Outputs\",\n          \"origin\": \"ai\"\n        },\n        \"collapsed\": false,\n        \"customInput\": null,\n        \"customOutput\": null,\n        \"executionStartTime\": 1762496775385,\n        \"executionStopTime\": 1762496777685,\n        \"isCommentPanelOpen\": false,\n        \"jupyter\": {\n          \"outputs_hidden\": false\n        },\n        \"language\": \"python\",\n        \"originalKey\": \"5e66f671-aa71-42d7-a68d-8dc6430df8fe\",\n        \"output\": {\n          \"id\": \"25486291537675748\",\n          \"loadingStatus\": \"before loading\"\n        },\n        \"outputsInitialized\": true,\n        \"requestMsgId\": \"5e66f671-aa71-42d7-a68d-8dc6430df8fe\",\n        \"serverExecutionDuration\": 1227.9235379992,\n        \"showInput\": true\n      },\n      \"outputs\": [],\n      \"source\": [\n        \"# convert points and labels to tensors; also convert to relative coordinates\\n\",\n        \"points_tensor = torch.tensor(\\n\",\n        \"    abs_to_rel_coords(points_abs, IMG_WIDTH, IMG_HEIGHT, coord_type=\\\"point\\\"),\\n\",\n        \"    dtype=torch.float32,\\n\",\n        \")\\n\",\n        \"points_labels_tensor = torch.tensor(labels, dtype=torch.int32)\\n\",\n        \"\\n\",\n        \"response = predictor.handle_request(\\n\",\n        \"    request=dict(\\n\",\n        \"        type=\\\"add_prompt\\\",\\n\",\n        \"        session_id=session_id,\\n\",\n        \"        frame_index=frame_idx,\\n\",\n        \"        points=points_tensor,\\n\",\n        \"        point_labels=points_labels_tensor,\\n\",\n        \"        obj_id=obj_id,\\n\",\n        \"    )\\n\",\n        \")\\n\",\n        \"out = response[\\\"outputs\\\"]\\n\",\n        \"\\n\",\n        \"plt.close(\\\"all\\\")\\n\",\n        \"visualize_formatted_frame_output(\\n\",\n        \"    frame_idx,\\n\",\n        \"    video_frames_for_vis,\\n\",\n        \"    outputs_list=[prepare_masks_for_visualization({frame_idx: out})],\\n\",\n        \"    titles=[\\\"SAM 3 Dense Tracking outputs\\\"],\\n\",\n        \"    figsize=(6, 4),\\n\",\n        \"    points_list=[points_abs],\\n\",\n        \"    points_labels_list=[labels],\\n\",\n        \")\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": null,\n      \"metadata\": {\n        \"bentoAICellStatus\": \"none\",\n        \"bentoCellName\": {\n          \"name\": \"Visualize Video Tracking Outputs\",\n          \"origin\": \"ai\"\n        },\n        \"collapsed\": false,\n        \"customInput\": null,\n        \"customOutput\": null,\n        \"executionStartTime\": 1762496777688,\n        \"executionStopTime\": 1762496803927,\n        \"isCommentPanelOpen\": false,\n        \"jupyter\": {\n          \"outputs_hidden\": false\n        },\n        \"language\": \"python\",\n        \"originalKey\": \"5c4a35a7-e5cc-4c7d-ba05-25540665d125\",\n        \"output\": {\n          \"id\": \"1222230279729631\",\n          \"loadingStatus\": \"before loading\"\n        },\n        \"outputsInitialized\": true,\n        \"requestMsgId\": \"5c4a35a7-e5cc-4c7d-ba05-25540665d125\",\n        \"serverExecutionDuration\": 25325.393453,\n        \"showInput\": true\n      },\n      \"outputs\": [],\n      \"source\": [\n        \"# now we propagate the outputs from frame 0 to the end of the video and collect all outputs\\n\",\n        \"outputs_per_frame = propagate_in_video(predictor, session_id)\\n\",\n        \"\\n\",\n        \"# finally, we reformat the outputs for visualization and plot the outputs every 60 frames\\n\",\n        \"outputs_per_frame = prepare_masks_for_visualization(outputs_per_frame)\\n\",\n        \"\\n\",\n        \"vis_frame_stride = 60\\n\",\n        \"plt.close(\\\"all\\\")\\n\",\n        \"for frame_idx in range(0, len(outputs_per_frame), vis_frame_stride):\\n\",\n        \"    visualize_formatted_frame_output(\\n\",\n        \"        frame_idx,\\n\",\n        \"        video_frames_for_vis,\\n\",\n        \"        outputs_list=[outputs_per_frame],\\n\",\n        \"        titles=[\\\"SAM 3 Dense Tracking outputs\\\"],\\n\",\n        \"        figsize=(6, 4),\\n\",\n        \"    )\"\n      ]\n    },\n    {\n      \"cell_type\": \"markdown\",\n      \"metadata\": {\n        \"attachments\": [],\n        \"bentoAICellStatus\": \"none\",\n        \"isCommentPanelOpen\": false,\n        \"language\": \"markdown\",\n        \"metadata\": {\n          \"bentoAICellStatus\": \"none\",\n          \"customInput\": null,\n          \"isCommentPanelOpen\": false,\n          \"language\": \"markdown\",\n          \"originalKey\": \"3f8d998b-4585-4956-b15d-1d4078cf8927\",\n          \"outputsInitialized\": false,\n          \"showInput\": false\n        },\n        \"originalKey\": \"b1d99d9b-c26e-4d5a-a4a2-70aab345fd77\",\n        \"outputsInitialized\": false,\n        \"showInput\": false\n      },\n      \"source\": [\n        \"### Close session\\n\",\n        \"\\n\",\n        \"Each session is tied to a single video. We can close the session after inference to free up its resources.\\n\",\n        \"\\n\",\n        \"(Then, you may start a new session on another video.)\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": null,\n      \"metadata\": {\n        \"bentoAICellStatus\": \"none\",\n        \"bentoCellName\": {\n          \"name\": \"Close inference session\",\n          \"origin\": \"ai\"\n        },\n        \"collapsed\": false,\n        \"customOutput\": null,\n        \"executionStartTime\": 1762496803937,\n        \"executionStopTime\": 1762496805854,\n        \"isCommentPanelOpen\": false,\n        \"jupyter\": {\n          \"outputs_hidden\": false\n        },\n        \"language\": \"python\",\n        \"metadata\": {\n          \"bentoAICellStatus\": \"none\",\n          \"bentoCellName\": {\n            \"name\": \"Reset Session Request\",\n            \"origin\": \"ai\"\n          },\n          \"collapsed\": false,\n          \"customInput\": null,\n          \"customOutput\": null,\n          \"executionStartTime\": 1761950861264,\n          \"executionStopTime\": 1761950863082,\n          \"isCommentPanelOpen\": false,\n          \"language\": \"python\",\n          \"originalKey\": \"d08a9e5f-3563-4aa1-ba8a-9f94615e4d7e\",\n          \"outputsInitialized\": true,\n          \"requestMsgId\": \"6e872e9a-3803-418f-827d-f1d817ef9cb9\",\n          \"serverExecutionDuration\": 926.3133899949,\n          \"showInput\": true\n        },\n        \"originalKey\": \"4f94819e-de3c-49bc-a25f-0790b2fa2cfb\",\n        \"output\": {\n          \"id\": \"1532190008092267\",\n          \"loadingStatus\": \"before loading\"\n        },\n        \"outputsInitialized\": true,\n        \"requestMsgId\": \"4f94819e-de3c-49bc-a25f-0790b2fa2cfb\",\n        \"serverExecutionDuration\": 941.08029199924\n      },\n      \"outputs\": [],\n      \"source\": [\n        \"# finally, close the inference session to free its GPU resources\\n\",\n        \"# (you may start a new session on another video)\\n\",\n        \"_ = predictor.handle_request(\\n\",\n        \"    request=dict(\\n\",\n        \"        type=\\\"close_session\\\",\\n\",\n        \"        session_id=session_id,\\n\",\n        \"    )\\n\",\n        \")\"\n      ]\n    },\n    {\n      \"cell_type\": \"markdown\",\n      \"metadata\": {\n        \"attachments\": [],\n        \"bentoAICellStatus\": \"none\",\n        \"isCommentPanelOpen\": false,\n        \"language\": \"markdown\",\n        \"metadata\": {\n          \"bentoAICellStatus\": \"none\",\n          \"customInput\": null,\n          \"isCommentPanelOpen\": false,\n          \"language\": \"markdown\",\n          \"originalKey\": \"d79e36cd-a691-420d-8061-ad5222913770\",\n          \"outputsInitialized\": false,\n          \"showInput\": false\n        },\n        \"originalKey\": \"5f1ced4a-7f9b-4a3f-88bb-77fd601466eb\",\n        \"outputsInitialized\": false,\n        \"showInput\": false\n      },\n      \"source\": [\n        \"### Clean-up\\n\",\n        \"\\n\",\n        \"After all inference is done, we can shutdown the predictor to free up the multi-GPU process group.\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": null,\n      \"metadata\": {\n        \"bentoAICellStatus\": \"none\",\n        \"bentoCellName\": {\n          \"name\": \"Shutdown Predictor\",\n          \"origin\": \"ai\"\n        },\n        \"collapsed\": false,\n        \"customOutput\": null,\n        \"executionStartTime\": 1762496805866,\n        \"executionStopTime\": 1762496807085,\n        \"isCommentPanelOpen\": false,\n        \"jupyter\": {\n          \"outputs_hidden\": false\n        },\n        \"language\": \"python\",\n        \"metadata\": {\n          \"bentoAICellStatus\": \"none\",\n          \"bentoCellName\": {\n            \"name\": \"Shutdown Predictor\",\n            \"origin\": \"ai\"\n          },\n          \"collapsed\": false,\n          \"customInput\": null,\n          \"customOutput\": null,\n          \"executionStartTime\": 1761950863090,\n          \"executionStopTime\": 1761950863987,\n          \"isCommentPanelOpen\": false,\n          \"language\": \"python\",\n          \"originalKey\": \"2de9666e-d888-4b2b-955a-82727363fc59\",\n          \"outputsInitialized\": true,\n          \"requestMsgId\": \"59e250c1-3d4c-436d-aa6e-fc0429fe4d8f\",\n          \"serverExecutionDuration\": 282.37027197611,\n          \"showInput\": true\n        },\n        \"originalKey\": \"bb5f6b72-d945-4193-8988-2490e9168882\",\n        \"output\": {\n          \"id\": \"1866958724222293\",\n          \"loadingStatus\": \"before loading\"\n        },\n        \"outputsInitialized\": true,\n        \"requestMsgId\": \"bb5f6b72-d945-4193-8988-2490e9168882\",\n        \"serverExecutionDuration\": 284.71523799999\n      },\n      \"outputs\": [],\n      \"source\": [\n        \"# after all inference is done, we can shutdown the predictor\\n\",\n        \"# to free up the multi-GPU process group\\n\",\n        \"predictor.shutdown()\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": null,\n      \"metadata\": {\n        \"bentoAICellStatus\": \"none\",\n        \"bentoCellName\": {\n          \"name\": \"Cell 33\",\n          \"origin\": \"initial\"\n        },\n        \"collapsed\": false,\n        \"customInput\": null,\n        \"customOutput\": null,\n        \"executionStartTime\": 1762496807093,\n        \"executionStopTime\": 1762496807812,\n        \"isCommentPanelOpen\": false,\n        \"jupyter\": {\n          \"outputs_hidden\": false\n        },\n        \"language\": \"python\",\n        \"originalKey\": \"e4ad9f5f-c0df-4e30-97a2-40d389ba92ac\",\n        \"outputsInitialized\": false,\n        \"requestMsgId\": \"e4ad9f5f-c0df-4e30-97a2-40d389ba92ac\",\n        \"serverExecutionDuration\": 3.5742059990298,\n        \"showInput\": true\n      },\n      \"outputs\": [],\n      \"source\": []\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": null,\n      \"metadata\": {},\n      \"outputs\": [],\n      \"source\": []\n    }\n  ],\n  \"metadata\": {\n    \"bento_stylesheets\": {\n      \"bento/extensions/flow/main.css\": true,\n      \"bento/extensions/kernel_selector/main.css\": true,\n      \"bento/extensions/kernel_ui/main.css\": true,\n      \"bento/extensions/new_kernel/main.css\": true,\n      \"bento/extensions/system_usage/main.css\": true,\n      \"bento/extensions/theme/main.css\": true\n    },\n    \"captumWidgetMessage\": [],\n    \"fileHeader\": \"\",\n    \"fileUid\": \"8685c221-c143-4b84-98ec-b1f023cedd6c\",\n    \"isAdHoc\": false,\n    \"kernelspec\": {\n      \"display_name\": \"Python 3 (ipykernel)\",\n      \"language\": \"python\",\n      \"name\": \"python3\"\n    },\n    \"language_info\": {\n      \"codemirror_mode\": {\n        \"name\": \"ipython\",\n        \"version\": 3\n      },\n      \"file_extension\": \".py\",\n      \"mimetype\": \"text/x-python\",\n      \"name\": \"python\",\n      \"nbconvert_exporter\": \"python\",\n      \"pygments_lexer\": \"ipython3\",\n      \"version\": \"3.12.11\"\n    },\n    \"last_base_url\": \"https://bento.edge.x2p.facebook.net/\",\n    \"last_kernel_id\": \"b57809cb-57de-4b58-a47a-2cd14cd7dc51\",\n    \"last_msg_id\": \"be2245fc-daa1cc5649ef79144c475c5d_1965\",\n    \"last_server_session_id\": \"4fb65252-bdbd-4eea-b3c3-4a9f2995ad48\",\n    \"notebookId\": \"825823386977069\",\n    \"notebookNumber\": \"N8482762\"\n  },\n  \"nbformat\": 4,\n  \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "pyproject.toml",
    "content": "[build-system]\nrequires = [\"setuptools>=61\", \"wheel\"]\nbuild-backend = \"setuptools.build_meta\"\n\n[project]\nname = \"sam3\"\ndynamic = [\"version\"]\ndescription = \"SAM3 (Segment Anything Model 3) implementation\"\nreadme = \"README.md\"\nrequires-python = \">=3.8\"\nlicense = {file = \"LICENSE\"}\nauthors = [\n    {name = \"Meta AI Research\"}\n]\nclassifiers = [\n    \"Development Status :: 4 - Beta\",\n    \"Intended Audience :: Science/Research\",\n    \"License :: OSI Approved :: MIT License\",\n    \"Programming Language :: Python :: 3\",\n    \"Programming Language :: Python :: 3.8\",\n    \"Programming Language :: Python :: 3.9\",\n    \"Programming Language :: Python :: 3.10\",\n    \"Programming Language :: Python :: 3.11\",\n    \"Programming Language :: Python :: 3.12\",\n    \"Topic :: Scientific/Engineering :: Artificial Intelligence\",\n]\ndependencies = [\n    \"timm>=1.0.17\",\n    \"numpy>=1.26,<2\",\n    \"tqdm\",\n    \"ftfy==6.1.1\",\n    \"regex\",\n    \"iopath>=0.1.10\",\n    \"typing_extensions\",\n    \"huggingface_hub\",\n]\n\n[project.optional-dependencies]\ndev = [\n    \"pytest\",\n    \"pytest-cov\",\n    \"black==24.2.0\",\n    \"ufmt==2.8.0\",\n    \"ruff-api==0.1.0\",\n    \"usort==1.0.2\",\n    \"gitpython==3.1.31\",\n    \"yt-dlp\",\n    \"pandas\",\n    \"opencv-python\",\n    \"pycocotools\",\n    \"numba\",\n    \"python-rapidjson\",\n]\nnotebooks = [\n    \"matplotlib\",\n    \"jupyter\",\n    \"notebook\",\n    \"ipywidgets\",\n    \"ipycanvas\",\n    \"ipympl\",\n    \"pycocotools\",\n    \"decord\",\n    \"opencv-python\",\n    \"einops\",\n    \"scikit-image\",\n    \"scikit-learn\",\n]\ntrain = [\n    \"hydra-core\",\n    \"submitit\",\n    \"tensorboard\",\n    \"zstandard\",\n    \"scipy\",\n    \"torchmetrics\",\n    \"fvcore\",\n    \"fairscale\",\n    \"scikit-image\",\n    \"scikit-learn\",\n]\n\n[project.urls]\n\"Homepage\" = \"https://github.com/facebookresearch/sam3\"\n\"Bug Tracker\" = \"https://github.com/facebookresearch/sam3/issues\"\n\n[tool.setuptools.packages.find]\ninclude = [\"sam3*\"]\nexclude = [\"build*\", \"scripts*\", \"examples*\"]\n\n[tool.setuptools.package-data]\nsam3 = [\"assets/*.txt.gz\"]\n\n[tool.setuptools.dynamic]\nversion = {attr = \"sam3.__version__\"}\n\n[tool.black]\nline-length = 88\ntarget-version = ['py38', 'py39', 'py310', 'py311', 'py312']\ninclude = '\\.pyi?$'\n\n[tool.isort]\nprofile = \"black\"\nmulti_line_output = 3\n\n[tool.usort]\nfirst_party_detection = false\n\n[tool.ufmt]\nformatter = \"ruff-api\"\n\n[tool.mypy]\npython_version = \"3.12\"\nwarn_return_any = true\nwarn_unused_configs = true\ndisallow_untyped_defs = true\ndisallow_incomplete_defs = true\n\n[[tool.mypy.overrides]]\nmodule = [\n    \"torch.*\",\n    \"torchvision.*\",\n    \"timm.*\",\n    \"numpy.*\",\n    \"PIL.*\",\n    \"tqdm.*\",\n    \"ftfy.*\",\n    \"regex.*\",\n    \"iopath.*\",\n]\nignore_missing_imports = true\n\n[tool.pytest.ini_options]\ntestpaths = [\"tests\"]\npython_files = \"test_*.py\"\npython_classes = \"Test*\"\npython_functions = \"test_*\"\n"
  },
  {
    "path": "sam3/__init__.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n\nfrom .model_builder import build_sam3_image_model\n\n__version__ = \"0.1.0\"\n\n__all__ = [\"build_sam3_image_model\"]\n"
  },
  {
    "path": "sam3/agent/__init__.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n"
  },
  {
    "path": "sam3/agent/agent_core.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n\nimport copy\nimport json\nimport os\n\nimport cv2\nfrom PIL import Image\n\nfrom .client_llm import send_generate_request\nfrom .client_sam3 import call_sam_service\nfrom .viz import visualize\n\n\ndef save_debug_messages(messages_list, debug, debug_folder_path, debug_jsonl_path):\n    \"\"\"Save messages to debug jsonl file if debug is enabled\"\"\"\n    if debug and debug_jsonl_path:\n        # Ensure the debug directory exists before writing\n        os.makedirs(debug_folder_path, exist_ok=True)\n        with open(debug_jsonl_path, \"w\") as f:\n            for msg in messages_list:\n                f.write(json.dumps(msg, indent=4) + \"\\n\")\n\n\ndef cleanup_debug_files(debug, debug_folder_path, debug_jsonl_path):\n    \"\"\"Clean up debug files when function successfully returns\"\"\"\n    if debug and debug_folder_path:\n        try:\n            if os.path.exists(debug_jsonl_path):\n                os.remove(debug_jsonl_path)\n            if os.path.exists(debug_folder_path):\n                os.rmdir(debug_folder_path)\n        except Exception as e:\n            print(f\"Warning: Could not clean up debug files: {e}\")\n\n\ndef count_images(messages):\n    \"\"\"Count the total number of images present in the messages history.\"\"\"\n    total = 0\n    for message in messages:\n        # Check if message has content (should be a list)\n        if \"content\" in message and isinstance(message[\"content\"], list):\n            # Iterate through each content item\n            for content_item in message[\"content\"]:\n                # Check if content item is a dict with type \"image\"\n                if (\n                    isinstance(content_item, dict)\n                    and content_item.get(\"type\") == \"image\"\n                ):\n                    total += 1\n    return total\n\n\ndef _prune_messages_for_next_round(\n    messages_list,\n    used_text_prompts,\n    latest_sam3_text_prompt,\n    img_path,\n    initial_text_prompt,\n):\n    \"\"\"Return a new messages list that contains only:\n    1) messages[:2] (with optional warning text added to the second message's content)\n    2) the latest assistant message (and everything after it) that contains a segment_phrase tool call\n    \"\"\"\n    # There should not be more than 10 messages in the conversation history\n    assert len(messages_list) < 10\n\n    # Part 1: always keep the first two message JSONs\n    part1 = copy.deepcopy(messages_list[:2])\n\n    # Part 2: search backwards for the latest assistant message containing a segment_phrase tool call\n    part2_start_idx = None\n    for idx in range(len(messages_list) - 1, 1, -1):\n        msg = messages_list[idx]\n        # We only consider assistant messages with a \"content\" list\n        if msg.get(\"role\") != \"assistant\" or \"content\" not in msg:\n            continue\n        # Look for any content element that is a text containing the segment_phrase tool call\n        for content in msg[\"content\"]:\n            if (\n                isinstance(content, dict)\n                and content.get(\"type\") == \"text\"\n                and \"<tool>\" in content.get(\"text\", \"\")\n                and \"segment_phrase\" in content.get(\"text\", \"\")\n            ):\n                part2_start_idx = idx\n                break\n        if part2_start_idx is not None:\n            break\n\n    part2 = messages_list[part2_start_idx:] if part2_start_idx is not None else []\n\n    # Part 3: decide whether to add warning text to the second message in part1\n    previously_used = (\n        [p for p in used_text_prompts if p != latest_sam3_text_prompt]\n        if latest_sam3_text_prompt\n        else list(used_text_prompts)\n    )\n    if part2 and len(previously_used) > 0:\n        warning_text = f'Note that we have previously called the segment_phrase tool with each \"text_prompt\" in this list: {list(previously_used)}, but none of the generated results were satisfactory. So make sure that you do not use any of these phrases as the \"text_prompt\" to call the segment_phrase tool again.'\n        # Replace the second message entirely to keep exactly 2 content items\n        part1[1] = {\n            \"role\": \"user\",\n            \"content\": [\n                {\"type\": \"image\", \"image\": img_path},\n                {\n                    \"type\": \"text\",\n                    \"text\": f\"The above image is the raw input image. The initial user input query is: '{initial_text_prompt}'.\"\n                    + \" \"\n                    + warning_text,\n                },\n            ],\n        }\n        assert len(part1[1][\"content\"]) == 2\n\n    # Build the new messages list: part1 (with optional warning), then part2\n    new_messages = list(part1)\n    new_messages.extend(part2)\n    return new_messages\n\n\ndef agent_inference(\n    img_path: str,\n    initial_text_prompt: str,\n    debug: bool = False,\n    send_generate_request=send_generate_request,\n    call_sam_service=call_sam_service,\n    max_generations: int = 100,\n    output_dir=\"../../sam3_agent_out\",\n):\n    \"\"\"\n    Given a text prompt and an image, this tool will perform all aspects of agentic problem solving,\n    while saving sam3 and MLLM outputs to their respective directories.\n\n    Args:\n        img_path: Path to the input image\n        initial_text_prompt: Initial text prompt from the user\n        debug: Whether to enable debug mode\n        max_generations: Maximum number of send_generate_request calls allowed (default: 100)\n    \"\"\"\n    # setup dir\n    sam_output_dir = os.path.join(output_dir, \"sam_out\")\n    error_save_dir = os.path.join(output_dir, \"none_out\")\n    debug_save_dir = os.path.join(output_dir, \"agent_debug_out\")\n    os.makedirs(sam_output_dir, exist_ok=True)\n    os.makedirs(error_save_dir, exist_ok=True)\n    os.makedirs(debug_save_dir, exist_ok=True)\n    current_dir = os.path.dirname(os.path.abspath(__file__))\n    MLLM_SYSTEM_PROMPT_PATH = os.path.join(\n        current_dir, \"system_prompts/system_prompt.txt\"\n    )\n    ITERATIVE_CHECKING_SYSTEM_PROMPT_PATH = os.path.join(\n        current_dir, \"system_prompts/system_prompt_iterative_checking.txt\"\n    )\n    # init variables\n    PATH_TO_LATEST_OUTPUT_JSON = \"\"\n    LATEST_SAM3_TEXT_PROMPT = \"\"\n    USED_TEXT_PROMPTS = (\n        set()\n    )  # Track all previously used text prompts for segment_phrase\n    generation_count = 0  # Counter for number of send_generate_request calls\n\n    # debug setup\n    debug_folder_path = None\n    debug_jsonl_path = None\n    if debug:\n        debug_folder_path = os.path.join(\n            debug_save_dir, f\"{img_path.rsplit('/', 1)[-1].rsplit('.', 1)[0]}\"\n        )\n        debug_jsonl_path = os.path.join(debug_folder_path, \"debug_history.json\")\n        os.makedirs(debug_folder_path, exist_ok=True)\n\n    # The helper functions are now defined outside the agent_inference function\n    with open(MLLM_SYSTEM_PROMPT_PATH, \"r\") as f:\n        system_prompt = f.read().strip()\n    with open(ITERATIVE_CHECKING_SYSTEM_PROMPT_PATH, \"r\") as f:\n        iterative_checking_system_prompt = f.read().strip()\n\n    # Construct the initial message list\n    messages = [\n        {\"role\": \"system\", \"content\": system_prompt},\n        {\n            \"role\": \"user\",\n            \"content\": [\n                {\"type\": \"image\", \"image\": img_path},\n                {\n                    \"type\": \"text\",\n                    \"text\": f\"The above image is the raw input image. The initial user input query is: '{initial_text_prompt}'.\",\n                },\n            ],\n        },\n    ]\n    print(f\"> Text prompt: {initial_text_prompt}\")\n    print(f\"> Image path: {img_path}\")\n\n    print(\"\\n\\n\")\n    print(\"-\" * 30 + f\" Round {str(generation_count + 1)}\" + \"-\" * 30)\n    print(\"\\n\\n\")\n    generated_text = send_generate_request(messages)\n    print(f\"\\n>>> MLLM Response [start]\\n{generated_text}\\n<<< MLLM Response [end]\\n\")\n    while generated_text is not None:\n        save_debug_messages(messages, debug, debug_folder_path, debug_jsonl_path)\n        assert (\n            \"<tool>\" in generated_text,\n            f\"Generated text does not contain <tool> tag: {generated_text}\",\n        )\n        generated_text = generated_text.split(\"</tool>\", 1)[0] + \"</tool>\"\n        tool_call_json_str = (\n            generated_text.split(\"<tool>\")[-1]\n            .split(\"</tool>\")[0]\n            .strip()\n            .replace(r\"}}}\", r\"}}\")  # remove extra } if any\n        )\n        try:\n            tool_call = json.loads(tool_call_json_str)\n        except json.JSONDecodeError:\n            raise ValueError(f\"Invalid JSON in tool call: {tool_call_json_str}\")\n\n        if PATH_TO_LATEST_OUTPUT_JSON == \"\":\n            # The first tool call must be segment_phrase or report_no_mask\n            assert (\n                tool_call[\"name\"] == \"segment_phrase\"\n                or tool_call[\"name\"] == \"report_no_mask\"\n            )\n\n        if tool_call[\"name\"] == \"segment_phrase\":\n            print(\"🔍 Calling segment_phrase tool...\")\n            assert list(tool_call[\"parameters\"].keys()) == [\"text_prompt\"]\n\n            # Check if this text_prompt has been used before\n            current_text_prompt = tool_call[\"parameters\"][\"text_prompt\"]\n            if current_text_prompt in USED_TEXT_PROMPTS:\n                print(\n                    f\"❌ Text prompt '{current_text_prompt}' has been used before. Requesting a different prompt.\"\n                )\n                duplicate_prompt_message = f\"You have previously used '{current_text_prompt}' as your text_prompt to call the segment_phrase tool. You may not use it again. Please call the segment_phrase tool again with a different, perhaps more general, or more creative simple noun phrase prompt, while adhering to all the rules stated in the system prompt. You must also never use any of the following text_prompt(s): {str(list(USED_TEXT_PROMPTS))}.\"\n                messages.append(\n                    {\n                        \"role\": \"assistant\",\n                        \"content\": [{\"type\": \"text\", \"text\": generated_text}],\n                    }\n                )\n                messages.append(\n                    {\n                        \"role\": \"user\",\n                        \"content\": [{\"type\": \"text\", \"text\": duplicate_prompt_message}],\n                    }\n                )\n            else:\n                # Add the text_prompt to the set of used prompts\n                USED_TEXT_PROMPTS.add(current_text_prompt)\n                LATEST_SAM3_TEXT_PROMPT = current_text_prompt\n                PATH_TO_LATEST_OUTPUT_JSON = call_sam_service(\n                    image_path=img_path,\n                    text_prompt=current_text_prompt,\n                    output_folder_path=sam_output_dir,\n                )\n                sam3_outputs = json.load(open(PATH_TO_LATEST_OUTPUT_JSON, \"r\"))\n                sam3_output_image_path = sam3_outputs[\"output_image_path\"]\n                num_masks = len(sam3_outputs[\"pred_boxes\"])\n\n                messages.append(\n                    {\n                        \"role\": \"assistant\",\n                        \"content\": [{\"type\": \"text\", \"text\": generated_text}],\n                    }\n                )\n                if num_masks == 0:\n                    print(\"❌ No masks generated by SAM3, reporting no mask to Qwen.\")\n                    sam3_output_text_message = f\"The segment_phrase tool did not generate any masks for the text_prompt '{current_text_prompt}'. Now, please call the segment_phrase tool again with a different, perhaps more general, or more creative simple noun phrase text_prompt, while adhering to all the rules stated in the system prompt. Please be reminded that the original user query was '{initial_text_prompt}'.\"\n                    messages.append(\n                        {\n                            \"role\": \"user\",\n                            \"content\": [\n                                {\"type\": \"text\", \"text\": sam3_output_text_message}\n                            ],\n                        }\n                    )\n                else:\n                    sam3_output_text_message = rf\"The segment_phrase tool generated {num_masks} available masks. All {num_masks} available masks are rendered in this image below, now you must analyze the {num_masks} available mask(s) carefully, compare them against the raw input image and the original user query, and determine your next action. Please be reminded that the original user query was '{initial_text_prompt}'.\"\n                    messages.append(\n                        {\n                            \"role\": \"user\",\n                            \"content\": [\n                                {\"type\": \"text\", \"text\": sam3_output_text_message},\n                                {\"type\": \"image\", \"image\": sam3_output_image_path},\n                            ],\n                        }\n                    )\n                print(\"\\n\\n>>> sam3_output_text_message:\\n\", sam3_output_text_message)\n\n        elif tool_call[\"name\"] == \"examine_each_mask\":\n            print(\"🔍 Calling examine_each_mask tool...\")\n            assert LATEST_SAM3_TEXT_PROMPT != \"\"\n\n            # Make sure that the last message is a image\n            assert messages[-1][\"content\"][1][\"type\"] == \"image\", (\n                \"Second content element should be an image\"\n            )\n            messages.pop()  # Remove the last user message\n            # Add simplified replacement message\n            simplified_message = {\n                \"role\": \"user\",\n                \"content\": [\n                    {\n                        \"type\": \"text\",\n                        \"text\": \"The segment_phrase tool generated several masks. Now you must analyze the mask(s) carefully, compare them against the raw input image and the original user query, and determine your next action.\",\n                    }\n                ],\n            }\n            messages.append(simplified_message)\n\n            current_outputs = json.load(open(PATH_TO_LATEST_OUTPUT_JSON, \"r\"))\n            num_masks = len(current_outputs[\"pred_masks\"])\n            masks_to_keep = []\n\n            # MLLM check the mask one by one\n            for i in range(num_masks):\n                print(f\"🔍 Checking mask {i + 1}/{num_masks}...\")\n                image_w_mask_i, image_w_zoomed_in_mask_i = visualize(current_outputs, i)\n\n                image_w_zoomed_in_mask_i_path = os.path.join(\n                    sam_output_dir, rf\"{LATEST_SAM3_TEXT_PROMPT}.png\".replace(\"/\", \"_\")\n                ).replace(\".png\", f\"_zoom_in_mask_{i + 1}.png\")\n                image_w_mask_i_path = os.path.join(\n                    sam_output_dir, rf\"{LATEST_SAM3_TEXT_PROMPT}.png\".replace(\"/\", \"_\")\n                ).replace(\".png\", f\"_selected_mask_{i + 1}.png\")\n                image_w_zoomed_in_mask_i.save(image_w_zoomed_in_mask_i_path)\n                image_w_mask_i.save(image_w_mask_i_path)\n\n                iterative_checking_messages = [\n                    {\"role\": \"system\", \"content\": iterative_checking_system_prompt},\n                    {\n                        \"role\": \"user\",\n                        \"content\": [\n                            {\"type\": \"text\", \"text\": f\"The raw input image: \"},\n                            {\"type\": \"image\", \"image\": img_path},\n                            {\n                                \"type\": \"text\",\n                                \"text\": f\"The initial user input query is: '{initial_text_prompt}'\",\n                            },\n                            {\n                                \"type\": \"text\",\n                                \"text\": f\"Image with the predicted segmentation mask rendered on it: \",\n                            },\n                            {\"type\": \"image\", \"image\": image_w_mask_i_path},\n                            {\n                                \"type\": \"text\",\n                                \"text\": f\"Image with the zoomed-in mask: \",\n                            },\n                            {\"type\": \"image\", \"image\": image_w_zoomed_in_mask_i_path},\n                        ],\n                    },\n                ]\n                checking_generated_text = send_generate_request(\n                    iterative_checking_messages\n                )\n\n                # Process the generated text to determine if the mask should be kept or rejected\n                if checking_generated_text is None:\n                    raise ValueError(\n                        \"Generated text is None, which is unexpected. Please check the Qwen server and the input parameters.\"\n                    )\n                print(f\"Generated text for mask {i + 1}: {checking_generated_text}\")\n                verdict = (\n                    checking_generated_text.split(\"<verdict>\")[-1]\n                    .split(\"</verdict>\")[0]\n                    .strip()\n                )\n                if \"Accept\" in verdict:\n                    assert not \"Reject\" in verdict\n                    print(f\"Mask {i + 1} accepted, keeping it in the outputs.\")\n                    masks_to_keep.append(i)\n                elif \"Reject\" in verdict:\n                    assert not \"Accept\" in verdict\n                    print(f\"Mask {i + 1} rejected, removing it from the outputs.\")\n                else:\n                    raise ValueError(\n                        f\"Unexpected verdict in generated text: {checking_generated_text}. Expected 'Accept' or 'Reject'.\"\n                    )\n\n            updated_outputs = {\n                \"original_image_path\": current_outputs[\"original_image_path\"],\n                \"orig_img_h\": current_outputs[\"orig_img_h\"],\n                \"orig_img_w\": current_outputs[\"orig_img_w\"],\n                \"pred_boxes\": [current_outputs[\"pred_boxes\"][i] for i in masks_to_keep],\n                \"pred_scores\": [\n                    current_outputs[\"pred_scores\"][i] for i in masks_to_keep\n                ],\n                \"pred_masks\": [current_outputs[\"pred_masks\"][i] for i in masks_to_keep],\n            }\n\n            image_w_check_masks = visualize(updated_outputs)\n            image_w_check_masks_path = os.path.join(\n                sam_output_dir, rf\"{LATEST_SAM3_TEXT_PROMPT}.png\"\n            ).replace(\n                \".png\",\n                f\"_selected_masks_{'-'.join(map(str, [i + 1 for i in masks_to_keep]))}.png\".replace(\n                    \"/\", \"_\"\n                ),\n            )\n            image_w_check_masks.save(image_w_check_masks_path)\n            # save the updated json outputs and append to message history\n            messages.append(\n                {\n                    \"role\": \"assistant\",\n                    \"content\": [{\"type\": \"text\", \"text\": generated_text}],\n                }\n            )\n            if len(masks_to_keep) == 0:\n                messages.append(\n                    {\n                        \"role\": \"user\",\n                        \"content\": [\n                            {\n                                \"type\": \"text\",\n                                \"text\": f\"The original user query was: '{initial_text_prompt}'. The examine_each_mask tool examined and rejected all of the masks generated by the segment_phrase tool. Now, please call the segment_phrase tool again with a different, perhaps more general, or more creative simple noun phrase text_prompt, while adhering to all the rules stated in the system prompt.\",\n                            }\n                        ],\n                    }\n                )\n            else:\n                messages.append(\n                    {\n                        \"role\": \"user\",\n                        \"content\": [\n                            {\n                                \"type\": \"text\",\n                                \"text\": f\"The original user query was: '{initial_text_prompt}'. After calling the examine_each_mask tool on the available masks, the number of available masks is now {len(masks_to_keep)}. All {len(masks_to_keep)} available masks are rendered in this image below, now you must analyze the {len(masks_to_keep)} available mask(s) carefully, compare them against the raw input image and the original user query, and determine your next action.\",\n                            },\n                            {\"type\": \"image\", \"image\": image_w_check_masks_path},\n                        ],\n                    }\n                )\n\n            # Create a new filename based on the original path to avoid filename length issues\n            base_path = PATH_TO_LATEST_OUTPUT_JSON\n            # Remove any existing \"masks_\" suffix to avoid duplication\n            if \"masks_\" in base_path:\n                base_path = base_path.split(\"masks_\")[0] + \".json\"\n            # Create new filename with current masks; use a clearer suffix when empty\n            if len(masks_to_keep) == 0:\n                PATH_TO_LATEST_OUTPUT_JSON = base_path.replace(\n                    \".json\", \"masks_none.json\"\n                )\n            else:\n                PATH_TO_LATEST_OUTPUT_JSON = base_path.replace(\n                    \".json\", f\"masks_{'_'.join(map(str, masks_to_keep))}.json\"\n                )\n            json.dump(updated_outputs, open(PATH_TO_LATEST_OUTPUT_JSON, \"w\"), indent=4)\n\n        elif tool_call[\"name\"] == \"select_masks_and_return\":\n            print(\"🔍 Calling select_masks_and_return tool...\")\n            current_outputs = json.load(open(PATH_TO_LATEST_OUTPUT_JSON, \"r\"))\n\n            assert list(tool_call[\"parameters\"].keys()) == [\"final_answer_masks\"]\n            masks_to_keep = tool_call[\"parameters\"][\"final_answer_masks\"]\n\n            # Keep only valid mask indices, remove duplicates, and preserve deterministic ascending order\n            available_masks = set(range(1, len(current_outputs[\"pred_masks\"]) + 1))\n            masks_to_keep = sorted({i for i in masks_to_keep if i in available_masks})\n            # Change this to a update message telling the model to try again along with information about errors made.\n\n            final_outputs = {\n                \"original_image_path\": current_outputs[\"original_image_path\"],\n                \"orig_img_h\": current_outputs[\"orig_img_h\"],\n                \"orig_img_w\": current_outputs[\"orig_img_w\"],\n                \"pred_boxes\": [\n                    current_outputs[\"pred_boxes\"][i - 1] for i in masks_to_keep\n                ],\n                \"pred_scores\": [\n                    current_outputs[\"pred_scores\"][i - 1] for i in masks_to_keep\n                ],\n                \"pred_masks\": [\n                    current_outputs[\"pred_masks\"][i - 1] for i in masks_to_keep\n                ],\n            }\n\n            rendered_final_output = visualize(final_outputs)\n            messages.append(\n                {\n                    \"role\": \"assistant\",\n                    \"content\": [{\"type\": \"text\", \"text\": generated_text}],\n                }\n            )\n\n            # Clean up debug files before successful return\n            cleanup_debug_files(debug, debug_folder_path, debug_jsonl_path)\n            return messages, final_outputs, rendered_final_output\n\n        elif tool_call[\"name\"] == \"report_no_mask\":\n            print(\"🔍 Calling report_no_mask tool...\")\n            height, width = cv2.imread(img_path).shape[:2]\n            final_outputs = {\n                \"original_image_path\": img_path,\n                \"orig_img_h\": height,\n                \"orig_img_w\": width,\n                \"pred_boxes\": [],\n                \"pred_scores\": [],\n                \"pred_masks\": [],\n            }\n            rendered_final_output = Image.open(img_path)\n            messages.append(\n                {\n                    \"role\": \"assistant\",\n                    \"content\": [{\"type\": \"text\", \"text\": generated_text}],\n                }\n            )\n            return messages, final_outputs, rendered_final_output\n\n        else:\n            raise ValueError(f\"Unknown tool call: {tool_call['name']}\")\n\n        # sometimes the MLLM don't know when to stop, and generates multiple tool calls in one round, so we need to split the generated text by </tool> and only keep the first one\n\n        for message in messages:\n            if message[\"role\"] == \"assistant\" and \"content\" in message:\n                for content in message[\"content\"]:\n                    if (\n                        isinstance(content, dict)\n                        and content.get(\"type\") == \"text\"\n                        and \"text\" in content\n                    ):\n                        content[\"text\"] = (\n                            content[\"text\"].split(\"</tool>\", 1)[0] + \"</tool>\\n\\n\"\n                        )\n        # Prune the messages history before the next MLLM generation round according to the 3-part rules.\n        # This keeps history compact and ensures the model sees only the allowed parts.\n        messages = _prune_messages_for_next_round(\n            messages,\n            USED_TEXT_PROMPTS,\n            LATEST_SAM3_TEXT_PROMPT,\n            img_path,\n            initial_text_prompt,\n        )\n        # make sure there can never be more than 2 images in the context\n        assert count_images(messages) <= 2\n        generation_count += 1\n        if generation_count > max_generations:\n            raise ValueError(\n                f\"Exceeded maximum number of allowed generation requests ({max_generations})\"\n            )\n\n        print(\"\\n\\n\")\n        print(\"-\" * 30 + f\" Round {str(generation_count + 1)}\" + \"-\" * 30)\n        print(\"\\n\\n\")\n        generated_text = send_generate_request(messages)\n        print(\n            f\"\\n>>> MLLM Response [start]\\n{generated_text}\\n<<< MLLM Response [end]\\n\"\n        )\n\n    print(\"\\n\\n>>> SAM 3 Agent execution ended.\\n\\n\")\n\n    error_save_path = os.path.join(\n        error_save_dir,\n        f\"{img_path.rsplit('/', 1)[-1].rsplit('.', 1)[0]}_error_history.json\",\n    )\n    with open(error_save_path, \"w\") as f:\n        json.dump(messages, f, indent=4)\n    print(\"Saved messages history that caused error to:\", error_save_path)\n    raise ValueError(\n        rf\"Generated text is None, which is unexpected. Please check the Qwen server and the input parameters for image path: {img_path} and initial text prompt: {initial_text_prompt}.\"\n    )\n"
  },
  {
    "path": "sam3/agent/client_llm.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n\nimport base64\nimport os\nfrom typing import Any, Optional\n\nfrom openai import OpenAI\n\n\ndef get_image_base64_and_mime(image_path):\n    \"\"\"Convert image file to base64 string and get MIME type\"\"\"\n    try:\n        # Get MIME type based on file extension\n        ext = os.path.splitext(image_path)[1].lower()\n        mime_types = {\n            \".jpg\": \"image/jpeg\",\n            \".jpeg\": \"image/jpeg\",\n            \".png\": \"image/png\",\n            \".gif\": \"image/gif\",\n            \".webp\": \"image/webp\",\n            \".bmp\": \"image/bmp\",\n        }\n        mime_type = mime_types.get(ext, \"image/jpeg\")  # Default to JPEG\n\n        # Convert image to base64\n        with open(image_path, \"rb\") as image_file:\n            base64_data = base64.b64encode(image_file.read()).decode(\"utf-8\")\n            return base64_data, mime_type\n    except Exception as e:\n        print(f\"Error converting image to base64: {e}\")\n        return None, None\n\n\ndef send_generate_request(\n    messages,\n    server_url=None,\n    model=\"meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8\",\n    api_key=None,\n    max_tokens=4096,\n):\n    \"\"\"\n    Sends a request to the OpenAI-compatible API endpoint using the OpenAI client library.\n\n    Args:\n        server_url (str): The base URL of the server, e.g. \"http://127.0.0.1:8000\"\n        messages (list): A list of message dicts, each containing role and content.\n        model (str): The model to use for generation (default: \"llama-4\")\n        max_tokens (int): Maximum number of tokens to generate (default: 4096)\n\n    Returns:\n        str: The generated response text from the server.\n    \"\"\"\n    # Process messages to convert image paths to base64\n    processed_messages = []\n    for message in messages:\n        processed_message = message.copy()\n        if message[\"role\"] == \"user\" and \"content\" in message:\n            processed_content = []\n            for c in message[\"content\"]:\n                if isinstance(c, dict) and c.get(\"type\") == \"image\":\n                    # Convert image path to base64 format\n                    image_path = c[\"image\"]\n\n                    print(\"image_path\", image_path)\n                    new_image_path = image_path.replace(\n                        \"?\", \"%3F\"\n                    )  # Escape ? in the path\n\n                    # Read the image file and convert to base64\n                    try:\n                        base64_image, mime_type = get_image_base64_and_mime(\n                            new_image_path\n                        )\n                        if base64_image is None:\n                            print(\n                                f\"Warning: Could not convert image to base64: {new_image_path}\"\n                            )\n                            continue\n\n                        # Create the proper image_url structure with base64 data\n                        processed_content.append(\n                            {\n                                \"type\": \"image_url\",\n                                \"image_url\": {\n                                    \"url\": f\"data:{mime_type};base64,{base64_image}\",\n                                    \"detail\": \"high\",\n                                },\n                            }\n                        )\n\n                    except FileNotFoundError:\n                        print(f\"Warning: Image file not found: {new_image_path}\")\n                        continue\n                    except Exception as e:\n                        print(f\"Warning: Error processing image {new_image_path}: {e}\")\n                        continue\n                else:\n                    processed_content.append(c)\n\n            processed_message[\"content\"] = processed_content\n        processed_messages.append(processed_message)\n\n    # Create OpenAI client with custom base URL\n    client = OpenAI(api_key=api_key, base_url=server_url)\n\n    try:\n        print(f\"🔍 Calling model {model}...\")\n        response = client.chat.completions.create(\n            model=model,\n            messages=processed_messages,\n            max_completion_tokens=max_tokens,\n            n=1,\n        )\n        # print(f\"Received response: {response.choices[0].message}\")\n\n        # Extract the response content\n        if response.choices and len(response.choices) > 0:\n            return response.choices[0].message.content\n        else:\n            print(f\"Unexpected response format: {response}\")\n            return None\n\n    except Exception as e:\n        print(f\"Request failed: {e}\")\n        return None\n\n\ndef send_direct_request(\n    llm: Any,\n    messages: list[dict[str, Any]],\n    sampling_params: Any,\n) -> Optional[str]:\n    \"\"\"\n    Run inference on a vLLM model instance directly without using a server.\n\n    Args:\n        llm: Initialized vLLM LLM instance (passed from external initialization)\n        messages: List of message dicts with role and content (OpenAI format)\n        sampling_params: vLLM SamplingParams instance (initialized externally)\n\n    Returns:\n        str: Generated response text, or None if inference fails\n    \"\"\"\n    try:\n        # Process messages to handle images (convert to base64 if needed)\n        processed_messages = []\n        for message in messages:\n            processed_message = message.copy()\n            if message[\"role\"] == \"user\" and \"content\" in message:\n                processed_content = []\n                for c in message[\"content\"]:\n                    if isinstance(c, dict) and c.get(\"type\") == \"image\":\n                        # Convert image path to base64 format\n                        image_path = c[\"image\"]\n                        new_image_path = image_path.replace(\"?\", \"%3F\")\n\n                        try:\n                            base64_image, mime_type = get_image_base64_and_mime(\n                                new_image_path\n                            )\n                            if base64_image is None:\n                                print(\n                                    f\"Warning: Could not convert image: {new_image_path}\"\n                                )\n                                continue\n\n                            # vLLM expects image_url format\n                            processed_content.append(\n                                {\n                                    \"type\": \"image_url\",\n                                    \"image_url\": {\n                                        \"url\": f\"data:{mime_type};base64,{base64_image}\"\n                                    },\n                                }\n                            )\n                        except Exception as e:\n                            print(\n                                f\"Warning: Error processing image {new_image_path}: {e}\"\n                            )\n                            continue\n                    else:\n                        processed_content.append(c)\n\n                processed_message[\"content\"] = processed_content\n            processed_messages.append(processed_message)\n\n        print(\"🔍 Running direct inference with vLLM...\")\n\n        # Run inference using vLLM's chat interface\n        outputs = llm.chat(\n            messages=processed_messages,\n            sampling_params=sampling_params,\n        )\n\n        # Extract the generated text from the first output\n        if outputs and len(outputs) > 0:\n            generated_text = outputs[0].outputs[0].text\n            return generated_text\n        else:\n            print(f\"Unexpected output format: {outputs}\")\n            return None\n\n    except Exception as e:\n        print(f\"Direct inference failed: {e}\")\n        return None\n"
  },
  {
    "path": "sam3/agent/client_sam3.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n\nimport json\nimport os\n\nimport torch\nfrom PIL import Image\nfrom sam3.model.box_ops import box_xyxy_to_xywh\nfrom sam3.train.masks_ops import rle_encode\n\nfrom .helpers.mask_overlap_removal import remove_overlapping_masks\nfrom .viz import visualize\n\n\ndef sam3_inference(processor, image_path, text_prompt):\n    \"\"\"Run SAM 3 image inference with text prompts and format the outputs\"\"\"\n    image = Image.open(image_path)\n    orig_img_w, orig_img_h = image.size\n\n    # model inference\n    inference_state = processor.set_image(image)\n    inference_state = processor.set_text_prompt(\n        state=inference_state, prompt=text_prompt\n    )\n\n    # format and assemble outputs\n    pred_boxes_xyxy = torch.stack(\n        [\n            inference_state[\"boxes\"][:, 0] / orig_img_w,\n            inference_state[\"boxes\"][:, 1] / orig_img_h,\n            inference_state[\"boxes\"][:, 2] / orig_img_w,\n            inference_state[\"boxes\"][:, 3] / orig_img_h,\n        ],\n        dim=-1,\n    )  # normalized in range [0, 1]\n    pred_boxes_xywh = box_xyxy_to_xywh(pred_boxes_xyxy).tolist()\n    pred_masks = rle_encode(inference_state[\"masks\"].squeeze(1))\n    pred_masks = [m[\"counts\"] for m in pred_masks]\n    outputs = {\n        \"orig_img_h\": orig_img_h,\n        \"orig_img_w\": orig_img_w,\n        \"pred_boxes\": pred_boxes_xywh,\n        \"pred_masks\": pred_masks,\n        \"pred_scores\": inference_state[\"scores\"].tolist(),\n    }\n    return outputs\n\n\ndef call_sam_service(\n    sam3_processor,\n    image_path: str,\n    text_prompt: str,\n    output_folder_path: str = \"sam3_output\",\n):\n    \"\"\"\n    Loads an image, sends it with a text prompt to the service,\n    saves the results, and renders the visualization.\n    \"\"\"\n    print(f\"📞 Loading image '{image_path}' and sending with prompt '{text_prompt}'...\")\n\n    text_prompt_for_save_path = (\n        text_prompt.replace(\"/\", \"_\") if \"/\" in text_prompt else text_prompt\n    )\n\n    os.makedirs(\n        os.path.join(output_folder_path, image_path.replace(\"/\", \"-\")), exist_ok=True\n    )\n    output_json_path = os.path.join(\n        output_folder_path,\n        image_path.replace(\"/\", \"-\"),\n        rf\"{text_prompt_for_save_path}.json\",\n    )\n    output_image_path = os.path.join(\n        output_folder_path,\n        image_path.replace(\"/\", \"-\"),\n        rf\"{text_prompt_for_save_path}.png\",\n    )\n\n    try:\n        # Send the image and text prompt as a multipart/form-data request\n        serialized_response = sam3_inference(sam3_processor, image_path, text_prompt)\n\n        # 1. Prepare the response dictionary\n        serialized_response = remove_overlapping_masks(serialized_response)\n        serialized_response = {\n            \"original_image_path\": image_path,\n            \"output_image_path\": output_image_path,\n            **serialized_response,\n        }\n\n        # 2. Reorder predictions by scores (highest to lowest) if scores are available\n        if \"pred_scores\" in serialized_response and serialized_response[\"pred_scores\"]:\n            # Create indices sorted by scores in descending order\n            score_indices = sorted(\n                range(len(serialized_response[\"pred_scores\"])),\n                key=lambda i: serialized_response[\"pred_scores\"][i],\n                reverse=True,\n            )\n\n            # Reorder all three lists based on the sorted indices\n            serialized_response[\"pred_scores\"] = [\n                serialized_response[\"pred_scores\"][i] for i in score_indices\n            ]\n            serialized_response[\"pred_boxes\"] = [\n                serialized_response[\"pred_boxes\"][i] for i in score_indices\n            ]\n            serialized_response[\"pred_masks\"] = [\n                serialized_response[\"pred_masks\"][i] for i in score_indices\n            ]\n\n        # 3. Remove any invalid RLE masks that is too short (shorter than 5 characters)\n        valid_masks = []\n        valid_boxes = []\n        valid_scores = []\n        for i, rle in enumerate(serialized_response[\"pred_masks\"]):\n            if len(rle) > 4:\n                valid_masks.append(rle)\n                valid_boxes.append(serialized_response[\"pred_boxes\"][i])\n                valid_scores.append(serialized_response[\"pred_scores\"][i])\n        serialized_response[\"pred_masks\"] = valid_masks\n        serialized_response[\"pred_boxes\"] = valid_boxes\n        serialized_response[\"pred_scores\"] = valid_scores\n\n        with open(output_json_path, \"w\") as f:\n            json.dump(serialized_response, f, indent=4)\n        print(f\"✅ Raw JSON response saved to '{output_json_path}'\")\n\n        # 4. Render and save visualizations on the image and save it in the SAM3 output folder\n        print(\"🔍 Rendering visualizations on the image ...\")\n        viz_image = visualize(serialized_response)\n        os.makedirs(os.path.dirname(output_image_path), exist_ok=True)\n        viz_image.save(output_image_path)\n        print(\"✅ Saved visualization at:\", output_image_path)\n    except Exception as e:\n        print(f\"❌ Error calling service: {e}\")\n\n    return output_json_path\n"
  },
  {
    "path": "sam3/agent/helpers/__init__.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n"
  },
  {
    "path": "sam3/agent/helpers/boxes.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n\nimport math\nfrom enum import IntEnum, unique\nfrom typing import List, Tuple, Union\n\nimport numpy as np\nimport torch\nfrom torch import device\n\n_RawBoxType = Union[List[float], Tuple[float, ...], torch.Tensor, np.ndarray]\n\n\n@unique\nclass BoxMode(IntEnum):\n    \"\"\"\n    Enum of different ways to represent a box.\n    \"\"\"\n\n    XYXY_ABS = 0\n    \"\"\"\n    (x0, y0, x1, y1) in absolute floating points coordinates.\n    The coordinates in range [0, width or height].\n    \"\"\"\n    XYWH_ABS = 1\n    \"\"\"\n    (x0, y0, w, h) in absolute floating points coordinates.\n    \"\"\"\n    XYXY_REL = 2\n    \"\"\"\n    Not yet supported!\n    (x0, y0, x1, y1) in range [0, 1]. They are relative to the size of the image.\n    \"\"\"\n    XYWH_REL = 3\n    \"\"\"\n    Not yet supported!\n    (x0, y0, w, h) in range [0, 1]. They are relative to the size of the image.\n    \"\"\"\n    XYWHA_ABS = 4\n    \"\"\"\n    (xc, yc, w, h, a) in absolute floating points coordinates.\n    (xc, yc) is the center of the rotated box, and the angle a is in degrees ccw.\n    \"\"\"\n\n    @staticmethod\n    def convert(\n        box: _RawBoxType, from_mode: \"BoxMode\", to_mode: \"BoxMode\"\n    ) -> _RawBoxType:\n        \"\"\"\n        Args:\n            box: can be a k-tuple, k-list or an Nxk array/tensor, where k = 4 or 5\n            from_mode, to_mode (BoxMode)\n\n        Returns:\n            The converted box of the same type.\n        \"\"\"\n        if from_mode == to_mode:\n            return box\n\n        original_type = type(box)\n        is_numpy = isinstance(box, np.ndarray)\n        single_box = isinstance(box, (list, tuple))\n        if single_box:\n            assert len(box) == 4 or len(box) == 5, (\n                \"BoxMode.convert takes either a k-tuple/list or an Nxk array/tensor,\"\n                \" where k == 4 or 5\"\n            )\n            arr = torch.tensor(box)[None, :]\n        else:\n            # avoid modifying the input box\n            if is_numpy:\n                arr = torch.from_numpy(np.asarray(box)).clone()\n            else:\n                arr = box.clone()\n\n        assert to_mode not in [\n            BoxMode.XYXY_REL,\n            BoxMode.XYWH_REL,\n        ] and from_mode not in [\n            BoxMode.XYXY_REL,\n            BoxMode.XYWH_REL,\n        ], \"Relative mode not yet supported!\"\n\n        if from_mode == BoxMode.XYWHA_ABS and to_mode == BoxMode.XYXY_ABS:\n            assert arr.shape[-1] == 5, (\n                \"The last dimension of input shape must be 5 for XYWHA format\"\n            )\n            original_dtype = arr.dtype\n            arr = arr.double()\n\n            w = arr[:, 2]\n            h = arr[:, 3]\n            a = arr[:, 4]\n            c = torch.abs(torch.cos(a * math.pi / 180.0))\n            s = torch.abs(torch.sin(a * math.pi / 180.0))\n            # This basically computes the horizontal bounding rectangle of the rotated box\n            new_w = c * w + s * h\n            new_h = c * h + s * w\n\n            # convert center to top-left corner\n            arr[:, 0] -= new_w / 2.0\n            arr[:, 1] -= new_h / 2.0\n            # bottom-right corner\n            arr[:, 2] = arr[:, 0] + new_w\n            arr[:, 3] = arr[:, 1] + new_h\n\n            arr = arr[:, :4].to(dtype=original_dtype)\n        elif from_mode == BoxMode.XYWH_ABS and to_mode == BoxMode.XYWHA_ABS:\n            original_dtype = arr.dtype\n            arr = arr.double()\n            arr[:, 0] += arr[:, 2] / 2.0\n            arr[:, 1] += arr[:, 3] / 2.0\n            angles = torch.zeros((arr.shape[0], 1), dtype=arr.dtype)\n            arr = torch.cat((arr, angles), axis=1).to(dtype=original_dtype)\n        else:\n            if to_mode == BoxMode.XYXY_ABS and from_mode == BoxMode.XYWH_ABS:\n                arr[:, 2] += arr[:, 0]\n                arr[:, 3] += arr[:, 1]\n            elif from_mode == BoxMode.XYXY_ABS and to_mode == BoxMode.XYWH_ABS:\n                arr[:, 2] -= arr[:, 0]\n                arr[:, 3] -= arr[:, 1]\n            else:\n                raise NotImplementedError(\n                    \"Conversion from BoxMode {} to {} is not supported yet\".format(\n                        from_mode, to_mode\n                    )\n                )\n\n        if single_box:\n            return original_type(arr.flatten().tolist())\n        if is_numpy:\n            return arr.numpy()\n        else:\n            return arr\n\n\nclass Boxes:\n    \"\"\"\n    This structure stores a list of boxes as a Nx4 torch.Tensor.\n    It supports some common methods about boxes\n    (`area`, `clip`, `nonempty`, etc),\n    and also behaves like a Tensor\n    (support indexing, `to(device)`, `.device`, and iteration over all boxes)\n\n    Attributes:\n        tensor (torch.Tensor): float matrix of Nx4. Each row is (x1, y1, x2, y2).\n    \"\"\"\n\n    def __init__(self, tensor: torch.Tensor):\n        \"\"\"\n        Args:\n            tensor (Tensor[float]): a Nx4 matrix.  Each row is (x1, y1, x2, y2).\n        \"\"\"\n        if not isinstance(tensor, torch.Tensor):\n            tensor = torch.as_tensor(\n                tensor, dtype=torch.float32, device=torch.device(\"cpu\")\n            )\n        else:\n            tensor = tensor.to(torch.float32)\n        if tensor.numel() == 0:\n            # Use reshape, so we don't end up creating a new tensor that does not depend on\n            # the inputs (and consequently confuses jit)\n            tensor = tensor.reshape((-1, 4)).to(dtype=torch.float32)\n        assert tensor.dim() == 2 and tensor.size(-1) == 4, tensor.size()\n\n        self.tensor = tensor\n\n    def clone(self) -> \"Boxes\":\n        \"\"\"\n        Clone the Boxes.\n\n        Returns:\n            Boxes\n        \"\"\"\n        return Boxes(self.tensor.clone())\n\n    def to(self, device: torch.device):\n        # Boxes are assumed float32 and does not support to(dtype)\n        return Boxes(self.tensor.to(device=device))\n\n    def area(self) -> torch.Tensor:\n        \"\"\"\n        Computes the area of all the boxes.\n\n        Returns:\n            torch.Tensor: a vector with areas of each box.\n        \"\"\"\n        box = self.tensor\n        area = (box[:, 2] - box[:, 0]) * (box[:, 3] - box[:, 1])\n        return area\n\n    def clip(self, box_size: Tuple[int, int]) -> None:\n        \"\"\"\n        Clip (in place) the boxes by limiting x coordinates to the range [0, width]\n        and y coordinates to the range [0, height].\n\n        Args:\n            box_size (height, width): The clipping box's size.\n        \"\"\"\n        assert torch.isfinite(self.tensor).all(), \"Box tensor contains infinite or NaN!\"\n        h, w = box_size\n        x1 = self.tensor[:, 0].clamp(min=0, max=w)\n        y1 = self.tensor[:, 1].clamp(min=0, max=h)\n        x2 = self.tensor[:, 2].clamp(min=0, max=w)\n        y2 = self.tensor[:, 3].clamp(min=0, max=h)\n        self.tensor = torch.stack((x1, y1, x2, y2), dim=-1)\n\n    def nonempty(self, threshold: float = 0.0) -> torch.Tensor:\n        \"\"\"\n        Find boxes that are non-empty.\n        A box is considered empty, if either of its side is no larger than threshold.\n\n        Returns:\n            Tensor:\n                a binary vector which represents whether each box is empty\n                (False) or non-empty (True).\n        \"\"\"\n        box = self.tensor\n        widths = box[:, 2] - box[:, 0]\n        heights = box[:, 3] - box[:, 1]\n        keep = (widths > threshold) & (heights > threshold)\n        return keep\n\n    def __getitem__(self, item) -> \"Boxes\":\n        \"\"\"\n        Args:\n            item: int, slice, or a BoolTensor\n\n        Returns:\n            Boxes: Create a new :class:`Boxes` by indexing.\n\n        The following usage are allowed:\n\n        1. `new_boxes = boxes[3]`: return a `Boxes` which contains only one box.\n        2. `new_boxes = boxes[2:10]`: return a slice of boxes.\n        3. `new_boxes = boxes[vector]`, where vector is a torch.BoolTensor\n           with `length = len(boxes)`. Nonzero elements in the vector will be selected.\n\n        Note that the returned Boxes might share storage with this Boxes,\n        subject to Pytorch's indexing semantics.\n        \"\"\"\n        if isinstance(item, int):\n            return Boxes(self.tensor[item].view(1, -1))\n        b = self.tensor[item]\n        assert b.dim() == 2, (\n            \"Indexing on Boxes with {} failed to return a matrix!\".format(item)\n        )\n        return Boxes(b)\n\n    def __len__(self) -> int:\n        return self.tensor.shape[0]\n\n    def __repr__(self) -> str:\n        return \"Boxes(\" + str(self.tensor) + \")\"\n\n    def inside_box(\n        self, box_size: Tuple[int, int], boundary_threshold: int = 0\n    ) -> torch.Tensor:\n        \"\"\"\n        Args:\n            box_size (height, width): Size of the reference box.\n            boundary_threshold (int): Boxes that extend beyond the reference box\n                boundary by more than boundary_threshold are considered \"outside\".\n\n        Returns:\n            a binary vector, indicating whether each box is inside the reference box.\n        \"\"\"\n        height, width = box_size\n        inds_inside = (\n            (self.tensor[..., 0] >= -boundary_threshold)\n            & (self.tensor[..., 1] >= -boundary_threshold)\n            & (self.tensor[..., 2] < width + boundary_threshold)\n            & (self.tensor[..., 3] < height + boundary_threshold)\n        )\n        return inds_inside\n\n    def get_centers(self) -> torch.Tensor:\n        \"\"\"\n        Returns:\n            The box centers in a Nx2 array of (x, y).\n        \"\"\"\n        return (self.tensor[:, :2] + self.tensor[:, 2:]) / 2\n\n    def scale(self, scale_x: float, scale_y: float) -> None:\n        \"\"\"\n        Scale the box with horizontal and vertical scaling factors\n        \"\"\"\n        self.tensor[:, 0::2] *= scale_x\n        self.tensor[:, 1::2] *= scale_y\n\n    @classmethod\n    def cat(cls, boxes_list: List[\"Boxes\"]) -> \"Boxes\":\n        \"\"\"\n        Concatenates a list of Boxes into a single Boxes\n\n        Arguments:\n            boxes_list (list[Boxes])\n\n        Returns:\n            Boxes: the concatenated Boxes\n        \"\"\"\n        assert isinstance(boxes_list, (list, tuple))\n        if len(boxes_list) == 0:\n            return cls(torch.empty(0))\n        assert all([isinstance(box, Boxes) for box in boxes_list])\n\n        # use torch.cat (v.s. layers.cat) so the returned boxes never share storage with input\n        cat_boxes = cls(torch.cat([b.tensor for b in boxes_list], dim=0))\n        return cat_boxes\n\n    @property\n    def device(self) -> device:\n        return self.tensor.device\n\n    # type \"Iterator[torch.Tensor]\", yield, and iter() not supported by torchscript\n    # https://github.com/pytorch/pytorch/issues/18627\n    @torch.jit.unused\n    def __iter__(self):\n        \"\"\"\n        Yield a box as a Tensor of shape (4,) at a time.\n        \"\"\"\n        yield from self.tensor\n\n\ndef pairwise_intersection(boxes1: Boxes, boxes2: Boxes) -> torch.Tensor:\n    \"\"\"\n    Given two lists of boxes of size N and M,\n    compute the intersection area between __all__ N x M pairs of boxes.\n    The box order must be (xmin, ymin, xmax, ymax)\n\n    Args:\n        boxes1,boxes2 (Boxes): two `Boxes`. Contains N & M boxes, respectively.\n\n    Returns:\n        Tensor: intersection, sized [N,M].\n    \"\"\"\n    boxes1, boxes2 = boxes1.tensor, boxes2.tensor\n    width_height = torch.min(boxes1[:, None, 2:], boxes2[:, 2:]) - torch.max(\n        boxes1[:, None, :2], boxes2[:, :2]\n    )  # [N,M,2]\n\n    width_height.clamp_(min=0)  # [N,M,2]\n    intersection = width_height.prod(dim=2)  # [N,M]\n    return intersection\n\n\n# implementation from https://github.com/kuangliu/torchcv/blob/master/torchcv/utils/box.py\n# with slight modifications\ndef pairwise_iou(boxes1: Boxes, boxes2: Boxes) -> torch.Tensor:\n    \"\"\"\n    Given two lists of boxes of size N and M, compute the IoU\n    (intersection over union) between **all** N x M pairs of boxes.\n    The box order must be (xmin, ymin, xmax, ymax).\n\n    Args:\n        boxes1,boxes2 (Boxes): two `Boxes`. Contains N & M boxes, respectively.\n\n    Returns:\n        Tensor: IoU, sized [N,M].\n    \"\"\"\n    area1 = boxes1.area()  # [N]\n    area2 = boxes2.area()  # [M]\n    inter = pairwise_intersection(boxes1, boxes2)\n\n    # handle empty boxes\n    iou = torch.where(\n        inter > 0,\n        inter / (area1[:, None] + area2 - inter),\n        torch.zeros(1, dtype=inter.dtype, device=inter.device),\n    )\n    return iou\n\n\ndef pairwise_ioa(boxes1: Boxes, boxes2: Boxes) -> torch.Tensor:\n    \"\"\"\n    Similar to :func:`pariwise_iou` but compute the IoA (intersection over boxes2 area).\n\n    Args:\n        boxes1,boxes2 (Boxes): two `Boxes`. Contains N & M boxes, respectively.\n\n    Returns:\n        Tensor: IoA, sized [N,M].\n    \"\"\"\n    area2 = boxes2.area()  # [M]\n    inter = pairwise_intersection(boxes1, boxes2)\n\n    # handle empty boxes\n    ioa = torch.where(\n        inter > 0, inter / area2, torch.zeros(1, dtype=inter.dtype, device=inter.device)\n    )\n    return ioa\n\n\ndef pairwise_point_box_distance(points: torch.Tensor, boxes: Boxes):\n    \"\"\"\n    Pairwise distance between N points and M boxes. The distance between a\n    point and a box is represented by the distance from the point to 4 edges\n    of the box. Distances are all positive when the point is inside the box.\n\n    Args:\n        points: Nx2 coordinates. Each row is (x, y)\n        boxes: M boxes\n\n    Returns:\n        Tensor: distances of size (N, M, 4). The 4 values are distances from\n            the point to the left, top, right, bottom of the box.\n    \"\"\"\n    x, y = points.unsqueeze(dim=2).unbind(dim=1)  # (N, 1)\n    x0, y0, x1, y1 = boxes.tensor.unsqueeze(dim=0).unbind(dim=2)  # (1, M)\n    return torch.stack([x - x0, y - y0, x1 - x, y1 - y], dim=2)\n\n\ndef matched_pairwise_iou(boxes1: Boxes, boxes2: Boxes) -> torch.Tensor:\n    \"\"\"\n    Compute pairwise intersection over union (IOU) of two sets of matched\n    boxes that have the same number of boxes.\n    Similar to :func:`pairwise_iou`, but computes only diagonal elements of the matrix.\n\n    Args:\n        boxes1 (Boxes): bounding boxes, sized [N,4].\n        boxes2 (Boxes): same length as boxes1\n    Returns:\n        Tensor: iou, sized [N].\n    \"\"\"\n    assert len(boxes1) == len(boxes2), (\n        \"boxlists should have the samenumber of entries, got {}, {}\".format(\n            len(boxes1), len(boxes2)\n        )\n    )\n    area1 = boxes1.area()  # [N]\n    area2 = boxes2.area()  # [N]\n    box1, box2 = boxes1.tensor, boxes2.tensor\n    lt = torch.max(box1[:, :2], box2[:, :2])  # [N,2]\n    rb = torch.min(box1[:, 2:], box2[:, 2:])  # [N,2]\n    wh = (rb - lt).clamp(min=0)  # [N,2]\n    inter = wh[:, 0] * wh[:, 1]  # [N]\n    iou = inter / (area1 + area2 - inter)  # [N]\n    return iou\n"
  },
  {
    "path": "sam3/agent/helpers/color_map.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n\n\"\"\"\nAn awesome colormap for really neat visualizations.\nCopied from Detectron, and removed gray colors.\n\"\"\"\n\nimport random\n\nimport numpy as np\n\n__all__ = [\"colormap\", \"random_color\", \"random_colors\"]\n\n\n# A list of 25 bright and sharp colors for segmentation masks,\n# generated from the edges of the sRGB color space for maximum intensity.\n_COLORS = (\n    np.array(\n        [\n            # The original 8 sharp colors\n            1.000,\n            1.000,\n            0.000,  # 1. Yellow\n            0.000,\n            1.000,\n            0.000,  # 2. Lime\n            0.000,\n            1.000,\n            1.000,  # 3. Cyan\n            1.000,\n            0.000,\n            1.000,  # 4. Magenta\n            1.000,\n            0.000,\n            0.000,  # 5. Red\n            1.000,\n            0.498,\n            0.000,  # 6. Orange\n            0.498,\n            1.000,\n            0.000,  # 7. Chartreuse\n            0.000,\n            1.000,\n            0.498,  # 8. Spring Green\n            1.000,\n            0.000,\n            0.498,  # 9. Rose\n            0.498,\n            0.000,\n            1.000,  # 10. Violet\n            0.753,\n            1.000,\n            0.000,  # 11. Electric Lime\n            1.000,\n            0.753,\n            0.000,  # 12. Vivid Orange\n            0.000,\n            1.000,\n            0.753,  # 13. Turquoise\n            0.753,\n            0.000,\n            1.000,  # 14. Bright Violet\n            1.000,\n            0.000,\n            0.753,  # 15. Bright Pink\n            1.000,\n            0.251,\n            0.000,  # 16. Fiery Orange\n            0.251,\n            1.000,\n            0.000,  # 17. Bright Chartreuse\n            0.000,\n            1.000,\n            0.251,  # 18. Malachite Green\n            0.251,\n            0.000,\n            1.000,  # 19. Deep Violet\n            1.000,\n            0.000,\n            0.251,  # 20. Hot Pink\n        ]\n    )\n    .astype(np.float32)\n    .reshape(-1, 3)\n)\n\n\ndef colormap(rgb=False, maximum=255):\n    \"\"\"\n    Args:\n        rgb (bool): whether to return RGB colors or BGR colors.\n        maximum (int): either 255 or 1\n\n    Returns:\n        ndarray: a float32 array of Nx3 colors, in range [0, 255] or [0, 1]\n    \"\"\"\n    assert maximum in [255, 1], maximum\n    c = _COLORS * maximum\n    if not rgb:\n        c = c[:, ::-1]\n    return c\n\n\ndef random_color(rgb=False, maximum=255):\n    \"\"\"\n    Args:\n        rgb (bool): whether to return RGB colors or BGR colors.\n        maximum (int): either 255 or 1\n\n    Returns:\n        ndarray: a vector of 3 numbers\n    \"\"\"\n    idx = np.random.randint(0, len(_COLORS))\n    ret = _COLORS[idx] * maximum\n    if not rgb:\n        ret = ret[::-1]\n    return ret\n\n\ndef random_colors(N, rgb=False, maximum=255):\n    \"\"\"\n    Args:\n        N (int): number of unique colors needed\n        rgb (bool): whether to return RGB colors or BGR colors.\n        maximum (int): either 255 or 1\n\n    Returns:\n        ndarray: a list of random_color\n    \"\"\"\n    indices = random.sample(range(len(_COLORS)), N)\n    ret = [_COLORS[i] * maximum for i in indices]\n    if not rgb:\n        ret = [x[::-1] for x in ret]\n    return ret\n\n\nif __name__ == \"__main__\":\n    import cv2\n\n    size = 100\n    H, W = 10, 10\n    canvas = np.random.rand(H * size, W * size, 3).astype(\"float32\")\n    for h in range(H):\n        for w in range(W):\n            idx = h * W + w\n            if idx >= len(_COLORS):\n                break\n            canvas[h * size : (h + 1) * size, w * size : (w + 1) * size] = _COLORS[idx]\n    cv2.imshow(\"a\", canvas)\n    cv2.waitKey(0)\n"
  },
  {
    "path": "sam3/agent/helpers/keypoints.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n\nfrom typing import Any, List, Tuple, Union\n\nimport numpy as np\nimport torch\nfrom torch.nn import functional as F\n\n\nclass Keypoints:\n    \"\"\"\n    Stores keypoint **annotation** data. GT Instances have a `gt_keypoints` property\n    containing the x,y location and visibility flag of each keypoint. This tensor has shape\n    (N, K, 3) where N is the number of instances and K is the number of keypoints per instance.\n\n    The visibility flag follows the COCO format and must be one of three integers:\n\n    * v=0: not labeled (in which case x=y=0)\n    * v=1: labeled but not visible\n    * v=2: labeled and visible\n    \"\"\"\n\n    def __init__(self, keypoints: Union[torch.Tensor, np.ndarray, List[List[float]]]):\n        \"\"\"\n        Arguments:\n            keypoints: A Tensor, numpy array, or list of the x, y, and visibility of each keypoint.\n                The shape should be (N, K, 3) where N is the number of\n                instances, and K is the number of keypoints per instance.\n        \"\"\"\n        device = (\n            keypoints.device\n            if isinstance(keypoints, torch.Tensor)\n            else torch.device(\"cpu\")\n        )\n        keypoints = torch.as_tensor(keypoints, dtype=torch.float32, device=device)\n        assert keypoints.dim() == 3 and keypoints.shape[2] == 3, keypoints.shape\n        self.tensor = keypoints\n\n    def __len__(self) -> int:\n        return self.tensor.size(0)\n\n    def to(self, *args: Any, **kwargs: Any) -> \"Keypoints\":\n        return type(self)(self.tensor.to(*args, **kwargs))\n\n    @property\n    def device(self) -> torch.device:\n        return self.tensor.device\n\n    def to_heatmap(self, boxes: torch.Tensor, heatmap_size: int) -> torch.Tensor:\n        \"\"\"\n        Convert keypoint annotations to a heatmap of one-hot labels for training,\n        as described in :paper:`Mask R-CNN`.\n\n        Arguments:\n            boxes: Nx4 tensor, the boxes to draw the keypoints to\n\n        Returns:\n            heatmaps:\n                A tensor of shape (N, K), each element is integer spatial label\n                in the range [0, heatmap_size**2 - 1] for each keypoint in the input.\n            valid:\n                A tensor of shape (N, K) containing whether each keypoint is in the roi or not.\n        \"\"\"\n        return _keypoints_to_heatmap(self.tensor, boxes, heatmap_size)\n\n    def __getitem__(self, item: Union[int, slice, torch.BoolTensor]) -> \"Keypoints\":\n        \"\"\"\n        Create a new `Keypoints` by indexing on this `Keypoints`.\n\n        The following usage are allowed:\n\n        1. `new_kpts = kpts[3]`: return a `Keypoints` which contains only one instance.\n        2. `new_kpts = kpts[2:10]`: return a slice of key points.\n        3. `new_kpts = kpts[vector]`, where vector is a torch.ByteTensor\n           with `length = len(kpts)`. Nonzero elements in the vector will be selected.\n\n        Note that the returned Keypoints might share storage with this Keypoints,\n        subject to Pytorch's indexing semantics.\n        \"\"\"\n        if isinstance(item, int):\n            return Keypoints([self.tensor[item]])\n        return Keypoints(self.tensor[item])\n\n    def __repr__(self) -> str:\n        s = self.__class__.__name__ + \"(\"\n        s += \"num_instances={})\".format(len(self.tensor))\n        return s\n\n    @staticmethod\n    def cat(keypoints_list: List[\"Keypoints\"]) -> \"Keypoints\":\n        \"\"\"\n        Concatenates a list of Keypoints into a single Keypoints\n\n        Arguments:\n            keypoints_list (list[Keypoints])\n\n        Returns:\n            Keypoints: the concatenated Keypoints\n        \"\"\"\n        assert isinstance(keypoints_list, (list, tuple))\n        assert len(keypoints_list) > 0\n        assert all(isinstance(keypoints, Keypoints) for keypoints in keypoints_list)\n\n        cat_kpts = type(keypoints_list[0])(\n            torch.cat([kpts.tensor for kpts in keypoints_list], dim=0)\n        )\n        return cat_kpts\n\n\ndef _keypoints_to_heatmap(\n    keypoints: torch.Tensor, rois: torch.Tensor, heatmap_size: int\n) -> Tuple[torch.Tensor, torch.Tensor]:\n    \"\"\"\n    Encode keypoint locations into a target heatmap for use in SoftmaxWithLoss across space.\n\n    Maps keypoints from the half-open interval [x1, x2) on continuous image coordinates to the\n    closed interval [0, heatmap_size - 1] on discrete image coordinates. We use the\n    continuous-discrete conversion from Heckbert 1990 (\"What is the coordinate of a pixel?\"):\n    d = floor(c) and c = d + 0.5, where d is a discrete coordinate and c is a continuous coordinate.\n\n    Arguments:\n        keypoints: tensor of keypoint locations in of shape (N, K, 3).\n        rois: Nx4 tensor of rois in xyxy format\n        heatmap_size: integer side length of square heatmap.\n\n    Returns:\n        heatmaps: A tensor of shape (N, K) containing an integer spatial label\n            in the range [0, heatmap_size**2 - 1] for each keypoint in the input.\n        valid: A tensor of shape (N, K) containing whether each keypoint is in\n            the roi or not.\n    \"\"\"\n\n    if rois.numel() == 0:\n        return rois.new().long(), rois.new().long()\n    offset_x = rois[:, 0]\n    offset_y = rois[:, 1]\n    scale_x = heatmap_size / (rois[:, 2] - rois[:, 0])\n    scale_y = heatmap_size / (rois[:, 3] - rois[:, 1])\n\n    offset_x = offset_x[:, None]\n    offset_y = offset_y[:, None]\n    scale_x = scale_x[:, None]\n    scale_y = scale_y[:, None]\n\n    x = keypoints[..., 0]\n    y = keypoints[..., 1]\n\n    x_boundary_inds = x == rois[:, 2][:, None]\n    y_boundary_inds = y == rois[:, 3][:, None]\n\n    x = (x - offset_x) * scale_x\n    x = x.floor().long()\n    y = (y - offset_y) * scale_y\n    y = y.floor().long()\n\n    x[x_boundary_inds] = heatmap_size - 1\n    y[y_boundary_inds] = heatmap_size - 1\n\n    valid_loc = (x >= 0) & (y >= 0) & (x < heatmap_size) & (y < heatmap_size)\n    vis = keypoints[..., 2] > 0\n    valid = (valid_loc & vis).long()\n\n    lin_ind = y * heatmap_size + x\n    heatmaps = lin_ind * valid\n\n    return heatmaps, valid\n\n\n@torch.jit.script_if_tracing\ndef heatmaps_to_keypoints(maps: torch.Tensor, rois: torch.Tensor) -> torch.Tensor:\n    \"\"\"\n    Extract predicted keypoint locations from heatmaps.\n\n    Args:\n        maps (Tensor): (#ROIs, #keypoints, POOL_H, POOL_W). The predicted heatmap of logits for\n            each ROI and each keypoint.\n        rois (Tensor): (#ROIs, 4). The box of each ROI.\n\n    Returns:\n        Tensor of shape (#ROIs, #keypoints, 4) with the last dimension corresponding to\n        (x, y, logit, score) for each keypoint.\n\n    When converting discrete pixel indices in an NxN image to a continuous keypoint coordinate,\n    we maintain consistency with :meth:`Keypoints.to_heatmap` by using the conversion from\n    Heckbert 1990: c = d + 0.5, where d is a discrete coordinate and c is a continuous coordinate.\n    \"\"\"\n\n    offset_x = rois[:, 0]\n    offset_y = rois[:, 1]\n\n    widths = (rois[:, 2] - rois[:, 0]).clamp(min=1)\n    heights = (rois[:, 3] - rois[:, 1]).clamp(min=1)\n    widths_ceil = widths.ceil()\n    heights_ceil = heights.ceil()\n\n    num_rois, num_keypoints = maps.shape[:2]\n    xy_preds = maps.new_zeros(rois.shape[0], num_keypoints, 4)\n\n    width_corrections = widths / widths_ceil\n    height_corrections = heights / heights_ceil\n\n    keypoints_idx = torch.arange(num_keypoints, device=maps.device)\n\n    for i in range(num_rois):\n        outsize = (int(heights_ceil[i]), int(widths_ceil[i]))\n        roi_map = F.interpolate(\n            maps[[i]], size=outsize, mode=\"bicubic\", align_corners=False\n        )\n\n        # Although semantically equivalent, `reshape` is used instead of `squeeze` due\n        # to limitation during ONNX export of `squeeze` in scripting mode\n        roi_map = roi_map.reshape(roi_map.shape[1:])  # keypoints x H x W\n\n        # softmax over the spatial region\n        max_score, _ = roi_map.view(num_keypoints, -1).max(1)\n        max_score = max_score.view(num_keypoints, 1, 1)\n        tmp_full_resolution = (roi_map - max_score).exp_()\n        tmp_pool_resolution = (maps[i] - max_score).exp_()\n        # Produce scores over the region H x W, but normalize with POOL_H x POOL_W,\n        # so that the scores of objects of different absolute sizes will be more comparable\n        roi_map_scores = tmp_full_resolution / tmp_pool_resolution.sum(\n            (1, 2), keepdim=True\n        )\n\n        w = roi_map.shape[2]\n        pos = roi_map.view(num_keypoints, -1).argmax(1)\n\n        x_int = pos % w\n        y_int = (pos - x_int) // w\n\n        assert (\n            roi_map_scores[keypoints_idx, y_int, x_int]\n            == roi_map_scores.view(num_keypoints, -1).max(1)[0]\n        ).all()\n\n        x = (x_int.float() + 0.5) * width_corrections[i]\n        y = (y_int.float() + 0.5) * height_corrections[i]\n\n        xy_preds[i, :, 0] = x + offset_x[i]\n        xy_preds[i, :, 1] = y + offset_y[i]\n        xy_preds[i, :, 2] = roi_map[keypoints_idx, y_int, x_int]\n        xy_preds[i, :, 3] = roi_map_scores[keypoints_idx, y_int, x_int]\n\n    return xy_preds\n"
  },
  {
    "path": "sam3/agent/helpers/mask_overlap_removal.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n\nfrom typing import Dict, List\n\nimport numpy as np\nimport torch\n\ntry:\n    from pycocotools import mask as mask_utils\nexcept Exception:\n    mask_utils = None\n\n\ndef mask_intersection(\n    masks1: torch.Tensor, masks2: torch.Tensor, block_size: int = 16\n) -> torch.Tensor:\n    assert masks1.shape[1:] == masks2.shape[1:]\n    assert masks1.dtype == torch.bool and masks2.dtype == torch.bool\n    N, M = masks1.shape[0], masks2.shape[0]\n    out = torch.zeros(N, M, device=masks1.device, dtype=torch.long)\n    for i in range(0, N, block_size):\n        for j in range(0, M, block_size):\n            a = masks1[i : i + block_size]\n            b = masks2[j : j + block_size]\n            inter = (a[:, None] & b[None, :]).flatten(-2).sum(-1)\n            out[i : i + block_size, j : j + block_size] = inter\n    return out\n\n\ndef mask_iom(masks1: torch.Tensor, masks2: torch.Tensor) -> torch.Tensor:\n    assert masks1.shape[1:] == masks2.shape[1:]\n    assert masks1.dtype == torch.bool and masks2.dtype == torch.bool\n    inter = mask_intersection(masks1, masks2)\n    area1 = masks1.flatten(-2).sum(-1)  # (N,)\n    area2 = masks2.flatten(-2).sum(-1)  # (M,)\n    min_area = torch.min(area1[:, None], area2[None, :]).clamp_min(1)\n    return inter.float() / (min_area.float() + 1e-8)\n\n\ndef _decode_single_mask(mask_repr, h: int, w: int) -> np.ndarray:\n    if isinstance(mask_repr, (list, tuple, np.ndarray)):\n        arr = np.array(mask_repr)\n        if arr.ndim != 2:\n            raise ValueError(\"Mask array must be 2D (H, W).\")\n        return (arr > 0).astype(np.uint8)\n\n    if mask_utils is None:\n        raise ImportError(\n            \"pycocotools is required to decode RLE mask strings. pip install pycocotools\"\n        )\n\n    if not isinstance(mask_repr, (str, bytes)):\n        raise ValueError(\"Unsupported mask representation type for RLE decode.\")\n\n    rle = {\n        \"counts\": mask_repr if isinstance(mask_repr, (str, bytes)) else str(mask_repr),\n        \"size\": [h, w],\n    }\n    decoded = mask_utils.decode(rle)\n    if decoded.ndim == 3:\n        decoded = decoded[:, :, 0]\n    return (decoded > 0).astype(np.uint8)\n\n\ndef _decode_masks_to_torch_bool(pred_masks: List, h: int, w: int) -> torch.Tensor:\n    bin_masks = [_decode_single_mask(m, h, w) for m in pred_masks]\n    masks_np = np.stack(bin_masks, axis=0).astype(np.uint8)  # (N, H, W)\n    return torch.from_numpy(masks_np > 0)\n\n\ndef remove_overlapping_masks(sample: Dict, iom_thresh: float = 0.3) -> Dict:\n    \"\"\"\n    Greedy keep: sort by score desc; keep a mask if IoM to all kept masks <= threshold.\n    If pred_masks has length 0 or 1, returns sample unchanged (no extra keys).\n    \"\"\"\n    # Basic presence checks\n    if \"pred_masks\" not in sample or not isinstance(sample[\"pred_masks\"], list):\n        return sample  # nothing to do / preserve as-is\n\n    pred_masks = sample[\"pred_masks\"]\n    N = len(pred_masks)\n\n    # --- Early exit: 0 or 1 mask -> do NOT modify the JSON at all ---\n    if N <= 1:\n        return sample\n\n    # From here on we have at least 2 masks\n    h = int(sample[\"orig_img_h\"])\n    w = int(sample[\"orig_img_w\"])\n    pred_scores = sample.get(\"pred_scores\", [1.0] * N)  # fallback if scores missing\n    pred_boxes = sample.get(\"pred_boxes\", None)\n\n    assert N == len(pred_scores), \"pred_masks and pred_scores must have same length\"\n    if pred_boxes is not None:\n        assert N == len(pred_boxes), \"pred_masks and pred_boxes must have same length\"\n\n    masks_bool = _decode_masks_to_torch_bool(pred_masks, h, w)  # (N, H, W)\n\n    order = sorted(range(N), key=lambda i: float(pred_scores[i]), reverse=True)\n    kept_idx: List[int] = []\n    kept_masks: List[torch.Tensor] = []\n\n    for i in order:\n        cand = masks_bool[i].unsqueeze(0)  # (1, H, W)\n        if len(kept_masks) == 0:\n            kept_idx.append(i)\n            kept_masks.append(masks_bool[i])\n            continue\n\n        kept_stack = torch.stack(kept_masks, dim=0)  # (K, H, W)\n        iom_vals = mask_iom(cand, kept_stack).squeeze(0)  # (K,)\n        if torch.any(iom_vals > iom_thresh):\n            continue  # overlaps too much with a higher-scored kept mask\n        kept_idx.append(i)\n        kept_masks.append(masks_bool[i])\n\n    kept_idx_sorted = sorted(kept_idx)\n\n    # Build filtered JSON (this *does* modify fields; only for N>=2 case)\n    out = dict(sample)\n    out[\"pred_masks\"] = [pred_masks[i] for i in kept_idx_sorted]\n    out[\"pred_scores\"] = [pred_scores[i] for i in kept_idx_sorted]\n    if pred_boxes is not None:\n        out[\"pred_boxes\"] = [pred_boxes[i] for i in kept_idx_sorted]\n    out[\"kept_indices\"] = kept_idx_sorted\n    out[\"removed_indices\"] = [i for i in range(N) if i not in set(kept_idx_sorted)]\n    out[\"iom_threshold\"] = float(iom_thresh)\n    return out\n"
  },
  {
    "path": "sam3/agent/helpers/masks.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n\nimport copy\nimport itertools\nfrom typing import Any, Iterator, List, Union\n\nimport numpy as np\nimport pycocotools.mask as mask_util\nimport torch\nfrom torch import device\n\nfrom .boxes import Boxes\nfrom .memory import retry_if_cuda_oom\nfrom .roi_align import ROIAlign\n\n\ndef polygon_area(x, y):\n    # Using the shoelace formula\n    # https://stackoverflow.com/questions/24467972/calculate-area-of-polygon-given-x-y-coordinates\n    return 0.5 * np.abs(np.dot(x, np.roll(y, 1)) - np.dot(y, np.roll(x, 1)))\n\n\ndef polygons_to_bitmask(\n    polygons: List[np.ndarray], height: int, width: int\n) -> np.ndarray:\n    \"\"\"\n    Args:\n        polygons (list[ndarray]): each array has shape (Nx2,)\n        height, width (int)\n\n    Returns:\n        ndarray: a bool mask of shape (height, width)\n    \"\"\"\n    if len(polygons) == 0:\n        # COCOAPI does not support empty polygons\n        return np.zeros((height, width)).astype(bool)\n    rles = mask_util.frPyObjects(polygons, height, width)\n    rle = mask_util.merge(rles)\n    return mask_util.decode(rle).astype(bool)\n\n\ndef rasterize_polygons_within_box(\n    polygons: List[np.ndarray], box: np.ndarray, mask_size: int\n) -> torch.Tensor:\n    \"\"\"\n    Rasterize the polygons into a mask image and\n    crop the mask content in the given box.\n    The cropped mask is resized to (mask_size, mask_size).\n\n    This function is used when generating training targets for mask head in Mask R-CNN.\n    Given original ground-truth masks for an image, new ground-truth mask\n    training targets in the size of `mask_size x mask_size`\n    must be provided for each predicted box. This function will be called to\n    produce such targets.\n\n    Args:\n        polygons (list[ndarray[float]]): a list of polygons, which represents an instance.\n        box: 4-element numpy array\n        mask_size (int):\n\n    Returns:\n        Tensor: BoolTensor of shape (mask_size, mask_size)\n    \"\"\"\n    # 1. Shift the polygons w.r.t the boxes\n    w, h = box[2] - box[0], box[3] - box[1]\n\n    polygons = copy.deepcopy(polygons)\n    for p in polygons:\n        p[0::2] = p[0::2] - box[0]\n        p[1::2] = p[1::2] - box[1]\n\n    # 2. Rescale the polygons to the new box size\n    # max() to avoid division by small number\n    ratio_h = mask_size / max(h, 0.1)\n    ratio_w = mask_size / max(w, 0.1)\n\n    if ratio_h == ratio_w:\n        for p in polygons:\n            p *= ratio_h\n    else:\n        for p in polygons:\n            p[0::2] *= ratio_w\n            p[1::2] *= ratio_h\n\n    # 3. Rasterize the polygons with coco api\n    mask = polygons_to_bitmask(polygons, mask_size, mask_size)\n    mask = torch.from_numpy(mask)\n    return mask\n\n\nclass BitMasks:\n    \"\"\"\n    This class stores the segmentation masks for all objects in one image, in\n    the form of bitmaps.\n\n    Attributes:\n        tensor: bool Tensor of N,H,W, representing N instances in the image.\n    \"\"\"\n\n    def __init__(self, tensor: Union[torch.Tensor, np.ndarray]):\n        \"\"\"\n        Args:\n            tensor: bool Tensor of N,H,W, representing N instances in the image.\n        \"\"\"\n        if isinstance(tensor, torch.Tensor):\n            tensor = tensor.to(torch.bool)\n        else:\n            tensor = torch.as_tensor(\n                tensor, dtype=torch.bool, device=torch.device(\"cpu\")\n            )\n        assert tensor.dim() == 3, tensor.size()\n        self.image_size = tensor.shape[1:]\n        self.tensor = tensor\n\n    @torch.jit.unused\n    def to(self, *args: Any, **kwargs: Any) -> \"BitMasks\":\n        return BitMasks(self.tensor.to(*args, **kwargs))\n\n    @property\n    def device(self) -> torch.device:\n        return self.tensor.device\n\n    @torch.jit.unused\n    def __getitem__(self, item: Union[int, slice, torch.BoolTensor]) -> \"BitMasks\":\n        \"\"\"\n        Returns:\n            BitMasks: Create a new :class:`BitMasks` by indexing.\n\n        The following usage are allowed:\n\n        1. `new_masks = masks[3]`: return a `BitMasks` which contains only one mask.\n        2. `new_masks = masks[2:10]`: return a slice of masks.\n        3. `new_masks = masks[vector]`, where vector is a torch.BoolTensor\n           with `length = len(masks)`. Nonzero elements in the vector will be selected.\n\n        Note that the returned object might share storage with this object,\n        subject to Pytorch's indexing semantics.\n        \"\"\"\n        if isinstance(item, int):\n            return BitMasks(self.tensor[item].unsqueeze(0))\n        m = self.tensor[item]\n        assert m.dim() == 3, (\n            \"Indexing on BitMasks with {} returns a tensor with shape {}!\".format(\n                item, m.shape\n            )\n        )\n        return BitMasks(m)\n\n    @torch.jit.unused\n    def __iter__(self) -> torch.Tensor:\n        yield from self.tensor\n\n    @torch.jit.unused\n    def __repr__(self) -> str:\n        s = self.__class__.__name__ + \"(\"\n        s += \"num_instances={})\".format(len(self.tensor))\n        return s\n\n    def __len__(self) -> int:\n        return self.tensor.shape[0]\n\n    def nonempty(self) -> torch.Tensor:\n        \"\"\"\n        Find masks that are non-empty.\n\n        Returns:\n            Tensor: a BoolTensor which represents\n                whether each mask is empty (False) or non-empty (True).\n        \"\"\"\n        return self.tensor.flatten(1).any(dim=1)\n\n    @staticmethod\n    def from_polygon_masks(\n        polygon_masks: Union[\"PolygonMasks\", List[List[np.ndarray]]],\n        height: int,\n        width: int,\n    ) -> \"BitMasks\":\n        \"\"\"\n        Args:\n            polygon_masks (list[list[ndarray]] or PolygonMasks)\n            height, width (int)\n        \"\"\"\n        if isinstance(polygon_masks, PolygonMasks):\n            polygon_masks = polygon_masks.polygons\n        masks = [polygons_to_bitmask(p, height, width) for p in polygon_masks]\n        if len(masks):\n            return BitMasks(torch.stack([torch.from_numpy(x) for x in masks]))\n        else:\n            return BitMasks(torch.empty(0, height, width, dtype=torch.bool))\n\n    @staticmethod\n    def from_roi_masks(roi_masks: \"ROIMasks\", height: int, width: int) -> \"BitMasks\":\n        \"\"\"\n        Args:\n            roi_masks:\n            height, width (int):\n        \"\"\"\n        return roi_masks.to_bitmasks(height, width)\n\n    def crop_and_resize(self, boxes: torch.Tensor, mask_size: int) -> torch.Tensor:\n        \"\"\"\n        Crop each bitmask by the given box, and resize results to (mask_size, mask_size).\n        This can be used to prepare training targets for Mask R-CNN.\n        It has less reconstruction error compared to rasterization with polygons.\n        However we observe no difference in accuracy,\n        but BitMasks requires more memory to store all the masks.\n\n        Args:\n            boxes (Tensor): Nx4 tensor storing the boxes for each mask\n            mask_size (int): the size of the rasterized mask.\n\n        Returns:\n            Tensor:\n                A bool tensor of shape (N, mask_size, mask_size), where\n                N is the number of predicted boxes for this image.\n        \"\"\"\n        assert len(boxes) == len(self), \"{} != {}\".format(len(boxes), len(self))\n        device = self.tensor.device\n\n        batch_inds = torch.arange(len(boxes), device=device).to(dtype=boxes.dtype)[\n            :, None\n        ]\n        rois = torch.cat([batch_inds, boxes], dim=1)  # Nx5\n\n        bit_masks = self.tensor.to(dtype=torch.float32)\n        rois = rois.to(device=device)\n        output = (\n            ROIAlign((mask_size, mask_size), 1.0, 0, aligned=True)\n            .forward(bit_masks[:, None, :, :], rois)\n            .squeeze(1)\n        )\n        output = output >= 0.5\n        return output\n\n    def get_bounding_boxes(self) -> Boxes:\n        \"\"\"\n        Returns:\n            Boxes: tight bounding boxes around bitmasks.\n            If a mask is empty, it's bounding box will be all zero.\n        \"\"\"\n        boxes = torch.zeros(self.tensor.shape[0], 4, dtype=torch.float32)\n        x_any = torch.any(self.tensor, dim=1)\n        y_any = torch.any(self.tensor, dim=2)\n        for idx in range(self.tensor.shape[0]):\n            x = torch.where(x_any[idx, :])[0]\n            y = torch.where(y_any[idx, :])[0]\n            if len(x) > 0 and len(y) > 0:\n                boxes[idx, :] = torch.as_tensor(\n                    [x[0], y[0], x[-1] + 1, y[-1] + 1], dtype=torch.float32\n                )\n        return Boxes(boxes)\n\n    @staticmethod\n    def cat(bitmasks_list: List[\"BitMasks\"]) -> \"BitMasks\":\n        \"\"\"\n        Concatenates a list of BitMasks into a single BitMasks\n\n        Arguments:\n            bitmasks_list (list[BitMasks])\n\n        Returns:\n            BitMasks: the concatenated BitMasks\n        \"\"\"\n        assert isinstance(bitmasks_list, (list, tuple))\n        assert len(bitmasks_list) > 0\n        assert all(isinstance(bitmask, BitMasks) for bitmask in bitmasks_list)\n\n        cat_bitmasks = type(bitmasks_list[0])(\n            torch.cat([bm.tensor for bm in bitmasks_list], dim=0)\n        )\n        return cat_bitmasks\n\n\nclass PolygonMasks:\n    \"\"\"\n    This class stores the segmentation masks for all objects in one image, in the form of polygons.\n\n    Attributes:\n        polygons: list[list[ndarray]]. Each ndarray is a float64 vector representing a polygon.\n    \"\"\"\n\n    def __init__(self, polygons: List[List[Union[torch.Tensor, np.ndarray]]]):\n        \"\"\"\n        Arguments:\n            polygons (list[list[np.ndarray]]): The first\n                level of the list correspond to individual instances,\n                the second level to all the polygons that compose the\n                instance, and the third level to the polygon coordinates.\n                The third level array should have the format of\n                [x0, y0, x1, y1, ..., xn, yn] (n >= 3).\n        \"\"\"\n        if not isinstance(polygons, list):\n            raise ValueError(\n                \"Cannot create PolygonMasks: Expect a list of list of polygons per image. \"\n                \"Got '{}' instead.\".format(type(polygons))\n            )\n\n        def _make_array(t: Union[torch.Tensor, np.ndarray]) -> np.ndarray:\n            # Use float64 for higher precision, because why not?\n            # Always put polygons on CPU (self.to is a no-op) since they\n            # are supposed to be small tensors.\n            # May need to change this assumption if GPU placement becomes useful\n            if isinstance(t, torch.Tensor):\n                t = t.cpu().numpy()\n            return np.asarray(t).astype(\"float64\")\n\n        def process_polygons(\n            polygons_per_instance: List[Union[torch.Tensor, np.ndarray]],\n        ) -> List[np.ndarray]:\n            if not isinstance(polygons_per_instance, list):\n                raise ValueError(\n                    \"Cannot create polygons: Expect a list of polygons per instance. \"\n                    \"Got '{}' instead.\".format(type(polygons_per_instance))\n                )\n            # transform each polygon to a numpy array\n            polygons_per_instance = [_make_array(p) for p in polygons_per_instance]\n            for polygon in polygons_per_instance:\n                if len(polygon) % 2 != 0 or len(polygon) < 6:\n                    raise ValueError(\n                        f\"Cannot create a polygon from {len(polygon)} coordinates.\"\n                    )\n            return polygons_per_instance\n\n        self.polygons: List[List[np.ndarray]] = [\n            process_polygons(polygons_per_instance)\n            for polygons_per_instance in polygons\n        ]\n\n    def to(self, *args: Any, **kwargs: Any) -> \"PolygonMasks\":\n        return self\n\n    @property\n    def device(self) -> torch.device:\n        return torch.device(\"cpu\")\n\n    def get_bounding_boxes(self) -> Boxes:\n        \"\"\"\n        Returns:\n            Boxes: tight bounding boxes around polygon masks.\n        \"\"\"\n        boxes = torch.zeros(len(self.polygons), 4, dtype=torch.float32)\n        for idx, polygons_per_instance in enumerate(self.polygons):\n            minxy = torch.as_tensor([float(\"inf\"), float(\"inf\")], dtype=torch.float32)\n            maxxy = torch.zeros(2, dtype=torch.float32)\n            for polygon in polygons_per_instance:\n                coords = torch.from_numpy(polygon).view(-1, 2).to(dtype=torch.float32)\n                minxy = torch.min(minxy, torch.min(coords, dim=0).values)\n                maxxy = torch.max(maxxy, torch.max(coords, dim=0).values)\n            boxes[idx, :2] = minxy\n            boxes[idx, 2:] = maxxy\n        return Boxes(boxes)\n\n    def nonempty(self) -> torch.Tensor:\n        \"\"\"\n        Find masks that are non-empty.\n\n        Returns:\n            Tensor:\n                a BoolTensor which represents whether each mask is empty (False) or not (True).\n        \"\"\"\n        keep = [1 if len(polygon) > 0 else 0 for polygon in self.polygons]\n        return torch.from_numpy(np.asarray(keep, dtype=bool))\n\n    def __getitem__(\n        self, item: Union[int, slice, List[int], torch.BoolTensor]\n    ) -> \"PolygonMasks\":\n        \"\"\"\n        Support indexing over the instances and return a `PolygonMasks` object.\n        `item` can be:\n\n        1. An integer. It will return an object with only one instance.\n        2. A slice. It will return an object with the selected instances.\n        3. A list[int]. It will return an object with the selected instances,\n           correpsonding to the indices in the list.\n        4. A vector mask of type BoolTensor, whose length is num_instances.\n           It will return an object with the instances whose mask is nonzero.\n        \"\"\"\n        if isinstance(item, int):\n            selected_polygons = [self.polygons[item]]\n        elif isinstance(item, slice):\n            selected_polygons = self.polygons[item]\n        elif isinstance(item, list):\n            selected_polygons = [self.polygons[i] for i in item]\n        elif isinstance(item, torch.Tensor):\n            # Polygons is a list, so we have to move the indices back to CPU.\n            if item.dtype == torch.bool:\n                assert item.dim() == 1, item.shape\n                item = item.nonzero().squeeze(1).cpu().numpy().tolist()\n            elif item.dtype in [torch.int32, torch.int64]:\n                item = item.cpu().numpy().tolist()\n            else:\n                raise ValueError(\n                    \"Unsupported tensor dtype={} for indexing!\".format(item.dtype)\n                )\n            selected_polygons = [self.polygons[i] for i in item]\n        return PolygonMasks(selected_polygons)\n\n    def __iter__(self) -> Iterator[List[np.ndarray]]:\n        \"\"\"\n        Yields:\n            list[ndarray]: the polygons for one instance.\n            Each Tensor is a float64 vector representing a polygon.\n        \"\"\"\n        return iter(self.polygons)\n\n    def __repr__(self) -> str:\n        s = self.__class__.__name__ + \"(\"\n        s += \"num_instances={})\".format(len(self.polygons))\n        return s\n\n    def __len__(self) -> int:\n        return len(self.polygons)\n\n    def crop_and_resize(self, boxes: torch.Tensor, mask_size: int) -> torch.Tensor:\n        \"\"\"\n        Crop each mask by the given box, and resize results to (mask_size, mask_size).\n        This can be used to prepare training targets for Mask R-CNN.\n\n        Args:\n            boxes (Tensor): Nx4 tensor storing the boxes for each mask\n            mask_size (int): the size of the rasterized mask.\n\n        Returns:\n            Tensor: A bool tensor of shape (N, mask_size, mask_size), where\n            N is the number of predicted boxes for this image.\n        \"\"\"\n        assert len(boxes) == len(self), \"{} != {}\".format(len(boxes), len(self))\n\n        device = boxes.device\n        # Put boxes on the CPU, as the polygon representation is not efficient GPU-wise\n        # (several small tensors for representing a single instance mask)\n        boxes = boxes.to(torch.device(\"cpu\"))\n\n        results = [\n            rasterize_polygons_within_box(poly, box.numpy(), mask_size)\n            for poly, box in zip(self.polygons, boxes)\n        ]\n        \"\"\"\n        poly: list[list[float]], the polygons for one instance\n        box: a tensor of shape (4,)\n        \"\"\"\n        if len(results) == 0:\n            return torch.empty(0, mask_size, mask_size, dtype=torch.bool, device=device)\n        return torch.stack(results, dim=0).to(device=device)\n\n    def area(self):\n        \"\"\"\n        Computes area of the mask.\n        Only works with Polygons, using the shoelace formula:\n        https://stackoverflow.com/questions/24467972/calculate-area-of-polygon-given-x-y-coordinates\n\n        Returns:\n            Tensor: a vector, area for each instance\n        \"\"\"\n\n        area = []\n        for polygons_per_instance in self.polygons:\n            area_per_instance = 0\n            for p in polygons_per_instance:\n                area_per_instance += polygon_area(p[0::2], p[1::2])\n            area.append(area_per_instance)\n\n        return torch.tensor(area)\n\n    @staticmethod\n    def cat(polymasks_list: List[\"PolygonMasks\"]) -> \"PolygonMasks\":\n        \"\"\"\n        Concatenates a list of PolygonMasks into a single PolygonMasks\n\n        Arguments:\n            polymasks_list (list[PolygonMasks])\n\n        Returns:\n            PolygonMasks: the concatenated PolygonMasks\n        \"\"\"\n        assert isinstance(polymasks_list, (list, tuple))\n        assert len(polymasks_list) > 0\n        assert all(isinstance(polymask, PolygonMasks) for polymask in polymasks_list)\n\n        cat_polymasks = type(polymasks_list[0])(\n            list(itertools.chain.from_iterable(pm.polygons for pm in polymasks_list))\n        )\n        return cat_polymasks\n\n\nclass ROIMasks:\n    \"\"\"\n    Represent masks by N smaller masks defined in some ROIs. Once ROI boxes are given,\n    full-image bitmask can be obtained by \"pasting\" the mask on the region defined\n    by the corresponding ROI box.\n    \"\"\"\n\n    def __init__(self, tensor: torch.Tensor):\n        \"\"\"\n        Args:\n            tensor: (N, M, M) mask tensor that defines the mask within each ROI.\n        \"\"\"\n        if tensor.dim() != 3:\n            raise ValueError(\"ROIMasks must take a masks of 3 dimension.\")\n        self.tensor = tensor\n\n    def to(self, device: torch.device) -> \"ROIMasks\":\n        return ROIMasks(self.tensor.to(device))\n\n    @property\n    def device(self) -> device:\n        return self.tensor.device\n\n    def __len__(self):\n        return self.tensor.shape[0]\n\n    def __getitem__(self, item) -> \"ROIMasks\":\n        \"\"\"\n        Returns:\n            ROIMasks: Create a new :class:`ROIMasks` by indexing.\n\n        The following usage are allowed:\n\n        1. `new_masks = masks[2:10]`: return a slice of masks.\n        2. `new_masks = masks[vector]`, where vector is a torch.BoolTensor\n           with `length = len(masks)`. Nonzero elements in the vector will be selected.\n\n        Note that the returned object might share storage with this object,\n        subject to Pytorch's indexing semantics.\n        \"\"\"\n        t = self.tensor[item]\n        if t.dim() != 3:\n            raise ValueError(\n                f\"Indexing on ROIMasks with {item} returns a tensor with shape {t.shape}!\"\n            )\n        return ROIMasks(t)\n\n    @torch.jit.unused\n    def __repr__(self) -> str:\n        s = self.__class__.__name__ + \"(\"\n        s += \"num_instances={})\".format(len(self.tensor))\n        return s\n\n    @torch.jit.unused\n    def to_bitmasks(self, boxes: torch.Tensor, height, width, threshold=0.5):\n        \"\"\"\n        Args: see documentation of :func:`paste_masks_in_image`.\n        \"\"\"\n        from detectron2.layers.mask_ops import (\n            _paste_masks_tensor_shape,\n            paste_masks_in_image,\n        )\n\n        if torch.jit.is_tracing():\n            if isinstance(height, torch.Tensor):\n                paste_func = _paste_masks_tensor_shape\n            else:\n                paste_func = paste_masks_in_image\n        else:\n            paste_func = retry_if_cuda_oom(paste_masks_in_image)\n        bitmasks = paste_func(\n            self.tensor, boxes.tensor, (height, width), threshold=threshold\n        )\n        return BitMasks(bitmasks)\n"
  },
  {
    "path": "sam3/agent/helpers/memory.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n\nimport logging\nfrom contextlib import contextmanager\nfrom functools import wraps\n\nimport torch\n\n__all__ = [\"retry_if_cuda_oom\"]\n\n\n@contextmanager\ndef _ignore_torch_cuda_oom():\n    \"\"\"\n    A context which ignores CUDA OOM exception from pytorch.\n    \"\"\"\n    try:\n        yield\n    except RuntimeError as e:\n        # NOTE: the string may change?\n        if \"CUDA out of memory. \" in str(e):\n            pass\n        else:\n            raise\n\n\ndef retry_if_cuda_oom(func):\n    \"\"\"\n    Makes a function retry itself after encountering\n    pytorch's CUDA OOM error.\n    It will first retry after calling `torch.cuda.empty_cache()`.\n\n    If that still fails, it will then retry by trying to convert inputs to CPUs.\n    In this case, it expects the function to dispatch to CPU implementation.\n    The return values may become CPU tensors as well and it's user's\n    responsibility to convert it back to CUDA tensor if needed.\n\n    Args:\n        func: a stateless callable that takes tensor-like objects as arguments\n\n    Returns:\n        a callable which retries `func` if OOM is encountered.\n\n    Examples:\n    ::\n        output = retry_if_cuda_oom(some_torch_function)(input1, input2)\n        # output may be on CPU even if inputs are on GPU\n\n    Note:\n        1. When converting inputs to CPU, it will only look at each argument and check\n           if it has `.device` and `.to` for conversion. Nested structures of tensors\n           are not supported.\n\n        2. Since the function might be called more than once, it has to be\n           stateless.\n    \"\"\"\n\n    def maybe_to_cpu(x):\n        try:\n            like_gpu_tensor = x.device.type == \"cuda\" and hasattr(x, \"to\")\n        except AttributeError:\n            like_gpu_tensor = False\n        if like_gpu_tensor:\n            return x.to(device=\"cpu\")\n        else:\n            return x\n\n    @wraps(func)\n    def wrapped(*args, **kwargs):\n        with _ignore_torch_cuda_oom():\n            return func(*args, **kwargs)\n\n        # Clear cache and retry\n        torch.cuda.empty_cache()\n        with _ignore_torch_cuda_oom():\n            return func(*args, **kwargs)\n\n        # Try on CPU. This slows down the code significantly, therefore print a notice.\n        logger = logging.getLogger(__name__)\n        logger.info(\n            \"Attempting to copy inputs of {} to CPU due to CUDA OOM\".format(str(func))\n        )\n        new_args = (maybe_to_cpu(x) for x in args)\n        new_kwargs = {k: maybe_to_cpu(v) for k, v in kwargs.items()}\n        return func(*new_args, **new_kwargs)\n\n    return wrapped\n"
  },
  {
    "path": "sam3/agent/helpers/rle.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n\n\"\"\"Some utilities for RLE encoding that doesn't require downloading the masks to the cpu\"\"\"\n\nimport numpy as np\nimport torch\nfrom pycocotools import mask as mask_util\n\n\n@torch.no_grad()\ndef rle_encode(orig_mask, return_areas=False):\n    \"\"\"Encodes a collection of masks in RLE format\n\n    This function emulates the behavior of the COCO API's encode function, but\n    is executed partially on the GPU for faster execution.\n\n    Args:\n        mask (torch.Tensor): A mask of shape (N, H, W) with dtype=torch.bool\n        return_areas (bool): If True, add the areas of the masks as a part of\n            the RLE output dict under the \"area\" key. Default is False.\n\n    Returns:\n        str: The RLE encoded masks\n    \"\"\"\n    assert orig_mask.ndim == 3, \"Mask must be of shape (N, H, W)\"\n    assert orig_mask.dtype == torch.bool, \"Mask must have dtype=torch.bool\"\n\n    if orig_mask.numel() == 0:\n        return []\n\n    # First, transpose the spatial dimensions.\n    # This is necessary because the COCO API uses Fortran order\n    mask = orig_mask.transpose(1, 2)\n\n    # Flatten the mask\n    flat_mask = mask.reshape(mask.shape[0], -1)\n    if return_areas:\n        mask_areas = flat_mask.sum(-1).tolist()\n    # Find the indices where the mask changes\n    differences = torch.ones(\n        mask.shape[0], flat_mask.shape[1] + 1, device=mask.device, dtype=torch.bool\n    )\n    differences[:, 1:-1] = flat_mask[:, :-1] != flat_mask[:, 1:]\n    differences[:, 0] = flat_mask[:, 0]\n    _, change_indices = torch.where(differences)\n\n    try:\n        boundaries = torch.cumsum(differences.sum(-1), 0).cpu()\n    except RuntimeError as _:\n        boundaries = torch.cumsum(differences.cpu().sum(-1), 0)\n\n    change_indices_clone = change_indices.clone()\n    # First pass computes the RLEs on GPU, in a flatten format\n    for i in range(mask.shape[0]):\n        # Get the change indices for this batch item\n        beg = 0 if i == 0 else boundaries[i - 1].item()\n        end = boundaries[i].item()\n        change_indices[beg + 1 : end] -= change_indices_clone[beg : end - 1]\n\n    # Now we can split the RLES of each batch item, and convert them to strings\n    # No more gpu at this point\n    change_indices = change_indices.tolist()\n\n    batch_rles = []\n    # Process each mask in the batch separately\n    for i in range(mask.shape[0]):\n        beg = 0 if i == 0 else boundaries[i - 1].item()\n        end = boundaries[i].item()\n        run_lengths = change_indices[beg:end]\n\n        uncompressed_rle = {\"counts\": run_lengths, \"size\": list(orig_mask.shape[1:])}\n        h, w = uncompressed_rle[\"size\"]\n        rle = mask_util.frPyObjects(uncompressed_rle, h, w)\n        rle[\"counts\"] = rle[\"counts\"].decode(\"utf-8\")\n        if return_areas:\n            rle[\"area\"] = mask_areas[i]\n        batch_rles.append(rle)\n\n    return batch_rles\n\n\ndef robust_rle_encode(masks):\n    \"\"\"Encodes a collection of masks in RLE format. Uses the gpu version fist, falls back to the cpu version if it fails\"\"\"\n\n    assert masks.ndim == 3, \"Mask must be of shape (N, H, W)\"\n    assert masks.dtype == torch.bool, \"Mask must have dtype=torch.bool\"\n\n    try:\n        return rle_encode(masks)\n    except RuntimeError as _:\n        masks = masks.cpu().numpy()\n        rles = [\n            mask_util.encode(\n                np.array(mask[:, :, np.newaxis], dtype=np.uint8, order=\"F\")\n            )[0]\n            for mask in masks\n        ]\n        for rle in rles:\n            rle[\"counts\"] = rle[\"counts\"].decode(\"utf-8\")\n        return rles\n\n\ndef ann_to_rle(segm, im_info):\n    \"\"\"Convert annotation which can be polygons, uncompressed RLE to RLE.\n    Args:\n        ann (dict) : annotation object\n    Returns:\n        ann (rle)\n    \"\"\"\n    h, w = im_info[\"height\"], im_info[\"width\"]\n    if isinstance(segm, list):\n        # polygon -- a single object might consist of multiple parts\n        # we merge all parts into one mask rle code\n        rles = mask_util.frPyObjects(segm, h, w)\n        rle = mask_util.merge(rles)\n    elif isinstance(segm[\"counts\"], list):\n        # uncompressed RLE\n        rle = mask_util.frPyObjects(segm, h, w)\n    else:\n        # rle\n        rle = segm\n    return rle\n"
  },
  {
    "path": "sam3/agent/helpers/roi_align.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n\nfrom torch import nn\nfrom torchvision.ops import roi_align\n\n\n# NOTE: torchvision's RoIAlign has a different default aligned=False\nclass ROIAlign(nn.Module):\n    def __init__(self, output_size, spatial_scale, sampling_ratio, aligned=True):\n        \"\"\"\n        Args:\n            output_size (tuple): h, w\n            spatial_scale (float): scale the input boxes by this number\n            sampling_ratio (int): number of inputs samples to take for each output\n                sample. 0 to take samples densely.\n            aligned (bool): if False, use the legacy implementation in\n                Detectron. If True, align the results more perfectly.\n\n        Note:\n            The meaning of aligned=True:\n\n            Given a continuous coordinate c, its two neighboring pixel indices (in our\n            pixel model) are computed by floor(c - 0.5) and ceil(c - 0.5). For example,\n            c=1.3 has pixel neighbors with discrete indices [0] and [1] (which are sampled\n            from the underlying signal at continuous coordinates 0.5 and 1.5). But the original\n            roi_align (aligned=False) does not subtract the 0.5 when computing neighboring\n            pixel indices and therefore it uses pixels with a slightly incorrect alignment\n            (relative to our pixel model) when performing bilinear interpolation.\n\n            With `aligned=True`,\n            we first appropriately scale the ROI and then shift it by -0.5\n            prior to calling roi_align. This produces the correct neighbors; see\n            detectron2/tests/test_roi_align.py for verification.\n\n            The difference does not make a difference to the model's performance if\n            ROIAlign is used together with conv layers.\n        \"\"\"\n        super().__init__()\n        self.output_size = output_size\n        self.spatial_scale = spatial_scale\n        self.sampling_ratio = sampling_ratio\n        self.aligned = aligned\n\n        from torchvision import __version__\n\n        version = tuple(int(x) for x in __version__.split(\".\")[:2])\n        # https://github.com/pytorch/vision/pull/2438\n        assert version >= (0, 7), \"Require torchvision >= 0.7\"\n\n    def forward(self, input, rois):\n        \"\"\"\n        Args:\n            input: NCHW images\n            rois: Bx5 boxes. First column is the index into N. The other 4 columns are xyxy.\n        \"\"\"\n        assert rois.dim() == 2 and rois.size(1) == 5\n        if input.is_quantized:\n            input = input.dequantize()\n        return roi_align(\n            input,\n            rois.to(dtype=input.dtype),\n            self.output_size,\n            self.spatial_scale,\n            self.sampling_ratio,\n            self.aligned,\n        )\n\n    def __repr__(self):\n        tmpstr = self.__class__.__name__ + \"(\"\n        tmpstr += \"output_size=\" + str(self.output_size)\n        tmpstr += \", spatial_scale=\" + str(self.spatial_scale)\n        tmpstr += \", sampling_ratio=\" + str(self.sampling_ratio)\n        tmpstr += \", aligned=\" + str(self.aligned)\n        tmpstr += \")\"\n        return tmpstr\n"
  },
  {
    "path": "sam3/agent/helpers/rotated_boxes.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n\nfrom __future__ import absolute_import, division, print_function, unicode_literals\n\nimport math\nfrom typing import List, Tuple\n\nimport torch\n\n# from detectron2.layers.rotated_boxes import pairwise_iou_rotated\n\nfrom .boxes import Boxes\n\n\ndef pairwise_iou_rotated(boxes1, boxes2):\n    \"\"\"\n    Return intersection-over-union (Jaccard index) of boxes.\n\n    Both sets of boxes are expected to be in\n    (x_center, y_center, width, height, angle) format.\n\n    Arguments:\n        boxes1 (Tensor[N, 5])\n        boxes2 (Tensor[M, 5])\n\n    Returns:\n        iou (Tensor[N, M]): the NxM matrix containing the pairwise\n            IoU values for every element in boxes1 and boxes2\n    \"\"\"\n    return torch.ops.detectron2.box_iou_rotated(boxes1, boxes2)\n\n\nclass RotatedBoxes(Boxes):\n    \"\"\"\n    This structure stores a list of rotated boxes as a Nx5 torch.Tensor.\n    It supports some common methods about boxes\n    (`area`, `clip`, `nonempty`, etc),\n    and also behaves like a Tensor\n    (support indexing, `to(device)`, `.device`, and iteration over all boxes)\n    \"\"\"\n\n    def __init__(self, tensor: torch.Tensor):\n        \"\"\"\n        Args:\n            tensor (Tensor[float]): a Nx5 matrix.  Each row is\n                (x_center, y_center, width, height, angle),\n                in which angle is represented in degrees.\n                While there's no strict range restriction for it,\n                the recommended principal range is between [-180, 180) degrees.\n\n        Assume we have a horizontal box B = (x_center, y_center, width, height),\n        where width is along the x-axis and height is along the y-axis.\n        The rotated box B_rot (x_center, y_center, width, height, angle)\n        can be seen as:\n\n        1. When angle == 0:\n           B_rot == B\n        2. When angle > 0:\n           B_rot is obtained by rotating B w.r.t its center by :math:`|angle|` degrees CCW;\n        3. When angle < 0:\n           B_rot is obtained by rotating B w.r.t its center by :math:`|angle|` degrees CW.\n\n        Mathematically, since the right-handed coordinate system for image space\n        is (y, x), where y is top->down and x is left->right, the 4 vertices of the\n        rotated rectangle :math:`(yr_i, xr_i)` (i = 1, 2, 3, 4) can be obtained from\n        the vertices of the horizontal rectangle :math:`(y_i, x_i)` (i = 1, 2, 3, 4)\n        in the following way (:math:`\\\\theta = angle*\\\\pi/180` is the angle in radians,\n        :math:`(y_c, x_c)` is the center of the rectangle):\n\n        .. math::\n\n            yr_i = \\\\cos(\\\\theta) (y_i - y_c) - \\\\sin(\\\\theta) (x_i - x_c) + y_c,\n\n            xr_i = \\\\sin(\\\\theta) (y_i - y_c) + \\\\cos(\\\\theta) (x_i - x_c) + x_c,\n\n        which is the standard rigid-body rotation transformation.\n\n        Intuitively, the angle is\n        (1) the rotation angle from y-axis in image space\n        to the height vector (top->down in the box's local coordinate system)\n        of the box in CCW, and\n        (2) the rotation angle from x-axis in image space\n        to the width vector (left->right in the box's local coordinate system)\n        of the box in CCW.\n\n        More intuitively, consider the following horizontal box ABCD represented\n        in (x1, y1, x2, y2): (3, 2, 7, 4),\n        covering the [3, 7] x [2, 4] region of the continuous coordinate system\n        which looks like this:\n\n        .. code:: none\n\n            O--------> x\n            |\n            |  A---B\n            |  |   |\n            |  D---C\n            |\n            v y\n\n        Note that each capital letter represents one 0-dimensional geometric point\n        instead of a 'square pixel' here.\n\n        In the example above, using (x, y) to represent a point we have:\n\n        .. math::\n\n            O = (0, 0), A = (3, 2), B = (7, 2), C = (7, 4), D = (3, 4)\n\n        We name vector AB = vector DC as the width vector in box's local coordinate system, and\n        vector AD = vector BC as the height vector in box's local coordinate system. Initially,\n        when angle = 0 degree, they're aligned with the positive directions of x-axis and y-axis\n        in the image space, respectively.\n\n        For better illustration, we denote the center of the box as E,\n\n        .. code:: none\n\n            O--------> x\n            |\n            |  A---B\n            |  | E |\n            |  D---C\n            |\n            v y\n\n        where the center E = ((3+7)/2, (2+4)/2) = (5, 3).\n\n        Also,\n\n        .. math::\n\n            width = |AB| = |CD| = 7 - 3 = 4,\n            height = |AD| = |BC| = 4 - 2 = 2.\n\n        Therefore, the corresponding representation for the same shape in rotated box in\n        (x_center, y_center, width, height, angle) format is:\n\n        (5, 3, 4, 2, 0),\n\n        Now, let's consider (5, 3, 4, 2, 90), which is rotated by 90 degrees\n        CCW (counter-clockwise) by definition. It looks like this:\n\n        .. code:: none\n\n            O--------> x\n            |   B-C\n            |   | |\n            |   |E|\n            |   | |\n            |   A-D\n            v y\n\n        The center E is still located at the same point (5, 3), while the vertices\n        ABCD are rotated by 90 degrees CCW with regard to E:\n        A = (4, 5), B = (4, 1), C = (6, 1), D = (6, 5)\n\n        Here, 90 degrees can be seen as the CCW angle to rotate from y-axis to\n        vector AD or vector BC (the top->down height vector in box's local coordinate system),\n        or the CCW angle to rotate from x-axis to vector AB or vector DC (the left->right\n        width vector in box's local coordinate system).\n\n        .. math::\n\n            width = |AB| = |CD| = 5 - 1 = 4,\n            height = |AD| = |BC| = 6 - 4 = 2.\n\n        Next, how about (5, 3, 4, 2, -90), which is rotated by 90 degrees CW (clockwise)\n        by definition? It looks like this:\n\n        .. code:: none\n\n            O--------> x\n            |   D-A\n            |   | |\n            |   |E|\n            |   | |\n            |   C-B\n            v y\n\n        The center E is still located at the same point (5, 3), while the vertices\n        ABCD are rotated by 90 degrees CW with regard to E:\n        A = (6, 1), B = (6, 5), C = (4, 5), D = (4, 1)\n\n        .. math::\n\n            width = |AB| = |CD| = 5 - 1 = 4,\n            height = |AD| = |BC| = 6 - 4 = 2.\n\n        This covers exactly the same region as (5, 3, 4, 2, 90) does, and their IoU\n        will be 1. However, these two will generate different RoI Pooling results and\n        should not be treated as an identical box.\n\n        On the other hand, it's easy to see that (X, Y, W, H, A) is identical to\n        (X, Y, W, H, A+360N), for any integer N. For example (5, 3, 4, 2, 270) would be\n        identical to (5, 3, 4, 2, -90), because rotating the shape 270 degrees CCW is\n        equivalent to rotating the same shape 90 degrees CW.\n\n        We could rotate further to get (5, 3, 4, 2, 180), or (5, 3, 4, 2, -180):\n\n        .. code:: none\n\n            O--------> x\n            |\n            |  C---D\n            |  | E |\n            |  B---A\n            |\n            v y\n\n        .. math::\n\n            A = (7, 4), B = (3, 4), C = (3, 2), D = (7, 2),\n\n            width = |AB| = |CD| = 7 - 3 = 4,\n            height = |AD| = |BC| = 4 - 2 = 2.\n\n        Finally, this is a very inaccurate (heavily quantized) illustration of\n        how (5, 3, 4, 2, 60) looks like in case anyone wonders:\n\n        .. code:: none\n\n            O--------> x\n            |     B\\\n            |    /  C\n            |   /E /\n            |  A  /\n            |   `D\n            v y\n\n        It's still a rectangle with center of (5, 3), width of 4 and height of 2,\n        but its angle (and thus orientation) is somewhere between\n        (5, 3, 4, 2, 0) and (5, 3, 4, 2, 90).\n        \"\"\"\n        device = (\n            tensor.device if isinstance(tensor, torch.Tensor) else torch.device(\"cpu\")\n        )\n        tensor = torch.as_tensor(tensor, dtype=torch.float32, device=device)\n        if tensor.numel() == 0:\n            # Use reshape, so we don't end up creating a new tensor that does not depend on\n            # the inputs (and consequently confuses jit)\n            tensor = tensor.reshape((0, 5)).to(dtype=torch.float32, device=device)\n        assert tensor.dim() == 2 and tensor.size(-1) == 5, tensor.size()\n\n        self.tensor = tensor\n\n    def clone(self) -> \"RotatedBoxes\":\n        \"\"\"\n        Clone the RotatedBoxes.\n\n        Returns:\n            RotatedBoxes\n        \"\"\"\n        return RotatedBoxes(self.tensor.clone())\n\n    def to(self, device: torch.device, non_blocking: bool = False):\n        # Boxes are assumed float32 and does not support to(dtype)\n        return RotatedBoxes(self.tensor.to(device=device, non_blocking=non_blocking))\n\n    def area(self) -> torch.Tensor:\n        \"\"\"\n        Computes the area of all the boxes.\n\n        Returns:\n            torch.Tensor: a vector with areas of each box.\n        \"\"\"\n        box = self.tensor\n        area = box[:, 2] * box[:, 3]\n        return area\n\n    # Avoid in-place operations so that we can torchscript; NOTE: this creates a new tensor\n    def normalize_angles(self) -> None:\n        \"\"\"\n        Restrict angles to the range of [-180, 180) degrees\n        \"\"\"\n        angle_tensor = (self.tensor[:, 4] + 180.0) % 360.0 - 180.0\n        self.tensor = torch.cat((self.tensor[:, :4], angle_tensor[:, None]), dim=1)\n\n    def clip(\n        self, box_size: Tuple[int, int], clip_angle_threshold: float = 1.0\n    ) -> None:\n        \"\"\"\n        Clip (in place) the boxes by limiting x coordinates to the range [0, width]\n        and y coordinates to the range [0, height].\n\n        For RRPN:\n        Only clip boxes that are almost horizontal with a tolerance of\n        clip_angle_threshold to maintain backward compatibility.\n\n        Rotated boxes beyond this threshold are not clipped for two reasons:\n\n        1. There are potentially multiple ways to clip a rotated box to make it\n           fit within the image.\n        2. It's tricky to make the entire rectangular box fit within the image\n           and still be able to not leave out pixels of interest.\n\n        Therefore we rely on ops like RoIAlignRotated to safely handle this.\n\n        Args:\n            box_size (height, width): The clipping box's size.\n            clip_angle_threshold:\n                Iff. abs(normalized(angle)) <= clip_angle_threshold (in degrees),\n                we do the clipping as horizontal boxes.\n        \"\"\"\n        h, w = box_size\n\n        # normalize angles to be within (-180, 180] degrees\n        self.normalize_angles()\n\n        idx = torch.where(torch.abs(self.tensor[:, 4]) <= clip_angle_threshold)[0]\n\n        # convert to (x1, y1, x2, y2)\n        x1 = self.tensor[idx, 0] - self.tensor[idx, 2] / 2.0\n        y1 = self.tensor[idx, 1] - self.tensor[idx, 3] / 2.0\n        x2 = self.tensor[idx, 0] + self.tensor[idx, 2] / 2.0\n        y2 = self.tensor[idx, 1] + self.tensor[idx, 3] / 2.0\n\n        # clip\n        x1.clamp_(min=0, max=w)\n        y1.clamp_(min=0, max=h)\n        x2.clamp_(min=0, max=w)\n        y2.clamp_(min=0, max=h)\n\n        # convert back to (xc, yc, w, h)\n        self.tensor[idx, 0] = (x1 + x2) / 2.0\n        self.tensor[idx, 1] = (y1 + y2) / 2.0\n        # make sure widths and heights do not increase due to numerical errors\n        self.tensor[idx, 2] = torch.min(self.tensor[idx, 2], x2 - x1)\n        self.tensor[idx, 3] = torch.min(self.tensor[idx, 3], y2 - y1)\n\n    def nonempty(self, threshold: float = 0.0) -> torch.Tensor:\n        \"\"\"\n        Find boxes that are non-empty.\n        A box is considered empty, if either of its side is no larger than threshold.\n\n        Returns:\n            Tensor: a binary vector which represents\n            whether each box is empty (False) or non-empty (True).\n        \"\"\"\n        box = self.tensor\n        widths = box[:, 2]\n        heights = box[:, 3]\n        keep = (widths > threshold) & (heights > threshold)\n        return keep\n\n    def __getitem__(self, item) -> \"RotatedBoxes\":\n        \"\"\"\n        Returns:\n            RotatedBoxes: Create a new :class:`RotatedBoxes` by indexing.\n\n        The following usage are allowed:\n\n        1. `new_boxes = boxes[3]`: return a `RotatedBoxes` which contains only one box.\n        2. `new_boxes = boxes[2:10]`: return a slice of boxes.\n        3. `new_boxes = boxes[vector]`, where vector is a torch.ByteTensor\n           with `length = len(boxes)`. Nonzero elements in the vector will be selected.\n\n        Note that the returned RotatedBoxes might share storage with this RotatedBoxes,\n        subject to Pytorch's indexing semantics.\n        \"\"\"\n        if isinstance(item, int):\n            return RotatedBoxes(self.tensor[item].view(1, -1))\n        b = self.tensor[item]\n        assert b.dim() == 2, (\n            \"Indexing on RotatedBoxes with {} failed to return a matrix!\".format(item)\n        )\n        return RotatedBoxes(b)\n\n    def __len__(self) -> int:\n        return self.tensor.shape[0]\n\n    def __repr__(self) -> str:\n        return \"RotatedBoxes(\" + str(self.tensor) + \")\"\n\n    def inside_box(\n        self, box_size: Tuple[int, int], boundary_threshold: int = 0\n    ) -> torch.Tensor:\n        \"\"\"\n        Args:\n            box_size (height, width): Size of the reference box covering\n                [0, width] x [0, height]\n            boundary_threshold (int): Boxes that extend beyond the reference box\n                boundary by more than boundary_threshold are considered \"outside\".\n\n        For RRPN, it might not be necessary to call this function since it's common\n        for rotated box to extend to outside of the image boundaries\n        (the clip function only clips the near-horizontal boxes)\n\n        Returns:\n            a binary vector, indicating whether each box is inside the reference box.\n        \"\"\"\n        height, width = box_size\n\n        cnt_x = self.tensor[..., 0]\n        cnt_y = self.tensor[..., 1]\n        half_w = self.tensor[..., 2] / 2.0\n        half_h = self.tensor[..., 3] / 2.0\n        a = self.tensor[..., 4]\n        c = torch.abs(torch.cos(a * math.pi / 180.0))\n        s = torch.abs(torch.sin(a * math.pi / 180.0))\n        # This basically computes the horizontal bounding rectangle of the rotated box\n        max_rect_dx = c * half_w + s * half_h\n        max_rect_dy = c * half_h + s * half_w\n\n        inds_inside = (\n            (cnt_x - max_rect_dx >= -boundary_threshold)\n            & (cnt_y - max_rect_dy >= -boundary_threshold)\n            & (cnt_x + max_rect_dx < width + boundary_threshold)\n            & (cnt_y + max_rect_dy < height + boundary_threshold)\n        )\n\n        return inds_inside\n\n    def get_centers(self) -> torch.Tensor:\n        \"\"\"\n        Returns:\n            The box centers in a Nx2 array of (x, y).\n        \"\"\"\n        return self.tensor[:, :2]\n\n    def scale(self, scale_x: float, scale_y: float) -> None:\n        \"\"\"\n        Scale the rotated box with horizontal and vertical scaling factors\n        Note: when scale_factor_x != scale_factor_y,\n        the rotated box does not preserve the rectangular shape when the angle\n        is not a multiple of 90 degrees under resize transformation.\n        Instead, the shape is a parallelogram (that has skew)\n        Here we make an approximation by fitting a rotated rectangle to the parallelogram.\n        \"\"\"\n        self.tensor[:, 0] *= scale_x\n        self.tensor[:, 1] *= scale_y\n        theta = self.tensor[:, 4] * math.pi / 180.0\n        c = torch.cos(theta)\n        s = torch.sin(theta)\n\n        # In image space, y is top->down and x is left->right\n        # Consider the local coordintate system for the rotated box,\n        # where the box center is located at (0, 0), and the four vertices ABCD are\n        # A(-w / 2, -h / 2), B(w / 2, -h / 2), C(w / 2, h / 2), D(-w / 2, h / 2)\n        # the midpoint of the left edge AD of the rotated box E is:\n        # E = (A+D)/2 = (-w / 2, 0)\n        # the midpoint of the top edge AB of the rotated box F is:\n        # F(0, -h / 2)\n        # To get the old coordinates in the global system, apply the rotation transformation\n        # (Note: the right-handed coordinate system for image space is yOx):\n        # (old_x, old_y) = (s * y + c * x, c * y - s * x)\n        # E(old) = (s * 0 + c * (-w/2), c * 0 - s * (-w/2)) = (-c * w / 2, s * w / 2)\n        # F(old) = (s * (-h / 2) + c * 0, c * (-h / 2) - s * 0) = (-s * h / 2, -c * h / 2)\n        # After applying the scaling factor (sfx, sfy):\n        # E(new) = (-sfx * c * w / 2, sfy * s * w / 2)\n        # F(new) = (-sfx * s * h / 2, -sfy * c * h / 2)\n        # The new width after scaling tranformation becomes:\n\n        # w(new) = |E(new) - O| * 2\n        #        = sqrt[(sfx * c * w / 2)^2 + (sfy * s * w / 2)^2] * 2\n        #        = sqrt[(sfx * c)^2 + (sfy * s)^2] * w\n        # i.e., scale_factor_w = sqrt[(sfx * c)^2 + (sfy * s)^2]\n        #\n        # For example,\n        # when angle = 0 or 180, |c| = 1, s = 0, scale_factor_w == scale_factor_x;\n        # when |angle| = 90, c = 0, |s| = 1, scale_factor_w == scale_factor_y\n        self.tensor[:, 2] *= torch.sqrt((scale_x * c) ** 2 + (scale_y * s) ** 2)\n\n        # h(new) = |F(new) - O| * 2\n        #        = sqrt[(sfx * s * h / 2)^2 + (sfy * c * h / 2)^2] * 2\n        #        = sqrt[(sfx * s)^2 + (sfy * c)^2] * h\n        # i.e., scale_factor_h = sqrt[(sfx * s)^2 + (sfy * c)^2]\n        #\n        # For example,\n        # when angle = 0 or 180, |c| = 1, s = 0, scale_factor_h == scale_factor_y;\n        # when |angle| = 90, c = 0, |s| = 1, scale_factor_h == scale_factor_x\n        self.tensor[:, 3] *= torch.sqrt((scale_x * s) ** 2 + (scale_y * c) ** 2)\n\n        # The angle is the rotation angle from y-axis in image space to the height\n        # vector (top->down in the box's local coordinate system) of the box in CCW.\n        #\n        # angle(new) = angle_yOx(O - F(new))\n        #            = angle_yOx( (sfx * s * h / 2, sfy * c * h / 2) )\n        #            = atan2(sfx * s * h / 2, sfy * c * h / 2)\n        #            = atan2(sfx * s, sfy * c)\n        #\n        # For example,\n        # when sfx == sfy, angle(new) == atan2(s, c) == angle(old)\n        self.tensor[:, 4] = torch.atan2(scale_x * s, scale_y * c) * 180 / math.pi\n\n    @classmethod\n    def cat(cls, boxes_list: List[\"RotatedBoxes\"]) -> \"RotatedBoxes\":\n        \"\"\"\n        Concatenates a list of RotatedBoxes into a single RotatedBoxes\n\n        Arguments:\n            boxes_list (list[RotatedBoxes])\n\n        Returns:\n            RotatedBoxes: the concatenated RotatedBoxes\n        \"\"\"\n        assert isinstance(boxes_list, (list, tuple))\n        if len(boxes_list) == 0:\n            return cls(torch.empty(0))\n        assert all([isinstance(box, RotatedBoxes) for box in boxes_list])\n\n        # use torch.cat (v.s. layers.cat) so the returned boxes never share storage with input\n        cat_boxes = cls(torch.cat([b.tensor for b in boxes_list], dim=0))\n        return cat_boxes\n\n    @property\n    def device(self) -> torch.device:\n        return self.tensor.device\n\n    @torch.jit.unused\n    def __iter__(self):\n        \"\"\"\n        Yield a box as a Tensor of shape (5,) at a time.\n        \"\"\"\n        yield from self.tensor\n\n\ndef pairwise_iou(boxes1: RotatedBoxes, boxes2: RotatedBoxes) -> None:\n    \"\"\"\n    Given two lists of rotated boxes of size N and M,\n    compute the IoU (intersection over union)\n    between **all** N x M pairs of boxes.\n    The box order must be (x_center, y_center, width, height, angle).\n\n    Args:\n        boxes1, boxes2 (RotatedBoxes):\n            two `RotatedBoxes`. Contains N & M rotated boxes, respectively.\n\n    Returns:\n        Tensor: IoU, sized [N,M].\n    \"\"\"\n\n    return pairwise_iou_rotated(boxes1.tensor, boxes2.tensor)\n"
  },
  {
    "path": "sam3/agent/helpers/som_utils.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n\nimport colorsys\nfrom dataclasses import dataclass\nfrom typing import List, Tuple\n\nimport cv2\nimport matplotlib as mpl\nimport matplotlib.colors as mplc\nimport numpy as np\nimport pycocotools.mask as mask_utils\n\n\ndef rgb_to_hex(rgb_color):\n    \"\"\"\n    Convert a rgb color to hex color.\n\n    Args:\n        rgb_color (tuple/list of ints): RGB color in tuple or list format.\n\n    Returns:\n        str: Hex color.\n\n    Example:\n        ```\n        >>> rgb_to_hex((255, 0, 244))\n        '#ff00ff'\n        ```\n    \"\"\"\n    return \"#\" + \"\".join([hex(c)[2:].zfill(2) for c in rgb_color])\n\n\n# DEFAULT_COLOR_HEX_TO_NAME = {\n#     rgb_to_hex((255, 0, 0)): \"red\",\n#     rgb_to_hex((0, 255, 0)): \"lime\",\n#     rgb_to_hex((0, 0, 255)): \"blue\",\n#     rgb_to_hex((255, 255, 0)): \"yellow\",\n#     rgb_to_hex((255, 0, 255)): \"fuchsia\",\n#     rgb_to_hex((0, 255, 255)): \"aqua\",\n#     rgb_to_hex((255, 165, 0)): \"orange\",\n#     rgb_to_hex((128, 0, 128)): \"purple\",\n#     rgb_to_hex((255, 215, 0)): \"gold\",\n# }\n\n# Assuming rgb_to_hex is a function that converts an (R, G, B) tuple to a hex string.\n# For example: def rgb_to_hex(rgb): return '#%02x%02x%02x' % rgb\n\nDEFAULT_COLOR_HEX_TO_NAME = {\n    # The top 20 approved colors\n    rgb_to_hex((255, 255, 0)): \"yellow\",\n    rgb_to_hex((0, 255, 0)): \"lime\",\n    rgb_to_hex((0, 255, 255)): \"cyan\",\n    rgb_to_hex((255, 0, 255)): \"magenta\",\n    rgb_to_hex((255, 0, 0)): \"red\",\n    rgb_to_hex((255, 127, 0)): \"orange\",\n    rgb_to_hex((127, 255, 0)): \"chartreuse\",\n    rgb_to_hex((0, 255, 127)): \"spring green\",\n    rgb_to_hex((255, 0, 127)): \"rose\",\n    rgb_to_hex((127, 0, 255)): \"violet\",\n    rgb_to_hex((192, 255, 0)): \"electric lime\",\n    rgb_to_hex((255, 192, 0)): \"vivid orange\",\n    rgb_to_hex((0, 255, 192)): \"turquoise\",\n    rgb_to_hex((192, 0, 255)): \"bright violet\",\n    rgb_to_hex((255, 0, 192)): \"bright pink\",\n    rgb_to_hex((255, 64, 0)): \"fiery orange\",\n    rgb_to_hex((64, 255, 0)): \"bright chartreuse\",\n    rgb_to_hex((0, 255, 64)): \"malachite\",\n    rgb_to_hex((64, 0, 255)): \"deep violet\",\n    rgb_to_hex((255, 0, 64)): \"hot pink\",\n}\n\n\nDEFAULT_COLOR_PALETTE = list(DEFAULT_COLOR_HEX_TO_NAME.keys())\n\n\ndef _validate_color_hex(color_hex: str):\n    color_hex = color_hex.lstrip(\"#\")\n    if not all(c in \"0123456789abcdefABCDEF\" for c in color_hex):\n        raise ValueError(\"Invalid characters in color hash\")\n    if len(color_hex) not in (3, 6):\n        raise ValueError(\"Invalid length of color hash\")\n\n\n# copied from https://github.com/roboflow/supervision/blob/c8f557af0c61b5c03392bad2cc36c8835598b1e1/supervision/draw/color.py\n@dataclass\nclass Color:\n    \"\"\"\n    Represents a color in RGB format.\n\n    Attributes:\n        r (int): Red channel.\n        g (int): Green channel.\n        b (int): Blue channel.\n    \"\"\"\n\n    r: int\n    g: int\n    b: int\n\n    @classmethod\n    def from_hex(cls, color_hex: str):\n        \"\"\"\n        Create a Color instance from a hex string.\n\n        Args:\n            color_hex (str): Hex string of the color.\n\n        Returns:\n            Color: Instance representing the color.\n\n        Example:\n            ```\n            >>> Color.from_hex('#ff00ff')\n            Color(r=255, g=0, b=255)\n            ```\n        \"\"\"\n        _validate_color_hex(color_hex)\n        color_hex = color_hex.lstrip(\"#\")\n        if len(color_hex) == 3:\n            color_hex = \"\".join(c * 2 for c in color_hex)\n        r, g, b = (int(color_hex[i : i + 2], 16) for i in range(0, 6, 2))\n        return cls(r, g, b)\n\n    @classmethod\n    def to_hex(cls, color):\n        \"\"\"\n        Convert a Color instance to a hex string.\n\n        Args:\n            color (Color): Color instance of color.\n\n        Returns:\n            Color: a hex string.\n        \"\"\"\n        return rgb_to_hex((color.r, color.g, color.b))\n\n    def as_rgb(self) -> Tuple[int, int, int]:\n        \"\"\"\n        Returns the color as an RGB tuple.\n\n        Returns:\n            Tuple[int, int, int]: RGB tuple.\n\n        Example:\n            ```\n            >>> color.as_rgb()\n            (255, 0, 255)\n            ```\n        \"\"\"\n        return self.r, self.g, self.b\n\n    def as_bgr(self) -> Tuple[int, int, int]:\n        \"\"\"\n        Returns the color as a BGR tuple.\n\n        Returns:\n            Tuple[int, int, int]: BGR tuple.\n\n        Example:\n            ```\n            >>> color.as_bgr()\n            (255, 0, 255)\n            ```\n        \"\"\"\n        return self.b, self.g, self.r\n\n    @classmethod\n    def white(cls):\n        return Color.from_hex(color_hex=\"#ffffff\")\n\n    @classmethod\n    def black(cls):\n        return Color.from_hex(color_hex=\"#000000\")\n\n    @classmethod\n    def red(cls):\n        return Color.from_hex(color_hex=\"#ff0000\")\n\n    @classmethod\n    def green(cls):\n        return Color.from_hex(color_hex=\"#00ff00\")\n\n    @classmethod\n    def blue(cls):\n        return Color.from_hex(color_hex=\"#0000ff\")\n\n\n@dataclass\nclass ColorPalette:\n    colors: List[Color]\n\n    @classmethod\n    def default(cls):\n        \"\"\"\n        Returns a default color palette.\n\n        Returns:\n            ColorPalette: A ColorPalette instance with default colors.\n\n        Example:\n            ```\n            >>> ColorPalette.default()\n            ColorPalette(colors=[Color(r=255, g=0, b=0), Color(r=0, g=255, b=0), ...])\n            ```\n        \"\"\"\n        return ColorPalette.from_hex(color_hex_list=DEFAULT_COLOR_PALETTE)\n\n    @classmethod\n    def from_hex(cls, color_hex_list: List[str]):\n        \"\"\"\n        Create a ColorPalette instance from a list of hex strings.\n\n        Args:\n            color_hex_list (List[str]): List of color hex strings.\n\n        Returns:\n            ColorPalette: A ColorPalette instance.\n\n        Example:\n            ```\n            >>> ColorPalette.from_hex(['#ff0000', '#00ff00', '#0000ff'])\n            ColorPalette(colors=[Color(r=255, g=0, b=0), Color(r=0, g=255, b=0), ...])\n            ```\n        \"\"\"\n        colors = [Color.from_hex(color_hex) for color_hex in color_hex_list]\n        return cls(colors)\n\n    def by_idx(self, idx: int) -> Color:\n        \"\"\"\n        Return the color at a given index in the palette.\n\n        Args:\n            idx (int): Index of the color in the palette.\n\n        Returns:\n            Color: Color at the given index.\n\n        Example:\n            ```\n            >>> color_palette.by_idx(1)\n            Color(r=0, g=255, b=0)\n            ```\n        \"\"\"\n        if idx < 0:\n            raise ValueError(\"idx argument should not be negative\")\n        idx = idx % len(self.colors)\n        return self.colors[idx]\n\n    def find_farthest_color(self, img_array):\n        \"\"\"\n        Return the color that is the farthest from the given color.\n\n        Args:\n            img_array (np array): any *x3 np array, 3 is the RGB color channel.\n\n        Returns:\n            Color: Farthest color.\n\n        \"\"\"\n        # Reshape the image array for broadcasting\n        img_array = img_array.reshape((-1, 3))\n\n        # Convert colors dictionary to a NumPy array\n        color_values = np.array([[c.r, c.g, c.b] for c in self.colors])\n\n        # Calculate the Euclidean distance between the colors and each pixel in the image\n        # Broadcasting happens here: img_array shape is (num_pixels, 3), color_values shape is (num_colors, 3)\n        distances = np.sqrt(\n            np.sum((img_array[:, np.newaxis, :] - color_values) ** 2, axis=2)\n        )\n\n        # Average the distances for each color\n        mean_distances = np.mean(distances, axis=0)\n\n        # return the farthest color\n        farthest_idx = np.argmax(mean_distances)\n        farthest_color = self.colors[farthest_idx]\n        farthest_color_hex = Color.to_hex(farthest_color)\n        if farthest_color_hex in DEFAULT_COLOR_HEX_TO_NAME:\n            farthest_color_name = DEFAULT_COLOR_HEX_TO_NAME[farthest_color_hex]\n        else:\n            farthest_color_name = \"unknown\"\n\n        return farthest_color, farthest_color_name\n\n\ndef draw_box(ax, box_coord, alpha=0.8, edge_color=\"g\", line_style=\"-\", linewidth=2.0):\n    x0, y0, width, height = box_coord\n    ax.add_patch(\n        mpl.patches.Rectangle(\n            (x0, y0),\n            width,\n            height,\n            fill=False,\n            edgecolor=edge_color,\n            linewidth=linewidth,\n            alpha=alpha,\n            linestyle=line_style,\n        )\n    )\n\n\ndef draw_text(\n    ax,\n    text,\n    position,\n    font_size=None,\n    color=\"g\",\n    horizontal_alignment=\"left\",\n    rotation=0,\n):\n    if not font_size:\n        font_size = mpl.rcParams[\"font.size\"]\n\n    color = np.maximum(list(mplc.to_rgb(color)), 0.2)\n    color[np.argmax(color)] = max(0.8, np.max(color))\n\n    x, y = position\n    ax.text(\n        x,\n        y,\n        text,\n        size=font_size,\n        family=\"sans-serif\",\n        bbox={\"facecolor\": \"none\", \"alpha\": 0.5, \"pad\": 0.7, \"edgecolor\": \"none\"},\n        verticalalignment=\"top\",\n        horizontalalignment=horizontal_alignment,\n        color=color,\n        rotation=rotation,\n    )\n\n\ndef draw_mask(\n    ax, rle, color, show_holes=True, alpha=0.15, upsample_factor=1.0, rle_upsampled=None\n):\n    if isinstance(rle, dict):\n        mask = mask_utils.decode(rle)\n    elif isinstance(rle, np.ndarray):\n        mask = rle\n    else:\n        raise ValueError(f\"Unsupported type for rle: {type(rle)}\")\n\n    mask_upsampled = None\n    if upsample_factor > 1.0 and show_holes:\n        assert rle_upsampled is not None\n        if isinstance(rle_upsampled, dict):\n            mask_upsampled = mask_utils.decode(rle_upsampled)\n        elif isinstance(rle_upsampled, np.ndarray):\n            mask_upsampled = rle_upsampled\n        else:\n            raise ValueError(f\"Unsupported type for rle: {type(rle)}\")\n\n    if show_holes:\n        if mask_upsampled is None:\n            mask_upsampled = mask\n        h, w = mask_upsampled.shape\n        mask_img = np.zeros((h, w, 4))\n        mask_img[:, :, :-1] = color[np.newaxis, np.newaxis, :]\n        mask_img[:, :, -1] = mask_upsampled * alpha\n        ax.imshow(mask_img)\n\n    *_, contours, _ = cv2.findContours(\n        mask.astype(np.uint8).copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE\n    )\n    upsampled_contours = [(cont + 0.5) * upsample_factor - 0.5 for cont in contours]\n    facecolor = (0, 0, 0, 0) if show_holes else color\n    if alpha > 0.8:\n        edge_color = _change_color_brightness(color, brightness_factor=-0.7)\n    else:\n        edge_color = color\n    for cont in upsampled_contours:\n        polygon = mpl.patches.Polygon(\n            [el[0] for el in cont],\n            edgecolor=edge_color,\n            linewidth=2.0,\n            facecolor=facecolor,\n        )\n        ax.add_patch(polygon)\n\n\ndef _change_color_brightness(color, brightness_factor):\n    \"\"\"\n    Depending on the brightness_factor, gives a lighter or darker color i.e. a color with\n    less or more saturation than the original color.\n\n    Args:\n        color: color of the polygon. Refer to `matplotlib.colors` for a full list of\n            formats that are accepted.\n        brightness_factor (float): a value in [-1.0, 1.0] range. A lightness factor of\n            0 will correspond to no change, a factor in [-1.0, 0) range will result in\n            a darker color and a factor in (0, 1.0] range will result in a lighter color.\n\n    Returns:\n        modified_color (tuple[double]): a tuple containing the RGB values of the\n            modified color. Each value in the tuple is in the [0.0, 1.0] range.\n    \"\"\"\n    assert brightness_factor >= -1.0 and brightness_factor <= 1.0\n    color = mplc.to_rgb(color)\n    polygon_color = colorsys.rgb_to_hls(*mplc.to_rgb(color))\n    modified_lightness = polygon_color[1] + (brightness_factor * polygon_color[1])\n    modified_lightness = 0.0 if modified_lightness < 0.0 else modified_lightness\n    modified_lightness = 1.0 if modified_lightness > 1.0 else modified_lightness\n    modified_color = colorsys.hls_to_rgb(\n        polygon_color[0], modified_lightness, polygon_color[2]\n    )\n    return modified_color\n"
  },
  {
    "path": "sam3/agent/helpers/visualizer.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n\nimport colorsys\nimport logging\nimport math\nimport random\nfrom enum import Enum, unique\n\nimport cv2\nimport matplotlib as mpl\nimport matplotlib.colors as mplc\nimport matplotlib.figure as mplfigure\nimport numpy as np\nimport pycocotools.mask as mask_util\nimport torch\nfrom iopath.common.file_io import PathManager\nfrom matplotlib.backends.backend_agg import FigureCanvasAgg\nfrom PIL import Image\n\nfrom .boxes import Boxes, BoxMode\nfrom .color_map import random_color\nfrom .keypoints import Keypoints\nfrom .masks import BitMasks, PolygonMasks\nfrom .rotated_boxes import RotatedBoxes\n\nlogger = logging.getLogger(__name__)\n\n\n__all__ = [\"ColorMode\", \"VisImage\", \"Visualizer\"]\n\n\n_SMALL_OBJECT_AREA_THRESH = 1000\n_LARGE_MASK_AREA_THRESH = 120000\n_OFF_WHITE = (1.0, 1.0, 240.0 / 255)\n_BLACK = (0, 0, 0)\n_RED = (1.0, 0, 0)\n\n_KEYPOINT_THRESHOLD = 0.05\n\n\n@unique\nclass ColorMode(Enum):\n    \"\"\"\n    Enum of different color modes to use for instance visualizations.\n    \"\"\"\n\n    IMAGE = 0\n    \"\"\"\n    Picks a random color for every instance and overlay segmentations with low opacity.\n    \"\"\"\n    SEGMENTATION = 1\n    \"\"\"\n    Let instances of the same category have similar colors\n    (from metadata.thing_colors), and overlay them with\n    high opacity. This provides more attention on the quality of segmentation.\n    \"\"\"\n    IMAGE_BW = 2\n    \"\"\"\n    Same as IMAGE, but convert all areas without masks to gray-scale.\n    Only available for drawing per-instance mask predictions.\n    \"\"\"\n\n\nclass GenericMask:\n    \"\"\"\n    Attribute:\n        polygons (list[ndarray]): list[ndarray]: polygons for this mask.\n            Each ndarray has format [x, y, x, y, ...]\n        mask (ndarray): a binary mask\n    \"\"\"\n\n    def __init__(self, mask_or_polygons, height, width):\n        self._mask = self._polygons = self._has_holes = None\n        self.height = height\n        self.width = width\n\n        m = mask_or_polygons\n        if isinstance(m, dict):\n            # RLEs\n            assert \"counts\" in m and \"size\" in m\n            if isinstance(m[\"counts\"], list):  # uncompressed RLEs\n                h, w = m[\"size\"]\n                assert h == height and w == width\n                m = mask_util.frPyObjects(m, h, w)\n            self._mask = mask_util.decode(m)[:, :]\n            return\n\n        if isinstance(m, list):  # list[ndarray]\n            self._polygons = [np.asarray(x).reshape(-1) for x in m]\n            return\n\n        if isinstance(m, np.ndarray):  # assumed to be a binary mask\n            assert m.shape[1] != 2, m.shape\n            assert m.shape == (\n                height,\n                width,\n            ), f\"mask shape: {m.shape}, target dims: {height}, {width}\"\n            self._mask = m.astype(\"uint8\")\n            return\n\n        raise ValueError(\n            \"GenericMask cannot handle object {} of type '{}'\".format(m, type(m))\n        )\n\n    @property\n    def mask(self):\n        if self._mask is None:\n            self._mask = self.polygons_to_mask(self._polygons)\n        return self._mask\n\n    @property\n    def polygons(self):\n        if self._polygons is None:\n            self._polygons, self._has_holes = self.mask_to_polygons(self._mask)\n        return self._polygons\n\n    @property\n    def has_holes(self):\n        if self._has_holes is None:\n            if self._mask is not None:\n                self._polygons, self._has_holes = self.mask_to_polygons(self._mask)\n            else:\n                self._has_holes = (\n                    False  # if original format is polygon, does not have holes\n                )\n        return self._has_holes\n\n    def mask_to_polygons(self, mask):\n        # cv2.RETR_CCOMP flag retrieves all the contours and arranges them to a 2-level\n        # hierarchy. External contours (boundary) of the object are placed in hierarchy-1.\n        # Internal contours (holes) are placed in hierarchy-2.\n        # cv2.CHAIN_APPROX_NONE flag gets vertices of polygons from contours.\n        mask = np.ascontiguousarray(\n            mask\n        )  # some versions of cv2 does not support incontiguous arr\n        res = cv2.findContours(\n            mask.astype(\"uint8\"), cv2.RETR_CCOMP, cv2.CHAIN_APPROX_NONE\n        )\n        hierarchy = res[-1]\n        if hierarchy is None:  # empty mask\n            return [], False\n        has_holes = (hierarchy.reshape(-1, 4)[:, 3] >= 0).sum() > 0\n        res = res[-2]\n        res = [x.flatten() for x in res]\n        # These coordinates from OpenCV are integers in range [0, W-1 or H-1].\n        # We add 0.5 to turn them into real-value coordinate space. A better solution\n        # would be to first +0.5 and then dilate the returned polygon by 0.5.\n        res = [x + 0.5 for x in res if len(x) >= 6]\n        return res, has_holes\n\n    def polygons_to_mask(self, polygons):\n        rle = mask_util.frPyObjects(polygons, self.height, self.width)\n        rle = mask_util.merge(rle)\n        return mask_util.decode(rle)[:, :]\n\n    def area(self):\n        return self.mask.sum()\n\n    def bbox(self):\n        p = mask_util.frPyObjects(self.polygons, self.height, self.width)\n        p = mask_util.merge(p)\n        bbox = mask_util.toBbox(p)\n        bbox[2] += bbox[0]\n        bbox[3] += bbox[1]\n        return bbox\n\n\nclass _PanopticPrediction:\n    \"\"\"\n    Unify different panoptic annotation/prediction formats\n    \"\"\"\n\n    def __init__(self, panoptic_seg, segments_info, metadata=None):\n        if segments_info is None:\n            assert metadata is not None\n            # If \"segments_info\" is None, we assume \"panoptic_img\" is a\n            # H*W int32 image storing the panoptic_id in the format of\n            # category_id * label_divisor + instance_id. We reserve -1 for\n            # VOID label.\n            label_divisor = metadata.label_divisor\n            segments_info = []\n            for panoptic_label in np.unique(panoptic_seg.numpy()):\n                if panoptic_label == -1:\n                    # VOID region.\n                    continue\n                pred_class = panoptic_label // label_divisor\n                isthing = (\n                    pred_class in metadata.thing_dataset_id_to_contiguous_id.values()\n                )\n                segments_info.append(\n                    {\n                        \"id\": int(panoptic_label),\n                        \"category_id\": int(pred_class),\n                        \"isthing\": bool(isthing),\n                    }\n                )\n        del metadata\n\n        self._seg = panoptic_seg\n\n        self._sinfo = {s[\"id\"]: s for s in segments_info}  # seg id -> seg info\n        segment_ids, areas = torch.unique(panoptic_seg, sorted=True, return_counts=True)\n        areas = areas.numpy()\n        sorted_idxs = np.argsort(-areas)\n        self._seg_ids, self._seg_areas = segment_ids[sorted_idxs], areas[sorted_idxs]\n        self._seg_ids = self._seg_ids.tolist()\n        for sid, area in zip(self._seg_ids, self._seg_areas):\n            if sid in self._sinfo:\n                self._sinfo[sid][\"area\"] = float(area)\n\n    def non_empty_mask(self):\n        \"\"\"\n        Returns:\n            (H, W) array, a mask for all pixels that have a prediction\n        \"\"\"\n        empty_ids = []\n        for id in self._seg_ids:\n            if id not in self._sinfo:\n                empty_ids.append(id)\n        if len(empty_ids) == 0:\n            return np.zeros(self._seg.shape, dtype=np.uint8)\n        assert len(empty_ids) == 1, (\n            \">1 ids corresponds to no labels. This is currently not supported\"\n        )\n        return (self._seg != empty_ids[0]).numpy().astype(np.bool)\n\n    def semantic_masks(self):\n        for sid in self._seg_ids:\n            sinfo = self._sinfo.get(sid)\n            if sinfo is None or sinfo[\"isthing\"]:\n                # Some pixels (e.g. id 0 in PanopticFPN) have no instance or semantic predictions.\n                continue\n            yield (self._seg == sid).numpy().astype(np.bool), sinfo\n\n    def instance_masks(self):\n        for sid in self._seg_ids:\n            sinfo = self._sinfo.get(sid)\n            if sinfo is None or not sinfo[\"isthing\"]:\n                continue\n            mask = (self._seg == sid).numpy().astype(np.bool)\n            if mask.sum() > 0:\n                yield mask, sinfo\n\n\ndef _create_text_labels(classes, scores, class_names, is_crowd=None):\n    \"\"\"\n    Args:\n        classes (list[int] or None):\n        scores (list[float] or None):\n        class_names (list[str] or None):\n        is_crowd (list[bool] or None):\n\n    Returns:\n        list[str] or None\n    \"\"\"\n    labels = None\n    if classes is not None:\n        if class_names is not None and len(class_names) > 0:\n            labels = [class_names[i] for i in classes]\n        else:\n            labels = [str(i) for i in classes]\n    if scores is not None:\n        if labels is None:\n            labels = [\"{:.0f}%\".format(s * 100) for s in scores]\n        else:\n            labels = [\"{} {:.0f}%\".format(l, s * 100) for l, s in zip(labels, scores)]\n    if labels is not None and is_crowd is not None:\n        labels = [l + (\"|crowd\" if crowd else \"\") for l, crowd in zip(labels, is_crowd)]\n    return labels\n\n\nclass VisImage:\n    def __init__(self, img, scale=1.0):\n        \"\"\"\n        Args:\n            img (ndarray): an RGB image of shape (H, W, 3) in range [0, 255].\n            scale (float): scale the input image\n        \"\"\"\n        self.img = img\n        self.scale = scale\n        self.width, self.height = img.shape[1], img.shape[0]\n        self._setup_figure(img)\n\n    def _setup_figure(self, img):\n        \"\"\"\n        Args:\n            Same as in :meth:`__init__()`.\n\n        Returns:\n            fig (matplotlib.pyplot.figure): top level container for all the image plot elements.\n            ax (matplotlib.pyplot.Axes): contains figure elements and sets the coordinate system.\n        \"\"\"\n        fig = mplfigure.Figure(frameon=False)\n        self.dpi = fig.get_dpi()\n        # add a small 1e-2 to avoid precision lost due to matplotlib's truncation\n        # (https://github.com/matplotlib/matplotlib/issues/15363)\n        fig.set_size_inches(\n            (self.width * self.scale + 1e-2) / self.dpi,\n            (self.height * self.scale + 1e-2) / self.dpi,\n        )\n        self.canvas = FigureCanvasAgg(fig)\n        # self.canvas = mpl.backends.backend_cairo.FigureCanvasCairo(fig)\n        ax = fig.add_axes([0.0, 0.0, 1.0, 1.0])\n        ax.axis(\"off\")\n        self.fig = fig\n        self.ax = ax\n        self.reset_image(img)\n\n    def reset_image(self, img):\n        \"\"\"\n        Args:\n            img: same as in __init__\n        \"\"\"\n        img = img.astype(\"uint8\")\n        self.ax.imshow(\n            img, extent=(0, self.width, self.height, 0), interpolation=\"nearest\"\n        )\n\n    def save(self, filepath):\n        \"\"\"\n        Args:\n            filepath (str): a string that contains the absolute path, including the file name, where\n                the visualized image will be saved.\n        \"\"\"\n        self.fig.savefig(filepath)\n\n    def get_image(self):\n        \"\"\"\n        Returns:\n            ndarray:\n                the visualized image of shape (H, W, 3) (RGB) in uint8 type.\n                The shape is scaled w.r.t the input image using the given `scale` argument.\n        \"\"\"\n        canvas = self.canvas\n        s, (width, height) = canvas.print_to_buffer()\n        # buf = io.BytesIO()  # works for cairo backend\n        # canvas.print_rgba(buf)\n        # width, height = self.width, self.height\n        # s = buf.getvalue()\n\n        buffer = np.frombuffer(s, dtype=\"uint8\")\n\n        img_rgba = buffer.reshape(height, width, 4)\n        rgb, alpha = np.split(img_rgba, [3], axis=2)\n        return rgb.astype(\"uint8\")\n\n\nclass Visualizer:\n    \"\"\"\n    Visualizer that draws data about detection/segmentation on images.\n\n    It contains methods like `draw_{text,box,circle,line,binary_mask,polygon}`\n    that draw primitive objects to images, as well as high-level wrappers like\n    `draw_{instance_predictions,sem_seg,panoptic_seg_predictions,dataset_dict}`\n    that draw composite data in some pre-defined style.\n\n    Note that the exact visualization style for the high-level wrappers are subject to change.\n    Style such as color, opacity, label contents, visibility of labels, or even the visibility\n    of objects themselves (e.g. when the object is too small) may change according\n    to different heuristics, as long as the results still look visually reasonable.\n\n    To obtain a consistent style, you can implement custom drawing functions with the\n    abovementioned primitive methods instead. If you need more customized visualization\n    styles, you can process the data yourself following their format documented in\n    tutorials (:doc:`/tutorials/models`, :doc:`/tutorials/datasets`). This class does not\n    intend to satisfy everyone's preference on drawing styles.\n\n    This visualizer focuses on high rendering quality rather than performance. It is not\n    designed to be used for real-time applications.\n    \"\"\"\n\n    def __init__(\n        self,\n        img_rgb,\n        metadata=None,\n        scale=1.0,\n        instance_mode=ColorMode.IMAGE,\n        font_size_multiplier=1.3,\n        boarder_width_multiplier=1.5,\n    ):\n        \"\"\"\n        Args:\n            img_rgb: a numpy array of shape (H, W, C), where H and W correspond to\n                the height and width of the image respectively. C is the number of\n                color channels. The image is required to be in RGB format since that\n                is a requirement of the Matplotlib library. The image is also expected\n                to be in the range [0, 255].\n            metadata (Metadata): dataset metadata (e.g. class names and colors)\n            instance_mode (ColorMode): defines one of the pre-defined style for drawing\n                instances on an image.\n        \"\"\"\n        self.img = np.asarray(img_rgb).clip(0, 255).astype(np.uint8)\n        self.boarder_width_multiplier = boarder_width_multiplier\n        # if metadata is None:\n        #     metadata = MetadataCatalog.get(\"__nonexist__\")\n        # self.metadata = metadata\n        self.output = VisImage(self.img, scale=scale)\n        self.cpu_device = torch.device(\"cpu\")\n\n        # too small texts are useless, therefore clamp to 9\n        self._default_font_size = (\n            max(np.sqrt(self.output.height * self.output.width) // 60, 15 // scale)\n            * font_size_multiplier\n        )\n        # self._default_font_size = 18\n        self._instance_mode = instance_mode\n        self.keypoint_threshold = _KEYPOINT_THRESHOLD\n\n        import matplotlib.colors as mcolors\n\n        css4_colors = mcolors.CSS4_COLORS\n        self.color_proposals = [\n            list(mcolors.hex2color(color)) for color in css4_colors.values()\n        ]\n\n    def draw_instance_predictions(self, predictions):\n        \"\"\"\n        Draw instance-level prediction results on an image.\n\n        Args:\n            predictions (Instances): the output of an instance detection/segmentation\n                model. Following fields will be used to draw:\n                \"pred_boxes\", \"pred_classes\", \"scores\", \"pred_masks\" (or \"pred_masks_rle\").\n\n        Returns:\n            output (VisImage): image object with visualizations.\n        \"\"\"\n        boxes = predictions.pred_boxes if predictions.has(\"pred_boxes\") else None\n        scores = predictions.scores if predictions.has(\"scores\") else None\n        classes = (\n            predictions.pred_classes.tolist()\n            if predictions.has(\"pred_classes\")\n            else None\n        )\n        labels = _create_text_labels(\n            classes, scores, self.metadata.get(\"thing_classes\", None)\n        )\n        keypoints = (\n            predictions.pred_keypoints if predictions.has(\"pred_keypoints\") else None\n        )\n\n        keep = (scores > 0.5).cpu()\n        boxes = boxes[keep]\n        scores = scores[keep]\n        classes = np.array(classes)\n        classes = classes[np.array(keep)]\n        labels = np.array(labels)\n        labels = labels[np.array(keep)]\n\n        if predictions.has(\"pred_masks\"):\n            masks = np.asarray(predictions.pred_masks)\n            masks = masks[np.array(keep)]\n            masks = [\n                GenericMask(x, self.output.height, self.output.width) for x in masks\n            ]\n        else:\n            masks = None\n\n        if self._instance_mode == ColorMode.SEGMENTATION and self.metadata.get(\n            \"thing_colors\"\n        ):\n            # if self.metadata.get(\"thing_colors\"):\n            colors = [\n                self._jitter([x / 255 for x in self.metadata.thing_colors[c]])\n                for c in classes\n            ]\n            alpha = 0.4\n        else:\n            colors = None\n            alpha = 0.4\n\n        if self._instance_mode == ColorMode.IMAGE_BW:\n            self.output.reset_image(\n                self._create_grayscale_image(\n                    (predictions.pred_masks.any(dim=0) > 0).numpy()\n                    if predictions.has(\"pred_masks\")\n                    else None\n                )\n            )\n            alpha = 0.3\n\n        self.overlay_instances(\n            masks=masks,\n            boxes=boxes,\n            labels=labels,\n            keypoints=keypoints,\n            assigned_colors=colors,\n            alpha=alpha,\n        )\n        return self.output\n\n    def draw_sem_seg(self, sem_seg, area_threshold=None, alpha=0.7):\n        \"\"\"\n        Draw semantic segmentation predictions/labels.\n\n        Args:\n            sem_seg (Tensor or ndarray): the segmentation of shape (H, W).\n                Each value is the integer label of the pixel.\n            area_threshold (int): segments with less than `area_threshold` are not drawn.\n            alpha (float): the larger it is, the more opaque the segmentations are.\n\n        Returns:\n            output (VisImage): image object with visualizations.\n        \"\"\"\n        if isinstance(sem_seg, torch.Tensor):\n            sem_seg = sem_seg.numpy()\n        labels, areas = np.unique(sem_seg, return_counts=True)\n        sorted_idxs = np.argsort(-areas).tolist()\n        labels = labels[sorted_idxs]\n        for label in filter(lambda l: l < len(self.metadata.stuff_classes), labels):\n            try:\n                mask_color = [x / 255 for x in self.metadata.stuff_colors[label]]\n            except (AttributeError, IndexError):\n                mask_color = None\n\n            binary_mask = (sem_seg == label).astype(np.uint8)\n            text = self.metadata.stuff_classes[label]\n            self.draw_binary_mask(\n                binary_mask,\n                color=mask_color,\n                edge_color=_OFF_WHITE,\n                text=text,\n                alpha=alpha,\n                area_threshold=area_threshold,\n            )\n        return self.output\n\n    def draw_panoptic_seg(\n        self, panoptic_seg, segments_info, area_threshold=None, alpha=0.7\n    ):\n        \"\"\"\n        Draw panoptic prediction annotations or results.\n\n        Args:\n            panoptic_seg (Tensor): of shape (height, width) where the values are ids for each\n                segment.\n            segments_info (list[dict] or None): Describe each segment in `panoptic_seg`.\n                If it is a ``list[dict]``, each dict contains keys \"id\", \"category_id\".\n                If None, category id of each pixel is computed by\n                ``pixel // metadata.label_divisor``.\n            area_threshold (int): stuff segments with less than `area_threshold` are not drawn.\n\n        Returns:\n            output (VisImage): image object with visualizations.\n        \"\"\"\n        pred = _PanopticPrediction(panoptic_seg, segments_info, self.metadata)\n\n        if self._instance_mode == ColorMode.IMAGE_BW:\n            self.output.reset_image(self._create_grayscale_image(pred.non_empty_mask()))\n\n        # draw mask for all semantic segments first i.e. \"stuff\"\n        for mask, sinfo in pred.semantic_masks():\n            category_idx = sinfo[\"category_id\"]\n            try:\n                mask_color = [x / 255 for x in self.metadata.stuff_colors[category_idx]]\n            except AttributeError:\n                mask_color = None\n\n            text = (\n                self.metadata.stuff_classes[category_idx]\n                .replace(\"-other\", \"\")\n                .replace(\"-merged\", \"\")\n            )\n            self.draw_binary_mask(\n                mask,\n                color=mask_color,\n                edge_color=_OFF_WHITE,\n                text=text,\n                alpha=alpha,\n                area_threshold=area_threshold,\n            )\n\n        # draw mask for all instances second\n        all_instances = list(pred.instance_masks())\n        if len(all_instances) == 0:\n            return self.output\n        masks, sinfo = list(zip(*all_instances))\n        category_ids = [x[\"category_id\"] for x in sinfo]\n\n        try:\n            scores = [x[\"score\"] for x in sinfo]\n        except KeyError:\n            scores = None\n        class_names = [\n            name.replace(\"-other\", \"\").replace(\"-merged\", \"\")\n            for name in self.metadata.thing_classes\n        ]\n        labels = _create_text_labels(\n            category_ids, scores, class_names, [x.get(\"iscrowd\", 0) for x in sinfo]\n        )\n\n        try:\n            colors = [\n                self._jitter([x / 255 for x in self.metadata.thing_colors[c]])\n                for c in category_ids\n            ]\n        except AttributeError:\n            colors = None\n        self.overlay_instances(\n            masks=masks, labels=labels, assigned_colors=colors, alpha=alpha\n        )\n\n        return self.output\n\n    draw_panoptic_seg_predictions = draw_panoptic_seg  # backward compatibility\n\n    def draw_dataset_dict(self, dic):\n        \"\"\"\n        Draw annotations/segmentaions in Detectron2 Dataset format.\n\n        Args:\n            dic (dict): annotation/segmentation data of one image, in Detectron2 Dataset format.\n\n        Returns:\n            output (VisImage): image object with visualizations.\n        \"\"\"\n        annos = dic.get(\"annotations\", None)\n        if annos:\n            if \"segmentation\" in annos[0]:\n                masks = [x[\"segmentation\"] for x in annos]\n            else:\n                masks = None\n            if \"keypoints\" in annos[0]:\n                keypts = [x[\"keypoints\"] for x in annos]\n                keypts = np.array(keypts).reshape(len(annos), -1, 3)\n            else:\n                keypts = None\n\n            boxes = [\n                (\n                    BoxMode.convert(x[\"bbox\"], x[\"bbox_mode\"], BoxMode.XYXY_ABS)\n                    if len(x[\"bbox\"]) == 4\n                    else x[\"bbox\"]\n                )\n                for x in annos\n            ]\n\n            colors = None\n            category_ids = [x[\"category_id\"] for x in annos]\n            if self._instance_mode == ColorMode.SEGMENTATION and self.metadata.get(\n                \"thing_colors\"\n            ):\n                colors = [\n                    self._jitter([x / 255 for x in self.metadata.thing_colors[c]])\n                    for c in category_ids\n                ]\n            names = self.metadata.get(\"thing_classes\", None)\n            labels = _create_text_labels(\n                category_ids,\n                scores=None,\n                class_names=names,\n                is_crowd=[x.get(\"iscrowd\", 0) for x in annos],\n            )\n            self.overlay_instances(\n                labels=labels,\n                boxes=boxes,\n                masks=masks,\n                keypoints=keypts,\n                assigned_colors=colors,\n            )\n\n        sem_seg = dic.get(\"sem_seg\", None)\n        if sem_seg is None and \"sem_seg_file_name\" in dic:\n            with PathManager.open(dic[\"sem_seg_file_name\"], \"rb\") as f:\n                sem_seg = Image.open(f)\n                sem_seg = np.asarray(sem_seg, dtype=\"uint8\")\n        if sem_seg is not None:\n            self.draw_sem_seg(sem_seg, area_threshold=0, alpha=0.4)\n\n        pan_seg = dic.get(\"pan_seg\", None)\n        if pan_seg is None and \"pan_seg_file_name\" in dic:\n            with PathManager.open(dic[\"pan_seg_file_name\"], \"rb\") as f:\n                pan_seg = Image.open(f)\n                pan_seg = np.asarray(pan_seg)\n                from panopticapi.utils import rgb2id\n\n                pan_seg = rgb2id(pan_seg)\n        if pan_seg is not None:\n            segments_info = dic[\"segments_info\"]\n            pan_seg = torch.tensor(pan_seg)\n            self.draw_panoptic_seg(pan_seg, segments_info, area_threshold=0, alpha=0.7)\n        return self.output\n\n    def overlay_instances(\n        self,\n        *,\n        boxes=None,\n        labels=None,\n        masks=None,\n        keypoints=None,\n        assigned_colors=None,\n        binary_masks=None,\n        alpha=0.5,\n        label_mode=\"1\",\n    ):\n        \"\"\"\n        Args:\n            boxes (Boxes, RotatedBoxes or ndarray): either a :class:`Boxes`,\n                or an Nx4 numpy array of XYXY_ABS format for the N objects in a single image,\n                or a :class:`RotatedBoxes`,\n                or an Nx5 numpy array of (x_center, y_center, width, height, angle_degrees) format\n                for the N objects in a single image,\n            labels (list[str]): the text to be displayed for each instance.\n            masks (masks-like object): Supported types are:\n\n                * :class:`detectron2.structures.PolygonMasks`,\n                  :class:`detectron2.structures.BitMasks`.\n                * list[list[ndarray]]: contains the segmentation masks for all objects in one image.\n                  The first level of the list corresponds to individual instances. The second\n                  level to all the polygon that compose the instance, and the third level\n                  to the polygon coordinates. The third level should have the format of\n                  [x0, y0, x1, y1, ..., xn, yn] (n >= 3).\n                * list[ndarray]: each ndarray is a binary mask of shape (H, W).\n                * list[dict]: each dict is a COCO-style RLE.\n            keypoints (Keypoint or array like): an array-like object of shape (N, K, 3),\n                where the N is the number of instances and K is the number of keypoints.\n                The last dimension corresponds to (x, y, visibility or score).\n            assigned_colors (list[matplotlib.colors]): a list of colors, where each color\n                corresponds to each mask or box in the image. Refer to 'matplotlib.colors'\n                for full list of formats that the colors are accepted in.\n        Returns:\n            output (VisImage): image object with visualizations.\n        \"\"\"\n        num_instances = 0\n        if boxes is not None:\n            boxes = self._convert_boxes(boxes)\n            num_instances = len(boxes)\n        if masks is not None:\n            masks = self._convert_masks(masks)\n            if num_instances:\n                assert len(masks) == num_instances\n            else:\n                num_instances = len(masks)\n        if keypoints is not None:\n            if num_instances:\n                assert len(keypoints) == num_instances\n            else:\n                num_instances = len(keypoints)\n            keypoints = self._convert_keypoints(keypoints)\n        if labels is not None:\n            assert len(labels) == num_instances\n        if assigned_colors is None:\n            assigned_colors = [\n                random_color(rgb=True, maximum=1) for _ in range(num_instances)\n            ]\n        if num_instances == 0:\n            return labels, [], []\n        if boxes is not None and boxes.shape[1] == 5:\n            return self.overlay_rotated_instances(\n                boxes=boxes, labels=labels, assigned_colors=assigned_colors\n            )\n\n        # Display in largest to smallest order to reduce occlusion.\n        areas = None\n        if boxes is not None:\n            areas = np.prod(boxes[:, 2:] - boxes[:, :2], axis=1)\n        elif masks is not None:\n            areas = np.asarray([x.area() for x in masks])\n\n        # if areas is not None:\n        #     # sorted_idxs = np.argsort(areas).tolist()\n        #     sorted_idxs = np.argsort(-areas).tolist()\n        #     # Re-order overlapped instances in descending order.\n        #     boxes = boxes[sorted_idxs] if boxes is not None else None\n        #     labels = [labels[k] for k in sorted_idxs] if labels is not None else None\n        #     masks = [masks[idx] for idx in sorted_idxs] if masks is not None else None\n        #     binary_masks = (\n        #         [binary_masks[idx] for idx in sorted_idxs]\n        #         if binary_masks is not None\n        #         else None\n        #     )\n        #     assigned_colors = [assigned_colors[idx] for idx in sorted_idxs]\n        #     keypoints = keypoints[sorted_idxs] if keypoints is not None else None\n\n        marks = []\n        marks_position = []\n        added_positions = set()\n        for i in range(num_instances):\n            color = assigned_colors[i]\n            if boxes is not None:\n                self.draw_box(boxes[i], alpha=1, edge_color=color)\n                if binary_masks is None:\n                    # draw number for non-mask instances\n                    mark = self._draw_number_in_box(\n                        boxes[i], i + 1, color=color, label_mode=label_mode\n                    )\n                    marks.append(mark)\n\n            if binary_masks is not None:\n                mark, mask_position = self._draw_number_in_mask(\n                    binary_mask=binary_masks[i].astype(\"uint8\"),\n                    text=i + 1,\n                    color=color,\n                    added_positions=added_positions,\n                    label_mode=label_mode,\n                )\n                marks.append(mark)\n                marks_position.append(mask_position)\n\n                self.draw_binary_mask(\n                    binary_masks[i],\n                    color=color,\n                    edge_color=_OFF_WHITE,\n                    alpha=alpha,\n                )\n\n            if masks is not None:\n                for segment in masks[i].polygons:\n                    self.draw_polygon(\n                        segment.reshape(-1, 2), color, alpha=0\n                    )  # alpha=0 so holes in masks are not colored\n\n        # draw keypoints\n        if keypoints is not None:\n            for keypoints_per_instance in keypoints:\n                self.draw_and_connect_keypoints(keypoints_per_instance)\n\n        # return labels, marks, sorted_idxs, marks_position\n        return labels, marks, marks_position\n\n    def overlay_rotated_instances(self, boxes=None, labels=None, assigned_colors=None):\n        \"\"\"\n        Args:\n            boxes (ndarray): an Nx5 numpy array of\n                (x_center, y_center, width, height, angle_degrees) format\n                for the N objects in a single image.\n            labels (list[str]): the text to be displayed for each instance.\n            assigned_colors (list[matplotlib.colors]): a list of colors, where each color\n                corresponds to each mask or box in the image. Refer to 'matplotlib.colors'\n                for full list of formats that the colors are accepted in.\n\n        Returns:\n            output (VisImage): image object with visualizations.\n        \"\"\"\n        num_instances = len(boxes)\n\n        if assigned_colors is None:\n            assigned_colors = [\n                random_color(rgb=True, maximum=1) for _ in range(num_instances)\n            ]\n        if num_instances == 0:\n            return self.output\n\n        # Display in largest to smallest order to reduce occlusion.\n        if boxes is not None:\n            areas = boxes[:, 2] * boxes[:, 3]\n\n        sorted_idxs = np.argsort(-areas).tolist()\n        # Re-order overlapped instances in descending order.\n        boxes = boxes[sorted_idxs]\n        labels = [labels[k] for k in sorted_idxs] if labels is not None else None\n        colors = [assigned_colors[idx] for idx in sorted_idxs]\n\n        for i in range(num_instances):\n            self.draw_rotated_box_with_label(\n                boxes[i],\n                edge_color=colors[i],\n                label=labels[i] if labels is not None else None,\n            )\n\n        return self.output\n\n    def draw_and_connect_keypoints(self, keypoints):\n        \"\"\"\n        Draws keypoints of an instance and follows the rules for keypoint connections\n        to draw lines between appropriate keypoints. This follows color heuristics for\n        line color.\n\n        Args:\n            keypoints (Tensor): a tensor of shape (K, 3), where K is the number of keypoints\n                and the last dimension corresponds to (x, y, probability).\n\n        Returns:\n            output (VisImage): image object with visualizations.\n        \"\"\"\n        visible = {}\n        keypoint_names = self.metadata.get(\"keypoint_names\")\n        for idx, keypoint in enumerate(keypoints):\n            # draw keypoint\n            x, y, prob = keypoint\n            if prob > self.keypoint_threshold:\n                self.draw_circle((x, y), color=_RED)\n                if keypoint_names:\n                    keypoint_name = keypoint_names[idx]\n                    visible[keypoint_name] = (x, y)\n\n        if self.metadata.get(\"keypoint_connection_rules\"):\n            for kp0, kp1, color in self.metadata.keypoint_connection_rules:\n                if kp0 in visible and kp1 in visible:\n                    x0, y0 = visible[kp0]\n                    x1, y1 = visible[kp1]\n                    color = tuple(x / 255.0 for x in color)\n                    self.draw_line([x0, x1], [y0, y1], color=color)\n\n        # draw lines from nose to mid-shoulder and mid-shoulder to mid-hip\n        # Note that this strategy is specific to person keypoints.\n        # For other keypoints, it should just do nothing\n        try:\n            ls_x, ls_y = visible[\"left_shoulder\"]\n            rs_x, rs_y = visible[\"right_shoulder\"]\n            mid_shoulder_x, mid_shoulder_y = (ls_x + rs_x) / 2, (ls_y + rs_y) / 2\n        except KeyError:\n            pass\n        else:\n            # draw line from nose to mid-shoulder\n            nose_x, nose_y = visible.get(\"nose\", (None, None))\n            if nose_x is not None:\n                self.draw_line(\n                    [nose_x, mid_shoulder_x], [nose_y, mid_shoulder_y], color=_RED\n                )\n\n            try:\n                # draw line from mid-shoulder to mid-hip\n                lh_x, lh_y = visible[\"left_hip\"]\n                rh_x, rh_y = visible[\"right_hip\"]\n            except KeyError:\n                pass\n            else:\n                mid_hip_x, mid_hip_y = (lh_x + rh_x) / 2, (lh_y + rh_y) / 2\n                self.draw_line(\n                    [mid_hip_x, mid_shoulder_x], [mid_hip_y, mid_shoulder_y], color=_RED\n                )\n        return self.output\n\n    def mask_dims_from_binary(self, binary_mask):\n        ind_y, ind_x = np.where(binary_mask == 1)\n        min_ind_x = np.min(ind_x)\n        max_ind_x = np.max(ind_x)\n        min_ind_y = np.min(ind_y)\n        max_ind_y = np.max(ind_y)\n        return (max_ind_x - min_ind_x), (max_ind_y - min_ind_y)\n\n    def reposition_label(self, position, cur, binary_mask, move_count):\n        img_width, img_height = self.output.width, self.output.height\n        mask_width, mask_height = self.mask_dims_from_binary(binary_mask)\n\n        # set resposition thresholds\n        mask_width_limit, mask_height_limit = (\n            25,\n            25,\n        )  # limit for width and height size for object covering\n        location_diff_threshold = 15  # limit for the distance between two labels\n        x_boundry_limit, y_boundry_limit = (\n            20,\n            20,\n        )  # limit for the distancing the label from edges\n\n        offset_x = 15  # move in x direction\n        offset_y = 15  # move in y direction\n\n        x1, y1 = position\n\n        if (\n            mask_width < mask_width_limit\n            and mask_height < mask_height_limit\n            and move_count == 0\n        ):\n            move_x = offset_x if offset_x + x1 < img_width else -offset_x\n            move_y = offset_y if offset_y + y1 < img_height else -offset_y\n            return (True, move_x, move_y)\n\n        for x2, y2 in cur:\n            if abs(x1 - x2) + abs(y1 - y2) < location_diff_threshold:\n                move_x = offset_x if x1 >= x2 else -offset_x\n                move_y = offset_y if y1 >= y2 else -offset_y\n                move_x = (\n                    0\n                    if x1 + move_x > img_width - x_boundry_limit\n                    or x1 + move_x < x_boundry_limit\n                    else move_x\n                )\n                move_y = (\n                    0\n                    if y1 + move_y > img_height - y_boundry_limit\n                    or y1 + move_y < y_boundry_limit\n                    else move_y\n                )\n                return (\n                    True,\n                    move_x,\n                    move_y,\n                )\n        return (False, 0, 0)\n\n    def locate_label_position(self, original_position, added_positions, binary_mask):\n        if added_positions is None or binary_mask is None:\n            return original_position\n\n        x, y = original_position\n\n        move_count = 0\n        reposition, x_move, y_move = self.reposition_label(\n            (x, y), added_positions, binary_mask, move_count\n        )\n        while reposition and move_count < 10:\n            x += x_move\n            y += y_move\n            move_count += 1\n            reposition, x_move, y_move = self.reposition_label(\n                (x, y), added_positions, binary_mask, move_count\n            )\n        added_positions.add((x, y))\n        return x, y\n\n    \"\"\"\n    Primitive drawing functions:\n    \"\"\"\n\n    def draw_text(\n        self,\n        text,\n        position,\n        added_positions=None,\n        binary_mask=None,\n        *,\n        font_size=None,\n        color=\"g\",\n        horizontal_alignment=\"center\",\n        rotation=0,\n    ):\n        \"\"\"\n        Args:\n            text (str): class label\n            position (tuple): a tuple of the x and y coordinates to place text on image.\n            font_size (int, optional): font of the text. If not provided, a font size\n                proportional to the image width is calculated and used.\n            color: color of the text. Refer to `matplotlib.colors` for full list\n                of formats that are accepted.\n            horizontal_alignment (str): see `matplotlib.text.Text`\n            rotation: rotation angle in degrees CCW\n\n        Returns:\n            output (VisImage): image object with text drawn.\n        \"\"\"\n        if not font_size:\n            font_size = self._default_font_size\n\n        # since the text background is dark, we don't want the text to be dark\n        color = np.maximum(list(mplc.to_rgb(color)), 0.15)\n        color[np.argmax(color)] = max(0.8, np.max(color))\n\n        def contrasting_color(rgb):\n            \"\"\"Returns 'white' or 'black' depending on which color contrasts more with the given RGB value.\"\"\"\n\n            # Decompose the RGB tuple\n            R, G, B = rgb\n\n            # Calculate the Y value\n            Y = 0.299 * R + 0.587 * G + 0.114 * B\n\n            # If Y value is greater than 128, it's closer to white so return black. Otherwise, return white.\n            return \"black\" if Y > 128 else \"white\"\n\n        bbox_background = contrasting_color(color * 255)\n\n        x, y = self.locate_label_position(\n            original_position=position,\n            added_positions=added_positions,\n            binary_mask=binary_mask,\n        )\n\n        self.output.ax.text(\n            x,\n            y,\n            text,\n            size=font_size * self.output.scale,\n            family=\"sans-serif\",\n            bbox={\n                \"facecolor\": bbox_background,\n                \"alpha\": 0.8,\n                \"pad\": 0.7,\n                \"edgecolor\": \"none\",\n            },\n            verticalalignment=\"top\",\n            horizontalalignment=horizontal_alignment,\n            color=color,\n            zorder=10,\n            rotation=rotation,\n        )\n        return self.output\n\n    def draw_box(self, box_coord, alpha=0.5, edge_color=\"g\", line_style=\"-\"):\n        \"\"\"\n        Args:\n            box_coord (tuple): a tuple containing x0, y0, x1, y1 coordinates, where x0 and y0\n                are the coordinates of the image's top left corner. x1 and y1 are the\n                coordinates of the image's bottom right corner.\n            alpha (float): blending efficient. Smaller values lead to more transparent masks.\n            edge_color: color of the outline of the box. Refer to `matplotlib.colors`\n                for full list of formats that are accepted.\n            line_style (string): the string to use to create the outline of the boxes.\n\n        Returns:\n            output (VisImage): image object with box drawn.\n        \"\"\"\n        x0, y0, x1, y1 = box_coord\n        width = x1 - x0\n        height = y1 - y0\n\n        linewidth = max(self._default_font_size / 12, 1) * self.boarder_width_multiplier\n\n        self.output.ax.add_patch(\n            mpl.patches.Rectangle(\n                (x0, y0),\n                width,\n                height,\n                fill=False,\n                edgecolor=edge_color,\n                linewidth=linewidth * self.output.scale,\n                alpha=alpha,\n                linestyle=line_style,\n            )\n        )\n        return self.output\n\n    def draw_rotated_box_with_label(\n        self, rotated_box, alpha=0.5, edge_color=\"g\", line_style=\"-\", label=None\n    ):\n        \"\"\"\n        Draw a rotated box with label on its top-left corner.\n\n        Args:\n            rotated_box (tuple): a tuple containing (cnt_x, cnt_y, w, h, angle),\n                where cnt_x and cnt_y are the center coordinates of the box.\n                w and h are the width and height of the box. angle represents how\n                many degrees the box is rotated CCW with regard to the 0-degree box.\n            alpha (float): blending efficient. Smaller values lead to more transparent masks.\n            edge_color: color of the outline of the box. Refer to `matplotlib.colors`\n                for full list of formats that are accepted.\n            line_style (string): the string to use to create the outline of the boxes.\n            label (string): label for rotated box. It will not be rendered when set to None.\n\n        Returns:\n            output (VisImage): image object with box drawn.\n        \"\"\"\n        cnt_x, cnt_y, w, h, angle = rotated_box\n        area = w * h\n        # use thinner lines when the box is small\n        linewidth = self._default_font_size / (\n            6 if area < _SMALL_OBJECT_AREA_THRESH * self.output.scale else 3\n        )\n\n        theta = angle * math.pi / 180.0\n        c = math.cos(theta)\n        s = math.sin(theta)\n        rect = [(-w / 2, h / 2), (-w / 2, -h / 2), (w / 2, -h / 2), (w / 2, h / 2)]\n        # x: left->right ; y: top->down\n        rotated_rect = [\n            (s * yy + c * xx + cnt_x, c * yy - s * xx + cnt_y) for (xx, yy) in rect\n        ]\n        for k in range(4):\n            j = (k + 1) % 4\n            self.draw_line(\n                [rotated_rect[k][0], rotated_rect[j][0]],\n                [rotated_rect[k][1], rotated_rect[j][1]],\n                color=edge_color,\n                linestyle=\"--\" if k == 1 else line_style,\n                linewidth=linewidth,\n            )\n\n        if label is not None:\n            text_pos = rotated_rect[1]  # topleft corner\n\n            height_ratio = h / np.sqrt(self.output.height * self.output.width)\n            label_color = self._change_color_brightness(\n                edge_color, brightness_factor=0.7\n            )\n            font_size = (\n                np.clip((height_ratio - 0.02) / 0.08 + 1, 1.2, 2)\n                * 0.5\n                * self._default_font_size\n            )\n            self.draw_text(\n                label, text_pos, color=label_color, font_size=font_size, rotation=angle\n            )\n\n        return self.output\n\n    def draw_circle(self, circle_coord, color, radius=3):\n        \"\"\"\n        Args:\n            circle_coord (list(int) or tuple(int)): contains the x and y coordinates\n                of the center of the circle.\n            color: color of the polygon. Refer to `matplotlib.colors` for a full list of\n                formats that are accepted.\n            radius (int): radius of the circle.\n\n        Returns:\n            output (VisImage): image object with box drawn.\n        \"\"\"\n        x, y = circle_coord\n        self.output.ax.add_patch(\n            mpl.patches.Circle(circle_coord, radius=radius, fill=True, color=color)\n        )\n        return self.output\n\n    def draw_line(self, x_data, y_data, color, linestyle=\"-\", linewidth=None):\n        \"\"\"\n        Args:\n            x_data (list[int]): a list containing x values of all the points being drawn.\n                Length of list should match the length of y_data.\n            y_data (list[int]): a list containing y values of all the points being drawn.\n                Length of list should match the length of x_data.\n            color: color of the line. Refer to `matplotlib.colors` for a full list of\n                formats that are accepted.\n            linestyle: style of the line. Refer to `matplotlib.lines.Line2D`\n                for a full list of formats that are accepted.\n            linewidth (float or None): width of the line. When it's None,\n                a default value will be computed and used.\n\n        Returns:\n            output (VisImage): image object with line drawn.\n        \"\"\"\n        if linewidth is None:\n            linewidth = self._default_font_size / 3\n        linewidth = max(linewidth, 1)\n        self.output.ax.add_line(\n            mpl.lines.Line2D(\n                x_data,\n                y_data,\n                linewidth=linewidth * self.output.scale,\n                color=color,\n                linestyle=linestyle,\n            )\n        )\n        return self.output\n\n    def draw_binary_mask(\n        self,\n        binary_mask,\n        color=None,\n        *,\n        edge_color=None,\n        text=None,\n        alpha=0.7,\n        area_threshold=10,\n    ):\n        \"\"\"\n        Args:\n            binary_mask (ndarray): numpy array of shape (H, W), where H is the image height and\n                W is the image width. Each value in the array is either a 0 or 1 value of uint8\n                type.\n            color: color of the mask. Refer to `matplotlib.colors` for a full list of\n                formats that are accepted. If None, will pick a random color.\n            edge_color: color of the polygon edges. Refer to `matplotlib.colors` for a\n                full list of formats that are accepted.\n            text (str): if None, will be drawn on the object\n            alpha (float): blending efficient. Smaller values lead to more transparent masks.\n            area_threshold (float): a connected component smaller than this area will not be shown.\n\n        Returns:\n            output (VisImage): image object with mask drawn.\n        \"\"\"\n        if color is None:\n            color = random_color(rgb=True, maximum=1)\n        color = mplc.to_rgb(color)\n\n        has_valid_segment = False\n        binary_mask = binary_mask.astype(\"uint8\")  # opencv needs uint8\n        mask = GenericMask(binary_mask, self.output.height, self.output.width)\n        shape2d = (binary_mask.shape[0], binary_mask.shape[1])\n\n        if not mask.has_holes:\n            # draw polygons for regular masks\n            for segment in mask.polygons:\n                area = mask_util.area(\n                    mask_util.frPyObjects([segment], shape2d[0], shape2d[1])\n                )\n                if area < (area_threshold or 0):\n                    continue\n                has_valid_segment = True\n                segment = segment.reshape(-1, 2)\n                self.draw_polygon(\n                    segment, color=color, edge_color=edge_color, alpha=alpha\n                )\n        else:\n            # https://stackoverflow.com/questions/8919719/how-to-plot-a-complex-polygon\n            rgba = np.zeros(shape2d + (4,), dtype=\"float32\")\n            rgba[:, :, :3] = color\n            rgba[:, :, 3] = (mask.mask == 1).astype(\"float32\") * alpha\n            has_valid_segment = True\n            self.output.ax.imshow(\n                rgba, extent=(0, self.output.width, self.output.height, 0)\n            )\n\n        if text is not None and has_valid_segment:\n            lighter_color = self._change_color_brightness(color, brightness_factor=0.7)\n            self._draw_text_in_mask(binary_mask, text, lighter_color)\n        return self.output\n\n    def draw_binary_mask_with_number(\n        self,\n        binary_mask,\n        color=None,\n        *,\n        edge_color=None,\n        text=None,\n        label_mode=\"1\",\n        alpha=0.1,\n        anno_mode=[\"Mask\"],\n        area_threshold=10,\n    ):\n        \"\"\"\n        Args:\n            binary_mask (ndarray): numpy array of shape (H, W), where H is the image height and\n                W is the image width. Each value in the array is either a 0 or 1 value of uint8\n                type.\n            color: color of the mask. Refer to `matplotlib.colors` for a full list of\n                formats that are accepted. If None, will pick a random color.\n            edge_color: color of the polygon edges. Refer to `matplotlib.colors` for a\n                full list of formats that are accepted.\n            text (str): if None, will be drawn on the object\n            alpha (float): blending efficient. Smaller values lead to more transparent masks.\n            area_threshold (float): a connected component smaller than this area will not be shown.\n\n        Returns:\n            output (VisImage): image object with mask drawn.\n        \"\"\"\n        if color is None:\n            randint = random.randint(0, len(self.color_proposals) - 1)\n            color = self.color_proposals[randint]\n        color = mplc.to_rgb(color)\n\n        has_valid_segment = True\n        binary_mask = binary_mask.astype(\"uint8\")  # opencv needs uint8\n        mask = GenericMask(binary_mask, self.output.height, self.output.width)\n        shape2d = (binary_mask.shape[0], binary_mask.shape[1])\n        bbox = mask.bbox()\n\n        if \"Mask\" in anno_mode:\n            if not mask.has_holes:\n                # draw polygons for regular masks\n                for segment in mask.polygons:\n                    area = mask_util.area(\n                        mask_util.frPyObjects([segment], shape2d[0], shape2d[1])\n                    )\n                    if area < (area_threshold or 0):\n                        continue\n                    has_valid_segment = True\n                    segment = segment.reshape(-1, 2)\n                    self.draw_polygon(\n                        segment, color=color, edge_color=edge_color, alpha=alpha\n                    )\n            else:\n                # https://stackoverflow.com/questions/8919719/how-to-plot-a-complex-polygon\n                rgba = np.zeros(shape2d + (4,), dtype=\"float32\")\n                rgba[:, :, :3] = color\n                rgba[:, :, 3] = (mask.mask == 1).astype(\"float32\") * alpha\n                has_valid_segment = True\n                self.output.ax.imshow(\n                    rgba, extent=(0, self.output.width, self.output.height, 0)\n                )\n\n        if \"Box\" in anno_mode:\n            self.draw_box(bbox, edge_color=color, alpha=0.75)\n\n        if \"Mark\" in anno_mode:\n            has_valid_segment = True\n        else:\n            has_valid_segment = False\n\n        if text is not None and has_valid_segment:\n            # lighter_color = tuple([x*0.2 for x in color])\n            lighter_color = [\n                1,\n                1,\n                1,\n            ]  # self._change_color_brightness(color, brightness_factor=0.7)\n            self._draw_number_in_mask(\n                binary_mask=binary_mask,\n                text=text,\n                color=lighter_color,\n                label_mode=label_mode,\n            )\n        return self.output\n\n    def draw_soft_mask(self, soft_mask, color=None, *, text=None, alpha=0.5):\n        \"\"\"\n        Args:\n            soft_mask (ndarray): float array of shape (H, W), each value in [0, 1].\n            color: color of the mask. Refer to `matplotlib.colors` for a full list of\n                formats that are accepted. If None, will pick a random color.\n            text (str): if None, will be drawn on the object\n            alpha (float): blending efficient. Smaller values lead to more transparent masks.\n\n        Returns:\n            output (VisImage): image object with mask drawn.\n        \"\"\"\n        if color is None:\n            color = random_color(rgb=True, maximum=1)\n        color = mplc.to_rgb(color)\n\n        shape2d = (soft_mask.shape[0], soft_mask.shape[1])\n        rgba = np.zeros(shape2d + (4,), dtype=\"float32\")\n        rgba[:, :, :3] = color\n        rgba[:, :, 3] = soft_mask * alpha\n        self.output.ax.imshow(\n            rgba, extent=(0, self.output.width, self.output.height, 0)\n        )\n\n        if text is not None:\n            lighter_color = self._change_color_brightness(color, brightness_factor=0.7)\n            binary_mask = (soft_mask > 0.5).astype(\"uint8\")\n            self._draw_text_in_mask(binary_mask, text, lighter_color)\n        return self.output\n\n    def draw_polygon(self, segment, color, edge_color=None, alpha=0.5):\n        \"\"\"\n        Args:\n            segment: numpy array of shape Nx2, containing all the points in the polygon.\n            color: color of the polygon. Refer to `matplotlib.colors` for a full list of\n                formats that are accepted.\n            edge_color: color of the polygon edges. Refer to `matplotlib.colors` for a\n                full list of formats that are accepted. If not provided, a darker shade\n                of the polygon color will be used instead.\n            alpha (float): blending efficient. Smaller values lead to more transparent masks.\n\n        Returns:\n            output (VisImage): image object with polygon drawn.\n        \"\"\"\n        if edge_color is None:\n            # make edge color darker than the polygon color\n            if alpha > 0.8:\n                edge_color = self._change_color_brightness(\n                    color, brightness_factor=-0.7\n                )\n            else:\n                edge_color = color\n        edge_color = mplc.to_rgb(edge_color) + (1,)\n\n        polygon = mpl.patches.Polygon(\n            segment,\n            fill=True,\n            facecolor=mplc.to_rgb(color) + (alpha,),\n            edgecolor=edge_color,\n            linewidth=max(self._default_font_size // 15 * self.output.scale, 1),\n        )\n        self.output.ax.add_patch(polygon)\n        return self.output\n\n    \"\"\"\n    Internal methods:\n    \"\"\"\n\n    def _jitter(self, color):\n        \"\"\"\n        Randomly modifies given color to produce a slightly different color than the color given.\n\n        Args:\n            color (tuple[double]): a tuple of 3 elements, containing the RGB values of the color\n                picked. The values in the list are in the [0.0, 1.0] range.\n\n        Returns:\n            jittered_color (tuple[double]): a tuple of 3 elements, containing the RGB values of the\n                color after being jittered. The values in the list are in the [0.0, 1.0] range.\n        \"\"\"\n        color = mplc.to_rgb(color)\n        # np.random.seed(0)\n        vec = np.random.rand(3)\n        # better to do it in another color space\n        vec = vec / np.linalg.norm(vec) * 0.5\n        res = np.clip(vec + color, 0, 1)\n        return tuple(res)\n\n    def _create_grayscale_image(self, mask=None):\n        \"\"\"\n        Create a grayscale version of the original image.\n        The colors in masked area, if given, will be kept.\n        \"\"\"\n        img_bw = self.img.astype(\"f4\").mean(axis=2)\n        img_bw = np.stack([img_bw] * 3, axis=2)\n        if mask is not None:\n            img_bw[mask] = self.img[mask]\n        return img_bw\n\n    def _change_color_brightness(self, color, brightness_factor):\n        \"\"\"\n        Depending on the brightness_factor, gives a lighter or darker color i.e. a color with\n        less or more saturation than the original color.\n\n        Args:\n            color: color of the polygon. Refer to `matplotlib.colors` for a full list of\n                formats that are accepted.\n            brightness_factor (float): a value in [-1.0, 1.0] range. A lightness factor of\n                0 will correspond to no change, a factor in [-1.0, 0) range will result in\n                a darker color and a factor in (0, 1.0] range will result in a lighter color.\n\n        Returns:\n            modified_color (tuple[double]): a tuple containing the RGB values of the\n                modified color. Each value in the tuple is in the [0.0, 1.0] range.\n        \"\"\"\n        assert brightness_factor >= -1.0 and brightness_factor <= 1.0\n        color = mplc.to_rgb(color)\n        polygon_color = colorsys.rgb_to_hls(*mplc.to_rgb(color))\n        modified_lightness = polygon_color[1] + (brightness_factor * polygon_color[1])\n        modified_lightness = 0.0 if modified_lightness < 0.0 else modified_lightness\n        modified_lightness = 1.0 if modified_lightness > 1.0 else modified_lightness\n        modified_color = colorsys.hls_to_rgb(\n            polygon_color[0], modified_lightness, polygon_color[2]\n        )\n        return modified_color\n\n    def _convert_boxes(self, boxes):\n        \"\"\"\n        Convert different format of boxes to an NxB array, where B = 4 or 5 is the box dimension.\n        \"\"\"\n        if isinstance(boxes, Boxes) or isinstance(boxes, RotatedBoxes):\n            return boxes.tensor.detach().numpy()\n        else:\n            return np.asarray(boxes)\n\n    def _convert_masks(self, masks_or_polygons):\n        \"\"\"\n        Convert different format of masks or polygons to a tuple of masks and polygons.\n\n        Returns:\n            list[GenericMask]:\n        \"\"\"\n\n        m = masks_or_polygons\n        if isinstance(m, PolygonMasks):\n            m = m.polygons\n        if isinstance(m, BitMasks):\n            m = m.tensor.numpy()\n        if isinstance(m, torch.Tensor):\n            m = m.numpy()\n        ret = []\n        for x in m:\n            if isinstance(x, GenericMask):\n                ret.append(x)\n            else:\n                ret.append(GenericMask(x, self.output.height, self.output.width))\n        return ret\n\n    def _draw_number_in_box(self, box, text, color, label_mode=\"1\"):\n        \"\"\"\n        Find proper places to draw text given a box.\n        \"\"\"\n        x0, y0, x1, y1 = box\n        text_pos = (x0, y0)  # if drawing boxes, put text on the box corner.\n        horiz_align = \"left\"\n        # for small objects, draw text at the side to avoid occlusion\n        instance_area = (y1 - y0) * (x1 - x0)\n        if (\n            instance_area < _SMALL_OBJECT_AREA_THRESH * self.output.scale\n            or y1 - y0 < 40 * self.output.scale\n        ):\n            if y1 >= self.output.height - 5:\n                text_pos = (x1, y0)\n            else:\n                text_pos = (x0, y1)\n\n        height_ratio = (y1 - y0) / np.sqrt(self.output.height * self.output.width)\n        lighter_color = self._change_color_brightness(color, brightness_factor=0.7)\n        font_size = (\n            np.clip((height_ratio - 0.02) / 0.08 + 1, 1.2, 2)\n            * 0.65\n            * self._default_font_size\n        )\n        if label_mode == \"a\":\n            text = self.number_to_string(int(text))\n        else:\n            text = text\n        self.draw_text(\n            text,\n            text_pos,\n            color=lighter_color,\n            horizontal_alignment=horiz_align,\n            font_size=font_size,\n        )\n\n        return str(text)\n\n    @staticmethod\n    def number_to_string(n):\n        chars = []\n        while n:\n            n, remainder = divmod(n - 1, 26)\n            chars.append(chr(97 + remainder))\n        return \"\".join(reversed(chars))\n\n    def _draw_number_in_mask(\n        self, binary_mask, text, color, added_positions=None, label_mode=\"1\"\n    ):\n        \"\"\"\n        Find proper places to draw text given a binary mask.\n        \"\"\"\n        binary_mask = np.pad(binary_mask, ((1, 1), (1, 1)), \"constant\")\n        mask_dt = cv2.distanceTransform(binary_mask, cv2.DIST_L2, 0)\n        mask_dt = mask_dt[1:-1, 1:-1]\n        max_dist = np.max(mask_dt)\n        coords_y, coords_x = np.where(mask_dt == max_dist)  # coords is [y, x]\n\n        if label_mode == \"a\":\n            text = self.number_to_string(int(text))\n        else:\n            text = text\n\n        text_position = (\n            coords_x[len(coords_x) // 2] + 2,\n            coords_y[len(coords_y) // 2] - 6,\n        )\n        self.draw_text(\n            text,\n            text_position,\n            added_positions=added_positions,\n            binary_mask=binary_mask,\n            color=color,\n        )\n\n        return str(text), text_position\n\n        # _num_cc, cc_labels, stats, centroids = cv2.connectedComponentsWithStats(binary_mask, 8)\n        # if stats[1:, -1].size == 0:\n        #     return\n        # largest_component_id = np.argmax(stats[1:, -1]) + 1\n\n        # # draw text on the largest component, as well as other very large components.\n        # for cid in range(1, _num_cc):\n        #     if cid == largest_component_id or stats[cid, -1] > _LARGE_MASK_AREA_THRESH:\n        #         # median is more stable than centroid\n        #         # center = centroids[largest_component_id]\n        #         center = np.median((cc_labels == cid).nonzero(), axis=1)[::-1]\n        #         # bottom=np.max((cc_labels == cid).nonzero(), axis=1)[::-1]\n        #         # center[1]=bottom[1]+2\n        #         self.draw_text(text, center, color=color)\n\n    def _draw_text_in_mask(self, binary_mask, text, color):\n        \"\"\"\n        Find proper places to draw text given a binary mask.\n        \"\"\"\n        _num_cc, cc_labels, stats, centroids = cv2.connectedComponentsWithStats(\n            binary_mask, 8\n        )\n        if stats[1:, -1].size == 0:\n            return\n        largest_component_id = np.argmax(stats[1:, -1]) + 1\n\n        # draw text on the largest component, as well as other very large components.\n        for cid in range(1, _num_cc):\n            if cid == largest_component_id or stats[cid, -1] > _LARGE_MASK_AREA_THRESH:\n                # median is more stable than centroid\n                # center = centroids[largest_component_id]\n                center = np.median((cc_labels == cid).nonzero(), axis=1)[::-1]\n                bottom = np.max((cc_labels == cid).nonzero(), axis=1)[::-1]\n                center[1] = bottom[1] + 2\n                self.draw_text(text, center, color=color)\n\n    def _convert_keypoints(self, keypoints):\n        if isinstance(keypoints, Keypoints):\n            keypoints = keypoints.tensor\n        keypoints = np.asarray(keypoints)\n        return keypoints\n\n    def get_output(self):\n        \"\"\"\n        Returns:\n            output (VisImage): the image output containing the visualizations added\n            to the image.\n        \"\"\"\n        return self.output\n"
  },
  {
    "path": "sam3/agent/helpers/zoom_in.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n\nimport io\nimport math\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pycocotools.mask as mask_utils\nfrom PIL import Image\n\nfrom .som_utils import ColorPalette, draw_box, draw_mask, draw_text\n\n\ndef render_zoom_in(\n    object_data,\n    image_file,\n    show_box: bool = True,\n    show_text: bool = False,\n    show_holes: bool = True,\n    mask_alpha: float = 0.15,\n):\n    \"\"\"\n    Render a two-panel visualization with a cropped original view (left/upper) and a zoomed-in\n    mask overlay (right/lower), then return it as a PIL.Image along with the chosen mask color (hex).\n\n    Parameters\n    ----------\n    object_data : dict\n        Dict containing \"labels\" and COCO RLE \"segmentation\".\n        Expected:\n          object_data[\"labels\"][0][\"noun_phrase\"] : str\n          object_data[\"segmentation\"] : COCO RLE (with \"size\": [H, W])\n    image_file : PIL.Image.Image\n        Source image (PIL).\n    show_box : bool\n        Whether to draw the bbox on the cropped original panel.\n    show_text : bool\n        Whether to draw the noun phrase label near the bbox.\n    show_holes : bool\n        Whether to render mask holes (passed through to draw_mask).\n    mask_alpha : float\n        Alpha for the mask overlay.\n\n    Returns\n    -------\n    pil_img : PIL.Image.Image\n        The composed visualization image.\n    color_hex : str\n        Hex string of the chosen mask color.\n    \"\"\"\n\n    # ---- local constants (avoid module-level globals) ----\n    _AREA_LARGE = 0.25\n    _AREA_MEDIUM = 0.05\n\n    # ---- local helpers (avoid name collisions in a larger class) ----\n    def _get_shift(x, w, w_new, w_img):\n        assert 0 <= w_new <= w_img\n        shift = (w_new - w) / 2\n        if x - shift + w_new > w_img:\n            shift = x + w_new - w_img\n        return min(x, shift)\n\n    def _get_zoom_in_box(mask_box_xywh, img_h, img_w, mask_area):\n        box_w, box_h = mask_box_xywh[2], mask_box_xywh[3]\n        w_new = min(box_w + max(0.2 * box_w, 16), img_w)\n        h_new = min(box_h + max(0.2 * box_h, 16), img_h)\n\n        mask_relative_area = mask_area / (w_new * h_new)\n\n        # zoom-in (larger box if mask is relatively big)\n        w_new_large, h_new_large = w_new, h_new\n        if mask_relative_area > _AREA_LARGE:\n            ratio_large = math.sqrt(mask_relative_area / _AREA_LARGE)\n            w_new_large = min(w_new * ratio_large, img_w)\n            h_new_large = min(h_new * ratio_large, img_h)\n\n        w_shift_large = _get_shift(\n            mask_box_xywh[0], mask_box_xywh[2], w_new_large, img_w\n        )\n        h_shift_large = _get_shift(\n            mask_box_xywh[1], mask_box_xywh[3], h_new_large, img_h\n        )\n        zoom_in_box = [\n            mask_box_xywh[0] - w_shift_large,\n            mask_box_xywh[1] - h_shift_large,\n            w_new_large,\n            h_new_large,\n        ]\n\n        # crop box for the original/cropped image\n        w_new_medium, h_new_medium = w_new, h_new\n        if mask_relative_area > _AREA_MEDIUM:\n            ratio_med = math.sqrt(mask_relative_area / _AREA_MEDIUM)\n            w_new_medium = min(w_new * ratio_med, img_w)\n            h_new_medium = min(h_new * ratio_med, img_h)\n\n        w_shift_medium = _get_shift(\n            mask_box_xywh[0], mask_box_xywh[2], w_new_medium, img_w\n        )\n        h_shift_medium = _get_shift(\n            mask_box_xywh[1], mask_box_xywh[3], h_new_medium, img_h\n        )\n        img_crop_box = [\n            mask_box_xywh[0] - w_shift_medium,\n            mask_box_xywh[1] - h_shift_medium,\n            w_new_medium,\n            h_new_medium,\n        ]\n        return zoom_in_box, img_crop_box\n\n    # ---- main body ----\n    # Input parsing\n    object_label = object_data[\"labels\"][0][\"noun_phrase\"]\n    img = image_file.convert(\"RGB\")\n    bbox_xywh = mask_utils.toBbox(object_data[\"segmentation\"])  # [x, y, w, h]\n\n    # Choose a stable, visually distant color based on crop\n    bbox_xyxy = [\n        bbox_xywh[0],\n        bbox_xywh[1],\n        bbox_xywh[0] + bbox_xywh[2],\n        bbox_xywh[1] + bbox_xywh[3],\n    ]\n    crop_img = img.crop(bbox_xyxy)\n    color_palette = ColorPalette.default()\n    color_obj, _ = color_palette.find_farthest_color(np.array(crop_img))\n    color = np.array([color_obj.r / 255, color_obj.g / 255, color_obj.b / 255])\n    color_hex = f\"#{color_obj.r:02x}{color_obj.g:02x}{color_obj.b:02x}\"\n\n    # Compute zoom-in / crop boxes\n    img_h, img_w = object_data[\"segmentation\"][\"size\"]\n    mask_area = mask_utils.area(object_data[\"segmentation\"])\n    zoom_in_box, img_crop_box = _get_zoom_in_box(bbox_xywh, img_h, img_w, mask_area)\n\n    # Layout choice\n    w, h = img_crop_box[2], img_crop_box[3]\n    if w < h:\n        fig, (ax1, ax2) = plt.subplots(1, 2)\n    else:\n        fig, (ax1, ax2) = plt.subplots(2, 1)\n\n    # Panel 1: cropped original with optional box/text\n    img_crop_box_xyxy = [\n        img_crop_box[0],\n        img_crop_box[1],\n        img_crop_box[0] + img_crop_box[2],\n        img_crop_box[1] + img_crop_box[3],\n    ]\n    img1 = img.crop(img_crop_box_xyxy)\n    bbox_xywh_rel = [\n        bbox_xywh[0] - img_crop_box[0],\n        bbox_xywh[1] - img_crop_box[1],\n        bbox_xywh[2],\n        bbox_xywh[3],\n    ]\n    ax1.imshow(img1)\n    ax1.axis(\"off\")\n    if show_box:\n        draw_box(ax1, bbox_xywh_rel, edge_color=color)\n    if show_text:\n        x0, y0 = bbox_xywh_rel[0] + 2, bbox_xywh_rel[1] + 2\n        draw_text(ax1, object_label, [x0, y0], color=color)\n\n    # Panel 2: zoomed-in mask overlay\n    binary_mask = mask_utils.decode(object_data[\"segmentation\"])\n    alpha = Image.fromarray((binary_mask * 255).astype(\"uint8\"))\n    img_rgba = img.convert(\"RGBA\")\n    img_rgba.putalpha(alpha)\n    zoom_in_box_xyxy = [\n        zoom_in_box[0],\n        zoom_in_box[1],\n        zoom_in_box[0] + zoom_in_box[2],\n        zoom_in_box[1] + zoom_in_box[3],\n    ]\n    img_with_alpha_zoomin = img_rgba.crop(zoom_in_box_xyxy)\n    alpha_zoomin = img_with_alpha_zoomin.split()[3]\n    binary_mask_zoomin = np.array(alpha_zoomin).astype(bool)\n\n    ax2.imshow(img_with_alpha_zoomin.convert(\"RGB\"))\n    ax2.axis(\"off\")\n    draw_mask(\n        ax2, binary_mask_zoomin, color=color, show_holes=show_holes, alpha=mask_alpha\n    )\n\n    plt.tight_layout()\n\n    # Buffer -> PIL.Image\n    buf = io.BytesIO()\n    fig.savefig(buf, format=\"png\", bbox_inches=\"tight\", pad_inches=0, dpi=100)\n    plt.close(fig)\n    buf.seek(0)\n    pil_img = Image.open(buf)\n\n    return pil_img, color_hex\n"
  },
  {
    "path": "sam3/agent/inference.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n\nimport json\nimport os\n\nfrom sam3.agent.agent_core import agent_inference\n\n\ndef run_single_image_inference(\n    image_path,\n    text_prompt,\n    llm_config,\n    send_generate_request,\n    call_sam_service,\n    output_dir=\"agent_output\",\n    debug=False,\n):\n    \"\"\"Run inference on a single image with provided prompt\"\"\"\n\n    llm_name = llm_config[\"name\"]\n\n    if not os.path.exists(image_path):\n        raise FileNotFoundError(f\"Image file not found: {image_path}\")\n\n    # Create output directory\n    os.makedirs(output_dir, exist_ok=True)\n\n    # Generate output file names\n    image_basename = os.path.splitext(os.path.basename(image_path))[0]\n    prompt_for_filename = text_prompt.replace(\"/\", \"_\").replace(\" \", \"_\")\n\n    base_filename = f\"{image_basename}_{prompt_for_filename}_agent_{llm_name}\"\n    output_json_path = os.path.join(output_dir, f\"{base_filename}_pred.json\")\n    output_image_path = os.path.join(output_dir, f\"{base_filename}_pred.png\")\n    agent_history_path = os.path.join(output_dir, f\"{base_filename}_history.json\")\n\n    # Check if output already exists and skip\n    if os.path.exists(output_json_path):\n        print(f\"Output JSON {output_json_path} already exists. Skipping.\")\n        return\n\n    print(f\"{'-' * 30} Starting SAM 3 Agent Session... {'-' * 30} \")\n    agent_history, final_output_dict, rendered_final_output = agent_inference(\n        image_path,\n        text_prompt,\n        send_generate_request=send_generate_request,\n        call_sam_service=call_sam_service,\n        output_dir=output_dir,\n        debug=debug,\n    )\n    print(f\"{'-' * 30} End of SAM 3 Agent Session... {'-' * 30} \")\n\n    final_output_dict[\"text_prompt\"] = text_prompt\n    final_output_dict[\"image_path\"] = image_path\n\n    # Save outputs\n    json.dump(final_output_dict, open(output_json_path, \"w\"), indent=4)\n    json.dump(agent_history, open(agent_history_path, \"w\"), indent=4)\n    rendered_final_output.save(output_image_path)\n\n    print(f\"\\n✅ Successfully processed single image!\")\n    print(f\"Output JSON: {output_json_path}\")\n    print(f\"Output Image: {output_image_path}\")\n    print(f\"Agent History: {agent_history_path}\")\n    return output_image_path\n"
  },
  {
    "path": "sam3/agent/system_prompts/system_prompt.txt",
    "content": "You are a helpful visual-concept grounding assistant capable of leveraging tool calls to ground concepts the user refers to, and providing structured JSON outputs and tool calls.\nThe user may provide you with a referring expression that matches some part(s) of the image, or a question whose answer points to some part(s) of the image.\nYou should observe and analyze the image along with the initial user input query very carefully, note all details in the image, think about what the user is actually referring to, how to leverage existing tools below to ground the target(s), and then call exactly one tool per turn.\nAt each turn, all available mask(s) will be renumbered and re-rendered on the most recent image provided to you. The numbering and coloring can be different from previous turns. You should only refer to mask(s) rendered on the most recent image using their currently assigned number.\nIf a tool call does not produce the intended output, do not give up; be creative and try calling the segment_phrase tool again with different parameters, or try a different tool. You may take as many turns as needed, but you must call exactly one tool per turn and then immediately stop. There is no need to rush to find a solution in the current turn, so take your time!\n\n\nHow you should understand the initial user input query and the raw input image:\n\n1. If there are multiple instances of the target object class in the image, you should read the initial user input query very carefully and think about whether the initial user input query applies broadly to all the instances or just one specific instance, and ground accordingly.\n2. You should think carefully and find the actual target object(s) the user is asking you to ground. Never call the segment_phrase tool to ground secondary object(s) in the initial user input query that only exist to help you identify the actual target. For example, given the initial user input query 'a giraffe with its head up', you should ground the whole 'giraffe' and not 'the head of the giraffe'. Given the initial user input query 'a person holding a blender with their left hand', you should ground 'person' instead of 'blender' or 'left hand'. Given the initial user input query 'two lovely ladies conversing while walking a dog, behind a bicycle', you should ground 'woman' instead of 'dog' or 'bicycle'. Given the initial user input query \"guy with white hat\", you should ground the \"guy\" and not the \"white hat\".\n3. Sometimes the user will mention or use non-target object(s) in their description to help identify the target object(s), you must make sure not to include mask(s) for those object(s) that are only used for identification purposes. For example, given the initial user input query \"a man carrying a young girl\", you should only ground the main target the \"man\" and not include the \"young girl\" in your final predicted mask(s). Given the initial user input query \"a small girl staring at something, along with her older sister\", you should only ground the \"small girl\" and not include her \"older sister\" in your final predicted mask(s).\n4. Sometimes the target object(s) are not directly named in the description but are clearly referenced, in which case you should focus only on grounding the clearly referenced target object(s). For example, given the initial user input query \"something that shows the man is playing golf\" and an image of a man holding a golf club, you should ground the phrase \"golf club\" and not the phrase \"man\" even though \"golf club\" is not directly named in the initial user input query.\n5. You must carefully examine all details in the raw input image and note them in your thinking, and reason step-by-step to determine if anything in the image could potentially match the initial user input query. You should not give up the grounding process and call the report_no_mask tool due to very small technicalities or small literal discrepancies. For example, if the user asks you to find a dry space, relatively dry areas like land would satisfy the constraint. If the user asks you to find object(s) that help you focus, headphones and even window shades could potentially serve the purpose. If the user asks you to find containers that can be used for holding hot water, cups or kettles can both work. You should only call the report_no_mask tool if there are very direct contradictions and/or hard constraints in the initial user input query that cause all objects in the raw input image to be invalid matches for the initial user input query.\n6. Sometimes the initial user input query can be slightly wrong but still very much related to the image. For example, the user may ask you to ground \"the red laptop\" when the laptop computer in the image is purple (in this case you should call segment_phrase on the \"text_prompt\" \"purple laptop computer\"); or the user may ask you to ground \"girl left\" when there is no girl on the left of the image but rather a woman on the left of the image (in this case you should call segment_phrase to ground the phrase \"left woman\"). In these cases, you should accommodate the user errors and still ground the object(s) in the image that best match the initial user input query. You may slightly modify the initial user input query based on your observation of the original image to better match the user’s intent.\n7. Sometimes the initial user input query may be grammatically incorrect, contain typos, or contain irrelevant information. In these cases, you should not blindly try to ground part(s) of the initial user input query using segment_phrase. Instead, you should reason step by step to think about what the user is actually referring to, and then modify the initial user input query based on your understanding and careful analysis of the raw input image. For example, you may see an initial user input query like \"left back to us guy\", which you can interpret as the man on the left who is facing the other direction (if you can see such a man exists in the image), and then call segment_phrase on \"man\" and then select the correct mask. You may also see an initial user input query like \"big maybe hotdog middle back taste good\", and there are just nine sandwiches in the image placed in three rows, then you can probably infer that the user is trying to ground the sandwich in the middle of the back row. You can then call segment_phrase to ground the phrase \"sandwich\" and use the select_masks_and_return tool to accurately choose only the sandwich in the middle of the back row in your \"final_answer_masks\" array.\n8. The correct \"final_answer_masks\" array should never contain any mask(s) whose number is greater than 100. For example, you may never select mask 102 or mask 114 in your \"final_answer_masks\" array. This also means that you are never allowed to select more than 100 masks in your \"final_answer_masks\" array.\n9. Please note that if the raw input image is composed of two individual sub-images concatenated visually; it still counts as only one image. If you find that there are \"two\" images in the chat context but the \"second image\" is not the same as the first image overlaid with numbered segmentation masks, this means that the \"second image\" is actually just a sub-image of the raw input image concatenated with the \"first image\" to serve as a combined raw input image. In this case, there is actually only one image in the chat context and you should follow the Scenario 1 instructions. This is very important!\n\nYou should always follow the response format defined below and complete the Steps for Each Turn as specified below. Never break the specified format for any reason.\n\n\nAvailable tools:\n\nsegment_phrase: Use the experimental Segment Anything 3 model to ground all instances of a simple noun phrase by generating segmentation mask(s) that cover those instances on the raw input image. At the same time, all previously generated mask(s) will be deleted and cannot be referred to in future messages.\nUse cases: \"Given a simple, direct, and singular noun phrase (not a referring expression that requires additional understanding/reasoning), segment_phrase will try to locate all object instance(s) on the raw input image that match the simple noun phrase you provided. The tool will also render all of the generated segmentation mask(s) onto the image for you to examine and decide the next step.\"\nParameters for segment_phrase: {\"type\": \"object\", \"properties\": {\"text_prompt\": {\"type\": \"string\", \"description\": \"A short and simple noun phrase, e.g., rope, bird beak, speed monitor, brown handbag, person torso\"}}, \"required\": [\"text_prompt\"]}\nReturn type: A new image with differently colored segmentation mask(s) rendered on it, and a text message indicating the number of mask(s) generated by the experimental Segment Anything 3 model for this \"text_prompt\" only.\nImportant rules for using the segment_phrase tool:\n1. You may use visual adjectives such as color to help identify the concept you want to ground, but do not use complicated descriptors like numbers or mention text that is written on the image as the segment_phrase tool does not have OCR capabilities. For example, use \"black ball\" instead of \"8-ball\" to ground a black ball with the number \"8\" written on it. If the user asks you to ground an object that can only be identified by the text or number written on it, you should generate mask(s) for all object(s) of that category and then cross-examine the original image against the masked image carefully to locate the exact mask(s) that match or answer the initial user input query and select only those mask(s).\n2. Do not try to directly ground words, letters, or numbers in written text on the image. For example, if there is text on a sign to ground, you should use \"sign\" as your \"text_prompt\" instead of using the actual text itself as your \"text_prompt\".\n3. If your call to segment_phrase does not generate any useful mask(s) or if the mask(s) are incomplete, you may want to try calling the segment_phrase tool again using a more general noun phrase. For example, if the \"text_prompt\" \"elementary school teacher\" does not give you any mask(s), you can call segment_phrase again with the \"text_prompt\": \"person\".\n4. You should avoid identifying concepts using actions, relationships, or comparatives; instead, call segment_phrase on a more general phrase and let the segment_phrase tool generate more mask(s) than you need. Then, in the next turn, you can use the select_masks_and_return tool to remove some mask(s). For example, use \"vase\" instead of \"the bigger vase\", use \"dog\" instead of \"the dog lying down\", and use \"brown pillow\" instead of \"the pillow on the chair\".\n5. If the results of segment_phrase are not what you expected, you can always call segment_phrase again using a different \"text_prompt\". For example, when grounding a dog's nose, you can try \"dog nose\" and \"black marking\" after \"nose\" does not work.\n6. Sometimes when the target object(s) are too niche and the segment_phrase tool does not provide any mask(s), you may want to try grounding a more general version of the object. For example, when \"sundial\" does not produce any mask(s), you can try grounding \"statue\".\n7. Be concise and get the right keywords; don't make your \"text_prompt\" long.\n8. Do not ever use the exact same \"text_prompt\" more than once. This is very important!\n9. Sometimes you may find that the user is referring to a person or some people as the main grounding target. In this case, you should absolutely avoid grounding identifying part(s) or attribute(s) of the person or people, even if these part(s) or component(s) are explicitly mentioned in the initial user input query. Instead, you should only call segment_phrase with general \"text_prompt\"s like \"person\", \"man\", \"girl\", \"firefighter\", etc. that refer to the person as a whole. Later you can refer back to these identifying part(s) or attribute(s) and look closely at the original image to help you select the correct mask(s).\n10. If a previously used \"text_prompt\" does not work, avoid using it again and think of a new, creative \"text_prompt\" that may be indirect but can achieve the target result. For example, when grounding the center of the cake with text written on it, try grounding \"birthday greeting\" instead.\n11. You should always call segment_phrase with a \"text_prompt\" that represents the entire grounding target to generate mask(s) that you can choose from (sometimes along with other entities of the same category if it is hard to avoid). Do not call segment_phrase with a \"text_prompt\" that refers to subpart(s) of the grounding target to narrow down your search, because your \"final_answer_masks\" array can only be composed of of mask(s) generated by segment_phrase. For example, when the grounding target is an adult, use the \"text_prompt\" \"adult person\" instead of \"adult hand\".\n12. If the initial user input query refers only to one specific object instance of a category, while there are other object instance(s) of the same category in the image that are not being referred to, you should call segment_phrase with a \"text_prompt\" that is the singular form of the category of object(s), and then use the select_masks_and_return and/or examine_each_mask tool to narrow down your \"final_answer_masks\".\n13. Every time you call the segment_phrase tool, all previously generated mask(s) will be deleted. You are forbidden from referring to mask(s) that exist only in previous images in the message history but have been deleted in the most recent turn (not rendered on the most recent image).\n14. You should only ground object(s) that fully match or answer the initial user input query, and ignore object(s) that only partially match the initial user input query. For example, if the user is asking for object(s) used for inputting data and controlling the computer, you should only ground the keyboard and not the mouse, since the mouse is only used for controlling the computer but not for inputting data.\n15. You should never propose a \"text_prompt\" that covers more area than the initial user input query, for example, if the initial user input query asks specifically for areas of the jeans that are broken, you should never propose the \"text_prompt\" \"jeans\" because it will definitely cover more area than the ground truth target.\n16. You should never propose a \"text_prompt\" that covers less area than the initial user input query, for example, if the initial user input query asks for the person holding a microphone, you should never propose the \"text_prompt\" \"microphone\" because it will definitely cover less area than the ground truth target.\n17. You should first try your best to propose a \"text_prompt\" that covers the exact same object(s) as referred to by the initial user input query, no more, no less. You may not propose a \"text_prompt\" that covers more object(s) than what is referred to by the initial user input query unless you have tried every creative \"text_prompt\" you can think of to cover exactly the correct object(s) and none of them worked.\n18. Be creative in your \"text_prompt\" choice; you may use synonyms and use visual common sense to think of different \"text_prompt\" choices. You have unlimited turns to call each tool, so take your time!\n\nexamine_each_mask: Use this tool when the segment_phrase tool generates multiple small or overlapping mask(s), making it difficult to distinguish the correct mask(s). examine_each_mask allows you to render and examine each mask independently to see small mask(s) clearly and avoid confusing overlapping mask(s). (examine_each_mask can only be called after segment_phrase has been called at least once.)\nUse cases: \"Sometimes there are multiple small mask(s) or overlapping mask(s) rendered on an image, making it difficult to distinguish each mask from others. In this case, you should call the examine_each_mask tool to individually verify each mask and filter out incorrect mask(s).\"\nParameters for examine_each_mask: None\nReturn type: A new image with colored segmentation mask(s) accepted by the examine_each_mask tool, and a text message indicating how many masks were accepted.\nImportant rules for using the examine_each_mask tool:\n1. You may only call the examine_each_mask tool when you have re-examined the raw input image and the most recent output image, and you are absolutely sure that all the correct mask(s) that match the initial user input query have been rendered on the most recent image, and there are no missing correct mask(s). You must state this explicitly before you call the examine_each_mask tool.\n2. Do not call the examine_each_mask tool if there is only one mask and the mask is not very small.\n3. Do not call the examine_each_mask tool when there are many masks in the image but they are neither very small nor overlapping.\n4. The purpose of calling examine_each_mask is to distinguish overlapping mask(s), to examine whether very small mask(s) are correct, or both.\n5. After you have carefully compared the generated mask(s) against the initial user input query and the original image, and stated that you are absolutely sure that all the correct mask(s) that match the initial user input query have been rendered on the most recent image, you may consider calling the examine_each_mask tool if there are multiple overlapping mask(s) generated and it is not easy for you to name the correct mask(s). For example, if the question is to ground \"the cookie behind the other cookie\", segment_phrase generates two mask(s) for the two cookies in the image, but they are overlapping. You can also call the examine_each_mask tool if there are one or more very small mask(s) that are generated and you are sure that some of them are correct, and it is not easy for you to directly decide the correct mask(s). For example, if the question is to ground \"sharp teeth\" and there are multiple small mask(s) generated but it is not easy for you to tell which ones are correct without zooming in on each mask.\n6. Do not call the examine_each_mask tool if there are many masks in the image but you can clearly tell each mask apart from all other mask(s), and there is no significant challenge in identifying the correct mask(s). For example, if the question is asking \"where people can sit\" and there are many masks for chairs, and you just need to list all the mask numbers for chairs.\n7. You may not call the examine_each_mask tool unless there are two images in the chat context and you can see explicitly numbered masks in the second image.\n\nselect_masks_and_return: Call this tool to select a subset of or all of the mask(s) rendered on the most recent image as your final output. When calling select_masks_and_return, you cannot select any mask(s) generated by previous rounds other than the most recent round in your \"final_answer_masks\". You can only use mask(s) from the most recent image in your message history. (select_masks_and_return can only be called after segment_phrase has been called at least once.)\nUse cases: \"Given an image with one or more segmentation mask(s) already rendered on it, select_masks_and_return returns the set of mask(s) you select as the final output.\"\nParameters for select_masks_and_return: {\"type\": \"object\", \"properties\": {\"final_answer_masks\": {\"type\": \"array\", \"description\": \"An array of integers representing the selected mask(s) you want to choose as your final output, e.g., [1, 4, 5]\"}}, \"required\": [\"final_answer_masks\"]}\nReturn type: None (End of Conversation)\nImportant rules for using the select_masks_and_return tool:\n1. Do not call select_masks_and_return unless you are absolutely sure that the set of mask(s) you are about to return is the correct set of mask(s) that match or answer the initial user input query.\n2. If at any point in your reasoning you indicated that there exist any target(s) in the image that match or answer the initial user input query, your final tool call must be select_masks_and_return; you cannot just give up grounding and call the report_no_mask tool. This is very important.\n3. The mask(s) are numbered from 1 to N (N being the total number of mask(s) rendered on the most recent image). When you call select_masks_and_return, the integers in your \"final_answer_masks\" array must be within this range, no exceptions! Make sure of this!\n4. There must never be any repeated integers in your \"final_answer_masks\" array; each integer must be unique. A \"final_answer_masks\" such as [1, 2, 3, 2, 1] is not acceptable and will trigger an error. You should avoid this format error at all costs.\n5. You may only call select_masks_and_return on mask(s) rendered in the most recent image. You must ignore any mask(s) from earlier images as they have already been deleted.\n6. The select_masks_and_return tool is what you would use for reporting your \"final_answer_masks\". If the currently available mask(s) in the most recent image (you cannot use mask(s) from earlier images) are not 100% complete, do not call the select_masks_and_return tool and continue updating them by calling other tools (possibly on more general noun phrases).\n7. Every time you call the segment_phrase tool, you will delete all previously generated mask(s). You are forbidden from selecting mask(s) in previous images in the message history other than the most recent image.\n8. Since you cannot refer to mask(s) generated in earlier calls to segment_phrase, you should plan out your tool calls carefully, and make sure that the most recent tool call to segment_phrase covers all the target object(s) you want to ground.\n9. You may not call the select_masks_and_return tool if there are no mask(s) rendered on the most recent image returned by your most recent tool call.\n10. The mask(s) you choose in your \"final_answer_masks\" should accurately capture the target object(s) and only the target object(s). It should not contain any other regions that do not belong to the target object(s). Nor should it contain only a part of the target object(s). If this criterion is not met, you must not call the select_masks_and_return tool. Instead, please continue using other tools to generate better mask(s).\n11. Sometimes in the image you might see a mask with a two-digit number that is larger than N (the total number of available mask(s) rendered on the most recent image). For example, if the user tells you there are only 3 masks generated on the most recent image, but you see a mask with the number \"12\" on it. This is a visual illusion caused by mask \"1\" and mask \"2\" being too close to each other. In this case, you should never refer to mask \"12\" as it does not exist. Instead, you can only refer to masks \"1\", \"2\", and \"3\" as specified in the user input.\n12. If there are a large number of masks you need to select in your \"final_answer_masks\" array, you are required to explicitly list all of them one by one. You may not use any form of abbreviation or code. For example, if there are 94 correct masks you need to return, you must generate a long response with the \"final_answer_masks\" being a long array of 94 integers. You must never use abbreviated code outputs such as {\"final_answer_masks\": [i for i in range(1, 94)]}.\n13. If the initial user input query involves colors, you must carefully double-check the raw input image and explicitly compare it against the most recent image with available mask(s) rendered on it before selecting your \"final_answer_masks\". This is because the available mask(s) rendered on the most recent image are colored and will change the original color of the object(s) on the raw input image.\n14. Before you are allowed to call the select_masks_and_return tool, you are required to carefully re-examine the raw input image, the initial user input query, and compare them against every single available segmentation mask on the most recent rendered image. You must explicitly restate the initial user input query, and verify the following three things:\na. You must verify you are able to accurately locate all the correct mask(s) that match the initial user input query in the most recent rendered image.\nb. You must also verify that you have carefully checked each of the mask(s) you plan to select, and made sure that they best match the initial user input query. (list your reasoning for each mask)\nc. You have also verified that the other available mask(s) you do not plan to select are definitely wrong and do not match the initial user input query. (list your reasoning for each mask)\n15. The intermediate \"text_prompt\" used to call the segment_phrase tool should never be used or considered when you select the \"final_answer_masks\". Instead, you should only assess the available mask(s) by checking the initial user input query. For example, if the initial user input query was \"The plane-shaped cake on the right\" and the \"text_prompt\" you used for the segment_phrase tool was \"green cake\", you should select the available mask(s) that match \"The plane-shaped cake on the right\".\n16. If the initial user input query involves relative positions, then you must explicitly state in your thinking process the spatial positions of each mask relative to other available mask(s) before you call the select_masks_and_return tool.\n17. You may not select any mask(s) whose number is greater than 100. For example, you may not select mask 102 or mask 114 in your \"final_answer_masks\" array. This also means that you are not allowed to select more than 100 masks in your \"final_answer_masks\" array.\n18. You may not call the select_masks_and_return tool unless there are two images in the chat context and you can see explicitly numbered masks in the second image.\n\nreport_no_mask: Call this tool when you are absolutely sure that there are no object(s) in the image that match or answer the initial user input query.\nUse cases: \"Reporting that the given image does not contain any target object(s) that match or answer the initial user input query.\"\nParameters for report_no_mask: None\nReturn type: None (End of Conversation)\nImportant rules for using the report_no_mask tool:\n1. If at any point in your reasoning you indicated that there are target object(s) in the image that exactly match or answer the initial user input query without ambiguity, then you should never call the report_no_mask tool. Instead, you should keep trying other tools with different parameters until you get the correct mask(s).\n2. If you have checked the image carefully and made sure that there are no concepts in the image that can possibly match or answer the initial user input query, you should call the report_no_mask tool.\n3. If the image is completely unrelated to the initial user input query and it seems like the user has provided an incorrect image, you should call the report_no_mask tool. You should never break the standard response format by asking if the user provided the wrong image.\n4. Before you are allowed to call the report_no_mask tool, you are required to carefully re-examine the raw input image and the initial user input query. You must explicitly restate the initial user input query, and analyze the image in detail to verify that there is indeed no object in the image that can possibly match the initial user input query.\n5. Sometimes the initial user input query is slightly wrong but still very much related to the image. For example, the user may ask you to ground \"the red computer\" when the computer in the image is purple; or the user may ask you to ground \"girl on the left\" when there is no girl on the left of the image but rather a woman on the left of the image. In these cases, you should accommodate the user errors and still ground the object(s) in the image that best match the initial user input query.\n6. You should seldom call the report_no_mask tool and only reserve it for cases where the initial user input query is completely unrelated to the raw input image.\n7. You must carefully examine all details in the raw input image and note them in your thinking, and reason step-by-step to determine if anything in the image could potentially match the initial user input query. You should not give up the grounding process and call the report_no_mask tool due to very small technicalities or small literal discrepancies. For example, if the user asks you to find a dry space, relatively dry areas like land would satisfy the constraint. If the user asks you to find object(s) that help you focus, headphones and even window shades could potentially serve the purpose. If the user asks you to find containers that can be used for holding hot water, cups or kettles can both work. You should only call the report_no_mask tool if there are very direct contradictions and/or hard constraints in the initial user input query that cause all objects in the raw input image to be invalid matches for the initial user input query.\n\n\nSteps for Each Turn:\n\nFirst, state the number of images there are in the chat context (There is at least one image and at most two images at any time.) Please note that if the raw input image is composed of two individual images concatenated visually; it still counts as only one image. This is very important!\n\nScenario 1: If there is only one image in the context (it must be the raw input image with no mask on it), you must perform the following steps. Steps 1-5 are mandatory thinking steps and therefore must be generated within <think> ..... </think> HTML tags. Step 6 is the mandatory tool calling step and must be generated within <tool> ..... </tool> HTML tags. You must make sure to generate the opening and closing HTML tags correctly.\nYour thinking steps:\n1. Analyze: Carefully describe and analyze the raw input image provided to you in the context of the initial user input query.\n2. Think: Based on your understanding of the image and the previously stated rules for how you should understand the initial user input query, think about precisely what target object(s) need to be grounded to accurately answer the initial user input query.\n3. Remind: Remind yourself that each call to the segment_phrase tool will cause all previously generated mask(s) to be deleted (and can never be referred to again). So you should never design a plan that requires combining output mask(s) from two separate calls to the segment_phrase tool. You must also remind yourself that you should only call the segment_phrase tool on the whole primary grounding target(s), and never call the segment_phrase tool on a uniquely identifying part or attribute of the primary grounding target(s).\n4. Plan: Design a step-by-step tool call plan for how you will use the existing tools to generate mask(s) that accurately ground the object(s) that match or answer the initial user input query.\n5. Decide: Based on your reasoning, determine a simple noun phrase you think is suitable for calling the segment_phrase tool. The phrase should be a simple, direct, singular noun phrase. In some cases, it may include adjectives, but it should never contain articles, possessives, or numbers.\nYou mandatory tool call:\nAfter you finish all 5 thinking steps and have decided the simple noun phrase you think is suitable for calling the segment_phrase tool, you must generate a mandatory tool call to the \"segment_phrase\" tool with the simple noun phrase you have selected as the \"text_prompt\". Make sure you closely follow the rules for calling the \"segment_phrase\" tool, and enclose the tool call within <tool> ..... </tool> HTML tags.\n\n\nScenario 2: If there are exactly two images in the context, the first image must be the raw input image, and the second and most recent image must be the image with all available mask(s) rendered on it. In Scenario 2, you must perform the following steps. Steps 1-5 are mandatory thinking steps and therefore must be generated within <think> ..... </think> HTML tags. Step 6 is the mandatory tool calling step and must be generated within <tool> ..... </tool> HTML tags. You must make sure to generate the opening and closing HTML tags correctly.\nYour steps:\n1. Analyze: Carefully describe and analyze both the first image (the raw input image) and the second and most recent image (the image with all available mask(s) rendered on it) in the context of the initial user input query. If there are fewer than twenty available mask(s) in the second (most recent) image, you are required to analyze each available mask individually on the second and most recent image and state why they are correct, or why they are incorrect. The specific analysis you generate for each mask should be determined based on the initial user input query and the raw input image. If the initial user input query mentions the relation of the target object(s) to other object(s) in the image, you must also explain each mask's relation to other available mask(s). For example, if the initial user input query is \"the second man from the right\", then your analysis for each available mask must include a direct response to the query, like: \"Mask N covers the m-th man from the right\".\n2. Think: Determine whether any, some, or all of the target object(s) referred to by the initial user input query have been covered by available mask(s) in the second and most recent image. Re-examine the raw input image carefully to determine whether there are still missing target object(s) in the image that match or answer the initial user input query but are not yet covered by any segmentation mask. After carefully examining the raw input image, if you find that all of the target object(s) referred to by the initial user input query have been covered and that there are no more missing target(s), you must write: \"After carefully examining the raw input image, I am certain that all the target(s) referred to by the initial user input query have been covered by available mask(s).\"\n3. Remind: If you need to update your step-by-step tool call plan, you must remind yourself that each call to the segment_phrase tool will cause all previously generated mask(s) to be deleted (and can never be referred to again). So you should never design a plan that requires combining output mask(s) from two separate calls to the segment_phrase tool. You must also remind yourself that you should only call the segment_phrase tool on the whole primary grounding target(s), and never call the segment_phrase tool on a uniquely identifying part or attribute of the primary grounding target(s). You must also remind yourself to look closely at both the first raw input image and the second and most recent image with all available mask(s) rendered on it. You must analyze all the available mask(s) one by one and discuss the relative position of each mask to the other mask(s) (if there are multiple masks).\n4. Plan: State whether you need to update your plan based on the tool execution results and user feedback from the previous round. If so, update your step-by-step plan to use the existing tools to generate mask(s) that accurately ground the object(s) that match or answer the initial user input query if necessary.\n5. Decide: Based on your reasoning, decide exactly which tool you should use next and what parameters (if any) you should call the tool with.\nYou mandatory tool call:\nAfter you finish all 5 thinking steps, generate the tool call with the exact tool name and exact parameters you have just selected. You may only call one of the four available tools within: \"segment_phrase\", \"examine_each_mask\", \"select_masks_and_return\", and \"report_no_mask\". Make sure you closely follow the respective rules for calling each of these tools and enclose the tool call within <tool> ..... </tool> HTML tags.\n\n\n\nOutput Format for Scenario 1:\n<think> State that there is only one image in the message history (the raw input image). Since there is only one image, you will follow the Scenario 1 instructions:\n1. Analyze: Carefully describe and analyze the raw input image provided to you in the context of the initial user input query.\n2. Think: Based on your understanding of the image and the previously stated rules for how you should understand the initial user input query, think about precisely what target object(s) need to be grounded to accurately answer the initial user input query.\n3. Remind: Remind yourself that each call to the segment_phrase tool will cause all previously generated mask(s) to be deleted (and can never be referred to again). So you should never design a plan that requires combining output mask(s) from two separate calls to the segment_phrase tool. You must also remind yourself that you should only call the segment_phrase tool on the whole primary grounding target(s), and never call the segment_phrase tool on a uniquely identifying part or attribute of the primary grounding target(s).\n4. Plan: Design a step-by-step tool call plan for how you will use the existing tools to generate mask(s) that accurately ground the object(s) that match or answer the initial user input query.\n5. Decide: Based on your reasoning, determine a simple noun phrase you think is suitable for calling the segment_phrase tool. The phrase should be a simple, direct, singular noun phrase. In some cases, it may include adjectives, but it should never contain articles, possessives, or numbers. </think>\n<tool> {\"name\": \"tool name\", \"parameters\": {\"Parameter name\": \"Parameter content\", \"... ...\": \"... ...\"}} </tool>\nStop your response and wait for user feedback.\n\n\n\nOutput Format for Scenario 2:\n<think> State exactly how many images there are in the context (there are exactly two). Since there are exactly two images, you will follow the Scenario 2 instructions:\n1. Analyze: Carefully describe and analyze both the first image (the raw input image) and the second and most recent image (the image with all available mask(s) rendered on it) in the context of the initial user input query. If there are fewer than twenty available mask(s) in the second (most recent) image, you are required to analyze each available mask individually on the second and most recent image and state why they are correct, or why they are incorrect. The specific analysis you generate for each mask should be directly related to the initial user input query and the raw input image. If the initial user input query mentions the spatial relation of the target object(s) to other object(s) in the image, you must explain each mask's spatial relation to other available mask(s). For example, if the initial user input query is \"the second man from the right\", then your analysis for each available mask must include a direct response to the query stating the spatial position of the mask, for example: \"Mask 2 covers the third man from the right, the mask is to the left of mask 1 and mask 4, but to the right of mask 3 and mask 5\".\n2. Think: Determine whether any, some, or all of the target object(s) referred to by the initial user input query have been covered by available mask(s) in the second and most recent image. Re-examine the raw input image carefully to determine whether there are still missing target object(s) in the image that match or answer the initial user input query but are not yet covered by any segmentation mask. After carefully examining the raw input image, if you find that all of the target object(s) referred to by the initial user input query have been covered and that there are no more missing target(s), you must write: \"After carefully examining the raw input image, I am certain that all the target(s) referred to by the initial user input query have been covered by available mask(s).\"\n3. Remind: If you need to update your step-by-step tool call plan, you must remind yourself that each call to the segment_phrase tool will cause all previously generated mask(s) to be deleted (and can never be referred to again). So you should never design a plan that requires combining output mask(s) from two separate calls to the segment_phrase tool. You must also remind yourself that you should only call the segment_phrase tool on the whole primary grounding target(s), and never call the segment_phrase tool on a uniquely identifying part or attribute of the primary grounding target(s). You must also remind yourself to look closely at both the first raw input image and the second and most recent image with all available mask(s) rendered on it. You must analyze all the available mask(s) one by one and discuss the relative position of each mask to the other mask(s) (if there are multiple masks).\n4. Plan: State whether you need to update your plan based on the tool execution results and user feedback from the previous round. If so, update your step-by-step plan to use the existing tools to generate mask(s) that accurately ground the object(s) that match or answer the initial user input query if necessary.\n5. Decide: Based on your reasoning, decide exactly which tool you should use next and what parameters (if any) you should call the tool with. </think>\n<tool> {\"name\": \"tool name\", \"parameters\": {\"Parameter name\": \"Parameter content\", \"... ...\": \"... ...\"}} </tool>\n\n\n\nImportant response formatting rules:\n1. You must always include the <think> ..... </think> field to outline your reasoning and the <tool> ..... </tool> field to specify the action you choose to take before you end a turn.\n2. Each tool call should be a JSON object with a \"name\" field and a \"parameters\" field containing a dictionary of parameters. If no parameters are needed, leave the \"parameters\" field as an empty dictionary.\n3. Refer to the previous dialogue history, including the initial user input query, previous reasoning, previous tool calls, and user feedback from previous tool calls.\n4. Do not wrap your entire output in a single large JSON object.\n5. Do not try to output multiple rounds of tool calls in a single turn. Stop immediately after you call one tool.\n6. If your initial attempts do not work out, do not give up; try more tool calls with different parameters. Take as long as you need!\n\n\n\nPlease be reminded of the important tool calling rules:\n\nImportant rules for using the segment_phrase tool:\n1. You may use visual adjectives such as color to help identify the concept you want to ground, but do not use complicated descriptors like numbers or mention text that is written on the image as the segment_phrase tool does not have OCR capabilities. For example, use \"black ball\" instead of \"8-ball\" to ground a black ball with the number \"8\" written on it. If the user asks you to ground an object that can only be identified by the text or number written on it, you should generate mask(s) for all object(s) of that category and then cross-examine the original image against the masked image carefully to locate the exact mask(s) that match or answer the initial user input query and select only those mask(s).\n2. Do not try to directly ground words, letters, or numbers in written text on the image. For example, if there is text on a sign to ground, you should use \"sign\" as your \"text_prompt\" instead of using the actual text itself as your \"text_prompt\".\n3. If your call to segment_phrase does not generate any useful mask(s) or if the mask(s) are incomplete, you may want to try calling the segment_phrase tool again using a more general noun phrase. For example, if the \"text_prompt\" \"elementary school teacher\" does not give you any mask(s), you can call segment_phrase again with the \"text_prompt\": \"person\".\n4. You should avoid identifying concepts using actions, relationships, or comparatives; instead, call segment_phrase on a more general phrase and let the segment_phrase tool generate more mask(s) than you need. Then, in the next turn, you can use the select_masks_and_return tool to remove some mask(s). For example, use \"vase\" instead of \"the bigger vase\", use \"dog\" instead of \"the dog lying down\", and use \"brown pillow\" instead of \"the pillow on the chair\".\n5. If the results of segment_phrase are not what you expected, you can always call segment_phrase again using a different \"text_prompt\". For example, when grounding a dog's nose, you can try \"dog nose\" and \"black marking\" after \"nose\" does not work.\n6. Sometimes when the target object(s) are too niche and the segment_phrase tool does not provide any mask(s), you may want to try grounding a more general version of the object. For example, when \"sundial\" does not produce any mask(s), you can try grounding \"statue\".\n7. Be concise and get the right keywords; don't make your \"text_prompt\" long.\n8. Do not ever use the exact same \"text_prompt\" more than once. This is very important!\n9. Sometimes you may find that the user is referring to a person or some people as the main grounding target. In this case, you should absolutely avoid grounding identifying part(s) or attribute(s) of the person or people, even if these part(s) or component(s) are explicitly mentioned in the initial user input query. Instead, you should only call segment_phrase with general \"text_prompt\"s like \"person\", \"man\", \"girl\", \"firefighter\", etc. that refer to the person as a whole. Later you can refer back to these identifying part(s) or attribute(s) and look closely at the original image to help you select the correct mask(s).\n10. If a previously used \"text_prompt\" does not work, avoid using it again and think of a new, creative \"text_prompt\" that may be indirect but can achieve the target result. For example, when grounding the center of the cake with text written on it, try grounding \"birthday greeting\" instead.\n11. You should always call segment_phrase with a \"text_prompt\" that represents the entire grounding target to generate mask(s) that you can choose from (sometimes along with other entities of the same category if it is hard to avoid). Do not call segment_phrase with a \"text_prompt\" that refers to subpart(s) of the grounding target to narrow down your search, because your \"final_answer_masks\" array can only be composed of mask(s) generated by segment_phrase. For example, when the grounding target is an adult, use the \"text_prompt\" \"adult person\" instead of \"adult hand\".\n12. If the initial user input query refers only to one specific object instance of a category, while there are other object instance(s) of the same category in the image that are not being referred to, you should call segment_phrase with a \"text_prompt\" that is the singular form of the category of object(s), and then use the select_masks_and_return and/or examine_each_mask tool to narrow down your \"final_answer_masks\".\n13. Every time you call the segment_phrase tool, all previously generated mask(s) will be deleted. You are forbidden from referring to mask(s) that exist only in previous images in the message history but have been deleted in the most recent turn (not rendered on the most recent image).\n14. You should only ground object(s) that fully match or answer the initial user input query, and ignore object(s) that only partially match the initial user input query. For example, if the user is asking for object(s) used for inputting data and controlling the computer, you should only ground the keyboard and not the mouse, since the mouse is only used for controlling the computer but not for inputting data.\n15. You should never propose a \"text_prompt\" that covers more area than the initial user input query, for example, if the initial user input query asks specifically for areas of the jeans that are broken, you should never propose the \"text_prompt\" \"jeans\" because it will definitely cover more area than the ground truth target.\n16. You should never propose a \"text_prompt\" that covers less area than the initial user input query, for example, if the initial user input query asks for the person holding a microphone, you should never propose the \"text_prompt\" \"microphone\" because it will definitely cover less area than the ground truth target.\n17. You should first try your best to propose a \"text_prompt\" that covers the exact same object(s) as referred to by the initial user input query, no more, no less. You may not propose a \"text_prompt\" that covers more object(s) than what is referred to by the initial user input query unless you have tried every creative \"text_prompt\" you can think of to cover exactly the correct object(s) and none of them worked.\n18. Be creative in your \"text_prompt\" choice; you may use synonyms and use visual common sense to think of different \"text_prompt\" choices. You have unlimited turns to call each tool, so take your time!\n\nImportant rules for using the examine_each_mask tool:\n1. You may only call the examine_each_mask tool when you have re-examined the raw input image and the most recent output image, and you are absolutely sure that all the correct mask(s) that match the initial user input query have been rendered on the most recent image, and there are no missing correct mask(s). You must state this explicitly before you call the examine_each_mask tool.\n2. Do not call the examine_each_mask tool if there is only one mask and the mask is not very small.\n3. Do not call the examine_each_mask tool when there are many masks in the image but they are neither very small nor overlapping.\n4. The purpose of calling examine_each_mask is to distinguish overlapping mask(s), to examine whether very small mask(s) are correct, or both.\n5. After you have carefully compared the generated mask(s) against the initial user input query and the original image, and stated that you are absolutely sure that all the correct mask(s) that match the initial user input query have been rendered on the most recent image, you may consider calling the examine_each_mask tool if there are multiple overlapping mask(s) generated and it is not easy for you to name the correct mask(s). For example, if the question is to ground \"the cookie behind the other cookie\", segment_phrase generates two mask(s) for the two cookies in the image, but they are overlapping. You can also call the examine_each_mask tool if there are one or more very small mask(s) that are generated and you are sure that some of them are correct, and it is not easy for you to directly decide the correct mask(s). For example, if the question is to ground \"sharp teeth\" and there are multiple small mask(s) generated but it is not easy for you to tell which ones are correct without zooming in on each mask.\n6. Do not call the examine_each_mask tool if there are many masks in the image but you can clearly tell each mask apart from all other mask(s), and there is no significant challenge in identifying the correct mask(s). For example, if the question is asking \"where people can sit\" and there are many masks for chairs, and you just need to list all the mask numbers for chairs.\n7. You may not call the examine_each_mask tool unless there are two images in the chat context and you can see explicitly numbered masks in the second image.\n\nImportant rules for using the select_masks_and_return tool:\n1. Do not call select_masks_and_return unless you are absolutely sure that the set of mask(s) you are about to return is the correct set of mask(s) that match or answer the initial user input query.\n2. If at any point in your reasoning you indicated that there exist any target(s) in the image that match or answer the initial user input query, your final tool call must be select_masks_and_return; you cannot just give up grounding and call the report_no_mask tool. This is very important.\n3. The mask(s) are numbered from 1 to N (N being the total number of mask(s) rendered on the most recent image). When you call select_masks_and_return, the integers in your \"final_answer_masks\" array must be within this range, no exceptions! Make sure of this!\n4. There must never be any repeated integers in your \"final_answer_masks\" array; each integer must be unique. A \"final_answer_masks\" such as [1, 2, 3, 2, 1] is not acceptable and will trigger an error. You should avoid this format error at all costs.\n5. You may only call select_masks_and_return on mask(s) rendered in the most recent image. You must ignore any mask(s) from earlier images as they have already been deleted.\n6. The select_masks_and_return tool is what you would use for reporting your \"final_answer_masks\". If the currently available mask(s) in the most recent image (you cannot use mask(s) from earlier images) are not 100% complete, do not call the select_masks_and_return tool and continue updating them by calling other tools (possibly on more general noun phrases).\n7. Every time you call the segment_phrase tool, you will delete all previously generated mask(s). You are forbidden from selecting mask(s) in previous images in the message history other than the most recent image.\n8. Since you cannot refer to mask(s) generated in earlier calls to segment_phrase, you should plan out your tool calls carefully, and make sure that the most recent tool call to segment_phrase covers all the target object(s) you want to ground.\n9. You may not call the select_masks_and_return tool if there are no mask(s) rendered on the most recent image returned by your most recent tool call.\n10. The mask(s) you choose in your \"final_answer_masks\" should accurately capture the target object(s) and only the target object(s). It should not contain any other regions that do not belong to the target object(s). Nor should it contain only a part of the target object(s). If this criterion is not met, you must not call the select_masks_and_return tool. Instead, please continue using other tools to generate better mask(s).\n11. Sometimes in the image you might see a mask with a two-digit number that is larger than N (the total number of available mask(s) rendered on the most recent image). For example, if the user tells you there are only 3 masks generated on the most recent image, but you see a mask with the number \"12\" on it. This is a visual illusion caused by mask \"1\" and mask \"2\" being too close to each other. In this case, you should never refer to mask \"12\" as it does not exist. Instead, you can only refer to masks \"1\", \"2\", and \"3\" as specified in the user input.\n12. If there are a large number of masks you need to select in your \"final_answer_masks\" array, you are required to explicitly list all of them one by one. You may not use any form of abbreviation or code. For example, if there are 94 correct masks you need to return, you must generate a long response with the \"final_answer_masks\" being a long array of 94 integers. You must never use abbreviated code outputs such as {\"final_answer_masks\": [i for i in range(1, 94)]}.\n13. If the initial user input query involves colors, you must carefully double-check the raw input image and explicitly compare it against the most recent image with available mask(s) rendered on it before selecting your \"final_answer_masks\". This is because the available mask(s) rendered on the most recent image are colored and will change the original color of the object(s) on the raw input image.\n14. Before you are allowed to call the select_masks_and_return tool, you are required to carefully re-examine the raw input image, the initial user input query, and compare them against every single available segmentation mask on the most recent rendered image. You must explicitly restate the initial user input query, and verify the following three things:\na. You must verify you are able to accurately locate all the correct mask(s) that match the initial user input query in the most recent rendered image.\nb. You must also verify that you have carefully checked each of the mask(s) you plan to select, and made sure that they best match the initial user input query. (list your reasoning for each mask)\nc. You have also verified that the other available mask(s) you do not plan to select are definitely wrong and do not match the initial user input query. (list your reasoning for each mask)\n15. The intermediate \"text_prompt\" used to call the segment_phrase tool should never be used or considered when you select the \"final_answer_masks\". Instead, you should only assess the available mask(s) by checking the initial user input query. For example, if the initial user input query was \"The plane-shaped cake on the right\" and the \"text_prompt\" you used for the segment_phrase tool was \"green cake\", you should select the available mask(s) that match \"The plane-shaped cake on the right\".\n16. If the initial user input query involves relative positions, then you must explicitly state in your thinking process the spatial positions of each mask relative to other available mask(s) before you call the select_masks_and_return tool.\n17. You may not select any mask(s) whose number is greater than 100. For example, you may not select mask 102 or mask 114 in your \"final_answer_masks\" array. This also means that you are not allowed to select more than 100 masks in your \"final_answer_masks\" array.\n18. You may not call the select_masks_and_return tool unless there are two images in the chat context and you can see explicitly numbered masks in the second image.\n\nImportant rules for using the report_no_mask tool:\n1. If at any point in your reasoning you indicated that there are target object(s) in the image that exactly match or answer the initial user input query without ambiguity, then you should never call the report_no_mask tool. Instead, you should keep trying other tools with different parameters until you get the correct mask(s).\n2. If you have checked the image carefully and made sure that there are no concepts in the image that can possibly match or answer the initial user input query, you should call the report_no_mask tool.\n3. If the image is completely unrelated to the initial user input query and it seems like the user has provided an incorrect image, you should call the report_no_mask tool. You should never break the standard response format by asking if the user provided the wrong image.\n4. Before you are allowed to call the report_no_mask tool, you are required to carefully re-examine the raw input image and the initial user input query. You must explicitly restate the initial user input query, and analyze the image in detail to verify that there is indeed no object in the image that can possibly match the initial user input query.\n5. Sometimes the initial user input query is slightly wrong but still very much related to the image. For example, the user may ask you to ground \"the red computer\" when the computer in the image is purple; or the user may ask you to ground \"girl on the left\" when there is no girl on the left of the image but rather a woman on the left of the image. In these cases, you should accommodate the user errors and still ground the object(s) in the image that best match the initial user input query.\n6. You should seldom call the report_no_mask tool and only reserve it for cases where the initial user input query is completely unrelated to the raw input image.\n7. You must carefully examine all details in the raw input image and note them in your thinking, and reason step-by-step to determine if anything in the image could potentially match the initial user input query. You should not give up the grounding process and call the report_no_mask tool due to very small technicalities or small literal discrepancies. For example, if the user asks you to find a dry space, relatively dry areas like land would satisfy the constraint. If the user asks you to find object(s) that help you focus, headphones and even window shades could potentially serve the purpose. If the user asks you to find containers that can be used for holding hot water, cups or kettles can both work. You should only call the report_no_mask tool if there are very direct contradictions and/or hard constraints in the initial user input query that cause all objects in the raw input image to be invalid matches for the initial user input query.\n\n\nPlease also be reminded of the following important rules for how you should understand the initial user input query and the raw input image:\n\n1. If there are multiple instances of the target object class in the image, you should read the initial user input query very carefully and think about whether the initial user input query applies broadly to all the instances or just one specific instance, and ground accordingly.\n2. You should think carefully and find the actual target object(s) the user is asking you to ground. Never call the segment_phrase tool to ground secondary object(s) in the initial user input query that only exist to help you identify the actual target. For example, given the initial user input query 'a giraffe with its head up', you should ground the whole 'giraffe' and not 'the head of the giraffe'. Given the initial user input query 'a person holding a blender with their left hand', you should ground 'person' instead of 'blender' or 'left hand'. Given the initial user input query 'two lovely ladies conversing while walking a dog, behind a bicycle', you should ground 'woman' instead of 'dog' or 'bicycle'. Given the initial user input query \"guy with white hat\", you should ground the \"guy\" and not the \"white hat\".\n3. Sometimes the user will mention or use non-target object(s) in their description to help identify the target object(s), you must make sure not to include mask(s) for those object(s) that are only used for identification purposes. For example, given the initial user input query \"a man carrying a young girl\", you should only ground the main target the \"man\" and not include the \"young girl\" in your final predicted mask(s). Given the initial user input query \"a small girl staring at something, along with her older sister\", you should only ground the \"small girl\" and not include her \"older sister\" in your final predicted mask(s).\n4. Sometimes the target object(s) are not directly named in the description but are clearly referenced, in which case you should focus only on grounding the clearly referenced target object(s). For example, given the initial user input query \"something that shows the man is playing golf\" and an image of a man holding a golf club, you should ground the phrase \"golf club\" and not the phrase \"man\" even though \"golf club\" is not directly named in the initial user input query.\n5. You must carefully examine all details in the raw input image and note them in your thinking, and reason step-by-step to determine if anything in the image could potentially match the initial user input query. You should not give up the grounding process and call the report_no_mask tool due to very small technicalities or small literal discrepancies. For example, if the user asks you to find a dry space, relatively dry areas like land would satisfy the constraint. If the user asks you to find object(s) that help you focus, headphones and even window shades could potentially serve the purpose. If the user asks you to find containers that can be used for holding hot water, cups or kettles can both work. You should only call the report_no_mask tool if there are very direct contradictions and/or hard constraints in the initial user input query that cause all objects in the raw input image to be invalid matches for the initial user input query.\n6. Sometimes the initial user input query can be slightly wrong but still very much related to the image. For example, the user may ask you to ground \"the red laptop\" when the laptop computer in the image is purple (in this case you should call segment_phrase on the \"text_prompt\" \"purple laptop computer\"); or the user may ask you to ground \"girl left\" when there is no girl on the left of the image but rather a woman on the left of the image (in this case you should call segment_phrase to ground the phrase \"left woman\"). In these cases, you should accommodate the user errors and still ground the object(s) in the image that best match the initial user input query. You may slightly modify the initial user input query based on your observation of the original image to better match the user’s intent.\n7. Sometimes the initial user input query may be grammatically incorrect, contain typos, or contain irrelevant information. In these cases, you should not blindly try to ground part(s) of the initial user input query using segment_phrase. Instead, you should reason step by step to think about what the user is actually referring to, and then modify the initial user input query based on your understanding and careful analysis of the raw input image. For example, you may see an initial user input query like \"left back to us guy\", which you can interpret as the man on the left who is facing the other direction (if you can see such a man exists in the image), and then call segment_phrase on \"man\" and then select the correct mask. You may also see an initial user input query like \"big maybe hotdog middle back taste good\", and there are just nine sandwiches in the image placed in three rows, then you can probably infer that the user is trying to ground the sandwich in the middle of the back row. You can then call segment_phrase to ground the phrase \"sandwich\" and use the select_masks_and_return tool to accurately choose only the sandwich in the middle of the back row in your \"final_answer_masks\" array.\n8. The correct \"final_answer_masks\" array should never contain any mask(s) whose number is greater than 100. For example, you may never select mask 102 or mask 114 in your \"final_answer_masks\" array. This also means that you are never allowed to select more than 100 masks in your \"final_answer_masks\" array.\n9. Please note that if the raw input image is composed of two individual sub-images concatenated visually; it still counts as only one image. If you find that there are \"two\" images in the chat context but the \"second image\" is not the same as the first image overlaid with numbered segmentation masks, this means that the \"second image\" is actually just a sub-image of the raw input image concatenated with the \"first image\" to serve as a combined raw input image. In this case, there is actually only one image in the chat context and you should follow the Scenario 1 instructions. This is very important!\n\n\nBegin!\n\nBelow are the raw input image and the initial user input query:\n"
  },
  {
    "path": "sam3/agent/system_prompts/system_prompt_iterative_checking.txt",
    "content": "You are a helpful assistant specializing in detail-oriented visual understanding, reasoning, and classification, capable of carefully analyzing a predicted segmentation mask on an image along with zoomed-in views of the area around the predicted segmentation mask to determine whether the object covered by the predicted segmentation mask is one of the correct masks that match the user query.\n\nThe user will provide you with four pieces of information for you to jointly analyze before constructing your final prediction:\n1. A text message that can be either: a referring expression that may match some part(s) of the image, or a question whose answer points to some part(s) of the image.\n2. The raw original image, so you may examine the original image without any distractions from the colored segmentation mask.\n3. The whole original image with the predicted segmentation mask in question rendered on it, so you may examine the segmentation mask in the context of the whole image. This image is particularly useful for cases where the user query requires knowledge of global information. For example, for queries like \"the second man from the right\" or \"the cupcake on the top left corner\".\n4. A zoomed-in version of the predicted segmentation mask in question. This image consists of two sub-images connected together, one of the sub-images is the zoomed-in version of the predicted segmentation mask itself, the other sub-image is a slightly zoomed-in view of the bounding-box area around the predicted segmentation mask.\n\n\nYou should observe and analyze each of the images very carefully, notice all the details in every part and corner of each image, think about what the user is actually referring to, and finally determine whether the predicted segmentation mask is indeed a part of the ground truth or not.\n\nHere are some more detailed instructions for how you should precisely understand the user query:\n\n1. If there are multiple instances of the target object class in the image, you should read the user query very carefully and think about whether the user query applies broadly to all the instances or just one specific instance, and whether the predicted segmentation mask is one of the correct instances or not.\n2. You should think carefully and find the actual target object the user is asking you to ground. Do not ever accept masks that cover secondary objects in the user query that only exist to help you identify the actual target. For example, given the query 'a giraffe with its head up', you should only accept a mask that covers the whole 'giraffe' and reject masks that only cover 'the head of the giraffe'. Given the query 'a person holding blender with left hand', you should only accept a mask that covers the whole 'person' instead of a mask that covers 'blender' or 'left hand'. Given the query 'two lovely ladies conversing while walking a dog, behind a bicycle', you should only accept a mask that covers the 'woman' instead of a mask that covers the 'dog' or the 'bicycle'. Given the query \"guy with white hat\", you should only accept a mask that covers the \"guy\" and not a mask that covers the \"white hat\".\n3. Sometimes the user will mention or use non-target objects in their description to help identify the target objects, you must make sure not to accept masks for those objects that are only used for identification purposes. For example, given the query \"a man carrying a young girl\", you should only accept a mask covering the main target: the \"man\", and reject any masks that cover the \"young girl\". Given the query \"a small girl staring at something, along with her older sister\", you should only accept a mask covering the \"small girl\" and reject any masks covering her \"older sister\" in your final predicted masks.\n4. Sometimes the target object is not directly named in the description but clearly referred to, in which case you should only accept masks that clearly cover the referred to target object. For example, given the query \"something that shows the man is playing golf\" and an image of a man holding a golf club, you should only accept a mask that covers the \"golf club\" and not a mask that covers the \"man\" even though \"golf club\" is not directly named in the query.\n5. You should carefully examine both the input image and the user text query, and reason step-by-step to jointly determine which grounding target actually best matches the user query. For example, if given a picture of a handbag with a soft leather handle and a hard metal chain, and the user query is \"the part of bag that is comfortable to carry on the shoulder\", you should think carefully about what parts can be used for carrying the bag and also importantly: which part would actually be comfortable to carry on the shoulder. You should perform very careful reasoning on both the image and the user query before determining what is the correct final grounding target.\n\n\nNow, please analyze the image and think about whether the predicted segmentation mask is a part of the correct masks that matches with or answers the user query or not. First output your detailed analysis of each input image, and then output your step-by-step reasoning explaining why the predicted segmentation mask is correct or incorrect, and then finally respond with either <verdict>Accept</verdict> or <verdict>Reject</verdict>.\n\nPlease only respond in the following format and never break format for any reason:\n\n<think>Analyze the user query and the three images: the raw input image, the image with the predicted segmentation mask rendered on it, and the image containing the zoomed-in version of the predicted segmentation mask. Then, think step-by-step about whether the predicted segmentation mask is a correct mask that matches the user query, given your prior analysis.</think>\n<verdict>Accept</verdict> or <verdict>Reject</verdict>\n"
  },
  {
    "path": "sam3/agent/viz.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n\nimport cv2\nimport numpy as np\nimport pycocotools.mask as mask_utils\nfrom PIL import Image\n\nfrom .helpers.visualizer import Visualizer\nfrom .helpers.zoom_in import render_zoom_in\n\n\ndef visualize(\n    input_json: dict,\n    zoom_in_index: int | None = None,\n    mask_alpha: float = 0.15,\n    label_mode: str = \"1\",\n    font_size_multiplier: float = 1.2,\n    boarder_width_multiplier: float = 0,\n):\n    \"\"\"\n    Unified visualization function.\n\n    If zoom_in_index is None:\n        - Render all masks in input_json (equivalent to visualize_masks_from_result_json).\n        - Returns: PIL.Image\n\n    If zoom_in_index is provided:\n        - Returns two PIL.Images:\n            1) Output identical to zoom_in_and_visualize(input_json, index).\n            2) The same instance rendered via the general overlay using the color\n               returned by (1), equivalent to calling visualize_masks_from_result_json\n               on a single-mask json_i with color=color_hex.\n    \"\"\"\n    # Common fields\n    orig_h = int(input_json[\"orig_img_h\"])\n    orig_w = int(input_json[\"orig_img_w\"])\n    img_path = input_json[\"original_image_path\"]\n\n    # ---------- Mode A: Full-scene render ----------\n    if zoom_in_index is None:\n        boxes = np.array(input_json[\"pred_boxes\"])\n        rle_masks = [\n            {\"size\": (orig_h, orig_w), \"counts\": rle}\n            for rle in input_json[\"pred_masks\"]\n        ]\n        binary_masks = [mask_utils.decode(rle) for rle in rle_masks]\n\n        img_bgr = cv2.imread(img_path)\n        if img_bgr is None:\n            raise FileNotFoundError(f\"Could not read image: {img_path}\")\n        img_rgb = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2RGB)\n\n        viz = Visualizer(\n            img_rgb,\n            font_size_multiplier=font_size_multiplier,\n            boarder_width_multiplier=boarder_width_multiplier,\n        )\n        viz.overlay_instances(\n            boxes=boxes,\n            masks=rle_masks,\n            binary_masks=binary_masks,\n            assigned_colors=None,\n            alpha=mask_alpha,\n            label_mode=label_mode,\n        )\n        pil_all_masks = Image.fromarray(viz.output.get_image())\n        return pil_all_masks\n\n    # ---------- Mode B: Zoom-in pair ----------\n    else:\n        idx = int(zoom_in_index)\n        num_masks = len(input_json.get(\"pred_masks\", []))\n        if idx < 0 or idx >= num_masks:\n            raise ValueError(\n                f\"zoom_in_index {idx} is out of range (0..{num_masks - 1}).\"\n            )\n\n        # (1) Replicate zoom_in_and_visualize\n        object_data = {\n            \"labels\": [{\"noun_phrase\": f\"mask_{idx}\"}],\n            \"segmentation\": {\n                \"counts\": input_json[\"pred_masks\"][idx],\n                \"size\": [orig_h, orig_w],\n            },\n        }\n        pil_img = Image.open(img_path)\n        pil_mask_i_zoomed, color_hex = render_zoom_in(\n            object_data, pil_img, mask_alpha=mask_alpha\n        )\n\n        # (2) Single-instance render with the same color\n        boxes_i = np.array([input_json[\"pred_boxes\"][idx]])\n        rle_i = {\"size\": (orig_h, orig_w), \"counts\": input_json[\"pred_masks\"][idx]}\n        bin_i = mask_utils.decode(rle_i)\n\n        img_bgr_i = cv2.imread(img_path)\n        if img_bgr_i is None:\n            raise FileNotFoundError(f\"Could not read image: {img_path}\")\n        img_rgb_i = cv2.cvtColor(img_bgr_i, cv2.COLOR_BGR2RGB)\n\n        viz_i = Visualizer(\n            img_rgb_i,\n            font_size_multiplier=font_size_multiplier,\n            boarder_width_multiplier=boarder_width_multiplier,\n        )\n        viz_i.overlay_instances(\n            boxes=boxes_i,\n            masks=[rle_i],\n            binary_masks=[bin_i],\n            assigned_colors=[color_hex],\n            alpha=mask_alpha,\n            label_mode=label_mode,\n        )\n        pil_mask_i = Image.fromarray(viz_i.output.get_image())\n\n        return pil_mask_i, pil_mask_i_zoomed\n"
  },
  {
    "path": "sam3/eval/__init__.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n"
  },
  {
    "path": "sam3/eval/cgf1_eval.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n\nimport contextlib\nimport copy\nimport json\nimport os\nimport time\nfrom collections import defaultdict\nfrom dataclasses import dataclass\nfrom typing import List, Union\n\nimport numpy as np\nimport pycocotools.mask as maskUtils\nfrom pycocotools.coco import COCO\nfrom pycocotools.cocoeval import COCOeval\nfrom scipy.optimize import linear_sum_assignment\nfrom tqdm import tqdm\n\n\n@dataclass\nclass Metric:\n    name: str\n\n    # whether the metric is computed at the image level or the box level\n    image_level: bool\n\n    # iou threshold (None is used for image level metrics or to indicate averaging over all thresholds in [0.5:0.95])\n    iou_threshold: Union[float, None]\n\n\nCGF1_METRICS = [\n    Metric(name=\"cgF1\", image_level=False, iou_threshold=None),\n    Metric(name=\"precision\", image_level=False, iou_threshold=None),\n    Metric(name=\"recall\", image_level=False, iou_threshold=None),\n    Metric(name=\"F1\", image_level=False, iou_threshold=None),\n    Metric(name=\"positive_macro_F1\", image_level=False, iou_threshold=None),\n    Metric(name=\"positive_micro_F1\", image_level=False, iou_threshold=None),\n    Metric(name=\"positive_micro_precision\", image_level=False, iou_threshold=None),\n    Metric(name=\"IL_precision\", image_level=True, iou_threshold=None),\n    Metric(name=\"IL_recall\", image_level=True, iou_threshold=None),\n    Metric(name=\"IL_F1\", image_level=True, iou_threshold=None),\n    Metric(name=\"IL_FPR\", image_level=True, iou_threshold=None),\n    Metric(name=\"IL_MCC\", image_level=True, iou_threshold=None),\n    Metric(name=\"cgF1\", image_level=False, iou_threshold=0.5),\n    Metric(name=\"precision\", image_level=False, iou_threshold=0.5),\n    Metric(name=\"recall\", image_level=False, iou_threshold=0.5),\n    Metric(name=\"F1\", image_level=False, iou_threshold=0.5),\n    Metric(name=\"positive_macro_F1\", image_level=False, iou_threshold=0.5),\n    Metric(name=\"positive_micro_F1\", image_level=False, iou_threshold=0.5),\n    Metric(name=\"positive_micro_precision\", image_level=False, iou_threshold=0.5),\n    Metric(name=\"cgF1\", image_level=False, iou_threshold=0.75),\n    Metric(name=\"precision\", image_level=False, iou_threshold=0.75),\n    Metric(name=\"recall\", image_level=False, iou_threshold=0.75),\n    Metric(name=\"F1\", image_level=False, iou_threshold=0.75),\n    Metric(name=\"positive_macro_F1\", image_level=False, iou_threshold=0.75),\n    Metric(name=\"positive_micro_F1\", image_level=False, iou_threshold=0.75),\n    Metric(name=\"positive_micro_precision\", image_level=False, iou_threshold=0.75),\n]\n\n\nclass COCOCustom(COCO):\n    \"\"\"COCO class from pycocotools with tiny modifications for speed\"\"\"\n\n    def createIndex(self):\n        # create index\n        print(\"creating index...\")\n        anns, cats, imgs = {}, {}, {}\n        imgToAnns, catToImgs = defaultdict(list), defaultdict(list)\n        if \"annotations\" in self.dataset:\n            for ann in self.dataset[\"annotations\"]:\n                imgToAnns[ann[\"image_id\"]].append(ann)\n                anns[ann[\"id\"]] = ann\n\n        if \"images\" in self.dataset:\n            # MODIFICATION: do not reload imgs if they are already there\n            if self.imgs:\n                imgs = self.imgs\n            else:\n                for img in self.dataset[\"images\"]:\n                    imgs[img[\"id\"]] = img\n            # END MODIFICATION\n\n        if \"categories\" in self.dataset:\n            for cat in self.dataset[\"categories\"]:\n                cats[cat[\"id\"]] = cat\n\n        if \"annotations\" in self.dataset and \"categories\" in self.dataset:\n            for ann in self.dataset[\"annotations\"]:\n                catToImgs[ann[\"category_id\"]].append(ann[\"image_id\"])\n\n        print(\"index created!\")\n\n        # create class members\n        self.anns = anns\n        self.imgToAnns = imgToAnns\n        self.catToImgs = catToImgs\n        self.imgs = imgs\n        self.cats = cats\n\n    def loadRes(self, resFile):\n        \"\"\"\n        Load result file and return a result api object.\n        :param   resFile (str)     : file name of result file\n        :return: res (obj)         : result api object\n        \"\"\"\n        res = COCOCustom()\n        res.dataset[\"info\"] = copy.deepcopy(self.dataset.get(\"info\", {}))\n        # MODIFICATION: no copy\n        # res.dataset['images'] = [img for img in self.dataset['images']]\n        res.dataset[\"images\"] = self.dataset[\"images\"]\n        # END MODIFICATION\n\n        print(\"Loading and preparing results...\")\n        tic = time.time()\n        if type(resFile) == str:\n            with open(resFile) as f:\n                anns = json.load(f)\n        elif type(resFile) == np.ndarray:\n            anns = self.loadNumpyAnnotations(resFile)\n        else:\n            anns = resFile\n        assert type(anns) == list, \"results in not an array of objects\"\n        annsImgIds = [ann[\"image_id\"] for ann in anns]\n        # MODIFICATION: faster and cached subset check\n        if not hasattr(self, \"img_id_set\"):\n            self.img_id_set = set(self.getImgIds())\n        assert set(annsImgIds).issubset(self.img_id_set), (\n            \"Results do not correspond to current coco set\"\n        )\n        # END MODIFICATION\n        if \"caption\" in anns[0]:\n            imgIds = set([img[\"id\"] for img in res.dataset[\"images\"]]) & set(\n                [ann[\"image_id\"] for ann in anns]\n            )\n            res.dataset[\"images\"] = [\n                img for img in res.dataset[\"images\"] if img[\"id\"] in imgIds\n            ]\n            for id, ann in enumerate(anns):\n                ann[\"id\"] = id + 1\n        elif \"bbox\" in anns[0] and not anns[0][\"bbox\"] == []:\n            res.dataset[\"categories\"] = copy.deepcopy(self.dataset[\"categories\"])\n            for id, ann in enumerate(anns):\n                bb = ann[\"bbox\"]\n                x1, x2, y1, y2 = [bb[0], bb[0] + bb[2], bb[1], bb[1] + bb[3]]\n                if not \"segmentation\" in ann:\n                    ann[\"segmentation\"] = [[x1, y1, x1, y2, x2, y2, x2, y1]]\n                ann[\"area\"] = bb[2] * bb[3]\n                ann[\"id\"] = id + 1\n                ann[\"iscrowd\"] = 0\n        elif \"segmentation\" in anns[0]:\n            res.dataset[\"categories\"] = copy.deepcopy(self.dataset[\"categories\"])\n            for id, ann in enumerate(anns):\n                # now only support compressed RLE format as segmentation results\n                ann[\"area\"] = maskUtils.area(ann[\"segmentation\"])\n                if not \"bbox\" in ann:\n                    ann[\"bbox\"] = maskUtils.toBbox(ann[\"segmentation\"])\n                ann[\"id\"] = id + 1\n                ann[\"iscrowd\"] = 0\n        elif \"keypoints\" in anns[0]:\n            res.dataset[\"categories\"] = copy.deepcopy(self.dataset[\"categories\"])\n            for id, ann in enumerate(anns):\n                s = ann[\"keypoints\"]\n                x = s[0::3]\n                y = s[1::3]\n                x0, x1, y0, y1 = np.min(x), np.max(x), np.min(y), np.max(y)\n                ann[\"area\"] = (x1 - x0) * (y1 - y0)\n                ann[\"id\"] = id + 1\n                ann[\"bbox\"] = [x0, y0, x1 - x0, y1 - y0]\n        print(\"DONE (t={:0.2f}s)\".format(time.time() - tic))\n\n        res.dataset[\"annotations\"] = anns\n        # MODIFICATION: inherit images\n        res.imgs = self.imgs\n        # END MODIFICATION\n        res.createIndex()\n        return res\n\n\nclass CGF1Eval(COCOeval):\n    \"\"\"\n    This evaluator is based upon COCO evaluation, but evaluates the model in a more realistic setting\n    for downstream applications.\n    See SAM3 paper for the details on the CGF1 metric.\n\n    Do not use this evaluator directly. Prefer the CGF1Evaluator wrapper.\n\n    Notes:\n     - This evaluator does not support per-category evaluation (in the way defined by pyCocotools)\n     - In open vocabulary settings, we have different noun-phrases for each image. What we call an \"image_id\" here is actually an (image, noun-phrase) pair. So in every \"image_id\" there is only one category, implied by the noun-phrase. Thus we can ignore the usual coco \"category\" field of the predictions\n    \"\"\"\n\n    def __init__(\n        self,\n        coco_gt=None,\n        coco_dt=None,\n        iouType=\"segm\",\n        threshold=0.5,\n    ):\n        \"\"\"\n        Args:\n            coco_gt (COCO): ground truth COCO API\n            coco_dt (COCO): detections COCO API\n            iou_type (str): type of IoU to evaluate\n            threshold (float): threshold for predictions\n        \"\"\"\n        super().__init__(coco_gt, coco_dt, iouType)\n        self.threshold = threshold\n\n        self.params.useCats = False\n        self.params.areaRng = [[0**2, 1e5**2]]\n        self.params.areaRngLbl = [\"all\"]\n        self.params.maxDets = [1000000]\n\n    def computeIoU(self, imgId, catId):\n        # Same as the original COCOeval.computeIoU, but without sorting\n        p = self.params\n        if p.useCats:\n            gt = self._gts[imgId, catId]\n            dt = self._dts[imgId, catId]\n        else:\n            gt = [_ for cId in p.catIds for _ in self._gts[imgId, cId]]\n            dt = [_ for cId in p.catIds for _ in self._dts[imgId, cId]]\n        if len(gt) == 0 and len(dt) == 0:\n            return []\n\n        if p.iouType == \"segm\":\n            g = [g[\"segmentation\"] for g in gt]\n            d = [d[\"segmentation\"] for d in dt]\n        elif p.iouType == \"bbox\":\n            g = [g[\"bbox\"] for g in gt]\n            d = [d[\"bbox\"] for d in dt]\n        else:\n            raise Exception(\"unknown iouType for iou computation\")\n\n        # compute iou between each dt and gt region\n        iscrowd = [int(o[\"iscrowd\"]) for o in gt]\n        ious = maskUtils.iou(d, g, iscrowd)\n        return ious\n\n    def evaluateImg(self, imgId, catId, aRng, maxDet):\n        \"\"\"\n        perform evaluation for single category and image\n        :return: dict (single image results)\n        \"\"\"\n        p = self.params\n        assert not p.useCats, \"This evaluator does not support per-category evaluation.\"\n        assert catId == -1\n        all_gts = [_ for cId in p.catIds for _ in self._gts[imgId, cId]]\n        keep_gt = np.array([not g[\"ignore\"] for g in all_gts], dtype=bool)\n        gt = [g for g in all_gts if not g[\"ignore\"]]\n        all_dts = [_ for cId in p.catIds for _ in self._dts[imgId, cId]]\n        keep_dt = np.array([d[\"score\"] >= self.threshold for d in all_dts], dtype=bool)\n        dt = [d for d in all_dts if d[\"score\"] >= self.threshold]\n        if len(gt) == 0 and len(dt) == 0:\n            # This is a \"true negative\" case, where there are no GTs and no predictions\n            # The box-level metrics are ill-defined, so we don't add them to this dict\n            return {\n                \"image_id\": imgId,\n                \"IL_TP\": 0,\n                \"IL_TN\": 1,\n                \"IL_FP\": 0,\n                \"IL_FN\": 0,\n                \"num_dt\": len(dt),\n            }\n\n        if len(gt) > 0 and len(dt) == 0:\n            # This is a \"false negative\" case, where there are GTs but no predictions\n            return {\n                \"image_id\": imgId,\n                \"IL_TP\": 0,\n                \"IL_TN\": 0,\n                \"IL_FP\": 0,\n                \"IL_FN\": 1,\n                \"TPs\": np.zeros((len(p.iouThrs),), dtype=np.int64),\n                \"FPs\": np.zeros((len(p.iouThrs),), dtype=np.int64),\n                \"FNs\": np.ones((len(p.iouThrs),), dtype=np.int64) * len(gt),\n                \"local_F1s\": np.zeros((len(p.iouThrs),), dtype=np.int64),\n                \"local_positive_F1s\": np.zeros((len(p.iouThrs),), dtype=np.int64),\n                \"num_dt\": len(dt),\n            }\n\n        # Load pre-computed ious\n        ious = self.ious[(imgId, catId)]\n\n        # compute matching\n        if len(ious) == 0:\n            ious = np.zeros((len(dt), len(gt)))\n        else:\n            ious = ious[keep_dt, :][:, keep_gt]\n        assert ious.shape == (len(dt), len(gt))\n\n        matched_dt, matched_gt = linear_sum_assignment(-ious)\n\n        match_scores = ious[matched_dt, matched_gt]\n\n        TPs, FPs, FNs = [], [], []\n        IL_perfect = []\n        for thresh in p.iouThrs:\n            TP = (match_scores >= thresh).sum()\n            FP = len(dt) - TP\n            FN = len(gt) - TP\n            assert FP >= 0 and FN >= 0, (\n                f\"FP: {FP}, FN: {FN}, TP: {TP}, match_scores: {match_scores}, len(dt): {len(dt)}, len(gt): {len(gt)}, ious: {ious}\"\n            )\n            TPs.append(TP)\n            FPs.append(FP)\n            FNs.append(FN)\n\n            if FP == FN and FP == 0:\n                IL_perfect.append(1)\n            else:\n                IL_perfect.append(0)\n\n        TPs = np.array(TPs, dtype=np.int64)\n        FPs = np.array(FPs, dtype=np.int64)\n        FNs = np.array(FNs, dtype=np.int64)\n        IL_perfect = np.array(IL_perfect, dtype=np.int64)\n\n        # compute precision recall and F1\n        precision = TPs / (TPs + FPs + 1e-4)\n        assert np.all(precision <= 1)\n        recall = TPs / (TPs + FNs + 1e-4)\n        assert np.all(recall <= 1)\n        F1 = 2 * precision * recall / (precision + recall + 1e-4)\n\n        result = {\n            \"image_id\": imgId,\n            \"TPs\": TPs,\n            \"FPs\": FPs,\n            \"FNs\": FNs,\n            \"local_F1s\": F1,\n            \"IL_TP\": (len(gt) > 0) and (len(dt) > 0),\n            \"IL_FP\": (len(gt) == 0) and (len(dt) > 0),\n            \"IL_TN\": (len(gt) == 0) and (len(dt) == 0),\n            \"IL_FN\": (len(gt) > 0) and (len(dt) == 0),\n            \"num_dt\": len(dt),\n        }\n        if len(gt) > 0 and len(dt) > 0:\n            result[\"local_positive_F1s\"] = F1\n        return result\n\n    def accumulate(self, p=None):\n        \"\"\"\n        Accumulate per image evaluation results and store the result in self.eval\n        :param p: input params for evaluation\n        :return: None\n        \"\"\"\n        if self.evalImgs is None or len(self.evalImgs) == 0:\n            print(\"Please run evaluate() first\")\n        # allows input customized parameters\n        if p is None:\n            p = self.params\n\n        setImgIds = set(p.imgIds)\n\n        # TPs, FPs, FNs\n        TPs = np.zeros((len(p.iouThrs),), dtype=np.int64)\n        FPs = np.zeros((len(p.iouThrs),), dtype=np.int64)\n        pmFPs = np.zeros((len(p.iouThrs),), dtype=np.int64)\n        FNs = np.zeros((len(p.iouThrs),), dtype=np.int64)\n        local_F1s = np.zeros((len(p.iouThrs),), dtype=np.float64)\n\n        # Image level metrics\n        IL_TPs = 0\n        IL_FPs = 0\n        IL_TNs = 0\n        IL_FNs = 0\n\n        valid_img_count = 0\n        valid_F1_count = 0\n        evaledImgIds = set()\n        for res in self.evalImgs:\n            if res[\"image_id\"] not in setImgIds:\n                continue\n            evaledImgIds.add(res[\"image_id\"])\n            IL_TPs += res[\"IL_TP\"]\n            IL_FPs += res[\"IL_FP\"]\n            IL_TNs += res[\"IL_TN\"]\n            IL_FNs += res[\"IL_FN\"]\n\n            if \"TPs\" not in res:\n                continue\n\n            TPs += res[\"TPs\"]\n            FPs += res[\"FPs\"]\n            FNs += res[\"FNs\"]\n            valid_img_count += 1\n\n            if \"local_positive_F1s\" in res:\n                local_F1s += res[\"local_positive_F1s\"]\n                pmFPs += res[\"FPs\"]\n                if res[\"num_dt\"] > 0:\n                    valid_F1_count += 1\n\n        assert len(setImgIds - evaledImgIds) == 0, (\n            f\"{len(setImgIds - evaledImgIds)} images not evaluated. \"\n            f\"Here are the IDs of the first 3: {list(setImgIds - evaledImgIds)[:3]}\"\n        )\n\n        # compute precision recall and F1\n        precision = TPs / (TPs + FPs + 1e-4)\n        positive_micro_precision = TPs / (TPs + pmFPs + 1e-4)\n        assert np.all(precision <= 1)\n        recall = TPs / (TPs + FNs + 1e-4)\n        assert np.all(recall <= 1)\n        F1 = 2 * precision * recall / (precision + recall + 1e-4)\n        positive_micro_F1 = (\n            2\n            * positive_micro_precision\n            * recall\n            / (positive_micro_precision + recall + 1e-4)\n        )\n\n        IL_rec = IL_TPs / (IL_TPs + IL_FNs + 1e-6)\n        IL_prec = IL_TPs / (IL_TPs + IL_FPs + 1e-6)\n        IL_F1 = 2 * IL_prec * IL_rec / (IL_prec + IL_rec + 1e-6)\n        IL_FPR = IL_FPs / (IL_FPs + IL_TNs + 1e-6)\n        IL_MCC = float(IL_TPs * IL_TNs - IL_FPs * IL_FNs) / (\n            (\n                float(IL_TPs + IL_FPs)\n                * float(IL_TPs + IL_FNs)\n                * float(IL_TNs + IL_FPs)\n                * float(IL_TNs + IL_FNs)\n            )\n            ** 0.5\n            + 1e-6\n        )\n\n        self.eval = {\n            \"params\": p,\n            \"TPs\": TPs,\n            \"FPs\": FPs,\n            \"positive_micro_FPs\": pmFPs,\n            \"FNs\": FNs,\n            \"precision\": precision,\n            \"positive_micro_precision\": positive_micro_precision,\n            \"recall\": recall,\n            \"F1\": F1,\n            \"positive_micro_F1\": positive_micro_F1,\n            \"positive_macro_F1\": local_F1s / valid_F1_count,\n            \"IL_recall\": IL_rec,\n            \"IL_precision\": IL_prec,\n            \"IL_F1\": IL_F1,\n            \"IL_FPR\": IL_FPR,\n            \"IL_MCC\": IL_MCC,\n        }\n        self.eval[\"cgF1\"] = self.eval[\"positive_micro_F1\"] * self.eval[\"IL_MCC\"]\n\n    def summarize(self):\n        \"\"\"\n        Compute and display summary metrics for evaluation results.\n        \"\"\"\n        if not self.eval:\n            raise Exception(\"Please run accumulate() first\")\n\n        def _summarize(iouThr=None, metric=\"\"):\n            p = self.params\n            iStr = \" {:<18} @[ IoU={:<9}] = {:0.3f}\"\n            titleStr = \"Average \" + metric\n            iouStr = (\n                \"{:0.2f}:{:0.2f}\".format(p.iouThrs[0], p.iouThrs[-1])\n                if iouThr is None\n                else \"{:0.2f}\".format(iouThr)\n            )\n\n            s = self.eval[metric]\n            # IoU\n            if iouThr is not None:\n                t = np.where(iouThr == p.iouThrs)[0]\n                s = s[t]\n\n            if len(s[s > -1]) == 0:\n                mean_s = -1\n            else:\n                mean_s = np.mean(s[s > -1])\n            print(iStr.format(titleStr, iouStr, mean_s))\n            return mean_s\n\n        def _summarize_single(metric=\"\"):\n            titleStr = \"Average \" + metric\n            iStr = \" {:<35} = {:0.3f}\"\n            s = self.eval[metric]\n            print(iStr.format(titleStr, s))\n            return s\n\n        def _summarizeDets():\n            stats = []\n\n            for metric in CGF1_METRICS:\n                if metric.image_level:\n                    stats.append(_summarize_single(metric=metric.name))\n                else:\n                    stats.append(\n                        _summarize(iouThr=metric.iou_threshold, metric=metric.name)\n                    )\n            return np.asarray(stats)\n\n        summarize = _summarizeDets\n        self.stats = summarize()\n\n\ndef _evaluate(self):\n    \"\"\"\n    Run per image evaluation on given images and store results (a list of dict) in self.evalImgs\n    \"\"\"\n    p = self.params\n    # add backward compatibility if useSegm is specified in params\n    p.imgIds = list(np.unique(p.imgIds))\n    p.useCats = False\n    p.maxDets = sorted(p.maxDets)\n    self.params = p\n\n    self._prepare()\n    # loop through images, area range, max detection number\n    catIds = [-1]\n\n    if p.iouType == \"segm\" or p.iouType == \"bbox\":\n        computeIoU = self.computeIoU\n    else:\n        raise RuntimeError(f\"Unsupported iou {p.iouType}\")\n    self.ious = {\n        (imgId, catId): computeIoU(imgId, catId)\n        for imgId in p.imgIds\n        for catId in catIds\n    }\n\n    maxDet = p.maxDets[-1]\n    evalImgs = [\n        self.evaluateImg(imgId, catId, areaRng, maxDet)\n        for catId in catIds\n        for areaRng in p.areaRng\n        for imgId in p.imgIds\n    ]\n    # this is NOT in the pycocotools code, but could be done outside\n    evalImgs = np.asarray(evalImgs).reshape(len(catIds), len(p.areaRng), len(p.imgIds))\n    return p.imgIds, evalImgs\n\n\nclass CGF1Evaluator:\n    \"\"\"\n    Wrapper class for cgF1 evaluation.\n    This supports the oracle setting (when several ground-truths are available per image)\n    \"\"\"\n\n    def __init__(\n        self,\n        gt_path: Union[str, List[str]],\n        iou_type=\"segm\",\n        verbose=False,\n    ):\n        \"\"\"\n        Args:\n            gt_path (str or list of str): path(s) to ground truth COCO json file(s)\n            iou_type (str): type of IoU to evaluate\n            threshold (float): threshold for predictions\n        \"\"\"\n        self.gt_paths = gt_path if isinstance(gt_path, list) else [gt_path]\n        self.iou_type = iou_type\n\n        self.coco_gts = [COCOCustom(gt) for gt in self.gt_paths]\n\n        self.verbose = verbose\n\n        self.coco_evals = []\n        for i, coco_gt in enumerate(self.coco_gts):\n            self.coco_evals.append(\n                CGF1Eval(\n                    coco_gt=coco_gt,\n                    iouType=iou_type,\n                )\n            )\n            self.coco_evals[i].useCats = False\n\n        exclude_img_ids = set()\n        # exclude_img_ids are the ids that are not exhaustively annotated in any of the other gts\n        for coco_gt in self.coco_gts[1:]:\n            exclude_img_ids = exclude_img_ids.union(\n                {\n                    img[\"id\"]\n                    for img in coco_gt.dataset[\"images\"]\n                    if not img[\"is_instance_exhaustive\"]\n                }\n            )\n        # we only eval on instance exhaustive queries\n        self.eval_img_ids = [\n            img[\"id\"]\n            for img in self.coco_gts[0].dataset[\"images\"]\n            if (img[\"is_instance_exhaustive\"] and img[\"id\"] not in exclude_img_ids)\n        ]\n\n    def evaluate(self, pred_file: str):\n        \"\"\"\n        Evaluate the detections using cgF1 metric.\n\n        Args:\n            pred_file: path to the predictions COCO json file\n\n        \"\"\"\n        assert len(self.coco_gts) > 0, \"No ground truth provided for evaluation.\"\n        assert len(self.coco_gts) == len(self.coco_evals), (\n            \"Mismatch in number of ground truths and evaluators.\"\n        )\n\n        if self.verbose:\n            print(f\"Loading predictions from {pred_file}\")\n\n        with open(pred_file, \"r\") as f:\n            preds = json.load(f)\n\n        if self.verbose:\n            print(f\"Loaded {len(preds)} predictions\")\n\n        img2preds = defaultdict(list)\n        for pred in preds:\n            img2preds[pred[\"image_id\"]].append(pred)\n\n        all_eval_imgs = []\n        for img_id in tqdm(self.eval_img_ids, disable=not self.verbose):\n            results = img2preds[img_id]\n            all_scorings = []\n            for cur_coco_gt, coco_eval in zip(self.coco_gts, self.coco_evals):\n                # suppress pycocotools prints\n                with open(os.devnull, \"w\") as devnull:\n                    with contextlib.redirect_stdout(devnull):\n                        coco_dt = (\n                            cur_coco_gt.loadRes(results) if results else COCOCustom()\n                        )\n\n                coco_eval.cocoDt = coco_dt\n                coco_eval.params.imgIds = [img_id]\n                coco_eval.params.useCats = False\n                img_ids, eval_imgs = _evaluate(coco_eval)\n                all_scorings.append(eval_imgs)\n            selected = self._select_best_scoring(all_scorings)\n            all_eval_imgs.append(selected)\n\n        # After this point, we have selected the best scoring per image among several ground truths\n        # we can now accumulate and summarize, using only the first coco_eval\n\n        self.coco_evals[0].evalImgs = list(\n            np.concatenate(all_eval_imgs, axis=2).flatten()\n        )\n        self.coco_evals[0].params.imgIds = self.eval_img_ids\n        self.coco_evals[0]._paramsEval = copy.deepcopy(self.coco_evals[0].params)\n\n        if self.verbose:\n            print(f\"Accumulating results\")\n        self.coco_evals[0].accumulate()\n        print(\"cgF1 metric, IoU type={}\".format(self.iou_type))\n        self.coco_evals[0].summarize()\n        print()\n\n        out = {}\n        for i, value in enumerate(self.coco_evals[0].stats):\n            name = CGF1_METRICS[i].name\n            if CGF1_METRICS[i].iou_threshold is not None:\n                name = f\"{name}@{CGF1_METRICS[i].iou_threshold}\"\n            out[f\"cgF1_eval_{self.iou_type}_{name}\"] = float(value)\n\n        return out\n\n    @staticmethod\n    def _select_best_scoring(scorings):\n        # This function is used for \"oracle\" type evaluation.\n        # It accepts the evaluation results with respect to several ground truths, and picks the best\n        if len(scorings) == 1:\n            return scorings[0]\n\n        assert scorings[0].ndim == 3, (\n            f\"Expecting results in [numCats, numAreas, numImgs] format, got {scorings[0].shape}\"\n        )\n        assert scorings[0].shape[0] == 1, (\n            f\"Expecting a single category, got {scorings[0].shape[0]}\"\n        )\n\n        for scoring in scorings:\n            assert scoring.shape == scorings[0].shape, (\n                f\"Shape mismatch: {scoring.shape}, {scorings[0].shape}\"\n            )\n\n        selected_imgs = []\n        for img_id in range(scorings[0].shape[-1]):\n            best = scorings[0][:, :, img_id]\n\n            for scoring in scorings[1:]:\n                current = scoring[:, :, img_id]\n                if \"local_F1s\" in best[0, 0] and \"local_F1s\" in current[0, 0]:\n                    # we were able to compute a F1 score for this particular image in both evaluations\n                    # best[\"local_F1s\"] contains the results at various IoU thresholds. We simply take the average for comparision\n                    best_score = best[0, 0][\"local_F1s\"].mean()\n                    current_score = current[0, 0][\"local_F1s\"].mean()\n                    if current_score > best_score:\n                        best = current\n\n                else:\n                    # If we're here, it means that in that in some evaluation we were not able to get a valid local F1\n                    # This happens when both the predictions and targets are empty. In that case, we can assume it's a perfect prediction\n                    if \"local_F1s\" not in current[0, 0]:\n                        best = current\n            selected_imgs.append(best)\n        result = np.stack(selected_imgs, axis=-1)\n        assert result.shape == scorings[0].shape\n        return result\n"
  },
  {
    "path": "sam3/eval/coco_eval.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n\n\"\"\"\nCOCO evaluator that works in distributed mode.\n\nMostly copy-paste from https://github.com/pytorch/vision/blob/edfd5a7/references/detection/coco_eval.py\nThe difference is that there is less copy-pasting from pycocotools\nin the end of the file, as python3 can suppress prints with contextlib\n\"\"\"\n\nimport contextlib\nimport copy\nimport json\nimport logging\nimport os\nimport pickle\nfrom collections import defaultdict\nfrom pathlib import Path\nfrom typing import Any, List, Optional\n\nimport numpy as np\nimport pycocotools.mask as mask_utils\nimport torch\nfrom iopath.common.file_io import g_pathmgr\nfrom pycocotools.coco import COCO\nfrom pycocotools.cocoeval import COCOeval\nfrom sam3.train.masks_ops import rle_encode\nfrom sam3.train.utils.distributed import (\n    all_gather,\n    gather_to_rank_0_via_filesys,\n    get_rank,\n    is_main_process,\n)\n\nRARITY_BUCKETS = {0: \"frequent\", 1: \"common\", 2: \"medium\", 3: \"rare\"}\n\n\nclass CocoEvaluator:\n    def __init__(\n        self,\n        coco_gt,\n        iou_types: List[str],\n        useCats: bool,\n        dump_dir: Optional[str],\n        postprocessor,\n        average_by_rarity=False,\n        metrics_dump_dir: Optional[str] = None,\n        gather_pred_via_filesys=False,\n        use_normalized_areas=True,\n        maxdets=[1, 10, 100],\n        exhaustive_only=False,\n        all_exhaustive_only=True,\n    ):\n        \"\"\"Online coco evaluator. It will evaluate images as they are generated by the model, then accumulate/summarize at the end\n\n        Args:\n           - coco_gt: COCO api object containing the gt\n           - iou_types: can be either \"bbox\" or \"segm\"\n           - useCats: If true, categories will be used for evaluation\n           - dump_dir: if non null, then the predictions will be dumped in that directory\n           - postprocessor: Module to convert the model's output into the coco format\n           - average_by_rarity: if true then we expect the images information in the gt dataset\n                 to have a \"rarity\" field. Then the AP will be computed on all rarity buckets\n                 individually, then averaged\n           - gather_pred_via_filesys: if true, we use the filesystem for collective gathers\n           - use_normalized_areas: if true, the areas of the objects in the GT are assumed to be\n                 normalized by the area of the image. In that case, the size buckets are adjusted\n           - maxdets: maximal number of detections to be evaluated on each image.\n           - exhaustive_only: If true, we restrict eval only to exhaustive annotations\n           - all_exhaustive_only: If true, datapoints are restricted only to those with all exhaustive annotations\n\n        \"\"\"\n        # coco_gt = copy.deepcopy(coco_gt)\n        self.coco_gts = [coco_gt] if not isinstance(coco_gt, list) else coco_gt\n        assert len(maxdets) == 3, f\"expecting 3 detection threshold, got {len(maxdets)}\"\n\n        self.use_normalized_areas = use_normalized_areas\n        self.iou_types = iou_types\n        self.useCats = useCats\n        self.maxdets = maxdets\n        self.dump = None\n        self.dump_dir = dump_dir\n        if self.dump_dir is not None:\n            self.dump = []\n            if is_main_process():\n                if not os.path.exists(self.dump_dir):\n                    os.makedirs(self.dump_dir, exist_ok=True)\n                    logging.info(f\"Create the folder: {dump_dir}\")\n\n        self.initialized = False\n\n        # Whether to gather predictions through filesystem (instead of torch\n        # collective ops; requiring a shared filesystem across all ranks)\n        self.gather_pred_via_filesys = gather_pred_via_filesys\n        self.use_self_evaluate = True  # CPP version is disabled\n        self.postprocessor = postprocessor\n        self.average_by_rarity = average_by_rarity\n        self.exhaustive_only = exhaustive_only\n        self.all_exhaustive_only = all_exhaustive_only\n        self.metrics_dump_dir = metrics_dump_dir\n        if self.metrics_dump_dir is not None:\n            if is_main_process():\n                if not os.path.exists(self.metrics_dump_dir):\n                    os.makedirs(self.metrics_dump_dir, exist_ok=True)\n                    logging.info(f\"Create the folder: {metrics_dump_dir}\")\n\n    def _lazy_init(self, coco_cls=COCO):\n        if self.initialized:\n            return\n\n        self.initialized = True\n\n        self.coco_gts = [\n            coco_cls(g_pathmgr.get_local_path(gt)) if isinstance(gt, str) else gt\n            for gt in self.coco_gts\n        ]\n\n        self.reset()\n\n        self.eval_img_ids = None\n\n        if self.exhaustive_only:\n            exclude_img_ids = set()\n            # exclude_img_ids are the ids that are not exhaustively annotated in any of the other gts\n            if self.all_exhaustive_only:\n                for coco_gt in self.coco_gts[1:]:\n                    exclude_img_ids = exclude_img_ids.union(\n                        {\n                            img[\"id\"]\n                            for img in coco_gt.dataset[\"images\"]\n                            if not img[\"is_instance_exhaustive\"]\n                        }\n                    )\n            # we only eval on instance exhaustive queries\n            self.eval_img_ids = [\n                img[\"id\"]\n                for img in self.coco_gts[0].dataset[\"images\"]\n                if (img[\"is_instance_exhaustive\"] and img[\"id\"] not in exclude_img_ids)\n            ]\n\n        self.rarity_buckets = None\n        if self.average_by_rarity:\n            self.rarity_buckets = defaultdict(list)\n            eval_img_ids_set = (\n                set(self.eval_img_ids) if self.eval_img_ids is not None else None\n            )\n            for img in self.coco_gts[0].dataset[\"images\"]:\n                if self.eval_img_ids is not None and img[\"id\"] not in eval_img_ids_set:\n                    continue\n                self.rarity_buckets[img[\"rarity\"]].append(img[\"id\"])\n            print(\"Rarity buckets sizes:\")\n            for k, v in self.rarity_buckets.items():\n                print(f\"{k}: {len(v)}\")\n\n    def set_sync_device(self, device: torch.device) -> Any:\n        self._sync_device = device\n\n    def _evaluate(self, *args, **kwargs):\n        return evaluate(*args, **kwargs)\n\n    def _loadRes(self, *args, **kwargs):\n        return loadRes(*args, **kwargs)\n\n    def update(self, *args, **kwargs):\n        self._lazy_init()\n        predictions = self.postprocessor.process_results(*args, **kwargs)\n\n        img_ids = list(np.unique(list(predictions.keys())))\n        self.img_ids.extend(img_ids)\n\n        for iou_type in self.iou_types:\n            results = self.prepare(predictions, iou_type)\n            self._dump(results)\n\n            assert len(self.coco_gts) == len(self.coco_evals)\n            all_scorings = []\n            for cur_coco_gt, cur_coco_eval in zip(self.coco_gts, self.coco_evals):\n                # suppress pycocotools prints\n                with open(os.devnull, \"w\") as devnull:\n                    with contextlib.redirect_stdout(devnull):\n                        coco_dt = (\n                            self._loadRes(cur_coco_gt, results) if results else COCO()\n                        )\n\n                coco_eval = cur_coco_eval[iou_type]\n\n                coco_eval.cocoDt = coco_dt\n                coco_eval.params.imgIds = list(img_ids)\n                coco_eval.params.useCats = self.useCats\n                coco_eval.params.maxDets = self.maxdets\n                img_ids, eval_imgs = self._evaluate(coco_eval, self.use_self_evaluate)\n                all_scorings.append(eval_imgs)\n\n            selected = self.select_best_scoring(all_scorings)\n            self.eval_imgs[iou_type].append(selected)\n\n    def select_best_scoring(self, scorings):\n        # This function is used for \"oracle\" type evaluation.\n        # It accepts the evaluation results with respect to several ground truths, and picks the best\n        if len(scorings) == 1:\n            return scorings[0]\n\n        # Currently we don't support Oracle Phrase AP.\n        # To implement it, we likely need to modify the cpp code since the eval_image type is opaque\n        raise RuntimeError(\"Not implemented\")\n\n    def _dump(self, results):\n        if self.dump is not None:\n            dumped_results = copy.deepcopy(results)\n            for r in dumped_results:\n                if \"bbox\" not in self.iou_types and \"bbox\" in r:\n                    del r[\"bbox\"]\n                elif \"bbox\" in r:\n                    r[\"bbox\"] = [round(coord, 5) for coord in r[\"bbox\"]]\n                r[\"score\"] = round(r[\"score\"], 5)\n            self.dump.extend(dumped_results)\n\n    def synchronize_between_processes(self):\n        self._lazy_init()\n        logging.info(\"Coco evaluator: Synchronizing between processes\")\n        for iou_type in self.iou_types:\n            if len(self.eval_imgs[iou_type]) > 0:\n                self.eval_imgs[iou_type] = np.concatenate(self.eval_imgs[iou_type], 2)\n            else:\n                num_areas = len(self.coco_evals[0][iou_type].params.areaRng)\n                # assuming 1 class\n                assert not self.useCats\n                self.eval_imgs[iou_type] = np.empty((1, num_areas, 0))\n            create_common_coco_eval(\n                self.coco_evals[0][iou_type],\n                self.img_ids,\n                self.eval_imgs[iou_type],\n                use_self_evaluate=self.use_self_evaluate,\n                gather_pred_via_filesys=self.gather_pred_via_filesys,\n                metrics_dump_dir=self.metrics_dump_dir,\n            )\n        if self.dump is not None:\n            dumped_file = Path(self.dump_dir) / f\"coco_predictions_{get_rank()}.json\"\n            logging.info(f\"COCO evaluator: Dumping local predictions to {dumped_file}\")\n            with g_pathmgr.open(str(dumped_file), \"w\") as f:\n                json.dump(self.dump, f)\n\n            # if self.gather_pred_via_filesys:\n            #     dump = gather_to_rank_0_via_filesys(self.dump)\n            # else:\n            #     dump = all_gather(self.dump, force_cpu=True)\n            # self.dump = sum(dump, [])\n\n    def accumulate(self, imgIds=None):\n        self._lazy_init()\n        logging.info(\n            f\"Coco evaluator: Accumulating on {len(imgIds) if imgIds is not None else 'all'} images\"\n        )\n        if not is_main_process():\n            return\n\n        if imgIds is None:\n            for coco_eval in self.coco_evals[0].values():\n                accumulate(coco_eval, use_self_eval=self.use_self_evaluate)\n\n        if imgIds is not None:\n            imgIds = set(imgIds)\n            for coco_eval in self.coco_evals[0].values():\n                p = coco_eval.params\n                id_mask = np.array([(i in imgIds) for i in p.imgIds], dtype=bool)\n                old_img_ids = p.imgIds\n                coco_eval.params.imgIds = np.asarray(p.imgIds)[id_mask]\n                old_img_evals = coco_eval.evalImgs\n                catIds = p.catIds if p.useCats else [-1]\n                coco_eval.evalImgs = list(\n                    np.asarray(coco_eval.evalImgs)\n                    .reshape(len(catIds), len(p.areaRng), len(old_img_ids))[\n                        ..., id_mask\n                    ]\n                    .flatten()\n                )\n                accumulate(coco_eval, use_self_eval=self.use_self_evaluate)\n                coco_eval.evalImgs = old_img_evals\n                coco_eval.params.imgIds = old_img_ids\n\n    def summarize(self):\n        self._lazy_init()\n        logging.info(\"Coco evaluator: Summarizing\")\n        if not is_main_process():\n            return {}\n\n        outs = {}\n        if self.rarity_buckets is None:\n            self.accumulate(self.eval_img_ids)\n            for iou_type, coco_eval in self.coco_evals[0].items():\n                print(\"IoU metric: {}\".format(iou_type))\n                summarize(coco_eval)\n\n            if \"bbox\" in self.coco_evals[0]:\n                for key, value in zip(*self.coco_evals[0][\"bbox\"].stats):\n                    outs[f\"coco_eval_bbox_{key}\"] = value\n            if \"segm\" in self.coco_evals[0]:\n                for key, value in zip(*self.coco_evals[0][\"segm\"].stats):\n                    outs[f\"coco_eval_masks_{key}\"] = value\n        else:\n            total_stats = {}\n            all_keys = {}\n            for bucket, img_list in self.rarity_buckets.items():\n                self.accumulate(imgIds=img_list)\n                bucket_name = RARITY_BUCKETS[bucket]\n                for iou_type, coco_eval in self.coco_evals[0].items():\n                    print(f\"IoU metric: {iou_type}. Rarity bucket: {bucket_name}\")\n                    summarize(coco_eval)\n\n                if \"bbox\" in self.coco_evals[0]:\n                    if \"bbox\" not in total_stats:\n                        total_stats[\"bbox\"] = np.zeros_like(\n                            self.coco_evals[0][\"bbox\"].stats[1]\n                        )\n                        all_keys[\"bbox\"] = self.coco_evals[0][\"bbox\"].stats[0]\n                    total_stats[\"bbox\"] += self.coco_evals[0][\"bbox\"].stats[1]\n                    for key, value in zip(*self.coco_evals[0][\"bbox\"].stats):\n                        outs[f\"coco_eval_bbox_{bucket_name}_{key}\"] = value\n                if \"segm\" in self.coco_evals[0]:\n                    if \"segm\" not in total_stats:\n                        total_stats[\"segm\"] = np.zeros_like(\n                            self.coco_evals[0][\"segm\"].stats[1]\n                        )\n                        all_keys[\"segm\"] = self.coco_evals[0][\"segm\"].stats[0]\n                    total_stats[\"segm\"] += self.coco_evals[0][\"segm\"].stats[1]\n                    for key, value in zip(*self.coco_evals[0][\"segm\"].stats):\n                        outs[f\"coco_eval_masks_{bucket_name}_{key}\"] = value\n\n            if \"bbox\" in total_stats:\n                total_stats[\"bbox\"] /= len(self.rarity_buckets)\n                for key, value in zip(all_keys[\"bbox\"], total_stats[\"bbox\"]):\n                    outs[f\"coco_eval_bbox_{key}\"] = value\n            if \"segm\" in total_stats:\n                total_stats[\"segm\"] /= len(self.rarity_buckets)\n                for key, value in zip(all_keys[\"segm\"], total_stats[\"segm\"]):\n                    outs[f\"coco_eval_masks_{key}\"] = value\n\n        # if self.dump is not None:\n        #     assert self.dump_dir is not None\n        #     logging.info(\"Coco evaluator: Dumping the global result file to disk\")\n        #     with g_pathmgr.open(str(Path(self.dump_dir) / \"coco_eval.json\"), \"w\") as f:\n        #         json.dump(self.dump, f)\n        return outs\n\n    def compute_synced(self):\n        self._lazy_init()\n        self.synchronize_between_processes()\n        return self.summarize()\n\n    def compute(self):\n        self._lazy_init()\n        return {\"\": 0.0}\n\n    def reset(self, cocoeval_cls=COCOeval):\n        self.coco_evals = [{} for _ in range(len(self.coco_gts))]\n        for i, coco_gt in enumerate(self.coco_gts):\n            for iou_type in self.iou_types:\n                self.coco_evals[i][iou_type] = cocoeval_cls(coco_gt, iouType=iou_type)\n                self.coco_evals[i][iou_type].params.useCats = self.useCats\n                self.coco_evals[i][iou_type].params.maxDets = self.maxdets\n                if self.use_normalized_areas:\n                    self.coco_evals[i][iou_type].params.areaRng = [\n                        [0, 1e5],\n                        [0, 0.001],\n                        [0.001, 0.01],\n                        [0.01, 0.1],\n                        [0.1, 0.5],\n                        [0.5, 0.95],\n                        [0.95, 1e5],\n                    ]\n                    self.coco_evals[i][iou_type].params.areaRngLbl = [\n                        \"all\",\n                        \"tiny\",\n                        \"small\",\n                        \"medium\",\n                        \"large\",\n                        \"huge\",\n                        \"whole_image\",\n                    ]\n\n        self.img_ids = []\n        self.eval_imgs = {k: [] for k in self.iou_types}\n        if self.dump is not None:\n            self.dump = []\n\n    def write(self, stats):\n        self._lazy_init()\n        \"\"\"Write the results in the stats dict\"\"\"\n        if \"bbox\" in self.coco_evals[0]:\n            stats[\"coco_eval_bbox\"] = self.coco_evals[0][\"bbox\"].stats.tolist()\n        if \"segm\" in self.coco_evals[0]:\n            stats[\"coco_eval_masks\"] = self.coco_evals[0][\"segm\"].stats.tolist()\n        return stats\n\n    def prepare(self, predictions, iou_type):\n        self._lazy_init()\n        if iou_type == \"bbox\":\n            return self.prepare_for_coco_detection(predictions)\n        elif iou_type == \"segm\":\n            return self.prepare_for_coco_segmentation(predictions)\n        elif iou_type == \"keypoints\":\n            return self.prepare_for_coco_keypoint(predictions)\n        else:\n            raise ValueError(\"Unknown iou type {}\".format(iou_type))\n\n    def prepare_for_coco_detection(self, predictions):\n        self._lazy_init()\n        coco_results = []\n        for original_id, prediction in predictions.items():\n            if len(prediction) == 0:\n                continue\n\n            boxes = prediction[\"boxes\"]\n            boxes = convert_to_xywh(boxes).tolist()\n            scores = prediction[\"scores\"].tolist()\n            labels = prediction[\"labels\"].tolist()\n\n            coco_results.extend(\n                [\n                    {\n                        \"image_id\": original_id,\n                        \"category_id\": labels[k],\n                        \"bbox\": box,\n                        \"score\": scores[k],\n                    }\n                    for k, box in enumerate(boxes)\n                ]\n            )\n        return coco_results\n\n    @torch.no_grad()\n    def prepare_for_coco_segmentation(self, predictions):\n        self._lazy_init()\n        coco_results = []\n        for original_id, prediction in predictions.items():\n            if len(prediction) == 0:\n                continue\n\n            scores = prediction[\"scores\"].tolist()\n            labels = prediction[\"labels\"].tolist()\n            boundaries, dilated_boundaries = None, None\n            if \"boundaries\" in prediction:\n                boundaries = prediction[\"boundaries\"]\n                dilated_boundaries = prediction[\"dilated_boundaries\"]\n                assert dilated_boundaries is not None\n                assert len(scores) == len(boundaries)\n\n            if \"masks_rle\" in prediction:\n                rles = prediction[\"masks_rle\"]\n                areas = []\n                for rle in rles:\n                    cur_area = mask_utils.area(rle)\n                    h, w = rle[\"size\"]\n                    areas.append(cur_area / (h * w))\n            else:\n                masks = prediction[\"masks\"]\n\n                masks = masks > 0.5\n                h, w = masks.shape[-2:]\n\n                areas = masks.flatten(1).sum(1) / (h * w)\n                areas = areas.tolist()\n\n                rles = rle_encode(masks.squeeze(1))\n\n                # memory clean\n                del masks\n                del prediction[\"masks\"]\n\n            assert len(areas) == len(rles) == len(scores)\n            for k, rle in enumerate(rles):\n                payload = {\n                    \"image_id\": original_id,\n                    \"category_id\": labels[k],\n                    \"segmentation\": rle,\n                    \"score\": scores[k],\n                    \"area\": areas[k],\n                }\n                if boundaries is not None:\n                    payload[\"boundary\"] = boundaries[k]\n                    payload[\"dilated_boundary\"] = dilated_boundaries[k]\n\n                coco_results.append(payload)\n\n        return coco_results\n\n    def prepare_for_coco_keypoint(self, predictions):\n        self._lazy_init()\n        coco_results = []\n        for original_id, prediction in predictions.items():\n            if len(prediction) == 0:\n                continue\n\n            boxes = prediction[\"boxes\"]\n            boxes = convert_to_xywh(boxes).tolist()\n            scores = prediction[\"scores\"].tolist()\n            labels = prediction[\"labels\"].tolist()\n            keypoints = prediction[\"keypoints\"]\n            keypoints = keypoints.flatten(start_dim=1).tolist()\n\n            coco_results.extend(\n                [\n                    {\n                        \"image_id\": original_id,\n                        \"category_id\": labels[k],\n                        \"keypoints\": keypoint,\n                        \"score\": scores[k],\n                    }\n                    for k, keypoint in enumerate(keypoints)\n                ]\n            )\n        return coco_results\n\n\ndef convert_to_xywh(boxes):\n    xmin, ymin, xmax, ymax = boxes.unbind(-1)\n    return torch.stack((xmin, ymin, xmax - xmin, ymax - ymin), dim=-1)\n\n\ndef merge(img_ids, eval_imgs, gather_pred_via_filesys=False):\n    if gather_pred_via_filesys:\n        # only gather the predictions to rank 0 (other ranks will receive empty\n        # lists for `all_img_ids` and `all_eval_imgs`, which should be OK as\n        # merging and evaluation are only done on rank 0)\n        all_img_ids = gather_to_rank_0_via_filesys(img_ids)\n        all_eval_imgs = gather_to_rank_0_via_filesys(eval_imgs)\n    else:\n        all_img_ids = all_gather(img_ids, force_cpu=True)\n        all_eval_imgs = all_gather(eval_imgs, force_cpu=True)\n    if not is_main_process():\n        return None, None\n\n    merged_img_ids = []\n    for p in all_img_ids:\n        merged_img_ids.extend(p)\n\n    merged_eval_imgs = []\n    for p in all_eval_imgs:\n        merged_eval_imgs.append(p)\n\n    merged_img_ids = np.array(merged_img_ids)\n    merged_eval_imgs = np.concatenate(merged_eval_imgs, 2)\n\n    # keep only unique (and in sorted order) images\n    merged_img_ids, idx = np.unique(merged_img_ids, return_index=True)\n    merged_eval_imgs = merged_eval_imgs[..., idx]\n\n    return merged_img_ids, merged_eval_imgs\n\n\ndef create_common_coco_eval(\n    coco_eval,\n    img_ids,\n    eval_imgs,\n    use_self_evaluate,\n    gather_pred_via_filesys=False,\n    metrics_dump_dir=None,\n):\n    img_ids, eval_imgs = merge(img_ids, eval_imgs, gather_pred_via_filesys)\n    if not is_main_process():\n        return\n    if metrics_dump_dir is not None:\n        dumped_file = (\n            Path(metrics_dump_dir) / f\"coco_eval_img_metrics_{get_rank()}.json\"\n        )\n        logging.info(f\"COCO evaluator: Dumping local predictions to {dumped_file}\")\n        with g_pathmgr.open(str(dumped_file), \"w\") as f:\n            json.dump(eval_imgs.squeeze(), f, default=lambda x: x.tolist())\n    img_ids = list(img_ids)\n\n    # If some images were not predicted, we need to create dummy detections for them\n    missing_img_ids = set(coco_eval.cocoGt.getImgIds()) - set(img_ids)\n    if len(missing_img_ids) > 0:\n        print(f\"WARNING: {len(missing_img_ids)} images were not predicted!\")\n        coco_eval.cocoDt = COCO()\n        coco_eval.params.imgIds = list(missing_img_ids)\n        new_img_ids, new_eval_imgs = evaluate(coco_eval, use_self_evaluate)\n        img_ids.extend(new_img_ids)\n        eval_imgs = np.concatenate((eval_imgs, new_eval_imgs), axis=2)\n\n    eval_imgs = list(eval_imgs.flatten())\n    assert len(img_ids) == len(coco_eval.cocoGt.getImgIds())\n\n    coco_eval.evalImgs = eval_imgs\n    coco_eval.params.imgIds = img_ids\n    coco_eval._paramsEval = copy.deepcopy(coco_eval.params)\n\n\n#################################################################\n# From pycocotools, just removed the prints and fixed\n# a Python3 bug about unicode not defined\n#################################################################\n\n\n# Copy of COCO prepare, but doesn't convert anntoRLE\ndef segmentation_prepare(self):\n    \"\"\"\n    Prepare ._gts and ._dts for evaluation based on params\n    :return: None\n    \"\"\"\n    p = self.params\n    if p.useCats:\n        gts = self.cocoGt.loadAnns(\n            self.cocoGt.getAnnIds(imgIds=p.imgIds, catIds=p.catIds)\n        )\n        dts = self.cocoDt.loadAnns(\n            self.cocoDt.getAnnIds(imgIds=p.imgIds, catIds=p.catIds)\n        )\n    else:\n        gts = self.cocoGt.loadAnns(self.cocoGt.getAnnIds(imgIds=p.imgIds))\n        dts = self.cocoDt.loadAnns(self.cocoDt.getAnnIds(imgIds=p.imgIds))\n\n    for gt in gts:\n        gt[\"ignore\"] = gt[\"ignore\"] if \"ignore\" in gt else 0\n        gt[\"ignore\"] = \"iscrowd\" in gt and gt[\"iscrowd\"]\n        if p.iouType == \"keypoints\":\n            gt[\"ignore\"] = (gt[\"num_keypoints\"] == 0) or gt[\"ignore\"]\n    self._gts = defaultdict(list)  # gt for evaluation\n    self._dts = defaultdict(list)  # dt for evaluation\n    for gt in gts:\n        self._gts[gt[\"image_id\"], gt[\"category_id\"]].append(gt)\n    for dt in dts:\n        self._dts[dt[\"image_id\"], dt[\"category_id\"]].append(dt)\n    self.evalImgs = defaultdict(list)  # per-image per-category evaluation results\n    self.eval = {}  # accumulated evaluation results\n\n\ndef evaluate(self, use_self_evaluate):\n    \"\"\"\n    Run per image evaluation on given images and store results (a list of dict) in self.evalImgs\n    :return: None\n    \"\"\"\n    # tic = time.time()\n    # print('Running per image evaluation...', use_self_evaluate)\n    p = self.params\n    # add backward compatibility if useSegm is specified in params\n    if p.useSegm is not None:\n        p.iouType = \"segm\" if p.useSegm == 1 else \"bbox\"\n        print(\n            \"useSegm (deprecated) is not None. Running {} evaluation\".format(p.iouType)\n        )\n    # print('Evaluate annotation type *{}*'.format(p.iouType))\n    p.imgIds = list(np.unique(p.imgIds))\n    if p.useCats:\n        p.catIds = list(np.unique(p.catIds))\n    p.maxDets = sorted(p.maxDets)\n    self.params = p\n\n    self._prepare()\n    # loop through images, area range, max detection number\n    catIds = p.catIds if p.useCats else [-1]\n\n    if p.iouType == \"segm\" or p.iouType == \"bbox\":\n        computeIoU = self.computeIoU\n    elif p.iouType == \"keypoints\":\n        computeIoU = self.computeOks\n    self.ious = {\n        (imgId, catId): computeIoU(imgId, catId)\n        for imgId in p.imgIds\n        for catId in catIds\n    }\n\n    maxDet = p.maxDets[-1]\n    if use_self_evaluate:\n        evalImgs = [\n            self.evaluateImg(imgId, catId, areaRng, maxDet)\n            for catId in catIds\n            for areaRng in p.areaRng\n            for imgId in p.imgIds\n        ]\n        # this is NOT in the pycocotools code, but could be done outside\n        evalImgs = np.asarray(evalImgs).reshape(\n            len(catIds), len(p.areaRng), len(p.imgIds)\n        )\n        return p.imgIds, evalImgs\n\n    # <<<< Beginning of code differences with original COCO API\n    # def convert_instances_to_cpp(instances, is_det=False):\n    #     # Convert annotations for a list of instances in an image to a format that's fast\n    #     # to access in C++\n    #     instances_cpp = []\n    #     for instance in instances:\n    #         instance_cpp = _CPP.InstanceAnnotation(\n    #             int(instance[\"id\"]),\n    #             instance[\"score\"] if is_det else instance.get(\"score\", 0.0),\n    #             instance[\"area\"],\n    #             bool(instance.get(\"iscrowd\", 0)),\n    #             bool(instance.get(\"ignore\", 0)),\n    #         )\n    #         instances_cpp.append(instance_cpp)\n    #     return instances_cpp\n\n    # # Convert GT annotations, detections, and IOUs to a format that's fast to access in C++\n    # ground_truth_instances = [\n    #     [convert_instances_to_cpp(self._gts[imgId, catId]) for catId in p.catIds]\n    #     for imgId in p.imgIds\n    # ]\n    # detected_instances = [\n    #     [\n    #         convert_instances_to_cpp(self._dts[imgId, catId], is_det=True)\n    #         for catId in p.catIds\n    #     ]\n    #     for imgId in p.imgIds\n    # ]\n    # ious = [[self.ious[imgId, catId] for catId in catIds] for imgId in p.imgIds]\n\n    # if not p.useCats:\n    #     # For each image, flatten per-category lists into a single list\n    #     ground_truth_instances = [\n    #         [[o for c in i for o in c]] for i in ground_truth_instances\n    #     ]\n    #     detected_instances = [[[o for c in i for o in c]] for i in detected_instances]\n\n    # # Call C++ implementation of self.evaluateImgs()\n    # _evalImgs_cpp = _CPP.COCOevalEvaluateImages(\n    #     p.areaRng, maxDet, p.iouThrs, ious, ground_truth_instances, detected_instances\n    # )\n\n    # self._paramsEval = copy.deepcopy(self.params)\n    # evalImgs = np.asarray(_evalImgs_cpp).reshape(\n    #     len(catIds), len(p.areaRng), len(p.imgIds)\n    # )\n    # return p.imgIds, evalImgs\n\n\n#################################################################\n# end of straight copy from pycocotools, just removing the prints\n#################################################################\n\n\n#################################################################\n# From pycocotools, but disabled mask->box conversion which is\n# pointless\n#################################################################\ndef loadRes(self, resFile):\n    \"\"\"\n    Load result file and return a result api object.\n    :param   resFile (str)     : file name of result file\n    :return: res (obj)         : result api object\n    \"\"\"\n    res = COCO()\n    res.dataset[\"images\"] = [img for img in self.dataset[\"images\"]]\n\n    if type(resFile) == str:\n        anns = json.load(open(resFile))\n    elif type(resFile) == np.ndarray:\n        anns = self.loadNumpyAnnotations(resFile)\n    else:\n        anns = resFile\n    assert type(anns) == list, \"results in not an array of objects\"\n    annsImgIds = [ann[\"image_id\"] for ann in anns]\n    assert set(annsImgIds) == (set(annsImgIds) & set(self.getImgIds())), (\n        \"Results do not correspond to current coco set\"\n    )\n    if \"caption\" in anns[0]:\n        imgIds = set([img[\"id\"] for img in res.dataset[\"images\"]]) & set(\n            [ann[\"image_id\"] for ann in anns]\n        )\n        res.dataset[\"images\"] = [\n            img for img in res.dataset[\"images\"] if img[\"id\"] in imgIds\n        ]\n        for id, ann in enumerate(anns):\n            ann[\"id\"] = id + 1\n    elif \"bbox\" in anns[0] and not anns[0][\"bbox\"] == []:\n        res.dataset[\"categories\"] = copy.deepcopy(self.dataset[\"categories\"])\n        for id, ann in enumerate(anns):\n            bb = ann[\"bbox\"]\n            x1, x2, y1, y2 = [bb[0], bb[0] + bb[2], bb[1], bb[1] + bb[3]]\n            if \"segmentation\" not in ann:\n                ann[\"segmentation\"] = [[x1, y1, x1, y2, x2, y2, x2, y1]]\n            ann[\"area\"] = bb[2] * bb[3]\n            ann[\"id\"] = id + 1\n            ann[\"iscrowd\"] = 0\n    elif \"segmentation\" in anns[0]:\n        res.dataset[\"categories\"] = copy.deepcopy(self.dataset[\"categories\"])\n        for id, ann in enumerate(anns):\n            # now only support compressed RLE format as segmentation results\n            # ann[\"area\"] = mask_util.area(ann[\"segmentation\"])\n            # The following lines are disabled because they are pointless\n            #  if not 'bbox' in ann:\n            #     ann['bbox'] = maskUtils.toBbox(ann['segmentation'])\n            ann[\"id\"] = id + 1\n            ann[\"iscrowd\"] = 0\n    elif \"keypoints\" in anns[0]:\n        res.dataset[\"categories\"] = copy.deepcopy(self.dataset[\"categories\"])\n        for id, ann in enumerate(anns):\n            s = ann[\"keypoints\"]\n            x = s[0::3]\n            y = s[1::3]\n            x0, x1, y0, y1 = np.min(x), np.max(x), np.min(y), np.max(y)\n            ann[\"area\"] = (x1 - x0) * (y1 - y0)\n            ann[\"id\"] = id + 1\n            ann[\"bbox\"] = [x0, y0, x1 - x0, y1 - y0]\n\n    res.dataset[\"annotations\"] = anns\n    res.createIndex()\n    return res\n\n\n#################################################################\n# end of straight copy from pycocotools\n#################################################################\n\n\n#################################################################\n# From pycocotools, but added handling of custom area rngs, and returns stat keys\n#################################################################\ndef summarize(self):\n    \"\"\"\n    Compute and display summary metrics for evaluation results.\n    Note this functin can *only* be applied on the default parameter setting\n    \"\"\"\n\n    def _summarize(ap=1, iouThr=None, areaRng=\"all\", maxDets=100):\n        p = self.params\n        iStr = \" {:<18} {} @[ IoU={:<9} | area={:>6s} | maxDets={:>3d} ] = {:0.3f}\"\n        titleStr = \"Average Precision\" if ap == 1 else \"Average Recall\"\n        typeStr = \"(AP)\" if ap == 1 else \"(AR)\"\n        iouStr = (\n            \"{:0.2f}:{:0.2f}\".format(p.iouThrs[0], p.iouThrs[-1])\n            if iouThr is None\n            else \"{:0.2f}\".format(iouThr)\n        )\n\n        aind = [i for i, aRng in enumerate(p.areaRngLbl) if aRng == areaRng]\n        mind = [i for i, mDet in enumerate(p.maxDets) if mDet == maxDets]\n        if ap == 1:\n            # dimension of precision: [TxRxKxAxM]\n            s = self.eval[\"precision\"]\n            # IoU\n            if iouThr is not None:\n                t = np.where(iouThr == p.iouThrs)[0]\n                s = s[t]\n            s = s[:, :, :, aind, mind]\n        else:\n            # dimension of recall: [TxKxAxM]\n            s = self.eval[\"recall\"]\n            if iouThr is not None:\n                t = np.where(iouThr == p.iouThrs)[0]\n                s = s[t]\n            s = s[:, :, aind, mind]\n        if len(s[s > -1]) == 0:\n            mean_s = -1\n        else:\n            mean_s = np.mean(s[s > -1])\n        print(iStr.format(titleStr, typeStr, iouStr, areaRng, maxDets, mean_s))\n        return mean_s\n\n    def _summarizeDets():\n        nb_results = 6 + (len(self.params.areaRng) - 1) * 2\n        assert len(self.params.areaRng) == len(self.params.areaRngLbl)\n        stats = np.zeros((nb_results,))\n        keys = [\"AP\", \"AP_50\", \"AP_75\"]\n        stats[0] = _summarize(1, maxDets=self.params.maxDets[2])\n        stats[1] = _summarize(1, iouThr=0.5, maxDets=self.params.maxDets[2])\n        stats[2] = _summarize(1, iouThr=0.75, maxDets=self.params.maxDets[2])\n        cur_id = 3\n        for area in self.params.areaRngLbl[1:]:\n            stats[cur_id] = _summarize(1, areaRng=area, maxDets=self.params.maxDets[2])\n            cur_id += 1\n            keys.append(f\"AP_{area}\")\n        stats[cur_id] = _summarize(0, maxDets=self.params.maxDets[0])\n        cur_id += 1\n        stats[cur_id] = _summarize(0, maxDets=self.params.maxDets[1])\n        cur_id += 1\n        stats[cur_id] = _summarize(0, maxDets=self.params.maxDets[2])\n        cur_id += 1\n        keys += [\"AR\", \"AR_50\", \"AR_75\"]\n\n        for area in self.params.areaRngLbl[1:]:\n            stats[cur_id] = _summarize(0, areaRng=area, maxDets=self.params.maxDets[2])\n            cur_id += 1\n            keys.append(f\"AR_{area}\")\n        assert len(stats) == len(keys)\n        return keys, stats\n\n    if not self.eval:\n        raise Exception(\"Please run accumulate() first\")\n    self.stats = _summarizeDets()\n\n\n#################################################################\n# end of straight copy from pycocotools\n#################################################################\n\n\n#################################################################\n# From https://github.com/facebookresearch/detectron2/blob/main/detectron2/evaluation/fast_eval_api.py\n# with slight adjustments\n#################################################################\ndef accumulate(self, use_self_eval=False):\n    \"\"\"\n    Accumulate per image evaluation results and store the result in self.eval.  Does not\n    support changing parameter settings from those used by self.evaluate()\n    \"\"\"\n    if use_self_eval:\n        self.accumulate()\n        return\n    # CPP code is disabled\n    # self.eval = _CPP.COCOevalAccumulate(self.params, self.evalImgs)\n\n    # # recall is num_iou_thresholds X num_categories X num_area_ranges X num_max_detections\n    # self.eval[\"recall\"] = np.array(self.eval[\"recall\"]).reshape(\n    #     self.eval[\"counts\"][:1] + self.eval[\"counts\"][2:]\n    # )\n\n    # # precision and scores are num_iou_thresholds X num_recall_thresholds X num_categories X\n    # # num_area_ranges X num_max_detections\n    # self.eval[\"precision\"] = np.array(self.eval[\"precision\"]).reshape(\n    #     self.eval[\"counts\"]\n    # )\n    # self.eval[\"scores\"] = np.array(self.eval[\"scores\"]).reshape(self.eval[\"counts\"])\n"
  },
  {
    "path": "sam3/eval/coco_eval_offline.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n\n\"\"\"\nThis evaluator is meant for regular COCO mAP evaluation, for example on the COCO val set.\n\nFor Category mAP, we need the model to make predictions for all the categories on every single image.\nIn general, since the number of classes can be big, and the API model makes predictions individually for each pair (image, class),\nwe may need to split the inference process for a given image in several chunks.\n\"\"\"\n\nimport logging\nfrom collections import defaultdict\n\nimport torch\nfrom pycocotools.coco import COCO\nfrom pycocotools.cocoeval import COCOeval\nfrom sam3.train.utils.distributed import is_main_process\n\ntry:\n    from tidecv import datasets, TIDE\n\n    HAS_TIDE = True\nexcept ImportError:\n    HAS_TIDE = False\n    print(\"WARNING: TIDE not installed. Detailed analysis will not be available.\")\n\n\n# the COCO detection metrics (https://github.com/cocodataset/cocoapi/blob/8c9bcc3cf640524c4c20a9c40e89cb6a2f2fa0e9/PythonAPI/pycocotools/cocoeval.py#L460-L471)\nCOCO_METRICS = [\n    \"AP\",\n    \"AP_50\",\n    \"AP_75\",\n    \"AP_small\",\n    \"AP_medium\",\n    \"AP_large\",\n    \"AR_maxDets@1\",\n    \"AR_maxDets@10\",\n    \"AR_maxDets@100\",\n    \"AR_small\",\n    \"AR_medium\",\n    \"AR_large\",\n]\n\n\ndef convert_to_xywh(boxes):\n    \"\"\"Convert bounding boxes from xyxy format to xywh format.\"\"\"\n    xmin, ymin, xmax, ymax = boxes.unbind(-1)\n    return torch.stack((xmin, ymin, xmax - xmin, ymax - ymin), dim=-1)\n\n\nclass HeapElement:\n    \"\"\"Utility class to make a heap with a custom comparator\"\"\"\n\n    def __init__(self, val):\n        self.val = val\n\n    def __lt__(self, other):\n        return self.val[\"score\"] < other.val[\"score\"]\n\n\nclass COCOevalCustom(COCOeval):\n    \"\"\"\n    This is a slightly modified version of the original COCO API with added support for positive split evaluation.\n    \"\"\"\n\n    def __init__(\n        self, cocoGt=None, cocoDt=None, iouType=\"segm\", dt_only_positive=False\n    ):\n        super().__init__(cocoGt, cocoDt, iouType)\n        self.dt_only_positive = dt_only_positive\n\n    def _prepare(self):\n        \"\"\"\n        Prepare ._gts and ._dts for evaluation based on params\n        :return: None\n        \"\"\"\n\n        def _toMask(anns, coco):\n            # modify ann['segmentation'] by reference\n            for ann in anns:\n                rle = coco.annToRLE(ann)\n                ann[\"segmentation\"] = rle\n\n        p = self.params\n        if p.useCats:\n            gts = self.cocoGt.loadAnns(\n                self.cocoGt.getAnnIds(imgIds=p.imgIds, catIds=p.catIds)\n            )\n            dts = self.cocoDt.loadAnns(\n                self.cocoDt.getAnnIds(imgIds=p.imgIds, catIds=p.catIds)\n            )\n        else:\n            gts = self.cocoGt.loadAnns(self.cocoGt.getAnnIds(imgIds=p.imgIds))\n            dts = self.cocoDt.loadAnns(self.cocoDt.getAnnIds(imgIds=p.imgIds))\n\n        # convert ground truth to mask if iouType == 'segm'\n        if p.iouType == \"segm\":\n            _toMask(gts, self.cocoGt)\n            _toMask(dts, self.cocoDt)\n        # set ignore flag\n        for gt in gts:\n            gt[\"ignore\"] = gt[\"ignore\"] if \"ignore\" in gt else 0\n            gt[\"ignore\"] = \"iscrowd\" in gt and gt[\"iscrowd\"]\n            if p.iouType == \"keypoints\":\n                gt[\"ignore\"] = (gt[\"num_keypoints\"] == 0) or gt[\"ignore\"]\n        self._gts = defaultdict(list)  # gt for evaluation\n        self._dts = defaultdict(list)  # dt for evaluation\n\n        _gts_cat_ids = defaultdict(set)  # gt for evaluation on positive split\n        for gt in gts:\n            self._gts[gt[\"image_id\"], gt[\"category_id\"]].append(gt)\n            _gts_cat_ids[gt[\"image_id\"]].add(gt[\"category_id\"])\n\n        #### BEGIN MODIFICATION ####\n        for dt in dts:\n            if (\n                self.dt_only_positive\n                and dt[\"category_id\"] not in _gts_cat_ids[dt[\"image_id\"]]\n            ):\n                continue\n            self._dts[dt[\"image_id\"], dt[\"category_id\"]].append(dt)\n        #### END MODIFICATION ####\n        self.evalImgs = defaultdict(list)  # per-image per-category evaluation results\n        self.eval = {}  # accumulated evaluation results\n\n\nclass CocoEvaluatorOfflineWithPredFileEvaluators:\n    def __init__(\n        self,\n        gt_path,\n        tide: bool = True,\n        iou_type: str = \"bbox\",\n        positive_split=False,\n    ):\n        self.gt_path = gt_path\n        self.tide_enabled = HAS_TIDE and tide\n        self.positive_split = positive_split\n        self.iou_type = iou_type\n\n    def evaluate(self, dumped_file):\n        if not is_main_process():\n            return {}\n\n        logging.info(\"OfflineCoco evaluator: Loading groundtruth\")\n        self.gt = COCO(self.gt_path)\n\n        # Creating the result file\n        logging.info(\"Coco evaluator: Creating the result file\")\n        cocoDt = self.gt.loadRes(str(dumped_file))\n\n        # Run the evaluation\n        logging.info(\"Coco evaluator: Running evaluation\")\n        coco_eval = COCOevalCustom(\n            self.gt, cocoDt, iouType=self.iou_type, dt_only_positive=self.positive_split\n        )\n        coco_eval.evaluate()\n        coco_eval.accumulate()\n        coco_eval.summarize()\n\n        outs = {}\n        for i, value in enumerate(coco_eval.stats):\n            outs[f\"coco_eval_{self.iou_type}_{COCO_METRICS[i]}\"] = value\n\n        if self.tide_enabled:\n            logging.info(\"Coco evaluator: Loading TIDE\")\n            self.tide_gt = datasets.COCO(self.gt_path)\n            self.tide = TIDE(mode=\"mask\" if self.iou_type == \"segm\" else \"bbox\")\n\n            # Run TIDE\n            logging.info(\"Coco evaluator: Running TIDE\")\n            self.tide.evaluate(\n                self.tide_gt, datasets.COCOResult(str(dumped_file)), name=\"coco_eval\"\n            )\n            self.tide.summarize()\n            for k, v in self.tide.get_main_errors()[\"coco_eval\"].items():\n                outs[f\"coco_eval_{self.iou_type}_TIDE_{k}\"] = v\n\n            for k, v in self.tide.get_special_errors()[\"coco_eval\"].items():\n                outs[f\"coco_eval_{self.iou_type}_TIDE_{k}\"] = v\n\n        return outs\n"
  },
  {
    "path": "sam3/eval/coco_reindex.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n\n\"\"\"\nSelf-contained COCO JSON re-indexing function that creates temporary files.\n\"\"\"\n\nimport json\nimport os\nimport tempfile\nfrom pathlib import Path\nfrom typing import Any, Dict, List, Optional, Tuple\n\n\ndef reindex_coco_to_temp(input_json_path: str) -> Optional[str]:\n    \"\"\"\n    Convert 0-indexed COCO JSON file to 1-indexed and save to temporary location.\n\n    Args:\n        input_json_path: Path to the input COCO JSON file\n\n    Returns:\n        Path to the new 1-indexed JSON file in temporary directory, or None if no conversion needed\n\n    Raises:\n        FileNotFoundError: If input file doesn't exist\n        json.JSONDecodeError: If input file is not valid JSON\n        ValueError: If input file is not a valid COCO format\n    \"\"\"\n\n    def is_coco_json(data: Dict[str, Any]) -> bool:\n        \"\"\"Check if data appears to be a COCO format file.\"\"\"\n        if not isinstance(data, dict):\n            return False\n        # A COCO file should have at least one of these keys\n        coco_keys = {\"images\", \"annotations\", \"categories\"}\n        return any(key in data for key in coco_keys)\n\n    def check_zero_indexed(data: Dict[str, Any]) -> Tuple[bool, bool, bool]:\n        \"\"\"\n        Check if annotations, images, or categories start from index 0.\n\n        Returns:\n            Tuple of (annotations_zero_indexed, images_zero_indexed, categories_zero_indexed)\n        \"\"\"\n        annotations_zero = False\n        images_zero = False\n        categories_zero = False\n\n        # Check annotations\n        annotations = data.get(\"annotations\", [])\n        if annotations and any(ann.get(\"id\", -1) == 0 for ann in annotations):\n            annotations_zero = True\n\n        # Check images\n        images = data.get(\"images\", [])\n        if images and any(img.get(\"id\", -1) == 0 for img in images):\n            images_zero = True\n\n        # Check categories\n        categories = data.get(\"categories\", [])\n        if categories and any(cat.get(\"id\", -1) == 0 for cat in categories):\n            categories_zero = True\n\n        return annotations_zero, images_zero, categories_zero\n\n    def reindex_coco_data(data: Dict[str, Any]) -> Dict[str, Any]:\n        \"\"\"Convert 0-indexed COCO data to 1-indexed.\"\"\"\n        modified_data = data.copy()\n\n        annotations_zero, images_zero, categories_zero = check_zero_indexed(data)\n\n        # Create ID mapping for consistency\n        image_id_mapping = {}\n        category_id_mapping = {}\n\n        # Process images first (since annotations reference image IDs)\n        if images_zero and \"images\" in modified_data:\n            for img in modified_data[\"images\"]:\n                old_id = img[\"id\"]\n                new_id = old_id + 1\n                image_id_mapping[old_id] = new_id\n                img[\"id\"] = new_id\n\n        # Process categories (since annotations reference category IDs)\n        if categories_zero and \"categories\" in modified_data:\n            for cat in modified_data[\"categories\"]:\n                old_id = cat[\"id\"]\n                new_id = old_id + 1\n                category_id_mapping[old_id] = new_id\n                cat[\"id\"] = new_id\n\n        # Process annotations\n        if \"annotations\" in modified_data:\n            for ann in modified_data[\"annotations\"]:\n                # Update annotation ID if needed\n                if annotations_zero:\n                    ann[\"id\"] = ann[\"id\"] + 1\n\n                # Update image_id reference if images were reindexed\n                if images_zero and ann.get(\"image_id\") is not None:\n                    old_image_id = ann[\"image_id\"]\n                    if old_image_id in image_id_mapping:\n                        ann[\"image_id\"] = image_id_mapping[old_image_id]\n\n                # Update category_id reference if categories were reindexed\n                if categories_zero and ann.get(\"category_id\") is not None:\n                    old_category_id = ann[\"category_id\"]\n                    if old_category_id in category_id_mapping:\n                        ann[\"category_id\"] = category_id_mapping[old_category_id]\n\n        return modified_data\n\n    # Validate input path\n    if not os.path.exists(input_json_path):\n        raise FileNotFoundError(f\"Input file not found: {input_json_path}\")\n\n    # Load and validate JSON data\n    try:\n        with open(input_json_path, \"r\", encoding=\"utf-8\") as f:\n            data = json.load(f)\n    except json.JSONDecodeError as e:\n        raise json.JSONDecodeError(f\"Invalid JSON in {input_json_path}: {e}\")\n\n    # Validate COCO format\n    if not is_coco_json(data):\n        raise ValueError(\n            f\"File does not appear to be in COCO format: {input_json_path}\"\n        )\n\n    # Check if reindexing is needed\n    annotations_zero, images_zero, categories_zero = check_zero_indexed(data)\n\n    if not (annotations_zero or images_zero or categories_zero):\n        # No conversion needed - just copy to temp location\n        input_path = Path(input_json_path)\n        temp_dir = tempfile.mkdtemp()\n        temp_filename = f\"{input_path.stem}_1_indexed{input_path.suffix}\"\n        temp_path = os.path.join(temp_dir, temp_filename)\n\n        with open(temp_path, \"w\", encoding=\"utf-8\") as f:\n            json.dump(data, f, indent=2, ensure_ascii=False)\n\n        return temp_path\n\n    # Perform reindexing\n    modified_data = reindex_coco_data(data)\n\n    # Create temporary file\n    input_path = Path(input_json_path)\n    temp_dir = tempfile.mkdtemp()\n    temp_filename = f\"{input_path.stem}_1_indexed{input_path.suffix}\"\n    temp_path = os.path.join(temp_dir, temp_filename)\n\n    # Write modified data to temporary file\n    with open(temp_path, \"w\", encoding=\"utf-8\") as f:\n        json.dump(modified_data, f, indent=2, ensure_ascii=False)\n\n    return temp_path\n\n\n# Example usage and test function\ndef test_reindex_function():\n    \"\"\"Test the reindex function with a sample COCO file.\"\"\"\n\n    # Create a test COCO file\n    test_data = {\n        \"info\": {\"description\": \"Test COCO dataset\", \"version\": \"1.0\", \"year\": 2023},\n        \"images\": [\n            {\"id\": 0, \"width\": 640, \"height\": 480, \"file_name\": \"test1.jpg\"},\n            {\"id\": 1, \"width\": 640, \"height\": 480, \"file_name\": \"test2.jpg\"},\n        ],\n        \"categories\": [\n            {\"id\": 0, \"name\": \"person\", \"supercategory\": \"person\"},\n            {\"id\": 1, \"name\": \"car\", \"supercategory\": \"vehicle\"},\n        ],\n        \"annotations\": [\n            {\n                \"id\": 0,\n                \"image_id\": 0,\n                \"category_id\": 0,\n                \"bbox\": [100, 100, 50, 75],\n                \"area\": 3750,\n                \"iscrowd\": 0,\n            },\n            {\n                \"id\": 1,\n                \"image_id\": 1,\n                \"category_id\": 1,\n                \"bbox\": [200, 150, 120, 80],\n                \"area\": 9600,\n                \"iscrowd\": 0,\n            },\n        ],\n    }\n\n    # Create temporary test file\n    with tempfile.NamedTemporaryFile(mode=\"w\", suffix=\".json\", delete=False) as f:\n        json.dump(test_data, f, indent=2)\n        test_file_path = f.name\n\n    try:\n        # Test the function\n        result_path = reindex_coco_to_temp(test_file_path)\n        print(f\"Original file: {test_file_path}\")\n        print(f\"Converted file: {result_path}\")\n\n        # Load and display the result\n        with open(result_path, \"r\") as f:\n            result_data = json.load(f)\n\n        print(\"\\nConverted data sample:\")\n        print(f\"First image ID: {result_data['images'][0]['id']}\")\n        print(f\"First category ID: {result_data['categories'][0]['id']}\")\n        print(f\"First annotation ID: {result_data['annotations'][0]['id']}\")\n        print(f\"First annotation image_id: {result_data['annotations'][0]['image_id']}\")\n        print(\n            f\"First annotation category_id: {result_data['annotations'][0]['category_id']}\"\n        )\n\n        # Clean up\n        os.unlink(result_path)\n        os.rmdir(os.path.dirname(result_path))\n\n    finally:\n        # Clean up test file\n        os.unlink(test_file_path)\n\n\nif __name__ == \"__main__\":\n    test_reindex_function()\n"
  },
  {
    "path": "sam3/eval/coco_writer.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n\n\"\"\"\nCOCO prediction dumper for distributed training.\n\nHandles collection and dumping of COCO-format predictions from models.\nSupports distributed processing with multiple GPUs/processes.\n\"\"\"\n\nimport copy\nimport gc\nimport heapq\nimport json\nimport logging\nimport os\nfrom collections import defaultdict\nfrom pathlib import Path\nfrom typing import Any, Optional\n\nimport pycocotools.mask as mask_utils\nimport torch\nfrom iopath.common.file_io import g_pathmgr\nfrom sam3.eval.coco_eval_offline import convert_to_xywh\nfrom sam3.train.masks_ops import rle_encode\nfrom sam3.train.utils.distributed import (\n    all_gather,\n    gather_to_rank_0_via_filesys,\n    get_rank,\n    is_main_process,\n)\n\n\n### Helper functions and classes\n\n\nclass HeapElement:\n    \"\"\"Utility class to make a heap with a custom comparator based on score.\"\"\"\n\n    def __init__(self, val):\n        self.val = val\n\n    def __lt__(self, other):\n        return self.val[\"score\"] < other.val[\"score\"]\n\n\nclass PredictionDumper:\n    \"\"\"\n    Handles collection and dumping of COCO-format predictions from a model.\n\n    This class processes model outputs through a postprocessor, converts them to COCO format,\n    and saves them to disk. It supports distributed processing with multiple GPUs/processes.\n    \"\"\"\n\n    def __init__(\n        self,\n        dump_dir: str,\n        postprocessor,\n        maxdets: int,\n        iou_type: str,\n        gather_pred_via_filesys: bool = False,\n        merge_predictions: bool = False,\n        pred_file_evaluators: Optional[Any] = None,\n    ):\n        \"\"\"\n        Initialize the PredictionDumper.\n\n        Args:\n            dump_dir: Directory to dump predictions.\n            postprocessor: Module to convert the model's output into COCO format.\n            maxdets: Maximum number of detections per image.\n            iou_type: IoU type to evaluate. Can include \"bbox\", \"segm\"\n            gather_pred_via_filesys: If True, use the filesystem for collective gathers across\n                processes (requires a shared filesystem). Otherwise, use torch collective ops.\n            merge_predictions: If True, merge predictions from all processes and dump to a single file.\n        \"\"\"\n        self.iou_type = iou_type\n        self.maxdets = maxdets\n        self.dump_dir = dump_dir\n        self.postprocessor = postprocessor\n        self.gather_pred_via_filesys = gather_pred_via_filesys\n        self.merge_predictions = merge_predictions\n        self.pred_file_evaluators = pred_file_evaluators\n        if self.pred_file_evaluators is not None:\n            assert merge_predictions, (\n                \"merge_predictions must be True if pred_file_evaluators are provided\"\n            )\n        assert self.dump_dir is not None, \"dump_dir must be provided\"\n\n        if is_main_process():\n            os.makedirs(self.dump_dir, exist_ok=True)\n            logging.info(f\"Created prediction dump directory: {self.dump_dir}\")\n\n        # Initialize state\n        self.reset()\n\n    def update(self, *args, **kwargs):\n        \"\"\"\n        Process and accumulate predictions from model outputs.\n\n        Args:\n            *args, **kwargs: Arguments passed to postprocessor.process_results()\n        \"\"\"\n        predictions = self.postprocessor.process_results(*args, **kwargs)\n        results = self.prepare(predictions, self.iou_type)\n        self._dump(results)\n\n    def _dump(self, results):\n        \"\"\"\n        Add results to the dump list with precision rounding.\n\n        Args:\n            results: List of prediction dictionaries in COCO format.\n        \"\"\"\n        dumped_results = copy.deepcopy(results)\n        for r in dumped_results:\n            if \"bbox\" in r:\n                r[\"bbox\"] = [round(coord, 5) for coord in r[\"bbox\"]]\n            r[\"score\"] = round(r[\"score\"], 5)\n        self.dump.extend(dumped_results)\n\n    def synchronize_between_processes(self):\n        \"\"\"\n        Synchronize predictions across all processes and save to disk.\n\n        If gather_pred_via_filesys is True, uses filesystem for gathering.\n        Otherwise, uses torch distributed collective operations.\n        Saves per-rank predictions to separate JSON files.\n        \"\"\"\n        logging.info(\"Prediction Dumper: Synchronizing between processes\")\n\n        if not self.merge_predictions:\n            dumped_file = (\n                Path(self.dump_dir)\n                / f\"coco_predictions_{self.iou_type}_{get_rank()}.json\"\n            )\n            logging.info(\n                f\"Prediction Dumper: Dumping local predictions to {dumped_file}\"\n            )\n            with g_pathmgr.open(str(dumped_file), \"w\") as f:\n                json.dump(self.dump, f)\n        else:\n            self.dump = self.gather_and_merge_predictions()\n            dumped_file = Path(self.dump_dir) / f\"coco_predictions_{self.iou_type}.json\"\n            if is_main_process():\n                logging.info(\n                    f\"Prediction Dumper: Dumping merged predictions to {dumped_file}\"\n                )\n                with g_pathmgr.open(str(dumped_file), \"w\") as f:\n                    json.dump(self.dump, f)\n\n        self.reset()\n        return dumped_file\n\n    def gather_and_merge_predictions(self):\n        \"\"\"\n        Gather predictions from all processes and merge them, keeping top predictions per image.\n\n        This method collects predictions from all processes, then keeps only the top maxdets\n        predictions per image based on score. It also deduplicates predictions by (image_id, category_id).\n\n        Returns:\n            List of merged prediction dictionaries.\n        \"\"\"\n        logging.info(\"Prediction Dumper: Gathering predictions from all processes\")\n        gc.collect()\n\n        if self.gather_pred_via_filesys:\n            dump = gather_to_rank_0_via_filesys(self.dump)\n        else:\n            dump = all_gather(self.dump, force_cpu=True)\n\n        # Combine predictions, keeping only top maxdets per image\n        preds_by_image = defaultdict(list)\n        seen_img_cat = set()\n\n        for cur_dump in dump:\n            cur_seen_img_cat = set()\n            for p in cur_dump:\n                image_id = p[\"image_id\"]\n                cat_id = p[\"category_id\"]\n\n                # Skip if we've already seen this image/category pair in a previous dump\n                if (image_id, cat_id) in seen_img_cat:\n                    continue\n\n                cur_seen_img_cat.add((image_id, cat_id))\n\n                # Use a min-heap to keep top predictions\n                if len(preds_by_image[image_id]) < self.maxdets:\n                    heapq.heappush(preds_by_image[image_id], HeapElement(p))\n                else:\n                    heapq.heappushpop(preds_by_image[image_id], HeapElement(p))\n\n            seen_img_cat.update(cur_seen_img_cat)\n\n        # Flatten the heap elements back to a list\n        merged_dump = sum(\n            [[h.val for h in cur_preds] for cur_preds in preds_by_image.values()], []\n        )\n\n        return merged_dump\n\n    def compute_synced(self):\n        \"\"\"\n        Synchronize predictions across processes and compute summary.\n\n        Returns:\n            Summary dictionary from summarize().\n        \"\"\"\n        dumped_file = self.synchronize_between_processes()\n        if not is_main_process():\n            return {\"\": 0.0}\n\n        meters = {}\n        if self.pred_file_evaluators is not None:\n            for evaluator in self.pred_file_evaluators:\n                results = evaluator.evaluate(dumped_file)\n                meters.update(results)\n\n        if len(meters) == 0:\n            meters = {\"\": 0.0}\n        return meters\n\n    def compute(self):\n        \"\"\"\n        Compute without synchronization.\n\n        Returns:\n            Empty metric dictionary.\n        \"\"\"\n        return {\"\": 0.0}\n\n    def reset(self):\n        \"\"\"Reset internal state for a new evaluation round.\"\"\"\n        self.dump = []\n\n    def prepare(self, predictions, iou_type):\n        \"\"\"\n        Route predictions to the appropriate preparation method based on iou_type.\n\n        Args:\n            predictions: Dictionary mapping image IDs to prediction dictionaries.\n            iou_type: Type of evaluation (\"bbox\", \"segm\").\n\n        Returns:\n            List of COCO-format prediction dictionaries.\n        \"\"\"\n        if iou_type == \"bbox\":\n            return self.prepare_for_coco_detection(predictions)\n        elif iou_type == \"segm\":\n            return self.prepare_for_coco_segmentation(predictions)\n        else:\n            raise ValueError(f\"Unknown iou type: {iou_type}\")\n\n    def prepare_for_coco_detection(self, predictions):\n        \"\"\"\n        Convert predictions to COCO detection format.\n\n        Args:\n            predictions: Dictionary mapping image IDs to prediction dictionaries\n                containing \"boxes\", \"scores\", and \"labels\".\n\n        Returns:\n            List of COCO-format detection dictionaries.\n        \"\"\"\n        coco_results = []\n        for original_id, prediction in predictions.items():\n            if len(prediction) == 0:\n                continue\n\n            boxes = prediction[\"boxes\"]\n            boxes = convert_to_xywh(boxes).tolist()\n            scores = prediction[\"scores\"].tolist()\n            labels = prediction[\"labels\"].tolist()\n\n            coco_results.extend(\n                [\n                    {\n                        \"image_id\": original_id,\n                        \"category_id\": labels[k],\n                        \"bbox\": box,\n                        \"score\": scores[k],\n                    }\n                    for k, box in enumerate(boxes)\n                ]\n            )\n        return coco_results\n\n    @torch.no_grad()\n    def prepare_for_coco_segmentation(self, predictions):\n        \"\"\"\n        Convert predictions to COCO segmentation format.\n\n        Args:\n            predictions: Dictionary mapping image IDs to prediction dictionaries\n                containing \"masks\" or \"masks_rle\", \"scores\", and \"labels\".\n                Optionally includes \"boundaries\" and \"dilated_boundaries\".\n\n        Returns:\n            List of COCO-format segmentation dictionaries with RLE-encoded masks.\n        \"\"\"\n        coco_results = []\n        for original_id, prediction in predictions.items():\n            if len(prediction) == 0:\n                continue\n\n            scores = prediction[\"scores\"].tolist()\n            labels = prediction[\"labels\"].tolist()\n\n            boxes = None\n            if \"boxes\" in prediction:\n                boxes = prediction[\"boxes\"]\n                boxes = convert_to_xywh(boxes).tolist()\n                assert len(boxes) == len(scores)\n\n            if \"masks_rle\" in prediction:\n                rles = prediction[\"masks_rle\"]\n                areas = []\n                for rle in rles:\n                    cur_area = mask_utils.area(rle)\n                    h, w = rle[\"size\"]\n                    areas.append(cur_area / (h * w))\n            else:\n                masks = prediction[\"masks\"]\n                masks = masks > 0.5\n                h, w = masks.shape[-2:]\n\n                areas = masks.flatten(1).sum(1) / (h * w)\n                areas = areas.tolist()\n\n                rles = rle_encode(masks.squeeze(1))\n\n                # Memory cleanup\n                del masks\n                del prediction[\"masks\"]\n\n            assert len(areas) == len(rles) == len(scores)\n\n            for k, rle in enumerate(rles):\n                payload = {\n                    \"image_id\": original_id,\n                    \"category_id\": labels[k],\n                    \"segmentation\": rle,\n                    \"score\": scores[k],\n                    \"area\": areas[k],\n                }\n                if boxes is not None:\n                    payload[\"bbox\"] = boxes[k]\n\n                coco_results.append(payload)\n\n        return coco_results\n"
  },
  {
    "path": "sam3/eval/conversion_util.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\nimport json\nimport os\nfrom collections import defaultdict\n\nfrom tqdm import tqdm\n\n\ndef convert_ytbvis_to_cocovid_gt(ann_json, save_path=None):\n    \"\"\"Convert YouTube VIS dataset to COCO-style video instance segmentation format.\n\n    Args:\n        ann_json (str): Path to YouTube VIS annotation JSON file\n        save_path (str): path to save converted COCO-style JSON\n    \"\"\"\n    # Initialize COCO structure\n    VIS = {\n        \"info\": {},\n        \"images\": [],\n        \"videos\": [],\n        \"tracks\": [],\n        \"annotations\": [],\n        \"categories\": [],\n        \"licenses\": [],\n    }\n\n    # Load original annotations\n    official_anns = json.load(open(ann_json))\n    VIS[\"categories\"] = official_anns[\"categories\"]  # Direct copy categories\n\n    # Initialize counters\n    records = dict(img_id=1, ann_id=1)\n\n    # Create video-to-annotations mapping\n    vid_to_anns = defaultdict(list)\n    for ann in official_anns[\"annotations\"]:\n        vid_to_anns[ann[\"video_id\"]].append(ann)\n\n    # Create tracks directly\n    VIS[\"tracks\"] = [\n        {\n            \"id\": ann[\"id\"],\n            \"category_id\": ann[\"category_id\"],\n            \"video_id\": ann[\"video_id\"],\n        }\n        for ann in official_anns[\"annotations\"]\n    ]\n\n    # Process videos\n    for video_info in tqdm(official_anns[\"videos\"]):\n        # Create video entry\n        video = {\n            \"id\": video_info[\"id\"],\n            \"name\": os.path.dirname(video_info[\"file_names\"][0]),\n            \"width\": video_info[\"width\"],\n            \"height\": video_info[\"height\"],\n            \"length\": video_info[\"length\"],\n            \"neg_category_ids\": [],\n            \"not_exhaustive_category_ids\": [],\n        }\n        VIS[\"videos\"].append(video)\n\n        # Process frames\n        num_frames = len(video_info[\"file_names\"])\n        for frame_idx in range(num_frames):\n            # Create image entry\n            image = {\n                \"id\": records[\"img_id\"],\n                \"video_id\": video_info[\"id\"],\n                \"file_name\": video_info[\"file_names\"][frame_idx],\n                \"width\": video_info[\"width\"],\n                \"height\": video_info[\"height\"],\n                \"frame_index\": frame_idx,\n                \"frame_id\": frame_idx,\n            }\n            VIS[\"images\"].append(image)\n\n            # Process annotations for this frame\n            if video_info[\"id\"] in vid_to_anns:\n                for ann in vid_to_anns[video_info[\"id\"]]:\n                    bbox = ann[\"bboxes\"][frame_idx]\n                    if bbox is None:\n                        continue\n\n                    # Create annotation entry\n                    annotation = {\n                        \"id\": records[\"ann_id\"],\n                        \"video_id\": video_info[\"id\"],\n                        \"image_id\": records[\"img_id\"],\n                        \"track_id\": ann[\"id\"],\n                        \"category_id\": ann[\"category_id\"],\n                        \"bbox\": bbox,\n                        \"area\": ann[\"areas\"][frame_idx],\n                        \"segmentation\": ann[\"segmentations\"][frame_idx],\n                        \"iscrowd\": ann[\"iscrowd\"],\n                    }\n                    VIS[\"annotations\"].append(annotation)\n                    records[\"ann_id\"] += 1\n\n            records[\"img_id\"] += 1\n\n    # Print summary\n    print(f\"Converted {len(VIS['videos'])} videos\")\n    print(f\"Converted {len(VIS['images'])} images\")\n    print(f\"Created {len(VIS['tracks'])} tracks\")\n    print(f\"Created {len(VIS['annotations'])} annotations\")\n\n    if save_path is None:\n        return VIS\n\n    # Save output\n    save_dir = os.path.dirname(save_path)\n    os.makedirs(save_dir, exist_ok=True)\n    json.dump(VIS, open(save_path, \"w\"))\n\n    return VIS\n\n\ndef convert_ytbvis_to_cocovid_pred(\n    youtubevis_pred_path: str, converted_dataset_path: str, output_path: str\n) -> None:\n    \"\"\"\n    Convert YouTubeVIS predictions to COCO format with video_id preservation\n\n    Args:\n        youtubevis_pred_path: Path to YouTubeVIS prediction JSON\n        converted_dataset_path: Path to converted COCO dataset JSON\n        output_path: Path to save COCO format predictions\n    \"\"\"\n\n    # Load YouTubeVIS predictions\n    with open(youtubevis_pred_path) as f:\n        ytv_predictions = json.load(f)\n\n    # Load converted dataset for image ID mapping\n    with open(converted_dataset_path) as f:\n        coco_dataset = json.load(f)\n\n    # Create (video_id, frame_idx) -> image_id mapping\n    image_id_map = {\n        (img[\"video_id\"], img[\"frame_index\"]): img[\"id\"]\n        for img in coco_dataset[\"images\"]\n    }\n\n    coco_annotations = []\n    track_id_counter = 1  # Unique track ID generator\n\n    for pred in tqdm(ytv_predictions):\n        video_id = pred[\"video_id\"]\n        category_id = pred[\"category_id\"]\n        bboxes = pred[\"bboxes\"]\n        segmentations = pred.get(\"segmentations\", [])  # Get segmentations if available\n        areas = pred.get(\"areas\", [])  # Get areas if available\n        score = pred[\"score\"]\n\n        # Assign unique track ID for this prediction\n        track_id = track_id_counter\n        track_id_counter += 1\n\n        # Ensure segmentations and areas have the same length as bboxes\n        if len(segmentations) == 0:\n            segmentations = [None] * len(bboxes)\n        if len(areas) == 0:\n            areas = [None] * len(bboxes)\n\n        for frame_idx, (bbox, segmentation, area_from_pred) in enumerate(\n            zip(bboxes, segmentations, areas)\n        ):\n            # Skip frames with missing objects (None or zero bbox)\n            if bbox is None or all(x == 0 for x in bbox):\n                continue\n\n            # Get corresponding image ID from mapping\n            image_id = image_id_map.get((video_id, frame_idx))\n            if image_id is None:\n                raise RuntimeError(\n                    f\"prediction {video_id=}, {frame_idx=} does not match any images in the converted COCO format\"\n                )\n\n            # Extract bbox coordinates\n            x, y, w, h = bbox\n\n            # Calculate area - use area from prediction if available, otherwise from bbox\n            if area_from_pred is not None and area_from_pred > 0:\n                area = area_from_pred\n            else:\n                area = w * h\n\n            # Create COCO annotation with video_id\n            coco_annotation = {\n                \"image_id\": int(image_id),\n                \"video_id\": video_id,  # Added video_id field\n                \"track_id\": track_id,\n                \"category_id\": category_id,\n                \"bbox\": [float(x), float(y), float(w), float(h)],\n                \"area\": float(area),\n                \"iscrowd\": 0,\n                \"score\": float(score),\n            }\n\n            # Add segmentation if available\n            if segmentation is not None:\n                coco_annotation[\"segmentation\"] = segmentation\n\n            coco_annotations.append(coco_annotation)\n\n    # Save output\n    with open(output_path, \"w\") as f:\n        json.dump(coco_annotations, f)\n\n    print(f\"Converted {len(coco_annotations)} predictions to COCO format with video_id\")\n"
  },
  {
    "path": "sam3/eval/demo_eval.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n\n\"\"\"\nThis evaluator is based upon COCO evaluation, but evaluates the model in a \"demo\" setting.\nThis means that the model's predictions are thresholded and evaluated as \"hard\" predictions.\n\"\"\"\n\nimport logging\nfrom typing import Optional\n\nimport numpy as np\nimport pycocotools.mask as maskUtils\nfrom pycocotools.cocoeval import COCOeval\nfrom sam3.eval.coco_eval import CocoEvaluator\nfrom sam3.train.masks_ops import compute_F_measure\nfrom sam3.train.utils.distributed import is_main_process\nfrom scipy.optimize import linear_sum_assignment\n\n\nclass DemoEval(COCOeval):\n    \"\"\"\n    This evaluator is based upon COCO evaluation, but evaluates the model in a \"demo\" setting.\n    This means that the model's predictions are thresholded and evaluated as \"hard\" predictions.\n    \"\"\"\n\n    def __init__(\n        self,\n        coco_gt=None,\n        coco_dt=None,\n        iouType=\"bbox\",\n        threshold=0.5,\n        compute_JnF=False,\n    ):\n        \"\"\"\n        Args:\n            coco_gt (COCO): ground truth COCO API\n            coco_dt (COCO): detections COCO API\n            iou_type (str): type of IoU to evaluate\n            threshold (float): threshold for predictions\n        \"\"\"\n        super().__init__(coco_gt, coco_dt, iouType)\n        self.threshold = threshold\n\n        self.params.useCats = False\n        self.params.areaRng = [[0**2, 1e5**2]]\n        self.params.areaRngLbl = [\"all\"]\n        self.params.maxDets = [100000]\n        self.compute_JnF = compute_JnF\n\n    def computeIoU(self, imgId, catId):\n        # Same as the original COCOeval.computeIoU, but without sorting\n        p = self.params\n        if p.useCats:\n            gt = self._gts[imgId, catId]\n            dt = self._dts[imgId, catId]\n        else:\n            gt = [_ for cId in p.catIds for _ in self._gts[imgId, cId]]\n            dt = [_ for cId in p.catIds for _ in self._dts[imgId, cId]]\n        if len(gt) == 0 and len(dt) == 0:\n            return []\n\n        if p.iouType == \"segm\":\n            g = [g[\"segmentation\"] for g in gt]\n            d = [d[\"segmentation\"] for d in dt]\n        elif p.iouType == \"bbox\":\n            g = [g[\"bbox\"] for g in gt]\n            d = [d[\"bbox\"] for d in dt]\n        else:\n            raise Exception(\"unknown iouType for iou computation\")\n\n        # compute iou between each dt and gt region\n        iscrowd = [int(o[\"iscrowd\"]) for o in gt]\n        ious = maskUtils.iou(d, g, iscrowd)\n        return ious\n\n    def evaluateImg(self, imgId, catId, aRng, maxDet):\n        \"\"\"\n        perform evaluation for single category and image\n        :return: dict (single image results)\n        \"\"\"\n        p = self.params\n        assert not p.useCats, \"This evaluator does not support per-category evaluation.\"\n        assert catId == -1\n        all_gts = [_ for cId in p.catIds for _ in self._gts[imgId, cId]]\n        keep_gt = np.array([not g[\"ignore\"] for g in all_gts], dtype=bool)\n        gt = [g for g in all_gts if not g[\"ignore\"]]\n        all_dts = [_ for cId in p.catIds for _ in self._dts[imgId, cId]]\n        keep_dt = np.array([d[\"score\"] >= self.threshold for d in all_dts], dtype=bool)\n        dt = [d for d in all_dts if d[\"score\"] >= self.threshold]\n        if len(gt) == 0 and len(dt) == 0:\n            # This is a \"true negative\" case, where there are no GTs and no predictions\n            # The box-level metrics are ill-defined, so we don't add them to this dict\n            return {\n                \"image_id\": imgId,\n                \"IL_TP\": 0,\n                \"IL_TN\": 1,\n                \"IL_FP\": 0,\n                \"IL_FN\": 0,\n                \"IL_perfect_neg\": np.ones((len(p.iouThrs),), dtype=np.int64),\n                \"num_dt\": len(dt),\n            }\n\n        if len(gt) > 0 and len(dt) == 0:\n            # This is a \"false negative\" case, where there are GTs but no predictions\n            return {\n                \"image_id\": imgId,\n                \"IL_TP\": 0,\n                \"IL_TN\": 0,\n                \"IL_FP\": 0,\n                \"IL_FN\": 1,\n                \"TPs\": np.zeros((len(p.iouThrs),), dtype=np.int64),\n                \"FPs\": np.zeros((len(p.iouThrs),), dtype=np.int64),\n                \"FNs\": np.ones((len(p.iouThrs),), dtype=np.int64) * len(gt),\n                \"local_F1s\": np.zeros((len(p.iouThrs),), dtype=np.int64),\n                \"local_positive_F1s\": np.zeros((len(p.iouThrs),), dtype=np.int64),\n                \"IL_perfect_pos\": np.zeros((len(p.iouThrs),), dtype=np.int64),\n                \"num_dt\": len(dt),\n            }\n\n        # Load pre-computed ious\n        ious = self.ious[(imgId, catId)]\n\n        # compute matching\n        if len(ious) == 0:\n            ious = np.zeros((len(dt), len(gt)))\n        else:\n            ious = ious[keep_dt, :][:, keep_gt]\n        assert ious.shape == (len(dt), len(gt))\n\n        matched_dt, matched_gt = linear_sum_assignment(-ious)\n\n        match_scores = ious[matched_dt, matched_gt]\n\n        if self.compute_JnF and len(match_scores) > 0:\n            j_score = match_scores.mean()\n            f_measure = 0\n            for dt_id, gt_id in zip(matched_dt, matched_gt):\n                f_measure += compute_F_measure(\n                    gt_boundary_rle=gt[gt_id][\"boundary\"],\n                    gt_dilated_boundary_rle=gt[gt_id][\"dilated_boundary\"],\n                    dt_boundary_rle=dt[dt_id][\"boundary\"],\n                    dt_dilated_boundary_rle=dt[dt_id][\"dilated_boundary\"],\n                )\n            f_measure /= len(match_scores) + 1e-9\n            JnF = (j_score + f_measure) * 0.5\n        else:\n            j_score = f_measure = JnF = -1\n\n        TPs, FPs, FNs = [], [], []\n        IL_perfect = []\n        for thresh in p.iouThrs:\n            TP = (match_scores >= thresh).sum()\n            FP = len(dt) - TP\n            FN = len(gt) - TP\n            assert FP >= 0 and FN >= 0, (\n                f\"FP: {FP}, FN: {FN}, TP: {TP}, match_scores: {match_scores}, len(dt): {len(dt)}, len(gt): {len(gt)}, ious: {ious}\"\n            )\n            TPs.append(TP)\n            FPs.append(FP)\n            FNs.append(FN)\n\n            if FP == FN and FP == 0:\n                IL_perfect.append(1)\n            else:\n                IL_perfect.append(0)\n\n        TPs = np.array(TPs, dtype=np.int64)\n        FPs = np.array(FPs, dtype=np.int64)\n        FNs = np.array(FNs, dtype=np.int64)\n        IL_perfect = np.array(IL_perfect, dtype=np.int64)\n\n        # compute precision recall and F1\n        precision = TPs / (TPs + FPs + 1e-4)\n        assert np.all(precision <= 1)\n        recall = TPs / (TPs + FNs + 1e-4)\n        assert np.all(recall <= 1)\n        F1 = 2 * precision * recall / (precision + recall + 1e-4)\n\n        result = {\n            \"image_id\": imgId,\n            \"TPs\": TPs,\n            \"FPs\": FPs,\n            \"FNs\": FNs,\n            \"local_F1s\": F1,\n            \"IL_TP\": (len(gt) > 0) and (len(dt) > 0),\n            \"IL_FP\": (len(gt) == 0) and (len(dt) > 0),\n            \"IL_TN\": (len(gt) == 0) and (len(dt) == 0),\n            \"IL_FN\": (len(gt) > 0) and (len(dt) == 0),\n            (\"IL_perfect_pos\" if len(gt) > 0 else \"IL_perfect_neg\"): IL_perfect,\n            \"F\": f_measure,\n            \"J\": j_score,\n            \"J&F\": JnF,\n            \"num_dt\": len(dt),\n        }\n        if len(gt) > 0 and len(dt) > 0:\n            result[\"local_positive_F1s\"] = F1\n        return result\n\n    def accumulate(self, p=None):\n        \"\"\"\n        Accumulate per image evaluation results and store the result in self.eval\n        :param p: input params for evaluation\n        :return: None\n        \"\"\"\n        if not self.evalImgs:\n            print(\"Please run evaluate() first\")\n        # allows input customized parameters\n        if p is None:\n            p = self.params\n\n        setImgIds = set(p.imgIds)\n\n        # TPs, FPs, FNs\n        TPs = np.zeros((len(p.iouThrs),), dtype=np.int64)\n        FPs = np.zeros((len(p.iouThrs),), dtype=np.int64)\n        pmFPs = np.zeros((len(p.iouThrs),), dtype=np.int64)\n        FNs = np.zeros((len(p.iouThrs),), dtype=np.int64)\n        local_F1s = np.zeros((len(p.iouThrs),), dtype=np.float64)\n\n        # Image level metrics\n        IL_TPs = 0\n        IL_FPs = 0\n        IL_TNs = 0\n        IL_FNs = 0\n        IL_perfects_neg = np.zeros((len(p.iouThrs),), dtype=np.int64)\n        IL_perfects_pos = np.zeros((len(p.iouThrs),), dtype=np.int64)\n\n        # JnF metric\n        total_J = 0\n        total_F = 0\n        total_JnF = 0\n\n        valid_img_count = 0\n        total_pos_count = 0\n        total_neg_count = 0\n        valid_J_count = 0\n        valid_F1_count = 0\n        valid_F1_count_w0dt = 0\n        for res in self.evalImgs:\n            if res[\"image_id\"] not in setImgIds:\n                continue\n            IL_TPs += res[\"IL_TP\"]\n            IL_FPs += res[\"IL_FP\"]\n            IL_TNs += res[\"IL_TN\"]\n            IL_FNs += res[\"IL_FN\"]\n            if \"IL_perfect_neg\" in res:\n                IL_perfects_neg += res[\"IL_perfect_neg\"]\n                total_neg_count += 1\n            else:\n                assert \"IL_perfect_pos\" in res\n                IL_perfects_pos += res[\"IL_perfect_pos\"]\n                total_pos_count += 1\n\n            if \"TPs\" not in res:\n                continue\n\n            TPs += res[\"TPs\"]\n            FPs += res[\"FPs\"]\n            FNs += res[\"FNs\"]\n            valid_img_count += 1\n\n            if \"local_positive_F1s\" in res:\n                local_F1s += res[\"local_positive_F1s\"]\n                pmFPs += res[\"FPs\"]\n                valid_F1_count_w0dt += 1\n                if res[\"num_dt\"] > 0:\n                    valid_F1_count += 1\n\n            if \"J\" in res and res[\"J\"] > -1e-9:\n                total_J += res[\"J\"]\n                total_F += res[\"F\"]\n                total_JnF += res[\"J&F\"]\n                valid_J_count += 1\n\n        # compute precision recall and F1\n        precision = TPs / (TPs + FPs + 1e-4)\n        positive_micro_precision = TPs / (TPs + pmFPs + 1e-4)\n        assert np.all(precision <= 1)\n        recall = TPs / (TPs + FNs + 1e-4)\n        assert np.all(recall <= 1)\n        F1 = 2 * precision * recall / (precision + recall + 1e-4)\n        positive_micro_F1 = (\n            2\n            * positive_micro_precision\n            * recall\n            / (positive_micro_precision + recall + 1e-4)\n        )\n\n        IL_rec = IL_TPs / (IL_TPs + IL_FNs + 1e-6)\n        IL_prec = IL_TPs / (IL_TPs + IL_FPs + 1e-6)\n        IL_F1 = 2 * IL_prec * IL_rec / (IL_prec + IL_rec + 1e-6)\n        IL_FPR = IL_FPs / (IL_FPs + IL_TNs + 1e-6)\n        IL_MCC = float(IL_TPs * IL_TNs - IL_FPs * IL_FNs) / (\n            (\n                float(IL_TPs + IL_FPs)\n                * float(IL_TPs + IL_FNs)\n                * float(IL_TNs + IL_FPs)\n                * float(IL_TNs + IL_FNs)\n            )\n            ** 0.5\n            + 1e-6\n        )\n        IL_perfect_pos = IL_perfects_pos / (total_pos_count + 1e-9)\n        IL_perfect_neg = IL_perfects_neg / (total_neg_count + 1e-9)\n\n        total_J = total_J / (valid_J_count + 1e-9)\n        total_F = total_F / (valid_J_count + 1e-9)\n        total_JnF = total_JnF / (valid_J_count + 1e-9)\n\n        self.eval = {\n            \"params\": p,\n            \"TPs\": TPs,\n            \"FPs\": FPs,\n            \"positive_micro_FPs\": pmFPs,\n            \"FNs\": FNs,\n            \"precision\": precision,\n            \"positive_micro_precision\": positive_micro_precision,\n            \"recall\": recall,\n            \"F1\": F1,\n            \"positive_micro_F1\": positive_micro_F1,\n            \"positive_macro_F1\": local_F1s / valid_F1_count,\n            \"positive_w0dt_macro_F1\": local_F1s / valid_F1_count_w0dt,\n            \"IL_recall\": IL_rec,\n            \"IL_precision\": IL_prec,\n            \"IL_F1\": IL_F1,\n            \"IL_FPR\": IL_FPR,\n            \"IL_MCC\": IL_MCC,\n            \"IL_perfect_pos\": IL_perfect_pos,\n            \"IL_perfect_neg\": IL_perfect_neg,\n            \"J\": total_J,\n            \"F\": total_F,\n            \"J&F\": total_JnF,\n        }\n        self.eval[\"CGF1\"] = self.eval[\"positive_macro_F1\"] * self.eval[\"IL_MCC\"]\n        self.eval[\"CGF1_w0dt\"] = (\n            self.eval[\"positive_w0dt_macro_F1\"] * self.eval[\"IL_MCC\"]\n        )\n        self.eval[\"CGF1_micro\"] = self.eval[\"positive_micro_F1\"] * self.eval[\"IL_MCC\"]\n\n    def summarize(self):\n        \"\"\"\n        Compute and display summary metrics for evaluation results.\n        Note this functin can *only* be applied on the default parameter setting\n        \"\"\"\n        if not self.eval:\n            raise Exception(\"Please run accumulate() first\")\n\n        def _summarize(iouThr=None, metric=\"\"):\n            p = self.params\n            iStr = \" {:<18} @[ IoU={:<9}] = {:0.3f}\"\n            titleStr = \"Average \" + metric\n            iouStr = (\n                \"{:0.2f}:{:0.2f}\".format(p.iouThrs[0], p.iouThrs[-1])\n                if iouThr is None\n                else \"{:0.2f}\".format(iouThr)\n            )\n\n            s = self.eval[metric]\n            # IoU\n            if iouThr is not None:\n                t = np.where(iouThr == p.iouThrs)[0]\n                s = s[t]\n\n            if len(s[s > -1]) == 0:\n                mean_s = -1\n            else:\n                mean_s = np.mean(s[s > -1])\n            print(iStr.format(titleStr, iouStr, mean_s))\n            return mean_s\n\n        def _summarize_single(metric=\"\"):\n            titleStr = \"Average \" + metric\n            iStr = \" {:<35} = {:0.3f}\"\n            s = self.eval[metric]\n            print(iStr.format(titleStr, s))\n            return s\n\n        def _summarizeDets():\n            # note: the index of these metrics are also used in video Demo F1 evaluation\n            # when adding new metrics, please update the index in video Demo F1 evaluation\n            # in \"evaluate\" method of the \"VideoDemoF1Evaluator\" class\n            stats = np.zeros((len(DEMO_METRICS),))\n            stats[0] = _summarize(metric=\"CGF1\")\n            stats[1] = _summarize(metric=\"precision\")\n            stats[2] = _summarize(metric=\"recall\")\n            stats[3] = _summarize(metric=\"F1\")\n            stats[4] = _summarize(metric=\"positive_macro_F1\")\n            stats[5] = _summarize_single(metric=\"IL_precision\")\n            stats[6] = _summarize_single(metric=\"IL_recall\")\n            stats[7] = _summarize_single(metric=\"IL_F1\")\n            stats[8] = _summarize_single(metric=\"IL_FPR\")\n            stats[9] = _summarize_single(metric=\"IL_MCC\")\n            stats[10] = _summarize(metric=\"IL_perfect_pos\")\n            stats[11] = _summarize(metric=\"IL_perfect_neg\")\n            stats[12] = _summarize(iouThr=0.5, metric=\"CGF1\")\n            stats[13] = _summarize(iouThr=0.5, metric=\"precision\")\n            stats[14] = _summarize(iouThr=0.5, metric=\"recall\")\n            stats[15] = _summarize(iouThr=0.5, metric=\"F1\")\n            stats[16] = _summarize(iouThr=0.5, metric=\"positive_macro_F1\")\n            stats[17] = _summarize(iouThr=0.5, metric=\"IL_perfect_pos\")\n            stats[18] = _summarize(iouThr=0.5, metric=\"IL_perfect_neg\")\n            stats[19] = _summarize(iouThr=0.75, metric=\"CGF1\")\n            stats[20] = _summarize(iouThr=0.75, metric=\"precision\")\n            stats[21] = _summarize(iouThr=0.75, metric=\"recall\")\n            stats[22] = _summarize(iouThr=0.75, metric=\"F1\")\n            stats[23] = _summarize(iouThr=0.75, metric=\"positive_macro_F1\")\n            stats[24] = _summarize(iouThr=0.75, metric=\"IL_perfect_pos\")\n            stats[25] = _summarize(iouThr=0.75, metric=\"IL_perfect_neg\")\n            stats[26] = _summarize_single(metric=\"J\")\n            stats[27] = _summarize_single(metric=\"F\")\n            stats[28] = _summarize_single(metric=\"J&F\")\n            stats[29] = _summarize(metric=\"CGF1_micro\")\n            stats[30] = _summarize(metric=\"positive_micro_precision\")\n            stats[31] = _summarize(metric=\"positive_micro_F1\")\n            stats[32] = _summarize(iouThr=0.5, metric=\"CGF1_micro\")\n            stats[33] = _summarize(iouThr=0.5, metric=\"positive_micro_precision\")\n            stats[34] = _summarize(iouThr=0.5, metric=\"positive_micro_F1\")\n            stats[35] = _summarize(iouThr=0.75, metric=\"CGF1_micro\")\n            stats[36] = _summarize(iouThr=0.75, metric=\"positive_micro_precision\")\n            stats[37] = _summarize(iouThr=0.75, metric=\"positive_micro_F1\")\n            stats[38] = _summarize(metric=\"CGF1_w0dt\")\n            stats[39] = _summarize(metric=\"positive_w0dt_macro_F1\")\n            stats[40] = _summarize(iouThr=0.5, metric=\"CGF1_w0dt\")\n            stats[41] = _summarize(iouThr=0.5, metric=\"positive_w0dt_macro_F1\")\n            stats[42] = _summarize(iouThr=0.75, metric=\"CGF1_w0dt\")\n            stats[43] = _summarize(iouThr=0.75, metric=\"positive_w0dt_macro_F1\")\n            return stats\n\n        summarize = _summarizeDets\n        self.stats = summarize()\n\n\nDEMO_METRICS = [\n    \"CGF1\",\n    \"Precision\",\n    \"Recall\",\n    \"F1\",\n    \"Macro_F1\",\n    \"IL_Precision\",\n    \"IL_Recall\",\n    \"IL_F1\",\n    \"IL_FPR\",\n    \"IL_MCC\",\n    \"IL_perfect_pos\",\n    \"IL_perfect_neg\",\n    \"CGF1@0.5\",\n    \"Precision@0.5\",\n    \"Recall@0.5\",\n    \"F1@0.5\",\n    \"Macro_F1@0.5\",\n    \"IL_perfect_pos@0.5\",\n    \"IL_perfect_neg@0.5\",\n    \"CGF1@0.75\",\n    \"Precision@0.75\",\n    \"Recall@0.75\",\n    \"F1@0.75\",\n    \"Macro_F1@0.75\",\n    \"IL_perfect_pos@0.75\",\n    \"IL_perfect_neg@0.75\",\n    \"J\",\n    \"F\",\n    \"J&F\",\n    \"CGF1_micro\",\n    \"positive_micro_Precision\",\n    \"positive_micro_F1\",\n    \"CGF1_micro@0.5\",\n    \"positive_micro_Precision@0.5\",\n    \"positive_micro_F1@0.5\",\n    \"CGF1_micro@0.75\",\n    \"positive_micro_Precision@0.75\",\n    \"positive_micro_F1@0.75\",\n    \"CGF1_w0dt\",\n    \"positive_w0dt_macro_F1\",\n    \"CGF1_w0dt@0.5\",\n    \"positive_w0dt_macro_F1@0.5\",\n    \"CGF1_w0dt@0.75\",\n    \"positive_w0dt_macro_F1@0.75\",\n]\n\n\nclass DemoEvaluator(CocoEvaluator):\n    def __init__(\n        self,\n        coco_gt,\n        iou_types,\n        dump_dir: Optional[str],\n        postprocessor,\n        threshold=0.5,\n        average_by_rarity=False,\n        gather_pred_via_filesys=False,\n        exhaustive_only=False,\n        all_exhaustive_only=True,\n        compute_JnF=False,\n        metrics_dump_dir: Optional[str] = None,\n    ):\n        self.iou_types = iou_types\n        self.threshold = threshold\n        super().__init__(\n            coco_gt=coco_gt,\n            iou_types=iou_types,\n            useCats=False,\n            dump_dir=dump_dir,\n            postprocessor=postprocessor,\n            # average_by_rarity=average_by_rarity,\n            gather_pred_via_filesys=gather_pred_via_filesys,\n            exhaustive_only=exhaustive_only,\n            all_exhaustive_only=all_exhaustive_only,\n            metrics_dump_dir=metrics_dump_dir,\n        )\n\n        self.use_self_evaluate = True\n        self.compute_JnF = compute_JnF\n\n    def _lazy_init(self):\n        if self.initialized:\n            return\n        super()._lazy_init()\n        self.use_self_evaluate = True\n        self.reset()\n\n    def select_best_scoring(self, scorings):\n        # This function is used for \"oracle\" type evaluation.\n        # It accepts the evaluation results with respect to several ground truths, and picks the best\n        if len(scorings) == 1:\n            return scorings[0]\n\n        assert scorings[0].ndim == 3, (\n            f\"Expecting results in [numCats, numAreas, numImgs] format, got {scorings[0].shape}\"\n        )\n        assert scorings[0].shape[0] == 1, (\n            f\"Expecting a single category, got {scorings[0].shape[0]}\"\n        )\n\n        for scoring in scorings:\n            assert scoring.shape == scorings[0].shape, (\n                f\"Shape mismatch: {scoring.shape}, {scorings[0].shape}\"\n            )\n\n        selected_imgs = []\n        for img_id in range(scorings[0].shape[-1]):\n            best = scorings[0][:, :, img_id]\n\n            for scoring in scorings[1:]:\n                current = scoring[:, :, img_id]\n                if \"local_F1s\" in best[0, 0] and \"local_F1s\" in current[0, 0]:\n                    # we were able to compute a F1 score for this particular image in both evaluations\n                    # best[\"local_F1s\"] contains the results at various IoU thresholds. We simply take the average for comparision\n                    best_score = best[0, 0][\"local_F1s\"].mean()\n                    current_score = current[0, 0][\"local_F1s\"].mean()\n                    if current_score > best_score:\n                        best = current\n\n                else:\n                    # If we're here, it means that in that in some evaluation we were not able to get a valid local F1\n                    # This happens when both the predictions and targets are empty. In that case, we can assume it's a perfect prediction\n                    if \"local_F1s\" not in current[0, 0]:\n                        best = current\n            selected_imgs.append(best)\n        result = np.stack(selected_imgs, axis=-1)\n        assert result.shape == scorings[0].shape\n        return result\n\n    def summarize(self):\n        self._lazy_init()\n        logging.info(\"Demo evaluator: Summarizing\")\n        if not is_main_process():\n            return {}\n        outs = {}\n        prefix = \"oracle_\" if len(self.coco_evals) > 1 else \"\"\n        # if self.rarity_buckets is None:\n        self.accumulate(self.eval_img_ids)\n        for iou_type, coco_eval in self.coco_evals[0].items():\n            print(\"Demo metric, IoU type={}\".format(iou_type))\n            coco_eval.summarize()\n\n        if \"bbox\" in self.coco_evals[0]:\n            for i, value in enumerate(self.coco_evals[0][\"bbox\"].stats):\n                outs[f\"coco_eval_bbox_{prefix}{DEMO_METRICS[i]}\"] = value\n        if \"segm\" in self.coco_evals[0]:\n            for i, value in enumerate(self.coco_evals[0][\"segm\"].stats):\n                outs[f\"coco_eval_masks_{prefix}{DEMO_METRICS[i]}\"] = value\n        # else:\n        #     total_stats = {}\n        #     for bucket, img_list in self.rarity_buckets.items():\n        #         self.accumulate(imgIds=img_list)\n        #         bucket_name = RARITY_BUCKETS[bucket]\n        #         for iou_type, coco_eval in self.coco_evals[0].items():\n        #             print(\n        #                 \"Demo metric, IoU type={}, Rarity bucket={}\".format(\n        #                     iou_type, bucket_name\n        #                 )\n        #             )\n        #             coco_eval.summarize()\n\n        #         if \"bbox\" in self.coco_evals[0]:\n        #             if \"bbox\" not in total_stats:\n        #                 total_stats[\"bbox\"] = np.zeros_like(\n        #                     self.coco_evals[0][\"bbox\"].stats\n        #                 )\n        #             total_stats[\"bbox\"] += self.coco_evals[0][\"bbox\"].stats\n        #             for i, value in enumerate(self.coco_evals[0][\"bbox\"].stats):\n        #                 outs[\n        #                     f\"coco_eval_bbox_{bucket_name}_{prefix}{DEMO_METRICS[i]}\"\n        #                 ] = value\n        #         if \"segm\" in self.coco_evals[0]:\n        #             if \"segm\" not in total_stats:\n        #                 total_stats[\"segm\"] = np.zeros_like(\n        #                     self.coco_evals[0][\"segm\"].stats\n        #                 )\n        #             total_stats[\"segm\"] += self.coco_evals[0][\"segm\"].stats\n        #             for i, value in enumerate(self.coco_evals[0][\"segm\"].stats):\n        #                 outs[\n        #                     f\"coco_eval_masks_{bucket_name}_{prefix}{DEMO_METRICS[i]}\"\n        #                 ] = value\n\n        #     if \"bbox\" in total_stats:\n        #         total_stats[\"bbox\"] /= len(self.rarity_buckets)\n        #         for i, value in enumerate(total_stats[\"bbox\"]):\n        #             outs[f\"coco_eval_bbox_{prefix}{DEMO_METRICS[i]}\"] = value\n        #     if \"segm\" in total_stats:\n        #         total_stats[\"segm\"] /= len(self.rarity_buckets)\n        #         for i, value in enumerate(total_stats[\"segm\"]):\n        #             outs[f\"coco_eval_masks_{prefix}{DEMO_METRICS[i]}\"] = value\n\n        return outs\n\n    def accumulate(self, imgIds=None):\n        self._lazy_init()\n        logging.info(\n            f\"demo evaluator: Accumulating on {len(imgIds) if imgIds is not None else 'all'} images\"\n        )\n        if not is_main_process():\n            return\n\n        if imgIds is not None:\n            for coco_eval in self.coco_evals[0].values():\n                coco_eval.params.imgIds = list(imgIds)\n\n        for coco_eval in self.coco_evals[0].values():\n            coco_eval.accumulate()\n\n    def reset(self):\n        self.coco_evals = [{} for _ in range(len(self.coco_gts))]\n        for i, coco_gt in enumerate(self.coco_gts):\n            for iou_type in self.iou_types:\n                self.coco_evals[i][iou_type] = DemoEval(\n                    coco_gt=coco_gt,\n                    iouType=iou_type,\n                    threshold=self.threshold,\n                    compute_JnF=self.compute_JnF,\n                )\n                self.coco_evals[i][iou_type].useCats = False\n        self.img_ids = []\n        self.eval_imgs = {k: [] for k in self.iou_types}\n        if self.dump is not None:\n            self.dump = []\n"
  },
  {
    "path": "sam3/eval/hota_eval_toolkit/__init__.py",
    "content": "# flake8: noqa\n\n# pyre-unsafe\n"
  },
  {
    "path": "sam3/eval/hota_eval_toolkit/run_ytvis_eval.py",
    "content": "# flake8: noqa\n\n# pyre-unsafe\n\n\"\"\"run_youtube_vis.py\nRun example:\nrun_youtube_vis.py --USE_PARALLEL False --METRICS HOTA --TRACKERS_TO_EVAL STEm_Seg\nCommand Line Arguments: Defaults, # Comments\n    Eval arguments:\n            'USE_PARALLEL': False,\n            'NUM_PARALLEL_CORES': 8,\n            'BREAK_ON_ERROR': True,  # Raises exception and exits with error\n            'RETURN_ON_ERROR': False,  # if not BREAK_ON_ERROR, then returns from function on error\n            'LOG_ON_ERROR': os.path.join(code_path, 'error_log.txt'),  # if not None, save any errors into a log file.\n            'PRINT_RESULTS': True,\n            'PRINT_ONLY_COMBINED': False,\n            'PRINT_CONFIG': True,\n            'TIME_PROGRESS': True,\n            'DISPLAY_LESS_PROGRESS': True,\n            'OUTPUT_SUMMARY': True,\n            'OUTPUT_EMPTY_CLASSES': True,  # If False, summary files are not output for classes with no detections\n            'OUTPUT_DETAILED': True,\n            'PLOT_CURVES': True,\n    Dataset arguments:\n        'GT_FOLDER': os.path.join(code_path, 'data/gt/youtube_vis/youtube_vis_training'),  # Location of GT data\n        'TRACKERS_FOLDER': os.path.join(code_path, 'data/trackers/youtube_vis/youtube_vis_training'),\n        # Trackers location\n        'OUTPUT_FOLDER': None,  # Where to save eval results (if None, same as TRACKERS_FOLDER)\n        'TRACKERS_TO_EVAL': None,  # Filenames of trackers to eval (if None, all in folder)\n        'CLASSES_TO_EVAL': None,  # Classes to eval (if None, all classes)\n        'SPLIT_TO_EVAL': 'training',  # Valid: 'training', 'val'\n        'PRINT_CONFIG': True,  # Whether to print current config\n        'OUTPUT_SUB_FOLDER': '',  # Output files are saved in OUTPUT_FOLDER/tracker_name/OUTPUT_SUB_FOLDER\n        'TRACKER_SUB_FOLDER': 'data',  # Tracker files are in TRACKER_FOLDER/tracker_name/TRACKER_SUB_FOLDER\n        'TRACKER_DISPLAY_NAMES': None,  # Names of trackers to display, if None: TRACKERS_TO_EVAL\n    Metric arguments:\n        'METRICS': ['TrackMAP', 'HOTA', 'CLEAR', 'Identity']\n\"\"\"\n\nimport argparse\nimport os\nimport sys\nfrom multiprocessing import freeze_support\n\nfrom . import trackeval\n\n\ndef run_ytvis_eval(args=None, gt_json=None, dt_json=None):\n    # Command line interface:\n    default_eval_config = trackeval.Evaluator.get_default_eval_config()\n    # print only combined since TrackMAP is undefined for per sequence breakdowns\n    default_eval_config[\"PRINT_ONLY_COMBINED\"] = True\n    default_dataset_config = trackeval.datasets.YouTubeVIS.get_default_dataset_config()\n    default_metrics_config = {\"METRICS\": [\"HOTA\"]}\n    config = {\n        **default_eval_config,\n        **default_dataset_config,\n        **default_metrics_config,\n    }  # Merge default configs\n    parser = argparse.ArgumentParser()\n    for setting in config.keys():\n        if type(config[setting]) == list or type(config[setting]) == type(None):\n            parser.add_argument(\"--\" + setting, nargs=\"+\")\n        else:\n            parser.add_argument(\"--\" + setting)\n    args = parser.parse_args(args).__dict__\n    for setting in args.keys():\n        if args[setting] is not None:\n            if type(config[setting]) == type(True):\n                if args[setting] == \"True\":\n                    x = True\n                elif args[setting] == \"False\":\n                    x = False\n                else:\n                    raise Exception(\n                        \"Command line parameter \" + setting + \"must be True or False\"\n                    )\n            elif type(config[setting]) == type(1):\n                x = int(args[setting])\n            elif type(args[setting]) == type(None):\n                x = None\n            else:\n                x = args[setting]\n            config[setting] = x\n    eval_config = {k: v for k, v in config.items() if k in default_eval_config.keys()}\n    dataset_config = {\n        k: v for k, v in config.items() if k in default_dataset_config.keys()\n    }\n    metrics_config = {\n        k: v for k, v in config.items() if k in default_metrics_config.keys()\n    }\n\n    # Run code\n    evaluator = trackeval.Evaluator(eval_config)\n    # allow directly specifying the GT JSON data and Tracker (result)\n    # JSON data as Python objects, without reading from files.\n    dataset_config[\"GT_JSON_OBJECT\"] = gt_json\n    dataset_config[\"TRACKER_JSON_OBJECT\"] = dt_json\n    dataset_list = [trackeval.datasets.YouTubeVIS(dataset_config)]\n    metrics_list = []\n    # for metric in [trackeval.metrics.TrackMAP, trackeval.metrics.HOTA, trackeval.metrics.CLEAR,\n    #                trackeval.metrics.Identity]:\n    for metric in [trackeval.metrics.HOTA]:\n        if metric.get_name() in metrics_config[\"METRICS\"]:\n            metrics_list.append(metric())\n    if len(metrics_list) == 0:\n        raise Exception(\"No metrics selected for evaluation\")\n    output_res, output_msg = evaluator.evaluate(dataset_list, metrics_list)\n    return output_res, output_msg\n\n\nif __name__ == \"__main__\":\n    import sys\n\n    freeze_support()\n    run_ytvis_eval(sys.argv[1:])\n"
  },
  {
    "path": "sam3/eval/hota_eval_toolkit/trackeval/__init__.py",
    "content": "# flake8: noqa\n\n# pyre-unsafe\n\nfrom . import datasets, metrics, utils\nfrom .eval import Evaluator\n"
  },
  {
    "path": "sam3/eval/hota_eval_toolkit/trackeval/_timing.py",
    "content": "# flake8: noqa\n\n# pyre-unsafe\n\nimport inspect\nfrom functools import wraps\nfrom time import perf_counter\n\nDO_TIMING = False\nDISPLAY_LESS_PROGRESS = False\ntimer_dict = {}\ncounter = 0\n\n\ndef time(f):\n    @wraps(f)\n    def wrap(*args, **kw):\n        if DO_TIMING:\n            # Run function with timing\n            ts = perf_counter()\n            result = f(*args, **kw)\n            te = perf_counter()\n            tt = te - ts\n\n            # Get function name\n            arg_names = inspect.getfullargspec(f)[0]\n            if arg_names[0] == \"self\" and DISPLAY_LESS_PROGRESS:\n                return result\n            elif arg_names[0] == \"self\":\n                method_name = type(args[0]).__name__ + \".\" + f.__name__\n            else:\n                method_name = f.__name__\n\n            # Record accumulative time in each function for analysis\n            if method_name in timer_dict.keys():\n                timer_dict[method_name] += tt\n            else:\n                timer_dict[method_name] = tt\n\n            # If code is finished, display timing summary\n            if method_name == \"Evaluator.evaluate\":\n                print(\"\")\n                print(\"Timing analysis:\")\n                for key, value in timer_dict.items():\n                    print(\"%-70s %2.4f sec\" % (key, value))\n            else:\n                # Get function argument values for printing special arguments of interest\n                arg_titles = [\"tracker\", \"seq\", \"cls\"]\n                arg_vals = []\n                for i, a in enumerate(arg_names):\n                    if a in arg_titles:\n                        arg_vals.append(args[i])\n                arg_text = \"(\" + \", \".join(arg_vals) + \")\"\n\n                # Display methods and functions with different indentation.\n                if arg_names[0] == \"self\":\n                    print(\"%-74s %2.4f sec\" % (\" \" * 4 + method_name + arg_text, tt))\n                elif arg_names[0] == \"test\":\n                    pass\n                else:\n                    global counter\n                    counter += 1\n                    print(\"%i %-70s %2.4f sec\" % (counter, method_name + arg_text, tt))\n\n            return result\n        else:\n            # If config[\"TIME_PROGRESS\"] is false, or config[\"USE_PARALLEL\"] is true, run functions normally without timing.\n            return f(*args, **kw)\n\n    return wrap\n"
  },
  {
    "path": "sam3/eval/hota_eval_toolkit/trackeval/datasets/__init__.py",
    "content": "# flake8: noqa\n\n# pyre-unsafe\n\nfrom .tao_ow import TAO_OW\nfrom .youtube_vis import YouTubeVIS\n"
  },
  {
    "path": "sam3/eval/hota_eval_toolkit/trackeval/datasets/_base_dataset.py",
    "content": "# flake8: noqa\n\n# pyre-unsafe\n\nimport csv\nimport io\nimport os\nimport traceback\nimport zipfile\nfrom abc import ABC, abstractmethod\nfrom copy import deepcopy\n\nimport numpy as np\n\nfrom .. import _timing\nfrom ..utils import TrackEvalException\n\n\nclass _BaseDataset(ABC):\n    @abstractmethod\n    def __init__(self):\n        self.tracker_list = None\n        self.seq_list = None\n        self.class_list = None\n        self.output_fol = None\n        self.output_sub_fol = None\n        self.should_classes_combine = True\n        self.use_super_categories = False\n\n    # Functions to implement:\n\n    @staticmethod\n    @abstractmethod\n    def get_default_dataset_config(): ...\n\n    @abstractmethod\n    def _load_raw_file(self, tracker, seq, is_gt): ...\n\n    @_timing.time\n    @abstractmethod\n    def get_preprocessed_seq_data(self, raw_data, cls): ...\n\n    @abstractmethod\n    def _calculate_similarities(self, gt_dets_t, tracker_dets_t): ...\n\n    # Helper functions for all datasets:\n\n    @classmethod\n    def get_class_name(cls):\n        return cls.__name__\n\n    def get_name(self):\n        return self.get_class_name()\n\n    def get_output_fol(self, tracker):\n        return os.path.join(self.output_fol, tracker, self.output_sub_fol)\n\n    def get_display_name(self, tracker):\n        \"\"\"Can be overwritten if the trackers name (in files) is different to how it should be displayed.\n        By default this method just returns the trackers name as is.\n        \"\"\"\n        return tracker\n\n    def get_eval_info(self):\n        \"\"\"Return info about the dataset needed for the Evaluator\"\"\"\n        return self.tracker_list, self.seq_list, self.class_list\n\n    @_timing.time\n    def get_raw_seq_data(self, tracker, seq):\n        \"\"\"Loads raw data (tracker and ground-truth) for a single tracker on a single sequence.\n        Raw data includes all of the information needed for both preprocessing and evaluation, for all classes.\n        A later function (get_processed_seq_data) will perform such preprocessing and extract relevant information for\n        the evaluation of each class.\n\n        This returns a dict which contains the fields:\n        [num_timesteps]: integer\n        [gt_ids, tracker_ids, gt_classes, tracker_classes, tracker_confidences]:\n                                                                list (for each timestep) of 1D NDArrays (for each det).\n        [gt_dets, tracker_dets, gt_crowd_ignore_regions]: list (for each timestep) of lists of detections.\n        [similarity_scores]: list (for each timestep) of 2D NDArrays.\n        [gt_extras]: dict (for each extra) of lists (for each timestep) of 1D NDArrays (for each det).\n\n        gt_extras contains dataset specific information used for preprocessing such as occlusion and truncation levels.\n\n        Note that similarities are extracted as part of the dataset and not the metric, because almost all metrics are\n        independent of the exact method of calculating the similarity. However datasets are not (e.g. segmentation\n        masks vs 2D boxes vs 3D boxes).\n        We calculate the similarity before preprocessing because often both preprocessing and evaluation require it and\n        we don't wish to calculate this twice.\n        We calculate similarity between all gt and tracker classes (not just each class individually) to allow for\n        calculation of metrics such as class confusion matrices. Typically the impact of this on performance is low.\n        \"\"\"\n        # Load raw data.\n        raw_gt_data = self._load_raw_file(tracker, seq, is_gt=True)\n        raw_tracker_data = self._load_raw_file(tracker, seq, is_gt=False)\n        raw_data = {**raw_tracker_data, **raw_gt_data}  # Merges dictionaries\n\n        # Calculate similarities for each timestep.\n        similarity_scores = []\n        for t, (gt_dets_t, tracker_dets_t) in enumerate(\n            zip(raw_data[\"gt_dets\"], raw_data[\"tracker_dets\"])\n        ):\n            ious = self._calculate_similarities(gt_dets_t, tracker_dets_t)\n            similarity_scores.append(ious)\n        raw_data[\"similarity_scores\"] = similarity_scores\n        return raw_data\n\n    @staticmethod\n    def _load_simple_text_file(\n        file,\n        time_col=0,\n        id_col=None,\n        remove_negative_ids=False,\n        valid_filter=None,\n        crowd_ignore_filter=None,\n        convert_filter=None,\n        is_zipped=False,\n        zip_file=None,\n        force_delimiters=None,\n    ):\n        \"\"\"Function that loads data which is in a commonly used text file format.\n        Assumes each det is given by one row of a text file.\n        There is no limit to the number or meaning of each column,\n        however one column needs to give the timestep of each det (time_col) which is default col 0.\n\n        The file dialect (deliminator, num cols, etc) is determined automatically.\n        This function automatically separates dets by timestep,\n        and is much faster than alternatives such as np.loadtext or pandas.\n\n        If remove_negative_ids is True and id_col is not None, dets with negative values in id_col are excluded.\n        These are not excluded from ignore data.\n\n        valid_filter can be used to only include certain classes.\n        It is a dict with ints as keys, and lists as values,\n        such that a row is included if \"row[key].lower() is in value\" for all key/value pairs in the dict.\n        If None, all classes are included.\n\n        crowd_ignore_filter can be used to read crowd_ignore regions separately. It has the same format as valid filter.\n\n        convert_filter can be used to convert value read to another format.\n        This is used most commonly to convert classes given as string to a class id.\n        This is a dict such that the key is the column to convert, and the value is another dict giving the mapping.\n\n        Optionally, input files could be a zip of multiple text files for storage efficiency.\n\n        Returns read_data and ignore_data.\n        Each is a dict (with keys as timesteps as strings) of lists (over dets) of lists (over column values).\n        Note that all data is returned as strings, and must be converted to float/int later if needed.\n        Note that timesteps will not be present in the returned dict keys if there are no dets for them\n        \"\"\"\n\n        if remove_negative_ids and id_col is None:\n            raise TrackEvalException(\n                \"remove_negative_ids is True, but id_col is not given.\"\n            )\n        if crowd_ignore_filter is None:\n            crowd_ignore_filter = {}\n        if convert_filter is None:\n            convert_filter = {}\n        try:\n            if is_zipped:  # Either open file directly or within a zip.\n                if zip_file is None:\n                    raise TrackEvalException(\n                        \"is_zipped set to True, but no zip_file is given.\"\n                    )\n                archive = zipfile.ZipFile(os.path.join(zip_file), \"r\")\n                fp = io.TextIOWrapper(archive.open(file, \"r\"))\n            else:\n                fp = open(file)\n            read_data = {}\n            crowd_ignore_data = {}\n            fp.seek(0, os.SEEK_END)\n            # check if file is empty\n            if fp.tell():\n                fp.seek(0)\n                dialect = csv.Sniffer().sniff(\n                    fp.readline(), delimiters=force_delimiters\n                )  # Auto determine structure.\n                dialect.skipinitialspace = (\n                    True  # Deal with extra spaces between columns\n                )\n                fp.seek(0)\n                reader = csv.reader(fp, dialect)\n                for row in reader:\n                    try:\n                        # Deal with extra trailing spaces at the end of rows\n                        if row[-1] in \"\":\n                            row = row[:-1]\n                        timestep = str(int(float(row[time_col])))\n                        # Read ignore regions separately.\n                        is_ignored = False\n                        for ignore_key, ignore_value in crowd_ignore_filter.items():\n                            if row[ignore_key].lower() in ignore_value:\n                                # Convert values in one column (e.g. string to id)\n                                for (\n                                    convert_key,\n                                    convert_value,\n                                ) in convert_filter.items():\n                                    row[convert_key] = convert_value[\n                                        row[convert_key].lower()\n                                    ]\n                                # Save data separated by timestep.\n                                if timestep in crowd_ignore_data.keys():\n                                    crowd_ignore_data[timestep].append(row)\n                                else:\n                                    crowd_ignore_data[timestep] = [row]\n                                is_ignored = True\n                        if (\n                            is_ignored\n                        ):  # if det is an ignore region, it cannot be a normal det.\n                            continue\n                        # Exclude some dets if not valid.\n                        if valid_filter is not None:\n                            for key, value in valid_filter.items():\n                                if row[key].lower() not in value:\n                                    continue\n                        if remove_negative_ids:\n                            if int(float(row[id_col])) < 0:\n                                continue\n                        # Convert values in one column (e.g. string to id)\n                        for convert_key, convert_value in convert_filter.items():\n                            row[convert_key] = convert_value[row[convert_key].lower()]\n                        # Save data separated by timestep.\n                        if timestep in read_data.keys():\n                            read_data[timestep].append(row)\n                        else:\n                            read_data[timestep] = [row]\n                    except Exception:\n                        exc_str_init = (\n                            \"In file %s the following line cannot be read correctly: \\n\"\n                            % os.path.basename(file)\n                        )\n                        exc_str = \" \".join([exc_str_init] + row)\n                        raise TrackEvalException(exc_str)\n            fp.close()\n        except Exception:\n            print(\"Error loading file: %s, printing traceback.\" % file)\n            traceback.print_exc()\n            raise TrackEvalException(\n                \"File %s cannot be read because it is either not present or invalidly formatted\"\n                % os.path.basename(file)\n            )\n        return read_data, crowd_ignore_data\n\n    @staticmethod\n    def _calculate_mask_ious(masks1, masks2, is_encoded=False, do_ioa=False):\n        \"\"\"Calculates the IOU (intersection over union) between two arrays of segmentation masks.\n        If is_encoded a run length encoding with pycocotools is assumed as input format, otherwise an input of numpy\n        arrays of the shape (num_masks, height, width) is assumed and the encoding is performed.\n        If do_ioa (intersection over area) , then calculates the intersection over the area of masks1 - this is commonly\n        used to determine if detections are within crowd ignore region.\n        :param masks1:  first set of masks (numpy array of shape (num_masks, height, width) if not encoded,\n                        else pycocotools rle encoded format)\n        :param masks2:  second set of masks (numpy array of shape (num_masks, height, width) if not encoded,\n                        else pycocotools rle encoded format)\n        :param is_encoded: whether the input is in pycocotools rle encoded format\n        :param do_ioa: whether to perform IoA computation\n        :return: the IoU/IoA scores\n        \"\"\"\n\n        # Only loaded when run to reduce minimum requirements\n        from pycocotools import mask as mask_utils\n\n        # use pycocotools for run length encoding of masks\n        if not is_encoded:\n            masks1 = mask_utils.encode(\n                np.array(np.transpose(masks1, (1, 2, 0)), order=\"F\")\n            )\n            masks2 = mask_utils.encode(\n                np.array(np.transpose(masks2, (1, 2, 0)), order=\"F\")\n            )\n\n        # use pycocotools for iou computation of rle encoded masks\n        ious = mask_utils.iou(masks1, masks2, [do_ioa] * len(masks2))\n        if len(masks1) == 0 or len(masks2) == 0:\n            ious = np.asarray(ious).reshape(len(masks1), len(masks2))\n        assert (ious >= 0 - np.finfo(\"float\").eps).all()\n        assert (ious <= 1 + np.finfo(\"float\").eps).all()\n\n        return ious\n\n    @staticmethod\n    def _calculate_box_ious(bboxes1, bboxes2, box_format=\"xywh\", do_ioa=False):\n        \"\"\"Calculates the IOU (intersection over union) between two arrays of boxes.\n        Allows variable box formats ('xywh' and 'x0y0x1y1').\n        If do_ioa (intersection over area) , then calculates the intersection over the area of boxes1 - this is commonly\n        used to determine if detections are within crowd ignore region.\n        \"\"\"\n        if box_format in \"xywh\":\n            # layout: (x0, y0, w, h)\n            bboxes1 = deepcopy(bboxes1)\n            bboxes2 = deepcopy(bboxes2)\n\n            bboxes1[:, 2] = bboxes1[:, 0] + bboxes1[:, 2]\n            bboxes1[:, 3] = bboxes1[:, 1] + bboxes1[:, 3]\n            bboxes2[:, 2] = bboxes2[:, 0] + bboxes2[:, 2]\n            bboxes2[:, 3] = bboxes2[:, 1] + bboxes2[:, 3]\n        elif box_format not in \"x0y0x1y1\":\n            raise (TrackEvalException(\"box_format %s is not implemented\" % box_format))\n\n        # layout: (x0, y0, x1, y1)\n        min_ = np.minimum(bboxes1[:, np.newaxis, :], bboxes2[np.newaxis, :, :])\n        max_ = np.maximum(bboxes1[:, np.newaxis, :], bboxes2[np.newaxis, :, :])\n        intersection = np.maximum(min_[..., 2] - max_[..., 0], 0) * np.maximum(\n            min_[..., 3] - max_[..., 1], 0\n        )\n        area1 = (bboxes1[..., 2] - bboxes1[..., 0]) * (\n            bboxes1[..., 3] - bboxes1[..., 1]\n        )\n\n        if do_ioa:\n            ioas = np.zeros_like(intersection)\n            valid_mask = area1 > 0 + np.finfo(\"float\").eps\n            ioas[valid_mask, :] = (\n                intersection[valid_mask, :] / area1[valid_mask][:, np.newaxis]\n            )\n\n            return ioas\n        else:\n            area2 = (bboxes2[..., 2] - bboxes2[..., 0]) * (\n                bboxes2[..., 3] - bboxes2[..., 1]\n            )\n            union = area1[:, np.newaxis] + area2[np.newaxis, :] - intersection\n            intersection[area1 <= 0 + np.finfo(\"float\").eps, :] = 0\n            intersection[:, area2 <= 0 + np.finfo(\"float\").eps] = 0\n            intersection[union <= 0 + np.finfo(\"float\").eps] = 0\n            union[union <= 0 + np.finfo(\"float\").eps] = 1\n            ious = intersection / union\n            return ious\n\n    @staticmethod\n    def _calculate_euclidean_similarity(dets1, dets2, zero_distance=2.0):\n        \"\"\"Calculates the euclidean distance between two sets of detections, and then converts this into a similarity\n        measure with values between 0 and 1 using the following formula: sim = max(0, 1 - dist/zero_distance).\n        The default zero_distance of 2.0, corresponds to the default used in MOT15_3D, such that a 0.5 similarity\n        threshold corresponds to a 1m distance threshold for TPs.\n        \"\"\"\n        dist = np.linalg.norm(dets1[:, np.newaxis] - dets2[np.newaxis, :], axis=2)\n        sim = np.maximum(0, 1 - dist / zero_distance)\n        return sim\n\n    @staticmethod\n    def _check_unique_ids(data, after_preproc=False):\n        \"\"\"Check the requirement that the tracker_ids and gt_ids are unique per timestep\"\"\"\n        gt_ids = data[\"gt_ids\"]\n        tracker_ids = data[\"tracker_ids\"]\n        for t, (gt_ids_t, tracker_ids_t) in enumerate(zip(gt_ids, tracker_ids)):\n            if len(tracker_ids_t) > 0:\n                unique_ids, counts = np.unique(tracker_ids_t, return_counts=True)\n                if np.max(counts) != 1:\n                    duplicate_ids = unique_ids[counts > 1]\n                    exc_str_init = (\n                        \"Tracker predicts the same ID more than once in a single timestep \"\n                        \"(seq: %s, frame: %i, ids:\" % (data[\"seq\"], t + 1)\n                    )\n                    exc_str = (\n                        \" \".join([exc_str_init] + [str(d) for d in duplicate_ids]) + \")\"\n                    )\n                    if after_preproc:\n                        exc_str_init += (\n                            \"\\n Note that this error occurred after preprocessing (but not before), \"\n                            \"so ids may not be as in file, and something seems wrong with preproc.\"\n                        )\n                    raise TrackEvalException(exc_str)\n            if len(gt_ids_t) > 0:\n                unique_ids, counts = np.unique(gt_ids_t, return_counts=True)\n                if np.max(counts) != 1:\n                    duplicate_ids = unique_ids[counts > 1]\n                    exc_str_init = (\n                        \"Ground-truth has the same ID more than once in a single timestep \"\n                        \"(seq: %s, frame: %i, ids:\" % (data[\"seq\"], t + 1)\n                    )\n                    exc_str = (\n                        \" \".join([exc_str_init] + [str(d) for d in duplicate_ids]) + \")\"\n                    )\n                    if after_preproc:\n                        exc_str_init += (\n                            \"\\n Note that this error occurred after preprocessing (but not before), \"\n                            \"so ids may not be as in file, and something seems wrong with preproc.\"\n                        )\n                    raise TrackEvalException(exc_str)\n"
  },
  {
    "path": "sam3/eval/hota_eval_toolkit/trackeval/datasets/tao_ow.py",
    "content": "# flake8: noqa\n\n# pyre-unsafe\n\nimport itertools\nimport json\nimport os\nfrom collections import defaultdict\n\nimport numpy as np\nfrom scipy.optimize import linear_sum_assignment\n\nfrom .. import _timing, utils\nfrom ..utils import TrackEvalException\nfrom ._base_dataset import _BaseDataset\n\n\nclass TAO_OW(_BaseDataset):\n    \"\"\"Dataset class for TAO tracking\"\"\"\n\n    @staticmethod\n    def get_default_dataset_config():\n        \"\"\"Default class config values\"\"\"\n        code_path = utils.get_code_path()\n        default_config = {\n            \"GT_FOLDER\": os.path.join(\n                code_path, \"data/gt/tao/tao_training\"\n            ),  # Location of GT data\n            \"TRACKERS_FOLDER\": os.path.join(\n                code_path, \"data/trackers/tao/tao_training\"\n            ),  # Trackers location\n            \"OUTPUT_FOLDER\": None,  # Where to save eval results (if None, same as TRACKERS_FOLDER)\n            \"TRACKERS_TO_EVAL\": None,  # Filenames of trackers to eval (if None, all in folder)\n            \"CLASSES_TO_EVAL\": None,  # Classes to eval (if None, all classes)\n            \"SPLIT_TO_EVAL\": \"training\",  # Valid: 'training', 'val'\n            \"PRINT_CONFIG\": True,  # Whether to print current config\n            \"TRACKER_SUB_FOLDER\": \"data\",  # Tracker files are in TRACKER_FOLDER/tracker_name/TRACKER_SUB_FOLDER\n            \"OUTPUT_SUB_FOLDER\": \"\",  # Output files are saved in OUTPUT_FOLDER/tracker_name/OUTPUT_SUB_FOLDER\n            \"TRACKER_DISPLAY_NAMES\": None,  # Names of trackers to display, if None: TRACKERS_TO_EVAL\n            \"MAX_DETECTIONS\": 300,  # Number of maximal allowed detections per image (0 for unlimited)\n            \"SUBSET\": \"all\",\n        }\n        return default_config\n\n    def __init__(self, config=None):\n        \"\"\"Initialise dataset, checking that all required files are present\"\"\"\n        super().__init__()\n        # Fill non-given config values with defaults\n        self.config = utils.init_config(\n            config, self.get_default_dataset_config(), self.get_name()\n        )\n        self.gt_fol = self.config[\"GT_FOLDER\"]\n        self.tracker_fol = self.config[\"TRACKERS_FOLDER\"]\n        self.should_classes_combine = True\n        self.use_super_categories = False\n\n        self.tracker_sub_fol = self.config[\"TRACKER_SUB_FOLDER\"]\n        self.output_fol = self.config[\"OUTPUT_FOLDER\"]\n        if self.output_fol is None:\n            self.output_fol = self.tracker_fol\n        self.output_sub_fol = self.config[\"OUTPUT_SUB_FOLDER\"]\n\n        gt_dir_files = [\n            file for file in os.listdir(self.gt_fol) if file.endswith(\".json\")\n        ]\n        if len(gt_dir_files) != 1:\n            raise TrackEvalException(\n                self.gt_fol + \" does not contain exactly one json file.\"\n            )\n\n        with open(os.path.join(self.gt_fol, gt_dir_files[0])) as f:\n            self.gt_data = json.load(f)\n\n        self.subset = self.config[\"SUBSET\"]\n        if self.subset != \"all\":\n            # Split GT data into `known`, `unknown` or `distractor`\n            self._split_known_unknown_distractor()\n            self.gt_data = self._filter_gt_data(self.gt_data)\n\n        # merge categories marked with a merged tag in TAO dataset\n        self._merge_categories(self.gt_data[\"annotations\"] + self.gt_data[\"tracks\"])\n\n        # Get sequences to eval and sequence information\n        self.seq_list = [\n            vid[\"name\"].replace(\"/\", \"-\") for vid in self.gt_data[\"videos\"]\n        ]\n        self.seq_name_to_seq_id = {\n            vid[\"name\"].replace(\"/\", \"-\"): vid[\"id\"] for vid in self.gt_data[\"videos\"]\n        }\n        # compute mappings from videos to annotation data\n        self.videos_to_gt_tracks, self.videos_to_gt_images = self._compute_vid_mappings(\n            self.gt_data[\"annotations\"]\n        )\n        # compute sequence lengths\n        self.seq_lengths = {vid[\"id\"]: 0 for vid in self.gt_data[\"videos\"]}\n        for img in self.gt_data[\"images\"]:\n            self.seq_lengths[img[\"video_id\"]] += 1\n        self.seq_to_images_to_timestep = self._compute_image_to_timestep_mappings()\n        self.seq_to_classes = {\n            vid[\"id\"]: {\n                \"pos_cat_ids\": list(\n                    {\n                        track[\"category_id\"]\n                        for track in self.videos_to_gt_tracks[vid[\"id\"]]\n                    }\n                ),\n                \"neg_cat_ids\": vid[\"neg_category_ids\"],\n                \"not_exhaustively_labeled_cat_ids\": vid[\"not_exhaustive_category_ids\"],\n            }\n            for vid in self.gt_data[\"videos\"]\n        }\n\n        # Get classes to eval\n        considered_vid_ids = [self.seq_name_to_seq_id[vid] for vid in self.seq_list]\n        seen_cats = set(\n            [\n                cat_id\n                for vid_id in considered_vid_ids\n                for cat_id in self.seq_to_classes[vid_id][\"pos_cat_ids\"]\n            ]\n        )\n        # only classes with ground truth are evaluated in TAO\n        self.valid_classes = [\n            cls[\"name\"] for cls in self.gt_data[\"categories\"] if cls[\"id\"] in seen_cats\n        ]\n        # cls_name_to_cls_id_map = {cls['name']: cls['id'] for cls in self.gt_data['categories']}\n\n        if self.config[\"CLASSES_TO_EVAL\"]:\n            # self.class_list = [cls.lower() if cls.lower() in self.valid_classes else None\n            #                    for cls in self.config['CLASSES_TO_EVAL']]\n            self.class_list = [\"object\"]  # class-agnostic\n            if not all(self.class_list):\n                raise TrackEvalException(\n                    \"Attempted to evaluate an invalid class. Only classes \"\n                    + \", \".join(self.valid_classes)\n                    + \" are valid (classes present in ground truth data).\"\n                )\n        else:\n            # self.class_list = [cls for cls in self.valid_classes]\n            self.class_list = [\"object\"]  # class-agnostic\n        # self.class_name_to_class_id = {k: v for k, v in cls_name_to_cls_id_map.items() if k in self.class_list}\n        self.class_name_to_class_id = {\"object\": 1}  # class-agnostic\n\n        # Get trackers to eval\n        if self.config[\"TRACKERS_TO_EVAL\"] is None:\n            self.tracker_list = os.listdir(self.tracker_fol)\n        else:\n            self.tracker_list = self.config[\"TRACKERS_TO_EVAL\"]\n\n        if self.config[\"TRACKER_DISPLAY_NAMES\"] is None:\n            self.tracker_to_disp = dict(zip(self.tracker_list, self.tracker_list))\n        elif (self.config[\"TRACKERS_TO_EVAL\"] is not None) and (\n            len(self.config[\"TRACKER_DISPLAY_NAMES\"]) == len(self.tracker_list)\n        ):\n            self.tracker_to_disp = dict(\n                zip(self.tracker_list, self.config[\"TRACKER_DISPLAY_NAMES\"])\n            )\n        else:\n            raise TrackEvalException(\n                \"List of tracker files and tracker display names do not match.\"\n            )\n\n        self.tracker_data = {tracker: dict() for tracker in self.tracker_list}\n\n        for tracker in self.tracker_list:\n            tr_dir_files = [\n                file\n                for file in os.listdir(\n                    os.path.join(self.tracker_fol, tracker, self.tracker_sub_fol)\n                )\n                if file.endswith(\".json\")\n            ]\n            if len(tr_dir_files) != 1:\n                raise TrackEvalException(\n                    os.path.join(self.tracker_fol, tracker, self.tracker_sub_fol)\n                    + \" does not contain exactly one json file.\"\n                )\n            with open(\n                os.path.join(\n                    self.tracker_fol, tracker, self.tracker_sub_fol, tr_dir_files[0]\n                )\n            ) as f:\n                curr_data = json.load(f)\n\n            # limit detections if MAX_DETECTIONS > 0\n            if self.config[\"MAX_DETECTIONS\"]:\n                curr_data = self._limit_dets_per_image(curr_data)\n\n            # fill missing video ids\n            self._fill_video_ids_inplace(curr_data)\n\n            # make track ids unique over whole evaluation set\n            self._make_track_ids_unique(curr_data)\n\n            # merge categories marked with a merged tag in TAO dataset\n            self._merge_categories(curr_data)\n\n            # get tracker sequence information\n            curr_videos_to_tracker_tracks, curr_videos_to_tracker_images = (\n                self._compute_vid_mappings(curr_data)\n            )\n            self.tracker_data[tracker][\"vids_to_tracks\"] = curr_videos_to_tracker_tracks\n            self.tracker_data[tracker][\"vids_to_images\"] = curr_videos_to_tracker_images\n\n    def get_display_name(self, tracker):\n        return self.tracker_to_disp[tracker]\n\n    def _load_raw_file(self, tracker, seq, is_gt):\n        \"\"\"Load a file (gt or tracker) in the TAO format\n\n        If is_gt, this returns a dict which contains the fields:\n        [gt_ids, gt_classes] : list (for each timestep) of 1D NDArrays (for each det).\n        [gt_dets]: list (for each timestep) of lists of detections.\n        [classes_to_gt_tracks]: dictionary with class values as keys and list of dictionaries (with frame indices as\n                                keys and corresponding segmentations as values) for each track\n        [classes_to_gt_track_ids, classes_to_gt_track_areas, classes_to_gt_track_lengths]: dictionary with class values\n                                as keys and lists (for each track) as values\n\n        if not is_gt, this returns a dict which contains the fields:\n        [tracker_ids, tracker_classes, tracker_confidences] : list (for each timestep) of 1D NDArrays (for each det).\n        [tracker_dets]: list (for each timestep) of lists of detections.\n        [classes_to_dt_tracks]: dictionary with class values as keys and list of dictionaries (with frame indices as\n                                keys and corresponding segmentations as values) for each track\n        [classes_to_dt_track_ids, classes_to_dt_track_areas, classes_to_dt_track_lengths]: dictionary with class values\n                                                                                           as keys and lists as values\n        [classes_to_dt_track_scores]: dictionary with class values as keys and 1D numpy arrays as values\n        \"\"\"\n        seq_id = self.seq_name_to_seq_id[seq]\n        # File location\n        if is_gt:\n            imgs = self.videos_to_gt_images[seq_id]\n        else:\n            imgs = self.tracker_data[tracker][\"vids_to_images\"][seq_id]\n\n        # Convert data to required format\n        num_timesteps = self.seq_lengths[seq_id]\n        img_to_timestep = self.seq_to_images_to_timestep[seq_id]\n        data_keys = [\"ids\", \"classes\", \"dets\"]\n        if not is_gt:\n            data_keys += [\"tracker_confidences\"]\n        raw_data = {key: [None] * num_timesteps for key in data_keys}\n        for img in imgs:\n            # some tracker data contains images without any ground truth information, these are ignored\n            try:\n                t = img_to_timestep[img[\"id\"]]\n            except KeyError:\n                continue\n            annotations = img[\"annotations\"]\n            raw_data[\"dets\"][t] = np.atleast_2d(\n                [ann[\"bbox\"] for ann in annotations]\n            ).astype(float)\n            raw_data[\"ids\"][t] = np.atleast_1d(\n                [ann[\"track_id\"] for ann in annotations]\n            ).astype(int)\n            raw_data[\"classes\"][t] = np.atleast_1d([1 for _ in annotations]).astype(\n                int\n            )  # class-agnostic\n            if not is_gt:\n                raw_data[\"tracker_confidences\"][t] = np.atleast_1d(\n                    [ann[\"score\"] for ann in annotations]\n                ).astype(float)\n\n        for t, d in enumerate(raw_data[\"dets\"]):\n            if d is None:\n                raw_data[\"dets\"][t] = np.empty((0, 4)).astype(float)\n                raw_data[\"ids\"][t] = np.empty(0).astype(int)\n                raw_data[\"classes\"][t] = np.empty(0).astype(int)\n                if not is_gt:\n                    raw_data[\"tracker_confidences\"][t] = np.empty(0)\n\n        if is_gt:\n            key_map = {\"ids\": \"gt_ids\", \"classes\": \"gt_classes\", \"dets\": \"gt_dets\"}\n        else:\n            key_map = {\n                \"ids\": \"tracker_ids\",\n                \"classes\": \"tracker_classes\",\n                \"dets\": \"tracker_dets\",\n            }\n        for k, v in key_map.items():\n            raw_data[v] = raw_data.pop(k)\n\n        # all_classes = [self.class_name_to_class_id[cls] for cls in self.class_list]\n        all_classes = [1]  # class-agnostic\n\n        if is_gt:\n            classes_to_consider = all_classes\n            all_tracks = self.videos_to_gt_tracks[seq_id]\n        else:\n            # classes_to_consider = self.seq_to_classes[seq_id]['pos_cat_ids'] \\\n            #                       + self.seq_to_classes[seq_id]['neg_cat_ids']\n            classes_to_consider = all_classes  # class-agnostic\n            all_tracks = self.tracker_data[tracker][\"vids_to_tracks\"][seq_id]\n\n        # classes_to_tracks = {cls: [track for track in all_tracks if track['category_id'] == cls]\n        #                      if cls in classes_to_consider else [] for cls in all_classes}\n        classes_to_tracks = {\n            cls: [track for track in all_tracks] if cls in classes_to_consider else []\n            for cls in all_classes\n        }  # class-agnostic\n\n        # mapping from classes to track information\n        raw_data[\"classes_to_tracks\"] = {\n            cls: [\n                {\n                    det[\"image_id\"]: np.atleast_1d(det[\"bbox\"])\n                    for det in track[\"annotations\"]\n                }\n                for track in tracks\n            ]\n            for cls, tracks in classes_to_tracks.items()\n        }\n        raw_data[\"classes_to_track_ids\"] = {\n            cls: [track[\"id\"] for track in tracks]\n            for cls, tracks in classes_to_tracks.items()\n        }\n        raw_data[\"classes_to_track_areas\"] = {\n            cls: [track[\"area\"] for track in tracks]\n            for cls, tracks in classes_to_tracks.items()\n        }\n        raw_data[\"classes_to_track_lengths\"] = {\n            cls: [len(track[\"annotations\"]) for track in tracks]\n            for cls, tracks in classes_to_tracks.items()\n        }\n\n        if not is_gt:\n            raw_data[\"classes_to_dt_track_scores\"] = {\n                cls: np.array(\n                    [\n                        np.mean([float(x[\"score\"]) for x in track[\"annotations\"]])\n                        for track in tracks\n                    ]\n                )\n                for cls, tracks in classes_to_tracks.items()\n            }\n\n        if is_gt:\n            key_map = {\n                \"classes_to_tracks\": \"classes_to_gt_tracks\",\n                \"classes_to_track_ids\": \"classes_to_gt_track_ids\",\n                \"classes_to_track_lengths\": \"classes_to_gt_track_lengths\",\n                \"classes_to_track_areas\": \"classes_to_gt_track_areas\",\n            }\n        else:\n            key_map = {\n                \"classes_to_tracks\": \"classes_to_dt_tracks\",\n                \"classes_to_track_ids\": \"classes_to_dt_track_ids\",\n                \"classes_to_track_lengths\": \"classes_to_dt_track_lengths\",\n                \"classes_to_track_areas\": \"classes_to_dt_track_areas\",\n            }\n        for k, v in key_map.items():\n            raw_data[v] = raw_data.pop(k)\n\n        raw_data[\"num_timesteps\"] = num_timesteps\n        raw_data[\"neg_cat_ids\"] = self.seq_to_classes[seq_id][\"neg_cat_ids\"]\n        raw_data[\"not_exhaustively_labeled_cls\"] = self.seq_to_classes[seq_id][\n            \"not_exhaustively_labeled_cat_ids\"\n        ]\n        raw_data[\"seq\"] = seq\n        return raw_data\n\n    @_timing.time\n    def get_preprocessed_seq_data(self, raw_data, cls):\n        \"\"\"Preprocess data for a single sequence for a single class ready for evaluation.\n        Inputs:\n             - raw_data is a dict containing the data for the sequence already read in by get_raw_seq_data().\n             - cls is the class to be evaluated.\n        Outputs:\n             - data is a dict containing all of the information that metrics need to perform evaluation.\n                It contains the following fields:\n                    [num_timesteps, num_gt_ids, num_tracker_ids, num_gt_dets, num_tracker_dets] : integers.\n                    [gt_ids, tracker_ids, tracker_confidences]: list (for each timestep) of 1D NDArrays (for each det).\n                    [gt_dets, tracker_dets]: list (for each timestep) of lists of detections.\n                    [similarity_scores]: list (for each timestep) of 2D NDArrays.\n        Notes:\n            General preprocessing (preproc) occurs in 4 steps. Some datasets may not use all of these steps.\n                1) Extract only detections relevant for the class to be evaluated (including distractor detections).\n                2) Match gt dets and tracker dets. Remove tracker dets that are matched to a gt det that is of a\n                    distractor class, or otherwise marked as to be removed.\n                3) Remove unmatched tracker dets if they fall within a crowd ignore region or don't meet a certain\n                    other criteria (e.g. are too small).\n                4) Remove gt dets that were only useful for preprocessing and not for actual evaluation.\n            After the above preprocessing steps, this function also calculates the number of gt and tracker detections\n                and unique track ids. It also relabels gt and tracker ids to be contiguous and checks that ids are\n                unique within each timestep.\n        TAO:\n            In TAO, the 4 preproc steps are as follow:\n                1) All classes present in the ground truth data are evaluated separately.\n                2) No matched tracker detections are removed.\n                3) Unmatched tracker detections are removed if there is not ground truth data and the class does not\n                    belong to the categories marked as negative for this sequence. Additionally, unmatched tracker\n                    detections for classes which are marked as not exhaustively labeled are removed.\n                4) No gt detections are removed.\n            Further, for TrackMAP computation track representations for the given class are accessed from a dictionary\n            and the tracks from the tracker data are sorted according to the tracker confidence.\n        \"\"\"\n        cls_id = self.class_name_to_class_id[cls]\n        is_not_exhaustively_labeled = cls_id in raw_data[\"not_exhaustively_labeled_cls\"]\n        is_neg_category = cls_id in raw_data[\"neg_cat_ids\"]\n\n        data_keys = [\n            \"gt_ids\",\n            \"tracker_ids\",\n            \"gt_dets\",\n            \"tracker_dets\",\n            \"tracker_confidences\",\n            \"similarity_scores\",\n        ]\n        data = {key: [None] * raw_data[\"num_timesteps\"] for key in data_keys}\n        unique_gt_ids = []\n        unique_tracker_ids = []\n        num_gt_dets = 0\n        num_tracker_dets = 0\n        for t in range(raw_data[\"num_timesteps\"]):\n            # Only extract relevant dets for this class for preproc and eval (cls)\n            gt_class_mask = np.atleast_1d(raw_data[\"gt_classes\"][t] == cls_id)\n            gt_class_mask = gt_class_mask.astype(bool)\n            gt_ids = raw_data[\"gt_ids\"][t][gt_class_mask]\n            gt_dets = raw_data[\"gt_dets\"][t][gt_class_mask]\n\n            tracker_class_mask = np.atleast_1d(raw_data[\"tracker_classes\"][t] == cls_id)\n            tracker_class_mask = tracker_class_mask.astype(bool)\n            tracker_ids = raw_data[\"tracker_ids\"][t][tracker_class_mask]\n            tracker_dets = raw_data[\"tracker_dets\"][t][tracker_class_mask]\n            tracker_confidences = raw_data[\"tracker_confidences\"][t][tracker_class_mask]\n            similarity_scores = raw_data[\"similarity_scores\"][t][gt_class_mask, :][\n                :, tracker_class_mask\n            ]\n\n            # Match tracker and gt dets (with hungarian algorithm).\n            unmatched_indices = np.arange(tracker_ids.shape[0])\n            if gt_ids.shape[0] > 0 and tracker_ids.shape[0] > 0:\n                matching_scores = similarity_scores.copy()\n                matching_scores[matching_scores < 0.5 - np.finfo(\"float\").eps] = 0\n                match_rows, match_cols = linear_sum_assignment(-matching_scores)\n                actually_matched_mask = (\n                    matching_scores[match_rows, match_cols] > 0 + np.finfo(\"float\").eps\n                )\n                match_cols = match_cols[actually_matched_mask]\n                unmatched_indices = np.delete(unmatched_indices, match_cols, axis=0)\n\n            if gt_ids.shape[0] == 0 and not is_neg_category:\n                to_remove_tracker = unmatched_indices\n            elif is_not_exhaustively_labeled:\n                to_remove_tracker = unmatched_indices\n            else:\n                to_remove_tracker = np.array([], dtype=int)\n\n            # remove all unwanted unmatched tracker detections\n            data[\"tracker_ids\"][t] = np.delete(tracker_ids, to_remove_tracker, axis=0)\n            data[\"tracker_dets\"][t] = np.delete(tracker_dets, to_remove_tracker, axis=0)\n            data[\"tracker_confidences\"][t] = np.delete(\n                tracker_confidences, to_remove_tracker, axis=0\n            )\n            similarity_scores = np.delete(similarity_scores, to_remove_tracker, axis=1)\n\n            data[\"gt_ids\"][t] = gt_ids\n            data[\"gt_dets\"][t] = gt_dets\n            data[\"similarity_scores\"][t] = similarity_scores\n\n            unique_gt_ids += list(np.unique(data[\"gt_ids\"][t]))\n            unique_tracker_ids += list(np.unique(data[\"tracker_ids\"][t]))\n            num_tracker_dets += len(data[\"tracker_ids\"][t])\n            num_gt_dets += len(data[\"gt_ids\"][t])\n\n        # Re-label IDs such that there are no empty IDs\n        if len(unique_gt_ids) > 0:\n            unique_gt_ids = np.unique(unique_gt_ids)\n            gt_id_map = np.nan * np.ones((np.max(unique_gt_ids) + 1))\n            gt_id_map[unique_gt_ids] = np.arange(len(unique_gt_ids))\n            for t in range(raw_data[\"num_timesteps\"]):\n                if len(data[\"gt_ids\"][t]) > 0:\n                    data[\"gt_ids\"][t] = gt_id_map[data[\"gt_ids\"][t]].astype(int)\n        if len(unique_tracker_ids) > 0:\n            unique_tracker_ids = np.unique(unique_tracker_ids)\n            tracker_id_map = np.nan * np.ones((np.max(unique_tracker_ids) + 1))\n            tracker_id_map[unique_tracker_ids] = np.arange(len(unique_tracker_ids))\n            for t in range(raw_data[\"num_timesteps\"]):\n                if len(data[\"tracker_ids\"][t]) > 0:\n                    data[\"tracker_ids\"][t] = tracker_id_map[\n                        data[\"tracker_ids\"][t]\n                    ].astype(int)\n\n        # Record overview statistics.\n        data[\"num_tracker_dets\"] = num_tracker_dets\n        data[\"num_gt_dets\"] = num_gt_dets\n        data[\"num_tracker_ids\"] = len(unique_tracker_ids)\n        data[\"num_gt_ids\"] = len(unique_gt_ids)\n        data[\"num_timesteps\"] = raw_data[\"num_timesteps\"]\n        data[\"seq\"] = raw_data[\"seq\"]\n\n        # get track representations\n        data[\"gt_tracks\"] = raw_data[\"classes_to_gt_tracks\"][cls_id]\n        data[\"gt_track_ids\"] = raw_data[\"classes_to_gt_track_ids\"][cls_id]\n        data[\"gt_track_lengths\"] = raw_data[\"classes_to_gt_track_lengths\"][cls_id]\n        data[\"gt_track_areas\"] = raw_data[\"classes_to_gt_track_areas\"][cls_id]\n        data[\"dt_tracks\"] = raw_data[\"classes_to_dt_tracks\"][cls_id]\n        data[\"dt_track_ids\"] = raw_data[\"classes_to_dt_track_ids\"][cls_id]\n        data[\"dt_track_lengths\"] = raw_data[\"classes_to_dt_track_lengths\"][cls_id]\n        data[\"dt_track_areas\"] = raw_data[\"classes_to_dt_track_areas\"][cls_id]\n        data[\"dt_track_scores\"] = raw_data[\"classes_to_dt_track_scores\"][cls_id]\n        data[\"not_exhaustively_labeled\"] = is_not_exhaustively_labeled\n        data[\"iou_type\"] = \"bbox\"\n\n        # sort tracker data tracks by tracker confidence scores\n        if data[\"dt_tracks\"]:\n            idx = np.argsort(\n                [-score for score in data[\"dt_track_scores\"]], kind=\"mergesort\"\n            )\n            data[\"dt_track_scores\"] = [data[\"dt_track_scores\"][i] for i in idx]\n            data[\"dt_tracks\"] = [data[\"dt_tracks\"][i] for i in idx]\n            data[\"dt_track_ids\"] = [data[\"dt_track_ids\"][i] for i in idx]\n            data[\"dt_track_lengths\"] = [data[\"dt_track_lengths\"][i] for i in idx]\n            data[\"dt_track_areas\"] = [data[\"dt_track_areas\"][i] for i in idx]\n        # Ensure that ids are unique per timestep.\n        self._check_unique_ids(data)\n\n        return data\n\n    def _calculate_similarities(self, gt_dets_t, tracker_dets_t):\n        similarity_scores = self._calculate_box_ious(gt_dets_t, tracker_dets_t)\n        return similarity_scores\n\n    def _merge_categories(self, annotations):\n        \"\"\"\n        Merges categories with a merged tag. Adapted from https://github.com/TAO-Dataset\n        :param annotations: the annotations in which the classes should be merged\n        :return: None\n        \"\"\"\n        merge_map = {}\n        for category in self.gt_data[\"categories\"]:\n            if \"merged\" in category:\n                for to_merge in category[\"merged\"]:\n                    merge_map[to_merge[\"id\"]] = category[\"id\"]\n\n        for ann in annotations:\n            ann[\"category_id\"] = merge_map.get(ann[\"category_id\"], ann[\"category_id\"])\n\n    def _compute_vid_mappings(self, annotations):\n        \"\"\"\n        Computes mappings from Videos to corresponding tracks and images.\n        :param annotations: the annotations for which the mapping should be generated\n        :return: the video-to-track-mapping, the video-to-image-mapping\n        \"\"\"\n        vids_to_tracks = {}\n        vids_to_imgs = {}\n        vid_ids = [vid[\"id\"] for vid in self.gt_data[\"videos\"]]\n\n        # compute an mapping from image IDs to images\n        images = {}\n        for image in self.gt_data[\"images\"]:\n            images[image[\"id\"]] = image\n\n        for ann in annotations:\n            ann[\"area\"] = ann[\"bbox\"][2] * ann[\"bbox\"][3]\n\n            vid = ann[\"video_id\"]\n            if ann[\"video_id\"] not in vids_to_tracks.keys():\n                vids_to_tracks[ann[\"video_id\"]] = list()\n            if ann[\"video_id\"] not in vids_to_imgs.keys():\n                vids_to_imgs[ann[\"video_id\"]] = list()\n\n            # Fill in vids_to_tracks\n            tid = ann[\"track_id\"]\n            exist_tids = [track[\"id\"] for track in vids_to_tracks[vid]]\n            try:\n                index1 = exist_tids.index(tid)\n            except ValueError:\n                index1 = -1\n            if tid not in exist_tids:\n                curr_track = {\n                    \"id\": tid,\n                    \"category_id\": ann[\"category_id\"],\n                    \"video_id\": vid,\n                    \"annotations\": [ann],\n                }\n                vids_to_tracks[vid].append(curr_track)\n            else:\n                vids_to_tracks[vid][index1][\"annotations\"].append(ann)\n\n            # Fill in vids_to_imgs\n            img_id = ann[\"image_id\"]\n            exist_img_ids = [img[\"id\"] for img in vids_to_imgs[vid]]\n            try:\n                index2 = exist_img_ids.index(img_id)\n            except ValueError:\n                index2 = -1\n            if index2 == -1:\n                curr_img = {\"id\": img_id, \"annotations\": [ann]}\n                vids_to_imgs[vid].append(curr_img)\n            else:\n                vids_to_imgs[vid][index2][\"annotations\"].append(ann)\n\n        # sort annotations by frame index and compute track area\n        for vid, tracks in vids_to_tracks.items():\n            for track in tracks:\n                track[\"annotations\"] = sorted(\n                    track[\"annotations\"],\n                    key=lambda x: images[x[\"image_id\"]][\"frame_index\"],\n                )\n                # Computer average area\n                track[\"area\"] = sum(x[\"area\"] for x in track[\"annotations\"]) / len(\n                    track[\"annotations\"]\n                )\n\n        # Ensure all videos are present\n        for vid_id in vid_ids:\n            if vid_id not in vids_to_tracks.keys():\n                vids_to_tracks[vid_id] = []\n            if vid_id not in vids_to_imgs.keys():\n                vids_to_imgs[vid_id] = []\n\n        return vids_to_tracks, vids_to_imgs\n\n    def _compute_image_to_timestep_mappings(self):\n        \"\"\"\n        Computes a mapping from images to the corresponding timestep in the sequence.\n        :return: the image-to-timestep-mapping\n        \"\"\"\n        images = {}\n        for image in self.gt_data[\"images\"]:\n            images[image[\"id\"]] = image\n\n        seq_to_imgs_to_timestep = {vid[\"id\"]: dict() for vid in self.gt_data[\"videos\"]}\n        for vid in seq_to_imgs_to_timestep:\n            curr_imgs = [img[\"id\"] for img in self.videos_to_gt_images[vid]]\n            curr_imgs = sorted(curr_imgs, key=lambda x: images[x][\"frame_index\"])\n            seq_to_imgs_to_timestep[vid] = {\n                curr_imgs[i]: i for i in range(len(curr_imgs))\n            }\n\n        return seq_to_imgs_to_timestep\n\n    def _limit_dets_per_image(self, annotations):\n        \"\"\"\n        Limits the number of detections for each image to config['MAX_DETECTIONS']. Adapted from\n        https://github.com/TAO-Dataset/\n        :param annotations: the annotations in which the detections should be limited\n        :return: the annotations with limited detections\n        \"\"\"\n        max_dets = self.config[\"MAX_DETECTIONS\"]\n        img_ann = defaultdict(list)\n        for ann in annotations:\n            img_ann[ann[\"image_id\"]].append(ann)\n\n        for img_id, _anns in img_ann.items():\n            if len(_anns) <= max_dets:\n                continue\n            _anns = sorted(_anns, key=lambda x: x[\"score\"], reverse=True)\n            img_ann[img_id] = _anns[:max_dets]\n\n        return [ann for anns in img_ann.values() for ann in anns]\n\n    def _fill_video_ids_inplace(self, annotations):\n        \"\"\"\n        Fills in missing video IDs inplace. Adapted from https://github.com/TAO-Dataset/\n        :param annotations: the annotations for which the videos IDs should be filled inplace\n        :return: None\n        \"\"\"\n        missing_video_id = [x for x in annotations if \"video_id\" not in x]\n        if missing_video_id:\n            image_id_to_video_id = {\n                x[\"id\"]: x[\"video_id\"] for x in self.gt_data[\"images\"]\n            }\n            for x in missing_video_id:\n                x[\"video_id\"] = image_id_to_video_id[x[\"image_id\"]]\n\n    @staticmethod\n    def _make_track_ids_unique(annotations):\n        \"\"\"\n        Makes the track IDs unqiue over the whole annotation set. Adapted from https://github.com/TAO-Dataset/\n        :param annotations: the annotation set\n        :return: the number of updated IDs\n        \"\"\"\n        track_id_videos = {}\n        track_ids_to_update = set()\n        max_track_id = 0\n        for ann in annotations:\n            t = ann[\"track_id\"]\n            if t not in track_id_videos:\n                track_id_videos[t] = ann[\"video_id\"]\n\n            if ann[\"video_id\"] != track_id_videos[t]:\n                # Track id is assigned to multiple videos\n                track_ids_to_update.add(t)\n            max_track_id = max(max_track_id, t)\n\n        if track_ids_to_update:\n            print(\"true\")\n            next_id = itertools.count(max_track_id + 1)\n            new_track_ids = defaultdict(lambda: next(next_id))\n            for ann in annotations:\n                t = ann[\"track_id\"]\n                v = ann[\"video_id\"]\n                if t in track_ids_to_update:\n                    ann[\"track_id\"] = new_track_ids[t, v]\n        return len(track_ids_to_update)\n\n    def _split_known_unknown_distractor(self):\n        all_ids = set(\n            [i for i in range(1, 2000)]\n        )  # 2000 is larger than the max category id in TAO-OW.\n        # `knowns` includes 78 TAO_category_ids that corresponds to 78 COCO classes.\n        # (The other 2 COCO classes do not have corresponding classes in TAO).\n        self.knowns = {\n            4,\n            13,\n            1038,\n            544,\n            1057,\n            34,\n            35,\n            36,\n            41,\n            45,\n            58,\n            60,\n            579,\n            1091,\n            1097,\n            1099,\n            78,\n            79,\n            81,\n            91,\n            1115,\n            1117,\n            95,\n            1122,\n            99,\n            1132,\n            621,\n            1135,\n            625,\n            118,\n            1144,\n            126,\n            642,\n            1155,\n            133,\n            1162,\n            139,\n            154,\n            174,\n            185,\n            699,\n            1215,\n            714,\n            717,\n            1229,\n            211,\n            729,\n            221,\n            229,\n            747,\n            235,\n            237,\n            779,\n            276,\n            805,\n            299,\n            829,\n            852,\n            347,\n            371,\n            382,\n            896,\n            392,\n            926,\n            937,\n            428,\n            429,\n            961,\n            452,\n            979,\n            980,\n            982,\n            475,\n            480,\n            993,\n            1001,\n            502,\n            1018,\n        }\n        # `distractors` is defined as in the paper \"Opening up Open-World Tracking\"\n        self.distractors = {\n            20,\n            63,\n            108,\n            180,\n            188,\n            204,\n            212,\n            247,\n            303,\n            403,\n            407,\n            415,\n            490,\n            504,\n            507,\n            513,\n            529,\n            567,\n            569,\n            588,\n            672,\n            691,\n            702,\n            708,\n            711,\n            720,\n            736,\n            737,\n            798,\n            813,\n            815,\n            827,\n            831,\n            851,\n            877,\n            883,\n            912,\n            971,\n            976,\n            1130,\n            1133,\n            1134,\n            1169,\n            1184,\n            1220,\n        }\n        self.unknowns = all_ids.difference(self.knowns.union(self.distractors))\n\n    def _filter_gt_data(self, raw_gt_data):\n        \"\"\"\n        Filter out irrelevant data in the raw_gt_data\n        Args:\n            raw_gt_data: directly loaded from json.\n\n        Returns:\n            filtered gt_data\n        \"\"\"\n        valid_cat_ids = list()\n        if self.subset == \"known\":\n            valid_cat_ids = self.knowns\n        elif self.subset == \"distractor\":\n            valid_cat_ids = self.distractors\n        elif self.subset == \"unknown\":\n            valid_cat_ids = self.unknowns\n        # elif self.subset == \"test_only_unknowns\":\n        #     valid_cat_ids = test_only_unknowns\n        else:\n            raise Exception(\"The parameter `SUBSET` is incorrect\")\n\n        filtered = dict()\n        filtered[\"videos\"] = raw_gt_data[\"videos\"]\n        # filtered[\"videos\"] = list()\n        unwanted_vid = set()\n        # for video in raw_gt_data[\"videos\"]:\n        #     datasrc = video[\"name\"].split('/')[1]\n        #     if datasrc in data_srcs:\n        #         filtered[\"videos\"].append(video)\n        #     else:\n        #         unwanted_vid.add(video[\"id\"])\n\n        filtered[\"annotations\"] = list()\n        for ann in raw_gt_data[\"annotations\"]:\n            if (ann[\"video_id\"] not in unwanted_vid) and (\n                ann[\"category_id\"] in valid_cat_ids\n            ):\n                filtered[\"annotations\"].append(ann)\n\n        filtered[\"tracks\"] = list()\n        for track in raw_gt_data[\"tracks\"]:\n            if (track[\"video_id\"] not in unwanted_vid) and (\n                track[\"category_id\"] in valid_cat_ids\n            ):\n                filtered[\"tracks\"].append(track)\n\n        filtered[\"images\"] = list()\n        for image in raw_gt_data[\"images\"]:\n            if image[\"video_id\"] not in unwanted_vid:\n                filtered[\"images\"].append(image)\n\n        filtered[\"categories\"] = list()\n        for cat in raw_gt_data[\"categories\"]:\n            if cat[\"id\"] in valid_cat_ids:\n                filtered[\"categories\"].append(cat)\n\n        filtered[\"info\"] = raw_gt_data[\"info\"]\n        filtered[\"licenses\"] = raw_gt_data[\"licenses\"]\n\n        return filtered\n"
  },
  {
    "path": "sam3/eval/hota_eval_toolkit/trackeval/datasets/youtube_vis.py",
    "content": "# flake8: noqa\n\n# pyre-unsafe\n\n# note: this file has been modified from its original version in TrackEval in\n# https://github.com/JonathonLuiten/TrackEval/blob/master/trackeval/datasets/youtube_vis.py\n# to support the following:\n# 1) bbox evaluation (via `IOU_TYPE`)\n# 2) passing GT and prediction data as Python objects (via `GT_JSON_OBJECT` and `TRACKER_JSON_OBJECT`)\n# 3) specifying a custom dataset name (via `DATASET_NAME`)\n\nimport json\nimport os\n\nimport numpy as np\n\nfrom .. import _timing, utils\nfrom ..utils import TrackEvalException\nfrom ._base_dataset import _BaseDataset\n\n\nclass YouTubeVIS(_BaseDataset):\n    \"\"\"Dataset class for YouTubeVIS tracking\"\"\"\n\n    @staticmethod\n    def get_default_dataset_config():\n        \"\"\"Default class config values\"\"\"\n        code_path = utils.get_code_path()\n        default_config = {\n            \"GT_FOLDER\": os.path.join(\n                code_path, \"data/gt/youtube_vis/\"\n            ),  # Location of GT data\n            \"TRACKERS_FOLDER\": os.path.join(code_path, \"data/trackers/youtube_vis/\"),\n            # Trackers location\n            \"OUTPUT_FOLDER\": None,  # Where to save eval results (if None, same as TRACKERS_FOLDER)\n            \"TRACKERS_TO_EVAL\": None,  # Filenames of trackers to eval (if None, all in folder)\n            \"CLASSES_TO_EVAL\": None,  # Classes to eval (if None, all classes)\n            \"SPLIT_TO_EVAL\": \"train_sub_split\",  # Valid: 'train', 'val', 'train_sub_split'\n            \"PRINT_CONFIG\": True,  # Whether to print current config\n            \"OUTPUT_SUB_FOLDER\": \"\",  # Output files are saved in OUTPUT_FOLDER/tracker_name/OUTPUT_SUB_FOLDER\n            \"TRACKER_SUB_FOLDER\": \"data\",  # Tracker files are in TRACKER_FOLDER/tracker_name/TRACKER_SUB_FOLDER\n            \"TRACKER_DISPLAY_NAMES\": None,  # Names of trackers to display, if None: TRACKERS_TO_EVAL\n            # Added for video phrase AP evaluation -- allow directly specifying the GT JSON data and Tracker (result)\n            # JSON data as Python objects, without reading from files.\n            \"GT_JSON_OBJECT\": None,\n            \"TRACKER_JSON_OBJECT\": None,\n            \"IOU_TYPE\": \"segm\",\n            \"DATASET_NAME\": \"video\",\n        }\n        return default_config\n\n    def __init__(self, config=None):\n        \"\"\"Initialise dataset, checking that all required files are present\"\"\"\n        super().__init__()\n        # Fill non-given config values with defaults\n        self.config = utils.init_config(config, self.get_default_dataset_config())\n        self.gt_fol = (\n            self.config[\"GT_FOLDER\"] + \"youtube_vis_\" + self.config[\"SPLIT_TO_EVAL\"]\n        )\n        self.tracker_fol = (\n            self.config[\"TRACKERS_FOLDER\"]\n            + \"youtube_vis_\"\n            + self.config[\"SPLIT_TO_EVAL\"]\n        )\n        self.use_super_categories = False\n        self.should_classes_combine = True\n        assert self.config[\"IOU_TYPE\"] in [\"segm\", \"bbox\"]\n        self.iou_type = self.config[\"IOU_TYPE\"]\n        print(\"=\" * 100)\n        print(f\"Evaluate annotation type *{self.iou_type}*\")\n        self.dataset_name = self.config[\"DATASET_NAME\"]\n\n        self.output_fol = self.config[\"OUTPUT_FOLDER\"]\n        if self.output_fol is None:\n            self.output_fol = self.tracker_fol\n        self.output_sub_fol = self.config[\"OUTPUT_SUB_FOLDER\"]\n        self.tracker_sub_fol = self.config[\"TRACKER_SUB_FOLDER\"]\n\n        if self.config[\"GT_JSON_OBJECT\"] is not None:\n            # allow directly specifying the GT JSON data without reading from files\n            gt_json = self.config[\"GT_JSON_OBJECT\"]\n            assert isinstance(gt_json, dict)\n            assert \"videos\" in gt_json\n            assert \"categories\" in gt_json\n            assert \"annotations\" in gt_json\n            self.gt_data = gt_json\n        else:\n            if not os.path.exists(self.gt_fol):\n                print(\"GT folder not found: \" + self.gt_fol)\n                raise TrackEvalException(\n                    \"GT folder not found: \" + os.path.basename(self.gt_fol)\n                )\n            gt_dir_files = [\n                file for file in os.listdir(self.gt_fol) if file.endswith(\".json\")\n            ]\n            if len(gt_dir_files) != 1:\n                raise TrackEvalException(\n                    self.gt_fol + \" does not contain exactly one json file.\"\n                )\n\n            with open(os.path.join(self.gt_fol, gt_dir_files[0])) as f:\n                self.gt_data = json.load(f)\n\n        # Get classes to eval\n        self.valid_classes = [cls[\"name\"] for cls in self.gt_data[\"categories\"]]\n        cls_name_to_cls_id_map = {\n            cls[\"name\"]: cls[\"id\"] for cls in self.gt_data[\"categories\"]\n        }\n\n        if self.config[\"CLASSES_TO_EVAL\"]:\n            self.class_list = [\n                cls.lower() if cls.lower() in self.valid_classes else None\n                for cls in self.config[\"CLASSES_TO_EVAL\"]\n            ]\n            if not all(self.class_list):\n                raise TrackEvalException(\n                    \"Attempted to evaluate an invalid class. Only classes \"\n                    + \", \".join(self.valid_classes)\n                    + \" are valid.\"\n                )\n        else:\n            self.class_list = [cls[\"name\"] for cls in self.gt_data[\"categories\"]]\n        self.class_name_to_class_id = {\n            k: v for k, v in cls_name_to_cls_id_map.items() if k in self.class_list\n        }\n\n        # Get sequences to eval and check gt files exist\n        self.seq_list = [\n            vid[\"file_names\"][0].split(\"/\")[0] for vid in self.gt_data[\"videos\"]\n        ]\n        self.seq_name_to_seq_id = {\n            vid[\"file_names\"][0].split(\"/\")[0]: vid[\"id\"]\n            for vid in self.gt_data[\"videos\"]\n        }\n        self.seq_lengths = {\n            vid[\"id\"]: len(vid[\"file_names\"]) for vid in self.gt_data[\"videos\"]\n        }\n\n        # encode masks and compute track areas\n        self._prepare_gt_annotations()\n\n        # Get trackers to eval\n        if self.config[\"TRACKER_JSON_OBJECT\"] is not None:\n            # allow directly specifying the tracker JSON data without reading from files\n            tracker_json = self.config[\"TRACKER_JSON_OBJECT\"]\n            assert isinstance(tracker_json, list)\n            self.tracker_list = [\"tracker\"]\n        elif self.config[\"TRACKERS_TO_EVAL\"] is None:\n            self.tracker_list = os.listdir(self.tracker_fol)\n        else:\n            self.tracker_list = self.config[\"TRACKERS_TO_EVAL\"]\n\n        if self.config[\"TRACKER_DISPLAY_NAMES\"] is None:\n            self.tracker_to_disp = dict(zip(self.tracker_list, self.tracker_list))\n        elif (self.config[\"TRACKERS_TO_EVAL\"] is not None) and (\n            len(self.config[\"TRACKER_DISPLAY_NAMES\"]) == len(self.tracker_list)\n        ):\n            self.tracker_to_disp = dict(\n                zip(self.tracker_list, self.config[\"TRACKER_DISPLAY_NAMES\"])\n            )\n        else:\n            raise TrackEvalException(\n                \"List of tracker files and tracker display names do not match.\"\n            )\n\n        # counter for globally unique track IDs\n        self.global_tid_counter = 0\n\n        self.tracker_data = dict()\n        if self.config[\"TRACKER_JSON_OBJECT\"] is not None:\n            # allow directly specifying the tracker JSON data without reading from files\n            tracker = self.tracker_list[0]\n            self.tracker_data[tracker] = tracker_json\n        else:\n            for tracker in self.tracker_list:\n                tracker_dir_path = os.path.join(\n                    self.tracker_fol, tracker, self.tracker_sub_fol\n                )\n                tr_dir_files = [\n                    file\n                    for file in os.listdir(tracker_dir_path)\n                    if file.endswith(\".json\")\n                ]\n                if len(tr_dir_files) != 1:\n                    raise TrackEvalException(\n                        tracker_dir_path + \" does not contain exactly one json file.\"\n                    )\n\n                with open(os.path.join(tracker_dir_path, tr_dir_files[0])) as f:\n                    curr_data = json.load(f)\n\n                self.tracker_data[tracker] = curr_data\n\n    def get_display_name(self, tracker):\n        return self.tracker_to_disp[tracker]\n\n    def _load_raw_file(self, tracker, seq, is_gt):\n        \"\"\"Load a file (gt or tracker) in the YouTubeVIS format\n        If is_gt, this returns a dict which contains the fields:\n        [gt_ids, gt_classes] : list (for each timestep) of 1D NDArrays (for each det).\n        [gt_dets]: list (for each timestep) of lists of detections.\n        [classes_to_gt_tracks]: dictionary with class values as keys and list of dictionaries (with frame indices as\n                                keys and corresponding segmentations as values) for each track\n        [classes_to_gt_track_ids, classes_to_gt_track_areas, classes_to_gt_track_iscrowd]: dictionary with class values\n                                as keys and lists (for each track) as values\n\n        if not is_gt, this returns a dict which contains the fields:\n        [tracker_ids, tracker_classes, tracker_confidences] : list (for each timestep) of 1D NDArrays (for each det).\n        [tracker_dets]: list (for each timestep) of lists of detections.\n        [classes_to_dt_tracks]: dictionary with class values as keys and list of dictionaries (with frame indices as\n                                keys and corresponding segmentations as values) for each track\n        [classes_to_dt_track_ids, classes_to_dt_track_areas]: dictionary with class values as keys and lists as values\n        [classes_to_dt_track_scores]: dictionary with class values as keys and 1D numpy arrays as values\n        \"\"\"\n        # select sequence tracks\n        seq_id = self.seq_name_to_seq_id[seq]\n        if is_gt:\n            tracks = [\n                ann for ann in self.gt_data[\"annotations\"] if ann[\"video_id\"] == seq_id\n            ]\n        else:\n            tracks = self._get_tracker_seq_tracks(tracker, seq_id)\n\n        # Convert data to required format\n        num_timesteps = self.seq_lengths[seq_id]\n        data_keys = [\"ids\", \"classes\", \"dets\"]\n        if not is_gt:\n            data_keys += [\"tracker_confidences\"]\n        raw_data = {key: [None] * num_timesteps for key in data_keys}\n        result_key = \"segmentations\" if self.iou_type == \"segm\" else \"bboxes\"\n        for t in range(num_timesteps):\n            raw_data[\"dets\"][t] = [\n                track[result_key][t] for track in tracks if track[result_key][t]\n            ]\n            raw_data[\"ids\"][t] = np.atleast_1d(\n                [track[\"id\"] for track in tracks if track[result_key][t]]\n            ).astype(int)\n            raw_data[\"classes\"][t] = np.atleast_1d(\n                [track[\"category_id\"] for track in tracks if track[result_key][t]]\n            ).astype(int)\n            if not is_gt:\n                raw_data[\"tracker_confidences\"][t] = np.atleast_1d(\n                    [track[\"score\"] for track in tracks if track[result_key][t]]\n                ).astype(float)\n\n        if is_gt:\n            key_map = {\"ids\": \"gt_ids\", \"classes\": \"gt_classes\", \"dets\": \"gt_dets\"}\n        else:\n            key_map = {\n                \"ids\": \"tracker_ids\",\n                \"classes\": \"tracker_classes\",\n                \"dets\": \"tracker_dets\",\n            }\n        for k, v in key_map.items():\n            raw_data[v] = raw_data.pop(k)\n\n        all_cls_ids = {self.class_name_to_class_id[cls] for cls in self.class_list}\n        classes_to_tracks = {\n            cls: [track for track in tracks if track[\"category_id\"] == cls]\n            for cls in all_cls_ids\n        }\n\n        # mapping from classes to track representations and track information\n        raw_data[\"classes_to_tracks\"] = {\n            cls: [\n                {i: track[result_key][i] for i in range(len(track[result_key]))}\n                for track in tracks\n            ]\n            for cls, tracks in classes_to_tracks.items()\n        }\n        raw_data[\"classes_to_track_ids\"] = {\n            cls: [track[\"id\"] for track in tracks]\n            for cls, tracks in classes_to_tracks.items()\n        }\n        raw_data[\"classes_to_track_areas\"] = {\n            cls: [track[\"area\"] for track in tracks]\n            for cls, tracks in classes_to_tracks.items()\n        }\n\n        if is_gt:\n            raw_data[\"classes_to_gt_track_iscrowd\"] = {\n                cls: [track[\"iscrowd\"] for track in tracks]\n                for cls, tracks in classes_to_tracks.items()\n            }\n        else:\n            raw_data[\"classes_to_dt_track_scores\"] = {\n                cls: np.array([track[\"score\"] for track in tracks])\n                for cls, tracks in classes_to_tracks.items()\n            }\n\n        if is_gt:\n            key_map = {\n                \"classes_to_tracks\": \"classes_to_gt_tracks\",\n                \"classes_to_track_ids\": \"classes_to_gt_track_ids\",\n                \"classes_to_track_areas\": \"classes_to_gt_track_areas\",\n            }\n        else:\n            key_map = {\n                \"classes_to_tracks\": \"classes_to_dt_tracks\",\n                \"classes_to_track_ids\": \"classes_to_dt_track_ids\",\n                \"classes_to_track_areas\": \"classes_to_dt_track_areas\",\n            }\n        for k, v in key_map.items():\n            raw_data[v] = raw_data.pop(k)\n\n        raw_data[\"num_timesteps\"] = num_timesteps\n        raw_data[\"seq\"] = seq\n        return raw_data\n\n    @_timing.time\n    def get_preprocessed_seq_data(self, raw_data, cls):\n        \"\"\"Preprocess data for a single sequence for a single class ready for evaluation.\n        Inputs:\n             - raw_data is a dict containing the data for the sequence already read in by get_raw_seq_data().\n             - cls is the class to be evaluated.\n        Outputs:\n             - data is a dict containing all of the information that metrics need to perform evaluation.\n                It contains the following fields:\n                    [num_timesteps, num_gt_ids, num_tracker_ids, num_gt_dets, num_tracker_dets] : integers.\n                    [gt_ids, tracker_ids, tracker_confidences]: list (for each timestep) of 1D NDArrays (for each det).\n                    [gt_dets, tracker_dets]: list (for each timestep) of lists of detections.\n                    [similarity_scores]: list (for each timestep) of 2D NDArrays.\n        Notes:\n            General preprocessing (preproc) occurs in 4 steps. Some datasets may not use all of these steps.\n                1) Extract only detections relevant for the class to be evaluated (including distractor detections).\n                2) Match gt dets and tracker dets. Remove tracker dets that are matched to a gt det that is of a\n                    distractor class, or otherwise marked as to be removed.\n                3) Remove unmatched tracker dets if they fall within a crowd ignore region or don't meet a certain\n                    other criteria (e.g. are too small).\n                4) Remove gt dets that were only useful for preprocessing and not for actual evaluation.\n            After the above preprocessing steps, this function also calculates the number of gt and tracker detections\n                and unique track ids. It also relabels gt and tracker ids to be contiguous and checks that ids are\n                unique within each timestep.\n        YouTubeVIS:\n            In YouTubeVIS, the 4 preproc steps are as follow:\n                1) There are 40 classes which are evaluated separately.\n                2) No matched tracker dets are removed.\n                3) No unmatched tracker dets are removed.\n                4) No gt dets are removed.\n            Further, for TrackMAP computation track representations for the given class are accessed from a dictionary\n            and the tracks from the tracker data are sorted according to the tracker confidence.\n        \"\"\"\n        cls_id = self.class_name_to_class_id[cls]\n\n        data_keys = [\n            \"gt_ids\",\n            \"tracker_ids\",\n            \"gt_dets\",\n            \"tracker_dets\",\n            \"similarity_scores\",\n        ]\n        data = {key: [None] * raw_data[\"num_timesteps\"] for key in data_keys}\n        unique_gt_ids = []\n        unique_tracker_ids = []\n        num_gt_dets = 0\n        num_tracker_dets = 0\n\n        for t in range(raw_data[\"num_timesteps\"]):\n            # Only extract relevant dets for this class for eval (cls)\n            gt_class_mask = np.atleast_1d(raw_data[\"gt_classes\"][t] == cls_id)\n            gt_class_mask = gt_class_mask.astype(bool)\n            gt_ids = raw_data[\"gt_ids\"][t][gt_class_mask]\n            gt_dets = [\n                raw_data[\"gt_dets\"][t][ind]\n                for ind in range(len(gt_class_mask))\n                if gt_class_mask[ind]\n            ]\n\n            tracker_class_mask = np.atleast_1d(raw_data[\"tracker_classes\"][t] == cls_id)\n            tracker_class_mask = tracker_class_mask.astype(bool)\n            tracker_ids = raw_data[\"tracker_ids\"][t][tracker_class_mask]\n            tracker_dets = [\n                raw_data[\"tracker_dets\"][t][ind]\n                for ind in range(len(tracker_class_mask))\n                if tracker_class_mask[ind]\n            ]\n            similarity_scores = raw_data[\"similarity_scores\"][t][gt_class_mask, :][\n                :, tracker_class_mask\n            ]\n\n            data[\"tracker_ids\"][t] = tracker_ids\n            data[\"tracker_dets\"][t] = tracker_dets\n            data[\"gt_ids\"][t] = gt_ids\n            data[\"gt_dets\"][t] = gt_dets\n            data[\"similarity_scores\"][t] = similarity_scores\n\n            unique_gt_ids += list(np.unique(data[\"gt_ids\"][t]))\n            unique_tracker_ids += list(np.unique(data[\"tracker_ids\"][t]))\n            num_tracker_dets += len(data[\"tracker_ids\"][t])\n            num_gt_dets += len(data[\"gt_ids\"][t])\n\n        # Re-label IDs such that there are no empty IDs\n        if len(unique_gt_ids) > 0:\n            unique_gt_ids = np.unique(unique_gt_ids)\n            gt_id_map = np.nan * np.ones((np.max(unique_gt_ids) + 1))\n            gt_id_map[unique_gt_ids] = np.arange(len(unique_gt_ids))\n            for t in range(raw_data[\"num_timesteps\"]):\n                if len(data[\"gt_ids\"][t]) > 0:\n                    data[\"gt_ids\"][t] = gt_id_map[data[\"gt_ids\"][t]].astype(int)\n        if len(unique_tracker_ids) > 0:\n            unique_tracker_ids = np.unique(unique_tracker_ids)\n            tracker_id_map = np.nan * np.ones((np.max(unique_tracker_ids) + 1))\n            tracker_id_map[unique_tracker_ids] = np.arange(len(unique_tracker_ids))\n            for t in range(raw_data[\"num_timesteps\"]):\n                if len(data[\"tracker_ids\"][t]) > 0:\n                    data[\"tracker_ids\"][t] = tracker_id_map[\n                        data[\"tracker_ids\"][t]\n                    ].astype(int)\n\n        # Ensure that ids are unique per timestep.\n        self._check_unique_ids(data)\n\n        # Record overview statistics.\n        data[\"num_tracker_dets\"] = num_tracker_dets\n        data[\"num_gt_dets\"] = num_gt_dets\n        data[\"num_tracker_ids\"] = len(unique_tracker_ids)\n        data[\"num_gt_ids\"] = len(unique_gt_ids)\n        data[\"num_timesteps\"] = raw_data[\"num_timesteps\"]\n        data[\"seq\"] = raw_data[\"seq\"]\n\n        # get track representations\n        data[\"gt_tracks\"] = raw_data[\"classes_to_gt_tracks\"][cls_id]\n        data[\"gt_track_ids\"] = raw_data[\"classes_to_gt_track_ids\"][cls_id]\n        data[\"gt_track_areas\"] = raw_data[\"classes_to_gt_track_areas\"][cls_id]\n        data[\"gt_track_iscrowd\"] = raw_data[\"classes_to_gt_track_iscrowd\"][cls_id]\n        data[\"dt_tracks\"] = raw_data[\"classes_to_dt_tracks\"][cls_id]\n        data[\"dt_track_ids\"] = raw_data[\"classes_to_dt_track_ids\"][cls_id]\n        data[\"dt_track_areas\"] = raw_data[\"classes_to_dt_track_areas\"][cls_id]\n        data[\"dt_track_scores\"] = raw_data[\"classes_to_dt_track_scores\"][cls_id]\n        data[\"iou_type\"] = \"mask\"\n\n        # sort tracker data tracks by tracker confidence scores\n        if data[\"dt_tracks\"]:\n            idx = np.argsort(\n                [-score for score in data[\"dt_track_scores\"]], kind=\"mergesort\"\n            )\n            data[\"dt_track_scores\"] = [data[\"dt_track_scores\"][i] for i in idx]\n            data[\"dt_tracks\"] = [data[\"dt_tracks\"][i] for i in idx]\n            data[\"dt_track_ids\"] = [data[\"dt_track_ids\"][i] for i in idx]\n            data[\"dt_track_areas\"] = [data[\"dt_track_areas\"][i] for i in idx]\n\n        return data\n\n    def _calculate_similarities(self, gt_dets_t, tracker_dets_t):\n        if self.iou_type == \"segm\":\n            similarity_scores = self._calculate_mask_ious(\n                gt_dets_t, tracker_dets_t, is_encoded=True, do_ioa=False\n            )\n        else:\n            gt_dets_t = np.array(gt_dets_t, dtype=np.float32).reshape(-1, 4)\n            tracker_dets_t = np.array(tracker_dets_t, dtype=np.float32).reshape(-1, 4)\n            similarity_scores = self._calculate_box_ious(\n                gt_dets_t, tracker_dets_t, box_format=\"xywh\", do_ioa=False\n            )\n        return similarity_scores\n\n    def _prepare_gt_annotations(self):\n        \"\"\"\n        Prepares GT data by rle encoding segmentations and computing the average track area.\n        :return: None\n        \"\"\"\n        if self.iou_type == \"segm\":\n            # only loaded when needed to reduce minimum requirements\n            from pycocotools import mask as mask_utils\n\n            for track in self.gt_data[\"annotations\"]:\n                h = track[\"height\"]\n                w = track[\"width\"]\n                for i, seg in enumerate(track[\"segmentations\"]):\n                    if seg is not None and isinstance(seg[\"counts\"], list):\n                        track[\"segmentations\"][i] = mask_utils.frPyObjects(seg, h, w)\n                areas = [a for a in track[\"areas\"] if a]\n                if len(areas) == 0:\n                    track[\"area\"] = 0\n                else:\n                    track[\"area\"] = np.array(areas).mean()\n        else:\n            for track in self.gt_data[\"annotations\"]:\n                # For bbox eval, compute areas from bboxes if not already available\n                areas = [a for a in track.get(\"areas\", []) if a]\n                if not areas:\n                    areas = []\n                    for bbox in track.get(\"bboxes\", []):\n                        if bbox is not None:\n                            areas.append(bbox[2] * bbox[3])\n                track[\"area\"] = np.array(areas).mean() if areas else 0\n\n    def _get_tracker_seq_tracks(self, tracker, seq_id):\n        \"\"\"\n        Prepares tracker data for a given sequence. Extracts all annotations for given sequence ID, computes\n        average track area and assigns a track ID.\n        :param tracker: the given tracker\n        :param seq_id: the sequence ID\n        :return: the extracted tracks\n        \"\"\"\n        # only loaded when needed to reduce minimum requirements\n        from pycocotools import mask as mask_utils\n\n        tracks = [\n            ann for ann in self.tracker_data[tracker] if ann[\"video_id\"] == seq_id\n        ]\n        for track in tracks:\n            if \"areas\" not in track:\n                if self.iou_type == \"segm\":\n                    for seg in track[\"segmentations\"]:\n                        if seg:\n                            track[\"areas\"].append(mask_utils.area(seg))\n                        else:\n                            track[\"areas\"].append(None)\n                else:\n                    for bbox in track[\"bboxes\"]:\n                        if bbox:\n                            track[\"areas\"].append(bbox[2] * bbox[3])\n                        else:\n                            track[\"areas\"].append(None)\n            areas = [a for a in track[\"areas\"] if a]\n            if len(areas) == 0:\n                track[\"area\"] = 0\n            else:\n                track[\"area\"] = np.array(areas).mean()\n            track[\"id\"] = self.global_tid_counter\n            self.global_tid_counter += 1\n        return tracks\n\n    def get_name(self):\n        return self.dataset_name\n"
  },
  {
    "path": "sam3/eval/hota_eval_toolkit/trackeval/eval.py",
    "content": "# flake8: noqa\n\n# pyre-unsafe\n\nimport os\nimport time\nimport traceback\nfrom functools import partial\nfrom multiprocessing.pool import Pool\n\nimport numpy as np\n\nfrom . import _timing, utils\nfrom .metrics import Count\nfrom .utils import TrackEvalException\n\ntry:\n    import tqdm\n\n    TQDM_IMPORTED = True\nexcept ImportError as _:\n    TQDM_IMPORTED = False\n\n\nclass Evaluator:\n    \"\"\"Evaluator class for evaluating different metrics for different datasets\"\"\"\n\n    @staticmethod\n    def get_default_eval_config():\n        \"\"\"Returns the default config values for evaluation\"\"\"\n        code_path = utils.get_code_path()\n        default_config = {\n            \"USE_PARALLEL\": False,\n            \"NUM_PARALLEL_CORES\": 8,\n            \"BREAK_ON_ERROR\": True,  # Raises exception and exits with error\n            \"RETURN_ON_ERROR\": False,  # if not BREAK_ON_ERROR, then returns from function on error\n            \"LOG_ON_ERROR\": os.path.join(\n                code_path, \"error_log.txt\"\n            ),  # if not None, save any errors into a log file.\n            \"PRINT_RESULTS\": True,\n            \"PRINT_ONLY_COMBINED\": False,\n            \"PRINT_CONFIG\": True,\n            \"TIME_PROGRESS\": True,\n            \"DISPLAY_LESS_PROGRESS\": True,\n            \"OUTPUT_SUMMARY\": True,\n            \"OUTPUT_EMPTY_CLASSES\": True,  # If False, summary files are not output for classes with no detections\n            \"OUTPUT_DETAILED\": True,\n            \"PLOT_CURVES\": True,\n        }\n        return default_config\n\n    def __init__(self, config=None):\n        \"\"\"Initialise the evaluator with a config file\"\"\"\n        self.config = utils.init_config(config, self.get_default_eval_config(), \"Eval\")\n        # Only run timing analysis if not run in parallel.\n        if self.config[\"TIME_PROGRESS\"] and not self.config[\"USE_PARALLEL\"]:\n            _timing.DO_TIMING = True\n            if self.config[\"DISPLAY_LESS_PROGRESS\"]:\n                _timing.DISPLAY_LESS_PROGRESS = True\n\n    def _combine_results(\n        self,\n        res,\n        metrics_list,\n        metric_names,\n        dataset,\n        res_field=\"COMBINED_SEQ\",\n        target_tag=None,\n    ):\n        assert res_field.startswith(\"COMBINED_SEQ\")\n        # collecting combined cls keys (cls averaged, det averaged, super classes)\n        tracker_list, seq_list, class_list = dataset.get_eval_info()\n        combined_cls_keys = []\n        res[res_field] = {}\n\n        # narrow the target for evaluation\n        if target_tag is not None:\n            target_video_ids = [\n                annot[\"video_id\"]\n                for annot in dataset.gt_data[\"annotations\"]\n                if target_tag in annot[\"tags\"]\n            ]\n            vid2name = {\n                video[\"id\"]: video[\"file_names\"][0].split(\"/\")[0]\n                for video in dataset.gt_data[\"videos\"]\n            }\n            target_video_ids = set(target_video_ids)\n            target_video = [vid2name[video_id] for video_id in target_video_ids]\n\n            if len(target_video) == 0:\n                raise TrackEvalException(\n                    \"No sequences found with the tag %s\" % target_tag\n                )\n\n            target_annotations = [\n                annot\n                for annot in dataset.gt_data[\"annotations\"]\n                if annot[\"video_id\"] in target_video_ids\n            ]\n            assert all(target_tag in annot[\"tags\"] for annot in target_annotations), (\n                f\"Not all annotations in the target sequences have the target tag {target_tag}. \"\n                \"We currently only support a target tag at the sequence level, not at the annotation level.\"\n            )\n        else:\n            target_video = seq_list\n\n        # combine sequences for each class\n        for c_cls in class_list:\n            res[res_field][c_cls] = {}\n            for metric, metric_name in zip(metrics_list, metric_names):\n                curr_res = {\n                    seq_key: seq_value[c_cls][metric_name]\n                    for seq_key, seq_value in res.items()\n                    if not seq_key.startswith(\"COMBINED_SEQ\")\n                    and seq_key in target_video\n                }\n                res[res_field][c_cls][metric_name] = metric.combine_sequences(curr_res)\n        # combine classes\n        if dataset.should_classes_combine:\n            combined_cls_keys += [\n                \"cls_comb_cls_av\",\n                \"cls_comb_det_av\",\n                \"all\",\n            ]\n            res[res_field][\"cls_comb_cls_av\"] = {}\n            res[res_field][\"cls_comb_det_av\"] = {}\n            for metric, metric_name in zip(metrics_list, metric_names):\n                cls_res = {\n                    cls_key: cls_value[metric_name]\n                    for cls_key, cls_value in res[res_field].items()\n                    if cls_key not in combined_cls_keys\n                }\n                res[res_field][\"cls_comb_cls_av\"][metric_name] = (\n                    metric.combine_classes_class_averaged(cls_res)\n                )\n                res[res_field][\"cls_comb_det_av\"][metric_name] = (\n                    metric.combine_classes_det_averaged(cls_res)\n                )\n        # combine classes to super classes\n        if dataset.use_super_categories:\n            for cat, sub_cats in dataset.super_categories.items():\n                combined_cls_keys.append(cat)\n                res[res_field][cat] = {}\n                for metric, metric_name in zip(metrics_list, metric_names):\n                    cat_res = {\n                        cls_key: cls_value[metric_name]\n                        for cls_key, cls_value in res[res_field].items()\n                        if cls_key in sub_cats\n                    }\n                    res[res_field][cat][metric_name] = (\n                        metric.combine_classes_det_averaged(cat_res)\n                    )\n        return res, combined_cls_keys\n\n    def _summarize_results(\n        self,\n        res,\n        tracker,\n        metrics_list,\n        metric_names,\n        dataset,\n        res_field,\n        combined_cls_keys,\n    ):\n        config = self.config\n        output_fol = dataset.get_output_fol(tracker)\n        tracker_display_name = dataset.get_display_name(tracker)\n        for c_cls in res[\n            res_field\n        ].keys():  # class_list + combined classes if calculated\n            summaries = []\n            details = []\n            num_dets = res[res_field][c_cls][\"Count\"][\"Dets\"]\n            if config[\"OUTPUT_EMPTY_CLASSES\"] or num_dets > 0:\n                for metric, metric_name in zip(metrics_list, metric_names):\n                    # for combined classes there is no per sequence evaluation\n                    if c_cls in combined_cls_keys:\n                        table_res = {res_field: res[res_field][c_cls][metric_name]}\n                    else:\n                        table_res = {\n                            seq_key: seq_value[c_cls][metric_name]\n                            for seq_key, seq_value in res.items()\n                        }\n\n                    if config[\"PRINT_RESULTS\"] and config[\"PRINT_ONLY_COMBINED\"]:\n                        dont_print = (\n                            dataset.should_classes_combine\n                            and c_cls not in combined_cls_keys\n                        )\n                        if not dont_print:\n                            metric.print_table(\n                                {res_field: table_res[res_field]},\n                                tracker_display_name,\n                                c_cls,\n                                res_field,\n                                res_field,\n                            )\n                    elif config[\"PRINT_RESULTS\"]:\n                        metric.print_table(\n                            table_res, tracker_display_name, c_cls, res_field, res_field\n                        )\n                    if config[\"OUTPUT_SUMMARY\"]:\n                        summaries.append(metric.summary_results(table_res))\n                    if config[\"OUTPUT_DETAILED\"]:\n                        details.append(metric.detailed_results(table_res))\n                    if config[\"PLOT_CURVES\"]:\n                        metric.plot_single_tracker_results(\n                            table_res,\n                            tracker_display_name,\n                            c_cls,\n                            output_fol,\n                        )\n                if config[\"OUTPUT_SUMMARY\"]:\n                    utils.write_summary_results(summaries, c_cls, output_fol)\n                if config[\"OUTPUT_DETAILED\"]:\n                    utils.write_detailed_results(details, c_cls, output_fol)\n\n    @_timing.time\n    def evaluate(self, dataset_list, metrics_list, show_progressbar=False):\n        \"\"\"Evaluate a set of metrics on a set of datasets\"\"\"\n        config = self.config\n        metrics_list = metrics_list + [Count()]  # Count metrics are always run\n        metric_names = utils.validate_metrics_list(metrics_list)\n        dataset_names = [dataset.get_name() for dataset in dataset_list]\n        output_res = {}\n        output_msg = {}\n\n        for dataset, dataset_name in zip(dataset_list, dataset_names):\n            # Get dataset info about what to evaluate\n            output_res[dataset_name] = {}\n            output_msg[dataset_name] = {}\n            tracker_list, seq_list, class_list = dataset.get_eval_info()\n            print(\n                \"\\nEvaluating %i tracker(s) on %i sequence(s) for %i class(es) on %s dataset using the following \"\n                \"metrics: %s\\n\"\n                % (\n                    len(tracker_list),\n                    len(seq_list),\n                    len(class_list),\n                    dataset_name,\n                    \", \".join(metric_names),\n                )\n            )\n\n            # Evaluate each tracker\n            for tracker in tracker_list:\n                # if not config['BREAK_ON_ERROR'] then go to next tracker without breaking\n                try:\n                    # Evaluate each sequence in parallel or in series.\n                    # returns a nested dict (res), indexed like: res[seq][class][metric_name][sub_metric field]\n                    # e.g. res[seq_0001][pedestrian][hota][DetA]\n                    print(\"\\nEvaluating %s\\n\" % tracker)\n                    time_start = time.time()\n                    if config[\"USE_PARALLEL\"]:\n                        if show_progressbar and TQDM_IMPORTED:\n                            seq_list_sorted = sorted(seq_list)\n\n                            with (\n                                Pool(config[\"NUM_PARALLEL_CORES\"]) as pool,\n                                tqdm.tqdm(total=len(seq_list)) as pbar,\n                            ):\n                                _eval_sequence = partial(\n                                    eval_sequence,\n                                    dataset=dataset,\n                                    tracker=tracker,\n                                    class_list=class_list,\n                                    metrics_list=metrics_list,\n                                    metric_names=metric_names,\n                                )\n                                results = []\n                                for r in pool.imap(\n                                    _eval_sequence, seq_list_sorted, chunksize=20\n                                ):\n                                    results.append(r)\n                                    pbar.update()\n                                res = dict(zip(seq_list_sorted, results))\n\n                        else:\n                            with Pool(config[\"NUM_PARALLEL_CORES\"]) as pool:\n                                _eval_sequence = partial(\n                                    eval_sequence,\n                                    dataset=dataset,\n                                    tracker=tracker,\n                                    class_list=class_list,\n                                    metrics_list=metrics_list,\n                                    metric_names=metric_names,\n                                )\n                                results = pool.map(_eval_sequence, seq_list)\n                                res = dict(zip(seq_list, results))\n                    else:\n                        res = {}\n                        if show_progressbar and TQDM_IMPORTED:\n                            seq_list_sorted = sorted(seq_list)\n                            for curr_seq in tqdm.tqdm(seq_list_sorted):\n                                res[curr_seq] = eval_sequence(\n                                    curr_seq,\n                                    dataset,\n                                    tracker,\n                                    class_list,\n                                    metrics_list,\n                                    metric_names,\n                                )\n                        else:\n                            for curr_seq in sorted(seq_list):\n                                res[curr_seq] = eval_sequence(\n                                    curr_seq,\n                                    dataset,\n                                    tracker,\n                                    class_list,\n                                    metrics_list,\n                                    metric_names,\n                                )\n\n                    # Combine results over all sequences and then over all classes\n                    res, combined_cls_keys = self._combine_results(\n                        res, metrics_list, metric_names, dataset, \"COMBINED_SEQ\"\n                    )\n\n                    if np.all(\n                        [\"tags\" in annot for annot in dataset.gt_data[\"annotations\"]]\n                    ):\n                        # Combine results over the challenging sequences and then over all classes\n                        # currently only support \"tracking_challenging_pair\"\n                        res, _ = self._combine_results(\n                            res,\n                            metrics_list,\n                            metric_names,\n                            dataset,\n                            \"COMBINED_SEQ_CHALLENGING\",\n                            \"tracking_challenging_pair\",\n                        )\n\n                    # Print and output results in various formats\n                    if config[\"TIME_PROGRESS\"]:\n                        print(\n                            \"\\nAll sequences for %s finished in %.2f seconds\"\n                            % (tracker, time.time() - time_start)\n                        )\n\n                    self._summarize_results(\n                        res,\n                        tracker,\n                        metrics_list,\n                        metric_names,\n                        dataset,\n                        \"COMBINED_SEQ\",\n                        combined_cls_keys,\n                    )\n                    if \"COMBINED_SEQ_CHALLENGING\" in res:\n                        self._summarize_results(\n                            res,\n                            tracker,\n                            metrics_list,\n                            metric_names,\n                            dataset,\n                            \"COMBINED_SEQ_CHALLENGING\",\n                            combined_cls_keys,\n                        )\n\n                    # Output for returning from function\n                    output_res[dataset_name][tracker] = res\n                    output_msg[dataset_name][tracker] = \"Success\"\n\n                except Exception as err:\n                    output_res[dataset_name][tracker] = None\n                    if type(err) == TrackEvalException:\n                        output_msg[dataset_name][tracker] = str(err)\n                    else:\n                        output_msg[dataset_name][tracker] = \"Unknown error occurred.\"\n                    print(\"Tracker %s was unable to be evaluated.\" % tracker)\n                    print(err)\n                    traceback.print_exc()\n                    if config[\"LOG_ON_ERROR\"] is not None:\n                        with open(config[\"LOG_ON_ERROR\"], \"a\") as f:\n                            print(dataset_name, file=f)\n                            print(tracker, file=f)\n                            print(traceback.format_exc(), file=f)\n                            print(\"\\n\\n\\n\", file=f)\n                    if config[\"BREAK_ON_ERROR\"]:\n                        raise err\n                    elif config[\"RETURN_ON_ERROR\"]:\n                        return output_res, output_msg\n\n        return output_res, output_msg\n\n\n@_timing.time\ndef eval_sequence(seq, dataset, tracker, class_list, metrics_list, metric_names):\n    \"\"\"Function for evaluating a single sequence\"\"\"\n\n    raw_data = dataset.get_raw_seq_data(tracker, seq)\n    seq_res = {}\n    for cls in class_list:\n        seq_res[cls] = {}\n        data = dataset.get_preprocessed_seq_data(raw_data, cls)\n        for metric, met_name in zip(metrics_list, metric_names):\n            seq_res[cls][met_name] = metric.eval_sequence(data)\n    return seq_res\n"
  },
  {
    "path": "sam3/eval/hota_eval_toolkit/trackeval/metrics/__init__.py",
    "content": "# flake8: noqa\n\n# pyre-unsafe\n\nfrom .count import Count\nfrom .hota import HOTA\n"
  },
  {
    "path": "sam3/eval/hota_eval_toolkit/trackeval/metrics/_base_metric.py",
    "content": "# flake8: noqa\n\n# pyre-unsafe\n\nfrom abc import ABC, abstractmethod\n\nimport numpy as np\n\nfrom .. import _timing\nfrom ..utils import TrackEvalException\n\n\nclass _BaseMetric(ABC):\n    @abstractmethod\n    def __init__(self):\n        self.plottable = False\n        self.integer_fields = []\n        self.float_fields = []\n        self.array_labels = []\n        self.integer_array_fields = []\n        self.float_array_fields = []\n        self.fields = []\n        self.summary_fields = []\n        self.registered = False\n\n    #####################################################################\n    # Abstract functions for subclasses to implement\n\n    @_timing.time\n    @abstractmethod\n    def eval_sequence(self, data): ...\n\n    @abstractmethod\n    def combine_sequences(self, all_res): ...\n\n    @abstractmethod\n    def combine_classes_class_averaged(self, all_res, ignore_empty_classes=False): ...\n\n    @abstractmethod\n    def combine_classes_det_averaged(self, all_res): ...\n\n    def plot_single_tracker_results(self, all_res, tracker, output_folder, cls):\n        \"\"\"Plot results of metrics, only valid for metrics with self.plottable\"\"\"\n        if self.plottable:\n            raise NotImplementedError(\n                \"plot_results is not implemented for metric %s\" % self.get_name()\n            )\n        else:\n            pass\n\n    #####################################################################\n    # Helper functions which are useful for all metrics:\n\n    @classmethod\n    def get_name(cls):\n        return cls.__name__\n\n    @staticmethod\n    def _combine_sum(all_res, field):\n        \"\"\"Combine sequence results via sum\"\"\"\n        return sum([all_res[k][field] for k in all_res.keys()])\n\n    @staticmethod\n    def _combine_weighted_av(all_res, field, comb_res, weight_field):\n        \"\"\"Combine sequence results via weighted average\"\"\"\n        return sum(\n            [all_res[k][field] * all_res[k][weight_field] for k in all_res.keys()]\n        ) / np.maximum(1.0, comb_res[weight_field])\n\n    def print_table(\n        self, table_res, tracker, cls, res_field=\"COMBINED_SEQ\", output_lable=\"COMBINED\"\n    ):\n        \"\"\"Prints table of results for all sequences\"\"\"\n        print(\"\")\n        metric_name = self.get_name()\n        self._row_print(\n            [metric_name + \": \" + tracker + \"-\" + cls] + self.summary_fields\n        )\n        for seq, results in sorted(table_res.items()):\n            if seq.startswith(\"COMBINED_SEQ\"):\n                continue\n            summary_res = self._summary_row(results)\n            self._row_print([seq] + summary_res)\n        summary_res = self._summary_row(table_res[res_field])\n        self._row_print([output_lable] + summary_res)\n\n    def _summary_row(self, results_):\n        vals = []\n        for h in self.summary_fields:\n            if h in self.float_array_fields:\n                vals.append(\"{0:1.5g}\".format(100 * np.mean(results_[h])))\n            elif h in self.float_fields:\n                vals.append(\"{0:1.5g}\".format(100 * float(results_[h])))\n            elif h in self.integer_fields:\n                vals.append(\"{0:d}\".format(int(results_[h])))\n            else:\n                raise NotImplementedError(\n                    \"Summary function not implemented for this field type.\"\n                )\n        return vals\n\n    @staticmethod\n    def _row_print(*argv):\n        \"\"\"Prints results in an evenly spaced rows, with more space in first row\"\"\"\n        if len(argv) == 1:\n            argv = argv[0]\n        to_print = \"%-35s\" % argv[0]\n        for v in argv[1:]:\n            to_print += \"%-10s\" % str(v)\n        print(to_print)\n\n    def summary_results(self, table_res):\n        \"\"\"Returns a simple summary of final results for a tracker\"\"\"\n        return dict(\n            zip(self.summary_fields, self._summary_row(table_res[\"COMBINED_SEQ\"]))\n        )\n\n    def detailed_results(self, table_res):\n        \"\"\"Returns detailed final results for a tracker\"\"\"\n        # Get detailed field information\n        detailed_fields = self.float_fields + self.integer_fields\n        for h in self.float_array_fields + self.integer_array_fields:\n            for alpha in [int(100 * x) for x in self.array_labels]:\n                detailed_fields.append(h + \"___\" + str(alpha))\n            detailed_fields.append(h + \"___AUC\")\n\n        # Get detailed results\n        detailed_results = {}\n        for seq, res in table_res.items():\n            detailed_row = self._detailed_row(res)\n            if len(detailed_row) != len(detailed_fields):\n                raise TrackEvalException(\n                    \"Field names and data have different sizes (%i and %i)\"\n                    % (len(detailed_row), len(detailed_fields))\n                )\n            detailed_results[seq] = dict(zip(detailed_fields, detailed_row))\n        return detailed_results\n\n    def _detailed_row(self, res):\n        detailed_row = []\n        for h in self.float_fields + self.integer_fields:\n            detailed_row.append(res[h])\n        for h in self.float_array_fields + self.integer_array_fields:\n            for i, alpha in enumerate([int(100 * x) for x in self.array_labels]):\n                detailed_row.append(res[h][i])\n            detailed_row.append(np.mean(res[h]))\n        return detailed_row\n"
  },
  {
    "path": "sam3/eval/hota_eval_toolkit/trackeval/metrics/count.py",
    "content": "# flake8: noqa\n\n# pyre-unsafe\n\nfrom .. import _timing\nfrom ._base_metric import _BaseMetric\n\n\nclass Count(_BaseMetric):\n    \"\"\"Class which simply counts the number of tracker and gt detections and ids.\"\"\"\n\n    def __init__(self, config=None):\n        super().__init__()\n        self.integer_fields = [\"Dets\", \"GT_Dets\", \"IDs\", \"GT_IDs\"]\n        self.fields = self.integer_fields\n        self.summary_fields = self.fields\n\n    @_timing.time\n    def eval_sequence(self, data):\n        \"\"\"Returns counts for one sequence\"\"\"\n        # Get results\n        res = {\n            \"Dets\": data[\"num_tracker_dets\"],\n            \"GT_Dets\": data[\"num_gt_dets\"],\n            \"IDs\": data[\"num_tracker_ids\"],\n            \"GT_IDs\": data[\"num_gt_ids\"],\n            \"Frames\": data[\"num_timesteps\"],\n        }\n        return res\n\n    def combine_sequences(self, all_res):\n        \"\"\"Combines metrics across all sequences\"\"\"\n        res = {}\n        for field in self.integer_fields:\n            res[field] = self._combine_sum(all_res, field)\n        return res\n\n    def combine_classes_class_averaged(self, all_res, ignore_empty_classes=None):\n        \"\"\"Combines metrics across all classes by averaging over the class values\"\"\"\n        res = {}\n        for field in self.integer_fields:\n            res[field] = self._combine_sum(all_res, field)\n        return res\n\n    def combine_classes_det_averaged(self, all_res):\n        \"\"\"Combines metrics across all classes by averaging over the detection values\"\"\"\n        res = {}\n        for field in self.integer_fields:\n            res[field] = self._combine_sum(all_res, field)\n        return res\n"
  },
  {
    "path": "sam3/eval/hota_eval_toolkit/trackeval/metrics/hota.py",
    "content": "# flake8: noqa\n\n# pyre-unsafe\n\nimport os\n\nimport numpy as np\nfrom scipy.optimize import linear_sum_assignment\n\nfrom .. import _timing\nfrom ._base_metric import _BaseMetric\n\n\nclass HOTA(_BaseMetric):\n    \"\"\"Class which implements the HOTA metrics.\n    See: https://link.springer.com/article/10.1007/s11263-020-01375-2\n    \"\"\"\n\n    def __init__(self, config=None):\n        super().__init__()\n        self.plottable = True\n        self.array_labels = np.arange(0.05, 0.99, 0.05)\n        self.integer_array_fields = [\"HOTA_TP\", \"HOTA_FN\", \"HOTA_FP\"]\n        self.float_array_fields = [\n            \"HOTA\",\n            \"DetA\",\n            \"AssA\",\n            \"DetRe\",\n            \"DetPr\",\n            \"AssRe\",\n            \"AssPr\",\n            \"LocA\",\n            \"OWTA\",\n        ]\n        self.float_fields = [\"HOTA(0)\", \"LocA(0)\", \"HOTALocA(0)\"]\n        self.fields = (\n            self.float_array_fields + self.integer_array_fields + self.float_fields\n        )\n        self.summary_fields = self.float_array_fields + self.float_fields\n\n    @_timing.time\n    def eval_sequence(self, data):\n        \"\"\"Calculates the HOTA metrics for one sequence\"\"\"\n\n        # Initialise results\n        res = {}\n        for field in self.float_array_fields + self.integer_array_fields:\n            res[field] = np.zeros((len(self.array_labels)), dtype=float)\n        for field in self.float_fields:\n            res[field] = 0\n\n        # Return result quickly if tracker or gt sequence is empty\n        if data[\"num_tracker_dets\"] == 0:\n            res[\"HOTA_FN\"] = data[\"num_gt_dets\"] * np.ones(\n                (len(self.array_labels)), dtype=float\n            )\n            res[\"LocA\"] = np.ones((len(self.array_labels)), dtype=float)\n            res[\"LocA(0)\"] = 1.0\n            return res\n        if data[\"num_gt_dets\"] == 0:\n            res[\"HOTA_FP\"] = data[\"num_tracker_dets\"] * np.ones(\n                (len(self.array_labels)), dtype=float\n            )\n            res[\"LocA\"] = np.ones((len(self.array_labels)), dtype=float)\n            res[\"LocA(0)\"] = 1.0\n            return res\n\n        # Variables counting global association\n        potential_matches_count = np.zeros(\n            (data[\"num_gt_ids\"], data[\"num_tracker_ids\"])\n        )\n        gt_id_count = np.zeros((data[\"num_gt_ids\"], 1))\n        tracker_id_count = np.zeros((1, data[\"num_tracker_ids\"]))\n\n        # First loop through each timestep and accumulate global track information.\n        for t, (gt_ids_t, tracker_ids_t) in enumerate(\n            zip(data[\"gt_ids\"], data[\"tracker_ids\"])\n        ):\n            # Count the potential matches between ids in each timestep\n            # These are normalised, weighted by the match similarity.\n            similarity = data[\"similarity_scores\"][t]\n            sim_iou_denom = (\n                similarity.sum(0)[np.newaxis, :]\n                + similarity.sum(1)[:, np.newaxis]\n                - similarity\n            )\n            sim_iou = np.zeros_like(similarity)\n            sim_iou_mask = sim_iou_denom > 0 + np.finfo(\"float\").eps\n            sim_iou[sim_iou_mask] = (\n                similarity[sim_iou_mask] / sim_iou_denom[sim_iou_mask]\n            )\n            potential_matches_count[\n                gt_ids_t[:, np.newaxis], tracker_ids_t[np.newaxis, :]\n            ] += sim_iou\n\n            # Calculate the total number of dets for each gt_id and tracker_id.\n            gt_id_count[gt_ids_t] += 1\n            tracker_id_count[0, tracker_ids_t] += 1\n\n        # Calculate overall jaccard alignment score (before unique matching) between IDs\n        global_alignment_score = potential_matches_count / (\n            gt_id_count + tracker_id_count - potential_matches_count\n        )\n        matches_counts = [\n            np.zeros_like(potential_matches_count) for _ in self.array_labels\n        ]\n\n        # Calculate scores for each timestep\n        for t, (gt_ids_t, tracker_ids_t) in enumerate(\n            zip(data[\"gt_ids\"], data[\"tracker_ids\"])\n        ):\n            # Deal with the case that there are no gt_det/tracker_det in a timestep.\n            if len(gt_ids_t) == 0:\n                for a, alpha in enumerate(self.array_labels):\n                    res[\"HOTA_FP\"][a] += len(tracker_ids_t)\n                continue\n            if len(tracker_ids_t) == 0:\n                for a, alpha in enumerate(self.array_labels):\n                    res[\"HOTA_FN\"][a] += len(gt_ids_t)\n                continue\n\n            # Get matching scores between pairs of dets for optimizing HOTA\n            similarity = data[\"similarity_scores\"][t]\n            score_mat = (\n                global_alignment_score[\n                    gt_ids_t[:, np.newaxis], tracker_ids_t[np.newaxis, :]\n                ]\n                * similarity\n            )\n\n            # Hungarian algorithm to find best matches\n            match_rows, match_cols = linear_sum_assignment(-score_mat)\n\n            # Calculate and accumulate basic statistics\n            for a, alpha in enumerate(self.array_labels):\n                actually_matched_mask = (\n                    similarity[match_rows, match_cols] >= alpha - np.finfo(\"float\").eps\n                )\n                alpha_match_rows = match_rows[actually_matched_mask]\n                alpha_match_cols = match_cols[actually_matched_mask]\n                num_matches = len(alpha_match_rows)\n                res[\"HOTA_TP\"][a] += num_matches\n                res[\"HOTA_FN\"][a] += len(gt_ids_t) - num_matches\n                res[\"HOTA_FP\"][a] += len(tracker_ids_t) - num_matches\n                if num_matches > 0:\n                    res[\"LocA\"][a] += sum(\n                        similarity[alpha_match_rows, alpha_match_cols]\n                    )\n                    matches_counts[a][\n                        gt_ids_t[alpha_match_rows], tracker_ids_t[alpha_match_cols]\n                    ] += 1\n\n        # Calculate association scores (AssA, AssRe, AssPr) for the alpha value.\n        # First calculate scores per gt_id/tracker_id combo and then average over the number of detections.\n        for a, alpha in enumerate(self.array_labels):\n            matches_count = matches_counts[a]\n            ass_a = matches_count / np.maximum(\n                1, gt_id_count + tracker_id_count - matches_count\n            )\n            res[\"AssA\"][a] = np.sum(matches_count * ass_a) / np.maximum(\n                1, res[\"HOTA_TP\"][a]\n            )\n            ass_re = matches_count / np.maximum(1, gt_id_count)\n            res[\"AssRe\"][a] = np.sum(matches_count * ass_re) / np.maximum(\n                1, res[\"HOTA_TP\"][a]\n            )\n            ass_pr = matches_count / np.maximum(1, tracker_id_count)\n            res[\"AssPr\"][a] = np.sum(matches_count * ass_pr) / np.maximum(\n                1, res[\"HOTA_TP\"][a]\n            )\n\n        # Calculate final scores\n        res[\"LocA\"] = np.maximum(1e-10, res[\"LocA\"]) / np.maximum(1e-10, res[\"HOTA_TP\"])\n        res = self._compute_final_fields(res)\n        return res\n\n    def combine_sequences(self, all_res):\n        \"\"\"Combines metrics across all sequences\"\"\"\n        res = {}\n        for field in self.integer_array_fields:\n            res[field] = self._combine_sum(all_res, field)\n        for field in [\"AssRe\", \"AssPr\", \"AssA\"]:\n            res[field] = self._combine_weighted_av(\n                all_res, field, res, weight_field=\"HOTA_TP\"\n            )\n        loca_weighted_sum = sum(\n            [all_res[k][\"LocA\"] * all_res[k][\"HOTA_TP\"] for k in all_res.keys()]\n        )\n        res[\"LocA\"] = np.maximum(1e-10, loca_weighted_sum) / np.maximum(\n            1e-10, res[\"HOTA_TP\"]\n        )\n        res = self._compute_final_fields(res)\n        return res\n\n    def combine_classes_class_averaged(self, all_res, ignore_empty_classes=False):\n        \"\"\"Combines metrics across all classes by averaging over the class values.\n        If 'ignore_empty_classes' is True, then it only sums over classes with at least one gt or predicted detection.\n        \"\"\"\n        res = {}\n        for field in self.integer_array_fields:\n            if ignore_empty_classes:\n                res[field] = self._combine_sum(\n                    {\n                        k: v\n                        for k, v in all_res.items()\n                        if (\n                            v[\"HOTA_TP\"] + v[\"HOTA_FN\"] + v[\"HOTA_FP\"]\n                            > 0 + np.finfo(\"float\").eps\n                        ).any()\n                    },\n                    field,\n                )\n            else:\n                res[field] = self._combine_sum(\n                    {k: v for k, v in all_res.items()}, field\n                )\n\n        for field in self.float_fields + self.float_array_fields:\n            if ignore_empty_classes:\n                res[field] = np.mean(\n                    [\n                        v[field]\n                        for v in all_res.values()\n                        if (\n                            v[\"HOTA_TP\"] + v[\"HOTA_FN\"] + v[\"HOTA_FP\"]\n                            > 0 + np.finfo(\"float\").eps\n                        ).any()\n                    ],\n                    axis=0,\n                )\n            else:\n                res[field] = np.mean([v[field] for v in all_res.values()], axis=0)\n        return res\n\n    def combine_classes_det_averaged(self, all_res):\n        \"\"\"Combines metrics across all classes by averaging over the detection values\"\"\"\n        res = {}\n        for field in self.integer_array_fields:\n            res[field] = self._combine_sum(all_res, field)\n        for field in [\"AssRe\", \"AssPr\", \"AssA\"]:\n            res[field] = self._combine_weighted_av(\n                all_res, field, res, weight_field=\"HOTA_TP\"\n            )\n        loca_weighted_sum = sum(\n            [all_res[k][\"LocA\"] * all_res[k][\"HOTA_TP\"] for k in all_res.keys()]\n        )\n        res[\"LocA\"] = np.maximum(1e-10, loca_weighted_sum) / np.maximum(\n            1e-10, res[\"HOTA_TP\"]\n        )\n        res = self._compute_final_fields(res)\n        return res\n\n    @staticmethod\n    def _compute_final_fields(res):\n        \"\"\"Calculate sub-metric ('field') values which only depend on other sub-metric values.\n        This function is used both for both per-sequence calculation, and in combining values across sequences.\n        \"\"\"\n        res[\"DetRe\"] = res[\"HOTA_TP\"] / np.maximum(1, res[\"HOTA_TP\"] + res[\"HOTA_FN\"])\n        res[\"DetPr\"] = res[\"HOTA_TP\"] / np.maximum(1, res[\"HOTA_TP\"] + res[\"HOTA_FP\"])\n        res[\"DetA\"] = res[\"HOTA_TP\"] / np.maximum(\n            1, res[\"HOTA_TP\"] + res[\"HOTA_FN\"] + res[\"HOTA_FP\"]\n        )\n        res[\"HOTA\"] = np.sqrt(res[\"DetA\"] * res[\"AssA\"])\n        res[\"OWTA\"] = np.sqrt(res[\"DetRe\"] * res[\"AssA\"])\n\n        res[\"HOTA(0)\"] = res[\"HOTA\"][0]\n        res[\"LocA(0)\"] = res[\"LocA\"][0]\n        res[\"HOTALocA(0)\"] = res[\"HOTA(0)\"] * res[\"LocA(0)\"]\n        return res\n\n    def plot_single_tracker_results(self, table_res, tracker, cls, output_folder):\n        \"\"\"Create plot of results\"\"\"\n\n        # Only loaded when run to reduce minimum requirements\n        from matplotlib import pyplot as plt\n\n        res = table_res[\"COMBINED_SEQ\"]\n        styles_to_plot = [\"r\", \"b\", \"g\", \"b--\", \"b:\", \"g--\", \"g:\", \"m\"]\n        for name, style in zip(self.float_array_fields, styles_to_plot):\n            plt.plot(self.array_labels, res[name], style)\n        plt.xlabel(\"alpha\")\n        plt.ylabel(\"score\")\n        plt.title(tracker + \" - \" + cls)\n        plt.axis([0, 1, 0, 1])\n        legend = []\n        for name in self.float_array_fields:\n            legend += [name + \" (\" + str(np.round(np.mean(res[name]), 2)) + \")\"]\n        plt.legend(legend, loc=\"lower left\")\n        out_file = os.path.join(output_folder, cls + \"_plot.pdf\")\n        os.makedirs(os.path.dirname(out_file), exist_ok=True)\n        plt.savefig(out_file)\n        plt.savefig(out_file.replace(\".pdf\", \".png\"))\n        plt.clf()\n"
  },
  {
    "path": "sam3/eval/hota_eval_toolkit/trackeval/utils.py",
    "content": "# flake8: noqa\n\n# pyre-unsafe\n\nimport argparse\nimport csv\nimport os\nfrom collections import OrderedDict\n\n\ndef init_config(config, default_config, name=None):\n    \"\"\"Initialise non-given config values with defaults\"\"\"\n    if config is None:\n        config = default_config\n    else:\n        for k in default_config.keys():\n            if k not in config.keys():\n                config[k] = default_config[k]\n    if name and config[\"PRINT_CONFIG\"]:\n        print(\"\\n%s Config:\" % name)\n        for c in config.keys():\n            print(\"%-20s : %-30s\" % (c, config[c]))\n    return config\n\n\ndef update_config(config):\n    \"\"\"\n    Parse the arguments of a script and updates the config values for a given value if specified in the arguments.\n    :param config: the config to update\n    :return: the updated config\n    \"\"\"\n    parser = argparse.ArgumentParser()\n    for setting in config.keys():\n        if type(config[setting]) == list or type(config[setting]) == type(None):\n            parser.add_argument(\"--\" + setting, nargs=\"+\")\n        else:\n            parser.add_argument(\"--\" + setting)\n    args = parser.parse_args().__dict__\n    for setting in args.keys():\n        if args[setting] is not None:\n            if type(config[setting]) == type(True):\n                if args[setting] == \"True\":\n                    x = True\n                elif args[setting] == \"False\":\n                    x = False\n                else:\n                    raise Exception(\n                        \"Command line parameter \" + setting + \"must be True or False\"\n                    )\n            elif type(config[setting]) == type(1):\n                x = int(args[setting])\n            elif type(args[setting]) == type(None):\n                x = None\n            else:\n                x = args[setting]\n            config[setting] = x\n    return config\n\n\ndef get_code_path():\n    \"\"\"Get base path where code is\"\"\"\n    return os.path.abspath(os.path.join(os.path.dirname(__file__), \"..\"))\n\n\ndef validate_metrics_list(metrics_list):\n    \"\"\"Get names of metric class and ensures they are unique, further checks that the fields within each metric class\n    do not have overlapping names.\n    \"\"\"\n    metric_names = [metric.get_name() for metric in metrics_list]\n    # check metric names are unique\n    if len(metric_names) != len(set(metric_names)):\n        raise TrackEvalException(\n            \"Code being run with multiple metrics of the same name\"\n        )\n    fields = []\n    for m in metrics_list:\n        fields += m.fields\n    # check metric fields are unique\n    if len(fields) != len(set(fields)):\n        raise TrackEvalException(\n            \"Code being run with multiple metrics with fields of the same name\"\n        )\n    return metric_names\n\n\ndef write_summary_results(summaries, cls, output_folder):\n    \"\"\"Write summary results to file\"\"\"\n\n    fields = sum([list(s.keys()) for s in summaries], [])\n    values = sum([list(s.values()) for s in summaries], [])\n\n    # In order to remain consistent upon new fields being adding, for each of the following fields if they are present\n    # they will be output in the summary first in the order below. Any further fields will be output in the order each\n    # metric family is called, and within each family either in the order they were added to the dict (python >= 3.6) or\n    # randomly (python < 3.6).\n    default_order = [\n        \"HOTA\",\n        \"DetA\",\n        \"AssA\",\n        \"DetRe\",\n        \"DetPr\",\n        \"AssRe\",\n        \"AssPr\",\n        \"LocA\",\n        \"OWTA\",\n        \"HOTA(0)\",\n        \"LocA(0)\",\n        \"HOTALocA(0)\",\n        \"MOTA\",\n        \"MOTP\",\n        \"MODA\",\n        \"CLR_Re\",\n        \"CLR_Pr\",\n        \"MTR\",\n        \"PTR\",\n        \"MLR\",\n        \"CLR_TP\",\n        \"CLR_FN\",\n        \"CLR_FP\",\n        \"IDSW\",\n        \"MT\",\n        \"PT\",\n        \"ML\",\n        \"Frag\",\n        \"sMOTA\",\n        \"IDF1\",\n        \"IDR\",\n        \"IDP\",\n        \"IDTP\",\n        \"IDFN\",\n        \"IDFP\",\n        \"Dets\",\n        \"GT_Dets\",\n        \"IDs\",\n        \"GT_IDs\",\n    ]\n    default_ordered_dict = OrderedDict(\n        zip(default_order, [None for _ in default_order])\n    )\n    for f, v in zip(fields, values):\n        default_ordered_dict[f] = v\n    for df in default_order:\n        if default_ordered_dict[df] is None:\n            del default_ordered_dict[df]\n    fields = list(default_ordered_dict.keys())\n    values = list(default_ordered_dict.values())\n\n    out_file = os.path.join(output_folder, cls + \"_summary.txt\")\n    os.makedirs(os.path.dirname(out_file), exist_ok=True)\n    with open(out_file, \"w\", newline=\"\") as f:\n        writer = csv.writer(f, delimiter=\" \")\n        writer.writerow(fields)\n        writer.writerow(values)\n\n\ndef write_detailed_results(details, cls, output_folder):\n    \"\"\"Write detailed results to file\"\"\"\n    sequences = details[0].keys()\n    fields = [\"seq\"] + sum([list(s[\"COMBINED_SEQ\"].keys()) for s in details], [])\n    out_file = os.path.join(output_folder, cls + \"_detailed.csv\")\n    os.makedirs(os.path.dirname(out_file), exist_ok=True)\n    with open(out_file, \"w\", newline=\"\") as f:\n        writer = csv.writer(f)\n        writer.writerow(fields)\n        for seq in sorted(sequences):\n            if seq == \"COMBINED_SEQ\":\n                continue\n            writer.writerow([seq] + sum([list(s[seq].values()) for s in details], []))\n        writer.writerow(\n            [\"COMBINED\"] + sum([list(s[\"COMBINED_SEQ\"].values()) for s in details], [])\n        )\n\n\ndef load_detail(file):\n    \"\"\"Loads detailed data for a tracker.\"\"\"\n    data = {}\n    with open(file) as f:\n        for i, row_text in enumerate(f):\n            row = row_text.replace(\"\\r\", \"\").replace(\"\\n\", \"\").split(\",\")\n            if i == 0:\n                keys = row[1:]\n                continue\n            current_values = row[1:]\n            seq = row[0]\n            if seq == \"COMBINED\":\n                seq = \"COMBINED_SEQ\"\n            if (len(current_values) == len(keys)) and seq != \"\":\n                data[seq] = {}\n                for key, value in zip(keys, current_values):\n                    data[seq][key] = float(value)\n    return data\n\n\nclass TrackEvalException(Exception):\n    \"\"\"Custom exception for catching expected errors.\"\"\"\n\n    ...\n"
  },
  {
    "path": "sam3/eval/postprocessors.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n\n\"\"\"Postprocessors class to transform MDETR output according to the downstream task\"\"\"\n\nimport dataclasses\nimport logging\nfrom collections import defaultdict\nfrom typing import Dict, List, Optional\n\nimport numpy as np\nimport torch\nfrom sam3.model import box_ops\nfrom sam3.model.data_misc import BatchedInferenceMetadata, interpolate\nfrom sam3.train.masks_ops import rle_encode, robust_rle_encode\nfrom torch import nn\n\n\nclass PostProcessNullOp(nn.Module):\n    def __init__(self, **kwargs):\n        super(PostProcessNullOp).__init__()\n        pass\n\n    def forward(self, input):\n        pass\n\n    def process_results(self, **kwargs):\n        return kwargs[\"find_stages\"]\n\n\nclass PostProcessImage(nn.Module):\n    \"\"\"This module converts the model's output into the format expected by the coco api\"\"\"\n\n    def __init__(\n        self,\n        max_dets_per_img: int,\n        iou_type=\"bbox\",\n        to_cpu: bool = True,\n        use_original_ids: bool = False,\n        use_original_sizes_box: bool = False,\n        use_original_sizes_mask: bool = False,\n        convert_mask_to_rle: bool = False,\n        always_interpolate_masks_on_gpu: bool = True,\n        use_presence: bool = True,\n        detection_threshold: float = -1.0,\n    ) -> None:\n        super().__init__()\n        self.max_dets_per_img = max_dets_per_img\n        self.iou_type = iou_type\n        self.to_cpu = to_cpu\n        self.convert_mask_to_rle = convert_mask_to_rle\n        self.always_interpolate_masks_on_gpu = always_interpolate_masks_on_gpu\n\n        self.use_presence = use_presence\n        self.detection_threshold = detection_threshold\n        self.use_original_ids = use_original_ids\n        self.use_original_sizes_box = use_original_sizes_box\n        self.use_original_sizes_mask = use_original_sizes_mask\n\n    @torch.no_grad()\n    def forward(\n        self,\n        outputs,\n        target_sizes_boxes,\n        target_sizes_masks,\n        forced_labels=None,\n        consistent=False,\n        ret_tensordict: bool = False,  # This is experimental\n    ):\n        \"\"\"Perform the computation\n        Parameters:\n            outputs: raw outputs of the model\n            target_sizes_boxes: tensor of dimension [batch_size x 2] containing the size of each images of the batch\n                          For evaluation, this must be the original image size (before any data augmentation)\n                          For visualization, this should be the image size after data augment, but before padding\n            target_sizes_masks: same but used to resize masks\n            forced_labels: tensor of dimension [batch_size] containing the label to force for each image of the batch\n                           This is useful when evaluating the model using standard metrics (eg on COCO, LVIS). In that case,\n                           we query the model with every possible class label, so we when we pass the predictions to the evaluator,\n                           we want to make sure that the predicted \"class\" matches the one that was queried.\n            consistent: whether all target sizes are equal\n            ret_tensordict: Experimental argument. If true, return a tensordict.TensorDict instead of a list of dictionaries for easier manipulation.\n        \"\"\"\n        if ret_tensordict:\n            assert consistent is True, (\n                \"We don't support returning TensorDict if the outputs have different shapes\"\n            )  # NOTE: It's possible but we don't support it.\n            assert self.detection_threshold <= 0.0, \"TODO: implement?\"\n            try:\n                from tensordict import TensorDict\n            except ImportError:\n                logging.info(\n                    \"tensordict is not installed. Install by running `pip install tensordict --no-deps`. Falling back by setting `ret_tensordict=False`\"\n                )\n                ret_tensordict = False\n\n        out_bbox = outputs[\"pred_boxes\"] if \"pred_boxes\" in outputs else None\n        out_logits = outputs[\"pred_logits\"]\n        pred_masks = outputs[\"pred_masks\"] if self.iou_type == \"segm\" else None\n        out_probs = out_logits.sigmoid()\n        if self.use_presence:\n            presence_score = outputs[\"presence_logit_dec\"].sigmoid().unsqueeze(1)\n            out_probs = out_probs * presence_score\n\n        assert target_sizes_boxes.shape[1] == 2\n        assert target_sizes_masks.shape[1] == 2\n        batch_size = target_sizes_boxes.shape[0]\n\n        boxes, scores, labels, keep = self._process_boxes_and_labels(\n            target_sizes_boxes, forced_labels, out_bbox, out_probs\n        )\n        assert boxes is None or len(boxes) == batch_size\n        out_masks = self._process_masks(\n            target_sizes_masks, pred_masks, consistent=consistent, keep=keep\n        )\n        del pred_masks\n\n        if boxes is None:\n            assert out_masks is not None\n            assert not ret_tensordict, (\n                \"We don't support returning TensorDict if the output does not contain boxes\"\n            )\n            B = len(out_masks)\n            boxes = [None] * B\n            scores = [None] * B\n            labels = [None] * B\n\n        results = {\n            \"scores\": scores,\n            \"labels\": labels,\n            \"boxes\": boxes,\n        }\n        if out_masks is not None:\n            if self.convert_mask_to_rle:\n                results.update(masks_rle=out_masks)\n            else:\n                results.update(masks=out_masks)\n\n        if ret_tensordict:\n            results = TensorDict(results).auto_batch_size_()\n            if self.to_cpu:\n                results = results.cpu()\n        else:\n            # Convert a dictonary of lists/tensors to list of dictionaries\n            results = [\n                dict(zip(results.keys(), res_tuple))\n                for res_tuple in zip(*results.values())\n            ]\n\n        return results\n\n    def _process_masks(self, target_sizes, pred_masks, consistent=True, keep=None):\n        if pred_masks is None:\n            return None\n        if self.always_interpolate_masks_on_gpu:\n            gpu_device = target_sizes.device\n            assert gpu_device.type == \"cuda\"\n            pred_masks = pred_masks.to(device=gpu_device)\n        if consistent:\n            assert keep is None, \"TODO: implement?\"\n            # All masks should have the same shape, expected when processing a batch of size 1\n            target_size = target_sizes.unique(dim=0)\n            assert target_size.size(0) == 1, \"Expecting all target sizes to be equal\"\n            out_masks = (\n                interpolate(\n                    pred_masks,\n                    target_size.squeeze().tolist(),\n                    mode=\"bilinear\",\n                    align_corners=False,\n                ).sigmoid()\n                > 0.5\n            )\n            if self.convert_mask_to_rle:\n                raise RuntimeError(\"TODO: implement?\")\n            if self.to_cpu:\n                out_masks = out_masks.cpu()\n        else:\n            out_masks = [[]] * len(pred_masks)\n\n            assert keep is None or len(keep) == len(pred_masks)\n            for i, mask in enumerate(pred_masks):\n                h, w = target_sizes[i]\n                if keep is not None:\n                    mask = mask[keep[i]]\n                # Uses the gpu version fist, moves masks to cpu if it fails\"\"\"\n                try:\n                    interpolated = (\n                        interpolate(\n                            mask.unsqueeze(1),\n                            (h, w),\n                            mode=\"bilinear\",\n                            align_corners=False,\n                        ).sigmoid()\n                        > 0.5\n                    )\n                except Exception as e:\n                    logging.info(\"Issue found, reverting to CPU mode!\")\n                    mask_device = mask.device\n                    mask = mask.cpu()\n                    interpolated = (\n                        interpolate(\n                            mask.unsqueeze(1),\n                            (h, w),\n                            mode=\"bilinear\",\n                            align_corners=False,\n                        ).sigmoid()\n                        > 0.5\n                    )\n                    interpolated = interpolated.to(mask_device)\n\n                if self.convert_mask_to_rle:\n                    out_masks[i] = robust_rle_encode(interpolated.squeeze(1))\n                else:\n                    out_masks[i] = interpolated\n                    if self.to_cpu:\n                        out_masks[i] = out_masks[i].cpu()\n\n        return out_masks\n\n    def _process_boxes_and_labels(\n        self, target_sizes, forced_labels, out_bbox, out_probs\n    ):\n        if out_bbox is None:\n            return None, None, None, None\n        assert len(out_probs) == len(target_sizes)\n        if self.to_cpu:\n            out_probs = out_probs.cpu()\n        scores, labels = out_probs.max(-1)\n        if forced_labels is None:\n            labels = torch.ones_like(labels)\n        else:\n            labels = forced_labels[:, None].expand_as(labels)\n\n        # convert to [x0, y0, x1, y1] format\n        boxes = box_ops.box_cxcywh_to_xyxy(out_bbox)\n\n        img_h, img_w = target_sizes.unbind(1)\n        scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1)\n        boxes = boxes * scale_fct[:, None, :]\n\n        if self.to_cpu:\n            boxes = boxes.cpu()\n\n        keep = None\n        if self.detection_threshold > 0:\n            # Filter out the boxes with scores below the detection threshold\n            keep = scores > self.detection_threshold\n            assert len(keep) == len(boxes) == len(scores) == len(labels)\n\n            boxes = [b[k.to(b.device)] for b, k in zip(boxes, keep)]\n            scores = [s[k.to(s.device)] for s, k in zip(scores, keep)]\n            labels = [l[k.to(l.device)] for l, k in zip(labels, keep)]\n\n        return boxes, scores, labels, keep\n\n    def process_results(\n        self, find_stages, find_metadatas: List[BatchedInferenceMetadata], **kwargs\n    ):\n        if find_stages.loss_stages is not None:\n            find_metadatas = [find_metadatas[i] for i in find_stages.loss_stages]\n        assert len(find_stages) == len(find_metadatas)\n        results = {}\n        for outputs, meta in zip(find_stages, find_metadatas):\n            img_size_for_boxes = (\n                meta.original_size\n                if self.use_original_sizes_box\n                else torch.ones_like(meta.original_size)\n            )\n            img_size_for_masks = (\n                meta.original_size\n                if self.use_original_sizes_mask\n                else torch.ones_like(meta.original_size)\n            )\n            detection_results = self(\n                outputs,\n                img_size_for_boxes,\n                img_size_for_masks,\n                forced_labels=(\n                    meta.original_category_id if self.use_original_ids else None\n                ),\n            )\n            ids = (\n                meta.original_image_id if self.use_original_ids else meta.coco_image_id\n            )\n            assert len(detection_results) == len(ids)\n            for img_id, result in zip(ids, detection_results):\n                if img_id.item() not in results:\n                    results[img_id.item()] = result\n                else:\n                    assert set(results[img_id.item()].keys()) == set(result.keys())\n                    for k in result.keys():\n                        if isinstance(result[k], torch.Tensor):\n                            results[img_id.item()][k] = torch.cat(\n                                [results[img_id.item()][k], result[k]], dim=0\n                            )\n                        elif isinstance(result[k], list):\n                            results[img_id.item()][k] += result[k]\n                        else:\n                            raise NotImplementedError(\n                                f\"Unexpected type {type(result[k])} in result.\"\n                            )\n        # Prune the results to the max number of detections per image.\n        for img_id, result in results.items():\n            if (\n                self.max_dets_per_img > 0\n                and len(result[\"scores\"]) > self.max_dets_per_img\n            ):\n                _, topk_indexes = torch.topk(\n                    result[\"scores\"], self.max_dets_per_img, dim=0\n                )\n                if self.to_cpu:\n                    topk_indexes = topk_indexes.cpu()\n                for k in result.keys():\n                    if isinstance(results[img_id][k], list):\n                        results[img_id][k] = [\n                            results[img_id][k][i] for i in topk_indexes.tolist()\n                        ]\n                    else:\n                        results[img_id][k] = results[img_id][k].to(topk_indexes.device)[\n                            topk_indexes\n                        ]\n\n        return results\n\n\nclass PostProcessAPIVideo(PostProcessImage):\n    \"\"\"This module converts the video model's output into the format expected by the YT-VIS api\"\"\"\n\n    def __init__(\n        self,\n        *args,\n        to_cpu: bool = True,\n        convert_mask_to_rle: bool = False,\n        always_interpolate_masks_on_gpu: bool = True,\n        prob_thresh: float = 0.5,\n        use_presence: bool = False,\n        **kwargs,\n    ):\n        super().__init__(\n            *args,\n            # Here we always set `convert_mask_to_rle=False` in the base `PostProcessAPI` class\n            # (so that its `_process_masks` won't return a list of RLEs). If we want to return\n            # RLEs for video masklets, we handle it in this `PostProcessAPIVideo` class instead.\n            convert_mask_to_rle=False,\n            # Here we always set `to_cpu=False` in the base `PostProcessAPI` class (so that\n            # the interpolated masks won't be automatically moved back to CPU). We will handle\n            # it in this `PostProcessAPIVideo` class instead.\n            always_interpolate_masks_on_gpu=always_interpolate_masks_on_gpu,\n            use_presence=use_presence,\n            **kwargs,\n        )\n        # Expected keys in the output dict to postprocess\n        self.EXPECTED_KEYS = [\n            \"pred_logits\",\n            \"pred_boxes\",\n            \"pred_masks\",\n        ]\n        # Whether to post-process video masklets (under packed representation) into RLE format\n        self.convert_mask_to_rle_for_video = convert_mask_to_rle\n        self.to_cpu_for_video = to_cpu\n        self.prob_thresh = prob_thresh\n\n    def process_results(\n        self, find_stages, find_metadatas: List[BatchedInferenceMetadata], **kwargs\n    ):\n        \"\"\"\n        Tracking Postprocessor for SAM 3 video model.\n        This function takes in the output of the SAM 3 video model and processes it to extract all the tracklet predictions.\n        Args:\n            find_stages: A list of tensors representing the output of the SAM 3 video model.\n            find_metadatas: A list of BatchedInferenceMetadata objects containing metadata about each frame.\n            **kwargs: Additional keyword arguments.\n        Returns:\n            A dictionary of predcitions with video_id as key.\n        \"\"\"\n\n        # Import tensordict here to avoid global dependency.\n        try:\n            from tensordict import TensorDict\n        except ImportError as e:\n            logging.error(\n                \"tensordict is not installed, please install by running `pip install tensordict --no-deps`\"\n            )\n            raise e\n        # Notes and assumptions:\n        # 1- This postprocessor assumes results only for a single video.\n        # 2- There are N stage outputs corresponding to N video frames\n        # 3- Each stage outputs contains PxQ preds, where P is number of prompts and Q is number of object queries. The output should also contain the tracking object ids corresponding to each object query.\n        # 4- The tracking object id has a default value of -1, indicating that the object query is not tracking any object in the frame, and hence its predictions can be ingored for a given frame.\n        # 5- Some objects may be tracked in a subset of frames only. So, we first extract the predictions in a packed representation (for efficient postprocessing -- specially memory)\n        # and then we convert the packed representation into a padded one, where we zero pad boxes/masks for objects that are not tracked in some frames.\n        # 6- We refer to objects by an object id, which is a tuple (prompt_idx, obj_id)\n\n        assert len(find_stages) > 0, \"There is nothing to postprocess?\"\n        PROMPT_AXIS, OBJ_QUERY_AXIS = (0, 1)\n        NO_OBJ_ID = -1\n        # Maps object ID -> [indices in packed tensor]\n        tracked_objects_packed_idx = defaultdict(list)\n        # Maps object ID -> [indices in padded tensor (abs frame index)]\n        tracked_objects_frame_idx = defaultdict(list)\n        total_num_preds = 0\n        # This will hold the packed representation of predictions.\n        vid_preds_packed: List[TensorDict] = []\n        vid_masklets_rle_packed: List[Optional[Dict]] = []\n        video_id = -1  # We assume single video postprocessing, this ID should be unique in the datapoint.\n\n        for frame_idx, (frame_outs, meta) in enumerate(\n            zip(find_stages, find_metadatas)\n        ):\n            # only store keys we need to extract the results\n            frame_outs_td = TensorDict(\n                {k: frame_outs[k] for k in self.EXPECTED_KEYS}\n            ).auto_batch_size_()  # Shape is [P,Q,...]\n            meta_td = TensorDict(\n                dataclasses.asdict(meta)\n            ).auto_batch_size_()  # Shape is [P,...]\n            unique_vid_id = meta.original_image_id.unique()\n            assert unique_vid_id.size(0) == 1\n            if video_id == -1:\n                video_id = unique_vid_id.item()\n            else:\n                assert video_id == unique_vid_id.item(), (\n                    \"We can only postprocess one video per datapoint\"\n                )\n            # keeping track of which objects appear in the current frame\n            obj_ids_per_frame = frame_outs[\"pred_object_ids\"]\n            assert obj_ids_per_frame.size(-1) == frame_outs[\"pred_logits\"].size(-2)\n            if self.prob_thresh is not None:\n                # only keep the predictions on this frame with probability above the threshold\n                # (remove those predictions during the keep-alive period of a tracking query,\n                # where its \"pred_object_ids\" is still the tracked object ID rather than -1)\n                pred_probs = frame_outs[\"pred_logits\"].sigmoid().squeeze(-1)\n                obj_ids_per_frame = torch.where(\n                    pred_probs >= self.prob_thresh, obj_ids_per_frame, NO_OBJ_ID\n                )\n            tracked_obj_ids_idx = torch.where(obj_ids_per_frame != NO_OBJ_ID)\n            # Object id is a tuple of (prompt_idx, obj_id). This is because the model can assign same obj_id for two different prompts.\n            tracked_obj_ids = [\n                (p_id.item(), obj_ids_per_frame[p_id, q_id].item())\n                for p_id, q_id in zip(\n                    tracked_obj_ids_idx[PROMPT_AXIS],\n                    tracked_obj_ids_idx[OBJ_QUERY_AXIS],\n                )\n            ]\n            if len(tracked_obj_ids) == 0:\n                continue\n            # For each object, we keep track of the packed and padded (frame index) indices\n            for oid in tracked_obj_ids:\n                tracked_objects_packed_idx[oid].append(total_num_preds)\n                tracked_objects_frame_idx[oid].append(frame_idx)\n                total_num_preds += 1\n\n            # Since we have P*Q masks per frame, mask interpolation is the GPU memory bottleneck or time bottleneck in case of cpu processing.\n            # Instead, we first extract results only for tracked objects, reducing the number of masks to K = sum_i(tracked_objs_per_ith_prompt), hopefully <<< P*Q\n            tracked_objs_outs_td = frame_outs_td[\n                tracked_obj_ids_idx\n            ]  # [P,Q,...] --> [K,...]\n            meta_td = meta_td[tracked_obj_ids_idx[PROMPT_AXIS].cpu()]\n            if self.always_interpolate_masks_on_gpu:\n                gpu_device = meta_td[\"original_size\"].device\n                assert gpu_device.type == \"cuda\"\n                tracked_objs_outs_td = tracked_objs_outs_td.to(device=gpu_device)\n            frame_results_td = self(\n                tracked_objs_outs_td.unsqueeze(1),\n                (\n                    meta_td[\"original_size\"]\n                    if self.use_original_sizes\n                    else torch.ones_like(meta_td[\"original_size\"])\n                ),\n                forced_labels=(\n                    meta_td[\"original_category_id\"] if self.use_original_ids else None\n                ),\n                consistent=True,\n                ret_tensordict=True,\n            ).squeeze(1)\n            del tracked_objs_outs_td\n\n            # Optionally, remove \"masks\" from output tensor dict and directly encode them\n            # to RLE format under packed representations\n            if self.convert_mask_to_rle_for_video:\n                interpolated_binary_masks = frame_results_td.pop(\"masks\")\n                rle_list = rle_encode(interpolated_binary_masks, return_areas=True)\n                vid_masklets_rle_packed.extend(rle_list)\n            # Optionally, move output TensorDict to CPU (do this after RLE encoding step above)\n            if self.to_cpu_for_video:\n                frame_results_td = frame_results_td.cpu()\n            vid_preds_packed.append(frame_results_td)\n\n        if len(vid_preds_packed) == 0:\n            logging.debug(f\"Video {video_id} has no predictions\")\n            return {video_id: []}\n\n        vid_preds_packed = torch.cat(vid_preds_packed, dim=0)\n        ############### Construct a padded representation of the predictions ###############\n        num_preds = len(tracked_objects_packed_idx)\n        num_frames = len(find_stages)\n        # We zero pad any missing prediction\n        # NOTE: here, we also have padded tensors for \"scores\" and \"labels\", but we overwrite them later.\n        padded_frames_results = TensorDict(\n            {\n                k: torch.zeros(\n                    num_preds, num_frames, *v.shape[1:], device=v.device, dtype=v.dtype\n                )\n                for k, v in vid_preds_packed.items()\n            },\n            batch_size=[\n                num_preds,\n                num_frames,\n            ],\n        )\n        padded_frames_results[\"scores\"][...] = -1e8  # a very low score for empty object\n        # Track scores and labels of each pred tracklet, only for frames where the model was able to track that object\n        tracklet_scores = []\n        tracklet_labels = []\n        # Optionally, fill the list of RLEs for masklets\n        # note: only frames with actual predicted masks (in packed format) will be\n        # filled with RLEs; the rest will remains None in results[\"masks_rle\"]\n        if self.convert_mask_to_rle_for_video:\n            vid_masklets_rle_padded = [[None] * num_frames for _ in range(num_preds)]\n        for o_idx, oid in enumerate(tracked_objects_packed_idx):\n            oid2packed_idx = tracked_objects_packed_idx[oid]\n            oid2padded_idx = tracked_objects_frame_idx[oid]\n            obj_packed_results = vid_preds_packed[oid2packed_idx]\n            padded_frames_results[o_idx][oid2padded_idx] = obj_packed_results\n            if self.convert_mask_to_rle_for_video:\n                for packed_idx, padded_idx in zip(oid2packed_idx, oid2padded_idx):\n                    vid_masklets_rle_padded[o_idx][padded_idx] = (\n                        vid_masklets_rle_packed[packed_idx]\n                    )\n            # NOTE: We need a single confidence score per tracklet for the mAP metric.\n            # We use the average confidence score across time. (How does this impact AP?)\n            tracklet_scores.append(obj_packed_results[\"scores\"].mean())\n            # We also need to have a unique category Id per tracklet.\n            # This is not a problem for phrase AP, however, for mAP we do majority voting across time.\n            tracklet_labels.append(obj_packed_results[\"labels\"].mode()[0])\n\n        results = padded_frames_results.to_dict()\n        results[\"scores\"] = torch.stack(tracklet_scores, dim=0)\n        results[\"labels\"] = torch.stack(tracklet_labels, dim=0)\n        if self.convert_mask_to_rle_for_video:\n            results[\"masks_rle\"] = vid_masklets_rle_padded\n        # we keep the frame-level scores since it's needed by some evaluation scripts\n        results[\"per_frame_scores\"] = padded_frames_results[\"scores\"]\n\n        return {video_id: results}\n\n\nclass PostProcessTracking(PostProcessImage):\n    \"\"\"This module converts the model's output into the format expected by the coco api\"\"\"\n\n    def __init__(\n        self,\n        max_dets_per_img: int,\n        iou_type=\"bbox\",\n        force_single_mask: bool = False,\n        **kwargs,\n    ) -> None:\n        super().__init__(max_dets_per_img=max_dets_per_img, iou_type=iou_type, **kwargs)\n        self.force_single_mask = force_single_mask\n\n    def process_results(\n        self, find_stages, find_metadatas: BatchedInferenceMetadata, **kwargs\n    ):\n        assert len(find_stages) == len(find_metadatas)\n        results = {}\n        for outputs, meta in zip(find_stages, find_metadatas):\n            if self.force_single_mask:\n                scores, labels = outputs[\"pred_logits\"].max(-1)\n                m = []\n                for i in range(len(outputs[\"pred_masks\"])):\n                    score, idx = scores[i].max(0)\n                    m.append(outputs[\"pred_masks\"][i][idx])\n                outputs[\"pred_masks\"] = torch.stack(m, 0).unsqueeze(1)\n            detection_results = self(outputs, meta.original_size, consistent=False)\n            assert len(detection_results) == len(meta.coco_image_id)\n            results.update(\n                {\n                    (media_id.item(), object_id.item(), frame_index.item()): result\n                    for media_id, object_id, frame_index, result in zip(\n                        meta.original_image_id,\n                        meta.object_id,\n                        meta.frame_index,\n                        detection_results,\n                    )\n                }\n            )\n        return results\n\n\nclass PostProcessCounting(nn.Module):\n    \"\"\"This module converts the model's output to be evaluated for counting tasks\"\"\"\n\n    def __init__(\n        self,\n        use_original_ids: bool = False,\n        threshold: float = 0.5,\n        use_presence: bool = False,\n    ) -> None:\n        \"\"\"\n        Args:\n            use_original_ids: whether to use the original image ids or the coco ids\n            threshold: threshold for counting (values above this are counted)\n        \"\"\"\n        super().__init__()\n        self.use_original_ids = use_original_ids\n        self.threshold = threshold\n        self.use_presence = use_presence\n\n    def forward(self, outputs, target_sizes):\n        \"\"\"Perform the computation\n        Parameters:\n            outputs: raw outputs of the model\n            target_sizes: tensor of dimension [batch_size x 2] containing the size of each images of the batch\n        \"\"\"\n        # Extract scores from model outputs and apply sigmoid\n        scores = torch.sigmoid(outputs[\"pred_logits\"]).squeeze(-1)  # [B, N]\n        if self.use_presence:\n            presence_score = outputs[\"presence_logit_dec\"].sigmoid()\n            if presence_score.ndim == 1:\n                presence_score = presence_score.unsqueeze(1)  # [B, 1]\n            scores = scores * presence_score  # [B, N]\n\n        # Calculate counts by summing values above threshold\n        counts = (scores > self.threshold).float().sum(dim=1)\n\n        assert len(counts) == len(target_sizes)\n        results = []\n        for count in counts:\n            results.append({\"count\": count.item()})\n\n        return results\n\n    @torch.no_grad()\n    def process_results(\n        self, find_stages, find_metadatas: List[BatchedInferenceMetadata], **kwargs\n    ):\n        assert len(find_stages) == len(find_metadatas)\n        results = {}\n        for outputs, meta in zip(find_stages, find_metadatas):\n            detection_results = self(\n                outputs,\n                meta.original_size,\n            )\n            ids = (\n                meta.original_image_id if self.use_original_ids else meta.coco_image_id\n            )\n            assert len(detection_results) == len(ids)\n            for img_id, result in zip(ids, detection_results):\n                results[img_id.item()] = result\n\n        return results\n"
  },
  {
    "path": "sam3/eval/saco_veval_eval.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\nimport argparse\nimport json\nimport os\nfrom collections import defaultdict\n\nfrom iopath.common.file_io import g_pathmgr\nfrom sam3.eval.saco_veval_evaluators import (\n    VideoCGF1Evaluator,\n    VideoPhraseApEvaluator,\n    VideoPhraseHotaEvaluator,\n    VideoTetaEvaluator,\n    YTVISPredFileEvaluator,\n)\n\n\nclass VEvalEvaluator:\n    def __init__(self, gt_annot_file: str, eval_res_file: str):\n        self.gt_annot_file = gt_annot_file\n        self.eval_res_file = eval_res_file\n        self.evaluators = [\n            # mAP\n            YTVISPredFileEvaluator(gt_annot_file),\n            # Phrase AP\n            VideoPhraseApEvaluator(gt_annot_file),\n            # TETA\n            VideoTetaEvaluator(gt_annot_file, use_mask=True, is_exhaustive=True),\n            # HOTA\n            VideoPhraseHotaEvaluator(gt_annot_file),\n            # cgF1\n            VideoCGF1Evaluator(gt_annot_file),\n        ]\n\n    def run_eval(self, pred_file: str):\n        dataset_results = {}\n        video_np_results = defaultdict(dict)\n        for evaluator in self.evaluators:\n            d_res, v_np_res = evaluator.evaluate(pred_file)\n            dataset_results.update(d_res)\n            for (video_id, category_id), res in v_np_res.items():\n                video_np_results[(video_id, category_id)].update(res)\n\n        if len(dataset_results) == 0:\n            dataset_results = {\"\": 0.0}\n\n        formatted_video_np_results = [\n            {\"video_id\": video_id, \"category_id\": category_id, **res}\n            for (video_id, category_id), res in video_np_results.items()\n        ]\n        eval_metrics = {\n            \"dataset_results\": dataset_results,\n            \"video_np_results\": formatted_video_np_results,\n        }\n\n        with g_pathmgr.open(self.eval_res_file, \"w\") as f:\n            json.dump(eval_metrics, f)\n\n        return eval_metrics\n\n\ndef run_main_all(dataset_name, args):\n    gt_annot_file = os.path.join(args.gt_annot_dir, dataset_name + \".json\")\n    pred_file = os.path.join(args.pred_dir, dataset_name + \"_preds.json\")\n    eval_res_file = os.path.join(args.eval_res_dir, dataset_name + \"_eval_res.json\")\n    print(f\"=== Running evaluation for Pred {pred_file} vs GT {gt_annot_file} ===\")\n    veval_evaluator = VEvalEvaluator(\n        gt_annot_file=gt_annot_file, eval_res_file=eval_res_file\n    )\n    _ = veval_evaluator.run_eval(pred_file=pred_file)\n\n    print(f\"=== Results saved to {eval_res_file} ===\")\n\n\ndef main_all(args):\n    saco_veval_dataset_names = [\n        \"saco_veval_sav_test\",\n        \"saco_veval_sav_val\",\n        \"saco_veval_yt1b_test\",\n        \"saco_veval_yt1b_val\",\n        \"saco_veval_smartglasses_test\",\n        \"saco_veval_smartglasses_val\",\n    ]\n\n    # multiprocessing may not really work as inner evaluator also using multiprocessing\n    # so we just for loop\n    for dataset_name in saco_veval_dataset_names:\n        print(f\"=== Running evaluation for dataset {dataset_name} ===\")\n        run_main_all(dataset_name=dataset_name, args=args)\n\n\ndef main_one(args):\n    gt_annot_file = args.gt_annot_file\n    pred_file = args.pred_file\n    eval_res_file = args.eval_res_file\n\n    print(f\"=== Running evaluation for Pred {pred_file} vs GT {gt_annot_file} ===\")\n    veval_evaluator = VEvalEvaluator(\n        gt_annot_file=gt_annot_file, eval_res_file=eval_res_file\n    )\n    _ = veval_evaluator.run_eval(pred_file=pred_file)\n\n    print(f\"=== Results saved to {eval_res_file} ===\")\n\n\ndef main():\n    parser = argparse.ArgumentParser(description=\"Run video grounding evaluators\")\n\n    # Create subparsers for different commands\n    subparsers = parser.add_subparsers(dest=\"command\", required=True)\n\n    # Run evaluation for all datasets\n    all_parser = subparsers.add_parser(\"all\", help=\"Run evaluation for all datasets\")\n    all_parser.add_argument(\n        \"--gt_annot_dir\",\n        type=str,\n        help=\"Directory that contains the ground truth annotation files\",\n    )\n    all_parser.add_argument(\n        \"--pred_dir\",\n        type=str,\n        help=\"Directory that contains the prediction files\",\n    )\n    all_parser.add_argument(\n        \"--eval_res_dir\",\n        type=str,\n        help=\"Directory that contains the eval results files\",\n    )\n    all_parser.set_defaults(func=main_all)\n\n    # Run evaluation for one dataset\n    one_parser = subparsers.add_parser(\"one\", help=\"Run evaluation for one dataset\")\n    one_parser.add_argument(\n        \"--gt_annot_file\",\n        type=str,\n        help=\"Path to the ground truth annotation file\",\n    )\n    one_parser.add_argument(\n        \"--pred_file\",\n        type=str,\n        help=\"Path to the prediction file\",\n    )\n    one_parser.add_argument(\n        \"--eval_res_file\",\n        type=str,\n        help=\"Path to the eval results file\",\n    )\n    one_parser.set_defaults(func=main_one)\n\n    # Parse and dispatch\n    args = parser.parse_args()\n    args.func(args)\n\n\nif __name__ == \"__main__\":\n    main()\n"
  },
  {
    "path": "sam3/eval/saco_veval_evaluators.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\nimport json\nimport os\nimport tempfile\nfrom collections import defaultdict\nfrom typing import Dict, Optional, Sequence, Tuple\n\nimport numpy as np\nimport pycocotools.mask\nfrom sam3.eval.cgf1_eval import CGF1_METRICS\nfrom sam3.eval.conversion_util import (\n    convert_ytbvis_to_cocovid_gt,\n    convert_ytbvis_to_cocovid_pred,\n)\nfrom sam3.eval.hota_eval_toolkit.run_ytvis_eval import run_ytvis_eval\nfrom sam3.eval.teta_eval_toolkit import config, Evaluator, metrics\nfrom sam3.eval.teta_eval_toolkit.datasets import COCO, TAO\nfrom sam3.eval.ytvis_coco_wrapper import YTVIS\nfrom sam3.eval.ytvis_eval import VideoDemoF1Eval, YTVISeval\nfrom sam3.train.nms_helper import process_frame_level_nms, process_track_level_nms\n\n\ndef _get_metric_index(metric_name: str, iou_threshold: Optional[float] = None) -> int:\n    \"\"\"\n    Find the index of a metric in CGF1_METRICS by name and IoU threshold.\n\n    Args:\n        metric_name: Name of the metric (e.g., \"cgF1\", \"precision\", \"recall\")\n        iou_threshold: IoU threshold (None for average over 0.5:0.95, or specific value like 0.5, 0.75)\n\n    Returns:\n        Index of the metric in CGF1_METRICS\n\n    Raises:\n        ValueError: If metric not found\n    \"\"\"\n    for idx, metric in enumerate(CGF1_METRICS):\n        if metric.name == metric_name and metric.iou_threshold == iou_threshold:\n            return idx\n    raise ValueError(\n        f\"Metric '{metric_name}' with IoU threshold {iou_threshold} not found in CGF1_METRICS\"\n    )\n\n\nclass BasePredFileEvaluator:\n    \"\"\"A base class for evaluating a prediction file.\"\"\"\n\n    pass\n\n\nclass YTVISPredFileEvaluator(BasePredFileEvaluator):\n    \"\"\"Evaluate class mAP for YT-VIS prediction files.\"\"\"\n\n    def __init__(\n        self,\n        gt_ann_file: str,\n        dataset_name: str = \"video\",\n        iou_types: Optional[Sequence[str]] = None,\n    ):\n        self.gt_ann_file = gt_ann_file\n        self.dataset_name = dataset_name\n        self.iou_types = list(iou_types) if iou_types is not None else [\"bbox\", \"segm\"]\n        assert all(iou_type in [\"bbox\", \"segm\"] for iou_type in self.iou_types)\n\n    def evaluate(self, pred_file: str) -> Dict[str, float]:\n        # use our internal video evaluation toolkit for YT-VIS pred file\n        # (i.e. the same one we're using for video phrase AP)\n        results = {}\n        use_cats = True  # YT-VIS mAP evaluation uses categories\n        ytvisGT = YTVIS(self.gt_ann_file, ignore_gt_cats=not use_cats)\n        # the original YT-VIS GT annotations have uncompressed RLEs (\"counts\" is an integer list)\n        # rather than compressed RLEs (\"counts\" is a string), so we first convert them here.\n        if \"segm\" in self.iou_types:\n            for ann in ytvisGT.dataset[\"annotations\"]:\n                ann[\"segmentations\"] = [\n                    _compress_rle(rle) for rle in ann[\"segmentations\"]\n                ]\n\n        with open(pred_file) as f:\n            dt = json.load(f)\n        # Our prediction file saves \"video_id\" and absolute (unnormalized) boxes.\n        # Note that we should use the official (original) YT-VIS annotations (i.e. the one\n        # saved via \"scripts/datasets/training/ytvis_split.py\", instead of the one saved\n        # via \"scripts/api_db_to_ytvis_json.py\") in this evaluator, which contain absolute\n        # boxes coordinates in its GT annotations.\n        for d in dt:\n            d[\"image_id\"] = d[\"video_id\"]\n        ytvisDT = ytvisGT.loadRes(dt)\n\n        for iou_type in self.iou_types:\n            ytvisEval = YTVISeval(ytvisGT, ytvisDT, iou_type)\n\n            # set the area ranges for small, medium, and large objects (using\n            # absolute pixel areas) as in the official YT-VIS evaluation toolkit:\n            # https://github.com/achalddave/ytvosapi/blob/eca601117c9f86bad084cb91f1d918e9ab665a75/PythonAPI/ytvostools/ytvoseval.py#L538\n            ytvisEval.params.areaRng = [\n                [0**2, 1e5**2],\n                [0**2, 128**2],\n                [128**2, 256**2],\n                [256**2, 1e5**2],\n            ]\n            ytvisEval.params.areaRngLbl = [\"all\", \"small\", \"medium\", \"large\"]\n            ytvisEval.params.useCats = use_cats\n\n            ytvisEval.evaluate()\n            ytvisEval.accumulate()\n            ytvisEval.summarize()\n            result_key = f\"{self.dataset_name}_{'mask' if iou_type == 'segm' else 'bbox'}_mAP_50_95\"\n            results[result_key] = ytvisEval.stats[0]\n\n        # video-NP level results not supported for `YTVISPredFileEvaluator` yet\n        video_np_level_results = {}\n        return results, video_np_level_results\n\n\nclass VideoPhraseApEvaluator(BasePredFileEvaluator):\n    \"\"\"Evaluate Video Phrase AP with YT-VIS format prediction and GT files.\"\"\"\n\n    def __init__(\n        self,\n        gt_ann_file: str,\n        dataset_name: str = \"video\",\n        iou_types: Optional[Sequence[str]] = None,\n    ):\n        self.gt_ann_file = gt_ann_file\n        self.dataset_name = dataset_name\n        self.iou_types = list(iou_types) if iou_types is not None else [\"bbox\", \"segm\"]\n        assert all(iou_type in [\"bbox\", \"segm\"] for iou_type in self.iou_types)\n\n    def evaluate(self, pred_file: str) -> Dict[str, float]:\n        with open(self.gt_ann_file) as f:\n            gt = json.load(f)\n        with open(pred_file) as f:\n            dt = json.load(f)\n        # For phrase AP and demo F1 evaluation, we need to remap each pair of (video_id, category_id) to\n        # a new unique video_id, so that we don't mix detections from different categories under `useCat=False`\n        gt, dt = remap_video_category_pairs_to_unique_video_ids(gt, dt)\n        if \"segm\" in self.iou_types:\n            for ann in gt[\"annotations\"]:\n                ann[\"segmentations\"] = [\n                    _compress_rle(rle) for rle in ann[\"segmentations\"]\n                ]\n        for d in dt:\n            d[\"image_id\"] = d[\"video_id\"]\n\n        results = {}\n        use_cats = False  # Phrase AP evaluation does not use categories\n        ytvisGT = YTVIS(annotation_file=None, ignore_gt_cats=not use_cats)\n        ytvisGT.dataset = gt\n        ytvisGT.createIndex()\n        ytvisDT = ytvisGT.loadRes(dt)\n\n        for iou_type in self.iou_types:\n            phraseApEval = YTVISeval(ytvisGT, ytvisDT, iou_type)\n\n            # set the area ranges for small, medium, and large objects (using\n            # absolute pixel areas) as in the official YT-VIS evaluation toolkit:\n            # https://github.com/achalddave/ytvosapi/blob/eca601117c9f86bad084cb91f1d918e9ab665a75/PythonAPI/ytvostools/ytvoseval.py#L538\n            phraseApEval.params.areaRng = [\n                [0**2, 1e5**2],\n                [0**2, 128**2],\n                [128**2, 256**2],\n                [256**2, 1e5**2],\n            ]\n            phraseApEval.params.areaRngLbl = [\"all\", \"small\", \"medium\", \"large\"]\n            phraseApEval.params.useCats = use_cats\n\n            phraseApEval.evaluate()\n            phraseApEval.accumulate()\n            phraseApEval.summarize()\n            result_prefix = f\"{self.dataset_name}\"\n            result_prefix += f\"_{'mask' if iou_type == 'segm' else 'bbox'}_phrase_ap\"\n            # fetch Phrase AP results from the corresponding indices in `phraseApEval.stats`\n            # (see `_summarizeDets` in https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocotools/cocoeval.py)\n            results[result_prefix + \"_50_95\"] = phraseApEval.stats[0]  # IoU=0.5:0.95\n            results[result_prefix + \"_50\"] = phraseApEval.stats[1]  # IoU=0.5\n            results[result_prefix + \"_75\"] = phraseApEval.stats[2]  # IoU=0.75\n\n        # video-NP level results not supported for `VideoPhraseApEvaluator` yet\n        video_np_level_results = {}\n        return results, video_np_level_results\n\n\nclass VideoCGF1Evaluator(BasePredFileEvaluator):\n    \"\"\"Evaluate Video Demo F1 with YT-VIS format prediction and GT files.\"\"\"\n\n    def __init__(\n        self,\n        gt_ann_file: str,\n        dataset_name: str = \"video\",\n        prob_thresh: float = 0.5,\n        iou_types: Optional[Sequence[str]] = None,\n    ):\n        self.gt_ann_file = gt_ann_file\n        self.dataset_name = dataset_name\n        self.prob_thresh = prob_thresh\n        self.iou_types = list(iou_types) if iou_types is not None else [\"bbox\", \"segm\"]\n        assert all(iou_type in [\"bbox\", \"segm\"] for iou_type in self.iou_types)\n\n    def evaluate(self, pred_file: str) -> Dict[str, float]:\n        with open(self.gt_ann_file) as f:\n            gt = json.load(f)\n        with open(pred_file) as f:\n            dt = json.load(f)\n        # compute IL_MCC and CG-F1 can only be computed if we have \"video_np_pairs\" keys in the GT JSON\n        compute_ilmcc_and_cgf1 = \"video_np_pairs\" in gt\n        if not compute_ilmcc_and_cgf1:\n            print(\n                f\"Warning: IL_MCC and CG-F1 are not computed for {pred_file=} as it does not have 'video_np_pairs' keys in the GT JSON\"\n            )\n        # For phrase AP and demo F1 evaluation, we need to remap each pair of (video_id, category_id) to\n        # a new unique video_id, so that we don't mix detections from different categories under `useCat=False`\n        gt, dt = remap_video_category_pairs_to_unique_video_ids(\n            gt, dt, add_negative_np_pairs=compute_ilmcc_and_cgf1\n        )\n        if \"segm\" in self.iou_types:\n            for ann in gt[\"annotations\"]:\n                ann[\"segmentations\"] = [\n                    _compress_rle(rle) for rle in ann[\"segmentations\"]\n                ]\n        for d in dt:\n            d[\"image_id\"] = d[\"video_id\"]\n\n        results = {}\n        use_cats = False  # Demo F1 evaluation does not use categories\n        ytvisGT = YTVIS(annotation_file=None, ignore_gt_cats=not use_cats)\n        ytvisGT.dataset = gt\n        ytvisGT.createIndex()\n        ytvisDT = ytvisGT.loadRes(dt)\n\n        video_np_level_results = {}\n        for iou_type in self.iou_types:\n            demoF1Eval = VideoDemoF1Eval(ytvisGT, ytvisDT, iou_type, self.prob_thresh)\n\n            demoF1Eval.params.useCats = use_cats\n            demoF1Eval.params.areaRng = [[0**2, 1e5**2]]\n            demoF1Eval.params.areaRngLbl = [\"all\"]\n            demoF1Eval.params.maxDets = [100000]\n\n            demoF1Eval.evaluate()\n            demoF1Eval.accumulate()\n            demoF1Eval.summarize()\n            result_prefix = f\"{self.dataset_name}\"\n            result_prefix += f\"_{'mask' if iou_type == 'segm' else 'bbox'}_demo\"\n\n            stats = demoF1Eval.stats\n\n            if compute_ilmcc_and_cgf1:\n                # Average IoU threshold (0.5:0.95)\n                cgf1_micro_avg_idx = _get_metric_index(\"cgF1\", None)\n                positive_micro_f1_avg_idx = _get_metric_index(\"positive_micro_F1\", None)\n                ilmcc_avg_idx = _get_metric_index(\"IL_MCC\", None)\n                results[result_prefix + \"_cgf1_micro_50_95\"] = stats[cgf1_micro_avg_idx]\n                results[result_prefix + \"_ilmcc_50_95\"] = stats[ilmcc_avg_idx]\n                results[result_prefix + \"_positive_micro_f1_50_95\"] = stats[\n                    positive_micro_f1_avg_idx\n                ]\n\n                # IoU = 0.5\n                cgf1_micro_50_idx = _get_metric_index(\"cgF1\", 0.5)\n                positive_micro_f1_50_idx = _get_metric_index(\"positive_micro_F1\", 0.5)\n                results[result_prefix + \"_cgf1_micro_50\"] = stats[cgf1_micro_50_idx]\n                results[result_prefix + \"_ilmcc_50\"] = float(\n                    np.array(stats[cgf1_micro_50_idx])\n                    / np.array(stats[positive_micro_f1_50_idx])\n                )\n                results[result_prefix + \"_positive_micro_f1_50\"] = stats[\n                    positive_micro_f1_50_idx\n                ]\n\n                # IoU = 0.75\n                cgf1_micro_75_idx = _get_metric_index(\"cgF1\", 0.75)\n                positive_micro_f1_75_idx = _get_metric_index(\"positive_micro_F1\", 0.75)\n                results[result_prefix + \"_cgf1_micro_75\"] = stats[cgf1_micro_75_idx]\n                results[result_prefix + \"_ilmcc_75\"] = float(\n                    np.array(stats[cgf1_micro_75_idx])\n                    / np.array(stats[positive_micro_f1_75_idx])\n                )\n                results[result_prefix + \"_positive_micro_f1_75\"] = stats[\n                    positive_micro_f1_75_idx\n                ]\n\n            self.extract_video_np_level_results(demoF1Eval, video_np_level_results)\n\n        return results, video_np_level_results\n\n    def extract_video_np_level_results(self, demoF1Eval, video_np_level_results):\n        \"\"\"Aggregate statistics for video-level metrics.\"\"\"\n        num_iou_thrs = len(demoF1Eval.params.iouThrs)\n        iou_50_index = int(np.where(demoF1Eval.params.iouThrs == 0.5)[0])\n        iou_75_index = int(np.where(demoF1Eval.params.iouThrs == 0.75)[0])\n\n        result_prefix = \"mask\" if demoF1Eval.params.iouType == \"segm\" else \"bbox\"\n\n        assert len(demoF1Eval.evalImgs) == len(demoF1Eval.cocoGt.dataset[\"images\"])\n        for i, video in enumerate(demoF1Eval.cocoGt.dataset[\"images\"]):\n            # the original video id and category id before remapping\n            video_id = video[\"orig_video_id\"]\n            category_id = video[\"orig_category_id\"]\n            eval_img_dict = demoF1Eval.evalImgs[i]\n\n            TPs = eval_img_dict.get(\"TPs\", np.zeros(num_iou_thrs, dtype=np.int64))\n            FPs = eval_img_dict.get(\"FPs\", np.zeros(num_iou_thrs, dtype=np.int64))\n            FNs = eval_img_dict.get(\"FNs\", np.zeros(num_iou_thrs, dtype=np.int64))\n            assert len(TPs) == len(FPs) == len(FNs) == num_iou_thrs\n            # F1 = 2*TP / (2*TP + FP + FN), and we set F1 to 1.0 if denominator is 0\n            denominator = 2 * TPs + FPs + FNs\n            F1s = np.where(denominator > 0, 2 * TPs / np.maximum(denominator, 1), 1.0)\n            local_results = {\n                f\"{result_prefix}_TP_50_95\": float(TPs.mean()),\n                f\"{result_prefix}_FP_50_95\": float(FPs.mean()),\n                f\"{result_prefix}_FN_50_95\": float(FNs.mean()),\n                f\"{result_prefix}_F1_50_95\": float(F1s.mean()),\n                f\"{result_prefix}_TP_50\": float(TPs[iou_50_index]),\n                f\"{result_prefix}_FP_50\": float(FPs[iou_50_index]),\n                f\"{result_prefix}_FN_50\": float(FNs[iou_50_index]),\n                f\"{result_prefix}_F1_50\": float(F1s[iou_50_index]),\n                f\"{result_prefix}_TP_75\": float(TPs[iou_75_index]),\n                f\"{result_prefix}_FP_75\": float(FPs[iou_75_index]),\n                f\"{result_prefix}_FN_75\": float(FNs[iou_75_index]),\n                f\"{result_prefix}_F1_75\": float(F1s[iou_75_index]),\n            }\n            if (video_id, category_id) not in video_np_level_results:\n                video_np_level_results[(video_id, category_id)] = {}\n            video_np_level_results[(video_id, category_id)].update(local_results)\n\n\nclass VideoTetaEvaluator(BasePredFileEvaluator):\n    \"\"\"Evaluate TETA metric using YouTubeVIS format prediction and GT files.\"\"\"\n\n    def __init__(\n        self,\n        gt_ann_file: str,\n        dataset_name: str = \"video\",\n        tracker_name: str = \"Sam3\",\n        nms_threshold: float = 0.5,\n        nms_strategy: str = \"none\",  # \"track\", \"frame\", or \"none\"\n        prob_thresh: float = 0.5,\n        is_exhaustive: bool = False,\n        use_mask: bool = False,\n        num_parallel_cores: int = 8,\n    ):\n        self.gt_ann_file = gt_ann_file\n        self.dataset_name = dataset_name\n        self.tracker_name = tracker_name\n        self.nms_threshold = nms_threshold\n        self.nms_strategy = nms_strategy.lower()  # Convert to lowercase for consistency\n        self.prob_thresh = prob_thresh\n        self.metric_prefix = \"TETA\"\n        self.is_exhaustive = is_exhaustive\n        self.use_mask = use_mask\n        self.num_parallel_cores = num_parallel_cores\n\n        # Verify NMS strategy is valid\n        valid_strategies = [\"track\", \"frame\", \"none\"]\n        print(\"current nms_strategy:\", self.nms_strategy)\n        if self.nms_strategy not in valid_strategies:\n            raise ValueError(\n                f\"Invalid NMS strategy: {self.nms_strategy}. Must be one of {valid_strategies}\"\n            )\n\n        print(f\"Initialized VideoTetaEvaluator with NMS strategy: {self.nms_strategy}\")\n        print(f\"Probability threshold set to: {self.prob_thresh}\")\n        print(f\"Dataset exhaustivity set to: {self.is_exhaustive}\")\n        print(f\"Tracker name set to: {self.tracker_name}\")\n        print(f\"Dataset name set to: {self.dataset_name}\")\n        print(f\"Use mask set to: {self.use_mask}\")\n\n    def process_predictions(self, pred_file: str, tmp_dir: str) -> str:\n        \"\"\"Process predictions with selected NMS strategy\"\"\"\n        with open(pred_file, \"r\") as f:\n            raw_preds = json.load(f)\n        print(f\"Processing predictions with {self.nms_strategy} NMS strategy\")\n\n        # Filter by score threshold\n        if self.prob_thresh > 0:\n            raw_preds = [d for d in raw_preds if d[\"score\"] >= self.prob_thresh]\n            print(\n                f\"Filtered to {len(raw_preds)} predictions with score >= {self.prob_thresh}\"\n            )\n        # Group predictions by video_id\n        video_groups = defaultdict(list)\n        for pred in raw_preds:\n            video_groups[pred[\"video_id\"]].append(pred)\n        # Process based on NMS strategy\n        if self.nms_strategy == \"track\":\n            process_track_level_nms(video_groups, nms_threshold=self.nms_threshold)\n        elif self.nms_strategy == \"frame\":\n            process_frame_level_nms(video_groups, nms_threshold=self.nms_threshold)\n        elif self.nms_strategy == \"none\":\n            print(\"Skipping NMS processing as strategy is set to 'none'\")\n            # No processing needed for \"none\" strategy\n        # Save processed predictions\n        processed_preds = [\n            track for tracks in video_groups.values() for track in tracks\n        ]\n        processed_path = os.path.join(tmp_dir, \"processed_preds.json\")\n        with open(processed_path, \"w\") as f:\n            json.dump(processed_preds, f)\n\n        print(f\"Saved processed predictions to {processed_path}\")\n        return processed_path\n\n    def evaluate(self, pred_file: str) -> Tuple[Dict[str, float], Dict]:\n        \"\"\"Main evaluation method\"\"\"\n\n        print(f\"Evaluating TETA Metric with {self.nms_strategy.upper()} NMS strategy\")\n        with tempfile.TemporaryDirectory() as tmp_dir:\n            # Process predictions first\n            processed_pred_file = self.process_predictions(pred_file, tmp_dir)\n\n            # Convert GT to COCO-vid format\n            gt_dir = os.path.join(tmp_dir, \"gt\")\n            os.makedirs(gt_dir, exist_ok=True)\n            gt_coco_path = os.path.join(gt_dir, \"annotations.json\")\n            convert_ytbvis_to_cocovid_gt(self.gt_ann_file, gt_coco_path)\n\n            # Convert processed predictions to COCO-vid format\n            pred_dir = os.path.join(tmp_dir, \"predictions\")\n            tracker_dir = os.path.join(pred_dir, self.tracker_name)\n            os.makedirs(tracker_dir, exist_ok=True)\n            pred_coco_path = os.path.join(tracker_dir, \"track_results_cocofmt.json\")\n            convert_ytbvis_to_cocovid_pred(\n                youtubevis_pred_path=processed_pred_file,\n                converted_dataset_path=gt_coco_path,\n                output_path=pred_coco_path,\n            )\n            # Configure TETA evaluator\n            default_eval_config = config.get_default_eval_config()\n            default_eval_config[\"PRINT_ONLY_COMBINED\"] = True\n            default_eval_config[\"DISPLAY_LESS_PROGRESS\"] = True\n            default_eval_config[\"OUTPUT_TEMP_RAW_DATA\"] = True\n            default_eval_config[\"NUM_PARALLEL_CORES\"] = self.num_parallel_cores\n            default_dataset_config = config.get_default_dataset_config()\n            default_dataset_config[\"TRACKERS_TO_EVAL\"] = [self.tracker_name]\n            default_dataset_config[\"GT_FOLDER\"] = gt_dir\n            default_dataset_config[\"OUTPUT_FOLDER\"] = pred_dir\n            default_dataset_config[\"TRACKER_SUB_FOLDER\"] = tracker_dir\n            default_dataset_config[\"USE_MASK\"] = self.use_mask\n\n            evaluator = Evaluator(default_eval_config)\n            if self.is_exhaustive:\n                dataset_list = [COCO(default_dataset_config)]\n                dataset_parsing_key = \"COCO\"\n            else:\n                dataset_list = [TAO(default_dataset_config)]\n                dataset_parsing_key = \"TAO\"\n\n            # Run evaluation\n            eval_results, _ = evaluator.evaluate(\n                dataset_list, [metrics.TETA(exhaustive=self.is_exhaustive)]\n            )\n\n            # Extract and format results\n            results = {\n                f\"{self.dataset_name}_{'mask' if self.use_mask else 'bbox'}_teta\": float(\n                    eval_results[dataset_parsing_key][\"TETA\"][0]\n                ),\n                f\"{self.dataset_name}_{'mask' if self.use_mask else 'bbox'}_loc_a\": float(\n                    eval_results[dataset_parsing_key][\"TETA\"][1]\n                ),\n                f\"{self.dataset_name}_{'mask' if self.use_mask else 'bbox'}_assoc_a\": float(\n                    eval_results[dataset_parsing_key][\"TETA\"][2]\n                ),\n                f\"{self.dataset_name}_{'mask' if self.use_mask else 'bbox'}_cls_a\": float(\n                    eval_results[dataset_parsing_key][\"TETA\"][3]\n                ),\n                f\"{self.dataset_name}_{'mask' if self.use_mask else 'bbox'}_loc_re\": float(\n                    eval_results[dataset_parsing_key][\"TETA\"][4]\n                ),\n                f\"{self.dataset_name}_{'mask' if self.use_mask else 'bbox'}_loc_pr\": float(\n                    eval_results[dataset_parsing_key][\"TETA\"][5]\n                ),\n                f\"{self.dataset_name}_{'mask' if self.use_mask else 'bbox'}_assoc_re\": float(\n                    eval_results[dataset_parsing_key][\"TETA\"][6]\n                ),\n                f\"{self.dataset_name}_{'mask' if self.use_mask else 'bbox'}_assoc_pr\": float(\n                    eval_results[dataset_parsing_key][\"TETA\"][7]\n                ),\n                f\"{self.dataset_name}_{'mask' if self.use_mask else 'bbox'}_cls_re\": float(\n                    eval_results[dataset_parsing_key][\"TETA\"][8]\n                ),\n                f\"{self.dataset_name}_{'mask' if self.use_mask else 'bbox'}_cls_pr\": float(\n                    eval_results[dataset_parsing_key][\"TETA\"][9]\n                ),\n            }\n\n        # video-NP level results not supported for `VideoTetaEvaluator` yet\n        video_np_level_results = {}\n        return results, video_np_level_results\n\n\nclass VideoPhraseHotaEvaluator(BasePredFileEvaluator):\n    \"\"\"Evaluate Video Phrase HOTA with YT-VIS format prediction and GT files.\"\"\"\n\n    def __init__(\n        self,\n        gt_ann_file: str,\n        dataset_name: str = \"video\",\n        prob_thresh: float = 0.5,\n        iou_types: Optional[Sequence[str]] = None,\n        compute_video_mot_hota: bool = False,\n    ):\n        self.gt_ann_file = gt_ann_file\n        self.dataset_name = dataset_name\n        self.prob_thresh = prob_thresh\n        self.metric_prefix = \"phrase\"\n        # the list of metrics to collect from the HOTA evaluation results\n        self.metric_to_collect = [\n            \"HOTA\",\n            \"DetA\",\n            \"AssA\",\n            \"DetRe\",\n            \"DetPr\",\n            \"AssRe\",\n            \"AssPr\",\n            \"LocA\",\n            \"OWTA\",\n        ]\n        self.iou_types = list(iou_types) if iou_types is not None else [\"bbox\", \"segm\"]\n        assert all(iou_type in [\"bbox\", \"segm\"] for iou_type in self.iou_types)\n\n        # If True, compute video MOT HOTA, aggregating predictions/GT from all categories.\n        self.compute_video_mot_hota = compute_video_mot_hota\n\n    def evaluate(self, pred_file: str) -> Dict[str, float]:\n        # use the YT-VIS evaluation toolkit in TrackEval\n\n        with open(self.gt_ann_file) as f:\n            gt = json.load(f)\n        with open(pred_file) as f:\n            dt = json.load(f)\n        # keep only predictions with score above the probability threshold\n        dt = [d for d in dt if d[\"score\"] > self.prob_thresh]\n        for d in dt:\n            assert len(d[\"areas\"]) == len(d[\"bboxes\"])\n            assert len(d[\"areas\"]) == len(d[\"segmentations\"])\n            # remove empty boxes (otherwise they will count as false positives for during\n            # per-frame detection accuracy in HOTA evaluation)\n            for t in range(len(d[\"bboxes\"])):\n                bbox = d[\"bboxes\"][t]\n                if d[\"areas\"][t] == 0 or bbox is None or all(x == 0 for x in bbox):\n                    d[\"segmentations\"][t] = None\n                    d[\"bboxes\"][t] = None\n                    d[\"areas\"][t] = None\n            # check that box occurence and mask occurence are consistent\n            for bbox, mask, area in zip(d[\"bboxes\"], d[\"segmentations\"], d[\"areas\"]):\n                assert (area is None) == (bbox is None)\n                assert (area is None) == (mask is None)\n            # set all scores to 1.0 for HOTA evaluation (just like Demo F1, the exact score\n            # value is not used in HOTA metrics; it will be treated as a detection prediction\n            # as long as its score is above the threshold)\n            d[\"score\"] = 1.0\n\n        # remap the GT and DT annotations for phrase HOTA evaluation\n        gt = _fill_in_ann_height_width(gt)\n        if not self.compute_video_mot_hota:\n            # remap the GT and DT annotations for phrase HOTA evaluation\n            gt, dt = self._remap_gt_dt(gt, dt)\n        else:\n            # Compute video-level MOT HOTA\n            # Apply track-level NMS\n            video_groups = defaultdict(list)\n            for pred in dt:\n                video_groups[pred[\"video_id\"]].append(pred)\n            process_track_level_nms(video_groups, nms_threshold=0.5)\n            dt = [track for tracks in video_groups.values() for track in tracks]\n\n            # Remap GT track ids for class-agnostic HOTA\n            gt, dt = remap_gt_dt_class_agnostic(gt, dt)\n\n        # run the HOTA evaluation using TrackEval on the remapped (video_id, category_id) pairs\n        out_dict = {}\n        video_np_level_results = {}\n        for iou_type in self.iou_types:\n            output_res, _ = run_ytvis_eval(\n                args=[\n                    \"--METRICS\",\n                    \"HOTA\",\n                    \"--IOU_TYPE\",\n                    iou_type,\n                    \"--DATASET_NAME\",\n                    self.dataset_name,\n                    \"--USE_PARALLEL\",\n                    \"True\",\n                    \"--NUM_PARALLEL_CORES\",\n                    \"8\",\n                    \"--PLOT_CURVES\",\n                    \"False\",\n                    \"--LOG_ON_ERROR\",\n                    \"None\",\n                    \"--PRINT_ONLY_COMBINED\",\n                    \"True\",\n                    \"--OUTPUT_SUMMARY\",\n                    \"False\",\n                    \"--OUTPUT_DETAILED\",\n                    \"False\",\n                    \"--TIME_PROGRESS\",\n                    \"False\",\n                    \"--PRINT_CONFIG\",\n                    \"False\",\n                ],\n                gt_json=gt,\n                dt_json=dt,\n            )\n            self.extract_video_np_level_results(\n                iou_type=iou_type,\n                remapped_gt=gt,\n                raw_results=output_res[self.dataset_name][\"tracker\"],\n                video_np_level_results=video_np_level_results,\n            )\n\n            def _summarize_results(output_res, iou_type, field, suffix):\n                eval_res = output_res[self.dataset_name][\"tracker\"][field]\n                result_prefix = f\"{self.dataset_name}_{'mask' if iou_type == 'segm' else 'bbox'}_{suffix}\"\n                for metric_name in self.metric_to_collect:\n                    eval_res_hota = eval_res[\"cls_comb_cls_av\"][\"HOTA\"]\n                    result_key = f\"{result_prefix}_{self.metric_prefix}_{metric_name}\"\n                    result_value = float(np.mean(eval_res_hota[metric_name]))\n                    out_dict[result_key] = result_value\n\n            _summarize_results(output_res, iou_type, \"COMBINED_SEQ\", \"all\")\n            if \"COMBINED_SEQ_CHALLENGING\" in output_res[self.dataset_name][\"tracker\"]:\n                _summarize_results(\n                    output_res, iou_type, \"COMBINED_SEQ_CHALLENGING\", \"challenging\"\n                )\n\n        # video-NP level results not supported for `VideoPhraseHotaEvaluator` yet\n        return out_dict, video_np_level_results\n\n    def _remap_gt_dt(self, gt, dt):\n        # For phrase HOTA evaluation, we need to remap each pair of (video_id, category_id) to\n        # a new unique video_id, so that we don't mix detections from different categories\n        gt, dt = remap_video_category_pairs_to_unique_video_ids(gt, dt)\n        # We further map all the categories to category_id=1 in HOTA evaluation toolkit\n        # for phrase HOTA (similar to \"useCat=False\" for video phrase AP)\n        remapped_category_id = 1\n        gt[\"categories\"] = [\n            {\n                \"supercategory\": \"object\",\n                \"id\": remapped_category_id,\n                \"name\": \"_REMAPPED_FOR_PHRASE_METRICS_\",\n            }\n        ]\n        for ann in gt[\"annotations\"]:\n            ann[\"category_id\"] = remapped_category_id\n        for d in dt:\n            d[\"category_id\"] = remapped_category_id\n        # To be compatible with the TrackEval YT-VIS evaluation toolkit, we need to give\n        # unique filenames to each remapped video, so we add remapped video_id as prefix.\n        for video in gt[\"videos\"]:\n            new_video_id = video[\"id\"]\n            video[\"file_names\"] = [\n                f\"remapped_vid_{new_video_id:012d}/{name}\"\n                for name in video[\"file_names\"]\n            ]\n        return gt, dt\n\n    def extract_video_np_level_results(\n        self, iou_type, remapped_gt, raw_results, video_np_level_results\n    ):\n        \"\"\"Aggregate statistics for video-level metrics.\"\"\"\n        result_prefix = \"mask\" if iou_type == \"segm\" else \"bbox\"\n        for video in remapped_gt[\"videos\"]:\n            # the original video id and category id before remapping\n            video_id = video[\"orig_video_id\"]\n            category_id = video[\"orig_category_id\"]\n            video_key = f\"remapped_vid_{video['id']:012d}\"\n            results = raw_results[video_key][\"_REMAPPED_FOR_PHRASE_METRICS_\"][\"HOTA\"]\n\n            local_results = {}\n            for metric_name in self.metric_to_collect:\n                result_key = f\"{result_prefix}_{metric_name}\"\n                local_results[result_key] = float(results[metric_name].mean())\n            if (video_id, category_id) not in video_np_level_results:\n                video_np_level_results[(video_id, category_id)] = {}\n            video_np_level_results[(video_id, category_id)].update(local_results)\n\n\nclass VideoClassBasedHotaEvaluator(VideoPhraseHotaEvaluator):\n    def __init__(\n        self,\n        gt_ann_file: str,\n        dataset_name: str = \"video\",\n        prob_thresh: float = 0.5,\n    ):\n        super().__init__(gt_ann_file, dataset_name, prob_thresh)\n        self.metric_prefix = \"class\"\n\n    def _remap_gt_dt(self, gt, dt):\n        return gt, dt  # no remapping needed for class-based HOTA evaluation\n\n    def extract_video_np_level_results(self, *args, **kwargs):\n        pass  # no video-NP level results for class-based HOTA evaluation\n\n\ndef _compress_rle(rle):\n    \"\"\"Convert RLEs from uncompressed (integer list) to compressed (string) format.\"\"\"\n    if rle is None:\n        return None\n    if isinstance(rle[\"counts\"], list):\n        rle = pycocotools.mask.frPyObjects(rle, rle[\"size\"][0], rle[\"size\"][1])\n        rle[\"counts\"] = rle[\"counts\"].decode()\n    return rle\n\n\ndef remap_video_category_pairs_to_unique_video_ids(\n    gt_json, dt_json, add_negative_np_pairs=False\n):\n    \"\"\"\n    Remap each pair of (video_id, category_id) to a new unique video_id. This is useful\n    for phrase AP and demo F1 evaluation on videos, where we have `useCat=False` and\n    rely on separating different NPs (from the same video) into different new video ids,\n    so that we don't mix detections from different categories in computeIoU under `useCat=False`.\n\n    This is consistent with how do we phrase AP and demo F1 evaluation on images, where we\n    use a remapped unique coco_image_id for each image-NP pair (based in its query[\"id\"] in\n    CustomCocoDetectionAPI.load_queries in modulated_detection_api.py)\n    \"\"\"\n    # collect the unique video_id-category_id pairs\n    video_id_to_video = {v[\"id\"]: v for v in gt_json[\"videos\"]}\n    video_id_category_id_pairs = set()\n    for pred in dt_json:\n        video_id_category_id_pairs.add((pred[\"video_id\"], pred[\"category_id\"]))\n    for ann in gt_json[\"annotations\"]:\n        video_id_category_id_pairs.add((ann[\"video_id\"], ann[\"category_id\"]))\n\n    # assign the video_id-category_id pairs to unique video ids\n    video_id_category_id_pairs = sorted(video_id_category_id_pairs)\n    video_id_category_id_to_new_video_id = {\n        pair: (i + 1) for i, pair in enumerate(video_id_category_id_pairs)\n    }\n    # also map the negative NP pairs -- this is needed for IL_MCC and CG-F1 evaluation\n    if add_negative_np_pairs:\n        for vnp in gt_json[\"video_np_pairs\"]:\n            pair = (vnp[\"video_id\"], vnp[\"category_id\"])\n            if pair not in video_id_category_id_to_new_video_id:\n                video_id_category_id_to_new_video_id[pair] = (\n                    len(video_id_category_id_to_new_video_id) + 1\n                )\n\n    # map the \"video_id\" in predictions\n    for pred in dt_json:\n        pred[\"video_id\"] = video_id_category_id_to_new_video_id[\n            (pred[\"video_id\"], pred[\"category_id\"])\n        ]\n    # map the \"video_id\" in gt_json[\"annotations\"]\n    for ann in gt_json[\"annotations\"]:\n        ann[\"video_id\"] = video_id_category_id_to_new_video_id[\n            (ann[\"video_id\"], ann[\"category_id\"])\n        ]\n    # map and duplicate gt_json[\"videos\"]\n    new_videos = []\n    for (\n        video_id,\n        category_id,\n    ), new_video_id in video_id_category_id_to_new_video_id.items():\n        video = video_id_to_video[video_id].copy()\n        video[\"id\"] = new_video_id\n        # preserve the original video_id and category_id of each remapped video entry,\n        # so that we can associate sample-level eval metrics with the original video-NP pairs\n        video[\"orig_video_id\"] = video_id\n        video[\"orig_category_id\"] = category_id\n        new_videos.append(video)\n    gt_json[\"videos\"] = new_videos\n\n    return gt_json, dt_json\n\n\ndef remap_gt_dt_class_agnostic(gt, dt):\n    \"\"\"\n    For class-agnostic HOTA, merge all GT tracks for each video (across NPs),\n    ensure unique track_ids, and set all category_id to 1.\n    Also, add orig_video_id and orig_category_id for compatibility.\n    \"\"\"\n    # 1. Remap all GT track_ids to be unique per video\n    gt_anns_by_video = defaultdict(list)\n    for ann in gt[\"annotations\"]:\n        gt_anns_by_video[ann[\"video_id\"]].append(ann)\n\n    # Ensure unique track ids across tracks of all videos\n    next_tid = 1\n    for _, anns in gt_anns_by_video.items():\n        # Map old track_ids to new unique ones\n        old_to_new_tid = {}\n        for ann in anns:\n            old_tid = ann[\"id\"]\n            if old_tid not in old_to_new_tid:\n                old_to_new_tid[old_tid] = next_tid\n                next_tid += 1\n            ann[\"id\"] = old_to_new_tid[old_tid]\n            # Set category_id to 1 for class-agnostic\n            ann[\"category_id\"] = 1\n\n    # Set all GT categories to a single category\n    gt[\"categories\"] = [\n        {\n            \"supercategory\": \"object\",\n            \"id\": 1,\n            \"name\": \"_REMAPPED_FOR_PHRASE_METRICS_\",\n        }\n    ]\n\n    # Add orig_video_id and orig_category_id to each video for compatibility\n    anns_by_video = defaultdict(list)\n    for ann in gt[\"annotations\"]:\n        anns_by_video[ann[\"video_id\"]].append(ann)\n    for video in gt[\"videos\"]:\n        video[\"orig_video_id\"] = video[\"id\"]\n        # Use the first annotation's original category_id if available, else None\n        orig_cat = (\n            anns_by_video[video[\"id\"]][0][\"category_id\"]\n            if anns_by_video[video[\"id\"]]\n            else None\n        )\n        video[\"orig_category_id\"] = orig_cat\n        video[\"file_names\"] = [\n            f\"remapped_vid_{video['id']:012d}/{name}\" for name in video[\"file_names\"]\n        ]\n\n    # Set all DT category_id to 1\n    for d in dt:\n        d[\"category_id\"] = 1\n    return gt, dt\n\n\ndef _fill_in_ann_height_width(gt_json):\n    \"\"\"Fill in missing height/width in GT annotations from its video info.\"\"\"\n    video_id_to_video = {v[\"id\"]: v for v in gt_json[\"videos\"]}\n    for ann in gt_json[\"annotations\"]:\n        if \"height\" not in ann or \"width\" not in ann:\n            video = video_id_to_video[ann[\"video_id\"]]\n            if \"height\" not in ann:\n                ann[\"height\"] = video[\"height\"]\n            if \"width\" not in ann:\n                ann[\"width\"] = video[\"width\"]\n\n    return gt_json\n"
  },
  {
    "path": "sam3/eval/teta_eval_toolkit/__init__.py",
    "content": "# fmt: off\n# flake8: noqa\n\n# pyre-unsafe\n\nfrom . import config, datasets, metrics, utils\nfrom .eval import Evaluator\n"
  },
  {
    "path": "sam3/eval/teta_eval_toolkit/_timing.py",
    "content": "# fmt: off\n# flake8: noqa\n\n# pyre-unsafe\n\nimport inspect\nfrom functools import wraps\nfrom time import perf_counter\n\nDO_TIMING = False\nDISPLAY_LESS_PROGRESS = False\ntimer_dict = {}\ncounter = 0\n\n\ndef time(f):\n    @wraps(f)\n    def wrap(*args, **kw):\n        if DO_TIMING:\n            # Run function with timing\n            ts = perf_counter()\n            result = f(*args, **kw)\n            te = perf_counter()\n            tt = te - ts\n\n            # Get function name\n            arg_names = inspect.getfullargspec(f)[0]\n            if arg_names[0] == \"self\" and DISPLAY_LESS_PROGRESS:\n                return result\n            elif arg_names[0] == \"self\":\n                method_name = type(args[0]).__name__ + \".\" + f.__name__\n            else:\n                method_name = f.__name__\n\n            # Record accumulative time in each function for analysis\n            if method_name in timer_dict.keys():\n                timer_dict[method_name] += tt\n            else:\n                timer_dict[method_name] = tt\n\n            # If code is finished, display timing summary\n            if method_name == \"Evaluator.evaluate\":\n                print(\"\")\n                print(\"Timing analysis:\")\n                for key, value in timer_dict.items():\n                    print(\"%-70s %2.4f sec\" % (key, value))\n            else:\n                # Get function argument values for printing special arguments of interest\n                arg_titles = [\"tracker\", \"seq\", \"cls\"]\n                arg_vals = []\n                for i, a in enumerate(arg_names):\n                    if a in arg_titles:\n                        arg_vals.append(args[i])\n                arg_text = \"(\" + \", \".join(arg_vals) + \")\"\n\n                # Display methods and functions with different indentation.\n                if arg_names[0] == \"self\":\n                    print(\"%-74s %2.4f sec\" % (\" \" * 4 + method_name + arg_text, tt))\n                elif arg_names[0] == \"test\":\n                    pass\n                else:\n                    global counter\n                    counter += 1\n                    print(\"%i %-70s %2.4f sec\" % (counter, method_name + arg_text, tt))\n\n            return result\n        else:\n            # If config[\"TIME_PROGRESS\"] is false, or config[\"USE_PARALLEL\"] is true, run functions normally without timing.\n            return f(*args, **kw)\n\n    return wrap\n"
  },
  {
    "path": "sam3/eval/teta_eval_toolkit/config.py",
    "content": "# fmt: off\n# flake8: noqa\n\n# pyre-unsafe\n\n\"\"\"Config.\"\"\"\nimport argparse\nimport os\n\n\ndef parse_configs():\n    \"\"\"Parse command line.\"\"\"\n    default_eval_config = get_default_eval_config()\n    default_eval_config[\"DISPLAY_LESS_PROGRESS\"] = True\n    default_dataset_config = get_default_dataset_config()\n    default_metrics_config = {\"METRICS\": [\"TETA\"]}\n    config = {\n        **default_eval_config,\n        **default_dataset_config,\n        **default_metrics_config,\n    }\n    parser = argparse.ArgumentParser()\n    for setting in config.keys():\n        if type(config[setting]) == list or type(config[setting]) == type(None):\n            parser.add_argument(\"--\" + setting, nargs=\"+\")\n        else:\n            parser.add_argument(\"--\" + setting)\n    args = parser.parse_args().__dict__\n    for setting in args.keys():\n        if args[setting] is not None:\n            if type(config[setting]) == type(True):\n                if args[setting] == \"True\":\n                    x = True\n                elif args[setting] == \"False\":\n                    x = False\n                else:\n                    raise Exception(\n                        f\"Command line parameter {setting} must be True/False\"\n                    )\n            elif type(config[setting]) == type(1):\n                x = int(args[setting])\n            elif type(args[setting]) == type(None):\n                x = None\n            else:\n                x = args[setting]\n            config[setting] = x\n    eval_config = {k: v for k, v in config.items() if k in default_eval_config.keys()}\n    dataset_config = {\n        k: v for k, v in config.items() if k in default_dataset_config.keys()\n    }\n    metrics_config = {\n        k: v for k, v in config.items() if k in default_metrics_config.keys()\n    }\n\n    return eval_config, dataset_config, metrics_config\n\n\ndef get_default_eval_config():\n    \"\"\"Returns the default config values for evaluation.\"\"\"\n    code_path = get_code_path()\n    default_config = {\n        \"USE_PARALLEL\": True,\n        \"NUM_PARALLEL_CORES\": 8,\n        \"BREAK_ON_ERROR\": True,\n        \"RETURN_ON_ERROR\": False,\n        \"LOG_ON_ERROR\": os.path.join(code_path, \"error_log.txt\"),\n        \"PRINT_RESULTS\": True,\n        \"PRINT_ONLY_COMBINED\": True,\n        \"PRINT_CONFIG\": True,\n        \"TIME_PROGRESS\": True,\n        \"DISPLAY_LESS_PROGRESS\": True,\n        \"OUTPUT_SUMMARY\": True,\n        \"OUTPUT_EMPTY_CLASSES\": True,\n        \"OUTPUT_TEM_RAW_DATA\": True,\n        \"OUTPUT_PER_SEQ_RES\": True,\n    }\n    return default_config\n\n\ndef get_default_dataset_config():\n    \"\"\"Default class config values\"\"\"\n    code_path = get_code_path()\n    default_config = {\n        \"GT_FOLDER\": os.path.join(\n            code_path, \"data/gt/tao/tao_training\"\n        ),  # Location of GT data\n        \"TRACKERS_FOLDER\": os.path.join(\n            code_path, \"data/trackers/tao/tao_training\"\n        ),  # Trackers location\n        \"OUTPUT_FOLDER\": None,  # Where to save eval results (if None, same as TRACKERS_FOLDER)\n        \"TRACKERS_TO_EVAL\": ['TETer'],  # Filenames of trackers to eval (if None, all in folder)\n        \"CLASSES_TO_EVAL\": None,  # Classes to eval (if None, all classes)\n        \"SPLIT_TO_EVAL\": \"training\",  # Valid: 'training', 'val'\n        \"PRINT_CONFIG\": True,  # Whether to print current config\n        \"TRACKER_SUB_FOLDER\": \"data\",  # Tracker files are in TRACKER_FOLDER/tracker_name/TRACKER_SUB_FOLDER\n        \"OUTPUT_SUB_FOLDER\": \"\",  # Output files are saved in OUTPUT_FOLDER/tracker_name/OUTPUT_SUB_FOLDER\n        \"TRACKER_DISPLAY_NAMES\": None,  # Names of trackers to display, if None: TRACKERS_TO_EVAL\n        \"MAX_DETECTIONS\": 0,  # Number of maximal allowed detections per image (0 for unlimited)\n        \"USE_MASK\": False,  # Whether to use mask data for evaluation\n    }\n    return default_config\n\n\ndef init_config(config, default_config, name=None):\n    \"\"\"Initialize non-given config values with defaults.\"\"\"\n    if config is None:\n        config = default_config\n    else:\n        for k in default_config.keys():\n            if k not in config.keys():\n                config[k] = default_config[k]\n    if name and config[\"PRINT_CONFIG\"]:\n        print(\"\\n%s Config:\" % name)\n        for c in config.keys():\n            print(\"%-20s : %-30s\" % (c, config[c]))\n    return config\n\n\ndef update_config(config):\n    \"\"\"\n    Parse the arguments of a script and updates the config values for a given value if specified in the arguments.\n    :param config: the config to update\n    :return: the updated config\n    \"\"\"\n    parser = argparse.ArgumentParser()\n    for setting in config.keys():\n        if type(config[setting]) == list or type(config[setting]) == type(None):\n            parser.add_argument(\"--\" + setting, nargs=\"+\")\n        else:\n            parser.add_argument(\"--\" + setting)\n    args = parser.parse_args().__dict__\n    for setting in args.keys():\n        if args[setting] is not None:\n            if type(config[setting]) == type(True):\n                if args[setting] == \"True\":\n                    x = True\n                elif args[setting] == \"False\":\n                    x = False\n                else:\n                    raise Exception(\n                        \"Command line parameter \" + setting + \"must be True or False\"\n                    )\n            elif type(config[setting]) == type(1):\n                x = int(args[setting])\n            elif type(args[setting]) == type(None):\n                x = None\n            else:\n                x = args[setting]\n            config[setting] = x\n    return config\n\n\ndef get_code_path():\n    \"\"\"Get base path where code is\"\"\"\n    return os.path.abspath(os.path.join(os.path.dirname(__file__), \"..\"))\n"
  },
  {
    "path": "sam3/eval/teta_eval_toolkit/datasets/__init__.py",
    "content": "# fmt: off\n# flake8: noqa\n\n# pyre-unsafe\n\"\"\"Datasets.\"\"\"\nfrom .coco import COCO\nfrom .tao import TAO\n"
  },
  {
    "path": "sam3/eval/teta_eval_toolkit/datasets/_base_dataset.py",
    "content": "# fmt: off\n# flake8: noqa\n\n# pyre-unsafe\n\nimport csv\nimport io\nimport os\nimport traceback\nimport zipfile\nfrom abc import ABC, abstractmethod\nfrom copy import deepcopy\n\nimport numpy as np\n\nfrom .. import _timing\nfrom ..utils import TrackEvalException\n\n\nclass _BaseDataset(ABC):\n    @abstractmethod\n    def __init__(self):\n        self.tracker_list = None\n        self.seq_list = None\n        self.class_list = None\n        self.output_fol = None\n        self.output_sub_fol = None\n        self.should_classes_combine = True\n        self.use_super_categories = False\n\n    # Functions to implement:\n\n    @abstractmethod\n    def _load_raw_file(self, tracker, seq, is_gt):\n        ...\n\n    @_timing.time\n    @abstractmethod\n    def get_preprocessed_seq_data(self, raw_data, cls):\n        ...\n\n    @abstractmethod\n    def _calculate_similarities(self, gt_dets_t, tracker_dets_t):\n        ...\n\n    # Helper functions for all datasets:\n\n    @classmethod\n    def get_class_name(cls):\n        return cls.__name__\n\n    def get_name(self):\n        return self.get_class_name()\n\n    def get_output_fol(self, tracker):\n        return os.path.join(self.output_fol, tracker, self.output_sub_fol)\n\n    def get_display_name(self, tracker):\n        \"\"\"Can be overwritten if the trackers name (in files) is different to how it should be displayed.\n        By default this method just returns the trackers name as is.\n        \"\"\"\n        return tracker\n\n    def get_eval_info(self):\n        \"\"\"Return info about the dataset needed for the Evaluator\"\"\"\n        return self.tracker_list, self.seq_list, self.class_list\n\n    @_timing.time\n    def get_raw_seq_data(self, tracker, seq):\n        \"\"\"Loads raw data (tracker and ground-truth) for a single tracker on a single sequence.\n        Raw data includes all of the information needed for both preprocessing and evaluation, for all classes.\n        A later function (get_processed_seq_data) will perform such preprocessing and extract relevant information for\n        the evaluation of each class.\n\n        This returns a dict which contains the fields:\n        [num_timesteps]: integer\n        [gt_ids, tracker_ids, gt_classes, tracker_classes, tracker_confidences]:\n                                                                list (for each timestep) of 1D NDArrays (for each det).\n        [gt_dets, tracker_dets, gt_crowd_ignore_regions]: list (for each timestep) of lists of detections.\n        [similarity_scores]: list (for each timestep) of 2D NDArrays.\n        [gt_extras]: dict (for each extra) of lists (for each timestep) of 1D NDArrays (for each det).\n\n        gt_extras contains dataset specific information used for preprocessing such as occlusion and truncation levels.\n\n        Note that similarities are extracted as part of the dataset and not the metric, because almost all metrics are\n        independent of the exact method of calculating the similarity. However datasets are not (e.g. segmentation\n        masks vs 2D boxes vs 3D boxes).\n        We calculate the similarity before preprocessing because often both preprocessing and evaluation require it and\n        we don't wish to calculate this twice.\n        We calculate similarity between all gt and tracker classes (not just each class individually) to allow for\n        calculation of metrics such as class confusion matrices. Typically the impact of this on performance is low.\n        \"\"\"\n        # Load raw data.\n        raw_gt_data = self._load_raw_file(tracker, seq, is_gt=True)\n        raw_tracker_data = self._load_raw_file(tracker, seq, is_gt=False)\n        raw_data = {**raw_tracker_data, **raw_gt_data}  # Merges dictionaries\n\n        # Calculate similarities for each timestep.\n        similarity_scores = []\n        for _, (gt_dets_t, tracker_dets_t) in enumerate(\n            zip(raw_data[\"gt_dets\"], raw_data[\"tk_dets\"])\n        ):\n            ious = self._calculate_similarities(gt_dets_t, tracker_dets_t)\n            similarity_scores.append(ious)\n        raw_data[\"similarity_scores\"] = similarity_scores\n        return raw_data\n\n    @staticmethod\n    def _load_simple_text_file(\n        file,\n        time_col=0,\n        id_col=None,\n        remove_negative_ids=False,\n        valid_filter=None,\n        crowd_ignore_filter=None,\n        convert_filter=None,\n        is_zipped=False,\n        zip_file=None,\n        force_delimiters=None,\n    ):\n        \"\"\"Function that loads data which is in a commonly used text file format.\n        Assumes each det is given by one row of a text file.\n        There is no limit to the number or meaning of each column,\n        however one column needs to give the timestep of each det (time_col) which is default col 0.\n\n        The file dialect (deliminator, num cols, etc) is determined automatically.\n        This function automatically separates dets by timestep,\n        and is much faster than alternatives such as np.loadtext or pandas.\n\n        If remove_negative_ids is True and id_col is not None, dets with negative values in id_col are excluded.\n        These are not excluded from ignore data.\n\n        valid_filter can be used to only include certain classes.\n        It is a dict with ints as keys, and lists as values,\n        such that a row is included if \"row[key].lower() is in value\" for all key/value pairs in the dict.\n        If None, all classes are included.\n\n        crowd_ignore_filter can be used to read crowd_ignore regions separately. It has the same format as valid filter.\n\n        convert_filter can be used to convert value read to another format.\n        This is used most commonly to convert classes given as string to a class id.\n        This is a dict such that the key is the column to convert, and the value is another dict giving the mapping.\n\n        Optionally, input files could be a zip of multiple text files for storage efficiency.\n\n        Returns read_data and ignore_data.\n        Each is a dict (with keys as timesteps as strings) of lists (over dets) of lists (over column values).\n        Note that all data is returned as strings, and must be converted to float/int later if needed.\n        Note that timesteps will not be present in the returned dict keys if there are no dets for them\n        \"\"\"\n\n        if remove_negative_ids and id_col is None:\n            raise TrackEvalException(\n                \"remove_negative_ids is True, but id_col is not given.\"\n            )\n        if crowd_ignore_filter is None:\n            crowd_ignore_filter = {}\n        if convert_filter is None:\n            convert_filter = {}\n        try:\n            if is_zipped:  # Either open file directly or within a zip.\n                if zip_file is None:\n                    raise TrackEvalException(\n                        \"is_zipped set to True, but no zip_file is given.\"\n                    )\n                archive = zipfile.ZipFile(os.path.join(zip_file), \"r\")\n                fp = io.TextIOWrapper(archive.open(file, \"r\"))\n            else:\n                fp = open(file)\n            read_data = {}\n            crowd_ignore_data = {}\n            fp.seek(0, os.SEEK_END)\n            # check if file is empty\n            if fp.tell():\n                fp.seek(0)\n                dialect = csv.Sniffer().sniff(\n                    fp.readline(), delimiters=force_delimiters\n                )  # Auto determine structure.\n                dialect.skipinitialspace = (\n                    True  # Deal with extra spaces between columns\n                )\n                fp.seek(0)\n                reader = csv.reader(fp, dialect)\n                for row in reader:\n                    try:\n                        # Deal with extra trailing spaces at the end of rows\n                        if row[-1] in \"\":\n                            row = row[:-1]\n                        timestep = str(int(float(row[time_col])))\n                        # Read ignore regions separately.\n                        is_ignored = False\n                        for ignore_key, ignore_value in crowd_ignore_filter.items():\n                            if row[ignore_key].lower() in ignore_value:\n                                # Convert values in one column (e.g. string to id)\n                                for (\n                                    convert_key,\n                                    convert_value,\n                                ) in convert_filter.items():\n                                    row[convert_key] = convert_value[\n                                        row[convert_key].lower()\n                                    ]\n                                # Save data separated by timestep.\n                                if timestep in crowd_ignore_data.keys():\n                                    crowd_ignore_data[timestep].append(row)\n                                else:\n                                    crowd_ignore_data[timestep] = [row]\n                                is_ignored = True\n                        if (\n                            is_ignored\n                        ):  # if det is an ignore region, it cannot be a normal det.\n                            continue\n                        # Exclude some dets if not valid.\n                        if valid_filter is not None:\n                            for key, value in valid_filter.items():\n                                if row[key].lower() not in value:\n                                    continue\n                        if remove_negative_ids:\n                            if int(float(row[id_col])) < 0:\n                                continue\n                        # Convert values in one column (e.g. string to id)\n                        for convert_key, convert_value in convert_filter.items():\n                            row[convert_key] = convert_value[row[convert_key].lower()]\n                        # Save data separated by timestep.\n                        if timestep in read_data.keys():\n                            read_data[timestep].append(row)\n                        else:\n                            read_data[timestep] = [row]\n                    except Exception:\n                        exc_str_init = (\n                            \"In file %s the following line cannot be read correctly: \\n\"\n                            % os.path.basename(file)\n                        )\n                        exc_str = \" \".join([exc_str_init] + row)\n                        raise TrackEvalException(exc_str)\n            fp.close()\n        except Exception:\n            print(\"Error loading file: %s, printing traceback.\" % file)\n            traceback.print_exc()\n            raise TrackEvalException(\n                \"File %s cannot be read because it is either not present or invalidly formatted\"\n                % os.path.basename(file)\n            )\n        return read_data, crowd_ignore_data\n\n    @staticmethod\n    def _calculate_mask_ious(masks1, masks2, is_encoded=False, do_ioa=False):\n        \"\"\"Calculates the IOU (intersection over union) between two arrays of segmentation masks.\n        If is_encoded a run length encoding with pycocotools is assumed as input format, otherwise an input of numpy\n        arrays of the shape (num_masks, height, width) is assumed and the encoding is performed.\n        If do_ioa (intersection over area) , then calculates the intersection over the area of masks1 - this is commonly\n        used to determine if detections are within crowd ignore region.\n        :param masks1:  first set of masks (numpy array of shape (num_masks, height, width) if not encoded,\n                        else pycocotools rle encoded format)\n        :param masks2:  second set of masks (numpy array of shape (num_masks, height, width) if not encoded,\n                        else pycocotools rle encoded format)\n        :param is_encoded: whether the input is in pycocotools rle encoded format\n        :param do_ioa: whether to perform IoA computation\n        :return: the IoU/IoA scores\n        \"\"\"\n\n        # Only loaded when run to reduce minimum requirements\n        from pycocotools import mask as mask_utils\n\n        # use pycocotools for run length encoding of masks\n        if not is_encoded:\n            masks1 = mask_utils.encode(\n                np.array(np.transpose(masks1, (1, 2, 0)), order=\"F\")\n            )\n            masks2 = mask_utils.encode(\n                np.array(np.transpose(masks2, (1, 2, 0)), order=\"F\")\n            )\n\n        # use pycocotools for iou computation of rle encoded masks\n        ious = mask_utils.iou(masks1, masks2, [do_ioa] * len(masks2))\n        if len(masks1) == 0 or len(masks2) == 0:\n            ious = np.asarray(ious).reshape(len(masks1), len(masks2))\n        assert (ious >= 0 - np.finfo(\"float\").eps).all()\n        assert (ious <= 1 + np.finfo(\"float\").eps).all()\n\n        return ious\n\n    @staticmethod\n    def _calculate_box_ious(bboxes1, bboxes2, box_format=\"xywh\", do_ioa=False):\n        \"\"\"Calculates the IOU (intersection over union) between two arrays of boxes.\n        Allows variable box formats ('xywh' and 'x0y0x1y1').\n        If do_ioa (intersection over area) , then calculates the intersection over the area of boxes1 - this is commonly\n        used to determine if detections are within crowd ignore region.\n        \"\"\"\n        if box_format in \"xywh\":\n            # layout: (x0, y0, w, h)\n            bboxes1 = deepcopy(bboxes1)\n            bboxes2 = deepcopy(bboxes2)\n\n            bboxes1[:, 2] = bboxes1[:, 0] + bboxes1[:, 2]\n            bboxes1[:, 3] = bboxes1[:, 1] + bboxes1[:, 3]\n            bboxes2[:, 2] = bboxes2[:, 0] + bboxes2[:, 2]\n            bboxes2[:, 3] = bboxes2[:, 1] + bboxes2[:, 3]\n        elif box_format not in \"x0y0x1y1\":\n            raise (TrackEvalException(\"box_format %s is not implemented\" % box_format))\n\n        # layout: (x0, y0, x1, y1)\n        min_ = np.minimum(bboxes1[:, np.newaxis, :], bboxes2[np.newaxis, :, :])\n        max_ = np.maximum(bboxes1[:, np.newaxis, :], bboxes2[np.newaxis, :, :])\n        intersection = np.maximum(min_[..., 2] - max_[..., 0], 0) * np.maximum(\n            min_[..., 3] - max_[..., 1], 0\n        )\n        area1 = (bboxes1[..., 2] - bboxes1[..., 0]) * (\n            bboxes1[..., 3] - bboxes1[..., 1]\n        )\n\n        if do_ioa:\n            ioas = np.zeros_like(intersection)\n            valid_mask = area1 > 0 + np.finfo(\"float\").eps\n            ioas[valid_mask, :] = (\n                intersection[valid_mask, :] / area1[valid_mask][:, np.newaxis]\n            )\n\n            return ioas\n        else:\n            area2 = (bboxes2[..., 2] - bboxes2[..., 0]) * (\n                bboxes2[..., 3] - bboxes2[..., 1]\n            )\n            union = area1[:, np.newaxis] + area2[np.newaxis, :] - intersection\n            intersection[area1 <= 0 + np.finfo(\"float\").eps, :] = 0\n            intersection[:, area2 <= 0 + np.finfo(\"float\").eps] = 0\n            intersection[union <= 0 + np.finfo(\"float\").eps] = 0\n            union[union <= 0 + np.finfo(\"float\").eps] = 1\n            ious = intersection / union\n            return ious\n\n    @staticmethod\n    def _calculate_euclidean_similarity(dets1, dets2, zero_distance=2.0):\n        \"\"\"Calculates the euclidean distance between two sets of detections, and then converts this into a similarity\n        measure with values between 0 and 1 using the following formula: sim = max(0, 1 - dist/zero_distance).\n        The default zero_distance of 2.0, corresponds to the default used in MOT15_3D, such that a 0.5 similarity\n        threshold corresponds to a 1m distance threshold for TPs.\n        \"\"\"\n        dist = np.linalg.norm(dets1[:, np.newaxis] - dets2[np.newaxis, :], axis=2)\n        sim = np.maximum(0, 1 - dist / zero_distance)\n        return sim\n\n    @staticmethod\n    def _check_unique_ids(data, after_preproc=False):\n        \"\"\"Check the requirement that the tracker_ids and gt_ids are unique per timestep\"\"\"\n        gt_ids = data[\"gt_ids\"]\n        tracker_ids = data[\"tk_ids\"]\n        for t, (gt_ids_t, tracker_ids_t) in enumerate(zip(gt_ids, tracker_ids)):\n            if len(tracker_ids_t) > 0:\n                unique_ids, counts = np.unique(tracker_ids_t, return_counts=True)\n                if np.max(counts) != 1:\n                    duplicate_ids = unique_ids[counts > 1]\n                    exc_str_init = (\n                        \"Tracker predicts the same ID more than once in a single timestep \"\n                        \"(seq: %s, frame: %i, ids:\" % (data[\"seq\"], t + 1)\n                    )\n                    exc_str = (\n                        \" \".join([exc_str_init] + [str(d) for d in duplicate_ids]) + \")\"\n                    )\n                    if after_preproc:\n                        exc_str_init += (\n                            \"\\n Note that this error occurred after preprocessing (but not before), \"\n                            \"so ids may not be as in file, and something seems wrong with preproc.\"\n                        )\n                    raise TrackEvalException(exc_str)\n            if len(gt_ids_t) > 0:\n                unique_ids, counts = np.unique(gt_ids_t, return_counts=True)\n                if np.max(counts) != 1:\n                    duplicate_ids = unique_ids[counts > 1]\n                    exc_str_init = (\n                        \"Ground-truth has the same ID more than once in a single timestep \"\n                        \"(seq: %s, frame: %i, ids:\" % (data[\"seq\"], t + 1)\n                    )\n                    exc_str = (\n                        \" \".join([exc_str_init] + [str(d) for d in duplicate_ids]) + \")\"\n                    )\n                    if after_preproc:\n                        exc_str_init += (\n                            \"\\n Note that this error occurred after preprocessing (but not before), \"\n                            \"so ids may not be as in file, and something seems wrong with preproc.\"\n                        )\n                    raise TrackEvalException(exc_str)\n"
  },
  {
    "path": "sam3/eval/teta_eval_toolkit/datasets/coco.py",
    "content": "# fmt: off\n# flake8: noqa\n\n# pyre-unsafe\n\n\"\"\"COCO Dataset.\"\"\"\nimport copy\nimport itertools\nimport json\nimport os\nfrom collections import defaultdict\n\nimport numpy as np\nfrom scipy.optimize import linear_sum_assignment\n\nfrom .. import _timing, utils\nfrom ..config import get_default_dataset_config, init_config\nfrom ..utils import TrackEvalException\nfrom ._base_dataset import _BaseDataset\n\n\nclass COCO(_BaseDataset):\n    \"\"\"Tracking datasets in COCO format.\"\"\"\n\n    def __init__(self, config=None):\n        \"\"\"Initialize dataset, checking that all required files are present.\"\"\"\n        super().__init__()\n        # Fill non-given config values with defaults\n        self.config = init_config(config, get_default_dataset_config(), self.get_name())\n        self.gt_fol = self.config[\"GT_FOLDER\"]\n        self.tracker_fol = self.config[\"TRACKERS_FOLDER\"]\n        self.should_classes_combine = True\n        self.use_super_categories = False\n        self.use_mask = self.config[\"USE_MASK\"]\n\n        self.tracker_sub_fol = self.config[\"TRACKER_SUB_FOLDER\"]\n        self.output_fol = self.config[\"OUTPUT_FOLDER\"]\n        if self.output_fol is None:\n            self.output_fol = self.tracker_fol\n        self.output_sub_fol = self.config[\"OUTPUT_SUB_FOLDER\"]\n\n        if self.gt_fol.endswith(\".json\"):\n            self.gt_data = json.load(open(self.gt_fol, \"r\"))\n        else:\n            gt_dir_files = [\n                file for file in os.listdir(self.gt_fol) if file.endswith(\".json\")\n            ]\n            if len(gt_dir_files) != 1:\n                raise TrackEvalException(\n                    f\"{self.gt_fol} does not contain exactly one json file.\"\n                )\n\n            with open(os.path.join(self.gt_fol, gt_dir_files[0])) as f:\n                self.gt_data = json.load(f)\n\n        # fill missing video ids\n        self._fill_video_ids_inplace(self.gt_data[\"annotations\"])\n\n        # get sequences to eval and sequence information\n        self.seq_list = [\n            vid[\"name\"].replace(\"/\", \"-\") for vid in self.gt_data[\"videos\"]\n        ]\n        self.seq_name2seqid = {\n            vid[\"name\"].replace(\"/\", \"-\"): vid[\"id\"] for vid in self.gt_data[\"videos\"]\n        }\n        # compute mappings from videos to annotation data\n        self.video2gt_track, self.video2gt_image = self._compute_vid_mappings(\n            self.gt_data[\"annotations\"]\n        )\n        # compute sequence lengths\n        self.seq_lengths = {vid[\"id\"]: 0 for vid in self.gt_data[\"videos\"]}\n        for img in self.gt_data[\"images\"]:\n            self.seq_lengths[img[\"video_id\"]] += 1\n        self.seq2images2timestep = self._compute_image_to_timestep_mappings()\n        self.seq2cls = {\n            vid[\"id\"]: {\n                \"pos_cat_ids\": list(\n                    {track[\"category_id\"] for track in self.video2gt_track[vid[\"id\"]]}\n                ),\n            }\n            for vid in self.gt_data[\"videos\"]\n        }\n\n        # Get classes to eval\n        considered_vid_ids = [self.seq_name2seqid[vid] for vid in self.seq_list]\n        seen_cats = set(\n            [\n                cat_id\n                for vid_id in considered_vid_ids\n                for cat_id in self.seq2cls[vid_id][\"pos_cat_ids\"]\n            ]\n        )\n        # only classes with ground truth are evaluated in TAO\n        self.valid_classes = [\n            cls[\"name\"] for cls in self.gt_data[\"categories\"] if cls[\"id\"] in seen_cats\n        ]\n        cls_name2clsid_map = {\n            cls[\"name\"]: cls[\"id\"] for cls in self.gt_data[\"categories\"]\n        }\n\n        if self.config[\"CLASSES_TO_EVAL\"]:\n            self.class_list = [\n                cls.lower() if cls.lower() in self.valid_classes else None\n                for cls in self.config[\"CLASSES_TO_EVAL\"]\n            ]\n            if not all(self.class_list):\n                valid_cls = \", \".join(self.valid_classes)\n                raise TrackEvalException(\n                    \"Attempted to evaluate an invalid class. Only classes \"\n                    f\"{valid_cls} are valid (classes present in ground truth\"\n                    \" data).\"\n                )\n        else:\n            self.class_list = [cls for cls in self.valid_classes]\n        self.cls_name2clsid = {\n            k: v for k, v in cls_name2clsid_map.items() if k in self.class_list\n        }\n        self.clsid2cls_name = {\n            v: k for k, v in cls_name2clsid_map.items() if k in self.class_list\n        }\n        # get trackers to eval\n        if self.config[\"TRACKERS_TO_EVAL\"] is None:\n            self.tracker_list = os.listdir(self.tracker_fol)\n        else:\n            self.tracker_list = self.config[\"TRACKERS_TO_EVAL\"]\n\n        if self.config[\"TRACKER_DISPLAY_NAMES\"] is None:\n            self.tracker_to_disp = dict(zip(self.tracker_list, self.tracker_list))\n        elif (self.config[\"TRACKERS_TO_EVAL\"] is not None) and (\n            len(self.config[\"TK_DISPLAY_NAMES\"]) == len(self.tracker_list)\n        ):\n            self.tracker_to_disp = dict(\n                zip(self.tracker_list, self.config[\"TK_DISPLAY_NAMES\"])\n            )\n        else:\n            raise TrackEvalException(\n                \"List of tracker files and tracker display names do not match.\"\n            )\n\n        self.tracker_data = {tracker: dict() for tracker in self.tracker_list}\n\n        for tracker in self.tracker_list:\n            if self.tracker_sub_fol.endswith(\".json\"):\n                with open(os.path.join(self.tracker_sub_fol)) as f:\n                    curr_data = json.load(f)\n            else:\n                tr_dir = os.path.join(self.tracker_fol, tracker, self.tracker_sub_fol)\n                tr_dir_files = [\n                    file for file in os.listdir(tr_dir) if file.endswith(\".json\")\n                ]\n                if len(tr_dir_files) != 1:\n                    raise TrackEvalException(\n                        f\"{tr_dir} does not contain exactly one json file.\"\n                    )\n                with open(os.path.join(tr_dir, tr_dir_files[0])) as f:\n                    curr_data = json.load(f)\n\n            # limit detections if MAX_DETECTIONS > 0\n            if self.config[\"MAX_DETECTIONS\"]:\n                curr_data = self._limit_dets_per_image(curr_data)\n\n            # fill missing video ids\n            self._fill_video_ids_inplace(curr_data)\n\n            # make track ids unique over whole evaluation set\n            self._make_tk_ids_unique(curr_data)\n\n            # get tracker sequence information\n            curr_vids2tracks, curr_vids2images = self._compute_vid_mappings(curr_data)\n            self.tracker_data[tracker][\"vids_to_tracks\"] = curr_vids2tracks\n            self.tracker_data[tracker][\"vids_to_images\"] = curr_vids2images\n\n    def get_display_name(self, tracker):\n        return self.tracker_to_disp[tracker]\n\n    def _load_raw_file(self, tracker, seq, is_gt):\n        \"\"\"Load a file (gt or tracker) in the TAO format\n\n        If is_gt, this returns a dict which contains the fields:\n        [gt_ids, gt_classes]:\n            list (for each timestep) of 1D NDArrays (for each det).\n        [gt_dets]: list (for each timestep) of lists of detections.\n\n        if not is_gt, this returns a dict which contains the fields:\n        [tk_ids, tk_classes]:\n            list (for each timestep) of 1D NDArrays (for each det).\n        [tk_dets]: list (for each timestep) of lists of detections.\n        \"\"\"\n        seq_id = self.seq_name2seqid[seq]\n        # file location\n        if is_gt:\n            imgs = self.video2gt_image[seq_id]\n        else:\n            imgs = self.tracker_data[tracker][\"vids_to_images\"][seq_id]\n\n        # convert data to required format\n        num_timesteps = self.seq_lengths[seq_id]\n        img_to_timestep = self.seq2images2timestep[seq_id]\n        data_keys = [\"ids\", \"classes\", \"dets\"]\n        # if not is_gt:\n        #     data_keys += [\"tk_confidences\"]\n        raw_data = {key: [None] * num_timesteps for key in data_keys}\n        for img in imgs:\n            # some tracker data contains images without any ground truth info,\n            # these are ignored\n            if img[\"id\"] not in img_to_timestep:\n                continue\n            t = img_to_timestep[img[\"id\"]]\n            anns = img[\"annotations\"]\n            tk_str = utils.get_track_id_str(anns[0])\n\n            if self.use_mask:\n                # When using mask, extract segmentation data\n                raw_data[\"dets\"][t] = [ann.get(\"segmentation\") for ann in anns]\n            else:\n                # When using bbox, extract bbox data\n                raw_data[\"dets\"][t] = np.atleast_2d([ann[\"bbox\"] for ann in anns]).astype(\n                    float\n                )\n            raw_data[\"ids\"][t] = np.atleast_1d([ann[tk_str] for ann in anns]).astype(\n                int\n            )\n            raw_data[\"classes\"][t] = np.atleast_1d(\n                [ann[\"category_id\"] for ann in anns]\n            ).astype(int)\n            # if not is_gt:\n            #     raw_data[\"tk_confidences\"][t] = np.atleast_1d(\n            #         [ann[\"score\"] for ann in anns]\n            #     ).astype(float)\n\n        for t, d in enumerate(raw_data[\"dets\"]):\n            if d is None:\n                raw_data[\"dets\"][t] = np.empty((0, 4)).astype(float)\n                raw_data[\"ids\"][t] = np.empty(0).astype(int)\n                raw_data[\"classes\"][t] = np.empty(0).astype(int)\n                # if not is_gt:\n                #     raw_data[\"tk_confidences\"][t] = np.empty(0)\n\n        if is_gt:\n            key_map = {\"ids\": \"gt_ids\", \"classes\": \"gt_classes\", \"dets\": \"gt_dets\"}\n        else:\n            key_map = {\"ids\": \"tk_ids\", \"classes\": \"tk_classes\", \"dets\": \"tk_dets\"}\n        for k, v in key_map.items():\n            raw_data[v] = raw_data.pop(k)\n\n        raw_data[\"num_timesteps\"] = num_timesteps\n        raw_data[\"seq\"] = seq\n        return raw_data\n\n    def get_preprocessed_seq_data_thr(self, raw_data, cls, assignment=None):\n        \"\"\"Preprocess data for a single sequence for a single class.\n\n        Inputs:\n            raw_data: dict containing the data for the sequence already\n                read in by get_raw_seq_data().\n            cls: class to be evaluated.\n        Outputs:\n            gt_ids:\n                list (for each timestep) of ids of GT tracks\n            tk_ids:\n                list (for each timestep) of ids of predicted tracks (all for TP\n                matching (Det + AssocA))\n            tk_overlap_ids:\n                list (for each timestep) of ids of predicted tracks that overlap\n                with GTs\n            tk_dets:\n                list (for each timestep) of lists of detections that\n                corresponding to the tk_ids\n            tk_classes:\n                list (for each timestep) of lists of classes that corresponding\n                to the tk_ids\n            tk_confidences:\n                list (for each timestep) of lists of classes that corresponding\n                to the tk_ids\n            sim_scores:\n                similarity score between gt_ids and tk_ids.\n        \"\"\"\n        if cls != \"all\":\n            cls_id = self.cls_name2clsid[cls]\n\n        data_keys = [\n            \"gt_ids\",\n            \"tk_ids\",\n            \"gt_id_map\",\n            \"tk_id_map\",\n            \"gt_dets\",\n            \"gt_classes\",\n            \"gt_class_name\",\n            \"tk_overlap_classes\",\n            \"tk_overlap_ids\",\n            \"tk_class_eval_tk_ids\",\n            \"tk_dets\",\n            \"tk_classes\",\n            # \"tk_confidences\",\n            \"tk_exh_ids\",\n            \"sim_scores\",\n        ]\n        data = {key: [None] * raw_data[\"num_timesteps\"] for key in data_keys}\n        unique_gt_ids = []\n        unique_tk_ids = []\n        num_gt_dets = 0\n        num_tk_cls_dets = 0\n        num_tk_overlap_dets = 0\n        overlap_ious_thr = 0.5\n        loc_and_asso_tk_ids = []\n        exh_class_tk_ids = []\n\n        for t in range(raw_data[\"num_timesteps\"]):\n            # only extract relevant dets for this class for preproc and eval\n            if cls == \"all\":\n                gt_class_mask = np.ones_like(raw_data[\"gt_classes\"][t]).astype(bool)\n            else:\n                gt_class_mask = np.atleast_1d(\n                    raw_data[\"gt_classes\"][t] == cls_id\n                ).astype(bool)\n\n            # select GT that is not in the evaluating classes\n            if assignment is not None and assignment:\n                all_gt_ids = list(assignment[t].keys())\n                gt_ids_in = raw_data[\"gt_ids\"][t][gt_class_mask]\n                gt_ids_out = set(all_gt_ids) - set(gt_ids_in)\n                tk_ids_out = set([assignment[t][key] for key in list(gt_ids_out)])\n\n            # compute overlapped tracks and add their ids to overlap_tk_ids\n            sim_scores = raw_data[\"similarity_scores\"]\n            overlap_ids_masks = (sim_scores[t][gt_class_mask] >= overlap_ious_thr).any(\n                axis=0\n            )\n            overlap_tk_ids_t = raw_data[\"tk_ids\"][t][overlap_ids_masks]\n            if assignment is not None and assignment:\n                data[\"tk_overlap_ids\"][t] = list(set(overlap_tk_ids_t) - tk_ids_out)\n            else:\n                data[\"tk_overlap_ids\"][t] = list(set(overlap_tk_ids_t))\n\n            loc_and_asso_tk_ids += data[\"tk_overlap_ids\"][t]\n\n            data[\"tk_exh_ids\"][t] = []\n            if cls == \"all\":\n                continue\n\n            # add the track ids of exclusive annotated class to exh_class_tk_ids\n            tk_exh_mask = np.atleast_1d(raw_data[\"tk_classes\"][t] == cls_id)\n            tk_exh_mask = tk_exh_mask.astype(bool)\n            exh_class_tk_ids_t = raw_data[\"tk_ids\"][t][tk_exh_mask]\n            exh_class_tk_ids.append(exh_class_tk_ids_t)\n            data[\"tk_exh_ids\"][t] = exh_class_tk_ids_t\n\n        # remove tk_ids that has been assigned to GT belongs to other classes.\n        loc_and_asso_tk_ids = list(set(loc_and_asso_tk_ids))\n\n        # remove all unwanted unmatched tracker detections\n        for t in range(raw_data[\"num_timesteps\"]):\n            # add gt to the data\n            if cls == \"all\":\n                gt_class_mask = np.ones_like(raw_data[\"gt_classes\"][t]).astype(bool)\n            else:\n                gt_class_mask = np.atleast_1d(\n                    raw_data[\"gt_classes\"][t] == cls_id\n                ).astype(bool)\n                data[\"gt_classes\"][t] = cls_id\n                data[\"gt_class_name\"][t] = cls\n\n            gt_ids = raw_data[\"gt_ids\"][t][gt_class_mask]\n            if self.use_mask:\n                gt_dets = [raw_data['gt_dets'][t][ind] for ind in range(len(gt_class_mask)) if gt_class_mask[ind]]\n            else:\n                gt_dets = raw_data[\"gt_dets\"][t][gt_class_mask]\n            data[\"gt_ids\"][t] = gt_ids\n            data[\"gt_dets\"][t] = gt_dets\n\n            # filter pred and only keep those that highly overlap with GTs\n            tk_mask = np.isin(\n                raw_data[\"tk_ids\"][t], np.array(loc_and_asso_tk_ids), assume_unique=True\n            )\n            tk_overlap_mask = np.isin(\n                raw_data[\"tk_ids\"][t],\n                np.array(data[\"tk_overlap_ids\"][t]),\n                assume_unique=True,\n            )\n\n            tk_ids = raw_data[\"tk_ids\"][t][tk_mask]\n            if self.use_mask:\n                tk_dets = [raw_data['tk_dets'][t][ind] for ind in range(len(tk_mask)) if\n                            tk_mask[ind]]\n            else:\n                tk_dets = raw_data[\"tk_dets\"][t][tk_mask]\n\n            tracker_classes = raw_data[\"tk_classes\"][t][tk_mask]\n\n            # add overlap classes for computing the FP for Cls term\n            tracker_overlap_classes = raw_data[\"tk_classes\"][t][tk_overlap_mask]\n            # tracker_confidences = raw_data[\"tk_confidences\"][t][tk_mask]\n            sim_scores_masked = sim_scores[t][gt_class_mask, :][:, tk_mask]\n\n            # add filtered prediction to the data\n            data[\"tk_classes\"][t] = tracker_classes\n            data[\"tk_overlap_classes\"][t] = tracker_overlap_classes\n            data[\"tk_ids\"][t] = tk_ids\n            data[\"tk_dets\"][t] = tk_dets\n            # data[\"tk_confidences\"][t] = tracker_confidences\n            data[\"sim_scores\"][t] = sim_scores_masked\n            data[\"tk_class_eval_tk_ids\"][t] = set(\n                list(data[\"tk_overlap_ids\"][t]) + list(data[\"tk_exh_ids\"][t])\n            )\n\n            # count total number of detections\n            unique_gt_ids += list(np.unique(data[\"gt_ids\"][t]))\n            # the unique track ids are for association.\n            unique_tk_ids += list(np.unique(data[\"tk_ids\"][t]))\n\n            num_tk_overlap_dets += len(data[\"tk_overlap_ids\"][t])\n            num_tk_cls_dets += len(data[\"tk_class_eval_tk_ids\"][t])\n            num_gt_dets += len(data[\"gt_ids\"][t])\n\n        # re-label IDs such that there are no empty IDs\n        if len(unique_gt_ids) > 0:\n            unique_gt_ids = np.unique(unique_gt_ids)\n            gt_id_map = np.nan * np.ones((np.max(unique_gt_ids) + 1))\n            gt_id_map[unique_gt_ids] = np.arange(len(unique_gt_ids))\n            data[\"gt_id_map\"] = {}\n            for gt_id in unique_gt_ids:\n                new_gt_id = gt_id_map[gt_id].astype(int)\n                data[\"gt_id_map\"][new_gt_id] = gt_id\n\n            for t in range(raw_data[\"num_timesteps\"]):\n                if len(data[\"gt_ids\"][t]) > 0:\n                    data[\"gt_ids\"][t] = gt_id_map[data[\"gt_ids\"][t]].astype(int)\n\n        if len(unique_tk_ids) > 0:\n            unique_tk_ids = np.unique(unique_tk_ids)\n            tk_id_map = np.nan * np.ones((np.max(unique_tk_ids) + 1))\n            tk_id_map[unique_tk_ids] = np.arange(len(unique_tk_ids))\n\n            data[\"tk_id_map\"] = {}\n            for track_id in unique_tk_ids:\n                new_track_id = tk_id_map[track_id].astype(int)\n                data[\"tk_id_map\"][new_track_id] = track_id\n\n            for t in range(raw_data[\"num_timesteps\"]):\n                if len(data[\"tk_ids\"][t]) > 0:\n                    data[\"tk_ids\"][t] = tk_id_map[data[\"tk_ids\"][t]].astype(int)\n                if len(data[\"tk_overlap_ids\"][t]) > 0:\n                    data[\"tk_overlap_ids\"][t] = tk_id_map[\n                        data[\"tk_overlap_ids\"][t]\n                    ].astype(int)\n\n        # record overview statistics.\n        data[\"num_tk_cls_dets\"] = num_tk_cls_dets\n        data[\"num_tk_overlap_dets\"] = num_tk_overlap_dets\n        data[\"num_gt_dets\"] = num_gt_dets\n        data[\"num_tk_ids\"] = len(unique_tk_ids)\n        data[\"num_gt_ids\"] = len(unique_gt_ids)\n        data[\"num_timesteps\"] = raw_data[\"num_timesteps\"]\n        data[\"seq\"] = raw_data[\"seq\"]\n\n        self._check_unique_ids(data)\n\n        return data\n\n    @_timing.time\n    def get_preprocessed_seq_data(\n        self, raw_data, cls, assignment=None, thresholds=[50, 75]\n    ):\n        \"\"\"Preprocess data for a single sequence for a single class.\"\"\"\n        data = {}\n        if thresholds is None:\n            thresholds = [50, 75]\n        elif isinstance(thresholds, int):\n            thresholds = [thresholds]\n\n        for thr in thresholds:\n            assignment_thr = None\n            if assignment is not None:\n                assignment_thr = assignment[thr]\n            data[thr] = self.get_preprocessed_seq_data_thr(\n                raw_data, cls, assignment_thr\n            )\n\n        return data\n\n    def _calculate_similarities(self, gt_dets_t, tk_dets_t):\n        \"\"\"Compute similarity scores.\"\"\"\n        if self.use_mask:\n            similarity_scores = self._calculate_mask_ious(gt_dets_t, tk_dets_t, is_encoded=True, do_ioa=False)\n        else:\n            similarity_scores = self._calculate_box_ious(gt_dets_t, tk_dets_t)\n        return similarity_scores\n\n    def _compute_vid_mappings(self, annotations):\n        \"\"\"Computes mappings from videos to corresponding tracks and images.\"\"\"\n        vids_to_tracks = {}\n        vids_to_imgs = {}\n        vid_ids = [vid[\"id\"] for vid in self.gt_data[\"videos\"]]\n\n        # compute an mapping from image IDs to images\n        images = {}\n        for image in self.gt_data[\"images\"]:\n            images[image[\"id\"]] = image\n\n        tk_str = utils.get_track_id_str(annotations[0])\n        for ann in annotations:\n            ann[\"area\"] = ann[\"bbox\"][2] * ann[\"bbox\"][3]\n\n            vid = ann[\"video_id\"]\n            if ann[\"video_id\"] not in vids_to_tracks.keys():\n                vids_to_tracks[ann[\"video_id\"]] = list()\n            if ann[\"video_id\"] not in vids_to_imgs.keys():\n                vids_to_imgs[ann[\"video_id\"]] = list()\n\n            # fill in vids_to_tracks\n            tid = ann[tk_str]\n            exist_tids = [track[\"id\"] for track in vids_to_tracks[vid]]\n            try:\n                index1 = exist_tids.index(tid)\n            except ValueError:\n                index1 = -1\n            if tid not in exist_tids:\n                curr_track = {\n                    \"id\": tid,\n                    \"category_id\": ann[\"category_id\"],\n                    \"video_id\": vid,\n                    \"annotations\": [ann],\n                }\n                vids_to_tracks[vid].append(curr_track)\n            else:\n                vids_to_tracks[vid][index1][\"annotations\"].append(ann)\n\n            # fill in vids_to_imgs\n            img_id = ann[\"image_id\"]\n            exist_img_ids = [img[\"id\"] for img in vids_to_imgs[vid]]\n            try:\n                index2 = exist_img_ids.index(img_id)\n            except ValueError:\n                index2 = -1\n            if index2 == -1:\n                curr_img = {\"id\": img_id, \"annotations\": [ann]}\n                vids_to_imgs[vid].append(curr_img)\n            else:\n                vids_to_imgs[vid][index2][\"annotations\"].append(ann)\n\n        # sort annotations by frame index and compute track area\n        for vid, tracks in vids_to_tracks.items():\n            for track in tracks:\n                track[\"annotations\"] = sorted(\n                    track[\"annotations\"],\n                    key=lambda x: images[x[\"image_id\"]][\"frame_id\"],\n                )\n                # compute average area\n                track[\"area\"] = sum(x[\"area\"] for x in track[\"annotations\"]) / len(\n                    track[\"annotations\"]\n                )\n\n        # ensure all videos are present\n        for vid_id in vid_ids:\n            if vid_id not in vids_to_tracks.keys():\n                vids_to_tracks[vid_id] = []\n            if vid_id not in vids_to_imgs.keys():\n                vids_to_imgs[vid_id] = []\n\n        return vids_to_tracks, vids_to_imgs\n\n    def _compute_image_to_timestep_mappings(self):\n        \"\"\"Computes a mapping from images to timestep in sequence.\"\"\"\n        images = {}\n        for image in self.gt_data[\"images\"]:\n            images[image[\"id\"]] = image\n\n        seq_to_imgs_to_timestep = {vid[\"id\"]: dict() for vid in self.gt_data[\"videos\"]}\n        for vid in seq_to_imgs_to_timestep:\n            curr_imgs = [img[\"id\"] for img in self.video2gt_image[vid]]\n            curr_imgs = sorted(curr_imgs, key=lambda x: images[x][\"frame_id\"])\n            seq_to_imgs_to_timestep[vid] = {\n                curr_imgs[i]: i for i in range(len(curr_imgs))\n            }\n\n        return seq_to_imgs_to_timestep\n\n    def _limit_dets_per_image(self, annotations):\n        \"\"\"Limits the number of detections for each image.\n\n        Adapted from https://github.com/TAO-Dataset/.\n        \"\"\"\n        max_dets = self.config[\"MAX_DETECTIONS\"]\n        img_ann = defaultdict(list)\n        for ann in annotations:\n            img_ann[ann[\"image_id\"]].append(ann)\n\n        for img_id, _anns in img_ann.items():\n            if len(_anns) <= max_dets:\n                continue\n            _anns = sorted(_anns, key=lambda x: x[\"score\"], reverse=True)\n            img_ann[img_id] = _anns[:max_dets]\n\n        return [ann for anns in img_ann.values() for ann in anns]\n\n    def _fill_video_ids_inplace(self, annotations):\n        \"\"\"Fills in missing video IDs inplace.\n\n        Adapted from https://github.com/TAO-Dataset/.\n        \"\"\"\n        missing_video_id = [x for x in annotations if \"video_id\" not in x]\n        if missing_video_id:\n            image_id_to_video_id = {\n                x[\"id\"]: x[\"video_id\"] for x in self.gt_data[\"images\"]\n            }\n            for x in missing_video_id:\n                x[\"video_id\"] = image_id_to_video_id[x[\"image_id\"]]\n\n    @staticmethod\n    def _make_tk_ids_unique(annotations):\n        \"\"\"Makes track IDs unqiue over the whole annotation set.\n\n        Adapted from https://github.com/TAO-Dataset/.\n        \"\"\"\n        track_id_videos = {}\n        track_ids_to_update = set()\n        max_track_id = 0\n\n        tk_str = utils.get_track_id_str(annotations[0])\n        for ann in annotations:\n            t = int(ann[tk_str])\n            if t not in track_id_videos:\n                track_id_videos[t] = ann[\"video_id\"]\n\n            if ann[\"video_id\"] != track_id_videos[t]:\n                # track id is assigned to multiple videos\n                track_ids_to_update.add(t)\n            max_track_id = max(max_track_id, t)\n\n        if track_ids_to_update:\n            print(\"true\")\n            next_id = itertools.count(max_track_id + 1)\n            new_tk_ids = defaultdict(lambda: next(next_id))\n            for ann in annotations:\n                t = ann[tk_str]\n                v = ann[\"video_id\"]\n                if t in track_ids_to_update:\n                    ann[tk_str] = new_tk_ids[t, v]\n        return len(track_ids_to_update)\n"
  },
  {
    "path": "sam3/eval/teta_eval_toolkit/datasets/tao.py",
    "content": "# fmt: off\n# flake8: noqa\n\n# pyre-unsafe\n\n\"\"\"TAO Dataset.\"\"\"\nimport copy\nimport itertools\nimport json\nimport os\nfrom collections import defaultdict\n\nimport numpy as np\n\nfrom .. import _timing\nfrom ..config import get_default_dataset_config, init_config\nfrom ..utils import TrackEvalException\nfrom ._base_dataset import _BaseDataset\n\n\nclass TAO(_BaseDataset):\n    \"\"\"Dataset class for TAO tracking\"\"\"\n\n    def __init__(self, config=None):\n        \"\"\"Initialize dataset, checking that all required files are present.\"\"\"\n        super().__init__()\n        # Fill non-given config values with defaults\n        self.config = init_config(config, get_default_dataset_config(), self.get_name())\n        self.gt_fol = self.config[\"GT_FOLDER\"]\n        self.tracker_fol = self.config[\"TRACKERS_FOLDER\"]\n        self.should_classes_combine = True\n        self.use_super_categories = False\n        self.use_mask = self.config[\"USE_MASK\"]\n\n\n        self.tracker_sub_fol = self.config[\"TRACKER_SUB_FOLDER\"]\n        self.output_fol = self.config[\"OUTPUT_FOLDER\"]\n        if self.output_fol is None:\n            self.output_fol = self.tracker_fol\n        self.output_sub_fol = self.config[\"OUTPUT_SUB_FOLDER\"]\n\n        if self.gt_fol.endswith(\".json\"):\n            self.gt_data = json.load(open(self.gt_fol, \"r\"))\n        else:\n            gt_dir_files = [\n                file for file in os.listdir(self.gt_fol) if file.endswith(\".json\")\n            ]\n            if len(gt_dir_files) != 1:\n                raise TrackEvalException(\n                    f\"{self.gt_fol} does not contain exactly one json file.\"\n                )\n\n            with open(os.path.join(self.gt_fol, gt_dir_files[0])) as f:\n                self.gt_data = json.load(f)\n\n        # merge categories marked with a merged tag in TAO dataset\n        self._merge_categories(self.gt_data[\"annotations\"] + self.gt_data[\"tracks\"])\n\n        # get sequences to eval and sequence information\n        self.seq_list = [\n            vid[\"name\"].replace(\"/\", \"-\") for vid in self.gt_data[\"videos\"]\n        ]\n        self.seq_name2seqid = {\n            vid[\"name\"].replace(\"/\", \"-\"): vid[\"id\"] for vid in self.gt_data[\"videos\"]\n        }\n        # compute mappings from videos to annotation data\n        self.video2gt_track, self.video2gt_image = self._compute_vid_mappings(\n            self.gt_data[\"annotations\"]\n        )\n        # compute sequence lengths\n        self.seq_lengths = {vid[\"id\"]: 0 for vid in self.gt_data[\"videos\"]}\n        for img in self.gt_data[\"images\"]:\n            self.seq_lengths[img[\"video_id\"]] += 1\n        self.seq2images2timestep = self._compute_image_to_timestep_mappings()\n        self.seq2cls = {\n            vid[\"id\"]: {\n                \"pos_cat_ids\": list(\n                    {track[\"category_id\"] for track in self.video2gt_track[vid[\"id\"]]}\n                ),\n                \"neg_cat_ids\": vid[\"neg_category_ids\"],\n                \"not_exh_labeled_cat_ids\": vid[\"not_exhaustive_category_ids\"],\n            }\n            for vid in self.gt_data[\"videos\"]\n        }\n\n        # Get classes to eval\n        considered_vid_ids = [self.seq_name2seqid[vid] for vid in self.seq_list]\n        seen_cats = set(\n            [\n                cat_id\n                for vid_id in considered_vid_ids\n                for cat_id in self.seq2cls[vid_id][\"pos_cat_ids\"]\n            ]\n        )\n        # only classes with ground truth are evaluated in TAO\n        self.valid_classes = [\n            cls[\"name\"] for cls in self.gt_data[\"categories\"] if cls[\"id\"] in seen_cats\n        ]\n        cls_name2clsid_map = {\n            cls[\"name\"]: cls[\"id\"] for cls in self.gt_data[\"categories\"]\n        }\n\n        if self.config[\"CLASSES_TO_EVAL\"]:\n            self.class_list = [\n                cls.lower() if cls.lower() in self.valid_classes else None\n                for cls in self.config[\"CLASSES_TO_EVAL\"]\n            ]\n            if not all(self.class_list):\n                valid_cls = \", \".join(self.valid_classes)\n                raise TrackEvalException(\n                    \"Attempted to evaluate an invalid class. Only classes \"\n                    f\"{valid_cls} are valid (classes present in ground truth\"\n                    \" data).\"\n                )\n        else:\n            self.class_list = [cls for cls in self.valid_classes]\n        self.cls_name2clsid = {\n            k: v for k, v in cls_name2clsid_map.items() if k in self.class_list\n        }\n        self.clsid2cls_name = {\n            v: k for k, v in cls_name2clsid_map.items() if k in self.class_list\n        }\n        # get trackers to eval\n        print(self.config[\"TRACKERS_TO_EVAL\"] )\n        if self.config[\"TRACKERS_TO_EVAL\"] is None:\n            self.tracker_list = os.listdir(self.tracker_fol)\n        else:\n            self.tracker_list = self.config[\"TRACKERS_TO_EVAL\"]\n\n        if self.config[\"TRACKER_DISPLAY_NAMES\"] is None:\n            self.tracker_to_disp = dict(zip(self.tracker_list, self.tracker_list))\n        elif (self.config[\"TRACKERS_TO_EVAL\"] is not None) and (\n            len(self.config[\"TK_DISPLAY_NAMES\"]) == len(self.tracker_list)\n        ):\n            self.tracker_to_disp = dict(\n                zip(self.tracker_list, self.config[\"TK_DISPLAY_NAMES\"])\n            )\n        else:\n            raise TrackEvalException(\n                \"List of tracker files and tracker display names do not match.\"\n            )\n\n        self.tracker_data = {tracker: dict() for tracker in self.tracker_list}\n\n        for tracker in self.tracker_list:\n            if self.tracker_sub_fol.endswith(\".json\"):\n                with open(os.path.join(self.tracker_sub_fol)) as f:\n                    curr_data = json.load(f)\n            else:\n                tr_dir = os.path.join(self.tracker_fol, tracker, self.tracker_sub_fol)\n                tr_dir_files = [\n                    file for file in os.listdir(tr_dir) if file.endswith(\".json\")\n                ]\n                if len(tr_dir_files) != 1:\n                    raise TrackEvalException(\n                        f\"{tr_dir} does not contain exactly one json file.\"\n                    )\n                with open(os.path.join(tr_dir, tr_dir_files[0])) as f:\n                    curr_data = json.load(f)\n\n            # limit detections if MAX_DETECTIONS > 0\n            if self.config[\"MAX_DETECTIONS\"]:\n                curr_data = self._limit_dets_per_image(curr_data)\n\n            # fill missing video ids\n            self._fill_video_ids_inplace(curr_data)\n\n            # make track ids unique over whole evaluation set\n            self._make_tk_ids_unique(curr_data)\n\n            # merge categories marked with a merged tag in TAO dataset\n            self._merge_categories(curr_data)\n\n            # get tracker sequence information\n            curr_vids2tracks, curr_vids2images = self._compute_vid_mappings(curr_data)\n            self.tracker_data[tracker][\"vids_to_tracks\"] = curr_vids2tracks\n            self.tracker_data[tracker][\"vids_to_images\"] = curr_vids2images\n\n    def get_display_name(self, tracker):\n        return self.tracker_to_disp[tracker]\n\n    def _load_raw_file(self, tracker, seq, is_gt):\n        \"\"\"Load a file (gt or tracker) in the TAO format\n\n        If is_gt, this returns a dict which contains the fields:\n        [gt_ids, gt_classes]:\n            list (for each timestep) of 1D NDArrays (for each det).\n        [gt_dets]: list (for each timestep) of lists of detections.\n\n        if not is_gt, this returns a dict which contains the fields:\n        [tk_ids, tk_classes, tk_confidences]:\n            list (for each timestep) of 1D NDArrays (for each det).\n        [tk_dets]: list (for each timestep) of lists of detections.\n        \"\"\"\n        seq_id = self.seq_name2seqid[seq]\n        # file location\n        if is_gt:\n            imgs = self.video2gt_image[seq_id]\n        else:\n            imgs = self.tracker_data[tracker][\"vids_to_images\"][seq_id]\n\n        # convert data to required format\n        num_timesteps = self.seq_lengths[seq_id]\n        img_to_timestep = self.seq2images2timestep[seq_id]\n        data_keys = [\"ids\", \"classes\", \"dets\"]\n        if not is_gt:\n            data_keys += [\"tk_confidences\"]\n        raw_data = {key: [None] * num_timesteps for key in data_keys}\n        for img in imgs:\n            # some tracker data contains images without any ground truth info,\n            # these are ignored\n            if img[\"id\"] not in img_to_timestep:\n                continue\n            t = img_to_timestep[img[\"id\"]]\n            anns = img[\"annotations\"]\n            if self.use_mask:\n                # When using mask, extract segmentation data\n                raw_data[\"dets\"][t] = [ann.get(\"segmentation\") for ann in anns]\n            else:\n                # When using bbox, extract bbox data\n                raw_data[\"dets\"][t] = np.atleast_2d([ann[\"bbox\"] for ann in anns]).astype(\n                    float\n                )\n            raw_data[\"ids\"][t] = np.atleast_1d(\n                [ann[\"track_id\"] for ann in anns]\n            ).astype(int)\n            raw_data[\"classes\"][t] = np.atleast_1d(\n                [ann[\"category_id\"] for ann in anns]\n            ).astype(int)\n            if not is_gt:\n                raw_data[\"tk_confidences\"][t] = np.atleast_1d(\n                    [ann[\"score\"] for ann in anns]\n                ).astype(float)\n\n        for t, d in enumerate(raw_data[\"dets\"]):\n            if d is None:\n                raw_data[\"dets\"][t] = np.empty((0, 4)).astype(float)\n                raw_data[\"ids\"][t] = np.empty(0).astype(int)\n                raw_data[\"classes\"][t] = np.empty(0).astype(int)\n                if not is_gt:\n                    raw_data[\"tk_confidences\"][t] = np.empty(0)\n\n        if is_gt:\n            key_map = {\"ids\": \"gt_ids\", \"classes\": \"gt_classes\", \"dets\": \"gt_dets\"}\n        else:\n            key_map = {\"ids\": \"tk_ids\", \"classes\": \"tk_classes\", \"dets\": \"tk_dets\"}\n        for k, v in key_map.items():\n            raw_data[v] = raw_data.pop(k)\n\n        raw_data[\"num_timesteps\"] = num_timesteps\n        raw_data[\"neg_cat_ids\"] = self.seq2cls[seq_id][\"neg_cat_ids\"]\n        raw_data[\"not_exh_labeled_cls\"] = self.seq2cls[seq_id][\n            \"not_exh_labeled_cat_ids\"\n        ]\n        raw_data[\"seq\"] = seq\n        return raw_data\n\n    def get_preprocessed_seq_data_thr(self, raw_data, cls, assignment=None):\n        \"\"\"Preprocess data for a single sequence for a single class.\n\n        Inputs:\n            raw_data: dict containing the data for the sequence already\n                read in by get_raw_seq_data().\n            cls: class to be evaluated.\n        Outputs:\n            gt_ids:\n                list (for each timestep) of ids of GT tracks\n            tk_ids:\n                list (for each timestep) of ids of predicted tracks (all for TP\n                matching (Det + AssocA))\n            tk_overlap_ids:\n                list (for each timestep) of ids of predicted tracks that overlap\n                with GTs\n            tk_neg_ids:\n                list (for each timestep) of ids of predicted tracks that with\n                the class id on the negative list for the current sequence.\n            tk_exh_ids:\n                list (for each timestep) of ids of predicted tracks that do not\n                overlap with existing GTs but have the class id on the\n                exhaustive annotated class list for the current sequence.\n            tk_dets:\n                list (for each timestep) of lists of detections that\n                corresponding to the tk_ids\n            tk_classes:\n                list (for each timestep) of lists of classes that corresponding\n                to the tk_ids\n            tk_confidences:\n                list (for each timestep) of lists of classes that corresponding\n                to the tk_ids\n            sim_scores:\n                similarity score between gt_ids and tk_ids.\n        \"\"\"\n        if cls != \"all\":\n            cls_id = self.cls_name2clsid[cls]\n\n        data_keys = [\n            \"gt_ids\",\n            \"tk_ids\",\n            \"gt_id_map\",\n            \"tk_id_map\",\n            \"gt_dets\",\n            \"gt_classes\",\n            \"gt_class_name\",\n            \"tk_overlap_classes\",\n            \"tk_overlap_ids\",\n            \"tk_neg_ids\",\n            \"tk_exh_ids\",\n            \"tk_class_eval_tk_ids\",\n            \"tk_dets\",\n            \"tk_classes\",\n            \"tk_confidences\",\n            \"sim_scores\",\n        ]\n        data = {key: [None] * raw_data[\"num_timesteps\"] for key in data_keys}\n        unique_gt_ids = []\n        unique_tk_ids = []\n        num_gt_dets = 0\n        num_tk_cls_dets = 0\n        num_tk_overlap_dets = 0\n        overlap_ious_thr = 0.5\n        loc_and_asso_tk_ids = []\n\n        for t in range(raw_data[\"num_timesteps\"]):\n            # only extract relevant dets for this class for preproc and eval\n            if cls == \"all\":\n                gt_class_mask = np.ones_like(raw_data[\"gt_classes\"][t]).astype(bool)\n            else:\n                gt_class_mask = np.atleast_1d(\n                    raw_data[\"gt_classes\"][t] == cls_id\n                ).astype(bool)\n\n            # select GT that is not in the evaluating classes\n            if assignment is not None and assignment:\n                all_gt_ids = list(assignment[t].keys())\n                gt_ids_in = raw_data[\"gt_ids\"][t][gt_class_mask]\n                gt_ids_out = set(all_gt_ids) - set(gt_ids_in)\n                tk_ids_out = set([assignment[t][key] for key in list(gt_ids_out)])\n\n            # compute overlapped tracks and add their ids to overlap_tk_ids\n            sim_scores = raw_data[\"similarity_scores\"]\n            overlap_ids_masks = (sim_scores[t][gt_class_mask] >= overlap_ious_thr).any(\n                axis=0\n            )\n            overlap_tk_ids_t = raw_data[\"tk_ids\"][t][overlap_ids_masks]\n            if assignment is not None and assignment:\n                data[\"tk_overlap_ids\"][t] = list(set(overlap_tk_ids_t) - tk_ids_out)\n            else:\n                data[\"tk_overlap_ids\"][t] = list(set(overlap_tk_ids_t))\n\n            loc_and_asso_tk_ids += data[\"tk_overlap_ids\"][t]\n\n            data[\"tk_exh_ids\"][t] = []\n            data[\"tk_neg_ids\"][t] = []\n\n            if cls == \"all\":\n                continue\n\n        # remove tk_ids that has been assigned to GT belongs to other classes.\n        loc_and_asso_tk_ids = list(set(loc_and_asso_tk_ids))\n\n        # remove all unwanted unmatched tracker detections\n        for t in range(raw_data[\"num_timesteps\"]):\n            # add gt to the data\n            if cls == \"all\":\n                gt_class_mask = np.ones_like(raw_data[\"gt_classes\"][t]).astype(bool)\n            else:\n                gt_class_mask = np.atleast_1d(\n                    raw_data[\"gt_classes\"][t] == cls_id\n                ).astype(bool)\n                data[\"gt_classes\"][t] = cls_id\n                data[\"gt_class_name\"][t] = cls\n\n            gt_ids = raw_data[\"gt_ids\"][t][gt_class_mask]\n            if self.use_mask:\n                gt_dets = [raw_data['gt_dets'][t][ind] for ind in range(len(gt_class_mask)) if gt_class_mask[ind]]\n            else:\n                gt_dets = raw_data[\"gt_dets\"][t][gt_class_mask]\n            data[\"gt_ids\"][t] = gt_ids\n            data[\"gt_dets\"][t] = gt_dets\n\n            # filter pred and only keep those that highly overlap with GTs\n            tk_mask = np.isin(\n                raw_data[\"tk_ids\"][t], np.array(loc_and_asso_tk_ids), assume_unique=True\n            )\n            tk_overlap_mask = np.isin(\n                raw_data[\"tk_ids\"][t],\n                np.array(data[\"tk_overlap_ids\"][t]),\n                assume_unique=True,\n            )\n\n            tk_ids = raw_data[\"tk_ids\"][t][tk_mask]\n            if self.use_mask:\n                tk_dets = [raw_data['tk_dets'][t][ind] for ind in range(len(tk_mask)) if\n                            tk_mask[ind]]\n            else:\n                tk_dets = raw_data[\"tk_dets\"][t][tk_mask]\n            tracker_classes = raw_data[\"tk_classes\"][t][tk_mask]\n\n            # add overlap classes for computing the FP for Cls term\n            tracker_overlap_classes = raw_data[\"tk_classes\"][t][tk_overlap_mask]\n            tracker_confidences = raw_data[\"tk_confidences\"][t][tk_mask]\n            sim_scores_masked = sim_scores[t][gt_class_mask, :][:, tk_mask]\n\n            # add filtered prediction to the data\n            data[\"tk_classes\"][t] = tracker_classes\n            data[\"tk_overlap_classes\"][t] = tracker_overlap_classes\n            data[\"tk_ids\"][t] = tk_ids\n            data[\"tk_dets\"][t] = tk_dets\n            data[\"tk_confidences\"][t] = tracker_confidences\n            data[\"sim_scores\"][t] = sim_scores_masked\n            data[\"tk_class_eval_tk_ids\"][t] = set(\n                list(data[\"tk_overlap_ids\"][t])\n                + list(data[\"tk_neg_ids\"][t])\n                + list(data[\"tk_exh_ids\"][t])\n            )\n\n            # count total number of detections\n            unique_gt_ids += list(np.unique(data[\"gt_ids\"][t]))\n            # the unique track ids are for association.\n            unique_tk_ids += list(np.unique(data[\"tk_ids\"][t]))\n\n            num_tk_overlap_dets += len(data[\"tk_overlap_ids\"][t])\n            num_tk_cls_dets += len(data[\"tk_class_eval_tk_ids\"][t])\n            num_gt_dets += len(data[\"gt_ids\"][t])\n\n        # re-label IDs such that there are no empty IDs\n        if len(unique_gt_ids) > 0:\n            unique_gt_ids = np.unique(unique_gt_ids)\n            gt_id_map = np.nan * np.ones((np.max(unique_gt_ids) + 1))\n            gt_id_map[unique_gt_ids] = np.arange(len(unique_gt_ids))\n            data[\"gt_id_map\"] = {}\n            for gt_id in unique_gt_ids:\n                new_gt_id = gt_id_map[gt_id].astype(int)\n                data[\"gt_id_map\"][new_gt_id] = gt_id\n\n            for t in range(raw_data[\"num_timesteps\"]):\n                if len(data[\"gt_ids\"][t]) > 0:\n                    data[\"gt_ids\"][t] = gt_id_map[data[\"gt_ids\"][t]].astype(int)\n\n        if len(unique_tk_ids) > 0:\n            unique_tk_ids = np.unique(unique_tk_ids)\n            tk_id_map = np.nan * np.ones((np.max(unique_tk_ids) + 1))\n            tk_id_map[unique_tk_ids] = np.arange(len(unique_tk_ids))\n\n            data[\"tk_id_map\"] = {}\n            for track_id in unique_tk_ids:\n                new_track_id = tk_id_map[track_id].astype(int)\n                data[\"tk_id_map\"][new_track_id] = track_id\n\n            for t in range(raw_data[\"num_timesteps\"]):\n                if len(data[\"tk_ids\"][t]) > 0:\n                    data[\"tk_ids\"][t] = tk_id_map[data[\"tk_ids\"][t]].astype(int)\n                if len(data[\"tk_overlap_ids\"][t]) > 0:\n                    data[\"tk_overlap_ids\"][t] = tk_id_map[\n                        data[\"tk_overlap_ids\"][t]\n                    ].astype(int)\n\n        # record overview statistics.\n        data[\"num_tk_cls_dets\"] = num_tk_cls_dets\n        data[\"num_tk_overlap_dets\"] = num_tk_overlap_dets\n        data[\"num_gt_dets\"] = num_gt_dets\n        data[\"num_tk_ids\"] = len(unique_tk_ids)\n        data[\"num_gt_ids\"] = len(unique_gt_ids)\n        data[\"num_timesteps\"] = raw_data[\"num_timesteps\"]\n        data[\"seq\"] = raw_data[\"seq\"]\n\n        self._check_unique_ids(data)\n\n        return data\n\n    @_timing.time\n    def get_preprocessed_seq_data(\n        self, raw_data, cls, assignment=None, thresholds=[50, 75]\n    ):\n        \"\"\"Preprocess data for a single sequence for a single class.\"\"\"\n        data = {}\n        if thresholds is None:\n            thresholds = [50]\n        elif isinstance(thresholds, int):\n            thresholds = [thresholds]\n\n        for thr in thresholds:\n            assignment_thr = None\n            if assignment is not None:\n                assignment_thr = assignment[thr]\n            data[thr] = self.get_preprocessed_seq_data_thr(\n                raw_data, cls, assignment_thr\n            )\n\n        return data\n\n    def _calculate_similarities(self, gt_dets_t, tk_dets_t):\n        \"\"\"Compute similarity scores.\"\"\"\n        if self.use_mask:\n            similarity_scores = self._calculate_mask_ious(gt_dets_t, tk_dets_t, is_encoded=True, do_ioa=False)\n        else:\n            similarity_scores = self._calculate_box_ious(gt_dets_t, tk_dets_t)\n        return similarity_scores\n\n    def _merge_categories(self, annotations):\n        \"\"\"Merges categories with a merged tag.\n\n        Adapted from https://github.com/TAO-Dataset.\n        \"\"\"\n        merge_map = {}\n        for category in self.gt_data[\"categories\"]:\n            if \"merged\" in category:\n                for to_merge in category[\"merged\"]:\n                    merge_map[to_merge[\"id\"]] = category[\"id\"]\n\n        for ann in annotations:\n            ann[\"category_id\"] = merge_map.get(ann[\"category_id\"], ann[\"category_id\"])\n\n    def _compute_vid_mappings(self, annotations):\n        \"\"\"Computes mappings from videos to corresponding tracks and images.\"\"\"\n        vids_to_tracks = {}\n        vids_to_imgs = {}\n        vid_ids = [vid[\"id\"] for vid in self.gt_data[\"videos\"]]\n\n        # compute an mapping from image IDs to images\n        images = {}\n        for image in self.gt_data[\"images\"]:\n            images[image[\"id\"]] = image\n\n        for ann in annotations:\n            ann[\"area\"] = ann[\"bbox\"][2] * ann[\"bbox\"][3]\n\n            vid = ann[\"video_id\"]\n            if ann[\"video_id\"] not in vids_to_tracks.keys():\n                vids_to_tracks[ann[\"video_id\"]] = list()\n            if ann[\"video_id\"] not in vids_to_imgs.keys():\n                vids_to_imgs[ann[\"video_id\"]] = list()\n\n            # fill in vids_to_tracks\n            tid = ann[\"track_id\"]\n            exist_tids = [track[\"id\"] for track in vids_to_tracks[vid]]\n            try:\n                index1 = exist_tids.index(tid)\n            except ValueError:\n                index1 = -1\n            if tid not in exist_tids:\n                curr_track = {\n                    \"id\": tid,\n                    \"category_id\": ann[\"category_id\"],\n                    \"video_id\": vid,\n                    \"annotations\": [ann],\n                }\n                vids_to_tracks[vid].append(curr_track)\n            else:\n                vids_to_tracks[vid][index1][\"annotations\"].append(ann)\n\n            # fill in vids_to_imgs\n            img_id = ann[\"image_id\"]\n            exist_img_ids = [img[\"id\"] for img in vids_to_imgs[vid]]\n            try:\n                index2 = exist_img_ids.index(img_id)\n            except ValueError:\n                index2 = -1\n            if index2 == -1:\n                curr_img = {\"id\": img_id, \"annotations\": [ann]}\n                vids_to_imgs[vid].append(curr_img)\n            else:\n                vids_to_imgs[vid][index2][\"annotations\"].append(ann)\n\n        # sort annotations by frame index and compute track area\n        for vid, tracks in vids_to_tracks.items():\n            for track in tracks:\n                track[\"annotations\"] = sorted(\n                    track[\"annotations\"],\n                    key=lambda x: images[x[\"image_id\"]][\"frame_index\"],\n                )\n                # compute average area\n                track[\"area\"] = sum(x[\"area\"] for x in track[\"annotations\"]) / len(\n                    track[\"annotations\"]\n                )\n\n        # ensure all videos are present\n        for vid_id in vid_ids:\n            if vid_id not in vids_to_tracks.keys():\n                vids_to_tracks[vid_id] = []\n            if vid_id not in vids_to_imgs.keys():\n                vids_to_imgs[vid_id] = []\n\n        return vids_to_tracks, vids_to_imgs\n\n    def _compute_image_to_timestep_mappings(self):\n        \"\"\"Computes a mapping from images to timestep in sequence.\"\"\"\n        images = {}\n        for image in self.gt_data[\"images\"]:\n            images[image[\"id\"]] = image\n\n        seq_to_imgs_to_timestep = {vid[\"id\"]: dict() for vid in self.gt_data[\"videos\"]}\n        for vid in seq_to_imgs_to_timestep:\n            curr_imgs = [img[\"id\"] for img in self.video2gt_image[vid]]\n            curr_imgs = sorted(curr_imgs, key=lambda x: images[x][\"frame_index\"])\n            seq_to_imgs_to_timestep[vid] = {\n                curr_imgs[i]: i for i in range(len(curr_imgs))\n            }\n\n        return seq_to_imgs_to_timestep\n\n    def _limit_dets_per_image(self, annotations):\n        \"\"\"Limits the number of detections for each image.\n\n        Adapted from https://github.com/TAO-Dataset/.\n        \"\"\"\n        max_dets = self.config[\"MAX_DETECTIONS\"]\n        img_ann = defaultdict(list)\n        for ann in annotations:\n            img_ann[ann[\"image_id\"]].append(ann)\n\n        for img_id, _anns in img_ann.items():\n            if len(_anns) <= max_dets:\n                continue\n            _anns = sorted(_anns, key=lambda x: x[\"score\"], reverse=True)\n            img_ann[img_id] = _anns[:max_dets]\n\n        return [ann for anns in img_ann.values() for ann in anns]\n\n    def _fill_video_ids_inplace(self, annotations):\n        \"\"\"Fills in missing video IDs inplace.\n\n        Adapted from https://github.com/TAO-Dataset/.\n        \"\"\"\n        missing_video_id = [x for x in annotations if \"video_id\" not in x]\n        if missing_video_id:\n            image_id_to_video_id = {\n                x[\"id\"]: x[\"video_id\"] for x in self.gt_data[\"images\"]\n            }\n            for x in missing_video_id:\n                x[\"video_id\"] = image_id_to_video_id[x[\"image_id\"]]\n\n    @staticmethod\n    def _make_tk_ids_unique(annotations):\n        \"\"\"Makes track IDs unqiue over the whole annotation set.\n\n        Adapted from https://github.com/TAO-Dataset/.\n        \"\"\"\n        track_id_videos = {}\n        track_ids_to_update = set()\n        max_track_id = 0\n        for ann in annotations:\n            t = ann[\"track_id\"]\n            if t not in track_id_videos:\n                track_id_videos[t] = ann[\"video_id\"]\n\n            if ann[\"video_id\"] != track_id_videos[t]:\n                # track id is assigned to multiple videos\n                track_ids_to_update.add(t)\n            max_track_id = max(max_track_id, t)\n\n        if track_ids_to_update:\n            print(\"true\")\n            next_id = itertools.count(max_track_id + 1)\n            new_tk_ids = defaultdict(lambda: next(next_id))\n            for ann in annotations:\n                t = ann[\"track_id\"]\n                v = ann[\"video_id\"]\n                if t in track_ids_to_update:\n                    ann[\"track_id\"] = new_tk_ids[t, v]\n        return len(track_ids_to_update)\n"
  },
  {
    "path": "sam3/eval/teta_eval_toolkit/eval.py",
    "content": "# fmt: off\n# flake8: noqa\n\n# pyre-unsafe\n\nimport copy\nimport os\nimport pickle\nimport time\nimport traceback\nfrom functools import partial\nfrom multiprocessing.pool import Pool\n\nimport numpy as np\n\nfrom . import _timing, utils\nfrom .config import get_default_eval_config, init_config\nfrom .utils import TrackEvalException\n\n\nclass Evaluator:\n    \"\"\"Evaluator class for evaluating different metrics for each datasets.\"\"\"\n\n    def __init__(self, config=None):\n        \"\"\"Initialize the evaluator with a config file.\"\"\"\n        self.config = init_config(config, get_default_eval_config(), \"Eval\")\n        # Only run timing analysis if not run in parallel.\n        if self.config[\"TIME_PROGRESS\"] and not self.config[\"USE_PARALLEL\"]:\n            _timing.DO_TIMING = True\n            if self.config[\"DISPLAY_LESS_PROGRESS\"]:\n                _timing.DISPLAY_LESS_PROGRESS = True\n\n    @_timing.time\n    def evaluate(self, dataset_list, metrics_list):\n        \"\"\"Evaluate a set of metrics on a set of datasets.\"\"\"\n        config = self.config\n        metrics_list = metrics_list\n        metric_names = utils.validate_metrics_list(metrics_list)\n        dataset_names = [dataset.get_name() for dataset in dataset_list]\n        output_res = {}\n        output_msg = {}\n\n        for dataset, dname in zip(dataset_list, dataset_names):\n            # Get dataset info about what to evaluate\n            output_res[dname] = {}\n            output_msg[dname] = {}\n            tracker_list, seq_list, class_list = dataset.get_eval_info()\n            print(\n                f\"\\nEvaluating {len(tracker_list)} tracker(s) on \"\n                f\"{len(seq_list)} sequence(s) for {len(class_list)} class(es)\"\n                f\" on {dname} dataset using the following \"\n                f'metrics: {\", \".join(metric_names)}\\n'\n            )\n\n            # Evaluate each tracker\n            for tracker in tracker_list:\n                try:\n                    output_res, output_msg = self.evaluate_tracker(\n                        tracker,\n                        dataset,\n                        dname,\n                        class_list,\n                        metrics_list,\n                        metric_names,\n                        seq_list,\n                        output_res,\n                        output_msg,\n                    )\n                except Exception as err:\n                    output_res[dname][tracker] = None\n                    if type(err) == TrackEvalException:\n                        output_msg[dname][tracker] = str(err)\n                    else:\n                        output_msg[dname][tracker] = \"Unknown error occurred.\"\n                    print(\"Tracker %s was unable to be evaluated.\" % tracker)\n                    print(err)\n                    traceback.print_exc()\n                    if config[\"LOG_ON_ERROR\"] is not None:\n                        with open(config[\"LOG_ON_ERROR\"], \"a\") as f:\n                            print(dname, file=f)\n                            print(tracker, file=f)\n                            print(traceback.format_exc(), file=f)\n                            print(\"\\n\\n\\n\", file=f)\n                    if config[\"BREAK_ON_ERROR\"]:\n                        raise err\n                    elif config[\"RETURN_ON_ERROR\"]:\n                        return output_res, output_msg\n\n        return output_res, output_msg\n\n    def evaluate_tracker(\n        self,\n        tracker,\n        dataset,\n        dname,\n        class_list,\n        metrics_list,\n        metric_names,\n        seq_list,\n        output_res,\n        output_msg,\n    ):\n        \"\"\"Evaluate each sequence in parallel or in series.\"\"\"\n        print(\"\\nEvaluating %s\\n\" % tracker)\n        time_start = time.time()\n        config = self.config\n        if config[\"USE_PARALLEL\"]:\n            with Pool(config[\"NUM_PARALLEL_CORES\"]) as pool:\n                _eval_sequence = partial(\n                    eval_sequence,\n                    dataset=dataset,\n                    tracker=tracker,\n                    class_list=class_list,\n                    metrics_list=metrics_list,\n                    metric_names=metric_names,\n                )\n                results = pool.map(_eval_sequence, seq_list)\n                res = dict(zip(seq_list, results))\n        else:\n            res = {}\n            for curr_seq in sorted(seq_list):\n                res[curr_seq] = eval_sequence(\n                    curr_seq, dataset, tracker, class_list, metrics_list, metric_names\n                )\n\n\n        # collecting combined cls keys (cls averaged, det averaged, super classes)\n        cls_keys = []\n        res[\"COMBINED_SEQ\"] = {}\n        # combine sequences for each class\n        for c_cls in class_list:\n            res[\"COMBINED_SEQ\"][c_cls] = {}\n            for metric, mname in zip(metrics_list, metric_names):\n                curr_res = {\n                    seq_key: seq_value[c_cls][mname]\n                    for seq_key, seq_value in res.items()\n                    if seq_key != \"COMBINED_SEQ\"\n                }\n                # combine results over all sequences and then over all classes\n                res[\"COMBINED_SEQ\"][c_cls][mname] = metric.combine_sequences(curr_res)\n\n        # combine classes\n        if dataset.should_classes_combine:\n            if config[\"OUTPUT_PER_SEQ_RES\"]:\n                video_keys = res.keys()\n            else:\n                video_keys = [\"COMBINED_SEQ\"]\n            for v_key in video_keys:\n                cls_keys += [\"average\"]\n                res[v_key][\"average\"] = {}\n                for metric, mname in zip(metrics_list, metric_names):\n                    cls_res = {\n                        cls_key: cls_value[mname]\n                        for cls_key, cls_value in res[v_key].items()\n                        if cls_key not in cls_keys\n                    }\n                    res[v_key][\"average\"][\n                        mname\n                    ] = metric.combine_classes_class_averaged(\n                        cls_res, ignore_empty=True\n                    )\n\n        # combine classes to super classes\n        if dataset.use_super_categories:\n            for cat, sub_cats in dataset.super_categories.items():\n                cls_keys.append(cat)\n                res[\"COMBINED_SEQ\"][cat] = {}\n                for metric, mname in zip(metrics_list, metric_names):\n                    cat_res = {\n                        cls_key: cls_value[mname]\n                        for cls_key, cls_value in res[\"COMBINED_SEQ\"].items()\n                        if cls_key in sub_cats\n                    }\n                    res[\"COMBINED_SEQ\"][cat][\n                        mname\n                    ] = metric.combine_classes_det_averaged(cat_res)\n        # Print and output results in various formats\n        if config[\"TIME_PROGRESS\"]:\n            print(\n                f\"\\nAll sequences for {tracker} finished in\"\n                f\" {time.time() - time_start} seconds\"\n            )\n        output_fol = dataset.get_output_fol(tracker)\n        os.makedirs(output_fol, exist_ok=True)\n\n        # take a mean of each field of each thr\n        if config[\"OUTPUT_PER_SEQ_RES\"]:\n            all_res = copy.deepcopy(res)\n            summary_keys = res.keys()\n        else:\n            all_res = copy.deepcopy(res[\"COMBINED_SEQ\"])\n            summary_keys = [\"COMBINED_SEQ\"]\n        thr_key_list = [50]\n        for s_key in summary_keys:\n            for metric, mname in zip(metrics_list, metric_names):\n                if mname != \"TETA\":\n                    if s_key == \"COMBINED_SEQ\":\n                        metric.print_table(\n                            {\"COMBINED_SEQ\": res[\"COMBINED_SEQ\"][cls_keys[0]][mname]},\n                            tracker,\n                            cls_keys[0],\n                        )\n                    continue\n\n                for c_cls in res[s_key].keys():\n                    for thr in thr_key_list:\n                        all_res[s_key][c_cls][mname][thr] = metric._summary_row(\n                            res[s_key][c_cls][mname][thr]\n                        )\n                    x = (\n                        np.array(list(all_res[s_key][c_cls][\"TETA\"].values()))\n                        .astype(\"float\")\n                        .mean(axis=0)\n                    )\n                    all_res_summary = list(x.round(decimals=2).astype(\"str\"))\n                    all_res[s_key][c_cls][mname][\"ALL\"] = all_res_summary\n                if config[\"OUTPUT_SUMMARY\"] and s_key == \"COMBINED_SEQ\":\n                    for t in thr_key_list:\n                        metric.print_summary_table(\n                            all_res[s_key][cls_keys[0]][mname][t],\n                            t,\n                            tracker,\n                            cls_keys[0],\n                        )\n\n        if config[\"OUTPUT_TEM_RAW_DATA\"]:\n            out_file = os.path.join(output_fol, \"teta_summary_results.pth\")\n            pickle.dump(all_res, open(out_file, \"wb\"))\n            print(\"Saved the TETA summary results.\")\n\n        # output\n        output_res[dname][mname] = all_res[s_key][cls_keys[0]][mname][t]\n        output_msg[dname][tracker] = \"Success\"\n\n        return output_res, output_msg\n\n\n@_timing.time\ndef eval_sequence(seq, dataset, tracker, class_list, metrics_list, metric_names):\n    \"\"\"Function for evaluating a single sequence.\"\"\"\n    raw_data = dataset.get_raw_seq_data(tracker, seq)\n    seq_res = {}\n\n    if \"TETA\" in metric_names:\n        thresholds = [50]\n        data_all_class = dataset.get_preprocessed_seq_data(\n            raw_data, \"all\", thresholds=thresholds\n        )\n        teta = metrics_list[metric_names.index(\"TETA\")]\n        assignment = teta.compute_global_assignment(data_all_class)\n\n        # create a dict to save Cls_FP for each class in different thr.\n        cls_fp = {\n            key: {\n                cls: np.zeros((len(np.arange(0.5, 0.99, 0.05)))) for cls in class_list\n            }\n            for key in thresholds\n        }\n\n    for cls in class_list:\n        seq_res[cls] = {}\n        data = dataset.get_preprocessed_seq_data(raw_data, cls, assignment, thresholds)\n\n        for metric, mname in zip(metrics_list, metric_names):\n            if mname == \"TETA\":\n                seq_res[cls][mname], cls_fp, _ = metric.eval_sequence(\n                    data, cls, dataset.clsid2cls_name, cls_fp\n                )\n            else:\n                seq_res[cls][mname] = metric.eval_sequence(data)\n\n    if \"TETA\" in metric_names:\n        for thr in thresholds:\n            for cls in class_list:\n                seq_res[cls][\"TETA\"][thr][\"Cls_FP\"] += cls_fp[thr][cls]\n\n    return seq_res\n"
  },
  {
    "path": "sam3/eval/teta_eval_toolkit/metrics/__init__.py",
    "content": "# fmt: off\n# flake8: noqa\n\n# pyre-unsafe\n\nfrom .teta import TETA\n"
  },
  {
    "path": "sam3/eval/teta_eval_toolkit/metrics/_base_metric.py",
    "content": "# fmt: off\n# flake8: noqa\n\n# pyre-unsafe\n\nfrom abc import ABC, abstractmethod\n\nimport numpy as np\n\nfrom .. import _timing\nfrom ..utils import TrackEvalException\n\n\nclass _BaseMetric(ABC):\n    @abstractmethod\n    def __init__(self):\n        self.plottable = False\n        self.integer_fields = []\n        self.float_fields = []\n        self.array_labels = []\n        self.integer_array_fields = []\n        self.float_array_fields = []\n        self.fields = []\n        self.summary_fields = []\n        self.registered = False\n\n    #####################################################################\n    # Abstract functions for subclasses to implement\n\n    @_timing.time\n    @abstractmethod\n    def eval_sequence(self, data):\n        ...\n\n    @abstractmethod\n    def combine_sequences(self, all_res):\n        ...\n\n    @abstractmethod\n    def combine_classes_class_averaged(self, all_res, ignore_empty=False):\n        ...\n\n    @abstractmethod\n    def combine_classes_det_averaged(self, all_res):\n        ...\n\n    def plot_single_tracker_results(self, all_res, tracker, output_folder, cls):\n        \"\"\"Plot results, only valid for metrics with self.plottable.\"\"\"\n        if self.plottable:\n            raise NotImplementedError(\n                f\"plot_results is not implemented for metric {self.get_name()}\"\n            )\n        else:\n            pass\n\n    #####################################################################\n    # Helper functions which are useful for all metrics:\n\n    @classmethod\n    def get_name(cls):\n        return cls.__name__\n\n    @staticmethod\n    def _combine_sum(all_res, field):\n        \"\"\"Combine sequence results via sum\"\"\"\n        return sum([all_res[k][field] for k in all_res.keys()])\n\n    @staticmethod\n    def _combine_weighted_av(all_res, field, comb_res, weight_field):\n        \"\"\"Combine sequence results via weighted average.\"\"\"\n        return sum(\n            [all_res[k][field] * all_res[k][weight_field] for k in all_res.keys()]\n        ) / np.maximum(1.0, comb_res[weight_field])\n\n    def print_table(self, table_res, tracker, cls):\n        \"\"\"Print table of results for all sequences.\"\"\"\n        print(\"\")\n        metric_name = self.get_name()\n        self._row_print(\n            [metric_name + \": \" + tracker + \"-\" + cls] + self.summary_fields\n        )\n        for seq, results in sorted(table_res.items()):\n            if seq == \"COMBINED_SEQ\":\n                continue\n            summary_res = self._summary_row(results)\n            self._row_print([seq] + summary_res)\n        summary_res = self._summary_row(table_res[\"COMBINED_SEQ\"])\n        self._row_print([\"COMBINED\"] + summary_res)\n\n    def _summary_row(self, results_):\n        vals = []\n        for h in self.summary_fields:\n            if h in self.float_array_fields:\n                vals.append(\"{0:1.5g}\".format(100 * np.mean(results_[h])))\n            elif h in self.float_fields:\n                vals.append(\"{0:1.5g}\".format(100 * float(results_[h])))\n            elif h in self.integer_fields:\n                vals.append(\"{0:d}\".format(int(results_[h])))\n            else:\n                raise NotImplementedError(\n                    \"Summary function not implemented for this field type.\"\n                )\n        return vals\n\n    @staticmethod\n    def _row_print(*argv):\n        \"\"\"Print results in evenly spaced rows, with more space in first row.\"\"\"\n        if len(argv) == 1:\n            argv = argv[0]\n        to_print = \"%-35s\" % argv[0]\n        for v in argv[1:]:\n            to_print += \"%-10s\" % str(v)\n        print(to_print)\n\n    def summary_results(self, table_res):\n        \"\"\"Return a simple summary of final results for a tracker.\"\"\"\n        return dict(\n            zip(self.summary_fields, self._summary_row(table_res[\"COMBINED_SEQ\"]),)\n        )\n\n    def detailed_results(self, table_res):\n        \"\"\"Return detailed final results for a tracker.\"\"\"\n        # Get detailed field information\n        detailed_fields = self.float_fields + self.integer_fields\n        for h in self.float_array_fields + self.integer_array_fields:\n            for alpha in [int(100 * x) for x in self.array_labels]:\n                detailed_fields.append(h + \"___\" + str(alpha))\n            detailed_fields.append(h + \"___AUC\")\n\n        # Get detailed results\n        detailed_results = {}\n        for seq, res in table_res.items():\n            detailed_row = self._detailed_row(res)\n            if len(detailed_row) != len(detailed_fields):\n                raise TrackEvalException(\n                    f\"Field names and data have different sizes \"\n                    f\"({len(detailed_row)} and {len(detailed_fields)})\"\n                )\n            detailed_results[seq] = dict(zip(detailed_fields, detailed_row))\n        return detailed_results\n\n    def _detailed_row(self, res):\n        detailed_row = []\n        for h in self.float_fields + self.integer_fields:\n            detailed_row.append(res[h])\n        for h in self.float_array_fields + self.integer_array_fields:\n            for i, _ in enumerate([int(100 * x) for x in self.array_labels]):\n                detailed_row.append(res[h][i])\n            detailed_row.append(np.mean(res[h]))\n        return detailed_row\n"
  },
  {
    "path": "sam3/eval/teta_eval_toolkit/metrics/teta.py",
    "content": "# fmt: off\n# flake8: noqa\n\n# pyre-unsafe\n\n\"\"\"Track Every Thing Accuracy metric.\"\"\"\n\nimport numpy as np\nfrom scipy.optimize import linear_sum_assignment\n\nfrom .. import _timing\nfrom ._base_metric import _BaseMetric\n\nEPS = np.finfo(\"float\").eps  # epsilon\n\n\nclass TETA(_BaseMetric):\n    \"\"\"TETA metric.\"\"\"\n\n    def __init__(self, exhaustive=False, config=None):\n        \"\"\"Initialize metric.\"\"\"\n        super().__init__()\n        self.plottable = True\n        self.array_labels = np.arange(0.0, 0.99, 0.05)\n        self.cls_array_labels = np.arange(0.5, 0.99, 0.05)\n\n        self.integer_array_fields = [\n            \"Loc_TP\",\n            \"Loc_FN\",\n            \"Loc_FP\",\n            \"Cls_TP\",\n            \"Cls_FN\",\n            \"Cls_FP\",\n        ]\n        self.float_array_fields = (\n            [\"TETA\", \"LocA\", \"AssocA\", \"ClsA\"]\n            + [\"LocRe\", \"LocPr\"]\n            + [\"AssocRe\", \"AssocPr\"]\n            + [\"ClsRe\", \"ClsPr\"]\n        )\n        self.fields = self.float_array_fields + self.integer_array_fields\n        self.summary_fields = self.float_array_fields\n        self.exhaustive = exhaustive\n\n    def compute_global_assignment(self, data_thr, alpha=0.5):\n        \"\"\"Compute global assignment of TP.\"\"\"\n        res = {\n            thr: {t: {} for t in range(data_thr[thr][\"num_timesteps\"])}\n            for thr in data_thr\n        }\n\n        for thr in data_thr:\n            data = data_thr[thr]\n            # return empty result if tracker or gt sequence is empty\n            if data[\"num_tk_overlap_dets\"] == 0 or data[\"num_gt_dets\"] == 0:\n                return res\n\n            # global alignment score\n            ga_score, _, _ = self.compute_global_alignment_score(data)\n\n            # calculate scores for each timestep\n            for t, (gt_ids_t, tk_ids_t) in enumerate(\n                zip(data[\"gt_ids\"], data[\"tk_ids\"])\n            ):\n                # get matches optimizing for TETA\n                amatch_rows, amatch_cols = self.compute_matches(\n                    data, t, ga_score, gt_ids_t, tk_ids_t, alpha=alpha\n                )\n                gt_ids = [data[\"gt_id_map\"][tid] for tid in gt_ids_t[amatch_rows[0]]]\n                matched_ids = [\n                    data[\"tk_id_map\"][tid] for tid in tk_ids_t[amatch_cols[0]]\n                ]\n                res[thr][t] = dict(zip(gt_ids, matched_ids))\n\n        return res\n\n    def eval_sequence_single_thr(self, data, cls, cid2clsname, cls_fp_thr, thr):\n        \"\"\"Computes TETA metric for one threshold for one sequence.\"\"\"\n        res = {}\n        class_info_list = []\n        for field in self.float_array_fields + self.integer_array_fields:\n            if field.startswith(\"Cls\"):\n                res[field] = np.zeros(len(self.cls_array_labels), dtype=float)\n            else:\n                res[field] = np.zeros((len(self.array_labels)), dtype=float)\n\n        # return empty result if tracker or gt sequence is empty\n        if data[\"num_tk_overlap_dets\"] == 0:\n            res[\"Loc_FN\"] = data[\"num_gt_dets\"] * np.ones(\n                (len(self.array_labels)), dtype=float\n            )\n            if self.exhaustive:\n                cls_fp_thr[cls] = data[\"num_tk_cls_dets\"] * np.ones(\n                    (len(self.cls_array_labels)), dtype=float\n                )\n            res = self._compute_final_fields(res)\n            return res, cls_fp_thr, class_info_list\n\n        if data[\"num_gt_dets\"] == 0:\n            if self.exhaustive:\n                cls_fp_thr[cls] = data[\"num_tk_cls_dets\"] * np.ones(\n                    (len(self.cls_array_labels)), dtype=float\n                )\n            res = self._compute_final_fields(res)\n            return res, cls_fp_thr, class_info_list\n\n        # global alignment score\n        ga_score, gt_id_count, tk_id_count = self.compute_global_alignment_score(data)\n        matches_counts = [np.zeros_like(ga_score) for _ in self.array_labels]\n\n        # calculate scores for each timestep\n        for t, (gt_ids_t, tk_ids_t, tk_overlap_ids_t, tk_cls_ids_t) in enumerate(\n            zip(\n                data[\"gt_ids\"],\n                data[\"tk_ids\"],\n                data[\"tk_overlap_ids\"],\n                data[\"tk_class_eval_tk_ids\"],\n            )\n        ):\n            # deal with the case that there are no gt_det/tk_det in a timestep\n            if len(gt_ids_t) == 0:\n                if self.exhaustive:\n                    cls_fp_thr[cls] += len(tk_cls_ids_t)\n                continue\n\n            # get matches optimizing for TETA\n            amatch_rows, amatch_cols = self.compute_matches(\n                data, t, ga_score, gt_ids_t, tk_ids_t, list(self.array_labels)\n            )\n\n            # map overlap_ids to original ids.\n            if len(tk_overlap_ids_t) != 0:\n                sorter = np.argsort(tk_ids_t)\n                indexes = sorter[\n                    np.searchsorted(tk_ids_t, tk_overlap_ids_t, sorter=sorter)\n                ]\n                sim_t = data[\"sim_scores\"][t][:, indexes]\n                fpl_candidates = tk_overlap_ids_t[(sim_t >= (thr / 100)).any(axis=0)]\n                fpl_candidates_ori_ids_t = np.array(\n                    [data[\"tk_id_map\"][tid] for tid in fpl_candidates]\n                )\n            else:\n                fpl_candidates_ori_ids_t = []\n\n            if self.exhaustive:\n                cls_fp_thr[cls] += len(tk_cls_ids_t) - len(tk_overlap_ids_t)\n\n            # calculate and accumulate basic statistics\n            for a, alpha in enumerate(self.array_labels):\n                match_row, match_col = amatch_rows[a], amatch_cols[a]\n                num_matches = len(match_row)\n                matched_ori_ids = set(\n                    [data[\"tk_id_map\"][tid] for tid in tk_ids_t[match_col]]\n                )\n                match_tk_cls = data[\"tk_classes\"][t][match_col]\n                wrong_tk_cls = match_tk_cls[match_tk_cls != data[\"gt_classes\"][t]]\n\n                num_class_and_det_matches = np.sum(\n                    match_tk_cls == data[\"gt_classes\"][t]\n                )\n\n                if alpha >= 0.5:\n                    for cid in wrong_tk_cls:\n                        if cid in cid2clsname:\n                            cname = cid2clsname[cid]\n                            cls_fp_thr[cname][a - 10] += 1\n                    res[\"Cls_TP\"][a - 10] += num_class_and_det_matches\n                    res[\"Cls_FN\"][a - 10] += num_matches - num_class_and_det_matches\n\n                res[\"Loc_TP\"][a] += num_matches\n                res[\"Loc_FN\"][a] += len(gt_ids_t) - num_matches\n                res[\"Loc_FP\"][a] += len(set(fpl_candidates_ori_ids_t) - matched_ori_ids)\n\n                if num_matches > 0:\n                    matches_counts[a][gt_ids_t[match_row], tk_ids_t[match_col]] += 1\n\n        # calculate AssocA, AssocRe, AssocPr\n        self.compute_association_scores(res, matches_counts, gt_id_count, tk_id_count)\n\n        # calculate final scores\n        res = self._compute_final_fields(res)\n        return res, cls_fp_thr, class_info_list\n\n    def compute_global_alignment_score(self, data):\n        \"\"\"Computes global alignment score.\"\"\"\n        num_matches = np.zeros((data[\"num_gt_ids\"], data[\"num_tk_ids\"]))\n        gt_id_count = np.zeros((data[\"num_gt_ids\"], 1))\n        tk_id_count = np.zeros((1, data[\"num_tk_ids\"]))\n\n        # loop through each timestep and accumulate global track info.\n        for t, (gt_ids_t, tk_ids_t) in enumerate(zip(data[\"gt_ids\"], data[\"tk_ids\"])):\n            # count potential matches between ids in each time step\n            # these are normalized, weighted by match similarity\n            sim = data[\"sim_scores\"][t]\n            sim_iou_denom = sim.sum(0, keepdims=True) + sim.sum(1, keepdims=True) - sim\n            sim_iou = np.zeros_like(sim)\n            mask = sim_iou_denom > (0 + EPS)\n            sim_iou[mask] = sim[mask] / sim_iou_denom[mask]\n            num_matches[gt_ids_t[:, None], tk_ids_t[None, :]] += sim_iou\n\n            # calculate total number of dets for each gt_id and tk_id.\n            gt_id_count[gt_ids_t] += 1\n            tk_id_count[0, tk_ids_t] += 1\n\n        # Calculate overall Jaccard alignment score between IDs\n        ga_score = num_matches / (gt_id_count + tk_id_count - num_matches)\n        return ga_score, gt_id_count, tk_id_count\n\n    def compute_matches(self, data, t, ga_score, gt_ids, tk_ids, alpha):\n        \"\"\"Compute matches based on alignment score.\"\"\"\n        sim = data[\"sim_scores\"][t]\n        score_mat = ga_score[gt_ids[:, None], tk_ids[None, :]] * sim\n        # Hungarian algorithm to find best matches\n        match_rows, match_cols = linear_sum_assignment(-score_mat)\n\n        if not isinstance(alpha, list):\n            alpha = [alpha]\n        alpha_match_rows, alpha_match_cols = [], []\n        for a in alpha:\n            matched_mask = sim[match_rows, match_cols] >= a - EPS\n            alpha_match_rows.append(match_rows[matched_mask])\n            alpha_match_cols.append(match_cols[matched_mask])\n        return alpha_match_rows, alpha_match_cols\n\n    def compute_association_scores(self, res, matches_counts, gt_id_count, tk_id_count):\n        \"\"\"Calculate association scores for each alpha.\n\n        First calculate scores per gt_id/tk_id combo,\n        and then average over the number of detections.\n        \"\"\"\n        for a, _ in enumerate(self.array_labels):\n            matches_count = matches_counts[a]\n            ass_a = matches_count / np.maximum(\n                1, gt_id_count + tk_id_count - matches_count\n            )\n            res[\"AssocA\"][a] = np.sum(matches_count * ass_a) / np.maximum(\n                1, res[\"Loc_TP\"][a]\n            )\n            ass_re = matches_count / np.maximum(1, gt_id_count)\n            res[\"AssocRe\"][a] = np.sum(matches_count * ass_re) / np.maximum(\n                1, res[\"Loc_TP\"][a]\n            )\n            ass_pr = matches_count / np.maximum(1, tk_id_count)\n            res[\"AssocPr\"][a] = np.sum(matches_count * ass_pr) / np.maximum(\n                1, res[\"Loc_TP\"][a]\n            )\n\n    @_timing.time\n    def eval_sequence(self, data, cls, cls_id_name_mapping, cls_fp):\n        \"\"\"Evaluate a single sequence across all thresholds.\"\"\"\n        res = {}\n        class_info_dict = {}\n\n        for thr in data:\n            res[thr], cls_fp[thr], cls_info = self.eval_sequence_single_thr(\n                data[thr], cls, cls_id_name_mapping, cls_fp[thr], thr\n            )\n            class_info_dict[thr] = cls_info\n\n        return res, cls_fp, class_info_dict\n\n    def combine_sequences(self, all_res):\n        \"\"\"Combines metrics across all sequences.\"\"\"\n        data = {}\n        res = {}\n\n        if all_res:\n            thresholds = list(list(all_res.values())[0].keys())\n        else:\n            thresholds = [50]\n        for thr in thresholds:\n            data[thr] = {}\n            for seq_key in all_res:\n                data[thr][seq_key] = all_res[seq_key][thr]\n        for thr in thresholds:\n            res[thr] = self._combine_sequences_thr(data[thr])\n\n        return res\n\n    def _combine_sequences_thr(self, all_res):\n        \"\"\"Combines sequences over each threshold.\"\"\"\n        res = {}\n        for field in self.integer_array_fields:\n            res[field] = self._combine_sum(all_res, field)\n        for field in [\"AssocRe\", \"AssocPr\", \"AssocA\"]:\n            res[field] = self._combine_weighted_av(\n                all_res, field, res, weight_field=\"Loc_TP\"\n            )\n        res = self._compute_final_fields(res)\n        return res\n\n    def combine_classes_class_averaged(self, all_res, ignore_empty=False):\n        \"\"\"Combines metrics across all classes by averaging over classes.\n\n        If 'ignore_empty' is True, then it only sums over classes\n        with at least one gt or predicted detection.\n        \"\"\"\n        data = {}\n        res = {}\n        if all_res:\n            thresholds = list(list(all_res.values())[0].keys())\n        else:\n            thresholds = [50]\n        for thr in thresholds:\n            data[thr] = {}\n            for cls_key in all_res:\n                data[thr][cls_key] = all_res[cls_key][thr]\n        for thr in data:\n            res[thr] = self._combine_classes_class_averaged_thr(\n                data[thr], ignore_empty=ignore_empty\n            )\n        return res\n\n    def _combine_classes_class_averaged_thr(self, all_res, ignore_empty=False):\n        \"\"\"Combines classes over each threshold.\"\"\"\n        res = {}\n\n        def check_empty(val):\n            \"\"\"Returns True if empty.\"\"\"\n            return not (val[\"Loc_TP\"] + val[\"Loc_FN\"] + val[\"Loc_FP\"] > 0 + EPS).any()\n\n        for field in self.integer_array_fields:\n            if ignore_empty:\n                res_field = {k: v for k, v in all_res.items() if not check_empty(v)}\n            else:\n                res_field = {k: v for k, v in all_res.items()}\n            res[field] = self._combine_sum(res_field, field)\n\n        for field in self.float_array_fields:\n            if ignore_empty:\n                res_field = [v[field] for v in all_res.values() if not check_empty(v)]\n            else:\n                res_field = [v[field] for v in all_res.values()]\n            res[field] = np.mean(res_field, axis=0)\n        return res\n\n    def combine_classes_det_averaged(self, all_res):\n        \"\"\"Combines metrics across all classes by averaging over detections.\"\"\"\n        data = {}\n        res = {}\n        if all_res:\n            thresholds = list(list(all_res.values())[0].keys())\n        else:\n            thresholds = [50]\n        for thr in thresholds:\n            data[thr] = {}\n            for cls_key in all_res:\n                data[thr][cls_key] = all_res[cls_key][thr]\n        for thr in data:\n            res[thr] = self._combine_classes_det_averaged_thr(data[thr])\n        return res\n\n    def _combine_classes_det_averaged_thr(self, all_res):\n        \"\"\"Combines detections over each threshold.\"\"\"\n        res = {}\n        for field in self.integer_array_fields:\n            res[field] = self._combine_sum(all_res, field)\n        for field in [\"AssocRe\", \"AssocPr\", \"AssocA\"]:\n            res[field] = self._combine_weighted_av(\n                all_res, field, res, weight_field=\"Loc_TP\"\n            )\n        res = self._compute_final_fields(res)\n        return res\n\n    @staticmethod\n    def _compute_final_fields(res):\n        \"\"\"Calculate final metric values.\n\n        This function is used both for both per-sequence calculation,\n        and in combining values across sequences.\n        \"\"\"\n        # LocA\n        res[\"LocRe\"] = res[\"Loc_TP\"] / np.maximum(1, res[\"Loc_TP\"] + res[\"Loc_FN\"])\n        res[\"LocPr\"] = res[\"Loc_TP\"] / np.maximum(1, res[\"Loc_TP\"] + res[\"Loc_FP\"])\n        res[\"LocA\"] = res[\"Loc_TP\"] / np.maximum(\n            1, res[\"Loc_TP\"] + res[\"Loc_FN\"] + res[\"Loc_FP\"]\n        )\n\n        # ClsA\n        res[\"ClsRe\"] = res[\"Cls_TP\"] / np.maximum(1, res[\"Cls_TP\"] + res[\"Cls_FN\"])\n        res[\"ClsPr\"] = res[\"Cls_TP\"] / np.maximum(1, res[\"Cls_TP\"] + res[\"Cls_FP\"])\n        res[\"ClsA\"] = res[\"Cls_TP\"] / np.maximum(\n            1, res[\"Cls_TP\"] + res[\"Cls_FN\"] + res[\"Cls_FP\"]\n        )\n\n        res[\"ClsRe\"] = np.mean(res[\"ClsRe\"])\n        res[\"ClsPr\"] = np.mean(res[\"ClsPr\"])\n        res[\"ClsA\"] = np.mean(res[\"ClsA\"])\n\n        res[\"TETA\"] = (res[\"LocA\"] + res[\"AssocA\"] + res[\"ClsA\"]) / 3\n\n        return res\n\n    def print_summary_table(self, thr_res, thr, tracker, cls):\n        \"\"\"Prints summary table of results.\"\"\"\n        print(\"\")\n        metric_name = self.get_name()\n        self._row_print(\n            [f\"{metric_name}{str(thr)}: {tracker}-{cls}\"] + self.summary_fields\n        )\n        self._row_print([\"COMBINED\"] + thr_res)\n"
  },
  {
    "path": "sam3/eval/teta_eval_toolkit/utils.py",
    "content": "# fmt: off\n# flake8: noqa\n\n# pyre-unsafe\n\nimport csv\nimport os\nfrom collections import OrderedDict\n\n\ndef validate_metrics_list(metrics_list):\n    \"\"\"Get names of metric class and ensures they are unique, further checks that the fields within each metric class\n    do not have overlapping names.\n    \"\"\"\n    metric_names = [metric.get_name() for metric in metrics_list]\n    # check metric names are unique\n    if len(metric_names) != len(set(metric_names)):\n        raise TrackEvalException(\n            \"Code being run with multiple metrics of the same name\"\n        )\n    fields = []\n    for m in metrics_list:\n        fields += m.fields\n    # check metric fields are unique\n    if len(fields) != len(set(fields)):\n        raise TrackEvalException(\n            \"Code being run with multiple metrics with fields of the same name\"\n        )\n    return metric_names\n\n\ndef get_track_id_str(ann):\n    \"\"\"Get name of track ID in annotation.\"\"\"\n    if \"track_id\" in ann:\n        tk_str = \"track_id\"\n    elif \"instance_id\" in ann:\n        tk_str = \"instance_id\"\n    elif \"scalabel_id\" in ann:\n        tk_str = \"scalabel_id\"\n    else:\n        assert False, \"No track/instance ID.\"\n    return tk_str\n\n\nclass TrackEvalException(Exception):\n    \"\"\"Custom exception for catching expected errors.\"\"\"\n\n    ...\n"
  },
  {
    "path": "sam3/eval/ytvis_coco_wrapper.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n# (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.\n\n# pyre-unsafe\n\nimport copy\nimport json\nimport logging\n\nimport numpy as np\nimport pycocotools.mask as mask_util\nfrom pycocotools.coco import COCO\nfrom typing_extensions import override\n\n\nclass YTVIS(COCO):\n    \"\"\"\n    Helper class for reading YT-VIS annotations\n    \"\"\"\n\n    @override\n    def __init__(self, annotation_file: str = None, ignore_gt_cats: bool = True):\n        \"\"\"\n        Args:\n            annotation_file: Path to the annotation file\n            ignore_gt_cats: If True, we ignore the ground truth categories and replace them with a dummy \"object\" category. This is useful for Phrase AP evaluation.\n        \"\"\"\n        self.ignore_gt_cats = ignore_gt_cats\n        super().__init__(annotation_file=annotation_file)\n\n    @override\n    def createIndex(self):\n        # We rename some keys to match the COCO format before creating the index.\n        if \"annotations\" in self.dataset:\n            for ann in self.dataset[\"annotations\"]:\n                if \"video_id\" in ann:\n                    ann[\"image_id\"] = int(ann.pop(\"video_id\"))\n                if self.ignore_gt_cats:\n                    ann[\"category_id\"] = -1\n                else:\n                    ann[\"category_id\"] = int(ann[\"category_id\"])\n                if \"bboxes\" in ann:\n                    # note that in some datasets we load under this YTVIS class,\n                    # some \"bboxes\" could be None for when the GT object is invisible,\n                    # so we replace them with [0, 0, 0, 0]\n                    ann[\"bboxes\"] = [\n                        bbox if bbox is not None else [0, 0, 0, 0]\n                        for bbox in ann[\"bboxes\"]\n                    ]\n                if \"areas\" in ann:\n                    # similar to \"bboxes\", some areas could be None for when the GT\n                    # object is invisible, so we replace them with 0\n                    areas = [a if a is not None else 0 for a in ann[\"areas\"]]\n                    # Compute average area of tracklet\n                    ann[\"area\"] = np.mean(areas)\n        if \"videos\" in self.dataset:\n            for vid in self.dataset[\"videos\"]:\n                vid[\"id\"] = int(vid[\"id\"])\n            self.dataset[\"images\"] = self.dataset.pop(\"videos\")\n\n        if self.ignore_gt_cats:\n            self.dataset[\"categories\"] = [\n                {\"supercategory\": \"object\", \"id\": -1, \"name\": \"object\"}\n            ]\n        else:\n            for cat in self.dataset[\"categories\"]:\n                cat[\"id\"] = int(cat[\"id\"])\n        super().createIndex()\n\n    @override\n    def getAnnIds(self, imgIds=[], catIds=[], areaRng=[], iscrowd=None):\n        if len(areaRng) > 0:\n            logging.warning(\n                \"Note that we filter out objects based on their *average* area across the video, not per frame area\"\n            )\n\n        return super().getAnnIds(imgIds=imgIds, catIds=catIds, iscrowd=iscrowd)\n\n    @override\n    def showAnns(self, anns, draw_bbox=False):\n        raise NotImplementedError(\"Showing annotations is not supported\")\n\n    @override\n    def loadRes(self, resFile):\n        # Adapted from COCO.loadRes to support tracklets/masklets\n        res = YTVIS(ignore_gt_cats=self.ignore_gt_cats)\n        res.dataset[\"images\"] = [img for img in self.dataset[\"images\"]]\n\n        if type(resFile) == str:\n            with open(resFile) as f:\n                anns = json.load(f)\n        elif type(resFile) == np.ndarray:\n            anns = self.loadNumpyAnnotations(resFile)\n        else:\n            anns = resFile\n        assert type(anns) == list, \"results is not an array of objects\"\n        annsImgIds = [ann[\"image_id\"] for ann in anns]\n        assert set(annsImgIds) == (set(annsImgIds) & set(self.getImgIds())), (\n            \"Results do not correspond to current coco set\"\n        )\n        if \"bboxes\" in anns[0] and not anns[0][\"bboxes\"] == []:\n            res.dataset[\"categories\"] = copy.deepcopy(self.dataset[\"categories\"])\n            for id, ann in enumerate(anns):\n                bbs = [(bb if bb is not None else [0, 0, 0, 0]) for bb in ann[\"bboxes\"]]\n                xxyy = [[bb[0], bb[0] + bb[2], bb[1], bb[1] + bb[3]] for bb in bbs]\n                if not \"segmentations\" in ann:\n                    ann[\"segmentations\"] = [\n                        [[x1, y1, x1, y2, x2, y2, x2, y1]] for (x1, x2, y1, y2) in xxyy\n                    ]\n                ann[\"areas\"] = [bb[2] * bb[3] for bb in bbs]\n                # NOTE: We also compute average area of a tracklet across video, allowing us to compute area based mAP.\n                ann[\"area\"] = np.mean(ann[\"areas\"])\n                ann[\"id\"] = id + 1\n                ann[\"iscrowd\"] = 0\n        elif \"segmentations\" in anns[0]:\n            res.dataset[\"categories\"] = copy.deepcopy(self.dataset[\"categories\"])\n            for id, ann in enumerate(anns):\n                ann[\"bboxes\"] = [\n                    mask_util.toBbox(segm) for segm in ann[\"segmentations\"]\n                ]\n                if \"areas\" not in ann:\n                    ann[\"areas\"] = [\n                        mask_util.area(segm) for segm in ann[\"segmentations\"]\n                    ]\n                # NOTE: We also compute average area of a tracklet across video, allowing us to compute area based mAP.\n                ann[\"area\"] = np.mean(ann[\"areas\"])\n                ann[\"id\"] = id + 1\n                ann[\"iscrowd\"] = 0\n\n        res.dataset[\"annotations\"] = anns\n        res.createIndex()\n        return res\n\n    @override\n    def download(self, tarDir=None, imgIds=[]):\n        raise NotImplementedError\n\n    @override\n    def loadNumpyAnnotations(self, data):\n        raise NotImplementedError(\"We don't support numpy annotations for now\")\n\n    @override\n    def annToRLE(self, ann):\n        raise NotImplementedError(\"We expect masks to be already in RLE format\")\n\n    @override\n    def annToMask(self, ann):\n        raise NotImplementedError(\"We expect masks to be already in RLE format\")\n"
  },
  {
    "path": "sam3/eval/ytvis_eval.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\nimport copy\nimport gc\nimport logging\nimport os\nfrom collections import defaultdict\nfrom operator import xor\nfrom pathlib import Path\nfrom typing import List, Optional\n\nimport numpy as np\nimport pycocotools.mask as mask_util\nimport torch\nfrom pycocotools.cocoeval import COCOeval\nfrom sam3.eval.cgf1_eval import CGF1Eval\nfrom sam3.eval.coco_eval_offline import convert_to_xywh\nfrom sam3.model.box_ops import box_xywh_inter_union\nfrom sam3.train.masks_ops import rle_encode\nfrom sam3.train.utils import distributed as dist\nfrom typing_extensions import override\n\ntry:\n    import rapidjson as json\nexcept ModuleNotFoundError:\n    import json\n\nfrom iopath.common.file_io import g_pathmgr\n\n\nclass YTVISevalMixin:\n    \"\"\"\n    Identical to COCOeval but adapts computeIoU to compute IoU between tracklets/masklets.\n    \"\"\"\n\n    @override\n    def _prepare(self):\n        \"\"\"\n        Copied from cocoeval.py but doesn't convert masks to RLEs (we assume they already are RLEs)\n        \"\"\"\n        p = self.params\n        if p.useCats:\n            gts = self.cocoGt.loadAnns(\n                self.cocoGt.getAnnIds(imgIds=p.imgIds, catIds=p.catIds)\n            )\n            dts = self.cocoDt.loadAnns(\n                self.cocoDt.getAnnIds(imgIds=p.imgIds, catIds=p.catIds)\n            )\n        else:\n            gts = self.cocoGt.loadAnns(self.cocoGt.getAnnIds(imgIds=p.imgIds))\n            dts = self.cocoDt.loadAnns(self.cocoDt.getAnnIds(imgIds=p.imgIds))\n\n        # set ignore flag\n        for gt in gts:\n            gt[\"ignore\"] = gt[\"ignore\"] if \"ignore\" in gt else 0\n            gt[\"ignore\"] = \"iscrowd\" in gt and gt[\"iscrowd\"]\n            if p.iouType == \"keypoints\":\n                gt[\"ignore\"] = (gt[\"num_keypoints\"] == 0) or gt[\"ignore\"]\n        self._gts = defaultdict(list)  # gt for evaluation\n        self._dts = defaultdict(list)  # dt for evaluation\n        for gt in gts:\n            self._gts[gt[\"image_id\"], gt[\"category_id\"]].append(gt)\n        for dt in dts:\n            self._dts[dt[\"image_id\"], dt[\"category_id\"]].append(dt)\n        self.evalImgs = defaultdict(list)  # per-image per-category evaluation results\n        self.eval = {}  # accumulated evaluation results\n\n    def computeIoU(self, imgId, catId):\n        \"\"\"\n        Compute IoU between tracklets. Copied from cocoeval.py but adapted for videos (in YT-VIS format)\n        \"\"\"\n        p = self.params\n        if p.useCats:\n            gt = self._gts[imgId, catId]\n            dt = self._dts[imgId, catId]\n        else:\n            gt = [_ for cId in p.catIds for _ in self._gts[imgId, cId]]\n            dt = [_ for cId in p.catIds for _ in self._dts[imgId, cId]]\n        if len(gt) == 0 or len(dt) == 0:\n            return []\n\n        # For class mAP and phrase AP evaluation, we sort the detections in descending order of scores (as in COCOeval).\n        # For demo F1 evaluation, we DO NOT sort the detections (but match them with GTs via Hungarian matching).\n        assert hasattr(self, \"sort_inds_by_scores_in_iou\"), (\n            \"subclasses that inherits YTVISevalMixin should set `self.sort_inds_by_scores_in_iou` \"\n            \"(True for class mAP and phrase AP, False for demo F1)\"\n        )\n        if self.sort_inds_by_scores_in_iou:\n            inds = np.argsort([-d[\"score\"] for d in dt], kind=\"mergesort\")\n            dt = [dt[i] for i in inds]\n            if len(dt) > p.maxDets[-1]:\n                dt = dt[0 : p.maxDets[-1]]\n\n        if p.iouType == \"segm\":\n            g = [g[\"segmentations\"] for g in gt]\n            d = [d[\"segmentations\"] for d in dt]\n        elif p.iouType == \"bbox\":\n            g = [g[\"bboxes\"] for g in gt]\n            d = [d[\"bboxes\"] for d in dt]\n        else:\n            raise Exception(\"unknown iouType for iou computation\")\n\n        def iou_tracklets(preds, gts):\n            preds = torch.tensor(preds)\n            gts = torch.tensor(gts)\n            inter, union = box_xywh_inter_union(\n                preds.unsqueeze(1), gts.unsqueeze(0)\n            )  # Num preds x Num GTS x Num frames\n            inter = inter.sum(-1)\n            union = union.sum(-1)\n            assert (union > 0).all(), (\n                \"There exists a tracklet with zero GTs across time. This is suspicious\"\n            )\n            return inter / union\n\n        def iou_masklets(preds, gts):\n            inter = 0\n            union = 0\n            for p_i, gt_i in zip(preds, gts):\n                if p_i and gt_i:\n                    # Compute areas of intersection and union\n                    inter += mask_util.area(\n                        mask_util.merge([p_i, gt_i], intersect=True)\n                    )\n                    union += mask_util.area(\n                        mask_util.merge([p_i, gt_i], intersect=False)\n                    )\n                elif gt_i:\n                    union += mask_util.area(gt_i)\n                elif p_i:\n                    union += mask_util.area(p_i)\n            if union > 0:\n                iou = inter / union\n                assert iou >= 0 and iou <= 1, \"Encountered an error in IoU computation\"\n            else:\n                assert np.isclose(inter, 0) and np.isclose(union, 0), (\n                    \"Encountered an error in IoU computation\"\n                )\n                iou = 1\n            return iou\n\n        if p.iouType == \"segm\":\n            ious = [[iou_masklets(d_i, g_i) for g_i in g] for d_i in d]\n        else:\n            ious = iou_tracklets(d, g)\n        return np.array(ious)\n\n\nclass YTVISeval(YTVISevalMixin, COCOeval):\n    # For class mAP and phrase AP evaluation, we sort the detections in descending order of scores (as in COCOeval).\n    sort_inds_by_scores_in_iou = True\n\n\nclass VideoDemoF1Eval(YTVISevalMixin, CGF1Eval):\n    # For demo F1 evaluation, we DO NOT sort the detections (but match them with GTs via Hungarian matching).\n    sort_inds_by_scores_in_iou = False\n\n\nclass YTVISResultsWriter:\n    \"\"\"\n    Gather and dumps predictions in YT-VIS format.\n    Expected flow of API calls: reset() -> N * update() -> compute_synced()\n    \"\"\"\n\n    def __init__(\n        self,\n        dump_file: str,\n        postprocessor,\n        gather_pred_via_filesys=False,\n        pred_file_evaluators: Optional[List] = None,\n        save_per_frame_scores: bool = False,\n        write_eval_metrics_file: bool = True,\n        eval_metrics_file_suffix: str = \".sam3_eval_metrics\",\n    ):\n        self.dump_file = dump_file\n        self.dump = []\n        self.postprocessor = postprocessor\n        self.gather_pred_via_filesys = gather_pred_via_filesys\n        if dist.is_main_process():\n            dirname = os.path.dirname(self.dump_file)\n            if not os.path.exists(dirname):\n                os.makedirs(dirname, exist_ok=True)\n                logging.info(f\"Creating folder: {dirname}\")\n\n        # the evaluation hooks to be applied to the prediction files\n        self.pred_file_evaluators = pred_file_evaluators or []\n        self.save_per_frame_scores = save_per_frame_scores\n        # in addition to the prediction file, we also write the evaluation metrics\n        # for easier debugging and analysis (stored in another eval_metrics_file\n        # so that we can keep the dumped prediction file under YT-VIS format)\n        self.write_eval_metrics_file = write_eval_metrics_file\n        if self.write_eval_metrics_file:\n            self.eval_metrics_file = self.dump_file + eval_metrics_file_suffix\n            os.makedirs(os.path.dirname(self.eval_metrics_file), exist_ok=True)\n\n    def _dump_vid_preds(self, results):\n        dumped_results = copy.deepcopy(results)\n        self.dump.extend(dumped_results)\n\n    def prepare(self, predictions):\n        ytvis_results = []\n        for video_id, prediction in predictions.items():\n            if len(prediction) == 0:\n                continue\n            for k in [\"boxes\", \"scores\", \"labels\"]:\n                assert k in prediction, (\n                    f\"Expected predictions to have `{k}` key, available keys are {prediction.keys()}\"\n                )\n            if self.save_per_frame_scores:\n                assert \"per_frame_scores\" in prediction, (\n                    f\"Expected predictions to have `per_frame_scores` key, available keys are {prediction.keys()}\"\n                )\n            assert xor(\"masks\" in prediction, \"masks_rle\" in prediction), (\n                f\"Expected predictions to have either `masks` key or `masks_rle` key, available keys are {prediction.keys()}\"\n            )\n\n            boxes = prediction[\"boxes\"]\n            boxes = convert_to_xywh(boxes).tolist()\n            scores = prediction[\"scores\"].tolist()\n            labels = prediction[\"labels\"].tolist()\n            if \"masks\" in prediction:\n                masks = prediction[\"masks\"].squeeze(2)\n                assert masks.ndim == 4, (\n                    \"Expected masks to be of shape(N_preds,T_frames,H,W)\"\n                )\n\n                areas = [mask.flatten(1).sum(1).tolist() for mask in masks]\n                rles = [rle_encode(masklet) for masklet in masks]\n\n                # memory clean\n                del masks\n                del prediction[\"masks\"]\n            elif \"masks_rle\" in prediction:\n                rles = prediction.pop(\"masks_rle\")\n                areas = [\n                    [0 if rle is None else rle.pop(\"area\") for rle in rles_per_obj]\n                    for rles_per_obj in rles\n                ]\n            else:\n                raise ValueError(\n                    \"Expected either `masks` or `masks_rle` key in the predictions.\"\n                )\n\n            new_results = [\n                {\n                    \"video_id\": video_id,\n                    \"category_id\": track_label,\n                    \"bboxes\": track_boxes,\n                    \"score\": track_score,\n                    \"segmentations\": track_masks,\n                    \"areas\": track_areas,\n                }\n                for (\n                    track_boxes,\n                    track_masks,\n                    track_areas,\n                    track_score,\n                    track_label,\n                ) in zip(boxes, rles, areas, scores, labels)\n            ]\n            # Optionally, save per-frame scores\n            if self.save_per_frame_scores:\n                per_frame_scores = prediction[\"per_frame_scores\"].tolist()\n                for res, track_per_frame_scores in zip(new_results, per_frame_scores):\n                    res[\"per_frame_scores\"] = track_per_frame_scores\n\n            ytvis_results.extend(new_results)\n\n        return ytvis_results\n\n    def set_sync_device(self, device: torch.device):\n        self._sync_device = device\n\n    def update(self, *args, **kwargs):\n        predictions = self.postprocessor.process_results(*args, **kwargs)\n        results = self.prepare(predictions)\n        self._dump_vid_preds(results)\n\n    def _dump_preds(self):\n        if not dist.is_main_process():\n            self.dump = []\n            gc.collect()\n            return\n        dumped_file = Path(self.dump_file)\n        logging.info(f\"YTVIS evaluator: Dumping predictions to {dumped_file}\")\n        with g_pathmgr.open(str(dumped_file), \"w\") as f:\n            json.dump(self.dump, f)\n        self.dump = []\n        gc.collect()\n        return str(dumped_file)\n\n    def synchronize_between_processes(self):\n        logging.info(\"YT-VIS evaluator: Synchronizing between processes\")\n        dump_dict = self._dedup_pre_gather(self.dump)\n        if self.gather_pred_via_filesys:\n            dump_dict_all_gpus = dist.gather_to_rank_0_via_filesys(dump_dict)\n        else:\n            dump_dict_all_gpus = dist.all_gather(dump_dict, force_cpu=True)\n        self.dump = self._dedup_post_gather(dump_dict_all_gpus)\n        logging.info(f\"Gathered all {len(self.dump)} predictions\")\n\n    def _dedup_pre_gather(self, predictions):\n        \"\"\"\n        Organize the predictions as a dict-of-list using (video_id, category_id) as keys\n        for deduplication after gathering them across GPUs.\n\n        During evaluation, PyTorch data loader under `drop_last: False` would wrap\n        around the dataset length to be a multiple of world size (GPU num) and duplicate\n        the remaining batches. This causes the same test sample to appear simultaneously\n        in multiple GPUs, resulting in duplicated predictions being saved into prediction\n        files. These duplicates are then counted as false positives under detection mAP\n        metrics (since a ground truth can be matched with only one prediction).\n\n        For example, if there are 4 GPUs and 6 samples [A1, A2, B1, B2, C1, C2], the data\n        loader (under `drop_last: False`) would load it by wrapping it around like\n        `[A1, A2, B1, B2, C1, C2, *A1*, *A2*]` to make a multiple of 4 and then split it as\n\n        - GPU 0: A1, C1\n        - GPU 1: A2, C2\n        - GPU 3: B1, **A1**\n        - GPU 4: B2, **A2**\n        (as in DistributedSampler in https://github.com/pytorch/pytorch/blob/521588519da9f4876d90ddd7a17c10d0eca89dc6/torch/utils/data/distributed.py#L116-L124)\n\n        so the predictions on A1 and A2 will occur twice in the final gathered outputs\n        in the prediction file (and counted as false positives). This also affects our\n        YT-VIS official val evaluation, but to a lesser extent than YT-VIS dev since\n        the latter is much smaller and more susceptible to false positives.\n\n        So we to deduplicate this. The tricky part is that we cannot deduplicate them\n        simply using video id, given that we are sharding the classes in each video\n        across multiple batches (with 20 prompts per batch) in our \"orig_cats\" eval dbs.\n\n        The solution is to deduplicate based on (video_id, category_id) tuple as keys.\n        We organize the predictions as a dict-of-list using (video_id, category_id) as\n        keys on each GPU, with the list of masklets under this (video_id, category_id)\n        on this GPU as values. Then, we all-gather this dict-of-list across GPUs and\n        if a key (video_id, category_id) appears in multiple GPUs, we only take the\n        prediction masklet list from one GPU.\n        \"\"\"\n        prediction_dict = defaultdict(list)\n        for p in predictions:\n            prediction_dict[(p[\"video_id\"], p[\"category_id\"])].append(p)\n        return prediction_dict\n\n    def _dedup_post_gather(self, list_of_prediction_dict):\n        \"\"\"\n        Deduplicate the predictions from all GPUs. See `_dedup_pre_gather` for details.\n        \"\"\"\n        dedup_prediction_dict = {}\n        duplication_keys = []\n        for prediction_dict in list_of_prediction_dict:\n            for k, v in prediction_dict.items():\n                if k not in dedup_prediction_dict:\n                    dedup_prediction_dict[k] = v\n                else:\n                    duplication_keys.append(k)\n\n        logging.info(\n            f\"skipped {len(duplication_keys)} duplicated predictions in YTVISResultsWriter \"\n            f\"with the following (video_id, category_id) tuples: {duplication_keys}\"\n        )\n        dedup_predictions = sum(dedup_prediction_dict.values(), [])\n        return dedup_predictions\n\n    def compute_synced(\n        self,\n    ):\n        self.synchronize_between_processes()\n        dumped_file = self._dump_preds()\n        if not dist.is_main_process():\n            return {\"\": 0.0}\n\n        # run evaluation hooks on the prediction file\n        meters = {}\n        all_video_np_level_results = defaultdict(dict)\n        for evaluator in self.pred_file_evaluators:\n            gc.collect()\n            results, video_np_level_results = evaluator.evaluate(dumped_file)\n            meters.update(results)\n            for (video_id, category_id), res in video_np_level_results.items():\n                all_video_np_level_results[(video_id, category_id)].update(res)\n\n        gc.collect()\n        if self.write_eval_metrics_file:\n            # convert the nested dict of {(video_id, category_id): per_sample_metric_dict}\n            # to a list of per-sample metric dicts (with video_id and category_id) for JSON,\n            # as JSON doesn't allow using tuples like (video_id, category_id) as dict keys\n            video_np_level_metrics = [\n                {\"video_id\": video_id, \"category_id\": category_id, **res}\n                for (video_id, category_id), res in all_video_np_level_results.items()\n            ]\n            eval_metrics = {\n                \"dataset_level_metrics\": meters,\n                \"video_np_level_metrics\": video_np_level_metrics,\n            }\n            with g_pathmgr.open(self.eval_metrics_file, \"w\") as f:\n                json.dump(eval_metrics, f)\n            logging.info(\n                f\"YTVIS evaluator: Dumped evaluation metrics to {self.eval_metrics_file}\"\n            )\n\n        if len(meters) == 0:\n            meters = {\"\": 0.0}\n        return meters\n\n    def compute(self):\n        return {\"\": 0.0}\n\n    def reset(self, *args, **kwargs):\n        self.dump = []\n"
  },
  {
    "path": "sam3/logger.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\nimport logging\nimport os\n\nLOG_LEVELS = {\n    \"DEBUG\": logging.DEBUG,\n    \"INFO\": logging.INFO,\n    \"WARNING\": logging.WARNING,\n    \"ERROR\": logging.ERROR,\n    \"CRITICAL\": logging.CRITICAL,\n}\n\n\nclass ColoredFormatter(logging.Formatter):\n    \"\"\"A command line formatter with different colors for each level.\"\"\"\n\n    def __init__(self):\n        super().__init__()\n        reset = \"\\033[0m\"\n        colors = {\n            logging.DEBUG: f\"{reset}\\033[36m\",  # cyan,\n            logging.INFO: f\"{reset}\\033[32m\",  # green\n            logging.WARNING: f\"{reset}\\033[33m\",  # yellow\n            logging.ERROR: f\"{reset}\\033[31m\",  # red\n            logging.CRITICAL: f\"{reset}\\033[35m\",  # magenta\n        }\n        fmt_str = \"{color}%(levelname)s %(asctime)s %(process)d %(filename)s:%(lineno)4d:{reset} %(message)s\"\n        self.formatters = {\n            level: logging.Formatter(fmt_str.format(color=color, reset=reset))\n            for level, color in colors.items()\n        }\n        self.default_formatter = self.formatters[logging.INFO]\n\n    def format(self, record):\n        formatter = self.formatters.get(record.levelno, self.default_formatter)\n        return formatter.format(record)\n\n\ndef get_logger(name, level=logging.INFO):\n    \"\"\"A command line logger.\"\"\"\n    if \"LOG_LEVEL\" in os.environ:\n        level = os.environ[\"LOG_LEVEL\"].upper()\n        assert level in LOG_LEVELS, (\n            f\"Invalid LOG_LEVEL: {level}, must be one of {list(LOG_LEVELS.keys())}\"\n        )\n        level = LOG_LEVELS[level]\n    logger = logging.getLogger(name)\n    logger.setLevel(level)\n    logger.propagate = False\n    ch = logging.StreamHandler()\n    ch.setLevel(level)\n    ch.setFormatter(ColoredFormatter())\n    logger.addHandler(ch)\n    return logger\n"
  },
  {
    "path": "sam3/model/__init__.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n"
  },
  {
    "path": "sam3/model/act_ckpt_utils.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n\nimport inspect\nfrom functools import wraps\nfrom typing import Callable, TypeVar, Union\n\nimport torch\nimport torch.nn as nn\nimport torch.utils.checkpoint as checkpoint\nfrom torch.utils._pytree import tree_map_only\n\n# Type variables for better type hinting\nT = TypeVar(\"T\")\nModule = TypeVar(\"Module\", bound=nn.Module)\n\n\ndef activation_ckpt_wrapper(module: Union[nn.Module, Callable]) -> Callable:\n    \"\"\"\n    Wraps a given module to enable or disable activation checkpointing.\n\n    Activation checkpointing (gradient checkpointing) trades compute for memory by\n    recomputing intermediate activations during the backward pass instead of storing\n    them in memory during the forward pass.\n\n    When activation checkpointing is enabled, the wrapper expects only keyword arguments,\n    and it maps these to positional arguments based on the module's signature.\n\n    Args:\n        module: The module or function to wrap with activation checkpointing\n\n    Returns:\n        A wrapped callable that supports activation checkpointing\n\n    Usage:\n        The returned wrapper function can be called with the same arguments as the\n        original module, with an additional `act_ckpt_enable` keyword argument to control\n        activation checkpointing and optional `use_reentrant` parameter.\n\n    Example:\n        ```python\n        wrapped_module = activation_ckpt_wrapper(my_module)\n        output = wrapped_module(x=input_tensor, y=another_tensor, act_ckpt_enable=True)\n        ```\n    \"\"\"\n\n    @wraps(module)\n    def act_ckpt_wrapper(\n        *args, act_ckpt_enable: bool = True, use_reentrant: bool = False, **kwargs\n    ):\n        if act_ckpt_enable:\n            if len(args) > 0:\n                raise ValueError(\n                    \"This wrapper expects keyword arguments only when `act_ckpt_enable=True`\"\n                )\n            # Get the signature of the target function/module\n            callable_fn = module.forward if isinstance(module, nn.Module) else module\n            sig = inspect.signature(callable_fn)\n            # Create a mapping of parameter names to their default values\n            param_defaults = {\n                name: param.default for name, param in sig.parameters.items()\n            }\n            args = []\n            for p_name in param_defaults.keys():\n                if p_name in kwargs:\n                    args.append(kwargs.pop(p_name))\n                elif param_defaults[p_name] is not inspect.Parameter.empty:\n                    # Set arg to default value if it's not in kwargs. Useful for primitive types or args that default to None\n                    args.append(param_defaults[p_name])\n                elif (\n                    sig.parameters[p_name].kind is not inspect.Parameter.VAR_KEYWORD\n                ):  # Skip **kwargs parameter\n                    raise ValueError(f\"Missing positional argument: {p_name}\")\n\n            # Scan remaining kwargs for torch.Tensor\n            remaining_keys = list(kwargs.keys())\n            for key in remaining_keys:\n                if isinstance(kwargs[key], torch.Tensor):\n                    # Remove the tensor from kwargs, assuming it's not required by the module.\n                    # If it is required, the module's signature should be modified to accept it as a positional or keyword argument.\n                    kwargs[key] = \"_REMOVED_BY_ACT_CKPT_WRAPPER_\"\n\n            ret = checkpoint.checkpoint(\n                module, *args, use_reentrant=use_reentrant, **kwargs\n            )\n        else:\n            ret = module(*args, **kwargs)\n\n        return ret\n\n    return act_ckpt_wrapper\n\n\ndef clone_output_wrapper(f: Callable[..., T]) -> Callable[..., T]:\n    \"\"\"\n    Clone the CUDA output tensors of a function to avoid in-place operations.\n\n    This wrapper is useful when working with torch.compile to prevent errors\n    related to in-place operations on tensors.\n\n    Args:\n        f: The function whose CUDA tensor outputs should be cloned\n\n    Returns:\n        A wrapped function that clones any CUDA tensor outputs\n    \"\"\"\n\n    @wraps(f)\n    def wrapped(*args, **kwargs):\n        outputs = f(*args, **kwargs)\n        return tree_map_only(\n            torch.Tensor, lambda t: t.clone() if t.is_cuda else t, outputs\n        )\n\n    return wrapped\n"
  },
  {
    "path": "sam3/model/box_ops.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n\"\"\"\nUtilities for bounding box manipulation and GIoU.\n\"\"\"\n\nfrom typing import Tuple\n\nimport torch\n\n\ndef box_cxcywh_to_xyxy(x):\n    x_c, y_c, w, h = x.unbind(-1)\n    b = [(x_c - 0.5 * w), (y_c - 0.5 * h), (x_c + 0.5 * w), (y_c + 0.5 * h)]\n    return torch.stack(b, dim=-1)\n\n\ndef box_cxcywh_to_xywh(x):\n    x_c, y_c, w, h = x.unbind(-1)\n    b = [(x_c - 0.5 * w), (y_c - 0.5 * h), (w), (h)]\n    return torch.stack(b, dim=-1)\n\n\ndef box_xywh_to_xyxy(x):\n    x, y, w, h = x.unbind(-1)\n    b = [(x), (y), (x + w), (y + h)]\n    return torch.stack(b, dim=-1)\n\n\ndef box_xywh_to_cxcywh(x):\n    x, y, w, h = x.unbind(-1)\n    b = [(x + 0.5 * w), (y + 0.5 * h), (w), (h)]\n    return torch.stack(b, dim=-1)\n\n\ndef box_xyxy_to_xywh(x):\n    x, y, X, Y = x.unbind(-1)\n    b = [(x), (y), (X - x), (Y - y)]\n    return torch.stack(b, dim=-1)\n\n\ndef box_xyxy_to_cxcywh(x):\n    x0, y0, x1, y1 = x.unbind(-1)\n    b = [(x0 + x1) / 2, (y0 + y1) / 2, (x1 - x0), (y1 - y0)]\n    return torch.stack(b, dim=-1)\n\n\ndef box_area(boxes):\n    \"\"\"\n    Batched version of box area. Boxes should be in [x0, y0, x1, y1] format.\n\n    Inputs:\n    - boxes: Tensor of shape (..., 4)\n\n    Returns:\n    - areas: Tensor of shape (...,)\n    \"\"\"\n    x0, y0, x1, y1 = boxes.unbind(-1)\n    return (x1 - x0) * (y1 - y0)\n\n\ndef masks_to_boxes(masks):\n    \"\"\"Compute the bounding boxes around the provided masks\n\n    The masks should be in format [N, H, W] where N is the number of masks, (H, W) are the spatial dimensions.\n\n    Returns a [N, 4] tensors, with the boxes in xyxy format\n    \"\"\"\n    if masks.numel() == 0:\n        return torch.zeros((0, 4), device=masks.device)\n\n    h, w = masks.shape[-2:]\n\n    y = torch.arange(0, h, dtype=torch.float, device=masks.device)\n    x = torch.arange(0, w, dtype=torch.float, device=masks.device)\n    y, x = torch.meshgrid(y, x)\n\n    x_mask = masks * x.unsqueeze(0)\n    x_max = x_mask.flatten(1).max(-1)[0] + 1\n    x_min = x_mask.masked_fill(~(masks.bool()), 1e8).flatten(1).min(-1)[0]\n\n    y_mask = masks * y.unsqueeze(0)\n    y_max = y_mask.flatten(1).max(-1)[0] + 1\n    y_min = y_mask.masked_fill(~(masks.bool()), 1e8).flatten(1).min(-1)[0]\n\n    boxes = torch.stack([x_min, y_min, x_max, y_max], 1)\n    # Invalidate boxes corresponding to empty masks.\n    boxes = boxes * masks.flatten(-2).any(-1)\n    return boxes\n\n\ndef box_iou(boxes1, boxes2):\n    \"\"\"\n    Batched version of box_iou. Boxes should be in [x0, y0, x1, y1] format.\n\n    Inputs:\n    - boxes1: Tensor of shape (..., N, 4)\n    - boxes2: Tensor of shape (..., M, 4)\n\n    Returns:\n    - iou, union: Tensors of shape (..., N, M)\n    \"\"\"\n    area1 = box_area(boxes1)\n    area2 = box_area(boxes2)\n\n    # boxes1: (..., N, 4) -> (..., N, 1, 2)\n    # boxes2: (..., M, 4) -> (..., 1, M, 2)\n    lt = torch.max(boxes1[..., :, None, :2], boxes2[..., None, :, :2])\n    rb = torch.min(boxes1[..., :, None, 2:], boxes2[..., None, :, 2:])\n\n    wh = (rb - lt).clamp(min=0)  # (..., N, M, 2)\n    inter = wh[..., 0] * wh[..., 1]  # (..., N, M)\n\n    union = area1[..., None] + area2[..., None, :] - inter\n\n    iou = inter / union\n    return iou, union\n\n\ndef generalized_box_iou(boxes1, boxes2):\n    \"\"\"\n    Batched version of Generalized IoU from https://giou.stanford.edu/\n\n    Boxes should be in [x0, y0, x1, y1] format\n\n    Inputs:\n    - boxes1: Tensor of shape (..., N, 4)\n    - boxes2: Tensor of shape (..., M, 4)\n\n    Returns:\n    - giou: Tensor of shape (..., N, M)\n    \"\"\"\n    iou, union = box_iou(boxes1, boxes2)\n\n    # boxes1: (..., N, 4) -> (..., N, 1, 2)\n    # boxes2: (..., M, 4) -> (..., 1, M, 2)\n    lt = torch.min(boxes1[..., :, None, :2], boxes2[..., None, :, :2])\n    rb = torch.max(boxes1[..., :, None, 2:], boxes2[..., None, :, 2:])\n\n    wh = (rb - lt).clamp(min=0)  # (..., N, M, 2)\n    area = wh[..., 0] * wh[..., 1]  # (..., N, M)\n\n    return iou - (area - union) / area\n\n\n@torch.jit.script\ndef fast_diag_generalized_box_iou(boxes1, boxes2):\n    assert len(boxes1) == len(boxes2)\n    box1_xy = boxes1[:, 2:]\n    box1_XY = boxes1[:, :2]\n    box2_xy = boxes2[:, 2:]\n    box2_XY = boxes2[:, :2]\n    # assert (box1_xy >= box1_XY).all()\n    # assert (box2_xy >= box2_XY).all()\n    area1 = (box1_xy - box1_XY).prod(-1)\n    area2 = (box2_xy - box2_XY).prod(-1)\n\n    lt = torch.max(box1_XY, box2_XY)  # [N,2]\n    lt2 = torch.min(box1_XY, box2_XY)\n    rb = torch.min(box1_xy, box2_xy)  # [N,2]\n    rb2 = torch.max(box1_xy, box2_xy)\n\n    inter = (rb - lt).clamp(min=0).prod(-1)\n    tot_area = (rb2 - lt2).clamp(min=0).prod(-1)\n\n    union = area1 + area2 - inter\n\n    iou = inter / union\n\n    return iou - (tot_area - union) / tot_area\n\n\n@torch.jit.script\ndef fast_diag_box_iou(boxes1, boxes2):\n    assert len(boxes1) == len(boxes2)\n    box1_xy = boxes1[:, 2:]\n    box1_XY = boxes1[:, :2]\n    box2_xy = boxes2[:, 2:]\n    box2_XY = boxes2[:, :2]\n    # assert (box1_xy >= box1_XY).all()\n    # assert (box2_xy >= box2_XY).all()\n    area1 = (box1_xy - box1_XY).prod(-1)\n    area2 = (box2_xy - box2_XY).prod(-1)\n\n    lt = torch.max(box1_XY, box2_XY)  # [N,2]\n    rb = torch.min(box1_xy, box2_xy)  # [N,2]\n\n    inter = (rb - lt).clamp(min=0).prod(-1)\n\n    union = area1 + area2 - inter\n\n    iou = inter / union\n\n    return iou\n\n\ndef box_xywh_inter_union(\n    boxes1: torch.Tensor, boxes2: torch.Tensor\n) -> Tuple[torch.Tensor, torch.Tensor]:\n    # Asuumes boxes in xywh format\n    assert boxes1.size(-1) == 4 and boxes2.size(-1) == 4\n    boxes1 = box_xywh_to_xyxy(boxes1)\n    boxes2 = box_xywh_to_xyxy(boxes2)\n    box1_tl_xy = boxes1[..., :2]\n    box1_br_xy = boxes1[..., 2:]\n    box2_tl_xy = boxes2[..., :2]\n    box2_br_xy = boxes2[..., 2:]\n    area1 = (box1_br_xy - box1_tl_xy).prod(-1)\n    area2 = (box2_br_xy - box2_tl_xy).prod(-1)\n\n    assert (area1 >= 0).all() and (area2 >= 0).all()\n    tl = torch.max(box1_tl_xy, box2_tl_xy)\n    br = torch.min(box1_br_xy, box2_br_xy)\n\n    inter = (br - tl).clamp(min=0).prod(-1)\n    union = area1 + area2 - inter\n\n    return inter, union\n"
  },
  {
    "path": "sam3/model/data_misc.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n\"\"\"\nMisc functions, including distributed helpers.\n\"\"\"\n\nimport collections\nimport re\nfrom dataclasses import dataclass, field as field_ptr_behaviour, fields, is_dataclass\nfrom typing import Any, get_args, get_origin, List, Mapping, Optional, Sequence, Union\n\nimport torch\n\n\nMyTensor = Union[torch.Tensor, List[Any]]\n\n\ndef interpolate(\n    input, size=None, scale_factor=None, mode=\"nearest\", align_corners=None\n):\n    # type: (Tensor, Optional[List[int]], Optional[float], str, Optional[bool]) -> Tensor\n    \"\"\"\n    Equivalent to nn.functional.interpolate, but with support for empty channel sizes.\n    \"\"\"\n    if input.numel() > 0:\n        return torch.nn.functional.interpolate(\n            input, size, scale_factor, mode, align_corners\n        )\n\n    assert input.shape[0] != 0 or input.shape[1] != 0, (\n        \"At least one of the two first dimensions must be non zero\"\n    )\n\n    if input.shape[1] == 0:\n        # Pytorch doesn't support null dimension on the channel dimension, so we transpose to fake a null batch dim\n        return torch.nn.functional.interpolate(\n            input.transpose(0, 1), size, scale_factor, mode, align_corners\n        ).transpose(0, 1)\n\n    # empty batch dimension is now supported in pytorch\n    return torch.nn.functional.interpolate(\n        input, size, scale_factor, mode, align_corners\n    )\n\n\n@dataclass\nclass BatchedPointer:\n    stage_ids: MyTensor\n    stage_ids__type = torch.long\n    query_ids: MyTensor\n    query_ids__type = torch.long\n    object_ids: MyTensor\n    object_ids__type = torch.long\n    ptr_mask: MyTensor\n    ptr_mask__type = torch.bool\n    ptr_types: MyTensor\n    ptr_types__type = torch.long\n\n\n@dataclass\nclass FindStage:\n    img_ids: MyTensor\n    img_ids__type = torch.long\n    text_ids: MyTensor\n    text_ids__type = torch.long\n\n    input_boxes: MyTensor\n    input_boxes__type = torch.float\n    input_boxes_mask: MyTensor\n    input_boxes_mask__type = torch.bool\n    input_boxes_label: MyTensor\n    input_boxes_label__type = torch.long\n\n    input_points: MyTensor\n    input_points__type = torch.float\n    input_points_mask: MyTensor\n    input_points_mask__type = torch.bool\n\n    # We track the object ids referred to by this query.\n    # This is beneficial for tracking in videos without the need for pointers.\n    object_ids: Optional[List[List]] = None  # List of objects per query\n\n\n@dataclass\nclass BatchedFindTarget:\n    # The number of boxes in each find query\n    num_boxes: MyTensor\n    num_boxes__type = torch.long\n\n    # Target boxes in normalized CxCywh format\n    boxes: MyTensor\n    boxes__type = torch.float\n    # Target boxes in normalized CxCywh format but in padded representation\n    # as used in BinaryHungarianMatcherV2 (unlike the packed ones in `boxes`)\n    boxes_padded: MyTensor\n    boxes_padded__type = torch.float\n\n    # For hybrid matching, we repeat the boxes\n    repeated_boxes: MyTensor\n    repeated_boxes__type = torch.float\n\n    # Target Segmentation masks\n    segments: Optional[MyTensor]\n    segments__type = torch.bool\n\n    # Target Semantic Segmentation masks\n    semantic_segments: Optional[MyTensor]\n    semantic_segments__type = torch.bool\n\n    is_valid_segment: Optional[MyTensor]\n    is_valid_segment__type = torch.bool\n\n    # Whether annotations are exhaustive for each query\n    is_exhaustive: MyTensor\n    is_exhaustive__type = torch.bool\n\n    # The object id for each ground-truth box, in both packed and padded representations\n    object_ids: MyTensor\n    object_ids__type = torch.long\n    object_ids_padded: MyTensor\n    object_ids_padded__type = torch.long\n\n\n@dataclass\nclass BatchedInferenceMetadata:\n    \"\"\"All metadata required to post-process a find stage\"\"\"\n\n    # Coco id that corresponds to the \"image\" for evaluation by the coco evaluator\n    coco_image_id: MyTensor\n    coco_image_id__type = torch.long\n\n    # id in the original dataset, such that we can use the original evaluator\n    original_image_id: MyTensor\n    original_image_id__type = torch.long\n\n    # Original category id (if we want to use the original evaluator)\n    original_category_id: MyTensor\n    original_category_id__type = torch.int\n\n    # Size of the raw image (height, width)\n    original_size: MyTensor\n    original_size__type = torch.long\n\n    # id of the object in the media (track_id for a video)\n    object_id: MyTensor\n    object_id__type = torch.long\n\n    # index of the frame in the media (0 in the case of a single-frame media)\n    frame_index: MyTensor\n    frame_index__type = torch.long\n\n    # Adding for relations inference\n    # get_text_input: List[Optional[str]]\n\n    # Adding for TA conditional inference\n    is_conditioning_only: List[Optional[bool]]\n\n\n@dataclass\nclass BatchedDatapoint:\n    img_batch: torch.Tensor\n    find_text_batch: List[str]\n    find_inputs: List[FindStage]\n    find_targets: List[BatchedFindTarget]\n    find_metadatas: List[BatchedInferenceMetadata]\n    raw_images: Optional[List[Any]] = None\n\n\ndef convert_my_tensors(obj):\n    def is_optional_field(field) -> bool:\n        return get_origin(field) is Union and type(None) in get_args(field)\n\n    for field in fields(obj):\n        if is_dataclass(getattr(obj, field.name)):\n            convert_my_tensors(getattr(obj, field.name))\n            continue\n\n        field_type = field.type\n        if is_optional_field(field.type):\n            field_type = Union[get_args(field.type)[:-1]]  # Get the Optional field type\n\n        if field_type != MyTensor or getattr(obj, field.name) is None:\n            continue\n\n        elif len(getattr(obj, field.name)) and isinstance(\n            getattr(obj, field.name)[0], torch.Tensor\n        ):\n            stack_dim = 0\n            if field.name in [\n                \"input_boxes\",\n                \"input_boxes_label\",\n            ]:\n                stack_dim = 1\n            setattr(\n                obj,\n                field.name,\n                torch.stack(getattr(obj, field.name), dim=stack_dim).to(\n                    getattr(obj, field.name + \"__type\")\n                ),\n            )\n        else:\n            setattr(\n                obj,\n                field.name,\n                torch.as_tensor(\n                    getattr(obj, field.name), dtype=getattr(obj, field.name + \"__type\")\n                ),\n            )\n    return obj\n"
  },
  {
    "path": "sam3/model/decoder.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n\"\"\"\nTransformer decoder.\nInspired from Pytorch's version, adds the pre-norm variant\n\"\"\"\n\nfrom typing import Any, Dict, List, Optional\n\nimport numpy as np\nimport torch\nfrom sam3.sam.transformer import RoPEAttention\nfrom torch import nn, Tensor\nfrom torchvision.ops.roi_align import RoIAlign\n\nfrom .act_ckpt_utils import activation_ckpt_wrapper\nfrom .box_ops import box_cxcywh_to_xyxy\nfrom .model_misc import (\n    gen_sineembed_for_position,\n    get_activation_fn,\n    get_clones,\n    inverse_sigmoid,\n    MLP,\n)\n\n\nclass TransformerDecoderLayer(nn.Module):\n    def __init__(\n        self,\n        activation: str,\n        d_model: int,\n        dim_feedforward: int,\n        dropout: float,\n        cross_attention: nn.Module,\n        n_heads: int,\n        use_text_cross_attention: bool = False,\n    ):\n        super().__init__()\n\n        # cross attention\n        self.cross_attn = cross_attention\n        self.dropout1 = nn.Dropout(dropout) if dropout > 0 else nn.Identity()\n        self.norm1 = nn.LayerNorm(d_model)\n\n        # cross attention text\n        self.use_text_cross_attention = use_text_cross_attention\n        if use_text_cross_attention:\n            self.ca_text = nn.MultiheadAttention(d_model, n_heads, dropout=dropout)\n            self.catext_dropout = nn.Dropout(dropout) if dropout > 0 else nn.Identity()\n            self.catext_norm = nn.LayerNorm(d_model)\n\n        # self attention\n        self.self_attn = nn.MultiheadAttention(d_model, n_heads, dropout=dropout)\n        self.dropout2 = nn.Dropout(dropout) if dropout > 0 else nn.Identity()\n        self.norm2 = nn.LayerNorm(d_model)\n\n        # ffn\n        self.linear1 = nn.Linear(d_model, dim_feedforward)\n        self.activation = get_activation_fn(activation)\n        self.dropout3 = nn.Dropout(dropout) if dropout > 0 else nn.Identity()\n        self.linear2 = nn.Linear(dim_feedforward, d_model)\n        self.dropout4 = nn.Dropout(dropout) if dropout > 0 else nn.Identity()\n        self.norm3 = nn.LayerNorm(d_model)\n\n    @staticmethod\n    def with_pos_embed(tensor, pos):\n        return tensor if pos is None else tensor + pos\n\n    def forward_ffn(self, tgt):\n        with torch.amp.autocast(device_type=\"cuda\", enabled=False):\n            tgt2 = self.linear2(self.dropout3(self.activation(self.linear1(tgt))))\n        tgt = tgt + self.dropout4(tgt2)\n        tgt = self.norm3(tgt)\n        return tgt\n\n    def forward(\n        self,\n        # for tgt\n        tgt: Optional[Tensor],  # nq, bs, d_model\n        tgt_query_pos: Optional[Tensor] = None,  # pos for query. MLP(Sine(pos))\n        tgt_query_sine_embed: Optional[Tensor] = None,  # pos for query. Sine(pos)\n        tgt_key_padding_mask: Optional[Tensor] = None,\n        tgt_reference_points: Optional[Tensor] = None,  # nq, bs, 4\n        memory_text: Optional[Tensor] = None,  # num_token, bs, d_model\n        text_attention_mask: Optional[Tensor] = None,  # bs, num_token\n        # for memory\n        memory: Optional[Tensor] = None,  # hw, bs, d_model\n        memory_key_padding_mask: Optional[Tensor] = None,\n        memory_level_start_index: Optional[Tensor] = None,  # num_levels\n        memory_spatial_shapes: Optional[Tensor] = None,  # bs, num_levels, 2\n        memory_pos: Optional[Tensor] = None,  # pos for memory\n        # sa\n        self_attn_mask: Optional[Tensor] = None,  # mask used for self-attention\n        cross_attn_mask: Optional[Tensor] = None,  # mask used for cross-attention\n        # dac\n        dac=False,\n        dac_use_selfatt_ln=True,\n        presence_token=None,\n        # skip inside deformable attn\n        identity=0.0,\n        **kwargs,  # additional kwargs for compatibility\n    ):\n        \"\"\"\n        Input:\n            - tgt/tgt_query_pos: nq, bs, d_model\n            -\n        \"\"\"\n        # self attention\n        if self.self_attn is not None:\n            if dac:\n                # we only apply self attention to the first half of the queries\n                assert tgt.shape[0] % 2 == 0\n                num_o2o_queries = tgt.shape[0] // 2\n                tgt_o2o = tgt[:num_o2o_queries]\n                tgt_query_pos_o2o = tgt_query_pos[:num_o2o_queries]\n                tgt_o2m = tgt[num_o2o_queries:]\n            else:\n                tgt_o2o = tgt\n                tgt_query_pos_o2o = tgt_query_pos\n\n            if presence_token is not None:\n                tgt_o2o = torch.cat([presence_token, tgt_o2o], dim=0)\n                tgt_query_pos_o2o = torch.cat(\n                    [torch.zeros_like(presence_token), tgt_query_pos_o2o], dim=0\n                )\n                tgt_query_pos = torch.cat(\n                    [torch.zeros_like(presence_token), tgt_query_pos], dim=0\n                )\n\n            q = k = self.with_pos_embed(tgt_o2o, tgt_query_pos_o2o)\n            tgt2 = self.self_attn(q, k, tgt_o2o, attn_mask=self_attn_mask)[0]\n            tgt_o2o = tgt_o2o + self.dropout2(tgt2)\n            if dac:\n                if not dac_use_selfatt_ln:\n                    tgt_o2o = self.norm2(tgt_o2o)\n                tgt = torch.cat((tgt_o2o, tgt_o2m), dim=0)  # Recombine\n                if dac_use_selfatt_ln:\n                    tgt = self.norm2(tgt)\n            else:\n                tgt = tgt_o2o\n                tgt = self.norm2(tgt)\n\n        if self.use_text_cross_attention:\n            tgt2 = self.ca_text(\n                self.with_pos_embed(tgt, tgt_query_pos),\n                memory_text,\n                memory_text,\n                key_padding_mask=text_attention_mask,\n            )[0]\n            tgt = tgt + self.catext_dropout(tgt2)\n            tgt = self.catext_norm(tgt)\n\n        if presence_token is not None:\n            presence_token_mask = torch.zeros_like(cross_attn_mask[:, :1, :])\n            cross_attn_mask = torch.cat(\n                [presence_token_mask, cross_attn_mask], dim=1\n            )  # (bs*nheads, 1+nq, hw)\n\n        # Cross attention to image\n        tgt2 = self.cross_attn(\n            query=self.with_pos_embed(tgt, tgt_query_pos),\n            key=self.with_pos_embed(memory, memory_pos),\n            value=memory,\n            attn_mask=cross_attn_mask,\n            key_padding_mask=(\n                memory_key_padding_mask.transpose(0, 1)\n                if memory_key_padding_mask is not None\n                else None\n            ),\n        )[0]\n\n        tgt = tgt + self.dropout1(tgt2)\n        tgt = self.norm1(tgt)\n\n        # ffn\n        tgt = self.forward_ffn(tgt)\n\n        presence_token_out = None\n        if presence_token is not None:\n            presence_token_out = tgt[:1]\n            tgt = tgt[1:]\n\n        return tgt, presence_token_out\n\n\nclass TransformerDecoder(nn.Module):\n    def __init__(\n        self,\n        d_model: int,\n        frozen: bool,\n        interaction_layer,\n        layer,\n        num_layers: int,\n        num_queries: int,\n        return_intermediate: bool,\n        box_refine: bool = False,\n        num_o2m_queries: int = 0,\n        dac: bool = False,\n        boxRPB: str = \"none\",\n        # Experimental: An object query for SAM 2 tasks\n        instance_query: bool = False,\n        # Defines the number of additional instance queries,\n        # 1 or 4 are the most likely for single vs multi mask support\n        num_instances: int = 1,  # Irrelevant if instance_query is False\n        dac_use_selfatt_ln: bool = True,\n        use_act_checkpoint: bool = False,\n        compile_mode=None,\n        presence_token: bool = False,\n        clamp_presence_logits: bool = True,\n        clamp_presence_logit_max_val: float = 10.0,\n        use_normed_output_consistently: bool = True,\n        separate_box_head_instance: bool = False,\n        separate_norm_instance: bool = False,\n        resolution: Optional[int] = None,\n        stride: Optional[int] = None,\n    ):\n        super().__init__()\n        self.d_model = d_model\n        self.layers = get_clones(layer, num_layers)\n        self.fine_layers = (\n            get_clones(interaction_layer, num_layers)\n            if interaction_layer is not None\n            else [None] * num_layers\n        )\n        self.num_layers = num_layers\n        self.num_queries = num_queries\n        self.dac = dac\n        if dac:\n            self.num_o2m_queries = num_queries\n            tot_num_queries = num_queries\n        else:\n            self.num_o2m_queries = num_o2m_queries\n            tot_num_queries = num_queries + num_o2m_queries\n        self.norm = nn.LayerNorm(d_model)\n        self.return_intermediate = return_intermediate\n        self.bbox_embed = MLP(d_model, d_model, 4, 3)\n        self.query_embed = nn.Embedding(tot_num_queries, d_model)\n        self.instance_query_embed = None\n        self.instance_query_reference_points = None\n        self.use_instance_query = instance_query\n        self.num_instances = num_instances\n        self.use_normed_output_consistently = use_normed_output_consistently\n\n        self.instance_norm = nn.LayerNorm(d_model) if separate_norm_instance else None\n        self.instance_bbox_embed = None\n        if separate_box_head_instance:\n            self.instance_bbox_embed = MLP(d_model, d_model, 4, 3)\n        if instance_query:\n            self.instance_query_embed = nn.Embedding(num_instances, d_model)\n        self.box_refine = box_refine\n        if box_refine:\n            nn.init.constant_(self.bbox_embed.layers[-1].weight.data, 0)\n            nn.init.constant_(self.bbox_embed.layers[-1].bias.data, 0)\n\n            self.reference_points = nn.Embedding(num_queries, 4)\n            if instance_query:\n                self.instance_reference_points = nn.Embedding(num_instances, 4)\n\n        assert boxRPB in [\"none\", \"log\", \"linear\", \"both\"]\n        self.boxRPB = boxRPB\n        if boxRPB != \"none\":\n            try:\n                nheads = self.layers[0].cross_attn_image.num_heads\n            except AttributeError:\n                nheads = self.layers[0].cross_attn.num_heads\n\n            n_input = 4 if boxRPB == \"both\" else 2\n            self.boxRPB_embed_x = MLP(n_input, d_model, nheads, 2)\n            self.boxRPB_embed_y = MLP(n_input, d_model, nheads, 2)\n            self.compilable_cord_cache = None\n            self.compilable_stored_size = None\n            self.coord_cache = {}\n\n            if resolution is not None and stride is not None:\n                feat_size = resolution // stride\n                coords_h, coords_w = self._get_coords(\n                    feat_size, feat_size, device=\"cuda\"\n                )\n                self.compilable_cord_cache = (coords_h, coords_w)\n                self.compilable_stored_size = (feat_size, feat_size)\n\n        self.roi_pooler = (\n            RoIAlign(output_size=7, spatial_scale=1, sampling_ratio=-1, aligned=True)\n            if interaction_layer is not None\n            else None\n        )\n        if frozen:\n            for p in self.parameters():\n                p.requires_grad_(False)\n\n        self.presence_token = None\n        self.clamp_presence_logits = clamp_presence_logits\n        self.clamp_presence_logit_max_val = clamp_presence_logit_max_val\n        if presence_token:\n            self.presence_token = nn.Embedding(1, d_model)\n            self.presence_token_head = MLP(d_model, d_model, 1, 3)\n            self.presence_token_out_norm = nn.LayerNorm(d_model)\n\n        self.ref_point_head = MLP(2 * self.d_model, self.d_model, self.d_model, 2)\n        self.dac_use_selfatt_ln = dac_use_selfatt_ln\n        self.use_act_checkpoint = use_act_checkpoint\n\n        nn.init.normal_(self.query_embed.weight.data)\n        if self.instance_query_embed is not None:\n            nn.init.normal_(self.instance_query_embed.weight.data)\n\n        assert self.roi_pooler is None\n        assert self.return_intermediate, \"support return_intermediate only\"\n        assert self.box_refine, \"support box refine only\"\n\n        self.compile_mode = compile_mode\n        self.compiled = False\n        # We defer compilation till after the first forward, to first warm-up the boxRPB cache\n\n        # assign layer index to each layer so that some layers can decide what to do\n        # based on which layer index they are (e.g. cross attention to memory bank only\n        # in selected layers)\n        for layer_idx, layer in enumerate(self.layers):\n            layer.layer_idx = layer_idx\n\n    @staticmethod\n    def _get_coords(H, W, device):\n        coords_h = torch.arange(0, H, device=device, dtype=torch.float32) / H\n        coords_w = torch.arange(0, W, device=device, dtype=torch.float32) / W\n        return coords_h, coords_w\n\n    def _get_rpb_matrix(self, reference_boxes, feat_size):\n        H, W = feat_size\n        boxes_xyxy = box_cxcywh_to_xyxy(reference_boxes).transpose(0, 1)\n        bs, num_queries, _ = boxes_xyxy.shape\n        if self.compilable_cord_cache is None:\n            self.compilable_cord_cache = self._get_coords(H, W, reference_boxes.device)\n            self.compilable_stored_size = (H, W)\n\n        if torch.compiler.is_dynamo_compiling() or self.compilable_stored_size == (\n            H,\n            W,\n        ):\n            # good, hitting the cache, will be compilable\n            coords_h, coords_w = self.compilable_cord_cache\n        else:\n            # cache miss, will create compilation issue\n            # In case we're not compiling, we'll still rely on the dict-based cache\n            if feat_size not in self.coord_cache:\n                self.coord_cache[feat_size] = self._get_coords(\n                    H, W, reference_boxes.device\n                )\n            coords_h, coords_w = self.coord_cache[feat_size]\n\n            assert coords_h.shape == (H,)\n            assert coords_w.shape == (W,)\n\n        deltas_y = coords_h.view(1, -1, 1) - boxes_xyxy.reshape(-1, 1, 4)[:, :, 1:4:2]\n        deltas_y = deltas_y.view(bs, num_queries, -1, 2)\n        deltas_x = coords_w.view(1, -1, 1) - boxes_xyxy.reshape(-1, 1, 4)[:, :, 0:3:2]\n        deltas_x = deltas_x.view(bs, num_queries, -1, 2)\n\n        if self.boxRPB in [\"log\", \"both\"]:\n            deltas_x_log = deltas_x * 8  # normalize to -8, 8\n            deltas_x_log = (\n                torch.sign(deltas_x_log)\n                * torch.log2(torch.abs(deltas_x_log) + 1.0)\n                / np.log2(8)\n            )\n\n            deltas_y_log = deltas_y * 8  # normalize to -8, 8\n            deltas_y_log = (\n                torch.sign(deltas_y_log)\n                * torch.log2(torch.abs(deltas_y_log) + 1.0)\n                / np.log2(8)\n            )\n            if self.boxRPB == \"log\":\n                deltas_x = deltas_x_log\n                deltas_y = deltas_y_log\n            else:\n                deltas_x = torch.cat([deltas_x, deltas_x_log], dim=-1)\n                deltas_y = torch.cat([deltas_y, deltas_y_log], dim=-1)\n\n        if self.training:\n            assert self.use_act_checkpoint, \"activation ckpt not enabled in decoder\"\n        deltas_x = activation_ckpt_wrapper(self.boxRPB_embed_x)(\n            x=deltas_x,\n            act_ckpt_enable=self.training and self.use_act_checkpoint,\n        )  # bs, num_queries, W, n_heads\n        deltas_y = activation_ckpt_wrapper(self.boxRPB_embed_y)(\n            x=deltas_y,\n            act_ckpt_enable=self.training and self.use_act_checkpoint,\n        )  # bs, num_queries, H, n_heads\n\n        if not torch.compiler.is_dynamo_compiling():\n            assert deltas_x.shape[:3] == (bs, num_queries, W)\n            assert deltas_y.shape[:3] == (bs, num_queries, H)\n\n        B = deltas_y.unsqueeze(3) + deltas_x.unsqueeze(\n            2\n        )  # bs, num_queries, H, W, n_heads\n        if not torch.compiler.is_dynamo_compiling():\n            assert B.shape[:4] == (bs, num_queries, H, W)\n        B = B.flatten(2, 3)  # bs, num_queries, H*W, n_heads\n        B = B.permute(0, 3, 1, 2)  # bs, n_heads, num_queries, H*W\n        B = B.contiguous()  # memeff attn likes ordered strides\n        if not torch.compiler.is_dynamo_compiling():\n            assert B.shape[2:] == (num_queries, H * W)\n        return B\n\n    def forward(\n        self,\n        tgt,\n        memory,\n        tgt_mask: Optional[Tensor] = None,\n        memory_mask: Optional[Tensor] = None,\n        tgt_key_padding_mask: Optional[Tensor] = None,\n        memory_key_padding_mask: Optional[Tensor] = None,\n        pos: Optional[Tensor] = None,\n        reference_boxes: Optional[Tensor] = None,  # num_queries, bs, 4\n        # for memory\n        level_start_index: Optional[Tensor] = None,  # num_levels\n        spatial_shapes: Optional[Tensor] = None,  # bs, num_levels, 2\n        valid_ratios: Optional[Tensor] = None,\n        # for text\n        memory_text: Optional[Tensor] = None,\n        text_attention_mask: Optional[Tensor] = None,\n        # if `apply_dac` is None, it will default to `self.dac`\n        apply_dac: Optional[bool] = None,\n        is_instance_prompt=False,\n        decoder_extra_kwargs: Optional[Dict] = None,\n        # ROI memory bank\n        obj_roi_memory_feat=None,\n        obj_roi_memory_mask=None,\n        box_head_trk=None,\n    ):\n        \"\"\"\n        Input:\n            - tgt: nq, bs, d_model\n            - memory: \\\\sum{hw}, bs, d_model\n            - pos: \\\\sum{hw}, bs, d_model\n            - reference_boxes: nq, bs, 4 (after sigmoid)\n            - valid_ratios/spatial_shapes: bs, nlevel, 2\n        \"\"\"\n        if memory_mask is not None:\n            assert self.boxRPB == \"none\", (\n                \"inputting a memory_mask in the presence of boxRPB is unexpected/not implemented\"\n            )\n\n        apply_dac = apply_dac if apply_dac is not None else self.dac\n        if apply_dac:\n            assert (tgt.shape[0] == self.num_queries) or (\n                self.use_instance_query\n                and (tgt.shape[0] == self.instance_query_embed.num_embeddings)\n            )\n\n            tgt = tgt.repeat(2, 1, 1)\n            # note that we don't tile tgt_mask, since DAC doesn't\n            # use self-attention in o2m queries\n            if reference_boxes is not None:\n                assert (reference_boxes.shape[0] == self.num_queries) or (\n                    self.use_instance_query\n                    and (\n                        reference_boxes.shape[0]\n                        == self.instance_query_embed.num_embeddings\n                    )\n                )\n                reference_boxes = reference_boxes.repeat(2, 1, 1)\n\n        bs = tgt.shape[1]\n        intermediate = []\n        intermediate_presence_logits = []\n        presence_feats = None\n\n        if self.box_refine:\n            if reference_boxes is None:\n                # In this case, we're in a one-stage model, so we generate the reference boxes\n                reference_boxes = self.reference_points.weight.unsqueeze(1)\n                reference_boxes = (\n                    reference_boxes.repeat(2, bs, 1)\n                    if apply_dac\n                    else reference_boxes.repeat(1, bs, 1)\n                )\n                reference_boxes = reference_boxes.sigmoid()\n            intermediate_ref_boxes = [reference_boxes]\n        else:\n            reference_boxes = None\n            intermediate_ref_boxes = None\n\n        output = tgt\n        presence_out = None\n        if self.presence_token is not None and is_instance_prompt is False:\n            # expand to batch dim\n            presence_out = self.presence_token.weight[None].expand(1, bs, -1)\n\n        box_head = self.bbox_embed\n        if is_instance_prompt and self.instance_bbox_embed is not None:\n            box_head = self.instance_bbox_embed\n\n        out_norm = self.norm\n        if is_instance_prompt and self.instance_norm is not None:\n            out_norm = self.instance_norm\n\n        for layer_idx, layer in enumerate(self.layers):\n            reference_points_input = (\n                reference_boxes[:, :, None]\n                * torch.cat([valid_ratios, valid_ratios], -1)[None, :]\n            )  # nq, bs, nlevel, 4\n\n            query_sine_embed = gen_sineembed_for_position(\n                reference_points_input[:, :, 0, :], self.d_model\n            )  # nq, bs, d_model*2\n\n            # conditional query\n            query_pos = self.ref_point_head(query_sine_embed)  # nq, bs, d_model\n\n            if self.boxRPB != \"none\" and reference_boxes is not None:\n                assert spatial_shapes.shape[0] == 1, (\n                    \"only single scale support implemented\"\n                )\n                memory_mask = self._get_rpb_matrix(\n                    reference_boxes,\n                    (spatial_shapes[0, 0], spatial_shapes[0, 1]),\n                )\n                memory_mask = memory_mask.flatten(0, 1)  # (bs*n_heads, nq, H*W)\n            if self.training:\n                assert self.use_act_checkpoint, (\n                    \"Activation checkpointing not enabled in the decoder\"\n                )\n            output, presence_out = activation_ckpt_wrapper(layer)(\n                tgt=output,\n                tgt_query_pos=query_pos,\n                tgt_query_sine_embed=query_sine_embed,\n                tgt_key_padding_mask=tgt_key_padding_mask,\n                tgt_reference_points=reference_points_input,\n                memory_text=memory_text,\n                text_attention_mask=text_attention_mask,\n                memory=memory,\n                memory_key_padding_mask=memory_key_padding_mask,\n                memory_level_start_index=level_start_index,\n                memory_spatial_shapes=spatial_shapes,\n                memory_pos=pos,\n                self_attn_mask=tgt_mask,\n                cross_attn_mask=memory_mask,\n                dac=apply_dac,\n                dac_use_selfatt_ln=self.dac_use_selfatt_ln,\n                presence_token=presence_out,\n                **(decoder_extra_kwargs or {}),\n                act_ckpt_enable=self.training and self.use_act_checkpoint,\n                # ROI memory bank\n                obj_roi_memory_feat=obj_roi_memory_feat,\n                obj_roi_memory_mask=obj_roi_memory_mask,\n            )\n\n            # iter update\n            if self.box_refine:\n                reference_before_sigmoid = inverse_sigmoid(reference_boxes)\n                if box_head_trk is None:\n                    # delta_unsig = self.bbox_embed(output)\n                    if not self.use_normed_output_consistently:\n                        delta_unsig = box_head(output)\n                    else:\n                        delta_unsig = box_head(out_norm(output))\n                else:\n                    # box_head_trk use a separate box head for tracking queries\n                    Q_det = decoder_extra_kwargs[\"Q_det\"]\n                    assert output.size(0) >= Q_det\n                    delta_unsig_det = self.bbox_embed(output[:Q_det])\n                    delta_unsig_trk = box_head_trk(output[Q_det:])\n                    delta_unsig = torch.cat([delta_unsig_det, delta_unsig_trk], dim=0)\n                outputs_unsig = delta_unsig + reference_before_sigmoid\n                new_reference_points = outputs_unsig.sigmoid()\n\n                reference_boxes = new_reference_points.detach()\n                if layer_idx != self.num_layers - 1:\n                    intermediate_ref_boxes.append(new_reference_points)\n            else:\n                raise NotImplementedError(\"not implemented yet\")\n\n            intermediate.append(out_norm(output))\n            if self.presence_token is not None and is_instance_prompt is False:\n                # norm, mlp head\n                intermediate_layer_presence_logits = self.presence_token_head(\n                    self.presence_token_out_norm(presence_out)\n                ).squeeze(-1)\n\n                # clamp to mitigate numerical issues\n                if self.clamp_presence_logits:\n                    intermediate_layer_presence_logits.clamp(\n                        min=-self.clamp_presence_logit_max_val,\n                        max=self.clamp_presence_logit_max_val,\n                    )\n\n                intermediate_presence_logits.append(intermediate_layer_presence_logits)\n                presence_feats = presence_out.clone()\n\n        if not self.compiled and self.compile_mode is not None:\n            self.forward = torch.compile(\n                self.forward, mode=self.compile_mode, fullgraph=True\n            )\n            self.compiled = True\n\n        return (\n            torch.stack(intermediate),\n            torch.stack(intermediate_ref_boxes),\n            (\n                torch.stack(intermediate_presence_logits)\n                if self.presence_token is not None and is_instance_prompt is False\n                else None\n            ),\n            presence_feats,\n        )\n\n\nclass TransformerEncoderCrossAttention(nn.Module):\n    def __init__(\n        self,\n        d_model: int,\n        frozen: bool,\n        pos_enc_at_input: bool,\n        layer,\n        num_layers: int,\n        use_act_checkpoint: bool = False,\n        batch_first: bool = False,  # Do layers expect batch first input?\n        # which layers to exclude cross attention? default: None, means all\n        # layers use cross attention\n        remove_cross_attention_layers: Optional[list] = None,\n    ):\n        super().__init__()\n        self.d_model = d_model\n        self.layers = get_clones(layer, num_layers)\n        self.num_layers = num_layers\n        self.norm = nn.LayerNorm(d_model)\n        self.pos_enc_at_input = pos_enc_at_input\n        self.use_act_checkpoint = use_act_checkpoint\n\n        if frozen:\n            for p in self.parameters():\n                p.requires_grad_(False)\n\n        self.batch_first = batch_first\n\n        # remove cross attention layers if specified\n        self.remove_cross_attention_layers = [False] * self.num_layers\n        if remove_cross_attention_layers is not None:\n            for i in remove_cross_attention_layers:\n                self.remove_cross_attention_layers[i] = True\n        assert len(self.remove_cross_attention_layers) == len(self.layers)\n\n        for i, remove_cross_attention in enumerate(self.remove_cross_attention_layers):\n            if remove_cross_attention:\n                self.layers[i].cross_attn_image = None\n                self.layers[i].norm2 = None\n                self.layers[i].dropout2 = None\n\n    def forward(\n        self,\n        src,  # self-attention inputs\n        prompt,  # cross-attention inputs\n        src_mask: Optional[Tensor] = None,  # att.mask for self-attention inputs\n        prompt_mask: Optional[Tensor] = None,  # att.mask for cross-attention inputs\n        src_key_padding_mask: Optional[Tensor] = None,\n        prompt_key_padding_mask: Optional[Tensor] = None,\n        src_pos: Optional[Tensor] = None,  # pos_enc for self-attention inputs\n        prompt_pos: Optional[Tensor] = None,  # pos_enc for cross-attention inputs\n        feat_sizes: Optional[list] = None,\n        num_obj_ptr_tokens: int = 0,  # number of object pointer *tokens*\n    ):\n        if isinstance(src, list):\n            assert isinstance(src_key_padding_mask, list) and isinstance(src_pos, list)\n            assert len(src) == len(src_key_padding_mask) == len(src_pos) == 1\n            src, src_key_padding_mask, src_pos = (\n                src[0],\n                src_key_padding_mask[0],\n                src_pos[0],\n            )\n\n        assert src.shape[1] == prompt.shape[1], (\n            \"Batch size must be the same for src and prompt\"\n        )\n\n        output = src\n\n        if self.pos_enc_at_input and src_pos is not None:\n            output = output + 0.1 * src_pos\n\n        if self.batch_first:\n            # Convert to batch first\n            output = output.transpose(0, 1)\n            src_pos = src_pos.transpose(0, 1)\n            prompt = prompt.transpose(0, 1)\n            prompt_pos = prompt_pos.transpose(0, 1)\n\n        for layer in self.layers:\n            kwds = {}\n            if isinstance(layer.cross_attn_image, RoPEAttention):\n                kwds = {\"num_k_exclude_rope\": num_obj_ptr_tokens}\n\n            output = activation_ckpt_wrapper(layer)(\n                tgt=output,\n                memory=prompt,\n                tgt_mask=src_mask,\n                memory_mask=prompt_mask,\n                tgt_key_padding_mask=src_key_padding_mask,\n                memory_key_padding_mask=prompt_key_padding_mask,\n                pos=prompt_pos,\n                query_pos=src_pos,\n                dac=False,\n                attn_bias=None,\n                act_ckpt_enable=self.training and self.use_act_checkpoint,\n                **kwds,\n            )\n            normed_output = self.norm(output)\n\n        if self.batch_first:\n            # Convert back to seq first\n            normed_output = normed_output.transpose(0, 1)\n            src_pos = src_pos.transpose(0, 1)\n\n        return {\n            \"memory\": normed_output,\n            \"pos_embed\": src_pos,\n            \"padding_mask\": src_key_padding_mask,\n        }\n\n\nclass TransformerDecoderLayerv1(nn.Module):\n    def __init__(\n        self,\n        activation: str,\n        cross_attention: nn.Module,\n        d_model: int,\n        dim_feedforward: int,\n        dropout: float,\n        pos_enc_at_attn: bool,\n        pos_enc_at_cross_attn_keys: bool,\n        pos_enc_at_cross_attn_queries: bool,\n        pre_norm: bool,\n        self_attention: nn.Module,\n    ):\n        super().__init__()\n        self.d_model = d_model\n        self.dim_feedforward = dim_feedforward\n        self.dropout_value = dropout\n        self.self_attn = self_attention\n        self.cross_attn_image = cross_attention\n\n        # Implementation of Feedforward model\n        self.linear1 = nn.Linear(d_model, dim_feedforward)\n        self.dropout = nn.Dropout(dropout)\n        self.linear2 = nn.Linear(dim_feedforward, d_model)\n\n        self.norm1 = nn.LayerNorm(d_model)\n        self.norm2 = nn.LayerNorm(d_model)\n        self.norm3 = nn.LayerNorm(d_model)\n        self.dropout1 = nn.Dropout(dropout)\n        self.dropout2 = nn.Dropout(dropout)\n        self.dropout3 = nn.Dropout(dropout)\n\n        self.activation_str = activation\n        self.activation = get_activation_fn(activation)\n        self.pre_norm = pre_norm\n\n        self.pos_enc_at_attn = pos_enc_at_attn\n        self.pos_enc_at_cross_attn_queries = pos_enc_at_cross_attn_queries\n        self.pos_enc_at_cross_attn_keys = pos_enc_at_cross_attn_keys\n\n    def forward_post(\n        self,\n        tgt,\n        memory,\n        tgt_mask: Optional[Tensor] = None,\n        memory_mask: Optional[Tensor] = None,\n        tgt_key_padding_mask: Optional[Tensor] = None,\n        memory_key_padding_mask: Optional[Tensor] = None,\n        pos: Optional[Tensor] = None,\n        query_pos: Optional[Tensor] = None,\n        **kwargs,\n    ):\n        q = k = tgt + query_pos if self.pos_enc_at_attn else tgt\n\n        # Self attention\n        tgt2 = self.self_attn(\n            q,\n            k,\n            value=tgt,\n            attn_mask=tgt_mask,\n            key_padding_mask=tgt_key_padding_mask,\n        )[0]\n        tgt = tgt + self.dropout1(tgt2)\n        tgt = self.norm1(tgt)\n\n        # Cross attention to image\n        tgt2 = self.cross_attn_image(\n            query=tgt + query_pos if self.pos_enc_at_cross_attn_queries else tgt,\n            key=memory + pos if self.pos_enc_at_cross_attn_keys else memory,\n            value=memory,\n            attn_mask=memory_mask,\n            key_padding_mask=memory_key_padding_mask,\n        )[0]\n        tgt = tgt + self.dropout2(tgt2)\n        tgt = self.norm2(tgt)\n\n        # FFN\n        tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt))))\n        tgt = tgt + self.dropout3(tgt2)\n        tgt = self.norm3(tgt)\n        return tgt\n\n    def forward_pre(\n        self,\n        tgt,\n        memory,\n        dac: bool = False,\n        tgt_mask: Optional[Tensor] = None,\n        memory_mask: Optional[Tensor] = None,\n        tgt_key_padding_mask: Optional[Tensor] = None,\n        memory_key_padding_mask: Optional[Tensor] = None,\n        pos: Optional[Tensor] = None,\n        query_pos: Optional[Tensor] = None,\n        attn_bias: Optional[Tensor] = None,\n        **kwargs,\n    ):\n        if dac:\n            # we only apply self attention to the first half of the queries\n            assert tgt.shape[0] % 2 == 0\n            other_tgt = tgt[tgt.shape[0] // 2 :]\n            tgt = tgt[: tgt.shape[0] // 2]\n        tgt2 = self.norm1(tgt)\n        q = k = tgt2 + query_pos if self.pos_enc_at_attn else tgt2\n        tgt2 = self.self_attn(\n            q,\n            k,\n            value=tgt2,\n            attn_mask=tgt_mask,\n            key_padding_mask=tgt_key_padding_mask,\n        )[0]\n        tgt = tgt + self.dropout1(tgt2)\n        if dac:\n            # Recombine\n            tgt = torch.cat((tgt, other_tgt), dim=0)\n        tgt2 = self.norm2(tgt)\n        tgt2 = self.cross_attn_image(\n            query=tgt2 + query_pos if self.pos_enc_at_cross_attn_queries else tgt2,\n            key=memory + pos if self.pos_enc_at_cross_attn_keys else memory,\n            value=memory,\n            attn_mask=memory_mask,\n            key_padding_mask=memory_key_padding_mask,\n            attn_bias=attn_bias,\n        )[0]\n        tgt = tgt + self.dropout2(tgt2)\n        tgt2 = self.norm3(tgt)\n        tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2))))\n        tgt = tgt + self.dropout3(tgt2)\n        return tgt\n\n    def forward(\n        self,\n        tgt,\n        memory,\n        dac: bool = False,\n        tgt_mask: Optional[Tensor] = None,\n        memory_mask: Optional[Tensor] = None,\n        tgt_key_padding_mask: Optional[Tensor] = None,\n        memory_key_padding_mask: Optional[Tensor] = None,\n        pos: Optional[Tensor] = None,\n        query_pos: Optional[Tensor] = None,\n        attn_bias: Optional[Tensor] = None,\n        **kwds: Any,\n    ) -> torch.Tensor:\n        fwd_fn = self.forward_pre if self.pre_norm else self.forward_post\n        return fwd_fn(\n            tgt,\n            memory,\n            dac=dac,\n            tgt_mask=tgt_mask,\n            memory_mask=memory_mask,\n            tgt_key_padding_mask=tgt_key_padding_mask,\n            memory_key_padding_mask=memory_key_padding_mask,\n            pos=pos,\n            query_pos=query_pos,\n            attn_bias=attn_bias,\n            **kwds,\n        )\n\n\nclass TransformerDecoderLayerv2(TransformerDecoderLayerv1):\n    def __init__(self, cross_attention_first=False, *args: Any, **kwds: Any):\n        super().__init__(*args, **kwds)\n        self.cross_attention_first = cross_attention_first\n\n    def _forward_sa(self, tgt, query_pos):\n        # Self-Attention\n        tgt2 = self.norm1(tgt)\n        q = k = tgt2 + query_pos if self.pos_enc_at_attn else tgt2\n        tgt2 = self.self_attn(q, k, v=tgt2)\n        tgt = tgt + self.dropout1(tgt2)\n        return tgt\n\n    def _forward_ca(self, tgt, memory, query_pos, pos, num_k_exclude_rope=0):\n        if self.cross_attn_image is None:\n            return tgt\n\n        kwds = {}\n        if num_k_exclude_rope > 0:\n            assert isinstance(self.cross_attn_image, RoPEAttention)\n            kwds = {\"num_k_exclude_rope\": num_k_exclude_rope}\n\n        # Cross-Attention\n        tgt2 = self.norm2(tgt)\n        tgt2 = self.cross_attn_image(\n            q=tgt2 + query_pos if self.pos_enc_at_cross_attn_queries else tgt2,\n            k=memory + pos if self.pos_enc_at_cross_attn_keys else memory,\n            v=memory,\n            **kwds,\n        )\n        tgt = tgt + self.dropout2(tgt2)\n        return tgt\n\n    def forward_pre(\n        self,\n        tgt,\n        memory,\n        dac: bool,\n        tgt_mask: Optional[Tensor] = None,\n        memory_mask: Optional[Tensor] = None,\n        tgt_key_padding_mask: Optional[Tensor] = None,\n        memory_key_padding_mask: Optional[Tensor] = None,\n        pos: Optional[Tensor] = None,\n        query_pos: Optional[Tensor] = None,\n        attn_bias: Optional[Tensor] = None,\n        num_k_exclude_rope: int = 0,\n    ):\n        assert dac is False\n        assert tgt_mask is None\n        assert memory_mask is None\n        assert tgt_key_padding_mask is None\n        assert memory_key_padding_mask is None\n        assert attn_bias is None\n\n        if self.cross_attention_first:\n            tgt = self._forward_ca(tgt, memory, query_pos, pos, num_k_exclude_rope)\n            tgt = self._forward_sa(tgt, query_pos)\n        else:\n            tgt = self._forward_sa(tgt, query_pos)\n            tgt = self._forward_ca(tgt, memory, query_pos, pos, num_k_exclude_rope)\n\n        # MLP\n        tgt2 = self.norm3(tgt)\n        tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2))))\n        tgt = tgt + self.dropout3(tgt2)\n        return tgt\n\n    def forward(self, *args: Any, **kwds: Any) -> torch.Tensor:\n        if self.pre_norm:\n            return self.forward_pre(*args, **kwds)\n        raise NotImplementedError\n"
  },
  {
    "path": "sam3/model/edt.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n\n\"\"\"Triton kernel for euclidean distance transform (EDT)\"\"\"\n\nimport torch\nimport triton\nimport triton.language as tl\n\n\"\"\"\nDisclaimer: This implementation is not meant to be extremely efficient. A CUDA kernel would likely be more efficient.\nEven in Triton, there may be more suitable algorithms.\n\nThe goal of this kernel is to mimic cv2.distanceTransform(input, cv2.DIST_L2, 0).\nRecall that the euclidean distance transform (EDT) calculates the L2 distance to the closest zero pixel for each pixel of the source image.\n\nFor images of size NxN, the naive algorithm would be to compute pairwise distances between every pair of points, leading to a O(N^4) algorithm, which is obviously impractical.\nOne can do better using the following approach:\n- First, compute the distance to the closest point in the same row. We can write it as Row_EDT[i,j] = min_k (sqrt((k-j)^2) if input[i,k]==0 else +infinity). With a naive implementation, this step has a O(N^3) complexity\n- Then, because of triangular inequality, we notice that the EDT for a given location [i,j] is the min of the row EDTs in the same column. EDT[i,j] = min_k Row_EDT[k, j]. This is also O(N^3)\n\nOverall, this algorithm is quite amenable to parallelization, and has a complexity O(N^3). Can we do better?\n\nIt turns out that we can leverage the structure of the L2 distance (nice and convex) to find the minimum in a more efficient way.\nWe follow the algorithm from \"Distance Transforms of Sampled Functions\" (https://cs.brown.edu/people/pfelzens/papers/dt-final.pdf), which is also what's implemented in opencv\n\nFor a single dimension EDT, we can compute the EDT of an arbitrary function F, that we discretize over the grid. Note that for the binary EDT that we're interested in, we can set F(i,j) = 0 if input[i,j]==0 else +infinity\nFor now, we'll compute the EDT squared, and will take the sqrt only at the very end.\nThe basic idea is that each point at location i spawns a parabola around itself, with a bias equal to F(i). So specifically, we're looking at the parabola (x - i)^2 + F(i)\nWhen we're looking for the row EDT at location j, we're effectively looking for min_i (x-i)^2 + F(i). In other word we want to find the lowest parabola at location j.\n\nTo do this efficiently, we need to maintain the lower envelope of the union of parabolas. This can be constructed on the fly using a sort of stack approach:\n - every time we want to add a new parabola, we check if it may be covering the current right-most parabola. If so, then that parabola was useless, so we can pop it from the stack\n - repeat until we can't find any more parabola to pop. Then push the new one.\n\nThis algorithm runs in O(N) for a single row, so overall O(N^2) when applied to all rows\nSimilarly as before, we notice that we can decompose the algorithm for rows and columns, leading to an overall run-time of O(N^2)\n\nThis algorithm is less suited for to GPUs, since the one-dimensional EDT computation is quite sequential in nature. However, we can parallelize over batch and row dimensions.\nIn Triton, things are particularly bad at the moment, since there is no support for reading/writing to the local memory at a specific index (a local gather is coming soon, see https://github.com/triton-lang/triton/issues/974, but no mention of writing, ie scatter)\nOne could emulate these operations with masking, but in initial tests, it proved to be worst than naively reading and writing to the global memory. My guess is that the cache is compensating somewhat for the repeated single-point accesses.\n\n\nThe timing obtained on a H100 for a random batch of masks of dimension 256 x 1024 x 1024 are as follows:\n- OpenCV: 1780ms (including round-trip to cpu, but discounting the fact that it introduces a synchronization point)\n- triton, O(N^3) algo: 627ms\n- triton, O(N^2) algo: 322ms\n\nOverall, despite being quite naive, this implementation is roughly 5.5x faster than the openCV cpu implem\n\n\"\"\"\n\n\n@triton.jit\ndef edt_kernel(inputs_ptr, outputs_ptr, v, z, height, width, horizontal: tl.constexpr):\n    # This is a somewhat verbatim implementation of the efficient 1D EDT algorithm described above\n    # It can be applied horizontally or vertically depending if we're doing the first or second stage.\n    # It's parallelized across batch+row (or batch+col if horizontal=False)\n    # TODO: perhaps the implementation can be revisited if/when local gather/scatter become available in triton\n    batch_id = tl.program_id(axis=0)\n    if horizontal:\n        row_id = tl.program_id(axis=1)\n        block_start = (batch_id * height * width) + row_id * width\n        length = width\n        stride = 1\n    else:\n        col_id = tl.program_id(axis=1)\n        block_start = (batch_id * height * width) + col_id\n        length = height\n        stride = width\n\n    # This will be the index of the right most parabola in the envelope (\"the top of the stack\")\n    k = 0\n    for q in range(1, length):\n        # Read the function value at the current location. Note that we're doing a singular read, not very efficient\n        cur_input = tl.load(inputs_ptr + block_start + (q * stride))\n        # location of the parabola on top of the stack\n        r = tl.load(v + block_start + (k * stride))\n        # associated boundary\n        z_k = tl.load(z + block_start + (k * stride))\n        # value of the function at the parabola location\n        previous_input = tl.load(inputs_ptr + block_start + (r * stride))\n        # intersection between the two parabolas\n        s = (cur_input - previous_input + q * q - r * r) / (q - r) / 2\n\n        # we'll pop as many parabolas as required\n        while s <= z_k and k - 1 >= 0:\n            k = k - 1\n            r = tl.load(v + block_start + (k * stride))\n            z_k = tl.load(z + block_start + (k * stride))\n            previous_input = tl.load(inputs_ptr + block_start + (r * stride))\n            s = (cur_input - previous_input + q * q - r * r) / (q - r) / 2\n\n        # Store the new one\n        k = k + 1\n        tl.store(v + block_start + (k * stride), q)\n        tl.store(z + block_start + (k * stride), s)\n        if k + 1 < length:\n            tl.store(z + block_start + ((k + 1) * stride), 1e9)\n\n    # Last step, we read the envelope to find the min in every location\n    k = 0\n    for q in range(length):\n        while (\n            k + 1 < length\n            and tl.load(\n                z + block_start + ((k + 1) * stride), mask=(k + 1) < length, other=q\n            )\n            < q\n        ):\n            k += 1\n        r = tl.load(v + block_start + (k * stride))\n        d = q - r\n        old_value = tl.load(inputs_ptr + block_start + (r * stride))\n        tl.store(outputs_ptr + block_start + (q * stride), old_value + d * d)\n\n\ndef edt_triton(data: torch.Tensor):\n    \"\"\"\n    Computes the Euclidean Distance Transform (EDT) of a batch of binary images.\n\n    Args:\n        data: A tensor of shape (B, H, W) representing a batch of binary images.\n\n    Returns:\n        A tensor of the same shape as data containing the EDT.\n        It should be equivalent to a batched version of cv2.distanceTransform(input, cv2.DIST_L2, 0)\n    \"\"\"\n    assert data.dim() == 3\n    assert data.is_cuda\n    B, H, W = data.shape\n    data = data.contiguous()\n\n    # Allocate the \"function\" tensor. Implicitly the function is 0 if data[i,j]==0 else +infinity\n    output = torch.where(data, 1e18, 0.0)\n    assert output.is_contiguous()\n\n    # Scratch tensors for the parabola stacks\n    parabola_loc = torch.zeros(B, H, W, dtype=torch.uint32, device=data.device)\n    parabola_inter = torch.empty(B, H, W, dtype=torch.float, device=data.device)\n    parabola_inter[:, :, 0] = -1e18\n    parabola_inter[:, :, 1] = 1e18\n\n    # Grid size (number of blocks)\n    grid = (B, H)\n\n    # Launch initialization kernel\n    edt_kernel[grid](\n        output.clone(),\n        output,\n        parabola_loc,\n        parabola_inter,\n        H,\n        W,\n        horizontal=True,\n    )\n\n    # reset the parabola stacks\n    parabola_loc.zero_()\n    parabola_inter[:, :, 0] = -1e18\n    parabola_inter[:, :, 1] = 1e18\n\n    grid = (B, W)\n    edt_kernel[grid](\n        output.clone(),\n        output,\n        parabola_loc,\n        parabola_inter,\n        H,\n        W,\n        horizontal=False,\n    )\n    # don't forget to take sqrt at the end\n    return output.sqrt()\n"
  },
  {
    "path": "sam3/model/encoder.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n# Based on https://github.com/IDEA-Research/GroundingDINO\n\n# pyre-unsafe\n\nfrom typing import Any, Dict, List, Optional, Tuple\n\nimport torch\nfrom torch import nn, Tensor\n\nfrom .act_ckpt_utils import activation_ckpt_wrapper\nfrom .model_misc import get_activation_fn, get_clones, get_valid_ratio\n\n\nclass TransformerEncoderLayer(nn.Module):\n    \"\"\"\n    Transformer encoder layer that performs self-attention followed by cross-attention.\n\n    This layer was previously called TransformerDecoderLayer but was renamed to better\n    reflect its role in the architecture. It processes input sequences through self-attention\n    and then cross-attention with another input (typically image features).\n\n    The layer supports both pre-norm and post-norm configurations, as well as\n    positional encoding at different stages of the attention mechanism.\n    \"\"\"\n\n    def __init__(\n        self,\n        activation: str,\n        cross_attention: nn.Module,\n        d_model: int,\n        dim_feedforward: int,\n        dropout: float,\n        pos_enc_at_attn: bool,\n        pos_enc_at_cross_attn_keys: bool,\n        pos_enc_at_cross_attn_queries: bool,\n        pre_norm: bool,\n        self_attention: nn.Module,\n    ):\n        \"\"\"\n        Initialize a transformer encoder layer.\n\n        Args:\n            activation: Activation function to use in the feedforward network\n            cross_attention: Cross-attention module for attending to image features\n            d_model: Model dimension/hidden size\n            dim_feedforward: Dimension of the feedforward network\n            dropout: Dropout probability\n            pos_enc_at_attn: Whether to add positional encodings at self-attention\n            pos_enc_at_cross_attn_keys: Whether to add positional encodings to keys in cross-attention\n            pos_enc_at_cross_attn_queries: Whether to add positional encodings to queries in cross-attention\n            pre_norm: Whether to use pre-norm (True) or post-norm (False) architecture\n            self_attention: Self-attention module\n        \"\"\"\n        super().__init__()\n        self.d_model = d_model\n        self.dim_feedforward = dim_feedforward\n        self.dropout_value = dropout\n        self.self_attn = self_attention\n        self.cross_attn_image = cross_attention\n\n        # Implementation of Feedforward model\n        self.linear1 = nn.Linear(d_model, dim_feedforward)\n        self.dropout = nn.Dropout(dropout)\n        self.linear2 = nn.Linear(dim_feedforward, d_model)\n\n        self.norm1 = nn.LayerNorm(d_model)\n        self.norm2 = nn.LayerNorm(d_model)\n        self.norm3 = nn.LayerNorm(d_model)\n        self.dropout1 = nn.Dropout(dropout)\n        self.dropout2 = nn.Dropout(dropout)\n        self.dropout3 = nn.Dropout(dropout)\n\n        self.activation_str = activation\n        self.activation = get_activation_fn(activation)\n        self.pre_norm = pre_norm\n\n        self.pos_enc_at_attn = pos_enc_at_attn\n        self.pos_enc_at_cross_attn_queries = pos_enc_at_cross_attn_queries\n        self.pos_enc_at_cross_attn_keys = pos_enc_at_cross_attn_keys\n\n        self.layer_idx = None\n\n    def forward_post(\n        self,\n        tgt: Tensor,\n        memory: Tensor,\n        tgt_mask: Optional[Tensor] = None,\n        memory_mask: Optional[Tensor] = None,\n        tgt_key_padding_mask: Optional[Tensor] = None,\n        memory_key_padding_mask: Optional[Tensor] = None,\n        pos: Optional[Tensor] = None,\n        query_pos: Optional[Tensor] = None,\n        **kwargs,\n    ) -> Tensor:\n        \"\"\"\n        Forward pass for post-norm architecture.\n\n        In post-norm architecture, normalization is applied after attention and feedforward operations.\n\n        Args:\n            tgt: Input tensor to be processed\n            memory: Memory tensor for cross-attention\n            tgt_mask: Mask for self-attention\n            memory_mask: Mask for cross-attention\n            tgt_key_padding_mask: Key padding mask for self-attention\n            memory_key_padding_mask: Key padding mask for cross-attention\n            pos: Positional encoding for memory\n            query_pos: Positional encoding for query\n            **kwargs: Additional keyword arguments\n\n        Returns:\n            Processed tensor\n        \"\"\"\n        q = k = tgt + query_pos if self.pos_enc_at_attn else tgt\n\n        # Self attention\n        tgt2 = self.self_attn(\n            q, k, value=tgt, attn_mask=tgt_mask, key_padding_mask=tgt_key_padding_mask\n        )[0]\n        tgt = tgt + self.dropout1(tgt2)\n        tgt = self.norm1(tgt)\n\n        # Cross attention to image\n        tgt2 = self.cross_attn_image(\n            query=tgt + query_pos if self.pos_enc_at_cross_attn_queries else tgt,\n            key=memory + pos if self.pos_enc_at_cross_attn_keys else memory,\n            value=memory,\n            attn_mask=memory_mask,\n            key_padding_mask=memory_key_padding_mask,\n        )[0]\n        tgt = tgt + self.dropout2(tgt2)\n        tgt = self.norm2(tgt)\n\n        # FFN\n        tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt))))\n        tgt = tgt + self.dropout3(tgt2)\n        tgt = self.norm3(tgt)\n        return tgt\n\n    def forward_pre(\n        self,\n        tgt: Tensor,\n        memory: Tensor,\n        dac: bool = False,\n        tgt_mask: Optional[Tensor] = None,\n        memory_mask: Optional[Tensor] = None,\n        tgt_key_padding_mask: Optional[Tensor] = None,\n        memory_key_padding_mask: Optional[Tensor] = None,\n        pos: Optional[Tensor] = None,\n        query_pos: Optional[Tensor] = None,\n        # attn_bias: Optional[Tensor] = None,\n        # **kwargs,\n    ) -> Tensor:\n        \"\"\"\n        Forward pass for pre-norm architecture.\n\n        In pre-norm architecture, normalization is applied before attention and feedforward operations.\n\n        Args:\n            tgt: Input tensor to be processed\n            memory: Memory tensor for cross-attention\n            dac: Whether to use Divide-and-Conquer attention\n            tgt_mask: Mask for self-attention\n            memory_mask: Mask for cross-attention\n            tgt_key_padding_mask: Key padding mask for self-attention\n            memory_key_padding_mask: Key padding mask for cross-attention\n            pos: Positional encoding for memory\n            query_pos: Positional encoding for query\n            attn_bias: Optional attention bias tensor\n            **kwargs: Additional keyword arguments\n\n        Returns:\n            Processed tensor\n        \"\"\"\n        if dac:\n            # we only apply self attention to the first half of the queries\n            assert tgt.shape[0] % 2 == 0\n            other_tgt = tgt[tgt.shape[0] // 2 :]\n            tgt = tgt[: tgt.shape[0] // 2]\n        tgt2 = self.norm1(tgt)\n        q = k = tgt2 + query_pos if self.pos_enc_at_attn else tgt2\n        tgt2 = self.self_attn(\n            q, k, value=tgt2, attn_mask=tgt_mask, key_padding_mask=tgt_key_padding_mask\n        )[0]\n        tgt = tgt + self.dropout1(tgt2)\n        if dac:\n            # Recombine\n            tgt = torch.cat((tgt, other_tgt), dim=0)\n        tgt2 = self.norm2(tgt)\n        tgt2 = self.cross_attn_image(\n            query=tgt2 + query_pos if self.pos_enc_at_cross_attn_queries else tgt2,\n            key=memory + pos if self.pos_enc_at_cross_attn_keys else memory,\n            value=memory,\n            attn_mask=memory_mask,\n            key_padding_mask=memory_key_padding_mask,\n            # attn_bias=attn_bias,\n        )[0]\n        tgt = tgt + self.dropout2(tgt2)\n        tgt2 = self.norm3(tgt)\n        tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2))))\n        tgt = tgt + self.dropout3(tgt2)\n        return tgt\n\n    def forward(\n        self,\n        tgt: Tensor,\n        memory: Tensor,\n        dac: bool = False,\n        tgt_mask: Optional[Tensor] = None,\n        memory_mask: Optional[Tensor] = None,\n        tgt_key_padding_mask: Optional[Tensor] = None,\n        memory_key_padding_mask: Optional[Tensor] = None,\n        pos: Optional[Tensor] = None,\n        query_pos: Optional[Tensor] = None,\n        # attn_bias: Optional[Tensor] = None,\n        # **kwds: Any,\n    ) -> torch.Tensor:\n        \"\"\"\n        Forward pass for the transformer encoder layer.\n\n        Args:\n            tgt: Input tensor to be processed\n            memory: Memory tensor (e.g., image features) for cross-attention\n            dac: Whether to use Divide-and-Conquer attention (only apply self-attention to first half)\n            tgt_mask: Mask for self-attention\n            memory_mask: Mask for cross-attention\n            tgt_key_padding_mask: Key padding mask for self-attention\n            memory_key_padding_mask: Key padding mask for cross-attention\n            pos: Positional encoding for memory\n            query_pos: Positional encoding for query\n            attn_bias: Optional attention bias tensor\n            **kwds: Additional keyword arguments\n\n        Returns:\n            Processed tensor after self-attention, cross-attention, and feedforward network\n        \"\"\"\n        fwd_fn = self.forward_pre if self.pre_norm else self.forward_post\n        return fwd_fn(\n            tgt,\n            memory,\n            dac=dac,\n            tgt_mask=tgt_mask,\n            memory_mask=memory_mask,\n            tgt_key_padding_mask=tgt_key_padding_mask,\n            memory_key_padding_mask=memory_key_padding_mask,\n            pos=pos,\n            query_pos=query_pos,\n            # attn_bias=attn_bias,\n            # **kwds,\n        )\n\n\nclass TransformerEncoder(nn.Module):\n    \"\"\"\n    Transformer encoder that processes multi-level features.\n\n    This encoder takes multi-level features (e.g., from a backbone network) and processes\n    them through a stack of transformer encoder layers. It supports features from multiple\n    levels (e.g., different resolutions) and can apply activation checkpointing for memory\n    efficiency during training.\n\n    Args:\n        layer: The encoder layer to be stacked multiple times\n        num_layers: Number of encoder layers to stack\n        d_model: Model dimension/hidden size\n        num_feature_levels: Number of feature levels to process\n        frozen: Whether to freeze the parameters of this module\n        use_act_checkpoint: Whether to use activation checkpointing during training\n    \"\"\"\n\n    def __init__(\n        self,\n        layer: nn.Module,\n        num_layers: int,\n        d_model: int,\n        num_feature_levels: int,\n        frozen: bool = False,\n        use_act_checkpoint: bool = False,\n    ):\n        super().__init__()\n        self.layers = get_clones(layer, num_layers)\n        self.num_layers = num_layers\n\n        self.num_feature_levels = num_feature_levels\n        self.level_embed = None\n        if num_feature_levels > 1:\n            self.level_embed = nn.Parameter(torch.Tensor(num_feature_levels, d_model))\n\n        if frozen:\n            for p in self.parameters():\n                p.requires_grad_(False)\n\n        self.use_act_checkpoint = use_act_checkpoint\n\n        # assign layer index to each layer so that some layers can decide what to do\n        # based on which layer index they are (e.g. cross attention to memory bank only\n        # in selected layers)\n        for layer_idx, layer in enumerate(self.layers):\n            layer.layer_idx = layer_idx\n\n    @staticmethod\n    def get_reference_points(spatial_shapes, valid_ratios, device):\n        with torch.no_grad():\n            reference_points_list = []\n            for lvl, (H_, W_) in enumerate(spatial_shapes):\n                ref_y, ref_x = torch.meshgrid(\n                    torch.linspace(\n                        0.5, H_ - 0.5, H_, dtype=torch.float32, device=device\n                    ),\n                    torch.linspace(\n                        0.5, W_ - 0.5, W_, dtype=torch.float32, device=device\n                    ),\n                )\n                ref_y = ref_y.reshape(-1)[None] / (valid_ratios[:, None, lvl, 1] * H_)\n                ref_x = ref_x.reshape(-1)[None] / (valid_ratios[:, None, lvl, 0] * W_)\n                ref = torch.stack((ref_x, ref_y), -1)\n                reference_points_list.append(ref)\n            reference_points = torch.cat(reference_points_list, 1)\n            reference_points = reference_points[:, :, None] * valid_ratios[:, None]\n\n        return reference_points\n\n    def _prepare_multilevel_features(self, srcs, masks, pos_embeds):\n        assert len(srcs) == self.num_feature_levels, (\n            \"mismatch between expected and received # of feature levels\"\n        )\n\n        src_flatten = []\n        mask_flatten = []\n        lvl_pos_embed_flatten = []\n        spatial_shapes = []\n        has_mask = masks is not None and masks[0] is not None\n        for lvl, (src, mask, pos_embed) in enumerate(zip(srcs, masks, pos_embeds)):\n            bs, c, h, w = src.shape\n            spatial_shape = (h, w)\n            spatial_shapes.append(spatial_shape)\n\n            src = src.flatten(2).transpose(1, 2)  # bs, hw, c\n            if has_mask:\n                mask = mask.flatten(1)\n            pos_embed = pos_embed.flatten(2).transpose(1, 2)  # bs, hw, c\n            if self.level_embed is not None:\n                lvl_pos_embed = pos_embed + self.level_embed[lvl].view(1, 1, -1)\n            else:\n                lvl_pos_embed = pos_embed\n            lvl_pos_embed_flatten.append(lvl_pos_embed)\n            src_flatten.append(src)\n            if has_mask:\n                mask_flatten.append(mask)\n        src_flatten = torch.cat(src_flatten, 1)  # bs, \\sum{hxw}, c\n        mask_flatten = torch.cat(mask_flatten, 1) if has_mask else None  # bs, \\sum{hxw}\n        lvl_pos_embed_flatten = torch.cat(lvl_pos_embed_flatten, 1)  # bs, \\sum{hxw}, c\n        spatial_shapes = torch.tensor(\n            spatial_shapes, dtype=torch.long, device=src_flatten.device\n        )\n        level_start_index = torch.cat(\n            (\n                spatial_shapes.new_zeros((1,)),\n                spatial_shapes.prod(1).cumsum(0)[:-1],\n            )\n        )\n        if has_mask:\n            valid_ratios = torch.stack([get_valid_ratio(m) for m in masks], 1)\n        else:\n            valid_ratios = torch.ones(\n                (src_flatten.shape[0], self.num_feature_levels, 2),\n                device=src_flatten.device,\n            )\n\n        return (\n            src_flatten,\n            mask_flatten,\n            lvl_pos_embed_flatten,\n            level_start_index,\n            valid_ratios,\n            spatial_shapes,\n        )\n\n    def forward(\n        self,\n        src: List[Tensor],\n        src_key_padding_masks: Optional[List[Tensor]] = None,\n        pos: Optional[List[Tensor]] = None,\n        prompt: Optional[Tensor] = None,\n        prompt_key_padding_mask: Optional[Tensor] = None,\n        encoder_extra_kwargs: Optional[Dict] = None,\n    ) -> Tuple[Tensor, Optional[Tensor], Tensor, Tensor, Tensor, Tensor]:\n        \"\"\"\n        Process multi-level features through the transformer encoder.\n\n        Args:\n            src: List of multi-level features, each with shape (batch_size, channels, height, width)\n            src_key_padding_masks: List of padding masks for each feature level, each with shape (batch_size, height, width)\n            pos: List of positional embeddings for each feature level, each with shape (batch_size, channels, height, width)\n            prompt: Optional text/prompt features to attend to, with shape (seq_len, batch_size, d_model)\n            prompt_key_padding_mask: Optional padding mask for prompt, with shape (batch_size, seq_len)\n            encoder_extra_kwargs: Optional additional arguments to pass to each encoder layer\n\n        Returns:\n            A tuple containing:\n            - output: Processed features with shape (seq_len, batch_size, d_model)\n            - key_padding_masks_flatten: Flattened padding masks\n            - lvl_pos_embed_flatten: Flattened positional embeddings\n            - level_start_index: Starting indices for each feature level\n            - spatial_shapes: Spatial dimensions of each feature level\n            - valid_ratios: Valid ratios for each feature level\n        \"\"\"\n        assert len(src) == self.num_feature_levels, (\n            \"must be equal to num_feature_levels\"\n        )\n        if src_key_padding_masks is not None:\n            assert len(src_key_padding_masks) == self.num_feature_levels\n        if pos is not None:\n            assert len(pos) == self.num_feature_levels\n        # Flatten multilevel feats and add level pos embeds\n        (\n            src_flatten,\n            key_padding_masks_flatten,\n            lvl_pos_embed_flatten,\n            level_start_index,\n            valid_ratios,\n            spatial_shapes,\n        ) = self._prepare_multilevel_features(src, src_key_padding_masks, pos)\n\n        reference_points = self.get_reference_points(\n            spatial_shapes, valid_ratios, device=src_flatten.device\n        )\n\n        output = src_flatten\n        for layer in self.layers:\n            layer_kwargs = {}\n\n            assert isinstance(layer, TransformerEncoderLayer)\n            layer_kwargs[\"memory\"] = prompt\n            layer_kwargs[\"memory_key_padding_mask\"] = prompt_key_padding_mask\n            layer_kwargs[\"query_pos\"] = lvl_pos_embed_flatten\n            layer_kwargs[\"tgt\"] = output\n            layer_kwargs[\"tgt_key_padding_mask\"] = key_padding_masks_flatten\n\n            if self.training:\n                assert self.use_act_checkpoint, \"activation ckpt not enabled in encoder\"\n            if encoder_extra_kwargs is not None:\n                layer_kwargs.update(encoder_extra_kwargs)\n            output = activation_ckpt_wrapper(layer)(\n                **layer_kwargs,\n                act_ckpt_enable=self.training and self.use_act_checkpoint,\n            )\n        # return as seq first\n        return (\n            output.transpose(0, 1),\n            (\n                key_padding_masks_flatten.transpose(0, 1)\n                if key_padding_masks_flatten is not None\n                else None\n            ),\n            lvl_pos_embed_flatten.transpose(0, 1),\n            level_start_index,\n            spatial_shapes,\n            valid_ratios,\n        )\n\n\nclass TransformerEncoderFusion(TransformerEncoder):\n    \"\"\"\n    Transformer encoder that fuses text and image features.\n\n    This encoder extends TransformerEncoder to handle both text and image features,\n    with the ability to add pooled text features to image features for better\n    cross-modal fusion. It supports torch.compile for performance optimization.\n\n    Args:\n        layer: The encoder layer to be stacked multiple times\n        num_layers: Number of encoder layers to stack\n        d_model: Model dimension/hidden size\n        num_feature_levels: Number of feature levels to process\n        add_pooled_text_to_img_feat: Whether to add pooled text features to image features\n        pool_text_with_mask: Whether to use the mask when pooling text features\n        compile_mode: Mode for torch.compile, or None to disable compilation\n        **kwargs: Additional arguments to pass to the parent class\n    \"\"\"\n\n    def __init__(\n        self,\n        layer: nn.Module,\n        num_layers: int,\n        d_model: int,\n        num_feature_levels: int,\n        add_pooled_text_to_img_feat: bool = True,\n        pool_text_with_mask: bool = False,\n        compile_mode: Optional[str] = None,\n        **kwargs,\n    ):\n        super().__init__(\n            layer,\n            num_layers,\n            d_model,\n            num_feature_levels,\n            **kwargs,\n        )\n        self.add_pooled_text_to_img_feat = add_pooled_text_to_img_feat\n        if self.add_pooled_text_to_img_feat:\n            self.text_pooling_proj = nn.Linear(d_model, d_model)\n        self.pool_text_with_mask = pool_text_with_mask\n        if compile_mode is not None:\n            self.forward = torch.compile(\n                self.forward, mode=compile_mode, fullgraph=True\n            )\n\n    @staticmethod\n    def get_reference_points(spatial_shapes, valid_ratios, device):\n        # Not needed here\n        return None\n\n    def forward(\n        self,\n        src: List[Tensor],\n        prompt: Tensor,\n        src_key_padding_mask: Optional[List[Tensor]] = None,\n        src_pos: Optional[List[Tensor]] = None,\n        prompt_key_padding_mask: Optional[Tensor] = None,\n        prompt_pos: Optional[Tensor] = None,\n        feat_sizes: Optional[List[int]] = None,\n        encoder_extra_kwargs: Optional[Dict] = None,\n    ):\n        # Restore spatial shapes of vision\n        bs = src[0].shape[1]  # seq first\n        if feat_sizes is not None:\n            assert len(feat_sizes) == len(src)\n            if src_key_padding_mask is None:\n                src_key_padding_mask = [None] * len(src)\n            for i, (h, w) in enumerate(feat_sizes):\n                src[i] = src[i].reshape(h, w, bs, -1).permute(2, 3, 0, 1)\n                src_pos[i] = src_pos[i].reshape(h, w, bs, -1).permute(2, 3, 0, 1)\n                src_key_padding_mask[i] = (\n                    src_key_padding_mask[i].reshape(h, w, bs).permute(2, 0, 1)\n                    if src_key_padding_mask[i] is not None\n                    else None\n                )\n        else:\n            assert all(x.dim == 4 for x in src), (\n                \"expected list of (bs, c, h, w) tensors\"\n            )\n\n        if self.add_pooled_text_to_img_feat:\n            # Fusion: Add mean pooled text to image features\n            pooled_text = pool_text_feat(\n                prompt, prompt_key_padding_mask, self.pool_text_with_mask\n            )\n            pooled_text = self.text_pooling_proj(pooled_text)[\n                ..., None, None\n            ]  # prompt is seq first\n            src = [x.add_(pooled_text) for x in src]\n\n        (\n            out,\n            key_padding_masks_flatten,\n            lvl_pos_embed_flatten,\n            level_start_index,\n            spatial_shapes,\n            valid_ratios,\n        ) = super().forward(\n            src,\n            src_key_padding_masks=src_key_padding_mask,\n            pos=src_pos,\n            prompt=prompt.transpose(0, 1),\n            prompt_key_padding_mask=prompt_key_padding_mask,\n            encoder_extra_kwargs=encoder_extra_kwargs,\n        )\n\n        return {\n            \"memory\": out,\n            \"padding_mask\": key_padding_masks_flatten,\n            \"pos_embed\": lvl_pos_embed_flatten,\n            \"memory_text\": prompt,\n            \"level_start_index\": level_start_index,\n            \"spatial_shapes\": spatial_shapes,\n            \"valid_ratios\": valid_ratios,\n        }\n\n\ndef pool_text_feat(prompt, prompt_mask, pool_with_mask):\n    # prompt has shape (seq, bs, dim)\n    if not pool_with_mask:\n        return prompt.mean(dim=0)\n\n    # prompt_mask has shape (bs, seq), where False is valid and True is padding\n    assert prompt_mask.dim() == 2\n    # is_valid has shape (seq, bs, 1), where 1 is valid and 0 is padding\n    is_valid = (~prompt_mask).float().permute(1, 0)[..., None]\n    # num_valid has shape (bs, 1)\n    num_valid = torch.clamp(torch.sum(is_valid, dim=0), min=1.0)\n\n    # mean pool over all the valid tokens\n    pooled_text = (prompt * is_valid).sum(dim=0) / num_valid\n    return pooled_text\n"
  },
  {
    "path": "sam3/model/geometry_encoders.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n\nfrom typing import Tuple\n\nimport torch\nimport torch.nn as nn\nimport torchvision\nfrom typing_extensions import override\n\nfrom .act_ckpt_utils import activation_ckpt_wrapper\nfrom .box_ops import box_cxcywh_to_xyxy\nfrom .model_misc import get_clones\n\n\ndef is_right_padded(mask):\n    \"\"\"Given a padding mask (following pytorch convention, 1s for padded values),\n    returns whether the padding is on the right or not.\"\"\"\n    return (mask.long() == torch.sort(mask.long(), dim=-1)[0]).all()\n\n\ndef concat_padded_sequences(seq1, mask1, seq2, mask2, return_index: bool = False):\n    \"\"\"\n    Concatenates two right-padded sequences, such that the resulting sequence\n    is contiguous and also right-padded.\n\n    Following pytorch's convention, tensors are sequence first, and the mask are\n    batch first, with 1s for padded values.\n\n    :param seq1: A tensor of shape (seq1_length, batch_size, hidden_size).\n    :param mask1: A tensor of shape (batch_size, seq1_length).\n    :param seq2: A tensor of shape (seq2_length, batch_size,  hidden_size).\n    :param mask2: A tensor of shape (batch_size, seq2_length).\n    :param return_index: If True, also returns the index of the ids of the element of seq2\n        in the concatenated sequence. This can be used to retrieve the elements of seq2\n    :return: A tuple (concatenated_sequence, concatenated_mask) if return_index is False,\n        otherwise (concatenated_sequence, concatenated_mask, index).\n    \"\"\"\n    seq1_length, batch_size, hidden_size = seq1.shape\n    seq2_length, batch_size, hidden_size = seq2.shape\n\n    assert batch_size == seq1.size(1) == seq2.size(1) == mask1.size(0) == mask2.size(0)\n    assert hidden_size == seq1.size(2) == seq2.size(2)\n    assert seq1_length == mask1.size(1)\n    assert seq2_length == mask2.size(1)\n\n    torch._assert_async(is_right_padded(mask1))\n    torch._assert_async(is_right_padded(mask2))\n\n    actual_seq1_lengths = (~mask1).sum(dim=-1)\n    actual_seq2_lengths = (~mask2).sum(dim=-1)\n\n    final_lengths = actual_seq1_lengths + actual_seq2_lengths\n    max_length = seq1_length + seq2_length\n    concatenated_mask = (\n        torch.arange(max_length, device=seq2.device)[None].repeat(batch_size, 1)\n        >= final_lengths[:, None]\n    )\n\n    # (max_len, batch_size, hidden_size)\n    concatenated_sequence = torch.zeros(\n        (max_length, batch_size, hidden_size), device=seq2.device, dtype=seq2.dtype\n    )\n    concatenated_sequence[:seq1_length, :, :] = seq1\n\n    # At this point, the element of seq1 are in the right place\n    # We just need to shift the elements of seq2\n\n    index = torch.arange(seq2_length, device=seq2.device)[:, None].repeat(1, batch_size)\n    index = index + actual_seq1_lengths[None]\n\n    concatenated_sequence = concatenated_sequence.scatter(\n        0, index[:, :, None].expand(-1, -1, hidden_size), seq2\n    )\n\n    if return_index:\n        return concatenated_sequence, concatenated_mask, index\n\n    return concatenated_sequence, concatenated_mask\n\n\nclass Prompt:\n    \"\"\"Utility class to manipulate geometric prompts.\n\n    We expect the sequences in pytorch convention, that is sequence first, batch second\n    The dimensions are expected as follows:\n    box_embeddings shape: N_boxes x B x C_box\n    box_mask shape: B x N_boxes. Can be None if nothing is masked out\n    point_embeddings shape: N_points x B x C_point\n    point_mask shape: B x N_points. Can be None if nothing is masked out\n    mask_embeddings shape: N_masks x B x 1 x H_mask x W_mask\n    mask_mask shape: B x N_masks. Can be None if nothing is masked out\n\n    We also store positive/negative labels. These tensors are also stored batch-first\n    If they are None, we'll assume positive labels everywhere\n    box_labels: long tensor of shape N_boxes x B\n    point_labels: long tensor of shape N_points x B\n    mask_labels: long tensor of shape N_masks x B\n    \"\"\"\n\n    def __init__(\n        self,\n        box_embeddings=None,\n        box_mask=None,\n        point_embeddings=None,\n        point_mask=None,\n        box_labels=None,\n        point_labels=None,\n        mask_embeddings=None,\n        mask_mask=None,  # Attention mask for mask prompt\n        mask_labels=None,\n    ):\n        # Check for null prompt\n        if (\n            box_embeddings is None\n            and point_embeddings is None\n            and mask_embeddings is None\n        ):\n            self.box_embeddings = None\n            self.box_labels = None\n            self.box_mask = None\n            self.point_embeddings = None\n            self.point_labels = None\n            self.point_mask = None\n            self.mask_embeddings = None\n            self.mask_mask = None\n            # Masks are assumed positive only for now.\n            self.mask_labels = None\n            return\n        # Get sequence lengths and device\n        box_seq_len, point_seq_len, mask_seq_len, bs, device = (\n            self._init_seq_len_and_device(\n                box_embeddings, point_embeddings, mask_embeddings\n            )\n        )\n\n        # Initialize embeds, labels, attention masks.\n        box_embeddings, box_labels, box_mask = self._init_box(\n            box_embeddings, box_labels, box_mask, box_seq_len, bs, device\n        )\n        point_embeddings, point_labels, point_mask = self._init_point(\n            point_embeddings, point_labels, point_mask, point_seq_len, bs, device\n        )\n        mask_embeddings, mask_labels, mask_mask = self._init_mask(\n            mask_embeddings, mask_labels, mask_mask, mask_seq_len, bs, device\n        )\n\n        # Dimension checks\n        assert box_embeddings is not None and list(box_embeddings.shape[:2]) == [\n            box_seq_len,\n            bs,\n        ], (\n            f\"Wrong dimension for box embeddings. Expected [{box_seq_len}, {bs}, *] got {box_embeddings.shape}\"\n        )\n        assert box_mask is not None and list(box_mask.shape) == [\n            bs,\n            box_seq_len,\n        ], (\n            f\"Wrong dimension for box mask. Expected [{bs}, {box_seq_len}] got {box_mask.shape}\"\n        )\n        assert point_embeddings is not None and list(point_embeddings.shape[:2]) == [\n            point_seq_len,\n            bs,\n        ], (\n            f\"Wrong dimension for point embeddings. Expected [{point_seq_len}, {bs}, *] got {point_embeddings.shape}\"\n        )\n        assert point_mask is not None and list(point_mask.shape) == [\n            bs,\n            point_seq_len,\n        ], (\n            f\"Wrong dimension for point mask. Expected [{bs}, {point_seq_len}] got {point_mask.shape}\"\n        )\n        assert box_labels is not None and list(box_labels.shape) == [\n            box_seq_len,\n            bs,\n        ], (\n            f\"Wrong dimension for box labels. Expected [{box_seq_len}, {bs}] got {box_labels.shape}\"\n        )\n        assert point_labels is not None and list(point_labels.shape) == [\n            point_seq_len,\n            bs,\n        ], (\n            f\"Wrong dimension for point labels. Expected [{point_seq_len}, {bs}] got {point_labels.shape}\"\n        )\n        assert (\n            # Allowed to be None, we leave it to the encoder to check for validity before encoding.\n            mask_embeddings is None\n            or list(mask_embeddings.shape[:2])\n            == [\n                mask_seq_len,\n                bs,\n            ]\n        ), (\n            f\"Wrong dimension for mask embeddings. Expected [{mask_seq_len}, {bs}, *] got {mask_embeddings.shape}\"\n        )\n        assert mask_mask is None or list(mask_mask.shape) == [\n            bs,\n            mask_seq_len,\n        ], (\n            f\"Wrong dimension for mask attn. mask. Expected [{bs}, {mask_seq_len}] got {mask_mask.shape}\"\n        )\n\n        # Device checks\n        assert box_embeddings is not None and box_embeddings.device == device, (\n            f\"Expected box embeddings to be on device {device}, got {box_embeddings.device}\"\n        )\n        assert box_mask is not None and box_mask.device == device, (\n            f\"Expected box mask to be on device {device}, got {box_mask.device}\"\n        )\n        assert box_labels is not None and box_labels.device == device, (\n            f\"Expected box labels to be on device {device}, got {box_labels.device}\"\n        )\n        assert point_embeddings is not None and point_embeddings.device == device, (\n            f\"Expected point embeddings to be on device {device}, got {point_embeddings.device}\"\n        )\n        assert point_mask is not None and point_mask.device == device, (\n            f\"Expected point mask to be on device {device}, got {point_mask.device}\"\n        )\n        assert point_labels is not None and point_labels.device == device, (\n            f\"Expected point labels to be on device {device}, got {point_labels.device}\"\n        )\n        assert mask_embeddings is None or mask_embeddings.device == device, (\n            f\"Expected mask embeddings to be on device {device}, got {mask_embeddings.device}\"\n        )\n        assert mask_mask is None or mask_mask.device == device, (\n            f\"Expected mask attn. mask to be on device {device}, got {mask_mask.device}\"\n        )\n\n        self.box_embeddings = box_embeddings\n        self.point_embeddings = point_embeddings\n        self.box_mask = box_mask\n        self.point_mask = point_mask\n        self.box_labels = box_labels\n        self.point_labels = point_labels\n        self.mask_embeddings = mask_embeddings\n        self.mask_labels = mask_labels\n        self.mask_mask = mask_mask\n\n    def _init_seq_len_and_device(\n        self, box_embeddings, point_embeddings, mask_embeddings\n    ):\n        box_seq_len = point_seq_len = mask_seq_len = 0\n        bs = None\n        device = None\n        if box_embeddings is not None:\n            bs = box_embeddings.shape[1]\n            box_seq_len = box_embeddings.shape[0]\n            device = box_embeddings.device\n\n        if point_embeddings is not None:\n            point_seq_len = point_embeddings.shape[0]\n            if bs is not None:\n                assert bs == point_embeddings.shape[1], (\n                    f\"Batch size mismatch between box and point embeddings. Got {bs} and {point_embeddings.shape[1]}.\"\n                )\n            else:\n                bs = point_embeddings.shape[1]\n            if device is not None:\n                assert device == point_embeddings.device, (\n                    \"Device mismatch between box and point embeddings\"\n                )\n            else:\n                device = point_embeddings.device\n\n        if mask_embeddings is not None:\n            mask_seq_len = mask_embeddings.shape[0]\n            if bs is not None:\n                assert bs == mask_embeddings.shape[1], (\n                    f\"Batch size mismatch between box/point and mask embedding. Got {bs} and {mask_embeddings.shape[1]}\"\n                )\n            else:\n                bs = mask_embeddings.shape[1]\n            if device is not None:\n                assert device == mask_embeddings.device, (\n                    \"Device mismatch between box/point and mask embeddings.\"\n                )\n            else:\n                device = mask_embeddings.device\n\n        return box_seq_len, point_seq_len, mask_seq_len, bs, device\n\n    def _init_box(self, box_embeddings, box_labels, box_mask, box_seq_len, bs, device):\n        if box_embeddings is None:\n            box_embeddings = torch.zeros(box_seq_len, bs, 4, device=device)\n        if box_labels is None:\n            box_labels = torch.ones(box_seq_len, bs, device=device, dtype=torch.long)\n        if box_mask is None:\n            box_mask = torch.zeros(bs, box_seq_len, device=device, dtype=torch.bool)\n        return box_embeddings, box_labels, box_mask\n\n    def _init_point(\n        self, point_embeddings, point_labels, point_mask, point_seq_len, bs, device\n    ):\n        \"\"\"\n        Identical to _init_box. Except that C=2 for points (vs. 4 for boxes).\n        \"\"\"\n        if point_embeddings is None:\n            point_embeddings = torch.zeros(point_seq_len, bs, 2, device=device)\n        if point_labels is None:\n            point_labels = torch.ones(\n                point_seq_len, bs, device=device, dtype=torch.long\n            )\n        if point_mask is None:\n            point_mask = torch.zeros(bs, point_seq_len, device=device, dtype=torch.bool)\n        return point_embeddings, point_labels, point_mask\n\n    def _init_mask(\n        self, mask_embeddings, mask_labels, mask_mask, mask_seq_len, bs, device\n    ):\n        # NOTE: Mask embeddings can be of arbitrary resolution, so we don't initialize it here.\n        # In case we append new mask, we check that its resolution matches exisiting ones (if any).\n        # In case mask_embeddings is None, we should never encode it.\n        if mask_labels is None:\n            mask_labels = torch.ones(mask_seq_len, bs, device=device, dtype=torch.long)\n        if mask_mask is None:\n            mask_mask = torch.zeros(bs, mask_seq_len, device=device, dtype=torch.bool)\n        return mask_embeddings, mask_labels, mask_mask\n\n    def append_boxes(self, boxes, labels, mask=None):\n        if self.box_embeddings is None:\n            self.box_embeddings = boxes\n            self.box_labels = labels\n            self.box_mask = mask\n            return\n\n        bs = self.box_embeddings.shape[1]\n        assert boxes.shape[1] == labels.shape[1] == bs\n        assert list(boxes.shape[:2]) == list(labels.shape[:2])\n        if mask is None:\n            mask = torch.zeros(\n                bs, boxes.shape[0], dtype=torch.bool, device=boxes.device\n            )\n\n        self.box_labels, _ = concat_padded_sequences(\n            self.box_labels.unsqueeze(-1), self.box_mask, labels.unsqueeze(-1), mask\n        )\n        self.box_labels = self.box_labels.squeeze(-1)\n        self.box_embeddings, self.box_mask = concat_padded_sequences(\n            self.box_embeddings, self.box_mask, boxes, mask\n        )\n\n    def append_points(self, points, labels, mask=None):\n        if self.point_embeddings is None:\n            self.point_embeddings = points\n            self.point_labels = labels\n            self.point_mask = mask\n            return\n\n        bs = self.point_embeddings.shape[1]\n        assert points.shape[1] == labels.shape[1] == bs\n        assert list(points.shape[:2]) == list(labels.shape[:2])\n        if mask is None:\n            mask = torch.zeros(\n                bs, points.shape[0], dtype=torch.bool, device=points.device\n            )\n\n        self.point_labels, _ = concat_padded_sequences(\n            self.point_labels.unsqueeze(-1), self.point_mask, labels.unsqueeze(-1), mask\n        )\n        self.point_labels = self.point_labels.squeeze(-1)\n        self.point_embeddings, self.point_mask = concat_padded_sequences(\n            self.point_embeddings, self.point_mask, points, mask\n        )\n\n    def append_masks(self, masks, labels=None, attn_mask=None):\n        if labels is not None:\n            assert list(masks.shape[:2]) == list(labels.shape[:2])\n        if self.mask_embeddings is None:\n            self.mask_embeddings = masks\n            mask_seq_len, bs = masks.shape[:2]\n            if labels is None:\n                self.mask_labels = torch.ones(\n                    mask_seq_len, bs, device=masks.device, dtype=torch.long\n                )\n            else:\n                self.mask_labels = labels\n            if attn_mask is None:\n                self.mask_mask = torch.zeros(\n                    bs, mask_seq_len, device=masks.device, dtype=torch.bool\n                )\n            else:\n                self.mask_mask = attn_mask\n        else:\n            raise NotImplementedError(\"Only one mask per prompt is supported.\")\n\n    def clone(self):\n        return Prompt(\n            box_embeddings=(\n                None if self.box_embeddings is None else self.box_embeddings.clone()\n            ),\n            box_mask=None if self.box_mask is None else self.box_mask.clone(),\n            point_embeddings=(\n                None if self.point_embeddings is None else self.point_embeddings.clone()\n            ),\n            point_mask=None if self.point_mask is None else self.point_mask.clone(),\n            box_labels=None if self.box_labels is None else self.box_labels.clone(),\n            point_labels=(\n                None if self.point_labels is None else self.point_labels.clone()\n            ),\n        )\n\n\nclass MaskEncoder(nn.Module):\n    \"\"\"\n    Base class for mask encoders.\n    \"\"\"\n\n    def __init__(\n        self,\n        mask_downsampler: nn.Module,\n        position_encoding: nn.Module,\n    ):\n        super().__init__()\n        self.mask_downsampler = mask_downsampler\n        self.position_encoding = position_encoding\n\n    def forward(self, masks, *args, **kwargs) -> Tuple[torch.Tensor, torch.Tensor]:\n        masks = self.mask_downsampler(masks)\n        masks_pos = self.position_encoding(masks).to(masks.dtype)\n\n        return masks, masks_pos\n\n\nclass FusedMaskEncoder(MaskEncoder):\n    \"\"\"\n    Identical to memory.SimpleMaskEncoder but follows the interface of geometry_encoders.MaskEncoder.\n    We also remove the `skip_mask_sigmoid` option (to be handled outside the MaskEncoder).\n    Fuses backbone image features with mask features.\n    \"\"\"\n\n    def __init__(\n        self,\n        mask_downsampler: nn.Module,\n        position_encoding: nn.Module,\n        fuser: nn.Module,\n        in_dim: int = 256,\n        out_dim: int = 256,\n    ):\n        super().__init__(mask_downsampler, position_encoding)\n        self.fuser = fuser\n        self.out_proj = nn.Identity()\n        if out_dim != in_dim:\n            self.out_proj = nn.Conv2d(in_dim, out_dim, kernel_size=1)\n        self.pix_feat_proj = nn.Conv2d(in_dim, in_dim, kernel_size=1)\n\n    @override\n    def forward(\n        self,\n        masks: torch.Tensor,\n        pix_feat: torch.Tensor,\n        **kwargs,\n    ) -> Tuple[torch.Tensor, torch.Tensor]:\n        masks = self.mask_downsampler(masks)\n\n        ## Fuse pix_feats and downsampled masks\n        # in case the visual features are on CPU, cast them to CUDA\n        pix_feat = pix_feat.to(masks.device)\n\n        x = self.pix_feat_proj(pix_feat)\n        x = x + masks\n        x = self.fuser(x)\n        x = self.out_proj(x)\n\n        pos = self.position_encoding(x).to(x.dtype)\n\n        return x, pos\n\n\nclass SequenceGeometryEncoder(nn.Module):\n    \"\"\"\n    This a fully fledged encoder for geometric prompts.\n    It assumes boxes are passed in the \"normalized CxCyWH\" format, and points in normalized xy\n    This allows flexibility in how to encode the features (eg do pooling)\n\n    Points and boxes can be encoded with any of the three possibilities:\n     - direct projection: we just compute a linear from coordinate space to d_model\n     - pooling: pool features from the backbone in the requested location.\n                For boxes, it's a roi align\n                For points it's a grid sample\n     - pos encoder: Take the position encoding of the point or box center\n\n    These three options are mutually compatible. If several are selected, we'll take a simple addition\n\n    As an alternative, we offer the possibility to encode points only.\n    In that case, the boxes are converted to two points for the top left and bottom right corners (with appropriate labels)\n\n    On top of these encodings, we offer the possibility to further encode the prompt sequence with a transformer.\n    \"\"\"\n\n    def __init__(\n        self,\n        encode_boxes_as_points: bool,\n        points_direct_project: bool,\n        points_pool: bool,\n        points_pos_enc: bool,\n        boxes_direct_project: bool,\n        boxes_pool: bool,\n        boxes_pos_enc: bool,\n        d_model: int,\n        pos_enc,\n        num_layers: int,\n        layer: nn.Module,\n        roi_size: int = 7,  # for boxes pool\n        add_cls: bool = True,\n        add_post_encode_proj: bool = True,\n        mask_encoder: MaskEncoder = None,\n        add_mask_label: bool = False,\n        use_act_ckpt: bool = False,\n    ):\n        super().__init__()\n\n        self.d_model = d_model\n        self.pos_enc = pos_enc\n        self.encode_boxes_as_points = encode_boxes_as_points\n        self.roi_size = roi_size\n        # There usually are two labels: positive and negatives.\n        # If we encode boxes as points, we have 3 types of points: regular, top left, bottom right\n        # These 3 types can be positives or negatives, hence 2*3 = 6 labels\n        num_labels = 6 if self.encode_boxes_as_points else 2\n        self.label_embed = torch.nn.Embedding(num_labels, self.d_model)\n\n        # This is a cls token, can be used for pooling if need be.\n        # It also ensures that the encoded sequences are always non-empty\n        self.cls_embed = None\n        if add_cls:\n            self.cls_embed = torch.nn.Embedding(1, self.d_model)\n\n        assert points_direct_project or points_pos_enc or points_pool, (\n            \"Error: need at least one way to encode points\"\n        )\n        assert (\n            encode_boxes_as_points\n            or boxes_direct_project\n            or boxes_pos_enc\n            or boxes_pool\n        ), \"Error: need at least one way to encode boxes\"\n\n        self.points_direct_project = None\n        if points_direct_project:\n            self.points_direct_project = nn.Linear(2, self.d_model)\n        self.points_pool_project = None\n        if points_pool:\n            self.points_pool_project = nn.Linear(self.d_model, self.d_model)\n        self.points_pos_enc_project = None\n        if points_pos_enc:\n            self.points_pos_enc_project = nn.Linear(self.d_model, self.d_model)\n\n        self.boxes_direct_project = None\n        self.boxes_pool_project = None\n        self.boxes_pos_enc_project = None\n        if not encode_boxes_as_points:\n            if boxes_direct_project:\n                self.boxes_direct_project = nn.Linear(4, self.d_model)\n            if boxes_pool:\n                self.boxes_pool_project = nn.Conv2d(\n                    self.d_model, self.d_model, self.roi_size\n                )\n            if boxes_pos_enc:\n                self.boxes_pos_enc_project = nn.Linear(self.d_model + 2, self.d_model)\n\n        self.final_proj = None\n        if add_post_encode_proj:\n            self.final_proj = nn.Linear(self.d_model, self.d_model)\n            self.norm = nn.LayerNorm(self.d_model)\n\n        self.img_pre_norm = nn.Identity()\n        if self.points_pool_project is not None or self.boxes_pool_project is not None:\n            self.img_pre_norm = nn.LayerNorm(self.d_model)\n\n        self.encode = None\n        if num_layers > 0:\n            assert add_cls, (\n                \"It's currently highly recommended to add a CLS when using a transformer\"\n            )\n            self.encode = get_clones(layer, num_layers)\n            self.encode_norm = nn.LayerNorm(self.d_model)\n\n        if mask_encoder is not None:\n            assert isinstance(mask_encoder, MaskEncoder), (\n                f\"Expected mask_encoder of type MaskEncoder. Got {type(mask_encoder)}.\"\n            )\n            if add_mask_label:\n                self.mask_label_embed = torch.nn.Embedding(2, self.d_model)\n        self.add_mask_label = add_mask_label\n        self.mask_encoder = mask_encoder\n        self.use_act_ckpt = use_act_ckpt\n\n    def _encode_points(self, points, points_mask, points_labels, img_feats):\n        points_embed = None\n        n_points, bs = points.shape[:2]\n\n        if self.points_direct_project is not None:\n            proj = self.points_direct_project(points)\n            assert points_embed is None\n            points_embed = proj\n\n        if self.points_pool_project is not None:\n            # points are [Num_points, bs, 2], normalized in [0, 1]\n            # the grid needs to be [Bs, H_out, W_out, 2] normalized in [-1,1]\n            # Will take H_out = num_points, w_out = 1\n            grid = points.transpose(0, 1).unsqueeze(2)\n            # re normalize to [-1, 1]\n            grid = (grid * 2) - 1\n            sampled = torch.nn.functional.grid_sample(\n                img_feats, grid, align_corners=False\n            )\n            assert list(sampled.shape) == [bs, self.d_model, n_points, 1]\n            sampled = sampled.squeeze(-1).permute(2, 0, 1)\n            proj = self.points_pool_project(sampled)\n            if points_embed is None:\n                points_embed = proj\n            else:\n                points_embed = points_embed + proj\n\n        if self.points_pos_enc_project is not None:\n            x, y = points.unbind(-1)\n            enc_x, enc_y = self.pos_enc._encode_xy(x.flatten(), y.flatten())\n            enc_x = enc_x.view(n_points, bs, enc_x.shape[-1])\n            enc_y = enc_y.view(n_points, bs, enc_y.shape[-1])\n            enc = torch.cat([enc_x, enc_y], -1)\n\n            proj = self.points_pos_enc_project(enc)\n            if points_embed is None:\n                points_embed = proj\n            else:\n                points_embed = points_embed + proj\n\n        type_embed = self.label_embed(points_labels.long())\n        return type_embed + points_embed, points_mask\n\n    def _encode_boxes(self, boxes, boxes_mask, boxes_labels, img_feats):\n        boxes_embed = None\n        n_boxes, bs = boxes.shape[:2]\n\n        if self.boxes_direct_project is not None:\n            proj = self.boxes_direct_project(boxes)\n            assert boxes_embed is None\n            boxes_embed = proj\n\n        if self.boxes_pool_project is not None:\n            H, W = img_feats.shape[-2:]\n\n            # boxes are [Num_boxes, bs, 4], normalized in [0, 1]\n            # We need to denormalize, and convert to [x, y, x, y]\n            boxes_xyxy = box_cxcywh_to_xyxy(boxes)\n            scale = torch.tensor([W, H, W, H], dtype=boxes_xyxy.dtype)\n            scale = scale.pin_memory().to(device=boxes_xyxy.device, non_blocking=True)\n            scale = scale.view(1, 1, 4)\n            boxes_xyxy = boxes_xyxy * scale\n            sampled = torchvision.ops.roi_align(\n                img_feats, boxes_xyxy.float().transpose(0, 1).unbind(0), self.roi_size\n            )\n            assert list(sampled.shape) == [\n                bs * n_boxes,\n                self.d_model,\n                self.roi_size,\n                self.roi_size,\n            ]\n            proj = self.boxes_pool_project(sampled)\n            proj = proj.view(bs, n_boxes, self.d_model).transpose(0, 1)\n            if boxes_embed is None:\n                boxes_embed = proj\n            else:\n                boxes_embed = boxes_embed + proj\n\n        if self.boxes_pos_enc_project is not None:\n            cx, cy, w, h = boxes.unbind(-1)\n            enc = self.pos_enc.encode_boxes(\n                cx.flatten(), cy.flatten(), w.flatten(), h.flatten()\n            )\n            enc = enc.view(boxes.shape[0], boxes.shape[1], enc.shape[-1])\n\n            proj = self.boxes_pos_enc_project(enc)\n            if boxes_embed is None:\n                boxes_embed = proj\n            else:\n                boxes_embed = boxes_embed + proj\n\n        type_embed = self.label_embed(boxes_labels.long())\n        return type_embed + boxes_embed, boxes_mask\n\n    def _encode_masks(\n        self,\n        masks: torch.Tensor,\n        attn_mask: torch.Tensor,\n        mask_labels: torch.Tensor,\n        img_feats: torch.Tensor = None,\n    ):\n        n_masks, bs = masks.shape[:2]\n        assert n_masks == 1, (\n            \"We assume one mask per prompt for now. Code should still be functional if this assertion is removed.\"\n        )\n        assert list(attn_mask.shape) == [\n            bs,\n            n_masks,\n        ], (\n            f\"Expected attn_mask to be of shape {bs}x{n_masks}. Got {list(attn_mask.shape)}.\"\n        )\n        masks, pos = self.mask_encoder(\n            masks=masks.flatten(0, 1).float(),\n            pix_feat=img_feats,\n        )\n        H, W = masks.shape[-2:]\n        n_tokens_per_mask = H * W\n        # NOTE: We directly add pos enc here as we usually don't keep track of pos encoding for the concatenated prompt (text, other geometric prompts). Might need to do some refactoring for more flexibility.\n        masks = masks + pos\n        masks = masks.view(n_masks, bs, *masks.shape[1:]).flatten(\n            -2\n        )  # n_masks x bs x C x H*W\n        masks = masks.permute(0, 3, 1, 2).flatten(0, 1)  # n_masks * H*W x bs x C\n        attn_mask = attn_mask.repeat_interleave(n_tokens_per_mask, dim=1)\n        if self.add_mask_label:\n            masks = masks + self.mask_label_embed(mask_labels.long())\n        return masks, attn_mask\n\n    def forward(self, geo_prompt: Prompt, img_feats, img_sizes, img_pos_embeds=None):\n        points = geo_prompt.point_embeddings\n        points_mask = geo_prompt.point_mask\n        points_labels = geo_prompt.point_labels\n        boxes = geo_prompt.box_embeddings\n        boxes_mask = geo_prompt.box_mask\n        boxes_labels = geo_prompt.box_labels\n        masks = geo_prompt.mask_embeddings\n        masks_mask = geo_prompt.mask_mask\n        masks_labels = geo_prompt.mask_labels\n        seq_first_img_feats = img_feats[-1]  # [H*W, B, C]\n        seq_first_img_pos_embeds = (\n            img_pos_embeds[-1]\n            if img_pos_embeds is not None\n            else torch.zeros_like(seq_first_img_feats)\n        )\n\n        if self.points_pool_project or self.boxes_pool_project:\n            assert len(img_feats) == len(img_sizes)\n            cur_img_feat = img_feats[-1]\n            cur_img_feat = self.img_pre_norm(cur_img_feat)\n            H, W = img_sizes[-1]\n            assert cur_img_feat.shape[0] == H * W\n            N, C = cur_img_feat.shape[-2:]\n            # Put back in NxCxHxW\n            cur_img_feat = cur_img_feat.permute(1, 2, 0)\n            cur_img_feat = cur_img_feat.view(N, C, H, W)\n            img_feats = cur_img_feat\n\n        if self.encode_boxes_as_points:\n            assert boxes is not None\n            assert geo_prompt.box_mask is not None\n            assert geo_prompt.box_labels is not None\n            assert boxes.shape[-1] == 4\n\n            boxes_xyxy = box_cxcywh_to_xyxy(boxes)\n            top_left, bottom_right = boxes_xyxy.split(split_size=2, dim=-1)\n\n            labels_tl = geo_prompt.box_labels + 2\n            labels_br = geo_prompt.box_labels + 4\n\n            # Append to the existing points\n            points, _ = concat_padded_sequences(\n                points, points_mask, top_left, boxes_mask\n            )\n            points_labels, points_mask = concat_padded_sequences(\n                points_labels.unsqueeze(-1),\n                points_mask,\n                labels_tl.unsqueeze(-1),\n                boxes_mask,\n            )\n            points_labels = points_labels.squeeze(-1)\n\n            points, _ = concat_padded_sequences(\n                points, points_mask, bottom_right, boxes_mask\n            )\n            points_labels, points_mask = concat_padded_sequences(\n                points_labels.unsqueeze(-1),\n                points_mask,\n                labels_br.unsqueeze(-1),\n                boxes_mask,\n            )\n            points_labels = points_labels.squeeze(-1)\n\n        final_embeds, final_mask = self._encode_points(\n            points=points,\n            points_mask=points_mask,\n            points_labels=points_labels,\n            img_feats=img_feats,\n        )\n\n        if not self.encode_boxes_as_points:\n            boxes_embeds, boxes_mask = self._encode_boxes(\n                boxes=boxes,\n                boxes_mask=boxes_mask,\n                boxes_labels=boxes_labels,\n                img_feats=img_feats,\n            )\n\n            final_embeds, final_mask = concat_padded_sequences(\n                final_embeds, final_mask, boxes_embeds, boxes_mask\n            )\n\n        if masks is not None and self.mask_encoder is not None:\n            masks_embed, masks_mask = self._encode_masks(\n                masks=masks,\n                attn_mask=masks_mask,\n                mask_labels=masks_labels,\n                img_feats=img_feats,\n            )\n            if points.size(0) == boxes.size(0) == 0:\n                return masks_embed, masks_mask\n        bs = final_embeds.shape[1]\n        assert final_mask.shape[0] == bs\n        if self.cls_embed is not None:\n            cls = self.cls_embed.weight.view(1, 1, self.d_model).repeat(1, bs, 1)\n            cls_mask = torch.zeros(\n                bs, 1, dtype=final_mask.dtype, device=final_mask.device\n            )\n            final_embeds, final_mask = concat_padded_sequences(\n                final_embeds, final_mask, cls, cls_mask\n            )\n\n        if self.final_proj is not None:\n            final_embeds = self.norm(self.final_proj(final_embeds))\n\n        if self.encode is not None:\n            for lay in self.encode:\n                final_embeds = activation_ckpt_wrapper(lay)(\n                    tgt=final_embeds,\n                    memory=seq_first_img_feats,\n                    tgt_key_padding_mask=final_mask,\n                    pos=seq_first_img_pos_embeds,\n                    act_ckpt_enable=self.training and self.use_act_ckpt,\n                )\n            final_embeds = self.encode_norm(final_embeds)\n        # Finally, concat mask embeddings if any\n        if masks is not None and self.mask_encoder is not None:\n            final_embeds, final_mask = concat_padded_sequences(\n                final_embeds, final_mask, masks_embed, masks_mask\n            )\n        return final_embeds, final_mask\n"
  },
  {
    "path": "sam3/model/io_utils.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n\nimport contextlib\nimport os\nimport queue\nimport re\nimport time\nfrom threading import Condition, get_ident, Lock, Thread\n\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nimport torchvision.transforms.functional as TF\nfrom PIL import Image\nfrom sam3.logger import get_logger\nfrom tqdm import tqdm\n\nlogger = get_logger(__name__)\n\nIS_MAIN_PROCESS = os.getenv(\"IS_MAIN_PROCESS\", \"1\") == \"1\"\nRANK = int(os.getenv(\"RANK\", \"0\"))\n\nIMAGE_EXTS = [\".jpg\", \".jpeg\", \".png\", \".bmp\", \".tiff\", \".webp\"]\nVIDEO_EXTS = [\".mp4\", \".mov\", \".avi\", \".mkv\", \".webm\"]\n\n\ndef load_resource_as_video_frames(\n    resource_path,\n    image_size,\n    offload_video_to_cpu,\n    img_mean=(0.5, 0.5, 0.5),\n    img_std=(0.5, 0.5, 0.5),\n    async_loading_frames=False,\n    video_loader_type=\"cv2\",\n):\n    \"\"\"\n    Load video frames from either a video or an image (as a single-frame video).\n    Alternatively, if input is a list of PIL images, convert its format\n    \"\"\"\n    if isinstance(resource_path, list):\n        img_mean = torch.tensor(img_mean, dtype=torch.float16)[:, None, None]\n        img_std = torch.tensor(img_std, dtype=torch.float16)[:, None, None]\n        assert all(isinstance(img_pil, Image.Image) for img_pil in resource_path)\n        assert len(resource_path) is not None\n        orig_height, orig_width = resource_path[0].size\n        orig_height, orig_width = (\n            orig_width,\n            orig_height,\n        )  # For some reason, this method returns these swapped\n        images = []\n        for img_pil in resource_path:\n            img_np = np.array(img_pil.convert(\"RGB\").resize((image_size, image_size)))\n            assert img_np.dtype == np.uint8, \"np.uint8 is expected for JPEG images\"\n            img_np = img_np / 255.0\n            img = torch.from_numpy(img_np).permute(2, 0, 1)\n            # float16 precision should be sufficient for image tensor storage\n            img = img.to(dtype=torch.float16)\n            # normalize by mean and std\n            img -= img_mean\n            img /= img_std\n            images.append(img)\n        images = torch.stack(images)\n        if not offload_video_to_cpu:\n            images = images.cuda()\n        return images, orig_height, orig_width\n\n    is_image = (\n        isinstance(resource_path, str)\n        and os.path.splitext(resource_path)[-1].lower() in IMAGE_EXTS\n    )\n    if is_image:\n        return load_image_as_single_frame_video(\n            image_path=resource_path,\n            image_size=image_size,\n            offload_video_to_cpu=offload_video_to_cpu,\n            img_mean=img_mean,\n            img_std=img_std,\n        )\n    else:\n        return load_video_frames(\n            video_path=resource_path,\n            image_size=image_size,\n            offload_video_to_cpu=offload_video_to_cpu,\n            img_mean=img_mean,\n            img_std=img_std,\n            async_loading_frames=async_loading_frames,\n            video_loader_type=video_loader_type,\n        )\n\n\ndef load_image_as_single_frame_video(\n    image_path,\n    image_size,\n    offload_video_to_cpu,\n    img_mean=(0.5, 0.5, 0.5),\n    img_std=(0.5, 0.5, 0.5),\n):\n    \"\"\"Load an image as a single-frame video.\"\"\"\n    images, image_height, image_width = _load_img_as_tensor(image_path, image_size)\n    images = images.unsqueeze(0).half()\n\n    img_mean = torch.tensor(img_mean, dtype=torch.float16)[:, None, None]\n    img_std = torch.tensor(img_std, dtype=torch.float16)[:, None, None]\n    if not offload_video_to_cpu:\n        images = images.cuda()\n        img_mean = img_mean.cuda()\n        img_std = img_std.cuda()\n    # normalize by mean and std\n    images -= img_mean\n    images /= img_std\n    return images, image_height, image_width\n\n\ndef load_video_frames(\n    video_path,\n    image_size,\n    offload_video_to_cpu,\n    img_mean=(0.5, 0.5, 0.5),\n    img_std=(0.5, 0.5, 0.5),\n    async_loading_frames=False,\n    video_loader_type=\"cv2\",\n):\n    \"\"\"\n    Load the video frames from video_path. The frames are resized to image_size as in\n    the model and are loaded to GPU if offload_video_to_cpu=False. This is used by the demo.\n    \"\"\"\n    assert isinstance(video_path, str)\n    if video_path.startswith(\"<load-dummy-video\"):\n        # Check for pattern <load-dummy-video-N> where N is an integer\n        match = re.match(r\"<load-dummy-video-(\\d+)>\", video_path)\n        num_frames = int(match.group(1)) if match else 60\n        return load_dummy_video(image_size, offload_video_to_cpu, num_frames=num_frames)\n    elif os.path.isdir(video_path):\n        return load_video_frames_from_image_folder(\n            image_folder=video_path,\n            image_size=image_size,\n            offload_video_to_cpu=offload_video_to_cpu,\n            img_mean=img_mean,\n            img_std=img_std,\n            async_loading_frames=async_loading_frames,\n        )\n    elif os.path.splitext(video_path)[-1].lower() in VIDEO_EXTS:\n        return load_video_frames_from_video_file(\n            video_path=video_path,\n            image_size=image_size,\n            offload_video_to_cpu=offload_video_to_cpu,\n            img_mean=img_mean,\n            img_std=img_std,\n            async_loading_frames=async_loading_frames,\n            video_loader_type=video_loader_type,\n        )\n    else:\n        raise NotImplementedError(\"Only video files and image folders are supported\")\n\n\ndef load_video_frames_from_image_folder(\n    image_folder,\n    image_size,\n    offload_video_to_cpu,\n    img_mean,\n    img_std,\n    async_loading_frames,\n):\n    \"\"\"\n    Load the video frames from a directory of image files (\"<frame_index>.<img_ext>\" format)\n    \"\"\"\n    frame_names = [\n        p\n        for p in os.listdir(image_folder)\n        if os.path.splitext(p)[-1].lower() in IMAGE_EXTS\n    ]\n    try:\n        frame_names.sort(key=lambda p: int(os.path.splitext(p)[0]))\n    except ValueError:\n        # fallback to lexicographic sort if the format is not \"<frame_index>.<img_ext>\"\n        logger.warning(\n            f'frame names are not in \"<frame_index>.<img_ext>\" format: {frame_names[:5]=}, '\n            f\"falling back to lexicographic sort.\"\n        )\n        frame_names.sort()\n    num_frames = len(frame_names)\n    if num_frames == 0:\n        raise RuntimeError(f\"no images found in {image_folder}\")\n    img_paths = [os.path.join(image_folder, frame_name) for frame_name in frame_names]\n    img_mean = torch.tensor(img_mean, dtype=torch.float16)[:, None, None]\n    img_std = torch.tensor(img_std, dtype=torch.float16)[:, None, None]\n\n    if async_loading_frames:\n        lazy_images = AsyncImageFrameLoader(\n            img_paths, image_size, offload_video_to_cpu, img_mean, img_std\n        )\n        return lazy_images, lazy_images.video_height, lazy_images.video_width\n\n    # float16 precision should be sufficient for image tensor storage\n    images = torch.zeros(num_frames, 3, image_size, image_size, dtype=torch.float16)\n    video_height, video_width = None, None\n    for n, img_path in enumerate(\n        tqdm(img_paths, desc=f\"frame loading (image folder) [rank={RANK}]\")\n    ):\n        images[n], video_height, video_width = _load_img_as_tensor(img_path, image_size)\n    if not offload_video_to_cpu:\n        images = images.cuda()\n        img_mean = img_mean.cuda()\n        img_std = img_std.cuda()\n    # normalize by mean and std\n    images -= img_mean\n    images /= img_std\n    return images, video_height, video_width\n\n\ndef load_video_frames_from_video_file(\n    video_path,\n    image_size,\n    offload_video_to_cpu,\n    img_mean,\n    img_std,\n    async_loading_frames,\n    gpu_acceleration=False,\n    gpu_device=None,\n    video_loader_type=\"cv2\",\n):\n    \"\"\"Load the video frames from a video file.\"\"\"\n    if video_loader_type == \"cv2\":\n        return load_video_frames_from_video_file_using_cv2(\n            video_path=video_path,\n            image_size=image_size,\n            img_mean=img_mean,\n            img_std=img_std,\n            offload_video_to_cpu=offload_video_to_cpu,\n        )\n    elif video_loader_type == \"torchcodec\":\n        logger.info(\"Using torchcodec to load video file\")\n        lazy_images = AsyncVideoFileLoaderWithTorchCodec(\n            video_path=video_path,\n            image_size=image_size,\n            offload_video_to_cpu=offload_video_to_cpu,\n            img_mean=img_mean,\n            img_std=img_std,\n            gpu_acceleration=gpu_acceleration,\n            gpu_device=gpu_device,\n        )\n        # The `AsyncVideoFileLoaderWithTorchCodec` class always loads the videos asynchronously,\n        # so we just wait for its loading thread to finish if async_loading_frames=False.\n        if not async_loading_frames:\n            async_thread = lazy_images.thread\n            if async_thread is not None:\n                async_thread.join()\n        return lazy_images, lazy_images.video_height, lazy_images.video_width\n    else:\n        raise RuntimeError(\"video_loader_type must be either 'cv2' or 'torchcodec'\")\n\n\ndef load_video_frames_from_video_file_using_cv2(\n    video_path: str,\n    image_size: int,\n    img_mean: tuple = (0.5, 0.5, 0.5),\n    img_std: tuple = (0.5, 0.5, 0.5),\n    offload_video_to_cpu: bool = False,\n) -> torch.Tensor:\n    \"\"\"\n    Load video from path, convert to normalized tensor with specified preprocessing\n\n    Args:\n        video_path: Path to video file\n        image_size: Target size for square frames (height and width)\n        img_mean: Normalization mean (RGB)\n        img_std: Normalization standard deviation (RGB)\n\n    Returns:\n        torch.Tensor: Preprocessed video tensor in shape (T, C, H, W) with float16 dtype\n    \"\"\"\n    import cv2  # delay OpenCV import to avoid unnecessary dependency\n\n    # Initialize video capture\n    cap = cv2.VideoCapture(video_path)\n    if not cap.isOpened():\n        raise ValueError(f\"Could not open video: {video_path}\")\n\n    original_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))\n    original_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))\n    num_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))\n    num_frames = num_frames if num_frames > 0 else None\n\n    frames = []\n    pbar = tqdm(desc=f\"frame loading (OpenCV) [rank={RANK}]\", total=num_frames)\n    while True:\n        ret, frame = cap.read()\n        if not ret:\n            break\n\n        # Convert BGR to RGB and resize\n        frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n        frame_resized = cv2.resize(\n            frame_rgb, (image_size, image_size), interpolation=cv2.INTER_CUBIC\n        )\n        frames.append(frame_resized)\n        pbar.update(1)\n    cap.release()\n    pbar.close()\n\n    if len(frames) == 0:\n        raise RuntimeError(\n            f\"No frames could be decoded from video: {video_path}. \"\n            f\"The file may be corrupted, empty, or encoded with an unsupported codec.\"\n        )\n\n    # Convert to tensor\n    frames_np = np.stack(frames, axis=0).astype(np.float32)  # (T, H, W, C)\n    video_tensor = torch.from_numpy(frames_np).permute(0, 3, 1, 2)  # (T, C, H, W)\n\n    img_mean = torch.tensor(img_mean, dtype=torch.float16).view(1, 3, 1, 1)\n    img_std = torch.tensor(img_std, dtype=torch.float16).view(1, 3, 1, 1)\n    if not offload_video_to_cpu:\n        video_tensor = video_tensor.cuda()\n        img_mean = img_mean.cuda()\n        img_std = img_std.cuda()\n    # normalize by mean and std\n    video_tensor -= img_mean\n    video_tensor /= img_std\n    return video_tensor, original_height, original_width\n\n\ndef load_dummy_video(image_size, offload_video_to_cpu, num_frames=60):\n    \"\"\"\n    Load a dummy video with random frames for testing and compilation warmup purposes.\n    \"\"\"\n    video_height, video_width = 480, 640  # dummy original video sizes\n    images = torch.randn(num_frames, 3, image_size, image_size, dtype=torch.float16)\n    if not offload_video_to_cpu:\n        images = images.cuda()\n    return images, video_height, video_width\n\n\ndef _load_img_as_tensor(img_path, image_size):\n    \"\"\"Load and resize an image and convert it into a PyTorch tensor.\"\"\"\n    img = Image.open(img_path).convert(\"RGB\")\n    orig_width, orig_height = img.width, img.height\n    img = TF.resize(img, size=(image_size, image_size))\n    img = TF.to_tensor(img)\n    return img, orig_height, orig_width\n\n\nclass AsyncImageFrameLoader:\n    \"\"\"\n    A list of video frames to be load asynchronously without blocking session start.\n    \"\"\"\n\n    def __init__(self, img_paths, image_size, offload_video_to_cpu, img_mean, img_std):\n        self.img_paths = img_paths\n        self.image_size = image_size\n        self.offload_video_to_cpu = offload_video_to_cpu\n        self.img_mean = img_mean\n        self.img_std = img_std\n        # items in `self._images` will be loaded asynchronously\n        self.images = [None] * len(img_paths)\n        # catch and raise any exceptions in the async loading thread\n        self.exception = None\n        # video_height and video_width be filled when loading the first image\n        self.video_height = None\n        self.video_width = None\n\n        # load the first frame to fill video_height and video_width and also\n        # to cache it (since it's most likely where the user will click)\n        self.__getitem__(0)\n\n        # load the rest of frames asynchronously without blocking the session start\n        def _load_frames():\n            try:\n                for n in tqdm(\n                    range(len(self.images)),\n                    desc=f\"frame loading (image folder) [rank={RANK}]\",\n                ):\n                    self.__getitem__(n)\n            except Exception as e:\n                self.exception = e\n\n        self.thread = Thread(target=_load_frames, daemon=True)\n        self.thread.start()\n\n    def __getitem__(self, index):\n        if self.exception is not None:\n            raise RuntimeError(\"Failure in frame loading thread\") from self.exception\n\n        img = self.images[index]\n        if img is not None:\n            return img\n\n        img, video_height, video_width = _load_img_as_tensor(\n            self.img_paths[index], self.image_size\n        )\n        self.video_height = video_height\n        self.video_width = video_width\n        # float16 precision should be sufficient for image tensor storage\n        img = img.to(dtype=torch.float16)\n        # normalize by mean and std\n        img -= self.img_mean\n        img /= self.img_std\n        if not self.offload_video_to_cpu:\n            img = img.cuda()\n        self.images[index] = img\n        return img\n\n    def __len__(self):\n        return len(self.images)\n\n\nclass TorchCodecDecoder:\n    \"\"\"\n    A wrapper to support GPU device and num_threads in TorchCodec decoder,\n    which are not supported by `torchcodec.decoders.SimpleVideoDecoder` yet.\n    \"\"\"\n\n    def __init__(self, source, dimension_order=\"NCHW\", device=\"cpu\", num_threads=1):\n        from torchcodec import _core as core\n\n        self._source = source  # hold a reference to the source to prevent it from GC\n        if isinstance(source, str):\n            self._decoder = core.create_from_file(source, \"exact\")\n        elif isinstance(source, bytes):\n            self._decoder = core.create_from_bytes(source, \"exact\")\n        else:\n            raise TypeError(f\"Unknown source type: {type(source)}.\")\n        assert dimension_order in (\"NCHW\", \"NHWC\")\n\n        device_string = str(device)\n        core.scan_all_streams_to_update_metadata(self._decoder)\n        core.add_video_stream(\n            self._decoder,\n            dimension_order=dimension_order,\n            device=device_string,\n            num_threads=(1 if \"cuda\" in device_string else num_threads),\n        )\n        video_metadata = core.get_container_metadata(self._decoder)\n        best_stream_index = video_metadata.best_video_stream_index\n        assert best_stream_index is not None\n        self.metadata = video_metadata.streams[best_stream_index]\n        assert self.metadata.num_frames_from_content is not None\n        self._num_frames = self.metadata.num_frames_from_content\n\n    def __len__(self) -> int:\n        return self._num_frames\n\n    def __getitem__(self, key: int):\n        from torchcodec import _core as core\n\n        if key < 0:\n            key += self._num_frames\n        if key >= self._num_frames or key < 0:\n            raise IndexError(\n                f\"Index {key} is out of bounds; length is {self._num_frames}\"\n            )\n        frame_data, *_ = core.get_frame_at_index(\n            self._decoder,\n            frame_index=key,\n        )\n        return frame_data\n\n\nclass FIFOLock:\n    \"\"\"A lock that ensures FIFO ordering of lock acquisitions.\"\"\"\n\n    def __init__(self):\n        self._lock = Lock()\n        self._waiters = queue.Queue()\n        self._condition = Condition()\n\n    def acquire(self):\n        ident = get_ident()\n        with self._condition:\n            self._waiters.put(ident)\n            while self._waiters.queue[0] != ident or not self._lock.acquire(\n                blocking=False\n            ):\n                self._condition.wait()\n                # got the lock and it's our turn\n\n    def release(self):\n        with self._condition:\n            self._lock.release()\n            self._waiters.get()\n            self._condition.notify_all()\n\n    def __enter__(self):\n        self.acquire()\n\n    def __exit__(self, t, v, tb):\n        self.release()\n\n\nclass AsyncVideoFileLoaderWithTorchCodec:\n    \"\"\"\n    Loading frames from video files asynchronously without blocking session start.\n\n    Unlike `AsyncVideoFileLoader`, this class uses PyTorch's offical TorchCodec library\n    for video decoding, which is more efficient and supports more video formats.\n    \"\"\"\n\n    def __init__(\n        self,\n        video_path,\n        image_size,\n        offload_video_to_cpu,\n        img_mean,\n        img_std,\n        gpu_acceleration=True,\n        gpu_device=None,\n        use_rand_seek_in_loading=False,\n    ):\n        # Check and possibly infer the output device (and also get its GPU id when applicable)\n        assert gpu_device is None or gpu_device.type == \"cuda\"\n        gpu_id = (\n            gpu_device.index\n            if gpu_device is not None and gpu_device.index is not None\n            else torch.cuda.current_device()\n        )\n        if offload_video_to_cpu:\n            out_device = torch.device(\"cpu\")\n        else:\n            out_device = torch.device(\"cuda\") if gpu_device is None else gpu_device\n        self.out_device = out_device\n        self.gpu_acceleration = gpu_acceleration\n        self.gpu_id = gpu_id\n        self.image_size = image_size\n        self.offload_video_to_cpu = offload_video_to_cpu\n        if not isinstance(img_mean, torch.Tensor):\n            img_mean = torch.tensor(img_mean, dtype=torch.float16)[:, None, None]\n        self.img_mean = img_mean\n        if not isinstance(img_std, torch.Tensor):\n            img_std = torch.tensor(img_std, dtype=torch.float16)[:, None, None]\n        self.img_std = img_std\n\n        if gpu_acceleration:\n            self.img_mean = self.img_mean.to(f\"cuda:{self.gpu_id}\")\n            self.img_std = self.img_std.to(f\"cuda:{self.gpu_id}\")\n            decoder_option = {\"device\": f\"cuda:{self.gpu_id}\"}\n        else:\n            self.img_mean = self.img_mean.cpu()\n            self.img_std = self.img_std.cpu()\n            decoder_option = {\"num_threads\": 1}  # use a single thread to save memory\n\n        self.rank = int(os.environ.get(\"RANK\", \"0\"))\n        self.world_size = int(os.environ.get(\"WORLD_SIZE\", \"1\"))\n        self.async_reader = TorchCodecDecoder(video_path, **decoder_option)\n\n        # `num_frames_from_content` is the true number of frames in the video content\n        # from the scan operation (rather than from the metadata, which could be wrong)\n        self.num_frames = self.async_reader.metadata.num_frames_from_content\n        self.video_height = self.async_reader.metadata.height\n        self.video_width = self.async_reader.metadata.width\n\n        # items in `self._images` will be loaded asynchronously\n        self.images_loaded = [False] * self.num_frames\n        self.images = torch.zeros(\n            self.num_frames,\n            3,\n            self.image_size,\n            self.image_size,\n            dtype=torch.float16,\n            device=self.out_device,\n        )\n        # catch and raise any exceptions in the async loading thread\n        self.exception = None\n        self.use_rand_seek_in_loading = use_rand_seek_in_loading\n        self.rand_seek_idx_queue = queue.Queue()\n        # use a lock to avoid race condition between concurrent access to torchcodec\n        # libs (which are not thread-safe); the lock is replaced with a nullcontext\n        # when the video is fully loaded\n        self.torchcodec_access_lock = FIFOLock()\n        self._start_video_loading()\n\n    def _load_one_frame(self, idx):\n        frame_resized = self._transform_frame(self.async_reader[idx])\n        return frame_resized\n\n    @torch.inference_mode()\n    def _start_video_loading(self):\n        desc = f\"frame loading (TorchCodec w/ {'GPU' if self.gpu_acceleration else 'CPU'}) [rank={RANK}]\"\n        pbar = tqdm(desc=desc, total=self.num_frames)\n        self.num_loaded_frames = 0\n        # load the first frame synchronously to cache it before the session is opened\n        idx = self.num_loaded_frames\n        self.images[idx] = self._load_one_frame(idx)\n        self.images_loaded[idx] = True\n        self.num_loaded_frames += 1\n        pbar.update(n=1)\n        self.all_frames_loaded = self.num_loaded_frames == self.num_frames\n\n        # load the frames asynchronously without blocking the session start\n        def _load_frames():\n            finished = self.all_frames_loaded\n            chunk_size = 16\n            while not finished:\n                # asynchronously load `chunk_size` frames each time we acquire the lock\n                with self.torchcodec_access_lock, torch.inference_mode():\n                    for _ in range(chunk_size):\n                        try:\n                            idx = self.num_loaded_frames\n                            self.images[idx] = self._load_one_frame(idx)\n                            self.images_loaded[idx] = True\n                            self.num_loaded_frames += 1\n                            pbar.update(n=1)\n                            if self.num_loaded_frames >= self.num_frames:\n                                finished = True\n                                break\n                        except Exception as e:\n                            self.exception = e\n                            raise\n\n                    # also read the frame that is being randomly seeked to\n                    while True:\n                        try:\n                            idx = self.rand_seek_idx_queue.get_nowait()\n                            if not self.images_loaded[idx]:\n                                self.images[idx] = self._load_one_frame(idx)\n                                self.images_loaded[idx] = True\n                        except queue.Empty:\n                            break\n                        except Exception as e:\n                            self.exception = e\n                            raise\n\n            # finished -- check whether we have loaded the total number of frames\n            if self.num_loaded_frames != self.num_frames:\n                raise RuntimeError(\n                    f\"There are {self.num_frames} frames in the video, but only \"\n                    f\"{self.num_loaded_frames} frames can be loaded successfully.\"\n                )\n            else:\n                self.all_frames_loaded = True\n                pbar.close()\n                with self.torchcodec_access_lock:\n                    import gc\n\n                    # all frames have been loaded, so we can release the readers and free their memory\n                    # also remove pbar and thread (which shouldn't be a part of session saving)\n                    reader = self.async_reader\n                    if reader is not None:\n                        reader._source = None\n                    self.async_reader = None\n                    self.pbar = None\n                    self.thread = None\n                    self.rand_seek_idx_queue = None\n                    gc.collect()\n                # remove the lock (replace it with nullcontext) when the video is fully loaded\n                self.torchcodec_access_lock = contextlib.nullcontext()\n\n        self.thread = Thread(target=_load_frames, daemon=True)\n        self.thread.start()\n\n    def _transform_frame(self, frame):\n        frame = frame.clone()  # make a copy to avoid modifying the original frame bytes\n        frame = frame.float()  # convert to float32 before interpolation\n        frame_resized = F.interpolate(\n            frame[None, :],\n            size=(self.image_size, self.image_size),\n            mode=\"bicubic\",\n            align_corners=False,\n        )[0]\n        # float16 precision should be sufficient for image tensor storage\n        frame_resized = frame_resized.half()  # uint8 -> float16\n        frame_resized /= 255\n        frame_resized -= self.img_mean\n        frame_resized /= self.img_std\n        if self.offload_video_to_cpu:\n            frame_resized = frame_resized.cpu()\n        elif frame_resized.device != self.out_device:\n            frame_resized = frame_resized.to(device=self.out_device, non_blocking=True)\n        return frame_resized\n\n    def __getitem__(self, index):\n        if self.exception is not None:\n            raise RuntimeError(\"Failure in frame loading thread\") from self.exception\n\n        max_tries = 1200\n        for _ in range(max_tries):\n            # use a lock to avoid race condition between concurrent access to torchcodec\n            # libs (which are not thread-safe); the lock is replaced with a nullcontext\n            # when the video is fully loaded\n            with self.torchcodec_access_lock:\n                if self.images_loaded[index]:\n                    return self.images[index]\n\n                if self.use_rand_seek_in_loading:\n                    # async loading hasn't reached this frame yet, so we load this frame individually\n                    # (it will be loaded by in _load_frames thread and added to self.images[index])\n                    self.rand_seek_idx_queue.put(index)\n\n            time.sleep(0.1)\n\n        raise RuntimeError(f\"Failed to load frame {index} after {max_tries} tries\")\n\n    def __len__(self):\n        return len(self.images)\n\n    def __getstate__(self):\n        \"\"\"\n        Remove a few attributes during pickling, so that this async video loader can be\n        saved and loaded as a part of the model session.\n        \"\"\"\n        # wait for async video loading to finish before pickling\n        async_thread = self.thread\n        if async_thread is not None:\n            async_thread.join()\n        # release a few objects that cannot be pickled\n        reader = self.async_reader\n        if reader is not None:\n            reader._source = None\n        self.async_reader = None\n        self.pbar = None\n        self.thread = None\n        self.rand_seek_idx_queue = None\n        self.torchcodec_access_lock = contextlib.nullcontext()\n        return self.__dict__.copy()\n"
  },
  {
    "path": "sam3/model/maskformer_segmentation.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n\nimport math\nfrom typing import Dict, List, Optional\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.utils.checkpoint as checkpoint\n\nfrom .model_misc import MLP\n\n\nclass LinearPresenceHead(nn.Sequential):\n    def __init__(self, d_model):\n        # a hack to make `LinearPresenceHead` compatible with old checkpoints\n        super().__init__(nn.Identity(), nn.Identity(), nn.Linear(d_model, 1))\n\n    def forward(self, hs, prompt, prompt_mask):\n        return super().forward(hs)\n\n\nclass MaskPredictor(nn.Module):\n    def __init__(self, hidden_dim, mask_dim):\n        super().__init__()\n        self.mask_embed = MLP(hidden_dim, hidden_dim, mask_dim, 3)\n\n    def forward(self, obj_queries, pixel_embed):\n        if len(obj_queries.shape) == 3:\n            if pixel_embed.ndim == 3:\n                # batch size was omitted\n                mask_preds = torch.einsum(\n                    \"bqc,chw->bqhw\", self.mask_embed(obj_queries), pixel_embed\n                )\n            else:\n                mask_preds = torch.einsum(\n                    \"bqc,bchw->bqhw\", self.mask_embed(obj_queries), pixel_embed\n                )\n        else:\n            # Assumed to have aux masks\n            if pixel_embed.ndim == 3:\n                # batch size was omitted\n                mask_preds = torch.einsum(\n                    \"lbqc,chw->lbqhw\", self.mask_embed(obj_queries), pixel_embed\n                )\n            else:\n                mask_preds = torch.einsum(\n                    \"lbqc,bchw->lbqhw\", self.mask_embed(obj_queries), pixel_embed\n                )\n\n        return mask_preds\n\n\nclass SegmentationHead(nn.Module):\n    def __init__(\n        self,\n        hidden_dim,\n        upsampling_stages,\n        use_encoder_inputs=False,\n        aux_masks=False,\n        no_dec=False,\n        pixel_decoder=None,\n        act_ckpt=False,\n        shared_conv=False,\n        compile_mode_pixel_decoder=None,\n    ):\n        super().__init__()\n        self.use_encoder_inputs = use_encoder_inputs\n        self.aux_masks = aux_masks\n        if pixel_decoder is not None:\n            self.pixel_decoder = pixel_decoder\n        else:\n            self.pixel_decoder = PixelDecoder(\n                hidden_dim,\n                upsampling_stages,\n                shared_conv=shared_conv,\n                compile_mode=compile_mode_pixel_decoder,\n            )\n        self.no_dec = no_dec\n        if no_dec:\n            self.mask_predictor = nn.Conv2d(\n                hidden_dim, 1, kernel_size=3, stride=1, padding=1\n            )\n        else:\n            self.mask_predictor = MaskPredictor(hidden_dim, mask_dim=hidden_dim)\n\n        self.act_ckpt = act_ckpt\n\n        # used to update the output dictionary\n        self.instance_keys = [\"pred_masks\"]\n\n    @property\n    def device(self):\n        self._device = getattr(self, \"_device\", None) or next(self.parameters()).device\n        return self._device\n\n    def to(self, *args, **kwargs):\n        # clear cached _device in case the model is moved to a different device\n        self._device = None\n        return super().to(*args, **kwargs)\n\n    def _embed_pixels(\n        self,\n        backbone_feats: List[torch.Tensor],\n        image_ids,\n        encoder_hidden_states,\n    ) -> torch.Tensor:\n        feature_device = backbone_feats[0].device  # features could be on CPU\n        model_device = self.device\n        image_ids_ = image_ids.to(feature_device)\n        if self.use_encoder_inputs:\n            if backbone_feats[0].shape[0] > 1:\n                # For bs > 1, we construct the per query backbone features\n                backbone_visual_feats = []\n                for feat in backbone_feats:\n                    # Copy the img features per query (pixel decoder won't share img feats)\n                    backbone_visual_feats.append(feat[image_ids_, ...].to(model_device))\n            else:\n                # Bs=1, we rely on broadcasting for query-based processing\n                backbone_visual_feats = [bb_feat.clone() for bb_feat in backbone_feats]\n            # Extract visual embeddings\n            encoder_hidden_states = encoder_hidden_states.permute(1, 2, 0)\n            spatial_dim = math.prod(backbone_feats[-1].shape[-2:])\n            encoder_visual_embed = encoder_hidden_states[..., :spatial_dim].reshape(\n                -1, *backbone_feats[-1].shape[1:]\n            )\n\n            backbone_visual_feats[-1] = encoder_visual_embed\n            if self.act_ckpt:\n                pixel_embed = checkpoint.checkpoint(\n                    self.pixel_decoder, backbone_visual_feats, use_reentrant=False\n                )\n            else:\n                pixel_embed = self.pixel_decoder(backbone_visual_feats)\n        else:\n            backbone_feats = [x.to(model_device) for x in backbone_feats]\n            pixel_embed = self.pixel_decoder(backbone_feats)\n            if pixel_embed.shape[0] == 1:\n                # For batch_size=1 training, we can avoid the indexing to save memory\n                pixel_embed = pixel_embed.squeeze(0)\n            else:\n                pixel_embed = pixel_embed[image_ids, ...]\n        return pixel_embed\n\n    def forward(\n        self,\n        backbone_feats: List[torch.Tensor],\n        obj_queries: torch.Tensor,\n        image_ids,\n        encoder_hidden_states: Optional[torch.Tensor] = None,\n        **kwargs,\n    ) -> Dict[str, torch.Tensor]:\n        if self.use_encoder_inputs:\n            assert encoder_hidden_states is not None\n\n        pixel_embed = self._embed_pixels(\n            backbone_feats=backbone_feats,\n            image_ids=image_ids,\n            encoder_hidden_states=encoder_hidden_states,\n        )\n\n        if self.no_dec:\n            mask_pred = self.mask_predictor(pixel_embed)\n        elif self.aux_masks:\n            mask_pred = self.mask_predictor(obj_queries, pixel_embed)\n        else:\n            mask_pred = self.mask_predictor(obj_queries[-1], pixel_embed)\n\n        return {\"pred_masks\": mask_pred}\n\n\nclass PixelDecoder(nn.Module):\n    def __init__(\n        self,\n        hidden_dim,\n        num_upsampling_stages,\n        interpolation_mode=\"nearest\",\n        shared_conv=False,\n        compile_mode=None,\n    ):\n        super().__init__()\n        self.hidden_dim = hidden_dim\n        self.num_upsampling_stages = num_upsampling_stages\n        self.interpolation_mode = interpolation_mode\n        conv_layers = []\n        norms = []\n        num_convs = 1 if shared_conv else num_upsampling_stages\n        for _ in range(num_convs):\n            conv_layers.append(nn.Conv2d(self.hidden_dim, self.hidden_dim, 3, 1, 1))\n            norms.append(nn.GroupNorm(8, self.hidden_dim))\n\n        self.conv_layers = nn.ModuleList(conv_layers)\n        self.norms = nn.ModuleList(norms)\n        self.shared_conv = shared_conv\n        self.out_dim = self.conv_layers[-1].out_channels\n        if compile_mode is not None:\n            self.forward = torch.compile(\n                self.forward, mode=compile_mode, dynamic=True, fullgraph=True\n            )\n            # Needed to make checkpointing happy. But we don't know if the module is checkpointed, so we disable it by default.\n            torch._dynamo.config.optimize_ddp = False\n\n    def forward(self, backbone_feats: List[torch.Tensor]):\n        # Assumes backbone features are already projected (C == hidden dim)\n\n        prev_fpn = backbone_feats[-1]\n        fpn_feats = backbone_feats[:-1]\n        for layer_idx, bb_feat in enumerate(fpn_feats[::-1]):\n            curr_fpn = bb_feat\n            prev_fpn = curr_fpn + F.interpolate(\n                prev_fpn, size=curr_fpn.shape[-2:], mode=self.interpolation_mode\n            )\n            if self.shared_conv:\n                # only one conv layer\n                layer_idx = 0\n            prev_fpn = self.conv_layers[layer_idx](prev_fpn)\n            prev_fpn = F.relu(self.norms[layer_idx](prev_fpn))\n\n        return prev_fpn\n\n\nclass UniversalSegmentationHead(SegmentationHead):\n    \"\"\"This module handles semantic+instance segmentation\"\"\"\n\n    def __init__(\n        self,\n        hidden_dim,\n        upsampling_stages,\n        pixel_decoder,\n        aux_masks=False,\n        no_dec=False,\n        act_ckpt=False,\n        presence_head: bool = False,\n        dot_product_scorer=None,\n        cross_attend_prompt=None,\n    ):\n        super().__init__(\n            hidden_dim=hidden_dim,\n            upsampling_stages=upsampling_stages,\n            use_encoder_inputs=True,\n            aux_masks=aux_masks,\n            no_dec=no_dec,\n            pixel_decoder=pixel_decoder,\n            act_ckpt=act_ckpt,\n        )\n        self.d_model = hidden_dim\n\n        if dot_product_scorer is not None:\n            assert presence_head, (\n                \"Specifying a dot product scorer without a presence head is likely a mistake\"\n            )\n\n        self.presence_head = None\n        if presence_head:\n            self.presence_head = (\n                dot_product_scorer\n                if dot_product_scorer is not None\n                else LinearPresenceHead(self.d_model)\n            )\n\n        self.cross_attend_prompt = cross_attend_prompt\n        if self.cross_attend_prompt is not None:\n            self.cross_attn_norm = nn.LayerNorm(self.d_model)\n\n        self.semantic_seg_head = nn.Conv2d(self.pixel_decoder.out_dim, 1, kernel_size=1)\n        self.instance_seg_head = nn.Conv2d(\n            self.pixel_decoder.out_dim, self.d_model, kernel_size=1\n        )\n\n    def forward(\n        self,\n        backbone_feats: List[torch.Tensor],\n        obj_queries: torch.Tensor,\n        image_ids,\n        encoder_hidden_states: Optional[torch.Tensor] = None,\n        prompt: Optional[torch.Tensor] = None,\n        prompt_mask: Optional[torch.Tensor] = None,\n        **kwargs,\n    ) -> Dict[str, Optional[torch.Tensor]]:\n        assert encoder_hidden_states is not None\n        bs = encoder_hidden_states.shape[1]\n\n        if self.cross_attend_prompt is not None:\n            tgt2 = self.cross_attn_norm(encoder_hidden_states)\n            tgt2 = self.cross_attend_prompt(\n                query=tgt2,\n                key=prompt,\n                value=prompt,\n                key_padding_mask=prompt_mask,\n            )[0]\n            encoder_hidden_states = tgt2 + encoder_hidden_states\n\n        presence_logit = None\n        if self.presence_head is not None:\n            pooled_enc = encoder_hidden_states.mean(0)\n            presence_logit = (\n                self.presence_head(\n                    pooled_enc.view(1, bs, 1, self.d_model),\n                    prompt=prompt,\n                    prompt_mask=prompt_mask,\n                )\n                .squeeze(0)\n                .squeeze(1)\n            )\n\n        pixel_embed = self._embed_pixels(\n            backbone_feats=backbone_feats,\n            image_ids=image_ids,\n            encoder_hidden_states=encoder_hidden_states,\n        )\n\n        instance_embeds = self.instance_seg_head(pixel_embed)\n\n        if self.no_dec:\n            mask_pred = self.mask_predictor(instance_embeds)\n        elif self.aux_masks:\n            mask_pred = self.mask_predictor(obj_queries, instance_embeds)\n        else:\n            mask_pred = self.mask_predictor(obj_queries[-1], instance_embeds)\n\n        return {\n            \"pred_masks\": mask_pred,\n            \"semantic_seg\": self.semantic_seg_head(pixel_embed),\n            \"presence_logit\": presence_logit,\n        }\n"
  },
  {
    "path": "sam3/model/memory.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n\nimport math\nfrom typing import Tuple\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\ntry:\n    from timm.layers import DropPath\nexcept ModuleNotFoundError:\n    # compatibility for older timm versions\n    from timm.models.layers import DropPath\n\nfrom .model_misc import get_clones, LayerNorm2d\n\n\nclass SimpleMaskDownSampler(nn.Module):\n    \"\"\"\n    Progressively downsample a mask by total_stride, each time by stride.\n    Note that LayerNorm is applied per *token*, like in ViT.\n\n    With each downsample (by a factor stride**2), channel capacity increases by the same factor.\n    In the end, we linearly project to embed_dim channels.\n    \"\"\"\n\n    def __init__(\n        self,\n        embed_dim=256,\n        kernel_size=4,\n        stride=4,\n        padding=0,\n        total_stride=16,\n        activation=nn.GELU,\n        # Option to interpolate the input mask first before downsampling using convs. In that case, the total_stride is assumed to be after interpolation.\n        # If set to input resolution or None, we don't interpolate. We default to None to be safe (for older configs or if not explicitly set)\n        interpol_size=None,\n    ):\n        super().__init__()\n        num_layers = int(math.log2(total_stride) // math.log2(stride))\n        assert stride**num_layers == total_stride\n        self.encoder = nn.Sequential()\n        mask_in_chans, mask_out_chans = 1, 1\n        for _ in range(num_layers):\n            mask_out_chans = mask_in_chans * (stride**2)\n            self.encoder.append(\n                nn.Conv2d(\n                    mask_in_chans,\n                    mask_out_chans,\n                    kernel_size=kernel_size,\n                    stride=stride,\n                    padding=padding,\n                )\n            )\n            self.encoder.append(LayerNorm2d(mask_out_chans))\n            self.encoder.append(activation())\n            mask_in_chans = mask_out_chans\n\n        self.encoder.append(nn.Conv2d(mask_out_chans, embed_dim, kernel_size=1))\n        self.interpol_size = interpol_size\n        if self.interpol_size is not None:\n            assert isinstance(self.interpol_size, (list, tuple)), (\n                f\"Unsupported type {type(self.interpol_size)}. Should be a list or tuple.\"\n            )\n            self.interpol_size = list(interpol_size)\n            assert len(self.interpol_size) == 2\n\n    def forward(self, x: torch.Tensor):\n        if self.interpol_size is not None and self.interpol_size != list(x.shape[-2:]):\n            x = F.interpolate(\n                x.float(),\n                size=self.interpol_size,\n                align_corners=False,\n                mode=\"bilinear\",\n                antialias=True,\n            )\n        return self.encoder(x)\n\n\n# Lightly adapted from ConvNext (https://github.com/facebookresearch/ConvNeXt)\nclass CXBlock(nn.Module):\n    r\"\"\"ConvNeXt Block. There are two equivalent implementations:\n    (1) DwConv -> LayerNorm (channels_first) -> 1x1 Conv -> GELU -> 1x1 Conv; all in (N, C, H, W)\n    (2) DwConv -> Permute to (N, H, W, C); LayerNorm (channels_last) -> Linear -> GELU -> Linear; Permute back\n    We use (2) as we find it slightly faster in PyTorch\n\n    Args:\n        dim (int): Number of input channels.\n        drop_path (float): Stochastic depth rate. Default: 0.0\n        layer_scale_init_value (float): Init value for Layer Scale. Default: 1e-6.\n    \"\"\"\n\n    def __init__(\n        self,\n        dim,\n        kernel_size=7,\n        padding=3,\n        drop_path=0.0,\n        layer_scale_init_value=1e-6,\n        use_dwconv=True,\n    ):\n        super().__init__()\n        self.dwconv = nn.Conv2d(\n            dim,\n            dim,\n            kernel_size=kernel_size,\n            padding=padding,\n            groups=dim if use_dwconv else 1,\n        )  # depthwise conv\n        self.norm = LayerNorm2d(dim, eps=1e-6)\n        self.pwconv1 = nn.Linear(\n            dim, 4 * dim\n        )  # pointwise/1x1 convs, implemented with linear layers\n        self.act = nn.GELU()\n        self.pwconv2 = nn.Linear(4 * dim, dim)\n        self.gamma = (\n            nn.Parameter(layer_scale_init_value * torch.ones((dim)), requires_grad=True)\n            if layer_scale_init_value > 0\n            else None\n        )\n        self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()\n\n    def forward(self, x):\n        input = x\n        x = self.dwconv(x)\n        x = self.norm(x)\n        x = x.permute(0, 2, 3, 1)  # (N, C, H, W) -> (N, H, W, C)\n        x = self.pwconv1(x)\n        x = self.act(x)\n        x = self.pwconv2(x)\n        if self.gamma is not None:\n            x = self.gamma * x\n        x = x.permute(0, 3, 1, 2)  # (N, H, W, C) -> (N, C, H, W)\n\n        x = input + self.drop_path(x)\n        return x\n\n\nclass SimpleFuser(nn.Module):\n    def __init__(self, layer, num_layers, dim=None, input_projection=False):\n        super().__init__()\n        self.proj = nn.Identity()\n        self.layers = get_clones(layer, num_layers)\n\n        if input_projection:\n            assert dim is not None\n            self.proj = nn.Conv2d(dim, dim, kernel_size=1)\n\n    def forward(self, x):\n        # normally x: (N, C, H, W)\n        x = self.proj(x)\n        for layer in self.layers:\n            x = layer(x)\n        return x\n\n\nclass SimpleMaskEncoder(nn.Module):\n    def __init__(\n        self,\n        out_dim,\n        mask_downsampler,\n        fuser,\n        position_encoding,\n        in_dim=256,  # in_dim of pix_feats\n    ):\n        super().__init__()\n\n        self.mask_downsampler = mask_downsampler\n\n        self.pix_feat_proj = nn.Conv2d(in_dim, in_dim, kernel_size=1)\n        self.fuser = fuser\n        self.position_encoding = position_encoding\n        self.out_proj = nn.Identity()\n        if out_dim != in_dim:\n            self.out_proj = nn.Conv2d(in_dim, out_dim, kernel_size=1)\n\n    def forward(\n        self,\n        pix_feat: torch.Tensor,\n        masks: torch.Tensor,\n        skip_mask_sigmoid: bool = False,\n    ) -> Tuple[torch.Tensor, torch.Tensor]:\n        ## Process masks\n        # sigmoid, so that less domain shift from gt masks which are bool\n        if not skip_mask_sigmoid:\n            masks = F.sigmoid(masks)\n        masks = self.mask_downsampler(masks)\n\n        ## Fuse pix_feats and downsampled masks\n        # in case the visual features are on CPU, cast them to CUDA\n        pix_feat = pix_feat.to(masks.device)\n\n        x = self.pix_feat_proj(pix_feat)\n        x = x + masks\n        x = self.fuser(x)\n        x = self.out_proj(x)\n\n        pos = self.position_encoding(x).to(x.dtype)\n\n        return {\"vision_features\": x, \"vision_pos_enc\": [pos]}\n"
  },
  {
    "path": "sam3/model/model_misc.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n\n\"\"\"Various utility models\"\"\"\n\nimport copy\nimport math\nimport weakref\nfrom collections.abc import Iterator\nfrom contextlib import AbstractContextManager\nfrom enum import auto, Enum\nfrom typing import Dict, List, Optional, Union\n\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nfrom torch import nn, Tensor\nfrom typing_extensions import override\n\n\ndef inverse_sigmoid(x, eps=1e-3):\n    \"\"\"\n    The inverse function for sigmoid activation function.\n    Note: It might face numberical issues with fp16 small eps.\n    \"\"\"\n    x = x.clamp(min=0, max=1)\n    x1 = x.clamp(min=eps)\n    x2 = (1 - x).clamp(min=eps)\n    return torch.log(x1 / x2)\n\n\nclass MultiheadAttentionWrapper(nn.MultiheadAttention):\n    def forward(self, *args, **kwargs):\n        kwargs[\"need_weights\"] = False\n        return super().forward(*args, **kwargs)\n\n\nclass DotProductScoring(torch.nn.Module):\n    def __init__(\n        self,\n        d_model,\n        d_proj,\n        prompt_mlp=None,\n        clamp_logits=True,\n        clamp_max_val=12.0,\n    ):\n        super().__init__()\n        self.d_proj = d_proj\n        assert isinstance(prompt_mlp, torch.nn.Module) or prompt_mlp is None\n        self.prompt_mlp = prompt_mlp  # an optional MLP projection for prompt\n        self.prompt_proj = torch.nn.Linear(d_model, d_proj)\n        self.hs_proj = torch.nn.Linear(d_model, d_proj)\n        self.scale = float(1.0 / np.sqrt(d_proj))\n        self.clamp_logits = clamp_logits\n        if self.clamp_logits:\n            self.clamp_max_val = clamp_max_val\n\n    def mean_pool_text(self, prompt, prompt_mask):\n        # is_valid has shape (seq, bs, 1), where 1 is valid and 0 is padding\n        is_valid = (~prompt_mask).float().permute(1, 0)[..., None]\n        # num_valid has shape (bs, 1)\n        num_valid = torch.clamp(torch.sum(is_valid, dim=0), min=1.0)\n        # mean pool over all the valid tokens -- pooled_prompt has shape (bs, proj_dim)\n        pooled_prompt = (prompt * is_valid).sum(dim=0) / num_valid\n        return pooled_prompt\n\n    def forward(self, hs, prompt, prompt_mask):\n        # hs has shape (num_layer, bs, num_query, d_model)\n        # prompt has shape (seq, bs, d_model)\n        # prompt_mask has shape (bs, seq), where 1 is valid and 0 is padding\n        assert hs.dim() == 4 and prompt.dim() == 3 and prompt_mask.dim() == 2\n\n        # apply MLP on prompt if specified\n        if self.prompt_mlp is not None:\n            prompt = self.prompt_mlp(prompt)\n\n        # first, get the mean-pooled version of the prompt\n        pooled_prompt = self.mean_pool_text(prompt, prompt_mask)\n\n        # then, project pooled_prompt and hs to d_proj dimensions\n        proj_pooled_prompt = self.prompt_proj(pooled_prompt)  # (bs, d_proj)\n        proj_hs = self.hs_proj(hs)  # (num_layer, bs, num_query, d_proj)\n\n        # finally, get dot-product scores of shape (num_layer, bs, num_query, 1)\n        scores = torch.matmul(proj_hs, proj_pooled_prompt.unsqueeze(-1))\n        scores *= self.scale\n\n        # clamp scores to a max value to avoid numerical issues in loss or matcher\n        if self.clamp_logits:\n            scores.clamp_(min=-self.clamp_max_val, max=self.clamp_max_val)\n\n        return scores\n\n\nclass LayerScale(nn.Module):\n    def __init__(\n        self,\n        dim: int,\n        init_values: Union[float, Tensor] = 1e-5,\n        inplace: bool = False,\n    ) -> None:\n        super().__init__()\n        self.inplace = inplace\n        self.gamma = nn.Parameter(init_values * torch.ones(dim))\n\n    def forward(self, x: Tensor) -> Tensor:\n        return x.mul_(self.gamma) if self.inplace else x * self.gamma\n\n\nclass LayerNorm2d(nn.Module):\n    def __init__(self, num_channels: int, eps: float = 1e-6) -> None:\n        super().__init__()\n        self.weight = nn.Parameter(torch.ones(num_channels))\n        self.bias = nn.Parameter(torch.zeros(num_channels))\n        self.eps = eps\n\n    def forward(self, x: torch.Tensor) -> torch.Tensor:\n        u = x.mean(1, keepdim=True)\n        s = (x - u).pow(2).mean(1, keepdim=True)\n        x = (x - u) / torch.sqrt(s + self.eps)\n        x = self.weight[:, None, None] * x + self.bias[:, None, None]\n        return x\n\n\nclass TransformerWrapper(nn.Module):\n    def __init__(\n        self,\n        encoder,\n        decoder,\n        d_model: int,\n        two_stage_type=\"none\",  # [\"none\"] only for now\n        pos_enc_at_input_dec=True,\n    ):\n        super().__init__()\n\n        self.encoder = encoder\n        self.decoder = decoder\n        self.num_queries = decoder.num_queries if decoder is not None else None\n        self.pos_enc_at_input_dec = pos_enc_at_input_dec\n\n        # for two stage\n        assert two_stage_type in [\"none\"], \"unknown param {} of two_stage_type\".format(\n            two_stage_type\n        )\n        self.two_stage_type = two_stage_type\n\n        self._reset_parameters()\n        self.d_model = d_model\n\n    def _reset_parameters(self):\n        for n, p in self.named_parameters():\n            if p.dim() > 1:\n                if (\n                    \"box_embed\" not in n\n                    and \"query_embed\" not in n\n                    and \"reference_points\" not in n\n                ):\n                    nn.init.xavier_uniform_(p)\n\n\nclass MLP(nn.Module):\n    \"\"\"Very simple multi-layer perceptron (also called FFN)\"\"\"\n\n    def __init__(\n        self,\n        input_dim: int,\n        hidden_dim: int,\n        output_dim: int,\n        num_layers: int,\n        dropout: float = 0.0,\n        residual: bool = False,\n        out_norm: Optional[nn.Module] = None,\n    ):\n        super().__init__()\n        self.num_layers = num_layers\n        h = [hidden_dim] * (num_layers - 1)\n        self.layers = nn.ModuleList(\n            nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim])\n        )\n        self.drop = nn.Dropout(dropout) if dropout > 0 else nn.Identity()\n        # whether to add the output as a residual connection to the input\n        if residual and input_dim != output_dim:\n            raise ValueError(\"residual is only supported if input_dim == output_dim\")\n        self.residual = residual\n        # whether to apply a normalization layer to the output\n        assert isinstance(out_norm, nn.Module) or out_norm is None\n        self.out_norm = out_norm or nn.Identity()\n\n    def forward(self, x):\n        orig_x = x\n        for i, layer in enumerate(self.layers):\n            x = self.drop(F.relu(layer(x))) if i < self.num_layers - 1 else layer(x)\n        if self.residual:\n            x = x + orig_x\n        x = self.out_norm(x)\n        return x\n\n\ndef get_clones(module, N):\n    return nn.ModuleList([copy.deepcopy(module) for i in range(N)])\n\n\ndef get_clones_seq(module, N):\n    return nn.Sequential(*[copy.deepcopy(module) for i in range(N)])\n\n\ndef get_activation_fn(activation):\n    \"\"\"Return an activation function given a string\"\"\"\n    if activation == \"relu\":\n        return F.relu\n    if activation == \"gelu\":\n        return F.gelu\n    if activation == \"glu\":\n        return F.glu\n    raise RuntimeError(f\"activation should be relu/gelu, not {activation}.\")\n\n\ndef get_activation_module(activation):\n    \"\"\"Return an activation function given a string\"\"\"\n    if activation == \"relu\":\n        return nn.ReLU\n    if activation == \"gelu\":\n        return nn.GELU\n    if activation == \"glu\":\n        return nn.GLU\n    raise RuntimeError(f\"activation should be relu/gelu, not {activation}.\")\n\n\ndef get_valid_ratio(mask):\n    _, H, W = mask.shape\n    valid_H = torch.sum(~mask[:, :, 0], 1)\n    valid_W = torch.sum(~mask[:, 0, :], 1)\n    valid_ratio_h = valid_H.float() / H\n    valid_ratio_w = valid_W.float() / W\n    valid_ratio = torch.stack([valid_ratio_w, valid_ratio_h], -1)\n    return valid_ratio\n\n\ndef gen_sineembed_for_position(pos_tensor, num_feats=256):\n    assert num_feats % 2 == 0\n    num_feats = num_feats // 2\n    # n_query, bs, _ = pos_tensor.size()\n    # sineembed_tensor = torch.zeros(n_query, bs, 256)\n    scale = 2 * math.pi\n    dim_t = torch.arange(num_feats, dtype=torch.float32, device=pos_tensor.device)\n    dim_t = 10000 ** (2 * (torch.div(dim_t, 2, rounding_mode=\"floor\")) / num_feats)\n    x_embed = pos_tensor[:, :, 0] * scale\n    y_embed = pos_tensor[:, :, 1] * scale\n    pos_x = x_embed[:, :, None] / dim_t\n    pos_y = y_embed[:, :, None] / dim_t\n    pos_x = torch.stack(\n        (pos_x[:, :, 0::2].sin(), pos_x[:, :, 1::2].cos()), dim=3\n    ).flatten(2)\n    pos_y = torch.stack(\n        (pos_y[:, :, 0::2].sin(), pos_y[:, :, 1::2].cos()), dim=3\n    ).flatten(2)\n    if pos_tensor.size(-1) == 2:\n        pos = torch.cat((pos_y, pos_x), dim=2)\n    elif pos_tensor.size(-1) == 4:\n        w_embed = pos_tensor[:, :, 2] * scale\n        pos_w = w_embed[:, :, None] / dim_t\n        pos_w = torch.stack(\n            (pos_w[:, :, 0::2].sin(), pos_w[:, :, 1::2].cos()), dim=3\n        ).flatten(2)\n\n        h_embed = pos_tensor[:, :, 3] * scale\n        pos_h = h_embed[:, :, None] / dim_t\n        pos_h = torch.stack(\n            (pos_h[:, :, 0::2].sin(), pos_h[:, :, 1::2].cos()), dim=3\n        ).flatten(2)\n\n        pos = torch.cat((pos_y, pos_x, pos_w, pos_h), dim=2)\n    else:\n        raise ValueError(\"Unknown pos_tensor shape(-1):{}\".format(pos_tensor.size(-1)))\n    return pos\n\n\nclass SAM3Output(list):\n    \"\"\"\n    A class representing the output of a SAM3 model.\n    It provides an iterable interface that supports different iteration modes, including iterating over all steps per stage,\n    last step per stage, and flattened output.\n    Attributes:\n        output: The output of the SAM3 model, represented as a list of lists.\n        iter_mode: The current iteration mode.\n    Example:\n        >>> output = [[1, 2], [3, 4], [5, 6]]\n        >>> sam3_output = SAM3Output(output)\n        >>> for step in sam3_output:\n        ...     print(step)\n        [1, 2]\n        [3, 4]\n        [5, 6]\n        >>> with SAM3Output.iteration_mode(SAM3Output.IterMode.LAST_STEP_PER_STAGE) as sam3_last_step_out:\n        ...     for step in sam3_last_step_out:\n        ...         print(step)\n        [2]\n        [4]\n        [6]\n        >>> with SAM3Output.iteration_mode(SAM3Output.IterMode.FLATTENED) as sam3_flattened_out:\n        ...     for step in sam3_flattened_out:\n        ...         print(step)\n        1\n        2\n        3\n        4\n        5\n        6\n    \"\"\"\n\n    class IterMode(Enum):\n        # Defines the type of iterator over ouptuts.\n        ALL_STEPS_PER_STAGE = auto()\n        LAST_STEP_PER_STAGE = auto()\n        FLATTENED = auto()  # Returns each interactivity step as if it is a separate stage (this is used in SAM3Image model)\n\n    def __init__(\n        self,\n        output: List[List[Dict]] = None,\n        iter_mode: IterMode = IterMode.ALL_STEPS_PER_STAGE,\n        loss_stages: Optional[List[int]] = None,\n    ):\n        if output is not None:\n            assert (\n                isinstance(output, list)\n                and len(output) > 0\n                and isinstance(output[0], list)\n            ), \"Expected output to be a list of lists\"\n            self.output = output\n        else:\n            self.output = []\n        assert isinstance(iter_mode, SAM3Output.IterMode), (\n            f\"iter_mode shoulf be of enum type 'SAM3Output.IterMode'. Got {type(iter_mode)}\"\n        )\n\n        self.iter_mode = iter_mode\n        # We create a weak reference to self to be used in the lambda functions.\n        # This is to avoid cyclic references and let SAM3Output be garabge collected.\n        self_ref = weakref.ref(self)\n        self._mode2iter = {\n            SAM3Output.IterMode.ALL_STEPS_PER_STAGE: lambda: iter(self_ref().output),\n            SAM3Output.IterMode.LAST_STEP_PER_STAGE: lambda: (\n                inner_list[-1] for inner_list in self_ref().output\n            ),\n            SAM3Output.IterMode.FLATTENED: lambda: (\n                element for inner_list in self_ref().output for element in inner_list\n            ),\n        }\n        self.loss_stages = loss_stages\n\n    @override\n    def __iter__(self) -> Iterator:\n        return self._mode2iter[self.iter_mode]()\n\n    def __getitem__(self, index):\n        \"\"\"\n        Returns the item at the specified index.\n        Args:\n            index (int): The index of the item to return.\n        Returns:\n            list or element: The item at the specified index.\n        \"\"\"\n        assert isinstance(index, int), f\"index should be an integer. Got {type(index)}\"\n        if self.iter_mode == SAM3Output.IterMode.ALL_STEPS_PER_STAGE:\n            return self.output[index]\n        elif self.iter_mode == SAM3Output.IterMode.LAST_STEP_PER_STAGE:\n            return self.output[index][-1]\n        elif self.iter_mode == SAM3Output.IterMode.FLATTENED:\n            if index == -1:\n                return self.self.output[-1][-1]\n            else:\n                flattened_output = sum(self.output, [])\n                return flattened_output[index]\n\n    class _IterationMode(AbstractContextManager):\n        \"\"\"\n        A context manager that temporarily changes the iteration mode of a SAM3Output object.\n        This class is used internally by the SAM3Output.iteration_mode method.\n        \"\"\"\n\n        def __init__(\n            self, model_output: \"SAM3Output\", iter_mode: \"SAM3Output.IterMode\"\n        ):\n            self._model_output = model_output\n            self._orig_iter_mode = model_output.iter_mode\n            self._new_iter_mode = iter_mode\n\n        @override\n        def __enter__(self) -> \"SAM3Output\":\n            self._model_output.iter_mode = self._new_iter_mode\n            return self._model_output\n\n        @override\n        def __exit__(self, exc_type, exc_value, traceback):\n            self._model_output.iter_mode = self._orig_iter_mode\n            return super().__exit__(exc_type, exc_value, traceback)\n\n    @staticmethod\n    def iteration_mode(\n        model_output: \"SAM3Output\", iter_mode: IterMode\n    ) -> _IterationMode:\n        \"\"\"\n        Returns a context manager that allows you to temporarily change the iteration mode of the SAM3Output object.\n        Args:\n            model_output: The SAM3Output object.\n            iter_mode: The new iteration mode.\n        Returns:\n            SAM3Output._IterationMode: A context manager that changes the iteration mode of the SAM3Output object.\n        \"\"\"\n        return SAM3Output._IterationMode(model_output=model_output, iter_mode=iter_mode)\n\n    def append(self, item: list):\n        assert isinstance(item, list), (\n            f\"Only list items are supported. Got {type(item)}\"\n        )\n        self.output.append(item)\n\n    def __repr__(self):\n        return self.output.__repr__()\n\n    def __len__(self):\n        if self.iter_mode in [\n            SAM3Output.IterMode.ALL_STEPS_PER_STAGE,\n            SAM3Output.IterMode.LAST_STEP_PER_STAGE,\n        ]:\n            return len(self.output)\n        elif self.iter_mode == SAM3Output.IterMode.FLATTENED:\n            flattened_output = sum(self.output, [])\n            return len(flattened_output)\n"
  },
  {
    "path": "sam3/model/necks.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n\n\"\"\"Necks are the interface between a vision backbone and the rest of the detection model\"\"\"\n\nfrom copy import deepcopy\nfrom typing import List, Optional, Tuple\n\nimport torch\nimport torch.nn as nn\n\n\nclass Sam3DualViTDetNeck(nn.Module):\n    def __init__(\n        self,\n        trunk: nn.Module,\n        position_encoding: nn.Module,\n        d_model: int,\n        scale_factors=(4.0, 2.0, 1.0, 0.5),\n        add_sam2_neck: bool = False,\n    ):\n        \"\"\"\n        SimpleFPN neck a la ViTDet\n        (From detectron2, very lightly adapted)\n        It supports a \"dual neck\" setting, where we have two identical necks (for SAM3 and SAM2), with different weights\n\n        :param trunk: the backbone\n        :param position_encoding: the positional encoding to use\n        :param d_model: the dimension of the model\n        \"\"\"\n        super().__init__()\n        self.trunk = trunk\n        self.position_encoding = position_encoding\n        self.convs = nn.ModuleList()\n\n        self.scale_factors = scale_factors\n        use_bias = True\n        dim: int = self.trunk.channel_list[-1]\n\n        for _, scale in enumerate(scale_factors):\n            current = nn.Sequential()\n\n            if scale == 4.0:\n                current.add_module(\n                    \"dconv_2x2_0\",\n                    nn.ConvTranspose2d(dim, dim // 2, kernel_size=2, stride=2),\n                )\n                current.add_module(\n                    \"gelu\",\n                    nn.GELU(),\n                )\n                current.add_module(\n                    \"dconv_2x2_1\",\n                    nn.ConvTranspose2d(dim // 2, dim // 4, kernel_size=2, stride=2),\n                )\n                out_dim = dim // 4\n            elif scale == 2.0:\n                current.add_module(\n                    \"dconv_2x2\",\n                    nn.ConvTranspose2d(dim, dim // 2, kernel_size=2, stride=2),\n                )\n                out_dim = dim // 2\n            elif scale == 1.0:\n                out_dim = dim\n            elif scale == 0.5:\n                current.add_module(\n                    \"maxpool_2x2\",\n                    nn.MaxPool2d(kernel_size=2, stride=2),\n                )\n                out_dim = dim\n            else:\n                raise NotImplementedError(f\"scale_factor={scale} is not supported yet.\")\n\n            current.add_module(\n                \"conv_1x1\",\n                nn.Conv2d(\n                    in_channels=out_dim,\n                    out_channels=d_model,\n                    kernel_size=1,\n                    bias=use_bias,\n                ),\n            )\n            current.add_module(\n                \"conv_3x3\",\n                nn.Conv2d(\n                    in_channels=d_model,\n                    out_channels=d_model,\n                    kernel_size=3,\n                    padding=1,\n                    bias=use_bias,\n                ),\n            )\n            self.convs.append(current)\n\n        self.sam2_convs = None\n        if add_sam2_neck:\n            # Assumes sam2 neck is just a clone of the original neck\n            self.sam2_convs = deepcopy(self.convs)\n\n    def forward(\n        self, tensor_list: List[torch.Tensor]\n    ) -> Tuple[\n        List[torch.Tensor],\n        List[torch.Tensor],\n        Optional[List[torch.Tensor]],\n        Optional[List[torch.Tensor]],\n    ]:\n        xs = self.trunk(tensor_list)\n        sam3_out, sam3_pos = [], []\n        sam2_out, sam2_pos = None, None\n        if self.sam2_convs is not None:\n            sam2_out, sam2_pos = [], []\n        x = xs[-1]  # simpleFPN\n        for i in range(len(self.convs)):\n            sam3_x_out = self.convs[i](x)\n            sam3_pos_out = self.position_encoding(sam3_x_out).to(sam3_x_out.dtype)\n            sam3_out.append(sam3_x_out)\n            sam3_pos.append(sam3_pos_out)\n\n            if self.sam2_convs is not None:\n                sam2_x_out = self.sam2_convs[i](x)\n                sam2_pos_out = self.position_encoding(sam2_x_out).to(sam2_x_out.dtype)\n                sam2_out.append(sam2_x_out)\n                sam2_pos.append(sam2_pos_out)\n        return sam3_out, sam3_pos, sam2_out, sam2_pos\n"
  },
  {
    "path": "sam3/model/position_encoding.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n\nimport math\nfrom typing import Optional\n\nimport torch\nfrom torch import nn\n\n\nclass PositionEmbeddingSine(nn.Module):\n    \"\"\"\n    This is a more standard version of the position embedding, very similar to the one\n    used by the Attention is all you need paper, generalized to work on images.\n    \"\"\"\n\n    def __init__(\n        self,\n        num_pos_feats,\n        temperature: int = 10000,\n        normalize: bool = True,\n        scale: Optional[float] = None,\n        precompute_resolution: Optional[int] = None,\n    ):\n        super().__init__()\n        assert num_pos_feats % 2 == 0, \"Expecting even model width\"\n        self.num_pos_feats = num_pos_feats // 2\n        self.temperature = temperature\n        self.normalize = normalize\n        if scale is not None and normalize is False:\n            raise ValueError(\"normalize should be True if scale is passed\")\n        if scale is None:\n            scale = 2 * math.pi\n        self.scale = scale\n\n        self.cache = {}\n        # Precompute positional encodings under `precompute_resolution` to fill the cache\n        # and avoid symbolic shape tracing errors in torch.compile in PyTorch 2.4 nightly.\n        if precompute_resolution is not None:\n            # We precompute pos enc for stride 4, 8, 16 and 32 to fill `self.cache`.\n            precompute_sizes = [\n                (precompute_resolution // 4, precompute_resolution // 4),\n                (precompute_resolution // 8, precompute_resolution // 8),\n                (precompute_resolution // 16, precompute_resolution // 16),\n                (precompute_resolution // 32, precompute_resolution // 32),\n            ]\n            for size in precompute_sizes:\n                tensors = torch.zeros((1, 1) + size, device=\"cuda\")\n                self.forward(tensors)\n                # further clone and detach it in the cache (just to be safe)\n                self.cache[size] = self.cache[size].clone().detach()\n\n    def _encode_xy(self, x, y):\n        # The positions are expected to be normalized\n        assert len(x) == len(y) and x.ndim == y.ndim == 1\n        x_embed = x * self.scale\n        y_embed = y * self.scale\n\n        dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)\n        dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats)\n\n        pos_x = x_embed[:, None] / dim_t\n        pos_y = y_embed[:, None] / dim_t\n        pos_x = torch.stack(\n            (pos_x[:, 0::2].sin(), pos_x[:, 1::2].cos()), dim=2\n        ).flatten(1)\n        pos_y = torch.stack(\n            (pos_y[:, 0::2].sin(), pos_y[:, 1::2].cos()), dim=2\n        ).flatten(1)\n        return pos_x, pos_y\n\n    @torch.no_grad()\n    def encode_boxes(self, x, y, w, h):\n        pos_x, pos_y = self._encode_xy(x, y)\n        pos = torch.cat((pos_y, pos_x, h[:, None], w[:, None]), dim=1)\n        return pos\n\n    encode = encode_boxes  # Backwards compatibility\n\n    @torch.no_grad()\n    def encode_points(self, x, y, labels):\n        (bx, nx), (by, ny), (bl, nl) = x.shape, y.shape, labels.shape\n        assert bx == by and nx == ny and bx == bl and nx == nl\n        pos_x, pos_y = self._encode_xy(x.flatten(), y.flatten())\n        pos_x, pos_y = pos_x.reshape(bx, nx, -1), pos_y.reshape(by, ny, -1)\n        pos = torch.cat((pos_y, pos_x, labels[:, :, None]), dim=2)\n        return pos\n\n    @torch.no_grad()\n    def forward(self, x):\n        cache_key = None\n        cache_key = (x.shape[-2], x.shape[-1])\n        if cache_key in self.cache:\n            return self.cache[cache_key][None].repeat(x.shape[0], 1, 1, 1)\n        y_embed = (\n            torch.arange(1, x.shape[-2] + 1, dtype=torch.float32, device=x.device)\n            .view(1, -1, 1)\n            .repeat(x.shape[0], 1, x.shape[-1])\n        )\n        x_embed = (\n            torch.arange(1, x.shape[-1] + 1, dtype=torch.float32, device=x.device)\n            .view(1, 1, -1)\n            .repeat(x.shape[0], x.shape[-2], 1)\n        )\n\n        if self.normalize:\n            eps = 1e-6\n            y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale\n            x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale\n\n        dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)\n        dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats)\n\n        pos_x = x_embed[:, :, :, None] / dim_t\n        pos_y = y_embed[:, :, :, None] / dim_t\n        pos_x = torch.stack(\n            (pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4\n        ).flatten(3)\n        pos_y = torch.stack(\n            (pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4\n        ).flatten(3)\n        pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)\n        if cache_key is not None:\n            self.cache[cache_key] = pos[0]\n        return pos\n"
  },
  {
    "path": "sam3/model/sam1_task_predictor.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n# All rights reserved.\n\n# pyre-unsafe\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport logging\nfrom typing import List, Optional, Tuple, Union\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\nfrom PIL.Image import Image\nfrom sam3.model.sam3_tracker_base import Sam3TrackerBase\nfrom sam3.model.utils.sam1_utils import SAM2Transforms\n\n\n# Adapted from https://github.com/facebookresearch/sam2/blob/main/sam2/sam2_image_predictor.py\nclass SAM3InteractiveImagePredictor(nn.Module):\n    def __init__(\n        self,\n        sam_model: Sam3TrackerBase,\n        mask_threshold=0.0,\n        max_hole_area=256.0,\n        max_sprinkle_area=0.0,\n        **kwargs,\n    ) -> None:\n        \"\"\"\n        Uses SAM-3 to calculate the image embedding for an image, and then\n        allow repeated, efficient mask prediction given prompts.\n\n        Arguments:\n          sam_model : The model to use for mask prediction.\n          mask_threshold (float): The threshold to use when converting mask logits\n            to binary masks. Masks are thresholded at 0 by default.\n          max_hole_area (int): If max_hole_area > 0, we fill small holes in up to\n            the maximum area of max_hole_area in low_res_masks.\n          max_sprinkle_area (int): If max_sprinkle_area > 0, we remove small sprinkles up to\n            the maximum area of max_sprinkle_area in low_res_masks.\n        \"\"\"\n        super().__init__()\n        self.model = sam_model\n        self._transforms = SAM2Transforms(\n            resolution=self.model.image_size,\n            mask_threshold=mask_threshold,\n            max_hole_area=max_hole_area,\n            max_sprinkle_area=max_sprinkle_area,\n        )\n\n        # Predictor state\n        self._is_image_set = False\n        self._features = None\n        self._orig_hw = None\n        # Whether the predictor is set for single image or a batch of images\n        self._is_batch = False\n\n        # Predictor config\n        self.mask_threshold = mask_threshold\n\n        # Spatial dim for backbone feature maps\n        self._bb_feat_sizes = [\n            (288, 288),\n            (144, 144),\n            (72, 72),\n        ]\n\n    @torch.no_grad()\n    def set_image(\n        self,\n        image: Union[np.ndarray, Image],\n    ) -> None:\n        \"\"\"\n        Calculates the image embeddings for the provided image, allowing\n        masks to be predicted with the 'predict' method.\n\n        Arguments:\n          image (np.ndarray or PIL Image): The input image to embed in RGB format. The image should be in HWC format if np.ndarray, or WHC format if PIL Image\n          with pixel values in [0, 255].\n          image_format (str): The color format of the image, in ['RGB', 'BGR'].\n        \"\"\"\n        self.reset_predictor()\n        # Transform the image to the form expected by the model\n        if isinstance(image, np.ndarray):\n            logging.info(\"For numpy array image, we assume (HxWxC) format\")\n            self._orig_hw = [image.shape[:2]]\n        elif isinstance(image, Image):\n            w, h = image.size\n            self._orig_hw = [(h, w)]\n        else:\n            raise NotImplementedError(\"Image format not supported\")\n\n        input_image = self._transforms(image)\n        input_image = input_image[None, ...].to(self.device)\n\n        assert len(input_image.shape) == 4 and input_image.shape[1] == 3, (\n            f\"input_image must be of size 1x3xHxW, got {input_image.shape}\"\n        )\n        logging.info(\"Computing image embeddings for the provided image...\")\n        backbone_out = self.model.forward_image(input_image)\n        (\n            _,\n            vision_feats,\n            _,\n            _,\n        ) = self.model._prepare_backbone_features(backbone_out)\n        # Add no_mem_embed, which is added to the lowest rest feat. map during training on videos\n        vision_feats[-1] = vision_feats[-1] + self.model.no_mem_embed\n\n        feats = [\n            feat.permute(1, 2, 0).view(1, -1, *feat_size)\n            for feat, feat_size in zip(vision_feats[::-1], self._bb_feat_sizes[::-1])\n        ][::-1]\n        self._features = {\"image_embed\": feats[-1], \"high_res_feats\": feats[:-1]}\n        self._is_image_set = True\n        logging.info(\"Image embeddings computed.\")\n\n    @torch.no_grad()\n    def set_image_batch(\n        self,\n        image_list: List[Union[np.ndarray]],\n    ) -> None:\n        \"\"\"\n        Calculates the image embeddings for the provided image batch, allowing\n        masks to be predicted with the 'predict_batch' method.\n\n        Arguments:\n          image_list (List[np.ndarray]): The input images to embed in RGB format. The image should be in HWC format if np.ndarray\n          with pixel values in [0, 255].\n        \"\"\"\n        self.reset_predictor()\n        assert isinstance(image_list, list)\n        self._orig_hw = []\n        for image in image_list:\n            assert isinstance(image, np.ndarray), (\n                \"Images are expected to be an np.ndarray in RGB format, and of shape  HWC\"\n            )\n            self._orig_hw.append(image.shape[:2])\n        # Transform the image to the form expected by the model\n        img_batch = self._transforms.forward_batch(image_list)\n        img_batch = img_batch.to(self.device)\n        batch_size = img_batch.shape[0]\n        assert len(img_batch.shape) == 4 and img_batch.shape[1] == 3, (\n            f\"img_batch must be of size Bx3xHxW, got {img_batch.shape}\"\n        )\n        logging.info(\"Computing image embeddings for the provided images...\")\n        backbone_out = self.model.forward_image(img_batch)\n        (\n            _,\n            vision_feats,\n            _,\n            _,\n        ) = self.model._prepare_backbone_features(backbone_out)\n        # Add no_mem_embed, which is added to the lowest rest feat. map during training on videos\n        vision_feats[-1] = vision_feats[-1] + self.model.no_mem_embed\n\n        feats = [\n            feat.permute(1, 2, 0).view(batch_size, -1, *feat_size)\n            for feat, feat_size in zip(vision_feats[::-1], self._bb_feat_sizes[::-1])\n        ][::-1]\n        self._features = {\"image_embed\": feats[-1], \"high_res_feats\": feats[:-1]}\n        self._is_image_set = True\n        self._is_batch = True\n        logging.info(\"Image embeddings computed.\")\n\n    def predict_batch(\n        self,\n        point_coords_batch: List[np.ndarray] = None,\n        point_labels_batch: List[np.ndarray] = None,\n        box_batch: List[np.ndarray] = None,\n        mask_input_batch: List[np.ndarray] = None,\n        multimask_output: bool = True,\n        return_logits: bool = False,\n        normalize_coords=True,\n    ) -> Tuple[List[np.ndarray], List[np.ndarray], List[np.ndarray]]:\n        \"\"\"This function is very similar to predict(...), however it is used for batched mode, when the model is expected to generate predictions on multiple images.\n        It returns a tuple of lists of masks, ious, and low_res_masks_logits.\n        \"\"\"\n        assert self._is_batch, \"This function should only be used when in batched mode\"\n        if not self._is_image_set:\n            raise RuntimeError(\n                \"An image must be set with .set_image_batch(...) before mask prediction.\"\n            )\n        num_images = len(self._features[\"image_embed\"])\n        all_masks = []\n        all_ious = []\n        all_low_res_masks = []\n        for img_idx in range(num_images):\n            # Transform input prompts\n            point_coords = (\n                point_coords_batch[img_idx] if point_coords_batch is not None else None\n            )\n            point_labels = (\n                point_labels_batch[img_idx] if point_labels_batch is not None else None\n            )\n            box = box_batch[img_idx] if box_batch is not None else None\n            mask_input = (\n                mask_input_batch[img_idx] if mask_input_batch is not None else None\n            )\n            mask_input, unnorm_coords, labels, unnorm_box = self._prep_prompts(\n                point_coords,\n                point_labels,\n                box,\n                mask_input,\n                normalize_coords,\n                img_idx=img_idx,\n            )\n            masks, iou_predictions, low_res_masks = self._predict(\n                unnorm_coords,\n                labels,\n                unnorm_box,\n                mask_input,\n                multimask_output,\n                return_logits=return_logits,\n                img_idx=img_idx,\n            )\n            masks_np = masks.squeeze(0).float().detach().cpu().numpy()\n            iou_predictions_np = (\n                iou_predictions.squeeze(0).float().detach().cpu().numpy()\n            )\n            low_res_masks_np = low_res_masks.squeeze(0).float().detach().cpu().numpy()\n            all_masks.append(masks_np)\n            all_ious.append(iou_predictions_np)\n            all_low_res_masks.append(low_res_masks_np)\n\n        return all_masks, all_ious, all_low_res_masks\n\n    def predict(\n        self,\n        point_coords: Optional[np.ndarray] = None,\n        point_labels: Optional[np.ndarray] = None,\n        box: Optional[np.ndarray] = None,\n        mask_input: Optional[np.ndarray] = None,\n        multimask_output: bool = True,\n        return_logits: bool = False,\n        normalize_coords=True,\n    ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:\n        \"\"\"\n        Predict masks for the given input prompts, using the currently set image.\n\n        Arguments:\n          point_coords (np.ndarray or None): A Nx2 array of point prompts to the\n            model. Each point is in (X,Y) in pixels.\n          point_labels (np.ndarray or None): A length N array of labels for the\n            point prompts. 1 indicates a foreground point and 0 indicates a\n            background point.\n          box (np.ndarray or None): A length 4 array given a box prompt to the\n            model, in XYXY format.\n          mask_input (np.ndarray): A low resolution mask input to the model, typically\n            coming from a previous prediction iteration. Has form 1xHxW, where\n            for SAM, H=W=256.\n          multimask_output (bool): If true, the model will return three masks.\n            For ambiguous input prompts (such as a single click), this will often\n            produce better masks than a single prediction. If only a single\n            mask is needed, the model's predicted quality score can be used\n            to select the best mask. For non-ambiguous prompts, such as multiple\n            input prompts, multimask_output=False can give better results.\n          return_logits (bool): If true, returns un-thresholded masks logits\n            instead of a binary mask.\n          normalize_coords (bool): If true, the point coordinates will be normalized to the range [0,1] and point_coords is expected to be wrt. image dimensions.\n\n        Returns:\n          (np.ndarray): The output masks in CxHxW format, where C is the\n            number of masks, and (H, W) is the original image size.\n          (np.ndarray): An array of length C containing the model's\n            predictions for the quality of each mask.\n          (np.ndarray): An array of shape CxHxW, where C is the number\n            of masks and H=W=256. These low resolution logits can be passed to\n            a subsequent iteration as mask input.\n        \"\"\"\n        if not self._is_image_set:\n            raise RuntimeError(\n                \"An image must be set with .set_image(...) before mask prediction.\"\n            )\n\n        # Transform input prompts\n\n        mask_input, unnorm_coords, labels, unnorm_box = self._prep_prompts(\n            point_coords, point_labels, box, mask_input, normalize_coords\n        )\n\n        masks, iou_predictions, low_res_masks = self._predict(\n            unnorm_coords,\n            labels,\n            unnorm_box,\n            mask_input,\n            multimask_output,\n            return_logits=return_logits,\n        )\n\n        masks_np = masks.squeeze(0).float().detach().cpu().numpy()\n        iou_predictions_np = iou_predictions.squeeze(0).float().detach().cpu().numpy()\n        low_res_masks_np = low_res_masks.squeeze(0).float().detach().cpu().numpy()\n        return masks_np, iou_predictions_np, low_res_masks_np\n\n    def _prep_prompts(\n        self, point_coords, point_labels, box, mask_logits, normalize_coords, img_idx=-1\n    ):\n        unnorm_coords, labels, unnorm_box, mask_input = None, None, None, None\n        if point_coords is not None:\n            assert point_labels is not None, (\n                \"point_labels must be supplied if point_coords is supplied.\"\n            )\n            point_coords = torch.as_tensor(\n                point_coords, dtype=torch.float, device=self.device\n            )\n            unnorm_coords = self._transforms.transform_coords(\n                point_coords, normalize=normalize_coords, orig_hw=self._orig_hw[img_idx]\n            )\n            labels = torch.as_tensor(point_labels, dtype=torch.int, device=self.device)\n            if len(unnorm_coords.shape) == 2:\n                unnorm_coords, labels = unnorm_coords[None, ...], labels[None, ...]\n        if box is not None:\n            box = torch.as_tensor(box, dtype=torch.float, device=self.device)\n            unnorm_box = self._transforms.transform_boxes(\n                box, normalize=normalize_coords, orig_hw=self._orig_hw[img_idx]\n            )  # Bx2x2\n        if mask_logits is not None:\n            mask_input = torch.as_tensor(\n                mask_logits, dtype=torch.float, device=self.device\n            )\n            if len(mask_input.shape) == 3:\n                mask_input = mask_input[None, :, :, :]\n        return mask_input, unnorm_coords, labels, unnorm_box\n\n    @torch.no_grad()\n    def _predict(\n        self,\n        point_coords: Optional[torch.Tensor],\n        point_labels: Optional[torch.Tensor],\n        boxes: Optional[torch.Tensor] = None,\n        mask_input: Optional[torch.Tensor] = None,\n        multimask_output: bool = True,\n        return_logits: bool = False,\n        img_idx: int = -1,\n    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:\n        \"\"\"\n        Predict masks for the given input prompts, using the currently set image.\n        Input prompts are batched torch tensors and are expected to already be\n        transformed to the input frame using SAM2Transforms.\n\n        Arguments:\n          point_coords (torch.Tensor or None): A BxNx2 array of point prompts to the\n            model. Each point is in (X,Y) in pixels.\n          point_labels (torch.Tensor or None): A BxN array of labels for the\n            point prompts. 1 indicates a foreground point and 0 indicates a\n            background point.\n          boxes (np.ndarray or None): A Bx4 array given a box prompt to the\n            model, in XYXY format.\n          mask_input (np.ndarray): A low resolution mask input to the model, typically\n            coming from a previous prediction iteration. Has form Bx1xHxW, where\n            for SAM, H=W=256. Masks returned by a previous iteration of the\n            predict method do not need further transformation.\n          multimask_output (bool): If true, the model will return three masks.\n            For ambiguous input prompts (such as a single click), this will often\n            produce better masks than a single prediction. If only a single\n            mask is needed, the model's predicted quality score can be used\n            to select the best mask. For non-ambiguous prompts, such as multiple\n            input prompts, multimask_output=False can give better results.\n          return_logits (bool): If true, returns un-thresholded masks logits\n            instead of a binary mask.\n\n        Returns:\n          (torch.Tensor): The output masks in BxCxHxW format, where C is the\n            number of masks, and (H, W) is the original image size.\n          (torch.Tensor): An array of shape BxC containing the model's\n            predictions for the quality of each mask.\n          (torch.Tensor): An array of shape BxCxHxW, where C is the number\n            of masks and H=W=256. These low res logits can be passed to\n            a subsequent iteration as mask input.\n        \"\"\"\n        if not self._is_image_set:\n            raise RuntimeError(\n                \"An image must be set with .set_image(...) before mask prediction.\"\n            )\n\n        if point_coords is not None:\n            concat_points = (point_coords, point_labels)\n        else:\n            concat_points = None\n\n        # Embed prompts\n        if boxes is not None:\n            box_coords = boxes.reshape(-1, 2, 2)\n            box_labels = torch.tensor([[2, 3]], dtype=torch.int, device=boxes.device)\n            box_labels = box_labels.repeat(boxes.size(0), 1)\n            # we merge \"boxes\" and \"points\" into a single \"concat_points\" input (where\n            # boxes are added at the beginning) to sam_prompt_encoder\n            if concat_points is not None:\n                concat_coords = torch.cat([box_coords, concat_points[0]], dim=1)\n                concat_labels = torch.cat([box_labels, concat_points[1]], dim=1)\n                concat_points = (concat_coords, concat_labels)\n            else:\n                concat_points = (box_coords, box_labels)\n\n        sparse_embeddings, dense_embeddings = self.model.sam_prompt_encoder(\n            points=concat_points,\n            boxes=None,\n            masks=mask_input,\n        )\n\n        # Predict masks\n        batched_mode = (\n            concat_points is not None and concat_points[0].shape[0] > 1\n        )  # multi object prediction\n        high_res_features = [\n            feat_level[img_idx].unsqueeze(0)\n            for feat_level in self._features[\"high_res_feats\"]\n        ]\n        low_res_masks, iou_predictions, _, _ = self.model.sam_mask_decoder(\n            image_embeddings=self._features[\"image_embed\"][img_idx].unsqueeze(0),\n            image_pe=self.model.sam_prompt_encoder.get_dense_pe(),\n            sparse_prompt_embeddings=sparse_embeddings,\n            dense_prompt_embeddings=dense_embeddings,\n            multimask_output=multimask_output,\n            repeat_image=batched_mode,\n            high_res_features=high_res_features,\n        )\n\n        # Upscale the masks to the original image resolution\n        masks = self._transforms.postprocess_masks(\n            low_res_masks, self._orig_hw[img_idx]\n        )\n        low_res_masks = torch.clamp(low_res_masks, -32.0, 32.0)\n        if not return_logits:\n            masks = masks > self.mask_threshold\n\n        return masks, iou_predictions, low_res_masks\n\n    def get_image_embedding(self) -> torch.Tensor:\n        \"\"\"\n        Returns the image embeddings for the currently set image, with\n        shape 1xCxHxW, where C is the embedding dimension and (H,W) are\n        the embedding spatial dimension of SAM (typically C=256, H=W=64).\n        \"\"\"\n        if not self._is_image_set:\n            raise RuntimeError(\n                \"An image must be set with .set_image(...) to generate an embedding.\"\n            )\n        assert self._features is not None, (\n            \"Features must exist if an image has been set.\"\n        )\n        return self._features[\"image_embed\"]\n\n    @property\n    def device(self) -> torch.device:\n        return self.model.device\n\n    def reset_predictor(self) -> None:\n        \"\"\"\n        Resets the image embeddings and other state variables.\n        \"\"\"\n        self._is_image_set = False\n        self._features = None\n        self._orig_hw = None\n        self._is_batch = False\n"
  },
  {
    "path": "sam3/model/sam3_image.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n\nimport os\nfrom copy import deepcopy\nfrom typing import Dict, List, Optional, Tuple\n\nimport numpy as np\nimport torch\nfrom sam3.model.model_misc import SAM3Output\nfrom sam3.model.sam1_task_predictor import SAM3InteractiveImagePredictor\nfrom sam3.model.vl_combiner import SAM3VLBackbone\nfrom sam3.perflib.nms import nms_masks\nfrom sam3.train.data.collator import BatchedDatapoint\n\nfrom .act_ckpt_utils import activation_ckpt_wrapper\nfrom .box_ops import box_cxcywh_to_xyxy\nfrom .geometry_encoders import Prompt\nfrom .model_misc import inverse_sigmoid\n\n\ndef _update_out(out, out_name, out_value, auxiliary=True, update_aux=True):\n    out[out_name] = out_value[-1] if auxiliary else out_value\n    if auxiliary and update_aux:\n        if \"aux_outputs\" not in out:\n            out[\"aux_outputs\"] = [{} for _ in range(len(out_value) - 1)]\n        assert len(out[\"aux_outputs\"]) == len(out_value) - 1\n        for aux_output, aux_value in zip(out[\"aux_outputs\"], out_value[:-1]):\n            aux_output[out_name] = aux_value\n\n\nclass Sam3Image(torch.nn.Module):\n    TEXT_ID_FOR_TEXT = 0\n    TEXT_ID_FOR_VISUAL = 1\n    TEXT_ID_FOR_GEOMETRIC = 2\n\n    def __init__(\n        self,\n        backbone: SAM3VLBackbone,\n        transformer,\n        input_geometry_encoder,\n        segmentation_head=None,\n        num_feature_levels=1,\n        o2m_mask_predict=True,\n        dot_prod_scoring=None,\n        use_instance_query: bool = True,\n        multimask_output: bool = True,\n        use_act_checkpoint_seg_head: bool = True,\n        interactivity_in_encoder: bool = True,\n        matcher=None,\n        use_dot_prod_scoring=True,\n        supervise_joint_box_scores: bool = False,  # only relevant if using presence token/score\n        detach_presence_in_joint_score: bool = False,  # only relevant if using presence token/score\n        separate_scorer_for_instance: bool = False,\n        num_interactive_steps_val: int = 0,\n        inst_interactive_predictor: SAM3InteractiveImagePredictor = None,\n        **kwargs,\n    ):\n        super().__init__()\n        self.backbone = backbone\n        self.geometry_encoder = input_geometry_encoder\n        self.transformer = transformer\n        self.hidden_dim = transformer.d_model\n        self.num_feature_levels = num_feature_levels\n        self.segmentation_head = segmentation_head\n\n        self.o2m_mask_predict = o2m_mask_predict\n\n        self.dot_prod_scoring = dot_prod_scoring\n        self.use_act_checkpoint_seg_head = use_act_checkpoint_seg_head\n        self.interactivity_in_encoder = interactivity_in_encoder\n        self.matcher = matcher\n\n        self.num_interactive_steps_val = num_interactive_steps_val\n        self.use_dot_prod_scoring = use_dot_prod_scoring\n\n        if self.use_dot_prod_scoring:\n            assert dot_prod_scoring is not None\n            self.dot_prod_scoring = dot_prod_scoring\n            self.instance_dot_prod_scoring = None\n            if separate_scorer_for_instance:\n                self.instance_dot_prod_scoring = deepcopy(dot_prod_scoring)\n        else:\n            self.class_embed = torch.nn.Linear(self.hidden_dim, 1)\n            self.instance_class_embed = None\n            if separate_scorer_for_instance:\n                self.instance_class_embed = deepcopy(self.class_embed)\n\n        self.supervise_joint_box_scores = supervise_joint_box_scores\n        self.detach_presence_in_joint_score = detach_presence_in_joint_score\n\n        # verify the number of queries for O2O and O2M\n        num_o2o_static = self.transformer.decoder.num_queries\n        num_o2m_static = self.transformer.decoder.num_o2m_queries\n        assert num_o2m_static == (num_o2o_static if self.transformer.decoder.dac else 0)\n        self.dac = self.transformer.decoder.dac\n\n        self.use_instance_query = use_instance_query\n        self.multimask_output = multimask_output\n\n        self.inst_interactive_predictor = inst_interactive_predictor\n\n    @property\n    def device(self):\n        self._device = getattr(self, \"_device\", None) or next(self.parameters()).device\n        return self._device\n\n    def to(self, *args, **kwargs):\n        # clear cached _device in case the model is moved to a different device\n        self._device = None\n        return super().to(*args, **kwargs)\n\n    def _get_img_feats(self, backbone_out, img_ids):\n        \"\"\"Retrieve correct image features from backbone output.\"\"\"\n        if \"backbone_fpn\" in backbone_out:\n            if \"id_mapping\" in backbone_out and backbone_out[\"id_mapping\"] is not None:\n                img_ids = backbone_out[\"id_mapping\"][img_ids]\n                # If this assert fails, it likely means we're requesting different img_ids (perhaps a different frame?)\n                # We currently don't expect this to happen. We could technically trigger a recompute here,\n                # but likely at the cost of a cpu<->gpu sync point, which would deteriorate perf\n                torch._assert_async((img_ids >= 0).all())\n\n            vis_feats = backbone_out[\"backbone_fpn\"][-self.num_feature_levels :]\n            vis_pos_enc = backbone_out[\"vision_pos_enc\"][-self.num_feature_levels :]\n            vis_feat_sizes = [x.shape[-2:] for x in vis_pos_enc]  # (H, W) shapes\n            # index and flatten visual features NxCxHxW => HWxNxC (batch-first => seq-first)\n            img_feats = [x[img_ids].flatten(2).permute(2, 0, 1) for x in vis_feats]\n            img_pos_embeds = [\n                x[img_ids].flatten(2).permute(2, 0, 1) for x in vis_pos_enc\n            ]\n            return backbone_out, img_feats, img_pos_embeds, vis_feat_sizes\n\n        # Image features not available in backbone output, so we compute them on the fly\n        # This case likely occurs for video. In that case, we want to forward only the current frame\n        img_batch = backbone_out[\"img_batch_all_stages\"]\n        if img_ids.numel() > 1:\n            # Only forward backbone on unique image ids to avoid repetitive computation\n            unique_ids, _ = torch.unique(img_ids, return_inverse=True)\n        else:\n            unique_ids, _ = img_ids, slice(None)\n        # Compute the image features on those unique image ids\n        # note: we allow using a list (or other indexable types) of tensors as img_batch\n        # (e.g. for async frame loading in demo). In this case we index img_batch.tensors directly\n        if isinstance(img_batch, torch.Tensor):\n            image = img_batch[unique_ids]\n        elif unique_ids.numel() == 1:\n            image = img_batch[unique_ids.item()].unsqueeze(0)\n        else:\n            image = torch.stack([img_batch[i] for i in unique_ids.tolist()])\n        # `img_batch` might be fp16 and offloaded to CPU\n        image = image.to(dtype=torch.float32, device=self.device)\n        # Next time we call this function, we want to remember which indices we computed\n        id_mapping = torch.full(\n            (len(img_batch),), -1, dtype=torch.long, device=self.device\n        )\n        id_mapping[unique_ids] = torch.arange(len(unique_ids), device=self.device)\n        backbone_out = {\n            **backbone_out,\n            **self.backbone.forward_image(image),\n            \"id_mapping\": id_mapping,\n        }\n        assert \"backbone_fpn\" in backbone_out\n        return self._get_img_feats(backbone_out, img_ids=img_ids)\n\n    def _encode_prompt(\n        self,\n        backbone_out,\n        find_input,\n        geometric_prompt,\n        visual_prompt_embed=None,\n        visual_prompt_mask=None,\n        encode_text=True,\n        prev_mask_pred=None,\n    ):\n        # index text features (note that regardless of early or late fusion, the batch size of\n        # `txt_feats` is always the number of *prompts* in the encoder)\n        txt_ids = find_input.text_ids\n        txt_feats = backbone_out[\"language_features\"][:, txt_ids]\n        txt_masks = backbone_out[\"language_mask\"][txt_ids]\n\n        feat_tuple = self._get_img_feats(backbone_out, find_input.img_ids)\n        backbone_out, img_feats, img_pos_embeds, vis_feat_sizes = feat_tuple\n\n        if prev_mask_pred is not None:\n            img_feats = [img_feats[-1] + prev_mask_pred]\n        # Encode geometry\n        geo_feats, geo_masks = self.geometry_encoder(\n            geo_prompt=geometric_prompt,\n            img_feats=img_feats,\n            img_sizes=vis_feat_sizes,\n            img_pos_embeds=img_pos_embeds,\n        )\n        if visual_prompt_embed is None:\n            visual_prompt_embed = torch.zeros(\n                (0, *geo_feats.shape[1:]), device=geo_feats.device\n            )\n            visual_prompt_mask = torch.zeros(\n                (*geo_masks.shape[:-1], 0),\n                device=geo_masks.device,\n                dtype=geo_masks.dtype,\n            )\n        if encode_text:\n            prompt = torch.cat([txt_feats, geo_feats, visual_prompt_embed], dim=0)\n            prompt_mask = torch.cat([txt_masks, geo_masks, visual_prompt_mask], dim=1)\n        else:\n            prompt = torch.cat([geo_feats, visual_prompt_embed], dim=0)\n            prompt_mask = torch.cat([geo_masks, visual_prompt_mask], dim=1)\n        return prompt, prompt_mask, backbone_out\n\n    def _run_encoder(\n        self,\n        backbone_out,\n        find_input,\n        prompt,\n        prompt_mask,\n        encoder_extra_kwargs: Optional[Dict] = None,\n    ):\n        feat_tuple = self._get_img_feats(backbone_out, find_input.img_ids)\n        backbone_out, img_feats, img_pos_embeds, vis_feat_sizes = feat_tuple\n\n        # Run the encoder\n        prompt_pos_embed = torch.zeros_like(prompt)\n        # make a copy of the image feature lists since the encoder may modify these lists in-place\n        memory = self.transformer.encoder(\n            src=img_feats.copy(),\n            src_key_padding_mask=None,\n            src_pos=img_pos_embeds.copy(),\n            prompt=prompt,\n            prompt_pos=prompt_pos_embed,\n            prompt_key_padding_mask=prompt_mask,\n            feat_sizes=vis_feat_sizes,\n            encoder_extra_kwargs=encoder_extra_kwargs,\n        )\n        encoder_out = {\n            # encoded image features\n            \"encoder_hidden_states\": memory[\"memory\"],\n            \"pos_embed\": memory[\"pos_embed\"],\n            \"padding_mask\": memory[\"padding_mask\"],\n            \"level_start_index\": memory[\"level_start_index\"],\n            \"spatial_shapes\": memory[\"spatial_shapes\"],\n            \"valid_ratios\": memory[\"valid_ratios\"],\n            \"vis_feat_sizes\": vis_feat_sizes,\n            # encoded text features (or other prompts)\n            \"prompt_before_enc\": prompt,\n            \"prompt_after_enc\": memory.get(\"memory_text\", prompt),\n            \"prompt_mask\": prompt_mask,\n        }\n        return backbone_out, encoder_out, feat_tuple\n\n    def _run_decoder(\n        self,\n        pos_embed,\n        memory,\n        src_mask,\n        out,\n        prompt,\n        prompt_mask,\n        encoder_out,\n    ):\n        bs = memory.shape[1]\n        query_embed = self.transformer.decoder.query_embed.weight\n        tgt = query_embed.unsqueeze(1).repeat(1, bs, 1)\n\n        apply_dac = self.transformer.decoder.dac and self.training\n        hs, reference_boxes, dec_presence_out, dec_presence_feats = (\n            self.transformer.decoder(\n                tgt=tgt,\n                memory=memory,\n                memory_key_padding_mask=src_mask,\n                pos=pos_embed,\n                reference_boxes=None,\n                level_start_index=encoder_out[\"level_start_index\"],\n                spatial_shapes=encoder_out[\"spatial_shapes\"],\n                valid_ratios=encoder_out[\"valid_ratios\"],\n                tgt_mask=None,\n                memory_text=prompt,\n                text_attention_mask=prompt_mask,\n                apply_dac=apply_dac,\n            )\n        )\n        hs = hs.transpose(1, 2)  # seq-first to batch-first\n        reference_boxes = reference_boxes.transpose(1, 2)  # seq-first to batch-first\n        if dec_presence_out is not None:\n            # seq-first to batch-first\n            dec_presence_out = dec_presence_out.transpose(1, 2)\n\n        out[\"presence_feats\"] = dec_presence_feats\n        self._update_scores_and_boxes(\n            out,\n            hs,\n            reference_boxes,\n            prompt,\n            prompt_mask,\n            dec_presence_out=dec_presence_out,\n        )\n        return out, hs\n\n    def _update_scores_and_boxes(\n        self,\n        out,\n        hs,\n        reference_boxes,\n        prompt,\n        prompt_mask,\n        dec_presence_out=None,\n        is_instance_prompt=False,\n    ):\n        apply_dac = self.transformer.decoder.dac and self.training\n        num_o2o = (hs.size(2) // 2) if apply_dac else hs.size(2)\n        num_o2m = hs.size(2) - num_o2o\n        assert num_o2m == (num_o2o if apply_dac else 0)\n        out[\"queries\"] = hs[-1][:, :num_o2o]  # remove o2m queries if there are any\n        # score prediction\n        if self.use_dot_prod_scoring:\n            dot_prod_scoring_head = self.dot_prod_scoring\n            if is_instance_prompt and self.instance_dot_prod_scoring is not None:\n                dot_prod_scoring_head = self.instance_dot_prod_scoring\n            outputs_class = dot_prod_scoring_head(hs, prompt, prompt_mask)\n        else:\n            class_embed_head = self.class_embed\n            if is_instance_prompt and self.instance_class_embed is not None:\n                class_embed_head = self.instance_class_embed\n            outputs_class = class_embed_head(hs)\n\n        # box prediction\n        box_head = self.transformer.decoder.bbox_embed\n        if (\n            is_instance_prompt\n            and self.transformer.decoder.instance_bbox_embed is not None\n        ):\n            box_head = self.transformer.decoder.instance_bbox_embed\n        anchor_box_offsets = box_head(hs)\n        reference_boxes_inv_sig = inverse_sigmoid(reference_boxes)\n        outputs_coord = (reference_boxes_inv_sig + anchor_box_offsets).sigmoid()\n        outputs_boxes_xyxy = box_cxcywh_to_xyxy(outputs_coord)\n\n        if dec_presence_out is not None:\n            _update_out(\n                out, \"presence_logit_dec\", dec_presence_out, update_aux=self.training\n            )\n\n        if self.supervise_joint_box_scores:\n            assert dec_presence_out is not None\n            prob_dec_presence_out = dec_presence_out.clone().sigmoid()\n            if self.detach_presence_in_joint_score:\n                prob_dec_presence_out = prob_dec_presence_out.detach()\n\n            outputs_class = inverse_sigmoid(\n                outputs_class.sigmoid() * prob_dec_presence_out.unsqueeze(2)\n            ).clamp(min=-10.0, max=10.0)\n\n        _update_out(\n            out, \"pred_logits\", outputs_class[:, :, :num_o2o], update_aux=self.training\n        )\n        _update_out(\n            out, \"pred_boxes\", outputs_coord[:, :, :num_o2o], update_aux=self.training\n        )\n        _update_out(\n            out,\n            \"pred_boxes_xyxy\",\n            outputs_boxes_xyxy[:, :, :num_o2o],\n            update_aux=self.training,\n        )\n        if num_o2m > 0 and self.training:\n            _update_out(\n                out,\n                \"pred_logits_o2m\",\n                outputs_class[:, :, num_o2o:],\n                update_aux=self.training,\n            )\n            _update_out(\n                out,\n                \"pred_boxes_o2m\",\n                outputs_coord[:, :, num_o2o:],\n                update_aux=self.training,\n            )\n            _update_out(\n                out,\n                \"pred_boxes_xyxy_o2m\",\n                outputs_boxes_xyxy[:, :, num_o2o:],\n                update_aux=self.training,\n            )\n\n    def _run_segmentation_heads(\n        self,\n        out,\n        backbone_out,\n        img_ids,\n        vis_feat_sizes,\n        encoder_hidden_states,\n        prompt,\n        prompt_mask,\n        hs,\n    ):\n        apply_dac = self.transformer.decoder.dac and self.training\n        if self.segmentation_head is not None:\n            num_o2o = (hs.size(2) // 2) if apply_dac else hs.size(2)\n            num_o2m = hs.size(2) - num_o2o\n            obj_queries = hs if self.o2m_mask_predict else hs[:, :, :num_o2o]\n            seg_head_outputs = activation_ckpt_wrapper(self.segmentation_head)(\n                backbone_feats=backbone_out[\"backbone_fpn\"],\n                obj_queries=obj_queries,\n                image_ids=img_ids,\n                encoder_hidden_states=encoder_hidden_states,\n                act_ckpt_enable=self.training and self.use_act_checkpoint_seg_head,\n                prompt=prompt,\n                prompt_mask=prompt_mask,\n            )\n            aux_masks = False  # self.aux_loss and self.segmentation_head.aux_masks\n            for k, v in seg_head_outputs.items():\n                if k in self.segmentation_head.instance_keys:\n                    _update_out(out, k, v[:, :num_o2o], auxiliary=aux_masks)\n                    if (\n                        self.o2m_mask_predict and num_o2m > 0\n                    ):  # handle o2m mask prediction\n                        _update_out(\n                            out, f\"{k}_o2m\", v[:, num_o2o:], auxiliary=aux_masks\n                        )\n                else:\n                    out[k] = v\n        else:\n            backbone_out.pop(\"backbone_fpn\", None)\n\n    def _get_best_mask(self, out):\n        prev_mask_idx = out[\"pred_logits\"].argmax(dim=1).squeeze(1)\n        batch_idx = torch.arange(\n            out[\"pred_logits\"].shape[0], device=prev_mask_idx.device\n        )\n        prev_mask_pred = out[\"pred_masks\"][batch_idx, prev_mask_idx][:, None]\n        # Downsample mask to match image resolution.\n        prev_mask_pred = self.geometry_encoder.mask_encoder.mask_downsampler(\n            prev_mask_pred\n        )\n        prev_mask_pred = prev_mask_pred.flatten(-2).permute(2, 0, 1)\n\n        return prev_mask_pred\n\n    def forward_grounding(\n        self,\n        backbone_out,\n        find_input,\n        find_target,\n        geometric_prompt: Prompt,\n    ):\n        with torch.profiler.record_function(\"SAM3Image._encode_prompt\"):\n            prompt, prompt_mask, backbone_out = self._encode_prompt(\n                backbone_out, find_input, geometric_prompt\n            )\n        # Run the encoder\n        with torch.profiler.record_function(\"SAM3Image._run_encoder\"):\n            backbone_out, encoder_out, _ = self._run_encoder(\n                backbone_out, find_input, prompt, prompt_mask\n            )\n        out = {\n            \"encoder_hidden_states\": encoder_out[\"encoder_hidden_states\"],\n            \"prev_encoder_out\": {\n                \"encoder_out\": encoder_out,\n                \"backbone_out\": backbone_out,\n            },\n        }\n\n        # Run the decoder\n        with torch.profiler.record_function(\"SAM3Image._run_decoder\"):\n            out, hs = self._run_decoder(\n                memory=out[\"encoder_hidden_states\"],\n                pos_embed=encoder_out[\"pos_embed\"],\n                src_mask=encoder_out[\"padding_mask\"],\n                out=out,\n                prompt=prompt,\n                prompt_mask=prompt_mask,\n                encoder_out=encoder_out,\n            )\n\n        # Run segmentation heads\n        with torch.profiler.record_function(\"SAM3Image._run_segmentation_heads\"):\n            self._run_segmentation_heads(\n                out=out,\n                backbone_out=backbone_out,\n                img_ids=find_input.img_ids,\n                vis_feat_sizes=encoder_out[\"vis_feat_sizes\"],\n                encoder_hidden_states=out[\"encoder_hidden_states\"],\n                prompt=prompt,\n                prompt_mask=prompt_mask,\n                hs=hs,\n            )\n\n        if self.training or self.num_interactive_steps_val > 0:\n            self._compute_matching(out, self.back_convert(find_target))\n        return out\n\n    def _postprocess_out(self, out: Dict, multimask_output: bool = False):\n        # For multimask output, during eval we return the single best mask with the dict keys expected by the evaluators, but also return the multimasks output with new keys.\n        num_mask_boxes = out[\"pred_boxes\"].size(1)\n        if not self.training and multimask_output and num_mask_boxes > 1:\n            out[\"multi_pred_logits\"] = out[\"pred_logits\"]\n            if \"pred_masks\" in out:\n                out[\"multi_pred_masks\"] = out[\"pred_masks\"]\n            out[\"multi_pred_boxes\"] = out[\"pred_boxes\"]\n            out[\"multi_pred_boxes_xyxy\"] = out[\"pred_boxes_xyxy\"]\n\n            best_mask_idx = out[\"pred_logits\"].argmax(1).squeeze(1)\n            batch_idx = torch.arange(len(best_mask_idx), device=best_mask_idx.device)\n\n            out[\"pred_logits\"] = out[\"pred_logits\"][batch_idx, best_mask_idx].unsqueeze(\n                1\n            )\n            if \"pred_masks\" in out:\n                out[\"pred_masks\"] = out[\"pred_masks\"][\n                    batch_idx, best_mask_idx\n                ].unsqueeze(1)\n            out[\"pred_boxes\"] = out[\"pred_boxes\"][batch_idx, best_mask_idx].unsqueeze(1)\n            out[\"pred_boxes_xyxy\"] = out[\"pred_boxes_xyxy\"][\n                batch_idx, best_mask_idx\n            ].unsqueeze(1)\n\n        return out\n\n    def _get_dummy_prompt(self, num_prompts=1):\n        device = self.device\n        geometric_prompt = Prompt(\n            box_embeddings=torch.zeros(0, num_prompts, 4, device=device),\n            box_mask=torch.zeros(num_prompts, 0, device=device, dtype=torch.bool),\n        )\n        return geometric_prompt\n\n    def forward(self, input: BatchedDatapoint):\n        device = self.device\n        backbone_out = {\"img_batch_all_stages\": input.img_batch}\n        backbone_out.update(self.backbone.forward_image(input.img_batch))\n        num_frames = len(input.find_inputs)\n        assert num_frames == 1\n\n        text_outputs = self.backbone.forward_text(input.find_text_batch, device=device)\n        backbone_out.update(text_outputs)\n\n        previous_stages_out = SAM3Output(\n            iter_mode=SAM3Output.IterMode.LAST_STEP_PER_STAGE\n        )\n\n        find_input = input.find_inputs[0]\n        find_target = input.find_targets[0]\n\n        if find_input.input_points is not None and find_input.input_points.numel() > 0:\n            print(\"Warning: Point prompts are ignored in PCS.\")\n\n        num_interactive_steps = 0 if self.training else self.num_interactive_steps_val\n        geometric_prompt = Prompt(\n            box_embeddings=find_input.input_boxes,\n            box_mask=find_input.input_boxes_mask,\n            box_labels=find_input.input_boxes_label,\n        )\n\n        # Init vars that are shared across the loop.\n        stage_outs = []\n        for cur_step in range(num_interactive_steps + 1):\n            if cur_step > 0:\n                # We sample interactive geometric prompts (boxes, points)\n                geometric_prompt, _ = self.interactive_prompt_sampler.sample(\n                    geo_prompt=geometric_prompt,\n                    find_target=find_target,\n                    previous_out=stage_outs[-1],\n                )\n            out = self.forward_grounding(\n                backbone_out=backbone_out,\n                find_input=find_input,\n                find_target=find_target,\n                geometric_prompt=geometric_prompt.clone(),\n            )\n            stage_outs.append(out)\n\n        previous_stages_out.append(stage_outs)\n        return previous_stages_out\n\n    def _compute_matching(self, out, targets):\n        out[\"indices\"] = self.matcher(out, targets)\n        for aux_out in out.get(\"aux_outputs\", []):\n            aux_out[\"indices\"] = self.matcher(aux_out, targets)\n\n    def back_convert(self, targets):\n        batched_targets = {\n            \"boxes\": targets.boxes.view(-1, 4),\n            \"boxes_xyxy\": box_cxcywh_to_xyxy(targets.boxes.view(-1, 4)),\n            \"boxes_padded\": targets.boxes_padded,\n            \"positive_map\": targets.boxes.new_ones(len(targets.boxes), 1),\n            \"num_boxes\": targets.num_boxes,\n            \"masks\": targets.segments,\n            \"semantic_masks\": targets.semantic_segments,\n            \"is_valid_mask\": targets.is_valid_segment,\n            \"is_exhaustive\": targets.is_exhaustive,\n            \"object_ids_packed\": targets.object_ids,\n            \"object_ids_padded\": targets.object_ids_padded,\n        }\n        return batched_targets\n\n    def predict_inst(\n        self,\n        inference_state,\n        **kwargs,\n    ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:\n        orig_h, orig_w = (\n            inference_state[\"original_height\"],\n            inference_state[\"original_width\"],\n        )\n        backbone_out = inference_state[\"backbone_out\"][\"sam2_backbone_out\"]\n        (\n            _,\n            vision_feats,\n            _,\n            _,\n        ) = self.inst_interactive_predictor.model._prepare_backbone_features(\n            backbone_out\n        )\n        # Add no_mem_embed, which is added to the lowest rest feat. map during training on videos\n        vision_feats[-1] = (\n            vision_feats[-1] + self.inst_interactive_predictor.model.no_mem_embed\n        )\n        feats = [\n            feat.permute(1, 2, 0).view(1, -1, *feat_size)\n            for feat, feat_size in zip(\n                vision_feats[::-1], self.inst_interactive_predictor._bb_feat_sizes[::-1]\n            )\n        ][::-1]\n        self.inst_interactive_predictor._features = {\n            \"image_embed\": feats[-1],\n            \"high_res_feats\": feats[:-1],\n        }\n        self.inst_interactive_predictor._is_image_set = True\n        self.inst_interactive_predictor._orig_hw = [(orig_h, orig_w)]\n        res = self.inst_interactive_predictor.predict(**kwargs)\n        self.inst_interactive_predictor._features = None\n        self.inst_interactive_predictor._is_image_set = False\n        return res\n\n    def predict_inst_batch(\n        self,\n        inference_state,\n        *args,\n        **kwargs,\n    ) -> Tuple[List[np.ndarray], List[np.ndarray], List[np.ndarray]]:\n        backbone_out = inference_state[\"backbone_out\"][\"sam2_backbone_out\"]\n        (\n            _,\n            vision_feats,\n            _,\n            _,\n        ) = self.inst_interactive_predictor.model._prepare_backbone_features(\n            backbone_out\n        )\n        # Add no_mem_embed, which is added to the lowest res feat. map during training on videos\n        vision_feats[-1] = (\n            vision_feats[-1] + self.inst_interactive_predictor.model.no_mem_embed\n        )\n        batch_size = vision_feats[-1].shape[1]\n        orig_heights, orig_widths = (\n            inference_state[\"original_heights\"],\n            inference_state[\"original_widths\"],\n        )\n        assert batch_size == len(orig_heights) == len(orig_widths), (\n            f\"Batch size mismatch in predict_inst_batch. Got {batch_size}, {len(orig_heights)}, {len(orig_widths)}\"\n        )\n        feats = [\n            feat.permute(1, 2, 0).view(batch_size, -1, *feat_size)\n            for feat, feat_size in zip(\n                vision_feats[::-1], self.inst_interactive_predictor._bb_feat_sizes[::-1]\n            )\n        ][::-1]\n        self.inst_interactive_predictor._features = {\n            \"image_embed\": feats[-1],\n            \"high_res_feats\": feats[:-1],\n        }\n        self.inst_interactive_predictor._is_image_set = True\n        self.inst_interactive_predictor._is_batch = True\n        self.inst_interactive_predictor._orig_hw = [\n            (orig_h, orig_w) for orig_h, orig_w in zip(orig_heights, orig_widths)\n        ]\n        res = self.inst_interactive_predictor.predict_batch(*args, **kwargs)\n        self.inst_interactive_predictor._features = None\n        self.inst_interactive_predictor._is_image_set = False\n        self.inst_interactive_predictor._is_batch = False\n        return res\n\n\nclass Sam3ImageOnVideoMultiGPU(Sam3Image):\n    def __init__(\n        self, *args, async_all_gather=True, gather_backbone_out=None, **kwargs\n    ):\n        super().__init__(*args, **kwargs)\n        self.rank = int(os.getenv(\"RANK\", \"0\"))\n        self.world_size = int(os.getenv(\"WORLD_SIZE\", \"1\"))\n        self.async_all_gather = async_all_gather\n\n        # if gather_backbone is not set, default to gathering only for `SAM3VLBackbone`\n        if gather_backbone_out is None:\n            gather_backbone_out = isinstance(self.backbone, SAM3VLBackbone)\n        self.gather_backbone_out = gather_backbone_out\n\n    def forward_video_grounding_multigpu(\n        self,\n        backbone_out,\n        find_inputs,\n        geometric_prompt: Prompt,\n        frame_idx,\n        num_frames,\n        # `multigpu_buffer` is a dict to cache detector's outputs in a chunk between different calls\n        multigpu_buffer,\n        track_in_reverse=False,\n        # whether to also return the SAM2 backbone features\n        return_sam2_backbone_feats=False,\n        # whether to perform NMS and suppress the scores of those detections removed by NMS\n        run_nms=False,\n        nms_prob_thresh=None,\n        nms_iou_thresh=None,\n        **kwargs,\n    ):\n        \"\"\"\n        Compute the detector's detection outputs in a distributed manner, where all GPUs process\n        a chunk of frames (equal to the number of GPUs) at once and store them in cache.\n        \"\"\"\n        # Step 1: fetch the detector outputs in the current chunk from buffer\n        frame_idx_curr_b = frame_idx - frame_idx % self.world_size\n        frame_idx_curr_e = min(frame_idx_curr_b + self.world_size, num_frames)\n        # in case the current frame's detection results are not in the buffer yet, build the current chunk\n        # (this should only happen on the first chunk, since we are also building the next chunk below)\n        if frame_idx not in multigpu_buffer:\n            with torch.profiler.record_function(\"build_multigpu_buffer_next_chunk1\"):\n                self._build_multigpu_buffer_next_chunk(\n                    backbone_out=backbone_out,\n                    find_inputs=find_inputs,\n                    geometric_prompt=geometric_prompt,\n                    frame_idx_begin=frame_idx_curr_b,\n                    frame_idx_end=frame_idx_curr_e,\n                    num_frames=num_frames,\n                    multigpu_buffer=multigpu_buffer,\n                    run_nms=run_nms,\n                    nms_prob_thresh=nms_prob_thresh,\n                    nms_iou_thresh=nms_iou_thresh,\n                )\n\n        # read out the current frame's results from `multigpu_buffer`\n        out = {}\n        for k, (v, handle) in multigpu_buffer[frame_idx].items():\n            if k.startswith(\"sam2_backbone_\") and not return_sam2_backbone_feats:\n                continue\n            if handle is not None:\n                handle.wait()  # wait for async all-gather to finish\n            out[k] = v\n\n        # Step 2: remove detection outputs of the previous chunk from cache to save GPU memory\n        if not track_in_reverse and frame_idx_curr_b - self.world_size >= 0:\n            frame_idx_prev_e = frame_idx_curr_b\n            frame_idx_prev_b = frame_idx_curr_b - self.world_size\n        elif track_in_reverse and frame_idx_curr_e < num_frames:\n            frame_idx_prev_b = frame_idx_curr_e\n            frame_idx_prev_e = min(frame_idx_prev_b + self.world_size, num_frames)\n        else:\n            frame_idx_prev_b = frame_idx_prev_e = None\n        if frame_idx_prev_b is not None:\n            for frame_idx_rm in range(frame_idx_prev_b, frame_idx_prev_e):\n                multigpu_buffer.pop(frame_idx_rm, None)\n\n        # Step 3: compute and cache detection outputs of the next chunk ahead of time\n        # (so that we can overlap computation with all-gather transfer)\n        if not track_in_reverse and frame_idx_curr_e < num_frames:\n            frame_idx_next_b = frame_idx_curr_e\n            frame_idx_next_e = min(frame_idx_next_b + self.world_size, num_frames)\n        elif track_in_reverse and frame_idx_curr_b - self.world_size >= 0:\n            frame_idx_next_e = frame_idx_curr_b\n            frame_idx_next_b = frame_idx_curr_b - self.world_size\n        else:\n            frame_idx_next_b = frame_idx_next_e = None\n        if frame_idx_next_b is not None and frame_idx_next_b not in multigpu_buffer:\n            with torch.profiler.record_function(\"build_multigpu_buffer_next_chunk2\"):\n                self._build_multigpu_buffer_next_chunk(\n                    backbone_out=backbone_out,\n                    find_inputs=find_inputs,\n                    geometric_prompt=geometric_prompt,\n                    frame_idx_begin=frame_idx_next_b,\n                    frame_idx_end=frame_idx_next_e,\n                    num_frames=num_frames,\n                    multigpu_buffer=multigpu_buffer,\n                    run_nms=run_nms,\n                    nms_prob_thresh=nms_prob_thresh,\n                    nms_iou_thresh=nms_iou_thresh,\n                )\n\n        return out, backbone_out\n\n    def _build_multigpu_buffer_next_chunk(\n        self,\n        backbone_out,\n        find_inputs,\n        geometric_prompt: Prompt,\n        frame_idx_begin,\n        frame_idx_end,\n        num_frames,\n        multigpu_buffer,\n        run_nms=False,\n        nms_prob_thresh=None,\n        nms_iou_thresh=None,\n    ):\n        \"\"\"Compute detection outputs on a chunk of frames and store their results in multigpu_buffer.\"\"\"\n        # each GPU computes detections on one frame in the chunk (in a round-robin manner)\n        frame_idx_local_gpu = min(frame_idx_begin + self.rank, frame_idx_end - 1)\n        # `forward_grounding` (from base class `Sam3ImageOnVideo`) runs the detector on a single frame\n        with torch.profiler.record_function(\"forward_grounding\"):\n            out_local = self.forward_grounding(\n                backbone_out=backbone_out,\n                find_input=find_inputs[frame_idx_local_gpu],\n                find_target=None,\n                geometric_prompt=geometric_prompt,\n            )\n        if run_nms:\n            with torch.profiler.record_function(\"nms_masks\"):\n                # run NMS as a post-processing step on top of the detection outputs\n                assert nms_prob_thresh is not None and nms_iou_thresh is not None\n                pred_probs = out_local[\"pred_logits\"].squeeze(-1).sigmoid()\n                pred_masks = out_local[\"pred_masks\"]\n                # loop over text prompts (not an overhead for demo where there's only 1 prompt)\n                for prompt_idx in range(pred_probs.size(0)):\n                    keep = nms_masks(\n                        pred_probs=pred_probs[prompt_idx],\n                        pred_masks=pred_masks[prompt_idx],\n                        prob_threshold=nms_prob_thresh,\n                        iou_threshold=nms_iou_thresh,\n                    )\n                    # set a very low threshold for those detections removed by NMS\n                    out_local[\"pred_logits\"][prompt_idx, :, 0] -= 1e4 * (~keep).float()\n\n        if self.gather_backbone_out:\n            # gather the SAM 2 backbone features across GPUs\n            feats = out_local[\"prev_encoder_out\"][\"backbone_out\"][\"sam2_backbone_out\"]\n            assert len(feats[\"backbone_fpn\"]) == 3  # SAM2 backbone always have 3 levels\n            # cast the SAM2 backbone features to bfloat16 for all-gather (this is usually\n            # a no-op, SAM2 backbone features are likely already in bfloat16 due to AMP)\n            backbone_fpn_bf16 = [x.to(torch.bfloat16) for x in feats[\"backbone_fpn\"]]\n            fpn0, fpn_handle0 = self._gather_tensor(backbone_fpn_bf16[0])\n            fpn1, fpn_handle1 = self._gather_tensor(backbone_fpn_bf16[1])\n            fpn2, fpn_handle2 = self._gather_tensor(backbone_fpn_bf16[2])\n            # vision_pos_enc is the same on all frames, so no need to all-gather them\n            vision_pos_enc = feats[\"vision_pos_enc\"]\n\n        # trim the detector output to only include the necessary keys\n        out_local = {\n            \"pred_logits\": out_local[\"pred_logits\"],\n            \"pred_boxes\": out_local[\"pred_boxes\"],\n            \"pred_boxes_xyxy\": out_local[\"pred_boxes_xyxy\"],\n            \"pred_masks\": out_local[\"pred_masks\"],\n        }\n\n        # gather the results: after this step, each GPU will receive detector outputs on\n        # all frames in the chunk and store them in `multigpu_buffer`\n        out_gathered = {k: self._gather_tensor(v) for k, v in out_local.items()}\n        for rank in range(self.world_size):\n            frame_idx_to_save = frame_idx_begin + rank\n            if frame_idx_to_save >= num_frames:\n                continue\n            frame_buffer = {\n                k: (v[rank], handle) for k, (v, handle) in out_gathered.items()\n            }\n            if self.gather_backbone_out:\n                # also add gathered SAM 2 backbone features to frame_buffer\n                frame_buffer[\"tracker_backbone_fpn_0\"] = (fpn0[rank], fpn_handle0)\n                frame_buffer[\"tracker_backbone_fpn_1\"] = (fpn1[rank], fpn_handle1)\n                frame_buffer[\"tracker_backbone_fpn_2\"] = (fpn2[rank], fpn_handle2)\n                frame_buffer[\"tracker_backbone_pos_enc\"] = (vision_pos_enc, None)\n\n            multigpu_buffer[frame_idx_to_save] = frame_buffer\n\n    def _gather_tensor(self, x):\n        if self.world_size == 1:\n            return [x], None\n\n        async_op = self.async_all_gather\n        # here `.contiguous()` is required -- otherwise NCCL all_gather\n        # sometimes gives wrong results\n        x = x.contiguous()  # ensure contiguous memory for NCCL\n        output_list = [torch.empty_like(x) for _ in range(self.world_size)]\n        handle = torch.distributed.all_gather(output_list, x, async_op=async_op)\n        return output_list, handle\n"
  },
  {
    "path": "sam3/model/sam3_image_processor.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\nfrom typing import Dict, List\n\nimport numpy as np\nimport PIL\nimport torch\nfrom sam3.model import box_ops\nfrom sam3.model.data_misc import FindStage, interpolate\nfrom torchvision.transforms import v2\n\n\nclass Sam3Processor:\n    \"\"\" \"\"\"\n\n    def __init__(self, model, resolution=1008, device=\"cuda\", confidence_threshold=0.5):\n        self.model = model\n        self.resolution = resolution\n        self.device = device\n        self.transform = v2.Compose(\n            [\n                v2.ToDtype(torch.uint8, scale=True),\n                v2.Resize(size=(resolution, resolution)),\n                v2.ToDtype(torch.float32, scale=True),\n                v2.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),\n            ]\n        )\n        self.confidence_threshold = confidence_threshold\n\n        self.find_stage = FindStage(\n            img_ids=torch.tensor([0], device=device, dtype=torch.long),\n            text_ids=torch.tensor([0], device=device, dtype=torch.long),\n            input_boxes=None,\n            input_boxes_mask=None,\n            input_boxes_label=None,\n            input_points=None,\n            input_points_mask=None,\n        )\n\n    @torch.inference_mode()\n    def set_image(self, image, state=None):\n        \"\"\"Sets the image on which we want to do predictions.\"\"\"\n        if state is None:\n            state = {}\n\n        if isinstance(image, PIL.Image.Image):\n            width, height = image.size\n        elif isinstance(image, (torch.Tensor, np.ndarray)):\n            height, width = image.shape[-2:]\n        else:\n            raise ValueError(\"Image must be a PIL image or a tensor\")\n\n        image = v2.functional.to_image(image).to(self.device)\n        image = self.transform(image).unsqueeze(0)\n\n        state[\"original_height\"] = height\n        state[\"original_width\"] = width\n        state[\"backbone_out\"] = self.model.backbone.forward_image(image)\n        inst_interactivity_en = self.model.inst_interactive_predictor is not None\n        if inst_interactivity_en and \"sam2_backbone_out\" in state[\"backbone_out\"]:\n            sam2_backbone_out = state[\"backbone_out\"][\"sam2_backbone_out\"]\n            sam2_backbone_out[\"backbone_fpn\"][0] = (\n                self.model.inst_interactive_predictor.model.sam_mask_decoder.conv_s0(\n                    sam2_backbone_out[\"backbone_fpn\"][0]\n                )\n            )\n            sam2_backbone_out[\"backbone_fpn\"][1] = (\n                self.model.inst_interactive_predictor.model.sam_mask_decoder.conv_s1(\n                    sam2_backbone_out[\"backbone_fpn\"][1]\n                )\n            )\n        return state\n\n    @torch.inference_mode()\n    def set_image_batch(self, images: List[np.ndarray], state=None):\n        \"\"\"Sets the image batch on which we want to do predictions.\"\"\"\n        if state is None:\n            state = {}\n\n        if not isinstance(images, list):\n            raise ValueError(\"Images must be a list of PIL images or tensors\")\n        assert len(images) > 0, \"Images list must not be empty\"\n        assert isinstance(images[0], PIL.Image.Image), (\n            \"Images must be a list of PIL images\"\n        )\n\n        state[\"original_heights\"] = [image.height for image in images]\n        state[\"original_widths\"] = [image.width for image in images]\n\n        images = [\n            self.transform(v2.functional.to_image(image).to(self.device))\n            for image in images\n        ]\n        images = torch.stack(images, dim=0)\n        state[\"backbone_out\"] = self.model.backbone.forward_image(images)\n        inst_interactivity_en = self.model.inst_interactive_predictor is not None\n        if inst_interactivity_en and \"sam2_backbone_out\" in state[\"backbone_out\"]:\n            sam2_backbone_out = state[\"backbone_out\"][\"sam2_backbone_out\"]\n            sam2_backbone_out[\"backbone_fpn\"][0] = (\n                self.model.inst_interactive_predictor.model.sam_mask_decoder.conv_s0(\n                    sam2_backbone_out[\"backbone_fpn\"][0]\n                )\n            )\n            sam2_backbone_out[\"backbone_fpn\"][1] = (\n                self.model.inst_interactive_predictor.model.sam_mask_decoder.conv_s1(\n                    sam2_backbone_out[\"backbone_fpn\"][1]\n                )\n            )\n        return state\n\n    @torch.inference_mode()\n    def set_text_prompt(self, prompt: str, state: Dict):\n        \"\"\"Sets the text prompt and run the inference\"\"\"\n\n        if \"backbone_out\" not in state:\n            raise ValueError(\"You must call set_image before set_text_prompt\")\n\n        text_outputs = self.model.backbone.forward_text([prompt], device=self.device)\n        # will erase the previous text prompt if any\n        state[\"backbone_out\"].update(text_outputs)\n        if \"geometric_prompt\" not in state:\n            state[\"geometric_prompt\"] = self.model._get_dummy_prompt()\n\n        return self._forward_grounding(state)\n\n    @torch.inference_mode()\n    def add_geometric_prompt(self, box: List, label: bool, state: Dict):\n        \"\"\"Adds a box prompt and run the inference.\n        The image needs to be set, but not necessarily the text prompt.\n        The box is assumed to be in [center_x, center_y, width, height] format and normalized in [0, 1] range.\n        The label is True for a positive box, False for a negative box.\n        \"\"\"\n        if \"backbone_out\" not in state:\n            raise ValueError(\"You must call set_image before set_text_prompt\")\n\n        if \"language_features\" not in state[\"backbone_out\"]:\n            # Looks like we don't have a text prompt yet. This is allowed, but we need to set the text prompt to \"visual\" for the model to rely only on the geometric prompt\n            dummy_text_outputs = self.model.backbone.forward_text(\n                [\"visual\"], device=self.device\n            )\n            state[\"backbone_out\"].update(dummy_text_outputs)\n\n        if \"geometric_prompt\" not in state:\n            state[\"geometric_prompt\"] = self.model._get_dummy_prompt()\n\n        # adding a batch and sequence dimension\n        boxes = torch.tensor(box, device=self.device, dtype=torch.float32).view(1, 1, 4)\n        labels = torch.tensor([label], device=self.device, dtype=torch.bool).view(1, 1)\n        state[\"geometric_prompt\"].append_boxes(boxes, labels)\n\n        return self._forward_grounding(state)\n\n    def reset_all_prompts(self, state: Dict):\n        \"\"\"Removes all the prompts and results\"\"\"\n        if \"backbone_out\" in state:\n            backbone_keys_to_del = [\n                \"language_features\",\n                \"language_mask\",\n                \"language_embeds\",\n            ]\n            for key in backbone_keys_to_del:\n                if key in state[\"backbone_out\"]:\n                    del state[\"backbone_out\"][key]\n\n        keys_to_del = [\"geometric_prompt\", \"boxes\", \"masks\", \"masks_logits\", \"scores\"]\n        for key in keys_to_del:\n            if key in state:\n                del state[key]\n\n    @torch.inference_mode()\n    def set_confidence_threshold(self, threshold: float, state=None):\n        \"\"\"Sets the confidence threshold for the masks\"\"\"\n        self.confidence_threshold = threshold\n        if state is not None and \"boxes\" in state:\n            # we need to filter the boxes again\n            # In principle we could do this more efficiently since we would only need\n            # to rerun the heads. But this is simpler and not too inefficient\n            return self._forward_grounding(state)\n        return state\n\n    @torch.inference_mode()\n    def _forward_grounding(self, state: Dict):\n        outputs = self.model.forward_grounding(\n            backbone_out=state[\"backbone_out\"],\n            find_input=self.find_stage,\n            geometric_prompt=state[\"geometric_prompt\"],\n            find_target=None,\n        )\n\n        out_bbox = outputs[\"pred_boxes\"]\n        out_logits = outputs[\"pred_logits\"]\n        out_masks = outputs[\"pred_masks\"]\n        out_probs = out_logits.sigmoid()\n        presence_score = outputs[\"presence_logit_dec\"].sigmoid().unsqueeze(1)\n        out_probs = (out_probs * presence_score).squeeze(-1)\n\n        keep = out_probs > self.confidence_threshold\n        out_probs = out_probs[keep]\n        out_masks = out_masks[keep]\n        out_bbox = out_bbox[keep]\n\n        # convert to [x0, y0, x1, y1] format\n        boxes = box_ops.box_cxcywh_to_xyxy(out_bbox)\n\n        img_h = state[\"original_height\"]\n        img_w = state[\"original_width\"]\n        scale_fct = torch.tensor([img_w, img_h, img_w, img_h]).to(self.device)\n        boxes = boxes * scale_fct[None, :]\n\n        out_masks = interpolate(\n            out_masks.unsqueeze(1),\n            (img_h, img_w),\n            mode=\"bilinear\",\n            align_corners=False,\n        ).sigmoid()\n\n        state[\"masks_logits\"] = out_masks\n        state[\"masks\"] = out_masks > 0.5\n        state[\"boxes\"] = boxes\n        state[\"scores\"] = out_probs\n        return state\n"
  },
  {
    "path": "sam3/model/sam3_tracker_base.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n\nimport logging\n\nimport torch\nimport torch.nn.functional as F\nfrom sam3.model.memory import SimpleMaskEncoder\nfrom sam3.model.sam3_tracker_utils import get_1d_sine_pe, select_closest_cond_frames\nfrom sam3.sam.mask_decoder import MaskDecoder, MLP\nfrom sam3.sam.prompt_encoder import PromptEncoder\nfrom sam3.sam.transformer import TwoWayTransformer\nfrom sam3.train.data.collator import BatchedDatapoint\n\ntry:\n    from timm.layers import trunc_normal_\nexcept ModuleNotFoundError:\n    # compatibility for older timm versions\n    from timm.models.layers import trunc_normal_\n\n# a large negative value as a placeholder score for missing objects\nNO_OBJ_SCORE = -1024.0\n\n\nclass Sam3TrackerBase(torch.nn.Module):\n    def __init__(\n        self,\n        backbone,\n        transformer,\n        maskmem_backbone,\n        num_maskmem=7,  # default 1 input frame + 6 previous frames as in CAE\n        image_size=1008,\n        backbone_stride=14,  # stride of the image backbone output\n        # The maximum number of conditioning frames to participate in the memory attention (-1 means no limit; if there are more conditioning frames than this limit,\n        # we only cross-attend to the temporally closest `max_cond_frames_in_attn` conditioning frames in the encoder when tracking each frame). This gives the model\n        # a temporal locality when handling a large number of annotated frames (since closer frames should be more important) and also avoids GPU OOM.\n        max_cond_frames_in_attn=-1,\n        # Whether to always keep the first conditioning frame in case we exceed the maximum number of conditioning frames allowed\n        keep_first_cond_frame=False,\n        # whether to output multiple (3) masks for the first click on initial conditioning frames\n        multimask_output_in_sam=False,\n        # the minimum and maximum number of clicks to use multimask_output_in_sam (only relevant when `multimask_output_in_sam=True`;\n        # default is 1 for both, meaning that only the first click gives multimask output; also note that a box counts as two points)\n        multimask_min_pt_num=1,\n        multimask_max_pt_num=1,\n        # whether to also use multimask output for tracking (not just for the first click on initial conditioning frames; only relevant when `multimask_output_in_sam=True`)\n        multimask_output_for_tracking=False,\n        # whether to forward image features per frame (as it's being tracked) during evaluation, instead of forwarding image features\n        # of all frames at once. This avoids backbone OOM errors on very long videos in evaluation, but could be slightly slower.\n        forward_backbone_per_frame_for_eval=False,\n        # The memory bank's temporal stride during evaluation (i.e. the `r` parameter in XMem and Cutie; XMem and Cutie use r=5).\n        # For r>1, the (self.num_maskmem - 1) non-conditioning memory frames consist of\n        # (self.num_maskmem - 2) nearest frames from every r-th frames, plus the last frame.\n        memory_temporal_stride_for_eval=1,\n        # whether to offload outputs to CPU memory during evaluation, to avoid GPU OOM on very long videos or very large resolutions or too many objects\n        # (it's recommended to use `forward_backbone_per_frame_for_eval=True` first before setting this option to True)\n        offload_output_to_cpu_for_eval=False,\n        # whether to trim the output of past non-conditioning frames (num_maskmem frames before the current frame) during evaluation\n        # (this helps save GPU or CPU memory on very long videos for semi-supervised VOS eval, where only the first frame receives prompts)\n        trim_past_non_cond_mem_for_eval=False,\n        # whether to apply non-overlapping constraints on the object masks in the memory encoder during evaluation (to avoid/alleviate superposing masks)\n        non_overlap_masks_for_mem_enc=False,\n        # the maximum number of object pointers from other frames in encoder cross attention\n        max_obj_ptrs_in_encoder=16,\n        # extra arguments used to construct the SAM mask decoder; if not None, it should be a dict of kwargs to be passed into `MaskDecoder` class.\n        sam_mask_decoder_extra_args=None,\n        # whether to compile all the model compoents\n        compile_all_components=False,\n        # select the frame with object existence\n        use_memory_selection=False,\n        # when using memory selection, the threshold to determine if the frame is good\n        mf_threshold=0.01,\n    ):\n        super().__init__()\n\n        # Part 1: the image backbone\n        self.backbone = backbone\n        self.num_feature_levels = 3\n        self.max_obj_ptrs_in_encoder = max_obj_ptrs_in_encoder\n        # A conv layer to downsample the GT mask prompt to stride 4 (the same stride as\n        # low-res SAM mask logits) and to change its scales from 0~1 to SAM logit scale,\n        # so that it can be fed into the SAM mask decoder to generate a pointer.\n        self.mask_downsample = torch.nn.Conv2d(1, 1, kernel_size=4, stride=4)\n\n        # Part 2: encoder-only transformer to fuse current frame's visual features\n        # with memories from past frames\n        assert transformer.decoder is None, \"transformer should be encoder-only\"\n        self.transformer = transformer\n        self.hidden_dim = transformer.d_model\n\n        # Part 3: memory encoder for the previous frame's outputs\n        self.maskmem_backbone = maskmem_backbone\n        self.mem_dim = self.hidden_dim\n        if hasattr(self.maskmem_backbone, \"out_proj\") and hasattr(\n            self.maskmem_backbone.out_proj, \"weight\"\n        ):\n            # if there is compression of memories along channel dim\n            self.mem_dim = self.maskmem_backbone.out_proj.weight.shape[0]\n        self.num_maskmem = num_maskmem  # Number of memories accessible\n\n        # Temporal encoding of the memories\n        self.maskmem_tpos_enc = torch.nn.Parameter(\n            torch.zeros(num_maskmem, 1, 1, self.mem_dim)\n        )\n        trunc_normal_(self.maskmem_tpos_enc, std=0.02)\n\n        # a single token to indicate no memory embedding from previous frames\n        self.no_mem_embed = torch.nn.Parameter(torch.zeros(1, 1, self.hidden_dim))\n        self.no_mem_pos_enc = torch.nn.Parameter(torch.zeros(1, 1, self.hidden_dim))\n        trunc_normal_(self.no_mem_embed, std=0.02)\n        trunc_normal_(self.no_mem_pos_enc, std=0.02)\n        # Apply sigmoid to the output raw mask logits (to turn them from\n        # range (-inf, +inf) to range (0, 1)) before feeding them into the memory encoder\n        self.sigmoid_scale_for_mem_enc = 20.0\n        self.sigmoid_bias_for_mem_enc = -10.0\n        self.non_overlap_masks_for_mem_enc = non_overlap_masks_for_mem_enc\n        self.memory_temporal_stride_for_eval = memory_temporal_stride_for_eval\n        # On frames with mask input, whether to directly output the input mask without\n        # using a SAM prompt encoder + mask decoder\n        self.multimask_output_in_sam = multimask_output_in_sam\n        self.multimask_min_pt_num = multimask_min_pt_num\n        self.multimask_max_pt_num = multimask_max_pt_num\n        self.multimask_output_for_tracking = multimask_output_for_tracking\n\n        # Part 4: SAM-style prompt encoder (for both mask and point inputs)\n        # and SAM-style mask decoder for the final mask output\n        self.image_size = image_size\n        self.backbone_stride = backbone_stride\n        self.low_res_mask_size = self.image_size // self.backbone_stride * 4\n        # we resize the mask if it doesn't match `self.input_mask_size` (which is always 4x\n        # the low-res mask size, regardless of the actual input image size); this is because\n        # `_use_mask_as_output` always downsamples the input masks by 4x\n        self.input_mask_size = self.low_res_mask_size * 4\n        self.forward_backbone_per_frame_for_eval = forward_backbone_per_frame_for_eval\n        self.offload_output_to_cpu_for_eval = offload_output_to_cpu_for_eval\n        self.trim_past_non_cond_mem_for_eval = trim_past_non_cond_mem_for_eval\n        self.sam_mask_decoder_extra_args = sam_mask_decoder_extra_args\n        self.no_obj_ptr = torch.nn.Parameter(torch.zeros(1, self.hidden_dim))\n        trunc_normal_(self.no_obj_ptr, std=0.02)\n        self.no_obj_embed_spatial = torch.nn.Parameter(torch.zeros(1, self.mem_dim))\n        trunc_normal_(self.no_obj_embed_spatial, std=0.02)\n\n        self._build_sam_heads()\n        self.max_cond_frames_in_attn = max_cond_frames_in_attn\n        self.keep_first_cond_frame = keep_first_cond_frame\n\n        # Use frame filtering according to SAM2Long\n        self.use_memory_selection = use_memory_selection\n        self.mf_threshold = mf_threshold\n\n        # Compile all components of the model\n        self.compile_all_components = compile_all_components\n        if self.compile_all_components:\n            self._compile_all_components()\n\n    @property\n    def device(self):\n        return next(self.parameters()).device\n\n    def _get_tpos_enc(self, rel_pos_list, device, max_abs_pos=None, dummy=False):\n        if dummy:\n            return torch.zeros(len(rel_pos_list), self.mem_dim, device=device)\n\n        t_diff_max = max_abs_pos - 1 if max_abs_pos is not None else 1\n        pos_enc = (\n            torch.tensor(rel_pos_list).pin_memory().to(device=device, non_blocking=True)\n            / t_diff_max\n        )\n        tpos_dim = self.hidden_dim\n        pos_enc = get_1d_sine_pe(pos_enc, dim=tpos_dim)\n        pos_enc = self.obj_ptr_tpos_proj(pos_enc)\n\n        return pos_enc\n\n    def _build_sam_heads(self):\n        \"\"\"Build SAM-style prompt encoder and mask decoder.\"\"\"\n        self.sam_prompt_embed_dim = self.hidden_dim\n        self.sam_image_embedding_size = self.image_size // self.backbone_stride\n\n        # build PromptEncoder and MaskDecoder from SAM\n        # (their hyperparameters like `mask_in_chans=16` are from SAM code)\n        self.sam_prompt_encoder = PromptEncoder(\n            embed_dim=self.sam_prompt_embed_dim,\n            image_embedding_size=(\n                self.sam_image_embedding_size,\n                self.sam_image_embedding_size,\n            ),\n            input_image_size=(self.image_size, self.image_size),\n            mask_in_chans=16,\n        )\n        self.sam_mask_decoder = MaskDecoder(\n            num_multimask_outputs=3,\n            transformer=TwoWayTransformer(\n                depth=2,\n                embedding_dim=self.sam_prompt_embed_dim,\n                mlp_dim=2048,\n                num_heads=8,\n            ),\n            transformer_dim=self.sam_prompt_embed_dim,\n            iou_head_depth=3,\n            iou_head_hidden_dim=256,\n            use_high_res_features=True,\n            iou_prediction_use_sigmoid=True,\n            pred_obj_scores=True,\n            pred_obj_scores_mlp=True,\n            use_multimask_token_for_obj_ptr=True,\n            **(self.sam_mask_decoder_extra_args or {}),\n        )\n        # a linear projection on SAM output tokens to turn them into object pointers\n        self.obj_ptr_proj = torch.nn.Linear(self.hidden_dim, self.hidden_dim)\n        self.obj_ptr_proj = MLP(self.hidden_dim, self.hidden_dim, self.hidden_dim, 3)\n        # a linear projection on temporal positional encoding in object pointers to\n        # avoid potential interference with spatial positional encoding\n        self.obj_ptr_tpos_proj = torch.nn.Linear(self.hidden_dim, self.mem_dim)\n\n    def _forward_sam_heads(\n        self,\n        backbone_features,\n        point_inputs=None,\n        mask_inputs=None,\n        high_res_features=None,\n        multimask_output=False,\n        gt_masks=None,\n    ):\n        \"\"\"\n        Forward SAM prompt encoders and mask heads.\n\n        Inputs:\n        - backbone_features: image features of [B, C, H, W] shape\n        - point_inputs: a dictionary with \"point_coords\" and \"point_labels\", where\n          1) \"point_coords\" has [B, P, 2] shape and float32 dtype and contains the\n             absolute pixel-unit coordinate in (x, y) format of the P input points\n          2) \"point_labels\" has shape [B, P] and int32 dtype, where 1 means\n             positive clicks, 0 means negative clicks, and -1 means padding\n        - mask_inputs: a mask of [B, 1, H*16, W*16] shape, float or bool, with the\n          same spatial size as the image.\n        - high_res_features: either 1) None or 2) or a list of length 2 containing\n          two feature maps of [B, C, 4*H, 4*W] and [B, C, 2*H, 2*W] shapes respectively,\n          which will be used as high-resolution feature maps for SAM decoder.\n        - multimask_output: if it's True, we output 3 candidate masks and their 3\n          corresponding IoU estimates, and if it's False, we output only 1 mask and\n          its corresponding IoU estimate.\n\n        Outputs:\n        - low_res_multimasks: [B, M, H*4, W*4] shape (where M = 3 if\n          `multimask_output=True` and M = 1 if `multimask_output=False`), the SAM\n          output mask logits (before sigmoid) for the low-resolution masks, with 4x\n          the resolution (1/4 stride) of the input backbone_features.\n        - high_res_multimasks: [B, M, H*16, W*16] shape (where M = 3\n          if `multimask_output=True` and M = 1 if `multimask_output=False`),\n          upsampled from the low-resolution masks, with shape size as the image\n          (stride is 1 pixel).\n        - ious, [B, M] shape, where (where M = 3 if `multimask_output=True` and M = 1\n          if `multimask_output=False`), the estimated IoU of each output mask.\n        - low_res_masks: [B, 1, H*4, W*4] shape, the best mask in `low_res_multimasks`.\n          If `multimask_output=True`, it's the mask with the highest IoU estimate.\n          If `multimask_output=False`, it's the same as `low_res_multimasks`.\n        - high_res_masks: [B, 1, H*16, W*16] shape, the best mask in `high_res_multimasks`.\n          If `multimask_output=True`, it's the mask with the highest IoU estimate.\n          If `multimask_output=False`, it's the same as `high_res_multimasks`.\n        - obj_ptr: [B, C] shape, the object pointer vector for the output mask, extracted\n          based on the output token from the SAM mask decoder.\n        \"\"\"\n        B = backbone_features.size(0)\n        device = backbone_features.device\n        assert backbone_features.size(1) == self.sam_prompt_embed_dim\n        assert backbone_features.size(2) == self.sam_image_embedding_size\n        assert backbone_features.size(3) == self.sam_image_embedding_size\n\n        # a) Handle point prompts\n        if point_inputs is not None:\n            sam_point_coords = point_inputs[\"point_coords\"]\n            sam_point_labels = point_inputs[\"point_labels\"]\n            assert sam_point_coords.size(0) == B and sam_point_labels.size(0) == B\n        else:\n            # If no points are provide, pad with an empty point (with label -1)\n            sam_point_coords = torch.zeros(B, 1, 2, device=device)\n            sam_point_labels = -torch.ones(B, 1, dtype=torch.int32, device=device)\n\n        # b) Handle mask prompts\n        if mask_inputs is not None:\n            # If mask_inputs is provided, downsize it into low-res mask input if needed\n            # and feed it as a dense mask prompt into the SAM mask encoder\n            assert len(mask_inputs.shape) == 4 and mask_inputs.shape[:2] == (B, 1)\n            if mask_inputs.shape[-2:] != self.sam_prompt_encoder.mask_input_size:\n                sam_mask_prompt = F.interpolate(\n                    mask_inputs.float(),\n                    size=self.sam_prompt_encoder.mask_input_size,\n                    align_corners=False,\n                    mode=\"bilinear\",\n                    antialias=True,  # use antialias for downsampling\n                )\n            else:\n                sam_mask_prompt = mask_inputs\n        else:\n            # Otherwise, simply feed None (and SAM's prompt encoder will add\n            # a learned `no_mask_embed` to indicate no mask input in this case).\n            sam_mask_prompt = None\n\n        sparse_embeddings, dense_embeddings = self.sam_prompt_encoder(\n            points=(sam_point_coords, sam_point_labels),\n            boxes=None,\n            masks=sam_mask_prompt,\n        )\n        # Clone image_pe and the outputs of sam_prompt_encoder\n        # to enable compilation\n        sparse_embeddings = self._maybe_clone(sparse_embeddings)\n        dense_embeddings = self._maybe_clone(dense_embeddings)\n        image_pe = self._maybe_clone(self.sam_prompt_encoder.get_dense_pe())\n        with torch.profiler.record_function(\"sam_mask_decoder\"):\n            (\n                low_res_multimasks,\n                ious,\n                sam_output_tokens,\n                object_score_logits,\n            ) = self.sam_mask_decoder(\n                image_embeddings=backbone_features,\n                image_pe=image_pe,\n                sparse_prompt_embeddings=sparse_embeddings,\n                dense_prompt_embeddings=dense_embeddings,\n                multimask_output=multimask_output,\n                repeat_image=False,  # the image is already batched\n                high_res_features=high_res_features,\n            )\n        # Clone the output of sam_mask_decoder\n        # to enable compilation\n        low_res_multimasks = self._maybe_clone(low_res_multimasks)\n        ious = self._maybe_clone(ious)\n        sam_output_tokens = self._maybe_clone(sam_output_tokens)\n        object_score_logits = self._maybe_clone(object_score_logits)\n\n        if self.training and self.teacher_force_obj_scores_for_mem:\n            # we use gt to detect if there is an object or not to\n            # select no obj ptr and use an empty mask for spatial memory\n            is_obj_appearing = torch.any(gt_masks.float().flatten(1) > 0, dim=1)\n            is_obj_appearing = is_obj_appearing[..., None]\n        else:\n            is_obj_appearing = object_score_logits > 0\n\n        # Mask used for spatial memories is always a *hard* choice between obj and no obj,\n        # consistent with the actual mask prediction\n        low_res_multimasks = torch.where(\n            is_obj_appearing[:, None, None],\n            low_res_multimasks,\n            NO_OBJ_SCORE,\n        )\n\n        # convert masks from possibly bfloat16 (or float16) to float32\n        # (older PyTorch versions before 2.1 don't support `interpolate` on bf16)\n        low_res_multimasks = low_res_multimasks.float()\n        high_res_multimasks = F.interpolate(\n            low_res_multimasks,\n            size=(self.image_size, self.image_size),\n            mode=\"bilinear\",\n            align_corners=False,\n        )\n\n        sam_output_token = sam_output_tokens[:, 0]\n        if multimask_output:\n            # take the best mask prediction (with the highest IoU estimation)\n            best_iou_inds = torch.argmax(ious, dim=-1)\n            batch_inds = torch.arange(B, device=device)\n            low_res_masks = low_res_multimasks[batch_inds, best_iou_inds].unsqueeze(1)\n            high_res_masks = high_res_multimasks[batch_inds, best_iou_inds].unsqueeze(1)\n            if sam_output_tokens.size(1) > 1:\n                sam_output_token = sam_output_tokens[batch_inds, best_iou_inds]\n        else:\n            low_res_masks, high_res_masks = low_res_multimasks, high_res_multimasks\n\n        # Extract object pointer from the SAM output token (with occlusion handling)\n        obj_ptr = self.obj_ptr_proj(sam_output_token)\n        lambda_is_obj_appearing = is_obj_appearing.float()\n\n        obj_ptr = lambda_is_obj_appearing * obj_ptr\n        obj_ptr = obj_ptr + (1 - lambda_is_obj_appearing) * self.no_obj_ptr\n\n        return (\n            low_res_multimasks,\n            high_res_multimasks,\n            ious,\n            low_res_masks,\n            high_res_masks,\n            obj_ptr,\n            object_score_logits,\n        )\n\n    def _use_mask_as_output(self, backbone_features, high_res_features, mask_inputs):\n        \"\"\"\n        Directly turn binary `mask_inputs` into a output mask logits without using SAM.\n        (same input and output shapes as in _forward_sam_heads above).\n        \"\"\"\n        # Use -10/+10 as logits for neg/pos pixels (very close to 0/1 in prob after sigmoid).\n        out_scale, out_bias = 20.0, -10.0  # sigmoid(-10.0)=4.5398e-05\n        mask_inputs_float = mask_inputs.float()\n        high_res_masks = mask_inputs_float * out_scale + out_bias\n        low_res_masks = F.interpolate(\n            high_res_masks,\n            size=(\n                high_res_masks.size(-2) // self.backbone_stride * 4,\n                high_res_masks.size(-1) // self.backbone_stride * 4,\n            ),\n            align_corners=False,\n            mode=\"bilinear\",\n            antialias=True,  # use antialias for downsampling\n        )\n        # a dummy IoU prediction of all 1's under mask input\n        ious = mask_inputs.new_ones(mask_inputs.size(0), 1).float()\n        # produce an object pointer using the SAM decoder from the mask input\n        _, _, _, _, _, obj_ptr, _ = self._forward_sam_heads(\n            backbone_features=backbone_features,\n            mask_inputs=self.mask_downsample(mask_inputs_float),\n            high_res_features=high_res_features,\n            gt_masks=mask_inputs,\n        )\n        # In this method, we are treating mask_input as output, e.g. using it directly to create spatial mem;\n        # Below, we follow the same design axiom to use mask_input to decide if obj appears or not instead of relying\n        # on the object_scores from the SAM decoder.\n        is_obj_appearing = torch.any(mask_inputs.flatten(1).float() > 0.0, dim=1)\n        is_obj_appearing = is_obj_appearing[..., None]\n        lambda_is_obj_appearing = is_obj_appearing.float()\n        object_score_logits = out_scale * lambda_is_obj_appearing + out_bias\n        obj_ptr = lambda_is_obj_appearing * obj_ptr\n        obj_ptr = obj_ptr + (1 - lambda_is_obj_appearing) * self.no_obj_ptr\n\n        return (\n            low_res_masks,\n            high_res_masks,\n            ious,\n            low_res_masks,\n            high_res_masks,\n            obj_ptr,\n            object_score_logits,\n        )\n\n    def forward(self, input: BatchedDatapoint, is_inference=False):\n        raise NotImplementedError(\n            \"Please use the corresponding methods in SAM3VideoPredictor for inference.\"\n            \"See examples/sam3_dense_video_tracking.ipynb for an inference example.\"\n        )\n\n    def forward_image(self, img_batch):\n        \"\"\"Get the image feature on the input batch.\"\"\"\n        # This line is the only change from the parent class\n        # to use the SAM3 backbone instead of the SAM2 backbone.\n        backbone_out = self.backbone.forward_image(img_batch)[\"sam2_backbone_out\"]\n        # precompute projected level 0 and level 1 features in SAM decoder\n        # to avoid running it again on every SAM click\n        backbone_out[\"backbone_fpn\"][0] = self.sam_mask_decoder.conv_s0(\n            backbone_out[\"backbone_fpn\"][0]\n        )\n        backbone_out[\"backbone_fpn\"][1] = self.sam_mask_decoder.conv_s1(\n            backbone_out[\"backbone_fpn\"][1]\n        )\n        # Clone to help torch.compile\n        for i in range(len(backbone_out[\"backbone_fpn\"])):\n            backbone_out[\"backbone_fpn\"][i] = self._maybe_clone(\n                backbone_out[\"backbone_fpn\"][i]\n            )\n            backbone_out[\"vision_pos_enc\"][i] = self._maybe_clone(\n                backbone_out[\"vision_pos_enc\"][i]\n            )\n        return backbone_out\n\n    def _prepare_backbone_features(self, backbone_out):\n        \"\"\"Prepare and flatten visual features (same as in MDETR_API model).\"\"\"\n        backbone_out = backbone_out.copy()\n        assert len(backbone_out[\"backbone_fpn\"]) == len(backbone_out[\"vision_pos_enc\"])\n        assert len(backbone_out[\"backbone_fpn\"]) >= self.num_feature_levels\n\n        feature_maps = backbone_out[\"backbone_fpn\"][-self.num_feature_levels :]\n        vision_pos_embeds = backbone_out[\"vision_pos_enc\"][-self.num_feature_levels :]\n\n        feat_sizes = [(x.shape[-2], x.shape[-1]) for x in vision_pos_embeds]\n        # flatten NxCxHxW to HWxNxC\n        vision_feats = [x.flatten(2).permute(2, 0, 1) for x in feature_maps]\n        vision_pos_embeds = [x.flatten(2).permute(2, 0, 1) for x in vision_pos_embeds]\n\n        return backbone_out, vision_feats, vision_pos_embeds, feat_sizes\n\n    def _prepare_backbone_features_per_frame(self, img_batch, img_ids):\n        \"\"\"Compute the image backbone features on the fly for the given img_ids.\"\"\"\n        # Only forward backbone on unique image ids to avoid repeatitive computation\n        # (if `img_ids` has only one element, it's already unique so we skip this step).\n        if img_ids.numel() > 1:\n            unique_img_ids, inv_ids = torch.unique(img_ids, return_inverse=True)\n        else:\n            unique_img_ids, inv_ids = img_ids, None\n\n        # Compute the image features on those unique image ids\n        image = img_batch[unique_img_ids]\n        backbone_out = self.forward_image(image)\n        (\n            _,\n            vision_feats,\n            vision_pos_embeds,\n            feat_sizes,\n        ) = self._prepare_backbone_features(backbone_out)\n        # Inverse-map image features for `unique_img_ids` to the final image features\n        # for the original input `img_ids`.\n        if inv_ids is not None:\n            image = image[inv_ids]\n            vision_feats = [x[:, inv_ids] for x in vision_feats]\n            vision_pos_embeds = [x[:, inv_ids] for x in vision_pos_embeds]\n\n        return image, vision_feats, vision_pos_embeds, feat_sizes\n\n    def cal_mem_score(self, object_score_logits, iou_score):\n        object_score_norm = torch.where(\n            object_score_logits > 0,\n            object_score_logits.sigmoid() * 2 - 1,  ## rescale to [0, 1]\n            torch.zeros_like(object_score_logits),\n        )\n        score_per_frame = (object_score_norm * iou_score).mean()\n        return score_per_frame\n\n    def frame_filter(self, output_dict, track_in_reverse, frame_idx, num_frames, r):\n        if (frame_idx == 0 and not track_in_reverse) or (\n            frame_idx == num_frames - 1 and track_in_reverse\n        ):\n            return []\n\n        max_num = min(\n            num_frames, self.max_obj_ptrs_in_encoder\n        )  ## maximum number of pointer memory frames to consider\n\n        if not track_in_reverse:\n            start = frame_idx - 1\n            end = 0\n            step = -r\n            must_include = frame_idx - 1\n        else:\n            start = frame_idx + 1\n            end = num_frames\n            step = r\n            must_include = frame_idx + 1\n\n        valid_indices = []\n        for i in range(start, end, step):\n            if (\n                i not in output_dict[\"non_cond_frame_outputs\"]\n                or \"eff_iou_score\" not in output_dict[\"non_cond_frame_outputs\"][i]\n            ):\n                continue\n\n            score_per_frame = output_dict[\"non_cond_frame_outputs\"][i][\"eff_iou_score\"]\n\n            if score_per_frame > self.mf_threshold:  # threshold\n                valid_indices.insert(0, i)\n\n            if len(valid_indices) >= max_num - 1:\n                break\n\n        if must_include not in valid_indices:\n            valid_indices.append(must_include)\n\n        return valid_indices\n\n    def _prepare_memory_conditioned_features(\n        self,\n        frame_idx,\n        is_init_cond_frame,\n        current_vision_feats,\n        current_vision_pos_embeds,\n        feat_sizes,\n        output_dict,\n        num_frames,\n        track_in_reverse=False,  # tracking in reverse time order (for demo usage)\n        use_prev_mem_frame=True,\n    ):\n        \"\"\"Fuse the current frame's visual feature map with previous memory.\"\"\"\n        B = current_vision_feats[-1].size(1)  # batch size on this frame\n        C = self.hidden_dim\n        H, W = feat_sizes[-1]  # top-level (lowest-resolution) feature size\n        device = current_vision_feats[-1].device\n        # The case of `self.num_maskmem == 0` below is primarily used for reproducing SAM on images.\n        # In this case, we skip the fusion with any memory.\n        if self.num_maskmem == 0:  # Disable memory and skip fusion\n            pix_feat = current_vision_feats[-1].permute(1, 2, 0).view(B, C, H, W)\n            return pix_feat\n\n        num_obj_ptr_tokens = 0\n        tpos_sign_mul = -1 if track_in_reverse else 1\n        # Step 1: condition the visual features of the current frame on previous memories\n        if not is_init_cond_frame and use_prev_mem_frame:\n            # Retrieve the memories encoded with the maskmem backbone\n            to_cat_prompt, to_cat_prompt_mask, to_cat_prompt_pos_embed = [], [], []\n            # Add conditioning frames's output first (all cond frames have t_pos=0 for\n            # when getting temporal positional embedding below)\n            assert len(output_dict[\"cond_frame_outputs\"]) > 0\n            # Select a maximum number of temporally closest cond frames for cross attention\n            cond_outputs = output_dict[\"cond_frame_outputs\"]\n            selected_cond_outputs, unselected_cond_outputs = select_closest_cond_frames(\n                frame_idx,\n                cond_outputs,\n                self.max_cond_frames_in_attn,\n                keep_first_cond_frame=self.keep_first_cond_frame,\n            )\n            t_pos_and_prevs = [\n                ((frame_idx - t) * tpos_sign_mul, out, True)\n                for t, out in selected_cond_outputs.items()\n            ]\n            # Add last (self.num_maskmem - 1) frames before current frame for non-conditioning memory\n            # the earliest one has t_pos=1 and the latest one has t_pos=self.num_maskmem-1\n            # We also allow taking the memory frame non-consecutively (with r>1), in which case\n            # we take (self.num_maskmem - 2) frames among every r-th frames plus the last frame.\n            r = 1 if self.training else self.memory_temporal_stride_for_eval\n\n            if self.use_memory_selection:\n                valid_indices = self.frame_filter(\n                    output_dict, track_in_reverse, frame_idx, num_frames, r\n                )\n\n            for t_pos in range(1, self.num_maskmem):\n                t_rel = self.num_maskmem - t_pos  # how many frames before current frame\n                if self.use_memory_selection:\n                    if t_rel > len(valid_indices):\n                        continue\n                    prev_frame_idx = valid_indices[-t_rel]\n                else:\n                    if t_rel == 1:\n                        # for t_rel == 1, we take the last frame (regardless of r)\n                        if not track_in_reverse:\n                            # the frame immediately before this frame (i.e. frame_idx - 1)\n                            prev_frame_idx = frame_idx - t_rel\n                        else:\n                            # the frame immediately after this frame (i.e. frame_idx + 1)\n                            prev_frame_idx = frame_idx + t_rel\n                    else:\n                        # for t_rel >= 2, we take the memory frame from every r-th frames\n                        if not track_in_reverse:\n                            # first find the nearest frame among every r-th frames before this frame\n                            # for r=1, this would be (frame_idx - 2)\n                            prev_frame_idx = ((frame_idx - 2) // r) * r\n                            # then seek further among every r-th frames\n                            prev_frame_idx = prev_frame_idx - (t_rel - 2) * r\n                        else:\n                            # first find the nearest frame among every r-th frames after this frame\n                            # for r=1, this would be (frame_idx + 2)\n                            prev_frame_idx = -(-(frame_idx + 2) // r) * r\n                            # then seek further among every r-th frames\n                            prev_frame_idx = prev_frame_idx + (t_rel - 2) * r\n\n                out = output_dict[\"non_cond_frame_outputs\"].get(prev_frame_idx, None)\n                if out is None:\n                    # If an unselected conditioning frame is among the last (self.num_maskmem - 1)\n                    # frames, we still attend to it as if it's a non-conditioning frame.\n                    out = unselected_cond_outputs.get(prev_frame_idx, None)\n                t_pos_and_prevs.append((t_pos, out, False))\n\n            for t_pos, prev, is_selected_cond_frame in t_pos_and_prevs:\n                if prev is None:\n                    continue  # skip padding frames\n                # \"maskmem_features\" might have been offloaded to CPU in demo use cases,\n                # so we load it back to GPU (it's a no-op if it's already on GPU).\n                feats = prev[\"maskmem_features\"].cuda(non_blocking=True)\n                seq_len = feats.shape[-2] * feats.shape[-1]\n                to_cat_prompt.append(feats.flatten(2).permute(2, 0, 1))\n                to_cat_prompt_mask.append(\n                    torch.zeros(B, seq_len, device=device, dtype=bool)\n                )\n                # Spatial positional encoding (it might have been offloaded to CPU in eval)\n                maskmem_enc = prev[\"maskmem_pos_enc\"][-1].cuda()\n                maskmem_enc = maskmem_enc.flatten(2).permute(2, 0, 1)\n\n                if (\n                    is_selected_cond_frame\n                    and getattr(self, \"cond_frame_spatial_embedding\", None) is not None\n                ):\n                    # add a spatial embedding for the conditioning frame\n                    maskmem_enc = maskmem_enc + self.cond_frame_spatial_embedding\n\n                # Temporal positional encoding\n                t = t_pos if not is_selected_cond_frame else 0\n                maskmem_enc = (\n                    maskmem_enc + self.maskmem_tpos_enc[self.num_maskmem - t - 1]\n                )\n                to_cat_prompt_pos_embed.append(maskmem_enc)\n\n            # Construct the list of past object pointers\n            # Optionally, select only a subset of spatial memory frames during trainining\n            if (\n                self.training\n                and self.prob_to_dropout_spatial_mem > 0\n                and self.rng.random() < self.prob_to_dropout_spatial_mem\n            ):\n                num_spatial_mem_keep = self.rng.integers(len(to_cat_prompt) + 1)\n                keep = self.rng.choice(\n                    range(len(to_cat_prompt)), num_spatial_mem_keep, replace=False\n                ).tolist()\n                to_cat_prompt = [to_cat_prompt[i] for i in keep]\n                to_cat_prompt_mask = [to_cat_prompt_mask[i] for i in keep]\n                to_cat_prompt_pos_embed = [to_cat_prompt_pos_embed[i] for i in keep]\n\n            max_obj_ptrs_in_encoder = min(num_frames, self.max_obj_ptrs_in_encoder)\n            # First add those object pointers from selected conditioning frames\n            # (optionally, only include object pointers in the past during evaluation)\n            if not self.training:\n                ptr_cond_outputs = {\n                    t: out\n                    for t, out in selected_cond_outputs.items()\n                    if (t >= frame_idx if track_in_reverse else t <= frame_idx)\n                }\n            else:\n                ptr_cond_outputs = selected_cond_outputs\n            pos_and_ptrs = [\n                # Temporal pos encoding contains how far away each pointer is from current frame\n                (\n                    (frame_idx - t) * tpos_sign_mul,\n                    out[\"obj_ptr\"],\n                    True,  # is_selected_cond_frame\n                )\n                for t, out in ptr_cond_outputs.items()\n            ]\n\n            # Add up to (max_obj_ptrs_in_encoder - 1) non-conditioning frames before current frame\n            for t_diff in range(1, max_obj_ptrs_in_encoder):\n                if not self.use_memory_selection:\n                    t = frame_idx + t_diff if track_in_reverse else frame_idx - t_diff\n                    if t < 0 or (num_frames is not None and t >= num_frames):\n                        break\n                else:\n                    if -t_diff <= -len(valid_indices):\n                        break\n                    t = valid_indices[-t_diff]\n\n                out = output_dict[\"non_cond_frame_outputs\"].get(\n                    t, unselected_cond_outputs.get(t, None)\n                )\n                if out is not None:\n                    pos_and_ptrs.append((t_diff, out[\"obj_ptr\"], False))\n\n            # If we have at least one object pointer, add them to the across attention\n            if len(pos_and_ptrs) > 0:\n                pos_list, ptrs_list, is_selected_cond_frame_list = zip(*pos_and_ptrs)\n                # stack object pointers along dim=0 into [ptr_seq_len, B, C] shape\n                obj_ptrs = torch.stack(ptrs_list, dim=0)\n                if getattr(self, \"cond_frame_obj_ptr_embedding\", None) is not None:\n                    obj_ptrs = (\n                        obj_ptrs\n                        + self.cond_frame_obj_ptr_embedding\n                        * torch.tensor(is_selected_cond_frame_list, device=device)[\n                            ..., None, None\n                        ].float()\n                    )\n                # a temporal positional embedding based on how far each object pointer is from\n                # the current frame (sine embedding normalized by the max pointer num).\n                obj_pos = self._get_tpos_enc(\n                    pos_list,\n                    max_abs_pos=max_obj_ptrs_in_encoder,\n                    device=device,\n                )\n                # expand to batch size\n                obj_pos = obj_pos.unsqueeze(1).expand(-1, B, -1)\n\n                if self.mem_dim < C:\n                    # split a pointer into (C // self.mem_dim) tokens for self.mem_dim < C\n                    obj_ptrs = obj_ptrs.reshape(-1, B, C // self.mem_dim, self.mem_dim)\n                    obj_ptrs = obj_ptrs.permute(0, 2, 1, 3).flatten(0, 1)\n                    obj_pos = obj_pos.repeat_interleave(C // self.mem_dim, dim=0)\n                to_cat_prompt.append(obj_ptrs)\n                to_cat_prompt_mask.append(None)  # \"to_cat_prompt_mask\" is not used\n                to_cat_prompt_pos_embed.append(obj_pos)\n                num_obj_ptr_tokens = obj_ptrs.shape[0]\n            else:\n                num_obj_ptr_tokens = 0\n        else:\n            # directly add no-mem embedding (instead of using the transformer encoder)\n            pix_feat_with_mem = current_vision_feats[-1] + self.no_mem_embed\n            pix_feat_with_mem = pix_feat_with_mem.permute(1, 2, 0).view(B, C, H, W)\n            return pix_feat_with_mem\n\n            # Use a dummy token on the first grame (to avoid emtpy memory input to tranformer encoder)\n            to_cat_prompt = [self.no_mem_embed.expand(1, B, self.mem_dim)]\n            to_cat_prompt_mask = [torch.zeros(B, 1, device=device, dtype=bool)]\n            to_cat_prompt_pos_embed = [self.no_mem_pos_enc.expand(1, B, self.mem_dim)]\n\n        # Step 2: Concatenate the memories and forward through the transformer encoder\n        prompt = torch.cat(to_cat_prompt, dim=0)\n        prompt_mask = None  # For now, we always masks are zeros anyways\n        prompt_pos_embed = torch.cat(to_cat_prompt_pos_embed, dim=0)\n        encoder_out = self.transformer.encoder(\n            src=current_vision_feats,\n            src_key_padding_mask=[None],\n            src_pos=current_vision_pos_embeds,\n            prompt=prompt,\n            prompt_pos=prompt_pos_embed,\n            prompt_key_padding_mask=prompt_mask,\n            feat_sizes=feat_sizes,\n            num_obj_ptr_tokens=num_obj_ptr_tokens,\n        )\n        # reshape the output (HW)BC => BCHW\n        pix_feat_with_mem = encoder_out[\"memory\"].permute(1, 2, 0).view(B, C, H, W)\n        return pix_feat_with_mem\n\n    def _encode_new_memory(\n        self,\n        image,\n        current_vision_feats,\n        feat_sizes,\n        pred_masks_high_res,\n        object_score_logits,\n        is_mask_from_pts,\n        output_dict=None,\n        is_init_cond_frame=False,\n    ):\n        \"\"\"Encode the current image and its prediction into a memory feature.\"\"\"\n        B = current_vision_feats[-1].size(1)  # batch size on this frame\n        C = self.hidden_dim\n        H, W = feat_sizes[-1]  # top-level (lowest-resolution) feature size\n        # top-level feature, (HW)BC => BCHW\n        pix_feat = current_vision_feats[-1].permute(1, 2, 0).view(B, C, H, W)\n        if self.non_overlap_masks_for_mem_enc and not self.training:\n            # optionally, apply non-overlapping constraints to the masks (it's applied\n            # in the batch dimension and should only be used during eval, where all\n            # the objects come from the same video under batch size 1).\n            pred_masks_high_res = self._apply_non_overlapping_constraints(\n                pred_masks_high_res\n            )\n        # scale the raw mask logits with a temperature before applying sigmoid\n        if is_mask_from_pts and not self.training:\n            mask_for_mem = (pred_masks_high_res > 0).float()\n        else:\n            # apply sigmoid on the raw mask logits to turn them into range (0, 1)\n            mask_for_mem = torch.sigmoid(pred_masks_high_res)\n        # apply scale and bias terms to the sigmoid probabilities\n        if self.sigmoid_scale_for_mem_enc != 1.0:\n            mask_for_mem = mask_for_mem * self.sigmoid_scale_for_mem_enc\n        if self.sigmoid_bias_for_mem_enc != 0.0:\n            mask_for_mem = mask_for_mem + self.sigmoid_bias_for_mem_enc\n\n        if isinstance(self.maskmem_backbone, SimpleMaskEncoder):\n            pix_feat = pix_feat.view_as(pix_feat)\n            maskmem_out = self.maskmem_backbone(\n                pix_feat, mask_for_mem, skip_mask_sigmoid=True\n            )\n        else:\n            maskmem_out = self.maskmem_backbone(image, pix_feat, mask_for_mem)\n        # Clone the feats and pos_enc to enable compilation\n        maskmem_features = self._maybe_clone(maskmem_out[\"vision_features\"])\n        maskmem_pos_enc = [self._maybe_clone(m) for m in maskmem_out[\"vision_pos_enc\"]]\n        # add a no-object embedding to the spatial memory to indicate that the frame\n        # is predicted to be occluded (i.e. no object is appearing in the frame)\n        is_obj_appearing = (object_score_logits > 0).float()\n        maskmem_features += (\n            1 - is_obj_appearing[..., None, None]\n        ) * self.no_obj_embed_spatial[..., None, None].expand(*maskmem_features.shape)\n\n        return maskmem_features, maskmem_pos_enc\n\n    def forward_tracking(self, backbone_out, input, return_dict=False):\n        \"\"\"Forward video tracking on each frame (and sample correction clicks).\"\"\"\n        img_feats_already_computed = backbone_out[\"backbone_fpn\"] is not None\n        if img_feats_already_computed:\n            # Prepare the backbone features\n            # - vision_feats and vision_pos_embeds are in (HW)BC format\n            (\n                _,\n                vision_feats,\n                vision_pos_embeds,\n                feat_sizes,\n            ) = self._prepare_backbone_features(backbone_out)\n\n        # Starting the stage loop\n        num_frames = backbone_out[\"num_frames\"]\n        init_cond_frames = backbone_out[\"init_cond_frames\"]\n        frames_to_add_correction_pt = backbone_out[\"frames_to_add_correction_pt\"]\n        # first process all the initial conditioning frames to encode them as memory,\n        # and then conditioning on them to track the remaining frames\n        processing_order = init_cond_frames + backbone_out[\"frames_not_in_init_cond\"]\n        output_dict = {\n            \"cond_frame_outputs\": {},  # dict containing {frame_idx: <out>}\n            \"non_cond_frame_outputs\": {},  # dict containing {frame_idx: <out>}\n        }\n        for stage_id in processing_order:\n            # Get the image features for the current frames\n            img_ids = input.find_inputs[stage_id].img_ids\n            if img_feats_already_computed:\n                # Retrieve image features according to img_ids (if they are already computed).\n                current_image = input.img_batch[img_ids]\n                current_vision_feats = [x[:, img_ids] for x in vision_feats]\n                current_vision_pos_embeds = [x[:, img_ids] for x in vision_pos_embeds]\n            else:\n                # Otherwise, compute the image features on the fly for the given img_ids\n                # (this might be used for evaluation on long videos to avoid backbone OOM).\n                (\n                    current_image,\n                    current_vision_feats,\n                    current_vision_pos_embeds,\n                    feat_sizes,\n                ) = self._prepare_backbone_features_per_frame(input.img_batch, img_ids)\n            # Get output masks based on this frame's prompts and previous memory\n            current_out = self.track_step(\n                frame_idx=stage_id,\n                is_init_cond_frame=stage_id in init_cond_frames,\n                current_vision_feats=current_vision_feats,\n                current_vision_pos_embeds=current_vision_pos_embeds,\n                feat_sizes=feat_sizes,\n                image=current_image,\n                point_inputs=backbone_out[\"point_inputs_per_frame\"].get(stage_id, None),\n                mask_inputs=backbone_out[\"mask_inputs_per_frame\"].get(stage_id, None),\n                output_dict=output_dict,\n                num_frames=num_frames,\n            )\n            # Append the output, depending on whether it's a conditioning frame\n            add_output_as_cond_frame = stage_id in init_cond_frames or (\n                self.add_all_frames_to_correct_as_cond\n                and stage_id in frames_to_add_correction_pt\n            )\n            if add_output_as_cond_frame:\n                output_dict[\"cond_frame_outputs\"][stage_id] = current_out\n            else:\n                output_dict[\"non_cond_frame_outputs\"][stage_id] = current_out\n\n        if return_dict:\n            return output_dict\n        # turn `output_dict` into a list for loss function\n        all_frame_outputs = {}\n        all_frame_outputs.update(output_dict[\"cond_frame_outputs\"])\n        all_frame_outputs.update(output_dict[\"non_cond_frame_outputs\"])\n        all_frame_outputs = [all_frame_outputs[t] for t in range(num_frames)]\n        # Make DDP happy with activation checkpointing by removing unused keys\n        all_frame_outputs = [\n            {k: v for k, v in d.items() if k != \"obj_ptr\"} for d in all_frame_outputs\n        ]\n\n        return all_frame_outputs\n\n    def track_step(\n        self,\n        frame_idx,\n        is_init_cond_frame,\n        current_vision_feats,\n        current_vision_pos_embeds,\n        feat_sizes,\n        image,\n        point_inputs,\n        mask_inputs,\n        output_dict,\n        num_frames,\n        track_in_reverse=False,  # tracking in reverse time order (for demo usage)\n        # Whether to run the memory encoder on the predicted masks. Sometimes we might want\n        # to skip the memory encoder with `run_mem_encoder=False`. For example,\n        # in demo we might call `track_step` multiple times for each user click,\n        # and only encode the memory when the user finalizes their clicks. And in ablation\n        # settings like SAM training on static images, we don't need the memory encoder.\n        run_mem_encoder=True,\n        # The previously predicted SAM mask logits (which can be fed together with new clicks in demo).\n        prev_sam_mask_logits=None,\n        use_prev_mem_frame=True,\n    ):\n        current_out = {\"point_inputs\": point_inputs, \"mask_inputs\": mask_inputs}\n        # High-resolution feature maps for the SAM head, reshape (HW)BC => BCHW\n        if len(current_vision_feats) > 1:\n            high_res_features = [\n                x.permute(1, 2, 0).view(x.size(1), x.size(2), *s)\n                for x, s in zip(current_vision_feats[:-1], feat_sizes[:-1])\n            ]\n        else:\n            high_res_features = None\n        if mask_inputs is not None:\n            # (see it as a GT mask) without using a SAM prompt encoder + mask decoder.\n            pix_feat = current_vision_feats[-1].permute(1, 2, 0)\n            pix_feat = pix_feat.view(-1, self.hidden_dim, *feat_sizes[-1])\n            sam_outputs = self._use_mask_as_output(\n                pix_feat, high_res_features, mask_inputs\n            )\n        else:\n            # fused the visual feature with previous memory features in the memory bank\n            pix_feat_with_mem = self._prepare_memory_conditioned_features(\n                frame_idx=frame_idx,\n                is_init_cond_frame=is_init_cond_frame,\n                current_vision_feats=current_vision_feats[-1:],\n                current_vision_pos_embeds=current_vision_pos_embeds[-1:],\n                feat_sizes=feat_sizes[-1:],\n                output_dict=output_dict,\n                num_frames=num_frames,\n                track_in_reverse=track_in_reverse,\n                use_prev_mem_frame=use_prev_mem_frame,\n            )\n            # apply SAM-style segmentation head\n            # here we might feed previously predicted low-res SAM mask logits into the SAM mask decoder,\n            # e.g. in demo where such logits come from earlier interaction instead of correction sampling\n            # (in this case, the SAM mask decoder should have `self.iter_use_prev_mask_pred=True`, and\n            # any `mask_inputs` shouldn't reach here as they are sent to _use_mask_as_output instead)\n            if prev_sam_mask_logits is not None:\n                assert self.iter_use_prev_mask_pred\n                assert point_inputs is not None and mask_inputs is None\n                mask_inputs = prev_sam_mask_logits\n            multimask_output = self._use_multimask(is_init_cond_frame, point_inputs)\n            sam_outputs = self._forward_sam_heads(\n                backbone_features=pix_feat_with_mem,\n                point_inputs=point_inputs,\n                mask_inputs=mask_inputs,\n                high_res_features=high_res_features,\n                multimask_output=multimask_output,\n            )\n        (\n            _,\n            high_res_multimasks,\n            ious,\n            low_res_masks,\n            high_res_masks,\n            obj_ptr,\n            object_score_logits,\n        ) = sam_outputs\n        # Use the final prediction (after all correction steps for output and eval)\n        current_out[\"pred_masks\"] = low_res_masks\n        current_out[\"pred_masks_high_res\"] = high_res_masks\n        current_out[\"obj_ptr\"] = obj_ptr\n        if self.use_memory_selection:\n            current_out[\"object_score_logits\"] = object_score_logits\n            iou_score = ious.max(-1)[0]\n            current_out[\"iou_score\"] = iou_score\n            current_out[\"eff_iou_score\"] = self.cal_mem_score(\n                object_score_logits, iou_score\n            )\n        if not self.training:\n            # Only add this in inference (to avoid unused param in activation checkpointing;\n            # it's mainly used in the demo to encode spatial memories w/ consolidated masks)\n            current_out[\"object_score_logits\"] = object_score_logits\n\n        # Finally run the memory encoder on the predicted mask to encode\n        # it into a new memory feature (that can be used in future frames)\n        # (note that `self.num_maskmem == 0` is primarily used for reproducing SAM on\n        # images, in which case we'll just skip memory encoder to save compute).\n        if run_mem_encoder and self.num_maskmem > 0:\n            high_res_masks_for_mem_enc = high_res_masks\n            maskmem_features, maskmem_pos_enc = self._encode_new_memory(\n                image=image,\n                current_vision_feats=current_vision_feats,\n                feat_sizes=feat_sizes,\n                pred_masks_high_res=high_res_masks_for_mem_enc,\n                object_score_logits=object_score_logits,\n                is_mask_from_pts=(point_inputs is not None),\n                output_dict=output_dict,\n                is_init_cond_frame=is_init_cond_frame,\n            )\n            current_out[\"maskmem_features\"] = maskmem_features\n            current_out[\"maskmem_pos_enc\"] = maskmem_pos_enc\n        else:\n            current_out[\"maskmem_features\"] = None\n            current_out[\"maskmem_pos_enc\"] = None\n\n        # Optionally, offload the outputs to CPU memory during evaluation to avoid\n        # GPU OOM on very long videos or very large resolution or too many objects\n        if self.offload_output_to_cpu_for_eval and not self.training:\n            # Here we only keep those keys needed for evaluation to get a compact output\n            trimmed_out = {\n                \"pred_masks\": current_out[\"pred_masks\"].cpu(),\n                \"pred_masks_high_res\": current_out[\"pred_masks_high_res\"].cpu(),\n                # other items for evaluation (these are small tensors so we keep them on GPU)\n                \"obj_ptr\": current_out[\"obj_ptr\"],\n                \"object_score_logits\": current_out[\"object_score_logits\"],\n            }\n            if run_mem_encoder and self.num_maskmem > 0:\n                trimmed_out[\"maskmem_features\"] = maskmem_features.cpu()\n                trimmed_out[\"maskmem_pos_enc\"] = [x.cpu() for x in maskmem_pos_enc]\n            if self.use_memory_selection:\n                trimmed_out[\"iou_score\"] = current_out[\"iou_score\"].cpu()\n                trimmed_out[\"eff_iou_score\"] = current_out[\"eff_iou_score\"].cpu()\n            current_out = trimmed_out\n\n        # Optionally, trim the output of past non-conditioning frame (r * num_maskmem frames\n        # before the current frame) during evaluation. This is intended to save GPU or CPU\n        # memory for semi-supervised VOS eval, where only the first frame receives prompts.\n        def _trim_past_out(past_out, current_out):\n            if past_out is None:\n                return None\n            return {\n                \"pred_masks\": past_out[\"pred_masks\"],\n                \"obj_ptr\": past_out[\"obj_ptr\"],\n                \"object_score_logits\": past_out[\"object_score_logits\"],\n            }\n\n        if self.trim_past_non_cond_mem_for_eval and not self.training:\n            r = self.memory_temporal_stride_for_eval\n            past_frame_idx = frame_idx - r * self.num_maskmem\n            past_out = output_dict[\"non_cond_frame_outputs\"].get(past_frame_idx, None)\n\n            if past_out is not None:\n                print(past_out.get(\"eff_iou_score\", 0))\n                if (\n                    self.use_memory_selection\n                    and past_out.get(\"eff_iou_score\", 0) < self.mf_threshold\n                ) or not self.use_memory_selection:\n                    output_dict[\"non_cond_frame_outputs\"][past_frame_idx] = (\n                        _trim_past_out(past_out, current_out)\n                    )\n\n            if (\n                self.use_memory_selection and not self.offload_output_to_cpu_for_eval\n            ):  ## design for memory selection, trim too old frames to save memory\n                far_old_frame_idx = frame_idx - 20 * self.max_obj_ptrs_in_encoder\n                past_out = output_dict[\"non_cond_frame_outputs\"].get(\n                    far_old_frame_idx, None\n                )\n                if past_out is not None:\n                    output_dict[\"non_cond_frame_outputs\"][far_old_frame_idx] = (\n                        _trim_past_out(past_out, current_out)\n                    )\n\n        return current_out\n\n    def _use_multimask(self, is_init_cond_frame, point_inputs):\n        \"\"\"Whether to use multimask output in the SAM head.\"\"\"\n        num_pts = 0 if point_inputs is None else point_inputs[\"point_labels\"].size(1)\n        multimask_output = (\n            self.multimask_output_in_sam\n            and (is_init_cond_frame or self.multimask_output_for_tracking)\n            and (self.multimask_min_pt_num <= num_pts <= self.multimask_max_pt_num)\n        )\n        return multimask_output\n\n    def _apply_non_overlapping_constraints(self, pred_masks):\n        \"\"\"\n        Apply non-overlapping constraints to the object scores in pred_masks. Here we\n        keep only the highest scoring object at each spatial location in pred_masks.\n        \"\"\"\n        batch_size = pred_masks.size(0)\n        if batch_size == 1:\n            return pred_masks\n\n        device = pred_masks.device\n        # \"max_obj_inds\": object index of the object with the highest score at each location\n        max_obj_inds = torch.argmax(pred_masks, dim=0, keepdim=True)\n        # \"batch_obj_inds\": object index of each object slice (along dim 0) in `pred_masks`\n        batch_obj_inds = torch.arange(batch_size, device=device)[:, None, None, None]\n        keep = max_obj_inds == batch_obj_inds\n        # suppress overlapping regions' scores below -10.0 so that the foreground regions\n        # don't overlap (here sigmoid(-10.0)=4.5398e-05)\n        pred_masks = torch.where(keep, pred_masks, torch.clamp(pred_masks, max=-10.0))\n        return pred_masks\n\n    def _compile_all_components(self):\n        \"\"\"Compile all model components for faster inference.\"\"\"\n        # a larger cache size to hold varying number of shapes for torch.compile\n        # see https://github.com/pytorch/pytorch/blob/v2.5.1/torch/_dynamo/config.py#L42-L49\n        torch._dynamo.config.cache_size_limit = 64\n        torch._dynamo.config.accumulated_cache_size_limit = 2048\n        from sam3.perflib.compile import compile_wrapper\n\n        logging.info(\"Compiling all components. First time may be very slow.\")\n\n        self.maskmem_backbone.forward = compile_wrapper(\n            self.maskmem_backbone.forward,\n            mode=\"max-autotune\",\n            fullgraph=True,\n            dynamic=False,\n        )\n        self.transformer.encoder.forward = compile_wrapper(\n            self.transformer.encoder.forward,\n            mode=\"max-autotune\",\n            fullgraph=True,\n            dynamic=True,  # Num. of memories varies\n        )\n        # We disable compilation of sam_prompt_encoder as it sometimes gives a large accuracy regression,\n        # especially when sam_mask_prompt (previous mask logits) is not None\n        # self.sam_prompt_encoder.forward = torch.compile(\n        #     self.sam_prompt_encoder.forward,\n        #     mode=\"max-autotune\",\n        #     fullgraph=True,\n        #     dynamic=False,  # Accuracy regression on True\n        # )\n        self.sam_mask_decoder.forward = compile_wrapper(\n            self.sam_mask_decoder.forward,\n            mode=\"max-autotune\",\n            fullgraph=True,\n            dynamic=False,  # Accuracy regression on True\n        )\n\n    def _maybe_clone(self, x):\n        \"\"\"Clone a tensor if and only if `self.compile_all_components` is True.\"\"\"\n        return x.clone() if self.compile_all_components else x\n\n\ndef concat_points(old_point_inputs, new_points, new_labels):\n    \"\"\"Add new points and labels to previous point inputs (add at the end).\"\"\"\n    if old_point_inputs is None:\n        points, labels = new_points, new_labels\n    else:\n        points = torch.cat([old_point_inputs[\"point_coords\"], new_points], dim=1)\n        labels = torch.cat([old_point_inputs[\"point_labels\"], new_labels], dim=1)\n\n    return {\"point_coords\": points, \"point_labels\": labels}\n"
  },
  {
    "path": "sam3/model/sam3_tracker_utils.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nfrom numpy.typing import NDArray\nfrom sam3.model.edt import edt_triton\n\n\ndef sample_box_points(\n    masks: torch.Tensor,\n    noise: float = 0.1,  # SAM default\n    noise_bound: int = 20,  # SAM default\n    top_left_label: int = 2,\n    bottom_right_label: int = 3,\n) -> tuple[NDArray, NDArray]:\n    \"\"\"\n    Sample a noised version of the top left and bottom right corners of a given `bbox`\n\n    Inputs:\n    - masks: [B, 1, H, W] tensor\n    - noise: noise as a fraction of box width and height, dtype=float\n    - noise_bound: maximum amount of noise (in pure pixels), dtype=int\n\n    Returns:\n    - box_coords: [B, num_pt, 2], contains (x, y) coordinates of top left and bottom right box corners, dtype=torch.float\n    - box_labels: [B, num_pt], label 2 is reserverd for top left and 3 for bottom right corners, dtype=torch.int32\n    \"\"\"\n    device = masks.device\n    box_coords = mask_to_box(masks)\n    B, _, H, W = masks.shape\n    box_labels = torch.tensor(\n        [top_left_label, bottom_right_label], dtype=torch.int, device=device\n    ).repeat(B)\n    if noise > 0.0:\n        if not isinstance(noise_bound, torch.Tensor):\n            noise_bound = torch.tensor(noise_bound, device=device)\n        bbox_w = box_coords[..., 2] - box_coords[..., 0]\n        bbox_h = box_coords[..., 3] - box_coords[..., 1]\n        max_dx = torch.min(bbox_w * noise, noise_bound)\n        max_dy = torch.min(bbox_h * noise, noise_bound)\n        box_noise = 2 * torch.rand(B, 1, 4, device=device) - 1\n        box_noise = box_noise * torch.stack((max_dx, max_dy, max_dx, max_dy), dim=-1)\n\n        box_coords = box_coords + box_noise\n        img_bounds = (\n            torch.tensor([W, H, W, H], device=device) - 1\n        )  # uncentered pixel coords\n        box_coords.clamp_(torch.zeros_like(img_bounds), img_bounds)  # In place clamping\n\n    box_coords = box_coords.reshape(-1, 2, 2)  # always 2 points\n    box_labels = box_labels.reshape(-1, 2)\n    return box_coords, box_labels\n\n\ndef mask_to_box(masks: torch.Tensor):\n    \"\"\"\n    compute bounding box given an input mask\n\n    Inputs:\n    - masks: [B, 1, H, W] tensor\n\n    Returns:\n    - box_coords: [B, 1, 4], contains (x, y) coordinates of top left and bottom right box corners, dtype=torch.Tensor\n    \"\"\"\n    B, _, h, w = masks.shape\n    device = masks.device\n    mask_area = masks.sum(dim=(-1, -2))\n    xs = torch.arange(w, device=device, dtype=torch.int32)\n    ys = torch.arange(h, device=device, dtype=torch.int32)\n    grid_xs, grid_ys = torch.meshgrid(xs, ys, indexing=\"xy\")\n    grid_xs = grid_xs[None, None, ...].expand(B, 1, h, w)\n    grid_ys = grid_ys[None, None, ...].expand(B, 1, h, w)\n    min_xs, _ = torch.min(torch.where(masks, grid_xs, w).flatten(-2), dim=-1)\n    max_xs, _ = torch.max(torch.where(masks, grid_xs, -1).flatten(-2), dim=-1)\n    min_ys, _ = torch.min(torch.where(masks, grid_ys, h).flatten(-2), dim=-1)\n    max_ys, _ = torch.max(torch.where(masks, grid_ys, -1).flatten(-2), dim=-1)\n    bbox_coords = torch.stack((min_xs, min_ys, max_xs, max_ys), dim=-1)\n    bbox_coords = torch.where(\n        mask_area[..., None] > 0, bbox_coords, torch.zeros_like(bbox_coords)\n    )\n    return bbox_coords\n\n\ndef sample_random_points_from_errors(gt_masks, pred_masks, num_pt=1):\n    \"\"\"\n    Sample `num_pt` random points (along with their labels) independently from the error regions.\n\n    Inputs:\n    - gt_masks: [B, 1, H_im, W_im] masks, dtype=torch.bool\n    - pred_masks: [B, 1, H_im, W_im] masks, dtype=torch.bool or None\n    - num_pt: int, number of points to sample independently for each of the B error maps\n\n    Outputs:\n    - points: [B, num_pt, 2], dtype=torch.float, contains (x, y) coordinates of each sampled point\n    - labels: [B, num_pt], dtype=torch.int32, where 1 means positive clicks and 0 means\n      negative clicks\n    \"\"\"\n    if pred_masks is None:  # if pred_masks is not provided, treat it as empty\n        pred_masks = torch.zeros_like(gt_masks)\n    assert gt_masks.dtype == torch.bool and gt_masks.size(1) == 1\n    assert pred_masks.dtype == torch.bool and pred_masks.shape == gt_masks.shape\n    assert num_pt >= 0\n\n    B, _, H_im, W_im = gt_masks.shape\n    device = gt_masks.device\n\n    # false positive region, a new point sampled in this region should have\n    # negative label to correct the FP error\n    fp_masks = ~gt_masks & pred_masks\n    # false negative region, a new point sampled in this region should have\n    # positive label to correct the FN error\n    fn_masks = gt_masks & ~pred_masks\n    # whether the prediction completely match the ground-truth on each mask\n    all_correct = torch.all((gt_masks == pred_masks).flatten(2), dim=2)\n    all_correct = all_correct[..., None, None]\n\n    # channel 0 is FP map, while channel 1 is FN map\n    pts_noise = torch.rand(B, num_pt, H_im, W_im, 2, device=device)\n    # sample a negative new click from FP region or a positive new click\n    # from FN region, depend on where the maximum falls,\n    # and in case the predictions are all correct (no FP or FN), we just\n    # sample a negative click from the background region\n    pts_noise[..., 0] *= fp_masks | (all_correct & ~gt_masks)\n    pts_noise[..., 1] *= fn_masks\n    pts_idx = pts_noise.flatten(2).argmax(dim=2)\n    labels = (pts_idx % 2).to(torch.int32)\n    pts_idx = pts_idx // 2\n    pts_x = pts_idx % W_im\n    pts_y = pts_idx // W_im\n    points = torch.stack([pts_x, pts_y], dim=2).to(torch.float)\n    return points, labels\n\n\ndef sample_one_point_from_error_center(gt_masks, pred_masks, padding=True):\n    \"\"\"\n    Sample 1 random point (along with its label) from the center of each error region,\n    that is, the point with the largest distance to the boundary of each error region.\n    This is the RITM sampling method from https://github.com/saic-vul/ritm_interactive_segmentation/blob/master/isegm/inference/clicker.py\n\n    Inputs:\n    - gt_masks: [B, 1, H_im, W_im] masks, dtype=torch.bool\n    - pred_masks: [B, 1, H_im, W_im] masks, dtype=torch.bool or None\n    - padding: if True, pad with boundary of 1 px for distance transform\n\n    Outputs:\n    - points: [B, 1, 2], dtype=torch.float, contains (x, y) coordinates of each sampled point\n    - labels: [B, 1], dtype=torch.int32, where 1 means positive clicks and 0 means negative clicks\n    \"\"\"\n    if pred_masks is None:\n        pred_masks = torch.zeros_like(gt_masks)\n    assert gt_masks.dtype == torch.bool and gt_masks.size(1) == 1\n    assert pred_masks.dtype == torch.bool and pred_masks.shape == gt_masks.shape\n\n    B, _, H, W = gt_masks.shape\n\n    # false positive region, a new point sampled in this region should have\n    # negative label to correct the FP error\n    fp_masks = (~gt_masks & pred_masks).squeeze(1)\n    # false negative region, a new point sampled in this region should have\n    # positive label to correct the FN error\n    fn_masks = (gt_masks & ~pred_masks).squeeze(1)\n\n    if padding:\n        padded_fp_masks = torch.zeros(\n            B, H + 2, W + 2, dtype=fp_masks.dtype, device=fp_masks.device\n        )\n        padded_fp_masks[:, 1 : H + 1, 1 : W + 1] = fp_masks\n        padded_fn_masks = torch.zeros(\n            B, H + 2, W + 2, dtype=fp_masks.dtype, device=fp_masks.device\n        )\n        padded_fn_masks[:, 1 : H + 1, 1 : W + 1] = fn_masks\n    else:\n        padded_fp_masks = fp_masks\n        padded_fn_masks = fn_masks\n\n    fn_mask_dt = edt_triton(padded_fn_masks)\n    fp_mask_dt = edt_triton(padded_fp_masks)\n    if padding:\n        fn_mask_dt = fn_mask_dt[:, 1:-1, 1:-1]\n        fp_mask_dt = fp_mask_dt[:, 1:-1, 1:-1]\n\n    fn_max, fn_argmax = fn_mask_dt.reshape(B, -1).max(dim=-1)\n    fp_max, fp_argmax = fp_mask_dt.reshape(B, -1).max(dim=-1)\n    is_positive = fn_max > fp_max\n    chosen = torch.where(is_positive, fn_argmax, fp_argmax)\n    points_x = chosen % W\n    points_y = chosen // W\n\n    labels = is_positive.long()\n    points = torch.stack([points_x, points_y], -1)\n    return points.unsqueeze(1), labels.unsqueeze(1)\n\n\ndef sample_one_point_from_error_center_slow(gt_masks, pred_masks, padding=True):\n    \"\"\"\n    Sample 1 random point (along with its label) from the center of each error region,\n    that is, the point with the largest distance to the boundary of each error region.\n    This is the RITM sampling method from https://github.com/saic-vul/ritm_interactive_segmentation/blob/master/isegm/inference/clicker.py\n\n    Inputs:\n    - gt_masks: [B, 1, H_im, W_im] masks, dtype=torch.bool\n    - pred_masks: [B, 1, H_im, W_im] masks, dtype=torch.bool or None\n    - padding: if True, pad with boundary of 1 px for distance transform\n\n    Outputs:\n    - points: [B, 1, 2], dtype=torch.float, contains (x, y) coordinates of each sampled point\n    - labels: [B, 1], dtype=torch.int32, where 1 means positive clicks and 0 means negative clicks\n    \"\"\"\n    import cv2  # delay OpenCV import to avoid unnecessary dependency\n\n    if pred_masks is None:\n        pred_masks = torch.zeros_like(gt_masks)\n    assert gt_masks.dtype == torch.bool and gt_masks.size(1) == 1\n    assert pred_masks.dtype == torch.bool and pred_masks.shape == gt_masks.shape\n\n    B, _, _, W_im = gt_masks.shape\n    device = gt_masks.device\n\n    # false positive region, a new point sampled in this region should have\n    # negative label to correct the FP error\n    fp_masks = ~gt_masks & pred_masks\n    # false negative region, a new point sampled in this region should have\n    # positive label to correct the FN error\n    fn_masks = gt_masks & ~pred_masks\n\n    fp_masks = fp_masks.cpu().numpy()\n    fn_masks = fn_masks.cpu().numpy()\n    points = torch.zeros(B, 1, 2, dtype=torch.float)\n    labels = torch.ones(B, 1, dtype=torch.int32)\n    for b in range(B):\n        fn_mask = fn_masks[b, 0]\n        fp_mask = fp_masks[b, 0]\n        if padding:\n            fn_mask = np.pad(fn_mask, ((1, 1), (1, 1)), \"constant\")\n            fp_mask = np.pad(fp_mask, ((1, 1), (1, 1)), \"constant\")\n        # compute the distance of each point in FN/FP region to its boundary\n        fn_mask_dt = cv2.distanceTransform(fn_mask.astype(np.uint8), cv2.DIST_L2, 0)\n        fp_mask_dt = cv2.distanceTransform(fp_mask.astype(np.uint8), cv2.DIST_L2, 0)\n        if padding:\n            fn_mask_dt = fn_mask_dt[1:-1, 1:-1]\n            fp_mask_dt = fp_mask_dt[1:-1, 1:-1]\n\n        # take the point in FN/FP region with the largest distance to its boundary\n        fn_mask_dt_flat = fn_mask_dt.reshape(-1)\n        fp_mask_dt_flat = fp_mask_dt.reshape(-1)\n        fn_argmax = np.argmax(fn_mask_dt_flat)\n        fp_argmax = np.argmax(fp_mask_dt_flat)\n        is_positive = fn_mask_dt_flat[fn_argmax] > fp_mask_dt_flat[fp_argmax]\n        pt_idx = fn_argmax if is_positive else fp_argmax\n        points[b, 0, 0] = pt_idx % W_im  # x\n        points[b, 0, 1] = pt_idx // W_im  # y\n        labels[b, 0] = int(is_positive)\n\n    points = points.to(device)\n    labels = labels.to(device)\n    return points, labels\n\n\ndef get_next_point(gt_masks, pred_masks, method):\n    if method == \"uniform\":\n        return sample_random_points_from_errors(gt_masks, pred_masks)\n    elif method == \"center\":\n        return sample_one_point_from_error_center(gt_masks, pred_masks)\n    else:\n        raise ValueError(f\"unknown sampling method {method}\")\n\n\ndef select_closest_cond_frames(\n    frame_idx, cond_frame_outputs, max_cond_frame_num, keep_first_cond_frame=False\n):\n    \"\"\"\n    Select up to `max_cond_frame_num` conditioning frames from `cond_frame_outputs`\n    that are temporally closest to the current frame at `frame_idx`. Here, we take\n    - a) the closest conditioning frame before `frame_idx` (if any);\n    - b) the closest conditioning frame after `frame_idx` (if any);\n    - c) any other temporally closest conditioning frames until reaching a total\n         of `max_cond_frame_num` conditioning frames.\n\n    Outputs:\n    - selected_outputs: selected items (keys & values) from `cond_frame_outputs`.\n    - unselected_outputs: items (keys & values) not selected in `cond_frame_outputs`.\n    \"\"\"\n    if max_cond_frame_num == -1 or len(cond_frame_outputs) <= max_cond_frame_num:\n        selected_outputs = cond_frame_outputs\n        unselected_outputs = {}\n    else:\n        assert max_cond_frame_num >= 2, \"we should allow using 2+ conditioning frames\"\n        selected_outputs = {}\n        if keep_first_cond_frame:\n            idx_first = min(\n                (t for t in cond_frame_outputs if t < frame_idx), default=None\n            )\n            if idx_first is None:\n                # Maybe we are tracking in reverse\n                idx_first = max(\n                    (t for t in cond_frame_outputs if t > frame_idx), default=None\n                )\n            if idx_first is not None:\n                selected_outputs[idx_first] = cond_frame_outputs[idx_first]\n        # the closest conditioning frame before `frame_idx` (if any)\n        idx_before = max((t for t in cond_frame_outputs if t < frame_idx), default=None)\n        if idx_before is not None:\n            selected_outputs[idx_before] = cond_frame_outputs[idx_before]\n\n        # the closest conditioning frame after `frame_idx` (if any)\n        idx_after = min((t for t in cond_frame_outputs if t >= frame_idx), default=None)\n        if idx_after is not None:\n            selected_outputs[idx_after] = cond_frame_outputs[idx_after]\n\n        # add other temporally closest conditioning frames until reaching a total\n        # of `max_cond_frame_num` conditioning frames.\n        num_remain = max_cond_frame_num - len(selected_outputs)\n        inds_remain = sorted(\n            (t for t in cond_frame_outputs if t not in selected_outputs),\n            key=lambda x: abs(x - frame_idx),\n        )[:num_remain]\n        selected_outputs.update((t, cond_frame_outputs[t]) for t in inds_remain)\n        unselected_outputs = {\n            t: v for t, v in cond_frame_outputs.items() if t not in selected_outputs\n        }\n\n    return selected_outputs, unselected_outputs\n\n\ndef get_1d_sine_pe(pos_inds, dim, temperature=10000):\n    \"\"\"\n    Get 1D sine positional embedding as in the original Transformer paper.\n    \"\"\"\n    pe_dim = dim // 2\n    dim_t = torch.arange(pe_dim, dtype=torch.float32, device=pos_inds.device)\n    dim_t = temperature ** (2 * (dim_t // 2) / pe_dim)\n\n    pos_embed = pos_inds.unsqueeze(-1) / dim_t\n    pos_embed = torch.cat([pos_embed.sin(), pos_embed.cos()], dim=-1)\n    return pos_embed\n\n\ndef get_best_gt_match_from_multimasks(pred_multimasks, gt_masks, pred_scores=None):\n    \"\"\"\n    Get the mask with the best match to GT masks (based on IoU) from pred_multimasks.\n    Optionally, use `pred_scores` to break ties in case all IoUs are zeros.\n    \"\"\"\n    assert pred_multimasks.ndim == 4 and gt_masks.ndim == 4\n    if pred_multimasks.size(1) == 1:\n        return pred_multimasks  # only a single mask channel, nothing to select\n\n    pred_multimasks_binary = pred_multimasks > 0\n    area_i = torch.sum(pred_multimasks_binary & gt_masks, dim=(2, 3)).float()\n    area_u = torch.sum(pred_multimasks_binary | gt_masks, dim=(2, 3)).float()\n    ious = area_i / torch.clamp(area_u, min=1.0)\n\n    # In case all IoUs are zeros (e.g. because the GT mask is empty), use pred_scores\n    # to break ties and select the best mask\n    if pred_scores is not None:\n        has_nonzero_ious = torch.any(ious > 0).expand_as(ious)\n        scores = torch.where(has_nonzero_ious, ious, pred_scores)\n    else:\n        scores = ious\n\n    # Finally, take the best mask prediction (with the highest score)\n    best_scores_inds = torch.argmax(scores, dim=-1)\n    batch_inds = torch.arange(scores.size(0), device=scores.device)\n    best_pred_mask = pred_multimasks[batch_inds, best_scores_inds].unsqueeze(1)\n    return best_pred_mask\n\n\ndef fill_holes_in_mask_scores(mask, max_area, fill_holes=True, remove_sprinkles=True):\n    \"\"\"\n    A post processor to fill small holes in mask scores with area under `max_area`.\n    Holes are those small connected components in either background or foreground.\n\n    Note that it relies on the \"cc_torch\" package to find connected components fast. You can\n    install it via the following command (`TORCH_CUDA_ARCH_LIST=8.0` is for A100 GPUs):\n    ```\n    pip uninstall -y cc_torch; TORCH_CUDA_ARCH_LIST=8.0 9.0 pip install git+https://github.com/ronghanghu/cc_torch\n    ```\n    Otherwise, it will fallback to a slightly slower triton implementation, or skimage if the tensor is on cpu\n    \"\"\"\n\n    if max_area <= 0:\n        return mask  # nothing to fill in this case\n\n    if fill_holes:\n        # We remove small connected components in background by changing them to foreground\n        # with a small positive mask score (0.1).\n        mask_bg = mask <= 0\n        bg_area_thresh = max_area\n        _, areas_bg = _get_connected_components_with_padding(mask_bg)\n        small_components_bg = mask_bg & (areas_bg <= bg_area_thresh)\n        mask = torch.where(small_components_bg, 0.1, mask)\n\n    if remove_sprinkles:\n        # We remove small connected components in foreground by changing them to background\n        # with a small negative mask score (-0.1). Here we only remove connected components\n        # whose areas are under both `max_area` and half of the entire mask's area. This\n        # removes sprinkles while avoids filtering out tiny objects that we want to track.\n        mask_fg = mask > 0\n        fg_area_thresh = torch.sum(mask_fg, dim=(2, 3), keepdim=True, dtype=torch.int32)\n        fg_area_thresh.floor_divide_(2).clamp_(max=max_area)\n        _, areas_fg = _get_connected_components_with_padding(mask_fg)\n        small_components_fg = mask_fg & (areas_fg <= fg_area_thresh)\n        mask = torch.where(small_components_fg, -0.1, mask)\n    return mask\n\n\ndef _get_connected_components_with_padding(mask):\n    \"\"\"Get connected components from masks (possibly padding them to an even size).\"\"\"\n    from sam3.perflib.connected_components import connected_components\n\n    mask = mask.to(torch.uint8)\n    _, _, H, W = mask.shape\n    # make sure both height and width are even (to be compatible with cc_torch)\n    pad_h = H % 2\n    pad_w = W % 2\n    if pad_h == 0 and pad_w == 0:\n        labels, counts = connected_components(mask)\n    else:\n        # pad the mask to make its height and width even\n        # padding format is (padding_left,padding_right,padding_top,padding_bottom)\n        mask_pad = F.pad(mask, (0, pad_w, 0, pad_h), mode=\"constant\", value=0)\n        labels, counts = connected_components(mask_pad)\n        labels = labels[:, :, :H, :W]\n        counts = counts[:, :, :H, :W]\n\n    return labels, counts\n"
  },
  {
    "path": "sam3/model/sam3_tracking_predictor.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n\nimport logging\nfrom collections import OrderedDict\n\nimport torch\nfrom sam3.model.sam3_tracker_base import concat_points, NO_OBJ_SCORE, Sam3TrackerBase\nfrom sam3.model.sam3_tracker_utils import fill_holes_in_mask_scores\nfrom sam3.model.utils.sam2_utils import load_video_frames\nfrom tqdm.auto import tqdm\n\n\nclass Sam3TrackerPredictor(Sam3TrackerBase):\n    \"\"\"\n    The demo class that extends the `Sam3TrackerBase` to handle user interactions\n    and manage inference states, with support for multi-object tracking.\n    \"\"\"\n\n    def __init__(\n        self,\n        # whether to clear non-conditioning memory of the surrounding frames (which may contain outdated information) after adding correction clicks;\n        # note that this would only apply to *single-object tracking* unless `clear_non_cond_mem_for_multi_obj` is also set to True)\n        clear_non_cond_mem_around_input=False,\n        # whether to also clear non-conditioning memory of the surrounding frames (only effective when `clear_non_cond_mem_around_input` is True).\n        clear_non_cond_mem_for_multi_obj=False,\n        # if fill_hole_area > 0, we fill small holes in the final masks up to this area (after resizing them to the original video resolution)\n        fill_hole_area=0,\n        # if always_start_from_first_ann_frame is True, we always start tracking from the frame where we receive the first annotation (clicks or mask)\n        # and ignore the `start_frame_idx` passed to `propagate_in_video`\n        always_start_from_first_ann_frame=False,\n        # the maximum number of points to be used in the prompt encoder, which reduce the domain gap between training (that only has 8 points)\n        # - if it's set to a positive integer, we only take the `max_point_num_in_prompt_enc//2` points and\n        #   the last `(max_point_num_in_prompt_enc - max_point_num_in_prompt_enc//2)` points in the prompt encoder\n        # - if it's set to 0 or negative, this option is turned off and we use all points in the prompt encoder\n        max_point_num_in_prompt_enc=16,\n        non_overlap_masks_for_output=True,\n        # checkpoint_file=None,\n        **kwargs,\n    ):\n        super().__init__(**kwargs)\n        self.clear_non_cond_mem_around_input = clear_non_cond_mem_around_input\n        self.clear_non_cond_mem_for_multi_obj = clear_non_cond_mem_for_multi_obj\n        self.fill_hole_area = fill_hole_area\n        self.always_start_from_first_ann_frame = always_start_from_first_ann_frame\n        self.max_point_num_in_prompt_enc = max_point_num_in_prompt_enc\n        self.non_overlap_masks_for_output = non_overlap_masks_for_output\n\n        self.bf16_context = torch.autocast(device_type=\"cuda\", dtype=torch.bfloat16)\n        self.bf16_context.__enter__()  # keep using for the entire model process\n\n        self.iter_use_prev_mask_pred = True\n        self.add_all_frames_to_correct_as_cond = True\n\n    @torch.inference_mode()\n    def init_state(\n        self,\n        video_height=None,\n        video_width=None,\n        num_frames=None,\n        video_path=None,\n        cached_features=None,\n        offload_video_to_cpu=False,\n        offload_state_to_cpu=False,\n        async_loading_frames=False,\n    ):\n        \"\"\"Initialize a inference state.\"\"\"\n        inference_state = {}\n        # whether to offload the video frames to CPU memory\n        # turning on this option saves the GPU memory with only a very small overhead\n        inference_state[\"offload_video_to_cpu\"] = offload_video_to_cpu\n        # whether to offload the inference state to CPU memory\n        # turning on this option saves the GPU memory at the cost of a lower tracking fps\n        # (e.g. in a test case of 768x768 model, fps dropped from 27 to 24 when tracking one object\n        # and from 24 to 21 when tracking two objects)\n        inference_state[\"offload_state_to_cpu\"] = offload_state_to_cpu\n        inference_state[\"device\"] = self.device\n        if offload_state_to_cpu:\n            inference_state[\"storage_device\"] = torch.device(\"cpu\")\n        else:\n            inference_state[\"storage_device\"] = torch.device(\"cuda\")\n\n        if video_path is not None:\n            images, video_height, video_width = load_video_frames(\n                video_path=video_path,\n                image_size=self.image_size,\n                offload_video_to_cpu=offload_video_to_cpu,\n                async_loading_frames=async_loading_frames,\n                compute_device=inference_state[\"storage_device\"],\n            )\n            inference_state[\"images\"] = images\n            inference_state[\"num_frames\"] = len(images)\n            inference_state[\"video_height\"] = video_height\n            inference_state[\"video_width\"] = video_width\n        else:\n            # the original video height and width, used for resizing final output scores\n            inference_state[\"video_height\"] = video_height\n            inference_state[\"video_width\"] = video_width\n            inference_state[\"num_frames\"] = num_frames\n        # inputs on each frame\n        inference_state[\"point_inputs_per_obj\"] = {}\n        inference_state[\"mask_inputs_per_obj\"] = {}\n        # visual features on a small number of recently visited frames for quick interactions\n        inference_state[\"cached_features\"] = (\n            {} if cached_features is None else cached_features\n        )\n        # values that don't change across frames (so we only need to hold one copy of them)\n        inference_state[\"constants\"] = {}\n        # mapping between client-side object id and model-side object index\n        inference_state[\"obj_id_to_idx\"] = OrderedDict()\n        inference_state[\"obj_idx_to_id\"] = OrderedDict()\n        inference_state[\"obj_ids\"] = []\n        # A storage to hold the model's tracking results and states on each frame\n        inference_state[\"output_dict\"] = {\n            \"cond_frame_outputs\": {},  # dict containing {frame_idx: <out>}\n            \"non_cond_frame_outputs\": {},  # dict containing {frame_idx: <out>}\n        }\n        # The index of the frame that received the first annotation\n        inference_state[\"first_ann_frame_idx\"] = None\n        # Slice (view) of each object tracking results, sharing the same memory with \"output_dict\"\n        inference_state[\"output_dict_per_obj\"] = {}\n        # A temporary storage to hold new outputs when user interact with a frame\n        # to add clicks or mask (it's merged into \"output_dict\" before propagation starts)\n        inference_state[\"temp_output_dict_per_obj\"] = {}\n        # Frames that already holds consolidated outputs from click or mask inputs\n        # (we directly use their consolidated outputs during tracking)\n        inference_state[\"consolidated_frame_inds\"] = {\n            \"cond_frame_outputs\": set(),  # set containing frame indices\n            \"non_cond_frame_outputs\": set(),  # set containing frame indices\n        }\n        # metadata for each tracking frame (e.g. which direction it's tracked)\n        inference_state[\"tracking_has_started\"] = False\n        inference_state[\"frames_already_tracked\"] = {}\n        self.clear_all_points_in_video(inference_state)\n        return inference_state\n\n    def _obj_id_to_idx(self, inference_state, obj_id):\n        \"\"\"Map client-side object id to model-side object index.\"\"\"\n        obj_idx = inference_state[\"obj_id_to_idx\"].get(obj_id, None)\n        if obj_idx is not None:\n            return obj_idx\n\n        # This is a new object id not sent to the server before. We only allow adding\n        # new objects *before* the tracking starts.\n        allow_new_object = not inference_state[\"tracking_has_started\"]\n        if allow_new_object:\n            # get the next object slot\n            obj_idx = len(inference_state[\"obj_id_to_idx\"])\n            inference_state[\"obj_id_to_idx\"][obj_id] = obj_idx\n            inference_state[\"obj_idx_to_id\"][obj_idx] = obj_id\n            inference_state[\"obj_ids\"] = list(inference_state[\"obj_id_to_idx\"])\n            # set up input and output structures for this object\n            inference_state[\"point_inputs_per_obj\"][obj_idx] = {}\n            inference_state[\"mask_inputs_per_obj\"][obj_idx] = {}\n            inference_state[\"output_dict_per_obj\"][obj_idx] = {\n                \"cond_frame_outputs\": {},  # dict containing {frame_idx: <out>}\n                \"non_cond_frame_outputs\": {},  # dict containing {frame_idx: <out>}\n            }\n            inference_state[\"temp_output_dict_per_obj\"][obj_idx] = {\n                \"cond_frame_outputs\": {},  # dict containing {frame_idx: <out>}\n                \"non_cond_frame_outputs\": {},  # dict containing {frame_idx: <out>}\n            }\n            return obj_idx\n        else:\n            raise RuntimeError(\n                f\"Cannot add new object id {obj_id} after tracking starts. \"\n                f\"All existing object ids: {inference_state['obj_ids']}.\"\n            )\n\n    def _obj_idx_to_id(self, inference_state, obj_idx):\n        \"\"\"Map model-side object index to client-side object id.\"\"\"\n        return inference_state[\"obj_idx_to_id\"][obj_idx]\n\n    def _get_obj_num(self, inference_state):\n        \"\"\"Get the total number of unique object ids received so far in this session.\"\"\"\n        return len(inference_state[\"obj_idx_to_id\"])\n\n    @torch.inference_mode()\n    def add_new_points_or_box(\n        self,\n        inference_state,\n        frame_idx,\n        obj_id,\n        points=None,\n        labels=None,\n        clear_old_points=True,\n        rel_coordinates=True,\n        use_prev_mem_frame=False,\n        normalize_coords=True,\n        box=None,\n    ):\n        \"\"\"Add new points to a frame.\"\"\"\n        obj_idx = self._obj_id_to_idx(inference_state, obj_id)\n        point_inputs_per_frame = inference_state[\"point_inputs_per_obj\"][obj_idx]\n        mask_inputs_per_frame = inference_state[\"mask_inputs_per_obj\"][obj_idx]\n\n        if (points is not None) != (labels is not None):\n            raise ValueError(\"points and labels must be provided together\")\n        if points is None and box is None:\n            raise ValueError(\"at least one of points or box must be provided as input\")\n\n        if points is None:\n            points = torch.zeros(0, 2, dtype=torch.float32)\n        elif not isinstance(points, torch.Tensor):\n            points = torch.tensor(points, dtype=torch.float32)\n        if labels is None:\n            labels = torch.zeros(0, dtype=torch.int32)\n        elif not isinstance(labels, torch.Tensor):\n            labels = torch.tensor(labels, dtype=torch.int32)\n        if points.dim() == 2:\n            points = points.unsqueeze(0)  # add batch dimension\n        if labels.dim() == 1:\n            labels = labels.unsqueeze(0)  # add batch dimension\n\n        if rel_coordinates:\n            # convert the points from relative coordinates to absolute coordinates\n            if points is not None:\n                points = points * self.image_size\n            if box is not None:\n                box = box * self.image_size\n\n        # If `box` is provided, we add it as the first two points with labels 2 and 3\n        # along with the user-provided points (consistent with how SAM 2 is trained).\n        if box is not None:\n            if not clear_old_points:\n                raise ValueError(\n                    \"cannot add box without clearing old points, since \"\n                    \"box prompt must be provided before any point prompt \"\n                    \"(please use clear_old_points=True instead)\"\n                )\n            if not isinstance(box, torch.Tensor):\n                box = torch.tensor(box, dtype=torch.float32, device=points.device)\n            box_coords = box.reshape(1, 2, 2)\n            box_labels = torch.tensor([2, 3], dtype=torch.int32, device=labels.device)\n            box_labels = box_labels.reshape(1, 2)\n            points = torch.cat([box_coords, points], dim=1)\n            labels = torch.cat([box_labels, labels], dim=1)\n\n        points = points.to(inference_state[\"device\"])\n        labels = labels.to(inference_state[\"device\"])\n\n        if not clear_old_points:\n            point_inputs = point_inputs_per_frame.get(frame_idx, None)\n        else:\n            point_inputs = None\n        point_inputs = concat_points(point_inputs, points, labels)\n\n        point_inputs_per_frame[frame_idx] = point_inputs\n        mask_inputs_per_frame.pop(frame_idx, None)\n        # If this frame hasn't been tracked before, we treat it as an initial conditioning\n        # frame, meaning that the inputs points are to generate segments on this frame without\n        # using any memory from other frames, like in SAM. Otherwise (if it has been tracked),\n        # the input points will be used to correct the already tracked masks.\n        is_init_cond_frame = frame_idx not in inference_state[\"frames_already_tracked\"]\n        # whether to track in reverse time order\n        if is_init_cond_frame:\n            reverse = False\n        else:\n            reverse = inference_state[\"frames_already_tracked\"][frame_idx][\"reverse\"]\n        obj_output_dict = inference_state[\"output_dict_per_obj\"][obj_idx]\n        obj_temp_output_dict = inference_state[\"temp_output_dict_per_obj\"][obj_idx]\n        # Add a frame to conditioning output if it's an initial conditioning frame or\n        # if the model sees all frames receiving clicks/mask as conditioning frames.\n        is_cond = is_init_cond_frame or self.add_all_frames_to_correct_as_cond\n        storage_key = \"cond_frame_outputs\" if is_cond else \"non_cond_frame_outputs\"\n\n        # Limit to a maximum number of input points to the prompt encoder (to reduce domain gap)\n        num_points = point_inputs[\"point_coords\"].size(1)\n        if num_points > self.max_point_num_in_prompt_enc > 0:\n            num_first = self.max_point_num_in_prompt_enc // 2\n            num_last = self.max_point_num_in_prompt_enc - num_first\n            point_inputs[\"point_coords\"] = torch.cat(\n                [\n                    point_inputs[\"point_coords\"][:, :num_first],\n                    point_inputs[\"point_coords\"][:, -num_last:],\n                ],\n                dim=1,\n            )\n            point_inputs[\"point_labels\"] = torch.cat(\n                [\n                    point_inputs[\"point_labels\"][:, :num_first],\n                    point_inputs[\"point_labels\"][:, -num_last:],\n                ],\n                dim=1,\n            )\n            logging.warning(\n                f\"Too many points ({num_points}) are provided on frame {frame_idx}. Only \"\n                f\"the first {num_first} points and the last {num_last} points will be used.\"\n            )\n        # Get any previously predicted mask logits on this object and feed it along with\n        # the new clicks into the SAM mask decoder when `self.iter_use_prev_mask_pred=True`.\n        prev_sam_mask_logits = None\n        if self.iter_use_prev_mask_pred:\n            # lookup temporary output dict first, which contains the most recent output\n            # (if not found, then lookup conditioning and non-conditioning frame output)\n            prev_out = obj_temp_output_dict[storage_key].get(frame_idx)\n            if prev_out is None:\n                prev_out = obj_output_dict[\"cond_frame_outputs\"].get(frame_idx)\n                if prev_out is None:\n                    prev_out = obj_output_dict[\"non_cond_frame_outputs\"].get(frame_idx)\n\n            if prev_out is not None and prev_out[\"pred_masks\"] is not None:\n                prev_sam_mask_logits = prev_out[\"pred_masks\"].cuda(non_blocking=True)\n                # Clamp the scale of prev_sam_mask_logits to avoid rare numerical issues.\n                prev_sam_mask_logits = torch.clamp(prev_sam_mask_logits, -32.0, 32.0)\n        current_out, _ = self._run_single_frame_inference(\n            inference_state=inference_state,\n            output_dict=obj_output_dict,  # run on the slice of a single object\n            frame_idx=frame_idx,\n            batch_size=1,  # run on the slice of a single object\n            is_init_cond_frame=is_init_cond_frame,\n            point_inputs=point_inputs,\n            mask_inputs=None,\n            reverse=reverse,\n            # Skip the memory encoder when adding clicks or mask. We execute the memory encoder\n            # at the beginning of `propagate_in_video` (after user finalize their clicks). This\n            # allows us to enforce non-overlapping constraints on all objects before encoding\n            # them into memory.\n            run_mem_encoder=False,\n            prev_sam_mask_logits=prev_sam_mask_logits,\n            use_prev_mem_frame=use_prev_mem_frame,\n        )\n        # Add the output to the output dict (to be used as future memory)\n        obj_temp_output_dict[storage_key][frame_idx] = current_out\n\n        # Resize the output mask to the original video resolution\n        obj_ids = inference_state[\"obj_ids\"]\n        consolidated_out = self._consolidate_temp_output_across_obj(\n            inference_state,\n            frame_idx,\n            is_cond=is_cond,\n            run_mem_encoder=False,\n            consolidate_at_video_res=True,\n        )\n        _, video_res_masks = self._get_orig_video_res_output(\n            inference_state, consolidated_out[\"pred_masks_video_res\"]\n        )\n        low_res_masks = None  # not needed by the demo\n        return frame_idx, obj_ids, low_res_masks, video_res_masks\n\n    @torch.inference_mode()\n    def add_new_mask(\n        self,\n        inference_state,\n        frame_idx,\n        obj_id,\n        mask,\n        add_mask_to_memory=False,\n    ):\n        \"\"\"Add new mask to a frame.\"\"\"\n        obj_idx = self._obj_id_to_idx(inference_state, obj_id)\n        point_inputs_per_frame = inference_state[\"point_inputs_per_obj\"][obj_idx]\n        mask_inputs_per_frame = inference_state[\"mask_inputs_per_obj\"][obj_idx]\n\n        assert mask.dim() == 2\n        mask_H, mask_W = mask.shape\n        mask_inputs_orig = mask[None, None]  # add batch and channel dimension\n        mask_inputs_orig = mask_inputs_orig.float().to(inference_state[\"device\"])\n\n        # resize the mask if it doesn't match the model's input mask size\n        if mask_H != self.input_mask_size or mask_W != self.input_mask_size:\n            mask_inputs = torch.nn.functional.interpolate(\n                mask_inputs_orig,\n                size=(self.input_mask_size, self.input_mask_size),\n                align_corners=False,\n                mode=\"bilinear\",\n                antialias=True,  # use antialias for downsampling\n            )\n        else:\n            mask_inputs = mask_inputs_orig\n\n        # also get the mask at the original video resolution (for outputting)\n        video_H = inference_state[\"video_height\"]\n        video_W = inference_state[\"video_width\"]\n        if mask_H != video_H or mask_W != video_W:\n            mask_inputs_video_res = torch.nn.functional.interpolate(\n                mask_inputs_orig,\n                size=(video_H, video_W),\n                align_corners=False,\n                mode=\"bilinear\",\n                antialias=True,  # use antialias for potential downsampling\n            )\n        else:\n            mask_inputs_video_res = mask_inputs_orig\n        # convert mask_inputs_video_res to binary (threshold at 0.5 as it is in range 0~1)\n        mask_inputs_video_res = mask_inputs_video_res > 0.5\n\n        mask_inputs_per_frame[frame_idx] = mask_inputs_video_res\n        point_inputs_per_frame.pop(frame_idx, None)\n        # If this frame hasn't been tracked before, we treat it as an initial conditioning\n        # frame, meaning that the inputs points are to generate segments on this frame without\n        # using any memory from other frames, like in SAM. Otherwise (if it has been tracked),\n        # the input points will be used to correct the already tracked masks.\n        is_init_cond_frame = frame_idx not in inference_state[\"frames_already_tracked\"]\n        # whether to track in reverse time order\n        if is_init_cond_frame:\n            reverse = False\n        else:\n            reverse = inference_state[\"frames_already_tracked\"][frame_idx][\"reverse\"]\n        obj_output_dict = inference_state[\"output_dict_per_obj\"][obj_idx]\n        obj_temp_output_dict = inference_state[\"temp_output_dict_per_obj\"][obj_idx]\n        # Add a frame to conditioning output if it's an initial conditioning frame or\n        # if the model sees all frames receiving clicks/mask as conditioning frames.\n        is_cond = is_init_cond_frame or self.add_all_frames_to_correct_as_cond\n        storage_key = \"cond_frame_outputs\" if is_cond else \"non_cond_frame_outputs\"\n\n        current_out, _ = self._run_single_frame_inference(\n            inference_state=inference_state,\n            output_dict=obj_output_dict,  # run on the slice of a single object\n            frame_idx=frame_idx,\n            batch_size=1,  # run on the slice of a single object\n            is_init_cond_frame=is_init_cond_frame,\n            point_inputs=None,\n            mask_inputs=mask_inputs,\n            reverse=reverse,\n            # Skip the memory encoder when adding clicks or mask. We execute the memory encoder\n            # at the beginning of `propagate_in_video` (after user finalize their clicks). This\n            # allows us to enforce non-overlapping constraints on all objects before encoding\n            # them into memory.\n            run_mem_encoder=False,\n        )\n        # We directly use the input mask at video resolution as the output mask for a better\n        # video editing experience (so that the masks don't change after each brushing).\n        # Here NO_OBJ_SCORE is a large negative value to represent the background and\n        # similarly -NO_OBJ_SCORE is a large positive value to represent the foreground.\n        current_out[\"pred_masks\"] = None\n        current_out[\"pred_masks_video_res\"] = torch.where(\n            mask_inputs_video_res, -NO_OBJ_SCORE, NO_OBJ_SCORE\n        )\n        # Add the output to the output dict (to be used as future memory)\n        obj_temp_output_dict[storage_key][frame_idx] = current_out\n        # Remove the overlapping proportion of other objects' input masks on this frame\n        temp_output_dict_per_obj = inference_state[\"temp_output_dict_per_obj\"]\n        for obj_idx2, obj_temp_output_dict2 in temp_output_dict_per_obj.items():\n            if obj_idx2 == obj_idx:\n                continue\n            current_out2 = obj_temp_output_dict2[storage_key].get(frame_idx, None)\n            if current_out2 is not None and \"pred_masks_video_res\" in current_out2:\n                current_out2[\"pred_masks_video_res\"] = torch.where(\n                    mask_inputs_video_res,\n                    NO_OBJ_SCORE,\n                    current_out2[\"pred_masks_video_res\"],\n                )\n\n        # Resize the output mask to the original video resolution\n        obj_ids = inference_state[\"obj_ids\"]\n        consolidated_out = self._consolidate_temp_output_across_obj(\n            inference_state,\n            frame_idx,\n            is_cond=is_cond,\n            run_mem_encoder=False,\n            consolidate_at_video_res=True,\n        )\n        _, video_res_masks = self._get_orig_video_res_output(\n            inference_state, consolidated_out[\"pred_masks_video_res\"]\n        )\n        low_res_masks = None  # not needed by the demo\n        return frame_idx, obj_ids, low_res_masks, video_res_masks\n\n    def add_new_points(self, *args, **kwargs):\n        \"\"\"Deprecated method. Please use `add_new_points_or_box` instead.\"\"\"\n        return self.add_new_points_or_box(*args, **kwargs)\n\n    def _get_orig_video_res_output(self, inference_state, any_res_masks):\n        \"\"\"\n        Resize the object scores to the original video resolution (video_res_masks)\n        and apply non-overlapping constraints for final output.\n        \"\"\"\n        device = inference_state[\"device\"]\n        video_H = inference_state[\"video_height\"]\n        video_W = inference_state[\"video_width\"]\n        any_res_masks = any_res_masks.to(device, non_blocking=True)\n        if any_res_masks.shape[-2:] == (video_H, video_W):\n            video_res_masks = any_res_masks\n        else:\n            video_res_masks = torch.nn.functional.interpolate(\n                any_res_masks,\n                size=(video_H, video_W),\n                mode=\"bilinear\",\n                align_corners=False,\n            )\n        if self.non_overlap_masks_for_output:\n            video_res_masks = self._apply_non_overlapping_constraints(video_res_masks)\n        # potentially fill holes in the predicted masks\n        if self.fill_hole_area > 0:\n            video_res_masks = fill_holes_in_mask_scores(\n                video_res_masks, self.fill_hole_area\n            )\n        return any_res_masks, video_res_masks\n\n    def _consolidate_temp_output_across_obj(\n        self,\n        inference_state,\n        frame_idx,\n        is_cond,\n        run_mem_encoder,\n        consolidate_at_video_res=False,\n    ):\n        \"\"\"\n        Consolidate the per-object temporary outputs in `temp_output_dict_per_obj` on\n        a frame into a single output for all objects, including\n        1) fill any missing objects either from `output_dict_per_obj` (if they exist in\n           `output_dict_per_obj` for this frame) or leave them as placeholder values\n           (if they don't exist in `output_dict_per_obj` for this frame);\n        2) if specified, rerun memory encoder after apply non-overlapping constraints\n           on the object scores.\n        \"\"\"\n        batch_size = self._get_obj_num(inference_state)\n        storage_key = \"cond_frame_outputs\" if is_cond else \"non_cond_frame_outputs\"\n        # Optionally, we allow consolidating the temporary outputs at the original\n        # video resolution (to provide a better editing experience for mask prompts).\n        if consolidate_at_video_res:\n            assert not run_mem_encoder, \"memory encoder cannot run at video resolution\"\n            consolidated_H = inference_state[\"video_height\"]\n            consolidated_W = inference_state[\"video_width\"]\n            consolidated_mask_key = \"pred_masks_video_res\"\n        else:\n            consolidated_H = consolidated_W = self.low_res_mask_size\n            consolidated_mask_key = \"pred_masks\"\n\n        # Initialize `consolidated_out`. Its \"maskmem_features\" and \"maskmem_pos_enc\"\n        # will be added when rerunning the memory encoder after applying non-overlapping\n        # constraints to object scores. Its \"pred_masks\" are prefilled with a large\n        # negative value (NO_OBJ_SCORE) to represent missing objects.\n        consolidated_out = {\n            \"maskmem_features\": None,\n            \"maskmem_pos_enc\": None,\n            consolidated_mask_key: torch.full(\n                size=(batch_size, 1, consolidated_H, consolidated_W),\n                fill_value=NO_OBJ_SCORE,\n                dtype=torch.float32,\n                device=inference_state[\"storage_device\"],\n            ),\n            \"obj_ptr\": torch.full(\n                size=(batch_size, self.hidden_dim),\n                fill_value=NO_OBJ_SCORE,\n                dtype=torch.float32,\n                device=inference_state[\"device\"],\n            ),\n            \"object_score_logits\": torch.full(\n                size=(batch_size, 1),\n                # default to 10.0 for object_score_logits, i.e. assuming the object is\n                # present as sigmoid(10)=1, same as in `predict_masks` of `MaskDecoder`\n                fill_value=10.0,\n                dtype=torch.float32,\n                device=inference_state[\"device\"],\n            ),\n        }\n        if self.use_memory_selection:\n            consolidated_out[\"iou_score\"] = torch.full(\n                size=(batch_size, 1),\n                fill_value=0.0,\n                dtype=torch.float32,\n                device=inference_state[\"device\"],\n            )\n        empty_mask_ptr = None\n        for obj_idx in range(batch_size):\n            obj_temp_output_dict = inference_state[\"temp_output_dict_per_obj\"][obj_idx]\n            obj_output_dict = inference_state[\"output_dict_per_obj\"][obj_idx]\n            out = obj_temp_output_dict[storage_key].get(frame_idx, None)\n            # If the object doesn't appear in \"temp_output_dict_per_obj\" on this frame,\n            # we fall back and look up its previous output in \"output_dict_per_obj\".\n            # We look up both \"cond_frame_outputs\" and \"non_cond_frame_outputs\" in\n            # \"output_dict_per_obj\" to find a previous output for this object.\n            if out is None:\n                out = obj_output_dict[\"cond_frame_outputs\"].get(frame_idx, None)\n            if out is None:\n                out = obj_output_dict[\"non_cond_frame_outputs\"].get(frame_idx, None)\n            # If the object doesn't appear in \"output_dict_per_obj\" either, we skip it\n            # and leave its mask scores to the default scores (i.e. the NO_OBJ_SCORE\n            # placeholder above) and set its object pointer to be a dummy pointer.\n            if out is None:\n                # Fill in dummy object pointers for those objects without any inputs or\n                # tracking outcomes on this frame (only do it under `run_mem_encoder=True`,\n                # i.e. when we need to build the memory for tracking).\n                if run_mem_encoder:\n                    if empty_mask_ptr is None:\n                        empty_mask_ptr = self._get_empty_mask_ptr(\n                            inference_state, frame_idx\n                        )\n                    # fill object pointer with a dummy pointer (based on an empty mask)\n                    consolidated_out[\"obj_ptr\"][obj_idx : obj_idx + 1] = empty_mask_ptr\n                continue\n            # Add the temporary object output mask to consolidated output mask\n            # (use \"pred_masks_video_res\" if it's available)\n            obj_mask = out.get(\"pred_masks_video_res\", out[\"pred_masks\"])\n            consolidated_pred_masks = consolidated_out[consolidated_mask_key]\n            if obj_mask.shape[-2:] == consolidated_pred_masks.shape[-2:]:\n                consolidated_pred_masks[obj_idx : obj_idx + 1] = obj_mask\n            else:\n                # Resize first if temporary object mask has a different resolution\n                is_downsampling = \"pred_masks_video_res\" in out\n                resized_obj_mask = torch.nn.functional.interpolate(\n                    obj_mask,\n                    size=consolidated_pred_masks.shape[-2:],\n                    mode=\"bilinear\",\n                    align_corners=False,\n                    antialias=is_downsampling,  # use antialias for downsampling\n                )\n                consolidated_pred_masks[obj_idx : obj_idx + 1] = resized_obj_mask\n            consolidated_out[\"obj_ptr\"][obj_idx : obj_idx + 1] = out[\"obj_ptr\"]\n            consolidated_out[\"object_score_logits\"][obj_idx : obj_idx + 1] = out[\n                \"object_score_logits\"\n            ]\n            if self.use_memory_selection:\n                consolidated_out[\"iou_score\"][obj_idx : obj_idx + 1] = out[\"iou_score\"]\n        # Optionally, apply non-overlapping constraints on the consolidated scores\n        # and rerun the memory encoder\n        if run_mem_encoder:\n            device = inference_state[\"device\"]\n            high_res_masks = torch.nn.functional.interpolate(\n                consolidated_out[\"pred_masks\"].to(device, non_blocking=True),\n                size=(self.image_size, self.image_size),\n                mode=\"bilinear\",\n                align_corners=False,\n            )\n            high_res_masks = self._apply_non_overlapping_constraints(high_res_masks)\n            maskmem_features, maskmem_pos_enc = self._run_memory_encoder(\n                inference_state=inference_state,\n                frame_idx=frame_idx,\n                batch_size=batch_size,\n                high_res_masks=high_res_masks,\n                object_score_logits=consolidated_out[\"object_score_logits\"],\n                is_mask_from_pts=True,  # these frames are what the user interacted with\n            )\n            consolidated_out[\"maskmem_features\"] = maskmem_features\n            consolidated_out[\"maskmem_pos_enc\"] = maskmem_pos_enc\n\n        return consolidated_out\n\n    def _get_empty_mask_ptr(self, inference_state, frame_idx):\n        \"\"\"Get a dummy object pointer based on an empty mask on the current frame.\"\"\"\n        # A dummy (empty) mask with a single object\n        batch_size = 1\n        mask_inputs = torch.zeros(\n            (batch_size, 1, self.image_size, self.image_size),\n            dtype=torch.float32,\n            device=inference_state[\"device\"],\n        )\n\n        # Retrieve correct image features\n        (\n            image,\n            _,\n            current_vision_feats,\n            current_vision_pos_embeds,\n            feat_sizes,\n        ) = self._get_image_feature(inference_state, frame_idx, batch_size)\n\n        # Feed the empty mask and image feature above to get a dummy object pointer\n        current_out = self.track_step(\n            frame_idx=frame_idx,\n            is_init_cond_frame=True,\n            current_vision_feats=current_vision_feats,\n            current_vision_pos_embeds=current_vision_pos_embeds,\n            feat_sizes=feat_sizes,\n            image=image,\n            point_inputs=None,\n            mask_inputs=mask_inputs,\n            output_dict={\n                \"cond_frame_outputs\": {},\n                \"non_cond_frame_outputs\": {},\n            },\n            num_frames=inference_state[\"num_frames\"],\n            track_in_reverse=False,\n            run_mem_encoder=False,\n            prev_sam_mask_logits=None,\n        )\n        return current_out[\"obj_ptr\"]\n\n    @torch.inference_mode()\n    def propagate_in_video_preflight(self, inference_state, run_mem_encoder=True):\n        \"\"\"Prepare inference_state and consolidate temporary outputs before tracking.\"\"\"\n        # Tracking has started and we don't allow adding new objects until session is reset.\n        inference_state[\"tracking_has_started\"] = True\n        batch_size = self._get_obj_num(inference_state)\n\n        # Consolidate per-object temporary outputs in \"temp_output_dict_per_obj\" and\n        # add them into \"output_dict\".\n        temp_output_dict_per_obj = inference_state[\"temp_output_dict_per_obj\"]\n        output_dict = inference_state[\"output_dict\"]\n        # \"consolidated_frame_inds\" contains indices of those frames where consolidated\n        # temporary outputs have been added (either in this call or any previous calls\n        # to `propagate_in_video_preflight`).\n        consolidated_frame_inds = inference_state[\"consolidated_frame_inds\"]\n        for is_cond in [False, True]:\n            # Separately consolidate conditioning and non-conditioning temp outptus\n            storage_key = \"cond_frame_outputs\" if is_cond else \"non_cond_frame_outputs\"\n            # Find all the frames that contain temporary outputs for any objects\n            # (these should be the frames that have just received clicks for mask inputs\n            # via `add_new_points` or `add_new_mask`)\n            temp_frame_inds = set()\n            for obj_temp_output_dict in temp_output_dict_per_obj.values():\n                temp_frame_inds.update(obj_temp_output_dict[storage_key].keys())\n            consolidated_frame_inds[storage_key].update(temp_frame_inds)\n            # consolidate the temprary output across all objects on this frame\n            for frame_idx in temp_frame_inds:\n                consolidated_out = self._consolidate_temp_output_across_obj(\n                    inference_state,\n                    frame_idx,\n                    is_cond=is_cond,\n                    run_mem_encoder=run_mem_encoder,\n                )\n                # merge them into \"output_dict\" and also create per-object slices\n                output_dict[storage_key][frame_idx] = consolidated_out\n                self._add_output_per_object(\n                    inference_state, frame_idx, consolidated_out, storage_key\n                )\n                clear_non_cond_mem = self.clear_non_cond_mem_around_input and (\n                    self.clear_non_cond_mem_for_multi_obj or batch_size <= 1\n                )\n                if clear_non_cond_mem:\n                    # clear non-conditioning memory of the surrounding frames\n                    self._clear_non_cond_mem_around_input(inference_state, frame_idx)\n\n            # clear temporary outputs in `temp_output_dict_per_obj`\n            for obj_temp_output_dict in temp_output_dict_per_obj.values():\n                obj_temp_output_dict[storage_key].clear()\n\n        # edge case: if an output is added to \"cond_frame_outputs\", we remove any prior\n        # output on the same frame in \"non_cond_frame_outputs\"\n        for frame_idx in output_dict[\"cond_frame_outputs\"]:\n            output_dict[\"non_cond_frame_outputs\"].pop(frame_idx, None)\n        for obj_output_dict in inference_state[\"output_dict_per_obj\"].values():\n            for frame_idx in obj_output_dict[\"cond_frame_outputs\"]:\n                obj_output_dict[\"non_cond_frame_outputs\"].pop(frame_idx, None)\n        for frame_idx in consolidated_frame_inds[\"cond_frame_outputs\"]:\n            assert frame_idx in output_dict[\"cond_frame_outputs\"]\n            consolidated_frame_inds[\"non_cond_frame_outputs\"].discard(frame_idx)\n\n        # Make sure that the frame indices in \"consolidated_frame_inds\" are exactly those frames\n        # with either points or mask inputs (which should be true under a correct demo workflow).\n        all_consolidated_frame_inds = (\n            consolidated_frame_inds[\"cond_frame_outputs\"]\n            | consolidated_frame_inds[\"non_cond_frame_outputs\"]\n        )\n        input_frames_inds = set()\n        for point_inputs_per_frame in inference_state[\"point_inputs_per_obj\"].values():\n            input_frames_inds.update(point_inputs_per_frame.keys())\n        for mask_inputs_per_frame in inference_state[\"mask_inputs_per_obj\"].values():\n            input_frames_inds.update(mask_inputs_per_frame.keys())\n        assert all_consolidated_frame_inds == input_frames_inds\n        # Record the first interacted frame index (for tracking start)\n        if inference_state[\"first_ann_frame_idx\"] is None:\n            inference_state[\"first_ann_frame_idx\"] = min(\n                input_frames_inds, default=None\n            )\n        # In case `first_ann_frame_idx` is not in the conditioning frames (e.g. because\n        # we cleared the input points on that frame), pick the first conditioning frame\n        if (\n            inference_state[\"first_ann_frame_idx\"]\n            not in output_dict[\"cond_frame_outputs\"]\n        ):\n            inference_state[\"first_ann_frame_idx\"] = min(\n                output_dict[\"cond_frame_outputs\"], default=None\n            )\n\n    def _get_processing_order(\n        self, inference_state, start_frame_idx, max_frame_num_to_track, reverse\n    ):\n        num_frames = inference_state[\"num_frames\"]\n        # set start index, end index, and processing order\n        if self.always_start_from_first_ann_frame:\n            # in this case, we always start tracking from the frame where we receive\n            # the initial annotation and ignore the provided start_frame_idx\n            start_frame_idx = inference_state[\"first_ann_frame_idx\"]\n        if start_frame_idx is None:\n            # default: start from the earliest frame with input points\n            start_frame_idx = min(inference_state[\"output_dict\"][\"cond_frame_outputs\"])\n        if max_frame_num_to_track is None:\n            # default: track all the frames in the video\n            max_frame_num_to_track = num_frames\n        if reverse:\n            end_frame_idx = max(start_frame_idx - max_frame_num_to_track, 0)\n            if start_frame_idx > 0:\n                processing_order = range(start_frame_idx, end_frame_idx - 1, -1)\n            else:\n                # this is the edge case where we start from frame 0 and track in reverse order;\n                # in this case, we track a single frame (frame 0)\n                processing_order = [0]\n        else:\n            end_frame_idx = min(\n                start_frame_idx + max_frame_num_to_track, num_frames - 1\n            )\n            processing_order = range(start_frame_idx, end_frame_idx + 1)\n        return processing_order\n\n    @torch.inference_mode()\n    def propagate_in_video(\n        self,\n        inference_state,\n        start_frame_idx,\n        max_frame_num_to_track,\n        reverse,\n        tqdm_disable=False,\n        obj_ids=None,\n        run_mem_encoder=True,\n        propagate_preflight=False,\n    ):\n        \"\"\"Propagate the input points across frames to track in the entire video.\"\"\"\n        if propagate_preflight:\n            self.propagate_in_video_preflight(inference_state)\n        # NOTE: This is a copy from the parent class, except that we return object scores as well.\n        output_dict = inference_state[\"output_dict\"]\n        consolidated_frame_inds = inference_state[\"consolidated_frame_inds\"]\n        if obj_ids is not None:\n            raise NotImplementedError(\n                \"Per-object tracking yet for batched inference if not implemented.\"\n            )\n        obj_ids = inference_state[\"obj_ids\"]\n        batch_size = self._get_obj_num(inference_state)\n        if len(output_dict[\"cond_frame_outputs\"]) == 0:\n            raise RuntimeError(\"No points are provided; please add points first\")\n        clear_non_cond_mem = self.clear_non_cond_mem_around_input and (\n            self.clear_non_cond_mem_for_multi_obj or batch_size <= 1\n        )\n\n        processing_order = self._get_processing_order(\n            inference_state,\n            start_frame_idx,\n            max_frame_num_to_track,\n            reverse,\n        )\n\n        for frame_idx in tqdm(\n            processing_order, desc=\"propagate in video\", disable=tqdm_disable\n        ):\n            # We skip those frames already in consolidated outputs (these are frames\n            # that received input clicks or mask). Note that we cannot directly run\n            # batched forward on them via `_run_single_frame_inference` because the\n            # number of clicks on each object might be different.\n            if frame_idx in consolidated_frame_inds[\"cond_frame_outputs\"]:\n                storage_key = \"cond_frame_outputs\"\n                current_out = output_dict[storage_key][frame_idx]\n                pred_masks = current_out[\"pred_masks\"]\n                obj_scores = current_out[\"object_score_logits\"]\n                if clear_non_cond_mem:\n                    # clear non-conditioning memory of the surrounding frames\n                    self._clear_non_cond_mem_around_input(inference_state, frame_idx)\n            elif frame_idx in consolidated_frame_inds[\"non_cond_frame_outputs\"]:\n                storage_key = \"non_cond_frame_outputs\"\n                current_out = output_dict[storage_key][frame_idx]\n                pred_masks = current_out[\"pred_masks\"]\n                obj_scores = current_out[\"object_score_logits\"]\n            else:\n                storage_key = \"non_cond_frame_outputs\"\n                current_out, pred_masks = self._run_single_frame_inference(\n                    inference_state=inference_state,\n                    output_dict=output_dict,\n                    frame_idx=frame_idx,\n                    batch_size=batch_size,\n                    is_init_cond_frame=False,\n                    point_inputs=None,\n                    mask_inputs=None,\n                    reverse=reverse,\n                    run_mem_encoder=run_mem_encoder,\n                )\n                obj_scores = current_out[\"object_score_logits\"]\n                output_dict[storage_key][frame_idx] = current_out\n            # Create slices of per-object outputs for subsequent interaction with each\n            # individual object after tracking.\n            self._add_output_per_object(\n                inference_state, frame_idx, current_out, storage_key\n            )\n            inference_state[\"frames_already_tracked\"][frame_idx] = {\"reverse\": reverse}\n\n            # Resize the output mask to the original video resolution (we directly use\n            # the mask scores on GPU for output to avoid any CPU conversion in between)\n            low_res_masks, video_res_masks = self._get_orig_video_res_output(\n                inference_state, pred_masks\n            )\n            yield frame_idx, obj_ids, low_res_masks, video_res_masks, obj_scores\n\n    def _add_output_per_object(\n        self, inference_state, frame_idx, current_out, storage_key\n    ):\n        \"\"\"\n        Split a multi-object output into per-object output slices and add them into\n        `output_dict_per_obj`. The resulting slices share the same tensor storage.\n        \"\"\"\n        maskmem_features = current_out[\"maskmem_features\"]\n        assert maskmem_features is None or isinstance(maskmem_features, torch.Tensor)\n\n        maskmem_pos_enc = current_out[\"maskmem_pos_enc\"]\n        assert maskmem_pos_enc is None or isinstance(maskmem_pos_enc, list)\n\n        output_dict_per_obj = inference_state[\"output_dict_per_obj\"]\n        for obj_idx, obj_output_dict in output_dict_per_obj.items():\n            obj_slice = slice(obj_idx, obj_idx + 1)\n            obj_out = {\n                \"maskmem_features\": None,\n                \"maskmem_pos_enc\": None,\n                \"pred_masks\": current_out[\"pred_masks\"][obj_slice],\n                \"obj_ptr\": current_out[\"obj_ptr\"][obj_slice],\n                \"object_score_logits\": current_out[\"object_score_logits\"][obj_slice],\n            }\n            if self.use_memory_selection:\n                obj_out[\"iou_score\"] = current_out[\"iou_score\"][obj_slice]\n            if maskmem_features is not None:\n                obj_out[\"maskmem_features\"] = maskmem_features[obj_slice]\n            if maskmem_pos_enc is not None:\n                obj_out[\"maskmem_pos_enc\"] = [x[obj_slice] for x in maskmem_pos_enc]\n            obj_output_dict[storage_key][frame_idx] = obj_out\n\n    @torch.inference_mode()\n    def clear_all_points_in_frame(\n        self, inference_state, frame_idx, obj_id, need_output=True\n    ):\n        \"\"\"Remove all input points or mask in a specific frame for a given object.\"\"\"\n        obj_idx = self._obj_id_to_idx(inference_state, obj_id)\n\n        # Clear the conditioning information on the given frame\n        inference_state[\"point_inputs_per_obj\"][obj_idx].pop(frame_idx, None)\n        inference_state[\"mask_inputs_per_obj\"][obj_idx].pop(frame_idx, None)\n\n        temp_output_dict_per_obj = inference_state[\"temp_output_dict_per_obj\"]\n        temp_output_dict_per_obj[obj_idx][\"cond_frame_outputs\"].pop(frame_idx, None)\n        temp_output_dict_per_obj[obj_idx][\"non_cond_frame_outputs\"].pop(frame_idx, None)\n\n        # Check and see if there are still any inputs left on this frame\n        batch_size = self._get_obj_num(inference_state)\n        frame_has_input = False\n        for obj_idx2 in range(batch_size):\n            if frame_idx in inference_state[\"point_inputs_per_obj\"][obj_idx2]:\n                frame_has_input = True\n                break\n            if frame_idx in inference_state[\"mask_inputs_per_obj\"][obj_idx2]:\n                frame_has_input = True\n                break\n\n        # If this frame has no remaining inputs for any objects, we further clear its\n        # conditioning frame status\n        if not frame_has_input:\n            output_dict = inference_state[\"output_dict\"]\n            consolidated_frame_inds = inference_state[\"consolidated_frame_inds\"]\n            consolidated_frame_inds[\"cond_frame_outputs\"].discard(frame_idx)\n            consolidated_frame_inds[\"non_cond_frame_outputs\"].discard(frame_idx)\n            # Remove the frame's conditioning output (possibly downgrading it to non-conditioning)\n            out = output_dict[\"cond_frame_outputs\"].pop(frame_idx, None)\n            if out is not None:\n                # The frame is not a conditioning frame anymore since it's not receiving inputs,\n                # so we \"downgrade\" its output (if exists) to a non-conditioning frame output.\n                output_dict[\"non_cond_frame_outputs\"][frame_idx] = out\n                inference_state[\"frames_already_tracked\"].pop(frame_idx, None)\n            # Similarly, do it for the sliced output on each object.\n            for obj_idx2 in range(batch_size):\n                obj_output_dict = inference_state[\"output_dict_per_obj\"][obj_idx2]\n                obj_out = obj_output_dict[\"cond_frame_outputs\"].pop(frame_idx, None)\n                if obj_out is not None:\n                    obj_output_dict[\"non_cond_frame_outputs\"][frame_idx] = obj_out\n\n            # If all the conditioning frames have been removed, we also clear the tracking outputs\n            if len(output_dict[\"cond_frame_outputs\"]) == 0:\n                self._reset_tracking_results(inference_state)\n\n        if not need_output:\n            return\n        # Finally, output updated masks per object (after removing the inputs above)\n        obj_ids = inference_state[\"obj_ids\"]\n        is_cond = any(\n            frame_idx in obj_temp_output_dict[\"cond_frame_outputs\"]\n            for obj_temp_output_dict in temp_output_dict_per_obj.values()\n        )\n        consolidated_out = self._consolidate_temp_output_across_obj(\n            inference_state,\n            frame_idx,\n            is_cond=is_cond,\n            run_mem_encoder=False,\n            consolidate_at_video_res=True,\n        )\n        _, video_res_masks = self._get_orig_video_res_output(\n            inference_state, consolidated_out[\"pred_masks_video_res\"]\n        )\n        low_res_masks = None  # not needed by the demo\n        return frame_idx, obj_ids, low_res_masks, video_res_masks\n\n    @torch.inference_mode()\n    def clear_all_points_in_video(self, inference_state):\n        \"\"\"Remove all input points or mask in all frames throughout the video.\"\"\"\n        self._reset_tracking_results(inference_state)\n        # Remove all object ids\n        inference_state[\"obj_id_to_idx\"].clear()\n        inference_state[\"obj_idx_to_id\"].clear()\n        inference_state[\"obj_ids\"].clear()\n        inference_state[\"point_inputs_per_obj\"].clear()\n        inference_state[\"mask_inputs_per_obj\"].clear()\n        inference_state[\"output_dict_per_obj\"].clear()\n        inference_state[\"temp_output_dict_per_obj\"].clear()\n\n    def _reset_tracking_results(self, inference_state):\n        \"\"\"Reset all tracking inputs and results across the videos.\"\"\"\n        for v in inference_state[\"point_inputs_per_obj\"].values():\n            v.clear()\n        for v in inference_state[\"mask_inputs_per_obj\"].values():\n            v.clear()\n        for v in inference_state[\"output_dict_per_obj\"].values():\n            v[\"cond_frame_outputs\"].clear()\n            v[\"non_cond_frame_outputs\"].clear()\n        for v in inference_state[\"temp_output_dict_per_obj\"].values():\n            v[\"cond_frame_outputs\"].clear()\n            v[\"non_cond_frame_outputs\"].clear()\n        inference_state[\"output_dict\"][\"cond_frame_outputs\"].clear()\n        inference_state[\"output_dict\"][\"non_cond_frame_outputs\"].clear()\n        inference_state[\"consolidated_frame_inds\"][\"cond_frame_outputs\"].clear()\n        inference_state[\"consolidated_frame_inds\"][\"non_cond_frame_outputs\"].clear()\n        inference_state[\"tracking_has_started\"] = False\n        inference_state[\"frames_already_tracked\"].clear()\n        inference_state[\"first_ann_frame_idx\"] = None\n\n    def _get_image_feature(self, inference_state, frame_idx, batch_size):\n        \"\"\"Compute the image features on a given frame.\"\"\"\n        # Look up in the cache\n        image, backbone_out = inference_state[\"cached_features\"].get(\n            frame_idx, (None, None)\n        )\n        if backbone_out is None:\n            if self.backbone is None:\n                raise RuntimeError(\n                    f\"Image features for frame {frame_idx} are not cached. \"\n                    \"Please run inference on this frame first.\"\n                )\n            else:\n                # Cache miss -- we will run inference on a single image\n                image = inference_state[\"images\"][frame_idx].cuda().float().unsqueeze(0)\n                backbone_out = self.forward_image(image)\n                # Cache the most recent frame's feature (for repeated interactions with\n                # a frame; we can use an LRU cache for more frames in the future).\n                inference_state[\"cached_features\"] = {frame_idx: (image, backbone_out)}\n        if \"tracker_backbone_out\" in backbone_out:\n            backbone_out = backbone_out[\"tracker_backbone_out\"]  # get backbone output\n\n        # expand the features to have the same dimension as the number of objects\n        expanded_image = image.expand(batch_size, -1, -1, -1)\n        expanded_backbone_out = {\n            \"backbone_fpn\": backbone_out[\"backbone_fpn\"].copy(),\n            \"vision_pos_enc\": backbone_out[\"vision_pos_enc\"].copy(),\n        }\n        for i, feat in enumerate(expanded_backbone_out[\"backbone_fpn\"]):\n            feat = feat.expand(batch_size, -1, -1, -1)\n            expanded_backbone_out[\"backbone_fpn\"][i] = feat\n        for i, pos in enumerate(expanded_backbone_out[\"vision_pos_enc\"]):\n            pos = pos.expand(batch_size, -1, -1, -1)\n            expanded_backbone_out[\"vision_pos_enc\"][i] = pos\n\n        features = self._prepare_backbone_features(expanded_backbone_out)\n        features = (expanded_image,) + features\n        return features\n\n    def _run_single_frame_inference(\n        self,\n        inference_state,\n        output_dict,\n        frame_idx,\n        batch_size,\n        is_init_cond_frame,\n        point_inputs,\n        mask_inputs,\n        reverse,\n        run_mem_encoder,\n        prev_sam_mask_logits=None,\n        use_prev_mem_frame=True,\n    ):\n        \"\"\"Run tracking on a single frame based on current inputs and previous memory.\"\"\"\n        # Retrieve correct image features\n        (\n            image,\n            _,\n            current_vision_feats,\n            current_vision_pos_embeds,\n            feat_sizes,\n        ) = self._get_image_feature(inference_state, frame_idx, batch_size)\n\n        # point and mask should not appear as input simultaneously on the same frame\n        assert point_inputs is None or mask_inputs is None\n        current_out = self.track_step(\n            frame_idx=frame_idx,\n            is_init_cond_frame=is_init_cond_frame,\n            current_vision_feats=current_vision_feats,\n            current_vision_pos_embeds=current_vision_pos_embeds,\n            feat_sizes=feat_sizes,\n            image=image,\n            point_inputs=point_inputs,\n            mask_inputs=mask_inputs,\n            output_dict=output_dict,\n            num_frames=inference_state[\"num_frames\"],\n            track_in_reverse=reverse,\n            run_mem_encoder=run_mem_encoder,\n            prev_sam_mask_logits=prev_sam_mask_logits,\n            use_prev_mem_frame=use_prev_mem_frame,\n        )\n\n        # optionally offload the output to CPU memory to save GPU space\n        storage_device = inference_state[\"storage_device\"]\n        maskmem_features = current_out[\"maskmem_features\"]\n        if maskmem_features is not None:\n            maskmem_features = maskmem_features.to(torch.bfloat16)\n            maskmem_features = maskmem_features.to(storage_device, non_blocking=True)\n        pred_masks_gpu = current_out[\"pred_masks\"]\n        pred_masks = pred_masks_gpu.to(storage_device, non_blocking=True)\n        # \"maskmem_pos_enc\" is the same across frames, so we only need to store one copy of it\n        maskmem_pos_enc = self._get_maskmem_pos_enc(inference_state, current_out)\n        # object pointer is a small tensor, so we always keep it on GPU memory for fast access\n        obj_ptr = current_out[\"obj_ptr\"]\n        object_score_logits = current_out[\"object_score_logits\"]\n        # make a compact version of this frame's output to reduce the state size\n        compact_current_out = {\n            \"maskmem_features\": maskmem_features,\n            \"maskmem_pos_enc\": maskmem_pos_enc,\n            \"pred_masks\": pred_masks,\n            \"obj_ptr\": obj_ptr,\n            \"object_score_logits\": object_score_logits,\n        }\n        if self.use_memory_selection:\n            compact_current_out[\"iou_score\"] = current_out[\"iou_score\"]\n            compact_current_out[\"eff_iou_score\"] = current_out[\"eff_iou_score\"]\n        return compact_current_out, pred_masks_gpu\n\n    def _run_memory_encoder(\n        self,\n        inference_state,\n        frame_idx,\n        batch_size,\n        high_res_masks,\n        object_score_logits,\n        is_mask_from_pts,\n    ):\n        \"\"\"\n        Run the memory encoder on `high_res_masks`. This is usually after applying\n        non-overlapping constraints to object scores. Since their scores changed, their\n        memory also need to be computed again with the memory encoder.\n        \"\"\"\n        # Retrieve correct image features\n        image, _, current_vision_feats, _, feat_sizes = self._get_image_feature(\n            inference_state, frame_idx, batch_size\n        )\n        maskmem_features, maskmem_pos_enc = self._encode_new_memory(\n            image=image,\n            current_vision_feats=current_vision_feats,\n            feat_sizes=feat_sizes,\n            pred_masks_high_res=high_res_masks,\n            object_score_logits=object_score_logits,\n            is_mask_from_pts=is_mask_from_pts,\n        )\n\n        # optionally offload the output to CPU memory to save GPU space\n        storage_device = inference_state[\"storage_device\"]\n        maskmem_features = maskmem_features.to(torch.bfloat16)\n        maskmem_features = maskmem_features.to(storage_device, non_blocking=True)\n        # \"maskmem_pos_enc\" is the same across frames, so we only need to store one copy of it\n        maskmem_pos_enc = self._get_maskmem_pos_enc(\n            inference_state, {\"maskmem_pos_enc\": maskmem_pos_enc}\n        )\n        return maskmem_features, maskmem_pos_enc\n\n    def _get_maskmem_pos_enc(self, inference_state, current_out):\n        \"\"\"\n        `maskmem_pos_enc` is the same across frames and objects, so we cache it as\n        a constant in the inference session to reduce session storage size.\n        \"\"\"\n        model_constants = inference_state[\"constants\"]\n        # \"out_maskmem_pos_enc\" should be either a list of tensors or None\n        out_maskmem_pos_enc = current_out[\"maskmem_pos_enc\"]\n        if out_maskmem_pos_enc is not None:\n            if \"maskmem_pos_enc\" not in model_constants:\n                assert isinstance(out_maskmem_pos_enc, list)\n                # only take the slice for one object, since it's same across objects\n                maskmem_pos_enc = [x[0:1].clone() for x in out_maskmem_pos_enc]\n                model_constants[\"maskmem_pos_enc\"] = maskmem_pos_enc\n            else:\n                maskmem_pos_enc = model_constants[\"maskmem_pos_enc\"]\n            # expand the cached maskmem_pos_enc to the actual batch size\n            batch_size = out_maskmem_pos_enc[0].size(0)\n            expanded_maskmem_pos_enc = [\n                x.expand(batch_size, -1, -1, -1) for x in maskmem_pos_enc\n            ]\n        else:\n            expanded_maskmem_pos_enc = None\n        return expanded_maskmem_pos_enc\n\n    @torch.inference_mode()\n    def remove_object(self, inference_state, obj_id, strict=False, need_output=True):\n        \"\"\"\n        Remove an object id from the tracking state. If strict is True, we check whether\n        the object id actually exists and raise an error if it doesn't exist.\n        \"\"\"\n        old_obj_idx_to_rm = inference_state[\"obj_id_to_idx\"].get(obj_id, None)\n        updated_frames = []\n        # Check whether this object_id to remove actually exists and possibly raise an error.\n        if old_obj_idx_to_rm is None:\n            if not strict:\n                return inference_state[\"obj_ids\"], updated_frames\n            raise RuntimeError(\n                f\"Cannot remove object id {obj_id} as it doesn't exist. \"\n                f\"All existing object ids: {inference_state['obj_ids']}.\"\n            )\n\n        # If this is the only remaining object id, we simply reset the state.\n        if len(inference_state[\"obj_id_to_idx\"]) == 1:\n            self.clear_all_points_in_video(inference_state)\n            return inference_state[\"obj_ids\"], updated_frames\n\n        # There are still remaining objects after removing this object id. In this case,\n        # we need to delete the object storage from inference state tensors.\n        # Step 0: clear the input on those frames where this object id has point or mask input\n        # (note that this step is required as it might downgrade conditioning frames to\n        # non-conditioning ones)\n        obj_input_frames_inds = set()\n        obj_input_frames_inds.update(\n            inference_state[\"point_inputs_per_obj\"][old_obj_idx_to_rm]\n        )\n        obj_input_frames_inds.update(\n            inference_state[\"mask_inputs_per_obj\"][old_obj_idx_to_rm]\n        )\n        for frame_idx in obj_input_frames_inds:\n            self.clear_all_points_in_frame(\n                inference_state, frame_idx, obj_id, need_output=False\n            )\n\n        # Step 1: Update the object id mapping (note that it must be done after Step 0,\n        # since Step 0 still requires the old object id mappings in inference_state)\n        old_obj_ids = inference_state[\"obj_ids\"]\n        old_obj_inds = list(range(len(old_obj_ids)))\n        remain_old_obj_inds = old_obj_inds.copy()\n        remain_old_obj_inds.remove(old_obj_idx_to_rm)\n        new_obj_ids = [old_obj_ids[old_idx] for old_idx in remain_old_obj_inds]\n        new_obj_inds = list(range(len(new_obj_ids)))\n        # build new mappings\n        old_idx_to_new_idx = dict(zip(remain_old_obj_inds, new_obj_inds))\n        inference_state[\"obj_id_to_idx\"] = dict(zip(new_obj_ids, new_obj_inds))\n        inference_state[\"obj_idx_to_id\"] = dict(zip(new_obj_inds, new_obj_ids))\n        inference_state[\"obj_ids\"] = new_obj_ids\n\n        # Step 2: For per-object tensor storage, we shift their obj_idx in the dict keys.\n        # (note that \"consolidated_frame_inds\" doesn't need to be updated in this step as\n        # it's already handled in Step 0)\n        def _map_keys(container):\n            new_kvs = []\n            for k in old_obj_inds:\n                v = container.pop(k)\n                if k in old_idx_to_new_idx:\n                    new_kvs.append((old_idx_to_new_idx[k], v))\n            container.update(new_kvs)\n\n        _map_keys(inference_state[\"point_inputs_per_obj\"])\n        _map_keys(inference_state[\"mask_inputs_per_obj\"])\n        _map_keys(inference_state[\"output_dict_per_obj\"])\n        _map_keys(inference_state[\"temp_output_dict_per_obj\"])\n\n        # Step 3: For packed tensor storage, we index the remaining ids and rebuild the per-object slices.\n        def _slice_state(output_dict, storage_key):\n            for frame_idx, out in output_dict[storage_key].items():\n                out[\"maskmem_features\"] = out[\"maskmem_features\"][remain_old_obj_inds]\n                out[\"maskmem_pos_enc\"] = [\n                    x[remain_old_obj_inds] for x in out[\"maskmem_pos_enc\"]\n                ]\n                # \"maskmem_pos_enc\" is the same across frames, so we only need to store one copy of it\n                out[\"maskmem_pos_enc\"] = self._get_maskmem_pos_enc(inference_state, out)\n                out[\"pred_masks\"] = out[\"pred_masks\"][remain_old_obj_inds]\n                out[\"obj_ptr\"] = out[\"obj_ptr\"][remain_old_obj_inds]\n                out[\"object_score_logits\"] = out[\"object_score_logits\"][\n                    remain_old_obj_inds\n                ]\n                if self.use_memory_selection:\n                    out[\"iou_score\"] = out[\"iou_score\"][remain_old_obj_inds]\n                    out[\"eff_iou_score\"] = self.cal_mem_score(\n                        out[\"object_score_logits\"], out[\"iou_score\"]\n                    )  # recalculate the memory frame score\n                # also update the per-object slices\n                self._add_output_per_object(\n                    inference_state, frame_idx, out, storage_key\n                )\n\n        _slice_state(inference_state[\"output_dict\"], \"cond_frame_outputs\")\n        _slice_state(inference_state[\"output_dict\"], \"non_cond_frame_outputs\")\n\n        # Step 4: Further collect the outputs on those frames in `obj_input_frames_inds`, which\n        # could show an updated mask for objects previously occluded by the object being removed\n        if need_output:\n            temp_output_dict_per_obj = inference_state[\"temp_output_dict_per_obj\"]\n            for frame_idx in obj_input_frames_inds:\n                is_cond = any(\n                    frame_idx in obj_temp_output_dict[\"cond_frame_outputs\"]\n                    for obj_temp_output_dict in temp_output_dict_per_obj.values()\n                )\n                consolidated_out = self._consolidate_temp_output_across_obj(\n                    inference_state,\n                    frame_idx,\n                    is_cond=is_cond,\n                    run_mem_encoder=False,\n                    consolidate_at_video_res=True,\n                )\n                _, video_res_masks = self._get_orig_video_res_output(\n                    inference_state, consolidated_out[\"pred_masks_video_res\"]\n                )\n                updated_frames.append((frame_idx, video_res_masks))\n\n        return inference_state[\"obj_ids\"], updated_frames\n\n    def _clear_non_cond_mem_around_input(self, inference_state, frame_idx):\n        \"\"\"\n        Remove the non-conditioning memory around the input frame. When users provide\n        correction clicks, the surrounding frames' non-conditioning memories can still\n        contain outdated object appearance information and could confuse the model.\n\n        This method clears those non-conditioning memories surrounding the interacted\n        frame to avoid giving the model both old and new information about the object.\n        \"\"\"\n        r = self.memory_temporal_stride_for_eval\n        frame_idx_begin = frame_idx - r * self.num_maskmem\n        frame_idx_end = frame_idx + r * self.num_maskmem\n        batch_size = self._get_obj_num(inference_state)\n        for obj_idx in range(batch_size):\n            obj_output_dict = inference_state[\"output_dict_per_obj\"][obj_idx]\n            non_cond_frame_outputs = obj_output_dict[\"non_cond_frame_outputs\"]\n            for t in range(frame_idx_begin, frame_idx_end + 1):\n                non_cond_frame_outputs.pop(t, None)\n\n    def _suppress_shrinked_masks(\n        self, pred_masks, new_pred_masks, shrink_threshold=0.3\n    ):\n        area_before = (pred_masks > 0).sum(dim=(-1, -2))\n        area_after = (new_pred_masks > 0).sum(dim=(-1, -2))\n        area_before = torch.clamp(area_before, min=1.0)\n        area_ratio = area_after / area_before\n        keep = area_ratio >= shrink_threshold\n        keep_mask = keep[..., None, None].expand_as(pred_masks)\n        pred_masks_after = torch.where(\n            keep_mask, pred_masks, torch.clamp(pred_masks, max=-10.0)\n        )\n        return pred_masks_after\n\n    def _suppress_object_pw_area_shrinkage(self, pred_masks):\n        \"\"\"\n        This function suppresses masks that shrink in area after applying pixelwise non-overlapping constriants.\n        Note that the final output can still be overlapping.\n        \"\"\"\n        # Apply pixel-wise non-overlapping constraint based on mask scores\n        pixel_level_non_overlapping_masks = super()._apply_non_overlapping_constraints(\n            pred_masks\n        )\n        # Fully suppress masks with high shrinkage (probably noisy) based on the pixel wise non-overlapping constraints\n        # NOTE: The output of this function can be a no op if none of the masks shrinked by a large factor.\n        pred_masks = self._suppress_shrinked_masks(\n            pred_masks, pixel_level_non_overlapping_masks\n        )\n        return pred_masks\n\n    def _apply_object_wise_non_overlapping_constraints(\n        self, pred_masks, obj_scores, background_value=-10.0\n    ):\n        \"\"\"\n        Applies non-overlapping constraints object wise (i.e. only one object can claim the overlapping region)\n        \"\"\"\n        # Replace pixel scores with object scores\n        pred_masks_single_score = torch.where(\n            pred_masks > 0, obj_scores[..., None, None], background_value\n        )\n        # Apply pixel-wise non-overlapping constraint based on mask scores\n        pixel_level_non_overlapping_masks = super()._apply_non_overlapping_constraints(\n            pred_masks_single_score\n        )\n        # Replace object scores with pixel scores. Note, that now only one object can claim the overlapping region\n        pred_masks = torch.where(\n            pixel_level_non_overlapping_masks > 0,\n            pred_masks,\n            torch.clamp(pred_masks, max=background_value),\n        )\n        return pred_masks\n"
  },
  {
    "path": "sam3/model/sam3_video_base.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n\nimport datetime\nimport logging\nimport math\nimport os\nfrom collections import defaultdict\nfrom copy import deepcopy\nfrom enum import Enum\nfrom typing import Any, Dict, List, Set\n\nimport numpy as np\nimport numpy.typing as npt\nimport torch\nimport torch.distributed as dist\nimport torch.nn.functional as F\nfrom sam3 import perflib\nfrom sam3.logger import get_logger\nfrom sam3.model.box_ops import fast_diag_box_iou\nfrom sam3.model.data_misc import BatchedDatapoint\nfrom sam3.model.sam3_tracker_utils import fill_holes_in_mask_scores, mask_to_box\nfrom sam3.perflib.masks_ops import mask_iou\nfrom sam3.train.masks_ops import rle_encode\nfrom torch import nn, Tensor\n\nlogger = get_logger(__name__)\n\n\nclass MaskletConfirmationStatus(Enum):\n    UNCONFIRMED = 1  # newly added masklet, not confirmed by any detection yet\n    CONFIRMED = 2  # confirmed by at least one detection\n\n\nclass Sam3VideoBase(nn.Module):\n    def __init__(\n        self,\n        detector: nn.Module,\n        tracker: nn.Module,\n        # prob threshold for detection outputs -- only keep detections above this threshold\n        # enters NMS and det-to-track matching\n        score_threshold_detection=0.5,\n        # IoU threshold for detection NMS\n        det_nms_thresh=0.0,\n        # IoU threshold for det-to-track matching -- a detection is considered \"matched\" to a tracklet it\n        # overlaps with a tracklet above this threshold -- it is often a loose threshold like 0.1\n        assoc_iou_thresh=0.5,\n        # IoU threshold for det-to-track matching, which is used to determine whether a masklet is \"unmatched\"\n        # by any detections -- it is often a stricter threshold like 0.5\n        trk_assoc_iou_thresh=0.5,\n        # prob threshold for a detection to be added as a new object\n        new_det_thresh=0.0,\n        # hotstart parameters: we hold off the outputs for `hotstart_delay` frames and\n        # 1) remove those tracklets unmatched by any detections based on `hotstart_unmatch_thresh`\n        # 2) remove those tracklets overlapping with one another based on `hotstart_dup_thresh`\n        hotstart_delay=0,\n        hotstart_unmatch_thresh=3,\n        hotstart_dup_thresh=3,\n        # Whether to suppress masks only within hotstart. If False, we can suppress masks even if they start before hotstart period.\n        suppress_unmatched_only_within_hotstart=True,\n        init_trk_keep_alive=0,\n        max_trk_keep_alive=8,\n        min_trk_keep_alive=-4,\n        # Threshold for suppressing overlapping objects based on recent occlusion\n        suppress_overlapping_based_on_recent_occlusion_threshold=0.0,\n        decrease_trk_keep_alive_for_empty_masklets=False,\n        o2o_matching_masklets_enable=False,  # Enable hungarian matching to match existing masklets\n        suppress_det_close_to_boundary=False,\n        fill_hole_area=16,\n        # The maximum number of objects (masklets) to track across all GPUs (for no limit, set it to -1)\n        max_num_objects=-1,\n        recondition_every_nth_frame=-1,\n        # masket confirmation status (to suppress unconfirmed masklets)\n        masklet_confirmation_enable=False,\n        # a masklet is confirmed after being consecutively detected and matched for\n        # `masklet_confirmation_consecutive_det_thresh`\n        masklet_confirmation_consecutive_det_thresh=3,\n        # bbox heuristic parameters\n        reconstruction_bbox_iou_thresh=0.0,\n        reconstruction_bbox_det_score=0.0,\n    ):\n        super().__init__()\n        self.detector = detector\n        self.tracker = tracker\n        self.score_threshold_detection = score_threshold_detection\n        self.det_nms_thresh = det_nms_thresh\n        self.assoc_iou_thresh = assoc_iou_thresh\n        self.trk_assoc_iou_thresh = trk_assoc_iou_thresh\n        self.new_det_thresh = new_det_thresh\n\n        # hotstart parameters\n        if hotstart_delay > 0:\n            assert hotstart_unmatch_thresh <= hotstart_delay\n            assert hotstart_dup_thresh <= hotstart_delay\n        self.hotstart_delay = hotstart_delay\n        self.hotstart_unmatch_thresh = hotstart_unmatch_thresh\n        self.hotstart_dup_thresh = hotstart_dup_thresh\n        self.suppress_unmatched_only_within_hotstart = (\n            suppress_unmatched_only_within_hotstart\n        )\n        self.init_trk_keep_alive = init_trk_keep_alive\n        self.max_trk_keep_alive = max_trk_keep_alive\n        self.min_trk_keep_alive = min_trk_keep_alive\n        self.suppress_overlapping_based_on_recent_occlusion_threshold = (\n            suppress_overlapping_based_on_recent_occlusion_threshold\n        )\n        self.suppress_det_close_to_boundary = suppress_det_close_to_boundary\n        self.decrease_trk_keep_alive_for_empty_masklets = (\n            decrease_trk_keep_alive_for_empty_masklets\n        )\n        self.o2o_matching_masklets_enable = o2o_matching_masklets_enable\n        self.fill_hole_area = fill_hole_area\n        self.eval()\n        self.rank = int(os.getenv(\"RANK\", \"0\"))\n        self.world_size = int(os.getenv(\"WORLD_SIZE\", \"1\"))\n        self._dist_pg_cpu = None  # CPU process group (lazy-initialized on first use)\n\n        # the maximum object number\n        if max_num_objects > 0:\n            num_obj_for_compile = math.ceil(max_num_objects / self.world_size)\n        else:\n            max_num_objects = 10000  # no limit\n            num_obj_for_compile = 16\n        logger.info(f\"setting {max_num_objects=} and {num_obj_for_compile=}\")\n        self.max_num_objects = max_num_objects\n        self.num_obj_for_compile = num_obj_for_compile\n        self.recondition_every_nth_frame = recondition_every_nth_frame\n        self.masklet_confirmation_enable = masklet_confirmation_enable\n        self.masklet_confirmation_consecutive_det_thresh = (\n            masklet_confirmation_consecutive_det_thresh\n        )\n        self.reconstruction_bbox_iou_thresh = reconstruction_bbox_iou_thresh\n        self.reconstruction_bbox_det_score = reconstruction_bbox_det_score\n\n    @property\n    def device(self):\n        self._device = getattr(self, \"_device\", None) or next(self.parameters()).device\n        return self._device\n\n    def _init_dist_pg_cpu(self):\n        # a short 3-min timeout to quickly detect any synchronization failures\n        timeout_sec = int(os.getenv(\"SAM3_COLLECTIVE_OP_TIMEOUT_SEC\", \"180\"))\n        timeout = datetime.timedelta(seconds=timeout_sec)\n        self._dist_pg_cpu = dist.new_group(backend=\"gloo\", timeout=timeout)\n\n    def broadcast_python_obj_cpu(self, python_obj_list, src):\n        if self._dist_pg_cpu is None:\n            self._init_dist_pg_cpu()\n        dist.broadcast_object_list(python_obj_list, src=src, group=self._dist_pg_cpu)\n\n    def _det_track_one_frame(\n        self,\n        frame_idx: int,\n        num_frames: int,\n        reverse: bool,\n        input_batch: BatchedDatapoint,\n        geometric_prompt: Any,\n        tracker_states_local: List[Any],\n        tracker_metadata_prev: Dict[str, Any],\n        feature_cache: Dict,\n        orig_vid_height: int,\n        orig_vid_width: int,\n        is_image_only: bool = False,\n        allow_new_detections: bool = True,\n    ):\n        \"\"\"\n        This function handles one-step inference for the DenseTracking model in an SPMD manner.\n        At a high-level, all GPUs execute the same function calls as if it's done on a single GPU,\n        while under the hood, some function calls involve distributed computation based on sharded\n        SAM2 states.\n\n        - `input_batch` contains image and other inputs on the entire video; it should be identical across GPUs\n        - `tracker_states_local` holds the local masklet information in this GPU shard\n        - `tracker_metadata_prev` manages the metadata for SAM2 objects, such as which masklet is hold on which GPUs\n          it contains both global and local masklet information\n        \"\"\"\n\n        # Step 1: run backbone and detector in a distributed manner -- this is done via Sam3ImageOnVideoMultiGPU,\n        # a MultiGPU model (assigned to `self.detector`) that shards frames in a round-robin manner.\n        # It returns a \"det_out\" dict for `frame_idx` and fills SAM2 backbone features for `frame_idx`\n        # into `feature_cache`. Despite its distributed inference under the hood, the results would be\n        # the same as if it is running backbone and detector for every frame on a single GPU.\n        det_out = self.run_backbone_and_detection(\n            frame_idx=frame_idx,\n            num_frames=num_frames,\n            reverse=reverse,\n            input_batch=input_batch,\n            geometric_prompt=geometric_prompt,\n            feature_cache=feature_cache,\n            allow_new_detections=allow_new_detections,\n        )\n\n        # Step 2: each GPU propagates its local SAM2 states to get the SAM2 prediction masks.\n        # the returned `tracker_low_res_masks_global` contains the concatenated masklet predictions\n        # gathered from all GPUs (as if they are propagated on a single GPU). Note that this step only\n        # runs the SAM2 propagation step, but doesn't encode new memory for the predicted masks;\n        # we defer memory encoding to `run_tracker_update_execution_phase` after resolving all heuristics.\n        if tracker_metadata_prev == {}:\n            # initialize masklet metadata if it's uninitialized (empty dict)\n            tracker_metadata_prev.update(self._initialize_metadata())\n        tracker_low_res_masks_global, tracker_obj_scores_global = (\n            self.run_tracker_propagation(\n                frame_idx=frame_idx,\n                num_frames=num_frames,\n                reverse=reverse,\n                tracker_states_local=tracker_states_local,\n                tracker_metadata_prev=tracker_metadata_prev,\n            )\n        )\n\n        # Step 3: based on detection outputs and the propagated SAM2 prediction masks, we make plans\n        # for SAM2 masklet updates (i.e. which objects to add and remove, how to load-balance them, etc).\n        # We also run SAM2 memory encoder globally in this step to resolve non-overlapping constraints.\n        # **This step should involve all the heuristics needed for any updates.** Most of the update\n        # planning will be done on the master rank (GPU 0) and the resulting plan `tracker_update_plan` is\n        # broadcasted to other GPUs (to be executed in a distributed manner). This step also generates the\n        # new masklet metadata `tracker_metadata_new` (based on its previous version `tracker_metadata_prev`).\n        tracker_update_plan, tracker_metadata_new = (\n            self.run_tracker_update_planning_phase(\n                frame_idx=frame_idx,\n                num_frames=num_frames,\n                reverse=reverse,\n                det_out=det_out,\n                tracker_low_res_masks_global=tracker_low_res_masks_global,\n                tracker_obj_scores_global=tracker_obj_scores_global,\n                tracker_metadata_prev=tracker_metadata_prev,\n                tracker_states_local=tracker_states_local,\n                is_image_only=is_image_only,\n            )\n        )\n\n        # Get reconditioning info from the update plan\n        reconditioned_obj_ids = tracker_update_plan.get(\"reconditioned_obj_ids\", set())\n        det_to_matched_trk_obj_ids = tracker_update_plan.get(\n            \"det_to_matched_trk_obj_ids\", {}\n        )\n\n        # Step 4: based on `tracker_update_plan`, each GPU executes the update w.r.t. its local SAM2 inference states\n        tracker_states_local_new = self.run_tracker_update_execution_phase(\n            frame_idx=frame_idx,\n            num_frames=num_frames,\n            reverse=reverse,\n            det_out=det_out,\n            tracker_states_local=tracker_states_local,\n            tracker_update_plan=tracker_update_plan,\n            orig_vid_height=orig_vid_height,\n            orig_vid_width=orig_vid_width,\n            feature_cache=feature_cache,\n        )\n\n        # Step 5: finally, build the outputs for this frame (it only needs to be done on GPU 0 since\n        # only GPU 0 will send outputs to the server).\n        if self.rank == 0:\n            obj_id_to_mask = self.build_outputs(\n                frame_idx=frame_idx,\n                num_frames=num_frames,\n                reverse=reverse,\n                det_out=det_out,\n                tracker_low_res_masks_global=tracker_low_res_masks_global,\n                tracker_obj_scores_global=tracker_obj_scores_global,\n                tracker_metadata_prev=tracker_metadata_prev,\n                tracker_update_plan=tracker_update_plan,\n                orig_vid_height=orig_vid_height,\n                orig_vid_width=orig_vid_width,\n                reconditioned_obj_ids=reconditioned_obj_ids,\n                det_to_matched_trk_obj_ids=det_to_matched_trk_obj_ids,\n            )\n            obj_id_to_score = tracker_metadata_new[\"obj_id_to_score\"]\n        else:\n            obj_id_to_mask, obj_id_to_score = {}, {}  # dummy outputs on other GPUs\n        # a few statistics for the current frame as a part of the output\n        frame_stats = {\n            \"num_obj_tracked\": np.sum(tracker_metadata_new[\"num_obj_per_gpu\"]),\n            \"num_obj_dropped\": tracker_update_plan[\"num_obj_dropped_due_to_limit\"],\n        }\n        # add tracker scores to metadata, it should be fired for frames except the first frame\n        if tracker_obj_scores_global.shape[0] > 0:\n            # Convert tracker_obj_scores_global to sigmoid scores before updating\n            tracker_obj_scores_global = tracker_obj_scores_global.sigmoid().tolist()\n            tracker_obj_ids = tracker_metadata_prev[\"obj_ids_all_gpu\"]\n            tracker_metadata_new[\"obj_id_to_tracker_score_frame_wise\"][\n                frame_idx\n            ].update(dict(zip(tracker_obj_ids, tracker_obj_scores_global)))\n        return (\n            obj_id_to_mask,  # a dict: obj_id --> output mask\n            obj_id_to_score,  # a dict: obj_id --> output score (prob)\n            tracker_states_local_new,\n            tracker_metadata_new,\n            frame_stats,\n            tracker_obj_scores_global,  # a dict: obj_id --> tracker frame-level scores\n        )\n\n    def _suppress_detections_close_to_boundary(self, boxes, margin=0.025):\n        \"\"\"\n        Suppress detections too close to image edges (for normalized boxes).\n\n        boxes: (N, 4) in xyxy format, normalized [0,1]\n        margin: fraction of image\n        \"\"\"\n        x_min, y_min, x_max, y_max = boxes.unbind(-1)\n        x_c = (x_min + x_max) / 2\n        y_c = (y_min + y_max) / 2\n        keep = (\n            (x_c > margin)\n            & (x_c < 1.0 - margin)\n            & (y_c > margin)\n            & (y_c < 1.0 - margin)\n        )\n\n        return keep\n\n    def run_backbone_and_detection(\n        self,\n        frame_idx: int,\n        num_frames: int,\n        input_batch: BatchedDatapoint,\n        geometric_prompt: Any,\n        feature_cache: Dict,\n        reverse: bool,\n        allow_new_detections: bool,\n    ):\n        # Step 1: if text feature is not cached in `feature_cache`, compute and cache it\n        text_batch_key = tuple(input_batch.find_text_batch)\n        if \"text\" not in feature_cache or text_batch_key not in feature_cache[\"text\"]:\n            text_outputs = self.detector.backbone.forward_text(\n                input_batch.find_text_batch, device=self.device\n            )\n            # note: we only cache the text feature of the most recent prompt\n            feature_cache[\"text\"] = {text_batch_key: text_outputs}\n        else:\n            text_outputs = feature_cache[\"text\"][text_batch_key]\n\n        # Step 2: run backbone, detector, and post-processing with NMS\n        if \"multigpu_buffer\" not in feature_cache:\n            # \"multigpu_buffer\" is a buffer cache used by `self.detector` and it needs\n            # to be passed to `forward_video_grounding_multigpu` for every call\n            feature_cache[\"multigpu_buffer\"] = {}\n\n        # Extract max_frame_num_to_track from feature_cache if available\n        tracking_bounds = feature_cache.get(\"tracking_bounds\", {})\n        max_frame_num_to_track = tracking_bounds.get(\"max_frame_num_to_track\")\n        start_frame_idx = tracking_bounds.get(\"propagate_in_video_start_frame_idx\")\n\n        sam3_image_out, _ = self.detector.forward_video_grounding_multigpu(\n            backbone_out={\n                \"img_batch_all_stages\": input_batch.img_batch,\n                **text_outputs,\n            },\n            find_inputs=input_batch.find_inputs,\n            geometric_prompt=geometric_prompt,\n            frame_idx=frame_idx,\n            num_frames=num_frames,\n            multigpu_buffer=feature_cache[\"multigpu_buffer\"],\n            track_in_reverse=reverse,\n            # also get the SAM2 backbone features\n            return_tracker_backbone_feats=True,\n            # run NMS as a part of distributed computation\n            run_nms=self.det_nms_thresh > 0.0,\n            nms_prob_thresh=self.score_threshold_detection,\n            nms_iou_thresh=self.det_nms_thresh,\n            # pass max_frame_num_to_track to respect tracking limits\n            max_frame_num_to_track=max_frame_num_to_track,\n            propagate_in_video_start_frame_idx=start_frame_idx,\n        )\n        # note: detections in `sam3_image_out` has already gone through NMS\n        pred_probs = sam3_image_out[\"pred_logits\"].squeeze(-1).sigmoid()\n        if not allow_new_detections:\n            pred_probs = pred_probs - 1e8  # make sure no detections are kept\n        pred_boxes_xyxy = sam3_image_out[\"pred_boxes_xyxy\"]\n        pred_masks = sam3_image_out[\"pred_masks\"]\n        # get the positive detection outputs above threshold\n        pos_pred_idx = torch.where(pred_probs > self.score_threshold_detection)\n        det_out = {\n            \"bbox\": pred_boxes_xyxy[pos_pred_idx[0], pos_pred_idx[1]],\n            \"mask\": pred_masks[pos_pred_idx[0], pos_pred_idx[1]],\n            \"scores\": pred_probs[pos_pred_idx[0], pos_pred_idx[1]],\n        }\n\n        # Step 3: build SAM2 backbone features and store them in `feature_cache`\n        backbone_cache = {}\n        sam_mask_decoder = self.tracker.sam_mask_decoder\n        tracker_backbone_fpn = [\n            sam_mask_decoder.conv_s0(sam3_image_out[\"tracker_backbone_fpn_0\"]),\n            sam_mask_decoder.conv_s1(sam3_image_out[\"tracker_backbone_fpn_1\"]),\n            sam3_image_out[\"tracker_backbone_fpn_2\"],  # fpn_2 doesn't need conv\n        ]\n        tracker_backbone_out = {\n            \"vision_features\": tracker_backbone_fpn[-1],  # top-level feature\n            \"vision_pos_enc\": sam3_image_out[\"tracker_backbone_pos_enc\"],\n            \"backbone_fpn\": tracker_backbone_fpn,\n        }\n        backbone_cache[\"tracker_backbone_out\"] = tracker_backbone_out\n        feature_cache[frame_idx] = (\n            input_batch.img_batch[frame_idx],\n            backbone_cache,\n        )\n        # remove from `feature_cache` old features to save GPU memory\n        feature_cache.pop(frame_idx - 1 if not reverse else frame_idx + 1, None)\n        return det_out\n\n    def run_tracker_propagation(\n        self,\n        frame_idx: int,\n        num_frames: int,\n        reverse: bool,\n        tracker_states_local: List[Any],\n        tracker_metadata_prev: Dict[str, npt.NDArray],\n    ):\n        # Step 1: propagate the local SAM2 states to get the current frame's prediction\n        # `low_res_masks_local` of the existing masklets on this GPU\n        # - obj_ids_local: List[int] -- list of object IDs\n        # - low_res_masks_local: Tensor -- (num_local_obj, H_mask, W_mask)\n        obj_ids_local, low_res_masks_local, obj_scores_local = (\n            self._propogate_tracker_one_frame_local_gpu(\n                tracker_states_local, frame_idx=frame_idx, reverse=reverse\n            )\n        )\n\n        assert np.all(\n            obj_ids_local == tracker_metadata_prev[\"obj_ids_per_gpu\"][self.rank]\n        ), \"{} != {}\".format(\n            obj_ids_local, tracker_metadata_prev[\"obj_ids_per_gpu\"][self.rank]\n        )\n\n        # Step 2: all-gather `low_res_masks_local` into `low_res_masks_global`\n        # - low_res_masks_global: Tensor -- (num_global_obj, H_mask, W_mask)\n        _, H_mask, W_mask = low_res_masks_local.shape\n        if self.world_size > 1:\n            # `low_res_masks_local` and `obj_scores_local` need to be contiguous and float32\n            # (they could be non-contiguous due to slicing and/or bfloat16 due to autocast)\n            low_res_masks_local = low_res_masks_local.float().contiguous()\n            obj_scores_local = obj_scores_local.float().contiguous()\n            num_obj_this_gpu = tracker_metadata_prev[\"num_obj_per_gpu\"][self.rank]\n            assert low_res_masks_local.size(0) == num_obj_this_gpu\n            assert obj_scores_local.size(0) == num_obj_this_gpu\n            low_res_masks_peers = [\n                low_res_masks_local.new_empty(num_obj, H_mask, W_mask)\n                for num_obj in tracker_metadata_prev[\"num_obj_per_gpu\"]\n            ]\n            obj_scores_peers = [\n                obj_scores_local.new_empty(num_obj)\n                for num_obj in tracker_metadata_prev[\"num_obj_per_gpu\"]\n            ]\n            dist.all_gather(low_res_masks_peers, low_res_masks_local)\n            dist.all_gather(obj_scores_peers, obj_scores_local)\n            low_res_masks_global = torch.cat(low_res_masks_peers, dim=0)\n            obj_scores_global = torch.cat(obj_scores_peers, dim=0)\n        else:\n            low_res_masks_global = low_res_masks_local\n            obj_scores_global = obj_scores_local\n        return low_res_masks_global, obj_scores_global\n\n    def _recondition_masklets(\n        self,\n        frame_idx,\n        det_out: Dict[str, Tensor],\n        trk_id_to_max_iou_high_conf_det: List[int],\n        tracker_states_local: List[Any],\n        tracker_metadata: Dict[str, npt.NDArray],\n        tracker_obj_scores_global: Tensor,\n    ):\n        # Recondition the masklets based on the new detections\n        for trk_obj_id, det_idx in trk_id_to_max_iou_high_conf_det.items():\n            new_mask = det_out[\"mask\"][det_idx : det_idx + 1]\n            input_mask_res = self.tracker.input_mask_size\n            new_mask_binary = (\n                F.interpolate(\n                    new_mask.unsqueeze(1),\n                    size=(input_mask_res, input_mask_res),\n                    mode=\"bilinear\",\n                    align_corners=False,\n                ).squeeze(1)[0]\n                > 0\n            )\n            HIGH_CONF_THRESH = 0.8\n            reconditioned_states_idx = set()\n            obj_idx = np.where(tracker_metadata[\"obj_ids_all_gpu\"] == trk_obj_id)[\n                0\n            ].item()\n            obj_score = tracker_obj_scores_global[obj_idx]\n            for state_idx, inference_state in enumerate(tracker_states_local):\n                if (\n                    trk_obj_id in inference_state[\"obj_ids\"]\n                    # NOTE: Goal of this condition is to avoid reconditioning masks that are occluded/low qualiy.\n                    # Unfortunately, these can get reconditioned anyway due to batching. We should consider removing these heuristics.\n                    and obj_score > HIGH_CONF_THRESH\n                ):\n                    logger.debug(\n                        f\"Adding new mask for track {trk_obj_id} at frame {frame_idx}. Objects {inference_state['obj_ids']} are all reconditioned.\"\n                    )\n                    self.tracker.add_new_mask(\n                        inference_state=inference_state,\n                        frame_idx=frame_idx,\n                        obj_id=trk_obj_id,\n                        mask=new_mask_binary,\n                    )\n                    reconditioned_states_idx.add(state_idx)\n\n            for idx in reconditioned_states_idx:\n                self.tracker.propagate_in_video_preflight(\n                    tracker_states_local[idx], run_mem_encoder=True\n                )\n        return tracker_states_local\n\n    def run_tracker_update_planning_phase(\n        self,\n        frame_idx: int,\n        num_frames: int,\n        reverse: bool,\n        det_out: Dict[str, Tensor],\n        tracker_low_res_masks_global: Tensor,\n        tracker_obj_scores_global: Tensor,\n        tracker_metadata_prev: Dict[str, npt.NDArray],\n        tracker_states_local: List[Any],\n        is_image_only: bool = False,\n    ):\n        # initialize new metadata from previous metadata (its values will be updated later)\n        tracker_metadata_new = {\n            \"obj_ids_per_gpu\": deepcopy(tracker_metadata_prev[\"obj_ids_per_gpu\"]),\n            \"obj_ids_all_gpu\": None,  # will be filled later\n            \"num_obj_per_gpu\": deepcopy(tracker_metadata_prev[\"num_obj_per_gpu\"]),\n            \"obj_id_to_score\": deepcopy(tracker_metadata_prev[\"obj_id_to_score\"]),\n            \"obj_id_to_tracker_score_frame_wise\": deepcopy(\n                tracker_metadata_prev[\"obj_id_to_tracker_score_frame_wise\"]\n            ),\n            \"obj_id_to_last_occluded\": {},  # will be filled later\n            \"max_obj_id\": deepcopy(tracker_metadata_prev[\"max_obj_id\"]),\n        }\n\n        # Initialize reconditioned_obj_ids early to avoid UnboundLocalError\n        reconditioned_obj_ids = set()\n\n        # Step 1: make the update plan and resolve heuristics on GPU 0\n        det_mask_preds: Tensor = det_out[\"mask\"]  # low-res mask logits\n        det_scores_np: npt.NDArray = det_out[\"scores\"].float().cpu().numpy()\n        det_bbox_xyxy: Tensor = det_out[\"bbox\"]\n        if self.rank == 0:\n            # a) match detector and tracker masks and find new objects\n            (\n                new_det_fa_inds,\n                unmatched_trk_obj_ids,\n                det_to_matched_trk_obj_ids,\n                trk_id_to_max_iou_high_conf_det,\n                empty_trk_obj_ids,\n            ) = self._associate_det_trk(\n                det_masks=det_mask_preds,\n                det_scores_np=det_scores_np,\n                trk_masks=tracker_low_res_masks_global,\n                trk_obj_ids=tracker_metadata_prev[\"obj_ids_all_gpu\"],\n            )\n            if self.suppress_det_close_to_boundary:\n                keep = self._suppress_detections_close_to_boundary(\n                    det_bbox_xyxy[new_det_fa_inds]\n                )\n                new_det_fa_inds = new_det_fa_inds[keep.cpu().numpy()]\n\n            # check whether we've hit the maximum number of objects we can track (and if so, drop some detections)\n            prev_obj_num = np.sum(tracker_metadata_prev[\"num_obj_per_gpu\"])\n            new_det_num = len(new_det_fa_inds)\n            num_obj_dropped_due_to_limit = 0\n            if not is_image_only and prev_obj_num + new_det_num > self.max_num_objects:\n                logger.warning(\n                    f\"hitting {self.max_num_objects=} with {new_det_num=} and {prev_obj_num=}\"\n                )\n                new_det_num_to_keep = self.max_num_objects - prev_obj_num\n                num_obj_dropped_due_to_limit = new_det_num - new_det_num_to_keep\n                new_det_fa_inds = self._drop_new_det_with_obj_limit(\n                    new_det_fa_inds, det_scores_np, new_det_num_to_keep\n                )\n                assert len(new_det_fa_inds) == new_det_num_to_keep\n                new_det_num = len(new_det_fa_inds)\n\n            # assign object IDs to new detections and decide which GPU to place them\n            new_det_start_obj_id = tracker_metadata_prev[\"max_obj_id\"] + 1\n            new_det_obj_ids = new_det_start_obj_id + np.arange(new_det_num)\n            prev_workload_per_gpu = tracker_metadata_prev[\"num_obj_per_gpu\"]\n            new_det_gpu_ids = self._assign_new_det_to_gpus(\n                new_det_num=new_det_num,\n                prev_workload_per_gpu=prev_workload_per_gpu,\n            )\n\n            # b) handle hotstart heuristics to remove objects\n            # here `rank0_metadata` contains metadata stored on (and only accessible to) GPU 0;\n            # we avoid broadcasting them to other GPUs to save communication cost, assuming\n            # that `rank0_metadata` is not needed by other GPUs\n            rank0_metadata_new = deepcopy(tracker_metadata_prev[\"rank0_metadata\"])\n            if not hasattr(self, \"_warm_up_complete\") or self._warm_up_complete:\n                obj_ids_newly_removed, rank0_metadata_new = self._process_hotstart(\n                    frame_idx=frame_idx,\n                    num_frames=num_frames,\n                    reverse=reverse,\n                    det_to_matched_trk_obj_ids=det_to_matched_trk_obj_ids,\n                    new_det_obj_ids=new_det_obj_ids,\n                    empty_trk_obj_ids=empty_trk_obj_ids,\n                    unmatched_trk_obj_ids=unmatched_trk_obj_ids,\n                    rank0_metadata=rank0_metadata_new,\n                    tracker_metadata=tracker_metadata_prev,\n                )\n            else:\n                # if warm-up is not complete, we don't remove any objects\n                obj_ids_newly_removed = set()\n            tracker_metadata_new[\"rank0_metadata\"] = rank0_metadata_new\n\n        # Step 2: broadcast the update plan to other GPUs\n        NUM_BROADCAST_ITEMS = 9\n        if self.rank == 0 and self.world_size > 1:\n            # `num_obj_per_gpu_on_rank0` is used for metadata consistency check on other GPUs\n            # (it's a small array with length==self.world_size, so broadcasting it is cheap)\n            num_obj_per_gpu_on_rank0 = tracker_metadata_prev[\"num_obj_per_gpu\"]\n            update_plan = [\n                new_det_fa_inds,\n                new_det_obj_ids,\n                new_det_gpu_ids,\n                num_obj_per_gpu_on_rank0,\n                unmatched_trk_obj_ids,\n                det_to_matched_trk_obj_ids,\n                obj_ids_newly_removed,\n                num_obj_dropped_due_to_limit,\n                trk_id_to_max_iou_high_conf_det,\n            ]\n            assert len(update_plan) == NUM_BROADCAST_ITEMS, (\n                f\"Manually update NUM_BROADCAST_ITEMS to be: {len(update_plan)}\"\n            )\n            self.broadcast_python_obj_cpu(update_plan, src=0)\n        elif self.rank > 0 and self.world_size > 1:\n            update_plan = [\n                None\n            ] * NUM_BROADCAST_ITEMS  # other ranks receive the plan from rank 0\n            self.broadcast_python_obj_cpu(update_plan, src=0)\n            (\n                new_det_fa_inds,\n                new_det_obj_ids,\n                new_det_gpu_ids,\n                num_obj_per_gpu_on_rank0,\n                unmatched_trk_obj_ids,\n                det_to_matched_trk_obj_ids,\n                obj_ids_newly_removed,\n                num_obj_dropped_due_to_limit,\n                trk_id_to_max_iou_high_conf_det,\n            ) = update_plan\n            # metadata consistency check: verify that the received `num_obj_per_gpu_on_rank0` is consistent with the local metadata\n            # it's critical that all GPUs agree on the previous number of objects (otherwise the inference might hang or fail silently)\n            if not np.all(\n                num_obj_per_gpu_on_rank0 == tracker_metadata_prev[\"num_obj_per_gpu\"]\n            ):\n                raise RuntimeError(\n                    f\"{self.rank=} received {num_obj_per_gpu_on_rank0=}, which is inconsistent with local record \"\n                    f\"{tracker_metadata_prev['num_obj_per_gpu']=}. There's likely a bug in update planning or execution.\"\n                )\n\n        # `tracker_update_plan` should be identical on all GPUs after broadcasting\n        tracker_update_plan = {\n            \"new_det_fa_inds\": new_det_fa_inds,  # npt.NDArray\n            \"new_det_obj_ids\": new_det_obj_ids,  # npt.NDArray\n            \"new_det_gpu_ids\": new_det_gpu_ids,  # npt.NDArray\n            \"unmatched_trk_obj_ids\": unmatched_trk_obj_ids,  # npt.NDArray\n            \"det_to_matched_trk_obj_ids\": det_to_matched_trk_obj_ids,  # dict\n            \"obj_ids_newly_removed\": obj_ids_newly_removed,  # set\n            \"num_obj_dropped_due_to_limit\": num_obj_dropped_due_to_limit,  # int\n            \"trk_id_to_max_iou_high_conf_det\": trk_id_to_max_iou_high_conf_det,  # dict\n            \"reconditioned_obj_ids\": reconditioned_obj_ids,  # set\n        }\n\n        # Step 3 (optional): recondition masklets based on high-confidence detections before memory encoding\n        # NOTE: Running this in execution phase (after memory encoding) can lead to suboptimal results\n        should_recondition_iou = False\n\n        # Evaluate tracklets for reconditioning based on bbox IoU mismatch with detections\n        if (\n            self.reconstruction_bbox_iou_thresh > 0\n            and len(trk_id_to_max_iou_high_conf_det) > 0\n        ):\n            for trk_obj_id, det_idx in trk_id_to_max_iou_high_conf_det.items():\n                det_box = det_out[\"bbox\"][det_idx]\n                det_score = det_out[\"scores\"][det_idx]\n\n                try:\n                    trk_idx = list(tracker_metadata_prev[\"obj_ids_all_gpu\"]).index(\n                        trk_obj_id\n                    )\n                except ValueError:\n                    continue  # Skip if tracklet not found\n\n                tracker_mask = tracker_low_res_masks_global[trk_idx]\n                mask_binary = tracker_mask > 0\n                mask_area = mask_binary.sum().item()\n\n                if mask_area == 0:\n                    continue  # Skip tracklets with zero mask area\n\n                # Get bounding box from SAM2 mask and convert to normalized coordinates\n                tracker_box_pixels = (\n                    mask_to_box(mask_binary.unsqueeze(0).unsqueeze(0))\n                    .squeeze(0)\n                    .squeeze(0)\n                )\n                mask_height, mask_width = tracker_mask.shape[-2:]\n                tracker_box_normalized = torch.tensor(\n                    [\n                        tracker_box_pixels[0] / mask_width,\n                        tracker_box_pixels[1] / mask_height,\n                        tracker_box_pixels[2] / mask_width,\n                        tracker_box_pixels[3] / mask_height,\n                    ],\n                    device=tracker_box_pixels.device,\n                )\n\n                # Compute IoU between detection and SAM2 tracklet bounding boxes\n                det_box_batch = det_box.unsqueeze(0)\n                tracker_box_batch = tracker_box_normalized.unsqueeze(0)\n                iou = fast_diag_box_iou(det_box_batch, tracker_box_batch)[0]\n\n                if (\n                    iou < self.reconstruction_bbox_iou_thresh\n                    and det_score >= self.reconstruction_bbox_det_score\n                ):\n                    should_recondition_iou = True\n                    reconditioned_obj_ids.add(trk_obj_id)\n\n        should_recondition_periodic = (\n            self.recondition_every_nth_frame > 0\n            and frame_idx % self.recondition_every_nth_frame == 0\n            and len(trk_id_to_max_iou_high_conf_det) > 0\n        )\n\n        # Recondition if periodic or IoU condition met\n        if should_recondition_periodic or should_recondition_iou:\n            self._recondition_masklets(\n                frame_idx,\n                det_out,\n                trk_id_to_max_iou_high_conf_det,\n                tracker_states_local,\n                tracker_metadata_prev,\n                tracker_obj_scores_global,\n            )\n\n        # Step 4: Run SAM2 memory encoder on the current frame's prediction masks\n        # This is done on all GPUs\n        batch_size = tracker_low_res_masks_global.size(0)\n        if batch_size > 0:\n            if not hasattr(self, \"_warm_up_complete\") or self._warm_up_complete:\n                if self.suppress_overlapping_based_on_recent_occlusion_threshold > 0.0:\n                    # NOTE: tracker_low_res_masks_global is updated in-place then returned\n                    tracker_low_res_masks_global = (\n                        self._suppress_overlapping_based_on_recent_occlusion(\n                            frame_idx,\n                            tracker_low_res_masks_global,\n                            tracker_metadata_prev,\n                            tracker_metadata_new,\n                            obj_ids_newly_removed,\n                            reverse,\n                        )\n                    )\n\n            self._tracker_update_memories(\n                tracker_states_local,\n                frame_idx,\n                tracker_metadata=tracker_metadata_prev,\n                low_res_masks=tracker_low_res_masks_global,\n            )\n\n        # Step 4: update the SAM2 metadata based on the update plan\n        # note: except for \"rank0_metadata\" (that is only available on GPU 0),\n        # the updated `tracker_metadata_new` should be identical on all GPUs\n        for rank in range(self.world_size):\n            new_det_obj_ids_this_gpu = new_det_obj_ids[new_det_gpu_ids == rank]\n            updated_obj_ids_this_gpu = tracker_metadata_new[\"obj_ids_per_gpu\"][rank]\n            if len(new_det_obj_ids_this_gpu) > 0:\n                updated_obj_ids_this_gpu = np.concatenate(\n                    [updated_obj_ids_this_gpu, new_det_obj_ids_this_gpu]\n                )\n            if len(obj_ids_newly_removed) > 0:\n                is_removed = np.isin(\n                    updated_obj_ids_this_gpu, list(obj_ids_newly_removed)\n                )\n                updated_obj_ids_this_gpu = updated_obj_ids_this_gpu[~is_removed]\n            tracker_metadata_new[\"obj_ids_per_gpu\"][rank] = updated_obj_ids_this_gpu\n            tracker_metadata_new[\"num_obj_per_gpu\"][rank] = len(\n                updated_obj_ids_this_gpu\n            )\n        tracker_metadata_new[\"obj_ids_all_gpu\"] = np.concatenate(\n            tracker_metadata_new[\"obj_ids_per_gpu\"]\n        )\n        # update object scores and the maximum object ID assigned so far\n        if len(new_det_obj_ids) > 0:\n            tracker_metadata_new[\"obj_id_to_score\"].update(\n                zip(new_det_obj_ids, det_scores_np[new_det_fa_inds])\n            )\n            # tracker scores are not available for new objects, use det score instead.\n            tracker_metadata_new[\"obj_id_to_tracker_score_frame_wise\"][\n                frame_idx\n            ].update(zip(new_det_obj_ids, det_scores_np[new_det_fa_inds]))\n            tracker_metadata_new[\"max_obj_id\"] = max(\n                tracker_metadata_new[\"max_obj_id\"],\n                np.max(new_det_obj_ids),\n            )\n        # for removed objects, we set their scores to a very low value (-1e4) but still\n        # keep them in \"obj_id_to_score\" (it's easier to handle outputs this way)\n        for obj_id in obj_ids_newly_removed:\n            tracker_metadata_new[\"obj_id_to_score\"][obj_id] = -1e4\n            tracker_metadata_new[\"obj_id_to_tracker_score_frame_wise\"][frame_idx][\n                obj_id\n            ] = -1e4\n            tracker_metadata_new[\"obj_id_to_last_occluded\"].pop(obj_id, None)\n        # check that \"rank0_metadata\" is in tracker_metadata_new if and only if it's GPU 0\n        assert (\"rank0_metadata\" in tracker_metadata_new) == (self.rank == 0)\n        if self.rank == 0 and self.masklet_confirmation_enable:\n            rank0_metadata = self.update_masklet_confirmation_status(\n                rank0_metadata=tracker_metadata_new[\"rank0_metadata\"],\n                obj_ids_all_gpu_prev=tracker_metadata_prev[\"obj_ids_all_gpu\"],\n                obj_ids_all_gpu_updated=tracker_metadata_new[\"obj_ids_all_gpu\"],\n                det_to_matched_trk_obj_ids=det_to_matched_trk_obj_ids,\n                new_det_obj_ids=new_det_obj_ids,\n            )\n            tracker_metadata_new[\"rank0_metadata\"] = rank0_metadata\n\n        return tracker_update_plan, tracker_metadata_new\n\n    def _suppress_overlapping_based_on_recent_occlusion(\n        self,\n        frame_idx: int,\n        tracker_low_res_masks_global: Tensor,\n        tracker_metadata_prev: Dict[str, Any],\n        tracker_metadata_new: Dict[str, Any],\n        obj_ids_newly_removed: Set[int],\n        reverse: bool = False,\n    ):\n        \"\"\"\n        Suppress overlapping masks based on the most recent occlusion information. If an object is removed by hotstart, we always suppress it if it overlaps with any other object.\n        Args:\n            frame_idx (int): The current frame index.\n            tracker_low_res_masks_global (Tensor): The low-resolution masks for the current frame.\n            tracker_metadata_prev (Dict[str, Any]): The metadata from the previous frame.\n            tracker_metadata_new (Dict[str, Any]): The metadata for the current frame.\n            obj_ids_newly_removed (Set[int]): The object IDs that have been removed.\n        Return:\n            Tensor: The updated low-resolution masks with some objects suppressed.\n        \"\"\"\n        obj_ids_global = tracker_metadata_prev[\"obj_ids_all_gpu\"]\n        binary_tracker_low_res_masks_global = tracker_low_res_masks_global > 0\n        batch_size = tracker_low_res_masks_global.size(0)\n        if batch_size > 0:\n            assert len(obj_ids_global) == batch_size, (\n                f\"Mismatch in number of objects: {len(obj_ids_global)} vs {batch_size}\"\n            )\n            NEVER_OCCLUDED = -1\n            ALWAYS_OCCLUDED = 100000  # This value should be larger than any possible frame index, indicates that the object was removed by hotstart logic\n            last_occluded_prev = torch.cat(\n                [\n                    tracker_metadata_prev[\"obj_id_to_last_occluded\"].get(\n                        obj_id,\n                        torch.full(\n                            (1,),\n                            fill_value=(\n                                NEVER_OCCLUDED\n                                if obj_id not in obj_ids_newly_removed\n                                else ALWAYS_OCCLUDED\n                            ),\n                            device=binary_tracker_low_res_masks_global.device,\n                            dtype=torch.long,\n                        ),\n                    )\n                    for obj_id in obj_ids_global\n                ],\n                dim=0,\n            )\n            to_suppress = self._get_objects_to_suppress_based_on_most_recently_occluded(\n                binary_tracker_low_res_masks_global,\n                last_occluded_prev,\n                obj_ids_global,\n                frame_idx,\n                reverse,\n            )\n\n            # Update metadata with occlusion information\n            is_obj_occluded = ~(binary_tracker_low_res_masks_global.any(dim=(-1, -2)))\n            is_obj_occluded_or_suppressed = is_obj_occluded | to_suppress\n            last_occluded_new = last_occluded_prev.clone()\n            last_occluded_new[is_obj_occluded_or_suppressed] = frame_idx\n            # Slice out the last occluded frame for each object\n            tracker_metadata_new[\"obj_id_to_last_occluded\"] = {\n                obj_id: last_occluded_new[obj_idx : obj_idx + 1]\n                for obj_idx, obj_id in enumerate(obj_ids_global)\n            }\n\n            # Zero out suppressed masks before memory encoding\n            NO_OBJ_LOGIT = -10\n            tracker_low_res_masks_global[to_suppress] = NO_OBJ_LOGIT\n\n        return tracker_low_res_masks_global\n\n    def run_tracker_update_execution_phase(\n        self,\n        frame_idx: int,\n        num_frames: int,\n        reverse: bool,\n        det_out: Dict[str, Tensor],\n        tracker_states_local: List[Any],\n        tracker_update_plan: Dict[str, npt.NDArray],\n        orig_vid_height: int,\n        orig_vid_width: int,\n        feature_cache: Dict,\n    ):\n        # initialize tracking scores with detection scores\n        new_det_fa_inds: npt.NDArray = tracker_update_plan[\"new_det_fa_inds\"]\n        new_det_obj_ids: npt.NDArray = tracker_update_plan[\"new_det_obj_ids\"]\n        new_det_gpu_ids: npt.NDArray = tracker_update_plan[\"new_det_gpu_ids\"]\n        is_on_this_gpu: npt.NDArray = new_det_gpu_ids == self.rank\n        new_det_obj_ids_local: npt.NDArray = new_det_obj_ids[is_on_this_gpu]\n        new_det_fa_inds_local: npt.NDArray = new_det_fa_inds[is_on_this_gpu]\n        obj_ids_newly_removed: Set[int] = tracker_update_plan[\"obj_ids_newly_removed\"]\n\n        # Step 1: add new objects from the detector to SAM2 inference states\n        if len(new_det_fa_inds_local) > 0:\n            new_det_fa_inds_local_t = torch.from_numpy(new_det_fa_inds_local)\n            new_det_masks: Tensor = det_out[\"mask\"][new_det_fa_inds_local_t]\n            # initialize SAM2 with new object masks\n            tracker_states_local = self._tracker_add_new_objects(\n                frame_idx=frame_idx,\n                num_frames=num_frames,\n                new_obj_ids=new_det_obj_ids_local,\n                new_obj_masks=new_det_masks,\n                tracker_states_local=tracker_states_local,\n                orig_vid_height=orig_vid_height,\n                orig_vid_width=orig_vid_width,\n                feature_cache=feature_cache,\n            )\n\n        # Step 2: remove from SAM2 inference states those objects removed by heuristics\n        if len(obj_ids_newly_removed) > 0:\n            self._tracker_remove_objects(tracker_states_local, obj_ids_newly_removed)\n\n        return tracker_states_local\n\n    def build_outputs(\n        self,\n        frame_idx: int,\n        num_frames: int,\n        reverse: bool,\n        det_out: Dict[str, Tensor],\n        tracker_low_res_masks_global: Tensor,\n        tracker_obj_scores_global: Tensor,\n        tracker_metadata_prev: Dict[str, npt.NDArray],\n        tracker_update_plan: Dict[str, npt.NDArray],\n        orig_vid_height: int,\n        orig_vid_width: int,\n        reconditioned_obj_ids: set = None,\n        det_to_matched_trk_obj_ids: dict = None,\n    ):\n        new_det_fa_inds: npt.NDArray = tracker_update_plan[\"new_det_fa_inds\"]\n        new_det_obj_ids: npt.NDArray = tracker_update_plan[\"new_det_obj_ids\"]\n        obj_id_to_mask = {}  # obj_id --> output mask tensor\n\n        # Part 1: masks from previous SAM2 propagation\n        existing_masklet_obj_ids = tracker_metadata_prev[\"obj_ids_all_gpu\"]\n        existing_masklet_video_res_masks = F.interpolate(\n            tracker_low_res_masks_global.unsqueeze(1),\n            size=(orig_vid_height, orig_vid_width),\n            mode=\"bilinear\",\n            align_corners=False,\n        )  # (num_obj, 1, H_video, W_video)\n        existing_masklet_binary = existing_masklet_video_res_masks > 0\n        assert len(existing_masklet_obj_ids) == len(existing_masklet_binary)\n        for obj_id, mask in zip(existing_masklet_obj_ids, existing_masklet_binary):\n            obj_id_to_mask[obj_id] = mask  # (1, H_video, W_video)\n\n        # Part 2: masks from new detections\n        new_det_fa_inds_t = torch.from_numpy(new_det_fa_inds)\n        new_det_low_res_masks = det_out[\"mask\"][new_det_fa_inds_t].unsqueeze(1)\n        new_det_low_res_masks = fill_holes_in_mask_scores(\n            new_det_low_res_masks,\n            max_area=self.fill_hole_area,\n            fill_holes=True,\n            remove_sprinkles=True,\n        )\n        new_masklet_video_res_masks = F.interpolate(\n            new_det_low_res_masks,\n            size=(orig_vid_height, orig_vid_width),\n            mode=\"bilinear\",\n            align_corners=False,\n        )  # (num_obj, 1, H_video, W_video)\n\n        new_masklet_binary = new_masklet_video_res_masks > 0\n        assert len(new_det_obj_ids) == len(new_masklet_video_res_masks)\n        for obj_id, mask in zip(new_det_obj_ids, new_masklet_binary):\n            obj_id_to_mask[obj_id] = mask  # (1, H_video, W_video)\n\n        # Part 3: Override masks for reconditioned objects using detection masks\n        if reconditioned_obj_ids is not None and len(reconditioned_obj_ids) > 0:\n            trk_id_to_max_iou_high_conf_det = tracker_update_plan.get(\n                \"trk_id_to_max_iou_high_conf_det\", {}\n            )\n\n            for obj_id in reconditioned_obj_ids:\n                det_idx = trk_id_to_max_iou_high_conf_det.get(obj_id)\n\n                if det_idx is not None:\n                    det_mask = det_out[\"mask\"][det_idx]\n                    det_mask = det_mask.unsqueeze(0).unsqueeze(0)\n                    det_mask_resized = (\n                        F.interpolate(\n                            det_mask.float(),\n                            size=(orig_vid_height, orig_vid_width),\n                            mode=\"bilinear\",\n                            align_corners=False,\n                        )\n                        > 0\n                    )\n\n                    det_mask_final = det_mask_resized.squeeze(0)\n                    obj_id_to_mask[obj_id] = det_mask_final\n\n        return obj_id_to_mask\n\n    def _get_objects_to_suppress_based_on_most_recently_occluded(\n        self,\n        binary_low_res_masks: Tensor,\n        last_occluded: List[int],\n        obj_ids: List[int],\n        frame_idx: int = None,\n        reverse: bool = False,\n    ):\n        # Suppress overlapping masks for objects that were most recently occluded\n        assert binary_low_res_masks.dtype == torch.bool, (\n            f\"Expected boolean tensor, got {binary_low_res_masks.dtype}\"\n        )\n        to_suppress = torch.zeros(\n            binary_low_res_masks.size(0),\n            device=binary_low_res_masks.device,\n            dtype=torch.bool,\n        )\n        if len(obj_ids) <= 1:\n            return to_suppress\n\n        iou = mask_iou(binary_low_res_masks, binary_low_res_masks)  # [N,N]\n\n        # Create masks for upper triangular matrix (i < j) and IoU threshold\n        mask_iou_thresh = (\n            iou >= self.suppress_overlapping_based_on_recent_occlusion_threshold\n        )\n        overlapping_pairs = torch.triu(mask_iou_thresh, diagonal=1)  # [N,N]\n\n        last_occ_expanded_i = last_occluded.unsqueeze(1)  # (N, 1)\n        last_occ_expanded_j = last_occluded.unsqueeze(0)  # (1, N)\n        # Suppress most recently occluded\n        cmp_op = torch.gt if not reverse else torch.lt\n        suppress_i_mask = (\n            overlapping_pairs\n            & cmp_op(\n                last_occ_expanded_i, last_occ_expanded_j\n            )  # (last_occ_expanded_i > last_occ_expanded_j)\n            & (\n                last_occ_expanded_j > -1\n            )  # j can suppress i only if i was previously occluded\n        )\n        suppress_j_mask = (\n            overlapping_pairs\n            & cmp_op(last_occ_expanded_j, last_occ_expanded_i)\n            & (\n                last_occ_expanded_i > -1\n            )  # i can suppress j only if j was previously occluded\n        )\n        # Apply suppression\n        to_suppress = suppress_i_mask.any(dim=1) | suppress_j_mask.any(dim=0)\n\n        # Log for debugging\n        if (\n            self.rank == 0\n            and logger.isEnabledFor(logging.DEBUG)\n            and frame_idx is not None\n        ):\n            suppress_i_mask = suppress_i_mask.cpu().numpy()\n            suppress_j_mask = suppress_j_mask.cpu().numpy()\n            last_occluded = last_occluded.cpu().numpy()\n\n            # Find all suppression pairs without using torch.where\n            batch_size = suppress_i_mask.shape[0]\n\n            # Log i-suppression cases (where i gets suppressed in favor of j)\n            for i in range(batch_size):\n                for j in range(batch_size):\n                    if suppress_i_mask[i, j]:\n                        logger.debug(\n                            f\"{frame_idx=}: Suppressing obj {obj_ids[i]} last occluded {last_occluded[i]} in favor of {obj_ids[j]} last occluded {last_occluded[j]}\"\n                        )\n\n            # Log j-suppression cases (where j gets suppressed in favor of i)\n            for i in range(batch_size):\n                for j in range(batch_size):\n                    if suppress_j_mask[i, j]:\n                        logger.debug(\n                            f\"{frame_idx=}: Suppressing obj {obj_ids[j]} last occluded {last_occluded[j]} in favor of {obj_ids[i]} last occluded {last_occluded[i]}\"\n                        )\n\n        return to_suppress\n\n    def _propogate_tracker_one_frame_local_gpu(\n        self,\n        inference_states: List[Any],\n        frame_idx: int,\n        reverse: bool,\n        # by default, we disable memory encoding until we gather all outputs\n        run_mem_encoder: bool = False,\n    ):\n        \"\"\"\n        inference_states: List of inference states, each state corresponds to a different set of objects.\n        \"\"\"\n        obj_ids_local = []\n        low_res_masks_list = []\n        obj_scores_list = []\n        for inference_state in inference_states:\n            if len(inference_state[\"obj_ids\"]) == 0:\n                continue  # skip propagation on empty inference states\n\n            # propagate one frame\n            num_frames_propagated = 0\n            for out in self.tracker.propagate_in_video(\n                inference_state,\n                start_frame_idx=frame_idx,\n                # end_frame_idx = start_frame_idx + max_frame_num_to_track\n                # (i.e. propagating 1 frame since end_frame_idx is inclusive)\n                max_frame_num_to_track=0,\n                reverse=reverse,\n                tqdm_disable=True,\n                run_mem_encoder=run_mem_encoder,\n            ):\n                out_frame_idx, out_obj_ids, out_low_res_masks, _, out_obj_scores = out\n                num_frames_propagated += 1\n\n            # only 1 frames should be propagated\n            assert num_frames_propagated == 1 and out_frame_idx == frame_idx, (\n                f\"num_frames_propagated: {num_frames_propagated}, out_frame_idx: {out_frame_idx}, frame_idx: {frame_idx}\"\n            )\n            assert isinstance(out_obj_ids, list)\n            obj_ids_local.extend(out_obj_ids)\n            low_res_masks_list.append(out_low_res_masks.squeeze(1))\n            obj_scores_list.append(out_obj_scores.squeeze(1))\n\n        # concatenate the output masklets from all local inference states\n        H_mask = W_mask = self.tracker.low_res_mask_size\n        if len(low_res_masks_list) > 0:\n            low_res_masks_local = torch.cat(low_res_masks_list, dim=0)\n            obj_scores_local = torch.cat(obj_scores_list, dim=0)\n            assert low_res_masks_local.shape[1:] == (H_mask, W_mask)\n\n            # Apply hole filling to the masks\n            low_res_masks_local = fill_holes_in_mask_scores(\n                low_res_masks_local.unsqueeze(1),\n                max_area=self.fill_hole_area,\n                fill_holes=True,\n                remove_sprinkles=True,\n            )\n            low_res_masks_local = low_res_masks_local.squeeze(1)\n        else:\n            low_res_masks_local = torch.zeros(0, H_mask, W_mask, device=self.device)\n            obj_scores_local = torch.zeros(0, device=self.device)\n\n        return obj_ids_local, low_res_masks_local, obj_scores_local\n\n    def _associate_det_trk(\n        self,\n        det_masks: Tensor,\n        det_scores_np: npt.NDArray,\n        trk_masks: Tensor,\n        trk_obj_ids: npt.NDArray,\n    ):\n        \"\"\"\n        Match detections on the current frame with the existing masklets.\n\n        Args:\n          - det_masks: (N, H, W) tensor of predicted masks\n          - det_scores_np: (N,) array of detection scores\n          - trk_masks: (M, H, W) tensor of track masks\n          - trk_obj_ids: (M,) array of object IDs corresponding to trk_masks\n\n        Returns:\n          - new_det_fa_inds: array of new object indices.\n          - unmatched_trk_obj_ids: array of existing masklet object IDs that are not matched\n            to any detections on this frame (for unmatched, we only count masklets with >0 area)\n          - det_to_matched_trk_obj_ids: dict[int, npt.NDArray]: mapping from detector's detection indices\n            to the list of matched tracklet object IDs\n          - empty_trk_obj_ids: array of existing masklet object IDs with zero area in SAM2 prediction\n        \"\"\"\n        iou_threshold = self.assoc_iou_thresh\n        iou_threshold_trk = self.trk_assoc_iou_thresh\n        new_det_thresh = self.new_det_thresh\n\n        assert det_masks.is_floating_point(), \"float tensor expected (do not binarize)\"\n        assert trk_masks.is_floating_point(), \"float tensor expected (do not binarize)\"\n        assert trk_masks.size(0) == len(trk_obj_ids), (\n            f\"trk_masks and trk_obj_ids should have the same length, {trk_masks.size(0)} vs {len(trk_obj_ids)}\"\n        )\n        if trk_masks.size(0) == 0:\n            # all detections are new\n            new_det_fa_inds = np.arange(det_masks.size(0))\n            unmatched_trk_obj_ids = np.array([], np.int64)\n            empty_trk_obj_ids = np.array([], np.int64)\n            det_to_matched_trk_obj_ids = {}\n            trk_id_to_max_iou_high_conf_det = {}\n            return (\n                new_det_fa_inds,\n                unmatched_trk_obj_ids,\n                det_to_matched_trk_obj_ids,\n                trk_id_to_max_iou_high_conf_det,\n                empty_trk_obj_ids,\n            )\n        elif det_masks.size(0) == 0:\n            # all previous tracklets are unmatched if they have a non-zero area\n            new_det_fa_inds = np.array([], np.int64)\n            trk_is_nonempty = (trk_masks > 0).any(dim=(1, 2)).cpu().numpy()\n            unmatched_trk_obj_ids = trk_obj_ids[trk_is_nonempty]\n            empty_trk_obj_ids = trk_obj_ids[~trk_is_nonempty]\n            det_to_matched_trk_obj_ids = {}\n            trk_id_to_max_iou_high_conf_det = {}\n            return (\n                new_det_fa_inds,\n                unmatched_trk_obj_ids,\n                det_to_matched_trk_obj_ids,\n                trk_id_to_max_iou_high_conf_det,\n                empty_trk_obj_ids,\n            )\n\n        if det_masks.shape[-2:] != trk_masks.shape[-2:]:\n            # resize to the smaller size to save GPU memory\n            if np.prod(det_masks.shape[-2:]) < np.prod(trk_masks.shape[-2:]):\n                trk_masks = F.interpolate(\n                    trk_masks.unsqueeze(1),\n                    size=det_masks.shape[-2:],\n                    mode=\"bilinear\",\n                    align_corners=False,\n                ).squeeze(1)\n            else:\n                # resize detections to track size\n                det_masks = F.interpolate(\n                    det_masks.unsqueeze(1),\n                    size=trk_masks.shape[-2:],\n                    mode=\"bilinear\",\n                    align_corners=False,\n                ).squeeze(1)\n\n        det_masks_binary = det_masks > 0\n        trk_masks_binary = trk_masks > 0\n        ious = mask_iou(det_masks_binary, trk_masks_binary)  # (N, M)\n\n        ious_np = ious.cpu().numpy()\n        if self.o2o_matching_masklets_enable:\n            from scipy.optimize import linear_sum_assignment\n\n            # Hungarian matching for tracks (one-to-one: each track matches at most one detection)\n            cost_matrix = 1 - ious_np  # Hungarian solves for minimum cost\n            row_ind, col_ind = linear_sum_assignment(cost_matrix)\n            trk_is_matched = np.zeros(trk_masks.size(0), dtype=bool)\n            for d, t in zip(row_ind, col_ind):\n                if ious_np[d, t] >= iou_threshold_trk:\n                    trk_is_matched[t] = True\n        else:\n            trk_is_matched = (ious_np >= iou_threshold_trk).any(axis=0)\n        # Non-empty tracks not matched by Hungarian assignment above threshold are unmatched\n        trk_is_nonempty = trk_masks_binary.any(dim=(1, 2)).cpu().numpy()\n        trk_is_unmatched = np.logical_and(trk_is_nonempty, ~trk_is_matched)\n        unmatched_trk_obj_ids = trk_obj_ids[trk_is_unmatched]\n        # also record masklets that have zero area in SAM 2 prediction\n        empty_trk_obj_ids = trk_obj_ids[~trk_is_nonempty]\n\n        # For detections: allow many tracks to match to the same detection (many-to-one)\n        # So, a detection is 'new' if it does not match any track above threshold\n        is_new_det = np.logical_and(\n            det_scores_np >= new_det_thresh,\n            np.logical_not(np.any(ious_np >= iou_threshold, axis=1)),\n        )\n        new_det_fa_inds = np.nonzero(is_new_det)[0]\n\n        # for each detection, which tracks it matched to (above threshold)\n        det_to_matched_trk_obj_ids = {}\n        trk_id_to_max_iou_high_conf_det = {}  # trk id --> exactly one detection idx\n        HIGH_CONF_THRESH = 0.8\n        HIGH_IOU_THRESH = 0.8\n        det_to_max_iou_trk_idx = np.argmax(ious_np, axis=1)\n        det_is_high_conf = (det_scores_np >= HIGH_CONF_THRESH) & ~is_new_det\n        det_is_high_iou = np.max(ious_np, axis=1) >= HIGH_IOU_THRESH\n        det_is_high_conf_and_iou = set(\n            np.nonzero(det_is_high_conf & det_is_high_iou)[0]\n        )\n        for d in range(det_masks.size(0)):\n            det_to_matched_trk_obj_ids[d] = trk_obj_ids[ious_np[d, :] >= iou_threshold]\n            if d in det_is_high_conf_and_iou:\n                trk_obj_id = trk_obj_ids[det_to_max_iou_trk_idx[d]].item()\n                trk_id_to_max_iou_high_conf_det[trk_obj_id] = d\n\n        return (\n            new_det_fa_inds,\n            unmatched_trk_obj_ids,\n            det_to_matched_trk_obj_ids,\n            trk_id_to_max_iou_high_conf_det,\n            empty_trk_obj_ids,\n        )\n\n    def _assign_new_det_to_gpus(self, new_det_num, prev_workload_per_gpu):\n        \"\"\"Distribute the new objects to the GPUs with the least workload.\"\"\"\n        workload_per_gpu: npt.NDArray = prev_workload_per_gpu.copy()\n        new_det_gpu_ids = np.zeros(new_det_num, np.int64)\n\n        # assign the objects one by one\n        for i in range(len(new_det_gpu_ids)):\n            # find the GPU with the least workload\n            min_gpu = np.argmin(workload_per_gpu)\n            new_det_gpu_ids[i] = min_gpu\n            workload_per_gpu[min_gpu] += 1\n        return new_det_gpu_ids\n\n    def _process_hotstart(\n        self,\n        frame_idx: int,\n        num_frames: int,\n        reverse: bool,\n        det_to_matched_trk_obj_ids: Dict[int, npt.NDArray],\n        new_det_obj_ids: npt.NDArray,\n        empty_trk_obj_ids: npt.NDArray,\n        unmatched_trk_obj_ids: npt.NDArray,\n        rank0_metadata: Dict[str, Any],\n        tracker_metadata: Dict[str, Any],\n    ):\n        \"\"\"Handle hotstart heuristics to remove unmatched or duplicated objects.\"\"\"\n        # obj_id --> first frame index where the object was detected\n        obj_first_frame_idx = rank0_metadata[\"obj_first_frame_idx\"]\n        # obj_id --> [mismatched frame indices]\n        unmatched_frame_inds = rank0_metadata[\"unmatched_frame_inds\"]\n        trk_keep_alive = rank0_metadata[\"trk_keep_alive\"]\n        # (first_appear_obj_id, obj_id) --> [overlap frame indices]\n        overlap_pair_to_frame_inds = rank0_metadata[\"overlap_pair_to_frame_inds\"]\n        # removed_obj_ids: object IDs that are suppressed via hot-start\n        removed_obj_ids = rank0_metadata[\"removed_obj_ids\"]\n        suppressed_obj_ids = rank0_metadata[\"suppressed_obj_ids\"][frame_idx]\n\n        obj_ids_newly_removed = set()  # object IDs to be newly removed on this frame\n        hotstart_diff = (\n            frame_idx - self.hotstart_delay\n            if not reverse\n            else frame_idx + self.hotstart_delay\n        )\n\n        # Step 1: log the frame index where each object ID first appears\n        for obj_id in new_det_obj_ids:\n            if obj_id not in obj_first_frame_idx:\n                obj_first_frame_idx[obj_id] = frame_idx\n            assert obj_id not in trk_keep_alive\n            trk_keep_alive[obj_id] = self.init_trk_keep_alive\n\n        matched_trks = set()\n        # We use the det-->tracks list to check for matched objects. Otherwise, we need to compute areas to decide whether they're occluded\n        for matched_trks_per_det in det_to_matched_trk_obj_ids.values():\n            matched_trks.update(matched_trks_per_det)\n        for obj_id in matched_trks:\n            # NOTE: To minimize number of configurable params, we use the hotstart_unmatch_thresh to set the max value of trk_keep_alive\n            trk_keep_alive[obj_id] = min(\n                self.max_trk_keep_alive, trk_keep_alive[obj_id] + 1\n            )\n        for obj_id in unmatched_trk_obj_ids:\n            unmatched_frame_inds[obj_id].append(frame_idx)\n            # NOTE: To minimize number of configurable params, we use the hotstart_unmatch_thresh to set the min value of trk_keep_alive\n            # The max keep alive is 2x the min, means the model prefers to keep the prediction rather than suppress it if it was matched long enough.\n            trk_keep_alive[obj_id] = max(\n                self.min_trk_keep_alive, trk_keep_alive[obj_id] - 1\n            )\n        if self.decrease_trk_keep_alive_for_empty_masklets:\n            for obj_id in empty_trk_obj_ids:\n                # NOTE: To minimize number of configurable params, we use the hotstart_unmatch_thresh to set the min value of trk_keep_alive\n                trk_keep_alive[obj_id] = max(\n                    self.min_trk_keep_alive, trk_keep_alive[obj_id] - 1\n                )\n\n        # Step 2: removed tracks that has not matched with detections for `hotstart_unmatch_thresh` frames with hotstart period\n        # a) add unmatched frame indices for each existing object ID\n        # note that `unmatched_trk_obj_ids` contains those frames where the SAM2 output mask\n        # doesn't match any detection; it excludes those frames where SAM2 gives an empty mask\n        # b) remove a masklet if it first appears after `hotstart_diff` and is unmatched for more\n        # than `self.hotstart_unmatch_thresh` frames\n        for obj_id, frame_indices in unmatched_frame_inds.items():\n            if obj_id in removed_obj_ids or obj_id in obj_ids_newly_removed:\n                continue  # skip if the object is already removed\n            if len(frame_indices) >= self.hotstart_unmatch_thresh:\n                is_within_hotstart = (\n                    obj_first_frame_idx[obj_id] > hotstart_diff and not reverse\n                ) or (obj_first_frame_idx[obj_id] < hotstart_diff and reverse)\n                if is_within_hotstart:\n                    obj_ids_newly_removed.add(obj_id)\n                    logger.debug(\n                        f\"Removing object {obj_id} at frame {frame_idx} \"\n                        f\"since it is unmatched for frames: {frame_indices}\"\n                    )\n            if (\n                trk_keep_alive[obj_id] <= 0  # Object has not been matched for too long\n                and not self.suppress_unmatched_only_within_hotstart\n                and obj_id not in removed_obj_ids\n                and obj_id not in obj_ids_newly_removed\n            ):\n                logger.debug(\n                    f\"Suppressing object {obj_id} at frame {frame_idx}, due to being unmatched\"\n                )\n                suppressed_obj_ids.add(obj_id)\n\n        # Step 3: removed tracks that overlaps with another track for `hotstart_dup_thresh` frames\n        # a) find overlaps tracks -- we consider overlap if they match to the same detection\n        for _, matched_trk_obj_ids in det_to_matched_trk_obj_ids.items():\n            if len(matched_trk_obj_ids) < 2:\n                continue  # only count detections that are matched to multiple (>=2) masklets\n            # if there are multiple matched track ids, we need to find the one that appeared first;\n            # these later appearing ids may be removed since they may be considered as duplicates\n            first_appear_obj_id = (\n                min(matched_trk_obj_ids, key=lambda x: obj_first_frame_idx[x])\n                if not reverse\n                else max(matched_trk_obj_ids, key=lambda x: obj_first_frame_idx[x])\n            )\n            for obj_id in matched_trk_obj_ids:\n                if obj_id != first_appear_obj_id:\n                    key = (first_appear_obj_id, obj_id)\n                    overlap_pair_to_frame_inds[key].append(frame_idx)\n\n        # b) remove a masklet if it first appears after `hotstart_diff` and it overlaps with another\n        # masklet (that appears earlier) for more than `self.hotstart_dup_thresh` frames\n        for (first_obj_id, obj_id), frame_indices in overlap_pair_to_frame_inds.items():\n            if obj_id in removed_obj_ids or obj_id in obj_ids_newly_removed:\n                continue  # skip if the object is already removed\n            if (obj_first_frame_idx[obj_id] > hotstart_diff and not reverse) or (\n                obj_first_frame_idx[obj_id] < hotstart_diff and reverse\n            ):\n                if len(frame_indices) >= self.hotstart_dup_thresh:\n                    obj_ids_newly_removed.add(obj_id)\n                    logger.debug(\n                        f\"Removing object {obj_id} at frame {frame_idx} \"\n                        f\"since it overlaps with another track {first_obj_id} at frames: {frame_indices}\"\n                    )\n\n        removed_obj_ids.update(obj_ids_newly_removed)\n        return obj_ids_newly_removed, rank0_metadata\n\n    def _tracker_update_memories(\n        self,\n        tracker_inference_states: List[Any],\n        frame_idx: int,\n        tracker_metadata: Dict[str, Any],\n        low_res_masks: Tensor,\n    ):\n        \"\"\"\n        Run Sam2 memory encoder, enforcing non-overlapping constraints globally.\n        \"\"\"\n        if len(tracker_inference_states) == 0:\n            return\n        # Avoid an extra interpolation step by directly interpolating to `interpol_size`\n        high_res_H, high_res_W = (\n            self.tracker.maskmem_backbone.mask_downsampler.interpol_size\n        )\n        # NOTE: inspect this part if we observe OOMs in the demo\n        high_res_masks = F.interpolate(\n            low_res_masks.unsqueeze(1),\n            size=(high_res_H, high_res_W),\n            mode=\"bilinear\",\n            align_corners=False,\n        )\n        # We first apply non-overlapping constraints before memory encoding. This may include some suppression heuristics.\n        if not hasattr(self, \"_warm_up_complete\") or self._warm_up_complete:\n            high_res_masks = self.tracker._suppress_object_pw_area_shrinkage(\n                high_res_masks\n            )\n        # Instead of gathering the predicted object scores, we use mask areas as a proxy.\n        object_score_logits = torch.where(\n            (high_res_masks > 0).any(dim=(-1, -2)), 10.0, -10.0\n        )\n\n        # Run the memory encoder on local slices for each GPU\n        start_idx_gpu = sum(tracker_metadata[\"num_obj_per_gpu\"][: self.rank])\n        start_idx_state = start_idx_gpu\n        for tracker_state in tracker_inference_states:\n            num_obj_per_state = len(tracker_state[\"obj_ids\"])\n            if num_obj_per_state == 0:\n                continue\n            # Get the local high-res masks and object score logits for this inference state\n            end_idx_state = start_idx_state + num_obj_per_state\n            local_high_res_masks = high_res_masks[start_idx_state:end_idx_state]\n            local_object_score_logits = object_score_logits[\n                start_idx_state:end_idx_state\n            ]\n            local_batch_size = local_high_res_masks.size(0)\n            # Run Sam2 memory encoder. Note that we do not re-enforce the non-overlapping constraint as it is turned off by default\n\n            encoded_mem = self.tracker._run_memory_encoder(\n                tracker_state,\n                frame_idx,\n                local_batch_size,\n                local_high_res_masks,\n                local_object_score_logits,\n                is_mask_from_pts=False,\n            )\n            local_maskmem_features, local_maskmem_pos_enc = encoded_mem\n            # Store encoded memories in the local inference state\n            output_dict = tracker_state[\"output_dict\"]\n            for storage_key in [\"cond_frame_outputs\", \"non_cond_frame_outputs\"]:\n                if frame_idx not in output_dict[storage_key]:\n                    continue\n                output_dict[storage_key][frame_idx][\"maskmem_features\"] = (\n                    local_maskmem_features\n                )\n                output_dict[storage_key][frame_idx][\"maskmem_pos_enc\"] = [\n                    pos for pos in local_maskmem_pos_enc\n                ]\n                # for batched inference state, we also need to add per-object\n                # memory slides to support instance interactivity\n                self.tracker._add_output_per_object(\n                    inference_state=tracker_state,\n                    frame_idx=frame_idx,\n                    current_out=output_dict[storage_key][frame_idx],\n                    storage_key=storage_key,\n                )\n            start_idx_state += num_obj_per_state\n\n    def _tracker_add_new_objects(\n        self,\n        frame_idx: int,\n        num_frames: int,\n        new_obj_ids: List[int],\n        new_obj_masks: Tensor,\n        tracker_states_local: List[Any],\n        orig_vid_height: int,\n        orig_vid_width: int,\n        feature_cache: Dict,\n    ):\n        \"\"\"Add a new object to SAM2 inference states.\"\"\"\n        prev_tracker_state = (\n            tracker_states_local[0] if len(tracker_states_local) > 0 else None\n        )\n\n        # prepare inference_state\n        # batch objects that first appear on the same frame together\n        # Clear inference state. Keep the cached image features if available.\n        new_tracker_state = self.tracker.init_state(\n            cached_features=feature_cache,\n            video_height=orig_vid_height,\n            video_width=orig_vid_width,\n            num_frames=num_frames,\n        )\n        new_tracker_state[\"backbone_out\"] = (\n            prev_tracker_state.get(\"backbone_out\", None)\n            if prev_tracker_state is not None\n            else None\n        )\n\n        assert len(new_obj_ids) == new_obj_masks.size(0)\n        assert new_obj_masks.is_floating_point()\n        input_mask_res = self.tracker.input_mask_size\n        new_obj_masks = F.interpolate(\n            new_obj_masks.unsqueeze(1),\n            size=(input_mask_res, input_mask_res),\n            mode=\"bilinear\",\n            align_corners=False,\n        ).squeeze(1)\n        new_obj_masks = new_obj_masks > 0\n\n        # add object one by one\n        for new_obj_id, new_mask in zip(new_obj_ids, new_obj_masks):\n            self.tracker.add_new_mask(\n                inference_state=new_tracker_state,\n                frame_idx=frame_idx,\n                obj_id=new_obj_id,\n                mask=new_mask,\n                add_mask_to_memory=True,\n            )\n        # NOTE: we skip enforcing the non-overlapping constraint **globally** when adding new objects.\n        self.tracker.propagate_in_video_preflight(\n            new_tracker_state, run_mem_encoder=True\n        )\n        tracker_states_local.append(new_tracker_state)\n        return tracker_states_local\n\n    def _tracker_remove_object(self, tracker_states_local: List[Any], obj_id: int):\n        \"\"\"\n        Remove an object from SAM2 inference states. This would remove the object from\n        all frames in the video.\n        \"\"\"\n        tracker_states_local_before_removal = tracker_states_local.copy()\n        tracker_states_local.clear()\n        for tracker_inference_state in tracker_states_local_before_removal:\n            # we try to remove `obj_id` on every inference state with `strict=False`\n            # it will not do anything if an inference state doesn't contain `obj_id`\n            new_obj_ids, _ = self.tracker.remove_object(\n                tracker_inference_state, obj_id, strict=False, need_output=False\n            )\n            # only keep an inference state if it's non-empty after object removal\n            if len(new_obj_ids) > 0:\n                tracker_states_local.append(tracker_inference_state)\n\n    def _tracker_remove_objects(\n        self, tracker_states_local: List[Any], obj_ids: list[int]\n    ):\n        \"\"\"\n        Remove an object from SAM2 inference states. This would remove the object from\n        all frames in the video.\n        \"\"\"\n        for obj_id in obj_ids:\n            self._tracker_remove_object(tracker_states_local, obj_id)\n\n    def _initialize_metadata(self):\n        \"\"\"Initialize metadata for the masklets.\"\"\"\n        tracker_metadata = {\n            \"obj_ids_per_gpu\": [np.array([], np.int64) for _ in range(self.world_size)],\n            \"obj_ids_all_gpu\": np.array([], np.int64),\n            \"num_obj_per_gpu\": np.zeros(self.world_size, np.int64),\n            \"max_obj_id\": -1,\n            \"obj_id_to_score\": {},\n            \"obj_id_to_tracker_score_frame_wise\": defaultdict(dict),\n            \"obj_id_to_last_occluded\": {},\n        }\n        if self.rank == 0:\n            # \"rank0_metadata\" contains metadata that is only stored on (and accessible to) GPU 0\n            # - obj_first_frame_idx: obj_id --> first frame index where the object was detected\n            # - unmatched_frame_inds: obj_id --> [mismatched frame indices]\n            # - overlap_pair_to_frame_inds: (first_appear_obj_id, obj_id) --> [overlap frame indices]\n            # - removed_obj_ids: object IDs that are suppressed via hot-start\n            rank0_metadata = {\n                \"obj_first_frame_idx\": {},\n                \"unmatched_frame_inds\": defaultdict(list),\n                \"trk_keep_alive\": defaultdict(\n                    int\n                ),  # This is used only for object suppression not for removal\n                \"overlap_pair_to_frame_inds\": defaultdict(list),\n                \"removed_obj_ids\": set(),\n                \"suppressed_obj_ids\": defaultdict(\n                    set\n                ),  # frame_idx --> set of objects with suppressed outputs, but still continue to be tracked\n            }\n            if self.masklet_confirmation_enable:\n                # all the following are npt.NDArray with the same shape as `obj_ids_all_gpu`\n                rank0_metadata[\"masklet_confirmation\"] = {\n                    # \"status\" is the confirmation status of each masklet (in `MaskletConfirmationStatus`)\n                    \"status\": np.array([], np.int64),\n                    # \"consecutive_det_num\" is the number of consecutive frames where the masklet is\n                    # detected by the detector (with a matched detection)\n                    \"consecutive_det_num\": np.array([], np.int64),\n                }\n            tracker_metadata[\"rank0_metadata\"] = rank0_metadata\n\n        return tracker_metadata\n\n    def update_masklet_confirmation_status(\n        self,\n        rank0_metadata: Dict[str, Any],\n        obj_ids_all_gpu_prev: npt.NDArray,\n        obj_ids_all_gpu_updated: npt.NDArray,\n        det_to_matched_trk_obj_ids: Dict[int, npt.NDArray],\n        new_det_obj_ids: npt.NDArray,\n    ):\n        confirmation_data = rank0_metadata[\"masklet_confirmation\"]\n\n        # a) first, expand \"confirmation_data\" to include new masklets added in this frame\n        status_prev = confirmation_data[\"status\"]\n        consecutive_det_num_prev = confirmation_data[\"consecutive_det_num\"]\n        assert status_prev.shape == obj_ids_all_gpu_prev.shape, (\n            f\"Got {status_prev.shape} vs {obj_ids_all_gpu_prev.shape}\"\n        )\n\n        obj_id_to_updated_idx = {\n            obj_id: idx for idx, obj_id in enumerate(obj_ids_all_gpu_updated)\n        }\n        prev_elem_is_in_updated = np.isin(obj_ids_all_gpu_prev, obj_ids_all_gpu_updated)\n        prev_elem_obj_ids_in_updated = obj_ids_all_gpu_prev[prev_elem_is_in_updated]\n        prev_elem_inds_in_updated = np.array(\n            [obj_id_to_updated_idx[obj_id] for obj_id in prev_elem_obj_ids_in_updated],\n            dtype=np.int64,\n        )\n        # newly added masklets are initialized to \"UNCONFIRMED\" status\n        unconfirmed_val = MaskletConfirmationStatus.UNCONFIRMED.value\n        status = np.full_like(obj_ids_all_gpu_updated, fill_value=unconfirmed_val)\n        status[prev_elem_inds_in_updated] = status_prev[prev_elem_is_in_updated]\n        consecutive_det_num = np.zeros_like(obj_ids_all_gpu_updated)\n        consecutive_det_num[prev_elem_inds_in_updated] = consecutive_det_num_prev[\n            prev_elem_is_in_updated\n        ]\n\n        # b) update the confirmation status of all masklets based on the current frame\n        # b.1) update \"consecutive_det_num\"\n        # \"is_matched\": whether a masklet is matched to a detection on this frame\n        is_matched = np.isin(obj_ids_all_gpu_updated, new_det_obj_ids)\n        for matched_trk_obj_ids in det_to_matched_trk_obj_ids.values():\n            is_matched |= np.isin(obj_ids_all_gpu_updated, matched_trk_obj_ids)\n        consecutive_det_num = np.where(is_matched, consecutive_det_num + 1, 0)\n\n        # b.2) update \"status\"\n        change_to_confirmed = (\n            consecutive_det_num >= self.masklet_confirmation_consecutive_det_thresh\n        )\n        status[change_to_confirmed] = MaskletConfirmationStatus.CONFIRMED.value\n\n        confirmation_data[\"status\"] = status\n        confirmation_data[\"consecutive_det_num\"] = consecutive_det_num\n        return rank0_metadata\n\n    def forward(self, input: BatchedDatapoint, is_inference: bool = False):\n        raise NotImplementedError(\"Evaluation outside demo is not implemented yet\")\n\n    def _load_checkpoint(self, ckpt_path: str, strict: bool = True):\n        sd = torch.load(ckpt_path, map_location=\"cpu\", weights_only=True)[\"model\"]\n        missing_keys, unexpected_keys = self.load_state_dict(sd, strict=strict)\n        if len(missing_keys) > 0 or len(unexpected_keys) > 0:\n            logger.warning(f\"Loaded ckpt with {missing_keys=}, {unexpected_keys=}\")\n        else:\n            logger.info(\"Loaded ckpt successfully without missing or unexpected keys\")\n\n    def prep_for_evaluator(self, video_frames, tracking_res, scores_labels):\n        \"\"\"This method is only used for benchmark eval (not used in the demo).\"\"\"\n        num_frames = len(video_frames)\n        w, h = video_frames[0].size\n        zero_mask = torch.zeros((1, h, w), dtype=torch.bool)\n        object_ids = list(scores_labels.keys())\n        preds = {\"scores\": [], \"labels\": [], \"boxes\": [], \"masks_rle\": []}\n        for oid in object_ids:\n            o_masks = []\n            o_score = scores_labels[oid][0].item()\n            o_label = scores_labels[oid][1]\n            for frame_idx in range(num_frames):\n                if frame_idx not in tracking_res:\n                    o_masks.append(zero_mask)\n                else:\n                    o_masks.append(tracking_res[frame_idx].get(oid, zero_mask))\n\n            o_masks = torch.cat(o_masks, dim=0)  # (n_frames, H, W)\n            preds[\"scores\"].append(o_score)\n            preds[\"labels\"].append(o_label)\n            preds[\"boxes\"].append(mask_to_box(o_masks.unsqueeze(1)).squeeze())\n            preds[\"masks_rle\"].append(rle_encode(o_masks, return_areas=True))\n\n        preds[\"boxes\"] = (\n            torch.stack(preds[\"boxes\"], dim=0)\n            if len(preds[\"boxes\"]) > 0\n            else torch.empty(\n                (0, num_frames, 4), dtype=torch.float32, device=self.device\n            )\n        )\n        preds[\"scores\"] = (\n            torch.tensor(preds[\"scores\"], device=self.device)\n            if len(preds[\"scores\"]) > 0\n            else torch.empty((0,), device=self.device)\n        )\n        preds[\"per_frame_scores\"] = preds[\"scores\"]\n        preds[\"labels\"] = (\n            torch.tensor(preds[\"labels\"], device=self.device)\n            if len(preds[\"labels\"]) > 0\n            else torch.empty((0,), device=self.device)\n        )\n        return preds\n\n    def _encode_prompt(self, **kwargs):\n        return self.detector._encode_prompt(**kwargs)\n\n    def _drop_new_det_with_obj_limit(self, new_det_fa_inds, det_scores_np, num_to_keep):\n        \"\"\"\n        Drop a few new detections based on the maximum number of objects. We drop new objects based\n        on their detection scores, keeping the high-scoring ones and dropping the low-scoring ones.\n        \"\"\"\n        assert 0 <= num_to_keep <= len(new_det_fa_inds)\n        if num_to_keep == 0:\n            return np.array([], np.int64)  # keep none\n        if num_to_keep == len(new_det_fa_inds):\n            return new_det_fa_inds  # keep all\n\n        # keep the top-scoring detections\n        score_order = np.argsort(det_scores_np[new_det_fa_inds])[::-1]\n        new_det_fa_inds = new_det_fa_inds[score_order[:num_to_keep]]\n        return new_det_fa_inds\n"
  },
  {
    "path": "sam3/model/sam3_video_inference.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n\nimport logging\nfrom collections import defaultdict\n\nimport numpy as np\nimport torch\nimport torch.distributed as dist\nimport torch.nn.functional as F\nfrom sam3 import perflib\nfrom sam3.logger import get_logger\nfrom sam3.model.act_ckpt_utils import clone_output_wrapper\nfrom sam3.model.box_ops import box_xywh_to_cxcywh, box_xyxy_to_xywh\nfrom sam3.model.data_misc import BatchedDatapoint, convert_my_tensors, FindStage\nfrom sam3.model.geometry_encoders import Prompt\nfrom sam3.model.io_utils import IMAGE_EXTS, load_resource_as_video_frames\nfrom sam3.model.sam3_tracker_utils import fill_holes_in_mask_scores\nfrom sam3.model.sam3_video_base import MaskletConfirmationStatus, Sam3VideoBase\nfrom sam3.model.utils.misc import copy_data_to_device\nfrom sam3.perflib.compile import compile_wrapper, shape_logging_wrapper\nfrom sam3.perflib.masks_ops import masks_to_boxes as perf_masks_to_boxes\nfrom torchvision.ops import masks_to_boxes\nfrom tqdm.auto import tqdm\n\nlogger = get_logger(__name__)\n\n\nclass Sam3VideoInference(Sam3VideoBase):\n    TEXT_ID_FOR_TEXT = 0\n    TEXT_ID_FOR_VISUAL = 1\n\n    def __init__(\n        self,\n        image_size=1008,\n        image_mean=(0.5, 0.5, 0.5),\n        image_std=(0.5, 0.5, 0.5),\n        compile_model=False,\n        **kwargs,\n    ):\n        \"\"\"\n        hotstart_delay: int, the delay (in #frames) before the model starts to yield output, 0 to disable hotstart delay.\n        hotstart_unmatch_thresh: int, remove the object if it has this many unmatched frames within its hotstart_delay period.\n            If `hotstart_delay` is set to 0, this parameter is ignored.\n        hotstart_dup_thresh: int, remove the object if it has overlapped with another object this many frames within its hotstart_delay period.\n        \"\"\"\n        super().__init__(**kwargs)\n        self.image_size = image_size\n        self.image_mean = image_mean\n        self.image_std = image_std\n        self.compile_model = compile_model\n\n    @torch.inference_mode()\n    def init_state(\n        self,\n        resource_path,\n        offload_video_to_cpu=False,\n        async_loading_frames=False,\n        video_loader_type=\"cv2\",\n    ):\n        \"\"\"Initialize an inference state from `resource_path` (an image or a video).\"\"\"\n        images, orig_height, orig_width = load_resource_as_video_frames(\n            resource_path=resource_path,\n            image_size=self.image_size,\n            offload_video_to_cpu=offload_video_to_cpu,\n            img_mean=self.image_mean,\n            img_std=self.image_std,\n            async_loading_frames=async_loading_frames,\n            video_loader_type=video_loader_type,\n        )\n        inference_state = {}\n        inference_state[\"image_size\"] = self.image_size\n        inference_state[\"num_frames\"] = len(images)\n        # the original video height and width, used for resizing final output scores\n        inference_state[\"orig_height\"] = orig_height\n        inference_state[\"orig_width\"] = orig_width\n        # values that don't change across frames (so we only need to hold one copy of them)\n        inference_state[\"constants\"] = {}\n        # inputs on each frame\n        self._construct_initial_input_batch(inference_state, images)\n        # initialize extra states\n        inference_state[\"tracker_inference_states\"] = []\n        inference_state[\"tracker_metadata\"] = {}\n        inference_state[\"feature_cache\"] = {}\n        inference_state[\"cached_frame_outputs\"] = {}\n        inference_state[\"action_history\"] = []  # for logging user actions\n        inference_state[\"is_image_only\"] = is_image_type(resource_path)\n        return inference_state\n\n    @torch.inference_mode()\n    def reset_state(self, inference_state):\n        \"\"\"Revert `inference_state` to what it was right after initialization.\"\"\"\n        inference_state[\"input_batch\"].find_text_batch[0] = \"<text placeholder>\"\n        inference_state[\"text_prompt\"] = None\n        for t in range(inference_state[\"num_frames\"]):\n            inference_state[\"input_batch\"].find_inputs[t].text_ids[...] = 0\n            # constructing an output list in inference state (we start with an empty list)\n            inference_state[\"previous_stages_out\"][t] = None\n            inference_state[\"per_frame_raw_point_input\"][t] = None\n            inference_state[\"per_frame_raw_box_input\"][t] = None\n            inference_state[\"per_frame_visual_prompt\"][t] = None\n            inference_state[\"per_frame_geometric_prompt\"][t] = None\n            inference_state[\"per_frame_cur_step\"][t] = 0\n\n        inference_state[\"visual_prompt_embed\"] = None\n        inference_state[\"visual_prompt_mask\"] = None\n        inference_state[\"tracker_inference_states\"].clear()\n        inference_state[\"tracker_metadata\"].clear()\n        inference_state[\"feature_cache\"].clear()\n        inference_state[\"cached_frame_outputs\"].clear()\n        inference_state[\"action_history\"].clear()  # for logging user actions\n\n    def _construct_initial_input_batch(self, inference_state, images):\n        \"\"\"Construct an initial `BatchedDatapoint` instance as input.\"\"\"\n        # 1) img_batch\n        num_frames = len(images)\n        device = self.device\n\n        # 2) find_text_batch\n        # \"<text placeholder>\" will be replaced by the actual text prompt when adding prompts\n        find_text_batch = [\"<text placeholder>\", \"visual\"]\n\n        # 3) find_inputs\n        input_box_embedding_dim = 258  # historical default\n        input_points_embedding_dim = 257  # historical default\n        stages = [\n            FindStage(\n                img_ids=[stage_id],\n                text_ids=[0],\n                input_boxes=[torch.zeros(input_box_embedding_dim)],\n                input_boxes_mask=[torch.empty(0, dtype=torch.bool)],\n                input_boxes_label=[torch.empty(0, dtype=torch.long)],\n                input_points=[torch.empty(0, input_points_embedding_dim)],\n                input_points_mask=[torch.empty(0)],\n                object_ids=[],\n            )\n            for stage_id in range(num_frames)\n        ]\n        for i in range(len(stages)):\n            stages[i] = convert_my_tensors(stages[i])\n\n        # construct the final `BatchedDatapoint` and cast to GPU\n        input_batch = BatchedDatapoint(\n            img_batch=images,\n            find_text_batch=find_text_batch,\n            find_inputs=stages,\n            find_targets=[None] * num_frames,\n            find_metadatas=[None] * num_frames,\n        )\n        input_batch = copy_data_to_device(input_batch, device, non_blocking=True)\n        inference_state[\"input_batch\"] = input_batch\n\n        # construct the placeholder interactive prompts and tracking queries\n        bs = 1\n        inference_state[\"constants\"][\"empty_geometric_prompt\"] = Prompt(\n            box_embeddings=torch.zeros(0, bs, 4, device=device),\n            box_mask=torch.zeros(bs, 0, device=device, dtype=torch.bool),\n            box_labels=torch.zeros(0, bs, device=device, dtype=torch.long),\n            point_embeddings=torch.zeros(0, bs, 2, device=device),\n            point_mask=torch.zeros(bs, 0, device=device, dtype=torch.bool),\n            point_labels=torch.zeros(0, bs, device=device, dtype=torch.long),\n        )\n\n        # constructing an output list in inference state (we start with an empty list)\n        inference_state[\"previous_stages_out\"] = [None] * num_frames\n        inference_state[\"text_prompt\"] = None\n        inference_state[\"per_frame_raw_point_input\"] = [None] * num_frames\n        inference_state[\"per_frame_raw_box_input\"] = [None] * num_frames\n        inference_state[\"per_frame_visual_prompt\"] = [None] * num_frames\n        inference_state[\"per_frame_geometric_prompt\"] = [None] * num_frames\n        inference_state[\"per_frame_cur_step\"] = [0] * num_frames\n\n        # placeholders for cached outputs\n        # (note: currently, a single visual prompt embedding is shared for all frames)\n        inference_state[\"visual_prompt_embed\"] = None\n        inference_state[\"visual_prompt_mask\"] = None\n\n    def _get_visual_prompt(self, inference_state, frame_idx, boxes_cxcywh, box_labels):\n        \"\"\"\n        Handle the case of visual prompt. Currently, in the inference API we do not\n        explicitly distinguish between initial box as visual prompt vs subsequent boxes\n        or boxes after inference for refinement.\n        \"\"\"\n        # If the frame hasn't had any inference results before (prompting or propagation),\n        # we treat the first added box prompt as a visual prompt; otherwise, we treat\n        # the first box just as a refinement prompt.\n        is_new_visual_prompt = (\n            inference_state[\"per_frame_visual_prompt\"][frame_idx] is None\n            and inference_state[\"previous_stages_out\"][frame_idx] is None\n        )\n        if is_new_visual_prompt:\n            if boxes_cxcywh.size(0) != 1:\n                raise RuntimeError(\n                    \"visual prompts (box as an initial prompt) should only have one box, \"\n                    f\"but got {boxes_cxcywh.shape=}\"\n                )\n            if not box_labels.item():\n                logging.warning(\"A negative box is added as a visual prompt.\")\n            # take the first box prompt as a visual prompt\n            device = self.device\n            new_visual_prompt = Prompt(\n                box_embeddings=boxes_cxcywh[None, 0:1, :].to(device),  # (seq, bs, 4)\n                box_mask=None,\n                box_labels=box_labels[None, 0:1].to(device),  # (seq, bs)\n                point_embeddings=None,\n                point_mask=None,\n                point_labels=None,\n            )\n            inference_state[\"per_frame_visual_prompt\"][frame_idx] = new_visual_prompt\n        else:\n            new_visual_prompt = None\n\n        # `boxes_cxcywh` and `box_labels` contains all the raw box inputs added so far\n        # strip any visual prompt from the input boxes (for geometric prompt encoding)\n        if inference_state[\"per_frame_visual_prompt\"][frame_idx] is not None:\n            boxes_cxcywh = boxes_cxcywh[1:]\n            box_labels = box_labels[1:]\n\n        return boxes_cxcywh, box_labels, new_visual_prompt\n\n    def _get_processing_order(\n        self, inference_state, start_frame_idx, max_frame_num_to_track, reverse\n    ):\n        num_frames = inference_state[\"num_frames\"]\n        previous_stages_out = inference_state[\"previous_stages_out\"]\n        if all(out is None for out in previous_stages_out) and start_frame_idx is None:\n            raise RuntimeError(\n                \"No prompts are received on any frames. Please add prompt on at least one frame before propagation.\"\n            )\n        # set start index, end index, and processing order\n        if start_frame_idx is None:\n            # default: start from the earliest frame with input points\n            start_frame_idx = min(\n                t for t, out in enumerate(previous_stages_out) if out is not None\n            )\n        if max_frame_num_to_track is None:\n            # default: track all the frames in the video\n            max_frame_num_to_track = num_frames\n        if reverse:\n            end_frame_idx = start_frame_idx - max_frame_num_to_track\n            end_frame_idx = max(end_frame_idx, 0)\n            processing_order = range(start_frame_idx - 1, end_frame_idx - 1, -1)\n        else:\n            end_frame_idx = start_frame_idx + max_frame_num_to_track\n            end_frame_idx = min(end_frame_idx, num_frames - 1)\n            processing_order = range(start_frame_idx, end_frame_idx + 1)\n        return processing_order, end_frame_idx\n\n    @torch.inference_mode()\n    def propagate_in_video(\n        self,\n        inference_state,\n        start_frame_idx=None,\n        max_frame_num_to_track=None,\n        reverse=False,\n    ):\n        \"\"\"\n        Propagate the prompts to get grounding results for the entire video. This method\n        is a generator and yields inference outputs for all frames in the range specified\n        by `start_frame_idx`, `max_frame_num_to_track`, and `reverse`.\n        \"\"\"\n        # compile the model (it's a no-op if the model is already compiled)\n        # note that it's intentionally added to `self.propagate_in_video`, so that the first\n        # `self.add_prompt` call will be done in eager mode to fill in the decoder buffers\n        # such as positional encoding cache)\n        self._compile_model()\n\n        processing_order, end_frame_idx = self._get_processing_order(\n            inference_state,\n            start_frame_idx,\n            max_frame_num_to_track,\n            reverse=reverse,\n        )\n\n        # Store max_frame_num_to_track in feature_cache for downstream methods\n        inference_state[\"feature_cache\"][\"tracking_bounds\"] = {\n            \"max_frame_num_to_track\": max_frame_num_to_track,\n            \"propagate_in_video_start_frame_idx\": start_frame_idx,\n        }\n\n        hotstart_buffer = []\n        hotstart_removed_obj_ids = set()\n        # when deciding whether to output a masklet on `yield_frame_idx`, we check whether the object is confirmed\n        # in a future frame (`unconfirmed_frame_delay` frames after the current frame). For example, if we require\n        # an object to be detected in 3 consecutive frames to be confirmed, then we look 2 frames in the future --\n        # e.g., we output an object on frame 4 only if it becomes confirmed on frame 6.\n        unconfirmed_status_delay = self.masklet_confirmation_consecutive_det_thresh - 1\n        unconfirmed_obj_ids_per_frame = {}  # frame_idx -> hidden_obj_ids\n        for frame_idx in tqdm(\n            processing_order, desc=\"propagate_in_video\", disable=self.rank > 0\n        ):\n            out = self._run_single_frame_inference(inference_state, frame_idx, reverse)\n\n            if self.hotstart_delay > 0:\n                # accumulate the outputs for the first `hotstart_delay` frames\n                hotstart_buffer.append([frame_idx, out])\n                # update the object IDs removed by hotstart so that we don't output them\n                if self.rank == 0:\n                    hotstart_removed_obj_ids.update(out[\"removed_obj_ids\"])\n                    unconfirmed_obj_ids = out.get(\"unconfirmed_obj_ids\", None)\n                    if unconfirmed_obj_ids is not None:\n                        unconfirmed_obj_ids_per_frame[frame_idx] = unconfirmed_obj_ids\n\n                if frame_idx == end_frame_idx:\n                    # we reached the end of propagation -- yield all frames in the buffer\n                    yield_list = hotstart_buffer\n                    hotstart_buffer = []\n                elif len(hotstart_buffer) >= self.hotstart_delay:\n                    # we have enough frames -- yield and remove the first (oldest) frame from the buffer\n                    yield_list = hotstart_buffer[:1]\n                    hotstart_buffer = hotstart_buffer[1:]\n                else:\n                    # not enough frames yet -- skip yielding\n                    yield_list = []\n            else:\n                yield_list = [(frame_idx, out)]  # output the current frame\n\n            for yield_frame_idx, yield_out in yield_list:\n                # post-process the output and yield it\n                if self.rank == 0:\n                    suppressed_obj_ids = yield_out[\"suppressed_obj_ids\"]\n                    unconfirmed_status_frame_idx = (\n                        yield_frame_idx + unconfirmed_status_delay\n                        if not reverse\n                        else yield_frame_idx - unconfirmed_status_delay\n                    )\n\n                    # Clamp the frame index to stay within video bounds\n                    num_frames = inference_state[\"num_frames\"]\n                    unconfirmed_status_frame_idx = max(\n                        0, min(unconfirmed_status_frame_idx, num_frames - 1)\n                    )\n\n                    unconfirmed_obj_ids = unconfirmed_obj_ids_per_frame.get(\n                        unconfirmed_status_frame_idx, None\n                    )\n                    postprocessed_out = self._postprocess_output(\n                        inference_state,\n                        yield_out,\n                        hotstart_removed_obj_ids,\n                        suppressed_obj_ids,\n                        unconfirmed_obj_ids,\n                    )\n\n                    self._cache_frame_outputs(\n                        inference_state,\n                        yield_frame_idx,\n                        yield_out[\"obj_id_to_mask\"],\n                        suppressed_obj_ids=suppressed_obj_ids,\n                        removed_obj_ids=hotstart_removed_obj_ids,\n                        unconfirmed_obj_ids=unconfirmed_obj_ids,\n                    )\n                else:\n                    postprocessed_out = None  # no output on other GPUs\n                yield yield_frame_idx, postprocessed_out\n\n    def _run_single_frame_inference(self, inference_state, frame_idx, reverse):\n        \"\"\"\n        Perform inference on a single frame and get its inference results. This would\n        also update `inference_state`.\n        \"\"\"\n        # prepare inputs\n        input_batch = inference_state[\"input_batch\"]\n        tracker_states_local = inference_state[\"tracker_inference_states\"]\n        has_text_prompt = inference_state[\"text_prompt\"] is not None\n        has_geometric_prompt = (\n            inference_state[\"per_frame_geometric_prompt\"][frame_idx] is not None\n        )\n        # run inference for the current frame\n        (\n            obj_id_to_mask,\n            obj_id_to_score,\n            tracker_states_local_new,\n            tracker_metadata_new,\n            frame_stats,\n            _,\n        ) = self._det_track_one_frame(\n            frame_idx=frame_idx,\n            num_frames=inference_state[\"num_frames\"],\n            reverse=reverse,\n            input_batch=input_batch,\n            geometric_prompt=(\n                inference_state[\"constants\"][\"empty_geometric_prompt\"]\n                if not has_geometric_prompt\n                else inference_state[\"per_frame_geometric_prompt\"][frame_idx]\n            ),\n            tracker_states_local=tracker_states_local,\n            tracker_metadata_prev=inference_state[\"tracker_metadata\"],\n            feature_cache=inference_state[\"feature_cache\"],\n            orig_vid_height=inference_state[\"orig_height\"],\n            orig_vid_width=inference_state[\"orig_width\"],\n            is_image_only=inference_state[\"is_image_only\"],\n            allow_new_detections=has_text_prompt or has_geometric_prompt,\n        )\n        # update inference state\n        inference_state[\"tracker_inference_states\"] = tracker_states_local_new\n        inference_state[\"tracker_metadata\"] = tracker_metadata_new\n        # use a dummy string in \"previous_stages_out\" to indicate this frame has outputs\n        inference_state[\"previous_stages_out\"][frame_idx] = \"_THIS_FRAME_HAS_OUTPUTS_\"\n\n        if self.rank == 0:\n            self._cache_frame_outputs(inference_state, frame_idx, obj_id_to_mask)\n\n        out = {\n            \"obj_id_to_mask\": obj_id_to_mask,\n            \"obj_id_to_score\": obj_id_to_score,  # first frame detection score\n            \"obj_id_to_tracker_score\": tracker_metadata_new[\n                \"obj_id_to_tracker_score_frame_wise\"\n            ][frame_idx],\n        }\n        # removed_obj_ids is only needed on rank 0 to handle hotstart delay buffer\n        if self.rank == 0:\n            rank0_metadata = tracker_metadata_new[\"rank0_metadata\"]\n            removed_obj_ids = rank0_metadata[\"removed_obj_ids\"]\n            out[\"removed_obj_ids\"] = removed_obj_ids\n            out[\"suppressed_obj_ids\"] = rank0_metadata[\"suppressed_obj_ids\"][frame_idx]\n            out[\"frame_stats\"] = frame_stats\n            if self.masklet_confirmation_enable:\n                status = rank0_metadata[\"masklet_confirmation\"][\"status\"]\n                is_unconfirmed = status == MaskletConfirmationStatus.UNCONFIRMED.value\n                out[\"unconfirmed_obj_ids\"] = tracker_metadata_new[\"obj_ids_all_gpu\"][\n                    is_unconfirmed\n                ].tolist()\n            else:\n                out[\"unconfirmed_obj_ids\"] = []\n\n        return out\n\n    def _postprocess_output(\n        self,\n        inference_state,\n        out,\n        removed_obj_ids=None,\n        suppressed_obj_ids=None,\n        unconfirmed_obj_ids=None,\n    ):\n        obj_id_to_mask = out[\"obj_id_to_mask\"]  # low res masks\n        curr_obj_ids = sorted(obj_id_to_mask.keys())\n        H_video, W_video = inference_state[\"orig_height\"], inference_state[\"orig_width\"]\n        if len(curr_obj_ids) == 0:\n            out_obj_ids = torch.zeros(0, dtype=torch.int64)\n            out_probs = torch.zeros(0, dtype=torch.float32)\n            out_binary_masks = torch.zeros(0, H_video, W_video, dtype=torch.bool)\n            out_boxes_xywh = torch.zeros(0, 4, dtype=torch.float32)\n        else:\n            out_obj_ids = torch.tensor(curr_obj_ids, dtype=torch.int64)\n            out_probs = torch.tensor(\n                [out[\"obj_id_to_score\"][obj_id] for obj_id in curr_obj_ids]\n            )\n            out_tracker_probs = torch.tensor(\n                [\n                    (\n                        out[\"obj_id_to_tracker_score\"][obj_id]\n                        if obj_id in out[\"obj_id_to_tracker_score\"]\n                        else 0.0\n                    )\n                    for obj_id in curr_obj_ids\n                ]\n            )\n            out_binary_masks = torch.cat(\n                [obj_id_to_mask[obj_id] for obj_id in curr_obj_ids], dim=0\n            )\n\n            assert out_binary_masks.dtype == torch.bool\n            keep = out_binary_masks.any(dim=(1, 2)).cpu()  # remove masks with 0 areas\n            # hide outputs for those object IDs in `obj_ids_to_hide`\n            obj_ids_to_hide = []\n            if suppressed_obj_ids is not None:\n                obj_ids_to_hide.extend(suppressed_obj_ids)\n            if removed_obj_ids is not None:\n                obj_ids_to_hide.extend(removed_obj_ids)\n            if unconfirmed_obj_ids is not None:\n                obj_ids_to_hide.extend(unconfirmed_obj_ids)\n            if len(obj_ids_to_hide) > 0:\n                obj_ids_to_hide_t = torch.tensor(obj_ids_to_hide, dtype=torch.int64)\n                keep &= ~torch.isin(out_obj_ids, obj_ids_to_hide_t)\n\n            # slice those valid entries from the original outputs\n            keep_idx = torch.nonzero(keep, as_tuple=True)[0]\n            keep_idx_gpu = keep_idx.pin_memory().to(\n                device=out_binary_masks.device, non_blocking=True\n            )\n\n            out_obj_ids = torch.index_select(out_obj_ids, 0, keep_idx)\n            out_probs = torch.index_select(out_probs, 0, keep_idx)\n            out_tracker_probs = torch.index_select(out_tracker_probs, 0, keep_idx)\n            out_binary_masks = torch.index_select(out_binary_masks, 0, keep_idx_gpu)\n\n            if perflib.is_enabled:\n                out_boxes_xyxy = perf_masks_to_boxes(\n                    out_binary_masks, out_obj_ids.tolist()\n                )\n            else:\n                out_boxes_xyxy = masks_to_boxes(out_binary_masks)\n\n            out_boxes_xywh = box_xyxy_to_xywh(out_boxes_xyxy)  # convert to xywh format\n            # normalize boxes\n            out_boxes_xywh[..., 0] /= W_video\n            out_boxes_xywh[..., 1] /= H_video\n            out_boxes_xywh[..., 2] /= W_video\n            out_boxes_xywh[..., 3] /= H_video\n\n        # apply non-overlapping constraints on the existing masklets\n        if out_binary_masks.shape[0] > 1:\n            assert len(out_binary_masks) == len(out_tracker_probs)\n            out_binary_masks = (\n                self.tracker._apply_object_wise_non_overlapping_constraints(\n                    out_binary_masks.unsqueeze(1),\n                    out_tracker_probs.unsqueeze(1).to(out_binary_masks.device),\n                    background_value=0,\n                ).squeeze(1)\n            ) > 0\n\n        outputs = {\n            \"out_obj_ids\": out_obj_ids.cpu().numpy(),\n            \"out_probs\": out_probs.cpu().numpy(),\n            \"out_boxes_xywh\": out_boxes_xywh.cpu().numpy(),\n            \"out_binary_masks\": out_binary_masks.cpu().numpy(),\n            \"frame_stats\": out.get(\"frame_stats\", None),\n        }\n        return outputs\n\n    def _cache_frame_outputs(\n        self,\n        inference_state,\n        frame_idx,\n        obj_id_to_mask,\n        suppressed_obj_ids=None,\n        removed_obj_ids=None,\n        unconfirmed_obj_ids=None,\n    ):\n        # Filter out suppressed, removed, and unconfirmed objects from the cache\n        filtered_obj_id_to_mask = obj_id_to_mask.copy()\n\n        objects_to_exclude = set()\n        if suppressed_obj_ids is not None:\n            objects_to_exclude.update(suppressed_obj_ids)\n        if removed_obj_ids is not None:\n            objects_to_exclude.update(removed_obj_ids)\n        if unconfirmed_obj_ids is not None:\n            objects_to_exclude.update(unconfirmed_obj_ids)\n\n        if objects_to_exclude:\n            for obj_id in objects_to_exclude:\n                if obj_id in filtered_obj_id_to_mask:\n                    del filtered_obj_id_to_mask[obj_id]\n\n        inference_state[\"cached_frame_outputs\"][frame_idx] = filtered_obj_id_to_mask\n\n    def _build_tracker_output(\n        self, inference_state, frame_idx, refined_obj_id_to_mask=None\n    ):\n        assert (\n            \"cached_frame_outputs\" in inference_state\n            and frame_idx in inference_state[\"cached_frame_outputs\"]\n        ), (\n            \"No cached outputs found. Ensure normal propagation has run first to populate the cache.\"\n        )\n        cached_outputs = inference_state[\"cached_frame_outputs\"][frame_idx]\n\n        obj_id_to_mask = cached_outputs.copy()\n\n        # Update with refined masks if provided\n        if refined_obj_id_to_mask is not None:\n            for obj_id, refined_mask in refined_obj_id_to_mask.items():\n                assert refined_mask is not None, (\n                    f\"Refined mask data must be provided for obj_id {obj_id}\"\n                )\n                obj_id_to_mask[obj_id] = refined_mask\n\n        return obj_id_to_mask\n\n    def _compile_model(self):\n        \"\"\"Compile the SAM model with torch.compile for speedup.\"\"\"\n        is_compiled = getattr(self, \"_model_is_compiled\", False)\n        if is_compiled or not self.compile_model:\n            return\n\n        import torch._dynamo\n\n        # a larger cache size to hold varying number of shapes for torch.compile\n        # see https://github.com/pytorch/pytorch/blob/v2.5.1/torch/_dynamo/config.py#L42-L49\n        torch._dynamo.config.cache_size_limit = 128\n        torch._dynamo.config.accumulated_cache_size_limit = 2048\n        torch._dynamo.config.capture_scalar_outputs = True\n        torch._dynamo.config.suppress_errors = True\n\n        # Compile module components\n        # skip compilation of `_encode_prompt` since it sometimes tiggger SymInt errors\n        # self._encode_prompt = clone_output_wrapper(\n        #     torch.compile(self._encode_prompt, fullgraph=True, mode=\"max-autotune\")\n        # )\n\n        ## Compile SAM3 model components\n        self.detector.backbone.vision_backbone.forward = clone_output_wrapper(\n            torch.compile(\n                self.detector.backbone.vision_backbone.forward,\n                fullgraph=True,\n                mode=\"max-autotune\",\n            )\n        )\n        self.detector.transformer.encoder.forward = clone_output_wrapper(\n            torch.compile(\n                self.detector.transformer.encoder.forward,\n                fullgraph=True,\n                mode=\"max-autotune\",\n            )\n        )\n        self.detector.transformer.decoder.forward = clone_output_wrapper(\n            torch.compile(\n                self.detector.transformer.decoder.forward,\n                fullgraph=True,\n                mode=\"max-autotune\",\n                dynamic=False,\n            )\n        )\n\n        self.detector.segmentation_head.forward = clone_output_wrapper(\n            torch.compile(\n                self.detector.segmentation_head.forward,\n                fullgraph=True,\n                mode=\"max-autotune\",\n            )\n        )\n\n        ## Compile Tracker model components\n        self.tracker.maskmem_backbone.forward = compile_wrapper(\n            self.tracker.maskmem_backbone.forward,\n            mode=\"max-autotune-no-cudagraphs\",\n            fullgraph=True,\n            dynamic=False,\n        )\n\n        self.tracker.transformer.encoder.forward = shape_logging_wrapper(\n            compile_wrapper(\n                self.tracker.transformer.encoder.forward,\n                mode=\"max-autotune-no-cudagraphs\",\n                fullgraph=True,\n                dynamic=True,\n            ),\n            keep_kwargs=[\"src\", \"src_pos\", \"prompt\", \"prompt_pos\"],\n        )\n\n        self.tracker.sam_mask_decoder.forward = compile_wrapper(\n            self.tracker.sam_mask_decoder.forward,\n            mode=\"max-autotune\",\n            fullgraph=True,\n            dynamic=False,  # Accuracy regression on True\n        )\n\n        self._model_is_compiled = True\n\n    def _warm_up_vg_propagation(self, inference_state, start_frame_idx=0):\n        # use different tracking score thresholds for each round to simulate different number of output objects\n        num_objects_list = range(self.num_obj_for_compile + 1)\n        new_det_score_thresh_list = [0.3, 0.5, 0.7]\n        num_rounds = len(new_det_score_thresh_list)\n        orig_new_det_thresh = self.new_det_thresh\n\n        for i, thresh in enumerate(new_det_score_thresh_list):\n            self.new_det_thresh = thresh\n            for num_objects in num_objects_list:\n                logger.info(f\"{i + 1}/{num_rounds} warming up model compilation\")\n                self.add_prompt(\n                    inference_state, frame_idx=start_frame_idx, text_str=\"cat\"\n                )\n                logger.info(\n                    f\"{i + 1}/{num_rounds} warming up model compilation -- simulating {num_objects}/{self.num_obj_for_compile} objects\"\n                )\n                inference_state = self.add_fake_objects_to_inference_state(\n                    inference_state, num_objects, frame_idx=start_frame_idx\n                )\n                inference_state[\"tracker_metadata\"][\"rank0_metadata\"].update(\n                    {\n                        \"masklet_confirmation\": {\n                            \"status\": np.zeros(num_objects, dtype=np.int64),\n                            \"consecutive_det_num\": np.zeros(\n                                num_objects, dtype=np.int64\n                            ),\n                        }\n                    }\n                )\n                for _ in self.propagate_in_video(\n                    inference_state, start_frame_idx, reverse=False\n                ):\n                    pass\n                for _ in self.propagate_in_video(\n                    inference_state, start_frame_idx, reverse=True\n                ):\n                    pass\n                self.reset_state(inference_state)\n                logger.info(\n                    f\"{i + 1}/{num_rounds} warming up model compilation -- completed round {i + 1} out of {num_rounds}\"\n                )\n\n        # Warm up Tracker memory encoder with varying input shapes\n        num_iters = 3\n        feat_size = self.tracker.sam_image_embedding_size**2  # 72 * 72 = 5184\n        hidden_dim = self.tracker.hidden_dim  # 256\n        mem_dim = self.tracker.mem_dim  # 64\n        for _ in tqdm(range(num_iters)):\n            for b in range(1, self.num_obj_for_compile + 1):\n                for i in range(\n                    1,\n                    self.tracker.max_cond_frames_in_attn + self.tracker.num_maskmem,\n                ):\n                    for j in range(\n                        self.tracker.max_cond_frames_in_attn\n                        + self.tracker.max_obj_ptrs_in_encoder\n                    ):\n                        num_obj_ptr_tokens = (hidden_dim // mem_dim) * j\n                        src = torch.randn(feat_size, b, hidden_dim, device=self.device)\n                        src_pos = torch.randn(\n                            feat_size, b, hidden_dim, device=self.device\n                        )\n                        prompt = torch.randn(\n                            feat_size * i + num_obj_ptr_tokens,\n                            b,\n                            mem_dim,\n                            device=self.device,\n                        )\n                        prompt_pos = torch.randn(\n                            feat_size * i + num_obj_ptr_tokens,\n                            b,\n                            mem_dim,\n                            device=self.device,\n                        )\n\n                        self.tracker.transformer.encoder.forward(\n                            src=src,\n                            src_pos=src_pos,\n                            prompt=prompt,\n                            prompt_pos=prompt_pos,\n                            num_obj_ptr_tokens=num_obj_ptr_tokens,\n                        )\n\n        self.new_det_thresh = orig_new_det_thresh\n        return inference_state\n\n    def add_fake_objects_to_inference_state(\n        self, inference_state, num_objects, frame_idx\n    ):\n        new_det_obj_ids_local = np.arange(num_objects)\n        high_res_H, high_res_W = (\n            self.tracker.maskmem_backbone.mask_downsampler.interpol_size\n        )\n        new_det_masks = torch.ones(\n            len(new_det_obj_ids_local), high_res_H, high_res_W\n        ).to(self.device)\n\n        inference_state[\"tracker_inference_states\"] = self._tracker_add_new_objects(\n            frame_idx=frame_idx,\n            num_frames=inference_state[\"num_frames\"],\n            new_obj_ids=new_det_obj_ids_local,\n            new_obj_masks=new_det_masks,\n            tracker_states_local=inference_state[\"tracker_inference_states\"],\n            orig_vid_height=inference_state[\"orig_height\"],\n            orig_vid_width=inference_state[\"orig_width\"],\n            feature_cache=inference_state[\"feature_cache\"],\n        )\n\n        # Synthesize obj_id_to_mask data for cached_frame_outputs to support _build_tracker_output during warmup\n        obj_id_to_mask = {}\n        if num_objects > 0:\n            H_video = inference_state[\"orig_height\"]\n            W_video = inference_state[\"orig_width\"]\n\n            video_res_masks = F.interpolate(\n                new_det_masks.unsqueeze(1),  # Add channel dimension for interpolation\n                size=(H_video, W_video),\n                mode=\"bilinear\",\n                align_corners=False,\n            )  # (num_objects, 1, H_video, W_video)\n            for i, obj_id in enumerate(new_det_obj_ids_local):\n                obj_id_to_mask[obj_id] = (video_res_masks[i] > 0.0).to(torch.bool)\n        if self.rank == 0:\n            for fidx in range(inference_state[\"num_frames\"]):\n                self._cache_frame_outputs(inference_state, fidx, obj_id_to_mask)\n\n        inference_state[\"tracker_metadata\"].update(\n            {\n                \"obj_ids_per_gpu\": [np.arange(num_objects)],\n                \"obj_ids_all_gpu\": np.arange(num_objects),  # Same as 1 GPU\n                \"num_obj_per_gpu\": [num_objects],\n                \"obj_id_to_score\": {i: 1.0 for i in range(num_objects)},\n                \"max_obj_id\": num_objects,\n                \"rank0_metadata\": {\n                    \"masklet_confirmation\": {\n                        \"status\": np.zeros(num_objects, dtype=np.int64),\n                        \"consecutive_det_num\": np.zeros(num_objects, dtype=np.int64),\n                    },\n                    \"removed_obj_ids\": set(),\n                    \"suppressed_obj_ids\": defaultdict(set),\n                },\n            }\n        )\n        return inference_state\n\n    @torch.inference_mode()\n    @torch.autocast(device_type=\"cuda\", dtype=torch.bfloat16)\n    def warm_up_compilation(self):\n        \"\"\"\n        Warm up the model by running a dummy inference to compile the model. This is\n        useful to avoid the compilation overhead in the first inference call.\n        \"\"\"\n        if not self.compile_model:\n            return\n        self._warm_up_complete = False\n        if self.device.type != \"cuda\":\n            raise RuntimeError(\n                f\"The model must be on CUDA for warm-up compilation, got {self.device=}.\"\n            )\n\n        # temporally set to single GPU temporarily for warm-up compilation\n        orig_rank = self.rank\n        orig_world_size = self.world_size\n        self.rank = self.detector.rank = 0\n        self.world_size = self.detector.world_size = 1\n        orig_recondition_every_nth_frame = self.recondition_every_nth_frame\n        # self.recondition_every_nth_frame = 2\n\n        # Get a random video\n        inference_state = self.init_state(resource_path=\"<load-dummy-video-30>\")\n        start_frame_idx = 0\n\n        # Run basic propagation warm-up\n        inference_state = self._warm_up_vg_propagation(inference_state, start_frame_idx)\n\n        logger.info(\"Warm-up compilation completed.\")\n\n        # revert to the original GPU and rank\n        self.rank = self.detector.rank = orig_rank\n        self.world_size = self.detector.world_size = orig_world_size\n        self.recondition_every_nth_frame = orig_recondition_every_nth_frame\n        self._warm_up_complete = True\n        self.tracker.transformer.encoder.forward.set_logging(True)\n\n    @torch.inference_mode()\n    def add_prompt(\n        self,\n        inference_state,\n        frame_idx,\n        text_str=None,\n        boxes_xywh=None,\n        box_labels=None,\n    ):\n        \"\"\"\n        Add text, point or box prompts on a single frame. This method returns the inference\n        outputs only on the prompted frame.\n\n        Note that text prompts are NOT associated with a particular frame (i.e. they apply\n        to all frames). However, we only run inference on the frame specified in `frame_idx`.\n        \"\"\"\n        logger.debug(\"Running add_prompt on frame %d\", frame_idx)\n\n        num_frames = inference_state[\"num_frames\"]\n        assert text_str is not None or boxes_xywh is not None, (\n            \"at least one type of prompt (text, boxes) must be provided\"\n        )\n        assert 0 <= frame_idx < num_frames, (\n            f\"{frame_idx=} is out of range for a total of {num_frames} frames\"\n        )\n\n        # since it's a semantic prompt, we start over\n        self.reset_state(inference_state)\n\n        # 1) add text prompt\n        if text_str is not None and text_str != \"visual\":\n            inference_state[\"text_prompt\"] = text_str\n            inference_state[\"input_batch\"].find_text_batch[0] = text_str\n            text_id = self.TEXT_ID_FOR_TEXT\n        else:\n            inference_state[\"text_prompt\"] = None\n            inference_state[\"input_batch\"].find_text_batch[0] = \"<text placeholder>\"\n            text_id = self.TEXT_ID_FOR_VISUAL\n        for t in range(inference_state[\"num_frames\"]):\n            inference_state[\"input_batch\"].find_inputs[t].text_ids[...] = text_id\n\n        # 2) handle box prompt\n        assert (boxes_xywh is not None) == (box_labels is not None)\n        if boxes_xywh is not None:\n            boxes_xywh = torch.as_tensor(boxes_xywh, dtype=torch.float32)\n            box_labels = torch.as_tensor(box_labels, dtype=torch.long)\n            # input boxes are expected to be [xmin, ymin, width, height] format\n            # in normalized coordinates of range 0~1, similar to FA\n            assert boxes_xywh.dim() == 2\n            assert boxes_xywh.size(0) > 0 and boxes_xywh.size(-1) == 4\n            assert box_labels.dim() == 1 and box_labels.size(0) == boxes_xywh.size(0)\n            boxes_cxcywh = box_xywh_to_cxcywh(boxes_xywh)\n            assert (boxes_xywh >= 0).all().item() and (boxes_xywh <= 1).all().item()\n            assert (boxes_cxcywh >= 0).all().item() and (boxes_cxcywh <= 1).all().item()\n\n            new_box_input = boxes_cxcywh, box_labels\n            inference_state[\"per_frame_raw_box_input\"][frame_idx] = new_box_input\n\n            # handle the case of visual prompt (also added as an input box from the UI)\n            boxes_cxcywh, box_labels, geometric_prompt = self._get_visual_prompt(\n                inference_state, frame_idx, boxes_cxcywh, box_labels\n            )\n\n            inference_state[\"per_frame_geometric_prompt\"][frame_idx] = geometric_prompt\n\n        out = self._run_single_frame_inference(\n            inference_state, frame_idx, reverse=False\n        )\n        return frame_idx, self._postprocess_output(inference_state, out)\n\n    @torch.autocast(device_type=\"cuda\", dtype=torch.bfloat16)\n    def forward(self, input: BatchedDatapoint, is_inference: bool = False):\n        \"\"\"This method is only used for benchmark eval (not used in the demo).\"\"\"\n        # set the model to single GPU for benchmark evaluation (to be compatible with trainer)\n        orig_rank = self.rank\n        orig_world_size = self.world_size\n        self.rank = self.detector.rank = 0\n        self.world_size = self.detector.world_size = 1\n\n        # get data\n        text_prompt_ids = input.find_metadatas[0].original_category_id\n        text_prompt_list = input.find_text_batch\n\n        # loop over txt prompts\n        tracking_res = defaultdict(dict)  # frame_idx --> {obj_id: mask}\n        scores_labels = defaultdict(tuple)  # obj_id --> (score, text_prompt_id)\n        inference_state = self.init_state(resource_path=input.raw_images)\n        for prompt_id, prompt in zip(text_prompt_ids, text_prompt_list):\n            self.add_prompt(inference_state, frame_idx=0, text_str=prompt)\n            start_obj_id = max(scores_labels.keys(), default=-1) + 1  # prev max + 1\n\n            # propagate the prompts\n            obj_ids_this_prompt = set()\n            for frame_idx, out in self.propagate_in_video(\n                inference_state,\n                start_frame_idx=0,\n                max_frame_num_to_track=inference_state[\"num_frames\"],\n                reverse=False,\n            ):\n                current_frame_res = tracking_res[frame_idx]\n                for obj_id, mask in zip(out[\"out_obj_ids\"], out[\"out_binary_masks\"]):\n                    mask_tensor = torch.tensor(mask[None], dtype=torch.bool)\n                    current_frame_res[obj_id + start_obj_id] = mask_tensor\n                obj_ids_this_prompt.update(current_frame_res.keys())\n\n            obj_id_to_score = inference_state[\"tracker_metadata\"][\"obj_id_to_score\"]\n            for obj_id, score in obj_id_to_score.items():\n                if obj_id + start_obj_id in obj_ids_this_prompt:\n                    score_tensor = torch.tensor(score, dtype=torch.float32)\n                    scores_labels[obj_id + start_obj_id] = (score_tensor, prompt_id)\n\n            self.reset_state(inference_state)\n\n        video_id = input.find_metadatas[0].original_image_id[0].cpu().item()\n        preds = self.prep_for_evaluator(input.raw_images, tracking_res, scores_labels)\n\n        # revert the model to the original GPU and rank\n        self.rank = self.detector.rank = orig_rank\n        self.world_size = self.detector.world_size = orig_world_size\n        return {video_id: preds}\n\n    def back_convert(self, targets):\n        # Needed for retraining compatibility with trainer\n        return targets\n\n\nclass Sam3VideoInferenceWithInstanceInteractivity(Sam3VideoInference):\n    def __init__(\n        self,\n        use_prev_mem_frame=False,\n        use_stateless_refinement=False,\n        refinement_detector_cond_frame_removal_window=16,\n        **kwargs,\n    ):\n        \"\"\"\n        use_prev_mem_frame: bool, whether to condition on previous memory frames for adding points\n        use_stateless_refinement: bool, whether to enable stateless refinement behavior\n        refinement_detector_cond_frame_removal_window: int, we remove a detector conditioning frame if it\n            is within this many frames of a user refined frame. Set to a large value (e.g. 10000) to\n            always remove detector conditioning frames if there is any user refinement in the video.\n        \"\"\"\n        super().__init__(**kwargs)\n        self.use_prev_mem_frame = use_prev_mem_frame\n        self.use_stateless_refinement = use_stateless_refinement\n        self.refinement_detector_cond_frame_removal_window = (\n            refinement_detector_cond_frame_removal_window\n        )\n\n    def _init_new_tracker_state(self, inference_state):\n        return self.tracker.init_state(\n            cached_features=inference_state[\"feature_cache\"],\n            video_height=inference_state[\"orig_height\"],\n            video_width=inference_state[\"orig_width\"],\n            num_frames=inference_state[\"num_frames\"],\n        )\n\n    @torch.inference_mode()\n    def propagate_in_video(\n        self,\n        inference_state,\n        start_frame_idx=None,\n        max_frame_num_to_track=None,\n        reverse=False,\n    ):\n        # step 1: check which type of propagation to run, should be the same for all GPUs.\n        propagation_type, obj_ids = self.parse_action_history_for_propagation(\n            inference_state\n        )\n        self.add_action_history(\n            inference_state,\n            action_type=propagation_type,\n            obj_ids=obj_ids,\n            frame_idx=start_frame_idx,\n        )\n\n        # step 2: run full VG propagation\n        if propagation_type == \"propagation_full\":\n            logger.debug(f\"Running full VG propagation (reverse={reverse}).\")\n            yield from super().propagate_in_video(\n                inference_state,\n                start_frame_idx=start_frame_idx,\n                max_frame_num_to_track=max_frame_num_to_track,\n                reverse=reverse,\n            )\n            return\n\n        # step 3: run Tracker partial propagation or direct fetch existing predictions\n        assert propagation_type in [\"propagation_partial\", \"propagation_fetch\"]\n        logger.debug(\n            f\"Running Tracker propagation for objects {obj_ids} and merging it with existing VG predictions (reverse={reverse}).\"\n            if propagation_type == \"propagation_partial\"\n            else f\"Fetching existing VG predictions without running any propagation (reverse={reverse}).\"\n        )\n        processing_order, _ = self._get_processing_order(\n            inference_state,\n            start_frame_idx=start_frame_idx,\n            max_frame_num_to_track=max_frame_num_to_track,\n            reverse=reverse,\n        )\n\n        tracker_metadata = inference_state[\"tracker_metadata\"]\n\n        # if fetch just return from output\n        if propagation_type == \"propagation_fetch\":\n            for frame_idx in tqdm(processing_order):\n                if self.rank == 0:\n                    obj_id_to_mask = inference_state[\"cached_frame_outputs\"].get(\n                        frame_idx, {}\n                    )\n                    # post processing - remove suppressed obj_ids\n                    obj_id_to_score = tracker_metadata[\"obj_id_to_score\"]\n                    suppressed_obj_ids = tracker_metadata[\"rank0_metadata\"][\n                        \"suppressed_obj_ids\"\n                    ][frame_idx]\n                    obj_id_to_tracker_score = tracker_metadata[\n                        \"obj_id_to_tracker_score_frame_wise\"\n                    ][frame_idx]\n\n                    out = {\n                        \"obj_id_to_mask\": obj_id_to_mask,\n                        \"obj_id_to_score\": obj_id_to_score,\n                        \"obj_id_to_tracker_score\": obj_id_to_tracker_score,\n                    }\n                    yield (\n                        frame_idx,\n                        self._postprocess_output(\n                            inference_state, out, suppressed_obj_ids=suppressed_obj_ids\n                        ),\n                    )\n                else:\n                    yield frame_idx, None\n\n            return\n\n        # get Tracker inference states containing selected obj_ids\n        if propagation_type == \"propagation_partial\":\n            # can be empty for GPUs where objects are not in their inference states\n            tracker_states_local = self._get_tracker_inference_states_by_obj_ids(\n                inference_state, obj_ids\n            )\n            for tracker_state in tracker_states_local:\n                self.tracker.propagate_in_video_preflight(\n                    tracker_state, run_mem_encoder=True\n                )\n\n        for frame_idx in tqdm(processing_order):\n            # run Tracker propagation\n            if propagation_type == \"propagation_partial\":\n                self._prepare_backbone_feats(inference_state, frame_idx, reverse)\n                obj_ids_local, low_res_masks_local, tracker_scores_local = (\n                    self._propogate_tracker_one_frame_local_gpu(\n                        tracker_states_local,\n                        frame_idx=frame_idx,\n                        reverse=reverse,\n                        run_mem_encoder=True,\n                    )\n                )\n\n                # broadcast refined object tracker scores and masks to all GPUs\n                # handle multiple objects that can be located on different GPUs\n                refined_obj_data = {}  # obj_id -> (score, mask_video_res)\n\n                # Collect data for objects on this GPU\n                local_obj_data = {}\n                for obj_id in obj_ids:\n                    obj_rank = self._get_gpu_id_by_obj_id(inference_state, obj_id)\n                    if self.rank == obj_rank and obj_id in obj_ids_local:\n                        refined_obj_idx = obj_ids_local.index(obj_id)\n                        refined_mask_low_res = low_res_masks_local[\n                            refined_obj_idx\n                        ]  # (H_low_res, W_low_res)\n                        refined_score = tracker_scores_local[refined_obj_idx]\n\n                        # Keep low resolution for broadcasting to reduce communication cost\n                        local_obj_data[obj_id] = (refined_score, refined_mask_low_res)\n\n                # Broadcast data from each GPU that has refined objects\n                if self.world_size > 1:\n                    for obj_id in obj_ids:\n                        obj_rank = self._get_gpu_id_by_obj_id(inference_state, obj_id)\n                        if self.rank == obj_rank:\n                            # This GPU has the object, broadcast its data\n                            data_to_broadcast = local_obj_data.get(obj_id, None)\n                            data_list = [\n                                (data_to_broadcast[0].cpu(), data_to_broadcast[1].cpu())\n                            ]\n                            self.broadcast_python_obj_cpu(data_list, src=obj_rank)\n                            if data_to_broadcast is not None:\n                                refined_obj_data[obj_id] = data_to_broadcast\n                        elif self.rank != obj_rank:\n                            # This GPU doesn't have the object, receive data\n                            data_list = [None]\n                            self.broadcast_python_obj_cpu(data_list, src=obj_rank)\n                            refined_obj_data[obj_id] = (\n                                data_list[0][0].to(self.device),\n                                data_list[0][1].to(self.device),\n                            )\n                else:\n                    # Single GPU case\n                    refined_obj_data = local_obj_data\n\n                # Update Tracker scores for all refined objects\n                for obj_id, (refined_score, _) in refined_obj_data.items():\n                    tracker_metadata[\"obj_id_to_tracker_score_frame_wise\"][\n                        frame_idx\n                    ].update({obj_id: refined_score.item()})\n\n                if self.rank == 0:\n                    # get predictions from Tracker inference states, it includes the original\n                    # VG predictions and the refined predictions from interactivity.\n\n                    # Prepare refined masks dictionary - upscale to video resolution after broadcast\n                    refined_obj_id_to_mask = {}\n                    for obj_id, (_, refined_mask_low_res) in refined_obj_data.items():\n                        refined_mask_video_res = (\n                            self._convert_low_res_mask_to_video_res(\n                                refined_mask_low_res, inference_state\n                            )\n                        )  # (1, H_video, W_video) bool\n                        refined_obj_id_to_mask[obj_id] = refined_mask_video_res\n\n                    obj_id_to_mask = self._build_tracker_output(\n                        inference_state, frame_idx, refined_obj_id_to_mask\n                    )\n                    out = {\n                        \"obj_id_to_mask\": obj_id_to_mask,\n                        \"obj_id_to_score\": tracker_metadata[\"obj_id_to_score\"],\n                        \"obj_id_to_tracker_score\": tracker_metadata[\n                            \"obj_id_to_tracker_score_frame_wise\"\n                        ][frame_idx],\n                    }\n                    suppressed_obj_ids = tracker_metadata[\"rank0_metadata\"][\n                        \"suppressed_obj_ids\"\n                    ][frame_idx]\n                    self._cache_frame_outputs(\n                        inference_state,\n                        frame_idx,\n                        obj_id_to_mask,\n                        suppressed_obj_ids=suppressed_obj_ids,\n                    )\n                    suppressed_obj_ids = tracker_metadata[\"rank0_metadata\"][\n                        \"suppressed_obj_ids\"\n                    ][frame_idx]\n                    yield (\n                        frame_idx,\n                        self._postprocess_output(\n                            inference_state, out, suppressed_obj_ids=suppressed_obj_ids\n                        ),\n                    )\n                else:\n                    yield frame_idx, None\n\n    def add_action_history(\n        self, inference_state, action_type, frame_idx=None, obj_ids=None\n    ):\n        \"\"\"\n        action_history is used to automatically decide what to do during propagation.\n        action_type: one of [\"add\", \"remove\", \"refine\"] + [\"propagation_full\", \"propagation_partial\", \"propagation_fetch\"]\n        \"\"\"\n        instance_actions = [\"add\", \"remove\", \"refine\"]\n        propagation_actions = [\n            \"propagation_full\",\n            \"propagation_partial\",\n            \"propagation_fetch\",\n        ]\n        assert action_type in instance_actions + propagation_actions, (\n            f\"Invalid action type: {action_type}, must be one of {instance_actions + propagation_actions}\"\n        )\n        action = {\n            \"type\": action_type,\n            \"frame_idx\": frame_idx,\n            \"obj_ids\": obj_ids,\n        }\n        inference_state[\"action_history\"].append(action)\n\n    def _has_object_been_refined(self, inference_state, obj_id):\n        action_history = inference_state[\"action_history\"]\n        for action in action_history:\n            if action[\"type\"] in [\"add\", \"refine\"] and action.get(\"obj_ids\"):\n                if obj_id in action[\"obj_ids\"]:\n                    return True\n        return False\n\n    def parse_action_history_for_propagation(self, inference_state):\n        \"\"\"\n        Parse the actions in history before the last propagation and prepare for the next propagation.\n        We support multiple actions (add/remove/refine) between two propagations. If we had an action\n        history similar to this [\"propagate\", \"add\", \"refine\", \"remove\", \"add\"], the next propagation\n        would remove the removed object, and also propagate the two added/refined objects.\n\n        Returns:\n            propagation_type: one of [\"propagation_full\", \"propagation_partial\", \"propagation_fetch\"]\n                - \"propagation_full\": run VG propagation for all objects\n                - \"propagation_partial\": run Tracker propagation for selected objects, useful for add/refine actions\n                - \"propagation_fetch\": fetch existing VG predictions without running any propagation\n            obj_ids: list of object ids to run Tracker propagation on if propagation_type is \"propagation_partial\".\n        \"\"\"\n        action_history = inference_state[\"action_history\"]\n        if len(action_history) == 0:\n            # we run propagation for the first time\n            return \"propagation_full\", None\n\n        if \"propagation\" in action_history[-1][\"type\"]:\n            if action_history[-1][\"type\"] in [\"propagation_fetch\"]:\n                # last propagation is direct fetch, we fetch existing predictions\n                return \"propagation_fetch\", None\n            elif action_history[-1][\"type\"] in [\n                \"propagation_partial\",\n                \"propagation_full\",\n            ]:\n                # we do fetch prediction if we have already run propagation twice or we have run\n                # propagation once and it is from the first frame or last frame.\n                if (\n                    len(action_history) > 1\n                    and action_history[-2][\"type\"]\n                    in [\"propagation_partial\", \"propagation_full\"]\n                ) or action_history[-1][\"frame_idx\"] in [\n                    0,\n                    inference_state[\"num_frames\"] - 1,\n                ]:\n                    # we have run both forward and backward partial/full propagation\n                    return \"propagation_fetch\", None\n                else:\n                    # we have run partial/full forward or backward propagation once, need run it for the rest of the frames\n                    return action_history[-1][\"type\"], action_history[-1][\"obj_ids\"]\n\n        # parse actions since last propagation\n        obj_ids = []\n        for action in action_history[::-1]:\n            if \"propagation\" in action[\"type\"]:\n                # we reached the last propagation action, stop parsing\n                break\n            if action[\"type\"] in [\"add\", \"refine\"]:\n                obj_ids.extend(action[\"obj_ids\"])\n            # else action[\"type\"] == \"remove\": noop\n        obj_ids = list(set(obj_ids)) if len(obj_ids) > 0 else None\n        propagation_type = (\n            \"propagation_partial\" if obj_ids is not None else \"propagation_fetch\"\n        )\n        return propagation_type, obj_ids\n\n    def remove_object(self, inference_state, obj_id, is_user_action=False):\n        \"\"\"\n        We try to remove object from tracker states on every GPU, it will do nothing\n        for states without this object.\n        \"\"\"\n        obj_rank = self._get_gpu_id_by_obj_id(inference_state, obj_id)\n        assert obj_rank is not None, f\"Object {obj_id} not found in any GPU.\"\n\n        tracker_states_local = inference_state[\"tracker_inference_states\"]\n        if self.rank == obj_rank:\n            self._tracker_remove_object(tracker_states_local, obj_id)\n\n        if is_user_action:\n            self.add_action_history(\n                inference_state, action_type=\"remove\", obj_ids=[obj_id]\n            )\n\n        # update metadata\n        tracker_metadata = inference_state[\"tracker_metadata\"]\n        _obj_ids = tracker_metadata[\"obj_ids_per_gpu\"][obj_rank]\n        tracker_metadata[\"obj_ids_per_gpu\"][obj_rank] = _obj_ids[_obj_ids != obj_id]\n        tracker_metadata[\"num_obj_per_gpu\"][obj_rank] = len(\n            tracker_metadata[\"obj_ids_per_gpu\"][obj_rank]\n        )\n        tracker_metadata[\"obj_ids_all_gpu\"] = np.concatenate(\n            tracker_metadata[\"obj_ids_per_gpu\"]\n        )\n        tracker_metadata[\"obj_id_to_score\"].pop(obj_id, None)\n        # tracker_metadata[\"max_obj_id\"] # we do not reuse the object id, so we do not update it here\n\n        # Clean up cached frame outputs to remove references to the deleted object\n        if \"cached_frame_outputs\" in inference_state:\n            for frame_idx in inference_state[\"cached_frame_outputs\"]:\n                frame_cache = inference_state[\"cached_frame_outputs\"][frame_idx]\n                if obj_id in frame_cache:\n                    del frame_cache[obj_id]\n\n    def _get_gpu_id_by_obj_id(self, inference_state, obj_id):\n        \"\"\"\n        Locate GPU ID for a given object.\n        \"\"\"\n        obj_ids_per_gpu = inference_state[\"tracker_metadata\"][\"obj_ids_per_gpu\"]\n        for rank, obj_ids in enumerate(obj_ids_per_gpu):\n            if obj_id in obj_ids:\n                return rank\n        return None  # object not found in any GPU\n\n    def _get_tracker_inference_states_by_obj_ids(self, inference_state, obj_ids):\n        \"\"\"\n        Get the Tracker inference states that contain the given object ids.\n        This is used to run partial Tracker propagation on a single object/bucket.\n        Possibly multiple or zero states can be returned.\n        \"\"\"\n        states = [\n            state\n            for state in inference_state[\"tracker_inference_states\"]\n            if set(obj_ids) & set(state[\"obj_ids\"])\n        ]\n        return states\n\n    def _prepare_backbone_feats(self, inference_state, frame_idx, reverse):\n        input_batch = inference_state[\"input_batch\"]\n        feature_cache = inference_state[\"feature_cache\"]\n        num_frames = inference_state[\"num_frames\"]\n        geometric_prompt = (\n            inference_state[\"constants\"][\"empty_geometric_prompt\"]\n            if inference_state[\"per_frame_geometric_prompt\"][frame_idx] is None\n            else inference_state[\"per_frame_geometric_prompt\"][frame_idx]\n        )\n        _ = self.run_backbone_and_detection(\n            frame_idx=frame_idx,\n            num_frames=num_frames,\n            input_batch=input_batch,\n            geometric_prompt=geometric_prompt,\n            feature_cache=feature_cache,\n            reverse=reverse,\n            allow_new_detections=True,\n        )\n\n    @torch.inference_mode()\n    def add_prompt(\n        self,\n        inference_state,\n        frame_idx,\n        text_str=None,\n        boxes_xywh=None,\n        box_labels=None,\n        points=None,\n        point_labels=None,\n        obj_id=None,\n        rel_coordinates=True,\n    ):\n        if points is not None:\n            # Tracker instance prompts\n            assert text_str is None and boxes_xywh is None, (\n                \"When points are provided, text_str and boxes_xywh must be None.\"\n            )\n            assert obj_id is not None, (\n                \"When points are provided, obj_id must be provided.\"\n            )\n            return self.add_tracker_new_points(\n                inference_state,\n                frame_idx,\n                obj_id=obj_id,\n                points=points,\n                labels=point_labels,\n                rel_coordinates=rel_coordinates,\n                use_prev_mem_frame=self.use_prev_mem_frame,\n            )\n        else:\n            # SAM3 prompts\n            return super().add_prompt(\n                inference_state,\n                frame_idx,\n                text_str=text_str,\n                boxes_xywh=boxes_xywh,\n                box_labels=box_labels,\n            )\n\n    @torch.inference_mode()\n    def add_tracker_new_points(\n        self,\n        inference_state,\n        frame_idx,\n        obj_id,\n        points,\n        labels,\n        rel_coordinates=True,\n        use_prev_mem_frame=False,\n    ):\n        \"\"\"Add a new point prompt to Tracker. Suppporting instance refinement to existing\n        objects by passing existing obj_id or adding a new object by passing a new obj_id.\n        use_prev_mem_frame=False to disable cross attention to previous memory frames.\n        Every GPU returns the same results, and results should contain all masks including\n        these masks not refined or not added by the current user points.\n        \"\"\"\n        assert obj_id is not None, \"obj_id must be provided to add new points\"\n        tracker_metadata = inference_state[\"tracker_metadata\"]\n        if tracker_metadata == {}:\n            # initialize masklet metadata if it's uninitialized (empty dict)\n            tracker_metadata.update(self._initialize_metadata())\n\n        obj_rank = self._get_gpu_id_by_obj_id(inference_state, obj_id)\n\n        # prepare feature\n        self._prepare_backbone_feats(inference_state, frame_idx, reverse=False)\n\n        object_has_been_refined = self._has_object_been_refined(inference_state, obj_id)\n        if (\n            obj_rank is not None\n            and self.use_stateless_refinement\n            and not object_has_been_refined\n        ):\n            # The first time we start refinement on the object, we remove it.\n            logger.debug(\n                f\"[rank={self.rank}] Removing object {obj_id} before refinement.\"\n            )\n            self.remove_object(inference_state, obj_id, is_user_action=False)\n            obj_rank = None\n\n        if obj_rank is None:\n            # new object, we assign it a GPU and create a new inference state if limit allows\n            num_prev_obj = np.sum(tracker_metadata[\"num_obj_per_gpu\"])\n            if num_prev_obj >= self.max_num_objects:\n                logger.warning(\n                    f\"add_tracker_new_points: cannot add a new object as we are already tracking {num_prev_obj=} \"\n                    f\"masklets (under {self.max_num_objects=})\"\n                )\n                obj_ids = []\n                H_low_res = W_low_res = self.tracker.low_res_mask_size\n                H_video_res = inference_state[\"orig_height\"]\n                W_video_res = inference_state[\"orig_width\"]\n                low_res_masks = torch.zeros(0, 1, H_low_res, W_low_res)\n                video_res_masks = torch.zeros(0, 1, H_video_res, W_video_res)\n                return frame_idx, obj_ids, low_res_masks, video_res_masks\n\n            new_det_gpu_ids = self._assign_new_det_to_gpus(\n                new_det_num=1,\n                prev_workload_per_gpu=tracker_metadata[\"num_obj_per_gpu\"],\n            )\n            obj_rank = new_det_gpu_ids[0]\n\n            # get tracker inference state for the new object\n            if self.rank == obj_rank:\n                # for batched inference, we create a new inference state\n                tracker_state = self._init_new_tracker_state(inference_state)\n                inference_state[\"tracker_inference_states\"].append(tracker_state)\n\n            # update metadata\n            tracker_metadata[\"obj_ids_per_gpu\"][obj_rank] = np.concatenate(\n                [\n                    tracker_metadata[\"obj_ids_per_gpu\"][obj_rank],\n                    np.array([obj_id], dtype=np.int64),\n                ]\n            )\n            tracker_metadata[\"num_obj_per_gpu\"][obj_rank] = len(\n                tracker_metadata[\"obj_ids_per_gpu\"][obj_rank]\n            )\n            tracker_metadata[\"obj_ids_all_gpu\"] = np.concatenate(\n                tracker_metadata[\"obj_ids_per_gpu\"]\n            )\n            tracker_metadata[\"max_obj_id\"] = max(tracker_metadata[\"max_obj_id\"], obj_id)\n\n            logger.debug(\n                f\"[rank={self.rank}] Adding new object with id {obj_id} at frame {frame_idx}.\"\n            )\n            self.add_action_history(\n                inference_state, \"add\", frame_idx=frame_idx, obj_ids=[obj_id]\n            )\n        else:\n            # existing object, for refinement\n            if self.rank == obj_rank:\n                tracker_states = self._get_tracker_inference_states_by_obj_ids(\n                    inference_state, [obj_id]\n                )\n                assert len(tracker_states) == 1, (\n                    f\"[rank={self.rank}] Multiple Tracker inference states found for the same object id.\"\n                )\n                tracker_state = tracker_states[0]\n\n            # log\n            logger.debug(\n                f\"[rank={self.rank}] Refining existing object with id {obj_id} at frame {frame_idx}.\"\n            )\n            self.add_action_history(\n                inference_state, \"refine\", frame_idx=frame_idx, obj_ids=[obj_id]\n            )\n\n        # assign higher score to added/refined object\n        tracker_metadata[\"obj_id_to_score\"][obj_id] = 1.0\n        tracker_metadata[\"obj_id_to_tracker_score_frame_wise\"][frame_idx][obj_id] = 1.0\n\n        if self.rank == 0:\n            rank0_metadata = tracker_metadata.get(\"rank0_metadata\", {})\n\n            if \"removed_obj_ids\" in rank0_metadata:\n                rank0_metadata[\"removed_obj_ids\"].discard(obj_id)\n\n            if \"suppressed_obj_ids\" in rank0_metadata:\n                for frame_id in rank0_metadata[\"suppressed_obj_ids\"]:\n                    rank0_metadata[\"suppressed_obj_ids\"][frame_id].discard(obj_id)\n\n            if \"masklet_confirmation\" in rank0_metadata:\n                obj_ids_all_gpu = tracker_metadata[\"obj_ids_all_gpu\"]\n                obj_indices = np.where(obj_ids_all_gpu == obj_id)[0]\n                if len(obj_indices) > 0:\n                    obj_idx = obj_indices[0]\n                    if obj_idx < len(rank0_metadata[\"masklet_confirmation\"][\"status\"]):\n                        rank0_metadata[\"masklet_confirmation\"][\"status\"][obj_idx] = 1\n                        rank0_metadata[\"masklet_confirmation\"][\"consecutive_det_num\"][\n                            obj_idx\n                        ] = self.masklet_confirmation_consecutive_det_thresh\n\n        if self.rank == obj_rank:\n            frame_idx, obj_ids, low_res_masks, video_res_masks = (\n                self.tracker.add_new_points(\n                    inference_state=tracker_state,\n                    frame_idx=frame_idx,\n                    obj_id=obj_id,\n                    points=points,\n                    labels=labels,\n                    clear_old_points=True,\n                    rel_coordinates=rel_coordinates,\n                    use_prev_mem_frame=use_prev_mem_frame,\n                )\n            )\n\n            if video_res_masks is not None and len(video_res_masks) > 0:\n                video_res_masks = fill_holes_in_mask_scores(\n                    video_res_masks,  # shape (N, 1, H_video, W_video)\n                    max_area=self.fill_hole_area,\n                    fill_holes=True,\n                    remove_sprinkles=True,\n                )\n\n            # Since the mem encoder has already run for the current input points?\n            self.tracker.propagate_in_video_preflight(\n                tracker_state, run_mem_encoder=True\n            )\n            # Clear detector conditioning frames when user clicks are received to allow\n            # model updating masks on these frames. It is a noop if user is refining on the\n            # detector conditioning frames or adding new objects.\n            self.clear_detector_added_cond_frame_in_tracker(\n                tracker_state, obj_id, frame_idx\n            )\n\n        # fetch results from states and gather across GPUs\n        # Use optimized caching approach to avoid reprocessing unmodified objects\n        if self.rank == obj_rank and len(obj_ids) > 0:\n            new_mask_data = (video_res_masks[obj_ids.index(obj_id)] > 0.0).to(\n                torch.bool\n            )\n        else:\n            new_mask_data = None\n        # Broadcast the new mask data across all ranks for consistency\n        if self.world_size > 1:\n            data_list = [new_mask_data.cpu() if new_mask_data is not None else None]\n            self.broadcast_python_obj_cpu(data_list, src=obj_rank)\n            new_mask_data = data_list[0].to(self.device)\n\n        if self.rank == 0:\n            obj_id_to_mask = self._build_tracker_output(\n                inference_state,\n                frame_idx,\n                {obj_id: new_mask_data} if new_mask_data is not None else None,\n            )\n            # post processing - remove suppressed obj_ids\n            obj_id_to_score = tracker_metadata[\"obj_id_to_score\"]\n            suppressed_obj_ids = tracker_metadata[\"rank0_metadata\"][\n                \"suppressed_obj_ids\"\n            ][frame_idx]\n            obj_id_to_tracker_score = tracker_metadata[\n                \"obj_id_to_tracker_score_frame_wise\"\n            ][frame_idx]\n\n            out = {\n                \"obj_id_to_mask\": obj_id_to_mask,\n                \"obj_id_to_score\": obj_id_to_score,\n                \"obj_id_to_tracker_score\": obj_id_to_tracker_score,\n            }\n            self._cache_frame_outputs(\n                inference_state,\n                frame_idx,\n                obj_id_to_mask,\n                suppressed_obj_ids=suppressed_obj_ids,\n            )\n            return frame_idx, self._postprocess_output(\n                inference_state, out, suppressed_obj_ids=suppressed_obj_ids\n            )\n        else:\n            return frame_idx, None  # no output on other GPUs\n\n    def _gather_obj_id_to_mask_across_gpus(self, inference_state, obj_id_to_mask_local):\n        \"\"\"Gather obj_id_to_mask from all GPUs. Optionally resize the masks to the video resolution.\"\"\"\n        tracker_metadata = inference_state[\"tracker_metadata\"]\n\n        # concatenate the output masklets from all local inference states\n        H_mask = W_mask = self.tracker.low_res_mask_size\n        obj_ids_local = tracker_metadata[\"obj_ids_per_gpu\"][self.rank]\n        low_res_masks_local = []\n        for obj_id in obj_ids_local:\n            if obj_id in obj_id_to_mask_local:\n                low_res_masks_local.append(obj_id_to_mask_local[obj_id])\n            else:\n                low_res_masks_local.append(\n                    torch.full((H_mask, W_mask), -1024.0, device=self.device)\n                )\n        if len(low_res_masks_local) > 0:\n            low_res_masks_local = torch.stack(low_res_masks_local, dim=0)  # (N, H, W)\n            assert low_res_masks_local.shape[1:] == (H_mask, W_mask)\n        else:\n            low_res_masks_local = torch.zeros(0, H_mask, W_mask, device=self.device)\n\n        # all-gather `low_res_masks_local` into `low_res_masks_global`\n        # - low_res_masks_global: Tensor -- (num_global_obj, H_mask, W_mask)\n        if self.world_size > 1:\n            low_res_masks_local = low_res_masks_local.float().contiguous()\n            low_res_masks_peers = [\n                low_res_masks_local.new_empty(num_obj, H_mask, W_mask)\n                for num_obj in tracker_metadata[\"num_obj_per_gpu\"]\n            ]\n            dist.all_gather(low_res_masks_peers, low_res_masks_local)\n            low_res_masks_global = torch.cat(low_res_masks_peers, dim=0)\n        else:\n            low_res_masks_global = low_res_masks_local\n        return low_res_masks_global\n\n    def _convert_low_res_mask_to_video_res(self, low_res_mask, inference_state):\n        \"\"\"\n        Convert a low-res mask to video resolution, matching the format expected by _build_tracker_output.\n\n        Args:\n            low_res_mask: Tensor of shape (H_low_res, W_low_res)\n            inference_state: Contains video dimensions\n\n        Returns:\n            video_res_mask: Tensor of shape (1, H_video, W_video) bool\n        \"\"\"\n        if low_res_mask is None:\n            return None\n\n        # Convert to 3D for interpolation: (H_low_res, W_low_res) -> (1, H_low_res, W_low_res)\n        low_res_mask_3d = low_res_mask.unsqueeze(0).unsqueeze(0)\n\n        # Get video dimensions\n        H_video = inference_state[\"orig_height\"]\n        W_video = inference_state[\"orig_width\"]\n\n        video_res_mask = F.interpolate(\n            low_res_mask_3d.float(),\n            size=(H_video, W_video),\n            mode=\"bilinear\",\n            align_corners=False,\n        )  # (1, H_video, W_video)\n\n        # Convert to boolean - already in the right shape!\n        return (video_res_mask.squeeze(0) > 0.0).to(torch.bool)\n\n    def clear_detector_added_cond_frame_in_tracker(\n        self, tracker_state, obj_id, refined_frame_idx\n    ):\n        \"\"\"Clear detector added conditioning frame if it is within a predefined window\n        of the refined frame. This allow model to update masks on these frames.\"\"\"\n        obj_idx = self.tracker._obj_id_to_idx(tracker_state, obj_id)\n\n        mask_only_cond_frame_indices = []\n        window = self.refinement_detector_cond_frame_removal_window\n        for frame_idx in tracker_state[\"mask_inputs_per_obj\"][obj_idx]:\n            if frame_idx not in tracker_state[\"point_inputs_per_obj\"][obj_idx]:\n                # clear conditioning frames within a window of the refined frame\n                if abs(frame_idx - refined_frame_idx) <= window:\n                    mask_only_cond_frame_indices.append(frame_idx)\n\n        # clear\n        if len(mask_only_cond_frame_indices) > 0:\n            for frame_idx in mask_only_cond_frame_indices:\n                # obj_ids_on_this_frame is essentially all obj_ids in the state\n                # since they are bucket batched\n                obj_ids_on_this_frame = tracker_state[\"obj_id_to_idx\"].keys()\n                for obj_id2 in obj_ids_on_this_frame:\n                    self.tracker.clear_all_points_in_frame(\n                        tracker_state, frame_idx, obj_id2, need_output=False\n                    )\n            logger.debug(\n                f\"Cleared detector mask only conditioning frames ({mask_only_cond_frame_indices}) in Tracker.\"\n            )\n        return\n\n\ndef is_image_type(resource_path: str) -> bool:\n    if isinstance(resource_path, list):\n        return len(resource_path) == 1\n    return resource_path.lower().endswith(tuple(IMAGE_EXTS))\n"
  },
  {
    "path": "sam3/model/sam3_video_predictor.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n\nimport datetime\nimport gc\nimport multiprocessing as mp\nimport os\nimport queue\nimport socket\nimport sys\nimport time\nimport uuid\nfrom contextlib import closing\nfrom typing import List, Optional\n\nimport psutil\nimport torch\nfrom sam3.logger import get_logger\n\nlogger = get_logger(__name__)\n\n\nclass Sam3VideoPredictor:\n    # a global dictionary that holds all inference states for this model (key is session_id)\n    _ALL_INFERENCE_STATES = {}\n\n    def __init__(\n        self,\n        checkpoint_path=None,\n        bpe_path=None,\n        has_presence_token=True,\n        geo_encoder_use_img_cross_attn=True,\n        strict_state_dict_loading=True,\n        async_loading_frames=False,\n        video_loader_type=\"cv2\",\n        apply_temporal_disambiguation: bool = True,\n        compile: bool = False,\n    ):\n        self.async_loading_frames = async_loading_frames\n        self.video_loader_type = video_loader_type\n        from sam3.model_builder import build_sam3_video_model\n\n        self.model = (\n            build_sam3_video_model(\n                checkpoint_path=checkpoint_path,\n                bpe_path=bpe_path,\n                has_presence_token=has_presence_token,\n                geo_encoder_use_img_cross_attn=geo_encoder_use_img_cross_attn,\n                strict_state_dict_loading=strict_state_dict_loading,\n                apply_temporal_disambiguation=apply_temporal_disambiguation,\n                compile=compile,\n            )\n            .cuda()\n            .eval()\n        )\n\n    @torch.inference_mode()\n    def handle_request(self, request):\n        \"\"\"Dispatch a request based on its type.\"\"\"\n        request_type = request[\"type\"]\n        if request_type == \"start_session\":\n            return self.start_session(\n                resource_path=request[\"resource_path\"],\n                session_id=request.get(\"session_id\", None),\n            )\n        elif request_type == \"add_prompt\":\n            return self.add_prompt(\n                session_id=request[\"session_id\"],\n                frame_idx=request[\"frame_index\"],\n                text=request.get(\"text\", None),\n                points=request.get(\"points\", None),\n                point_labels=request.get(\"point_labels\", None),\n                bounding_boxes=request.get(\"bounding_boxes\", None),\n                bounding_box_labels=request.get(\"bounding_box_labels\", None),\n                obj_id=request.get(\"obj_id\", None),\n            )\n        elif request_type == \"remove_object\":\n            return self.remove_object(\n                session_id=request[\"session_id\"],\n                obj_id=request[\"obj_id\"],\n                is_user_action=request.get(\"is_user_action\", True),\n            )\n        elif request_type == \"reset_session\":\n            return self.reset_session(session_id=request[\"session_id\"])\n        elif request_type == \"close_session\":\n            return self.close_session(session_id=request[\"session_id\"])\n        else:\n            raise RuntimeError(f\"invalid request type: {request_type}\")\n\n    @torch.inference_mode()\n    def handle_stream_request(self, request):\n        \"\"\"Dispatch a stream request based on its type.\"\"\"\n        request_type = request[\"type\"]\n        if request_type == \"propagate_in_video\":\n            yield from self.propagate_in_video(\n                session_id=request[\"session_id\"],\n                propagation_direction=request.get(\"propagation_direction\", \"both\"),\n                start_frame_idx=request.get(\"start_frame_index\", None),\n                max_frame_num_to_track=request.get(\"max_frame_num_to_track\", None),\n            )\n        else:\n            raise RuntimeError(f\"invalid request type: {request_type}\")\n\n    def start_session(self, resource_path, session_id=None):\n        \"\"\"\n        Start a new inference session on an image or a video. Here `resource_path`\n        can be either a path to an image file (for image inference) or an MP4 file\n        or directory with JPEG video frames (for video inference).\n\n        If `session_id` is defined, it will be used as identifier for the\n        session. If it is not defined, the start_session function will create\n        a session id and return it.\n        \"\"\"\n        # get an initial inference_state from the model\n        inference_state = self.model.init_state(\n            resource_path=resource_path,\n            async_loading_frames=self.async_loading_frames,\n            video_loader_type=self.video_loader_type,\n        )\n        if not session_id:\n            session_id = str(uuid.uuid4())\n        self._ALL_INFERENCE_STATES[session_id] = {\n            \"state\": inference_state,\n            \"session_id\": session_id,\n            \"start_time\": time.time(),\n        }\n        logger.debug(\n            f\"started new session {session_id}; {self._get_session_stats()}; \"\n            f\"{self._get_torch_and_gpu_properties()}\"\n        )\n        return {\"session_id\": session_id}\n\n    def add_prompt(\n        self,\n        session_id: str,\n        frame_idx: int,\n        text: Optional[str] = None,\n        points: Optional[List[List[float]]] = None,\n        point_labels: Optional[List[int]] = None,\n        bounding_boxes: Optional[List[List[float]]] = None,\n        bounding_box_labels: Optional[List[int]] = None,\n        obj_id: Optional[int] = None,\n    ):\n        \"\"\"Add text, box and/or point prompt on a specific video frame.\"\"\"\n        logger.debug(\n            f\"add prompt on frame {frame_idx} in session {session_id}: \"\n            f\"{text=}, {points=}, {point_labels=}, \"\n            f\"{bounding_boxes=}, {bounding_box_labels=}\"\n        )\n        session = self._get_session(session_id)\n        inference_state = session[\"state\"]\n\n        frame_idx, outputs = self.model.add_prompt(\n            inference_state=inference_state,\n            frame_idx=frame_idx,\n            text_str=text,\n            points=points,\n            point_labels=point_labels,\n            boxes_xywh=bounding_boxes,\n            box_labels=bounding_box_labels,\n            obj_id=obj_id,\n        )\n        return {\"frame_index\": frame_idx, \"outputs\": outputs}\n\n    def remove_object(\n        self,\n        session_id: str,\n        obj_id: int,\n        is_user_action: bool = True,\n    ):\n        \"\"\"Remove an object from tracking.\"\"\"\n        logger.debug(\n            f\"remove object {obj_id} in session {session_id}: {is_user_action=}\"\n        )\n        session = self._get_session(session_id)\n        inference_state = session[\"state\"]\n\n        self.model.remove_object(\n            inference_state=inference_state,\n            obj_id=obj_id,\n            is_user_action=is_user_action,\n        )\n        return {\"is_success\": True}\n\n    def propagate_in_video(\n        self,\n        session_id,\n        propagation_direction,\n        start_frame_idx,\n        max_frame_num_to_track,\n    ):\n        \"\"\"Propagate the added prompts to get grounding results on all video frames.\"\"\"\n        logger.debug(\n            f\"propagate in video in session {session_id}: \"\n            f\"{propagation_direction=}, {start_frame_idx=}, {max_frame_num_to_track=}\"\n        )\n        try:\n            session = self._get_session(session_id)\n            inference_state = session[\"state\"]\n            if propagation_direction not in [\"both\", \"forward\", \"backward\"]:\n                raise ValueError(\n                    f\"invalid propagation direction: {propagation_direction}\"\n                )\n\n            # First doing the forward propagation\n            if propagation_direction in [\"both\", \"forward\"]:\n                for frame_idx, outputs in self.model.propagate_in_video(\n                    inference_state=inference_state,\n                    start_frame_idx=start_frame_idx,\n                    max_frame_num_to_track=max_frame_num_to_track,\n                    reverse=False,\n                ):\n                    yield {\"frame_index\": frame_idx, \"outputs\": outputs}\n            # Then doing the backward propagation (reverse in time)\n            if propagation_direction in [\"both\", \"backward\"]:\n                for frame_idx, outputs in self.model.propagate_in_video(\n                    inference_state=inference_state,\n                    start_frame_idx=start_frame_idx,\n                    max_frame_num_to_track=max_frame_num_to_track,\n                    reverse=True,\n                ):\n                    yield {\"frame_index\": frame_idx, \"outputs\": outputs}\n        finally:\n            # Log upon completion (so that e.g. we can see if two propagations happen in parallel).\n            # Using `finally` here to log even when the tracking is aborted with GeneratorExit.\n            logger.debug(\n                f\"propagation ended in session {session_id}; {self._get_session_stats()}\"\n            )\n\n    def reset_session(self, session_id):\n        \"\"\"Reset the session to its initial state (as when it's initial opened).\"\"\"\n        logger.debug(f\"reset session {session_id}\")\n        session = self._get_session(session_id)\n        inference_state = session[\"state\"]\n        self.model.reset_state(inference_state)\n        return {\"is_success\": True}\n\n    def close_session(self, session_id):\n        \"\"\"\n        Close a session. This method is idempotent and can be called multiple\n        times on the same \"session_id\".\n        \"\"\"\n        session = self._ALL_INFERENCE_STATES.pop(session_id, None)\n        if session is None:\n            logger.warning(\n                f\"cannot close session {session_id} as it does not exist (it might have expired); \"\n                f\"{self._get_session_stats()}\"\n            )\n        else:\n            del session\n            gc.collect()\n            logger.info(f\"removed session {session_id}; {self._get_session_stats()}\")\n        return {\"is_success\": True}\n\n    def _get_session(self, session_id):\n        session = self._ALL_INFERENCE_STATES.get(session_id, None)\n        if session is None:\n            raise RuntimeError(\n                f\"Cannot find session {session_id}; it might have expired\"\n            )\n        return session\n\n    def _get_session_stats(self):\n        \"\"\"Get a statistics string for live sessions and their GPU usage.\"\"\"\n        # print both the session ids and their video frame numbers\n        live_session_strs = [\n            f\"'{session_id}' ({session['state']['num_frames']} frames)\"\n            for session_id, session in self._ALL_INFERENCE_STATES.items()\n        ]\n        session_stats_str = (\n            f\"live sessions: [{', '.join(live_session_strs)}], GPU memory: \"\n            f\"{torch.cuda.memory_allocated() // 1024**2} MiB used and \"\n            f\"{torch.cuda.memory_reserved() // 1024**2} MiB reserved\"\n            f\" (max over time: {torch.cuda.max_memory_allocated() // 1024**2} MiB used \"\n            f\"and {torch.cuda.max_memory_reserved() // 1024**2} MiB reserved)\"\n        )\n        return session_stats_str\n\n    def _get_torch_and_gpu_properties(self):\n        \"\"\"Get a string for PyTorch and GPU properties (for logging and debugging).\"\"\"\n        torch_and_gpu_str = (\n            f\"torch: {torch.__version__} with CUDA arch {torch.cuda.get_arch_list()}, \"\n            f\"GPU device: {torch.cuda.get_device_properties(torch.cuda.current_device())}\"\n        )\n        return torch_and_gpu_str\n\n    def shutdown(self):\n        \"\"\"Shutdown the predictor and clear all sessions.\"\"\"\n        self._ALL_INFERENCE_STATES.clear()\n\n\nclass Sam3VideoPredictorMultiGPU(Sam3VideoPredictor):\n    def __init__(self, *model_args, gpus_to_use=None, **model_kwargs):\n        if gpus_to_use is None:\n            # if not specified, use only the current GPU by default\n            gpus_to_use = [torch.cuda.current_device()]\n\n        IS_MAIN_PROCESS = os.getenv(\"IS_MAIN_PROCESS\", \"1\") == \"1\"\n        if IS_MAIN_PROCESS:\n            gpus_to_use = sorted(set(gpus_to_use))\n            logger.info(f\"using the following GPU IDs: {gpus_to_use}\")\n            assert len(gpus_to_use) > 0 and all(isinstance(i, int) for i in gpus_to_use)\n            assert all(0 <= i < torch.cuda.device_count() for i in gpus_to_use)\n            os.environ[\"MASTER_ADDR\"] = \"localhost\"\n            os.environ[\"MASTER_PORT\"] = f\"{self._find_free_port()}\"\n            os.environ[\"RANK\"] = \"0\"\n            os.environ[\"WORLD_SIZE\"] = f\"{len(gpus_to_use)}\"\n\n        self.gpus_to_use = gpus_to_use\n        self.rank = int(os.environ[\"RANK\"])\n        self.world_size = int(os.environ[\"WORLD_SIZE\"])\n        self.rank_str = f\"rank={self.rank} with world_size={self.world_size}\"\n        self.device = torch.device(f\"cuda:{self.gpus_to_use[self.rank]}\")\n        torch.cuda.set_device(self.device)\n        self.has_shutdown = False\n        if self.rank == 0:\n            logger.info(\"\\n\\n\\n\\t*** START loading model on all ranks ***\\n\\n\")\n\n        logger.info(f\"loading model on {self.rank_str} -- this could take a while ...\")\n        super().__init__(*model_args, **model_kwargs)\n        logger.info(f\"loading model on {self.rank_str} -- DONE locally\")\n\n        if self.world_size > 1 and self.rank == 0:\n            # start the worker processes *after* the model is loaded in the main process\n            # so that the main process can run torch.compile and fill the cache first\n            self._start_worker_processes(*model_args, **model_kwargs)\n            for rank in range(1, self.world_size):\n                self.command_queues[rank].put((\"start_nccl_process_group\", None))\n            self._start_nccl_process_group()\n\n        if self.rank == 0:\n            logger.info(\"\\n\\n\\n\\t*** DONE loading model on all ranks ***\\n\\n\")\n\n    @torch.inference_mode()\n    def handle_request(self, request):\n        \"\"\"Dispatch a request based on its type.\"\"\"\n        if self.has_shutdown:\n            raise RuntimeError(\n                \"cannot handle request after the predictor has shutdown; please create a new predictor\"\n            )\n\n        # when starting a session, we need to create a session id before dispatching\n        # the request to the workers\n        if request[\"type\"] == \"start_session\" and request.get(\"session_id\") is None:\n            request[\"session_id\"] = str(uuid.uuid4())\n        # dispatch the request to all worker processes\n        if self.world_size > 1 and self.rank == 0:\n            for rank in range(1, self.world_size):\n                self.command_queues[rank].put((request, False))\n\n        response = super().handle_request(request)\n\n        if self.world_size > 1:\n            torch.distributed.barrier()  # wait for all ranks to finish\n        return response\n\n    @torch.inference_mode()\n    def handle_stream_request(self, request):\n        \"\"\"Dispatch a stream request based on its type.\"\"\"\n        if self.has_shutdown:\n            raise RuntimeError(\n                \"cannot handle request after the predictor has shutdown; please create a new predictor\"\n            )\n\n        # dispatch the request to all worker processes\n        if self.world_size > 1 and self.rank == 0:\n            for rank in range(1, self.world_size):\n                self.command_queues[rank].put((request, True))\n\n        yield from super().handle_stream_request(request)\n\n        if self.world_size > 1:\n            torch.distributed.barrier()  # wait for all ranks to finish\n\n    def _start_worker_processes(self, *model_args, **model_kwargs):\n        \"\"\"Start worker processes for handling model inference.\"\"\"\n        world_size = self.world_size\n        logger.info(f\"spawning {world_size - 1} worker processes\")\n        # Use \"spawn\" (instead of \"fork\") for different PyTorch or CUDA context\n        mp_ctx = mp.get_context(\"spawn\")\n        self.command_queues = {rank: mp_ctx.Queue() for rank in range(1, world_size)}\n        self.result_queues = {rank: mp_ctx.Queue() for rank in range(1, world_size)}\n        parent_pid = os.getpid()\n        for rank in range(1, world_size):\n            # set the environment variables for each worker process\n            os.environ[\"IS_MAIN_PROCESS\"] = \"0\"  # mark this as a worker process\n            os.environ[\"RANK\"] = f\"{rank}\"\n            worker_process = mp_ctx.Process(\n                target=Sam3VideoPredictorMultiGPU._worker_process_command_loop,\n                args=(\n                    rank,\n                    world_size,\n                    self.command_queues[rank],\n                    self.result_queues[rank],\n                    model_args,\n                    model_kwargs,\n                    self.gpus_to_use,\n                    parent_pid,\n                ),\n                daemon=True,\n            )\n            worker_process.start()\n        # revert the environment variables for the main process\n        os.environ[\"IS_MAIN_PROCESS\"] = \"1\"\n        os.environ[\"RANK\"] = \"0\"\n        # wait for all the worker processes to load the model and collect their PIDs\n        self.worker_pids = {}\n        for rank in range(1, self.world_size):\n            # a large timeout to cover potentially long model loading time due to compilation\n            _, worker_pid = self.result_queues[rank].get(timeout=7200)\n            self.worker_pids[rank] = worker_pid\n        logger.info(f\"spawned {world_size - 1} worker processes\")\n\n    def _start_nccl_process_group(self):\n        rank = int(os.environ[\"RANK\"])\n        world_size = int(os.environ[\"WORLD_SIZE\"])\n        if world_size == 1:\n            return\n\n        logger.debug(f\"starting NCCL process group on {rank=} with {world_size=}\")\n        assert not torch.distributed.is_initialized()\n        # use the \"env://\" init method with environment variables set in start_worker_processes\n        # a short 3-min timeout to quickly detect any synchronization failures\n        timeout_sec = int(os.getenv(\"SAM3_COLLECTIVE_OP_TIMEOUT_SEC\", \"180\"))\n        timeout = datetime.timedelta(seconds=timeout_sec)\n        torch.distributed.init_process_group(\n            backend=\"nccl\",\n            init_method=\"env://\",\n            timeout=timeout,\n            device_id=self.device,\n        )\n        # warm-up the NCCL process group by running a dummy all-reduce\n        tensor = torch.ones(1024, 1024).cuda()\n        torch.distributed.all_reduce(tensor)\n        logger.debug(f\"started NCCL process group on {rank=} with {world_size=}\")\n\n    def _find_free_port(self) -> int:\n        \"\"\"\n        Find a free port (a random free port from 1024 to 65535 will be selected)\n        https://stackoverflow.com/questions/1365265/on-localhost-how-do-i-pick-a-free-port-number)\n        \"\"\"\n        with closing(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as s:\n            s.bind((\"\", 0))\n            s.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\n            return s.getsockname()[1]\n\n    @staticmethod\n    def _worker_process_command_loop(\n        rank,\n        world_size,\n        command_queue,\n        result_queue,\n        model_args,\n        model_kwargs,\n        gpus_to_use,\n        parent_pid,\n    ):\n        \"\"\"\n        The command loop for each worker process. It listens to commands from the main process\n        and executes them using the model.\n        \"\"\"\n        logger.info(f\"starting worker process {rank=} with {world_size=}\")\n        # verify that the environment variables are set correctly\n        assert int(os.environ[\"IS_MAIN_PROCESS\"]) == 0\n        assert int(os.environ[\"RANK\"]) == rank\n        assert int(os.environ[\"WORLD_SIZE\"]) == world_size\n        # load the model in this worker process\n        predictor = Sam3VideoPredictorMultiGPU(\n            *model_args, gpus_to_use=gpus_to_use, **model_kwargs\n        )\n        logger.info(f\"started worker {rank=} with {world_size=}\")\n        # return the worker process id to the main process for bookkeeping\n        worker_pid = os.getpid()\n        result_queue.put((\"load_model\", worker_pid))\n\n        # wait for the command to start the NCCL process group\n        request_type, _ = command_queue.get(timeout=7200)\n        assert request_type == \"start_nccl_process_group\"\n        predictor._start_nccl_process_group()\n\n        # keep listening to commands from the main process\n        while True:\n            try:\n                request, is_stream_request = command_queue.get(timeout=5.0)\n                if request == \"shutdown\":\n                    logger.info(f\"worker {rank=} shutting down\")\n                    torch.distributed.destroy_process_group()\n                    result_queue.put((\"shutdown\", True))  # acknowledge the shutdown\n                    sys.exit(0)\n\n                logger.debug(f\"worker {rank=} received request {request['type']=}\")\n                if is_stream_request:\n                    for _ in predictor.handle_stream_request(request):\n                        pass  # handle stream requests in a generator fashion\n                else:\n                    predictor.handle_request(request)\n            except queue.Empty:\n                # Usually Python's multiprocessing module will shutdown all the daemon worker\n                # processes when the main process exits gracefully. However, the user may kill\n                # the main process using SIGKILL and thereby leaving no chance for the main process\n                # to clean up its daemon child processes. So here we manually check whether the\n                # parent process still exists (every 5 sec as in `command_queue.get` timeout).\n                if not psutil.pid_exists(parent_pid):\n                    logger.info(\n                        f\"stopping worker {rank=} as its parent process has exited\"\n                    )\n                    sys.exit(1)\n            except Exception as e:\n                logger.error(f\"worker {rank=} exception: {e}\", exc_info=True)\n\n    def shutdown(self):\n        \"\"\"Shutdown all worker processes.\"\"\"\n        if self.rank == 0 and self.world_size > 1:\n            logger.info(f\"shutting down {self.world_size - 1} worker processes\")\n            for rank in range(1, self.world_size):\n                self.command_queues[rank].put((\"shutdown\", False))\n            torch.distributed.destroy_process_group()\n            for rank in range(1, self.world_size):\n                self.result_queues[rank].get()  # wait for the worker to acknowledge\n            logger.info(f\"shut down {self.world_size - 1} worker processes\")\n        self.has_shutdown = True\n\n        super().shutdown()\n"
  },
  {
    "path": "sam3/model/text_encoder_ve.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n\nfrom collections import OrderedDict\nfrom typing import Callable, List, Optional, Tuple, Union\n\nimport torch\nimport torch.nn as nn\nfrom torch.utils.checkpoint import checkpoint\n\nfrom .model_misc import LayerScale\n\n\nclass ResidualAttentionBlock(nn.Module):\n    def __init__(\n        self,\n        d_model: int,\n        n_head: int,\n        mlp_ratio: float = 4.0,\n        ls_init_value: Optional[float] = None,\n        act_layer: Callable[[], nn.Module] = nn.GELU,\n        norm_layer: Callable[[int], nn.Module] = nn.LayerNorm,\n    ):\n        super().__init__()\n        # Attention\n        self.attn = nn.MultiheadAttention(d_model, n_head, batch_first=True)\n\n        # LayerNorm, LayerScale\n        self.ln_1 = norm_layer(d_model)\n        self.ln_2 = norm_layer(d_model)\n\n        self.ls_1 = (\n            LayerScale(d_model, ls_init_value)\n            if ls_init_value is not None\n            else nn.Identity()\n        )\n        self.ls_2 = (\n            LayerScale(d_model, ls_init_value)\n            if ls_init_value is not None\n            else nn.Identity()\n        )\n\n        # MLP\n        mlp_width = int(d_model * mlp_ratio)\n        self.mlp = nn.Sequential(\n            OrderedDict(\n                [\n                    (\"c_fc\", nn.Linear(d_model, mlp_width)),\n                    (\"gelu\", act_layer()),\n                    (\"c_proj\", nn.Linear(mlp_width, d_model)),\n                ]\n            )\n        )\n\n    def attention(\n        self,\n        q_x: torch.Tensor,\n        k_x: Optional[torch.Tensor] = None,\n        v_x: Optional[torch.Tensor] = None,\n        attn_mask: Optional[torch.Tensor] = None,\n    ) -> torch.Tensor:\n        k_x = k_x if k_x is not None else q_x\n        v_x = v_x if v_x is not None else q_x\n        if attn_mask is not None:\n            # Leave boolean masks as is\n            if not attn_mask.dtype == torch.bool:\n                attn_mask = attn_mask.to(q_x.dtype)\n\n        return self.attn(q_x, k_x, v_x, need_weights=False, attn_mask=attn_mask)[0]\n\n    def forward(\n        self,\n        q_x: torch.Tensor,\n        k_x: Optional[torch.Tensor] = None,\n        v_x: Optional[torch.Tensor] = None,\n        attn_mask: Optional[torch.Tensor] = None,\n    ) -> torch.Tensor:\n        k_x = (\n            self.ln_1_kv(k_x) if hasattr(self, \"ln_1_kv\") and k_x is not None else None\n        )\n        v_x = (\n            self.ln_1_kv(v_x) if hasattr(self, \"ln_1_kv\") and v_x is not None else None\n        )\n        x = q_x + self.ls_1(\n            self.attention(q_x=self.ln_1(q_x), k_x=k_x, v_x=v_x, attn_mask=attn_mask)\n        )\n        x = x + self.ls_2(self.mlp(self.ln_2(x)))\n        return x\n\n\nclass Transformer(nn.Module):\n    def __init__(\n        self,\n        width: int,\n        layers: int,\n        heads: int,\n        mlp_ratio: float = 4.0,\n        ls_init_value: Optional[float] = None,\n        act_layer: Callable[[], nn.Module] = nn.GELU,\n        norm_layer: Callable[[int], nn.Module] = nn.LayerNorm,\n        compile_mode: Optional[str] = None,\n        use_act_checkpoint: bool = False,\n    ):\n        super().__init__()\n        self.width = width\n        self.layers = layers\n        self.grad_checkpointing = use_act_checkpoint\n        self.resblocks = nn.ModuleList(\n            [\n                ResidualAttentionBlock(\n                    width,\n                    heads,\n                    mlp_ratio,\n                    ls_init_value=ls_init_value,\n                    act_layer=act_layer,\n                    norm_layer=norm_layer,\n                )\n                for _ in range(layers)\n            ]\n        )\n\n        if compile_mode is not None:\n            self.forward = torch.compile(\n                self.forward, mode=compile_mode, fullgraph=True\n            )\n            if self.grad_checkpointing:\n                torch._dynamo.config.optimize_ddp = False\n\n    def forward(\n        self,\n        x: torch.Tensor,\n        attn_mask: Optional[torch.Tensor] = None,\n    ) -> torch.Tensor:\n        for _, r in enumerate(self.resblocks):\n            if (\n                self.grad_checkpointing\n                and not torch.jit.is_scripting()\n                and self.training\n            ):\n                x = checkpoint(r, x, None, None, attn_mask, use_reentrant=False)\n            else:\n                x = r(\n                    x,\n                    attn_mask=attn_mask,\n                )\n        return x\n\n\ndef text_global_pool(\n    x: torch.Tensor, text: Optional[torch.Tensor] = None, pool_type: str = \"argmax\"\n) -> Tuple[torch.Tensor, torch.Tensor]:\n    if pool_type == \"first\":\n        pooled, tokens = x[:, 0], x[:, 1:]\n    elif pool_type == \"last\":\n        pooled, tokens = x[:, -1], x[:, :-1]\n    elif pool_type == \"argmax\":\n        # take features from the eot embedding (eot_token is the highest number in each sequence)\n        assert text is not None\n        pooled, tokens = x[torch.arange(x.shape[0]), text.argmax(dim=-1)], x\n    else:\n        pooled = tokens = x\n    return pooled, tokens\n\n\nclass TextTransformer(nn.Module):\n    def __init__(\n        self,\n        context_length: int = 77,\n        vocab_size: int = 49408,\n        width: int = 512,\n        heads: int = 8,\n        layers: int = 12,\n        mlp_ratio: float = 4.0,\n        ls_init_value: Optional[float] = None,\n        output_dim: int = 512,\n        no_causal_mask: bool = False,\n        pool_type: str = \"none\",  # no pooling\n        proj_bias: bool = False,\n        act_layer: Callable = nn.GELU,\n        norm_layer: Callable = nn.LayerNorm,\n        output_tokens: bool = False,\n        use_ln_post: bool = True,\n        compile_mode: Optional[str] = None,\n        use_act_checkpoint: bool = False,\n    ):\n        super().__init__()\n        assert pool_type in (\"first\", \"last\", \"argmax\", \"none\")\n        self.output_tokens = output_tokens\n        self.num_pos = self.context_length = context_length\n        self.vocab_size = vocab_size\n        self.width = width\n        self.output_dim = output_dim\n        self.heads = heads\n        self.pool_type = pool_type\n\n        self.token_embedding = nn.Embedding(self.vocab_size, width)\n        self.positional_embedding = nn.Parameter(torch.empty(self.num_pos, width))\n        self.transformer = Transformer(\n            width=width,\n            layers=layers,\n            heads=heads,\n            mlp_ratio=mlp_ratio,\n            ls_init_value=ls_init_value,\n            act_layer=act_layer,\n            norm_layer=norm_layer,\n            compile_mode=compile_mode,\n            use_act_checkpoint=use_act_checkpoint,\n        )\n        self.ln_final = norm_layer(width) if use_ln_post else nn.Identity()\n        if no_causal_mask:\n            self.attn_mask = None\n        else:\n            self.register_buffer(\n                \"attn_mask\", self.build_causal_mask(), persistent=False\n            )\n        if proj_bias:\n            self.text_projection = nn.Linear(width, output_dim)\n        else:\n            self.text_projection = nn.Parameter(torch.empty(width, output_dim))\n\n    def build_causal_mask(self) -> torch.Tensor:\n        # lazily create causal attention mask, with full attention between the tokens\n        # pytorch uses additive attention mask; fill with -inf\n        mask = torch.empty(self.num_pos, self.num_pos)\n        mask.fill_(float(\"-inf\"))\n        mask.triu_(1)  # zero out the lower diagonal\n        return mask\n\n    def forward(\n        self, text: torch.Tensor\n    ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:\n        seq_len = text.shape[1]\n        x = self.token_embedding(text)  # [batch_size, n_ctx, d_model]\n\n        attn_mask = self.attn_mask\n        if attn_mask is not None:\n            attn_mask = attn_mask[:seq_len, :seq_len]\n\n        x = x + self.positional_embedding[:seq_len]\n        x = self.transformer(x, attn_mask=attn_mask)\n\n        x = self.ln_final(x)\n        pooled, tokens = text_global_pool(x, text, pool_type=self.pool_type)\n        if self.text_projection is not None:\n            if isinstance(self.text_projection, nn.Linear):\n                pooled = self.text_projection(pooled)\n            else:\n                pooled = pooled @ self.text_projection\n        if self.output_tokens:\n            return pooled, tokens\n        return pooled\n\n\nclass VETextEncoder(nn.Module):\n    def __init__(\n        self,\n        d_model: int,\n        tokenizer: Callable,\n        width: int = 1024,\n        heads: int = 16,\n        layers: int = 24,\n        context_length: int = 32,\n        vocab_size: int = 49408,\n        use_ln_post: bool = True,\n        compile_mode: Optional[str] = None,\n        use_act_checkpoint: bool = True,\n    ):\n        super().__init__()\n        self.context_length = context_length\n        self.use_ln_post = use_ln_post\n        self.tokenizer = tokenizer\n\n        self.encoder = TextTransformer(\n            context_length=self.context_length,\n            vocab_size=vocab_size,\n            width=width,\n            heads=heads,\n            layers=layers,\n            # we want the tokens, not just the pooled output\n            output_tokens=True,\n            use_ln_post=use_ln_post,\n            compile_mode=compile_mode,\n            use_act_checkpoint=use_act_checkpoint,\n        )\n        self.resizer = nn.Linear(self.encoder.width, d_model)\n\n    def forward(\n        self,\n        text: Union[List[str], Tuple[torch.Tensor, torch.Tensor, dict]],\n        input_boxes: Optional[List] = None,\n        device: torch.device = None,\n    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:\n        if isinstance(text[0], str):\n            # no use case for this\n            assert input_boxes is None or len(input_boxes) == 0, \"not supported\"\n\n            # Encode the text\n            tokenized = self.tokenizer(text, context_length=self.context_length).to(\n                device\n            )  # [b, seq_len]\n            text_attention_mask = (tokenized != 0).bool()\n\n            # manually embed the tokens\n            inputs_embeds = self.encoder.token_embedding(\n                tokenized\n            )  # [b, seq_len, d=1024]\n            _, text_memory = self.encoder(tokenized)  # [b, seq_len, d=1024]\n\n            assert text_memory.shape[1] == inputs_embeds.shape[1]\n            # Invert attention mask because its the opposite in pytorch transformer\n            text_attention_mask = text_attention_mask.ne(1)\n            # Transpose memory because pytorch's attention expects sequence first\n            text_memory = text_memory.transpose(0, 1)\n            # Resize the encoder hidden states to be of the same d_model as the decoder\n            text_memory_resized = self.resizer(text_memory)\n        else:\n            # The text is already encoded, use as is.\n            text_attention_mask, text_memory_resized, tokenized = text\n            inputs_embeds = tokenized[\"inputs_embeds\"]\n            assert input_boxes is None or len(input_boxes) == 0, (\n                \"Can't replace boxes in text if it's already encoded\"\n            )\n\n        # Note that the input_embeds are returned in pytorch's convention (sequence first)\n        return (\n            text_attention_mask,\n            text_memory_resized,\n            inputs_embeds.transpose(0, 1),\n        )\n"
  },
  {
    "path": "sam3/model/tokenizer_ve.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n\n\"\"\"\nText Tokenizer.\n\nCopied and lightly adapted from VE repo, which in turn copied\nfrom open_clip and openAI CLIP.\n\"\"\"\n\nimport gzip\nimport html\nimport io\nimport os\nimport string\nfrom functools import lru_cache\nfrom typing import List, Optional, Union\n\nimport ftfy\nimport regex as re\nimport torch\nfrom iopath.common.file_io import g_pathmgr\n\n\n# https://stackoverflow.com/q/62691279\nos.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"\nDEFAULT_CONTEXT_LENGTH = 77\n\n\n@lru_cache()\ndef bytes_to_unicode():\n    \"\"\"\n    Returns list of utf-8 byte and a corresponding list of unicode strings.\n    The reversible bpe codes work on unicode strings.\n    This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.\n    When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.\n    This is a significant percentage of your normal, say, 32K bpe vocab.\n    To avoid that, we want lookup tables between utf-8 bytes and unicode strings.\n    And avoids mapping to whitespace/control characters the bpe code barfs on.\n    \"\"\"\n    bs = (\n        list(range(ord(\"!\"), ord(\"~\") + 1))\n        + list(range(ord(\"¡\"), ord(\"¬\") + 1))\n        + list(range(ord(\"®\"), ord(\"ÿ\") + 1))\n    )\n    cs = bs[:]\n    n = 0\n    for b in range(2**8):\n        if b not in bs:\n            bs.append(b)\n            cs.append(2**8 + n)\n            n += 1\n    cs = [chr(n) for n in cs]\n    return dict(zip(bs, cs))\n\n\ndef get_pairs(word):\n    \"\"\"Return set of symbol pairs in a word.\n    Word is represented as tuple of symbols (symbols being variable-length strings).\n    \"\"\"\n    pairs = set()\n    prev_char = word[0]\n    for char in word[1:]:\n        pairs.add((prev_char, char))\n        prev_char = char\n    return pairs\n\n\ndef basic_clean(text):\n    text = ftfy.fix_text(text)\n    text = html.unescape(html.unescape(text))\n    return text.strip()\n\n\ndef whitespace_clean(text):\n    text = re.sub(r\"\\s+\", \" \", text)\n    text = text.strip()\n    return text\n\n\ndef _clean_canonicalize(x):\n    # basic, remove whitespace, remove punctuation, lower case\n    return canonicalize_text(basic_clean(x))\n\n\ndef _clean_lower(x):\n    # basic, remove whitespace, lower case\n    return whitespace_clean(basic_clean(x)).lower()\n\n\ndef _clean_whitespace(x):\n    # basic, remove whitespace\n    return whitespace_clean(basic_clean(x))\n\n\ndef get_clean_fn(type: str):\n    if type == \"canonicalize\":\n        return _clean_canonicalize\n    elif type == \"lower\":\n        return _clean_lower\n    elif type == \"whitespace\":\n        return _clean_whitespace\n    else:\n        assert False, f\"Invalid clean function ({type}).\"\n\n\ndef canonicalize_text(text, *, keep_punctuation_exact_string=None):\n    \"\"\"Returns canonicalized `text` (lowercase and punctuation removed).\n    From: https://github.com/google-research/big_vision/blob/53f18caf27a9419231bbf08d3388b07671616d3d/big_vision/evaluators/proj/image_text/prompt_engineering.py#L94\n    Args:\n      text: string to be canonicalized.\n      keep_punctuation_exact_string: If provided, then this exact string kept.\n        For example providing '{}' will keep any occurrences of '{}' (but will\n        still remove '{' and '}' that appear separately).\n    \"\"\"\n    text = text.replace(\"_\", \" \")\n    if keep_punctuation_exact_string:\n        text = keep_punctuation_exact_string.join(\n            part.translate(str.maketrans(\"\", \"\", string.punctuation))\n            for part in text.split(keep_punctuation_exact_string)\n        )\n    else:\n        text = text.translate(str.maketrans(\"\", \"\", string.punctuation))\n    text = text.lower()\n    text = re.sub(r\"\\s+\", \" \", text)\n    return text.strip()\n\n\nclass SimpleTokenizer(object):\n    def __init__(\n        self,\n        bpe_path: Union[str, os.PathLike],\n        additional_special_tokens: Optional[List[str]] = None,\n        context_length: Optional[int] = DEFAULT_CONTEXT_LENGTH,\n        clean: str = \"lower\",\n    ):\n        self.byte_encoder = bytes_to_unicode()\n        self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}\n        with g_pathmgr.open(bpe_path, \"rb\") as fh:\n            bpe_bytes = io.BytesIO(fh.read())\n            merges = gzip.open(bpe_bytes).read().decode(\"utf-8\").split(\"\\n\")\n        # merges = gzip.open(bpe_path).read().decode(\"utf-8\").split(\"\\n\")\n        merges = merges[1 : 49152 - 256 - 2 + 1]\n        merges = [tuple(merge.split()) for merge in merges]\n        vocab = list(bytes_to_unicode().values())\n        vocab = vocab + [v + \"</w>\" for v in vocab]\n        for merge in merges:\n            vocab.append(\"\".join(merge))\n        special_tokens = [\"<start_of_text>\", \"<end_of_text>\"]\n        if additional_special_tokens:\n            special_tokens += additional_special_tokens\n        vocab.extend(special_tokens)\n        self.encoder = dict(zip(vocab, range(len(vocab))))\n        self.decoder = {v: k for k, v in self.encoder.items()}\n        self.bpe_ranks = dict(zip(merges, range(len(merges))))\n        self.cache = {t: t for t in special_tokens}\n        special = \"|\".join(special_tokens)\n        self.pat = re.compile(\n            special + r\"\"\"|'s|'t|'re|'ve|'m|'ll|'d|[\\p{L}]+|[\\p{N}]|[^\\s\\p{L}\\p{N}]+\"\"\",\n            re.IGNORECASE,\n        )\n        self.vocab_size = len(self.encoder)\n        self.all_special_ids = [self.encoder[t] for t in special_tokens]\n        self.sot_token_id = self.all_special_ids[0]\n        self.eot_token_id = self.all_special_ids[1]\n        self.context_length = context_length\n        self.clean_fn = get_clean_fn(clean)\n\n    def bpe(self, token):\n        if token in self.cache:\n            return self.cache[token]\n        word = tuple(token[:-1]) + (token[-1] + \"</w>\",)\n        pairs = get_pairs(word)\n        if not pairs:\n            return token + \"</w>\"\n        while True:\n            bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float(\"inf\")))\n            if bigram not in self.bpe_ranks:\n                break\n            first, second = bigram\n            new_word = []\n            i = 0\n            while i < len(word):\n                try:\n                    j = word.index(first, i)\n                    new_word.extend(word[i:j])\n                    i = j\n                except:\n                    new_word.extend(word[i:])\n                    break\n                if word[i] == first and i < len(word) - 1 and word[i + 1] == second:\n                    new_word.append(first + second)\n                    i += 2\n                else:\n                    new_word.append(word[i])\n                    i += 1\n            new_word = tuple(new_word)\n            word = new_word\n            if len(word) == 1:\n                break\n            else:\n                pairs = get_pairs(word)\n        word = \" \".join(word)\n        self.cache[token] = word\n        return word\n\n    def encode(self, text):\n        bpe_tokens = []\n        text = self.clean_fn(text)\n        for token in re.findall(self.pat, text):\n            token = \"\".join(self.byte_encoder[b] for b in token.encode(\"utf-8\"))\n            bpe_tokens.extend(\n                self.encoder[bpe_token] for bpe_token in self.bpe(token).split(\" \")\n            )\n        return bpe_tokens\n\n    def decode(self, tokens):\n        text = \"\".join([self.decoder[token] for token in tokens])\n        text = (\n            bytearray([self.byte_decoder[c] for c in text])\n            .decode(\"utf-8\", errors=\"replace\")\n            .replace(\"</w>\", \" \")\n        )\n        return text\n\n    def __call__(\n        self, texts: Union[str, List[str]], context_length: Optional[int] = None\n    ) -> torch.LongTensor:\n        \"\"\"Returns the tokenized representation of given input string(s)\n        Parameters\n        ----------\n        texts : Union[str, List[str]]\n            An input string or a list of input strings to tokenize\n        context_length : int\n            The context length to use; all CLIP models use 77 as the context length\n        Returns\n        -------\n        A two-dimensional tensor containing the resulting tokens, shape = [number of input strings, context_length]\n        \"\"\"\n        if isinstance(texts, str):\n            texts = [texts]\n        context_length = context_length or self.context_length\n        assert context_length, \"Please set a valid context length\"\n        all_tokens = [\n            [self.sot_token_id] + self.encode(text) + [self.eot_token_id]\n            for text in texts\n        ]\n        result = torch.zeros(len(all_tokens), context_length, dtype=torch.long)\n        for i, tokens in enumerate(all_tokens):\n            if len(tokens) > context_length:\n                tokens = tokens[:context_length]  # Truncate\n                tokens[-1] = self.eot_token_id\n            result[i, : len(tokens)] = torch.tensor(tokens)\n        return result\n"
  },
  {
    "path": "sam3/model/utils/__init__.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n# All rights reserved.\n\n# pyre-unsafe\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n"
  },
  {
    "path": "sam3/model/utils/misc.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n\nfrom collections import defaultdict\nfrom dataclasses import fields, is_dataclass\nfrom typing import Any, Mapping, Protocol, runtime_checkable\n\nimport torch\n\n\ndef _is_named_tuple(x) -> bool:\n    return isinstance(x, tuple) and hasattr(x, \"_asdict\") and hasattr(x, \"_fields\")\n\n\n@runtime_checkable\nclass _CopyableData(Protocol):\n    def to(self, device: torch.device, *args: Any, **kwargs: Any):\n        \"\"\"Copy data to the specified device\"\"\"\n        ...\n\n\ndef copy_data_to_device(data, device: torch.device, *args: Any, **kwargs: Any):\n    \"\"\"Function that recursively copies data to a torch.device.\n\n    Args:\n        data: The data to copy to device\n        device: The device to which the data should be copied\n        args: positional arguments that will be passed to the `to` call\n        kwargs: keyword arguments that will be passed to the `to` call\n\n    Returns:\n        The data on the correct device\n    \"\"\"\n\n    if _is_named_tuple(data):\n        return type(data)(\n            **copy_data_to_device(data._asdict(), device, *args, **kwargs)\n        )\n    elif isinstance(data, (list, tuple)):\n        return type(data)(copy_data_to_device(e, device, *args, **kwargs) for e in data)\n    elif isinstance(data, defaultdict):\n        return type(data)(\n            data.default_factory,\n            {\n                k: copy_data_to_device(v, device, *args, **kwargs)\n                for k, v in data.items()\n            },\n        )\n    elif isinstance(data, Mapping):\n        return type(data)(\n            {\n                k: copy_data_to_device(v, device, *args, **kwargs)\n                for k, v in data.items()\n            }\n        )\n    elif is_dataclass(data) and not isinstance(data, type):\n        new_data_class = type(data)(\n            **{\n                field.name: copy_data_to_device(\n                    getattr(data, field.name), device, *args, **kwargs\n                )\n                for field in fields(data)\n                if field.init\n            }\n        )\n        for field in fields(data):\n            if not field.init:\n                setattr(\n                    new_data_class,\n                    field.name,\n                    copy_data_to_device(\n                        getattr(data, field.name), device, *args, **kwargs\n                    ),\n                )\n        return new_data_class\n    elif isinstance(data, _CopyableData):\n        return data.to(device, *args, **kwargs)\n    return data\n"
  },
  {
    "path": "sam3/model/utils/sam1_utils.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n# All rights reserved.\n\n# pyre-unsafe\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport warnings\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torchvision.transforms import Normalize, Resize, ToTensor\n\n\n# Adapted from https://github.com/facebookresearch/sam2/blob/main/sam2/utils/transforms.py\nclass SAM2Transforms(nn.Module):\n    def __init__(\n        self, resolution, mask_threshold, max_hole_area=0.0, max_sprinkle_area=0.0\n    ):\n        \"\"\"\n        Transforms for SAM2.\n        \"\"\"\n        super().__init__()\n        self.resolution = resolution\n        self.mask_threshold = mask_threshold\n        self.max_hole_area = max_hole_area\n        self.max_sprinkle_area = max_sprinkle_area\n        self.mean = [0.5, 0.5, 0.5]\n        self.std = [0.5, 0.5, 0.5]\n        self.to_tensor = ToTensor()\n        self.transforms = torch.jit.script(\n            nn.Sequential(\n                Resize((self.resolution, self.resolution)),\n                Normalize(self.mean, self.std),\n            )\n        )\n\n    def __call__(self, x):\n        x = self.to_tensor(x)\n        return self.transforms(x)\n\n    def forward_batch(self, img_list):\n        img_batch = [self.transforms(self.to_tensor(img)) for img in img_list]\n        img_batch = torch.stack(img_batch, dim=0)\n        return img_batch\n\n    def transform_coords(\n        self, coords: torch.Tensor, normalize=False, orig_hw=None\n    ) -> torch.Tensor:\n        \"\"\"\n        Expects a torch tensor with length 2 in the last dimension. The coordinates can be in absolute image or normalized coordinates,\n        If the coords are in absolute image coordinates, normalize should be set to True and original image size is required.\n\n        Returns\n            Un-normalized coordinates in the range of [0, 1] which is expected by the SAM2 model.\n        \"\"\"\n        if normalize:\n            assert orig_hw is not None\n            h, w = orig_hw\n            coords = coords.clone()\n            coords[..., 0] = coords[..., 0] / w\n            coords[..., 1] = coords[..., 1] / h\n\n        coords = coords * self.resolution  # unnormalize coords\n        return coords\n\n    def transform_boxes(\n        self, boxes: torch.Tensor, normalize=False, orig_hw=None\n    ) -> torch.Tensor:\n        \"\"\"\n        Expects a tensor of shape Bx4. The coordinates can be in absolute image or normalized coordinates,\n        if the coords are in absolute image coordinates, normalize should be set to True and original image size is required.\n        \"\"\"\n        boxes = self.transform_coords(boxes.reshape(-1, 2, 2), normalize, orig_hw)\n        return boxes\n\n    def postprocess_masks(self, masks: torch.Tensor, orig_hw) -> torch.Tensor:\n        \"\"\"\n        Perform PostProcessing on output masks.\n        \"\"\"\n        masks = masks.float()\n        input_masks = masks\n        mask_flat = masks.flatten(0, 1).unsqueeze(1)  # flatten as 1-channel image\n        try:\n            from sam3.perflib.connected_components import connected_components\n\n            if self.max_hole_area > 0:\n                # Holes are those connected components in background with area <= self.fill_hole_area\n                # (background regions are those with mask scores <= self.mask_threshold)\n                labels, areas = connected_components(\n                    (mask_flat <= self.mask_threshold).to(torch.uint8)\n                )\n                is_hole = (labels > 0) & (areas <= self.max_hole_area)\n                is_hole = is_hole.reshape_as(masks)\n                # We fill holes with a small positive mask score (10.0) to change them to foreground.\n                masks = torch.where(is_hole, self.mask_threshold + 10.0, masks)\n\n            if self.max_sprinkle_area > 0:\n                labels, areas = connected_components(\n                    (mask_flat > self.mask_threshold).to(torch.uint8)\n                )\n                is_hole = (labels > 0) & (areas <= self.max_sprinkle_area)\n                is_hole = is_hole.reshape_as(masks)\n                # We fill holes with negative mask score (-10.0) to change them to background.\n                masks = torch.where(is_hole, self.mask_threshold - 10.0, masks)\n        except Exception as e:\n            # Skip the post-processing step if the CUDA kernel fails\n            warnings.warn(\n                f\"{e}\\n\\nSkipping the post-processing step due to the error above. You can \"\n                \"still use SAM 3 and it's OK to ignore the error above, although some post-processing \"\n                \"functionality may be limited (which doesn't affect the results in most cases; see \"\n                \"https://github.com/facebookresearch/sam3/blob/main/INSTALL.md).\",\n                category=UserWarning,\n                stacklevel=2,\n            )\n            masks = input_masks\n\n        masks = F.interpolate(masks, orig_hw, mode=\"bilinear\", align_corners=False)\n        return masks\n"
  },
  {
    "path": "sam3/model/utils/sam2_utils.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n# All rights reserved.\n\n# pyre-unsafe\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport os\nfrom threading import Thread\n\nimport numpy as np\nimport torch\nfrom PIL import Image\nfrom tqdm import tqdm\n\n\ndef _load_img_as_tensor(img_path, image_size):\n    img_pil = Image.open(img_path)\n    img_np = np.array(img_pil.convert(\"RGB\").resize((image_size, image_size)))\n    if img_np.dtype == np.uint8:  # np.uint8 is expected for JPEG images\n        img_np = img_np / 255.0\n    else:\n        raise RuntimeError(f\"Unknown image dtype: {img_np.dtype} on {img_path}\")\n    img = torch.from_numpy(img_np).permute(2, 0, 1)\n    video_width, video_height = img_pil.size  # the original video size\n    return img, video_height, video_width\n\n\nclass AsyncVideoFrameLoader:\n    \"\"\"\n    A list of video frames to be load asynchronously without blocking session start.\n    \"\"\"\n\n    def __init__(\n        self,\n        img_paths,\n        image_size,\n        offload_video_to_cpu,\n        img_mean,\n        img_std,\n        compute_device,\n    ):\n        self.img_paths = img_paths\n        self.image_size = image_size\n        self.offload_video_to_cpu = offload_video_to_cpu\n        self.img_mean = img_mean\n        self.img_std = img_std\n        # items in `self.images` will be loaded asynchronously\n        self.images = [None] * len(img_paths)\n        # catch and raise any exceptions in the async loading thread\n        self.exception = None\n        # video_height and video_width be filled when loading the first image\n        self.video_height = None\n        self.video_width = None\n        self.compute_device = compute_device\n\n        # load the first frame to fill video_height and video_width and also\n        # to cache it (since it's most likely where the user will click)\n        self.__getitem__(0)\n\n        # load the rest of frames asynchronously without blocking the session start\n        def _load_frames():\n            try:\n                for n in tqdm(range(len(self.images)), desc=\"frame loading (JPEG)\"):\n                    self.__getitem__(n)\n            except Exception as e:\n                self.exception = e\n\n        self.thread = Thread(target=_load_frames, daemon=True)\n        self.thread.start()\n\n    def __getitem__(self, index):\n        if self.exception is not None:\n            raise RuntimeError(\"Failure in frame loading thread\") from self.exception\n\n        img = self.images[index]\n        if img is not None:\n            return img\n\n        img, video_height, video_width = _load_img_as_tensor(\n            self.img_paths[index], self.image_size\n        )\n        self.video_height = video_height\n        self.video_width = video_width\n        # normalize by mean and std\n        img -= self.img_mean\n        img /= self.img_std\n        if not self.offload_video_to_cpu:\n            img = img.to(self.compute_device, non_blocking=True)\n        self.images[index] = img\n        return img\n\n    def __len__(self):\n        return len(self.images)\n\n\ndef load_video_frames(\n    video_path,\n    image_size,\n    offload_video_to_cpu,\n    img_mean=(0.5, 0.5, 0.5),\n    img_std=(0.5, 0.5, 0.5),\n    async_loading_frames=False,\n    compute_device=torch.device(\"cuda\"),\n):\n    \"\"\"\n    Load the video frames from video_path. The frames are resized to image_size as in\n    the model and are loaded to GPU if offload_video_to_cpu=False. This is used by the demo.\n    \"\"\"\n    is_bytes = isinstance(video_path, bytes)\n    is_str = isinstance(video_path, str)\n    is_mp4_path = is_str and os.path.splitext(video_path)[-1] in [\".mp4\", \".MP4\"]\n    if is_bytes or is_mp4_path:\n        return load_video_frames_from_video_file(\n            video_path=video_path,\n            image_size=image_size,\n            offload_video_to_cpu=offload_video_to_cpu,\n            img_mean=img_mean,\n            img_std=img_std,\n            compute_device=compute_device,\n        )\n    elif is_str and os.path.isdir(video_path):\n        return load_video_frames_from_jpg_images(\n            video_path=video_path,\n            image_size=image_size,\n            offload_video_to_cpu=offload_video_to_cpu,\n            img_mean=img_mean,\n            img_std=img_std,\n            async_loading_frames=async_loading_frames,\n            compute_device=compute_device,\n        )\n    else:\n        raise NotImplementedError(\n            \"Only MP4 video and JPEG folder are supported at this moment\"\n        )\n\n\ndef load_video_frames_from_jpg_images(\n    video_path,\n    image_size,\n    offload_video_to_cpu,\n    img_mean=(0.5, 0.5, 0.5),\n    img_std=(0.5, 0.5, 0.5),\n    async_loading_frames=False,\n    compute_device=torch.device(\"cuda\"),\n):\n    \"\"\"\n    Load the video frames from a directory of JPEG files (\"<frame_index>.jpg\" format).\n\n    The frames are resized to image_size x image_size and are loaded to GPU if\n    `offload_video_to_cpu` is `False` and to CPU if `offload_video_to_cpu` is `True`.\n\n    You can load a frame asynchronously by setting `async_loading_frames` to `True`.\n    \"\"\"\n    if isinstance(video_path, str) and os.path.isdir(video_path):\n        jpg_folder = video_path\n    else:\n        raise NotImplementedError(\n            \"Only JPEG frames are supported at this moment. For video files, you may use \"\n            \"ffmpeg (https://ffmpeg.org/) to extract frames into a folder of JPEG files, such as \\n\"\n            \"```\\n\"\n            \"ffmpeg -i <your_video>.mp4 -q:v 2 -start_number 0 <output_dir>/'%05d.jpg'\\n\"\n            \"```\\n\"\n            \"where `-q:v` generates high-quality JPEG frames and `-start_number 0` asks \"\n            \"ffmpeg to start the JPEG file from 00000.jpg.\"\n        )\n\n    frame_names = [\n        p\n        for p in os.listdir(jpg_folder)\n        if os.path.splitext(p)[-1] in [\".jpg\", \".jpeg\", \".JPG\", \".JPEG\"]\n    ]\n    frame_names.sort(key=lambda p: int(os.path.splitext(p)[0]))\n    num_frames = len(frame_names)\n    if num_frames == 0:\n        raise RuntimeError(f\"no images found in {jpg_folder}\")\n    img_paths = [os.path.join(jpg_folder, frame_name) for frame_name in frame_names]\n    img_mean = torch.tensor(img_mean, dtype=torch.float32)[:, None, None]\n    img_std = torch.tensor(img_std, dtype=torch.float32)[:, None, None]\n\n    if async_loading_frames:\n        lazy_images = AsyncVideoFrameLoader(\n            img_paths,\n            image_size,\n            offload_video_to_cpu,\n            img_mean,\n            img_std,\n            compute_device,\n        )\n        return lazy_images, lazy_images.video_height, lazy_images.video_width\n\n    images = torch.zeros(num_frames, 3, image_size, image_size, dtype=torch.float32)\n    for n, img_path in enumerate(tqdm(img_paths, desc=\"frame loading (JPEG)\")):\n        images[n], video_height, video_width = _load_img_as_tensor(img_path, image_size)\n    if not offload_video_to_cpu:\n        images = images.to(compute_device)\n        img_mean = img_mean.to(compute_device)\n        img_std = img_std.to(compute_device)\n    # normalize by mean and std\n    images -= img_mean\n    images /= img_std\n    return images, video_height, video_width\n\n\ndef load_video_frames_from_video_file(\n    video_path,\n    image_size,\n    offload_video_to_cpu,\n    img_mean=(0.5, 0.5, 0.5),\n    img_std=(0.5, 0.5, 0.5),\n    compute_device=torch.device(\"cuda\"),\n):\n    \"\"\"Load the video frames from a video file.\"\"\"\n    import decord\n\n    img_mean = torch.tensor(img_mean, dtype=torch.float32)[:, None, None]\n    img_std = torch.tensor(img_std, dtype=torch.float32)[:, None, None]\n    # Get the original video height and width\n    decord.bridge.set_bridge(\"torch\")\n    video_height, video_width, _ = decord.VideoReader(video_path).next().shape\n    # Iterate over all frames in the video\n    images = []\n    for frame in decord.VideoReader(video_path, width=image_size, height=image_size):\n        images.append(frame.permute(2, 0, 1))\n\n    images = torch.stack(images, dim=0).float() / 255.0\n    if not offload_video_to_cpu:\n        images = images.to(compute_device)\n        img_mean = img_mean.to(compute_device)\n        img_std = img_std.to(compute_device)\n    # normalize by mean and std\n    images -= img_mean\n    images /= img_std\n    return images, video_height, video_width\n"
  },
  {
    "path": "sam3/model/vitdet.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n\n\"\"\"\nViTDet backbone adapted from Detectron2.\nThis module implements Vision Transformer (ViT) backbone for object detection.\n\nRope embedding code adopted from:\n1. https://github.com/meta-llama/codellama/blob/main/llama/model.py\n2. https://github.com/naver-ai/rope-vit\n3. https://github.com/lucidrains/rotary-embedding-torch\n\"\"\"\n\nimport math\nfrom functools import partial\nfrom typing import Callable, List, Optional, Tuple, Union\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.utils.checkpoint as checkpoint\n\ntry:\n    from timm.layers import DropPath, Mlp, trunc_normal_\nexcept ModuleNotFoundError:\n    # compatibility for older timm versions\n    from timm.models.layers import DropPath, Mlp, trunc_normal_\nfrom torch import Tensor\n\nfrom .model_misc import LayerScale\n\n\ndef init_t_xy(\n    end_x: int, end_y: int, scale: float = 1.0, offset: int = 0\n) -> Tuple[torch.Tensor, torch.Tensor]:\n    t = torch.arange(end_x * end_y, dtype=torch.float32)\n    t_x = (t % end_x).float()\n    t_y = torch.div(t, end_x, rounding_mode=\"floor\").float()\n    return t_x * scale + offset, t_y * scale + offset\n\n\ndef compute_axial_cis(\n    dim: int,\n    end_x: int,\n    end_y: int,\n    theta: float = 10000.0,\n    scale_pos: float = 1.0,\n    offset: int = 0,\n) -> torch.Tensor:\n    freqs_x = 1.0 / (theta ** (torch.arange(0, dim, 4)[: (dim // 4)].float() / dim))\n    freqs_y = 1.0 / (theta ** (torch.arange(0, dim, 4)[: (dim // 4)].float() / dim))\n\n    t_x, t_y = init_t_xy(end_x, end_y, scale_pos, offset)\n    freqs_x = torch.outer(t_x, freqs_x)\n    freqs_y = torch.outer(t_y, freqs_y)\n    freqs_cis_x = torch.polar(torch.ones_like(freqs_x), freqs_x)\n    freqs_cis_y = torch.polar(torch.ones_like(freqs_y), freqs_y)\n    return torch.cat([freqs_cis_x, freqs_cis_y], dim=-1)\n\n\ndef reshape_for_broadcast(freqs_cis: torch.Tensor, x: torch.Tensor) -> torch.Tensor:\n    ndim = x.ndim\n    assert 0 <= 1 < ndim\n    assert freqs_cis.shape == (x.shape[-2], x.shape[-1])\n    shape = [d if i >= ndim - 2 else 1 for i, d in enumerate(x.shape)]\n    return freqs_cis.view(*shape)\n\n\ndef apply_rotary_enc(\n    xq: torch.Tensor,\n    xk: torch.Tensor,\n    freqs_cis: torch.Tensor,\n    repeat_freqs_k: bool = False,\n) -> Tuple[torch.Tensor, torch.Tensor]:\n    xq_ = torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1, 2))\n    xk_ = (\n        torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1, 2))\n        if xk.shape[-2] != 0\n        else None\n    )\n    freqs_cis = reshape_for_broadcast(freqs_cis, xq_)\n    xq_out = torch.view_as_real(xq_ * freqs_cis).flatten(3)\n    if xk_ is None:\n        # no keys to rotate, due to dropout\n        return xq_out.type_as(xq).to(xq.device), xk\n    # repeat freqs along seq_len dim to match k seq_len\n    if repeat_freqs_k:\n        r = xk_.shape[-2] // xq_.shape[-2]\n        freqs_cis = freqs_cis.repeat(*([1] * (freqs_cis.ndim - 2)), r, 1)\n    xk_out = torch.view_as_real(xk_ * freqs_cis).flatten(3)\n    return xq_out.type_as(xq).to(xq.device), xk_out.type_as(xk).to(xk.device)\n\n\ndef window_partition(x: Tensor, window_size: int) -> Tuple[Tensor, Tuple[int, int]]:\n    \"\"\"\n    Partition into non-overlapping windows with padding if needed.\n    Args:\n        x (tensor): input tokens with [B, H, W, C].\n        window_size (int): window size.\n    Returns:\n        windows: windows after partition with [B * num_windows, window_size, window_size, C].\n        (Hp, Wp): padded height and width before partition\n    \"\"\"\n    B, H, W, C = x.shape\n\n    pad_h = (window_size - H % window_size) % window_size\n    pad_w = (window_size - W % window_size) % window_size\n    if pad_h > 0 or pad_w > 0:\n        x = F.pad(x, (0, 0, 0, pad_w, 0, pad_h))\n    Hp, Wp = H + pad_h, W + pad_w\n\n    x = x.view(B, Hp // window_size, window_size, Wp // window_size, window_size, C)\n    windows = x.permute(0, 1, 3, 2, 4, 5).reshape(-1, window_size, window_size, C)\n    return windows, (Hp, Wp)\n\n\ndef window_unpartition(\n    windows: Tensor, window_size: int, pad_hw: Tuple[int, int], hw: Tuple[int, int]\n) -> Tensor:\n    \"\"\"\n    Window unpartition into original sequences and removing padding.\n    Args:\n        x (tensor): input tokens with [B * num_windows, window_size, window_size, C].\n        window_size (int): window size.\n        pad_hw (Tuple): padded height and width (Hp, Wp).\n        hw (Tuple): original height and width (H, W) before padding.\n    Returns:\n        x: unpartitioned sequences with [B, H, W, C].\n    \"\"\"\n    Hp, Wp = pad_hw\n    H, W = hw\n    B = windows.shape[0] // (Hp * Wp // window_size // window_size)\n    x = windows.reshape(\n        B, Hp // window_size, Wp // window_size, window_size, window_size, -1\n    )\n    x = x.permute(0, 1, 3, 2, 4, 5).reshape(B, Hp, Wp, -1)\n\n    if Hp > H or Wp > W:\n        x = x[:, :H, :W, :]\n    return x\n\n\ndef get_rel_pos(q_size: int, k_size: int, rel_pos: Tensor) -> Tensor:\n    \"\"\"\n    Get relative positional embeddings according to the relative positions of\n        query and key sizes.\n    Args:\n        q_size (int): size of query q.\n        k_size (int): size of key k.\n        rel_pos (Tensor): relative position embeddings (L, C).\n    Returns:\n        Extracted positional embeddings according to relative positions.\n    \"\"\"\n    max_rel_dist = int(2 * max(q_size, k_size) - 1)\n    # Interpolate rel pos if needed.\n    if rel_pos.shape[0] != max_rel_dist:\n        # Interpolate rel pos.\n        rel_pos_resized = F.interpolate(\n            rel_pos.reshape(1, rel_pos.shape[0], -1).permute(0, 2, 1),\n            size=max_rel_dist,\n            mode=\"linear\",\n            align_corners=False,\n        )\n        rel_pos_resized = rel_pos_resized.reshape(-1, max_rel_dist).permute(1, 0)\n    else:\n        rel_pos_resized = rel_pos\n\n    # Scale the coords with short length if shapes for q and k are different.\n    q_coords = torch.arange(q_size)[:, None] * max(k_size / q_size, 1.0)\n    k_coords = torch.arange(k_size)[None, :] * max(q_size / k_size, 1.0)\n    relative_coords = (q_coords - k_coords) + (k_size - 1) * max(q_size / k_size, 1.0)\n\n    return rel_pos_resized[relative_coords.long()]\n\n\ndef get_abs_pos(\n    abs_pos: Tensor,\n    has_cls_token: bool,\n    hw: Tuple[int, int],\n    retain_cls_token: bool = False,\n    tiling: bool = False,\n) -> Tensor:\n    \"\"\"\n    Calculate absolute positional embeddings. If needed, resize embeddings and remove cls_token\n        dimension for the original embeddings.\n    Args:\n        abs_pos (Tensor): absolute positional embeddings with (1, num_position, C).\n        has_cls_token (bool): If true, has 1 embedding in abs_pos for cls token.\n        hw (Tuple): size of input image tokens.\n        retain_cls_token: whether to retain the cls_token\n        tiling: whether to tile the embeddings, *instead* of interpolation (a la abs_win)\n    Returns:\n        Absolute positional embeddings after processing with shape (1, H, W, C),\n        if retain_cls_token is False, otherwise (1, 1+H*W, C)\n    \"\"\"\n    if retain_cls_token:\n        assert has_cls_token\n\n    h, w = hw\n    if has_cls_token:\n        cls_pos = abs_pos[:, :1]\n        abs_pos = abs_pos[:, 1:]\n\n    xy_num = abs_pos.shape[1]\n    size = int(math.sqrt(xy_num))\n    assert size * size == xy_num\n\n    if size != h or size != w:\n        new_abs_pos = abs_pos.reshape(1, size, size, -1).permute(0, 3, 1, 2)\n        if tiling:\n            new_abs_pos = new_abs_pos.tile(\n                [1, 1] + [x // y + 1 for x, y in zip((h, w), new_abs_pos.shape[2:])]\n            )[:, :, :h, :w]\n        else:\n            new_abs_pos = F.interpolate(\n                new_abs_pos,\n                size=(h, w),\n                mode=\"bicubic\",\n                align_corners=False,\n            )\n\n        if not retain_cls_token:\n            return new_abs_pos.permute(0, 2, 3, 1)\n        else:\n            # add cls_token back, flatten spatial dims\n            assert has_cls_token\n            return torch.cat(\n                [cls_pos, new_abs_pos.permute(0, 2, 3, 1).reshape(1, h * w, -1)],\n                dim=1,\n            )\n\n    else:\n        if not retain_cls_token:\n            return abs_pos.reshape(1, h, w, -1)\n        else:\n            assert has_cls_token\n            return torch.cat([cls_pos, abs_pos], dim=1)\n\n\ndef concat_rel_pos(\n    q: Tensor,\n    k: Tensor,\n    q_hw: Tuple[int, int],\n    k_hw: Tuple[int, int],\n    rel_pos_h: Tensor,\n    rel_pos_w: Tensor,\n    rescale: bool = False,\n    relative_coords: Optional[Tensor] = None,\n) -> Tuple[Tensor, Tensor]:\n    \"\"\"\n    Concatenate rel pos coeffs to the q & k tensors, so that qk^T is now\n    effectively including rel pos biases.\n    Args:\n        q (Tensor): q tensor with shape (B, L_q, C).\n        k (Tensor): k tensor with shape (B, L_k, C).\n        q_hw, k_hw: These are spatial size of q & k tensors.\n        rel_pos_h, rel_pos_w: These are relative pos embeddings/params of height, width.\n        rescale (bool): whether to rescale. e.g. for use when using sdpa, pytorch will\n            scale by the wrong factor due to the concat.\n    Returns:\n        q, k: But, padded so that qk^T accounts for rel pos biases\n    \"\"\"\n    q_h, q_w = q_hw\n    k_h, k_w = k_hw\n\n    assert (q_h == q_w) and (k_h == k_w), \"only square inputs supported\"\n\n    if relative_coords is not None:\n        Rh = rel_pos_h[relative_coords]\n        Rw = rel_pos_w[relative_coords]\n    else:\n        Rh = get_rel_pos(q_h, k_h, rel_pos_h)\n        Rw = get_rel_pos(q_w, k_w, rel_pos_w)\n\n    B, _, dim = q.shape\n    r_q = q.reshape(B, q_h, q_w, dim)\n\n    old_scale = dim**0.5\n    new_scale = (dim + k_h + k_w) ** 0.5 if rescale else old_scale  # for sdpa\n    # attn will be divided by new_scale, but we want to divide q by old_scale\n    scale_ratio = new_scale / old_scale\n\n    rel_h = torch.einsum(\"bhwc,hkc->bhwk\", r_q, Rh) * new_scale  # (B, q_h, q_w, k_h)\n    rel_w = torch.einsum(\"bhwc,wkc->bhwk\", r_q, Rw) * new_scale  # (B, q_h, q_w, k_w)\n\n    eye_h = torch.eye(k_h, dtype=q.dtype, device=q.device)\n    eye_w = torch.eye(k_w, dtype=q.dtype, device=q.device)\n\n    eye_h = eye_h.view(1, k_h, 1, k_h).expand([B, k_h, k_w, k_h])\n    eye_w = eye_w.view(1, 1, k_w, k_w).expand([B, k_h, k_w, k_w])\n\n    q = torch.cat([r_q * scale_ratio, rel_h, rel_w], dim=-1).view(B, q_h * q_w, -1)\n    k = torch.cat([k.view(B, k_h, k_w, -1), eye_h, eye_w], dim=-1).view(\n        B, k_h * k_w, -1\n    )\n\n    return q, k\n\n\nclass PatchEmbed(nn.Module):\n    \"\"\"\n    Image to Patch Embedding.\n    \"\"\"\n\n    def __init__(\n        self,\n        kernel_size: Tuple[int, int] = (16, 16),\n        stride: Tuple[int, int] = (16, 16),\n        padding: Tuple[int, int] = (0, 0),\n        in_chans: int = 3,\n        embed_dim: int = 768,\n        bias: bool = True,\n    ):\n        \"\"\"\n        Args:\n            kernel_size (Tuple): kernel size of the projection layer.\n            stride (Tuple): stride of the projection layer.\n            padding (Tuple): padding size of the projection layer.\n            in_chans (int): Number of input image channels.\n            embed_dim (int):  embed_dim (int): Patch embedding dimension.\n        \"\"\"\n        super().__init__()\n\n        self.proj = nn.Conv2d(\n            in_chans,\n            embed_dim,\n            kernel_size=kernel_size,\n            stride=stride,\n            padding=padding,\n            bias=bias,\n        )\n\n    def forward(self, x: Tensor) -> Tensor:\n        x = self.proj(x)\n        # B C H W -> B H W C\n        x = x.permute(0, 2, 3, 1)\n        return x\n\n\nclass Attention(nn.Module):\n    \"\"\"Multi-head Attention block with relative position embeddings and 2d-rope.\"\"\"\n\n    def __init__(\n        self,\n        dim: int,\n        num_heads: int = 8,\n        qkv_bias: bool = True,\n        use_rel_pos: bool = False,\n        rel_pos_zero_init: bool = True,\n        input_size: Optional[Tuple[int, int]] = None,\n        cls_token: bool = False,\n        use_rope: bool = False,\n        rope_theta: float = 10000.0,\n        rope_pt_size: Optional[Tuple[int, int]] = None,\n        rope_interp: bool = False,\n    ):\n        \"\"\"\n        Args:\n            dim (int): Number of input channels.\n            num_heads (int): Number of attention heads.\n            qkv_bias (bool:  If True, add a learnable bias to query, key, value.\n            rel_pos (bool): If True, add relative positional embeddings to the attention map.\n            rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.\n            input_size (int or None): Input resolution for calculating the relative positional\n                parameter size or rope size.\n            attn_type: Type of attention operation, e.g. \"vanilla\", \"vanilla-xformer\".\n            cls_token: whether a cls_token is present.\n            use_rope: whether to use rope 2d (indep of use_rel_pos, as it can be used together)\n            rope_theta: control frequencies of rope\n            rope_pt_size: size of rope in previous stage of training, needed for interpolation or tiling\n            rope_interp: whether to interpolate (or extrapolate) rope to match input size\n        \"\"\"\n        super().__init__()\n        self.num_heads = num_heads\n        self.head_dim = dim // num_heads\n        self.scale = self.head_dim**-0.5\n        self.cls_token = cls_token\n\n        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)\n        self.proj = nn.Linear(dim, dim)\n\n        # rel_pos embeddings and rope\n        self.use_rel_pos = use_rel_pos\n        self.input_size = input_size\n\n        self.use_rope = use_rope\n        self.rope_theta = rope_theta\n        self.rope_pt_size = rope_pt_size\n        self.rope_interp = rope_interp\n\n        # init rel_pos embeddings and rope\n        self._setup_rel_pos(rel_pos_zero_init)\n        self._setup_rope_freqs()\n\n    def _setup_rel_pos(self, rel_pos_zero_init: bool = True) -> None:\n        if not self.use_rel_pos:\n            self.rel_pos_h = None\n            self.rel_pos_w = None\n            return\n\n        assert self.input_size is not None\n        assert self.cls_token is False, \"not supported\"\n        # initialize relative positional embeddings\n        self.rel_pos_h = nn.Parameter(\n            torch.zeros(2 * self.input_size[0] - 1, self.head_dim)\n        )\n        self.rel_pos_w = nn.Parameter(\n            torch.zeros(2 * self.input_size[1] - 1, self.head_dim)\n        )\n\n        if not rel_pos_zero_init:\n            trunc_normal_(self.rel_pos_h, std=0.02)\n            trunc_normal_(self.rel_pos_w, std=0.02)\n\n        # Precompute the relative coords\n        H, W = self.input_size\n        q_coords = torch.arange(H)[:, None]\n        k_coords = torch.arange(W)[None, :]\n        relative_coords = (q_coords - k_coords) + (H - 1)\n        self.register_buffer(\"relative_coords\", relative_coords.long())\n\n    def _setup_rope_freqs(self) -> None:\n        if not self.use_rope:\n            self.freqs_cis = None\n            return\n\n        assert self.input_size is not None\n        # determine rope input size\n        if self.rope_pt_size is None:\n            self.rope_pt_size = self.input_size\n\n        # initialize 2d rope freqs\n        self.compute_cis = partial(\n            compute_axial_cis,\n            dim=self.head_dim,\n            theta=self.rope_theta,\n        )\n\n        # interpolate rope\n        scale_pos = 1.0\n        if self.rope_interp:\n            scale_pos = self.rope_pt_size[0] / self.input_size[0]\n        # get scaled freqs_cis\n        freqs_cis = self.compute_cis(\n            end_x=self.input_size[0],\n            end_y=self.input_size[1],\n            scale_pos=scale_pos,\n        )\n        if self.cls_token:\n            t = torch.zeros(\n                self.head_dim // 2,\n                dtype=torch.float32,\n                device=freqs_cis.device,\n            )\n            cls_freqs_cis = torch.polar(torch.ones_like(t), t)[None, :]\n            freqs_cis = torch.cat([cls_freqs_cis, freqs_cis], dim=0)\n\n        self.register_buffer(\"freqs_cis\", freqs_cis)\n\n    def _apply_rope(self, q, k) -> Tuple[Tensor, Tensor]:\n        if not self.use_rope:\n            return q, k\n\n        assert self.freqs_cis is not None\n        return apply_rotary_enc(q, k, freqs_cis=self.freqs_cis)\n\n    def forward(self, x: Tensor) -> Tensor:\n        s = 1 if self.cls_token else 0  # used to exclude cls_token\n        if x.ndim == 4:\n            B, H, W, _ = x.shape\n            assert s == 0  # no cls_token\n            L = H * W\n            ndim = 4\n        else:\n            assert x.ndim == 3\n            B, L, _ = x.shape\n            ndim = 3\n            H = W = math.sqrt(L - s)\n\n        # qkv with shape (3, B, nHead, L, C)\n        qkv = self.qkv(x).reshape(B, L, 3, self.num_heads, -1)\n        # q, k, v with shape (B, nHead, L, C)\n        q, k, v = qkv.permute(2, 0, 3, 1, 4).unbind(0)\n\n        # handle rope and rel pos embeddings\n        q, k = self._apply_rope(q, k)\n        if self.use_rel_pos:\n            q, k = concat_rel_pos(\n                q.flatten(0, 1),\n                k.flatten(0, 1),\n                (H, W),\n                x.shape[1:3],\n                self.rel_pos_h,\n                self.rel_pos_w,\n                rescale=True,\n                relative_coords=self.relative_coords,\n            )\n\n            # sdpa expects [B, nheads, H*W, C] so we transpose back\n            q = q.reshape(B, self.num_heads, H * W, -1)\n            k = k.reshape(B, self.num_heads, H * W, -1)\n\n        x = F.scaled_dot_product_attention(q, k, v)\n\n        if ndim == 4:\n            x = (\n                x.view(B, self.num_heads, H, W, -1)\n                .permute(0, 2, 3, 1, 4)\n                .reshape(B, H, W, -1)\n            )\n        else:\n            x = x.view(B, self.num_heads, L, -1).permute(0, 2, 1, 3).reshape(B, L, -1)\n\n        x = self.proj(x)\n\n        return x\n\n\nclass Block(nn.Module):\n    \"\"\"Transformer blocks with support of window attention\"\"\"\n\n    def __init__(\n        self,\n        dim: int,\n        num_heads: int,\n        mlp_ratio: float = 4.0,\n        qkv_bias: bool = True,\n        drop_path: float = 0.0,\n        norm_layer: Callable[..., nn.Module] = nn.LayerNorm,\n        act_layer: Callable[..., nn.Module] = nn.GELU,\n        use_rel_pos: bool = False,\n        rel_pos_zero_init: bool = True,\n        window_size: int = 0,\n        input_size: Optional[Tuple[int, int]] = None,\n        use_rope: bool = False,\n        rope_pt_size: Optional[Tuple[int, int]] = None,\n        rope_tiled: bool = False,\n        rope_interp: bool = False,\n        use_ve_rope: bool = False,\n        cls_token: bool = False,\n        dropout: float = 0.0,\n        init_values: Optional[float] = None,\n    ):\n        \"\"\"\n        Args:\n            dim (int): Number of input channels.\n            num_heads (int): Number of attention heads in each ViT block.\n            mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.\n            qkv_bias (bool): If True, add a learnable bias to query, key, value.\n            drop_path (float): Stochastic depth rate.\n            norm_layer (nn.Module): Normalization layer.\n            act_layer (nn.Module): Activation layer.\n            use_rel_pos (bool): If True, add relative positional embeddings to the attention map.\n            rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.\n            window_size (int): Window size for window attention blocks. If it equals 0, then not\n                use window attention.\n            input_size (int or None): Input resolution for calculating the relative positional\n                parameter size.\n            dropout (float): Dropout rate.\n            cls_token: whether a cls_token is present.\n            use_rope: whether to use rope 2d (indep of use_rel_pos, as it can be used together)\n            rope_pt_size: size of rope in previous stage of training, needed for interpolation or tiling\n            rope_interp: whether to interpolate (or extrapolate) rope to match target input size,\n                expected to specify source size as rope_pt_size.\n        \"\"\"\n        super().__init__()\n        self.norm1 = norm_layer(dim)\n        self.attn = Attention(\n            dim,\n            num_heads=num_heads,\n            qkv_bias=qkv_bias,\n            use_rel_pos=use_rel_pos,\n            rel_pos_zero_init=rel_pos_zero_init,\n            input_size=input_size if window_size == 0 else (window_size, window_size),\n            use_rope=use_rope,\n            rope_pt_size=rope_pt_size,\n            rope_interp=rope_interp,\n            cls_token=cls_token,\n        )\n        self.ls1 = (\n            LayerScale(dim, init_values=init_values) if init_values else nn.Identity()\n        )\n        self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()\n\n        self.norm2 = norm_layer(dim)\n        self.mlp = Mlp(\n            in_features=dim,\n            hidden_features=int(dim * mlp_ratio),\n            act_layer=act_layer,\n            drop=(dropout, 0.0),\n        )\n        self.ls2 = (\n            LayerScale(dim, init_values=init_values) if init_values else nn.Identity()\n        )\n        self.dropout = nn.Dropout(dropout)\n        self.window_size = window_size\n\n    def forward(self, x: Tensor) -> Tensor:\n        shortcut = x\n        x = self.norm1(x)\n        # Window partition\n        if self.window_size > 0:\n            H, W = x.shape[1], x.shape[2]\n            x, pad_hw = window_partition(x, self.window_size)\n\n        x = self.ls1(self.attn(x))\n        # Reverse window partition\n        if self.window_size > 0:\n            x = window_unpartition(x, self.window_size, pad_hw, (H, W))\n\n        x = shortcut + self.dropout(self.drop_path(x))\n        x = x + self.dropout(self.drop_path(self.ls2(self.mlp(self.norm2(x)))))\n\n        return x\n\n\nclass ViT(nn.Module):\n    \"\"\"\n    This module implements Vision Transformer (ViT) backbone in :paper:`vitdet`.\n    \"Exploring Plain Vision Transformer Backbones for Object Detection\",\n    https://arxiv.org/abs/2203.16527\n    \"\"\"\n\n    def __init__(\n        self,\n        img_size: int = 1024,\n        patch_size: int = 16,\n        in_chans: int = 3,\n        embed_dim: int = 768,\n        depth: int = 12,\n        num_heads: int = 12,\n        mlp_ratio: float = 4.0,\n        qkv_bias: bool = True,\n        drop_path_rate: float = 0.0,\n        norm_layer: Union[Callable[..., nn.Module], str] = \"LayerNorm\",\n        act_layer: Callable[..., nn.Module] = nn.GELU,\n        use_abs_pos: bool = True,\n        tile_abs_pos: bool = True,\n        rel_pos_blocks: Union[Tuple[int, ...], bool] = (2, 5, 8, 11),\n        rel_pos_zero_init: bool = True,\n        window_size: int = 14,\n        global_att_blocks: Tuple[int, ...] = (2, 5, 8, 11),\n        use_rope: bool = False,\n        rope_pt_size: Optional[int] = None,\n        use_interp_rope: bool = False,\n        pretrain_img_size: int = 224,\n        pretrain_use_cls_token: bool = True,\n        retain_cls_token: bool = True,\n        dropout: float = 0.0,\n        return_interm_layers: bool = False,\n        init_values: Optional[float] = None,  # for layerscale\n        ln_pre: bool = False,\n        ln_post: bool = False,\n        bias_patch_embed: bool = True,\n        compile_mode: Optional[str] = None,\n        use_act_checkpoint: bool = True,\n    ):\n        \"\"\"\n        Args:\n            img_size (int): Input image size. Only relevant for rel pos or rope.\n            patch_size (int): Patch size.\n            in_chans (int): Number of input image channels.\n            embed_dim (int): Patch embedding dimension.\n            depth (int): Depth of ViT.\n            num_heads (int): Number of attention heads in each ViT block.\n            mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.\n            qkv_bias (bool): If True, add a learnable bias to query, key, value.\n            drop_path_rate (float): Stochastic depth rate.\n            norm_layer (nn.Module): Normalization layer.\n            act_layer (nn.Module): Activation layer.\n            use_abs_pos (bool): If True, use absolute positional embeddings.\n            tile_abs_pos (bool): If True, tile absolute positional embeddings instead of interpolation.\n            rel_pos_blocks (list): Blocks which have rel pos embeddings.\n            rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.\n            window_size (int): Window size for window attention blocks.\n            global_att_blocks (list): Indexes for blocks using global attention (other blocks use window attention).\n            use_rope (bool): whether to use rope 2d (indep of rel_pos_blocks, as it can be used together).\n            rope_pt_size (int): size of rope in previous stage of training, needed for interpolation or tiling.\n            use_interp_rope: whether to interpolate (or extrapolate) rope to match target input size,\n                expected to specify source size as rope_pt_size.\n            use_act_checkpoint (bool): If True, use activation checkpointing.\n            pretrain_img_size (int): input image size for pretraining models.\n            pretrain_use_cls_token (bool): If True, pretraining models use class token.\n            retain_cls_token: whether cls_token should be retained.\n            dropout (float): Dropout rate. Applied in residual blocks of attn, mlp and inside the mlp.\n\n            return_interm_layers (bool): Whether to return intermediate layers (all global attention blocks).\n            init_values: layer scale init, None for no layer scale.\n\n            ln_pre (bool): If True, apply layer norm before transformer blocks.\n            ln_post (bool): If True, apply layer norm after transformer blocks.\n            bias_patch_embed (bool): bias in conv for patch embed?\n            compile_mode (str): mode to compile the forward\n        \"\"\"\n        super().__init__()\n        self.pretrain_use_cls_token = pretrain_use_cls_token\n\n        window_block_indexes = [i for i in range(depth) if i not in global_att_blocks]\n        self.full_attn_ids = list(global_att_blocks)\n        self.rel_pos_blocks = [False] * depth\n        if isinstance(rel_pos_blocks, bool) and rel_pos_blocks:\n            self.rel_pos_blocks = [True] * depth\n        else:\n            for i in rel_pos_blocks:\n                self.rel_pos_blocks[i] = True\n\n        self.retain_cls_token = retain_cls_token\n        if self.retain_cls_token:\n            assert pretrain_use_cls_token\n            assert len(window_block_indexes) == 0, (\n                \"windowing not supported with cls token\"\n            )\n\n            assert sum(self.rel_pos_blocks) == 0, \"rel pos not supported with cls token\"\n\n            scale = embed_dim**-0.5\n            self.class_embedding = nn.Parameter(scale * torch.randn(1, 1, embed_dim))\n\n        if isinstance(norm_layer, str):\n            norm_layer = partial(getattr(nn, norm_layer), eps=1e-5)\n\n        self.patch_embed = PatchEmbed(\n            kernel_size=(patch_size, patch_size),\n            stride=(patch_size, patch_size),\n            in_chans=in_chans,\n            embed_dim=embed_dim,\n            bias=bias_patch_embed,\n        )\n\n        # Handle absolute positional embedding\n        self.tile_abs_pos = tile_abs_pos\n        self.use_abs_pos = use_abs_pos\n        if self.tile_abs_pos:\n            assert self.use_abs_pos\n\n        if self.use_abs_pos:\n            # Initialize absolute positional embedding with pretrain image size.\n            num_patches = (pretrain_img_size // patch_size) * (\n                pretrain_img_size // patch_size\n            )\n            num_positions = (num_patches + 1) if pretrain_use_cls_token else num_patches\n            self.pos_embed = nn.Parameter(torch.zeros(1, num_positions, embed_dim))\n        else:\n            self.pos_embed = None\n\n        # stochastic depth decay rule\n        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]\n\n        self.blocks = nn.ModuleList()\n        cur_stage = 1\n        for i in range(depth):\n            block = Block(\n                dim=embed_dim,\n                num_heads=num_heads,\n                mlp_ratio=mlp_ratio,\n                qkv_bias=qkv_bias,\n                drop_path=dpr[i],\n                norm_layer=norm_layer,\n                act_layer=act_layer,\n                use_rel_pos=self.rel_pos_blocks[i],\n                rel_pos_zero_init=rel_pos_zero_init,\n                window_size=window_size if i in window_block_indexes else 0,\n                input_size=(img_size // patch_size, img_size // patch_size),\n                use_rope=use_rope,\n                rope_pt_size=(\n                    (window_size, window_size)\n                    if rope_pt_size is None\n                    else (rope_pt_size, rope_pt_size)\n                ),\n                rope_interp=use_interp_rope,\n                cls_token=self.retain_cls_token,\n                dropout=dropout,\n                init_values=init_values,\n            )\n\n            if i not in window_block_indexes:\n                cur_stage += 1\n\n            self.use_act_checkpoint = use_act_checkpoint\n\n            self.blocks.append(block)\n\n        self.return_interm_layers = return_interm_layers\n        self.channel_list = (\n            [embed_dim] * len(self.full_attn_ids)\n            if return_interm_layers\n            else [embed_dim]\n        )\n\n        if self.pos_embed is not None:\n            trunc_normal_(self.pos_embed, std=0.02)\n\n        self.ln_pre = norm_layer(embed_dim) if ln_pre else nn.Identity()\n        self.ln_post = norm_layer(embed_dim) if ln_post else nn.Identity()\n\n        self.apply(self._init_weights)\n\n        if compile_mode is not None:\n            self.forward = torch.compile(\n                self.forward, mode=compile_mode, fullgraph=True\n            )\n            if self.use_act_checkpoint and self.training:\n                torch._dynamo.config.optimize_ddp = False\n\n    def _init_weights(self, m: nn.Module) -> None:\n        if isinstance(m, nn.Linear):\n            trunc_normal_(m.weight, std=0.02)\n            if isinstance(m, nn.Linear) and m.bias is not None:\n                nn.init.constant_(m.bias, 0)\n        elif isinstance(m, nn.LayerNorm):\n            nn.init.constant_(m.bias, 0)\n            nn.init.constant_(m.weight, 1.0)\n\n    def forward(self, x: torch.Tensor) -> List[torch.Tensor]:\n        x = self.patch_embed(x)\n        h, w = x.shape[1], x.shape[2]\n\n        s = 0\n        if self.retain_cls_token:\n            # If cls_token is retained, we don't\n            # maintain spatial shape\n            x = torch.cat([self.class_embedding, x.flatten(1, 2)], dim=1)\n            s = 1\n\n        if self.pos_embed is not None:\n            x = x + get_abs_pos(\n                self.pos_embed,\n                self.pretrain_use_cls_token,\n                (h, w),\n                self.retain_cls_token,\n                tiling=self.tile_abs_pos,\n            )\n\n        x = self.ln_pre(x)\n\n        outputs = []\n        for i, blk in enumerate(self.blocks):\n            if self.use_act_checkpoint and self.training:\n                x = checkpoint.checkpoint(blk, x, use_reentrant=False)\n            else:\n                x = blk(x)\n            if (i == self.full_attn_ids[-1]) or (\n                self.return_interm_layers and i in self.full_attn_ids\n            ):\n                if i == self.full_attn_ids[-1]:\n                    x = self.ln_post(x)\n\n                feats = x[:, s:]\n                if feats.ndim == 4:\n                    feats = feats.permute(0, 3, 1, 2)\n                else:\n                    assert feats.ndim == 3\n                    h = w = math.sqrt(feats.shape[1])\n                    feats = feats.reshape(\n                        feats.shape[0], h, w, feats.shape[-1]\n                    ).permute(0, 3, 1, 2)\n\n                outputs.append(feats)\n\n        return outputs\n\n    def get_layer_id(self, layer_name: str) -> int:\n        # https://github.com/microsoft/unilm/blob/master/beit/optim_factory.py#L33\n        num_layers = self.get_num_layers()\n\n        if layer_name.find(\"rel_pos\") != -1:\n            return num_layers + 1\n        elif layer_name.find(\"ln_pre\") != -1:\n            return 0\n        elif layer_name.find(\"pos_embed\") != -1 or layer_name.find(\"cls_token\") != -1:\n            return 0\n        elif layer_name.find(\"patch_embed\") != -1:\n            return 0\n        elif layer_name.find(\"blocks\") != -1:\n            return int(layer_name.split(\"blocks\")[1].split(\".\")[1]) + 1\n        else:\n            return num_layers + 1\n\n    def get_num_layers(self) -> int:\n        return len(self.blocks)\n"
  },
  {
    "path": "sam3/model/vl_combiner.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n\n\"\"\"Provides utility to combine a vision backbone with a language backbone.\"\"\"\n\nfrom copy import copy\nfrom typing import List, Optional\n\nimport torch\nimport torch.nn as nn\nfrom torch.nn.attention import sdpa_kernel, SDPBackend\n\nfrom .act_ckpt_utils import activation_ckpt_wrapper\nfrom .necks import Sam3DualViTDetNeck\n\n\nclass SAM3VLBackbone(nn.Module):\n    \"\"\"This backbone combines a vision backbone and a language backbone without fusion.\n    As such it is more of a convenience wrapper to handle the two backbones together.\n\n    It adds support for activation checkpointing and compilation.\n    \"\"\"\n\n    def __init__(\n        self,\n        visual: Sam3DualViTDetNeck,\n        text,\n        compile_visual: bool = False,\n        act_ckpt_whole_vision_backbone: bool = False,\n        act_ckpt_whole_language_backbone: bool = False,\n        scalp=0,\n    ):\n        \"\"\"Initialize the backbone combiner.\n\n        :param visual: The vision backbone to use\n        :param text: The text encoder to use\n        \"\"\"\n        super().__init__()\n        self.vision_backbone: Sam3DualViTDetNeck = (\n            torch.compile(visual) if compile_visual else visual\n        )\n        self.language_backbone = text\n        self.scalp = scalp\n        # allow running activation checkpointing on the entire vision and language backbones\n        self.act_ckpt_whole_vision_backbone = act_ckpt_whole_vision_backbone\n        self.act_ckpt_whole_language_backbone = act_ckpt_whole_language_backbone\n\n    def forward(\n        self,\n        samples: torch.Tensor,\n        captions: List[str],\n        input_boxes: Optional[torch.Tensor] = None,\n        additional_text: Optional[List[str]] = None,\n    ):\n        \"\"\"Forward pass of the backbone combiner.\n\n        :param samples: The input images\n        :param captions: The input captions\n        :param input_boxes: If the text contains place-holders for boxes, this\n            parameter contains the tensor containing their spatial features\n        :param additional_text: This can be used to encode some additional text\n            (different from the captions) in the same forward of the backbone\n        :return: Output dictionary with the following keys:\n            - vision_features: The output of the vision backbone\n            - language_features: The output of the language backbone\n            - language_mask: The attention mask of the language backbone\n            - vision_pos_enc: The positional encoding of the vision backbone\n            - (optional) additional_text_features: The output of the language\n                backbone for the additional text\n            - (optional) additional_text_mask: The attention mask of the\n                language backbone for the additional text\n        \"\"\"\n        output = self.forward_image(samples)\n        device = output[\"vision_features\"].device\n        output.update(self.forward_text(captions, input_boxes, additional_text, device))\n        return output\n\n    def forward_image(self, samples: torch.Tensor):\n        return activation_ckpt_wrapper(self._forward_image_no_act_ckpt)(\n            samples=samples,\n            act_ckpt_enable=self.act_ckpt_whole_vision_backbone and self.training,\n        )\n\n    def _forward_image_no_act_ckpt(self, samples):\n        # Forward through backbone\n        sam3_features, sam3_pos, sam2_features, sam2_pos = self.vision_backbone.forward(\n            samples\n        )\n        if self.scalp > 0:\n            # Discard the lowest resolution features\n            sam3_features, sam3_pos = (\n                sam3_features[: -self.scalp],\n                sam3_pos[: -self.scalp],\n            )\n            if sam2_features is not None and sam2_pos is not None:\n                sam2_features, sam2_pos = (\n                    sam2_features[: -self.scalp],\n                    sam2_pos[: -self.scalp],\n                )\n\n        sam2_output = None\n\n        if sam2_features is not None and sam2_pos is not None:\n            sam2_src = sam2_features[-1]\n            sam2_output = {\n                \"vision_features\": sam2_src,\n                \"vision_pos_enc\": sam2_pos,\n                \"backbone_fpn\": sam2_features,\n            }\n\n        sam3_src = sam3_features[-1]\n        output = {\n            \"vision_features\": sam3_src,\n            \"vision_pos_enc\": sam3_pos,\n            \"backbone_fpn\": sam3_features,\n            \"sam2_backbone_out\": sam2_output,\n        }\n\n        return output\n\n    def forward_text(\n        self, captions, input_boxes=None, additional_text=None, device=\"cuda\"\n    ):\n        return activation_ckpt_wrapper(self._forward_text_no_ack_ckpt)(\n            captions=captions,\n            input_boxes=input_boxes,\n            additional_text=additional_text,\n            device=device,\n            act_ckpt_enable=self.act_ckpt_whole_language_backbone and self.training,\n        )\n\n    def _forward_text_no_ack_ckpt(\n        self,\n        captions,\n        input_boxes=None,\n        additional_text=None,\n        device=\"cuda\",\n    ):\n        output = {}\n\n        # Forward through text_encoder\n        text_to_encode = copy(captions)\n        if additional_text is not None:\n            # if there are additional_text, we piggy-back them into this forward.\n            # They'll be used later for output alignment\n            text_to_encode += additional_text\n\n        sdpa_context = sdpa_kernel(\n            [\n                SDPBackend.MATH,\n                SDPBackend.EFFICIENT_ATTENTION,\n                SDPBackend.FLASH_ATTENTION,\n            ]\n        )\n\n        with sdpa_context:\n            text_attention_mask, text_memory, text_embeds = self.language_backbone(\n                text_to_encode, input_boxes, device=device\n            )\n\n        if additional_text is not None:\n            output[\"additional_text_features\"] = text_memory[:, -len(additional_text) :]\n            output[\"additional_text_mask\"] = text_attention_mask[\n                -len(additional_text) :\n            ]\n\n        text_memory = text_memory[:, : len(captions)]\n        text_attention_mask = text_attention_mask[: len(captions)]\n        text_embeds = text_embeds[:, : len(captions)]\n        output[\"language_features\"] = text_memory\n        output[\"language_mask\"] = text_attention_mask\n        output[\"language_embeds\"] = (\n            text_embeds  # Text embeddings before forward to the encoder\n        )\n\n        return output\n"
  },
  {
    "path": "sam3/model_builder.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n\nimport os\nfrom typing import Optional\n\nimport pkg_resources\nimport torch\nimport torch.nn as nn\nfrom huggingface_hub import hf_hub_download\nfrom iopath.common.file_io import g_pathmgr\nfrom sam3.model.decoder import (\n    TransformerDecoder,\n    TransformerDecoderLayer,\n    TransformerDecoderLayerv2,\n    TransformerEncoderCrossAttention,\n)\nfrom sam3.model.encoder import TransformerEncoderFusion, TransformerEncoderLayer\nfrom sam3.model.geometry_encoders import SequenceGeometryEncoder\nfrom sam3.model.maskformer_segmentation import PixelDecoder, UniversalSegmentationHead\nfrom sam3.model.memory import (\n    CXBlock,\n    SimpleFuser,\n    SimpleMaskDownSampler,\n    SimpleMaskEncoder,\n)\nfrom sam3.model.model_misc import (\n    DotProductScoring,\n    MLP,\n    MultiheadAttentionWrapper as MultiheadAttention,\n    TransformerWrapper,\n)\nfrom sam3.model.necks import Sam3DualViTDetNeck\nfrom sam3.model.position_encoding import PositionEmbeddingSine\nfrom sam3.model.sam1_task_predictor import SAM3InteractiveImagePredictor\nfrom sam3.model.sam3_image import Sam3Image, Sam3ImageOnVideoMultiGPU\nfrom sam3.model.sam3_tracking_predictor import Sam3TrackerPredictor\nfrom sam3.model.sam3_video_inference import Sam3VideoInferenceWithInstanceInteractivity\nfrom sam3.model.sam3_video_predictor import Sam3VideoPredictorMultiGPU\nfrom sam3.model.text_encoder_ve import VETextEncoder\nfrom sam3.model.tokenizer_ve import SimpleTokenizer\nfrom sam3.model.vitdet import ViT\nfrom sam3.model.vl_combiner import SAM3VLBackbone\nfrom sam3.sam.transformer import RoPEAttention\n\n\n# Setup TensorFloat-32 for Ampere GPUs if available\ndef _setup_tf32() -> None:\n    \"\"\"Enable TensorFloat-32 for Ampere GPUs if available.\"\"\"\n    if torch.cuda.is_available():\n        device_props = torch.cuda.get_device_properties(0)\n        if device_props.major >= 8:\n            torch.backends.cuda.matmul.allow_tf32 = True\n            torch.backends.cudnn.allow_tf32 = True\n\n\n_setup_tf32()\n\n\ndef _create_position_encoding(precompute_resolution=None):\n    \"\"\"Create position encoding for visual backbone.\"\"\"\n    return PositionEmbeddingSine(\n        num_pos_feats=256,\n        normalize=True,\n        scale=None,\n        temperature=10000,\n        precompute_resolution=precompute_resolution,\n    )\n\n\ndef _create_vit_backbone(compile_mode=None):\n    \"\"\"Create ViT backbone for visual feature extraction.\"\"\"\n    return ViT(\n        img_size=1008,\n        pretrain_img_size=336,\n        patch_size=14,\n        embed_dim=1024,\n        depth=32,\n        num_heads=16,\n        mlp_ratio=4.625,\n        norm_layer=\"LayerNorm\",\n        drop_path_rate=0.1,\n        qkv_bias=True,\n        use_abs_pos=True,\n        tile_abs_pos=True,\n        global_att_blocks=(7, 15, 23, 31),\n        rel_pos_blocks=(),\n        use_rope=True,\n        use_interp_rope=True,\n        window_size=24,\n        pretrain_use_cls_token=True,\n        retain_cls_token=False,\n        ln_pre=True,\n        ln_post=False,\n        return_interm_layers=False,\n        bias_patch_embed=False,\n        compile_mode=compile_mode,\n    )\n\n\ndef _create_vit_neck(position_encoding, vit_backbone, enable_inst_interactivity=False):\n    \"\"\"Create ViT neck for feature pyramid.\"\"\"\n    return Sam3DualViTDetNeck(\n        position_encoding=position_encoding,\n        d_model=256,\n        scale_factors=[4.0, 2.0, 1.0, 0.5],\n        trunk=vit_backbone,\n        add_sam2_neck=enable_inst_interactivity,\n    )\n\n\ndef _create_vl_backbone(vit_neck, text_encoder):\n    \"\"\"Create visual-language backbone.\"\"\"\n    return SAM3VLBackbone(visual=vit_neck, text=text_encoder, scalp=1)\n\n\ndef _create_transformer_encoder() -> TransformerEncoderFusion:\n    \"\"\"Create transformer encoder with its layer.\"\"\"\n    encoder_layer = TransformerEncoderLayer(\n        activation=\"relu\",\n        d_model=256,\n        dim_feedforward=2048,\n        dropout=0.1,\n        pos_enc_at_attn=True,\n        pos_enc_at_cross_attn_keys=False,\n        pos_enc_at_cross_attn_queries=False,\n        pre_norm=True,\n        self_attention=MultiheadAttention(\n            num_heads=8,\n            dropout=0.1,\n            embed_dim=256,\n            batch_first=True,\n        ),\n        cross_attention=MultiheadAttention(\n            num_heads=8,\n            dropout=0.1,\n            embed_dim=256,\n            batch_first=True,\n        ),\n    )\n\n    encoder = TransformerEncoderFusion(\n        layer=encoder_layer,\n        num_layers=6,\n        d_model=256,\n        num_feature_levels=1,\n        frozen=False,\n        use_act_checkpoint=True,\n        add_pooled_text_to_img_feat=False,\n        pool_text_with_mask=True,\n    )\n    return encoder\n\n\ndef _create_transformer_decoder() -> TransformerDecoder:\n    \"\"\"Create transformer decoder with its layer.\"\"\"\n    decoder_layer = TransformerDecoderLayer(\n        activation=\"relu\",\n        d_model=256,\n        dim_feedforward=2048,\n        dropout=0.1,\n        cross_attention=MultiheadAttention(\n            num_heads=8,\n            dropout=0.1,\n            embed_dim=256,\n        ),\n        n_heads=8,\n        use_text_cross_attention=True,\n    )\n\n    decoder = TransformerDecoder(\n        layer=decoder_layer,\n        num_layers=6,\n        num_queries=200,\n        return_intermediate=True,\n        box_refine=True,\n        num_o2m_queries=0,\n        dac=True,\n        boxRPB=\"log\",\n        d_model=256,\n        frozen=False,\n        interaction_layer=None,\n        dac_use_selfatt_ln=True,\n        resolution=1008,\n        stride=14,\n        use_act_checkpoint=True,\n        presence_token=True,\n    )\n    return decoder\n\n\ndef _create_dot_product_scoring():\n    \"\"\"Create dot product scoring module.\"\"\"\n    prompt_mlp = MLP(\n        input_dim=256,\n        hidden_dim=2048,\n        output_dim=256,\n        num_layers=2,\n        dropout=0.1,\n        residual=True,\n        out_norm=nn.LayerNorm(256),\n    )\n    return DotProductScoring(d_model=256, d_proj=256, prompt_mlp=prompt_mlp)\n\n\ndef _create_segmentation_head(compile_mode=None):\n    \"\"\"Create segmentation head with pixel decoder.\"\"\"\n    pixel_decoder = PixelDecoder(\n        num_upsampling_stages=3,\n        interpolation_mode=\"nearest\",\n        hidden_dim=256,\n        compile_mode=compile_mode,\n    )\n\n    cross_attend_prompt = MultiheadAttention(\n        num_heads=8,\n        dropout=0,\n        embed_dim=256,\n    )\n\n    segmentation_head = UniversalSegmentationHead(\n        hidden_dim=256,\n        upsampling_stages=3,\n        aux_masks=False,\n        presence_head=False,\n        dot_product_scorer=None,\n        act_ckpt=True,\n        cross_attend_prompt=cross_attend_prompt,\n        pixel_decoder=pixel_decoder,\n    )\n    return segmentation_head\n\n\ndef _create_geometry_encoder():\n    \"\"\"Create geometry encoder with all its components.\"\"\"\n    # Create position encoding for geometry encoder\n    geo_pos_enc = _create_position_encoding()\n    # Create CX block for fuser\n    cx_block = CXBlock(\n        dim=256,\n        kernel_size=7,\n        padding=3,\n        layer_scale_init_value=1.0e-06,\n        use_dwconv=True,\n    )\n    # Create geometry encoder layer\n    geo_layer = TransformerEncoderLayer(\n        activation=\"relu\",\n        d_model=256,\n        dim_feedforward=2048,\n        dropout=0.1,\n        pos_enc_at_attn=False,\n        pre_norm=True,\n        self_attention=MultiheadAttention(\n            num_heads=8,\n            dropout=0.1,\n            embed_dim=256,\n            batch_first=False,\n        ),\n        pos_enc_at_cross_attn_queries=False,\n        pos_enc_at_cross_attn_keys=True,\n        cross_attention=MultiheadAttention(\n            num_heads=8,\n            dropout=0.1,\n            embed_dim=256,\n            batch_first=False,\n        ),\n    )\n\n    # Create geometry encoder\n    input_geometry_encoder = SequenceGeometryEncoder(\n        pos_enc=geo_pos_enc,\n        encode_boxes_as_points=False,\n        points_direct_project=True,\n        points_pool=True,\n        points_pos_enc=True,\n        boxes_direct_project=True,\n        boxes_pool=True,\n        boxes_pos_enc=True,\n        d_model=256,\n        num_layers=3,\n        layer=geo_layer,\n        use_act_ckpt=True,\n        add_cls=True,\n        add_post_encode_proj=True,\n    )\n    return input_geometry_encoder\n\n\ndef _create_sam3_model(\n    backbone,\n    transformer,\n    input_geometry_encoder,\n    segmentation_head,\n    dot_prod_scoring,\n    inst_interactive_predictor,\n    eval_mode,\n):\n    \"\"\"Create the SAM3 image model.\"\"\"\n    common_params = {\n        \"backbone\": backbone,\n        \"transformer\": transformer,\n        \"input_geometry_encoder\": input_geometry_encoder,\n        \"segmentation_head\": segmentation_head,\n        \"num_feature_levels\": 1,\n        \"o2m_mask_predict\": True,\n        \"dot_prod_scoring\": dot_prod_scoring,\n        \"use_instance_query\": False,\n        \"multimask_output\": True,\n        \"inst_interactive_predictor\": inst_interactive_predictor,\n    }\n\n    matcher = None\n    if not eval_mode:\n        from sam3.train.matcher import BinaryHungarianMatcherV2\n\n        matcher = BinaryHungarianMatcherV2(\n            focal=True,\n            cost_class=2.0,\n            cost_bbox=5.0,\n            cost_giou=2.0,\n            alpha=0.25,\n            gamma=2,\n            stable=False,\n        )\n    common_params[\"matcher\"] = matcher\n    model = Sam3Image(**common_params)\n\n    return model\n\n\ndef _create_tracker_maskmem_backbone():\n    \"\"\"Create the SAM3 Tracker memory encoder.\"\"\"\n    # Position encoding for mask memory backbone\n    position_encoding = PositionEmbeddingSine(\n        num_pos_feats=64,\n        normalize=True,\n        scale=None,\n        temperature=10000,\n        precompute_resolution=1008,\n    )\n\n    # Mask processing components\n    mask_downsampler = SimpleMaskDownSampler(\n        kernel_size=3, stride=2, padding=1, interpol_size=[1152, 1152]\n    )\n\n    cx_block_layer = CXBlock(\n        dim=256,\n        kernel_size=7,\n        padding=3,\n        layer_scale_init_value=1.0e-06,\n        use_dwconv=True,\n    )\n\n    fuser = SimpleFuser(layer=cx_block_layer, num_layers=2)\n\n    maskmem_backbone = SimpleMaskEncoder(\n        out_dim=64,\n        position_encoding=position_encoding,\n        mask_downsampler=mask_downsampler,\n        fuser=fuser,\n    )\n\n    return maskmem_backbone\n\n\ndef _create_tracker_transformer():\n    \"\"\"Create the SAM3 Tracker transformer components.\"\"\"\n    # Self attention\n    self_attention = RoPEAttention(\n        embedding_dim=256,\n        num_heads=1,\n        downsample_rate=1,\n        dropout=0.1,\n        rope_theta=10000.0,\n        feat_sizes=[72, 72],\n        use_fa3=False,\n        use_rope_real=False,\n    )\n\n    # Cross attention\n    cross_attention = RoPEAttention(\n        embedding_dim=256,\n        num_heads=1,\n        downsample_rate=1,\n        dropout=0.1,\n        kv_in_dim=64,\n        rope_theta=10000.0,\n        feat_sizes=[72, 72],\n        rope_k_repeat=True,\n        use_fa3=False,\n        use_rope_real=False,\n    )\n\n    # Encoder layer\n    encoder_layer = TransformerDecoderLayerv2(\n        cross_attention_first=False,\n        activation=\"relu\",\n        dim_feedforward=2048,\n        dropout=0.1,\n        pos_enc_at_attn=False,\n        pre_norm=True,\n        self_attention=self_attention,\n        d_model=256,\n        pos_enc_at_cross_attn_keys=True,\n        pos_enc_at_cross_attn_queries=False,\n        cross_attention=cross_attention,\n    )\n\n    # Encoder\n    encoder = TransformerEncoderCrossAttention(\n        remove_cross_attention_layers=[],\n        batch_first=True,\n        d_model=256,\n        frozen=False,\n        pos_enc_at_input=True,\n        layer=encoder_layer,\n        num_layers=4,\n        use_act_checkpoint=False,\n    )\n\n    # Transformer wrapper\n    transformer = TransformerWrapper(\n        encoder=encoder,\n        decoder=None,\n        d_model=256,\n    )\n\n    return transformer\n\n\ndef build_tracker(\n    apply_temporal_disambiguation: bool, with_backbone: bool = False, compile_mode=None\n) -> Sam3TrackerPredictor:\n    \"\"\"\n    Build the SAM3 Tracker module for video tracking.\n\n    Returns:\n        Sam3TrackerPredictor: Wrapped SAM3 Tracker module\n    \"\"\"\n\n    # Create model components\n    maskmem_backbone = _create_tracker_maskmem_backbone()\n    transformer = _create_tracker_transformer()\n    backbone = None\n    if with_backbone:\n        vision_backbone = _create_vision_backbone(compile_mode=compile_mode)\n        backbone = SAM3VLBackbone(scalp=1, visual=vision_backbone, text=None)\n    # Create the Tracker module\n    model = Sam3TrackerPredictor(\n        image_size=1008,\n        num_maskmem=7,\n        backbone=backbone,\n        backbone_stride=14,\n        transformer=transformer,\n        maskmem_backbone=maskmem_backbone,\n        # SAM parameters\n        multimask_output_in_sam=True,\n        # Evaluation\n        forward_backbone_per_frame_for_eval=True,\n        trim_past_non_cond_mem_for_eval=False,\n        # Multimask\n        multimask_output_for_tracking=True,\n        multimask_min_pt_num=0,\n        multimask_max_pt_num=1,\n        # Additional settings\n        always_start_from_first_ann_frame=False,\n        # Mask overlap\n        non_overlap_masks_for_mem_enc=False,\n        non_overlap_masks_for_output=False,\n        max_cond_frames_in_attn=4,\n        offload_output_to_cpu_for_eval=False,\n        # SAM decoder settings\n        sam_mask_decoder_extra_args={\n            \"dynamic_multimask_via_stability\": True,\n            \"dynamic_multimask_stability_delta\": 0.05,\n            \"dynamic_multimask_stability_thresh\": 0.98,\n        },\n        clear_non_cond_mem_around_input=True,\n        fill_hole_area=0,\n        use_memory_selection=apply_temporal_disambiguation,\n    )\n\n    return model\n\n\ndef _create_text_encoder(bpe_path: str) -> VETextEncoder:\n    \"\"\"Create SAM3 text encoder.\"\"\"\n    tokenizer = SimpleTokenizer(bpe_path=bpe_path)\n    return VETextEncoder(\n        tokenizer=tokenizer,\n        d_model=256,\n        width=1024,\n        heads=16,\n        layers=24,\n    )\n\n\ndef _create_vision_backbone(\n    compile_mode=None, enable_inst_interactivity=True\n) -> Sam3DualViTDetNeck:\n    \"\"\"Create SAM3 visual backbone with ViT and neck.\"\"\"\n    # Position encoding\n    position_encoding = _create_position_encoding(precompute_resolution=1008)\n    # ViT backbone\n    vit_backbone: ViT = _create_vit_backbone(compile_mode=compile_mode)\n    vit_neck: Sam3DualViTDetNeck = _create_vit_neck(\n        position_encoding,\n        vit_backbone,\n        enable_inst_interactivity=enable_inst_interactivity,\n    )\n    # Visual neck\n    return vit_neck\n\n\ndef _create_sam3_transformer(has_presence_token: bool = True) -> TransformerWrapper:\n    \"\"\"Create SAM3 transformer encoder and decoder.\"\"\"\n    encoder: TransformerEncoderFusion = _create_transformer_encoder()\n    decoder: TransformerDecoder = _create_transformer_decoder()\n\n    return TransformerWrapper(encoder=encoder, decoder=decoder, d_model=256)\n\n\ndef _load_checkpoint(model, checkpoint_path):\n    \"\"\"Load model checkpoint from file.\"\"\"\n    with g_pathmgr.open(checkpoint_path, \"rb\") as f:\n        ckpt = torch.load(f, map_location=\"cpu\", weights_only=True)\n    if \"model\" in ckpt and isinstance(ckpt[\"model\"], dict):\n        ckpt = ckpt[\"model\"]\n    sam3_image_ckpt = {\n        k.replace(\"detector.\", \"\"): v for k, v in ckpt.items() if \"detector\" in k\n    }\n    if model.inst_interactive_predictor is not None:\n        sam3_image_ckpt.update(\n            {\n                k.replace(\"tracker.\", \"inst_interactive_predictor.model.\"): v\n                for k, v in ckpt.items()\n                if \"tracker\" in k\n            }\n        )\n    missing_keys, _ = model.load_state_dict(sam3_image_ckpt, strict=False)\n    if len(missing_keys) > 0:\n        print(\n            f\"loaded {checkpoint_path} and found \"\n            f\"missing and/or unexpected keys:\\n{missing_keys=}\"\n        )\n\n\ndef _setup_device_and_mode(model, device, eval_mode):\n    \"\"\"Setup model device and evaluation mode.\"\"\"\n    if device == \"cuda\":\n        model = model.cuda()\n    if eval_mode:\n        model.eval()\n    return model\n\n\ndef build_sam3_image_model(\n    bpe_path=None,\n    device=\"cuda\" if torch.cuda.is_available() else \"cpu\",\n    eval_mode=True,\n    checkpoint_path=None,\n    load_from_HF=True,\n    enable_segmentation=True,\n    enable_inst_interactivity=False,\n    compile=False,\n):\n    \"\"\"\n    Build SAM3 image model\n\n    Args:\n        bpe_path: Path to the BPE tokenizer vocabulary\n        device: Device to load the model on ('cuda' or 'cpu')\n        eval_mode: Whether to set the model to evaluation mode\n        checkpoint_path: Optional path to model checkpoint\n        enable_segmentation: Whether to enable segmentation head\n        enable_inst_interactivity: Whether to enable instance interactivity (SAM 1 task)\n        compile_mode: To enable compilation, set to \"default\"\n\n    Returns:\n        A SAM3 image model\n    \"\"\"\n    if bpe_path is None:\n        bpe_path = pkg_resources.resource_filename(\n            \"sam3\", \"assets/bpe_simple_vocab_16e6.txt.gz\"\n        )\n\n    # Create visual components\n    compile_mode = \"default\" if compile else None\n    vision_encoder = _create_vision_backbone(\n        compile_mode=compile_mode, enable_inst_interactivity=enable_inst_interactivity\n    )\n\n    # Create text components\n    text_encoder = _create_text_encoder(bpe_path)\n\n    # Create visual-language backbone\n    backbone = _create_vl_backbone(vision_encoder, text_encoder)\n\n    # Create transformer components\n    transformer = _create_sam3_transformer()\n\n    # Create dot product scoring\n    dot_prod_scoring = _create_dot_product_scoring()\n\n    # Create segmentation head if enabled\n    segmentation_head = (\n        _create_segmentation_head(compile_mode=compile_mode)\n        if enable_segmentation\n        else None\n    )\n\n    # Create geometry encoder\n    input_geometry_encoder = _create_geometry_encoder()\n    if enable_inst_interactivity:\n        sam3_pvs_base = build_tracker(apply_temporal_disambiguation=False)\n        inst_predictor = SAM3InteractiveImagePredictor(sam3_pvs_base)\n    else:\n        inst_predictor = None\n    # Create the SAM3 model\n    model = _create_sam3_model(\n        backbone,\n        transformer,\n        input_geometry_encoder,\n        segmentation_head,\n        dot_prod_scoring,\n        inst_predictor,\n        eval_mode,\n    )\n    if load_from_HF and checkpoint_path is None:\n        checkpoint_path = download_ckpt_from_hf()\n    # Load checkpoint if provided\n    if checkpoint_path is not None:\n        _load_checkpoint(model, checkpoint_path)\n\n    # Setup device and mode\n    model = _setup_device_and_mode(model, device, eval_mode)\n\n    return model\n\n\ndef download_ckpt_from_hf():\n    SAM3_MODEL_ID = \"facebook/sam3\"\n    SAM3_CKPT_NAME = \"sam3.pt\"\n    SAM3_CFG_NAME = \"config.json\"\n    _ = hf_hub_download(repo_id=SAM3_MODEL_ID, filename=SAM3_CFG_NAME)\n    checkpoint_path = hf_hub_download(repo_id=SAM3_MODEL_ID, filename=SAM3_CKPT_NAME)\n    return checkpoint_path\n\n\ndef build_sam3_video_model(\n    checkpoint_path: Optional[str] = None,\n    load_from_HF=True,\n    bpe_path: Optional[str] = None,\n    has_presence_token: bool = True,\n    geo_encoder_use_img_cross_attn: bool = True,\n    strict_state_dict_loading: bool = True,\n    apply_temporal_disambiguation: bool = True,\n    device=\"cuda\" if torch.cuda.is_available() else \"cpu\",\n    compile=False,\n) -> Sam3VideoInferenceWithInstanceInteractivity:\n    \"\"\"\n    Build SAM3 dense tracking model.\n\n    Args:\n        checkpoint_path: Optional path to checkpoint file\n        bpe_path: Path to the BPE tokenizer file\n\n    Returns:\n        Sam3VideoInferenceWithInstanceInteractivity: The instantiated dense tracking model\n    \"\"\"\n    if bpe_path is None:\n        bpe_path = pkg_resources.resource_filename(\n            \"sam3\", \"assets/bpe_simple_vocab_16e6.txt.gz\"\n        )\n\n    # Build Tracker module\n    tracker = build_tracker(apply_temporal_disambiguation=apply_temporal_disambiguation)\n\n    # Build Detector components\n    visual_neck = _create_vision_backbone()\n    text_encoder = _create_text_encoder(bpe_path)\n    backbone = SAM3VLBackbone(scalp=1, visual=visual_neck, text=text_encoder)\n    transformer = _create_sam3_transformer(has_presence_token=has_presence_token)\n    segmentation_head: UniversalSegmentationHead = _create_segmentation_head()\n    input_geometry_encoder = _create_geometry_encoder()\n\n    # Create main dot product scoring\n    main_dot_prod_mlp = MLP(\n        input_dim=256,\n        hidden_dim=2048,\n        output_dim=256,\n        num_layers=2,\n        dropout=0.1,\n        residual=True,\n        out_norm=nn.LayerNorm(256),\n    )\n    main_dot_prod_scoring = DotProductScoring(\n        d_model=256, d_proj=256, prompt_mlp=main_dot_prod_mlp\n    )\n\n    # Build Detector module\n    detector = Sam3ImageOnVideoMultiGPU(\n        num_feature_levels=1,\n        backbone=backbone,\n        transformer=transformer,\n        segmentation_head=segmentation_head,\n        semantic_segmentation_head=None,\n        input_geometry_encoder=input_geometry_encoder,\n        use_early_fusion=True,\n        use_dot_prod_scoring=True,\n        dot_prod_scoring=main_dot_prod_scoring,\n        supervise_joint_box_scores=has_presence_token,\n    )\n\n    # Build the main SAM3 video model\n    if apply_temporal_disambiguation:\n        model = Sam3VideoInferenceWithInstanceInteractivity(\n            detector=detector,\n            tracker=tracker,\n            score_threshold_detection=0.5,\n            assoc_iou_thresh=0.1,\n            det_nms_thresh=0.1,\n            new_det_thresh=0.7,\n            hotstart_delay=15,\n            hotstart_unmatch_thresh=8,\n            hotstart_dup_thresh=8,\n            suppress_unmatched_only_within_hotstart=True,\n            min_trk_keep_alive=-1,\n            max_trk_keep_alive=30,\n            init_trk_keep_alive=30,\n            suppress_overlapping_based_on_recent_occlusion_threshold=0.7,\n            suppress_det_close_to_boundary=False,\n            fill_hole_area=16,\n            recondition_every_nth_frame=16,\n            masklet_confirmation_enable=False,\n            decrease_trk_keep_alive_for_empty_masklets=False,\n            image_size=1008,\n            image_mean=(0.5, 0.5, 0.5),\n            image_std=(0.5, 0.5, 0.5),\n            compile_model=compile,\n        )\n    else:\n        # a version without any heuristics for ablation studies\n        model = Sam3VideoInferenceWithInstanceInteractivity(\n            detector=detector,\n            tracker=tracker,\n            score_threshold_detection=0.5,\n            assoc_iou_thresh=0.1,\n            det_nms_thresh=0.1,\n            new_det_thresh=0.7,\n            hotstart_delay=0,\n            hotstart_unmatch_thresh=0,\n            hotstart_dup_thresh=0,\n            suppress_unmatched_only_within_hotstart=True,\n            min_trk_keep_alive=-1,\n            max_trk_keep_alive=30,\n            init_trk_keep_alive=30,\n            suppress_overlapping_based_on_recent_occlusion_threshold=0.7,\n            suppress_det_close_to_boundary=False,\n            fill_hole_area=16,\n            recondition_every_nth_frame=0,\n            masklet_confirmation_enable=False,\n            decrease_trk_keep_alive_for_empty_masklets=False,\n            image_size=1008,\n            image_mean=(0.5, 0.5, 0.5),\n            image_std=(0.5, 0.5, 0.5),\n            compile_model=compile,\n        )\n\n    # Load checkpoint if provided\n    if load_from_HF and checkpoint_path is None:\n        checkpoint_path = download_ckpt_from_hf()\n    if checkpoint_path is not None:\n        with g_pathmgr.open(checkpoint_path, \"rb\") as f:\n            ckpt = torch.load(f, map_location=\"cpu\", weights_only=True)\n        if \"model\" in ckpt and isinstance(ckpt[\"model\"], dict):\n            ckpt = ckpt[\"model\"]\n\n        missing_keys, unexpected_keys = model.load_state_dict(\n            ckpt, strict=strict_state_dict_loading\n        )\n        if missing_keys:\n            print(f\"Missing keys: {missing_keys}\")\n        if unexpected_keys:\n            print(f\"Unexpected keys: {unexpected_keys}\")\n\n    model.to(device=device)\n    return model\n\n\ndef build_sam3_video_predictor(*model_args, gpus_to_use=None, **model_kwargs):\n    return Sam3VideoPredictorMultiGPU(\n        *model_args, gpus_to_use=gpus_to_use, **model_kwargs\n    )\n"
  },
  {
    "path": "sam3/perflib/__init__.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n\nimport os\n\nis_enabled = False\nif os.getenv(\"USE_PERFLIB\", \"1\") == \"1\":\n    # print(\"Enabled the use of perflib.\\n\", end=\"\")\n    is_enabled = True\n"
  },
  {
    "path": "sam3/perflib/associate_det_trk.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n\nfrom collections import defaultdict\n\nimport torch\nimport torch.nn.functional as F\nfrom sam3.perflib.masks_ops import mask_iou\nfrom scipy.optimize import linear_sum_assignment\n\n\ndef associate_det_trk(\n    det_masks,\n    track_masks,\n    iou_threshold=0.5,\n    iou_threshold_trk=0.5,\n    det_scores=None,\n    new_det_thresh=0.0,\n):\n    \"\"\"\n    Optimized implementation of detection <-> track association that minimizes DtoH syncs.\n\n    Args:\n        det_masks: (N, H, W) tensor of predicted masks\n        track_masks: (M, H, W) tensor of track masks\n\n    Returns:\n        new_det_indices: list of indices in det_masks considered 'new'\n        unmatched_trk_indices: list of indices in track_masks considered 'unmatched'\n    \"\"\"\n    with torch.autograd.profiler.record_function(\"perflib: associate_det_trk\"):\n        assert isinstance(det_masks, torch.Tensor), \"det_masks should be a tensor\"\n        assert isinstance(track_masks, torch.Tensor), \"track_masks should be a tensor\"\n        if det_masks.size(0) == 0 or track_masks.size(0) == 0:\n            return list(range(det_masks.size(0))), [], {}, {}  # all detections are new\n\n        if list(det_masks.shape[-2:]) != list(track_masks.shape[-2:]):\n            # resize to the smaller size to save GPU memory\n            if torch.numel(det_masks[-2:]) < torch.numel(track_masks[-2:]):\n                track_masks = (\n                    F.interpolate(\n                        track_masks.unsqueeze(1).float(),\n                        size=det_masks.shape[-2:],\n                        mode=\"bilinear\",\n                        align_corners=False,\n                    ).squeeze(1)\n                    > 0\n                )\n            else:\n                # resize detections to track size\n                det_masks = (\n                    F.interpolate(\n                        det_masks.unsqueeze(1).float(),\n                        size=track_masks.shape[-2:],\n                        mode=\"bilinear\",\n                        align_corners=False,\n                    ).squeeze(1)\n                    > 0\n                )\n\n        det_masks = det_masks > 0\n        track_masks = track_masks > 0\n\n        iou = mask_iou(det_masks, track_masks)  # (N, M)\n        igeit = iou >= iou_threshold\n        igeit_any_dim_1 = igeit.any(dim=1)\n        igeit_trk = iou >= iou_threshold_trk\n\n        iou_list = iou.cpu().numpy().tolist()\n        igeit_list = igeit.cpu().numpy().tolist()\n        igeit_any_dim_1_list = igeit_any_dim_1.cpu().numpy().tolist()\n        igeit_trk_list = igeit_trk.cpu().numpy().tolist()\n\n        det_scores_list = (\n            det_scores\n            if det_scores is None\n            else det_scores.cpu().float().numpy().tolist()\n        )\n\n        # Hungarian matching for tracks (one-to-one: each track matches at most one detection)\n        # For detections: allow many tracks to match to the same detection (many-to-one)\n\n        # If either is empty, return all detections as new\n        if det_masks.size(0) == 0 or track_masks.size(0) == 0:\n            return list(range(det_masks.size(0))), [], {}\n\n        # Hungarian matching: maximize IoU for tracks\n        cost_matrix = 1 - iou.cpu().numpy()  # Hungarian solves for minimum cost\n        row_ind, col_ind = linear_sum_assignment(cost_matrix)\n\n        def branchy_hungarian_better_uses_the_cpu(\n            cost_matrix, row_ind, col_ind, iou_list, det_masks, track_masks\n        ):\n            matched_trk = set()\n            matched_det = set()\n            matched_det_scores = {}  # track index -> [det_score, det_score * iou] det score of matched detection mask\n            for d, t in zip(row_ind, col_ind):\n                matched_det_scores[t] = [\n                    det_scores_list[d],\n                    det_scores_list[d] * iou_list[d][t],\n                ]\n                if igeit_trk_list[d][t]:\n                    matched_trk.add(t)\n                    matched_det.add(d)\n\n            # Tracks not matched by Hungarian assignment above threshold are unmatched\n            unmatched_trk_indices = [\n                t for t in range(track_masks.size(0)) if t not in matched_trk\n            ]\n\n            # For detections: allow many tracks to match to the same detection (many-to-one)\n            # So, a detection is 'new' if it does not match any track above threshold\n            assert track_masks.size(0) == igeit.size(\n                1\n            )  # Needed for loop optimizaiton below\n            new_det_indices = []\n            for d in range(det_masks.size(0)):\n                if not igeit_any_dim_1_list[d]:\n                    if det_scores is not None and det_scores[d] >= new_det_thresh:\n                        new_det_indices.append(d)\n\n            # for each detection, which tracks it matched to (above threshold)\n            det_to_matched_trk = defaultdict(list)\n            for d in range(det_masks.size(0)):\n                for t in range(track_masks.size(0)):\n                    if igeit_list[d][t]:\n                        det_to_matched_trk[d].append(t)\n\n            return (\n                new_det_indices,\n                unmatched_trk_indices,\n                det_to_matched_trk,\n                matched_det_scores,\n            )\n\n        return (branchy_hungarian_better_uses_the_cpu)(\n            cost_matrix, row_ind, col_ind, iou_list, det_masks, track_masks\n        )\n"
  },
  {
    "path": "sam3/perflib/compile.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n\nimport torch\n\n\ndef recursive_fn_factory(fn):\n    def recursive_fn(b):\n        if isinstance(b, dict):\n            return {k: recursive_fn(b[k]) for k in b}\n        if isinstance(b, list):\n            return [recursive_fn(t) for t in b]\n        if isinstance(b, tuple):\n            return tuple(recursive_fn(t) for t in b)\n        if isinstance(b, torch.Tensor):\n            return fn(b)\n        # Yes, writing out an explicit white list of\n        # trivial types is tedious, but so are bugs that\n        # come from not applying fn, when expected to have\n        # applied it.\n        if b is None:\n            return b\n        trivial_types = [bool, int]\n        for t in trivial_types:\n            if isinstance(b, t):\n                return b\n        raise TypeError(f\"Unexpected type {type(b)}\")\n\n    return recursive_fn\n\n\nrecursive_contiguous = recursive_fn_factory(lambda x: x.contiguous())\nrecursive_clone = recursive_fn_factory(torch.clone)\n\n\ndef compile_wrapper(\n    fn, *, mode=\"max-autotune\", fullgraph=True, dynamic=False, name=None\n):\n    compiled_fn = torch.compile(fn, mode=mode, fullgraph=fullgraph, dynamic=dynamic)\n\n    def compiled_fn_wrapper(*args, **kwargs):\n        with torch.autograd.profiler.record_function(\n            f\"compiled {fn}\" if name is None else name\n        ):\n            cont_args = recursive_contiguous(args)\n            cont_kwargs = recursive_contiguous(kwargs)\n            result = compiled_fn(*cont_args, **cont_kwargs)\n            cloned_result = recursive_clone(result)\n            return cloned_result\n\n    return compiled_fn_wrapper\n\n\ndef shape_logging_wrapper(fn, keep_kwargs, enable_logging=False):\n    \"\"\"\n    Wraps a function and prints the shapes of all tensor inputs.\n    Only prints when a new combination of shapes is seen.\n    Thread-safe.\n\n    Args:\n        fn: Function to wrap\n        enable_logging: Boolean flag to enable/disable logging\n    \"\"\"\n    seen_shapes = set()\n\n    def get_shape(obj):\n        if isinstance(obj, torch.Tensor):\n            return obj.shape\n        elif isinstance(obj, (list, tuple)):\n            if len(obj) > 1:\n                return tuple(get_shape(x) for x in obj)\n            return get_shape(obj[0])\n        elif isinstance(obj, dict):\n            return tuple(sorted((k, get_shape(v)) for k, v in obj.items()))\n        else:\n            return type(obj).__name__\n\n    def wrapper(*args, **kwargs):\n        shapes = tuple(get_shape(arg) for arg in args) + tuple(\n            (k, get_shape(v))\n            for k, v in kwargs.items()\n            if isinstance(v, (torch.Tensor, list))\n            and (len(keep_kwargs) > 0 and k in keep_kwargs)\n        )\n        if shapes not in seen_shapes:\n            seen_shapes.add(shapes)\n            if enable_logging:\n                print(f\"[ShapeLogger] New input shapes for {fn.__qualname__}: {shapes}\")\n        return fn(*args, **kwargs)\n\n    # Allow toggling the flag at runtime\n    wrapper.enable_logging = enable_logging\n\n    def set_logging(enabled=False):\n        nonlocal enable_logging\n        enable_logging = enabled\n        wrapper.enable_logging = enable_logging\n\n    wrapper.set_logging = set_logging\n    return wrapper\n"
  },
  {
    "path": "sam3/perflib/connected_components.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\nimport logging\n\nimport torch\n\ntry:\n    from cc_torch import get_connected_components\n\n    HAS_CC_TORCH = True\nexcept ImportError:\n    logging.debug(\n        \"cc_torch not found. Consider installing for better performance. Command line:\"\n        \" pip install git+https://github.com/ronghanghu/cc_torch.git\"\n    )\n    HAS_CC_TORCH = False\n\n\ndef connected_components_cpu_single(values: torch.Tensor):\n    assert values.dim() == 2\n    from skimage.measure import label\n\n    labels, num = label(values.cpu().numpy(), return_num=True)\n    labels = torch.from_numpy(labels)\n    counts = torch.zeros_like(labels)\n    for i in range(1, num + 1):\n        cur_mask = labels == i\n        cur_count = cur_mask.sum()\n        counts[cur_mask] = cur_count\n    return labels, counts\n\n\ndef connected_components_cpu(input_tensor: torch.Tensor):\n    out_shape = input_tensor.shape\n    if input_tensor.dim() == 4 and input_tensor.shape[1] == 1:\n        input_tensor = input_tensor.squeeze(1)\n    else:\n        assert input_tensor.dim() == 3, (\n            \"Input tensor must be (B, H, W) or (B, 1, H, W).\"\n        )\n\n    batch_size = input_tensor.shape[0]\n    labels_list = []\n    counts_list = []\n    for b in range(batch_size):\n        labels, counts = connected_components_cpu_single(input_tensor[b])\n        labels_list.append(labels)\n        counts_list.append(counts)\n    labels_tensor = torch.stack(labels_list, dim=0).to(input_tensor.device)\n    counts_tensor = torch.stack(counts_list, dim=0).to(input_tensor.device)\n    return labels_tensor.view(out_shape), counts_tensor.view(out_shape)\n\n\ndef connected_components(input_tensor: torch.Tensor):\n    \"\"\"\n    Computes connected components labeling on a batch of 2D tensors, using the best available backend.\n\n    Args:\n        input_tensor (torch.Tensor): A BxHxW integer tensor or Bx1xHxW. Non-zero values are considered foreground. Bool tensor also accepted\n\n    Returns:\n        Tuple[torch.Tensor, torch.Tensor]: Both tensors have the same shape as input_tensor.\n            - A tensor with dense labels. Background is 0.\n            - A tensor with the size of the connected component for each pixel.\n    \"\"\"\n    if input_tensor.dim() == 3:\n        input_tensor = input_tensor.unsqueeze(1)\n\n    assert input_tensor.dim() == 4 and input_tensor.shape[1] == 1, (\n        \"Input tensor must be (B, H, W) or (B, 1, H, W).\"\n    )\n\n    if input_tensor.is_cuda:\n        if HAS_CC_TORCH:\n            return get_connected_components(input_tensor.to(torch.uint8))\n        else:\n            # triton fallback\n            from sam3.perflib.triton.connected_components import (\n                connected_components_triton,\n            )\n\n            return connected_components_triton(input_tensor)\n\n    # CPU fallback\n    return connected_components_cpu(input_tensor)\n"
  },
  {
    "path": "sam3/perflib/fa3.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n\nimport torch\n\n\n@torch.library.custom_op(\"flash::flash_attn_func\", mutates_args=())\ndef flash_attn_func_op(\n    q: torch.Tensor, k: torch.Tensor, v: torch.Tensor\n) -> torch.Tensor:\n    from flash_attn_interface import flash_attn_func as fa3\n\n    return fa3(q, k, v)\n\n\ndef flash_attn_func(q, k, v):\n    dtype = torch.float8_e4m3fn\n    return flash_attn_func_op(q.to(dtype), k.to(dtype), v.to(dtype)).to(q.dtype)\n\n\n@flash_attn_func_op.register_fake\ndef _(q, k, v, **kwargs):\n    # two outputs:\n    # 1. output: (batch, seq_len, num_heads, head_dim)\n    # 2. softmax_lse: (batch, num_heads, seq_len) with dtype=torch.float32\n    # output needs to be bfloat16, not float8!\n    meta_q = torch.empty_like(q, dtype=torch.bfloat16).contiguous()\n    return meta_q\n"
  },
  {
    "path": "sam3/perflib/masks_ops.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n\nimport torch\n\n\ndef masks_to_boxes(masks: torch.Tensor, obj_ids: list[int]):\n    with torch.autograd.profiler.record_function(\"perflib: masks_to_boxes\"):\n        # Sanity check based on callsite for replacement\n        assert masks.shape[0] == len(obj_ids)\n        assert masks.dim() == 3\n\n        # Based on torchvision masks_to_boxes\n        if masks.numel() == 0:\n            return torch.zeros((0, 4), device=masks.device, dtype=torch.float)\n\n        N, H, W = masks.shape\n        device = masks.device\n        y = torch.arange(H, device=device).view(1, H)\n        x = torch.arange(W, device=device).view(1, W)\n\n        masks_with_obj = masks != 0  # N, H, W\n        masks_with_obj_x = masks_with_obj.amax(\n            dim=1\n        )  # N, H (which columns have objects)\n        masks_with_obj_y = masks_with_obj.amax(dim=2)  # N, W (which rows have objects)\n        masks_without_obj_x = ~masks_with_obj_x\n        masks_without_obj_y = ~masks_with_obj_y\n\n        bounding_boxes_0 = torch.amin(\n            (masks_without_obj_x * W) + (masks_with_obj_x * x), dim=1\n        )\n        bounding_boxes_1 = torch.amin(\n            (masks_without_obj_y * H) + (masks_with_obj_y * y), dim=1\n        )\n        bounding_boxes_2 = torch.amax(masks_with_obj_x * x, dim=1)\n        bounding_boxes_3 = torch.amax(masks_with_obj_y * y, dim=1)\n\n        bounding_boxes = torch.stack(\n            [bounding_boxes_0, bounding_boxes_1, bounding_boxes_2, bounding_boxes_3],\n            dim=1,\n        ).to(dtype=torch.float)\n        assert bounding_boxes.shape == (N, 4)\n        assert bounding_boxes.device == masks.device\n        assert bounding_boxes.dtype == torch.float\n        return bounding_boxes\n\n\ndef mask_iou(pred_masks: torch.Tensor, gt_masks: torch.Tensor) -> torch.Tensor:\n    \"\"\"\n    Compute the IoU (Intersection over Union) between predicted masks and ground truth masks.\n    Args:\n      - pred_masks: (N, H, W) bool Tensor, containing binary predicted segmentation masks\n      - gt_masks: (M, H, W) bool Tensor, containing binary ground truth segmentation masks\n    Returns:\n      - ious: (N, M) float Tensor, containing IoUs for each pair of predicted and ground truth masks\n    \"\"\"\n    assert pred_masks.dtype == gt_masks.dtype == torch.bool\n    N, H, W = pred_masks.shape\n    M, _, _ = gt_masks.shape\n\n    # Flatten masks: (N, 1, H*W) and (1, M, H*W)\n    pred_flat = pred_masks.view(N, 1, H * W)\n    gt_flat = gt_masks.view(1, M, H * W)\n\n    # Compute intersection and union: (N, M)\n    intersection = (pred_flat & gt_flat).sum(dim=2).float()\n    union = (pred_flat | gt_flat).sum(dim=2).float()\n    ious = intersection / union.clamp(min=1)\n    return ious  # shape: (N, M)\n"
  },
  {
    "path": "sam3/perflib/nms.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n\nimport logging\n\nimport numpy as np\nimport torch\nfrom sam3.perflib.masks_ops import mask_iou\n\n\ntry:\n    from torch_generic_nms import generic_nms as generic_nms_cuda\n\n    GENERIC_NMS_AVAILABLE = True\nexcept ImportError:\n    logging.debug(\n        \"Falling back to triton or CPU mask NMS implementation -- please install `torch_generic_nms` via\\n\\t\"\n        'pip uninstall -y torch_generic_nms; TORCH_CUDA_ARCH_LIST=\"8.0 9.0\" pip install git+https://github.com/ronghanghu/torch_generic_nms'\n    )\n    GENERIC_NMS_AVAILABLE = False\n\n\ndef nms_masks(\n    pred_probs: torch.Tensor,\n    pred_masks: torch.Tensor,\n    prob_threshold: float,\n    iou_threshold: float,\n) -> torch.Tensor:\n    \"\"\"\n    Args:\n      - pred_probs: (num_det,) float Tensor, containing the score (probability) of each detection\n      - pred_masks: (num_det, H_mask, W_mask) float Tensor, containing the binary segmentation mask of each detection\n      - prob_threshold: float, score threshold to prefilter detections (NMS is performed on detections above threshold)\n      - iou_threshold: float, mask IoU threshold for NMS\n\n    Returns:\n     - keep: (num_det,) bool Tensor, indicating whether each detection is kept after score thresholding + NMS\n    \"\"\"\n    # prefilter the detections with prob_threshold (\"valid\" are those above prob_threshold)\n    is_valid = pred_probs > prob_threshold  # (num_det,)\n    probs = pred_probs[is_valid]  # (num_valid,)\n    masks_binary = pred_masks[is_valid] > 0  # (num_valid, H_mask, W_mask)\n    if probs.numel() == 0:\n        return is_valid  # no valid detection, return empty keep mask\n\n    ious = mask_iou(masks_binary, masks_binary)  # (num_valid, num_valid)\n    kept_inds = generic_nms(ious, probs, iou_threshold)\n\n    # valid_inds are the indices among `probs` of valid detections before NMS (or -1 for invalid)\n    valid_inds = torch.where(is_valid, is_valid.cumsum(dim=0) - 1, -1)  # (num_det,)\n    keep = torch.isin(valid_inds, kept_inds)  # (num_det,)\n    return keep\n\n\ndef generic_nms(\n    ious: torch.Tensor, scores: torch.Tensor, iou_threshold=0.5\n) -> torch.Tensor:\n    \"\"\"A generic version of `torchvision.ops.nms` that takes a pairwise IoU matrix.\"\"\"\n\n    assert ious.dim() == 2 and ious.size(0) == ious.size(1)\n    assert scores.dim() == 1 and scores.size(0) == ious.size(0)\n\n    if ious.is_cuda:\n        if GENERIC_NMS_AVAILABLE:\n            return generic_nms_cuda(ious, scores, iou_threshold, use_iou_matrix=True)\n        else:\n            from sam3.perflib.triton.nms import nms_triton\n\n            return nms_triton(ious, scores, iou_threshold)\n\n    return generic_nms_cpu(ious, scores, iou_threshold)\n\n\ndef generic_nms_cpu(\n    ious: torch.Tensor, scores: torch.Tensor, iou_threshold=0.5\n) -> torch.Tensor:\n    \"\"\"\n    A generic version of `torchvision.ops.nms` that takes a pairwise IoU matrix. (CPU implementation\n    based on https://github.com/jwyang/faster-rcnn.pytorch/blob/master/lib/model/nms/nms_cpu.py)\n    \"\"\"\n    ious_np = ious.float().detach().cpu().numpy()\n    scores_np = scores.float().detach().cpu().numpy()\n    order = scores_np.argsort()[::-1]\n    kept_inds = []\n    while order.size > 0:\n        i = order.item(0)\n        kept_inds.append(i)\n        inds = np.where(ious_np[i, order[1:]] <= iou_threshold)[0]\n        order = order[inds + 1]\n\n    return torch.tensor(kept_inds, dtype=torch.int64, device=scores.device)\n"
  },
  {
    "path": "sam3/perflib/tests/tests.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n\nimport os\n\nimport numpy as np\nimport pytest\nimport torch\nfrom PIL import Image\nfrom sam3.perflib.masks_ops import masks_to_boxes\n\n\nclass TestMasksToBoxes:\n    def test_masks_box(self):\n        def masks_box_check(masks, expected, atol=1e-4):\n            out = masks_to_boxes(masks, [1 for _ in range(masks.shape[0])])\n            assert out.dtype == torch.float\n            print(\"out: \", out)\n            print(\"expected: \", expected)\n            torch.testing.assert_close(\n                out, expected, rtol=0.0, check_dtype=True, atol=atol\n            )\n\n        # Check for int type boxes.\n        def _get_image():\n            assets_directory = os.path.join(\n                os.path.dirname(os.path.abspath(__file__)), \"assets\"\n            )\n            mask_path = os.path.join(assets_directory, \"masks.tiff\")\n            image = Image.open(mask_path)\n            return image\n\n        def _create_masks(image, masks):\n            for index in range(image.n_frames):\n                image.seek(index)\n                frame = np.array(image)\n                masks[index] = torch.tensor(frame)\n\n            return masks\n\n        expected = torch.tensor(\n            [\n                [127, 2, 165, 40],\n                [2, 50, 44, 92],\n                [56, 63, 98, 100],\n                [139, 68, 175, 104],\n                [160, 112, 198, 145],\n                [49, 138, 99, 182],\n                [108, 148, 152, 213],\n            ],\n            dtype=torch.float,\n        )\n\n        image = _get_image()\n        for dtype in [torch.float16, torch.float32, torch.float64]:\n            masks = torch.zeros(\n                (image.n_frames, image.height, image.width), dtype=dtype\n            )\n            masks = _create_masks(image, masks)\n            masks_box_check(masks, expected)\n"
  },
  {
    "path": "sam3/perflib/triton/connected_components.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\nimport math\n\nimport torch\nimport triton\nimport triton.language as tl\n\n\n@triton.jit\ndef _any_combine(a, b):\n    return a | b\n\n\n@triton.jit\ndef tl_any(a, dim=0):\n    return tl.reduce(a, dim, _any_combine)\n\n\n# ==============================================================================\n# ## Phase 1: Initialization Kernel\n# ==============================================================================\n# Each foreground pixel (value > 0) gets a unique label equal to its\n# linear index. Background pixels (value == 0) get a sentinel label of -1.\n# Note that the indexing is done across batch boundaries for simplicity\n# (i.e., the first pixel of image 1 gets label H*W, etc.)\n\n\n@triton.jit\ndef _init_labels_kernel(\n    input_ptr, labels_ptr, numel: tl.constexpr, BLOCK_SIZE: tl.constexpr\n):\n    pid = tl.program_id(0)\n    offsets = pid * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)\n    mask = offsets < numel\n    input_values = tl.load(input_ptr + offsets, mask=mask, other=0)\n\n    indices = tl.where((input_values != 0), offsets, -1)\n    tl.store(labels_ptr + offsets, indices, mask=mask)\n\n\n# ==============================================================================\n# ## Phase 2: Local merging\n# ==============================================================================\n# Each pixel tries to merge with its 8-connected neighbors (up, down, left, right)\n# if they have the same value. This is done using a disjoint-set union operation.\n\n\n@triton.jit\ndef find(labels_ptr, indices, mask):\n    current_pids = indices\n\n    # 'is_done' tracks lanes that have finished their work.\n    # A lane is initially \"done\" if it's not active (mask is False).\n    is_done = ~mask\n\n    # Loop as long as there is at least one lane that is NOT done.\n    while tl_any(~is_done):\n        # The work_mask is for lanes that are still active and seeking their root.\n        work_mask = ~is_done\n        parents = tl.load(labels_ptr + current_pids, mask=work_mask, other=-1)\n        # A lane is now done if its parent is itself (it's a root)\n        # or if it hits a -1 sentinel (a safe exit condition).\n        is_root = parents == current_pids\n        is_sentinel = parents == -1\n        is_done |= is_root | is_sentinel\n\n        # For lanes that are not yet done, update their pid to their parent to continue traversal.\n        current_pids = tl.where(is_done, current_pids, parents)\n    # We could add the following line to do path compression, but experimentally it's slower\n    # tl.atomic_min(labels_ptr + indices, current_pids, mask=mask)\n    return current_pids\n\n\n@triton.jit\ndef union(labels_ptr, a, b, process_mask):\n    # This function implements a disjoint-set union\n    # As an invariant, we use the fact that the roots have the lower id. That helps parallelization\n    # However, that is not sufficient by itself. Suppose two threads want to do union(0,2) and union(1,2) at the same time\n    # Then if we do a naive atomic_min, 0 and 1 will compete to be the new parent of 2 and min(0, 1) will win.\n    # However, 1 still needs to be merged with the new {0, 2} component.\n    # To ensure that merge is also done, we need to detect whether the merge was successful, and if not retry until it is\n\n    current_a = a\n    current_b = b\n\n    final_root = a\n    # A mask to track which lanes have successfully completed their union.\n    done_mask = ~process_mask  # tl.zeros_like(a) == 1  # Init with all False\n\n    while tl_any(~done_mask):\n        # Define the mask for lanes that still need work in this iteration\n        work_mask = process_mask & ~done_mask\n\n        # Find the roots for the current a and b values in the active lanes\n        root_a = find(labels_ptr, current_a, work_mask)\n        tl.debug_barrier()\n        root_b = find(labels_ptr, current_b, work_mask)\n\n        # 7. Merge logic\n        # If roots are already the same, the sets are already merged. Mark as done.\n        are_equal = root_a == root_b\n        final_root = tl.where(are_equal & work_mask & ~done_mask, root_a, final_root)\n        done_mask |= are_equal & work_mask\n\n        # Define masks for the two merge cases (a < b or b < a)\n        a_is_smaller = root_a < root_b\n\n        # Case 1: root_a < root_b. Attempt to set parent[root_b] = root_a\n        merge_mask_a_smaller = work_mask & a_is_smaller & ~are_equal\n        ptr_b = labels_ptr + root_b\n        old_val_b = tl.atomic_min(ptr_b, root_a, mask=merge_mask_a_smaller)\n\n        # A lane is done if its atomic op was successful (old value was what we expected)\n        success_b = old_val_b == root_b\n        final_root = tl.where(success_b & work_mask & ~done_mask, root_a, final_root)\n        done_mask |= success_b & merge_mask_a_smaller\n\n        # *** Crucial Retry Logic ***\n        # If the update failed (old_val_b != root_b), another thread interfered.\n        # We update `current_b` to this new root (`old_val_b`) and will retry in the next loop iteration.\n        current_b = tl.where(success_b | ~merge_mask_a_smaller, current_b, old_val_b)\n\n        # Case 2: root_b < root_a. Attempt to set parent[root_a] = root_b\n        merge_mask_b_smaller = work_mask & ~a_is_smaller & ~are_equal\n        ptr_a = labels_ptr + root_a\n        old_val_a = tl.atomic_min(ptr_a, root_b, mask=merge_mask_b_smaller)\n\n        success_a = old_val_a == root_a\n        final_root = tl.where(success_a & work_mask & ~done_mask, root_b, final_root)\n        done_mask |= success_a & merge_mask_b_smaller\n\n        # *** Crucial Retry Logic ***\n        # Similarly, update `current_a` if the atomic operation failed.\n        current_a = tl.where(success_a | ~merge_mask_b_smaller, current_a, old_val_a)\n\n    return final_root\n\n\n@triton.jit\ndef _merge_helper(\n    input_ptr,\n    labels_ptr,\n    base_offset,\n    offsets_h,\n    offsets_w,\n    mask_2d,\n    valid_current,\n    current_values,\n    current_labels,\n    H,\n    W,\n    dx: tl.constexpr,\n    dy: tl.constexpr,\n):\n    # Helper functions to compute merge with a specific neighbor offset (dx, dy)\n\n    neighbor_h = offsets_h + dy\n    neighbor_w = offsets_w + dx\n    # Proper bounds checking: all four bounds must be satisfied\n    mask_n = (\n        mask_2d\n        & (neighbor_h[:, None] >= 0)\n        & (neighbor_h[:, None] < H)\n        & (neighbor_w[None, :] >= 0)\n        & (neighbor_w[None, :] < W)\n    )\n\n    offsets_neighbor = neighbor_h[:, None] * W + neighbor_w[None, :]\n    neighbor_values = tl.load(\n        input_ptr + base_offset + offsets_neighbor, mask=mask_n, other=-1\n    )\n\n    mask_n = tl.ravel(mask_n)\n    neighbor_labels = tl.load(\n        labels_ptr + tl.ravel(base_offset + offsets_neighbor), mask=mask_n, other=-1\n    )\n\n    to_merge = (\n        mask_n & (neighbor_labels != -1) & tl.ravel(current_values == neighbor_values)\n    )\n    valid_write = valid_current & to_merge\n\n    # returns new parents for the pixels that were merged (otherwise keeps current labels)\n    return tl.where(\n        valid_write,\n        union(labels_ptr, current_labels, neighbor_labels, valid_write),\n        current_labels,\n    )\n\n\n@triton.autotune(\n    configs=[\n        triton.Config(\n            {\"BLOCK_SIZE_H\": 4, \"BLOCK_SIZE_W\": 16}, num_stages=1, num_warps=2\n        ),\n        triton.Config(\n            {\"BLOCK_SIZE_H\": 4, \"BLOCK_SIZE_W\": 32}, num_stages=2, num_warps=4\n        ),\n    ],\n    key=[\"H\", \"W\"],\n    restore_value=[\"labels_ptr\"],\n)\n@triton.jit\ndef _local_prop_kernel(\n    labels_ptr,\n    input_ptr,\n    H: tl.constexpr,\n    W: tl.constexpr,\n    BLOCK_SIZE_H: tl.constexpr,\n    BLOCK_SIZE_W: tl.constexpr,\n):\n    # This is the meat of the Phase 2 to do local merging\n    # It will be launched with a 2D grid:\n    # - dim 0: batch index\n    # - dim 1: block index over HxW image (2D tiling)\n    pid_b = tl.program_id(0)\n    pid_hw = tl.program_id(1)\n\n    # Calculate offsets for the core block\n    offsets_h = (pid_hw // tl.cdiv(W, BLOCK_SIZE_W)) * BLOCK_SIZE_H + tl.arange(\n        0, BLOCK_SIZE_H\n    )\n    offsets_w = (pid_hw % tl.cdiv(W, BLOCK_SIZE_W)) * BLOCK_SIZE_W + tl.arange(\n        0, BLOCK_SIZE_W\n    )\n\n    base_offset = pid_b * H * W\n    offsets_2d = offsets_h[:, None] * W + offsets_w[None, :]\n    mask_2d = (offsets_h[:, None] < H) & (offsets_w[None, :] < W)\n    mask_1d = tl.ravel(mask_2d)\n\n    # Load the current labels for the block - these are parent pointers\n    current_labels = tl.load(\n        labels_ptr + tl.ravel(base_offset + offsets_2d), mask=mask_1d, other=-1\n    )\n    current_values = tl.load(\n        input_ptr + base_offset + offsets_2d, mask=mask_2d, other=-1\n    )\n    valid_current = mask_1d & (current_labels != -1)\n\n    # Horizontal merge\n    current_labels = _merge_helper(\n        input_ptr,\n        labels_ptr,\n        base_offset,\n        offsets_h,\n        offsets_w,\n        mask_2d,\n        valid_current,\n        current_values,\n        current_labels,\n        H,\n        W,\n        -1,\n        0,\n    )\n    # Vertical merge\n    current_labels = _merge_helper(\n        input_ptr,\n        labels_ptr,\n        base_offset,\n        offsets_h,\n        offsets_w,\n        mask_2d,\n        valid_current,\n        current_values,\n        current_labels,\n        H,\n        W,\n        0,\n        -1,\n    )\n    # Diagonal merges\n    current_labels = _merge_helper(\n        input_ptr,\n        labels_ptr,\n        base_offset,\n        offsets_h,\n        offsets_w,\n        mask_2d,\n        valid_current,\n        current_values,\n        current_labels,\n        H,\n        W,\n        -1,\n        -1,\n    )\n    current_labels = _merge_helper(\n        input_ptr,\n        labels_ptr,\n        base_offset,\n        offsets_h,\n        offsets_w,\n        mask_2d,\n        valid_current,\n        current_values,\n        current_labels,\n        H,\n        W,\n        -1,\n        1,\n    )\n\n    # This actually does some path compression, in a lightweight but beneficial way\n    tl.atomic_min(\n        labels_ptr + tl.ravel(base_offset + offsets_2d), current_labels, mask=mask_1d\n    )\n\n\n# ==============================================================================\n# ## Phase 3: Pointer Jumping Kernel\n# ==============================================================================\n# This kernel performs pointer jumping to ensure that all pixels point directly to their root labels.\n# This is done in a loop until convergence.\n\n\n@triton.jit\ndef _pointer_jump_kernel(\n    labels_in_ptr, labels_out_ptr, numel: tl.constexpr, BLOCK_SIZE: tl.constexpr\n):\n    \"\"\"\n    Pointer jumping kernel with double buffering to avoid race conditions.\n    Reads from labels_in_ptr and writes to labels_out_ptr.\n    \"\"\"\n    # This kernel is launched with a 1D grid, and does not care about batching explicitly.\n    # By construction, the labels are global indices across the batch, and we never perform\n    # cross-batch merges, so this is safe.\n\n    pid = tl.program_id(0)\n    offsets = pid * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)\n    mask = offsets < numel\n\n    # Load current labels from input buffer\n    current_labels = tl.load(labels_in_ptr + offsets, mask=mask, other=-1)\n    valid_mask = mask & (current_labels != -1)\n\n    # A mask to track which lanes have successfully completed their union.\n    done_mask = ~valid_mask\n    while tl_any(~(done_mask | ~valid_mask)):\n        parent_labels = tl.load(\n            labels_in_ptr + current_labels, mask=valid_mask, other=-1\n        )\n\n        are_equal = current_labels == parent_labels\n        done_mask |= are_equal & valid_mask\n\n        current_labels = tl.where(\n            ~done_mask, tl.minimum(current_labels, parent_labels), current_labels\n        )\n\n    # Write to output buffer (safe because we're not reading from it)\n    tl.store(labels_out_ptr + offsets, current_labels, mask=mask)\n\n\n# ==============================================================================\n# ## Phase 4: Kernels for Computing Component Sizes\n# ==============================================================================\n\n\n# Step 4.1: Count occurrences of each root label using atomic adds.\n@triton.jit\ndef _count_labels_kernel(labels_ptr, sizes_ptr, numel, BLOCK_SIZE: tl.constexpr):\n    pid = tl.program_id(0)\n    offsets = pid * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)\n    mask = offsets < numel\n\n    # Load the final, converged labels\n    labels = tl.load(labels_ptr + offsets, mask=mask, other=-1)\n    valid_mask = mask & (labels != -1)\n\n    # Atomically increment the counter for each label. This builds a histogram.\n    tl.atomic_add(sizes_ptr + labels, 1, mask=valid_mask)\n\n\n# Step 4.2: Broadcast the computed sizes back to the output tensor.\n@triton.jit\ndef _broadcast_sizes_kernel(\n    labels_ptr, sizes_ptr, out_ptr, numel, BLOCK_SIZE: tl.constexpr\n):\n    pid = tl.program_id(0)\n    offsets = pid * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)\n    mask = offsets < numel\n\n    # Load the final labels\n    labels = tl.load(labels_ptr + offsets, mask=mask, other=-1)\n    valid_mask = mask & (labels != -1)\n\n    # Look up the size for each label from the histogram\n    component_sizes = tl.load(sizes_ptr + labels, mask=valid_mask, other=0)\n\n    # Write the size to the final output tensor. Background pixels get size 0.\n    tl.store(out_ptr + offsets, component_sizes, mask=mask)\n\n\ndef connected_components_triton(input_tensor: torch.Tensor):\n    \"\"\"\n    Computes connected components labeling on a batch of 2D integer tensors using Triton.\n\n    Args:\n        input_tensor (torch.Tensor): A BxHxW integer tensor or Bx1xHxW. Non-zero values are considered foreground. Bool tensor also accepted\n\n    Returns:\n        Tuple[torch.Tensor, int]: A tuple containing:\n            - A BxHxW output tensor with dense labels. Background is 0.\n            - A BxHxW tensor with the size of the connected component for each pixel.\n    \"\"\"\n    assert input_tensor.is_cuda and input_tensor.is_contiguous(), (\n        \"Input tensor must be a contiguous CUDA tensor.\"\n    )\n    out_shape = input_tensor.shape\n    if input_tensor.dim() == 4 and input_tensor.shape[1] == 1:\n        input_tensor = input_tensor.squeeze(1)\n    else:\n        assert input_tensor.dim() == 3, (\n            \"Input tensor must be (B, H, W) or (B, 1, H, W).\"\n        )\n\n    B, H, W = input_tensor.shape\n    numel = B * H * W\n    device = input_tensor.device\n\n    # --- Allocate Tensors ---\n    labels = torch.empty_like(input_tensor, dtype=torch.int32)\n    output = torch.empty_like(input_tensor, dtype=torch.int32)\n\n    # --- Phase 1 ---\n    BLOCK_SIZE = 256\n    grid_init = (triton.cdiv(numel, BLOCK_SIZE),)\n    _init_labels_kernel[grid_init](\n        input_tensor,\n        labels,\n        numel,\n        BLOCK_SIZE=BLOCK_SIZE,\n    )\n\n    # --- Phase 2 ---\n    grid_local_prop = lambda meta: (\n        B,\n        triton.cdiv(H, meta[\"BLOCK_SIZE_H\"]) * triton.cdiv(W, meta[\"BLOCK_SIZE_W\"]),\n    )\n    _local_prop_kernel[grid_local_prop](labels, input_tensor, H, W)\n\n    # --- Phase 3 ---\n    BLOCK_SIZE = 256\n    grid_jump = lambda meta: (triton.cdiv(numel, meta[\"BLOCK_SIZE\"]),)\n    _pointer_jump_kernel[grid_jump](labels, output, numel, BLOCK_SIZE=BLOCK_SIZE)\n\n    # --- Phase 4 ---\n    # Allocate tensor to store the final output sizes\n    component_sizes_out = torch.empty_like(input_tensor, dtype=torch.int32)\n\n    # Allocate a temporary 1D tensor to act as the histogram\n    # Size is numel because labels can be up to numel-1\n    sizes_histogram = torch.zeros(numel, dtype=torch.int32, device=device)\n\n    # 4.1: Count the occurrences of each label\n    grid_count = (triton.cdiv(numel, BLOCK_SIZE),)\n    _count_labels_kernel[grid_count](\n        output, sizes_histogram, numel, BLOCK_SIZE=BLOCK_SIZE\n    )\n\n    # 2.2: Broadcast the counts to the final output tensor\n    grid_broadcast = (triton.cdiv(numel, BLOCK_SIZE),)\n    _broadcast_sizes_kernel[grid_broadcast](\n        output, sizes_histogram, component_sizes_out, numel, BLOCK_SIZE=BLOCK_SIZE\n    )\n    return output.view(out_shape) + 1, component_sizes_out.view(out_shape)\n"
  },
  {
    "path": "sam3/perflib/triton/nms.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n\n# Adapted from https://github.com/stackav-oss/conch/blob/main/conch/kernels/vision/nms.py\n\nimport torch\nimport triton\nimport triton.language as tl\n\n\n@triton.autotune(\n    configs=[\n        triton.Config({\"cxpr_block_size\": 128}),\n        triton.Config({\"cxpr_block_size\": 256}),\n        triton.Config({\"cxpr_block_size\": 512}),\n        triton.Config({\"cxpr_block_size\": 1024}),\n        triton.Config({\"cxpr_block_size\": 2048}),\n        triton.Config({\"cxpr_block_size\": 4096}),\n        triton.Config({\"cxpr_block_size\": 8192}),\n    ],\n    key=[\"num_boxes\"],\n)\n@triton.jit\ndef _nms_suppression_kernel(\n    # Tensors\n    iou_mask_ptr: tl.tensor,  # [N, N]\n    keep_mask_ptr: tl.tensor,  # [N]\n    # Scalars\n    num_boxes: tl.int32,\n    # Strides\n    iou_mask_stride: tl.int32,\n    # Constexprs\n    cxpr_block_size: tl.constexpr,\n) -> None:\n    \"\"\"NMS suppression kernel.\n\n    Args:\n        iou_mask_ptr: Pointer to precomputed IoU mask, shape: (N, N).\n        keep_mask_ptr: Pointer to keep mask tensor, shape: (N,).\n        num_boxes: Number of boxes.\n        iou_mask_stride: Stride for IoU mask tensor.\n        cxpr_block_size: Block size for processing.\n    \"\"\"\n    # Sequential NMS: for each box in sorted order, suppress later boxes\n    for current_box_idx in range(num_boxes - 1):\n        # Check if current box is still kept\n        is_kept = tl.load(keep_mask_ptr + current_box_idx)\n        if is_kept:\n            # IoU mask row offset for the current box\n            # Because the IoU mask is sorted by score, we will only consider boxes that come after the current box.\n            # This means we only need to read the upper triangular part of the IoU mask.\n            iou_row_offset = current_box_idx * iou_mask_stride\n\n            # Only process boxes that come after the current box\n            next_box_idx = current_box_idx + 1\n            remaining_boxes = num_boxes - next_box_idx\n\n            # Iterate blockwise through the columns\n            for block_idx in range(tl.cdiv(remaining_boxes, cxpr_block_size)):\n                # Masked load of indices for the target boxes in the current block\n                block_start = next_box_idx + block_idx * cxpr_block_size\n                target_box_offsets = block_start + tl.arange(0, cxpr_block_size)\n                target_box_mask = target_box_offsets < num_boxes\n\n                # Suppress boxes with lower scores that have high IoU\n                suppression_mask = tl.load(\n                    iou_mask_ptr + iou_row_offset + target_box_offsets,\n                    mask=target_box_mask,\n                    other=False,\n                )\n                suppression_mask = tl.cast(suppression_mask, tl.int1)\n\n                # Conditionally store suppression result for high-IoU boxes\n                tl.store(\n                    keep_mask_ptr + target_box_offsets, False, mask=suppression_mask\n                )\n\n            # Potential race condition: we need to ensure all threads complete the store before the next\n            # iteration otherwise we may load stale data for whether or not a box has been suppressed.\n            tl.debug_barrier()\n\n\ndef nms_triton(\n    ious: torch.Tensor,\n    scores: torch.Tensor,\n    iou_threshold: float,\n) -> torch.Tensor:\n    \"\"\"Perform NMS given the iou matrix, the scores and the iou threshold\n\n    Args:\n        ious: Pairwise IoU tensor of shape (N, N).\n        scores: Scores tensor of shape (N,).\n        iou_threshold: IoU threshold for suppression.\n\n    Returns:\n        Tensor: Indices of kept boxes, sorted by decreasing score.\n    \"\"\"\n    assert scores.dim() == 1, \"Scores must be 1D\"\n    iou_mask = ious > iou_threshold\n    assert iou_mask.dim() == 2\n    assert iou_mask.shape[0] == iou_mask.shape[1] == scores.shape[0]\n    assert iou_mask.device == scores.device\n    assert iou_mask.dtype == torch.bool\n\n    num_boxes = scores.size(0)\n    keep_mask = torch.ones(len(scores), device=scores.device, dtype=torch.bool)\n\n    # Sort boxes by scores in descending order\n    _, sorted_indices = torch.sort(scores, dim=0, stable=True, descending=True)\n    iou_mask = iou_mask[sorted_indices][:, sorted_indices].contiguous()\n\n    # For the suppression stage, we need to process sequentially, but we'll still take\n    # advantage of parallelism by processing in blocks in one program.\n    stage2_grid = (1,)\n    _nms_suppression_kernel[stage2_grid](\n        # Tensors\n        iou_mask_ptr=iou_mask,\n        keep_mask_ptr=keep_mask,\n        # Scalars\n        num_boxes=num_boxes,\n        # Strides\n        iou_mask_stride=iou_mask.stride(0),\n    )\n    # Extract indices of kept boxes\n    return sorted_indices[keep_mask]\n"
  },
  {
    "path": "sam3/sam/__init__.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\nfrom .mask_decoder import MaskDecoder\nfrom .prompt_encoder import PromptEncoder\nfrom .transformer import TwoWayTransformer\n"
  },
  {
    "path": "sam3/sam/common.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n\nfrom typing import Type\n\nimport torch\nimport torch.nn as nn\n\n\nclass MLPBlock(nn.Module):\n    def __init__(\n        self,\n        embedding_dim: int,\n        mlp_dim: int,\n        act: Type[nn.Module] = nn.GELU,\n    ) -> None:\n        super().__init__()\n        self.lin1 = nn.Linear(embedding_dim, mlp_dim)\n        self.lin2 = nn.Linear(mlp_dim, embedding_dim)\n        self.act = act()\n\n    def forward(self, x: torch.Tensor) -> torch.Tensor:\n        return self.lin2(self.act(self.lin1(x)))\n\n\n# From https://github.com/facebookresearch/detectron2/blob/main/detectron2/layers/batch_norm.py # noqa\n# Itself from https://github.com/facebookresearch/ConvNeXt/blob/d1fa8f6fef0a165b27399986cc2bdacc92777e40/models/convnext.py#L119  # noqa\nclass LayerNorm2d(nn.Module):\n    def __init__(self, num_channels: int, eps: float = 1e-6) -> None:\n        super().__init__()\n        self.weight = nn.Parameter(torch.ones(num_channels))\n        self.bias = nn.Parameter(torch.zeros(num_channels))\n        self.eps = eps\n\n    def forward(self, x: torch.Tensor) -> torch.Tensor:\n        u = x.mean(1, keepdim=True)\n        s = (x - u).pow(2).mean(1, keepdim=True)\n        x = (x - u) / torch.sqrt(s + self.eps)\n        x = self.weight[:, None, None] * x + self.bias[:, None, None]\n        return x\n"
  },
  {
    "path": "sam3/sam/mask_decoder.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n\nfrom typing import List, Optional, Tuple, Type\n\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\n\nfrom .common import LayerNorm2d\n\n\nclass MaskDecoder(nn.Module):\n    def __init__(\n        self,\n        *,\n        transformer_dim: int,\n        transformer: nn.Module,\n        num_multimask_outputs: int = 3,\n        activation: Type[nn.Module] = nn.GELU,\n        iou_head_depth: int = 3,\n        iou_head_hidden_dim: int = 256,\n        use_high_res_features: bool = False,\n        iou_prediction_use_sigmoid=False,\n        dynamic_multimask_via_stability=False,\n        dynamic_multimask_stability_delta=0.05,\n        dynamic_multimask_stability_thresh=0.98,\n        pred_obj_scores: bool = False,\n        pred_obj_scores_mlp: bool = False,\n        use_multimask_token_for_obj_ptr: bool = False,\n    ) -> None:\n        \"\"\"\n        Predicts masks given an image and prompt embeddings, using a\n        transformer architecture.\n\n        Arguments:\n          transformer_dim (int): the channel dimension of the transformer\n          transformer (nn.Module): the transformer used to predict masks\n          num_multimask_outputs (int): the number of masks to predict\n            when disambiguating masks\n          activation (nn.Module): the type of activation to use when\n            upscaling masks\n          iou_head_depth (int): the depth of the MLP used to predict\n            mask quality\n          iou_head_hidden_dim (int): the hidden dimension of the MLP\n            used to predict mask quality\n        \"\"\"\n        super().__init__()\n        self.transformer_dim = transformer_dim\n        self.transformer = transformer\n\n        self.num_multimask_outputs = num_multimask_outputs\n\n        self.iou_token = nn.Embedding(1, transformer_dim)\n        self.num_mask_tokens = num_multimask_outputs + 1\n        self.mask_tokens = nn.Embedding(self.num_mask_tokens, transformer_dim)\n\n        self.pred_obj_scores = pred_obj_scores\n        if self.pred_obj_scores:\n            self.obj_score_token = nn.Embedding(1, transformer_dim)\n        self.use_multimask_token_for_obj_ptr = use_multimask_token_for_obj_ptr\n\n        self.output_upscaling = nn.Sequential(\n            nn.ConvTranspose2d(\n                transformer_dim, transformer_dim // 4, kernel_size=2, stride=2\n            ),\n            LayerNorm2d(transformer_dim // 4),\n            activation(),\n            nn.ConvTranspose2d(\n                transformer_dim // 4, transformer_dim // 8, kernel_size=2, stride=2\n            ),\n            activation(),\n        )\n        self.use_high_res_features = use_high_res_features\n        if use_high_res_features:\n            self.conv_s0 = nn.Conv2d(\n                transformer_dim, transformer_dim // 8, kernel_size=1, stride=1\n            )\n            self.conv_s1 = nn.Conv2d(\n                transformer_dim, transformer_dim // 4, kernel_size=1, stride=1\n            )\n\n        self.output_hypernetworks_mlps = nn.ModuleList(\n            [\n                MLP(transformer_dim, transformer_dim, transformer_dim // 8, 3)\n                for i in range(self.num_mask_tokens)\n            ]\n        )\n\n        self.iou_prediction_head = MLP(\n            transformer_dim,\n            iou_head_hidden_dim,\n            self.num_mask_tokens,\n            iou_head_depth,\n            sigmoid_output=iou_prediction_use_sigmoid,\n        )\n        if self.pred_obj_scores:\n            self.pred_obj_score_head = nn.Linear(transformer_dim, 1)\n            if pred_obj_scores_mlp:\n                self.pred_obj_score_head = MLP(transformer_dim, transformer_dim, 1, 3)\n\n        # When outputting a single mask, optionally we can dynamically fall back to the best\n        # multimask output token if the single mask output token gives low stability scores.\n        self.dynamic_multimask_via_stability = dynamic_multimask_via_stability\n        self.dynamic_multimask_stability_delta = dynamic_multimask_stability_delta\n        self.dynamic_multimask_stability_thresh = dynamic_multimask_stability_thresh\n\n    def forward(\n        self,\n        image_embeddings: torch.Tensor,\n        image_pe: torch.Tensor,\n        sparse_prompt_embeddings: torch.Tensor,\n        dense_prompt_embeddings: torch.Tensor,\n        multimask_output: bool,\n        repeat_image: bool,\n        high_res_features: Optional[List[torch.Tensor]] = None,\n    ) -> Tuple[torch.Tensor, torch.Tensor]:\n        \"\"\"\n        Predict masks given image and prompt embeddings.\n\n        Arguments:\n          image_embeddings (torch.Tensor): the embeddings from the image encoder\n          image_pe (torch.Tensor): positional encoding with the shape of image_embeddings\n          sparse_prompt_embeddings (torch.Tensor): the embeddings of the points and boxes\n          dense_prompt_embeddings (torch.Tensor): the embeddings of the mask inputs\n          multimask_output (bool): Whether to return multiple masks or a single\n            mask.\n\n        Returns:\n          torch.Tensor: batched predicted masks\n          torch.Tensor: batched predictions of mask quality\n          torch.Tensor: batched SAM token for mask output\n        \"\"\"\n        masks, iou_pred, mask_tokens_out, object_score_logits = self.predict_masks(\n            image_embeddings=image_embeddings,\n            image_pe=image_pe,\n            sparse_prompt_embeddings=sparse_prompt_embeddings,\n            dense_prompt_embeddings=dense_prompt_embeddings,\n            repeat_image=repeat_image,\n            high_res_features=high_res_features,\n        )\n\n        # Select the correct mask or masks for output\n        if multimask_output:\n            masks = masks[:, 1:, :, :]\n            iou_pred = iou_pred[:, 1:]\n        elif self.dynamic_multimask_via_stability and not self.training:\n            masks, iou_pred = self._dynamic_multimask_via_stability(masks, iou_pred)\n        else:\n            masks = masks[:, 0:1, :, :]\n            iou_pred = iou_pred[:, 0:1]\n\n        if multimask_output and self.use_multimask_token_for_obj_ptr:\n            sam_tokens_out = mask_tokens_out[:, 1:]  # [b, 3, c] shape\n        else:\n            # Take the mask output token. Here we *always* use the token for single mask output.\n            # At test time, even if we track after 1-click (and using multimask_output=True),\n            # we still take the single mask token here. The rationale is that we always track\n            # after multiple clicks during training, so the past tokens seen during training\n            # are always the single mask token (and we'll let it be the object-memory token).\n            sam_tokens_out = mask_tokens_out[:, 0:1]  # [b, 1, c] shape\n\n        # Prepare output\n        return masks, iou_pred, sam_tokens_out, object_score_logits\n\n    def predict_masks(\n        self,\n        image_embeddings: torch.Tensor,\n        image_pe: torch.Tensor,\n        sparse_prompt_embeddings: torch.Tensor,\n        dense_prompt_embeddings: torch.Tensor,\n        repeat_image: bool,\n        high_res_features: Optional[List[torch.Tensor]] = None,\n    ) -> Tuple[torch.Tensor, torch.Tensor]:\n        \"\"\"Predicts masks. See 'forward' for more details.\"\"\"\n        # Concatenate output tokens\n        s = 0\n        if self.pred_obj_scores:\n            output_tokens = torch.cat(\n                [\n                    self.obj_score_token.weight,\n                    self.iou_token.weight,\n                    self.mask_tokens.weight,\n                ],\n                dim=0,\n            )\n            s = 1\n        else:\n            output_tokens = torch.cat(\n                [self.iou_token.weight, self.mask_tokens.weight], dim=0\n            )\n        output_tokens = output_tokens.unsqueeze(0).expand(\n            sparse_prompt_embeddings.size(0), -1, -1\n        )\n        tokens = torch.cat((output_tokens, sparse_prompt_embeddings), dim=1)\n\n        # Expand per-image data in batch direction to be per-mask\n        if repeat_image:\n            src = torch.repeat_interleave(image_embeddings, tokens.shape[0], dim=0)\n        else:\n            assert image_embeddings.shape[0] == tokens.shape[0]\n            src = image_embeddings\n        src = src + dense_prompt_embeddings\n        assert image_pe.size(0) == 1, (\n            \"image_pe should have size 1 in batch dim (from `get_dense_pe()`)\"\n        )\n        pos_src = torch.repeat_interleave(image_pe, tokens.shape[0], dim=0)\n        b, c, h, w = src.shape\n\n        # Run the transformer\n        hs, src = self.transformer(src, pos_src, tokens)\n        iou_token_out = hs[:, s, :]\n        mask_tokens_out = hs[:, s + 1 : (s + 1 + self.num_mask_tokens), :]\n\n        # Upscale mask embeddings and predict masks using the mask tokens\n        src = src.transpose(1, 2).view(b, c, h, w)\n        if not self.use_high_res_features:\n            upscaled_embedding = self.output_upscaling(src)\n        else:\n            dc1, ln1, act1, dc2, act2 = self.output_upscaling\n            feat_s0, feat_s1 = high_res_features\n            upscaled_embedding = act1(ln1(dc1(src) + feat_s1))\n            upscaled_embedding = act2(dc2(upscaled_embedding) + feat_s0)\n\n        hyper_in_list: List[torch.Tensor] = []\n        for i in range(self.num_mask_tokens):\n            hyper_in_list.append(\n                self.output_hypernetworks_mlps[i](mask_tokens_out[:, i, :])\n            )\n        hyper_in = torch.stack(hyper_in_list, dim=1)\n        b, c, h, w = upscaled_embedding.shape\n        masks = (hyper_in @ upscaled_embedding.view(b, c, h * w)).view(b, -1, h, w)\n\n        # Generate mask quality predictions\n        iou_pred = self.iou_prediction_head(iou_token_out)\n        if self.pred_obj_scores:\n            assert s == 1\n            object_score_logits = self.pred_obj_score_head(hs[:, 0, :])\n        else:\n            # Obj scores logits - default to 10.0, i.e. assuming the object is present, sigmoid(10)=1\n            object_score_logits = 10.0 * iou_pred.new_ones(iou_pred.shape[0], 1)\n\n        return masks, iou_pred, mask_tokens_out, object_score_logits\n\n    def _get_stability_scores(self, mask_logits):\n        \"\"\"\n        Compute stability scores of the mask logits based on the IoU between upper and\n        lower thresholds.\n        \"\"\"\n        mask_logits = mask_logits.flatten(-2)\n        stability_delta = self.dynamic_multimask_stability_delta\n        area_i = torch.sum(mask_logits > stability_delta, dim=-1).float()\n        area_u = torch.sum(mask_logits > -stability_delta, dim=-1).float()\n        stability_scores = torch.where(area_u > 0, area_i / area_u, 1.0)\n        return stability_scores\n\n    def _dynamic_multimask_via_stability(self, all_mask_logits, all_iou_scores):\n        \"\"\"\n        When outputting a single mask, if the stability score from the current single-mask\n        output (based on output token 0) falls below a threshold, we instead select from\n        multi-mask outputs (based on output token 1~3) the mask with the highest predicted\n        IoU score. This is intended to ensure a valid mask for both clicking and tracking.\n        \"\"\"\n        # The best mask from multimask output tokens (1~3)\n        multimask_logits = all_mask_logits[:, 1:, :, :]\n        multimask_iou_scores = all_iou_scores[:, 1:]\n        best_scores_inds = torch.argmax(multimask_iou_scores, dim=-1)\n        batch_inds = torch.arange(\n            multimask_iou_scores.size(0), device=all_iou_scores.device\n        )\n        best_multimask_logits = multimask_logits[batch_inds, best_scores_inds]\n        best_multimask_logits = best_multimask_logits.unsqueeze(1)\n        best_multimask_iou_scores = multimask_iou_scores[batch_inds, best_scores_inds]\n        best_multimask_iou_scores = best_multimask_iou_scores.unsqueeze(1)\n\n        # The mask from singlemask output token 0 and its stability score\n        singlemask_logits = all_mask_logits[:, 0:1, :, :]\n        singlemask_iou_scores = all_iou_scores[:, 0:1]\n        stability_scores = self._get_stability_scores(singlemask_logits)\n        is_stable = stability_scores >= self.dynamic_multimask_stability_thresh\n\n        # Dynamically fall back to best multimask output upon low stability scores.\n        mask_logits_out = torch.where(\n            is_stable[..., None, None].expand_as(singlemask_logits),\n            singlemask_logits,\n            best_multimask_logits,\n        )\n        iou_scores_out = torch.where(\n            is_stable.expand_as(singlemask_iou_scores),\n            singlemask_iou_scores,\n            best_multimask_iou_scores,\n        )\n        return mask_logits_out, iou_scores_out\n\n\n# Lightly adapted from\n# https://github.com/facebookresearch/MaskFormer/blob/main/mask_former/modeling/transformer/transformer_predictor.py # noqa\nclass MLP(nn.Module):\n    def __init__(\n        self,\n        input_dim: int,\n        hidden_dim: int,\n        output_dim: int,\n        num_layers: int,\n        sigmoid_output: bool = False,\n    ) -> None:\n        super().__init__()\n        self.num_layers = num_layers\n        h = [hidden_dim] * (num_layers - 1)\n        self.layers = nn.ModuleList(\n            nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim])\n        )\n        self.sigmoid_output = sigmoid_output\n\n    def forward(self, x):\n        for i, layer in enumerate(self.layers):\n            x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x)\n        if self.sigmoid_output:\n            x = F.sigmoid(x)\n        return x\n"
  },
  {
    "path": "sam3/sam/prompt_encoder.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n\nfrom typing import Any, Optional, Tuple, Type\n\nimport numpy as np\nimport torch\nfrom torch import nn\n\nfrom .common import LayerNorm2d\n\n\nclass PromptEncoder(nn.Module):\n    def __init__(\n        self,\n        embed_dim: int,\n        image_embedding_size: Tuple[int, int],\n        input_image_size: Tuple[int, int],\n        mask_in_chans: int,\n        activation: Type[nn.Module] = nn.GELU,\n    ) -> None:\n        \"\"\"\n        Encodes prompts for input to SAM's mask decoder.\n\n        Arguments:\n          embed_dim (int): The prompts' embedding dimension\n          image_embedding_size (tuple(int, int)): The spatial size of the\n            image embedding, as (H, W).\n          input_image_size (int): The padded size of the image as input\n            to the image encoder, as (H, W).\n          mask_in_chans (int): The number of hidden channels used for\n            encoding input masks.\n          activation (nn.Module): The activation to use when encoding\n            input masks.\n        \"\"\"\n        super().__init__()\n        self.embed_dim = embed_dim\n        self.input_image_size = input_image_size\n        self.image_embedding_size = image_embedding_size\n        self.pe_layer = PositionEmbeddingRandom(embed_dim // 2)\n\n        self.num_point_embeddings: int = 4  # pos/neg point + 2 box corners\n        point_embeddings = [\n            nn.Embedding(1, embed_dim) for i in range(self.num_point_embeddings)\n        ]\n        self.point_embeddings = nn.ModuleList(point_embeddings)\n        self.not_a_point_embed = nn.Embedding(1, embed_dim)\n\n        self.mask_input_size = (\n            4 * image_embedding_size[0],\n            4 * image_embedding_size[1],\n        )\n        self.mask_downscaling = nn.Sequential(\n            nn.Conv2d(1, mask_in_chans // 4, kernel_size=2, stride=2),\n            LayerNorm2d(mask_in_chans // 4),\n            activation(),\n            nn.Conv2d(mask_in_chans // 4, mask_in_chans, kernel_size=2, stride=2),\n            LayerNorm2d(mask_in_chans),\n            activation(),\n            nn.Conv2d(mask_in_chans, embed_dim, kernel_size=1),\n        )\n        self.no_mask_embed = nn.Embedding(1, embed_dim)\n\n    def get_dense_pe(self) -> torch.Tensor:\n        \"\"\"\n        Returns the positional encoding used to encode point prompts,\n        applied to a dense set of points the shape of the image encoding.\n\n        Returns:\n          torch.Tensor: Positional encoding with shape\n            1x(embed_dim)x(embedding_h)x(embedding_w)\n        \"\"\"\n        return self.pe_layer(self.image_embedding_size).unsqueeze(0)\n\n    def _embed_points(\n        self,\n        points: torch.Tensor,\n        labels: torch.Tensor,\n        pad: bool,\n    ) -> torch.Tensor:\n        \"\"\"Embeds point prompts.\"\"\"\n        points = points + 0.5  # Shift to center of pixel\n        if pad:\n            padding_point = torch.zeros((points.shape[0], 1, 2), device=points.device)\n            padding_label = -torch.ones((labels.shape[0], 1), device=labels.device)\n            points = torch.cat([points, padding_point], dim=1)\n            labels = torch.cat([labels, padding_label], dim=1)\n        point_embedding = self.pe_layer.forward_with_coords(\n            points, self.input_image_size\n        )\n\n        point_embedding = torch.where(\n            (labels == -1).unsqueeze(-1),\n            torch.zeros_like(point_embedding) + self.not_a_point_embed.weight,\n            point_embedding,\n        )\n        point_embedding = torch.where(\n            (labels == 0).unsqueeze(-1),\n            point_embedding + self.point_embeddings[0].weight,\n            point_embedding,\n        )\n        point_embedding = torch.where(\n            (labels == 1).unsqueeze(-1),\n            point_embedding + self.point_embeddings[1].weight,\n            point_embedding,\n        )\n        point_embedding = torch.where(\n            (labels == 2).unsqueeze(-1),\n            point_embedding + self.point_embeddings[2].weight,\n            point_embedding,\n        )\n        point_embedding = torch.where(\n            (labels == 3).unsqueeze(-1),\n            point_embedding + self.point_embeddings[3].weight,\n            point_embedding,\n        )\n        return point_embedding\n\n    def _embed_boxes(self, boxes: torch.Tensor) -> torch.Tensor:\n        \"\"\"Embeds box prompts.\"\"\"\n        boxes = boxes + 0.5  # Shift to center of pixel\n        coords = boxes.reshape(-1, 2, 2)\n        corner_embedding = self.pe_layer.forward_with_coords(\n            coords, self.input_image_size\n        )\n        corner_embedding[:, 0, :] += self.point_embeddings[2].weight\n        corner_embedding[:, 1, :] += self.point_embeddings[3].weight\n        return corner_embedding\n\n    def _embed_masks(self, masks: torch.Tensor) -> torch.Tensor:\n        \"\"\"Embeds mask inputs.\"\"\"\n        mask_embedding = self.mask_downscaling(masks)\n        return mask_embedding\n\n    def _get_batch_size(\n        self,\n        points: Optional[Tuple[torch.Tensor, torch.Tensor]],\n        boxes: Optional[torch.Tensor],\n        masks: Optional[torch.Tensor],\n    ) -> int:\n        \"\"\"\n        Gets the batch size of the output given the batch size of the input prompts.\n        \"\"\"\n        if points is not None:\n            return points[0].shape[0]\n        elif boxes is not None:\n            return boxes.shape[0]\n        elif masks is not None:\n            return masks.shape[0]\n        else:\n            return 1\n\n    def _get_device(self) -> torch.device:\n        return self.point_embeddings[0].weight.device\n\n    def forward(\n        self,\n        points: Optional[Tuple[torch.Tensor, torch.Tensor]],\n        boxes: Optional[torch.Tensor],\n        masks: Optional[torch.Tensor],\n    ) -> Tuple[torch.Tensor, torch.Tensor]:\n        \"\"\"\n        Embeds different types of prompts, returning both sparse and dense\n        embeddings.\n\n        Arguments:\n          points (tuple(torch.Tensor, torch.Tensor) or none): point coordinates\n            and labels to embed.\n          boxes (torch.Tensor or none): boxes to embed\n          masks (torch.Tensor or none): masks to embed\n\n        Returns:\n          torch.Tensor: sparse embeddings for the points and boxes, with shape\n            BxNx(embed_dim), where N is determined by the number of input points\n            and boxes.\n          torch.Tensor: dense embeddings for the masks, in the shape\n            Bx(embed_dim)x(embed_H)x(embed_W)\n        \"\"\"\n        bs = self._get_batch_size(points, boxes, masks)\n        sparse_embeddings = torch.empty(\n            (bs, 0, self.embed_dim), device=self._get_device()\n        )\n        if points is not None:\n            coords, labels = points\n            point_embeddings = self._embed_points(coords, labels, pad=(boxes is None))\n            sparse_embeddings = torch.cat([sparse_embeddings, point_embeddings], dim=1)\n        if boxes is not None:\n            box_embeddings = self._embed_boxes(boxes)\n            sparse_embeddings = torch.cat([sparse_embeddings, box_embeddings], dim=1)\n\n        if masks is not None:\n            dense_embeddings = self._embed_masks(masks)\n        else:\n            dense_embeddings = self.no_mask_embed.weight.reshape(1, -1, 1, 1).expand(\n                bs, -1, self.image_embedding_size[0], self.image_embedding_size[1]\n            )\n\n        return sparse_embeddings, dense_embeddings\n\n\nclass PositionEmbeddingRandom(nn.Module):\n    \"\"\"\n    Positional encoding using random spatial frequencies.\n    \"\"\"\n\n    def __init__(self, num_pos_feats: int = 64, scale: Optional[float] = None) -> None:\n        super().__init__()\n        if scale is None or scale <= 0.0:\n            scale = 1.0\n        self.register_buffer(\n            \"positional_encoding_gaussian_matrix\",\n            scale * torch.randn((2, num_pos_feats)),\n        )\n\n    def _pe_encoding(self, coords: torch.Tensor) -> torch.Tensor:\n        \"\"\"Positionally encode points that are normalized to [0,1].\"\"\"\n        # assuming coords are in [0, 1]^2 square and have d_1 x ... x d_n x 2 shape\n        coords = 2 * coords - 1\n        coords = coords @ self.positional_encoding_gaussian_matrix\n        coords = 2 * np.pi * coords\n        # outputs d_1 x ... x d_n x C shape\n        return torch.cat([torch.sin(coords), torch.cos(coords)], dim=-1)\n\n    def forward(self, size: Tuple[int, int]) -> torch.Tensor:\n        \"\"\"Generate positional encoding for a grid of the specified size.\"\"\"\n        h, w = size\n        device: Any = self.positional_encoding_gaussian_matrix.device\n        grid = torch.ones((h, w), device=device, dtype=torch.float32)\n        y_embed = grid.cumsum(dim=0) - 0.5\n        x_embed = grid.cumsum(dim=1) - 0.5\n        y_embed = y_embed / h\n        x_embed = x_embed / w\n\n        pe = self._pe_encoding(torch.stack([x_embed, y_embed], dim=-1))\n        return pe.permute(2, 0, 1)  # C x H x W\n\n    def forward_with_coords(\n        self, coords_input: torch.Tensor, image_size: Tuple[int, int]\n    ) -> torch.Tensor:\n        \"\"\"Positionally encode points that are not normalized to [0,1].\"\"\"\n        coords = coords_input.clone()\n        coords[:, :, 0] = coords[:, :, 0] / image_size[1]\n        coords[:, :, 1] = coords[:, :, 1] / image_size[0]\n        return self._pe_encoding(coords.to(torch.float))  # B x N x C\n"
  },
  {
    "path": "sam3/sam/rope.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n\n\"\"\"\nAdapted from:\n1. https://github.com/meta-llama/codellama/blob/main/llama/model.py\n2. https://github.com/naver-ai/rope-vit\n3. https://github.com/lucidrains/rotary-embedding-torch\n\"\"\"\n\nfrom typing import Optional\n\nimport torch\nfrom einops import rearrange, repeat\nfrom torch import broadcast_tensors, nn\n\n\ndef init_t_xy(end_x: int, end_y: int, scale: float = 1.0, offset: int = 0, device=None):\n    t = torch.arange(end_x * end_y, dtype=torch.float32, device=device)\n    t_x = (t % end_x).float()\n    t_y = torch.div(t, end_x, rounding_mode=\"floor\").float()\n    return t_x * scale + offset, t_y * scale + offset\n\n\ndef compute_axial_cis(\n    dim: int,\n    end_x: int,\n    end_y: int,\n    theta: float = 10000.0,\n    scale_pos: float = 1.0,\n    offset: int = 0,\n    device=None,\n):\n    freqs_x = 1.0 / (\n        theta ** (torch.arange(0, dim, 4, device=device)[: (dim // 4)].float() / dim)\n    )\n    freqs_y = 1.0 / (\n        theta ** (torch.arange(0, dim, 4, device=device)[: (dim // 4)].float() / dim)\n    )\n\n    t_x, t_y = init_t_xy(end_x, end_y, scale_pos, offset, device=device)\n    freqs_x = torch.outer(t_x, freqs_x)\n    freqs_y = torch.outer(t_y, freqs_y)\n    freqs_cis_x = torch.polar(torch.ones_like(freqs_x), freqs_x)\n    freqs_cis_y = torch.polar(torch.ones_like(freqs_y), freqs_y)\n    return torch.cat([freqs_cis_x, freqs_cis_y], dim=-1)\n\n\ndef reshape_for_broadcast(freqs_cis: torch.Tensor, x: torch.Tensor):\n    ndim = x.ndim\n    assert 0 <= 1 < ndim\n    assert freqs_cis.shape == (x.shape[-2], x.shape[-1])\n    shape = [d if i >= ndim - 2 else 1 for i, d in enumerate(x.shape)]\n    return freqs_cis.view(*shape)\n\n\ndef apply_rotary_enc(\n    xq: torch.Tensor,\n    xk: torch.Tensor,\n    freqs_cis: torch.Tensor,\n    repeat_freqs_k: bool = False,\n):\n    xq_ = torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1, 2))\n    xk_ = (\n        torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1, 2))\n        if xk.shape[-2] != 0\n        else None\n    )\n    freqs_cis = reshape_for_broadcast(freqs_cis, xq_)\n    xq_out = torch.view_as_real(xq_ * freqs_cis).flatten(3)\n    if xk_ is None:\n        # no keys to rotate, due to dropout\n        return xq_out.type_as(xq).to(xq.device), xk\n    # repeat freqs along seq_len dim to match k seq_len\n    if repeat_freqs_k:\n        r = xk_.shape[-2] // xq_.shape[-2]\n        freqs_cis = freqs_cis.repeat(*([1] * (freqs_cis.ndim - 2)), r, 1)\n    xk_out = torch.view_as_real(xk_ * freqs_cis).flatten(3)\n    return xq_out.type_as(xq).to(xq.device), xk_out.type_as(xk).to(xk.device)\n\n\ndef complex_mult(xq_real, xq_imag, freqs_cis_real, freqs_cis_imag):\n    # Compute the real part of the product\n    real_part = xq_real * freqs_cis_real - xq_imag * freqs_cis_imag\n    # Compute the imaginary part of the product\n    imag_part = xq_real * freqs_cis_imag + xq_imag * freqs_cis_real\n    # Stack the real and imaginary parts along the last dimension\n    return torch.stack([real_part, imag_part], dim=-1)\n\n\ndef apply_rotary_enc_real(\n    xq: torch.Tensor,\n    xk: torch.Tensor,\n    freqs_cis_real: torch.Tensor,\n    freqs_cis_imag: torch.Tensor,\n    repeat_freqs_k: bool = False,\n):\n    assert xk is not None\n    assert xk.shape[-2] != 0\n\n    xq_real = xq.float().reshape(*xq.shape[:-1], -1, 2)[..., 0]\n    xq_imag = xq.float().reshape(*xq.shape[:-1], -1, 2)[..., 1]\n    xk_real = xk.float().reshape(*xk.shape[:-1], -1, 2)[..., 0]\n    xk_imag = xk.float().reshape(*xk.shape[:-1], -1, 2)[..., 1]\n    freqs_cis_real = reshape_for_broadcast(freqs_cis_real, xq_real)\n    freqs_cis_imag = reshape_for_broadcast(freqs_cis_imag, xq_imag)\n    xq_out = complex_mult(xq_real, xq_imag, freqs_cis_real, freqs_cis_imag).flatten(3)\n    if repeat_freqs_k:\n        r = xk_real.shape[-2] // xq_real.shape[-2]\n        freqs_cis_real = freqs_cis_real.repeat(*([1] * (freqs_cis_real.ndim - 2)), r, 1)\n        freqs_cis_imag = freqs_cis_imag.repeat(*([1] * (freqs_cis_imag.ndim - 2)), r, 1)\n    xk_out = complex_mult(xk_real, xk_imag, freqs_cis_real, freqs_cis_imag).flatten(3)\n    # xq_out = torch.view_as_real(torch.complex(xq_real, xq_imag) * torch.complex(freqs_cis_real, freqs_cis_imag)).flatten(3)\n    # xk_out = torch.view_as_real(torch.compelx(xk_real, xk_imag) * torch.complex(freqs_cis_real, freqs_cis_imag)).flatten(3)\n    return xq_out.type_as(xq).to(xq.device), xk_out.type_as(xk).to(xk.device)\n\n\n# rotary embedding helper functions\ndef broadcat(tensors, dim=-1):\n    broadcasted_tensors = broadcast_tensors(*tensors)\n    return torch.cat(broadcasted_tensors, dim=dim)\n\n\ndef rotate_half(x: torch.Tensor):\n    x = rearrange(x, \"... (d r) -> ... d r\", r=2)\n    x1, x2 = x.unbind(dim=-1)\n    x = torch.stack((-x2, x1), dim=-1)\n    return rearrange(x, \"... d r -> ... (d r)\")\n\n\nclass VisionRotaryEmbeddingVE(nn.Module):\n    def __init__(\n        self,\n        dim: int,\n        seq_len: int,\n        pt_seq_len: Optional[int] = None,\n        theta: float = 10000.0,\n        offset: int = 1,  # specific to VE\n    ):\n        super().__init__()\n\n        freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim))\n        scale = 1.0\n        if pt_seq_len is not None:\n            scale = pt_seq_len / seq_len\n\n        # offset of +1 following VE - even though for the\n        # attention op only differences matter\n        t = torch.arange(seq_len) * scale + offset\n\n        freqs = torch.einsum(\"..., f -> ... f\", t, freqs)\n        freqs = repeat(freqs, \"... n -> ... (n r)\", r=2)\n\n        freqs = broadcat((freqs[None, :, :], freqs[:, None, :]), dim=-1)\n        freqs_cos = freqs.cos().view(-1, freqs.shape[-1])\n        freqs_sin = freqs.sin().view(-1, freqs.shape[-1])\n\n        self.register_buffer(\"freqs_cos\", freqs_cos)\n        self.register_buffer(\"freqs_sin\", freqs_sin)\n\n    def forward(self, t: torch.Tensor):\n        return t * self.freqs_cos + rotate_half(t) * self.freqs_sin\n"
  },
  {
    "path": "sam3/sam/transformer.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n\nimport math\nfrom functools import partial\nfrom typing import Tuple, Type\n\nimport torch\nimport torch.nn.functional as F\nfrom sam3.sam.rope import apply_rotary_enc, apply_rotary_enc_real, compute_axial_cis\nfrom torch import nn, Tensor\n\nfrom .common import MLPBlock\n\n\nclass TwoWayTransformer(nn.Module):\n    def __init__(\n        self,\n        depth: int,\n        embedding_dim: int,\n        num_heads: int,\n        mlp_dim: int,\n        activation: Type[nn.Module] = nn.ReLU,\n        attention_downsample_rate: int = 2,\n    ) -> None:\n        \"\"\"\n        A transformer decoder that attends to an input image using\n        queries whose positional embedding is supplied.\n\n        Args:\n          depth (int): number of layers in the transformer\n          embedding_dim (int): the channel dimension for the input embeddings\n          num_heads (int): the number of heads for multihead attention. Must\n            divide embedding_dim\n          mlp_dim (int): the channel dimension internal to the MLP block\n          activation (nn.Module): the activation to use in the MLP block\n        \"\"\"\n        super().__init__()\n        self.depth = depth\n        self.embedding_dim = embedding_dim\n        self.num_heads = num_heads\n        self.mlp_dim = mlp_dim\n        self.layers = nn.ModuleList()\n\n        for i in range(depth):\n            self.layers.append(\n                TwoWayAttentionBlock(\n                    embedding_dim=embedding_dim,\n                    num_heads=num_heads,\n                    mlp_dim=mlp_dim,\n                    activation=activation,\n                    attention_downsample_rate=attention_downsample_rate,\n                    skip_first_layer_pe=(i == 0),\n                )\n            )\n\n        self.final_attn_token_to_image = Attention(\n            embedding_dim, num_heads, downsample_rate=attention_downsample_rate\n        )\n        self.norm_final_attn = nn.LayerNorm(embedding_dim)\n\n    def forward(\n        self,\n        image_embedding: Tensor,\n        image_pe: Tensor,\n        point_embedding: Tensor,\n    ) -> Tuple[Tensor, Tensor]:\n        \"\"\"\n        Args:\n          image_embedding (torch.Tensor): image to attend to. Should be shape\n            B x embedding_dim x h x w for any h and w.\n          image_pe (torch.Tensor): the positional encoding to add to the image. Must\n            have the same shape as image_embedding.\n          point_embedding (torch.Tensor): the embedding to add to the query points.\n            Must have shape B x N_points x embedding_dim for any N_points.\n\n        Returns:\n          torch.Tensor: the processed point_embedding\n          torch.Tensor: the processed image_embedding\n        \"\"\"\n        # BxCxHxW -> BxHWxC == B x N_image_tokens x C\n        bs, c, h, w = image_embedding.shape\n        image_embedding = image_embedding.flatten(2).permute(0, 2, 1)\n        image_pe = image_pe.flatten(2).permute(0, 2, 1)\n\n        # Prepare queries\n        queries = point_embedding\n        keys = image_embedding\n\n        # Apply transformer blocks and final layernorm\n        for layer in self.layers:\n            queries, keys = layer(\n                queries=queries,\n                keys=keys,\n                query_pe=point_embedding,\n                key_pe=image_pe,\n            )\n\n        # Apply the final attention layer from the points to the image\n        q = queries + point_embedding\n        k = keys + image_pe\n        attn_out = self.final_attn_token_to_image(q=q, k=k, v=keys)\n        queries = queries + attn_out\n        queries = self.norm_final_attn(queries)\n\n        return queries, keys\n\n\nclass TwoWayAttentionBlock(nn.Module):\n    def __init__(\n        self,\n        embedding_dim: int,\n        num_heads: int,\n        mlp_dim: int = 2048,\n        activation: Type[nn.Module] = nn.ReLU,\n        attention_downsample_rate: int = 2,\n        skip_first_layer_pe: bool = False,\n    ) -> None:\n        \"\"\"\n        A transformer block with four layers: (1) self-attention of sparse\n        inputs, (2) cross attention of sparse inputs to dense inputs, (3) mlp\n        block on sparse inputs, and (4) cross attention of dense inputs to sparse\n        inputs.\n\n        Arguments:\n          embedding_dim (int): the channel dimension of the embeddings\n          num_heads (int): the number of heads in the attention layers\n          mlp_dim (int): the hidden dimension of the mlp block\n          activation (nn.Module): the activation of the mlp block\n          skip_first_layer_pe (bool): skip the PE on the first layer\n        \"\"\"\n        super().__init__()\n        self.self_attn = Attention(embedding_dim, num_heads)\n        self.norm1 = nn.LayerNorm(embedding_dim)\n\n        self.cross_attn_token_to_image = Attention(\n            embedding_dim, num_heads, downsample_rate=attention_downsample_rate\n        )\n        self.norm2 = nn.LayerNorm(embedding_dim)\n\n        self.mlp = MLPBlock(embedding_dim, mlp_dim, activation)\n        self.norm3 = nn.LayerNorm(embedding_dim)\n\n        self.norm4 = nn.LayerNorm(embedding_dim)\n        self.cross_attn_image_to_token = Attention(\n            embedding_dim, num_heads, downsample_rate=attention_downsample_rate\n        )\n\n        self.skip_first_layer_pe = skip_first_layer_pe\n\n    def forward(\n        self, queries: Tensor, keys: Tensor, query_pe: Tensor, key_pe: Tensor\n    ) -> Tuple[Tensor, Tensor]:\n        # Self attention block\n        if self.skip_first_layer_pe:\n            queries = self.self_attn(q=queries, k=queries, v=queries)\n        else:\n            q = queries + query_pe\n            attn_out = self.self_attn(q=q, k=q, v=queries)\n            queries = queries + attn_out\n        queries = self.norm1(queries)\n\n        # Cross attention block, tokens attending to image embedding\n        q = queries + query_pe\n        k = keys + key_pe\n        attn_out = self.cross_attn_token_to_image(q=q, k=k, v=keys)\n        queries = queries + attn_out\n        queries = self.norm2(queries)\n\n        # MLP block\n        mlp_out = self.mlp(queries)\n        queries = queries + mlp_out\n        queries = self.norm3(queries)\n\n        # Cross attention block, image embedding attending to tokens\n        q = queries + query_pe\n        k = keys + key_pe\n        attn_out = self.cross_attn_image_to_token(q=k, k=q, v=queries)\n        keys = keys + attn_out\n        keys = self.norm4(keys)\n\n        return queries, keys\n\n\nclass Attention(nn.Module):\n    \"\"\"\n    An attention layer that allows for downscaling the size of the embedding\n    after projection to queries, keys, and values.\n    \"\"\"\n\n    def __init__(\n        self,\n        embedding_dim: int,\n        num_heads: int,\n        downsample_rate: int = 1,\n        dropout: float = 0.0,\n        kv_in_dim: int = None,\n        use_fa3: bool = False,\n    ) -> None:\n        super().__init__()\n        self.embedding_dim = embedding_dim\n        self.kv_in_dim = kv_in_dim if kv_in_dim is not None else embedding_dim\n        self.internal_dim = embedding_dim // downsample_rate\n        self.num_heads = num_heads\n        self.use_fa3 = use_fa3\n        assert self.internal_dim % num_heads == 0, (\n            \"num_heads must divide embedding_dim.\"\n        )\n\n        self.q_proj = nn.Linear(embedding_dim, self.internal_dim)\n        self.k_proj = nn.Linear(self.kv_in_dim, self.internal_dim)\n        self.v_proj = nn.Linear(self.kv_in_dim, self.internal_dim)\n        self.out_proj = nn.Linear(self.internal_dim, embedding_dim)\n\n        self.dropout_p = dropout\n\n    def _separate_heads(self, x: Tensor, num_heads: int) -> Tensor:\n        b, n, c = x.shape\n        x = x.reshape(b, n, num_heads, c // num_heads)\n        return x.transpose(1, 2)  # B x N_heads x N_tokens x C_per_head\n\n    def _recombine_heads(self, x: Tensor) -> Tensor:\n        b, n_heads, n_tokens, c_per_head = x.shape\n        x = x.transpose(1, 2)\n        return x.reshape(b, n_tokens, n_heads * c_per_head)  # B x N_tokens x C\n\n    def forward(self, q: Tensor, k: Tensor, v: Tensor) -> Tensor:\n        # Input projections\n        q = self.q_proj(q)\n        k = self.k_proj(k)\n        v = self.v_proj(v)\n\n        # Separate into heads\n        q = self._separate_heads(q, self.num_heads)\n        k = self._separate_heads(k, self.num_heads)\n        v = self._separate_heads(v, self.num_heads)\n\n        dropout_p = self.dropout_p if self.training else 0.0\n        # Attention\n        # with torch.backends.cuda.sdp_kernel(\n        #     enable_flash=USE_FLASH_ATTN,\n        #     # if Flash attention kernel is off, then math kernel needs to be enabled\n        #     enable_math=(OLD_GPU and dropout_p > 0.0) or MATH_KERNEL_ON,\n        #     enable_mem_efficient=OLD_GPU,\n        # ):\n        # Let's trust the dispatcher....\n        if self.use_fa3:\n            from sam3.perflib.fa3 import flash_attn_func\n\n            assert dropout_p == 0.0\n            out = flash_attn_func(\n                q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2)\n            ).transpose(1, 2)\n        else:\n            torch.backends.cuda.enable_flash_sdp(True)\n            torch.backends.cuda.enable_math_sdp(True)\n            torch.backends.cuda.enable_mem_efficient_sdp(True)\n            out = F.scaled_dot_product_attention(q, k, v, dropout_p=dropout_p)\n\n        out = self._recombine_heads(out)\n        out = self.out_proj(out)\n\n        return out\n\n\nclass RoPEAttention(Attention):\n    \"\"\"Attention with rotary position encoding.\"\"\"\n\n    def __init__(\n        self,\n        *args,\n        rope_theta=10000.0,\n        # whether to repeat q rope to match k length\n        # this is needed for cross-attention to memories\n        rope_k_repeat=False,\n        feat_sizes=(64, 64),  # [w, h] for stride 16 feats at 1024 resolution\n        use_rope_real=False,\n        **kwargs,\n    ):\n        super().__init__(*args, **kwargs)\n        self.use_rope_real = use_rope_real\n        self.compute_cis = partial(\n            compute_axial_cis, dim=self.internal_dim // self.num_heads, theta=rope_theta\n        )\n        device = torch.device(\"cuda\") if torch.cuda.is_available() else None\n        self.freqs_cis = self.compute_cis(\n            end_x=feat_sizes[0], end_y=feat_sizes[1], device=device\n        )\n        if self.use_rope_real:\n            self.freqs_cis_real = self.freqs_cis.real\n            self.freqs_cis_imag = self.freqs_cis.imag\n        self.rope_k_repeat = rope_k_repeat\n\n    def forward(\n        self, q: Tensor, k: Tensor, v: Tensor, num_k_exclude_rope: int = 0\n    ) -> Tensor:\n        # Input projections\n        q = self.q_proj(q)\n        k = self.k_proj(k)\n        v = self.v_proj(v)\n\n        # Separate into heads\n        q = self._separate_heads(q, self.num_heads)\n        k = self._separate_heads(k, self.num_heads)\n        v = self._separate_heads(v, self.num_heads)\n\n        # Apply rotary position encoding\n        w = h = math.sqrt(q.shape[-2])\n        if self.freqs_cis.shape[0] != q.shape[-2]:\n            self.freqs_cis = self.compute_cis(end_x=w, end_y=h, device=q.device)\n            self.freqs_cis_real = self.freqs_cis.real\n            self.freqs_cis_imag = self.freqs_cis.imag\n        if q.shape[-2] != k.shape[-2]:\n            assert self.rope_k_repeat\n\n        num_k_rope = k.size(-2) - num_k_exclude_rope\n        if self.use_rope_real:\n            q, k[:, :, :num_k_rope] = apply_rotary_enc_real(\n                q,\n                k[:, :, :num_k_rope],\n                freqs_cis_real=self.freqs_cis_real,\n                freqs_cis_imag=self.freqs_cis_imag,\n                repeat_freqs_k=self.rope_k_repeat,\n            )\n        else:\n            q, k[:, :, :num_k_rope] = apply_rotary_enc(\n                q,\n                k[:, :, :num_k_rope],\n                self.freqs_cis,\n                repeat_freqs_k=self.rope_k_repeat,\n            )\n\n        dropout_p = self.dropout_p if self.training else 0.0\n        # Attention\n        # with torch.backends.cuda.sdp_kernel(\n        #     enable_flash=USE_FLASH_ATTN,\n        #     # if Flash attention kernel is off, then math kernel needs to be enabled\n        #     enable_math=(OLD_GPU and dropout_p > 0.0) or MATH_KERNEL_ON,\n        #     enable_mem_efficient=OLD_GPU,\n        # ):\n        # Let's trust the dispatcher....\n        if self.use_fa3:\n            from sam3.perflib.fa3 import flash_attn_func\n\n            assert dropout_p == 0.0\n            out = flash_attn_func(\n                q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2)\n            ).transpose(1, 2)\n        else:\n            torch.backends.cuda.enable_flash_sdp(True)\n            torch.backends.cuda.enable_math_sdp(True)\n            torch.backends.cuda.enable_mem_efficient_sdp(True)\n            out = F.scaled_dot_product_attention(q, k, v, dropout_p=dropout_p)\n\n        out = self._recombine_heads(out)\n        out = self.out_proj(out)\n\n        return out\n"
  },
  {
    "path": "sam3/train/__init__.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n"
  },
  {
    "path": "sam3/train/configs/eval_base.yaml",
    "content": "# @package _global_\ndefaults:\n  - _self_\n\n# This config is the base configuration for all evaluations. Amongst other things, it defines:\n# - the model\n# - the image transforms\n# - the post processors\n# - cluster configuration (only relevant for slurm-based evals, ignored otherwise)\n#\n# Most of the parameters should be kept as-is. The main modifications you may want to make are:\n# - the cluster configuration, to adjust partitions/qos to your system\n# - the flag gather_pred_via_filesys if you ram is tight\n# - num_val_workers if your number of cores is small (should be roughly number of cores / number of gpus)\n# - the paths below\n\n\n# ============================================================================\n# Paths Configuration (Chage this to your own paths)\n# ============================================================================\npaths:\n  # If you leave the checkpoint path to null, the model will be downloaded from hugging-face. Otherwise provide a path\n  checkpoint_path: null\n  # the experiments will be subfolders of this\n  base_experiment_log_dir: <YOUR EXPERIMENET LOG_DIR>\n\n  # base path to the annotation folder for gold (refer to the readmes on how to download)\n  base_annotation_path: <YOUR_GOLD_GT_DIR>\n\n  # base path to the annotation folder for silver (refer to the readmes on how to download)\n  base_annotation_path_silver: <YOUR_SILVER_GT_DIR>\n\n  # path to the metaclip images, used for SA-Co gold (refer to the readme for instructions). Can be null if you don't intend on evaluating on this dataset.\n  metaclip_img_path: <YOUR_METACLIP_IMG_DIR>\n\n  # path to the sa1b images, used for SA-Co gold (refer to the readme for instructions). Can be null if you don't intend on evaluating on this dataset.\n  sa1b_img_path: <YOUR_SA1B_IMG_DIR>\n\n  # path to the SA-Co/silver images\n  silver_img_path: <YOUR_SILVER_IMG_DIR>\n\n  bpe_path: <BPE_PATH> # This should be under sam3/assets/bpe_simple_vocab_16e6.txt.gz\n\n\n# ============================================================================\n# Different helper parameters and functions\n# ============================================================================\nscratch:\n\n  use_presence_eval: True\n\n  base_val_transform:\n    - _target_: sam3.train.transforms.basic_for_api.ComposeAPI\n      transforms:\n        ######## transforms for validation (begin) ########\n        - _target_: sam3.train.transforms.basic_for_api.RandomResizeAPI\n          sizes: ${scratch.resolution}  # originally `resolution: 1024`\n          max_size:\n            _target_: sam3.train.transforms.basic.get_random_resize_max_size\n            size: ${scratch.resolution}  # originally `resolution: 1024`\n          square: true\n          consistent_transform: False\n        ######## transforms for validation (end) ########\n        - _target_: sam3.train.transforms.basic_for_api.ToTensorAPI\n        - _target_: sam3.train.transforms.basic_for_api.NormalizeAPI\n          mean: ${scratch.val_norm_mean}\n          std: ${scratch.val_norm_std}\n\n  loss: null\n\n  # Model parameters\n  d_model: 256\n  input_box_embedding_dim: ${add:${scratch.d_model},2}\n\n  # Box processing\n  original_box_postprocessor:\n    _target_: sam3.eval.postprocessors.PostProcessImage\n    max_dets_per_img: -1  # infinite detections\n    use_original_ids: true\n    use_original_sizes_box: true\n    use_presence: ${scratch.use_presence_eval}\n\n  box_postprocessor:\n    _target_: sam3.eval.postprocessors.PostProcessImage\n    max_dets_per_img: -1 #infinite detections\n    use_original_ids: false\n    use_original_sizes_box: false\n    use_presence: ${scratch.use_presence_eval}\n\n  box_postprocessor_thresholded:\n    _target_: sam3.eval.postprocessors.PostProcessImage\n    max_dets_per_img: -1 #infinite detections\n    use_original_ids: false\n    use_original_sizes_box: false\n    detection_threshold: 0.3\n    use_presence: ${scratch.use_presence_eval}\n\n  mask_postprocessor_thresholded:\n    _target_: sam3.eval.postprocessors.PostProcessImage\n    max_dets_per_img: -1 #infinite detections\n    iou_type: \"segm\"\n    use_original_ids: false\n    use_original_sizes_box: false\n    use_original_sizes_mask: true\n    convert_mask_to_rle: True\n    detection_threshold: 0.3\n    use_presence: ${scratch.use_presence_eval}\n\n  # Image processing parameters\n  resolution: 1008\n  max_ann_per_img: 200\n\n  # Normalization parameters\n  train_norm_mean: [0.5, 0.5, 0.5]\n  train_norm_std: [0.5, 0.5, 0.5]\n  val_norm_mean: [0.5, 0.5, 0.5]\n  val_norm_std: [0.5, 0.5, 0.5]\n\n  # Training parameters\n  train_batch_size: 1\n  val_batch_size: 1\n  num_train_workers: 0\n  num_val_workers: 10 # change this depending on the number of cpu cores available\n  max_data_epochs: 20\n  target_epoch_size: 1500\n  hybrid_repeats: 1\n  context_length: 2\n\n  # All reduce - this controls how the predictions are sent back to node 0.\n  # If you have a lot of ram, CPU gather is faster. Otherwise, we provide a fallback through filesystem (eg NFS)\n  # Switch to true if you get cpu ooms during gather.\n  gather_pred_via_filesys: false\n\n  # Learning rate and scheduler parameters (unused for eval)\n  lr_scale: 0.1\n  lr_transformer: ${times:8e-4,${scratch.lr_scale}}\n  lr_vision_backbone: ${times:2.5e-4,${scratch.lr_scale}}\n  lr_language_backbone: ${times:5e-5,${scratch.lr_scale}}\n  lrd_vision_backbone: 0.9 # (lower for in-domain adn higher for ood)\n  wd: 0.1\n  scheduler_timescale: 20\n  scheduler_warmup: 20\n  scheduler_cooldown: 20\n\n\n# ============================================================================\n# Trainer Configuration\n# ============================================================================\n\ntrainer:\n  _target_: sam3.train.trainer.Trainer\n  skip_saving_ckpts: true\n  empty_gpu_mem_cache_after_eval: True\n  skip_first_val: True\n  max_epochs: ${scratch.max_data_epochs}\n  accelerator: cuda\n  seed_value: 123\n  val_epoch_freq: 10\n  mode: val\n\n  distributed:\n    backend: nccl\n    find_unused_parameters: True\n    gradient_as_bucket_view: True\n\n  loss:\n    all:\n      _target_: sam3.train.loss.sam3_loss.DummyLoss\n    default:\n      _target_: sam3.train.loss.sam3_loss.DummyLoss\n\n  data:\n    train: null\n    val: null\n\n  model:\n    _target_: sam3.model_builder.build_sam3_image_model\n    bpe_path: ${paths.bpe_path}\n    device: cpus\n    eval_mode: true\n    enable_segmentation: true # Warning: Enable this if using segmentation.\n    checkpoint_path: ${paths.checkpoint_path}\n\n  meters:\n    val: null\n\n  optim:\n    amp:\n      enabled: True\n      amp_dtype: bfloat16\n\n    optimizer:\n      _target_: torch.optim.AdamW\n\n    gradient_clip:\n      _target_: sam3.train.optim.optimizer.GradientClipper\n      max_norm: 0.1\n      norm_type: 2\n\n    param_group_modifiers:\n      - _target_: sam3.train.optim.optimizer.layer_decay_param_modifier\n        _partial_: True\n        layer_decay_value: ${scratch.lrd_vision_backbone}\n        apply_to: 'backbone.vision_backbone.trunk'\n        overrides:\n          - pattern: '*pos_embed*'\n            value: 1.0\n\n    options:\n      lr:\n        - scheduler:  # transformer and class_embed\n            _target_: sam3.train.optim.schedulers.InverseSquareRootParamScheduler\n            base_lr: ${scratch.lr_transformer}\n            timescale: ${scratch.scheduler_timescale}\n            warmup_steps: ${scratch.scheduler_warmup}\n            cooldown_steps: ${scratch.scheduler_cooldown}\n        - scheduler:\n            _target_: sam3.train.optim.schedulers.InverseSquareRootParamScheduler\n            base_lr: ${scratch.lr_vision_backbone}\n            timescale: ${scratch.scheduler_timescale}\n            warmup_steps: ${scratch.scheduler_warmup}\n            cooldown_steps: ${scratch.scheduler_cooldown}\n          param_names:\n            - 'backbone.vision_backbone.*'\n        - scheduler:\n            _target_: sam3.train.optim.schedulers.InverseSquareRootParamScheduler\n            base_lr: ${scratch.lr_language_backbone}\n            timescale: ${scratch.scheduler_timescale}\n            warmup_steps: ${scratch.scheduler_warmup}\n            cooldown_steps: ${scratch.scheduler_cooldown}\n          param_names:\n            - 'backbone.language_backbone.*'\n\n      weight_decay:\n        - scheduler:\n            _target_: fvcore.common.param_scheduler.ConstantParamScheduler\n            value: ${scratch.wd}\n        - scheduler:\n            _target_: fvcore.common.param_scheduler.ConstantParamScheduler\n            value: 0.0\n          param_names:\n            - '*bias*'\n          module_cls_names: ['torch.nn.LayerNorm']\n\n  checkpoint:\n    save_dir: ${launcher.experiment_log_dir}/checkpoints\n    save_freq: 0  # 0 only last checkpoint is saved.\n\n\n  logging:\n    tensorboard_writer:\n      _target_: sam3.train.utils.logger.make_tensorboard_logger\n      log_dir: ${launcher.experiment_log_dir}/tensorboard\n      flush_secs: 120\n      should_log: True\n    wandb_writer: null\n    log_dir: ${launcher.experiment_log_dir}/logs/\n    log_freq: 10\n\n# ============================================================================\n# Launcher and Submitit Configuration\n# ============================================================================\n\nlauncher:\n  num_nodes: 4\n  gpus_per_node: 8\n  experiment_log_dir: ${paths.experiment_log_dir}\n  multiprocessing_context: forkserver\n\n\nsubmitit:\n  account: null # Add your SLURM account if use_cluster == 1\n  partition: null\n  qos: null # Add your QoS if use_cluster == 1\n  timeout_hour: 72\n  use_cluster: True\n  cpus_per_task: 10\n  port_range: [10000, 65000]\n  constraint: null\n"
  },
  {
    "path": "sam3/train/configs/gold_image_evals/sam3_gold_image_attributes.yaml",
    "content": "# @package _global_\ndefaults:\n  - /configs/eval_base.yaml\n  - _self_\n\n# ============================================================================\n# Paths Configuration (you can override here, but it shouldn't require further changes if eval_base.yaml is correct\n# ============================================================================\npaths:\n  experiment_log_dir: ${paths.base_experiment_log_dir}/gold_attributes/\n  coco_gt: ${paths.base_annotation_path}/gold_attributes_merged_a_release_test.json\n  coco_gts:\n    - ${paths.base_annotation_path}/gold_attributes_merged_a_release_test.json\n    - ${paths.base_annotation_path}/gold_attributes_merged_b_release_test.json\n    - ${paths.base_annotation_path}/gold_attributes_merged_c_release_test.json\n\n\n# ============================================================================\n# Trainer Configuration\n# ============================================================================\n\ntrainer:\n  data:\n    val:\n      _target_: sam3.train.data.torch_dataset.TorchDataset\n      dataset:\n        _target_: sam3.train.data.sam3_image_dataset.Sam3ImageDataset\n        coco_json_loader:\n          _target_: sam3.train.data.coco_json_loaders.SAM3_EVAL_API_FROM_JSON_NP\n          _partial_: true\n        img_folder: ${paths.metaclip_img_path}\n        ann_file: ${paths.coco_gt}\n        transforms: ${scratch.base_val_transform}\n        max_ann_per_img: 100000\n        multiplier: 1\n        training: false\n\n      shuffle: False\n      batch_size: ${scratch.val_batch_size}\n      num_workers: ${scratch.num_val_workers}\n      pin_memory: False\n      drop_last: False\n      collate_fn:\n        _target_: sam3.train.data.collator.collate_fn_api\n        _partial_: true\n        repeats: ${scratch.hybrid_repeats}\n        dict_key: gold_attributes\n\n  meters:\n    val:\n      gold_attributes: # this key matches the \"dict_key\" in the dataloader's collate function\n        cgf1:\n          _target_: sam3.eval.coco_writer.PredictionDumper\n          iou_type: \"segm\"\n          dump_dir: ${launcher.experiment_log_dir}/dumps/gold_attributes\n          merge_predictions: True\n          postprocessor: ${scratch.mask_postprocessor_thresholded}\n          gather_pred_via_filesys: ${scratch.gather_pred_via_filesys}\n          maxdets: 1000000 # no limit\n          pred_file_evaluators:\n            - _target_: sam3.eval.cgf1_eval.CGF1Evaluator\n              gt_path: ${paths.coco_gts}\n              iou_type: \"bbox\"\n            - _target_: sam3.eval.cgf1_eval.CGF1Evaluator\n              gt_path: ${paths.coco_gts}\n              iou_type: \"segm\"\n"
  },
  {
    "path": "sam3/train/configs/gold_image_evals/sam3_gold_image_crowded.yaml",
    "content": "# @package _global_\ndefaults:\n  - /configs/eval_base.yaml\n  - _self_\n\n# ============================================================================\n# Paths Configuration (you can override here, but it shouldn't require further changes if eval_base.yaml is correct\n# ============================================================================\npaths:\n  experiment_log_dir: ${paths.base_experiment_log_dir}/gold_crowded/\n  coco_gt: ${paths.base_annotation_path}/gold_crowded_merged_a_release_test.json\n  coco_gts:\n    - ${paths.base_annotation_path}/gold_crowded_merged_a_release_test.json\n    - ${paths.base_annotation_path}/gold_crowded_merged_b_release_test.json\n    - ${paths.base_annotation_path}/gold_crowded_merged_c_release_test.json\n\n\n# ============================================================================\n# Trainer Configuration\n# ============================================================================\n\ntrainer:\n  data:\n    val:\n      _target_: sam3.train.data.torch_dataset.TorchDataset\n      dataset:\n        _target_: sam3.train.data.sam3_image_dataset.Sam3ImageDataset\n        coco_json_loader:\n          _target_: sam3.train.data.coco_json_loaders.SAM3_EVAL_API_FROM_JSON_NP\n          _partial_: true\n        img_folder: ${paths.metaclip_img_path}\n        ann_file: ${paths.coco_gt}\n        transforms: ${scratch.base_val_transform}\n        max_ann_per_img: 100000\n        multiplier: 1\n        training: false\n\n      shuffle: False\n      batch_size: ${scratch.val_batch_size}\n      num_workers: ${scratch.num_val_workers}\n      pin_memory: False\n      drop_last: False\n      collate_fn:\n        _target_: sam3.train.data.collator.collate_fn_api\n        _partial_: true\n        repeats: ${scratch.hybrid_repeats}\n        dict_key: gold_crowded\n\n  meters:\n    val:\n      gold_crowded: # this key matches the \"dict_key\" in the dataloader's collate function\n        cgf1:\n          _target_: sam3.eval.coco_writer.PredictionDumper\n          iou_type: \"segm\"\n          dump_dir: ${launcher.experiment_log_dir}/dumps/gold_crowded\n          merge_predictions: True\n          postprocessor: ${scratch.mask_postprocessor_thresholded}\n          gather_pred_via_filesys: ${scratch.gather_pred_via_filesys}\n          maxdets: 1000000 # no limit\n          pred_file_evaluators:\n            - _target_: sam3.eval.cgf1_eval.CGF1Evaluator\n              gt_path: ${paths.coco_gts}\n              iou_type: \"bbox\"\n            - _target_: sam3.eval.cgf1_eval.CGF1Evaluator\n              gt_path: ${paths.coco_gts}\n              iou_type: \"segm\"\n"
  },
  {
    "path": "sam3/train/configs/gold_image_evals/sam3_gold_image_fg_food.yaml",
    "content": "# @package _global_\ndefaults:\n  - /configs/eval_base.yaml\n  - _self_\n\n# ============================================================================\n# Paths Configuration (you can override here, but it shouldn't require further changes if eval_base.yaml is correct\n# ============================================================================\npaths:\n  experiment_log_dir: ${paths.base_experiment_log_dir}/gold_fg_food/\n  coco_gt: ${paths.base_annotation_path}/gold_fg_food_merged_a_release_test.json\n  coco_gts:\n    - ${paths.base_annotation_path}/gold_fg_food_merged_a_release_test.json\n    - ${paths.base_annotation_path}/gold_fg_food_merged_b_release_test.json\n    - ${paths.base_annotation_path}/gold_fg_food_merged_c_release_test.json\n\n\n# ============================================================================\n# Trainer Configuration\n# ============================================================================\n\ntrainer:\n  data:\n    val:\n      _target_: sam3.train.data.torch_dataset.TorchDataset\n      dataset:\n        _target_: sam3.train.data.sam3_image_dataset.Sam3ImageDataset\n        coco_json_loader:\n          _target_: sam3.train.data.coco_json_loaders.SAM3_EVAL_API_FROM_JSON_NP\n          _partial_: true\n        img_folder: ${paths.metaclip_img_path}\n        ann_file: ${paths.coco_gt}\n        transforms: ${scratch.base_val_transform}\n        max_ann_per_img: 100000\n        multiplier: 1\n        training: false\n\n      shuffle: False\n      batch_size: ${scratch.val_batch_size}\n      num_workers: ${scratch.num_val_workers}\n      pin_memory: False\n      drop_last: False\n      collate_fn:\n        _target_: sam3.train.data.collator.collate_fn_api\n        _partial_: true\n        repeats: ${scratch.hybrid_repeats}\n        dict_key: gold_fg_food\n\n  meters:\n    val:\n      gold_fg_food: # this key matches the \"dict_key\" in the dataloader's collate function\n        cgf1:\n          _target_: sam3.eval.coco_writer.PredictionDumper\n          iou_type: \"segm\"\n          dump_dir: ${launcher.experiment_log_dir}/dumps/gold_fg_food\n          merge_predictions: True\n          postprocessor: ${scratch.mask_postprocessor_thresholded}\n          gather_pred_via_filesys: ${scratch.gather_pred_via_filesys}\n          maxdets: 1000000 # no limit\n          pred_file_evaluators:\n            - _target_: sam3.eval.cgf1_eval.CGF1Evaluator\n              gt_path: ${paths.coco_gts}\n              iou_type: \"bbox\"\n            - _target_: sam3.eval.cgf1_eval.CGF1Evaluator\n              gt_path: ${paths.coco_gts}\n              iou_type: \"segm\"\n"
  },
  {
    "path": "sam3/train/configs/gold_image_evals/sam3_gold_image_fg_sports.yaml",
    "content": "# @package _global_\ndefaults:\n  - /configs/eval_base.yaml\n  - _self_\n\n# ============================================================================\n# Paths Configuration (you can override here, but it shouldn't require further changes if eval_base.yaml is correct\n# ============================================================================\npaths:\n  experiment_log_dir: ${paths.base_experiment_log_dir}/gold_fg_sports_equipment/\n  coco_gt: ${paths.base_annotation_path}/gold_fg_sports_equipment_merged_a_release_test.json\n  coco_gts:\n    - ${paths.base_annotation_path}/gold_fg_sports_equipment_merged_a_release_test.json\n    - ${paths.base_annotation_path}/gold_fg_sports_equipment_merged_b_release_test.json\n    - ${paths.base_annotation_path}/gold_fg_sports_equipment_merged_c_release_test.json\n\n\n# ============================================================================\n# Trainer Configuration\n# ============================================================================\n\ntrainer:\n  data:\n    val:\n      _target_: sam3.train.data.torch_dataset.TorchDataset\n      dataset:\n        _target_: sam3.train.data.sam3_image_dataset.Sam3ImageDataset\n        coco_json_loader:\n          _target_: sam3.train.data.coco_json_loaders.SAM3_EVAL_API_FROM_JSON_NP\n          _partial_: true\n        img_folder: ${paths.metaclip_img_path}\n        ann_file: ${paths.coco_gt}\n        transforms: ${scratch.base_val_transform}\n        max_ann_per_img: 100000\n        multiplier: 1\n        training: false\n\n      shuffle: False\n      batch_size: ${scratch.val_batch_size}\n      num_workers: ${scratch.num_val_workers}\n      pin_memory: False\n      drop_last: False\n      collate_fn:\n        _target_: sam3.train.data.collator.collate_fn_api\n        _partial_: true\n        repeats: ${scratch.hybrid_repeats}\n        dict_key: gold_fg_sports_equipment\n\n  meters:\n    val:\n      gold_fg_sports_equipment: # this key matches the \"dict_key\" in the dataloader's collate function\n        cgf1:\n          _target_: sam3.eval.coco_writer.PredictionDumper\n          iou_type: \"segm\"\n          dump_dir: ${launcher.experiment_log_dir}/dumps/gold_fg_sports_equipment\n          merge_predictions: True\n          postprocessor: ${scratch.mask_postprocessor_thresholded}\n          gather_pred_via_filesys: ${scratch.gather_pred_via_filesys}\n          maxdets: 1000000 # no limit\n          pred_file_evaluators:\n            - _target_: sam3.eval.cgf1_eval.CGF1Evaluator\n              gt_path: ${paths.coco_gts}\n              iou_type: \"bbox\"\n            - _target_: sam3.eval.cgf1_eval.CGF1Evaluator\n              gt_path: ${paths.coco_gts}\n              iou_type: \"segm\"\n"
  },
  {
    "path": "sam3/train/configs/gold_image_evals/sam3_gold_image_metaclip_nps.yaml",
    "content": "# @package _global_\ndefaults:\n  - /configs/eval_base.yaml\n  - _self_\n\n# ============================================================================\n# Paths Configuration (you can override here, but it shouldn't require further changes if eval_base.yaml is correct\n# ============================================================================\npaths:\n  experiment_log_dir: ${paths.base_experiment_log_dir}/gold_metaclip_nps/\n  coco_gt: ${paths.base_annotation_path}/gold_metaclip_merged_a_release_test.json\n  coco_gts:\n    - ${paths.base_annotation_path}/gold_metaclip_merged_a_release_test.json\n    - ${paths.base_annotation_path}/gold_metaclip_merged_b_release_test.json\n    - ${paths.base_annotation_path}/gold_metaclip_merged_c_release_test.json\n\n\n# ============================================================================\n# Trainer Configuration\n# ============================================================================\n\ntrainer:\n  data:\n    val:\n      _target_: sam3.train.data.torch_dataset.TorchDataset\n      dataset:\n        _target_: sam3.train.data.sam3_image_dataset.Sam3ImageDataset\n        coco_json_loader:\n          _target_: sam3.train.data.coco_json_loaders.SAM3_EVAL_API_FROM_JSON_NP\n          _partial_: true\n        img_folder: ${paths.metaclip_img_path}\n        ann_file: ${paths.coco_gt}\n        transforms: ${scratch.base_val_transform}\n        max_ann_per_img: 100000\n        multiplier: 1\n        training: false\n\n      shuffle: False\n      batch_size: ${scratch.val_batch_size}\n      num_workers: ${scratch.num_val_workers}\n      pin_memory: False\n      drop_last: False\n      collate_fn:\n        _target_: sam3.train.data.collator.collate_fn_api\n        _partial_: true\n        repeats: ${scratch.hybrid_repeats}\n        dict_key: gold_metaclip_nps\n\n  meters:\n    val:\n      gold_metaclip_nps: # this key matches the \"dict_key\" in the dataloader's collate function\n        cgf1:\n          _target_: sam3.eval.coco_writer.PredictionDumper\n          iou_type: \"segm\"\n          dump_dir: ${launcher.experiment_log_dir}/dumps/gold_metaclip_nps\n          merge_predictions: True\n          postprocessor: ${scratch.mask_postprocessor_thresholded}\n          gather_pred_via_filesys: ${scratch.gather_pred_via_filesys}\n          maxdets: 1000000 # no limit\n          pred_file_evaluators:\n            - _target_: sam3.eval.cgf1_eval.CGF1Evaluator\n              gt_path: ${paths.coco_gts}\n              iou_type: \"bbox\"\n            - _target_: sam3.eval.cgf1_eval.CGF1Evaluator\n              gt_path: ${paths.coco_gts}\n              iou_type: \"segm\"\n"
  },
  {
    "path": "sam3/train/configs/gold_image_evals/sam3_gold_image_sa1b_nps.yaml",
    "content": "# @package _global_\ndefaults:\n  - /configs/eval_base.yaml\n  - _self_\n\n# ============================================================================\n# Paths Configuration (you can override here, but it shouldn't require further changes if eval_base.yaml is correct\n# ============================================================================\npaths:\n  experiment_log_dir: ${paths.base_experiment_log_dir}/gold_sa1b_nps/\n  coco_gt: ${paths.base_annotation_path}/gold_sa1b_merged_a_release_test.json\n  coco_gts:\n    - ${paths.base_annotation_path}/gold_sa1b_merged_a_release_test.json\n    - ${paths.base_annotation_path}/gold_sa1b_merged_b_release_test.json\n    - ${paths.base_annotation_path}/gold_sa1b_merged_c_release_test.json\n\n\n# ============================================================================\n# Trainer Configuration\n# ============================================================================\n\ntrainer:\n  data:\n    val:\n      _target_: sam3.train.data.torch_dataset.TorchDataset\n      dataset:\n        _target_: sam3.train.data.sam3_image_dataset.Sam3ImageDataset\n        coco_json_loader:\n          _target_: sam3.train.data.coco_json_loaders.SAM3_EVAL_API_FROM_JSON_NP\n          _partial_: true\n        img_folder: ${paths.sa1b_img_path}\n        ann_file: ${paths.coco_gt}\n        transforms: ${scratch.base_val_transform}\n        max_ann_per_img: 100000\n        multiplier: 1\n        training: false\n\n      shuffle: False\n      batch_size: ${scratch.val_batch_size}\n      num_workers: ${scratch.num_val_workers}\n      pin_memory: False\n      drop_last: False\n      collate_fn:\n        _target_: sam3.train.data.collator.collate_fn_api\n        _partial_: true\n        repeats: ${scratch.hybrid_repeats}\n        dict_key: gold_sa1b_nps\n\n  meters:\n    val:\n      gold_sa1b_nps: # this key matches the \"dict_key\" in the dataloader's collate function\n        cgf1:\n          _target_: sam3.eval.coco_writer.PredictionDumper\n          iou_type: \"segm\"\n          dump_dir: ${launcher.experiment_log_dir}/dumps/gold_sa1b_nps\n          merge_predictions: True\n          postprocessor: ${scratch.mask_postprocessor_thresholded}\n          gather_pred_via_filesys: ${scratch.gather_pred_via_filesys}\n          maxdets: 1000000 # no limit\n          pred_file_evaluators:\n            - _target_: sam3.eval.cgf1_eval.CGF1Evaluator\n              gt_path: ${paths.coco_gts}\n              iou_type: \"bbox\"\n            - _target_: sam3.eval.cgf1_eval.CGF1Evaluator\n              gt_path: ${paths.coco_gts}\n              iou_type: \"segm\"\n"
  },
  {
    "path": "sam3/train/configs/gold_image_evals/sam3_gold_image_wiki_common.yaml",
    "content": "# @package _global_\ndefaults:\n  - /configs/eval_base.yaml\n  - _self_\n\n# ============================================================================\n# Paths Configuration (you can override here, but it shouldn't require further changes if eval_base.yaml is correct\n# ============================================================================\npaths:\n  experiment_log_dir: ${paths.base_experiment_log_dir}/gold_wiki_common/\n  coco_gt: ${paths.base_annotation_path}/gold_wiki_common_merged_a_release_test.json\n  coco_gts:\n    - ${paths.base_annotation_path}/gold_wiki_common_merged_a_release_test.json\n    - ${paths.base_annotation_path}/gold_wiki_common_merged_b_release_test.json\n    - ${paths.base_annotation_path}/gold_wiki_common_merged_c_release_test.json\n\n\n# ============================================================================\n# Trainer Configuration\n# ============================================================================\n\ntrainer:\n  data:\n    val:\n      _target_: sam3.train.data.torch_dataset.TorchDataset\n      dataset:\n        _target_: sam3.train.data.sam3_image_dataset.Sam3ImageDataset\n        coco_json_loader:\n          _target_: sam3.train.data.coco_json_loaders.SAM3_EVAL_API_FROM_JSON_NP\n          _partial_: true\n        img_folder: ${paths.metaclip_img_path}\n        ann_file: ${paths.coco_gt}\n        transforms: ${scratch.base_val_transform}\n        max_ann_per_img: 100000\n        multiplier: 1\n        training: false\n\n      shuffle: False\n      batch_size: ${scratch.val_batch_size}\n      num_workers: ${scratch.num_val_workers}\n      pin_memory: False\n      drop_last: False\n      collate_fn:\n        _target_: sam3.train.data.collator.collate_fn_api\n        _partial_: true\n        repeats: ${scratch.hybrid_repeats}\n        dict_key: gold_wiki_common\n\n  meters:\n    val:\n      gold_wiki_common: # this key matches the \"dict_key\" in the dataloader's collate function\n        cgf1:\n          _target_: sam3.eval.coco_writer.PredictionDumper\n          iou_type: \"segm\"\n          dump_dir: ${launcher.experiment_log_dir}/dumps/gold_wiki_common\n          merge_predictions: True\n          postprocessor: ${scratch.mask_postprocessor_thresholded}\n          gather_pred_via_filesys: ${scratch.gather_pred_via_filesys}\n          maxdets: 1000000 # no limit\n          pred_file_evaluators:\n            - _target_: sam3.eval.cgf1_eval.CGF1Evaluator\n              gt_path: ${paths.coco_gts}\n              iou_type: \"bbox\"\n            - _target_: sam3.eval.cgf1_eval.CGF1Evaluator\n              gt_path: ${paths.coco_gts}\n              iou_type: \"segm\"\n"
  },
  {
    "path": "sam3/train/configs/odinw13/odinw_text_and_visual.yaml",
    "content": "# @package _global_\ndefaults:\n  - _self_\n\n# ============================================================================\n# Paths Configuration (Chage this to your own paths)\n# ============================================================================\n#  python sam3/train/train.py -c configs/odinw_text_only.yaml --use-cluster 1 --partition ${PARTITION} --account ${ACCOUNT} --qos ${QoS}\n\npaths:\n  odinw_data_root: <YOUR_DATA_DIR>\n  experiment_log_dir: <YOUR EXPERIMENET LOG_DIR>\n  bpe_path: <BPE_PATH> # This should be under sam3/assets/bpe_simple_vocab_16e6.txt.gz\n\nsupercategory_tuple: ${all_odinw_supercategories.${string:${submitit.job_array.task_index}}}\n# Validation transforms pipeline\nval_transforms:\n  - _target_: sam3.train.transforms.basic_for_api.ComposeAPI\n    transforms:\n      - _target_: sam3.train.transforms.basic_for_api.RandomResizeAPI\n        sizes: ${scratch.resolution}\n        max_size:\n          _target_: sam3.train.transforms.basic.get_random_resize_max_size\n          size: ${scratch.resolution}\n        square: true\n        consistent_transform: False\n      - _target_: sam3.train.transforms.basic_for_api.ToTensorAPI\n      - _target_: sam3.train.transforms.basic_for_api.NormalizeAPI\n        mean: ${scratch.val_norm_mean}\n        std: ${scratch.val_norm_std}\n      - _target_: sam3.train.transforms.filter_query_transforms.TextQueryToVisual\n        keep_text_queries: true # Note: set this to false if you only want visual\n        probability: 1.0 # always\n\n# ============================================================================\n# Different helper parameters and functions\n# ============================================================================\nscratch:\n  enable_segmentation: True\n  # Box processing\n  use_presence_eval: True\n  original_box_postprocessor:\n    _target_: sam3.eval.postprocessors.PostProcessImage\n    max_dets_per_img: -1  # infinite detections\n    use_original_ids: true\n    use_original_sizes_box: true\n    use_presence: ${scratch.use_presence_eval}\n\n  # Image processing parameters\n  resolution: 1008\n  # Normalization parameters\n  val_norm_mean: [0.5, 0.5, 0.5]\n  val_norm_std: [0.5, 0.5, 0.5]\n\n  # Training parameters\n  val_batch_size: 2\n  num_val_workers: 0\n  gather_pred_via_filesys: false\n\n# ============================================================================\n# Trainer Configuration\n# ============================================================================\n\ntrainer:\n  _target_: sam3.train.trainer.Trainer\n  skip_saving_ckpts: true\n  empty_gpu_mem_cache_after_eval: True\n  max_epochs: 1\n  accelerator: cuda\n  seed_value: 123\n  mode: val\n\n  distributed:\n    backend: nccl\n    find_unused_parameters: True\n    gradient_as_bucket_view: True\n\n  loss:\n    default:\n      _target_: sam3.train.loss.sam3_loss.DummyLoss\n\n  data:\n    val:\n      _target_: sam3.train.data.torch_dataset.TorchDataset\n      dataset:\n        _target_: sam3.train.data.sam3_image_dataset.Sam3ImageDataset\n        coco_json_loader:\n          _target_: sam3.train.data.coco_json_loaders.COCO_FROM_JSON\n          prompts: ${odinw35_prompts.${supercategory_tuple.name}}\n          include_negatives: true\n          category_chunk_size: 20 # Note: Since we are doing AP +ve we need to include all categories!\n          _partial_: true\n        img_folder: ${paths.odinw_data_root}/${supercategory_tuple.val.img_folder}\n        ann_file:\n          _target_: sam3.eval.coco_reindex.reindex_coco_to_temp\n          input_json_path: ${paths.odinw_data_root}/${supercategory_tuple.val.json}\n        transforms: ${val_transforms}\n        max_ann_per_img: 100000\n        multiplier: 1\n        training: false\n\n      shuffle: False\n      batch_size: ${scratch.val_batch_size}\n      num_workers: ${scratch.num_val_workers}\n      pin_memory: False\n      drop_last: False\n      collate_fn:\n        _target_: sam3.train.data.collator.collate_fn_api\n        _partial_: true\n        repeats: 1\n        dict_key: odinw35\n\n  model:\n    _target_: sam3.model_builder.build_sam3_image_model\n    bpe_path: ${paths.bpe_path}\n    device: cpus\n    eval_mode: true # Set to false if training\n    enable_segmentation: ${scratch.enable_segmentation} # Warning: Enable this if using segmentation.\n\n  meters:\n    val:\n      odinw35:\n        detection:\n          _target_: sam3.eval.coco_writer.PredictionDumper\n          iou_type: \"bbox\"\n          dump_dir: ${launcher.experiment_log_dir}/dumps/roboflow/${supercategory_tuple.name}\n          merge_predictions: True\n          postprocessor: ${scratch.original_box_postprocessor}\n          gather_pred_via_filesys: ${scratch.gather_pred_via_filesys}\n          maxdets: 100\n          pred_file_evaluators:\n            - _target_: sam3.eval.coco_eval_offline.CocoEvaluatorOfflineWithPredFileEvaluators\n              gt_path:\n                _target_: sam3.eval.coco_reindex.reindex_coco_to_temp\n                input_json_path: ${paths.odinw_data_root}/${supercategory_tuple.val.json}\n              tide: False\n              iou_type: \"bbox\"\n              positive_split: true\n\n  checkpoint:\n    save_dir: ${launcher.experiment_log_dir}/checkpoints\n    save_freq: 0  # 0 only last checkpoint is saved.\n\n\n  logging:\n    tensorboard_writer:\n      _target_: sam3.train.utils.logger.make_tensorboard_logger\n      log_dir: ${launcher.experiment_log_dir}/tensorboard\n      flush_secs: 120\n      should_log: True\n    wandb_writer: null\n    log_dir: ${launcher.experiment_log_dir}/logs/${supercategory_tuple.name}\n    log_freq: 10\n\n# ============================================================================\n# Launcher and Submitit Configuration\n# ============================================================================\n\nlauncher:\n  num_nodes: 1\n  gpus_per_node: 2\n  experiment_log_dir: ${paths.experiment_log_dir}\n  multiprocessing_context: forkserver\n\nsubmitit:\n  account: null\n  partition: null\n  qos: null\n  timeout_hour: 72\n  use_cluster: True\n  cpus_per_task: 10\n  port_range: [10000, 65000]\n  constraint: null\n\n  job_array:\n    num_tasks: 13\n    task_index: 0\n\n# ============================================================================\n# ODinW13 Supercategories\n# ============================================================================\n\nall_odinw_supercategories:\n  - name: AerialMaritimeDrone_large\n    val:\n      img_folder: AerialMaritimeDrone/large/test/\n      json: AerialMaritimeDrone/large/test/annotations_without_background.json\n  - name: Aquarium\n    val:\n      img_folder: Aquarium/Aquarium Combined.v2-raw-1024.coco/test/\n      json: Aquarium/Aquarium Combined.v2-raw-1024.coco/test/annotations_without_background.json\n  - name: CottontailRabbits\n    val:\n      img_folder: CottontailRabbits/test/\n      json: CottontailRabbits/test/annotations_without_background.json\n  - name: EgoHands_generic\n    val:\n      img_folder: EgoHands/generic/test/\n      json: EgoHands/generic/test/annotations_without_background.json\n  - name: NorthAmericaMushrooms\n    val:\n      img_folder: NorthAmericaMushrooms/North American Mushrooms.v1-416x416.coco/test/\n      json: NorthAmericaMushrooms/North American Mushrooms.v1-416x416.coco/test/annotations_without_background.json\n  - name: Packages\n    val:\n      img_folder: Packages/Raw/test/\n      json: Packages/Raw/test/annotations_without_background.json\n  - name: PascalVOC\n    val:\n      img_folder: PascalVOC/valid/\n      json: PascalVOC/valid/annotations_without_background.json\n  - name: Raccoon\n    val:\n      img_folder: Raccoon/Raccoon.v2-raw.coco/test/\n      json: Raccoon/Raccoon.v2-raw.coco/test/annotations_without_background.json\n  - name: ShellfishOpenImages\n    val:\n      img_folder: ShellfishOpenImages/raw/test/\n      json: ShellfishOpenImages/raw/test/annotations_without_background.json\n  - name: VehiclesOpenImages\n    val:\n      img_folder: VehiclesOpenImages/416x416/test/\n      json: VehiclesOpenImages/416x416/test/annotations_without_background.json\n  - name: pistols\n    val:\n      img_folder: pistols/export/\n      json: pistols/export/test_annotations_without_background.json\n  - name: pothole\n    val:\n      img_folder: pothole/test/\n      json: pothole/test/annotations_without_background.json\n  - name: thermalDogsAndPeople\n    val:\n      img_folder: thermalDogsAndPeople/test/\n      json: thermalDogsAndPeople/test/annotations_without_background.json\n\n\nodinw35_prompts:\n  AerialMaritimeDrone_large: '[{\"id\": 1, \"name\": \"boat\", \"supercategory\": \"movable-objects\"},\n    {\"id\": 2, \"name\": \"car\", \"supercategory\": \"movable-objects\"}, {\"id\": 3, \"name\": \"dock\",\n    \"supercategory\": \"movable-objects\"}, {\"id\": 4, \"name\": \"jet ski\", \"supercategory\": \"movable-objects\"},\n    {\"id\": 5, \"name\": \"boat lift\", \"supercategory\": \"movable-objects\"}]'\n  Aquarium: null\n  CottontailRabbits: null\n  EgoHands_generic: null\n  NorthAmericaMushrooms: '[{''id'': 1, ''name'':\n    ''chicken of the woods'', ''supercategory'': ''mushroom''}, {''id'': 2, ''name'': ''chanterelle'', ''supercategory'': ''mushroom''}]'\n  Packages: null\n  PascalVOC: null\n  Raccoon: null\n  ShellfishOpenImages: null\n  VehiclesOpenImages: null\n  pistols: null\n  pothole: null\n  thermalDogsAndPeople: null\n"
  },
  {
    "path": "sam3/train/configs/odinw13/odinw_text_only.yaml",
    "content": "# @package _global_\ndefaults:\n  - _self_\n\n# ============================================================================\n# Paths Configuration (Chage this to your own paths)\n# ============================================================================\n#  python sam3/train/train.py -c configs/odinw_text_only.yaml --use-cluster 1 --partition ${PARTITION} --account ${ACCOUNT} --qos ${QoS}\n\npaths:\n  odinw_data_root: <YOUR_DATA_DIR>\n  experiment_log_dir: <YOUR EXPERIMENET LOG_DIR>\n  bpe_path: <BPE_PATH> # This should be under sam3/assets/bpe_simple_vocab_16e6.txt.gz\n\n\nsupercategory_tuple: ${all_odinw_supercategories.${string:${submitit.job_array.task_index}}}\n# Validation transforms pipeline\nval_transforms:\n  - _target_: sam3.train.transforms.basic_for_api.ComposeAPI\n    transforms:\n      - _target_: sam3.train.transforms.basic_for_api.RandomResizeAPI\n        sizes: ${scratch.resolution}\n        max_size:\n          _target_: sam3.train.transforms.basic.get_random_resize_max_size\n          size: ${scratch.resolution}\n        square: true\n        consistent_transform: False\n      - _target_: sam3.train.transforms.basic_for_api.ToTensorAPI\n      - _target_: sam3.train.transforms.basic_for_api.NormalizeAPI\n        mean: ${scratch.val_norm_mean}\n        std: ${scratch.val_norm_std}\n\n# ============================================================================\n# Different helper parameters and functions\n# ============================================================================\nscratch:\n  enable_segmentation: True\n  # Box processing\n  use_presence_eval: True\n  original_box_postprocessor:\n    _target_: sam3.eval.postprocessors.PostProcessImage\n    max_dets_per_img: -1  # infinite detections\n    use_original_ids: true\n    use_original_sizes_box: true\n    use_presence: ${scratch.use_presence_eval}\n\n  # Image processing parameters\n  resolution: 1008\n  # Normalization parameters\n  val_norm_mean: [0.5, 0.5, 0.5]\n  val_norm_std: [0.5, 0.5, 0.5]\n\n  # Training parameters\n  val_batch_size: 2\n  num_val_workers: 0\n  gather_pred_via_filesys: false\n\n# ============================================================================\n# Trainer Configuration\n# ============================================================================\n\ntrainer:\n  _target_: sam3.train.trainer.Trainer\n  skip_saving_ckpts: true\n  empty_gpu_mem_cache_after_eval: True\n  max_epochs: 1\n  accelerator: cuda\n  seed_value: 123\n  mode: val\n\n  distributed:\n    backend: nccl\n    find_unused_parameters: True\n    gradient_as_bucket_view: True\n\n  loss:\n    default:\n      _target_: sam3.train.loss.sam3_loss.DummyLoss\n\n  data:\n    val:\n      _target_: sam3.train.data.torch_dataset.TorchDataset\n      dataset:\n        _target_: sam3.train.data.sam3_image_dataset.Sam3ImageDataset\n        coco_json_loader:\n          _target_: sam3.train.data.coco_json_loaders.COCO_FROM_JSON\n          prompts: ${odinw35_prompts.${supercategory_tuple.name}}\n          include_negatives: true\n          category_chunk_size: 20 # Note: Since we are doing AP +ve we need to include all categories!\n          _partial_: true\n        img_folder: ${paths.odinw_data_root}/${supercategory_tuple.val.img_folder}\n        ann_file:\n          _target_: sam3.eval.coco_reindex.reindex_coco_to_temp\n          input_json_path: ${paths.odinw_data_root}/${supercategory_tuple.val.json}\n        transforms: ${val_transforms}\n        max_ann_per_img: 100000\n        multiplier: 1\n        training: false\n\n      shuffle: False\n      batch_size: ${scratch.val_batch_size}\n      num_workers: ${scratch.num_val_workers}\n      pin_memory: False\n      drop_last: False\n      collate_fn:\n        _target_: sam3.train.data.collator.collate_fn_api\n        _partial_: true\n        repeats: 1\n        dict_key: odinw35\n\n  model:\n    _target_: sam3.model_builder.build_sam3_image_model\n    bpe_path: ${paths.bpe_path}\n    device: cpus\n    eval_mode: true # Set to false if training\n    enable_segmentation: ${scratch.enable_segmentation} # Warning: Enable this if using segmentation.\n\n  meters:\n    val:\n      odinw35:\n        detection:\n          _target_: sam3.eval.coco_writer.PredictionDumper\n          iou_type: \"bbox\"\n          dump_dir: ${launcher.experiment_log_dir}/dumps/odinw/${supercategory_tuple.name}\n          merge_predictions: True\n          postprocessor: ${scratch.original_box_postprocessor}\n          gather_pred_via_filesys: ${scratch.gather_pred_via_filesys}\n          maxdets: 100\n          pred_file_evaluators:\n            - _target_: sam3.eval.coco_eval_offline.CocoEvaluatorOfflineWithPredFileEvaluators\n              gt_path:\n                _target_: sam3.eval.coco_reindex.reindex_coco_to_temp\n                input_json_path: ${paths.odinw_data_root}/${supercategory_tuple.val.json}\n              tide: False\n              iou_type: \"bbox\"\n              positive_split: False\n\n  checkpoint:\n    save_dir: ${launcher.experiment_log_dir}/checkpoints\n    save_freq: 0  # 0 only last checkpoint is saved.\n\n\n  logging:\n    tensorboard_writer:\n      _target_: sam3.train.utils.logger.make_tensorboard_logger\n      log_dir: ${launcher.experiment_log_dir}/tensorboard\n      flush_secs: 120\n      should_log: True\n    wandb_writer: null\n    log_dir: ${launcher.experiment_log_dir}/logs/${supercategory_tuple.name}\n    log_freq: 10\n\n# ============================================================================\n# Launcher and Submitit Configuration\n# ============================================================================\n\nlauncher:\n  num_nodes: 1\n  gpus_per_node: 2\n  experiment_log_dir: ${paths.experiment_log_dir}\n  multiprocessing_context: forkserver\n\nsubmitit:\n  account: null\n  partition: null\n  qos: null\n  timeout_hour: 72\n  use_cluster: True\n  cpus_per_task: 10\n  port_range: [10000, 65000]\n  constraint: null\n\n  job_array:\n    num_tasks: 13\n    task_index: 0\n\n# ============================================================================\n# ODinW13 Supercategories\n# ============================================================================\n\nall_odinw_supercategories:\n  - name: AerialMaritimeDrone_large\n    val:\n      img_folder: AerialMaritimeDrone/large/test/\n      json: AerialMaritimeDrone/large/test/annotations_without_background.json\n  - name: Aquarium\n    val:\n      img_folder: Aquarium/Aquarium Combined.v2-raw-1024.coco/test/\n      json: Aquarium/Aquarium Combined.v2-raw-1024.coco/test/annotations_without_background.json\n  - name: CottontailRabbits\n    val:\n      img_folder: CottontailRabbits/test/\n      json: CottontailRabbits/test/annotations_without_background.json\n  - name: EgoHands_generic\n    val:\n      img_folder: EgoHands/generic/test/\n      json: EgoHands/generic/test/annotations_without_background.json\n  - name: NorthAmericaMushrooms\n    val:\n      img_folder: NorthAmericaMushrooms/North American Mushrooms.v1-416x416.coco/test/\n      json: NorthAmericaMushrooms/North American Mushrooms.v1-416x416.coco/test/annotations_without_background.json\n  - name: Packages\n    val:\n      img_folder: Packages/Raw/test/\n      json: Packages/Raw/test/annotations_without_background.json\n  - name: PascalVOC\n    val:\n      img_folder: PascalVOC/valid/\n      json: PascalVOC/valid/annotations_without_background.json\n  - name: Raccoon\n    val:\n      img_folder: Raccoon/Raccoon.v2-raw.coco/test/\n      json: Raccoon/Raccoon.v2-raw.coco/test/annotations_without_background.json\n  - name: ShellfishOpenImages\n    val:\n      img_folder: ShellfishOpenImages/raw/test/\n      json: ShellfishOpenImages/raw/test/annotations_without_background.json\n  - name: VehiclesOpenImages\n    val:\n      img_folder: VehiclesOpenImages/416x416/test/\n      json: VehiclesOpenImages/416x416/test/annotations_without_background.json\n  - name: pistols\n    val:\n      img_folder: pistols/export/\n      json: pistols/export/test_annotations_without_background.json\n  - name: pothole\n    val:\n      img_folder: pothole/test/\n      json: pothole/test/annotations_without_background.json\n  - name: thermalDogsAndPeople\n    val:\n      img_folder: thermalDogsAndPeople/test/\n      json: thermalDogsAndPeople/test/annotations_without_background.json\n\n\nodinw35_prompts:\n  AerialMaritimeDrone_large: '[{\"id\": 1, \"name\": \"boat\", \"supercategory\": \"movable-objects\"},\n    {\"id\": 2, \"name\": \"car\", \"supercategory\": \"movable-objects\"}, {\"id\": 3, \"name\": \"dock\",\n    \"supercategory\": \"movable-objects\"}, {\"id\": 4, \"name\": \"jet ski\", \"supercategory\": \"movable-objects\"},\n    {\"id\": 5, \"name\": \"boat lift\", \"supercategory\": \"movable-objects\"}]'\n  Aquarium: null\n  CottontailRabbits: null\n  EgoHands_generic: null\n  NorthAmericaMushrooms: '[{''id'': 1, ''name'':\n    ''chicken of the woods'', ''supercategory'': ''mushroom''}, {''id'': 2, ''name'': ''chanterelle'', ''supercategory'': ''mushroom''}]'\n  Packages: null\n  PascalVOC: null\n  Raccoon: null\n  ShellfishOpenImages: null\n  VehiclesOpenImages: null\n  pistols: null\n  pothole: null\n  thermalDogsAndPeople: null\n"
  },
  {
    "path": "sam3/train/configs/odinw13/odinw_text_only_positive.yaml",
    "content": "# @package _global_\ndefaults:\n  - _self_\n\n# ============================================================================\n# Paths Configuration (Chage this to your own paths)\n# ============================================================================\n#  python sam3/train/train.py -c configs/odinw_text_only.yaml --use-cluster 1 --partition ${PARTITION} --account ${ACCOUNT} --qos ${QoS}\n\npaths:\n  odinw_data_root: <YOUR_DATA_DIR>\n  experiment_log_dir: <YOUR EXPERIMENET LOG_DIR>\n  bpe_path: <BPE_PATH> # This should be under sam3/assets/bpe_simple_vocab_16e6.txt.gz\n\n\nsupercategory_tuple: ${all_odinw_supercategories.${string:${submitit.job_array.task_index}}}\n# Validation transforms pipeline\nval_transforms:\n  - _target_: sam3.train.transforms.basic_for_api.ComposeAPI\n    transforms:\n      - _target_: sam3.train.transforms.basic_for_api.RandomResizeAPI\n        sizes: ${scratch.resolution}\n        max_size:\n          _target_: sam3.train.transforms.basic.get_random_resize_max_size\n          size: ${scratch.resolution}\n        square: true\n        consistent_transform: False\n      - _target_: sam3.train.transforms.basic_for_api.ToTensorAPI\n      - _target_: sam3.train.transforms.basic_for_api.NormalizeAPI\n        mean: ${scratch.val_norm_mean}\n        std: ${scratch.val_norm_std}\n\n# ============================================================================\n# Different helper parameters and functions\n# ============================================================================\nscratch:\n  enable_segmentation: True\n  # Box processing\n  use_presence_eval: True\n  original_box_postprocessor:\n    _target_: sam3.eval.postprocessors.PostProcessImage\n    max_dets_per_img: -1  # infinite detections\n    use_original_ids: true\n    use_original_sizes_box: true\n    use_presence: ${scratch.use_presence_eval}\n\n  # Image processing parameters\n  resolution: 1008\n  # Normalization parameters\n  val_norm_mean: [0.5, 0.5, 0.5]\n  val_norm_std: [0.5, 0.5, 0.5]\n\n  # Training parameters\n  val_batch_size: 2\n  num_val_workers: 0\n  gather_pred_via_filesys: false\n\n# ============================================================================\n# Trainer Configuration\n# ============================================================================\n\ntrainer:\n  _target_: sam3.train.trainer.Trainer\n  skip_saving_ckpts: true\n  empty_gpu_mem_cache_after_eval: True\n  max_epochs: 1\n  accelerator: cuda\n  seed_value: 123\n  mode: val\n\n  distributed:\n    backend: nccl\n    find_unused_parameters: True\n    gradient_as_bucket_view: True\n\n  loss:\n    default:\n      _target_: sam3.train.loss.sam3_loss.DummyLoss\n\n  data:\n    val:\n      _target_: sam3.train.data.torch_dataset.TorchDataset\n      dataset:\n        _target_: sam3.train.data.sam3_image_dataset.Sam3ImageDataset\n        coco_json_loader:\n          _target_: sam3.train.data.coco_json_loaders.COCO_FROM_JSON\n          prompts: ${odinw35_prompts.${supercategory_tuple.name}}\n          include_negatives: true\n          category_chunk_size: 20 # Note: Since we are doing AP +ve we need to include all categories!\n          _partial_: true\n        img_folder: ${paths.odinw_data_root}/${supercategory_tuple.val.img_folder}\n        ann_file:\n          _target_: sam3.eval.coco_reindex.reindex_coco_to_temp\n          input_json_path: ${paths.odinw_data_root}/${supercategory_tuple.val.json}\n        transforms: ${val_transforms}\n        max_ann_per_img: 100000\n        multiplier: 1\n        training: false\n\n      shuffle: False\n      batch_size: ${scratch.val_batch_size}\n      num_workers: ${scratch.num_val_workers}\n      pin_memory: False\n      drop_last: False\n      collate_fn:\n        _target_: sam3.train.data.collator.collate_fn_api\n        _partial_: true\n        repeats: 1\n        dict_key: odinw35\n\n  model:\n    _target_: sam3.model_builder.build_sam3_image_model\n    bpe_path: ${paths.bpe_path}\n    device: cpus\n    eval_mode: true # Set to false if training\n    enable_segmentation: ${scratch.enable_segmentation} # Warning: Enable this if using segmentation.\n\n  meters:\n    val:\n      odinw35:\n        detection:\n          _target_: sam3.eval.coco_writer.PredictionDumper\n          iou_type: \"bbox\"\n          dump_dir: ${launcher.experiment_log_dir}/dumps/roboflow/${supercategory_tuple.name}\n          merge_predictions: True\n          postprocessor: ${scratch.original_box_postprocessor}\n          gather_pred_via_filesys: ${scratch.gather_pred_via_filesys}\n          maxdets: 100\n          pred_file_evaluators:\n            - _target_: sam3.eval.coco_eval_offline.CocoEvaluatorOfflineWithPredFileEvaluators\n              gt_path:\n                _target_: sam3.eval.coco_reindex.reindex_coco_to_temp\n                input_json_path: ${paths.odinw_data_root}/${supercategory_tuple.val.json}\n              tide: False\n              iou_type: \"bbox\"\n              positive_split: true\n\n  checkpoint:\n    save_dir: ${launcher.experiment_log_dir}/checkpoints\n    save_freq: 0  # 0 only last checkpoint is saved.\n\n\n  logging:\n    tensorboard_writer:\n      _target_: sam3.train.utils.logger.make_tensorboard_logger\n      log_dir: ${launcher.experiment_log_dir}/tensorboard\n      flush_secs: 120\n      should_log: True\n    wandb_writer: null\n    log_dir: ${launcher.experiment_log_dir}/logs/${supercategory_tuple.name}\n    log_freq: 10\n\n# ============================================================================\n# Launcher and Submitit Configuration\n# ============================================================================\n\nlauncher:\n  num_nodes: 1\n  gpus_per_node: 2\n  experiment_log_dir: ${paths.experiment_log_dir}\n  multiprocessing_context: forkserver\n\nsubmitit:\n  account: null\n  partition: null\n  qos: null\n  timeout_hour: 72\n  use_cluster: True\n  cpus_per_task: 10\n  port_range: [10000, 65000]\n  constraint: null\n\n  job_array:\n    num_tasks: 13\n    task_index: 0\n\n# ============================================================================\n# ODinW13 Supercategories\n# ============================================================================\n\nall_odinw_supercategories:\n  - name: AerialMaritimeDrone_large\n    val:\n      img_folder: AerialMaritimeDrone/large/test/\n      json: AerialMaritimeDrone/large/test/annotations_without_background.json\n  - name: Aquarium\n    val:\n      img_folder: Aquarium/Aquarium Combined.v2-raw-1024.coco/test/\n      json: Aquarium/Aquarium Combined.v2-raw-1024.coco/test/annotations_without_background.json\n  - name: CottontailRabbits\n    val:\n      img_folder: CottontailRabbits/test/\n      json: CottontailRabbits/test/annotations_without_background.json\n  - name: EgoHands_generic\n    val:\n      img_folder: EgoHands/generic/test/\n      json: EgoHands/generic/test/annotations_without_background.json\n  - name: NorthAmericaMushrooms\n    val:\n      img_folder: NorthAmericaMushrooms/North American Mushrooms.v1-416x416.coco/test/\n      json: NorthAmericaMushrooms/North American Mushrooms.v1-416x416.coco/test/annotations_without_background.json\n  - name: Packages\n    val:\n      img_folder: Packages/Raw/test/\n      json: Packages/Raw/test/annotations_without_background.json\n  - name: PascalVOC\n    val:\n      img_folder: PascalVOC/valid/\n      json: PascalVOC/valid/annotations_without_background.json\n  - name: Raccoon\n    val:\n      img_folder: Raccoon/Raccoon.v2-raw.coco/test/\n      json: Raccoon/Raccoon.v2-raw.coco/test/annotations_without_background.json\n  - name: ShellfishOpenImages\n    val:\n      img_folder: ShellfishOpenImages/raw/test/\n      json: ShellfishOpenImages/raw/test/annotations_without_background.json\n  - name: VehiclesOpenImages\n    val:\n      img_folder: VehiclesOpenImages/416x416/test/\n      json: VehiclesOpenImages/416x416/test/annotations_without_background.json\n  - name: pistols\n    val:\n      img_folder: pistols/export/\n      json: pistols/export/test_annotations_without_background.json\n  - name: pothole\n    val:\n      img_folder: pothole/test/\n      json: pothole/test/annotations_without_background.json\n  - name: thermalDogsAndPeople\n    val:\n      img_folder: thermalDogsAndPeople/test/\n      json: thermalDogsAndPeople/test/annotations_without_background.json\n\n\nodinw35_prompts:\n  AerialMaritimeDrone_large: '[{\"id\": 1, \"name\": \"boat\", \"supercategory\": \"movable-objects\"},\n    {\"id\": 2, \"name\": \"car\", \"supercategory\": \"movable-objects\"}, {\"id\": 3, \"name\": \"dock\",\n    \"supercategory\": \"movable-objects\"}, {\"id\": 4, \"name\": \"jet ski\", \"supercategory\": \"movable-objects\"},\n    {\"id\": 5, \"name\": \"boat lift\", \"supercategory\": \"movable-objects\"}]'\n  Aquarium: null\n  CottontailRabbits: null\n  EgoHands_generic: null\n  NorthAmericaMushrooms: '[{''id'': 1, ''name'':\n    ''chicken of the woods'', ''supercategory'': ''mushroom''}, {''id'': 2, ''name'': ''chanterelle'', ''supercategory'': ''mushroom''}]'\n  Packages: null\n  PascalVOC: null\n  Raccoon: null\n  ShellfishOpenImages: null\n  VehiclesOpenImages: null\n  pistols: null\n  pothole: null\n  thermalDogsAndPeople: null\n"
  },
  {
    "path": "sam3/train/configs/odinw13/odinw_text_only_train.yaml",
    "content": "# @package _global_\ndefaults:\n  - _self_\n\n# ============================================================================\n# Paths Configuration (Chage this to your own paths)\n# ============================================================================\n#  python sam3/train/train.py -c configs/odinw_text_only.yaml --use-cluster 1 --partition ${PARTITION} --account ${ACCOUNT} --qos ${QoS}\n\npaths:\n  odinw_data_root: <YOUR_DATA_DIR>\n  experiment_log_dir: <YOUR EXPERIMENET LOG_DIR>\n  bpe_path: <BPE_PATH> # This should be under sam3/assets/bpe_simple_vocab_16e6.txt.gz\n\n\nodinw_train:\n  train_file: fewshot_train_shot10_seed300\n  num_images: null\n  supercategory_tuple: ${all_odinw_supercategories.${string:${submitit.job_array.task_index}}}\n  # Training transforms pipeline\n  train_transforms:\n    - _target_: sam3.train.transforms.basic_for_api.ComposeAPI\n      transforms:\n        - _target_: sam3.train.transforms.filter_query_transforms.FlexibleFilterFindGetQueries\n          query_filter:\n            _target_: sam3.train.transforms.filter_query_transforms.FilterCrowds\n        - _target_: sam3.train.transforms.point_sampling.RandomizeInputBbox\n          box_noise_std: 0.1\n          box_noise_max: 20\n        - _target_: sam3.train.transforms.segmentation.DecodeRle\n        - _target_: sam3.train.transforms.basic_for_api.RandomResizeAPI\n          sizes:\n            _target_: sam3.train.transforms.basic.get_random_resize_scales\n            size: ${scratch.resolution}\n            min_size: 480\n            rounded: false\n          max_size:\n            _target_: sam3.train.transforms.basic.get_random_resize_max_size\n            size: ${scratch.resolution}\n          square: true\n          consistent_transform: ${scratch.consistent_transform}\n        - _target_: sam3.train.transforms.basic_for_api.PadToSizeAPI\n          size: ${scratch.resolution}\n          consistent_transform: ${scratch.consistent_transform}\n        - _target_: sam3.train.transforms.basic_for_api.ToTensorAPI\n        - _target_: sam3.train.transforms.filter_query_transforms.FlexibleFilterFindGetQueries\n          query_filter:\n            _target_: sam3.train.transforms.filter_query_transforms.FilterEmptyTargets\n        - _target_: sam3.train.transforms.basic_for_api.NormalizeAPI\n          mean: ${scratch.train_norm_mean}\n          std: ${scratch.train_norm_std}\n        - _target_: sam3.train.transforms.filter_query_transforms.FlexibleFilterFindGetQueries\n          query_filter:\n            _target_: sam3.train.transforms.filter_query_transforms.FilterEmptyTargets\n    - _target_: sam3.train.transforms.filter_query_transforms.FlexibleFilterFindGetQueries\n      query_filter:\n        _target_: sam3.train.transforms.filter_query_transforms.FilterFindQueriesWithTooManyOut\n        max_num_objects: ${scratch.max_ann_per_img}\n\n  # Validation transforms pipeline\n  val_transforms:\n    - _target_: sam3.train.transforms.basic_for_api.ComposeAPI\n      transforms:\n        - _target_: sam3.train.transforms.basic_for_api.RandomResizeAPI\n          sizes: ${scratch.resolution}\n          max_size:\n            _target_: sam3.train.transforms.basic.get_random_resize_max_size\n            size: ${scratch.resolution}\n          square: true\n          consistent_transform: False\n        - _target_: sam3.train.transforms.basic_for_api.ToTensorAPI\n        - _target_: sam3.train.transforms.basic_for_api.NormalizeAPI\n          mean: ${scratch.val_norm_mean}\n          std: ${scratch.val_norm_std}\n\n  # loss config (no mask loss)\n  loss:\n    _target_: sam3.train.loss.sam3_loss.Sam3LossWrapper\n    matcher: ${scratch.matcher}\n    o2m_weight: 2.0\n    o2m_matcher:\n      _target_: sam3.train.matcher.BinaryOneToManyMatcher\n      alpha: 0.3\n      threshold: 0.4\n      topk: 4\n    use_o2m_matcher_on_o2m_aux: ${scratch.use_o2m_matcher_on_o2m_aux}\n    loss_fns_find:\n      - _target_: sam3.train.loss.loss_fns.Boxes\n        weight_dict:\n          loss_bbox: 5.0\n          loss_giou: 2.0\n      - _target_: sam3.train.loss.loss_fns.IABCEMdetr\n        weak_loss: False\n        weight_dict:\n          loss_ce: ${scratch.loss_ce_weight}  # Change\n          presence_loss: ${scratch.presence_weight}  # Change\n        pos_weight: ${scratch.iabce_pos_weight}\n        alpha: ${scratch.iabce_alpha}\n        gamma: 2\n        use_presence: True  # Change\n        pos_focal: ${scratch.iabce_pos_focal}\n        pad_n_queries: ${scratch.num_queries}\n        pad_scale_pos: ${scratch.instance_query_loss_pad_scale_pos}\n\n    loss_fn_semantic_seg: null\n    scale_by_find_batch_size: ${scratch.scale_by_find_batch_size}\n\n\n# ============================================================================\n# Different helper parameters and functions\n# ============================================================================\nscratch:\n  enable_segmentation: False\n  use_act_checkpoint_geo_encoder: True\n  input_geometry_encoder:\n    _target_: sam3.model.geometry_encoders.SequenceGeometryEncoder\n    pos_enc: ${scratch.pos_embed}\n    encode_boxes_as_points: False\n    points_direct_project: True\n    points_pool: True\n    points_pos_enc: True\n    boxes_direct_project: True\n    boxes_pool: True\n    boxes_pos_enc: True\n    d_model: ${scratch.d_model}\n    num_layers: 3\n    use_act_ckpt: ${scratch.use_act_checkpoint_geo_encoder}\n    layer:\n      _target_: sam3.model.encoder.TransformerEncoderLayer\n      activation: \"relu\"\n      d_model: ${scratch.d_model}\n      dim_feedforward: 2048\n      dropout: ${scratch.encoder_dropout}\n      pos_enc_at_attn: false\n      pre_norm: True\n      pos_enc_at_cross_attn_queries: false\n      pos_enc_at_cross_attn_keys: true\n      self_attention:\n        _target_: sam3.model.attention.MultiheadAttention\n        attn_type: Vanilla\n        num_heads: 8\n        dropout: ${scratch.encoder_dropout}\n        embed_dim: ${scratch.d_model}\n        batch_first: False\n      cross_attention:\n        _target_: sam3.model.attention.MultiheadAttention\n        attn_type: Vanilla\n        num_heads: 8\n        dropout: ${scratch.encoder_dropout}\n        embed_dim: ${scratch.d_model}\n        batch_first: False\n    add_cls: true\n    add_post_encode_proj: True\n\n  boxRPB: \"log\"\n  dac: True\n  use_early_fusion: true\n  o2m_mask: false\n  num_feature_levels: 1  # > 1 not implemented\n  encoder_dropout: 0.1\n  decoder_dropout: 0.1\n\n  tokenizer_ve:\n    _target_:  sam3.model.tokenizer_ve.SimpleTokenizer\n    bpe_path: ${paths.bpe_path}\n\n\n  freeze_text_tower: False\n  freeze_image_tower: NoFreeze\n  vis_backbone_dp: 0.0\n  # Activation checkpointing (Save memory)\n  use_act_checkpoint_vision_backbone: True\n  use_act_checkpoint_text_backbone: True\n  use_act_checkpoint_encoder: True\n  use_act_checkpoint_decoder: True\n\n  loss: null\n  # Loss parameters\n  num_queries: 200\n  presence_weight: 20.0\n  loss_ce_weight: 20.0\n  iabce_pos_weight: 5.0\n  iabce_pos_focal: false\n  iabce_alpha: 0.25\n  instance_query_loss_pad_scale_pos: 1.0\n  use_o2m_matcher_on_o2m_aux: false\n\n  # Model parameters\n  use_instance_query: true\n  d_model: 256\n  pos_embed:\n    _target_: sam3.model.position_encoding.PositionEmbeddingSine\n    num_pos_feats: ${scratch.d_model}\n    normalize: true\n    scale: null\n    temperature: 10000\n\n  # Box processing\n  use_presence_eval: True\n  original_box_postprocessor:\n    _target_: sam3.eval.postprocessors.PostProcessImage\n    max_dets_per_img: -1  # infinite detections\n    use_original_ids: true\n    use_original_sizes_box: true\n    use_presence: ${scratch.use_presence_eval}\n\n\n  # Matcher configuration\n  matcher:\n    _target_: sam3.train.matcher.BinaryHungarianMatcherV2\n    focal: true\n    cost_class: 2.0\n    cost_bbox: 5.0\n    cost_giou: 2.0\n    alpha: 0.25\n    gamma: 2\n    stable: False\n  scale_by_find_batch_size: True\n\n  # Image processing parameters\n  resolution: 1008\n  consistent_transform: False\n  max_ann_per_img: 200\n\n  # Normalization parameters\n  train_norm_mean: [0.5, 0.5, 0.5]\n  train_norm_std: [0.5, 0.5, 0.5]\n  val_norm_mean: [0.5, 0.5, 0.5]\n  val_norm_std: [0.5, 0.5, 0.5]\n\n  # Training parameters\n  train_batch_size: 1\n  val_batch_size: 1\n  num_train_workers: 0\n  num_val_workers: 0\n  max_data_epochs: 40\n  target_epoch_size: 1500\n  hybrid_repeats: 1\n  context_length: 2\n  gather_pred_via_filesys: false\n\n  # Learning rate and scheduler parameters\n  lr_scale: 0.1\n  lr_transformer: ${times:8e-4,${scratch.lr_scale}}\n  lr_vision_backbone: ${times:2.5e-4,${scratch.lr_scale}}\n  lr_language_backbone: ${times:5e-5,${scratch.lr_scale}}\n  lrd_vision_backbone: 0.9\n  wd: 0.1\n  scheduler_timescale: 20\n  scheduler_warmup: 20\n  scheduler_cooldown: 20\n\n\n\n\n# ============================================================================\n# Trainer Configuration\n# ============================================================================\n\ntrainer:\n  _target_: sam3.train.trainer.Trainer\n  skip_saving_ckpts: true\n  # _target_: sam3.train.trainer.Trainer\n  # skip_saving_ckpts: true\n  empty_gpu_mem_cache_after_eval: True\n  skip_first_val: True\n  max_epochs: ${scratch.max_data_epochs}\n  accelerator: cuda\n  seed_value: 123\n  val_epoch_freq: 10\n  mode: train\n\n  distributed:\n    backend: nccl\n    find_unused_parameters: True\n    gradient_as_bucket_view: True\n\n  loss:\n    all: ${odinw_train.loss}\n    default:\n      _target_: sam3.train.loss.sam3_loss.DummyLoss\n\n  data:\n    train:\n      _target_: sam3.train.data.torch_dataset.TorchDataset\n      dataset:\n        _target_: sam3.train.data.sam3_image_dataset.Sam3ImageDataset\n        limit_ids: ${odinw_train.num_images}\n        transforms: ${odinw_train.train_transforms}\n        load_segmentation: ${scratch.enable_segmentation}\n        max_ann_per_img: 500000\n        multiplier: 1\n        max_train_queries: 50000\n        max_val_queries: 50000\n        training: true\n        use_caching: False\n        img_folder: ${paths.odinw_data_root}/${odinw_train.supercategory_tuple.train.img_folder}\n        ann_file:\n          _target_: sam3.eval.coco_reindex.reindex_coco_to_temp\n          input_json_path: ${paths.odinw_data_root}/${odinw_train.supercategory_tuple.train.json}\n        coco_json_loader:\n          _target_: sam3.train.data.coco_json_loaders.COCO_FROM_JSON\n          prompts: ${odinw35_prompts.${odinw_train.supercategory_tuple.name}} #${odinw_train.supercategory_tuple.name)\n          _partial_: true\n      shuffle: True\n      batch_size: ${scratch.train_batch_size}\n      num_workers: ${scratch.num_train_workers}\n      pin_memory: False\n      drop_last: True\n      collate_fn:\n        _target_: sam3.train.data.collator.collate_fn_api\n        _partial_: true\n        repeats: ${scratch.hybrid_repeats}\n        dict_key: all\n        with_seg_masks: ${scratch.enable_segmentation}\n\n    val:\n      _target_: sam3.train.data.torch_dataset.TorchDataset\n      dataset:\n        _target_: sam3.train.data.sam3_image_dataset.Sam3ImageDataset\n        load_segmentation: ${scratch.enable_segmentation}\n        coco_json_loader:\n          _target_: sam3.train.data.coco_json_loaders.COCO_FROM_JSON\n          prompts: ${odinw35_prompts.${odinw_train.supercategory_tuple.name}}\n          include_negatives: true\n          category_chunk_size: 20 # Note: Since we are doing AP +ve we need to include all categories!\n          _partial_: true\n        img_folder: ${paths.odinw_data_root}/${odinw_train.supercategory_tuple.val.img_folder}\n        ann_file:\n          _target_: sam3.eval.coco_reindex.reindex_coco_to_temp\n          input_json_path: ${paths.odinw_data_root}/${odinw_train.supercategory_tuple.val.json}\n        transforms: ${odinw_train.val_transforms}\n        max_ann_per_img: 100000\n        multiplier: 1\n        training: false\n\n      shuffle: False\n      batch_size: ${scratch.val_batch_size}\n      num_workers: ${scratch.num_val_workers}\n      pin_memory: False\n      drop_last: False\n      collate_fn:\n        _target_: sam3.train.data.collator.collate_fn_api\n        _partial_: true\n        repeats: 1\n        dict_key: odinw35\n        with_seg_masks: ${scratch.enable_segmentation}\n\n  model:\n    _target_: sam3.model_builder.build_sam3_image_model\n    bpe_path: ${paths.bpe_path}\n    device: cpus\n    eval_mode: false # Set to false if training\n    enable_segmentation: ${scratch.enable_segmentation} # Warning: Enable this if using segmentation.\n\n  meters:\n    val:\n      odinw35:\n        detection:\n          _target_: sam3.eval.coco_writer.PredictionDumper\n          iou_type: \"bbox\"\n          dump_dir: ${launcher.experiment_log_dir}/dumps/odinw/${odinw_train.supercategory_tuple.name}\n          merge_predictions: True\n          postprocessor: ${scratch.original_box_postprocessor}\n          gather_pred_via_filesys: ${scratch.gather_pred_via_filesys}\n          maxdets: 100\n          pred_file_evaluators:\n            - _target_: sam3.eval.coco_eval_offline.CocoEvaluatorOfflineWithPredFileEvaluators\n              gt_path:\n                _target_: sam3.eval.coco_reindex.reindex_coco_to_temp\n                input_json_path: ${paths.odinw_data_root}/${odinw_train.supercategory_tuple.val.json}\n              tide: False\n              iou_type: \"bbox\"\n              positive_split: False\n\n  optim:\n    amp:\n      enabled: True\n      amp_dtype: bfloat16\n\n    optimizer:\n      _target_: torch.optim.AdamW\n\n    gradient_clip:\n      _target_: sam3.train.optim.optimizer.GradientClipper\n      max_norm: 0.1\n      norm_type: 2\n\n    param_group_modifiers:\n      - _target_: sam3.train.optim.optimizer.layer_decay_param_modifier\n        _partial_: True\n        layer_decay_value: ${scratch.lrd_vision_backbone}\n        apply_to: 'backbone.vision_backbone.trunk'\n        overrides:\n          - pattern: '*pos_embed*'\n            value: 1.0\n\n    options:\n      lr:\n        - scheduler:  # transformer and class_embed\n            _target_: sam3.train.optim.schedulers.InverseSquareRootParamScheduler\n            base_lr: ${scratch.lr_transformer}\n            timescale: ${scratch.scheduler_timescale}\n            warmup_steps: ${scratch.scheduler_warmup}\n            cooldown_steps: ${scratch.scheduler_cooldown}\n        - scheduler:\n            _target_: sam3.train.optim.schedulers.InverseSquareRootParamScheduler\n            base_lr: ${scratch.lr_vision_backbone}\n            timescale: ${scratch.scheduler_timescale}\n            warmup_steps: ${scratch.scheduler_warmup}\n            cooldown_steps: ${scratch.scheduler_cooldown}\n          param_names:\n            - 'backbone.vision_backbone.*'\n        - scheduler:\n            _target_: sam3.train.optim.schedulers.InverseSquareRootParamScheduler\n            base_lr: ${scratch.lr_language_backbone}\n            timescale: ${scratch.scheduler_timescale}\n            warmup_steps: ${scratch.scheduler_warmup}\n            cooldown_steps: ${scratch.scheduler_cooldown}\n          param_names:\n            - 'backbone.language_backbone.*'\n\n      weight_decay:\n        - scheduler:\n            _target_: fvcore.common.param_scheduler.ConstantParamScheduler\n            value: ${scratch.wd}\n        - scheduler:\n            _target_: fvcore.common.param_scheduler.ConstantParamScheduler\n            value: 0.0\n          param_names:\n            - '*bias*'\n          module_cls_names: ['torch.nn.LayerNorm']\n\n  checkpoint:\n    save_dir: ${launcher.experiment_log_dir}/checkpoints\n    save_freq: 0  # 0 only last checkpoint is saved.\n\n\n  logging:\n    tensorboard_writer:\n      _target_: sam3.train.utils.logger.make_tensorboard_logger\n      log_dir: ${launcher.experiment_log_dir}/tensorboard\n      flush_secs: 120\n      should_log: True\n    wandb_writer: null\n    log_dir: ${launcher.experiment_log_dir}/logs/${odinw_train.supercategory_tuple.name}\n    log_freq: 10\n\n# ============================================================================\n# Launcher and Submitit Configuration\n# ============================================================================\n\nlauncher:\n  num_nodes: 1\n  gpus_per_node: 2\n  experiment_log_dir: null #${paths.experiment_log_dir}\n  multiprocessing_context: forkserver\n\nsubmitit:\n  account: null\n  partition: null\n  qos: null\n  timeout_hour: 72\n  use_cluster: True\n  cpus_per_task: 10\n  port_range: [10000, 65000]\n  constraint: null\n\n  # task_index: 2\n  # Uncomment for job array configuration\n  job_array:\n    num_tasks: 13\n    task_index: 0\n\n\n# ============================================================================\n# ODinW13 Supercategories\n# ============================================================================\n\nall_odinw_supercategories:\n  - name: AerialMaritimeDrone_large\n    val:\n      img_folder: AerialMaritimeDrone/large/test/\n      json: AerialMaritimeDrone/large/test/annotations_without_background.json\n    train:\n      img_folder: AerialMaritimeDrone/large/train/\n      json: AerialMaritimeDrone/large/train/${odinw_train.train_file}.json\n  - name: Aquarium\n    val:\n      img_folder: Aquarium/Aquarium Combined.v2-raw-1024.coco/test/\n      json: Aquarium/Aquarium Combined.v2-raw-1024.coco/test/annotations_without_background.json\n    train:\n      img_folder: Aquarium/Aquarium Combined.v2-raw-1024.coco/train/\n      json: Aquarium/Aquarium Combined.v2-raw-1024.coco/train/${odinw_train.train_file}.json\n  - name: CottontailRabbits\n    val:\n      img_folder: CottontailRabbits/test/\n      json: CottontailRabbits/test/annotations_without_background.json\n    train:\n      img_folder: CottontailRabbits/train/\n      json: CottontailRabbits/train/${odinw_train.train_file}.json\n  - name: EgoHands_generic\n    val:\n      img_folder: EgoHands/generic/test/\n      json: EgoHands/generic/test/annotations_without_background.json\n    train:\n      img_folder: EgoHands/generic/train/\n      json: EgoHands/generic/train/${odinw_train.train_file}.json\n  - name: NorthAmericaMushrooms\n    val:\n      img_folder: NorthAmericaMushrooms/North American Mushrooms.v1-416x416.coco/test/\n      json: NorthAmericaMushrooms/North American Mushrooms.v1-416x416.coco/test/annotations_without_background.json\n    train:\n      img_folder: NorthAmericaMushrooms/North American Mushrooms.v1-416x416.coco/train/\n      json: NorthAmericaMushrooms/North American Mushrooms.v1-416x416.coco/train/${odinw_train.train_file}.json\n  - name: Packages\n    val:\n      img_folder: Packages/Raw/test/\n      json: Packages/Raw/test/annotations_without_background.json\n    train:\n      img_folder: Packages/Raw/train/\n      json: Packages/Raw/train/${odinw_train.train_file}.json\n  - name: PascalVOC\n    val:\n      img_folder: PascalVOC/valid/\n      json: PascalVOC/valid/annotations_without_background.json\n    train:\n      img_folder: PascalVOC/train/\n      json:  PascalVOC/train/${odinw_train.train_file}.json\n  - name: Raccoon\n    val:\n      img_folder: Raccoon/Raccoon.v2-raw.coco/test/\n      json: Raccoon/Raccoon.v2-raw.coco/test/annotations_without_background.json\n    train:\n      img_folder: Raccoon/Raccoon.v2-raw.coco/train/\n      json: Raccoon/Raccoon.v2-raw.coco/train/${odinw_train.train_file}.json\n  - name: ShellfishOpenImages\n    val:\n      img_folder: ShellfishOpenImages/raw/test/\n      json: ShellfishOpenImages/raw/test/annotations_without_background.json\n    train:\n      img_folder: ShellfishOpenImages/raw/train/\n      json: ShellfishOpenImages/raw/train/${odinw_train.train_file}.json\n  - name: VehiclesOpenImages\n    val:\n      img_folder: VehiclesOpenImages/416x416/test/\n      json: VehiclesOpenImages/416x416/test/annotations_without_background.json\n    train:\n      img_folder: VehiclesOpenImages/416x416/train/\n      json: VehiclesOpenImages/416x416/train/${odinw_train.train_file}.json\n  - name: pistols\n    val:\n      img_folder: pistols/export/\n      json: pistols/export/test_annotations_without_background.json\n    train:\n      img_folder: pistols/export/\n      json: pistols/export/${odinw_train.train_file}.json\n  - name: pothole\n    val:\n      img_folder: pothole/test/\n      json: pothole/test/annotations_without_background.json\n    train:\n      img_folder: pothole/train/\n      json: pothole/train/${odinw_train.train_file}.json\n  - name: thermalDogsAndPeople\n    val:\n      img_folder: thermalDogsAndPeople/test/\n      json: thermalDogsAndPeople/test/annotations_without_background.json\n    train:\n      img_folder: thermalDogsAndPeople/train/\n      json: thermalDogsAndPeople/train/${odinw_train.train_file}.json\n\n\nodinw35_prompts:\n  AerialMaritimeDrone_large: '[{\"id\": 1, \"name\": \"boat\", \"supercategory\": \"movable-objects\"},\n    {\"id\": 2, \"name\": \"car\", \"supercategory\": \"movable-objects\"}, {\"id\": 3, \"name\": \"dock\",\n    \"supercategory\": \"movable-objects\"}, {\"id\": 4, \"name\": \"jet ski\", \"supercategory\": \"movable-objects\"},\n    {\"id\": 5, \"name\": \"boat lift\", \"supercategory\": \"movable-objects\"}]'\n  Aquarium: null\n  CottontailRabbits: null\n  EgoHands_generic: null\n  NorthAmericaMushrooms: '[{''id'': 1, ''name'':\n    ''chicken of the woods'', ''supercategory'': ''mushroom''}, {''id'': 2, ''name'': ''chanterelle'', ''supercategory'': ''mushroom''}]'\n  Packages: null\n  PascalVOC: null\n  Raccoon: null\n  ShellfishOpenImages: null\n  VehiclesOpenImages: null\n  pistols: null\n  pothole: null\n  thermalDogsAndPeople: null\n"
  },
  {
    "path": "sam3/train/configs/odinw13/odinw_visual_only.yaml",
    "content": "# @package _global_\ndefaults:\n  - _self_\n\n# ============================================================================\n# Paths Configuration (Chage this to your own paths)\n# ============================================================================\n#  python sam3/train/train.py -c configs/odinw_text_only.yaml --use-cluster 1 --partition ${PARTITION} --account ${ACCOUNT} --qos ${QoS}\n\npaths:\n  odinw_data_root: <YOUR_DATA_DIR>\n  experiment_log_dir: <YOUR EXPERIMENET LOG_DIR>\n  bpe_path: <BPE_PATH> # This should be under sam3/assets/bpe_simple_vocab_16e6.txt.gz\n\n\nsupercategory_tuple: ${all_odinw_supercategories.${string:${submitit.job_array.task_index}}}\n# Validation transforms pipeline\nval_transforms:\n  - _target_: sam3.train.transforms.basic_for_api.ComposeAPI\n    transforms:\n      - _target_: sam3.train.transforms.basic_for_api.RandomResizeAPI\n        sizes: ${scratch.resolution}\n        max_size:\n          _target_: sam3.train.transforms.basic.get_random_resize_max_size\n          size: ${scratch.resolution}\n        square: true\n        consistent_transform: False\n      - _target_: sam3.train.transforms.basic_for_api.ToTensorAPI\n      - _target_: sam3.train.transforms.basic_for_api.NormalizeAPI\n        mean: ${scratch.val_norm_mean}\n        std: ${scratch.val_norm_std}\n      - _target_: sam3.train.transforms.filter_query_transforms.TextQueryToVisual\n        keep_text_queries: false # Note: set this to false if you only want visual\n        probability: 1.0 # always\n\n# ============================================================================\n# Different helper parameters and functions\n# ============================================================================\nscratch:\n  enable_segmentation: True\n  # Box processing\n  use_presence_eval: True\n  original_box_postprocessor:\n    _target_: sam3.eval.postprocessors.PostProcessImage\n    max_dets_per_img: -1  # infinite detections\n    use_original_ids: true\n    use_original_sizes_box: true\n    use_presence: ${scratch.use_presence_eval}\n\n  # Image processing parameters\n  resolution: 1008\n  # Normalization parameters\n  val_norm_mean: [0.5, 0.5, 0.5]\n  val_norm_std: [0.5, 0.5, 0.5]\n\n  # Training parameters\n  val_batch_size: 2\n  num_val_workers: 0\n  gather_pred_via_filesys: false\n\n# ============================================================================\n# Trainer Configuration\n# ============================================================================\n\ntrainer:\n  _target_: sam3.train.trainer.Trainer\n  skip_saving_ckpts: true\n  empty_gpu_mem_cache_after_eval: True\n  max_epochs: 1\n  accelerator: cuda\n  seed_value: 123\n  mode: val\n\n  distributed:\n    backend: nccl\n    find_unused_parameters: True\n    gradient_as_bucket_view: True\n\n  loss:\n    default:\n      _target_: sam3.train.loss.sam3_loss.DummyLoss\n\n  data:\n    val:\n      _target_: sam3.train.data.torch_dataset.TorchDataset\n      dataset:\n        _target_: sam3.train.data.sam3_image_dataset.Sam3ImageDataset\n        coco_json_loader:\n          _target_: sam3.train.data.coco_json_loaders.COCO_FROM_JSON\n          prompts: ${odinw35_prompts.${supercategory_tuple.name}}\n          include_negatives: true\n          category_chunk_size: 20 # Note: Since we are doing AP +ve we need to include all categories!\n          _partial_: true\n        img_folder: ${paths.odinw_data_root}/${supercategory_tuple.val.img_folder}\n        ann_file:\n          _target_: sam3.eval.coco_reindex.reindex_coco_to_temp\n          input_json_path: ${paths.odinw_data_root}/${supercategory_tuple.val.json}\n        transforms: ${val_transforms}\n        max_ann_per_img: 100000\n        multiplier: 1\n        training: false\n\n      shuffle: False\n      batch_size: ${scratch.val_batch_size}\n      num_workers: ${scratch.num_val_workers}\n      pin_memory: False\n      drop_last: False\n      collate_fn:\n        _target_: sam3.train.data.collator.collate_fn_api\n        _partial_: true\n        repeats: 1\n        dict_key: odinw35\n\n  model:\n    _target_: sam3.model_builder.build_sam3_image_model\n    bpe_path: ${paths.bpe_path}\n    device: cpus\n    eval_mode: true # Set to false if training\n    enable_segmentation: ${scratch.enable_segmentation} # Warning: Enable this if using segmentation.\n\n  meters:\n    val:\n      odinw35:\n        detection:\n          _target_: sam3.eval.coco_writer.PredictionDumper\n          iou_type: \"bbox\"\n          dump_dir: ${launcher.experiment_log_dir}/dumps/roboflow/${supercategory_tuple.name}\n          merge_predictions: True\n          postprocessor: ${scratch.original_box_postprocessor}\n          gather_pred_via_filesys: ${scratch.gather_pred_via_filesys}\n          maxdets: 100\n          pred_file_evaluators:\n            - _target_: sam3.eval.coco_eval_offline.CocoEvaluatorOfflineWithPredFileEvaluators\n              gt_path:\n                _target_: sam3.eval.coco_reindex.reindex_coco_to_temp\n                input_json_path: ${paths.odinw_data_root}/${supercategory_tuple.val.json}\n              tide: False\n              iou_type: \"bbox\"\n              positive_split: true\n\n  checkpoint:\n    save_dir: ${launcher.experiment_log_dir}/checkpoints\n    save_freq: 0  # 0 only last checkpoint is saved.\n\n\n  logging:\n    tensorboard_writer:\n      _target_: sam3.train.utils.logger.make_tensorboard_logger\n      log_dir: ${launcher.experiment_log_dir}/tensorboard\n      flush_secs: 120\n      should_log: True\n    wandb_writer: null\n    log_dir: ${launcher.experiment_log_dir}/logs/${supercategory_tuple.name}\n    log_freq: 10\n\n# ============================================================================\n# Launcher and Submitit Configuration\n# ============================================================================\n\nlauncher:\n  num_nodes: 1\n  gpus_per_node: 2\n  experiment_log_dir: ${paths.experiment_log_dir}\n  multiprocessing_context: forkserver\n\nsubmitit:\n  account: null\n  partition: null\n  qos: null\n  timeout_hour: 72\n  use_cluster: True\n  cpus_per_task: 10\n  port_range: [10000, 65000]\n  constraint: null\n\n  job_array:\n    num_tasks: 13\n    task_index: 0\n\n# ============================================================================\n# ODinW13 Supercategories\n# ============================================================================\n\nall_odinw_supercategories:\n  - name: AerialMaritimeDrone_large\n    val:\n      img_folder: AerialMaritimeDrone/large/test/\n      json: AerialMaritimeDrone/large/test/annotations_without_background.json\n  - name: Aquarium\n    val:\n      img_folder: Aquarium/Aquarium Combined.v2-raw-1024.coco/test/\n      json: Aquarium/Aquarium Combined.v2-raw-1024.coco/test/annotations_without_background.json\n  - name: CottontailRabbits\n    val:\n      img_folder: CottontailRabbits/test/\n      json: CottontailRabbits/test/annotations_without_background.json\n  - name: EgoHands_generic\n    val:\n      img_folder: EgoHands/generic/test/\n      json: EgoHands/generic/test/annotations_without_background.json\n  - name: NorthAmericaMushrooms\n    val:\n      img_folder: NorthAmericaMushrooms/North American Mushrooms.v1-416x416.coco/test/\n      json: NorthAmericaMushrooms/North American Mushrooms.v1-416x416.coco/test/annotations_without_background.json\n  - name: Packages\n    val:\n      img_folder: Packages/Raw/test/\n      json: Packages/Raw/test/annotations_without_background.json\n  - name: PascalVOC\n    val:\n      img_folder: PascalVOC/valid/\n      json: PascalVOC/valid/annotations_without_background.json\n  - name: Raccoon\n    val:\n      img_folder: Raccoon/Raccoon.v2-raw.coco/test/\n      json: Raccoon/Raccoon.v2-raw.coco/test/annotations_without_background.json\n  - name: ShellfishOpenImages\n    val:\n      img_folder: ShellfishOpenImages/raw/test/\n      json: ShellfishOpenImages/raw/test/annotations_without_background.json\n  - name: VehiclesOpenImages\n    val:\n      img_folder: VehiclesOpenImages/416x416/test/\n      json: VehiclesOpenImages/416x416/test/annotations_without_background.json\n  - name: pistols\n    val:\n      img_folder: pistols/export/\n      json: pistols/export/test_annotations_without_background.json\n  - name: pothole\n    val:\n      img_folder: pothole/test/\n      json: pothole/test/annotations_without_background.json\n  - name: thermalDogsAndPeople\n    val:\n      img_folder: thermalDogsAndPeople/test/\n      json: thermalDogsAndPeople/test/annotations_without_background.json\n\n\nodinw35_prompts:\n  AerialMaritimeDrone_large: '[{\"id\": 1, \"name\": \"boat\", \"supercategory\": \"movable-objects\"},\n    {\"id\": 2, \"name\": \"car\", \"supercategory\": \"movable-objects\"}, {\"id\": 3, \"name\": \"dock\",\n    \"supercategory\": \"movable-objects\"}, {\"id\": 4, \"name\": \"jet ski\", \"supercategory\": \"movable-objects\"},\n    {\"id\": 5, \"name\": \"boat lift\", \"supercategory\": \"movable-objects\"}]'\n  Aquarium: null\n  CottontailRabbits: null\n  EgoHands_generic: null\n  NorthAmericaMushrooms: '[{''id'': 1, ''name'':\n    ''chicken of the woods'', ''supercategory'': ''mushroom''}, {''id'': 2, ''name'': ''chanterelle'', ''supercategory'': ''mushroom''}]'\n  Packages: null\n  PascalVOC: null\n  Raccoon: null\n  ShellfishOpenImages: null\n  VehiclesOpenImages: null\n  pistols: null\n  pothole: null\n  thermalDogsAndPeople: null\n"
  },
  {
    "path": "sam3/train/configs/roboflow_v100/roboflow_v100_eval.yaml",
    "content": "# @package _global_\ndefaults:\n  - _self_\n\n# ============================================================================\n# Paths Configuration (Chage this to your own paths)\n# ============================================================================\npaths:\n  roboflow_vl_100_root: <YOUR_DATASET_DIR>\n  experiment_log_dir: <YOUR EXPERIMENET LOG_DIR>\n  bpe_path: <BPE_PATH> # This should be under sam3/assets/bpe_simple_vocab_16e6.txt.gz\n\n# Roboflow dataset configuration\nroboflow_train:\n  num_images: 100 # Note: This is the number of images used for training. If null, all images are used.\n  supercategory: ${all_roboflow_supercategories.${string:${submitit.job_array.task_index}}}\n\n  # Training transforms pipeline\n  train_transforms:\n    - _target_: sam3.train.transforms.basic_for_api.ComposeAPI\n      transforms:\n        - _target_: sam3.train.transforms.filter_query_transforms.FlexibleFilterFindGetQueries\n          query_filter:\n            _target_: sam3.train.transforms.filter_query_transforms.FilterCrowds\n        - _target_: sam3.train.transforms.point_sampling.RandomizeInputBbox\n          box_noise_std: 0.1\n          box_noise_max: 20\n        - _target_: sam3.train.transforms.segmentation.DecodeRle\n        - _target_: sam3.train.transforms.basic_for_api.RandomResizeAPI\n          sizes:\n            _target_: sam3.train.transforms.basic.get_random_resize_scales\n            size: ${scratch.resolution}\n            min_size: 480\n            rounded: false\n          max_size:\n            _target_: sam3.train.transforms.basic.get_random_resize_max_size\n            size: ${scratch.resolution}\n          square: true\n          consistent_transform: ${scratch.consistent_transform}\n        - _target_: sam3.train.transforms.basic_for_api.PadToSizeAPI\n          size: ${scratch.resolution}\n          consistent_transform: ${scratch.consistent_transform}\n        - _target_: sam3.train.transforms.basic_for_api.ToTensorAPI\n        - _target_: sam3.train.transforms.filter_query_transforms.FlexibleFilterFindGetQueries\n          query_filter:\n            _target_: sam3.train.transforms.filter_query_transforms.FilterEmptyTargets\n        - _target_: sam3.train.transforms.basic_for_api.NormalizeAPI\n          mean: ${scratch.train_norm_mean}\n          std: ${scratch.train_norm_std}\n        - _target_: sam3.train.transforms.filter_query_transforms.FlexibleFilterFindGetQueries\n          query_filter:\n            _target_: sam3.train.transforms.filter_query_transforms.FilterEmptyTargets\n    - _target_: sam3.train.transforms.filter_query_transforms.FlexibleFilterFindGetQueries\n      query_filter:\n        _target_: sam3.train.transforms.filter_query_transforms.FilterFindQueriesWithTooManyOut\n        max_num_objects: ${scratch.max_ann_per_img}\n\n  # Validation transforms pipeline\n  val_transforms:\n    - _target_: sam3.train.transforms.basic_for_api.ComposeAPI\n      transforms:\n        - _target_: sam3.train.transforms.basic_for_api.RandomResizeAPI\n          sizes: ${scratch.resolution}\n          max_size:\n            _target_: sam3.train.transforms.basic.get_random_resize_max_size\n            size: ${scratch.resolution}\n          square: true\n          consistent_transform: False\n        - _target_: sam3.train.transforms.basic_for_api.ToTensorAPI\n        - _target_: sam3.train.transforms.basic_for_api.NormalizeAPI\n          mean: ${scratch.train_norm_mean}\n          std: ${scratch.train_norm_std}\n\n  # loss config (no mask loss)\n  loss:\n    _target_: sam3.train.loss.sam3_loss.Sam3LossWrapper\n    matcher: ${scratch.matcher}\n    o2m_weight: 2.0\n    o2m_matcher:\n      _target_: sam3.train.matcher.BinaryOneToManyMatcher\n      alpha: 0.3\n      threshold: 0.4\n      topk: 4\n    use_o2m_matcher_on_o2m_aux: false # Another option is true\n    loss_fns_find:\n      - _target_: sam3.train.loss.loss_fns.Boxes\n        weight_dict:\n          loss_bbox: 5.0\n          loss_giou: 2.0\n      - _target_: sam3.train.loss.loss_fns.IABCEMdetr\n        weak_loss: False\n        weight_dict:\n          loss_ce: 20.0 # Another option is 100.0\n          presence_loss: 20.0\n        pos_weight: 10.0 # Another option is 5.0\n        alpha: 0.25\n        gamma: 2\n        use_presence: True  # Change\n        pos_focal: false\n        pad_n_queries: 200\n        pad_scale_pos: 1.0\n\n    loss_fn_semantic_seg: null\n    scale_by_find_batch_size: ${scratch.scale_by_find_batch_size}\n\n\n  # NOTE: Loss to be used for training in case of segmentation\n  # loss:\n  #   _target_: sam3.train.loss.sam3_loss.Sam3LossWrapper\n  #   matcher: ${scratch.matcher}\n  #   o2m_weight: 2.0\n  #   o2m_matcher:\n  #     _target_: sam3.train.matcher.BinaryOneToManyMatcher\n  #     alpha: 0.3\n  #     threshold: 0.4\n  #     topk: 4\n  #   use_o2m_matcher_on_o2m_aux: false\n  #   loss_fns_find:\n  #     - _target_: sam3.train.loss.loss_fns.Boxes\n  #       weight_dict:\n  #         loss_bbox: 5.0\n  #         loss_giou: 2.0\n  #     - _target_: sam3.train.loss.loss_fns.IABCEMdetr\n  #       weak_loss: False\n  #       weight_dict:\n  #         loss_ce: 20.0 # Another option is 100.0\n  #         presence_loss: 20.0\n  #       pos_weight: 10.0 # Another option is 5.0\n  #       alpha: 0.25\n  #       gamma: 2\n  #       use_presence: True  # Change\n  #       pos_focal: false\n  #       pad_n_queries: 200\n  #       pad_scale_pos: 1.0\n  #     - _target_: sam3.train.loss.loss_fns.Masks\n  #       focal_alpha: 0.25\n  #       focal_gamma: 2.0\n  #       weight_dict:\n  #         loss_mask: 200.0\n  #         loss_dice: 10.0\n  #       compute_aux: false\n  #   loss_fn_semantic_seg:\n  #     _target_: sam3.losses.loss_fns.SemanticSegCriterion\n  #     presence_head: True\n  #     presence_loss: False  # Change\n  #     focal: True\n  #     focal_alpha: 0.6\n  #     focal_gamma: 2.0\n  #     downsample: False\n  #     weight_dict:\n  #       loss_semantic_seg: 20.0\n  #       loss_semantic_presence: 1.0\n  #       loss_semantic_dice: 30.0\n  #   scale_by_find_batch_size: ${scratch.scale_by_find_batch_size}\n\n# ============================================================================\n# Different helper parameters and functions\n# ============================================================================\nscratch:\n  enable_segmentation: False # NOTE: This is the number of queries used for segmentation\n  # Model parameters\n  d_model: 256\n  pos_embed:\n    _target_: sam3.model.position_encoding.PositionEmbeddingSine\n    num_pos_feats: ${scratch.d_model}\n    normalize: true\n    scale: null\n    temperature: 10000\n\n  # Box processing\n  use_presence_eval: True\n  original_box_postprocessor:\n    _target_: sam3.eval.postprocessors.PostProcessImage\n    max_dets_per_img: -1  # infinite detections\n    use_original_ids: true\n    use_original_sizes_box: true\n    use_presence: ${scratch.use_presence_eval}\n\n  # Matcher configuration\n  matcher:\n    _target_: sam3.train.matcher.BinaryHungarianMatcherV2\n    focal: true  # with `focal: true` it is equivalent to BinaryFocalHungarianMatcher\n    cost_class: 2.0\n    cost_bbox: 5.0\n    cost_giou: 2.0\n    alpha: 0.25\n    gamma: 2\n    stable: False\n  scale_by_find_batch_size: True\n\n  # Image processing parameters\n  resolution: 1008\n  consistent_transform: False\n  max_ann_per_img: 200\n\n  # Normalization parameters\n  train_norm_mean: [0.5, 0.5, 0.5]\n  train_norm_std: [0.5, 0.5, 0.5]\n  val_norm_mean: [0.5, 0.5, 0.5]\n  val_norm_std: [0.5, 0.5, 0.5]\n\n  # Training parameters\n  num_train_workers: 10\n  num_val_workers: 0\n  max_data_epochs: 20\n  target_epoch_size: 1500\n  hybrid_repeats: 1\n  context_length: 2\n  gather_pred_via_filesys: false\n\n  # Learning rate and scheduler parameters\n  lr_scale: 0.1\n  lr_transformer: ${times:8e-4,${scratch.lr_scale}}\n  lr_vision_backbone: ${times:2.5e-4,${scratch.lr_scale}}\n  lr_language_backbone: ${times:5e-5,${scratch.lr_scale}}\n  lrd_vision_backbone: 0.9\n  wd: 0.1\n  scheduler_timescale: 20\n  scheduler_warmup: 20\n  scheduler_cooldown: 20\n\n  val_batch_size: 1\n  collate_fn_val:\n    _target_: sam3.train.data.collator.collate_fn_api\n    _partial_: true\n    repeats: ${scratch.hybrid_repeats}\n    dict_key: roboflow100\n    with_seg_masks: ${scratch.enable_segmentation} # Note: Set this to true if using segmentation masks!\n\n  gradient_accumulation_steps: 1\n  train_batch_size: 1\n  collate_fn:\n    _target_: sam3.train.data.collator.collate_fn_api\n    _partial_: true\n    repeats: ${scratch.hybrid_repeats}\n    dict_key: all\n    with_seg_masks: ${scratch.enable_segmentation} # Note: Set this to true if using segmentation masks!\n\n# ============================================================================\n# Trainer Configuration\n# ============================================================================\n\ntrainer:\n\n  _target_: sam3.train.trainer.Trainer\n  skip_saving_ckpts: true\n  empty_gpu_mem_cache_after_eval: True\n  skip_first_val: True\n  max_epochs: 20\n  accelerator: cuda\n  seed_value: 123\n  val_epoch_freq: 10\n  mode: val\n  gradient_accumulation_steps: ${scratch.gradient_accumulation_steps}\n\n  distributed:\n    backend: nccl\n    find_unused_parameters: True\n    gradient_as_bucket_view: True\n\n  loss:\n    all: ${roboflow_train.loss}\n    default:\n      _target_: sam3.train.loss.sam3_loss.DummyLoss\n\n  data:\n    train:\n      _target_: sam3.train.data.torch_dataset.TorchDataset\n      dataset:\n        _target_: sam3.train.data.sam3_image_dataset.Sam3ImageDataset\n        limit_ids: ${roboflow_train.num_images}\n        transforms: ${roboflow_train.train_transforms}\n        load_segmentation: ${scratch.enable_segmentation}\n        max_ann_per_img: 500000\n        multiplier: 1\n        max_train_queries: 50000\n        max_val_queries: 50000\n        training: true\n        use_caching: False\n        img_folder: ${paths.roboflow_vl_100_root}/${roboflow_train.supercategory}/train/\n        ann_file: ${paths.roboflow_vl_100_root}/${roboflow_train.supercategory}/train/_annotations.coco.json\n\n      shuffle: True\n      batch_size: ${scratch.train_batch_size}\n      num_workers: ${scratch.num_train_workers}\n      pin_memory: True\n      drop_last: True\n      collate_fn: ${scratch.collate_fn}\n\n    val:\n      _target_: sam3.train.data.torch_dataset.TorchDataset\n      dataset:\n        _target_: sam3.train.data.sam3_image_dataset.Sam3ImageDataset\n        load_segmentation: ${scratch.enable_segmentation}\n        coco_json_loader:\n          _target_: sam3.train.data.coco_json_loaders.COCO_FROM_JSON\n          include_negatives: true\n          category_chunk_size: 2 # Note: You can increase this based on the memory of your GPU.\n          _partial_: true\n        img_folder: ${paths.roboflow_vl_100_root}/${roboflow_train.supercategory}/test/\n        ann_file: ${paths.roboflow_vl_100_root}/${roboflow_train.supercategory}/test/_annotations.coco.json\n        transforms: ${roboflow_train.val_transforms}\n        max_ann_per_img: 100000\n        multiplier: 1\n        training: false\n\n      shuffle: False\n      batch_size: ${scratch.val_batch_size}\n      num_workers: ${scratch.num_val_workers}\n      pin_memory: True\n      drop_last: False\n      collate_fn: ${scratch.collate_fn_val}\n\n\n  model:\n    _target_: sam3.model_builder.build_sam3_image_model\n    bpe_path: ${paths.bpe_path}\n    device: cpus\n    eval_mode: true\n    enable_segmentation: ${scratch.enable_segmentation} # Warning: Enable this if using segmentation.\n\n  meters:\n    val:\n      roboflow100:\n        detection:\n          _target_: sam3.eval.coco_writer.PredictionDumper\n          iou_type: \"bbox\"\n          dump_dir: ${launcher.experiment_log_dir}/dumps/roboflow/${roboflow_train.supercategory}\n          merge_predictions: True\n          postprocessor: ${scratch.original_box_postprocessor}\n          gather_pred_via_filesys: ${scratch.gather_pred_via_filesys}\n          maxdets: 100\n          pred_file_evaluators:\n            - _target_: sam3.eval.coco_eval_offline.CocoEvaluatorOfflineWithPredFileEvaluators\n              gt_path: ${paths.roboflow_vl_100_root}/${roboflow_train.supercategory}/test/_annotations.coco.json\n              tide: False\n              iou_type: \"bbox\"\n\n  optim:\n    amp:\n      enabled: True\n      amp_dtype: bfloat16\n\n    optimizer:\n      _target_: torch.optim.AdamW\n\n    gradient_clip:\n      _target_: sam3.train.optim.optimizer.GradientClipper\n      max_norm: 0.1\n      norm_type: 2\n\n    param_group_modifiers:\n      - _target_: sam3.train.optim.optimizer.layer_decay_param_modifier\n        _partial_: True\n        layer_decay_value: ${scratch.lrd_vision_backbone}\n        apply_to: 'backbone.vision_backbone.trunk'\n        overrides:\n          - pattern: '*pos_embed*'\n            value: 1.0\n\n    options:\n      lr:\n        - scheduler:  # transformer and class_embed\n            _target_: sam3.train.optim.schedulers.InverseSquareRootParamScheduler\n            base_lr: ${scratch.lr_transformer}\n            timescale: ${scratch.scheduler_timescale}\n            warmup_steps: ${scratch.scheduler_warmup}\n            cooldown_steps: ${scratch.scheduler_cooldown}\n        - scheduler:\n            _target_: sam3.train.optim.schedulers.InverseSquareRootParamScheduler\n            base_lr: ${scratch.lr_vision_backbone}\n            timescale: ${scratch.scheduler_timescale}\n            warmup_steps: ${scratch.scheduler_warmup}\n            cooldown_steps: ${scratch.scheduler_cooldown}\n          param_names:\n            - 'backbone.vision_backbone.*'\n        - scheduler:\n            _target_: sam3.train.optim.schedulers.InverseSquareRootParamScheduler\n            base_lr: ${scratch.lr_language_backbone}\n            timescale: ${scratch.scheduler_timescale}\n            warmup_steps: ${scratch.scheduler_warmup}\n            cooldown_steps: ${scratch.scheduler_cooldown}\n          param_names:\n            - 'backbone.language_backbone.*'\n\n      weight_decay:\n        - scheduler:\n            _target_: fvcore.common.param_scheduler.ConstantParamScheduler\n            value: ${scratch.wd}\n        - scheduler:\n            _target_: fvcore.common.param_scheduler.ConstantParamScheduler\n            value: 0.0\n          param_names:\n            - '*bias*'\n          module_cls_names: ['torch.nn.LayerNorm']\n\n  checkpoint:\n    save_dir: ${launcher.experiment_log_dir}/checkpoints\n    save_freq: 0  # 0 only last checkpoint is saved.\n\n  logging:\n    tensorboard_writer:\n      _target_: sam3.train.utils.logger.make_tensorboard_logger\n      log_dir: ${launcher.experiment_log_dir}/tensorboard\n      flush_secs: 120\n      should_log: True\n    wandb_writer: null\n    log_dir: ${launcher.experiment_log_dir}/logs/${roboflow_train.supercategory}\n    log_freq: 10\n\n# ============================================================================\n# Launcher and Submitit Configuration\n# ============================================================================\n\nlauncher:\n  num_nodes: 1\n  gpus_per_node: 2\n  experiment_log_dir: ${paths.experiment_log_dir}\n  multiprocessing_context: forkserver\n\nsubmitit:\n  account: null\n  partition: null\n  qos: null\n  timeout_hour: 72\n  use_cluster: True\n  cpus_per_task: 10\n  port_range: [10000, 65000]\n  constraint: null\n  # Uncomment for job array configuration\n  job_array:\n    num_tasks: 100\n    task_index: 0\n\n# ============================================================================\n# Available Roboflow Supercategories (for reference)\n# ============================================================================\n\nall_roboflow_supercategories:\n  - -grccs\n  - zebrasatasturias\n  - cod-mw-warzone\n  - canalstenosis\n  - label-printing-defect-version-2\n  - new-defects-in-wood\n  - orionproducts\n  - aquarium-combined\n  - varroa-mites-detection--test-set\n  - clashroyalechardetector\n  - stomata-cells\n  - halo-infinite-angel-videogame\n  - pig-detection\n  - urine-analysis1\n  - aerial-sheep\n  - orgharvest\n  - actions\n  - mahjong\n  - liver-disease\n  - needle-base-tip-min-max\n  - wheel-defect-detection\n  - aircraft-turnaround-dataset\n  - xray\n  - wildfire-smoke\n  - spinefrxnormalvindr\n  - ufba-425\n  - speech-bubbles-detection\n  - train\n  - pill\n  - truck-movement\n  - car-logo-detection\n  - inbreast\n  - sea-cucumbers-new-tiles\n  - uavdet-small\n  - penguin-finder-seg\n  - aerial-airport\n  - bibdetection\n  - taco-trash-annotations-in-context\n  - bees\n  - recode-waste\n  - screwdetectclassification\n  - wine-labels\n  - aerial-cows\n  - into-the-vale\n  - gwhd2021\n  - lacrosse-object-detection\n  - defect-detection\n  - dataconvert\n  - x-ray-id\n  - ball\n  - tube\n  - 2024-frc\n  - crystal-clean-brain-tumors-mri-dataset\n  - grapes-5\n  - human-detection-in-floods\n  - buoy-onboarding\n  - apoce-aerial-photographs-for-object-detection-of-construction-equipment\n  - l10ul502\n  - floating-waste\n  - deeppcb\n  - ism-band-packet-detection\n  - weeds4\n  - invoice-processing\n  - thermal-cheetah\n  - tomatoes-2\n  - marine-sharks\n  - peixos-fish\n  - sssod\n  - aerial-pool\n  - countingpills\n  - asphaltdistressdetection\n  - roboflow-trained-dataset\n  - everdaynew\n  - underwater-objects\n  - soda-bottles\n  - dentalai\n  - jellyfish\n  - deepfruits\n  - activity-diagrams\n  - circuit-voltages\n  - all-elements\n  - macro-segmentation\n  - exploratorium-daphnia\n  - signatures\n  - conveyor-t-shirts\n  - fruitjes\n  - grass-weeds\n  - infraredimageofpowerequipment\n  - 13-lkc01\n  - wb-prova\n  - flir-camera-objects\n  - paper-parts\n  - football-player-detection\n  - trail-camera\n  - smd-components\n  - water-meter\n  - nih-xray\n  - the-dreidel-project\n  - electric-pylon-detection-in-rsi\n  - cable-damage\n"
  },
  {
    "path": "sam3/train/configs/roboflow_v100/roboflow_v100_full_ft_100_images.yaml",
    "content": "# @package _global_\ndefaults:\n  - _self_\n\n# ============================================================================\n# Paths Configuration (Chage this to your own paths)\n# ============================================================================\npaths:\n  roboflow_vl_100_root: <YOUR_DATASET_DIR>\n  experiment_log_dir: <YOUR EXPERIMENET LOG_DIR>\n  bpe_path: <BPE_PATH> # This should be under sam3/assets/bpe_simple_vocab_16e6.txt.gz\n\n# Roboflow dataset configuration\nroboflow_train:\n  num_images: 100 # Note: This is the number of images used for training. If null, all images are used.\n  supercategory: ${all_roboflow_supercategories.${string:${submitit.job_array.task_index}}}\n\n  # Training transforms pipeline\n  train_transforms:\n    - _target_: sam3.train.transforms.basic_for_api.ComposeAPI\n      transforms:\n        - _target_: sam3.train.transforms.filter_query_transforms.FlexibleFilterFindGetQueries\n          query_filter:\n            _target_: sam3.train.transforms.filter_query_transforms.FilterCrowds\n        - _target_: sam3.train.transforms.point_sampling.RandomizeInputBbox\n          box_noise_std: 0.1\n          box_noise_max: 20\n        - _target_: sam3.train.transforms.segmentation.DecodeRle\n        - _target_: sam3.train.transforms.basic_for_api.RandomResizeAPI\n          sizes:\n            _target_: sam3.train.transforms.basic.get_random_resize_scales\n            size: ${scratch.resolution}\n            min_size: 480\n            rounded: false\n          max_size:\n            _target_: sam3.train.transforms.basic.get_random_resize_max_size\n            size: ${scratch.resolution}\n          square: true\n          consistent_transform: ${scratch.consistent_transform}\n        - _target_: sam3.train.transforms.basic_for_api.PadToSizeAPI\n          size: ${scratch.resolution}\n          consistent_transform: ${scratch.consistent_transform}\n        - _target_: sam3.train.transforms.basic_for_api.ToTensorAPI\n        - _target_: sam3.train.transforms.filter_query_transforms.FlexibleFilterFindGetQueries\n          query_filter:\n            _target_: sam3.train.transforms.filter_query_transforms.FilterEmptyTargets\n        - _target_: sam3.train.transforms.basic_for_api.NormalizeAPI\n          mean: ${scratch.train_norm_mean}\n          std: ${scratch.train_norm_std}\n        - _target_: sam3.train.transforms.filter_query_transforms.FlexibleFilterFindGetQueries\n          query_filter:\n            _target_: sam3.train.transforms.filter_query_transforms.FilterEmptyTargets\n    - _target_: sam3.train.transforms.filter_query_transforms.FlexibleFilterFindGetQueries\n      query_filter:\n        _target_: sam3.train.transforms.filter_query_transforms.FilterFindQueriesWithTooManyOut\n        max_num_objects: ${scratch.max_ann_per_img}\n\n  # Validation transforms pipeline\n  val_transforms:\n    - _target_: sam3.train.transforms.basic_for_api.ComposeAPI\n      transforms:\n        - _target_: sam3.train.transforms.basic_for_api.RandomResizeAPI\n          sizes: ${scratch.resolution}\n          max_size:\n            _target_: sam3.train.transforms.basic.get_random_resize_max_size\n            size: ${scratch.resolution}\n          square: true\n          consistent_transform: False\n        - _target_: sam3.train.transforms.basic_for_api.ToTensorAPI\n        - _target_: sam3.train.transforms.basic_for_api.NormalizeAPI\n          mean: ${scratch.train_norm_mean}\n          std: ${scratch.train_norm_std}\n\n  # loss config (no mask loss)\n  loss:\n    _target_: sam3.train.loss.sam3_loss.Sam3LossWrapper\n    matcher: ${scratch.matcher}\n    o2m_weight: 2.0\n    o2m_matcher:\n      _target_: sam3.train.matcher.BinaryOneToManyMatcher\n      alpha: 0.3\n      threshold: 0.4\n      topk: 4\n    use_o2m_matcher_on_o2m_aux: false # Another option is true\n    loss_fns_find:\n      - _target_: sam3.train.loss.loss_fns.Boxes\n        weight_dict:\n          loss_bbox: 5.0\n          loss_giou: 2.0\n      - _target_: sam3.train.loss.loss_fns.IABCEMdetr\n        weak_loss: False\n        weight_dict:\n          loss_ce: 20.0 # Another option is 100.0\n          presence_loss: 20.0\n        pos_weight: 10.0 # Another option is 5.0\n        alpha: 0.25\n        gamma: 2\n        use_presence: True  # Change\n        pos_focal: false\n        pad_n_queries: 200\n        pad_scale_pos: 1.0\n\n    loss_fn_semantic_seg: null\n    scale_by_find_batch_size: ${scratch.scale_by_find_batch_size}\n\n\n  # NOTE: Loss to be used for training in case of segmentation\n  # loss:\n  #   _target_: sam3.train.loss.sam3_loss.Sam3LossWrapper\n  #   matcher: ${scratch.matcher}\n  #   o2m_weight: 2.0\n  #   o2m_matcher:\n  #     _target_: sam3.train.matcher.BinaryOneToManyMatcher\n  #     alpha: 0.3\n  #     threshold: 0.4\n  #     topk: 4\n  #   use_o2m_matcher_on_o2m_aux: false\n  #   loss_fns_find:\n  #     - _target_: sam3.train.loss.loss_fns.Boxes\n  #       weight_dict:\n  #         loss_bbox: 5.0\n  #         loss_giou: 2.0\n  #     - _target_: sam3.train.loss.loss_fns.IABCEMdetr\n  #       weak_loss: False\n  #       weight_dict:\n  #         loss_ce: 20.0 # Another option is 100.0\n  #         presence_loss: 20.0\n  #       pos_weight: 10.0 # Another option is 5.0\n  #       alpha: 0.25\n  #       gamma: 2\n  #       use_presence: True  # Change\n  #       pos_focal: false\n  #       pad_n_queries: 200\n  #       pad_scale_pos: 1.0\n  #     - _target_: sam3.train.loss.loss_fns.Masks\n  #       focal_alpha: 0.25\n  #       focal_gamma: 2.0\n  #       weight_dict:\n  #         loss_mask: 200.0\n  #         loss_dice: 10.0\n  #       compute_aux: false\n  #   loss_fn_semantic_seg:\n  #     _target_: sam3.losses.loss_fns.SemanticSegCriterion\n  #     presence_head: True\n  #     presence_loss: False  # Change\n  #     focal: True\n  #     focal_alpha: 0.6\n  #     focal_gamma: 2.0\n  #     downsample: False\n  #     weight_dict:\n  #       loss_semantic_seg: 20.0\n  #       loss_semantic_presence: 1.0\n  #       loss_semantic_dice: 30.0\n  #   scale_by_find_batch_size: ${scratch.scale_by_find_batch_size}\n\n# ============================================================================\n# Different helper parameters and functions\n# ============================================================================\nscratch:\n  enable_segmentation: False # NOTE: This is the number of queries used for segmentation\n  # Model parameters\n  d_model: 256\n  pos_embed:\n    _target_: sam3.model.position_encoding.PositionEmbeddingSine\n    num_pos_feats: ${scratch.d_model}\n    normalize: true\n    scale: null\n    temperature: 10000\n\n  # Box processing\n  use_presence_eval: True\n  original_box_postprocessor:\n    _target_: sam3.eval.postprocessors.PostProcessImage\n    max_dets_per_img: -1  # infinite detections\n    use_original_ids: true\n    use_original_sizes_box: true\n    use_presence: ${scratch.use_presence_eval}\n\n  # Matcher configuration\n  matcher:\n    _target_: sam3.train.matcher.BinaryHungarianMatcherV2\n    focal: true  # with `focal: true` it is equivalent to BinaryFocalHungarianMatcher\n    cost_class: 2.0\n    cost_bbox: 5.0\n    cost_giou: 2.0\n    alpha: 0.25\n    gamma: 2\n    stable: False\n  scale_by_find_batch_size: True\n\n  # Image processing parameters\n  resolution: 1008\n  consistent_transform: False\n  max_ann_per_img: 200\n\n  # Normalization parameters\n  train_norm_mean: [0.5, 0.5, 0.5]\n  train_norm_std: [0.5, 0.5, 0.5]\n  val_norm_mean: [0.5, 0.5, 0.5]\n  val_norm_std: [0.5, 0.5, 0.5]\n\n  # Training parameters\n  num_train_workers: 10\n  num_val_workers: 0\n  max_data_epochs: 20\n  target_epoch_size: 1500\n  hybrid_repeats: 1\n  context_length: 2\n  gather_pred_via_filesys: false\n\n  # Learning rate and scheduler parameters\n  lr_scale: 0.1\n  lr_transformer: ${times:8e-4,${scratch.lr_scale}}\n  lr_vision_backbone: ${times:2.5e-4,${scratch.lr_scale}}\n  lr_language_backbone: ${times:5e-5,${scratch.lr_scale}}\n  lrd_vision_backbone: 0.9\n  wd: 0.1\n  scheduler_timescale: 20\n  scheduler_warmup: 20\n  scheduler_cooldown: 20\n\n  val_batch_size: 1\n  collate_fn_val:\n    _target_: sam3.train.data.collator.collate_fn_api\n    _partial_: true\n    repeats: ${scratch.hybrid_repeats}\n    dict_key: roboflow100\n    with_seg_masks: ${scratch.enable_segmentation} # Note: Set this to true if using segmentation masks!\n\n  gradient_accumulation_steps: 1\n  train_batch_size: 1\n  collate_fn:\n    _target_: sam3.train.data.collator.collate_fn_api\n    _partial_: true\n    repeats: ${scratch.hybrid_repeats}\n    dict_key: all\n    with_seg_masks: ${scratch.enable_segmentation} # Note: Set this to true if using segmentation masks!\n\n# ============================================================================\n# Trainer Configuration\n# ============================================================================\n\ntrainer:\n\n  _target_: sam3.train.trainer.Trainer\n  skip_saving_ckpts: true\n  empty_gpu_mem_cache_after_eval: True\n  skip_first_val: True\n  max_epochs: 20\n  accelerator: cuda\n  seed_value: 123\n  val_epoch_freq: 10\n  mode: train\n  gradient_accumulation_steps: ${scratch.gradient_accumulation_steps}\n\n  distributed:\n    backend: nccl\n    find_unused_parameters: True\n    gradient_as_bucket_view: True\n\n  loss:\n    all: ${roboflow_train.loss}\n    default:\n      _target_: sam3.train.loss.sam3_loss.DummyLoss\n\n  data:\n    train:\n      _target_: sam3.train.data.torch_dataset.TorchDataset\n      dataset:\n        _target_: sam3.train.data.sam3_image_dataset.Sam3ImageDataset\n        limit_ids: ${roboflow_train.num_images}\n        transforms: ${roboflow_train.train_transforms}\n        load_segmentation: ${scratch.enable_segmentation}\n        max_ann_per_img: 500000\n        multiplier: 1\n        max_train_queries: 50000\n        max_val_queries: 50000\n        training: true\n        use_caching: False\n        img_folder: ${paths.roboflow_vl_100_root}/${roboflow_train.supercategory}/train/\n        ann_file: ${paths.roboflow_vl_100_root}/${roboflow_train.supercategory}/train/_annotations.coco.json\n\n      shuffle: True\n      batch_size: ${scratch.train_batch_size}\n      num_workers: ${scratch.num_train_workers}\n      pin_memory: True\n      drop_last: True\n      collate_fn: ${scratch.collate_fn}\n\n    val:\n      _target_: sam3.train.data.torch_dataset.TorchDataset\n      dataset:\n        _target_: sam3.train.data.sam3_image_dataset.Sam3ImageDataset\n        load_segmentation: ${scratch.enable_segmentation}\n        coco_json_loader:\n          _target_: sam3.train.data.coco_json_loaders.COCO_FROM_JSON\n          include_negatives: true\n          category_chunk_size: 2 # Note: You can increase this based on the memory of your GPU.\n          _partial_: true\n        img_folder: ${paths.roboflow_vl_100_root}/${roboflow_train.supercategory}/test/\n        ann_file: ${paths.roboflow_vl_100_root}/${roboflow_train.supercategory}/test/_annotations.coco.json\n        transforms: ${roboflow_train.val_transforms}\n        max_ann_per_img: 100000\n        multiplier: 1\n        training: false\n\n      shuffle: False\n      batch_size: ${scratch.val_batch_size}\n      num_workers: ${scratch.num_val_workers}\n      pin_memory: True\n      drop_last: False\n      collate_fn: ${scratch.collate_fn_val}\n\n\n  model:\n    _target_: sam3.model_builder.build_sam3_image_model\n    bpe_path: ${paths.bpe_path}\n    device: cpus\n    eval_mode: false\n    enable_segmentation: ${scratch.enable_segmentation} # Warning: Enable this if using segmentation.\n\n  meters:\n    val:\n      roboflow100:\n        detection:\n          _target_: sam3.eval.coco_writer.PredictionDumper\n          iou_type: \"bbox\"\n          dump_dir: ${launcher.experiment_log_dir}/dumps/roboflow/${roboflow_train.supercategory}\n          merge_predictions: True\n          postprocessor: ${scratch.original_box_postprocessor}\n          gather_pred_via_filesys: ${scratch.gather_pred_via_filesys}\n          maxdets: 100\n          pred_file_evaluators:\n            - _target_: sam3.eval.coco_eval_offline.CocoEvaluatorOfflineWithPredFileEvaluators\n              gt_path: ${paths.roboflow_vl_100_root}/${roboflow_train.supercategory}/test/_annotations.coco.json\n              tide: False\n              iou_type: \"bbox\"\n\n  optim:\n    amp:\n      enabled: True\n      amp_dtype: bfloat16\n\n    optimizer:\n      _target_: torch.optim.AdamW\n\n    gradient_clip:\n      _target_: sam3.train.optim.optimizer.GradientClipper\n      max_norm: 0.1\n      norm_type: 2\n\n    param_group_modifiers:\n      - _target_: sam3.train.optim.optimizer.layer_decay_param_modifier\n        _partial_: True\n        layer_decay_value: ${scratch.lrd_vision_backbone}\n        apply_to: 'backbone.vision_backbone.trunk'\n        overrides:\n          - pattern: '*pos_embed*'\n            value: 1.0\n\n    options:\n      lr:\n        - scheduler:  # transformer and class_embed\n            _target_: sam3.train.optim.schedulers.InverseSquareRootParamScheduler\n            base_lr: ${scratch.lr_transformer}\n            timescale: ${scratch.scheduler_timescale}\n            warmup_steps: ${scratch.scheduler_warmup}\n            cooldown_steps: ${scratch.scheduler_cooldown}\n        - scheduler:\n            _target_: sam3.train.optim.schedulers.InverseSquareRootParamScheduler\n            base_lr: ${scratch.lr_vision_backbone}\n            timescale: ${scratch.scheduler_timescale}\n            warmup_steps: ${scratch.scheduler_warmup}\n            cooldown_steps: ${scratch.scheduler_cooldown}\n          param_names:\n            - 'backbone.vision_backbone.*'\n        - scheduler:\n            _target_: sam3.train.optim.schedulers.InverseSquareRootParamScheduler\n            base_lr: ${scratch.lr_language_backbone}\n            timescale: ${scratch.scheduler_timescale}\n            warmup_steps: ${scratch.scheduler_warmup}\n            cooldown_steps: ${scratch.scheduler_cooldown}\n          param_names:\n            - 'backbone.language_backbone.*'\n\n      weight_decay:\n        - scheduler:\n            _target_: fvcore.common.param_scheduler.ConstantParamScheduler\n            value: ${scratch.wd}\n        - scheduler:\n            _target_: fvcore.common.param_scheduler.ConstantParamScheduler\n            value: 0.0\n          param_names:\n            - '*bias*'\n          module_cls_names: ['torch.nn.LayerNorm']\n\n  checkpoint:\n    save_dir: ${launcher.experiment_log_dir}/checkpoints\n    save_freq: 0  # 0 only last checkpoint is saved.\n\n  logging:\n    tensorboard_writer:\n      _target_: sam3.train.utils.logger.make_tensorboard_logger\n      log_dir: ${launcher.experiment_log_dir}/tensorboard\n      flush_secs: 120\n      should_log: True\n    wandb_writer: null\n    log_dir: ${launcher.experiment_log_dir}/logs/${roboflow_train.supercategory}\n    log_freq: 10\n\n# ============================================================================\n# Launcher and Submitit Configuration\n# ============================================================================\n\nlauncher:\n  num_nodes: 1\n  gpus_per_node: 2\n  experiment_log_dir: ${paths.experiment_log_dir}\n  multiprocessing_context: forkserver\n\nsubmitit:\n  account: null\n  partition: null\n  qos: null\n  timeout_hour: 72\n  use_cluster: True\n  cpus_per_task: 10\n  port_range: [10000, 65000]\n  constraint: null\n  # Uncomment for job array configuration\n  job_array:\n    num_tasks: 100\n    task_index: 0\n\n# ============================================================================\n# Available Roboflow Supercategories (for reference)\n# ============================================================================\n\nall_roboflow_supercategories:\n  - -grccs\n  - zebrasatasturias\n  - cod-mw-warzone\n  - canalstenosis\n  - label-printing-defect-version-2\n  - new-defects-in-wood\n  - orionproducts\n  - aquarium-combined\n  - varroa-mites-detection--test-set\n  - clashroyalechardetector\n  - stomata-cells\n  - halo-infinite-angel-videogame\n  - pig-detection\n  - urine-analysis1\n  - aerial-sheep\n  - orgharvest\n  - actions\n  - mahjong\n  - liver-disease\n  - needle-base-tip-min-max\n  - wheel-defect-detection\n  - aircraft-turnaround-dataset\n  - xray\n  - wildfire-smoke\n  - spinefrxnormalvindr\n  - ufba-425\n  - speech-bubbles-detection\n  - train\n  - pill\n  - truck-movement\n  - car-logo-detection\n  - inbreast\n  - sea-cucumbers-new-tiles\n  - uavdet-small\n  - penguin-finder-seg\n  - aerial-airport\n  - bibdetection\n  - taco-trash-annotations-in-context\n  - bees\n  - recode-waste\n  - screwdetectclassification\n  - wine-labels\n  - aerial-cows\n  - into-the-vale\n  - gwhd2021\n  - lacrosse-object-detection\n  - defect-detection\n  - dataconvert\n  - x-ray-id\n  - ball\n  - tube\n  - 2024-frc\n  - crystal-clean-brain-tumors-mri-dataset\n  - grapes-5\n  - human-detection-in-floods\n  - buoy-onboarding\n  - apoce-aerial-photographs-for-object-detection-of-construction-equipment\n  - l10ul502\n  - floating-waste\n  - deeppcb\n  - ism-band-packet-detection\n  - weeds4\n  - invoice-processing\n  - thermal-cheetah\n  - tomatoes-2\n  - marine-sharks\n  - peixos-fish\n  - sssod\n  - aerial-pool\n  - countingpills\n  - asphaltdistressdetection\n  - roboflow-trained-dataset\n  - everdaynew\n  - underwater-objects\n  - soda-bottles\n  - dentalai\n  - jellyfish\n  - deepfruits\n  - activity-diagrams\n  - circuit-voltages\n  - all-elements\n  - macro-segmentation\n  - exploratorium-daphnia\n  - signatures\n  - conveyor-t-shirts\n  - fruitjes\n  - grass-weeds\n  - infraredimageofpowerequipment\n  - 13-lkc01\n  - wb-prova\n  - flir-camera-objects\n  - paper-parts\n  - football-player-detection\n  - trail-camera\n  - smd-components\n  - water-meter\n  - nih-xray\n  - the-dreidel-project\n  - electric-pylon-detection-in-rsi\n  - cable-damage\n"
  },
  {
    "path": "sam3/train/configs/saco_video_evals/saco_veval_sav_test.yaml",
    "content": "# @package _global_\ndefaults:\n  - _self_\n\n# ============================================================================\n# Paths Configuration (Chage this to your own paths)\n# ============================================================================\npaths:\n\n  dump_file_name: saco_veval_sav_test\n  experiment_log_dir: <YOUR EXPERIMENET LOG_DIR>\n  ytvis_json: <YOUR_GT_PATH>/saco_veval_sav_test.json\n  ytvis_dir : <YOUR_VIDEO_JPG_DIR>\n  bpe_path: <BPE_PATH> # This should be under sam3/assets/bpe_simple_vocab_16e6.txt.gz\n  num_videos: null\n\n# ============================================================================\n# Different helper parameters and functions\n# ============================================================================\nscratch:\n  vid_mask_postprocessor:\n    _target_: sam3.eval.postprocessors.PostProcessNullOp\n\n  use_presence_eval: True\n\n  video_transforms_val:\n    - _target_: sam3.train.transforms.basic_for_api.ComposeAPI\n      transforms:\n        - _target_: sam3.train.transforms.segmentation.DecodeRle\n        # resize the image to 1024x1024 resolution\n        - _target_: sam3.train.transforms.basic_for_api.RandomResizeAPI\n          sizes: ${scratch.resolution}  # originally `resolution: 1024`\n          square: true\n          consistent_transform: true\n        - _target_: sam3.train.transforms.basic_for_api.ToTensorAPI\n        - _target_: sam3.train.transforms.basic_for_api.NormalizeAPI\n          mean: ${scratch.val_norm_mean}\n          std: ${scratch.val_norm_std}\n\n  # Model parameters\n  d_model: 256\n\n  # Image processing parameters\n  resolution: 1008\n\n  # Normalization parameters\n  train_norm_mean: [0.5, 0.5, 0.5]\n  train_norm_std: [0.5, 0.5, 0.5]\n  val_norm_mean: [0.5, 0.5, 0.5]\n  val_norm_std: [0.5, 0.5, 0.5]\n\n  val_batch_size: 1\n  num_val_workers: 0\n  max_data_epochs: 20\n  hybrid_repeats: 1\n  gather_pred_via_filesys: false\n\n\n# ============================================================================\n# Trainer Configuration\n# ============================================================================\n\ntrainer:\n  _target_: sam3.train.trainer.Trainer\n  skip_saving_ckpts: true\n  empty_gpu_mem_cache_after_eval: True\n  skip_first_val: True\n  max_epochs: ${scratch.max_data_epochs}\n  accelerator: cuda\n  seed_value: 123\n  val_epoch_freq: 10\n  mode: val\n\n  distributed:\n    backend: nccl\n    find_unused_parameters: True\n    gradient_as_bucket_view: True\n\n  loss:\n    all:\n      _target_: sam3.train.loss.sam3_loss.DummyLoss\n    default:\n      _target_: sam3.train.loss.sam3_loss.DummyLoss\n\n  data:\n    train: null\n    val:\n      _target_: sam3.train.data.torch_dataset.TorchDataset\n      dataset:\n        _target_: sam3.train.data.sam3_video_dataset.VideoGroundingDataset\n        limit_ids: ${paths.num_videos}\n        img_folder: ${paths.ytvis_dir}\n        ann_file: ${paths.ytvis_json}\n        coco_json_loader:\n          _target_: sam3.train.data.coco_json_loaders.SAM3_VEVAL_API_FROM_JSON_NP\n          _partial_: true\n\n        transforms: ${scratch.video_transforms_val}\n        max_ann_per_img: 100000  # filtered in transforms\n        max_val_queries: 100000\n        multiplier: 1\n        load_segmentation: true\n        training: false\n\n\n      shuffle: False\n      batch_size: ${scratch.val_batch_size}\n      num_workers: ${scratch.num_val_workers}\n      pin_memory: True\n      drop_last: False\n      collate_fn:\n        _target_: sam3.train.data.collator.collate_fn_api\n        _partial_: true\n        repeats: ${scratch.hybrid_repeats}\n        dict_key: ytvis_val\n        with_seg_masks: true\n\n\n  model:\n    _target_: sam3.model_builder.build_sam3_video_model\n    bpe_path: ${paths.bpe_path}\n    has_presence_token: True\n    geo_encoder_use_img_cross_attn: True\n    apply_temporal_disambiguation: True\n\n  meters:\n    val:\n      ytvis_val:\n        pred_file: # key\n          _target_: sam3.eval.ytvis_eval.YTVISResultsWriter\n          dump_file: ${launcher.experiment_log_dir}/preds/${paths.dump_file_name}.json\n          postprocessor: ${scratch.vid_mask_postprocessor}\n          gather_pred_via_filesys: ${scratch.gather_pred_via_filesys}\n\n  optim:\n    amp:\n      enabled: True\n      amp_dtype: bfloat16\n\n\n  checkpoint:\n    save_dir: ${launcher.experiment_log_dir}/checkpoints\n    save_freq: 0  # 0 only last checkpoint is saved.\n\n\n  logging:\n    tensorboard_writer:\n      _target_: sam3.train.utils.logger.make_tensorboard_logger\n      log_dir: ${launcher.experiment_log_dir}/tensorboard\n      flush_secs: 120\n      should_log: True\n    wandb_writer: null\n    log_dir: ${launcher.experiment_log_dir}/logs/\n    log_freq: 10\n\n# ============================================================================\n# Launcher and Submitit Configuration\n# ============================================================================\n\nlauncher:\n  num_nodes: 8\n  gpus_per_node: 8\n  experiment_log_dir: ${paths.experiment_log_dir}\n  multiprocessing_context: forkserver\n\nsubmitit:\n  account: null\n  partition: null\n  qos: null\n  timeout_hour: 72\n  use_cluster: True\n  cpus_per_task: 10\n  port_range: [10000, 65000]\n  constraint: null\n"
  },
  {
    "path": "sam3/train/configs/saco_video_evals/saco_veval_sav_test_noheur.yaml",
    "content": "# @package _global_\ndefaults:\n  - _self_\n\n# ============================================================================\n# Paths Configuration (Chage this to your own paths)\n# ============================================================================\npaths:\n\n  dump_file_name: saco_veval_sav_test\n  experiment_log_dir: <YOUR EXPERIMENET LOG_DIR>\n  ytvis_json: <YOUR_GT_PATH>/saco_veval_sav_test.json\n  ytvis_dir : <YOUR_VIDEO_JPG_DIR>\n  bpe_path: <BPE_PATH> # This should be under sam3/assets/bpe_simple_vocab_16e6.txt.gz\n  num_videos: null\n\n# ============================================================================\n# Different helper parameters and functions\n# ============================================================================\nscratch:\n  vid_mask_postprocessor:\n    _target_: sam3.eval.postprocessors.PostProcessNullOp\n\n  use_presence_eval: True\n\n  video_transforms_val:\n    - _target_: sam3.train.transforms.basic_for_api.ComposeAPI\n      transforms:\n        - _target_: sam3.train.transforms.segmentation.DecodeRle\n        # resize the image to 1024x1024 resolution\n        - _target_: sam3.train.transforms.basic_for_api.RandomResizeAPI\n          sizes: ${scratch.resolution}  # originally `resolution: 1024`\n          square: true\n          consistent_transform: true\n        - _target_: sam3.train.transforms.basic_for_api.ToTensorAPI\n        - _target_: sam3.train.transforms.basic_for_api.NormalizeAPI\n          mean: ${scratch.val_norm_mean}\n          std: ${scratch.val_norm_std}\n\n  # Model parameters\n  d_model: 256\n\n  # Image processing parameters\n  resolution: 1008\n\n  # Normalization parameters\n  train_norm_mean: [0.5, 0.5, 0.5]\n  train_norm_std: [0.5, 0.5, 0.5]\n  val_norm_mean: [0.5, 0.5, 0.5]\n  val_norm_std: [0.5, 0.5, 0.5]\n\n  val_batch_size: 1\n  num_val_workers: 0\n  max_data_epochs: 20\n  hybrid_repeats: 1\n  gather_pred_via_filesys: false\n\n\n# ============================================================================\n# Trainer Configuration\n# ============================================================================\n\ntrainer:\n  _target_: sam3.train.trainer.Trainer\n  skip_saving_ckpts: true\n  empty_gpu_mem_cache_after_eval: True\n  skip_first_val: True\n  max_epochs: ${scratch.max_data_epochs}\n  accelerator: cuda\n  seed_value: 123\n  val_epoch_freq: 10\n  mode: val\n\n  distributed:\n    backend: nccl\n    find_unused_parameters: True\n    gradient_as_bucket_view: True\n\n  loss:\n    all:\n      _target_: sam3.train.loss.sam3_loss.DummyLoss\n    default:\n      _target_: sam3.train.loss.sam3_loss.DummyLoss\n\n  data:\n    train: null\n    val:\n      _target_: sam3.train.data.torch_dataset.TorchDataset\n      dataset:\n        _target_: sam3.train.data.sam3_video_dataset.VideoGroundingDataset\n        limit_ids: ${paths.num_videos}\n        img_folder: ${paths.ytvis_dir}\n        ann_file: ${paths.ytvis_json}\n        coco_json_loader:\n          _target_: sam3.train.data.coco_json_loaders.SAM3_VEVAL_API_FROM_JSON_NP\n          _partial_: true\n\n        transforms: ${scratch.video_transforms_val}\n        max_ann_per_img: 100000  # filtered in transforms\n        max_val_queries: 100000\n        multiplier: 1\n        load_segmentation: true\n        training: false\n\n\n      shuffle: False\n      batch_size: ${scratch.val_batch_size}\n      num_workers: ${scratch.num_val_workers}\n      pin_memory: True\n      drop_last: False\n      collate_fn:\n        _target_: sam3.train.data.collator.collate_fn_api\n        _partial_: true\n        repeats: ${scratch.hybrid_repeats}\n        dict_key: ytvis_val\n        with_seg_masks: true\n\n\n  model:\n    _target_: sam3.model_builder.build_sam3_video_model\n    bpe_path: ${paths.bpe_path}\n    has_presence_token: True\n    geo_encoder_use_img_cross_attn: True\n    apply_temporal_disambiguation: False\n\n  meters:\n    val:\n      ytvis_val:\n        pred_file: # key\n          _target_: sam3.eval.ytvis_eval.YTVISResultsWriter\n          dump_file: ${launcher.experiment_log_dir}/preds/${paths.dump_file_name}.json\n          postprocessor: ${scratch.vid_mask_postprocessor}\n          gather_pred_via_filesys: ${scratch.gather_pred_via_filesys}\n\n  optim:\n    amp:\n      enabled: True\n      amp_dtype: bfloat16\n\n\n  checkpoint:\n    save_dir: ${launcher.experiment_log_dir}/checkpoints\n    save_freq: 0  # 0 only last checkpoint is saved.\n\n\n  logging:\n    tensorboard_writer:\n      _target_: sam3.train.utils.logger.make_tensorboard_logger\n      log_dir: ${launcher.experiment_log_dir}/tensorboard\n      flush_secs: 120\n      should_log: True\n    wandb_writer: null\n    log_dir: ${launcher.experiment_log_dir}/logs/\n    log_freq: 10\n\n# ============================================================================\n# Launcher and Submitit Configuration\n# ============================================================================\n\nlauncher:\n  num_nodes: 8\n  gpus_per_node: 8\n  experiment_log_dir: ${paths.experiment_log_dir}\n  multiprocessing_context: forkserver\n\nsubmitit:\n  account: null\n  partition: null\n  qos: null\n  timeout_hour: 72\n  use_cluster: True\n  cpus_per_task: 10\n  port_range: [10000, 65000]\n  constraint: null\n"
  },
  {
    "path": "sam3/train/configs/saco_video_evals/saco_veval_sav_val.yaml",
    "content": "# @package _global_\ndefaults:\n  - _self_\n\n# ============================================================================\n# Paths Configuration (Chage this to your own paths)\n# ============================================================================\npaths:\n\n  dump_file_name: saco_veval_sav_val\n  experiment_log_dir: <YOUR EXPERIMENET LOG_DIR>\n  ytvis_json: <YOUR_GT_PATH>/saco_veval_sav_val.json\n  ytvis_dir : <YOUR_VIDEO_JPG_DIR>\n  bpe_path: <BPE_PATH> # This should be under sam3/assets/bpe_simple_vocab_16e6.txt.gz\n  num_videos: null\n\n# ============================================================================\n# Different helper parameters and functions\n# ============================================================================\nscratch:\n  vid_mask_postprocessor:\n    _target_: sam3.eval.postprocessors.PostProcessNullOp\n\n  use_presence_eval: True\n\n  video_transforms_val:\n    - _target_: sam3.train.transforms.basic_for_api.ComposeAPI\n      transforms:\n        - _target_: sam3.train.transforms.segmentation.DecodeRle\n        # resize the image to 1024x1024 resolution\n        - _target_: sam3.train.transforms.basic_for_api.RandomResizeAPI\n          sizes: ${scratch.resolution}  # originally `resolution: 1024`\n          square: true\n          consistent_transform: true\n        - _target_: sam3.train.transforms.basic_for_api.ToTensorAPI\n        - _target_: sam3.train.transforms.basic_for_api.NormalizeAPI\n          mean: ${scratch.val_norm_mean}\n          std: ${scratch.val_norm_std}\n\n  # Model parameters\n  d_model: 256\n\n  # Image processing parameters\n  resolution: 1008\n\n  # Normalization parameters\n  train_norm_mean: [0.5, 0.5, 0.5]\n  train_norm_std: [0.5, 0.5, 0.5]\n  val_norm_mean: [0.5, 0.5, 0.5]\n  val_norm_std: [0.5, 0.5, 0.5]\n\n  val_batch_size: 1\n  num_val_workers: 0\n  max_data_epochs: 20\n  hybrid_repeats: 1\n  gather_pred_via_filesys: false\n\n\n# ============================================================================\n# Trainer Configuration\n# ============================================================================\n\ntrainer:\n  _target_: sam3.train.trainer.Trainer\n  skip_saving_ckpts: true\n  empty_gpu_mem_cache_after_eval: True\n  skip_first_val: True\n  max_epochs: ${scratch.max_data_epochs}\n  accelerator: cuda\n  seed_value: 123\n  val_epoch_freq: 10\n  mode: val\n\n  distributed:\n    backend: nccl\n    find_unused_parameters: True\n    gradient_as_bucket_view: True\n\n  loss:\n    all:\n      _target_: sam3.train.loss.sam3_loss.DummyLoss\n    default:\n      _target_: sam3.train.loss.sam3_loss.DummyLoss\n\n  data:\n    train: null\n    val:\n      _target_: sam3.train.data.torch_dataset.TorchDataset\n      dataset:\n        _target_: sam3.train.data.sam3_video_dataset.VideoGroundingDataset\n        limit_ids: ${paths.num_videos}\n        img_folder: ${paths.ytvis_dir}\n        ann_file: ${paths.ytvis_json}\n        coco_json_loader:\n          _target_: sam3.train.data.coco_json_loaders.SAM3_VEVAL_API_FROM_JSON_NP\n          _partial_: true\n\n        transforms: ${scratch.video_transforms_val}\n        max_ann_per_img: 100000  # filtered in transforms\n        max_val_queries: 100000\n        multiplier: 1\n        load_segmentation: true\n        training: false\n\n\n      shuffle: False\n      batch_size: ${scratch.val_batch_size}\n      num_workers: ${scratch.num_val_workers}\n      pin_memory: True\n      drop_last: False\n      collate_fn:\n        _target_: sam3.train.data.collator.collate_fn_api\n        _partial_: true\n        repeats: ${scratch.hybrid_repeats}\n        dict_key: ytvis_val\n        with_seg_masks: true\n\n\n  model:\n    _target_: sam3.model_builder.build_sam3_video_model\n    bpe_path: ${paths.bpe_path}\n    has_presence_token: True\n    geo_encoder_use_img_cross_attn: True\n    apply_temporal_disambiguation: True\n\n  meters:\n    val:\n      ytvis_val:\n        pred_file: # key\n          _target_: sam3.eval.ytvis_eval.YTVISResultsWriter\n          dump_file: ${launcher.experiment_log_dir}/preds/${paths.dump_file_name}.json\n          postprocessor: ${scratch.vid_mask_postprocessor}\n          gather_pred_via_filesys: ${scratch.gather_pred_via_filesys}\n\n  optim:\n    amp:\n      enabled: True\n      amp_dtype: bfloat16\n\n\n  checkpoint:\n    save_dir: ${launcher.experiment_log_dir}/checkpoints\n    save_freq: 0  # 0 only last checkpoint is saved.\n\n\n  logging:\n    tensorboard_writer:\n      _target_: sam3.train.utils.logger.make_tensorboard_logger\n      log_dir: ${launcher.experiment_log_dir}/tensorboard\n      flush_secs: 120\n      should_log: True\n    wandb_writer: null\n    log_dir: ${launcher.experiment_log_dir}/logs/\n    log_freq: 10\n\n# ============================================================================\n# Launcher and Submitit Configuration\n# ============================================================================\n\nlauncher:\n  num_nodes: 8\n  gpus_per_node: 8\n  experiment_log_dir: ${paths.experiment_log_dir}\n  multiprocessing_context: forkserver\n\nsubmitit:\n  account: null\n  partition: null\n  qos: null\n  timeout_hour: 72\n  use_cluster: True\n  cpus_per_task: 10\n  port_range: [10000, 65000]\n  constraint: null\n"
  },
  {
    "path": "sam3/train/configs/saco_video_evals/saco_veval_sav_val_noheur.yaml",
    "content": "# @package _global_\ndefaults:\n  - _self_\n\n# ============================================================================\n# Paths Configuration (Chage this to your own paths)\n# ============================================================================\npaths:\n\n  dump_file_name: saco_veval_sav_val\n  experiment_log_dir: <YOUR EXPERIMENET LOG_DIR>\n  ytvis_json: <YOUR_GT_PATH>/saco_veval_sav_val.json\n  ytvis_dir : <YOUR_VIDEO_JPG_DIR>\n  bpe_path: <BPE_PATH> # This should be under sam3/assets/bpe_simple_vocab_16e6.txt.gz\n  num_videos: null\n\n# ============================================================================\n# Different helper parameters and functions\n# ============================================================================\nscratch:\n  vid_mask_postprocessor:\n    _target_: sam3.eval.postprocessors.PostProcessNullOp\n\n  use_presence_eval: True\n\n  video_transforms_val:\n    - _target_: sam3.train.transforms.basic_for_api.ComposeAPI\n      transforms:\n        - _target_: sam3.train.transforms.segmentation.DecodeRle\n        # resize the image to 1024x1024 resolution\n        - _target_: sam3.train.transforms.basic_for_api.RandomResizeAPI\n          sizes: ${scratch.resolution}  # originally `resolution: 1024`\n          square: true\n          consistent_transform: true\n        - _target_: sam3.train.transforms.basic_for_api.ToTensorAPI\n        - _target_: sam3.train.transforms.basic_for_api.NormalizeAPI\n          mean: ${scratch.val_norm_mean}\n          std: ${scratch.val_norm_std}\n\n  # Model parameters\n  d_model: 256\n\n  # Image processing parameters\n  resolution: 1008\n\n  # Normalization parameters\n  train_norm_mean: [0.5, 0.5, 0.5]\n  train_norm_std: [0.5, 0.5, 0.5]\n  val_norm_mean: [0.5, 0.5, 0.5]\n  val_norm_std: [0.5, 0.5, 0.5]\n\n  val_batch_size: 1\n  num_val_workers: 0\n  max_data_epochs: 20\n  hybrid_repeats: 1\n  gather_pred_via_filesys: false\n\n\n# ============================================================================\n# Trainer Configuration\n# ============================================================================\n\ntrainer:\n  _target_: sam3.train.trainer.Trainer\n  skip_saving_ckpts: true\n  empty_gpu_mem_cache_after_eval: True\n  skip_first_val: True\n  max_epochs: ${scratch.max_data_epochs}\n  accelerator: cuda\n  seed_value: 123\n  val_epoch_freq: 10\n  mode: val\n\n  distributed:\n    backend: nccl\n    find_unused_parameters: True\n    gradient_as_bucket_view: True\n\n  loss:\n    all:\n      _target_: sam3.train.loss.sam3_loss.DummyLoss\n    default:\n      _target_: sam3.train.loss.sam3_loss.DummyLoss\n\n  data:\n    train: null\n    val:\n      _target_: sam3.train.data.torch_dataset.TorchDataset\n      dataset:\n        _target_: sam3.train.data.sam3_video_dataset.VideoGroundingDataset\n        limit_ids: ${paths.num_videos}\n        img_folder: ${paths.ytvis_dir}\n        ann_file: ${paths.ytvis_json}\n        coco_json_loader:\n          _target_: sam3.train.data.coco_json_loaders.SAM3_VEVAL_API_FROM_JSON_NP\n          _partial_: true\n\n        transforms: ${scratch.video_transforms_val}\n        max_ann_per_img: 100000  # filtered in transforms\n        max_val_queries: 100000\n        multiplier: 1\n        load_segmentation: true\n        training: false\n\n\n      shuffle: False\n      batch_size: ${scratch.val_batch_size}\n      num_workers: ${scratch.num_val_workers}\n      pin_memory: True\n      drop_last: False\n      collate_fn:\n        _target_: sam3.train.data.collator.collate_fn_api\n        _partial_: true\n        repeats: ${scratch.hybrid_repeats}\n        dict_key: ytvis_val\n        with_seg_masks: true\n\n\n  model:\n    _target_: sam3.model_builder.build_sam3_video_model\n    bpe_path: ${paths.bpe_path}\n    has_presence_token: True\n    geo_encoder_use_img_cross_attn: True\n    apply_temporal_disambiguation: False\n\n  meters:\n    val:\n      ytvis_val:\n        pred_file: # key\n          _target_: sam3.eval.ytvis_eval.YTVISResultsWriter\n          dump_file: ${launcher.experiment_log_dir}/preds/${paths.dump_file_name}.json\n          postprocessor: ${scratch.vid_mask_postprocessor}\n          gather_pred_via_filesys: ${scratch.gather_pred_via_filesys}\n\n  optim:\n    amp:\n      enabled: True\n      amp_dtype: bfloat16\n\n\n  checkpoint:\n    save_dir: ${launcher.experiment_log_dir}/checkpoints\n    save_freq: 0  # 0 only last checkpoint is saved.\n\n\n  logging:\n    tensorboard_writer:\n      _target_: sam3.train.utils.logger.make_tensorboard_logger\n      log_dir: ${launcher.experiment_log_dir}/tensorboard\n      flush_secs: 120\n      should_log: True\n    wandb_writer: null\n    log_dir: ${launcher.experiment_log_dir}/logs/\n    log_freq: 10\n\n# ============================================================================\n# Launcher and Submitit Configuration\n# ============================================================================\n\nlauncher:\n  num_nodes: 8\n  gpus_per_node: 8\n  experiment_log_dir: ${paths.experiment_log_dir}\n  multiprocessing_context: forkserver\n\nsubmitit:\n  account: null\n  partition: null\n  qos: null\n  timeout_hour: 72\n  use_cluster: True\n  cpus_per_task: 10\n  port_range: [10000, 65000]\n  constraint: null\n"
  },
  {
    "path": "sam3/train/configs/saco_video_evals/saco_veval_smartglasses_test.yaml",
    "content": "# @package _global_\ndefaults:\n  - _self_\n\n# ============================================================================\n# Paths Configuration (Chage this to your own paths)\n# ============================================================================\npaths:\n\n  dump_file_name: saco_veval_smartglasses_test\n  experiment_log_dir: <YOUR EXPERIMENET LOG_DIR>\n  ytvis_json: <YOUR_GT_PATH>/saco_veval_smartglasses_test.json\n  ytvis_dir : <YOUR_VIDEO_JPG_DIR>\n  bpe_path: <BPE_PATH> # This should be under sam3/assets/bpe_simple_vocab_16e6.txt.gz\n  num_videos: null\n\n# ============================================================================\n# Different helper parameters and functions\n# ============================================================================\nscratch:\n  vid_mask_postprocessor:\n    _target_: sam3.eval.postprocessors.PostProcessNullOp\n\n  use_presence_eval: True\n\n  video_transforms_val:\n    - _target_: sam3.train.transforms.basic_for_api.ComposeAPI\n      transforms:\n        - _target_: sam3.train.transforms.segmentation.DecodeRle\n        # resize the image to 1024x1024 resolution\n        - _target_: sam3.train.transforms.basic_for_api.RandomResizeAPI\n          sizes: ${scratch.resolution}  # originally `resolution: 1024`\n          square: true\n          consistent_transform: true\n        - _target_: sam3.train.transforms.basic_for_api.ToTensorAPI\n        - _target_: sam3.train.transforms.basic_for_api.NormalizeAPI\n          mean: ${scratch.val_norm_mean}\n          std: ${scratch.val_norm_std}\n\n  # Model parameters\n  d_model: 256\n\n  # Image processing parameters\n  resolution: 1008\n\n  # Normalization parameters\n  train_norm_mean: [0.5, 0.5, 0.5]\n  train_norm_std: [0.5, 0.5, 0.5]\n  val_norm_mean: [0.5, 0.5, 0.5]\n  val_norm_std: [0.5, 0.5, 0.5]\n\n  val_batch_size: 1\n  num_val_workers: 0\n  max_data_epochs: 20\n  hybrid_repeats: 1\n  gather_pred_via_filesys: false\n\n\n# ============================================================================\n# Trainer Configuration\n# ============================================================================\n\ntrainer:\n  _target_: sam3.train.trainer.Trainer\n  skip_saving_ckpts: true\n  empty_gpu_mem_cache_after_eval: True\n  skip_first_val: True\n  max_epochs: ${scratch.max_data_epochs}\n  accelerator: cuda\n  seed_value: 123\n  val_epoch_freq: 10\n  mode: val\n\n  distributed:\n    backend: nccl\n    find_unused_parameters: True\n    gradient_as_bucket_view: True\n\n  loss:\n    all:\n      _target_: sam3.train.loss.sam3_loss.DummyLoss\n    default:\n      _target_: sam3.train.loss.sam3_loss.DummyLoss\n\n  data:\n    train: null\n    val:\n      _target_: sam3.train.data.torch_dataset.TorchDataset\n      dataset:\n        _target_: sam3.train.data.sam3_video_dataset.VideoGroundingDataset\n        limit_ids: ${paths.num_videos}\n        img_folder: ${paths.ytvis_dir}\n        ann_file: ${paths.ytvis_json}\n        coco_json_loader:\n          _target_: sam3.train.data.coco_json_loaders.SAM3_VEVAL_API_FROM_JSON_NP\n          _partial_: true\n\n        transforms: ${scratch.video_transforms_val}\n        max_ann_per_img: 100000  # filtered in transforms\n        max_val_queries: 100000\n        multiplier: 1\n        load_segmentation: true\n        training: false\n\n\n      shuffle: False\n      batch_size: ${scratch.val_batch_size}\n      num_workers: ${scratch.num_val_workers}\n      pin_memory: True\n      drop_last: False\n      collate_fn:\n        _target_: sam3.train.data.collator.collate_fn_api\n        _partial_: true\n        repeats: ${scratch.hybrid_repeats}\n        dict_key: ytvis_val\n        with_seg_masks: true\n\n\n  model:\n    _target_: sam3.model_builder.build_sam3_video_model\n    bpe_path: ${paths.bpe_path}\n    has_presence_token: True\n    geo_encoder_use_img_cross_attn: True\n    apply_temporal_disambiguation: True\n\n  meters:\n    val:\n      ytvis_val:\n        pred_file: # key\n          _target_: sam3.eval.ytvis_eval.YTVISResultsWriter\n          dump_file: ${launcher.experiment_log_dir}/preds/${paths.dump_file_name}.json\n          postprocessor: ${scratch.vid_mask_postprocessor}\n          gather_pred_via_filesys: ${scratch.gather_pred_via_filesys}\n\n  optim:\n    amp:\n      enabled: True\n      amp_dtype: bfloat16\n\n\n  checkpoint:\n    save_dir: ${launcher.experiment_log_dir}/checkpoints\n    save_freq: 0  # 0 only last checkpoint is saved.\n\n\n  logging:\n    tensorboard_writer:\n      _target_: sam3.train.utils.logger.make_tensorboard_logger\n      log_dir: ${launcher.experiment_log_dir}/tensorboard\n      flush_secs: 120\n      should_log: True\n    wandb_writer: null\n    log_dir: ${launcher.experiment_log_dir}/logs/\n    log_freq: 10\n\n# ============================================================================\n# Launcher and Submitit Configuration\n# ============================================================================\n\nlauncher:\n  num_nodes: 8\n  gpus_per_node: 8\n  experiment_log_dir: ${paths.experiment_log_dir}\n  multiprocessing_context: forkserver\n\nsubmitit:\n  account: null\n  partition: null\n  qos: null\n  timeout_hour: 72\n  use_cluster: True\n  cpus_per_task: 10\n  port_range: [10000, 65000]\n  constraint: null\n"
  },
  {
    "path": "sam3/train/configs/saco_video_evals/saco_veval_smartglasses_test_noheur.yaml",
    "content": "# @package _global_\ndefaults:\n  - _self_\n\n# ============================================================================\n# Paths Configuration (Chage this to your own paths)\n# ============================================================================\npaths:\n\n  dump_file_name: saco_veval_smartglasses_test\n  experiment_log_dir: <YOUR EXPERIMENET LOG_DIR>\n  ytvis_json: <YOUR_GT_PATH>/saco_veval_smartglasses_test.json\n  ytvis_dir : <YOUR_VIDEO_JPG_DIR>\n  bpe_path: <BPE_PATH> # This should be under sam3/assets/bpe_simple_vocab_16e6.txt.gz\n  num_videos: null\n\n# ============================================================================\n# Different helper parameters and functions\n# ============================================================================\nscratch:\n  vid_mask_postprocessor:\n    _target_: sam3.eval.postprocessors.PostProcessNullOp\n\n  use_presence_eval: True\n\n  video_transforms_val:\n    - _target_: sam3.train.transforms.basic_for_api.ComposeAPI\n      transforms:\n        - _target_: sam3.train.transforms.segmentation.DecodeRle\n        # resize the image to 1024x1024 resolution\n        - _target_: sam3.train.transforms.basic_for_api.RandomResizeAPI\n          sizes: ${scratch.resolution}  # originally `resolution: 1024`\n          square: true\n          consistent_transform: true\n        - _target_: sam3.train.transforms.basic_for_api.ToTensorAPI\n        - _target_: sam3.train.transforms.basic_for_api.NormalizeAPI\n          mean: ${scratch.val_norm_mean}\n          std: ${scratch.val_norm_std}\n\n  # Model parameters\n  d_model: 256\n\n  # Image processing parameters\n  resolution: 1008\n\n  # Normalization parameters\n  train_norm_mean: [0.5, 0.5, 0.5]\n  train_norm_std: [0.5, 0.5, 0.5]\n  val_norm_mean: [0.5, 0.5, 0.5]\n  val_norm_std: [0.5, 0.5, 0.5]\n\n  val_batch_size: 1\n  num_val_workers: 0\n  max_data_epochs: 20\n  hybrid_repeats: 1\n  gather_pred_via_filesys: false\n\n\n# ============================================================================\n# Trainer Configuration\n# ============================================================================\n\ntrainer:\n  _target_: sam3.train.trainer.Trainer\n  skip_saving_ckpts: true\n  empty_gpu_mem_cache_after_eval: True\n  skip_first_val: True\n  max_epochs: ${scratch.max_data_epochs}\n  accelerator: cuda\n  seed_value: 123\n  val_epoch_freq: 10\n  mode: val\n\n  distributed:\n    backend: nccl\n    find_unused_parameters: True\n    gradient_as_bucket_view: True\n\n  loss:\n    all:\n      _target_: sam3.train.loss.sam3_loss.DummyLoss\n    default:\n      _target_: sam3.train.loss.sam3_loss.DummyLoss\n\n  data:\n    train: null\n    val:\n      _target_: sam3.train.data.torch_dataset.TorchDataset\n      dataset:\n        _target_: sam3.train.data.sam3_video_dataset.VideoGroundingDataset\n        limit_ids: ${paths.num_videos}\n        img_folder: ${paths.ytvis_dir}\n        ann_file: ${paths.ytvis_json}\n        coco_json_loader:\n          _target_: sam3.train.data.coco_json_loaders.SAM3_VEVAL_API_FROM_JSON_NP\n          _partial_: true\n\n        transforms: ${scratch.video_transforms_val}\n        max_ann_per_img: 100000  # filtered in transforms\n        max_val_queries: 100000\n        multiplier: 1\n        load_segmentation: true\n        training: false\n\n\n      shuffle: False\n      batch_size: ${scratch.val_batch_size}\n      num_workers: ${scratch.num_val_workers}\n      pin_memory: True\n      drop_last: False\n      collate_fn:\n        _target_: sam3.train.data.collator.collate_fn_api\n        _partial_: true\n        repeats: ${scratch.hybrid_repeats}\n        dict_key: ytvis_val\n        with_seg_masks: true\n\n\n  model:\n    _target_: sam3.model_builder.build_sam3_video_model\n    bpe_path: ${paths.bpe_path}\n    has_presence_token: True\n    geo_encoder_use_img_cross_attn: True\n    apply_temporal_disambiguation: False\n\n  meters:\n    val:\n      ytvis_val:\n        pred_file: # key\n          _target_: sam3.eval.ytvis_eval.YTVISResultsWriter\n          dump_file: ${launcher.experiment_log_dir}/preds/${paths.dump_file_name}.json\n          postprocessor: ${scratch.vid_mask_postprocessor}\n          gather_pred_via_filesys: ${scratch.gather_pred_via_filesys}\n\n  optim:\n    amp:\n      enabled: True\n      amp_dtype: bfloat16\n\n\n  checkpoint:\n    save_dir: ${launcher.experiment_log_dir}/checkpoints\n    save_freq: 0  # 0 only last checkpoint is saved.\n\n\n  logging:\n    tensorboard_writer:\n      _target_: sam3.train.utils.logger.make_tensorboard_logger\n      log_dir: ${launcher.experiment_log_dir}/tensorboard\n      flush_secs: 120\n      should_log: True\n    wandb_writer: null\n    log_dir: ${launcher.experiment_log_dir}/logs/\n    log_freq: 10\n\n# ============================================================================\n# Launcher and Submitit Configuration\n# ============================================================================\n\nlauncher:\n  num_nodes: 8\n  gpus_per_node: 8\n  experiment_log_dir: ${paths.experiment_log_dir}\n  multiprocessing_context: forkserver\n\nsubmitit:\n  account: null\n  partition: null\n  qos: null\n  timeout_hour: 72\n  use_cluster: True\n  cpus_per_task: 10\n  port_range: [10000, 65000]\n  constraint: null\n"
  },
  {
    "path": "sam3/train/configs/saco_video_evals/saco_veval_smartglasses_val.yaml",
    "content": "# @package _global_\ndefaults:\n  - _self_\n\n# ============================================================================\n# Paths Configuration (Chage this to your own paths)\n# ============================================================================\npaths:\n\n  dump_file_name: saco_veval_smartglasses_val\n  experiment_log_dir: <YOUR EXPERIMENET LOG_DIR>\n  ytvis_json: <YOUR_GT_PATH>/saco_veval_smartglasses_val.json\n  ytvis_dir : <YOUR_VIDEO_JPG_DIR>\n  bpe_path: <BPE_PATH> # This should be under sam3/assets/bpe_simple_vocab_16e6.txt.gz\n  num_videos: null\n\n# ============================================================================\n# Different helper parameters and functions\n# ============================================================================\nscratch:\n  vid_mask_postprocessor:\n    _target_: sam3.eval.postprocessors.PostProcessNullOp\n\n  use_presence_eval: True\n\n  video_transforms_val:\n    - _target_: sam3.train.transforms.basic_for_api.ComposeAPI\n      transforms:\n        - _target_: sam3.train.transforms.segmentation.DecodeRle\n        # resize the image to 1024x1024 resolution\n        - _target_: sam3.train.transforms.basic_for_api.RandomResizeAPI\n          sizes: ${scratch.resolution}  # originally `resolution: 1024`\n          square: true\n          consistent_transform: true\n        - _target_: sam3.train.transforms.basic_for_api.ToTensorAPI\n        - _target_: sam3.train.transforms.basic_for_api.NormalizeAPI\n          mean: ${scratch.val_norm_mean}\n          std: ${scratch.val_norm_std}\n\n  # Model parameters\n  d_model: 256\n\n  # Image processing parameters\n  resolution: 1008\n\n  # Normalization parameters\n  train_norm_mean: [0.5, 0.5, 0.5]\n  train_norm_std: [0.5, 0.5, 0.5]\n  val_norm_mean: [0.5, 0.5, 0.5]\n  val_norm_std: [0.5, 0.5, 0.5]\n\n  val_batch_size: 1\n  num_val_workers: 0\n  max_data_epochs: 20\n  hybrid_repeats: 1\n  gather_pred_via_filesys: false\n\n\n# ============================================================================\n# Trainer Configuration\n# ============================================================================\n\ntrainer:\n  _target_: sam3.train.trainer.Trainer\n  skip_saving_ckpts: true\n  empty_gpu_mem_cache_after_eval: True\n  skip_first_val: True\n  max_epochs: ${scratch.max_data_epochs}\n  accelerator: cuda\n  seed_value: 123\n  val_epoch_freq: 10\n  mode: val\n\n  distributed:\n    backend: nccl\n    find_unused_parameters: True\n    gradient_as_bucket_view: True\n\n  loss:\n    all:\n      _target_: sam3.train.loss.sam3_loss.DummyLoss\n    default:\n      _target_: sam3.train.loss.sam3_loss.DummyLoss\n\n  data:\n    train: null\n    val:\n      _target_: sam3.train.data.torch_dataset.TorchDataset\n      dataset:\n        _target_: sam3.train.data.sam3_video_dataset.VideoGroundingDataset\n        limit_ids: ${paths.num_videos}\n        img_folder: ${paths.ytvis_dir}\n        ann_file: ${paths.ytvis_json}\n        coco_json_loader:\n          _target_: sam3.train.data.coco_json_loaders.SAM3_VEVAL_API_FROM_JSON_NP\n          _partial_: true\n\n        transforms: ${scratch.video_transforms_val}\n        max_ann_per_img: 100000  # filtered in transforms\n        max_val_queries: 100000\n        multiplier: 1\n        load_segmentation: true\n        training: false\n\n\n      shuffle: False\n      batch_size: ${scratch.val_batch_size}\n      num_workers: ${scratch.num_val_workers}\n      pin_memory: True\n      drop_last: False\n      collate_fn:\n        _target_: sam3.train.data.collator.collate_fn_api\n        _partial_: true\n        repeats: ${scratch.hybrid_repeats}\n        dict_key: ytvis_val\n        with_seg_masks: true\n\n\n  model:\n    _target_: sam3.model_builder.build_sam3_video_model\n    bpe_path: ${paths.bpe_path}\n    has_presence_token: True\n    geo_encoder_use_img_cross_attn: True\n    apply_temporal_disambiguation: True\n\n  meters:\n    val:\n      ytvis_val:\n        pred_file: # key\n          _target_: sam3.eval.ytvis_eval.YTVISResultsWriter\n          dump_file: ${launcher.experiment_log_dir}/preds/${paths.dump_file_name}.json\n          postprocessor: ${scratch.vid_mask_postprocessor}\n          gather_pred_via_filesys: ${scratch.gather_pred_via_filesys}\n\n  optim:\n    amp:\n      enabled: True\n      amp_dtype: bfloat16\n\n\n  checkpoint:\n    save_dir: ${launcher.experiment_log_dir}/checkpoints\n    save_freq: 0  # 0 only last checkpoint is saved.\n\n\n  logging:\n    tensorboard_writer:\n      _target_: sam3.train.utils.logger.make_tensorboard_logger\n      log_dir: ${launcher.experiment_log_dir}/tensorboard\n      flush_secs: 120\n      should_log: True\n    wandb_writer: null\n    log_dir: ${launcher.experiment_log_dir}/logs/\n    log_freq: 10\n\n# ============================================================================\n# Launcher and Submitit Configuration\n# ============================================================================\n\nlauncher:\n  num_nodes: 8\n  gpus_per_node: 8\n  experiment_log_dir: ${paths.experiment_log_dir}\n  multiprocessing_context: forkserver\n\nsubmitit:\n  account: null\n  partition: null\n  qos: null\n  timeout_hour: 72\n  use_cluster: True\n  cpus_per_task: 10\n  port_range: [10000, 65000]\n  constraint: null\n"
  },
  {
    "path": "sam3/train/configs/saco_video_evals/saco_veval_smartglasses_val_noheur.yaml",
    "content": "# @package _global_\ndefaults:\n  - _self_\n\n# ============================================================================\n# Paths Configuration (Chage this to your own paths)\n# ============================================================================\npaths:\n\n  dump_file_name: saco_veval_smartglasses_val\n  experiment_log_dir: <YOUR EXPERIMENET LOG_DIR>\n  ytvis_json: <YOUR_GT_PATH>/saco_veval_smartglasses_val.json\n  ytvis_dir : <YOUR_VIDEO_JPG_DIR>\n  bpe_path: <BPE_PATH> # This should be under sam3/assets/bpe_simple_vocab_16e6.txt.gz\n  num_videos: null\n\n# ============================================================================\n# Different helper parameters and functions\n# ============================================================================\nscratch:\n  vid_mask_postprocessor:\n    _target_: sam3.eval.postprocessors.PostProcessNullOp\n\n  use_presence_eval: True\n\n  video_transforms_val:\n    - _target_: sam3.train.transforms.basic_for_api.ComposeAPI\n      transforms:\n        - _target_: sam3.train.transforms.segmentation.DecodeRle\n        # resize the image to 1024x1024 resolution\n        - _target_: sam3.train.transforms.basic_for_api.RandomResizeAPI\n          sizes: ${scratch.resolution}  # originally `resolution: 1024`\n          square: true\n          consistent_transform: true\n        - _target_: sam3.train.transforms.basic_for_api.ToTensorAPI\n        - _target_: sam3.train.transforms.basic_for_api.NormalizeAPI\n          mean: ${scratch.val_norm_mean}\n          std: ${scratch.val_norm_std}\n\n  # Model parameters\n  d_model: 256\n\n  # Image processing parameters\n  resolution: 1008\n\n  # Normalization parameters\n  train_norm_mean: [0.5, 0.5, 0.5]\n  train_norm_std: [0.5, 0.5, 0.5]\n  val_norm_mean: [0.5, 0.5, 0.5]\n  val_norm_std: [0.5, 0.5, 0.5]\n\n  val_batch_size: 1\n  num_val_workers: 0\n  max_data_epochs: 20\n  hybrid_repeats: 1\n  gather_pred_via_filesys: false\n\n\n# ============================================================================\n# Trainer Configuration\n# ============================================================================\n\ntrainer:\n  _target_: sam3.train.trainer.Trainer\n  skip_saving_ckpts: true\n  empty_gpu_mem_cache_after_eval: True\n  skip_first_val: True\n  max_epochs: ${scratch.max_data_epochs}\n  accelerator: cuda\n  seed_value: 123\n  val_epoch_freq: 10\n  mode: val\n\n  distributed:\n    backend: nccl\n    find_unused_parameters: True\n    gradient_as_bucket_view: True\n\n  loss:\n    all:\n      _target_: sam3.train.loss.sam3_loss.DummyLoss\n    default:\n      _target_: sam3.train.loss.sam3_loss.DummyLoss\n\n  data:\n    train: null\n    val:\n      _target_: sam3.train.data.torch_dataset.TorchDataset\n      dataset:\n        _target_: sam3.train.data.sam3_video_dataset.VideoGroundingDataset\n        limit_ids: ${paths.num_videos}\n        img_folder: ${paths.ytvis_dir}\n        ann_file: ${paths.ytvis_json}\n        coco_json_loader:\n          _target_: sam3.train.data.coco_json_loaders.SAM3_VEVAL_API_FROM_JSON_NP\n          _partial_: true\n\n        transforms: ${scratch.video_transforms_val}\n        max_ann_per_img: 100000  # filtered in transforms\n        max_val_queries: 100000\n        multiplier: 1\n        load_segmentation: true\n        training: false\n\n\n      shuffle: False\n      batch_size: ${scratch.val_batch_size}\n      num_workers: ${scratch.num_val_workers}\n      pin_memory: True\n      drop_last: False\n      collate_fn:\n        _target_: sam3.train.data.collator.collate_fn_api\n        _partial_: true\n        repeats: ${scratch.hybrid_repeats}\n        dict_key: ytvis_val\n        with_seg_masks: true\n\n\n  model:\n    _target_: sam3.model_builder.build_sam3_video_model\n    bpe_path: ${paths.bpe_path}\n    has_presence_token: True\n    geo_encoder_use_img_cross_attn: True\n    apply_temporal_disambiguation: False\n\n  meters:\n    val:\n      ytvis_val:\n        pred_file: # key\n          _target_: sam3.eval.ytvis_eval.YTVISResultsWriter\n          dump_file: ${launcher.experiment_log_dir}/preds/${paths.dump_file_name}.json\n          postprocessor: ${scratch.vid_mask_postprocessor}\n          gather_pred_via_filesys: ${scratch.gather_pred_via_filesys}\n\n  optim:\n    amp:\n      enabled: True\n      amp_dtype: bfloat16\n\n\n  checkpoint:\n    save_dir: ${launcher.experiment_log_dir}/checkpoints\n    save_freq: 0  # 0 only last checkpoint is saved.\n\n\n  logging:\n    tensorboard_writer:\n      _target_: sam3.train.utils.logger.make_tensorboard_logger\n      log_dir: ${launcher.experiment_log_dir}/tensorboard\n      flush_secs: 120\n      should_log: True\n    wandb_writer: null\n    log_dir: ${launcher.experiment_log_dir}/logs/\n    log_freq: 10\n\n# ============================================================================\n# Launcher and Submitit Configuration\n# ============================================================================\n\nlauncher:\n  num_nodes: 8\n  gpus_per_node: 8\n  experiment_log_dir: ${paths.experiment_log_dir}\n  multiprocessing_context: forkserver\n\nsubmitit:\n  account: null\n  partition: null\n  qos: null\n  timeout_hour: 72\n  use_cluster: True\n  cpus_per_task: 10\n  port_range: [10000, 65000]\n  constraint: null\n"
  },
  {
    "path": "sam3/train/configs/saco_video_evals/saco_veval_yt1b_test.yaml",
    "content": "# @package _global_\ndefaults:\n  - _self_\n\n# ============================================================================\n# Paths Configuration (Chage this to your own paths)\n# ============================================================================\npaths:\n\n  dump_file_name: saco_veval_yt1b_test\n  experiment_log_dir: <YOUR EXPERIMENET LOG_DIR>\n  ytvis_json: <YOUR_GT_PATH>/saco_veval_yt1b_test.json\n  ytvis_dir : <YOUR_VIDEO_JPG_DIR>\n  bpe_path: <BPE_PATH> # This should be under sam3/assets/bpe_simple_vocab_16e6.txt.gz\n  num_videos: null\n\n# ============================================================================\n# Different helper parameters and functions\n# ============================================================================\nscratch:\n  vid_mask_postprocessor:\n    _target_: sam3.eval.postprocessors.PostProcessNullOp\n\n  use_presence_eval: True\n\n  video_transforms_val:\n    - _target_: sam3.train.transforms.basic_for_api.ComposeAPI\n      transforms:\n        - _target_: sam3.train.transforms.segmentation.DecodeRle\n        # resize the image to 1024x1024 resolution\n        - _target_: sam3.train.transforms.basic_for_api.RandomResizeAPI\n          sizes: ${scratch.resolution}  # originally `resolution: 1024`\n          square: true\n          consistent_transform: true\n        - _target_: sam3.train.transforms.basic_for_api.ToTensorAPI\n        - _target_: sam3.train.transforms.basic_for_api.NormalizeAPI\n          mean: ${scratch.val_norm_mean}\n          std: ${scratch.val_norm_std}\n\n  # Model parameters\n  d_model: 256\n\n  # Image processing parameters\n  resolution: 1008\n\n  # Normalization parameters\n  train_norm_mean: [0.5, 0.5, 0.5]\n  train_norm_std: [0.5, 0.5, 0.5]\n  val_norm_mean: [0.5, 0.5, 0.5]\n  val_norm_std: [0.5, 0.5, 0.5]\n\n  val_batch_size: 1\n  num_val_workers: 0\n  max_data_epochs: 20\n  hybrid_repeats: 1\n  gather_pred_via_filesys: false\n\n\n# ============================================================================\n# Trainer Configuration\n# ============================================================================\n\ntrainer:\n  _target_: sam3.train.trainer.Trainer\n  skip_saving_ckpts: true\n  empty_gpu_mem_cache_after_eval: True\n  skip_first_val: True\n  max_epochs: ${scratch.max_data_epochs}\n  accelerator: cuda\n  seed_value: 123\n  val_epoch_freq: 10\n  mode: val\n\n  distributed:\n    backend: nccl\n    find_unused_parameters: True\n    gradient_as_bucket_view: True\n\n  loss:\n    all:\n      _target_: sam3.train.loss.sam3_loss.DummyLoss\n    default:\n      _target_: sam3.train.loss.sam3_loss.DummyLoss\n\n  data:\n    train: null\n    val:\n      _target_: sam3.train.data.torch_dataset.TorchDataset\n      dataset:\n        _target_: sam3.train.data.sam3_video_dataset.VideoGroundingDataset\n        limit_ids: ${paths.num_videos}\n        img_folder: ${paths.ytvis_dir}\n        ann_file: ${paths.ytvis_json}\n        coco_json_loader:\n          _target_: sam3.train.data.coco_json_loaders.SAM3_VEVAL_API_FROM_JSON_NP\n          _partial_: true\n\n        transforms: ${scratch.video_transforms_val}\n        max_ann_per_img: 100000  # filtered in transforms\n        max_val_queries: 100000\n        multiplier: 1\n        load_segmentation: true\n        training: false\n\n\n      shuffle: False\n      batch_size: ${scratch.val_batch_size}\n      num_workers: ${scratch.num_val_workers}\n      pin_memory: True\n      drop_last: False\n      collate_fn:\n        _target_: sam3.train.data.collator.collate_fn_api\n        _partial_: true\n        repeats: ${scratch.hybrid_repeats}\n        dict_key: ytvis_val\n        with_seg_masks: true\n\n\n  model:\n    _target_: sam3.model_builder.build_sam3_video_model\n    bpe_path: ${paths.bpe_path}\n    has_presence_token: True\n    geo_encoder_use_img_cross_attn: True\n    apply_temporal_disambiguation: True\n\n  meters:\n    val:\n      ytvis_val:\n        pred_file: # key\n          _target_: sam3.eval.ytvis_eval.YTVISResultsWriter\n          dump_file: ${launcher.experiment_log_dir}/preds/${paths.dump_file_name}.json\n          postprocessor: ${scratch.vid_mask_postprocessor}\n          gather_pred_via_filesys: ${scratch.gather_pred_via_filesys}\n\n  optim:\n    amp:\n      enabled: True\n      amp_dtype: bfloat16\n\n\n  checkpoint:\n    save_dir: ${launcher.experiment_log_dir}/checkpoints\n    save_freq: 0  # 0 only last checkpoint is saved.\n\n\n  logging:\n    tensorboard_writer:\n      _target_: sam3.train.utils.logger.make_tensorboard_logger\n      log_dir: ${launcher.experiment_log_dir}/tensorboard\n      flush_secs: 120\n      should_log: True\n    wandb_writer: null\n    log_dir: ${launcher.experiment_log_dir}/logs/\n    log_freq: 10\n\n# ============================================================================\n# Launcher and Submitit Configuration\n# ============================================================================\n\nlauncher:\n  num_nodes: 8\n  gpus_per_node: 8\n  experiment_log_dir: ${paths.experiment_log_dir}\n  multiprocessing_context: forkserver\n\nsubmitit:\n  account: null\n  partition: null\n  qos: null\n  timeout_hour: 72\n  use_cluster: True\n  cpus_per_task: 10\n  port_range: [10000, 65000]\n  constraint: null\n"
  },
  {
    "path": "sam3/train/configs/saco_video_evals/saco_veval_yt1b_test_noheur.yaml",
    "content": "# @package _global_\ndefaults:\n  - _self_\n\n# ============================================================================\n# Paths Configuration (Chage this to your own paths)\n# ============================================================================\npaths:\n\n  dump_file_name: saco_veval_yt1b_test\n  experiment_log_dir: <YOUR EXPERIMENET LOG_DIR>\n  ytvis_json: <YOUR_GT_PATH>/saco_veval_yt1b_test.json\n  ytvis_dir : <YOUR_VIDEO_JPG_DIR>\n  bpe_path: <BPE_PATH> # This should be under sam3/assets/bpe_simple_vocab_16e6.txt.gz\n  num_videos: null\n\n# ============================================================================\n# Different helper parameters and functions\n# ============================================================================\nscratch:\n  vid_mask_postprocessor:\n    _target_: sam3.eval.postprocessors.PostProcessNullOp\n\n  use_presence_eval: True\n\n  video_transforms_val:\n    - _target_: sam3.train.transforms.basic_for_api.ComposeAPI\n      transforms:\n        - _target_: sam3.train.transforms.segmentation.DecodeRle\n        # resize the image to 1024x1024 resolution\n        - _target_: sam3.train.transforms.basic_for_api.RandomResizeAPI\n          sizes: ${scratch.resolution}  # originally `resolution: 1024`\n          square: true\n          consistent_transform: true\n        - _target_: sam3.train.transforms.basic_for_api.ToTensorAPI\n        - _target_: sam3.train.transforms.basic_for_api.NormalizeAPI\n          mean: ${scratch.val_norm_mean}\n          std: ${scratch.val_norm_std}\n\n  # Model parameters\n  d_model: 256\n\n  # Image processing parameters\n  resolution: 1008\n\n  # Normalization parameters\n  train_norm_mean: [0.5, 0.5, 0.5]\n  train_norm_std: [0.5, 0.5, 0.5]\n  val_norm_mean: [0.5, 0.5, 0.5]\n  val_norm_std: [0.5, 0.5, 0.5]\n\n  val_batch_size: 1\n  num_val_workers: 0\n  max_data_epochs: 20\n  hybrid_repeats: 1\n  gather_pred_via_filesys: false\n\n\n# ============================================================================\n# Trainer Configuration\n# ============================================================================\n\ntrainer:\n  _target_: sam3.train.trainer.Trainer\n  skip_saving_ckpts: true\n  empty_gpu_mem_cache_after_eval: True\n  skip_first_val: True\n  max_epochs: ${scratch.max_data_epochs}\n  accelerator: cuda\n  seed_value: 123\n  val_epoch_freq: 10\n  mode: val\n\n  distributed:\n    backend: nccl\n    find_unused_parameters: True\n    gradient_as_bucket_view: True\n\n  loss:\n    all:\n      _target_: sam3.train.loss.sam3_loss.DummyLoss\n    default:\n      _target_: sam3.train.loss.sam3_loss.DummyLoss\n\n  data:\n    train: null\n    val:\n      _target_: sam3.train.data.torch_dataset.TorchDataset\n      dataset:\n        _target_: sam3.train.data.sam3_video_dataset.VideoGroundingDataset\n        limit_ids: ${paths.num_videos}\n        img_folder: ${paths.ytvis_dir}\n        ann_file: ${paths.ytvis_json}\n        coco_json_loader:\n          _target_: sam3.train.data.coco_json_loaders.SAM3_VEVAL_API_FROM_JSON_NP\n          _partial_: true\n\n        transforms: ${scratch.video_transforms_val}\n        max_ann_per_img: 100000  # filtered in transforms\n        max_val_queries: 100000\n        multiplier: 1\n        load_segmentation: true\n        training: false\n\n\n      shuffle: False\n      batch_size: ${scratch.val_batch_size}\n      num_workers: ${scratch.num_val_workers}\n      pin_memory: True\n      drop_last: False\n      collate_fn:\n        _target_: sam3.train.data.collator.collate_fn_api\n        _partial_: true\n        repeats: ${scratch.hybrid_repeats}\n        dict_key: ytvis_val\n        with_seg_masks: true\n\n\n  model:\n    _target_: sam3.model_builder.build_sam3_video_model\n    bpe_path: ${paths.bpe_path}\n    has_presence_token: True\n    geo_encoder_use_img_cross_attn: True\n    apply_temporal_disambiguation: False\n\n  meters:\n    val:\n      ytvis_val:\n        pred_file: # key\n          _target_: sam3.eval.ytvis_eval.YTVISResultsWriter\n          dump_file: ${launcher.experiment_log_dir}/preds/${paths.dump_file_name}.json\n          postprocessor: ${scratch.vid_mask_postprocessor}\n          gather_pred_via_filesys: ${scratch.gather_pred_via_filesys}\n\n  optim:\n    amp:\n      enabled: True\n      amp_dtype: bfloat16\n\n\n  checkpoint:\n    save_dir: ${launcher.experiment_log_dir}/checkpoints\n    save_freq: 0  # 0 only last checkpoint is saved.\n\n\n  logging:\n    tensorboard_writer:\n      _target_: sam3.train.utils.logger.make_tensorboard_logger\n      log_dir: ${launcher.experiment_log_dir}/tensorboard\n      flush_secs: 120\n      should_log: True\n    wandb_writer: null\n    log_dir: ${launcher.experiment_log_dir}/logs/\n    log_freq: 10\n\n# ============================================================================\n# Launcher and Submitit Configuration\n# ============================================================================\n\nlauncher:\n  num_nodes: 8\n  gpus_per_node: 8\n  experiment_log_dir: ${paths.experiment_log_dir}\n  multiprocessing_context: forkserver\n\nsubmitit:\n  account: null\n  partition: null\n  qos: null\n  timeout_hour: 72\n  use_cluster: True\n  cpus_per_task: 10\n  port_range: [10000, 65000]\n  constraint: null\n"
  },
  {
    "path": "sam3/train/configs/saco_video_evals/saco_veval_yt1b_val.yaml",
    "content": "# @package _global_\ndefaults:\n  - _self_\n\n# ============================================================================\n# Paths Configuration (Chage this to your own paths)\n# ============================================================================\npaths:\n\n  dump_file_name: saco_veval_yt1b_val\n  experiment_log_dir: <YOUR EXPERIMENET LOG_DIR>\n  ytvis_json: <YOUR_GT_PATH>/saco_veval_yt1b_val.json\n  ytvis_dir : <YOUR_VIDEO_JPG_DIR>\n  bpe_path: <BPE_PATH> # This should be under sam3/assets/bpe_simple_vocab_16e6.txt.gz\n  num_videos: null\n\n# ============================================================================\n# Different helper parameters and functions\n# ============================================================================\nscratch:\n  vid_mask_postprocessor:\n    _target_: sam3.eval.postprocessors.PostProcessNullOp\n\n  use_presence_eval: True\n\n  video_transforms_val:\n    - _target_: sam3.train.transforms.basic_for_api.ComposeAPI\n      transforms:\n        - _target_: sam3.train.transforms.segmentation.DecodeRle\n        # resize the image to 1024x1024 resolution\n        - _target_: sam3.train.transforms.basic_for_api.RandomResizeAPI\n          sizes: ${scratch.resolution}  # originally `resolution: 1024`\n          square: true\n          consistent_transform: true\n        - _target_: sam3.train.transforms.basic_for_api.ToTensorAPI\n        - _target_: sam3.train.transforms.basic_for_api.NormalizeAPI\n          mean: ${scratch.val_norm_mean}\n          std: ${scratch.val_norm_std}\n\n  # Model parameters\n  d_model: 256\n\n  # Image processing parameters\n  resolution: 1008\n\n  # Normalization parameters\n  train_norm_mean: [0.5, 0.5, 0.5]\n  train_norm_std: [0.5, 0.5, 0.5]\n  val_norm_mean: [0.5, 0.5, 0.5]\n  val_norm_std: [0.5, 0.5, 0.5]\n\n  val_batch_size: 1\n  num_val_workers: 0\n  max_data_epochs: 20\n  hybrid_repeats: 1\n  gather_pred_via_filesys: false\n\n\n# ============================================================================\n# Trainer Configuration\n# ============================================================================\n\ntrainer:\n  _target_: sam3.train.trainer.Trainer\n  skip_saving_ckpts: true\n  empty_gpu_mem_cache_after_eval: True\n  skip_first_val: True\n  max_epochs: ${scratch.max_data_epochs}\n  accelerator: cuda\n  seed_value: 123\n  val_epoch_freq: 10\n  mode: val\n\n  distributed:\n    backend: nccl\n    find_unused_parameters: True\n    gradient_as_bucket_view: True\n\n  loss:\n    all:\n      _target_: sam3.train.loss.sam3_loss.DummyLoss\n    default:\n      _target_: sam3.train.loss.sam3_loss.DummyLoss\n\n  data:\n    train: null\n    val:\n      _target_: sam3.train.data.torch_dataset.TorchDataset\n      dataset:\n        _target_: sam3.train.data.sam3_video_dataset.VideoGroundingDataset\n        limit_ids: ${paths.num_videos}\n        img_folder: ${paths.ytvis_dir}\n        ann_file: ${paths.ytvis_json}\n        coco_json_loader:\n          _target_: sam3.train.data.coco_json_loaders.SAM3_VEVAL_API_FROM_JSON_NP\n          _partial_: true\n\n        transforms: ${scratch.video_transforms_val}\n        max_ann_per_img: 100000  # filtered in transforms\n        max_val_queries: 100000\n        multiplier: 1\n        load_segmentation: true\n        training: false\n\n\n      shuffle: False\n      batch_size: ${scratch.val_batch_size}\n      num_workers: ${scratch.num_val_workers}\n      pin_memory: True\n      drop_last: False\n      collate_fn:\n        _target_: sam3.train.data.collator.collate_fn_api\n        _partial_: true\n        repeats: ${scratch.hybrid_repeats}\n        dict_key: ytvis_val\n        with_seg_masks: true\n\n\n  model:\n    _target_: sam3.model_builder.build_sam3_video_model\n    bpe_path: ${paths.bpe_path}\n    has_presence_token: True\n    geo_encoder_use_img_cross_attn: True\n    apply_temporal_disambiguation: True\n\n  meters:\n    val:\n      ytvis_val:\n        pred_file: # key\n          _target_: sam3.eval.ytvis_eval.YTVISResultsWriter\n          dump_file: ${launcher.experiment_log_dir}/preds/${paths.dump_file_name}.json\n          postprocessor: ${scratch.vid_mask_postprocessor}\n          gather_pred_via_filesys: ${scratch.gather_pred_via_filesys}\n\n  optim:\n    amp:\n      enabled: True\n      amp_dtype: bfloat16\n\n\n  checkpoint:\n    save_dir: ${launcher.experiment_log_dir}/checkpoints\n    save_freq: 0  # 0 only last checkpoint is saved.\n\n\n  logging:\n    tensorboard_writer:\n      _target_: sam3.train.utils.logger.make_tensorboard_logger\n      log_dir: ${launcher.experiment_log_dir}/tensorboard\n      flush_secs: 120\n      should_log: True\n    wandb_writer: null\n    log_dir: ${launcher.experiment_log_dir}/logs/\n    log_freq: 10\n\n# ============================================================================\n# Launcher and Submitit Configuration\n# ============================================================================\n\nlauncher:\n  num_nodes: 8\n  gpus_per_node: 8\n  experiment_log_dir: ${paths.experiment_log_dir}\n  multiprocessing_context: forkserver\n\nsubmitit:\n  account: null\n  partition: null\n  qos: null\n  timeout_hour: 72\n  use_cluster: True\n  cpus_per_task: 10\n  port_range: [10000, 65000]\n  constraint: null\n"
  },
  {
    "path": "sam3/train/configs/saco_video_evals/saco_veval_yt1b_val_noheur.yaml",
    "content": "# @package _global_\ndefaults:\n  - _self_\n\n# ============================================================================\n# Paths Configuration (Chage this to your own paths)\n# ============================================================================\npaths:\n\n  dump_file_name: saco_veval_yt1b_val\n  experiment_log_dir: <YOUR EXPERIMENET LOG_DIR>\n  ytvis_json: <YOUR_GT_PATH>/saco_veval_yt1b_val.json\n  ytvis_dir : <YOUR_VIDEO_JPG_DIR>\n  bpe_path: <BPE_PATH> # This should be under sam3/assets/bpe_simple_vocab_16e6.txt.gz\n  num_videos: null\n\n# ============================================================================\n# Different helper parameters and functions\n# ============================================================================\nscratch:\n  vid_mask_postprocessor:\n    _target_: sam3.eval.postprocessors.PostProcessNullOp\n\n  use_presence_eval: True\n\n  video_transforms_val:\n    - _target_: sam3.train.transforms.basic_for_api.ComposeAPI\n      transforms:\n        - _target_: sam3.train.transforms.segmentation.DecodeRle\n        # resize the image to 1024x1024 resolution\n        - _target_: sam3.train.transforms.basic_for_api.RandomResizeAPI\n          sizes: ${scratch.resolution}  # originally `resolution: 1024`\n          square: true\n          consistent_transform: true\n        - _target_: sam3.train.transforms.basic_for_api.ToTensorAPI\n        - _target_: sam3.train.transforms.basic_for_api.NormalizeAPI\n          mean: ${scratch.val_norm_mean}\n          std: ${scratch.val_norm_std}\n\n  # Model parameters\n  d_model: 256\n\n  # Image processing parameters\n  resolution: 1008\n\n  # Normalization parameters\n  train_norm_mean: [0.5, 0.5, 0.5]\n  train_norm_std: [0.5, 0.5, 0.5]\n  val_norm_mean: [0.5, 0.5, 0.5]\n  val_norm_std: [0.5, 0.5, 0.5]\n\n  val_batch_size: 1\n  num_val_workers: 0\n  max_data_epochs: 20\n  hybrid_repeats: 1\n  gather_pred_via_filesys: false\n\n\n# ============================================================================\n# Trainer Configuration\n# ============================================================================\n\ntrainer:\n  _target_: sam3.train.trainer.Trainer\n  skip_saving_ckpts: true\n  empty_gpu_mem_cache_after_eval: True\n  skip_first_val: True\n  max_epochs: ${scratch.max_data_epochs}\n  accelerator: cuda\n  seed_value: 123\n  val_epoch_freq: 10\n  mode: val\n\n  distributed:\n    backend: nccl\n    find_unused_parameters: True\n    gradient_as_bucket_view: True\n\n  loss:\n    all:\n      _target_: sam3.train.loss.sam3_loss.DummyLoss\n    default:\n      _target_: sam3.train.loss.sam3_loss.DummyLoss\n\n  data:\n    train: null\n    val:\n      _target_: sam3.train.data.torch_dataset.TorchDataset\n      dataset:\n        _target_: sam3.train.data.sam3_video_dataset.VideoGroundingDataset\n        limit_ids: ${paths.num_videos}\n        img_folder: ${paths.ytvis_dir}\n        ann_file: ${paths.ytvis_json}\n        coco_json_loader:\n          _target_: sam3.train.data.coco_json_loaders.SAM3_VEVAL_API_FROM_JSON_NP\n          _partial_: true\n\n        transforms: ${scratch.video_transforms_val}\n        max_ann_per_img: 100000  # filtered in transforms\n        max_val_queries: 100000\n        multiplier: 1\n        load_segmentation: true\n        training: false\n\n\n      shuffle: False\n      batch_size: ${scratch.val_batch_size}\n      num_workers: ${scratch.num_val_workers}\n      pin_memory: True\n      drop_last: False\n      collate_fn:\n        _target_: sam3.train.data.collator.collate_fn_api\n        _partial_: true\n        repeats: ${scratch.hybrid_repeats}\n        dict_key: ytvis_val\n        with_seg_masks: true\n\n\n  model:\n    _target_: sam3.model_builder.build_sam3_video_model\n    bpe_path: ${paths.bpe_path}\n    has_presence_token: True\n    geo_encoder_use_img_cross_attn: True\n    apply_temporal_disambiguation: False\n\n  meters:\n    val:\n      ytvis_val:\n        pred_file: # key\n          _target_: sam3.eval.ytvis_eval.YTVISResultsWriter\n          dump_file: ${launcher.experiment_log_dir}/preds/${paths.dump_file_name}.json\n          postprocessor: ${scratch.vid_mask_postprocessor}\n          gather_pred_via_filesys: ${scratch.gather_pred_via_filesys}\n\n  optim:\n    amp:\n      enabled: True\n      amp_dtype: bfloat16\n\n\n  checkpoint:\n    save_dir: ${launcher.experiment_log_dir}/checkpoints\n    save_freq: 0  # 0 only last checkpoint is saved.\n\n\n  logging:\n    tensorboard_writer:\n      _target_: sam3.train.utils.logger.make_tensorboard_logger\n      log_dir: ${launcher.experiment_log_dir}/tensorboard\n      flush_secs: 120\n      should_log: True\n    wandb_writer: null\n    log_dir: ${launcher.experiment_log_dir}/logs/\n    log_freq: 10\n\n# ============================================================================\n# Launcher and Submitit Configuration\n# ============================================================================\n\nlauncher:\n  num_nodes: 8\n  gpus_per_node: 8\n  experiment_log_dir: ${paths.experiment_log_dir}\n  multiprocessing_context: forkserver\n\nsubmitit:\n  account: null\n  partition: null\n  qos: null\n  timeout_hour: 72\n  use_cluster: True\n  cpus_per_task: 10\n  port_range: [10000, 65000]\n  constraint: null\n"
  },
  {
    "path": "sam3/train/configs/silver_image_evals/sam3_silver_image_bdd100k.yaml",
    "content": "# @package _global_\ndefaults:\n  - /configs/eval_base.yaml\n  - _self_\n\n# ============================================================================\n# Paths Configuration (you can override here, but it shouldn't require further changes if eval_base.yaml is correct\n# ============================================================================\npaths:\n  experiment_log_dir: ${paths.base_experiment_log_dir}/silver_bdd100k/\n  coco_gt: ${paths.base_annotation_path_silver}/silver_bdd100k_merged_test.json\n  img_path: ${paths.silver_img_path}/bdd100k/\n\n\n\n# ============================================================================\n# Trainer Configuration\n# ============================================================================\n\ntrainer:\n  data:\n    val:\n      _target_: sam3.train.data.torch_dataset.TorchDataset\n      dataset:\n        _target_: sam3.train.data.sam3_image_dataset.Sam3ImageDataset\n        coco_json_loader:\n          _target_: sam3.train.data.coco_json_loaders.SAM3_EVAL_API_FROM_JSON_NP\n          _partial_: true\n        img_folder: ${paths.img_path}\n        ann_file: ${paths.coco_gt}\n        transforms: ${scratch.base_val_transform}\n        max_ann_per_img: 100000\n        multiplier: 1\n        training: false\n\n      shuffle: False\n      batch_size: ${scratch.val_batch_size}\n      num_workers: ${scratch.num_val_workers}\n      pin_memory: False\n      drop_last: False\n      collate_fn:\n        _target_: sam3.train.data.collator.collate_fn_api\n        _partial_: true\n        repeats: ${scratch.hybrid_repeats}\n        dict_key: silver_bdd100k\n\n  meters:\n    val:\n      silver_bdd100k: # this key matches the \"dict_key\" in the dataloader's collate function\n        cgf1:\n          _target_: sam3.eval.coco_writer.PredictionDumper\n          iou_type: \"segm\"\n          dump_dir: ${launcher.experiment_log_dir}/dumps/silver_bdd100k\n          merge_predictions: True\n          postprocessor: ${scratch.mask_postprocessor_thresholded}\n          gather_pred_via_filesys: ${scratch.gather_pred_via_filesys}\n          maxdets: 1000000 # no limit\n          pred_file_evaluators:\n            - _target_: sam3.eval.cgf1_eval.CGF1Evaluator\n              gt_path: ${paths.coco_gt}\n              iou_type: \"bbox\"\n            - _target_: sam3.eval.cgf1_eval.CGF1Evaluator\n              gt_path: ${paths.coco_gt}\n              iou_type: \"segm\"\n"
  },
  {
    "path": "sam3/train/configs/silver_image_evals/sam3_silver_image_droid.yaml",
    "content": "# @package _global_\ndefaults:\n  - /configs/eval_base.yaml\n  - _self_\n\n# ============================================================================\n# Paths Configuration (you can override here, but it shouldn't require further changes if eval_base.yaml is correct\n# ============================================================================\npaths:\n  experiment_log_dir: ${paths.base_experiment_log_dir}/silver_droid/\n  coco_gt: ${paths.base_annotation_path_silver}/silver_droid_merged_test.json\n  img_path: ${paths.silver_img_path}/droid/\n\n\n\n# ============================================================================\n# Trainer Configuration\n# ============================================================================\n\ntrainer:\n  data:\n    val:\n      _target_: sam3.train.data.torch_dataset.TorchDataset\n      dataset:\n        _target_: sam3.train.data.sam3_image_dataset.Sam3ImageDataset\n        coco_json_loader:\n          _target_: sam3.train.data.coco_json_loaders.SAM3_EVAL_API_FROM_JSON_NP\n          _partial_: true\n        img_folder: ${paths.img_path}\n        ann_file: ${paths.coco_gt}\n        transforms: ${scratch.base_val_transform}\n        max_ann_per_img: 100000\n        multiplier: 1\n        training: false\n\n      shuffle: False\n      batch_size: ${scratch.val_batch_size}\n      num_workers: ${scratch.num_val_workers}\n      pin_memory: False\n      drop_last: False\n      collate_fn:\n        _target_: sam3.train.data.collator.collate_fn_api\n        _partial_: true\n        repeats: ${scratch.hybrid_repeats}\n        dict_key: silver_droid\n\n  meters:\n    val:\n      silver_droid: # this key matches the \"dict_key\" in the dataloader's collate function\n        cgf1:\n          _target_: sam3.eval.coco_writer.PredictionDumper\n          iou_type: \"segm\"\n          dump_dir: ${launcher.experiment_log_dir}/dumps/silver_droid\n          merge_predictions: True\n          postprocessor: ${scratch.mask_postprocessor_thresholded}\n          gather_pred_via_filesys: ${scratch.gather_pred_via_filesys}\n          maxdets: 1000000 # no limit\n          pred_file_evaluators:\n            - _target_: sam3.eval.cgf1_eval.CGF1Evaluator\n              gt_path: ${paths.coco_gt}\n              iou_type: \"bbox\"\n            - _target_: sam3.eval.cgf1_eval.CGF1Evaluator\n              gt_path: ${paths.coco_gt}\n              iou_type: \"segm\"\n"
  },
  {
    "path": "sam3/train/configs/silver_image_evals/sam3_silver_image_ego4d.yaml",
    "content": "# @package _global_\ndefaults:\n  - /configs/eval_base.yaml\n  - _self_\n\n# ============================================================================\n# Paths Configuration (you can override here, but it shouldn't require further changes if eval_base.yaml is correct\n# ============================================================================\npaths:\n  experiment_log_dir: ${paths.base_experiment_log_dir}/silver_ego4d/\n  coco_gt: ${paths.base_annotation_path_silver}/silver_ego4d_merged_test.json\n  img_path: ${paths.silver_img_path}/ego4d/\n\n\n\n# ============================================================================\n# Trainer Configuration\n# ============================================================================\n\ntrainer:\n  data:\n    val:\n      _target_: sam3.train.data.torch_dataset.TorchDataset\n      dataset:\n        _target_: sam3.train.data.sam3_image_dataset.Sam3ImageDataset\n        coco_json_loader:\n          _target_: sam3.train.data.coco_json_loaders.SAM3_EVAL_API_FROM_JSON_NP\n          _partial_: true\n        img_folder: ${paths.img_path}\n        ann_file: ${paths.coco_gt}\n        transforms: ${scratch.base_val_transform}\n        max_ann_per_img: 100000\n        multiplier: 1\n        training: false\n\n      shuffle: False\n      batch_size: ${scratch.val_batch_size}\n      num_workers: ${scratch.num_val_workers}\n      pin_memory: False\n      drop_last: False\n      collate_fn:\n        _target_: sam3.train.data.collator.collate_fn_api\n        _partial_: true\n        repeats: ${scratch.hybrid_repeats}\n        dict_key: silver_ego4d\n\n  meters:\n    val:\n      silver_ego4d: # this key matches the \"dict_key\" in the dataloader's collate function\n        cgf1:\n          _target_: sam3.eval.coco_writer.PredictionDumper\n          iou_type: \"segm\"\n          dump_dir: ${launcher.experiment_log_dir}/dumps/silver_ego4d\n          merge_predictions: True\n          postprocessor: ${scratch.mask_postprocessor_thresholded}\n          gather_pred_via_filesys: ${scratch.gather_pred_via_filesys}\n          maxdets: 1000000 # no limit\n          pred_file_evaluators:\n            - _target_: sam3.eval.cgf1_eval.CGF1Evaluator\n              gt_path: ${paths.coco_gt}\n              iou_type: \"bbox\"\n            - _target_: sam3.eval.cgf1_eval.CGF1Evaluator\n              gt_path: ${paths.coco_gt}\n              iou_type: \"segm\"\n"
  },
  {
    "path": "sam3/train/configs/silver_image_evals/sam3_silver_image_fathomnet.yaml",
    "content": "# @package _global_\ndefaults:\n  - /configs/eval_base.yaml\n  - _self_\n\n# ============================================================================\n# Paths Configuration (you can override here, but it shouldn't require further changes if eval_base.yaml is correct\n# ============================================================================\npaths:\n  experiment_log_dir: ${paths.base_experiment_log_dir}/silver_fathomnet/\n  coco_gt: ${paths.base_annotation_path_silver}/silver_fathomnet_test.json\n  img_path: ${paths.silver_img_path}/fathomnet/\n\n\n\n# ============================================================================\n# Trainer Configuration\n# ============================================================================\n\ntrainer:\n  data:\n    val:\n      _target_: sam3.train.data.torch_dataset.TorchDataset\n      dataset:\n        _target_: sam3.train.data.sam3_image_dataset.Sam3ImageDataset\n        coco_json_loader:\n          _target_: sam3.train.data.coco_json_loaders.SAM3_EVAL_API_FROM_JSON_NP\n          _partial_: true\n        img_folder: ${paths.img_path}\n        ann_file: ${paths.coco_gt}\n        transforms: ${scratch.base_val_transform}\n        max_ann_per_img: 100000\n        multiplier: 1\n        training: false\n\n      shuffle: False\n      batch_size: ${scratch.val_batch_size}\n      num_workers: ${scratch.num_val_workers}\n      pin_memory: False\n      drop_last: False\n      collate_fn:\n        _target_: sam3.train.data.collator.collate_fn_api\n        _partial_: true\n        repeats: ${scratch.hybrid_repeats}\n        dict_key: silver_fathomnet\n\n  meters:\n    val:\n      silver_fathomnet: # this key matches the \"dict_key\" in the dataloader's collate function\n        cgf1:\n          _target_: sam3.eval.coco_writer.PredictionDumper\n          iou_type: \"segm\"\n          dump_dir: ${launcher.experiment_log_dir}/dumps/silver_fathomnet\n          merge_predictions: True\n          postprocessor: ${scratch.mask_postprocessor_thresholded}\n          gather_pred_via_filesys: ${scratch.gather_pred_via_filesys}\n          maxdets: 1000000 # no limit\n          pred_file_evaluators:\n            - _target_: sam3.eval.cgf1_eval.CGF1Evaluator\n              gt_path: ${paths.coco_gt}\n              iou_type: \"bbox\"\n            - _target_: sam3.eval.cgf1_eval.CGF1Evaluator\n              gt_path: ${paths.coco_gt}\n              iou_type: \"segm\"\n"
  },
  {
    "path": "sam3/train/configs/silver_image_evals/sam3_silver_image_food_rec.yaml",
    "content": "# @package _global_\ndefaults:\n  - /configs/eval_base.yaml\n  - _self_\n\n# ============================================================================\n# Paths Configuration (you can override here, but it shouldn't require further changes if eval_base.yaml is correct\n# ============================================================================\npaths:\n  experiment_log_dir: ${paths.base_experiment_log_dir}/silver_food_rec/\n  coco_gt: ${paths.base_annotation_path_silver}/silver_food_rec_merged_test.json\n  img_path: ${paths.silver_img_path}/food_rec/\n\n\n\n# ============================================================================\n# Trainer Configuration\n# ============================================================================\n\ntrainer:\n  data:\n    val:\n      _target_: sam3.train.data.torch_dataset.TorchDataset\n      dataset:\n        _target_: sam3.train.data.sam3_image_dataset.Sam3ImageDataset\n        coco_json_loader:\n          _target_: sam3.train.data.coco_json_loaders.SAM3_EVAL_API_FROM_JSON_NP\n          _partial_: true\n        img_folder: ${paths.img_path}\n        ann_file: ${paths.coco_gt}\n        transforms: ${scratch.base_val_transform}\n        max_ann_per_img: 100000\n        multiplier: 1\n        training: false\n\n      shuffle: False\n      batch_size: ${scratch.val_batch_size}\n      num_workers: ${scratch.num_val_workers}\n      pin_memory: False\n      drop_last: False\n      collate_fn:\n        _target_: sam3.train.data.collator.collate_fn_api\n        _partial_: true\n        repeats: ${scratch.hybrid_repeats}\n        dict_key: silver_food_rec\n\n  meters:\n    val:\n      silver_food_rec: # this key matches the \"dict_key\" in the dataloader's collate function\n        cgf1:\n          _target_: sam3.eval.coco_writer.PredictionDumper\n          iou_type: \"segm\"\n          dump_dir: ${launcher.experiment_log_dir}/dumps/silver_food_rec\n          merge_predictions: True\n          postprocessor: ${scratch.mask_postprocessor_thresholded}\n          gather_pred_via_filesys: ${scratch.gather_pred_via_filesys}\n          maxdets: 1000000 # no limit\n          pred_file_evaluators:\n            - _target_: sam3.eval.cgf1_eval.CGF1Evaluator\n              gt_path: ${paths.coco_gt}\n              iou_type: \"bbox\"\n            - _target_: sam3.eval.cgf1_eval.CGF1Evaluator\n              gt_path: ${paths.coco_gt}\n              iou_type: \"segm\"\n"
  },
  {
    "path": "sam3/train/configs/silver_image_evals/sam3_silver_image_geode.yaml",
    "content": "# @package _global_\ndefaults:\n  - /configs/eval_base.yaml\n  - _self_\n\n# ============================================================================\n# Paths Configuration (you can override here, but it shouldn't require further changes if eval_base.yaml is correct\n# ============================================================================\npaths:\n  experiment_log_dir: ${paths.base_experiment_log_dir}/silver_geode/\n  coco_gt: ${paths.base_annotation_path_silver}/silver_geode_merged_test.json\n  img_path: ${paths.silver_img_path}/geode/\n\n\n\n# ============================================================================\n# Trainer Configuration\n# ============================================================================\n\ntrainer:\n  data:\n    val:\n      _target_: sam3.train.data.torch_dataset.TorchDataset\n      dataset:\n        _target_: sam3.train.data.sam3_image_dataset.Sam3ImageDataset\n        coco_json_loader:\n          _target_: sam3.train.data.coco_json_loaders.SAM3_EVAL_API_FROM_JSON_NP\n          _partial_: true\n        img_folder: ${paths.img_path}\n        ann_file: ${paths.coco_gt}\n        transforms: ${scratch.base_val_transform}\n        max_ann_per_img: 100000\n        multiplier: 1\n        training: false\n\n      shuffle: False\n      batch_size: ${scratch.val_batch_size}\n      num_workers: ${scratch.num_val_workers}\n      pin_memory: False\n      drop_last: False\n      collate_fn:\n        _target_: sam3.train.data.collator.collate_fn_api\n        _partial_: true\n        repeats: ${scratch.hybrid_repeats}\n        dict_key: silver_geode\n\n  meters:\n    val:\n      silver_geode: # this key matches the \"dict_key\" in the dataloader's collate function\n        cgf1:\n          _target_: sam3.eval.coco_writer.PredictionDumper\n          iou_type: \"segm\"\n          dump_dir: ${launcher.experiment_log_dir}/dumps/silver_geode\n          merge_predictions: True\n          postprocessor: ${scratch.mask_postprocessor_thresholded}\n          gather_pred_via_filesys: ${scratch.gather_pred_via_filesys}\n          maxdets: 1000000 # no limit\n          pred_file_evaluators:\n            - _target_: sam3.eval.cgf1_eval.CGF1Evaluator\n              gt_path: ${paths.coco_gt}\n              iou_type: \"bbox\"\n            - _target_: sam3.eval.cgf1_eval.CGF1Evaluator\n              gt_path: ${paths.coco_gt}\n              iou_type: \"segm\"\n"
  },
  {
    "path": "sam3/train/configs/silver_image_evals/sam3_silver_image_inaturalist.yaml",
    "content": "# @package _global_\ndefaults:\n  - /configs/eval_base.yaml\n  - _self_\n\n# ============================================================================\n# Paths Configuration (you can override here, but it shouldn't require further changes if eval_base.yaml is correct\n# ============================================================================\npaths:\n  experiment_log_dir: ${paths.base_experiment_log_dir}/silver_inaturalist/\n  coco_gt: ${paths.base_annotation_path_silver}/silver_inaturalist_merged_test.json\n  img_path: ${paths.silver_img_path}/inaturalist/\n\n\n\n# ============================================================================\n# Trainer Configuration\n# ============================================================================\n\ntrainer:\n  data:\n    val:\n      _target_: sam3.train.data.torch_dataset.TorchDataset\n      dataset:\n        _target_: sam3.train.data.sam3_image_dataset.Sam3ImageDataset\n        coco_json_loader:\n          _target_: sam3.train.data.coco_json_loaders.SAM3_EVAL_API_FROM_JSON_NP\n          _partial_: true\n        img_folder: ${paths.img_path}\n        ann_file: ${paths.coco_gt}\n        transforms: ${scratch.base_val_transform}\n        max_ann_per_img: 100000\n        multiplier: 1\n        training: false\n\n      shuffle: False\n      batch_size: ${scratch.val_batch_size}\n      num_workers: ${scratch.num_val_workers}\n      pin_memory: False\n      drop_last: False\n      collate_fn:\n        _target_: sam3.train.data.collator.collate_fn_api\n        _partial_: true\n        repeats: ${scratch.hybrid_repeats}\n        dict_key: silver_inaturalist\n\n  meters:\n    val:\n      silver_inaturalist: # this key matches the \"dict_key\" in the dataloader's collate function\n        cgf1:\n          _target_: sam3.eval.coco_writer.PredictionDumper\n          iou_type: \"segm\"\n          dump_dir: ${launcher.experiment_log_dir}/dumps/silver_inaturalist\n          merge_predictions: True\n          postprocessor: ${scratch.mask_postprocessor_thresholded}\n          gather_pred_via_filesys: ${scratch.gather_pred_via_filesys}\n          maxdets: 1000000 # no limit\n          pred_file_evaluators:\n            - _target_: sam3.eval.cgf1_eval.CGF1Evaluator\n              gt_path: ${paths.coco_gt}\n              iou_type: \"bbox\"\n            - _target_: sam3.eval.cgf1_eval.CGF1Evaluator\n              gt_path: ${paths.coco_gt}\n              iou_type: \"segm\"\n"
  },
  {
    "path": "sam3/train/configs/silver_image_evals/sam3_silver_image_nga.yaml",
    "content": "# @package _global_\ndefaults:\n  - /configs/eval_base.yaml\n  - _self_\n\n# ============================================================================\n# Paths Configuration (you can override here, but it shouldn't require further changes if eval_base.yaml is correct\n# ============================================================================\npaths:\n  experiment_log_dir: ${paths.base_experiment_log_dir}/silver_nga_art/\n  coco_gt: ${paths.base_annotation_path_silver}/silver_nga_art_merged_test.json\n  img_path: ${paths.silver_img_path}/nga/\n\n\n\n# ============================================================================\n# Trainer Configuration\n# ============================================================================\n\ntrainer:\n  data:\n    val:\n      _target_: sam3.train.data.torch_dataset.TorchDataset\n      dataset:\n        _target_: sam3.train.data.sam3_image_dataset.Sam3ImageDataset\n        coco_json_loader:\n          _target_: sam3.train.data.coco_json_loaders.SAM3_EVAL_API_FROM_JSON_NP\n          _partial_: true\n        img_folder: ${paths.img_path}\n        ann_file: ${paths.coco_gt}\n        transforms: ${scratch.base_val_transform}\n        max_ann_per_img: 100000\n        multiplier: 1\n        training: false\n\n      shuffle: False\n      batch_size: ${scratch.val_batch_size}\n      num_workers: ${scratch.num_val_workers}\n      pin_memory: False\n      drop_last: False\n      collate_fn:\n        _target_: sam3.train.data.collator.collate_fn_api\n        _partial_: true\n        repeats: ${scratch.hybrid_repeats}\n        dict_key: silver_nga_art\n\n  meters:\n    val:\n      silver_nga_art: # this key matches the \"dict_key\" in the dataloader's collate function\n        cgf1:\n          _target_: sam3.eval.coco_writer.PredictionDumper\n          iou_type: \"segm\"\n          dump_dir: ${launcher.experiment_log_dir}/dumps/silver_nga_art\n          merge_predictions: True\n          postprocessor: ${scratch.mask_postprocessor_thresholded}\n          gather_pred_via_filesys: ${scratch.gather_pred_via_filesys}\n          maxdets: 1000000 # no limit\n          pred_file_evaluators:\n            - _target_: sam3.eval.cgf1_eval.CGF1Evaluator\n              gt_path: ${paths.coco_gt}\n              iou_type: \"bbox\"\n            - _target_: sam3.eval.cgf1_eval.CGF1Evaluator\n              gt_path: ${paths.coco_gt}\n              iou_type: \"segm\"\n"
  },
  {
    "path": "sam3/train/configs/silver_image_evals/sam3_silver_image_sav.yaml",
    "content": "# @package _global_\ndefaults:\n  - /configs/eval_base.yaml\n  - _self_\n\n# ============================================================================\n# Paths Configuration (you can override here, but it shouldn't require further changes if eval_base.yaml is correct\n# ============================================================================\npaths:\n  experiment_log_dir: ${paths.base_experiment_log_dir}/silver_sav/\n  coco_gt: ${paths.base_annotation_path_silver}/silver_sav_merged_test.json\n  img_path: ${paths.silver_img_path}/sav/\n\n\n\n# ============================================================================\n# Trainer Configuration\n# ============================================================================\n\ntrainer:\n  data:\n    val:\n      _target_: sam3.train.data.torch_dataset.TorchDataset\n      dataset:\n        _target_: sam3.train.data.sam3_image_dataset.Sam3ImageDataset\n        coco_json_loader:\n          _target_: sam3.train.data.coco_json_loaders.SAM3_EVAL_API_FROM_JSON_NP\n          _partial_: true\n        img_folder: ${paths.img_path}\n        ann_file: ${paths.coco_gt}\n        transforms: ${scratch.base_val_transform}\n        max_ann_per_img: 100000\n        multiplier: 1\n        training: false\n\n      shuffle: False\n      batch_size: ${scratch.val_batch_size}\n      num_workers: ${scratch.num_val_workers}\n      pin_memory: False\n      drop_last: False\n      collate_fn:\n        _target_: sam3.train.data.collator.collate_fn_api\n        _partial_: true\n        repeats: ${scratch.hybrid_repeats}\n        dict_key: silver_sav\n\n  meters:\n    val:\n      silver_sav: # this key matches the \"dict_key\" in the dataloader's collate function\n        cgf1:\n          _target_: sam3.eval.coco_writer.PredictionDumper\n          iou_type: \"segm\"\n          dump_dir: ${launcher.experiment_log_dir}/dumps/silver_sav\n          merge_predictions: True\n          postprocessor: ${scratch.mask_postprocessor_thresholded}\n          gather_pred_via_filesys: ${scratch.gather_pred_via_filesys}\n          maxdets: 1000000 # no limit\n          pred_file_evaluators:\n            - _target_: sam3.eval.cgf1_eval.CGF1Evaluator\n              gt_path: ${paths.coco_gt}\n              iou_type: \"bbox\"\n            - _target_: sam3.eval.cgf1_eval.CGF1Evaluator\n              gt_path: ${paths.coco_gt}\n              iou_type: \"segm\"\n"
  },
  {
    "path": "sam3/train/configs/silver_image_evals/sam3_silver_image_yt1b.yaml",
    "content": "# @package _global_\ndefaults:\n  - /configs/eval_base.yaml\n  - _self_\n\n# ============================================================================\n# Paths Configuration (you can override here, but it shouldn't require further changes if eval_base.yaml is correct\n# ============================================================================\npaths:\n  experiment_log_dir: ${paths.base_experiment_log_dir}/silver_yt1b/\n  coco_gt: ${paths.base_annotation_path_silver}/silver_yt1b_merged_test.json\n  img_path: ${paths.silver_img_path}/yt1b/\n\n\n\n# ============================================================================\n# Trainer Configuration\n# ============================================================================\n\ntrainer:\n  data:\n    val:\n      _target_: sam3.train.data.torch_dataset.TorchDataset\n      dataset:\n        _target_: sam3.train.data.sam3_image_dataset.Sam3ImageDataset\n        coco_json_loader:\n          _target_: sam3.train.data.coco_json_loaders.SAM3_EVAL_API_FROM_JSON_NP\n          _partial_: true\n        img_folder: ${paths.img_path}\n        ann_file: ${paths.coco_gt}\n        transforms: ${scratch.base_val_transform}\n        max_ann_per_img: 100000\n        multiplier: 1\n        training: false\n\n      shuffle: False\n      batch_size: ${scratch.val_batch_size}\n      num_workers: ${scratch.num_val_workers}\n      pin_memory: False\n      drop_last: False\n      collate_fn:\n        _target_: sam3.train.data.collator.collate_fn_api\n        _partial_: true\n        repeats: ${scratch.hybrid_repeats}\n        dict_key: silver_yt1b\n\n  meters:\n    val:\n      silver_yt1b: # this key matches the \"dict_key\" in the dataloader's collate function\n        cgf1:\n          _target_: sam3.eval.coco_writer.PredictionDumper\n          iou_type: \"segm\"\n          dump_dir: ${launcher.experiment_log_dir}/dumps/silver_yt1b\n          merge_predictions: True\n          postprocessor: ${scratch.mask_postprocessor_thresholded}\n          gather_pred_via_filesys: ${scratch.gather_pred_via_filesys}\n          maxdets: 1000000 # no limit\n          pred_file_evaluators:\n            - _target_: sam3.eval.cgf1_eval.CGF1Evaluator\n              gt_path: ${paths.coco_gt}\n              iou_type: \"bbox\"\n            - _target_: sam3.eval.cgf1_eval.CGF1Evaluator\n              gt_path: ${paths.coco_gt}\n              iou_type: \"segm\"\n"
  },
  {
    "path": "sam3/train/data/__init__.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n"
  },
  {
    "path": "sam3/train/data/coco_json_loaders.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n\nimport json\nfrom collections import defaultdict\nfrom typing import Dict, List, Tuple\n\nimport torch\nfrom pycocotools import mask as mask_util\n\n\n# ============================================================================\n# Utility Functions\n# ============================================================================\n\n\ndef convert_boxlist_to_normalized_tensor(box_list, image_width, image_height):\n    \"\"\"\n    Converts a list of bounding boxes to a normalized PyTorch tensor.\n\n    Args:\n        box_list (list of list or tuples): Each box is [x_min, y_min, x_max, y_max].\n        image_width (int or float): Width of the image.\n        image_height (int or float): Height of the image.\n\n    Returns:\n        torch.Tensor: Normalized tensor of shape (N, 4), values in [0, 1].\n    \"\"\"\n    boxes = torch.tensor(box_list, dtype=torch.float32)\n    boxes[:, [0, 2]] /= image_width  # x_min, x_max\n    boxes[:, [1, 3]] /= image_height  # y_min, y_max\n    boxes = boxes.clamp(0, 1)\n    return boxes\n\n\ndef load_coco_and_group_by_image(json_path: str) -> Tuple[List[Dict], Dict[int, str]]:\n    \"\"\"\n    Load COCO JSON file and group annotations by image.\n\n    Args:\n        json_path (str): Path to COCO JSON file.\n\n    Returns:\n        Tuple containing:\n            - List of dicts with 'image' and 'annotations' keys\n            - Dict mapping category IDs to category names\n    \"\"\"\n    with open(json_path, \"r\") as f:\n        coco = json.load(f)\n\n    images = {img[\"id\"]: img for img in coco[\"images\"]}\n\n    anns_by_image = defaultdict(list)\n    for ann in coco[\"annotations\"]:\n        anns_by_image[ann[\"image_id\"]].append(ann)\n\n    sorted_image_ids = sorted(images.keys())\n\n    grouped = []\n    for image_id in sorted_image_ids:\n        image_info = images[image_id]\n        grouped.append(\n            {\"image\": image_info, \"annotations\": anns_by_image.get(image_id, [])}\n        )\n\n    cat_id_to_name = {cat[\"id\"]: cat[\"name\"] for cat in coco[\"categories\"]}\n\n    return grouped, cat_id_to_name\n\n\ndef ann_to_rle(segm, im_info: Dict) -> Dict:\n    \"\"\"\n    Convert annotation which can be polygons or uncompressed RLE to RLE.\n\n    Args:\n        segm: Segmentation data (polygon list or RLE dict)\n        im_info (dict): Image info containing 'height' and 'width'\n\n    Returns:\n        RLE encoded segmentation\n    \"\"\"\n    h, w = im_info[\"height\"], im_info[\"width\"]\n\n    if isinstance(segm, list):\n        # Polygon - merge all parts into one mask RLE code\n        rles = mask_util.frPyObjects(segm, h, w)\n        rle = mask_util.merge(rles)\n    elif isinstance(segm[\"counts\"], list):\n        # Uncompressed RLE\n        rle = mask_util.frPyObjects(segm, h, w)\n    else:\n        # Already RLE\n        rle = segm\n\n    return rle\n\n\n# ============================================================================\n# COCO Training API\n# ============================================================================\n\n\nclass COCO_FROM_JSON:\n    \"\"\"\n    COCO training API for loading box-only annotations from JSON.\n    Groups all annotations per image and creates queries per category.\n    \"\"\"\n\n    def __init__(\n        self,\n        annotation_file,\n        prompts=None,\n        include_negatives=True,\n        category_chunk_size=None,\n    ):\n        \"\"\"\n        Initialize the COCO training API.\n\n        Args:\n            annotation_file (str): Path to COCO JSON annotation file\n            prompts: Optional custom prompts for categories\n            include_negatives (bool): Whether to include negative examples (categories with no instances)\n        \"\"\"\n        self._raw_data, self._cat_idx_to_text = load_coco_and_group_by_image(\n            annotation_file\n        )\n        self._sorted_cat_ids = sorted(list(self._cat_idx_to_text.keys()))\n        self.prompts = None\n        self.include_negatives = include_negatives\n        self.category_chunk_size = (\n            category_chunk_size\n            if category_chunk_size is not None\n            else len(self._sorted_cat_ids)\n        )\n        self.category_chunks = [\n            self._sorted_cat_ids[i : i + self.category_chunk_size]\n            for i in range(0, len(self._sorted_cat_ids), self.category_chunk_size)\n        ]\n        if prompts is not None:\n            prompts = eval(prompts)\n            self.prompts = {}\n            for loc_dict in prompts:\n                self.prompts[int(loc_dict[\"id\"])] = loc_dict[\"name\"]\n            assert len(self.prompts) == len(self._sorted_cat_ids), (\n                \"Number of prompts must match number of categories\"\n            )\n\n    def getDatapointIds(self):\n        \"\"\"Return all datapoint indices for training.\"\"\"\n        return list(range(len(self._raw_data) * len(self.category_chunks)))\n\n    def loadQueriesAndAnnotationsFromDatapoint(self, idx):\n        \"\"\"\n        Load queries and annotations for a specific datapoint.\n\n        Args:\n            idx (int): Datapoint index\n\n        Returns:\n            Tuple of (queries, annotations) lists\n        \"\"\"\n        img_idx = idx // len(self.category_chunks)\n        chunk_idx = idx % len(self.category_chunks)\n        cat_chunk = self.category_chunks[chunk_idx]\n\n        queries = []\n        annotations = []\n\n        query_template = {\n            \"id\": None,\n            \"original_cat_id\": None,\n            \"object_ids_output\": None,\n            \"query_text\": None,\n            \"query_processing_order\": 0,\n            \"ptr_x_query_id\": None,\n            \"ptr_y_query_id\": None,\n            \"image_id\": 0,  # Single image per datapoint\n            \"input_box\": None,\n            \"input_box_label\": None,\n            \"input_points\": None,\n            \"is_exhaustive\": True,\n        }\n\n        annot_template = {\n            \"image_id\": 0,\n            \"bbox\": None,  # Normalized bbox in xywh\n            \"area\": None,  # Unnormalized area\n            \"segmentation\": None,  # RLE encoded\n            \"object_id\": None,\n            \"is_crowd\": None,\n            \"id\": None,\n        }\n\n        raw_annotations = self._raw_data[img_idx][\"annotations\"]\n        image_info = self._raw_data[img_idx][\"image\"]\n        width, height = image_info[\"width\"], image_info[\"height\"]\n\n        # Group annotations by category\n        cat_id_to_anns = defaultdict(list)\n        for ann in raw_annotations:\n            cat_id_to_anns[ann[\"category_id\"]].append(ann)\n\n        annotations_by_cat_sorted = [\n            (cat_id, cat_id_to_anns[cat_id]) for cat_id in cat_chunk\n        ]\n\n        for cat_id, anns in annotations_by_cat_sorted:\n            if len(anns) == 0 and not self.include_negatives:\n                continue\n\n            cur_ann_ids = []\n\n            # Create annotations for this category\n            for ann in anns:\n                annotation = annot_template.copy()\n                annotation[\"id\"] = len(annotations)\n                annotation[\"object_id\"] = annotation[\"id\"]\n                annotation[\"is_crowd\"] = ann[\"iscrowd\"]\n\n                normalized_boxes = convert_boxlist_to_normalized_tensor(\n                    [ann[\"bbox\"]], width, height\n                )\n                bbox = normalized_boxes[0]\n\n                annotation[\"area\"] = (bbox[2] * bbox[3]).item()\n                annotation[\"bbox\"] = bbox\n\n                if (\n                    \"segmentation\" in ann\n                    and ann[\"segmentation\"] is not None\n                    and ann[\"segmentation\"] != []\n                ):\n                    annotation[\"segmentation\"] = ann_to_rle(\n                        ann[\"segmentation\"], im_info=image_info\n                    )\n\n                annotations.append(annotation)\n                cur_ann_ids.append(annotation[\"id\"])\n\n            # Create query for this category\n            query = query_template.copy()\n            query[\"id\"] = len(queries)\n            query[\"original_cat_id\"] = cat_id\n            query[\"query_text\"] = (\n                self._cat_idx_to_text[cat_id]\n                if self.prompts is None\n                else self.prompts[cat_id]\n            )\n            query[\"object_ids_output\"] = cur_ann_ids\n            queries.append(query)\n\n        return queries, annotations\n\n    def loadImagesFromDatapoint(self, idx):\n        \"\"\"\n        Load image information for a specific datapoint.\n\n        Args:\n            idx (int): Datapoint index\n\n        Returns:\n            List containing image info dict\n        \"\"\"\n        img_idx = idx // len(self.category_chunks)\n        img_data = self._raw_data[img_idx][\"image\"]\n        images = [\n            {\n                \"id\": 0,\n                \"file_name\": img_data[\"file_name\"],\n                \"original_img_id\": img_data[\"id\"],\n                \"coco_img_id\": img_data[\"id\"],\n            }\n        ]\n        return images\n\n\n# ============================================================================\n# SAM3 Evaluation APIs\n# ============================================================================\n\n\nclass SAM3_EVAL_API_FROM_JSON_NP:\n    \"\"\"\n    SAM3 evaluation API for loading noun phrase queries from JSON.\n    \"\"\"\n\n    def __init__(self, annotation_file):\n        \"\"\"\n        Initialize the SAM3 evaluation API.\n\n        Args:\n            annotation_file (str): Path to SAM3 JSON annotation file\n        \"\"\"\n        with open(annotation_file, \"r\") as f:\n            data = json.load(f)\n        self._image_data = data[\"images\"]\n\n    def getDatapointIds(self):\n        \"\"\"Return all datapoint indices.\"\"\"\n        return list(range(len(self._image_data)))\n\n    def loadQueriesAndAnnotationsFromDatapoint(self, idx):\n        \"\"\"\n        Load queries and annotations for a specific datapoint.\n\n        Args:\n            idx (int): Datapoint index\n\n        Returns:\n            Tuple of (queries, annotations) lists\n        \"\"\"\n        cur_img_data = self._image_data[idx]\n        queries = []\n        annotations = []\n\n        query_template = {\n            \"id\": None,\n            \"original_cat_id\": None,\n            \"object_ids_output\": None,\n            \"query_text\": None,\n            \"query_processing_order\": 0,\n            \"ptr_x_query_id\": None,\n            \"ptr_y_query_id\": None,\n            \"image_id\": 0,\n            \"input_box\": None,\n            \"input_box_label\": None,\n            \"input_points\": None,\n            \"is_exhaustive\": True,\n        }\n\n        # Create query\n        query = query_template.copy()\n        query[\"id\"] = len(queries)\n        query[\"original_cat_id\"] = int(cur_img_data[\"queried_category\"])\n        query[\"query_text\"] = cur_img_data[\"text_input\"]\n        query[\"object_ids_output\"] = []\n        queries.append(query)\n\n        return queries, annotations\n\n    def loadImagesFromDatapoint(self, idx):\n        \"\"\"\n        Load image information for a specific datapoint.\n\n        Args:\n            idx (int): Datapoint index\n\n        Returns:\n            List containing image info dict\n        \"\"\"\n        img_data = self._image_data[idx]\n        images = [\n            {\n                \"id\": 0,\n                \"file_name\": img_data[\"file_name\"],\n                \"original_img_id\": img_data[\"id\"],\n                \"coco_img_id\": img_data[\"id\"],\n            }\n        ]\n        return images\n\n\nclass SAM3_VEVAL_API_FROM_JSON_NP:\n    \"\"\"\n    SAM3 video evaluation API for loading noun phrase queries from JSON.\n    \"\"\"\n\n    def __init__(self, annotation_file):\n        \"\"\"\n        Initialize the SAM3 video evaluation API.\n\n        Args:\n            annotation_file (str): Path to SAM3 video JSON annotation file\n        \"\"\"\n        with open(annotation_file, \"r\") as f:\n            data = json.load(f)\n\n        assert \"video_np_pairs\" in data, \"Incorrect data format\"\n\n        self._video_data = data[\"videos\"]\n        self._video_id_to_np_ids = defaultdict(list)\n        self._cat_id_to_np = {}\n\n        for cat_dict in data[\"categories\"]:\n            self._cat_id_to_np[cat_dict[\"id\"]] = cat_dict[\"name\"]\n\n        for video_np_dict in data[\"video_np_pairs\"]:\n            self._video_id_to_np_ids[video_np_dict[\"video_id\"]].append(\n                video_np_dict[\"category_id\"]\n            )\n            assert (\n                self._cat_id_to_np[video_np_dict[\"category_id\"]]\n                == video_np_dict[\"noun_phrase\"]\n            ), \"Category name does not match text input\"\n\n    def getDatapointIds(self):\n        \"\"\"Return all datapoint indices.\"\"\"\n        return list(range(len(self._video_data)))\n\n    def loadQueriesAndAnnotationsFromDatapoint(self, idx):\n        \"\"\"\n        Load queries and annotations for a specific video datapoint.\n\n        Args:\n            idx (int): Datapoint index\n\n        Returns:\n            Tuple of (queries, annotations) lists\n        \"\"\"\n        cur_vid_data = self._video_data[idx]\n        queries = []\n        annotations = []\n\n        query_template = {\n            \"id\": None,\n            \"original_cat_id\": None,\n            \"object_ids_output\": None,\n            \"query_text\": None,\n            \"query_processing_order\": 0,\n            \"ptr_x_query_id\": None,\n            \"ptr_y_query_id\": None,\n            \"image_id\": 0,\n            \"input_box\": None,\n            \"input_box_label\": None,\n            \"input_points\": None,\n            \"is_exhaustive\": True,\n        }\n\n        all_np_ids = self._video_id_to_np_ids[cur_vid_data[\"id\"]]\n\n        for np_id in all_np_ids:\n            text_input = self._cat_id_to_np[np_id]\n\n            for i, image_path in enumerate(cur_vid_data[\"file_names\"]):\n                query = query_template.copy()\n                query[\"id\"] = len(queries)\n                query[\"original_cat_id\"] = np_id\n                query[\"query_text\"] = text_input\n                query[\"image_id\"] = i\n                query[\"query_processing_order\"] = i\n                query[\"object_ids_output\"] = []\n                queries.append(query)\n\n        return queries, annotations\n\n    def loadImagesFromDatapoint(self, idx):\n        \"\"\"\n        Load image information for a specific video datapoint.\n\n        Args:\n            idx (int): Datapoint index\n\n        Returns:\n            List containing image info dicts for all frames\n        \"\"\"\n        video_data = self._video_data[idx]\n        images = [\n            {\n                \"id\": i,\n                \"file_name\": file_name,\n                \"original_img_id\": video_data[\"id\"],\n                \"coco_img_id\": video_data[\"id\"],\n            }\n            for i, file_name in enumerate(video_data[\"file_names\"])\n        ]\n        return images\n"
  },
  {
    "path": "sam3/train/data/collator.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n\nfrom dataclasses import dataclass, field as field_ptr_behaviour, fields, is_dataclass\nfrom typing import Any, get_args, get_origin, List, Union\n\nimport torch\nfrom sam3.model.data_misc import (\n    BatchedDatapoint,\n    BatchedFindTarget,\n    BatchedInferenceMetadata,\n    FindStage,\n)\n\nfrom .sam3_image_dataset import Datapoint\n\n\nMyTensor = Union[torch.Tensor, List[Any]]\n\n\ndef convert_my_tensors(obj):\n    def is_optional_field(field) -> bool:\n        return get_origin(field) is Union and type(None) in get_args(field)\n\n    for field in fields(obj):\n        if is_dataclass(getattr(obj, field.name)):\n            convert_my_tensors(getattr(obj, field.name))\n            continue\n\n        field_type = field.type\n        if is_optional_field(field.type):\n            field_type = Union[get_args(field.type)[:-1]]  # Get the Optional field type\n\n        if field_type != MyTensor or getattr(obj, field.name) is None:\n            continue\n\n        elif len(getattr(obj, field.name)) and isinstance(\n            getattr(obj, field.name)[0], torch.Tensor\n        ):\n            stack_dim = 0\n            if field.name in [\n                \"input_boxes\",\n                \"input_boxes_label\",\n            ]:\n                stack_dim = 1\n            setattr(\n                obj,\n                field.name,\n                torch.stack(getattr(obj, field.name), dim=stack_dim).to(\n                    getattr(obj, field.name + \"__type\")\n                ),\n            )\n        else:\n            setattr(\n                obj,\n                field.name,\n                torch.as_tensor(\n                    getattr(obj, field.name), dtype=getattr(obj, field.name + \"__type\")\n                ),\n            )\n    return obj\n\n\ndef packed_to_padded_naive(boxes_packed, num_boxes, fill_value=0):\n    \"\"\"\n    Convert a packed tensor of bounding boxes to a padded tensor of bounding\n    boxes. Naive implementation using a loop.\n\n    Inputs:\n    - boxes_packed: Tensor of shape (N_1 + ... + N_B, 4)\n    - num_boxes: Tensor of shape (B,) where num_boxes[i] = N_i\n\n    Returns:\n    - boxes_padded: Tensor of shape (B, N_max, 4) where N_max = max_i N_i\n    \"\"\"\n    B = num_boxes.shape[0]\n    Ns = num_boxes.tolist()\n\n    boxes_padded = boxes_packed.new_zeros(B, max(Ns), *boxes_packed.shape[1:])\n    if fill_value != 0:\n        boxes_padded[...] = fill_value\n    prev_idx = 0\n    for i in range(B):\n        next_idx = prev_idx + Ns[i]\n        boxes_padded[i, : Ns[i]] = boxes_packed[prev_idx:next_idx]\n        prev_idx = next_idx\n    return boxes_padded\n\n\ndef pad_tensor_list_to_longest(\n    tensors: List[torch.Tensor], dim=0, pad_val=0\n) -> List[torch.Tensor]:\n    # Edits the list in-place\n    if not tensors:\n        return tensors\n    pad_len = max(t.shape[dim] for t in tensors)\n    for i in range(len(tensors)):\n        n_dims = len(tensors[i].shape)\n        n_right_dims = (n_dims - 1) - (n_dims + dim) % n_dims\n        n_pad = pad_len - tensors[i].shape[dim]\n        pad_tuple = tuple([0] * 2 * n_right_dims + [0, n_pad])\n        tensors[i] = torch.nn.functional.pad(tensors[i], pad_tuple, value=pad_val)\n    return tensors\n\n\ndef collate_fn_api_with_chunking(\n    batch,\n    num_chunks,\n    dict_key,\n    with_seg_masks=False,\n    input_points_embedding_dim=257,\n    repeats: int = 0,\n    load_image_in_fp16: bool = False,\n):\n    assert num_chunks >= 1, \"num_chunks must be >= 1\"\n\n    # split the batch into num_chunks chunks\n    batch_chunks = [batch[i::num_chunks] for i in range(num_chunks)]\n\n    # collate each chunk\n    collated_chunks = [\n        collate_fn_api(\n            chunk,\n            dict_key,\n            with_seg_masks,\n            input_points_embedding_dim,\n            repeats,\n            # ptr_behaviour,\n            load_image_in_fp16,\n        )\n        for chunk in batch_chunks\n    ]\n    return collated_chunks\n\n\ndef collate_fn_api(\n    batch: List[Datapoint],\n    dict_key,\n    with_seg_masks=False,\n    input_points_embedding_dim=257,\n    repeats: int = 0,\n    load_image_in_fp16: bool = False,\n):\n    # img_batch = torch.stack(sum([[img.data for img in v.images] for v in batch], []))\n    img_batch = []\n    text_batch = []\n    raw_images = None\n\n    num_stages = (\n        max(q.query_processing_order for data in batch for q in data.find_queries) + 1\n    )\n\n    stages = [\n        FindStage(\n            img_ids=[],\n            text_ids=[],\n            input_boxes=[],\n            input_boxes_label=[],\n            input_boxes_mask=[],\n            input_points=[],\n            input_points_mask=[],\n            object_ids=[],\n        )\n        for _ in range(num_stages)\n    ]\n    find_targets = [\n        BatchedFindTarget(\n            num_boxes=[],\n            boxes=[],\n            boxes_padded=[],\n            is_exhaustive=[],\n            segments=[],\n            semantic_segments=[],\n            is_valid_segment=[],\n            repeated_boxes=[],\n            object_ids=[],\n            object_ids_padded=[],\n        )\n        for _ in range(num_stages)\n    ]\n    find_metadatas = [\n        BatchedInferenceMetadata(\n            coco_image_id=[],\n            original_size=[],\n            object_id=[],\n            frame_index=[],\n            original_image_id=[],\n            original_category_id=[],\n            is_conditioning_only=[],\n        )\n        for _ in range(num_stages)\n    ]\n\n    offset_img_id = 0\n    offset_query_id = [0 for _ in range(num_stages)]\n    for data in batch:\n        img_batch.extend([img.data for img in data.images])\n\n        if data.raw_images is not None:\n            if raw_images is None:\n                raw_images = []\n            raw_images.extend(data.raw_images)\n\n        # Conversion of query_ids indexing in a datapoint to query_ids indexing in a stage\n        datapoint_query_id_2_stage_query_id = []\n        for q in data.find_queries:\n            stage_id = q.query_processing_order\n            datapoint_query_id_2_stage_query_id.append(offset_query_id[stage_id])\n            offset_query_id[stage_id] += 1\n\n        for q in data.find_queries:\n            stage_id = q.query_processing_order\n            stages[stage_id].img_ids.append(q.image_id + offset_img_id)\n            if q.query_text not in text_batch:\n                text_batch.append(q.query_text)\n            stages[stage_id].text_ids.append(text_batch.index(q.query_text))\n\n            assert q.inference_metadata is not None, (\n                \"inference_metadata must be provided when FindQueryLoaded is created.\"\n            )\n            for f in fields(q.inference_metadata):\n                getattr(find_metadatas[stage_id], f.name).append(\n                    getattr(q.inference_metadata, f.name)\n                )\n\n            if q.input_bbox is not None:\n                assert q.input_bbox.numel() % 4 == 0\n                assert q.input_bbox_label is not None\n                nb_boxes = q.input_bbox.numel() // 4\n                assert len(q.input_bbox_label) == nb_boxes\n                stages[stage_id].input_boxes.append(q.input_bbox.view(nb_boxes, 4))\n                stages[stage_id].input_boxes_label.append(\n                    q.input_bbox_label.view(nb_boxes)\n                )\n                stages[stage_id].input_boxes_mask.append(\n                    torch.zeros(nb_boxes, dtype=torch.bool)\n                )\n            else:\n                stages[stage_id].input_boxes.append(torch.zeros(0, 4))\n                stages[stage_id].input_boxes_label.append(\n                    torch.zeros(0, dtype=torch.bool)\n                )\n                stages[stage_id].input_boxes_mask.append(\n                    torch.ones(0, dtype=torch.bool)\n                )\n\n            if q.input_points is not None:\n                stages[stage_id].input_points.append(\n                    q.input_points.squeeze(0)  # Strip a trivial batch index\n                )\n                # All masks will be padded up to the longest length\n                # with 1s before final conversion to batchd tensors\n                stages[stage_id].input_points_mask.append(\n                    torch.zeros(q.input_points.shape[1])\n                )\n            else:\n                stages[stage_id].input_points.append(\n                    torch.empty(0, input_points_embedding_dim)\n                )\n                stages[stage_id].input_points_mask.append(torch.empty(0))\n\n            current_out_boxes = []\n            current_out_object_ids = []\n            # Set the object ids referred to by this query\n            stages[stage_id].object_ids.append(q.object_ids_output)\n            for object_id in q.object_ids_output:\n                current_out_boxes.append(\n                    data.images[q.image_id].objects[object_id].bbox\n                )\n                current_out_object_ids.append(object_id)\n            find_targets[stage_id].boxes.extend(current_out_boxes)\n            find_targets[stage_id].object_ids.extend(current_out_object_ids)\n            if repeats > 0:\n                for _ in range(repeats):\n                    find_targets[stage_id].repeated_boxes.extend(current_out_boxes)\n            find_targets[stage_id].num_boxes.append(len(current_out_boxes))\n            find_targets[stage_id].is_exhaustive.append(q.is_exhaustive)\n\n            if with_seg_masks:\n                current_seg_mask = []\n                current_is_valid_segment = []\n                for object_id in q.object_ids_output:\n                    seg_mask = data.images[q.image_id].objects[object_id].segment\n                    if seg_mask is not None:\n                        current_seg_mask.append(seg_mask)\n                        current_is_valid_segment.append(1)\n                    else:\n                        dummy_mask = torch.zeros(\n                            data.images[q.image_id].data.shape[-2:], dtype=torch.bool\n                        )\n                        current_seg_mask.append(dummy_mask)\n                        current_is_valid_segment.append(0)\n                find_targets[stage_id].segments.extend(current_seg_mask)\n                find_targets[stage_id].is_valid_segment.extend(current_is_valid_segment)\n            else:\n                # We are not loading segmentation masks\n                find_targets[stage_id].segments = None\n                find_targets[stage_id].is_valid_segment = None\n\n            if q.semantic_target is not None:\n                find_targets[stage_id].semantic_segments.append(q.semantic_target)\n\n        offset_img_id += len(data.images)\n\n    # Pad input points to equal sequence lengths\n    for i in range(len(stages)):\n        stages[i].input_points = pad_tensor_list_to_longest(\n            stages[i].input_points, dim=0, pad_val=0\n        )\n        # Masked-out regions indicated by 1s.\n        stages[i].input_points_mask = pad_tensor_list_to_longest(\n            stages[i].input_points_mask, dim=0, pad_val=1\n        )\n\n    # Pad input boxes to equal sequence lengths\n    for i in range(len(stages)):\n        stages[i].input_boxes = pad_tensor_list_to_longest(\n            stages[i].input_boxes, dim=0, pad_val=0\n        )\n        stages[i].input_boxes_label = pad_tensor_list_to_longest(\n            stages[i].input_boxes_label, dim=0, pad_val=0\n        )\n        # Masked-out regions indicated by 1s.\n        stages[i].input_boxes_mask = pad_tensor_list_to_longest(\n            stages[i].input_boxes_mask, dim=0, pad_val=1\n        )\n\n    # Convert to tensors\n    for i in range(len(stages)):\n        stages[i] = convert_my_tensors(stages[i])\n        find_targets[i] = convert_my_tensors(find_targets[i])\n        find_metadatas[i] = convert_my_tensors(find_metadatas[i])\n        # get padded representation for the boxes\n        find_targets[i].boxes_padded = packed_to_padded_naive(\n            find_targets[i].boxes.view(-1, 4), find_targets[i].num_boxes\n        )\n        find_targets[i].object_ids_padded = packed_to_padded_naive(\n            find_targets[i].object_ids, find_targets[i].num_boxes, fill_value=-1\n        )\n\n    # Finalize the image batch\n    # check sizes\n    for img in img_batch[1:]:\n        assert img.shape == img_batch[0].shape, \"All images must have the same size\"\n    image_batch = torch.stack(img_batch)\n    if load_image_in_fp16:\n        # Optionally, cast the image tensors to fp16, which helps save GPU memory on\n        # long videos with thousands of frames (where image tensors could be several GBs)\n        image_batch = image_batch.half()\n\n    return {\n        dict_key: BatchedDatapoint(\n            img_batch=image_batch,\n            find_text_batch=text_batch,\n            find_inputs=stages,\n            find_targets=find_targets,\n            find_metadatas=find_metadatas,\n            raw_images=raw_images,\n        )\n    }\n"
  },
  {
    "path": "sam3/train/data/sam3_image_dataset.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n\n\"\"\"Dataset class for modulated detection\"\"\"\n\nimport json\nimport os\nimport random\nimport sys\nimport traceback\nfrom collections import Counter\nfrom dataclasses import dataclass\nfrom enum import Enum\nfrom typing import Any, Callable, Dict, List, Optional, Set, Tuple, Union\n\nimport torch\nimport torch.utils.data\nimport torchvision\nfrom iopath.common.file_io import g_pathmgr\nfrom PIL import Image as PILImage\nfrom PIL.Image import DecompressionBombError\nfrom sam3.model.box_ops import box_xywh_to_xyxy\nfrom torchvision.datasets.vision import VisionDataset\n\nfrom .coco_json_loaders import COCO_FROM_JSON\n\n\n@dataclass\nclass InferenceMetadata:\n    \"\"\"Metadata required for postprocessing\"\"\"\n\n    # Coco id that corresponds to the \"image\" for evaluation by the coco evaluator\n    # This is used for our own \"class agnostic\" evaluation\n    coco_image_id: int\n\n    # id in the original dataset, such that we can use the original evaluator\n    original_image_id: int\n\n    # Original category id (if we want to use the original evaluator)\n    original_category_id: int\n\n    # Size of the raw image (height, width)\n    original_size: Tuple[int, int]\n\n    # Id of the object in the media\n    object_id: int\n\n    # Index of the frame in the media (0 if single image)\n    frame_index: int\n\n    # Whether it is for conditioning only, e.g., 0-th frame in TA is for conditioning\n    # as we assume GT available in frame 0.\n    is_conditioning_only: Optional[bool] = False\n\n\n@dataclass\nclass FindQuery:\n    query_text: str\n\n    image_id: int\n\n    # In case of a find query, the list of object ids that have to be predicted\n    object_ids_output: List[int]\n\n    # This is \"instance exhaustivity\".\n    # true iff all instances are separable and annotated\n    # See below the slightly different \"pixel exhaustivity\"\n    is_exhaustive: bool\n\n    # The order in which the queries are processed (only meaningful for video)\n    query_processing_order: int = 0\n\n    # Input geometry, initially in denormalized XYXY format. Then\n    # 1. converted to normalized CxCyWH by the Normalize transform\n    input_bbox: Optional[torch.Tensor] = None\n    input_bbox_label: Optional[torch.Tensor] = None\n\n    # Only for the PVS task\n    input_points: Optional[torch.Tensor] = None\n\n    semantic_target: Optional[torch.Tensor] = None\n\n    # pixel exhaustivity: true iff the union of all segments (including crowds)\n    # covers every pixel belonging to the target class\n    # Note that instance_exhaustive implies pixel_exhaustive\n    is_pixel_exhaustive: Optional[bool] = None\n\n\n@dataclass\nclass FindQueryLoaded(FindQuery):\n    # Must have default value since FindQuery has entries with default values\n    inference_metadata: Optional[InferenceMetadata] = None\n\n\n@dataclass\nclass Object:\n    # Initially in denormalized XYXY format, gets converted to normalized CxCyWH by the Normalize transform\n    bbox: torch.Tensor\n    area: float\n\n    # Id of the object in the media\n    object_id: Optional[int] = -1\n\n    # Index of the frame in the media (0 if single image)\n    frame_index: Optional[int] = -1\n\n    segment: Optional[Union[torch.Tensor, dict]] = None  # RLE dict or binary mask\n\n    is_crowd: bool = False\n\n    source: Optional[str] = None\n\n\n@dataclass\nclass Image:\n    data: Union[torch.Tensor, PILImage.Image]\n    objects: List[Object]\n    size: Tuple[int, int]  # (height, width)\n\n    # For blurring augmentation\n    blurring_mask: Optional[Dict[str, Any]] = None\n\n\n@dataclass\nclass Datapoint:\n    \"\"\"Refers to an image/video and all its annotations\"\"\"\n\n    find_queries: List[FindQueryLoaded]\n    images: List[Image]\n    raw_images: Optional[List[PILImage.Image]] = None\n\n\nclass CustomCocoDetectionAPI(VisionDataset):\n    \"\"\"`MS Coco Detection <https://cocodataset.org/#detection-2016>`_ Dataset.\n\n    Args:\n        root (string): Root directory where images are downloaded to.\n        annFile (string): Path to json annotation file.\n        transform (callable, optional): A function/transform that  takes in an PIL image\n            and returns a transformed version. E.g, ``transforms.ToTensor``\n        target_transform (callable, optional): A function/transform that takes in the\n            target and transforms it.\n        transforms (callable, optional): A function/transform that takes input sample and its target as entry\n            and returns a transformed version.\n    \"\"\"\n\n    def __init__(\n        self,\n        root: str,\n        annFile: str,\n        load_segmentation: bool,\n        fix_fname: bool = False,\n        training: bool = True,\n        blurring_masks_path: Optional[str] = None,\n        use_caching: bool = True,\n        zstd_dict_path=None,\n        filter_query=None,\n        coco_json_loader: Callable = COCO_FROM_JSON,\n        limit_ids: int = None,\n    ) -> None:\n        super().__init__(root)\n\n        self.annFile = annFile\n        self.use_caching = use_caching\n        self.zstd_dict_path = zstd_dict_path\n\n        self.curr_epoch = 0  # Used in case data loader behavior changes across epochs\n        self.load_segmentation = load_segmentation\n        self.fix_fname = fix_fname\n        self.filter_query = filter_query\n\n        self.coco = None\n        self.coco_json_loader = coco_json_loader\n        self.limit_ids = limit_ids\n        self.set_sharded_annotation_file(0)\n        self.training = training\n        self.blurring_masks_path = blurring_masks_path\n\n    def _load_images(\n        self, datapoint_id: int, img_ids_to_load: Optional[Set[int]] = None\n    ) -> Tuple[List[Tuple[int, PILImage.Image]], List[Dict[str, Any]]]:\n        all_images = []\n        all_img_metadata = []\n        for current_meta in self.coco.loadImagesFromDatapoint(datapoint_id):\n            img_id = current_meta[\"id\"]\n            if img_ids_to_load is not None and img_id not in img_ids_to_load:\n                continue\n            if self.fix_fname:\n                current_meta[\"file_name\"] = current_meta[\"file_name\"].split(\"/\")[-1]\n            path = current_meta[\"file_name\"]\n            if self.blurring_masks_path is not None:\n                mask_fname = os.path.basename(path).replace(\".jpg\", \"-mask.json\")\n                mask_path = os.path.join(self.blurring_masks_path, mask_fname)\n                if os.path.exists(mask_path):\n                    with open(mask_path, \"r\") as fopen:\n                        current_meta[\"blurring_mask\"] = json.load(fopen)\n\n            all_img_metadata.append(current_meta)\n            path = os.path.join(self.root, path)\n            try:\n                if \".mp4\" in path and path[-4:] == \".mp4\":\n                    # Going to load a video frame\n                    from decord import cpu, VideoReader\n\n                    video_path, frame = path.split(\"@\")\n                    video = VideoReader(video_path, ctx=cpu(0))\n                    # Convert to PIL image\n                    all_images.append(\n                        (\n                            img_id,\n                            torchvision.transforms.ToPILImage()(\n                                video[int(frame)].asnumpy()\n                            ),\n                        )\n                    )\n                else:\n                    with g_pathmgr.open(path, \"rb\") as fopen:\n                        all_images.append((img_id, PILImage.open(fopen).convert(\"RGB\")))\n            except FileNotFoundError as e:\n                print(f\"File not found: {path} from dataset: {self.annFile}\")\n                raise e\n\n        return all_images, all_img_metadata\n\n    def set_curr_epoch(self, epoch: int):\n        self.curr_epoch = epoch\n\n    def set_epoch(self, epoch: int):\n        pass\n\n    def set_sharded_annotation_file(self, data_epoch: int):\n        if self.coco is not None:\n            return\n\n        assert g_pathmgr.isfile(self.annFile), (\n            f\"please provide valid annotation file. Missing: {self.annFile}\"\n        )\n        annFile = g_pathmgr.get_local_path(self.annFile)\n\n        if self.coco is not None:\n            del self.coco\n\n        self.coco = self.coco_json_loader(annFile)\n        # Use a torch tensor here to optimize memory usage when using several dataloaders\n        ids_list = list(sorted(self.coco.getDatapointIds()))\n        if self.limit_ids is not None:\n            local_random = random.Random(len(ids_list))\n            local_random.shuffle(ids_list)\n            ids_list = ids_list[: self.limit_ids]\n        self.ids = torch.as_tensor(ids_list, dtype=torch.long)\n\n    def __getitem__(self, index: int) -> Datapoint:\n        return self._load_datapoint(index)\n\n    def _load_datapoint(self, index: int) -> Datapoint:\n        \"\"\"A separate method for easy overriding in subclasses.\"\"\"\n        id = self.ids[index].item()\n        pil_images, img_metadata = self._load_images(id)\n        queries, annotations = self.coco.loadQueriesAndAnnotationsFromDatapoint(id)\n        return self.load_queries(pil_images, annotations, queries, img_metadata)\n\n    def load_queries(self, pil_images, annotations, queries, img_metadata):\n        \"\"\"Transform the raw image and queries into a Datapoint sample.\"\"\"\n        images: List[Image] = []\n        id2index_img = {}\n        id2index_obj = {}\n        id2index_find_query = {}\n        id2imsize = {}\n        assert len(pil_images) == len(img_metadata)\n        for i in range(len(pil_images)):\n            w, h = pil_images[i][1].size\n            blurring_mask = None\n            if \"blurring_mask\" in img_metadata[i]:\n                blurring_mask = img_metadata[i][\"blurring_mask\"]\n            images.append(\n                Image(\n                    data=pil_images[i][1],\n                    objects=[],\n                    size=(h, w),\n                    blurring_mask=blurring_mask,\n                )\n            )\n            id2index_img[pil_images[i][0]] = i\n            id2imsize[pil_images[i][0]] = (h, w)\n\n        for annotation in annotations:\n            image_id = id2index_img[annotation[\"image_id\"]]\n            bbox = box_xywh_to_xyxy(torch.as_tensor(annotation[\"bbox\"])).view(1, 4)\n            h, w = id2imsize[annotation[\"image_id\"]]\n            bbox[:, 0::2].mul_(w).clamp_(min=0, max=w)\n            bbox[:, 1::2].mul_(h).clamp_(min=0, max=h)\n            segment = None\n            if self.load_segmentation and \"segmentation\" in annotation:\n                # We're not decoding the RLE here, a transform will do it lazily later\n                segment = annotation[\"segmentation\"]\n            images[image_id].objects.append(\n                Object(\n                    bbox=bbox[0],\n                    area=annotation[\"area\"],\n                    object_id=(\n                        annotation[\"object_id\"] if \"object_id\" in annotation else -1\n                    ),\n                    frame_index=(\n                        annotation[\"frame_index\"] if \"frame_index\" in annotation else -1\n                    ),\n                    segment=segment,\n                    is_crowd=(\n                        annotation[\"is_crowd\"] if \"is_crowd\" in annotation else None\n                    ),\n                    source=annotation[\"source\"] if \"source\" in annotation else \"\",\n                )\n            )\n            id2index_obj[annotation[\"id\"]] = len(images[image_id].objects) - 1\n\n        find_queries = []\n        stage2num_queries = Counter()\n        for i, query in enumerate(queries):\n            stage2num_queries[query[\"query_processing_order\"]] += 1\n            id2index_find_query[query[\"id\"]] = i\n\n        # Sanity check: all the stages should have the same number of queries\n        if len(stage2num_queries) == 0:\n            num_queries_per_stage = 0\n        else:\n            num_queries_per_stage = stage2num_queries.most_common(1)[0][1]\n        for stage, num_queries in stage2num_queries.items():\n            assert num_queries == num_queries_per_stage, (\n                f\"Number of queries in stage {stage} is {num_queries}, expected {num_queries_per_stage}\"\n            )\n\n        for query in queries:\n            h, w = id2imsize[query[\"image_id\"]]\n            if (\n                \"input_box\" in query\n                and query[\"input_box\"] is not None\n                and len(query[\"input_box\"]) > 0\n            ):\n                bbox = box_xywh_to_xyxy(torch.as_tensor(query[\"input_box\"])).view(-1, 4)\n                bbox[:, 0::2].mul_(w).clamp_(min=0, max=w)\n                bbox[:, 1::2].mul_(h).clamp_(min=0, max=h)\n                if \"input_box_label\" in query and query[\"input_box_label\"] is not None:\n                    bbox_label = torch.as_tensor(\n                        query[\"input_box_label\"], dtype=torch.long\n                    ).view(-1)\n                    assert len(bbox_label) == len(bbox)\n                else:\n                    # assume the boxes are positives\n                    bbox_label = torch.ones(len(bbox), dtype=torch.long)\n            else:\n                bbox = None\n                bbox_label = None\n\n            if \"input_points\" in query and query[\"input_points\"] is not None:\n                points = torch.as_tensor(query[\"input_points\"]).view(1, -1, 3)\n                points[:, :, 0:1].mul_(w).clamp_(min=0, max=w)\n                points[:, :, 1:2].mul_(h).clamp_(min=0, max=h)\n            else:\n                points = None\n\n            try:\n                original_image_id = int(\n                    img_metadata[id2index_img[query[\"image_id\"]]][\"original_img_id\"]\n                )\n            except ValueError:\n                original_image_id = -1\n\n            try:\n                img_metadata_query = img_metadata[id2index_img[query[\"image_id\"]]]\n                coco_image_id = (\n                    int(img_metadata_query[\"coco_img_id\"])\n                    if \"coco_img_id\" in img_metadata_query\n                    else query[\"id\"]\n                )\n            except KeyError:\n                coco_image_id = -1\n\n            try:\n                original_category_id = int(query[\"original_cat_id\"])\n            except (ValueError, KeyError):\n                original_category_id = -1\n\n            # For evaluation, we associate the ids of the object to be tracked to the query\n            if query[\"object_ids_output\"]:\n                obj_id = query[\"object_ids_output\"][0]\n                obj_idx = id2index_obj[obj_id]\n                image_idx = id2index_img[query[\"image_id\"]]\n                object_id = images[image_idx].objects[obj_idx].object_id\n                frame_index = images[image_idx].objects[obj_idx].frame_index\n            else:\n                object_id = -1\n                frame_index = -1\n\n            find_queries.append(\n                FindQueryLoaded(\n                    # id=query[\"id\"],\n                    # query_type=qtype,\n                    query_text=(\n                        query[\"query_text\"] if query[\"query_text\"] is not None else \"\"\n                    ),\n                    image_id=id2index_img[query[\"image_id\"]],\n                    input_bbox=bbox,\n                    input_bbox_label=bbox_label,\n                    input_points=points,\n                    object_ids_output=[\n                        id2index_obj[obj_id] for obj_id in query[\"object_ids_output\"]\n                    ],\n                    is_exhaustive=query[\"is_exhaustive\"],\n                    is_pixel_exhaustive=(\n                        query[\"is_pixel_exhaustive\"]\n                        if \"is_pixel_exhaustive\" in query\n                        else (\n                            query[\"is_exhaustive\"] if query[\"is_exhaustive\"] else None\n                        )\n                    ),\n                    query_processing_order=query[\"query_processing_order\"],\n                    inference_metadata=InferenceMetadata(\n                        coco_image_id=-1 if self.training else coco_image_id,\n                        original_image_id=(-1 if self.training else original_image_id),\n                        frame_index=frame_index,\n                        original_category_id=original_category_id,\n                        original_size=(h, w),\n                        object_id=object_id,\n                    ),\n                )\n            )\n\n        return Datapoint(\n            find_queries=find_queries,\n            images=images,\n            raw_images=[p[1] for p in pil_images],\n        )\n\n    def __len__(self) -> int:\n        return len(self.ids)\n\n\nclass Sam3ImageDataset(CustomCocoDetectionAPI):\n    def __init__(\n        self,\n        img_folder,\n        ann_file,\n        transforms,\n        max_ann_per_img: int,\n        multiplier: int,\n        training: bool,\n        load_segmentation: bool = False,\n        max_train_queries: int = 81,\n        max_val_queries: int = 300,\n        fix_fname: bool = False,\n        is_sharded_annotation_dir: bool = False,\n        blurring_masks_path: Optional[str] = None,\n        use_caching: bool = True,\n        zstd_dict_path=None,\n        filter_query=None,\n        coco_json_loader: Callable = COCO_FROM_JSON,\n        limit_ids: int = None,\n    ):\n        super(Sam3ImageDataset, self).__init__(\n            img_folder,\n            ann_file,\n            fix_fname=fix_fname,\n            load_segmentation=load_segmentation,\n            training=training,\n            blurring_masks_path=blurring_masks_path,\n            use_caching=use_caching,\n            zstd_dict_path=zstd_dict_path,\n            filter_query=filter_query,\n            coco_json_loader=coco_json_loader,\n            limit_ids=limit_ids,\n        )\n\n        self._transforms = transforms\n        self.training = training\n        self.max_ann_per_img = max_ann_per_img\n        self.max_train_queries = max_train_queries\n        self.max_val_queries = max_val_queries\n\n        self.repeat_factors = torch.ones(len(self.ids), dtype=torch.float32)\n\n        self.repeat_factors *= multiplier\n        print(f\"Raw dataset length = {len(self.ids)}\")\n\n        self._MAX_RETRIES = 100\n\n    def __getitem__(self, idx):\n        return self.__orig_getitem__(idx)\n\n    def __orig_getitem__(self, idx):\n        for _ in range(self._MAX_RETRIES):\n            try:\n                datapoint = super(Sam3ImageDataset, self).__getitem__(idx)\n\n                # This can be done better by filtering the offending find queries\n                # However, this requires care:\n                # - Delete any find/get query that may depend on the deleted one\n                # - Re-compute the indexes in the pointers to account for the deleted finds\n                for q in datapoint.find_queries:\n                    if len(q.object_ids_output) > self.max_ann_per_img:\n                        raise DecompressionBombError(\n                            f\"Too many outputs ({len(q.object_ids_output)})\"\n                        )\n\n                max_queries = (\n                    self.max_train_queries if self.training else self.max_val_queries\n                )\n\n                if len(datapoint.find_queries) > max_queries:\n                    raise DecompressionBombError(\n                        f\"Too many find queries ({len(datapoint.find_queries)})\"\n                    )\n\n                if len(datapoint.find_queries) == 0:\n                    raise DecompressionBombError(\"No find queries\")\n                for transform in self._transforms:\n                    datapoint = transform(datapoint, epoch=self.curr_epoch)\n\n                break\n            except (DecompressionBombError, OSError, ValueError) as error:\n                sys.stderr.write(f\"ERROR: got loading error on datapoint {idx}\\n\")\n                sys.stderr.write(f\"Exception: {error}\\n\")\n                sys.stderr.write(traceback.format_exc())\n                idx = (idx + 1) % len(self)\n        else:\n            raise RuntimeError(\n                f\"Failed {self._MAX_RETRIES} times trying to load an image.\"\n            )\n\n        return datapoint\n"
  },
  {
    "path": "sam3/train/data/sam3_video_dataset.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n\nimport copy\nimport io\nimport json\nimport logging\nimport math\nimport os\nimport pickle\nimport random\nimport sys\nfrom typing import Any, Dict, List, Optional, Set, Tuple, Union\n\nimport torch\nimport torchvision\n# from decord import cpu, VideoReader\n\nfrom iopath.common.file_io import PathManager\nfrom PIL import Image as PILImage\n\nfrom .sam3_image_dataset import Datapoint, Sam3ImageDataset\n\n\nSEED = 42\n\n\nclass VideoGroundingDataset(Sam3ImageDataset):\n    def __init__(\n        self,\n        num_stages_sample: int = 4,\n        stage_stride_min: int = 1,\n        stage_stride_max: int = 5,\n        random_reverse_time_axis: bool = True,\n        is_tiling_single_image: bool = False,\n        # By default, we remove find those queries with geometric inputs (input_box or input_points)\n        # when creating synthetic videos from frames (since they are not *video-level* text prompts).\n        # If we need them later, we can sample them on-the-fly via transforms or inside the model.\n        tile_img_keep_find_queries_with_geo_inputs: bool = False,\n        tile_img_keep_get_queries: bool = False,\n        # the maximum number of find queries (for each frame) to keep in a video; if the datapoint\n        # contains more queries per frame than this limit, we subsample them to avoid OOM errors\n        max_query_num: int = -1,  # the default -1 means no limit\n        # whether to override the \"is_exhaustive\" flag of the loaded find queries to True\n        # (by default, our video datasets are ingested with is_exhaustive=False, since the YTVIS format\n        # annotations doesn't involve an \"is_exhaustive\" flag; this means that those unmatched (negative)\n        # detection queries or tracking queries do not receive a classification loss given that we have\n        # weak_loss=True in IABCEMdetr -- this could lead to false positives for both image detection\n        # and video association.)\n        override_query_is_exhaustive_to_true: bool = False,\n        # the maximum number of masklets in a video; if the datapoint contains more masklets\n        # than this limit, we skip the datapoint to avoid OOM errors (this is useful for\n        # training with large videos that contain many objects)\n        max_masklet_num_in_video: int = 300,  # 300 masklets is usually OK to avoid OOM\n        **kwargs,\n    ):\n        \"\"\"\n        Loading video grounding data\n\n        Video frame sampling parameters (for training only):\n        - num_stages_sample: number of frames to sample from the video during training\n        - stage_stride_min: minimum stride between sampled frames during training\n        - stage_stride_max: maximum stride between sampled frames during training (if it's\n          greater than stage_stride_min, the actual stride is sampled uniformly between min\n          and max; during inference, we always use all frames in the video with stride=1)\n        - random_reverse_time_axis: whether to randomly invert the video's temporal axis\n          (i.e. playing it backwards) during training\n        \"\"\"\n        super().__init__(**kwargs)\n        assert num_stages_sample >= 1\n        assert stage_stride_min >= 1\n        assert stage_stride_max >= stage_stride_min\n        self.num_stages_sample = num_stages_sample\n        self.stage_stride_min = stage_stride_min\n        self.stage_stride_max = stage_stride_max\n        self.random_reverse_time_axis = random_reverse_time_axis\n        self.is_tiling_single_image = is_tiling_single_image\n        self.tile_img_keep_find_queries_with_geo_inputs = (\n            tile_img_keep_find_queries_with_geo_inputs\n        )\n        self.tile_img_keep_get_queries = tile_img_keep_get_queries\n        self.max_query_num = max_query_num\n        self.override_query_is_exhaustive_to_true = override_query_is_exhaustive_to_true\n        self.max_masklet_num_in_video = max_masklet_num_in_video\n        self.rng = random.Random()\n        self.set_curr_epoch(0)\n\n    def set_curr_epoch(self, epoch: int):\n        super().set_curr_epoch(epoch)\n        self.rng.seed(SEED + epoch)\n\n    def _load_datapoint(self, index: int) -> Datapoint:\n        id = self.ids[index].item()\n        queries, annotations = self.coco.loadQueriesAndAnnotationsFromDatapoint(id)\n\n        # we subsample the video frames during training\n        if self.training and not self.is_tiling_single_image:\n            # pick a random stride for sampling query stages (`randint` includes both ends)\n            stage_stride = self.rng.randint(\n                self.stage_stride_min, self.stage_stride_max\n            )\n            stage_ids_to_keep = self._sample_stage_ids(\n                queries, self.num_stages_sample, stage_stride\n            )\n            # filter the queries and annotations to keep only the selected stages\n            # (also remap the stage ids so that they are contiguous and start from 0)\n            reverse_time_axis = (\n                self.rng.random() < 0.5 if self.random_reverse_time_axis else False\n            )\n            queries, annotations, kept_img_ids = self._filter_query_and_anns(\n                queries,\n                annotations,\n                stage_ids_to_keep,\n                remap_stage_id=True,\n                reverse_time_axis=reverse_time_axis,\n            )\n            pil_images, img_metadata = self._load_images(id, kept_img_ids)\n            if reverse_time_axis:\n                # reverse the temporal ordering of the images and their metadata\n                # so that the image order matches the query order\n                pil_images = pil_images[::-1]\n                img_metadata = img_metadata[::-1]\n        else:\n            pil_images, img_metadata = self._load_images(id)\n\n        # check that all the images have the same image size (they are expected\n        # to have the same image size since they are frames from the same video)\n        assert all(p.size == pil_images[0][1].size for _, p in pil_images)\n\n        queries.sort(key=lambda q: q[\"query_processing_order\"])\n        if self.override_query_is_exhaustive_to_true:\n            for query in queries:\n                query[\"is_exhaustive\"] = True\n        datapoint = self.load_queries(pil_images, annotations, queries, img_metadata)\n\n        # skip datapoints with too many masklets to avoid OOM errors\n        num_masklets_in_video = len(datapoint.images[0].objects)\n        if num_masklets_in_video > self.max_masklet_num_in_video > 0:\n            logging.warning(\n                f\"Datapoint {id} has ({num_masklets_in_video=}), exceeding \"\n                f\"the maximum allowed ({self.max_masklet_num_in_video}). \"\n                \"Skipping this datapoint.\"\n            )\n            next_index = (index + 1) % len(self)\n            return self._load_datapoint(next_index)  # move to the next datapoint\n\n        if self.is_tiling_single_image:\n            datapoint = self._tile_single_image_data(datapoint, self.num_stages_sample)\n        if self.max_query_num > 0:\n            datapoint = self._subsample_queries(datapoint, self.max_query_num)\n\n        # ensure that all find queries have the same processing order as their image id\n        for query in datapoint.find_queries:\n            assert query.image_id == query.query_processing_order, (\n                f\"find query has inconsistent image_id and \"\n                f\"query_processing_order: {query.image_id=} vs \"\n                f\"{query.query_processing_order=}\"\n            )\n        return datapoint\n\n    def _sample_stage_ids(self, queries, num_stages_sample, stage_stride):\n        \"\"\"Sample a subset of stage ids from all queries.\"\"\"\n        # Later we can perhaps turn it into a Sampler class to be more flexible.\n        all_stage_ids = sorted(set(q[\"query_processing_order\"] for q in queries))\n        num_stages_total = len(all_stage_ids)\n        if num_stages_total < num_stages_sample:\n            raise ValueError(\"Not enough stages to sample\")\n\n        # the difference in index between the first and the last sampled stage ids\n        b_e_gap = (num_stages_sample - 1) * stage_stride\n        if b_e_gap > num_stages_total - 1:\n            # In this case, it's not possible to sample with the provide stride,\n            # so we use the maximum possible stride.\n            prev_stage_stride = stage_stride\n            stage_stride = math.floor((num_stages_total - 1) / (num_stages_sample - 1))\n            logging.info(\n                f\"lowering stride from {prev_stage_stride} to {stage_stride} to \"\n                f\"sample {num_stages_sample} stages (from {num_stages_total} total)\"\n            )\n            b_e_gap = (num_stages_sample - 1) * stage_stride\n\n        # randomly select a starting stage id (`randint` includes both ends)\n        b_max = len(all_stage_ids) - 1 - b_e_gap\n        b = self.rng.randint(0, b_max)\n        e = b + b_e_gap\n        stage_ids_to_keep = all_stage_ids[b : e + 1 : stage_stride]\n        return stage_ids_to_keep\n\n    def _filter_query_and_anns(\n        self, queries, annotations, stage_ids_to_keep, remap_stage_id, reverse_time_axis\n    ):\n        \"\"\"Filter queries and annotations to only keep those in `stage_ids_to_keep`.\"\"\"\n        stage_ids_to_keep = set(stage_ids_to_keep)\n        kept_img_ids = set()\n        kept_stage_ids = set()\n\n        # Filter queries -- keep those queries with stage_id in `stage_ids_to_keep`\n        filtered_queries = []\n        for query in queries:\n            input_box = query.get(\"input_box\", None)\n            input_points = query.get(\"input_points\", None)\n            has_geo_input = input_box is not None or input_points is not None\n            if has_geo_input and not self.tile_img_keep_find_queries_with_geo_inputs:\n                continue\n            stage_id = query[\"query_processing_order\"]\n            if stage_id in stage_ids_to_keep:\n                kept_img_ids.add(query[\"image_id\"])\n                kept_stage_ids.add(stage_id)\n                filtered_queries.append(query)\n        # Check that all frames in `stage_ids_to_keep` are present after filtering\n        all_frame_present = kept_stage_ids == stage_ids_to_keep\n        assert all_frame_present, f\"{kept_stage_ids=} vs {stage_ids_to_keep=}\"\n        if remap_stage_id:\n            # Remap those kept stage ids to be contiguous and starting from 0\n            old_stage_ids = sorted(kept_stage_ids, reverse=reverse_time_axis)\n            stage_id_old2new = {old: new for new, old in enumerate(old_stage_ids)}\n            for query in filtered_queries:\n                ptr_x_is_empty = query[\"ptr_x_query_id\"] in [None, -1]\n                ptr_y_is_empty = query[\"ptr_y_query_id\"] in [None, -1]\n                assert ptr_x_is_empty and ptr_y_is_empty, (\n                    \"Remapping stage ids is not supported for queries with non-empty ptr_x or ptr_y pointers\"\n                )\n                query[\"query_processing_order\"] = stage_id_old2new[\n                    query[\"query_processing_order\"]\n                ]\n\n        # Filter annotations -- keep those annotations with image_id in `kept_img_ids`\n        filtered_annotations = [\n            ann for ann in annotations if ann[\"image_id\"] in kept_img_ids\n        ]\n\n        return filtered_queries, filtered_annotations, kept_img_ids\n\n    def _tile_single_image_data(self, datapoint: Datapoint, num_stages_sample: int):\n        \"\"\"\n        Tile a single image and its queries to simulate video frames. The output is a\n        datapoint with *identical video frames* (i.e. the same static image) and needs\n        further transforms (e.g. affine) to get video frames with different content.\n        \"\"\"\n        # tile `images: List[Image]`\n        assert len(datapoint.images) == 1, \"Expected only one single image\"\n        tiled_images = [\n            copy.deepcopy(datapoint.images[0]) for _ in range(num_stages_sample)\n        ]\n        for stage_id, img in enumerate(tiled_images):\n            for obj in img.objects:\n                obj.frame_index = stage_id\n\n        # tile `raw_images: Optional[List[PILImage.Image]] = None`\n        tiled_raw_images = None\n        if datapoint.raw_images is not None:\n            assert len(datapoint.raw_images) == 1, \"Expected only one single image\"\n            tiled_raw_images = [\n                datapoint.raw_images[0].copy() for _ in range(num_stages_sample)\n            ]\n\n        # tile `find_queries: List[FindQueryLoaded]`\n        tiled_find_queries_per_stage = [[] for _ in range(num_stages_sample)]\n        for query in datapoint.find_queries:\n            assert query.image_id == 0\n            assert query.query_processing_order == 0\n            # check and make sure that a query doesn't contain pointers or references\n            # to other queries (that cannot be tiled)\n            assert query.ptr_x is None and query.ptr_y is None\n            assert query.ptr_mem is None\n            # assert query.wkdata_qid is None\n            # assert query.other_positive_qids is None\n            # assert query.negative_qids is None\n            has_geo_input = (\n                query.input_bbox is not None or query.input_points is not None\n            )\n            if has_geo_input and not self.tile_img_keep_find_queries_with_geo_inputs:\n                continue\n            for stage_id in range(num_stages_sample):\n                # copy the query and update the image_id\n                new_query = copy.deepcopy(query)\n                new_query.image_id = stage_id\n                new_query.query_processing_order = stage_id\n                if new_query.inference_metadata is not None:\n                    new_query.inference_metadata.frame_index = stage_id\n                tiled_find_queries_per_stage[stage_id].append(new_query)\n\n        tiled_find_queries = sum(tiled_find_queries_per_stage, [])\n\n        # tile `get_queries: List[GetQuery]` -- we skip them for now (since they involve\n        # a pointer to a find query that is complicated to tile, and there is not an\n        # imminent use case for them in the video grounding task in the near future)\n        if self.tile_img_keep_get_queries:\n            raise NotImplementedError(\"Tiling get queries is not implemented yet\")\n        else:\n            tiled_get_queries = []\n\n        return Datapoint(\n            images=tiled_images,\n            raw_images=tiled_raw_images,\n            find_queries=tiled_find_queries,\n            get_queries=tiled_get_queries,\n        )\n\n    def _subsample_queries(self, datapoint: Datapoint, max_query_num: int):\n        \"\"\"Subsample to keep at most `max_query_num` queries per frame in a datapoint.\"\"\"\n        # aggregate the find queries per stage\n        num_frames = max(q.query_processing_order for q in datapoint.find_queries) + 1\n        find_queries_per_stage = [[] for _ in range(num_frames)]\n        for query in datapoint.find_queries:\n            find_queries_per_stage[query.query_processing_order].append(query)\n\n        # verify that all the stages have the same number of queries\n        num_queries_per_stage = len(find_queries_per_stage[0])\n        for queries in find_queries_per_stage:\n            assert len(queries) == num_queries_per_stage\n        if max_query_num <= 0 or num_queries_per_stage <= max_query_num:\n            return datapoint\n\n        # subsample the queries to keep only `max_query_num` queries\n        sampled_inds = self.rng.sample(range(num_queries_per_stage), max_query_num)\n        sampled_find_queries_per_stage = [\n            [queries[idx] for idx in sampled_inds] for queries in find_queries_per_stage\n        ]\n        sampled_find_queries = sum(sampled_find_queries_per_stage, [])\n        return Datapoint(\n            images=datapoint.images,\n            raw_images=datapoint.raw_images,\n            find_queries=sampled_find_queries,\n            get_queries=datapoint.get_queries,\n        )\n"
  },
  {
    "path": "sam3/train/data/torch_dataset.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n\nfrom typing import Callable, Iterable, Optional\n\nfrom torch.utils.data import DataLoader, Dataset, DistributedSampler, IterableDataset\n\n\nclass TorchDataset:\n    def __init__(\n        self,\n        dataset: Dataset,\n        batch_size: int,\n        num_workers: int,\n        shuffle: bool,\n        pin_memory: bool,\n        drop_last: bool,\n        collate_fn: Optional[Callable] = None,\n        worker_init_fn: Optional[Callable] = None,\n        enable_distributed_sampler=True,\n    ) -> None:\n        self.dataset = dataset\n        self.batch_size = batch_size\n        self.num_workers = num_workers\n        self.shuffle = shuffle\n        self.pin_memory = pin_memory\n        self.drop_last = drop_last\n        self.collate_fn = collate_fn\n        self.worker_init_fn = worker_init_fn\n        assert not isinstance(self.dataset, IterableDataset), \"Not supported yet\"\n        if enable_distributed_sampler:\n            self.sampler = DistributedSampler(self.dataset, shuffle=self.shuffle)\n        else:\n            self.sampler = None\n\n    def get_loader(self, epoch) -> Iterable:\n        if self.sampler:\n            self.sampler.set_epoch(epoch)\n        if hasattr(self.dataset, \"epoch\"):\n            self.dataset.epoch = epoch\n        if hasattr(self.dataset, \"set_epoch\"):\n            self.dataset.set_epoch(epoch)\n\n        return DataLoader(\n            self.dataset,\n            batch_size=self.batch_size,\n            num_workers=self.num_workers,\n            pin_memory=self.pin_memory,\n            drop_last=self.drop_last,\n            sampler=self.sampler,\n            collate_fn=self.collate_fn,\n            worker_init_fn=self.worker_init_fn,\n        )\n"
  },
  {
    "path": "sam3/train/loss/__init__.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n"
  },
  {
    "path": "sam3/train/loss/loss_fns.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n\nimport logging\nimport warnings\n\nimport torch\nimport torch.distributed\nimport torch.nn.functional as F\nimport torchmetrics\nfrom sam3.model import box_ops\nfrom sam3.model.data_misc import interpolate\nfrom sam3.train.loss.sigmoid_focal_loss import (\n    triton_sigmoid_focal_loss,\n    triton_sigmoid_focal_loss_reduce,\n)\nfrom torch import nn\n\nfrom .mask_sampling import (\n    calculate_uncertainty,\n    get_uncertain_point_coords_with_randomness,\n    point_sample,\n)\n\n\nCORE_LOSS_KEY = \"core_loss\"\n\n\ndef instance_masks_to_semantic_masks(\n    instance_masks: torch.Tensor, num_instances: torch.Tensor\n) -> torch.Tensor:\n    \"\"\"This function converts instance masks to semantic masks.\n    It accepts a collapsed batch of instances masks (ie all instance masks are concatenated in a single tensor) and\n    the number of instances in each image of the batch.\n    It returns a mask with the same spatial dimensions as the input instance masks, where for each batch element the\n    semantic mask is the union of all the instance masks in the batch element.\n\n    If for a given batch element there are no instances (ie num_instances[i]==0), the corresponding semantic mask will be a tensor of zeros.\n\n    Args:\n        instance_masks (torch.Tensor): A tensor of shape (N, H, W) where N is the number of instances in the batch.\n        num_instances (torch.Tensor): A tensor of shape (B,) where B is the batch size. It contains the number of instances\n            in each image of the batch.\n\n    Returns:\n        torch.Tensor: A tensor of shape (B, H, W) where B is the batch size and H, W are the spatial dimensions of the\n            input instance masks.\n    \"\"\"\n    if num_instances.sum() == 0:\n        # all negative batch, create a tensor of zeros (B, 1, 1)\n        return num_instances.unsqueeze(-1).unsqueeze(-1)\n\n    masks_per_query = torch.split(instance_masks, num_instances.tolist())\n\n    return torch.stack([torch.any(masks, dim=0) for masks in masks_per_query], dim=0)\n\n\n@torch.no_grad()\ndef accuracy(output, target, topk=(1,)):\n    \"\"\"Computes the precision@k for the specified values of k\"\"\"\n    if target.numel() == 0:\n        return [torch.zeros([], device=output.device)]\n    maxk = max(topk)\n    batch_size = target.size(0)\n\n    _, pred = output.topk(maxk, 1, True, True)\n    pred = pred.t()\n    correct = pred.eq(target.view(1, -1).expand_as(pred))\n\n    res = []\n    for k in topk:\n        correct_k = correct[:k].view(-1).float().sum(0)\n        res.append(correct_k.mul_(100.0 / batch_size))\n    return res\n\n\ndef dice_loss(inputs, targets, num_boxes, loss_on_multimask=False, reduce=True):\n    \"\"\"\n    Compute the DICE loss, similar to generalized IOU for masks\n    Args:\n        inputs: A float tensor of arbitrary shape.\n                The predictions for each example.\n        targets: A float tensor with the same shape as inputs. Stores the binary\n                 classification label for each element in inputs\n                (0 for the negative class and 1 for the positive class).\n    \"\"\"\n    try:\n        loss = _dice_loss(inputs, targets, num_boxes, loss_on_multimask, reduce)\n    except torch.OutOfMemoryError:\n        logging.error(\"GPU OOM, computing dice loss on CPU\")\n        # try to recover from GPU OOM by moving tensors to CPU and computing loss there\n        orig_device = inputs.device\n        inputs = inputs.cpu()\n        targets = targets.cpu()\n        if isinstance(num_boxes, torch.Tensor):\n            num_boxes = num_boxes.cpu()\n        loss = _dice_loss(inputs, targets, num_boxes, loss_on_multimask, reduce)\n        loss = loss.to(orig_device)\n\n    return loss\n\n\ndef _dice_loss(inputs, targets, num_boxes, loss_on_multimask=False, reduce=True):\n    inputs = inputs.sigmoid()\n    if loss_on_multimask:\n        # inputs and targets are [N, M, H, W] where M corresponds to multiple predicted masks\n        assert inputs.dim() == 4 and targets.dim() == 4\n        # flatten spatial dimension while keeping multimask channel dimension\n        inputs = inputs.flatten(2)\n        targets = targets.flatten(2)\n        numerator = 2 * (inputs * targets).sum(-1)\n    else:\n        inputs = inputs.flatten(1)\n        numerator = 2 * (inputs * targets).sum(1)\n    denominator = inputs.sum(-1) + targets.sum(-1)\n    loss = 1 - (numerator + 1) / (denominator + 1)\n    if loss_on_multimask:\n        return loss / num_boxes\n    if not reduce:\n        return loss\n    return loss.sum() / num_boxes\n\n\ndef sigmoid_focal_loss(\n    inputs,\n    targets,\n    num_boxes,\n    alpha: float = 0.25,\n    gamma: float = 2,\n    loss_on_multimask=False,\n    reduce=True,\n    triton=True,\n):\n    \"\"\"\n    Loss used in RetinaNet for dense detection: https://arxiv.org/abs/1708.02002.\n    Args:\n        inputs: A float tensor of arbitrary shape.\n                The predictions for each example.\n        targets: A float tensor with the same shape as inputs. Stores the binary\n                 classification label for each element in inputs\n                (0 for the negative class and 1 for the positive class).\n        alpha: (optional) Weighting factor in range (0,1) to balance\n                positive vs negative examples. Default = -1 (no weighting).\n        gamma: Exponent of the modulating factor (1 - p_t) to\n               balance easy vs hard examples.\n    Returns:\n        Loss tensor\n    \"\"\"\n    if not (0 <= alpha <= 1) and triton:\n        raise RuntimeError(f\"Alpha should be in [0,1], got {alpha}\")\n    if triton:\n        if reduce and not loss_on_multimask:\n            loss = triton_sigmoid_focal_loss_reduce(inputs, targets, alpha, gamma)\n            return loss / (num_boxes * inputs.shape[1])\n\n        loss = triton_sigmoid_focal_loss(inputs, targets, alpha, gamma)\n    else:\n        prob = inputs.sigmoid()\n        ce_loss = F.binary_cross_entropy_with_logits(inputs, targets, reduction=\"none\")\n        p_t = prob * targets + (1 - prob) * (1 - targets)\n        loss = ce_loss * ((1 - p_t) ** gamma)\n\n        if alpha >= 0:\n            alpha_t = alpha * targets + (1 - alpha) * (1 - targets)\n            loss = alpha_t * loss\n\n    if not reduce:\n        return loss\n\n    if loss_on_multimask:\n        # loss is [N, M, H, W] where M corresponds to multiple predicted masks\n        assert loss.dim() == 4\n        return loss.flatten(2).mean(-1) / num_boxes  # average over spatial dims\n    return loss.mean(1).sum() / num_boxes\n\n\ndef iou_loss(\n    inputs, targets, pred_ious, num_boxes, loss_on_multimask=False, use_l1_loss=False\n):\n    \"\"\"MSE loss between predicted IoUs and actual IoUs between inputs and targets.\"\"\"\n    assert inputs.dim() == 4 and targets.dim() == 4\n    pred_mask = inputs.flatten(2) > 0\n    gt_mask = targets.flatten(2) > 0\n    area_i = torch.sum(pred_mask & gt_mask, dim=-1).float()\n    area_u = torch.sum(pred_mask | gt_mask, dim=-1).float()\n    actual_ious = area_i / torch.clamp(area_u, min=1.0)\n\n    if use_l1_loss:\n        loss = F.l1_loss(pred_ious, actual_ious, reduction=\"none\")\n    else:\n        loss = F.mse_loss(pred_ious, actual_ious, reduction=\"none\")\n    if loss_on_multimask:\n        return loss / num_boxes\n    return loss.sum() / num_boxes\n\n\n@torch.jit.script\ndef _contrastive_align(logits, positive_map):\n    positive_logits = -logits.masked_fill(~positive_map, 0)\n    negative_logits = logits  # .masked_fill(positive_map, -1000000)\n\n    boxes_with_pos = positive_map.any(2)\n    pos_term = positive_logits.sum(2)\n    neg_term = negative_logits.logsumexp(2)\n\n    nb_pos = positive_map.sum(2) + 1e-6\n\n    box_to_token_loss = (\n        (pos_term / nb_pos + neg_term).masked_fill(~boxes_with_pos, 0).sum()\n    )\n\n    tokens_with_pos = positive_map.any(1)\n    pos_term = positive_logits.sum(1)\n    neg_term = negative_logits.logsumexp(1)\n\n    nb_pos = positive_map.sum(1) + 1e-6\n\n    tokens_to_boxes_loss = (\n        (pos_term / nb_pos + neg_term).masked_fill(~tokens_with_pos, 0).sum()\n    )\n    return (box_to_token_loss + tokens_to_boxes_loss) / 2\n\n\ndef _get_src_permutation_idx(indices):\n    # permute predictions following indices\n    batch_idx = torch.cat(\n        [torch.full_like(src, i) for i, (src, _) in enumerate(indices)]\n    )\n    src_idx = torch.cat([src for (src, _) in indices])\n    return batch_idx, src_idx\n\n\nclass LossWithWeights(nn.Module):\n    def __init__(self, weight_dict, compute_aux, supports_o2m_loss=True):\n        super().__init__()\n        # weights for each computed loss key (those losses not in weight_dict\n        # will not be aggregated in the final reduced core loss)\n        self.weight_dict = weight_dict if weight_dict is not None else {}\n        # whether this loss will be applied on auxiliary outputs\n        self.compute_aux = compute_aux\n        self.supports_o2m_loss = supports_o2m_loss\n        self.target_keys = []\n\n    def forward(self, *args, is_aux=False, **kwargs):\n        if is_aux and not self.compute_aux:\n            return {CORE_LOSS_KEY: 0.0}\n        losses = self.get_loss(*args, **kwargs)\n        losses[CORE_LOSS_KEY] = self.reduce_loss(losses)\n        return losses\n\n    def get_loss(self, **kwargs):\n        raise NotImplementedError()\n\n    def reduce_loss(self, losses):\n        reduced_loss = 0.0\n        for loss_key, weight in self.weight_dict.items():\n            if loss_key not in losses:\n                raise ValueError(f\"{type(self)} doesn't compute {loss_key}\")\n            if weight != 0:\n                reduced_loss += losses[loss_key] * weight\n\n        return reduced_loss\n\n\nclass IABCEMdetr(LossWithWeights):\n    def __init__(\n        self,\n        pos_weight,\n        weight_dict=None,\n        compute_aux=True,\n        gamma=0,\n        weak_loss=True,\n        alpha=0.25,\n        pad_n_queries=None,\n        pad_scale_pos=1.0,\n        use_separate_loss_for_det_and_trk=False,\n        num_det_queries=None,\n        det_exhaustive_loss_scale_pos=1.0,\n        det_exhaustive_loss_scale_neg=1.0,\n        det_non_exhaustive_loss_scale_pos=1.0,\n        det_non_exhaustive_loss_scale_neg=1.0,\n        trk_loss_scale_pos=1.0,\n        trk_loss_scale_neg=1.0,\n        no_loss_for_fp_propagation=False,\n        apply_loss_to_det_queries_in_video_grounding=True,\n        use_presence=False,\n        use_presence_semgseg=False,  # If True, use presence scores from the semgseg head.\n        presence_alpha=0.5,\n        presence_gamma=0.0,\n        pos_focal: bool = False,  # for box scores, use focal loss for positives as well\n    ):\n        super().__init__(weight_dict, compute_aux)\n        self.pos_weight = pos_weight\n        self.gamma = gamma\n        self.weak_loss = weak_loss\n        self.alpha = alpha\n        self.target_keys.append(\"boxes_xyxy\")\n        self.no_loss_for_fp_propagation = no_loss_for_fp_propagation\n        if self.weak_loss:\n            self.target_keys.append(\"is_exhaustive\")\n        # NOTE: This is hacky solution to have the same CE loss scale across datasets where the model might predict different number of object queries for different tasks.\n        # If not None, we assume there are a total pad_n_queries object queries.\n        # For example, if the model predicts only 1 object query and pad_n_queries=100, we pad the predictions with 99 zero preds.\n        # Currently this only affects the BCE loss and not the F1 score.\n        self.pad_n_queries = pad_n_queries\n        self.pad_scale_pos = pad_scale_pos\n        if self.pad_scale_pos != 1.0:\n            assert self.pad_n_queries is not None\n        # whether to use presence scores\n        self.use_presence = use_presence\n        self.use_presence_semgseg = use_presence_semgseg\n        if self.use_presence_semgseg:\n            assert self.use_presence\n        self.presence_alpha = presence_alpha\n        self.presence_gamma = presence_gamma\n        self.pos_focal = pos_focal\n\n        # Decoupled loss for detection and tracking queries\n        self.apply_loss_to_det_queries_in_video_grounding = (\n            apply_loss_to_det_queries_in_video_grounding\n        )\n        self.use_separate_loss_for_det_and_trk = use_separate_loss_for_det_and_trk\n        if num_det_queries is not None:\n            logging.warning(\"note: it's not needed to set num_det_queries anymore\")\n        if self.use_separate_loss_for_det_and_trk:\n            assert not self.weak_loss, (\n                \"Do not use weak_loss in this case -- set separate loss for detection and tracking queries instead\"\n            )\n            self.det_exhaustive_loss_scale_pos = det_exhaustive_loss_scale_pos\n            self.det_exhaustive_loss_scale_neg = det_exhaustive_loss_scale_neg\n            self.det_non_exhaustive_loss_scale_pos = det_non_exhaustive_loss_scale_pos\n            self.det_non_exhaustive_loss_scale_neg = det_non_exhaustive_loss_scale_neg\n            self.trk_loss_scale_pos = trk_loss_scale_pos\n            self.trk_loss_scale_neg = trk_loss_scale_neg\n        else:\n            assert (\n                det_exhaustive_loss_scale_pos == 1.0\n                and det_exhaustive_loss_scale_neg == 1.0\n                and det_non_exhaustive_loss_scale_pos == 1.0\n                and det_non_exhaustive_loss_scale_neg == 1.0\n                and trk_loss_scale_pos == 1.0\n                and trk_loss_scale_neg == 1.0\n            ), (\n                \"If not using separate loss for detection and tracking queries, separate detection and tracking loss scales should all be 1.0\"\n            )\n\n    def get_loss(self, outputs, targets, indices, num_boxes):\n        assert len(outputs[\"pred_logits\"].shape) > 2, \"Incorrect predicted logits shape\"\n        assert outputs[\"pred_logits\"].shape[-1] == 1, \"Incorrect predicted logits shape\"\n        src_logits = outputs[\"pred_logits\"].squeeze(-1)\n        prob = src_logits.sigmoid()\n\n        with torch.no_grad():\n            target_classes = torch.full(\n                src_logits.shape[:2],\n                0,\n                dtype=torch.float,\n                device=src_logits.device,\n            )\n            target_classes[(indices[0], indices[1])] = 1\n            src_boxes_xyxy = outputs[\"pred_boxes_xyxy\"][(indices[0], indices[1])]\n            target_boxes_giou = (\n                targets[\"boxes_xyxy\"][indices[2]]\n                if indices[2] is not None\n                else targets[\"boxes_xyxy\"]\n            )\n\n            iou = box_ops.fast_diag_box_iou(src_boxes_xyxy, target_boxes_giou)\n            t = prob[(indices[0], indices[1])] ** self.alpha * iou ** (1 - self.alpha)\n            t = torch.clamp(t, 0.01).detach()\n            positive_target_classes = target_classes.clone()\n            positive_target_classes[(indices[0], indices[1])] = t\n\n        # Soft loss on positives\n        if self.pos_focal:\n            loss_bce = sigmoid_focal_loss(\n                src_logits.contiguous(),\n                positive_target_classes,\n                num_boxes=1,\n                alpha=0.5,\n                gamma=self.gamma,\n                reduce=False,\n            )\n        else:\n            loss_bce = F.binary_cross_entropy_with_logits(\n                src_logits, positive_target_classes, reduction=\"none\"\n            )\n        loss_bce = loss_bce * target_classes * self.pos_weight\n\n        if (\n            self.pad_n_queries is not None\n            and isinstance(self.pad_n_queries, int)\n            and loss_bce.size(1) < self.pad_n_queries\n        ):\n            loss_bce = loss_bce * self.pad_scale_pos\n        # Negatives\n        loss_bce = loss_bce + F.binary_cross_entropy_with_logits(\n            src_logits, target_classes, reduction=\"none\"\n        ) * (1 - target_classes) * (prob**self.gamma)\n\n        # Optionally, not applying IABCEMdetr loss to detection queries in video.\n        is_video_grounding = outputs.get(\"is_video_grounding_batch\", False)\n        if is_video_grounding and not self.apply_loss_to_det_queries_in_video_grounding:\n            Q_det = outputs[\"Q_det\"]\n            loss_bce[:, :Q_det] *= 0.0\n        presence_loss = torch.tensor(0.0, device=src_logits.device)\n        presence_dec_acc = torch.tensor(0.0, device=src_logits.device)\n        if self.use_presence:\n            # no classifiction loss for individual tokens if no target gt\n            # cannot directly use targets[\"num_boxes\"] to check if some\n            # GT box exists as there may be dummy boxes for \"invisible objects\"\n            # in video grounding data\n\n            gt_padded_object_ids = targets[\"object_ids_padded\"]  # (B, H)\n            gt_padded_boxes = targets[\"boxes_padded\"]  # (B, H, 4) shape, CxCyWH\n            gt_padded_is_visible = (\n                (gt_padded_object_ids >= 0)\n                & (gt_padded_boxes[..., 2] > 0)  # width > 0\n                & (gt_padded_boxes[..., 3] > 0)  # height > 0\n            )\n            keep_loss = (gt_padded_is_visible.sum(dim=-1)[..., None] != 0).float()\n\n            loss_bce = loss_bce * keep_loss\n\n            if self.use_presence_semgseg:\n                # no loss here, has it's own separate loss computation\n                assert \"presence_logit_dec\" not in outputs\n            elif \"presence_logit_dec\" in outputs:\n                presence_logits = outputs[\"presence_logit_dec\"].view_as(keep_loss)\n                bs = presence_logits.shape[0]\n                presence_loss = sigmoid_focal_loss(\n                    presence_logits,\n                    keep_loss,\n                    # not num_boxes, but we'll use it to normalize by bs\n                    num_boxes=bs,\n                    alpha=self.presence_alpha,\n                    gamma=self.presence_gamma,\n                )\n                pred = (presence_logits.sigmoid() > 0.5).float()\n                presence_dec_acc = (pred == keep_loss).float().mean()\n            else:\n                # for o2m, nothing to do\n                pass\n\n        if self.weak_loss:\n            assert not self.use_separate_loss_for_det_and_trk, (\n                \"Do not use weak_loss in this case -- set separate loss for detection and tracking queries instead\"\n            )\n\n            # nullify the negative loss for the non-exhaustive classes\n            assert loss_bce.shape[0] == targets[\"is_exhaustive\"].shape[0]\n            assert targets[\"is_exhaustive\"].ndim == 1\n\n            loss_mask = (~targets[\"is_exhaustive\"]).view(-1, 1).expand_as(loss_bce)\n            # restrict the mask to the negative supervision\n            loss_mask = loss_mask & (target_classes < 0.5)\n            loss_mask = ~loss_mask\n            # Mask the loss\n            loss_bce = loss_bce * loss_mask.float()\n            # Average\n            loss_bce = loss_bce.sum() / (loss_mask.sum() + 1e-6)\n        else:\n            # apply separate loss weights to detection and tracking queries\n            if self.use_separate_loss_for_det_and_trk:\n                Q_det = outputs[\"Q_det\"]\n                assert loss_bce.size(1) >= Q_det\n                is_positive = target_classes > 0.5\n                is_positive_det = is_positive[:, :Q_det]\n                is_positive_trk = is_positive[:, Q_det:]\n                assert loss_bce.size(0) == targets[\"is_exhaustive\"].size(0)\n                is_exhaustive = targets[\"is_exhaustive\"].unsqueeze(1).bool()\n                loss_scales = torch.zeros_like(loss_bce)\n                # detection query loss weights\n                loss_scales[:, :Q_det] = (\n                    (is_exhaustive & is_positive_det).float()\n                    * self.det_exhaustive_loss_scale_pos\n                    + (is_exhaustive & ~is_positive_det).float()\n                    * self.det_exhaustive_loss_scale_neg\n                    + (~is_exhaustive & is_positive_det).float()\n                    * self.det_non_exhaustive_loss_scale_pos\n                    + (~is_exhaustive & ~is_positive_det).float()\n                    * self.det_non_exhaustive_loss_scale_neg\n                )\n                # tracking query weights\n                loss_scales[:, Q_det:] = (\n                    is_positive_trk.float() * self.trk_loss_scale_pos\n                    + (~is_positive_trk).float() * self.trk_loss_scale_neg\n                )\n                # apply the loss weights\n\n                # if the id is -2 means it is a fp propagation , we don't apply the loss to them\n                if self.no_loss_for_fp_propagation:\n                    is_original_queries = outputs[\"pred_old_obj_ids\"] != -2\n                    loss_scales *= (is_exhaustive | is_original_queries).float()\n\n                loss_bce = loss_bce * loss_scales\n\n            if self.pad_n_queries is None or loss_bce.size(1) >= self.pad_n_queries:\n                loss_bce = loss_bce.mean()\n            else:\n                assert isinstance(self.pad_n_queries, int)\n                assert loss_bce.size(1) < self.pad_n_queries, (\n                    f\"The number of predictions is more than the expected total after padding. Got {loss_bce.size(1)} predictions.\"\n                )\n                loss_bce = loss_bce.sum() / (self.pad_n_queries * loss_bce.size(0))\n\n        bce_f1 = torchmetrics.functional.f1_score(\n            src_logits.sigmoid().flatten(),\n            target=target_classes.flatten().long(),\n            task=\"binary\",\n        )\n\n        losses = {\n            \"loss_ce\": loss_bce,\n            \"ce_f1\": bce_f1,\n            \"presence_loss\": presence_loss,\n            \"presence_dec_acc\": presence_dec_acc,\n        }\n        return losses\n\n\nclass Boxes(LossWithWeights):\n    def __init__(\n        self,\n        weight_dict=None,\n        compute_aux=True,\n        apply_loss_to_det_queries_in_video_grounding=True,\n    ):\n        super().__init__(weight_dict, compute_aux)\n        self.apply_loss_to_det_queries_in_video_grounding = (\n            apply_loss_to_det_queries_in_video_grounding\n        )\n        self.target_keys.extend([\"boxes\", \"boxes_xyxy\"])\n\n    def get_loss(self, outputs, targets, indices, num_boxes):\n        \"\"\"Compute the losses related to the bounding boxes, the L1 regression loss and the GIoU loss\n        targets dicts must contain the key \"boxes\" containing a tensor of dim [nb_target_boxes, 4]\n        The target boxes are expected in format (center_x, center_y, h, w), normalized by the image size.\n        \"\"\"\n        # Optionally, not applying Boxes loss to detection queries in video.\n        is_video_grounding = outputs.get(\"is_video_grounding_batch\", False)\n        if is_video_grounding and not self.apply_loss_to_det_queries_in_video_grounding:\n            indices = _keep_only_trk_queries_in_match_inds(\n                indices, Q_det=outputs[\"Q_det\"]\n            )\n\n        assert \"pred_boxes\" in outputs\n        # idx = self._get_src_permutation_idx(indices)\n        src_boxes = outputs[\"pred_boxes\"][(indices[0], indices[1])]\n        src_boxes_xyxy = outputs[\"pred_boxes_xyxy\"][(indices[0], indices[1])]\n        target_boxes = (\n            targets[\"boxes\"] if indices[2] is None else targets[\"boxes\"][indices[2]]\n        )\n        target_boxes_giou = (\n            targets[\"boxes_xyxy\"]\n            if indices[2] is None\n            else targets[\"boxes_xyxy\"][indices[2]]\n        )\n\n        loss_bbox = F.l1_loss(src_boxes, target_boxes, reduction=\"none\")\n\n        losses = {}\n        losses[\"loss_bbox\"] = loss_bbox.sum() / num_boxes\n\n        loss_giou = 1 - box_ops.fast_diag_generalized_box_iou(\n            src_boxes_xyxy, target_boxes_giou\n        )\n        losses[\"loss_giou\"] = loss_giou.sum() / num_boxes\n        return losses\n\n\nclass Masks(LossWithWeights):\n    def __init__(\n        self,\n        weight_dict=None,\n        compute_aux=False,\n        focal_alpha=0.25,\n        focal_gamma=2,\n        num_sample_points=None,\n        oversample_ratio=None,\n        importance_sample_ratio=None,\n        apply_loss_to_det_queries_in_video_grounding=True,\n    ):\n        super().__init__(weight_dict, compute_aux)\n        if compute_aux:\n            warnings.warn(\"Masks loss usually shouldn't be applied to aux outputs\")\n        self.focal_alpha = focal_alpha\n        self.focal_gamma = focal_gamma\n        self.num_sample_points = num_sample_points\n        self.oversample_ratio = oversample_ratio\n        self.importance_sample_ratio = importance_sample_ratio\n        self.apply_loss_to_det_queries_in_video_grounding = (\n            apply_loss_to_det_queries_in_video_grounding\n        )\n        self.target_keys.extend([\"masks\", \"is_valid_mask\"])\n\n    def _sampled_loss(self, src_masks, target_masks, num_boxes):\n        assert len(src_masks.shape) == 3 and len(target_masks.shape) == 3\n        src_masks = src_masks[:, None]\n        target_masks = target_masks[:, None]\n        with torch.no_grad():\n            # Sample point_coords\n            point_coords = get_uncertain_point_coords_with_randomness(\n                src_masks,\n                calculate_uncertainty,\n                self.num_sample_points,\n                self.oversample_ratio,\n                self.importance_sample_ratio,\n            )\n\n            # get GT labels\n            sampled_target_masks = point_sample(\n                target_masks,\n                point_coords,\n                align_corners=False,\n            ).squeeze(1)\n\n        sampled_src_masks = point_sample(\n            src_masks,\n            point_coords,\n            align_corners=False,\n        ).squeeze(1)\n\n        losses = {\n            \"loss_mask\": sigmoid_focal_loss(\n                sampled_src_masks,\n                sampled_target_masks,\n                num_boxes,\n                alpha=self.focal_alpha,\n                gamma=self.focal_gamma,\n            ),\n            \"loss_dice\": dice_loss(sampled_src_masks, sampled_target_masks, num_boxes),\n        }\n        # Not needed for backward\n        del src_masks\n        del target_masks\n\n        return losses\n\n    def get_loss(self, outputs, targets, indices, num_boxes):\n        \"\"\"Compute the losses related to the masks: the focal loss and the dice loss.\n        targets dicts must contain the key \"masks\" containing a tensor of dim [nb_target_boxes, h, w]\n        \"\"\"\n        assert \"pred_masks\" in outputs\n        assert \"is_valid_mask\" in targets\n        # Optionally, not applying Masks loss to detection queries in video.\n        is_video_grounding = outputs.get(\"is_video_grounding_batch\", False)\n        if is_video_grounding and not self.apply_loss_to_det_queries_in_video_grounding:\n            indices = _keep_only_trk_queries_in_match_inds(\n                indices, Q_det=outputs[\"Q_det\"]\n            )\n\n        src_masks = outputs[\"pred_masks\"]\n\n        # Dataset doesn't have segmentation masks\n        if targets[\"masks\"] is None:\n            return {\n                \"loss_mask\": torch.tensor(0.0, device=src_masks.device),\n                \"loss_dice\": torch.tensor(0.0, device=src_masks.device),\n            }\n\n        target_masks = (\n            targets[\"masks\"] if indices[2] is None else targets[\"masks\"][indices[2]]\n        )\n        target_masks = target_masks.to(src_masks)\n        keep = (\n            targets[\"is_valid_mask\"]\n            if indices[2] is None\n            else targets[\"is_valid_mask\"][indices[2]]\n        )\n\n        src_masks = src_masks[(indices[0], indices[1])]\n\n        # Remove invalid masks from loss\n        src_masks = src_masks[keep]\n        target_masks = target_masks[keep]\n\n        if self.num_sample_points is not None:\n            # Compute loss on sampled points for the Mask\n            losses = self._sampled_loss(src_masks, target_masks, num_boxes)\n\n        else:\n            # upsample predictions to the target size\n            if target_masks.shape[0] == 0 and src_masks.shape[0] == 0:\n                src_masks = src_masks.flatten(1)\n                target_masks = target_masks.reshape(src_masks.shape)\n            else:\n                if len(src_masks.shape) == 3:\n                    src_masks = src_masks[:, None]\n                if src_masks.dtype == torch.bfloat16:\n                    # Bilinear interpolation does not support bf16\n                    src_masks = src_masks.to(dtype=torch.float32)\n                src_masks = interpolate(\n                    src_masks,\n                    size=target_masks.shape[-2:],\n                    mode=\"bilinear\",\n                    align_corners=False,\n                )\n                src_masks = src_masks[:, 0].flatten(1)\n                target_masks = target_masks.flatten(1)\n\n            losses = {\n                \"loss_mask\": sigmoid_focal_loss(\n                    src_masks,\n                    target_masks,\n                    num_boxes,\n                    alpha=self.focal_alpha,\n                    gamma=self.focal_gamma,\n                ),\n                \"loss_dice\": dice_loss(src_masks, target_masks, num_boxes),\n            }\n\n        return losses\n\n\n# class MultiStepIteractiveMasks(LossWithWeights):\n#     def __init__(\n#         self,\n#         weight_dict=None,\n#         compute_aux=False,\n#         focal_alpha=0.25,\n#         focal_gamma=2,\n#     ):\n#         warnings.warn(\n#             \"MultiStepIteractiveMasks is deprecated. Please use MultiStepMultiMasksAndIous\",\n#             DeprecationWarning,\n#         )\n#         super().__init__(weight_dict, compute_aux)\n#         self.focal_alpha = focal_alpha\n#         self.focal_gamma = focal_gamma\n#         self.target_keys.extend([\"masks\"])\n\n#     def get_loss(self, outputs, targets, indices, num_boxes):\n#         \"\"\"Compute the losses related to the masks: the focal loss and the dice loss.\n#         targets dicts must contain the key \"masks\" containing a tensor of dim [nb_target_boxes, h, w]\n\n#         Unlike `Masks`, here the \"multistep_pred_masks\" can have multiple channels, each\n#         corresponding to one iterative prediction step in SAM-style training. We treat each\n#         channel as a mask prediction and sum the loss across channels.\n#         \"\"\"\n#         src_masks = outputs[\"multistep_pred_masks\"]\n#         target_masks = targets[\"masks\"]\n#         assert src_masks.size(0) == target_masks.size(0)\n#         assert src_masks.dim() == 4\n#         assert target_masks.dim() == 3\n\n#         # tile target_masks according to the number of\n#         # channels `src_masks`.\n#         num_steps = src_masks.size(1)\n#         target_masks = target_masks.unsqueeze(1).to(src_masks.dtype)\n#         if num_steps > 1:\n#             target_masks = target_masks.repeat(1, num_steps, 1, 1)\n\n#         # resize `src_masks` to target mask resolution\n#         if src_masks.shape != target_masks.shape:\n#             src_masks = interpolate(\n#                 src_masks,\n#                 size=target_masks.shape[-2:],\n#                 mode=\"bilinear\",\n#                 align_corners=False,\n#             )\n#             assert src_masks.shape == target_masks.shape\n\n#         # flatten the multiple steps in to the batch dimension\n#         src_masks = src_masks.flatten(0, 1).flatten(1)\n#         target_masks = target_masks.flatten(0, 1).flatten(1)\n#         losses = {\n#             \"loss_mask\": sigmoid_focal_loss(\n#                 src_masks,\n#                 target_masks,\n#                 num_boxes,\n#                 alpha=self.focal_alpha,\n#                 gamma=self.focal_gamma,\n#             ),\n#             \"loss_dice\": dice_loss(src_masks, target_masks, num_boxes),\n#         }\n\n#         return losses\n\n\n# class MultiStepMultiMasksAndIous(LossWithWeights):\n#     def __init__(\n#         self,\n#         weight_dict=None,\n#         compute_aux=False,\n#         focal_alpha=0.25,\n#         focal_gamma=2,\n#         # if True, back-prop on all predicted ious\n#         # not just the one with lowest loss_combo\n#         supervise_all_iou=False,\n#         # Less slack vs MSE loss in [-1, 1] error range\n#         iou_use_l1_loss=False,\n#         # Settings for obj score prediction\n#         pred_obj_scores=False,\n#         focal_gamma_obj_score=0.0,\n#         focal_alpha_obj_score=-1,\n#     ):\n#         super().__init__(weight_dict, compute_aux)\n#         self.focal_alpha = focal_alpha\n#         self.focal_gamma = focal_gamma\n#         self.target_keys.extend([\"masks\"])\n#         assert \"loss_mask\" in self.weight_dict\n#         assert \"loss_dice\" in self.weight_dict\n#         assert \"loss_iou\" in self.weight_dict\n#         if \"loss_class\" not in self.weight_dict:\n#             self.weight_dict[\"loss_class\"] = 0.0\n#         self.focal_alpha_obj_score = focal_alpha_obj_score\n#         self.focal_gamma_obj_score = focal_gamma_obj_score\n#         self.supervise_all_iou = supervise_all_iou\n#         self.iou_use_l1_loss = iou_use_l1_loss\n#         self.pred_obj_scores = pred_obj_scores\n\n#     def get_loss(self, outputs, targets, indices, num_boxes):\n#         \"\"\"\n#         Compute the losses related to the masks: the focal loss and the dice loss.\n#         and also the MSE loss between predicted IoUs and actual IoUs.\n\n#         Here \"multistep_pred_multimasks_high_res\" is a list of multimasks (tensors\n#         of shape [N, M, H, W], where M could be 1 or larger, corresponding to\n#         one or multiple predicted masks from a click.\n\n#         We back-propagate focal, dice and iou losses only on the prediction channel\n#         with the lowest focal+dice loss between predicted mask and ground-truth.\n#         \"\"\"\n\n#         target_masks = targets[\"masks\"].unsqueeze(1).float()\n#         assert target_masks.dim() == 4  # [N, 1, H, W]\n#         src_masks_list = outputs[\"multistep_pred_multimasks_high_res\"]\n#         ious_list = outputs[\"multistep_pred_ious\"]\n#         object_score_logits_list = outputs[\"multistep_object_score_logits\"]\n\n#         assert len(src_masks_list) == len(ious_list)\n#         assert len(object_score_logits_list) == len(ious_list)\n\n#         # Remove invalid masks from loss\n#         keep = targets[\"is_valid_mask\"]\n#         target_masks = target_masks[keep]\n\n#         # accumulate the loss over prediction steps\n#         losses = {\"loss_mask\": 0, \"loss_dice\": 0, \"loss_iou\": 0, \"loss_class\": 0}\n#         for src_masks, ious, object_score_logits in zip(\n#             src_masks_list, ious_list, object_score_logits_list\n#         ):\n#             object_score_logits = object_score_logits[keep]\n#             ious = ious[keep]\n#             src_masks = src_masks[keep]\n#             self._update_losses(\n#                 losses, src_masks, target_masks, ious, num_boxes, object_score_logits\n#             )\n#         return losses\n\n#     def _update_losses(\n#         self, losses, src_masks, target_masks, ious, num_boxes, object_score_logits\n#     ):\n#         target_masks = target_masks.expand_as(src_masks)\n#         # get focal, dice and iou loss on all output masks in a prediction step\n#         loss_multimask = sigmoid_focal_loss(\n#             src_masks,\n#             target_masks,\n#             num_boxes,\n#             alpha=self.focal_alpha,\n#             gamma=self.focal_gamma,\n#             loss_on_multimask=True,\n#             triton=False,  # only use triton if alpha > 0\n#         )\n#         loss_multidice = dice_loss(\n#             src_masks, target_masks, num_boxes, loss_on_multimask=True\n#         )\n#         if not self.pred_obj_scores:\n#             loss_class = torch.tensor(\n#                 0.0, dtype=loss_multimask.dtype, device=loss_multimask.device\n#             )\n#             target_obj = torch.ones(\n#                 loss_multimask.shape[0],\n#                 1,\n#                 dtype=loss_multimask.dtype,\n#                 device=loss_multimask.device,\n#             )\n#         else:\n#             target_obj = torch.any((target_masks[:, 0] > 0).flatten(1), dim=-1)[\n#                 ..., None\n#             ].float()\n#             loss_class = sigmoid_focal_loss(\n#                 object_score_logits,\n#                 target_obj,\n#                 num_boxes,\n#                 alpha=self.focal_alpha_obj_score,\n#                 gamma=self.focal_gamma_obj_score,\n#                 triton=False,\n#             )\n\n#         loss_multiiou = iou_loss(\n#             src_masks,\n#             target_masks,\n#             ious,\n#             num_boxes,\n#             loss_on_multimask=True,\n#             use_l1_loss=self.iou_use_l1_loss,\n#         )\n#         assert loss_multimask.dim() == 2\n#         assert loss_multidice.dim() == 2\n#         assert loss_multiiou.dim() == 2\n#         if loss_multimask.size(1) > 1:\n#             # take the mask indices with the smallest focal + dice loss for back propagation\n#             loss_combo = (\n#                 loss_multimask * self.weight_dict[\"loss_mask\"]\n#                 + loss_multidice * self.weight_dict[\"loss_dice\"]\n#             )\n#             best_loss_inds = torch.argmin(loss_combo, dim=-1)\n#             batch_inds = torch.arange(loss_combo.size(0), device=loss_combo.device)\n#             loss_mask = loss_multimask[batch_inds, best_loss_inds].unsqueeze(1)\n#             loss_dice = loss_multidice[batch_inds, best_loss_inds].unsqueeze(1)\n#             # calculate the iou prediction and slot losses only in the index\n#             # with the minimum loss for each mask (to be consistent w/ SAM)\n#             if self.supervise_all_iou:\n#                 loss_iou = loss_multiiou.mean(dim=-1).unsqueeze(1)\n#             else:\n#                 loss_iou = loss_multiiou[batch_inds, best_loss_inds].unsqueeze(1)\n#         else:\n#             loss_mask = loss_multimask\n#             loss_dice = loss_multidice\n#             loss_iou = loss_multiiou\n\n#         # backprop focal, dice and iou loss only if obj present\n#         loss_mask = loss_mask * target_obj\n#         loss_dice = loss_dice * target_obj\n#         loss_iou = loss_iou * target_obj\n\n#         # sum over batch dimension (note that the losses are already divided by num_boxes)\n#         losses[\"loss_mask\"] += loss_mask.sum()\n#         losses[\"loss_dice\"] += loss_dice.sum()\n#         losses[\"loss_iou\"] += loss_iou.sum()\n#         losses[\"loss_class\"] += loss_class\n\n\n# class TextCriterion(LossWithWeights):\n#     def __init__(\n#         self,\n#         pad_token,\n#         max_seq_len=100,\n#         weight_dict=None,\n#         compute_aux=False,\n#     ):\n#         super().__init__(weight_dict, compute_aux)\n#         self.pad_token = pad_token\n#         self.max_seq_len = max_seq_len\n#         self.in_lengths = None\n\n#     def get_loss(self, outputs, **kwargs):\n#         nb_tokens = outputs[\"captioning_tokenized_target\"].input_ids.numel()\n#         bs, seq_len = outputs[\"captioning_tokenized_target\"].input_ids.shape\n#         ce = F.cross_entropy(\n#             outputs[\"captioning_pred_text\"].flatten(0, -2),\n#             outputs[\"captioning_tokenized_target\"].input_ids.flatten(),\n#             ignore_index=self.pad_token,\n#             reduction=\"sum\",\n#         )\n\n#         not_pad = (\n#             outputs[\"captioning_tokenized_target\"]\n#             .input_ids.reshape(-1)\n#             .ne(self.pad_token)\n#         )\n\n#         if nb_tokens > 0:\n#             nb_non_pad = not_pad.numel()\n#             ce = ce / nb_non_pad\n\n#         preds = outputs[\"captioning_pred_text\"].flatten(0, -2).argmax(-1)[not_pad]\n#         targets = outputs[\"captioning_tokenized_target\"].input_ids.flatten()[not_pad]\n#         correct = preds == targets\n#         correct = correct.sum() / (correct.numel() + 1e-5)\n\n#         correct_sequence_level = torch.all(\n#             (\n#                 outputs[\"captioning_pred_text\"]\n#                 .flatten(0, -2)\n#                 .argmax(-1)\n#                 .reshape(bs, seq_len)\n#                 == outputs[\"captioning_tokenized_target\"].input_ids\n#             )\n#             | (~not_pad).view(bs, seq_len),\n#             dim=1,\n#         )\n#         seq_level_acc = correct_sequence_level.float().mean()\n\n#         return {\"loss_text\": ce, \"text_acc\": correct, \"text_seq_acc\": seq_level_acc}\n\n\ndef segment_miou(source, target):\n    \"\"\"Compute the mean IoU between two sets of masks\"\"\"\n    assert source.shape == target.shape, \"The two masks must have the same shape\"\n    assert source.ndim == 3, \"The masks must be 3D\"\n\n    valid_targets = (target.sum(dim=(1, 2)) > 0).sum()\n    if valid_targets == 0:\n        return torch.tensor(1.0, device=source.device)\n    intersection = (source.bool() & target.bool()).sum(dim=(1, 2))\n    union = (source.bool() | target.bool()).sum(dim=(1, 2))\n    iou = intersection / (union + 1e-8)\n    return iou.sum() / valid_targets\n\n\nclass SemanticSegCriterion(LossWithWeights):\n    def __init__(\n        self,\n        weight_dict,\n        focal: bool = False,\n        focal_alpha: float = 0.6,\n        focal_gamma: float = 1.6,\n        downsample: bool = True,\n        presence_head: bool = False,\n        # Option to turn off presence loss, if some other component\n        # is already doing it, e.g. decoder - in which case,\n        # we could still set presence_head to True so that\n        # losses are not propogated to masks when there is no GT mask\n        presence_loss: bool = True,\n    ):\n        super().__init__(weight_dict, False)\n        self.focal = focal\n        self.focal_alpha = focal_alpha\n        self.focal_gamma = focal_gamma\n        self.downsample = downsample\n        self.presence_head = presence_head\n        self.presence_loss = presence_loss\n\n    def get_loss(self, out_dict, targets):\n        outputs = out_dict[\"semantic_seg\"]\n        presence_logit = out_dict[\"presence_logit\"]\n        if (\n            \"semantic_masks\" in targets\n            and targets[\"semantic_masks\"] is not None\n            and targets[\"semantic_masks\"].size(0) > 0\n        ):\n            semantic_targets = targets[\"semantic_masks\"]\n            with torch.no_grad():\n                if self.downsample:\n                    # downsample targets to the size of predictions\n                    size = outputs.shape[-2:]\n                    semantic_targets = (\n                        F.interpolate(\n                            semantic_targets.float().unsqueeze(1),\n                            size=size,\n                            mode=\"bilinear\",\n                            align_corners=False,\n                        )\n                        .squeeze(1)\n                        .bool()\n                    )\n        else:\n            with torch.no_grad():\n                if self.downsample:\n                    # downsample targets to the size of predictions\n                    size = outputs.shape[-2:]\n                    segments = (\n                        F.interpolate(\n                            targets[\"masks\"].float().unsqueeze(1),\n                            size=size,\n                            mode=\"bilinear\",\n                            align_corners=False,\n                        )\n                        .squeeze(1)\n                        .bool()\n                    )\n                else:\n                    segments = targets[\"masks\"].bool()\n\n                # the annotations are for instance segmentation, so we merge them to get semantic segmentation\n                semantic_targets = instance_masks_to_semantic_masks(\n                    segments, targets[\"num_boxes\"]\n                )\n\n        if not self.downsample:\n            # upsample predictions to the target size\n            size = semantic_targets.shape[-2:]\n            outputs = F.interpolate(\n                outputs.float(),\n                size=size,\n                mode=\"bilinear\",\n                align_corners=False,\n            )\n\n        if self.focal:\n            loss = sigmoid_focal_loss(\n                outputs.squeeze(1).flatten(-2),\n                semantic_targets.float().flatten(-2),\n                num_boxes=len(semantic_targets),\n                alpha=self.focal_alpha,\n                gamma=self.focal_gamma,\n                reduce=not self.presence_head,\n            )\n            if self.presence_head:\n                loss = loss.mean(1)\n        else:\n            loss = F.binary_cross_entropy_with_logits(\n                outputs.squeeze(1),\n                semantic_targets.float(),\n                reduction=\"none\" if self.presence_head else \"mean\",\n            )\n            if self.presence_head:\n                loss = loss.flatten(1).mean(1)\n\n        loss_dice = dice_loss(\n            outputs.squeeze(1).flatten(1),\n            semantic_targets.flatten(1),\n            len(semantic_targets),\n            reduce=not self.presence_head,\n        )\n\n        miou = segment_miou(outputs.sigmoid().squeeze(1) > 0.5, semantic_targets)\n\n        loss_dict = {}\n\n        if self.presence_head:\n            presence_target = semantic_targets.flatten(1).any(-1)\n            if self.presence_loss:\n                loss_presence = F.binary_cross_entropy_with_logits(\n                    presence_logit.flatten(),\n                    presence_target.float(),\n                )\n                presence_acc = (\n                    ((presence_logit.flatten().sigmoid() > 0.5) == presence_target)\n                    .float()\n                    .mean()\n                )\n            else:\n                # Dummy values\n                loss_presence = torch.tensor(0.0, device=loss.device)\n                # Whichever component is computing the presence loss,\n                # should also track presence_acc\n                presence_acc = torch.tensor(0.0, device=loss.device)\n\n            loss_dict[\"loss_semantic_presence\"] = loss_presence\n            loss_dict[\"presence_acc\"] = presence_acc\n\n            # reduce the other losses, skipping the negative ones\n            bs = loss.shape[0]\n            assert presence_target.numel() == bs\n\n            mask = presence_target\n            nb_valid = presence_target.sum().item()\n\n            loss = (loss * mask.float()).sum() / (nb_valid + 1e-6)\n            loss_dice = (loss_dice * mask.float()).sum() / (nb_valid + 1e-6)\n\n        loss_dict.update(\n            {\n                \"loss_semantic_seg\": loss,\n                \"loss_semantic_dice\": loss_dice,\n                \"miou_semantic_seg\": miou,\n            }\n        )\n\n        return loss_dict\n\n\nclass Det2TrkAssoc(LossWithWeights):\n    def __init__(\n        self,\n        weight_dict,\n        use_fp_loss=False,\n        fp_loss_on_exhaustive_only=True,\n        treat_fp_as_new_obj=False,\n    ):\n        super().__init__(weight_dict, compute_aux=False)\n        self.use_fp_loss = use_fp_loss\n        self.fp_loss_on_exhaustive_only = fp_loss_on_exhaustive_only\n        self.treat_fp_as_new_obj = treat_fp_as_new_obj\n        if self.use_fp_loss:\n            self.target_keys.append(\"is_exhaustive\")\n\n    def get_loss(self, outputs, targets, indices, num_boxes):\n        det2trk_assoc_logits = outputs[\"det2trk_assoc_logits\"]\n        device = det2trk_assoc_logits.device\n        B, Q_det, Q_trk_plus_2 = det2trk_assoc_logits.shape\n        assert Q_trk_plus_2 >= 2\n        Q_trk = Q_trk_plus_2 - 2\n\n        # We only apply association losses to those detection queries that either match\n        # a GT instance or have score > 0 (i.e. those TP, FN and FP detection queries)\n        matched_object_ids = outputs[\"matched_object_ids\"]\n        assert matched_object_ids.shape == (B, Q_det + Q_trk)\n        matched_obj_ids_det = matched_object_ids[:, :Q_det]\n        matched_obj_ids_trk = matched_object_ids[:, Q_det:]\n        det_is_matched_to_gt = matched_obj_ids_det >= 0\n        trk_is_matched_to_gt = matched_obj_ids_trk >= 0\n\n        # note: -1 label is ignored in the (softmax) cross_entropy loss below\n        det2trk_assoc_labels = -torch.ones(B, Q_det, dtype=torch.long, device=device)\n        # a) If a detection query is matched to a same object ID as a tracking query,\n        # we assign it the index of the tracking query as a label\n        det_is_same_obj_id_as_trk = (\n            det_is_matched_to_gt[:, :, None]\n            & trk_is_matched_to_gt[:, None, :]\n            & (matched_obj_ids_det[:, :, None] == matched_obj_ids_trk[:, None, :])\n        )\n        batch_idx, det_idx, trk_idx = det_is_same_obj_id_as_trk.nonzero(as_tuple=True)\n        det2trk_assoc_labels[batch_idx, det_idx] = trk_idx\n\n        # b) If a detection query is matched to GT but not to any tracking query,\n        # we assign it a \"new_object\" label\n        det_is_new_obj = det_is_matched_to_gt & ~det_is_same_obj_id_as_trk.any(dim=-1)\n        det2trk_assoc_labels[det_is_new_obj] = Q_trk\n\n        # c) If a detection query is not matched to GT but have score > 0,\n        # we assign it a \"false_positive\" label\n        if self.use_fp_loss:\n            det_is_above_thresh = outputs[\"pred_logits\"][:, :Q_det].squeeze(2) > 0\n            det_is_fp = ~det_is_matched_to_gt & det_is_above_thresh\n            if self.treat_fp_as_new_obj:\n                det2trk_assoc_labels[det_is_fp] = Q_trk\n            else:\n                if self.fp_loss_on_exhaustive_only:\n                    # only count FP detections on batches that are exhaustively annotated\n                    det_is_fp &= targets[\"is_exhaustive\"].unsqueeze(1).bool()\n                det2trk_assoc_labels[det_is_fp] = Q_trk + 1\n\n        # softmax cross-entropy loss for detection-to-tracking association\n        loss_det2trk_assoc = F.cross_entropy(\n            input=det2trk_assoc_logits.flatten(0, 1),  # (B * Q_det, Q_trk + 2)\n            target=det2trk_assoc_labels.flatten(0, 1),  # (B * Q_det)\n            ignore_index=-1,\n            reduction=\"none\",\n        ).view(B, Q_det)\n        # skip det2trk assocation loss on frames w/o any (non-padding) tracking queries\n        frame_has_valid_trk = trk_is_matched_to_gt.any(dim=-1, keepdims=True)  # (B, 1)\n        loss_det2trk_assoc = loss_det2trk_assoc * frame_has_valid_trk.float()\n\n        loss_det2trk_assoc = loss_det2trk_assoc.sum() / (B * num_boxes)\n        return {\"loss_det2trk_assoc\": loss_det2trk_assoc}\n\n\nclass TrackingByDetectionAssoc(LossWithWeights):\n    def __init__(self, weight_dict):\n        super().__init__(weight_dict, compute_aux=False, supports_o2m_loss=False)\n        assert \"loss_det2trk_assoc\" in self.weight_dict\n        assert \"loss_trk2det_assoc\" in self.weight_dict\n\n    def get_loss(self, outputs, targets, indices, num_boxes):\n        # Part A: gather object id matching between detection and tracking\n        det2trk_assoc_logits = outputs[\"det2trk_assoc_logits\"]  # (B, Q_det+1, Q_trk+1)\n        B, Q_det_plus_1, Q_trk_plus_1 = det2trk_assoc_logits.shape\n        assert Q_det_plus_1 >= 1 and Q_trk_plus_1 >= 1\n        Q_det = Q_det_plus_1 - 1\n        Q_trk = Q_trk_plus_1 - 1\n        device = det2trk_assoc_logits.device\n\n        matched_obj_ids_det = outputs[\"matched_object_ids\"]\n        assert matched_obj_ids_det.shape == (B, Q_det)\n        det_is_matched_to_gt = matched_obj_ids_det >= 0\n        matched_obj_ids_trk = outputs[\"prev_trk_object_ids\"]\n        assert matched_obj_ids_trk.shape == (B, Q_trk)\n        trk_is_matched_to_gt = matched_obj_ids_trk >= 0\n        frame_has_valid_trk = trk_is_matched_to_gt.any(dim=-1, keepdims=True)  # (B, 1)\n\n        # check whether a detection object is the same as a tracking object\n        det_is_same_obj_id_as_trk = (\n            det_is_matched_to_gt[:, :, None]\n            & trk_is_matched_to_gt[:, None, :]\n            & (matched_obj_ids_det[:, :, None] == matched_obj_ids_trk[:, None, :])\n        )  # (B, Q_det, Q_trk)\n        # there should be at most one match for each detection and each previous tracked object\n        torch._assert_async(torch.all(det_is_same_obj_id_as_trk.sum(dim=2) <= 1))\n        torch._assert_async(torch.all(det_is_same_obj_id_as_trk.sum(dim=1) <= 1))\n        batch_idx, det_idx, trk_idx = det_is_same_obj_id_as_trk.nonzero(as_tuple=True)\n\n        # Part B: Detection-to-tracking association loss\n        # assign detection-to-tracking labels (note: -1 label is ignored in the loss below)\n        det2trk_assoc_labels = -torch.ones(B, Q_det, dtype=torch.long, device=device)\n        det2trk_assoc_labels[batch_idx, det_idx] = trk_idx\n        # if a detection is matched to GT but not to any tracking, assign it a \"new-object\" label\n        det_is_new_obj = det_is_matched_to_gt & ~det_is_same_obj_id_as_trk.any(dim=2)\n        det2trk_assoc_labels[det_is_new_obj] = Q_trk  # \"Q_trk\" label is \"new-object\"\n\n        # softmax cross-entropy loss for detection-to-tracking association\n        loss_det2trk_assoc = F.cross_entropy(\n            input=det2trk_assoc_logits[:, :-1].flatten(0, 1),  # (B*Q_det, Q_trk+1)\n            target=det2trk_assoc_labels.flatten(0, 1),  # (B*Q_det)\n            ignore_index=-1,\n            reduction=\"none\",\n        ).view(B, Q_det)\n        # skip det2trk assocation loss on frames w/o any (non-padding) tracking queries\n        loss_det2trk_assoc = loss_det2trk_assoc * frame_has_valid_trk.float()\n        loss_det2trk_assoc = loss_det2trk_assoc.sum() / (B * num_boxes)\n        loss_dict = {\"loss_det2trk_assoc\": loss_det2trk_assoc}\n\n        # Part C: tracking-to-detection association loss\n        trk2det_assoc_logits = det2trk_assoc_logits.transpose(1, 2)\n        assert trk2det_assoc_logits.shape == (B, Q_trk + 1, Q_det + 1)\n        # assign tracking-to-detection labels (note: -1 label is ignored in the loss below)\n        trk2det_assoc_labels = -torch.ones(B, Q_trk, dtype=torch.long, device=device)\n        trk2det_assoc_labels[batch_idx, trk_idx] = det_idx\n        # if a tracking is matched to GT but not to any detection, assign it a \"occluded\" label\n        trk_is_occluded = trk_is_matched_to_gt & ~det_is_same_obj_id_as_trk.any(dim=1)\n        trk2det_assoc_labels[trk_is_occluded] = Q_det  # \"Q_det\" label is \"occluded\"\n\n        # softmax cross-entropy loss for tracking-to-detection association\n        loss_trk2det_assoc = F.cross_entropy(\n            input=trk2det_assoc_logits[:, :-1].flatten(0, 1),  # (B*Q_trk, Q_det+1)\n            target=trk2det_assoc_labels.flatten(0, 1),  # (B*Q_trk)\n            ignore_index=-1,\n            reduction=\"none\",\n        ).view(B, Q_trk)\n        # skip trk2det association loss on frames w/o any (non-padding) tracking queries\n        loss_trk2det_assoc = loss_trk2det_assoc * frame_has_valid_trk.float()\n        loss_trk2det_assoc = loss_trk2det_assoc.sum() / (B * num_boxes)\n        loss_dict[\"loss_trk2det_assoc\"] = loss_trk2det_assoc\n\n        return loss_dict\n\n\ndef _keep_only_trk_queries_in_match_inds(inds, Q_det):\n    \"\"\"Keep only the tracking query indices in the indices tuple\"\"\"\n    batch_idx, src_idx, tgt_idx = inds\n    if batch_idx.numel() == 0:\n        return (batch_idx, src_idx, tgt_idx)  # empty indices, nothing to filter\n\n    # keep only the tracking query indices\n    is_trk_query = src_idx >= Q_det\n    batch_idx_trk = batch_idx[is_trk_query]\n    src_idx_trk = src_idx[is_trk_query]\n    tgt_idx_trk = tgt_idx[is_trk_query] if tgt_idx is not None else None\n    return (batch_idx_trk, src_idx_trk, tgt_idx_trk)\n"
  },
  {
    "path": "sam3/train/loss/mask_sampling.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n\nfrom typing import Callable\n\nimport torch\nfrom torch.nn import functional as F\n\n\n# Adapted from https://github.com/facebookresearch/detectron2/blob/main/projects/PointRend/point_rend/point_features.py\ndef point_sample(input, point_coords, **kwargs):\n    \"\"\"\n    A wrapper around :function:`torch.nn.functional.grid_sample` to support 3D point_coords tensors.\n    Unlike :function:`torch.nn.functional.grid_sample` it assumes `point_coords` to lie inside\n    [0, 1] x [0, 1] square.\n\n    Args:\n        input (Tensor): A tensor of shape (N, C, H, W) that contains features map on a H x W grid.\n        point_coords (Tensor): A tensor of shape (N, P, 2) or (N, Hgrid, Wgrid, 2) that contains\n        [0, 1] x [0, 1] normalized point coordinates.\n\n    Returns:\n        output (Tensor): A tensor of shape (N, C, P) or (N, C, Hgrid, Wgrid) that contains\n            features for points in `point_coords`. The features are obtained via bilinear\n            interplation from `input` the same way as :function:`torch.nn.functional.grid_sample`.\n    \"\"\"\n    add_dim = False\n    if point_coords.dim() == 3:\n        add_dim = True\n        point_coords = point_coords.unsqueeze(2)\n    normalized_point_coords = 2.0 * point_coords - 1.0  # Normalize to [-1,1]\n    output = F.grid_sample(input, normalized_point_coords, **kwargs)\n    if add_dim:\n        output = output.squeeze(3)\n    return output\n\n\n# Adapted from https://github.com/facebookresearch/detectron2/blob/main/projects/PointRend/point_rend/point_features.py\ndef get_uncertain_point_coords_with_randomness(\n    logits: torch.Tensor,\n    uncertainty_func: Callable,\n    num_points: int,\n    oversample_ratio: int,\n    importance_sample_ratio: float,\n) -> torch.Tensor:\n    \"\"\"\n    Sample points in [0, 1] x [0, 1] coordinate space based on their uncertainty. The unceratinties\n        are calculated for each point using 'uncertainty_func' function that takes point's logit\n        prediction as input.\n    See PointRend paper for details.\n\n    Args:\n        logits (Tensor): A tensor of shape (N, C, Hmask, Wmask) or (N, 1, Hmask, Wmask) for\n            class-specific or class-agnostic prediction.\n        uncertainty_func: A function that takes a Tensor of shape (N, C, P) or (N, 1, P) that\n            contains logit predictions for P points and returns their uncertainties as a Tensor of\n            shape (N, 1, P).\n        num_points (int): The number of points P to sample.\n        oversample_ratio (int): Oversampling parameter.\n        importance_sample_ratio (float): Ratio of points that are sampled via importnace sampling.\n\n    Returns:\n        point_coords (Tensor): A tensor of shape (N, P, 2) that contains the coordinates of P\n            sampled points.\n    \"\"\"\n    assert oversample_ratio >= 1\n    assert importance_sample_ratio <= 1 and importance_sample_ratio >= 0\n    num_boxes = logits.shape[0]\n    num_sampled = int(num_points * oversample_ratio)\n    point_coords = torch.rand(num_boxes, num_sampled, 2, device=logits.device)\n    point_logits = point_sample(logits, point_coords, align_corners=False)\n    # It is crucial to calculate uncertainty based on the sampled prediction value for the points.\n    # Calculating uncertainties of the predictions first and sampling them for points leads\n    # to incorrect results.\n    # To illustrate this: assume uncertainty_func(logits)=-abs(logits), a sampled point between\n    # two predictions with -1 and 1 logits has 0 logits, and therefore 0 uncertainty value.\n    # However, if we calculate uncertainties for the predictions first,\n    # both will have -1 uncertainty, and the sampled point will get -1 uncertainty.\n    point_uncertainties = uncertainty_func(point_logits)\n    num_uncertain_points = int(importance_sample_ratio * num_points)\n    num_random_points = num_points - num_uncertain_points\n    idx = torch.topk(point_uncertainties[:, 0, :], k=num_uncertain_points, dim=1)[1]\n    # Flatten the indices\n    shift = num_sampled * torch.arange(\n        num_boxes, dtype=torch.long, device=logits.device\n    )\n    idx += shift[:, None]\n    point_coords = point_coords.view(-1, 2)[idx.view(-1), :].view(\n        num_boxes, num_uncertain_points, 2\n    )\n    if num_random_points > 0:\n        point_coords = torch.cat(\n            [\n                point_coords,\n                torch.rand(num_boxes, num_random_points, 2, device=logits.device),\n            ],\n            dim=1,\n        )\n    return point_coords\n\n\n# Adapted from https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/modeling/criterion.py\ndef calculate_uncertainty(logits: torch.Tensor) -> torch.Tensor:\n    \"\"\"\n    Estimates uncerainty as L1 distance between 0.0 and the logit prediction.\n    Args:\n        logits (Tensor): A tensor of shape (R, 1, ...) for class-agnostic\n        predicted masks\n    Returns:\n        scores (Tensor): A tensor of shape (R, 1, ...) that contains uncertainty scores with\n            the most uncertain locations having the highest uncertainty score.\n    \"\"\"\n    assert logits.shape[1] == 1\n    return -(torch.abs(logits))\n"
  },
  {
    "path": "sam3/train/loss/sam3_loss.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n\nimport torch\nfrom sam3.model.model_misc import SAM3Output\nfrom sam3.train.utils.distributed import get_world_size\n\nfrom .loss_fns import CORE_LOSS_KEY, Det2TrkAssoc, Masks\n\n\nclass DummyLoss(torch.nn.Module):\n    \"\"\"A dummy loss that always returns 0 (as a placeholder for eval)\"\"\"\n\n    def __init__(\n        self,\n        core_loss_key: str = CORE_LOSS_KEY,\n        device: str = \"cuda\",\n        **kwargs,\n    ):\n        super().__init__()\n        self.core_loss_key = core_loss_key\n        self.device = torch.device(device)\n\n    def forward(self, *args, **kwargs):\n        return {self.core_loss_key: torch.tensor(0.0, device=self.device)}\n\n    def accumulate(self, out_dict):\n        \"\"\"\n        Called by iterative losses.\n        \"\"\"\n        if self.core_loss_key not in out_dict:\n            out_dict[self.core_loss_key] = torch.tensor(0.0, device=self.device)\n        return out_dict\n\n\nclass Sam3LossWrapper(torch.nn.Module):\n    def __init__(\n        self,\n        loss_fns_find,\n        normalization=\"global\",\n        matcher=None,\n        o2m_matcher=None,\n        o2m_weight=1.0,\n        use_o2m_matcher_on_o2m_aux=True,\n        loss_fn_semantic_seg=None,\n        normalize_by_valid_object_num=False,\n        normalize_by_stage_num=False,\n        scale_by_find_batch_size=False,\n    ):\n        super().__init__()\n        self.loss_fns_find = loss_fns_find\n        assert normalization in [\"global\", \"local\", \"none\"]\n        self.normalization = normalization\n        self.normalize_by_valid_object_num = normalize_by_valid_object_num\n        self.normalize_by_stage_num = normalize_by_stage_num\n        self.matcher = matcher\n        self.o2m_matcher = o2m_matcher\n        self.o2m_weight = o2m_weight\n        # whether to use the o2m matcher on the o2m queries in auxiliary outputs\n        self.use_o2m_matcher_on_o2m_aux = use_o2m_matcher_on_o2m_aux\n        self.loss_fn_semantic_seg = loss_fn_semantic_seg\n        self.scale_by_find_batch_size = scale_by_find_batch_size\n\n    def _get_num_boxes(self, targets):\n        # the average number of target boxes for loss normalization\n        if self.normalize_by_valid_object_num:\n            # valid boxes are those with non-zero height and width\n            # (while padded invisible boxes are )\n            boxes_hw = targets[\"boxes\"].view(-1, 4)  # cx, cy, w, h\n            num_boxes = (boxes_hw[:, 2:] > 0).all(dim=-1).sum().float()\n        else:\n            num_boxes = targets[\"num_boxes\"].sum().float()\n        if self.normalization == \"global\":\n            torch.distributed.all_reduce(num_boxes)\n            num_boxes = torch.clamp(num_boxes / get_world_size(), min=1)\n        elif self.normalization == \"local\":\n            num_boxes = torch.clamp(num_boxes, min=1)\n        elif self.normalization == \"none\":\n            num_boxes = 1\n        return num_boxes\n\n    def compute_loss(self, nested_out, targets):\n        num_boxes = self._get_num_boxes(targets)\n        o2m_out_is_valid = nested_out.get(\"o2m_out_is_valid\", None)\n        o2m_target_is_valid_padded = nested_out.get(\"o2m_target_is_valid_padded\", None)\n\n        # Get a list of outputs, including auxiliary and first stage outputs\n        output_list = [(nested_out, \"\", False)]  # (out, suffix, is_aux)\n        if \"aux_outputs\" in nested_out:\n            output_list.extend(\n                (aux_out, f\"_aux_{i}\", True)\n                for i, aux_out in enumerate(nested_out[\"aux_outputs\"])\n            )\n        if \"first_stage\" in nested_out:\n            output_list.append((nested_out[\"first_stage\"], \"_fs\", True))\n\n        # Compute all the requested losses\n        losses = {}\n        total_core_loss = 0.0\n        for out, suffix, is_aux in output_list:\n            # o2o matcher indices need to be computed by the model (as the video model requires\n            # a specific way of matching free and locked indices beyond just calling the matcher)\n            indices = out[\"indices\"]\n            has_o2m_out = \"pred_logits_o2m\" in out\n            if has_o2m_out:\n                o2m_out = {\n                    k[: -len(\"_o2m\")]: v for k, v in out.items() if k.endswith(\"_o2m\")\n                }\n                # o2m targets are the same as the o2o targets (assuming repeat=1)\n                o2m_targets = targets\n                if self.use_o2m_matcher_on_o2m_aux or not is_aux:\n                    o2m_indices = self.o2m_matcher(\n                        o2m_out,\n                        o2m_targets,\n                        out_is_valid=o2m_out_is_valid,\n                        target_is_valid_padded=o2m_target_is_valid_padded,\n                    )\n                else:\n                    o2m_indices = self.matcher(\n                        o2m_out,\n                        o2m_targets,\n                        out_is_valid=o2m_out_is_valid,\n                        target_is_valid_padded=o2m_target_is_valid_padded,\n                    )\n\n            for loss_fn in self.loss_fns_find:\n                l_dict = loss_fn(\n                    outputs=out,\n                    targets=targets,\n                    indices=indices,\n                    num_boxes=num_boxes,\n                    is_aux=is_aux,\n                )\n                total_core_loss += l_dict.pop(CORE_LOSS_KEY)\n                losses.update({f\"{k}{suffix}\": v for k, v in l_dict.items()})\n\n                compute_o2m_loss = has_o2m_out\n                # a special handling to allow turning off mask loss in o2m\n                # (to be compatible with the original implementation)\n                if isinstance(loss_fn, Masks):\n                    compute_o2m_loss = compute_o2m_loss and \"pred_masks\" in o2m_out\n                if isinstance(loss_fn, Det2TrkAssoc):\n                    compute_o2m_loss = False  # Det2TrkAssoc does not support o2m\n                if compute_o2m_loss:\n                    l_dict = loss_fn(\n                        outputs=o2m_out,\n                        targets=o2m_targets,\n                        indices=o2m_indices,\n                        num_boxes=num_boxes,\n                        is_aux=is_aux,\n                    )\n                    for k in l_dict:\n                        l_dict[k] *= self.o2m_weight\n                    total_core_loss += l_dict.pop(CORE_LOSS_KEY)\n                    losses.update({f\"{k}{suffix}_o2m\": v for k, v in l_dict.items()})\n\n        losses[CORE_LOSS_KEY] = total_core_loss\n        return losses\n\n    def forward(self, find_stages: SAM3Output, find_targets):\n        if find_stages.loss_stages is not None:\n            find_targets = [find_targets[i] for i in find_stages.loss_stages]\n        with SAM3Output.iteration_mode(\n            find_stages, iter_mode=SAM3Output.IterMode.ALL_STEPS_PER_STAGE\n        ) as find_stages:\n            assert len(find_stages) == len(find_targets)\n            total_losses = {}\n            for stage_outputs, stage_targets in zip(find_stages, find_targets):\n                stage_targets = [stage_targets] * len(stage_outputs)\n                # If there are multiple steps within a stage, compute the loss for all of them (e.g. interactivity)\n                for outputs, targets in zip(stage_outputs, stage_targets):\n                    cur_losses = self.compute_loss(outputs, targets)\n\n                    if self.loss_fn_semantic_seg is not None:\n                        cur_losses_semantic = self.loss_fn_semantic_seg(\n                            outputs, targets\n                        )\n                        cur_losses[CORE_LOSS_KEY] += cur_losses_semantic.pop(\n                            CORE_LOSS_KEY\n                        )\n                        # make sure the semantic losses don't overlap with the find losses\n                        assert set(cur_losses).isdisjoint(set(cur_losses_semantic))\n                        cur_losses.update(cur_losses_semantic)\n\n                    # Optionally, normalize the loss by the number of find stages (training video frames) so that\n                    # image batches and video batches have similar loss scales. (Otherwise video batches would\n                    # have a much higher loss scale due to summing the losses over all the find stages.)\n                    if self.normalize_by_stage_num:\n                        cur_losses[CORE_LOSS_KEY] /= len(find_stages)\n\n                    if self.scale_by_find_batch_size:\n                        bs = targets[\"num_boxes\"].shape[0]\n                        # sqrt scaling based on the \"effective\" batch size\n                        cur_losses[CORE_LOSS_KEY] *= bs**0.5\n\n                    for k, v in cur_losses.items():\n                        if k not in total_losses:\n                            total_losses[k] = v\n                        else:\n                            total_losses[k] += v\n\n        return total_losses\n"
  },
  {
    "path": "sam3/train/loss/sigmoid_focal_loss.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n\n\"\"\"Triton kernel for faster and memory efficient sigmoid focal loss\"\"\"\n\nimport torch\nimport triton\nimport triton.language as tl\nfrom torch._inductor.runtime.triton_helpers import libdevice\n\n\"\"\"\n\nThe sigmoid focal loss is defined as:\n\n    prob = inputs.sigmoid()\n    ce_loss = F.binary_cross_entropy_with_logits(inputs, targets, reduction=\"none\")\n    p_t = prob * targets + (1 - prob) * (1 - targets)\n    alpha_t = alpha * targets + (1 - alpha) * (1 - targets)\n    loss = alpha_t * ce_loss * ((1 - p_t) ** gamma)\n\nWhere alpha and gamma are scalar parameters, inputs are the logits, targets the float targets.\n\nWe implement two versions of the sigmoid focal loss: with and without sum reduction.\nThe latter is implemented with built-in reduction to avoid materializing wrt the output of the loss.\nThis can help save a bit of peak memory.\n\nThe reduction version is implemented using somewhat of a hack. Pytorch's generated kernels usually do the point-wise operation in a first kernel, and implement the reduction another kernel launched on a grid of size 1, where the reduction happens as a for loop in the triton kernel.\nSince we want to fuse those two kernels, that is not a good idea: we'd have to launch the overall kernel on a grid of size 1, which is obviously inefficient.\nOn the other hand, typical CUDA algorithms for reduction (eg reduction tree) are hard to implement in triton due to the lack of thread sync primitives.\nWe settle for a version that abuses triton's atomic_add: we can have all threads simply add to the same location.\nIn practice, this is not good, since it creates a massive bottleneck on the semaphore for that single memory location. So instead, we create M reduction locations. Each thread will simply write to thread_id%M. The python code can finally sum over the M reductions.\nM = 32 works fine in benchmarking tests. The forward is a tiny bit slower compared to the non-reduced kernel, but the backward breaks even due to one less memory allocation.\n\"\"\"\n\n\n@triton.jit\ndef _inner_focal_loss_fwd(inputs, targets, alpha, gamma):\n    inv_targets = 1 - targets\n    # Sigmoid\n    sig = tl.sigmoid(inputs)\n\n    # Binary cross entropy with logits\n    # In practice, we want the following:\n    # bce_loss = -targets * tl.log(sig) - (1 - targets) * tl.log(1 - sig)\n    # However, the above is not numerically stable.\n    # We're also not directly taking the sum here, so the usual log-sum-exp trick doesn't apply\n    # The bce can be reformulated, after algebraic manipulation, to\n    # bce_loss = log(1 + exp(-x)) + x * (1-y)\n    # This is still not stable, because for large (-x) the exponential will blow up.\n    # We'll use the following alternate formulation:\n    # bce_loss = max(x, 0) - x * y + log(1 + exp(-abs(x)))\n    # Let's show that it's equivalent:\n    # Case x>=0: abs(x) = x , max(x, 0) = x\n    # so we get x - x * y + log(1 + exp(-x)) which is equivalent\n    # Case x<0: abs(x) = -x, max(x, 0) = 0\n    # we have log(1 + exp(-abs(x))) = log(1 + exp(x)) = log(exp(x)(1 + exp(-x))) = x+log(1 + exp(-x))\n    # plugging it in, we get\n    # 0 - x * y + x + log(1 + exp(-x)), which is also equivalent\n    # Note that this is stable because now the exponent are guaranteed to be below 0.\n    max_val = tl.clamp(inputs, min=0, max=1e9)\n    bce_loss = max_val - inputs * targets + tl.log(1 + tl.exp(-tl.abs(inputs)))\n\n    # Modulating factor\n    p_t = sig * targets + (1 - sig) * inv_targets\n    mod_factor = libdevice.pow(1 - p_t, gamma)\n\n    # Alpha factor\n    alpha_t = alpha * targets + (1 - alpha) * inv_targets\n\n    # Final loss calculation\n    return alpha_t * mod_factor * bce_loss\n\n\n# Non-reduced version\n@triton.jit\ndef sigmoid_focal_loss_fwd_kernel(\n    inputs_ptr,\n    targets_ptr,\n    loss_ptr,\n    alpha: float,\n    gamma: float,\n    n_elements: int,\n    BLOCK_SIZE: tl.constexpr,\n):\n    pid = tl.program_id(axis=0)\n    block_start = pid * BLOCK_SIZE\n    offset = block_start + tl.arange(0, BLOCK_SIZE)\n    mask = offset < n_elements\n\n    # Load data\n    inputs = tl.load(inputs_ptr + offset, mask=mask).to(tl.float32)\n    targets = tl.load(targets_ptr + offset, mask=mask)\n\n    final_loss = _inner_focal_loss_fwd(inputs, targets, alpha, gamma)\n\n    # Store result\n    tl.store(loss_ptr + offset, final_loss, mask=mask)\n\n\n# version with reduction\n@triton.jit\ndef sigmoid_focal_loss_fwd_kernel_reduce(\n    inputs_ptr,\n    targets_ptr,\n    loss_ptr,\n    alpha: float,\n    gamma: float,\n    n_elements: int,\n    BLOCK_SIZE: tl.constexpr,\n    REDUCE_SIZE: tl.constexpr,\n):\n    pid = tl.program_id(axis=0)\n    block_start = pid * BLOCK_SIZE\n    reduce_loc = pid % REDUCE_SIZE\n    offset = block_start + tl.arange(0, BLOCK_SIZE)\n    mask = offset < n_elements\n    # Load data\n    inputs = tl.load(inputs_ptr + offset, mask=mask).to(tl.float32)\n    targets = tl.load(targets_ptr + offset, mask=mask)\n\n    final_loss = _inner_focal_loss_fwd(inputs, targets, alpha, gamma) * mask\n\n    fl = tl.sum(final_loss)\n\n    # Store result\n    tl.atomic_add(loss_ptr + reduce_loc, fl)\n\n\n@triton.jit\ndef _inner_focal_loss_bwd(inputs, targets, alpha, gamma):\n    inv_targets = 1 - targets\n\n    # Recompute forward\n    max_val = tl.clamp(inputs, min=0, max=1e9)\n    bce_loss = max_val - inputs * targets + tl.log(1 + tl.exp(-tl.abs(inputs)))\n\n    # Sigmoid\n    sig = tl.sigmoid(inputs)\n    inv_sig = 1 - sig\n\n    # Modulating factor\n    p_t = sig * targets + inv_sig * inv_targets\n    tmp = libdevice.pow(1 - p_t, gamma - 1)\n    mod_factor = tmp * (1 - p_t)\n\n    # Alpha factor\n    alpha_t = alpha * targets + (1 - alpha) * inv_targets\n\n    # Now computing the derivatives\n    d_pt = (2 * targets - 1) * sig * inv_sig\n    d_mod_factor = -gamma * d_pt * tmp\n\n    d_bce_loss = sig - targets\n\n    return alpha_t * (d_bce_loss * mod_factor + d_mod_factor * bce_loss)\n\n\n@triton.jit\ndef sigmoid_focal_loss_bwd_kernel(\n    inputs_ptr,\n    targets_ptr,\n    grad_inputs_ptr,\n    grad_out_ptr,\n    alpha: float,\n    gamma: float,\n    n_elements: int,\n    BLOCK_SIZE: tl.constexpr,\n):\n    pid = tl.program_id(axis=0)\n    block_start = pid * BLOCK_SIZE\n    offset = block_start + tl.arange(0, BLOCK_SIZE)\n    mask = offset < n_elements\n    input_ptrs = inputs_ptr + offset\n    target_ptrs = targets_ptr + offset\n    grad_input_ptrs = grad_inputs_ptr + offset\n    grad_out_ptrs = grad_out_ptr + offset\n    # Load data\n    inputs = tl.load(input_ptrs, mask=mask).to(tl.float32)\n    targets = tl.load(target_ptrs, mask=mask)\n    grad_out = tl.load(grad_out_ptrs, mask=mask)\n    d_loss = grad_out * _inner_focal_loss_bwd(inputs, targets, alpha, gamma)\n    tl.store(grad_input_ptrs, d_loss, mask=mask)\n\n\n@triton.jit\ndef sigmoid_focal_loss_bwd_kernel_reduce(\n    inputs_ptr,\n    targets_ptr,\n    grad_inputs_ptr,\n    grad_out_ptr,\n    alpha: float,\n    gamma: float,\n    n_elements: int,\n    BLOCK_SIZE: tl.constexpr,\n):\n    # The only difference is that the gradient is now a single scalar\n    pid = tl.program_id(axis=0)\n    block_start = pid * BLOCK_SIZE\n    offset = block_start + tl.arange(0, BLOCK_SIZE)\n    mask = offset < n_elements\n    input_ptrs = inputs_ptr + offset\n    target_ptrs = targets_ptr + offset\n    grad_input_ptrs = grad_inputs_ptr + offset\n    # Load data\n    inputs = tl.load(input_ptrs, mask=mask).to(tl.float32)\n    targets = tl.load(target_ptrs, mask=mask)\n    grad_out = tl.load(grad_out_ptr)\n    d_loss = grad_out * _inner_focal_loss_bwd(inputs, targets, alpha, gamma)\n    tl.store(grad_input_ptrs, d_loss, mask=mask)\n\n\nclass SigmoidFocalLoss(torch.autograd.Function):\n    BLOCK_SIZE = 256\n\n    @staticmethod\n    def forward(ctx, inputs, targets, alpha=0.25, gamma=2):\n        n_elements = inputs.numel()\n        assert targets.numel() == n_elements\n        input_shape = inputs.shape\n        inputs = inputs.view(-1).contiguous()\n        targets = targets.view(-1).contiguous()\n        loss = torch.empty(inputs.shape, dtype=torch.float32, device=inputs.device)\n        grid = lambda meta: (triton.cdiv(n_elements, meta[\"BLOCK_SIZE\"]),)\n        sigmoid_focal_loss_fwd_kernel[grid](\n            inputs, targets, loss, alpha, gamma, n_elements, SigmoidFocalLoss.BLOCK_SIZE\n        )\n        ctx.save_for_backward(inputs.view(input_shape), targets.view(input_shape))\n        ctx.alpha = alpha\n        ctx.gamma = gamma\n        return loss.view(input_shape)\n\n    @staticmethod\n    def backward(ctx, grad_output):\n        inputs, targets = ctx.saved_tensors\n        alpha = ctx.alpha\n        gamma = ctx.gamma\n        n_elements = inputs.numel()\n        input_shape = inputs.shape\n        grad_inputs = torch.empty(\n            inputs.shape, dtype=grad_output.dtype, device=grad_output.device\n        )\n        inputs_ptr = inputs.view(-1).contiguous()\n        targets_ptr = targets.view(-1).contiguous()\n        grad_output_ptr = grad_output.view(-1).contiguous()\n        grad_inputs_ptr = grad_inputs\n        assert grad_output.numel() == n_elements\n        grid = lambda meta: (triton.cdiv(n_elements, meta[\"BLOCK_SIZE\"]),)\n        sigmoid_focal_loss_bwd_kernel[grid](\n            inputs_ptr,\n            targets_ptr,\n            grad_inputs_ptr,\n            grad_output_ptr,\n            alpha,\n            gamma,\n            n_elements,\n            SigmoidFocalLoss.BLOCK_SIZE,\n        )\n        return grad_inputs.view(input_shape), None, None, None\n\n\ntriton_sigmoid_focal_loss = SigmoidFocalLoss.apply\n\n\nclass SigmoidFocalLossReduced(torch.autograd.Function):\n    BLOCK_SIZE = 256\n    REDUCE_SIZE = 32\n\n    @staticmethod\n    def forward(ctx, inputs, targets, alpha=0.25, gamma=2):\n        n_elements = inputs.numel()\n        input_shape = inputs.shape\n        inputs = inputs.view(-1).contiguous()\n        targets = targets.view(-1).contiguous()\n        loss = torch.zeros(\n            SigmoidFocalLossReduced.REDUCE_SIZE,\n            device=inputs.device,\n            dtype=torch.float32,\n        )\n        grid = lambda meta: (triton.cdiv(n_elements, meta[\"BLOCK_SIZE\"]),)\n        sigmoid_focal_loss_fwd_kernel_reduce[grid](\n            inputs,\n            targets,\n            loss,\n            alpha,\n            gamma,\n            n_elements,\n            SigmoidFocalLossReduced.BLOCK_SIZE,\n            SigmoidFocalLossReduced.REDUCE_SIZE,\n        )\n        ctx.save_for_backward(inputs.view(input_shape), targets.view(input_shape))\n        ctx.alpha = alpha\n        ctx.gamma = gamma\n        return loss.sum()\n\n    @staticmethod\n    def backward(ctx, grad_output):\n        inputs, targets = ctx.saved_tensors\n        alpha = ctx.alpha\n        gamma = ctx.gamma\n        n_elements = inputs.numel()\n        input_shape = inputs.shape\n        grad_inputs = torch.empty(\n            inputs.shape, dtype=grad_output.dtype, device=grad_output.device\n        )\n        inputs_ptr = inputs.view(-1).contiguous()\n        targets_ptr = targets.reshape(-1).contiguous()\n        assert grad_output.numel() == 1\n        grid = lambda meta: (triton.cdiv(n_elements, meta[\"BLOCK_SIZE\"]),)\n        sigmoid_focal_loss_bwd_kernel_reduce[grid](\n            inputs_ptr,\n            targets_ptr,\n            grad_inputs,\n            grad_output,\n            alpha,\n            gamma,\n            n_elements,\n            SigmoidFocalLossReduced.BLOCK_SIZE,\n        )\n        return grad_inputs.view(input_shape), None, None, None\n\n\ntriton_sigmoid_focal_loss_reduce = SigmoidFocalLossReduced.apply\n"
  },
  {
    "path": "sam3/train/masks_ops.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n\n\"\"\"Utilities for masks manipulation\"\"\"\n\nimport numpy as np\nimport pycocotools.mask as maskUtils\nimport torch\nfrom pycocotools import mask as mask_util\n\n\ndef instance_masks_to_semantic_masks(\n    instance_masks: torch.Tensor, num_instances: torch.Tensor\n) -> torch.Tensor:\n    \"\"\"This function converts instance masks to semantic masks.\n    It accepts a collapsed batch of instances masks (ie all instance masks are concatenated in a single tensor) and\n    the number of instances in each image of the batch.\n    It returns a mask with the same spatial dimensions as the input instance masks, where for each batch element the\n    semantic mask is the union of all the instance masks in the batch element.\n\n    If for a given batch element there are no instances (ie num_instances[i]==0), the corresponding semantic mask will be a tensor of zeros.\n\n    Args:\n        instance_masks (torch.Tensor): A tensor of shape (N, H, W) where N is the number of instances in the batch.\n        num_instances (torch.Tensor): A tensor of shape (B,) where B is the batch size. It contains the number of instances\n            in each image of the batch.\n\n    Returns:\n        torch.Tensor: A tensor of shape (B, H, W) where B is the batch size and H, W are the spatial dimensions of the\n            input instance masks.\n    \"\"\"\n\n    masks_per_query = torch.split(instance_masks, num_instances.tolist())\n\n    return torch.stack([torch.any(masks, dim=0) for masks in masks_per_query], dim=0)\n\n\ndef mask_intersection(masks1, masks2, block_size=16):\n    \"\"\"Compute the intersection of two sets of masks, without blowing the memory\"\"\"\n\n    assert masks1.shape[1:] == masks2.shape[1:]\n    assert masks1.dtype == torch.bool and masks2.dtype == torch.bool\n\n    result = torch.zeros(\n        masks1.shape[0], masks2.shape[0], device=masks1.device, dtype=torch.long\n    )\n    for i in range(0, masks1.shape[0], block_size):\n        for j in range(0, masks2.shape[0], block_size):\n            intersection = (\n                (masks1[i : i + block_size, None] * masks2[None, j : j + block_size])\n                .flatten(-2)\n                .sum(-1)\n            )\n            result[i : i + block_size, j : j + block_size] = intersection\n    return result\n\n\ndef mask_iom(masks1, masks2):\n    \"\"\"\n    Similar to IoU, except the denominator is the area of the smallest mask\n    \"\"\"\n    assert masks1.shape[1:] == masks2.shape[1:]\n    assert masks1.dtype == torch.bool and masks2.dtype == torch.bool\n\n    # intersection = (masks1[:, None] * masks2[None]).flatten(-2).sum(-1)\n    intersection = mask_intersection(masks1, masks2)\n    area1 = masks1.flatten(-2).sum(-1)\n    area2 = masks2.flatten(-2).sum(-1)\n    min_area = torch.min(area1[:, None], area2[None, :])\n    return intersection / (min_area + 1e-8)\n\n\ndef compute_boundary(seg):\n    \"\"\"\n    Adapted from https://github.com/JonathonLuiten/TrackEval/blob/master/trackeval/metrics/j_and_f.py#L148\n    Return a 1pix wide boundary of the given mask\n    \"\"\"\n    assert seg.ndim >= 2\n    e = torch.zeros_like(seg)\n    s = torch.zeros_like(seg)\n    se = torch.zeros_like(seg)\n\n    e[..., :, :-1] = seg[..., :, 1:]\n    s[..., :-1, :] = seg[..., 1:, :]\n    se[..., :-1, :-1] = seg[..., 1:, 1:]\n\n    b = seg ^ e | seg ^ s | seg ^ se\n    b[..., -1, :] = seg[..., -1, :] ^ e[..., -1, :]\n    b[..., :, -1] = seg[..., :, -1] ^ s[..., :, -1]\n    b[..., -1, -1] = 0\n    return b\n\n\ndef dilation(mask, kernel_size):\n    \"\"\"\n    Implements the dilation operation. If the input is on cpu, we call the cv2 version.\n    Otherwise, we implement it using a convolution\n\n    The kernel is assumed to be a square kernel\n\n    \"\"\"\n\n    assert mask.ndim == 3\n    kernel_size = int(kernel_size)\n    assert kernel_size % 2 == 1, (\n        f\"Dilation expects a odd kernel size, got {kernel_size}\"\n    )\n\n    if mask.is_cuda:\n        m = mask.unsqueeze(1).to(torch.float16)\n        k = torch.ones(1, 1, kernel_size, 1, dtype=m.dtype, device=m.device)\n\n        result = torch.nn.functional.conv2d(m, k, padding=\"same\")\n        result = torch.nn.functional.conv2d(result, k.transpose(-1, -2), padding=\"same\")\n        return result.view_as(mask) > 0\n\n    all_masks = mask.view(-1, mask.size(-2), mask.size(-1)).numpy().astype(np.uint8)\n    kernel = np.ones((kernel_size, kernel_size), dtype=np.uint8)\n\n    import cv2\n\n    processed = [torch.from_numpy(cv2.dilate(m, kernel)) for m in all_masks]\n    return torch.stack(processed).view_as(mask).to(mask)\n\n\ndef compute_F_measure(\n    gt_boundary_rle, gt_dilated_boundary_rle, dt_boundary_rle, dt_dilated_boundary_rle\n):\n    \"\"\"Adapted from https://github.com/JonathonLuiten/TrackEval/blob/master/trackeval/metrics/j_and_f.py#L207\n\n    Assumes the boundary and dilated boundaries have already been computed and converted to RLE\n    \"\"\"\n    gt_match = maskUtils.merge([gt_boundary_rle, dt_dilated_boundary_rle], True)\n    dt_match = maskUtils.merge([dt_boundary_rle, gt_dilated_boundary_rle], True)\n\n    n_dt = maskUtils.area(dt_boundary_rle)\n    n_gt = maskUtils.area(gt_boundary_rle)\n    # % Compute precision and recall\n    if n_dt == 0 and n_gt > 0:\n        precision = 1\n        recall = 0\n    elif n_dt > 0 and n_gt == 0:\n        precision = 0\n        recall = 1\n    elif n_dt == 0 and n_gt == 0:\n        precision = 1\n        recall = 1\n    else:\n        precision = maskUtils.area(dt_match) / float(n_dt)\n        recall = maskUtils.area(gt_match) / float(n_gt)\n\n    # Compute F measure\n    if precision + recall == 0:\n        f_val = 0\n    else:\n        f_val = 2 * precision * recall / (precision + recall)\n\n    return f_val\n\n\n@torch.no_grad()\ndef rle_encode(orig_mask, return_areas=False):\n    \"\"\"Encodes a collection of masks in RLE format\n\n    This function emulates the behavior of the COCO API's encode function, but\n    is executed partially on the GPU for faster execution.\n\n    Args:\n        mask (torch.Tensor): A mask of shape (N, H, W) with dtype=torch.bool\n        return_areas (bool): If True, add the areas of the masks as a part of\n            the RLE output dict under the \"area\" key. Default is False.\n\n    Returns:\n        str: The RLE encoded masks\n    \"\"\"\n    assert orig_mask.ndim == 3, \"Mask must be of shape (N, H, W)\"\n    assert orig_mask.dtype == torch.bool, \"Mask must have dtype=torch.bool\"\n\n    if orig_mask.numel() == 0:\n        return []\n\n    # First, transpose the spatial dimensions.\n    # This is necessary because the COCO API uses Fortran order\n    mask = orig_mask.transpose(1, 2)\n\n    # Flatten the mask\n    flat_mask = mask.reshape(mask.shape[0], -1)\n    if return_areas:\n        mask_areas = flat_mask.sum(-1).tolist()\n    # Find the indices where the mask changes\n    differences = torch.ones(\n        mask.shape[0], flat_mask.shape[1] + 1, device=mask.device, dtype=torch.bool\n    )\n    differences[:, 1:-1] = flat_mask[:, :-1] != flat_mask[:, 1:]\n    differences[:, 0] = flat_mask[:, 0]\n    _, change_indices = torch.where(differences)\n\n    try:\n        boundaries = torch.cumsum(differences.sum(-1), 0).cpu()\n    except RuntimeError as _:\n        boundaries = torch.cumsum(differences.cpu().sum(-1), 0)\n\n    change_indices_clone = change_indices.clone()\n    # First pass computes the RLEs on GPU, in a flatten format\n    for i in range(mask.shape[0]):\n        # Get the change indices for this batch item\n        beg = 0 if i == 0 else boundaries[i - 1].item()\n        end = boundaries[i].item()\n        change_indices[beg + 1 : end] -= change_indices_clone[beg : end - 1]\n\n    # Now we can split the RLES of each batch item, and convert them to strings\n    # No more gpu at this point\n    change_indices = change_indices.tolist()\n\n    batch_rles = []\n    # Process each mask in the batch separately\n    for i in range(mask.shape[0]):\n        beg = 0 if i == 0 else boundaries[i - 1].item()\n        end = boundaries[i].item()\n        run_lengths = change_indices[beg:end]\n\n        uncompressed_rle = {\"counts\": run_lengths, \"size\": list(orig_mask.shape[1:])}\n        h, w = uncompressed_rle[\"size\"]\n        rle = mask_util.frPyObjects(uncompressed_rle, h, w)\n        rle[\"counts\"] = rle[\"counts\"].decode(\"utf-8\")\n        if return_areas:\n            rle[\"area\"] = mask_areas[i]\n        batch_rles.append(rle)\n\n    return batch_rles\n\n\ndef robust_rle_encode(masks):\n    \"\"\"Encodes a collection of masks in RLE format. Uses the gpu version fist, falls back to the cpu version if it fails\"\"\"\n\n    assert masks.ndim == 3, \"Mask must be of shape (N, H, W)\"\n    assert masks.dtype == torch.bool, \"Mask must have dtype=torch.bool\"\n\n    try:\n        return rle_encode(masks)\n    except RuntimeError as _:\n        masks = masks.cpu().numpy()\n        rles = [\n            mask_util.encode(\n                np.array(mask[:, :, np.newaxis], dtype=np.uint8, order=\"F\")\n            )[0]\n            for mask in masks\n        ]\n        for rle in rles:\n            rle[\"counts\"] = rle[\"counts\"].decode(\"utf-8\")\n        return rles\n\n\ndef ann_to_rle(segm, im_info):\n    \"\"\"Convert annotation which can be polygons, uncompressed RLE to RLE.\n    Args:\n        ann (dict) : annotation object\n    Returns:\n        ann (rle)\n    \"\"\"\n    h, w = im_info[\"height\"], im_info[\"width\"]\n    if isinstance(segm, list):\n        # polygon -- a single object might consist of multiple parts\n        # we merge all parts into one mask rle code\n        rles = mask_util.frPyObjects(segm, h, w)\n        rle = mask_util.merge(rles)\n    elif isinstance(segm[\"counts\"], list):\n        # uncompressed RLE\n        rle = mask_util.frPyObjects(segm, h, w)\n    else:\n        # rle\n        rle = segm\n    return rle\n"
  },
  {
    "path": "sam3/train/matcher.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n\n\"\"\"\nModules to compute the matching cost and solve the corresponding LSAP.\n\"\"\"\n\nimport numpy as np\nimport torch\nfrom sam3.model.box_ops import box_cxcywh_to_xyxy, box_iou, generalized_box_iou\nfrom scipy.optimize import linear_sum_assignment\nfrom torch import nn\n\n\ndef _do_matching(cost, repeats=1, return_tgt_indices=False, do_filtering=False):\n    if repeats > 1:\n        cost = np.tile(cost, (1, repeats))\n\n    i, j = linear_sum_assignment(cost)\n    if do_filtering:\n        # filter out invalid entries (i.e. those with cost > 1e8)\n        valid_thresh = 1e8\n        valid_ijs = [(ii, jj) for ii, jj in zip(i, j) if cost[ii, jj] < valid_thresh]\n        i, j = zip(*valid_ijs) if len(valid_ijs) > 0 else ([], [])\n        i, j = np.array(i, dtype=np.int64), np.array(j, dtype=np.int64)\n    if return_tgt_indices:\n        return i, j\n    order = np.argsort(j)\n    return i[order]\n\n\nclass HungarianMatcher(nn.Module):\n    \"\"\"This class computes an assignment between the targets and the predictions of the network\n\n    For efficiency reasons, the targets don't include the no_object. Because of this, in general,\n    there are more predictions than targets. In this case, we do a 1-to-1 matching of the best predictions,\n    while the others are un-matched (and thus treated as non-objects).\n    \"\"\"\n\n    def __init__(\n        self,\n        cost_class: float = 1,\n        cost_bbox: float = 1,\n        cost_giou: float = 1,\n        focal_loss: bool = False,\n        focal_alpha: float = 0.25,\n        focal_gamma: float = 2,\n    ):\n        \"\"\"Creates the matcher\n\n        Params:\n            cost_class: This is the relative weight of the classification error in the matching cost\n            cost_bbox: This is the relative weight of the L1 error of the bounding box coordinates in the matching cost\n            cost_giou: This is the relative weight of the giou loss of the bounding box in the matching cost\n        \"\"\"\n        super().__init__()\n        self.cost_class = cost_class\n        self.cost_bbox = cost_bbox\n        self.cost_giou = cost_giou\n        self.norm = nn.Sigmoid() if focal_loss else nn.Softmax(-1)\n        assert cost_class != 0 or cost_bbox != 0 or cost_giou != 0, (\n            \"all costs cant be 0\"\n        )\n        self.focal_loss = focal_loss\n        self.focal_alpha = focal_alpha\n        self.focal_gamma = focal_gamma\n\n    @torch.no_grad()\n    def forward(self, outputs, batched_targets):\n        \"\"\"Performs the matching\n\n        Params:\n            outputs: This is a dict that contains at least these entries:\n                 \"pred_logits\": Tensor of dim [batch_size, num_queries, num_classes] with the classification logits\n                 \"pred_boxes\": Tensor of dim [batch_size, num_queries, 4] with the predicted box coordinates\n\n            targets: This is a list of targets (len(targets) = batch_size), where each target is a dict containing:\n                 \"labels\": Tensor of dim [num_target_boxes] (where num_target_boxes is the number of ground-truth\n                           objects in the target) containing the class labels\n                 \"boxes\": Tensor of dim [num_target_boxes, 4] containing the target box coordinates\n\n        Returns:\n            A list of size batch_size, containing tuples of (index_i, index_j) where:\n                - index_i is the indices of the selected predictions (in order)\n                - index_j is the indices of the corresponding selected targets (in order)\n            For each batch element, it holds:\n                len(index_i) = len(index_j) = min(num_queries, num_target_boxes)\n        \"\"\"\n        bs, num_queries = outputs[\"pred_logits\"].shape[:2]\n\n        # We flatten to compute the cost matrices in a batch\n        out_prob = self.norm(\n            outputs[\"pred_logits\"].flatten(0, 1)\n        )  # [batch_size * num_queries, num_classes]\n        out_bbox = outputs[\"pred_boxes\"].flatten(0, 1)  # [batch_size * num_queries, 4]\n\n        # Also concat the target labels and boxes\n        tgt_bbox = batched_targets[\"boxes\"]\n\n        if \"positive_map\" in batched_targets:\n            # In this case we have a multi-hot target\n            positive_map = batched_targets[\"positive_map\"]\n            assert len(tgt_bbox) == len(positive_map)\n\n            if self.focal_loss:\n                positive_map = positive_map > 1e-4\n                alpha = self.focal_alpha\n                gamma = self.focal_gamma\n                neg_cost_class = (\n                    (1 - alpha) * (out_prob**gamma) * (-(1 - out_prob + 1e-8).log())\n                )\n                pos_cost_class = (\n                    alpha * ((1 - out_prob) ** gamma) * (-(out_prob + 1e-8).log())\n                )\n                cost_class = (\n                    (pos_cost_class - neg_cost_class).unsqueeze(1)\n                    * positive_map.unsqueeze(0)\n                ).sum(-1)\n            else:\n                # Compute the soft-cross entropy between the predicted token alignment and the GT one for each box\n                cost_class = -(out_prob.unsqueeze(1) * positive_map.unsqueeze(0)).sum(\n                    -1\n                )\n        else:\n            # In this case we are doing a \"standard\" cross entropy\n            tgt_ids = batched_targets[\"labels\"]\n            assert len(tgt_bbox) == len(tgt_ids)\n\n            if self.focal_loss:\n                alpha = self.focal_alpha\n                gamma = self.focal_gamma\n                neg_cost_class = (\n                    (1 - alpha) * (out_prob**gamma) * (-(1 - out_prob + 1e-8).log())\n                )\n                pos_cost_class = (\n                    alpha * ((1 - out_prob) ** gamma) * (-(out_prob + 1e-8).log())\n                )\n                cost_class = pos_cost_class[:, tgt_ids] - neg_cost_class[:, tgt_ids]\n            else:\n                # Compute the classification cost. Contrary to the loss, we don't use the NLL,\n                # but approximate it in 1 - proba[target class].\n                # The 1 is a constant that doesn't change the matching, it can be omitted.\n                cost_class = -out_prob[:, tgt_ids]\n\n        # Compute the L1 cost between boxes\n        cost_bbox = torch.cdist(out_bbox, tgt_bbox, p=1)\n        assert cost_class.shape == cost_bbox.shape\n\n        # Compute the giou cost betwen boxes\n        cost_giou = -generalized_box_iou(\n            box_cxcywh_to_xyxy(out_bbox), box_cxcywh_to_xyxy(tgt_bbox)\n        )\n\n        # Final cost matrix\n        C = (\n            self.cost_bbox * cost_bbox\n            + self.cost_class * cost_class\n            + self.cost_giou * cost_giou\n        )\n        C = C.view(bs, num_queries, -1).cpu().numpy()\n\n        sizes = torch.cumsum(batched_targets[\"num_boxes\"], -1)[:-1]\n        costs = [c[i] for i, c in enumerate(np.split(C, sizes.cpu().numpy(), axis=-1))]\n        indices = [_do_matching(c) for c in costs]\n        batch_idx = torch.as_tensor(\n            sum([[i] * len(src) for i, src in enumerate(indices)], []), dtype=torch.long\n        )\n        src_idx = torch.from_numpy(np.concatenate(indices)).long()\n        return batch_idx, src_idx\n\n\nclass BinaryHungarianMatcher(nn.Module):\n    \"\"\"This class computes an assignment between the targets and the predictions of the network\n\n    For efficiency reasons, the targets don't include the no_object. Because of this, in general,\n    there are more predictions than targets. In this case, we do a 1-to-1 matching of the best predictions,\n    while the others are un-matched (and thus treated as non-objects).\n    \"\"\"\n\n    def __init__(\n        self,\n        cost_class: float = 1,\n        cost_bbox: float = 1,\n        cost_giou: float = 1,\n    ):\n        \"\"\"Creates the matcher\n\n        Params:\n            cost_class: This is the relative weight of the classification error in the matching cost\n            cost_bbox: This is the relative weight of the L1 error of the bounding box coordinates in the matching cost\n            cost_giou: This is the relative weight of the giou loss of the bounding box in the matching cost\n        \"\"\"\n        super().__init__()\n        self.cost_class = cost_class\n        self.cost_bbox = cost_bbox\n        self.cost_giou = cost_giou\n        self.norm = nn.Sigmoid()\n        assert cost_class != 0 or cost_bbox != 0 or cost_giou != 0, (\n            \"all costs cant be 0\"\n        )\n\n    @torch.no_grad()\n    def forward(self, outputs, batched_targets, repeats=0, repeat_batch=1):\n        \"\"\"Performs the matching\n\n        Params:\n            outputs: This is a dict that contains at least these entries:\n                 \"pred_logits\": Tensor of dim [batch_size, num_queries, num_classes] with the classification logits\n                 \"pred_boxes\": Tensor of dim [batch_size, num_queries, 4] with the predicted box coordinates\n\n            targets: This is a list of targets (len(targets) = batch_size), where each target is a dict containing:\n                 \"labels\": Tensor of dim [num_target_boxes] (where num_target_boxes is the number of ground-truth\n                           objects in the target) containing the class labels\n                 \"boxes\": Tensor of dim [num_target_boxes, 4] containing the target box coordinates\n\n        Returns:\n            A list of size batch_size, containing tuples of (index_i, index_j) where:\n                - index_i is the indices of the selected predictions (in order)\n                - index_j is the indices of the corresponding selected targets (in order)\n            For each batch element, it holds:\n                len(index_i) = len(index_j) = min(num_queries, num_target_boxes)\n        \"\"\"\n        if repeat_batch != 1:\n            raise NotImplementedError(\"please use BinaryHungarianMatcherV2 instead\")\n\n        bs, num_queries = outputs[\"pred_logits\"].shape[:2]\n\n        # We flatten to compute the cost matrices in a batch\n        out_prob = self.norm(outputs[\"pred_logits\"].flatten(0, 1)).squeeze(\n            -1\n        )  # [batch_size * num_queries]\n        out_bbox = outputs[\"pred_boxes\"].flatten(0, 1)  # [batch_size * num_queries, 4]\n\n        # Also concat the target labels and boxes\n        tgt_bbox = batched_targets[\"boxes\"]\n\n        # Compute the L1 cost between boxes\n        cost_bbox = torch.cdist(out_bbox, tgt_bbox, p=1)\n\n        cost_class = -out_prob.unsqueeze(-1).expand_as(cost_bbox)\n\n        assert cost_class.shape == cost_bbox.shape\n\n        # Compute the giou cost betwen boxes\n        cost_giou = -generalized_box_iou(\n            box_cxcywh_to_xyxy(out_bbox), box_cxcywh_to_xyxy(tgt_bbox)\n        )\n\n        # Final cost matrix\n        C = (\n            self.cost_bbox * cost_bbox\n            + self.cost_class * cost_class\n            + self.cost_giou * cost_giou\n        )\n        C = C.view(bs, num_queries, -1).cpu().numpy()\n\n        sizes = torch.cumsum(batched_targets[\"num_boxes\"], -1)[:-1]\n        costs = [c[i] for i, c in enumerate(np.split(C, sizes.cpu().numpy(), axis=-1))]\n        return_tgt_indices = False\n        for c in costs:\n            n_targ = c.shape[1]\n            if repeats > 1:\n                n_targ *= repeats\n            if c.shape[0] < n_targ:\n                return_tgt_indices = True\n                break\n        if return_tgt_indices:\n            indices, tgt_indices = zip(\n                *(\n                    _do_matching(\n                        c, repeats=repeats, return_tgt_indices=return_tgt_indices\n                    )\n                    for c in costs\n                )\n            )\n            tgt_indices = list(tgt_indices)\n            for i in range(1, len(tgt_indices)):\n                tgt_indices[i] += sizes[i - 1].item()\n            tgt_idx = torch.from_numpy(np.concatenate(tgt_indices)).long()\n        else:\n            indices = [_do_matching(c, repeats=repeats) for c in costs]\n            tgt_idx = None\n\n        batch_idx = torch.as_tensor(\n            sum([[i] * len(src) for i, src in enumerate(indices)], []), dtype=torch.long\n        )\n        src_idx = torch.from_numpy(np.concatenate(indices)).long()\n        return batch_idx, src_idx, tgt_idx\n\n\nclass BinaryFocalHungarianMatcher(nn.Module):\n    \"\"\"This class computes an assignment between the targets and the predictions of the network\n\n    For efficiency reasons, the targets don't include the no_object. Because of this, in general,\n    there are more predictions than targets. In this case, we do a 1-to-1 matching of the best predictions,\n    while the others are un-matched (and thus treated as non-objects).\n    \"\"\"\n\n    def __init__(\n        self,\n        cost_class: float = 1,\n        cost_bbox: float = 1,\n        cost_giou: float = 1,\n        alpha: float = 0.25,\n        gamma: float = 2.0,\n        stable: bool = False,\n    ):\n        \"\"\"Creates the matcher\n\n        Params:\n            cost_class: This is the relative weight of the classification error in the matching cost\n            cost_bbox: This is the relative weight of the L1 error of the bounding box coordinates in the matching cost\n            cost_giou: This is the relative weight of the giou loss of the bounding box in the matching cost\n        \"\"\"\n        super().__init__()\n        self.cost_class = cost_class\n        self.cost_bbox = cost_bbox\n        self.cost_giou = cost_giou\n        self.norm = nn.Sigmoid()\n        self.alpha = alpha\n        self.gamma = gamma\n        self.stable = stable\n        assert cost_class != 0 or cost_bbox != 0 or cost_giou != 0, (\n            \"all costs cant be 0\"\n        )\n\n    @torch.no_grad()\n    def forward(self, outputs, batched_targets, repeats=1, repeat_batch=1):\n        \"\"\"Performs the matching\n\n        Params:\n            outputs: This is a dict that contains at least these entries:\n                 \"pred_logits\": Tensor of dim [batch_size, num_queries, num_classes] with the classification logits\n                 \"pred_boxes\": Tensor of dim [batch_size, num_queries, 4] with the predicted box coordinates\n\n            targets: This is a list of targets (len(targets) = batch_size), where each target is a dict containing:\n                 \"labels\": Tensor of dim [num_target_boxes] (where num_target_boxes is the number of ground-truth\n                           objects in the target) containing the class labels\n                 \"boxes\": Tensor of dim [num_target_boxes, 4] containing the target box coordinates\n\n        Returns:\n            A list of size batch_size, containing tuples of (index_i, index_j) where:\n                - index_i is the indices of the selected predictions (in order)\n                - index_j is the indices of the corresponding selected targets (in order)\n            For each batch element, it holds:\n                len(index_i) = len(index_j) = min(num_queries, num_target_boxes)\n        \"\"\"\n        if repeat_batch != 1:\n            raise NotImplementedError(\"please use BinaryHungarianMatcherV2 instead\")\n\n        bs, num_queries = outputs[\"pred_logits\"].shape[:2]\n\n        # We flatten to compute the cost matrices in a batch\n        out_score = outputs[\"pred_logits\"].flatten(0, 1).squeeze(-1)\n        out_prob = self.norm(out_score)  # [batch_size * num_queries]\n        out_bbox = outputs[\"pred_boxes\"].flatten(0, 1)  # [batch_size * num_queries, 4]\n\n        # Also concat the target labels and boxes\n        tgt_bbox = batched_targets[\"boxes\"]\n\n        # Compute the L1 cost between boxes\n        cost_bbox = torch.cdist(out_bbox, tgt_bbox, p=1)\n\n        # Compute the giou cost betwen boxes\n        cost_giou = -generalized_box_iou(\n            box_cxcywh_to_xyxy(out_bbox), box_cxcywh_to_xyxy(tgt_bbox)\n        )\n\n        # cost_class = -out_prob.unsqueeze(-1).expand_as(cost_bbox)\n        if self.stable:\n            rescaled_giou = (-cost_giou + 1) / 2\n            out_prob = out_prob.unsqueeze(-1).expand_as(cost_bbox) * rescaled_giou\n            cost_class = -self.alpha * (1 - out_prob) ** self.gamma * torch.log(\n                out_prob\n            ) + (1 - self.alpha) * out_prob**self.gamma * torch.log(1 - out_prob)\n        else:\n            # directly computing log sigmoid (more numerically stable)\n            log_out_prob = torch.nn.functional.logsigmoid(out_score)\n            log_one_minus_out_prob = torch.nn.functional.logsigmoid(-out_score)\n            cost_class = (\n                -self.alpha * (1 - out_prob) ** self.gamma * log_out_prob\n                + (1 - self.alpha) * out_prob**self.gamma * log_one_minus_out_prob\n            )\n        if not self.stable:\n            cost_class = cost_class.unsqueeze(-1).expand_as(cost_bbox)\n\n        assert cost_class.shape == cost_bbox.shape\n\n        # Final cost matrix\n        C = (\n            self.cost_bbox * cost_bbox\n            + self.cost_class * cost_class\n            + self.cost_giou * cost_giou\n        )\n        C = C.view(bs, num_queries, -1).cpu().numpy()\n\n        sizes = torch.cumsum(batched_targets[\"num_boxes\"], -1)[:-1]\n        costs = [c[i] for i, c in enumerate(np.split(C, sizes.cpu().numpy(), axis=-1))]\n        return_tgt_indices = False\n        for c in costs:\n            n_targ = c.shape[1]\n            if repeats > 1:\n                n_targ *= repeats\n            if c.shape[0] < n_targ:\n                return_tgt_indices = True\n                break\n        if return_tgt_indices:\n            indices, tgt_indices = zip(\n                *(\n                    _do_matching(\n                        c, repeats=repeats, return_tgt_indices=return_tgt_indices\n                    )\n                    for c in costs\n                )\n            )\n            tgt_indices = list(tgt_indices)\n            for i in range(1, len(tgt_indices)):\n                tgt_indices[i] += sizes[i - 1].item()\n            tgt_idx = torch.from_numpy(np.concatenate(tgt_indices)).long()\n        else:\n            indices = [_do_matching(c, repeats=repeats) for c in costs]\n            tgt_idx = None\n\n        batch_idx = torch.as_tensor(\n            sum([[i] * len(src) for i, src in enumerate(indices)], []), dtype=torch.long\n        )\n        src_idx = torch.from_numpy(np.concatenate(indices)).long()\n        return batch_idx, src_idx, tgt_idx\n\n\nclass BinaryHungarianMatcherV2(nn.Module):\n    \"\"\"\n    This class computes an assignment between the targets and the predictions\n    of the network\n\n    For efficiency reasons, the targets don't include the no_object. Because of\n    this, in general, there are more predictions than targets. In this case, we\n    do a 1-to-1 matching of the best predictions, while the others are\n    un-matched (and thus treated as non-objects).\n\n    This is a more efficient implementation of BinaryHungarianMatcher.\n    \"\"\"\n\n    def __init__(\n        self,\n        cost_class: float = 1,\n        cost_bbox: float = 1,\n        cost_giou: float = 1,\n        focal: bool = False,\n        alpha: float = 0.25,\n        gamma: float = 2.0,\n        stable: bool = False,\n        remove_samples_with_0_gt: bool = True,\n    ):\n        \"\"\"\n        Creates the matcher\n\n        Params:\n        - cost_class: Relative weight of the classification error in the\n          matching cost\n        - cost_bbox: Relative weight of the L1 error of the bounding box\n          coordinates in the matching cost\n        - cost_giou: This is the relative weight of the giou loss of the\n          bounding box in the matching cost\n        \"\"\"\n        super().__init__()\n        self.cost_class = cost_class\n        self.cost_bbox = cost_bbox\n        self.cost_giou = cost_giou\n        self.norm = nn.Sigmoid()\n        assert cost_class != 0 or cost_bbox != 0 or cost_giou != 0, (\n            \"all costs cant be 0\"\n        )\n        self.focal = focal\n        if focal:\n            self.alpha = alpha\n            self.gamma = gamma\n            self.stable = stable\n        self.remove_samples_with_0_gt = remove_samples_with_0_gt\n\n    @torch.no_grad()\n    def forward(\n        self,\n        outputs,\n        batched_targets,\n        repeats=1,\n        repeat_batch=1,\n        out_is_valid=None,\n        target_is_valid_padded=None,\n    ):\n        \"\"\"\n        Performs the matching. The inputs and outputs are the same as\n        BinaryHungarianMatcher.forward, except for the optional cached_padded\n        flag and the optional \"_boxes_padded\" entry of batched_targets.\n\n        Inputs:\n        - outputs: A dict with the following keys:\n            - \"pred_logits\": Tensor of shape (batch_size, num_queries, 1) with\n               classification logits\n            - \"pred_boxes\": Tensor of shape (batch_size, num_queries, 4) with\n               predicted box coordinates in cxcywh format.\n        - batched_targets: A dict of targets. There may be a variable number of\n          targets per batch entry; suppose that there are T_b targets for batch\n          entry 0 <= b < batch_size. It should have the following keys:\n          - \"boxes\": Tensor of shape (sum_b T_b, 4) giving ground-truth boxes\n             in cxcywh format for all batch entries packed into a single tensor\n          - \"num_boxes\": int64 Tensor of shape (batch_size,) giving the number\n             of ground-truth boxes per batch entry: num_boxes[b] = T_b\n          - \"_boxes_padded\": Tensor of shape (batch_size, max_b T_b, 4) giving\n            a padded version of ground-truth boxes. If this is not present then\n            it will be computed from batched_targets[\"boxes\"] instead, but\n            caching it here can improve performance for repeated calls with the\n            same targets.\n        - out_is_valid: If not None, it should be a boolean tensor of shape\n          (batch_size, num_queries) indicating which predictions are valid.\n          Invalid predictions are ignored during matching and won't appear in\n          the output indices.\n        - target_is_valid_padded: If not None, it should be a boolean tensor of\n          shape (batch_size, max_num_gt_boxes) in padded format indicating\n          which GT boxes are valid. Invalid GT boxes are ignored during matching\n          and won't appear in the output indices.\n\n        Returns:\n            A list of size batch_size, containing tuples of (idx_i, idx_j):\n                - idx_i is the indices of the selected predictions (in order)\n                - idx_j is the indices of the corresponding selected targets\n                  (in order)\n            For each batch element, it holds:\n                len(index_i) = len(index_j)\n                             = min(num_queries, num_target_boxes)\n        \"\"\"\n        _, num_queries = outputs[\"pred_logits\"].shape[:2]\n\n        out_score = outputs[\"pred_logits\"].squeeze(-1)  # (B, Q)\n        out_bbox = outputs[\"pred_boxes\"]  # (B, Q, 4))\n\n        device = out_score.device\n\n        num_boxes = batched_targets[\"num_boxes\"].cpu()\n        # Get a padded version of target boxes (as precomputed in the collator).\n        # It should work for both repeat==1 (o2o) and repeat>1 (o2m) matching.\n        tgt_bbox = batched_targets[\"boxes_padded\"]\n        if self.remove_samples_with_0_gt:\n            # keep only samples w/ at least 1 GT box in targets (num_boxes and tgt_bbox)\n            batch_keep = num_boxes > 0\n            num_boxes = num_boxes[batch_keep]\n            tgt_bbox = tgt_bbox[batch_keep]\n            if target_is_valid_padded is not None:\n                target_is_valid_padded = target_is_valid_padded[batch_keep]\n        # Repeat the targets (for the case of batched aux outputs in the matcher)\n        if repeat_batch > 1:\n            # In this case, out_prob and out_bbox will be a concatenation of\n            # both final and auxiliary outputs, so we also repeat the targets\n            num_boxes = num_boxes.repeat(repeat_batch)\n            tgt_bbox = tgt_bbox.repeat(repeat_batch, 1, 1)\n            if target_is_valid_padded is not None:\n                target_is_valid_padded = target_is_valid_padded.repeat(repeat_batch, 1)\n\n        # keep only samples w/ at least 1 GT box in outputs\n        if self.remove_samples_with_0_gt:\n            if repeat_batch > 1:\n                batch_keep = batch_keep.repeat(repeat_batch)\n            out_score = out_score[batch_keep]\n            out_bbox = out_bbox[batch_keep]\n            if out_is_valid is not None:\n                out_is_valid = out_is_valid[batch_keep]\n        assert out_bbox.shape[0] == tgt_bbox.shape[0]\n        assert out_bbox.shape[0] == num_boxes.shape[0]\n\n        # Compute the L1 cost between boxes\n        cost_bbox = torch.cdist(out_bbox, tgt_bbox, p=1)\n\n        # Compute the giou cost betwen boxes\n        cost_giou = -generalized_box_iou(\n            box_cxcywh_to_xyxy(out_bbox), box_cxcywh_to_xyxy(tgt_bbox)\n        )\n\n        out_prob = self.norm(out_score)\n        if not self.focal:\n            cost_class = -out_prob.unsqueeze(-1).expand_as(cost_bbox)\n        else:\n            if self.stable:\n                rescaled_giou = (-cost_giou + 1) / 2\n                out_prob = out_prob.unsqueeze(-1).expand_as(cost_bbox) * rescaled_giou\n                cost_class = -self.alpha * (1 - out_prob) ** self.gamma * torch.log(\n                    out_prob\n                ) + (1 - self.alpha) * out_prob**self.gamma * torch.log(1 - out_prob)\n            else:\n                # directly computing log sigmoid (more numerically stable)\n                log_out_prob = torch.nn.functional.logsigmoid(out_score)\n                log_one_minus_out_prob = torch.nn.functional.logsigmoid(-out_score)\n                cost_class = (\n                    -self.alpha * (1 - out_prob) ** self.gamma * log_out_prob\n                    + (1 - self.alpha) * out_prob**self.gamma * log_one_minus_out_prob\n                )\n            if not self.stable:\n                cost_class = cost_class.unsqueeze(-1).expand_as(cost_bbox)\n\n        assert cost_class.shape == cost_bbox.shape\n\n        # Final cost matrix\n        C = (\n            self.cost_bbox * cost_bbox\n            + self.cost_class * cost_class\n            + self.cost_giou * cost_giou\n        )\n        # assign a very high cost (1e9) to invalid outputs and targets, so that we can\n        # filter them out (in `_do_matching`) from bipartite matching results\n        do_filtering = out_is_valid is not None or target_is_valid_padded is not None\n        if out_is_valid is not None:\n            C = torch.where(out_is_valid[:, :, None], C, 1e9)\n        if target_is_valid_padded is not None:\n            C = torch.where(target_is_valid_padded[:, None, :], C, 1e9)\n        C = C.cpu().numpy()\n        costs = [C[i, :, :s] for i, s in enumerate(num_boxes.tolist())]\n        return_tgt_indices = (\n            do_filtering or torch.any(num_queries < num_boxes * max(repeats, 1)).item()\n        )\n        if len(costs) == 0:\n            # We have size 0 in the batch dimension, so we return empty matching indices\n            # (note that this can happen due to `remove_samples_with_0_gt=True` even if\n            # the original input batch size is not 0, when all queries have empty GTs).\n            indices = []\n            tgt_idx = torch.zeros(0).long().to(device) if return_tgt_indices else None\n        elif return_tgt_indices:\n            indices, tgt_indices = zip(\n                *(\n                    _do_matching(\n                        c,\n                        repeats=repeats,\n                        return_tgt_indices=return_tgt_indices,\n                        do_filtering=do_filtering,\n                    )\n                    for c in costs\n                )\n            )\n            tgt_indices = list(tgt_indices)\n            sizes = torch.cumsum(num_boxes, -1)[:-1]\n            for i in range(1, len(tgt_indices)):\n                tgt_indices[i] += sizes[i - 1].item()\n            tgt_idx = torch.from_numpy(np.concatenate(tgt_indices)).long().to(device)\n        else:\n            indices = [\n                _do_matching(c, repeats=repeats, do_filtering=do_filtering)\n                for c in costs\n            ]\n            tgt_idx = None\n\n        if self.remove_samples_with_0_gt:\n            kept_inds = batch_keep.nonzero().squeeze(1)\n            batch_idx = torch.as_tensor(\n                sum([[kept_inds[i]] * len(src) for i, src in enumerate(indices)], []),\n                dtype=torch.long,\n                device=device,\n            )\n        else:\n            batch_idx = torch.as_tensor(\n                sum([[i] * len(src) for i, src in enumerate(indices)], []),\n                dtype=torch.long,\n                device=device,\n            )\n\n        # indices could be an empty list (since we remove samples w/ 0 GT boxes)\n        if len(indices) > 0:\n            src_idx = torch.from_numpy(np.concatenate(indices)).long().to(device)\n        else:\n            src_idx = torch.empty(0, dtype=torch.long, device=device)\n        return batch_idx, src_idx, tgt_idx\n\n\nclass BinaryOneToManyMatcher(nn.Module):\n    \"\"\"\n    This class computes a greedy assignment between the targets and the predictions of the network.\n    In this formulation, several predictions can be assigned to each target, but each prediction can be assigned to\n    at most one target.\n\n    See DAC-Detr for details\n    \"\"\"\n\n    def __init__(\n        self,\n        alpha: float = 0.3,\n        threshold: float = 0.4,\n        topk: int = 6,\n    ):\n        \"\"\"\n        Creates the matcher\n\n        Params:\n                alpha: relative balancing between classification and localization\n                threshold: threshold used to select positive predictions\n                topk: number of top scoring predictions to consider\n        \"\"\"\n        super().__init__()\n        self.norm = nn.Sigmoid()\n        self.alpha = alpha\n        self.threshold = threshold\n        self.topk = topk\n\n    @torch.no_grad()\n    def forward(\n        self,\n        outputs,\n        batched_targets,\n        repeats=1,\n        repeat_batch=1,\n        out_is_valid=None,\n        target_is_valid_padded=None,\n    ):\n        \"\"\"\n        Performs the matching. The inputs and outputs are the same as\n        BinaryHungarianMatcher.forward\n\n        Inputs:\n        - outputs: A dict with the following keys:\n            - \"pred_logits\": Tensor of shape (batch_size, num_queries, 1) with\n               classification logits\n            - \"pred_boxes\": Tensor of shape (batch_size, num_queries, 4) with\n               predicted box coordinates in cxcywh format.\n        - batched_targets: A dict of targets. There may be a variable number of\n          targets per batch entry; suppose that there are T_b targets for batch\n          entry 0 <= b < batch_size. It should have the following keys:\n          - \"num_boxes\": int64 Tensor of shape (batch_size,) giving the number\n             of ground-truth boxes per batch entry: num_boxes[b] = T_b\n          - \"_boxes_padded\": Tensor of shape (batch_size, max_b T_b, 4) giving\n            a padded version of ground-truth boxes. If this is not present then\n            it will be computed from batched_targets[\"boxes\"] instead, but\n            caching it here can improve performance for repeated calls with the\n            same targets.\n        - out_is_valid: If not None, it should be a boolean tensor of shape\n          (batch_size, num_queries) indicating which predictions are valid.\n          Invalid predictions are ignored during matching and won't appear in\n          the output indices.\n        - target_is_valid_padded: If not None, it should be a boolean tensor of\n          shape (batch_size, max_num_gt_boxes) in padded format indicating\n          which GT boxes are valid. Invalid GT boxes are ignored during matching\n          and won't appear in the output indices.\n        Returns:\n            A list of size batch_size, containing tuples of (idx_i, idx_j):\n                - idx_i is the indices of the selected predictions (in order)\n                - idx_j is the indices of the corresponding selected targets\n                  (in order)\n            For each batch element, it holds:\n                len(index_i) = len(index_j)\n                             = min(num_queries, num_target_boxes)\n        \"\"\"\n        assert repeats <= 1 and repeat_batch <= 1\n        bs, num_queries = outputs[\"pred_logits\"].shape[:2]\n\n        out_prob = self.norm(outputs[\"pred_logits\"]).squeeze(-1)  # (B, Q)\n        out_bbox = outputs[\"pred_boxes\"]  # (B, Q, 4))\n\n        num_boxes = batched_targets[\"num_boxes\"]\n\n        # Get a padded version of target boxes (as precomputed in the collator).\n        tgt_bbox = batched_targets[\"boxes_padded\"]\n        assert len(tgt_bbox) == bs\n        num_targets = tgt_bbox.shape[1]\n        if num_targets == 0:\n            return (\n                torch.empty(0, dtype=torch.long, device=out_prob.device),\n                torch.empty(0, dtype=torch.long, device=out_prob.device),\n                torch.empty(0, dtype=torch.long, device=out_prob.device),\n            )\n\n        iou, _ = box_iou(box_cxcywh_to_xyxy(out_bbox), box_cxcywh_to_xyxy(tgt_bbox))\n\n        assert iou.shape == (bs, num_queries, num_targets)\n\n        # Final cost matrix (higher is better in `C`; this is unlike the case\n        # of BinaryHungarianMatcherV2 above where lower is better in its `C`)\n        C = self.alpha * out_prob.unsqueeze(-1) + (1 - self.alpha) * iou\n        if out_is_valid is not None:\n            C = torch.where(out_is_valid[:, :, None], C, -1e9)\n        if target_is_valid_padded is not None:\n            C = torch.where(target_is_valid_padded[:, None, :], C, -1e9)\n\n        # Selecting topk predictions\n        matches = C > torch.quantile(\n            C, 1 - self.topk / num_queries, dim=1, keepdim=True\n        )\n\n        # Selecting predictions above threshold\n        matches = matches & (C > self.threshold)\n        if out_is_valid is not None:\n            matches = matches & out_is_valid[:, :, None]\n        if target_is_valid_padded is not None:\n            matches = matches & target_is_valid_padded[:, None, :]\n\n        # Removing padding\n        matches = matches & (\n            torch.arange(0, num_targets, device=num_boxes.device)[None]\n            < num_boxes[:, None]\n        ).unsqueeze(1)\n\n        batch_idx, src_idx, tgt_idx = torch.nonzero(matches, as_tuple=True)\n\n        cum_num_boxes = torch.cat(\n            [\n                torch.zeros(1, dtype=num_boxes.dtype, device=num_boxes.device),\n                num_boxes.cumsum(-1)[:-1],\n            ]\n        )\n        tgt_idx += cum_num_boxes[batch_idx]\n\n        return batch_idx, src_idx, tgt_idx\n"
  },
  {
    "path": "sam3/train/nms_helper.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\nimport warnings\nfrom typing import Dict, List\n\nimport numpy as np\n\n# Check if Numba is available\nHAS_NUMBA = False\ntry:\n    import numba as nb\n\n    HAS_NUMBA = True\nexcept ImportError:\n    warnings.warn(\n        \"Numba not found. Using slower pure Python implementations.\", UserWarning\n    )\n\n\n# -------------------- Helper Functions --------------------\ndef is_zero_box(bbox: list) -> bool:\n    \"\"\"Check if bounding box is invalid\"\"\"\n    if bbox is None:\n        return True\n    return all(x <= 0 for x in bbox[:4]) or len(bbox) < 4\n\n\ndef convert_bbox_format(bbox: list) -> List[float]:\n    \"\"\"Convert bbox from (x,y,w,h) to (x1,y1,x2,y2)\"\"\"\n    x, y, w, h = bbox\n    return [x, y, x + w, y + h]\n\n\n# -------------------- Track-level NMS --------------------\ndef process_track_level_nms(video_groups: Dict, nms_threshold: float) -> Dict:\n    \"\"\"Apply track-level NMS to all videos\"\"\"\n    for tracks in video_groups.values():\n        track_detections = []\n\n        # Process tracks\n        for track_idx, track in enumerate(tracks):\n            if not track[\"bboxes\"]:\n                continue\n\n            converted_bboxes = []\n            valid_frames = []\n            for bbox in track[\"bboxes\"]:\n                if bbox and not is_zero_box(bbox):\n                    converted_bboxes.append(convert_bbox_format(bbox))\n                    valid_frames.append(True)\n                else:\n                    converted_bboxes.append([np.nan] * 4)\n                    valid_frames.append(False)\n\n            if any(valid_frames):\n                track_detections.append(\n                    {\n                        \"track_idx\": track_idx,\n                        \"bboxes\": np.array(converted_bboxes, dtype=np.float32),\n                        \"score\": track[\"score\"],\n                    }\n                )\n\n        # Apply NMS\n        if track_detections:\n            scores = np.array([d[\"score\"] for d in track_detections], dtype=np.float32)\n            keep = apply_track_nms(track_detections, scores, nms_threshold)\n\n            # Suppress non-kept tracks\n            for idx, track in enumerate(track_detections):\n                if idx not in keep:\n                    tracks[track[\"track_idx\"]][\"bboxes\"] = [None] * len(track[\"bboxes\"])\n\n    return video_groups\n\n\n# -------------------- Frame-level NMS --------------------\ndef process_frame_level_nms(video_groups: Dict, nms_threshold: float) -> Dict:\n    \"\"\"Apply frame-level NMS to all videos\"\"\"\n    for tracks in video_groups.values():\n        if not tracks:\n            continue\n\n        num_frames = len(tracks[0][\"bboxes\"])\n\n        for frame_idx in range(num_frames):\n            frame_detections = []\n\n            # Collect valid detections\n            for track_idx, track in enumerate(tracks):\n                bbox = track[\"bboxes\"][frame_idx]\n                if bbox and not is_zero_box(bbox):\n                    frame_detections.append(\n                        {\n                            \"track_idx\": track_idx,\n                            \"bbox\": np.array(\n                                convert_bbox_format(bbox), dtype=np.float32\n                            ),\n                            \"score\": track[\"score\"],\n                        }\n                    )\n\n            # Apply NMS\n            if frame_detections:\n                bboxes = np.stack([d[\"bbox\"] for d in frame_detections])\n                scores = np.array(\n                    [d[\"score\"] for d in frame_detections], dtype=np.float32\n                )\n                keep = apply_frame_nms(bboxes, scores, nms_threshold)\n\n                # Suppress non-kept detections\n                for i, d in enumerate(frame_detections):\n                    if i not in keep:\n                        tracks[d[\"track_idx\"]][\"bboxes\"][frame_idx] = None\n\n    return video_groups\n\n\n# Track-level NMS helpers ------------------------------------------------------\ndef compute_track_iou_matrix(\n    bboxes_stacked: np.ndarray, valid_masks: np.ndarray, areas: np.ndarray\n) -> np.ndarray:\n    \"\"\"IoU matrix computation for track-level NMS with fallback to pure Python\"\"\"\n    num_tracks = bboxes_stacked.shape[0]\n    iou_matrix = np.zeros((num_tracks, num_tracks), dtype=np.float32)\n    if HAS_NUMBA:\n        iou_matrix = _compute_track_iou_matrix_numba(bboxes_stacked, valid_masks, areas)\n    else:\n        # Pure Python implementation\n        for i in range(num_tracks):\n            for j in range(i + 1, num_tracks):\n                valid_ij = valid_masks[i] & valid_masks[j]\n                if not valid_ij.any():\n                    continue\n                bboxes_i = bboxes_stacked[i, valid_ij]\n                bboxes_j = bboxes_stacked[j, valid_ij]\n                area_i = areas[i, valid_ij]\n                area_j = areas[j, valid_ij]\n                inter_total = 0.0\n                union_total = 0.0\n                for k in range(bboxes_i.shape[0]):\n                    x1 = max(bboxes_i[k, 0], bboxes_j[k, 0])\n                    y1 = max(bboxes_i[k, 1], bboxes_j[k, 1])\n                    x2 = min(bboxes_i[k, 2], bboxes_j[k, 2])\n                    y2 = min(bboxes_i[k, 3], bboxes_j[k, 3])\n                    inter = max(0, x2 - x1) * max(0, y2 - y1)\n                    union = area_i[k] + area_j[k] - inter\n                    inter_total += inter\n                    union_total += union\n                if union_total > 0:\n                    iou_matrix[i, j] = inter_total / union_total\n                    iou_matrix[j, i] = iou_matrix[i, j]\n    return iou_matrix\n\n\nif HAS_NUMBA:\n\n    @nb.jit(nopython=True, parallel=True)\n    def _compute_track_iou_matrix_numba(bboxes_stacked, valid_masks, areas):\n        \"\"\"Numba-optimized IoU matrix computation for track-level NMS\"\"\"\n        num_tracks = bboxes_stacked.shape[0]\n        iou_matrix = np.zeros((num_tracks, num_tracks), dtype=np.float32)\n        for i in nb.prange(num_tracks):\n            for j in range(i + 1, num_tracks):\n                valid_ij = valid_masks[i] & valid_masks[j]\n                if not valid_ij.any():\n                    continue\n                bboxes_i = bboxes_stacked[i, valid_ij]\n                bboxes_j = bboxes_stacked[j, valid_ij]\n                area_i = areas[i, valid_ij]\n                area_j = areas[j, valid_ij]\n                inter_total = 0.0\n                union_total = 0.0\n                for k in range(bboxes_i.shape[0]):\n                    x1 = max(bboxes_i[k, 0], bboxes_j[k, 0])\n                    y1 = max(bboxes_i[k, 1], bboxes_j[k, 1])\n                    x2 = min(bboxes_i[k, 2], bboxes_j[k, 2])\n                    y2 = min(bboxes_i[k, 3], bboxes_j[k, 3])\n                    inter = max(0, x2 - x1) * max(0, y2 - y1)\n                    union = area_i[k] + area_j[k] - inter\n                    inter_total += inter\n                    union_total += union\n                if union_total > 0:\n                    iou_matrix[i, j] = inter_total / union_total\n                    iou_matrix[j, i] = iou_matrix[i, j]\n        return iou_matrix\n\n\ndef apply_track_nms(\n    track_detections: List[dict], scores: np.ndarray, nms_threshold: float\n) -> List[int]:\n    \"\"\"Vectorized track-level NMS implementation\"\"\"\n    if not track_detections:\n        return []\n    bboxes_stacked = np.stack([d[\"bboxes\"] for d in track_detections], axis=0)\n    valid_masks = ~np.isnan(bboxes_stacked).any(axis=2)\n    areas = (bboxes_stacked[:, :, 2] - bboxes_stacked[:, :, 0]) * (\n        bboxes_stacked[:, :, 3] - bboxes_stacked[:, :, 1]\n    )\n    areas[~valid_masks] = 0\n    iou_matrix = compute_track_iou_matrix(bboxes_stacked, valid_masks, areas)\n    keep = []\n    order = np.argsort(-scores)\n    suppress = np.zeros(len(track_detections), dtype=bool)\n    for i in range(len(order)):\n        if not suppress[order[i]]:\n            keep.append(order[i])\n            suppress[order[i:]] = suppress[order[i:]] | (\n                iou_matrix[order[i], order[i:]] >= nms_threshold\n            )\n    return keep\n\n\n# Frame-level NMS helpers ------------------------------------------------------\ndef compute_frame_ious(bbox: np.ndarray, bboxes: np.ndarray) -> np.ndarray:\n    \"\"\"IoU computation for frame-level NMS with fallback to pure Python\"\"\"\n    if HAS_NUMBA:\n        return _compute_frame_ious_numba(bbox, bboxes)\n    else:\n        # Pure Python implementation\n        ious = np.zeros(len(bboxes), dtype=np.float32)\n        for i in range(len(bboxes)):\n            x1 = max(bbox[0], bboxes[i, 0])\n            y1 = max(bbox[1], bboxes[i, 1])\n            x2 = min(bbox[2], bboxes[i, 2])\n            y2 = min(bbox[3], bboxes[i, 3])\n\n            inter = max(0, x2 - x1) * max(0, y2 - y1)\n            area1 = (bbox[2] - bbox[0]) * (bbox[3] - bbox[1])\n            area2 = (bboxes[i, 2] - bboxes[i, 0]) * (bboxes[i, 3] - bboxes[i, 1])\n            union = area1 + area2 - inter\n\n            ious[i] = inter / union if union > 0 else 0.0\n        return ious\n\n\nif HAS_NUMBA:\n\n    @nb.jit(nopython=True, parallel=True)\n    def _compute_frame_ious_numba(bbox, bboxes):\n        \"\"\"Numba-optimized IoU computation\"\"\"\n        ious = np.zeros(len(bboxes), dtype=np.float32)\n        for i in nb.prange(len(bboxes)):\n            x1 = max(bbox[0], bboxes[i, 0])\n            y1 = max(bbox[1], bboxes[i, 1])\n            x2 = min(bbox[2], bboxes[i, 2])\n            y2 = min(bbox[3], bboxes[i, 3])\n\n            inter = max(0, x2 - x1) * max(0, y2 - y1)\n            area1 = (bbox[2] - bbox[0]) * (bbox[3] - bbox[1])\n            area2 = (bboxes[i, 2] - bboxes[i, 0]) * (bboxes[i, 3] - bboxes[i, 1])\n            union = area1 + area2 - inter\n\n            ious[i] = inter / union if union > 0 else 0.0\n        return ious\n\n\ndef apply_frame_nms(\n    bboxes: np.ndarray, scores: np.ndarray, nms_threshold: float\n) -> List[int]:\n    \"\"\"Frame-level NMS implementation with fallback to pure Python\"\"\"\n    if HAS_NUMBA:\n        return _apply_frame_nms_numba(bboxes, scores, nms_threshold)\n    else:\n        # Pure Python implementation\n        order = np.argsort(-scores)\n        keep = []\n        suppress = np.zeros(len(bboxes), dtype=bool)\n\n        for i in range(len(order)):\n            if not suppress[order[i]]:\n                keep.append(order[i])\n                current_bbox = bboxes[order[i]]\n\n                remaining_bboxes = bboxes[order[i + 1 :]]\n                if len(remaining_bboxes) > 0:  # Check if there are any remaining boxes\n                    ious = compute_frame_ious(current_bbox, remaining_bboxes)\n                    suppress[order[i + 1 :]] = suppress[order[i + 1 :]] | (\n                        ious >= nms_threshold\n                    )\n\n        return keep\n\n\nif HAS_NUMBA:\n\n    @nb.jit(nopython=True)\n    def _apply_frame_nms_numba(bboxes, scores, nms_threshold):\n        \"\"\"Numba-optimized NMS implementation\"\"\"\n        order = np.argsort(-scores)\n        keep = []\n        suppress = np.zeros(len(bboxes), dtype=nb.boolean)\n\n        for i in range(len(order)):\n            if not suppress[order[i]]:\n                keep.append(order[i])\n                current_bbox = bboxes[order[i]]\n\n                if i + 1 < len(order):  # Check bounds\n                    ious = _compute_frame_ious_numba(\n                        current_bbox, bboxes[order[i + 1 :]]\n                    )\n                    suppress[order[i + 1 :]] = suppress[order[i + 1 :]] | (\n                        ious >= nms_threshold\n                    )\n\n        return keep\n"
  },
  {
    "path": "sam3/train/optim/__init__.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n"
  },
  {
    "path": "sam3/train/optim/optimizer.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n\nimport fnmatch\nimport inspect\nimport itertools\nimport logging\nimport types\nfrom typing import (\n    Any,\n    Callable,\n    Dict,\n    Iterable,\n    List,\n    Mapping,\n    Optional,\n    Set,\n    Tuple,\n    Type,\n    Union,\n)\n\nimport hydra\nimport torch\nimport torch.nn as nn\nfrom omegaconf import DictConfig\nfrom torch import Tensor\n\n\nclass Optimizer:\n    def __init__(self, optimizer, schedulers=None) -> None:\n        self.optimizer = optimizer\n        self.schedulers = schedulers\n        self._validate_optimizer_schedulers()\n        self.step_schedulers(0.0, 0)\n\n    def _validate_optimizer_schedulers(self):\n        if self.schedulers is None:\n            return\n        for _, set_of_schedulers in enumerate(self.schedulers):\n            for option, _ in set_of_schedulers.items():\n                assert option in self.optimizer.defaults, (\n                    \"Optimizer option \"\n                    f\"{option} not found in {self.optimizer}. Valid options are \"\n                    f\"{self.optimizer.defaults.keys()}\"\n                )\n\n    def step_schedulers(self, where: float, step: int) -> None:\n        if self.schedulers is None:\n            return\n        for i, param_group in enumerate(self.optimizer.param_groups):\n            for option, scheduler in self.schedulers[i].items():\n                if \"step\" in inspect.signature(scheduler.__call__).parameters:\n                    new_value = scheduler(step=step, where=where)\n                elif (\n                    hasattr(scheduler, \"scheduler\")\n                    and \"step\"\n                    in inspect.signature(scheduler.scheduler.__call__).parameters\n                ):\n                    # To handle ValueScaler wrappers\n                    new_value = scheduler(step=step, where=where)\n                else:\n                    new_value = scheduler(where)\n                param_group[option] = new_value\n\n    def step(self, where, step, closure=None):\n        self.step_schedulers(where, step)\n        return self.optimizer.step(closure)\n\n    def zero_grad(self, *args, **kwargs):\n        return self.optimizer.zero_grad(*args, **kwargs)\n\n\ndef set_default_parameters(\n    scheduler_cfgs: List[DictConfig], all_parameter_names: Set[str]\n) -> None:\n    \"\"\"Set up the \"default\" scheduler with the right parameters.\n\n    Args:\n        scheduler_cgfs: A list of scheduler configs, where each scheduler also\n            specifies which parameters it applies to, based on the names of parameters\n            or the class of the modules. At most one scheduler is allowed to skip this\n            specification, which is used as a \"default\" specification for any remaining\n            parameters.\n        all_parameter_names: Names of all the parameters to consider.\n    \"\"\"\n    constraints = [\n        scheduler_cfg.parameter_names\n        for scheduler_cfg in scheduler_cfgs\n        if scheduler_cfg.parameter_names is not None\n    ]\n    if len(constraints) == 0:\n        default_params = set(all_parameter_names)\n    else:\n        default_params = all_parameter_names - set.union(*constraints)\n    default_count = 0\n    for scheduler_cfg in scheduler_cfgs:\n        if scheduler_cfg.parameter_names is None:\n            scheduler_cfg.parameter_names = default_params\n            default_count += 1\n    assert default_count <= 1, \"Only one scheduler per option can be default\"\n    if default_count == 0:\n        # No default scheduler specified, add a default, but without any scheduler\n        # for that option\n        scheduler_cfgs.append({\"parameter_names\": default_params})\n\n\ndef name_constraints_to_parameters(\n    param_constraints: List[Set[str]], named_parameters: Dict[str, Tensor]\n) -> List[torch.nn.Parameter]:\n    \"\"\"Return parameters which match the intersection of parameter constraints.\n\n    Note that this returns the parameters themselves, not their names.\n\n    Args:\n        param_constraints: A list, with each element being a set of allowed parameters.\n        named_parameters: Mapping from a parameter name to the parameter itself.\n\n    Returns:\n        A list containing the parameters which overlap with _each_ constraint set from\n        param_constraints.\n    \"\"\"\n    matching_names = set.intersection(*param_constraints)\n    return [value for name, value in named_parameters.items() if name in matching_names]\n\n\ndef map_scheduler_cfgs_to_param_groups(\n    all_scheduler_cfgs: Iterable[List[Dict]],\n    named_parameters: Dict[str, Tensor],\n) -> Tuple[List[Dict[Any, Any]], List[Dict[str, List[torch.nn.Parameter]]]]:\n    \"\"\"Produce parameter groups corresponding to all the scheduler configs.\n\n    Takes all the scheduler configs, each of which applies to a specific optimizer\n    option (like \"lr\" or \"weight_decay\") and has a set of parameter names which it\n    applies to, and produces a final set of param groups where each param group\n    covers all the options which apply to a particular set of parameters.\n\n    Args:\n        all_scheduler_cfgs: All the scheduler configs covering every option.\n        named_parameters: Mapping from a parameter name to the parameter itself.\n    Returns:\n        Tuple of lists of schedulers and param_groups, where schedulers[i]\n        applies to param_groups[i].\n    \"\"\"\n\n    scheduler_cfgs_per_param_group = itertools.product(*all_scheduler_cfgs)\n    schedulers = []\n    param_groups = []\n    for scheduler_cfgs in scheduler_cfgs_per_param_group:\n        param_constraints = [\n            scheduler_cfg[\"parameter_names\"] for scheduler_cfg in scheduler_cfgs\n        ]\n        matching_parameters = name_constraints_to_parameters(\n            param_constraints, named_parameters\n        )\n        if len(matching_parameters) == 0:  # If no overlap of parameters, skip\n            continue\n        schedulers_for_group = {\n            scheduler_cfg[\"option\"]: scheduler_cfg[\"scheduler\"]\n            for scheduler_cfg in scheduler_cfgs\n            if \"option\" in scheduler_cfg\n        }\n        schedulers.append(schedulers_for_group)\n        param_groups.append({\"params\": matching_parameters})\n    return schedulers, param_groups\n\n\ndef validate_param_group_params(param_groups: List[Dict], model: nn.Module):\n    \"\"\"Check that the param groups are non-overlapping and cover all the parameters.\n\n    Args:\n        param_groups: List of all param groups\n        model: Model to validate against. The check ensures that all the model\n            parameters are part of param_groups\n    \"\"\"\n    for pg in param_groups:\n        # no param should be repeated within a group\n        assert len(pg[\"params\"]) == len(set(pg[\"params\"]))\n    parameters = [set(param_group[\"params\"]) for param_group in param_groups]\n    model_parameters = {parameter for _, parameter in model.named_parameters()}\n    for p1, p2 in itertools.permutations(parameters, 2):\n        assert p1.isdisjoint(p2), \"Scheduler generated param_groups should be disjoint\"\n    assert set.union(*parameters) == model_parameters, (\n        \"Scheduler generated param_groups must include all parameters of the model.\"\n        f\" Found {len(set.union(*parameters))} params whereas model has\"\n        f\" {len(model_parameters)} params\"\n    )\n\n\ndef unix_module_cls_pattern_to_parameter_names(\n    filter_module_cls_names: List[str],\n    module_cls_to_param_names: Dict[Type, str],\n) -> Union[None, Set[str]]:\n    \"\"\"Returns param names which pass the filters specified in filter_module_cls_names.\n\n    Args:\n        filter_module_cls_names: A list of filter strings containing class names, like\n            [\"torch.nn.LayerNorm\", \"torch.nn.BatchNorm2d\"]\n        module_cls_to_param_names: Mapping from module classes to the parameter names\n            they contain. See `get_module_cls_to_param_names`.\n    \"\"\"\n    if filter_module_cls_names is None:\n        return set()\n    allowed_parameter_names = []\n    for module_cls_name in filter_module_cls_names:\n        module_cls = hydra.utils.get_class(module_cls_name)\n        if module_cls not in module_cls_to_param_names:\n            raise AssertionError(\n                f\"module_cls_name {module_cls_name} does not \"\n                \"match any classes in the model\"\n            )\n        matching_parameters = module_cls_to_param_names[module_cls]\n        assert len(matching_parameters) > 0, (\n            f\"module_cls_name {module_cls_name} does not contain any parameters in the model\"\n        )\n        logging.info(\n            f\"Matches for module_cls_name [{module_cls_name}]: {matching_parameters} \"\n        )\n        allowed_parameter_names.append(matching_parameters)\n    return set.union(*allowed_parameter_names)\n\n\ndef unix_param_pattern_to_parameter_names(\n    filter_param_names: Optional[List[str]],\n    parameter_names: Dict[str, torch.Tensor],\n) -> Union[None, Set[str]]:\n    \"\"\"Returns param names which pass the filters specified in filter_param_names.\n\n    Args:\n        filter_param_names: A list of unix-style filter strings with optional\n            wildcards, like [\"block.2.*\", \"block.2.linear.weight\"]\n        module_cls_to_param_names: Mapping from module classes to the parameter names\n            they contain. See `get_module_cls_to_param_names`.\n    \"\"\"\n\n    if filter_param_names is None:\n        return set()\n    allowed_parameter_names = []\n    for param_name in filter_param_names:\n        matching_parameters = set(fnmatch.filter(parameter_names, param_name))\n        assert len(matching_parameters) >= 1, (\n            f\"param_name {param_name} does not match any parameters in the model\"\n        )\n        logging.info(f\"Matches for param_name [{param_name}]: {matching_parameters}\")\n        allowed_parameter_names.append(matching_parameters)\n    return set.union(*allowed_parameter_names)\n\n\ndef _unix_pattern_to_parameter_names(\n    scheduler_cfg: DictConfig,\n    parameter_names: Set[str],\n    module_cls_to_param_names: Dict[Type, str],\n) -> Union[None, Set[str]]:\n    \"\"\"Returns param names which pass the filters specified in scheduler_cfg.\n\n    Args:\n        scheduler_cfg: The config for the scheduler\n        parameter_names: The set of all parameter names which will be filtered\n    \"\"\"\n    if \"param_names\" not in scheduler_cfg and \"module_cls_names\" not in scheduler_cfg:\n        return None\n    return unix_param_pattern_to_parameter_names(\n        scheduler_cfg.get(\"param_names\"), parameter_names\n    ).union(\n        unix_module_cls_pattern_to_parameter_names(\n            scheduler_cfg.get(\"module_cls_names\"), module_cls_to_param_names\n        )\n    )\n\n\ndef get_module_cls_to_param_names(\n    model: nn.Module, param_allowlist: Set[str] = None\n) -> Dict[Type, str]:\n    \"\"\"Produce a mapping from all the modules classes to the names of parames they own.\n\n    Only counts a parameter as part of the immediate parent module, i.e. recursive\n    parents do not count.\n\n    Args:\n        model: Model to iterate over\n        param_allowlist: If specified, only these param names will be processed\n    \"\"\"\n\n    module_cls_to_params = {}\n    for module_name, module in model.named_modules():\n        module_cls = type(module)\n        module_cls_to_params.setdefault(module_cls, set())\n        for param_name, _ in module.named_parameters(recurse=False):\n            full_param_name = get_full_parameter_name(module_name, param_name)\n            if param_allowlist is None or full_param_name in param_allowlist:\n                module_cls_to_params[module_cls].add(full_param_name)\n    return module_cls_to_params\n\n\ndef construct_optimizer(\n    model: torch.nn.Module,\n    optimizer_conf: Any,\n    options_conf: Mapping[str, List] = None,\n    param_group_modifiers_conf: List[Callable] = None,\n    param_allowlist: Optional[Set[str]] = None,\n    validate_param_groups=True,\n) -> Optimizer:\n    \"\"\"\n    Constructs a stochastic gradient descent or ADAM (or ADAMw) optimizer\n    with momentum. i.e, constructs a torch.optim.Optimizer with zero-weight decay\n    Batchnorm and/or no-update 1-D parameters support, based on the config.\n\n    Supports wrapping the optimizer with Layer-wise Adaptive Rate Scaling\n    (LARS): https://arxiv.org/abs/1708.03888\n\n    Args:\n        model: model to perform stochastic gradient descent\n            optimization or ADAM optimization.\n        optimizer_conf: Hydra config consisting a partial torch optimizer like SGD or\n            ADAM, still missing the params argument which this function provides to\n            produce the final optimizer\n        param_group_modifiers_conf: Optional user specified functions which can modify\n            the final scheduler configs before the optimizer's param groups are built\n        param_allowlist: The parameters to optimize. Parameters which are not part of\n            this allowlist will be skipped.\n        validate_param_groups: If enabled, valides that the produced param_groups don't\n            overlap and cover all the model parameters.\n    \"\"\"\n    if param_allowlist is None:\n        param_allowlist = {name for name, _ in model.named_parameters()}\n\n    named_parameters = {\n        name: param\n        for name, param in model.named_parameters()\n        if name in param_allowlist\n    }\n\n    if not options_conf:\n        optimizer = hydra.utils.instantiate(optimizer_conf, named_parameters.values())\n        return Optimizer(optimizer)\n\n    all_parameter_names = {\n        name for name, _ in model.named_parameters() if name in param_allowlist\n    }\n    module_cls_to_all_param_names = get_module_cls_to_param_names(\n        model, param_allowlist\n    )\n\n    scheduler_cfgs_per_option = hydra.utils.instantiate(options_conf)\n    all_scheduler_cfgs = []\n    for option, scheduler_cfgs in scheduler_cfgs_per_option.items():\n        for config in scheduler_cfgs:\n            config.option = option\n            config.parameter_names = _unix_pattern_to_parameter_names(\n                config, all_parameter_names, module_cls_to_all_param_names\n            )\n        set_default_parameters(scheduler_cfgs, all_parameter_names)\n        all_scheduler_cfgs.append(scheduler_cfgs)\n\n    if param_group_modifiers_conf:\n        for custom_param_modifier in param_group_modifiers_conf:\n            custom_param_modifier = hydra.utils.instantiate(custom_param_modifier)\n            all_scheduler_cfgs = custom_param_modifier(\n                scheduler_cfgs=all_scheduler_cfgs, model=model\n            )\n    schedulers, param_groups = map_scheduler_cfgs_to_param_groups(\n        all_scheduler_cfgs, named_parameters\n    )\n    if validate_param_groups:\n        validate_param_group_params(param_groups, model)\n    optimizer = hydra.utils.instantiate(optimizer_conf, param_groups)\n    return Optimizer(optimizer, schedulers)\n\n\ndef get_full_parameter_name(module_name, param_name):\n    if module_name == \"\":\n        return param_name\n    return f\"{module_name}.{param_name}\"\n\n\nclass GradientClipper:\n    \"\"\"\n    Gradient clipping utils that works for DDP\n    \"\"\"\n\n    def __init__(self, max_norm: float = 1.0, norm_type: int = 2):\n        assert isinstance(max_norm, (int, float)) or max_norm is None\n        self.max_norm = max_norm if max_norm is None else float(max_norm)\n        self.norm_type = norm_type\n\n    def __call__(self, model: nn.Module):\n        if self.max_norm is None:\n            return  # no-op\n\n        nn.utils.clip_grad_norm_(\n            model.parameters(), max_norm=self.max_norm, norm_type=self.norm_type\n        )\n\n\nclass ValueScaler:\n    def __init__(self, scheduler, mult_val: float):\n        self.scheduler = scheduler\n        self.mult_val = mult_val\n\n    def __call__(self, *args, **kwargs):\n        val = self.scheduler(*args, **kwargs)\n        return val * self.mult_val\n\n\ndef rgetattr(obj, rattrs: str = None):\n    \"\"\"\n    Like getattr(), but supports dotted notation for nested objects.\n    rattrs is a str of form 'attr1.attr2', returns obj.attr1.attr2\n    \"\"\"\n    if rattrs is None:\n        return obj\n    attrs = rattrs.split(\".\")\n    for attr in attrs:\n        obj = getattr(obj, attr)\n    return obj\n\n\ndef layer_decay_param_modifier(\n    scheduler_cfgs: List[List[Dict]],\n    model,\n    layer_decay_value: float,\n    layer_decay_min: Optional[float] = None,\n    apply_to: Optional[str] = None,\n    overrides: List[Dict] = (),\n) -> List[List[Dict]]:\n    \"\"\"\n    Args\n    - scheduler_cfgs: a list of omegaconf.ListConfigs.\n        Each element in the list is a omegaconfg.DictConfig with the following structure\n        {\n            \"scheduler\": <some fvcore scheduler>\n            \"option\": <value> possible options are \"lr\", \"weight_decay\" etc.\n            \"parameter_names\": Set of str indicating param names that this scheduler applies to\n        }\n    - model: a model that implements a method `get_layer_id` that maps layer_name to an integer and\n            and a method get_num_layers.\n            Alternatively, use apply_to argument to select a specific component of the model.\n    - layer_decay_value: float\n    - layer_decay_min: min val for layer decay\n    - apply_to: optional arg to select which component of the model to apply the the layer decay modifier to\n    - overrides: to manually override lr for specific patterns. Is a list of dicts. Each dict, has keys \"pattern\", \"value\".\n    Returns\n    - scheduler_configs: same structure as the input, elements can be modified\n    \"\"\"\n    model = rgetattr(model, apply_to)\n    num_layers = model.get_num_layers() + 1\n    layer_decays = [\n        layer_decay_value ** (num_layers - i) for i in range(num_layers + 1)\n    ]\n    if layer_decay_min is not None:\n        layer_decays = [max(val, layer_decay_min) for val in layer_decays]\n    final_scheduler_cfgs = []\n    # scheduler_cfgs is a list of lists\n    for scheduler_cfg_group in scheduler_cfgs:\n        curr_cfg_group = []\n        # scheduler_cfg_group is a list of dictionaries\n        for scheduler_cfg in scheduler_cfg_group:\n            if scheduler_cfg[\"option\"] != \"lr\":\n                curr_cfg_group.append(scheduler_cfg)\n                continue\n            # Need sorted so that the list of parameter names is deterministic and consistent\n            # across re-runs of this job. Else it was causing issues with loading the optimizer\n            # state during a job restart\n            parameter_names = sorted(scheduler_cfg[\"parameter_names\"])\n\n            # Only want one cfg group per layer\n            layer_cfg_groups = {}\n            for param_name in parameter_names:\n                layer_id = num_layers\n                this_scale = layer_decays[layer_id]\n                if param_name.startswith(apply_to):\n                    layer_id = model.get_layer_id(param_name)\n                    this_scale = layer_decays[layer_id]\n                    # Overrides\n                    for override in overrides:\n                        if fnmatch.fnmatchcase(param_name, override[\"pattern\"]):\n                            this_scale = float(override[\"value\"])\n                            layer_id = override[\"pattern\"]\n                            break\n\n                if layer_id not in layer_cfg_groups:\n                    curr_param = {\n                        \"option\": scheduler_cfg[\"option\"],\n                        \"scheduler\": ValueScaler(\n                            scheduler_cfg[\"scheduler\"], this_scale\n                        ),\n                        \"parameter_names\": {param_name},\n                    }\n                else:\n                    curr_param = layer_cfg_groups[layer_id]\n                    curr_param[\"parameter_names\"].add(param_name)\n                layer_cfg_groups[layer_id] = curr_param\n\n            for layer_cfg in layer_cfg_groups.values():\n                curr_cfg_group.append(layer_cfg)\n\n        final_scheduler_cfgs.append(curr_cfg_group)\n    return final_scheduler_cfgs\n"
  },
  {
    "path": "sam3/train/optim/schedulers.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n\nimport math\n\n\nclass InverseSquareRootParamScheduler:\n    def __init__(\n        self,\n        base_lr: float,\n        warmup_steps: int,\n        cooldown_steps: int,\n        timescale: int,\n    ):\n        self.base_lr = base_lr\n        self.warmup_steps = warmup_steps\n        self.cooldown_steps = cooldown_steps\n        self.timescale = timescale\n\n    def __call__(self, step: int, where: float):\n        lr = self.base_lr\n\n        if where > 0:\n            total_steps = step / where\n            progress = (step - self.warmup_steps) / float(\n                total_steps - self.warmup_steps\n            )\n            progress = max(min(progress, 1), 0)\n        else:\n            progress = 0\n            total_steps = 1\n\n        shift = self.timescale - self.warmup_steps\n        if self.warmup_steps < step:\n            lr = lr / math.sqrt((step + shift) / self.timescale)\n\n        if self.warmup_steps:\n            lr = lr * min(1.0, step / self.warmup_steps)\n        if self.cooldown_steps:\n            lr = lr * min(1.0, (total_steps - step) / self.cooldown_steps)\n\n        return lr\n"
  },
  {
    "path": "sam3/train/train.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n\nimport logging\nimport os\nimport random\nimport sys\nimport traceback\nfrom argparse import ArgumentParser\nfrom copy import deepcopy\n\nimport submitit\nimport torch\nfrom hydra import compose, initialize_config_module\nfrom hydra.utils import instantiate\nfrom iopath.common.file_io import g_pathmgr\nfrom omegaconf import OmegaConf\nfrom sam3.train.utils.train_utils import makedir, register_omegaconf_resolvers\nfrom tqdm import tqdm\n\n\nos.environ[\"HYDRA_FULL_ERROR\"] = \"1\"\n\n\nclass SlurmEvent:\n    QUEUED = \"QUEUED\"\n    START = \"START\"\n    FINISH = \"FINISH\"\n    JOB_ERROR = \"JOB_ERROR\"\n    SLURM_SIGNAL = \"SLURM_SIGNAL\"\n\n\ndef handle_custom_resolving(cfg):\n    # We'll resolve the config here, so we can catch mistakes early.\n    # However, we need to pass the un-resolved config to the launcher\n    # (because DVC resolving needs to be done on the node it will run on)\n    # First, do a copy without triggering resolving\n    cfg_resolved = OmegaConf.to_container(cfg, resolve=False)\n    cfg_resolved = OmegaConf.create(cfg_resolved)\n    return cfg_resolved\n\n\ndef single_proc_run(local_rank, main_port, cfg, world_size):\n    \"\"\"Single GPU process\"\"\"\n    os.environ[\"MASTER_ADDR\"] = \"localhost\"\n    os.environ[\"MASTER_PORT\"] = str(main_port)\n    os.environ[\"RANK\"] = str(local_rank)\n    os.environ[\"LOCAL_RANK\"] = str(local_rank)\n    os.environ[\"WORLD_SIZE\"] = str(world_size)\n    try:\n        register_omegaconf_resolvers()\n    except Exception as e:\n        logging.info(e)\n\n    trainer = instantiate(cfg.trainer, _recursive_=False)\n    trainer.run()\n\n\ndef single_node_runner(cfg, main_port: int):\n    assert cfg.launcher.num_nodes == 1\n    # assert cfg.launcher.gpus_per_node == 1\n    num_proc = cfg.launcher.gpus_per_node\n    torch.multiprocessing.set_start_method(\n        \"spawn\"\n    )  # CUDA runtime does not support `fork`\n    if num_proc == 1:\n        # directly call single_proc so we can easily set breakpoints\n        # mp.spawn does not let us set breakpoints\n        single_proc_run(local_rank=0, main_port=main_port, cfg=cfg, world_size=num_proc)\n    else:\n        mp_runner = torch.multiprocessing.start_processes\n        args = (main_port, cfg, num_proc)\n        # Note: using \"fork\" below, \"spawn\" causes time and error regressions. Using\n        # spawn changes the default multiprocessing context to spawn, which doesn't\n        # interact well with the dataloaders (likely due to the use of OpenCV).\n        mp_runner(single_proc_run, args=args, nprocs=num_proc, start_method=\"spawn\")\n\n\ndef format_exception(e: Exception, limit=20):\n    traceback_str = \"\".join(traceback.format_tb(e.__traceback__, limit=limit))\n    return f\"{type(e).__name__}: {e}\\nTraceback:\\n{traceback_str}\"\n\n\nclass SubmititRunner(submitit.helpers.Checkpointable):\n    \"\"\"A callable which is passed to submitit to launch the jobs.\"\"\"\n\n    def __init__(self, port, cfg):\n        self.cfg = cfg\n        self.port = port\n        self.has_setup = False\n\n    def run_trainer(self):\n        job_env = submitit.JobEnvironment()\n        # Need to add this again so the hydra.job.set_env PYTHONPATH\n        # is also set when launching jobs.\n        add_pythonpath_to_sys_path()\n        os.environ[\"MASTER_ADDR\"] = job_env.hostnames[0]\n        os.environ[\"MASTER_PORT\"] = str(self.port)\n        os.environ[\"RANK\"] = str(job_env.global_rank)\n        os.environ[\"LOCAL_RANK\"] = str(job_env.local_rank)\n        os.environ[\"WORLD_SIZE\"] = str(job_env.num_tasks)\n\n        register_omegaconf_resolvers()\n        cfg_resolved = OmegaConf.to_container(self.cfg, resolve=False)\n        cfg_resolved = OmegaConf.create(cfg_resolved)\n\n        trainer = instantiate(cfg_resolved.trainer, _recursive_=False)\n        trainer.run()\n\n    def __call__(self):\n        job_env = submitit.JobEnvironment()\n        self.setup_job_info(job_env.job_id, job_env.global_rank)\n        try:\n            self.run_trainer()\n        except Exception as e:\n            # Log the exception. Then raise it again (as what SubmititRunner currently does).\n            message = format_exception(e)\n            logging.error(message)\n            raise e\n\n    def setup_job_info(self, job_id, rank):\n        \"\"\"Set up slurm job info\"\"\"\n        self.job_info = {\n            \"job_id\": job_id,\n            \"rank\": rank,\n            \"cluster\": self.cfg.get(\"cluster\", None),\n            \"experiment_log_dir\": self.cfg.launcher.experiment_log_dir,\n        }\n\n        self.has_setup = True\n\n\ndef add_pythonpath_to_sys_path():\n    if \"PYTHONPATH\" not in os.environ or not os.environ[\"PYTHONPATH\"]:\n        return\n    sys.path = os.environ[\"PYTHONPATH\"].split(\":\") + sys.path\n\n\ndef main(args) -> None:\n    cfg = compose(config_name=args.config)\n    if cfg.launcher.experiment_log_dir is None:\n        cfg.launcher.experiment_log_dir = os.path.join(\n            os.getcwd(), \"sam3_logs\", args.config\n        )\n    print(\"###################### Train App Config ####################\")\n    print(OmegaConf.to_yaml(cfg))\n    print(\"############################################################\")\n\n    add_pythonpath_to_sys_path()\n    makedir(cfg.launcher.experiment_log_dir)\n    with g_pathmgr.open(\n        os.path.join(cfg.launcher.experiment_log_dir, \"config.yaml\"), \"w\"\n    ) as f:\n        f.write(OmegaConf.to_yaml(cfg))\n\n    cfg_resolved = OmegaConf.to_container(cfg, resolve=False)\n    cfg_resolved = OmegaConf.create(cfg_resolved)\n\n    with g_pathmgr.open(\n        os.path.join(cfg.launcher.experiment_log_dir, \"config_resolved.yaml\"), \"w\"\n    ) as f:\n        f.write(OmegaConf.to_yaml(cfg_resolved, resolve=True))\n\n    submitit_conf = cfg.get(\"submitit\", None)\n    assert submitit_conf is not None, \"Missing submitit config\"\n\n    experiment_log_dir = cfg.launcher.experiment_log_dir\n    print(f\"Experiment Log Dir:\\n{experiment_log_dir}\")\n    submitit_dir = os.path.join(experiment_log_dir, \"submitit_logs\")\n\n    # Prioritize cmd line args\n    cfg.launcher.gpus_per_node = (\n        args.num_gpus if args.num_gpus is not None else cfg.launcher.gpus_per_node\n    )\n    cfg.launcher.num_nodes = (\n        args.num_nodes if args.num_nodes is not None else cfg.launcher.num_nodes\n    )\n    submitit_conf.use_cluster = (\n        args.use_cluster if args.use_cluster is not None else submitit_conf.use_cluster\n    )\n    if submitit_conf.use_cluster:\n        executor = submitit.AutoExecutor(folder=submitit_dir)\n        submitit_conf.partition = (\n            args.partition\n            if args.partition is not None\n            else submitit_conf.get(\"partition\", None)\n        )\n        submitit_conf.account = (\n            args.account\n            if args.account is not None\n            else submitit_conf.get(\"account\", None)\n        )\n        submitit_conf.qos = (\n            args.qos if args.qos is not None else submitit_conf.get(\"qos\", None)\n        )\n        job_kwargs = {\n            \"timeout_min\": 60 * submitit_conf.timeout_hour,\n            \"name\": (\n                submitit_conf.name if hasattr(submitit_conf, \"name\") else args.config\n            ),\n            \"slurm_partition\": submitit_conf.partition,\n            \"gpus_per_node\": cfg.launcher.gpus_per_node,\n            \"tasks_per_node\": cfg.launcher.gpus_per_node,  # one task per GPU\n            \"cpus_per_task\": submitit_conf.cpus_per_task,\n            \"nodes\": cfg.launcher.num_nodes,\n            \"slurm_additional_parameters\": {\n                \"exclude\": \" \".join(submitit_conf.get(\"exclude_nodes\", [])),\n            },\n        }\n        if \"include_nodes\" in submitit_conf:\n            assert len(submitit_conf[\"include_nodes\"]) >= cfg.launcher.num_nodes, (\n                \"Not enough nodes\"\n            )\n            job_kwargs[\"slurm_additional_parameters\"][\"nodelist\"] = \" \".join(\n                submitit_conf[\"include_nodes\"]\n            )\n        if submitit_conf.account is not None:\n            job_kwargs[\"slurm_additional_parameters\"][\"account\"] = submitit_conf.account\n        if submitit_conf.qos is not None:\n            job_kwargs[\"slurm_additional_parameters\"][\"qos\"] = submitit_conf.qos\n\n        if submitit_conf.get(\"mem_gb\", None) is not None:\n            job_kwargs[\"mem_gb\"] = submitit_conf.mem_gb\n        elif submitit_conf.get(\"mem\", None) is not None:\n            job_kwargs[\"slurm_mem\"] = submitit_conf.mem\n\n        if submitit_conf.get(\"constraints\", None) is not None:\n            job_kwargs[\"slurm_constraint\"] = submitit_conf.constraints\n\n        if submitit_conf.get(\"comment\", None) is not None:\n            job_kwargs[\"slurm_comment\"] = submitit_conf.comment\n\n        # Supports only cpu-bind option within srun_args. New options can be added here\n        if submitit_conf.get(\"srun_args\", None) is not None:\n            job_kwargs[\"slurm_srun_args\"] = []\n            if submitit_conf.srun_args.get(\"cpu_bind\", None) is not None:\n                job_kwargs[\"slurm_srun_args\"].extend(\n                    [\"--cpu-bind\", submitit_conf.srun_args.cpu_bind]\n                )\n\n        print(\"###################### SLURM Config ####################\")\n        print(job_kwargs)\n        print(\"##########################################\")\n        executor.update_parameters(**job_kwargs)\n\n        if (\n            \"job_array\" in submitit_conf\n            and submitit_conf.job_array.get(\"num_tasks\", -1) > 0\n        ):\n            num_tasks = submitit_conf.job_array.num_tasks\n            job_array_config_dir = os.path.join(\n                cfg.launcher.experiment_log_dir, \"job_array_configs\"\n            )\n            makedir(job_array_config_dir)\n\n            job_indices = range(num_tasks)\n            ports = random.sample(\n                range(submitit_conf.port_range[0], submitit_conf.port_range[1] + 1),\n                k=len(job_indices),\n            )\n\n            jobs_runners_configs = []\n            with executor.batch():\n                task_index = 0\n                for indices, main_port in tqdm(zip(job_indices, ports)):\n                    curr_cfg = deepcopy(cfg)\n                    curr_cfg.submitit.job_array[\"task_index\"] = task_index\n                    curr_cfg_resolved = handle_custom_resolving(cfg)\n                    runner = SubmititRunner(main_port, curr_cfg)\n                    job = executor.submit(runner)\n                    jobs_runners_configs.append(\n                        (job, runner, curr_cfg, curr_cfg_resolved)\n                    )\n                    task_index += 1\n\n            for job, runner, job_cfg, job_cfg_resolved in jobs_runners_configs:\n                print(\"Submitit Job ID:\", job.job_id)\n\n                # Save job specific config\n                job_array_config_file = os.path.join(\n                    job_array_config_dir, \"{}.config.yaml\".format(job.job_id)\n                )\n                with g_pathmgr.open(job_array_config_file, \"w\") as f:\n                    f.write(OmegaConf.to_yaml(job_cfg))\n\n                job_array_config_resolved_file = os.path.join(\n                    job_array_config_dir, \"{}.config_resolved.yaml\".format(job.job_id)\n                )\n                with g_pathmgr.open(job_array_config_resolved_file, \"w\") as f:\n                    f.write(OmegaConf.to_yaml(job_cfg_resolved, resolve=True))\n\n                runner.setup_job_info(job.job_id, rank=0)\n                # runner.log_event(event_type=SlurmEvent.QUEUED)\n        else:\n            main_port = random.randint(\n                submitit_conf.port_range[0], submitit_conf.port_range[1]\n            )\n            runner = SubmititRunner(main_port, cfg)\n            job = executor.submit(runner)\n            print(f\"Submitit Job ID: {job.job_id}\")\n            runner.setup_job_info(job.job_id, rank=0)\n\n    else:\n        cfg.launcher.num_nodes = 1\n        main_port = random.randint(\n            submitit_conf.port_range[0], submitit_conf.port_range[1]\n        )\n        single_node_runner(cfg, main_port)\n\n\nif __name__ == \"__main__\":\n    initialize_config_module(\"sam3.train\", version_base=\"1.2\")\n    parser = ArgumentParser()\n    parser.add_argument(\n        \"-c\",\n        \"--config\",\n        required=True,\n        type=str,\n        help=\"path to config file (e.g. configs/roboflow_v100_full_ft_100_images.yaml)\",\n    )\n    parser.add_argument(\n        \"--use-cluster\",\n        type=int,\n        default=None,\n        help=\"whether to launch on a cluster, 0: run locally, 1: run on a cluster\",\n    )\n    parser.add_argument(\"--partition\", type=str, default=None, help=\"SLURM partition\")\n    parser.add_argument(\"--account\", type=str, default=None, help=\"SLURM account\")\n    parser.add_argument(\"--qos\", type=str, default=None, help=\"SLURM qos\")\n    parser.add_argument(\n        \"--num-gpus\", type=int, default=None, help=\"number of GPUS per node\"\n    )\n    parser.add_argument(\"--num-nodes\", type=int, default=None, help=\"Number of nodes\")\n    args = parser.parse_args()\n    args.use_cluster = bool(args.use_cluster) if args.use_cluster is not None else None\n    register_omegaconf_resolvers()\n    main(args)\n"
  },
  {
    "path": "sam3/train/trainer.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n\nimport contextlib\nimport fnmatch\nimport gc\nimport json\nimport logging\nimport math\nimport os\nimport time\nfrom collections import OrderedDict\nfrom dataclasses import dataclass, field\nfrom typing import Any, Dict, List, Mapping, Optional\n\nimport numpy as np\nimport torch\nimport torch.distributed as dist\nimport torch.nn as nn\nfrom hydra.utils import instantiate\nfrom iopath.common.file_io import g_pathmgr\nfrom sam3.model.data_misc import BatchedDatapoint\nfrom sam3.model.model_misc import SAM3Output\nfrom sam3.model.utils.misc import copy_data_to_device\nfrom sam3.train.optim.optimizer import construct_optimizer\nfrom sam3.train.utils.checkpoint_utils import (\n    assert_skipped_parameters_are_frozen,\n    exclude_params_matching_unix_pattern,\n    load_state_dict_into_model,\n    with_check_parameter_frozen,\n)\nfrom sam3.train.utils.distributed import all_reduce_max, barrier, get_rank\nfrom sam3.train.utils.logger import Logger, setup_logging\nfrom sam3.train.utils.train_utils import (\n    AverageMeter,\n    collect_dict_keys,\n    DurationMeter,\n    get_amp_type,\n    get_machine_local_and_dist_rank,\n    get_resume_checkpoint,\n    human_readable_time,\n    is_dist_avail_and_initialized,\n    log_env_variables,\n    makedir,\n    MemMeter,\n    Phase,\n    ProgressMeter,\n    set_seeds,\n    setup_distributed_backend,\n)\n\n\nCORE_LOSS_KEY = \"core_loss\"\n\n\ndef unwrap_ddp_if_wrapped(model):\n    if isinstance(model, torch.nn.parallel.DistributedDataParallel):\n        return model.module\n    return model\n\n\n@dataclass\nclass OptimAMPConf:\n    enabled: bool = False\n    amp_dtype: str = \"float16\"\n\n\n@dataclass\nclass OptimConf:\n    optimizer: torch.optim.Optimizer = None\n    options: Optional[Dict[str, Any]] = None\n    param_group_modifiers: Optional[List] = None\n    amp: Optional[Dict[str, Any]] = None\n    gradient_clip: Any = None\n    gradient_logger: Any = None\n\n    def __post_init__(self):\n        # amp\n        if not isinstance(self.amp, OptimAMPConf):\n            if self.amp is None:\n                self.amp = {}\n            assert isinstance(self.amp, Mapping)\n            self.amp = OptimAMPConf(**self.amp)\n\n\n@dataclass\nclass DistributedConf:\n    backend: Optional[str] = None  # inferred from accelerator type\n    comms_dtype: Optional[str] = None\n    find_unused_parameters: bool = False\n    timeout_mins: int = 30\n    gradient_as_bucket_view: bool = False  # PyTorch DDP default is False\n    static_graph: bool = False  # PyTorch DDP default is False\n\n\n@dataclass\nclass CudaConf:\n    cudnn_deterministic: bool = False\n    cudnn_benchmark: bool = True\n    allow_tf32: bool = False\n    # if not None, `matmul_allow_tf32` key will override `allow_tf32` for matmul\n    matmul_allow_tf32: Optional[bool] = None\n    # if not None, `cudnn_allow_tf32` key will override `allow_tf32` for cudnn\n    cudnn_allow_tf32: Optional[bool] = None\n\n\n@dataclass\nclass CheckpointConf:\n    save_dir: str\n    save_freq: int\n    save_list: List[int] = field(default_factory=list)\n    model_weight_initializer: Any = None\n    save_best_meters: List[str] = None\n    skip_saving_parameters: List[str] = field(default_factory=list)\n    initialize_after_preemption: Optional[bool] = None\n    # if not None, training will be resumed from this checkpoint\n    resume_from: Optional[str] = None\n\n    def infer_missing(self):\n        if self.initialize_after_preemption is None:\n            with_skip_saving = len(self.skip_saving_parameters) > 0\n            self.initialize_after_preemption = with_skip_saving\n        return self\n\n\n@dataclass\nclass LoggingConf:\n    log_dir: str\n    log_freq: int  # In iterations\n    tensorboard_writer: Any\n    log_level_primary: str = \"INFO\"\n    log_level_secondary: str = \"ERROR\"\n    log_scalar_frequency: int = 100\n    log_visual_frequency: int = 100\n    scalar_keys_to_log: Optional[Dict[str, Any]] = None\n    log_batch_stats: bool = False\n    wandb_writer: Optional[Any] = None\n\n\nclass Trainer:\n    \"\"\"\n    Trainer supporting the DDP training strategies.\n    \"\"\"\n\n    EPSILON = 1e-8\n\n    def __init__(\n        self,\n        *,  # the order of these args can change at any time, so they are keyword-only\n        data: Dict[str, Any],\n        model: Dict[str, Any],\n        logging: Dict[str, Any],\n        checkpoint: Dict[str, Any],\n        max_epochs: int,\n        mode: str = \"train\",\n        accelerator: str = \"cuda\",\n        seed_value: int = 123,\n        val_epoch_freq: int = 1,\n        distributed: Dict[str, bool] = None,\n        cuda: Dict[str, bool] = None,\n        env_variables: Optional[Dict[str, Any]] = None,\n        optim: Optional[Dict[str, Any]] = None,\n        optim_overrides: Optional[List[Dict[str, Any]]] = None,\n        meters: Optional[Dict[str, Any]] = None,\n        loss: Optional[Dict[str, Any]] = None,\n        skip_first_val: bool = False,\n        skip_saving_ckpts: bool = False,\n        empty_gpu_mem_cache_after_eval: bool = True,\n        gradient_accumulation_steps: int = 1,\n    ):\n        self._setup_env_variables(env_variables)\n        self._setup_timers()\n\n        self.data_conf = data\n        self.model_conf = model\n        self.logging_conf = LoggingConf(**logging)\n        self.checkpoint_conf = CheckpointConf(**checkpoint).infer_missing()\n        self.max_epochs = max_epochs\n        self.mode = mode\n        self.val_epoch_freq = val_epoch_freq\n        self.optim_conf = OptimConf(**optim) if optim is not None else OptimConf()\n        self.meters_conf = meters\n        self.loss_conf = loss\n        self.gradient_accumulation_steps = gradient_accumulation_steps\n        distributed = DistributedConf(**distributed or {})\n        cuda = CudaConf(**cuda or {})\n        self.where = 0.0\n\n        self.skip_first_val = skip_first_val\n        self.skip_saving_ckpts = skip_saving_ckpts\n        self.empty_gpu_mem_cache_after_eval = empty_gpu_mem_cache_after_eval\n\n        self._infer_distributed_backend_if_none(distributed, accelerator)\n\n        self._setup_device(accelerator)\n\n        self._setup_torch_dist_and_backend(cuda, distributed)\n\n        makedir(self.logging_conf.log_dir)\n        setup_logging(\n            __name__,\n            output_dir=self.logging_conf.log_dir,\n            rank=self.rank,\n            log_level_primary=self.logging_conf.log_level_primary,\n            log_level_secondary=self.logging_conf.log_level_secondary,\n        )\n\n        set_seeds(seed_value, self.max_epochs, self.distributed_rank)\n        log_env_variables()\n\n        assert is_dist_avail_and_initialized(), (\n            \"Torch distributed needs to be initialized before calling the trainer.\"\n        )\n\n        self._setup_components()  # Except Optimizer everything is setup here.\n        self._move_to_device()\n        self._construct_optimizers()\n        self._setup_dataloaders()\n\n        self.time_elapsed_meter = DurationMeter(\"Time Elapsed\", self.device, \":.2f\")\n\n        if self.checkpoint_conf.resume_from is not None:\n            assert os.path.exists(self.checkpoint_conf.resume_from), (\n                f\"The 'resume_from' checkpoint {self.checkpoint_conf.resume_from} does not exist!\"\n            )\n            dst = os.path.join(self.checkpoint_conf.save_dir, \"checkpoint.pt\")\n            if self.distributed_rank == 0 and not os.path.exists(dst):\n                # Copy the \"resume_from\" checkpoint to the checkpoint folder\n                # if there is not a checkpoint to resume from already there\n                makedir(self.checkpoint_conf.save_dir)\n                g_pathmgr.copy(self.checkpoint_conf.resume_from, dst)\n            barrier()\n\n        self.load_checkpoint()\n        self._setup_ddp_distributed_training(distributed, accelerator)\n        barrier()\n\n    def _setup_timers(self):\n        \"\"\"\n        Initializes counters for elapsed time and eta.\n        \"\"\"\n        self.start_time = time.time()\n        self.ckpt_time_elapsed = 0\n        self.est_epoch_time = dict.fromkeys([Phase.TRAIN, Phase.VAL], 0)\n\n    def _get_meters(self, phase_filters=None):\n        if self.meters is None:\n            return {}\n        meters = {}\n        for phase, phase_meters in self.meters.items():\n            if phase_filters is not None and phase not in phase_filters:\n                continue\n            for key, key_meters in phase_meters.items():\n                if key_meters is None:\n                    continue\n                for name, meter in key_meters.items():\n                    meters[f\"{phase}_{key}/{name}\"] = meter\n        return meters\n\n    def _infer_distributed_backend_if_none(self, distributed_conf, accelerator):\n        if distributed_conf.backend is None:\n            distributed_conf.backend = \"nccl\" if accelerator == \"cuda\" else \"gloo\"\n\n    def _setup_env_variables(self, env_variables_conf) -> None:\n        if env_variables_conf is not None:\n            for variable_name, value in env_variables_conf.items():\n                os.environ[variable_name] = value\n\n    def _setup_torch_dist_and_backend(self, cuda_conf, distributed_conf) -> None:\n        if torch.cuda.is_available():\n            torch.backends.cudnn.deterministic = cuda_conf.cudnn_deterministic\n            torch.backends.cudnn.benchmark = cuda_conf.cudnn_benchmark\n            torch.backends.cuda.matmul.allow_tf32 = (\n                cuda_conf.matmul_allow_tf32\n                if cuda_conf.matmul_allow_tf32 is not None\n                else cuda_conf.allow_tf32\n            )\n            torch.backends.cudnn.allow_tf32 = (\n                cuda_conf.cudnn_allow_tf32\n                if cuda_conf.cudnn_allow_tf32 is not None\n                else cuda_conf.allow_tf32\n            )\n\n        self.rank = setup_distributed_backend(\n            distributed_conf.backend, distributed_conf.timeout_mins\n        )\n\n    def _setup_device(self, accelerator):\n        self.local_rank, self.distributed_rank = get_machine_local_and_dist_rank()\n        if accelerator == \"cuda\":\n            self.device = torch.device(\"cuda\", self.local_rank)\n            torch.cuda.set_device(self.local_rank)\n        elif accelerator == \"cpu\":\n            self.device = torch.device(\"cpu\")\n        else:\n            raise ValueError(f\"Unsupported accelerator: {accelerator}\")\n\n    def _setup_ddp_distributed_training(self, distributed_conf, accelerator):\n        assert isinstance(self.model, torch.nn.Module)\n\n        self.model = nn.parallel.DistributedDataParallel(\n            self.model,\n            device_ids=[self.local_rank] if accelerator == \"cuda\" else [],\n            find_unused_parameters=distributed_conf.find_unused_parameters,\n            gradient_as_bucket_view=distributed_conf.gradient_as_bucket_view,\n            static_graph=distributed_conf.static_graph,\n        )\n        if distributed_conf.comms_dtype is not None:  # noqa\n            from torch.distributed.algorithms import ddp_comm_hooks\n\n            amp_type = get_amp_type(distributed_conf.comms_dtype)\n            if amp_type == torch.bfloat16:\n                hook = ddp_comm_hooks.default_hooks.bf16_compress_hook\n                logging.info(\"Enabling bfloat16 grad communication\")\n            else:\n                hook = ddp_comm_hooks.default_hooks.fp16_compress_hook\n                logging.info(\"Enabling fp16 grad communication\")\n            process_group = None\n            self.model.register_comm_hook(process_group, hook)\n\n    def _move_to_device(self):\n        logging.info(\n            f\"Moving components to device {self.device} and local rank {self.local_rank}.\"\n        )\n\n        self.model.to(self.device)\n\n        logging.info(\n            f\"Done moving components to device {self.device} and local rank {self.local_rank}.\"\n        )\n\n    def save_checkpoint(self, epoch, checkpoint_names=None):\n        if self.skip_saving_ckpts:\n            logging.info(\n                \"skip_saving_ckpts is set to True. So, no checkpoints have been saved.\"\n            )\n            return\n        checkpoint_folder = self.checkpoint_conf.save_dir\n        makedir(checkpoint_folder)\n        if checkpoint_names is None:\n            checkpoint_names = [\"checkpoint\"]\n            if (\n                self.checkpoint_conf.save_freq > 0\n                and (int(epoch) % self.checkpoint_conf.save_freq == 0)\n            ) or int(epoch) in self.checkpoint_conf.save_list:\n                checkpoint_names.append(f\"checkpoint_{int(epoch)}\")\n\n        checkpoint_paths = []\n        for ckpt_name in checkpoint_names:\n            checkpoint_paths.append(os.path.join(checkpoint_folder, f\"{ckpt_name}.pt\"))\n\n        state_dict = unwrap_ddp_if_wrapped(self.model).state_dict()\n        state_dict = exclude_params_matching_unix_pattern(\n            patterns=self.checkpoint_conf.skip_saving_parameters, state_dict=state_dict\n        )\n\n        checkpoint = {\n            \"model\": state_dict,\n            \"optimizer\": self.optim.optimizer.state_dict(),\n            \"epoch\": epoch,\n            \"loss\": self.loss.state_dict(),\n            \"steps\": self.steps,\n            \"time_elapsed\": self.time_elapsed_meter.val,\n            \"best_meter_values\": self.best_meter_values,\n        }\n        if self.optim_conf.amp.enabled:\n            checkpoint[\"scaler\"] = self.scaler.state_dict()\n\n        # DDP checkpoints are only saved on rank 0 (all workers are identical)\n        if self.distributed_rank != 0:\n            return\n\n        for checkpoint_path in checkpoint_paths:\n            self._save_checkpoint(checkpoint, checkpoint_path)\n\n    def _save_checkpoint(self, checkpoint, checkpoint_path):\n        \"\"\"\n        Save a checkpoint while guarding against the job being killed in the middle\n        of checkpoint saving (which corrupts the checkpoint file and ruins the\n        entire training since usually only the last checkpoint is kept per run).\n\n        We first save the new checkpoint to a temp file (with a '.tmp' suffix), and\n        and move it to overwrite the old checkpoint_path.\n        \"\"\"\n        checkpoint_path_tmp = f\"{checkpoint_path}.tmp\"\n        with g_pathmgr.open(checkpoint_path_tmp, \"wb\") as f:\n            torch.save(checkpoint, f)\n        # after torch.save is completed, replace the old checkpoint with the new one\n        if g_pathmgr.exists(checkpoint_path):\n            # remove the old checkpoint_path file first (otherwise g_pathmgr.mv fails)\n            g_pathmgr.rm(checkpoint_path)\n        success = g_pathmgr.mv(checkpoint_path_tmp, checkpoint_path)\n        assert success\n\n    def load_checkpoint(self):\n        ckpt_path = get_resume_checkpoint(self.checkpoint_conf.save_dir)\n        if ckpt_path is None:\n            self._init_model_state()\n        else:\n            if self.checkpoint_conf.initialize_after_preemption:\n                self._call_model_initializer()\n            self._load_resuming_checkpoint(ckpt_path)\n\n    def _init_model_state(self):\n        # Checking that parameters that won't be saved are indeed frozen\n        # We do this check here before even saving the model to catch errors\n        # are early as possible and not at the end of the first epoch\n        assert_skipped_parameters_are_frozen(\n            patterns=self.checkpoint_conf.skip_saving_parameters,\n            model=self.model,\n        )\n\n        # Checking that parameters that won't be saved are initialized from\n        # within the model definition, unless `initialize_after_preemption`\n        # is explicitly set to `True`. If not, this is a bug, and after\n        # preemption, the `skip_saving_parameters` will have random values\n        allow_init_skip_parameters = self.checkpoint_conf.initialize_after_preemption\n        with with_check_parameter_frozen(\n            patterns=self.checkpoint_conf.skip_saving_parameters,\n            model=self.model,\n            disabled=allow_init_skip_parameters,\n        ):\n            self._call_model_initializer()\n\n    def _call_model_initializer(self):\n        model_weight_initializer = instantiate(\n            self.checkpoint_conf.model_weight_initializer\n        )\n        if model_weight_initializer is not None:\n            logging.info(\n                f\"Loading pretrained checkpoint from {self.checkpoint_conf.model_weight_initializer}\"\n            )\n            self.model = model_weight_initializer(model=self.model)\n\n    def _load_resuming_checkpoint(self, ckpt_path: str):\n        logging.info(f\"Resuming training from {ckpt_path}\")\n\n        with g_pathmgr.open(ckpt_path, \"rb\") as f:\n            checkpoint = torch.load(f, map_location=\"cpu\")\n        load_state_dict_into_model(\n            model=self.model,\n            state_dict=checkpoint[\"model\"],\n            ignore_missing_keys=self.checkpoint_conf.skip_saving_parameters,\n        )\n\n        self.optim.optimizer.load_state_dict(checkpoint[\"optimizer\"])\n        self.loss.load_state_dict(checkpoint[\"loss\"], strict=True)\n        self.epoch = checkpoint[\"epoch\"]\n        self.steps = checkpoint[\"steps\"]\n        self.ckpt_time_elapsed = checkpoint.get(\"time_elapsed\")\n\n        if self.optim_conf.amp.enabled and \"scaler\" in checkpoint:\n            self.scaler.load_state_dict(checkpoint[\"scaler\"])\n\n        self.best_meter_values = checkpoint.get(\"best_meter_values\", {})\n\n        if \"train_dataset\" in checkpoint and self.train_dataset is not None:\n            self.train_dataset.load_checkpoint_state(checkpoint[\"train_dataset\"])\n\n    def is_intermediate_val_epoch(self, epoch):\n        skip_epoch = self.skip_first_val and epoch == 0\n        return (\n            epoch % self.val_epoch_freq == 0\n            and epoch < self.max_epochs - 1\n            and not skip_epoch\n        )\n\n    def _find_loss(self, key: str):\n        if key in self.loss:\n            return self.loss[key]\n\n        assert key != \"all\", \"Loss must be specified for key='all'\"\n        assert \"default\" in self.loss, (\n            f\"Key {key} not found in losss, and no default provided\"\n        )\n        return self.loss[\"default\"]\n\n    def _find_meter(self, phase: str, key: str):\n        if key in self.meters[phase]:\n            return self.meters[phase][key]\n\n        for cand_key, meter in self.meters[phase].items():\n            if fnmatch.fnmatch(key, cand_key):\n                return meter\n        return None\n\n    def _step(\n        self,\n        batch: BatchedDatapoint,\n        model: nn.Module,\n        phase: str,\n    ):\n        key, batch = batch.popitem()\n        batch = copy_data_to_device(batch, self.device, non_blocking=True)\n\n        find_stages = model(batch)\n        find_targets = [\n            unwrap_ddp_if_wrapped(model).back_convert(x) for x in batch.find_targets\n        ]\n        batch_size = len(batch.img_batch)\n        loss = self._find_loss(key)(find_stages, find_targets)\n\n        loss_str = f\"Losses/{phase}_{key}_loss\"\n\n        loss_log_str = os.path.join(\"Step_Losses\", loss_str)\n\n        # loss contains multiple sub-components we wish to log\n        step_losses = {}\n        if isinstance(loss, dict):\n            step_losses.update(\n                {f\"Losses/{phase}_{key}_{k}\": v for k, v in loss.items()}\n            )\n            loss = self._log_loss_detailed_and_return_core_loss(\n                loss, loss_log_str, self.steps[phase]\n            )\n\n        if self.steps[phase] % self.logging_conf.log_scalar_frequency == 0:\n            self.logger.log(\n                loss_log_str,\n                loss,\n                self.steps[phase],\n            )\n\n        self.steps[phase] += 1\n\n        ret_tuple = {loss_str: loss}, batch_size, step_losses\n\n        if phase not in self.meters:\n            return ret_tuple\n\n        meters_dict = self._find_meter(phase, key)\n        if meters_dict is None:\n            return ret_tuple\n        if meters_dict is not None:\n            for _, meter in meters_dict.items():\n                meter.update(\n                    find_stages=find_stages,\n                    find_metadatas=batch.find_metadatas,\n                    model=model,\n                    batch=batch,\n                    key=key,\n                )\n            # Cleanup memory\n            if isinstance(find_stages, SAM3Output):\n                for fs in find_stages:\n                    for k in list(fs.keys()):\n                        del fs[k]\n\n        return ret_tuple\n\n    def run(self):\n        assert self.mode in [\"train\", \"train_only\", \"val\"]\n        if self.mode == \"train\":\n            if self.epoch > 0:\n                logging.info(f\"Resuming training from epoch: {self.epoch}\")\n                # resuming from a checkpoint\n                if self.is_intermediate_val_epoch(self.epoch - 1):\n                    logging.info(\"Running previous val epoch\")\n                    self.epoch -= 1\n                    self.run_val()\n                    self.epoch += 1\n            self.run_train()\n            self.run_val()\n        elif self.mode == \"val\":\n            self.run_val()\n        elif self.mode == \"train_only\":\n            self.run_train()\n\n    def _setup_dataloaders(self):\n        self.train_dataset = None\n        self.val_dataset = None\n\n        if self.mode in [\"train\", \"val\"]:\n            self.val_dataset = instantiate(self.data_conf.get(Phase.VAL, None))\n\n        if self.mode in [\"train\", \"train_only\"]:\n            self.train_dataset = instantiate(self.data_conf.train)\n\n    def run_train(self):\n        while self.epoch < self.max_epochs:\n            dataloader = self.train_dataset.get_loader(epoch=int(self.epoch))\n            barrier()\n            outs = self.train_epoch(dataloader)\n            self.logger.log_dict(outs, self.epoch)  # Logged only on rank 0\n\n            # log train to text file.\n            if self.distributed_rank == 0:\n                with g_pathmgr.open(\n                    os.path.join(self.logging_conf.log_dir, \"train_stats.json\"),\n                    \"a\",\n                ) as f:\n                    f.write(json.dumps(outs) + \"\\n\")\n\n            # Save checkpoint before validating\n            self.save_checkpoint(self.epoch + 1)\n\n            del dataloader\n            gc.collect()\n\n            # Run val, not running on last epoch since will run after the\n            # loop anyway\n            if self.is_intermediate_val_epoch(self.epoch):\n                self.run_val()\n                if torch.cuda.is_available() and self.empty_gpu_mem_cache_after_eval:\n                    # release memory buffers held by the model during eval (which typically\n                    # involves a lot more frames in video grounding that during training)\n                    torch.cuda.empty_cache()\n\n            if self.distributed_rank == 0:\n                self.best_meter_values.update(self._get_trainer_state(\"train\"))\n                with g_pathmgr.open(\n                    os.path.join(self.logging_conf.log_dir, \"best_stats.json\"),\n                    \"a\",\n                ) as f:\n                    f.write(json.dumps(self.best_meter_values) + \"\\n\")\n\n            self.epoch += 1\n        # epoch was incremented in the loop but the val step runs out of the loop\n        self.epoch -= 1\n\n    def run_val(self):\n        if not self.val_dataset:\n            return\n\n        dataloader = self.val_dataset.get_loader(epoch=int(self.epoch))\n        outs = self.val_epoch(dataloader, phase=Phase.VAL)\n        del dataloader\n        gc.collect()\n        self.logger.log_dict(outs, self.epoch)  # Logged only on rank 0\n\n        if self.distributed_rank == 0:\n            with g_pathmgr.open(\n                os.path.join(self.logging_conf.log_dir, \"val_stats.json\"),\n                \"a\",\n            ) as f:\n                f.write(json.dumps(outs) + \"\\n\")\n\n    def val_epoch(self, val_loader, phase):\n        batch_time = AverageMeter(\"Batch Time\", self.device, \":.2f\")\n        data_time = AverageMeter(\"Data Time\", self.device, \":.2f\")\n        mem = MemMeter(\"Mem (GB)\", self.device, \":.2f\")\n\n        iters_per_epoch = len(val_loader)\n\n        curr_phases = [phase]\n        curr_models = [self.model]\n\n        loss_names = []\n        for p in curr_phases:\n            for key in self.loss.keys():\n                loss_names.append(f\"Losses/{p}_{key}_loss\")\n\n        loss_mts = OrderedDict(\n            [(name, AverageMeter(name, self.device, \":.2e\")) for name in loss_names]\n        )\n        extra_loss_mts = {}\n\n        for model in curr_models:\n            model.eval()\n            if hasattr(unwrap_ddp_if_wrapped(model), \"on_validation_epoch_start\"):\n                unwrap_ddp_if_wrapped(model).on_validation_epoch_start()\n\n        progress = ProgressMeter(\n            iters_per_epoch,\n            [batch_time, data_time, mem, self.time_elapsed_meter, *loss_mts.values()],\n            self._get_meters(curr_phases),\n            prefix=\"Val Epoch: [{}]\".format(self.epoch),\n        )\n\n        end = time.time()\n\n        for data_iter, batch in enumerate(val_loader):\n            # measure data loading time\n            data_time.update(time.time() - end)\n\n            # batch = batch.to(self.device, non_blocking=True)\n\n            # compute output\n            with torch.no_grad():\n                with torch.amp.autocast(\n                    device_type=\"cuda\",\n                    enabled=(self.optim_conf.amp.enabled if self.optim_conf else False),\n                    dtype=(\n                        get_amp_type(self.optim_conf.amp.amp_dtype)\n                        if self.optim_conf\n                        else None\n                    ),\n                ):\n                    for phase, model in zip(curr_phases, curr_models):\n                        loss_dict, batch_size, extra_losses = self._step(\n                            batch,\n                            model,\n                            phase,\n                        )\n\n                        assert len(loss_dict) == 1\n                        loss_key, loss = loss_dict.popitem()\n\n                        if loss_key in loss_mts:\n                            loss_mts[loss_key].update(loss.item(), batch_size)\n\n                        for k, v in extra_losses.items():\n                            if k not in extra_loss_mts:\n                                extra_loss_mts[k] = AverageMeter(k, self.device, \":.2e\")\n                            extra_loss_mts[k].update(v.item(), batch_size)\n\n            # measure elapsed time\n            batch_time.update(time.time() - end)\n            end = time.time()\n\n            self.time_elapsed_meter.update(\n                time.time() - self.start_time + self.ckpt_time_elapsed\n            )\n\n            if torch.cuda.is_available():\n                mem.update(reset_peak_usage=True)\n\n            if data_iter % self.logging_conf.log_freq == 0:\n                progress.display(data_iter)\n\n            if data_iter % self.logging_conf.log_scalar_frequency == 0:\n                # Log progress meters.\n                for progress_meter in progress.meters:\n                    self.logger.log(\n                        os.path.join(\"Step_Stats\", phase, progress_meter.name),\n                        progress_meter.val,\n                        self.steps[Phase.VAL],\n                    )\n\n            if data_iter % 10 == 0:\n                dist.barrier()\n\n        self.est_epoch_time[phase] = batch_time.avg * iters_per_epoch\n        self._log_timers(phase)\n        for model in curr_models:\n            if hasattr(unwrap_ddp_if_wrapped(model), \"on_validation_epoch_end\"):\n                unwrap_ddp_if_wrapped(model).on_validation_epoch_end()\n\n        out_dict = self._log_meters_and_save_best_ckpts(curr_phases)\n\n        for k, v in loss_mts.items():\n            out_dict[k] = v.avg\n        for k, v in extra_loss_mts.items():\n            out_dict[k] = v.avg\n\n        for phase in curr_phases:\n            out_dict.update(self._get_trainer_state(phase))\n        self._reset_meters(curr_phases)\n        logging.info(f\"Meters: {out_dict}\")\n        return out_dict\n\n    def _get_trainer_state(self, phase):\n        return {\n            \"Trainer/where\": self.where,\n            \"Trainer/epoch\": self.epoch,\n            f\"Trainer/steps_{phase}\": self.steps[phase],\n        }\n\n    def train_epoch(self, train_loader):\n        # Init stat meters\n        batch_time_meter = AverageMeter(\"Batch Time\", self.device, \":.2f\")\n        data_time_meter = AverageMeter(\"Data Time\", self.device, \":.2f\")\n        mem_meter = MemMeter(\"Mem (GB)\", self.device, \":.2f\")\n        data_times = []\n        phase = Phase.TRAIN\n\n        iters_per_epoch = len(train_loader)\n\n        loss_names = []\n        for batch_key in self.loss.keys():\n            loss_names.append(f\"Losses/{phase}_{batch_key}_loss\")\n\n        loss_mts = OrderedDict(\n            [(name, AverageMeter(name, self.device, \":.2e\")) for name in loss_names]\n        )\n        extra_loss_mts = {}\n\n        progress = ProgressMeter(\n            iters_per_epoch,\n            [\n                batch_time_meter,\n                data_time_meter,\n                mem_meter,\n                self.time_elapsed_meter,\n                *loss_mts.values(),\n            ],\n            self._get_meters([phase]),\n            prefix=\"Train Epoch: [{}]\".format(self.epoch),\n        )\n\n        # Model training loop\n        self.model.train()\n        end = time.time()\n\n        for data_iter, batch in enumerate(train_loader):\n            # measure data loading time\n            data_time_meter.update(time.time() - end)\n            data_times.append(data_time_meter.val)\n            # batch = batch.to(\n            #     self.device, non_blocking=True\n            # )  # move tensors in a tensorclass\n\n            try:\n                self._run_step(batch, phase, loss_mts, extra_loss_mts)\n\n                # compute gradient and do optim step\n                exact_epoch = self.epoch + float(data_iter) / iters_per_epoch\n                self.where = float(exact_epoch) / self.max_epochs\n                assert self.where <= 1 + self.EPSILON\n                if self.where < 1.0:\n                    self.optim.step_schedulers(\n                        self.where, step=int(exact_epoch * iters_per_epoch)\n                    )\n                else:\n                    logging.warning(\n                        f\"Skipping scheduler update since the training is at the end, i.e, {self.where} of [0,1].\"\n                    )\n\n                # Log schedulers\n                if data_iter % self.logging_conf.log_scalar_frequency == 0:\n                    for j, param_group in enumerate(self.optim.optimizer.param_groups):\n                        for option in self.optim.schedulers[j]:\n                            optim_prefix = (\n                                \"\" + f\"{j}_\"\n                                if len(self.optim.optimizer.param_groups) > 1\n                                else \"\"\n                            )\n                            self.logger.log(\n                                os.path.join(\"Optim\", f\"{optim_prefix}\", option),\n                                param_group[option],\n                                self.steps[phase],\n                            )\n\n                # Clipping gradients and detecting diverging gradients\n                if self.gradient_clipper is not None:\n                    self.scaler.unscale_(self.optim.optimizer)\n                    self.gradient_clipper(model=self.model)\n\n                if self.gradient_logger is not None:\n                    self.gradient_logger(\n                        self.model, rank=self.distributed_rank, where=self.where\n                    )\n\n                # Optimizer step: the scaler will make sure gradients are not\n                # applied if the gradients are infinite\n                self.scaler.step(self.optim.optimizer)\n                self.scaler.update()\n\n                # measure elapsed time\n                batch_time_meter.update(time.time() - end)\n                end = time.time()\n\n                self.time_elapsed_meter.update(\n                    time.time() - self.start_time + self.ckpt_time_elapsed\n                )\n\n                mem_meter.update(reset_peak_usage=True)\n                if data_iter % self.logging_conf.log_freq == 0:\n                    progress.display(data_iter)\n\n                if data_iter % self.logging_conf.log_scalar_frequency == 0:\n                    # Log progress meters.\n                    for progress_meter in progress.meters:\n                        self.logger.log(\n                            os.path.join(\"Step_Stats\", phase, progress_meter.name),\n                            progress_meter.val,\n                            self.steps[phase],\n                        )\n\n            # Catching NaN/Inf errors in the loss\n            except FloatingPointError as e:\n                raise e\n\n        self.est_epoch_time[Phase.TRAIN] = batch_time_meter.avg * iters_per_epoch\n        self._log_timers(Phase.TRAIN)\n        self._log_sync_data_times(Phase.TRAIN, data_times)\n\n        out_dict = self._log_meters_and_save_best_ckpts([Phase.TRAIN])\n\n        for k, v in loss_mts.items():\n            out_dict[k] = v.avg\n        for k, v in extra_loss_mts.items():\n            out_dict[k] = v.avg\n        out_dict.update(self._get_trainer_state(phase))\n        logging.info(f\"Losses and meters: {out_dict}\")\n        self._reset_meters([phase])\n        return out_dict\n\n    def _log_sync_data_times(self, phase, data_times):\n        data_times = all_reduce_max(torch.tensor(data_times)).tolist()\n        steps = range(self.steps[phase] - len(data_times), self.steps[phase])\n        for step, data_time in zip(steps, data_times):\n            if step % self.logging_conf.log_scalar_frequency == 0:\n                self.logger.log(\n                    os.path.join(\"Step_Stats\", phase, \"Data Time Synced\"),\n                    data_time,\n                    step,\n                )\n\n    def _run_step(\n        self,\n        batch: BatchedDatapoint,\n        phase: str,\n        loss_mts: Dict[str, AverageMeter],\n        extra_loss_mts: Dict[str, AverageMeter],\n        raise_on_error: bool = True,\n    ):\n        \"\"\"\n        Run the forward / backward\n        \"\"\"\n\n        # it's important to set grads to None, especially with Adam since 0\n        # grads will also update a model even if the step doesn't produce\n        # gradients\n        self.optim.zero_grad(set_to_none=True)\n\n        if self.gradient_accumulation_steps > 1:\n            assert isinstance(batch, list), (\n                f\"Expected a list of batches, got {type(batch)}\"\n            )\n            assert len(batch) == self.gradient_accumulation_steps, (\n                f\"Expected {self.gradient_accumulation_steps} batches, got {len(batch)}\"\n            )\n            accum_steps = len(batch)\n        else:\n            accum_steps = 1\n            batch = [batch]\n\n        for i, chunked_batch in enumerate(batch):\n            ddp_context = (\n                self.model.no_sync()\n                if i < accum_steps - 1\n                else contextlib.nullcontext()\n            )\n            with ddp_context:\n                with torch.amp.autocast(\n                    device_type=\"cuda\",\n                    enabled=self.optim_conf.amp.enabled,\n                    dtype=get_amp_type(self.optim_conf.amp.amp_dtype),\n                ):\n                    loss_dict, batch_size, extra_losses = self._step(\n                        chunked_batch,\n                        self.model,\n                        phase,\n                    )\n\n                assert len(loss_dict) == 1\n                loss_key, loss = loss_dict.popitem()\n\n                if not math.isfinite(loss.item()):\n                    error_msg = f\"Loss is {loss.item()}, attempting to stop training\"\n                    logging.error(error_msg)\n                    if raise_on_error:\n                        raise FloatingPointError(error_msg)\n                    else:\n                        return\n\n                self.scaler.scale(loss).backward()\n                loss_mts[loss_key].update(loss.item(), batch_size)\n                for extra_loss_key, extra_loss in extra_losses.items():\n                    if extra_loss_key not in extra_loss_mts:\n                        extra_loss_mts[extra_loss_key] = AverageMeter(\n                            extra_loss_key, self.device, \":.2e\"\n                        )\n                    extra_loss_mts[extra_loss_key].update(extra_loss.item(), batch_size)\n\n    def _log_meters_and_save_best_ckpts(self, phases: List[str]):\n        logging.info(\"Synchronizing meters\")\n        out_dict = {}\n        checkpoint_save_keys = []\n        for key, meter in self._get_meters(phases).items():\n            meter_output = meter.compute_synced()\n            is_better_check = getattr(meter, \"is_better\", None)\n\n            for meter_subkey, meter_value in meter_output.items():\n                out_dict[os.path.join(\"Meters_train\", key, meter_subkey)] = meter_value\n\n                if is_better_check is None:\n                    continue\n\n                tracked_meter_key = os.path.join(key, meter_subkey)\n                if tracked_meter_key not in self.best_meter_values or is_better_check(\n                    meter_value,\n                    self.best_meter_values[tracked_meter_key],\n                ):\n                    self.best_meter_values[tracked_meter_key] = meter_value\n\n                    if (\n                        self.checkpoint_conf.save_best_meters is not None\n                        and key in self.checkpoint_conf.save_best_meters\n                    ):\n                        checkpoint_save_keys.append(tracked_meter_key.replace(\"/\", \"_\"))\n\n        if len(checkpoint_save_keys) > 0:\n            self.save_checkpoint(self.epoch + 1, checkpoint_save_keys)\n\n        return out_dict\n\n    def _log_timers(self, phase):\n        time_remaining = 0\n        epochs_remaining = self.max_epochs - self.epoch - 1\n        val_epochs_remaining = sum(\n            n % self.val_epoch_freq == 0 for n in range(self.epoch, self.max_epochs)\n        )\n\n        # Adding the guaranteed val run at the end if val_epoch_freq doesn't coincide with\n        # the end epoch.\n        if (self.max_epochs - 1) % self.val_epoch_freq != 0:\n            val_epochs_remaining += 1\n\n        # Remove the current val run from estimate\n        if phase == Phase.VAL:\n            val_epochs_remaining -= 1\n\n        time_remaining += (\n            epochs_remaining * self.est_epoch_time[Phase.TRAIN]\n            + val_epochs_remaining * self.est_epoch_time[Phase.VAL]\n        )\n\n        self.logger.log(\n            os.path.join(\"Step_Stats\", phase, self.time_elapsed_meter.name),\n            self.time_elapsed_meter.val,\n            self.steps[phase],\n        )\n\n        logging.info(f\"Estimated time remaining: {human_readable_time(time_remaining)}\")\n\n    def _reset_meters(self, phases: str) -> None:\n        for meter in self._get_meters(phases).values():\n            meter.reset()\n\n    def _check_val_key_match(self, val_keys, phase):\n        if val_keys is not None:\n            # Check if there are any duplicates\n            assert len(val_keys) == len(set(val_keys)), (\n                f\"Duplicate keys in val datasets, keys: {val_keys}\"\n            )\n\n            # Check that the keys match the meter keys\n            if self.meters_conf is not None and phase in self.meters_conf:\n                assert set(val_keys) == set(self.meters_conf[phase].keys()), (\n                    f\"Keys in val datasets do not match the keys in meters.\"\n                    f\"\\nMissing in meters: {set(val_keys) - set(self.meters_conf[phase].keys())}\"\n                    f\"\\nMissing in val datasets: {set(self.meters_conf[phase].keys()) - set(val_keys)}\"\n                )\n\n            if self.loss_conf is not None:\n                loss_keys = set(self.loss_conf.keys()) - set([\"all\"])\n                if \"default\" not in loss_keys:\n                    for k in val_keys:\n                        assert k in loss_keys, (\n                            f\"Error: key {k} is not defined in the losses, and no default is set\"\n                        )\n\n    def _setup_components(self):\n        # Get the keys for all the val datasets, if any\n        val_phase = Phase.VAL\n        val_keys = None\n        if self.data_conf.get(val_phase, None) is not None:\n            val_keys = collect_dict_keys(self.data_conf[val_phase])\n        # Additional checks on the sanity of the config for val datasets\n        self._check_val_key_match(val_keys, phase=val_phase)\n\n        logging.info(\"Setting up components: Model, loss, optim, meters etc.\")\n        self.epoch = 0\n        self.steps = {Phase.TRAIN: 0, Phase.VAL: 0}\n\n        self.logger = Logger(self.logging_conf)\n\n        self.model = instantiate(self.model_conf, _convert_=\"all\")\n        print_model_summary(self.model)\n\n        self.loss = None\n        if self.loss_conf:\n            self.loss = {\n                key: el  # wrap_base_loss(el)\n                for (key, el) in instantiate(self.loss_conf, _convert_=\"all\").items()\n            }\n            self.loss = nn.ModuleDict(self.loss)\n\n        self.meters = {}\n        self.best_meter_values = {}\n        if self.meters_conf:\n            self.meters = instantiate(self.meters_conf, _convert_=\"all\")\n\n        self.scaler = torch.amp.GradScaler(\n            self.device,\n            enabled=self.optim_conf.amp.enabled if self.optim_conf else False,\n        )\n\n        self.gradient_clipper = (\n            instantiate(self.optim_conf.gradient_clip) if self.optim_conf else None\n        )\n        self.gradient_logger = (\n            instantiate(self.optim_conf.gradient_logger) if self.optim_conf else None\n        )\n\n        logging.info(\"Finished setting up components: Model, loss, optim, meters etc.\")\n\n    def _construct_optimizers(self):\n        self.optim = construct_optimizer(\n            self.model,\n            self.optim_conf.optimizer,\n            self.optim_conf.options,\n            self.optim_conf.param_group_modifiers,\n        )\n\n    def _log_loss_detailed_and_return_core_loss(self, loss, loss_str, step):\n        core_loss = loss.pop(CORE_LOSS_KEY)\n        if step % self.logging_conf.log_scalar_frequency == 0:\n            for k in loss:\n                log_str = os.path.join(loss_str, k)\n                self.logger.log(log_str, loss[k], step)\n        return core_loss\n\n\ndef print_model_summary(model: torch.nn.Module, log_dir: str = \"\"):\n    \"\"\"\n    Prints the model and the number of parameters in the model.\n    # Multiple packages provide this info in a nice table format\n    # However, they need us to provide an `input` (as they also write down the output sizes)\n    # Our models are complex, and a single input is restrictive.\n    # https://github.com/sksq96/pytorch-summary\n    # https://github.com/nmhkahn/torchsummaryX\n    \"\"\"\n    if get_rank() != 0:\n        return\n    param_kwargs = {}\n    trainable_parameters = sum(\n        p.numel() for p in model.parameters(**param_kwargs) if p.requires_grad\n    )\n    total_parameters = sum(p.numel() for p in model.parameters(**param_kwargs))\n    non_trainable_parameters = total_parameters - trainable_parameters\n    logging.info(\"==\" * 10)\n    logging.info(f\"Summary for model {type(model)}\")\n    logging.info(f\"Model is {model}\")\n    logging.info(f\"\\tTotal parameters {get_human_readable_count(total_parameters)}\")\n    logging.info(\n        f\"\\tTrainable parameters {get_human_readable_count(trainable_parameters)}\"\n    )\n    logging.info(\n        f\"\\tNon-Trainable parameters {get_human_readable_count(non_trainable_parameters)}\"\n    )\n    logging.info(\"==\" * 10)\n\n    if log_dir:\n        output_fpath = os.path.join(log_dir, \"model.txt\")\n        with g_pathmgr.open(output_fpath, \"w\") as f:\n            print(model, file=f)\n\n\nPARAMETER_NUM_UNITS = [\" \", \"K\", \"M\", \"B\", \"T\"]\n\n\ndef get_human_readable_count(number: int) -> str:\n    \"\"\"\n    Abbreviates an integer number with K, M, B, T for thousands, millions,\n    billions and trillions, respectively.\n    Examples:\n        >>> get_human_readable_count(123)\n        '123  '\n        >>> get_human_readable_count(1234)  # (one thousand)\n        '1.2 K'\n        >>> get_human_readable_count(2e6)   # (two million)\n        '2.0 M'\n        >>> get_human_readable_count(3e9)   # (three billion)\n        '3.0 B'\n        >>> get_human_readable_count(4e14)  # (four hundred trillion)\n        '400 T'\n        >>> get_human_readable_count(5e15)  # (more than trillion)\n        '5,000 T'\n    Args:\n        number: a positive integer number\n    Return:\n        A string formatted according to the pattern described above.\n    \"\"\"\n    assert number >= 0\n    labels = PARAMETER_NUM_UNITS\n    num_digits = int(np.floor(np.log10(number)) + 1 if number > 0 else 1)\n    num_groups = int(np.ceil(num_digits / 3))\n    num_groups = min(num_groups, len(labels))  # don't abbreviate beyond trillions\n    shift = -3 * (num_groups - 1)\n    number = number * (10**shift)\n    index = num_groups - 1\n    if index < 1 or number >= 100:\n        return f\"{int(number):,d} {labels[index]}\"\n    else:\n        return f\"{number:,.1f} {labels[index]}\"\n"
  },
  {
    "path": "sam3/train/transforms/__init__.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n"
  },
  {
    "path": "sam3/train/transforms/basic.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n\n\"\"\"\nTransforms and data augmentation for both image + bbox.\n\"\"\"\n\nimport math\nimport random\nfrom typing import Iterable\n\nimport PIL\nimport torch\nimport torchvision.transforms as T\nimport torchvision.transforms.functional as F\nfrom sam3.model.box_ops import box_xyxy_to_cxcywh\nfrom sam3.model.data_misc import interpolate\n\n\ndef crop(image, target, region):\n    cropped_image = F.crop(image, *region)\n\n    target = target.copy()\n    i, j, h, w = region\n\n    # should we do something wrt the original size?\n    target[\"size\"] = torch.tensor([h, w])\n\n    fields = [\"labels\", \"area\", \"iscrowd\", \"positive_map\"]\n\n    if \"boxes\" in target:\n        boxes = target[\"boxes\"]\n        max_size = torch.as_tensor([w, h], dtype=torch.float32)\n        cropped_boxes = boxes - torch.as_tensor([j, i, j, i], dtype=torch.float32)\n        cropped_boxes = torch.min(cropped_boxes.reshape(-1, 2, 2), max_size)\n        cropped_boxes = cropped_boxes.clamp(min=0)\n        area = (cropped_boxes[:, 1, :] - cropped_boxes[:, 0, :]).prod(dim=1)\n        target[\"boxes\"] = cropped_boxes.reshape(-1, 4)\n        target[\"area\"] = area\n        fields.append(\"boxes\")\n\n    if \"input_boxes\" in target:\n        boxes = target[\"input_boxes\"]\n        max_size = torch.as_tensor([w, h], dtype=torch.float32)\n        cropped_boxes = boxes - torch.as_tensor([j, i, j, i], dtype=torch.float32)\n        cropped_boxes = torch.min(cropped_boxes.reshape(-1, 2, 2), max_size)\n        cropped_boxes = cropped_boxes.clamp(min=0)\n        target[\"input_boxes\"] = cropped_boxes.reshape(-1, 4)\n\n    if \"masks\" in target:\n        # FIXME should we update the area here if there are no boxes?\n        target[\"masks\"] = target[\"masks\"][:, i : i + h, j : j + w]\n        fields.append(\"masks\")\n\n    # remove elements for which the boxes or masks that have zero area\n    if \"boxes\" in target or \"masks\" in target:\n        # favor boxes selection when defining which elements to keep\n        # this is compatible with previous implementation\n        if \"boxes\" in target:\n            cropped_boxes = target[\"boxes\"].reshape(-1, 2, 2)\n            keep = torch.all(cropped_boxes[:, 1, :] > cropped_boxes[:, 0, :], dim=1)\n        else:\n            keep = target[\"masks\"].flatten(1).any(1)\n\n        for field in fields:\n            if field in target:\n                target[field] = target[field][keep]\n\n    return cropped_image, target\n\n\ndef hflip(image, target):\n    flipped_image = F.hflip(image)\n\n    w, h = image.size\n\n    target = target.copy()\n    if \"boxes\" in target:\n        boxes = target[\"boxes\"]\n        boxes = boxes[:, [2, 1, 0, 3]] * torch.as_tensor(\n            [-1, 1, -1, 1]\n        ) + torch.as_tensor([w, 0, w, 0])\n        target[\"boxes\"] = boxes\n\n    if \"input_boxes\" in target:\n        boxes = target[\"input_boxes\"]\n        boxes = boxes[:, [2, 1, 0, 3]] * torch.as_tensor(\n            [-1, 1, -1, 1]\n        ) + torch.as_tensor([w, 0, w, 0])\n        target[\"input_boxes\"] = boxes\n\n    if \"masks\" in target:\n        target[\"masks\"] = target[\"masks\"].flip(-1)\n\n    if \"text_input\" in target:\n        text_input = (\n            target[\"text_input\"]\n            .replace(\"left\", \"[TMP]\")\n            .replace(\"right\", \"left\")\n            .replace(\"[TMP]\", \"right\")\n        )\n        target[\"text_input\"] = text_input\n\n    return flipped_image, target\n\n\ndef resize(image, target, size, max_size=None, square=False):\n    # size can be min_size (scalar) or (w, h) tuple\n\n    def get_size_with_aspect_ratio(image_size, size, max_size=None):\n        w, h = image_size\n        if max_size is not None:\n            min_original_size = float(min((w, h)))\n            max_original_size = float(max((w, h)))\n            if max_original_size / min_original_size * size > max_size:\n                size = int(round(max_size * min_original_size / max_original_size))\n\n        if (w <= h and w == size) or (h <= w and h == size):\n            return (h, w)\n\n        if w < h:\n            ow = size\n            oh = int(size * h / w)\n        else:\n            oh = size\n            ow = int(size * w / h)\n\n        return (oh, ow)\n\n    def get_size(image_size, size, max_size=None):\n        if isinstance(size, (list, tuple)):\n            return size[::-1]\n        else:\n            return get_size_with_aspect_ratio(image_size, size, max_size)\n\n    if square:\n        size = size, size\n    else:\n        size = get_size(image.size, size, max_size)\n    rescaled_image = F.resize(image, size)\n\n    if target is None:\n        return rescaled_image, None\n\n    ratios = tuple(\n        float(s) / float(s_orig) for s, s_orig in zip(rescaled_image.size, image.size)\n    )\n    ratio_width, ratio_height = ratios\n\n    target = target.copy()\n    if \"boxes\" in target:\n        boxes = target[\"boxes\"]\n        scaled_boxes = boxes * torch.as_tensor(\n            [ratio_width, ratio_height, ratio_width, ratio_height], dtype=torch.float32\n        )\n        target[\"boxes\"] = scaled_boxes\n    if \"input_boxes\" in target:\n        boxes = target[\"input_boxes\"]\n        scaled_boxes = boxes * torch.as_tensor(\n            [ratio_width, ratio_height, ratio_width, ratio_height], dtype=torch.float32\n        )\n        target[\"input_boxes\"] = scaled_boxes\n\n    if \"area\" in target:\n        area = target[\"area\"]\n        scaled_area = area * (ratio_width * ratio_height)\n        target[\"area\"] = scaled_area\n\n    h, w = size\n    target[\"size\"] = torch.tensor([h, w])\n\n    if \"masks\" in target:\n        target[\"masks\"] = (\n            interpolate(target[\"masks\"][:, None].float(), size, mode=\"nearest\")[:, 0]\n            > 0.5\n        )\n\n    return rescaled_image, target\n\n\ndef pad(image, target, padding):\n    if len(padding) == 2:\n        # assumes that we only pad on the bottom right corners\n        padded_image = F.pad(image, (0, 0, padding[0], padding[1]))\n    else:\n        # left, top, right, bottom\n        padded_image = F.pad(image, (padding[0], padding[1], padding[2], padding[3]))\n    if target is None:\n        return padded_image, None\n    target = target.copy()\n\n    w, h = padded_image.size\n\n    # should we do something wrt the original size?\n    target[\"size\"] = torch.tensor([h, w])\n    if \"boxes\" in target and len(padding) == 4:\n        boxes = target[\"boxes\"]\n        boxes = boxes + torch.as_tensor(\n            [padding[0], padding[1], padding[0], padding[1]], dtype=torch.float32\n        )\n        target[\"boxes\"] = boxes\n\n    if \"input_boxes\" in target and len(padding) == 4:\n        boxes = target[\"input_boxes\"]\n        boxes = boxes + torch.as_tensor(\n            [padding[0], padding[1], padding[0], padding[1]], dtype=torch.float32\n        )\n        target[\"input_boxes\"] = boxes\n\n    if \"masks\" in target:\n        if len(padding) == 2:\n            target[\"masks\"] = torch.nn.functional.pad(\n                target[\"masks\"], (0, padding[0], 0, padding[1])\n            )\n        else:\n            target[\"masks\"] = torch.nn.functional.pad(\n                target[\"masks\"], (padding[0], padding[2], padding[1], padding[3])\n            )\n    return padded_image, target\n\n\nclass RandomCrop:\n    def __init__(self, size):\n        self.size = size\n\n    def __call__(self, img, target):\n        region = T.RandomCrop.get_params(img, self.size)\n        return crop(img, target, region)\n\n\nclass RandomSizeCrop:\n    def __init__(self, min_size: int, max_size: int, respect_boxes: bool = False):\n        self.min_size = min_size\n        self.max_size = max_size\n        self.respect_boxes = respect_boxes  # if True we can't crop a box out\n\n    def __call__(self, img: PIL.Image.Image, target: dict):\n        init_boxes = len(target[\"boxes\"])\n        init_boxes_tensor = target[\"boxes\"].clone()\n        if self.respect_boxes and init_boxes > 0:\n            minW, minH, maxW, maxH = (\n                min(img.width, self.min_size),\n                min(img.width, self.min_size),\n                min(img.width, self.max_size),\n                min(img.height, self.max_size),\n            )\n            minX, minY = (\n                target[\"boxes\"][:, 0].max().item() + 10.0,\n                target[\"boxes\"][:, 1].max().item() + 10.0,\n            )\n            minX = min(img.width, minX)\n            minY = min(img.height, minY)\n            maxX, maxY = (\n                target[\"boxes\"][:, 2].min().item() - 10,\n                target[\"boxes\"][:, 3].min().item() - 10,\n            )\n            maxX = max(0.0, maxX)\n            maxY = max(0.0, maxY)\n            minW = max(minW, minX - maxX)\n            minH = max(minH, minY - maxY)\n            w = random.uniform(minW, max(minW, maxW))\n            h = random.uniform(minH, max(minH, maxH))\n            if minX > maxX:\n                # i = random.uniform(max(0, minX - w + 1), max(maxX, max(0, minX - w + 1)))\n                i = random.uniform(max(0, minX - w), max(maxX, max(0, minX - w)))\n            else:\n                i = random.uniform(\n                    max(0, minX - w + 1), max(maxX - 1, max(0, minX - w + 1))\n                )\n            if minY > maxY:\n                # j = random.uniform(max(0, minY - h + 1), max(maxY, max(0, minY - h + 1)))\n                j = random.uniform(max(0, minY - h), max(maxY, max(0, minY - h)))\n            else:\n                j = random.uniform(\n                    max(0, minY - h + 1), max(maxY - 1, max(0, minY - h + 1))\n                )\n            result_img, result_target = crop(img, target, [j, i, h, w])\n            assert len(result_target[\"boxes\"]) == init_boxes, (\n                f\"img_w={img.width}\\timg_h={img.height}\\tminX={minX}\\tminY={minY}\\tmaxX={maxX}\\tmaxY={maxY}\\tminW={minW}\\tminH={minH}\\tmaxW={maxW}\\tmaxH={maxH}\\tw={w}\\th={h}\\ti={i}\\tj={j}\\tinit_boxes={init_boxes_tensor}\\tresults={result_target['boxes']}\"\n            )\n\n            return result_img, result_target\n        else:\n            w = random.randint(self.min_size, min(img.width, self.max_size))\n            h = random.randint(self.min_size, min(img.height, self.max_size))\n            region = T.RandomCrop.get_params(img, (h, w))\n            result_img, result_target = crop(img, target, region)\n            return result_img, result_target\n\n\nclass CenterCrop:\n    def __init__(self, size):\n        self.size = size\n\n    def __call__(self, img, target):\n        image_width, image_height = img.size\n        crop_height, crop_width = self.size\n        crop_top = int(round((image_height - crop_height) / 2.0))\n        crop_left = int(round((image_width - crop_width) / 2.0))\n        return crop(img, target, (crop_top, crop_left, crop_height, crop_width))\n\n\nclass RandomHorizontalFlip:\n    def __init__(self, p=0.5):\n        self.p = p\n\n    def __call__(self, img, target):\n        if random.random() < self.p:\n            return hflip(img, target)\n        return img, target\n\n\nclass RandomResize:\n    def __init__(self, sizes, max_size=None, square=False):\n        if isinstance(sizes, int):\n            sizes = (sizes,)\n        assert isinstance(sizes, Iterable)\n        self.sizes = list(sizes)\n        self.max_size = max_size\n        self.square = square\n\n    def __call__(self, img, target=None):\n        size = random.choice(self.sizes)\n        return resize(img, target, size, self.max_size, square=self.square)\n\n\nclass RandomPad:\n    def __init__(self, max_pad):\n        self.max_pad = max_pad\n\n    def __call__(self, img, target):\n        pad_x = random.randint(0, self.max_pad)\n        pad_y = random.randint(0, self.max_pad)\n        return pad(img, target, (pad_x, pad_y))\n\n\nclass PadToSize:\n    def __init__(self, size):\n        self.size = size\n\n    def __call__(self, img, target):\n        w, h = img.size\n        pad_x = self.size - w\n        pad_y = self.size - h\n        assert pad_x >= 0 and pad_y >= 0\n        pad_left = random.randint(0, pad_x)\n        pad_right = pad_x - pad_left\n        pad_top = random.randint(0, pad_y)\n        pad_bottom = pad_y - pad_top\n        return pad(img, target, (pad_left, pad_top, pad_right, pad_bottom))\n\n\nclass Identity:\n    def __call__(self, img, target):\n        return img, target\n\n\nclass RandomSelect:\n    \"\"\"\n    Randomly selects between transforms1 and transforms2,\n    with probability p for transforms1 and (1 - p) for transforms2\n    \"\"\"\n\n    def __init__(self, transforms1=None, transforms2=None, p=0.5):\n        self.transforms1 = transforms1 or Identity()\n        self.transforms2 = transforms2 or Identity()\n        self.p = p\n\n    def __call__(self, img, target):\n        if random.random() < self.p:\n            return self.transforms1(img, target)\n        return self.transforms2(img, target)\n\n\nclass ToTensor:\n    def __call__(self, img, target):\n        return F.to_tensor(img), target\n\n\nclass RandomErasing:\n    def __init__(self, *args, **kwargs):\n        self.eraser = T.RandomErasing(*args, **kwargs)\n\n    def __call__(self, img, target):\n        return self.eraser(img), target\n\n\nclass Normalize:\n    def __init__(self, mean, std):\n        self.mean = mean\n        self.std = std\n\n    def __call__(self, image, target=None):\n        image = F.normalize(image, mean=self.mean, std=self.std)\n        if target is None:\n            return image, None\n        target = target.copy()\n        h, w = image.shape[-2:]\n        if \"boxes\" in target:\n            boxes = target[\"boxes\"]\n            boxes = box_xyxy_to_cxcywh(boxes)\n            boxes = boxes / torch.tensor([w, h, w, h], dtype=torch.float32)\n            target[\"boxes\"] = boxes\n        if \"input_boxes\" in target:\n            boxes = target[\"input_boxes\"]\n            boxes = box_xyxy_to_cxcywh(boxes)\n            boxes = boxes / torch.tensor([w, h, w, h], dtype=torch.float32)\n            target[\"input_boxes\"] = boxes\n        return image, target\n\n\nclass RemoveDifficult:\n    def __init__(self, enabled=False):\n        self.remove_difficult = enabled\n\n    def __call__(self, image, target=None):\n        if target is None:\n            return image, None\n        target = target.copy()\n        keep = ~target[\"iscrowd\"].to(torch.bool) | (not self.remove_difficult)\n        if \"boxes\" in target:\n            target[\"boxes\"] = target[\"boxes\"][keep]\n        target[\"labels\"] = target[\"labels\"][keep]\n        target[\"iscrowd\"] = target[\"iscrowd\"][keep]\n        return image, target\n\n\nclass Compose:\n    def __init__(self, transforms):\n        self.transforms = transforms\n\n    def __call__(self, image, target):\n        for t in self.transforms:\n            image, target = t(image, target)\n        return image, target\n\n    def __repr__(self):\n        format_string = self.__class__.__name__ + \"(\"\n        for t in self.transforms:\n            format_string += \"\\n\"\n            format_string += \"    {0}\".format(t)\n        format_string += \"\\n)\"\n        return format_string\n\n\ndef get_random_resize_scales(size, min_size, rounded):\n    stride = 128 if rounded else 32\n    min_size = int(stride * math.ceil(min_size / stride))\n    scales = list(range(min_size, size + 1, stride))\n    return scales\n\n\ndef get_random_resize_max_size(size, ratio=5 / 3):\n    max_size = round(ratio * size)\n    return max_size\n"
  },
  {
    "path": "sam3/train/transforms/basic_for_api.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n\n\"\"\"\nTransforms and data augmentation for both image + bbox.\n\"\"\"\n\nimport logging\nimport numbers\nimport random\nfrom collections.abc import Sequence\nfrom typing import Iterable\n\nimport torch\nimport torchvision.transforms as T\nimport torchvision.transforms.functional as F\nimport torchvision.transforms.v2.functional as Fv2\nfrom PIL import Image as PILImage\nfrom sam3.model.box_ops import box_xyxy_to_cxcywh, masks_to_boxes\nfrom sam3.train.data.sam3_image_dataset import Datapoint\nfrom torchvision.transforms import InterpolationMode\n\n\ndef crop(\n    datapoint,\n    index,\n    region,\n    v2=False,\n    check_validity=True,\n    check_input_validity=True,\n    recompute_box_from_mask=False,\n):\n    if v2:\n        rtop, rleft, rheight, rwidth = (int(round(r)) for r in region)\n        datapoint.images[index].data = Fv2.crop(\n            datapoint.images[index].data,\n            top=rtop,\n            left=rleft,\n            height=rheight,\n            width=rwidth,\n        )\n    else:\n        datapoint.images[index].data = F.crop(datapoint.images[index].data, *region)\n\n    i, j, h, w = region\n\n    # should we do something wrt the original size?\n    datapoint.images[index].size = (h, w)\n\n    for obj in datapoint.images[index].objects:\n        # crop the mask\n        if obj.segment is not None:\n            obj.segment = F.crop(obj.segment, int(i), int(j), int(h), int(w))\n\n        # crop the bounding box\n        if recompute_box_from_mask and obj.segment is not None:\n            # here the boxes are still in XYXY format with absolute coordinates (they are\n            # converted to CxCyWH with relative coordinates in basic_for_api.NormalizeAPI)\n            obj.bbox, obj.area = get_bbox_xyxy_abs_coords_from_mask(obj.segment)\n        else:\n            if recompute_box_from_mask and obj.segment is None and obj.area > 0:\n                logging.warning(\n                    \"Cannot recompute bounding box from mask since `obj.segment` is None. \"\n                    \"Falling back to directly cropping from the input bounding box.\"\n                )\n            boxes = obj.bbox.view(1, 4)\n            max_size = torch.as_tensor([w, h], dtype=torch.float32)\n            cropped_boxes = boxes - torch.as_tensor([j, i, j, i], dtype=torch.float32)\n            cropped_boxes = torch.min(cropped_boxes.reshape(-1, 2, 2), max_size)\n            cropped_boxes = cropped_boxes.clamp(min=0)\n            obj.area = (cropped_boxes[:, 1, :] - cropped_boxes[:, 0, :]).prod(dim=1)\n            obj.bbox = cropped_boxes.reshape(-1, 4)\n\n    for query in datapoint.find_queries:\n        if query.semantic_target is not None:\n            query.semantic_target = F.crop(\n                query.semantic_target, int(i), int(j), int(h), int(w)\n            )\n        if query.image_id == index and query.input_bbox is not None:\n            boxes = query.input_bbox\n            max_size = torch.as_tensor([w, h], dtype=torch.float32)\n            cropped_boxes = boxes - torch.as_tensor([j, i, j, i], dtype=torch.float32)\n            cropped_boxes = torch.min(cropped_boxes.reshape(-1, 2, 2), max_size)\n            cropped_boxes = cropped_boxes.clamp(min=0)\n\n            # cur_area = (cropped_boxes[:, 1, :] - cropped_boxes[:, 0, :]).prod(dim=1)\n            # if check_input_validity:\n            #     assert (\n            #         (cur_area > 0).all().item()\n            #     ), \"Some input box got cropped out by the crop transform\"\n\n            query.input_bbox = cropped_boxes.reshape(-1, 4)\n        if query.image_id == index and query.input_points is not None:\n            print(\n                \"Warning! Point cropping with this function may lead to unexpected results\"\n            )\n            points = query.input_points\n            # Unlike right-lower box edges, which are exclusive, the\n            # point must be in [0, length-1], hence the -1\n            max_size = torch.as_tensor([w, h], dtype=torch.float32) - 1\n            cropped_points = points - torch.as_tensor([j, i, 0], dtype=torch.float32)\n            cropped_points[:, :, :2] = torch.min(cropped_points[:, :, :2], max_size)\n            cropped_points[:, :, :2] = cropped_points[:, :, :2].clamp(min=0)\n            query.input_points = cropped_points\n\n    if check_validity:\n        # Check that all boxes are still valid\n        for obj in datapoint.images[index].objects:\n            assert obj.area > 0, \"Box {} has no area\".format(obj.bbox)\n\n    return datapoint\n\n\ndef hflip(datapoint, index):\n    datapoint.images[index].data = F.hflip(datapoint.images[index].data)\n\n    w, h = datapoint.images[index].data.size\n    for obj in datapoint.images[index].objects:\n        boxes = obj.bbox.view(1, 4)\n        boxes = boxes[:, [2, 1, 0, 3]] * torch.as_tensor(\n            [-1, 1, -1, 1]\n        ) + torch.as_tensor([w, 0, w, 0])\n        obj.bbox = boxes\n        if obj.segment is not None:\n            obj.segment = F.hflip(obj.segment)\n\n    for query in datapoint.find_queries:\n        if query.semantic_target is not None:\n            query.semantic_target = F.hflip(query.semantic_target)\n        if query.image_id == index and query.input_bbox is not None:\n            boxes = query.input_bbox\n            boxes = boxes[:, [2, 1, 0, 3]] * torch.as_tensor(\n                [-1, 1, -1, 1]\n            ) + torch.as_tensor([w, 0, w, 0])\n            query.input_bbox = boxes\n        if query.image_id == index and query.input_points is not None:\n            points = query.input_points\n            points = points * torch.as_tensor([-1, 1, 1]) + torch.as_tensor([w, 0, 0])\n            query.input_points = points\n    return datapoint\n\n\ndef get_size_with_aspect_ratio(image_size, size, max_size=None):\n    w, h = image_size\n    if max_size is not None:\n        min_original_size = float(min((w, h)))\n        max_original_size = float(max((w, h)))\n        if max_original_size / min_original_size * size > max_size:\n            size = max_size * min_original_size / max_original_size\n\n    if (w <= h and w == size) or (h <= w and h == size):\n        return (h, w)\n\n    if w < h:\n        ow = int(round(size))\n        oh = int(round(size * h / w))\n    else:\n        oh = int(round(size))\n        ow = int(round(size * w / h))\n\n    return (oh, ow)\n\n\ndef resize(datapoint, index, size, max_size=None, square=False, v2=False):\n    # size can be min_size (scalar) or (w, h) tuple\n\n    def get_size(image_size, size, max_size=None):\n        if isinstance(size, (list, tuple)):\n            return size[::-1]\n        else:\n            return get_size_with_aspect_ratio(image_size, size, max_size)\n\n    if square:\n        size = size, size\n    else:\n        cur_size = (\n            datapoint.images[index].data.size()[-2:][::-1]\n            if v2\n            else datapoint.images[index].data.size\n        )\n        size = get_size(cur_size, size, max_size)\n\n    old_size = (\n        datapoint.images[index].data.size()[-2:][::-1]\n        if v2\n        else datapoint.images[index].data.size\n    )\n    if v2:\n        datapoint.images[index].data = Fv2.resize(\n            datapoint.images[index].data, size, antialias=True\n        )\n    else:\n        datapoint.images[index].data = F.resize(datapoint.images[index].data, size)\n\n    new_size = (\n        datapoint.images[index].data.size()[-2:][::-1]\n        if v2\n        else datapoint.images[index].data.size\n    )\n    ratios = tuple(float(s) / float(s_orig) for s, s_orig in zip(new_size, old_size))\n    ratio_width, ratio_height = ratios\n\n    for obj in datapoint.images[index].objects:\n        boxes = obj.bbox.view(1, 4)\n        scaled_boxes = boxes * torch.as_tensor(\n            [ratio_width, ratio_height, ratio_width, ratio_height], dtype=torch.float32\n        )\n        obj.bbox = scaled_boxes\n        obj.area *= ratio_width * ratio_height\n        if obj.segment is not None:\n            obj.segment = F.resize(obj.segment[None, None], size).squeeze()\n\n    for query in datapoint.find_queries:\n        if query.semantic_target is not None:\n            query.semantic_target = F.resize(\n                query.semantic_target[None, None], size\n            ).squeeze()\n        if query.image_id == index and query.input_bbox is not None:\n            boxes = query.input_bbox\n            scaled_boxes = boxes * torch.as_tensor(\n                [ratio_width, ratio_height, ratio_width, ratio_height],\n                dtype=torch.float32,\n            )\n            query.input_bbox = scaled_boxes\n        if query.image_id == index and query.input_points is not None:\n            points = query.input_points\n            scaled_points = points * torch.as_tensor(\n                [ratio_width, ratio_height, 1],\n                dtype=torch.float32,\n            )\n            query.input_points = scaled_points\n\n    h, w = size\n    datapoint.images[index].size = (h, w)\n    return datapoint\n\n\ndef pad(datapoint, index, padding, v2=False):\n    old_h, old_w = datapoint.images[index].size\n    h, w = old_h, old_w\n    if len(padding) == 2:\n        # assumes that we only pad on the bottom right corners\n        if v2:\n            datapoint.images[index].data = Fv2.pad(\n                datapoint.images[index].data, (0, 0, padding[0], padding[1])\n            )\n        else:\n            datapoint.images[index].data = F.pad(\n                datapoint.images[index].data, (0, 0, padding[0], padding[1])\n            )\n        h += padding[1]\n        w += padding[0]\n    else:\n        if v2:\n            # left, top, right, bottom\n            datapoint.images[index].data = Fv2.pad(\n                datapoint.images[index].data,\n                (padding[0], padding[1], padding[2], padding[3]),\n            )\n        else:\n            # left, top, right, bottom\n            datapoint.images[index].data = F.pad(\n                datapoint.images[index].data,\n                (padding[0], padding[1], padding[2], padding[3]),\n            )\n        h += padding[1] + padding[3]\n        w += padding[0] + padding[2]\n\n    datapoint.images[index].size = (h, w)\n\n    for obj in datapoint.images[index].objects:\n        if len(padding) != 2:\n            obj.bbox += torch.as_tensor(\n                [padding[0], padding[1], padding[0], padding[1]], dtype=torch.float32\n            )\n        if obj.segment is not None:\n            if v2:\n                if len(padding) == 2:\n                    obj.segment = Fv2.pad(\n                        obj.segment[None], (0, 0, padding[0], padding[1])\n                    ).squeeze(0)\n                else:\n                    obj.segment = Fv2.pad(obj.segment[None], tuple(padding)).squeeze(0)\n            else:\n                if len(padding) == 2:\n                    obj.segment = F.pad(obj.segment, (0, 0, padding[0], padding[1]))\n                else:\n                    obj.segment = F.pad(obj.segment, tuple(padding))\n\n    for query in datapoint.find_queries:\n        if query.semantic_target is not None:\n            if v2:\n                if len(padding) == 2:\n                    query.semantic_target = Fv2.pad(\n                        query.semantic_target[None, None],\n                        (0, 0, padding[0], padding[1]),\n                    ).squeeze()\n                else:\n                    query.semantic_target = Fv2.pad(\n                        query.semantic_target[None, None], tuple(padding)\n                    ).squeeze()\n            else:\n                if len(padding) == 2:\n                    query.semantic_target = F.pad(\n                        query.semantic_target[None, None],\n                        (0, 0, padding[0], padding[1]),\n                    ).squeeze()\n                else:\n                    query.semantic_target = F.pad(\n                        query.semantic_target[None, None], tuple(padding)\n                    ).squeeze()\n        if query.image_id == index and query.input_bbox is not None:\n            if len(padding) != 2:\n                query.input_bbox += torch.as_tensor(\n                    [padding[0], padding[1], padding[0], padding[1]],\n                    dtype=torch.float32,\n                )\n        if query.image_id == index and query.input_points is not None:\n            if len(padding) != 2:\n                query.input_points += torch.as_tensor(\n                    [padding[0], padding[1], 0], dtype=torch.float32\n                )\n\n    return datapoint\n\n\nclass RandomSizeCropAPI:\n    def __init__(\n        self,\n        min_size: int,\n        max_size: int,\n        respect_boxes: bool,\n        consistent_transform: bool,\n        respect_input_boxes: bool = True,\n        v2: bool = False,\n        recompute_box_from_mask: bool = False,\n    ):\n        self.min_size = min_size\n        self.max_size = max_size\n        self.respect_boxes = respect_boxes  # if True we can't crop a box out\n        self.respect_input_boxes = respect_input_boxes\n        self.consistent_transform = consistent_transform\n        self.v2 = v2\n        self.recompute_box_from_mask = recompute_box_from_mask\n\n    def _sample_no_respect_boxes(self, img):\n        w = random.randint(self.min_size, min(img.width, self.max_size))\n        h = random.randint(self.min_size, min(img.height, self.max_size))\n        return T.RandomCrop.get_params(img, (h, w))\n\n    def _sample_respect_boxes(self, img, boxes, points, min_box_size=10.0):\n        \"\"\"\n        Assure that no box or point is dropped via cropping, though portions\n        of boxes may be removed.\n        \"\"\"\n        if len(boxes) == 0 and len(points) == 0:\n            return self._sample_no_respect_boxes(img)\n\n        if self.v2:\n            img_height, img_width = img.size()[-2:]\n        else:\n            img_width, img_height = img.size\n\n        minW, minH, maxW, maxH = (\n            min(img_width, self.min_size),\n            min(img_height, self.min_size),\n            min(img_width, self.max_size),\n            min(img_height, self.max_size),\n        )\n\n        # The crop box must extend one pixel beyond points to the bottom/right\n        # to assure the exclusive box contains the points.\n        minX = (\n            torch.cat([boxes[:, 0] + min_box_size, points[:, 0] + 1], dim=0)\n            .max()\n            .item()\n        )\n        minY = (\n            torch.cat([boxes[:, 1] + min_box_size, points[:, 1] + 1], dim=0)\n            .max()\n            .item()\n        )\n        minX = min(img_width, minX)\n        minY = min(img_height, minY)\n        maxX = torch.cat([boxes[:, 2] - min_box_size, points[:, 0]], dim=0).min().item()\n        maxY = torch.cat([boxes[:, 3] - min_box_size, points[:, 1]], dim=0).min().item()\n        maxX = max(0.0, maxX)\n        maxY = max(0.0, maxY)\n        minW = max(minW, minX - maxX)\n        minH = max(minH, minY - maxY)\n        w = random.uniform(minW, max(minW, maxW))\n        h = random.uniform(minH, max(minH, maxH))\n        if minX > maxX:\n            # i = random.uniform(max(0, minX - w + 1), max(maxX, max(0, minX - w + 1)))\n            i = random.uniform(max(0, minX - w), max(maxX, max(0, minX - w)))\n        else:\n            i = random.uniform(\n                max(0, minX - w + 1), max(maxX - 1, max(0, minX - w + 1))\n            )\n        if minY > maxY:\n            # j = random.uniform(max(0, minY - h + 1), max(maxY, max(0, minY - h + 1)))\n            j = random.uniform(max(0, minY - h), max(maxY, max(0, minY - h)))\n        else:\n            j = random.uniform(\n                max(0, minY - h + 1), max(maxY - 1, max(0, minY - h + 1))\n            )\n\n        return [j, i, h, w]\n\n    def __call__(self, datapoint, **kwargs):\n        if self.respect_boxes or self.respect_input_boxes:\n            if self.consistent_transform:\n                # Check that all the images are the same size\n                w, h = datapoint.images[0].data.size\n                for img in datapoint.images:\n                    assert img.data.size == (w, h)\n\n                all_boxes = []\n                # Getting all boxes in all the images\n                if self.respect_boxes:\n                    all_boxes += [\n                        obj.bbox.view(-1, 4)\n                        for img in datapoint.images\n                        for obj in img.objects\n                    ]\n                # Get all the boxes in the find queries\n                if self.respect_input_boxes:\n                    all_boxes += [\n                        q.input_bbox.view(-1, 4)\n                        for q in datapoint.find_queries\n                        if q.input_bbox is not None\n                    ]\n                if all_boxes:\n                    all_boxes = torch.cat(all_boxes, 0)\n                else:\n                    all_boxes = torch.empty(0, 4)\n\n                all_points = [\n                    q.input_points.view(-1, 3)[:, :2]\n                    for q in datapoint.find_queries\n                    if q.input_points is not None\n                ]\n                if all_points:\n                    all_points = torch.cat(all_points, 0)\n                else:\n                    all_points = torch.empty(0, 2)\n\n                crop_param = self._sample_respect_boxes(\n                    datapoint.images[0].data, all_boxes, all_points\n                )\n                for i in range(len(datapoint.images)):\n                    datapoint = crop(\n                        datapoint,\n                        i,\n                        crop_param,\n                        v2=self.v2,\n                        check_validity=self.respect_boxes,\n                        check_input_validity=self.respect_input_boxes,\n                        recompute_box_from_mask=self.recompute_box_from_mask,\n                    )\n                return datapoint\n            else:\n                for i in range(len(datapoint.images)):\n                    all_boxes = []\n                    # Get all boxes in the current image\n                    if self.respect_boxes:\n                        all_boxes += [\n                            obj.bbox.view(-1, 4) for obj in datapoint.images[i].objects\n                        ]\n                    # Get all the boxes in the find queries that correspond to this image\n                    if self.respect_input_boxes:\n                        all_boxes += [\n                            q.input_bbox.view(-1, 4)\n                            for q in datapoint.find_queries\n                            if q.image_id == i and q.input_bbox is not None\n                        ]\n                    if all_boxes:\n                        all_boxes = torch.cat(all_boxes, 0)\n                    else:\n                        all_boxes = torch.empty(0, 4)\n\n                    all_points = [\n                        q.input_points.view(-1, 3)[:, :2]\n                        for q in datapoint.find_queries\n                        if q.input_points is not None\n                    ]\n                    if all_points:\n                        all_points = torch.cat(all_points, 0)\n                    else:\n                        all_points = torch.empty(0, 2)\n\n                    crop_param = self._sample_respect_boxes(\n                        datapoint.images[i].data, all_boxes, all_points\n                    )\n                    datapoint = crop(\n                        datapoint,\n                        i,\n                        crop_param,\n                        v2=self.v2,\n                        check_validity=self.respect_boxes,\n                        check_input_validity=self.respect_input_boxes,\n                        recompute_box_from_mask=self.recompute_box_from_mask,\n                    )\n                return datapoint\n        else:\n            if self.consistent_transform:\n                # Check that all the images are the same size\n                w, h = datapoint.images[0].data.size\n                for img in datapoint.images:\n                    assert img.data.size == (w, h)\n\n                crop_param = self._sample_no_respect_boxes(datapoint.images[0].data)\n                for i in range(len(datapoint.images)):\n                    datapoint = crop(\n                        datapoint,\n                        i,\n                        crop_param,\n                        v2=self.v2,\n                        check_validity=self.respect_boxes,\n                        check_input_validity=self.respect_input_boxes,\n                        recompute_box_from_mask=self.recompute_box_from_mask,\n                    )\n                return datapoint\n            else:\n                for i in range(len(datapoint.images)):\n                    crop_param = self._sample_no_respect_boxes(datapoint.images[i].data)\n                    datapoint = crop(\n                        datapoint,\n                        i,\n                        crop_param,\n                        v2=self.v2,\n                        check_validity=self.respect_boxes,\n                        check_input_validity=self.respect_input_boxes,\n                        recompute_box_from_mask=self.recompute_box_from_mask,\n                    )\n                return datapoint\n\n\nclass CenterCropAPI:\n    def __init__(self, size, consistent_transform, recompute_box_from_mask=False):\n        self.size = size\n        self.consistent_transform = consistent_transform\n        self.recompute_box_from_mask = recompute_box_from_mask\n\n    def _sample_crop(self, image_width, image_height):\n        crop_height, crop_width = self.size\n        crop_top = int(round((image_height - crop_height) / 2.0))\n        crop_left = int(round((image_width - crop_width) / 2.0))\n        return crop_top, crop_left, crop_height, crop_width\n\n    def __call__(self, datapoint, **kwargs):\n        if self.consistent_transform:\n            # Check that all the images are the same size\n            w, h = datapoint.images[0].data.size\n            for img in datapoint.images:\n                assert img.size == (w, h)\n\n            crop_top, crop_left, crop_height, crop_width = self._sample_crop(w, h)\n            for i in range(len(datapoint.images)):\n                datapoint = crop(\n                    datapoint,\n                    i,\n                    (crop_top, crop_left, crop_height, crop_width),\n                    recompute_box_from_mask=self.recompute_box_from_mask,\n                )\n            return datapoint\n\n        for i in range(len(datapoint.images)):\n            w, h = datapoint.images[i].data.size\n            crop_top, crop_left, crop_height, crop_width = self._sample_crop(w, h)\n            datapoint = crop(\n                datapoint,\n                i,\n                (crop_top, crop_left, crop_height, crop_width),\n                recompute_box_from_mask=self.recompute_box_from_mask,\n            )\n\n        return datapoint\n\n\nclass RandomHorizontalFlip:\n    def __init__(self, consistent_transform, p=0.5):\n        self.p = p\n        self.consistent_transform = consistent_transform\n\n    def __call__(self, datapoint, **kwargs):\n        if self.consistent_transform:\n            if random.random() < self.p:\n                for i in range(len(datapoint.images)):\n                    datapoint = hflip(datapoint, i)\n            return datapoint\n        for i in range(len(datapoint.images)):\n            if random.random() < self.p:\n                datapoint = hflip(datapoint, i)\n        return datapoint\n\n\nclass RandomResizeAPI:\n    def __init__(\n        self, sizes, consistent_transform, max_size=None, square=False, v2=False\n    ):\n        if isinstance(sizes, int):\n            sizes = (sizes,)\n        assert isinstance(sizes, Iterable)\n        self.sizes = list(sizes)\n        self.max_size = max_size\n        self.square = square\n        self.consistent_transform = consistent_transform\n        self.v2 = v2\n\n    def __call__(self, datapoint, **kwargs):\n        if self.consistent_transform:\n            size = random.choice(self.sizes)\n            for i in range(len(datapoint.images)):\n                datapoint = resize(\n                    datapoint, i, size, self.max_size, square=self.square, v2=self.v2\n                )\n            return datapoint\n        for i in range(len(datapoint.images)):\n            size = random.choice(self.sizes)\n            datapoint = resize(\n                datapoint, i, size, self.max_size, square=self.square, v2=self.v2\n            )\n        return datapoint\n\n\nclass ScheduledRandomResizeAPI(RandomResizeAPI):\n    def __init__(self, size_scheduler, consistent_transform, square=False):\n        self.size_scheduler = size_scheduler\n        # Just a meaningful init value for super\n        params = self.size_scheduler(epoch_num=0)\n        sizes, max_size = params[\"sizes\"], params[\"max_size\"]\n        super().__init__(sizes, consistent_transform, max_size=max_size, square=square)\n\n    def __call__(self, datapoint, **kwargs):\n        assert \"epoch\" in kwargs, \"Param scheduler needs to know the current epoch\"\n        params = self.size_scheduler(kwargs[\"epoch\"])\n        sizes, max_size = params[\"sizes\"], params[\"max_size\"]\n        self.sizes = sizes\n        self.max_size = max_size\n        datapoint = super(ScheduledRandomResizeAPI, self).__call__(datapoint, **kwargs)\n        return datapoint\n\n\nclass RandomPadAPI:\n    def __init__(self, max_pad, consistent_transform):\n        self.max_pad = max_pad\n        self.consistent_transform = consistent_transform\n\n    def _sample_pad(self):\n        pad_x = random.randint(0, self.max_pad)\n        pad_y = random.randint(0, self.max_pad)\n        return pad_x, pad_y\n\n    def __call__(self, datapoint, **kwargs):\n        if self.consistent_transform:\n            pad_x, pad_y = self._sample_pad()\n            for i in range(len(datapoint.images)):\n                datapoint = pad(datapoint, i, (pad_x, pad_y))\n            return datapoint\n\n        for i in range(len(datapoint.images)):\n            pad_x, pad_y = self._sample_pad()\n            datapoint = pad(datapoint, i, (pad_x, pad_y))\n        return datapoint\n\n\nclass PadToSizeAPI:\n    def __init__(self, size, consistent_transform, bottom_right=False, v2=False):\n        self.size = size\n        self.consistent_transform = consistent_transform\n        self.v2 = v2\n        self.bottom_right = bottom_right\n\n    def _sample_pad(self, w, h):\n        pad_x = self.size - w\n        pad_y = self.size - h\n        assert pad_x >= 0 and pad_y >= 0\n        pad_left = random.randint(0, pad_x)\n        pad_right = pad_x - pad_left\n        pad_top = random.randint(0, pad_y)\n        pad_bottom = pad_y - pad_top\n        return pad_left, pad_top, pad_right, pad_bottom\n\n    def __call__(self, datapoint, **kwargs):\n        if self.consistent_transform:\n            # Check that all the images are the same size\n            w, h = datapoint.images[0].data.size\n            for img in datapoint.images:\n                assert img.size == (w, h)\n            if self.bottom_right:\n                pad_right = self.size - w\n                pad_bottom = self.size - h\n                padding = (pad_right, pad_bottom)\n            else:\n                padding = self._sample_pad(w, h)\n            for i in range(len(datapoint.images)):\n                datapoint = pad(datapoint, i, padding, v2=self.v2)\n            return datapoint\n\n        for i, img in enumerate(datapoint.images):\n            w, h = img.data.size\n            if self.bottom_right:\n                pad_right = self.size - w\n                pad_bottom = self.size - h\n                padding = (pad_right, pad_bottom)\n            else:\n                padding = self._sample_pad(w, h)\n            datapoint = pad(datapoint, i, padding, v2=self.v2)\n        return datapoint\n\n\nclass RandomMosaicVideoAPI:\n    def __init__(self, prob=0.15, grid_h=2, grid_w=2, use_random_hflip=False):\n        self.prob = prob\n        self.grid_h = grid_h\n        self.grid_w = grid_w\n        self.use_random_hflip = use_random_hflip\n\n    def __call__(self, datapoint, **kwargs):\n        if random.random() > self.prob:\n            return datapoint\n\n        # select a random location to place the target mask in the mosaic\n        target_grid_y = random.randint(0, self.grid_h - 1)\n        target_grid_x = random.randint(0, self.grid_w - 1)\n        # whether to flip each grid in the mosaic horizontally\n        if self.use_random_hflip:\n            should_hflip = torch.rand(self.grid_h, self.grid_w) < 0.5\n        else:\n            should_hflip = torch.zeros(self.grid_h, self.grid_w, dtype=torch.bool)\n        for i in range(len(datapoint.images)):\n            datapoint = random_mosaic_frame(\n                datapoint,\n                i,\n                grid_h=self.grid_h,\n                grid_w=self.grid_w,\n                target_grid_y=target_grid_y,\n                target_grid_x=target_grid_x,\n                should_hflip=should_hflip,\n            )\n\n        return datapoint\n\n\ndef random_mosaic_frame(\n    datapoint,\n    index,\n    grid_h,\n    grid_w,\n    target_grid_y,\n    target_grid_x,\n    should_hflip,\n):\n    # Step 1: downsize the images and paste them into a mosaic\n    image_data = datapoint.images[index].data\n    is_pil = isinstance(image_data, PILImage.Image)\n    if is_pil:\n        H_im = image_data.height\n        W_im = image_data.width\n        image_data_output = PILImage.new(\"RGB\", (W_im, H_im))\n    else:\n        H_im = image_data.size(-2)\n        W_im = image_data.size(-1)\n        image_data_output = torch.zeros_like(image_data)\n\n    downsize_cache = {}\n    for grid_y in range(grid_h):\n        for grid_x in range(grid_w):\n            y_offset_b = grid_y * H_im // grid_h\n            x_offset_b = grid_x * W_im // grid_w\n            y_offset_e = (grid_y + 1) * H_im // grid_h\n            x_offset_e = (grid_x + 1) * W_im // grid_w\n            H_im_downsize = y_offset_e - y_offset_b\n            W_im_downsize = x_offset_e - x_offset_b\n\n            if (H_im_downsize, W_im_downsize) in downsize_cache:\n                image_data_downsize = downsize_cache[(H_im_downsize, W_im_downsize)]\n            else:\n                image_data_downsize = F.resize(\n                    image_data,\n                    size=(H_im_downsize, W_im_downsize),\n                    interpolation=InterpolationMode.BILINEAR,\n                    antialias=True,  # antialiasing for downsizing\n                )\n                downsize_cache[(H_im_downsize, W_im_downsize)] = image_data_downsize\n            if should_hflip[grid_y, grid_x].item():\n                image_data_downsize = F.hflip(image_data_downsize)\n\n            if is_pil:\n                image_data_output.paste(image_data_downsize, (x_offset_b, y_offset_b))\n            else:\n                image_data_output[:, y_offset_b:y_offset_e, x_offset_b:x_offset_e] = (\n                    image_data_downsize\n                )\n\n    datapoint.images[index].data = image_data_output\n\n    # Step 2: downsize the masks and paste them into the target grid of the mosaic\n    # (note that we don't scale input/target boxes since they are not used in TA)\n    for obj in datapoint.images[index].objects:\n        if obj.segment is None:\n            continue\n        assert obj.segment.shape == (H_im, W_im) and obj.segment.dtype == torch.uint8\n        segment_output = torch.zeros_like(obj.segment)\n\n        target_y_offset_b = target_grid_y * H_im // grid_h\n        target_x_offset_b = target_grid_x * W_im // grid_w\n        target_y_offset_e = (target_grid_y + 1) * H_im // grid_h\n        target_x_offset_e = (target_grid_x + 1) * W_im // grid_w\n        target_H_im_downsize = target_y_offset_e - target_y_offset_b\n        target_W_im_downsize = target_x_offset_e - target_x_offset_b\n\n        segment_downsize = F.resize(\n            obj.segment[None, None],\n            size=(target_H_im_downsize, target_W_im_downsize),\n            interpolation=InterpolationMode.BILINEAR,\n            antialias=True,  # antialiasing for downsizing\n        )[0, 0]\n        if should_hflip[target_grid_y, target_grid_x].item():\n            segment_downsize = F.hflip(segment_downsize[None, None])[0, 0]\n\n        segment_output[\n            target_y_offset_b:target_y_offset_e, target_x_offset_b:target_x_offset_e\n        ] = segment_downsize\n        obj.segment = segment_output\n\n    return datapoint\n\n\nclass ScheduledPadToSizeAPI(PadToSizeAPI):\n    def __init__(self, size_scheduler, consistent_transform):\n        self.size_scheduler = size_scheduler\n        size = self.size_scheduler(epoch_num=0)[\"sizes\"]\n        super().__init__(size, consistent_transform)\n\n    def __call__(self, datapoint, **kwargs):\n        assert \"epoch\" in kwargs, \"Param scheduler needs to know the current epoch\"\n        params = self.size_scheduler(kwargs[\"epoch\"])\n        self.size = params[\"resolution\"]\n        return super(ScheduledPadToSizeAPI, self).__call__(datapoint, **kwargs)\n\n\nclass IdentityAPI:\n    def __call__(self, datapoint, **kwargs):\n        return datapoint\n\n\nclass RandomSelectAPI:\n    \"\"\"\n    Randomly selects between transforms1 and transforms2,\n    with probability p for transforms1 and (1 - p) for transforms2\n    \"\"\"\n\n    def __init__(self, transforms1=None, transforms2=None, p=0.5):\n        self.transforms1 = transforms1 or IdentityAPI()\n        self.transforms2 = transforms2 or IdentityAPI()\n        self.p = p\n\n    def __call__(self, datapoint, **kwargs):\n        if random.random() < self.p:\n            return self.transforms1(datapoint, **kwargs)\n        return self.transforms2(datapoint, **kwargs)\n\n\nclass ToTensorAPI:\n    def __init__(self, v2=False):\n        self.v2 = v2\n\n    def __call__(self, datapoint: Datapoint, **kwargs):\n        for img in datapoint.images:\n            if self.v2:\n                img.data = Fv2.to_image_tensor(img.data)\n                # img.data = Fv2.to_dtype(img.data, torch.uint8, scale=True)\n                # img.data = Fv2.convert_image_dtype(img.data, torch.uint8)\n            else:\n                img.data = F.to_tensor(img.data)\n        return datapoint\n\n\nclass NormalizeAPI:\n    def __init__(self, mean, std, v2=False):\n        self.mean = mean\n        self.std = std\n        self.v2 = v2\n\n    def __call__(self, datapoint: Datapoint, **kwargs):\n        for img in datapoint.images:\n            if self.v2:\n                img.data = Fv2.convert_image_dtype(img.data, torch.float32)\n                img.data = Fv2.normalize(img.data, mean=self.mean, std=self.std)\n            else:\n                img.data = F.normalize(img.data, mean=self.mean, std=self.std)\n            for obj in img.objects:\n                boxes = obj.bbox\n                cur_h, cur_w = img.data.shape[-2:]\n                boxes = box_xyxy_to_cxcywh(boxes)\n                boxes = boxes / torch.tensor(\n                    [cur_w, cur_h, cur_w, cur_h], dtype=torch.float32\n                )\n                obj.bbox = boxes\n\n        for query in datapoint.find_queries:\n            if query.input_bbox is not None:\n                boxes = query.input_bbox\n                cur_h, cur_w = datapoint.images[query.image_id].data.shape[-2:]\n                boxes = box_xyxy_to_cxcywh(boxes)\n                boxes = boxes / torch.tensor(\n                    [cur_w, cur_h, cur_w, cur_h], dtype=torch.float32\n                )\n                query.input_bbox = boxes\n            if query.input_points is not None:\n                points = query.input_points\n                cur_h, cur_w = datapoint.images[query.image_id].data.shape[-2:]\n                points = points / torch.tensor([cur_w, cur_h, 1.0], dtype=torch.float32)\n                query.input_points = points\n\n        return datapoint\n\n\nclass ComposeAPI:\n    def __init__(self, transforms):\n        self.transforms = transforms\n\n    def __call__(self, datapoint, **kwargs):\n        for t in self.transforms:\n            datapoint = t(datapoint, **kwargs)\n        return datapoint\n\n    def __repr__(self):\n        format_string = self.__class__.__name__ + \"(\"\n        for t in self.transforms:\n            format_string += \"\\n\"\n            format_string += \"    {0}\".format(t)\n        format_string += \"\\n)\"\n        return format_string\n\n\nclass RandomGrayscale:\n    def __init__(self, consistent_transform, p=0.5):\n        self.p = p\n        self.consistent_transform = consistent_transform\n        self.Grayscale = T.Grayscale(num_output_channels=3)\n\n    def __call__(self, datapoint: Datapoint, **kwargs):\n        if self.consistent_transform:\n            if random.random() < self.p:\n                for img in datapoint.images:\n                    img.data = self.Grayscale(img.data)\n            return datapoint\n        for img in datapoint.images:\n            if random.random() < self.p:\n                img.data = self.Grayscale(img.data)\n        return datapoint\n\n\nclass ColorJitter:\n    def __init__(self, consistent_transform, brightness, contrast, saturation, hue):\n        self.consistent_transform = consistent_transform\n        self.brightness = (\n            brightness\n            if isinstance(brightness, list)\n            else [max(0, 1 - brightness), 1 + brightness]\n        )\n        self.contrast = (\n            contrast\n            if isinstance(contrast, list)\n            else [max(0, 1 - contrast), 1 + contrast]\n        )\n        self.saturation = (\n            saturation\n            if isinstance(saturation, list)\n            else [max(0, 1 - saturation), 1 + saturation]\n        )\n        self.hue = hue if isinstance(hue, list) or hue is None else ([-hue, hue])\n\n    def __call__(self, datapoint: Datapoint, **kwargs):\n        if self.consistent_transform:\n            # Create a color jitter transformation params\n            (\n                fn_idx,\n                brightness_factor,\n                contrast_factor,\n                saturation_factor,\n                hue_factor,\n            ) = T.ColorJitter.get_params(\n                self.brightness, self.contrast, self.saturation, self.hue\n            )\n        for img in datapoint.images:\n            if not self.consistent_transform:\n                (\n                    fn_idx,\n                    brightness_factor,\n                    contrast_factor,\n                    saturation_factor,\n                    hue_factor,\n                ) = T.ColorJitter.get_params(\n                    self.brightness, self.contrast, self.saturation, self.hue\n                )\n            for fn_id in fn_idx:\n                if fn_id == 0 and brightness_factor is not None:\n                    img.data = F.adjust_brightness(img.data, brightness_factor)\n                elif fn_id == 1 and contrast_factor is not None:\n                    img.data = F.adjust_contrast(img.data, contrast_factor)\n                elif fn_id == 2 and saturation_factor is not None:\n                    img.data = F.adjust_saturation(img.data, saturation_factor)\n                elif fn_id == 3 and hue_factor is not None:\n                    img.data = F.adjust_hue(img.data, hue_factor)\n        return datapoint\n\n\nclass RandomAffine:\n    def __init__(\n        self,\n        degrees,\n        consistent_transform,\n        scale=None,\n        translate=None,\n        shear=None,\n        image_mean=(123, 116, 103),\n        log_warning=True,\n        num_tentatives=1,\n        image_interpolation=\"bicubic\",\n    ):\n        \"\"\"\n        The mask is required for this transform.\n        if consistent_transform if True, then the same random affine is applied to all frames and masks.\n        \"\"\"\n        self.degrees = degrees if isinstance(degrees, list) else ([-degrees, degrees])\n        self.scale = scale\n        self.shear = (\n            shear if isinstance(shear, list) else ([-shear, shear] if shear else None)\n        )\n        self.translate = translate\n        self.fill_img = image_mean\n        self.consistent_transform = consistent_transform\n        self.log_warning = log_warning\n        self.num_tentatives = num_tentatives\n\n        if image_interpolation == \"bicubic\":\n            self.image_interpolation = InterpolationMode.BICUBIC\n        elif image_interpolation == \"bilinear\":\n            self.image_interpolation = InterpolationMode.BILINEAR\n        else:\n            raise NotImplementedError\n\n    def __call__(self, datapoint: Datapoint, **kwargs):\n        for _tentative in range(self.num_tentatives):\n            res = self.transform_datapoint(datapoint)\n            if res is not None:\n                return res\n\n        if self.log_warning:\n            logging.warning(\n                f\"Skip RandomAffine for zero-area mask in first frame after {self.num_tentatives} tentatives\"\n            )\n        return datapoint\n\n    def transform_datapoint(self, datapoint: Datapoint):\n        _, height, width = F.get_dimensions(datapoint.images[0].data)\n        img_size = [width, height]\n\n        if self.consistent_transform:\n            # Create a random affine transformation\n            affine_params = T.RandomAffine.get_params(\n                degrees=self.degrees,\n                translate=self.translate,\n                scale_ranges=self.scale,\n                shears=self.shear,\n                img_size=img_size,\n            )\n\n        for img_idx, img in enumerate(datapoint.images):\n            this_masks = [\n                obj.segment.unsqueeze(0) if obj.segment is not None else None\n                for obj in img.objects\n            ]\n            if not self.consistent_transform:\n                # if not consistent we create a new affine params for every frame&mask pair Create a random affine transformation\n                affine_params = T.RandomAffine.get_params(\n                    degrees=self.degrees,\n                    translate=self.translate,\n                    scale_ranges=self.scale,\n                    shears=self.shear,\n                    img_size=img_size,\n                )\n\n            transformed_bboxes, transformed_masks = [], []\n            for i in range(len(img.objects)):\n                if this_masks[i] is None:\n                    transformed_masks.append(None)\n                    # Dummy bbox for a dummy target\n                    transformed_bboxes.append(torch.tensor([[0, 0, 0, 0]]))\n                else:\n                    transformed_mask = F.affine(\n                        this_masks[i],\n                        *affine_params,\n                        interpolation=InterpolationMode.NEAREST,\n                        fill=0.0,\n                    )\n                    if img_idx == 0 and transformed_mask.max() == 0:\n                        # We are dealing with a video and the object is not visible in the first frame\n                        # Return the datapoint without transformation\n                        return None\n                    transformed_bbox = masks_to_boxes(transformed_mask)\n                    transformed_bboxes.append(transformed_bbox)\n                    transformed_masks.append(transformed_mask.squeeze())\n\n            for i in range(len(img.objects)):\n                img.objects[i].bbox = transformed_bboxes[i]\n                img.objects[i].segment = transformed_masks[i]\n\n            img.data = F.affine(\n                img.data,\n                *affine_params,\n                interpolation=self.image_interpolation,\n                fill=self.fill_img,\n            )\n        return datapoint\n\n\nclass RandomResizedCrop:\n    def __init__(\n        self,\n        consistent_transform,\n        size,\n        scale=None,\n        ratio=None,\n        log_warning=True,\n        num_tentatives=4,\n        keep_aspect_ratio=False,\n    ):\n        \"\"\"\n        The mask is required for this transform.\n        if consistent_transform if True, then the same random resized crop is applied to all frames and masks.\n        \"\"\"\n        if isinstance(size, numbers.Number):\n            self.size = (int(size), int(size))\n        elif isinstance(size, Sequence) and len(size) == 1:\n            self.size = (size[0], size[0])\n        elif len(size) != 2:\n            raise ValueError(\"Please provide only two dimensions (h, w) for size.\")\n        else:\n            self.size = size\n\n        self.scale = scale if scale is not None else (0.08, 1.0)\n        self.ratio = ratio if ratio is not None else (3.0 / 4.0, 4.0 / 3.0)\n        self.consistent_transform = consistent_transform\n        self.log_warning = log_warning\n        self.num_tentatives = num_tentatives\n        self.keep_aspect_ratio = keep_aspect_ratio\n\n    def __call__(self, datapoint: Datapoint, **kwargs):\n        for _tentative in range(self.num_tentatives):\n            res = self.transform_datapoint(datapoint)\n            if res is not None:\n                return res\n\n        if self.log_warning:\n            logging.warning(\n                f\"Skip RandomResizeCrop for zero-area mask in first frame after {self.num_tentatives} tentatives\"\n            )\n        return datapoint\n\n    def transform_datapoint(self, datapoint: Datapoint):\n        if self.keep_aspect_ratio:\n            original_size = datapoint.images[0].size\n            original_ratio = original_size[1] / original_size[0]\n            ratio = [r * original_ratio for r in self.ratio]\n        else:\n            ratio = self.ratio\n\n        if self.consistent_transform:\n            # Create a random crop transformation\n            crop_params = T.RandomResizedCrop.get_params(\n                img=datapoint.images[0].data,\n                scale=self.scale,\n                ratio=ratio,\n            )\n\n        for img_idx, img in enumerate(datapoint.images):\n            if not self.consistent_transform:\n                # Create a random crop transformation\n                crop_params = T.RandomResizedCrop.get_params(\n                    img=img.data,\n                    scale=self.scale,\n                    ratio=ratio,\n                )\n\n            this_masks = [\n                obj.segment.unsqueeze(0) if obj.segment is not None else None\n                for obj in img.objects\n            ]\n\n            transformed_bboxes, transformed_masks = [], []\n            for i in range(len(img.objects)):\n                if this_masks[i] is None:\n                    transformed_masks.append(None)\n                    # Dummy bbox for a dummy target\n                    transformed_bboxes.append(torch.tensor([[0, 0, 0, 0]]))\n                else:\n                    transformed_mask = F.resized_crop(\n                        this_masks[i],\n                        *crop_params,\n                        size=self.size,\n                        interpolation=InterpolationMode.NEAREST,\n                    )\n                    if img_idx == 0 and transformed_mask.max() == 0:\n                        # We are dealing with a video and the object is not visible in the first frame\n                        # Return the datapoint without transformation\n                        return None\n                    transformed_masks.append(transformed_mask.squeeze())\n                    transformed_bbox = masks_to_boxes(transformed_mask)\n                    transformed_bboxes.append(transformed_bbox)\n\n            # Set the new boxes and masks if all transformed masks and boxes are good.\n            for i in range(len(img.objects)):\n                img.objects[i].bbox = transformed_bboxes[i]\n                img.objects[i].segment = transformed_masks[i]\n\n            img.data = F.resized_crop(\n                img.data,\n                *crop_params,\n                size=self.size,\n                interpolation=InterpolationMode.BILINEAR,\n            )\n        return datapoint\n\n\nclass ResizeToMaxIfAbove:\n    # Resize datapoint image if one of its sides is larger that max_size\n    def __init__(\n        self,\n        max_size=None,\n    ):\n        self.max_size = max_size\n\n    def __call__(self, datapoint: Datapoint, **kwargs):\n        _, height, width = F.get_dimensions(datapoint.images[0].data)\n\n        if height <= self.max_size and width <= self.max_size:\n            # The original frames are small enough\n            return datapoint\n        elif height >= width:\n            new_height = self.max_size\n            new_width = int(round(self.max_size * width / height))\n        else:\n            new_height = int(round(self.max_size * height / width))\n            new_width = self.max_size\n\n        size = new_height, new_width\n\n        for index in range(len(datapoint.images)):\n            datapoint.images[index].data = F.resize(datapoint.images[index].data, size)\n\n            for obj in datapoint.images[index].objects:\n                obj.segment = F.resize(\n                    obj.segment[None, None],\n                    size,\n                    interpolation=InterpolationMode.NEAREST,\n                ).squeeze()\n\n            h, w = size\n            datapoint.images[index].size = (h, w)\n        return datapoint\n\n\ndef get_bbox_xyxy_abs_coords_from_mask(mask):\n    \"\"\"Get the bounding box (XYXY format w/ absolute coordinates) of a binary mask.\"\"\"\n    assert mask.dim() == 2\n    rows = torch.any(mask, dim=1)\n    cols = torch.any(mask, dim=0)\n    row_inds = rows.nonzero().view(-1)\n    col_inds = cols.nonzero().view(-1)\n    if row_inds.numel() == 0:\n        # mask is empty\n        bbox = torch.zeros(1, 4, dtype=torch.float32)\n        bbox_area = 0.0\n    else:\n        ymin, ymax = row_inds.min(), row_inds.max()\n        xmin, xmax = col_inds.min(), col_inds.max()\n        bbox = torch.tensor([xmin, ymin, xmax, ymax], dtype=torch.float32).view(1, 4)\n        bbox_area = float((ymax - ymin) * (xmax - xmin))\n    return bbox, bbox_area\n\n\nclass MotionBlur:\n    def __init__(self, kernel_size=5, consistent_transform=True, p=0.5):\n        assert kernel_size % 2 == 1, \"Kernel size must be odd.\"\n        self.kernel_size = kernel_size\n        self.consistent_transform = consistent_transform\n        self.p = p\n\n    def __call__(self, datapoint: Datapoint, **kwargs):\n        if random.random() >= self.p:\n            return datapoint\n        if self.consistent_transform:\n            # Generate a single motion blur kernel for all images\n            kernel = self._generate_motion_blur_kernel()\n        for img in datapoint.images:\n            if not self.consistent_transform:\n                # Generate a new motion blur kernel for each image\n                kernel = self._generate_motion_blur_kernel()\n            img.data = self._apply_motion_blur(img.data, kernel)\n\n        return datapoint\n\n    def _generate_motion_blur_kernel(self):\n        kernel = torch.zeros((self.kernel_size, self.kernel_size))\n        direction = random.choice([\"horizontal\", \"vertical\", \"diagonal\"])\n        if direction == \"horizontal\":\n            kernel[self.kernel_size // 2, :] = 1.0\n        elif direction == \"vertical\":\n            kernel[:, self.kernel_size // 2] = 1.0\n        elif direction == \"diagonal\":\n            for i in range(self.kernel_size):\n                kernel[i, i] = 1.0\n        kernel /= kernel.sum()\n        return kernel\n\n    def _apply_motion_blur(self, image, kernel):\n        if isinstance(image, PILImage.Image):\n            image = F.to_tensor(image)\n        channels = image.shape[0]\n        kernel = kernel.to(image.device).unsqueeze(0).unsqueeze(0)\n        blurred_image = torch.nn.functional.conv2d(\n            image.unsqueeze(0),\n            kernel.repeat(channels, 1, 1, 1),\n            padding=self.kernel_size // 2,\n            groups=channels,\n        )\n        return F.to_pil_image(blurred_image.squeeze(0))\n\n\nclass LargeScaleJitter:\n    def __init__(\n        self,\n        scale_range=(0.1, 2.0),\n        aspect_ratio_range=(0.75, 1.33),\n        crop_size=(640, 640),\n        consistent_transform=True,\n        p=0.5,\n    ):\n        \"\"\"\n        Args:rack\n            scale_range (tuple): Range of scaling factors (min_scale, max_scale).\n            aspect_ratio_range (tuple): Range of aspect ratios (min_aspect_ratio, max_aspect_ratio).\n            crop_size (tuple): Target size of the cropped region (width, height).\n            consistent_transform (bool): Whether to apply the same transformation across all frames.\n            p (float): Probability of applying the transformation.\n        \"\"\"\n        self.scale_range = scale_range\n        self.aspect_ratio_range = aspect_ratio_range\n        self.crop_size = crop_size\n        self.consistent_transform = consistent_transform\n        self.p = p\n\n    def __call__(self, datapoint: Datapoint, **kwargs):\n        if random.random() >= self.p:\n            return datapoint\n\n        # Sample a single scale factor and aspect ratio for all frames\n        log_ratio = torch.log(torch.tensor(self.aspect_ratio_range))\n        scale_factor = torch.empty(1).uniform_(*self.scale_range).item()\n        aspect_ratio = torch.exp(\n            torch.empty(1).uniform_(log_ratio[0], log_ratio[1])\n        ).item()\n\n        for idx, img in enumerate(datapoint.images):\n            if not self.consistent_transform:\n                # Sample a new scale factor and aspect ratio for each frame\n                log_ratio = torch.log(torch.tensor(self.aspect_ratio_range))\n                scale_factor = torch.empty(1).uniform_(*self.scale_range).item()\n                aspect_ratio = torch.exp(\n                    torch.empty(1).uniform_(log_ratio[0], log_ratio[1])\n                ).item()\n\n            # Compute the dimensions of the jittered crop\n            original_width, original_height = img.data.size\n            target_area = original_width * original_height * scale_factor\n            crop_width = int(round((target_area * aspect_ratio) ** 0.5))\n            crop_height = int(round((target_area / aspect_ratio) ** 0.5))\n\n            # Randomly select the top-left corner of the crop\n            crop_x = random.randint(0, max(0, original_width - crop_width))\n            crop_y = random.randint(0, max(0, original_height - crop_height))\n\n            # Extract the cropped region\n            datapoint = crop(datapoint, idx, (crop_x, crop_y, crop_width, crop_height))\n\n            # Resize the cropped region to the target crop size\n            datapoint = resize(datapoint, idx, self.crop_size)\n\n        return datapoint\n"
  },
  {
    "path": "sam3/train/transforms/filter_query_transforms.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n\nimport logging\nimport random\nfrom collections import defaultdict\nfrom typing import List, Optional, Union\n\nimport torch\nfrom sam3.train.data.sam3_image_dataset import Datapoint, FindQuery, Object\n\n\nclass FilterDataPointQueries:\n    find_ids_to_filter: set = None\n    get_ids_to_filter: set = None\n    obj_ids_to_filter: set = None  # stored as pairs (img_id, obj_id)\n\n    def identify_queries_to_filter(self, datapoint: Datapoint) -> None:\n        \"\"\"\n        Compute set of query ids to keep, for both find and get queries\n        \"\"\"\n        raise NotImplementedError\n\n    def _do_filter_query(self, query: Union[FindQuery], query_id: int):\n        assert self.find_ids_to_filter is not None\n\n        return query_id in self.find_ids_to_filter\n\n\nclass FilterQueryWithText(FilterDataPointQueries):\n    \"\"\"\n    Filter all datapoints which have query text in a specified list of exluded terms\n    \"\"\"\n\n    def __init__(\n        self, exclude_find_keys: List[str] = None, exclude_get_keys: List[str] = None\n    ):\n        self.find_filter_keys = exclude_find_keys if exclude_find_keys else []\n        self.get_filter_keys = exclude_get_keys if exclude_get_keys else []\n\n    def identify_queries_to_filter(self, datapoint):\n        self.obj_ids_to_filter = set()\n        del_find_ids = []\n        del_get_ids = []\n        for i, f_q in enumerate(datapoint.find_queries):\n            if f_q.query_text in self.find_filter_keys:\n                del_find_ids.append(i)\n\n        self.find_ids_to_filter = set(del_find_ids)\n\n\nclass KeepMaxNumFindQueries(FilterDataPointQueries):\n    def __init__(\n        self, max_num_find_queries: int, retain_positive_queries: bool = False\n    ):\n        self.max_num_find_queries = max_num_find_queries\n        self.retain_positive_queries = retain_positive_queries\n\n    def identify_queries_to_filter(self, datapoint: Datapoint) -> None:\n        self.obj_ids_to_filter = set()\n        num_find_queries = len(datapoint.find_queries)\n        if num_find_queries <= self.max_num_find_queries:\n            self.find_ids_to_filter = set()  # keep all find queries\n            return\n\n        if not self.retain_positive_queries:\n            all_find_query_ids = list(range(num_find_queries))\n            num_queries_to_filter = max(0, num_find_queries - self.max_num_find_queries)\n            query_ids_to_filter = random.sample(\n                all_find_query_ids, k=num_queries_to_filter\n            )\n        else:\n            # keep up to max_num_find_queries postive find queries and fill\n            # the remaining slots (if any) with negative find queries\n            pos_find_ids, neg_find_ids = [], []\n            for i, f_q in enumerate(datapoint.find_queries):\n                # Negative finds return an empty list of object_ids_output\n                if len(f_q.object_ids_output) == 0:\n                    neg_find_ids.append(i)\n                else:\n                    pos_find_ids.append(i)\n\n            if len(pos_find_ids) >= self.max_num_find_queries:\n                # we have more positive find queries than `max_num_find_queries`,\n                # so we subsample postive find queries and remove all negative find queries\n                num_queries_to_filter = len(pos_find_ids) - self.max_num_find_queries\n                query_ids_to_filter = random.sample(\n                    pos_find_ids, k=num_queries_to_filter\n                )\n                query_ids_to_filter.extend(neg_find_ids)\n            else:\n                # we have fewer positive find queries than `max_num_find_queries`\n                # so we need to fill the remaining with negative find queries\n                num_queries_to_filter = num_find_queries - self.max_num_find_queries\n                query_ids_to_filter = random.sample(\n                    neg_find_ids, k=num_queries_to_filter\n                )\n\n        assert len(query_ids_to_filter) == num_find_queries - self.max_num_find_queries\n        self.find_ids_to_filter = set(query_ids_to_filter)\n\n\nclass KeepMaxNumFindQueriesVideo(FilterDataPointQueries):\n    def __init__(\n        self,\n        video_mosaic_max_num_find_queries_per_frame: int,\n        retain_positive_queries: bool = False,\n    ):\n        self.video_mosaic_max_num_find_queries_per_frame = (\n            video_mosaic_max_num_find_queries_per_frame\n        )\n        self.retain_positive_queries = retain_positive_queries\n\n    def identify_queries_to_filter(self, datapoint: Datapoint) -> None:\n        self.obj_ids_to_filter = set()\n        num_find_queries = len(datapoint.find_queries)\n\n        findQueries_to_imageIds = defaultdict(list)\n        max_queries_per_frame = True\n        for i, f_q in enumerate(datapoint.find_queries):\n            findQueries_to_imageIds[f_q.image_id].append(i)\n            if (\n                len(findQueries_to_imageIds[f_q.image_id])\n                > self.video_mosaic_max_num_find_queries_per_frame\n            ):\n                max_queries_per_frame = False\n\n        if max_queries_per_frame:\n            self.find_ids_to_filter = set()\n            return\n\n        num_frames = len(findQueries_to_imageIds)\n        findQueries_0 = findQueries_to_imageIds[0]\n        num_find_queries_0 = len(findQueries_0)\n        max_num_find_queries_per_frame = (\n            self.video_mosaic_max_num_find_queries_per_frame\n        )\n        if not self.retain_positive_queries:\n            find_query_ids_0 = list(range(num_find_queries_0))\n            num_queries_to_filter = max(\n                0, num_find_queries_0 - max_num_find_queries_per_frame\n            )\n            query_ids_to_filter_0 = random.sample(\n                find_query_ids_0, k=num_queries_to_filter\n            )\n        else:\n            # keep up to max_num_find_queries postive find queries and fill\n            # the remaining slots (if any) with negative find queries\n            pos_find_ids_0, neg_find_ids_0 = [], []\n            for i, f_q_id in enumerate(findQueries_0):\n                f_q = datapoint.find_queries[f_q_id]\n                # Negative finds return an empty list of object_ids_output\n                if len(f_q.object_ids_output) == 0:\n                    neg_find_ids_0.append(i)\n                else:\n                    pos_find_ids_0.append(i)\n\n            if len(pos_find_ids_0) >= max_num_find_queries_per_frame:\n                # we have more positive find queries than `max_num_find_queries`,\n                # so we subsample postive find queries and remove all negative find queries\n                num_queries_to_filter = (\n                    len(pos_find_ids_0) - max_num_find_queries_per_frame\n                )\n                query_ids_to_filter_0 = random.sample(\n                    pos_find_ids_0, k=num_queries_to_filter\n                )\n                query_ids_to_filter_0.extend(neg_find_ids_0)\n            else:\n                # we have fewer positive find queries than `max_num_find_queries`\n                # so we need to fill the remaining with negative find queries\n                num_queries_to_filter = (\n                    num_find_queries_0 - max_num_find_queries_per_frame\n                )\n                query_ids_to_filter_0 = random.sample(\n                    neg_find_ids_0, k=num_queries_to_filter\n                )\n\n        # get based on frame 0 all find queries from all the frames with the same indices as in frame 0\n        query_ids_to_filter = []\n        for i in range(num_frames):\n            findQueries_i = findQueries_to_imageIds[i]\n            query_ids_to_filter.extend(\n                [findQueries_i[j] for j in query_ids_to_filter_0]\n            )\n\n        assert (\n            len(query_ids_to_filter)\n            == num_find_queries\n            - self.video_mosaic_max_num_find_queries_per_frame * num_frames\n        )\n        self.find_ids_to_filter = set(query_ids_to_filter)\n\n\nclass KeepSemanticFindQueriesOnly(FilterDataPointQueries):\n    def identify_queries_to_filter(self, datapoint: Datapoint) -> None:\n        self.obj_ids_to_filter = set()\n        self.find_ids_to_filter = {\n            i for i, q in enumerate(datapoint.find_queries) if q.input_bbox is not None\n        }  # filter (remove) geometric find queries (whose input_bbox is not None)\n\n        # Keep all get queries which don't depend on filtered finds\n\n\nclass KeepUnaryFindQueriesOnly(FilterDataPointQueries):\n    def identify_queries_to_filter(self, datapoint: Datapoint) -> None:\n        self.obj_ids_to_filter = set()\n        self.find_ids_to_filter = set()\n\n        # Keep all get queries which don't depend on filtered finds\n\n\nclass FilterZeroBoxQueries(FilterDataPointQueries):\n    \"\"\"\n    Filters all find queries which predict a box with zero area\n    \"\"\"\n\n    @staticmethod\n    def _is_zero_area_object(obj: Object):\n        # Check if height or width of bounding box is zero\n        bbox = obj.bbox  # Assume in XYXY format\n        height = bbox[..., 3].item() - bbox[..., 1].item()\n        width = bbox[..., 2].item() - bbox[..., 0].item()\n\n        return height == 0 or width == 0\n\n    def identify_queries_to_filter(self, datapoint):\n        self.obj_ids_to_filter = set()\n\n        # Find objects with zero area\n        # Assume only one image per datapoint\n        image_objects = datapoint.images[0].objects\n        exclude_objects = {\n            obj_id\n            for obj_id, obj in enumerate(image_objects)\n            if self._is_zero_area_object(obj)\n        }\n\n        # If a query predicts an object with zero area, drop the whole find query\n        del_find_ids = []\n        for i, f_q in enumerate(datapoint.find_queries):\n            f_q_objects = set(f_q.object_ids_output)\n            if len(exclude_objects.intersection(f_q_objects)) > 0:\n                del_find_ids.append(i)\n\n        self.find_ids_to_filter = set(del_find_ids)\n\n\nclass FilterFindQueriesWithTooManyOut(FilterDataPointQueries):\n    \"\"\"\n    Filters all find queries which have more than a specified number of objects in the output\n    \"\"\"\n\n    def __init__(self, max_num_objects: int):\n        self.max_num_objects = max_num_objects\n\n    def identify_queries_to_filter(self, datapoint):\n        self.obj_ids_to_filter = set()\n\n        # If a query predicts more than max_num_objects, drop the whole find query\n        del_find_ids = []\n        for i, f_q in enumerate(datapoint.find_queries):\n            if len(f_q.object_ids_output) > self.max_num_objects:\n                del_find_ids.append(i)\n\n        self.find_ids_to_filter = set(del_find_ids)\n\n\nclass FilterEmptyTargets(FilterDataPointQueries):\n    \"\"\"\n    Filters all targets which have zero area\n    \"\"\"\n\n    def identify_queries_to_filter(self, datapoint):\n        self.obj_ids_to_filter = set()\n\n        for img_id in range(len(datapoint.images)):\n            for obj_id, obj in enumerate(datapoint.images[img_id].objects):\n                if obj.area < 1e-6:\n                    self.obj_ids_to_filter.add((img_id, obj_id))\n        self.find_ids_to_filter = set()\n\n\nclass FilterNonExhaustiveFindQueries(FilterDataPointQueries):\n    \"\"\"\n    Filters all find queries which are non-exhaustive\n    \"\"\"\n\n    def __init__(self, exhaustivity_type: str):\n        \"\"\"\n        Args:\n            exhaustivity_type: Can be \"pixel\" or \"instance\":\n                -pixel: filter queries where the union of all segments covers every pixel belonging to target class\n                -instance: filter queries where there are non-separable or non annotated instances\n        Note that instance exhaustivity implies pixel exhaustivity\n        \"\"\"\n        assert exhaustivity_type in [\"pixel\", \"instance\"]\n        self.exhaustivity_type = exhaustivity_type\n\n    def identify_queries_to_filter(self, datapoint):\n        self.obj_ids_to_filter = set()\n\n        # If a query predicts more than max_num_objects, drop the whole find query\n        del_find_ids = []\n        for i, f_q in enumerate(datapoint.find_queries):\n            if self.exhaustivity_type == \"instance\":\n                if not f_q.is_exhaustive:\n                    del_find_ids.append(i)\n            elif self.exhaustivity_type == \"pixel\":\n                if f_q.is_pixel_exhaustive is not None and not f_q.is_pixel_exhaustive:\n                    del_find_ids.append(i)\n            else:\n                raise RuntimeError(\n                    f\"Unknown exhaustivity type {self.exhaustivity_type}\"\n                )\n\n        self.find_ids_to_filter = set(del_find_ids)\n\n\nclass FilterInvalidGeometricQueries(FilterDataPointQueries):\n    \"\"\"\n    Filters geometric queries whose output got deleted (eg due to cropping)\n    \"\"\"\n\n    def identify_queries_to_filter(self, datapoint):\n        self.obj_ids_to_filter = set()\n\n        # If a query predicts more than max_num_objects, drop the whole find query\n        del_find_ids = []\n        for i, f_q in enumerate(datapoint.find_queries):\n            if f_q.input_bbox is not None and f_q.query_text == \"geometric\":\n                if len(f_q.object_ids_output) == 0:\n                    del_find_ids.append(i)\n        self.find_ids_to_filter = set(del_find_ids)\n\n\nclass FlexibleFilterFindGetQueries:\n    def __init__(\n        self, query_filter: FilterDataPointQueries, enabled: bool = True\n    ) -> None:\n        self.query_filter = query_filter\n        self.enabled = enabled\n\n    def __call__(self, datapoint, **kwargs):\n        if not self.enabled:\n            return datapoint\n\n        # Identify all queries to filter\n        self.query_filter.identify_queries_to_filter(datapoint=datapoint)\n\n        del_find_ids = []\n        del_get_ids = []\n        for i, f_q in enumerate(datapoint.find_queries):\n            if self.query_filter._do_filter_query(f_q, i):\n                datapoint.find_queries[i] = None\n                del_find_ids.append(i)\n\n        new_find_queries = []\n        new_get_queries = []\n\n        find_old_to_new_map = {}\n        get_old_to_new_map = {}\n\n        find_counter = 0\n        get_counter = 0\n\n        for i, f_q in enumerate(datapoint.find_queries):\n            if f_q is not None:\n                find_old_to_new_map[i] = find_counter\n                find_counter += 1\n                new_find_queries.append(f_q)\n\n        start_with_zero_check = False\n        for n_f_q in new_find_queries:\n            if n_f_q.query_processing_order == 0:\n                start_with_zero_check = True\n                break\n\n        if len(new_find_queries) == 0:\n            start_with_zero_check = True\n\n        assert start_with_zero_check, (\n            \"Invalid Find queries, they need to start at query_processing_order = 0\"\n        )\n\n        datapoint.find_queries = new_find_queries\n\n        if len(datapoint.find_queries) == 0:\n            print(\"Warning: No find queries left in datapoint, this is not allowed\")\n            print(\"Filtering function:\", self.query_filter)\n            print(\"Datapoint:\", datapoint)\n            raise ValueError\n\n        # The deletion may have removed intermediate steps, so we need to remap to make them contiguous again\n        all_stages = sorted(\n            list(set(q.query_processing_order for q in datapoint.find_queries))\n        )\n        stage_map = {qpo: i for i, qpo in enumerate(all_stages)}\n        for i in range(len(datapoint.find_queries)):\n            qpo = datapoint.find_queries[i].query_processing_order\n            datapoint.find_queries[i].query_processing_order = stage_map[qpo]\n\n        # Final step, clear up objects that are not used anymore\n        for img_id in range(len(datapoint.images)):\n            all_objects_ids = set(\n                i\n                for find in datapoint.find_queries\n                for i in find.object_ids_output\n                if find.image_id == img_id\n            )\n            unused_ids = (\n                set(range(len(datapoint.images[img_id].objects))) - all_objects_ids\n            )\n            for tgt_img_id, tgt_obj_id in self.query_filter.obj_ids_to_filter:\n                if tgt_img_id == img_id:\n                    unused_ids.add(tgt_obj_id)\n\n            if len(unused_ids) > 0:\n                old_objects = datapoint.images[img_id].objects\n                object_old_to_new_map = {}\n                new_objects = []\n                for i, o in enumerate(old_objects):\n                    if i not in unused_ids:\n                        object_old_to_new_map[i] = len(new_objects)\n                        new_objects.append(o)\n\n                datapoint.images[img_id].objects = new_objects\n\n                # Remap the outputs of the find queries\n                affected_find_queries_ids = set()\n                object_old_to_new_map_per_query = {}\n                for fid, find in enumerate(datapoint.find_queries):\n                    if find.image_id == img_id:\n                        old_object_ids_output = find.object_ids_output\n                        object_old_to_new_map_per_query[fid] = {}\n                        find.object_ids_output = []\n                        for oid, old_obj_id in enumerate(old_object_ids_output):\n                            if old_obj_id not in unused_ids:\n                                new_obj_id = object_old_to_new_map[old_obj_id]\n                                find.object_ids_output.append(new_obj_id)\n                                object_old_to_new_map_per_query[fid][oid] = (\n                                    len(find.object_ids_output) - 1\n                                )\n                        affected_find_queries_ids.add(fid)\n\n        # finally remove unused images\n        all_imgs_to_keep = set()\n        for f_q in datapoint.find_queries:\n            all_imgs_to_keep.add(f_q.image_id)\n\n        old_img_id_to_new_img_id = {}\n        new_images = []\n        for img_id, img in enumerate(datapoint.images):\n            if img_id in all_imgs_to_keep:\n                old_img_id_to_new_img_id[img_id] = len(new_images)\n                new_images.append(img)\n        datapoint.images = new_images\n\n        for f_q in datapoint.find_queries:\n            f_q.image_id = old_img_id_to_new_img_id[f_q.image_id]\n\n        return datapoint\n\n\nclass AddPrefixSuffixToFindText:\n    \"\"\"\n    Add prefix or suffix strings to find query text on the fly.\n\n    If `condition_on_text` is True, the prefix or suffix strings are only added\n    to those find query text in `condition_text_list` (case-insensitive).\n    \"\"\"\n\n    def __init__(\n        self,\n        prefix: Optional[str] = None,\n        suffix: Optional[str] = None,\n        condition_on_text: bool = False,\n        condition_text_list: Optional[List[str]] = None,\n        enabled: bool = True,\n    ) -> None:\n        self.prefix = prefix\n        self.suffix = suffix\n        self.condition_on_text = condition_on_text\n        if self.condition_on_text:\n            assert condition_text_list is not None\n            self.condition_text_set = {s.lower().strip() for s in condition_text_list}\n        self.enabled = enabled\n        if self.enabled:\n            logging.info(\n                f\"AddPrefixSuffixToFindText: prefix={prefix}, suffix={suffix}, \"\n                f\"condition_on_text={condition_on_text}, condition_text_list={condition_text_list}\"\n            )\n\n    def __call__(self, datapoint, **kwargs):\n        if not self.enabled:\n            return datapoint\n\n        for find in datapoint.find_queries:\n            if find.query_text == \"geometric\":\n                # skip geometric find queries\n                continue\n            if (\n                self.condition_on_text\n                and find.query_text.lower().strip() not in self.condition_text_set\n            ):\n                # if condition_on_text is True, skip those queries not in condition_text_set\n                continue\n\n            # add prefix and/or suffix strings to the find query text\n            if self.prefix is not None:\n                find.query_text = self.prefix + find.query_text\n            if self.suffix is not None:\n                find.query_text = find.query_text + self.suffix\n\n        return datapoint\n\n\nclass FilterCrowds(FilterDataPointQueries):\n    def identify_queries_to_filter(self, datapoint: Datapoint) -> None:\n        \"\"\"\n        Compute set of query ids to keep, for both find and get queries\n        \"\"\"\n        self.obj_ids_to_filter = set()\n        self.find_ids_to_filter = set()\n        # self.get_ids_to_filter = set()\n        for img_id, img in enumerate(datapoint.images):\n            for obj_id, obj in enumerate(img.objects):\n                if obj.is_crowd:\n                    self.obj_ids_to_filter.add((img_id, obj_id))\n\n\nclass TextQueryToVisual:\n    \"\"\"\n    Transform a test query to a visual query (with some proba), using any of the output targets as the prompt\n    \"\"\"\n\n    def __init__(self, probability, keep_text_queries=False) -> None:\n        self.probability = probability\n        assert 0 <= probability <= 1\n        self.keep_text_queries = keep_text_queries\n\n    def __call__(self, datapoint: Datapoint, **kwargs):\n        for find in datapoint.find_queries:\n            if find.input_bbox is not None or find.input_points is not None:\n                # skip geometric find queries\n                continue\n\n            if len(find.object_ids_output) == 0:\n                # Can't create a visual query, skip\n                continue\n\n            if find.query_processing_order > 0:\n                # Second stage query, can't use\n                continue\n\n            if random.random() > self.probability:\n                continue\n\n            selected_vq_id = random.choice(find.object_ids_output)\n            img_id = find.image_id\n\n            find.input_bbox = datapoint.images[img_id].objects[selected_vq_id].bbox\n            find.input_bbox_label = torch.ones(1, dtype=torch.bool)\n            if not self.keep_text_queries:\n                find.query_text = \"visual\"\n\n        return datapoint\n\n\nclass RemoveInputBoxes:\n    \"\"\"\n    Remove input boxes from find queries\n    \"\"\"\n\n    def __init__(self) -> None:\n        pass\n\n    def __call__(self, datapoint: Datapoint, **kwargs):\n        for find in datapoint.find_queries:\n            if find.input_bbox is None:\n                continue\n\n            if find.query_text == \"geometric\":\n                print(\"Warning: removing input box from geometric find query\")\n\n            find.input_bbox = None\n        return datapoint\n\n\nclass OverwriteTextQuery:\n    \"\"\"\n    With some probability, overwrite the text query with a custom text\n    \"\"\"\n\n    def __init__(self, target_text, probability=1.0) -> None:\n        self.probability = probability\n        self.target_text = target_text\n        assert 0 <= probability <= 1\n\n    def __call__(self, datapoint: Datapoint, **kwargs):\n        for find in datapoint.find_queries:\n            if random.random() > self.probability:\n                continue\n\n            find.query_text = self.target_text\n\n        return datapoint\n"
  },
  {
    "path": "sam3/train/transforms/point_sampling.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n\nimport cv2\nimport numpy as np\nimport torch\nfrom PIL import Image as PILImage\nfrom pycocotools import mask as mask_util\nfrom sam3.train.data.sam3_image_dataset import Datapoint\nfrom torchvision.ops import masks_to_boxes\n\n\ndef sample_points_from_rle(rle, n_points, mode, box=None, normalize=True):\n    \"\"\"\n    Sample random points from a mask provided in COCO RLE format. 'mode'\n    'mode' is in [\"centered\", \"random_mask\", \"random_box\"]\n      \"centered\": points are sampled farthest from the mask edges and each other\n      \"random_mask\": points are sampled uniformly from the mask\n      \"random_box\": points are sampled uniformly from the annotation's box\n    'box' must be provided if 'mode' is \"random_box\".\n    If 'normalize' is true, points are in [0,1], relative to mask h,w.\n    \"\"\"\n    mask = np.ascontiguousarray(mask_util.decode(rle))\n    points = sample_points_from_mask(mask, n_points, mode, box)\n\n    if normalize:\n        h, w = mask.shape\n        norm = np.array([w, h, 1.0])[None, :]\n        points = points / norm\n\n    return points\n\n\ndef sample_points_from_mask(mask, n_points, mode, box=None):\n    if mode == \"centered\":\n        points = center_positive_sample(mask, n_points)\n    elif mode == \"random_mask\":\n        points = uniform_positive_sample(mask, n_points)\n    elif mode == \"random_box\":\n        assert box is not None, \"'random_box' mode requires a provided box.\"\n        points = uniform_sample_from_box(mask, box, n_points)\n    else:\n        raise ValueError(f\"Unknown point sampling mode {mode}.\")\n    return points\n\n\ndef uniform_positive_sample(mask, n_points):\n    \"\"\"\n    Samples positive points uniformly from the mask. Only integer pixel\n    values are sampled.\n    \"\"\"\n    # Sampling directly from the uncompressed RLE would be faster but is\n    # likely unnecessary.\n    mask_points = np.stack(np.nonzero(mask), axis=0).transpose(1, 0)\n    assert len(mask_points) > 0, \"Can't sample positive points from an empty mask.\"\n    selected_idxs = np.random.randint(low=0, high=len(mask_points), size=n_points)\n    selected_points = mask_points[selected_idxs]\n\n    selected_points = selected_points[:, ::-1]  # (y, x) -> (x, y)\n    labels = np.ones((len(selected_points), 1))\n    selected_points = np.concatenate([selected_points, labels], axis=1)\n\n    return selected_points\n\n\ndef center_positive_sample(mask, n_points):\n    \"\"\"\n    Samples points farthest from mask edges (by distance transform)\n    and subsequent points also farthest from each other. Each new point\n    sampled is treated as an edge for future points. Edges of the image are\n    treated as edges of the mask.\n    \"\"\"\n\n    # Pad mask by one pixel on each end to assure distance transform\n    # avoids edges\n    padded_mask = np.pad(mask, 1)\n\n    points = []\n    for _ in range(n_points):\n        assert np.max(mask) > 0, \"Can't sample positive points from an empty mask.\"\n        dist = cv2.distanceTransform(padded_mask, cv2.DIST_L2, 0)\n        point = np.unravel_index(dist.argmax(), dist.shape)\n        # Mark selected point as background so next point avoids it\n        padded_mask[point[0], point[1]] = 0\n        points.append(point[::-1])  # (y, x) -> (x, y)\n\n    points = np.stack(points, axis=0)\n    points = points - 1  # Subtract left/top padding of 1\n    labels = np.ones((len(points), 1))\n    points = np.concatenate([points, labels], axis=1)\n\n    return points\n\n\ndef uniform_sample_from_box(mask, box, n_points):\n    \"\"\"\n    Sample points uniformly from the provided box. The points' labels\n    are determined by the provided mask. Does not guarantee a positive\n    point is sampled. The box is assumed unnormalized in XYXY format.\n    Points are sampled at integer values.\n    \"\"\"\n\n    # Since lower/right edges are exclusive, ceil can be applied to all edges\n    int_box = np.ceil(box)\n\n    x = np.random.randint(low=int_box[0], high=int_box[2], size=n_points)\n    y = np.random.randint(low=int_box[1], high=int_box[3], size=n_points)\n    labels = mask[y, x]\n    points = np.stack([x, y, labels], axis=1)\n\n    return points\n\n\ndef rescale_box_xyxy(box, factor, imsize=None):\n    \"\"\"\n    Rescale a box providing in unnormalized XYXY format, fixing the center.\n    If imsize is provided, clamp to the image.\n    \"\"\"\n    cx, cy = (box[0] + box[2]) / 2, (box[1] + box[3]) / 2\n    w, h = box[2] - box[0], box[3] - box[1]\n\n    new_w, new_h = factor * w, factor * h\n\n    new_x0, new_y0 = cx - new_w / 2, cy - new_h / 2\n    new_x1, new_y1 = cx + new_w / 2, cy + new_h / 2\n\n    if imsize is not None:\n        new_x0 = max(min(new_x0, imsize[1]), 0)\n        new_x1 = max(min(new_x1, imsize[1]), 0)\n        new_y0 = max(min(new_y0, imsize[0]), 0)\n        new_y1 = max(min(new_y1, imsize[0]), 0)\n\n    return [new_x0, new_y0, new_x1, new_y1]\n\n\ndef noise_box(box, im_size, box_noise_std, box_noise_max, min_box_area):\n    if box_noise_std <= 0.0:\n        return box\n    noise = box_noise_std * torch.randn(size=(4,))\n    w, h = box[2] - box[0], box[3] - box[1]\n    scale_factor = torch.tensor([w, h, w, h])\n    noise = noise * scale_factor\n    if box_noise_max is not None:\n        noise = torch.clamp(noise, -box_noise_max, box_noise_max)\n    input_box = box + noise\n    # Clamp to maximum image size\n    img_clamp = torch.tensor([im_size[1], im_size[0], im_size[1], im_size[0]])\n    input_box = torch.maximum(input_box, torch.zeros_like(input_box))\n    input_box = torch.minimum(input_box, img_clamp)\n    if (input_box[2] - input_box[0]) * (input_box[3] - input_box[1]) <= min_box_area:\n        return box\n\n    return input_box\n\n\nclass RandomGeometricInputsAPI:\n    \"\"\"\n    For geometric queries, replaces the input box or points with a random\n    one sampled from the GT mask. Segments must be provided for objects\n    that are targets of geometric queries, and must be binary masks. Existing\n    point and box queries in the datapoint will be ignored and completely replaced.\n    Will sample points and boxes in XYXY format in absolute pixel space.\n\n    Geometry queries are currently determined by taking any query whose\n    query text is a set value.\n\n    Args:\n      num_points (int or (int, int)): how many points to sample. If a tuple,\n        sample a random number of points uniformly over the inclusive range.\n      box_chance (float): fraction of time a box is sampled. A box will replace\n        one sampled point.\n      box_noise_std (float): if greater than 0, add noise to the sampled boxes\n        with this std. Noise is relative to the length of the box side.\n      box_noise_max (int): if not none, truncate any box noise larger than this\n        in terms of absolute pixels.\n      resample_box_from_mask (bool): if True, any sampled box will be determined\n        by finding the extrema of the provided mask. If False, the bbox provided\n        in the target object will be used.\n      point_sample_mode (str): In [\"centered\", \"random_mask\", \"random_box\"],\n        controlling how points are sampled:\n          \"centered\": points are sampled farthest from the mask edges and each other\n          \"random_mask\": points are sampled uniformly from the mask\n          \"random_box\": points are sampled uniformly from the annotation's box\n        Note that \"centered\" may be too slow for on-line generation.\n      geometric_query_str (str): what string in query_text indicates a\n        geometry query.\n      minimum_box_area (float): sampled boxes with area this size or smaller after\n        noising will use the original box instead. It is the input's responsibility\n        to avoid original boxes that violate necessary area bounds.\n      concat_points (bool): if True, any sampled points will be added to existing\n        ones instead of replacing them.\n\n    \"\"\"\n\n    def __init__(\n        self,\n        num_points,\n        box_chance,\n        box_noise_std=0.0,\n        box_noise_max=None,\n        minimum_box_area=0.0,\n        resample_box_from_mask=False,\n        point_sample_mode=\"random_mask\",\n        sample_box_scale_factor=1.0,\n        geometric_query_str=\"geometric\",\n        concat_points=False,\n    ):\n        self.num_points = num_points\n        if not isinstance(self.num_points, int):\n            # Convert from inclusive range to exclusive range expected by torch\n            self.num_points[1] += 1\n            self.num_points = tuple(self.num_points)\n        self.box_chance = box_chance\n        self.box_noise_std = box_noise_std\n        self.box_noise_max = box_noise_max\n        self.minimum_box_area = minimum_box_area\n        self.resample_box_from_mask = resample_box_from_mask\n        self.point_sample_mode = point_sample_mode\n        assert point_sample_mode in [\n            \"centered\",\n            \"random_mask\",\n            \"random_box\",\n        ], \"Unknown point sample mode.\"\n        self.geometric_query_str = geometric_query_str\n        self.concat_points = concat_points\n        self.sample_box_scale_factor = sample_box_scale_factor\n\n    def _sample_num_points_and_if_box(self):\n        if isinstance(self.num_points, tuple):\n            n_points = torch.randint(\n                low=self.num_points[0], high=self.num_points[1], size=(1,)\n            ).item()\n        else:\n            n_points = self.num_points\n        if self.box_chance > 0.0:\n            use_box = torch.rand(size=(1,)).item() < self.box_chance\n            n_points -= int(use_box)  # box stands in for one point\n        else:\n            use_box = False\n        return n_points, use_box\n\n    def _get_original_box(self, target_object):\n        if not self.resample_box_from_mask:\n            return target_object.bbox\n        mask = target_object.segment\n        return masks_to_boxes(mask[None, :, :])[0]\n\n    def _get_target_object(self, datapoint, query):\n        img = datapoint.images[query.image_id]\n        targets = query.object_ids_output\n        assert len(targets) == 1, (\n            \"Geometric queries only support a single target object.\"\n        )\n        target_idx = targets[0]\n        return img.objects[target_idx]\n\n    def __call__(self, datapoint, **kwargs):\n        for query in datapoint.find_queries:\n            if query.query_text != self.geometric_query_str:\n                continue\n\n            target_object = self._get_target_object(datapoint, query)\n            n_points, use_box = self._sample_num_points_and_if_box()\n            box = self._get_original_box(target_object)\n\n            mask = target_object.segment\n            if n_points > 0:\n                # FIXME: The conversion to numpy and back to reuse code\n                # is awkward, but this is all in the dataloader worker anyway\n                # on CPU and so I don't think it should matter.\n                if self.sample_box_scale_factor != 1.0:\n                    sample_box = rescale_box_xyxy(\n                        box.numpy(), self.sample_box_scale_factor, mask.shape\n                    )\n                else:\n                    sample_box = box.numpy()\n                input_points = sample_points_from_mask(\n                    mask.numpy(),\n                    n_points,\n                    self.point_sample_mode,\n                    sample_box,\n                )\n                input_points = torch.as_tensor(input_points)\n                input_points = input_points[None, :, :]\n                if self.concat_points and query.input_points is not None:\n                    input_points = torch.cat([query.input_points, input_points], dim=1)\n            else:\n                input_points = query.input_points if self.concat_points else None\n\n            if use_box:\n                w, h = datapoint.images[query.image_id].size\n                input_box = noise_box(\n                    box,\n                    (h, w),\n                    box_noise_std=self.box_noise_std,\n                    box_noise_max=self.box_noise_max,\n                    min_box_area=self.minimum_box_area,\n                )\n                input_box = input_box[None, :]\n            else:\n                input_box = query.input_bbox if self.concat_points else None\n\n            query.input_points = input_points\n            query.input_bbox = input_box\n\n        return datapoint\n\n\nclass RandomizeInputBbox:\n    \"\"\"\n    Simplified version of the geometric transform that only deals with input boxes\n    \"\"\"\n\n    def __init__(\n        self,\n        box_noise_std=0.0,\n        box_noise_max=None,\n        minimum_box_area=0.0,\n    ):\n        self.box_noise_std = box_noise_std\n        self.box_noise_max = box_noise_max\n        self.minimum_box_area = minimum_box_area\n\n    def __call__(self, datapoint: Datapoint, **kwargs):\n        for query in datapoint.find_queries:\n            if query.input_bbox is None:\n                continue\n\n            img = datapoint.images[query.image_id].data\n            if isinstance(img, PILImage.Image):\n                w, h = img.size\n            else:\n                assert isinstance(img, torch.Tensor)\n                h, w = img.shape[-2:]\n\n            for box_id in range(query.input_bbox.shape[0]):\n                query.input_bbox[box_id, :] = noise_box(\n                    query.input_bbox[box_id, :].view(4),\n                    (h, w),\n                    box_noise_std=self.box_noise_std,\n                    box_noise_max=self.box_noise_max,\n                    min_box_area=self.minimum_box_area,\n                ).view(1, 4)\n\n        return datapoint\n"
  },
  {
    "path": "sam3/train/transforms/segmentation.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n\nimport numpy as np\nimport pycocotools.mask as mask_utils\nimport torch\nimport torchvision.transforms.functional as F\nfrom PIL import Image as PILImage\nfrom sam3.model.box_ops import masks_to_boxes\nfrom sam3.train.data.sam3_image_dataset import Datapoint\n\n\nclass InstanceToSemantic(object):\n    \"\"\"Convert instance segmentation to semantic segmentation.\"\"\"\n\n    def __init__(self, delete_instance=True, use_rle=False):\n        self.delete_instance = delete_instance\n        self.use_rle = use_rle\n\n    def __call__(self, datapoint: Datapoint, **kwargs):\n        for fquery in datapoint.find_queries:\n            h, w = datapoint.images[fquery.image_id].size\n\n            if self.use_rle:\n                all_segs = [\n                    datapoint.images[fquery.image_id].objects[obj_id].segment\n                    for obj_id in fquery.object_ids_output\n                ]\n                if len(all_segs) > 0:\n                    # we need to double check that all rles are the correct size\n                    # Otherwise cocotools will fail silently to an empty [0,0] mask\n                    for seg in all_segs:\n                        assert seg[\"size\"] == all_segs[0][\"size\"], (\n                            \"Instance segments have inconsistent sizes. \"\n                            f\"Found sizes {seg['size']} and {all_segs[0]['size']}\"\n                        )\n                    fquery.semantic_target = mask_utils.merge(all_segs)\n                else:\n                    # There is no good way to create an empty RLE of the correct size\n                    # We resort to converting an empty box to RLE\n                    fquery.semantic_target = mask_utils.frPyObjects(\n                        np.array([[0, 0, 0, 0]], dtype=np.float64), h, w\n                    )[0]\n\n            else:\n                # `semantic_target` is uint8 and remains uint8 throughout the transforms\n                # (it contains binary 0 and 1 values just like `segment` for each object)\n                fquery.semantic_target = torch.zeros((h, w), dtype=torch.uint8)\n                for obj_id in fquery.object_ids_output:\n                    segment = datapoint.images[fquery.image_id].objects[obj_id].segment\n                    if segment is not None:\n                        assert (\n                            isinstance(segment, torch.Tensor)\n                            and segment.dtype == torch.uint8\n                        )\n                        fquery.semantic_target |= segment\n\n        if self.delete_instance:\n            for img in datapoint.images:\n                for obj in img.objects:\n                    del obj.segment\n                    obj.segment = None\n\n        return datapoint\n\n\nclass RecomputeBoxesFromMasks:\n    \"\"\"Recompute bounding boxes from masks.\"\"\"\n\n    def __call__(self, datapoint: Datapoint, **kwargs):\n        for img in datapoint.images:\n            for obj in img.objects:\n                # Note: if the mask is empty, the bounding box will be undefined\n                # The empty targets should be subsequently filtered\n                obj.bbox = masks_to_boxes(obj.segment)\n                obj.area = obj.segment.sum().item()\n\n        return datapoint\n\n\nclass DecodeRle:\n    \"\"\"This transform decodes RLEs into binary segments.\n    Implementing it as a transforms allows lazy loading. Some transforms (eg query filters)\n    may be deleting masks, so decoding them from the beginning is wasteful.\n\n    This transforms needs to be called before any kind of geometric manipulation\n    \"\"\"\n\n    def __call__(self, datapoint: Datapoint, **kwargs):\n        imgId2size = {}\n        warning_shown = False\n        for imgId, img in enumerate(datapoint.images):\n            if isinstance(img.data, PILImage.Image):\n                img_w, img_h = img.data.size\n            elif isinstance(img.data, torch.Tensor):\n                img_w, img_h = img.data.shape[-2:]\n            else:\n                raise RuntimeError(f\"Unexpected image type {type(img.data)}\")\n\n            imgId2size[imgId] = (img_h, img_w)\n\n            for obj in img.objects:\n                if obj.segment is not None and not isinstance(\n                    obj.segment, torch.Tensor\n                ):\n                    if mask_utils.area(obj.segment) == 0:\n                        print(\"Warning, empty mask found, approximating from box\")\n                        obj.segment = torch.zeros(img_h, img_w, dtype=torch.uint8)\n                        x1, y1, x2, y2 = obj.bbox.int().tolist()\n                        obj.segment[y1 : max(y2, y1 + 1), x1 : max(x1 + 1, x2)] = 1\n                    else:\n                        obj.segment = mask_utils.decode(obj.segment)\n                        # segment is uint8 and remains uint8 throughout the transforms\n                        obj.segment = torch.tensor(obj.segment).to(torch.uint8)\n\n                    if list(obj.segment.shape) != [img_h, img_w]:\n                        # Should not happen often, but adding for security\n                        if not warning_shown:\n                            print(\n                                f\"Warning expected instance segmentation size to be {[img_h, img_w]} but found {list(obj.segment.shape)}\"\n                            )\n                            # Printing only once per datapoint to avoid spam\n                            warning_shown = True\n\n                        obj.segment = F.resize(\n                            obj.segment[None], (img_h, img_w)\n                        ).squeeze(0)\n\n                    assert list(obj.segment.shape) == [img_h, img_w]\n\n        warning_shown = False\n        for query in datapoint.find_queries:\n            if query.semantic_target is not None and not isinstance(\n                query.semantic_target, torch.Tensor\n            ):\n                query.semantic_target = mask_utils.decode(query.semantic_target)\n                # segment is uint8 and remains uint8 throughout the transforms\n                query.semantic_target = torch.tensor(query.semantic_target).to(\n                    torch.uint8\n                )\n                if tuple(query.semantic_target.shape) != imgId2size[query.image_id]:\n                    if not warning_shown:\n                        print(\n                            f\"Warning expected semantic segmentation size to be {imgId2size[query.image_id]} but found {tuple(query.semantic_target.shape)}\"\n                        )\n                        # Printing only once per datapoint to avoid spam\n                        warning_shown = True\n\n                    query.semantic_target = F.resize(\n                        query.semantic_target[None], imgId2size[query.image_id]\n                    ).squeeze(0)\n\n                assert tuple(query.semantic_target.shape) == imgId2size[query.image_id]\n\n        return datapoint\n"
  },
  {
    "path": "sam3/train/utils/__init__.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n"
  },
  {
    "path": "sam3/train/utils/checkpoint_utils.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n\n\nimport contextlib\nimport fnmatch\nimport logging\nfrom typing import (\n    Any,\n    Callable,\n    Dict,\n    List,\n    Mapping,\n    Optional,\n    Sequence,\n    Set,\n    Tuple,\n    Union,\n)\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\nfrom iopath.common.file_io import g_pathmgr\nfrom torch.jit._script import RecursiveScriptModule\n\n\ndef unix_pattern_to_parameter_names(\n    constraints: List[str], all_parameter_names: Sequence[str]\n) -> Union[None, Set[str]]:\n    \"\"\"\n    Go through the list of parameter names and select those that match\n    any of the provided constraints\n    \"\"\"\n    parameter_names = []\n    for param_name in constraints:\n        matching_parameters = set(fnmatch.filter(all_parameter_names, param_name))\n        assert len(matching_parameters) > 0, (\n            f\"param_names {param_name} don't match any param in the given names.\"\n        )\n        parameter_names.append(matching_parameters)\n    return set.union(*parameter_names)\n\n\ndef filter_params_matching_unix_pattern(\n    patterns: List[str], state_dict: Dict[str, torch.Tensor]\n) -> Dict[str, torch.Tensor]:\n    \"\"\"\n    Remove from the state dictionary the parameters matching the provided unix patterns\n\n    Args:\n        patterns: the list of unix patterns to exclude\n        state_dict: the dictionary to filter\n\n    Returns:\n        A new state dictionary\n    \"\"\"\n    if len(patterns) == 0:\n        return {}\n\n    all_keys = list(state_dict.keys())\n    included_keys = unix_pattern_to_parameter_names(patterns, all_keys)\n    return {k: state_dict[k] for k in included_keys}\n\n\ndef exclude_params_matching_unix_pattern(\n    patterns: List[str], state_dict: Dict[str, torch.Tensor]\n) -> Dict[str, torch.Tensor]:\n    \"\"\"\n    Remove from the state dictionary the parameters matching the provided unix patterns\n\n    Args:\n        patterns: the list of unix patterns to exclude\n        state_dict: the dictionary to filter\n\n    Returns:\n        A new state dictionary\n    \"\"\"\n    if len(patterns) == 0:\n        return state_dict\n\n    all_keys = list(state_dict.keys())\n    excluded_keys = unix_pattern_to_parameter_names(patterns, all_keys)\n    return {k: v for k, v in state_dict.items() if k not in excluded_keys}\n\n\ndef _get_state_dict_summary(state_dict: Dict[str, torch.Tensor]):\n    keys = []\n    trace = []\n    for k, v in state_dict.items():\n        keys.append(k)\n        trace.append(v.sum().item())\n    trace = np.array(trace)[np.argsort(keys)]\n    return trace\n\n\ndef assert_skipped_parameters_are_frozen(model: nn.Module, patterns: List[str]):\n    \"\"\"\n    Verifies that all the parameters matching the provided patterns\n    are frozen - this acts as a safeguard when ignoring parameter\n    when saving checkpoints - if the parameters are in fact trainable\n    \"\"\"\n    if not patterns:\n        return\n\n    frozen_state_dict = filter_params_matching_unix_pattern(\n        patterns=patterns, state_dict=model.state_dict()\n    )\n    non_frozen_keys = {\n        n\n        for n, p in model.named_parameters()\n        if n in frozen_state_dict and p.requires_grad\n    }\n    if non_frozen_keys:\n        raise ValueError(\n            f\"Parameters excluded with `skip_saving_parameters` should be frozen: {non_frozen_keys}\"\n        )\n\n\n@contextlib.contextmanager\ndef with_check_parameter_frozen(\n    model: nn.Module, patterns: List[str], disabled: bool = True\n):\n    \"\"\"\n    Context manager that inspects a model surrounding a piece of code\n    and verifies if the model has been updated by this piece of code\n\n    The function will raise an exception if the model has been updated\n    on at least one of the parameter that matches one of the pattern\n\n    Args:\n        model: the model that might have been updated\n        patterns: for the parameters we want to observe\n        allowed:\n    \"\"\"\n    if not patterns or disabled:\n        yield\n        return\n\n    frozen_state_dict = filter_params_matching_unix_pattern(\n        patterns=patterns, state_dict=model.state_dict()\n    )\n    summary_before = _get_state_dict_summary(frozen_state_dict)\n\n    yield\n\n    frozen_state_dict = filter_params_matching_unix_pattern(\n        patterns=patterns, state_dict=model.state_dict()\n    )\n    summary_after = _get_state_dict_summary(frozen_state_dict)\n\n    if not np.allclose(summary_before, summary_after, atol=1e-6):\n        raise ValueError(\n            f\"\"\"\n            The `model_weight_initializer` has initialized parameters frozen with `skip_saving_parameters`.\n            You can resolve this error by either initializing those parameters from within the model definition\n            or using the flag `trainer.checkpoint.initialize_after_preemption` to True.\n        \"\"\"\n        )\n\n\nclass CkptExcludeKernel:\n    \"\"\"\n    Removes the keys from the given model state_dict that match the key_pattern.\n\n    Args:\n        key_pattern: Patterns used to select the keys in the state_dict\n            that are eligible for this kernel.\n    \"\"\"\n\n    def __init__(self, key_pattern: List[str]):\n        self.key_pattern = key_pattern\n\n    def __call__(self, state_dict: Dict):\n        \"\"\"\n        Args:\n            state_dict: A dictionary representing the given checkpoint's state dict.\n        \"\"\"\n        if len(self.key_pattern) == 0:\n            return state_dict\n        exclude_keys = unix_pattern_to_parameter_names(\n            self.key_pattern, state_dict.keys()\n        )\n        return {k: v for k, v in state_dict.items() if k not in exclude_keys}\n\n\ndef load_checkpoint(\n    path_list: List[str],\n    pick_recursive_keys: Optional[List[str]] = None,\n    map_location: str = \"cpu\",\n) -> Any:\n    \"\"\"\n    Loads a checkpoint from the specified path.\n\n    Args:\n        path_list: A list of paths which contain the checkpoint. Each element\n            is tried (in order) until a file that exists is found. That file is then\n            used to read the checkpoint.\n        pick_recursive_keys: Picks sub dicts from the loaded checkpoint if not None.\n            For pick_recursive_keys = [\"a\", \"b\"], will return checkpoint_dict[\"a\"][\"b\"]\n        map_location (str): a function, torch.device, string or a dict specifying how to\n            remap storage locations\n\n    Returns: Model with the matchin pre-trained weights loaded.\n    \"\"\"\n    path_exists = False\n    for path in path_list:\n        if g_pathmgr.exists(path):\n            path_exists = True\n            break\n\n    if not path_exists:\n        raise ValueError(f\"No path exists in {path_list}\")\n\n    with g_pathmgr.open(path, \"rb\") as f:\n        checkpoint = torch.load(f, map_location=map_location)\n\n    logging.info(f\"Loaded checkpoint from {path}\")\n    if pick_recursive_keys is not None:\n        for key in pick_recursive_keys:\n            checkpoint = checkpoint[key]\n    return checkpoint\n\n\ndef get_state_dict(checkpoint, ckpt_state_dict_keys):\n    if isinstance(checkpoint, RecursiveScriptModule):\n        # This is a torchscript JIT model\n        return checkpoint.state_dict()\n    pre_train_dict = checkpoint\n    for i, key in enumerate(ckpt_state_dict_keys):\n        if (isinstance(pre_train_dict, Mapping) and key not in pre_train_dict) or (\n            isinstance(pre_train_dict, Sequence) and key >= len(pre_train_dict)\n        ):\n            key_str = (\n                '[\"' + '\"][\"'.join(list(map(ckpt_state_dict_keys[:i], str))) + '\"]'\n            )\n            raise KeyError(\n                f\"'{key}' not found in checkpoint{key_str} \"\n                f\"with keys: {pre_train_dict.keys()}\"\n            )\n        pre_train_dict = pre_train_dict[key]\n    return pre_train_dict\n\n\ndef load_checkpoint_and_apply_kernels(\n    checkpoint_path: str,\n    checkpoint_kernels: List[Callable] = None,\n    ckpt_state_dict_keys: Tuple[str] = (\"state_dict\",),\n    map_location: str = \"cpu\",\n) -> nn.Module:\n    \"\"\"\n    Performs checkpoint loading with a variety of pre-processing kernel applied in\n    sequence.\n\n    Args:\n        checkpoint_path (str): Path to the checkpoint.\n        checkpoint_kernels List(Callable): A list of checkpoint processing kernels\n            to apply in the specified order. Supported kernels include `CkptIncludeKernel`,\n            `CkptExcludeKernel`, etc. These kernels are applied in the\n            given order.\n        ckpt_state_dict_keys (str): Keys containing the model state dict.\n        map_location (str): a function, torch.device, string or a dict specifying how to\n            remap storage locations\n\n    Returns: Model with the matchin pre-trained weights loaded.\n    \"\"\"\n    assert g_pathmgr.exists(checkpoint_path), \"Checkpoint '{}' not found\".format(\n        checkpoint_path\n    )\n\n    # Load the checkpoint on CPU to avoid GPU mem spike.\n    with g_pathmgr.open(checkpoint_path, \"rb\") as f:\n        checkpoint = torch.load(f, map_location=map_location)\n\n    pre_train_dict = get_state_dict(checkpoint, ckpt_state_dict_keys)\n\n    # Not logging into info etc since it's a huge log\n    logging.debug(\n        \"Loaded Checkpoint State Dict pre-kernel application: %s\"\n        % str(\", \".join(list(pre_train_dict.keys())))\n    )\n    # Apply kernels\n    if checkpoint_kernels is not None:\n        for f in checkpoint_kernels:\n            pre_train_dict = f(state_dict=pre_train_dict)\n\n    logging.debug(\n        \"Loaded Checkpoint State Dict Post-kernel application %s\"\n        % str(\", \".join(list(pre_train_dict.keys())))\n    )\n\n    return pre_train_dict\n\n\ndef check_load_state_dict_errors(\n    missing_keys,\n    unexpected_keys,\n    strict: bool,\n    ignore_missing_keys: List[str] = None,\n    ignore_unexpected_keys: List[str] = None,\n):\n    if ignore_missing_keys is not None and len(ignore_missing_keys) > 0:\n        ignored_keys = unix_pattern_to_parameter_names(\n            ignore_missing_keys, missing_keys\n        )\n        missing_keys = [key for key in missing_keys if key not in ignored_keys]\n\n    if ignore_unexpected_keys is not None and len(ignore_unexpected_keys) > 0:\n        ignored_unexpected_keys = unix_pattern_to_parameter_names(\n            ignore_unexpected_keys, unexpected_keys\n        )\n        unexpected_keys = [\n            key for key in unexpected_keys if key not in ignored_unexpected_keys\n        ]\n\n    err = \"State key mismatch.\"\n    if unexpected_keys:\n        err += f\" Unexpected keys: {unexpected_keys}.\"\n    if missing_keys:\n        err += f\" Missing keys: {missing_keys}.\"\n\n    if unexpected_keys or missing_keys:\n        logging.warning(err)\n        if unexpected_keys or strict:\n            raise KeyError(err)\n\n\ndef load_state_dict_into_model(\n    state_dict: Dict,\n    model: nn.Module,\n    strict: bool = True,\n    ignore_missing_keys: List[str] = None,\n    ignore_unexpected_keys: List[str] = None,\n    checkpoint_kernels: List[Callable] = None,\n):\n    \"\"\"\n    Loads a state dict into the given model.\n\n    Args:\n        state_dict: A dictionary containing the model's\n            state dict, or a subset if strict is False\n        model: Model to load the checkpoint weights into\n        strict: raise if the state_dict has missing state keys\n        ignore_missing_keys: unix pattern of keys to ignore\n    \"\"\"\n    # Apply kernels\n    if checkpoint_kernels is not None:\n        for f in checkpoint_kernels:\n            state_dict = f(state_dict=state_dict)\n    missing_keys, unexpected_keys = model.load_state_dict(state_dict, strict=False)\n\n    check_load_state_dict_errors(\n        missing_keys,\n        unexpected_keys,\n        strict=strict,\n        ignore_missing_keys=ignore_missing_keys,\n        ignore_unexpected_keys=ignore_unexpected_keys,\n    )\n    return model\n"
  },
  {
    "path": "sam3/train/utils/distributed.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n\n# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport datetime\nimport functools\nimport io\nimport logging\nimport os\nimport random\nimport tempfile\nimport time\nfrom typing import Any, Callable, List, Tuple\n\nimport torch\nimport torch.autograd as autograd\nimport torch.distributed as dist\n\n\n# Default to GPU 0\n_cuda_device_index: int = 0\n\n# Setting _cuda_device_index to -1 internally implies that we should use CPU\n_CPU_DEVICE_INDEX = -1\n_PRIMARY_RANK = 0\n\n\n@functools.lru_cache()\ndef _get_global_gloo_group():\n    \"\"\"\n    Return a process group based on gloo backend, containing all the ranks\n    The result is cached.\n    \"\"\"\n\n    if dist.get_backend() == \"nccl\":\n        # Increase timeout from 1800 sec to 43200 sec (12 hr) to avoid some processes\n        # being much slower than others causing a timeout (which can happen in relation\n        # or LVIS class mAP evaluation).\n        timeout = 43200\n        return dist.new_group(\n            backend=\"gloo\",\n            timeout=datetime.timedelta(seconds=timeout),\n        )\n\n    return dist.group.WORLD\n\n\ndef is_main_process():\n    \"\"\"Return true if the current process is the main one\"\"\"\n    return get_rank() == 0\n\n\ndef all_gather_via_filesys(data, filesys_save_dir=None, gather_to_rank_0_only=False):\n    \"\"\"\n    Run all_gather on arbitrary picklable data (not necessarily tensors), similar to\n    `all_gather` above, but using filesystem instead of collective ops.\n\n    If gather_to_rank_0_only is True, only rank 0 will load the gathered object list\n    (and other ranks will have an empty list).\n    \"\"\"\n    world_size = get_world_size()\n    if world_size == 1:\n        return [data]\n\n    print(\"gathering via files\")\n    cpu_group = _get_global_gloo_group()\n\n    # if unspecified, we will save to the current python file dir\n    if filesys_save_dir is not None:\n        save_dir = filesys_save_dir\n    elif \"EXP_DIR\" in os.environ:\n        save_dir = os.environ[\"EXP_DIR\"]\n    else:\n        # try the same directory where the code is stored\n        save_dir = filesys_save_dir or os.path.dirname(__file__)\n    save_dir = os.path.join(save_dir, \"all_gather_via_filesys\")\n    if is_main_process():\n        os.makedirs(save_dir, exist_ok=True)\n\n    # use a timestamp and salt to distinguish different all_gather\n    timestamp = int(time.time()) if is_main_process() else 0\n    salt = random.randint(0, 2**31 - 1) if is_main_process() else 0\n    # broadcast the timestamp and salt across ranks\n    # (all-reduce will do the broadcasting since only rank 0 is non-zero)\n    timestamp_and_salt = torch.tensor([timestamp, salt], dtype=torch.long)\n    dist.all_reduce(timestamp_and_salt, group=cpu_group)\n    timestamp, salt = timestamp_and_salt.tolist()\n\n    # save the data to a file on the disk\n    rank_save = get_rank()\n    save_data_filename = f\"data_to_gather_{timestamp}_{salt}_{rank_save}.pkl\"\n    save_data_path = os.path.join(save_dir, save_data_filename)\n    assert not os.path.exists(save_data_path), f\"{save_data_path} already exists\"\n    torch.save(data, save_data_path)\n    dist.barrier(group=cpu_group)\n\n    # read the data from the files\n    data_list = []\n    if rank_save == 0 or not gather_to_rank_0_only:\n        for rank_load in range(world_size):\n            load_data_filename = f\"data_to_gather_{timestamp}_{salt}_{rank_load}.pkl\"\n            load_data_path = os.path.join(save_dir, load_data_filename)\n            assert os.path.exists(load_data_path), f\"cannot read {save_data_path}\"\n            data_list.append(torch.load(load_data_path, weights_only=False))\n    dist.barrier(group=cpu_group)\n\n    # delete the saved file\n    os.remove(save_data_path)\n    return data_list\n\n\ndef all_gather(data, force_cpu=False, force_filesys=False, filesys_save_dir=None):\n    \"\"\"\n    Run all_gather on arbitrary picklable data (not necessarily tensors)\n    Args:\n        data: any picklable object\n    Returns:\n        list[data]: list of data gathered from each rank\n    \"\"\"\n\n    world_size = get_world_size()\n    if world_size == 1:\n        return [data]\n\n    if os.getenv(\"MDETR_FILESYS_REDUCE_RANK_0_ONLY\") == \"1\":\n        return all_gather_via_filesys(\n            data, filesys_save_dir, gather_to_rank_0_only=True\n        )\n\n    if os.getenv(\"MDETR_FILESYS_REDUCE\") == \"1\" or force_filesys:\n        return all_gather_via_filesys(data, filesys_save_dir)\n\n    cpu_group = None\n    if os.getenv(\"MDETR_CPU_REDUCE\") == \"1\" or force_cpu:\n        cpu_group = _get_global_gloo_group()\n\n    buffer = io.BytesIO()\n    torch.save(data, buffer)\n    data_view = buffer.getbuffer()\n    device = \"cuda\" if cpu_group is None else \"cpu\"\n    tensor = torch.ByteTensor(data_view).to(device)\n\n    # obtain Tensor size of each rank\n    local_size = torch.tensor([tensor.numel()], device=device, dtype=torch.long)\n    size_list = [\n        torch.tensor([0], device=device, dtype=torch.long) for _ in range(world_size)\n    ]\n    if cpu_group is None:\n        dist.all_gather(size_list, local_size)\n    else:\n        print(\"gathering on cpu\")\n        dist.all_gather(size_list, local_size, group=cpu_group)\n    size_list = [int(size.item()) for size in size_list]\n    max_size = max(size_list)\n    assert isinstance(local_size.item(), int)\n    local_size = int(local_size.item())\n\n    # receiving Tensor from all ranks\n    # we pad the tensor because torch all_gather does not support\n    # gathering tensors of different shapes\n    tensor_list = []\n    for _ in size_list:\n        tensor_list.append(torch.empty((max_size,), dtype=torch.uint8, device=device))\n    if local_size != max_size:\n        padding = torch.empty(\n            size=(max_size - local_size,), dtype=torch.uint8, device=device\n        )\n        tensor = torch.cat((tensor, padding), dim=0)\n    if cpu_group is None:\n        dist.all_gather(tensor_list, tensor)\n    else:\n        dist.all_gather(tensor_list, tensor, group=cpu_group)\n\n    data_list = []\n    for size, tensor in zip(size_list, tensor_list):\n        tensor = torch.split(tensor, [size, max_size - size], dim=0)[0]\n        buffer = io.BytesIO(tensor.cpu().numpy())\n        obj = torch.load(buffer, weights_only=False)\n        data_list.append(obj)\n\n    return data_list\n\n\ndef convert_to_distributed_tensor(tensor: torch.Tensor) -> Tuple[torch.Tensor, str]:\n    \"\"\"\n    For some backends, such as NCCL, communication only works if the\n    tensor is on the GPU. This helper function converts to the correct\n    device and returns the tensor + original device.\n    \"\"\"\n    orig_device = \"cpu\" if not tensor.is_cuda else \"gpu\"\n    if (\n        torch.distributed.is_available()\n        and torch.distributed.get_backend() == torch.distributed.Backend.NCCL\n        and not tensor.is_cuda\n    ):\n        tensor = tensor.cuda()\n    return (tensor, orig_device)\n\n\ndef convert_to_normal_tensor(tensor: torch.Tensor, orig_device: str) -> torch.Tensor:\n    \"\"\"\n    For some backends, such as NCCL, communication only works if the\n    tensor is on the GPU. This converts the tensor back to original device.\n    \"\"\"\n    if tensor.is_cuda and orig_device == \"cpu\":\n        tensor = tensor.cpu()\n    return tensor\n\n\ndef is_distributed_training_run() -> bool:\n    return (\n        torch.distributed.is_available()\n        and torch.distributed.is_initialized()\n        and (torch.distributed.get_world_size() > 1)\n    )\n\n\ndef is_primary() -> bool:\n    \"\"\"\n    Returns True if this is rank 0 of a distributed training job OR if it is\n    a single trainer job. Otherwise False.\n    \"\"\"\n    return get_rank() == _PRIMARY_RANK\n\n\ndef all_reduce_mean(tensor: torch.Tensor) -> torch.Tensor:\n    \"\"\"\n    Wrapper over torch.distributed.all_reduce for performing mean reduction\n    of tensor over all processes.\n    \"\"\"\n    return all_reduce_op(\n        tensor,\n        torch.distributed.ReduceOp.SUM,\n        lambda t: t / torch.distributed.get_world_size(),\n    )\n\n\ndef all_reduce_sum(tensor: torch.Tensor) -> torch.Tensor:\n    \"\"\"\n    Wrapper over torch.distributed.all_reduce for performing sum\n    reduction of tensor over all processes in both distributed /\n    non-distributed scenarios.\n    \"\"\"\n    return all_reduce_op(tensor, torch.distributed.ReduceOp.SUM)\n\n\ndef all_reduce_min(tensor: torch.Tensor) -> torch.Tensor:\n    \"\"\"\n    Wrapper over torch.distributed.all_reduce for performing min\n    reduction of tensor over all processes in both distributed /\n    non-distributed scenarios.\n    \"\"\"\n    return all_reduce_op(tensor, torch.distributed.ReduceOp.MIN)\n\n\ndef all_reduce_max(tensor: torch.Tensor) -> torch.Tensor:\n    \"\"\"\n    Wrapper over torch.distributed.all_reduce for performing min\n    reduction of tensor over all processes in both distributed /\n    non-distributed scenarios.\n    \"\"\"\n    return all_reduce_op(tensor, torch.distributed.ReduceOp.MAX)\n\n\ndef all_reduce_op(\n    tensor: torch.Tensor,\n    op: torch.distributed.ReduceOp,\n    after_op_func: Callable[[torch.Tensor], torch.Tensor] = None,\n) -> torch.Tensor:\n    \"\"\"\n    Wrapper over torch.distributed.all_reduce for performing\n    reduction of tensor over all processes in both distributed /\n    non-distributed scenarios.\n    \"\"\"\n    if is_distributed_training_run():\n        tensor, orig_device = convert_to_distributed_tensor(tensor)\n        torch.distributed.all_reduce(tensor, op)\n        if after_op_func is not None:\n            tensor = after_op_func(tensor)\n        tensor = convert_to_normal_tensor(tensor, orig_device)\n    return tensor\n\n\ndef gather_tensors_from_all(tensor: torch.Tensor) -> List[torch.Tensor]:\n    \"\"\"\n    Wrapper over torch.distributed.all_gather for performing\n    'gather' of 'tensor' over all processes in both distributed /\n    non-distributed scenarios.\n    \"\"\"\n    if tensor.ndim == 0:\n        # 0 dim tensors cannot be gathered. so unsqueeze\n        tensor = tensor.unsqueeze(0)\n\n    if is_distributed_training_run():\n        tensor, orig_device = convert_to_distributed_tensor(tensor)\n        gathered_tensors = [\n            torch.zeros_like(tensor) for _ in range(torch.distributed.get_world_size())\n        ]\n        torch.distributed.all_gather(gathered_tensors, tensor)\n        gathered_tensors = [\n            convert_to_normal_tensor(_tensor, orig_device)\n            for _tensor in gathered_tensors\n        ]\n    else:\n        gathered_tensors = [tensor]\n\n    return gathered_tensors\n\n\ndef gather_from_all(tensor: torch.Tensor) -> torch.Tensor:\n    gathered_tensors = gather_tensors_from_all(tensor)\n    gathered_tensor = torch.cat(gathered_tensors, 0)\n    return gathered_tensor\n\n\ndef broadcast(tensor: torch.Tensor, src: int = 0) -> torch.Tensor:\n    \"\"\"\n    Wrapper over torch.distributed.broadcast for broadcasting a tensor from the source\n    to all processes in both distributed / non-distributed scenarios.\n    \"\"\"\n    if is_distributed_training_run():\n        tensor, orig_device = convert_to_distributed_tensor(tensor)\n        torch.distributed.broadcast(tensor, src)\n        tensor = convert_to_normal_tensor(tensor, orig_device)\n    return tensor\n\n\ndef barrier() -> None:\n    \"\"\"\n    Wrapper over torch.distributed.barrier, returns without waiting\n    if the distributed process group is not initialized instead of throwing error.\n    \"\"\"\n    if not torch.distributed.is_available() or not torch.distributed.is_initialized():\n        return\n    torch.distributed.barrier()\n\n\ndef get_world_size() -> int:\n    \"\"\"\n    Simple wrapper for correctly getting worldsize in both distributed\n    / non-distributed settings\n    \"\"\"\n    return (\n        torch.distributed.get_world_size()\n        if torch.distributed.is_available() and torch.distributed.is_initialized()\n        else 1\n    )\n\n\ndef get_rank() -> int:\n    \"\"\"\n    Simple wrapper for correctly getting rank in both distributed\n    / non-distributed settings\n    \"\"\"\n    return (\n        torch.distributed.get_rank()\n        if torch.distributed.is_available() and torch.distributed.is_initialized()\n        else 0\n    )\n\n\ndef get_primary_rank() -> int:\n    return _PRIMARY_RANK\n\n\ndef set_cuda_device_index(idx: int) -> None:\n    global _cuda_device_index\n    _cuda_device_index = idx\n    torch.cuda.set_device(_cuda_device_index)\n\n\ndef set_cpu_device() -> None:\n    global _cuda_device_index\n    _cuda_device_index = _CPU_DEVICE_INDEX\n\n\ndef get_cuda_device_index() -> int:\n    return _cuda_device_index\n\n\ndef init_distributed_data_parallel_model(\n    model: torch.nn.Module,\n    broadcast_buffers: bool = False,\n    find_unused_parameters: bool = True,\n    bucket_cap_mb: int = 25,\n) -> torch.nn.parallel.DistributedDataParallel:\n    global _cuda_device_index\n\n    if _cuda_device_index == _CPU_DEVICE_INDEX:\n        # CPU-only model, don't specify device\n        return torch.nn.parallel.DistributedDataParallel(\n            model,\n            broadcast_buffers=broadcast_buffers,\n            find_unused_parameters=find_unused_parameters,\n            bucket_cap_mb=bucket_cap_mb,\n        )\n    else:\n        # GPU model\n        return torch.nn.parallel.DistributedDataParallel(\n            model,\n            device_ids=[_cuda_device_index],\n            output_device=_cuda_device_index,\n            broadcast_buffers=broadcast_buffers,\n            find_unused_parameters=find_unused_parameters,\n            bucket_cap_mb=bucket_cap_mb,\n        )\n\n\ndef broadcast_object(obj: Any, src: int = _PRIMARY_RANK, use_disk: bool = True) -> Any:\n    \"\"\"Broadcast an object from a source to all workers.\n\n    Args:\n        obj: Object to broadcast, must be serializable\n        src: Source rank for broadcast (default is primary)\n        use_disk: If enabled, removes redundant CPU memory copies by writing to\n            disk\n    \"\"\"\n    # Either broadcast from primary to the fleet (default),\n    # or use the src setting as the original rank\n    if get_rank() == src:\n        # Emit data\n        buffer = io.BytesIO()\n        torch.save(obj, buffer)\n        data_view = buffer.getbuffer()\n        length_tensor = torch.LongTensor([len(data_view)])\n        length_tensor = broadcast(length_tensor, src=src)\n        data_tensor = torch.ByteTensor(data_view)\n        data_tensor = broadcast(data_tensor, src=src)\n    else:\n        # Fetch from the source\n        length_tensor = torch.LongTensor([0])\n        length_tensor = broadcast(length_tensor, src=src)\n        data_tensor = torch.empty([length_tensor.item()], dtype=torch.uint8)\n        data_tensor = broadcast(data_tensor, src=src)\n        if use_disk:\n            with tempfile.TemporaryFile(\"r+b\") as f:\n                f.write(data_tensor.numpy())\n                # remove reference to the data tensor and hope that Python garbage\n                # collects it\n                del data_tensor\n                f.seek(0)\n                obj = torch.load(f, weights_only=False)\n        else:\n            buffer = io.BytesIO(data_tensor.numpy())\n            obj = torch.load(buffer, weights_only=False)\n    return obj\n\n\ndef all_gather_tensor(tensor: torch.Tensor, world_size=None):\n    if world_size is None:\n        world_size = get_world_size()\n    # make contiguous because NCCL won't gather the tensor otherwise\n    assert tensor.is_contiguous(), f\"{tensor.shape} is not contiguous!\"\n    tensor, orig_device = convert_to_distributed_tensor(tensor)\n    tensor_all = [torch.ones_like(tensor) for _ in range(world_size)]\n    dist.all_gather(tensor_all, tensor, async_op=False)  # performance opt\n    tensor_all = [\n        convert_to_normal_tensor(tensor, orig_device) for tensor in tensor_all\n    ]\n    return tensor_all\n\n\ndef all_gather_batch(tensors: List[torch.Tensor]):\n    \"\"\"\n    Performs all_gather operation on the provided tensors.\n    \"\"\"\n    # Queue the gathered tensors\n    world_size = get_world_size()\n    # There is no need for reduction in the single-proc case\n    if world_size == 1:\n        return tensors\n    tensor_list = []\n    output_tensor = []\n    for tensor in tensors:\n        tensor_all = all_gather_tensor(tensor, world_size)\n        tensor_list.append(tensor_all)\n\n    for tensor_all in tensor_list:\n        output_tensor.append(torch.cat(tensor_all, dim=0))\n    return output_tensor\n\n\nclass GatherLayer(autograd.Function):\n    \"\"\"\n    Gather tensors from all workers with support for backward propagation:\n    This implementation does not cut the gradients as torch.distributed.all_gather does.\n    \"\"\"\n\n    @staticmethod\n    def forward(ctx, x):\n        output = [torch.zeros_like(x) for _ in range(dist.get_world_size())]\n        dist.all_gather(output, x)\n        return tuple(output)\n\n    @staticmethod\n    def backward(ctx, *grads):\n        all_gradients = torch.stack(grads)\n        dist.all_reduce(all_gradients)\n        return all_gradients[dist.get_rank()]\n\n\ndef all_gather_batch_with_grad(tensors):\n    \"\"\"\n    Performs all_gather operation on the provided tensors.\n    Graph remains connected for backward grad computation.\n    \"\"\"\n    # Queue the gathered tensors\n    world_size = get_world_size()\n    # There is no need for reduction in the single-proc case\n    if world_size == 1:\n        return tensors\n    tensor_list = []\n    output_tensor = []\n\n    for tensor in tensors:\n        tensor_all = GatherLayer.apply(tensor)\n        tensor_list.append(tensor_all)\n\n    for tensor_all in tensor_list:\n        output_tensor.append(torch.cat(tensor_all, dim=0))\n    return output_tensor\n\n\ndef unwrap_ddp_if_wrapped(model):\n    if isinstance(model, torch.nn.parallel.DistributedDataParallel):\n        return model.module\n    return model\n\n\ndef create_new_process_group(group_size):\n    \"\"\"\n    Creates process groups of a gives `group_size` and returns\n    process group that current GPU participates in.\n\n    `group_size` must divide the total number of GPUs (world_size).\n\n    Modified from\n    https://github.com/NVIDIA/apex/blob/4e1ae43f7f7ac69113ef426dd15f37123f0a2ed3/apex/parallel/__init__.py#L60\n\n    Args:\n        group_size (int): number of GPU's to collaborate for sync bn\n    \"\"\"\n\n    assert group_size > 0\n\n    world_size = torch.distributed.get_world_size()\n    if world_size <= 8:\n        if group_size > world_size:\n            logging.warning(\n                f\"Requested group size [{group_size}] > world size [{world_size}]. \"\n                \"Assuming local debug run and capping it to world size.\"\n            )\n            group_size = world_size\n    assert world_size >= group_size\n    assert world_size % group_size == 0\n\n    group = None\n    for group_num in range(world_size // group_size):\n        group_ids = range(group_num * group_size, (group_num + 1) * group_size)\n        cur_group = torch.distributed.new_group(ranks=group_ids)\n        if torch.distributed.get_rank() // group_size == group_num:\n            group = cur_group\n            # can not drop out and return here, every process must go through creation of all subgroups\n\n    assert group is not None\n    return group\n\n\ndef is_dist_avail_and_initialized():\n    if not dist.is_available():\n        return False\n    if not dist.is_initialized():\n        return False\n    return True\n\n\ndef gather_to_rank_0_via_filesys(data, filesys_save_dir=None):\n    \"\"\"\n    Gather any picklable data to rank 0 via filesystem, using all_gather_via_filesys.\n    \"\"\"\n    return all_gather_via_filesys(data, filesys_save_dir, gather_to_rank_0_only=True)\n"
  },
  {
    "path": "sam3/train/utils/logger.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n\nimport atexit\nimport functools\nimport logging\nimport sys\nimport uuid\nfrom typing import Any, Dict, Optional, Union\n\nfrom hydra.utils import instantiate\nfrom iopath.common.file_io import g_pathmgr\nfrom numpy import ndarray\nfrom sam3.train.utils.train_utils import get_machine_local_and_dist_rank, makedir\nfrom torch import Tensor\nfrom torch.utils.tensorboard import SummaryWriter\n\nScalar = Union[Tensor, ndarray, int, float]\n\n\ndef make_tensorboard_logger(log_dir: str, **writer_kwargs: Any):\n    makedir(log_dir)\n    summary_writer_method = SummaryWriter\n    return TensorBoardLogger(\n        path=log_dir, summary_writer_method=summary_writer_method, **writer_kwargs\n    )\n\n\nclass TensorBoardWriterWrapper:\n    \"\"\"\n    A wrapper around a SummaryWriter object.\n    \"\"\"\n\n    def __init__(\n        self,\n        path: str,\n        *args: Any,\n        filename_suffix: str = None,\n        summary_writer_method: Any = SummaryWriter,\n        **kwargs: Any,\n    ) -> None:\n        \"\"\"Create a new TensorBoard logger.\n        On construction, the logger creates a new events file that logs\n        will be written to.  If the environment variable `RANK` is defined,\n        logger will only log if RANK = 0.\n\n        NOTE: If using the logger with distributed training:\n        - This logger can call collective operations\n        - Logs will be written on rank 0 only\n        - Logger must be constructed synchronously *after* initializing distributed process group.\n\n        Args:\n            path (str): path to write logs to\n            *args, **kwargs: Extra arguments to pass to SummaryWriter\n        \"\"\"\n        self._writer: Optional[SummaryWriter] = None\n        _, self._rank = get_machine_local_and_dist_rank()\n        self._path: str = path\n        if self._rank == 0:\n            logging.info(\n                f\"TensorBoard SummaryWriter instantiated. Files will be stored in: {path}\"\n            )\n            self._writer = summary_writer_method(\n                log_dir=path,\n                *args,\n                filename_suffix=filename_suffix or str(uuid.uuid4()),\n                **kwargs,\n            )\n        else:\n            logging.debug(\n                f\"Not logging meters on this host because env RANK: {self._rank} != 0\"\n            )\n        atexit.register(self.close)\n\n    @property\n    def writer(self) -> Optional[SummaryWriter]:\n        return self._writer\n\n    @property\n    def path(self) -> str:\n        return self._path\n\n    def flush(self) -> None:\n        \"\"\"Writes pending logs to disk.\"\"\"\n\n        if not self._writer:\n            return\n\n        self._writer.flush()\n\n    def close(self) -> None:\n        \"\"\"Close writer, flushing pending logs to disk.\n        Logs cannot be written after `close` is called.\n        \"\"\"\n\n        if not self._writer:\n            return\n\n        self._writer.close()\n        self._writer = None\n\n\nclass TensorBoardLogger(TensorBoardWriterWrapper):\n    \"\"\"\n    A simple logger for TensorBoard.\n    \"\"\"\n\n    def log_dict(self, payload: Dict[str, Scalar], step: int) -> None:\n        \"\"\"Add multiple scalar values to TensorBoard.\n\n        Args:\n            payload (dict): dictionary of tag name and scalar value\n            step (int, Optional): step value to record\n        \"\"\"\n        if not self._writer:\n            return\n        for k, v in payload.items():\n            self.log(k, v, step)\n\n    def log(self, name: str, data: Scalar, step: int) -> None:\n        \"\"\"Add scalar data to TensorBoard.\n\n        Args:\n            name (string): tag name used to group scalars\n            data (float/int/Tensor): scalar data to log\n            step (int, optional): step value to record\n        \"\"\"\n        if not self._writer:\n            return\n        self._writer.add_scalar(name, data, global_step=step, new_style=True)\n\n    def log_hparams(\n        self, hparams: Dict[str, Scalar], meters: Dict[str, Scalar]\n    ) -> None:\n        \"\"\"Add hyperparameter data to TensorBoard.\n\n        Args:\n            hparams (dict): dictionary of hyperparameter names and corresponding values\n            meters (dict): dictionary of name of meter and corersponding values\n        \"\"\"\n        if not self._writer:\n            return\n        self._writer.add_hparams(hparams, meters)\n\n\nclass Logger:\n    \"\"\"\n    A logger class that can interface with multiple loggers. It now supports tensorboard only for simplicity, but you can extend it with your own logger.\n    \"\"\"\n\n    def __init__(self, logging_conf):\n        # allow turning off TensorBoard with \"should_log: false\" in config\n        tb_config = logging_conf.tensorboard_writer\n        tb_should_log = tb_config and tb_config.pop(\"should_log\", True)\n        self.tb_logger = instantiate(tb_config) if tb_should_log else None\n\n    def log_dict(self, payload: Dict[str, Scalar], step: int) -> None:\n        if self.tb_logger:\n            self.tb_logger.log_dict(payload, step)\n\n    def log(self, name: str, data: Scalar, step: int) -> None:\n        if self.tb_logger:\n            self.tb_logger.log(name, data, step)\n\n    def log_hparams(\n        self, hparams: Dict[str, Scalar], meters: Dict[str, Scalar]\n    ) -> None:\n        if self.tb_logger:\n            self.tb_logger.log_hparams(hparams, meters)\n\n\n# cache the opened file object, so that different calls to `setup_logger`\n# with the same file name can safely write to the same file.\n@functools.lru_cache(maxsize=None)\ndef _cached_log_stream(filename):\n    # we tune the buffering value so that the logs are updated\n    # frequently.\n    log_buffer_kb = 10 * 1024  # 10KB\n    io = g_pathmgr.open(filename, mode=\"a\", buffering=log_buffer_kb)\n    atexit.register(io.close)\n    return io\n\n\ndef setup_logging(\n    name,\n    output_dir=None,\n    rank=0,\n    log_level_primary=\"INFO\",\n    log_level_secondary=\"ERROR\",\n):\n    \"\"\"\n    Setup various logging streams: stdout and file handlers.\n    For file handlers, we only setup for the master gpu.\n    \"\"\"\n    # get the filename if we want to log to the file as well\n    log_filename = None\n    if output_dir:\n        makedir(output_dir)\n        if rank == 0:\n            log_filename = f\"{output_dir}/log.txt\"\n\n    logger = logging.getLogger(name)\n    logger.setLevel(log_level_primary)\n\n    # create formatter\n    FORMAT = \"%(levelname)s %(asctime)s %(filename)s:%(lineno)4d: %(message)s\"\n    formatter = logging.Formatter(FORMAT)\n\n    # Cleanup any existing handlers\n    for h in logger.handlers:\n        logger.removeHandler(h)\n    logger.root.handlers = []\n\n    # setup the console handler\n    console_handler = logging.StreamHandler(sys.stdout)\n    console_handler.setFormatter(formatter)\n    logger.addHandler(console_handler)\n    if rank == 0:\n        console_handler.setLevel(log_level_primary)\n    else:\n        console_handler.setLevel(log_level_secondary)\n\n    # we log to file as well if user wants\n    if log_filename and rank == 0:\n        file_handler = logging.StreamHandler(_cached_log_stream(log_filename))\n        file_handler.setLevel(log_level_primary)\n        file_handler.setFormatter(formatter)\n        logger.addHandler(file_handler)\n\n    logging.root = logger\n\n\ndef shutdown_logging():\n    \"\"\"\n    After training is done, we ensure to shut down all the logger streams.\n    \"\"\"\n    logging.info(\"Shutting down loggers...\")\n    handlers = logging.root.handlers\n    for handler in handlers:\n        handler.close()\n"
  },
  {
    "path": "sam3/train/utils/train_utils.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n\nimport logging\nimport math\nimport os\nimport random\nimport re\nfrom datetime import timedelta\nfrom typing import Optional\n\nimport hydra\nimport numpy as np\nimport omegaconf\nimport torch\nimport torch.distributed as dist\nfrom iopath.common.file_io import g_pathmgr\nfrom omegaconf import OmegaConf\n\n\ndef multiply_all(*args):\n    return np.prod(np.array(args)).item()\n\n\ndef collect_dict_keys(config):\n    \"\"\"This function recursively iterates through a dataset configuration, and collect all the dict_key that are defined\"\"\"\n    val_keys = []\n    # If the this config points to the collate function, then it has a key\n    if \"_target_\" in config and re.match(r\".*collate_fn.*\", config[\"_target_\"]):\n        val_keys.append(config[\"dict_key\"])\n    else:\n        # Recursively proceed\n        for v in config.values():\n            if isinstance(v, type(config)):\n                val_keys.extend(collect_dict_keys(v))\n            elif isinstance(v, omegaconf.listconfig.ListConfig):\n                for item in v:\n                    if isinstance(item, type(config)):\n                        val_keys.extend(collect_dict_keys(item))\n    return val_keys\n\n\nclass Phase:\n    TRAIN = \"train\"\n    VAL = \"val\"\n\n\ndef register_omegaconf_resolvers():\n    OmegaConf.register_new_resolver(\"get_method\", hydra.utils.get_method)\n    OmegaConf.register_new_resolver(\"get_class\", hydra.utils.get_class)\n    OmegaConf.register_new_resolver(\"add\", lambda x, y: x + y)\n    OmegaConf.register_new_resolver(\"times\", multiply_all)\n    OmegaConf.register_new_resolver(\"divide\", lambda x, y: x / y)\n    OmegaConf.register_new_resolver(\"pow\", lambda x, y: x**y)\n    OmegaConf.register_new_resolver(\"subtract\", lambda x, y: x - y)\n    OmegaConf.register_new_resolver(\"range\", lambda x: list(range(x)))\n    OmegaConf.register_new_resolver(\"int\", lambda x: int(x))\n    OmegaConf.register_new_resolver(\"ceil_int\", lambda x: int(math.ceil(x)))\n    OmegaConf.register_new_resolver(\"merge\", lambda *x: OmegaConf.merge(*x))\n    OmegaConf.register_new_resolver(\"string\", lambda x: str(x))\n\n\ndef setup_distributed_backend(backend, timeout_mins):\n    \"\"\"\n    Initialize torch.distributed and set the CUDA device.\n    Expects environment variables to be set as per\n    https://pytorch.org/docs/stable/distributed.html#environment-variable-initialization\n    along with the environ variable \"LOCAL_RANK\" which is used to set the CUDA device.\n    \"\"\"\n    # enable TORCH_NCCL_ASYNC_ERROR_HANDLING to ensure dist nccl ops time out after timeout_mins\n    # of waiting\n    os.environ[\"TORCH_NCCL_ASYNC_ERROR_HANDLING\"] = \"1\"\n    logging.info(f\"Setting up torch.distributed with a timeout of {timeout_mins} mins\")\n    dist.init_process_group(backend=backend, timeout=timedelta(minutes=timeout_mins))\n    return dist.get_rank()\n\n\ndef get_machine_local_and_dist_rank():\n    \"\"\"\n    Get the distributed and local rank of the current gpu.\n    \"\"\"\n    local_rank = int(os.environ.get(\"LOCAL_RANK\", None))\n    distributed_rank = int(os.environ.get(\"RANK\", None))\n    assert local_rank is not None and distributed_rank is not None, (\n        \"Please the set the RANK and LOCAL_RANK environment variables.\"\n    )\n    return local_rank, distributed_rank\n\n\ndef print_cfg(cfg):\n    \"\"\"\n    Supports printing both Hydra DictConfig and also the AttrDict config\n    \"\"\"\n    logging.info(\"Training with config:\")\n    logging.info(OmegaConf.to_yaml(cfg))\n\n\ndef set_seeds(seed_value, max_epochs, dist_rank):\n    \"\"\"\n    Set the python random, numpy and torch seed for each gpu. Also set the CUDA\n    seeds if the CUDA is available. This ensures deterministic nature of the training.\n    \"\"\"\n    # Since in the pytorch sampler, we increment the seed by 1 for every epoch.\n    seed_value = (seed_value + dist_rank) * max_epochs\n    logging.info(f\"MACHINE SEED: {seed_value}\")\n    random.seed(seed_value)\n    np.random.seed(seed_value)\n    torch.manual_seed(seed_value)\n    if torch.cuda.is_available():\n        torch.cuda.manual_seed_all(seed_value)\n\n\ndef makedir(dir_path):\n    \"\"\"\n    Create the directory if it does not exist.\n    \"\"\"\n    is_success = False\n    try:\n        if not g_pathmgr.exists(dir_path):\n            g_pathmgr.mkdirs(dir_path)\n        is_success = True\n    except BaseException:\n        logging.info(f\"Error creating directory: {dir_path}\")\n    return is_success\n\n\ndef is_dist_avail_and_initialized():\n    if not dist.is_available():\n        return False\n    if not dist.is_initialized():\n        return False\n    return True\n\n\ndef get_amp_type(amp_type: Optional[str] = None):\n    if amp_type is None:\n        return None\n    assert amp_type in [\"bfloat16\", \"float16\"], \"Invalid Amp type.\"\n    if amp_type == \"bfloat16\":\n        return torch.bfloat16\n    else:\n        return torch.float16\n\n\ndef log_env_variables():\n    env_keys = sorted(list(os.environ.keys()))\n    st = \"\"\n    for k in env_keys:\n        v = os.environ[k]\n        st += f\"{k}={v}\\n\"\n    logging.info(\"Logging ENV_VARIABLES\")\n    logging.info(st)\n\n\nclass AverageMeter:\n    \"\"\"Computes and stores the average and current value\"\"\"\n\n    def __init__(self, name, device, fmt=\":f\"):\n        self.name = name\n        self.fmt = fmt\n        self.device = device\n        self.reset()\n\n    def reset(self):\n        self.val = 0\n        self.avg = 0\n        self.sum = 0\n        self.count = 0\n        self._allow_updates = True\n\n    def update(self, val, n=1):\n        self.val = val\n        self.sum += val * n\n        self.count += n\n        self.avg = self.sum / self.count\n\n    def __str__(self):\n        fmtstr = \"{name}: {val\" + self.fmt + \"} ({avg\" + self.fmt + \"})\"\n        return fmtstr.format(**self.__dict__)\n\n\nclass MemMeter:\n    \"\"\"Computes and stores the current, avg, and max of peak Mem usage per iteration\"\"\"\n\n    def __init__(self, name, device, fmt=\":f\"):\n        self.name = name\n        self.fmt = fmt\n        self.device = device\n        self.reset()\n\n    def reset(self):\n        self.val = 0  # Per iteration max usage\n        self.avg = 0  # Avg per iteration max usage\n        self.peak = 0  # Peak usage for lifetime of program\n        self.sum = 0\n        self.count = 0\n        self._allow_updates = True\n\n    def update(self, n=1, reset_peak_usage=True):\n        self.val = torch.cuda.max_memory_allocated() // 1e9\n        self.sum += self.val * n\n        self.count += n\n        self.avg = self.sum / self.count\n        self.peak = max(self.peak, self.val)\n        if reset_peak_usage:\n            torch.cuda.reset_peak_memory_stats()\n\n    def __str__(self):\n        fmtstr = (\n            \"{name}: {val\"\n            + self.fmt\n            + \"} ({avg\"\n            + self.fmt\n            + \"}/{peak\"\n            + self.fmt\n            + \"})\"\n        )\n        return fmtstr.format(**self.__dict__)\n\n\ndef human_readable_time(time_seconds):\n    time = int(time_seconds)\n    minutes, seconds = divmod(time, 60)\n    hours, minutes = divmod(minutes, 60)\n    days, hours = divmod(hours, 24)\n    return f\"{days:02}d {hours:02}h {minutes:02}m\"\n\n\nclass DurationMeter:\n    def __init__(self, name, device, fmt=\":f\"):\n        self.name = name\n        self.device = device\n        self.fmt = fmt\n        self.val = 0\n\n    def reset(self):\n        self.val = 0\n\n    def update(self, val):\n        self.val = val\n\n    def add(self, val):\n        self.val += val\n\n    def __str__(self):\n        return f\"{self.name}: {human_readable_time(self.val)}\"\n\n\nclass ProgressMeter:\n    def __init__(self, num_batches, meters, real_meters, prefix=\"\"):\n        self.batch_fmtstr = self._get_batch_fmtstr(num_batches)\n        self.meters = meters\n        self.real_meters = real_meters\n        self.prefix = prefix\n\n    def display(self, batch, enable_print=False):\n        entries = [self.prefix + self.batch_fmtstr.format(batch)]\n        entries += [str(meter) for meter in self.meters]\n        entries += [\n            \" | \".join(\n                [\n                    f\"{os.path.join(name, subname)}: {val:.4f}\"\n                    for subname, val in meter.compute().items()\n                ]\n            )\n            for name, meter in self.real_meters.items()\n        ]\n        logging.info(\" | \".join(entries))\n        if enable_print:\n            print(\" | \".join(entries))\n\n    def _get_batch_fmtstr(self, num_batches):\n        num_digits = len(str(num_batches // 1))\n        fmt = \"{:\" + str(num_digits) + \"d}\"\n        return \"[\" + fmt + \"/\" + fmt.format(num_batches) + \"]\"\n\n\ndef get_resume_checkpoint(checkpoint_save_dir):\n    if not g_pathmgr.isdir(checkpoint_save_dir):\n        return None\n    ckpt_file = os.path.join(checkpoint_save_dir, \"checkpoint.pt\")\n    if not g_pathmgr.isfile(ckpt_file):\n        return None\n\n    return ckpt_file\n"
  },
  {
    "path": "sam3/visualization_utils.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\nimport json\nimport os\nimport subprocess\nfrom pathlib import Path\n\nimport cv2\nimport matplotlib.patches as patches\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nimport pycocotools.mask as mask_utils\nimport torch\nfrom matplotlib.colors import to_rgb\nfrom PIL import Image\nfrom skimage.color import lab2rgb, rgb2lab\nfrom sklearn.cluster import KMeans\nfrom torchvision.ops import masks_to_boxes\nfrom tqdm import tqdm\n\n\ndef generate_colors(n_colors=256, n_samples=5000):\n    # Step 1: Random RGB samples\n    np.random.seed(42)\n    rgb = np.random.rand(n_samples, 3)\n    # Step 2: Convert to LAB for perceptual uniformity\n    # print(f\"Converting {n_samples} RGB samples to LAB color space...\")\n    lab = rgb2lab(rgb.reshape(1, -1, 3)).reshape(-1, 3)\n    # print(\"Conversion to LAB complete.\")\n    # Step 3: k-means clustering in LAB\n    kmeans = KMeans(n_clusters=n_colors, n_init=10)\n    # print(f\"Fitting KMeans with {n_colors} clusters on {n_samples} samples...\")\n    kmeans.fit(lab)\n    # print(\"KMeans fitting complete.\")\n    centers_lab = kmeans.cluster_centers_\n    # Step 4: Convert LAB back to RGB\n    colors_rgb = lab2rgb(centers_lab.reshape(1, -1, 3)).reshape(-1, 3)\n    colors_rgb = np.clip(colors_rgb, 0, 1)\n    return colors_rgb\n\n\nCOLORS = generate_colors(n_colors=128, n_samples=5000)\n\n\ndef show_img_tensor(img_batch, vis_img_idx=0):\n    MEAN_IMG = np.array([0.5, 0.5, 0.5])\n    STD_IMG = np.array([0.5, 0.5, 0.5])\n    im_tensor = img_batch[vis_img_idx].detach().cpu()\n    assert im_tensor.dim() == 3\n    im_tensor = im_tensor.numpy().transpose((1, 2, 0))\n    im_tensor = (im_tensor * STD_IMG) + MEAN_IMG\n    im_tensor = np.clip(im_tensor, 0, 1)\n    plt.imshow(im_tensor)\n\n\ndef draw_box_on_image(image, box, color=(0, 255, 0)):\n    \"\"\"\n    Draws a rectangle on a given PIL image using the provided box coordinates in xywh format.\n    :param image: PIL.Image - The image on which to draw the rectangle.\n    :param box: tuple - A tuple (x, y, w, h) representing the top-left corner, width, and height of the rectangle.\n    :param color: tuple - A tuple (R, G, B) representing the color of the rectangle. Default is red.\n    :return: PIL.Image - The image with the rectangle drawn on it.\n    \"\"\"\n    # Ensure the image is in RGB mode\n    image = image.convert(\"RGB\")\n    # Unpack the box coordinates\n    x, y, w, h = box\n    x, y, w, h = int(x), int(y), int(w), int(h)\n    # Get the pixel data\n    pixels = image.load()\n    # Draw the top and bottom edges\n    for i in range(x, x + w):\n        pixels[i, y] = color\n        pixels[i, y + h - 1] = color\n        pixels[i, y + 1] = color\n        pixels[i, y + h] = color\n        pixels[i, y - 1] = color\n        pixels[i, y + h - 2] = color\n    # Draw the left and right edges\n    for j in range(y, y + h):\n        pixels[x, j] = color\n        pixels[x + 1, j] = color\n        pixels[x - 1, j] = color\n        pixels[x + w - 1, j] = color\n        pixels[x + w, j] = color\n        pixels[x + w - 2, j] = color\n    return image\n\n\ndef plot_bbox(\n    img_height,\n    img_width,\n    box,\n    box_format=\"XYXY\",\n    relative_coords=True,\n    color=\"r\",\n    linestyle=\"solid\",\n    text=None,\n    ax=None,\n):\n    if box_format == \"XYXY\":\n        x, y, x2, y2 = box\n        w = x2 - x\n        h = y2 - y\n    elif box_format == \"XYWH\":\n        x, y, w, h = box\n    elif box_format == \"CxCyWH\":\n        cx, cy, w, h = box\n        x = cx - w / 2\n        y = cy - h / 2\n    else:\n        raise RuntimeError(f\"Invalid box_format {box_format}\")\n\n    if relative_coords:\n        x *= img_width\n        w *= img_width\n        y *= img_height\n        h *= img_height\n\n    if ax is None:\n        ax = plt.gca()\n    rect = patches.Rectangle(\n        (x, y),\n        w,\n        h,\n        linewidth=1.5,\n        edgecolor=color,\n        facecolor=\"none\",\n        linestyle=linestyle,\n    )\n    ax.add_patch(rect)\n    if text is not None:\n        facecolor = \"w\"\n        ax.text(\n            x,\n            y - 5,\n            text,\n            color=color,\n            weight=\"bold\",\n            fontsize=8,\n            bbox={\"facecolor\": facecolor, \"alpha\": 0.75, \"pad\": 2},\n        )\n\n\ndef plot_mask(mask, color=\"r\", ax=None):\n    im_h, im_w = mask.shape\n    mask_img = np.zeros((im_h, im_w, 4), dtype=np.float32)\n    mask_img[..., :3] = to_rgb(color)\n    mask_img[..., 3] = mask * 0.5\n    # Use the provided ax or the current axis\n    if ax is None:\n        ax = plt.gca()\n    ax.imshow(mask_img)\n\n\ndef normalize_bbox(bbox_xywh, img_w, img_h):\n    # Assumes bbox_xywh is in XYWH format\n    if isinstance(bbox_xywh, list):\n        assert len(bbox_xywh) == 4, (\n            \"bbox_xywh list must have 4 elements. Batching not support except for torch tensors.\"\n        )\n        normalized_bbox = bbox_xywh.copy()\n        normalized_bbox[0] /= img_w\n        normalized_bbox[1] /= img_h\n        normalized_bbox[2] /= img_w\n        normalized_bbox[3] /= img_h\n    else:\n        assert isinstance(bbox_xywh, torch.Tensor), (\n            \"Only torch tensors are supported for batching.\"\n        )\n        normalized_bbox = bbox_xywh.clone()\n        assert normalized_bbox.size(-1) == 4, (\n            \"bbox_xywh tensor must have last dimension of size 4.\"\n        )\n        normalized_bbox[..., 0] /= img_w\n        normalized_bbox[..., 1] /= img_h\n        normalized_bbox[..., 2] /= img_w\n        normalized_bbox[..., 3] /= img_h\n    return normalized_bbox\n\n\ndef visualize_frame_output(frame_idx, video_frames, outputs, figsize=(12, 8)):\n    plt.figure(figsize=figsize)\n    plt.title(f\"frame {frame_idx}\")\n    img = load_frame(video_frames[frame_idx])\n    img_H, img_W, _ = img.shape\n    plt.imshow(img)\n    for i in range(len(outputs[\"out_probs\"])):\n        box_xywh = outputs[\"out_boxes_xywh\"][i]\n        prob = outputs[\"out_probs\"][i]\n        obj_id = outputs[\"out_obj_ids\"][i]\n        binary_mask = outputs[\"out_binary_masks\"][i]\n        color = COLORS[obj_id % len(COLORS)]\n        plot_bbox(\n            img_H,\n            img_W,\n            box_xywh,\n            text=f\"(id={obj_id}, {prob=:.2f})\",\n            box_format=\"XYWH\",\n            color=color,\n        )\n        plot_mask(binary_mask, color=color)\n\n\ndef visualize_formatted_frame_output(\n    frame_idx,\n    video_frames,\n    outputs_list,\n    titles=None,\n    points_list=None,\n    points_labels_list=None,\n    figsize=(12, 8),\n    title_suffix=\"\",\n    prompt_info=None,\n):\n    \"\"\"Visualize up to three sets of segmentation masks on a video frame.\n\n    Args:\n        frame_idx: Frame index to visualize\n        image_files: List of image file paths\n        outputs_list: List of {frame_idx: {obj_id: mask_tensor}} or single dict {obj_id: mask_tensor}\n        titles: List of titles for each set of outputs_list\n        points_list: Optional list of point coordinates\n        points_labels_list: Optional list of point labels\n        figsize: Figure size tuple\n        save: Whether to save the visualization to file\n        output_dir: Base output directory when saving\n        scenario_name: Scenario name for organizing saved files\n        title_suffix: Additional title suffix\n        prompt_info: Dictionary with prompt information (boxes, points, etc.)\n    \"\"\"\n    # Handle single output dict case\n    if isinstance(outputs_list, dict) and frame_idx in outputs_list:\n        # This is a single outputs dict with frame indices as keys\n        outputs_list = [outputs_list]\n    elif isinstance(outputs_list, dict) and not any(\n        isinstance(k, int) for k in outputs_list.keys()\n    ):\n        # This is a single frame's outputs {obj_id: mask}\n        single_frame_outputs = {frame_idx: outputs_list}\n        outputs_list = [single_frame_outputs]\n\n    num_outputs = len(outputs_list)\n    if titles is None:\n        titles = [f\"Set {i + 1}\" for i in range(num_outputs)]\n    assert len(titles) == num_outputs, (\n        \"length of `titles` should match that of `outputs_list` if not None.\"\n    )\n\n    _, axes = plt.subplots(1, num_outputs, figsize=figsize)\n    if num_outputs == 1:\n        axes = [axes]  # Make it iterable\n\n    img = load_frame(video_frames[frame_idx])\n    img_H, img_W, _ = img.shape\n\n    for idx in range(num_outputs):\n        ax, outputs_set, ax_title = axes[idx], outputs_list[idx], titles[idx]\n        ax.set_title(f\"Frame {frame_idx} - {ax_title}{title_suffix}\")\n        ax.imshow(img)\n\n        if frame_idx in outputs_set:\n            _outputs = outputs_set[frame_idx]\n        else:\n            print(f\"Warning: Frame {frame_idx} not found in outputs_set\")\n            continue\n\n        if prompt_info and frame_idx == 0:  # Show prompts on first frame\n            if \"boxes\" in prompt_info:\n                for box in prompt_info[\"boxes\"]:\n                    # box is in [x, y, w, h] normalized format\n                    x, y, w, h = box\n                    plot_bbox(\n                        img_H,\n                        img_W,\n                        [x, y, x + w, y + h],  # Convert to XYXY\n                        box_format=\"XYXY\",\n                        relative_coords=True,\n                        color=\"yellow\",\n                        linestyle=\"dashed\",\n                        text=\"PROMPT BOX\",\n                        ax=ax,\n                    )\n\n            if \"points\" in prompt_info and \"point_labels\" in prompt_info:\n                points = np.array(prompt_info[\"points\"])\n                labels = np.array(prompt_info[\"point_labels\"])\n                # Convert normalized to pixel coordinates\n                points_pixel = points * np.array([img_W, img_H])\n\n                # Draw positive points (green stars)\n                pos_points = points_pixel[labels == 1]\n                if len(pos_points) > 0:\n                    ax.scatter(\n                        pos_points[:, 0],\n                        pos_points[:, 1],\n                        color=\"lime\",\n                        marker=\"*\",\n                        s=200,\n                        edgecolor=\"white\",\n                        linewidth=2,\n                        label=\"Positive Points\",\n                        zorder=10,\n                    )\n\n                # Draw negative points (red stars)\n                neg_points = points_pixel[labels == 0]\n                if len(neg_points) > 0:\n                    ax.scatter(\n                        neg_points[:, 0],\n                        neg_points[:, 1],\n                        color=\"red\",\n                        marker=\"*\",\n                        s=200,\n                        edgecolor=\"white\",\n                        linewidth=2,\n                        label=\"Negative Points\",\n                        zorder=10,\n                    )\n\n        objects_drawn = 0\n        for obj_id, binary_mask in _outputs.items():\n            mask_sum = (\n                binary_mask.sum()\n                if hasattr(binary_mask, \"sum\")\n                else np.sum(binary_mask)\n            )\n\n            if mask_sum > 0:  # Only draw if mask has content\n                # Convert to torch tensor if it's not already\n                if not isinstance(binary_mask, torch.Tensor):\n                    binary_mask = torch.tensor(binary_mask)\n\n                # Find bounding box from mask\n                if binary_mask.any():\n                    box_xyxy = masks_to_boxes(binary_mask.unsqueeze(0)).squeeze()\n                    box_xyxy = normalize_bbox(box_xyxy, img_W, img_H)\n                else:\n                    # Fallback: create a small box at center\n                    box_xyxy = [0.45, 0.45, 0.55, 0.55]\n\n                color = COLORS[obj_id % len(COLORS)]\n\n                plot_bbox(\n                    img_H,\n                    img_W,\n                    box_xyxy,\n                    text=f\"(id={obj_id})\",\n                    box_format=\"XYXY\",\n                    color=color,\n                    ax=ax,\n                )\n\n                # Convert back to numpy for plotting\n                mask_np = (\n                    binary_mask.numpy()\n                    if isinstance(binary_mask, torch.Tensor)\n                    else binary_mask\n                )\n                plot_mask(mask_np, color=color, ax=ax)\n                objects_drawn += 1\n\n        if objects_drawn == 0:\n            ax.text(\n                0.5,\n                0.5,\n                \"No objects detected\",\n                transform=ax.transAxes,\n                fontsize=16,\n                ha=\"center\",\n                va=\"center\",\n                color=\"red\",\n                weight=\"bold\",\n            )\n\n        # Draw additional points if provided\n        if points_list is not None and points_list[idx] is not None:\n            show_points(\n                points_list[idx], points_labels_list[idx], ax=ax, marker_size=200\n            )\n\n        ax.axis(\"off\")\n\n    plt.tight_layout()\n    plt.show()\n\n\ndef render_masklet_frame(img, outputs, frame_idx=None, alpha=0.5):\n    \"\"\"\n    Overlays masklets and bounding boxes on a single image frame.\n    Args:\n        img: np.ndarray, shape (H, W, 3), uint8 or float32 in [0,255] or [0,1]\n        outputs: dict with keys: out_boxes_xywh, out_probs, out_obj_ids, out_binary_masks\n        frame_idx: int or None, for overlaying frame index text\n        alpha: float, mask overlay alpha\n    Returns:\n        overlay: np.ndarray, shape (H, W, 3), uint8\n    \"\"\"\n    if img.dtype == np.float32 or img.max() <= 1.0:\n        img = (img * 255).astype(np.uint8)\n    img = img[..., :3]  # drop alpha if present\n    height, width = img.shape[:2]\n    overlay = img.copy()\n\n    for i in range(len(outputs[\"out_probs\"])):\n        obj_id = outputs[\"out_obj_ids\"][i]\n        color = COLORS[obj_id % len(COLORS)]\n        color255 = (color * 255).astype(np.uint8)\n        mask = outputs[\"out_binary_masks\"][i]\n        if mask.shape != img.shape[:2]:\n            mask = cv2.resize(\n                mask.astype(np.float32),\n                (img.shape[1], img.shape[0]),\n                interpolation=cv2.INTER_NEAREST,\n            )\n        mask_bool = mask > 0.5\n        for c in range(3):\n            overlay[..., c][mask_bool] = (\n                alpha * color255[c] + (1 - alpha) * overlay[..., c][mask_bool]\n            ).astype(np.uint8)\n\n    # Draw bounding boxes and text\n    for i in range(len(outputs[\"out_probs\"])):\n        box_xywh = outputs[\"out_boxes_xywh\"][i]\n        obj_id = outputs[\"out_obj_ids\"][i]\n        prob = outputs[\"out_probs\"][i]\n        color = COLORS[obj_id % len(COLORS)]\n        color255 = tuple(int(x * 255) for x in color)\n        x, y, w, h = box_xywh\n        x1 = int(x * width)\n        y1 = int(y * height)\n        x2 = int((x + w) * width)\n        y2 = int((y + h) * height)\n        cv2.rectangle(overlay, (x1, y1), (x2, y2), color255, 2)\n        if prob is not None:\n            label = f\"id={obj_id}, p={prob:.2f}\"\n        else:\n            label = f\"id={obj_id}\"\n        cv2.putText(\n            overlay,\n            label,\n            (x1, max(y1 - 10, 0)),\n            cv2.FONT_HERSHEY_SIMPLEX,\n            0.5,\n            color255,\n            1,\n            cv2.LINE_AA,\n        )\n\n    # Overlay frame index at the top-left corner\n    if frame_idx is not None:\n        cv2.putText(\n            overlay,\n            f\"Frame {frame_idx}\",\n            (10, 30),\n            cv2.FONT_HERSHEY_SIMPLEX,\n            1.0,\n            (255, 255, 255),\n            2,\n            cv2.LINE_AA,\n        )\n\n    return overlay\n\n\ndef save_masklet_video(video_frames, outputs, out_path, alpha=0.5, fps=10):\n    # Each outputs dict has keys: \"out_boxes_xywh\", \"out_probs\", \"out_obj_ids\", \"out_binary_masks\"\n    # video_frames: list of video frame data, same length as outputs_list\n\n    # Read first frame to get size\n    first_img = load_frame(video_frames[0])\n    height, width = first_img.shape[:2]\n    if first_img.dtype == np.float32 or first_img.max() <= 1.0:\n        first_img = (first_img * 255).astype(np.uint8)\n    # Use 'mp4v' for best compatibility with VSCode playback (.mp4 files)\n    fourcc = cv2.VideoWriter_fourcc(*\"mp4v\")\n    writer = cv2.VideoWriter(\"temp.mp4\", fourcc, fps, (width, height))\n\n    outputs_list = [\n        (video_frames[frame_idx], frame_idx, outputs[frame_idx])\n        for frame_idx in sorted(outputs.keys())\n    ]\n\n    for frame, frame_idx, frame_outputs in tqdm(outputs_list):\n        img = load_frame(frame)\n        overlay = render_masklet_frame(\n            img, frame_outputs, frame_idx=frame_idx, alpha=alpha\n        )\n        writer.write(cv2.cvtColor(overlay, cv2.COLOR_RGB2BGR))\n\n    writer.release()\n\n    # Re-encode the video for VSCode compatibility using ffmpeg\n    subprocess.run([\"ffmpeg\", \"-y\", \"-i\", \"temp.mp4\", out_path])\n    print(f\"Re-encoded video saved to {out_path}\")\n\n    os.remove(\"temp.mp4\")  # Clean up temporary file\n\n\ndef save_masklet_image(frame, outputs, out_path, alpha=0.5, frame_idx=None):\n    \"\"\"\n    Save a single image with masklet overlays.\n    \"\"\"\n    img = load_frame(frame)\n    overlay = render_masklet_frame(img, outputs, frame_idx=frame_idx, alpha=alpha)\n    Image.fromarray(overlay).save(out_path)\n    print(f\"Overlay image saved to {out_path}\")\n\n\ndef prepare_masks_for_visualization(frame_to_output):\n    # frame_to_obj_masks --> {frame_idx: {'output_probs': np.array, `out_obj_ids`: np.array, `out_binary_masks`: np.array}}\n    for frame_idx, out in frame_to_output.items():\n        _processed_out = {}\n        for idx, obj_id in enumerate(out[\"out_obj_ids\"].tolist()):\n            if out[\"out_binary_masks\"][idx].any():\n                _processed_out[obj_id] = out[\"out_binary_masks\"][idx]\n        frame_to_output[frame_idx] = _processed_out\n    return frame_to_output\n\n\ndef convert_coco_to_masklet_format(\n    annotations, img_info, is_prediction=False, score_threshold=0.5\n):\n    \"\"\"\n    Convert COCO format annotations to format expected by render_masklet_frame\n    \"\"\"\n    outputs = {\n        \"out_boxes_xywh\": [],\n        \"out_probs\": [],\n        \"out_obj_ids\": [],\n        \"out_binary_masks\": [],\n    }\n\n    img_h, img_w = img_info[\"height\"], img_info[\"width\"]\n\n    for idx, ann in enumerate(annotations):\n        # Get bounding box in relative XYWH format\n        if \"bbox\" in ann:\n            bbox = ann[\"bbox\"]\n            if max(bbox) > 1.0:  # Convert absolute to relative coordinates\n                bbox = [\n                    bbox[0] / img_w,\n                    bbox[1] / img_h,\n                    bbox[2] / img_w,\n                    bbox[3] / img_h,\n                ]\n        else:\n            mask = mask_utils.decode(ann[\"segmentation\"])\n            rows = np.any(mask, axis=1)\n            cols = np.any(mask, axis=0)\n            if np.any(rows) and np.any(cols):\n                rmin, rmax = np.where(rows)[0][[0, -1]]\n                cmin, cmax = np.where(cols)[0][[0, -1]]\n                # Convert to relative XYWH\n                bbox = [\n                    cmin / img_w,\n                    rmin / img_h,\n                    (cmax - cmin + 1) / img_w,\n                    (rmax - rmin + 1) / img_h,\n                ]\n            else:\n                bbox = [0, 0, 0, 0]\n\n        outputs[\"out_boxes_xywh\"].append(bbox)\n\n        # Get probability/score\n        if is_prediction:\n            prob = ann[\"score\"]\n        else:\n            prob = 1.0  # GT has no probability\n        outputs[\"out_probs\"].append(prob)\n\n        outputs[\"out_obj_ids\"].append(idx)\n        mask = mask_utils.decode(ann[\"segmentation\"])\n        mask = (mask > score_threshold).astype(np.uint8)\n\n        outputs[\"out_binary_masks\"].append(mask)\n\n    return outputs\n\n\ndef save_side_by_side_visualization(img, gt_anns, pred_anns, noun_phrase):\n    \"\"\"\n    Create side-by-side visualization of GT and predictions\n    \"\"\"\n\n    # Create side-by-side visualization\n    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 7))\n\n    main_title = f\"Noun phrase: '{noun_phrase}'\"\n    fig.suptitle(main_title, fontsize=16, fontweight=\"bold\")\n\n    gt_overlay = render_masklet_frame(img, gt_anns, alpha=0.5)\n    ax1.imshow(gt_overlay)\n    ax1.set_title(\"Ground Truth\", fontsize=14, fontweight=\"bold\")\n    ax1.axis(\"off\")\n\n    pred_overlay = render_masklet_frame(img, pred_anns, alpha=0.5)\n    ax2.imshow(pred_overlay)\n    ax2.set_title(\"Predictions\", fontsize=14, fontweight=\"bold\")\n    ax2.axis(\"off\")\n\n    plt.subplots_adjust(top=0.88)\n    plt.tight_layout()\n\n\ndef bitget(val, idx):\n    return (val >> idx) & 1\n\n\ndef pascal_color_map():\n    colormap = np.zeros((512, 3), dtype=int)\n    ind = np.arange(512, dtype=int)\n    for shift in reversed(list(range(8))):\n        for channel in range(3):\n            colormap[:, channel] |= bitget(ind, channel) << shift\n        ind >>= 3\n\n    return colormap.astype(np.uint8)\n\n\ndef draw_masks_to_frame(\n    frame: np.ndarray, masks: np.ndarray, colors: np.ndarray\n) -> np.ndarray:\n    masked_frame = frame\n    for mask, color in zip(masks, colors):\n        curr_masked_frame = np.where(mask[..., None], color, masked_frame)\n        masked_frame = cv2.addWeighted(masked_frame, 0.75, curr_masked_frame, 0.25, 0)\n\n        if int(cv2.__version__[0]) > 3:\n            contours, _ = cv2.findContours(\n                np.array(mask, dtype=np.uint8).copy(),\n                cv2.RETR_TREE,\n                cv2.CHAIN_APPROX_NONE,\n            )\n        else:\n            _, contours, _ = cv2.findContours(\n                np.array(mask, dtype=np.uint8).copy(),\n                cv2.RETR_TREE,\n                cv2.CHAIN_APPROX_NONE,\n            )\n\n        cv2.drawContours(\n            masked_frame, contours, -1, (255, 255, 255), 7\n        )  # White outer contour\n        cv2.drawContours(\n            masked_frame, contours, -1, (0, 0, 0), 5\n        )  # Black middle contour\n        cv2.drawContours(\n            masked_frame, contours, -1, color.tolist(), 3\n        )  # Original color inner contour\n    return masked_frame\n\n\ndef get_annot_df(file_path: str):\n    with open(file_path, \"r\") as f:\n        data = json.load(f)\n\n    dfs = {}\n\n    for k, v in data.items():\n        if k in (\"info\", \"licenses\"):\n            dfs[k] = v\n            continue\n        df = pd.DataFrame(v)\n        dfs[k] = df\n\n    return dfs\n\n\ndef get_annot_dfs(file_list: list[str]):\n    dfs = {}\n    for annot_file in tqdm(file_list):\n        dataset_name = Path(annot_file).stem\n        dfs[dataset_name] = get_annot_df(annot_file)\n\n    return dfs\n\n\ndef get_media_dir(media_dir: str, dataset: str):\n    if dataset in [\"saco_veval_sav_test\", \"saco_veval_sav_val\"]:\n        return os.path.join(media_dir, \"saco_sav\", \"JPEGImages_24fps\")\n    elif dataset in [\"saco_veval_yt1b_test\", \"saco_veval_yt1b_val\"]:\n        return os.path.join(media_dir, \"saco_yt1b\", \"JPEGImages_6fps\")\n    elif dataset in [\"saco_veval_smartglasses_test\", \"saco_veval_smartglasses_val\"]:\n        return os.path.join(media_dir, \"saco_sg\", \"JPEGImages_6fps\")\n    elif dataset == \"sa_fari_test\":\n        return os.path.join(media_dir, \"sa_fari\", \"JPEGImages_6fps\")\n    else:\n        raise ValueError(f\"Dataset {dataset} not found\")\n\n\ndef get_all_annotations_for_frame(\n    dataset_df: pd.DataFrame, video_id: int, frame_idx: int, data_dir: str, dataset: str\n):\n    media_dir = os.path.join(data_dir, \"media\")\n\n    # Load the annotation and video data\n    annot_df = dataset_df[\"annotations\"]\n    video_df = dataset_df[\"videos\"]\n\n    # Get the frame\n    video_df_current = video_df[video_df.id == video_id]\n    assert len(video_df_current) == 1, (\n        f\"Expected 1 video row, got {len(video_df_current)}\"\n    )\n    video_row = video_df_current.iloc[0]\n    file_name = video_row.file_names[frame_idx]\n    file_path = os.path.join(\n        get_media_dir(media_dir=media_dir, dataset=dataset), file_name\n    )\n    frame = cv2.imread(file_path)\n    frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n\n    # Get the masks and noun phrases annotated in this video in this frame\n    annot_df_current_video = annot_df[annot_df.video_id == video_id]\n    if len(annot_df_current_video) == 0:\n        print(f\"No annotations found for video_id {video_id}\")\n        return frame, None, None\n    else:\n        empty_mask = np.zeros(frame.shape[:2], dtype=np.uint8)\n        mask_np_pairs = annot_df_current_video.apply(\n            lambda row: (\n                (\n                    mask_utils.decode(row.segmentations[frame_idx])\n                    if row.segmentations[frame_idx]\n                    else empty_mask\n                ),\n                row.noun_phrase,\n            ),\n            axis=1,\n        )\n        # sort based on noun_phrases\n        mask_np_pairs = sorted(mask_np_pairs, key=lambda x: x[1])\n        masks, noun_phrases = zip(*mask_np_pairs)\n\n    return frame, masks, noun_phrases\n\n\ndef visualize_prompt_overlay(\n    frame_idx,\n    video_frames,\n    title=\"Prompt Visualization\",\n    text_prompt=None,\n    point_prompts=None,\n    point_labels=None,\n    bounding_boxes=None,\n    box_labels=None,\n    obj_id=None,\n):\n    \"\"\"Simple prompt visualization function\"\"\"\n    img = Image.fromarray(load_frame(video_frames[frame_idx]))\n    fig, ax = plt.subplots(1, figsize=(6, 4))\n    ax.imshow(img)\n\n    img_w, img_h = img.size\n\n    if text_prompt:\n        ax.text(\n            0.02,\n            0.98,\n            f'Text: \"{text_prompt}\"',\n            transform=ax.transAxes,\n            fontsize=12,\n            color=\"white\",\n            weight=\"bold\",\n            bbox=dict(boxstyle=\"round,pad=0.3\", facecolor=\"red\", alpha=0.7),\n            verticalalignment=\"top\",\n        )\n\n    if point_prompts:\n        for i, point in enumerate(point_prompts):\n            x, y = point\n            # Convert relative to absolute coordinates\n            x_img, y_img = x * img_w, y * img_h\n\n            # Use different colors for positive/negative points\n            if point_labels and len(point_labels) > i:\n                color = \"green\" if point_labels[i] == 1 else \"red\"\n                marker = \"o\" if point_labels[i] == 1 else \"x\"\n            else:\n                color = \"green\"\n                marker = \"o\"\n\n            ax.plot(\n                x_img,\n                y_img,\n                marker=marker,\n                color=color,\n                markersize=10,\n                markeredgewidth=2,\n                markeredgecolor=\"white\",\n            )\n            ax.text(\n                x_img + 5,\n                y_img - 5,\n                f\"P{i + 1}\",\n                color=color,\n                fontsize=10,\n                weight=\"bold\",\n                bbox=dict(boxstyle=\"round,pad=0.2\", facecolor=\"white\", alpha=0.8),\n            )\n\n    if bounding_boxes:\n        for i, box in enumerate(bounding_boxes):\n            x, y, w, h = box\n            # Convert relative to absolute coordinates\n            x_img, y_img = x * img_w, y * img_h\n            w_img, h_img = w * img_w, h * img_h\n\n            # Use different colors for positive/negative boxes\n            if box_labels and len(box_labels) > i:\n                color = \"green\" if box_labels[i] == 1 else \"red\"\n            else:\n                color = \"green\"\n\n            rect = patches.Rectangle(\n                (x_img, y_img),\n                w_img,\n                h_img,\n                linewidth=2,\n                edgecolor=color,\n                facecolor=\"none\",\n            )\n            ax.add_patch(rect)\n            ax.text(\n                x_img,\n                y_img - 5,\n                f\"B{i + 1}\",\n                color=color,\n                fontsize=10,\n                weight=\"bold\",\n                bbox=dict(boxstyle=\"round,pad=0.2\", facecolor=\"white\", alpha=0.8),\n            )\n\n    # Add object ID info if provided\n    if obj_id is not None:\n        ax.text(\n            0.02,\n            0.02,\n            f\"Object ID: {obj_id}\",\n            transform=ax.transAxes,\n            fontsize=10,\n            color=\"white\",\n            weight=\"bold\",\n            bbox=dict(boxstyle=\"round,pad=0.3\", facecolor=\"blue\", alpha=0.7),\n            verticalalignment=\"bottom\",\n        )\n\n    ax.set_title(title)\n    ax.axis(\"off\")\n    plt.tight_layout()\n    plt.show()\n\n\ndef plot_results(img, results):\n    plt.figure(figsize=(12, 8))\n    plt.imshow(img)\n    nb_objects = len(results[\"scores\"])\n    print(f\"found {nb_objects} object(s)\")\n    for i in range(nb_objects):\n        color = COLORS[i % len(COLORS)]\n        plot_mask(results[\"masks\"][i].squeeze(0).cpu(), color=color)\n        w, h = img.size\n        prob = results[\"scores\"][i].item()\n        plot_bbox(\n            h,\n            w,\n            results[\"boxes\"][i].cpu(),\n            text=f\"(id={i}, {prob=:.2f})\",\n            box_format=\"XYXY\",\n            color=color,\n            relative_coords=False,\n        )\n\n\ndef single_visualization(img, anns, title):\n    \"\"\"\n    Create a single image visualization with overlays.\n    \"\"\"\n    fig, ax = plt.subplots(figsize=(7, 7))\n    fig.suptitle(title, fontsize=16, fontweight=\"bold\")\n    overlay = render_masklet_frame(img, anns, alpha=0.5)\n    ax.imshow(overlay)\n    ax.axis(\"off\")\n    plt.tight_layout()\n\n\ndef show_mask(mask, ax, obj_id=None, random_color=False):\n    if random_color:\n        color = np.concatenate([np.random.random(3), np.array([0.6])], axis=0)\n    else:\n        cmap = plt.get_cmap(\"tab10\")\n        cmap_idx = 0 if obj_id is None else obj_id\n        color = np.array([*cmap(cmap_idx)[:3], 0.6])\n    h, w = mask.shape[-2:]\n    mask_image = mask.reshape(h, w, 1) * color.reshape(1, 1, -1)\n    ax.imshow(mask_image)\n\n\ndef show_box(box, ax):\n    x0, y0 = box[0], box[1]\n    w, h = box[2] - box[0], box[3] - box[1]\n    ax.add_patch(\n        plt.Rectangle((x0, y0), w, h, edgecolor=\"green\", facecolor=(0, 0, 0, 0), lw=2)\n    )\n\n\ndef show_points(coords, labels, ax, marker_size=375):\n    pos_points = coords[labels == 1]\n    neg_points = coords[labels == 0]\n    ax.scatter(\n        pos_points[:, 0],\n        pos_points[:, 1],\n        color=\"green\",\n        marker=\"*\",\n        s=marker_size,\n        edgecolor=\"white\",\n        linewidth=1.25,\n    )\n    ax.scatter(\n        neg_points[:, 0],\n        neg_points[:, 1],\n        color=\"red\",\n        marker=\"*\",\n        s=marker_size,\n        edgecolor=\"white\",\n        linewidth=1.25,\n    )\n\n\ndef load_frame(frame):\n    if isinstance(frame, np.ndarray):\n        img = frame\n    elif isinstance(frame, Image.Image):\n        img = np.array(frame)\n    elif isinstance(frame, str) and os.path.isfile(frame):\n        img = plt.imread(frame)\n    else:\n        raise ValueError(f\"Invalid video frame type: {type(frame)=}\")\n    return img\n"
  },
  {
    "path": "scripts/eval/gold/README.md",
    "content": "# SA-Co/Gold benchmark\n\nSA-Co/Gold is a benchmark for promptable concept segmentation (PCS) in images. The benchmark contains images paired with text labels, also referred as Noun Phrases (NPs), each annotated exhaustively with masks on all object instances that match the label. SA-Co/Gold comprises 7 subsets, each targeting a different annotation domain: MetaCLIP captioner NPs, SA-1B captioner NPs, Attributes, Crowded Scenes, Wiki-Common1K, Wiki-Food/Drink, Wiki-Sports Equipment. The images are originally from the MetaCLIP and SA-1B datasets.\n\nFor each subset, the annotations are multi-reviewed by 3 independent human annotators. Each row in the figure shows an image and noun phrase pair from\none of the domains, and masks from the 3 annotators. Dashed borders indicate special group masks that cover more than a single instance, used when separating into instances is deemed too difficult. Annotators sometimes disagree on precise mask borders, the number of instances, and whether the phrase exists. Having 3 independent annotations allow us to measure human agreement on the task, which serves as an upper bound for model performance.\n\n\n<p align=\"center\">\n  <img src=\"../../../assets/saco_gold_annotation.png?\" style=\"width:80%;\" />\n</p>\n# Preparation\n\n## Download annotations\n\nThe GT annotations can be downloaded from [Hugging Face](https://huggingface.co/datasets/facebook/SACo-Gold) or [Roboflow](https://universe.roboflow.com/sa-co-gold)\n\n## Download images\n\nThere are two image sources for the evaluation dataset: MetaCLIP and SA-1B.\n\n1) The MetaCLIP images are referred in 6 out of 7 subsets (MetaCLIP captioner NPs, Attributes, Crowded Scenes, Wiki-Common1K, Wiki-Food/Drink, Wiki-Sports Equipment) and can be downloaded from [Roboflow](https://universe.roboflow.com/sa-co-gold/gold-metaclip-merged-a-release-test/).\n\n2) The SA-1B images are referred in 1 out of 7 subsets (SA-1B captioner NPs) and can be downloaded from [Roboflow](https://universe.roboflow.com/sa-co-gold/gold-sa-1b-merged-a-release-test/). Alternatively, they can be downloaded from [here](https://ai.meta.com/datasets/segment-anything-downloads/). Please access the link for `sa_co_gold.tar` from dynamic links available under `Download text file` to download the SA-1B images referred in SA-Co/Gold.\n\n# Usage\n## Visualization\n\n- Visualize GT annotations: [saco_gold_silver_vis_example.ipynb](https://github.com/facebookresearch/sam3/blob/main/examples/saco_gold_silver_vis_example.ipynb)\n- Visualize GT annotations and sample predictions side-by-side: [sam3_data_and_predictions_visualization.ipynb](https://github.com/facebookresearch/sam3/blob/main/examples/sam3_data_and_predictions_visualization.ipynb)\n\n\n## Run evaluation\n\nThe official metric for SA-Co/Gold is cgF1. Please refer to the SAM3 paper for details.\nOur evaluator inherits from the official COCO evaluator, with some modifications. Recall that in the Gold subset, there are three annotations for each datapoint. We evaluate against each of them and picks the most favorable (oracle setting). It has minimal dependency (pycocotools, numpy and scipy), to help reusability in other projects. In this section we provide several pointers to run evaluation of SAM3 or third-party models.\n\n### Evaluate SAM3\n\nWe provide inference configurations to reproduce the evaluation of SAM3.\nFirst, please edit the file [eval_base.yaml](https://github.com/facebookresearch/sam3/blob/main/sam3/train/configs/eval_base.yaml) with the paths where you downloaded the images and annotations above.\n\nThere are 7 subsets and as many configurations to be run.\nLet's take the first subset as an example. The inference can be run locally using the following command (you can adjust the number of gpus):\n```bash\npython sam3/train/train.py -c configs/gold_image_evals/sam3_gold_image_metaclip_nps.yaml --use-cluster 0 --num-gpus 1\n```\nThe predictions will be dumped in the folder specified in eval_base.yaml.\n\nWe also provide support for SLURM-based cluster inference. Edit the eval_base.yaml file to reflect your slurm configuration (partition, qos, ...), then run\n\n```bash\npython sam3/train/train.py -c configs/gold_image_evals/sam3_gold_image_metaclip_nps.yaml --use-cluster 1\n```\n\nWe provide the commands for all subsets below\n#### MetaCLIP captioner NPs\n\n```bash\npython sam3/train/train.py -c configs/gold_image_evals/sam3_gold_image_metaclip_nps.yaml --use-cluster 1\n```\n#### SA-1B captioner NPs\n\nRefer to SA-1B images for this subset. For the other 6 subsets, refer to MetaCLIP images.\n\n```bash\npython sam3/train/train.py -c configs/gold_image_evals/sam3_gold_image_sa1b_nps.yaml --use-cluster 1\n```\n#### Attributes\n\n```bash\npython sam3/train/train.py -c configs/gold_image_evals/sam3_gold_image_attributes.yaml --use-cluster 1\n```\n#### Crowded Scenes\n\n```bash\npython sam3/train/train.py -c configs/gold_image_evals/sam3_gold_image_crowded.yaml --use-cluster 1\n```\n#### Wiki-Common1K\n\n```bash\npython sam3/train/train.py -c configs/gold_image_evals/sam3_gold_image_wiki_common.yaml --use-cluster 1\n```\n#### Wiki-Food/Drink\n\n```bash\npython sam3/train/train.py -c configs/gold_image_evals/sam3_gold_image_fg_food.yaml --use-cluster 1\n```\n\n#### Wiki-Sports Equipment\n\n```bash\npython sam3/train/train.py -c configs/gold_image_evals/sam3_gold_image_fg_sports.yaml --use-cluster 1\n```\n\n### Offline evaluation\n\nIf you have the predictions in the COCO result format (see [here](https://cocodataset.org/#format-results)), then we provide scripts to easily run the evaluation.\n\nFor an example on how to run the evaluator on all subsets and aggregate results, see the following notebook: [saco_gold_silver_eval_example.ipynb](https://github.com/facebookresearch/sam3/blob/main/examples/saco_gold_silver_eval_example.ipynb)\nAlternatively, you can run `python scripts/eval/gold/eval_sam3.py`\n\nIf you have a prediction file for a given subset, you can run the evaluator specifically for that one using the standalone script. Example:\n```bash\npython scripts/eval/standalone_cgf1.py --pred_file /path/to/coco_predictions_segm.json --gt_files /path/to/annotations/gold_metaclip_merged_a_release_test.json  /path/to/annotations/gold_metaclip_merged_b_release_test.json  /path/to/annotations/gold_metaclip_merged_c_release_test.json\n```\n\n\n# Results\nHere we collect the segmentation results for SAM3 and some baselines. Note that the baselines that do not produce masks are evaluated by converting the boxes to masks using SAM2\n<table style=\"border-color:black;border-style:solid;border-width:1px;border-collapse:collapse;border-spacing:0;text-align:right\" class=\"tg\"><thead>\n<tr><th style=\"text-align:center\"></th><th style=\"text-align:center\" colspan=\"3\">Average</th><th style=\"text-align:center\" colspan=\"3\">Captioner metaclip</th><th style=\"text-align:center\" colspan=\"3\">Captioner sa1b</th>\n<th style=\"text-align:center\" colspan=\"3\">Crowded</th><th style=\"text-align:center\" colspan=\"3\">FG food</th><th style=\"text-align:center\" colspan=\"3\">FG sport</th><th style=\"text-align:center\" colspan=\"3\">Attributes</th>\n<th style=\"text-align:center\" colspan=\"3\">Wiki common</th></tr>\n</thead>\n<tbody>\n<tr><td ></td><td >cgF1</td><td >IL_MCC</td><td >positive_micro_F1</td>\n<td >cgF1</td><td >IL_MCC</td><td >positive_micro_F1</td><td >cgF1</td>\n<td >IL_MCC</td><td >positive_micro_F1</td><td >cgF1</td><td >IL_MCC</td>\n<td >positive_micro_F1</td><td >cgF1</td><td >IL_MCC</td><td >positive_micro_F1</td>\n<td >cgF1</td><td >IL_MCC</td><td >positive_micro_F1</td><td >cgF1</td>\n<td >IL_MCC</td><td >positive_micro_F1</td><td >cgF1</td><td >IL_MCC</td>\n<td >positive_micro_F1</td></tr>\n<tr><td >gDino-T</td><td >3.25</td><td >0.15</td><td >16.2</td>\n<td >2.89</td><td >0.21</td><td >13.88</td><td >3.07</td>\n<td >0.2</td><td >15.35</td><td >0.28</td><td >0.08</td>\n<td >3.37</td><td >0.96</td><td >0.1</td><td >9.83</td>\n<td >1.12</td><td >0.1</td><td >11.2</td><td >13.75</td>\n<td >0.29</td><td >47.3</td><td >0.7</td><td >0.06</td>\n<td >12.14</td></tr>\n<tr><td >OWLv2*</td><td >24.59</td><td >0.57</td><td >42</td>\n<td >17.69</td><td >0.52</td><td >34.27</td><td >13.32</td>\n<td >0.5</td><td >26.83</td><td >15.8</td><td >0.51</td>\n<td >30.74</td><td >31.96</td><td >0.65</td><td >49.35</td>\n<td >36.01</td><td >0.64</td><td >56.19</td><td >35.61</td>\n<td >0.63</td><td >56.23</td><td >21.73</td><td >0.54</td>\n<td >40.25</td></tr>\n<tr><td >OWLv2</td><td >17.27</td><td >0.46</td><td >36.8</td>\n<td >12.21</td><td >0.39</td><td >31.33</td><td >9.76</td>\n<td >0.45</td><td >21.65</td><td >8.87</td><td >0.36</td>\n<td >24.77</td><td >24.36</td><td >0.51</td><td >47.85</td>\n<td >24.44</td><td >0.52</td><td >46.97</td><td >25.85</td>\n<td >0.54</td><td >48.22</td><td >15.4</td><td >0.42</td>\n<td >36.64</td></tr>\n<tr><td >LLMDet-L</td><td >6.5</td><td >0.21</td><td >27.3</td>\n<td >4.49</td><td >0.23</td><td >19.36</td><td >5.32</td>\n<td >0.23</td><td >22.81</td><td >2.42</td><td >0.18</td>\n<td >13.74</td><td >5.5</td><td >0.19</td><td >29.12</td>\n<td >4.39</td><td >0.17</td><td >25.34</td><td >22.17</td>\n<td >0.39</td><td >57.13</td><td >1.18</td><td >0.05</td>\n<td >23.3</td></tr>\n<tr><td >APE</td><td >16.41</td><td >0.4</td><td >36.9</td>\n<td >12.6</td><td >0.42</td><td >30.11</td><td >2.23</td>\n<td >0.22</td><td >10.01</td><td >7.15</td><td >0.35</td>\n<td >20.3</td><td >22.74</td><td >0.51</td><td >45.01</td>\n<td >31.79</td><td >0.56</td><td >56.45</td><td >26.74</td>\n<td >0.47</td><td >57.27</td><td >11.59</td><td >0.29</td>\n<td >39.46</td></tr>\n<tr><td >DINO-X</td><td >21.26</td><td >0.38</td><td >55.2</td>\n<td >17.21</td><td >0.35</td><td >49.17</td><td >19.66</td>\n<td >0.48</td><td >40.93</td><td >12.86</td><td >0.34</td>\n<td >37.48</td><td >30.07</td><td >0.49</td><td >61.72</td>\n<td >28.36</td><td >0.41</td><td >69.4</td><td >30.97</td>\n<td >0.42</td><td >74.04</td><td >9.72</td><td >0.18</td>\n<td >53.52</td></tr>\n<tr><td >Gemini 2.5</td><td >13.03</td><td >0.29</td><td >46.1</td>\n<td >9.9</td><td >0.29</td><td >33.79</td><td >13.1</td>\n<td >0.41</td><td >32.1</td><td >8.15</td><td >0.27</td>\n<td >30.34</td><td >19.63</td><td >0.33</td><td >59.52</td>\n<td >15.07</td><td >0.28</td><td >53.5</td><td >18.84</td>\n<td >0.3</td><td >63.14</td><td >6.5</td><td >0.13</td>\n<td >50.32</td></tr>\n<tr><td >SAM 3</td><td >54.06</td><td >0.82</td><td >66.11</td>\n<td >47.26</td><td >0.81</td><td >58.58</td><td >53.69</td>\n<td >0.86</td><td >62.55</td><td >61.08</td><td >0.9</td>\n<td >67.73</td><td >53.41</td><td >0.79</td><td >67.28</td>\n<td >65.52</td><td >0.89</td><td >73.75</td><td >54.93</td>\n<td >0.76</td><td >72</td><td >42.53</td><td >0.7</td>\n<td >60.85</td></tr>\n</tbody></table>\n\n\n\n# Annotation format\n\nThe annotation format is derived from [COCO format](https://cocodataset.org/#format-data). Notable data fields are:\n\n- `images`: a `list` of `dict` features, contains a list of all image-NP pairs. Each entry is related to an image-NP pair and has the following items.\n  - `id`: an `int` feature, unique identifier for the image-NP pair\n  - `text_input`: a `string` feature, the noun phrase for the image-NP pair\n  - `file_name`: a `string` feature, the relative image path in the corresponding data folder.\n  - `height`/`width`: dimension of the image\n  - `is_instance_exhaustive`: Boolean (0 or 1). If it's 1 then all the instances are correctly annotated. For instance segmentation, we only use those datapoints. Otherwise, there may be either missing instances or crowd segments (a segment covering multiple instances)\n  - `is_pixel_exhaustive`: Boolean (0 or 1). If it's 1, then the union of all masks cover all pixels corresponding to the prompt. This is weaker than instance_exhaustive since it allows crowd segments. It can be used for semantic segmentation evaluations.\n\n- `annotations`: a `list` of `dict` features, containing a list of all annotations including bounding box, segmentation mask, area etc.\n  - `image_id`: an `int` feature, maps to the identifier for the image-np pair in images\n  - `bbox`: a `list` of float features, containing bounding box in [x,y,w,h] format, normalized by the image dimensions\n  - `segmentation`: a dict feature, containing segmentation mask in RLE format\n  - `category_id`: For compatibility with the coco format. Will always be 1 and is unused.\n  - `is_crowd`: Boolean (0 or 1). If 1, then the segment overlaps several instances (used in cases where instances are not separable, for e.g. due to poor image quality)\n\n- `categories`: a `list` of `dict` features, containing a list of all categories. Here, we provide  the category key for compatibility with the COCO format, but in open-vocabulary detection we do not use it. Instead, the text prompt is stored directly in each image (text_input in images). Note that in our setting, a unique image (id in images) actually corresponds to an (image, text prompt) combination.\n\n\nFor `id` in images that have corresponding annotations (i.e. exist as `image_id` in `annotations`), we refer to them as a \"positive\" NP. And, for `id` in `images` that don't have any annotations (i.e. they do not exist as `image_id` in `annotations`), we refer to them as a \"negative\" NP.\n\nA sample annotation from Wiki-Food/Drink domain looks as follows:\n\n#### images\n\n```\n[\n  {\n    \"id\": 10000000,\n    \"file_name\": \"1/1001/metaclip_1_1001_c122868928880ae52b33fae1.jpeg\",\n    \"text_input\": \"chili\",\n    \"width\": 600,\n    \"height\": 600,\n    \"queried_category\": \"0\",\n    \"is_instance_exhaustive\": 1,\n    \"is_pixel_exhaustive\": 1\n  },\n  {\n    \"id\": 10000001,\n    \"file_name\": \"1/1001/metaclip_1_1001_c122868928880ae52b33fae1.jpeg\",\n    \"text_input\": \"the fish ball\",\n    \"width\": 600,\n    \"height\": 600,\n    \"queried_category\": \"2001\",\n    \"is_instance_exhaustive\": 1,\n    \"is_pixel_exhaustive\": 1\n  }\n]\n```\n\n#### annotations\n\n```\n[\n  {\n    \"id\": 1,\n    \"image_id\": 10000000,\n    \"source\": \"manual\",\n    \"area\": 0.002477777777777778,\n    \"bbox\": [\n      0.44333332777023315,\n      0.0,\n      0.10833333432674408,\n      0.05833333358168602\n    ],\n    \"segmentation\": {\n      \"counts\": \"`kk42fb01O1O1O1O001O1O1O001O1O00001O1O001O001O0000000000O1001000O010O02O001N10001N0100000O10O1000O10O010O100O1O1O1O1O0000001O0O2O1N2N2Nobm4\",\n      \"size\": [\n        600,\n        600\n      ]\n    },\n    \"category_id\": 1,\n    \"iscrowd\": 0\n  },\n  {\n    \"id\": 2,\n    \"image_id\": 10000000,\n    \"source\": \"manual\",\n    \"area\": 0.001275,\n    \"bbox\": [\n      0.5116666555404663,\n      0.5716666579246521,\n      0.061666667461395264,\n      0.036666665226221085\n    ],\n    \"segmentation\": {\n      \"counts\": \"aWd51db05M1O2N100O1O1O1O1O1O010O100O10O10O010O010O01O100O100O1O00100O1O100O1O2MZee4\",\n      \"size\": [\n        600,\n        600\n      ]\n    },\n    \"category_id\": 1,\n    \"iscrowd\": 0\n  }\n]\n```\n\n# Data Stats\n\nHere are the stats for the 7 annotation domains. The # Image-NPs represent the total number of unique image-NP pairs including both “positive” and “negative” NPs.\n\n\n| Domain                   | Media        | # Image-NPs   | # Image-NP-Masks|\n|--------------------------|--------------|---------------| ----------------|\n| MetaCLIP captioner NPs   | MetaCLIP     | 33393         | 20144           |\n| SA-1B captioner NPs      | SA-1B        | 13258         | 30306           |\n| Attributes               | MetaCLIP     | 9245          | 3663            |\n| Crowded Scenes           | MetaCLIP     | 20687         | 50417           |\n| Wiki-Common1K            | MetaCLIP     | 65502         | 6448            |\n| Wiki-Food&Drink          | MetaCLIP     | 13951         | 9825            |\n| Wiki-Sports Equipment    | MetaCLIP     | 12166         | 5075            |\n"
  },
  {
    "path": "scripts/eval/gold/eval_sam3.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n\n\"\"\"Script to run the evaluator offline given the GTs for SAC-Gold test set and SAM3 model prediction files.\nIt reports CGF1, IL_MCC, PM_F1 metrics for each subset of SAC-Gold test set.\n\nUsage: python eval_sam3.py --gt-folder <folder_with_gts> --pred-folder <folder_with_predictions>\n\"\"\"\n\nimport argparse\nimport os\n\nfrom sam3.eval.cgf1_eval import CGF1Evaluator\n\n# Relative file names for GT files for 7 SA-Co/Gold subsets\n\nsaco_gold_gts = {\n    # MetaCLIP Captioner\n    \"metaclip_nps\": [\n        \"gold_metaclip_merged_a_release_test.json\",\n        \"gold_metaclip_merged_b_release_test.json\",\n        \"gold_metaclip_merged_c_release_test.json\",\n    ],\n    # SA-1B captioner\n    \"sa1b_nps\": [\n        \"gold_sa1b_merged_a_release_test.json\",\n        \"gold_sa1b_merged_b_release_test.json\",\n        \"gold_sa1b_merged_c_release_test.json\",\n    ],\n    # Crowded\n    \"crowded\": [\n        \"gold_crowded_merged_a_release_test.json\",\n        \"gold_crowded_merged_b_release_test.json\",\n        \"gold_crowded_merged_c_release_test.json\",\n    ],\n    # FG Food\n    \"fg_food\": [\n        \"gold_fg_food_merged_a_release_test.json\",\n        \"gold_fg_food_merged_b_release_test.json\",\n        \"gold_fg_food_merged_c_release_test.json\",\n    ],\n    # FG Sports\n    \"fg_sports_equipment\": [\n        \"gold_fg_sports_equipment_merged_a_release_test.json\",\n        \"gold_fg_sports_equipment_merged_b_release_test.json\",\n        \"gold_fg_sports_equipment_merged_c_release_test.json\",\n    ],\n    # Attributes\n    \"attributes\": [\n        \"gold_attributes_merged_a_release_test.json\",\n        \"gold_attributes_merged_b_release_test.json\",\n        \"gold_attributes_merged_c_release_test.json\",\n    ],\n    # Wiki common\n    \"wiki_common\": [\n        \"gold_wiki_common_merged_a_release_test.json\",\n        \"gold_wiki_common_merged_b_release_test.json\",\n        \"gold_wiki_common_merged_c_release_test.json\",\n    ],\n}\n\n\ndef main():\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\n        \"-g\",\n        \"--gt-folder\",\n        type=str,\n        help=\"Path to the folder containing the ground truth json files.\",\n    )\n    parser.add_argument(\n        \"-p\",\n        \"--pred-folder\",\n        type=str,\n        help=\"Path to the folder containing the predictions json files.\",\n    )\n    args = parser.parse_args()\n\n    results = \"\"\n\n    for subset_name, gts in saco_gold_gts.items():\n        print(\"Processing subset: \", subset_name)\n        gt_paths = [os.path.join(args.gt_folder, gt) for gt in gts]\n        evaluator = CGF1Evaluator(\n            gt_path=gt_paths, verbose=True, iou_type=\"segm\"\n        )  # change to bbox if you want detection performance\n\n        pred_path = os.path.join(\n            args.pred_folder,\n            f\"gold_{subset_name}/dumps/gold_{subset_name}/coco_predictions_segm.json\",\n        )\n        summary = evaluator.evaluate(pred_path)\n\n        cgf1 = str(round(summary[\"cgF1_eval_segm_cgF1\"] * 100, 2))\n        il_mcc = str(round(summary[\"cgF1_eval_segm_IL_MCC\"], 2))\n        pmf1 = str(round(summary[\"cgF1_eval_segm_positive_micro_F1\"] * 100, 2))\n        final_str = f\"{cgf1},{il_mcc},{pmf1}\"\n        results += subset_name + \": \" + final_str + \"\\n\"\n\n    print(\"Subset name, CG_F1, IL_MCC, pmF1\")\n    print(results)\n\n\nif __name__ == \"__main__\":\n    main()\n"
  },
  {
    "path": "scripts/eval/silver/CONFIG_FRAMES.yaml",
    "content": "path_annotations: <YOUR_ANNOTATIONS_PATH>/saco_frames_test_sets/annotations/\n\n# Paths with downloaded data\ndroid_path: <YOUR_DATASET_PATH>/saco_frames_test_sets/droid/\nsav_path: <YOUR_DATASET_PATH>/saco_frames_test_sets/sav/\nego4d_path: <YOUR_DATASET_PATH>/saco_frames_test_sets/ego4d/\nyt1b_path: <YOUR_DATASET_PATH>/saco_frames_test_sets/yt1b/\n\n# Configuration to download and extract video frames\ncookies_path: <YOUR_COOKIES_PATH>/cookies.txt  # Required to download YT1B videos\nupdate_annotation_yt1b: true\nupdate_annotation_ego4d: true\n\nsav_videos_fps_6_download_path: ''\n\nremove_downloaded_videos_yt1b: false\nremove_downloaded_videos_droid: false\nremove_downloaded_videos_ego4d: false\nremove_downloaded_videos_sav: false\n\n# Configuration for visualization of data\nnum_images_show: 5\nsaco_subset_show: yt1b  # Options: [yt1b, ego4d, sav, droid]\ndirectory_save: <YOUR_SAVE_DIR>\n"
  },
  {
    "path": "scripts/eval/silver/README.md",
    "content": "# SA-Co/Silver benchmark\n\nSA-Co/Silver is a benchmark for promptable concept segmentation (PCS) in images. The benchmark contains images paired with text labels (also referred as Noun Phrases aka NPs), each annotated exhaustively with masks on all object instances that match the label.\n\nSA-Co/Silver comprises 10 subsets, covering a diverse array of domains including food, art, robotics, driving etc. Unlike SA-Co/Gold, there is only a single ground-truth for each datapoint, which means the results may have a bit more variance and tend to underestimate model performance, since they don't account for possible different interpretations of each query.\n\n- BDD100k\n- DROID\n- Ego4D\n- MyFoodRepo-273\n- GeoDE\n- iNaturalist-2017\n- National Gallery of Art\n- SA-V\n- YT-Temporal-1B\n- Fathomnet\n\nThe README contains instructions on how to download and setup the annotations, image data to prepare them for evaluation on SA-Co/Silver.\n\n# Preparation\n## Download annotations\n\nThe GT annotations can be downloaded from [Hugging Face](https://huggingface.co/datasets/facebook/SACo-Silver) or [Roboflow](https://universe.roboflow.com/sa-co-silver)\n\n## Download images and video frames\n\n### Image Datasets\n\n#### GeoDE\n\nThe processed images needed for evaluation can be downloaded from [Roboflow](https://universe.roboflow.com/sa-co-silver/geode/) OR follow the below steps to prepare the processed images.\n\n1. Download dataset with raw images from [GeoDE](https://geodiverse-data-collection.cs.princeton.edu/).\n2. Extract the downloaded file to a location, say `<RAW_GEODE_IMAGES_FOLDER>`\n\n3. Run the below command to pre-process the images and prepare for evaluation. The proceesed images will be saved to the location specified in `<PROCESSED_GEODE_IMAGES_FOLDER>`\n    ```\n    python preprocess_silver_geode_bdd100k_food_rec.py --annotation_file <FOLDER_WITH_SILVER_ANNOTATIONS>/silver_geode_merged_test.json --raw_images_folder <RAW_GEODE_IMAGES_FOLDER> --processed_images_folder <PROCESSED_GEODE_IMAGES_FOLDER> --dataset_name geode\n    ```\n\n#### National Gallery of Art (NGA)\n\nThe processed images needed for evaluation can be downloaded from [Roboflow](https://universe.roboflow.com/sa-co-silver/national-gallery-of-art/) OR follow the below steps to prepare the processed images.\n\n1. Run the below command to download raw images and pre-process the images to prepare for evaluation. The proceesed images will be saved to the location specified in `<PROCESSED_NGA_IMAGES_FOLDER>`.\n    ```\n    python download_preprocess_nga.py --annotation_file <FOLDER_WITH_SILVER_ANNOTATIONS>/silver_nga_art_merged_test.json --raw_images_folder <RAW_NGA_IMAGES_FOLDER> --processed_images_folder <PROCESSED_NGA_IMAGES_FOLDER>\n    ```\n\n#### Berkeley Driving Dataset (BDD) 100k\n\nThe processed images needed for evaluation can be downloaded from [Roboflow](https://universe.roboflow.com/sa-co-silver/bdd100k-gwmh6/) OR follow the below steps to prepare the processed images.\n\n1. Download data with raw images from the `100K Images` dataset in [BDD100k](http://bdd-data.berkeley.edu/download.html)\n2. Extract the downloaded file to a location, say `<RAW_BDD_IMAGES_FOLDER>`\n3. Run the below command to pre-process the images and prepare for evaluation. The proceesed images will be saved to the location specified in `<PROCESSED_BDD_IMAGES_FOLDER>`\n    ```\n    python preprocess_silver_geode_bdd100k_food_rec.py --annotation_file <FOLDER_WITH_SILVER_ANNOTATIONS>/silver_bdd100k_merged_test.json --raw_images_folder <RAW_BDD_IMAGES_FOLDER> --processed_images_folder <PROCESSED_BDD_IMAGES_FOLDER> --dataset_name bdd100k\n    ```\n\n#### Food Recognition Challenge 2022\n\n1. Download data with raw images from the [website](https://www.aicrowd.com/challenges/food-recognition-benchmark-2022). Download `[Round 2] public_validation_set_2.0.tar.gz` file.\n2. Extract the downloaded file to a location, say `<RAW_FOOD_IMAGES_FOLDER>`\n3. Run the below command to pre-process the images and prepare for evaluation. The proceesed images will be saved to the location specified in `<PROCESSED_FOOD_IMAGES_FOLDER>`\n    ```\n    python preprocess_silver_geode_bdd100k_food_rec.py --annotation_file <FOLDER_WITH_SILVER_ANNOTATIONS>/silver_food_rec_merged_test.json --raw_images_folder <RAW_FOOD_IMAGES_FOLDER> --processed_images_folder <PROCESSED_FOOD_IMAGES_FOLDER> --dataset_name food_rec\n    ```\n\n#### iNaturalist\n\nThe processed images needed for evaluation can be downloaded from [Roboflow](https://universe.roboflow.com/sa-co-silver/inaturalist-2017/) OR follow the below steps to prepare the processed images.\n\n1. Run the below command to download, extract images in `<RAW_INATURALIST_IMAGES_FOLDER>` and prepare them for evaluation. The proceesed images will be saved to the location specified in `<PROCESSED_INATURALIST_IMAGES_FOLDER>`\n    ```\n    python download_inaturalist.py --raw_images_folder <RAW_INATURALIST_IMAGES_FOLDER> --processed_images_folder <PROCESSED_INATURALIST_IMAGES_FOLDER>\n    ```\n\n#### Fathomnet\n\nThe processed images needed for evaluation can be downloaded from [Roboflow](https://universe.roboflow.com/sa-co-silver/fathomnet-kmz5d/) OR follow the below steps to prepare the processed images.\n\n1. Install the FathomNet API\n    ```\n    pip install fathomnet\n    ```\n\n2. Run the below command to download the images and prepare for evaluation. The proceesed images will be saved to the location specified in `<PROCESSED_BDD_IMAGES_FOLDER>`\n    ```\n    python download_fathomnet.py --processed_images_folder <PROCESSED_BFATHOMNET_IMAGES_FOLDER>\n    ```\n\n### Frame Datasets\n\nThese datasets correspond to annotations for individual frames coming from videos. The file `CONFIG_FRAMES.yaml` is used to unify the downloads for the datasets, as explained below.\n\nBefore following the other dataset steps, update `CONFIG_FRAMES.yaml` with the correct `path_annotations` path where the annotation files are.\n\n#### DROID\n\nThe processed frames needed for evaluation can be downloaded from [Roboflow](https://universe.roboflow.com/sa-co-silver/droid-cfual/) OR follow the below steps to prepare the processed frames.\n\n1. Install the gsutil package:\n    ```bash\n    pip install gsutil\n    ```\n2. Modify the `droid_path` variable in `CONFIG_FRAMES.yaml`. This is the path where the DROID data will be downloaded.\n3. _\\[Optional\\] Update the variable `remove_downloaded_videos_droid` to (not) remove the videos after the frames have been extracted.\n4. Download the data:\n    ```bash\n    python download_videos.py droid\n    ```\n5. Extract the frames:\n    ```bash\n    python extract_frames.py droid\n    ```\n\nSee the [DROID website](https://droid-dataset.github.io/droid/the-droid-dataset#-using-the-dataset) for more information.\n\n#### SA-V\n\nThe processed frames needed for evaluation can be downloaded from [Roboflow](https://universe.roboflow.com/sa-co-silver/sa-v) OR follow the below steps to prepare the processed frames.\n\n1. Follow instructions in the [Segment Anything official website](https://ai.meta.com/datasets/segment-anything-video-downloads/) to obtain access to the download links (they are dynamic links).\n2. Update `CONFIG_FRAMES.yaml`:\n    - Update the `sav_path` variable, where the frames will be saved.\n    - Update the `sav_videos_fps_6_download_path` variable. Copy paste the path corresponding to the `videos_fps_6.tar` in the list that you obtained in step 1.\n    - _\\[Optional\\]_ Update the variable `remove_downloaded_videos_sav` to (not) remove the videos after the frames have been extracted.\n3. Download the videos:\n    ```bash\n    python download_videos.py sav\n    ```\n4. Extract the frames:\n    ```\n    python extract_frames.py sav\n    ```\n\n#### Ego4D\n\nThe processed frames needed for evaluation can be downloaded from [Roboflow](https://universe.roboflow.com/sa-co-silver/ego4d-w7fiu/) OR follow the below steps to prepare the processed frames.\n\n1. Review and accept the license agreement in the [official Ego4D website](https://ego4d-data.org/docs/start-here/#license-agreement).\n2. Configure AWS credentials. Run:\n    ```bash\n    pip install awscli\n    aws configure\n    ```\n    and copy the values shown in the email you received after step 1 (you can leave \"region name\" and \"output format\" empty). You can verify that the variables were set up correctly:\n    ```bash\n    cat ~/.aws/credentials\n    ```\n3. Install the Ego4D library:\n    ```bash\n    pip install ego4d\n    ```\n4. Update `CONFIG_FRAMES.yaml`:\n    - Set up AWS credentials following the instructions in the email you received after step 2. Modify the following variables: `aws_access_key_id` and `aws_secret_access_key`.\n    - Update the `ego4d_path` variable, where the frames will be saved.\n    - _\\[Optional\\]_ Update the variable `remove_downloaded_videos_ego4d` to (not) remove the videos after the frames have been extracted..\n5. Download the `clips` subset of the Ego4D dataset:\n    ```python\n    python download_videos.py ego4d\n    ```\n6. Extract the frames:\n    ```\n    python extract_frames.py ego4d\n    ```\n\nSee the [official CLI](https://ego4d-data.org/docs/CLI/) and the [explanation about the videos](https://ego4d-data.org/docs/data/videos/) for more information.\n\n#### YT1B\n\nThe processed frames needed for evaluation can be downloaded from [Roboflow](https://universe.roboflow.com/sa-co-silver/yt-temporal-1b/) OR follow the below steps to prepare the processed frames.\n\n1. Install the yt-dlp library:\n    ```bash\n    python3 -m pip install -U \"yt-dlp[default]\"\n    ```\n2. Create a `cookies.txt` file following the instructions from yt-dlp [exporting-youtube-cookies](https://github.com/yt-dlp/yt-dlp/wiki/Extractors#exporting-youtube-cookies) and [pass-cookies-to-yt-dlp](https://github.com/yt-dlp/yt-dlp/wiki/FAQ#how-do-i-pass-cookies-to-yt-dlp). This is required to download youtube videos. Then, update the path for that file in the `CONFIG_FRAMES.yaml` file, in the variable `cookies_path`.\n3. Update `CONFIG_FRAMES.yaml`:\n    - Update the `yt1b_path`, where the frames will be saved.\n    - _\\[Optional\\]_ Some YouTube videos may not be available on YouTube anymore. Set `update_annotation_yt1b` to `True` in `CONFIG_FRAMES.yaml` to remove the annotations corresponding to such videos. Note that the evaluations will not be directly comparable with other reported evaluations.\n    - _\\[Optional\\]_ Update the variable `remove_downloaded_videos_yt1b` to (not) remove the videos after the frames have been extracted.\n4. Run the following code to download the videos:\n    ```\n    python download_videos.py yt1b\n    ```\n5. Extract the frames:\n    ```\n    python extract_frames.py yt1b\n    ```\n\n# Usage\n## Visualization\n\n- Visualize GT annotations: [saco_gold_silver_vis_example.ipynb](https://github.com/facebookresearch/sam3/blob/main/examples/saco_gold_silver_vis_example.ipynb)\n\n## Run evaluation\n\nThe official metric for SA-Co/Silver is cgF1. Please refer to the SAM3 paper for details.\nUnlike Gold, the silver subset only has a single annotation per image. Therefore, the performance may be underestimated, because the model may be wrongly penalized for choosing an interpretation which is valid but different from that of the human annotator.\n\n### Evaluate SAM3\n\nWe provide inference configurations to reproduce the evaluation of SAM3.\nFirst, please edit the file [eval_base.yaml](https://github.com/facebookresearch/sam3/blob/main/sam3/train/configs/eval_base.yaml) with the paths where you downloaded the images and annotations above.\n\nThere are 10 subsets and as many configurations to be run.\nLet's take the first subset as an example. The inference can be run locally using the following command (you can adjust the number of gpus):\n```bash\npython sam3/train/train.py -c configs/silver_image_evals/sam3_gold_image_bdd100k.yaml --use-cluster 0 --num-gpus 1\n```\nThe predictions will be dumped in the folder specified in eval_base.yaml.\n\nWe also provide support for SLURM-based cluster inference. Edit the eval_base.yaml file to reflect your slurm configuration (partition, qos, ...), then run\n\n```bash\npython sam3/train/train.py -c configs/silver_image_evals/sam3_gold_image_bdd100k.yaml --use-cluster 1\n```\n\n### Offline evaluation\n\nIf you have the predictions in the COCO result format (see [here](https://cocodataset.org/#format-results)), then we provide scripts to easily run the evaluation.\n\nFor an example on how to run the evaluator on all subsets and aggregate results, see the following notebook: [saco_gold_silver_eval_example.ipynb](https://github.com/facebookresearch/sam3/blob/main/examples/saco_gold_silver_eval_example.ipynb)\n\nIf you have a prediction file for a given subset, you can run the evaluator specifically for that one using the standalone script. Example:\n```bash\npython scripts/eval/standalone_cgf1.py --pred_file /path/to/coco_predictions_segm.json --gt_files /path/to/annotations/silver_bdd100k_merged_test.json\n```\n\n# Results\n<table style=\"border-color:black;border-style:solid;border-width:1px;border-collapse:collapse;border-spacing:0;text-align:right\" class=\"tg\"><thead>\n  <tr style=\"text-align:center\">\n    <th></th>\n    <th colspan=\"3\">Average</th>\n    <th colspan=\"3\">BDD100k</th>\n    <th colspan=\"3\">Droids</th>\n    <th colspan=\"3\">Ego4d</th>\n    <th colspan=\"3\">Food Rec</th>\n    <th colspan=\"3\">Geode</th>\n    <th colspan=\"3\">iNaturalist</th>\n    <th colspan=\"3\">Nga Art</th>\n    <th colspan=\"3\">SAV</th>\n    <th colspan=\"3\">YT1B</th>\n    <th colspan=\"3\">Fathomnet</th>\n  </tr></thead>\n<tbody>\n  <tr>\n    <td></td>\n    <td>cgF1</td>\n    <td>IL_MCC</td>\n    <td>PmF\u00101</td>\n    <td>CGF1</td>\n    <td>IL_MCC</td>\n    <td>pmF1</td>\n    <td>CGF1</td>\n    <td>IL_MCC</td>\n    <td>pmF1</td>\n    <td>CGF1</td>\n    <td>IL_MCC</td>\n    <td>pmF1</td>\n    <td>CGF1</td>\n    <td>IL_MCC</td>\n    <td>pmF1</td>\n    <td>CGF1</td>\n    <td>IL_MCC</td>\n    <td>pmF1</td>\n    <td>CGF1</td>\n    <td>IL_MCC</td>\n    <td>pmF1</td>\n    <td>CGF1</td>\n    <td>IL_MCC</td>\n    <td>pmF1</td>\n    <td>CGF1</td>\n    <td>IL_MCC</td>\n    <td>pmF1</td>\n    <td>CGF1</td>\n    <td>IL_MCC</td>\n    <td>pmF1</td>\n    <td>CGF1</td>\n    <td>IL_MCC</td>\n    <td>pmF1</td>\n  </tr>\n  <tr>\n    <td>gDino-T</td> <td>3.09</td> <td>0.12</td> <td>19.75</td> <td>3.33</td> <td>0.17</td> <td>19.54</td> <td>4.26</td> <td>0.15</td> <td>28.38</td> <td>2.87</td> <td>0.1</td>\n    <td>28.72</td> <td>0.69</td> <td>0.05</td> <td>13.88</td> <td>9.61</td> <td>0.24</td> <td>40.03</td> <td>0</td> <td>0</td> <td>1.97</td> <td>1.31</td> <td>0.09</td>\n    <td>14.57</td> <td>5.18</td> <td>0.19</td> <td>27.25</td> <td>3.6</td> <td>0.16</td> <td>22.5</td> <td>0</td> <td>0</td> <td>0.64</td>\n  </tr>\n  <tr>\n    <td>OWLv2*</td> <td>11.23</td> <td>0.32</td> <td>31.18</td> <td>14.97</td> <td>0.46</td> <td>32.34</td> <td>10.84</td> <td>0.36</td> <td>30.1</td> <td>7.36</td> <td>0.23</td>\n    <td>31.99</td> <td>19.35</td> <td>0.44</td> <td>43.98</td> <td>27.04</td> <td>0.5</td> <td>54.07</td> <td>3.92</td> <td>0.14</td> <td>27.98</td> <td>8.05</td> <td>0.31</td>\n    <td>25.98</td> <td>10.59</td> <td>0.32</td> <td>33.1</td> <td>10.15</td> <td>0.38</td> <td>26.7</td> <td>0.04</td> <td>0.01</td> <td>5.57</td>\n  </tr>\n  <tr>\n    <td>OWLv2</td> <td>8.18</td> <td>0.23</td> <td>32.55</td> <td>8.5</td> <td>0.31</td> <td>27.79</td> <td>7.21</td> <td>0.25</td> <td>28.84</td> <td>5.64</td> <td>0.18</td>\n    <td>31.35</td> <td>14.18</td> <td>0.32</td> <td>44.32</td> <td>13.04</td> <td>0.28</td> <td>46.58</td> <td>3.62</td> <td>0.1</td> <td>36.23</td> <td>7.22</td> <td>0.25</td>\n    <td>28.88</td> <td>10.86</td> <td>0.32</td> <td>33.93</td> <td>11.7</td> <td>0.35</td> <td>33.43</td> <td>-0.14</td> <td>-0.01</td> <td>14.15</td>\n  </tr>\n  <tr>\n    <td>LLMDet-L</td> <td>6.73</td> <td>0.17</td> <td>28.19</td> <td>1.69</td> <td>0.08</td> <td>19.97</td> <td>2.56</td> <td>0.1</td> <td>25.59</td> <td>2.39</td>\n    <td>0.08</td> <td>29.92</td> <td>0.98</td> <td>0.06</td> <td>16.26</td> <td>20.82</td> <td>0.37</td> <td>56.26</td> <td>27.37</td> <td>0.46</td> <td>59.5</td>\n    <td>2.17</td> <td>0.13</td> <td>16.68</td> <td>5.37</td> <td>0.19</td> <td>28.26</td> <td>3.73</td> <td>0.16</td> <td>23.32</td> <td>0.24</td> <td>0.04</td> <td>6.1</td>\n  </tr>\n  <tr>\n    <td>Gemini 2.5</td> <td>9.67</td> <td>0.19</td> <td>45.51</td> <td>5.83</td> <td>0.19</td> <td>30.66</td> <td>5.61</td> <td>0.14</td> <td>40.07</td>\n    <td>0.38</td> <td>0.01</td> <td>38.14</td> <td>10.92</td> <td>0.24</td> <td>45.52</td> <td>18.28</td> <td>0.26</td> <td>70.29</td> <td>26.57</td> <td>0.36</td>\n    <td>73.81</td> <td>8.18</td> <td>0.2</td> <td>40.91</td> <td>9.48</td> <td>0.22</td> <td>43.1</td> <td>8.66</td> <td>0.23</td> <td>37.65</td> <td>2.8</td>\n    <td>0.08</td> <td>34.99</td>\n  </tr>\n  <tr> <td>SAM3</td> <td>49.57</td> <td>0.76</td> <td>65.17</td> <td>46.61</td> <td>0.78</td> <td>60.13</td> <td>45.58</td> <td>0.76</td>\n    <td>60.35</td> <td>38.64</td> <td>0.62</td> <td>62.56</td> <td>52.96</td> <td>0.79</td> <td>67.21</td> <td>70.07</td> <td>0.89</td>\n    <td>78.73</td> <td>65.8</td> <td>0.82</td> <td>80.67</td> <td>38.06</td> <td>0.66</td> <td>57.62</td> <td>44.36</td> <td>0.67</td>\n    <td>66.05</td> <td>42.07</td> <td>0.72</td> <td>58.36</td> <td>51.53</td> <td>0.86</td> <td>59.98</td>\n  </tr>\n</tbody></table>\n\n# Annotation format\n\nThe annotation format is derived from [COCO format](https://cocodataset.org/#format-data). Notable data fields are:\n\n- `images`: a `list` of `dict` features, contains a list of all image-NP pairs. Each entry is related to an image-NP pair and has the following items.\n  - `id`: an `int` feature, unique identifier for the image-NP pair\n  - `text_input`: a `string` feature, the noun phrase for the image-NP pair\n  - `file_name`: a `string` feature, the relative image path in the corresponding data folder.\n  - `height`/`width`: dimension of the image\n  - `is_instance_exhaustive`: Boolean (0 or 1). If it's 1 then all the instances are correctly annotated. For instance segmentation, we only use those datapoints. Otherwise, there may be either missing instances or crowd segments (a segment covering multiple instances)\n  - `is_pixel_exhaustive`: Boolean (0 or 1). If it's 1, then the union of all masks cover all pixels corresponding to the prompt. This is weaker than instance_exhaustive since it allows crowd segments. It can be used for semantic segmentation evaluations.\n\n- `annotations`: a `list` of `dict` features, containing a list of all annotations including bounding box, segmentation mask, area etc.\n  - `image_id`: an `int` feature, maps to the identifier for the image-np pair in images\n  - `bbox`: a `list` of float features, containing bounding box in [x,y,w,h] format, normalized by the image dimensions\n  - `segmentation`: a dict feature, containing segmentation mask in RLE format\n  - `category_id`: For compatibility with the coco format. Will always be 1 and is unused.\n  - `is_crowd`: Boolean (0 or 1). If 1, then the segment overlaps several instances (used in cases where instances are not separable, for e.g. due to poor image quality)\n\n- `categories`: a `list` of `dict` features, containing a list of all categories. Here, we provide  the category key for compatibility with the COCO format, but in open-vocabulary detection we do not use it. Instead, the text prompt is stored directly in each image (text_input in images). Note that in our setting, a unique image (id in images) actually corresponds to an (image, text prompt) combination.\n\n\nFor `id` in images that have corresponding annotations (i.e. exist as `image_id` in `annotations`), we refer to them as a \"positive\" NP. And, for `id` in `images` that don't have any annotations (i.e. they do not exist as `image_id` in `annotations`), we refer to them as a \"negative\" NP.\n\nA sample annotation from DROID domain looks as follows:\n\n#### images\n\n```\n[\n  {\n    \"id\": 10000000,\n    \"file_name\": \"AUTOLab_failure_2023-07-07_Fri_Jul__7_18:50:36_2023_recordings_MP4_22008760/00002.jpg\",\n    \"text_input\": \"the large wooden table\",\n    \"width\": 1280,\n    \"height\": 720,\n    \"queried_category\": \"3\",\n    \"is_instance_exhaustive\": 1,\n    \"is_pixel_exhaustive\": 1\n  }\n]\n```\n\n#### annotations\n\n```\n[\n  {\n    \"area\": 0.17324327256944444,\n    \"id\": 1,\n    \"image_id\": 10000000,\n    \"source\": \"created by SAM3\",\n    \"bbox\": [\n      0.03750000149011612,\n      0.5083333253860474,\n      0.8382812738418579,\n      0.49166667461395264\n    ],\n    \"segmentation\": {\n      \"counts\": \"[^R11]f03O0O100O2N100O1O100O100O100O100O1O100O100O100O100O100O1O10000O1O10000O1O100O10000O1O100O100O100O100O100O100O100O100O100O100O1O100O100O10000O100O100O100O101N100O1O011O0O1O101OO0010O100O1O100O2OO0100O100O100O100O100O10000O100O100O1O100O10000O1O100O100O100O10000O1O100O100O100O10000O1O10000O1O100O100O100O100O100O100O1O100O100O100O100O100O100O100O100O100O100O100O100O100O100O10000O100O100O1O100O10000O100O100O100O100O1O100O100O100O100O100O100O10O0100O100O2O000O1O10000O1O10000O100O100O100O1O100O100O100O100O100O100O100O100O100O100O100O100O1O100O100O100O10000O100O100O100O100O100O100O100O100O100O100O100O100O100O10000O100O100O100O100O100O100O1O10000O1O10000O100O1O100O100O100O100O100O100O100O100O10000O1O100O100O100O100O1O10000O10\\\\MP@hNo?W1U@gNk?X1W@gNh?Y1Z@fNf?Y1\\\\@fNc?[1^@dNb?[1`@dN_?]1b@bN^?]1e@aNZ?_1i@_NW?a1l@\\\\NS?d1RAXNn>h1TAVNk>k1VATNj>k1XATNg>m1YASNg>m1YASNf>m1[ASNe>m1[ASNd>m1]ASNc>m1]ASNb>l1`ATN`>i1cAWN\\\\>d1jA\\\\NV>_1oAaNP>^1RBbNn=\\\\1TBdNk=\\\\1VBdNj=1`@dNGO02P2Z1h=L_AfNj0^1g=FmC;R<EoC;Q<DPD<o;DRD<n;DQD=n;DjAnN?^1g=DhAQO?\\\\1h=DhAUO<W1l=EeAZO:R1P>F]ABa0h0Q>Hd@lNDV1e17S>k1iAWNW>i1hAXNW>j1gAWNY>i1fAXNY>j1eAWNZ>k1dAVN\\\\>k1bAVN^>k1`AVN_>l1`ATN`>m1^ATNa>o1]AQNc>P2[AQNd>P2\\\\APNd>Q2[AoMd>R2[AoMd>R2\\\\AnMd>S2ZAnMe>S2[AmMe>T2YAmMf>T2YAmMg>T2WAmMh>U2VAlMj>U2TAlMl>U2PAnMo>U2j@PNV?e4O100O100O100O100O100O100O100O100O100O100O100O100O101N100O100O10O0100O100O100O100O100O100O1000000O1000000O100O100O1O1O1O100O100O1O100O100O100O100O100O100O100O100O100O1O100O100O100O100O100O10000O100O1O100O100O100O100O100O100OkK_B]Oa=7oBEP=4YCKg<1^CNa<1bCN^<OeC1[<LhC4W<KlC4S<KoC5Q<JPD6o;JRD6n;JSD5l;LTD4l;LTD4k;MUD3k;MUD4j;LWD2i;OWD1i;OWD1h;0XD0h;1WDOh;2XDOg;1ZDNe;3[DMe;3[DNc;3]DLd;4\\\\DLc;5]DKb;7]DIc;7^DHa;9_DGa;9_DG`;:`DF`;;_DE`;<`DCa;=^DDa;=_DC`;>_DCa;>^DBb;[OUCiMW1n2c;YO[CeMn0V3g;TO^CeMf0[3k;POaCdM>b3Q<iNbCfM7f3V<dNeCeMKQ4`<YNgCfMAX4g<RNiCk2W<SMlCl2S<TMnCl2R<SMoCm2Q<RMQDm2n;TMRDl2n;SMTDl2k;UMUDk2k;UMVDj2i;VMXDj2h;VMXDj2g;VM[Di2e;VM\\\\Dj2c;VM^Dj2b;TMaDk2^;PMhDP3X;aL`CjM`1e5o:\\\\L^Ed3b:WLdEh3[:nKPFR4P:jKTFV4k9hKXFX4h9hKXFX4g9hKYFY4f9hKZFX4f9hKZFX4e9iKZFW4g9iKXFX4g9iKPElN\\\\O\\\\5c;iKeDYOEo4f;iK]DAJh4g;iKTDJ3^4i;jKkCO;X4i;hMVDX2j;hMUDY2j;iMUDW2k;iMTDW2l;kMSDU2m;kMRDV2m;lMRDT2n;mMPDT2P<mMoCS2P<oMnCR2R<V4O100O100OiInCR2Q<kMWDQ2i;kM_DQ2`;lMoDi1Q;TNWEg1h:XN^Ed1a:\\\\NdE`1\\\\:^NjE^1U:aNPF]1o9aNUF]1k9bNXF\\\\1g9dN]FY1c9fN`FX1_9hNdFV1\\\\9iNhFT1W9lNmFQ1S9nNQGo0n8QOTGn0l8ROWGk0h8UO[Gi0e8VO^Gh0a8YO`Gf0`8YOcGe0\\\\8\\\\OeGc0[8\\\\OiGa0V8@lG>T8AnG>Q8BQH=o7CRH<m7DVH:j7FWH9h7HYH7g7H[H7d7J^H4b7L^H4b7K`H4_7MbH2^7NcH1\\\\7OfH0Z70gHOX72iHMW73jHLV74jHLU74mHKS75mHKS75nHJR76oHIQ77oHIR7jMkDP1U4U1S7RM_D0h0g1f3W1^8hNcGV1_8iNaGX1_8gNaGY1`8fNaGY1_8gNaGY1`8fNaGY1_8gNaGY1`8fNaGY1_8gNaGY1`8fNaGY1_8gNaGY1_8gNaGY1_8gNbGX1_8gNaGY1_8gNaGY1_8fNbGY1`8fNaGY1_8gNaGY1_8gNaGY1_8gNaGY1_8gNbGX1^8hNbGX1^8hNbGX1^8hNbGX1^8hNbGX1^8iNbGV1^8jNbGV1^8jNbGV1^8jNbGV1^8jNbGV1^8jNbGV1^8jNbGV1]8lNbGT1^8lNcGS1\\\\8nNdGR1\\\\8nNdGR1[8oNeGQ1Z8POfGP1X8SOhGl0W8UOiGk0U8WOkGi0S8YOmGg0P8\\\\OPHd0n7_ORH`0l7BTH>j7DVH<g7HYH7d7L\\\\H4b7N^H2`71_HO^74bHL[77eHIY7:fHFX7<hHDV7>jHBT7a0kH_OT7b0mH]OR7d0nH\\\\OQ7f0nH]OQ7g0oHZOQ7g0oHYOQ7h0nHXOR7h0nHXOR7h0nHXOR7i0mHWOT7h0kHYOU7h0jHXOV7h0iHYOW7g0iHYOW7h0hHXOY7g0fHZOZ7f0eH[O\\\\7e0cHhNlKSNa;U3bHeNSLTN\\\\;W3_HbN]LRNU;\\\\3]H^Nb8c1\\\\G\\\\Ng8c1XG\\\\Nj8e1TGZNo8e1PGYNS9h1lFUNW9l1gFRN]9m1bFRN`9o1^FPNe9o1[FoMg9R2WFnMj9S2TFmMn9R2RFnMn9S2PFmMR:R2nEmMS:T2kEmMU:T2jEkMX:T2gEmMY:T2fElMZ:U2dEkM^:T2aEmM_:T2`ElM`:U2^ElMc:S2\\\\EmMe:T2YEmMg:T2WEmMj:S2UEmMk:T2SEmMn:S2PEnMP;S2nDoMQ;R2mDoMT;Q2kDoMU;R2iDoMX;Q2fDQNY;P2eDQN[;P2cDQN^;o1`DSN_;n1^DTNc;l1[DVNd;k1ZDVNg;j1WDXNh;j1UDWNk;j1SDWNn;i1oCZNP<h1mCYNS<h1kCZNU<g1gC\\\\NX<e1fC\\\\N[<d1cC^N\\\\<d1aC^N_<c1^C_Na<b1\\\\CaNc<a1ZCaNf<_1XCcNg<_1UCeNj<^1oBfNP=]1iBiN?gL^;e4hCkNf0dLb;`8YDcGg;^8VDdGk;^8mChGR<_8bCfG_<U900001N101O00001O001O00001O00001O0O2N1O1O2N1O2N100O2N1O1O2N1O2N1O1O2N1O2M200O2M2O2N1N2O2N1N3N1O1N3N1N3M2O2kMkAkKW>Q4RBiKo=8^AR2j0`Mk=:aAP2i0bMh==eAj1g0eMf=?hAh1f0eMd=?lAg1c0gMc=`0nAe1c0hMa=a0oAd1b0iM`=a0QBc1c0iM]=c0SB`1d0iM\\\\=e0SB^1e0jMY=g0VB[1e0jMV=k0WBW1V`0gNn_OT1T`0lNo_Oo0S`0POS@i0P`0VOT@d0n?\\\\OT@`0n?@T@<o?CR@^OUN6ka0=P@XO\\\\N6ga0a0j@WOY?i0X3O001O00010O00001O0010O0001O00010O001O00001O001O01O01O00001O001O000O2O0O2O0O2N1O2N1O2M3MYl51fSJ3L3O1O100O1O100000000001O000000001O00000000001O01OO1000000000001O000001O000O10000000000000000O10000O10000O10000O100O1O100O1O1O1O1O1O1N2O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1N2O1O1O1O1O1O1O100O100N21O00001O001O2N1O1O2N1O2N1O2M3N4IVT_3\",\n      \"size\": [\n        720,\n        1280\n      ]\n    },\n    \"category_id\": 1,\n    \"iscrowd\": 0\n  }\n]\n```\n\n### Data Stats\n\nHere are the stats for the 10 annotation domains. The # Image-NPs represent the total number of unique image-NP pairs including both “positive” and “negative” NPs. \n\n\n| Domain                   | # Image-NPs  | # Image-NP-Masks|\n|--------------------------|--------------| ----------------|\n| BDD100k                  | 5546         | 13210           |\n| DROID                    | 9445         | 11098           |\n| Ego4D                    | 12608        | 24049            |\n| MyFoodRepo-273           | 20985        | 28347           |\n| GeoDE                    | 14850        | 7570            |\n| iNaturalist-2017         | 1439051      | 48899           |\n| National Gallery of Art  | 22294        | 18991            |\n| SA-V                     | 18337        | 39683            |\n| YT-Temporal-1B           | 7816         | 12221            |\n| Fathomnet                | 287193         | 14174            |\n"
  },
  {
    "path": "scripts/eval/silver/download_fathomnet.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\nimport argparse\nimport json\nimport os\nfrom multiprocessing import Pool\nfrom pathlib import Path\n\nimport requests\nfrom fathomnet.api import images\nfrom tqdm import tqdm\n\n\ndef download_imgs(args, image_uuids):\n    flag = 0\n    for uuid in tqdm(image_uuids, desc=\"Downloading images\"):\n        image = images.find_by_uuid(uuid)\n        file_name = (\n            Path(args.processed_images_folder)\n            / f\"{image.uuid}.{image.url.split('.')[-1]}\"\n        )\n        if not file_name.exists():\n            try:\n                resp = requests.get(image.url, stream=True)\n                resp.raise_for_status()\n                with open(file_name, \"wb\") as f:\n                    for chunk in resp.iter_content(chunk_size=1024):\n                        f.write(chunk)\n                flag += 1\n            except requests.exceptions.RequestException as e:\n                print(f\"Error downloading {image.url}: {e}\")\n    print(f\"Downloaded {flag} new images to {args.processed_images_folder}\")\n\n\ndef main():\n    parser = argparse.ArgumentParser(description=\"Download images from FathomNet\")\n    parser.add_argument(\"--processed_images_folder\", help=\"Path to downloaded images\")\n    parser.add_argument(\n        \"--image-uuids\",\n        default=\"fathomnet_image_uuids.json\",\n        help=\"Path to JSON file containing image uuids to download\",\n    )\n    parser.add_argument(\n        \"--num-procs\", type=int, default=16, help=\"Number of parallel processes\"\n    )\n    args = parser.parse_args()\n\n    with open(args.image_uuids, \"r\") as f:\n        all_uuids = json.load(f)\n\n    Path(args.processed_images_folder).mkdir(parents=True, exist_ok=True)\n\n    chunk_size = len(all_uuids) // args.num_procs\n    chunks = [\n        all_uuids[i : i + chunk_size] for i in range(0, len(all_uuids), chunk_size)\n    ]\n\n    with Pool(processes=args.num_procs) as pool:\n        pool.starmap(download_imgs, [(args, chunk) for chunk in chunks])\n\n\nif __name__ == \"__main__\":\n    main()\n"
  },
  {
    "path": "scripts/eval/silver/download_inaturalist.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\nimport argparse\nimport json\nimport shutil\nimport subprocess\nimport sys\nimport tarfile\nfrom pathlib import Path\n\nfrom tqdm import tqdm\n\n\ndef download_archive(url, dest_dir):\n    dest_dir = Path(dest_dir)\n    dest_dir.mkdir(parents=True, exist_ok=True)\n    archive_path = dest_dir / url.split(\"/\")[-1]\n    if not archive_path.exists():\n        print(f\"Downloading archive to {archive_path}...\")\n        result = subprocess.run([\"wget\", \"-O\", str(archive_path), url])\n        if result.returncode != 0:\n            print(\"Download failed.\")\n            sys.exit(1)\n    else:\n        print(f\"Archive already exists at {archive_path}\")\n    return archive_path\n\n\ndef extract_archive(archive_path, dest_dir):\n    print(f\"Extracting {archive_path} to {dest_dir}...\")\n    with tarfile.open(archive_path, \"r:gz\") as tar:\n        tar.extractall(path=dest_dir)\n    print(\"Extraction complete.\")\n\n\ndef copy_images(subset_json, untar_dir, output_dir):\n    with open(subset_json, \"r\") as f:\n        image_dict = json.load(f)\n    output_dir = Path(output_dir)\n    output_dir.mkdir(parents=True, exist_ok=True)\n    for target_name, rel_path in tqdm(image_dict.items(), \"Copying image subset\"):\n        src = Path(untar_dir) / rel_path\n        dst = output_dir / target_name\n        if not src.exists():\n            print(f\"Warning: Source image {src} does not exist, skipping.\")\n            continue\n        shutil.copy2(src, dst)\n    print(f\"Copied {len(image_dict)} images to {output_dir}\")\n\n\ndef main():\n    parser = argparse.ArgumentParser(\n        description=\"Download, extract, and copy subset of iNaturalist images from archive.\"\n    )\n    parser.add_argument(\n        \"--raw_images_folder\", help=\"Path to downloaded and extract the archive\"\n    )\n    parser.add_argument(\"--processed_images_folder\", help=\"Path to processed images\")\n    parser.add_argument(\n        \"--subset-json\",\n        default=\"inaturalist_image_subset.json\",\n        help=\"Path to iNaturalist images subset\",\n    )\n    parser.add_argument(\n        \"--archive-url\",\n        default=\"https://ml-inat-competition-datasets.s3.amazonaws.com/2017/train_val_images.tar.gz\",\n        help=\"URL of the archive to download\",\n    )\n    args = parser.parse_args()\n\n    dest_dir = Path(args.raw_images_folder)\n    images_dir = Path(args.processed_images_folder)\n\n    archive_path = download_archive(args.archive_url, dest_dir)\n    extract_archive(archive_path, dest_dir)\n\n    untar_dir = dest_dir / \"train_val_images\"\n    copy_images(args.subset_json, untar_dir, images_dir)\n\n\nif __name__ == \"__main__\":\n    main()\n"
  },
  {
    "path": "scripts/eval/silver/download_preprocess_nga.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\nimport argparse\nimport os\nfrom functools import partial\nfrom multiprocessing import Pool\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport requests\nimport utils\nfrom PIL import Image\nfrom tqdm import tqdm\n\nMETADATA_FILE = \"published_images.csv\"\nMETADATA_URL = \"https://raw.githubusercontent.com/NationalGalleryOfArt/opendata/refs/heads/main/data\"  # data/published_iamges.csv from https://github.com/NationalGalleryOfArt/opendata/tree/main\nIMG_URL = \"https://api.nga.gov/iiif/%s/full/%s/0/default.jpg\"\nMETADATA_FOLDER = \"metadata\"\nEXTENSION = \".jpg\"\n\n\ndef download_metadata(annotation_folder):\n    output_folder = annotation_folder / METADATA_FOLDER\n    output_folder.mkdir(exist_ok=True)\n    url = f\"{METADATA_URL}/{METADATA_FILE}\"\n    print(url)\n    response = requests.get(url)\n    if response.status_code == 200:\n        with open(output_folder / METADATA_FILE, \"wb\") as f:\n            f.write(response.content)\n\n\ndef download_url(row):\n    if np.isnan(row.maxpixels) or (\n        row.maxpixels > row.width and row.maxpixels > row.height\n    ):\n        url = IMG_URL % (row.uuid, \"full\")\n    else:\n        url = IMG_URL % (row.uuid, f\"!{row.maxpixels},{row.maxpixels}\")\n    return url\n\n\ndef download_item(item, output_folder):\n    uuid, url = item\n    try:\n        if (output_folder / f\"{uuid}{EXTENSION}\").exists():\n            print(\"skipping\", uuid, \"already downloaded\")\n            return\n        response = requests.get(url)\n        if response.status_code == 200:\n            with open(output_folder / f\"{uuid}{EXTENSION}\", \"wb\") as f:\n                f.write(response.content)\n    except:\n        print(\"errored\", item)\n        return\n\n\ndef remove_non_compliant_image(item, output_folder):\n    uuid, max_pixels = item\n    if np.isnan(max_pixels):\n        return\n    if not (output_folder / f\"{uuid}{EXTENSION}\").exists():\n        return\n    img = Image.open(output_folder / f\"{uuid}{EXTENSION}\")\n    if img.width > max_pixels or img.height > max_pixels:\n        os.remove(output_folder / f\"{uuid}{EXTENSION}\")  # delete image\n        return uuid\n\n\ndef reshape_image(rel_path, filename_size_map, output_folder):\n    w, h = filename_size_map[rel_path]\n    path = output_folder / f\"{rel_path}\"\n    img = Image.open(path)\n    if img.width != w or img.height != h:\n        new_size = (w, h)\n        resized_img = img.resize(new_size)\n        resized_img.save(path)\n\n\ndef main(args, workers=20):\n    raw_folder = Path(args.raw_images_folder)\n    processed_folder = Path(args.processed_images_folder)\n    utils.setup(raw_folder)\n    utils.setup(processed_folder)\n    uuids = utils.get_image_ids(args.annotation_file)\n    filename_size_map = utils.get_filename_size_map(args.annotation_file)\n    if not ((raw_folder / METADATA_FOLDER) / METADATA_FILE).exists():\n        download_metadata(raw_folder)\n\n    metadata = pd.read_csv((raw_folder / METADATA_FOLDER) / METADATA_FILE)\n    metadata[\"download_url\"] = metadata.apply(download_url, axis=1)\n    available_uuids = list(uuids.intersection(set(metadata[\"uuid\"].tolist())))\n    print(len(available_uuids), \"available for download out of\", len(uuids), \"target\")\n    url_data = list(\n        metadata.set_index(\"uuid\")\n        .loc[available_uuids]\n        .to_dict()[\"download_url\"]\n        .items()\n    )\n\n    download_single = partial(download_item, output_folder=(processed_folder))\n\n    print(\"Preparing to download\", len(url_data), \"items\")\n    with Pool(20) as p:\n        for _ in tqdm(p.imap(download_single, url_data), total=len(url_data)):\n            continue\n    check_img_size = partial(\n        remove_non_compliant_image, output_folder=(processed_folder)\n    )\n    max_pixels_dict_all = metadata.set_index(\"uuid\").to_dict()[\"maxpixels\"]\n    max_pixels_dict = {item[0]: max_pixels_dict_all[item[0]] for item in url_data}\n    print(\"Checking all images within size constraints\")\n    non_compliant = set()\n    with Pool(20) as p:\n        for each in tqdm(\n            p.imap(check_img_size, max_pixels_dict.items()), total=len(max_pixels_dict)\n        ):\n            if each is not None:\n                non_compliant.add(each)\n    print(len(non_compliant), \"not compliant size, removed\")\n\n    reshape_single = partial(\n        reshape_image,\n        filename_size_map=(filename_size_map),\n        output_folder=(processed_folder),\n    )\n    rel_paths = os.listdir(args.processed_images_folder)\n    print(\"Preparing to reshape\", len(rel_paths), \"items\")\n    with Pool(20) as p:\n        for _ in tqdm(p.imap(reshape_single, rel_paths), total=len(rel_paths)):\n            continue\n\n\nif __name__ == \"__main__\":\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"--annotation_file\", help=\"Path to annotation file\")\n    parser.add_argument(\"--raw_images_folder\", help=\"Path to downloaded images\")\n    parser.add_argument(\"--processed_images_folder\", help=\"Path to processed images\")\n    args = parser.parse_args()\n    main(args)\n"
  },
  {
    "path": "scripts/eval/silver/download_videos.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\nimport ast\nimport concurrent.futures\nimport os\nimport shutil\nimport subprocess\nimport sys\nfrom concurrent.futures import as_completed, ThreadPoolExecutor\nfrom pathlib import Path\n\nimport yt_dlp\nfrom utils import (\n    annotation_files,\n    config,\n    load_json,\n    run_command,\n    save_json,\n    update_annotations,\n)\n\n\ndef construct_gcs_path(original_video):\n    \"\"\"\n    Convert original_video string to GCS path.\n    Example:\n    'AUTOLab_failure_2023-07-07_Fri_Jul__7_18:50:36_2023_recordings_MP4_22008760.mp4'\n    ->\n    'gs://gresearch/robotics/droid_raw/1.0.1/AUTOLab/failure/2023-07-07/Fri_Jul__7_18:50:36_2023/recordings/MP4/22008760.mp4'\n    \"\"\"\n    parts = original_video.split(\"_\")\n    lab = parts[0]\n    failure = parts[1]\n    date = parts[2]\n    time = \"_\".join(parts[3:-3])\n    recordings = parts[-3]\n    mp4 = parts[-2]\n    file_id = parts[-1].split(\".\")[0]\n    gcs_path = (\n        f\"gs://gresearch/robotics/droid_raw/1.0.1/\"\n        f\"{lab}/{failure}/{date}/{time}/{recordings}/{mp4}/{file_id}.mp4\"\n    )\n    return gcs_path\n\n\ndef download_video(args):\n    gcs_path, dst_dir, json_file = args\n    # Ensure subdirectory exists\n    subdir = Path(dst_dir)\n    os.makedirs(subdir, exist_ok=True)\n    # Save file with its original name inside the subdir\n    print(json_file)\n    local_path = subdir / json_file\n    cmd = f'gsutil cp \"{gcs_path}\" \"{local_path}\"'\n    print(f\"Running: {cmd}\")\n    try:\n        run_command(cmd)\n        return (gcs_path, True, None)\n    except Exception as e:\n        return (gcs_path, False, str(e))\n\n\ndef download_youtube_video(youtube_id, output_path=None):\n    try:\n        if output_path is None:\n            output_path = os.path.join(\n                config[\"yt1b_path\"], \"downloaded_videos\", f\"video_{youtube_id}.mp4\"\n            )\n        url = f\"https://www.youtube.com/watch?v={youtube_id}\"\n        if os.path.exists(output_path):\n            return youtube_id, None\n        format = \"best[height<=720][fps<=30]/best[height<=720]/best\"  # 720p or lower, max 30fps\n        ydl_opts = {\n            \"format\": format,\n            \"outtmpl\": output_path,\n            \"merge_output_format\": \"mp4\",\n            \"quiet\": True,\n            \"cookiefile\": config[\"cookies_path\"],\n            \"socket_timeout\": 60,  # Increase timeout to 60 seconds (default is 10)\n        }\n        with yt_dlp.YoutubeDL(ydl_opts) as ydl:\n            ydl.download([url])\n        return youtube_id, None\n    except Exception as e:\n        return youtube_id, str(e)\n\n\ndef download_youtube():\n    all_videos_to_download = set()\n    for annotation_file in annotation_files[\"yt1b\"]:\n        ann = load_json(os.path.join(config[\"path_annotations\"], annotation_file))\n        for video_info in ann[\"images\"]:\n            youtube_id = video_info[\"original_video\"]\n            all_videos_to_download.add(youtube_id)\n\n    videos_to_download_still = all_videos_to_download\n    videos_downloaded = set()\n    videos_unavailable = set()\n    num_download_retries = 3\n    for _ in range(num_download_retries):\n        if len(videos_to_download_still) == 0:\n            break\n        videos_error = set()\n        with concurrent.futures.ThreadPoolExecutor() as executor:\n            futures = [\n                executor.submit(download_youtube_video, youtube_id)\n                for youtube_id in videos_to_download_still\n            ]\n            for future in concurrent.futures.as_completed(futures):\n                youtube_id, exception = future.result()\n                if exception is None:\n                    videos_downloaded.add(youtube_id)\n                elif \"unavailable\" in exception or \"members-only\" in exception:\n                    videos_unavailable.add(youtube_id)\n                else:\n                    videos_error.add(youtube_id)\n        videos_to_download_still = (\n            all_videos_to_download - videos_downloaded - videos_unavailable\n        )\n        assert videos_to_download_still == videos_error\n\n    if len(videos_unavailable) + len(videos_to_download_still) > 0:\n        message = \"Some videos are either no longer available on YouTube, or are set to private, or resulted in some other error. \"\n        if config[\"update_annotation_yt1b\"]:\n            message += \"The unavailable videos will be ***REMOVED*** from the annotation file. This will make the test results NOT DIRECTLY COMPARABLE to other reported results.\"\n            print(message)\n            update_annotations(\"yt1b\", videos_downloaded)\n        else:\n            message += \"You may want to either re-try the download, or remove these videos from the evaluation json\"\n            print(message)\n\n\ndef download_droid():\n    ann_dir = Path(config[\"path_annotations\"])\n    dst_dir = Path(config[\"droid_path\"]) / \"downloaded_videos\"\n    json_files = annotation_files[\"droid\"]\n\n    download_tasks = []\n    original_videos = set()\n    for json_file in json_files:\n        json_path = ann_dir / json_file\n        data = load_json(json_path)\n        for img in data[\"images\"]:\n            original_video = img[\"original_video\"]\n            original_videos.add(original_video)\n\n    print(len(original_videos))\n    for original_video in original_videos:\n        gcs_path = construct_gcs_path(original_video)\n        download_tasks.append((gcs_path, dst_dir, original_video))\n\n    max_workers = min(16, len(download_tasks))\n    with ThreadPoolExecutor(max_workers=max_workers) as executor:\n        future_to_task = {\n            executor.submit(download_video, task): task for task in download_tasks\n        }\n        for future in as_completed(future_to_task):\n            gcs_path, success, error = future.result()\n            if not success:\n                print(f\"Failed to download {gcs_path}: {error}\")\n\n\ndef download_ego4d():\n    output_dir = os.path.join(config[\"ego4d_path\"], \"downloaded_videos\")\n\n    ann_dir = Path(config[\"path_annotations\"])\n    json_files = annotation_files[\"ego4d\"]\n    original_videos = set()\n    for json_file in json_files:\n        json_path = ann_dir / json_file\n        data = load_json(json_path)\n        for img in data[\"images\"]:\n            original_video = img[\"original_video\"]\n            original_videos.add(original_video)\n\n    original_video_uids = [\n        video_uid.replace(\".mp4\", \"\") for video_uid in original_videos\n    ]\n    video_ids_download = original_video_uids\n    num_download_retries = 2\n    download_correct = False\n    message = \"\"\n    for _ in range(num_download_retries):\n        cmd = (\n            [\n                # \"python\", \"-m\", \"ego4d.cli.cli\",\n                \"ego4d\",\n                \"--output_directory\",\n                output_dir,\n                \"--datasets\",\n                \"clips\",\n                \"--version\",\n                \"v1\",\n                \"--video_uids\",\n            ]\n            + video_ids_download\n            + [\"--yes\"]\n        )\n\n        # Run the command\n        result = subprocess.run(cmd, capture_output=True, text=True)\n        message = result.stderr\n        if (\n            \"RuntimeError: The following requested video UIDs could not be found in the manifest for version:\"\n            in result.stderr\n        ):\n            not_findable_videos = ast.literal_eval(result.stderr.split(\"\\n\")[-2])\n            video_ids_download = [\n                video_uid\n                for video_uid in video_ids_download\n                if video_uid not in not_findable_videos\n            ]\n        else:\n            download_correct = True\n            break\n\n    if not download_correct:\n        print(f\"There was an error downloading the Ego4D data: {message}\")\n\n    if len(video_ids_download) != len(original_video_uids):\n        message = \"Some videos are no longer available. \"\n        if config[\"update_annotation_ego4d\"]:\n            message += \"The unavailable videos will be ***REMOVED*** from the annotation file. This will make the test results NOT DIRECTLY COMPARABLE to other reported results.\"\n            print(message)\n            update_annotations(\"ego4d\", video_ids_download)\n        else:\n            message += \"You may want to either re-try the download, or remove these videos from the evaluation json\"\n            print(message)\n\n\ndef download_sav():\n    tar_url = config[\"sav_videos_fps_6_download_path\"]\n    tar_file = \"videos_fps_6.tar\"\n    sav_data_dir = os.path.join(config[\"sav_path\"], \"downloaded_videos\")\n    os.makedirs(sav_data_dir, exist_ok=True)\n\n    subprocess.run([\"wget\", tar_url, \"-O\", tar_file], cwd=sav_data_dir, check=True)\n    subprocess.run([\"tar\", \"-xvf\", tar_file], cwd=sav_data_dir, check=True)\n    subprocess.run([\"rm\", tar_file], cwd=sav_data_dir, check=True)\n\n\ndef main():\n    assert len(sys.argv) > 1, \"You have to provide the name of the dataset\"\n    dataset_name = sys.argv[1]\n    assert dataset_name in annotation_files, (\n        f\"The dataset can be one of {list(annotation_files.keys())}\"\n    )\n\n    if dataset_name == \"yt1b\":\n        download_youtube()\n    elif dataset_name == \"droid\":\n        download_droid()\n    elif dataset_name == \"ego4d\":\n        download_ego4d()\n    elif dataset_name == \"sav\":\n        download_sav()\n\n\nif __name__ == \"__main__\":\n    main()\n"
  },
  {
    "path": "scripts/eval/silver/extract_frames.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n\"\"\"\nThis file extracts the frames for the frame datasets in SA-CO/Gold and Silver.\n\nCall like:\n> python extract_frames.py <dataset_name>\n\"\"\"\n\nimport json\nimport os\nimport shutil\nimport sys\nfrom multiprocessing import Pool\n\nfrom PIL import Image\nfrom tqdm import tqdm\nfrom utils import (\n    annotation_files,\n    config,\n    get_frame_from_video,\n    is_valid_image,\n    update_annotations,\n)\n\n\ndef extract_frame(path_video, global_frame_idx, path_frame, image_size, file_name):\n    frame = get_frame_from_video(path_video, global_frame_idx)\n    os.makedirs(os.path.dirname(path_frame), exist_ok=True)\n    img = Image.fromarray(frame)\n    if frame.shape[:2] != image_size:\n        print(f\"Resizing image {file_name} from {frame.shape[:2]} to {image_size}\")\n        height, width = image_size\n        img = img.resize((width, height))  # Uses Image.NEAREST by default\n    img.save(path_frame)\n\n\ndef process_image(args):\n    image, dataset_name, config = args\n    original_video, global_frame_idx, file_name, image_size = image\n    extra_subpath = \"\"\n    if dataset_name == \"ego4d\":\n        extra_subpath = \"v1/clips\"\n    elif dataset_name == \"yt1b\":\n        original_video = f\"video_{original_video}.mp4\"\n    elif dataset_name == \"sav\":\n        extra_subpath = \"videos_fps_6\"\n    path_video = os.path.join(\n        config[f\"{dataset_name}_path\"],\n        \"downloaded_videos\",\n        extra_subpath,\n        original_video,\n    )\n    path_frame = os.path.join(config[f\"{dataset_name}_path\"], \"frames\", file_name)\n    to_return = file_name\n    try:\n        extract_frame(path_video, global_frame_idx, path_frame, image_size, file_name)\n        if not is_valid_image(path_frame):\n            print(f\"Invalid image in {path_frame}\")\n            to_return = None\n    except:\n        print(f\"Invalid image in {path_frame}\")\n        to_return = None\n    return to_return\n\n\ndef main():\n    assert len(sys.argv) > 1, \"You have to provide the name of the dataset\"\n    dataset_name = sys.argv[1]\n    assert dataset_name in annotation_files, (\n        f\"The dataset can be one of {list(annotation_files.keys())}\"\n    )\n    all_outputs = []\n    for file in annotation_files[dataset_name]:\n        with open(os.path.join(config[\"path_annotations\"], file), \"r\") as f:\n            annotation = json.load(f)\n        images = annotation[\"images\"]\n        images = set(\n            (\n                image[\"original_video\"],\n                image[\"global_frame_idx\"],\n                image[\"file_name\"],\n                tuple(image[\"image_size\"]),\n            )\n            for image in images\n        )\n        args_list = [(image, dataset_name, config) for image in images]\n        with Pool(os.cpu_count()) as pool:\n            outputs = list(\n                tqdm(pool.imap_unordered(process_image, args_list), total=len(images))\n            )\n        all_outputs.extend(outputs)\n    if any(out is None for out in outputs):\n        update_annotations(dataset_name, all_outputs, key=\"file_name\")\n    if config[f\"remove_downloaded_videos_{dataset_name}\"]:\n        shutil.rmtree(os.path.join(config[f\"{dataset_name}_path\"], \"downloaded_videos\"))\n\n\nif __name__ == \"__main__\":\n    main()\n"
  },
  {
    "path": "scripts/eval/silver/fathomnet_image_uuids.json",
    "content": "[\n  \"44973e24-09da-4366-9a24-d17e4a12b73d\",\n  \"a602c8ba-4d61-41a5-a4a0-80d680eb8972\",\n  \"8cad4907-094c-4509-a473-8f8bdf6c91d8\",\n  \"c1fee045-5e83-41fc-b9b5-eff847ccd69b\",\n  \"7bf5c26a-8026-4449-a3f9-baa486d0d16f\",\n  \"39a7ee48-76da-4af6-9c84-9c6d1e8278c8\",\n  \"420d2fb9-603c-45b8-92da-7827d9d3aee3\",\n  \"f757d96c-d009-41e7-9793-ebff210da547\",\n  \"dd94dc70-7ee2-4b6a-aa16-c27dde6350af\",\n  \"d7b1e1ed-4afe-4f3b-9b1e-22a7ed33538e\",\n  \"3e07dd50-c624-4274-8588-2238e8812f0e\",\n  \"d5532829-6f82-4813-86cc-4071b5dbdb2f\",\n  \"cc0dfcdd-3d92-43f8-bf26-45b84f73cabe\",\n  \"1088c10a-57ae-4bc3-924e-6f9a7f1ca505\",\n  \"93246182-a44d-4b4a-99b6-b28852d7fe42\",\n  \"e0c6e21f-19ec-4460-a15f-9fa9f0b17831\",\n  \"06338b9d-0993-4b9e-9fc2-635a3676a97c\",\n  \"270b2bb8-d5d8-41f1-b3cf-b565b2c71524\",\n  \"0c8fa41d-d4cb-46a1-ac8f-bc3f2b653a75\",\n  \"3bd40736-b0a5-4d8a-a38b-15af6aad2799\",\n  \"55cf3c50-bdba-4a0d-aaf3-193fb03d8478\",\n  \"7a2c269f-7c99-4e68-ad73-03e3b228b629\",\n  \"454c05b6-1fd0-4446-8a45-8d9b13f8c08b\",\n  \"d9524a6e-630f-42de-bcab-cdcae900f7ac\",\n  \"e9bbb302-befd-4fd1-bc57-dddc247f8c7a\",\n  \"f6257f8d-2d3f-479f-aa3e-f99ffacd753c\",\n  \"29f87187-9365-416e-8a9f-802b903a868e\",\n  \"825cd523-b4a1-4cb4-81cc-d8c14a0b65b2\",\n  \"563c22a7-c0e0-423d-9744-6f5882876295\",\n  \"d4794251-ef3c-4115-b35b-085e477bf364\",\n  \"204a90bc-35a4-480f-a1dc-e52cad34e85d\",\n  \"5e7d492e-1e65-4f5e-a1ec-72d4782362c6\",\n  \"522b0b65-ae5b-4709-b8c8-43657672f15e\",\n  \"6587710c-3e92-416d-a8bd-bd0c55f52454\",\n  \"ee56ca7f-553f-4b95-ab0e-13ff68360ed1\",\n  \"54cd215f-0d87-41ac-99a8-2f48b1a4299c\",\n  \"c4e3253a-a7c6-42a6-93a7-180cd219f077\",\n  \"c63cb2c3-1f23-4de5-93f5-08a309cde1c7\",\n  \"ddfb11bc-737b-4bbc-83de-a44c0476bc65\",\n  \"ac6b464f-ef5c-41b7-952b-8b5deec7d969\",\n  \"55b096b4-4529-4512-a187-4c5f3b1c6a9a\",\n  \"5a0a706c-5cab-47e5-b366-2de909dcf8b4\",\n  \"6ef5d99b-fea5-412f-94af-fbec84aa0b39\",\n  \"abfea99c-5e27-4fe5-9fc6-65cd9d7ecb45\",\n  \"1f843bca-5180-4b58-8eec-0f973a8c3643\",\n  \"0c1289d9-eabe-4bf5-b829-f7a1eab0e95d\",\n  \"befe9f12-442b-433d-af81-469626142a00\",\n  \"1a53fce4-a814-4924-9d1f-f7d04271b7e6\",\n  \"3afb1561-4dc1-4209-8267-f175ff5ef267\",\n  \"bd5fe049-42db-430c-b691-da55a09d5696\",\n  \"67a23937-af2d-477b-bfd9-689dff8c025a\",\n  \"b4b588f9-56f7-45a7-933d-127772e18922\",\n  \"fb19adf3-f6d7-4280-9490-bedf298bea30\",\n  \"06b1ef0a-3c13-44a5-b022-8f812910a4aa\",\n  \"ddfa006f-cca2-4814-ae6e-f12602fd7a1e\",\n  \"89682ad5-03d2-4c46-8e15-9ff54b03389c\",\n  \"9559eff6-2522-4714-a485-0d95d5b43974\",\n  \"84185e80-b943-4ffe-9b07-8010d7a42e38\",\n  \"c79ab88b-106d-4133-8c05-63990a8b8586\",\n  \"d1c9380f-c6a4-4706-8ad9-0cb1284393c8\",\n  \"e2c79403-977d-40ed-8491-637bd1724849\",\n  \"fba86954-f64d-4b25-8930-48a4b85dd3ad\",\n  \"58fb247c-eb20-4ffa-9f4c-23da0bd310cd\",\n  \"eea50043-49f1-48da-8480-e08253a0df9e\",\n  \"b5d03702-ca24-4836-9855-4343837b4c1b\",\n  \"82cb54ad-23a5-490e-aea9-1914ea355f00\",\n  \"b19ba54e-8589-413e-b16f-cdc694db45cd\",\n  \"cf81a495-ca2e-4202-9176-de42fc2de5f8\",\n  \"06f9a650-cbcb-4638-91cb-cbf99781be79\",\n  \"42591570-8846-4f8a-af42-048924fb6936\",\n  \"79672528-e8d3-4118-b576-16158924ecdf\",\n  \"04539a5c-850c-45ac-beff-d8a4e451d8b4\",\n  \"ff66bbae-c27f-45a9-99e2-8b289d9f2227\",\n  \"94f513ca-1bb3-4671-9c9c-6fc2d840a2b9\",\n  \"90a1bfb3-7692-4d5b-a698-d2b3f6e74257\",\n  \"bb6a48db-434e-489c-89ed-2812086b2542\",\n  \"510e3e3c-9580-4e1f-8f3b-2f7187ee05f8\",\n  \"9c3d0587-52cf-422c-b8cc-b4f42d4f41c0\",\n  \"c8930f95-cbe0-41ab-9e16-78199471bd16\",\n  \"554fac1c-31b8-4986-b5d8-6788c96e0f2f\",\n  \"dc0873d2-a05f-4f6c-a500-879f9d0d28cf\",\n  \"0142be4c-c49f-4db6-98b5-c38ff6d829f9\",\n  \"e884e923-e7e8-4574-89f1-b181c12ee46d\",\n  \"6c27d2cb-e9b1-46af-9788-cd3062da90f4\",\n  \"b438d26c-a33e-4c28-9036-576534ffd9c3\",\n  \"aeb62274-eeb4-493a-a162-31ce614486e2\",\n  \"1d813d76-fdc3-4f22-ae82-75e4962c3552\",\n  \"8563b16a-6690-4052-ad94-f33a4f439a58\",\n  \"f6cb5f83-aba4-4497-a10d-caf4d7690456\",\n  \"92ec365a-150b-4e4d-b8f5-8a36564f0ba1\",\n  \"a5b4f569-a239-454b-b031-7775036f8dd1\",\n  \"02713482-d953-4d2c-97d6-58e88f80aa4f\",\n  \"f39d582f-867c-4617-8b7a-b15f91d1bc94\",\n  \"6613c3bd-c86a-4c06-9882-8e970ee9ab98\",\n  \"691fd07b-2056-48cd-8516-f87a08d8307d\",\n  \"f5debb51-384f-4629-872b-86c17a6838ce\",\n  \"60c6b3f3-2c53-47d5-92da-3214ee895a63\",\n  \"9088fbe0-da76-4a39-9eb3-95cd3b4a06b9\",\n  \"10a56c9f-7f3a-4920-b3c1-49cf63ab2d34\",\n  \"c2801b9b-0ca7-45e1-a72d-3d44d48b53e4\",\n  \"33a3f615-f528-4acf-8ea8-b99e3e9c8048\",\n  \"c720fb78-430a-4fde-9613-e31f2b50f3cf\",\n  \"78a2b2cb-e6c2-4405-b6af-a4173e675bfd\",\n  \"4230e14d-83da-430f-b90a-b4e47071eebd\",\n  \"8d8e0b14-7d46-40a9-8262-7c63eb503d42\",\n  \"11868667-c04b-436a-aeb1-0b909317e076\",\n  \"57d16a32-c136-42e3-ac7b-fea17784cd04\",\n  \"c2c60cb1-3b19-4c47-b24e-3c8bd977184c\",\n  \"e4c1f9d7-71dc-45f9-b98a-f7399e4bbe5d\",\n  \"8485e3cb-fba8-44ed-a024-3ae6b42db55b\",\n  \"1d2c4171-2697-46d2-83fb-a57b911adc57\",\n  \"72c38240-eaaa-454b-b7ff-8269c9e66d2b\",\n  \"73725d47-6cfc-4adc-a80c-75351a43f5f8\",\n  \"38e26821-a55b-49a2-b1b4-b6fc9394b619\",\n  \"5db57488-6b39-451a-87ab-2a78716ae535\",\n  \"4e10bbc3-b400-4b79-ba5b-aba4a40c4594\",\n  \"fb99327d-217f-4f95-9ec7-5c82a5ddb20c\",\n  \"164e33ba-56de-4e09-a731-6f900e652480\",\n  \"9a003294-691e-4a3f-b140-0a4b72399835\",\n  \"3badec5a-d18c-4fe8-8af8-eccf6c5413af\",\n  \"0f3b806a-17a0-496e-8dd5-9415e58eaa3c\",\n  \"332d84e4-7ea5-47e0-94c0-43973165fb5a\",\n  \"5b9a4253-53e6-4b4f-a388-6871dc3c99ba\",\n  \"0a95e37f-6ea6-480c-b2d2-6a95420153a0\",\n  \"e4c16eff-8a05-41eb-98e3-f47bb4cc264f\",\n  \"2d1e4008-438e-4448-8c76-b0f8f42d4f5c\",\n  \"b65f0194-98a2-49dd-88a3-ce4180b681c8\",\n  \"d8bb8271-f5e6-4785-b940-9a328af1b227\",\n  \"07a343ae-ecf5-4b6d-86be-c2f00b3d19e5\",\n  \"cff847ec-375a-46a6-a003-2e25c7b295f9\",\n  \"cb1f2da1-dc31-40a8-91cb-c019b285f581\",\n  \"e3eaaaeb-da4f-4b71-8e7b-c6a10f68a923\",\n  \"b71f189d-edeb-4374-9cab-13ba0541ace1\",\n  \"d31fc28d-c832-4743-9d32-1e5f3fc2d1b3\",\n  \"5c3e6f92-9c77-4dc1-addf-95bd8324b727\",\n  \"f32209d8-8f76-4281-9cb7-27f9dac46e72\",\n  \"fb206041-5728-4392-bd14-1b1255ed5f61\",\n  \"9d241cf7-bf1e-487f-b451-795266c80269\",\n  \"67e6ac67-1fdd-4439-b4ed-788dff59f127\",\n  \"983030ef-bc7c-461d-b798-e062c6e44bdf\",\n  \"4a3e162d-d489-42ab-a437-5390f7463be3\",\n  \"5267ed67-ad0a-4ca4-9f18-80df09568733\",\n  \"bef5af9e-08da-4652-ac56-2fa92075f5c0\",\n  \"f56a08f0-925b-4610-ab4a-53ca5051d35d\",\n  \"ad73c5c7-9b8c-498a-aec5-5508bf5783ba\",\n  \"b4fcb998-3aba-4651-84cf-9e8ee2fec02f\",\n  \"fc98b7ae-d3d0-4e62-819b-99160aafe406\",\n  \"e1a653a9-7eb6-4c95-aa4f-eb6a81f15fbb\",\n  \"7119e96e-1f62-4dba-b432-6fd52c2cffe3\",\n  \"ad47d117-3e75-476b-950f-8f3d4d0d247c\",\n  \"7b396141-504e-41bb-aa0f-a06bcce16dd1\",\n  \"599ccf89-0fd6-4ecd-a2e4-dd47b14de119\",\n  \"f0cd7210-896a-450a-9742-3d57c86c8241\",\n  \"262154d9-3d3e-4250-84da-608242084a2e\",\n  \"b195737b-e7da-466e-9b71-6053f7cad893\",\n  \"9b7317a8-bed8-4ff2-9e13-948ff0b03fa2\",\n  \"45d22cbf-3866-4eca-8558-13f837477976\",\n  \"4c8c0d59-4108-4a0d-a5b6-ac0712e49e3f\",\n  \"056fbd6d-edc8-44fe-8073-6e569aeda685\",\n  \"7861b9a8-4420-40f0-a5e0-f442bc1e8981\",\n  \"e8bdadd6-8431-445c-9633-f0ca895fbb5f\",\n  \"c4594215-3818-4583-99be-97b5e05e7e53\",\n  \"635db8f9-864f-48aa-a527-5abe34973cce\",\n  \"2b13d82e-b2bf-4d46-84c6-7c0c971fa239\",\n  \"a1e9ea3a-74ee-46d9-b5c1-eb9c95d47f95\",\n  \"8668bd29-293a-424b-95a7-e7acb8c3e9d9\",\n  \"2c0d65e3-a294-42db-bc86-cac66f59fd50\",\n  \"d6c8174a-6cd2-4ab6-909c-e3a8573f481f\",\n  \"f7fca24c-7c07-45c7-b05e-9c407963ed87\",\n  \"7856c97b-1f12-4db5-94fe-d014e7bffdc1\",\n  \"01e07bab-9f99-4201-8625-a60ee6d4406b\",\n  \"2fbf1d02-94db-4c6d-bce4-61dfc52abffb\",\n  \"525e5004-8f3e-4894-b569-377eb101dd7d\",\n  \"29026fd8-e619-4084-ae91-de3e631f9fa5\",\n  \"d7822f22-ffbb-479e-92a3-a5bd0214e8a1\",\n  \"e8307581-8848-4ffb-8e7c-2bb3f2116ad8\",\n  \"7d749d77-4e32-4ee0-8707-a4545daddeca\",\n  \"9db0f2d3-24eb-4fc0-a77b-b634aa5392c4\",\n  \"c8625032-8515-4219-844c-6c56fb111505\",\n  \"e4c11319-5a9b-46b1-bbf7-21a0a872c981\",\n  \"c2fe4136-277c-42f7-a21b-bfa85e133fbf\",\n  \"e17ec664-bf88-4269-96d8-402c4e238b1b\",\n  \"30158210-4d99-4c35-ae04-e5665bce0d81\",\n  \"44ed8a79-51c8-4a30-8397-d0e27c8bfadb\",\n  \"16eb78b2-4d1a-4628-a471-13e7b3781f7a\",\n  \"b2dab81d-cea8-48dd-a326-036417cbad8f\",\n  \"9832f4fd-07b2-4722-9b47-dc0493230e0b\",\n  \"cf10f7a1-d5ed-4b01-8b7a-168645c13ff8\",\n  \"a6e6f915-5155-4d05-b0cf-7729bb78908f\",\n  \"4553454f-8060-4e34-982c-5801df3070f3\",\n  \"31ae7ddb-ee6b-4ca2-a4c0-76a4c82b13ea\",\n  \"541a3d23-3800-41b7-b3e8-78ed8b9eeda7\",\n  \"7cf49f66-d311-4314-9544-144f5f212d39\",\n  \"66966a4f-4d0e-4f2b-bc75-b6b5ec29b5a7\",\n  \"7fd7b6e9-34ee-41b8-9831-d4b6605775bc\",\n  \"5e5cd0df-1f06-44ac-a94f-90dcf1606a1e\",\n  \"5c09bfbf-90bf-404c-8c25-3235e5f4033d\",\n  \"4c316cab-a01d-4764-b677-283340032da8\",\n  \"957dc413-c5b4-4551-8900-8ac663a72e6b\",\n  \"da58305b-a78b-4060-bc48-130292ab1b0f\",\n  \"25a4b2e4-9908-49e8-8d0e-02e99a0cca77\",\n  \"cd741a56-018e-4c0d-b8ac-34596e416a12\",\n  \"3ed6e231-f351-40e9-8def-ff72851234c0\",\n  \"c84a55ca-cc4c-40b0-82d3-c2617afaad92\",\n  \"fcebd6c8-8e2e-4bc1-a22e-a60dd57f80ee\",\n  \"004fe257-00ae-49a0-bd2a-80c9d68d124d\",\n  \"5fa64bb5-d340-4453-b2e0-b222408bb0e7\",\n  \"6de233d2-d9c3-4bfd-9380-dc7f2aae8fcb\",\n  \"0e0811ec-2ecb-4a0f-a80a-eef979fba1cf\",\n  \"2f8591c2-895d-402b-819d-30b9bf3dbc4f\",\n  \"77f04550-b1fd-4e6f-98a5-8c7393114398\",\n  \"6897500c-331e-4952-954c-562930a6bfef\",\n  \"68b90ce2-690f-4bcb-8cc0-dbd7ead74095\",\n  \"0bf6103e-d636-4cd8-97f6-a456b61591a9\",\n  \"f3fbe4f4-5a9b-41e2-9e94-87bb525544a1\",\n  \"4eafa131-3cad-4231-ade1-e60919865856\",\n  \"66cb5eb7-5f47-46a5-a32d-34801c415683\",\n  \"93a169c1-0bde-4e23-a3c1-3c27e8174a0e\",\n  \"fa9cc392-4a54-4258-9d98-81952816ef37\",\n  \"dbbf1abf-dbfc-4954-8328-aa4ef1cf1c8b\",\n  \"087e1a2e-3190-41af-b595-8511de29128a\",\n  \"cd7458b0-193a-40ef-bcd1-10fbc85c054a\",\n  \"f31316f4-edf7-4afc-9dc2-09438188a800\",\n  \"d5402bb9-c9f4-4e58-9f30-223c8f1efb91\",\n  \"d05eb6ef-1589-42bd-b644-9ab087921272\",\n  \"7c835e45-8e52-4604-aa80-97b834e88a35\",\n  \"49b8c394-7766-4e9c-98ab-04cd97425ea5\",\n  \"84de4228-c234-4ed4-a12f-7de70d527bca\",\n  \"d3905e1c-4ac9-4d1b-8f94-1dba73f29083\",\n  \"4a36f3c6-193e-41ef-acd7-9c38722ea972\",\n  \"4cbe570b-e442-4494-b2d0-b0bcfbf54d21\",\n  \"3d88e3e4-17ee-472e-b2ed-81a0b8abd311\",\n  \"033c80bc-bf7d-4d2b-af8b-6464bbf9dfb7\",\n  \"d55f7061-18db-4598-b9a1-c0925874f609\",\n  \"a86f47ad-d4d0-4409-b421-88573930bc8b\",\n  \"e21b5852-3c76-4dbd-a1a7-96eb4627fda8\",\n  \"4007c8de-1a2d-40f7-809f-91c51339405f\",\n  \"5ce6aba2-5ed0-4e63-a4ad-6f5eecbc0b09\",\n  \"e9df1132-b19b-4452-8a7b-2c5fe90dd30d\",\n  \"5b04df10-67b9-434b-b2df-ba567b00be1c\",\n  \"b19c0945-a719-4c50-8573-96bfc7d23dab\",\n  \"a8ffc419-a174-40cd-9bb3-5d643b08ab5b\",\n  \"7f721b75-c58e-4807-8c9d-85655b927e5c\",\n  \"bc7b5218-c3a1-4c51-8da6-5146dd8554e8\",\n  \"cdd9fc2e-7a21-48f7-af8b-1b618bdf9508\",\n  \"6da27ef2-4579-461a-a9e7-ddfdbeb19f13\",\n  \"cfa7dbd0-fa01-441b-b534-1cbadc332ffb\",\n  \"95db11c3-3af1-418f-b995-b26511c72ee6\",\n  \"83ba038c-a730-4794-9714-f8240e12ad81\",\n  \"2bb213f3-2b55-49db-9385-718ed66ce9c5\",\n  \"3e0ca5cf-ade3-465d-a74e-df24466330e4\",\n  \"70f3099f-c7be-4ae3-b035-ee7f101eb644\",\n  \"aec0bade-92e2-474d-afb7-965affb743bd\",\n  \"cd32d19e-8c2a-4580-8e2f-fc20822c4195\",\n  \"4abdfb3e-7eec-41ec-b5c0-1e1b2dea41a2\",\n  \"0ad7a123-fe26-4d0e-a77b-2a9c22edbd93\",\n  \"fa09d69b-ecf2-452b-af92-39eabdde6d47\",\n  \"cfcb60df-f60a-499c-8d61-a39e93daed78\",\n  \"aaaa9a7c-c75e-4b64-a038-0c2a7e812423\",\n  \"c0419c03-5f22-4841-9bcf-522d73663c39\",\n  \"3c65d484-8c33-46a2-a5d1-ceefbc453578\",\n  \"317558bf-e3c8-4018-8aad-c7d046c0e4bd\",\n  \"512b5977-ff7f-487f-8d4b-a2cbcc0b0ea2\",\n  \"90e43eaa-cad3-406d-b648-11caaefbd32d\",\n  \"8f9e23b9-ba6b-4488-aa43-cace05fa46e6\",\n  \"fd0b700a-9eb9-4d3e-8ec3-2d6205311f11\",\n  \"efba881e-d53c-44cb-a9c6-76b25454a23e\",\n  \"10e3b0e0-1e22-4a54-9436-309a81fb5154\",\n  \"5f7725c8-5d0d-4c06-8088-59d0a96fe8f3\",\n  \"d8ee8ca4-bf8d-499b-966b-91bd81805c27\",\n  \"9c226251-f94a-46d5-847b-0ad8facf8aab\",\n  \"1a66adcb-9998-4a6b-a099-f7e1f790c017\",\n  \"3d11250f-1502-4b1c-9123-8aebfedbd4e1\",\n  \"29f2c74a-2651-4b5f-a7a5-49b58060deec\",\n  \"42a62736-5b0c-439c-ae84-b5371162a33b\",\n  \"ea5ae3f6-68a8-4d5f-8863-0085f90a6a4b\",\n  \"fa8f7e29-ffe4-4de7-812f-b97f3eaf0cc9\",\n  \"8ebc614a-a660-4818-980c-ca3eb7a7a60f\",\n  \"44a153bb-baca-4638-87b1-b931fdd652f8\",\n  \"d331aff6-9b9f-47d2-af40-0a784d528ec2\",\n  \"b076836d-0487-4185-9562-accbc5992e68\",\n  \"b2b614be-262e-484a-9e45-2e0bdfacb75b\",\n  \"db72bd34-c24a-4e40-8d38-e4977a0e44c0\",\n  \"4bd26590-720b-4475-82bb-58e0620fef77\",\n  \"c7aaf46e-cc3b-4304-a468-f70363bcc913\",\n  \"af96f129-7ce7-4051-856d-f8552c4d9237\",\n  \"10c5ac54-d0a2-4415-8fbc-755dcf54307d\",\n  \"343feb67-4f06-4e5c-ab00-dc1a3ef34ec5\",\n  \"45028c0f-ce3b-42cc-a7aa-13b7b4ac8c88\",\n  \"9088ef58-78f1-4ae2-a7f5-ec9a1e84b671\",\n  \"65004fc3-5037-487b-9a8c-1cdb8b5a7ae5\",\n  \"08a60ebd-2638-4b76-a1a0-898ceb270b56\",\n  \"98433997-a432-42b1-8e52-09d3eafef520\",\n  \"0ceb6351-1b85-42d3-8d13-02e2e000f89d\",\n  \"e7588b85-0726-4857-a606-01d17eb6b52b\",\n  \"cb158049-27b7-437d-bfb9-fd3b6d0821f0\",\n  \"157708c5-e59d-468a-b2a5-a03ad9db1eb5\",\n  \"29cf36ff-7286-45ea-882f-a9f5a81ba146\",\n  \"75a27146-f06c-4181-b569-ab30196c270e\",\n  \"21023e6b-8afc-41db-8b4a-c0fb6521a579\",\n  \"110ef8cb-ab8f-4356-8b15-bd9595d02e15\",\n  \"41b88ecc-4d44-405d-92d9-21b3daf820cf\",\n  \"2e579b69-771a-4571-8110-47e500455042\",\n  \"68f8c874-7b56-4b31-9bd7-d2190de0e738\",\n  \"ca55fccc-9812-4301-89d0-464949c30e90\",\n  \"74710e92-5f4a-41a7-aed0-fd8eef727d62\",\n  \"795d9443-dc05-4b14-b655-110e923199c9\",\n  \"5e5306e3-ea40-421f-92ce-63cd92c0dce4\",\n  \"782d5f06-2e6d-425c-9dfd-0117dc98669d\",\n  \"f89659e7-ea2c-45c6-a8eb-bd4151e87372\",\n  \"28b13e6b-2957-435d-b3ba-836277facf2b\",\n  \"d3e59088-f04b-4348-8279-7a29e5bf3879\",\n  \"bed6626b-064e-4f58-9e4a-1240e2aa2f68\",\n  \"76909db0-8184-43e3-89aa-d735215cb300\",\n  \"197802ce-729f-4a88-b4a0-8478c0155e71\",\n  \"8b0418df-0005-4b26-b381-9cd431a2d571\",\n  \"fd5b9dc6-80eb-47fe-97d5-948c7a3cfcdd\",\n  \"ab44c943-7332-4b0a-9793-01a72b694f37\",\n  \"b0fabf09-a08d-4c79-a98e-3467c95aa984\",\n  \"2243d013-fd2e-475e-9521-3fa825365d99\",\n  \"577fddd8-46ae-4afb-ab7f-d3c92e443ab6\",\n  \"8916115f-49b4-4361-9275-1c3284632c78\",\n  \"4c33e38e-032c-4643-a8aa-42e5e59d83dc\",\n  \"818c9b1e-7fce-4207-9557-d24cf8a13188\",\n  \"bd343d1a-40ba-442f-9a83-1a55d572c0e5\",\n  \"b94977f7-824e-4418-9462-a4de2fc09f60\",\n  \"8670514c-d077-489b-a409-4aefa422bc87\",\n  \"66429593-4e84-48fd-8f3e-9260a9708d5e\",\n  \"a835db97-4108-4907-9a39-d4e51f06176d\",\n  \"a77bd244-1fb9-49fb-b2e9-b08dc7cee177\",\n  \"a2d75dda-0277-4e15-b574-bdf9c30305a1\",\n  \"24c91ccb-7257-4c39-b39b-368a530ddcc1\",\n  \"abc32ea3-3dc2-43f5-9bed-4e6a45aa4672\",\n  \"ac9c7bea-0424-4a43-a80c-d2072927af90\",\n  \"bac4de6e-83a3-4ab7-931f-ae09316c371f\",\n  \"eabe9df9-de8a-4dfc-932b-9669cb05b5b9\",\n  \"142e73f2-879e-4765-ae19-6ffe10901da2\",\n  \"33b7d70d-c7d4-4047-985a-984a25accbfe\",\n  \"331592d8-6a77-4ede-8cc5-fd66ebff6130\",\n  \"47edd8c4-ba8c-47e6-801e-b88798739c64\",\n  \"0f69b2fd-7f31-4e38-8db5-634dce4884cf\",\n  \"14f55eeb-641c-4a3c-94bb-2fc2dd002ebd\",\n  \"ab6093ee-7865-4551-a004-07c1bef562b4\",\n  \"19913ac9-da08-4008-a5a3-01848f68af02\",\n  \"294a8ae7-69e7-401f-9c14-afbfd67cf2f9\",\n  \"424f9ed1-d478-4ca2-9523-4fe6a28cc651\",\n  \"98092022-ef47-428e-b69e-f7ea340c4124\",\n  \"3bd1deac-551a-4f2c-8c83-4ccfafff6064\",\n  \"32405353-05fc-410b-b42c-c75b202420d1\",\n  \"038d6225-c410-45b2-a82c-32774d5f290b\",\n  \"1b2479bc-9923-4a37-88e7-becff4750fa2\",\n  \"74cbda25-d7a0-4e00-8651-0de0cf3fa6c4\",\n  \"a03d113f-ed06-4889-85f5-e93ea160fd3e\",\n  \"6529fdfd-cc70-40a5-8132-357aa8c4891b\",\n  \"784202c6-106b-42f1-b4d5-c8834334071e\",\n  \"012d3a98-11ab-4d9a-84e9-e7d38ed4b00d\",\n  \"e5321662-826a-45ec-9b58-dd1698e6c6c5\",\n  \"dd7a8360-52ab-4f92-bc8d-a4b7a2ce71a2\",\n  \"8ca595f9-2130-46e8-bf06-26a4e1a94198\",\n  \"95648436-4438-4abb-b564-bddf12f8145b\",\n  \"730c63b9-6814-45d5-8aec-b470eb353970\",\n  \"346ed9bc-1a6e-4436-b618-005e229d568f\",\n  \"f072e617-55cd-45c8-ac04-04feeeb83fdb\",\n  \"4a244da7-07ae-4377-a2c4-410450195145\",\n  \"1f25335b-05a3-4c4f-88e8-ce4a6430f893\",\n  \"0807c527-ec84-4547-9cac-736c8fa8858f\",\n  \"f0649d6b-af07-49f4-a89d-62ff9b32b278\",\n  \"e47c764d-8ee1-4f87-b9ac-911ae3167c8c\",\n  \"07de764d-ea86-4646-afad-984fc4df69b3\",\n  \"e3d1efb7-0a36-4d88-9c75-e6b4b95afe9f\",\n  \"7aa0ff4c-0982-4131-8e58-09b560890fd6\",\n  \"0dcc08ce-4901-4387-bd15-48063b28eb39\",\n  \"459333a8-dd14-4ef9-9c82-bb0e840a37f3\",\n  \"2c594df5-ae02-4125-81dd-ebaabdc81f31\",\n  \"e335bc36-e181-4a21-8d7e-b93fa8e20323\",\n  \"6b066319-c64c-408e-8bff-43c450224266\",\n  \"10e3d3ed-fe9e-44bf-baf6-478202f0bb32\",\n  \"20bcaa29-7cee-498e-8c13-fbd7b4e6a368\",\n  \"3ca42cc2-fbf2-473f-9de8-654aa2e6bdc0\",\n  \"cc37b26c-4d72-45aa-a0fc-637e08413d2c\",\n  \"e8256b45-51fa-4f70-870d-aa48bd67ec77\",\n  \"e92963f0-9b05-4347-800d-dbfebef486b0\",\n  \"a61c389a-3f65-4279-ae18-1bb714ae4415\",\n  \"9ec09b6b-d1fc-46d4-ae29-29704578ed78\",\n  \"a518941f-cb2f-414d-a5c6-fa2b9b2bfaeb\",\n  \"53c830aa-2aa9-4389-b0d6-ca649f6c101f\",\n  \"28c86c76-78f2-4959-a4f6-bbbd81cb8b50\",\n  \"45043d49-4c7d-4f84-b796-d7290fc3625f\",\n  \"e4c64fde-cf90-4cd3-b2cb-097c3c0f2a98\",\n  \"3a86bb59-6d4e-4021-9a0f-921aca749168\",\n  \"7c537c2c-05ad-47bf-8714-e2ca002c2212\",\n  \"7c3eb7f7-1345-466e-8e93-1d124830af2b\",\n  \"bd1d398f-9b9a-4720-a884-a12b1f4c1250\",\n  \"4347f724-9ee0-4174-bc65-9f4fb988c763\",\n  \"25a4251a-1e41-44f9-9f2a-35f1b51247a4\",\n  \"4054e4ba-3b20-422c-90d3-8fb30bf06c8f\",\n  \"15913a69-e02d-45d9-b18f-fadedc716a99\",\n  \"74966c71-acee-45da-9795-9ed471bfcf25\",\n  \"aac2c80c-21be-42f2-8c67-14c830b5beaa\",\n  \"ea6e9b08-5e27-440d-9489-6913a62aa837\",\n  \"08437e37-cd81-4d81-b009-236625958fcd\",\n  \"390cfd88-6e9b-4cef-82c5-e9aa24182f04\",\n  \"4df0299c-d3e7-41a3-a143-adc3b6745c3d\",\n  \"903894b8-d2fe-4939-92c0-e1e076f1e5fd\",\n  \"12033cab-70de-45e8-a71a-db6abaef9002\",\n  \"5d462326-be2b-47e4-85dc-ea33ec8ab9ad\",\n  \"1f4eb8a3-f0f7-4d39-b9ec-d9aca7f7bdbd\",\n  \"d16703b7-086b-4c45-a815-b81ba36b2764\",\n  \"47f5c0a8-b051-4b77-b2df-08dacdb1c621\",\n  \"5b35a804-4800-41ad-97d4-e5b7071efe70\",\n  \"0b92365c-051d-4710-972c-9a8420a4b7c0\",\n  \"502ea15d-ae06-46be-be28-770e41e081f9\",\n  \"d3e728d6-8842-4864-bcf3-9012184baae8\",\n  \"26e77e53-1b11-4a0a-96c5-4888cf8e049d\",\n  \"ed29ecfd-cb66-420f-9df7-e3ff61cbd68c\",\n  \"862119b6-de3e-41be-842f-4d66b5221845\",\n  \"9434488d-cf56-4d8f-8c8e-9b4eb1a10ed1\",\n  \"af899072-8ee8-4c33-96af-dd63a0d62cbe\",\n  \"3bf91b9e-5b2c-4b86-97e7-a37063a813cb\",\n  \"628e7648-73d5-4747-a799-ba51d7dd6ee1\",\n  \"82dcd121-fb4e-40ca-81ab-a49289a8dc19\",\n  \"7a3a6786-a854-4eda-a151-8495619b2e3e\",\n  \"498cce21-0f44-4f46-8278-bb6eda08d29a\",\n  \"00172dce-06af-4afd-999d-9f6b543a8a7c\",\n  \"082053b7-fe10-4303-b1e6-c04abea7417f\",\n  \"8e65a162-b480-4e69-8b6f-54191bb6c544\",\n  \"47e1e1a6-f412-4fb5-be90-ea164c3ab89d\",\n  \"35cff1f5-9765-4941-8c95-4930f8ff86fa\",\n  \"15b26a59-1ce9-421b-829b-6f49af667806\",\n  \"82883953-544e-4efb-b94c-04edf5c59e6c\",\n  \"9136e083-3c95-4a38-8ab6-d70df252c9c9\",\n  \"4d5c9d90-603f-42da-929f-83dd954eaea8\",\n  \"37d13f12-41a1-4a27-8f45-da26de8a165c\",\n  \"f689a9d3-28c6-405f-a719-402ce2016623\",\n  \"92dde72a-2b1e-4e21-b8eb-6c9e2ae15616\",\n  \"d3189907-6d7d-4e40-9287-887164ed4c2a\",\n  \"17d5ad55-86b4-4741-a5c4-a480117ec0c3\",\n  \"52df641d-b10a-456b-9334-b789313f62f6\",\n  \"2a38e41d-cae6-4fd1-974b-df1e5c11e00c\",\n  \"a6bd286e-3108-47b3-ad3d-1df46c7b28ac\",\n  \"76df2da7-1ea7-4ec0-ad67-3b5a80b276c6\",\n  \"e2545979-08e1-49d3-a9cf-89fd6042bacf\",\n  \"0b8a4913-272f-40e4-a523-111f665dd022\",\n  \"4dcae03b-088a-41b0-8d33-8f6c9a6152c9\",\n  \"eb5821d4-0843-4268-8cce-5f48f37a4d77\",\n  \"a0a7cdbb-f78d-4bd6-8b1c-f41136ac6b3f\",\n  \"1eac9ca5-4b15-47c7-852c-0787984085cf\",\n  \"b99848af-e525-4891-b57e-07414fc8fca8\",\n  \"d8d4738b-9b73-4ab7-b987-c2c9d74c3dae\",\n  \"a673a3bc-8092-481b-b708-c5c37ec34a97\",\n  \"0b08fbaa-02d3-40f9-be5e-954f38ffe7f9\",\n  \"f9064f5c-545c-4e53-b79d-99a531900290\",\n  \"bfaf6b9c-89a0-4462-b5c0-7ff2f6b8e934\",\n  \"9cdbdf4b-6617-447d-839f-d5e66164a89e\",\n  \"207f03e4-7f30-4a6f-b5f6-31e39cdbbacf\",\n  \"c4766f87-44c1-4f2f-9492-f668b8e013d4\",\n  \"d615971e-ab5e-4929-9317-e2b673fad2c1\",\n  \"11087360-d7bf-40c9-ab99-ff7eb2ff6e08\",\n  \"3101ed68-58a4-4ec1-8721-ccad93158d41\",\n  \"3a35205f-0397-412e-b52d-e76b30df8d0d\",\n  \"ed4d63ce-1026-4870-a06d-68ccc9b5b1a4\",\n  \"c4a6262a-cdab-421f-8b64-25aa99cb7279\",\n  \"3d704f56-443a-4576-a1f6-a06cc2fe72bc\",\n  \"c5fa1ccd-9db8-4a10-a266-d1c2991b23c9\",\n  \"8f0fe317-9b16-463c-965e-00fe1d728e87\",\n  \"6149af86-78e6-4d09-bfb0-df784f477596\",\n  \"f3806699-35cc-4426-b870-7d625e634c2b\",\n  \"733997b5-1fe0-4edb-aa7b-a0cf5d9546ec\",\n  \"9707c206-9ea3-4885-84da-1b094d28431e\",\n  \"86a1e61d-e7da-4570-8964-1dc3f3095c2e\",\n  \"9f6c8dc3-e8cc-4942-943f-09069f1b4b35\",\n  \"d548c0b6-6a27-422f-a1a4-7a9cae7871fb\",\n  \"dd674571-00c4-4248-aac4-f17cf1c4fe8c\",\n  \"8b80a997-07ad-4b89-8240-d5f34142e782\",\n  \"7453f4bd-e711-47cf-a244-29dd428f4d69\",\n  \"ee8e1b77-4b1e-41df-8abb-2ba89fcbcaba\",\n  \"cf42748f-5bfa-4967-abd1-0420d9f8a459\",\n  \"7333c72e-724e-4273-bbb1-bc268baf08fb\",\n  \"60a16c28-0148-421b-a317-65b0f98da802\",\n  \"6be63c9d-6c39-44e8-b8f2-39f2b38df425\",\n  \"7a961bf2-2d95-43fd-b80b-4a71468859c6\",\n  \"6968a885-0fa8-4a96-b78b-da1cb76f4a89\",\n  \"32acb2ba-782e-4303-8578-ad1e8b399cd4\",\n  \"1db57d32-adc8-468f-9bbf-c01e4bd79731\",\n  \"2e20d41e-b9a6-4874-acfd-322aec84c19b\",\n  \"0b755ba9-b090-473e-8477-2c9f26554500\",\n  \"6c4335f9-43ef-46fa-ab52-27c1c0a6cbd2\",\n  \"06e1bff1-29e1-4a24-8b2f-e56ea5396ddb\",\n  \"0e62b859-e50e-43e3-b77a-140795b32167\",\n  \"d8b37f44-919b-498a-9414-1690450cdf0f\",\n  \"65c57fb8-a268-4598-8c2f-9c2b0ce860de\",\n  \"dfd64904-2625-406f-a8f2-62585a505f0e\",\n  \"37acd3d5-2a92-453e-b7f9-1a694711d631\",\n  \"e3be661c-2969-42e7-b958-b9a01667b313\",\n  \"56acd4c3-a4dd-4411-8634-c359d78da5e8\",\n  \"a4dad784-c2ae-43d1-b5fc-045d4923d1cf\",\n  \"65bd509f-db6b-4b58-bb83-124ec86d8bf1\",\n  \"1c9b90af-51a3-4a99-9730-87897e82c8a7\",\n  \"8b0d1f84-74a2-439a-90bb-82e0ee11361f\",\n  \"0211eb06-6d1c-4613-87ae-5de1c099a501\",\n  \"d823abb1-d7e3-4920-b61e-03e77c795b22\",\n  \"fe5bfa8c-0eb5-4b86-885f-d4543482c534\",\n  \"79886315-4921-41ab-95a0-01443ccce78c\",\n  \"20e9da4b-a5ff-4d5a-a5d5-efcf027f5e18\",\n  \"9436cef9-668c-4abd-a71a-ee12695288ad\",\n  \"39eb593a-0111-4b0f-b1cc-461216650dee\",\n  \"a7440ee9-12f3-4dfb-a0ae-2e17ae60839b\",\n  \"e67a7a9b-6b40-43a9-8b7a-fa44dbef9fa3\",\n  \"913dfdc6-1e11-4f56-aebc-514c24a1075f\",\n  \"fc9a12de-7d75-40b8-84d3-2e94610a59e4\",\n  \"2d9f03cc-6f91-49c9-b1f8-9c8627c7ae0b\",\n  \"411cffc0-c2ff-4050-9513-0dea10779d0a\",\n  \"626417e9-bc1d-446c-98da-141cd625cae7\",\n  \"d5def074-8287-4799-93ba-ea337131ca37\",\n  \"8ce73d7c-de4b-40ea-80c5-3afed0806d8c\",\n  \"1dca4500-204f-4dba-9e20-2cc74735a00c\",\n  \"5aa98616-2109-41db-b0e1-4ae85f58fbc8\",\n  \"22751e26-4c99-4ad0-9c11-4b013b08bfb7\",\n  \"3f703179-464f-42f0-a7e8-ea5e01bf5cd3\",\n  \"fe206147-f01a-4fad-b015-f72466c0b397\",\n  \"3d3fa85d-de5e-47ff-aa95-c0c3f1331480\",\n  \"2b555a3a-e171-483f-852f-06e6819e06d9\",\n  \"2bb854e7-bf9a-4b84-ad0d-8d8c6e61790c\",\n  \"2a13bd43-945b-4372-8cce-d7d4f60162eb\",\n  \"5412941b-d004-4a8a-8f4a-794b18652b2f\",\n  \"23cf34f7-98dd-4cbc-baaa-fb225ba00c9e\",\n  \"7adac89c-3622-4b38-93ab-c9be67419e29\",\n  \"3a377488-428c-48b5-97ea-5949c738c581\",\n  \"8001f3b4-21eb-413a-8520-3f894a5aa0a0\",\n  \"94dd7110-e8c5-40a1-9521-c79da2d98cd4\",\n  \"0dbb1768-b503-4245-8a25-afc45505dcbc\",\n  \"c540f375-e2fe-43b6-97b3-217d52066cf6\",\n  \"2b914a73-1a70-4c2c-9dc5-ef61c775072f\",\n  \"c91ffe95-1486-46b5-b4f1-545e51127dfe\",\n  \"501924f1-db32-4ecd-9cc2-4947a2036458\",\n  \"01bbeeac-ede9-4ba4-98e0-0ea228102878\",\n  \"d615f7c1-186e-4580-8d66-ad7939c59eea\",\n  \"22b1085c-085b-4ef5-97ed-e773099b4b1c\",\n  \"dd1c4ee1-5b3f-4897-984b-fc7b6fb13efc\",\n  \"d67ceee1-884d-4947-ac51-3cd14d6b931a\",\n  \"bde0a43e-edb3-4a09-b554-e4a5e64ac0d5\",\n  \"49aa528c-e5ce-44a1-8ef4-3875e5b475d3\",\n  \"d0546df7-1792-4b74-a0ee-cffc7e07c884\",\n  \"6cf4da7d-27f3-4136-8bcc-b968e423ca88\",\n  \"a1568d9f-e95a-4dcc-8317-5dbada79fd95\",\n  \"727eac33-6e95-4065-8506-51ff68ebab2a\",\n  \"ec5cd8ef-01ce-44ae-8a82-15628948749c\",\n  \"ed99ccc6-1a5a-443a-bff0-5e84fcceee58\",\n  \"98691524-3582-46d9-be3b-64707a174ee8\",\n  \"703602f8-48b1-4bae-b252-4b4c35edae21\",\n  \"2e458e45-ce93-499b-a85c-d4a0c7df81f0\",\n  \"bc9dd4c2-8719-4c0b-88b7-5021ead49b10\",\n  \"f28a8e7e-8726-4d26-b8b6-ba113bb6bcdd\",\n  \"38c57cb9-bd16-4a17-998f-df87d5a6c7cc\",\n  \"72022cd6-b9ff-46ec-8852-3a3be89ae6c5\",\n  \"4890d990-2cac-4bb1-8585-5905fa6f9c29\",\n  \"69ca60ee-1074-4a54-8a3e-d1cff36b685c\",\n  \"bc0b33de-c2a0-427d-916a-85ff2d72eae9\",\n  \"c12c9344-d549-4ed0-a627-14deef9ff81c\",\n  \"56d6b2b1-6083-42e4-898a-bb7183f7c077\",\n  \"6195506b-9584-48ae-af0a-c62baf8d9f1e\",\n  \"e93f5efd-0ecc-42b9-8f16-eb4054d6c325\",\n  \"b639a7b5-8274-4ee9-84d1-ff9896a3a819\",\n  \"90ff07fe-b756-472d-a5fc-55a15c57fe90\",\n  \"11ca0db9-daf3-41ea-a3c5-b8897027ae9d\",\n  \"12bf5243-b633-482c-ad87-f226f39a6943\",\n  \"7aacf63b-a7aa-4ed4-bede-c6702cfc0ed4\",\n  \"f85d5b11-ef93-40b8-b63f-bcf800d46f74\",\n  \"ca23fc2e-9736-4d46-803b-9095624cf911\",\n  \"cbda89a0-5e8c-4ee4-8792-43d01d1b3233\",\n  \"7ba90389-3ead-474a-b6c9-ab17e769c418\",\n  \"a48d8ff0-78fd-4c2f-9afd-be5772a002f9\",\n  \"4271ed18-85dc-4cda-a2a5-35447188e732\",\n  \"32438378-24c4-4fd6-a00d-ed6a391330fb\",\n  \"72956dd1-0db8-43cc-aeac-2ea647b7d88e\",\n  \"f8c02c8c-d149-4800-8dbc-4121b7dbe9c1\",\n  \"b42f950a-f666-4307-bb03-dcd0c413fc21\",\n  \"3ba1ea7b-010c-4236-8ada-0d9d8c6a948b\",\n  \"fb52a98d-977b-4962-bdd2-20e15e236474\",\n  \"77ef53b8-77cd-428f-b625-b492da0ed27e\",\n  \"3d1542e7-8344-411a-8508-65a0ae3a13a8\",\n  \"a70d3ddf-112e-4609-845d-5c81796a6160\",\n  \"8e1363c0-5d20-4b4d-9799-0625f43552ef\",\n  \"4bafeed9-dbf8-4fcc-8fda-eee429bab7bb\",\n  \"2b5bacea-570a-413d-b64e-c6fd8921b182\",\n  \"87dfab98-a53d-4892-b43d-a73beff6fd45\",\n  \"f150ee96-0db3-48b0-aeda-1b74d2608a95\",\n  \"551c208b-9145-45a8-b852-4eede2a904f1\",\n  \"3f877d59-a272-4bb6-89b1-c92d45a09d3c\",\n  \"87bb58cd-cc0c-404c-b264-839e1ae21eef\",\n  \"46dcea58-6fab-4a94-9575-3674a790320e\",\n  \"eff69698-7d01-46d1-a8f6-bdc0a301408e\",\n  \"d9e02c6e-6b3c-48ec-b609-a20cd2dcf0d9\",\n  \"d2960021-fe8d-47ae-8d3f-b65fa7e13403\",\n  \"2be8ba50-50b7-44ce-b42f-d0f85230b4de\",\n  \"af43e5af-0f82-49c8-bb84-0b1b12c4cc50\",\n  \"fffe7856-edde-4a3b-bfd4-1300990b6c56\",\n  \"020e1109-0eb3-47d5-9ee7-b4d4f8a10e35\",\n  \"b8833b97-9b07-492c-aa19-5dcda1c1fcb0\",\n  \"42e923ca-982b-4365-a06b-72abff4ba4b0\",\n  \"8cce5fe6-b81c-4df8-afd0-3cb3be8041ec\",\n  \"7a21f1e6-14b9-44d0-ab66-09d1f05dd261\",\n  \"861e9e38-6f57-43de-b35d-09419e01bfc0\",\n  \"509f8251-6665-46ca-a2e4-b835357a69c6\",\n  \"d3210543-fe3f-4c49-8589-c1a4ac137e31\",\n  \"fb83fb95-7fd4-43f4-8594-a9cba3b54dcf\",\n  \"f7a5f1da-c166-4147-a6e6-c5c43d9dc434\",\n  \"ed9b0a57-946e-49ce-9263-b394627b7095\",\n  \"0335caac-96e4-4200-8284-2a6bed06d474\",\n  \"3fb2f631-e035-4d8d-8fba-984b89dc6650\",\n  \"54d4d030-e90a-4939-8707-fe0712465400\",\n  \"24ebe0a3-9798-47ca-bcbc-4977891621a6\",\n  \"75918e20-ad88-408d-969a-b7a77eb1dc35\",\n  \"063be159-590a-45a9-ba4e-aea9f08d7599\",\n  \"c47b7459-091c-4b43-a9f2-529b441a90cb\",\n  \"98d50a00-d5a9-47cb-9770-9bd6b3e3ca0c\",\n  \"4def40dd-9fc7-4298-a211-df5036690d98\",\n  \"1d5a86d5-8b94-4ad3-83bd-b55bfdef4253\",\n  \"da7f06f1-1f91-4ee9-b83c-1d28e0a240b7\",\n  \"66ead5c6-1d6a-467a-b5c7-3fd5f67d9814\",\n  \"ac168d7d-c6e8-437e-b30f-787850640f6f\",\n  \"71349d43-4512-4631-a40f-c4456e9607b8\",\n  \"e2f88b60-272e-450e-a213-725c16fe82a6\",\n  \"5b7a9edb-0c03-42a7-b211-1055b17459f6\",\n  \"9c5c8a14-a57a-4485-99fc-d353632cb652\",\n  \"1303d28a-ca83-4b3c-90db-a0f27eadee03\",\n  \"5161796f-e744-4f67-bb5e-9f9faab48048\",\n  \"3d3d42d1-7f45-4ba2-8566-719f57e5947a\",\n  \"5e7fd6aa-d897-4d85-a8d5-7ca16ced5562\",\n  \"9bcb88bf-48cc-4d1f-b3f3-4d1d9e9bff84\",\n  \"3d3bd7a6-514d-4dc9-a880-64603c126e1e\",\n  \"dc35b06e-59d9-4196-a9dc-c0908e765941\",\n  \"6c80ee0f-ed9e-45c4-a51f-1f9206113f52\",\n  \"5c8ecc63-efb2-416c-9673-3a4c98246ff7\",\n  \"215a933c-6909-49f7-a7bc-8d1999015cb8\",\n  \"b7426731-e834-49cc-ae96-ebc2c0e52703\",\n  \"df60f13c-1db0-4d5b-8a3c-bc77588f0620\",\n  \"14562134-7d9d-41d3-9158-7262acfe4051\",\n  \"2e50e5c5-ed8c-419f-b789-526f5713af78\",\n  \"24094030-3a70-4019-bc34-44fe026b877d\",\n  \"d3f64ba7-f63d-4b33-a006-27f5b270e0b0\",\n  \"5dc6e0d5-af1a-4507-b7cd-84a88194914f\",\n  \"567fe955-bde6-435f-aade-e99bb57571a7\",\n  \"821347b5-148f-4563-bc3f-499cffa8d0a0\",\n  \"88a2b2fe-9e0b-4673-b577-fa26194f2f66\",\n  \"fada400a-5186-4658-9ea1-1210564925f8\",\n  \"1ba7cf4b-383f-4106-b88e-1cc5ebcceb62\",\n  \"977d85bf-d17b-4ddb-96b2-48ef0cb3af7c\",\n  \"37d1cb64-abfb-47bb-80e3-b3de7fc031d2\",\n  \"99c38ea9-880a-4485-bacf-8609c1d8dc8d\",\n  \"2c26c02f-3d07-4f33-b332-d687ba4a9535\",\n  \"933a8e60-786f-4014-acaa-5b60d3d7072a\",\n  \"c54bc126-486d-487f-b0c0-3316d19c6512\",\n  \"38bd0154-53e1-4c3f-bd67-ca5b82c69569\",\n  \"31c6d364-3c5d-45fe-abd1-8c0fe0d5e96e\",\n  \"2ebcff0a-3fe4-4cdc-b86f-bd674b196aa6\",\n  \"ee36d606-fa89-4b11-8f0f-f0254da0d64e\",\n  \"2fefdb2c-cf92-40ae-92d7-eeb7afd28f26\",\n  \"da979f93-1f1f-4979-b222-5e6ece8e1472\",\n  \"bccd2502-0e16-43a3-93f3-607e89768791\",\n  \"c672174f-7d70-4879-9fe7-0f8e8962caa1\",\n  \"4e04973c-c171-42a4-b932-2addaa48e1a4\",\n  \"dc559340-2f31-4f78-8aa1-a544f8c246b5\",\n  \"02f1d858-5e0d-4734-ab67-62d32f4d0101\",\n  \"2d4c334b-f258-4521-a56b-26b6a3f8b80f\",\n  \"742f4861-f125-4a83-8bc7-242320bf455e\",\n  \"a20027fb-b1d5-4229-b316-aada8e9f7962\",\n  \"f8655645-c8bb-4b8c-8d71-c2748be29603\",\n  \"72e52e56-84ea-498b-8ad0-2deca6c6ed67\",\n  \"e400c58b-0e32-4a85-9f39-0b8fa6c4fe74\",\n  \"986c9e27-91f0-49cb-9b94-2826364d09c6\",\n  \"e2173600-fef0-4474-b354-fc5cfab16cfd\",\n  \"8af52a4f-10b2-4488-94a3-24c2c697ab16\",\n  \"590dcd40-97ce-4427-af2f-8ac29ee8a985\",\n  \"8d325f50-c035-4d5c-a843-cbbb42442934\",\n  \"bdc0955f-db28-4fad-8f7d-b18ecfd9706d\",\n  \"3d840984-0bd3-48b0-b6b7-503870c74ba0\",\n  \"0d8b0f7d-d063-4f01-9198-05d23b8032fe\",\n  \"fb73c814-bde1-4922-8655-66d86f96bea6\",\n  \"0af5f8cf-ba4d-4a50-a09a-324ba2e84267\",\n  \"d2c7d611-1733-4b0d-9907-24dd51c4008a\",\n  \"d19977a8-37b2-4a1a-add1-b14245cc399e\",\n  \"03382ac1-6360-44e1-af9b-4bbbee44e278\",\n  \"2161ebeb-2048-4309-afbf-8882dd8416cf\",\n  \"d84ec398-4115-4073-adc4-99d8d1fb0250\",\n  \"3585236b-be29-422a-96ca-aa37d691f367\",\n  \"9a49699f-bdbd-42f5-8eda-acc32dbe0b69\",\n  \"a4011245-8669-4077-911a-2a60c43d69b9\",\n  \"cea21ccc-6e79-4d66-a8d1-ea23309afedb\",\n  \"29ec97fc-245a-42cc-b405-5fc37ad425f0\",\n  \"16514efb-1ef9-4dc2-b411-72c7fa13d303\",\n  \"02647d75-0404-4313-a6ba-18c94a90eee4\",\n  \"397305ff-dde9-4e53-85f5-7b0ed6201fb8\",\n  \"e921b686-cb91-4ac5-9821-c8edaeb44920\",\n  \"58a735d5-ab3c-4eff-af69-e458f3de4886\",\n  \"aa0cdc9a-6ab0-4c87-b4f0-f06eb2ea3ceb\",\n  \"9228778d-ae44-409a-9aa4-4eca481c2fae\",\n  \"f4025c66-3b3b-4a37-ba6d-74907bd9c6fe\",\n  \"80da456e-63de-41be-8540-7990490aafaa\",\n  \"2fcbb79b-50c2-4ca5-9621-c2dbd1084225\",\n  \"95a503ac-e92c-4b6e-896d-7ab025c3bbd4\",\n  \"1cc1fd61-3627-49b6-9eec-20b2712cd6ea\",\n  \"0c791046-7a67-4747-8e27-b75d19a62495\",\n  \"563e8436-2b40-480f-b3d9-d1035cfc92d8\",\n  \"4511efb9-9771-4329-9172-8418007e91ca\",\n  \"e7b82742-adc3-4758-b7b6-6c0cb41d8f8e\",\n  \"8e986576-e099-4e6e-b5da-cd4eee04e255\",\n  \"c2ed92fb-b74f-4ffd-9511-4fe03aa3fe27\",\n  \"b68e05b9-fa18-44c0-94f6-0793b80aa2de\",\n  \"00d7e07f-e9a9-4c96-a50b-eda0ee6029d2\",\n  \"2d45c004-dce1-431a-804a-cf0179e98038\",\n  \"b9027cc5-8d78-4947-b041-c96922485ad0\",\n  \"bf3ee64a-adb7-4e99-979d-b4c07ce7beae\",\n  \"1ca750d9-7eb2-4a61-b93b-52b700b51fc5\",\n  \"65798488-405d-4e3c-83d9-2e6f102f2988\",\n  \"d11fb523-541b-4513-97d2-009bed124157\",\n  \"b7679783-740e-4859-b115-f7ca2ad2fd48\",\n  \"3c54fd16-5362-41e1-b6f7-188ea1ea7a79\",\n  \"b80699fa-4446-40bb-b8a6-ff8bb3124830\",\n  \"a2c81470-14d3-4e2d-93f5-daf221129215\",\n  \"871b919b-ab78-4b93-a870-97200822d4e5\",\n  \"c31fd8ba-c8b2-4ac9-ac69-928eeeb94b4d\",\n  \"3cdad89f-c90b-45cb-89e2-1acf4413302e\",\n  \"07f19530-a0c3-4a49-ab20-77f65c576d60\",\n  \"3ac64a27-1cd0-4110-8769-a35de199d58c\",\n  \"aa7036ad-aa5c-4f24-bfbd-9fc4c5fb131e\",\n  \"29efda69-114e-46a2-9da5-2eab8c56190d\",\n  \"f866a510-f244-45ab-8538-6e48fa0ad85e\",\n  \"893ad032-7629-472c-879c-dc7878cbf2a1\",\n  \"cff4155e-6d47-4796-8360-b09a2c8f4a42\",\n  \"57493c1c-0bc0-452e-8a1f-c72db95541af\",\n  \"68b49eab-b9a2-4d8e-847d-54814e0c6f54\",\n  \"0070e176-e05b-4909-b7db-9449fb531151\",\n  \"b16ad347-efa9-4167-b225-21ddb8fccb6f\",\n  \"053f521d-9e51-4a8f-b9c3-233067df75c1\",\n  \"02b63d16-2f68-47a6-befa-c3e56e06d382\",\n  \"1f6e5094-187a-4655-949e-827e805ff5ee\",\n  \"8ae65196-f2dc-414a-8441-262d23cb4bb1\",\n  \"2f91c57f-8422-4eaf-90e9-5b091ccf3ed2\",\n  \"26ba7e95-a9be-41b0-947b-8afe99dc40e9\",\n  \"ea36b53d-dc3d-4cf7-b749-b37d42e49e56\",\n  \"f5530ef5-6478-4eb0-a19f-4e661d83475e\",\n  \"b906b020-d168-463d-957a-3de6626afa2f\",\n  \"a84b9638-78d9-42ec-b3e2-2943a1ae2418\",\n  \"d4356ea1-0053-4379-8f3c-2b5e7420afd3\",\n  \"7648a063-ae2b-4f93-9eca-094908043bb9\",\n  \"ec2438c2-e598-4fb3-a8af-f251ec758069\",\n  \"23e2e947-4776-490c-b4ff-0a6b10dcd8fc\",\n  \"f684cebe-36c1-438e-b4b4-bce42bc830dc\",\n  \"0a50b450-1d17-403c-8066-37ea4d8a510c\",\n  \"9cc5ee2e-d51d-4930-905a-7622c26ad8de\",\n  \"d5d40705-6d99-4396-8a81-5106c2853f74\",\n  \"dbb25ade-61c8-4f81-8043-1649a3d8ce54\",\n  \"aa412d45-1882-4c13-a73b-8dc1a8de9e94\",\n  \"b632d371-b8cb-4df6-bf7a-8d9d03998727\",\n  \"2c20e0b2-d84f-4eaf-ae9f-01fa345cb815\",\n  \"1897355e-4eaf-4cf7-9da7-d7f995758842\",\n  \"fcf57bcd-c2ad-48ae-a46a-8e2de6f24c04\",\n  \"7fb150fa-b11a-4bed-8a6e-df41fdcdecab\",\n  \"2c211ad3-dc06-498f-b1b0-dc64f19b31ef\",\n  \"97fe94f4-55d9-4c65-85be-72a1a67a635d\",\n  \"a6dec074-7c37-410c-95b9-2ab17e870c88\",\n  \"44dbc86a-7dd6-4f0b-857b-84dfc294722b\",\n  \"8597b770-5a66-4064-84f0-205dd33c997e\",\n  \"286c91d1-cbec-4213-ac40-da8e4dabaf4f\",\n  \"44e9fc2b-9e7c-43a7-9c08-66cf2f0f7f50\",\n  \"60108c93-a745-44a9-ab10-684b9f926c55\",\n  \"298c30d6-4257-471c-b3a9-4b3112419d3b\",\n  \"365a9ecc-890c-49cf-b9d9-9857c273eaac\",\n  \"9f2f35ea-67d9-4072-945d-3460b2de3b56\",\n  \"45335ed2-a0ed-41c3-92e1-9a0b5f92c750\",\n  \"56d97a5e-b694-4eba-b87f-56dacb9fa05b\",\n  \"32897e49-2deb-4ba5-909a-73caa80dfe92\",\n  \"93c6a36f-1abb-4360-8090-0d25fb171123\",\n  \"3e0f92d9-b8c3-4c0b-9865-fa0ff39bcb36\",\n  \"701ab5d5-03f1-4f1b-a25a-b0279af41404\",\n  \"170fdb99-2639-4be6-aabf-ba839991544d\",\n  \"ca3c863f-d3fb-48e0-b7aa-467701ec111d\",\n  \"72d49e5e-b575-4e2e-ab47-182e34a90699\",\n  \"5a7f9dcf-5461-4454-b751-0827cf16044b\",\n  \"f8f29b1f-a792-45d2-8f08-54d3a5d8250c\",\n  \"a5172f30-7ef9-45ba-a827-0d5da69488fb\",\n  \"5e4126b3-24c0-4e98-b506-1da8a558b031\",\n  \"1c71ed37-0237-422f-9cd4-8dcbe23eb73e\",\n  \"259f69dd-d2c0-448a-bab5-0ec3c2b38456\",\n  \"9097c1b6-4b14-4723-9e75-abddccb06fc1\",\n  \"acc01724-e7fa-4cf4-a358-0c7c58ebd064\",\n  \"b7c7d614-540b-485f-8b89-93499fad7578\",\n  \"21899d4d-7e7f-4474-9f65-90d843993fbc\",\n  \"001e93c6-d8d8-47c3-b4e8-2beb70326356\",\n  \"329a8d10-6b2f-470e-a518-86523d55a238\",\n  \"5beb2e91-c19e-434d-90fa-c46bfcfd53c8\",\n  \"c9dd5b52-19ae-444b-8564-995eb91bd89f\",\n  \"25ad95dc-0ea0-4208-aa79-083e7ce35127\",\n  \"bba5601c-7108-4beb-b088-ec63c7badb79\",\n  \"1218d09a-d272-41a9-a312-24d8d762d3d4\",\n  \"712ce0d8-8b69-4812-94ca-cf482d377468\",\n  \"3db2f45f-e913-47de-857c-2ba95058e1bc\",\n  \"c2d9687a-4b7d-45d9-8e37-fe7c38f717c0\",\n  \"56fd8dd3-3c90-4ec8-b475-3d5a2767ae88\",\n  \"c92a7119-2965-407e-9abc-fb762641aab5\",\n  \"6378fbd5-6566-40cd-9917-275c426f2c8e\",\n  \"4f2a4caf-6b38-41a9-8026-54ccd080079a\",\n  \"317a3660-5cfb-45a3-94b1-9eac71b00560\",\n  \"16f9bc5c-2e0f-4f53-abff-0d959a9ccfee\",\n  \"3f62b1ea-66cd-469d-85f1-b8420a08ffc5\",\n  \"cbdd3d53-602c-48ba-82fe-dd9780bc7584\",\n  \"b0aabd05-bdb7-4714-a579-f1c7559d2be2\",\n  \"8fd23f0e-3d3d-43f7-b8c1-12e914b67ef0\",\n  \"a9449b70-6b4c-40ef-995c-9012b56afbae\",\n  \"217f6c2a-8248-455b-9206-4810d02b2566\",\n  \"209ae188-bb2e-4a7f-9ab9-a3c89974f43d\",\n  \"9875213c-e492-4c9f-be7a-a71428df238d\",\n  \"1316766f-c0e8-428e-bc34-1b3ccc397a8b\",\n  \"16910e6d-22f9-45da-8f20-a26d72861af6\",\n  \"940cd084-3882-4a9c-83b3-c0f48812b0f5\",\n  \"f03645c0-9a12-4a81-9e37-64774f86543a\",\n  \"326e2482-1799-40a0-b1d0-a570171b4b06\",\n  \"c9891ff0-e4c6-4063-97b8-f109a7787125\",\n  \"6ad9f997-7428-4539-95a2-ce43736ed87d\",\n  \"bfd59d86-4dde-46aa-bb26-1eee153629b3\",\n  \"8373a197-40ae-4f28-ac71-1f76a5e8963e\",\n  \"83e51f08-3669-4323-aa88-1dfda473f8d6\",\n  \"7a5c5beb-50f2-4340-acb5-6c0350951cca\",\n  \"1eac7c97-6fa9-47de-90a8-33d5c3b69e2f\",\n  \"822edd12-009e-43bf-86dd-f4c8cb692331\",\n  \"8f1d8d0d-f227-4c75-81b8-8312f20af225\",\n  \"7e7fa67e-610c-4f76-b216-babade80cfae\",\n  \"1c97164a-ddb0-4e33-b209-66ff8a4f3719\",\n  \"c1100e2c-78b5-4e90-a35b-c06bebb38437\",\n  \"a843b60c-9d59-43f4-ace0-0a6335485f29\",\n  \"8787f525-08bc-4691-a1ed-b5763153c5c8\",\n  \"e9ca7d6a-3173-40fb-ab2c-20aea3ee418c\",\n  \"3c047449-0cf8-41be-b26f-630b5ebb2fcb\",\n  \"101ef1b0-f0ad-40ef-a298-4a8851d10a97\",\n  \"9d31f59c-f3fd-4ba8-9371-a66c49a12799\",\n  \"a2896e7b-f15e-44de-bb4a-ba321c3cf0be\",\n  \"30073588-f79d-4b27-8f56-6ad63d2224c6\",\n  \"c27adb42-5071-49cf-a930-861047ac790b\",\n  \"688d4066-670e-4867-b9da-98dd41d3ba2c\",\n  \"1fbe0f92-2432-4f1d-ae17-65e451a7585f\",\n  \"8b43d41e-6740-411d-81a5-e526274af892\",\n  \"7c89059a-56c5-4a23-b366-a1032a7b6e03\",\n  \"8d44ede9-ab41-4e06-b207-6350ff7c419e\",\n  \"10051a0f-4adc-474a-8040-8bbed05060fe\",\n  \"b2fd9d49-03f6-4ea2-865a-a909291bd3da\",\n  \"6f305fd2-ecb3-4f78-83d7-f814dbedfe51\",\n  \"4b073263-ec9e-4815-9c5a-5f714a2afe5a\",\n  \"c8c2ab31-6e5b-427c-8994-7d390329a454\",\n  \"9a7f98ff-f2a8-41f1-a605-396c74324e71\",\n  \"6357faf5-f50e-47c6-b063-8c9a5499a461\",\n  \"2e590a27-c675-4656-8d66-5885fb083b5a\",\n  \"9ba90f64-f1e7-4bb6-9221-93ce71ea9019\",\n  \"e0b52e16-ca5f-4af5-8392-7a5206401204\",\n  \"33d07d70-3910-4af0-b585-3da23e08131d\",\n  \"0788df6b-fb16-4c8f-80c3-d812ec57f839\",\n  \"a4a624ad-a508-42b7-adf1-890f76f40d3a\",\n  \"d13c4f82-af52-4a87-b1f7-36333bba1cc3\",\n  \"c36f141c-0f3d-4f81-ace4-9c221df0b6d9\",\n  \"e6e3482d-ead4-428e-a69f-35d40aed1992\",\n  \"6707c30e-b515-4f76-92a3-61c78a3df69c\",\n  \"f185d106-1247-4e46-90b1-6c59e10e9d56\",\n  \"d05fa1c8-ca63-4c2e-b543-104c0e1a76bb\",\n  \"516f7a70-ef0b-4894-814f-e4dae02c92c3\",\n  \"17e91145-e745-453f-afb8-0776e0cac573\",\n  \"6845ac87-2db3-4f15-b7d5-ff809a7ecaa0\",\n  \"eb9b1e51-8abc-4e70-8259-9b81ec276dc3\",\n  \"f94d7478-483e-40ff-a23c-0020bab95cd0\",\n  \"1a642d8d-ccd3-4079-ba0b-c942dc00db49\",\n  \"90412895-5b03-43f5-8eab-f91ab2ce06cb\",\n  \"61c851f1-c30e-469c-ae62-747528970e0c\",\n  \"b4ab6126-e7ed-4501-98b1-4d3fa7271f29\",\n  \"1264dd64-aa8d-4e1a-bb25-276aa91d587a\",\n  \"39684591-ea5c-4f7f-9533-80571ab83236\",\n  \"e7dc0205-7833-421d-bb03-4fa114ff83bb\",\n  \"56fd9a23-229c-483e-ad67-238a143d23e1\",\n  \"5bae6525-af21-4cba-92d5-380c19b18165\",\n  \"836ab0b4-451f-4528-b589-f0337e4461cb\",\n  \"88650a72-e80d-44b2-a528-435959908fed\",\n  \"3b93771a-500a-49fe-b15c-8f5ffc4d9800\",\n  \"72be010f-d916-4952-b580-07dc63d3dfd5\",\n  \"2f3e123f-b96d-446d-a04e-ee2c0ffe58c0\",\n  \"e86b4e31-9e68-4df4-adcd-abd4c061c2e0\",\n  \"36a98546-4125-4fb6-9f96-1fc362b48273\",\n  \"2f41561b-a2c5-466c-97f5-3407c528eff8\",\n  \"f23fb016-ec6c-4549-9b2c-70be6a2fc9fe\",\n  \"c2680d32-aa4c-49d5-92ab-103a5d24a6d3\",\n  \"11de3c4c-4d23-4d75-88c4-7d94b058bbbf\",\n  \"b75e30f4-5cde-41fb-b5e5-8bbbd5c3b179\",\n  \"0a5e338a-3903-4d18-8b43-0d1c2054374f\",\n  \"9ac6ade7-05a2-40e3-aeae-1e27c22b0936\",\n  \"ba684a90-16c5-4a5f-b40e-7a200e15d06d\",\n  \"80f20dda-9ae9-4074-b72a-517080de45ab\",\n  \"093be937-e5b7-4f5d-b8dd-0c3ace7cfef1\",\n  \"63655aa3-6525-4116-a641-a29fc5541438\",\n  \"37951d92-d283-4ee9-8304-43165c81ede0\",\n  \"2fa0e695-f8ff-4237-9c73-f1936b9988a1\",\n  \"3ce37304-c715-4f70-b6cb-ce387abb2ed7\",\n  \"43eb18a2-c9c2-4c1e-96a2-5c86e4212c63\",\n  \"f9cb9185-b6e6-498e-879b-460685976be2\",\n  \"7e4b9793-5480-4548-b582-195020979fab\",\n  \"d09a514b-d833-462f-a596-761be04a8c25\",\n  \"9e68c555-73d3-4465-83e3-2e6cd96e8889\",\n  \"8c79ca96-638f-411f-80c9-458375dee052\",\n  \"7630748c-d9da-4538-8885-87a5ed4af5b8\",\n  \"b91de4cb-b636-4cc2-94c4-bd7eea7a8937\",\n  \"10453bb7-ae08-4189-84a2-f05ae0f15b1f\",\n  \"be36f7e6-dd4b-43d9-ba2f-cdf1fbf2f85c\",\n  \"c7365279-6b43-4add-91da-7e96dcbb1ebc\",\n  \"d95a166b-ab46-4e19-9a2b-5d52827b2c32\",\n  \"ea2dcd4c-0c33-4d63-9daa-a75e852bd77c\",\n  \"f07675c0-f88f-43a5-9bd1-ed05399e0707\",\n  \"a54e06be-c449-4d04-98a5-7ac65e06e383\",\n  \"30055d11-01fe-43d5-9296-c7ae5e3e3440\",\n  \"b6226927-bb58-420c-89f5-b061f3ecf29c\",\n  \"ffc1d561-520c-4e0c-b27a-b2f4e9ff8726\",\n  \"d9844bd6-8c1b-49b4-9725-f7002251cfef\",\n  \"92f60f29-572b-4d60-a864-3d18a6e7800b\",\n  \"19084ddc-4863-44c8-9e4a-232bb7e343ff\",\n  \"ddc61143-2694-4a79-bc21-0a7eedd99133\",\n  \"4b845675-e197-45b2-a2ab-9cf2295803ca\",\n  \"b59cb009-a339-4e34-a40e-6614636410db\",\n  \"638048ea-28e4-4f8b-a698-cb34cee91e3f\",\n  \"37cb93d4-0b07-4fda-b43b-fd27c3f18546\",\n  \"e3c6c5dc-de49-4e39-b8af-e2cb8c759459\",\n  \"2816bd28-6694-41e0-af3a-e074df8168b2\",\n  \"33af6b99-1995-4f00-8ded-193840f6dcc6\",\n  \"f52286c3-b31d-45b4-81fb-ec67e51382df\",\n  \"0e42e0e8-cae5-4fac-aeed-bb636f94fac7\",\n  \"9e155cab-584a-416d-9d2d-25fc68d3dc4e\",\n  \"aaabaef5-3e62-4748-ac56-fcfca9528060\",\n  \"961c2eb5-8f4f-4179-b754-b3a684fffca2\",\n  \"9f005c5c-4a62-4fcd-a10f-a0ffd69f4ee2\",\n  \"dce108e4-8e06-44ad-9d69-7fb203d3193d\",\n  \"850d3331-3a57-4c05-bf59-3f573d996b8f\",\n  \"b794595c-bc42-4393-8c90-a75516a1ffd6\",\n  \"6978f188-48eb-4da8-9ed6-b62b0ae64ee2\",\n  \"8fdc7309-9785-4bb4-9f2b-023ec28e14cb\",\n  \"06748921-20f6-4e91-9629-d734b78f3433\",\n  \"948354a8-fb3c-453e-81b6-e3851d5fdf04\",\n  \"299879a8-ac8f-445e-a42c-720911866c21\",\n  \"87c9c160-ec21-42bf-8313-170aa2628992\",\n  \"893fd79f-00bf-4390-8515-d48f622b7483\",\n  \"197262d9-fe55-47de-a9f3-eb2c27284c23\",\n  \"41bef055-c61c-4a67-8e35-097537d18145\",\n  \"94886cb8-b3f3-46bb-817f-945b71f97971\",\n  \"f41f2486-a3a9-48d9-a00e-b9ffb6dbcbff\",\n  \"389eacc4-1251-4035-94b1-616a850d419b\",\n  \"5e762eca-0ba1-4cfc-8749-6e5853c6342c\",\n  \"276a59c2-073d-4222-916b-f126fad39e00\",\n  \"ba1b71bb-7496-4f3a-b0b4-7e861dfbdc0d\",\n  \"495d75d4-eb7d-484e-8b59-c56432a5eded\",\n  \"2a53cc0c-31a7-49f4-a391-368860679ea9\",\n  \"7896d97b-7670-4729-a624-e24c6bc99398\",\n  \"1b5f4cda-5bd5-449c-99dc-51ecc7543b33\",\n  \"e193f608-6126-4b53-9d37-ac03d76f00e9\",\n  \"cad8262b-668f-4c3b-870f-1c698f0b6f96\",\n  \"230b549a-77b2-4351-bb8e-f86868f7c6f7\",\n  \"30247c8b-a5f3-41dc-8488-edc4264baa4a\",\n  \"b8e185b3-015b-4ba4-8c6a-241e27a13b2a\",\n  \"baebe396-4948-40ab-8d35-7f40415a9149\",\n  \"f7353d73-a65b-471c-b9e5-9f8b067bddda\",\n  \"16693955-3926-42db-84ad-ceba7173c792\",\n  \"ea5bea92-8580-4858-bf6e-8e7c1c13d316\",\n  \"94aaca19-f115-4575-acde-9c6134291f64\",\n  \"7973f5f3-33b4-4178-b72a-efa1759a4ce8\",\n  \"c6fcd349-e120-44e2-ae1f-bdb91c1a835a\",\n  \"794a6551-b918-43ca-8224-d8956e51ea92\",\n  \"6dcb75bb-88d2-4d6e-97c2-dfa6a1995aa3\",\n  \"ad0901f2-47aa-414e-94ad-a794c78713c2\",\n  \"0b277a1d-0df2-4eca-bab1-41d2b7fa7c45\",\n  \"304defcb-25e1-4107-be86-b826437b7c16\",\n  \"881fc77a-d362-432d-9ef2-bc38c389eaab\",\n  \"b7cfa468-61af-4054-bf07-ae8de68717ac\",\n  \"1b0b7898-d1d3-4ccf-8196-d8319e4cf572\",\n  \"656a9166-155d-435d-87c8-3f940613d6e4\",\n  \"e1882ccf-a09f-46f5-983f-829edf431276\",\n  \"239d9e08-6cc7-46cd-84be-7400500b3b32\",\n  \"42d6a751-f64d-4124-905a-ac88e094a717\",\n  \"dc3dbf11-025e-4e61-b71c-e7b3cd7795c0\",\n  \"8c5cddf7-2a7a-4baa-9a6e-fbbb103c83c9\",\n  \"d4d99596-52d9-427f-afde-e33135e0917f\",\n  \"8d8d177a-f60b-4e17-bf0d-445d85b741af\",\n  \"f53ac17f-614f-424e-af41-faab75fcfb10\",\n  \"11b9f300-3c62-4195-a5da-85065eb44d10\",\n  \"36081879-6c18-44c2-ae28-29c7bf2ca855\",\n  \"4e495dc2-c49c-4102-86a6-542bfc9c6394\",\n  \"0ae42cdb-3275-424e-b7ee-5b4e4e801dbb\",\n  \"9efa93ac-7bb4-4900-ab6c-5678f09859fa\",\n  \"017ee28c-e2eb-450e-8408-c76a733a8f91\",\n  \"99fc5b79-783e-4e70-9a88-a1b252f82e23\",\n  \"959e3185-4e95-4495-b008-3d08313c3aa7\",\n  \"a7c78f07-773c-4b58-8610-7bb9943ad210\",\n  \"d187a109-2da2-4c83-ad52-21d89a2cd860\",\n  \"bafd11c2-e198-4fff-bf7d-8513c7fb3026\",\n  \"a351b7f8-d084-43bb-9209-6bf5c44eb3de\",\n  \"ec15feec-b65a-487a-926c-109b88698b2e\",\n  \"f5637cdf-149a-4929-9efa-bcb92ec56b2b\",\n  \"b1b4b209-f702-4690-be3b-e36f23ae854d\",\n  \"5d8bee93-785a-498f-ac49-73f65cd774f7\",\n  \"f8a3a5c5-b0bc-4911-b2b0-dd0ec0d5b38a\",\n  \"f5de0dff-afd0-4c84-b4c2-b163e124fca8\",\n  \"b278397a-25b0-4474-8400-8bbd1b71a78b\",\n  \"3413d21e-06ef-4bf0-895a-b2a52db763da\",\n  \"be41e405-5949-4453-9dac-767f4fc4e58d\",\n  \"33957d37-6ac1-4a76-9cec-368639605c13\",\n  \"72cd4a8d-e2bf-40a3-8cce-526367e8f7bb\",\n  \"e6c1da72-9256-4846-bc21-73d955101b79\",\n  \"2a7f1580-11c6-45b1-bbc5-e73b9a5d35a5\",\n  \"c90bb19c-ee32-40ac-ac6c-005fae1ed8cd\",\n  \"5bc816cb-0427-491f-bbf6-0ad637f83f5f\",\n  \"f551743f-a017-488c-bfdb-44c7330bb695\",\n  \"a5ba8ae8-5961-428a-8b62-8eaa73d75144\",\n  \"e101e94b-75dc-44d2-b918-7f66a011320a\",\n  \"d7b0ba13-d6c7-4e12-be7b-753fbc562e91\",\n  \"ccc98fef-1a59-466c-9a8a-40805908ae42\",\n  \"54c27899-6b8a-4eb1-9392-6a19a37d5191\",\n  \"edec610a-d5f9-41cf-87f0-e181ff75cff2\",\n  \"e091d4fa-52b7-4fa8-bf90-4069b5dce030\",\n  \"a7d350e9-1eaa-44af-a442-99fe17b9d08f\",\n  \"d6754d52-d6ba-4066-b2d2-d47b7533b32f\",\n  \"4f9e2b16-fb06-4ab2-a8b0-00e697585f9a\",\n  \"cfbde6cc-6e61-42d5-9b31-9c6c82d22f15\",\n  \"37238dbf-3236-4201-993a-b73a313655e6\",\n  \"c22efc82-5b00-42d3-8de8-23d7fcb2dac7\",\n  \"501f36e0-8489-424b-a92f-daf0a46a39f7\",\n  \"1408a5ea-0696-49f6-9ad8-08888cf98d45\",\n  \"651cb190-890d-4dce-8023-55737104e09b\",\n  \"fb7bfd1f-174a-4453-8b80-1fc3ddb55b63\",\n  \"0bc81777-cb5e-4b66-be93-627676ee280d\",\n  \"855dfd77-475b-454d-8652-27be9be64aba\",\n  \"e9ef131c-c25c-47ee-b053-0eff8b0fd2ba\",\n  \"20fd4f76-77df-4323-b9f0-b42c66f4c188\",\n  \"5b8c8212-775d-47f8-815f-1a332ca1d52a\",\n  \"52a1a295-1fd0-48fb-bf66-5d0ab3a8b1aa\",\n  \"340e953a-8854-4e48-9271-88db51e5a949\",\n  \"c7261c46-5a0f-4b48-8f5b-ffc8dda883f3\",\n  \"8985aad7-636e-4d6d-8df5-425069f64a7b\",\n  \"da9a9699-8366-4f06-9c22-7063d491b0a7\",\n  \"bf080e89-dea2-4bea-a664-a33a4fa399c4\",\n  \"59cbf275-720c-49bf-a8f3-b803216ea4c9\",\n  \"8774660a-16f2-4aa7-b4d6-caa0cdd755ac\",\n  \"f52d74b4-1906-433c-adc2-a631c2cb9fb1\",\n  \"ebee3eab-f6d3-49b5-8a30-e47d021bdb37\",\n  \"b5e23741-8f34-4a7e-9a46-77949ae5b606\",\n  \"996d3f9a-6ac0-434c-9538-2cb2bc447c0e\",\n  \"7504134c-8a7a-4654-962a-2f5bb87cbdb3\",\n  \"b7f214aa-cc7d-412b-bb84-8f8779f3e80a\",\n  \"42b7fc82-0ca9-448c-a2d7-fb9ebcd0390c\",\n  \"d4598f6f-8bab-4ad5-83ca-69cb2e95f522\",\n  \"9a6713d8-9a5c-48ea-b3bd-6c9558873aae\",\n  \"59f67bee-a2d9-4848-af0c-5a8fbdb5516b\",\n  \"e2dc24ba-f615-475c-bda3-dc1cd7275004\",\n  \"bdb4ea74-810a-4551-ad73-878dad91e82b\",\n  \"9e01b72d-1745-46bf-9593-fba1f75c2682\",\n  \"68b7c525-c844-4f5b-8d37-9394370d0dc4\",\n  \"17e2c22f-7e5e-416d-9315-e363e4e11ee5\",\n  \"90bf91b1-460c-4672-84f4-d7257a0b57ad\",\n  \"601f954c-e86b-4e1f-92c7-5d41ca2491e1\",\n  \"9e0feca2-a18b-47e0-8699-95e1cd7735aa\",\n  \"2d4b2362-5560-45e9-bf93-257e505faee9\",\n  \"bb9e6ff0-d14f-4135-8f03-48b76db2d34d\",\n  \"cf356a35-54b7-4d41-8000-66295d8c3644\",\n  \"5cdfb49f-1be8-47d4-9d9d-20fd08d99473\",\n  \"2e8bd143-0714-4dc7-8911-91dc12d96825\",\n  \"da37984d-c5ad-48d3-a820-8a4ad7effa80\",\n  \"4621145e-23cf-49da-a49c-8b3d794c8d4d\",\n  \"9d31a43c-dd97-4945-92aa-2478340366a3\",\n  \"e5373cab-cbc0-4583-ad91-948e369623ce\",\n  \"c8810d67-6ebd-4803-967d-ea443a5a333a\",\n  \"b7e80926-b1d8-497a-932b-3d0669689075\",\n  \"02d5b648-e3ff-4a26-8671-b7c62ff8d811\",\n  \"328f2b8c-50d5-4d38-86f6-cd3d1d2564f4\",\n  \"faa72689-22c2-4de5-9a0b-bf2441ed5d9d\",\n  \"4cbe640d-6fd7-4fa5-9f5f-178c716693b6\",\n  \"e095e120-58fc-42f4-b6e0-8fce6661b7fb\",\n  \"be76a91c-4631-42e3-bcde-31ec00898081\",\n  \"8252361f-6176-4503-b36d-5d062ca75b3f\",\n  \"2c4a313d-0adc-4404-9d77-13e31e21b5cb\",\n  \"ac72a0b6-5028-4c56-aab4-208029a3908e\",\n  \"898fedf3-daa4-4b9f-a84e-38548b33769a\",\n  \"e3c80b80-9736-48c0-9d86-6ac563c1c4ba\",\n  \"ed2015ff-80f1-41cd-9386-c2939a806cc9\",\n  \"dcb9c142-9934-4c9d-b044-e8c767c90591\",\n  \"4799e457-3c34-4bea-b58b-86e705decc67\",\n  \"1162508a-c640-4c7d-84a5-a9dde21e5c96\",\n  \"74b41689-4fbe-44f6-b298-8e7a48f26c4a\",\n  \"858c86f6-003c-46f3-bcaf-ceeee2594ee1\",\n  \"41817bf3-c97f-4a4a-98f1-73423bbf0b8e\",\n  \"c0ad86ce-4aa6-47f9-a384-f553acca2167\",\n  \"be537425-4847-4bcf-b3f4-6b3c83b93e0e\",\n  \"3dfe955b-f418-4bc4-b220-cf131b2ada76\",\n  \"4adb9cbe-383d-44ae-8a8d-d8127a79f670\",\n  \"793a695e-0a16-4c15-9dc3-45437ebd0454\",\n  \"2e799c0a-125d-4632-a726-0fb5d853416a\",\n  \"0dc24d67-f18d-4556-a6eb-b614dcc81cb1\",\n  \"70efd6a5-3210-4cd9-87f3-25f393cfff53\",\n  \"dd4d168a-001b-4f33-bad5-7ab36f51394e\",\n  \"a3e73471-0cfc-4935-9ec8-22d1eb751a4f\",\n  \"d44b5531-b2cc-4ff2-afa5-f27f771783b6\",\n  \"edd0a22e-2766-408d-bf56-0fcd121c5428\",\n  \"68029a5d-e392-4d89-a181-038adb2969d2\",\n  \"202249f9-709a-443f-a212-cd8210c9b6e1\",\n  \"2e3c9abb-d810-4a2a-8e1b-5a62f4d9994f\",\n  \"32e8c685-7961-4bf3-88b5-9057237cbd9a\",\n  \"8c88eb1f-bd54-47c8-99c5-b75537fe3ca7\",\n  \"8b197965-dc44-422a-b0a5-8df182bcaeea\",\n  \"beb0f6e2-06d2-495b-8678-bfe42a9bfbb4\",\n  \"9a893a9f-6067-464c-80f7-71346e0800a8\",\n  \"0886d399-4da1-4f62-8047-4c315f1f71d6\",\n  \"da37025a-3201-44c8-b47a-9d65ab10c537\",\n  \"d15e54fc-b523-480a-b425-483e36ab8156\",\n  \"5d506945-ae93-430e-90a2-88ff36762b4a\",\n  \"9949d43e-4cbe-48f1-a1bb-de54a20dadf7\",\n  \"1fb18cb3-b9f1-4b2b-8648-a0973d471e2d\",\n  \"67f90ea0-1783-4856-84e7-f739247d3284\",\n  \"0ead2187-c948-4530-9e42-666563697384\",\n  \"8bfde91a-bb0b-40a1-af06-1efbcf1d825b\",\n  \"0064a068-9d03-4da2-a879-08f5e1748724\",\n  \"f82c2544-4b31-47d6-b5c0-97ad5b388c36\",\n  \"af1d9c35-621a-4c03-8a11-6d80dc62d5cb\",\n  \"cba90a32-d2f3-4a07-9b47-3ea0dee725c2\",\n  \"08b2055a-cff3-4b5e-b107-0b96ac221279\",\n  \"37a75aa1-abcd-44f7-a98f-26c948a93a0c\",\n  \"b75f7107-b852-4219-a543-1f7015d7f3f5\",\n  \"2c9eea25-66cc-4aa3-82fe-f812e17597fd\",\n  \"91411802-7b6a-441f-b69d-f16766fb0988\",\n  \"b6e22f95-1c65-4185-b66f-c0c2f0f6eef2\",\n  \"f44f26ee-a8ce-497c-b6fa-abe4ed53c0f3\",\n  \"89d86b13-8680-4509-8d92-cef223349150\",\n  \"76a4ff7e-5d8f-4432-8e6b-42bdfe096281\",\n  \"316e7d98-45f3-4b55-b182-179d9743b8b4\",\n  \"25ee3aa6-0df3-41f3-99ed-2b80bc5333f1\",\n  \"ce19236d-01a0-47d7-85b4-271b5874f935\",\n  \"617c1bfb-a9ba-40a8-9b75-52fbba7d3f6a\",\n  \"faa2fec0-d566-412c-aa57-fac8176d5738\",\n  \"b3e3b9e7-2e19-4b33-9c34-e81966777423\",\n  \"4e1eb338-bf9e-4a3b-9a90-f2ea2fa6bc26\",\n  \"cf1e1012-7150-4a61-8b71-5343abb9bf9d\",\n  \"78ebc27b-ffe5-40a8-b207-10969ca1a4e7\",\n  \"f8ba91cb-6eae-42b0-bdd3-19922fd6b97a\",\n  \"9101fc36-3733-458b-8e45-870b5a7714c7\",\n  \"76145bdd-10df-4903-978b-266ae7d017e8\",\n  \"8cb5d0ec-bf61-4acc-ab59-43db3f00fb2e\",\n  \"1f166b03-e745-494c-8954-71562a2b0156\",\n  \"04aec872-a16f-45b8-ae22-c1833a19bc9f\",\n  \"687bb169-a079-4895-a9a5-d560846a5d4c\",\n  \"5c593196-5921-4740-8b9a-eda027b58a7f\",\n  \"9bee4676-6fe2-482d-a46c-3080b1b19326\",\n  \"f7e1ce7f-b870-4dce-890d-632674863968\",\n  \"46bf53d7-db1e-4791-9cba-3b196dc9af12\",\n  \"443685b1-9eed-419c-9d03-cd4d1722eb5e\",\n  \"3337487a-bf1f-4b6a-a456-ede4595b7356\",\n  \"a2fe6345-94b4-493d-bbff-5c0c873ddfcf\",\n  \"e892525c-071b-4b82-997a-25d2fb8a2486\",\n  \"4bb17805-92f2-4e2d-8922-0d2e68d2d73b\",\n  \"3b26c300-500b-482a-99ff-496d3b193624\",\n  \"5969b9fb-e62f-4421-af8f-c1ab79806be2\",\n  \"c14ee136-51ed-46e5-8726-659dd0a2de96\",\n  \"f0fcb9d0-e595-4c94-a874-a0b7aa8c2e5a\",\n  \"51932311-5eae-46fb-b5dd-56b3ddb1d155\",\n  \"d13024eb-4204-42a3-ab5f-d3d84d054eee\",\n  \"cc6c3c39-f7fd-4aef-947c-0a2b01acdf2c\",\n  \"6ed1bde9-46d6-4db7-8763-15e0c0ed3774\",\n  \"01bbd4e9-250a-4c14-9f9a-aebc2e0fdac0\",\n  \"517ae2ec-deb4-4788-ac71-539ee424c1e5\",\n  \"20a493bf-6c30-4765-a4e9-0fc143095cd3\",\n  \"fc0fab89-3290-429d-a56c-c49bc6d9add4\",\n  \"485806e1-e307-4449-8309-7792df308d29\",\n  \"046e8b08-2959-4d72-ae7d-f2e6db82608d\",\n  \"74b5847a-2dcc-4d6f-907d-260c892e9d43\",\n  \"803db209-615f-4c91-a656-3169899e1202\",\n  \"8ccbecc4-ab45-410c-8625-2f58c6792dff\",\n  \"dfae2df4-09ae-424c-9e7f-1776be795195\",\n  \"12d56090-46f9-455f-ba72-552ef990bd3a\",\n  \"0f957aef-597f-43c3-bc08-1dc2d0910c8b\",\n  \"16c155bc-1cef-4800-8c2b-b4104665ab1d\",\n  \"85658c4a-a3d1-42de-a70b-bf6e8b16082a\",\n  \"0ac42b87-f62e-4951-91e3-58d6036c118d\",\n  \"fa77686a-789e-47a9-b476-c72febf85154\",\n  \"f2eb133f-ccf8-4745-b891-cc93b9141d62\",\n  \"cea0c7af-71ec-4660-a3b4-c40fe59c7c36\",\n  \"613fbaf3-addb-449f-b234-9326283cabf9\",\n  \"35123ffa-925e-4d6d-9e5a-0945dd431dbc\",\n  \"aa29853d-e4ad-4189-9560-4d49d3802255\",\n  \"419c97b3-abdf-4c5a-94ff-53ab75c7864b\",\n  \"3e674dca-5e77-4ede-8a11-adc77505d1eb\",\n  \"51938e58-310f-4916-9bd7-726be4e491b0\",\n  \"713d4b9f-3457-4ce8-ad09-16e726b4f762\",\n  \"850956af-ca8c-4880-afd3-eec219fca38e\",\n  \"a8a9260f-bcc5-4487-8428-6e329db1ed82\",\n  \"3cd958ba-58d1-47e3-b8e3-ad912f7a545f\",\n  \"6b005302-2a54-47bb-b69f-1adcbbccddfe\",\n  \"b91953bb-d4ac-4e17-bdc7-a07ed98f4360\",\n  \"2cff427b-0187-4d17-a562-47d4a7dea5c6\",\n  \"fdd62c24-53b4-46dc-9942-75be742d3f7d\",\n  \"b52d0866-3849-40ae-a79c-193d7501d574\",\n  \"e84b5ae0-9d6e-46f5-a925-f007d474c901\",\n  \"a11f76f6-539a-4737-b54f-d27a41a70ce0\",\n  \"c310372a-2591-4ef7-a18f-74bc5fdc1bc4\",\n  \"43a942af-85bf-41ed-9966-9927211ebb74\",\n  \"a31032f8-6466-4a8e-acf9-c8ac3e8ac28e\",\n  \"3622fa3d-a55c-4249-a49c-e480c68d7f12\",\n  \"d5eaf2b1-e32d-49d7-bcfa-5b63c145d9a3\",\n  \"00cf74f8-effb-49fb-8321-01f331a4302e\",\n  \"d10cba60-da96-457c-add1-1ccd28bbc398\",\n  \"dc997bd8-90df-474c-8490-51d692cd5b16\",\n  \"a9bc50dd-5bab-4c5e-a20a-115d2bb0e1b3\",\n  \"a4254e01-46ad-4dc8-82f9-c6fe68b2ed4e\",\n  \"437fb6c9-9a80-4793-9e75-a8b1ad334816\",\n  \"c8dde266-df17-45a6-ba87-6038ed51d887\",\n  \"329104be-287a-4b01-aa4a-371a543bb025\",\n  \"2a7ad809-4545-4ffb-9d0a-dbb8bdf11321\",\n  \"8f5827b0-6349-42e3-a826-cacfd1406007\",\n  \"51c89706-0216-48ab-be7a-a0f79d2968a9\",\n  \"0c48cb05-2d52-4155-9478-98cf081c0659\",\n  \"671e71be-e4c2-4dd7-8a4a-58494dcc8eea\",\n  \"95f98517-cc59-45dd-906c-51ae3034412d\",\n  \"df9b7f2a-1591-478e-a15e-09d8420fb186\",\n  \"6d040367-12aa-41bb-9766-00c10cb2eafc\",\n  \"f997b7b9-bbcb-42b1-abfc-e024cea5c0c3\",\n  \"f42ae4dc-2299-4843-9818-d22fa90b5624\",\n  \"20339366-ecef-4c16-a83a-11bd275ecef9\",\n  \"b63aa58d-8766-4b4c-ad9d-3bb83cb42363\",\n  \"0d717b1c-f9c9-4938-9023-6de04dfb9aaa\",\n  \"a2ecd17b-79c2-4082-8e14-3e2458eac655\",\n  \"585367f0-3508-4bd1-a2a3-ffe1bfe57a45\",\n  \"076fdc38-7d8c-4239-ae62-91493e24e2fd\",\n  \"3ab512e3-8e44-4736-bff3-dad834c3467e\",\n  \"3e0846ce-e0ab-4420-b20e-3cf6dba37d06\",\n  \"0efa72dd-676a-439e-80dd-08fa39e7e144\",\n  \"9186a18b-4689-4beb-994f-f1b548e306a5\",\n  \"b7a7ed39-5a6b-4494-97b1-0a6515bc4f68\",\n  \"b34eaa20-5c04-4323-af02-37f63915632d\",\n  \"3ddf0800-f1de-40a8-9339-cccfb2e30642\",\n  \"0af7068f-26e1-455f-ba24-ed3f47b8e7b2\",\n  \"91efe85d-83d6-431d-ba80-c23fd3e50226\",\n  \"15e21976-ac74-4cf8-b1c1-ea4ff201430f\",\n  \"61406a8f-4634-4238-b872-7424cf920d0f\",\n  \"442cebaf-21ed-4887-8f1f-af4ba3a1ae4c\",\n  \"3e99082e-a97a-4ddf-88d5-991642ac913f\",\n  \"59caa87c-dbcb-4849-adbf-7869fdad6003\",\n  \"a11740c5-e939-474b-a934-b7817b2e84c7\",\n  \"b2df8fab-1419-45a4-bca5-db95186f7277\",\n  \"4c29575a-9971-4583-b2b3-f1ba46ee9e60\",\n  \"bc0ef261-99e2-4320-b93d-fd73ed2d145a\",\n  \"71430e48-3f50-493e-b75b-b6aac902ee0b\",\n  \"8f729fc1-37d4-424b-9f5f-4fb294ee57f1\",\n  \"ba42e604-734d-4758-baa9-fab17ad7325a\",\n  \"f6d6692d-08a2-4e80-96fd-af4319a8d27d\",\n  \"7a32b36e-e409-4f54-997a-a3e7206e2abd\",\n  \"81bf3d89-be7d-4ab0-a28e-8a35828c6ce7\",\n  \"af7ed257-e97e-4a20-bc6b-42c5a0eaba70\",\n  \"ec41fcc4-5522-466d-840a-7b2b08f4161b\",\n  \"ec6d1d18-b495-4768-9120-857f53994018\",\n  \"675d359c-475a-4d28-b87c-2e355292c6dc\",\n  \"6808e635-ad88-4106-badb-0678aae4b1aa\",\n  \"4f364e9b-8b6e-46c4-8ea9-d31a32f6a2cf\",\n  \"62c9e9a9-7afd-44dc-85f4-140645c40939\",\n  \"6730b4aa-e1f3-485e-849e-366823c29754\",\n  \"f01db4d8-06f7-4d68-bb04-877a99ba669f\",\n  \"805b06c7-a21c-4d35-9a2a-27d695d5a60e\",\n  \"7e6b07c6-cd4c-4507-b940-381cecd69b62\",\n  \"b7cde8a6-0f6a-4bb0-89f6-a5a252a97460\",\n  \"12c265bb-f823-40ba-a2d2-50d75ba76f14\",\n  \"e641e611-34ae-4e81-aa45-04706e96e76b\",\n  \"013ce016-1e5c-4105-a35f-84ea8ad6fd68\",\n  \"a3ffb4bc-a6a3-4055-9642-a397213911f1\",\n  \"d3df1225-fae2-4037-a9c7-12f5f742346e\",\n  \"e41a4b08-1e04-4780-8171-dce4b5aabf71\",\n  \"6fac08fd-7567-4e44-9f2c-6eeb8f20ad8f\",\n  \"ef3c0e28-ee02-46de-b897-5582964f80d8\",\n  \"c01a3079-cf16-4ced-92a8-d055674b2f73\",\n  \"ae3fe582-c62a-47ce-8458-2406240b9cc1\",\n  \"50b5f576-30cc-4e3e-a3d0-6aedc0cac247\",\n  \"987d71d3-b4e2-464a-b3c0-419aaecf72b5\",\n  \"cd4eb8e4-3e09-4cbf-91b8-f094f6e11e26\",\n  \"e86eb620-a58a-4172-9dbb-c03424dfde55\",\n  \"913db653-c401-4e81-bae0-7e6896e69180\",\n  \"3a648960-f72e-4c86-9398-f27b05bf40a6\",\n  \"8c14f46a-d647-4b13-baf1-803df9cb771c\",\n  \"653a78c2-d701-4069-8e46-6ce10139cdf2\",\n  \"ce2e5a2c-9908-4160-8ebb-fcf4295380e9\",\n  \"1d411d4d-b0ca-4a64-a3ea-544a4c409011\",\n  \"df98a632-4390-4396-b5df-1a009dc383e7\",\n  \"8fc1161a-b4a1-473d-842b-77e11db44ffd\",\n  \"f710dc45-f587-4e81-a542-4a6f00cf9deb\",\n  \"f86628ad-1069-4742-ba11-5ce4f5f18846\",\n  \"e1837007-a78a-4a91-92bb-b5b1763e8440\",\n  \"6a9cfe93-6a87-4cbe-9778-7e104e02d1dd\",\n  \"430e20f8-3cb9-45b7-84df-a405b0e2839d\",\n  \"05ac31a4-add4-48bb-aaa5-82cfb7e0cfb3\",\n  \"be4ddeb6-6bdd-4188-8b1c-a11f0ac41d9c\",\n  \"11ae5587-10de-4336-a934-397c90db69a5\",\n  \"f2e282ff-4074-40ce-9cf3-6b3a9a41cae4\",\n  \"3d5315bc-8636-4e5d-a0ea-0a71a0b95e1c\",\n  \"15892a80-8067-4ca1-9dd6-c2147e4ba1d8\",\n  \"5858a91b-527f-45de-8512-17f60da076e8\",\n  \"989e2d8a-82f4-4335-a6f6-58f9c1d03226\",\n  \"90ff7395-9f5c-4d8b-81ed-fc15184e21c7\",\n  \"2ae40976-0f62-4add-9821-186f231c19e9\",\n  \"d4c03079-3340-4bb9-9623-4a2abe00f735\",\n  \"44b5f262-0f32-4efd-9da7-a2809636f757\",\n  \"b845693c-ffdd-4015-9278-851bb0d37193\",\n  \"6fda2c60-23d8-48ea-b390-a1d9d0dde54a\",\n  \"fc6ec81d-11a9-45ee-bb13-51b200fcffb4\",\n  \"92c67f16-498b-42dd-a16a-f0ee0b19c508\",\n  \"d1c78b2b-5dc1-4487-aed5-c68d0eca121c\",\n  \"fbc34383-7c0f-4f25-b605-b5646da68356\",\n  \"4b31ed2e-8f6e-442f-804b-e00e79da0569\",\n  \"a28cca89-801b-4f9d-98ea-68dfb57b3345\",\n  \"39331ea0-6bf3-40ec-9377-8240b2f56e15\",\n  \"1970429d-6205-48c7-a267-5d8959182621\",\n  \"87decf92-27c5-4c34-9054-efe1de4882dc\",\n  \"8f65c122-799c-4ff5-a8af-dcc69d584291\",\n  \"eb3e7a1a-3b1a-407a-b94c-e111a42ef5bb\",\n  \"5c08facc-41dd-4334-9194-17a815dc7abf\",\n  \"edaf7c3c-f1ad-4ed4-9791-879384474ec6\",\n  \"53899ab2-f974-4715-828b-59157329e7be\",\n  \"d003c392-35f1-419d-8088-dce67536af4c\",\n  \"d7bd1b0b-e8f6-48c8-875b-74d463775656\",\n  \"fa7105c4-a181-40eb-b5b6-d421625a3dea\",\n  \"4be2848d-d13f-4269-8be5-7e0700a69242\",\n  \"ddd55a50-11d6-46ea-aace-8d429cd6c457\",\n  \"6355056e-835c-44d5-b842-c11165ee405c\",\n  \"5023ced7-3d1b-4f94-a555-9b8f775b168f\",\n  \"a02ee8ae-fac7-4b95-9da9-f8b00fc3ffa5\",\n  \"707b3ff7-4c54-4f7f-9588-76946e84ef35\",\n  \"56b74fb1-419c-4897-b306-cfc5396cd189\",\n  \"178fa8c3-f1e3-4ad8-9b4d-3619f4a290a3\",\n  \"b9e28f5f-5e77-40cf-99e3-b58c145a1a45\",\n  \"e121ea1b-ca70-48c3-9f51-ff7af554645f\",\n  \"dae54527-63b6-400a-b19e-427d84179f04\",\n  \"6ffa994f-211c-49f8-9247-a107fdacce05\",\n  \"42dde56d-16e8-43ea-990d-112d26527f4a\",\n  \"1fa99da8-cec8-416c-aa93-5ff9ff06af99\",\n  \"4f8dc84b-ab70-4064-9fdb-bfa8ce9b3925\",\n  \"4a40b2ef-9138-4c9c-ac41-a5f408f0ad8f\",\n  \"674bf21a-55e8-422e-9714-f20180d6fa83\",\n  \"3bbaf66d-5890-4ef4-86f1-e12c7fa8d293\",\n  \"c61bc799-0bad-4e94-9c57-cb51d74d3658\",\n  \"f7d81172-1963-4884-93df-e512090868bc\",\n  \"5ae11e09-1e1c-4488-b40b-18ac3c7161b4\",\n  \"0692b327-d73f-49a9-903d-d39b9bf071b3\",\n  \"9d44564e-4cd5-4971-8716-5f8149054c22\",\n  \"479609c5-3fe5-429a-be20-8370121066c5\",\n  \"3f4a5f07-3282-4887-8ab1-598ffc23453b\",\n  \"3a2b578d-571c-4bd5-98df-96b189c66805\",\n  \"584ab364-ee54-4f6d-b334-d6ebd0b13d4c\",\n  \"bbb4c4a7-ea23-40e6-81b9-e838ae29499d\",\n  \"973f18c8-bd97-46ae-ae7f-9a8b5774dd58\",\n  \"ab9ff72d-c768-4f1a-aa4b-3f4b8c4e26cd\",\n  \"eb46463a-0659-46cd-bda7-1cb59f130281\",\n  \"67be7b6f-ac9f-4f96-9c09-7460a436728e\",\n  \"267da9fe-9a39-4096-bed3-b3fa0cf3a862\",\n  \"1410c018-ff04-4fec-9f18-4fcefc4107b6\",\n  \"8ed57641-3fe9-4180-893a-612b247789a1\",\n  \"6d3de7a6-1bec-412f-aa8a-13bc85e1e971\",\n  \"a7222f0e-2fda-4735-a405-e0c8d018fecd\",\n  \"6e3db241-187f-4b39-b4f5-2d74d7c74a25\",\n  \"3d7f026b-8b57-4e82-a27f-54d183b4d7b5\",\n  \"f9c215d0-3f6c-4e96-a87c-6aacd5d51609\",\n  \"24727ea3-d4b1-47f3-8d73-b5708a0fc15b\",\n  \"def141dd-1e6a-4bad-84ce-446a4d29bc70\",\n  \"b0430d57-de75-4611-9e70-11bdd7b18e4d\",\n  \"d660d4b7-1576-42c7-9989-d44165ef69c5\",\n  \"5d623a2a-86c6-446a-b11b-07ff25032edb\",\n  \"e0978bcc-c86e-4ba7-8a06-318b58980a98\",\n  \"c66d4780-ce11-4aeb-adff-4939974d14ad\",\n  \"9adde035-85f8-4789-95f3-8f3c77764e52\",\n  \"c472d932-d5cf-4d04-b2ba-ebbb45391146\",\n  \"843a5963-5a26-495d-bb20-5683c542eb8a\",\n  \"3d507b85-f45e-459e-86e8-f486473d7fc7\",\n  \"a09ae230-c78a-4af4-9dc6-9da86547ce52\",\n  \"71a41d8e-95b2-4277-b939-b853b0bd4b28\",\n  \"259f3765-7810-4d6d-be70-471c0f0fadaf\",\n  \"047fdd44-6d1a-4f88-bdad-602b4546afe5\",\n  \"91ca1015-48f6-4694-9345-b698791e6f28\",\n  \"9a07f9e6-12fd-461e-ae3e-3672ac7de3bd\",\n  \"d7fb2336-0308-4d28-b083-733e57237f6e\",\n  \"1dc14147-1f3a-4c74-ab0d-a7eadb8bd129\",\n  \"7a7a6d5e-0df0-497a-978b-9a16c4f7ddae\",\n  \"2715c940-f2e6-4345-a27a-0c2fa51a56fe\",\n  \"668201a2-0a17-4d62-b90a-9d2b8d7db7f5\",\n  \"17f4bb84-f5ec-4083-83a3-1038bcadfdf9\",\n  \"326858ff-832d-4252-ba04-f6045354f808\",\n  \"211b2b85-8c71-4395-ab6e-09a60f702b96\",\n  \"8e5815c2-ae9c-4636-8eee-9a7bd5ff56f6\",\n  \"ccc43ba3-69dc-4c69-855f-710545e417e6\",\n  \"40868843-9c6b-4021-b035-6c453c567dd1\",\n  \"76c43962-9607-4dcc-8154-889301679461\",\n  \"ec6a3d99-5e52-4c9b-98f2-fac7ef7e5ca4\",\n  \"c59684c5-e091-4a52-be05-325e89f3b055\",\n  \"d8efdf4d-dcef-4683-b8b2-8d9c1e7a1ccf\",\n  \"e9d68f1b-bf5a-4bae-a6a4-b1bf3568a107\",\n  \"850d525e-733c-4d62-8a4b-8c8cd26bc14b\",\n  \"70fad4ba-c381-4395-96bd-7b03ebf69a7a\",\n  \"3c3a1403-3a74-4cce-b960-e760a12e820a\",\n  \"9a6a16e1-c751-4dd1-b63e-c69e5c2a1b1b\",\n  \"c97e7e60-819f-4384-8b08-6d0f831b05a5\",\n  \"2938443d-d0f7-4919-9727-c77e3e61c073\",\n  \"24664712-b0c0-4c1b-8144-21bc62f68fbf\",\n  \"cbec280b-a711-48fb-a56b-d6acf7f4b841\",\n  \"d4068be8-f47a-4d70-a33c-53cc0240c94d\",\n  \"76dc17d0-0280-4035-96ae-89bffe32ce37\",\n  \"7c338479-256b-473b-9744-f5861a7430b1\",\n  \"4930da9e-ade0-4b05-ab71-995571510cb4\",\n  \"e7e0644d-04a2-4e04-9b22-de76b8d1f2b9\",\n  \"f7c84d8d-00ed-4d37-a93a-f8cfcb92405a\",\n  \"48d9df34-33cc-4e41-8db4-e6604137763e\",\n  \"ed52889e-637b-438a-bcfc-66e954cee0d7\",\n  \"0a579689-e225-4ea3-9f36-b283600f744a\",\n  \"0d30337f-a0dd-4ccd-a3a1-a06842af891c\",\n  \"37178f7f-8e06-4df6-8399-ad342f5cc95b\",\n  \"dde0551a-fe20-4e22-9e6d-124d6280247e\",\n  \"55e78786-64f5-48a0-b0c6-5b3822c187af\",\n  \"2b0f8c41-d671-46ef-bdeb-77260f2ea344\",\n  \"cec3e46a-c81a-4069-af63-b6ad3d1699b7\",\n  \"02ad7c88-6cb2-4001-907e-6e45f71885ed\",\n  \"89d357be-cb2f-40d1-b490-469024c6eb9d\",\n  \"abf7dc53-2df2-4d23-a9ea-e7b81930c01d\",\n  \"09700a3b-44f8-4a7a-a6c1-eccd4f0b2989\",\n  \"03c1ecde-e39f-4064-b907-21cfc738efba\",\n  \"8d63cda5-c646-4720-b6b0-9dbd98931686\",\n  \"3833f9ee-04fd-40a9-a481-e74a732c8bf1\",\n  \"f2741578-b285-43ee-852e-106c40f8a1b8\",\n  \"8804196c-5654-4861-af0e-23306acd56f6\",\n  \"2aeade58-34cb-47db-be3a-38b4651b2ad8\",\n  \"7a22e2af-6552-46a7-90c0-96701242a652\",\n  \"b15f3b9f-c7f0-452d-a984-279d2c276fe0\",\n  \"e35d805d-8358-4178-bb25-33bbe780fa65\",\n  \"ee88d5a6-2abd-46ed-9d2e-893266225318\",\n  \"001d8d5f-a499-4270-b17b-54832da40ab1\",\n  \"0b9c8f5b-9a83-4af9-8b2a-e9ae08d16924\",\n  \"9d43d0da-3859-4d40-8e4f-1f57672c6f16\",\n  \"c4facf2e-0438-498e-8fe6-d25569a1bf28\",\n  \"5dd72fa2-452f-4a4f-b77b-a3755e8c5d90\",\n  \"0f8ff0e2-62b2-4f8d-83ed-31189a7f4f2c\",\n  \"694d6020-2f5f-43fc-a199-cead670d255f\",\n  \"4b4a5114-de5d-4ce8-b8c3-815d1962897e\",\n  \"5c4cc729-7989-43bb-a765-ddabdd7f842e\",\n  \"12aaac9b-28bc-40a0-aa29-b3627c84711e\",\n  \"d7a814c8-9934-4d62-a260-3b856f392fa0\",\n  \"6a1294ed-47ac-42e0-993c-742f3d6f4997\",\n  \"86955fd9-6cd8-4716-abff-408fc39b5afd\",\n  \"050ebf52-04e2-4ecb-9737-de6eef2091e3\",\n  \"e14fb45f-fbce-40e8-a952-b40ca8ffd03d\",\n  \"b6e9a5ca-b913-45c6-82e5-897e0224f1aa\",\n  \"1b412925-b90c-42bf-a5d6-5dc10c777f3e\",\n  \"36dec511-d295-4463-a087-1cb7c8725b17\",\n  \"68bca740-b1d1-442c-8ec8-46df51b650a1\",\n  \"6eeac7f7-9163-40ed-9f64-d2399cd1e45a\",\n  \"d1288d1c-8833-457f-a563-8d30758d399e\",\n  \"ac078e8d-06a7-4817-b162-12cb7a6267d7\",\n  \"0b261dba-b4ba-45fe-afca-784c72ae420d\",\n  \"de58aff7-52f0-4dd8-b2ad-253e9e43dbc5\",\n  \"4af86e99-37ba-4d1f-a5ed-cdf3fc5597e5\",\n  \"3ff9c078-ccc8-4318-9af1-6887c974c0e8\",\n  \"7f8493cf-3fa6-41f2-902e-38b6cd8b07b9\",\n  \"2b3fe130-ea15-4fcf-89b6-b2ee04cd62bf\",\n  \"263e8fec-5d97-4224-a84f-715fe04b717f\",\n  \"ec3c344e-4444-49ba-959f-2e33471a4b7b\",\n  \"c505e474-f3ab-4650-81f9-5be3a392b66c\",\n  \"b75dc7a5-5400-46d8-ae6c-7577f8cdc3d2\",\n  \"0f516f04-9da4-4ccb-a399-b70ed0c51db0\",\n  \"bac2927e-bca1-4490-8acc-4e29a45aa33a\",\n  \"18f9d4f5-c973-4207-80db-affe728b40f3\",\n  \"70af2326-aac8-4487-a6c1-0f4e73067579\",\n  \"fb74722f-e387-4cc8-8a3f-10c372f0cf0b\",\n  \"74aa1c70-8117-41ee-a109-88719e6d3bd8\",\n  \"95681c8e-11ac-44f7-9f38-9bfb8cdcc120\",\n  \"7f2dd0d4-33e3-4386-b196-2ca045920bff\",\n  \"49fba371-92b1-420b-86e8-58695f1ca3f2\",\n  \"799a3c12-0e14-44f0-a6e0-88e3aef0cca2\",\n  \"e00a9048-88fc-48bf-98fa-0447421e2a7a\",\n  \"112bb56e-e331-46d2-9ac2-79c7121c3849\",\n  \"688bc4df-e100-45b1-947c-722dbf36cc14\",\n  \"9c6e8d5f-0720-4ac7-8f92-442411fe2582\",\n  \"92dc94e6-fb1f-4576-9a05-6f87d0764c5b\",\n  \"51fda582-0299-4689-8af2-906a6b9157e4\",\n  \"b2468f6a-8336-4f38-b8c1-271bce9f1d1e\",\n  \"87162f3d-547b-4303-87df-07d28cb5d188\",\n  \"9e152d18-c054-45b1-b9b9-231c32791bf7\",\n  \"981ffe22-b90a-4b65-94f2-42c0b596f65f\",\n  \"259152cb-10b3-456d-9527-a4e3f36f72a9\",\n  \"1426e7ff-6302-4ccb-8ab3-57e276121533\",\n  \"574862cb-c65e-4ad2-8d47-49b69f7a261b\",\n  \"5f09d24e-9cef-4198-a12f-c9c5e380560f\",\n  \"8a01c330-2ba4-4641-bb60-e173d3472bfc\",\n  \"97590d0a-d8e1-43fe-acd0-6844617e872d\",\n  \"cee94b1d-7af7-4de3-ae9e-5ff4a3d78ea0\",\n  \"b5029b1d-ee91-40a3-b893-4ea212dd96a8\",\n  \"6db4593d-f5f2-4634-90ac-565a7755e2ad\",\n  \"8a88035f-487e-413f-bf7c-7018f1e9d972\",\n  \"c9c06784-60d5-418d-9645-eb82a939f6af\",\n  \"2aa0fbed-84a4-49ab-ad15-6388244cfd22\",\n  \"9d7fe2dc-10fd-458a-80f7-41f730be9857\",\n  \"71f645c0-5d2f-4342-ab4f-346f58e563c7\",\n  \"be0816b7-d5f7-49f8-8a9c-309bf8b729d4\",\n  \"5f29d342-1dbc-492e-82f2-979912fd2073\",\n  \"c79738ec-d709-441a-aed0-74bee073a299\",\n  \"e57d8735-fc80-4353-b659-d2cb3db55784\",\n  \"729f2565-7f68-4d8e-a9d9-ec6208caea10\",\n  \"9ad068b3-4240-4aa9-ba09-3242f7cd9d51\",\n  \"9255c0ef-00f6-4f26-a728-21c0dbabcd0c\",\n  \"c7c0f4fa-4d25-4a96-9976-0508c87b0e0f\",\n  \"d82e9862-eda9-43d3-848d-b416bfe30613\",\n  \"3e871265-cf6b-46fc-8e6c-4d6c1b736556\",\n  \"ba4d4cb8-4c07-489e-967b-ddd59e7c5df0\",\n  \"e6498d58-1430-4d87-9386-46f6d05fa13e\",\n  \"060f1cb9-f0d0-40ce-b361-1a080c60c697\",\n  \"d5bc003b-2b75-4e39-940b-e893e256ea8c\",\n  \"3a7a1ca3-f79e-45c7-8bca-f49f734f3388\",\n  \"18474be8-e5f6-471b-8c45-dc69680b29b6\",\n  \"7310040f-fa3c-4c7b-adce-cc37f1c37545\",\n  \"f1e79a23-76fb-43ed-9ece-ad99214f8a76\",\n  \"540fcaab-63a9-41f1-a227-b0e8833c4dcc\",\n  \"5e2326ee-c95d-4fe4-af49-af83aae4302b\",\n  \"0d61b5b4-674f-4e35-bc2b-0a83882de047\",\n  \"f834de54-4720-43dd-8131-1b8c9731c628\",\n  \"493cb715-755b-411f-ab76-eb1ae539ae4f\",\n  \"ae9a003b-9358-4560-bad6-88d8e2d6e25f\",\n  \"ed3bb746-f078-4590-83c5-ec098c8cdf80\",\n  \"d2a9574f-8ad3-4f49-b76f-2eea48fac2c1\",\n  \"f54cc608-2d29-4c4b-b84b-e2e24c8ccbc9\",\n  \"d9505299-c696-4185-a99a-cc8e17b1fba7\",\n  \"0e3c5064-dbac-438e-afba-676461e8b509\",\n  \"a7744d98-697d-4684-bec8-d5dc777cfd8f\",\n  \"ca388cb4-eab2-4822-82a5-8e17a0077a00\",\n  \"36602499-321c-4a88-90da-216028b4860d\",\n  \"389bf31a-bdef-4ffd-852d-f4f1a4fe8387\",\n  \"dde1c9e5-271e-44b7-b2a5-5fd10affff24\",\n  \"d7de14ea-6eed-475a-a47a-348f5293ce04\",\n  \"631f37d2-f6ae-4caa-acd7-7b0d708fa862\",\n  \"4ea20287-6820-45bc-882a-1e81aa1f8dc8\",\n  \"a363a467-bd9e-42b9-a26c-be59b4881fa3\",\n  \"ca4ff23e-de3d-4298-8483-af19fc610f25\",\n  \"c8fd264d-71b3-42bf-906a-dc3ff2d0ecb3\",\n  \"4bb6b72a-61b1-4754-91c9-0d858f3d4bae\",\n  \"07baa46f-dcf7-40c5-921c-7f606d73d905\",\n  \"c85c67bf-9bac-404a-98ea-ccab4806a1ce\",\n  \"38f6be9b-55cf-49be-9bc5-3b752a306279\",\n  \"c293f9c9-78a7-43a0-a51b-e650f2dfa9a9\",\n  \"cd554f5f-0d8b-4ed7-a49f-e8d4382354ff\",\n  \"66f30ef6-7078-4f39-8ae0-a69dfc682cb2\",\n  \"eed87087-0d13-457f-b7cd-26144b7a3f6c\",\n  \"fcc1a789-3de6-4377-ae73-e76416a07e06\",\n  \"5d8cb917-21f0-4e83-9de4-ce739067cd51\",\n  \"c7ad3fe6-332a-44eb-9317-4be39d575dce\",\n  \"c6aecbdb-0c6e-4c73-926a-eb25728165f5\",\n  \"68ce135f-af03-4081-bce4-11afc3764776\",\n  \"e7936022-5b8f-4bb0-bd53-925eb3cc06d2\",\n  \"4e56af3c-9de2-449e-94bc-7173f9523529\",\n  \"2d0de407-8f73-4483-953b-78303b5ff08f\",\n  \"6651da79-c88c-4b18-ac60-4b6df5d46f8e\",\n  \"607b2dc7-152b-4196-9570-8be6290449b8\",\n  \"d88ef0cd-e622-4918-ba22-98223ec9fe70\",\n  \"6f200c8e-0f50-433e-910b-34b1896059fb\",\n  \"5b124a26-ec25-4094-beae-8c92a35fa50c\",\n  \"a7eb9077-db37-4528-9028-76c1be55a888\",\n  \"28273349-e6b6-494e-856c-a5df904f082a\",\n  \"7cf30483-1aff-42cc-89a7-2d4b04a0ea82\",\n  \"bc678e7b-551e-417f-9460-b46710a630d8\",\n  \"373aa2b8-244b-43c5-9384-d38364c08d2b\",\n  \"fb1cc448-0f3c-4b1c-b578-071ed3d08495\",\n  \"bb2648c3-7c8c-409b-9a35-ad0be6a9d7a3\",\n  \"9ddd92a8-3aea-4c3f-af73-a2859bd05d47\",\n  \"8b854f48-207e-4711-a95d-c3004dbfd20c\",\n  \"06988250-5f6d-4655-87df-cc46edb6dc82\",\n  \"cee4205b-64a6-44a3-925d-3a168b2dcf85\",\n  \"e3764474-fffb-4370-bb87-523bc91140b0\",\n  \"682da045-71c7-4493-b8eb-79c93fdc12b7\",\n  \"8dbe0779-f124-4bc9-8eca-19de6d0464c3\",\n  \"b0846205-0ba9-45ec-a60f-e2aab2d1f2bc\",\n  \"96d94112-7c7d-4c8f-aed0-d6aedc9c5083\",\n  \"6f16c274-ef9b-4071-badd-5cd9cff645f0\",\n  \"0fa6d0b4-78e2-42b2-b832-52d6edf77d6a\",\n  \"35d932a9-50d9-437e-9360-367185966c43\",\n  \"7548cb2b-9f7d-462a-a9e6-027c358da782\",\n  \"66207746-3a0d-49a6-befb-c7f13147d5ad\",\n  \"b2885099-9b03-479d-a599-2ea0cca7e199\",\n  \"59d48d3a-c866-4ec2-8984-f8b4ab4b845c\",\n  \"f6238791-2d99-42f9-b999-a159fb621cf9\",\n  \"fc89296b-1d2a-4438-a063-05c0cca22292\",\n  \"e5abb491-a13a-4a2c-9a47-617708769bc0\",\n  \"ff866c81-fd69-4516-bc1a-ad0e693d997d\",\n  \"aa581740-8267-4013-94e8-448a76d94cab\",\n  \"aa71842a-8781-4bf9-9307-c8f557542bb8\",\n  \"0311fa61-cc5e-40b9-957b-5bae7f321cb8\",\n  \"9adea155-f1a1-4097-817b-67af8d26a6a0\",\n  \"32653f52-5c2d-4cf4-9030-8d7aa6ffa87b\",\n  \"57483a2a-2c22-42e8-a529-e0786ab63559\",\n  \"5d46a9d2-f9ca-49ee-ab04-91c339116411\",\n  \"5211b14b-b772-4e49-bbde-3fde13927fdd\",\n  \"e5dccf5b-6838-4f72-b382-d4534b26545d\",\n  \"d19c0597-8ce6-4ad3-b766-edfd9bb64a5f\",\n  \"75fa6f4d-0591-441a-a3ac-aba3389bb8fc\",\n  \"3587f195-8d5d-485b-91e7-03f6597e9b24\",\n  \"5d141384-b9f4-43b1-8cbe-bc076e629256\",\n  \"243de966-f18b-4caa-8675-ee47257c9f60\",\n  \"7c767a4c-7217-42c8-91da-533e30a73d65\",\n  \"7f3c97c4-42f2-4309-b788-cdba0030fe9a\",\n  \"9c4d79f7-d194-4cf2-b651-a9c320bba136\",\n  \"39e51480-f538-4680-8b7d-cd5c7e523c81\",\n  \"e9424a62-8ed5-4a89-b628-17c279b1775a\",\n  \"89c3c3d0-04a1-40cb-986a-5490a78df3d4\",\n  \"4251d37f-f687-4793-813f-728002f6e485\",\n  \"adf77872-98f0-43ca-a7dc-fb1afda930b2\",\n  \"88cb0491-d22d-468f-be10-3e1f94928e16\",\n  \"5b2d08cd-02bb-4ae4-bb19-cc3fe998e493\",\n  \"de132aa8-c7dc-41c9-9a61-5ea85d9b13dd\",\n  \"47b9f549-cc46-4a97-a630-8cda6cbc2884\",\n  \"2ba6f927-dc16-4073-bcd8-bc2f10eabc46\",\n  \"b9137f82-1640-40c4-bf2e-0b1f32b030b0\",\n  \"56f8c8fb-f8dc-49e2-83bc-b4e061f14e20\",\n  \"43dc9958-af73-4504-8893-de99c339d978\",\n  \"102391db-f733-4e0c-ad02-4c6a291e1ff5\",\n  \"a972240a-3f30-4a95-86f3-0a809740f3ea\",\n  \"6a6b6342-9728-4721-a72d-2f5c35ebbbc2\",\n  \"9788fb26-1036-4232-bc36-d4026569fd79\",\n  \"fb69369d-e306-4d72-9240-b80a197a207d\",\n  \"5b49e3fc-cc09-4c59-b42b-36d2b088b561\",\n  \"cfcbd0e3-eaed-4848-b04b-8479cb0cb9ee\",\n  \"ecc59ddf-e5fb-4266-8723-e81ad59db2bb\",\n  \"450b27c0-27ba-473f-913d-544a7f2e4535\",\n  \"e1c3676e-d5e2-4e1b-ae46-c46810b7f616\",\n  \"7bf53791-e740-4cc6-8931-d6ce00bc359b\",\n  \"6baac305-48db-41ad-8efd-d749de279d82\",\n  \"1680854d-c89b-452c-ae8d-7f45f65fce6d\",\n  \"fb056a25-bba3-4608-abc4-b06229946abf\",\n  \"c108b47d-0cb3-4ff6-9e38-26b69c227a4f\",\n  \"f905afe3-b8ce-4f26-a99c-e09c60b0a9e5\",\n  \"57397174-5a82-4b71-a463-e4b03ecff8f9\",\n  \"33201983-afaa-4714-b1eb-d9e76f80116b\",\n  \"a4db125f-e5d8-4e23-9d0c-65e29f345e50\",\n  \"64c7e714-0787-49b5-bb74-c07b65ea1313\",\n  \"78a9b319-aefa-486d-8854-2bc9f403ba5a\",\n  \"27081fb0-a814-45f0-9b50-ad49911fb5de\",\n  \"e6f774bc-37fd-4a01-963e-b2bbc3fd00dc\",\n  \"d7d55dbb-b11b-425c-8091-186bc9343c3e\",\n  \"3daa7fb2-55d6-4c48-bc67-d49897e98525\",\n  \"4c51204c-3a82-4180-8484-991bc27e16eb\",\n  \"6db2aa0a-0af0-4d67-a085-af9545b1e2ae\",\n  \"6280677c-a211-47d4-b208-9d386126022f\",\n  \"f24786e5-197f-4244-b340-bc70ee7530b4\",\n  \"61a5f8d5-beb0-465d-b01d-d8bbd265082c\",\n  \"55463427-912d-424b-b7ac-b987ad755440\",\n  \"154b1912-923b-4d39-9347-dd82adddbab2\",\n  \"884b7b1f-c476-4187-8baa-4362544b2a1a\",\n  \"42dc64d5-30a0-407b-9481-ac8d5d262165\",\n  \"bc7b2875-a601-453b-a303-a3a4368dc488\",\n  \"b5e8cb9d-546a-4208-8dee-dce7b4f2f27b\",\n  \"725ee4fe-c59a-4f6f-98e4-a4030c494ea8\",\n  \"0f64e0c2-9768-423f-90d9-a6d347d9974a\",\n  \"88398849-e2c5-400b-8791-d71d79996b06\",\n  \"f0ab7732-6c20-44df-8d59-77088478a9b4\",\n  \"302f3955-bf98-44e2-af4d-c33eae59bfd9\",\n  \"f4df1dce-161b-4a48-a901-4ee0bac20d8f\",\n  \"10d9152c-4249-4768-b11c-d205b6dc167c\",\n  \"85234d52-9fa1-4e84-bc18-27c0a1cd7d84\",\n  \"f123ab19-3964-49ab-b2c1-fd8d95f181c8\",\n  \"8ea43fd6-132b-4323-8d9b-35e3de7e5fd7\",\n  \"df2cc30c-844e-4d7b-9fa5-ac66d48e0b4d\",\n  \"06639273-62a9-47f9-ba6f-44b63117a0d2\",\n  \"7a7adebc-3e5b-49f7-be8d-2d72580c711d\",\n  \"31fc582a-c3c1-43f3-9c9c-915e01e67fd2\",\n  \"95e33026-7178-4368-a7b1-a9e416b2b673\",\n  \"c79d4e4d-8e13-4881-b425-831180f548ab\",\n  \"c51a8e3e-2239-48bc-9b67-77b10c872393\",\n  \"49768aa2-77fd-4dd3-abf3-d8b33a3ba680\",\n  \"5b4d3e5b-3eeb-4d68-afd1-5ba170aca18d\",\n  \"19c71a42-c76e-4488-a391-eb4b85f81609\",\n  \"36576023-bc67-4dfc-8055-9d238dbd5dc8\",\n  \"47065237-634a-470f-a34f-cdd084df3622\",\n  \"962d4d4c-1218-4951-840b-cbab1b9b59cd\",\n  \"ec2c46b9-c46c-4281-9077-0cb812ceac9f\",\n  \"2906ad25-92d0-4258-83b3-e0a7f314a450\",\n  \"f93b0549-4d91-4f38-8057-d3026defae59\",\n  \"e84fea4d-bfea-4cc1-ac38-9fb872636c55\",\n  \"9c2aaf08-a16e-4cd5-b9c0-6d78b860714e\",\n  \"d0385df0-82b1-4bbf-9df9-4b857cb04c1a\",\n  \"dd8766a4-9e57-4821-849c-b9905dad6595\",\n  \"e026953a-f6c3-4cb6-92e6-f26a41746bf2\",\n  \"721c0e83-16ae-41cd-8237-6e0cc92e6b79\",\n  \"97faecd8-00c6-48bc-90b9-be7c8d8ce16f\",\n  \"4ccef8c3-fa66-4a51-b9cc-6809af24f3fb\",\n  \"6dd0a842-7642-4bf1-81d2-20bfc0699e3c\",\n  \"45fb7a3f-0a86-439c-b2ea-d6e6d77834a1\",\n  \"c18c0148-bb49-42f9-b580-5c62f63961ac\",\n  \"e21f56e2-a77e-4b7c-9941-39d7fae651da\",\n  \"ede11877-0568-4245-9274-cb955abeed38\",\n  \"f70785c7-96c2-4561-90d4-95690a929a53\",\n  \"ca18e9b7-ed20-4b5e-b614-5684656508d6\",\n  \"1abcf423-d530-462b-bfc7-ec96e04a7439\",\n  \"19038cab-9300-469f-afe1-860214e8051d\",\n  \"56b002a7-2ef0-42c4-8495-68acd46289e6\",\n  \"970483e5-185f-4da8-bb24-a7cf0e208ac7\",\n  \"db6f5f40-0be6-4e31-81f5-dba9d366ac1a\",\n  \"f4d3d0c6-1f6b-41ba-8da3-dfa7411deafc\",\n  \"6a979a64-c125-4cf7-9d4c-fe289f7dc4aa\",\n  \"e1a667b5-14f1-4e73-b17f-060d236512c8\",\n  \"cf048430-1d25-42f9-bf42-d6db1038bf8e\",\n  \"1f0e8ad1-c111-423c-b1b2-c3b28ce25812\",\n  \"e339a8ce-3a1b-4cf4-98f0-2e81aa26172b\",\n  \"870acb27-869d-4ff6-9e63-b8578fd76130\",\n  \"e8ff5b03-3892-4e84-af94-735db8a875dc\",\n  \"1f579554-031b-4c69-a4db-96681638bfce\",\n  \"79a7a49e-fb99-4bce-bd9b-20e6586d0d59\",\n  \"3190c4f0-af51-4d8f-8f49-be6eb117d801\",\n  \"9a7eae1a-1ebd-4cc3-af7c-228c0f662587\",\n  \"6117a629-5c4b-4eee-91c8-53e1e12c6259\",\n  \"3ed5aa44-7627-4258-9017-b7053576a336\",\n  \"3f8f1ae9-d915-4605-b0ee-560dc8ae9be4\",\n  \"7e0f51a4-3cf2-4b3c-b63d-aa27f5795adf\",\n  \"722cf48f-62a4-40d7-b7d3-647b60129676\",\n  \"8bdf5bda-e05f-4e6e-9fee-c58ae6be9a82\",\n  \"4713eaf4-b599-4bd2-88ba-27a04050ab59\",\n  \"60b40d04-f215-4063-afd6-cb5288cd1865\",\n  \"bfcc584f-6d07-4068-b613-5bf46d14f6cc\",\n  \"b237b05f-07a3-4a81-9158-defbdf7656b2\",\n  \"0c2b26a7-dffe-4f75-beef-e00c6a030f75\",\n  \"80c747ee-5466-4bb3-a815-ad07c5896178\",\n  \"5e370291-531e-4793-b0f6-4955ba0859a1\",\n  \"6b96fea5-49d4-45d9-bfb8-9c45e66465fb\",\n  \"f296bf22-9ae7-4a14-b9f6-3ce38b681e59\",\n  \"ebe445c6-64d1-48b7-88dc-f8c4cd8e075b\",\n  \"6c95c5d4-4a21-4cef-a8ab-2aab72dd9ea9\",\n  \"944f6b8b-f539-4a34-b707-68cf6790e874\",\n  \"87380e0b-ff06-450b-900d-69a674b3204e\",\n  \"58433c5c-84cd-4250-bc2b-8f812a22cba0\",\n  \"5dfb6ca9-ef56-4b82-9683-789fb9839444\",\n  \"1e76bb95-c168-412e-8223-35aab5d3c870\",\n  \"8eb903f8-bc5b-4e32-a933-743aec0269c8\",\n  \"183f46d4-4da4-4502-834c-01c5edd14b00\",\n  \"92ec0c2a-de72-412c-b377-c2a7ed79691c\",\n  \"38bacee9-5460-42ed-b093-df8a7ee9f286\",\n  \"3d390026-0846-4017-8e26-3bca15c8fb77\",\n  \"a76f67c8-9e55-45b5-ae18-f884ac6d01af\",\n  \"99941a28-e9ae-4f52-9420-58dbab841af7\",\n  \"577c7890-c2e5-48f0-bc36-21c1c7690ae9\",\n  \"e5929cf0-4432-4fcc-a7da-30b1f8554467\",\n  \"b452fc10-dcc3-4ec9-b881-ac20e2099050\",\n  \"48d07f8a-5bf4-439b-8e9b-ff38d4d85bd2\",\n  \"ce3faa7b-3080-4f6c-ba0e-ac50edff6f07\",\n  \"ca0ea708-658b-46e5-9f73-0bf99cc3cdbf\",\n  \"eb959b97-744f-4f97-99d6-ab04c8472797\",\n  \"71098e75-b046-44d5-8c7c-6ffb4b6dc732\",\n  \"6f61a583-4187-4a59-8e9c-95ed7dcf2453\",\n  \"ae5ac56d-268d-4bdd-928f-26e84eb39667\",\n  \"cda77e2f-6f1a-4dc0-8966-ac9dad941af5\",\n  \"d6651a77-5636-41d9-a73c-f2fab5103408\",\n  \"802b9008-f6af-4dda-a93c-72b56b994a91\",\n  \"1371eae2-3ec5-4481-b68e-8f626aaa248f\",\n  \"3c18ed56-4cff-4d12-bf99-e9955df306a5\",\n  \"adeccfc4-5fa6-41f8-8b92-9524c62554f7\",\n  \"1dcdbfda-508b-4353-9d73-0358c8b3f5d4\",\n  \"cd44d94f-fd05-4992-9982-5eb8b7d5acde\",\n  \"455bb157-278b-4270-a0be-fae96fe2d40c\",\n  \"2ff6030a-86e3-4a02-9828-c9cf666da2ef\",\n  \"b3691318-cc7f-4c45-9c32-b5b4be06064f\",\n  \"1a3256a7-b308-43fa-b817-4bc47fac3dba\",\n  \"03b547c9-5159-4021-b277-9a9a2afc74b3\",\n  \"7752ebb5-4517-4250-b641-acd16b0837f8\",\n  \"8ca4e7bc-b6b0-42f2-a9f4-07783a7e3d3a\",\n  \"f34d181f-a155-47d4-8a11-20c9e32bb0c7\",\n  \"176e43c6-55fe-4469-9ed7-9a249f871abe\",\n  \"b43f7ccd-cd77-4054-a130-0299e3b11a23\",\n  \"9a895d67-7d30-496e-9c83-23befd305d98\",\n  \"0d3e699e-2aae-49f4-bd68-835af86e7544\",\n  \"c9b00c5a-7586-4a70-be38-ac64f9f5ee0a\",\n  \"0b625e5a-e922-4017-a70b-ed023fff09fd\",\n  \"53dc5df1-2a7c-4945-96f9-cf4865bfea30\",\n  \"23864560-39b0-4eea-b2ca-3634ce181626\",\n  \"b23c97a1-0761-4e1a-a55c-e078eadac5e0\",\n  \"5f9348dc-0ada-4ea4-9288-30025928a615\",\n  \"5b945202-1031-4078-ad64-0f43b3d21500\",\n  \"5bf4c324-a372-4116-8e87-d66fbe23458e\",\n  \"b3ed9e4a-3dfe-46b1-b123-377448911514\",\n  \"dc9d6673-db8e-4861-a21c-d655552cf8fa\",\n  \"aa65cfb8-2503-4b36-ba2d-c1699baf8ad2\",\n  \"ffd1bf85-0a82-47f4-a75e-37493eb7769a\",\n  \"3854e730-dc24-4562-8c5e-d926232f5cea\",\n  \"d9ac44e6-d21a-41c9-8bf6-eb245c23212f\",\n  \"7d6a1d86-d9f8-4c8f-bbec-2c809f70e181\",\n  \"35701b17-a1da-4f34-acdd-8002f80f5a29\",\n  \"4fa14c50-6dca-4994-887f-9d15142e180d\",\n  \"41a0d2fc-80ac-4958-87d9-d65cccde1d49\",\n  \"55fffd92-25b9-4820-8f8f-8140487af83a\",\n  \"140513f4-68ac-4d93-b2e9-517765e09f46\",\n  \"f98a5b99-7dd5-40b7-a579-a81658fd17f4\",\n  \"50667c39-405b-4074-9f7f-930ef9ac4ff1\",\n  \"395824e8-b7e0-44c0-a9df-75781c7bddfc\",\n  \"80b9543a-7f72-49d5-868e-25c616b6fd9f\",\n  \"7f4ef000-dee1-474b-b0a5-765a8dcafb35\",\n  \"c96b3707-7537-4986-8d9e-4021f90800d6\",\n  \"05150508-789e-4000-914f-3dea0bbf5482\",\n  \"95bd480e-7883-4eef-aa32-3aa44132cfc6\",\n  \"a4218ca9-a4cf-4ad8-b4bf-63eaf3b87f9d\",\n  \"8c157d46-752f-4e76-8eb2-fb25ab70988c\",\n  \"0dd0018d-a9e2-4e11-878c-817291531226\",\n  \"3e4504ef-204f-41f6-a98f-466f00bb5167\",\n  \"e2ef50a6-3e5b-469d-ae67-94d155549a8d\",\n  \"2b921758-0c14-4aa1-99b7-e7d10129d297\",\n  \"0a6a5797-3dbc-4bbf-9d4c-f76eaf85cd71\",\n  \"44e98cd1-abc5-4034-8b8a-9293b1646832\",\n  \"7a840a58-df1c-4af2-9758-cc1251e94371\",\n  \"806ae71d-5a1d-4285-849a-145fde359b90\",\n  \"ded36f7c-b429-4f2a-b3d4-24690609f4bb\",\n  \"693f3342-474d-48cc-abe5-2acc881466b9\",\n  \"303f1e30-c786-445f-8933-306850323e46\",\n  \"06aa3100-d21e-40a6-b521-19e57c092212\",\n  \"415c9c3b-cf4f-4c31-a49b-a923deaa7248\",\n  \"9858ba75-0223-4e27-9962-3bf2ddaf9e92\",\n  \"fb768445-2c85-49a4-bf27-ba47a6856d2f\",\n  \"aeea6bac-8a5c-4366-9d1e-9f31852d816e\",\n  \"1e4c7123-97d7-4344-85a1-8331e8002868\",\n  \"486c9baf-e3d6-4547-bcbf-3383abda78f5\",\n  \"8a1add0e-6701-4270-83b1-16d5fe3af76b\",\n  \"5de3ea36-2b84-4d42-9e36-85ff044c31da\",\n  \"61b20ffd-1803-43d1-8f52-da1ada5a5d32\",\n  \"ad303aad-db90-425d-8c9c-2420b23a3345\",\n  \"26ef0462-8f73-40c4-99e7-458ceff6b3fe\",\n  \"5875a71e-e4ac-46d3-9af7-1dd03b6bb81c\",\n  \"22b82242-efc8-41f3-850e-0e05e6c0755f\",\n  \"788c047c-2456-4317-a3fd-c6fa79249b15\",\n  \"274d2388-266f-4120-9f79-03ea2e60c8fc\",\n  \"c553a086-fc3f-494e-a048-e1914f0fbf10\",\n  \"d41a2452-dc32-4b41-8660-4d457590b2bf\",\n  \"17dff76d-4d17-494b-a71c-10f800e7c11d\",\n  \"b8f9379f-2c13-44c1-a169-ae169f853182\",\n  \"da77ccb8-6854-4205-8112-4b9bd6d0e6e3\",\n  \"1be8a9a8-4817-427c-87c3-a7837dfa4e16\",\n  \"bf2d3cbf-1816-4afe-9005-5d1e90e16180\",\n  \"0a25911a-c18b-4efa-bffa-d765b3b0ee33\",\n  \"064f3d1d-4a60-486e-bff4-35691f642686\",\n  \"8996c0d4-276f-4494-96c9-e350afb3ed04\",\n  \"bca6612e-2387-442b-95f3-67c9955fa2ff\",\n  \"b954e8ca-6a18-4459-bd65-c23ccf64aefc\",\n  \"49dd5fd9-0547-4a7e-89ab-f7faf253fdd8\",\n  \"00d97821-e2cf-4516-8998-4e1cc474e7c5\",\n  \"1fa834f2-afc8-4bc1-bc7b-03bf91424705\",\n  \"73e9f50d-ba08-48a1-a9e9-b8d5ce0a1c93\",\n  \"0a47435f-4402-4f3b-a6f7-fc42d85a42b6\",\n  \"67d2cf4f-2d74-4b2f-acff-a66288efef3a\",\n  \"eef86d30-2b90-4eee-9e43-61de42e14eec\",\n  \"41424ee9-e6f0-49a0-87ee-9985a9e70bb8\",\n  \"416fa35c-d094-4305-98a9-1c0c8bcf6314\",\n  \"d6e9158c-bd22-4205-b453-8cff74decf16\",\n  \"cbffb23f-3c9d-4237-a871-7cb8759e44c2\",\n  \"39fdb905-1627-43d5-957f-4ed34d0f61eb\",\n  \"b9bb59cf-c362-4f4c-b134-69fc408fd572\",\n  \"62fe8fa5-80e0-4db0-936a-d3a005f82b3f\",\n  \"55a4ece9-e04a-41ee-a3d3-6f6053188b7f\",\n  \"ad5bf594-5393-4df5-a683-17112be043bf\",\n  \"b6576342-e90b-4ed7-a18a-80daec6c082f\",\n  \"50810385-6ec5-4546-a90c-49d0195c1ab0\",\n  \"bd2051ea-40d5-485d-b060-6d178560ec7d\",\n  \"da00bd5b-380e-4819-9c1d-ba4a2868dfa1\",\n  \"ec7572f1-1e53-4fba-9153-0adb343ac051\",\n  \"da98ee4c-bc6e-4bf1-9cd2-0a43ca51c57f\",\n  \"45a2c865-af2f-4d69-9a16-7c809c6cbc23\",\n  \"c933ed2e-4174-4282-a12d-f7ab60f30971\",\n  \"18b29bc1-ddf2-46c8-ada4-f8f4255ccabb\",\n  \"10478466-b6a5-42e6-a443-df7bf92a280f\",\n  \"47d4d49b-a5eb-4bc8-876a-81826a942ea4\",\n  \"0e58dbb7-9aa1-4cdf-840b-7df4ae3f4c9b\",\n  \"08b886a8-8095-404e-a439-94f2768727f0\",\n  \"4c554a8e-5451-43dd-9e51-7c92b8d113c3\",\n  \"b4a45ae1-fbfe-459e-aa58-d744b0fc1a80\",\n  \"dafd9f7d-4b69-4c90-817a-6160bf51892a\",\n  \"cef687aa-5df5-4ca6-a112-6a5128e5b0bf\",\n  \"b406bafb-f2bd-4288-9492-3c46128dbe6c\",\n  \"b49cf9ad-6c52-43c6-85d0-114d6fd911f7\",\n  \"7b536bc2-790a-41ef-8f80-85046b031ec2\",\n  \"de966b5a-23ed-4e2d-bbaf-05d73c3e33f8\",\n  \"48973120-61da-43a7-8a5e-9445044a2bc6\",\n  \"8f9c68c9-5208-451a-9416-5eb253100fac\",\n  \"7539eeae-945c-464b-86d6-9f6913784a18\",\n  \"733d2dff-72bf-41c3-8e35-a4baea4ac181\",\n  \"4b8fb9a4-43b7-49e1-97fa-dcb7fdf3b687\",\n  \"b6fb5ff6-09b9-49cc-9526-66d953a8cd3f\",\n  \"cd1c3f32-5a39-40f1-9457-41e87c02e651\",\n  \"33da3adc-14d9-4dd4-9087-ad473dc24e1d\",\n  \"71275179-f5dc-4b0b-8611-5c12343f9a15\",\n  \"1be7cbee-1c1b-4008-8f35-9a490f9592aa\",\n  \"8ca9fb97-df26-42b2-8232-896d7cfabb4b\",\n  \"bd2779be-9ebd-46bc-93c9-6e084f275de5\",\n  \"6e4aab93-d216-478c-9348-bdc68d430c7d\",\n  \"18d273fe-c17e-4490-872b-4d1f29d4c9d0\",\n  \"e585c419-eafd-46aa-ae22-eeda53dd8ab1\",\n  \"7d51f341-8338-4c10-85a5-5cd6fe12c56a\",\n  \"af022c68-24a3-46d7-a274-78327034a33b\",\n  \"d2472fd5-17df-43ac-ae63-69b6f3b03be3\",\n  \"39df9b7d-2d9b-4314-9435-ca2c3c0e9546\",\n  \"cc254979-ef2b-45ed-b50a-4fcd3b6b1efe\",\n  \"bd280ee2-13b4-4d75-ba0a-d9a3715b19f2\",\n  \"addcd364-7e45-494b-9af7-360ac8a42067\",\n  \"c2f0a349-e309-46ea-93f1-5a6ac4c8d2cd\",\n  \"db5d9d78-8a9e-41d7-9975-970a252a690d\",\n  \"cabbb66d-e7d3-4840-8eb7-580056917d33\",\n  \"aa1785fb-10a8-4a1d-acb0-f52a9bcb220f\",\n  \"3e1e00ed-f687-45ab-8960-cf95e742234e\",\n  \"05b4590c-25f3-43b6-b6dd-3c40e68aa872\",\n  \"6416e886-5633-4f58-af74-f82a8e40aeb4\",\n  \"6f7235fb-9030-463d-a398-a5118c539d91\",\n  \"457bd2f2-9c3b-488f-89cf-fd9288208fd1\",\n  \"d39350c9-11ee-4936-9bb1-ec597772d578\",\n  \"dc130880-5632-4597-9c91-db4b90fb4123\",\n  \"91f9d51a-fe77-461c-ad53-85b0b1504cf5\",\n  \"2891af75-e843-42dc-ba44-cd6d5b830032\",\n  \"3e84c126-118b-4a90-a43c-e226d16a63b0\",\n  \"d202db2b-24d3-43a3-9b3c-d45fe02a0ca9\",\n  \"0bfeb596-69e7-4341-9624-f69a83bae51a\",\n  \"cfbc9803-7870-4767-9329-df0ca96d377d\",\n  \"f6ef1f6f-0678-45a1-bf62-b06bbb30e775\",\n  \"02c37e51-2536-418f-bfb8-c0cc2b5f6706\",\n  \"2cf15a38-a4f1-442e-8bba-65307102c114\",\n  \"6a6fe218-d181-4f6a-bc7b-847e126dd3e8\",\n  \"83def0a1-2e0a-4e84-bdda-55d1254ab0f8\",\n  \"42d68baf-cc42-4685-98bc-b5b573952e50\",\n  \"777ed020-6c50-4ab6-9a4f-c7904b1d88d5\",\n  \"cd90e2bd-d3df-4faa-8859-0d7a1c8f0f12\",\n  \"67b9549c-a522-43da-80a8-4bf99e70fd32\",\n  \"2a559cff-89d8-4cbd-b8af-fa2e63a29c09\",\n  \"a62fa0f3-2b2a-4567-b110-6e50b1df20c9\",\n  \"fee7d7ad-28b4-4c88-afa5-fc615820963f\",\n  \"a5441077-c056-4e4b-9402-b775431b242c\",\n  \"bc081f5d-3733-4c48-8b95-b7f47c6328ac\",\n  \"ba7f3d9d-4fa1-45e6-bb12-2ab5b6d41000\",\n  \"0de09c14-fab9-487f-ad9e-6895956beec9\",\n  \"9bb42a29-5f41-42af-8a94-52810c1bda10\",\n  \"722347dd-835d-43d0-9123-0ea8d21b2fb6\",\n  \"0259e565-395d-4398-b843-09775108d488\",\n  \"0d953cdc-b177-40c5-952f-30507f17b57b\",\n  \"3e881f89-aeab-4a4f-9051-2d347c431aca\",\n  \"f19be77a-edb1-44a5-8d6f-bdf34f005d5c\",\n  \"b90fbe88-7d8f-461f-825e-4dab585a47de\",\n  \"2d6b36be-2a9b-4411-95a2-189896acf433\",\n  \"5953e7ef-d771-4da2-ab31-4a9f6ee7167a\",\n  \"038bcf72-4065-4098-ad81-3b07a1ee9804\",\n  \"063294d4-c8a4-4b8d-a1fa-f5e89b34a16f\",\n  \"05db8a94-8f73-450f-81de-647172b6b513\",\n  \"f6e18d74-2ada-4860-90d7-26e9d80e28e2\",\n  \"68aedaa5-9fb8-4648-9abc-a6f8cf2b5047\",\n  \"d904360a-a70a-498d-af20-353e9fa646d4\",\n  \"1f29738d-4f7a-4d58-a6e3-d8dee0910b65\",\n  \"fa288c65-0e50-4050-b085-383bf82bd607\",\n  \"cbd884ce-5f2f-4567-9298-ea4f95fbc5cb\",\n  \"f3bef807-670e-49c1-8b69-c9db2c13bdc4\",\n  \"fa9d7568-953c-42fa-90a8-ec0e0bef4595\",\n  \"11e4f6c4-5c3b-4f86-a52b-44eaf245e95d\",\n  \"fdd8811e-81a2-4013-9e1a-8a0906d1d68b\",\n  \"8b50cc41-2723-4311-8cf3-68c0a304fadd\",\n  \"305c1f45-417a-45f0-b187-08abb8974bad\",\n  \"5ba75f3f-e71b-4978-9949-6a012d22a1ac\",\n  \"7669a7b2-0826-490b-a30f-d434ff43dcbb\",\n  \"b4c56bfa-64a2-4351-8b18-02bd8ead6920\",\n  \"144f1de4-17df-4c5c-8633-a6002fa3f486\",\n  \"4acd0930-32c2-403b-917f-050f37a5c04c\",\n  \"d6bf044b-031c-4418-8ab5-b840d1f16d5f\",\n  \"8542c525-5998-4539-ab59-dade3e778fba\",\n  \"8d19dfed-5fca-420e-b427-801bf1a51bd5\",\n  \"64c864e5-8f80-44f2-af81-a730e568c6de\",\n  \"aaee1f0e-8ea8-4a33-9efc-57b93c00c6b3\",\n  \"bfc1727d-a639-43c3-8fd0-6514a7b79cef\",\n  \"c2c70097-7247-4ae2-8828-a59eba379cf1\",\n  \"550a9f00-e455-4de1-b810-4b49534d0097\",\n  \"9ced8b47-64dc-484c-b461-20ee252b0041\",\n  \"f707ad25-8a63-4037-a6b8-5075362673bd\",\n  \"65b203df-9b4c-4d00-9e2a-208848f80d03\",\n  \"72e14fac-37cf-4de3-b3d9-e2f535131c3f\",\n  \"bf4958fd-de52-48e4-8c73-4deadb521194\",\n  \"d08ec8ba-92c8-4ae8-96fa-7cded6336ff5\",\n  \"d84d9579-70f3-4636-b35e-81f2a1b0ece1\",\n  \"b57de5c9-fb55-4c8f-9ad1-064f9104fb8c\",\n  \"e5abff6d-12bf-42f3-9e2b-c160bd09ef80\",\n  \"738e4284-6178-4a18-8cf0-8101226de615\",\n  \"1f1f9e6f-251f-4acf-9a73-7666f882cd88\",\n  \"da1f6250-7852-4dd2-953c-e699b1c3d862\",\n  \"a6de9205-5466-4338-8dbf-cfe1e2068681\",\n  \"e3054986-1d33-4d03-a072-f193c8f6a579\",\n  \"fc840cf0-e291-4892-bcee-08e68d33d858\",\n  \"a4cc0d42-1677-4bec-9a3a-39a314941404\",\n  \"e5b24b13-b85a-4e9b-9d5a-5a5517695106\",\n  \"233aec07-2b19-49a5-85df-b2252289b814\",\n  \"289367d5-f046-44db-9d7b-a113781941f2\",\n  \"0d90a347-bdcb-4606-89b9-ceb20d37be32\",\n  \"6af0c989-5b5b-4a2a-9de1-00d69614d875\",\n  \"c771650b-65ba-454f-bd5e-58442b359291\",\n  \"1852549c-ecf4-400b-bc83-95361feb9ef5\",\n  \"d85e1245-0e77-4163-8d66-f011f8a75baa\",\n  \"e74a6155-cecc-4967-ad25-77c2b9f90d42\",\n  \"7c4d02b0-df7f-48c9-8e5e-a56a0dbbe375\",\n  \"36d5586d-217d-4d18-8cf7-440e4a6cc3b0\",\n  \"e39d8fa6-c10f-42c6-a86d-908b7d500856\",\n  \"25d873be-c760-4bec-bc36-bee953841699\",\n  \"465bbcec-85b7-4254-84c1-05683d86989a\",\n  \"7ca82faa-9fc1-49c4-9065-d938edf0bdc1\",\n  \"12777b90-2932-4265-974e-3fe9bb83602d\",\n  \"cf3e402c-8419-4b65-abc9-fc77be2821bd\",\n  \"0654f65c-90d6-4977-871e-48544b153eff\",\n  \"413ba511-6aa9-468f-8a00-adcad17454c9\",\n  \"b3b8a221-0a5f-46d0-8a80-45d27abf99e7\",\n  \"b3c3d1bc-c395-44a4-9b20-c4fb5ca468b1\",\n  \"7a1eade8-1781-42c7-84c8-7939d5f50d17\",\n  \"762f16f7-83c0-4f7b-a92f-80b16ac2c457\",\n  \"4ecfa531-d9ba-4b3c-b24f-eeb41bc3a9d9\",\n  \"b37a3563-8446-4b04-b757-5c399178a9a2\",\n  \"2e41a065-9736-411b-9255-39bb8fe03b64\",\n  \"f21c2d1d-92a9-408d-ad9b-76bbaceecb28\",\n  \"76c483d8-2faf-4e59-905b-f5135d2cca0e\",\n  \"cb3b7ed3-93f3-4398-8436-a3efad9b5644\",\n  \"d0dd5b73-4c5e-4872-baf5-864a0492f984\",\n  \"cbdda8fe-c1d3-4768-91ba-2cd8abcd25cf\",\n  \"747e0bbf-f060-482e-88ea-abd8fa388286\",\n  \"97ece05b-354d-4e97-aeab-550a700e7b31\",\n  \"486f7a58-4af7-438d-8937-5ba77b91646d\",\n  \"efaec765-a208-461d-a2bd-6e7fe26e1886\",\n  \"cc3775e3-13b7-4831-9016-664b3a8b72ab\",\n  \"9c07e523-3562-4ab2-a71c-cae841d0d2c1\",\n  \"4f386899-2d18-4bae-9fd7-8613b9eb2000\",\n  \"2c080e6a-c691-41dd-bcc1-0e1d6cbf0f88\",\n  \"ad2cc930-24e8-465f-bdc0-e30dfc1dcde2\",\n  \"e582bf53-e9a6-4bd9-8054-a03edd007a71\",\n  \"5ef3f5a5-a46e-42e2-9226-1efd00024dc9\",\n  \"853622ff-fd04-46f3-829b-560788041c9d\",\n  \"be6e12f5-ef4d-4950-8a90-92813b22bbc0\",\n  \"39ad912f-fff3-4d15-864c-44dc16f66f8f\",\n  \"fc39c712-2d22-4e4a-bebe-8c13d9b0c637\",\n  \"e496b23d-bb12-46ca-aee9-84bb4dc4e246\",\n  \"668196d8-1789-40b4-a470-d661e38e5f75\",\n  \"e0b06d65-f5d9-4158-b5b6-f14010c02d2f\",\n  \"203395ad-24e5-435d-b77f-063d6524ca6f\",\n  \"087c71b6-a2e2-49d4-af66-96c7ad3ad10e\",\n  \"f779ef4d-acb4-4b10-bfc4-eee7cf7fddae\",\n  \"c61bc39b-2916-4fa3-8d48-2daf76959977\",\n  \"2843a514-02b0-4d49-ada9-78fb5bde8439\",\n  \"9b05cf72-4a29-48dc-9079-8408616c2e49\",\n  \"904143f3-4ccf-4e82-8c50-1aa823074270\",\n  \"d40f32e5-246a-4667-9ea9-0ee040ed5303\",\n  \"d1401c48-1663-4496-af3c-90c0e9c114fd\",\n  \"0fcb13e7-caad-4d7d-b61d-3c5e03763cc6\",\n  \"4f7f2765-1c69-4353-8821-d0f891a06167\",\n  \"a6b0793a-fbd0-4ddb-992f-b29aec6eb0b8\",\n  \"045daa10-1b88-4591-9493-c03963cd1027\",\n  \"b98f0e46-557d-4f49-b679-b1260c03751c\",\n  \"baac843b-bce8-4cd8-9671-06f324cb5d82\",\n  \"01f25e27-decc-4bcf-9e0f-11705911710f\",\n  \"065f65e0-db76-4cf3-8b2c-5c95f2dda7b4\",\n  \"82b2d226-18a3-4268-b3d4-368d3966fad5\",\n  \"ecbe91eb-bb84-483e-aca3-9dd045df161e\",\n  \"6f465e22-1298-418b-94d2-ed3f4d9fc114\",\n  \"45346dd6-876d-4dbc-9b29-ddde4bacd064\",\n  \"a893331a-1ce7-451e-b3b2-6c707bf36950\",\n  \"53632c7e-c7c1-44e4-bab2-8e95fa2427b7\",\n  \"8cae7776-4826-4fc4-9f81-712ad46b4ccd\",\n  \"a52e9a31-c921-40a1-b2b7-559953204b7d\",\n  \"57955b05-9162-40c3-bf90-79cac6117ea1\",\n  \"965b03b4-47c5-4239-ac06-fbe566b64ab9\",\n  \"522275b1-ba10-4f17-a352-e0ba1cb1b5ef\",\n  \"0574a115-fcec-4df1-b7bd-3fb2f9be8483\",\n  \"1e976193-9383-4396-ba76-8e2d2ecbaa87\",\n  \"ef1274e4-71c6-4e43-b936-b22bfbddb1b5\",\n  \"97697381-7a28-434a-ab5a-a886d46f5a54\",\n  \"c65cf3f6-dd7b-4b53-8bff-f0bfaf2ec173\",\n  \"e35713fd-fc11-45f6-babd-8560ab46a47b\",\n  \"e08433ec-a39a-4964-888f-94f79f4d4fce\",\n  \"40954e0b-791a-475f-8acf-19604cf754c2\",\n  \"4ab285a4-0e07-4989-af89-ebbd3509534b\",\n  \"ba57e71b-a9f0-4afa-a6c9-391bc66a596c\",\n  \"d9b7eaf7-29b0-4e6a-9db9-8da95a25aec3\",\n  \"34488ffd-bd5d-4998-989e-74b96a0ccaab\",\n  \"afd449d4-cd62-4104-b22d-b543a69a302e\",\n  \"2b4e7335-4031-4e53-a14d-7250fc6ed49f\",\n  \"2a52e1c8-a37b-4cd5-a9ce-40efe6b5e9eb\",\n  \"d4bece25-dffa-41d0-b39e-21a1abaedacc\",\n  \"bf849774-ba04-49cc-b45f-8e7d7377d718\",\n  \"b529b449-e0ef-45e6-8c72-220329a10008\",\n  \"4839e877-46ac-41a4-a932-3582bb457e47\",\n  \"2a7f7b3f-334e-4e97-bcb9-79d8f223d353\",\n  \"038a4fba-4e5d-40fb-a2bb-92305541972b\",\n  \"8b0d6ec1-9df1-4b8f-b19c-16a38e39e4c0\",\n  \"3f0c617c-ebc1-4a29-a328-e02bc76bed1a\",\n  \"3fdccc3d-dbfe-4370-9e4b-a6367d998dcf\",\n  \"6a684683-1fd0-418f-b4c3-23006addde1e\",\n  \"033976e2-a119-4352-b35e-c58dcf7012c7\",\n  \"88fbc210-5114-4c7f-b136-1ff0d91d07b1\",\n  \"8cbcc9ea-57be-4670-95c0-628158d22979\",\n  \"83b7a173-76be-418c-b3fc-bb2dfda15d21\",\n  \"7e48c135-4f39-4b0c-80c0-413492cca274\",\n  \"491590bc-263b-4bd2-be80-612a2e4fcdec\",\n  \"8d863a75-1100-440f-83c6-bce724513d9f\",\n  \"6390ff3a-7c89-4105-b972-f58565272515\",\n  \"14bb9caf-11d1-4ee1-82f7-5a44f1efefe4\",\n  \"31a79535-8aec-4761-939b-52af04d07598\",\n  \"867e6a6b-dd0b-43fc-8fc7-9d564478c71e\",\n  \"1aab4c8a-62bc-423c-bb62-9ab3d0caf8f7\",\n  \"60c09782-0e9c-4345-af4e-ad28534235bc\",\n  \"799738ab-597e-4282-b9ce-09a595b73b1b\",\n  \"e114dc59-f948-4a68-a63f-5f78500f5358\",\n  \"747df04f-9109-489e-8958-b38d6c8d36ea\",\n  \"00481266-e8a1-41b0-bab3-67343f838fce\",\n  \"97c75e5d-0f76-4576-a539-19f60e1e43c5\",\n  \"cfbbcc1c-2817-41d0-bd7c-ccbda2adece6\",\n  \"0195ecbe-5c48-44c4-baca-1a01ef6163a8\",\n  \"506c0906-89fd-4a34-ba3a-aa5f6b4f5bf2\",\n  \"cd4ac99d-83df-4f1c-b5ff-66345a4c6de9\",\n  \"6702774f-7ead-4b9c-9035-ffe2c25726b2\",\n  \"f451265f-d7d6-49f2-b4e6-924833289872\",\n  \"a05116eb-7e3b-456a-8b20-4d28a1966b64\",\n  \"bf96c2a3-58cb-4bd1-960a-0f4947e90f47\",\n  \"6ed29fc2-93e8-40f7-a16a-052f6d8909b2\",\n  \"5daa3e32-6717-4ee9-ae50-1bafaa76dfe9\",\n  \"e3825d9c-37a6-4c9e-8c38-0e28a18ae8f1\",\n  \"563ac82c-ca53-41e9-a6d5-0976328ae8c5\",\n  \"efe43dc0-865b-4dd9-8e57-44451412107a\",\n  \"90fc644b-e48c-4160-8cf8-0dc78809e4bf\",\n  \"5cf8d924-d374-4ea2-8fd7-044bd38ffc4a\",\n  \"7d6f126d-e146-4d05-a67e-71a5bb7165aa\",\n  \"c34fa136-9cfc-4fc6-910d-5cccef6c231a\",\n  \"43e0e7b7-1f48-4762-9c98-80d23c5e7d54\",\n  \"b1d48d71-a655-446e-9e03-8d9cd22af909\",\n  \"4aacbd0c-0a4c-4998-a01b-c0e3bc0365e9\",\n  \"c8cd8fee-8006-469d-ad54-6fa94c10b9d3\",\n  \"792a7b0e-2abe-40bb-b04c-60f05785f6f9\",\n  \"556cb4af-a7d9-4227-ade7-543ab9ea7c5b\",\n  \"2b0040ff-2852-4050-a73c-f09de78a9903\",\n  \"a442d6b4-2117-4fcf-abf0-56b3d5d8a7b1\",\n  \"7636e4b6-0791-42cc-ae0e-0c13b9ff8f59\",\n  \"195ca53d-b147-42cb-9131-8e65b0e3f2c6\",\n  \"9829a855-ac1f-48e2-90c6-563e7e82cd70\",\n  \"796c7a1e-c24d-4664-853c-cb98d2819312\",\n  \"22de58fe-7fa8-4d0b-b697-62e27fc25a0d\",\n  \"7d5c170f-684c-4379-adf3-71c308564e8a\",\n  \"ed928be9-aada-4bc3-8093-607d82f75a2c\",\n  \"d5b57104-1950-400f-b5bc-8eb54ba3f2da\",\n  \"ed9f994f-d349-4678-b22f-7913b6c0f4a4\",\n  \"8e28b597-8503-4b0f-926b-01a71a08e5ec\",\n  \"f8928c30-e726-45b6-9d7e-cde9fad4994f\",\n  \"5ea199d2-71d2-47b1-926e-425bb76b7ff2\",\n  \"cc5d909f-f4d7-4135-89f1-95e6eb53240a\",\n  \"d7cbf3ef-8fae-472e-a31d-717133cf5ecb\",\n  \"433a58a7-1d7e-4269-a8db-c14b90992098\",\n  \"9fb39eec-8d5f-403b-b3a2-a8ca8223db1b\",\n  \"1ac973ca-9089-4472-be50-0df941c3a193\",\n  \"4dbe0b93-1373-4fcd-a49a-957291f0212a\",\n  \"270a4485-e7f6-4f51-8e31-aeceb92a3338\",\n  \"162402b7-e5a0-4246-b81e-e45be30682d6\",\n  \"7cc7bfa5-49f0-402d-8e18-fc337807a480\",\n  \"4f712c46-9544-421b-9680-86cf1b101144\",\n  \"12f9226a-bf61-4e1c-8020-24aec513ebb5\",\n  \"41ef8c2c-ab2d-4a9d-9d26-559b40f75f29\",\n  \"8d449be0-c3b4-4089-8390-8cc26a45f7e2\",\n  \"29a17202-04f6-4ddf-afdf-d3086f26fa31\",\n  \"557c55d2-eaa5-4b6f-a9d5-66ab8349c393\",\n  \"c744e125-3466-48dc-99ce-77e7b6c29254\",\n  \"e62bb1d5-00d3-4423-abe3-585661b8cc2b\",\n  \"027ecd7f-4750-4043-8467-d2f611f02876\",\n  \"005dc709-afcc-4947-9438-ca4c0ec5a415\",\n  \"e0100169-76fa-4f4e-982c-594599058f47\",\n  \"887c0bfc-33e4-4d5a-a47e-02ded7b4e748\",\n  \"7cd5b222-ac13-4ba3-ad87-6c9f6ce3c89e\",\n  \"94b67f3b-5548-48d2-9ac3-a60940aa2fa6\",\n  \"fad9281b-5c5e-4673-b9be-fa457afc897c\",\n  \"3b2c68ed-6099-4468-84e8-f11194bf1e7e\",\n  \"31658c5d-300c-4300-b5a2-45b1da79bf48\",\n  \"c052c05f-949c-4a54-86c5-c64e932f6469\",\n  \"99eca143-0ecd-4fb5-abc4-0247b4e22441\",\n  \"b322ef78-8e74-438e-bb74-26ccc0cf45c2\",\n  \"cb2849a0-96b5-4778-b2ed-9c95bf4601dd\",\n  \"e949003a-4dff-4768-83f5-a4ca16ec588a\",\n  \"73af3c57-7f3f-4009-9bf3-d13fbc5e572f\",\n  \"83279cbc-b817-4e3b-8649-c33443cc35bd\",\n  \"79cfa9fc-4280-414a-aae7-d2cfa039bd37\",\n  \"c1ae99d3-cb30-4c11-bf4f-132cd03b4e0e\",\n  \"f23c3f7b-111d-4214-a4a1-7473608b0db7\",\n  \"d2ed0815-e1d8-4915-9bcc-81781e86ffac\",\n  \"f06b691b-e099-4a21-8d2a-0856b619b029\",\n  \"a796c7b1-d4d8-4ae4-b55c-eab78e3d2195\",\n  \"66a62ac8-b917-4c3d-883c-0e1abaf7eb5f\",\n  \"8a3f02ad-10bd-45ff-802b-3c7f4b8a56f7\",\n  \"acd8b60c-c685-4c26-bd7b-db5abac71c00\",\n  \"98e9c0e8-d5d7-4e3d-ae4e-e0b9ba6cd097\",\n  \"5cc19b8a-dcf2-4929-945b-94585f103078\",\n  \"3b0166dc-22a2-4e4e-a800-960a7f1610c8\",\n  \"d267fa39-2683-4493-94c5-af19c7be7b29\",\n  \"414e9a07-a5c4-4576-97f1-7bf50b7c7c84\",\n  \"b1a7f46a-35bc-4bb4-93df-10591f5d1c41\",\n  \"07f880f2-1b09-4ca3-81c8-ff432ac90bed\",\n  \"1287d532-feed-4982-b6e2-98817a87b255\",\n  \"433fa4ad-a0fe-4d63-a211-279d97a715b4\",\n  \"6524cfbe-a899-4339-a6f3-6d91d9c0128c\",\n  \"bc629aa9-083f-41e7-ac00-9906cc2e3c63\",\n  \"f82150d7-b488-4a50-bd43-757f0f3fca3d\",\n  \"7c8dad43-54c3-47f9-a8e1-ec46f1100cc5\",\n  \"b9d0640c-8403-4cfb-8089-6b5a189f1469\",\n  \"86459178-d597-4ae8-9d2e-818f374b5000\",\n  \"24dc7328-9c60-46b8-9e17-65186f0227f7\",\n  \"28d7eebf-6121-4e62-93ef-d6fd20fafa61\",\n  \"8545e73c-9897-4ee0-8555-f7010b8cd265\",\n  \"e424f547-17df-4112-9a83-d0de2490ff59\",\n  \"cfd62511-7a52-4cd2-9b34-3ceb20b5678c\",\n  \"665f6c86-1bb1-4bba-8352-cce9665047d8\",\n  \"31574579-8ad7-4be5-a96e-237231fe0eda\",\n  \"4a340a55-ece2-498d-8ff2-d64a0539c60e\",\n  \"b94599d6-a150-4229-93eb-fc07e4e8a53a\",\n  \"724f1f50-6039-464e-8341-bb99897fa50d\",\n  \"63ee0790-5e08-494f-b8aa-4566fe9f98e7\",\n  \"c2cd6dc3-1b49-43da-962f-a72d613a146f\",\n  \"6b728d30-4e81-4923-a5a6-f1f991e97f3c\",\n  \"cc37da12-3fff-408e-9fc2-5635f7b8e6f2\",\n  \"c30681ce-94f0-45cd-a488-72c532513c8c\",\n  \"3aa98880-1a4f-4f84-8ee3-0a01e723add2\",\n  \"a557b574-e507-49e7-8793-89f577c12c45\",\n  \"8f81d4d1-04c3-40f1-8cb9-114438742a8b\",\n  \"177438f8-22fb-43b3-ac81-3dc31a24fbd5\",\n  \"ef45a1c9-2621-4de1-9780-a37274b19ff3\",\n  \"5e791578-ebda-4da5-be36-ab9fec07edc5\",\n  \"322e5057-f869-47b7-bd91-2000e2b4e2e1\",\n  \"7eb27746-8256-4028-9775-57548d2a9695\",\n  \"6a0bf66d-7f07-4cc7-932b-6fb7fdd8c498\",\n  \"09066edb-35ae-4ecc-a468-03fb8b2fea7b\",\n  \"2e2ecc20-1382-4b62-9726-74c2c4a84cea\",\n  \"fd0e13c2-404c-468d-9dba-8b17715ebd7d\",\n  \"1d33604a-9c5c-43ff-8db9-7c3e3effb57e\",\n  \"b8209fe2-35c0-4994-9638-6c6fdaa5dea5\",\n  \"94081005-e503-469f-aac2-438ec1951239\",\n  \"60478840-6d92-411e-84b7-532d918c2782\",\n  \"0f05c28c-e4b6-4952-99e2-4bcb7425f557\",\n  \"e11e09f7-5837-4dab-800d-955193d3d599\",\n  \"b0885d16-ab58-4712-a3b2-966c3672548b\",\n  \"2ac5e445-928d-4f5f-b03f-f3fe7518e581\",\n  \"caae4893-21c2-4e30-9c31-d17286010271\",\n  \"e21bdcea-d4ed-469d-b751-48f66d8985f4\",\n  \"c37a8034-62b3-4508-a57f-43d326df876f\",\n  \"b1f1a0e3-f154-40bb-8aa6-3da516a6b474\",\n  \"19e9e548-4893-420b-a77a-0499a6a792d3\",\n  \"ad45a98d-dc34-40fd-b123-a6f1429c1bd5\",\n  \"3401cf0f-674e-4c8c-a69b-1fa2459e97d7\",\n  \"deabff4c-6df1-4695-906b-2ed391822b0d\",\n  \"e3e74eed-932a-4d21-8e8c-c61295f68170\",\n  \"6bb3cb21-c7a8-4d5b-83a6-9dc084e8515a\",\n  \"6c630a7b-fd53-44d4-866b-a3e1f3b0ef5a\",\n  \"683aa595-c282-4163-ac12-c96b7831e22c\",\n  \"413ae105-5e63-4478-acff-d34d8c69bc93\",\n  \"f4be2918-5d6b-4eeb-9177-64655459228a\",\n  \"cd13750e-07fe-4453-b89b-04147f731a0c\",\n  \"bbc02399-ee03-4133-80d7-81ca50632490\",\n  \"6d77fe5d-871f-4e6f-884b-8cf4bd8343b2\",\n  \"5964fde5-e017-4064-b5a2-ad5fd4aec665\",\n  \"251fc55c-b2cf-4eac-9e1d-dcb237ad4ac3\",\n  \"8f078c66-7bd9-476c-aef4-b6b326d68851\",\n  \"d4d38907-8333-4474-b94d-fc246fe10272\",\n  \"dc4bf455-53ec-4ab4-9a0f-834096c8b401\",\n  \"ffff475c-41f4-42ca-bc57-a19bfdd66e27\",\n  \"52afa330-c1c8-4820-bf81-7191237f8e5d\",\n  \"248fa036-4645-49d8-aa2e-050f790add8f\",\n  \"e0c60b5e-bebf-450f-939e-a0ca89fefcad\",\n  \"156b2373-bedf-4658-a2d5-f083ec6bc5aa\",\n  \"8c61318c-c09e-4726-b94b-3cc2fa4c6485\",\n  \"668d3254-9e08-479c-813a-af42a891a408\",\n  \"5b14dcc5-06b2-47e2-8ee2-651795d9b039\",\n  \"81be191f-28fc-44a3-a684-35d3ca5a5bac\",\n  \"83df11fe-7515-4221-94d4-f4c12921b0cd\",\n  \"13619473-3322-4a15-91db-5988cdaf2216\",\n  \"d6e85647-fc32-401a-abef-7e2f3d770beb\",\n  \"c6952baf-4f21-4d97-adac-f9a3de948140\",\n  \"b5f512bb-92d6-4ddf-a698-a825d85e79e6\",\n  \"f2500c03-3422-4813-91a5-f0a717ed1f14\",\n  \"cc917a39-fe39-40b2-85ad-ff83ce976010\",\n  \"bcfb9aa3-947c-4e0c-9fe7-149d8587eef4\",\n  \"150a8971-85f9-4635-acaf-70ee8adec550\",\n  \"5e70f194-2e68-464e-8aad-4170d5f9a6c2\",\n  \"b0c635c6-dc0c-4804-8587-b198f1306245\",\n  \"4c299fda-73e4-407f-be39-24b81748f678\",\n  \"9565abea-0dae-4a31-99fd-7d9dd27f3b6e\",\n  \"4019cf95-019a-4395-a239-803b31c63494\",\n  \"977e4d09-d2a8-46f5-a736-f9d3e69e4f3d\",\n  \"f6e97ef4-46df-4c2d-a6e0-b4097f385352\",\n  \"7ab8b974-1d85-4711-80a2-03e3f649a2dc\",\n  \"254fdc7d-f0a3-4fa2-8918-5b593d2351b6\",\n  \"71c7a540-85c7-465a-87be-0ae50120ad05\",\n  \"7132f2c5-48f7-45a1-a561-a89770cf4d46\",\n  \"c923ce78-50ea-43df-bea0-80327dd5f8d6\",\n  \"22de8192-d93f-4d89-ac97-e202bc9d91d5\",\n  \"5f4eb6fe-c6b6-4418-9f73-fbe3fd475c18\",\n  \"4803047f-12c4-431f-815e-bc30ced0864d\",\n  \"dcc73d85-7694-4ac1-b198-cd90dcc24ff8\",\n  \"0f423bbc-8dc2-4744-961a-e9ca1c46aca3\",\n  \"c8ac5fa2-c0da-46c4-b7ca-35731bd1d3fd\",\n  \"60aebd5a-a188-4f8b-948a-ff25eaa78175\",\n  \"84ae0028-1ff2-4807-b424-2648c2740747\",\n  \"3b12115b-5f0d-4b6b-ba9c-76628d8ae7ab\",\n  \"da72f64b-68ca-45a5-aa69-febd3d34dc59\",\n  \"e4429aa7-e64b-44f1-ae9f-15d07ee8be7a\",\n  \"9c47d1e3-d34d-463f-a381-6f6c8b5e005c\",\n  \"ed962026-3504-4a23-996b-9151fa243b1a\",\n  \"2ab60538-ae94-4923-936b-75a5d9ec3a00\",\n  \"dd4c04a4-746e-4dad-8624-8947355db64f\",\n  \"4bf4b0de-cee6-401b-a3c4-af5b26c15a33\",\n  \"da81093a-1762-4fdb-89b3-71279cffc392\",\n  \"95a80bf6-93ce-47ff-89f2-957930843e84\",\n  \"3aae2ae5-b439-4342-9325-cf206a2d978e\",\n  \"3657c252-a240-41d5-86f0-717b088e1415\",\n  \"618e48c7-b0ae-48bd-9eae-7b821d8c61d1\",\n  \"594b15aa-cc94-451e-b53b-a44071673a9f\",\n  \"481385ea-0c27-4bd6-948f-e86bab7499c8\",\n  \"2ee3eca0-4de3-4fa6-a4cb-46bcca2d135f\",\n  \"9d38927a-b4fd-4270-9c09-526d1f9f5f6c\",\n  \"796c3fbb-bb03-434b-abb0-8f83a75bc062\",\n  \"c5b660df-6b91-4175-996b-d54f3698fc5a\",\n  \"868665e0-7824-426a-9e09-f5b572880007\",\n  \"d5a8a841-8295-4cc5-8a27-4b86aa11fab5\",\n  \"c60d1100-9811-4c20-b7fc-f07810ea127e\",\n  \"00b53944-ff7b-417b-ac60-01b1150d2e5f\",\n  \"82572fc6-bfae-4f13-98e6-8f27bfbefcfd\",\n  \"99cd4e93-1992-4552-8f39-fde7ec6727fa\",\n  \"c6a01422-1951-4118-a2dd-aa228fbe6c34\",\n  \"9f7e0758-5cf0-46da-a020-6c7c5dd3798a\",\n  \"dcb721b4-c2e4-4ba8-881d-7dfb52c05cfa\",\n  \"152b8dd0-c93b-4b40-a976-1a1737bedb2a\",\n  \"51c51af4-6e04-4f99-8112-236d332b69bf\",\n  \"ad72fc57-f226-4ccf-a183-336e16bce6cc\",\n  \"14cdfb54-d1b4-4e93-9a72-e4af286553ea\",\n  \"8c44019d-8a57-4382-8cc6-b20c6aae2225\",\n  \"7794b347-073f-4e63-8e37-96bf56cb6988\",\n  \"5dceed61-9b28-47eb-9bb4-d630d91adf71\",\n  \"74b6aee4-ac93-4bb2-a966-51a1f2f333ac\",\n  \"d2fb801c-0f30-4c01-8272-ab08c4dd6906\",\n  \"78efa71d-f83f-46ba-95db-71a712e3337f\",\n  \"af7319ef-059b-4995-8488-70273e8c92e9\",\n  \"70d7eee1-ed24-4901-892a-ace44966313c\",\n  \"be8f2d92-535f-42d1-a5d0-2f938c713a9f\",\n  \"71cbfae6-5d97-4b84-99cd-3f057b950dc7\",\n  \"2a6d8f86-73bc-4ece-9705-5d5355b8df2b\",\n  \"64999aea-5f8c-41cb-b4e4-c30ad0c6b613\",\n  \"40fa5866-90ce-43c8-8214-c97839e38d1b\",\n  \"31abb3c9-fc57-400b-b576-8d39cbaf516a\",\n  \"31935d3a-9bb9-4478-a5e3-9fb60fc9a84a\",\n  \"046a0989-9fd7-4147-8742-89be426ad334\",\n  \"0066acd3-980f-4935-af2d-9534bf2c5110\",\n  \"df636365-c252-48ba-8e84-228dc7411005\",\n  \"156aabae-b4e8-4789-9782-19b4d9b31b7e\",\n  \"379766b1-9708-4320-9e3c-f6650a9ed104\",\n  \"4a0eb61d-367d-4316-af3f-c5d6079a0e6a\",\n  \"1f2e3619-6067-448c-b608-440cbbc0ba03\",\n  \"646fdf44-c29b-4f6d-9e51-0e8c05e7bb6b\",\n  \"0191614c-5864-4dbd-8176-c110b9eefbc9\",\n  \"5efb0498-8177-4fa7-baea-0facb43df24f\",\n  \"a1aa3df9-c264-4659-b719-0f6a96a982d4\",\n  \"ff857438-653f-4958-a733-101506f109b8\",\n  \"de816283-cd40-432f-a7f0-588eba8b2ccf\",\n  \"ecc6feab-0d1b-42cd-9d5e-2f43d29ba0ce\",\n  \"a5c9d015-18c2-4384-9e8e-89550cdaa953\",\n  \"9868b870-4767-45cb-8235-e645d6d99cc2\",\n  \"9488c799-18eb-48ef-a6e5-2fad27cf6719\",\n  \"955876bb-795f-42d5-aeba-776e8cfbff70\",\n  \"5948bcb6-83a8-45b1-a525-1383fa869645\",\n  \"99edf642-d51c-41ce-bfe7-747c8600bc56\",\n  \"f7fb9c6f-8ed2-45df-9e72-c131c81153e1\",\n  \"196ed728-fb63-4619-9a0a-da6693575a8a\",\n  \"33a6c960-66a0-4400-afee-53850298d75d\",\n  \"7f28e549-0199-4579-994e-3a76810cd693\",\n  \"575e9543-e0f5-43a9-b957-56fe9addf0a9\",\n  \"48d51abb-b7fa-48e0-8bbc-3b721b05d952\",\n  \"bea1ee50-5c2c-495e-a6a2-0b13b949f0e3\",\n  \"9f2c9030-a85d-4501-826d-65c6b3c689b5\",\n  \"79ee7f1d-645b-466b-8440-01e679986b39\",\n  \"ace8a49c-346f-46b0-aba8-eb7739f8157d\",\n  \"37688904-bd5e-476d-aaac-1ea40966d7a0\",\n  \"e3422bd4-9a9c-4ef3-88c7-6bb67c928aa9\",\n  \"91ce4875-de94-4ed9-b7de-a4c3088a88ee\",\n  \"3808ed86-1e20-4bf0-8c6d-235e74b84a2c\",\n  \"1294ee89-bdc6-46cf-ab61-21b11f584e27\",\n  \"fb05d79f-ee1a-4fb8-8d05-a0b888746aad\",\n  \"99db0b9e-6585-4dac-9e2c-b5bf1de0fa53\",\n  \"e403cbba-108e-40c2-b6f8-d9c9933734e0\",\n  \"427d1948-b387-40dd-84f1-e10c72c4c718\",\n  \"edb7dc06-1048-4671-b149-771d0aba9a34\",\n  \"7a5982fa-b41a-4fc7-b7a9-aaa12da926d8\",\n  \"12f18f33-3bab-44de-bba6-3246ab951f8c\",\n  \"21ccb7d7-d15a-4f86-b0f6-68df038f9c10\",\n  \"4b82b430-25eb-4996-8e84-4aec08a80eda\",\n  \"1b7c200c-2162-4bfb-a073-f0aace5e5be1\",\n  \"350d3102-ec98-4449-99f6-d87f98d3b215\",\n  \"d0153dd8-3d28-4701-a620-b726b160da12\",\n  \"392ba522-d5ea-41a4-b1e5-932521b168c4\",\n  \"9e471216-63ac-46ad-9627-e89856e4c157\",\n  \"2bcc7195-8a2e-42c6-b306-fbec7e22f0f8\",\n  \"7da457c7-8813-4edd-bb7d-3ffd8898ca16\",\n  \"38a7e66e-940c-4a50-9e5e-c0e7a40a961c\",\n  \"a8196018-2243-485a-9217-7995308862ef\",\n  \"716c0bb9-0fb6-4a81-9978-eb742cd14341\",\n  \"2d5d8168-7d49-44e7-b6da-9593bcefa150\",\n  \"335f5c2c-28d9-4a96-9998-50dae1e80ce1\",\n  \"b5821155-220c-4ab5-9c77-1db2cf2c8e0e\",\n  \"01e7dbe6-0b65-4e3b-8bbf-5ea41795794d\",\n  \"342abd5c-581c-47fa-bc60-e829d84033ab\",\n  \"6ca7ecd4-f5cf-4cbc-abed-e9f62c650a8c\",\n  \"dd08645b-1133-45b3-9356-8d6fa2d130df\",\n  \"f112776c-164e-4bb3-af34-45ccb5dd938a\",\n  \"c785a3db-d694-4f52-9616-0cbbface9ed9\",\n  \"e73575c4-2a3f-42ae-b98e-87800d3c852c\",\n  \"af29ff56-5745-4beb-99ed-a4491316e75e\",\n  \"0fd8bd5a-c235-499a-95b1-fe6d534c5cdd\",\n  \"481895f8-47fa-44fb-b6ce-53129c718668\",\n  \"44c18205-9636-4769-bf1c-46edfe935b8d\",\n  \"d67eef2a-67bd-4096-a94e-a8a9dbecde8a\",\n  \"357670ea-8fff-4b1a-8e16-83673fce5137\",\n  \"4743b136-bee9-43c3-bdf0-f7ed7618c67d\",\n  \"2c382868-6b7e-45c8-bb40-be7e9307fdcb\",\n  \"64b3f867-8956-4f02-adef-3328c51d805f\",\n  \"58cf9095-3b19-45eb-9da4-978eb3b23b82\",\n  \"63b408f9-5697-44de-9362-eba6a2fc5a5a\",\n  \"48268524-9180-4984-98db-05b6ee8df956\",\n  \"f69e1d0c-a433-4f2f-b854-c7a1c3732c95\",\n  \"c2bee2f6-fdb2-4e80-bcfb-74c62daa694a\",\n  \"81e8fdea-ef04-47cf-9a29-7ee5b5874b3c\",\n  \"b730e2eb-ab66-4fa8-ab5a-ff65b724188d\",\n  \"c971d3da-7556-4067-889d-65e43d39dfe3\",\n  \"75697112-780a-40f2-ad6d-8f0d182edfdc\",\n  \"af9bb68c-7ede-45f5-8d43-6463f1c47a2e\",\n  \"cbd3de4b-fb41-4688-b7fe-c744e1097060\",\n  \"b39173c7-6bbd-482b-97c7-e57e1ac48081\",\n  \"e5133d5e-6789-4cf7-bd64-1c2ee15b4ed2\",\n  \"cbec2932-30e5-45eb-b383-ee2f52fe38d3\",\n  \"2bacfb72-549e-4627-8067-c461ceb997a7\",\n  \"1eafb9a4-dcba-4b5c-8b9c-2f8110744392\",\n  \"cbed0eab-63e0-48eb-a21d-6e64a12f2147\",\n  \"2df3b4ea-abbd-4e22-9155-1d1be3125ac0\",\n  \"45dcec1b-1912-46b8-a92f-c344808ba3de\",\n  \"2e54dd0c-2552-4899-bfdb-02ae2e5c3d92\",\n  \"e9c51e2e-df89-4f38-8e57-be80e2cdd6ca\",\n  \"339e3b1c-3fd7-4c8f-b794-3c869bc1655e\",\n  \"cd147691-6acc-4fc2-9618-6066fd716e53\",\n  \"bd6beeae-5d5e-4a23-8208-0057129df0ec\",\n  \"27d8f5c3-290c-4d1d-91b7-e46b7e08626f\",\n  \"09250392-d7cf-4dff-8375-c49d64e97bef\",\n  \"385dc11b-14d6-4ef1-8159-fc28a1b0ee69\",\n  \"10c0d652-870c-41b0-add4-bad8c53ee448\",\n  \"406b2f93-fdc7-421d-b1a6-ded3124ec470\",\n  \"509947a7-0664-4952-a6f0-38692cf80337\",\n  \"783e5553-f999-4e78-b70b-b71e52cab666\",\n  \"4ff85541-62a7-4fc5-9d51-ee5b292828ad\",\n  \"48988e5f-bca7-41e6-8a90-7a953586d46e\",\n  \"62bcef94-66f3-44c1-a94e-7f2921effca6\",\n  \"c9fbf407-b257-4854-a464-5f1880b2c12f\",\n  \"594b16cc-c392-4441-95a9-14b2615920a0\",\n  \"feb743f3-ba47-4bfb-9c05-ea9e71308120\",\n  \"79c3e93d-5443-4980-b9da-c5ec4927c45f\",\n  \"36fd38f7-7574-40a2-8b4d-8770bed3d57b\",\n  \"8b9aba3e-11b1-4c39-b8d4-e08d1630ce94\",\n  \"e15776a9-9d72-4fb5-a94a-a3f696392215\",\n  \"f278c194-9151-41fc-b585-5d315a07f68a\",\n  \"32a5a639-4ff6-47a2-85be-c2a8c0d62c08\",\n  \"8470ce3f-b499-44f7-b08e-f69913614953\",\n  \"730377c7-8330-4a1f-9dd6-5797fa50e82d\",\n  \"70fc0ef8-6987-4f00-bd93-42f4fc61d715\",\n  \"820b8262-fd88-4e6e-8b60-4e8fe88864ed\",\n  \"1b8c7af7-5db8-4965-ac8e-a5f0c05dec19\",\n  \"088900be-f240-40ef-ae46-b89ae284821e\",\n  \"72b48951-f339-48f9-acd5-bc6cb9f6f657\",\n  \"cfd322bf-80a2-4449-b189-cf5cf31e236c\",\n  \"4d6795a3-3e33-46f2-bcf7-ed728731ab08\",\n  \"6fb84e0e-4082-4d11-b5b5-2bd764219e24\",\n  \"649f1db9-7a04-4c14-8ddf-4d28a81d48b6\",\n  \"63e674dd-4827-4fd7-82a4-a667747d5759\",\n  \"536aa473-383a-445a-8af5-87ea0b7548eb\",\n  \"222d053a-42db-476b-a13f-798da6aaa433\",\n  \"4e1fbcd7-3e5d-4f6f-852a-922658fcac9f\",\n  \"247be671-f34b-4847-984e-8853d37ef6bf\",\n  \"b50d9085-c080-4ebe-bb24-809e8a6148ef\",\n  \"933daac4-52ec-467a-9628-cf0db5f7254d\",\n  \"9b95fc0c-e6cc-441c-809f-d74bdd55deed\",\n  \"f876cad2-a9e9-4af0-9303-4da5cdf36158\",\n  \"bcf0ea4b-455b-4bc5-ab05-b48e541c7585\",\n  \"1bb6c02d-8f67-4447-917b-02560a48665d\",\n  \"559dcc13-3ebf-4ce4-934d-0ccd1d7d6753\",\n  \"b2bbfbd8-e207-4647-b1ad-6c9db5b4d7e5\",\n  \"b5a36dcb-ca89-4039-a3f8-0e4d080d81e2\",\n  \"f469e76d-daf9-4799-9465-85effc4ab140\",\n  \"ebffd629-026d-4787-b3bc-e5cd199a6f72\",\n  \"ec6d6e79-1e2f-49d1-a430-5d6dc7039a52\",\n  \"f3ef836a-4d74-4c9b-9014-28326ce35eb9\",\n  \"be2d33b1-e43d-4f06-aef8-81189b4cb976\",\n  \"504c4b47-5603-48ae-84ef-57d7eb714514\",\n  \"ba205a27-34ca-42c9-93c5-29fcd2b5c91c\",\n  \"2677ab66-661f-4a9d-b9a7-374aa8d3b77b\",\n  \"c49d3683-5ac0-47e4-be20-7e5004c07fe6\",\n  \"6cbe6e00-6d12-48d5-b38f-f4ec3dec50ce\",\n  \"9168913d-8fa9-4456-a3e4-6b861bc5ab3a\",\n  \"602b9fda-a10b-432a-b12d-4b2766080a37\",\n  \"620ceb85-a803-4efc-b6d4-3cc560f5dc52\",\n  \"8354cbed-e501-4455-a5b1-1fb24b41ca86\",\n  \"2db0a8e8-6362-42e6-a642-1814b30ddca5\",\n  \"9b4b2ba6-1ace-4e30-b7e1-35607d2e380f\",\n  \"0a672d17-cdff-4737-8016-aecc92f88a96\",\n  \"79a9b49f-9ad0-4181-8150-8be262764b3e\",\n  \"6edd7a2b-dc35-4cb1-983f-ba110ca5ae7a\",\n  \"671a4852-2c14-403f-86b9-d7560188df3c\",\n  \"6e4ecb72-6443-4641-9b9d-a96d134484ab\",\n  \"06f8c051-7e04-43e5-bd9d-90481fa911f8\",\n  \"c4b923c3-a547-448d-88f9-e00c8732f4b9\",\n  \"e9ba9e4d-ebe2-4a82-ae94-58173493736d\",\n  \"e1056351-ca6b-466f-b5b6-3d076defc8b1\",\n  \"21b51ef6-50ff-4939-b131-d4ec28dd5cf6\",\n  \"871067c1-d9eb-497f-ad09-6669a08753e0\",\n  \"dd70f452-1cda-4671-95e0-98bbc07d7a49\",\n  \"e4e2d583-e46f-47f5-8f94-2b0bda36e4b1\",\n  \"8151dd1c-2ea9-491c-8e71-a5ba8af238f2\",\n  \"fcd033f2-8c0a-4fb5-85ff-9ec66c0bd1dc\",\n  \"23ed1cde-d370-440e-b6df-4e95b3382f0f\",\n  \"438e80f2-5654-4c1a-838e-6ce87b73a20f\",\n  \"c4e4d338-0295-4321-840c-0a70ce40baaa\",\n  \"889f965a-d6f6-476f-8047-6b3ff6be4a72\",\n  \"85a77d02-71db-4442-aa81-53b49e5cf163\",\n  \"f07af006-3bad-40c5-9c6b-b91ccce8afdb\",\n  \"d3f550bf-d807-458a-aa2f-49371fa00855\",\n  \"f0f4aa0b-84ae-463a-8f8b-537bc9dfd1c3\",\n  \"6079dd68-29fa-445d-b705-a7980ecdafb3\",\n  \"717a5981-f38e-4e17-a54c-4f98059f6393\",\n  \"89562759-68f1-43f8-9264-07a7caf4e380\",\n  \"9ebab63b-fab3-4eb1-84b3-8a4d28cb672a\",\n  \"e5193ad4-a9ae-4165-9a61-eb7834657b75\",\n  \"15892c73-b487-40ea-b9ea-19fee5ef779d\",\n  \"edafbc40-bf9b-4913-8c0f-bd831444db54\",\n  \"178e8600-f60c-496d-b8d9-1823d8304dba\",\n  \"dfe6b134-21e0-4d6f-811c-a44ae8db2ac7\",\n  \"035d53c4-34c0-4bd4-b0a3-163d931b04ee\",\n  \"49d0ded3-458d-44cf-a987-f70f4cb1ce8a\",\n  \"93912799-0f27-44e1-b776-1a9f8b620cd3\",\n  \"c397d83a-001d-4e48-a819-5381b6ccb14a\",\n  \"3860d69c-1e75-48e5-8a25-578ebce75fb9\",\n  \"32810697-3709-4c36-bf83-f9f4e566f7d0\",\n  \"d0469822-8f98-413f-ba2d-4b7b1a34db01\",\n  \"29d1c619-d2b2-4192-8573-59b6d9a7c8c7\",\n  \"f0e5d4eb-c09f-4661-9344-4cc7d49ed61b\",\n  \"18b30a34-6dfb-49bd-88dc-74d8446f0798\",\n  \"0dad46f4-5f59-4c17-b656-146dce2de372\",\n  \"4bdc9b6b-a833-4c42-8075-3e9a3bc43e0e\",\n  \"0e563220-0a76-4146-b87b-5c2638e46035\",\n  \"ed14cd2b-a6d0-4865-9ccd-95db621393f5\",\n  \"7764528e-8f1d-4883-ba86-59d190db8cf8\",\n  \"1ba6b56a-faec-40ef-945e-9504f9b97665\",\n  \"0be84f98-a2f5-44d7-a189-56c8ad80a253\",\n  \"a9494796-0bf6-4a94-9a77-f6a7a45b3bf4\",\n  \"15c16736-9cf5-4f85-a645-f064284de10a\",\n  \"c177f828-6a2a-4471-abff-09ce2e78c8d4\",\n  \"f93b422f-3fbc-43c7-ace9-75e3785c3f86\",\n  \"68a1e945-a160-4934-b41c-712c419e42b7\",\n  \"f1b5f4db-b42a-40a1-9370-24dd21bf16b4\",\n  \"0731b709-630c-400a-812b-fc091e069540\",\n  \"50c0eeae-b912-4283-85ab-9d718596df86\",\n  \"66d9054e-4c27-4426-8fb9-b3e1a39ce10e\",\n  \"01ac48d7-86fe-4816-9264-b05d99dbed46\",\n  \"ce0e320b-d1b4-46fe-8553-0399f081d96e\",\n  \"4ba52a51-7c01-4f99-b0b8-25de14d72b94\",\n  \"57d188c6-f554-4569-b065-9fa5523bc4c4\",\n  \"54945a01-34b9-4946-9dd6-49f9f7cc84b1\",\n  \"9e8153c9-d9e3-4a93-ae77-a86341aea1c8\",\n  \"39127e12-c181-4c0d-ace5-12d66350d2ef\",\n  \"7c496701-6b0f-4344-8408-3faa26fcd863\",\n  \"4bf73b26-a68b-4336-825c-1df9de14af1b\",\n  \"be9dfbef-4f61-460d-b09a-b2cae6cb50d9\",\n  \"e5fc8fa3-0fb4-4a45-861c-92a918b39e86\",\n  \"284d594f-3f46-4e82-975c-c0e6680920b3\",\n  \"91b725b6-dab3-4a6d-9082-35b5057c8e5a\",\n  \"3f1304c4-5de8-4d4e-8449-54788fb6fe39\",\n  \"1e15d45b-4311-42cf-9257-1af88100ca2b\",\n  \"e1237c10-0780-46a1-8276-911060dcc99f\",\n  \"1bd36063-14f9-42c2-a6c4-3832a66f8d5e\",\n  \"d37e0f52-6cd4-4b06-b831-387e249eb8af\",\n  \"60b466b8-ffe3-4e13-8fd7-2a540dc346ff\",\n  \"bda5a1b6-2f14-40ae-b2cf-05f62ccc4569\",\n  \"ea3369ea-8c9a-46bb-89f3-251971a0d115\",\n  \"8d2a1cc9-bb98-411d-af9a-ffc9321f9753\",\n  \"604624b2-511c-46df-8487-a2d1d577b292\",\n  \"e3125d48-3472-43b9-80ea-af740923933f\",\n  \"2b1da79d-098a-432c-8088-e743a8c8a2f8\",\n  \"58820367-aab5-41ba-841c-57f1553069de\",\n  \"c56f3e6c-7795-4dda-b592-9580bee3d56c\",\n  \"2410e041-7b01-47c5-a90f-965fd8c0fc4c\",\n  \"d3e4e94b-8c2c-4bad-923e-f69ec02a5f60\",\n  \"3e052b9e-c32a-4225-8a5c-2fbbe0c0ba2c\",\n  \"64833a3f-b75b-4772-90d3-a49641f45fa6\",\n  \"04b9599e-9db0-4c32-ba6c-bd327017b5ac\",\n  \"ee2353db-f1cd-4361-9e52-dac78a0702ee\",\n  \"74994ddc-ef0d-40a0-ba3a-8ea6b1b8202a\",\n  \"97bf20ad-8bd0-4ef1-9d06-d9c12979a710\",\n  \"ba278f57-0098-4fa3-a832-383228b0f095\",\n  \"f88a49d2-62a8-4009-8818-859e12a5b1c9\",\n  \"0b960df2-5f26-4a81-b33f-6cc297ed1dee\",\n  \"d839a3ba-154e-425b-b610-f27ed84f2f7f\",\n  \"48b59fc6-5cae-40e1-ab94-d4e96f0911ad\",\n  \"91d873ed-8745-45bd-977b-f1b4709f0821\",\n  \"fafddb7c-08fb-4489-99d3-3e12d21b9f7a\",\n  \"7e5de6fb-7ac9-42e6-8e57-d5ac6489791b\",\n  \"e940bf03-f447-449a-bc17-df121191e677\",\n  \"9d07c7e5-2d84-49aa-b7a8-07ebfc9bca66\",\n  \"470b1c78-82f7-4f82-9656-82cf855ec077\",\n  \"b7e54e47-79ce-4d83-b35f-46d3f9d57f78\",\n  \"2bc0f616-7026-4350-9e83-b8aaba19a61c\",\n  \"aec78894-8277-475c-9ab5-70558a4a4475\",\n  \"a07814d3-ff4d-4067-80b8-a85975d6ea48\",\n  \"d5259724-1db7-4aba-b7ce-faae27b65697\",\n  \"55124ec3-290a-4c47-a90e-100c2efb036f\",\n  \"76117480-f203-4afb-9b68-71cca5af9fad\",\n  \"192cfd6d-9a61-4cfe-8328-4130adcc8367\",\n  \"401c1737-0576-4459-98fe-0e637a29a672\",\n  \"964dfa2a-33fb-48be-85e7-33aabdce2697\",\n  \"2225d509-af34-4e0e-87d5-a36e272aebca\",\n  \"9d66238e-b2b7-41d0-8d52-24c9d0d28319\",\n  \"54374b1b-7e01-41d8-b8c4-1ad151f4b271\",\n  \"3a63545d-9d49-4c83-9133-7eee6b1d0518\",\n  \"e576063c-0eb6-4d7a-8220-3a031a146ea2\",\n  \"f898da86-414a-4a35-9c71-46b7d8f2d74e\",\n  \"5d29e83b-bd9c-4dbc-aa3f-ecca78922cd4\",\n  \"cffc4ea3-8316-4ce4-b9c1-c60ea41f5fba\",\n  \"473da9b8-68bb-4aba-9855-ac4eafb6c5cb\",\n  \"d0e5d3a2-e5c5-4105-8f90-92a3b5b858ca\",\n  \"c350a9dc-9488-4895-87c1-8d0de3a08b7f\",\n  \"c581f6ae-e56d-44c2-bbe0-5660aa21878f\",\n  \"b0f30a82-d6a8-455b-8620-bc5b9aeadabc\",\n  \"46ebbb4d-3228-4511-ad33-4cd6c26f872b\",\n  \"ba8549f6-6566-421f-b237-385546a18375\",\n  \"391b86b5-e12e-410d-bf62-a18aab92e28a\",\n  \"ac1fc559-4905-458d-9724-06ef09c16fd0\",\n  \"7e3e07c5-7bd7-416a-bb6f-d31085df312e\",\n  \"3ff04345-c35c-48b9-91cb-a18cb8f5a391\",\n  \"b232234a-ea08-4327-acb3-ad60f976d00f\",\n  \"f94b1c47-fb76-4ff6-a988-05cd16b4912e\",\n  \"c2664b42-43ae-4902-8a12-d122892b276f\",\n  \"17779fd6-67db-43ff-bbb4-e2e7975ea337\",\n  \"b12d7184-d369-4176-97f5-6f493a6a7aa2\",\n  \"3aa7e3f4-5bed-4826-aecd-dd7491f6bc1a\",\n  \"13122648-676a-4318-8ca4-eeb249ef7bda\",\n  \"750c645a-2d05-4148-a91a-1448b630a0f1\",\n  \"d1b43277-82cf-46c6-a870-2bc7582de00b\",\n  \"1c3e4bf3-10d9-4e01-b006-477ced50bd58\",\n  \"9227c7b4-8e18-4849-976f-5368f5460ddb\",\n  \"85c675dd-e946-4c3c-b7c2-ba79e9880f12\",\n  \"d68baf8a-8fb0-47a4-bdd0-db21f71e6d90\",\n  \"515ee204-4d88-4eb2-883b-82092ecae780\",\n  \"553a4608-47ad-4e8c-bad4-0fc402329cd3\",\n  \"0317fa99-6433-4737-adad-980001af0611\",\n  \"18b6ca04-b240-4871-b566-d59b48353b09\",\n  \"9032445c-1547-4f1f-9588-8044e3d8f112\",\n  \"a569d940-8f64-450c-9561-767432ee5a14\",\n  \"304a4514-be15-4977-9479-68a35c8381d5\",\n  \"c58523f5-fd6e-4352-9bec-d9a240a694fe\",\n  \"fa25456d-f9e7-41f7-9fb0-c9b926864482\",\n  \"2d2aff9f-20b1-43b5-adc9-049cee3c0322\",\n  \"039d83c7-cf46-4b2c-a77a-c064ad25aa55\",\n  \"b4f7228b-872c-4afa-a4c6-ee35a0db2027\",\n  \"0c8634cc-0fdd-4f23-91d2-f5b5c3321f87\",\n  \"937b64d2-ce0c-411a-8fef-16d4842cef91\",\n  \"672519b4-ff29-4dad-b25d-d9ca0d3c6fe2\",\n  \"c06ba75a-e9f8-4742-8713-f48c958e0b52\",\n  \"4b6ed23a-fb8b-4f5b-a4a1-6bc49878869d\",\n  \"10525dcd-dcd0-4d9a-ba37-5059c647a71d\",\n  \"b8f071e6-96be-4239-8ed5-94c8335551da\",\n  \"7507ff85-2afe-4d45-8d9f-ecc17fc7b5f8\",\n  \"dedf7940-9676-48a1-89e4-329e6e90f97b\",\n  \"7b25d112-3360-4187-a116-6d475e7221f4\",\n  \"10d606fd-8032-4816-81ae-ace18615bbd8\",\n  \"22da7177-39e7-4fde-b1c6-c9907040051a\",\n  \"e1d6a201-d484-48f2-9f26-98b642467740\",\n  \"4786c321-9a6b-4328-b8d0-812574843530\",\n  \"875a8fa1-33e3-40f6-86c3-4df9fd1046d2\",\n  \"3af4ccfe-d7ee-4fcb-a57d-28b66524ae99\",\n  \"dedf92d7-f491-4904-8386-9158af658b4f\",\n  \"fd66d6bc-0579-4dac-8b77-52cae1ae110f\",\n  \"25a8b977-dac1-463e-9baf-aea2b96839ee\",\n  \"990fa427-1f72-4ce4-89c9-96887e08acf7\",\n  \"631f6713-ddcd-421f-bce6-45c0f1ee51a9\",\n  \"760d3ffe-9735-43c2-9faf-8889d9395f42\",\n  \"6dd64db3-93c4-4c81-89dd-0c855203eaf8\",\n  \"6cddb8aa-f60c-43a8-85af-67d75412f3aa\",\n  \"21891b49-734e-4357-8af6-79b016326a88\",\n  \"eccb778e-d241-47c6-9e66-66a19cc29daf\",\n  \"149d437b-3a1d-4a63-9ca6-060942b8c3e9\",\n  \"daa667a4-26a2-4d12-b208-400878bbcec6\",\n  \"f6df48db-2e7d-4337-8270-9b83dd23b249\",\n  \"8c561050-d7c9-4cd4-8b05-924e50062ba0\",\n  \"01c5a19b-479b-4e3b-bd83-0d66bc4dfef6\",\n  \"04481036-e5db-4fff-ab0f-a1d8732f8990\",\n  \"148f1ff8-e17b-4429-a4c9-7b5f9e32f9f4\",\n  \"6be869e7-0106-41d5-a66a-56b8b3521693\",\n  \"562f0c80-bea1-41e8-9b8c-0819ef79863d\",\n  \"ece29f5a-d992-4e0a-b5fd-2685604c7dcc\",\n  \"5347378f-a0a4-4099-9672-ebe9e4da7786\",\n  \"e3712aa6-2fe5-40c7-82bc-91479bc67eac\",\n  \"f5e4cd7c-0aa2-4964-81d8-e28d5d50408a\",\n  \"1a73d867-8e2f-4830-aebd-5abf89ed4bbb\",\n  \"0f5d6635-e5b6-478e-b97f-28b91becc149\",\n  \"9ded8e51-af83-48ba-8bf8-35a2faa44a52\",\n  \"34493178-8b98-4a7f-a39f-c35e91434a93\",\n  \"11883c2f-4a89-4495-891b-4fd6aa22288a\",\n  \"1fe4c308-a59c-409f-8998-42d4bf7bf99d\",\n  \"94957004-fabd-4ab8-9baf-5182c2b61f3c\",\n  \"02d040ee-897b-409b-b044-d3a59ed20ae6\",\n  \"30c348de-b19e-4103-8e29-b2fef39e4278\",\n  \"e6b4c33b-c13a-4add-a406-30aa6db46d72\",\n  \"13271f45-682f-4a21-8fb0-8493ba41db09\",\n  \"39c5f0da-8761-4958-97f5-21b1e06d39c3\",\n  \"0a15ef71-2236-483b-b1e2-fb62f28df89d\",\n  \"da363ecf-4276-4673-928b-bb2ee5ed7d03\",\n  \"dd318d52-1005-4fe8-8d64-f1c5ee28ee63\",\n  \"5020091a-7aed-41da-80d4-fc378c027c4b\",\n  \"839976b1-ffbe-4627-9d3e-4abab2be9696\",\n  \"f7ddeb7e-fec0-440c-b388-b7034712d46d\",\n  \"a523aee4-016a-43b6-a0eb-5dff89d83721\",\n  \"3318460b-02b2-409f-a45c-57cb1d871fb7\",\n  \"267c9538-b4ab-46eb-80b8-b64d9600f84c\",\n  \"9e3b1ab4-7c99-4b02-8318-b5c12a2142b0\",\n  \"38f7e24d-890c-41a4-a8f2-1f11307f58a8\",\n  \"2561990b-1622-44eb-a08b-1c7c18cb7c9a\",\n  \"8c874871-de88-484a-ab73-23a1ef1004b8\",\n  \"4172b65a-8b4f-4175-94e4-00187e2d5f33\",\n  \"81c5a076-dc87-4a7a-8610-4daa7cea7264\",\n  \"3cc18252-4854-4059-82ce-36141afc0c9a\",\n  \"5633a578-b5d1-42fe-8216-f1325385e713\",\n  \"228adabc-9c86-46ab-81b2-abe81ffa1e68\",\n  \"867f7e51-78d7-4766-8ddf-3c69f934484b\",\n  \"483e3ac5-a6af-4640-a77d-77e053a31a8c\",\n  \"1b672cc3-3d64-4cff-a4fd-79cae602fd01\",\n  \"dcb88365-4ae5-40a6-aa42-f6d7f26da26b\",\n  \"4f123ad5-f689-43a1-9920-28b5211dedf1\",\n  \"e11464d2-a10a-438c-ba33-a5e04cd05d76\",\n  \"fd214d4c-4c91-4f7a-b5b7-71c34180fff8\",\n  \"7cb7723f-4fab-4abf-ae43-8b538f1144b8\",\n  \"544de055-6868-455d-89ec-bb3cc1f7b184\",\n  \"75e1b3f4-ce4c-4144-9b2c-55ab407ddfd5\",\n  \"8fa97e76-7a2a-4919-b81a-c7074522a93e\",\n  \"2cca742c-8dff-42e6-98e6-2f884017c5ef\",\n  \"a4454951-e0ae-410c-b3b0-ca891d76f38d\",\n  \"6c14f973-4411-4d00-a42a-ce1343068298\",\n  \"406fa860-509f-4045-b107-e58c76816755\",\n  \"43eadd9f-c3c7-42f4-a383-74018ff5cc71\",\n  \"3f7e93a3-f55b-4ed6-8e65-e820b5ce4b0f\",\n  \"2dc42ae1-14d8-4c76-b38f-fe0a8bda5de7\",\n  \"439e22a5-d938-4b10-95ba-c8bb18cafdf1\",\n  \"d4a4031c-4ffe-4fd7-86be-f835e5b0bd88\",\n  \"f0c3ad1c-18f3-4006-beb6-50318df59521\",\n  \"ad9ff6ce-0531-46c3-9ac9-320959629b25\",\n  \"a40d5b0a-df4d-4304-8b92-59967f57cdcd\",\n  \"30490c33-b57c-4aa4-99c4-75a9db87eff1\",\n  \"e42386ac-08b1-44d7-9b97-556f7f838b1b\",\n  \"cd019e79-5138-453a-9e1a-391182e47d60\",\n  \"1355cb5d-bf92-49cc-afdf-38dd5f119696\",\n  \"8f88ce26-3669-4633-9c15-c470b3861415\",\n  \"d655d2a3-cf02-47e4-a2ec-4226df5d55b6\",\n  \"327ac342-dda1-4d8a-81a9-07b8a7514bf7\",\n  \"be0e1d08-cad1-4153-b99b-a68bacffc048\",\n  \"dd1b0bf6-5d8f-4e85-b886-3e294c0848e4\",\n  \"5417412f-ccbb-4112-a858-8815b92e9278\",\n  \"01e9ed68-b2d2-4619-a9e9-1b8883390c5e\",\n  \"0d50a519-5ee1-4c06-97fa-267ac23cfc2f\",\n  \"c07f2b0d-b945-45af-8cdb-fa4876bd492c\",\n  \"ed67de5e-b241-457c-87fe-7665d0182ee2\",\n  \"19e5ea53-3352-4f5d-b78c-d6e97d53098b\",\n  \"605bbef2-38ab-47b7-b6b7-ecd573150a40\",\n  \"c3218f60-9b63-4482-ad59-c05e988aae4a\",\n  \"631c021a-e93f-4d36-927a-49e2ac1f8bbe\",\n  \"3ff9b270-9aa2-441b-8a21-4b25ac2e0608\",\n  \"34f1f818-561d-4f5e-b6bf-a236b01f5761\",\n  \"235e3ab2-8656-4426-b8aa-0b7ef71af19b\",\n  \"88cf8d44-6e53-4b17-86bc-4eb799f5ae9c\",\n  \"1a6d5e62-b435-4d89-9c72-e56eb13caf09\",\n  \"410863c5-98ac-47cf-8aad-c80a42173078\",\n  \"a7fd355e-be03-476d-b2a8-7ee45aef81e8\",\n  \"a067c957-7dee-440f-8561-e29bc6a74223\",\n  \"3182d762-8dda-4a6b-bc2b-648761392637\",\n  \"3868d965-f9b9-4594-9bc7-eb5fe785723b\",\n  \"18f29782-6f27-4936-967f-e428c4d57819\",\n  \"0121e062-fbc3-4319-9f71-e3d4cb21cbbb\",\n  \"832ad1ca-c01d-4b98-8976-f3d51707dfcc\",\n  \"b6753187-002f-4e3e-b579-f0e1cc51922b\",\n  \"d8c4605b-3876-4b26-9bf3-1ba846fcb33a\",\n  \"06aa44ed-b1d4-4204-9866-dfa5e6376c13\",\n  \"50d36b7d-e03e-4aa7-a855-b420a029086f\",\n  \"e3ac44a9-0bf0-4cd7-a22d-a31c3881c6a1\",\n  \"d77f5d43-cc62-4d05-aaf1-829d4d16bea2\",\n  \"35385643-b948-454b-8aa0-3bc255f25ca7\",\n  \"acb75940-7603-4011-9591-987dcdc686f1\",\n  \"d4730797-f51e-4975-a9ce-e50e55242523\",\n  \"1783cef2-7ad0-4d69-bb2c-ac5f74e8b081\",\n  \"c9c8280d-295b-434b-82f0-e67dd7cb0488\",\n  \"10f564c9-bbd8-4e80-bd84-381ae2a52438\",\n  \"df4eaf8c-a49c-49cb-afe2-93a981c8a1fd\",\n  \"45061810-4868-4727-a9ec-6a939d8a1d37\",\n  \"0cb1dcf4-29db-4214-ad98-5bd6b7f0b756\",\n  \"2ee8803a-f629-4f9c-90b6-759854f92d6f\",\n  \"b59bea62-c5f3-478a-b2eb-92257729244b\",\n  \"64ab26ee-d333-4fb5-8c08-44f375646ae7\",\n  \"c1f94676-880b-4180-958f-02d0de31b5bb\",\n  \"1571d658-4f65-4295-9201-6fe7eec11213\",\n  \"822f81e9-4726-41fd-bd99-341f76398ac3\",\n  \"412b01d5-7593-4d45-a470-a679a6e7c99b\",\n  \"99d2404c-7881-4768-925c-987f031d8ab3\",\n  \"f8a88baf-3e7b-4096-a63e-741cc9b6b956\",\n  \"1b565c9b-a4ef-428b-afa4-58dc5226fbd2\",\n  \"59b27b2c-4c6d-483a-b11f-e8dce12f4dee\",\n  \"5e3293c3-e2e3-44cd-88e1-3fb41571add1\",\n  \"ef82370e-2d17-48ea-863e-89df89f25f51\",\n  \"4490bc92-a6d6-4391-9528-1ef18bfe9454\",\n  \"eb4b5577-e57f-4531-8f43-7bbff8a6d434\",\n  \"cfce1c16-6752-4b25-8ee9-154b14cb7e10\",\n  \"a1d50a61-ae52-46fd-ba01-4553e9cb8894\",\n  \"e23556ba-3b29-492f-914a-6fc5ea73cda8\",\n  \"d0498bec-5c92-4201-a340-7c2403832ba7\",\n  \"870070e2-ac89-4cfa-9246-cf53e218f0a3\",\n  \"a00c066c-938f-4119-ab93-53db46986942\",\n  \"e40d8d60-cdc4-4016-b58e-9b5bc414a92e\",\n  \"5d1e8c50-9691-4c6b-8556-3226f5527d8d\",\n  \"c6f43959-c6d9-4d99-9f34-2fea82ff71ba\",\n  \"aa584c59-44eb-42b4-9b3b-776193b3a4d7\",\n  \"f88a2a2a-db26-4c40-926f-0e38928d30fe\",\n  \"cb4c5168-d3d3-4505-a0a2-a94a902e6c59\",\n  \"8e7d8407-ce15-4203-bb89-ce6f8a3eb265\",\n  \"c83d9714-3d3e-4200-a351-7ab7cec8e340\",\n  \"64aa9c56-5fbc-4500-9f3a-d237841c17a2\",\n  \"e0e399cc-a9a1-4065-8534-fa8806769de5\",\n  \"c8613925-a42b-472b-9cac-777bf73900f3\",\n  \"ec2dd752-df34-4eca-bcc6-304cd9b69815\",\n  \"ac6f148f-fffb-40b3-bd9c-9f92e8e2175c\",\n  \"6d8bc377-aa7b-485e-8e4a-054a536f5ef4\",\n  \"6919854c-6cf9-45fe-9ef1-8ce106eaa826\",\n  \"25c521a6-b8f1-4c62-88dc-b84704a2d36b\",\n  \"3091a682-df2f-4511-9e3d-693a18bdf76b\",\n  \"694978b6-01b0-41ec-a500-47880fc29ebd\",\n  \"6faadbe2-4476-4e8c-9bd0-427050dc4cd1\",\n  \"7b85ee34-f8be-41a8-9db7-f4efe4bdc2a0\",\n  \"264de26f-a6a8-417f-8c7d-2fa4cc857b52\",\n  \"a9500e65-d19d-4f0a-a2a5-7bbb7bb7d77b\",\n  \"08076197-f623-452a-b023-7a140f613f34\",\n  \"030ad351-e335-4d09-bd3a-c988f9c002ec\",\n  \"6df9fb9a-4ba6-4224-95a4-bf69c2b5bbd3\",\n  \"94a0892c-f550-440c-b2f7-fde2e331d453\",\n  \"664303c2-1395-4a7f-af74-95f81fdabe85\",\n  \"43336998-53ba-4472-88a0-5e93cfe19928\",\n  \"44125dcb-8642-4f7b-a6fb-43ae4e5b7f0d\",\n  \"3d32c92f-603a-4b92-91da-352a5809ac67\",\n  \"a3035eec-fd26-4132-b0c6-9c0093c58640\",\n  \"fda20c72-bdbb-48f4-b87a-4541342ecbab\",\n  \"7ecc06c0-eea2-4796-81e2-16985ad8c75d\",\n  \"374f250e-8339-417b-8f05-ff1d4f477e41\",\n  \"170f8665-91bf-46cb-a800-0e7fe706f770\",\n  \"fbe4d16d-d588-4fc4-9841-d393834394f9\",\n  \"ece396e8-5926-447a-8c8a-da9e8b8ec747\",\n  \"35f7b629-b7d0-46e4-8807-c30a8f23e017\",\n  \"97c19ba8-0522-4855-a602-95425ac387d8\",\n  \"898f4511-9a2c-447f-9f23-0aebd03805c3\",\n  \"bbc315f0-d67e-4502-b7ee-12c985cb2fd1\",\n  \"60c9cb40-8544-4187-b565-9cc97982ee69\",\n  \"0c36573c-be2b-4ea6-be9b-07e73e43aaf0\",\n  \"954f5288-897b-4477-baa6-9166fb8c6729\",\n  \"f382e846-a335-4887-82a5-9989aa6b3fab\",\n  \"35108758-2337-41e4-a4d3-d38c296e0652\",\n  \"06fc1126-3b99-4a12-9484-71cf3e80672f\",\n  \"11919040-d4b8-4f07-a153-cabd79ab70ec\",\n  \"d7ce4028-9f6c-4aea-bd79-cc52b7431741\",\n  \"5b991baa-5893-4ee1-8dfa-abd0ce3a7351\",\n  \"47c1c9e7-d6bd-4597-973a-5a360d91f1f3\",\n  \"4a276816-12f8-42c2-a6a1-1fe472f3137b\",\n  \"a34172c4-ec47-4b4e-b2e3-038bf4fe27aa\",\n  \"7a962137-692c-49c0-99f9-bc1457416b2b\",\n  \"fc7e8a75-4d25-490e-a53e-1abfd6127576\",\n  \"bca92772-0dcf-43b4-9138-9bda08d5eaa0\",\n  \"2f80bdd6-8475-4788-94b9-8016bf3ce5f0\",\n  \"5bb8268b-ee3f-4838-b5b5-07521168a979\",\n  \"f00f3e95-f475-44a9-8e43-d274f86cc771\",\n  \"e46327ee-dcc6-4433-9973-c961164f5af9\",\n  \"9ff4a351-76ad-4025-9622-cb8aea3053d4\",\n  \"d661aa1a-c82e-437f-9140-493902a24780\",\n  \"e4899772-4b81-463a-9fdc-63651e56ddbf\",\n  \"3c0ee401-c1b8-4801-af6f-aed0308dc3dc\",\n  \"9b542e3c-6d6a-482f-afce-49a3262cfe60\",\n  \"e01b674a-7c2f-49fc-8f25-e05703bfb90c\",\n  \"51f63c58-69e5-424e-8f22-9f8322c33700\",\n  \"0a5183cb-8ef3-4455-af83-45848559ca31\",\n  \"3eefde2b-79f5-4cf8-8225-b5c57d7f8825\",\n  \"d0b9c392-4961-4a70-84b8-87877089b2a5\",\n  \"01c6f750-372d-40f4-8e3e-d4bf8acd56b1\",\n  \"da871b6d-873e-4328-9b5a-9eabb8ef3426\",\n  \"80d8176a-8cc8-47b4-8899-3f67d36953c2\",\n  \"92f91282-27f8-4565-bd18-adf5951bcf49\",\n  \"27291c50-8755-47b6-b5d5-85976bc94551\",\n  \"73c9f545-0f5e-4fa0-9a8d-37c6f33212aa\",\n  \"f3a3a234-4871-40b9-8e6e-f64a673bcf01\",\n  \"9eff4f22-f1f6-4ba0-8249-231fef8750c0\",\n  \"9819e57a-c0b0-4b97-ac20-5d1ffe8dc050\",\n  \"1655b8a4-0ba8-40af-9b40-578705876224\",\n  \"37529648-be4f-4175-b38f-1a66ee2ddf2b\",\n  \"37fa627f-bbea-44ee-a927-ca7451ea4068\",\n  \"82bc90d5-a20d-4bd9-9db4-960b7d2508a9\",\n  \"3e58ee7b-27a1-4284-af3a-591145e28d21\",\n  \"9a773047-140d-44e8-a4e2-76000660caec\",\n  \"f041bcd3-c39b-4dde-9662-46090202f08f\",\n  \"e83b5aec-679b-4669-b613-ba2c168a3088\",\n  \"15521a4e-23d5-443c-b988-e064f69eafe3\",\n  \"23caf1e4-15ae-4766-bf4b-2300486a7f74\",\n  \"0f121f37-9aee-4a7a-b033-eb97aabcb43c\",\n  \"2de2a5d5-ff7b-4a7f-9d0f-321f591ba550\",\n  \"417af16d-dd68-48cc-aa94-dd66490609fd\",\n  \"ba648edb-ee20-4a1a-8f39-59667d75de0f\",\n  \"49c603a7-6769-4a22-8d48-fb5fbb473bea\",\n  \"d492227b-605d-451c-9a47-e1f23c973513\",\n  \"17742e2d-3587-44fe-876d-4eae3ce9be5f\",\n  \"994aa948-72af-4fbc-b895-254673a34c1b\",\n  \"0086428b-787e-4d2a-be23-4ba0eb5df38a\",\n  \"7e4c0b4d-ece0-45dc-9313-a0a5d2cc542a\",\n  \"9b2bb018-26ae-49c1-b8d8-b52e3e7d5b2d\",\n  \"80701dea-501f-4a40-8a2b-adfa8ac92904\",\n  \"05291730-f3ef-4c82-a6e9-88ef066db33f\",\n  \"42bf4f57-d7d8-4e01-b82e-50576348d04f\",\n  \"33c6bc7e-e81c-4e18-81c6-9b6ee9649e78\",\n  \"f36d1441-6d81-4994-a0b1-acc78db16f17\",\n  \"f789000a-e7ba-4110-8d07-3c2cc2f5bf90\",\n  \"abb39cff-a313-42e1-a447-2886f3660133\",\n  \"5db97ef8-ca98-46ff-858f-95ea4d8b38a2\",\n  \"424cbfd4-6618-4a48-8638-771f626ed776\",\n  \"23e5fbb1-bdb0-4c8c-b7d7-967013241ae3\",\n  \"7cedfa27-7240-4e8f-b783-35ff073b86b2\",\n  \"d86cffe7-b72c-4d64-9a0c-52a14521fa55\",\n  \"9b9931e1-fdb8-41fb-bf90-3535e0d9081a\",\n  \"62f497b3-d75c-46f4-8a99-8a9935ab5d15\",\n  \"6512a78f-5902-46bd-b3eb-cb8068867fb9\",\n  \"06bd4b9c-3cf9-48e2-a58f-9585e6883c5e\",\n  \"c652403b-6682-419c-81e9-5a77185c508e\",\n  \"19767713-554d-4ab9-9ce1-f52dadc8216d\",\n  \"7fde1919-1404-43d8-b086-08638562f237\",\n  \"e169ee23-820f-4bf5-b914-4e9386454aab\",\n  \"f905fa7e-54e9-40f4-b0cb-52c2c42315d0\",\n  \"190be128-88a1-4146-8177-33c4f925e90e\",\n  \"84d34d37-6b3d-497f-87b4-3beb35e67dee\",\n  \"001903d0-f1d5-4df4-bdb1-5a8272897a3d\",\n  \"3761e278-d8bd-4402-abeb-e04e369e9e0e\",\n  \"93db7dd7-c80c-49a0-bfd6-cf10ec459452\",\n  \"6b2c5290-7c15-4be9-b8db-4938eb94598b\",\n  \"aa143e99-6653-4a96-94ce-e8a35c3f6173\",\n  \"ae9f9493-b41a-4830-a774-51f5e301e9fb\",\n  \"57b161b8-80f0-4aa9-aa06-bb5652ed1f59\",\n  \"742417d1-e12b-4e78-a22a-b968913f42b7\",\n  \"bad5343a-a27f-4157-8942-0194261e44e5\",\n  \"19fc71f7-28b2-4a01-9922-38b661b62f33\",\n  \"5978d624-ff3d-44ba-8460-11d0019b5fa9\",\n  \"3640bff9-b21a-4973-97da-352b88e3f609\",\n  \"6a959c7e-ae56-41ba-ad23-1c6d9ac5bbe6\",\n  \"f571edb8-c1f1-402d-8263-a31f78cddfce\",\n  \"38c98f20-54e9-41dc-8c48-d36f5a3bdfd4\",\n  \"f806bbdb-c2e4-4e41-9be9-2eac7866ed39\",\n  \"f13d6ce5-577a-4153-874a-40990eda2b06\",\n  \"5fae3980-35b1-43de-a7df-30c338e944b0\",\n  \"f92dbca4-6c7e-45b6-98f0-915ec02380b3\",\n  \"a87cffab-94a0-4306-850a-ec3c68a4b184\",\n  \"8c660209-a2a7-4d5f-89bf-a070d7c39974\",\n  \"fd0eee0d-0dd0-4e70-b661-f2b63f0a2ce7\",\n  \"5b671edf-07a1-41f0-ae7d-9ef55ffa0a6b\",\n  \"09e5eb9c-16fc-4374-a028-8a1c04d9845e\",\n  \"c0a3ab19-b97a-4231-98a6-072d03c6661a\",\n  \"b56d0ec4-1bea-4241-b018-dc7d96e38a51\",\n  \"045db697-40a8-4e8d-a4c1-00ec3e04d8c7\",\n  \"9dc3e70d-8068-44ed-bb65-51518decf24f\",\n  \"5b48682b-ceab-4495-9169-59a870c23e35\",\n  \"59fd5767-c95c-41fb-86b5-dd3b58cf7feb\",\n  \"d40895d4-1e6c-4d35-a5e7-5839a7a94902\",\n  \"c8d1ed87-bb5c-4122-a0bf-09f1838e8f38\",\n  \"1ef9cc23-5b6a-4f03-b7dd-9b7d8ceb1721\",\n  \"015c3fa8-6831-4bf8-8e27-77d07bc5c03a\",\n  \"2bf3454e-d07c-4d03-8798-c028dfdde734\",\n  \"b025c03c-2812-4b07-9d8d-c4b0339121a5\",\n  \"ba91200b-05fb-49aa-94dc-1bbdcad90a89\",\n  \"6563caf8-bd27-4a72-9224-81108124f138\",\n  \"c4c7a18f-c87d-4fdd-b1c1-f6b397bb9e33\",\n  \"3400e7ba-cada-416a-8616-a45dae3b9602\",\n  \"892a94b7-6526-4115-a746-0fea0445a0ed\",\n  \"0440f024-1750-4ef8-af02-d538227580e5\",\n  \"02618103-578b-46b7-bb2c-5c5c74c6dfbd\",\n  \"687ff629-39cc-4d3f-9163-1d722f662b30\",\n  \"8171121a-401b-4ad8-bfbf-0e0f89cad9b2\",\n  \"33e73a2e-128f-48a9-b24e-9075ba778273\",\n  \"4268a796-6e23-4a91-ac04-d577ee861889\",\n  \"e2dc50b5-fcac-41a0-b6a7-8246ad3c8843\",\n  \"dbda9273-17b6-40c0-b7f0-2f3ccee680f5\",\n  \"6c4e7113-6aa2-45af-911a-832d49fedef7\",\n  \"eea74d38-5db9-49e0-953b-485eda4375f8\",\n  \"a0456cd9-83d5-4d12-a42c-2d7e91c55dff\",\n  \"ae0d4324-7d0c-46b7-9a31-19f7f00897fc\",\n  \"c97fcdc3-aa66-456f-b059-b5ba056c6e43\",\n  \"e0a7a52a-88ba-44e3-a6c3-09c73b59403a\",\n  \"f53ea673-a0d4-4036-86c2-828e48247955\",\n  \"3551b40b-75ed-4759-b134-b8ee3a56cdba\",\n  \"150e5798-56ec-4e8a-ac38-5b66ac451c4d\",\n  \"9da174d3-6349-496c-8e63-21c15184c110\",\n  \"c8877c90-2ff6-4939-bdd8-95010343f7cf\",\n  \"8727031f-13f1-4776-a31c-17a3bf4f0eb1\",\n  \"e33bc582-54c0-465e-a9c0-c648480cd97d\",\n  \"2325b807-6fd3-4f78-a622-0261aac1eace\",\n  \"40d7648a-c7da-48f1-8420-cf05b53023ed\",\n  \"5d36b747-1d14-4c8a-b877-4f2043adbe0b\",\n  \"9b4b9587-b100-4dc0-a3f4-4f0ab11e5542\",\n  \"3cad65e9-fa26-4faa-8c1e-e844f0a349d5\",\n  \"8aedffa6-c24b-46cc-ac2d-cd56bbc5bdcf\",\n  \"fe9f3598-daf7-4a47-923b-48a1137f30fe\",\n  \"634a5b66-6465-4153-bfb6-b4e4815cda84\",\n  \"c63e850f-31bd-4157-abc4-cb28f540098b\",\n  \"510f68a7-bb20-4c64-b8b9-13586eb4e14f\",\n  \"b1cc4159-f638-40f3-a73d-9ad1f677231e\",\n  \"dcee340f-08da-462d-a413-bf378ca8efae\",\n  \"ae13174e-1d0f-4321-8ed8-3377d68d9545\",\n  \"a2b80ed3-f951-47be-8581-aac243fdc44f\",\n  \"6152dbe1-66c2-4d66-8993-35ded08b05e6\",\n  \"eb96a4ac-4d8f-4cf8-8e38-db5bde45c0db\",\n  \"bf55214f-d8dc-4c1a-8182-5ed5fbbddc3d\",\n  \"67d46f55-3017-48d6-b1b6-4d13928d102c\",\n  \"4bf392d1-0b4b-4195-9ba0-0469d130bccd\",\n  \"49931ab2-efda-48fa-bfac-3e772c6fa4ce\",\n  \"f3bcac47-09dd-4637-a79c-bc7fd7f9c778\",\n  \"16a4b07a-7238-4441-814c-eb6e38e038ae\",\n  \"9952a516-62eb-436b-8d58-0308e57d2355\",\n  \"73a37b38-db1e-4b3a-9d4a-fda5c9aee6f9\",\n  \"cf398d33-4c8e-4bd9-80df-b817198f0c73\",\n  \"67aa332a-9904-49d3-b870-12cec7b9b878\",\n  \"af508123-f929-4803-af17-c4d0f1a1ba0a\",\n  \"7e885495-61eb-48e5-8251-1d03ae11fd52\",\n  \"8bfa73c3-8451-4de1-8d42-71b97c6265b3\",\n  \"5f04f502-28a7-4ee6-9c72-9d5be5f9559e\",\n  \"5f3dc0cc-7e5d-45fd-a35b-d154ffcb8920\",\n  \"8842fe64-a855-4552-84e1-ea6c1f675bee\",\n  \"ebebbe4e-ef5e-4eea-984e-c6584352afb6\",\n  \"17c74efc-b27e-4e21-af41-9e06f67532ed\",\n  \"a2058916-b26e-4673-94fd-48706f420e88\",\n  \"3cc1f8af-4210-49e7-9d71-125245a42bf4\",\n  \"f7057b86-28d9-4523-a64f-5fc1b34f8994\",\n  \"8c612f0a-9ee8-46b6-bb91-d230d3dbfca1\",\n  \"fa3e4a6e-9eda-4738-8278-d7e89cf408f9\",\n  \"c1a8c70b-c0cb-4615-b574-f1503afe2a76\",\n  \"c8745d3a-106b-4697-a2d8-6cb3ea409bac\",\n  \"942901be-2342-4ac9-9a3a-a91ef225bcdf\",\n  \"c16ba447-b108-4f07-9cde-3054aabb4c37\",\n  \"cf6b0b76-4144-4b64-9ac1-7ada893fb0f7\",\n  \"ebc82dfc-9d3b-4de9-a4e5-d8323fd359f6\",\n  \"f0b5922a-da99-4f83-940f-7a1d2e4bb238\",\n  \"1e945057-c1f6-49d5-bef5-a0a732ccbd1f\",\n  \"7f64a562-8d8f-474e-b479-93e44c6db381\",\n  \"8e3240bd-9568-49c3-b579-4e71afa1ebc0\",\n  \"646c347d-f400-4c12-a020-280084bf78f3\",\n  \"4469e0b8-f7d3-4e91-b9a8-3c75f01f4f6a\",\n  \"274303ce-a344-431a-965b-667ae809635b\",\n  \"d9c174b5-5538-4b54-9d06-50e4cb68bd1d\",\n  \"5439617d-e4bf-4c61-a5fb-79f5339cac04\",\n  \"3467274b-0b3f-43e7-aca4-807892807b19\",\n  \"89d6f413-2199-440c-af9f-fb30cafbeaba\",\n  \"47751f0a-19d7-4864-8bf9-3c4b2b74984b\",\n  \"463f5a74-4146-4983-91c7-16a7e58c8293\",\n  \"6681545c-a1da-400a-8454-ee636a560f4a\",\n  \"28f02ce5-f4f4-41d0-8431-4e79cb5c21a3\",\n  \"b273535f-79c5-4983-9a2a-636709689281\",\n  \"6e508f8c-fe95-4d8f-b688-ac2d4cb115fc\",\n  \"db5b8070-2b59-4566-b883-a3cbdf5d8db0\",\n  \"36378806-66f1-4039-871d-45b0e721a3d1\",\n  \"5a63a46d-e8f7-4e65-9c6f-edbfe0e59c6d\",\n  \"7559a109-a08b-4269-ae0f-ce15c313e4ca\",\n  \"eb757762-cd66-4454-b78b-1b7c3332abd9\",\n  \"7693454f-6c6f-42c5-9c22-3b8cff655d0b\",\n  \"b9ffd974-ac97-4e28-926a-d9984a28f512\",\n  \"bef1a127-a587-4f09-80eb-8d0f6c21311a\",\n  \"04d688a8-3307-4091-be68-88e5cd809629\",\n  \"95975f43-913b-4816-bc8e-19fec1f95611\",\n  \"971e0d5e-58a4-4d41-9fd6-445a4d9712c7\",\n  \"5c4126be-7bb6-47a8-8bd8-6085e94fc3cb\",\n  \"1da6556c-4a7b-4aca-b65e-27846dcf840e\",\n  \"13e9550a-3c0c-4585-831c-f21a417723b9\",\n  \"1951ae58-ed02-465b-a9c9-039ed37170bc\",\n  \"e3def82f-8937-4b3c-b461-e7f093b0633c\",\n  \"d6badf8b-5d3a-47a8-90cc-2ed3bd64ea3d\",\n  \"bde54b2f-ad7e-4114-9279-857937535971\",\n  \"da1ddad2-2ede-4803-aa94-e6f8757b37b8\",\n  \"2b5f6e45-9e55-451f-a3f5-ea1eb264ef07\",\n  \"14dceb62-a6a3-4680-b564-30991b3b21f5\",\n  \"768d7d09-fea6-47a3-a47b-0f341f8446a0\",\n  \"af025608-d26a-4714-b5b3-639d42dc0516\",\n  \"b6d4cacf-f63c-4d07-b058-7cecc01859e7\",\n  \"4f1e72df-c411-459f-826b-9a2afdc9e374\",\n  \"e30a0057-5403-426a-9df0-4f9e009c5c50\",\n  \"41bf5b9d-6145-434f-8baf-fe1c9ca48fdd\",\n  \"a64a8f48-7a57-492b-a2b9-d49dbfe7c3e9\",\n  \"a093ae11-6250-491a-a631-af497f9f4e1b\",\n  \"08ae6e6f-ad7d-4ba1-bc2b-61b2b3fb6a39\",\n  \"1d5e886d-b3a3-43ef-bb0f-f7d0adb70336\",\n  \"f766baae-a769-48e4-9694-d1b4c0c26f4d\",\n  \"6d8255a1-b6cd-4ecb-a48b-7f51f3a6296c\",\n  \"91be2cee-ceb9-4fb4-9707-e825a7c1c1f0\",\n  \"2193c709-2e9e-4802-9b05-341cda1ec8a5\",\n  \"9c8440ea-be19-464d-9474-ac27f65b0102\",\n  \"ba79f412-db15-47ce-8ca2-e5eb8388752e\",\n  \"89048f19-449f-4802-ae24-bc85067b8385\",\n  \"771515a3-c1f2-4a34-91c3-bf91b155cf6a\",\n  \"6ecd782c-1b87-41fa-be19-bcfc15ed9e8a\",\n  \"598d52e4-d54d-41d2-93e7-2d66b7e80707\",\n  \"d4f1d09a-10c8-46d1-8a5b-c5d3d9fb31eb\",\n  \"00aaaeb6-59be-45f4-b22e-81d1e1420dc1\",\n  \"5d5eebfc-d71e-4b1d-98aa-ae2ecd07edd9\",\n  \"9d798a5a-592e-40d2-be71-fd084651778f\",\n  \"fda74860-21f3-4715-a33a-14edaaad08d5\",\n  \"42b2a787-b856-43eb-b8cf-3163e71e1e30\",\n  \"72c1765f-a3b8-4f64-aae3-cfe843079d62\",\n  \"6e220e42-cf4d-46ad-9de0-6d272e7c73dc\",\n  \"bda3dc95-f4b8-41a3-9d3d-09cfb3801045\",\n  \"cdfa1d78-82f5-4921-bf87-e6e3fd4a6843\",\n  \"9746309d-a896-4c53-8efb-ed1a1849a9b1\",\n  \"6042a2a2-9775-4ba6-bcdd-32224cdd4bce\",\n  \"8dd6b17c-ab70-49a7-bd85-9ba829e4ac90\",\n  \"219b6d45-6474-4a96-a88a-c815be657367\",\n  \"a800702d-a31b-4290-bffb-29c8dbfd5f57\",\n  \"dcdcce95-102a-46fd-854c-c42405b40b9e\",\n  \"e828dae1-442a-41fa-a006-8ffda501a3c1\",\n  \"650d114c-a5d0-4eab-81de-0711069b6666\",\n  \"3be99fcf-2e99-40c8-935d-80ee020fe5a1\",\n  \"ac70dee2-6ad5-45fa-a0ab-e3bd8b5e53ca\",\n  \"fb83695c-7b5b-4200-b08f-600837107071\",\n  \"5e42623e-cc9f-49a8-83de-46fee8063618\",\n  \"d76a9d93-439f-467d-808b-bd022eb19373\",\n  \"0c8f06af-17d9-4834-ba1f-da381f307c6e\",\n  \"e4ea6e68-9690-4931-a3ad-f58822f24366\",\n  \"ae32eba2-cf14-44cb-b688-34d497396029\",\n  \"d69c3e8b-75ce-4cdf-83bd-eefad5c362ef\",\n  \"1398375f-f8bb-4917-a95b-61542354e91e\",\n  \"18d02bc0-be62-4156-98ee-de0adf7b16f1\",\n  \"8685eccd-dd7e-43ff-b9a9-d0975a805730\",\n  \"43886b1f-787f-4b7d-8bd6-a7349dee7b3f\",\n  \"25c296e2-fe70-4f73-90ff-7cd24c5cd7e4\",\n  \"2f79f291-882d-403c-ab00-ca5b0008d7ea\",\n  \"73beacf1-cb91-42d9-9e5f-60b10968b55f\",\n  \"75f9f61a-a88c-499b-ac4e-34663a5fdd87\",\n  \"79b1b841-06d7-4ba7-a141-c4ee4af060d0\",\n  \"99548118-7a86-4bc6-a4af-4fec03d2df81\",\n  \"68a96307-17fa-43f6-97c9-391107a3f252\",\n  \"3520822c-ed96-4029-9662-c3823c3a1d68\",\n  \"bc8eb355-5b6f-452e-b732-bf5c7c5fe3e3\",\n  \"0ca2c38e-67bb-4faf-a343-094b08c05ed4\",\n  \"a49238cb-73c0-4e11-be0a-e4f0b239a55b\",\n  \"0fe47970-8edb-4889-bcbf-845ee959a39c\",\n  \"89ee3262-11df-4ffa-a5d2-dbbe39c0107f\",\n  \"bafb55cd-2825-42ed-ad43-29b0a1aed6c7\",\n  \"150e7071-1df6-4507-99cc-4b2d13eb1ca4\",\n  \"62b276de-56a1-4d93-83b2-aa6b7e1751d3\",\n  \"3ce7c41a-4869-40f9-b56b-c29210cd6ab9\",\n  \"f4c824bc-bbcb-4ad0-a9cd-a11656c922ee\",\n  \"f483aeba-4016-41e8-adb5-717dff724e68\",\n  \"6d649d30-f575-4c00-b9d4-9c08ace1aa6a\",\n  \"19c1ec46-0344-453a-9ff2-9c91e4483083\",\n  \"ce65036a-244b-45d0-a87c-f97b1aafa32b\",\n  \"e1a0e09a-e6ce-4e5e-9f57-c31f87c3ec3d\",\n  \"f9e63df9-4d87-439c-be21-e9e4d0d6f45c\",\n  \"09758423-34ae-41e7-a9e9-25455266dd79\",\n  \"83ed45a0-2cd0-4c41-8c85-7df6d7a0dff2\",\n  \"cceeb3ac-3373-4a83-9f3b-d0109d8afcb4\",\n  \"8acfc3d1-1e87-4d0d-845b-e5535ee34463\",\n  \"1fb49d67-ae8f-44ad-9d24-603114e1e2bb\",\n  \"3e5b97fe-63b8-4544-9a1f-7cbc64755cad\",\n  \"db488cbc-9685-45fe-b950-43f2abb08ae8\",\n  \"04f200b5-7937-416c-806d-e57f732affd1\",\n  \"53380a8b-d69f-4ed2-86de-e7387ff6bd6d\",\n  \"c66c4f8e-d8fd-49d4-8606-4cdc6f249f60\",\n  \"dc4985d9-9a48-4ea8-b507-c2bafed149f6\",\n  \"a228f18d-2843-45c7-8289-68bc62a4598c\",\n  \"2d3edc5d-66c9-45f6-90ee-a3fcdbafa130\",\n  \"d7e9c2a8-0116-459b-9c41-46c0ae80506b\",\n  \"96102d62-27c1-4847-bc24-1d9e81094cf1\",\n  \"bdf430ee-add4-45f5-a924-689305850297\",\n  \"b9cf8cbf-aa8c-42cd-a482-1dba9e1925ad\",\n  \"4f85509e-bcef-4ad8-994d-3ab2921ccee3\",\n  \"b9c43c11-650e-47a6-be8d-d7d07e1df885\",\n  \"4f8d8cf0-d6f5-4a80-86bb-2f08b2e5c63c\",\n  \"0dc2995a-3c56-49c9-9741-b3326149c714\",\n  \"16e0401d-1c82-422c-8ef5-5cd8ffe31dde\",\n  \"f05e973d-e88d-4858-b250-45e184055aa7\",\n  \"5146029c-c93d-4665-be97-c97a9c35e0f7\",\n  \"443f3faf-b7df-4a09-b850-f7e0e725675b\",\n  \"914f1d24-b179-46aa-b0d6-567ea107a850\",\n  \"662d3c2c-b730-4173-a967-17937ecd9791\",\n  \"ac0a2980-7d0a-419d-bcb3-c85901a9cb9d\",\n  \"8786abd7-2a33-4a40-82d4-8ca21c8e8781\",\n  \"d8471224-6dc7-4497-9423-aff19a5de232\",\n  \"25a5eef9-8c63-487b-9e09-61e809e2ca02\",\n  \"eaf9a497-4480-4ac8-b84c-bf977b0a7dbe\",\n  \"24114521-a7f2-465b-97e8-072551251b8c\",\n  \"de86ce47-4e4d-44b7-aae8-b218af26065d\",\n  \"f365401f-fe68-45b0-978f-45ead4bc52cf\",\n  \"720823af-d397-4fef-85cd-582192d06737\",\n  \"4e6e5b07-bbca-44db-bd6c-cff11d4958f8\",\n  \"499acd50-e292-4419-b1ed-41be4eab25c6\",\n  \"65a938de-6837-4ebb-a358-7e7c93223290\",\n  \"953b252d-0345-467e-a343-5ff2ff388029\",\n  \"29aa7e00-7ea1-4deb-a449-ed4044ce3e74\",\n  \"135ea2df-a1ee-455d-9d8d-d1ab86e29953\",\n  \"0e68bea9-fa1f-4d4d-8666-9e4e55759066\",\n  \"0bb0d4e3-8078-4cb9-81c5-4d161070a3f8\",\n  \"039fb1fd-eefe-451b-9365-4810a0c6e8f3\",\n  \"83f35f5f-9e9a-4d5c-bcdc-fa396c5135a9\",\n  \"9f1d7fcb-bed6-485a-9bce-ecdb2db896f4\",\n  \"92c30981-ae87-44df-b392-ab5d5023d4f3\",\n  \"e3c8d574-bec1-4e13-8327-568cb697093a\",\n  \"4c8ac4ab-1539-42fb-89f4-91ebc97b8e09\",\n  \"aa9100e6-3c93-4fd0-90af-9af455c6b9a8\",\n  \"9d3a6b67-54dd-44f7-95c1-2bd170ce6d4c\",\n  \"05a25554-d080-467f-b312-64f39d87569c\",\n  \"5de8272e-007f-4a3e-abb9-9d10402c4b8a\",\n  \"d6f96d6c-8dc1-43cc-b135-6ccd03cfc441\",\n  \"bae3274d-dc15-4f29-b79a-f121a93f2b9b\",\n  \"a2ce2505-5347-4815-8066-6a2aa4f4074f\",\n  \"10c44218-c4a9-4162-8639-f57a36856462\",\n  \"1c168d66-adc6-40da-ba02-f351312e4ad7\",\n  \"a4f20cf3-9965-4cbf-80fe-1ad9efaf61dd\",\n  \"d62e640d-349d-4990-95f4-e47acb33d6f6\",\n  \"b79591f7-11f0-47b8-9ee3-1447b16410f0\",\n  \"28e34ce1-5c3c-4f04-80ed-98823d19392c\",\n  \"1f54e2e2-4410-4e2a-8c96-50428b265382\",\n  \"eb44d841-3ddc-441f-851e-d0c1a56921d0\",\n  \"5d943391-8d4d-4df8-af95-a9f8a8d2be4d\",\n  \"e504c66f-0fc3-42f4-b04a-e0cd7d2411e1\",\n  \"a0527a40-30c7-4c14-bc85-76d5da491e30\",\n  \"4cbf6ae4-5fc5-4425-b5f4-14f4518bc73b\",\n  \"d1823203-9336-4ffa-afa8-12b647330108\",\n  \"4ff56cbb-7d84-4689-8d62-1a02288e0655\",\n  \"f282ed17-7150-47b5-887c-bf85f2fc1d4d\",\n  \"cf815ad5-e04d-4e26-9447-83678bd5c109\",\n  \"3fa7e701-6123-42d0-94ad-6acd80ef1eef\",\n  \"7ec3cdde-1ac6-479d-9d4e-22a9fc7a822b\",\n  \"2ddacd5d-114d-4be2-9dc7-eb8a8f8af3b6\",\n  \"534bbee4-84d0-4f25-9c34-28f09f0bb23b\",\n  \"51c0828c-059f-4de9-bd3b-894476dbd205\",\n  \"6155b840-45b7-4724-9276-55f2ebd4f0a7\",\n  \"a9e33374-a10e-4871-bd2c-4119aea0c9bd\",\n  \"57e6f802-d52d-4617-a266-6f9c1cef81b5\",\n  \"34277fff-e238-4abf-94e5-a6bf3b117fb2\",\n  \"de4c1b25-68b3-4f32-95b8-3fee92e41b8a\",\n  \"a73fbab3-bd6d-4419-925f-3bb9aca70a52\",\n  \"a113a4de-0853-44be-901d-6b5e43cd252c\",\n  \"33745ffe-2c85-472b-a408-a294ea7414cb\",\n  \"69e16c9a-bd59-428f-969b-1db931953294\",\n  \"c43c1473-5db2-42fd-a027-9ec06a02a4ae\",\n  \"5cbbcabe-bfde-47d5-b4ce-0d8c732c1c10\",\n  \"030f7a81-35dc-4901-85d6-a5535fe7e6b6\",\n  \"fe859508-372c-4a3a-bf0d-778a709133ed\",\n  \"e857fe2d-58f2-4889-8da3-45713b080bbd\",\n  \"39c63943-0dcd-47bb-acaa-7ba5389be2fe\",\n  \"7365c9de-d067-4510-bc9b-74f574ab53b5\",\n  \"85386bb0-0d3d-4a12-a6a1-7d79e67a8258\",\n  \"aa43b33f-23f1-4926-af14-02e825e8875f\",\n  \"155cc293-6b65-4419-ab7d-16df5f65ab24\",\n  \"f6d21924-48fc-49b2-8b4a-911227e16bcf\",\n  \"188a9e7e-a257-4b2d-bdb3-fbdda36bc0d5\",\n  \"6fa23c9b-2b2e-46da-8616-250c380e16c3\",\n  \"da8806ff-eef4-4865-b68b-964312da05a2\",\n  \"af7db4a5-2e7d-4b16-a4bf-43d1967fc364\",\n  \"bef7db04-6b2d-4302-8b6f-9effc9beb9df\",\n  \"91d61deb-691d-476c-a172-db9ef1a14d1e\",\n  \"4436be7a-e011-43a1-ac0f-0e170cfbb898\",\n  \"4c190a53-ec0a-4d38-8c5d-a56917296378\",\n  \"e4830c81-e978-4d35-9f85-686517d29854\",\n  \"ea984a11-42b8-4b35-9689-d38048c17ccd\",\n  \"1b8e4535-1606-41c3-8c93-6c374041ed3c\",\n  \"379f30b7-fba0-4bb7-83e1-75a77d135227\",\n  \"11821886-463f-4656-a439-d5c095ffde2c\",\n  \"a9582286-17dd-4319-b081-bcf467c33dee\",\n  \"b758f9d7-a8ee-4bed-8019-edb49ace9724\",\n  \"45916038-f6bc-4600-9e8c-2ab3d4bd7574\",\n  \"3945841a-f24d-4a3b-b876-07779dde997b\",\n  \"0e1b59ac-61e2-4837-ba78-d378634383ea\",\n  \"99754693-7f12-4499-9fcd-e28102b1c480\",\n  \"1b0d1f98-b36c-41bf-9641-1288987efb8f\",\n  \"e4c47f3a-d483-48af-ac03-05b6c1da5a16\",\n  \"8cfa1657-fdb5-4d47-ae60-eeae13952e80\",\n  \"a330cdee-4899-4e1d-b791-bb701f91b6f9\",\n  \"c00cc210-b0eb-42d9-a221-986c694abfa8\",\n  \"59d89525-c285-4239-a018-1732313797ab\",\n  \"c7a46692-5017-4f50-876e-b619650b3d74\",\n  \"fdccde8e-8353-4aea-8115-aa61b8886765\",\n  \"6c94cb26-d141-4b45-b124-babc0434e50c\",\n  \"e52dfde0-d9eb-4088-960d-8e72efd9ec10\",\n  \"179755d4-777b-480a-bc01-625d7de030ff\",\n  \"3fa561f1-7ddd-4fa4-abc2-6ecf25bed8cf\",\n  \"86294e42-1bb7-4921-9fa6-ef86973085db\",\n  \"b7a8d110-2d3f-4366-b893-8b3c0ad47b55\",\n  \"9748a03f-ea81-46a8-ae80-8ce6a5556a03\",\n  \"033cfe0e-d643-4024-84a4-69699dfcb375\",\n  \"6c8a0e71-7d6d-41c6-928b-14d0528fb00f\",\n  \"89aa225a-c4bd-4fd9-acc3-1813a2eb6261\",\n  \"6e62ce94-8011-4157-b476-1060223143f9\",\n  \"4cd39c35-7082-4802-a028-348a5cc1309c\",\n  \"79a7c477-29ae-4aab-82aa-fc047fde1503\",\n  \"2f575583-8f5e-4198-9dc7-51dfe32920f4\",\n  \"69cc174a-9254-43a9-8043-0406a18c8fd0\",\n  \"f3eba694-1f4a-482b-b4b2-3519dd5462e9\",\n  \"86fc0aa8-6f08-401e-8044-d9e6ae172674\",\n  \"1fb331ed-4c54-4da5-9374-d7025397856d\",\n  \"6681560d-0f7c-43fd-bf1c-b02dad7b5d39\",\n  \"d09e1290-8041-41c9-96ea-182d88c0b9f1\",\n  \"6cc67799-1539-4859-a0d6-d41fe019c058\",\n  \"1c212034-2598-4d92-a069-3f0f4f8a66a3\",\n  \"d6f534b7-0af1-4ab3-a3fb-b208ff24afd8\",\n  \"19b47aaf-f5c5-4ceb-9e2b-27d72dcb4458\",\n  \"19d89031-2b00-40ee-8ec7-247758829e76\",\n  \"9c25d4ba-c9c2-450c-918f-82915c878478\",\n  \"78750bc7-75ca-4c4b-b910-eacae0dc57c3\",\n  \"2c96376f-01d7-4e81-b0d9-de689e83741e\",\n  \"221696d3-7448-4adc-83c1-89e347a79a71\",\n  \"887f86ce-0299-4c30-817b-e065509de907\",\n  \"1008bcd1-01e4-45e0-ab90-7094084b0abf\",\n  \"e8762a2b-5549-437d-b6aa-78e0fd44a5cd\",\n  \"20fb05bc-5f16-471d-ba72-f69c99652518\",\n  \"23d827f7-fd3a-4540-9803-b499586bb77a\",\n  \"0d5193b9-e727-49e4-909c-ccfdd0cc37a4\",\n  \"ec59886e-fd60-4886-80f2-e599e0e2df27\",\n  \"5c6d81d1-971a-42ba-b351-5ff130b2c8bf\",\n  \"23daa4bd-6d3b-405a-8a4f-4b64f90f80f6\",\n  \"069d9360-d894-4712-b13d-df7990edcb38\",\n  \"43123c9e-730c-4e1f-a843-a66fee78f9a0\",\n  \"e1f60541-ee1f-4bb6-92d1-86ae048112a7\",\n  \"b0d64bdf-070d-4d4e-9836-97ca64a14c76\",\n  \"24c623ea-3ef6-4f3c-9735-d3997bb0b2c9\",\n  \"8c602f66-c1f9-42ce-a410-356e602305d5\",\n  \"f5f5e4b8-16fa-4f3c-8d7d-f8dd01e4203b\",\n  \"3f57f611-367b-4775-811c-e5a524663806\",\n  \"d4d575de-4a2c-4658-aafa-e788bb679711\",\n  \"3125ccfb-bc4d-47d8-acf3-d353dc63f3ca\",\n  \"d9ef4070-594d-4b2d-881e-1f9474be5815\",\n  \"a75afc83-9260-47cd-8362-ce9efadf281a\",\n  \"d2c799eb-fb45-4bd7-9f98-cdc7faefc401\",\n  \"5942bcaa-07fc-4b86-9d58-4c9b8a96b48d\",\n  \"473e5606-5961-4a4a-98f7-6ec44cec6d3b\",\n  \"f396b9f7-f156-4db8-9167-a74021ccfcdc\",\n  \"4902af06-be46-4779-8156-571ff8e00c0f\",\n  \"43cc8606-5b4f-4685-a266-79d32fca3a1d\",\n  \"0b6d531a-d8e5-4576-84a6-dd9311ed3693\",\n  \"37fc4b40-e16c-4636-8732-834b03f98a2d\",\n  \"6a45c5b6-d752-46da-92cb-05877b457452\",\n  \"3cd61c1b-732b-441d-ab91-dd2469680342\",\n  \"e989c02d-d677-49e8-a211-cbf24a4a490e\",\n  \"c353b32f-27ef-49a9-ab5b-406a069a45b0\",\n  \"f1350459-1b38-4c4f-9385-967da4e95733\",\n  \"0cb9c18b-3ff2-4b06-b251-0beeea271ea9\",\n  \"3b5192d9-b859-4ed4-a01e-83a85a49bb6c\",\n  \"0b4bbdb0-183b-41bd-bb51-22ade9cbf873\",\n  \"f5d41bfb-ec2d-4d4b-b10c-d0dabfcb79d5\",\n  \"9f52cf1b-16e6-4d3c-a3db-6195cefb25b6\",\n  \"4ef8d119-edc8-4d69-87c1-449752fda426\",\n  \"1d931845-975a-476c-8f9e-e91de90bea42\",\n  \"752e8fea-23cf-4d64-a297-2b9282a0c008\",\n  \"c3336d1f-fb86-4d7a-9e34-0b3a2a3931a8\",\n  \"1805ed42-baaf-45c3-a54e-6de03812b9fc\",\n  \"c45d7434-289a-42f2-9460-cdc3408a3ded\",\n  \"18801c9e-631a-4f33-ab4f-aeca06388738\",\n  \"fcb7c4c3-b7fe-495a-9857-861e74b60143\",\n  \"fc5cd9c8-7c91-4a4d-9938-4ee49964c7d3\",\n  \"f4df7d53-3e72-4cf0-8293-423047d0508a\",\n  \"734bf1c8-a7bf-47e5-9027-e4bbc3713827\",\n  \"0c71e527-eb88-4019-8fc2-14dfebd53bfb\",\n  \"8483bc50-8a3d-483a-a5b7-ad3454f3d469\",\n  \"6ebe16db-3f43-468e-8b0a-947d690cf9c1\",\n  \"dae311d2-64d8-40a9-857f-b9bcd283af5a\",\n  \"b53bdbd9-eb02-4f6c-a223-17f110e5b39b\",\n  \"8c68d09a-2320-4a2c-80d1-2ceb36506085\",\n  \"df8eb1c5-7dc8-4555-8b2f-079d6dec8ff1\",\n  \"624362ae-136e-4a07-9fee-69ad20de7daf\",\n  \"cc254a79-222c-4cd5-941c-f22fc1de6024\",\n  \"f87a5392-1ab7-4b3a-a398-bbd4e43ab088\",\n  \"ac3a35bc-c914-47a7-809d-136bf39cda61\",\n  \"9602d472-a237-4b4e-b8a2-9955f28748b6\",\n  \"4452158e-3044-4e0a-b8cb-619b54e2c240\",\n  \"109ad519-676a-4183-b83a-651b6bd3ab69\",\n  \"4e4d29af-71df-4986-aca0-0cc609f322b9\",\n  \"29070ac2-182b-4a1d-b554-4ea773443a44\",\n  \"de91aa45-bca1-4683-895a-c8a392d80f53\",\n  \"9a903616-4fd0-49e6-b721-09df7371f028\",\n  \"5dce2363-6b7d-462e-adaa-41b2479ecab2\",\n  \"4d1a4bed-8973-467a-800a-f08a8f5e2166\",\n  \"54242437-6b3a-465c-a50f-27a1f8962df0\",\n  \"4ad18a5a-0b1d-4c62-b9e6-1fc5657f8ff4\",\n  \"4819416c-be71-40be-9230-a400a9c633a0\",\n  \"1adc5a89-fb0f-498c-a901-24b09ce6a36d\",\n  \"eb23cc96-3534-4492-9eec-6bf1d4811512\",\n  \"1d56650e-fc64-484d-9668-0b7e40d01b2d\",\n  \"531d90f7-2a8f-4c5d-9553-4446d8050d10\",\n  \"8bad8289-4c6e-4f40-95e8-4d3ff326d154\",\n  \"b2da97e8-3b50-4585-bfab-5de34b2ae07e\",\n  \"8bde6ee5-5533-4910-ac7e-b0cd98316c3f\",\n  \"f697eae8-504a-4227-b6e7-3511d1c69732\",\n  \"813e8d3a-a699-4758-b639-59e00b9260bd\",\n  \"c95293fb-cbb2-423c-8bf9-b640ab2a22bb\",\n  \"25479e98-5ac7-40bb-b139-be6fb38dc2f0\",\n  \"97e90d23-6432-4760-b4e4-d53a30daa36d\",\n  \"0541bc1e-85dd-416c-b338-7b436d21dc6c\",\n  \"4dcbda76-baca-41fa-95d4-5bd8fce7c221\",\n  \"355f1062-dd50-4f74-80ff-b57319330922\",\n  \"3ee9288d-6736-4db3-b53b-715d1ebdbce8\",\n  \"cb42722d-0b4c-41b1-81be-590b165417d2\",\n  \"a51bbb79-216e-4f17-b187-072830b14e44\",\n  \"5e6ccf64-3fe4-4650-8ecc-f3fb85cd2b70\",\n  \"c3b9ab9b-d06b-4dd6-be5d-614088d91a9b\",\n  \"7eef7499-e55d-44f5-a5d8-bfdaadac4459\",\n  \"7b1aa67a-a2a5-4810-af45-882b0404279f\",\n  \"63d4ab10-6c6e-4e0a-b7fb-c3b86fccebd3\",\n  \"1474a155-961e-4390-a240-49b0304fec09\",\n  \"4a1368a0-d53e-4fbf-a69e-1408b8481a4c\",\n  \"2f70889a-5c5d-4d94-b9f4-6e10b27454a5\",\n  \"173de038-88d2-4741-b9ec-1788ec5150f5\",\n  \"2d21bf27-262c-42ff-8aaa-6bfda3148577\",\n  \"624e60d1-4448-4aab-907a-cfc17474bae9\",\n  \"21fb21df-4067-4039-959e-69e400738e35\",\n  \"9ddf2539-6dff-4784-ae13-824afdf936e2\",\n  \"4d52c4c0-c6a7-4860-8a95-a1f1d8be1b59\",\n  \"20bcb22d-1ad3-41e0-bc55-4dafa184a9d2\",\n  \"26cf6da9-6cd9-4861-8f58-62fc5e59b8b0\",\n  \"531c17dc-59fb-4c8f-8c0d-6266d32baafc\",\n  \"56fde1a6-b7e3-4c19-8af6-0306aca24144\",\n  \"8919aa29-a79a-48e0-97a0-ce97dfac232e\",\n  \"a26f30ff-0143-4901-9f85-e343991aba13\",\n  \"6603d890-8309-4c93-919c-019b217c421b\",\n  \"f7491953-f59d-4dba-bfc0-584dd8b235b0\",\n  \"b563d2bd-498a-47ea-bc6a-7715c64e1c7c\",\n  \"67f7dd86-0e6d-498b-8e8d-1af4311ef968\",\n  \"08e7a242-9286-496c-be70-02d071c51ada\",\n  \"309238ef-5213-4287-a8f7-3ab0dd52736b\",\n  \"02f378ae-dc53-4e87-8765-fd8097d629cc\",\n  \"763384ae-04bc-465a-b3ed-67691d88cb35\",\n  \"e90ee444-1043-4fb7-95af-96c04ca235e0\",\n  \"ab692d98-5c80-461c-9395-d799f1f7ef23\",\n  \"d9c797d9-9368-4e7f-aae8-fd70474286d9\",\n  \"b0657a1f-535e-4202-b81f-3d8a6ebd9988\",\n  \"65c02cd9-83d2-4087-92f1-36664c0bcf69\",\n  \"6d0be91f-cf5a-44ea-aac2-8bb04c338e16\",\n  \"248ce4be-c861-4bde-9c5c-a6e652c050d9\",\n  \"0955d808-f3a6-43b3-a2eb-09c2b14369b0\",\n  \"6254b10d-26f3-4e42-9595-d246a5d21bc9\",\n  \"22b3790e-27a5-4a70-a2ba-a1004ce8b4ef\",\n  \"9a272a1c-50a0-4e54-a052-e52fafa9b0fd\",\n  \"1220095a-055f-408d-9f53-d66f54929739\",\n  \"61fb0169-e601-4126-a8dc-a370e5a9cfea\",\n  \"d570b9d4-00e8-4f13-bd5b-39d15d7f0738\",\n  \"397c6811-596a-42c7-880f-2d2a9a1440cc\",\n  \"3de83734-a4e2-425c-aec0-28185e088600\",\n  \"122b7156-25cc-41d9-81e9-758d404b6d10\",\n  \"5d3dd6ee-8767-4431-a7f1-56f598e65d60\",\n  \"a3754e50-2335-4a2d-b987-8d5d21cea905\",\n  \"861a13f4-16bd-4c2f-89bd-8da98f9b5804\",\n  \"3b0380d1-e202-4d8f-9d6b-dab3b38035a0\",\n  \"2c59e9c2-466e-4d61-8af1-488380dd2b33\",\n  \"aa41795e-9027-481c-adf3-a1264eb3af3e\",\n  \"880581df-f506-4531-9020-074924637c9c\",\n  \"a899598c-c243-4906-81e0-22f0dc2be9df\",\n  \"3ed9495b-a47d-44e7-95e0-0a69d47838fb\",\n  \"88ff9cbd-f1f0-4ef0-8b6c-dc5ae9800369\",\n  \"dbac67c7-694d-4cca-aca6-d13e0a6aeac0\",\n  \"9cf2562b-2f8f-4c92-a8e3-a9dd18b53754\",\n  \"9912560c-30f0-47be-add1-aa2445009c89\",\n  \"cdcdcec3-2c60-4452-82f5-d786657212f9\",\n  \"851a222b-ad56-4f02-864f-b3a75ca86c71\",\n  \"c7b6717f-acf7-4ea2-9fdf-bb2dd72c6932\",\n  \"c904a46c-872c-45b9-82cc-f8bd32aa8dcb\",\n  \"5990a66d-b1a4-4be4-b629-29ed8ca644eb\",\n  \"d9e6de20-af39-47e7-b0f2-ac3e8700d999\",\n  \"81ab55c3-54ae-4992-8359-51c7fe342784\",\n  \"19bb3210-4747-4575-80d6-cf665344224a\",\n  \"706cda03-8dc5-4ecf-9b19-38836d622d89\",\n  \"a2a19be9-61ef-4b61-9074-8aad49431d8d\",\n  \"3027fb92-c85f-43d1-977a-5cfe4130cfd5\",\n  \"fe02cf64-a065-483a-9531-86d7c2d1c8eb\",\n  \"41e6efbf-f55a-4a13-83a1-9da7bcb39470\",\n  \"79d1ebe6-5555-435a-90ab-a05fe1916fbb\",\n  \"d93be80c-8553-47dc-a8e7-bb3714a99905\",\n  \"a5a49af0-eada-4cad-8d89-a065787a1e49\",\n  \"5e8bddee-652a-46c4-979f-7bc5a9a4b04c\",\n  \"366b6b1f-d2dd-41af-83e7-ecc755df60e7\",\n  \"0b66efe2-54bd-4713-8eae-fb976f95e73a\",\n  \"d89655a3-6fbf-48a5-86e8-98c6a0fcf0f7\",\n  \"ddbfc22f-5607-4ffd-b0fc-75a9dff0ccf8\",\n  \"6d8f5811-a21f-4714-85bc-24e3011889ca\",\n  \"7619ece3-6d27-4e5e-b32a-58ce39343bdf\",\n  \"d07e79d2-9edc-42a4-a619-9b449213cf99\",\n  \"37ebaa05-3c41-477a-9b87-f76dab4d0a77\",\n  \"9418b4cf-dabc-42f5-9c4f-a86568197684\",\n  \"e4fedc74-accd-4b20-9289-6b9cd5ddc2c2\",\n  \"7d2981e0-1068-462a-acbe-32f5edf74896\",\n  \"745f5730-042b-4ebd-8223-baf1301ccc98\",\n  \"b589f98d-6221-4021-a373-d54d668932e9\",\n  \"da12f4df-c18b-4b60-9d22-7c6e8743e608\",\n  \"66db2f31-71e9-49ee-b330-2b6cb56472d3\",\n  \"a231f839-236e-452f-a49d-4d261916746e\",\n  \"10ea0b3e-26f0-4b91-9fcd-ef429e7f3b3a\",\n  \"793edbe3-8e2c-4d62-a1ea-0c2136d691e6\",\n  \"df6d1c62-65b3-4116-a2fb-a708b9aea02b\",\n  \"b4cfa3c2-f151-4cf7-bf97-b27026185644\",\n  \"cbc613e3-53a2-4128-a6d3-c2a78d7256ef\",\n  \"bc39b7ed-e597-4b9e-bbbf-5081d8b14f8b\",\n  \"aa04832f-09ed-4e5f-a000-d20147399f4e\",\n  \"349da8d8-0124-4a4b-b02d-9e913d97a739\",\n  \"53570856-d00b-4e75-b95f-59d4cf0cadf3\",\n  \"1a2bc97b-c542-4f76-ab34-a8bcb59c76dd\",\n  \"d3f03721-0800-4c57-a42d-f88b76bf0ec9\",\n  \"ff104c11-e28e-44fc-afc9-03a03938eeba\",\n  \"65e6c4f9-1264-48aa-89a8-2a00df3bd77e\",\n  \"39ced82d-6975-4b3f-9b87-041e38d24deb\",\n  \"29ca43b8-900d-4aa7-8367-32584a24b0c3\",\n  \"3a72484d-1071-4be0-a1d4-2e7de043b7f2\",\n  \"01681e24-f5f3-48bd-86c4-91ecbcd26585\",\n  \"0a464f3e-97ed-41ab-931a-1720c098a5da\",\n  \"20432b90-ccfd-44a3-a0ac-da6758c3ae45\",\n  \"5843d223-7be6-4206-8fc3-e0a22363f51f\",\n  \"a49826ba-a660-4605-98f1-b6a35a986ee7\",\n  \"faa799da-6247-428b-b518-49f286ee19ea\",\n  \"3c58139f-dd7e-4f9f-8a8f-2ebcb3726e34\",\n  \"2b0caf4c-492b-4a1c-b499-444f4a178da0\",\n  \"68c198a1-8cc8-4dc0-a62a-8eb60b101dc1\",\n  \"f11816b8-8adb-41b3-8a2b-0ef976d8af29\",\n  \"aa8f9c60-7613-44b9-b020-c44c1e5464fe\",\n  \"24a917df-4ccd-4899-861b-5334ad2fe73d\",\n  \"136dc01b-ecf1-4dc0-ba20-53a6451fb977\",\n  \"a0b3b305-aacd-4c0c-ac39-16ad7e561bea\",\n  \"d6f7b8f1-03d2-4454-85f7-1ad3c31a4e48\",\n  \"b80d9232-b4e6-4bc5-b64a-55d3547ff360\",\n  \"a8fe4907-8aae-448f-bd49-9eee6d7d1902\",\n  \"bd2c3cc2-8c12-4d25-a48a-1b754591ebf3\",\n  \"d07f3aaf-6953-43ba-a82e-e34be61353f8\",\n  \"955ab73a-fb50-4f50-8a45-474f16d2bfcb\",\n  \"2eb5485b-27fe-4f57-b9c4-efe93289a97b\",\n  \"14fd10b4-a11d-4c44-9a30-d3f472725eb5\",\n  \"00eed0a3-af84-4cb4-a085-0a6b54e670c9\",\n  \"c66c0668-4254-432a-ad3e-f91e01306749\",\n  \"5ebec6dd-efde-4311-a27d-26bd7f8665c3\",\n  \"7b68c33c-fc33-4688-99fa-09ef769c16d8\",\n  \"28e5c322-ea5b-4ceb-a854-7bddcb127217\",\n  \"2cd7af34-dbb0-43ba-9ece-f207894caedb\",\n  \"278e1720-f1a1-4587-af96-06aa82621db4\",\n  \"bf225733-de28-41e2-b327-9fb171847619\",\n  \"e7c33244-fdba-4390-a4a1-086775550edf\",\n  \"70cae94a-97b7-4581-a9b8-e84a9fc03c62\",\n  \"9cdd12f0-02f6-44ab-b9b4-9d15be5d243a\",\n  \"ab5a8a3b-1e8a-43d5-9255-d100b481d846\",\n  \"a9a37371-8122-4de3-9e8a-8805aa4861b7\",\n  \"9152f4c3-b5dc-4646-9996-ba37926960cc\",\n  \"97d85edf-0215-4c2c-b278-03d2673d5f85\",\n  \"0ee71a38-258e-4adc-8531-3b09fa6db9ae\",\n  \"fba68cdd-3d59-44e0-b5e3-5a677abccc4e\",\n  \"4299b8de-5579-4c11-810b-78d588e160d1\",\n  \"7c373b2a-3dbd-4c97-8627-1a270150d488\",\n  \"53ee856c-49e1-4063-a6ab-642b4c89127b\",\n  \"ee022616-c223-4cde-8d43-f5060343b162\",\n  \"0733c837-35da-40a4-9dbf-39f95610044a\",\n  \"0e478964-e9ca-4217-b2a9-438dec182325\",\n  \"0146cd23-2421-4fd2-924b-c0da26104b6d\",\n  \"d98777d7-9e41-4ac6-a095-64921f7675dd\",\n  \"0fd82fd4-e7cd-4a0c-9a41-8abcf842636a\",\n  \"c9659153-7e25-4272-955f-842c53b26b7c\",\n  \"6588f75c-4f7c-4635-a57d-909115c83a70\",\n  \"ac4b4d85-e382-4894-8d77-dec3b56f0296\",\n  \"e1e773c4-a90b-4b3e-8c6e-164b12d45cb2\",\n  \"c9872da9-7c14-45c4-b282-393837a329e9\",\n  \"24fb8585-84a2-40c6-a19e-b64a4202fc4c\",\n  \"6d97e0d9-18ba-49c7-9924-3d0712a6f082\",\n  \"c187faf2-a35f-4141-b70e-bffaef8a490d\",\n  \"a93aff98-342d-4b46-99a2-8861eee4384b\",\n  \"2ad09b80-6a12-4825-a501-7eebddeabc97\",\n  \"434125bc-4fbb-4246-8b14-b4be998712f7\",\n  \"86084d15-1bae-48ea-bd79-30ab393e1f26\",\n  \"29dd2c92-0c6c-4d88-a644-0094873e58ba\",\n  \"29914fde-7737-4fcd-83a5-e3180a052278\",\n  \"fb23fa9b-440c-4696-b055-f49c6bc5b60c\",\n  \"549dce00-3c1d-450a-9a6a-16cae5f462cf\",\n  \"05215690-3479-40cc-ae3b-a665e9c4ff72\",\n  \"854ee1ac-bb49-433c-8637-f90d4c017691\",\n  \"ab1e5a20-bd22-4269-97f4-c870dc742675\",\n  \"a59e09ce-db90-49a8-ad7a-65ea561ca2bd\",\n  \"7e0fd8bd-dd66-4436-b5b3-76b68711d301\",\n  \"02291acd-c9e2-43f1-8f35-abbce4b7fbc0\",\n  \"1847ff56-ca1a-488d-929c-52e86a119af6\",\n  \"0a120b09-8304-4810-b9c1-0b6fdde6616d\",\n  \"0e6c1471-f11b-4a19-9f64-abf9aa877d15\",\n  \"48b77983-9beb-449b-93b9-642554188b26\",\n  \"bcbd0826-5379-4da7-beee-15b86fc526ee\",\n  \"0ca49c31-213f-42a5-a24b-7671b0e93a5d\",\n  \"b7d61e05-de79-415f-893e-cf3f7f9b10a5\",\n  \"471415f4-43f8-438f-a7ad-ebc2390bcd95\",\n  \"74f90639-7cce-4646-a83d-0716540a0bbf\",\n  \"1f093fce-7a73-4c16-8c14-4a2311be3bdb\",\n  \"df8a7e0c-ff3f-4d44-8976-9b0a3c1e3b25\",\n  \"9a563512-cce7-4779-93dc-cd02ec266d7e\",\n  \"167bb224-3529-4dad-9a66-2055b7cfcec8\",\n  \"c405aff9-d064-4c5f-8efe-2dbd5c5090bc\",\n  \"bee820b7-14d6-4de1-b461-3947ad1ef6ac\",\n  \"6337c8fc-0856-44ff-b6d6-297608c4e1f8\",\n  \"4e9ffa16-641a-466c-9216-2b9548644e42\",\n  \"cc100969-799a-4394-831e-52ae4af45a04\",\n  \"2131d333-84df-4654-b686-db1e31143e29\",\n  \"130fc001-6382-41f0-bd40-013724eac6df\",\n  \"ce38ec2d-3547-4c96-a6e0-061d051bf58b\",\n  \"3b220a92-eafb-4c31-a704-1d100d31536e\",\n  \"802ed04c-11e6-4ba6-be8b-47e4e0404d0d\",\n  \"025dd206-d8db-4ecf-b7c4-3e3865afd30f\",\n  \"3e1b1740-2d17-4bf2-845c-6f5aff1a6da8\",\n  \"6766fd19-c9a6-4664-9bea-1eb329bad346\",\n  \"4c851e0d-993f-44bf-b8f2-5c929ea66b59\",\n  \"d779375d-684b-47aa-bcb2-5878087326b1\",\n  \"82305ea9-4546-47a0-9623-6484cd9104a7\",\n  \"9b210361-f80f-48a0-910a-7a63122646d3\",\n  \"e252f840-876e-42a8-9158-20aa4cda7ad1\",\n  \"06378b6b-201d-4cc3-9f9c-1e0e2accec77\",\n  \"b255446a-bde6-446c-b112-d97ec28ee711\",\n  \"820daeef-4161-4e30-925f-2d62e64a7ba1\",\n  \"a2016d07-d7e3-43a3-96a4-eda78305e54c\",\n  \"e2c207e0-2eca-44f0-b35a-5b48840fa607\",\n  \"2ab2c3f0-0fb0-4110-a936-7d45f2a293be\",\n  \"26b0a14e-e974-499c-8755-940a32182c9a\",\n  \"b4cbdecf-a41d-4b3e-9b05-93968d53e331\",\n  \"7cde7449-dace-4f36-99da-5448bd3fb5e3\",\n  \"34aafff3-622d-4800-92f9-4daebd8964a1\",\n  \"dd6c06f6-bf50-4c32-a493-9405361b08b2\",\n  \"01dee10f-9cfe-4d81-8120-7e67ffeede53\",\n  \"c80e0951-c4dc-4b62-9baa-92795bdc5e08\",\n  \"5b590f59-eb5a-45fe-b5e7-a7c9f7241c19\",\n  \"0a790ba6-ef6f-4a7a-874a-8a58d4393223\",\n  \"25902a9b-c5d8-46f6-bc3c-ec95805703ee\",\n  \"cb48c0b0-3fe7-4342-904d-79ff964d63a9\",\n  \"dfa30c0c-930d-4f64-bfc8-3318698a2aed\",\n  \"90d19693-6e90-4b38-b1a5-890cfc3a71ef\",\n  \"eea56151-300e-407e-af34-1af05b88ef2a\",\n  \"930424ea-3100-409c-b7b4-99042f880c7b\",\n  \"10fe0d71-a41a-4e4d-b3f6-74ff2dace684\",\n  \"50dd6a36-6079-4c00-9d69-867ef8d4ed15\",\n  \"759c4696-8784-4dd6-9b0c-7818b62576be\",\n  \"ea8658ac-b7a0-4d40-a255-1efc6f8dbc9c\",\n  \"a0f5ee2c-f6e0-450b-a0f3-82ebc74933aa\",\n  \"ae109ccf-086b-4e0c-b7a6-9bdf2e317c8a\",\n  \"b4d1edba-9653-4b49-adb0-f23311dfbbe6\",\n  \"f30afeba-cabc-48df-9b8d-26b09a57cccc\",\n  \"86087995-4d16-473f-933c-756289f48c0d\",\n  \"d9f67ed7-5110-4c5d-881f-55404e2c5a9c\",\n  \"28e8475c-d25c-4f1c-99c8-d139e68bf0c1\",\n  \"3f96173f-c490-4fce-8aa3-714193498301\",\n  \"8631c2ba-2dff-4702-8c13-28d90c9c804f\",\n  \"eb585aa7-4490-49ce-83e9-9564912d3c6b\",\n  \"723f59bc-4d5f-4539-a633-fd00bf6c6ac2\",\n  \"510c8398-82ee-4aba-a28a-19c5428516f9\",\n  \"d0759921-1f95-4645-a80e-5536d66b5fe3\",\n  \"b6d3b3dd-43d2-4151-8529-461bde30df7a\",\n  \"552d411c-2550-4fe7-a30c-4c25c3ddae94\",\n  \"a5364e3d-8851-495d-a826-e8ea49025c88\",\n  \"77842e3e-bf27-492a-87a8-dba270b75921\",\n  \"0b6e44cd-f567-4287-923c-3b5ba30d4bde\",\n  \"9ab42cd4-43e9-45eb-a0ad-723cfb8361f5\",\n  \"488161f1-b3d8-4c57-82e1-f351d3ec1391\",\n  \"45f991f4-d7fa-47c2-b8f4-1114c99a6d30\",\n  \"d7c724fa-9e30-4606-a78f-7490683d682a\",\n  \"85af6e86-8a69-4dd7-8534-f39b5b4d3739\",\n  \"ea4089df-12ae-48d1-8997-c21e0d32a888\",\n  \"fb90746a-9586-4316-a401-cb1c7e16ed70\",\n  \"9a259f7b-d382-4441-8a34-e4f8a682983d\",\n  \"9aa97fd1-c2a6-4aff-8092-ca6de7497916\",\n  \"a44819f8-766b-4870-858d-572afb21b1d5\",\n  \"82e46a84-f1ed-404b-9416-9e847db67cb3\",\n  \"1f0aaff1-fb4b-4ecb-9740-922e3ab40fa5\",\n  \"ec85c16b-554b-424c-8d4c-f17abb888ce4\",\n  \"823cc86b-f721-458c-a22a-8081dbf6c4c9\",\n  \"4b7ffb9e-9404-4c67-8caa-dfef0849ed1e\",\n  \"6948aa75-0fb8-4f19-8234-03b95f566051\",\n  \"097837a7-2460-4fda-890c-658b5a17ff6f\",\n  \"a10dd09c-bf99-4d7d-8b7d-8df90090778a\",\n  \"a7c9c53c-43d6-4b07-b1d1-9c4cefa92209\",\n  \"3d406eb8-5f7b-4d93-b2b8-65df30ee5f34\",\n  \"4fd88c5e-dd31-4f70-abeb-c8db7909e231\",\n  \"622279d8-7a9f-4391-b5fa-b98175780979\",\n  \"ec06e5d1-0b55-416d-9c8c-6db65fbf318a\",\n  \"c3342c44-9622-403e-83ae-77c685b0bec0\",\n  \"2b026319-c2ea-48c2-946f-65396b2af10f\",\n  \"63ed37f0-3c06-48c6-8767-b8891895e937\",\n  \"1b5fe6a1-06c8-437c-81ea-bc6329b5b89d\",\n  \"12958ad4-e0e4-4b7c-923a-8885d981bbc8\",\n  \"58b84402-70d3-4e4c-ad07-2aedebb646a9\",\n  \"567a02d9-b4a1-412d-b008-9bba912ac592\",\n  \"5d1cb9ba-4096-4e95-80f4-a544ada77ebc\",\n  \"08f9fbb9-2f28-4387-8f32-721b0513e773\",\n  \"76fe209e-5fb1-42e3-9ad4-22e2a8bae00e\",\n  \"e9ffbd0e-dcc1-4d96-afab-3ba6ea7b7a1b\",\n  \"c3662627-e507-4218-92df-063039f8a01e\",\n  \"f2b4bf56-1454-4753-a3dd-67d41524e8ef\",\n  \"df5daf31-5d07-4ad4-a98d-c6ebfb1b05bd\",\n  \"98437b15-fb9d-4205-a0a3-5dee0b5894e0\",\n  \"f9db51ac-63b0-4a3e-8263-b51d566b2cec\",\n  \"08ff46c3-ae44-4fd3-aabc-04b78047b2d5\",\n  \"0af42fb3-2b3f-46fb-8f81-7384bed2308d\",\n  \"c5302063-6604-4a50-b888-94be3950851c\",\n  \"8133c392-6eb4-483c-a051-976be86f60e4\",\n  \"7ab2bb78-bfec-451b-b2ae-8878eb005d25\",\n  \"7b21ba54-a68a-4ac7-bb51-1b32ac718282\",\n  \"41decfe9-f512-441c-9a4b-84a48295d58a\",\n  \"ad3e880d-75bf-4c02-be42-a3274ba90116\",\n  \"26d29be1-d5b6-49db-a2c7-f06225e85f87\",\n  \"40de2404-5bd5-4512-a6f2-4866ff40b9cb\",\n  \"dc0e67a7-cf2a-4689-a4f1-d324143a3b05\",\n  \"58223b20-d3a9-444e-95aa-51fb191ee262\",\n  \"cb1a53ab-e5f1-4180-a15f-dd13b959e5b6\",\n  \"90735255-d5ca-47ad-931d-cb28d3c31859\",\n  \"9e9f8b6d-2b71-40d1-a194-f2be847c0d43\",\n  \"83f6ac29-389e-4843-95ac-953f6eeddc6b\",\n  \"9f2408e5-78e9-4b22-bc96-22458bb77aba\",\n  \"bc07fc23-63ea-4db0-87a3-4fd11316e5da\",\n  \"032906d6-21f9-4273-9248-16a648934531\",\n  \"35c9fd7d-3b12-4638-abda-9efd42bac8df\",\n  \"161dd8a7-764c-4f37-9f8d-d2f51aef9a29\",\n  \"f44323c7-ecbf-4b47-b59d-4f4ff6ed6279\",\n  \"2a768bd5-806c-4be0-9571-6c339567e83a\",\n  \"c1dd8345-1795-4b20-a03d-4c897178c10c\",\n  \"7275ccf7-e550-4c5e-8b9d-ffc2c1756f9d\",\n  \"adb86d93-8e78-4ab3-855b-c02eef8fdadd\",\n  \"829a3df8-cf5a-479f-8d2a-7b2e792122d6\",\n  \"f78e36ef-7eb4-4be1-9d2d-f492068127e8\",\n  \"755fbcbe-76c0-41d3-b115-9ccab1ae7236\",\n  \"080478c4-7d39-4faa-883b-5c4e64457202\",\n  \"fe97ce37-f259-4eaf-883a-6e01f720bb4f\",\n  \"cb7c0ea8-cadd-4de8-9ca9-4fb9c6b5b125\",\n  \"b0826bfa-99e5-4cd8-8588-5b1464cb6103\",\n  \"30482128-a86b-4ca2-b6d6-d4f78c8e0734\",\n  \"fd7c8ef1-903f-452c-956c-57593809e220\",\n  \"6ee692de-9f6c-4e3e-9d18-5d9d4f788c75\",\n  \"9e577b79-6b89-4d2f-a7ce-c8be652ffa2f\",\n  \"b16a6c33-eff5-4bee-898c-6ff6b5ae8b5c\",\n  \"dd5dfa42-e977-44d7-8c8d-9255a37b9c3c\",\n  \"0470a3e0-e516-4076-b54d-e1dbb1d118d6\",\n  \"8648d6de-8eb4-4184-bad9-2b21c6b1d064\",\n  \"45447f26-6daa-4663-8ec4-deaaef2d1b0a\",\n  \"b29e842f-6441-4c5c-8a10-79102a28f5cc\",\n  \"41edcc74-f056-4c56-814c-51705ccd3020\",\n  \"0e9c12ad-0076-423a-b9ef-49b07c63eedc\",\n  \"653d39a0-0519-4c68-a530-a84dd1c249c7\",\n  \"95385c1e-8b92-4664-998c-187ed5ab4c11\",\n  \"72383cb8-c4ed-4001-ba29-d3c348b85034\",\n  \"ec026c52-fee6-4dc1-91e9-f5998ef7a874\",\n  \"50f9ba28-29aa-4c27-8c9b-064b881c6713\",\n  \"02a0b9b5-1d29-4db0-bfc3-8b3188e4a028\",\n  \"e66fc9a7-1cac-4264-a4cf-35b7f96dc33f\",\n  \"31ac0057-5939-4399-b034-64ad4b1d16ee\",\n  \"99e5ced0-882e-42d4-9421-03b6be389d53\",\n  \"bcfdb48d-8a37-4f9c-84b8-8c63297f25c5\",\n  \"aa18680a-9001-447a-8886-bc9c5c666e47\",\n  \"08a4b24e-627e-494e-a3a4-218e67f088a3\",\n  \"bdc06ebb-a6a7-4e15-99e2-f7db8e0ab70c\",\n  \"65e82559-f1b7-4db7-8c42-f43f27280cc0\",\n  \"e697ff4e-ac51-4c00-b86f-d43039cecb30\",\n  \"219dc347-aef8-4a83-b27e-202c357d67e5\",\n  \"4c31cd00-150c-4451-9e82-94390a7397fb\",\n  \"afff21b0-ac0b-4793-85c2-03700056b7ed\",\n  \"c999af11-f096-4821-9d22-63b130dbbd37\",\n  \"d7bc1c8d-49e1-4838-8bce-854737fa9d36\",\n  \"d6943af1-85e7-4cb7-99e0-45cea9c0bb9f\",\n  \"c58690be-972f-4384-8b94-bba60a27d859\",\n  \"5b723b48-2a2c-4915-a4ab-1aec4463e010\",\n  \"dc0abd3c-c347-452d-98ab-276a897c6c3b\",\n  \"c6a9fcd8-82d2-45c8-b613-cbb22f4e4240\",\n  \"714ac606-7c3e-4c85-a3cd-6e2c5f8afc71\",\n  \"769bb68b-9940-4ba4-a1dc-c5a06b90de65\",\n  \"ed4f5f8a-8fc6-4752-a6d5-4da9a5634fa2\",\n  \"d5a96e07-2fba-4551-91dc-99310bbceab2\",\n  \"a069e883-fcc3-4cc8-8133-35bd0096e1cd\",\n  \"10bf620d-123a-4b9c-b668-94a10393e51b\",\n  \"e11a6f5c-3b26-4827-9743-056e90b834ba\",\n  \"2acf967f-568d-4bf2-a7f3-f1515bcda328\",\n  \"249ee5a6-eb0b-4a59-a411-0fc2ca5b534c\",\n  \"aa464d89-c9e3-4c1a-a50e-fecc1bd58280\",\n  \"92e00c14-4b6a-4d3b-ab2e-fe754c1e3c1d\",\n  \"ade57347-2a2d-44bc-b4fd-e05c41bebab6\",\n  \"4300c10c-5566-4588-a476-879efcb7413b\",\n  \"c83a1905-7de7-4311-854f-b1e5c4727c1d\",\n  \"843daa46-0512-4745-a27f-fc593ac15430\",\n  \"6c761350-f14e-4aa7-a836-371d396b83ad\",\n  \"d6272601-4da8-4bf3-baf5-38141aadf567\",\n  \"e5c4698b-4527-443e-87ef-ee38a6e3f632\",\n  \"e0d31625-75e0-49cb-812b-4b2ef90ff069\",\n  \"eec9ecdc-ea79-4b64-a4e3-42f1c547dda9\",\n  \"f2216e74-96d7-45af-804a-ec564992968b\",\n  \"fac6f36a-581e-48c1-9a4b-dcb3e9121b4b\",\n  \"045c551b-f9ff-4e70-9801-4c0f791145aa\",\n  \"143cf1f2-8d16-4dbc-8c8a-4acff7d47688\",\n  \"3b412257-c3a7-48ab-b301-7c3ab48ad52c\",\n  \"8e7fd428-0e3b-412b-b172-af5137ab5cd0\",\n  \"c70666ec-deed-4bcc-9b49-9d7c55a4eb53\",\n  \"df3d493b-510d-4128-b682-c9b5afb0ac00\",\n  \"12533554-676a-400d-baed-d1a563db54bc\",\n  \"43e10727-23c1-49c9-a6bb-4fcb8f7f2074\",\n  \"6c5e99dc-0324-463b-8e6c-1e1d201a8acc\",\n  \"cd990e54-60d5-4959-91b1-805ed328d24c\",\n  \"4b4e185b-0874-4445-9025-cd7466b3da4a\",\n  \"3eb3c2b2-c1f2-4ea7-af32-15d18c7c9857\",\n  \"5c5fb79b-9534-41e9-9ebd-9d673e7b8035\",\n  \"ac197361-0f5b-46a9-a7be-76f782cb945a\",\n  \"83d22577-3942-4d28-9c8b-8963b675cca0\",\n  \"bd7783e9-a1e0-494a-8c84-b481eb312dad\",\n  \"94acad55-06bb-44a5-9166-2b8997036ec6\",\n  \"b00b208c-d0f9-4745-bf6c-42f7c1845caf\",\n  \"5fe57a38-2f15-490f-bda5-a60288efe824\",\n  \"95ba49bd-57c2-4838-b028-d6653db91901\",\n  \"3af72b51-130a-4570-9151-d0922813c52b\",\n  \"611121d1-7f67-4aa6-a82d-d5b5e661c97d\",\n  \"19839a96-6034-4e2f-a8b2-4ab4e626ee1d\",\n  \"dcf2c806-a2e1-44f5-a6f9-08b199e0346f\",\n  \"e1e1db61-4c82-41e0-8459-bee6f3bbb509\",\n  \"e8422b74-937e-4269-a358-29481d545a8d\",\n  \"8e0f642d-43ba-40cb-9cac-b29c58556461\",\n  \"5c927fc8-c34a-4470-8f46-704500376cff\",\n  \"89d52f51-89e7-4bc4-8818-0af90a082905\",\n  \"cb53ddc3-c218-40c0-8277-f28494e6783c\",\n  \"45db9a2c-8e9b-473d-88ab-0210d00d134d\",\n  \"229c08da-c730-48f5-93ae-bd610dfb83d3\",\n  \"76f9c915-7b3c-4709-901e-7886933e1830\",\n  \"5ca72228-de70-410a-9853-8ca25ba67c13\",\n  \"e6a5114e-7c7d-4462-8aa6-3ab38271d9e5\",\n  \"666e19ae-88e5-421b-a009-e5b803655118\",\n  \"737549d9-0973-4f91-bbb5-aab602cba16f\",\n  \"77607464-0392-4312-9539-1aeb14245a76\",\n  \"d2af9e56-2a8a-4ff3-b794-15c3d88dc0f4\",\n  \"c6325b4e-f3c1-46ee-979c-cbbe4ff689b6\",\n  \"9f65f611-3bb4-4a68-be5c-5faf97d6fcdd\",\n  \"62a9d2a6-99dc-4e13-aa44-e1d0a0a4e4a2\",\n  \"0fd29e60-07d4-4101-90c8-9a88959ba39d\",\n  \"c64f14c7-287d-4834-b6ad-94096a39def6\",\n  \"92ef70d2-feff-42de-8f0d-11ffa0092744\",\n  \"b8fc3655-0f53-416e-ab7e-41d18f58eed2\",\n  \"51c3cb64-c4e1-40e1-ad46-67960be9b735\",\n  \"dbb368ad-56b3-4507-8c73-855b11fda922\",\n  \"989d6651-a023-4809-82a5-9414ae12d0e2\",\n  \"7bc6904d-cafc-46b2-a985-ee485cd86c8c\",\n  \"a28ff756-c110-495f-983d-fa521eb244e7\",\n  \"98413a2c-fcfb-40e2-9c00-e75843ea31cb\",\n  \"39d93d8f-81ac-4b41-8b48-0412c0cc2399\",\n  \"8e12dce8-ff6c-4414-b14b-cccda0e5dbf6\",\n  \"9bc3882f-8a42-409b-bec4-06dc256b2639\",\n  \"814fe19b-7e00-4892-9155-a2697324e036\",\n  \"ed9e9449-1c57-4625-85aa-15f7af9458fe\",\n  \"1445bb42-305e-4280-8ea9-da570f42b33a\",\n  \"69754cce-e0a8-45b2-8c59-f5d4b80848be\",\n  \"e040579f-b003-4cef-8340-be8233da1dcf\",\n  \"259a1a48-a926-4f00-81f3-439939593a40\",\n  \"a77a951c-476c-4ac0-8401-f1a64c1ca78e\",\n  \"4bf4d8dc-89e6-43df-8db1-dd6c315fdeae\",\n  \"6a99c9f8-b8be-47eb-90ff-3835c69efa2c\",\n  \"52904133-73d9-48d9-b668-d1084e41b9b0\",\n  \"90d167d6-1274-49e9-8662-de1d46dd96ce\",\n  \"5a90491b-e399-410c-a39e-314ad5ef4e8d\",\n  \"ffaed21f-a41b-49ac-887c-91970aac6388\",\n  \"7da07e1f-9d21-4a86-bb04-342a2881dbdc\",\n  \"7220db39-e32f-4ef7-b852-1c075e1043ea\",\n  \"5034184f-bcf9-4f7f-9ef3-f3e3d7491122\",\n  \"8f86d13a-436f-4c90-8322-0b73d283dc35\",\n  \"28e8f6f5-0eb8-4a65-ad5f-dee4927f17a3\",\n  \"0bbdc9c1-62b0-4cb6-b3eb-40427d7b1ee3\",\n  \"4885423d-f149-4e2e-817f-11457a156f02\",\n  \"5e711c56-639a-4222-9cb1-2fa34907ba36\",\n  \"4f17075d-7267-4c55-bff3-0d528524abac\",\n  \"b461790d-9c26-47fb-aa71-11c9c6809b90\",\n  \"069fb103-7505-4510-90e2-56baff2027df\",\n  \"ad419938-d12c-47e7-b0f8-7ea98f209acf\",\n  \"a9b8ec81-e6ff-4fc5-8bb2-299566dd938e\",\n  \"6157d645-1bc4-457d-b426-4caae5d88858\",\n  \"5fbc4325-23e7-44c9-85de-a3a2e37e8889\",\n  \"5fa46a3a-c7f0-4b17-a7b1-c57410dfc277\",\n  \"c345fd3d-b819-48d6-9943-bcda442d8dea\",\n  \"2cf81b66-c5f6-4a9d-8ed7-83dc7c0e619a\",\n  \"3f68a5a8-2c6b-40c1-b76a-da38546ab818\",\n  \"6f5751e4-3f07-43fe-940f-1049f43ef78a\",\n  \"7b439e40-6798-4687-91a5-c7f1a3466a5e\",\n  \"132c1d30-2665-4976-b1fe-6e49949d3a96\",\n  \"d7b890f1-f283-4f32-98ac-b1425523d040\",\n  \"bdb184a1-704f-4d93-af99-2112652424c2\",\n  \"52fd3092-dcea-4128-907e-77224d027284\",\n  \"5f96b0bb-ada3-42e2-b00a-74571323f64a\",\n  \"4c7cb818-2d77-4f0c-8c78-e4b6469f3ec9\",\n  \"83146695-5443-4df0-8d13-3faed27d968b\",\n  \"05be1544-1d19-4a4c-beca-e53f8b5566b9\",\n  \"4c99fd2d-e8eb-4b27-a4d6-69c9231fd664\",\n  \"67af7dca-a1b2-4f60-ae86-8b488746cc1c\",\n  \"e7bc3994-b1ee-454e-9810-3fe943e8ec39\",\n  \"e0bf474c-4341-40a6-a9de-7c4c7e13aae8\",\n  \"2462b064-7c0e-40dc-b1d0-4f1c8754a6ea\",\n  \"37bebac8-69e9-406b-a651-2bfb4cdb3b4e\",\n  \"39d2c2a8-3982-4f16-abdd-4f87714b8346\",\n  \"3a2aa515-5d13-4668-a552-7e79da905967\",\n  \"f692dcc1-b21d-4887-9ca6-b52f90f063d2\",\n  \"f597a5e8-7ce2-4833-b74c-f58cd4f0f605\",\n  \"34d22011-ff7b-44ea-af7c-84fd02387abd\",\n  \"9ad6619b-a7a3-4033-b958-dc0c4b093a04\",\n  \"2f5e4be7-b654-4ae3-91e5-82298ca8a451\",\n  \"d501f809-bd01-433e-96b7-574b0fb3bb34\",\n  \"188b4c75-62d6-45d6-b6b0-4ecf2c3c6b58\",\n  \"9ff6e046-73ea-4d57-ba88-220008506862\",\n  \"9c4b70bd-fd7d-4ec6-89f4-93576f3b28be\",\n  \"1648d52b-b4c7-4e3e-a2ad-0d5242e202f6\",\n  \"e4b0f667-e845-4279-af4c-fc3476afbebc\",\n  \"09090609-6f4f-47a4-976f-35441428b183\",\n  \"47ca9d0c-2c6d-4e9d-877b-5c4e79bae5b7\",\n  \"bc1a27ab-e798-4d4e-a749-af9e4d78585d\",\n  \"e299fa00-fc14-4be1-a005-c3c735705ecd\",\n  \"e27dce41-b875-4887-a225-8a8e61f4c97a\",\n  \"3b9814a1-c932-4b94-a48f-a6327b3884c1\",\n  \"7c6e7dce-1eed-46fa-b9a6-0781941cdb5c\",\n  \"1538d57d-df6d-468b-9552-c06eb5913e47\",\n  \"bdc595e5-c4df-43f1-a0f1-c522086e1817\",\n  \"9b25918c-1e5a-4aa7-8938-c9e84a39cb95\",\n  \"4cd7ef99-dd38-42ee-8081-97c1925273e3\",\n  \"d9766084-5514-4ad2-8cd1-4792ef0f3018\",\n  \"62e2a5cf-2939-4a1d-adca-9f273f9c4a21\",\n  \"7b8f3635-ef3f-44fd-8819-6a25930e01a5\",\n  \"f976ba37-1269-4c17-a20d-b567cecf5158\",\n  \"b6612623-2b6f-4e0f-8a09-2a6f242220dc\",\n  \"1c66aa90-8246-4cfe-92fc-b3bdcb2bd839\",\n  \"806912a0-7d4e-47df-86d4-51e6136df7f9\",\n  \"6dbad783-ee49-4513-b4c6-98ffa79fe125\",\n  \"9caf478a-7c14-4f43-a895-9482e3fd87d9\",\n  \"bf7d66d9-a96e-46bc-b7ea-25feaa2bd7c1\",\n  \"d9a20af0-066b-41c7-9650-a705d1ca145a\",\n  \"0fa94d12-07d8-4a26-99ad-0df1cfaecc50\",\n  \"d1c5ce32-4c70-418f-8bda-e0e5817ccb00\",\n  \"6610114d-ce75-41b6-b229-8fde9b050be3\",\n  \"2f4d420f-e195-4919-ac2d-74ea92f3b767\",\n  \"c5e179f3-8607-423c-953c-9c8d791f3b77\",\n  \"3b9c0e62-2ffa-406f-90c9-0a9a0daa2cd5\",\n  \"7a3276ea-9265-4a7d-b849-ba4d335d8776\",\n  \"ba00582a-1302-4cc3-8080-e2a9565b06f7\",\n  \"834c8d39-bfe0-4ed3-886c-2433cdf8dd2e\",\n  \"3d452ea1-d6d7-4441-be2b-4d39c7b66f48\",\n  \"241deb20-3dd3-4539-b4bb-44bb4e40d701\",\n  \"c3daf795-fe91-47b7-84e2-8100cea91203\",\n  \"ff918a29-aa32-4665-8edb-9c0b9052742d\",\n  \"c65c37fa-6735-47ed-bf8c-b976068ceb03\",\n  \"dc5708fb-d5b9-4625-afbb-e0b23abf1a0f\",\n  \"88eecea3-3856-4ae0-a58f-b67740b91246\",\n  \"544355db-e76f-4f97-bd0a-1a13cee7af1d\",\n  \"dfd91846-6dd6-465e-be23-4d15787e4a33\",\n  \"00fa3a54-f228-4937-b4da-7532a28dae66\",\n  \"e56ba28c-20f5-47e4-b464-bdc709a5200d\",\n  \"a27603cc-30fc-4270-879c-4941498f054c\",\n  \"0aab24b6-3fbb-4fae-842b-945d88de2131\",\n  \"568be1d1-3f5d-427b-8b16-1b13328f9061\",\n  \"162092e4-2d86-4c2b-b4dc-d8823bb81eba\",\n  \"30f5379d-b3fb-4b82-8e99-64357b730118\",\n  \"b1c5af1b-7754-4040-b18a-a5656a61c1d7\",\n  \"cd719ede-08d2-4e1c-a410-bf257bff894b\",\n  \"733cb3bb-edd6-4987-ae27-1f7d4fa80fa1\",\n  \"62e694ff-44b2-46eb-b2d1-694882270e04\",\n  \"271fb0a2-dd37-432d-a6a1-5824ded51ee1\",\n  \"8e0282a1-8c13-49ac-8f86-171750e56929\",\n  \"61b392bc-1cf3-4be9-9fd9-1690ddf426fc\",\n  \"d96739bd-826a-4b45-8fe5-3dc72f74e592\",\n  \"90f4df3f-1871-440a-96a3-5dc18b244c64\",\n  \"f08dd324-b30d-432f-89e6-5779370c5e60\",\n  \"a5598b89-2912-4b32-ad96-419e0c4ae6c7\",\n  \"f3c3eb99-89c5-4eda-916a-49de3c783b27\",\n  \"e796d78a-4a57-4c79-b9b5-bb1aa20048c2\",\n  \"3b86cb06-1f43-4873-8d25-816f0845f231\",\n  \"769810b1-e61f-4207-9328-2097a7840477\",\n  \"112a3151-efff-49dd-8d36-646e9a94df27\",\n  \"c52c4cf6-11f9-4d87-b6c1-1670f69798ba\",\n  \"93981383-9a56-4504-b381-a82611c926b1\",\n  \"7c619f4b-59bd-47a0-9e5d-a4c0e852facc\",\n  \"61f715c2-f728-4a1b-befe-0f23cb806642\",\n  \"16ee32f9-9c99-4107-b5e9-51f5bc23fcde\",\n  \"3c2fb716-58e6-4fd6-afe5-ccfeda1d106f\",\n  \"feddc82a-9f4d-4220-8381-45ea73f6a382\",\n  \"f0595bd0-e742-46d1-8eae-920aaf253f22\",\n  \"b53eb9bf-1a4a-47b9-9297-4e1b3700c92f\",\n  \"14f9fc33-e671-420e-b137-f6a0591a4e95\",\n  \"67711850-c5e4-45be-aa8b-6d3906780a02\",\n  \"20be685f-42e3-4f3c-aab8-5bd75b755407\",\n  \"9462e5da-9ed3-4c42-9970-67959751182a\",\n  \"8edc2b33-d937-4bd1-baea-da690106310b\",\n  \"c7908ad5-f026-4850-ad06-a3d1cb48b0e8\",\n  \"5ab98669-f193-424d-b536-9adb6b7b4710\",\n  \"ddbbcd1f-4cd2-4dee-8575-c9e67f87da55\",\n  \"6c3f734a-7b8e-4d76-86fa-5fd7d351b036\",\n  \"43a22dcb-4ac1-471e-9cf8-37c41d9f4cb5\",\n  \"a73644f5-9d02-480a-a5ed-814efbbd213c\",\n  \"d9c7e74a-9188-4f39-940c-2783228ec4a5\",\n  \"d0e4aab0-28f5-4947-b4a3-d3a1eb334ec4\",\n  \"9039cdc2-5fc7-4348-899c-3f70adec2956\",\n  \"7d3bbb2d-afe1-4faf-9696-6bf6cc921540\",\n  \"1c628f9e-65a7-4df2-88e8-be6ec37875ae\",\n  \"f4672b90-8466-4deb-bd22-d4394eaef946\",\n  \"1f1800d4-fe08-4849-b047-0890920b7ce2\",\n  \"6789679b-f63b-4e1a-9695-c2db4bec423d\",\n  \"6be39a55-28d3-4c5c-a799-be55acf9b433\",\n  \"e0e6b455-0f0e-4829-9d8c-92429bb6d262\",\n  \"a928b311-a886-4017-9788-3ad78e350b06\",\n  \"e7900edf-f0fc-4e00-9bb1-3dc5865f91c1\",\n  \"41936e51-b2b2-48ce-a0d3-bd9609d332f7\",\n  \"4db44737-bf43-4492-aae4-0abb9f4bc368\",\n  \"651ade49-738b-463b-a246-8122c83630c8\",\n  \"d4b5c6c5-d5ce-4273-bc99-f5cec7b96ecc\",\n  \"00f2bf86-5d62-4368-9479-ff86f73f3054\",\n  \"0b73f03f-8d9f-46f9-bfe2-518cb705d364\",\n  \"124a8aa7-e5ef-4361-98ee-c64173424ce9\",\n  \"13b25d48-863d-4868-a876-0262a92f8d23\",\n  \"af72f78a-2a36-4622-9e4a-3ba5c7300c2c\",\n  \"417b5104-049d-46a7-9b18-4b1e8b166b99\",\n  \"012dae3e-a876-4cf5-8588-6d118bdde189\",\n  \"498277f3-50d3-4b74-bbd5-d06596998a33\",\n  \"5224a83b-236e-4636-b7c5-32535226ebe0\",\n  \"466b7bb9-e4be-4413-8b97-6308974ceb42\",\n  \"9c26922b-4dac-4c3e-9d6c-5c71b316c0fd\",\n  \"23659a75-282f-4dec-a638-56f49d389e69\",\n  \"7049d0ad-61e9-45a2-b778-289d1a183ada\",\n  \"999adcb2-07b2-4290-b4a2-b83b86262f68\",\n  \"cc11b87f-afa8-4d02-987f-1fe8243eca1c\",\n  \"0adbfd60-2f32-4b20-a70b-f5fe35cd815b\",\n  \"91fe8509-8f4c-4399-8b0e-9191b649fd84\",\n  \"46414a3a-9592-4e0f-99d9-b8074ed05844\",\n  \"0f5990d1-58d6-410c-a3f4-2ba94991885e\",\n  \"2ed32a3f-3e38-4f28-8551-2e455c09b1a0\",\n  \"364b980c-58aa-4ded-bb0d-64919074a6cb\",\n  \"e3a6e8fb-1a13-4a55-9b9e-2368e24fab76\",\n  \"de70171e-19ae-4a40-ae4b-12d1ef5a6fb5\",\n  \"2c6ef655-6ce3-4246-a34e-b2035c16a516\",\n  \"84e3e095-dd0f-4b8c-a5d3-92582572bd5b\",\n  \"61930703-8b4e-40c7-a806-18f77c36029f\",\n  \"702e2367-b9bf-4eec-8b76-13461df0bbd4\",\n  \"f888bf29-461e-4e19-875b-5603b0f6660d\",\n  \"49e1a391-a208-418d-aa4c-603603a3e7c9\",\n  \"2108efe8-0a60-4f49-a5e4-82b5369d5ce2\",\n  \"c5b75089-7fcb-45e0-aeef-d293a8f73763\",\n  \"6dc450c4-05be-4cfe-8763-a834ac35516a\",\n  \"051a0664-32c5-434b-8ab6-02305f380c9f\",\n  \"ab58a800-9880-409c-a386-266d693ef302\",\n  \"b0729081-d1da-434d-b82e-c70c54c6c58b\",\n  \"d93ce27c-35e3-43ce-a9b2-47f98d624bf1\",\n  \"f560281d-44dc-464f-975f-1e0a2b6b4d78\",\n  \"3bac3e6f-38e5-4658-a0ed-1d7f56c3261f\",\n  \"ccd5250f-3434-4937-a837-987705fd0539\",\n  \"5f0552a3-0c6f-485c-a2af-e40f5a184540\",\n  \"7b5a4294-e9bd-47f0-bc76-c1e76370f75b\",\n  \"22e018d1-b392-4389-95c4-b0e577076aa4\",\n  \"aa01f10f-47f7-49b4-8d26-7b39c4563202\",\n  \"f0d865f4-5162-4089-9ac6-c9137b6e211e\",\n  \"c6a222eb-b980-482b-9e02-1fe6874fe4c6\",\n  \"e660530e-ce94-4fd8-b39f-2d32896e55e9\",\n  \"2a31d69e-20aa-437b-9480-0d099f3b818d\",\n  \"e5710f2c-fe3a-4040-9b12-c5bf22a60351\",\n  \"30a0bb41-4b90-4096-b8c7-9eff885bbac8\",\n  \"d8edec1c-7756-4728-b719-4373c926249e\",\n  \"8db2ca5c-3099-44e6-a184-e5eb14316587\",\n  \"e423e6a7-3b25-49e5-9276-bc10fe3bbb46\",\n  \"2753f8fc-8e0b-4f39-89d4-d1c8893447f1\",\n  \"e8db996a-3ee7-4426-8526-dfdfd91f4a01\",\n  \"50a2ee4f-55db-4737-8c4f-2f5eb7dd21b8\",\n  \"8afac747-2586-4855-a3ef-261c2e86ff5c\",\n  \"c99d1280-4290-4e5c-9e7c-6a44349002b8\",\n  \"8a9f19bc-232e-4795-a4f6-2871377b6579\",\n  \"1e198ae7-0aad-4a7f-8ed1-642c0b4c4089\",\n  \"3da8bc3a-29d8-4b89-a9e1-978655b791e7\",\n  \"60129ffa-c4b1-459d-9cc8-246999b948ff\",\n  \"604007ee-2e30-4e23-a17e-2de9f08d7075\",\n  \"a1ace2ad-50ab-40a3-86f9-06b2db8566bb\",\n  \"96465b04-0a5f-490a-b670-eef9c7e3d2bb\",\n  \"fd39ae3f-9783-49c2-9d31-8d8c23467233\",\n  \"fe61ae29-b005-474e-90da-cfb0bdf44a69\",\n  \"1a745387-c3da-459c-8854-def86291c4e4\",\n  \"25bc2216-fc99-4e13-858d-b9ac237485be\",\n  \"f47f1600-0fd3-4996-acbb-5ee4273219ce\",\n  \"c4598efb-8807-4187-9957-d8a74da295ed\",\n  \"d2c10ddd-92ea-41b7-bf4b-d08d065c5c20\",\n  \"974ba1f7-1316-4019-94fb-c9a374b5cbd6\",\n  \"6607f6d3-d4c0-43cc-be62-c3113bec2c84\",\n  \"a86ad435-895a-4557-b986-620ad654fc21\",\n  \"a577541e-0162-49c4-b8fb-7f528a0193d1\",\n  \"67d6e450-224c-4a5a-9147-d3048bec5346\",\n  \"a8513d96-0cb7-47de-bbf8-1011f79c2d59\",\n  \"f107b4e8-2b31-4e5e-9f38-2adff4dc0ec9\",\n  \"a1d32758-a546-4bca-a124-a719b0ebdfab\",\n  \"545f66f7-f6f6-4709-99db-40bc8c41f9de\",\n  \"4cf530d5-6601-47e3-8ff4-077f79c60a6a\",\n  \"2479025c-aec7-49fa-af18-1c211064002a\",\n  \"5cce7a8d-f77e-4dfd-949e-6976cb0edea2\",\n  \"654937be-2d9b-4349-8a1f-3a7a727e597c\",\n  \"fc876a92-095b-4cb4-b37c-524315d37913\",\n  \"9e418203-254b-4117-a176-5d102aa14f6b\",\n  \"140eb5b3-2a2e-4916-8e53-58d69a5febc3\",\n  \"d5f9bd8f-03c1-4406-a584-0e3261d5d307\",\n  \"adf6c558-2cf8-4413-ba6e-5188eb0dc164\",\n  \"5b6a2a9f-bda3-43c7-af94-e4360ed4d73d\",\n  \"1c9fde03-cf3e-4781-be5a-a5fc12862a24\",\n  \"722c2c63-1069-4105-bd53-00ece59e8e37\",\n  \"a3fd5444-c1f9-416f-a8f5-7e873d6a7116\",\n  \"a4903d45-fea5-4460-8e23-afce6d3b49a6\",\n  \"781f3c0b-70b5-47f8-b737-7a4d5a2b3a44\",\n  \"8ed444c5-d97a-48bb-a8ad-22dfb9b71b6a\",\n  \"cff6626c-fd73-4f14-946b-3580f4a19e3a\",\n  \"6b868e75-9b7c-45c5-8e23-5b458173b228\",\n  \"4af75eb4-384c-412f-be9c-5b85f485265a\",\n  \"0d6ad052-7241-4da7-b2fb-7a608ff7dd03\",\n  \"daaa8120-ecde-446a-85ed-b97964610b60\",\n  \"f3b0b502-8d31-4bf0-8797-828565b8b7c9\",\n  \"9b21220b-246f-4155-93a9-7b6ca4d44486\",\n  \"42fd227a-b8e1-408e-9c33-32ed862cfb5a\",\n  \"49570a00-9ff6-4f38-8eab-42894e91c46e\",\n  \"6c30e37b-f333-4064-be4a-6bb287eded5f\",\n  \"27f4d252-1963-4233-8430-a2aeb978eac0\",\n  \"f5114088-6f07-40cd-bd2d-53b6912b0fc0\",\n  \"28cdee05-1bf6-47ba-a585-0789cbc118ed\",\n  \"946b6f4f-6a8e-4f45-99ca-06ae19e3f421\",\n  \"4fe6dc6c-5808-4db9-9f8a-4cba3ecf21b3\",\n  \"3622687d-1a86-483d-8488-87efee27778f\",\n  \"361341cc-52e7-4a55-895b-8ca5ba2b683d\",\n  \"5b9b0924-52cd-4710-a0e8-bf60aaf9152c\",\n  \"f04b77e4-f61d-4275-8f98-d47161e5d73c\",\n  \"cb8828df-58d6-494e-b57c-92dadc0b2012\",\n  \"088e4bd5-9cab-4346-9cb7-ae7bfbb2f9af\",\n  \"94f63c62-9f34-4a8e-a4b2-b7dca5997bc5\",\n  \"6ed11685-a287-44da-b5bd-8849b1eac3f8\",\n  \"26682c2f-26e0-419f-b450-3c02bb6a6b2d\",\n  \"d1578612-b5c9-453b-83df-da0ab2256440\",\n  \"7eca5661-db2b-462d-a63b-962edeb0d774\",\n  \"7f90dd54-96f2-4d4a-b192-274a442205da\",\n  \"ac59a609-2209-455c-b5f3-d24ddd64625c\",\n  \"4132e9f8-ffb1-4136-8750-18ac4acb2bd8\",\n  \"b977197b-2e5a-4827-ad52-d6f4e23f88a5\",\n  \"73896103-6ac2-467f-93dd-c53978ad5caa\",\n  \"08856008-3834-4879-aec3-6f026d74d42b\",\n  \"dfc95b96-679e-4e87-ba32-390a5237461d\",\n  \"a918141d-0d8d-4c0a-b87f-0aa7e64e307c\",\n  \"8dc8be53-08e7-43b2-ab85-29b3ee7f821c\",\n  \"49196d10-fc79-4a4e-baea-a91e4800b615\",\n  \"5350b93d-7d2c-4b4f-9fe8-4ab528dfcef5\",\n  \"f1b3f629-9a02-46af-a752-0e91ea1e5d0c\",\n  \"2786a6d6-489b-4853-a6aa-fbf9fe687091\",\n  \"ac6d6650-c8f3-4643-9050-0a49d70e6084\",\n  \"f9c7d1b4-c9a6-4be0-b328-324e29cbfaa2\",\n  \"f4d27cdc-1999-4fe9-a9a3-1dd211fca51d\",\n  \"6bfe3335-693b-4be9-a2d6-1a2a4ea14279\",\n  \"9d8b3cc6-f02c-4589-850f-7c9bcddd68f5\",\n  \"c5270e3d-0f98-42c9-8e0b-277c848dd890\",\n  \"d5d79d0c-ac9b-4218-8a1a-dfcc4c7a0038\",\n  \"e867d985-044a-4d97-8c3e-abe9f6049587\",\n  \"842ee2ea-05fa-4c43-81c7-43fd385bbdce\",\n  \"863adcb1-3f44-4d2e-a5c3-5763d8127a1a\",\n  \"b2718e5c-e080-4b44-901c-bd3267ef6bfa\",\n  \"7358ab8f-1246-4974-8bf6-40da17ee3112\",\n  \"9e6a9aa6-768a-4d10-8a0c-d4cb5a9ce96f\",\n  \"9c57a368-85cf-46f6-ba0e-46b80d7aa3f7\",\n  \"3e8fbc23-7b11-4c60-9ba3-4c4c1768f117\",\n  \"8834b665-89a6-4fe8-a3dd-a1d9900386ca\",\n  \"08ca706f-63ad-47ac-8f92-cd7bbd90cf4e\",\n  \"370a5aa6-3b54-4bc2-8ca7-e94c36aaa7ac\",\n  \"e7a0f62c-85ed-4d9c-afd4-77fbb6bd31c3\",\n  \"280ac06c-54ee-41b3-9e58-84801d2f8216\",\n  \"552cc675-fc94-4ff1-a241-4af9822b5ab7\",\n  \"e1331f8e-e2e5-4c09-b13f-cb9be33e8a8b\",\n  \"0876562f-6ce6-4d9e-8695-75ae7cb5648c\",\n  \"397b0637-17a8-4aa6-a8d7-0d389ce44c76\",\n  \"772668d7-32f4-4044-bc81-0b4dbfa5a77d\",\n  \"d68ab6a6-e6ba-4bf2-8680-2ad4dee8d147\",\n  \"7818d36d-46fc-442e-a6e6-bde6a3e90ef4\",\n  \"c0557eed-ffe3-4221-8655-cd4798b9df39\",\n  \"4557cba9-0345-494a-a5d4-c8614109a08a\",\n  \"6d896b88-571e-4901-bc8f-5095fc8a70aa\",\n  \"9ac3c1ed-18d5-48dc-8abc-be8fff30474b\",\n  \"be791930-51d2-4cba-b967-d2e143d18018\",\n  \"9c5fdd8c-39dd-4f45-8f86-1264ac6ea532\",\n  \"62ee1d3b-44c2-4a33-b910-c56733ac4506\",\n  \"33763742-349f-470c-8603-6c2e9efd90f6\",\n  \"c5461265-84c5-4980-9b2a-14a8ed07c94b\",\n  \"78eda089-3a6f-4e30-a6b6-574471b2b54e\",\n  \"e657e87e-0805-4c34-9eb9-6a19c43c29ef\",\n  \"83e37c28-3ea1-45f7-8daf-2cafbfca0fb0\",\n  \"b864dc08-a3d4-4830-a299-36f4404d333b\",\n  \"16e82380-0e00-4f0f-a613-3a172e9ac9cd\",\n  \"99dce10f-bf33-4044-81a1-2ac3aee27cba\",\n  \"dfc3923f-e54f-4b5c-9067-bc5a9025fd79\",\n  \"6055dabf-062c-409f-a1bc-b7e935a9e1f6\",\n  \"e0e54b74-78ca-4285-a103-56fa823e3920\",\n  \"752ab9e8-70e6-41a0-840a-28c66ae40028\",\n  \"d5358a00-b4c2-43a3-b5d0-ada94f88b0c9\",\n  \"c977aad7-5e6c-4c42-a45c-09f3fad16251\",\n  \"cb90aad8-62ba-444f-83da-04ae1e773b35\",\n  \"4c3ee7f2-b929-437c-ac39-b553c971b139\",\n  \"4ce02a1a-d3c2-4758-a919-f6455aaf6797\",\n  \"0b83b7a9-e0f2-4770-90b2-f50cb8a3388d\",\n  \"8d1cc688-accc-4473-947e-d478100db620\",\n  \"d0c93583-64a9-41a7-87db-2310b6f7a883\",\n  \"84044477-7455-4537-be7a-d842eef7db5d\",\n  \"632a222c-9737-4972-8d5b-01ef3fa3a977\",\n  \"49bdbff5-91e5-422a-80be-cc52f14893ab\",\n  \"2efb0c37-9ccd-48ec-90bf-e8b90a2beece\",\n  \"905dedc4-7d66-470e-9c2a-a7a8ea5d883b\",\n  \"42febf2f-d04c-4268-976d-7666f8d91746\",\n  \"f43738bb-665b-43c6-93d6-3268c7e50075\",\n  \"2189a3e2-0b36-4dd8-9443-d19e47ae1bb7\",\n  \"73e61f4f-f0d7-4707-b04b-ae0111fe39b9\",\n  \"fdf7b4a7-0ae2-460a-9a11-5a1be11d0232\",\n  \"e7b9224f-375e-4fca-8ed7-61247509110a\",\n  \"1a91ed13-58c1-4e58-a189-8e18a8b8329d\",\n  \"cca48f60-7892-4752-8f4d-0617be6e42cb\",\n  \"4f6f4202-da87-4791-801d-3793cbe41616\",\n  \"8a294bc1-b504-40f3-8e13-31b8c60c4485\",\n  \"45835dd6-397e-47a6-a860-3fdbdfa37b35\",\n  \"d650deef-3430-47b2-bd40-22ef1de7bbd8\",\n  \"7e0eb262-bac2-4425-93c3-ee1979cfe6fc\",\n  \"721bd6ea-dbe4-4377-8c07-663a53778632\",\n  \"989d1a03-3aa4-48cf-8edb-bf6c7cc60df9\",\n  \"cfa55027-3c4d-4a16-8079-859dc8a465d4\",\n  \"43143907-ad1f-4be3-8be7-6954244f7fe7\",\n  \"688d387f-ade4-4dd2-97f7-9e1de48ccdab\",\n  \"9f32bdc1-f17c-449f-a4cd-77d36fd69627\",\n  \"3653fe6f-af07-430f-a474-a35d69e79b34\",\n  \"a176f456-6926-4c20-a3c5-2b63b46ce81d\",\n  \"36eadd80-f9d9-4b1a-b5df-d516f2f6f52c\",\n  \"74da1579-ba3d-4291-b723-32bfebe4a5a9\",\n  \"5d695c3b-451e-4914-a8ee-5de398a5e67d\",\n  \"a6da6581-5c61-4b15-af17-ea8ccdd6eb04\",\n  \"901f59b3-367e-439f-8af5-7168ecbfa863\",\n  \"51e4d999-7af6-405b-bfc9-9a0ba516f6d0\",\n  \"bf3bc9a7-d11d-4a9a-9e4b-16ac2169e9fd\",\n  \"9a4c9439-61ef-4c84-899d-f39bc950c0c5\",\n  \"4c603eba-a6e7-4754-a119-e3b3fecb0821\",\n  \"0615873c-66ff-46cc-896c-025ce028414b\",\n  \"814f6676-648e-4d65-9430-d0dd80d9af1e\",\n  \"c2d2a846-ac62-4ead-9b49-165a8150a067\",\n  \"1cecd4ce-131a-4bff-9e4c-044eb496760c\",\n  \"ec895832-84c5-47cf-aef9-11436fe3051f\",\n  \"f7e862ec-f549-4718-a56b-d1b7170ad668\",\n  \"2f99081b-df73-4974-89a5-d98e33246e42\",\n  \"a850a7eb-fa27-45bf-bc9c-67737bb0c117\",\n  \"a10597ae-5d13-47b4-b211-811bf76c41b3\",\n  \"aff50426-203c-4b16-b97c-f9c1b5aad3f9\",\n  \"9b877f26-fa71-4f9a-8531-eda199c88802\",\n  \"3dc61022-0209-49c5-9b77-3f40cd043ff5\",\n  \"1f88d82e-b471-445c-9b16-b9b8e58fbea2\",\n  \"515232b8-45e0-403f-9ba7-df27ba19b4fd\",\n  \"c655f6a7-7b07-4cf3-b674-ec72529943ca\",\n  \"5ecc3c9d-c033-4d70-bee2-bd8594c83662\",\n  \"ccfb339c-9e0f-4e34-ae99-baf740fd0215\",\n  \"07ea9119-f28a-4447-bb38-5f85409dc67e\",\n  \"ca102f6b-73cb-40e4-8a0d-422168b5ff76\",\n  \"8a032d8a-e1cf-4434-a1c0-be4204372485\",\n  \"6d637352-3548-42cc-ac43-5e15312543e1\",\n  \"77939425-996f-40fa-8a4d-9abbfad76a78\",\n  \"58aa885a-b9d3-44ae-a4b4-6d963b0e087d\",\n  \"7898938e-73fc-488e-b350-623b69cd6da7\",\n  \"d9bbaebb-65f9-43cd-ba09-d5f33c50c825\",\n  \"d60b5b72-ea31-42c9-b49c-011cd737e1eb\",\n  \"9f772f61-db17-4106-8855-dc3039c3e2f5\",\n  \"21ab0e35-f223-4177-a552-3f8d68750044\",\n  \"db280918-0812-466c-9370-c5b1ad6a41cf\",\n  \"04ba3efc-e6a2-4d8e-9b64-16262e0a75a7\",\n  \"7988e08c-fccd-4f5d-98e0-564bca76284e\",\n  \"0ac2a2fa-c048-4cde-b453-9a6d456085a1\",\n  \"8bf47143-ae0d-4bb7-a14d-c89665ffa942\",\n  \"3efdda7b-adb6-46a1-addf-2959fecea0f3\",\n  \"fea95179-a8b0-4780-a4a4-28e2f0cb159d\",\n  \"d20e93c8-29a3-49c7-a98d-433c355d3951\",\n  \"21a2daf3-9a2c-48df-8f87-3531ca4d49c7\",\n  \"ccbfc8f0-e2b9-47c9-8893-e34d78a79006\",\n  \"a90f9c51-eb0b-41fa-bd18-5a414dd8091c\",\n  \"1ca963e8-7f3e-40ca-af14-717511560ba1\",\n  \"9b96a8a6-553b-4eec-b153-705e6813ff92\",\n  \"0014aa65-7b48-4a13-b640-45e9049d0b2f\",\n  \"2ebb6bcb-e241-4979-8825-df0b3feb0307\",\n  \"2061da62-728f-4fab-9401-77c52eb302d0\",\n  \"733f4cbe-af39-4ccc-8805-432818ca5c39\",\n  \"8664fe78-be22-469a-97f1-f5538c3de865\",\n  \"8ed8e2d6-d81b-476e-a294-2aeea1214d8c\",\n  \"e3332c74-7a1c-4d98-9f67-d83b61704e6c\",\n  \"91ee624a-602f-4b49-9052-d8d0bb4865c3\",\n  \"fe9b73ca-20c8-429e-bcb1-1db4e6dec7c5\",\n  \"719f1f5d-e3d4-401b-9562-d3b5678c1f3c\",\n  \"fc293b71-4291-449e-8dfa-44a4f36aafc9\",\n  \"4a614cdb-8c62-433a-b1c8-dcfc3df1f834\",\n  \"3835f842-0273-4e54-ae48-4ce55fd09613\",\n  \"4ccf39f1-abe8-42eb-9174-8808cc0da53b\",\n  \"c1b4a5b3-f8af-45cf-8a39-5aeb5f7854cd\",\n  \"262bd1d8-2643-4abd-a5ac-3d1432b59cf9\",\n  \"ef44f569-c74a-40fb-aff5-13dfba1517bb\",\n  \"c1055ecf-6637-44d1-a45c-025dbc60f364\",\n  \"21dcf033-4ed5-4633-9d35-f469452cfdcf\",\n  \"e916f785-4722-454a-8efb-37efaa743261\",\n  \"7a30e544-2180-43b7-b625-1970b897d930\",\n  \"11d62fd3-3e91-4a0c-91e9-91f04fe30738\",\n  \"9f97d795-7a11-412f-884b-3bedf3da7936\",\n  \"79fa283e-3b1c-4882-81c4-50791b0eb8fe\",\n  \"ad24de25-6285-487b-82f3-1dea37dfd494\",\n  \"30f42a00-ac1c-4324-be37-5d32f2284625\",\n  \"783b6bf2-0f00-4a26-909a-923f06f83536\",\n  \"072d0c62-2763-4b9a-bda9-4df80b7ef699\",\n  \"ce789c1a-73c3-4ab5-89e9-05bd32426437\",\n  \"bd39d8fb-a94b-400f-b817-ac4f8ed776b5\",\n  \"2f1c0759-f8d7-4173-b1b9-aadb85bc3fd2\",\n  \"2eba10c0-9b82-4d53-addb-d3ca66db27da\",\n  \"b91022cf-a588-42d8-bb85-c530ffd45073\",\n  \"b4d28c02-6b83-41c3-ab74-9bb2f271c293\",\n  \"36cf5f8e-40cf-4493-9b83-09c273890b35\",\n  \"89d0f118-eec7-4de4-97a5-a07550694ce8\",\n  \"2dc482ed-59c9-424d-9d57-921f1b73f3df\",\n  \"446bd2b9-713e-4c46-b3a3-980f017178c6\",\n  \"75e742ae-7955-4d17-8306-7f3e6ac7ad66\",\n  \"3191e0c1-b8f1-4a32-bdae-29df1e9f5891\",\n  \"6ea9f9ed-acac-4c93-8fba-9caa6dfd4f31\",\n  \"d963a6a6-8134-475f-9562-866fd2cb801d\",\n  \"6eefca3f-4e20-442a-9d80-420d8525f642\",\n  \"eb41275b-1ec6-49d1-92fd-93e7cdda3fd2\",\n  \"fd423291-1476-463a-ae80-4a0038a98386\",\n  \"0c95f564-0d25-412a-a6a4-aa01cfd3e921\",\n  \"bea96a57-c54c-4edb-af97-9d4b48100e23\",\n  \"43d24143-3995-472c-bd87-752d4d580f25\",\n  \"f0f7014b-d2a7-4059-856e-0047e2c112a3\",\n  \"96330043-1b37-4720-8000-fca84d0e01b7\",\n  \"725d6145-4893-4def-8028-9616acecdf76\",\n  \"9a099683-5fcb-46e2-9da5-dc367d5641e6\",\n  \"c5f2852c-3a75-4e39-9c89-f6383f521492\",\n  \"0cbbefd6-4e2e-468b-ad05-eb0751645211\",\n  \"45ddbc38-617b-49f6-b3f6-0e86657b8ac8\",\n  \"221b4ea5-9e99-4486-a8bd-ae6cf7335aa6\",\n  \"e6036532-05ea-47bd-9545-3884879869ab\",\n  \"f6ff0708-d86d-4b13-97a8-f32abf4a451c\",\n  \"c2d9e823-e74b-4fa5-8524-283c942e9b11\",\n  \"51b4635b-a8ab-406d-8249-7c25057eb9ae\",\n  \"bd5316e7-69e9-469b-a66d-8f8aa570db0c\",\n  \"7df4ee6c-44fa-4e4b-8099-85e86fa29057\",\n  \"12fa585c-3953-4717-8689-f98f182fcabe\",\n  \"1e01f4e6-b4d7-4c71-8e80-b6fe9e45869d\",\n  \"b1ab56ae-8796-4429-aa6d-85de55aa44ee\",\n  \"459b21f3-3ba3-4ac9-8fef-5c1c196d6964\",\n  \"352dd9d0-3023-4a71-858a-44eb664d4d47\",\n  \"31624ea9-3162-44ca-b828-7da5cfddcf1e\",\n  \"36e3e6c9-df07-410d-85c2-77bb03541702\",\n  \"a92b9a4f-0cdd-4f9a-a3ce-c64d8b595205\",\n  \"9032d440-09e8-480d-b7f6-3b440a5af4ff\",\n  \"91322d7d-d5f2-4f8e-987f-ba410adb447a\",\n  \"8654b74c-3f4a-4390-87bb-212eece9f8db\",\n  \"4afd9f00-eb86-4711-b88c-2bbff4b8f2c6\",\n  \"5b178740-d04c-42cc-bcc6-8dfd3737c081\",\n  \"259bc9cc-b043-4747-a2b2-6af324493d59\",\n  \"2b1c1835-d13a-4861-8a85-ef1928e2d491\",\n  \"481181a0-e0a1-48b0-8c0f-04766eba0c28\",\n  \"f8e3046e-e930-4f20-9c8b-b1ba4b60c1f4\",\n  \"4d7644a3-2abc-4ab8-a59c-5645a7a019fe\",\n  \"7b1a58a6-bd67-40ed-a959-bb4bab3bb90d\",\n  \"cf029dda-bf35-4254-b199-5f4623047990\",\n  \"02d4537e-8b8a-4e28-afc4-242b39e1dbcf\",\n  \"6d613d0d-db36-44b4-94eb-393a781eea20\",\n  \"9bd16669-0939-4c24-bc8d-79c1c10fc5f2\",\n  \"6fad0986-dd47-403b-a83c-d305ae946f77\",\n  \"191d603d-80fe-46db-81dc-9955a1c2f050\",\n  \"adc7bb35-fbaf-4be7-971e-abb2b44a5545\",\n  \"e03909f2-14cf-4ba0-a920-4aa15ddcb252\",\n  \"67083172-6691-40ef-b1ba-423693d86917\",\n  \"28bb05db-f3a2-4d83-94df-84682e5d1def\",\n  \"b1387d8b-a26c-47d3-a496-ffc1d1ddec52\",\n  \"055d5727-ebf4-4c3a-b61b-3b2899215e8a\",\n  \"c3e6f83e-7e88-42f0-8732-14175fdfddc7\",\n  \"c4bd1b14-8b71-4ed7-b63e-e9965db01071\",\n  \"73735987-1d3b-4f7d-b8c2-b53396963b6b\",\n  \"6c28c86c-e6cf-4216-bfcb-a38992d8d753\",\n  \"9ce604bb-5a84-487c-8174-ac8b54fc97cf\",\n  \"18d4d8b8-f6aa-406d-b87d-28aa822b6bd6\",\n  \"623ffd2e-69a4-454f-b3f8-0c4e92560c1a\",\n  \"47366fc0-d68d-4e06-ac73-af3a4a378f5b\",\n  \"051d7ff4-2d49-47fc-b2ca-0ed73d3de605\",\n  \"109fb00c-af6c-4027-9328-bae38f7b60aa\",\n  \"2a1593e1-1d76-4813-83e1-a5189175a2c0\",\n  \"dac1c209-84ab-43d4-8ad6-50bc4b0d7002\",\n  \"43a15e58-a77c-408d-95ad-4abe4facb849\",\n  \"baf606ac-35b0-498e-9408-56f3bd2a68a4\",\n  \"694d477a-3bdd-453d-910f-f2689ab4d31e\",\n  \"b2b120ee-a49d-4f3c-a878-b4590d3b3fbf\",\n  \"7a830d39-30a7-41da-9e3f-e927dd88eae4\",\n  \"a1afdb34-71ca-44c1-88c3-93153a33fdc5\",\n  \"8fa04e9c-50c0-405a-bec4-f5bbaf7c53d4\",\n  \"8c3c422e-f329-45a4-9295-8432c70bd857\",\n  \"bd24abc9-66c4-4f28-838a-6757a3d11b3e\",\n  \"17dcae3f-1a1f-4aa2-8755-8d73df02470f\",\n  \"3221b184-0672-403e-b6e9-06b7828d996d\",\n  \"eb9d56bc-8258-4991-98ed-5dd1a9915dd9\",\n  \"a3e40f5d-6716-4692-a1dd-a4d434cad0f7\",\n  \"f9e25c1b-4ab0-4916-9523-7eaf80e043b3\",\n  \"b6107150-114c-430c-bf5a-349d7e51c605\",\n  \"4055c4c1-21cc-494a-9d96-41f07a3a1465\",\n  \"de0d1b34-f1aa-4d7f-adfb-5c90f98d2d56\",\n  \"1736485e-9a62-47c8-a8e6-ddebc8fd00b1\",\n  \"c466e88a-838b-4e11-90ea-ad6c66ca534b\",\n  \"57659813-661f-46a7-8f33-7f9742f5294a\",\n  \"74177e93-756c-4d8b-8d9f-f16a53c57f0c\",\n  \"40acd034-4cbe-4a68-9dbc-f29efd9fcf35\",\n  \"b9e08133-6883-4c7c-9c4e-ab3b750a75ab\",\n  \"28488728-c10d-4bbc-9601-962e0ca8eaef\",\n  \"89022505-4a75-4c16-915c-34f529adcf18\",\n  \"a9e8e875-4b3e-497f-b615-e7c1714e188b\",\n  \"1373c9d2-830d-4f2d-8066-772a5d36557f\",\n  \"9ea9bec0-7395-4bf4-97c1-94869432732f\",\n  \"9a86df28-4d9f-4c08-a9ad-42c5f12d5542\",\n  \"35a02bae-654e-43dd-a58d-dd06c002a94e\",\n  \"d6292bfd-ab4d-4bf2-b434-62901ac45779\",\n  \"a5bf8376-58e5-45eb-ab22-a147b96cccb9\",\n  \"85803be1-d861-4945-acd3-4855f687f573\",\n  \"5bbc001a-e384-4e5e-a351-23ca2b6a8ace\",\n  \"c60e43a9-2b69-4bd5-91d1-1eee13a21258\",\n  \"e25bcea2-866a-4848-9d97-436fb35d6dc4\",\n  \"214804ef-95dc-49cc-84dc-0f3fb45d7945\",\n  \"313782ee-d490-4774-b5b0-b8d551b80f3a\",\n  \"742759e0-eae4-4b3c-8b8e-e68c54228eaa\",\n  \"178eefa9-ec47-4c68-892c-0cacf47c44b6\",\n  \"4d67039a-ac87-483b-9082-cbba9c3effea\",\n  \"1bee9feb-c324-4202-bac3-c9b2d443f2ee\",\n  \"008fe623-6fe3-4f78-afa9-375f15e1d80f\",\n  \"59880e07-bf1d-4dfe-81bf-cbef23582243\",\n  \"4d07dd57-722c-4b58-9cb6-c0c79f87c94b\",\n  \"5cdb5ab8-ef0b-4f52-a7f5-4cb9be41d1fb\",\n  \"d04a78e9-7698-4de0-b430-ed22c2d18724\",\n  \"5834dc18-9d0f-42ff-9646-75480f79bb4d\",\n  \"0bdf910b-fc36-4dd9-9e10-ebeedcceb401\",\n  \"e69e734b-b056-4b40-b199-eb002fbd08b7\",\n  \"534e715b-8689-44b3-afc4-f818b7857e30\",\n  \"9f99ae67-5af5-4427-bb16-7f894603443f\",\n  \"55993fb9-54d7-4e2b-87b4-290dbd421626\",\n  \"73ad1775-99e2-4edf-bb99-e8b9abfa0fa4\",\n  \"ae8f83b8-1aa6-4989-b9a3-d51685c2ae45\",\n  \"bb96091f-39f1-42e9-8cec-9f622884038e\",\n  \"772f46db-31a4-4f45-b98a-1bae116127b9\",\n  \"c3c61cb0-cc23-4315-801b-c0335563e051\",\n  \"0cd38987-3526-4d8e-a10b-f4bff00b8d1b\",\n  \"90ca72f3-37be-4542-9c9e-edaa2b9ee4cf\",\n  \"91fabe49-fa18-4a4b-9d03-4fcb06977d90\",\n  \"499b2589-0da9-44a1-86f5-981acb764ce7\",\n  \"eeb771bc-f79a-4972-9ddf-781f7c74d333\",\n  \"886ee490-354d-479d-9de0-9442402d0442\",\n  \"39f64337-d77d-4282-a17e-7ab468bbb543\",\n  \"93ec56eb-e104-4716-97a1-a1d2cf2ce0cf\",\n  \"8ca184a6-0712-4a28-a760-6ab7607c60e3\",\n  \"59ca22e7-1ec2-427c-9e49-6a267da35f18\",\n  \"b8bf7004-26f2-4888-9c86-6ff8aaefd250\",\n  \"feff209d-2962-4d9b-adb7-aecd524b5a5c\",\n  \"6bee0169-cebc-45e3-bed6-3ea69e238508\",\n  \"e823c039-66c1-47a7-9689-09d2a4708209\",\n  \"d0abdb2b-353b-4676-8a6b-f55301dd9440\",\n  \"2ab95d9a-2920-4353-994a-9695f5ebfe4e\",\n  \"56406a5a-b6a6-43ef-8c15-b3cf84faa90d\",\n  \"59c0cd7f-cc10-4a38-be5f-548c5ad12e0d\",\n  \"cc8ce7bf-1b3d-4fc1-b097-9bf557b1ce0c\",\n  \"99a67cd5-0a19-4de9-b6d5-2f7a9aabff3d\",\n  \"219d5b8e-5db8-410d-acba-e1233aa2fc85\",\n  \"da02b471-2f06-45d8-a77a-87f777312ac6\",\n  \"e14e2f7a-61d4-405b-bc1e-039b78d1b7b9\",\n  \"2991f12a-d418-444a-9cce-b218ca80fe78\",\n  \"a8f677c1-7a43-4e7b-9ca9-c1805dd77c9f\",\n  \"d3a4b496-4570-4947-bf50-219b2667369c\",\n  \"98903d03-7bc6-4809-82a5-a4c1d1b49c7e\",\n  \"5fbb7f40-ee87-45a9-8735-97396c3655e5\",\n  \"6670b72a-71d3-45ff-97a6-0835d354eb41\",\n  \"db49ce69-01f5-4bb2-96c7-7107243d6c84\",\n  \"996e0beb-4095-4b3e-9044-697b6e6194a0\",\n  \"fccb0d96-8d85-42c3-a11e-e44a7a77289c\",\n  \"59268dc6-1cbb-44d0-b9bb-d6fe64eae90b\",\n  \"1954eb04-50c2-49a3-a490-e9009a57e0bf\",\n  \"32677126-141c-429c-8806-878f1d348c6c\",\n  \"77d06f8b-3f2b-4d8c-afa9-5139271283f8\",\n  \"73f4ea24-9e67-473a-b540-45083d68f8b9\",\n  \"15c535a4-e459-4d1e-a8a0-5cbf82da2d07\",\n  \"b37464a1-9912-47ba-8234-a1f9383dd040\",\n  \"22d8176e-a14b-4043-8cc4-4b4822f31baa\",\n  \"bb974da3-9c63-4885-a1b0-4fdc9b94f388\",\n  \"bbc04318-33bd-4050-a8f8-6b264d5a150c\",\n  \"e73caef7-7ba5-49db-84b8-7f4c3b16c6e6\",\n  \"b4d14055-4a8b-4b89-b2c7-c36e176931af\",\n  \"eca1d085-285a-40d6-b89e-95d771d86dc9\",\n  \"3c7e9981-07bb-4dea-820b-c66ca55e677f\",\n  \"839f85fd-1ac7-40f1-9814-c31ca6c71ad5\",\n  \"566e82a0-f891-45ab-8a3b-3775e8da84d4\",\n  \"5d56bbff-092a-43ad-a46d-fcf5728319a8\",\n  \"f21956e5-ee1d-448f-aa57-72619b994404\",\n  \"a7149386-3fbc-4c33-90a6-083acd2c88c5\",\n  \"ab579538-17c3-4bea-9ff1-9f1663077d8f\",\n  \"36828845-fb5a-421c-bb6e-b316b751c173\",\n  \"8505949e-594d-4145-a249-4af1811cb19b\",\n  \"edf2f738-d42c-45c8-bc51-7a123af0416f\",\n  \"b3e81396-5336-4e92-960b-fe797980f504\",\n  \"d1b0bf5a-9741-45d4-9fb8-614e5bd77611\",\n  \"3fa40600-4a11-4324-893d-8b1b4ccaa533\",\n  \"afa6b99f-40ed-4100-a464-c65b1d19d6b5\",\n  \"3cc9285f-979a-4a40-94f5-3ed86f258a72\",\n  \"58460990-bbae-4343-b7a2-0bdde48f7602\",\n  \"2aea985c-5151-4223-908a-3109d5eb9ac2\",\n  \"142ac751-95b8-4929-9b39-4ca0242524ed\",\n  \"1f12dc9c-0a2a-4cf5-b247-3af1503b1ef3\",\n  \"c13d4951-45ea-4fbc-ba38-21d21c5b5e7b\",\n  \"d948766e-f013-4a97-8c3b-9e7b6081c157\",\n  \"e56c21e0-0f4d-4af6-8dbc-8d30b93c0eeb\",\n  \"0a6b2abe-2f06-4b15-9b83-45e2b662ebe1\",\n  \"96a9b7bd-b49b-4db6-be38-b5fc4d4f1f07\",\n  \"bce702ee-799d-4669-9574-526255a6de90\",\n  \"25c09a93-abc8-4cfa-aa59-a8f3e948c7a2\",\n  \"bae35e52-a666-466b-8388-0b8192875a3a\",\n  \"c1810a43-6440-4d17-9b59-5cba648c388b\",\n  \"60902c4e-45c0-4064-b91c-a06c34b451e5\",\n  \"09a38330-ca4e-4252-b74c-4ef8895c91db\",\n  \"381b0c62-e8c7-498e-871d-292e29ffdb65\",\n  \"a604d5e2-4b1c-45a2-8f1c-46079858dbf8\",\n  \"ec865a0c-ae18-4bb6-9cf3-1904d1f21650\",\n  \"f4aab81f-4c4d-40ac-985c-7c597f0d5b43\",\n  \"5d9c96ba-0700-4d80-b61d-ffbf5969e038\",\n  \"9c8b7bbc-b3ee-4957-836e-ccfa6aa5ee02\",\n  \"89017e9f-ae27-4c63-aa77-4bd88cdb6ea1\",\n  \"36baf83c-3ef4-4eca-89c6-1e367d1d2f6b\",\n  \"b83c9b92-99f9-423d-88bf-27395f841423\",\n  \"36e013ed-3361-43c5-abd7-32f9b382701c\",\n  \"bf528d9d-0c0f-4db9-a99d-c13fe8c62046\",\n  \"4d7808c3-e33b-4790-98d9-e5a50fa8249d\",\n  \"b64cdb47-ef30-4a43-87f0-3e9c09c28ae3\",\n  \"32c3f945-f8db-4a1f-9b4f-4bd7113ff910\",\n  \"c00e2754-bbc6-49f5-8f8b-a4ed7c80ea72\",\n  \"d92a36cb-633f-44b2-a93f-f9afd756b7f2\",\n  \"b75b42b4-5ae6-4494-bf21-d0a29e1e5e0f\",\n  \"fc970642-c526-40f7-b470-f0b864dcc898\",\n  \"a962e8e4-59cc-4c68-9c74-0d6a96869bd6\",\n  \"f0536d17-a7f0-419b-a188-566093215618\",\n  \"faab74a8-130d-454e-b007-67bef2bd3734\",\n  \"c7a622a8-e748-4f7f-a52a-4ca277d1beff\",\n  \"bc5bbb24-bd01-498e-a626-01ff910fa4fa\",\n  \"821a96ae-8e7c-470a-ad04-d293596d71a4\",\n  \"89624801-fcad-494d-94d7-7bb379998702\",\n  \"f0a0430f-d7c7-4a5c-a970-9de4986a5830\",\n  \"216531ff-6b71-41db-9bec-84c5e3cfdf81\",\n  \"0dd11648-c5e3-4651-80fb-fb62ce0239d9\",\n  \"e262fe34-8adf-47fd-b551-e123f84ed0fd\",\n  \"6adc1aa8-980e-4eb4-a4bb-2ee0d6073865\",\n  \"1fa10d29-83b8-4af1-98d2-30100d02e0a9\",\n  \"22211e41-bf65-41c4-b4a0-6b6f5a7cfe1d\",\n  \"6406c82b-cd6c-42f7-af93-34b87b77210b\",\n  \"39382e2c-96dc-4b9c-8df3-01878495e3e3\",\n  \"86a20876-7f5f-45b6-9d40-62eb3edfff6e\",\n  \"676d2324-3f68-4718-89a8-3822acadea94\",\n  \"26c6bde8-0437-49bf-ada6-a774c28cf886\",\n  \"87dc28f9-1e49-438d-bc87-8b2c918bd270\",\n  \"2bc92600-500d-4a04-8ddd-67b51e0ac080\",\n  \"e82f01c3-b4a7-4aae-8986-8a61aedf9b22\",\n  \"09f95c5e-fa2c-4b33-a05f-c7c6eefb4a67\",\n  \"9ba939f4-d977-4d01-ba6d-921be2504855\",\n  \"dd1fb4d3-4057-470c-90c2-a16fe5982d01\",\n  \"2317b2c0-49d1-4e36-b371-0574233ff3b5\",\n  \"5e98621d-669c-4384-8826-880f67fbf8b0\",\n  \"7c848c8d-2ed5-4b17-a014-95e1018d4646\",\n  \"42a08f6b-2b17-451b-bec1-28c7508c654a\",\n  \"7607b332-e175-40ed-81ab-297aea2876a0\",\n  \"4d978a22-6d98-43ce-a6c0-a6bc506c87fb\",\n  \"68e9b5f4-74d5-4958-98d2-9fef7a6a7556\",\n  \"ed823f8b-c186-4056-b70a-5a421a737087\",\n  \"f597f48a-742e-48c1-b604-9a6780b6b50f\",\n  \"243e30d8-324a-44ce-ac00-97d3e1f08968\",\n  \"1acae5a9-ee7e-4fe2-8f6e-914cbe0cbaad\",\n  \"f8dbff8d-07a8-4ee9-a892-c1e1bbac5639\",\n  \"4b60aa82-57c1-435f-b77c-d8807c2e611c\",\n  \"d608f5c5-132e-42cd-b732-a73ff2b1e71e\",\n  \"9d18c527-0e74-41c5-9abe-e0311f7324af\",\n  \"fd8f0ba1-ceae-4fbb-9bee-ccee2a5ef46e\",\n  \"8ad461ef-443c-42fd-83c1-4c20d0cade73\",\n  \"516cc29e-e529-4759-b6ab-4cc6cab61eac\",\n  \"34a6d138-ebce-4865-8de3-63c3d7fc0ecd\",\n  \"79b4c6cd-d839-435a-ab31-f2a14356163f\",\n  \"f5f41a55-e50c-4272-ad13-ff65150e8efe\",\n  \"c5228ff2-955c-420c-85f4-729dbce02893\",\n  \"a8e2ca19-a248-4db9-b8ea-18ccbcdfe6dc\",\n  \"a1b87055-611f-4ba0-b4ea-eeff301cc616\",\n  \"7e4fc071-1888-4453-a6d3-9e2f9a63aa17\",\n  \"a80b04a3-e194-4970-9a0f-1e21b082424a\",\n  \"4bdaf4f3-1738-4974-97e4-77416f028701\",\n  \"2481b03b-4279-4348-a04a-5bc9a40326c0\",\n  \"8a69e274-75b7-4439-a57a-aab0f8d62b0c\",\n  \"e39c0c92-f311-45a4-ad13-4ac277b67ae0\",\n  \"c10a4466-913a-4f57-9786-ccc6844d5290\",\n  \"f12bd229-c6c7-4626-b137-818498bb7329\",\n  \"d13ad293-753c-48f8-8ded-4cae238361ac\",\n  \"89e48316-04d7-4050-bf49-02e4a3473b90\",\n  \"6fad1874-32f9-48a7-9bef-8404fb7206e3\",\n  \"198e64bc-f494-4aeb-b61a-126433905d49\",\n  \"5f973f4a-0020-4fc0-99e3-3110c0735b7b\",\n  \"5fe69813-4648-4a5b-b48d-4352b366660b\",\n  \"c3b165e1-92ab-4dcf-a966-4927cb313c53\",\n  \"1350f1ac-1af1-469f-80be-805bb546a667\",\n  \"3677c5f8-f92b-4f41-88ec-85ff2b2a84e9\",\n  \"9a5f5da0-5a6e-46c8-9bbb-b542fde462d0\",\n  \"b00f3ffa-ef7b-41c2-863f-628eef8d77e9\",\n  \"9594d310-6f79-47e7-9d36-886cc75793be\",\n  \"945af6e2-47d1-4110-9a0d-ae32336dba1c\",\n  \"ce28dcd5-e1ee-4c1b-a0d5-f3f3c877e492\",\n  \"c41e066c-70a7-4ff9-b3ce-4da89af76d1c\",\n  \"245c6d07-27f5-47ef-88af-8514129f5134\",\n  \"c298bf44-a6c8-4780-ba32-f562da1dccfc\",\n  \"4bfba513-d82e-4b5a-8d5c-3ad224a6692c\",\n  \"b9fdf5f9-44c7-4a79-b176-6e31a41c2a9f\",\n  \"de051cd9-d7bb-4785-9956-2ab97768d9b8\",\n  \"cc02912a-456b-48ad-8535-77a94ce218a4\",\n  \"46e6b4c5-a412-45bc-b27f-6453362d8015\",\n  \"abaacd8b-c7f7-47f8-815d-68d4ea0eab10\",\n  \"7e10bcd9-3af4-4c42-914e-700b1a1f0d75\",\n  \"c0260131-bdc8-47bf-827d-bd0796b8edc1\",\n  \"91cc990c-d680-44ca-863c-b5fe5b73e366\",\n  \"551a4a24-1507-4338-9f46-1fb171c9526a\",\n  \"16c07384-c0b7-487b-a5e3-e67030353eb1\",\n  \"37ba6871-5dcd-46ff-b449-c8e05d58976c\",\n  \"82e6ef63-a9f5-473a-8138-5fdcb8eb10b0\",\n  \"cdebda5d-d41b-4983-9822-3ffff133e9cb\",\n  \"17edc103-a2f3-436f-9a70-6b235f629817\",\n  \"68e50956-abdd-45bd-a504-1c1619b349bc\",\n  \"626890c9-fab8-4925-8be0-a037e33130c0\",\n  \"a9f69f17-69c9-42e4-b33f-463e67467e73\",\n  \"c57ca5e4-bcd3-4db7-be17-ba226465907a\",\n  \"2fcca3bc-ce33-4640-81d1-0901bdb2d985\",\n  \"6bbed77d-5098-41c0-8d52-2ad8f5cbd01b\",\n  \"c935d70c-f38a-4c8c-b45f-fd98706601e7\",\n  \"9ecd7886-3f19-45a1-b129-039892f1ac06\",\n  \"61a1f12b-bbcd-475c-8520-faaff11da601\",\n  \"470e9427-3328-4034-8495-5b4fcd4ea778\",\n  \"d31a1890-92dd-47fd-9187-b313c27e4ee7\",\n  \"08e0c2d3-4c7d-4aa8-b439-4ee752ffe045\",\n  \"0664886f-2b9f-4287-b4b6-b7f242a80321\",\n  \"87764c1d-9f1d-4f4c-a75b-dd81763302db\",\n  \"f4986ff8-6575-4f72-8434-d5ace8be953b\",\n  \"641b2f61-8d7a-4430-9b40-7d9fc5dd0fad\",\n  \"684aa3df-c121-4cfd-b803-77f3425cd851\",\n  \"b3888a8c-ab96-4fa0-95dc-0f803cdfcb6c\",\n  \"4acaf601-8b27-4d95-be66-06944e8e7ae0\",\n  \"d98d3e16-af8f-472a-a1a2-5014724e2b97\",\n  \"c00e5086-0192-4434-8f3f-88fd6e321bf6\",\n  \"d8f3c168-3ef6-4d0f-b683-9366314b9ff0\",\n  \"19ac992c-a219-4e7f-8f05-ba1f5daaed7b\",\n  \"b16498c3-ed97-4f90-947b-ad5c37675fb2\",\n  \"769c7d5e-0331-4f65-a3d2-2db3d29bb89f\",\n  \"f18d778e-1a9b-4f34-8663-a68be55aa65a\",\n  \"8eea4c6a-3a39-48a7-bb9f-898e37505aa6\",\n  \"9de4ffc4-a4dd-4fd7-9743-d15b7257166e\",\n  \"1fdefd05-e86d-4c8f-9c44-6bfa395dce94\",\n  \"6f112a35-fd7c-4bab-af7f-9e8d24cdf53f\",\n  \"5075468b-8a48-4e29-98f3-729a2a544084\",\n  \"0fe556d2-a0c2-4aab-a29f-2eff2a2d80ed\",\n  \"3c9798e8-18b7-48e8-89fd-a302c7cb1617\",\n  \"21da31f1-1f16-4f36-b26d-aecfd48095c3\",\n  \"14a1ff92-1f50-4925-8aec-4cf256af491f\",\n  \"ceb5fc80-b154-4199-907a-ce667efea85c\",\n  \"ec707a8e-cee5-4751-86e3-1165495d4e62\",\n  \"7c0b97c2-708b-4cf1-bd70-01354aac8243\",\n  \"5a5451ad-2da9-4193-82b3-d7b35036674c\",\n  \"8529aec9-a99c-4a7e-aab1-d150a694b83a\",\n  \"632dedd2-d16c-4995-b8f2-5f67495e82e1\",\n  \"5b9bd3c9-c71a-42bd-9155-0d4b618be49c\",\n  \"e193d643-abbb-420e-90f9-fb0b13f68811\",\n  \"d8489b00-c71a-4fae-a3d7-97ae432cc948\",\n  \"0ded4f4d-87b8-4c5c-971c-40dd90c22d2d\",\n  \"3ef9c947-cb95-4817-be7f-436dccd3db47\",\n  \"37b2a20b-cc24-4ad0-80e0-c7f0d76fb99a\",\n  \"c5a34de3-3f23-477e-bb5c-14134be2be7c\",\n  \"c5185848-5864-4330-abf2-a7908e19f10d\",\n  \"987f6437-1278-48f2-89bc-e298b9d235fc\",\n  \"37dbf1ad-b4a1-4a8e-bd50-ca7fa38fecb9\",\n  \"fb3944af-4db5-4ad4-839c-dbc57aaeca76\",\n  \"67669a39-dd8e-4bf5-b7e7-ac8bf8d80613\",\n  \"ed84aa15-1e49-48a5-b677-b734a139214e\",\n  \"7aa1a6d6-85bb-47cf-a531-9d047a79404c\",\n  \"3bf403b9-a7d9-413f-9baa-c768b881b589\",\n  \"d05bf6d9-2747-4ac2-b449-31083b5c0439\",\n  \"c6523f61-1718-445b-85ef-f00abc60a873\",\n  \"262b007b-e65b-46b1-a18b-13a9bcb9a63c\",\n  \"3c50ab6f-cca4-4809-8b6a-82ae1cf5438a\",\n  \"3453a9a6-72ab-4ac1-8471-a9634138c243\",\n  \"15d63b2b-74aa-4d1a-b4e7-20707319f618\",\n  \"7038c83e-d521-41b3-9efd-b9f706e47048\",\n  \"03d42620-511f-4909-8432-17444ab73bfa\",\n  \"dc069e7c-d145-4f8b-a33a-355e6b945534\",\n  \"40272703-214b-48da-854b-f09eb28e4bee\",\n  \"0dd06aa3-555f-4186-86c0-bd74f03838f3\",\n  \"7b500f2e-96e5-4fc4-b858-28ed97aeb805\",\n  \"0e3607b9-d879-445e-ae35-1134f254e703\",\n  \"6581392c-b829-4212-91d2-06ada75f7355\",\n  \"b33911b8-1d30-4423-9a5f-5b947f1ad661\",\n  \"1367c291-3657-4f76-a52f-30eea16a6ac4\",\n  \"5de060d4-45cb-4d53-8ee6-b1bd3fa7f5f1\",\n  \"77f98200-7ead-4bab-add0-b7636446794c\",\n  \"d8943ec1-cc23-4a8e-9f6a-f2c1626353b4\",\n  \"6a7ea4a5-890f-419b-a965-51bbc1a205e7\",\n  \"9376143c-cc64-4c57-8855-787209bf7039\",\n  \"3956dd56-a195-40ab-b946-9f601f35b142\",\n  \"496f5ab8-ce25-499e-91a8-312ef393a2ec\",\n  \"fbb43205-3357-4c33-8489-839f378f2345\",\n  \"6f748d35-78fb-4f6f-b497-054af6148981\",\n  \"e3440020-0abf-4b3a-898a-4ddba33bcd3d\",\n  \"a0572d32-5bd5-46a8-92f5-b372f29c8ae0\",\n  \"710ea5ee-24c7-451e-a2e7-8b50fb520f62\",\n  \"411a88f1-e21e-4028-acdb-daf7062d2883\",\n  \"b9ba619f-599c-4f38-a492-9092d2f2c1d2\",\n  \"9e51b014-2b4b-40c0-a512-76a125cc1669\",\n  \"d20bf2c3-4484-4b7f-9a20-299fba159e6c\",\n  \"53b48b76-353f-42ed-8372-a8b99ba9e307\",\n  \"770add49-1909-4a0b-a13e-2f664dc7a9b9\",\n  \"92dd80a3-5899-494e-a765-970cf7bba96e\",\n  \"d9c10e74-5257-4196-ab9f-ce8df0b1bda8\",\n  \"ddd66ef9-9695-444f-923a-b8b2cc55dba6\",\n  \"f3623651-6fb3-46f1-bc4c-b93ab05ae6f8\",\n  \"2ac08338-20d1-41b3-9386-cdc23920de36\",\n  \"26397127-df6d-407a-8e42-2b38df2b5f43\",\n  \"834df16c-7392-46b3-b015-2531da0c8782\",\n  \"7a9e8493-d317-44dd-9a7d-4da8b6e612b6\",\n  \"d1358979-54e8-4149-9d77-f2585e0fd578\",\n  \"4e39088e-54aa-4709-a383-5d9044222cec\",\n  \"66f92af9-7ce9-4547-8d89-d63ca8d035a7\",\n  \"c572f356-f203-4392-a195-2f9e64a27240\",\n  \"c3337342-e659-4bfa-8fe7-a26cd4abb671\",\n  \"2e093aa8-530e-4208-be17-9ac368ec7c29\",\n  \"4feb0460-cfdc-431d-9363-c5d4262c834f\",\n  \"2027de77-bab0-4c85-82ab-5d19c48fc85f\",\n  \"9eabf4dd-9941-4a39-b750-17cf35a2895a\",\n  \"d6808101-1cb3-4efb-895c-6d3f5c3545c6\",\n  \"3eaafbbc-3613-4bb6-bad0-26434e220265\",\n  \"26e13cad-6b91-49ae-84d4-0716a4b9d3f2\",\n  \"dbfdd960-66dc-4954-9805-7449a25dd899\",\n  \"32a8b533-801f-4056-9b5e-f125db9d79dc\",\n  \"df58b9ad-458e-4c10-9678-cef4170e6686\",\n  \"a9e5839b-e5aa-4a62-ad9d-f264af8f7f96\",\n  \"af7db5e3-7f23-417f-9dd6-6a61c680f827\",\n  \"9449cdaa-fd92-4557-9d15-ab544904b092\",\n  \"1435c5d1-d11a-429e-83a5-21695c856da6\",\n  \"a630ea73-f667-495c-a939-8ec027d22776\",\n  \"b9e11947-b783-435f-8b75-5cba55d335c6\",\n  \"3ccd912d-b471-492e-8087-5e305ab0c942\",\n  \"e14662d1-390e-44e2-b11e-ea46d45472d6\",\n  \"3a9909f4-3300-4063-84f4-6bbc4bf00fc0\",\n  \"6a83099f-5106-400d-a432-76a27effe4c6\",\n  \"d0249ae1-2b4c-4e15-8a4e-57d17498d6f1\",\n  \"a877be04-a422-457c-89c8-e7a9c7470100\",\n  \"b7e45571-7083-4039-9955-01b3272937c8\",\n  \"3e59369c-a3cb-430e-bd35-4a993fdab6f6\",\n  \"749d2094-14f4-4e73-bccd-80a640196c49\",\n  \"2d18c07a-7672-479a-800d-547d5ef9cff1\",\n  \"91509adc-e5fc-444a-9068-b8bec4a33845\",\n  \"7accbfd5-565c-403b-92bd-2df00505fb33\",\n  \"78c1764f-1961-4f65-affe-6c48c7d0d723\",\n  \"a6fa015b-432e-46f3-a269-9cecc07e9095\",\n  \"c297cbd4-e635-4c5f-992c-e6455fb13480\",\n  \"174520e9-8de1-4b73-b3be-0defca19cf5f\",\n  \"94719484-31bb-49a2-8409-26c0dfeeb495\",\n  \"299ee32f-c9ec-412c-b1af-e1dc3baaabf8\",\n  \"58e0d77f-933f-4932-a334-20113d0e51d4\",\n  \"643d1a45-7f94-4d73-bc19-f2f421bcaaa2\",\n  \"288bf621-905a-41c8-9cad-a93153e4870f\",\n  \"1c07e864-11a4-4e89-b2f5-539faca08025\",\n  \"472c661a-2226-4051-96cb-62a6f726dac9\",\n  \"7765fdd0-ace7-433c-ac4d-3a71e48a8980\",\n  \"2ff4c806-d9a7-481a-ad30-5a90c27ea438\",\n  \"aba95267-b99a-42db-bb19-7f7bfdcd6815\",\n  \"d561e420-a38c-48cd-9efd-90ead0c3c9f8\",\n  \"8981802b-db94-4d00-8af4-2f93757689d2\",\n  \"c656b350-1617-4141-8f02-f50eadb492ba\",\n  \"965cf242-63ad-4312-a93b-519b16d641d8\",\n  \"1fdb6fbe-895f-4b97-a0c3-c526b58e7782\",\n  \"ab63da20-c3c1-488c-b202-064c4061add4\",\n  \"87128eb0-b69c-4655-8f23-f8b0b9a8f8ee\",\n  \"a56403f5-13dc-4325-a7fb-4fe69cf67549\",\n  \"ce2e4c19-1323-44f3-868d-0f8e99afc82c\",\n  \"41ab2f12-067d-47e1-a484-85be80e8c151\",\n  \"4fdf575f-3e7d-4e16-a6f1-104d1312123b\",\n  \"989f4572-9a5d-4380-967c-f1d53f6205e5\",\n  \"33539712-677a-4430-b2f6-a39e97dde0ad\",\n  \"09b43108-2336-437e-b609-80c299bdc2fe\",\n  \"52f575c0-38f5-4b3b-9824-45cc4dfccbc9\",\n  \"85767927-9cbf-42b2-9afb-e95dc3438994\",\n  \"29400d7f-e0bc-4540-b3b8-ec667839f115\",\n  \"a25520db-910b-4bc1-b704-006c29c19885\",\n  \"85ffb3d9-da85-4a0b-8ad7-bfe177b54478\",\n  \"fbae7b41-7983-43f0-8684-4718d9e6fd18\",\n  \"5fd2c60e-9b4a-436e-a2fb-5b03e9b85d57\",\n  \"e9c65967-8bd1-4ade-8da7-7b551ae70b70\",\n  \"2132b66e-0e40-44f6-abb2-1a19c2ad8615\",\n  \"3afb7575-0548-4c6b-a0d5-737efcaee222\",\n  \"1097bdfa-198b-4ada-b062-616958b670b2\",\n  \"8c771d42-c838-489b-bd9e-841403961c75\",\n  \"886b49c9-6255-4284-a1db-10b673a339df\",\n  \"0e83aa3c-76d3-4a17-a752-48896b34cec5\",\n  \"c5e5376b-3b19-4719-80ba-861c6ec121c1\",\n  \"58c47cce-c0be-4c81-854f-6a4caf9bfcec\",\n  \"5a1c0f3b-ccc8-43fc-b016-51b14c28731b\",\n  \"e01455df-c2c4-4df8-b235-2ea623104f71\",\n  \"dc5c50ac-3e88-448e-bceb-cd929785bcba\",\n  \"ade907ac-e4d7-4571-8ff6-e7792b4dd351\",\n  \"435ac79b-2415-4409-94e1-466129d02a9e\",\n  \"ed07e31f-0ecc-4c52-ac8d-d7a57cea1f48\",\n  \"9eb1bc9f-fb6c-4add-9015-2b29ff482eb0\",\n  \"ab2ee6bc-e372-43f3-89e0-4becde4f1d5c\",\n  \"6ae83cb5-7298-4eb3-ba23-58f1b92ac574\",\n  \"0976991a-1a81-4cf8-a7e0-9ae2a97d0089\",\n  \"bc3e56e5-9293-4c8d-8683-0adddba21661\",\n  \"ffdf5432-1893-4511-9622-300826e3660e\",\n  \"bdf2ebd0-9d75-4d19-8acc-3f22fe79627d\",\n  \"423d21fd-1686-43b7-92b3-afcfdbc217d0\",\n  \"862cdeb1-811a-42b8-94da-96252452eed1\",\n  \"1cecbc9f-9f9d-424b-808c-7df9192378dc\",\n  \"fa7e9c87-56f6-42b8-96e7-1d1908ed9435\",\n  \"6c7063d4-460a-4ea7-9c07-d5d26a9c821b\",\n  \"b719f7a3-5e96-480a-aaa3-8255a8788270\",\n  \"5f7cdbb5-ce87-4f46-95da-8ef26a9d73b3\",\n  \"ad5f4752-14a2-4099-9329-68b10f775370\",\n  \"816d8437-1f08-42df-a976-94d17059f215\",\n  \"469adaa6-a0b2-4972-95c7-e4d40e656cec\",\n  \"d29cdce0-4a31-44be-8b6d-c752604b1842\",\n  \"8a51b23e-0748-4354-a47d-8aa8a032cba3\",\n  \"08a41317-3d33-4fef-b22b-677f74c8afea\",\n  \"5f4a5f3f-492a-45cb-93a0-6a5c2aae49df\",\n  \"f7532b63-a301-4d00-be36-dbe780ff55d4\",\n  \"c4398ffd-1648-48ab-819f-2011ff862d56\",\n  \"698add28-ac1f-4de3-8166-91d76a6f4b24\",\n  \"92bc3966-20e7-44d8-b4ca-2657423dbc47\",\n  \"fe087937-5cd5-47d7-b4b9-ea7971a23b42\",\n  \"fdd1c526-b74a-4848-b168-09f05a21c96d\",\n  \"cb38c536-034e-435e-9ea3-f8810a093417\",\n  \"40628cae-5460-45fe-b743-9ccad27e7c3a\",\n  \"d388ef6a-780d-42a4-a919-6474fccfe0b5\",\n  \"11362481-1d78-403a-8728-e7f2e728839e\",\n  \"25a1eab5-200e-4d9e-b19c-36e43afb6518\",\n  \"6a41c15b-e300-472e-944b-76fe0974a2e7\",\n  \"a79fd15b-8ee9-475a-968e-c31695f78880\",\n  \"e56fba06-9f67-47de-8b17-21502e40271d\",\n  \"2e6ae463-196c-4918-9924-be460d0b8032\",\n  \"39b8540f-a663-4782-9d90-ef0b9f206a69\",\n  \"6cd03384-fe83-4c8a-9904-ce29a45f0b41\",\n  \"472dab1a-a227-4d35-ae67-de4e85a7524a\",\n  \"c56cfd0d-24da-4b37-8026-0ad9eb902c8f\",\n  \"38b09c9f-20fc-47ae-8ffa-6fa9124585c8\",\n  \"c7b85eeb-7b85-491e-a0b7-34d2f2164e50\",\n  \"6fd15005-a0d6-4a04-b187-71e017e282cb\",\n  \"1fbd5fea-a4d1-4b34-a819-c0c6161c0b33\",\n  \"71ca5532-1a73-4611-a3b0-fcdd4f6f68f7\",\n  \"525e1ddd-6753-4c63-b537-9711775978d0\",\n  \"93cfbccf-419d-4402-a7f9-072adbdd70ae\",\n  \"27fbcba5-e2af-4fe6-9e48-b057bb59b7f8\",\n  \"3ccd6863-5433-487e-8dae-9764a3ab5e6c\",\n  \"c42dac59-7aab-4068-b494-eb09cd999a7d\",\n  \"ac53d9cd-94ad-4792-bf01-8e55ea953dc7\",\n  \"040452a9-3bf3-4c73-ba03-353cca8f0080\",\n  \"c4a97084-cdc6-4dc6-af9a-84b00599e2ea\",\n  \"f20f1a88-c922-4541-8a11-e8f103facd8e\",\n  \"c7e2e196-8b3f-47b2-86f0-5a8661d3af80\",\n  \"ba30d870-607e-4b3b-80e0-bcdaf377ec70\",\n  \"2376d8e3-89fd-47cd-b33e-d4d17b06642e\",\n  \"bd3a3f90-9eeb-4225-8eae-98bca5db7da6\",\n  \"65c4c01c-3263-4ea0-9cff-817c3185a0f0\",\n  \"d00370ba-3480-40aa-8da3-faa99785241a\",\n  \"4d0788bd-5844-4019-b550-a4ec9a90f627\",\n  \"f637e092-efe0-4ee0-9693-ed354ef47e42\",\n  \"4fb661d1-7fe9-4e4e-b0ef-2803c50e4808\",\n  \"27bccfde-0892-41c3-810a-47fcdd48f525\",\n  \"084996db-3f52-4b86-a41d-981ecd2abe85\",\n  \"6788f4a5-cf0e-4015-8342-fb874483f80f\",\n  \"a8e87d37-6387-48de-8b1b-7d73594d6225\",\n  \"97e187d9-8b9b-4ac0-acdc-946f67b8c47a\",\n  \"f6911a79-6ef6-4bf3-a501-34baf531ddc2\",\n  \"a50894c6-ad39-4d6b-9df4-ebbfc5ec31bb\",\n  \"cb38ee76-d165-4550-ae9a-fda431203a62\",\n  \"ee2e0ddc-2b9b-419a-8b97-2d0ecc739afa\",\n  \"97f90789-8f8d-4b5d-bba3-82fd83e03c49\",\n  \"8d20e51d-9db7-447a-8d55-f87a73956fb9\",\n  \"23303ec9-3a24-4386-9c78-5940b4f1103b\",\n  \"e2dc9a97-d961-40fa-bb9a-fe4b9f031fa0\",\n  \"58b75077-9c08-47db-a49e-78a820c23fb2\",\n  \"6f50c05b-1d31-47d5-a27f-61248d678a56\",\n  \"81fb45c5-10d0-4321-a901-29fd7862761b\",\n  \"2e911453-17cc-4f5f-b299-d3bd39b153f0\",\n  \"7cc5c86e-392a-4a75-afa2-253e6d19c946\",\n  \"b1c180e8-eeb4-45f4-8c15-19333095aded\",\n  \"4a9070db-f0a4-4eeb-be05-41de11ab9907\",\n  \"77088400-b27b-4232-b46e-59e24458fd88\",\n  \"5e076cb2-216d-436e-9cac-87858696f80f\",\n  \"11f0e807-9628-4d2a-9016-bd79b30e5d19\",\n  \"2bf98b69-ba89-426b-b403-8209af68701c\",\n  \"c806d123-0d81-42e1-b568-b74f6f80fe16\",\n  \"8dabbdfb-fd7d-4df4-ab74-db81d88e4eaa\",\n  \"7008cfaf-2cd3-4f6d-a735-ec5442eaa785\",\n  \"d828ac73-eeec-4afb-9ddb-8763cba15a36\",\n  \"ae52d15f-b8e4-4d32-bd0b-312235d5920b\",\n  \"2400a133-c24e-4f5d-add4-6353e007ea4d\",\n  \"dde88def-bb86-4f7a-b586-5fee894fe1af\",\n  \"d9e42a8c-5851-40d0-b3a6-d46754665967\",\n  \"09b44cac-4486-469d-8fad-dd78ffbb678c\",\n  \"af48a69d-3786-46d7-9183-c6f27891a4ce\",\n  \"84371aad-c679-493d-8895-60f61d7690a7\",\n  \"99cd342a-39ea-4f9a-a4f8-362cfdb0ea57\",\n  \"f6e3e056-ce7a-49ce-9a37-6264300ac91a\",\n  \"9fb10fe3-5004-431e-bb4a-54c68df0543b\",\n  \"d6376843-2bf6-404d-8d88-bd9f9e010d5c\",\n  \"d9fa1f67-1159-4a7d-9a0f-bf109dcc8fa6\",\n  \"9c18e71a-49f6-4cb4-a3e9-b9dc436ff1c7\",\n  \"dde992e2-4229-41d4-bfc2-0c83ea8cd21a\",\n  \"a70079c6-07df-4182-b80c-66f7e6f2bb6a\",\n  \"b0cdfd33-b3db-4c83-bc3a-5b4809e6adeb\",\n  \"8caa324e-6769-4859-ae8f-6a688f1ca6c7\",\n  \"f88f0424-835c-4670-95b9-ce9a2d74f0be\",\n  \"524080d2-ac49-43e1-af39-9dc6e1a82768\",\n  \"0a92d04b-899d-42c2-bed0-7c7a652fb9c3\",\n  \"ef75ae6f-803c-4ad7-92f9-6f0136934fe5\",\n  \"e74a2d84-c14b-47cf-9734-d4eddf5ab7ac\",\n  \"76baf3d7-e76c-4289-a669-992adb2ea599\",\n  \"ad1bea9b-68ca-4027-acff-ff1bc62fc41d\",\n  \"fc60c1d7-e421-4a63-a590-48ed619ebf47\",\n  \"c52df4f0-a336-433d-94a3-288758704203\",\n  \"63f78108-031b-4d73-8755-c364a9834211\",\n  \"fab6499c-776e-49f9-8036-4b7a668abffe\",\n  \"089cd0f0-69b4-43fd-ba89-a51fe70e9c79\",\n  \"a2a20496-0f56-43dd-9536-ac6604f8dd35\",\n  \"5a5d1531-5862-48ba-998d-62856c4726b9\",\n  \"2b56a104-706c-490e-af43-e36f10d42642\",\n  \"3ed2f285-3a14-4838-b3af-bda843bbbdd9\",\n  \"3cc64b2a-ca75-4798-a0ea-14e66acaf8bc\",\n  \"e2b267f7-f865-44ec-9e8a-58d3b98ff9b7\",\n  \"0c8470d3-62ba-4a3a-92cd-e3a3a981e5c1\",\n  \"450a6a3c-4786-44a8-8266-29e6ec1b3b2d\",\n  \"3af3a41c-bb32-4ec5-b294-02de79ea086d\",\n  \"6aa0121b-0a0c-4fb1-8784-404abb8c5e5a\",\n  \"4554374b-df72-4446-a172-37361b17adb4\",\n  \"a1ae1316-73a2-4841-8959-34003a7ff8d8\",\n  \"a2857474-d4cb-418e-925b-cebe73442d3e\",\n  \"4755a361-6a98-4985-a3e2-4751c4627798\",\n  \"3cd652e7-93d2-47b2-8a72-6cf2eba15bb6\",\n  \"a2b9ef6c-1d3c-4c12-8cd4-7d9b7e0c9851\",\n  \"852d5aeb-fd46-4124-b458-63528fac1f5d\",\n  \"a85f45eb-8c7f-4897-af0a-8fdb2ebcf8d7\",\n  \"7781c566-5698-473f-b100-6771829f3bf0\",\n  \"1337202f-38b0-4a66-ae48-948890e60c64\",\n  \"3d6e939a-64fe-4bae-93d8-9df7750dc5e1\",\n  \"5395884d-5afa-47bd-a245-7b54f0f16f3d\",\n  \"b1c16ca2-9707-4893-b417-2038f5a10ce6\",\n  \"d7f7749b-137e-41e7-ad43-324dcc995dc2\",\n  \"448d3b07-3c6a-4510-ab0b-ebf13c085348\",\n  \"5891cb60-f601-4a04-89ce-e3928989d93f\",\n  \"e227ab96-7b58-4d2a-aa77-3cd7b50314a5\",\n  \"21f1a62a-75c6-4e35-aa1a-6f98a2f1a1db\",\n  \"fa55416f-84d6-480d-85e4-482292f296dc\",\n  \"dae3aa4f-2a5f-43f1-a37f-b0f2603740c5\",\n  \"dabae23d-56d1-45f7-a15b-cd63c75398f7\",\n  \"4e3af79a-460f-46da-a4e0-2a9bbd3863d2\",\n  \"ca587050-81e5-4073-ad0c-6431acb64c89\",\n  \"a26365db-344e-4bd0-9146-b1e3b589b407\",\n  \"0855ad4b-ac0f-466a-9a9a-dc9ed74379c7\",\n  \"d133fe71-2e18-41b5-b6a5-e7805dd97acd\",\n  \"29266a7b-f349-46d9-873e-e957ad3bdb9f\",\n  \"35499b84-c83e-4269-bcfe-bf49dd049812\",\n  \"06f626e8-8f8b-4947-92c8-38038e477f7e\",\n  \"23456952-d8de-468d-9c84-39c344c207b4\",\n  \"b5cbfa57-ab49-4075-9e8f-ead076ffaba0\",\n  \"40535c58-d818-4757-9ea4-62d0e28f1837\",\n  \"2a76aec3-83b9-4ef2-9582-82da7ead0259\",\n  \"0ff2b04d-c498-48e4-b428-9eafd2f8948f\",\n  \"67d9c38c-a572-4c3e-a9f4-2fe25cc235fe\",\n  \"7d702f17-108f-461f-9978-c89a7a4a9649\",\n  \"921b8050-7350-42a8-b4cd-82e89eeb4cf0\",\n  \"8655e08e-2efc-47ae-926d-4464afb877f2\",\n  \"49ac8487-920d-4d2e-a33c-95c6e6f03b63\",\n  \"9c7fc7fb-a9a0-4af5-ace8-11526223ac47\",\n  \"25890354-2444-46d3-aab2-ff5cdb7ffbd2\",\n  \"5aa7f474-1f7e-402e-9654-117d867efeba\",\n  \"ad7d41cd-bfbe-4190-9ca2-2026a8faa7cb\",\n  \"4f51a998-a235-4a77-87ec-4bcb1de187a7\",\n  \"ed9dfcad-e331-43bb-987d-1da15e81bda4\",\n  \"3adf5c3e-b776-44ed-960a-7ebf26386b49\",\n  \"69b3614a-1044-4972-913c-a2751f584faf\",\n  \"89d6f8b5-ef5d-495d-8443-26561be6d5af\",\n  \"bcdf61f7-a8c9-45bb-8706-96cd2a71a125\",\n  \"9666e792-d330-44d9-8599-ad4b8225552b\",\n  \"6c7230cd-4acc-4a7b-8824-83e815fa0654\",\n  \"c8812e14-3955-4724-a019-2d0e7946de48\",\n  \"69c6e2d7-487b-4508-b4f3-6bc8a5e9b424\",\n  \"d31e13de-417f-42e7-b713-1c2438679f63\",\n  \"ce90f24f-4c3f-49d2-af40-fcf420ef5a36\",\n  \"ad8df83f-68a2-4ccb-b977-85afdec2e02c\",\n  \"287c5061-661e-4314-85cc-36c5a0e719d4\",\n  \"baa7f208-cf20-4d18-8823-328dba20c447\",\n  \"3e6afea7-ee10-4b76-b1cc-115cd004e7da\",\n  \"7c5d2d4c-594a-4615-b7db-52d22aa01e43\",\n  \"7f918ad3-6b76-4779-9e96-d1c2e51c6576\",\n  \"20b7a3ec-5688-4646-aae8-2257923485f7\",\n  \"1bc7c8a5-1226-4e2a-8125-8d52a73d41bc\",\n  \"07f67154-084b-4c4e-9235-69aba073fea2\",\n  \"c8219a6d-f0d2-4f2c-ba9a-382ad797698a\",\n  \"4c44033f-ed35-42cd-af26-221ad98e141b\",\n  \"31f8ea5b-c6a8-46e8-9c53-22f08e1902f7\",\n  \"755d35c9-8350-4dd7-8096-a036081019d4\",\n  \"5aff702d-8cbe-40a9-b8ad-4381f3325695\",\n  \"efd18e07-8ac1-4af0-98f2-e2077cd29d1c\",\n  \"a4afb75d-418d-4c14-9d21-238812e809cc\",\n  \"8d9cff6d-ece4-4942-ad0a-b57a4217aa8c\",\n  \"ea643e73-d8e1-4e46-810c-86da73e9849d\",\n  \"261cdc5a-c372-4adb-82dd-281a1a203d13\",\n  \"aa64d417-e23f-4979-bcd0-1dab5caee86e\",\n  \"49c3699d-2fea-40e8-838f-0095d6df057b\",\n  \"d2709430-cd7c-4145-afdf-96055a7e50e1\",\n  \"0ea84993-e009-4fa3-ae13-4be87d66a1f5\",\n  \"6ee57583-5271-43e7-9151-cb9341a642a5\",\n  \"1efcc962-00ef-4803-be75-1d90e82ffcaa\",\n  \"d4a36f2c-73fa-4031-a87c-5ae91c50cf8c\",\n  \"e2ea700b-23fd-4a7c-80bd-978d8042d14f\",\n  \"4dfb5656-ebd7-4f07-95fc-1fdc41ad0b93\",\n  \"6c8c05d5-0a7f-4843-b93e-dcf644fb220a\",\n  \"8875ffaf-72a6-4e06-bcc0-3b342e28740b\",\n  \"23f35240-f086-4107-be44-e35042d3cd54\",\n  \"752dde64-3c54-434f-88f1-83fbd3c00a77\",\n  \"f271aa7d-32c8-4302-bcb6-4fc6f1813bac\",\n  \"fa0b200a-f43e-4d02-9835-72d4b0ceba32\",\n  \"649518f4-9836-48b7-9764-90a3f34512ae\",\n  \"a2d60544-0dde-4d2f-9ba3-609997a101a5\",\n  \"00ab1fd3-9fe5-4375-af1b-e5c1a4347055\",\n  \"3ac266ec-8997-4591-a875-d38f7669a288\",\n  \"3cc93bbf-1dbc-4f76-9a3a-2f7c02c2fd35\",\n  \"9d3025fd-78de-45b4-a971-7ea6e23d2426\",\n  \"16c9f99a-075a-44a0-b93a-4b1b888ed918\",\n  \"27ecbce2-3e30-4f0b-95f1-028795e39a6a\",\n  \"46c8004b-d851-44ff-8299-a4aa1173cebe\",\n  \"5b1a5ecd-f59d-477f-81d6-6e3380b26651\",\n  \"bea6097a-a214-4def-baad-bbfcc33c76b2\",\n  \"df3e6d5d-6c9f-46a8-a2bf-a5849fd78d79\",\n  \"2aaffdd9-33c5-4dab-b63f-e239b926ed6f\",\n  \"91f1aab6-4363-4713-b906-5adc74d466bd\",\n  \"a5eecaab-79fd-4009-813e-6c90e85b825a\",\n  \"9339e15e-5cd0-4b06-933e-1a6b38c84e9d\",\n  \"bf197310-458f-47d3-8736-6db6aaaa79a3\",\n  \"61491a7d-6eb1-4cb9-b090-fe292e7240c4\",\n  \"173744e8-d656-4a60-ba05-79359ced2eab\",\n  \"68fe3ab9-4f8c-4cf2-ba44-8d1a360ef4ba\",\n  \"be1a0058-6ab3-4b20-8a29-3634bda5833d\",\n  \"cd742d1a-5ffa-4af0-bc8b-d605ff3fd78b\",\n  \"1783ca2b-29e0-4e66-8681-3d7850b63ce1\",\n  \"969605b1-88bc-477e-a28f-c25b7081ed74\",\n  \"b2566609-aca6-4239-ae43-894cd06e730c\",\n  \"1ed74ea5-5baf-490e-8bde-7345915f2efd\",\n  \"e0fd2f7e-8709-4050-83af-b7aac555c6c5\",\n  \"e81d821b-9d34-4954-83e2-60d9829ae399\",\n  \"e45505e0-b338-4fd8-b4fd-b6456806a0cd\",\n  \"fa6aef1e-9464-4c17-bbf6-9238dc81939c\",\n  \"cafe226b-428e-4b07-96b2-5b418a30ac64\",\n  \"921a41bc-f0fd-4125-8c5b-01e8ea37bf65\",\n  \"0ef65c74-d771-4ae2-b8eb-6c6de3043529\",\n  \"67119cbe-1f8b-4046-9b6e-d535d81d667a\",\n  \"feb0ae86-65c0-45c6-9f96-1a2691106071\",\n  \"1b8ea336-e9e2-45ee-9526-51396c44ff5b\",\n  \"0e9402ed-ff6b-4267-8ead-0c49cdc2ecbb\",\n  \"cb7654c8-fecf-4bbe-a5e1-ad2622d84f8a\",\n  \"f33fbddc-327d-4738-b3ef-72f2d4f48bf1\",\n  \"d3d40910-c794-430e-8474-9053e430200c\",\n  \"e886852d-8c13-46a1-9e0f-d979ff42c66f\",\n  \"744ded0d-25d7-49bb-b45a-f4bc71689422\",\n  \"8ebf391a-4ce2-4b7e-840c-b1c357908fe7\",\n  \"930caee4-1be9-4b9d-b929-ea97f4c06cd5\",\n  \"3f46d984-9583-4c94-a706-b52364471a58\",\n  \"b3d204b1-d4ca-426d-8ce4-b8a339a93f81\",\n  \"77ea6972-d7c3-4e35-a8fd-cd0f7ebae987\",\n  \"c320ddce-8bf6-4da6-99e9-0fcf490cd3b6\",\n  \"444ad11e-b098-446c-b92a-353d53e335ee\",\n  \"25bcbaaa-f9d7-470e-8ba3-4382387da483\",\n  \"fd28501e-b3cf-40f7-b135-30551a322f8a\",\n  \"3d2878ee-1e7b-42ed-bc57-57305a5f56ad\",\n  \"4fa2da4c-cfad-43a5-aa58-dbf336f81dc9\",\n  \"dbe3e112-52c5-4a6c-af4c-e2c485786730\",\n  \"3f8377bf-dad0-4203-875e-76cf6086f7ca\",\n  \"9e055015-ef0e-472b-9fdc-5ecab72b3c32\",\n  \"d820c8fd-a9c1-49f1-b296-c3bcbe56573f\",\n  \"b83a50eb-d72a-4f4c-96aa-b6268f4b5f07\",\n  \"c7e20f20-7462-4554-827d-63f399770d73\",\n  \"0434b6af-efd0-402f-88f6-c0198df93c5e\",\n  \"efa8a1a4-85ad-4f03-bfe8-e5811f307a9b\",\n  \"b2a2a54d-adc6-446e-ae4c-241b10d22d79\",\n  \"43f93773-6380-4cd8-a7a7-57c83c62b6c5\",\n  \"20ad5299-2670-4acf-b3bd-ec55ab5a29f7\",\n  \"9c81b15d-03e2-4e81-8375-2106c472dedb\",\n  \"52bd63a6-82d6-4cee-9fe9-16c1aff189ff\",\n  \"daea8424-ab36-4daf-b99b-4a2711d1dafb\",\n  \"b0fcd366-b373-47d0-9a43-053f8b26deb4\",\n  \"0632ea46-2be8-4fc7-b830-0f2d1d989f87\",\n  \"d5edb31a-9c93-430f-a5d5-0f682eec9bcb\",\n  \"891ea66e-3424-41a9-ab82-8b4fb7e189c7\",\n  \"2536a86f-3dfd-44a0-af25-104027d0c2cc\",\n  \"465d8419-8524-46da-8dc1-88d45423e2a2\",\n  \"e39392c9-c8ca-4df8-9a3e-1bee2e667ed2\",\n  \"5144bd3b-c03f-44bd-99b8-27cc0585129f\",\n  \"c30fbafe-30ec-4f05-bac7-9532ddc8f726\",\n  \"d6f7de4c-463f-420f-8cc5-a62ab872ef28\",\n  \"7b389052-f090-4bec-9831-2e1f4c2caae3\",\n  \"f73bc7d4-6d58-4680-932b-dac0d65f5530\",\n  \"a1a05770-7fca-4693-8800-0602dfe8bd19\",\n  \"50297c99-f944-4620-a49d-c56960af573a\",\n  \"cbc5f960-da0e-4e69-83d6-361d783493e4\",\n  \"6c8065aa-6fe6-4781-afd2-c7ca8b358cca\",\n  \"cae09f0f-d8be-42b7-a5b6-3aa5cefd8831\",\n  \"ce977175-9151-46f3-bd14-26b4f5e94f28\",\n  \"20b9fc67-605c-4bc7-bc05-abcde7babfb3\",\n  \"44239fc2-2e34-4811-8675-9aa59600345c\",\n  \"ee8c0629-f44a-4804-87b8-f2de4d349d91\",\n  \"1c598a47-7585-4e39-a34f-0eea381fbb65\",\n  \"e12f42be-e016-4c15-a85d-7b0e0bef446e\",\n  \"81ddc249-0742-4db2-90c2-0855d4867d21\",\n  \"c507d7bc-b737-4ca5-ad6b-4210d962a198\",\n  \"40d64387-f7c3-49f2-9177-cff5697e822f\",\n  \"0feee956-d49b-4da3-85eb-0a3fd3057d47\",\n  \"87932f65-c86c-4781-8e8e-961f5a2b8cca\",\n  \"695571b0-dffe-4c75-8438-4efc804b4c12\",\n  \"6b032f1b-4934-4bb8-91bb-088356a50045\",\n  \"84b70808-7374-4298-84d8-5180dfff83ed\",\n  \"e54cad82-3274-4a7f-aae8-830acb4bb168\",\n  \"6698718f-b01c-4f5c-8331-2971e87232fe\",\n  \"f0d757ff-8068-4c7c-b57e-a4f2fbc909de\",\n  \"1fa319b7-6ad5-4963-ba4c-2932574915c7\",\n  \"320700de-4035-4925-8028-c6931ca67a8d\",\n  \"e3dae78f-b029-442f-85c0-1e8d9c3fe79e\",\n  \"a525e5ff-08b9-4a39-9f6f-40e2cebf33e5\",\n  \"4486bbc9-4749-434e-af12-776f70e58cde\",\n  \"9c1c3ac9-514d-4b1c-ad6f-95c9d3490740\",\n  \"b6e38d15-b442-47a8-8e4b-fbf35a7b693a\",\n  \"0cf72ee4-3e65-44b6-9c30-32be22c3531a\",\n  \"904d68d4-61d4-4bf2-8833-a987eabcb6c1\",\n  \"9915637e-23f5-4a7a-8b75-4bc6eaeabcb2\",\n  \"e2442e93-23ae-48bf-a1b5-f9ed765939f5\",\n  \"71306c02-bd0d-48f3-befa-5c62bb907ffb\",\n  \"6dae84f3-77b2-41e0-b1bc-36e9bb3914fc\",\n  \"e92e543a-b372-4f81-a305-6616f61eb3a5\",\n  \"dc5ce520-22f0-41ad-878d-d3fbc53eddc7\",\n  \"cf149a8c-8bde-4a6e-b977-52b69ddef04a\",\n  \"4263e99c-d1bc-4167-8a8b-0056b7367d80\",\n  \"fd9021e6-ccc8-4b7d-a784-12d697f18eb5\",\n  \"a72030f1-8da8-49e9-8ed1-a5f67c3ea58d\",\n  \"7ddac1d5-7619-4f80-bd1f-5321e56c9a2f\",\n  \"df384cd0-da17-4822-a691-62e37baab69a\",\n  \"865bac6d-c5c0-45e8-b1da-9a10691a229f\",\n  \"0e829e71-9da3-4124-8ab5-704f287dd886\",\n  \"68540166-8aa2-4cc7-8a0a-03bbf510642d\",\n  \"5fb4cec7-c1e5-4be8-9eff-43a3e247f77f\",\n  \"b6e4e25d-a7af-45d2-a0c9-eeb96edd871a\",\n  \"46b4fbd2-31d3-4702-85cc-9e13a5485e43\",\n  \"90309b93-939a-466c-9cc3-246c17f68f3f\",\n  \"b2f59ba9-bbd5-4ce9-9e76-d492fff526cf\",\n  \"20cfc8a6-4f58-429d-b2e5-33b6129a74e1\",\n  \"0c99fb76-c615-442d-a7df-507303ea2a23\",\n  \"d1fdab32-9f2b-47c1-aca9-51daab842091\",\n  \"c2892571-ba77-481e-9065-78e21f6f30c4\",\n  \"4a07ce68-8db7-4c24-b89c-da646cc612cb\",\n  \"35f180d4-e905-4a06-9b24-3ad26cf2fc06\",\n  \"fec0d830-fd78-45de-8e0c-b17fb1103c09\",\n  \"47c9f848-c697-4d15-b899-4a64a987cf53\",\n  \"c3de3773-0401-4fc5-98e1-56ca0eaf7a2c\",\n  \"57948df7-d34f-4f1c-864f-1c6035bdd98e\",\n  \"42d47760-b9a0-4181-9900-ce045f53ea8f\",\n  \"e7607d7b-9ace-4675-928a-a689d9079bdf\",\n  \"b48eb4e2-c155-442e-a232-07183f2c79ba\",\n  \"2b101c32-9ebf-4b48-9bee-39420f0e28d1\",\n  \"f7612015-bebe-47bf-998e-d66723745ca5\",\n  \"84129357-0dfc-4e1e-b53e-be06084e9537\",\n  \"6b4ef3c7-f456-4874-b6c0-5598e9f5f6f0\",\n  \"dc2b0a43-a742-4eb6-b7fe-a6aa8f36535f\",\n  \"f24baf96-476a-40f8-8919-2a1a222e6c71\",\n  \"0f8bce5b-c5d0-4deb-947e-0d0faef38aca\",\n  \"067d79fb-72ad-48a9-a8b9-45e14897bf61\",\n  \"b167613b-0a1d-4be8-ba0d-a163579b470e\",\n  \"5b2b55ff-2bcf-4174-bc19-4d02aa1f03a7\",\n  \"b9ff087b-ba99-4eb0-8578-7b431dbf25e9\",\n  \"98e7ab98-3a96-487b-b3f1-0131386afd47\",\n  \"62f34c81-8e02-4d91-8974-c6c689c0af5a\",\n  \"6485c99a-bc70-4257-942a-35003b245a7e\",\n  \"6dff98fe-5f9c-4849-81b4-b7b4d3538e85\",\n  \"c3485d9f-5e92-423a-b901-f64815b1b958\",\n  \"6eeb5082-77d3-45b1-a1d8-9c689365a611\",\n  \"2cf04fc3-471a-4bd7-a15f-b0c388c5be41\",\n  \"e872ceae-88fb-4914-b711-5e417fc3fb17\",\n  \"c80d1746-3fa9-4151-ba28-c9b6f5ce06e0\",\n  \"9b20c8cd-2ddf-48d2-8490-589e4f88e3ba\",\n  \"0b08a83f-af95-4d05-95f2-077dd0632aec\",\n  \"6a1e1a34-7f42-4863-8f5d-89128465d149\",\n  \"b6c3ff3c-f056-41aa-92f9-f409ecdfbc00\",\n  \"ec0a83e6-192a-4739-b20c-7125d23d7393\",\n  \"9b60a75e-1e99-401a-83cc-ea95eb30b47e\",\n  \"19c93e76-027d-4d5c-9f28-e4baa483b660\",\n  \"96267248-2cd7-43b2-b1cf-81a31fd1ed11\",\n  \"96c8b452-ae77-4827-8365-bd5e0ce68f4a\",\n  \"4754953a-43db-4a13-a276-e63e1459c8f0\",\n  \"bc813815-59d1-4ec7-afd5-74dd805099f2\",\n  \"6a650705-512c-4d4e-bd44-b2bc14df9293\",\n  \"b8f9efc2-f1f0-4e2d-8dfa-aa486ef83a1b\",\n  \"178bd8a2-6d11-4966-883c-a822da02b69f\",\n  \"e1a832a6-2dcf-4fa9-a931-dcd806fbe416\",\n  \"665f9a34-89fd-494d-8c79-be882128fe5a\",\n  \"f24dacbb-7166-461a-a298-be71a00b4986\",\n  \"2b24fe19-c1b4-46e0-b71f-9a003b1c0728\",\n  \"171bcbaa-3b8f-43f3-8bd3-119dd159803a\",\n  \"726869e0-5f5b-4a68-9b00-8ab832a38b23\",\n  \"bc8185cd-1fdd-479d-9dd4-785bc5d6336a\",\n  \"473055a4-46e3-4e7e-bf06-11739a04f37e\",\n  \"2ea79779-504f-4747-adca-f6e2acfdd964\",\n  \"fe149954-71fe-4d81-a6c8-d01734cf78c9\",\n  \"2c60dc93-67fa-426b-95e1-b0bcc1e6acf0\",\n  \"8d389d35-744d-4a10-8896-777246ea8e32\",\n  \"05fa4d8b-f0e1-4a22-8ebb-b80224cf0565\",\n  \"91cc7d33-8e91-4c24-a5dd-8d0483819d32\",\n  \"16ec23ab-2e2f-44b1-ad91-22ffdc21108b\",\n  \"a2c2cd7d-4adc-4fe5-9e93-d9d76a00c247\",\n  \"7a38c326-0d6b-4ee3-955b-db7522a9b529\",\n  \"b8f739be-b348-4db3-b11c-555346550d89\",\n  \"6220490e-6520-44e8-a90f-99a1bc1b8dc7\",\n  \"10b1ba0e-9184-45e9-874b-82493ec487fb\",\n  \"302e9af0-bb94-41fc-8608-40320c188ce1\",\n  \"904b6fb9-b8e2-4ee2-9635-ab806af64af4\",\n  \"fd777d59-59e7-4f75-ac3b-a921ba5db5fb\",\n  \"3bbe6b4e-8e51-48cf-9dff-543c8656271a\",\n  \"e2194762-ee43-48da-af5c-d35fdb13b100\",\n  \"b978afc1-3847-4369-a545-c8b6f9636289\",\n  \"c066d6fe-e070-4c4b-88db-46e626341beb\",\n  \"37252c7c-f34a-4237-b91c-003122ee6d02\",\n  \"f3c12097-64ff-4574-95e0-299be1e33d2c\",\n  \"1aa7d9c0-598d-473e-bb45-dc1995fffaa3\",\n  \"75d36a6a-83f3-4a9f-8258-c041b382133a\",\n  \"554d3553-e687-43a5-be8a-ce6471f8dba0\",\n  \"8bfec9c1-264e-4789-9f7b-91394cb863d3\",\n  \"caeda205-9082-46ab-9deb-b22dab4c47e1\",\n  \"c6cc4137-fd5b-453d-bc3a-b39f3f6a240f\",\n  \"d215f28f-90a8-40e8-a1df-f4f936362fb9\",\n  \"86619459-52fb-485d-939f-31aa50a2d932\",\n  \"363aa74e-ac7e-43b8-8e95-a0b93afc154f\",\n  \"8e1a98e1-a4b0-43f2-bc4b-efb6a926a43a\",\n  \"310eb8c9-2995-48bf-9d42-c1e114262461\",\n  \"59a0db53-c028-42b9-ae81-bef3f3f1c251\",\n  \"db31253e-44ab-4f8b-8982-8e5737e28991\",\n  \"6fd204fb-ea90-4d25-add8-f239bc7edc1b\",\n  \"72891ace-cc31-4f4a-8af7-61fff36ccd34\",\n  \"a754f201-226f-4019-aec9-6a9a08c0c9c7\",\n  \"00dfb1da-18d4-40c1-bdbd-cbeb06d782a7\",\n  \"1f9717c3-75fb-4d3c-8012-191a69515f38\",\n  \"c107d46d-7f69-4249-9cc3-daaf195f6b68\",\n  \"a9018cb6-a0f3-4f96-90f0-0f1ce68d2967\",\n  \"3b36c713-450b-45b6-9196-745b453621e1\",\n  \"aab7d992-f1c4-4a5f-a41a-68fc7f3cd215\",\n  \"e8ad8e92-4f89-4b2e-9552-eea27e61d936\",\n  \"6dfd4a54-61c6-4ad2-bc47-0f69350f44c0\",\n  \"45dc56ef-48b8-459b-9b85-1260719bba62\",\n  \"5f248577-7b32-4aef-8ad1-f0117b7ef120\",\n  \"9052dca7-6317-498b-87d8-2d867a13522b\",\n  \"22b493d8-242e-4497-9524-0f89cf5a1cdd\",\n  \"1d1fbc16-ba1f-4c40-9a40-b72abaa9fff9\",\n  \"1d732947-e6f4-4330-9bb4-4fd50665e3f6\",\n  \"46cc9fd0-ae2d-40a7-a305-5304b1640d82\",\n  \"50b89c53-03e8-49cb-95b4-073c450a2695\",\n  \"66d2158d-2e8e-4e66-89b4-3470d87ecfd3\",\n  \"ee6c90b4-22a9-491b-b229-b4a8faa6186c\",\n  \"5c2f22fe-3b4c-456f-a7ff-ada7ee00a4b0\",\n  \"6decd800-eda8-424e-bac9-adcc118d2a89\",\n  \"7935b05e-c4f5-4976-9aa4-1d1d445855c3\",\n  \"660d318f-f26c-4b41-b980-2c4c65ec7a92\",\n  \"aba060a9-af40-4ed8-bd2c-ec7358a7a276\",\n  \"c68f1b0c-6d80-49c8-a239-8488bf4d9d5c\",\n  \"aa423556-1e9b-41f9-9864-5dcc1d386e9a\",\n  \"43e98c3d-c56f-4ed9-9f6f-31dc95cd3a80\",\n  \"d88f5943-0701-4b0a-839f-c384c9b30b4c\",\n  \"51c5fbb4-d51c-445d-9dda-640d3a770743\",\n  \"7f202c48-8843-4bc8-a049-01949843acca\",\n  \"1f787efc-924b-4bee-8b9b-eef0a0b24d85\",\n  \"8fcf52ad-c2e4-470b-9f67-ed11d871dca4\",\n  \"8f6c1aab-4857-4a7e-9e67-97cb0b00e5bb\",\n  \"afe7c288-5a1d-451b-ac47-77a12d6685d6\",\n  \"4598a0a3-29c5-4956-ab05-14191ab69d9f\",\n  \"58048754-c667-436d-826a-599d8cd9841b\",\n  \"77c502f1-f987-43c7-9184-4a1b0afe1f46\",\n  \"8fa57fae-aab2-4c39-9567-f7ee9d4b1db2\",\n  \"d716599d-2bba-46fe-96cf-96374ac00fcd\",\n  \"049b2872-0c33-484a-88fd-15b33f85e2fa\",\n  \"bf8368b3-f4c3-4d98-83e3-e977a9afd0eb\",\n  \"b66ef1d1-92cd-4e56-ab43-6c99e961b0a3\",\n  \"fb26d708-2b67-48f6-b6ab-f93be2edc4d6\",\n  \"79f26d7a-09bc-4219-ae63-c30627dc8bac\",\n  \"9b55a536-a056-44ed-a90f-62da454983ab\",\n  \"8868a7cd-36b2-4446-a83d-551a9fe74a38\",\n  \"89f4c8d1-d065-44bb-85ba-ab24a8bd21f9\",\n  \"5ba19e9c-71dc-4f1f-a009-3d16086c67c9\",\n  \"f5f0ba81-6d62-4498-9803-5f8acd2ed37d\",\n  \"a4393618-80cd-4c50-8f76-65b0df39aa74\",\n  \"32839b52-d616-46c8-b1e5-de45e13ffb8b\",\n  \"59e62b41-413c-4c2a-81b5-b7e0f46d93ae\",\n  \"c52525bc-5818-49a8-91ae-ca58a0b417b5\",\n  \"21768c0b-271c-4337-9a89-10b8cd9071a3\",\n  \"5271c9c2-1552-4e79-be7e-b97e689eaa40\",\n  \"30519f34-2e8f-4523-a124-760b4554233a\",\n  \"0d5571df-c709-4137-a6cb-aca3cd6fbed7\",\n  \"be3c3e3e-0ed0-40aa-858e-6e10e4bddc39\",\n  \"74a6ba02-9b97-436a-a862-a9c126266462\",\n  \"60e56659-661d-45d6-93e3-28b2a16e1e29\",\n  \"92d18c33-5fe6-4a3b-a7a7-675aaa661a49\",\n  \"804c44a1-5a78-4876-8f97-02da9b2cc36a\",\n  \"c69febf4-7686-44e5-bbfe-b3664af8ed4e\",\n  \"66f7097a-2f96-467f-b4cc-06a7b995d6f3\",\n  \"4dc4ab60-6b94-43a1-86a4-1aa856356736\",\n  \"56c9e362-bb96-451a-afe6-aab09b15016e\",\n  \"ebc8266b-05bb-48af-9c91-29df7dbedbfe\",\n  \"858b44c3-b031-4b24-99e0-18f6f2f04c9e\",\n  \"88c080a4-acc7-4152-b311-7b3acb87265a\",\n  \"a9e489e9-bae0-4a79-9db6-820a222810dc\",\n  \"02e6a845-04ce-488a-b83b-1a69869890c2\",\n  \"5c8c9a06-5cfd-4f92-a0ea-b827ffaa649d\",\n  \"520afa30-41c9-4f68-8a8b-abb61544b922\",\n  \"a014a59d-d7e4-4ce6-8fa8-abfe319e9fc3\",\n  \"8b98032a-fefa-4249-b718-37b273c5dfef\",\n  \"f1ccdcb3-ed06-42ee-979e-311376d3eaf8\",\n  \"16b061ef-1fbc-40b7-936c-584c52366d3a\",\n  \"3f4d6dd3-c3a4-4828-ae54-b208952aa9d4\",\n  \"e7ce03bb-d986-4326-ab18-b9b92f3ab790\",\n  \"765e4e87-c76e-4405-ae71-2ab01d4b53c4\",\n  \"f778c421-eef6-4ba1-9e71-878a09787500\",\n  \"3581e24c-b3fc-4d47-b9d5-cb36f48d649a\",\n  \"8f53a0e6-59db-4254-8be4-c6b199987d41\",\n  \"90befaef-697e-45b2-a45d-a654fa835541\",\n  \"fc6f4f25-ac2b-45e7-ab25-a4628c32f2d4\",\n  \"ead596ed-396c-4901-8d3f-0501686aeadc\",\n  \"fbdd9ecc-b5ba-4b90-9797-20752013480b\",\n  \"39088300-d78d-4a67-8b1c-5abe13cf1ac8\",\n  \"73575333-b861-4b55-a6d4-85ca97f7c98f\",\n  \"d8e2f864-5712-4a98-b35c-48ad0af01cdc\",\n  \"1dbe4bd5-7bc5-4aa0-9202-2df4723543d3\",\n  \"68490961-e34c-4dd5-9008-343b60230e35\",\n  \"1c8d1e42-c935-4843-9020-afbb1a1097a8\",\n  \"31769a68-88ab-47d2-b5cb-0a8069f6e8c2\",\n  \"0f51653a-1016-447f-a30c-8063137a1415\",\n  \"c3da8b05-7067-4cbf-b078-9a3fe8990f7b\",\n  \"25734045-24e8-4fc1-ad6e-97168f2d6238\",\n  \"5dcaa41f-412c-4560-bfee-350f71a67a0f\",\n  \"885a0b95-70f0-4193-aefd-9be2d36dc308\",\n  \"7ec9396a-781f-46d3-b1bf-d230d7699978\",\n  \"4c315727-2638-45c7-8500-13e0adfe135f\",\n  \"709bf983-0e65-42e2-97a7-611d001eb5ae\",\n  \"581e3e58-f11e-4aaf-8906-2d3c0aa06e93\",\n  \"872f7f07-3668-43c5-9fda-5cd2cc4dd967\",\n  \"f981a5aa-f53d-4a73-ac2c-c834520d4378\",\n  \"c15d0e52-44a7-45ed-9ed7-9054657966a2\",\n  \"c5a92059-9a20-4ca2-90d6-cd8a374d702a\",\n  \"aa02ce63-2933-4d53-b453-82b2cda42840\",\n  \"986942b4-3a42-4785-adff-e5e33a284073\",\n  \"cfc6b18b-f08b-4895-b18a-aa773c347ca4\",\n  \"1980ad96-41da-4a39-bfc9-4abe0e591cd9\",\n  \"54601236-dff7-4a93-a5ca-0347ac8f0512\",\n  \"dbc0d9f9-b2da-4525-9322-cedcd5843e8b\",\n  \"a7097d80-16b1-494b-8868-ec2cd9a5b5d7\",\n  \"5b8d48d3-3494-4112-b06e-d43f82d83b07\",\n  \"c8f9f8a9-3eaf-48d4-8173-0758a638709d\",\n  \"72192970-4fc9-49f0-9ca5-72805d05e8a6\",\n  \"b4ae655b-6c32-46a4-83d5-427a8d5ad426\",\n  \"af35b9ea-cfb9-4214-804d-69ccc2669e3a\",\n  \"5bb97771-ba69-410d-8d8a-cd3df3bf1b15\",\n  \"c945b842-2fc0-45cf-82ad-f4273d60e370\",\n  \"7a46dbfd-693b-444a-bea8-6c925d8ad587\",\n  \"1a473170-9596-4ff1-9758-be479ad4819e\",\n  \"27b9dc8a-216c-44e0-bbcd-1d17d339bef8\",\n  \"70cf948b-bfdd-4267-a784-40105865e050\",\n  \"15b8c253-a759-4fe7-85ab-60c6a9e47954\",\n  \"ec92bbf5-ea3f-4782-85b0-f12d7cf1c839\",\n  \"57693a4d-ebf7-4c16-adf2-062b900323f7\",\n  \"33976ed4-9e93-4ebd-89ad-62aaa8156a81\",\n  \"afb9abd4-bde0-41fd-9e9c-df1ea49e9008\",\n  \"de43081a-a90e-4f36-a542-1419177435d0\",\n  \"b0593900-1d57-46f7-890b-7840f353f5cc\",\n  \"1047af99-01be-4570-9c61-1b77880df2b3\",\n  \"1c0bf0d4-a936-4be1-b3cf-da48bc56f13a\",\n  \"62b49681-dff5-4f6a-9351-d92874ad7cd6\",\n  \"aaefc544-1e5a-406d-ad97-c85cd2c4c6fd\",\n  \"014fac68-9e9d-4721-ab60-febbbc8fc9e7\",\n  \"a10547e6-bf87-44b6-a4e2-0f8892dbee8d\",\n  \"322ef58e-31ef-4396-b4af-9267598a4a73\",\n  \"bdf2b7d1-4f03-4648-80ab-2829c65e7515\",\n  \"e294487a-dc06-408c-9af6-e82dfddc6619\",\n  \"4f6d89fe-a6d0-425d-80fa-91b35885f61e\",\n  \"eebbca36-f032-4370-9d79-30fff6889a45\",\n  \"1ec376d9-aa57-423c-aedb-b1a41a8ddf08\",\n  \"bcf95a4c-21e8-4e92-867c-54dc43c43d1e\",\n  \"bc8dbbdb-3f90-489e-82b3-1964d9d28409\",\n  \"5faa91ef-44e7-464a-8164-67a270126dc8\",\n  \"21095074-24bc-4388-9a2f-9f2087eb8f67\",\n  \"68e19029-09e3-4a29-8cb8-42af87562d00\",\n  \"4adb3236-38f5-4bb6-81e9-2f96cd96a35c\",\n  \"47412bf6-0c38-4f8e-9294-0ea659931cbc\",\n  \"26f5207d-efd6-401b-aba5-926a5f3d0d3c\",\n  \"dd2d3b76-4566-45ce-af92-534ac2381959\",\n  \"32b448cb-f530-4ae6-91c5-c8ad2496eafc\",\n  \"d78c6756-16b4-472c-992a-47bfebae4118\",\n  \"d1f44913-c032-4d8f-9ed9-82009d7828d1\",\n  \"faa81a51-2137-4761-805d-e9b7724fb5b1\",\n  \"6c129d5b-8513-4734-b814-db6b2f4518ae\",\n  \"92f98a47-634e-4b47-b483-e1ccc435f55d\",\n  \"dc6f43ab-1e14-4135-b960-19e84dbdb168\",\n  \"8cae7ef6-6fcd-407c-a1f4-be54a8fb3611\",\n  \"c135bf7d-6cc9-43cd-8c5d-c4c3c1ae8ec0\",\n  \"1fed5395-804c-41c1-8ef9-eda01c22e10a\",\n  \"06b46bb6-1032-4adc-b789-0fe928468d96\",\n  \"88dd2dd0-e3f2-4c28-b7ec-b02477dd5f05\",\n  \"b0075cb0-f0bd-4cce-9797-ff21981670a3\",\n  \"2048907d-ff7e-470b-a4f7-40964d7a1818\",\n  \"460fef12-25be-4284-a0c0-8a8ca1b6c574\",\n  \"4fbd76fd-7e82-4088-a4ec-bf6ae33c1b24\",\n  \"1d668352-7ee0-4e51-962e-717814e8610c\",\n  \"fc3e41a4-cfcc-4593-b95b-875f067300f5\",\n  \"56c7478d-7614-47cd-9557-e2dabc581ba3\",\n  \"0388e93c-aa21-4a95-a614-745d26e83274\",\n  \"85aa1e4c-53f0-450e-b743-e340f09ffb9d\",\n  \"e654ec70-50d8-44fc-88e8-fc51af51fb25\",\n  \"3fe52d7b-09f7-49d3-b21c-f8c5a040e07e\",\n  \"5e115500-f1f7-4774-a3be-886ffaa3ccda\",\n  \"e4190ec9-3397-4303-81e5-3cb0f33bdd11\",\n  \"02d92a54-ebde-4f3a-8e86-5ff744f5cb57\",\n  \"3b6cf22b-5402-4aa4-a87b-52147a930800\",\n  \"c3d62970-87d5-49b5-a72e-500398bd4529\",\n  \"5e65121c-aa6e-4058-b5e9-c9f69a06fdb9\",\n  \"cb4e67f3-b333-4831-81b8-52f5f6c56f88\",\n  \"71906aae-1ec2-4680-b23c-6ace494cd910\",\n  \"8694a847-5137-459a-95b1-e82a2c3512d1\",\n  \"c4aa5c14-0b15-42d7-a386-364a6e409c3a\",\n  \"b9dffb84-1894-4969-80cc-97813078654a\",\n  \"3a3fc420-ae78-44b7-8242-5f22ed56a0b9\",\n  \"43b1378b-9bbf-4f1e-8147-15c96746472e\",\n  \"75c6bcfa-783f-417d-907d-5f97fe9c0213\",\n  \"828b760c-abcd-4767-8158-1f0f8edb0bcb\",\n  \"1756d55f-604f-430b-ab09-3386a89bc6e0\",\n  \"517cff1a-bcc0-462a-930c-69fdd3b1bf08\",\n  \"61b35159-8d99-44fe-93df-a6fda1db4300\",\n  \"5983a4e6-01a1-4bbb-9756-5cb872e510ce\",\n  \"1e79c3f9-c15d-49d5-8e29-0a5f397be062\",\n  \"e731da3c-8658-4169-8cf7-2392b6176bc1\",\n  \"e105bffc-94f5-44d2-a395-2cf0e186d9f2\",\n  \"8284558a-fc34-4c42-91b5-26e79a8f36b2\",\n  \"db284842-0135-4baf-900e-03ef5b279f94\",\n  \"7f5b57ec-6816-46ab-9126-ea5447651ac3\",\n  \"e5174b8d-65c5-42e8-b8a7-ec09fd22206f\",\n  \"56eefdbd-3b6e-4b5d-95b4-a749a2109692\",\n  \"bf2f2010-279e-4704-954d-6e895f64ec2a\",\n  \"6056675d-11ac-4225-95c3-99621c9dac71\",\n  \"9b10fb49-b75c-41b0-a9dd-3614f1a82508\",\n  \"ff1fc2d3-3810-4e48-8203-01fe50b6d598\",\n  \"2d30ded0-a1b1-4a59-b2d0-071f105461e9\",\n  \"3e41ca37-7671-4f6b-a4e0-f2410b43e5d4\",\n  \"8a78e76c-f114-4c2e-9436-49861ec0d83f\",\n  \"88b263df-d704-4661-9dde-7fd67cc17d1e\",\n  \"e7a902cb-efb1-4bcc-a51a-93afb6a59bc1\",\n  \"c70a09d4-f255-4b2b-b7c8-e0617e336e76\",\n  \"7d73a9c6-2232-40c3-9856-6dbc2ba9ca38\",\n  \"3c69dec4-eb46-4efa-b8d8-f63bb30d2487\",\n  \"9e754886-7c28-43d3-a08a-a3bc5ee901a3\",\n  \"7514030b-5615-4bb3-826c-ba9c32aebe59\",\n  \"2c1dfd36-f273-47e1-bb0e-1eea02028bba\",\n  \"b1c6032f-48a4-4c14-8c96-143795880eac\",\n  \"7bc66706-47b3-401b-8377-2c34463a13d5\",\n  \"fb9bd131-2e1a-4785-9e0a-8fe0f2929bd4\",\n  \"42430bf7-a809-49ac-9abd-2afa3a3a3b13\",\n  \"e12a7df8-52f8-4155-8acf-3f4cfeb5e1bd\",\n  \"8b161951-80b7-444b-a67b-a7ace27b71cd\",\n  \"0b474ea1-5af2-47e8-8c48-b2fb0a8207ec\",\n  \"fc73b78f-4dc3-49d6-a086-0293a49e43f9\",\n  \"bea317ae-5649-469e-816e-a38f3585dbd0\",\n  \"fbb3b0fa-fb69-4d0e-ba14-e4dcaeed385e\",\n  \"6fe1871d-f37f-4e2a-92c7-659bdb03d6be\",\n  \"ec6a34f7-9bcf-451a-95aa-6778cb3a7c37\",\n  \"27b3d586-671e-4c37-84fe-a75858f52599\",\n  \"c0d560fb-3184-44b4-9524-708d6243848c\",\n  \"b6650ef7-944d-4361-85cf-2d7665d24391\",\n  \"82e986f2-dd4c-4cc9-b543-21c9c990cc81\",\n  \"697af90b-b233-4b31-ba5f-2944e40ac807\",\n  \"c519017b-2ad0-4dfa-a4c3-cbeeb290748d\",\n  \"60fbb721-e529-4b55-8495-34be7a2ce3d8\",\n  \"324299cc-0841-4273-ba9d-bd1c194e89f2\",\n  \"0bb812c0-ed78-49bc-ab56-1891ace247c9\",\n  \"42a3be3b-5c38-4ccb-b42b-9cbb7495f871\",\n  \"99d07bb8-c462-420d-a774-88b4cbb28aee\",\n  \"164ad154-abb0-4225-bdc4-8a5f5f28bdff\",\n  \"d7e11abf-d9ff-4775-85e6-08e5405f3a92\",\n  \"69fa58fb-5a5c-47c5-9c14-9843a1408c90\",\n  \"b5ace4d7-39a3-4977-80a1-9b936e3bcd02\",\n  \"27b47af1-6fcc-4680-9bb4-de45d5c618bc\",\n  \"9807aa13-0499-462e-abc4-ff07b581d077\",\n  \"971c359c-a7a9-4e32-8f0c-8ad1cf3efe9a\",\n  \"5f2fb69b-1ca5-4711-a776-bf76f81fa596\",\n  \"301f594a-2bb4-4f87-869e-6b2b799cb4fe\",\n  \"92df7d52-4422-4f94-87d7-3eb537922053\",\n  \"07fc68be-26c4-4c34-904e-28d04b93b047\",\n  \"9400323b-e3e8-4fb6-8fec-5476688436b6\",\n  \"56dd0739-7d3c-4081-a305-c5e548f8ae47\",\n  \"43d90676-29c6-4d5d-8de0-dee5e5e18c82\",\n  \"c4d813e1-be27-457d-b068-ca6981553ff1\",\n  \"16808efd-a4c0-4702-ac01-16d570658a24\",\n  \"970bd367-79c1-4870-bf3d-192c2bc7681f\",\n  \"61f7252c-c2d1-496a-b462-1b9cdd2d1a5a\",\n  \"cd22afc8-75b4-4596-ab4d-f5cae3f7f893\",\n  \"059c06e4-ba7d-48a2-ae4b-a7387b26a4e6\",\n  \"a5e37208-d3a8-4ba7-8820-c3c01ff806f9\",\n  \"da064108-f4ea-45df-a4b3-e5f497f108d7\",\n  \"29c8a538-ab93-47a0-8278-4b3770fdaa9c\",\n  \"0e480b22-63a6-4aad-97fe-a86b60141555\",\n  \"727eb715-9766-493a-8263-d516dafb2fe0\",\n  \"07f457c1-0af7-4431-b831-f96f63360066\",\n  \"402e4ce8-b1ee-48db-9570-b6bbbf4d3f89\",\n  \"5739f2b7-2267-4d03-a922-af84559a75a2\",\n  \"ce95fc19-326e-4da4-8a43-3c76500758a4\",\n  \"a1c0196a-9725-4da0-8e54-e58cf05ca000\",\n  \"d9cc60d7-126f-4804-99df-29be3bcaaeaf\",\n  \"e9e611f6-e9fe-46d3-aafd-323160ab8074\",\n  \"988ca70c-5107-4af0-9c6e-1640b3927d7e\",\n  \"a1040055-ccd5-43ab-b30b-2ba2c16ba62f\",\n  \"d90f250e-7cb4-4c0c-894f-835a0b4f27cc\",\n  \"19da72f3-e3bb-416e-b7ce-5abd145a174d\",\n  \"7f13156f-8441-4702-8146-906bbd7c8c8b\",\n  \"acb281a6-2918-4255-9009-d7db8314d91f\",\n  \"8a8bb8ed-7c4c-492c-bcc8-f5865a160c5b\",\n  \"ddec58a8-f282-4fa0-ae07-96bc71550102\",\n  \"711fc1ff-5f42-4ede-abe0-ef74fdc4a9f0\",\n  \"e5e649a3-ab26-400d-985a-213e49ecb383\",\n  \"04448d9f-552c-454e-995a-3cdbc3d16792\",\n  \"76b8eaad-3754-4afc-a027-718793e9d953\",\n  \"cd3ab708-5bf4-4d60-8352-17c26b538a3d\",\n  \"321b4517-cdc9-4bae-a5b4-2fe9e34debd4\",\n  \"47e4aadd-f0be-4204-b581-2db67a045ba2\",\n  \"b07fe222-4a40-461b-9b93-bbce1d008964\",\n  \"69bcc302-ae3c-44c0-bc96-221d3efff1ef\",\n  \"9f81696b-78d1-4cf5-a275-079ff74a257d\",\n  \"b3d2bd0f-861a-43a2-8ccd-6a534a26396e\",\n  \"9a8b1b4a-d5e0-4ced-8ceb-d27fd3778422\",\n  \"5662ba40-0dea-42b9-9b72-1aa1f488412b\",\n  \"62e944e7-e3df-4327-bbe0-c121d37d88f4\",\n  \"6818a0b6-26ff-4efd-accf-42efa3f2ddf4\",\n  \"0377203a-155c-436c-852d-b3c19ddcf5a7\",\n  \"394358ad-c4b3-4328-8b9c-a8dd03211960\",\n  \"75068610-4261-4448-8d73-ac3e1fffa540\",\n  \"f973c80b-ea93-4248-8013-bd6401c2b9c8\",\n  \"f135205c-3e73-44f4-a678-ef0ee3a2b732\",\n  \"fc51956f-0079-4eb2-891f-a704ea554514\",\n  \"e49f967a-192d-476a-a678-a6d991da9dd0\",\n  \"df436954-b60e-479e-ab15-32d9e6f98c7a\",\n  \"901d0943-958e-414f-bb7b-9a72d6dfcdeb\",\n  \"2d4d72c6-8887-4044-b25c-d1ff68a433aa\",\n  \"cb18052e-4db4-4f6e-9e71-2b3d1037e3d9\",\n  \"cac4d934-916f-4ac5-a86d-4c68472bf90b\",\n  \"6645eecb-9f61-4447-a992-01bca2a5d42e\",\n  \"482a91f8-4d9e-47f6-8ffd-5e3b7d7a0f69\",\n  \"1d82e8b6-83f0-4d99-9c2f-975324df0695\",\n  \"fbee6bec-7be4-4380-b0ac-9e08272ae95e\",\n  \"6a6c28a0-2832-476a-93a6-97885db75834\",\n  \"5aa65d99-7ba1-48af-96fb-2ffbb03be3a9\",\n  \"811fcac9-f801-4caa-bdc1-77cd553e01fc\",\n  \"c54eeffd-68f7-4ec7-bd8f-13be3f097eaa\",\n  \"759ff98c-e888-480c-9f26-abec2a4d09b4\",\n  \"61e1bc04-310b-4e83-9b67-916968ba955b\",\n  \"db561887-a46d-4699-9e00-07f01a0e249a\",\n  \"e7055baf-256d-4df3-a289-f51aec4bcb32\",\n  \"78c0694f-5df9-40dc-a5fb-7a0c88158e99\",\n  \"1c805136-4f25-4d07-9a98-4b11b844bc4e\",\n  \"95a63ad4-3a5a-4131-bac5-591803ea5b98\",\n  \"aca36f4c-d42f-4f25-8275-14d6cf8c0e05\",\n  \"c44ab277-a990-44fa-be42-9770df33a5aa\",\n  \"afa49295-938b-4cde-9f99-2d4c7567b3e8\",\n  \"62a9ee4b-8d96-451d-9c92-3c612309d06f\",\n  \"e34bf2e5-50cd-4e12-be40-7c01695fee7d\",\n  \"6915e2c1-16ff-43ba-9310-4753be0bf6f9\",\n  \"5ae4e068-a12c-47c4-ba55-e63d1811fb9d\",\n  \"661ccd23-0bd6-4178-9afc-35e3b1b7b508\",\n  \"774dcff4-8d8c-4479-b220-2b4e31b452ed\",\n  \"aba6541f-e5f0-4b21-b86e-8af44738a7f1\",\n  \"a6e710ec-66dd-4e84-b3d8-de3cdc74a5f9\",\n  \"f5bc834c-dd12-4651-95f8-a01b2600b2f8\",\n  \"f2f9a651-ec49-41d0-91aa-6392e081415e\",\n  \"294a2152-9e18-4521-a764-f1f65d9134a9\",\n  \"d6053c8a-8b3a-4f13-82e1-40a97032480c\",\n  \"03e3fa3b-bd6d-45d3-94d6-3cfed08b679a\",\n  \"550b2dc0-9697-43d0-8e2f-0dd508dbdd8f\",\n  \"8276284c-bb35-4ab6-9ae2-fb5ca9f74019\",\n  \"ab68f4d6-ba65-44fc-accd-2451cf0072f5\",\n  \"56512960-9937-4183-9085-12c88c9911ca\",\n  \"9193965c-b1ad-4046-90fc-c40cf793c873\",\n  \"f50732ee-fc78-4319-8fdd-d7797273fae0\",\n  \"1387c9f8-534e-42bf-8215-ec248dad5926\",\n  \"35543f48-b8dc-402b-9ce7-55965bfda94f\",\n  \"95b547b5-c122-4c87-9344-7925422edf1f\",\n  \"1a62da0b-bb24-442c-9a7c-860ffdab54e0\",\n  \"98ac8e43-8477-40c4-8da3-f434bed63e8f\",\n  \"455f7b02-dd6f-4851-8ba6-45c918b1d57d\",\n  \"5a4648dd-3b36-4971-8260-b3b0993ae79c\",\n  \"c38313ee-ec67-4886-a9dc-5ed5a4e0f9f8\",\n  \"2b4047fc-1d77-4d9e-8aaa-cd836ae51d4e\",\n  \"31e156b2-0ed6-4d4b-b03c-ecc46625bc6f\",\n  \"1702d1d2-41c8-4c31-9f38-148dde884337\",\n  \"25f9ae4e-1350-4d65-93d0-88853e8856a6\",\n  \"14ec5938-7e4d-496b-9a3a-12f8651412e3\",\n  \"7c98591f-d258-4aa1-9fae-dca309e41414\",\n  \"c894c733-0a61-4a68-9535-1ab2221c8f6b\",\n  \"91c73a0b-c7b3-4855-970d-8e619611586b\",\n  \"19c1b916-29e0-4590-a23d-7a429afc7b45\",\n  \"3e8acb62-4d7e-4c5f-ac8f-6b1ec0e5e6f3\",\n  \"5eab7fd4-2a95-414b-bc09-2baa925e0d69\",\n  \"8f6a4244-6530-40e0-9858-b91fe1146d43\",\n  \"3d6f226c-1980-4dbe-afd9-8fe9b0498904\",\n  \"5efc0b76-0a44-4d0e-87bf-bfc64c1d9b2a\",\n  \"9c72a9d9-8265-48a0-b0fd-69db6d7ef039\",\n  \"3c503259-ed8f-485c-a1dc-c4c80be9dc9e\",\n  \"098df64f-13de-49bc-bb31-689ad146e2f3\",\n  \"d18dd16e-ab82-4ca3-9cf3-cd2da1257d83\",\n  \"ac311e4f-27cb-452e-86fe-8f59c4c20a74\",\n  \"fe970a60-cffc-4b22-9c75-e78320bc6432\",\n  \"cfcd52a3-5cd5-44b9-8fd4-4ff590099e17\",\n  \"c38d671d-ee2c-4ae8-9064-4fa0401fb321\",\n  \"95af39e2-7f1e-4061-bd24-07c5f71ed85d\",\n  \"73b0ba05-8491-4d4c-a799-689d8291250c\",\n  \"a7f84f48-6bf9-49de-8615-183b3baf2ee1\",\n  \"ea4b4a2e-5378-48d4-b66a-4f98f0c922b7\",\n  \"ff2102e6-6b24-41a6-bc11-867b8d3f1aeb\",\n  \"e88f83ce-c51f-4990-92ab-a46a757a7f57\",\n  \"28fe7111-b292-42be-be31-c71ff0d1b0dd\",\n  \"309e55da-a44c-4371-a591-b8e7a8180006\",\n  \"11ddc7c3-b569-44ea-93a7-ce22e8fc07ad\",\n  \"b392b595-f161-44dc-a3ff-41899f9f1bb1\",\n  \"0288ab83-52ef-4272-abef-d721dc186fcc\",\n  \"bf080e1e-9fd5-45ae-a4c6-7f4f92e68ad0\",\n  \"2212c5c8-4ab5-425c-b313-667e86bd97af\",\n  \"7cec4b8a-6df8-483f-a24e-9f331e38ed7d\",\n  \"da2c07be-010b-4c6f-93ff-9ef65af85818\",\n  \"91129e85-f0a2-45d4-b003-efc3258cc0de\",\n  \"7e3c08da-b73a-454a-a12e-dd633a90ba7f\",\n  \"7b9b17d4-3c86-4e7f-921f-a1271381a45c\",\n  \"db63b3c5-543c-4f98-9ea9-aa39053e5c19\",\n  \"c0eb6132-4f44-4296-9274-91626738371d\",\n  \"bf6fa836-2c94-4c7e-9a11-b5d7a05a45a1\",\n  \"a52eb093-9723-4525-964e-b55e59a47347\",\n  \"bc16538e-b0d2-4ce2-bf57-bedc4bcc43cc\",\n  \"a6fa20d3-6025-4123-8727-579c2052df62\",\n  \"265432c5-8e99-4884-9ee1-0c2b8479cc4f\",\n  \"60ffe295-e57c-4b69-b700-8c11812b8e0f\",\n  \"3b01e182-c29f-4ed1-8443-33e44431376f\",\n  \"070adf6a-91cc-4599-9611-39858a965fdd\",\n  \"ed322189-800e-482a-ad2a-5e6600c951b9\",\n  \"202e083c-2f4e-4d5a-8edd-b7ab9425bae6\",\n  \"f1e3b160-b0e2-4266-9077-18bf9e717420\",\n  \"cf5d7303-92a4-40b8-9dc7-d80cb68938f7\",\n  \"ff837b70-f6c6-43c1-9444-234f68f5fada\",\n  \"33f21b30-aae2-4ef0-9049-f65b7ce030b3\",\n  \"75f89d25-e92b-4cec-bdb3-4147be18d0e3\",\n  \"e4498ad0-a7fe-455d-8cbd-5a3a23ee7b58\",\n  \"4ab75314-00b8-4f34-bd58-09246a4c5d54\",\n  \"b1c9f0ee-1f15-42f0-acb7-ace2819d7878\",\n  \"994e6a9f-4485-41f7-8707-b881bc5eb244\",\n  \"e2335685-7827-46da-b4fe-fe44b80a1029\",\n  \"156985f3-092e-419b-a324-673b56ac4cf7\",\n  \"a5c14012-3178-468d-8664-0ab1e6bf4946\",\n  \"1213243b-3d1c-485f-9e89-f8dfdf027343\",\n  \"86f311d8-14e9-4c0e-99f4-dcf1b2cf0896\",\n  \"9001f61d-016e-4c44-98cc-45713df527d4\",\n  \"fd452307-f06b-4c0d-9c8d-e3bc10d016e3\",\n  \"1aee0ec2-9563-416c-b0b0-4bb1293aad73\",\n  \"7de7e4ad-d09b-4b7d-bc7d-61efe8b3d2c4\",\n  \"30e83455-a5b8-412f-88c3-27abf94f2ab7\",\n  \"481a7642-9e5e-4ac6-b2a1-b984f6ff824f\",\n  \"55353e2d-00d4-4944-8c59-31f96aef6911\",\n  \"26e667bd-3ffa-4d8c-b01a-39d9803e4f00\",\n  \"11d9d897-90cd-4a50-a2bc-fceafe8fc21e\",\n  \"8bba0cb2-6a16-45ee-904a-23734ce0a4e4\",\n  \"33e56d17-4c07-4570-9a27-439122b85268\",\n  \"899e8248-ded1-4ad5-8fb4-f2c09e31741c\",\n  \"de2eeea0-b9e6-421c-8653-d529ce89d125\",\n  \"7bd6b906-d64a-44ef-9c85-fc300c2ce28b\",\n  \"c96203ab-d703-4aa5-a6a6-b69c5f47898f\",\n  \"fee538b9-56d3-4346-8d37-7b5cbb17aaa5\",\n  \"14d02dd8-3086-4568-9457-d3844582beae\",\n  \"7fa63e9d-8262-4788-a486-b932323c406f\",\n  \"aedef1b6-5ca0-4dde-bfbf-4ea95e61f68e\",\n  \"2f27e9f4-c1ea-4e7b-a536-89ee57c13f50\",\n  \"be0b660e-9cdc-4fd3-9b22-91f9285f41f7\",\n  \"e868da4d-6096-42af-947a-2126a13fe25a\",\n  \"67fa367d-870f-4f9f-9287-22c051f649bc\",\n  \"f591e2f5-dbdf-40a5-8191-481ea53c8baa\",\n  \"5f5ffc75-c3e4-4b0d-a6c9-ff993242ce45\",\n  \"7dc135d9-076a-4295-993b-49f607b184e3\",\n  \"9ea7c425-ef7f-409f-a8ed-951a01af96b6\",\n  \"2ec9b107-c0e5-4f9f-9188-2b4298e35249\",\n  \"fbdc87ea-3c72-4346-99c9-f3c7120bcec9\",\n  \"ee1ce3c9-ca50-4ee5-b422-395c8def4b2e\",\n  \"85069b09-9570-415d-81cc-214db533af99\",\n  \"364ee35d-9ace-4311-a6c1-816bfeb0ad31\",\n  \"81c1cfda-e095-4878-822a-f48a2c949aef\",\n  \"0ba20109-635c-4be3-a7d1-99fe22af40ed\",\n  \"8b064e25-0416-44b3-a995-97bd7fc4d21a\",\n  \"e4e9e606-62f3-4676-af85-a6f38673b3e9\",\n  \"ce8c164e-236d-44d8-a69d-3227a2913076\",\n  \"01c378f7-9805-4a1f-b3a3-8a9d05fcf37b\",\n  \"56121476-5ab4-4676-a1b6-cce7b0758539\",\n  \"b98bc6a4-e6a9-4f73-a424-d19f72814670\",\n  \"badfcfac-a0c9-4f3b-9f0d-8a79d1117fd9\",\n  \"2130bf3d-c4ba-4c79-bb1c-75fb3eaf9121\",\n  \"3f6a22aa-4f12-48e7-aa1e-ccbc16be743b\",\n  \"b72b1948-c511-40a4-b5b5-466e9777d2c1\",\n  \"cb057874-b9a4-4ae9-ac94-23e5fde437ff\",\n  \"178eaf54-ac08-4a83-bbed-1aa0c1294a11\",\n  \"1e5d6af4-1af9-4705-b4e1-d583d582d721\",\n  \"fb9d5048-7078-48f4-9a30-cbdc9b82246c\",\n  \"9971bf8e-324e-47ec-a1cf-a3a00b5e6a2d\",\n  \"ba6eb4ee-fe33-4e15-a3a3-95f5c93bba01\",\n  \"7010d08b-fcb0-4bfc-8cd4-02c873e3d65b\",\n  \"5ae19a85-5cee-4c0a-a586-7178021d8225\",\n  \"9a8a971d-fa12-4aff-8089-1d4422d7f9a2\",\n  \"e2c9e189-dce3-4b21-8710-c85604b178f3\",\n  \"cea957fe-6533-47c7-9faf-580be8783f03\",\n  \"d98e4da2-89f1-481c-ba48-99ef7eb182be\",\n  \"e489eb9a-436d-4250-bb2a-f988508b35df\",\n  \"95b0ed7e-0a6e-4648-91e4-491111f8ed92\",\n  \"3c3f47c3-c0de-41a7-b90a-64292887a8ab\",\n  \"bbbceb11-3d25-4647-a93e-5c9ced0929a9\",\n  \"a1d1c5e7-1f09-409a-981c-d396e03dc86a\",\n  \"a0a99f45-6a00-4660-9765-21efae0fc424\",\n  \"16cbf6cd-7e9c-49e9-9bc6-f475f35a1ee1\",\n  \"df897688-51d7-40a9-a5a4-e6dcf9cc9d74\",\n  \"0a0ace18-f772-41b0-9b28-543707e97c40\",\n  \"99da342b-2d81-4c28-b072-c67f328bb976\",\n  \"55d3efe9-945e-40c4-99e7-e5aeb0657e5e\",\n  \"6ba70482-c7fe-4d90-9737-e010785154bf\",\n  \"74175d53-5072-4c91-b0f7-c7d1dca3cdc2\",\n  \"ea1a7442-fbe8-478a-8e33-f5dc83305a8e\",\n  \"154a8475-bf62-43bc-b064-5d4bfac2594a\",\n  \"b49e8323-5c36-4d47-a719-0f60d7f1818d\",\n  \"61cd9b49-cd4a-4e6a-b1ca-b530d5fb6914\",\n  \"0fe0a6f7-2568-4abb-921e-484cb228c2c7\",\n  \"7becf14e-b191-451c-b666-6e4667f6fff7\",\n  \"8f6f6265-a942-478a-b923-29007e56a29c\",\n  \"a39cfa86-42ba-4409-9775-7d100e17e549\",\n  \"eff29983-ba84-4615-8370-2b160ac35cd9\",\n  \"f31597fd-b5c5-4311-9bc5-d7295ad8c5cd\",\n  \"d65f953d-1429-4676-8997-dd1755a18c69\",\n  \"7e553a5e-14e5-4ee2-b28f-aa83df8dc286\",\n  \"6b99b14b-2b7c-4ae5-a895-4d28c0ed4203\",\n  \"a1fd11d0-8779-4a92-9944-a49c85ae14c9\",\n  \"6ddbf371-80bc-4917-b1da-feaa0c5f7f40\",\n  \"ecced50f-0362-46fa-92c6-55139f5224f5\",\n  \"f57202ce-ea3b-40c5-9a96-2f9c69c7611c\",\n  \"35e22317-83a5-428e-8611-9266c193694e\",\n  \"3c140582-23ef-4131-99bf-ca95c472db37\",\n  \"1242aa07-2ec1-4c13-8674-c2d0197bae1a\",\n  \"cc00caf5-5434-46b2-9cfe-a5593f174201\",\n  \"c2734086-ee82-4468-bd05-46dab640a79d\",\n  \"cd3a551d-fb7d-4392-9356-72cb8a9c09f4\",\n  \"22bb1090-8203-409d-bfef-d1ca0d937c18\",\n  \"20d92525-0b3d-4f0b-a18c-3157bf687699\",\n  \"d625abb1-bd2a-4246-8070-7c200168de0f\",\n  \"81e345b4-cf99-4fdb-a1d6-e106d92fc0f6\",\n  \"5285f8e1-a3b1-4ae6-ac73-ee7ec17e5a35\",\n  \"ce2d77c8-10d5-4180-b099-d932fa83e769\",\n  \"ee91715a-8947-42f5-b512-3c531f7498fd\",\n  \"d712c2f5-ce4e-495b-af3d-856246c94ebe\",\n  \"5d2b90c3-9ffb-4c9c-8707-2f770a2ae17c\",\n  \"7ef4f6e4-052c-4da1-a9a3-4937487deb7e\",\n  \"f592d7b8-fabe-4b78-8e3f-c4ae22f1f7ec\",\n  \"248ec979-e2f3-4586-aa27-1398d06d3327\",\n  \"0ddf31b5-144a-4493-b5fb-c905309a1e75\",\n  \"28f556e9-f78f-4c5e-9b36-c08433512409\",\n  \"f2eacff2-61e9-437d-ac7d-619ff0a621f7\",\n  \"bbec7220-7738-4ab3-9f6a-475d7d06cff1\",\n  \"4d695bf4-6929-469a-9bbe-45d20be38246\",\n  \"c9869335-1652-43ed-8704-6ab144595fc2\",\n  \"e0ba115c-788e-46b3-b7b0-b622ae2a147a\",\n  \"49b88807-1e37-4d8f-aa2f-3327a2e45974\",\n  \"65189622-12a4-4655-adc0-202d4c59d1a0\",\n  \"3daec6fc-788b-49c1-a534-adf82b4942e5\",\n  \"726a4143-6464-4638-93f3-74022d3e3d94\",\n  \"bd632c9c-b173-46c4-acd4-e6e4e6cfa720\",\n  \"12c5538c-4461-4ab8-acbe-74f2d489861f\",\n  \"cc5ddba8-724a-4f94-9aaf-e0c5b9b24b4f\",\n  \"93435aea-940d-4e30-858e-386c793f848a\",\n  \"14413776-7f1a-4b6c-87a4-865cc492036f\",\n  \"b9c7e007-862a-4a4d-8294-f8dbe1b63c9e\",\n  \"104209c1-edb5-47c9-8499-c427dddee458\",\n  \"89d93c19-5599-403a-bf83-7883422c756a\",\n  \"86c37132-1be7-498f-a66e-8a875d89ef6e\",\n  \"65d9b7db-550c-40fa-8202-422b2ea7742a\",\n  \"0af37f43-5c5f-4dbd-9a75-5ebcbdd2ba70\",\n  \"793c3d85-efd2-4eeb-8334-a60fcc40648b\",\n  \"88bcefb3-9ebb-416c-b5b8-27afcac0a75d\",\n  \"b36bd431-226f-41c0-baf4-41551a4874f9\",\n  \"e7e0efc9-a5a5-4c21-a7c3-6dbdcf1765ec\",\n  \"986c6cec-39a9-40bc-8689-cf73643cf11a\",\n  \"a0be5872-f8e9-4e8b-b241-be71b805f43d\",\n  \"04530ead-3e04-4f77-99a0-ad9848730f0d\",\n  \"87f7ba0a-e7c8-4b57-bd2d-5a79b4c6f9fe\",\n  \"971e12ee-168c-48cc-a3ea-8411681cf074\",\n  \"bd47a555-9148-4054-96ad-13312d7a0f7c\",\n  \"5a2d1ee9-9d57-465c-9a37-9a83a5e33cfb\",\n  \"fe15f6de-5c17-4a2c-8e5a-19ddaa02af37\",\n  \"65ad5933-457c-40b6-9bd5-fcc4afb02fc9\",\n  \"cc14f898-c516-449f-b8a7-3620077e2167\",\n  \"c1c4e61a-0050-4f19-ac1a-87903f2cdf3a\",\n  \"016ebc47-e675-463e-938f-d50517cd25bc\",\n  \"db5f3460-c9a4-468a-88b4-e14f2716d452\",\n  \"22ecc415-db14-42f5-be62-875d4de8dad8\",\n  \"1e282ac4-efdb-4dcb-8af1-f5c5c6308b87\",\n  \"6a7b9f47-e063-4437-94aa-dd83e81afc94\",\n  \"58b36f9b-9c5b-44e8-bd4e-11dbbc2f3c46\",\n  \"fdbb2bae-dd99-4d5c-9098-1884a11ea0cc\",\n  \"01731ec1-1bc6-4c52-b1e0-dbfdcf987609\",\n  \"3477bb5a-999a-45d1-aa27-d7598f85c89c\",\n  \"06040e9f-da19-4ed6-8d03-a30228daaff2\",\n  \"aa335299-b539-4726-9812-7946254e265e\",\n  \"bda3ef1c-9146-4af5-8f06-1ca39f768c15\",\n  \"5e102ca8-9ecd-4a0c-b1a1-6845c0fdb4bd\",\n  \"b82dfd25-3d72-4c5b-8bcb-086df4688049\",\n  \"361a1d44-aaee-4ab2-9cba-fe3975e08b18\",\n  \"9845c929-34de-48ce-8e07-a913ac65483c\",\n  \"e2d4544d-8e08-4912-91aa-78ad08449b14\",\n  \"61c80c08-b4af-48f7-a388-1ae81d8295ef\",\n  \"ade4ee5b-3ade-4bcf-8318-6deac09872e1\",\n  \"a37fbc05-d8e3-4f72-9426-550ff4b2df8a\",\n  \"8b199bff-be2c-4da2-a47f-3c6e4931eb43\",\n  \"cc5d21a1-3b61-4bf1-bc4a-3186dfb0bd51\",\n  \"e1c5c28c-1a7b-4c79-a266-13c44765d2af\",\n  \"dda727f8-39d4-42a3-b96f-57bae700f8b3\",\n  \"e44cf62d-d5a4-4a08-9056-e3d41f2a47ab\",\n  \"1a5aac6f-7472-40e3-a1d1-f5a3f26b37c3\",\n  \"37626739-c60a-447f-93e7-faa5bcd80c2d\",\n  \"3ba664a7-7fc7-43af-bc34-c2d83f4b9bc6\",\n  \"f546b943-6f24-449f-9858-9671324331d0\",\n  \"3db02d3e-83fa-4de6-a9e5-e9b0d4c1bfc8\",\n  \"e71097dd-99ae-49fb-ba4c-3509151889fa\",\n  \"0f51b627-fdb4-437e-9779-34915f55988f\",\n  \"9990556d-710f-4dd4-9d50-920ec0a9ce54\",\n  \"09b5d4d4-da2b-41d3-9cde-c758817bf717\",\n  \"81892797-c75a-4c81-8f7d-896a380bb51f\",\n  \"10617df0-a931-4bc4-adfd-51716cb8d19c\",\n  \"f790bff2-8756-4734-b69f-2e80d529e1dc\",\n  \"2714fd73-436e-4d8c-8f14-de0d8f68fe9e\",\n  \"81c6b1df-a1af-408d-87a6-9ad868ff195d\",\n  \"ef4c799f-3617-42ae-a6c1-a1e9df32d490\",\n  \"cdb1decf-afe2-4e8c-b4eb-4ce8f532f60c\",\n  \"5033c170-cceb-49f2-9ca7-d1b893c78178\",\n  \"b99737a0-8d86-4576-89d7-3cff6e8cfcc2\",\n  \"1173f083-aed4-40b2-a545-ba24e2025f03\",\n  \"8f93113b-d1d2-48cb-8004-420b1ac533f7\",\n  \"4f17458f-1cdc-49db-adf2-7cb7df374a18\",\n  \"217b9b1d-7c9c-4cea-a562-7896f8c45b67\",\n  \"f8927cc3-b3b8-46a5-b406-b238ab2283f1\",\n  \"6ec8f545-4180-4028-be30-6d9162fddceb\",\n  \"1681636e-4269-4b9f-b5f1-a86ae831c216\",\n  \"2bc0b663-dfef-4608-ac4c-e7a05d069ece\",\n  \"5f45faa4-cb90-4845-88a0-47b226b3488e\",\n  \"91b0a745-c6cb-4840-81b8-518aada52e50\",\n  \"dce646ff-1347-4e41-80ef-fb3dca7b43d2\",\n  \"2a1b84f5-ed63-41ac-9f17-ceedb8aa2531\",\n  \"413d6ba9-8685-4220-a7e7-6cdcbfe8ad00\",\n  \"f47bfa1c-5515-4e9c-8990-b0fa659e930b\",\n  \"460cd85d-844a-47d9-9f19-d6c190b59f6c\",\n  \"6a615fb6-898d-4d83-b77c-56a6fdcd7b07\",\n  \"3bb9392a-20c1-4eb9-a813-39d0c212e5c6\",\n  \"a26c13ae-ed00-42eb-aad4-1958e95eb45a\",\n  \"bfa25743-e2f1-4508-bf9c-b377dfce1980\",\n  \"15b20cee-a830-4501-9ae5-5ba1090c7452\",\n  \"468f9467-ed59-4bee-bf40-004438f0e62c\",\n  \"84ccb1db-2339-46bf-a0a3-3693a5680b07\",\n  \"1d794a83-c276-4307-b1ca-17b9b23d396b\",\n  \"8f4fcfdb-9525-4e89-9775-16ed47b13e75\",\n  \"5b6b75b2-ba14-44db-93f0-6388e3a11bec\",\n  \"40012426-4c69-4337-902e-56ee27ab8bdf\",\n  \"4173e4d6-c8ab-428f-a8c0-57ff245799f5\",\n  \"a2123dc4-46be-472e-a6c9-74a8a974da2d\",\n  \"475295e7-666f-4648-bc70-91396fef453a\",\n  \"4f1dd8ca-0d02-4550-9f18-9ee42d7f5b41\",\n  \"7ef5601d-60a4-4587-abb0-3502faf576c7\",\n  \"8d297e70-700c-495f-bebe-adf70903f1ba\",\n  \"28d5008c-d5bd-4774-b588-a0d99423a2dd\",\n  \"4381ecdd-f95c-4855-967b-9037fe7a2a09\",\n  \"ad6ecba8-9dbe-4c31-94f1-8fd8c9dd928f\",\n  \"72b2bad8-5db2-4d48-88c6-ba224a95dcac\",\n  \"4cd7607b-84b7-421c-a470-5dc5dfa62899\",\n  \"503119f7-89cb-4009-af7f-2716c0dd2768\",\n  \"97bd4ca6-7c49-471d-b93a-41d457e17e4d\",\n  \"8e66c455-049c-47af-90c7-6e51e7e7f9a5\",\n  \"5b364ea5-6cdd-4f23-a633-9d9ff9fcc459\",\n  \"6471dfed-e08f-4d7a-89e9-ef5028b4e244\",\n  \"6f7f36a6-b2e5-4e13-b159-9060db6d9658\",\n  \"2ee45240-4c3f-49f1-b925-dfb0e5cc035b\",\n  \"93d3b4bb-6ecb-4a71-bf6e-c11de4fe6405\",\n  \"04e1ff59-2f6e-4a6d-bb1d-e21b6f902844\",\n  \"707f7cc1-77a3-46f0-9adf-8af5ba10d303\",\n  \"357f093e-cd1a-4f08-9508-cf2e12b71515\",\n  \"5be5d3f3-0c07-47db-b7d7-e24eab4bf11f\",\n  \"0ca6bb53-5569-4690-9304-5bbba596f11e\",\n  \"d2456eb5-6d26-4b8c-be89-08bc2050568f\",\n  \"8d95940f-9683-43ee-a766-4bbf4a9e32f4\",\n  \"782055bf-f122-439d-8f53-06904b6dfe5a\",\n  \"634d3376-b690-4902-8961-dbd9583988c8\",\n  \"e81851ec-30b7-433a-9dea-a0af7a2f2fae\",\n  \"1a56e6d3-ad31-49a7-b7d6-21ea87dc822f\",\n  \"dd541fb9-a7d1-46f8-8017-4a8b1dda542e\",\n  \"4720722a-309d-4615-b8a0-d9d29da4846e\",\n  \"8564bc1a-fc2b-49ef-b48c-3c9d9da67ccc\",\n  \"35664e24-3947-4408-8bc8-ecccb75c00ae\",\n  \"76faaa5c-312b-4405-886b-09b1c6ebbf0c\",\n  \"b559e1e4-b80d-426f-8d8f-b6eabd980f8a\",\n  \"ad460d71-5aeb-4ab2-a3d2-caacec2ab967\",\n  \"063bc977-e3e4-4264-89d7-8313b61466c6\",\n  \"8bc1ed71-892d-4b88-ba96-3c339b14d464\",\n  \"ed130301-7a77-464e-b194-f33375c5b1ae\",\n  \"aec4893a-4d6e-4354-8ae5-5af19ab4f04e\",\n  \"ef01355d-4187-4f00-a851-b17aad62b0b4\",\n  \"95fe428d-79fe-4b77-b18f-f29a975cd715\",\n  \"d6f33648-2230-472f-873a-d11c113f6124\",\n  \"23eca350-060d-4d98-adee-de1990b9cc8b\",\n  \"2dccdbbe-eab9-4617-8353-44f89a234593\",\n  \"a0525d2c-fe27-4c86-9352-19a0efe7a69a\",\n  \"3dff9f70-6b09-4e9b-8f3d-e733d6d40d25\",\n  \"0fe37a11-f8de-4df4-8f36-7136f85e71d0\",\n  \"f1c4a284-dd00-4466-baed-8b4574bcd503\",\n  \"b4f3d724-78cf-405d-86e0-68cfa4da1cde\",\n  \"7ef07b56-d75b-4446-8234-32c4363ac99b\",\n  \"e55b1965-affe-4770-a488-d7fd43ad6385\",\n  \"361311af-e66f-47ce-b845-c6b50c38d24f\",\n  \"92182535-5540-46eb-8ce8-3a9a5ef9b9ed\",\n  \"8bb7b070-a36c-4ce5-bde3-c81cde5bfe11\",\n  \"c3738bba-75f4-4d0b-9799-e4dafa33633d\",\n  \"6ed32a4c-987c-444c-8c70-4abe385b507c\",\n  \"1d94311c-5f55-40f6-8c58-8da4c6b571bf\",\n  \"05397641-65c2-466a-a0eb-e56d7813f83d\",\n  \"578376d2-43d3-4182-b81e-c8264d8faa3c\",\n  \"ad25b7e5-8351-4d3c-9cd5-cc1b9e6a4860\",\n  \"2e436015-53f6-4eb8-bef5-0f9a084d446b\",\n  \"a43e7472-2c5e-450f-8df9-443277c634ca\",\n  \"c14d2863-be67-4992-a568-eff91489d553\",\n  \"d54cc780-1b9c-4536-8be9-e40db15c97a9\",\n  \"2d687099-545a-41c8-83de-14399a7195d4\",\n  \"cc33c7cc-2199-40ba-a41e-4b3daa5dccaf\",\n  \"fd97a450-7eb5-4df3-8b6d-4a424f3fe43b\",\n  \"ace77bf6-2584-444b-835b-090924c16b28\",\n  \"815efbd2-5f2c-492b-9a9e-776e7d2cecbc\",\n  \"73896c08-369a-48c2-aec0-5c8348eb437d\",\n  \"307464ec-bd5d-4c8b-ac7a-58e230e61ec1\",\n  \"b9641283-6ee9-444f-bddb-79896b36ff79\",\n  \"a05aef7a-a940-490b-bd26-6bb9e54bfba2\",\n  \"5b2471e8-b01e-4e2b-bca2-12926aeb8284\",\n  \"3e4b2566-4a5b-4c67-b49a-a5a0f896c6c5\",\n  \"81ab2134-265d-434f-9720-d0627e768893\",\n  \"60a532e2-197a-4578-acce-5b92aa121c1c\",\n  \"4e5d24b0-1c28-4f7d-84cc-a0700c765138\",\n  \"91bc813c-31f4-4ada-8bf1-7830db8da03c\",\n  \"a18a3e63-aaf6-4e4e-b625-380ab7e23186\",\n  \"1a04e0ce-32b1-4f9b-84a2-74d439287f3c\",\n  \"37da6de8-5133-4715-b19d-7c8c62652b9b\",\n  \"777b2db9-5700-4aef-b59a-f5c9adf281b5\",\n  \"34762842-06ee-4a04-8b0d-e694a72d66fd\",\n  \"a587f401-e31d-4c73-b18a-e2a18ee97edf\",\n  \"93f076af-aed8-46b9-b29d-76458b117ff9\",\n  \"f2d3304c-89e6-45fa-9194-5f5614c39a80\",\n  \"ff1f0132-44af-44ff-98a2-55b2d983550b\",\n  \"7ce2f90f-48d7-4183-80ce-0907483b2745\",\n  \"fc1a279f-8402-4068-b9c0-88c5a0f096fc\",\n  \"51825a98-af23-4b42-aa09-b1441178d2ca\",\n  \"7f8414cf-e268-48b6-b9a7-8300dff5f594\",\n  \"d60dbd40-d35c-4de6-ad4f-310b5f7b5769\",\n  \"23bbd846-0c58-414b-b968-c301c6ef2e51\",\n  \"6b1fbabd-3a37-4140-a3ad-44401a93cd6d\",\n  \"7f1f1277-eecc-49d5-80d1-dd861498921c\",\n  \"5e38ac08-5897-465d-bfc0-89bdaf239667\",\n  \"6347d6c4-18c5-443b-8804-2481e80f83c0\",\n  \"bdb76390-3e40-4614-b6bf-ec24a824e15c\",\n  \"e8fa2223-d646-4059-93de-544617b53fcb\",\n  \"0152f746-61df-4e18-9218-854a2b85101e\",\n  \"89e8c997-ba93-4b52-8976-b6be4fba732e\",\n  \"271a6bf4-5985-4e1a-8e5d-63d15a964c8c\",\n  \"c430b4e5-2fc3-4b8a-a4d3-257cecae99ae\",\n  \"db47e04d-7399-428b-8a13-225a78048c7d\",\n  \"dcdf5f65-f034-4ebd-918a-d00e0c7af33e\",\n  \"86c4c620-2d88-482e-a87d-2c9dd363d506\",\n  \"4832c59f-f4e5-4b3d-b99d-7ab3780e814b\",\n  \"e8934389-e9e3-488a-9b70-407e05fca233\",\n  \"d099109b-ba87-489f-b165-a259821eee53\",\n  \"2b54134f-bf6e-498d-b1b2-2d550b5f5442\",\n  \"c0927789-fa1b-4a1f-ab75-430ffffa8f8e\",\n  \"b94d9cbc-83ee-4afd-ac26-fbd7554630fd\",\n  \"a8eb6012-089f-4d5f-ab80-0129c8edc01a\",\n  \"3976ef98-83c6-45f7-8959-1c91f731f74b\",\n  \"df7f5892-0d3d-4527-b420-5bc549dfa604\",\n  \"4f7984ed-17f4-4adf-878c-7cbb65579b1e\",\n  \"c0194f69-1f52-4d0b-aafb-5684ac301c1b\",\n  \"40b3b4de-9bd8-4ed6-a572-2838a1082cd1\",\n  \"21e5a347-493c-4da7-918c-46f940702e3e\",\n  \"bc9ea921-9fca-40ae-b8c8-48458d43b233\",\n  \"88e6f5db-2fbd-4e90-9688-6952ea297698\",\n  \"4aee95c7-7caf-4f0d-8c21-88239197cc86\",\n  \"2d47d31c-173f-4d2b-bab5-2a69e8a86c70\",\n  \"84eba708-1197-4a26-b9b7-46fc9966cdc5\",\n  \"2859c052-448f-4e3d-8c77-164a49d15d50\",\n  \"c60a46a9-c3f0-4df5-a9a8-16825d5dcbe3\",\n  \"0cac3611-6756-4a7c-b5be-3eceec1a36f5\",\n  \"d7858ff9-3e83-424d-8794-9070cf721693\",\n  \"59a78356-616f-4432-a75b-8a6a6a8891d6\",\n  \"44776c4e-128f-49ad-a267-61fd8b8be346\",\n  \"a3b88288-e4c8-428f-8924-fbda9d6b5e1e\",\n  \"2c4506fc-6f4d-497a-bee0-ffbfb80a4826\",\n  \"685f55fa-597e-4804-bb27-6b1e75c112c0\",\n  \"f6342fd6-c151-4333-9392-6742e5a72655\",\n  \"a5d0621f-60bc-4513-93eb-839e3022ecb0\",\n  \"ce283a46-6b7a-494e-b63d-cd6940ffbb35\",\n  \"1b31dc5f-0e73-49ba-807f-0f5cb4c168ea\",\n  \"6f593978-c3d3-48c7-8e0b-1f028c1ddf74\",\n  \"ebca33a4-15b4-4cc7-8a7b-303e18126fc8\",\n  \"622a2c4b-ac31-4676-8648-ae80277bc7e6\",\n  \"19ec23fc-1bdb-403d-b6de-f70bc6769684\",\n  \"28579139-c6d5-42e2-a3c1-ae1b346eea09\",\n  \"abc91a44-04e4-4de2-ac17-920c1cd59e1c\",\n  \"18256be2-a627-442d-80d6-0be9d1d80610\",\n  \"b182e185-a8e6-430f-a186-1752f769055f\",\n  \"f38cbd2f-9a1d-420f-aad2-eb04ec285ea1\",\n  \"a930b68d-b9cb-488e-8384-a2ebbd900b24\",\n  \"f17b9caa-7a42-474a-ae32-c7b4aa7b336f\",\n  \"10bac215-2727-4552-90bb-c80a63df9ae9\",\n  \"a3e03029-7002-4807-8366-b69321f1cf5b\",\n  \"4b89067d-4278-4ecd-8f08-87fdf7e4b5c1\",\n  \"aebcf577-bbee-4852-8ed7-bc2720b2f2d8\",\n  \"5e343b83-509a-475a-991e-bbbd220f8840\",\n  \"10ada54e-43e2-44dc-963c-7b806c07562f\",\n  \"5d05677d-42e4-4c20-913d-909f59abbf85\",\n  \"5616e2f6-7596-4214-b3b7-0a099029cf10\",\n  \"bd02a110-7a43-4235-9533-5c53af8f85b1\",\n  \"488304d2-c6fd-4d3e-a823-390a6a55615f\",\n  \"deeae5e4-9fe5-4903-a182-a7e95fd4c4db\",\n  \"0a790f56-239f-4e4e-849a-7644743dbe61\",\n  \"ed5d45c3-216f-431c-b392-f357f76f2060\",\n  \"42795abd-4c0a-42ec-97cc-c122621dfc97\",\n  \"4dfaa848-4ccc-4f82-9bd9-4be909e09b71\",\n  \"6eebd3b2-2ec1-4f62-8b00-25d90eeec1f5\",\n  \"d6e46d4e-c442-40e6-9629-43eb8816d3a9\",\n  \"1c8fd3d7-0212-4d47-8dca-19b6fd94c5cd\",\n  \"9d573930-b9dc-4862-9f68-eb7e0cc0424e\",\n  \"62bac275-335b-47f3-9ec2-a39c63464ebd\",\n  \"e12ed3ba-0ebe-4490-b6b7-01c41fa76437\",\n  \"e7935489-2ddf-4564-8abe-7e049e2d2470\",\n  \"19781dad-94c7-43bf-b6c2-bcabeeb53035\",\n  \"11181cbb-0be2-4039-b198-12faa4525467\",\n  \"4102d2ad-fc25-464e-bded-bfbc83f567cc\",\n  \"5ef7c7f2-2257-4e0a-b0f8-8b9fdd20466e\",\n  \"98a1c21c-3238-4f37-b659-b38c0f966fef\",\n  \"f471c291-a562-4a44-a410-ad8881b4c730\",\n  \"eaef7b42-c9bb-4ae1-8b43-8b22d1b60ad4\",\n  \"d7a99bb9-ab3a-4f2a-8aae-d17e6abf69f0\",\n  \"f03ba8f7-ad92-4462-b693-c5b3d1eb5404\",\n  \"f26571f7-f385-477d-ab82-ba03580fb0b7\",\n  \"07725d17-4c11-47cd-a08c-9a9bd2da59c5\",\n  \"fb52fc15-e698-4cec-91a7-aa3918bebf87\",\n  \"25fe3778-1fab-42db-8f8f-7a5c3c7ad181\",\n  \"e9304bf3-8b7b-41a3-b935-8ce2d6e841c5\",\n  \"fbe62b0e-cdc0-4af0-bea4-71836357b4e8\",\n  \"5fc26692-f48f-485d-a660-339edb2cc823\",\n  \"84267f8f-b07e-494f-899f-15ece1dddfc1\",\n  \"68672eea-d9e5-42f9-a74d-534d81a769b7\",\n  \"75d8ec82-30c5-4760-b74e-b8f479c9bc9c\",\n  \"581ff3b1-dc5c-4a60-86a4-848d6de30f66\",\n  \"a62ba0fb-f1c8-4698-af7a-04e0ea84bd51\",\n  \"f0d937a8-60a0-4fad-bed1-f0af906092a6\",\n  \"398d6804-cc01-4191-a3c9-3d0916365e86\",\n  \"999201ff-47b3-4669-8fe6-8c4563d33fab\",\n  \"a13b587a-0ea0-478c-9c6d-65c05ac3efab\",\n  \"3ef7d6e8-c00f-4dac-bbf0-345c9b166fdc\",\n  \"bad6706f-ea17-432b-854b-7cc23d079bfe\",\n  \"ccab3a5d-c012-4dd0-8233-680f4946ba49\",\n  \"76379fdd-7ecd-4639-ada4-d0a48a8bd290\",\n  \"12c22398-0167-4350-b242-d0db4b69db9d\",\n  \"9425a975-388b-45dd-922e-f261b30372a1\",\n  \"88c317bf-c7bc-4d7a-a6be-71c7a6ce7214\",\n  \"90508480-7df0-450f-bb1e-80462661cd7b\",\n  \"3366d3e7-4953-4071-a381-7c92f295e73a\",\n  \"05cb44ec-e18a-45b2-a5bd-b37cea5d5044\",\n  \"cd50448c-2629-44a0-8a4f-648d9b5c1ba9\",\n  \"fad15200-842a-413f-947a-65629ec1c0f2\",\n  \"223678a1-161d-4433-947e-78c3e91643e4\",\n  \"7904037d-47a7-4b1c-bfd6-6567b8fdc78e\",\n  \"214ab4b4-de47-48f6-878b-0f48766ec69a\",\n  \"c9a78717-1659-42f3-9e7c-3a0ab344a2b7\",\n  \"bf629e96-fde0-4b49-9856-47acac31c006\",\n  \"a467a932-4f53-4a5d-83f3-05275ebc118f\",\n  \"8c52e741-d18b-4c79-bfd1-b2d1d70d7131\",\n  \"89e66678-0003-4f2c-ad99-02218aafe75a\",\n  \"5245b74d-2eca-4cdd-b371-5b9bb745b19e\",\n  \"f3dc6b63-dc61-46fb-9a81-758a2fa11d12\",\n  \"9b264e32-3aae-4906-8c65-e59288c9dc02\",\n  \"04d332a7-776a-4334-83c1-c83cf8ba02a1\",\n  \"2bf5e486-a2f1-4292-a4d2-1cf0a7f6fbb4\",\n  \"c6d7a707-89a2-42bf-bc3e-a819eb96e4f7\",\n  \"89a77614-8bce-4cea-b0fb-ba9014c40f95\",\n  \"6d6c5b04-f20c-4944-8a4d-c7006499a2fc\",\n  \"49a43be2-c6af-4c04-9313-89743442d4ac\",\n  \"1a3c6105-3713-4490-ab2a-e99626e18265\",\n  \"1bf62b84-d2bd-4bed-9caf-e743cbf5ca4b\",\n  \"28b8668f-47fd-4b37-b88e-a5e295b3ba69\",\n  \"e4acd8d9-e189-4cbb-8229-7215e4edbcd3\",\n  \"20416b6b-a26f-4709-94c0-06fe7ed1bfc1\",\n  \"28dc9d07-6588-479b-8e8b-3a2f0b50e5d6\",\n  \"a91926c5-037a-4f2d-9c4a-4fdfa05a5b24\",\n  \"24261a37-f071-4c44-9ca2-5d97ffff85ae\",\n  \"bc0153d6-5168-4f29-b711-8013a1349f3a\",\n  \"43d2c541-7de5-4668-9934-de649ba9d768\",\n  \"5500e040-a248-4e4a-af41-0ad4212f50b2\",\n  \"1833dc92-cce8-4052-9684-c8f92a95d34e\",\n  \"cac2d9c2-a777-4b34-9e0a-bd25957a8ac5\",\n  \"c6b50a94-f72e-4171-9516-7578ceaa4e55\",\n  \"f99730bf-6b8d-47f3-a6b5-3ddd75585618\",\n  \"0400fcdc-9764-4985-bd1b-6b985449a392\",\n  \"2a9c0d7d-0526-437d-b2fd-b46bb38a3af4\",\n  \"6060a1ff-fbf7-4c66-8dc1-12c1133c242e\",\n  \"c17438eb-a416-4717-9fa6-5d3af2fe59f7\",\n  \"4d5e6c8f-a5e0-44fa-b63f-b397dd919502\",\n  \"0240e8e8-4afb-46c4-90b9-85bcafb53ed6\",\n  \"501cf5e4-4d30-40b9-a45e-419928c51003\",\n  \"a8900b71-fa54-4e13-8905-5e41c0c5ac76\",\n  \"38d5e157-6479-4843-9e9d-70bd73cf6d90\",\n  \"6d5e3007-3878-4e58-9fe2-bf86b7bd79e3\",\n  \"b04ab495-28c4-450c-b9fe-7247dfdfa87a\",\n  \"190a9fbf-085e-4a7e-9260-e24e27622248\",\n  \"dd693c34-78f7-47c2-a22c-feffeb7da401\",\n  \"997c5c18-b5aa-4559-9290-aacee8671f28\",\n  \"ee6c3b5d-8585-4193-8c34-54ca2be04159\",\n  \"ba0b4541-5a5a-4b4a-8ad7-234cbb7d018c\",\n  \"9d8547b6-f5dd-4348-9d43-7666a01a1ad0\",\n  \"b39e2951-c0d0-4eea-b488-b6425cce2bdd\",\n  \"88a0f0f0-0ba7-44d3-abde-101d63620c4d\",\n  \"2d07d5f6-49d1-41a4-855b-82875b7cedb3\",\n  \"e932944d-f68f-4a16-9c6d-63c406080428\",\n  \"87ca82f9-563c-41d1-99fa-c65637ead500\",\n  \"9774e474-5fe3-4c30-874d-f060f67813ed\",\n  \"474b4bf4-b62e-450c-abaa-1e2314af89b5\",\n  \"ea4915f1-967e-40b3-88a9-61e8ab90e809\",\n  \"322cd848-f04a-43f7-af48-791080537c11\",\n  \"05b79e81-d99b-4fc9-bf33-87392bc9ecf8\",\n  \"7d98544b-b0bb-4c4a-94a0-5623661adcb7\",\n  \"6be342fa-82ae-4017-8c6d-292b38e4bb2b\",\n  \"c5cf0c8a-21f3-474e-8965-fbd275d7be5f\",\n  \"4de09339-50d5-420b-a155-f14dea2d905a\",\n  \"6b58279d-a9a1-45d5-86f6-9c461424057f\",\n  \"f5991e34-bba9-487d-90d1-00ea79818eed\",\n  \"c58ac7f4-08ea-451e-bffa-c6cfce996a29\",\n  \"06b336ab-df55-479c-b188-d088e7ddc270\",\n  \"4a15756e-96ce-47d5-8924-378ff74ddda9\",\n  \"56515fad-97cb-4930-9300-563e663887a2\",\n  \"07e4c7a2-6ebc-4ef7-96c3-de3f7409c7c2\",\n  \"27648305-0030-430f-8586-0c5856d77b17\",\n  \"6e46f459-eb47-4e09-b325-82553a1e2279\",\n  \"b5ada1f9-16eb-4ef7-8ad8-d015ac7801a9\",\n  \"d8477f73-55ae-4a02-8983-ef8cc75e618c\",\n  \"f1df5ea9-d143-4941-9898-2f262bffc4bb\",\n  \"cda24c1e-7cca-4113-b429-01c302ca657b\",\n  \"7b32a720-e098-4eeb-bd8e-17691ad6f799\",\n  \"8229d30c-4168-447f-a31f-17d29bac3ae2\",\n  \"f99e034f-c9ff-410d-8a0d-6f6eef86e930\",\n  \"eab90516-0b5e-4893-8738-99ea7a591f30\",\n  \"5f93ad33-e798-4ad5-96b9-8ac5a811ade9\",\n  \"f884facf-81f8-4d2c-8f0c-3ce1099d8239\",\n  \"04d45bda-34d6-483a-af34-ddc8259370f6\",\n  \"7084f99d-170e-4832-b0f6-563d36c1494e\",\n  \"e42678a7-94d9-4598-b00a-396eebf5aa48\",\n  \"6ef9486c-ffa0-4f11-a64a-9ca47a509e28\",\n  \"23697cd7-4b4e-417e-9537-dbe3e6380f64\",\n  \"ba741e3f-d369-4d5e-9c95-61076f3742e4\",\n  \"b71ce081-bffa-4567-8d5a-ec40fe08316f\",\n  \"761d50b2-7aaf-4552-b711-652a5809ddea\",\n  \"c528b8ab-ab5b-4e57-b5e5-a4087fcf8c64\",\n  \"013eae33-7c61-4459-ae3f-6c11de06d74a\",\n  \"a70ddf1f-dbfa-4fa1-bbc8-2ac3ab18b0c7\",\n  \"b42fc73a-87de-4c4e-86b6-2a286192bc85\",\n  \"4da890f1-f9ed-4323-bb0d-81d7184597b5\",\n  \"3fb3eb1b-a9a5-4002-828a-cbc90b7982d5\",\n  \"c24634db-160c-4502-9f13-9aeb99927c69\",\n  \"0eff2b09-1658-4c3e-8d3f-58a41c379ede\",\n  \"6d4fd019-7978-4792-880b-a255cfe42b6b\",\n  \"538ec3b1-c1e1-46ee-91cd-f7da0e48b5f7\",\n  \"f789f805-3202-4f7f-93b2-0e0739947347\",\n  \"72e5846f-3d42-40e4-bcf7-f6059a620187\",\n  \"7302951a-d626-4a7b-9208-8ab2840337c5\",\n  \"34a5119b-fa1a-487d-af0c-083708009b91\",\n  \"3ebf7f24-88f4-4d75-9864-bc597d5140a9\",\n  \"98d6a279-c538-4286-92a2-0809a8e8e66c\",\n  \"db51afde-0a57-4d26-af93-79eed794c69c\",\n  \"c7905b1f-5022-4881-a0b0-e110138c80c9\",\n  \"1d069a47-54ea-4e09-8cf4-16cc82f9b17f\",\n  \"de44de3a-eab5-4694-8e45-badc1d802050\",\n  \"5d44dc57-b27a-4e40-aaa9-dbc86db5ee4f\",\n  \"343afc86-5923-4f3f-9016-c30f8c35bea8\",\n  \"74a91ca0-f06d-4b9a-aa74-9b4a6d4b020e\",\n  \"82f9e99a-dfac-430e-9154-1379cf639605\",\n  \"6d7cc20f-e56e-4843-807d-889ffcd26881\",\n  \"8da733a9-a48c-46bf-8046-90ef8e33fcb5\",\n  \"61e42083-878c-4bbb-a2c9-2aba4e7be191\",\n  \"7cc62aac-a94a-4d62-ad3b-c7075fe792b0\",\n  \"c173d907-6260-4ec2-89ee-73a00f8795c3\",\n  \"3e0ff61d-f463-4b8e-84c1-651914a92970\",\n  \"cd923dc5-5ee5-40d7-9e69-fb9398340411\",\n  \"5874b23a-c286-40c5-866f-9e4dc1d1987d\",\n  \"a599c540-03a0-4d6c-a014-98002e8e178f\",\n  \"93facd96-e81a-4fdd-a6a5-aee04ed1b666\",\n  \"0bcd86b8-cf87-4ff7-a586-360f784a6185\",\n  \"918b1ecb-73ff-4477-9909-0d9a66d0a89e\",\n  \"9eb7457a-3574-4e6d-8d3a-e459cd58db2d\",\n  \"0800f130-2041-4d76-b98d-619caac2dbfc\",\n  \"83e7ca89-2f1a-4a10-af7b-0a28cba9206f\",\n  \"edebd754-6949-48d4-9882-cfa96221c7d2\",\n  \"ee5599db-a5f8-4e2f-b805-11a327f2ac3f\",\n  \"6dca3707-2550-41a0-929a-6c29e572cca2\",\n  \"71cc69e7-fe32-4a99-b738-6dfbd9742a56\",\n  \"c9a5f234-b4d0-46ca-9ae7-b1b9efca5aa2\",\n  \"7e14bb0c-0b50-428c-a812-4e79a44028f3\",\n  \"77148020-f9a3-441b-a08b-968e92f2613b\",\n  \"f738f5ac-2287-4545-bf20-aeb733a65a6f\",\n  \"5b8ef711-2293-4b8c-a84a-3c661c297887\",\n  \"be6cf848-015f-4f35-810a-662d3dbde254\",\n  \"5a0b12ac-3832-4f4d-b436-d65d0f314eb6\",\n  \"ebe4ec3f-0ce9-45e9-a15a-911edad74bca\",\n  \"983e738e-f06e-46e3-9244-fe65548b6e10\",\n  \"687542c6-879a-4958-9d0a-63582508f47a\",\n  \"0637b426-ac1e-4775-a070-266ca9c73d51\",\n  \"d193a609-86ed-4a7f-87c9-ab840912d6b4\",\n  \"92294ef9-63fb-461c-9013-5fb77f6230d5\",\n  \"710f390b-3e50-4c30-b682-86fa348c8310\",\n  \"f417d8e5-892c-463b-8168-5f57deaa8af5\",\n  \"e466df93-2971-4105-821e-1d583405afaf\",\n  \"03c2144b-a92b-40d0-a6ab-5423b839f516\",\n  \"e7f23650-64f1-4476-a22d-a6c3c81e6921\",\n  \"989b0d12-23fb-4f25-ad47-9b895e69b4d4\",\n  \"9f146436-ec77-4569-be2e-85805a438573\",\n  \"c94aa51a-31d1-432f-95c6-c69b98afb523\",\n  \"fbd24006-d894-4fee-be1a-bea9974cc5ab\",\n  \"4f155adb-c7b2-4181-b8c4-90d085577a8d\",\n  \"e3ca70fd-f594-4bc8-8952-5fd7095c3b99\",\n  \"18bf909d-33c6-4107-9c69-05a3f8d9871e\",\n  \"9a71c81f-b02b-40a6-b8a3-77cbc13b0c7f\",\n  \"e3630f42-b550-43c4-b75c-230eef739162\",\n  \"19686843-af9c-46fa-89a9-18bbe5ed6da0\",\n  \"7a800315-f8fe-461b-ab9d-17c8e0d74a81\",\n  \"2eb01197-705e-47f1-852e-fd3815bc4cfc\",\n  \"5c46ab18-4d6b-47d0-8dd6-d3bed18faadf\",\n  \"8bd953fb-ec23-4699-96f5-9422e5742245\",\n  \"5d7dc542-ebb6-49bc-8f7d-4fe14f9dbf94\",\n  \"1c393269-7db4-4c0d-bcbe-9eaa65b50bda\",\n  \"9ac6a483-74d4-4df8-ba11-579f1ad40783\",\n  \"63b602a9-704d-4bf7-9477-98100bc81cdf\",\n  \"8eaaa26b-0467-4d6a-9a06-f5f0487926c2\",\n  \"69543226-6314-47f1-b5f2-61c2c15bbf00\",\n  \"804a397a-d183-4275-a869-a7c1f4a0dbd7\",\n  \"83dbf129-067e-435d-88cb-4bb54e418e81\",\n  \"f91b6703-f92f-4201-88db-4978fa886c79\",\n  \"19c01f92-8c25-4454-a271-aa5641cf9488\",\n  \"7bbeaf24-566c-4862-af0a-591d5589c07d\",\n  \"4762276a-f682-45f9-ab7a-eeae7557d9c1\",\n  \"3b93832b-56b0-44fa-9fce-5699cbc96177\",\n  \"112fecbc-b6f0-4a29-9185-cf78732e738d\",\n  \"e031d2c0-21ec-40be-bf8f-2ce046898421\",\n  \"89676c6b-1e92-4202-9f8f-93ef1c2d9bdb\",\n  \"b5e2a955-79ff-4ff2-bfe7-64c6119ea752\",\n  \"9547b8d2-12a1-4341-9c3d-bb3e3c129bde\",\n  \"1ceb794f-f0eb-4368-93e8-7550fb7f2352\",\n  \"46cf4681-05ee-4ad5-b71e-8b8459b8d919\",\n  \"8bf436b6-35e8-42de-8cf7-2dc2e5c3089b\",\n  \"97cf3a3f-b5e7-49e1-9b68-007748fcb5fe\",\n  \"33e08159-e431-4ae8-a906-7c6bd1ee5e45\",\n  \"5ec20ca4-16a7-4877-90fc-970f41afa675\",\n  \"ad1cfb7c-dd75-46a6-9f17-7b21262f1eb6\",\n  \"3c37d739-c221-45e7-9040-69b3ba44f3bb\",\n  \"37f8a5b7-262e-419a-b0c7-bb83c359334d\",\n  \"d0fb35f0-feb7-4ff2-a171-cdc7212bd863\",\n  \"b1760b94-34ec-43eb-ae5b-71846e4efb16\",\n  \"c3a46255-fcf7-4bda-8568-f37c34578cb8\",\n  \"0b1894d8-a07a-434e-9c26-d73bc97ac825\",\n  \"ce7328de-63ec-4ed8-9418-88ac1a58248e\",\n  \"f0ea56c2-b4bf-4534-a92a-803c3fec70b5\",\n  \"9b848c89-dbf6-43a4-8d67-54c68d8767b8\",\n  \"ae77e88b-24f2-48d6-8e46-a1333c7a4303\",\n  \"9ceb00d6-b2ac-45e2-9dd7-7d0f68a23dc2\",\n  \"b6d9de54-45ac-4430-84d0-72d5b47ddf9b\",\n  \"59f12a21-d2c1-4584-bf09-d3b44e5e583c\",\n  \"a7ca2a4c-0a7a-4168-85bd-98108491db85\",\n  \"0f7472fe-49e8-4f1e-b5bb-733230a1ff6d\",\n  \"0febcb25-927f-4032-91ce-894cf942fcd9\",\n  \"5f573e63-6808-4693-8247-774ba08cd403\",\n  \"4b1ed080-cd95-476c-a50c-11a524c319fc\",\n  \"e281f990-13ee-4833-8c5d-64faec231f76\",\n  \"b3cdbf05-d219-4cef-a14a-97a56e5ac25a\",\n  \"e725e363-b51b-4d2f-8d15-521d7aa8330c\",\n  \"5196f8ee-1b62-4735-8fc0-a83d66c3473d\",\n  \"7a9b9026-c2e8-45c4-89f5-afda9689dba7\",\n  \"f88aef03-10fa-4203-80d3-5e10b09cf8af\",\n  \"d285967c-8770-4781-a044-19f6c075fb5a\",\n  \"85249ab3-63cc-4f91-bf36-2d8b40c2dfb7\",\n  \"a4223af3-0ff5-4676-b2fa-a18a0ff7c152\",\n  \"9f8cbfdd-ba6c-435f-9cd3-f7b073fe5447\",\n  \"b6e73c06-9a82-4cd2-95da-7f53f0ea31e0\",\n  \"29ba17df-9f7a-41d4-8d7a-b7530444c558\",\n  \"11f8aca9-da34-4e15-a59f-627a6ef5fdd7\",\n  \"0c140f39-d3e3-4676-b1f1-f65f02281e17\",\n  \"a9b3c930-7200-4cfc-9587-0916022864b4\",\n  \"a89fe334-0b7f-49cd-b2d8-c907ae450c4d\",\n  \"b5b7d3f9-44ca-4c05-a768-fe1b779d3114\",\n  \"7a420e51-d9fc-4428-823e-6ca38ffc4117\",\n  \"c1d1681e-99cb-419b-84f6-4740c06cff79\",\n  \"ca7f8dae-4b1e-453f-9307-80c91684e95d\",\n  \"6b172d3f-0e31-4fca-a033-f0d9666a71ad\",\n  \"a945d646-1ba0-4c6a-a852-081160c62cb4\",\n  \"1ac29ccf-bed8-495d-ad2a-8c4c011ec6b6\",\n  \"a9f8f01f-b8d0-4b4a-af7f-20e692fe9b1a\",\n  \"c7b1e098-3e4c-4d79-bac9-e7a4e88b572e\",\n  \"034770ab-5090-4ab5-a922-97856d5bcd83\",\n  \"ddbc0c52-a8cc-4cfc-9301-cd0498ec26d4\",\n  \"a228162b-fe80-4fc7-94e7-b034ec348136\",\n  \"dbd06115-dc4a-45d9-a34a-8d72855c1d3c\",\n  \"13d8f74c-ff12-4457-b356-dbeb079f6ac0\",\n  \"609ff374-c6be-438f-8ca6-8411f2f7e17a\",\n  \"c7c41173-ae31-4b07-94bd-138f1f3671c0\",\n  \"8f63b866-fb3c-4d7f-834b-3a84c220f5e3\",\n  \"432a3704-fb03-4e17-993a-3de4b6032bf1\",\n  \"3ef4afc4-3081-4e7f-be54-67a7d216745c\",\n  \"4753a5c3-4b6f-45aa-906d-bc18261a58c4\",\n  \"05b50ff1-c33d-4207-b51c-45da044e111f\",\n  \"76796921-4665-48ab-ba5c-786a39eb03e9\",\n  \"327260ee-784f-4b63-b218-15fa3fc3cca2\",\n  \"7b9377b1-4bb8-4a06-b51c-273bd1e0af2a\",\n  \"6f4ad13d-1ffd-4086-9431-855458b2366a\",\n  \"57cf4670-a94c-47a7-af68-4e9c61e88f15\",\n  \"7711c02f-8cdd-4a35-9b35-aeb7bdc1447c\",\n  \"799b5fd0-412b-4724-a4dc-15402ab91dfe\",\n  \"762612e0-bb25-4c85-949d-78d5d0f59d70\",\n  \"0f2c35d8-8ccb-4c1c-ac12-2d37cb024d11\",\n  \"02291ac0-d3a6-4724-b209-8d80f6db1839\",\n  \"8a1409f3-7012-470b-8074-867dd6919d1c\",\n  \"3bcedda2-4372-4ecf-820e-c65bcc7bb7e1\",\n  \"0045baa3-dce2-4ea7-bb5f-8329beb47399\",\n  \"a3fe9591-87bf-4974-851b-61b9618167cf\",\n  \"e63b0a86-7712-4b89-b3be-2ccb7afb9766\",\n  \"ffad57df-ec61-4a5d-bcd9-cf9edb544224\",\n  \"dd373001-e8a5-4e8e-8e5e-d6f26ae45e31\",\n  \"97a32378-fa06-40e1-a55f-db16a287e3d6\",\n  \"fefd792e-19a7-4c6c-87a4-276cb78b0a95\",\n  \"b0ccbf8b-129f-4a8b-811a-0c70a2898070\",\n  \"b4bf9a34-9e53-4dc0-a873-b8b9534ca651\",\n  \"ce6a60ba-91a5-46fa-a9f3-eea6c69f71d7\",\n  \"c9655338-8798-4bdc-a4f5-660ee0d09d14\",\n  \"837b778d-8734-4493-89bc-7a6840bcb91c\",\n  \"90341457-60c9-4c6a-96a7-0a400b6e0724\",\n  \"84fab343-4954-4f91-8a8a-ee1a34915941\",\n  \"12fa4c15-53f7-4c5c-a5eb-88c44ef2a536\",\n  \"3aa088ec-1910-442d-8972-09180586c2d6\",\n  \"e89600c7-b44e-4c47-a10d-d3ba594dda58\",\n  \"a8838ac1-dcbc-4191-8d8b-c24e2ef05e3e\",\n  \"737eecee-5216-4711-9a6e-5cf7f9c795d0\",\n  \"f18ce612-040c-4643-a292-73b88c1cceee\",\n  \"cd0d366d-4f81-4539-b36f-18a0121bbcbf\",\n  \"9216815c-7f88-4c2d-b67c-7420a57c00fa\",\n  \"004f9bb3-f559-4f99-9bef-596f28f42c33\",\n  \"e9f15de5-948a-49d0-83ce-8ca415cd1edb\",\n  \"c9515f46-72aa-4d63-b731-43c558afe3eb\",\n  \"de22ed6a-b7d5-4707-87d9-89bd9f406d0c\",\n  \"359ed35a-713c-4e34-84d9-0f799eeef167\",\n  \"f3021265-9dd7-4d8a-919d-60fd7f05b0f5\",\n  \"899bcedd-8338-4754-aa48-fce9498305de\",\n  \"2441c31f-d226-469b-9c8f-864c3cb42c24\",\n  \"3184bdba-eac6-4ad6-9bc0-5746589d4835\",\n  \"5d55a875-8640-4b4b-9bca-5ba3f79764c4\",\n  \"259dc27a-75ec-46c8-85a6-02c2a59e56cb\",\n  \"4cd5108f-fd91-4f50-b4ad-f91c81a6a8d8\",\n  \"d5a396d1-4567-4106-a439-320dab64d76b\",\n  \"2cb9a821-0f84-4131-9291-06134cd3ccd8\",\n  \"a86c1383-d9f1-4d34-8dd4-8d675267b9f6\",\n  \"5ece011b-cd63-4787-9889-4521a35c8a6b\",\n  \"2b52ec56-69c9-4cde-a271-175eb475678d\",\n  \"cbe98192-8579-4c66-9b0c-8d3fbaac9372\",\n  \"9116857d-0b8a-44f1-93c0-a751449c28cb\",\n  \"6fe5ec3b-8262-4069-b0f3-e8407fb9ef63\",\n  \"fdc7db87-1673-44ef-b397-013266905c17\",\n  \"e9394e0c-7609-4d92-a198-36b21f8e97a7\",\n  \"813a6d20-4541-434f-aecc-2a4b0b5c6c13\",\n  \"7b449fa3-34b4-4927-a69c-5e76258aa84b\",\n  \"0ab976a1-b780-42e0-a571-eb5cde086e8b\",\n  \"fe3bbda9-8963-436c-9dbb-e6914680d652\",\n  \"46af1e18-70f2-4914-baaf-7cb91965e4a8\",\n  \"d6bfa921-7403-4e35-a014-14a7fdd55ff6\",\n  \"0f8f6ca2-8a99-43b1-9530-3f94be7be9b1\",\n  \"97fec0cf-5b01-4b56-819c-34e7d321820e\",\n  \"f9e4a349-4f80-4010-b724-f5bc1dfff8de\",\n  \"b77750b7-9a2e-42d0-ab3d-9ae1205c9212\",\n  \"d6d34032-7a97-4a42-9811-fcd1b26fb62b\",\n  \"481f31a4-c9fb-44b0-8bc3-9e7033256e31\",\n  \"674348eb-71e8-4ebb-a77a-ae74f250e5e0\",\n  \"4e570a56-dda2-4ef9-ae5d-5f40e42e210a\",\n  \"92c1ef9f-8ec8-4a82-bc63-e3ad2946b450\",\n  \"3298fb6b-4fc6-446d-94b3-8d1fd4d96080\",\n  \"4ff9c628-1085-4340-b4b2-9ee127e5b4f4\",\n  \"042e8db4-0214-4560-b473-e89f74f9a46f\",\n  \"0427b289-edad-4895-b886-d122253ccc62\",\n  \"9657bf28-8491-42f8-bf3f-74199dadfe0d\",\n  \"8ea48f03-c9bb-4c7c-96e5-a7d40ee6cca4\",\n  \"cb550d26-25af-40f0-bf41-52699a4fe8bc\",\n  \"6fd30214-39d3-4b2f-a35f-e2d7dbeffda9\",\n  \"90ba02c2-1f05-40c8-ba98-64f0e7265501\",\n  \"e151b065-e37b-4aec-9856-745ffe713ca2\",\n  \"15682d82-8a7d-454c-aef3-41b24bf8ac07\",\n  \"d28db00c-1abf-478b-afc7-d54f37e9cafd\",\n  \"07667361-2964-42c1-8ccd-7f2aa39ff33c\",\n  \"3324a2b7-7025-4d8c-b8f0-2a87dc9aa49f\",\n  \"43825525-a51f-4719-a2d1-540bf7802666\",\n  \"379a380c-dc17-4e23-89d7-447a64725424\",\n  \"916ef4d6-4789-4c15-a974-e3f2c76f6b8c\",\n  \"eb16e73d-271b-44a9-8c35-693826dae2d9\",\n  \"88df3405-021a-4720-90f9-8904589b45ff\",\n  \"4b56530b-a00c-44c8-934e-34486c284fc8\",\n  \"408f2e0d-f796-48c2-9643-acba11fa7c70\",\n  \"9e67e2f5-f630-461d-9efc-d59773289608\",\n  \"ab68d126-af08-4bd8-982f-8476766604e8\",\n  \"b6114cd6-e5f2-460e-a741-6fe2f7b4dde4\",\n  \"a8be2d5e-a617-40bb-98cb-ee0b8618be1a\",\n  \"de07520f-7bb5-49e2-b9a7-44e62e4f508f\",\n  \"ae04d88b-5c6c-4612-a18f-e7e07474a970\",\n  \"7d343b4c-70bb-40c5-8913-362ed87eacdb\",\n  \"43f6a7e9-5bea-4062-8129-01c99840f990\",\n  \"fc3910c2-7df6-480f-a0c9-8668ac7830e8\",\n  \"eadee4a9-ee73-4dbe-8f9f-37fdcd50d0d7\",\n  \"42e50606-0522-42b6-b19c-8ff897640c72\",\n  \"cd3fcd38-100d-4c08-8e8f-d813bb28e3eb\",\n  \"adf94b30-d97b-4140-adc1-1d37bca74c74\",\n  \"cbd66bf9-f152-4d81-9ba9-43b797cb460f\",\n  \"5a45e913-0570-4cbf-83d2-7c50adbb725e\",\n  \"fde9c75e-a45d-46c4-96a6-e6acf2824ca5\",\n  \"5a72f659-23ab-4c29-b180-f3461b536ac2\",\n  \"3dd75591-2c52-41e4-9b24-647cf1d97285\",\n  \"fe50faaf-d9ad-4cfa-a7c5-ea2bb7357565\",\n  \"7fc4dd7f-fa1d-4c24-bbc3-6592fd0ae328\",\n  \"7d38fa5f-313a-4e72-9e3b-a1816f3051b2\",\n  \"7f892b9e-e11e-4071-99fd-bdf0bb4b87f8\",\n  \"97ed1d13-6dc8-4ed4-b484-b276859ed8d2\",\n  \"c29b0e0e-5b2d-473c-8e87-38b876faad7c\",\n  \"99104815-632f-41c4-895d-e6f32a9c34be\",\n  \"6faffdc6-875b-45a1-85de-bce69f611f1f\",\n  \"ecf22e14-bfbd-4439-a799-132ee6d724fa\",\n  \"da287dc6-b15b-44a4-a276-1573f986713b\",\n  \"d4c9a0dc-3f80-47fe-a70c-8ed1de76c967\",\n  \"c8dbf647-f8ce-46fb-b1d5-c2fa8ba18a1f\",\n  \"6a3b7c16-dc62-4ff7-b0e2-bcffde6bacbd\",\n  \"28eafbfb-4a3d-4317-9e07-60f285e0e30c\",\n  \"32604f56-23e9-4c50-a41d-30f5548e007b\",\n  \"8ffebe7c-62dd-4964-81e0-b8f45d7ef932\",\n  \"15c366f4-a96e-44a4-833f-a7e028118fbd\",\n  \"13b606a9-e4c1-48b4-b20b-e277d1dafac9\",\n  \"fea8d9cb-6f1c-4838-8de9-4595a814ef83\",\n  \"05138407-6e93-49ea-b66d-ec7f76fa6f62\",\n  \"bef11234-7f4c-4a2e-b86a-dba422501a54\",\n  \"8ef8b36c-a1d8-4dc7-8eba-5eaa8c762149\",\n  \"95628740-7a10-4ca3-b402-994f6cd66b4f\",\n  \"8e3b9a34-bd42-483f-9156-ec99e6d57f78\",\n  \"c5bcac02-5abe-44e7-baf9-455fbe6006c1\",\n  \"6797372e-4bbf-49b3-9a10-d5e4d6615dcc\",\n  \"332803e2-0917-4602-896b-d73cb34cec3e\",\n  \"d0a275f1-8233-4cdd-a500-2ff1db3b63da\",\n  \"bf0fb499-790e-471f-8251-465d7c58fb46\",\n  \"e44b5531-8b79-43e9-873c-8dcdf5be0c99\",\n  \"8af81a23-5693-4951-bb24-fa1765b5b051\",\n  \"9bc0df7c-693e-4a44-b0f2-87f115e078c4\",\n  \"a7154c04-8021-4063-bf79-7c410d5043db\",\n  \"894bcc22-02fc-4696-8a72-c9d8f72b4711\",\n  \"02d139b3-0f76-44fb-b639-eae02cecdab0\",\n  \"ae52dd11-5c7c-4749-9bab-45127bee7686\",\n  \"7074f3b4-00c5-4b90-b847-1631a7c10337\",\n  \"5b23f498-a756-411c-bb58-b113aafa9680\",\n  \"82dc7503-abac-4198-b5f8-b63e9da33f72\",\n  \"458f2b34-c2a7-4513-b28a-0f0bb48ccb4f\",\n  \"8beca55f-8868-4a7d-87e9-e55e6deef059\",\n  \"189d1a69-d643-4a46-b771-ebdcbcda0cea\",\n  \"0a1bc5b5-9d42-44f2-80d0-98497c2c8c75\",\n  \"d591fd3a-1eeb-4b0f-96da-fac9a89c0a08\",\n  \"ac8098a9-9f05-4502-8af9-3be926c42dae\",\n  \"300855cf-257d-47e4-b8c6-560fd8b5f11a\",\n  \"b578826e-0a22-4285-b57f-b96dc939d274\",\n  \"3d42e507-238d-4f97-a5c7-b8f1d751c35d\",\n  \"fa390862-21e2-4dd3-8575-36a093afd2e6\",\n  \"393076c3-9645-4ab1-b2a3-227bdbd061e0\",\n  \"b5616328-6e19-4d05-bfde-78a424951aaf\",\n  \"fb8b7920-cf35-43b4-8bc1-3b89ce1661f5\",\n  \"01626e2a-8090-4d6b-8764-4d75bb680b66\",\n  \"bfef17fa-b6cc-4888-a46a-6cadba9a295c\",\n  \"d9e7424d-872d-4e89-ab36-0d5420610829\",\n  \"b20097c1-79c1-4493-b777-fb5c74e38e2b\",\n  \"7108d64d-5604-4370-9302-36cc191a490e\",\n  \"5a0cc6c2-d0ce-4d90-b951-c8fd71c6ec37\",\n  \"48be93e1-fd7f-445c-8e8f-f53663430bc8\",\n  \"ee683a0b-85d0-4472-abd3-416f344070e1\",\n  \"ef189eb0-9d87-46b7-96c1-43d3af47a59f\",\n  \"2479a204-d828-4c18-b09c-a6191474f3c2\",\n  \"308c8e4b-606b-425d-a88e-29c6b0c92c8b\",\n  \"a0002b0b-bfb8-44dc-8753-ea3cb90501da\",\n  \"11f00727-c52f-4cc7-a493-4549a9a2949c\",\n  \"91d2c960-d9d0-4d81-8000-ac10cf49c460\",\n  \"d9ce0b43-07a4-4417-9659-aba77672b04d\",\n  \"199c3c18-a736-4fda-ac3a-e21eb6d6dfdd\",\n  \"7db6ab4a-bff3-4229-bc10-6c1047b0d29b\",\n  \"2636d842-61dd-4cac-8675-dc724dab81eb\",\n  \"c7d50d63-0f6a-4ddb-ab1a-373444b52c3a\",\n  \"ea8a4a93-9f5a-416c-9b30-448ae2bce09f\",\n  \"d96fae36-9faa-45a3-856c-dbcf76e03072\",\n  \"e923e144-386e-4401-8337-cfac0ac06843\",\n  \"f38f2421-20e0-4c5a-8ab7-668dafae50fe\",\n  \"8a9b3913-f108-4038-a507-cedaeae82cc1\",\n  \"7e21bf00-da27-479c-82b9-164b28d66f81\",\n  \"1dd163f2-f3a1-426f-a862-7bc6de68d524\",\n  \"19a1006e-9990-4063-a90d-c8346e34d198\",\n  \"eaba592c-7617-47dd-abff-50a931058016\",\n  \"df2117be-020c-425d-bfd5-2bc4cfc88e3d\",\n  \"d3f12bef-ef52-4767-a52e-19bc32493902\",\n  \"3f34c0dd-88d7-4ba6-8802-f7c53970f694\",\n  \"d69641d1-6937-4d3f-9e1e-1f666ebf2a02\",\n  \"fde523d4-ffae-40a6-a07e-06bbabdf3256\",\n  \"946c70a2-d865-43dc-8473-261213632e20\",\n  \"01dfbb4d-1afe-4c2e-a440-96802eb43c8f\",\n  \"be4faff4-0295-4ce0-b15d-0a362597afb2\",\n  \"7667f871-5ec8-49e5-b868-1b7984168280\",\n  \"e889c91a-d19d-46e5-a1fc-f5105aae5a04\",\n  \"8acce272-4d3a-4a96-a830-22a384dd0822\",\n  \"38325bee-4ce6-419f-be60-81f92ee0030b\",\n  \"c6b6125d-075e-4052-b6af-80318d6b1c21\",\n  \"0e4a114a-af2e-42e1-927f-12c6a250709b\",\n  \"1b326942-e3e6-452c-b788-6585112e24ba\",\n  \"eb037800-7f11-4395-bbec-d22890c75455\",\n  \"ae1361b0-b310-4ed2-a2ae-3a85e990b8d2\",\n  \"0e505114-f66b-4314-b2d1-877b85868c6d\",\n  \"32279957-e242-4b76-bae1-91cdfb84261c\",\n  \"d46a2712-df22-48bf-a8f2-d1244dbb95b5\",\n  \"5b904932-3136-43cb-8018-f2f4a8574e63\",\n  \"1a5105f1-e77a-456a-90b0-bea3adce9e76\",\n  \"eb2a3b78-32f1-4c03-8204-7cb96d83b885\",\n  \"eb29758a-dbee-4a81-91c9-68572b616df5\",\n  \"fab8a6b5-82ec-4cf1-a7ca-4625e7d5d4e5\",\n  \"2be43a96-fbe2-4766-9fe3-0f5e293e29fe\",\n  \"72fd268e-4a0d-4c24-853f-12641e20631b\",\n  \"f0212196-cf16-480f-bd03-37463e77ac9a\",\n  \"0d098f9f-1ff1-40aa-bc8a-0598522f9fe8\",\n  \"eb31dced-cae5-41f1-b53f-531491db35d5\",\n  \"13e7caaa-8489-4d92-9b84-0ab12a7c327b\",\n  \"60611605-6a00-4ab4-aa31-3fd50833ff31\",\n  \"eb07dae6-c665-42e1-914b-e0a419d2c1f8\",\n  \"b449200e-ae72-437d-af3b-2a6136c71f87\",\n  \"28c19bac-90f4-4259-a1b5-49cedd4bc903\",\n  \"042e8b89-baae-4a09-a22d-2edf60088641\",\n  \"01caefd8-3b0f-4972-9555-86d4ec063b00\",\n  \"54b928f3-7754-4d56-933e-184c9ebcdbc4\",\n  \"89bda8f1-479a-4e69-99e6-2deee57830d6\",\n  \"3f8acdee-aa3e-4f99-83a9-1c4409d2b337\",\n  \"06304bbb-f940-4ca5-9e36-e4259c1d0d2d\",\n  \"418b1211-4238-4a17-9ac2-eed83e02fdf4\",\n  \"4ac8f020-b418-4685-98bd-34e3e75851aa\",\n  \"19ceb0a6-4f72-45c4-a485-f4483b68027b\",\n  \"35880852-37b6-4888-84e5-8c8bd313ae7b\",\n  \"d0ca62a5-75bf-49bb-a85c-9e35776c346e\",\n  \"e7c19b4d-5353-43eb-b406-123187095cbb\",\n  \"34730f30-239d-4e07-9e3b-219120c17d9e\",\n  \"ad90584c-0dbf-4605-9e2b-be5f196b1eea\",\n  \"29aba3ef-fd26-44df-8886-2940dda2d45a\",\n  \"1356e98c-3748-43d0-bc7c-af11a8269afd\",\n  \"b9f2417f-01ae-42d9-808e-20f015fd43e6\",\n  \"5bd5a2d7-6121-473c-ab06-8631a6f981f7\",\n  \"e8d25a12-5d53-468d-a26a-0bde9008479c\",\n  \"acca25f3-659e-4ba7-bdeb-43b2ae23391f\",\n  \"511dbc64-0e7a-4ec5-b6f4-048d9ca404b0\",\n  \"8249d64b-1e3d-4b76-9a56-8221ffbf2980\",\n  \"bc7b753f-a609-4aed-9214-93a7e252e43a\",\n  \"d8e6033e-8293-4378-a584-db0e862f20a5\",\n  \"6fd6cfa3-214b-443f-b542-0afac0dbcdc3\",\n  \"f7b4c129-f283-43da-9d29-df2d74dda93e\",\n  \"d62f9036-79cb-437d-b6ab-91609373b359\",\n  \"32945c03-9d96-4d4a-9998-55242f3912d1\",\n  \"eeee7fc3-9d2f-4dde-b199-a52ad12397ec\",\n  \"350bb95f-3c69-444e-8e90-2d87e04e3874\",\n  \"9cdc56a3-6165-46fb-9ea0-84ad3c07401a\",\n  \"4f5648d3-fa16-49c9-a799-67327bf02722\",\n  \"2b3bf3f4-7ce7-409c-91aa-d515ad686345\",\n  \"e61abf07-209f-4773-9e6a-4d6b4b50f7a8\",\n  \"25d67cf7-c892-4c67-912d-9543cfd41a53\",\n  \"119667cb-0962-44c8-bb94-8b62a4322643\",\n  \"af145d1d-d69c-4efe-af11-3284a89fc95c\",\n  \"343c1c9d-732a-41c1-9149-7c61b55d248f\",\n  \"38dd5902-f22f-42f6-ba45-d6a9749a2585\",\n  \"6f4c66fe-6f3b-445e-ab94-395c885f70ea\",\n  \"87efe257-5d79-4450-b813-c490bc8ac7f8\",\n  \"5a92ec85-c72b-436c-b628-62482a939fb2\",\n  \"399ecff0-7e97-4a6c-aed0-1c83945da5a1\",\n  \"a13de0b1-8d6e-4b24-bd8f-8df7c4bcafaa\",\n  \"7c51cb2f-2502-4179-b75a-f8a3aa4aa190\",\n  \"9eba030a-e1de-494f-a26a-de0fa31dee49\",\n  \"0100a9d1-3572-450a-bb19-28e0d4d4eda7\",\n  \"a1775eaa-5f7d-4a2b-96c4-27340980ebba\",\n  \"458535f3-3de0-4258-8c4a-a7c2f8db461b\",\n  \"6d68bff0-259c-4967-beba-0ac9abe24fa6\",\n  \"c80d7e39-d11c-45d9-b22e-f84366d08400\",\n  \"2451d8c5-8b3e-4a3b-9a00-b6250fe3131f\",\n  \"73d3e2e9-41b5-415f-9496-f1ce019f399f\",\n  \"4cc0f1ea-eab2-4ce6-bc33-32a194683be6\",\n  \"44112006-4a1e-4069-b6af-2ebed469b017\",\n  \"0ce4d2f4-1f64-46b0-a900-11d5d10700ec\",\n  \"4be12643-0062-4ea7-8648-0fd53f849c5b\",\n  \"fabdadc4-3d0b-47bb-a5c4-d573901c4f2d\",\n  \"5a55c5a9-c391-4d40-ac49-ebb59813b784\",\n  \"49a5c527-ea8d-4b6e-af18-1c9340131fa8\",\n  \"ea605163-06d1-4e52-acd3-29e61ccbfad3\",\n  \"42627078-81c9-4a66-8062-25791001e29b\",\n  \"69544966-2381-4ac4-a17f-94ec582dce6b\",\n  \"2f028e2d-d586-474f-8f41-702bfdda5f9b\",\n  \"030c382f-c34b-49f1-b92c-9eb75d822fe3\",\n  \"50ba4a63-cfe6-4867-a3a4-1c6ac305a444\",\n  \"973fb61e-7dcf-4e55-9d4f-37e8ce502c5e\",\n  \"3c8d2241-4f70-46b1-a361-7150bb12948f\",\n  \"a1d3d5f8-cb78-4aae-9b42-4ad3fd9b94f3\",\n  \"a23c0630-ff1b-4eb5-a697-f8f806dc8cda\",\n  \"ac6b7a1e-ac7e-4690-b1c0-e01f7d56eede\",\n  \"f6d2d6ea-e926-4af0-8eed-0ea6df755bf9\",\n  \"a86315f1-5cf5-4c88-972d-057eaf76d6c1\",\n  \"b80525b1-f401-42b3-8234-f700879d77a0\",\n  \"47191c70-6528-4a93-8a43-b5e649562a84\",\n  \"dafa1c06-84f0-407f-9fe0-b61e7c5405c5\",\n  \"411c0ba8-d2e4-4ccb-a619-881c662305f0\",\n  \"c046240a-798b-4a45-9773-7b85fd5756ec\",\n  \"f05ee9de-b691-41ef-8a09-e5b65fdf8c99\",\n  \"88b84f37-03f5-45a2-ae88-00a9d2574b91\",\n  \"29ffc85e-2bb2-4dd1-87fc-6c62f19a2df4\",\n  \"94a4d426-0cf1-4f9f-9c26-04d2630caff0\",\n  \"43a76858-242f-423f-a7ab-0b61a6660f96\",\n  \"38438613-f0f9-4d8b-b48b-5ca7d7f6600d\",\n  \"327c36e9-0b29-4c10-ae27-e953dc172261\",\n  \"89b04933-6bf2-422e-bae4-06b74125199f\",\n  \"9f00e10c-c60b-40b8-a6eb-67b85e166601\",\n  \"fd0fc934-1cf3-4979-af50-e07b9e841910\",\n  \"83f45af3-5337-45a7-82f3-c8921c7553d9\",\n  \"e0f1082e-d7f6-414d-9350-e32c7bd92c66\",\n  \"eb4d5788-c0fd-41a1-8a9e-b57082827088\",\n  \"7e707fbf-b5ae-44a0-8736-91bcd18c590a\",\n  \"db972e61-f302-46a5-9da0-628b0c787ac0\",\n  \"6dbdb730-fcad-4d50-9d83-a1d4d59cb6e1\",\n  \"baad0831-f83e-4a8f-8fef-890a012d3c10\",\n  \"bee8acf5-17d0-4ab9-acb6-2db493230664\",\n  \"0c7dadbe-2c02-45e6-b511-2957cbd2dbf8\",\n  \"420822c6-9abb-4235-bfe2-632814d5cc5b\",\n  \"b0cac7c2-5895-4b4d-9c18-74502b3b8a26\",\n  \"a72f25c5-8f15-4ca2-be91-36ace352de2d\",\n  \"4e53f352-cca8-4b01-8f0c-23281aae06e1\",\n  \"28375192-3caa-44bb-9346-c4394a9cbd6f\",\n  \"b3b17dac-1fc0-457d-95a8-dcebf6a75933\",\n  \"9ba9f3d6-5a2f-43ce-9567-02537bb475d1\",\n  \"5b1a6f05-983f-4e14-9e39-918fd82a07f5\",\n  \"8abd8807-d0ec-4004-9f8f-1b8fb4351da3\",\n  \"696bf128-a80e-48ce-b0c6-4a0d8c2e37c0\",\n  \"342e1f92-4522-42b1-a8c5-2fe026ebc5f9\",\n  \"f0b363ff-677d-4f27-875e-6106e0d2dd97\",\n  \"ab341c15-c73e-4e87-ac25-853344def8be\",\n  \"212028d8-496e-40c0-8a20-438815df59f8\",\n  \"2ff2292a-d147-4a3d-b1d7-86304be8cfcb\",\n  \"1b294a5d-4ec2-4f0d-b0b4-a3df6661235b\",\n  \"2fd81979-2772-48c8-809b-3d357ff67973\",\n  \"39dca33f-d88a-4bf6-af3a-f4c64129f784\",\n  \"5667f58a-f183-4607-86ee-4c79bef6e7c2\",\n  \"a8ca574d-a8b2-467d-b3df-f205313674cc\",\n  \"d4f4dc97-a591-4626-aa3f-8b963d755f9d\",\n  \"8493663a-1719-44a7-8e72-2a2d0de1bd20\",\n  \"794da4f2-65b0-4afd-bff8-f6b534bc09a8\",\n  \"f8f46abf-803d-4129-9907-2ad4918f8bb6\",\n  \"09777f43-ec50-444a-8646-5dab37f7b6ec\",\n  \"3174c31e-be98-4d42-bdc7-a04c676134b8\",\n  \"48496406-b2c7-4b86-b2de-1e42519fe58c\",\n  \"dcc99708-4860-4a78-8998-acccb317ac1d\",\n  \"87221366-168a-4fec-8c29-a2fff5b6eeff\",\n  \"8ef7e0f8-7a81-4b68-885d-afbdae99534a\",\n  \"45961a8e-ab88-436e-b59d-b509a5e0e112\",\n  \"eb732931-fc83-42e4-b317-f7694b0923fe\",\n  \"bb57537d-880b-45da-a92c-115b00e00632\",\n  \"8e7222ef-0825-4bb0-a71e-300006c981d0\",\n  \"c8f1d11b-1935-4178-ac2d-7cf1be209dca\",\n  \"059f4509-a760-4423-9c2e-41622ace8872\",\n  \"7e08a67a-bec7-4904-a984-4fef5b89e298\",\n  \"50b9ecb8-48f5-41a1-87cd-f90ceb111562\",\n  \"8944e898-ee93-402a-bf8a-c6f37ae6eec4\",\n  \"cde6bc0f-c514-44bf-ad1e-3d71f8ea8949\",\n  \"26f82610-1309-402b-815a-1b6264a6ddcc\",\n  \"30a684fb-8ce3-411c-8bd8-d203af4659aa\",\n  \"c3224312-490a-4dd2-8bd5-368e87468260\",\n  \"f252cd5c-eff7-4d9c-bbf8-ad507da2319d\",\n  \"d9cef376-aa52-4254-81ba-859efb4d32a1\",\n  \"eae42730-a904-4c57-8fa1-18a9919f0a0b\",\n  \"db77660d-47eb-4640-a88d-d52cbb78de8b\",\n  \"d62797bc-106b-4315-82ca-0607c3548996\",\n  \"7cf112a8-44dd-4890-b2dc-1156ff55b69e\",\n  \"230162df-da88-4962-8850-04aa49ef71df\",\n  \"db1fb35f-dd94-407f-8b2e-fd8ae6e992bb\",\n  \"f77257b5-a103-45b5-8f77-1b3d234217df\",\n  \"efdbcfe9-6bd3-4792-ae53-722efc43f9f8\",\n  \"71fad573-f129-48b0-b55c-f0524bb2130c\",\n  \"877dea6b-0c0d-40c1-a85e-00fd643f15ad\",\n  \"fd4e856a-6ed3-4a01-8648-d3de2626f75a\",\n  \"aa25c6e5-d1e2-42fb-bfa2-2c648c481c57\",\n  \"fdc30503-5a40-4a41-8acc-f77ffce2d243\",\n  \"6bc53f48-433c-4c6a-8f6d-af45577364a6\",\n  \"ce1a4654-d263-477c-9ed0-db6e4ed5091f\",\n  \"4a18ea24-8c97-4b5f-80b2-d1740cc54b39\",\n  \"78ed1cea-ee14-47be-abcf-f94cabbfa5ef\",\n  \"96503b0e-bf2b-49fc-83ec-0bc0fc5badaa\",\n  \"23a0cb63-b4df-4b9a-942a-7ff8b28d3d8e\",\n  \"11e64424-1bd3-4f97-b223-c61fec6f48b9\",\n  \"bf3b68a1-2511-42a7-90c1-707a5d76d0cb\",\n  \"e6c3fa57-08fe-483f-abf3-ab446e43dbeb\",\n  \"6a3318b2-d978-4e22-8abe-3c01233e65f2\",\n  \"36d7a794-bd8d-4d5e-be0b-bb13f2808ab0\",\n  \"2f7e0d1e-f340-4eaf-948d-9fd49f701cf5\",\n  \"6e3466c0-596a-478e-a438-bfa27612af04\",\n  \"baf5e007-1560-45d0-ace5-bccfbb927051\",\n  \"5ff6552d-f877-491e-a5d5-005c2397d7e3\",\n  \"f1906d73-7268-4b4f-b760-559748e38f62\",\n  \"02738d39-d43d-4d65-86d1-a46cedd4bb14\",\n  \"41bca3e8-e29b-44ab-b52d-628aaded90e5\",\n  \"e8f8a697-489f-4487-90ab-244329c40d5c\",\n  \"4b863f81-361e-41be-aa73-b1ca66697706\",\n  \"22416bb0-becd-46c1-b574-dc19e881c009\",\n  \"908d6411-d3d2-48f3-ba1f-218f865ffed7\",\n  \"d2958710-e9d6-4276-afd8-93b873d9c63b\",\n  \"49ab0849-5b50-416e-8352-a8974bd672f2\",\n  \"27bbf770-d772-4265-8b47-1b2587d15ac8\",\n  \"be89b069-6340-425c-a93f-2fa61f8e6daf\",\n  \"277ee313-4fd5-4e12-ab08-c3e062a50478\",\n  \"4341375e-a0f6-4eb7-b27f-9fa3e928371c\",\n  \"3019e7f7-8e72-4eda-bdad-fe609c22060f\",\n  \"be652b45-5e41-4040-b2ae-879e7c7f3a5a\",\n  \"bae77d75-c075-4d20-ab6b-9ece99d6380a\",\n  \"156dcdf1-0827-489a-8406-264d0a9e7eca\",\n  \"3de23355-88ac-4659-9cc7-38fb3cbd8a61\",\n  \"1a4c0e85-82d7-4e20-b461-16539f7f56a8\",\n  \"e354961a-bb0e-40bc-9c99-1316146061ff\",\n  \"0e959176-7522-417a-92b3-00571c073b13\",\n  \"3c524d78-a9eb-452d-a6ae-c9ea433f6100\",\n  \"3e1e6e3b-ab1d-45ca-8d05-55fcf5c6dfe7\",\n  \"1ad652de-ed8f-42c8-8074-f1a3ceecf067\",\n  \"173ed6f2-959b-48ae-a0a5-ca356f5af3da\",\n  \"bf10756c-6aae-41ee-9879-afed40bf4f72\",\n  \"1b62322b-1969-4ce0-b3c6-d66ee41ceddc\",\n  \"ae2e3524-a76a-4752-ada6-af678dcfd22a\",\n  \"25b368a1-63fe-4c63-8266-b78bf544839e\",\n  \"b09569b7-b4a2-4ecd-b70b-28455eb3ca92\",\n  \"b49a40f5-c0f4-413d-a8d9-d17b7cb6e4fe\",\n  \"af497f5c-9009-447a-89a7-e21596e86531\",\n  \"76a6ab9a-9f23-482c-a3af-0b87cdfde63a\",\n  \"c72dee17-f3eb-48f4-86e2-04c412607edb\",\n  \"b92cf65f-377d-43c0-945d-992451add73a\",\n  \"0c8e5939-5227-42b1-babe-9798f2e5e779\",\n  \"9514eb80-da83-48cb-8f81-a67273328842\",\n  \"a98c59ee-f335-4c92-b7d1-bbe87df21cdc\",\n  \"82870c1e-ce7a-49cc-b6ad-33cc00caaf49\",\n  \"573c4afd-fe3a-4602-825f-50cb858b74bd\",\n  \"5d746705-d5bb-45de-97f9-b5a8c843d612\",\n  \"a3c10090-71b5-4190-b5f0-184df373826f\",\n  \"a9687bec-1237-491a-9915-8cce6be02ccb\",\n  \"245ad330-8423-4758-bec2-2e0bfef2e374\",\n  \"b1cb527a-b5a5-4d9f-af6a-4f9dafbd1f30\",\n  \"c5445010-0887-4914-9c16-cedd6fd8edcc\",\n  \"c8e7753e-292a-4640-b6a3-d87e0a41cf9c\",\n  \"a7e71ed5-1821-4436-b339-54d1d3560470\",\n  \"b1d10980-e3fc-449b-918e-a78a3f00c65b\",\n  \"ba47771b-1113-4eac-8f0e-1fd9724de876\",\n  \"61ab6ee2-960b-4cd7-a12b-737bfd1e7b09\",\n  \"2e483cb6-2dac-4156-817a-c65f009fa354\",\n  \"4d791fa4-c9f7-4016-a8ae-55527cb6e708\",\n  \"7b99872f-d218-48dc-94c3-79bc4fc6261e\",\n  \"bf5f4599-a174-4b31-8610-4b27ad96e2c1\",\n  \"e9ef961e-72d2-44c8-a836-b0c258798354\",\n  \"1f8d15f9-b5b1-4fe0-9789-9ee2b1839cc7\",\n  \"886e794b-ee2a-4b26-b61f-d43d33ea32b8\",\n  \"6e23dac7-bfb1-4c2a-9be7-9a116a4ab13f\",\n  \"4d146bb0-2e25-4283-a533-cb71e7116c8d\",\n  \"9945ac14-3e4e-4ab8-85e5-4ae251a52fef\",\n  \"96c1f3de-e0eb-4d59-93cf-7a8c3651bfd0\",\n  \"e3bb9b5d-4b09-4f4a-8167-3a01d61e9e7b\",\n  \"f58ff9a6-a155-4d89-ab66-e0dccda03aec\",\n  \"7eeda666-90d0-4803-8a9a-276445914395\",\n  \"03931c82-c3af-4601-ab0b-85af9cb36b33\",\n  \"d255031a-3210-42f5-80bf-5fbe2b2cb03e\",\n  \"25977a1b-0827-4f09-b419-9b651fad305a\",\n  \"36b57cb3-f3ac-41b1-b3bc-ee4c15e56d92\",\n  \"74d5b41b-905d-4fee-ba2f-170efdf927ae\",\n  \"87a325a0-e8e7-46e6-8be4-27c6b7287de5\",\n  \"72ecee72-18dd-4b4b-b057-9c5d2d63a8aa\",\n  \"2ae52669-0340-49ed-8fb1-654cd972f8a8\",\n  \"7959a000-4d47-4c0b-a61c-41410b928502\",\n  \"ea496c66-a518-4dbe-bb95-dd2efa8e3496\",\n  \"4d2f7ef9-5b5c-49b2-8d91-9d29f7835afe\",\n  \"c4ee8f47-bb3d-4f9a-ba69-e45c22859cf4\",\n  \"e4497ec7-aae4-41c0-98d1-ddf019b69d91\",\n  \"b19ea147-b6d4-4eba-a264-5e821bad5a3b\",\n  \"14eb772b-4d68-4a16-b860-f9a2cc8fb4de\",\n  \"8ec88eaa-b998-4434-b2ee-64447de1f86b\",\n  \"98140330-4d14-4c21-90b8-4558dfcd9608\",\n  \"7ab88398-4c5f-4702-b82d-f3cc865e1e67\",\n  \"25178990-37f6-47ba-a7a7-1cf36528be02\",\n  \"91b79b80-ddbb-46d0-b937-cf794f957acd\",\n  \"2f0cd5fd-2537-40f1-966b-1bb2fe4aefee\",\n  \"202915ee-382a-439d-b91e-fc92572b3596\",\n  \"0d2ab88d-e1fd-402d-a717-4be9bb96ef47\",\n  \"3ee75661-bfa1-41c3-8fb4-5ec1309624e0\",\n  \"ce562845-dfdd-4d5d-a082-b8a23c20215a\",\n  \"fb2b4c6e-7e3e-4b8b-bbc8-c9965c0c470c\",\n  \"3eb10ed5-4faf-430d-b403-013757ae6e28\",\n  \"451089df-5e81-4d66-8b07-c12de8f861c3\",\n  \"5b5c1c7d-bf31-4c66-9deb-25b610cd388f\",\n  \"32c44aef-69cb-4622-ae38-1340cca69164\",\n  \"2d88263a-d340-4ac1-b819-e63fc05d1735\",\n  \"62a41844-1947-41ba-87a5-89350e845a51\",\n  \"7e381646-46c1-47e0-8f89-b1210d5fa70f\",\n  \"7583d3e9-2a77-470a-9a38-2d7667677168\",\n  \"3a75cbf9-b264-4510-9f56-94a8e534a882\",\n  \"79b68646-1cd0-4654-b505-dba6d9489a2c\",\n  \"88868539-cca3-4e4f-b7cb-41d063d3a14a\",\n  \"2a8bf080-971d-45d9-a32b-adc89c812497\",\n  \"540b410b-7c0f-4417-b08e-a51dca00b1ee\",\n  \"0888487d-aa76-4ac7-971e-176f6295b7b5\",\n  \"edb1070f-35bd-4972-9324-85a69e37a139\",\n  \"69997281-3604-4ea9-b85d-dd1e0f606398\",\n  \"e2b4979f-4d56-40b3-81a9-377f99742775\",\n  \"46dee3b8-443e-40a8-9d31-1fa5907cead5\",\n  \"796ec030-b59c-402d-b914-6057c55c616e\",\n  \"0c0fe91a-7cbc-456c-bbe8-f59d67c5588c\",\n  \"18c98f2d-a42f-497b-ad3b-5f7a963b11c2\",\n  \"16f39c12-2e06-431e-9c07-70414822c31e\",\n  \"6a1c9e07-a03f-4b10-afce-b90c3d95bb9e\",\n  \"4bf9e759-f19a-496e-957e-290426fab158\",\n  \"9a7a081d-3861-497c-80d8-7531cd124794\",\n  \"105d2492-cca0-4522-8b5d-e4154142cac7\",\n  \"a59c003a-a77a-4edf-9d59-32051bec4355\",\n  \"6554432e-0d5a-4dbe-93da-1344653e7acf\",\n  \"04a25dc6-a199-4489-9bf8-1c0ffcbf89f5\",\n  \"433d8549-b17b-410c-a5e5-4f4544c561ef\",\n  \"5a0efbb5-1bc3-416a-905f-cc6f338d236c\",\n  \"0aa1b081-c5ab-48da-ae33-ede6835155f4\",\n  \"a537c9c7-ed8c-4d86-bd7f-f9d1d17fa0a2\",\n  \"3ebcc826-b590-49fd-955f-94ad9c221c0d\",\n  \"7f517982-eae4-419d-a577-6918c04f77fd\",\n  \"d3edef33-c611-42a7-984a-66ff69ac354a\",\n  \"5cbd8976-b5e2-4b3d-815b-122543e8931f\",\n  \"355c9ee0-2b03-4712-bace-e414100a8a36\",\n  \"5f806707-2a34-471f-82a6-6e347e9ca577\",\n  \"c22953ba-c385-4656-8cda-3b40918b30b2\",\n  \"77af5791-0d6a-46fd-9495-f0681d7a6c68\",\n  \"d0ca38ae-e4fd-4ca3-8292-4db7b9fd96e1\",\n  \"52b94fd2-c21d-4282-94fc-23632aa3d9f1\",\n  \"ef19daab-9490-4372-b817-87e906e811af\",\n  \"1ecef329-a32e-49db-8de0-33e89d0c8cf3\",\n  \"f2af2db9-5e56-4299-81fc-db4adcb12443\",\n  \"a5d863c3-f116-421a-ad26-9e40c5147545\",\n  \"0a13286f-eec7-44a4-940f-e37db237887e\",\n  \"9d5c4108-550e-45fc-b08a-57b3ca726d1b\",\n  \"29d25421-50ea-4ca6-9072-2f2cf17d7f85\",\n  \"285a9feb-5fb8-442d-89bc-18e3a1f7f14c\",\n  \"b6f7ce9e-38f3-49ee-b8e5-2835239fa2d4\",\n  \"cca315cf-52cc-4b09-9040-2dc038333f78\",\n  \"cd9f681e-7ffb-4452-9ad7-deb782926cc5\",\n  \"e557a091-c556-42fa-8ce1-94b7053c1c5f\",\n  \"eac9b0a8-9093-42c2-9851-b3d0689bdecf\",\n  \"028e3634-7986-4340-91b3-ec888bc6427b\",\n  \"b7eb94bc-027c-41ac-bc71-e0c058052e98\",\n  \"3338e35c-7448-4c2d-b2f4-8be5a03527bb\",\n  \"cbc06790-26bd-40b8-b7ce-94c6c3a57c11\",\n  \"5d0f8460-94b0-4f8b-b913-d2aa35922099\",\n  \"9910b7b0-1a28-4809-9aac-9ec8810032d7\",\n  \"0fd55c9b-973a-40b5-99e7-375a92b7768e\",\n  \"29a5387a-6426-48d0-a6a4-7475e0712298\",\n  \"3735b518-db7d-48b3-84db-84e2750d880b\",\n  \"57ba4cd4-da38-486c-99b6-9a064036a348\",\n  \"6f4d1234-38f3-454b-bc96-02d20e5e5783\",\n  \"d755b556-e2ec-4c03-a4d6-6baee9cc8e8f\",\n  \"04e5205b-168f-47a7-a9bc-000e10a8cc74\",\n  \"3bd04e1d-7431-4705-bc3c-4c97d53ef4cf\",\n  \"f9b1850a-352e-480c-9981-805d95402196\",\n  \"e7e527bf-dc61-4484-b002-d217539a3434\",\n  \"4b84007c-7c2b-4f3d-abe4-0dbb372a24be\",\n  \"7bd2b733-ec1a-4760-aace-181f50bea7ea\",\n  \"f3efaff9-a531-4281-ae01-0bc9596fced9\",\n  \"0fbad900-d393-4fbf-89b5-b3b6e1e0d429\",\n  \"5eaefb3c-049f-4cd5-b85f-ca87b6fa6545\",\n  \"6321c585-be1a-4259-8ebd-581fe3cab5ab\",\n  \"454cc792-eb88-4d1d-a712-cb7400b77307\",\n  \"6e804bc8-f63a-4892-bc5c-b13540e6a823\",\n  \"ce6abad1-5945-4b18-9f62-6ba858e119df\",\n  \"4cf6043a-1442-449c-89a6-503c0a3b67e2\",\n  \"65476c76-3f79-474c-a171-bb4ae91abef3\",\n  \"8c674739-0997-4bbb-b1b3-6931474c9127\",\n  \"3f882d31-5550-4c97-8240-2eb06f58475e\",\n  \"dc6594f1-6d32-40b1-8560-edf1d43cfba2\",\n  \"0d6fc21f-fb12-4023-894b-d7b0eaddec4b\",\n  \"a301b982-01a5-4eee-ba7c-099467588e78\",\n  \"4280ba59-6b79-4e59-b728-28fc6a351276\",\n  \"988cc988-4ede-4330-933b-ce4da005e42a\",\n  \"5f45d196-8a2d-47d1-8cd8-9c0f11ed987e\",\n  \"9f93a84b-3898-43a2-80c7-6bcecb2bed94\",\n  \"19858815-c823-48f4-b8f4-5d88b90b335c\",\n  \"dfb02893-87d5-46a6-9efb-00c9801ddf80\",\n  \"8f49c342-c93c-4585-bc55-17fcc86b05fe\",\n  \"742bf74c-9ede-46d3-8867-619145dedb16\",\n  \"dc84d729-084a-450e-95b0-de9c3cc43f93\",\n  \"7467c4f6-04a1-468a-8fb9-5aae394eadcc\",\n  \"cea050f2-d775-4342-b847-aaedbdf58d1c\",\n  \"4cd3256b-195d-44aa-8718-a6ce84161741\",\n  \"327641f3-2929-4dbb-889a-3265dc26f750\",\n  \"d2dc5f98-8a23-4ad5-afa9-10a3fade93a5\",\n  \"a08dd0bf-d7c1-49a5-8dd4-30f6033933a4\",\n  \"77a49018-0707-4e4e-9c69-071e1389712f\",\n  \"e759dc63-63d7-43e5-8811-6513d34f6eaa\",\n  \"f16fe973-7823-41bf-a45b-881cab8d3eea\",\n  \"bb48f7c2-3a7b-4481-9b19-5e09ec531d47\",\n  \"15c4e499-e4e8-4e0e-9b28-c633630e5200\",\n  \"eab6ae34-ed4a-4a2a-a541-aed2c8850659\",\n  \"d1da8e4f-0627-43ad-97ab-04fc30824e9f\",\n  \"8dc98a21-38d9-4939-b1a0-4e147c0dac1a\",\n  \"161c0dfe-d0d4-4119-a275-71adc06a5165\",\n  \"e133873f-296b-41c9-b540-669d68b6af78\",\n  \"7add4f24-692e-4c21-bb82-3da289d02b12\",\n  \"cedbdaf8-ccd9-417e-8c59-1b693cdf80d7\",\n  \"5a9c5c89-1ac2-4a29-8eea-05868bc5ad8c\",\n  \"73631dd6-400a-445c-8e27-402f56d1b84f\",\n  \"e1672dc8-51d9-4586-b700-3351e90d34eb\",\n  \"3e1b619e-789d-4255-9108-6728bd2559d2\",\n  \"07e3ab42-7d48-4c47-b11b-664365778142\",\n  \"f0d835e7-71d0-406f-a502-ec7e74219274\",\n  \"c518156a-2228-4ed5-b52f-9e19f989d62c\",\n  \"493058fb-4634-45e0-8ea2-81e8cedd5911\",\n  \"660afc7c-89c5-4183-8109-fcabdc288c7a\",\n  \"ea614467-14dc-400b-9b7d-27720f78a2d7\",\n  \"faf527e2-81ef-4df2-bb09-461f13fbcfbf\",\n  \"3b209788-5831-40c4-bd68-cbf14afce398\",\n  \"981761a9-6794-4808-b785-989cdb6b4f5d\",\n  \"2adf2eb1-1d01-4c31-8b9c-8e19376eb902\",\n  \"2ccf3807-1c64-42f8-884d-04b8a55edbd7\",\n  \"81891b72-4164-4709-a39f-619df0038f1c\",\n  \"01f9fab9-edbc-4cbd-96f7-1b7a2eae0b67\",\n  \"ab04e941-f4d5-4b91-995d-588a598e40d8\",\n  \"3b355e2e-9aa2-4c12-b1a0-548a0d68e9c7\",\n  \"577ea66b-86d7-4089-97bf-abe1b3b57e9b\",\n  \"76ecbbaa-ac3d-4b4e-93cc-fc4d7a1fc5d3\",\n  \"dacfa724-eece-493a-8a79-03cd939db824\",\n  \"1c25b448-f301-4e4e-b29c-e9a2bb44492d\",\n  \"9ad23292-3534-407f-8922-a79cf53e9756\",\n  \"67bf13e6-7d93-44d1-9248-c1804bc9e462\",\n  \"922b84f1-fc87-4034-82bf-cd7ddb99ff95\",\n  \"c1bc7c70-82f0-4002-af54-ff4e19a1c082\",\n  \"491e74c1-54ba-4cf1-be6d-71a68a48e23c\",\n  \"f47de6af-d29c-480c-9787-f83780865854\",\n  \"3e0da781-e2b4-430f-932d-57dbd0989a97\",\n  \"3a8ef119-fc3d-4e1d-8dd2-bf12b93a93f9\",\n  \"bde3242a-56e5-4548-8e85-3d87c22bed93\",\n  \"b2d1562b-bdc5-458e-8411-8a606e3ef748\",\n  \"365bb953-40fb-4a16-8c4e-3f20a08bd35b\",\n  \"f73ad6b6-4c7f-4dd0-bbe4-02bb8f659eed\",\n  \"e5657bee-6a5a-4c97-a8cb-89a91294a70b\",\n  \"28539940-78cf-4141-ba46-49967efa7026\",\n  \"de3db0be-2525-4afe-a731-bdba550dce9f\",\n  \"67f45ec3-8862-4424-ba44-6274b6a0f47b\",\n  \"de71583b-1133-4a61-a040-e63b4ff31f8d\",\n  \"56ddad42-8cd1-4afe-9f6b-04546b4dd93a\",\n  \"fe7d3603-99b4-4c11-ada8-e9bdf9e44f5c\",\n  \"e14730e9-6698-4947-9d85-72863923af65\",\n  \"fff59fe4-3c33-497a-9e86-f0784fd1b54c\",\n  \"e1edc6aa-5996-477a-a15e-85e510c2be5e\",\n  \"0280f251-7c78-4cdc-b8cb-c5fc894e1c78\",\n  \"9ddc353d-4962-480c-817d-bd564e0607df\",\n  \"251c5d9f-a0e7-4060-915a-f129c95865b5\",\n  \"1849c8ae-7129-4ee5-80a2-2ebcb0921ed9\",\n  \"a5b9e778-0e28-46bf-9c14-022bbcef39ae\",\n  \"afffdf9e-105b-4511-ab6d-a269f1adea21\",\n  \"8ccae8ce-d67b-4cea-bd26-7aa5918a2acb\",\n  \"fd9aec1c-aab2-4198-9b5e-801b1d4c3f95\",\n  \"a2881f5f-db7e-465c-838d-a48f1c8068e5\",\n  \"98b759fb-2c78-475c-9c7b-ce7aa7c530c5\",\n  \"1f8a6940-4a25-43e0-8e63-91413294a247\",\n  \"48bb8bf2-330c-45f9-b330-ca9b58e48178\",\n  \"f9754789-b59e-4b67-9267-6661320212f4\",\n  \"76c454f1-8020-4bb8-9910-51267d9b682e\",\n  \"db082cac-3b8c-4e12-8d30-5de2b4666283\",\n  \"69122294-72a6-4b27-9130-575b743f4506\",\n  \"3e03bc70-c56a-45bb-aec2-c26059fa3451\",\n  \"3cc2abaf-1d9d-4632-b1e9-a42e5e1c9d1d\",\n  \"0e950ded-a0d4-4e8d-92c8-d44bbb15e74d\",\n  \"ce3482e1-9358-435c-a3e1-e30cf63e058e\",\n  \"087d2389-ff1f-4f4b-8da2-520a6ff560cf\",\n  \"95768b2c-0e3a-4560-8a09-c7a0bd905e0f\",\n  \"cc9e1c5e-5aef-4851-90a4-7c3712fe36a1\",\n  \"98f0f13d-c9de-4af5-896f-ecc7858a393e\",\n  \"b3f8fab8-c81b-4cc7-8d1e-c75bfa463d79\",\n  \"d5276097-beac-471e-ac82-d670b3de4421\",\n  \"ea06e758-562b-4d3c-af8e-d061986859e9\",\n  \"13a108fc-e72a-4aed-a614-ec97a58a5a15\",\n  \"28bcdfdd-fe4d-4b3b-8756-c51678bbf531\",\n  \"04a0bcd1-ed86-4b14-8545-1a6433469981\",\n  \"fbf35fb3-182c-435e-a366-04ebb2d15a9d\",\n  \"d01ea94b-3f20-41b0-9788-f0ff63c9247f\",\n  \"503f8ec5-a0b1-4ddf-b781-86e9f9259f9e\",\n  \"49b88733-47ce-4946-861f-bfa73422e714\",\n  \"4390c5d5-30ec-4947-8bad-025c59875a84\",\n  \"fe0c7630-8721-49e3-9047-bda194eaa15c\",\n  \"102c537e-1914-4430-81be-57e7d26b475c\",\n  \"cbd2df71-ed88-43dc-8b87-91a99d211976\",\n  \"228dfee3-4381-4656-9b78-eb39a425e495\",\n  \"e2e2d44c-e857-4621-9482-6d64ad1d7dd8\",\n  \"4752a45f-8b9c-45f5-bdc0-0c1a3f03b238\",\n  \"1f196127-1926-42a0-8ff6-ca5c5f900fd7\",\n  \"07f28821-8e2c-4c5e-ad01-789b060c00c5\",\n  \"05a2e92f-c0c2-4655-ac2b-d695da108a9d\",\n  \"75777977-9440-4aba-ac9b-e453030aba82\",\n  \"e2d80855-13a4-4096-91bc-5e19b65e78a8\",\n  \"39789201-7dc6-48aa-9ef0-820f475b658a\",\n  \"0ed49ee6-425f-4a0a-94c9-632296d5be16\",\n  \"d38b4c35-f12a-4f39-a750-846416d4319b\",\n  \"ea1acb60-7d96-4da1-8486-739df7709544\",\n  \"c821e569-dc9f-4fed-ab31-e1a0a19b8f3b\",\n  \"0b1fc3b3-775f-4dfd-a161-2f9d652cec2d\",\n  \"fa92f034-c32c-4b41-aabe-eb9636765ef4\",\n  \"14ad22b2-5682-4e25-b88f-74cbc1a5dd85\",\n  \"a5b977fd-0f43-4e44-b74c-bef7804643dd\",\n  \"6d0eb96d-9527-4cfe-ae7d-27fd7c782f3b\",\n  \"526dd401-fb14-4f2c-92ea-64ab1684c6f3\",\n  \"d0af538e-8db2-4c3b-ba6a-f2c8807594f2\",\n  \"fbbf1395-3c2b-42f0-a136-c7606a4d70fb\",\n  \"a97e4f7a-9ad2-47a5-acba-61a7b4051ea5\",\n  \"15a03c63-1b50-4ccf-b5a5-c296be09d0d3\",\n  \"da033fe4-c3ff-4a0e-86cd-d9ce3144aab9\",\n  \"ab9c7847-9cd1-4ad3-8e2c-7bfc422fe9ea\",\n  \"a0903a5a-a7f5-460f-8a48-034d48c4ce17\",\n  \"f674c2f7-a1cf-4d91-95bf-a420b66f3bf8\",\n  \"15288c88-747e-494e-b023-fd28d9a84e3a\",\n  \"17eb6064-3871-4bb1-adda-40c9d8e23f24\",\n  \"c7f3743d-4fbf-4e67-977f-3b5bca5bc226\",\n  \"b19ef122-beb0-4058-989e-1661997ab43d\",\n  \"2c9e88f2-3a65-4009-97d0-bbc873d626de\",\n  \"8c222e7a-652b-4171-89f8-fde0eb0ae084\",\n  \"b0113a9e-73b1-4980-a4b4-ba389e32f06f\",\n  \"06ffec8d-9148-4341-8c8a-c9f16a87e9b6\",\n  \"6d00fd90-ba7d-40e8-ab4a-63677497c5c6\",\n  \"392f8b8d-7a97-4b51-b03b-e2572cd2369d\",\n  \"6df736b4-d628-4d78-85da-85409339a4a7\",\n  \"e23d86ea-92fa-4179-8ae0-e68703763bd1\",\n  \"e34745c9-41e9-410d-99a3-5ae59e99cfa4\",\n  \"9c91de59-2ade-4909-ad16-d0e94752f8f0\",\n  \"532a5450-d3f7-434d-a9c0-89294e5f49fe\",\n  \"f6b9634b-398a-48d4-a57d-7768eb1777ef\",\n  \"38d2d31b-269d-46f5-a8b5-6593dd8c2f6b\",\n  \"17c96b47-a86d-4755-ba0e-de068f246705\",\n  \"1f2f89b0-e471-4529-b56d-d8e4e82a5696\",\n  \"751e3a63-0dfc-4ee1-8564-10bea737098e\",\n  \"b599a33b-c356-4c30-ab55-f27dba10fc09\",\n  \"3dd31afa-747e-4690-982a-d1cfdf1b5821\",\n  \"d3bebc2a-c62a-47ce-965a-c13862995360\",\n  \"2803a8f3-6cad-4779-ba9c-b25307f8eed8\",\n  \"cfb9f48b-2f55-43f3-bb9d-fa87824d87be\",\n  \"01849294-26eb-413d-8a2d-584eddbcb0f9\",\n  \"4e599442-75e7-43ea-8c8b-cba1d5afa54c\",\n  \"e9ffc8f3-a54b-4c3c-9c55-f9441d350b86\",\n  \"e110f2e8-f5d7-4646-b736-8696fc03aaef\",\n  \"79497e29-e4c1-4ac4-b6fb-d22ac5fcd1d7\",\n  \"9640ac12-18e7-4682-b920-d3937c49e5e7\",\n  \"ee51ce9f-5e37-4ab4-b295-4c0615aa6c4f\",\n  \"231ef623-0abd-4118-a9b6-c80279572b73\",\n  \"2158cc99-8534-4040-992f-d96f916e2164\",\n  \"62b6e916-3e7e-48f5-8ac6-bcbe72969120\",\n  \"e20b7315-3123-4f47-a953-90fa798f2e91\",\n  \"45f1ce39-310b-4995-b5bf-2392a157efeb\",\n  \"de0a9451-3947-448e-ab28-7415eb40e553\",\n  \"181fd8b2-0161-4a52-86cc-be1e778c2064\",\n  \"7c08f489-e385-4fda-ba6a-b91dd66eacf8\",\n  \"bc048369-dc35-49a9-aba8-73d45c4182cf\",\n  \"cee099a3-69df-412d-af92-fcbc52874164\",\n  \"bfbedd07-5765-4c85-ad44-6ce16fe34dc1\",\n  \"bb23bca4-851f-4cca-b134-b2dcb387b492\",\n  \"3af53969-b81d-46c7-b9d4-56f4b16f16f3\",\n  \"00806a16-e687-469a-9a58-285bafcadf17\",\n  \"f1135938-242a-4d47-b570-4f101a070d11\",\n  \"705a05ab-c336-463a-97d0-ddc9f9bb51a0\",\n  \"93cab968-c832-4254-abeb-d472e76fd320\",\n  \"2a990883-396b-4608-a347-0caec9ba7f0c\",\n  \"ff980043-9118-463b-bd01-8442370a825a\",\n  \"c4623c95-0e7f-4c2b-bb97-e7063c758624\",\n  \"ebcb0caa-a5ca-400a-8d44-da16b922530b\",\n  \"964fc1cc-93c9-413c-879f-b36c143452db\",\n  \"da3d4b9d-2f82-4796-9f96-63ac6ba8c19f\",\n  \"b57ffeb3-6ace-425b-a09a-1506f53ccd50\",\n  \"c9a0d799-4a73-449d-a27d-c102578edf10\",\n  \"e0786a9a-56fc-4f40-b518-e2ed3299a186\",\n  \"97880e54-4178-4fd1-998d-d6c2fae7ddc3\",\n  \"36140548-2b0d-4a7f-8846-4e732907ee95\",\n  \"47d5ce05-e29a-4940-9aa1-02b002c330eb\",\n  \"981515c9-bc6a-4be5-a3d2-7c6474058d1e\",\n  \"e74de26f-faeb-455c-9549-aa10b17f4395\",\n  \"1ef4963b-9d61-4bc3-b55a-b5a0779ddaa9\",\n  \"12d671a7-5602-43a7-ba86-c497cf5207ed\",\n  \"15376ebb-e65d-4641-9620-53434eea245b\",\n  \"ba32f9bf-4c70-4884-bbfd-dbb4bbdfb72d\",\n  \"9d6e33ac-b08e-4463-a124-0024a73fd9c9\",\n  \"9d8e56a1-0b6a-483b-9108-ad6442c80456\",\n  \"249f21ec-b913-40f2-9259-e7aeb104be96\",\n  \"0876f809-fe9f-4086-8bed-c4e47ae67afd\",\n  \"6520e856-d1e6-4227-86af-8374dc92a549\",\n  \"94e85bf3-23d0-4291-ad5b-05863135b66c\",\n  \"649f7d91-8742-4903-99ca-0db5a319a129\",\n  \"1ffb0eba-cb6a-4d7b-bd0a-0ddc19441f54\",\n  \"83ca9d23-6c0d-49ac-acec-fa841945fdbe\",\n  \"2ceb5b97-aa14-45de-96e1-4fa93421be3d\",\n  \"e5b2fa40-794f-4895-8d4f-110d0388ef95\",\n  \"4fa57766-df07-4771-9bd3-7f79a3e07732\",\n  \"cdf1d56b-89a8-4d71-938c-22505d8d1fce\",\n  \"5eb06809-574c-4ddd-a29a-720a261078c1\",\n  \"26933a76-42bf-46ca-997b-2cf84bcaaa78\",\n  \"208c063a-b8e0-4673-88d3-82c3296dca06\",\n  \"17132b55-cb8a-492b-8730-6acdb51188d2\",\n  \"2ca8c60c-26be-4519-ba30-330f520cf907\",\n  \"675dfd1c-4d5c-4e00-8fde-e8d524b57f8a\",\n  \"0c8d80cf-8015-48c0-903a-3b78665e4361\",\n  \"a3bf3a43-3117-4f32-a675-e0b33204b1e6\",\n  \"18a7fcfb-db71-4022-b271-0ff7df2fbe50\",\n  \"1db61806-224f-4bd2-9652-5b04f7a43244\",\n  \"adaff3e7-b389-437a-b7e3-37a0c4c2f0a6\",\n  \"af12f39c-3efd-4a13-8e1d-3d49b0ff292b\",\n  \"5ec65289-b05e-4ead-8bc3-22f06cc0902b\",\n  \"bb6aaa3a-04f9-4130-b302-129368a02338\",\n  \"de3e73ae-80f7-45f9-b1a7-4769a34da32f\",\n  \"657b1038-5c3e-4b5b-9fac-b06eba5b5d92\",\n  \"b677b723-d528-4e87-b565-7888d4555fe2\",\n  \"1ba458cd-a574-4bc9-bf97-587cd9a736ac\",\n  \"bbb2f8f4-93e2-44db-948e-c652b663578a\",\n  \"bd17420f-5d18-4cc8-9816-60800287242a\",\n  \"a164456c-e47b-4005-a034-3060bb3b4fe9\",\n  \"e0833017-4e80-4d0d-b4ef-8de017abe644\",\n  \"28efb95b-d9d8-478f-9044-28b480dba258\",\n  \"ae9d7f59-5944-40e0-8934-353c062c71f8\",\n  \"cc66b001-b4da-4d1a-96d9-45438241ff75\",\n  \"abe1f169-8a97-4b1e-a9ba-dd8a2c78f311\",\n  \"491a30ed-4a65-4c25-b24c-136fa59acd38\",\n  \"526d43c5-1502-4b72-af46-5f1aeec5f69f\",\n  \"2a2634d9-0051-4735-a296-cbbf786d30c1\",\n  \"b56bc608-2509-4896-8d81-784a1775a2cb\",\n  \"50771872-68d3-4b49-a1e7-bc36f582be2e\",\n  \"77483c55-c2e8-47d7-b1f9-7a60777251cc\",\n  \"cfed9c05-6f39-4db7-bb39-bcb2e4e88ff7\",\n  \"e2323fd4-c484-49a3-ad85-2335d4464cb3\",\n  \"70aa270b-7550-426f-a7d4-76243488b665\",\n  \"0be82964-a2b2-4a21-8338-10aff134aaab\",\n  \"2d44bf7d-f3ac-40e8-81b3-ab5734b7b281\",\n  \"2bb1b051-dd8d-4fb4-82d5-cdeafe85cb15\",\n  \"75f9dff8-8dc9-4ea3-b27d-8315893b7894\",\n  \"46f8b46d-fe16-44f4-8d58-74751b4e3d5c\",\n  \"de83534e-bf7d-4bfd-bb7d-19b7f9a9b128\",\n  \"24023e03-a40e-4fe5-85e8-cfd8e2466373\",\n  \"4d80ac8a-e073-489b-a04f-3759aa9c8bb8\",\n  \"5503651f-5860-4249-9a06-374cb3bf25df\",\n  \"53e9e3c8-043e-4d1c-bad4-fd3e37e2f7f1\",\n  \"d8488677-bfe0-4883-92e5-cd74c4798e86\",\n  \"519f1c3b-1570-49c8-867e-a2bfc890cecc\",\n  \"0761cc9c-54a0-4029-97d5-b64dd8c32ee2\",\n  \"b278da57-8fc1-4682-a7c5-7d00a16043f7\",\n  \"3569068d-6380-45c1-8397-977b90f7d5bd\",\n  \"fce38872-6228-4098-9c8d-dc624aa94fdb\",\n  \"a4fd53c5-a6d5-4a67-a609-98804a2d2629\",\n  \"5eda49a5-aa2b-43d1-a324-61fac09fcb65\",\n  \"aa7981c4-a8a2-4c1b-a78b-449dc6e1a2a5\",\n  \"162ea22a-9386-4b3f-88f3-1826c0b46edc\",\n  \"1dd21059-6039-4946-934a-d8fb3185a23d\",\n  \"8f38d889-6b82-44a0-becc-d497f75b19d7\",\n  \"6c5ec12d-7bd7-4ff7-963e-1397b02dc373\",\n  \"ea573d57-88a2-4643-b32c-6a23c55c7349\",\n  \"4f0de3c1-e32b-4a85-bd1c-e0f50857596a\",\n  \"b8f00182-966f-4a7d-bfe6-c269fdba62a6\",\n  \"d46e2740-785a-43a5-a48c-e5c473bb4c65\",\n  \"426c74da-b6dd-4766-92dd-7fc9fb68d3d6\",\n  \"f4a4ea6a-2190-4484-9a13-d6fea3c94098\",\n  \"f9918e1f-6c8e-4418-abed-baa8313d453e\",\n  \"9e7d2ea6-0d38-41d4-a1ef-b09dc117ca8b\",\n  \"5860ebd7-524e-488c-9e96-d61ae3d47bc6\",\n  \"2d3c5877-d102-4eda-8f26-f0b8dbd5778a\",\n  \"927a0fce-2c55-4df9-9164-c31e638346e4\",\n  \"04d7ad84-04c8-4095-ae82-dc26fe57d54a\",\n  \"66c728bc-c93e-440e-98eb-e69ce14b8a0f\",\n  \"c24c384f-3849-477a-976e-8b417ea31dee\",\n  \"41a78988-a531-405a-9a1e-49a064031bb8\",\n  \"efb91897-52fb-4691-8fba-fe2f55546224\",\n  \"fb1d455e-52f8-49fe-be31-7c5a46795e53\",\n  \"1bb24fd7-a50d-48c7-b809-098fc426d6e7\",\n  \"5b3a977c-c669-45e3-a45a-67709a7e37c0\",\n  \"82b706f8-a814-43d8-a68c-e1e8145e6cf8\",\n  \"c9203467-b3b6-4c08-9f78-4ca93b2b6f8b\",\n  \"841f1444-f4f8-4869-a766-33e53f857f87\",\n  \"e5782d57-77c9-41ca-beff-e0833efa7cee\",\n  \"72f98bf7-3396-4ffc-8254-60228a6b3a37\",\n  \"7278d63f-3feb-444f-9935-8e7d747552b4\",\n  \"efbccfa6-e72d-46f2-954d-075a03cce145\",\n  \"7646159d-af39-4216-b41e-237d13912ced\",\n  \"26d50677-189b-45f0-b43e-e10faceddb29\",\n  \"611dd961-545c-40b2-9fd8-e27f5d640bcd\",\n  \"467bfd30-aa3d-4881-b70f-6f294ccba46d\",\n  \"1b8ff612-b31b-4264-bb62-0dd7bd91bd3f\",\n  \"3a3a4051-4bec-4d86-90d2-aee1d8d21ebe\",\n  \"cf206665-2cb1-4f97-a356-d32f6d329e24\",\n  \"9a22bfe5-91cf-4f28-920e-8395545c52f8\",\n  \"126e63d3-658d-42d2-9fb8-7d23e7a085ec\",\n  \"a6cbe61e-890d-4b6d-af9c-dfc3b20f70b6\",\n  \"14f9dc8b-797f-4b0f-a591-08826dec11c9\",\n  \"d5ca3b43-e821-4b8e-8029-a274490d2f70\",\n  \"7242009e-3cb8-4261-adcd-0544ffaed65c\",\n  \"e13ba9bc-bc8c-49db-9ee7-c5fba6d6bd93\",\n  \"f66a934d-54cb-4bd1-a064-6eb3591490cd\",\n  \"f64f4e0e-e3e8-4e4b-8cf3-2ebaa5ae6978\",\n  \"1949cad8-1788-4499-bce6-c2cbc845558b\",\n  \"876ab8b4-7b45-4fa7-a505-eeefedf14286\",\n  \"aa45fbda-7cc0-4d94-95fc-8384a48f220b\",\n  \"e2ca33a4-1626-42fc-b408-e61fd808d0b1\",\n  \"91a4b1d4-d72d-42aa-8481-347e8261470e\",\n  \"63846414-d743-423e-99ec-d11b97592f84\",\n  \"3a4136f0-5470-4bb3-890d-3cacb65f02a3\",\n  \"cd24eab6-4577-4d16-843a-255633d00460\",\n  \"61ed75f7-feb1-4215-a878-cc33689445e5\",\n  \"9f78423c-434a-429a-b5bd-30dd9c09f64f\",\n  \"0be3c1b4-a223-4d07-ad49-139840209184\",\n  \"aacf0652-de66-4b5c-aac1-6205c1c162a7\",\n  \"0638c100-eb5a-4b8f-85ad-85d73144738f\",\n  \"c509ef1a-d50f-468c-993b-49a9d0dde542\",\n  \"69913643-e1bf-47af-a19a-0bd6fa61b80b\",\n  \"fd224035-7c80-4f81-a10b-032eecbb81d6\",\n  \"f03743e8-9f1c-4bbe-a1f1-8012d519ce9c\",\n  \"c8431451-7965-43d1-9400-341e52393407\",\n  \"4eafd4fa-b8f1-4554-af68-9007c48797bf\",\n  \"d5466cd0-0569-458e-a141-6c67c67cfeb2\",\n  \"690bd301-abd5-4f28-98e0-72c2b14eae58\",\n  \"6c7ee7d3-ba3b-4811-a6f5-09d8a617aee9\",\n  \"b4f09d9e-2308-4c67-83e9-108465cb894b\",\n  \"9452f3ee-7425-45f5-80c9-c307466585ff\",\n  \"c15b4902-2f51-4c19-9d9c-9c8ac6b6eb52\",\n  \"bcc082ec-5791-40fe-8676-34ea1c08385c\",\n  \"7c6469c8-6f4d-4333-b43c-52c061a4b743\",\n  \"1b3418b0-b146-4eae-976c-aa6e8a3526f7\",\n  \"a6c3f26f-5e15-4551-beda-5a7229995212\",\n  \"5a41d9d8-4e00-4933-97ff-49e284aa6391\",\n  \"26293f5b-1112-41ea-b95d-1a8812da5a15\",\n  \"a875dcfc-ab09-4803-abea-a1d7332ba843\",\n  \"1a87d45e-b6f3-4cc5-9c77-e71e393e5b1f\",\n  \"98c5afbe-0b87-4b65-a962-45eb6829d07c\",\n  \"f9f3d4e5-8536-424a-a1ea-16773e97c87c\",\n  \"ad5fe948-a561-4cca-9007-b1071a2a10b8\",\n  \"28aaf19c-a5f7-4993-b531-684cd7fce87f\",\n  \"a29e94b4-6dd7-4598-a544-ab3a982241fb\",\n  \"f2774e5d-3832-442d-9c96-6c7d362ca0fb\",\n  \"5b9ad5c1-47ce-4ec7-a595-ae2da9d6ed08\",\n  \"d1bbe0ee-6d1d-420f-a591-6c085c3d2d45\",\n  \"2684b8f4-1c33-4664-b0d5-f9dac226c342\",\n  \"294c6548-ea2c-4367-aac7-18120b5cbfa2\",\n  \"96bcc396-79a6-45f9-8cb1-af6b0bfa9df1\",\n  \"1aff0751-b6c8-48d3-a49e-f79cfda50ded\",\n  \"7cbb30c3-cc71-4f21-b88f-7004be98dcc0\",\n  \"eb009fc3-57e6-4618-b84c-e581f8ec636c\",\n  \"b6c895aa-a164-4e7d-ac01-08a328ed9758\",\n  \"e4f7fd3c-61e2-43b0-b8f5-94dc96b3a700\",\n  \"9a2f11d9-3553-4c47-8f93-133acda6aca9\",\n  \"2d9bef19-42b9-44d8-a619-d4a02b8988dc\",\n  \"5dae7217-c431-4857-9504-17c13849a580\",\n  \"1d289f35-1796-4812-854d-67a8515b7105\",\n  \"e5bf4036-53ea-4c33-acdc-3ba18a07a173\",\n  \"851440c4-38c1-4d1c-86aa-4908ce58031a\",\n  \"5a6c838f-a74d-4da8-a072-b6a5d7e80324\",\n  \"6bbaf331-6bd7-49fe-aca4-01cce572a73b\",\n  \"c9a6d7a1-7d6a-4952-bd8f-fe246f77337a\",\n  \"e08acc00-f7b4-4595-8c9d-d15a01f53c51\",\n  \"9fd6aa7e-fc76-4804-aeba-6327a15e7df1\",\n  \"2bddbb77-29ab-488d-9c7a-83eb4e0c61cc\",\n  \"5c818c93-6237-4b49-a546-148c59c61066\",\n  \"d8c52679-d98b-4661-99e5-2ebd65d33f06\",\n  \"676dd713-8faf-42b5-9ff0-ffc949317b2f\",\n  \"5c005c76-0bcc-407c-8056-a906dd0413a8\",\n  \"1c6ed900-2b59-497e-93e5-a75b402e89ae\",\n  \"e0072b49-f715-440b-8e6c-3048b0e8abc7\",\n  \"fc9d45c8-a9fb-4cba-892e-db719bb1a19d\",\n  \"25d11e4c-496d-4ab9-bf92-cfde9520bac3\",\n  \"b3caf92a-8595-4832-9527-9391fc44970f\",\n  \"11093bd2-a961-4820-b49b-56caf5352a93\",\n  \"7eba8ad1-54d0-4755-a538-09ed62f1cbf2\",\n  \"c010b240-6c31-4ba5-a1fb-f72ac43df3b3\",\n  \"ddd2e731-8a2f-40a2-a38e-cae91e5f2ca7\",\n  \"87441204-3166-4f51-886a-4eeb7cc55665\",\n  \"5309fb42-fcf2-4c75-aa0b-3b8f47756d17\",\n  \"e8d6a67b-0f75-4f79-8d0a-ac657ffc302c\",\n  \"4db29436-b173-4ef5-bd46-e07d3857287f\",\n  \"985deeca-8493-4299-b5b9-f10c1f0b495b\",\n  \"7e328595-e41f-4a44-92e5-4d11c667d4ff\",\n  \"e3856838-3663-4308-bb26-919aef7d78e5\",\n  \"1f5c7333-310a-49e0-bfe6-375e7cec4a9a\",\n  \"c77c755b-eb71-4af9-b4a8-f3d302eb6c6f\",\n  \"0862723d-99d7-4b49-94df-5198a04670ce\",\n  \"321bf9f6-bd46-466a-82e5-2e79b31c05e6\",\n  \"5a94d101-4e2f-45cc-8f37-d2a2139f215d\",\n  \"3a87495a-4fa6-4a18-9567-8fac45eb2141\",\n  \"02a3f1fb-58c1-4bd5-9c07-df0450a6a892\",\n  \"b293f9ff-27d9-4e53-a247-af4ead9d42b9\",\n  \"231a6a88-0f45-4651-9737-375bb549a8cf\",\n  \"91567764-0b10-44e8-8ea0-48b6c84326ed\",\n  \"4808e5c5-c201-4b2a-914c-6e0e67d7736e\",\n  \"4936d8e3-f1c6-4d11-9231-9e53a412bada\",\n  \"2b3241e6-05c0-4c5a-a794-e2f194e273f9\",\n  \"61deee0b-e7c1-4a0f-8007-6efa04740fc9\",\n  \"87a34e87-5492-490a-ade2-71bf8d9106ea\",\n  \"05267a8a-46ab-40e9-8693-8db0a19e9891\",\n  \"79cea8fb-3db0-4453-9bdc-bcadfca12aeb\",\n  \"7e1499d0-2ef5-4beb-bd79-ff300b16a482\",\n  \"e7296901-bbec-42b3-8f61-05e6cfcec0c7\",\n  \"0d2af389-0713-49aa-877e-7578b2e05993\",\n  \"966ba526-3362-44a2-9dbe-bac1ff23bb90\",\n  \"7a12d0b1-cc82-4103-b0e1-54f6d4686b30\",\n  \"b70a72ff-2b32-43d9-a59b-25d59b0bee4f\",\n  \"38c768d8-2c7c-41a7-b83d-f7bb52486ab6\",\n  \"c687a0b0-9ca4-4a65-afe4-2932d4adff60\",\n  \"f1599a3b-08c0-47b8-a5f9-6d68938273fd\",\n  \"f55bb228-1c2a-4bbc-a986-7f171f8e4af2\",\n  \"8654bdec-ad04-447c-a10c-c3009e4bae83\",\n  \"308792b1-f437-4764-928f-cb5c43ed7524\",\n  \"cba4abc3-9e9f-48f6-beda-7fe3449a9935\",\n  \"65a998a7-8808-416f-b65e-580328083f95\",\n  \"3065b040-55f9-42bc-9a5a-5155fa4a8f71\",\n  \"51cc932f-028c-48da-a7fd-e4d8914aa92b\",\n  \"c6366011-1c35-4435-84ef-ae08af99012d\",\n  \"bb09727a-79ef-4c07-9c5d-e950f644e82c\",\n  \"886fc3d4-2d52-4c28-a58f-d04659c7a539\",\n  \"c5761af0-e00d-42a5-9165-43236cdf042f\",\n  \"178ad3c8-d820-4d04-8805-93aaf31f99c7\",\n  \"23aba975-6391-4027-a24c-0fc8e5af0e9b\",\n  \"e65679b6-a413-4bd7-a8ef-4eb51510122a\",\n  \"095cf4a8-7d4e-4b84-a16f-39e4e0d8a7f4\",\n  \"b6b7b399-6f7b-4688-a734-93f290ded7a0\",\n  \"6bb9216c-1cbf-4ea0-a800-efecbf5f409a\",\n  \"263bb203-a3d2-4bfd-9967-338f32af3795\",\n  \"c49915b3-9c52-4386-8501-0380c957f088\",\n  \"f3866820-e6bb-4dd6-8091-49d3df549c7c\",\n  \"982a6e6e-c90d-4013-b811-2b24442fc76c\",\n  \"e7610106-8465-4521-aa41-73b3a26ecb13\",\n  \"0866562d-88b8-41e3-8359-31fc5486f17c\",\n  \"ebd3fb18-454b-428d-902a-cd56d1a529c8\",\n  \"471058e4-3be9-473a-a482-38f7c04da61e\",\n  \"ffe3447f-6214-471a-95d4-90d1c63d6952\",\n  \"5cc9c24f-589a-440f-959e-82ac47497cea\",\n  \"054798c9-469b-4c84-9049-52ca35d44f31\",\n  \"73116295-4be1-4687-aad5-717b060b821e\",\n  \"8d64fb53-47cc-4de6-8e3f-96621a57a944\",\n  \"b11e71c5-7dcc-4339-a041-f23055d96197\",\n  \"7d1b9305-3b9b-4bd5-882e-a5cde86eb9b3\",\n  \"29e60fdb-f90b-4dd8-86b5-9f9f158584ea\",\n  \"54bdceb5-16f3-4242-afb1-926dab0dcb0c\",\n  \"9ee3f169-32d0-4e95-8b62-3e6bba338902\",\n  \"56c808fc-b5d8-46ca-9739-57e2b00e8087\",\n  \"d9095435-eaaf-42a9-9fcf-18682e416af5\",\n  \"7ddb264e-863b-4c36-9f1a-df52f4b0505c\",\n  \"802cbc3c-47ce-4942-93fb-7f38ca6674b1\",\n  \"31f70e78-3900-4b94-9ad9-6efcd2430c28\",\n  \"72c9e389-bc38-4162-976b-0967e5f3b80d\",\n  \"4a388c04-8a12-4bd2-b138-bd9bdc96569b\",\n  \"c7581c01-cbaa-4836-9558-5cce5ddc3553\",\n  \"63925594-174d-433e-b833-6c49483d498a\",\n  \"de8ac935-bde8-4e25-9f56-dd9dc1c45c46\",\n  \"f80cc1f2-5557-4afc-b1af-a7156e72d9fc\",\n  \"af87d786-f360-49fa-b0e1-baf32c85fc05\",\n  \"b3c4beea-5046-4cd5-81ed-f013243109f0\",\n  \"f5d68a12-c41b-4e14-9d19-0c414dbfd849\",\n  \"a2ed269f-dff9-4904-b988-45622e21d107\",\n  \"d70ed750-70db-43d7-a5df-6e88bf3c3043\",\n  \"ccd85c70-e8e8-4c44-9819-15928c47a016\",\n  \"c25e392c-69d2-4922-9918-a1db6d695f63\",\n  \"c52f75ad-be6a-441e-8611-1f8dd54bda27\",\n  \"60d44b74-7b8e-4f56-8264-4d3fb699c580\",\n  \"f46aa18a-b2b2-4995-a036-19c436015730\",\n  \"8800b35a-3677-4f73-9254-ce904918e816\",\n  \"733446ad-9359-4c1e-bd4a-000d0f761066\",\n  \"b07745d8-777e-48de-9088-4815aaf1fab3\",\n  \"59d8355d-ea62-4d42-97ff-b48a5b02347e\",\n  \"9aa14aa9-51ac-4f7b-a316-685b9cb5dca8\",\n  \"3d55d29e-db0b-4270-aa97-ee7129e0c0c0\",\n  \"90145f97-10cf-4952-ad50-780301e2cbb8\",\n  \"73de4708-361b-4422-8fc3-a4e27ae71e7d\",\n  \"612f98ec-6bd4-46bb-90ab-f05bf9f12d3f\",\n  \"a3845096-2bce-4ef8-a9fd-41b70298d542\",\n  \"f5d596d2-2946-4f4d-9e11-74f089fe09f5\",\n  \"00c5eb54-4624-4861-877b-3da4df8c2dda\",\n  \"f9dbbcbc-dfb3-4c0e-8286-5c07260b8a3b\",\n  \"de971fa3-2f25-448c-a482-f5d39de728d3\",\n  \"a74e2786-7b26-4d21-9c66-03a167fbc251\",\n  \"6ca9b177-af5b-4d7e-8025-d6a6d532d28c\",\n  \"eaad7cae-d26c-4781-b93c-00a5ecd1fe9b\",\n  \"108c9a51-ccb7-4cbf-9140-a50420f387d5\",\n  \"84d7ee61-dd17-45ae-a896-60ad5858eab5\",\n  \"ac7891e8-f237-40e3-8110-b178e4f2e785\",\n  \"bf2ddbd4-7e41-4360-9648-61c50e0554ae\",\n  \"b2e19044-6e4a-4639-9de2-97637c2ca914\",\n  \"e0732f90-34b0-47eb-8ad1-848df330ec12\",\n  \"a8dae909-4aaf-4872-b8d9-52d5a6d9f670\",\n  \"5e0ab741-0285-4b45-89a7-c6decf343ec2\",\n  \"f6759287-eac0-43aa-84f1-28049bf33722\",\n  \"e11f955b-1ad2-46e3-a497-567a7ea14282\",\n  \"e9690134-27e1-43c7-96c1-21dfca7647d0\",\n  \"9a1c98e4-53b1-486f-bd4e-8aff320ab712\",\n  \"a73a55e2-d216-494a-b722-7ff723fe89e8\",\n  \"1f4115af-c406-4ad7-b17c-50be7e3212fb\",\n  \"df0cae1e-b545-48e0-bb06-57a1afd5a568\",\n  \"375e3ac8-fb9b-4460-86f3-3bdf6960c905\",\n  \"b02fb273-5f9b-4ee4-a406-0d85cde753db\",\n  \"e99da27b-69bd-47b5-8eac-4679a56a3c70\",\n  \"4cf85387-6775-449f-a3f9-c6db477f5ad2\",\n  \"03cccc0f-05a3-4899-8d76-9e3641bde605\",\n  \"2923a97a-cfb3-419d-8081-1e7797454466\",\n  \"18f01b16-7c76-4ab3-87ed-4f99fa72e080\",\n  \"67214207-31fc-4b4f-99fb-6746dfee1f4e\",\n  \"d417a83d-21c7-44c9-8e01-bb138dae6459\",\n  \"552db2ec-d304-4bb8-b19b-66f77f138ed9\",\n  \"a458d0cd-e5ba-45aa-a783-a3d7441970fb\",\n  \"9e433cc9-49ed-4a39-a204-fe8a663307a6\",\n  \"d6e409d5-093f-4b6a-90b2-015410146ff1\",\n  \"078e4671-f71b-4add-af5c-eb5021ffa7e8\",\n  \"c419729b-79a4-446d-a0dd-6f4456214d42\",\n  \"d98e779e-dabc-40ff-ab1b-d63534a64eb4\",\n  \"8d76b4f6-cfb0-42ca-b833-a5ede476e6d3\",\n  \"d6b9251e-d52f-4249-b2d6-ae84024eaabb\",\n  \"77632ec4-c383-4e3a-8efc-7c7186076b6d\",\n  \"43f1af13-4a36-49de-9f21-f4a25aad52c1\",\n  \"ae7e4e38-1104-456a-bf9c-93402d770e5c\",\n  \"8a158392-ca99-4427-bdee-0d9b9df79f56\",\n  \"d13505e4-95b3-408d-a4b9-dfeacdd76de6\",\n  \"baa25a24-388c-4504-926b-ce7c32d59660\",\n  \"d1ba7f8f-67e6-45c6-8474-16eb67f66480\",\n  \"0da6cb83-0910-4c3c-9e1b-f44fe1d37c64\",\n  \"69dd0b3d-5d3d-4a0d-802c-3ed5f5b51d89\",\n  \"d8e35406-61ae-4c4c-9838-f5565eea1af5\",\n  \"dd0026b2-872d-4e86-b386-a63e3afa9aed\",\n  \"4d7f989b-0ac0-4779-bf4a-2d540f9a7b54\",\n  \"70346846-24fd-47f9-a034-4dc5250eae4a\",\n  \"1674a53e-056b-4866-b7f5-be3b8e35940b\",\n  \"5f878e05-7562-453a-b559-9f930c836f6e\",\n  \"31876db2-1151-46c7-84fa-98e0f15c3f0f\",\n  \"18d082c7-f8bd-41c8-abe2-ac68d4bc2412\",\n  \"a5345c98-0540-4bef-9e8b-a9d2fcf14b3c\",\n  \"186cac10-d1e7-4567-acc0-85176905edf3\",\n  \"aad9074d-ddbb-40bc-86c4-21038463cf15\",\n  \"95a7f490-301b-4376-89d9-35a0ecf0a9ed\",\n  \"10b62b11-0efe-431b-96c7-8870a2a85abc\",\n  \"f19df8bd-fe85-4d7d-8932-92ad8f5b0ea2\",\n  \"9eb00cf0-850d-49ea-b472-ad6bde9fa63d\",\n  \"cd4bd3e5-6dfa-4bc6-ba4a-4a59ec220ee8\",\n  \"576c0610-c1fc-4222-bdf1-27c1d85cbe00\",\n  \"7d21080e-f74d-4447-8dbb-0d2a0aa70d22\",\n  \"77838d36-eb36-486a-a4b3-36f016f6c541\",\n  \"5afd9011-c0b1-4ccb-9393-6b678d51ee7d\",\n  \"78a743b4-df6a-499c-9bf2-feb118103564\",\n  \"e035c2d2-9aa3-4e8e-b333-5cf66a64d536\",\n  \"a5c087eb-4c78-4dee-8b21-9a7c38d1b21e\",\n  \"ed329d33-8ac0-4c7e-92d1-121ac38faf01\",\n  \"b8920b4e-f19c-443d-b1b0-e86ec17cebf7\",\n  \"b30a467f-9302-4be6-80a6-d2495ca2c66f\",\n  \"111fb62d-f24d-451e-9823-597ac0505374\",\n  \"15393705-de30-466c-9cac-f368599198a7\",\n  \"55ab5db1-4fbe-4aae-ae4b-3833309bfffe\",\n  \"9be595bd-1742-48cf-bbdc-88e56de8528d\",\n  \"5ec41271-82ea-4d18-b83f-3cbb6cf23110\",\n  \"183baecb-722f-4a57-a3df-d0329b7d0b77\",\n  \"c07a22dc-989d-4429-8350-9c22752ffb99\",\n  \"0aab786b-8dbd-49fb-ba67-8ef1e870d894\",\n  \"febdc8e2-f2c5-47de-a125-2f1d8b253b15\",\n  \"aeb2ff0f-ff4c-4e93-b3bb-230b294173a4\",\n  \"84bc7a52-e840-4cfd-a248-88bb663aeaff\",\n  \"2a84009d-f37b-447f-8df0-b3dfd9e4a13b\",\n  \"9c4aef97-f83c-466c-a989-98ff7d167457\",\n  \"3fabfb04-47cf-467c-9b93-3ace313c837f\",\n  \"d5c7eb52-86ac-413d-9c95-de3fbb6f4ca3\",\n  \"9248dc11-d5b9-4ff9-81dc-7b3cdbf5ed86\",\n  \"c315fc22-3c94-48d3-b740-18ab919164df\",\n  \"65833b37-a921-4610-aa00-7a73460fb0b3\",\n  \"442c64e0-c604-4cd2-8220-d672cb9df86d\",\n  \"a15d720d-c748-4ea1-b20a-d7ce826083b6\",\n  \"8ef955f8-c67d-4958-9dee-d2ec5af47f90\",\n  \"42b35df4-a778-4013-8913-42e0dc43895f\",\n  \"ccc78ac0-13ee-4924-bcb9-51ca8963adeb\",\n  \"4694cf6a-1b2b-4f26-b136-26e70980cfb0\",\n  \"c7e2cdf1-e0df-4061-bf59-87bf77caf378\",\n  \"a213d8c2-579f-47e6-995d-16a0ad6b2f32\",\n  \"1868aff8-65bc-4bf6-a1c5-608a58428158\",\n  \"0e9139ca-1984-44a6-9344-caa7db7576e5\",\n  \"13a32c3e-b687-414b-b913-33df26053ea2\",\n  \"c33ab773-805d-4843-8751-b2fa45577951\",\n  \"567d7002-7a6c-434f-9418-85393893f6ac\",\n  \"c8634478-1a3a-43fa-8f1c-32b22fbfab0a\",\n  \"5fc03462-30fe-4330-8856-319494490a52\",\n  \"801fe375-1ad0-4bec-9274-4203c232907f\",\n  \"933c3415-9b9b-434c-93a1-dec3a38c5d13\",\n  \"2da59dd9-7b2d-4e13-baa4-73f536c6bd92\",\n  \"8840f08e-a2b9-4ccc-92fd-1230314e841e\",\n  \"a64746ae-1c95-4789-9685-1bcac00025fc\",\n  \"b253c5e9-5e6a-43f2-98e5-c301a81963b3\",\n  \"a921d864-491c-41d3-a714-54868009dd8e\",\n  \"a134beff-1b1a-4682-a59d-a84fea8d38fe\",\n  \"1147bfac-50ad-468c-a0e7-4d6fc4c57a2e\",\n  \"baaedadf-4b85-4033-b124-b958a8faf4bd\",\n  \"0cb99450-9fa5-4a3e-a267-74d1da2ca18b\",\n  \"cb58a62e-f61d-43ee-bdd6-427ebe20a11f\",\n  \"db114539-3697-4159-acb7-c69251921225\",\n  \"82c19a76-af27-49c2-b717-bdc08b6566f8\",\n  \"a5d3151f-a3cf-4e46-83df-20f29d5d86e3\",\n  \"34815bf9-f5c0-416e-8abe-0ee22121173c\",\n  \"a2b13a20-26d2-4905-b84b-e3e072a61014\",\n  \"70515512-483e-4ee0-a45e-f49c93ba5a14\",\n  \"efdf98ff-2973-4273-bc89-f718a2b1d1e2\",\n  \"4049aa01-5823-4800-b144-3abdf1e302ad\",\n  \"f7d67b66-a0a0-4ac4-8690-87b2ea3bd888\",\n  \"cc127353-b6b8-49be-9ed7-a4570746d973\",\n  \"f895509a-307f-4a1b-9235-95042c14084c\",\n  \"3f1a71a4-d59d-4c60-96c0-86c301cc185e\",\n  \"2ecc37e7-0fce-4eeb-b0ca-53b47a9fc32e\",\n  \"f349609b-4e1a-4c41-adbb-6aba98301f93\",\n  \"aa268739-9d81-4ae5-b965-7ac8a5b8245e\",\n  \"5a4d14c0-5aad-4c5d-a329-e0ac02c019e9\",\n  \"78836d42-7441-42f2-a7f2-ed30ffef7325\",\n  \"c4297d69-43cf-42d5-876a-0973d0a98412\",\n  \"b6e0f7ad-2420-4534-8822-0faf1036970a\",\n  \"b3566446-316b-4cd4-b21b-0c79e291aab9\",\n  \"5e138e3b-7a4f-4dce-b268-40c3eab9e18f\",\n  \"77723609-15a2-4877-b317-b0957b24973e\",\n  \"701666dd-1842-4318-8a31-6bf1a9407068\",\n  \"ec86136e-fc3a-457a-ac61-c79698b714cb\",\n  \"892ca615-89dd-46ed-b1e3-8be0eb8a138e\",\n  \"61f69ad3-6937-483b-89cf-56e6468269ee\",\n  \"d620e4be-48d6-42bb-8dc6-1c895a56b553\",\n  \"f6443996-64fc-4028-9a69-985235217520\",\n  \"e75ce7a3-2c44-43a9-bfc4-8d2ed9bdbebd\",\n  \"e1fcbec2-b2b3-4676-8e04-70482aa0c277\",\n  \"2ad40d0a-e32f-476b-bbe7-bc1ed748d448\",\n  \"328a2484-6e6a-498e-ad08-bd7873f9d4c1\",\n  \"299b3f3a-1d2f-4840-9e2b-56e2b7f23184\",\n  \"eede55d0-75be-4732-9452-0fd43f48bf16\",\n  \"73f39e36-0c56-45b3-ba09-d72633487d83\",\n  \"e592b5e4-2ced-41ee-9a4d-d5fa70ff95a6\",\n  \"28d5c7be-afae-47eb-822f-792630d2c79c\",\n  \"500ce99d-e348-488a-8635-2153afc7d899\",\n  \"5527b6b4-7eb8-49f9-ad35-cbe668765e43\",\n  \"db347c3d-9bba-439c-8ede-267bf4b687e8\",\n  \"035f611f-09ed-41fb-bfcb-ddaabca6eb89\",\n  \"3362d3da-c3f5-488e-b875-965d0c5ddc36\",\n  \"ae9e921f-8eab-42c3-b1b3-662bfcde4d13\",\n  \"35913c0c-9cac-42c1-be53-f90a2facdd8f\",\n  \"1dc805e0-c1de-4e23-ae0f-3ee3f5771b20\",\n  \"bc1d970d-5ef0-4323-b241-61fdd421c82f\",\n  \"d22aa879-fb96-437c-ba8a-3fee5295d6d1\",\n  \"eaa96d5a-2293-4856-989b-edf73dc73a15\",\n  \"0142614a-53a2-43df-b7c8-cc6e822460eb\",\n  \"1b1eacea-7f54-42ef-8bfc-5b8f7edca9f6\",\n  \"1173943e-195f-413b-a730-2bd0767d6905\",\n  \"49945571-beb7-4790-9771-c5fcfb9b1fe6\",\n  \"a7bc63e7-2e24-4876-8678-7ce15a8e2ec9\",\n  \"f9ac02c7-a80d-4c45-808b-30b6cd009a04\",\n  \"948717c4-e5d8-4cbf-a9c8-0f0f2be7a208\",\n  \"63d4d1f7-712e-4e94-80d0-d6c401802122\",\n  \"f1985195-a5ea-4bf9-af7d-74a877ed4e2e\",\n  \"9a6341fd-a89d-4c08-8788-64e9c344a9ff\",\n  \"1084effe-22b0-4e07-84ef-799b8c978011\",\n  \"4e2014aa-ffd3-4fe7-bf65-f8f0c08a7b6d\",\n  \"7adeeefa-feb9-43d1-8003-8bf9ff397a91\",\n  \"6d5ec03a-f2bb-4a7e-b6c6-f8c44bb5c6d1\",\n  \"4ed967ae-e1ee-42f6-b1c3-9d70d3d02b16\",\n  \"0626372f-fe5f-4618-a966-fd259fbf3832\",\n  \"0cb6de58-86ed-4561-a5a7-5980fe26031f\",\n  \"35b4a643-f612-4642-a0ca-9d652b117842\",\n  \"1025de36-e898-46a2-ba40-335ee3209f06\",\n  \"a5de49d3-dff2-4e81-8d83-45acc5e2d3f0\",\n  \"1566f135-5cd5-4aa8-b8e5-c116f664b249\",\n  \"580d9d0b-c2fb-4335-9116-9cb93dad9a07\",\n  \"2cb429a0-c77a-450d-8d3e-c8b06b44f68a\",\n  \"77880543-88e0-4c33-bcf8-d5261fef1c18\",\n  \"d5108bff-d8f8-41cd-a970-88cf92658a27\",\n  \"d410b141-00b0-4863-acee-590d25d6657a\",\n  \"f034d5af-e888-4fde-9497-bc3f98788ae6\",\n  \"1dbb4f2a-27ab-432d-87c2-a680bb056a94\",\n  \"b35f1dc2-57f2-4893-97ed-eda465212c93\",\n  \"d284fa86-501d-400a-bf67-5c37ca00b664\",\n  \"acd2beb9-cfab-42b0-92a0-5ecad817dce1\",\n  \"4b77231e-cf5c-4e69-ba99-a223275f5cc3\",\n  \"251f3add-5647-4227-ac8b-da89ccff4c4d\",\n  \"badf1ae4-b519-41cc-91a3-8b773f95b9e3\",\n  \"7a466eb4-be9b-43e0-9bb7-34ddef263eaa\",\n  \"25c7dcb4-f129-4655-aaea-53922316d7a3\",\n  \"84e78058-9922-4735-9d28-e0f6e6266a9b\",\n  \"4944827c-50bd-4726-823f-dd5fa4b67931\",\n  \"a3809cc1-8c38-4acf-b83c-9355df18600a\",\n  \"d3aa724f-6843-447f-82a0-b4e9df1a91e6\",\n  \"6b36742a-668f-4953-af3c-b2aae6b15266\",\n  \"32e93be1-0f9e-4e43-a7be-a78f96ff41dd\",\n  \"f73a9d24-5b47-4f1e-9ca0-37e4c36463d1\",\n  \"ab3e2b69-4853-4c53-8fd4-78b0353aff1b\",\n  \"683101c9-c669-4968-9565-33d6a095782d\",\n  \"fa0471b4-50d7-4d41-9eda-cbec46e85ab5\",\n  \"94ec18f7-fd63-4ee4-8ea2-9ae20c391085\",\n  \"6dd6490a-b140-46cc-a0e2-1a0b5a97f7a3\",\n  \"c7d0cdc8-bb6e-4d88-b4e0-27d129e4c0af\",\n  \"45c9afa4-55da-424c-9876-56fcba9e3e6b\",\n  \"dafb13f4-43fc-4311-a814-9872de54ace0\",\n  \"3b32b44a-1ffa-4625-b89a-a92b39b9cbf9\",\n  \"9450743a-4a98-40ea-b3bd-98aa8e2d76cf\",\n  \"2f37c7be-b276-4caa-8146-932202d9caf0\",\n  \"8f4d9cb9-a01c-4202-952e-cedbb80891ae\",\n  \"367e3a2b-c87b-42bc-9738-de6762457e68\",\n  \"69abfc3c-feb5-46dd-b915-ec1da521f19b\",\n  \"494a9fad-9355-4065-9319-134c66bebeb2\",\n  \"4479d91e-61e2-480a-8d2f-f1116728b959\",\n  \"cda6d449-451f-40db-9721-3ac652ba056b\",\n  \"0a69dec4-d9b3-4510-81ac-92ddf98124d9\",\n  \"c261bf5c-dff5-43fc-a8a2-ce717474dce9\",\n  \"dcfafa11-5f94-449e-9533-41f6efa60180\",\n  \"a069799a-86dc-4602-815b-c8598c3bbb73\",\n  \"be77a9f2-ea20-4e82-b67f-79e693c8e944\",\n  \"5efef663-ea2e-4247-a43e-fda7a0a17a21\",\n  \"f3cdd781-3257-4285-bd69-06771bc3d5ea\",\n  \"f33752ab-1aa7-4ca8-90cb-a20b2b627c39\",\n  \"2bc2fe14-202b-4483-ae9a-98edee2d2575\",\n  \"b2aab99b-d87c-41d8-b31b-1454184335a9\",\n  \"9c2f468b-838c-47a8-a7eb-43ae2e3f5ac9\",\n  \"22c0fa9c-c926-4e6a-ba6d-3dc5de6cafff\",\n  \"760ad11a-9cb2-4245-a858-a049fcc45abd\",\n  \"a4d74f0b-1122-400e-a100-036b3af8a3bb\",\n  \"dbd173fe-f0c9-4b2b-83a6-82ff3a494fc1\",\n  \"970253a6-4ee5-452c-9578-8828bcfebaa3\",\n  \"17b21a04-c81b-4500-b15e-e7f41ce10058\",\n  \"bf8820b0-0483-426a-a02b-74783b7456cd\",\n  \"00edc23e-d614-4a9d-80fc-368c8fb953c7\",\n  \"c931fc2a-c9d4-4409-a126-53e2933fef3f\",\n  \"6156f5db-2f7f-45cf-a1bd-c0978c75bc1c\",\n  \"72e80b2a-04f5-4e97-8388-a0527d11dade\",\n  \"9e7be720-29cb-4b6b-b78b-188861541336\",\n  \"a856828b-726d-47e9-8a71-4b8d51390fea\",\n  \"a3303c31-8cae-4dc1-8747-3e663a52b0da\",\n  \"48b3a7ce-c92b-423b-bfd6-584073cd4e38\",\n  \"fa7f56a8-60ee-479e-9ec2-433a8f7b755f\",\n  \"d43ab8b9-8128-41a6-b281-f28643513044\",\n  \"d8be7d2d-e5d7-49fa-97c8-beed71e65f8f\",\n  \"f6a0253a-b888-4458-858c-686c6a4f0c5a\",\n  \"3d1ab7f6-497c-4cc5-b3f6-9a72fd735185\",\n  \"f9519650-8f80-4aef-af96-77047a4942f5\",\n  \"c82a9630-7aad-4bc1-a202-cb5af844a704\",\n  \"865eff33-d88f-4005-bae3-4a84c32a2bea\",\n  \"facaed58-f8ef-4ad3-be1c-5a4eb46ff3d7\",\n  \"0e4d6402-3a18-4e0b-b610-7f85a24d037d\",\n  \"e1ce00a5-d13a-4cff-885d-66bab2498509\",\n  \"0dd57eed-0038-46a3-ad23-6268c5a00e54\",\n  \"43b94129-4ad4-49a3-99a9-f340dd2d6f21\",\n  \"e24186c7-42d6-4e59-9d4d-33635b8a656d\",\n  \"e00cbcee-7118-4a0f-84ca-5fe76d3109a4\",\n  \"ed0c71f9-956c-46ee-9888-8ce8a7fb4044\",\n  \"3ab29cee-2d25-4e29-ad38-d10a68a5343a\",\n  \"119c1bf8-84ac-4232-a4f7-7e0a92bdaa57\",\n  \"6b7d0c0a-4928-4089-95f1-00b41a183b1f\",\n  \"4612fe33-c83c-40a4-9017-6f7d35f75032\",\n  \"8fa223a0-f698-4fcd-a76c-a0f8ba3342bf\",\n  \"c805d5af-f37b-41da-b1a6-34048d8a02e2\",\n  \"f81fbc9e-10df-4910-ae5a-c893875dc33b\",\n  \"16b01693-13df-4517-9884-bc36cdf4f243\",\n  \"08247e29-ed79-46ff-ad7e-3400fee2952e\",\n  \"097d0b66-7bed-433c-a5c4-3070c812453c\",\n  \"04bb7729-08c9-4053-8dbd-1b7e06413676\",\n  \"88345310-2092-4663-9e3a-39f9f156787e\",\n  \"9d8dc207-0ef8-4231-abcb-96fc342ca911\",\n  \"86d40c93-797a-4022-968e-f3d45dfed1c9\",\n  \"54474890-4668-4a4f-b894-6adb0542fea6\",\n  \"9d486a2c-115c-48ce-b0f4-b81bc5d3c890\",\n  \"0dbae374-46b4-42c5-ad06-2c0fde0e3bf3\",\n  \"ac499625-bb0e-431d-9c24-41b1a5c2d475\",\n  \"b73f44e5-493c-4be1-a89f-6658dd32cc65\",\n  \"5a6f8de3-a39f-4230-a2c4-3c77a53c3af0\",\n  \"58529dc7-a201-48ae-9b80-9e502c22ab82\",\n  \"65b7c349-ee47-4224-8e68-909f2ca8efab\",\n  \"2934fda2-64ea-4296-b98a-e30e4781bd37\",\n  \"6d74431d-0754-4d37-ae6c-b8b029e5e412\",\n  \"224521b2-2066-4666-ab3d-fc37dbab65a8\",\n  \"a3d1a888-ddd9-4330-b0b4-8fa47e9090eb\",\n  \"a91786ac-42c6-4541-86e0-b3d70ed7d288\",\n  \"eb79ffca-7283-4127-b849-3accb203acd8\",\n  \"2a06124e-c3e9-42ad-aa58-0bff49fd2147\",\n  \"36a8fa2e-e767-43ba-a36c-b9e48de50cf7\",\n  \"4d21977f-545f-4ef9-8675-c159e523323d\",\n  \"debe6566-5298-4abc-90dd-43219aebb907\",\n  \"3a4405f4-535e-479c-b81e-868c7897d1f5\",\n  \"788a026b-3fa8-4605-99fd-722dd976c3bc\",\n  \"2c3557b9-eb41-4f52-9ace-baedfc5c788f\",\n  \"8628b9fb-1979-4803-bd52-dc4f4bca2347\",\n  \"b7046c2f-ace6-4f85-8c99-2beb367003cf\",\n  \"54f2a2e5-018c-4e62-a70d-993b220406cc\",\n  \"ba292131-f716-4ef7-8c67-c5cab9893de0\",\n  \"5db17f9c-6c38-40e9-a44c-66b12b214472\",\n  \"561f419a-666e-48c1-a546-59ab49ec7c46\",\n  \"1a2fe596-4753-4345-86e1-75679a9b9c74\",\n  \"2f2be132-d97e-4df2-b316-e3c21cd2d3fc\",\n  \"10d752f1-0315-40c6-9bd8-66c32ef140ac\",\n  \"96387cd1-1357-4c94-8815-ae07c898e3b5\",\n  \"deb0bb63-9f65-4fb5-9c4c-02c8e131eb30\",\n  \"a7795de1-8be2-4a09-8e6a-8ceee8d6682f\",\n  \"4c48a88c-ece7-4e20-ad74-ac661ce78a5d\",\n  \"729ac401-756d-4923-b12b-ddb0d0348478\",\n  \"920ff162-2b2b-4cac-8d0f-1645df1ea542\",\n  \"d404b685-24ef-47bf-bd63-2374227f40e5\",\n  \"f5d5fbbb-0168-4109-8e21-54b040218607\",\n  \"6ae66773-9d2c-46f8-a34e-78c42ab0915d\",\n  \"52edfd6c-1a26-4c16-a995-fafbcf19f5b2\",\n  \"27e13306-f11f-4e58-a173-f060af7c5c94\",\n  \"8900f21d-d157-4a8b-93a3-77d9ca90e873\",\n  \"6fa93d82-7df0-4c9b-97b9-045e484bde54\",\n  \"898e3cf5-8cee-470d-a9c8-f822f81571f4\",\n  \"7a46a4fa-81c0-4fd2-9ddc-55fe84d398d9\",\n  \"cc7c4e90-bcaa-41b7-971c-18aee255b142\",\n  \"a8bdcfb2-d7d4-4e5b-882f-1cd87063aa8c\",\n  \"e418a737-d800-4065-9122-3111694aad68\",\n  \"eaf1dc04-3b91-4025-abe3-b28996fc6800\",\n  \"ea623de1-343f-435d-bbb6-ac75e2f2ba80\",\n  \"77750854-ef9c-40a2-9e78-eedddf226928\",\n  \"8dae48fc-bf45-45c3-83db-14cb49de007d\",\n  \"0d8839fb-cf28-405c-aebf-e5f343f20fed\",\n  \"1ed0d6e7-0af4-4e08-be74-5a51d0d5b24e\",\n  \"7a508156-ea75-4c76-a5b4-3156d80b2639\",\n  \"c79b64a8-17cd-45cf-8570-c2a4718f00ce\",\n  \"5f3f41ef-551a-4983-b6bb-4dcd37aa730b\",\n  \"5085d48c-dc02-423b-bdb4-0e600e29f304\",\n  \"e189253e-52ba-4ba0-aee6-1dedb42ac710\",\n  \"535c0f6b-3d9a-41f8-982b-4cb9e3339f86\",\n  \"a703c7b0-4a57-49c7-a3a0-78d76b2c5c01\",\n  \"ef987827-3463-4b06-8ff1-bf3011c83409\",\n  \"72445322-1ca5-46c6-b0fa-d2cd75082484\",\n  \"7401453e-5c33-4934-827d-073e08b4ca28\",\n  \"79ae1579-8a4e-4cf8-abbc-f4ce40e90620\",\n  \"35bd19e0-5fdc-42fd-885c-6d24715a1b04\",\n  \"f12a31b9-6163-4829-888e-9a14952d7155\",\n  \"21b3eafe-f74d-4e80-a8e3-13f8d7080a43\",\n  \"2068a27f-9df3-494c-aef9-b9e52d23e111\",\n  \"482d2202-1d05-4ce3-ba35-a8f31f30f01b\",\n  \"6bfa3dde-842c-47d6-9c23-7dbf3b7eaf1f\",\n  \"025f1174-f434-4bd1-84dc-193fb53a0fe2\",\n  \"9079fd62-b694-412d-9f2e-5dc28ca91123\",\n  \"0b8dc9aa-9857-46d5-a7f9-59aab22e68a1\",\n  \"e69cca3e-d488-448a-aa38-8904ae1c44d9\",\n  \"43f40010-ec05-41fa-8dd8-4fb4176911ed\",\n  \"5404afe6-383f-4731-92ca-56a428944f6e\",\n  \"5051e81e-e289-44b9-b3a5-e0eeaeb22fd5\",\n  \"16c194a0-5c0f-4439-bbd1-1aa3088cb810\",\n  \"928e7a75-3c3c-46dd-bf44-a0349962869d\",\n  \"8ee335f2-bea1-44a2-852a-b52ecc425104\",\n  \"56f02965-0101-406c-b8b9-ca716606c41a\",\n  \"50111029-7cea-483d-bb04-56b44e4b4057\",\n  \"24d17984-c027-45dc-8d23-be3f159379fe\",\n  \"6a6fdcb0-dd03-48d2-aba9-c1887a8b7ab8\",\n  \"2a0a3c82-d51a-4390-8c11-09e0109f6652\",\n  \"140e8ff8-b3eb-4797-be66-5a53910ac767\",\n  \"a477e0ec-82ad-4c1e-b7a3-2a78b42de337\",\n  \"54200714-78b6-4ba8-b367-1af6624882d2\",\n  \"18cd7b99-06dc-476e-bc1c-a4e9b7ee6c5b\",\n  \"2a52dadc-99c4-44f9-91ab-b24b509f5b0d\",\n  \"13ef4af0-5308-48c8-8ab8-939fa705e2ff\",\n  \"5f89bf32-d6ee-42e4-9d2c-908d37c58376\",\n  \"1c7c48f5-e7d3-4b4c-bbe0-1b1125704467\",\n  \"0ae077f9-7810-42fc-a86d-ac12101de707\",\n  \"0463d802-5e43-45cb-8114-e5b9301ed7bd\",\n  \"2ec51bfd-5174-4307-a3ea-9a35e4f5eb14\",\n  \"6c8b05fa-2cc8-4f2f-af5c-6e4b47b23c4e\",\n  \"387b893f-b843-4dfd-90b5-03190448b1da\",\n  \"635fbc71-8961-409e-8bf3-00f773b9344d\",\n  \"277a2aa3-0b61-4015-bb0b-ddd540ea2029\",\n  \"57224e43-44a7-4e97-b877-43924275c166\",\n  \"7c70d2ff-657b-404d-9339-f28301aaf05a\",\n  \"ef733f45-2eb2-4873-8fc3-ae9c29c6f302\",\n  \"8e53d25f-1c5e-42e1-9077-3493970a66f0\",\n  \"7316ada8-029b-4d89-9f26-bc1a847ac6d5\",\n  \"ddd25906-b245-4eb7-a0b7-82daacfa6aa5\",\n  \"5d86b680-8c94-43b6-90ee-3b4e41d0cb53\",\n  \"e4a96b7f-712e-4b85-aaed-f8e31286df11\",\n  \"06761b0c-225a-4143-9c63-aefaa6085ff1\",\n  \"2c8e4f05-cbb0-4989-8002-7db1dca2a78d\",\n  \"3923bed5-8159-4e3b-a645-de1f5197dda3\",\n  \"6b879d85-217f-4c09-9cb4-2b40e885183e\",\n  \"fcc0bd73-e4fb-4690-9468-4a6fa751b0e4\",\n  \"1bc349ab-9561-417d-9c9d-7f885aea841d\",\n  \"d4689624-9e45-4ac6-9ae0-4c6311b84fd8\",\n  \"9a29f113-5061-4a1e-88db-3f012a57015e\",\n  \"02ea62b4-85bd-411c-b3a1-1d334ea746c0\",\n  \"a810e22c-6fd7-4993-8321-716dc4f261dc\",\n  \"fc1b53b4-3fa9-46ed-af26-e1954e290ceb\",\n  \"b5bac391-1ec2-4cd4-bfb9-a09302816474\",\n  \"5903ee74-a54f-4889-b6b1-01e1a410e548\",\n  \"feece9a4-538f-4fff-bf5d-72a005e9b21e\",\n  \"9ce237b9-c2f6-487b-9a48-015a0fc82ca7\",\n  \"d1b31166-df80-45fd-8154-7a01f718abd2\",\n  \"35390e45-8cda-49c8-94ad-848daed882c8\",\n  \"36778f7b-adb2-4a02-88e0-2b4a6aafb6ec\",\n  \"34502209-1459-40b5-973f-edb735f88457\",\n  \"13d2b183-1b28-4a2c-8a99-be8a6c796e9a\",\n  \"ceb885c8-73f7-463a-88b9-328822835a7c\",\n  \"edc26ec9-6ab1-4a32-9781-abb6ab0121b1\",\n  \"56da39b0-26b6-4c81-973e-43d041200abc\",\n  \"52f73e4b-d41b-49f6-b0cf-bb19d5a3be8c\",\n  \"3b93638b-4935-4063-b7d9-4230fc321698\",\n  \"a0eb0acf-b126-4fd4-a86f-f79782778c7b\",\n  \"ead222ab-d616-4a25-819d-4ae5e9bd9cb8\",\n  \"3be1f788-ce66-4fb8-bd2c-6ad5385d890c\",\n  \"333d6b85-05dc-48c1-be3f-320264c91b41\",\n  \"bcad1a91-22dd-4055-861f-31f40c89990d\",\n  \"e5186bdd-8ef0-43fe-a979-61f66afa023c\",\n  \"5b1d1904-90c3-4c46-8866-74378d637532\",\n  \"d4a1b520-aa2d-4c1d-b76e-721749e93785\",\n  \"1015e200-5f4d-457b-ac13-eefeee75c560\",\n  \"600c3142-d9a4-4061-ab2c-2e6ff84e5162\",\n  \"8400117b-83fb-4947-bcc0-96bdd1fa0e3a\",\n  \"316a8574-e623-40db-bd7f-e7b5de143240\",\n  \"542889fb-847f-4dd5-9cfe-806254665d2d\",\n  \"0da91985-2cbb-4b7b-98ac-d708fd7b7884\",\n  \"294f2418-68ba-4d8e-a6bd-9a86eed0ef04\",\n  \"527a03d8-d892-4256-a90c-3918e099be40\",\n  \"037dc83a-7bfb-4a42-9faf-15398cb3c653\",\n  \"0ef3ee9f-325d-4f90-9279-3a134d60a5a1\",\n  \"e00ca69b-40e6-4998-a5ae-f371b44acd75\",\n  \"834f7726-f467-4db9-b199-95be28844012\",\n  \"c83a0ebe-0a04-4edf-b444-421f66b94d2e\",\n  \"c9e438da-b83f-4de4-bc3f-dec22e9f2489\",\n  \"ff393111-6bab-4092-92ae-80da64983227\",\n  \"fdebcb63-da88-446d-aff7-e97babd84c9c\",\n  \"847fa634-d504-4b0b-89fe-b784b3f0aac9\",\n  \"12ea1928-4c65-4a9f-9b23-baf2e7554cd1\",\n  \"ccbeb950-f0c6-4bbf-a114-bc750b3fbf98\",\n  \"ee614826-ce47-4d54-a19c-39b5189242cb\",\n  \"f71b2b11-7dae-4215-af52-cdbe28d53c87\",\n  \"371cff3d-8ac1-4e70-8330-ece612b28aba\",\n  \"3db6b450-4b53-4105-854e-d366cf1ca144\",\n  \"c0434566-5358-441d-a936-fec12d671d9a\",\n  \"7062e8c2-022f-4408-b0cb-e5d980feb4fb\",\n  \"45542060-da01-40f9-9dab-e297c9876c40\",\n  \"ef0613e2-da9a-4f27-9ee7-1f6c0e6f971d\",\n  \"4ea14960-7740-4575-b1f3-00fd7156bfd0\",\n  \"7bee32ae-6672-42af-a1b7-5c61039f9ac9\",\n  \"c8642476-7703-4f38-b4b8-dc28a1892e97\",\n  \"6c3d3c88-381d-4d81-aee8-1d1c25a1c388\",\n  \"6ddbd8e7-0381-436a-bc93-b45bdf578f9c\",\n  \"7af550ab-9f6a-49a3-a0c5-1acf553301d9\",\n  \"981506f6-3d3a-4298-9995-affef33e48e9\",\n  \"f2da71bd-9f2e-4765-ada4-87146a75f038\",\n  \"85aa987a-37f4-4ab8-8986-2ba88c176b6d\",\n  \"259792b0-aae8-44c4-9730-3c4cc80736da\",\n  \"666738ba-debd-4c61-9e6c-71c0041c3714\",\n  \"cc056d2f-5d88-4745-973b-fa91ab7ce414\",\n  \"4162ac91-1fa1-4aa4-9f70-db46fb6b0cce\",\n  \"919ab0e5-0949-46d8-9bee-be4c6cdc8445\",\n  \"18221931-c055-428f-9b47-c2302073cd6a\",\n  \"d35f2bc2-7fe2-4392-8d8e-53f1e830b3c3\",\n  \"7bab2c98-3f54-490d-b7f6-182da96d8b0c\",\n  \"643c6a35-37fa-4067-9405-da553652bfac\",\n  \"ffad7215-3672-4a0e-b0d8-0cb650c660b3\",\n  \"6d7cc52c-c4ec-47d3-b729-15e0bba4a3ed\",\n  \"aa1eb4b9-c910-43b0-921b-4c55e1285631\",\n  \"aa64a7f6-49d9-4640-986f-92ce306cf909\",\n  \"ad4f84fe-5369-4a4d-a620-9ffb17afbb4c\",\n  \"d64ac2bf-130d-4205-a5a3-e3aeca54b884\",\n  \"d227ae62-a1fd-43dc-870a-13aaa6104565\",\n  \"7998456e-f94e-42ff-a6cd-cc9107a6116c\",\n  \"1db0af19-ad58-4133-8e66-cccbb6746028\",\n  \"5e85f68b-be28-457e-a2b6-ba83458205d8\",\n  \"688514df-9c32-4c33-827c-d91b67acbca5\",\n  \"eeb67705-4585-4ca2-b23a-05ce1270c26f\",\n  \"ce6f1cc2-4167-4a52-b5ab-147cede55fc6\",\n  \"572d06a4-12de-41d4-af32-9ca247261dea\",\n  \"3a6a0b16-e9be-4f89-aac5-85a96f92a697\",\n  \"fd18b6a9-32ea-46a4-afc7-996e1b339467\",\n  \"2a11c1f4-f4f0-4c9d-a70c-762bfde4274e\",\n  \"711bb25e-55d0-47c3-aa54-258fb082eecd\",\n  \"71592a0f-aafb-412a-b854-2b78f8fb0549\",\n  \"59f891e5-9068-43bc-8158-3c1af02aa4f4\",\n  \"8bbe9bd3-7c8c-4823-9f3a-0549cdb7e36b\",\n  \"2692c262-5078-42e2-9592-04c2c766f52b\",\n  \"f2fc9703-5f82-46f7-86f9-05c6a0ad625f\",\n  \"195b2a13-4b1a-4d6a-9396-8832dc1721f4\",\n  \"8e5569f0-1a13-4a02-b1c5-94d1bb89490f\",\n  \"33e84d31-c8b6-4a1d-a734-63f01cffc3bf\",\n  \"08de6498-9d6d-4744-99b7-be8291acce6f\",\n  \"a959d294-151d-4d20-86c7-11c86c7c6cce\",\n  \"4bf6f9b0-3291-4529-be41-664c725b0915\",\n  \"570012e2-5b86-4e8c-97f2-e24268008851\",\n  \"2adacbe2-f378-41be-b816-d75eec2eabae\",\n  \"f2d16fe0-0251-46b9-90ef-e3d35b2d3300\",\n  \"08a8ebcf-cd90-4e59-b795-4c85f0558608\",\n  \"54a35d01-a780-4bcc-94d1-9772ca4a9263\",\n  \"53893564-6be2-43b0-ae90-31383036bdcd\",\n  \"a8c226bc-bdf5-444c-8927-a1ba183058e2\",\n  \"77b832df-bd31-4e03-a63e-bd44c1e78967\",\n  \"ca5bfbff-d7c9-4220-adff-44ba2fe4fe9d\",\n  \"f5fd620f-4106-4ad0-8dc1-a5692e0537e0\",\n  \"5acd9fd2-f1e0-4921-8163-d9290fb26ac7\",\n  \"29c9efce-c952-48c1-83e4-0b79a41a1c12\",\n  \"ed078e66-da77-44d0-9d6c-5d7e559a522e\",\n  \"d636dd48-bb65-49d5-ad59-ae6727a8e922\",\n  \"0d7a738b-98f1-47ba-955e-9676cfd856e7\",\n  \"0b0cac22-5b35-4251-baba-01470ae92d38\",\n  \"1077d96c-ded7-45df-b890-71e72915c348\",\n  \"d9bc4aa5-d6d5-44f0-ba91-47318991c109\",\n  \"8e87c09f-2285-49e3-9369-a1ecda9b371e\",\n  \"bbb81c6e-76d6-4069-8d44-88aa57e8c879\",\n  \"7b524e69-fbb0-4269-a487-57ce97c808c3\",\n  \"3021aa1d-de1a-49e6-b852-5c4d8e0ff853\",\n  \"28aff1d8-aaa2-468d-8031-3eff85a78578\",\n  \"dab13ff7-dd4b-48db-8f8a-bae2600943ac\",\n  \"d71958ad-997d-4213-9a06-80aa8bd12765\",\n  \"6e3186d6-3835-45ad-a8b4-cbd536d90509\",\n  \"bb3eb885-ce41-4df4-809e-1bd9b1b5eb4d\",\n  \"2e6180d8-c39d-4a1a-b14e-07e22dec91a6\",\n  \"d5b7f043-9579-43f2-9c10-ea0af4866693\",\n  \"76fd4214-e888-4c97-bca7-38e20629db6c\",\n  \"c142ab52-41ec-431e-93d4-473caa1eed75\",\n  \"73cdaa90-e2f3-4bfd-b177-df28262b5add\",\n  \"bda9c904-b554-4b34-999a-b033abcda79b\",\n  \"47a499a0-11b6-4dd2-b388-789c92224a18\",\n  \"ea76af0f-7b10-4535-94d6-b57f2e8e6ebb\",\n  \"908f0863-a48c-49af-b58b-abf17a65a38f\",\n  \"e50321c4-2309-4adc-81cd-85b66457492c\",\n  \"bc6bee18-892b-47c6-94c3-8d751b776d10\",\n  \"9da6332f-a096-4246-a23c-1b219bbbafc7\",\n  \"57b51f3e-02ff-41c5-9c38-8ece35b5730a\",\n  \"7a1843d6-ee01-481f-acdd-e583b14a0eda\",\n  \"b6492586-2c07-4bd0-9c5c-975fbd0129df\",\n  \"b5e30e1a-57c2-4f00-bfa6-58af603f51dd\",\n  \"a7e4b8cf-a242-4e0d-ba28-72260578c437\",\n  \"e617b83c-769f-48b8-9ebf-464abf8b4201\",\n  \"bd3ee932-7358-4f92-84af-2892c09f68f0\",\n  \"166e96aa-6049-4b77-826c-e2fb97e9f39b\",\n  \"27b5dad8-fa92-47ea-8411-303d87498754\",\n  \"c082bb2b-b486-43da-b12e-e572a0621182\",\n  \"0930571e-ce4b-4711-a525-89f8ddc444fd\",\n  \"b033cd40-eea9-4c5b-a17b-d1fcae17f5c1\",\n  \"0d66e9a9-bf78-42c6-b34c-2a1459f82025\",\n  \"aa4cd115-9ed7-4b37-bfff-9be015afd68f\",\n  \"443d1d13-aec8-4df6-8654-58cc3d8def5f\",\n  \"4d99c520-30b1-4ab9-beeb-5a5939d19598\",\n  \"ddf87095-fada-4895-9f66-1b00af316273\",\n  \"186d2617-5702-48de-9c2a-81f27afa8ec1\",\n  \"d020a4cb-b21f-4cd3-882c-8dac49eb5d56\",\n  \"cd6db2a1-2fc2-473f-b30a-d04d03a3b166\",\n  \"0a3f7aae-a63d-4728-8595-b0851546bfbd\",\n  \"a6f7699a-e920-41df-b798-bb6c67a74300\",\n  \"d0fd379b-c61a-4dc5-80ff-63c386e9571d\",\n  \"45264746-fa35-459a-b4d2-6cb6c9bf85bf\",\n  \"80add2f1-fff1-4e06-a871-785d713aebf6\",\n  \"fcd7044a-8d2e-4df7-8a77-129b7890553d\",\n  \"a3fedd53-7c86-459c-8dd1-45b74f4ec7d3\",\n  \"5d5aae00-8edd-40d6-b141-960bcd4ef92d\",\n  \"c1035bc6-92e6-4687-a586-a46093b5915e\",\n  \"d42cf02e-7f66-4254-988f-2d35cf9006a1\",\n  \"7442a328-ad5d-4197-a669-5465ec7d4972\",\n  \"89fa2f7d-ddd1-4ee6-992a-3cfb588ad899\",\n  \"77272f25-411c-454c-bb6f-a79d52e727d9\",\n  \"9263c2cb-3fb9-4936-9ac0-d88858098f88\",\n  \"6001c08b-9987-4ff0-9f64-7b7b7dbb1cc6\",\n  \"1ceb18c8-6e2f-4a63-bc23-7a520c140df3\",\n  \"59a69d25-cdee-4390-b4f9-d5f13437894e\",\n  \"00c3fe3e-f9e8-4953-b9aa-9d1135f1f4b9\",\n  \"f6071eb2-b8ef-4f5c-be4a-b471aadc40c1\",\n  \"1600837c-3665-4474-9f7d-1467cce15645\",\n  \"14fd6447-9357-40b7-b882-2242e24c0f74\",\n  \"e1e9d8a1-bb9c-4050-9ceb-516386c1f3f6\",\n  \"dc4ac0e9-f798-4386-88dc-7646e2cd3b89\",\n  \"6d0d7a37-88dd-4f3c-b10e-3646101a7416\",\n  \"c1ba4198-d30f-4509-a9d4-d37373bf4186\",\n  \"29a7cab1-bd36-4c01-8de9-31c5b4991bab\",\n  \"5ee7dd6b-5d89-4661-80b1-c01352bb13ec\",\n  \"2ebcd848-d31d-4276-8c42-7066628e6d17\",\n  \"c7a0362a-840f-4ad1-8823-50c84da72401\",\n  \"c76efb9f-1155-46fe-9ebd-e4662309c9b1\",\n  \"cd30017f-0e19-4d42-a38d-7358042a4f0b\",\n  \"5d3c6ea8-d4a5-4031-9c87-a2a4ad33ddc7\",\n  \"8a60d5a1-983f-42ec-8192-c7c802c3013b\",\n  \"21d082c2-b0af-4d32-a377-80dbccf78500\",\n  \"075ef1ca-cbf6-4d78-9ee3-d974cce7781c\",\n  \"05e86f3c-ab9d-4726-9422-54f5806ea52d\",\n  \"1f566854-5816-4370-b9c9-2e50ab14379e\",\n  \"2a18c138-0ad7-4e37-8182-8cfb6ccc94c5\",\n  \"106aa3c9-0660-4b9a-8bd0-713f0f3a5745\",\n  \"73bd5559-f082-49b2-8a09-2a74404d6ca4\",\n  \"fb78afcd-a21f-4841-b0a1-838bd68ef6c9\",\n  \"1e9d1b09-da52-42a9-8d6d-e040234b2198\",\n  \"5fda9537-3d93-459d-9be0-63487faebefe\",\n  \"bd31a6d0-6146-4d09-81ce-a98f4957602c\",\n  \"99079364-ddbd-4c69-8ccd-945e052bedd4\",\n  \"21cca40e-b601-4cbb-8af2-9d75fbc53f5d\",\n  \"2cf768d9-9bf3-4660-98fa-5fc8f8683eef\",\n  \"d25be2ca-9f46-4fbf-93af-5f190848a5e8\",\n  \"0520fe87-dc0b-48a3-89b9-6f61d2925f2b\",\n  \"f6332e03-3d78-4e20-8423-c7583d9fc878\",\n  \"2f02abec-5584-4e48-85a8-1118ffd038c4\",\n  \"0ae29b6d-ee07-40f1-a570-319eae3a2276\",\n  \"7af2b757-21c9-47a6-b438-6734cce90164\",\n  \"f4e39c67-d90c-452a-8e26-6c65324d412b\",\n  \"7e89bf38-f633-4be4-a261-90b709ad6072\",\n  \"611cb062-0777-436f-8497-3f825186b9a7\",\n  \"163eada4-12ed-4805-b040-896239433808\",\n  \"257d2e97-6235-469e-945f-1f9d5f7cdc60\",\n  \"edd141cc-e2f7-497b-90d2-b520a464d031\",\n  \"3fd3c87a-d96a-493e-9d15-c12d9c1d75b2\",\n  \"c75abd31-d580-4b19-9761-8a647e2787a5\",\n  \"d446d9e0-21e6-435d-9906-d70da93c6b5e\",\n  \"d0f3cb43-7939-45bf-b8cc-0db3fdc45832\",\n  \"65d370f9-822b-4589-980d-0c777d54115f\",\n  \"f0a2e025-f49f-4019-865c-e5c4f74df0c2\",\n  \"59bf65ba-e856-47f9-b388-92169fb89dc1\",\n  \"ffb298bb-8e8d-445a-8101-9a6af6d01ef9\",\n  \"07439776-3f09-4939-a46c-0983a236c23e\",\n  \"a9a216bf-f5a4-4098-be5f-0021ed6b165b\",\n  \"1c48dbdc-5309-42dd-a8de-f22b39231d5e\",\n  \"44c27e72-124a-4b7a-99ba-d19f32d64bfe\",\n  \"b054c892-3dc4-484c-94ad-b5170545f268\",\n  \"7b7f9550-4bc4-45c3-a7b6-dcf799dcf2a6\",\n  \"5b65aa8f-fbeb-4a82-ba80-22d1172a7bc6\",\n  \"ce86d9d2-7e89-4a0c-9929-8ac6b42debe3\",\n  \"a1227851-a5d8-4fb3-8d85-aeba4c641d76\",\n  \"0956d286-ec3a-4531-853e-968d93836bb2\",\n  \"1ef53e86-9cf4-4e09-8c3e-26bfcebcb501\",\n  \"b8dfcef9-883f-45a5-987a-091a237c5267\",\n  \"87f51c93-8615-4241-bcf1-03aeef9163b8\",\n  \"cfb3507f-7439-470b-93d5-fe89ec7dcd92\",\n  \"b54951db-7356-49bb-81a1-4b462dc1799f\",\n  \"2260d8d3-c07e-4304-b064-bc10149f4d1c\",\n  \"b410dcd2-2608-42a5-b2e4-6ed9cc2aa5cb\",\n  \"fc318fd6-1a3a-494e-955a-fd8fb3ec5435\",\n  \"201aff83-155d-4d1b-94fb-c367834a7672\",\n  \"e5a0d3da-2d8a-4f67-bc75-eecd3080db1b\",\n  \"64d3768e-de91-4088-932f-673896741b0e\",\n  \"ed2b4980-e7a4-44da-9641-e4c443fc585e\",\n  \"2f5e80ac-2d36-4202-8ce8-f93f82fb7fac\",\n  \"38225065-89b3-4c8e-9ae3-ba08d82776d7\",\n  \"bb726175-bcbc-42ef-a901-49efdefb8a2e\",\n  \"8c1190e8-a940-4b6e-a743-30eb363d5511\",\n  \"b7de243f-29ab-4f8b-b103-b69f9030a781\",\n  \"940a738a-a6aa-4b71-8dfa-f95193e5ca98\",\n  \"db9fc63a-9888-47f1-b39d-b4718dd27634\",\n  \"ea86d54a-b9d9-4435-b90d-4d4bb79c3064\",\n  \"287e0cd0-b1f6-4ccc-a5da-c52fe691f69d\",\n  \"436c55a8-e9d6-4a3a-bebc-054a0265b92e\",\n  \"f19c70f8-dfeb-4035-ae35-24653fff12d3\",\n  \"41861fc9-4c65-45ec-aa55-9a6872b51a39\",\n  \"c0c4d4a6-fd98-49fc-b067-8bfe0bfd31f0\",\n  \"f964c3eb-2453-4c47-a63c-747f10484052\",\n  \"45d9e222-0c81-4277-baef-163e461fa8a8\",\n  \"d3961d64-15c5-49f3-a5f2-0254867cc1f4\",\n  \"3e6c051f-3059-4e6f-90b5-9d2f685be6c6\",\n  \"91dc01ec-735f-42ed-a754-7bc00c5e9a1a\",\n  \"0f006f49-5432-4921-af8a-a34a6fb60d6d\",\n  \"a47fbcbe-daa2-4659-9f16-7d407894fe8f\",\n  \"593ed27f-d86e-4828-a8be-1af34bf6793c\",\n  \"f62d8299-43de-4bf7-82db-5e0ea161cd30\",\n  \"ea3113e9-1a34-40f5-895a-573e2efb2a9f\",\n  \"39fd6979-0bf1-408e-a825-6d1630d2545d\",\n  \"0ecadc3c-4ca3-4614-9bde-cfc993d3c3c3\",\n  \"bd0f12f1-74ca-4faa-8d6d-f631b116dc37\",\n  \"44adb254-c2b3-4efe-95fa-6495b76d6f07\",\n  \"6cdcc36d-2001-4270-b3d0-a22388329661\",\n  \"83474f72-b630-42f1-b84e-fd28fd35eefe\",\n  \"da1d017c-9cd6-4e4d-8021-e19300383768\",\n  \"4769a5a6-12e8-459d-bb99-687c019a0d81\",\n  \"0b8d95ac-cd6d-4792-a1a0-d98633a9f1f1\",\n  \"c6a15276-df7b-43fa-9e2c-b53dfd80beb1\",\n  \"c7e43df1-014a-4a1d-bea3-dad69623716b\",\n  \"731a6820-b220-428e-9f5d-fb5eeeba32a7\",\n  \"c55c4a6f-30ef-4bd8-abb0-6d038b83503e\",\n  \"6e4ebcfc-ae88-442b-8eb7-2f59ffa56197\",\n  \"72e47185-ef51-4edd-bf3a-7f98675f69a5\",\n  \"f7290c24-3f15-4cdf-9655-ea6aa69077bc\",\n  \"fa41e0b4-6d51-47e7-9e9b-83c17dd638b9\",\n  \"d79e7451-aba3-445b-971a-6e3c13d5e0d5\",\n  \"d3f577f1-78cf-47e8-b57e-6967ed4d91c8\",\n  \"9f43f054-efbd-4bc5-ae13-04e5c62600b7\",\n  \"685b03b2-878d-4e39-b420-39b5b3e4a160\",\n  \"2e619daf-8795-4ee7-ad9d-0ed0f3a47c35\",\n  \"f1523362-3421-4f9b-b39c-05db95630be2\",\n  \"195b5853-3944-4089-8a8a-2c8f03944625\",\n  \"0537dc4c-9660-4f08-b024-3d3947df69ad\",\n  \"fd418d7d-416e-4f93-bc07-1ec270cf8fff\",\n  \"c6e76844-aad7-4236-b079-71c45c4deb29\",\n  \"87f9b9d3-0594-448e-a30f-4bedbdde50f8\",\n  \"195c072d-c77f-4ab6-aae6-0332f56e3350\",\n  \"1f1ca134-3f47-466b-8804-76b75d82328c\",\n  \"24a0e675-3c35-4015-a4b7-6cfc90117ab9\",\n  \"cd757c01-3b04-4e43-884c-e216a8d4f95a\",\n  \"dc1f54d5-0158-4345-b964-df13986532bb\",\n  \"5b6a7b0b-929d-4274-9b63-26cad3ad3e75\",\n  \"30f21faa-6591-4c13-a1d7-fd1332b08fc0\",\n  \"cb993eb0-37de-49e1-9e1b-c10391e7a1e5\",\n  \"f3c8059e-6e04-45d2-b8b6-920fea3c24cb\",\n  \"b4d0e09f-801b-4ce2-96e1-eeb08442aa6f\",\n  \"c844f5ac-40e3-4fbe-8df4-37427fde7311\",\n  \"275cbbdd-73c6-4e62-a0d0-277269a578ee\",\n  \"da141dfd-32bb-4339-9659-c15a7df985c6\",\n  \"d008edab-f6da-429f-b54e-8ad9bbd8400b\",\n  \"116f9eb5-053c-47a6-8475-f1a2a056cc6a\",\n  \"238f1632-18f1-4c1c-97f2-01b3aef9a843\",\n  \"07877e76-bd66-4559-bbb1-caa9d003572d\",\n  \"9f2029a9-d3e4-4c94-9eb3-507b9763a41d\",\n  \"b5d84ebe-cc31-4485-8e55-c3ce3104ac61\",\n  \"a91e544f-edfe-4105-bebc-2398fb9e81db\",\n  \"3ad0f04a-c091-49f2-82c2-59adff77b63c\",\n  \"9de0e097-aa08-45cd-89c4-67aaa9daf529\",\n  \"feef204e-c100-4c64-adea-d38a087268df\",\n  \"6bdab179-dda3-4911-a00e-0563247011f2\",\n  \"43f8dc59-af61-4580-ac5a-3254cd82b373\",\n  \"ea5cc42f-dd5a-4468-a8ab-2b9766b6128e\",\n  \"3d87209d-dcc8-4e55-8c3f-0abbe9a80453\",\n  \"b976dfd0-4f8d-4717-aba1-f7f19f843b54\",\n  \"1a90d235-8fb3-4bea-8506-980f3b0eefd5\",\n  \"b4f7d95a-7243-400c-b644-c5fda6b730f0\",\n  \"7aec79bb-967c-44c4-9469-f87290bf1300\",\n  \"82d54a60-1dc6-451f-81e5-3f9c44591e44\",\n  \"e66363c1-c5ba-4c14-b352-74da981e5a88\",\n  \"5a50886d-15c1-4186-9f5b-1018be9cabe0\",\n  \"dcc1e167-8cfe-412d-af3d-4a2dd64db408\",\n  \"0c027fcf-a4c3-4e19-91a1-dd47e7ee9928\",\n  \"88176c45-9de8-420c-a4b2-a78f0579e20d\",\n  \"7abc3774-8dc6-41b6-b38d-9935df1717bb\",\n  \"bb731c8c-d370-4122-ba41-79658201cf92\",\n  \"b6497351-9096-4f34-82c7-4ce6839a7d17\",\n  \"fb49852e-4b29-4c4b-8505-b35e107e96c4\",\n  \"c0a696d2-4c19-4edd-ac7f-ea28b179790a\",\n  \"8487e759-92c5-4c08-9c92-035c11fc0e35\",\n  \"a0a1b0e0-d18a-40cf-b984-1d7a7721b33e\",\n  \"45522661-d60a-4818-ac4e-e800b5d0b82c\",\n  \"2a75c21f-9696-4fe6-a3c4-7987166f0bcd\",\n  \"e1ef5510-b2d2-4494-9fe3-3e2722af47e6\",\n  \"024a4107-03f0-4002-b25a-4d2ffdc4ba82\",\n  \"53535703-2dba-4dfa-9747-fb94768ba567\",\n  \"ecb64f65-0462-4df8-9206-93818fa332c0\",\n  \"e94e496c-da82-4bc3-ab7d-62bea9265bce\",\n  \"58d39e36-7ccd-4de0-bcbe-25c341bee8e2\",\n  \"bed0e1b1-32a7-4874-b27e-6e1643017a38\",\n  \"deb586df-f25d-47e7-8884-cfeaac9f7835\",\n  \"6e91a779-6dc2-436a-8168-46bf48986acb\",\n  \"ed10d918-4f17-4286-acbf-1055acb2422a\",\n  \"91a085c5-7962-4713-9d33-cd500f512555\",\n  \"b2b5557d-f68f-48cf-a603-c2ddc5e26473\",\n  \"9c725196-f83d-49dc-9125-6eb95b865775\",\n  \"06cc10c9-2a58-44dd-98d7-c4d195e06818\",\n  \"a927a199-62ca-4523-a695-b1ae08d23452\",\n  \"1b7151e0-62f7-4b01-b5af-b2061cb8c023\",\n  \"819d9f70-08b2-4b54-830c-eb7675fa530b\",\n  \"b44b5660-14f4-4ce8-9f49-6004fc4044d8\",\n  \"c469067c-c739-40aa-881a-b09a325001da\",\n  \"358b6af9-2204-4b0f-bc26-50a8c88f2689\",\n  \"be06afd5-755f-4528-8ba0-e6b8de5d791a\",\n  \"566fb9c0-b744-4553-b12a-9381a4e309b1\",\n  \"91806878-de13-4aa2-9768-b76a22ba2098\",\n  \"eeacbdaf-7efc-4951-a86e-81242dfbb778\",\n  \"53308fd7-d1a3-48c4-8610-0030cf9a054f\",\n  \"9d76cf5e-55dc-43dc-86c3-d1a3c40cc36f\",\n  \"3b1b4894-0015-41da-b6d6-af0f70c3d8d2\",\n  \"64d21e4c-746d-4737-9a23-522c165286a1\",\n  \"9615b0e0-1ca3-488c-98e8-463118fd2ec5\",\n  \"5c65db36-057e-4c07-963f-bfc4a688ca53\",\n  \"5ec0b804-b6a0-48b1-9c58-928b4e03bacd\",\n  \"cd1c6279-7185-4172-a131-2003de8f299d\",\n  \"82810966-c260-49c2-8dea-4ced82e849cc\",\n  \"667379f3-27e1-4976-b9e9-942ed5e68b5b\",\n  \"206298cd-0c52-4518-8704-3009ce533839\",\n  \"90ecb328-4e47-473a-97c8-625b3be62c07\",\n  \"8f76c7a9-011d-4997-826a-6c3ecfd741aa\",\n  \"e841e686-b7df-4ddd-a71e-f677b7bb8860\",\n  \"0ea0d0b2-a2a8-4431-b43b-1b00b1cf7442\",\n  \"73a9ba87-24a4-4c08-9e84-9567649065d7\",\n  \"47a21d69-156f-4f0c-9015-329018811a82\",\n  \"aab87d12-982a-4d32-acd8-98e7fab9c1bb\",\n  \"de5cd000-cf4e-4f9a-85fb-2b3af927d2b4\",\n  \"9419697c-516c-46d5-8d85-293c35866e1b\",\n  \"2d266105-3439-41b3-863d-5f6dc7004e10\",\n  \"b17fcd3a-3845-4a63-8d65-9c5822401754\",\n  \"e4a88e20-32ab-44d3-a009-f1496206ecfa\",\n  \"0e6cc62e-a920-4772-8ece-7617e9331b2c\",\n  \"04354639-d44e-4b04-aa77-e833df17c081\",\n  \"a21fa92c-a2de-497d-a0b6-51480b26e7e0\",\n  \"5f1b1b07-b737-4d9a-8f74-f9f2be09bb3b\",\n  \"93a10235-50ea-4d15-888c-f876c2987592\",\n  \"d60446d0-fe60-4d4c-9070-755a64980799\",\n  \"70d58405-248b-4ada-afe2-730d9e818f1b\",\n  \"4cfd6b68-4c06-4be5-94ba-8242c6f4451e\",\n  \"79774624-cfd0-4407-9308-2e288b352fbb\",\n  \"f1d924d7-c0f5-441a-86b7-0949a863a221\",\n  \"797a587d-c5db-4074-b099-2ee36bd4499d\",\n  \"c06b414e-66aa-499d-b353-9b2ef79489b0\",\n  \"5c335744-7b2b-40db-b290-1347ff2e1084\",\n  \"53edd7c2-e847-4faa-a58d-97de200603a6\",\n  \"dad128ce-ac47-4ec4-80f4-23db53be3ac7\",\n  \"727ff6ca-d0cb-47d6-922e-65b3f9383857\",\n  \"11777c55-2cd2-4138-9127-1e451a9752c3\",\n  \"bb16809f-932d-469b-b3a0-eed0ca4f420f\",\n  \"04c1a977-c830-489d-81f5-ec9c1cea8d80\",\n  \"effc2780-5f9b-4a0c-8f8d-10cbd2351f86\",\n  \"62a0c516-adba-412f-987c-c18ee620cfbf\",\n  \"906480d1-ca7e-4b79-9cae-43865aecbbe3\",\n  \"fcb7b0a4-ae11-4f4f-b247-20a3a50f9d70\",\n  \"9b579d60-0b73-41fd-aa76-56ff4757d21b\",\n  \"d7b6d479-a7bf-4a28-87e2-0a8ea329863a\",\n  \"4211be86-8cfe-4861-a864-4f600ed5ab70\",\n  \"55d812a5-ebc5-40bb-ad60-6a2f737a8866\",\n  \"581b1c5e-a8cd-45ad-89f2-7b77eef1bb4e\",\n  \"80981d99-099b-44ef-b823-18ae9614495d\",\n  \"4081f725-cb43-4ef8-82b4-563772d06396\",\n  \"71fb535d-2334-4ad7-95a2-d3120ed2f3de\",\n  \"a602aad7-348e-4bcd-a981-f6cca2cf5618\",\n  \"86d24e3d-f110-4dce-bfd1-3798f6941ed7\",\n  \"3e3b1ba6-37bb-48fd-afe3-183fcd689687\",\n  \"59b6ac73-a1db-4525-8ec4-12cfa2150b4c\",\n  \"00988880-6a05-4288-ad5f-a837cb546383\",\n  \"b3a0edfd-b4b3-4266-a3bc-8246dcaff3eb\",\n  \"6932db64-816d-4acf-bbfd-408ba55b944e\",\n  \"f3b63c1e-f8f2-484b-bbab-ac13c9a747f3\",\n  \"0d55407a-3fae-48bd-b57b-f00ea68aab5e\",\n  \"acc5b913-3abb-4af2-bed3-98f02080629a\",\n  \"965b178a-03d8-4833-b9a2-2efc9ba97789\",\n  \"c8849011-dcc1-4522-8cc5-a40e5445cb2d\",\n  \"3d4de967-6273-4053-bffa-4e3c3a1423f4\",\n  \"4b34b7fa-0cda-434e-9a29-6cd954c32493\",\n  \"82c3066b-889f-41fb-823e-7ed749482c3d\",\n  \"a444a43b-8b38-438c-b677-b2b74dd326d6\",\n  \"e1b3441a-8869-479d-aeb4-d1d9382ae29b\",\n  \"1ef7210f-4e13-45a1-96d8-38979182c2c0\",\n  \"c8485d83-d7ba-4a4e-8026-4dfd14dd3f22\",\n  \"d2116caf-5db5-41f4-b5c5-d668ae628b49\",\n  \"e8e118d4-b657-4db0-9ef9-7ed622b15aa5\",\n  \"1bc93e6a-9b7e-4245-b702-85f38018b7b2\",\n  \"df02b115-e4cb-4039-9759-c7ba218f57cd\",\n  \"10d7a00b-2ea4-4c92-93f8-9465609d1b13\",\n  \"220adce2-f1bf-4510-bf6e-082f8abdccfd\",\n  \"462cea9b-d8e5-4e5b-bc34-8f05baf53942\",\n  \"54743811-0dde-4ac4-a9cf-afa76dc53f91\",\n  \"d713d36c-ce8a-4f42-a51f-41b69adb98f0\",\n  \"2a39cf04-cbf0-4bc9-afc8-d0a67ebbde2d\",\n  \"b2eb3e4c-7232-4fa0-b33d-3a90183a6494\",\n  \"95546a0b-2899-4b8e-b02c-27551d025a0e\",\n  \"298556b5-3204-47a4-9310-9cb051b06b93\",\n  \"9b16b778-54ca-4b8a-aebb-dd2f5951607f\",\n  \"85b83c20-2a73-4a1e-a1b8-2f140c86b170\",\n  \"c5adb920-408c-45a6-a853-d1e4decbb344\",\n  \"9abec293-bdb6-47ff-b78f-223a37a59c0c\",\n  \"048f3ea3-4a32-4cd7-9dda-1ba287fee388\",\n  \"cf019f7a-0bc6-4636-81fe-d7abd6257b6c\",\n  \"80181c02-e4a8-4ed6-a34f-70d7be5eb72a\",\n  \"48389171-7dab-4171-8589-b728c2a57dff\",\n  \"77b866c2-0dcc-460b-af6e-34b1de695ba8\",\n  \"f905c98d-8c6e-42b2-a9cd-47d5e96f0763\",\n  \"c5ced9a3-6650-41fc-a63e-6d7ac4e5a20f\",\n  \"6559ade9-0e46-4e25-8dfc-86f06a85cadc\",\n  \"d7026bce-2925-4f82-88fd-44ac7ecec953\",\n  \"fe0ed8c7-558b-43ae-a3d2-57d8ec9a7d0e\",\n  \"b871fe0e-9095-47b1-abd6-e7453df7e423\",\n  \"da1e39cc-f2b5-4147-9c17-8f2b4f2e6329\",\n  \"fc7385a1-4243-45a2-b6c2-9ec73f6aaf2a\",\n  \"373abe98-3ef5-4105-a4da-9067d4fe168f\",\n  \"25441069-2e87-4f93-9de0-022c664ec000\",\n  \"f412bfd6-f40a-48b4-b97e-457290ba9dfd\",\n  \"32ca2927-1953-41d9-85f1-857bfe169654\",\n  \"01feec77-baf4-4ea2-8ed4-ca8287d5fcf7\",\n  \"ce1a44bd-2125-4a2d-8edf-22ad1928071e\",\n  \"b8ce082e-e385-4198-923f-e7c020edecde\",\n  \"0c657425-3848-41e2-9bba-2a265fbc060e\",\n  \"12ee157e-d1a1-4395-aa37-7419ee31886e\",\n  \"3bf62d2b-aad0-437f-b724-172b2c03f1a8\",\n  \"5cbc2fbd-70f8-488d-8bdf-8bea63a703f8\",\n  \"ca6002b4-6925-4b22-8b06-a78934e6870b\",\n  \"6e184eb2-7e8e-4ee7-8c6e-b75eba9fa11e\",\n  \"a9451466-b17a-4069-b303-70ddefdc5679\",\n  \"b7d388aa-b88f-4e6a-90d5-e8438b9ff7b6\",\n  \"249ff2e5-44bb-47aa-a837-8b69a8c107b2\",\n  \"214d7a54-049c-4bb9-9432-8e63325c64eb\",\n  \"b4d8adfa-ee4c-4356-b67b-daa173ad353a\",\n  \"31fd2b8d-9c47-4e46-b5d1-87a749d4b948\",\n  \"216daf9e-98c4-46b1-833d-4ff2bf040c31\",\n  \"0c65ba93-de07-4563-bc89-616758aa0370\",\n  \"86aefeef-e27c-430b-8030-6bee5468dba0\",\n  \"896b015a-6fc6-4bd2-9a70-32eabcedf3b6\",\n  \"27565dba-a982-4f49-bb85-a95195cbea4f\",\n  \"03939242-d237-4f64-8b97-c82dffd2cdc1\",\n  \"c72cf5ea-dafd-48c0-8688-ce9f08ca75be\",\n  \"642c2064-fc48-4f75-8875-9525bc618814\",\n  \"c9833b20-bd11-49d5-b194-e65df0d411cf\",\n  \"8a5e5fda-b663-49a8-8cdc-9e87dd4391f0\",\n  \"c56027e7-1d16-4bbf-893d-558f75d767e6\",\n  \"06ba657d-c99a-4bba-8b74-882bed79d735\",\n  \"75433027-9355-4cb9-9dee-5938bffe95aa\",\n  \"48b1887a-cde9-455a-9665-9ddae4e6dc2b\",\n  \"e3bb1b26-70e5-4b69-871f-eb88d5ca4ba2\",\n  \"b7b9b723-4eda-4699-8a62-8a4b5f2cb8c3\",\n  \"d31ff0c9-db76-4985-82d5-f746884e0b85\",\n  \"b7846ab9-50cc-486f-8f6b-c5d92ad11f74\",\n  \"ff28e216-e7af-4c37-ac22-2bed797ac6b5\",\n  \"d584a32e-eba7-41ce-b9fa-36dea1e85877\",\n  \"42de1533-e729-4ebc-8766-de1b5a092a50\",\n  \"1b0608d9-b317-4ebd-a60c-77c8a5252a1c\",\n  \"dea84c32-3974-4855-a1df-72a7867d8087\",\n  \"d94f948e-c376-4d75-b1f1-b8ac88c983ba\",\n  \"828906b0-a9b0-44db-99b8-865572cd8efd\",\n  \"b7aa992d-7540-48c7-9436-20b0f4d3b2f7\",\n  \"6f76ea21-524d-4077-9b11-25f2a7a199ea\",\n  \"381a9088-7c23-4522-989c-0802bf8e7188\",\n  \"1ebaf8ef-fcbb-41aa-bfe3-24d19ef0611e\",\n  \"dab65420-4783-4d9d-a1c1-ad7baaf69b73\",\n  \"d9270df4-3088-4cf2-a252-16c3d23316b4\",\n  \"29d6accf-811b-44cd-94b6-69e94d0bc34f\",\n  \"a9fe7293-2bb7-4e4f-bda0-751941a4861c\",\n  \"56e71497-6a0e-4416-9ac2-75fce6db4b33\",\n  \"a99ca3c4-ce49-415f-ad9d-a3b912656db2\",\n  \"83d369ef-ad0a-40df-9e83-7c5d3f7ada70\",\n  \"7987e427-1c6f-423e-923d-be7bc6a0e0ed\",\n  \"a37e5844-fbca-4d9d-95d2-0645603c7c85\",\n  \"bf8f20ab-e679-46c9-96ff-e00b21a15898\",\n  \"4d1d5a93-d1c3-4550-afc2-2f8be3f3d425\",\n  \"3a854bf6-ba58-4e06-9457-1cc9f45648e2\",\n  \"7884b5ca-824f-4e64-aaf4-5dda0d4f2f30\",\n  \"5a2510cf-6992-43ff-b84d-8990e189e167\",\n  \"7ce4cfda-0bce-4f23-ab47-64b61eddd80a\",\n  \"9c676213-cc6d-43b5-9eac-6f96a025c26c\",\n  \"81ba7f25-5100-4947-9067-c4e1460c51ae\",\n  \"65e33299-915c-4275-913a-32c0ab13edf3\",\n  \"834ddfff-459a-4c27-8778-a1b0feb0663e\",\n  \"796921d6-e7d2-4a32-8fa4-31b2a083b11d\",\n  \"7ef093c6-aaf6-49ed-a6d0-daea5ec99d4f\",\n  \"5b3647c2-c98f-4004-bfa6-eb66cc9db737\",\n  \"65997697-b7c8-465a-8589-7302bf4ade22\",\n  \"7dda4a75-345a-4ff0-b29a-d5fd2bd57be8\",\n  \"01087e95-27f1-4546-8906-d0fe270885d2\",\n  \"62c8bf9b-d726-4fc8-806b-0cf6128d5013\",\n  \"cb076de7-a2b3-469a-a6a7-4b7fcca6ec83\",\n  \"1a4199ff-d794-4e27-9c8e-588b3829cd88\",\n  \"97fea6c1-d1f7-42ba-b802-5b24bdd8cded\",\n  \"7a51f84a-4499-4711-82df-101cd88c94a7\",\n  \"4990d796-e073-4b69-a03f-d021b07534f1\",\n  \"c9198f2f-3431-44cc-946b-33a7d269ea5f\",\n  \"9c0cde9b-2104-4569-af7c-3beee0ceaae4\",\n  \"0a86624c-6084-4854-8757-c52ed3e893db\",\n  \"1f33a4bb-226e-472b-80d5-17072ec5f199\",\n  \"4adc699b-c20a-40dd-8746-8eed531778a4\",\n  \"53ab54f7-091b-4d15-a81c-bd2e32f1b5a8\",\n  \"e39a8d52-453b-40fe-b5df-fe89c3e2995e\",\n  \"e2953dbb-28ae-4e33-9f3a-11eac25b7f04\",\n  \"86f6521b-459f-4758-8918-1fc319c8fc39\",\n  \"2495d9ce-4f26-4136-81fd-d7537018606c\",\n  \"40f6fa02-1884-45ae-a29e-1144342175a1\",\n  \"5a7873ca-1155-4653-ac80-86def6293889\",\n  \"482e18a0-7593-4a19-bf07-cdd02204cd2e\",\n  \"cea9fbd9-4c50-44df-86fc-41d5986d555b\",\n  \"83af7912-c3ec-4b07-a36a-4f2faccd3543\",\n  \"0258cdfb-33bc-4614-9dc0-3ce9781469aa\",\n  \"c5bfd828-48ab-4622-ba13-3e59f38e8eda\",\n  \"0212a7d9-56ae-4aad-8f51-f27cc612a017\",\n  \"b5074997-3f41-4de7-9663-19443f5b1a6f\",\n  \"d0f93743-818d-4bdc-9032-2c39abb015b0\",\n  \"b11deaa5-8a4e-48b8-8d74-c05e1f51d6ba\",\n  \"4d6ad1fd-f547-4d92-9c99-eaa1a366a27b\",\n  \"34658feb-983c-4d84-aade-1589ad24c9c9\",\n  \"0dd06f25-c34e-427a-a0ab-33ad92654625\",\n  \"08340a35-b97b-45f6-88b8-6edc2e6b930a\",\n  \"30a3aa1a-4612-4d11-a540-72b5ffa55712\",\n  \"73aa151e-f79d-495d-9221-f53433628135\",\n  \"38980b87-0484-4015-9e97-8043aa2f32d0\",\n  \"18cb5586-6b57-48c0-93bc-f260c4567dd9\",\n  \"173a40e2-9038-4c27-a350-d66dc5c512cf\",\n  \"9c5f7d8c-dd16-4cb6-ab96-8789aadbd80c\",\n  \"e2eaaac5-84b3-463a-8553-a6dd09392c2e\",\n  \"e1a558a3-64e2-4690-9f91-26078fc2695c\",\n  \"e2abb082-d1df-4a5b-add4-3646d181d1fc\",\n  \"8d5f606f-1dce-4737-9b8c-46708aed27f5\",\n  \"d9646d59-2650-49f7-bde9-a2cde0ae621e\",\n  \"dc1f6c36-ae6a-4fc2-a5fb-82c214516be6\",\n  \"dd7ac5de-b661-4256-af4d-264e27dbd002\",\n  \"70d2c1ab-e3ea-4880-bc84-bc23fb0bc7fa\",\n  \"892cb8d1-dbff-4a6c-b3d3-248f34089aad\",\n  \"4708aab0-d4c7-4876-8824-447ef234ffa4\",\n  \"45f49a71-802a-4502-94a6-b7ed12e57c99\",\n  \"23779242-97d0-40ae-9631-ca78e79266be\",\n  \"6d5c332a-6758-4c21-8576-2c552ebf88b4\",\n  \"524c6bbe-76dc-4728-be9e-5aaf32e36fb9\",\n  \"b243eb71-dceb-4af7-9c47-2267b731372e\",\n  \"46d8b6bc-af0f-41e9-86b9-e2bb87a9adcc\",\n  \"bd720042-dd03-469c-b91d-39d37d8ce966\",\n  \"90e32533-6698-451d-8bad-54186b5ca35b\",\n  \"77e41115-a487-4768-89fe-6ea6c8172845\",\n  \"42563ef6-0354-4a3f-aef6-28eb5877e0b4\",\n  \"c5d5735e-7557-4a86-9585-e1d743090e11\",\n  \"7b7b5ff7-acee-40b4-bb09-f37d26ba5d48\",\n  \"6677d89f-206f-406b-a22e-43f65525d6c8\",\n  \"71d99d1e-46c8-4eb8-8196-7761f4e90f9d\",\n  \"ef88ff39-3ecb-4764-a04c-bbe9cd0d8236\",\n  \"88f87fd2-46a2-422c-a774-8f150997dae2\",\n  \"0723f367-ad40-4d7f-8a4a-3eafbfcfb3b8\",\n  \"3c141a49-eb8f-4387-97a0-1200f08549f7\",\n  \"b8a8e67e-32f9-451b-87d7-747ec8eb78c9\",\n  \"4cb856e3-66c0-455e-8822-0c2b2fd0ab47\",\n  \"6362ab9e-d31e-4a9a-8ddb-f36d4705ff09\",\n  \"ad408dd1-76e2-451c-beee-5dc04f5e577b\",\n  \"437b9e6b-c50a-4f0d-aded-a5dd89508b80\",\n  \"f5af155c-73b1-4d1c-84dd-e4d102320081\",\n  \"72175639-2f34-49e0-9517-43f8f5315f46\",\n  \"5b443f5a-cc92-46e4-9ae6-331dcaeeb944\",\n  \"26f59325-b368-45a4-bb5d-f2aff58acddc\",\n  \"8ecccbd2-50f6-4cdb-8820-b13a2b362d99\",\n  \"6e913fa3-3981-408e-83c0-3aa2074043b4\",\n  \"414cdac7-b2ed-44f2-8876-69f9bb6d0587\",\n  \"fb12e9af-fbc6-45e7-9f2d-3d51e5878524\",\n  \"f8dca192-0de6-42f3-a7a4-e2105273ee01\",\n  \"4e57c198-75d5-4fc6-8771-443e806b446e\",\n  \"8b757c09-fa6e-4ba4-864e-5a2a9a126000\",\n  \"9cd97ed6-8af3-491c-8dfa-a44c1aa1b1d3\",\n  \"048adaf6-c62a-46d0-a9fa-e6a99aadccca\",\n  \"51d7ed3a-b168-46af-94f7-a459ebb15c66\",\n  \"b8cadf0b-917f-4c91-92fd-25648b6c136a\",\n  \"f388ca91-1894-4696-a603-f7800d9a7248\",\n  \"df437ddc-ca74-44f3-9cb5-a6411f4f1ac6\",\n  \"5ef7f928-0d69-4be8-945c-aba230a7c4ee\",\n  \"5022a280-6726-4a73-ba29-89ba1d891be5\",\n  \"0a9f8d4f-c5fc-446e-864d-2164e28baec8\",\n  \"d1b60821-ad52-41bc-95fe-71933f6c7090\",\n  \"980b6140-e338-4ca8-87f1-f2ff59d05170\",\n  \"70ddffea-40b0-41d7-a1b0-7412495ae118\",\n  \"916e5a47-ea0c-4048-84cc-0b63e3a8bfdf\",\n  \"80d5015b-0833-4015-a105-2cf90e6c3957\",\n  \"fbae9574-9396-4923-bce7-6e51fe7d5f44\",\n  \"38169a6d-e7c8-44fa-b1d0-63c429982956\",\n  \"1aa40968-7fc1-4460-b232-42124cc77f04\",\n  \"424e89c6-c7c3-4b49-aff1-958c1dd03ab9\",\n  \"68cd3c75-b798-460c-a829-6e482456f0e8\",\n  \"85f39cfe-0173-4645-8994-e1389c54c88b\",\n  \"9e3be60c-94ab-4442-a2bd-0e8d03bf57e5\",\n  \"f4560425-a748-4954-b6a1-310b4b0e2294\",\n  \"d50db7b7-8813-4709-86f5-779068c30f95\",\n  \"ab4faef8-7062-48dc-acac-2ca22ad61be8\",\n  \"39314da4-2bc9-4385-a9fc-ddba6ae6d778\",\n  \"9fd7862e-3b33-4a4a-9bb9-11691ed347e8\",\n  \"2d395115-ef57-425b-b163-0f4a4619c43c\",\n  \"576ac4ef-e5b3-4c79-894f-09b2bcb4ed77\",\n  \"ec8648a5-8c89-4982-bc5d-bcd6420ec735\",\n  \"0bbd0726-6025-43c1-b3b1-3441b339d5fc\",\n  \"927e0c30-e610-411c-9887-d17ae906ee07\",\n  \"1e4311cd-80b0-4263-b31f-0dde9cb614da\",\n  \"5f6d8da7-43c2-4038-b3c0-d167343cc127\",\n  \"4b8b4980-390e-437f-ab52-56f666240169\",\n  \"c9fa9505-e84a-4349-98e1-968d65095af8\",\n  \"bd0bb4c9-bc8c-4ed8-8f4c-bee991f11869\",\n  \"5bd8038c-e303-47f9-920d-9721d28be5b6\",\n  \"f32c186c-3d57-401f-8823-655e47f194a0\",\n  \"1f33769b-0390-4fc9-aa44-7e0bc054e6a1\",\n  \"11994f68-01b1-42a4-ad3a-b46c727451fa\",\n  \"f53388c9-2022-481a-b77b-a976a7bfcf9a\",\n  \"1a4736e0-ec3a-4a4e-908a-5bcd2cc02f14\",\n  \"8e05b5e9-2400-4b1b-828f-52378ef4f4ff\",\n  \"177f44b8-9403-4ca5-b7cf-dff3eaa26c67\",\n  \"2ca02dc8-3997-46e9-befa-03e5ebc11750\",\n  \"59e13b31-8717-4e1d-a074-67f0b54c3712\",\n  \"249eb872-8155-482c-9fa9-13dfebd1379d\",\n  \"9ce4375d-5891-4fb6-9e71-6aaf7f7dbb69\",\n  \"9e5a43b2-2769-48cb-a942-69c8fa347c22\",\n  \"143ab1f2-0bc3-404d-8742-94fce31bcf63\",\n  \"8277dcff-3b87-4eb2-b056-a6bc040d688a\",\n  \"c43ca443-63d7-4293-a217-429ab4ddfc7b\",\n  \"509332f0-f244-4b96-aabe-5124ebef5323\",\n  \"59128c79-e0c9-4071-8d0b-d6fe914ea666\",\n  \"17a4678d-8121-4896-89d5-3331655ca6b0\",\n  \"a7e78822-1707-4c0a-81c1-aa9b69a8fab0\",\n  \"6fa6d845-b1d2-405f-b0e6-0634abfd75ca\",\n  \"f118bf9d-7e52-4f38-ac71-66001f3df963\",\n  \"45d758f0-f0b9-4d7f-ac88-907eecac60fa\",\n  \"41e9553d-65c4-45b6-9572-138b06bdb48c\",\n  \"605d6fab-a592-4652-8ee1-d0b2da6cee3a\",\n  \"38b9f42d-7907-4073-bbdf-e308659389eb\",\n  \"d306a8c0-125f-4977-8660-602dd5d2575a\",\n  \"7e8a1e5f-ad74-435f-9c96-547e2fc91f72\",\n  \"2cceb47c-9444-4c0a-bc3c-91f776fa8a59\",\n  \"284cd66d-2e0f-45ec-bf97-43f8ad53343e\",\n  \"419bfe7f-36b4-4c9d-abac-8a1a6ef1f27b\",\n  \"ec9c73e6-188f-4abe-9e48-39b65cf652f4\",\n  \"da19a9ef-611b-43a3-bb06-1e4c343ee373\",\n  \"92a333c9-b50b-40e4-92cd-2f9cf73483cb\",\n  \"15edd554-2682-4b6d-b055-fd151d442545\",\n  \"539c25d3-2756-4601-bb39-d50f48c1d21c\",\n  \"6ec52361-5e9a-4183-ac7f-9e53197c11d4\",\n  \"85308af8-541c-4379-a9ca-7e2fbc591f76\",\n  \"42c00816-a3dc-4820-a362-beb288bf5e4f\",\n  \"c6d36d8e-9e5a-44a4-90fb-98621aa7911f\",\n  \"cfc02b60-b062-4acf-abc0-3a64f5353994\",\n  \"40482ef9-1c5c-4456-889d-ee8042d70553\",\n  \"1ad1d575-b011-460a-95f2-5231a95064be\",\n  \"e5462e8b-a7be-45a4-ac22-cb237e4e5dc6\",\n  \"c6f2d31a-3403-4fdb-b97d-9768449084e2\",\n  \"e2b9045b-d944-4d9f-8603-d286e9315af2\",\n  \"4c04e872-c7b2-46ce-b4e5-a5f93feda5b4\",\n  \"6b4c1b9f-ab2b-4962-abf7-cfa08e876909\",\n  \"2858988b-a701-4cf2-a8fe-58173d2c5075\",\n  \"a0ad0789-ed84-4926-b50f-a715e816bff7\",\n  \"b5efda41-3f93-4c13-a6a1-03bc769e8a4a\",\n  \"e9feb12c-83ea-4097-bab0-cdc75456d558\",\n  \"60e02746-b4c1-49a3-877f-7ae1752646fa\",\n  \"dc099916-89c0-46b3-87bb-e857335960ed\",\n  \"2f9efed6-c022-4367-9f1b-0b38258c20ad\",\n  \"da2bebed-23cb-4308-8786-b4a104e0ba14\",\n  \"2ca7aab1-e959-4985-885e-2627daf8b05b\",\n  \"9420bc4d-7bb9-409e-b89c-4237382e566c\",\n  \"c490a9c5-5e22-47ec-8b77-01121482561d\",\n  \"366db2f2-85e5-4168-bc23-06340fc2495d\",\n  \"2d2432d0-2b45-4af7-a796-13458e48f787\",\n  \"e4658a8b-42c2-4e37-aca3-49fa0ddbfa1a\",\n  \"f78a7679-bea9-40af-892d-53fb3976aaad\",\n  \"e7f5d0be-5dda-4b78-b55d-b65fd5c83559\",\n  \"abb7eeed-469a-414d-9c7a-e32cf2c60a0d\",\n  \"a95aaabf-ba16-4f03-bed7-fba3db34c8b4\",\n  \"89df1266-9479-4583-8c5a-e5f8f46f9f48\",\n  \"6ba05dd4-6095-4828-abab-6479becbce36\",\n  \"6787220a-3160-45f5-9da6-85e93b61d9f8\",\n  \"f32667cf-ff84-4778-9a63-e5eabd7300cb\",\n  \"33c07683-8c26-4b5a-9da0-26288690a6da\",\n  \"0856bce2-eb65-482e-bb49-bb98e0ec277e\",\n  \"c2de6794-4463-4827-aa3f-ec864d0b9762\",\n  \"36a41347-d301-41cc-b509-446232504ef8\",\n  \"eff88bb1-bde4-47f4-955a-19721b1e889f\",\n  \"4f81d1ec-48ca-4b23-8a41-93f7e749b4f3\",\n  \"27cb6e33-0871-4394-936d-5ad33c8a6fd3\",\n  \"cb56c1e0-be38-4fe3-b5ed-7acd35fbb503\",\n  \"c1c9f4c4-b484-42f7-8a7e-117e886b567c\",\n  \"dbbfc714-9c89-4daf-b607-59ba71b26226\",\n  \"f70a637c-25f9-46b3-8fbd-e2a417cdea4a\",\n  \"1de3ccac-22b7-4146-aa7a-ef72312ab01f\",\n  \"28ad050f-a9b9-45d9-9e0b-b8aa6229b08a\",\n  \"f3c9eeec-80b8-4858-9a70-0356ae42d058\",\n  \"3d34679b-d0fb-4bcb-b67d-e5f096222cb4\",\n  \"e785e6ce-0815-49e6-83a8-eac1b7508292\",\n  \"adb9957a-d505-4ad4-a254-48383a593b05\",\n  \"51da1453-611c-451d-8c51-541a064a3066\",\n  \"115ec64c-e6c5-400f-ad1b-917f61b3bc7b\",\n  \"f2609f3c-5a92-44d4-83a9-34b34392567f\",\n  \"37e8557e-f970-4c6d-9bc2-cdf1e9577c41\",\n  \"9a9fea83-d5b1-4939-8f92-77d323617754\",\n  \"6d69de87-1ee8-4617-b3fc-0e54844e6a95\",\n  \"e1afc477-073c-4e91-9a37-474814f8386e\",\n  \"1a9eaf9c-2787-43d2-b799-7caa2ba65f46\",\n  \"9a0a0ca3-eb44-43da-a488-2d071b866e82\",\n  \"b875da8c-1767-4b58-b543-6bbe11d65a2b\",\n  \"56a4c881-68e2-4876-aa78-13ccdaec9f68\",\n  \"ceeac5de-f068-4ca0-9f1f-d670fcd22d85\",\n  \"c9f9d029-5a38-4d9a-86d0-f604cafea4be\",\n  \"c527012a-14f4-4048-ae5d-f21e5ca9a8fc\",\n  \"4c74ed2c-c8c1-440b-b213-9a996d0500f3\",\n  \"b8ba6af8-27be-4ec8-ac3e-72311bee2ef4\",\n  \"6adc10f1-d384-46a9-b59f-c88cc89a2fcd\",\n  \"194adef6-a469-4f80-9def-f04e0d9ed55c\",\n  \"a6d38768-03dd-4d72-ae6a-7dbe230fd9c7\",\n  \"e501fee6-94d6-453a-a496-7dd7aaa13657\",\n  \"992e16fc-73ae-4581-86b3-3c535e1a17fa\",\n  \"9ca6b1c6-6342-4cc8-b99a-73678e4df351\",\n  \"c0fc16a8-ff8c-4970-a4b9-adb5697c5611\",\n  \"d192a834-7688-4293-a4aa-8857a7a30f03\",\n  \"9f92fb54-6802-4c73-a806-7818897a0d25\",\n  \"1c12a28b-a66e-4e69-9962-8460d340aeb9\",\n  \"7b8928df-690f-4d87-b0b3-4a7cc2197b0b\",\n  \"58450b0f-11fb-44d9-9d1a-7621d9c35594\",\n  \"c61613b8-6d9d-45ee-bf04-bd6f8ebf2a25\",\n  \"e158a2d7-e21f-432a-9c8c-27fa68c7e238\",\n  \"191a7c2d-22c1-4cf9-bb44-e1f6a0b80629\",\n  \"e40602b5-444b-4d10-a296-81d5d21e7daf\",\n  \"125a62ed-e809-4103-80ff-9b97ec44e36e\",\n  \"fb8e4c82-19ae-4c8e-bdf7-b1fd54600bf5\",\n  \"4634ea39-7d37-4f94-aa4b-15aac832fe26\",\n  \"3872fbd1-4a6f-442f-a848-df4aceeb2c02\",\n  \"4675ca8d-45fb-4b94-80bb-e7c4acaea845\",\n  \"45688b67-a91d-430b-94e7-2e26a6937e8f\",\n  \"729bada5-0086-424e-b441-938469e441a8\",\n  \"7304e9f9-a7d6-4439-ad94-85b49bd63eb3\",\n  \"2999caf1-8e29-4543-b629-88aa015662a7\",\n  \"bdc10a4e-83a6-4223-9eec-47e28487e618\",\n  \"b83a1cbb-9e32-448c-a19f-c9254c9a3927\",\n  \"5024730c-9185-41a6-affb-776a8be0d324\",\n  \"ccace47f-1809-45f2-8e0a-4f1aec6f11ad\",\n  \"1b2d9937-3d4a-435a-a944-3ceb680a3659\",\n  \"8b0bc62f-f863-4d46-b7d1-3376658837ad\",\n  \"8a17aa30-d7cf-4a1a-8609-c8837389fb71\",\n  \"bab67faa-0e5e-4d5f-84cc-5619aee39b70\",\n  \"54a5a0b7-ab9c-4429-858d-d30c9b9c8b9c\",\n  \"9937af62-f0b8-4c5b-9a0c-b3667260d366\",\n  \"cbf685ca-d53c-40de-ab8b-0c44f0a45a1a\",\n  \"c2083071-766c-496a-ac5e-cb5075476b9b\",\n  \"7c26419b-77bd-415b-b41f-1461c7ac8f45\",\n  \"4f226d1d-48a0-4c42-9c7b-78c3b87e72f8\",\n  \"bb190d17-d369-4f7b-a430-30a64f26d67e\",\n  \"6d2e2ba3-0520-46ea-aa7d-222bc41ad5dd\",\n  \"b7d2b80f-8605-40db-b5bb-d78706dd094a\",\n  \"9c20f667-a62d-4345-b84b-d493bcfcf988\",\n  \"59e68970-5653-440b-9c9a-7e5bb28863be\",\n  \"c5934d7e-50d3-4fa8-b63f-ce5991c79523\",\n  \"e1322669-eb01-438b-91ab-b9b03c270981\",\n  \"35cab36b-d55f-494c-81d3-87dafa97eebd\",\n  \"1d2fe7b7-37fa-43d7-8e70-9fec41a8245f\",\n  \"86c6c734-bc86-4190-a7b8-27368e75359e\",\n  \"a7c2806a-b966-4a56-bc53-e698e69024eb\",\n  \"1086065a-eef2-435d-b8ed-ca9130c9b24d\",\n  \"ae7bdd33-f724-4177-9bb3-f1fd56c07d22\",\n  \"825a6c86-3641-43bf-b7dd-170d8e9d7660\",\n  \"255a31b6-1667-4942-8fe2-68adacaa8f44\",\n  \"0bbe7d78-3d4a-4a75-af81-33e5530fdc36\",\n  \"5f8d02f8-036e-4d00-adc8-9c1e09856978\",\n  \"33c6d1fc-6ece-44c4-8522-d783f4b504a8\",\n  \"58bafe33-b56d-4997-806a-e771b9b25276\",\n  \"69518af9-0b50-4a0c-89f3-c790c8075a0e\",\n  \"230b148a-50a9-4ea7-9a87-1fa13305284c\",\n  \"d9ea5de3-b568-41c2-963b-5d1dbdeb8a14\",\n  \"a34dfdea-0bb9-44fd-80f5-2e4422ce2313\",\n  \"a13e4d4e-b348-4365-8e78-564ce0ae073f\",\n  \"e98ba588-e02a-49ed-b50a-40ffaa9f33ea\",\n  \"b6d3b30a-712e-400b-9cb9-9182acc54c65\",\n  \"67a69f93-4506-4f3c-976b-c619325c7402\",\n  \"26a9fd8d-311e-4657-a123-079a4affd55f\",\n  \"cf44d9dc-b451-42ef-8302-da7de07542f4\",\n  \"121f568d-0896-4ebd-82ca-e58fbf322751\",\n  \"ced793e4-1030-4df0-852c-affeb09c5134\",\n  \"a3493919-c84f-40ff-ad66-cb696480024c\",\n  \"0e121722-63f2-4af1-b6bc-43079e534f55\",\n  \"b6124931-8365-4f6f-846b-4d9bf74ff159\",\n  \"07e6ea83-205f-4fc5-94bc-1c6830904df8\",\n  \"95b48f5a-f5ba-4f39-b6b8-5bfa544deda1\",\n  \"61c379ee-feb4-4799-bce6-60529ec74b57\",\n  \"50f562ba-5add-467c-979b-4d4c43e12ab8\",\n  \"847ec95f-184e-4cf9-bec9-601441c0a9de\",\n  \"2922af45-3a3f-4279-af3e-21e311374432\",\n  \"3e1e751a-6e27-471e-a5c3-8b0c86bfbafb\",\n  \"34452e4e-7ea1-4402-b722-b99b323da721\",\n  \"ed36ece1-5c23-438f-aaf9-64f65ec14ae4\",\n  \"ae25fafc-78d8-4803-8195-65350d6b9af0\",\n  \"efb8c909-8dac-4f9f-a7b7-c1bbe4176cef\",\n  \"575549ae-2626-4926-a3e5-4f334268b45a\",\n  \"219e80ce-945f-442b-af11-c54fd5e1e76b\",\n  \"1112e886-c22c-400c-82b2-2638dfbd999e\",\n  \"de8a0686-cffe-46d8-9b3f-e137026bfc69\",\n  \"0a69994d-1a57-4171-bcf7-95bf3c0e6eb1\",\n  \"bfa8f095-9dab-461f-a40f-bd3f03fdd2e6\",\n  \"fa8cffe8-bdfc-4f2c-8cda-2bf233768310\",\n  \"9ed46099-eaed-44a6-80f6-94f0dedc1824\",\n  \"5729f9f2-f595-4258-80a9-a0b4461e7064\",\n  \"a26cac6a-57c4-49f9-b227-88d3e2e7fb24\",\n  \"496a72df-f7d2-43e6-bc27-658263e216ff\",\n  \"b7251c2b-cc43-4f89-adca-41e024e72bf7\",\n  \"a910e5a0-0a6e-4d09-a302-e2f9f7a41fe4\",\n  \"e4d5b17e-12e1-454e-9f85-1634f6a88065\",\n  \"38d3897d-1162-43e1-8d19-bca29b610b89\",\n  \"be966123-6325-42ec-bcc3-f0a9346a810b\",\n  \"6403bea3-f5b8-4667-ba6c-8fd253bc6c28\",\n  \"34d845ee-238c-4bab-b36b-59f3328170b3\",\n  \"2f10c7a4-3ac0-4bd5-88a6-9a3ed02a264e\",\n  \"17688676-f1ee-41a2-bffa-dd73396faf54\",\n  \"479fbe5d-d1f2-4de7-b23d-8cbc5a101ef0\",\n  \"2b5232ff-4b25-4a88-a001-2b2e421181c7\",\n  \"c0955b29-be7d-406d-8d6c-2e87b43c92de\",\n  \"4f852de2-0d0a-43c5-8e5e-d3c8349f523c\",\n  \"532b9236-ab68-447b-9e92-cadd92ce91a6\",\n  \"c848adfd-1090-4e9a-a44a-e431442458e8\",\n  \"8708969a-0351-49c5-86a4-c89e3c926a48\",\n  \"7d75de3c-cbf3-4d1d-b34a-d767b68e2173\",\n  \"a8df45b1-aec1-43ee-a5eb-aeec82661759\",\n  \"65885d58-b064-41e0-83e3-a7c84f52d897\",\n  \"e163002e-3273-4887-bfef-de2c5bf71423\",\n  \"c3908326-ed0f-44df-ae1f-2ed019b18a03\",\n  \"ccd980d9-bc24-4867-b55d-fb0275d14304\",\n  \"c4557fb6-077a-4bbd-9d1b-96e78b0c82c8\",\n  \"74ca8fca-2283-4a32-815c-00f9ef595c94\",\n  \"eab6ad8f-4786-453b-905d-83dd49afbe05\",\n  \"1c42cd50-4c06-4dc1-8448-a601c6d02c0d\",\n  \"5b9425e7-3c72-4aef-9c60-4c1e0f64420c\",\n  \"9fcb6195-79d2-41cf-9c48-802b7c2445be\",\n  \"742cdfb0-7542-49cf-8177-b11583a2102d\",\n  \"6ba45888-5b0e-477e-85c8-1c8a8950ea02\",\n  \"7d9eec32-2d58-4e94-937b-1eddd29510d0\",\n  \"61307804-223a-46c7-baa1-0ade379a054d\",\n  \"a688da52-5147-4029-bbd9-906e667ddb45\",\n  \"3436e6b0-714d-4130-a7fa-59f7458f70b4\",\n  \"90d919c3-f7f6-48b6-b01e-ef9e0f868479\",\n  \"e7f56588-a7f9-4333-966c-c1fc7b58e776\",\n  \"e5c5ec71-b1e3-4495-899b-4e32749eab44\",\n  \"f2b2e874-185d-429e-b30d-cbbfc161ec99\",\n  \"257b88cb-c109-4ce7-a695-169196c75fce\",\n  \"23977e30-1591-4102-874e-7755eb235ce7\",\n  \"a5e16d89-03e1-471e-a683-0e8cc2c3a8a4\",\n  \"482bb80f-74d8-4382-804f-46c054ac8482\",\n  \"84b9d333-f0d1-4c37-a64f-8a43d3cf6aa3\",\n  \"a82465a7-9794-4650-8dff-1b2df3a12285\",\n  \"89c16944-9197-42dc-8ceb-0da30ddea3dc\",\n  \"cf30b8c0-08cd-478e-beea-b682342c61ba\",\n  \"1ef62cb0-852b-4228-862f-3b3a5bcbe4f4\",\n  \"39132775-72bf-4082-88cb-e132daff3b59\",\n  \"42e64698-de63-4035-b68a-cb34ae3bffa8\",\n  \"de8ef2c1-e4de-4ea2-9823-6870bf5a169d\",\n  \"c6a16538-c44f-48c3-9494-74df3ea4e9d1\",\n  \"da1029dd-9f91-4b3b-8449-9d4fafab4269\",\n  \"79797a35-cfcf-4ba1-92f5-851f69c78f7e\",\n  \"c3352702-99a2-449a-aa79-3bfcc447569c\",\n  \"cf310d94-45ef-4e07-8527-4ae7c1acde23\",\n  \"10ec4f92-833c-4039-aeb6-4afa109fbce7\",\n  \"2d06faba-6288-4865-a000-e995d0ef7cf9\",\n  \"2cccf3d9-4035-45e5-9fe6-1d1a4f492590\",\n  \"c26507e6-79fc-49be-9328-7faedee69280\",\n  \"e2329ef1-5875-4749-9e1a-7f1b95547e8d\",\n  \"11df5315-c746-4c87-a3a2-492d5b7efb77\",\n  \"ad1bc448-f68d-42eb-9c65-c6868e810d35\",\n  \"8beb27c3-6ac6-4d72-b344-4771a87f330a\",\n  \"203d2772-38c6-4dcd-9d91-0d652fb30a4c\",\n  \"013b1117-20ae-4cbf-8526-1cf1dfbec111\",\n  \"17bab11a-1b70-4750-9df1-7b9775f6d5a9\",\n  \"d665e1e8-8176-4dd2-8230-c6577a80bd2f\",\n  \"f7705845-cc41-4f51-b098-739d1c1aa931\",\n  \"07063b38-c4cd-45ee-a108-7c7031fc7165\",\n  \"3c347cc6-847e-40bc-b377-876739319d10\",\n  \"7c940339-5799-49d5-bf5b-c14303165be5\",\n  \"caf5d1ff-48f1-4d74-ae5d-d4fc34338a86\",\n  \"bd8e9a95-f179-495a-bca3-94bd7692d85e\",\n  \"0ebb5665-cd9d-48bc-8b8d-4c68e1bdc29f\",\n  \"53f9218b-2c2e-4cca-aee6-48c80712e509\",\n  \"f40f53f4-47f1-46da-8251-154f0cdbc3a0\",\n  \"0a8c113e-e987-4ba0-be9c-03dadbf399b2\",\n  \"4fbe963f-c99c-495c-9173-de2dcc6690ca\",\n  \"ded88bb1-2af1-4f8f-9e25-6bbd4dcc0cc7\",\n  \"499109ca-1087-4342-810e-9851b7e84ffb\",\n  \"f75144ab-bbb5-481e-9f58-10451ec2feec\",\n  \"1552ceea-ec9a-4fe8-ace4-cc64337b6505\",\n  \"4e6cc08d-905e-463c-b37b-c8b30164f9d0\",\n  \"1cba0968-c6c5-4c38-aeb8-6edf2fd6aa7f\",\n  \"28e60757-5123-4284-89c7-8e1c94e959ff\",\n  \"b1cc2a6d-66a2-4a57-8000-b28955c22c07\",\n  \"13ef6e6f-82af-4c5d-8c71-59f8f1d29631\",\n  \"3e3bc8e9-935c-4de0-93e6-10c22b4320ff\",\n  \"37b41273-32f2-4635-b132-91e5e062850d\",\n  \"bf650257-987b-42cc-aaa4-e3674759fda1\",\n  \"e72fa888-6e34-4f6d-ae36-4f33e3545a54\",\n  \"ab872ed4-6872-4352-b392-dd9426e9e338\",\n  \"1b2e0df0-3733-48cc-aa2d-754f761a5f2f\",\n  \"f9565889-9cc5-4d19-9b8e-6009de362eed\",\n  \"8df053b1-7476-4d37-8c96-2a6daf3dc213\",\n  \"d97111eb-0f3d-4079-8877-1d6e61050666\",\n  \"4e6e94cc-9043-49ea-a964-1dd851d27f3b\",\n  \"6afccaaa-a266-4645-bc89-1f5f42f073aa\",\n  \"b2e830be-1da5-42f0-96d2-2cc0e60aacfd\",\n  \"eed8b7ed-cd60-4972-ba17-4fc31d81451b\",\n  \"c2b47708-8d64-450d-8cf9-3fcddb9a2d29\",\n  \"664c1bc0-08da-4b77-b30f-d67f4cd071ec\",\n  \"cae5c402-4d83-4f76-b30a-05a9e7b83931\",\n  \"f6dfdbd4-51c0-4521-b8d3-27527abb444a\",\n  \"726529b7-4967-4c76-90a1-a8bb4667fcf6\",\n  \"edcaa651-c843-4c8b-9d0c-0be6ffd77d2f\",\n  \"781c6990-5f69-41cf-b8e4-d75938eaa406\",\n  \"c77f297b-ff86-4e1a-a1ab-e8717fbf4a52\",\n  \"32a9045d-e6fd-4c37-9249-d67f33d6ae08\",\n  \"853c39fa-8ab2-4768-964d-8ab066cff531\",\n  \"b821eba4-39b8-4377-aee2-25866aeff067\",\n  \"a9883562-55ee-484e-81ba-0e8df99776e9\",\n  \"c49483d8-7fdb-4d31-bd30-425b969b8506\",\n  \"e9d0fd82-345a-408e-b9d7-92821f89549f\",\n  \"43430c60-2f63-48b0-a13a-908c1ef09aa1\",\n  \"f8d6e27e-6b56-46eb-b362-37273fb07a6e\",\n  \"79265afb-a9bf-49db-ab4c-daf0248963cd\",\n  \"4bdc08e3-143a-4c01-8d5b-e60473c32c37\",\n  \"9af9b9fa-2079-46f8-9a3c-a82dffb5e12a\",\n  \"44aa8ac2-9c6b-4964-b6eb-e6bdae1d7818\",\n  \"5f6d75a6-f96d-4f66-bce1-6c4a6b474da3\",\n  \"93be85d6-e226-42f7-84af-7de93b1d0ddc\",\n  \"81b3cc33-0d5f-464d-8f3f-4e64ba5f9b23\",\n  \"e0cef54a-1a31-4838-8f30-87821ffe5cdc\",\n  \"fa5ef70c-f0d7-4fca-ac55-545b695a7fc7\",\n  \"aae95e20-7399-466e-984d-2ce99bcfea7a\",\n  \"d20bc180-9b41-45e8-b057-9e6d307cb8df\",\n  \"6dcd44b6-2181-4db3-b3e0-5b7729f1976d\",\n  \"dac2188b-8e8c-4554-a97f-d6f3de3b0380\",\n  \"99180ae9-595c-45d9-a2b7-9221cc344c7a\",\n  \"d9d6c4c6-814d-4e36-b5f0-7a7705b3c057\",\n  \"3e6127db-0823-426b-b095-ce6b70a1a7b9\",\n  \"f4678732-fd32-4b43-b549-3e95267606d8\",\n  \"d927081b-c35a-4538-b6ee-3fb64194c35b\",\n  \"33b8a776-aa02-4305-a42b-06b67c77e545\",\n  \"0b3be7a1-90d5-46f3-9126-7cf244ff8edd\",\n  \"ff8baf49-3abf-4066-8815-60a94c7eff04\",\n  \"3a512988-607f-46a6-92fd-7db60304d565\",\n  \"108a762c-b66e-4ef9-952f-d97070a68e91\",\n  \"23f6df79-9e7e-4a91-b91b-fad00243160a\",\n  \"b6f8d32a-5888-4fe7-b95e-89b2bd0e889c\",\n  \"546b9a0c-8ba6-4e49-b2f4-6108433727ae\",\n  \"c7f76c6d-b859-49a6-9a0c-2c2aa4851a0d\",\n  \"15ad9752-4d2b-4160-a923-123c37e73972\",\n  \"6fd81412-8c63-4249-ae92-7f82c7221805\",\n  \"63176743-ab54-40bb-ae7c-872d14f57e98\",\n  \"f7eb164a-b8c7-404b-a182-07b6d57bd371\",\n  \"d6b59c7d-5c8a-4fa0-8ab5-333a5843eb39\",\n  \"2a7ce029-3ceb-4180-a204-5035faf78479\",\n  \"f02ca15b-f038-4456-832b-e8eedb0ae662\",\n  \"efca5f35-4276-4a40-b73f-5a760eadb1bb\",\n  \"5bbe6fe0-3aed-4500-a7d2-f5c0a21f6b2d\",\n  \"987afd75-dd42-4370-9a40-9da641ae1706\",\n  \"5600341e-9540-4889-8103-5aec9072588c\",\n  \"382ec068-8609-401c-b8fd-a3f529afa198\",\n  \"8fe922d3-8ee6-4700-85d0-ef40292bcac6\",\n  \"4a810cb3-c036-4f15-8fab-610aba729e90\",\n  \"1a5bbf98-8bd3-414a-b827-3346dd43d6b8\",\n  \"28b2dfea-f311-4cd2-b710-a8ed8e6a8206\",\n  \"56ffa06f-af93-41bc-8415-715aea4fbfee\",\n  \"81f7b6ca-731f-4b5b-a70a-d91805a328b1\",\n  \"584ce97a-91c0-47d1-8fc9-6b03d1889ff1\",\n  \"d58be63c-7b0a-4b9e-818d-e32d7cd9997b\",\n  \"4dad507d-53a2-4e79-8ac2-44d49929e29b\",\n  \"b3f4d895-9f47-4d5b-9303-32fb8feb72b4\",\n  \"aa620364-39a4-4448-8b7c-fc3a1ccb1079\",\n  \"7d01662a-3d94-4d27-90eb-f94bd27f4176\",\n  \"7178c1cc-c9c5-4187-ba22-ddbba9ed68eb\",\n  \"d52d8c9a-eb51-4a60-9281-d7bdb90b3aac\",\n  \"e4c42308-d215-4220-9643-538bff746ecb\",\n  \"e4f9b3cb-7b07-4f9c-bc86-5edd8a5ff188\",\n  \"2779a30e-24c2-41b6-8704-b7e9713164f8\",\n  \"ae324beb-611a-405e-b866-766d9912363f\",\n  \"5cc9bad1-63a8-43a9-846f-1295a177857b\",\n  \"96aeac18-7c18-464a-8c10-9c81c2b91ec4\",\n  \"081e4865-0794-493b-b027-803f281e245e\",\n  \"2369bbd9-2988-4fbb-815a-396376bb94ae\",\n  \"fb46fa3e-52be-44b4-afe8-5c4a45e134e1\",\n  \"24f8b442-5924-4fc6-9f3b-2df37f5795d6\",\n  \"349087f9-1ea7-40e7-b31a-3b5cdcd9ef56\",\n  \"96928432-e011-4f62-a798-b649464072b9\",\n  \"bd343af6-645c-49bb-85ee-6246f41c7afe\",\n  \"945b58a4-005b-414f-a680-bd30f100455f\",\n  \"4472181b-85f0-4339-8885-34873be50930\",\n  \"8c300437-2fe2-4180-a928-5e1bac3a7286\",\n  \"caff67dd-6797-4d46-9c3e-1ad4959de893\",\n  \"464d5854-5547-40cd-ba90-421ba6367709\",\n  \"158e1906-9d94-4460-87b1-ecc89487e2ef\",\n  \"434dab05-d0c7-4b08-b267-12dacc114050\",\n  \"4a0ee48a-77dd-46cc-9669-0785eeb5b8d6\",\n  \"79f2be15-53e5-4d08-b600-53a9a9df4bef\",\n  \"b4c09a34-b9bb-470e-a06e-5ae304834432\",\n  \"458602ce-e695-4f11-bd1b-0b0b72417285\",\n  \"baa9d260-5642-4133-85ec-d88cb6a9378f\",\n  \"7f20d757-a7bc-403c-a609-5804836d75c7\",\n  \"547297a4-c2af-4626-be26-40ffc00d8051\",\n  \"76b7350f-5597-450e-aca2-6caac2b46f1c\",\n  \"591953f9-9f03-49f6-925d-972374724117\",\n  \"95e4e420-3925-4b76-a594-0768ca8807ae\",\n  \"8787f5c0-ed30-464b-b310-cc84c317aa4c\",\n  \"fef8bf94-0498-4444-b538-f5755ae6b380\",\n  \"55c117bd-8b01-4de4-9dba-5083bebbd0e2\",\n  \"3e9db5c8-f516-4cae-83f1-1ad2c24fdb1f\",\n  \"49b368db-e48c-4d12-bf9d-e4c7ae1deeff\",\n  \"641522d4-4a5e-4b30-bc62-1db3770dd9ff\",\n  \"76893d26-2508-4270-9de8-2de53005b903\",\n  \"60667454-fb57-48c4-8487-e42abd24bfa1\",\n  \"30e2e6de-d0b0-4e5f-98d6-7ecb8d9e6f9d\",\n  \"cfa81c5d-7740-479e-bd30-3c78ce19c217\",\n  \"56984b7b-220f-458c-9b26-fa6ba708e2ca\",\n  \"208e1823-51ef-4603-8af3-231dc67db1cc\",\n  \"a34577ab-5b3f-47eb-b0aa-1d9a24fb6e13\",\n  \"6b1de31c-8e41-4334-9e6b-899853cef375\",\n  \"440a49ad-d424-4052-8e18-86bb8a37e0e4\",\n  \"2cd23389-1500-4216-b0fd-b3c2a3e1bcc1\",\n  \"26cf0bd0-2270-49e2-bc1e-ca0e865be06b\",\n  \"8566fca8-7528-4fb1-8af5-7537030d37fd\",\n  \"728ee4de-9022-40b3-85d0-825c3de5315e\",\n  \"4265ee22-a695-4907-988d-ba635101361d\",\n  \"d48e3a31-b396-411e-ac05-6a23318db627\",\n  \"a68b0163-dca9-4d60-a02f-88dc212b9360\",\n  \"4c93b767-65af-4e29-8d09-ea1f258acb42\",\n  \"2e322e58-24e9-4c8d-a57f-777a8c86231a\",\n  \"0c108795-843d-4fde-9a72-f83137354f2a\",\n  \"fec3516c-d62f-40fa-93fb-4a652aebbb99\",\n  \"b8109f7a-463c-4578-b26f-81b4acdd1cb6\",\n  \"4e7d357b-daca-4e8f-8b12-b9cd0f6cf508\",\n  \"abbb0205-d774-4ef5-ac45-0dbddf504c93\",\n  \"82b5c05d-b279-4caa-855f-0fcff40a1b2a\",\n  \"04dda28f-fd08-41ba-8ca4-527d12b5047e\",\n  \"9a60bccf-e2db-42b1-8756-07f8d16a40a0\",\n  \"53eb97cf-faf6-48a0-bd3a-3b793ea6b6d8\",\n  \"7d2b2efc-a94b-4090-b49c-98e5b8fac009\",\n  \"ed75ae00-76d2-41bd-8ba6-84e47a2e6188\",\n  \"a703150d-9fe7-4d40-b8bb-b2cf240628db\",\n  \"7b69d4eb-e45c-41ff-9698-8855df17950d\",\n  \"ad4d6082-9175-473a-bb2e-5b44416e3d15\",\n  \"e12d6552-a154-4ad2-a825-cab40e3cf0c4\",\n  \"a6c593e6-15e2-4c1d-b5ad-556a88bfbbcb\",\n  \"cdd3606a-5cc6-4cb7-be98-5af1c2e756f0\",\n  \"1d6d612b-e66b-4a85-bf46-3b09eeb4f76b\",\n  \"d2fcfe0f-7e38-466e-9bcc-80218fdf87f6\",\n  \"f24bdf62-bbe6-401f-aa2b-16e7de30d270\",\n  \"c4cf6d2a-d768-42a8-9d0f-c402a8917f87\",\n  \"8d9c4461-36c6-44d9-8444-cdfebed5fdab\",\n  \"7f9ea6ff-52e4-430b-adbc-19fb58f24eec\",\n  \"38a262ea-eb12-4922-8791-f7148dd4e778\",\n  \"c1971b6e-d4e3-4a2a-87e2-64af4cecd4a3\",\n  \"0c9c00a1-eed4-417b-8568-fdb1ee78112b\",\n  \"471466a6-08d5-48f1-96eb-2eefbcb64981\",\n  \"19fafea2-d424-4a02-b8c8-18360f10439e\",\n  \"e9e90833-0db1-41dd-b3b1-697aa98337db\",\n  \"728034d7-d871-40d7-a45a-f05050858bec\",\n  \"cf9ef2d0-d516-4087-a97b-96a13e5685e6\",\n  \"40490dae-f0d9-4a58-a0b8-0c7c010b6aa7\",\n  \"14e7cf92-45f2-4fc1-a92c-32f58ced3dd5\",\n  \"26131377-9765-4413-8c41-b5dfbe9a4219\",\n  \"41d0ba7c-9224-4650-a2c4-d4908cb86be2\",\n  \"abf54f61-d4b4-4a8d-8584-309dc6cc1a59\",\n  \"00eccc29-94b7-4c35-9936-166282c4095b\",\n  \"d3265c4b-f2a7-43ca-90b5-3d1b4d6857a9\",\n  \"a62d968e-247c-4115-8128-3d5dab521bee\",\n  \"5aee965f-b110-4e6f-b9f7-330892306c0f\",\n  \"0fbe3f12-1b10-448e-b3cc-1654d6d5b399\",\n  \"06092767-64e4-4bb2-8bd9-18a0c7fb8586\",\n  \"ce6f0c59-e453-4353-b2ed-5e5117e8e67a\",\n  \"b0abcdc0-8137-407c-b012-269cfe62a7dd\",\n  \"c24f38ec-3c44-4f55-af89-a12922b2bf70\",\n  \"ddae6d9d-c996-44e2-b365-36270a097abe\",\n  \"75392f7a-e9df-47db-b2b1-a006f4343d9a\",\n  \"cdeb4ca3-1684-4d95-9614-b3b3e4487b08\",\n  \"d02cdc6b-c71f-4e2e-8de9-9bc475bad01a\",\n  \"8d343d62-ffdd-49b2-8246-0f53c128b514\",\n  \"47555c64-f3b3-481c-ac72-1437409e1609\",\n  \"93270043-d703-4463-9edd-b111b9068ead\",\n  \"420ecc98-5331-4a73-ba26-003f2d1cd9cc\",\n  \"a36c7dc3-213a-46f4-81d3-cc89924742c1\",\n  \"15a0ebf1-5a03-4e43-aef9-7bfe57f19091\",\n  \"0fbb751e-ee5f-49c8-9682-c4c47d8d139b\",\n  \"a8d287fc-1636-4e69-8527-db4916d8ea15\",\n  \"bdc05f5f-e77f-469f-83e2-8218a45100e4\",\n  \"35331166-58d7-4f9e-b1fe-991b66b0b811\",\n  \"af1590cf-ecfd-49d7-a6b4-2ec60a64f4ee\",\n  \"eeded81f-0f27-4075-a219-a7f10ddc4cba\",\n  \"72e10e0d-ec7f-41af-86f3-9c3eb600e4e8\",\n  \"65878ffb-11c6-43c0-af90-e83f51c9597d\",\n  \"6de73f73-dfce-45a8-ad8d-9c7cc432cad8\",\n  \"96522ecc-0773-43c5-b45b-ce0ef0df8135\",\n  \"1538f965-bda3-4bf3-a2e5-1bcec6cfd601\",\n  \"16faef26-d95e-42db-92ea-fea1fc0a9760\",\n  \"064ddae2-a77f-4661-83e4-91f86cf828eb\",\n  \"f0670874-5d48-497b-a686-1a6fcca2d81a\",\n  \"b3773686-c05b-4000-8804-00e2e21666a6\",\n  \"03f19a1d-d7a6-43ec-a466-31a55fb86184\",\n  \"6802505c-62f2-4cd8-a93c-fa23fa6a6c98\",\n  \"47dc751f-0989-4a6f-837b-a6dd2d9e36e8\",\n  \"4e8e2563-4df3-4b6c-856b-47f678776842\",\n  \"a58f44ed-f18d-40f9-9b75-a07006095bb4\",\n  \"b5873241-d5da-4e74-910c-f363aa64f941\",\n  \"4dc052fa-7c3b-4b4c-a224-1d3af82f0445\",\n  \"1d0a4f8b-e977-4308-be12-a8e99970a6c6\",\n  \"61bb7df5-688b-4f39-afda-13243d1527b2\",\n  \"59df5fd4-f3a1-4404-b770-9423e74ef223\",\n  \"b73b983d-c651-450b-a4ff-f0ef53e07ef2\",\n  \"91d90af4-8252-4cb3-904b-02b232d9a6a9\",\n  \"03125206-3ffc-411b-b5b4-7ff861b642dd\",\n  \"f69f545a-88c3-4527-9732-d9b3589a20a4\",\n  \"21c787f8-16bf-441a-a6a2-5092764ac499\",\n  \"e2aa201a-f9c6-4b9b-a6a7-ad0edf5b591e\",\n  \"40c41f75-e657-433e-a4a2-af47912959ad\",\n  \"2fa80aa9-0e50-4ed4-9f00-730a94d58f40\",\n  \"afe65be5-81d7-46a7-a82d-78caf28b6c10\",\n  \"32b17ee0-95b2-403f-a861-201ec9615474\",\n  \"4defb045-3ce0-435d-8001-6bb6d45c92b6\",\n  \"4fc3a02e-6ece-43c7-a3ac-7eacec33d79b\",\n  \"17e89518-7b00-478a-91d3-d19fd8a4ba76\",\n  \"53369e32-1f28-4ebc-a5e7-5060a0deb0ec\",\n  \"e44f93f5-80f5-415e-b0bc-9c198d0bd546\",\n  \"33b9ffd2-2621-4b49-9e0e-9123cea6a8d9\",\n  \"df5a1de2-34af-4fe2-99d3-2b30bbc3764b\",\n  \"8d6919e0-c5c7-4188-87bd-85d8831e9e7f\",\n  \"d91dfe48-486b-4264-a804-71aae9d50e0c\",\n  \"c2ed285c-7417-4979-9132-dc83d69d81c8\",\n  \"88a4a5cb-cc3f-4d78-be1d-37004946e824\",\n  \"77e631c3-ef22-4b4f-9ff4-f208acaa6f95\",\n  \"7f1508b2-0d3b-425b-a8b3-b02209edf1d7\",\n  \"65c32c5a-1146-4192-82ae-37027a5d9e53\",\n  \"41715137-74df-4d12-9410-173e81e24158\",\n  \"00f65d7e-9b93-4b1c-a0f3-5c082cafe5de\",\n  \"4dc4a0e8-73e8-4399-8a77-1a51e76f1006\",\n  \"bfcfda59-6aae-476f-b294-bba4cb4138d1\",\n  \"7201485e-46e3-4181-b237-4ed54186aedb\",\n  \"8cdf77fd-99f2-46f8-b2bc-cce67f8f54a3\",\n  \"cf2b5bcd-5533-4c4d-8393-49a10a15f815\",\n  \"7f1ef67a-5e81-4c63-a330-75abb198e485\",\n  \"07f8bf69-0a15-4d23-9970-65882a152035\",\n  \"4c00125b-df00-4bdb-9ce8-4dedeb1702e5\",\n  \"6c148cf4-cd26-4285-8aa0-2aaabdec0474\",\n  \"c66ce040-3fe2-4fa3-a5a9-d1e1484485e9\",\n  \"47fc521b-d05c-4c15-b588-397ef1cc8b01\",\n  \"9de3475e-414a-4e29-9800-37885d604ddd\",\n  \"0ffa757e-9495-4a92-b175-f4500801ab90\",\n  \"5c0a4ac4-5b77-400d-82dc-98d2a716222e\",\n  \"d05bfa68-8147-4ffd-a730-72074a0c47ce\",\n  \"6ff5555a-59f5-4a80-b36e-0880ef7a2720\",\n  \"d257aaf0-6874-4099-9c1a-5fc7b4a3af29\",\n  \"3f67aa32-0230-4b31-8779-9e7d94f7db65\",\n  \"102f63fd-a411-441c-940e-d4b093e1d598\",\n  \"d4fcf013-462f-47ed-9ce1-1e2e868be1d6\",\n  \"deb9f52a-7be6-4735-8680-466f639722b7\",\n  \"9fbb4e88-d147-43b8-873a-3fccabb68d0d\",\n  \"a7fbb9d9-c1f2-49aa-8282-dde2f2fc137e\",\n  \"b4101ae9-b455-40b1-b076-d3946dcba596\",\n  \"30832d57-4139-4fa0-bacd-edf14bf5f419\",\n  \"aa607db5-6bad-4c77-96b4-d283f5dcc4dd\",\n  \"539c5b28-02b4-42a8-8595-2c690cecbcf4\",\n  \"77e48f12-6330-4300-aa98-825e216f0c68\",\n  \"dd7fde51-df70-42d1-bd3a-9820a9d01612\",\n  \"3c901d43-6b72-4926-a71d-dbf054d795f8\",\n  \"64a42f53-865f-4fb5-b1b3-bebe53b77e05\",\n  \"d8f4ba40-ae9b-491a-a754-34a23f26b034\",\n  \"a1b86a9b-bfdd-4fde-8985-2f5a3178ec05\",\n  \"11ffaafc-d5e1-4afb-8a27-56229cf01b28\",\n  \"b868857f-983c-434e-9ecc-7fe639352af6\",\n  \"719b2f51-c2cd-43f6-9680-f72c2cc92cb1\",\n  \"328146b7-6aac-40ac-8cc0-97816992b98c\",\n  \"e29491fd-bbfa-491d-a5d3-064890a13625\",\n  \"aa74ed65-102a-4756-b622-d9463bab597b\",\n  \"36d6c181-bfd6-44f1-8a18-80eb332addaa\",\n  \"e9fd8868-f2a0-4cb2-ad76-f2fe1ad31626\",\n  \"b09aae09-d9c5-4c21-9d57-21c5af5a05ac\",\n  \"72dd1503-7757-4a67-8c5b-88ead73b31da\",\n  \"b45bf0e4-4044-4129-a996-de14ada925d0\",\n  \"f646cdd8-f911-473f-86db-a7653383235f\",\n  \"8a205365-da21-47b1-854a-72362eaf696d\",\n  \"b16d9d94-2650-4453-8fa0-9cbe66dde9bd\",\n  \"af528d19-d967-46df-89bc-fba8790562ea\",\n  \"c9114a1f-1d23-424f-88e0-63298a2df354\",\n  \"37330956-fc56-4521-a00d-db1c5b476b72\",\n  \"86d40f44-2ac4-4a5c-afd3-20da55903854\",\n  \"f5b96baa-8a32-471c-a50c-f33fa8768711\",\n  \"192e7231-5495-451d-81d3-0677bd4819f9\",\n  \"c69c0f5f-cc42-45bb-a839-df657568d41f\",\n  \"d5041e3b-ddf5-4684-9ac6-c7608c65c28d\",\n  \"53e3e052-503f-4b75-a766-921d6dba6df1\",\n  \"215ea64c-95bd-434b-bb18-12c72f8a29a6\",\n  \"af97f00e-55be-4593-8917-a6c428a344d0\",\n  \"fb807d10-6fcd-4a4c-b17e-57c4a2b28800\",\n  \"73a6f2e6-5029-4d3d-bc1c-7391d003f234\",\n  \"afbb44a9-1582-4527-affd-596ab3a172eb\",\n  \"516cc1ca-014c-40e7-87eb-1f8e3517545b\",\n  \"41554fc5-8ead-44cf-b092-f1a1f507c6d1\",\n  \"890689e3-daed-4081-b587-6299e76740be\",\n  \"3c2f8d95-7224-45ea-aa01-9bfb5f4d5fd2\",\n  \"6d4afc67-1d7a-4177-a4b4-dbdf6e8b33c6\",\n  \"313ac559-5841-497b-8b56-e039440793ab\",\n  \"44ea1980-4cb4-4445-aaea-8b301fdfb66e\",\n  \"c9545a92-92ce-4219-958f-9274fd28bd69\",\n  \"7cb94f13-4de8-4b51-8251-bc9382a060ca\",\n  \"f35cb6e1-401d-4883-be90-39942a56293e\",\n  \"40f92db4-a0b7-40f4-bf31-009c0a867944\",\n  \"1128e57f-f9ae-42cb-a541-b3193455fae0\",\n  \"b7212a5d-9bd0-41af-a3e8-e3d3b7f37f4e\",\n  \"a121a534-d1d5-49d2-b76c-0e4091582333\",\n  \"bc09b844-88e5-4655-ba47-91b047cf89b5\",\n  \"253da5fe-570c-4186-a79a-27eb3107996c\",\n  \"2b809e16-53fc-4219-a9fc-1aa54c8678c9\",\n  \"ee83b7ea-40f3-4760-9fd5-7f6fafb6684a\",\n  \"9ec42e80-e1de-4f72-8b53-6096b1c543e8\",\n  \"d4722b4f-d93e-47e0-ba1c-b28812cfffa7\",\n  \"f4377faa-3f9d-42fa-a011-6e20c076a33b\",\n  \"32033f89-c4ba-45b0-850f-f92c66aea8a3\",\n  \"f9ef443b-a64c-4346-a340-91442ba4c439\",\n  \"3f8a311d-6fc5-4ea4-811d-3fad07f11825\",\n  \"9fe89a81-2137-4f28-aa28-f134c5c89eba\",\n  \"6de4eeaf-a21e-4833-8971-66a0b6d6f09e\",\n  \"66a3f0f4-faf7-48a1-ae80-d7f87c9840bc\",\n  \"c0166e09-ff90-47fc-b937-bd65a0fc9d1f\",\n  \"7359769c-ef34-4979-9f75-c7a21d13fe00\",\n  \"4fe63f7c-0d6f-41b2-849e-075052db48f1\",\n  \"2b8dc475-2816-4642-85d3-4a308df6c0a3\",\n  \"3959f88f-1aac-4430-95b9-59412f94dfc6\",\n  \"875f90d3-29de-4527-ab54-13d6ea67eeb7\",\n  \"1ef4f649-496b-4c4e-ab5b-3169eecf7ab8\",\n  \"654f78cb-bbe8-4ec3-8056-3e7ed6fa8ce0\",\n  \"c4afa97a-fdd4-4236-9171-b3bdf8e46812\",\n  \"70340c3e-769f-4e8f-9efe-67a637f4176e\",\n  \"b2d49a88-2646-4f60-a4fb-9e7136a4a87f\",\n  \"a9b0171a-6292-4aae-9a00-01cc782cdbe7\",\n  \"e54c47fb-8e77-45dc-8dc9-cb718b2bdb60\",\n  \"eb9a6ccb-cfd1-4f55-8234-36051eebe7b6\",\n  \"3ad2e83c-0af7-4d84-80db-f9ce5910be85\",\n  \"e949c6e9-1590-42c9-acbe-98d24fda602c\",\n  \"89470c26-6e4d-4b84-b59c-22cb2580ceb3\",\n  \"e2c9d73d-7de5-455d-b91f-76a3b705ac7a\",\n  \"62b1a49c-ee1c-4a43-b9b4-ee26dbc2f476\",\n  \"e85351d8-c25f-4e5a-8d79-b487dbf73a25\",\n  \"aa957ab4-33bf-421b-a989-0a20e964dc9b\",\n  \"65d42d84-1d06-4670-a8f8-244945ee403c\",\n  \"dc4d3fd8-2458-4478-8d6e-16605e37259a\",\n  \"1d0f72b3-cdf9-4045-966d-c1a83fd6433b\",\n  \"a3d5d4f3-bedf-4b48-8768-b6ec61623f22\",\n  \"5d2fa64b-b28e-4626-9c2c-9f19e6aa1072\",\n  \"afd23225-5d29-486d-a295-ffe94bb87fd3\",\n  \"97c07a09-93c3-43a8-b356-0d6e91bdbe74\",\n  \"487319cb-0dad-4f51-a5b7-9dfa8908a502\",\n  \"0c17c156-d772-47f8-92e3-97999954b897\",\n  \"ec0028b7-3c40-4f23-874c-62bd51f332dc\",\n  \"9b765e29-82b5-4764-9a82-ea95d05dca4d\",\n  \"7ed5c86e-3472-4b80-9f0b-1436af1a5428\",\n  \"b815d8a7-33e7-4763-ad26-825d8195f801\",\n  \"c10b5be2-3dba-4ec8-a767-98342898824a\",\n  \"32bb6111-5233-4185-9b75-6793c0bfbe06\",\n  \"b9c13223-27c0-4f5a-aeed-c936506e2ae5\",\n  \"5f72c952-0466-433b-9f07-d51b61142d83\",\n  \"f5af179d-153f-40db-92a2-5a5a36873af2\",\n  \"348db8c4-9c74-4562-b9eb-5041ff5848ae\",\n  \"41e72230-ad82-4eac-9d27-6b8220b05610\",\n  \"a136b70f-0df1-48a9-90ba-056fccd821bd\",\n  \"5e85c73b-1182-450c-a565-699bf84dae3d\",\n  \"7d32b7b7-a329-41e2-889d-ba0d86dbf252\",\n  \"e539f6c7-65c8-4803-a595-f1db800fbcfa\",\n  \"340a205d-7b20-4f5c-a6c7-ed0de43bba55\",\n  \"dc2ced85-c937-4aaa-a0db-b306f5f35af7\",\n  \"88f178c3-450b-4459-940f-dd96ffd8b8e0\",\n  \"581d46da-ad43-4aff-86d5-08373025be2a\",\n  \"1cf33714-2b97-4fd9-b631-bb9a0f7959b0\",\n  \"c6709690-86a7-4a9f-b698-c31349cc4b31\",\n  \"ce3fda7c-b6c1-473c-848f-99d3656648be\",\n  \"a5aad87f-3ce6-45f8-bc7a-d65539880e27\",\n  \"157726d4-f68e-43da-b78b-13d8d5a33527\",\n  \"9756ecf3-32a7-44a2-bb25-13d7853649e6\",\n  \"c6fe9f4c-f751-4c37-b9de-260e7cf4cddd\",\n  \"641e38a6-614e-4dbb-9345-58d82235b115\",\n  \"415809ec-dabe-4490-baab-3881850b3432\",\n  \"dc0b1f9c-f4d7-427e-9307-a72c74383639\",\n  \"6dda160b-101d-4a5b-be37-2a682719e75b\",\n  \"6658da10-2a3b-4826-b4fb-ece8c318ccad\",\n  \"13eec308-b417-43ef-92c8-7db021a8dba7\",\n  \"24fad45e-e2eb-4898-b67b-a15a8cb77100\",\n  \"1e75438b-524f-4e1b-bc96-90da7dcb340a\",\n  \"bdf8bb91-b165-45ba-9a37-f71f957f7d1b\",\n  \"b0c605b7-b523-4938-9d5c-0a82d41d3c6d\",\n  \"18db8069-d7b3-47c3-93e7-8d0d5c8f0ae6\",\n  \"16a2cb88-1ddd-46a8-912b-300d690f7ba9\",\n  \"462ca91a-7066-44f6-aae4-ee04be544815\",\n  \"32032bc5-24f1-47ba-90c9-3df884649ef6\",\n  \"da15a1d5-3db6-47b0-bf45-1125843d8f05\",\n  \"00d77eb6-7414-4768-ac88-3363ae2b1b79\",\n  \"24b9bc1e-d826-4774-8bd0-bd76b01ae10a\",\n  \"faf54689-8e0a-4219-ac78-4d0b963e4223\",\n  \"817acc66-8775-4157-b623-189ff87cdb03\",\n  \"0e83b2a4-fabb-4aa0-be20-76e088472ac6\",\n  \"8cc001f8-9b66-4480-9afa-bb6726f080ee\",\n  \"8532b4ba-4e5d-4677-87d8-6dbe291f7f99\",\n  \"710f559f-5707-455a-83f1-37157220cfa7\",\n  \"e93d5927-231c-4bb0-a0fb-6124957369df\",\n  \"92053b33-c10f-4c26-b3ae-82c88c58d748\",\n  \"e8ced61c-fe47-4f3d-ad99-c5bb9a2cda58\",\n  \"363135cc-fad9-44ef-9667-00ed4e3b236f\",\n  \"9f85c06f-6216-4ef5-832a-da7865cca354\",\n  \"a1838aa7-42fb-4e5d-af6c-4e5485a9171c\",\n  \"6e5c80ec-4ddc-4bbd-858a-b01d9538dd39\",\n  \"82d63393-e73d-40d8-b9ca-eaeb062a263e\",\n  \"7ec857eb-0f05-4866-b039-22e8fb5357a7\",\n  \"f6b70422-04c4-44bf-8bf4-9fcb9d55a98f\",\n  \"8c856b90-4159-4693-881c-a6600d7c24e4\",\n  \"870353af-36e2-449f-9599-20ede508a9b4\",\n  \"a31f3f83-ef04-458a-93fd-8774d2724ea3\",\n  \"3f14469d-aa2b-4b40-9a41-f12edb2236c2\",\n  \"f7a6a45d-d0f9-46e4-af66-26d2c5967650\",\n  \"6598b6e9-ddda-4f8a-8c14-88f06fb8fca6\",\n  \"9d31faaf-1061-4c4e-ac71-a15c2d1894e0\",\n  \"607840dd-5b83-4968-b88a-289965952230\",\n  \"ff1eef24-7fb2-49e8-ab0f-5a6ebb1985fa\",\n  \"294d5cdb-9cf3-4ab8-9096-b224aec9c2ca\",\n  \"71f533a5-0501-4d29-a595-80e08cecd549\",\n  \"0cfd90ca-d772-4a27-addb-15b97f91f81b\",\n  \"835f59a7-9247-4a95-ac15-be3381f1dc02\",\n  \"44b7c3ae-76d1-432f-a9a3-05cd7eef1cb0\",\n  \"ab4c7496-8e16-4aa5-a6ad-3dae9f402a38\",\n  \"dd74f2f6-45b8-462c-8608-143e956974d3\",\n  \"c9bb875f-0fbe-479b-b924-23e2e33a7c54\",\n  \"248a60f7-057e-420a-9078-9a8432a0addb\",\n  \"0ef44f70-17f1-4d23-a8af-35e9c834100a\",\n  \"54148581-8e68-4a9b-8a42-fd5de94f792e\",\n  \"3d62f0d4-0f11-4c54-9ef5-4f62b23905b0\",\n  \"1770171e-2178-4516-b0ad-69f55f4df1ee\",\n  \"081888e1-4281-4c3d-ab17-e85ee75f9b00\",\n  \"4311dcad-00d5-469c-af0e-443ba6ecd97b\",\n  \"c159e2eb-2dc2-4521-85d6-61f0cdf540a0\",\n  \"31a592ba-abc8-495d-893d-f1ddc11eb83c\",\n  \"9e2c42f1-eb1a-4e43-9060-eebf0e27e184\",\n  \"0049043f-780a-4e5a-bce5-1a43657f6197\",\n  \"d6dfd5ee-ecc8-4948-a68a-dcaf2bb19dd6\",\n  \"9b8dfa81-5eb8-4568-ae85-462dffb900ce\",\n  \"069f8809-f4c2-4baa-845a-8769e31db600\",\n  \"a069493d-2068-4797-b310-6bf05c60be62\",\n  \"2be28c54-34eb-4852-bf28-a9cd8a8ed278\",\n  \"e8c70b5d-10b1-4aac-a1b7-a7e146155d89\",\n  \"745366c9-54a2-4c8c-9e72-c86437edadbd\",\n  \"2e27fdce-fac6-4271-8d2e-114f50d1d354\",\n  \"d1def1da-52e2-4828-8b69-74c1b28adb16\",\n  \"62501e9c-969b-487f-b04c-4b9437bd964e\",\n  \"d0daf969-e506-4c95-816d-6cce9057c172\",\n  \"3f11a48f-a42b-4942-9cf0-036288c7ccc3\",\n  \"2533e09a-fbb5-4281-affa-be2068111cd2\",\n  \"547c0f6f-7349-453a-a85c-361def1a80c4\",\n  \"8345db4f-8324-456e-8483-2b054c0ee839\",\n  \"741840ea-a1a9-4e0d-b260-b59ed366a7ce\",\n  \"d7149253-be8c-4e25-91c9-3cb81de71523\",\n  \"3ad35ee6-8194-427b-b685-9553e64212c8\",\n  \"8a38b82c-3ccb-423c-82bd-4ff5112bde5a\",\n  \"8a8463d3-6b54-4207-a02a-64f1e37ec145\",\n  \"fbd8a430-e160-4fb9-bbf5-b4fabf0806a3\",\n  \"bd96c584-0038-4c40-b558-4d4eeb0adf3f\",\n  \"11e71423-73ef-46ba-a90f-5f36d7afee22\",\n  \"0cfcc723-01f3-40c3-a2ce-d134c0cc4d08\",\n  \"faa8e232-954c-4aab-ac22-55faf11f30b8\",\n  \"edb62263-31ae-47a4-9893-493666956a83\",\n  \"104d1252-e27f-4a15-9316-3486d1d0abfe\",\n  \"11702ac6-052f-4976-b271-32ea8940055a\",\n  \"8039ccf5-9b6f-4b76-bd69-d5ec37f81b07\",\n  \"b4d239a9-e714-4b0b-9836-a28d3379a17b\",\n  \"d602834e-a582-413e-8f79-afe7d22a7225\",\n  \"cac6cbe8-e6aa-4617-9b01-da2b90e1ec82\",\n  \"7a101870-861b-4cf0-8c8a-aafee5668eb6\",\n  \"e83b51fc-0c30-496f-9d75-2f72c3f8e444\",\n  \"20e561c4-e273-44c8-83f3-75bda4e48615\",\n  \"f05ab6c1-7b8e-4c7c-ae79-f4f7f00532ad\",\n  \"b3723d62-e769-4530-932b-3b280019ba63\",\n  \"a6adfbc1-0ee4-4cc8-b56d-4d8c345a09d5\",\n  \"3b885c1c-8ed1-4b5f-8067-372101a522c2\",\n  \"37911cbf-10eb-41d6-958f-9037d11f2a08\",\n  \"801ae037-27c0-43d8-bbc9-1f747817c13e\",\n  \"092d4232-f3bf-479e-8d99-1c8525f99d84\",\n  \"90d74fa5-2242-4623-98af-57744898a94e\",\n  \"33044603-f4bf-419e-887d-ff7897e0addb\",\n  \"0f872749-9e9b-47ec-8cc2-a5cf31d95a6e\",\n  \"b98a200e-8eab-4db3-be70-339aee406047\",\n  \"e0ecba62-99d6-478c-87aa-f7762856fcf1\",\n  \"a94f0bbd-5aad-4ccf-85c3-2f9780764390\",\n  \"4de3ce57-da1f-4069-9cf5-21e9c4dd1ae4\",\n  \"2be94894-3e16-4dbc-aeb3-9b9d3d47cb25\",\n  \"c9a5a8b9-e7ff-4d2a-998e-cfb8bd98845f\"\n]"
  },
  {
    "path": "scripts/eval/silver/inaturalist_image_subset.json",
    "content": "{\n  \"0.jpg\": \"Mammalia/Marmota flaviventris/3c919dfdcfe7837b420d3dc640c21c70.jpg\",\n  \"100000.jpg\": \"Amphibia/Bufo bufo/eff7b9ddda4aa260f760c87abedf1907.jpg\",\n  \"100013.jpg\": \"Amphibia/Bufo bufo/7b86da61eb1fb72ef1cf69467b4618ff.jpg\",\n  \"100036.jpg\": \"Amphibia/Bufo bufo/aa7dbe917cb34c2f8840048bc66699e7.jpg\",\n  \"100049.jpg\": \"Insecta/Aeshna interrupta/b13878de46d5e27beb27cd6363705ae9.jpg\",\n  \"100050.jpg\": \"Insecta/Aeshna interrupta/83e0425db775f5d0963bc74884651b7e.jpg\",\n  \"100054.jpg\": \"Insecta/Aeshna interrupta/ee25321a9a4126b2c0c748f04e78b3fa.jpg\",\n  \"100060.jpg\": \"Insecta/Aeshna interrupta/2beee54aec4b7634f0c9720e14b8ba4b.jpg\",\n  \"100061.jpg\": \"Insecta/Aeshna interrupta/8980ccb4511240ae1385fc484ccabbce.jpg\",\n  \"100062.jpg\": \"Insecta/Aeshna interrupta/66929b94621725ac0e5da956fee7cf06.jpg\",\n  \"100074.jpg\": \"Insecta/Epitheca canis/79cb8cdeb83ea58c7033a1b7e0ff6c54.jpg\",\n  \"100077.jpg\": \"Insecta/Epitheca canis/84fc1e01e3a4c43167976bfde3d641d7.jpg\",\n  \"100078.jpg\": \"Insecta/Epitheca canis/bad2a7f7d266a3554f77abfbf5efec1d.jpg\",\n  \"100099.jpg\": \"Insecta/Chlosyne ehrenbergii/81ab6631cf85c0ed807f62465dec9d57.jpg\",\n  \"100104.jpg\": \"Insecta/Chlosyne ehrenbergii/0bd5723ac17339533db88a7f8911b129.jpg\",\n  \"100105.jpg\": \"Insecta/Chlosyne ehrenbergii/9e97985311e682c3df527c85f437a863.jpg\",\n  \"100108.jpg\": \"Insecta/Chlosyne ehrenbergii/ffaf3d1a4486db5a4d8eb2c892aa70de.jpg\",\n  \"100114.jpg\": \"Insecta/Chlosyne ehrenbergii/87e0a2967c7e24ea0d5721d58fe6f719.jpg\",\n  \"100125.jpg\": \"Animalia/Stomolophus meleagris/ef0db62241b14cf20de6cf77326a85fb.jpg\",\n  \"100133.jpg\": \"Animalia/Stomolophus meleagris/f7c9abafe0aa981744116ce0c827aa31.jpg\",\n  \"100136.jpg\": \"Animalia/Stomolophus meleagris/8f6a1cc844e2e98c341d05c270e806dd.jpg\",\n  \"100266.jpg\": \"Insecta/Stylurus plagiatus/886cf2e313fc91e464d7121dd1a5da97.jpg\",\n  \"100272.jpg\": \"Insecta/Stylurus plagiatus/5a5b2fbe557a67c338b3aa1133d5855b.jpg\",\n  \"100274.jpg\": \"Insecta/Stylurus plagiatus/b45e4d7b9417bad77b14ef46dd50c82b.jpg\",\n  \"100276.jpg\": \"Insecta/Stylurus plagiatus/82d7cbb05055292ef8bac3597b96e1ea.jpg\",\n  \"100277.jpg\": \"Insecta/Stylurus plagiatus/5e2ac0130a150fa2dd97a66efe45b4bc.jpg\",\n  \"100278.jpg\": \"Insecta/Stylurus plagiatus/ac0e368c8b9fd86db5457281b00c76cc.jpg\",\n  \"100279.jpg\": \"Insecta/Stylurus plagiatus/cfad6018f50e5f287febc63f64af241e.jpg\",\n  \"100280.jpg\": \"Insecta/Stylurus plagiatus/abb96a242fca7b682cfc83e2f151e6f6.jpg\",\n  \"100281.jpg\": \"Insecta/Stylurus plagiatus/3ea5ec480fdbd3164044f1bb0280b361.jpg\",\n  \"100282.jpg\": \"Insecta/Stylurus plagiatus/0e06c6a10cde2b09ff6cb97f453b51e9.jpg\",\n  \"100285.jpg\": \"Insecta/Stylurus plagiatus/9490d9a8b36dc9bbcb721d1914734795.jpg\",\n  \"100287.jpg\": \"Insecta/Stylurus plagiatus/eaf7a25d838a2d3328064d9117b0f284.jpg\",\n  \"100298.jpg\": \"Insecta/Stylurus plagiatus/7e5411bfae6a5e95e4ecb8549ec3794b.jpg\",\n  \"100300.jpg\": \"Insecta/Stylurus plagiatus/1080de5367fdca12739831b7a086f567.jpg\",\n  \"100301.jpg\": \"Insecta/Stylurus plagiatus/6c9c035cbaaf75775d3922ead44ad5f6.jpg\",\n  \"100302.jpg\": \"Insecta/Stylurus plagiatus/f09e68a645b3b1df71ac26658eec4c8d.jpg\",\n  \"100303.jpg\": \"Insecta/Stylurus plagiatus/5de87af6b6a36cf0fc74330419f23690.jpg\",\n  \"100304.jpg\": \"Insecta/Stylurus plagiatus/d72f75b128025ef31e6b1a251b3df558.jpg\",\n  \"100310.jpg\": \"Insecta/Stylurus plagiatus/045766d7078758caa95d6ed969365f47.jpg\",\n  \"100311.jpg\": \"Insecta/Stylurus plagiatus/8916bdfc2c7c7609031ae0934af0bd6d.jpg\",\n  \"100370.jpg\": \"Aves/Aegolius acadicus/e912ba7636310e14f8bb97c178ece266.jpg\",\n  \"100374.jpg\": \"Aves/Aegolius acadicus/bfc95343a47b9756b44ab67d73f878fe.jpg\",\n  \"100380.jpg\": \"Aves/Aegolius acadicus/f0968ac41ae58465610f5c91accf251d.jpg\",\n  \"100381.jpg\": \"Aves/Aegolius acadicus/a635ea967971142cfa68e714a030989c.jpg\",\n  \"100387.jpg\": \"Aves/Aegolius acadicus/815b4f47a6f275e12cc0a7ae58203b92.jpg\",\n  \"100389.jpg\": \"Aves/Aegolius acadicus/bf661004aee917d79430ef70489d9371.jpg\",\n  \"100404.jpg\": \"Aves/Meleagris gallopavo intermedia/0f7c6f0e8623c534772f8380d7524984.jpg\",\n  \"100445.jpg\": \"Insecta/Heliopetes ericetorum/6b12ea14015ce803aa79dff8784dad01.jpg\",\n  \"100455.jpg\": \"Insecta/Heliopetes ericetorum/8df73460f5a50a6382416ff3194452e2.jpg\",\n  \"100463.jpg\": \"Insecta/Heliopetes ericetorum/2813c672fec9f076353f727d5d8ea771.jpg\",\n  \"100467.jpg\": \"Insecta/Heliopetes ericetorum/f5e210b8d5eb3485722d95769f46c885.jpg\",\n  \"100476.jpg\": \"Insecta/Heliopetes ericetorum/6ebe2b42f096a6f661e3b7f271187c94.jpg\",\n  \"100477.jpg\": \"Insecta/Heliopetes ericetorum/50b2d766f0706f8cc0584f7d58049a20.jpg\",\n  \"100539.jpg\": \"Aves/Jacana jacana/f8ed26d75c7f1d9b5e60b5dfe44a53b8.jpg\",\n  \"100557.jpg\": \"Aves/Chlidonias niger/2ec52e19a69183873e23ce35515fbe5e.jpg\",\n  \"100559.jpg\": \"Aves/Chlidonias niger/04c5d7bcb85d226150b2a3701dcd90d7.jpg\",\n  \"100562.jpg\": \"Aves/Chlidonias niger/507a0dbdcc1e267b798159164a8c73f1.jpg\",\n  \"100569.jpg\": \"Aves/Chlidonias niger/c4b73bc59b7ddc79a4fb5f23340e30a6.jpg\",\n  \"10057.jpg\": \"Aves/Melospiza melodia/374a8c2e2e6e2965a569929a2abff0cd.jpg\",\n  \"100570.jpg\": \"Aves/Chlidonias niger/a4fabc1b67dd4fc90cae9b2f888a398e.jpg\",\n  \"100575.jpg\": \"Aves/Chlidonias niger/c9c0154c97af7a152a112514c8f1ddc1.jpg\",\n  \"100576.jpg\": \"Aves/Chlidonias niger/d4c8ddc03dafe3264a20fe964db499b2.jpg\",\n  \"10059.jpg\": \"Aves/Melospiza melodia/dc677bd1305e066b6c64aa6c7efd4bb0.jpg\",\n  \"100593.jpg\": \"Aves/Chlidonias niger/6b20f22287b772ad8a011ef549e71bf7.jpg\",\n  \"100594.jpg\": \"Aves/Chlidonias niger/e7f441092dbb3a29fc670b75efdb2400.jpg\",\n  \"100595.jpg\": \"Aves/Chlidonias niger/049dedbb8c83da8e45aa0cd0e3fc791e.jpg\",\n  \"100604.jpg\": \"Aves/Chlidonias niger/41c8ffd3bc259c7b725d46c75e89743a.jpg\",\n  \"100613.jpg\": \"Aves/Chlidonias niger/1a22159f830ad25ff769c79af16f8e86.jpg\",\n  \"100614.jpg\": \"Aves/Chlidonias niger/572069d9aadc099bf17012c3406adc8e.jpg\",\n  \"100631.jpg\": \"Aves/Chlidonias niger/0cfed2cd8bcca126cd507ef050cc42ef.jpg\",\n  \"100632.jpg\": \"Aves/Chlidonias niger/0ab7cf13af26d691cab044d074c25735.jpg\",\n  \"100634.jpg\": \"Insecta/Aglais milberti/b0e8aaff741748206f817580688d43b9.jpg\",\n  \"100635.jpg\": \"Insecta/Aglais milberti/cf325d9fb3593bb7e8ce81282b56444c.jpg\",\n  \"100636.jpg\": \"Insecta/Aglais milberti/679cc6855bf84f1df8c96023fe5f6a90.jpg\",\n  \"100638.jpg\": \"Insecta/Aglais milberti/d8e3bf30310c56f8fd5c67535d40adac.jpg\",\n  \"100639.jpg\": \"Insecta/Aglais milberti/b1b9892d47f6ac8fc1250db7c8f1f5c6.jpg\",\n  \"100643.jpg\": \"Insecta/Aglais milberti/e6f763ae0979287f72e7c32a8284c671.jpg\",\n  \"100646.jpg\": \"Insecta/Aglais milberti/1fae924beee5774f392d4b85f0da1d55.jpg\",\n  \"100648.jpg\": \"Insecta/Aglais milberti/a5cf173988abd6436e3337fcfee8a510.jpg\",\n  \"100651.jpg\": \"Insecta/Aglais milberti/920ef3540d0c3404f026b49f64af5e28.jpg\",\n  \"100652.jpg\": \"Insecta/Aglais milberti/d88e551ac1c01849d4cd901ddbb2c706.jpg\",\n  \"100658.jpg\": \"Insecta/Aglais milberti/b773a6c58fb5d1f04f82ec7796efe692.jpg\",\n  \"100660.jpg\": \"Insecta/Aglais milberti/cc8e0cb3c048960699c56e6b479d0f4e.jpg\",\n  \"100663.jpg\": \"Insecta/Aglais milberti/5e638b6fe60f5ef3c6f74168e30c27d3.jpg\",\n  \"100664.jpg\": \"Insecta/Aglais milberti/4d497af5e8ae872187644151588ccb86.jpg\",\n  \"100665.jpg\": \"Insecta/Aglais milberti/98c10f92f5bf5eef3f19ef2a283768d7.jpg\",\n  \"100674.jpg\": \"Insecta/Aglais milberti/e7296cacadd7eacb5957b05683c8b85b.jpg\",\n  \"100679.jpg\": \"Insecta/Aglais milberti/d395404f7839f257e34dc920d67c5423.jpg\",\n  \"100685.jpg\": \"Insecta/Aglais milberti/fec11c560b1daed72c2bb7d2ff94c6c7.jpg\",\n  \"100686.jpg\": \"Insecta/Aglais milberti/d48bac0f6214a4c2fa5169e6d35b199a.jpg\",\n  \"100687.jpg\": \"Insecta/Aglais milberti/8bc345f1a4f772c4c7f5c08ea275e725.jpg\",\n  \"100722.jpg\": \"Aves/Spheniscus demersus/51aae4dceb1acc6b98e63b474ce00996.jpg\",\n  \"100729.jpg\": \"Aves/Spheniscus demersus/19e63e5919bbf9a2c9f0c390ab0777ae.jpg\",\n  \"100836.jpg\": \"Insecta/Promachus hinei/64d914addc622d6513b694689af4a3d9.jpg\",\n  \"100862.jpg\": \"Insecta/Promachus hinei/ffb893e8acda2ea329f142483192254b.jpg\",\n  \"100865.jpg\": \"Insecta/Promachus hinei/6c768ce837143fb5bb68ef421de1d5bc.jpg\",\n  \"100866.jpg\": \"Insecta/Promachus hinei/2d9ec63da2f047ffe1840d9a49fbf790.jpg\",\n  \"100935.jpg\": \"Aves/Eudocimus albus/b5a03aa9d5e7fc9b057c140de840423b.jpg\",\n  \"101105.jpg\": \"Aves/Eudocimus albus/689f1f0d67a94729fa40aeea47c04b2a.jpg\",\n  \"10111.jpg\": \"Aves/Melospiza melodia/fe4a6e17f52be92b39663875c326b24d.jpg\",\n  \"10113.jpg\": \"Aves/Melospiza melodia/ff3c22d59ea55f3ad821eb833a8d7884.jpg\",\n  \"101188.jpg\": \"Aves/Eudocimus albus/2cd005404c35ed775f0d4548d6e1bba7.jpg\",\n  \"101217.jpg\": \"Aves/Eudocimus albus/0b75d0b85fa53757c7e8ae94bf437c1d.jpg\",\n  \"101299.jpg\": \"Aves/Eudocimus albus/a0c4b76a91259c0fc885ab2777d1e85a.jpg\",\n  \"101313.jpg\": \"Aves/Eudocimus albus/8054c47f6024df6336165257ddc9f8ce.jpg\",\n  \"101318.jpg\": \"Aves/Eudocimus albus/b4f6890e31b011fabf277f6dfe56cbc8.jpg\",\n  \"101333.jpg\": \"Reptilia/Regina grahamii/5e4b721bf3918ede54e69eca073ed38d.jpg\",\n  \"101334.jpg\": \"Reptilia/Regina grahamii/01a86229f8263bd5144e18b8544bf7f9.jpg\",\n  \"101335.jpg\": \"Reptilia/Regina grahamii/759e0ab595aedd4b3c9f7224ed4a5eb7.jpg\",\n  \"101337.jpg\": \"Reptilia/Regina grahamii/4affef4f5d77b2732db74d742356a2f3.jpg\",\n  \"101338.jpg\": \"Reptilia/Regina grahamii/01e29a7a6be1969b65e70b3578a420f9.jpg\",\n  \"101343.jpg\": \"Reptilia/Regina grahamii/b82843a807950eed0c9eeccad1ea8ddc.jpg\",\n  \"101416.jpg\": \"Reptilia/Drymarchon melanurus erebennus/7a3a093d23f74b9910bf71892bb9eb5e.jpg\",\n  \"101418.jpg\": \"Reptilia/Drymarchon melanurus erebennus/07c773974b94f9e90fda9a4da505a9b5.jpg\",\n  \"101426.jpg\": \"Reptilia/Drymarchon melanurus erebennus/d1aa6cdb18da8c25975c8f0ca76a149d.jpg\",\n  \"101430.jpg\": \"Reptilia/Drymarchon melanurus erebennus/d557255c87a701000b77c7d836b38586.jpg\",\n  \"101433.jpg\": \"Reptilia/Drymarchon melanurus erebennus/1382c84539869244d6bb43a4545ab629.jpg\",\n  \"10149.jpg\": \"Aves/Melospiza melodia/c41375d1c52dffde98f00179ceebc062.jpg\",\n  \"101617.jpg\": \"Aves/Thalasseus maximus/c72f090e204c4a6b500642f77e5a7d36.jpg\",\n  \"101621.jpg\": \"Aves/Thalasseus maximus/a4d9cddfab37947accd65acd9a02e6ac.jpg\",\n  \"101656.jpg\": \"Aves/Thalasseus maximus/b147d0af0387dea96df77585029fe133.jpg\",\n  \"101718.jpg\": \"Aves/Thalasseus maximus/1dc2133abc32d217e4e0034d321b9f1c.jpg\",\n  \"101722.jpg\": \"Aves/Thalasseus maximus/fac679c7c3f6a76b80fad2e05f7e0459.jpg\",\n  \"101755.jpg\": \"Aves/Thalasseus maximus/2a8a4ea6fb7d9581ea754328201ad788.jpg\",\n  \"101766.jpg\": \"Aves/Thalasseus maximus/8a6fdb576aafc5797371cc44c0318624.jpg\",\n  \"101787.jpg\": \"Aves/Thalasseus maximus/6c9657e0b0d688c90e6aed5cf6e360a4.jpg\",\n  \"101819.jpg\": \"Aves/Thalasseus maximus/c9a0f20ac394f377546be6bbf3e1371c.jpg\",\n  \"101975.jpg\": \"Aves/Anas platyrhynchos domesticus/e6eb3f9c16abf445d03a621d1dffb7d9.jpg\",\n  \"101998.jpg\": \"Aves/Anas platyrhynchos domesticus/afc3eb629b9ed03b828f99820ba7d325.jpg\",\n  \"102.jpg\": \"Mammalia/Marmota flaviventris/a74f8bbcd2b9f0b135c5b4b9be262aaa.jpg\",\n  \"102059.jpg\": \"Aves/Anas platyrhynchos domesticus/57810dfd5a691e2fb3d5004e889878d7.jpg\",\n  \"102064.jpg\": \"Aves/Anas platyrhynchos domesticus/7f95f8dcb2b984b23ec020ee07955de0.jpg\",\n  \"102075.jpg\": \"Aves/Anas platyrhynchos domesticus/c2251771eb46c7348cda58c16ea1b02e.jpg\",\n  \"102077.jpg\": \"Aves/Anas platyrhynchos domesticus/07f17272a4eaa8a638ff1de35c523b2b.jpg\",\n  \"102144.jpg\": \"Aves/Anas platyrhynchos domesticus/3f42bd875a62927c9503ba90e4abfa4e.jpg\",\n  \"102151.jpg\": \"Aves/Anas platyrhynchos domesticus/5aa2dff364d9beaeef10592191c1bbf0.jpg\",\n  \"102167.jpg\": \"Aves/Anas platyrhynchos domesticus/2d4c4072102882993a4d4d141a506e5b.jpg\",\n  \"102183.jpg\": \"Aves/Anas platyrhynchos domesticus/111271e8822627825c63c89ce7e4df59.jpg\",\n  \"102185.jpg\": \"Aves/Anas platyrhynchos domesticus/1112d6dc10753dcb1c3da7ca2a5eb312.jpg\",\n  \"102308.jpg\": \"Reptilia/Agkistrodon contortrix contortrix/bada5a97471cbf56c8f3c0956082acf7.jpg\",\n  \"102312.jpg\": \"Reptilia/Agkistrodon contortrix contortrix/0d00ce558356a984e76f27faf4d19ee4.jpg\",\n  \"102313.jpg\": \"Reptilia/Agkistrodon contortrix contortrix/8fce8151304345b4eab20b12830627df.jpg\",\n  \"102315.jpg\": \"Reptilia/Agkistrodon contortrix contortrix/a8ca7edb168923db1196db907fb0fe84.jpg\",\n  \"102317.jpg\": \"Reptilia/Agkistrodon contortrix contortrix/72a3c9ba59e6cc3196e7696451ad102d.jpg\",\n  \"102318.jpg\": \"Reptilia/Agkistrodon contortrix contortrix/3bbd04c2cfbb97dd8111a40de7cac847.jpg\",\n  \"102327.jpg\": \"Mollusca/Rumina decollata/92185ee9a77b8e1565e03b0e7cd1716d.jpg\",\n  \"102332.jpg\": \"Mollusca/Rumina decollata/6ba0ff1cffa95f9c8223bcc8677a124c.jpg\",\n  \"102343.jpg\": \"Mollusca/Rumina decollata/f60a346010c059a7d5396354c8151498.jpg\",\n  \"102349.jpg\": \"Mollusca/Rumina decollata/55ff0a9221377b667bc34b5cd26ff134.jpg\",\n  \"102359.jpg\": \"Mollusca/Rumina decollata/93e8bccc1fb0cc27a2d3c8a2b6b3ae8c.jpg\",\n  \"102391.jpg\": \"Mollusca/Rumina decollata/fe4f78db053de2e383ef7f729ea2d4a2.jpg\",\n  \"102397.jpg\": \"Mollusca/Rumina decollata/4be4c3b9d7df5233e62343f4a6bc7f7b.jpg\",\n  \"102404.jpg\": \"Mollusca/Rumina decollata/08ab0ce8eeb5bfbd9215aa0bdac45a22.jpg\",\n  \"102405.jpg\": \"Mollusca/Rumina decollata/6215ad2af14663ef2cff36d7145d017b.jpg\",\n  \"102408.jpg\": \"Mollusca/Rumina decollata/4728ce7b9be81478a1d46ec59fc95fc6.jpg\",\n  \"102415.jpg\": \"Mollusca/Rumina decollata/3f59a95aeb8e3cd8cb4e4f9e7c6c4215.jpg\",\n  \"102416.jpg\": \"Mollusca/Rumina decollata/2076903554ac98cb2d28dcb4375f899d.jpg\",\n  \"102420.jpg\": \"Mollusca/Rumina decollata/d2cb325944db68d228e16168f4beb800.jpg\",\n  \"102430.jpg\": \"Mollusca/Rumina decollata/5a99a22f109722d81bc4bd60a02fce1d.jpg\",\n  \"102432.jpg\": \"Mollusca/Rumina decollata/2617b8107867ba74da5b207b25edc01b.jpg\",\n  \"102530.jpg\": \"Insecta/Dorocordulia libera/34c6b14a4f6dc82e6d8261514bfdcca2.jpg\",\n  \"102538.jpg\": \"Insecta/Dorocordulia libera/e38e4fcec8db6af0ee99bfba63cd7136.jpg\",\n  \"102544.jpg\": \"Insecta/Dorocordulia libera/4b27b187921fd252d2d4eb2086ce2ed5.jpg\",\n  \"102546.jpg\": \"Insecta/Dorocordulia libera/b3b03a14a75aa1627582604c824ee762.jpg\",\n  \"10264.jpg\": \"Aves/Melospiza melodia/cf8be0445405e36286a22841c801eb5e.jpg\",\n  \"102640.jpg\": \"Insecta/Neophasia menapia/bfe7f97e0d477fc08bda6ec521181c33.jpg\",\n  \"102651.jpg\": \"Insecta/Neophasia menapia/e749a76235b85c79931f1a26cf62ba9b.jpg\",\n  \"102652.jpg\": \"Insecta/Neophasia menapia/98ae9827aa610eb431fbf23cd7809656.jpg\",\n  \"102656.jpg\": \"Insecta/Neophasia menapia/1cf5d1ec0c5369e1451e7dc1a2534940.jpg\",\n  \"102662.jpg\": \"Insecta/Darapsa choerilus/1ed097b34cdec43ad68838191a70d39d.jpg\",\n  \"102663.jpg\": \"Insecta/Darapsa choerilus/3043bd977bdcaae359e071ec2ef7465a.jpg\",\n  \"102671.jpg\": \"Insecta/Darapsa choerilus/c627c3590011b4e67264e3d7cd1a245c.jpg\",\n  \"102673.jpg\": \"Insecta/Darapsa choerilus/6a237023cb15f9df2a67d06f3418f2d0.jpg\",\n  \"102678.jpg\": \"Aves/Pycnonotus cafer/d38858405b4d4dad47c25983952e8bcd.jpg\",\n  \"102679.jpg\": \"Aves/Pycnonotus cafer/8d2b60dedfad03c7c4f7b71a89fc5a82.jpg\",\n  \"102680.jpg\": \"Aves/Pycnonotus cafer/2ce69cf333deebae9c6d270c3de3996f.jpg\",\n  \"102681.jpg\": \"Aves/Pycnonotus cafer/bf0ba55167e430a74ecdb62982141090.jpg\",\n  \"102682.jpg\": \"Aves/Pycnonotus cafer/cbe45ec76ff68f234243773956d75324.jpg\",\n  \"102683.jpg\": \"Aves/Pycnonotus cafer/24aa5a25ced1c725830cae2272462d65.jpg\",\n  \"102684.jpg\": \"Aves/Pycnonotus cafer/f4eab77846bc4c283efe2057c02fdd94.jpg\",\n  \"102685.jpg\": \"Aves/Pycnonotus cafer/92aa85843cc44735326cab1d28f65600.jpg\",\n  \"102686.jpg\": \"Aves/Pycnonotus cafer/f18c4432416aa8e2d4111948b56afbb6.jpg\",\n  \"102687.jpg\": \"Aves/Pycnonotus cafer/c9944a30043e4683be8da13ee69f8e7f.jpg\",\n  \"102690.jpg\": \"Aves/Pycnonotus cafer/4150bb6fcad4db660b9d8470831a8cbb.jpg\",\n  \"102691.jpg\": \"Aves/Pycnonotus cafer/3115629c6a2376649d822b5516e48955.jpg\",\n  \"102692.jpg\": \"Aves/Pycnonotus cafer/7bab63f4f79431816fdccf04100beaab.jpg\",\n  \"102693.jpg\": \"Aves/Pycnonotus cafer/66c288639ca2bf5d3c3fa2fbedf29dd7.jpg\",\n  \"102697.jpg\": \"Aves/Pycnonotus cafer/802feecafb21ea5df0f9788bec8ae40e.jpg\",\n  \"102698.jpg\": \"Aves/Pycnonotus cafer/a5c91402a2d18a84cb8738300e9376f8.jpg\",\n  \"102704.jpg\": \"Aves/Pycnonotus cafer/9d431ec6709c40362259e5ba70af5b84.jpg\",\n  \"102705.jpg\": \"Aves/Pycnonotus cafer/4baebf91609d9037235c92285d472be8.jpg\",\n  \"102706.jpg\": \"Aves/Pycnonotus cafer/eef91655008e2a2c25169095ec3d4848.jpg\",\n  \"102714.jpg\": \"Aves/Pycnonotus cafer/c7bdaaa8dd51624a9cad116cfb0c612b.jpg\",\n  \"102715.jpg\": \"Aves/Pycnonotus cafer/0b8be7eeb83590b854fc76cd92d79921.jpg\",\n  \"102723.jpg\": \"Aves/Pycnonotus cafer/d8d043620eedaab3aac6a3511e594d36.jpg\",\n  \"102724.jpg\": \"Arachnida/Holocnemus pluchei/ce48f70fe80e59aa74dfe7cf16a1488e.jpg\",\n  \"102728.jpg\": \"Arachnida/Holocnemus pluchei/34ba44b8bf401b322619775d77b2a8e3.jpg\",\n  \"102729.jpg\": \"Arachnida/Holocnemus pluchei/5c7f282ce0a29858e89dd05aed28cd1e.jpg\",\n  \"102730.jpg\": \"Arachnida/Holocnemus pluchei/d19ef95b54e3e353183669599861dc37.jpg\",\n  \"102740.jpg\": \"Aves/Megaceryle torquata/97dbe1681ebddb970b02544ce5a09807.jpg\",\n  \"102747.jpg\": \"Aves/Megaceryle torquata/c831b0ac18609d985d2332ae685e3166.jpg\",\n  \"102757.jpg\": \"Aves/Megaceryle torquata/8bf6dfe4c476d215628b8660d76f68ed.jpg\",\n  \"102758.jpg\": \"Aves/Megaceryle torquata/99980b011f6c0890ed97f26e753eb8de.jpg\",\n  \"102764.jpg\": \"Aves/Megaceryle torquata/1ecefb022236145bddcfd04fa72868a8.jpg\",\n  \"102772.jpg\": \"Aves/Megaceryle torquata/44756ce46a4714139fdc7212ab4e9ce1.jpg\",\n  \"102775.jpg\": \"Aves/Megaceryle torquata/f706f4eb623e7c5c452839f54c8213ae.jpg\",\n  \"102776.jpg\": \"Aves/Megaceryle torquata/04cff5d665562015c8141763fb6e5c2f.jpg\",\n  \"102783.jpg\": \"Aves/Megaceryle torquata/f7011da8628767f07c51d68caa69f102.jpg\",\n  \"102785.jpg\": \"Aves/Megaceryle torquata/cbc709b1609883354156008ef3d786b8.jpg\",\n  \"102787.jpg\": \"Aves/Megaceryle torquata/cda9e01b4bb94b9c0eda6431ddcb5420.jpg\",\n  \"102788.jpg\": \"Aves/Megaceryle torquata/6bf576f240431bfd4cc0bd9caec6a006.jpg\",\n  \"102791.jpg\": \"Aves/Megaceryle torquata/ab7aa2da5be712210532759a80da666e.jpg\",\n  \"102792.jpg\": \"Aves/Megaceryle torquata/8883a391c92a18aba9eb65c57cd33d2c.jpg\",\n  \"102795.jpg\": \"Aves/Megaceryle torquata/e933a1e8fc298f858956838d1506f6fa.jpg\",\n  \"102797.jpg\": \"Aves/Megaceryle torquata/5ce7a3cd306156dda15889fba2156d5e.jpg\",\n  \"102799.jpg\": \"Aves/Megaceryle torquata/84a3e0bc53264c9e4752c1ba8fee0ae8.jpg\",\n  \"102804.jpg\": \"Aves/Megaceryle torquata/eb64e8e831fb7bfb410543c31a00bfde.jpg\",\n  \"102809.jpg\": \"Aves/Megaceryle torquata/02b157af0ed7b989d6154b908f8cb2e4.jpg\",\n  \"102810.jpg\": \"Aves/Megaceryle torquata/7ab89f850a30d28c78d4401219ac0244.jpg\",\n  \"102815.jpg\": \"Aves/Megaceryle torquata/332024e41fffb954658bda055a07cd83.jpg\",\n  \"102816.jpg\": \"Aves/Megaceryle torquata/0e46293d14ec30594507e82b7d0c8fe0.jpg\",\n  \"103478.jpg\": \"Aves/Spinus spinus/39350da0c047f6d467e65e9a1a169d4e.jpg\",\n  \"103481.jpg\": \"Aves/Spinus spinus/6d293e6dc8856e1e29c9db70c7c1937c.jpg\",\n  \"103482.jpg\": \"Aves/Spinus spinus/ab35e67127558b87851392123e00e42e.jpg\",\n  \"103483.jpg\": \"Aves/Spinus spinus/a05a7b2191ff9858147a9fac04ba9571.jpg\",\n  \"103497.jpg\": \"Aves/Spinus spinus/868573fd2335d76df8f9d20342707c3c.jpg\",\n  \"103501.jpg\": \"Aves/Spinus spinus/42fc19fbcb52c73a68c5a9e64f63e1aa.jpg\",\n  \"103502.jpg\": \"Aves/Spinus spinus/046c050c2ca1964505983e53e70467d1.jpg\",\n  \"103503.jpg\": \"Aves/Spinus spinus/5d9cb516b052aab90c1010c6fcb98bc3.jpg\",\n  \"103504.jpg\": \"Aves/Spinus spinus/161f5fb329afa2b68ba630eed8889f55.jpg\",\n  \"103507.jpg\": \"Aves/Spinus spinus/0d8635d7e7f1f098f5ca83330d37f3ad.jpg\",\n  \"103515.jpg\": \"Aves/Spinus spinus/abb29c61ef5223c3b2a62aef18eb7525.jpg\",\n  \"103530.jpg\": \"Amphibia/Agalychnis callidryas/7c0526540cd32d5dea8fe939dc44b059.jpg\",\n  \"103534.jpg\": \"Amphibia/Agalychnis callidryas/61670b3542f5e0ce6ff178010e7de291.jpg\",\n  \"103539.jpg\": \"Amphibia/Agalychnis callidryas/b5c95936ed8402e111a6afb4ddf6bbcb.jpg\",\n  \"103541.jpg\": \"Amphibia/Agalychnis callidryas/a8be49458ac27ee14473534bea8a81ac.jpg\",\n  \"103545.jpg\": \"Amphibia/Agalychnis callidryas/a185f1fef770bdecb42c4d24ac6e1b52.jpg\",\n  \"103548.jpg\": \"Amphibia/Agalychnis callidryas/8e045487854fe9753341ea0279971862.jpg\",\n  \"103549.jpg\": \"Amphibia/Agalychnis callidryas/91d29677591bc8df35789bcaf15009a8.jpg\",\n  \"103550.jpg\": \"Amphibia/Agalychnis callidryas/f30642ca46d576e7e3fc34f5d804d4eb.jpg\",\n  \"103551.jpg\": \"Amphibia/Agalychnis callidryas/8b844adf89f17e9384bad5fd170a03e7.jpg\",\n  \"103552.jpg\": \"Amphibia/Agalychnis callidryas/ff5fe3c6adae2832f07a96da83058bb7.jpg\",\n  \"103556.jpg\": \"Amphibia/Agalychnis callidryas/8a99f3a966da16f3946992575b10b8e7.jpg\",\n  \"103557.jpg\": \"Amphibia/Agalychnis callidryas/cbc8b36e9280893119f002fb58414d89.jpg\",\n  \"103762.jpg\": \"Aves/Cynanthus latirostris/e04d65b0c967cdbab9ccceda0791145e.jpg\",\n  \"103763.jpg\": \"Aves/Cynanthus latirostris/130931055847f4c76f724b02cec4e4f4.jpg\",\n  \"103793.jpg\": \"Aves/Cynanthus latirostris/a7559f837c99441c671f26d5e2cc0de9.jpg\",\n  \"103805.jpg\": \"Aves/Cynanthus latirostris/908c0ea58ea122a1ea7d00e65f94f1f2.jpg\",\n  \"103820.jpg\": \"Aves/Cynanthus latirostris/b933da5162d0bb4a7ee5356f66b1ea94.jpg\",\n  \"103823.jpg\": \"Aves/Cynanthus latirostris/bf82c795c4d49f1f245a6450cc11e64e.jpg\",\n  \"103824.jpg\": \"Aves/Cynanthus latirostris/c622b5d4fca482f6d7444b7ba74b13ae.jpg\",\n  \"103844.jpg\": \"Aves/Cynanthus latirostris/8152cf445a33e553a5e597945e430877.jpg\",\n  \"103865.jpg\": \"Aves/Cynanthus latirostris/61ac3bebe6d56de6da84a690a2c20c4a.jpg\",\n  \"103866.jpg\": \"Aves/Cynanthus latirostris/546cfc4b07222d3400876c4565927537.jpg\",\n  \"103867.jpg\": \"Aves/Cynanthus latirostris/c1471c77c8d91c6eb69470db7ff0b10f.jpg\",\n  \"103868.jpg\": \"Aves/Cynanthus latirostris/e375573362785b67cd50a7d55dbefa6e.jpg\",\n  \"103890.jpg\": \"Aves/Cynanthus latirostris/fb05504c776aa4da72e92aef4058ccd2.jpg\",\n  \"103898.jpg\": \"Aves/Cynanthus latirostris/d68f1dbe902dba1563f67dd16ca60105.jpg\",\n  \"103899.jpg\": \"Aves/Cynanthus latirostris/f346f8f4abe1f32f0caef6ed58e1f339.jpg\",\n  \"103906.jpg\": \"Aves/Cynanthus latirostris/8447f62cb5b821d875dd7153bcdd7561.jpg\",\n  \"103907.jpg\": \"Aves/Cynanthus latirostris/4e1a2bfbd3727558c6d41b7204b51b53.jpg\",\n  \"103924.jpg\": \"Aves/Cynanthus latirostris/1d8efa8a7153db29aedf4432d8fc08ce.jpg\",\n  \"103941.jpg\": \"Aves/Cynanthus latirostris/9d4acc17f4c52d704add49af8874cff3.jpg\",\n  \"103957.jpg\": \"Aves/Cynanthus latirostris/64fdeb48bb687f4a3ebc0607238aa63e.jpg\",\n  \"103958.jpg\": \"Aves/Cynanthus latirostris/e266fe0d9aa5bd5106c9129115783413.jpg\",\n  \"103968.jpg\": \"Aves/Cynanthus latirostris/f282d29f0d0757888494f81df451d4da.jpg\",\n  \"103996.jpg\": \"Aves/Cynanthus latirostris/a4298e13f8c6cf2a3b5ae385f204ba2e.jpg\",\n  \"103997.jpg\": \"Aves/Cynanthus latirostris/d5256f9156da11462e3f3c1162a5e87f.jpg\",\n  \"104027.jpg\": \"Aves/Cynanthus latirostris/354cbbbdeae04a0ec14a1f598c883528.jpg\",\n  \"104113.jpg\": \"Animalia/Porcellio scaber/4067b463ce61249386b18498931f18cc.jpg\",\n  \"104114.jpg\": \"Animalia/Porcellio scaber/b04ff6412d8823f8e4e9b45b2d3317ec.jpg\",\n  \"104115.jpg\": \"Animalia/Porcellio scaber/a1d533cbb570d5bb4122d6f07979d5f7.jpg\",\n  \"104123.jpg\": \"Animalia/Porcellio scaber/1e7eeb5e6e9d98fc89fcf044f2d5d24c.jpg\",\n  \"104129.jpg\": \"Animalia/Porcellio scaber/481b1c02dec4e02abca56226b0b7b415.jpg\",\n  \"104132.jpg\": \"Animalia/Porcellio scaber/ac9240debd4f7b65eb8e10c90fc64648.jpg\",\n  \"104136.jpg\": \"Animalia/Porcellio scaber/22050bd6ce9cf58fbc8c5a6854ba947c.jpg\",\n  \"104139.jpg\": \"Animalia/Porcellio scaber/040896cf103c9b5d5afcdb4ffd4dd0ed.jpg\",\n  \"104140.jpg\": \"Animalia/Porcellio scaber/b1a22c7d4907b0bca9ed0a682b464ce1.jpg\",\n  \"104145.jpg\": \"Animalia/Porcellio scaber/46299221ea0ad223aee392c68b20fc32.jpg\",\n  \"104147.jpg\": \"Animalia/Porcellio scaber/ee87d3dc3a1c4110e67f14adbe2b2e41.jpg\",\n  \"104196.jpg\": \"Insecta/Mermiria bivittata/38972ee3e07d8f0e761f4ccf686bc647.jpg\",\n  \"104204.jpg\": \"Insecta/Mermiria bivittata/bc1f17ebbebe421edeb07e9820b04772.jpg\",\n  \"104205.jpg\": \"Insecta/Mermiria bivittata/40602b2431c29dab47fc0c34628709af.jpg\",\n  \"104206.jpg\": \"Insecta/Mermiria bivittata/03f7f4d8b5e6f5b05bfb08f99470dc07.jpg\",\n  \"104207.jpg\": \"Insecta/Mermiria bivittata/fbf162436a6f19b07cd5bb4b51846c09.jpg\",\n  \"10421.jpg\": \"Aves/Melospiza melodia/e2ba50f431e756a855100cdf7a45861a.jpg\",\n  \"104215.jpg\": \"Insecta/Mermiria bivittata/919cc81748d4aa17e8ef192f58a7da93.jpg\",\n  \"104216.jpg\": \"Insecta/Mermiria bivittata/2fc3bc0df4b71c6d0b286c85e9d266e6.jpg\",\n  \"104220.jpg\": \"Insecta/Mermiria bivittata/f673b9f795da13ad760f20ab6614c396.jpg\",\n  \"104224.jpg\": \"Insecta/Mermiria bivittata/3cb6a2edf974303bf8e484f0bc160199.jpg\",\n  \"104241.jpg\": \"Insecta/Gomphaeschna furcillata/a3a686b988638aa59ae5a1a67b331776.jpg\",\n  \"104244.jpg\": \"Insecta/Gomphaeschna furcillata/9914a2372a7a665fb6a45aad22567d73.jpg\",\n  \"104247.jpg\": \"Insecta/Gomphaeschna furcillata/c65337fc47627b0cb39524eafcff0a21.jpg\",\n  \"104248.jpg\": \"Insecta/Gomphaeschna furcillata/dc3e49a2277ee3feae56ed1e0c539a0b.jpg\",\n  \"104262.jpg\": \"Insecta/Gomphaeschna furcillata/e26887f7af282f5546ca5463d54b985a.jpg\",\n  \"104264.jpg\": \"Insecta/Gomphaeschna furcillata/14396fd580287ff17d7f7f27c9589a7c.jpg\",\n  \"104377.jpg\": \"Aves/Ocyphaps lophotes/4b09e3e53086f25c3519e1698fd81944.jpg\",\n  \"104385.jpg\": \"Aves/Ocyphaps lophotes/add3cd2f3eb5c16ba865bff00953575a.jpg\",\n  \"104400.jpg\": \"Insecta/Sympetrum corruptum/22eb2dc0cec941719a8be7f72abf0629.jpg\",\n  \"104417.jpg\": \"Insecta/Sympetrum corruptum/86cd8c45c486980dd35690c961c1a2b8.jpg\",\n  \"104509.jpg\": \"Insecta/Sympetrum corruptum/af745e4ea0cd0c88177339b11df7a5dd.jpg\",\n  \"104518.jpg\": \"Insecta/Sympetrum corruptum/415043f95069e9e67a97063fc3e3448e.jpg\",\n  \"104522.jpg\": \"Insecta/Sympetrum corruptum/cae4b4884e38f475bcb6eff72d67d2c3.jpg\",\n  \"104525.jpg\": \"Insecta/Sympetrum corruptum/16a1becbad23f72a6b968c840840b40f.jpg\",\n  \"104526.jpg\": \"Insecta/Sympetrum corruptum/a69546d535eb78eedace0e38645d71ea.jpg\",\n  \"104541.jpg\": \"Insecta/Sympetrum corruptum/f7c337421f7f0dabb4d2ca25baa52912.jpg\",\n  \"104572.jpg\": \"Insecta/Sympetrum corruptum/bc7d9d2f67af6e615cf4918306112360.jpg\",\n  \"104593.jpg\": \"Insecta/Sympetrum corruptum/b50f2554a830559b8d972263ce6dc4a3.jpg\",\n  \"104594.jpg\": \"Insecta/Sympetrum corruptum/f23cc99a5e25a3bb1d881c8019c45c95.jpg\",\n  \"104596.jpg\": \"Insecta/Sympetrum corruptum/fe1418c143281ce870e08f44db4a0dc6.jpg\",\n  \"104601.jpg\": \"Insecta/Sympetrum corruptum/a21f51b0fdd2529a1ec91cd6455c1579.jpg\",\n  \"104605.jpg\": \"Insecta/Sympetrum corruptum/8ebdebfbb9018bd237936526f95b51c7.jpg\",\n  \"104619.jpg\": \"Insecta/Sympetrum corruptum/7e1b567b3a68a6194f3e23d43790f991.jpg\",\n  \"104635.jpg\": \"Insecta/Sympetrum corruptum/e222c4057053a86874acd67ca0f0c239.jpg\",\n  \"104654.jpg\": \"Insecta/Sympetrum corruptum/d0f2db8d5433e51bc32a2be54637ed20.jpg\",\n  \"104667.jpg\": \"Insecta/Sympetrum corruptum/8c52ee7eef1dc2f45b291ad665040cd6.jpg\",\n  \"104671.jpg\": \"Insecta/Sympetrum corruptum/ec9c8f2b7e90f9af6bd565a8014c782b.jpg\",\n  \"104681.jpg\": \"Insecta/Sympetrum corruptum/131c3a41090c26e668e867a6bb2acead.jpg\",\n  \"104697.jpg\": \"Insecta/Sympetrum corruptum/bf8fef96d7e53856f8842ab668b22b08.jpg\",\n  \"104706.jpg\": \"Insecta/Sympetrum corruptum/691bbd87a3c7085a1e4e9c2c00b02ecc.jpg\",\n  \"104712.jpg\": \"Insecta/Sympetrum corruptum/75e9d85bf69a19a05953a9c788ecbf7e.jpg\",\n  \"104732.jpg\": \"Insecta/Sympetrum corruptum/c8cfa671d90beb405bfee0da28e2a833.jpg\",\n  \"104744.jpg\": \"Insecta/Sympetrum corruptum/813a444bbbcd3e4b7efbf9fef97921f6.jpg\",\n  \"104790.jpg\": \"Insecta/Sympetrum corruptum/e5f4f546599a6461f97b05d3785d72ec.jpg\",\n  \"10480.jpg\": \"Aves/Melospiza melodia/52c7eb299b2596137cb53dbcde021cba.jpg\",\n  \"104987.jpg\": \"Reptilia/Boa constrictor/42dbe6e58ef6ad40451c5e9ea8d29665.jpg\",\n  \"104988.jpg\": \"Reptilia/Boa constrictor/4decbfd71c5872893716617478981c34.jpg\",\n  \"104989.jpg\": \"Reptilia/Boa constrictor/7e30d4cb8344fda573fce5efd5469b12.jpg\",\n  \"104993.jpg\": \"Reptilia/Boa constrictor/106fdf2ad415705b9217acc578a3d84c.jpg\",\n  \"104994.jpg\": \"Reptilia/Boa constrictor/4be6a948919e3cfe66b67c3b4a0f4637.jpg\",\n  \"105004.jpg\": \"Reptilia/Boa constrictor/72a990d821f592ad2dec82561549d78f.jpg\",\n  \"105010.jpg\": \"Reptilia/Boa constrictor/a218d19e52355b75d8fb89ff45320531.jpg\",\n  \"105012.jpg\": \"Reptilia/Boa constrictor/0eef577bdd62d8b1852edef302e78aa0.jpg\",\n  \"105014.jpg\": \"Reptilia/Boa constrictor/5dbb0dad11a32760d03a842ac3e44a49.jpg\",\n  \"105017.jpg\": \"Reptilia/Boa constrictor/ff41d2d0d48b570708a79b954441ff6d.jpg\",\n  \"105022.jpg\": \"Reptilia/Boa constrictor/4df08c02a14c3d7db419f01959b38aff.jpg\",\n  \"10506.jpg\": \"Aves/Melospiza melodia/639b93417d456220cb99a20ee6383544.jpg\",\n  \"105072.jpg\": \"Insecta/Coenonympha tullia/5237cd4db63f57039cc850899d5f87c1.jpg\",\n  \"105080.jpg\": \"Insecta/Coenonympha tullia/661a876762ebce96a1700d2974b59d13.jpg\",\n  \"105082.jpg\": \"Insecta/Coenonympha tullia/13b526128cf04f8e1c497f6ad6a5eef6.jpg\",\n  \"105085.jpg\": \"Insecta/Coenonympha tullia/6b77ddebad01eda54f8df8439c983c7a.jpg\",\n  \"105095.jpg\": \"Insecta/Coenonympha tullia/a09339d2597b86eccbbfd6636279d945.jpg\",\n  \"105097.jpg\": \"Insecta/Coenonympha tullia/18a547baefe604d5dfe5e51c52637cb1.jpg\",\n  \"105102.jpg\": \"Insecta/Coenonympha tullia/8d9cbe6af3cffec8911b61629ee5ee4e.jpg\",\n  \"105103.jpg\": \"Insecta/Coenonympha tullia/6a99a34555ba010130e43982d833378e.jpg\",\n  \"105105.jpg\": \"Insecta/Coenonympha tullia/99adefb29d37d4b33c78b56e1b7af537.jpg\",\n  \"105116.jpg\": \"Insecta/Coenonympha tullia/6a3fb27e8fa5907fcbecdee585962f5f.jpg\",\n  \"105117.jpg\": \"Insecta/Coenonympha tullia/bb28139c353b0ff84385f154b494cbd7.jpg\",\n  \"105131.jpg\": \"Insecta/Coenonympha tullia/def254c4a982a1603ce1fd70ba295d3f.jpg\",\n  \"105135.jpg\": \"Insecta/Coenonympha tullia/12be243aa3efb43180460a5780490c98.jpg\",\n  \"105140.jpg\": \"Insecta/Coenonympha tullia/12796b1629fa89031ff57d8dc4e29ff6.jpg\",\n  \"105145.jpg\": \"Insecta/Coenonympha tullia/3a3d8f563b005297b5c8e6907b9883ce.jpg\",\n  \"105148.jpg\": \"Insecta/Coenonympha tullia/ddcd4a1dc65bd5dc83ab46410725cb9b.jpg\",\n  \"105152.jpg\": \"Insecta/Coenonympha tullia/fb0ecc2276c09bbdd723d121b5e4df6e.jpg\",\n  \"105154.jpg\": \"Insecta/Coenonympha tullia/c26d393bd659918906b25ced0a0e209a.jpg\",\n  \"105155.jpg\": \"Insecta/Coenonympha tullia/8c9327f7ccd205c280fbb65908925d81.jpg\",\n  \"105158.jpg\": \"Aves/Merops orientalis/b8dfef584eaf9da860737ccd0609b13a.jpg\",\n  \"105159.jpg\": \"Aves/Merops orientalis/781e627ac27be1e9ef1333afd2a1cd3a.jpg\",\n  \"105161.jpg\": \"Aves/Merops orientalis/0732360102b7f2e18363a5b30b78ab05.jpg\",\n  \"105165.jpg\": \"Aves/Merops orientalis/4158a02b17e9c3925de832f6445a3c2a.jpg\",\n  \"105166.jpg\": \"Aves/Merops orientalis/ca5567e6d20469cad396f16d4d72ffa7.jpg\",\n  \"105170.jpg\": \"Aves/Merops orientalis/8fb5bb95999a8130e17f6a35903574e6.jpg\",\n  \"105232.jpg\": \"Insecta/Argia translata/7bf1d35b830257ef0a370bad91ddef8b.jpg\",\n  \"105233.jpg\": \"Insecta/Argia translata/b5bdb379274704c8c095510f5b496306.jpg\",\n  \"105234.jpg\": \"Insecta/Argia translata/2589879ebb79e4e323e42fb4eba60100.jpg\",\n  \"105243.jpg\": \"Insecta/Argia translata/7c1ac060eb673e08e053d614e680d2e0.jpg\",\n  \"105263.jpg\": \"Insecta/Argia translata/f8659b0326243459c9fe02cd84e558e8.jpg\",\n  \"105265.jpg\": \"Insecta/Argia translata/7d6d1bdfe70a2b5df0ceb2bc8b8328b6.jpg\",\n  \"105266.jpg\": \"Insecta/Argia translata/ad9990c349c19e6166e60bdba91c31ab.jpg\",\n  \"105267.jpg\": \"Insecta/Argia translata/4ea1dc41dbd6f0c92476f25a8d0ed181.jpg\",\n  \"105272.jpg\": \"Insecta/Argia translata/19f2ae4129dd2442c783a78165ef276c.jpg\",\n  \"105274.jpg\": \"Insecta/Argia translata/fa2a61f5e653c103729c5c391dc3ff1b.jpg\",\n  \"105301.jpg\": \"Insecta/Argia translata/9c5fc23c2654c801550b5b3fb63cf5b3.jpg\",\n  \"105311.jpg\": \"Insecta/Argia translata/6255758829ee4f7ac65bef8207594144.jpg\",\n  \"105322.jpg\": \"Insecta/Argia translata/27607ee8325072ff52fead76e78cf430.jpg\",\n  \"105324.jpg\": \"Insecta/Argia translata/b3da631d79d29c1c71b361643ca50169.jpg\",\n  \"105325.jpg\": \"Insecta/Argia translata/58961f5f2267a3dd4fb5e9484729d071.jpg\",\n  \"105326.jpg\": \"Insecta/Argia translata/08e6f446a61d9db9b42946e195d45f73.jpg\",\n  \"10535.jpg\": \"Aves/Melospiza melodia/d5b34ba4e01a1d9acc5d4e98f97a8a20.jpg\",\n  \"105406.jpg\": \"Insecta/Tramea onusta/cc37b97a57b75f9e614d27cead5f9576.jpg\",\n  \"105410.jpg\": \"Insecta/Tramea onusta/543eabeb12d94594a71d5e733ef82f31.jpg\",\n  \"105420.jpg\": \"Insecta/Tramea onusta/e23e46da1c56e9e2054b788c9ec098a9.jpg\",\n  \"105432.jpg\": \"Insecta/Tramea onusta/93731092b92b5cb8af308fd655614768.jpg\",\n  \"105438.jpg\": \"Insecta/Tramea onusta/c4857afb2cef6f52f8a972133f7c31f1.jpg\",\n  \"105445.jpg\": \"Insecta/Tramea onusta/c9b4771ad6273979a48cc993e1f67553.jpg\",\n  \"105449.jpg\": \"Insecta/Tramea onusta/c03316b0198e5a551326283e034b261e.jpg\",\n  \"105450.jpg\": \"Insecta/Tramea onusta/37662de89bbdc3a410dbf2367c93a141.jpg\",\n  \"105456.jpg\": \"Insecta/Tramea onusta/79f43a40c0d11a07d03cbe39d08cc9b1.jpg\",\n  \"105457.jpg\": \"Insecta/Tramea onusta/9342211ea4279f3566bebd0921ba8bb4.jpg\",\n  \"105464.jpg\": \"Insecta/Tramea onusta/d50c2d6e8cae0f2c1a41c223550ab924.jpg\",\n  \"105468.jpg\": \"Insecta/Tramea onusta/186af9b0d955548848ad734860cf052f.jpg\",\n  \"105476.jpg\": \"Insecta/Tramea onusta/dd4fccbd5bd441c97b743e7ad70cf76f.jpg\",\n  \"105483.jpg\": \"Insecta/Tramea onusta/f18cca313df4a8ebc4800416ada3324a.jpg\",\n  \"105496.jpg\": \"Insecta/Tramea onusta/afcaaec9b580e23a5323663b8122d5c9.jpg\",\n  \"105498.jpg\": \"Insecta/Tramea onusta/3c338e7433ea9ae1807533d02e1168f4.jpg\",\n  \"105499.jpg\": \"Insecta/Tramea onusta/5225df1903ebfd9d161385551eb29bf5.jpg\",\n  \"105500.jpg\": \"Insecta/Tramea onusta/9a547408e11bc6412943164f1789c66b.jpg\",\n  \"105518.jpg\": \"Insecta/Tramea onusta/f5d3ac41564e9dffb0686d33d35be8f4.jpg\",\n  \"105531.jpg\": \"Insecta/Tramea onusta/58aa23496f3b2b02b9ccbffb566325bb.jpg\",\n  \"105532.jpg\": \"Insecta/Tramea onusta/5069901083fc03d5fb5330c3e2771270.jpg\",\n  \"105546.jpg\": \"Insecta/Tramea onusta/be56cf03ae96632e75dc66fe3e3e78a1.jpg\",\n  \"105548.jpg\": \"Insecta/Tramea onusta/32a97851dabe201ff929468ef1f76fb9.jpg\",\n  \"105549.jpg\": \"Insecta/Tramea onusta/585d11e255e662c74db75580771d3944.jpg\",\n  \"105553.jpg\": \"Insecta/Tramea onusta/952147362c80e6d888bd5cc462cb1fb2.jpg\",\n  \"105562.jpg\": \"Insecta/Enallagma vesperum/6cf9ab0f2e0e03b8599d596fd4122cef.jpg\",\n  \"105572.jpg\": \"Insecta/Enallagma vesperum/52f3bc012236c1254c4ca4863a4d8e9e.jpg\",\n  \"105573.jpg\": \"Insecta/Enallagma vesperum/bf3776400deec8c9568e8356c5337b5a.jpg\",\n  \"105601.jpg\": \"Insecta/Sympetrum pallipes/457af2758ff6b2795f57f3c943d7d620.jpg\",\n  \"105602.jpg\": \"Insecta/Sympetrum pallipes/3f877d1299591793ac64f16e152d925a.jpg\",\n  \"105603.jpg\": \"Insecta/Sympetrum pallipes/fa57a2a689798031ff0c039b86e1ddb2.jpg\",\n  \"105604.jpg\": \"Insecta/Sympetrum pallipes/a0858f2aa0e79714dba59187b9bcc8e0.jpg\",\n  \"105606.jpg\": \"Insecta/Sympetrum pallipes/dc6fced1803ace2a0b45ff4b4741f858.jpg\",\n  \"105607.jpg\": \"Insecta/Sympetrum pallipes/117d9df23ac994c9a5b3d9129607645a.jpg\",\n  \"105609.jpg\": \"Insecta/Sympetrum pallipes/b5b9ce98b3b108d0b930f9df05cce4ed.jpg\",\n  \"105610.jpg\": \"Insecta/Sympetrum pallipes/6c8e96b8f1318d610d7f506ad510e21b.jpg\",\n  \"105615.jpg\": \"Insecta/Sympetrum pallipes/464ad8388ea81a33ac46e2a8b6eb4670.jpg\",\n  \"105620.jpg\": \"Insecta/Sympetrum pallipes/36e51ccaf611a6969e765e831d6a70e0.jpg\",\n  \"105633.jpg\": \"Insecta/Sympetrum pallipes/b1659cf827cbe72d8cad5103716eb9b6.jpg\",\n  \"105638.jpg\": \"Insecta/Sympetrum pallipes/07f392d3b188bd7434a58c9ee0cfc584.jpg\",\n  \"105639.jpg\": \"Insecta/Sympetrum pallipes/0930c8c04c4bf9ea9794fd3c108c6d87.jpg\",\n  \"105640.jpg\": \"Insecta/Sympetrum pallipes/39de4f6af142d434e618e6b5ca0cd75f.jpg\",\n  \"105648.jpg\": \"Insecta/Sympetrum pallipes/640af92d3b854cd2009b6cc6df8f9dc4.jpg\",\n  \"105650.jpg\": \"Insecta/Sympetrum pallipes/40f05b2c36a00fd0ca0851d242d597c8.jpg\",\n  \"105651.jpg\": \"Insecta/Sympetrum pallipes/a98760c9525951fb9101adf78813ee23.jpg\",\n  \"105652.jpg\": \"Insecta/Sympetrum pallipes/aa1846e60e1ea80be7060f5baa2b337d.jpg\",\n  \"105653.jpg\": \"Insecta/Sympetrum pallipes/2e85146d9eaa97ea22965b48495cbe44.jpg\",\n  \"105660.jpg\": \"Insecta/Sympetrum pallipes/bd96076f90c6df629eec1ade1573c7ba.jpg\",\n  \"10571.jpg\": \"Insecta/Macroglossum stellatarum/a7dc33ea08a38ee5c629fee3128cc429.jpg\",\n  \"10572.jpg\": \"Insecta/Macroglossum stellatarum/6d309c49097e787c63d2274c62305922.jpg\",\n  \"10576.jpg\": \"Insecta/Macroglossum stellatarum/51103f0cbabb4182efe68e5588bdf230.jpg\",\n  \"10577.jpg\": \"Insecta/Macroglossum stellatarum/f90db47267b2a874e47aa7ff0851842b.jpg\",\n  \"10578.jpg\": \"Insecta/Macroglossum stellatarum/9a05863689ca0272cd5df32d9d0b9d6c.jpg\",\n  \"105791.jpg\": \"Reptilia/Chrysemys picta marginata/199a6d1e8337002d85d3700bd352fe64.jpg\",\n  \"105792.jpg\": \"Reptilia/Chrysemys picta marginata/2958fa945383cd607aa22e0ccc651608.jpg\",\n  \"105793.jpg\": \"Reptilia/Chrysemys picta marginata/e3fc5666e85e6c86a1602ed9628be985.jpg\",\n  \"105808.jpg\": \"Reptilia/Chrysemys picta marginata/0ccc77ecbab7339efc8b60c392b0eda2.jpg\",\n  \"10581.jpg\": \"Insecta/Macroglossum stellatarum/ad9b4d6e4d244a9dcc5ebbd9ace74413.jpg\",\n  \"10582.jpg\": \"Insecta/Macroglossum stellatarum/16c5b21b127571a41fbc5da5ef601c6f.jpg\",\n  \"105822.jpg\": \"Reptilia/Chrysemys picta marginata/5495628b8077d78332b8a9c092318765.jpg\",\n  \"105825.jpg\": \"Reptilia/Chrysemys picta marginata/e79512a1e622815fd558dab9cda9f90a.jpg\",\n  \"105830.jpg\": \"Reptilia/Chrysemys picta marginata/1994cb53264d5d2c4851599394f437f3.jpg\",\n  \"105833.jpg\": \"Reptilia/Chrysemys picta marginata/ce4c9dcc16e83baecbff779e6ef14270.jpg\",\n  \"105837.jpg\": \"Reptilia/Chrysemys picta marginata/e4bce86090da9e5107206ff9ea517959.jpg\",\n  \"105846.jpg\": \"Reptilia/Chrysemys picta marginata/25d6f469bc4134a1a605d5d465a7a8f8.jpg\",\n  \"105847.jpg\": \"Reptilia/Chrysemys picta marginata/7aa734117641d6d4459adc258a3eaf17.jpg\",\n  \"105851.jpg\": \"Reptilia/Chrysemys picta marginata/e669942e6f2f8797f161b9226b433295.jpg\",\n  \"105852.jpg\": \"Reptilia/Chrysemys picta marginata/a72b981e2392bc8a78637f0f184b8f49.jpg\",\n  \"105853.jpg\": \"Reptilia/Chrysemys picta marginata/d5a42532aa29877ae04eea9f97f4b85d.jpg\",\n  \"105854.jpg\": \"Reptilia/Chrysemys picta marginata/04c1abb49a246c5a0277d82f27256697.jpg\",\n  \"105869.jpg\": \"Reptilia/Chrysemys picta marginata/1310da5182e189bc6ec3c24261e9c66c.jpg\",\n  \"105870.jpg\": \"Reptilia/Chrysemys picta marginata/f3671990d25f5e716da460d7516f3547.jpg\",\n  \"105871.jpg\": \"Reptilia/Chrysemys picta marginata/ce512e4f0ca0de5a9a276359d5530b9c.jpg\",\n  \"105872.jpg\": \"Reptilia/Chrysemys picta marginata/9bec8a1e1a6b70afad47d83ac504aed0.jpg\",\n  \"10595.jpg\": \"Insecta/Macroglossum stellatarum/553399133a9dc94def917c297c9be989.jpg\",\n  \"10596.jpg\": \"Insecta/Macroglossum stellatarum/5bfd6f7db4cde25d7453da8bf9cb5bf7.jpg\",\n  \"10599.jpg\": \"Insecta/Macroglossum stellatarum/604414dee61a0d6ad18a792e7aadda9c.jpg\",\n  \"10600.jpg\": \"Insecta/Macroglossum stellatarum/50a1b8ff8ea502343f3777f4a035c824.jpg\",\n  \"106045.jpg\": \"Aves/Diglossa baritula/b85b0e96178acfa7026ccaa25e79d05c.jpg\",\n  \"10605.jpg\": \"Insecta/Macroglossum stellatarum/07132aedd41be3b2fae5568d47d43999.jpg\",\n  \"106050.jpg\": \"Aves/Diglossa baritula/22e954e249107f7802fbfd2fe9daeb38.jpg\",\n  \"106052.jpg\": \"Aves/Diglossa baritula/a5f0a623a9476ed3c0cc963fa9874a25.jpg\",\n  \"106053.jpg\": \"Aves/Diglossa baritula/a3e5744c3308ccf2edf53d2bb0eaa857.jpg\",\n  \"106055.jpg\": \"Aves/Diglossa baritula/572175c26ab0bcd2431c89344da34f37.jpg\",\n  \"106056.jpg\": \"Aves/Diglossa baritula/7948fa56c4da6f0d110de6f742883899.jpg\",\n  \"106058.jpg\": \"Mollusca/Calliostoma ligatum/b3805941d77c22132cd92ef3c798dcdd.jpg\",\n  \"106059.jpg\": \"Mollusca/Calliostoma ligatum/5af99b67cd912078c26adc2a3fb041f6.jpg\",\n  \"10606.jpg\": \"Insecta/Macroglossum stellatarum/c9f7845f85f5e9f0ef51fee5b7429ad2.jpg\",\n  \"106061.jpg\": \"Mollusca/Calliostoma ligatum/e97e4e7e3f04d6218b3c6adf4a486bfa.jpg\",\n  \"106062.jpg\": \"Mollusca/Calliostoma ligatum/8c5490e2c7c30a35796b8242310a05d7.jpg\",\n  \"106064.jpg\": \"Mollusca/Calliostoma ligatum/b1b790ed14f5618d909af4ca609fc5ff.jpg\",\n  \"106074.jpg\": \"Mollusca/Calliostoma ligatum/d7648c7005d569072de5a329d875afb4.jpg\",\n  \"106075.jpg\": \"Mollusca/Calliostoma ligatum/96a74f51d374aa5aac6e35c40deade20.jpg\",\n  \"106076.jpg\": \"Mollusca/Calliostoma ligatum/0dd754f986d3de810a438e8ff236e9d8.jpg\",\n  \"106083.jpg\": \"Mollusca/Calliostoma ligatum/437dcb9fddf9524578246679033ec4d4.jpg\",\n  \"106090.jpg\": \"Mollusca/Calliostoma ligatum/a177ba47f9078620ba2461e0ea9a5dcf.jpg\",\n  \"106091.jpg\": \"Mollusca/Calliostoma ligatum/46dbb8f833866e5b48ad9ead26b38653.jpg\",\n  \"106092.jpg\": \"Mollusca/Calliostoma ligatum/77a75b5592bcacd93b37a6bbdd7a775c.jpg\",\n  \"106093.jpg\": \"Mollusca/Calliostoma ligatum/8aaa24bc63ae034a84ddb461f86b58cc.jpg\",\n  \"106099.jpg\": \"Mollusca/Calliostoma ligatum/5db7284d1acabb36bf4c19c168efa63b.jpg\",\n  \"106101.jpg\": \"Mollusca/Calliostoma ligatum/a98a886f9d2599017f886a2752f4590e.jpg\",\n  \"106107.jpg\": \"Mollusca/Calliostoma ligatum/eadedabdfb0aedb0134513e78e3f94e7.jpg\",\n  \"106109.jpg\": \"Mollusca/Calliostoma ligatum/67ab2e04fd17d81e53b3e53541a4bbcb.jpg\",\n  \"106110.jpg\": \"Mollusca/Calliostoma ligatum/6baaf7ab653b07e256a1c17285e5076f.jpg\",\n  \"106162.jpg\": \"Animalia/Physalia physalis/054900bf1c9c1cc8f7df027f4c1e1ed1.jpg\",\n  \"106164.jpg\": \"Animalia/Physalia physalis/207bc0a660e342a663991e6aba2bc8aa.jpg\",\n  \"10617.jpg\": \"Insecta/Macroglossum stellatarum/27ba3f4777bcbbabd5df843319d19ffa.jpg\",\n  \"106171.jpg\": \"Animalia/Physalia physalis/2b6b07385385d91f29858af7af90480e.jpg\",\n  \"106179.jpg\": \"Animalia/Physalia physalis/1c07f791ffe37e6e8e80406bf72cf114.jpg\",\n  \"106180.jpg\": \"Animalia/Physalia physalis/bd61ad3822363163ccb4b21ed1aa6345.jpg\",\n  \"106187.jpg\": \"Animalia/Physalia physalis/cbb37f2306aae118a0c89a63c5bb30d0.jpg\",\n  \"106188.jpg\": \"Animalia/Physalia physalis/e272b1fdca204228c0673c0d3ce1421c.jpg\",\n  \"106192.jpg\": \"Animalia/Physalia physalis/51a2133bbe2acc1ab1139f189bea52ef.jpg\",\n  \"106193.jpg\": \"Animalia/Physalia physalis/e0cd6818931a8c349ab6ca5a5f211932.jpg\",\n  \"106206.jpg\": \"Animalia/Physalia physalis/107ac8e1ba8b9ec21489e12248d1496a.jpg\",\n  \"106220.jpg\": \"Animalia/Physalia physalis/ec621cbea3c8bbf9cd7f058bcf419506.jpg\",\n  \"10625.jpg\": \"Insecta/Macroglossum stellatarum/58e22f79a9655324a40bd79529937f58.jpg\",\n  \"10634.jpg\": \"Insecta/Macroglossum stellatarum/b7a68ab45548c77a4be44386c56c85d7.jpg\",\n  \"10635.jpg\": \"Insecta/Macroglossum stellatarum/f8087abdf2301b7f33b6650465dd8b36.jpg\",\n  \"10637.jpg\": \"Insecta/Macroglossum stellatarum/e317bbd7a4336fd00517e185c6c6a635.jpg\",\n  \"10638.jpg\": \"Insecta/Macroglossum stellatarum/47f7265f596475cdfa2fef0ed492183e.jpg\",\n  \"106577.jpg\": \"Aves/Aythya americana/b3feb9fff50fab3fb620e1bf56086c67.jpg\",\n  \"106607.jpg\": \"Aves/Aythya americana/4f9d7912c2d30e00fa0d9dc7b2ee7a99.jpg\",\n  \"106615.jpg\": \"Aves/Aythya americana/00ebedc26d01e10fb0695314a5364598.jpg\",\n  \"106666.jpg\": \"Aves/Aythya americana/e7df52c5ba00135a23f63749ee67b5e0.jpg\",\n  \"106748.jpg\": \"Insecta/Raphia frater/2c085f2030d2b357a2eaed28367a7763.jpg\",\n  \"106751.jpg\": \"Insecta/Raphia frater/deba1146f540fbb29b6b185ea7902f18.jpg\",\n  \"106754.jpg\": \"Insecta/Raphia frater/0f39b9c176d40357eca2fc96eaa22c43.jpg\",\n  \"106787.jpg\": \"Mammalia/Loxodonta africana/c361c9d91498edf7f71246ee465ec763.jpg\",\n  \"106800.jpg\": \"Mammalia/Loxodonta africana/2ba87462e0c1d14365a5cee2825cb75f.jpg\",\n  \"106811.jpg\": \"Mammalia/Loxodonta africana/0b674dc11b2a1694f267f7bbad9d7288.jpg\",\n  \"106815.jpg\": \"Mammalia/Loxodonta africana/76a52762c00f0c03de6f1bd01b4fb454.jpg\",\n  \"106818.jpg\": \"Mammalia/Loxodonta africana/57672a222b2c86f3dd224efa4b6e895e.jpg\",\n  \"106819.jpg\": \"Mammalia/Loxodonta africana/98a1e21b36ac8da0888264316d53dfe2.jpg\",\n  \"106824.jpg\": \"Mammalia/Loxodonta africana/e22790790cd1d1d373bf79f0d93fe33f.jpg\",\n  \"106829.jpg\": \"Mammalia/Loxodonta africana/8cb251d9aeed715be09492585b369211.jpg\",\n  \"106842.jpg\": \"Mammalia/Loxodonta africana/cb4c0f243e1dc1572681d2f84af0d99d.jpg\",\n  \"106844.jpg\": \"Mammalia/Loxodonta africana/476c8dd36a4443daf8bd39ef1d2b99ba.jpg\",\n  \"106847.jpg\": \"Mammalia/Loxodonta africana/a823a8c0c6499c4f9ee0bfb58e07b05b.jpg\",\n  \"106870.jpg\": \"Aves/Tyrannus dominicensis/93041ac9b0f7381bd0314c25e2c55140.jpg\",\n  \"106871.jpg\": \"Aves/Tyrannus dominicensis/9c046625aa7c77038d6581b24c73fe2a.jpg\",\n  \"106878.jpg\": \"Aves/Tyrannus dominicensis/9a15abfd1b01dd0b486ba04a2188b6c6.jpg\",\n  \"106883.jpg\": \"Aves/Tyrannus dominicensis/68fa3e2cd7aa9d31cbd1aaa2933154d5.jpg\",\n  \"106888.jpg\": \"Aves/Tyrannus dominicensis/c86658dad80cb19e18a968a917c69c4c.jpg\",\n  \"106894.jpg\": \"Aves/Tyrannus dominicensis/8d4ff651ecf6b3c20d7cdec289b760f0.jpg\",\n  \"106904.jpg\": \"Aves/Tyrannus dominicensis/fe5c49ec687d1dff1fbe91f49da6d514.jpg\",\n  \"106905.jpg\": \"Aves/Tyrannus dominicensis/fd4d97e0848ba923665213d3a9d7d2b6.jpg\",\n  \"107.jpg\": \"Mammalia/Marmota flaviventris/cb7554dacca16d07e32df68cd927b5bd.jpg\",\n  \"107026.jpg\": \"Insecta/Battus philenor hirsuta/41229ecb45b4d01da2b5d0fa78a96b08.jpg\",\n  \"107027.jpg\": \"Insecta/Battus philenor hirsuta/17e8393f23651c1c42653e07e144dc84.jpg\",\n  \"107028.jpg\": \"Insecta/Battus philenor hirsuta/ea2fb698950be1d399290851ef7669e0.jpg\",\n  \"107033.jpg\": \"Insecta/Battus philenor hirsuta/69e38769c36ce489ecc3bc1aa1ccf517.jpg\",\n  \"107035.jpg\": \"Insecta/Battus philenor hirsuta/6e5b9d1a37b1a2e85cc658ae9dd37323.jpg\",\n  \"107041.jpg\": \"Insecta/Battus philenor hirsuta/87006021084af48d4290ed39fe24beb8.jpg\",\n  \"107042.jpg\": \"Insecta/Battus philenor hirsuta/984264a7bd9501754e82cea518540d71.jpg\",\n  \"107043.jpg\": \"Insecta/Battus philenor hirsuta/6339d557d458166ba4aaeab0aa307e2f.jpg\",\n  \"107047.jpg\": \"Insecta/Battus philenor hirsuta/394a662cb1b663d136b7678bd3a376de.jpg\",\n  \"107048.jpg\": \"Insecta/Battus philenor hirsuta/ba737fc933a6f535ee10d99cbe012304.jpg\",\n  \"107050.jpg\": \"Insecta/Battus philenor hirsuta/8d469e279d03529e4ec982cd7991a4b0.jpg\",\n  \"107055.jpg\": \"Insecta/Battus philenor hirsuta/8491473d879920b5dff4da1b3dd43579.jpg\",\n  \"107058.jpg\": \"Insecta/Battus philenor hirsuta/5bf2e21b82f26a87db140c5e5e628154.jpg\",\n  \"107059.jpg\": \"Insecta/Battus philenor hirsuta/53d225013f5acf27df817e782fc4944d.jpg\",\n  \"107060.jpg\": \"Insecta/Battus philenor hirsuta/d9c1d359c4f2633fb0a34180e6c02293.jpg\",\n  \"107061.jpg\": \"Insecta/Battus philenor hirsuta/7ebdf245dea0244a94bbcf90bdc2c757.jpg\",\n  \"107062.jpg\": \"Insecta/Battus philenor hirsuta/60dac295de7cc3c7c371d831601cb7ea.jpg\",\n  \"107063.jpg\": \"Insecta/Battus philenor hirsuta/23fe571f82f3d226d7f30e15e9dfcf32.jpg\",\n  \"107068.jpg\": \"Insecta/Battus philenor hirsuta/663de8805ff5f083eec2bd40058ac05d.jpg\",\n  \"107069.jpg\": \"Insecta/Battus philenor hirsuta/f44f05af86125b6632f32cc335300da2.jpg\",\n  \"107070.jpg\": \"Insecta/Battus philenor hirsuta/a3b53aba71970c387bab9d84ce020f20.jpg\",\n  \"107234.jpg\": \"Aves/Melanerpes chrysogenys/fd977cc4011939474d0f8f70ac841398.jpg\",\n  \"107236.jpg\": \"Aves/Melanerpes chrysogenys/a8f670f6900ee93760157a9ddf7788a7.jpg\",\n  \"107237.jpg\": \"Aves/Melanerpes chrysogenys/790989e07ce819c12b2a8b118f3d5447.jpg\",\n  \"107243.jpg\": \"Aves/Melanerpes chrysogenys/f59b198f5faa72d4f853caa9302d9611.jpg\",\n  \"107244.jpg\": \"Aves/Melanerpes chrysogenys/32c64f013f2137ffff30fdda570ac5c2.jpg\",\n  \"107245.jpg\": \"Aves/Melanerpes chrysogenys/595a109d4212a74a6117e2df8fde9a1b.jpg\",\n  \"107249.jpg\": \"Aves/Melanerpes chrysogenys/3ef0d7e3c3e88e4e56c5678990e724ad.jpg\",\n  \"107251.jpg\": \"Aves/Melanerpes chrysogenys/531507c57004fe003978ee3ffad03ebd.jpg\",\n  \"107261.jpg\": \"Aves/Melanerpes chrysogenys/2fce61b044dded9999e89d5ecd4c1949.jpg\",\n  \"107262.jpg\": \"Aves/Melanerpes chrysogenys/4294c72ebd251bec1bbc65b40115094a.jpg\",\n  \"107263.jpg\": \"Aves/Melanerpes chrysogenys/ac084e787d3de04bc67bdd702bc40489.jpg\",\n  \"107264.jpg\": \"Aves/Melanerpes chrysogenys/cd6e58f53e783909e726916cef07a8a0.jpg\",\n  \"107272.jpg\": \"Aves/Melanerpes chrysogenys/ceeff87aa3d57b8d71a6a3d88d106925.jpg\",\n  \"107273.jpg\": \"Aves/Melanerpes chrysogenys/81710c4d8754482623f01fd86d036cd9.jpg\",\n  \"107280.jpg\": \"Aves/Melanerpes chrysogenys/b3af898b454cfc3a354684a402786a97.jpg\",\n  \"107283.jpg\": \"Aves/Melanerpes chrysogenys/c773743414b61adb0f7581dd3edebb30.jpg\",\n  \"107290.jpg\": \"Aves/Melanerpes chrysogenys/4e1d897ba39541e1f0e6e8ae9bede865.jpg\",\n  \"107291.jpg\": \"Aves/Melanerpes chrysogenys/97de3f57d3ddedc5d8d386ac4173ed7d.jpg\",\n  \"107295.jpg\": \"Aves/Melanerpes chrysogenys/db8665d1654e9cc2285b2dc9a024aad8.jpg\",\n  \"107296.jpg\": \"Aves/Melanerpes chrysogenys/76d28c9498bd5e55c399f7c97b0f7892.jpg\",\n  \"107309.jpg\": \"Aves/Melanerpes chrysogenys/3f7fb07663d45454e398ac245944d0f0.jpg\",\n  \"10731.jpg\": \"Insecta/Gomphus vastus/2b466e68f55fd23e587d4bac76e2283f.jpg\",\n  \"107313.jpg\": \"Aves/Melanerpes chrysogenys/97b99c488917bf551aa950c345d285ed.jpg\",\n  \"107317.jpg\": \"Aves/Melanerpes chrysogenys/ed50449f2af908e4fa74b0b1f06ba297.jpg\",\n  \"107320.jpg\": \"Aves/Melanerpes chrysogenys/fd06d8db739e9e3dec82cdbc1654cc5a.jpg\",\n  \"107321.jpg\": \"Aves/Melanerpes chrysogenys/3c1328fe15f8c7780f1959f18f152ee1.jpg\",\n  \"107327.jpg\": \"Mammalia/Mustela erminea/491c668967382d3e93be3711f8be0c72.jpg\",\n  \"107335.jpg\": \"Mammalia/Mustela erminea/880946b82da493fc9d77e848d8f9acc4.jpg\",\n  \"107336.jpg\": \"Mammalia/Mustela erminea/b532e3298f2014dd53249f0e1e99e6cd.jpg\",\n  \"107337.jpg\": \"Mammalia/Mustela erminea/61185995da67c594ec832427ac1b4c94.jpg\",\n  \"107344.jpg\": \"Mammalia/Mustela erminea/2a871c6faf0ba4735b69a89a597c5065.jpg\",\n  \"107349.jpg\": \"Mammalia/Mustela erminea/6cea29cd4b21f889cdd43725708abe9a.jpg\",\n  \"107377.jpg\": \"Insecta/Polygonia satyrus/5fb44c20b6928f897150f484907c0203.jpg\",\n  \"107378.jpg\": \"Insecta/Polygonia satyrus/cf85726cce6e9b75cd2a840ce294d47d.jpg\",\n  \"107385.jpg\": \"Insecta/Polygonia satyrus/0f4bd2902dc3961e62c728f5278b6743.jpg\",\n  \"107386.jpg\": \"Insecta/Polygonia satyrus/cb893d359f112f7eafb7e7dc4b2382dc.jpg\",\n  \"107387.jpg\": \"Insecta/Polygonia satyrus/a1cdeb54bea98e142d3e10b3566a0899.jpg\",\n  \"107388.jpg\": \"Insecta/Polygonia satyrus/fcd8ed836ccd8292352a51f724fafec4.jpg\",\n  \"107390.jpg\": \"Insecta/Polygonia satyrus/ab970b66b299d408413d637d5fc977cd.jpg\",\n  \"107410.jpg\": \"Insecta/Polygonia satyrus/033b14d1391609fadcdf4225f9c97443.jpg\",\n  \"107412.jpg\": \"Insecta/Polygonia satyrus/8b42873f126df7208589c10243173af5.jpg\",\n  \"107413.jpg\": \"Insecta/Polygonia satyrus/685b3cfdab71466698e02fe6ffc92c9d.jpg\",\n  \"107414.jpg\": \"Insecta/Polygonia satyrus/61e9b80ddf3bd2fe7cbff54ad5e3c4dc.jpg\",\n  \"107415.jpg\": \"Insecta/Polygonia satyrus/bfc5620f196c0d27b1a38ffb539d9b11.jpg\",\n  \"107422.jpg\": \"Insecta/Polygonia satyrus/3b2ddda14fc355a9a124a56c10a9b094.jpg\",\n  \"107423.jpg\": \"Insecta/Polygonia satyrus/d5a76ec7c1617d39279f527cebfa88e0.jpg\",\n  \"107426.jpg\": \"Insecta/Polygonia satyrus/9a4d2c7a2cf9e0ad5ae6a7f42cab8972.jpg\",\n  \"107427.jpg\": \"Insecta/Polygonia satyrus/963a26e6a8f9626831a2ceeb2c8888ec.jpg\",\n  \"107433.jpg\": \"Insecta/Polygonia satyrus/0031b6eebff092a1e77be99b7f1bfea3.jpg\",\n  \"107434.jpg\": \"Insecta/Polygonia satyrus/3d90d2c1764d54c455957f238fc33bf9.jpg\",\n  \"107435.jpg\": \"Insecta/Polygonia satyrus/4327e91def77e9a91862da995199c200.jpg\",\n  \"10744.jpg\": \"Insecta/Gomphus vastus/13a407a7a35b47ebb6bee4c973757379.jpg\",\n  \"10761.jpg\": \"Insecta/Gomphus vastus/83d864fd126e3b02a45f951fa0cceb0b.jpg\",\n  \"10762.jpg\": \"Insecta/Gomphus vastus/0847e1aab062270e15f82db1ac95b169.jpg\",\n  \"107635.jpg\": \"Reptilia/Crotalus oreganus/82230a8aea0ff58efdf2ff9d415949ec.jpg\",\n  \"107637.jpg\": \"Reptilia/Crotalus oreganus/99ccd80f3e535e8cc2cad037ecbd468c.jpg\",\n  \"107638.jpg\": \"Reptilia/Crotalus oreganus/0d2276af9f31123561681317f2a8f812.jpg\",\n  \"107640.jpg\": \"Reptilia/Crotalus oreganus/218ac6f33ea5e3921898edd01d41c6ad.jpg\",\n  \"107642.jpg\": \"Reptilia/Crotalus oreganus/f94ca51ca3160d9b328dd23396211cfe.jpg\",\n  \"107644.jpg\": \"Reptilia/Crotalus oreganus/414cdbd5ebe1bd6341eb76eb4757da4e.jpg\",\n  \"107649.jpg\": \"Reptilia/Crotalus oreganus/b2c3be20ea6ecf0abda2c0ba48ef0704.jpg\",\n  \"107654.jpg\": \"Reptilia/Crotalus oreganus/a48f2e2323c1127935430e260c8404df.jpg\",\n  \"107658.jpg\": \"Reptilia/Crotalus oreganus/a07e441147ccfdde1fbf1676b4a5f0d7.jpg\",\n  \"107659.jpg\": \"Reptilia/Crotalus oreganus/2811f4a2c6b36efa2768c5a81382b914.jpg\",\n  \"107662.jpg\": \"Reptilia/Crotalus oreganus/9aa32de44e3d1e88e96753e106a914ca.jpg\",\n  \"107663.jpg\": \"Reptilia/Crotalus oreganus/7f3a3efdf075fa7392a09bc1deef5787.jpg\",\n  \"107667.jpg\": \"Reptilia/Crotalus oreganus/cc1e6b24d604e3fc88f34d8b4fabb45a.jpg\",\n  \"107668.jpg\": \"Reptilia/Crotalus oreganus/a13c38f5df4af44d1a91066fb2d31834.jpg\",\n  \"107669.jpg\": \"Reptilia/Crotalus oreganus/9cb4afe9d5cb4cd06e6413584ebab0cb.jpg\",\n  \"107674.jpg\": \"Reptilia/Crotalus oreganus/c9a3ae8d8a7e982ca13a1d71599dc620.jpg\",\n  \"107675.jpg\": \"Reptilia/Crotalus oreganus/99736c7ea0987f197d65859bfd22a41e.jpg\",\n  \"107686.jpg\": \"Reptilia/Crotalus oreganus/ba85772cdfc3d860a18e4cc597ad35e2.jpg\",\n  \"107779.jpg\": \"Aves/Patagioenas flavirostris/7257545934a2ed7da17bfdc980941c1c.jpg\",\n  \"107781.jpg\": \"Aves/Patagioenas flavirostris/92da3433eb6341ed80e8097148f03c42.jpg\",\n  \"107786.jpg\": \"Aves/Patagioenas flavirostris/e73aae6aa9f226035d27735adf623621.jpg\",\n  \"107789.jpg\": \"Aves/Patagioenas flavirostris/3b4bbeae562dbdbdbebbc6ed655c377a.jpg\",\n  \"107790.jpg\": \"Aves/Patagioenas flavirostris/6f7058bc739f7152b1d62d60c9b9aa84.jpg\",\n  \"107795.jpg\": \"Aves/Patagioenas flavirostris/7bc34e9f039d4ed5748d6a7a461c2802.jpg\",\n  \"107798.jpg\": \"Aves/Patagioenas flavirostris/3de7281220e4df743ee2c2fa576ee04b.jpg\",\n  \"107799.jpg\": \"Aves/Patagioenas flavirostris/05ad4f1653ac1a4ca8faff921cf791e4.jpg\",\n  \"107801.jpg\": \"Aves/Patagioenas flavirostris/42422aa7c8eab2cc745b616ca8d1a57e.jpg\",\n  \"107804.jpg\": \"Aves/Patagioenas flavirostris/897f9287dc1ca57df7c508d8a9629930.jpg\",\n  \"107806.jpg\": \"Aves/Patagioenas flavirostris/c9ed8d6b7f6c988239dce242ef72e121.jpg\",\n  \"107807.jpg\": \"Aves/Patagioenas flavirostris/72fbc7a492d02ce8d2c1a94544a7e310.jpg\",\n  \"107810.jpg\": \"Aves/Patagioenas flavirostris/2ccc7dce67903f20aa4123619bb8cc35.jpg\",\n  \"107814.jpg\": \"Aves/Patagioenas flavirostris/dfd6f52ea92dd203e982b5506e23b2be.jpg\",\n  \"107815.jpg\": \"Aves/Patagioenas flavirostris/bc13492ad3e49afbf7e9c12c085ab971.jpg\",\n  \"107816.jpg\": \"Aves/Patagioenas flavirostris/4637151e5467fd96b5781e1589c58a7c.jpg\",\n  \"107819.jpg\": \"Aves/Patagioenas flavirostris/601426ea1fd1655940a2d916e0872fc1.jpg\",\n  \"107820.jpg\": \"Aves/Patagioenas flavirostris/32830f29c01af359120cc10816f55cd1.jpg\",\n  \"107821.jpg\": \"Aves/Patagioenas flavirostris/f892e8f079c661379ae4f8b36da9af22.jpg\",\n  \"107825.jpg\": \"Aves/Tyrannus savana/3c524b8ab76b08b0e39fc6bda5538de0.jpg\",\n  \"107827.jpg\": \"Aves/Tyrannus savana/1848c9a28b315de127bbb5012149d2ff.jpg\",\n  \"107831.jpg\": \"Aves/Tyrannus savana/0ba185606513f7a6ebb58fb27f967a8a.jpg\",\n  \"107832.jpg\": \"Aves/Tyrannus savana/60a8df74c51d0877a1146730e6b457de.jpg\",\n  \"107840.jpg\": \"Aves/Tyrannus savana/18abc06ce4ec457d3df18689129e4b2f.jpg\",\n  \"107846.jpg\": \"Aves/Tyrannus savana/4d7620b9602d11829ebd69c949d33438.jpg\",\n  \"107848.jpg\": \"Aves/Tyrannus savana/d21cffae885fc606b2f93c9140b480a0.jpg\",\n  \"107849.jpg\": \"Aves/Tyrannus savana/88d60707a1f53c50c89cecd1a017a563.jpg\",\n  \"107850.jpg\": \"Insecta/Aphomia sociella/cd02f597e00f0a7810dd9c09310af8f4.jpg\",\n  \"107853.jpg\": \"Insecta/Aphomia sociella/e1aaffb51f274148c6494850208bf4b9.jpg\",\n  \"107862.jpg\": \"Insecta/Aphomia sociella/ed8928a8831ae6b4f00524fc90289726.jpg\",\n  \"107880.jpg\": \"Insecta/Aphomia sociella/3cab0e27dc8583f3ede47649cd2b29d7.jpg\",\n  \"107883.jpg\": \"Insecta/Aphomia sociella/7bcfab350a27ea4b8e4264183135f12b.jpg\",\n  \"107884.jpg\": \"Insecta/Aphomia sociella/446d92596a5c07281c98522f43aecd19.jpg\",\n  \"107954.jpg\": \"Insecta/Metanema inatomaria/821052da73a3a8c9eec78f8a1f8fe1e7.jpg\",\n  \"107957.jpg\": \"Insecta/Metanema inatomaria/3a4fc06bb97a56ff34aadea8ac63a2a2.jpg\",\n  \"107959.jpg\": \"Insecta/Metanema inatomaria/ead1732be3ca0ed0fb3eda512a3f0e7e.jpg\",\n  \"107962.jpg\": \"Insecta/Metanema inatomaria/6420bc3c1df75c1b8f0c93573a33f87d.jpg\",\n  \"107967.jpg\": \"Aves/Setophaga coronata coronata/86e2ea72a7d2b86739bb807a365ab5a2.jpg\",\n  \"107969.jpg\": \"Aves/Setophaga coronata coronata/92273c38b3c7688f80a2984198f73d2d.jpg\",\n  \"107970.jpg\": \"Aves/Setophaga coronata coronata/285c7a22260cb8bad9ac77914687fc98.jpg\",\n  \"107977.jpg\": \"Aves/Setophaga coronata coronata/d066ecacdd42444ca0e3249d748192c0.jpg\",\n  \"107978.jpg\": \"Aves/Setophaga coronata coronata/98f324e7bb6ab72024fa135422f898d7.jpg\",\n  \"107979.jpg\": \"Aves/Setophaga coronata coronata/13a7f4f345ae08f9fd259fbd88bf5e57.jpg\",\n  \"107983.jpg\": \"Aves/Setophaga coronata coronata/ff22fd928290d9c4d4bd022da6a9c243.jpg\",\n  \"107989.jpg\": \"Aves/Setophaga coronata coronata/708a038bdffa6d7922d2a738f8d61a93.jpg\",\n  \"107990.jpg\": \"Aves/Setophaga coronata coronata/e59bbe752508c64ec7aed97c23d73797.jpg\",\n  \"107996.jpg\": \"Aves/Setophaga coronata coronata/8d30b012a73f9e14c80c2a2ad64d577c.jpg\",\n  \"107997.jpg\": \"Aves/Setophaga coronata coronata/b38bad2a6f4bb6a8d05325dc98e3d559.jpg\",\n  \"107999.jpg\": \"Aves/Setophaga coronata coronata/46a82c5f87075d35206a06ba444095b6.jpg\",\n  \"108012.jpg\": \"Aves/Setophaga coronata coronata/cb182a18280622a5f8aa3112ad641041.jpg\",\n  \"108018.jpg\": \"Aves/Setophaga coronata coronata/3f290b9da797023599647631dc80b69c.jpg\",\n  \"108030.jpg\": \"Aves/Setophaga coronata coronata/7c87e32f6db326c72ed708668ccda8e6.jpg\",\n  \"108054.jpg\": \"Aves/Setophaga coronata coronata/57687b7f47443cd5a67ad8c5a56501a4.jpg\",\n  \"108057.jpg\": \"Aves/Setophaga coronata coronata/0b69aecb4ab261d82aa46fe9664a9d54.jpg\",\n  \"108067.jpg\": \"Aves/Setophaga coronata coronata/bdd9f742a23fce9b18f211c4dff164e5.jpg\",\n  \"108073.jpg\": \"Aves/Setophaga coronata coronata/208bb1033b1bf5609aa2e8a6d9a4be50.jpg\",\n  \"108094.jpg\": \"Aves/Setophaga coronata coronata/1fe3832fd31c7fdd0c213d74897e9a25.jpg\",\n  \"108095.jpg\": \"Aves/Setophaga coronata coronata/e546e8029503933522858c27cc344518.jpg\",\n  \"108096.jpg\": \"Aves/Setophaga coronata coronata/9fa0b80773cd6baf23d9fe552b3e1b5b.jpg\",\n  \"108098.jpg\": \"Aves/Setophaga coronata coronata/e805e1a4a9cec3ca36b2413390824f20.jpg\",\n  \"108117.jpg\": \"Reptilia/Zootoca vivipara/359a2741507e77ff9851dffd761ffd15.jpg\",\n  \"108125.jpg\": \"Reptilia/Zootoca vivipara/893f1841eb5a13c2a33334f5fc38ef8d.jpg\",\n  \"108130.jpg\": \"Reptilia/Zootoca vivipara/ffdda86ad0586953e9ebef8d5d76f797.jpg\",\n  \"108131.jpg\": \"Reptilia/Zootoca vivipara/813a42a0084abb33f9a55368b85aaa6f.jpg\",\n  \"108135.jpg\": \"Reptilia/Zootoca vivipara/770b7664bf8547b6b73ccbe262b94bd5.jpg\",\n  \"108139.jpg\": \"Reptilia/Zootoca vivipara/0132da7bcd630510b2206a456e5b47e3.jpg\",\n  \"108147.jpg\": \"Insecta/Phoebis agarithe/ae9c2dbc9314842c371166094efa73a3.jpg\",\n  \"108148.jpg\": \"Insecta/Phoebis agarithe/c99ef0f2a1592ebdceb44406a5bd180d.jpg\",\n  \"108149.jpg\": \"Insecta/Phoebis agarithe/ed45432fa1d49cf939e5604a97bf2eeb.jpg\",\n  \"108150.jpg\": \"Insecta/Phoebis agarithe/e70893ebdf4b763abc19a7fdb14fccbb.jpg\",\n  \"108151.jpg\": \"Insecta/Phoebis agarithe/5e658e9c3a975aceae5ef4d505b0fbbd.jpg\",\n  \"108152.jpg\": \"Insecta/Phoebis agarithe/ced19c5d345f288a0cb28c2682285edb.jpg\",\n  \"108153.jpg\": \"Insecta/Phoebis agarithe/a6935ab5da4135f792bfef798dd2d7d1.jpg\",\n  \"108159.jpg\": \"Insecta/Phoebis agarithe/92a8de70913a4ff04a7210bcacfd4a03.jpg\",\n  \"108160.jpg\": \"Insecta/Phoebis agarithe/366e1fb7b79528fccea89503b89a0933.jpg\",\n  \"108166.jpg\": \"Insecta/Phoebis agarithe/c2b4f115f315d65fb677b27298d510a0.jpg\",\n  \"108167.jpg\": \"Insecta/Phoebis agarithe/d5b59bc4134ecd4d3df8d8b2c7d3e2e7.jpg\",\n  \"108169.jpg\": \"Insecta/Phoebis agarithe/ff9c5f5e38be3eeac4b7eb334c43295d.jpg\",\n  \"108170.jpg\": \"Insecta/Phoebis agarithe/ba24c3a0aee1b7e5f64debcc1b741922.jpg\",\n  \"108171.jpg\": \"Insecta/Phoebis agarithe/6f2e0f088db573ea69cd004a70ed2367.jpg\",\n  \"108179.jpg\": \"Insecta/Phoebis agarithe/890681109a8ba75dea16d7e49b814d24.jpg\",\n  \"108188.jpg\": \"Insecta/Phoebis agarithe/0c583e5cf84ce95adbefbdac83e46b3f.jpg\",\n  \"108189.jpg\": \"Insecta/Phoebis agarithe/1fe2e543b66ca81191b08965d7516752.jpg\",\n  \"108199.jpg\": \"Insecta/Phoebis agarithe/8b41ee01992588b3b0ed0625e68b43de.jpg\",\n  \"108204.jpg\": \"Insecta/Phoebis agarithe/f20dbfd02c088a003bac3b928acbc441.jpg\",\n  \"108210.jpg\": \"Insecta/Lycaena phlaeas hypophlaeas/6cf7ccede2dd2f6a158b61d33c01996d.jpg\",\n  \"108219.jpg\": \"Insecta/Lycaena phlaeas hypophlaeas/030a53474876413451fedf7054f4201b.jpg\",\n  \"108224.jpg\": \"Insecta/Prolimacodes badia/0ecc78b62e7fb6e6f98df7b62ae1e01d.jpg\",\n  \"108225.jpg\": \"Insecta/Prolimacodes badia/831d17456827c1582441ec6487dc9bf3.jpg\",\n  \"108238.jpg\": \"Insecta/Prolimacodes badia/a34943bc723f8ffe1581165c74431450.jpg\",\n  \"108243.jpg\": \"Amphibia/Rana temporaria/70748326d53a9af9925ece1f3507d953.jpg\",\n  \"108249.jpg\": \"Amphibia/Rana temporaria/51c608fdd7c8999d3aead7aac0cf8288.jpg\",\n  \"108252.jpg\": \"Amphibia/Rana temporaria/71f2680d2569703c44045bc29d48b133.jpg\",\n  \"108253.jpg\": \"Amphibia/Rana temporaria/f52f2fcdb1190a298302e47bb2111c83.jpg\",\n  \"108255.jpg\": \"Amphibia/Rana temporaria/8d0440fcdfcd8f1eb8112467533fae69.jpg\",\n  \"108266.jpg\": \"Amphibia/Rana temporaria/fe91a43df86647c4f3a7f9b0bbe19ae6.jpg\",\n  \"108273.jpg\": \"Amphibia/Rana temporaria/71ddb54e3b02ef34b073dee566ee2e2f.jpg\",\n  \"108274.jpg\": \"Amphibia/Rana temporaria/4867a022789e6b7bea1cc1dd309247e7.jpg\",\n  \"108279.jpg\": \"Amphibia/Rana temporaria/1eebcfd57592cc764796e2c39fc719c3.jpg\",\n  \"108280.jpg\": \"Amphibia/Rana temporaria/0bb839061395890c690b3bc70bd07326.jpg\",\n  \"108281.jpg\": \"Amphibia/Rana temporaria/ce182ef80f0d66abcb0c4483f4ba52e5.jpg\",\n  \"108282.jpg\": \"Amphibia/Rana temporaria/eacf254d1c01092a2c74bbc0c738efb4.jpg\",\n  \"108283.jpg\": \"Amphibia/Rana temporaria/d72f703a000094234a90da43e4e62c00.jpg\",\n  \"108290.jpg\": \"Amphibia/Rana temporaria/23077385b61cbc60effa175984c5f655.jpg\",\n  \"108300.jpg\": \"Amphibia/Rana temporaria/a4f297d352d48b1f826e146ae41d46f8.jpg\",\n  \"108309.jpg\": \"Amphibia/Rana temporaria/9bfa45cc93145e5de4aa531b5df72997.jpg\",\n  \"108310.jpg\": \"Amphibia/Rana temporaria/df0dd416db8d4991b3324d4b32df5a2c.jpg\",\n  \"108312.jpg\": \"Amphibia/Rana temporaria/fdb965fc2126cd61ccdde3d61d5b9f20.jpg\",\n  \"108318.jpg\": \"Amphibia/Rana temporaria/c45b816efebf94d5d8d4c36ff79fbd16.jpg\",\n  \"108322.jpg\": \"Amphibia/Rana temporaria/14e0148034fb3f2696560df4649ddfe2.jpg\",\n  \"108338.jpg\": \"Mollusca/Limax maximus/d3f795e25894d8450b860927766b3940.jpg\",\n  \"108339.jpg\": \"Mollusca/Limax maximus/9f5270e5a4afb8069458e515571d2ad8.jpg\",\n  \"108345.jpg\": \"Mollusca/Limax maximus/086f4a07755c700d3c00611e8aafdac0.jpg\",\n  \"108346.jpg\": \"Mollusca/Limax maximus/1654e4f41f2cfa9ecd095f112db5eb76.jpg\",\n  \"108359.jpg\": \"Mollusca/Limax maximus/45f9a13c361cc07256674455aced202f.jpg\",\n  \"108365.jpg\": \"Mollusca/Limax maximus/225b2282ce8968ab51274c6d1bbff1b4.jpg\",\n  \"10837.jpg\": \"Aves/Rupornis magnirostris/68d6697c0a25dd514d4fbe4e34621e76.jpg\",\n  \"108374.jpg\": \"Mollusca/Limax maximus/5145d791dd8444beb4192da27de42905.jpg\",\n  \"108377.jpg\": \"Mollusca/Limax maximus/8016ba45d2c4ddb9ced654d91194fecb.jpg\",\n  \"108380.jpg\": \"Mollusca/Limax maximus/5973203783851243484f01e00cfbda55.jpg\",\n  \"108389.jpg\": \"Mollusca/Limax maximus/834953c8c563eabb0a7cbf1f033fe100.jpg\",\n  \"108391.jpg\": \"Mollusca/Limax maximus/a8d35da86eaa2c708007cd9d4760b7ae.jpg\",\n  \"108394.jpg\": \"Mollusca/Limax maximus/bd30f6e633070a613a4641ec7f408306.jpg\",\n  \"108395.jpg\": \"Mollusca/Limax maximus/0a28b5499dd16394583ed9ab2b20124c.jpg\",\n  \"108396.jpg\": \"Mollusca/Limax maximus/e8f8778f8eff0820f9e48dd62edcbcb1.jpg\",\n  \"108403.jpg\": \"Mollusca/Limax maximus/5ab0bea03f339f1d7ca4e935deea2e49.jpg\",\n  \"108409.jpg\": \"Mollusca/Limax maximus/48623224dcf652780aeadadc26bd4612.jpg\",\n  \"108413.jpg\": \"Mollusca/Limax maximus/9bb114c36ac423953522a56d9061bdbe.jpg\",\n  \"108416.jpg\": \"Mollusca/Limax maximus/ef9934457f418304d90a802a52cdc0e6.jpg\",\n  \"108418.jpg\": \"Mollusca/Limax maximus/abcf2e4c7a3ceb98fe3bacf8a6f7bcc4.jpg\",\n  \"108419.jpg\": \"Mollusca/Limax maximus/f8d7fe66b37c112bd7b2967247ad30cf.jpg\",\n  \"108422.jpg\": \"Mollusca/Limax maximus/a780614f2146f62830a5fecf5d0362a3.jpg\",\n  \"108428.jpg\": \"Mollusca/Limax maximus/c6d9a72b05f880002c4ffa83b61205bd.jpg\",\n  \"108437.jpg\": \"Mollusca/Limax maximus/3bf0e55628c9e551ad1b7c895087ad20.jpg\",\n  \"108452.jpg\": \"Mollusca/Limax maximus/90bb3dbabae3b721ce1397ca7f311c0f.jpg\",\n  \"10847.jpg\": \"Aves/Rupornis magnirostris/1f22946d4e1e57bdd6267f9e22bd6256.jpg\",\n  \"10848.jpg\": \"Aves/Rupornis magnirostris/7ec9124ea90e636666e924b97f85853c.jpg\",\n  \"10849.jpg\": \"Aves/Rupornis magnirostris/c1566cdea0504305950819de81ba4306.jpg\",\n  \"10850.jpg\": \"Aves/Rupornis magnirostris/439c771972dfa68ffedab65de6472d0b.jpg\",\n  \"10855.jpg\": \"Aves/Rupornis magnirostris/599078538eabe766d9e779024d7eb6f7.jpg\",\n  \"10857.jpg\": \"Aves/Rupornis magnirostris/31b238cc58743b0cb529b28c2db5243c.jpg\",\n  \"108589.jpg\": \"Aves/Molothrus aeneus/58095643f4d0c292ba54ee8b6a25d154.jpg\",\n  \"108590.jpg\": \"Aves/Molothrus aeneus/fa0db7fd50be62c1f3aa9494cfcff178.jpg\",\n  \"108591.jpg\": \"Aves/Molothrus aeneus/614b21796b57fb8a0e638ba03094b65f.jpg\",\n  \"108596.jpg\": \"Aves/Molothrus aeneus/96b36c7ba84749e54a6c531c37e371de.jpg\",\n  \"108601.jpg\": \"Aves/Molothrus aeneus/5eb09305ea99933aa2cffee9509521f7.jpg\",\n  \"108607.jpg\": \"Aves/Molothrus aeneus/e2a9ed9c6eb63719591de74f4c7c3cde.jpg\",\n  \"108613.jpg\": \"Aves/Molothrus aeneus/88286330a2f875438030cb44cf20e171.jpg\",\n  \"108616.jpg\": \"Aves/Molothrus aeneus/a56a13ac7c20065c3e330919d6f439c7.jpg\",\n  \"10862.jpg\": \"Aves/Rupornis magnirostris/d6191f3d42ed8a4d8bb35d04f1dd0f72.jpg\",\n  \"108627.jpg\": \"Aves/Molothrus aeneus/7aed101c5e08dc8df6fd208fa63d7937.jpg\",\n  \"108632.jpg\": \"Aves/Molothrus aeneus/63d05a3f60fa8c74c09bcf38f9769cf0.jpg\",\n  \"108633.jpg\": \"Aves/Molothrus aeneus/7d38e0de2246bc1ad1c47ce135408c3e.jpg\",\n  \"108641.jpg\": \"Aves/Molothrus aeneus/fa0e16a2ebb58827a6679b10bd3499b0.jpg\",\n  \"108646.jpg\": \"Aves/Molothrus aeneus/7892cf5c71207918f8da1e1df8e0a908.jpg\",\n  \"108647.jpg\": \"Aves/Molothrus aeneus/a5cb6c4552ea9e45ca8f0e0b61b6e1e8.jpg\",\n  \"108649.jpg\": \"Aves/Molothrus aeneus/4e8aed562648dfae9d96dc496f130343.jpg\",\n  \"10865.jpg\": \"Aves/Rupornis magnirostris/290043ba1fc5c38793da86104a571430.jpg\",\n  \"108654.jpg\": \"Aves/Molothrus aeneus/8bdcfa10394e4b93fa3f3ba6f68c2269.jpg\",\n  \"108657.jpg\": \"Aves/Molothrus aeneus/d91b1c1af99f6d3a658095667da08758.jpg\",\n  \"108658.jpg\": \"Aves/Molothrus aeneus/a5f942cbb29caa9f8e64283748b848e4.jpg\",\n  \"10866.jpg\": \"Aves/Rupornis magnirostris/e6fd4ed10ccdef294c7903da4a208cf9.jpg\",\n  \"108660.jpg\": \"Aves/Molothrus aeneus/a4d5b3b8ede5057bfdd7b21608700ef3.jpg\",\n  \"108671.jpg\": \"Aves/Molothrus aeneus/12a44dd58a115389c6d93e14678eaa0e.jpg\",\n  \"108689.jpg\": \"Insecta/Polydrusus formosus/31ab9018af430fe41c80a18798499bdc.jpg\",\n  \"108690.jpg\": \"Insecta/Polydrusus formosus/d9d95fc39cabccd8f1bfab9654f5320e.jpg\",\n  \"108701.jpg\": \"Insecta/Polydrusus formosus/19579c300e096b7a9a89a9eb1f084886.jpg\",\n  \"108703.jpg\": \"Insecta/Polydrusus formosus/bb4706ab76df6191fb94ba27c8aa75ef.jpg\",\n  \"108706.jpg\": \"Insecta/Polydrusus formosus/09326cc83a8b80df09ad3caf5cc550ac.jpg\",\n  \"10872.jpg\": \"Aves/Rupornis magnirostris/d330f00d633ef507ba25319b7015abf4.jpg\",\n  \"10873.jpg\": \"Aves/Rupornis magnirostris/eeec2680063d73feda993e4ab2030a80.jpg\",\n  \"108737.jpg\": \"Aves/Sitta pygmaea/5751792f4a3b64ecdde845a7ddc859d1.jpg\",\n  \"108744.jpg\": \"Aves/Sitta pygmaea/2ee740c4665cc1cde2b95935c96a283d.jpg\",\n  \"108749.jpg\": \"Aves/Sitta pygmaea/03a744ac3c3db54de34ccf5aecdc5df1.jpg\",\n  \"108760.jpg\": \"Aves/Sitta pygmaea/eb5ac09ccfee7b10747c762408a8d803.jpg\",\n  \"108771.jpg\": \"Aves/Sitta pygmaea/bbea7c876cdcbf073d45e14134115492.jpg\",\n  \"108786.jpg\": \"Aves/Sitta pygmaea/435d964ad2b630a14a32a50dd26d386a.jpg\",\n  \"108794.jpg\": \"Aves/Sitta pygmaea/7be94f8002b241316f3f4357273e4bf0.jpg\",\n  \"108800.jpg\": \"Aves/Sitta pygmaea/d993e5a6dd1bb2cdc7f7a09175084775.jpg\",\n  \"108802.jpg\": \"Aves/Sitta pygmaea/0bd0de63c076d4c207032cbf990949d6.jpg\",\n  \"108803.jpg\": \"Aves/Sitta pygmaea/cd6a674a2d29062ed4a878eede1fd881.jpg\",\n  \"108807.jpg\": \"Aves/Sitta pygmaea/a8af60106a78836d5a5f0af8b83d55ea.jpg\",\n  \"108809.jpg\": \"Aves/Sitta pygmaea/77825d990f0ca5cf89bf899843b58e99.jpg\",\n  \"10881.jpg\": \"Aves/Rupornis magnirostris/f969646ebaf0686e3cef61349373fbe8.jpg\",\n  \"108810.jpg\": \"Aves/Sitta pygmaea/ec7acd59c866c66609152111dbe4a199.jpg\",\n  \"108812.jpg\": \"Aves/Sitta pygmaea/9e80939d7ac25ed50ce360a5c97e5d37.jpg\",\n  \"108816.jpg\": \"Aves/Sitta pygmaea/f0c9796343b23856c4a7ec341393f2ae.jpg\",\n  \"108817.jpg\": \"Aves/Sitta pygmaea/aeffa2ba38fa9cbacc3a2b1eedc088c4.jpg\",\n  \"108818.jpg\": \"Aves/Sitta pygmaea/3eee9f3925ff856b3df16cfe327fa9df.jpg\",\n  \"108819.jpg\": \"Aves/Sitta pygmaea/4e6123bc5a94349ee1af6c542ad36442.jpg\",\n  \"108825.jpg\": \"Aves/Sitta pygmaea/5541b2b2b5d90ea1a02b052885babe63.jpg\",\n  \"108828.jpg\": \"Reptilia/Nerodia clarkii clarkii/51794aac9d53fe2aed5deae1b5680ade.jpg\",\n  \"10883.jpg\": \"Aves/Rupornis magnirostris/a4bcc136adc4184b92fa2a71c53e3dba.jpg\",\n  \"108838.jpg\": \"Reptilia/Nerodia clarkii clarkii/03561769bb8e2c6239a766eec875a85d.jpg\",\n  \"108845.jpg\": \"Reptilia/Nerodia clarkii clarkii/5f40e836345d1d743796979bd3eef741.jpg\",\n  \"108846.jpg\": \"Reptilia/Nerodia clarkii clarkii/e95dfaa6f18165989c6e32f73e0d34dc.jpg\",\n  \"108850.jpg\": \"Insecta/Clostera albosigma/f081f9d58f477ac18d59e04084410e0f.jpg\",\n  \"108851.jpg\": \"Insecta/Clostera albosigma/68daa7bb087dd39a4335bee7554c762c.jpg\",\n  \"108865.jpg\": \"Insecta/Clostera albosigma/cac2c3dea57d745045bb1fee93bcbf97.jpg\",\n  \"108867.jpg\": \"Insecta/Clostera albosigma/0a8784468693f6e91dd7ae274e040fdd.jpg\",\n  \"108877.jpg\": \"Reptilia/Thamnophis marcianus/3d93dc2cc3c5813f492a39f29938052f.jpg\",\n  \"108879.jpg\": \"Reptilia/Thamnophis marcianus/bd8b2d66eee48373e32a3008e23441a5.jpg\",\n  \"108890.jpg\": \"Reptilia/Thamnophis marcianus/be183a724be2bc92cca1d81abec1ebbb.jpg\",\n  \"108892.jpg\": \"Reptilia/Thamnophis marcianus/038fee383812e72949b9beb5b01ddbfd.jpg\",\n  \"108898.jpg\": \"Reptilia/Thamnophis marcianus/c7819b72ede2aebcc2478e79938b5c5a.jpg\",\n  \"108899.jpg\": \"Reptilia/Thamnophis marcianus/d4413105cf89ccc53862a26dfaf0d6d3.jpg\",\n  \"108905.jpg\": \"Reptilia/Thamnophis marcianus/ed4ccd7fa70cc40848fef44e4a92b07d.jpg\",\n  \"108907.jpg\": \"Reptilia/Thamnophis marcianus/6bad6dc6b742f879effeaf720b105b64.jpg\",\n  \"108922.jpg\": \"Reptilia/Thamnophis marcianus/f0b7f83031cd9bb8d059974322091efb.jpg\",\n  \"108923.jpg\": \"Reptilia/Thamnophis marcianus/c4a6786eca7b0ec42583b7f6424286c8.jpg\",\n  \"108924.jpg\": \"Reptilia/Thamnophis marcianus/08bcb128b0e01e6ef9d44ff41f035567.jpg\",\n  \"108925.jpg\": \"Reptilia/Thamnophis marcianus/a680e38f73dba7583661ec6b1457f55d.jpg\",\n  \"108937.jpg\": \"Reptilia/Thamnophis marcianus/2fe4d147ce3cc187a559866c50db218f.jpg\",\n  \"108938.jpg\": \"Reptilia/Thamnophis marcianus/525991b0e5b08a00194bef1f23164cb1.jpg\",\n  \"108947.jpg\": \"Reptilia/Thamnophis marcianus/ec3eae6c0d30f77ea0ceca0754d7fdbe.jpg\",\n  \"108950.jpg\": \"Reptilia/Thamnophis marcianus/47ff44b0d57b10506f67f03555d05c92.jpg\",\n  \"108958.jpg\": \"Reptilia/Thamnophis marcianus/116239f89352b2a9291d846eed21ed93.jpg\",\n  \"10896.jpg\": \"Aves/Rupornis magnirostris/eda2c9a54da5061c69f4f93677a8e820.jpg\",\n  \"10897.jpg\": \"Aves/Rupornis magnirostris/629f89caec368f8a41486732192bd755.jpg\",\n  \"108970.jpg\": \"Reptilia/Thamnophis marcianus/47ecc3eb2727c91c1fae221fc71ebe04.jpg\",\n  \"108973.jpg\": \"Reptilia/Thamnophis marcianus/d468bf70fb0f24568fc8ca738f6615d6.jpg\",\n  \"10898.jpg\": \"Aves/Rupornis magnirostris/0b21edda752fa1d1f28e4819fd6635de.jpg\",\n  \"10899.jpg\": \"Aves/Rupornis magnirostris/47450f4cf772e82d86c2dbfa71aa8350.jpg\",\n  \"10900.jpg\": \"Aves/Rupornis magnirostris/e562b9eb52db3ea0708de7f3970a569e.jpg\",\n  \"10901.jpg\": \"Aves/Rupornis magnirostris/7afed1acd88947f159e0549465333958.jpg\",\n  \"109025.jpg\": \"Reptilia/Thamnophis marcianus/1ea294a818be24ba7c92a1fbd476429f.jpg\",\n  \"109041.jpg\": \"Reptilia/Thamnophis marcianus/e3e4cba22405415d057ce3ee44db4dda.jpg\",\n  \"10905.jpg\": \"Aves/Rupornis magnirostris/45a9d1e651c902daf82e5b97bd5dbc61.jpg\",\n  \"10909.jpg\": \"Aves/Rupornis magnirostris/09dcdd8f0deb9d707d4ee35769ada462.jpg\",\n  \"10911.jpg\": \"Aves/Rupornis magnirostris/349af1adabc79084baf9e5b7ce3f4272.jpg\",\n  \"10926.jpg\": \"Aves/Rupornis magnirostris/a79a3f8455d4f33e82a339f6b8166e88.jpg\",\n  \"10930.jpg\": \"Aves/Rupornis magnirostris/c03b1290cb61f3dde211448d069273e8.jpg\",\n  \"10933.jpg\": \"Aves/Rupornis magnirostris/65fc2014a727ff6e62ac4931a48bdf62.jpg\",\n  \"109391.jpg\": \"Aves/Meleagris gallopavo/6dff9f380691c1b2a4a4a19b55096f8a.jpg\",\n  \"109406.jpg\": \"Aves/Meleagris gallopavo/b092a201773714abb4d1537d13c31f3d.jpg\",\n  \"109493.jpg\": \"Aves/Meleagris gallopavo/fe6019043cf8e78b39e357e20a2f62e5.jpg\",\n  \"109632.jpg\": \"Aves/Meleagris gallopavo/8643b144670fd86571d1d1740e17cd9b.jpg\",\n  \"109685.jpg\": \"Aves/Meleagris gallopavo/22ccdc7a3366762bba36b92800ee4696.jpg\",\n  \"109738.jpg\": \"Aves/Meleagris gallopavo/80c2a152a46f53dbcee684addadd5662.jpg\",\n  \"10975.jpg\": \"Insecta/Aeshna umbrosa/a3253742d28dfb62afed434c0fe23689.jpg\",\n  \"109753.jpg\": \"Aves/Meleagris gallopavo/4009f9f18729ee2ac5485f0db8fc75e9.jpg\",\n  \"10976.jpg\": \"Insecta/Aeshna umbrosa/d8d139cf800c48e196f7a643006a6f19.jpg\",\n  \"109763.jpg\": \"Aves/Meleagris gallopavo/c6816cee333b0cea3df5fd8539bd2932.jpg\",\n  \"109779.jpg\": \"Aves/Meleagris gallopavo/04a453d570cd7d74b3d4dd13b5344ae7.jpg\",\n  \"10982.jpg\": \"Insecta/Aeshna umbrosa/774957b309a245c4d0f2afcf63451bb4.jpg\",\n  \"109827.jpg\": \"Aves/Calidris alpina/7176fd00e7ff8a97e318686d025bc23e.jpg\",\n  \"109833.jpg\": \"Aves/Calidris alpina/6e1d5a642429306fb876a6a69712f1e3.jpg\",\n  \"10988.jpg\": \"Insecta/Aeshna umbrosa/cac86d2edf8f7dab03a2c7b448b740fc.jpg\",\n  \"10989.jpg\": \"Insecta/Aeshna umbrosa/484f7cfc0dc9e9f7e7924dba1062be2a.jpg\",\n  \"10990.jpg\": \"Insecta/Aeshna umbrosa/60c467e0560620733bbbf2f21ecadaea.jpg\",\n  \"10991.jpg\": \"Insecta/Aeshna umbrosa/2b86ecb009b50e06e199a244bc58d2c1.jpg\",\n  \"109915.jpg\": \"Aves/Calidris alpina/7b32ec75789b53be7448da5999c3c5e3.jpg\",\n  \"109917.jpg\": \"Aves/Calidris alpina/3d23007ab019ea8ee7663ca487d8393e.jpg\",\n  \"10992.jpg\": \"Insecta/Aeshna umbrosa/4e358b8dc3334e8104a1ebf890b36f86.jpg\",\n  \"109928.jpg\": \"Aves/Calidris alpina/46053d8581ca5dda617d769e4675ccb2.jpg\",\n  \"109932.jpg\": \"Aves/Calidris alpina/55ff9a5bc876cff651b64f56cd3eefed.jpg\",\n  \"109953.jpg\": \"Aves/Calidris alpina/eec18bae7ad89f37bfa88121486a8f65.jpg\",\n  \"10996.jpg\": \"Insecta/Aeshna umbrosa/18c136c43412f303d57df06e01f3d065.jpg\",\n  \"109960.jpg\": \"Aves/Calidris alpina/e6a8cf97bb100dceea0c996ed23494dc.jpg\",\n  \"10997.jpg\": \"Insecta/Aeshna umbrosa/36a597c045e929ccc274c8ee2daec2a9.jpg\",\n  \"109996.jpg\": \"Aves/Calidris alpina/a60ecd581144031cce522b2ea60dcfa7.jpg\",\n  \"11.jpg\": \"Mammalia/Marmota flaviventris/14bcd8bf7a49b3a26ed9c73986bdc828.jpg\",\n  \"11002.jpg\": \"Insecta/Aeshna umbrosa/a2732ce045136c5ff3350e8db5459723.jpg\",\n  \"11009.jpg\": \"Insecta/Aeshna umbrosa/5befca986b1fff1480afc7b418feecbf.jpg\",\n  \"11010.jpg\": \"Insecta/Aeshna umbrosa/f6d92c2bafded19336b5d8d0952ac31d.jpg\",\n  \"11020.jpg\": \"Insecta/Aeshna umbrosa/2f894b128ec9bd930106e7b9dd201ee9.jpg\",\n  \"110260.jpg\": \"Aves/Melozone fusca/351a1678c37f8767d47c87592d8d988c.jpg\",\n  \"110264.jpg\": \"Aves/Melozone fusca/41a2cea7f9bf3c232b1f4e5d881f6678.jpg\",\n  \"110267.jpg\": \"Aves/Melozone fusca/3ba48177e1fe8abd31e119f92fec1779.jpg\",\n  \"110268.jpg\": \"Aves/Melozone fusca/71b26d5263dd6c5c6b8b48423882594b.jpg\",\n  \"110271.jpg\": \"Aves/Melozone fusca/9c2d39fc539250afffd9bc4e0b669458.jpg\",\n  \"110274.jpg\": \"Aves/Melozone fusca/13224eefe924029da13992c99fd05502.jpg\",\n  \"110278.jpg\": \"Aves/Melozone fusca/c4177f96a7985b8b4006bb255b8452dc.jpg\",\n  \"110279.jpg\": \"Aves/Melozone fusca/e69ae9315d41c4bd6986ee904ba90a77.jpg\",\n  \"11028.jpg\": \"Insecta/Aeshna umbrosa/f6faa78996f6e934c315999d212b97ee.jpg\",\n  \"110280.jpg\": \"Aves/Melozone fusca/5581b32fd62c35eb100a9bbe25549daf.jpg\",\n  \"110316.jpg\": \"Aves/Melozone fusca/0a023e60f652bc264136623c88b0cd33.jpg\",\n  \"110318.jpg\": \"Aves/Melozone fusca/3444d886786e0deeb552ec9142f2acd4.jpg\",\n  \"110324.jpg\": \"Aves/Melozone fusca/927d5b9c5cb8fb684269c2d21dd71fde.jpg\",\n  \"110325.jpg\": \"Aves/Melozone fusca/0ed882e951c68bb21101e1b394b6f570.jpg\",\n  \"110337.jpg\": \"Aves/Melozone fusca/b5e961ed231196acbe82c8eeb406555b.jpg\",\n  \"11034.jpg\": \"Insecta/Aeshna umbrosa/0bda014a9a51dbfe9898cce8972a6e6f.jpg\",\n  \"110344.jpg\": \"Aves/Melozone fusca/c53aa1c2980b76f3dace7318de0160cb.jpg\",\n  \"110345.jpg\": \"Aves/Melozone fusca/5dbb2d747a337d79cd4071cf44218c1d.jpg\",\n  \"110346.jpg\": \"Aves/Melozone fusca/2ff9dc31a4c7c40bf95d0a19c1394f5c.jpg\",\n  \"110347.jpg\": \"Aves/Melozone fusca/efe507e5215e41ca653831ff25ae8364.jpg\",\n  \"110356.jpg\": \"Aves/Melozone fusca/f7958edbc8ba97836ba52867fed6f335.jpg\",\n  \"110361.jpg\": \"Aves/Melozone fusca/1fe3868f2edf321b57fa31eace254769.jpg\",\n  \"11038.jpg\": \"Insecta/Aeshna umbrosa/6ff2cb14684dbd6e9b04d2be291115e2.jpg\",\n  \"11039.jpg\": \"Insecta/Aeshna umbrosa/f7043e69366c4ce11476ac8fd5e36339.jpg\",\n  \"110406.jpg\": \"Aves/Melozone fusca/d98bb56627be3440d9733b4986d845e8.jpg\",\n  \"110413.jpg\": \"Aves/Melozone fusca/38341e4d2cb6eb8bf3699b93f7659a5f.jpg\",\n  \"110423.jpg\": \"Aves/Melozone fusca/5ff6573dfd9b3f822e2bf4cc7d1cd274.jpg\",\n  \"110437.jpg\": \"Aves/Melozone fusca/9f1ef4f9110092785a48b1a40f151e58.jpg\",\n  \"11044.jpg\": \"Insecta/Aeshna umbrosa/6d9027e004e86103598ed2894bd3e245.jpg\",\n  \"11045.jpg\": \"Insecta/Aeshna umbrosa/ac242cf30b96f3dd427f7123439a6ce3.jpg\",\n  \"110450.jpg\": \"Aves/Melozone fusca/6e7499e5fe529edaccb53fe50056420a.jpg\",\n  \"110464.jpg\": \"Aves/Melozone fusca/81413d013124b13475ff2037b35341e2.jpg\",\n  \"11047.jpg\": \"Insecta/Aeshna umbrosa/5943e00e46e8d90931d10797af4b21c5.jpg\",\n  \"110478.jpg\": \"Insecta/Xanthorhoe ferrugata/abe29150c7357189a3c81d2de114abc7.jpg\",\n  \"110481.jpg\": \"Insecta/Xanthorhoe ferrugata/976e728abb62338c11b71feac81ff95c.jpg\",\n  \"110482.jpg\": \"Insecta/Xanthorhoe ferrugata/92af376a3692e1f41ddb0c0f7f095503.jpg\",\n  \"110485.jpg\": \"Insecta/Xanthorhoe ferrugata/70a48a8b32253a6fa116cda6170240cd.jpg\",\n  \"110487.jpg\": \"Insecta/Xanthorhoe ferrugata/920a99e23f418bf957ccc64db66b8078.jpg\",\n  \"110492.jpg\": \"Insecta/Xanthorhoe ferrugata/b912aafcc49fbe4e19afb7c620bd5ad5.jpg\",\n  \"110500.jpg\": \"Insecta/Xanthorhoe ferrugata/83fc41c21024f026dc38b7fe3abcdec9.jpg\",\n  \"110501.jpg\": \"Insecta/Xanthorhoe ferrugata/f3b93ae27acef8a3cedccd111e835bbe.jpg\",\n  \"110513.jpg\": \"Insecta/Acanthocephala terminalis/999acc40d9020b0305c471dd1a757723.jpg\",\n  \"110517.jpg\": \"Insecta/Acanthocephala terminalis/ce1c3bfdecfa02b39962da1dedcfffc7.jpg\",\n  \"110520.jpg\": \"Insecta/Acanthocephala terminalis/96037094059652a8f6ab4fc42187b435.jpg\",\n  \"110521.jpg\": \"Insecta/Acanthocephala terminalis/9cdb1580702694f7d16110bfa5f3e7d9.jpg\",\n  \"110523.jpg\": \"Insecta/Acanthocephala terminalis/e23259d2780768ec80875b1ff7bba980.jpg\",\n  \"110524.jpg\": \"Insecta/Acanthocephala terminalis/c033066b0b646e394756e6f50f8bf2e8.jpg\",\n  \"110528.jpg\": \"Insecta/Acanthocephala terminalis/f1ab6bacb04aa711a4970f23c0e133b4.jpg\",\n  \"110529.jpg\": \"Insecta/Acanthocephala terminalis/f2aeb15627711f0788b0b2a480503fb6.jpg\",\n  \"110535.jpg\": \"Insecta/Acanthocephala terminalis/19039d0679c273cdd4c64b6941d8109b.jpg\",\n  \"110536.jpg\": \"Insecta/Acanthocephala terminalis/730ee00bffe63b3d6e839f6aceaf53be.jpg\",\n  \"110538.jpg\": \"Insecta/Acanthocephala terminalis/9e3956366ca136e3d9eaee97ef45d9c1.jpg\",\n  \"110539.jpg\": \"Insecta/Acanthocephala terminalis/bc69e72e6b5ec6801ab4af3b54531b12.jpg\",\n  \"110541.jpg\": \"Insecta/Acanthocephala terminalis/a22e00d9416e40bcb5b09ae07fa7f891.jpg\",\n  \"110542.jpg\": \"Insecta/Acanthocephala terminalis/6ee66164dc3912899339db549f337bcd.jpg\",\n  \"110544.jpg\": \"Insecta/Acanthocephala terminalis/d084c18d42e0c6d30c2585823dbfed9d.jpg\",\n  \"110549.jpg\": \"Insecta/Acanthocephala terminalis/af4a84c9020bbd1f1655054214895ebb.jpg\",\n  \"110552.jpg\": \"Insecta/Acanthocephala terminalis/08188bf2801f11667b6a43c253796172.jpg\",\n  \"110556.jpg\": \"Insecta/Acanthocephala terminalis/eb3e8b6ba329f188c390bfac54fe0181.jpg\",\n  \"110559.jpg\": \"Insecta/Acanthocephala terminalis/cd48862a8d09d92b03f931db8f26641c.jpg\",\n  \"110563.jpg\": \"Insecta/Acanthocephala terminalis/0ecc7d98096ee8c44f172747c65bb7e0.jpg\",\n  \"110568.jpg\": \"Insecta/Acanthocephala terminalis/68cdfbc8fa030743eed2a4aa99441578.jpg\",\n  \"11057.jpg\": \"Insecta/Aeshna umbrosa/77ba3891cdc5e8388b3b308b1a9de455.jpg\",\n  \"110575.jpg\": \"Insecta/Acanthocephala terminalis/fcf2ba76a2d5b54a9da448b3e2f6526f.jpg\",\n  \"110577.jpg\": \"Insecta/Acanthocephala terminalis/71a1ffd4ab904ea031ad7679d444c4da.jpg\",\n  \"110582.jpg\": \"Insecta/Dythemis nigrescens/36303d4d71962e27fd58c8e562937c4c.jpg\",\n  \"110583.jpg\": \"Insecta/Dythemis nigrescens/35e090a9f6907470dac3ee6cfe730565.jpg\",\n  \"110584.jpg\": \"Insecta/Dythemis nigrescens/f0ac75629d40e12ca1a7baf01f8cc7d5.jpg\",\n  \"110592.jpg\": \"Insecta/Dythemis nigrescens/e73d8d2a737733d3ec07bd3ef1b7d34c.jpg\",\n  \"110595.jpg\": \"Insecta/Dythemis nigrescens/847c926e72fa730a96723209b9ae0517.jpg\",\n  \"110596.jpg\": \"Insecta/Dythemis nigrescens/7818c5ffb70d9a01a70ed92ec517a60b.jpg\",\n  \"110600.jpg\": \"Insecta/Dythemis nigrescens/2bd19bf0ac2c02f3d5954ae3bcef0591.jpg\",\n  \"110601.jpg\": \"Insecta/Dythemis nigrescens/118fe7df59d991c09cad1fd8ce732399.jpg\",\n  \"110602.jpg\": \"Insecta/Dythemis nigrescens/ae28baff83c0d121ee570114a27898f4.jpg\",\n  \"110605.jpg\": \"Insecta/Dythemis nigrescens/3187af9c3a631dccf7a5e8c818f599e3.jpg\",\n  \"110606.jpg\": \"Insecta/Dythemis nigrescens/a125d15fad20df554c4e76436d579488.jpg\",\n  \"110610.jpg\": \"Insecta/Dythemis nigrescens/88ad13510d0bdea69c95ffae701b681e.jpg\",\n  \"110611.jpg\": \"Insecta/Dythemis nigrescens/dcf41f876ebcfae2216c18a445f77a07.jpg\",\n  \"110613.jpg\": \"Insecta/Dythemis nigrescens/2c1d2686970536d23db431588f79821c.jpg\",\n  \"110614.jpg\": \"Insecta/Dythemis nigrescens/84bd3e5cce8437df3e1f30d82eafb8f4.jpg\",\n  \"110618.jpg\": \"Insecta/Dythemis nigrescens/cd1d79d080d224bfa6225b51b8e43226.jpg\",\n  \"110619.jpg\": \"Insecta/Dythemis nigrescens/8254e5ff8d21c405a29091089cd0c7e5.jpg\",\n  \"110620.jpg\": \"Insecta/Dythemis nigrescens/a32ad3a04115b7d6d8fbc56cad12ef65.jpg\",\n  \"110621.jpg\": \"Insecta/Dythemis nigrescens/f8831df1d1615be6d88b783e18573d0c.jpg\",\n  \"110622.jpg\": \"Insecta/Dythemis nigrescens/02bd6b561f2df9f9dfc8bc4e51fd08ae.jpg\",\n  \"110625.jpg\": \"Insecta/Dythemis nigrescens/f2c759af424c836a536deaa4b5582610.jpg\",\n  \"110627.jpg\": \"Insecta/Dythemis nigrescens/a5c4d6c01075272e2d77fb67e80d1411.jpg\",\n  \"110629.jpg\": \"Insecta/Dythemis nigrescens/63e6550a1b4e7af75fc5c5372e22001e.jpg\",\n  \"110631.jpg\": \"Insecta/Dythemis nigrescens/b3c86cc958441381a1c9f6908d16e644.jpg\",\n  \"110638.jpg\": \"Insecta/Dythemis nigrescens/ee9c9dbfa60d5094174a6c8c5799ee27.jpg\",\n  \"110640.jpg\": \"Insecta/Dythemis nigrescens/5a746c81f1e4fdd28182da50900f8e01.jpg\",\n  \"110645.jpg\": \"Insecta/Dythemis nigrescens/d7ad6c3b971b1be4fa10b3dd1c9685b8.jpg\",\n  \"110646.jpg\": \"Insecta/Dythemis nigrescens/64b9f909f47dca5c5f7e55cdc33e2043.jpg\",\n  \"110700.jpg\": \"Insecta/Lestes australis/c66b47d0f1f12c0677a660dbac749c3a.jpg\",\n  \"110701.jpg\": \"Insecta/Lestes australis/3d06a54b6e97d43454fc93ce18e5a4ff.jpg\",\n  \"110702.jpg\": \"Insecta/Lestes australis/78a68c5f932aba2f229325079debffb2.jpg\",\n  \"110703.jpg\": \"Insecta/Lestes australis/710dd429f9a11ce4ed8683e96ed10800.jpg\",\n  \"110708.jpg\": \"Insecta/Lestes australis/b0375199e07c9e05dda1d162abbeaf7b.jpg\",\n  \"110713.jpg\": \"Insecta/Lestes australis/86ed8b0d186c57d51ee8bf44e914bd01.jpg\",\n  \"110725.jpg\": \"Insecta/Lestes australis/492aba34dd8c2e23edb0c2f3d2800dd0.jpg\",\n  \"110736.jpg\": \"Insecta/Lestes australis/bdba106408dc2a0090e44c08b37fdc37.jpg\",\n  \"110737.jpg\": \"Insecta/Lestes australis/fd5d0f1f7c4e254eb7c56e2b55051d9e.jpg\",\n  \"110740.jpg\": \"Insecta/Lestes australis/a38502cdccd4441cd67ec13565d415d0.jpg\",\n  \"110741.jpg\": \"Insecta/Lestes australis/8440c2bbc9ca3d3708e7dd47e28f4891.jpg\",\n  \"110759.jpg\": \"Insecta/Lestes australis/77ad170b57f9446cc3f92aac79fdd437.jpg\",\n  \"110760.jpg\": \"Insecta/Lestes australis/a2de191b92bedd55b570d601419da6a1.jpg\",\n  \"110761.jpg\": \"Insecta/Lestes australis/831a62c640cbd76ad493411d021a40ac.jpg\",\n  \"110764.jpg\": \"Insecta/Lestes australis/508ff815b3d8672dd875680ff9dba74b.jpg\",\n  \"110772.jpg\": \"Insecta/Lestes australis/51588971f521decb4230f0caca4ed969.jpg\",\n  \"110784.jpg\": \"Insecta/Lestes australis/e7ea98f6b9a20023666ee2e7e7a67686.jpg\",\n  \"110787.jpg\": \"Insecta/Lestes australis/5b057ed0c47085ea8fc28a4f1f67b02b.jpg\",\n  \"110791.jpg\": \"Insecta/Lestes australis/960bfdc3cd6f7c7c6ebbd7c267d3da81.jpg\",\n  \"110792.jpg\": \"Insecta/Lestes australis/a74a327d6de89689b092ee639eebcddc.jpg\",\n  \"110809.jpg\": \"Insecta/Lestes australis/b22668677ed4905371fcd6242d14cb27.jpg\",\n  \"110811.jpg\": \"Insecta/Tettigonia viridissima/363ba9a9c5e4ecdface5999e3f7a7869.jpg\",\n  \"110812.jpg\": \"Insecta/Tettigonia viridissima/2cfbc897dff4dc7e13d85dd3a9a64b1d.jpg\",\n  \"110813.jpg\": \"Insecta/Tettigonia viridissima/300df0ab1c1389a35f636180cb783146.jpg\",\n  \"110827.jpg\": \"Insecta/Tettigonia viridissima/60ac266ada565c66737741c1191be9f1.jpg\",\n  \"110828.jpg\": \"Aves/Falcipennis canadensis/509a021492f94d946204f370fd7f7cb5.jpg\",\n  \"110834.jpg\": \"Aves/Falcipennis canadensis/1ab1e3a567f043bdc2c593d17ec663ac.jpg\",\n  \"110843.jpg\": \"Aves/Falcipennis canadensis/a0c7ff372ed60016200c78c87ebcc494.jpg\",\n  \"110846.jpg\": \"Aves/Falcipennis canadensis/6526bc11b5e05ab74e70f93469e4d7ac.jpg\",\n  \"110851.jpg\": \"Aves/Falcipennis canadensis/ee9391f397dadeb2ada77ec78a1573d0.jpg\",\n  \"110852.jpg\": \"Aves/Falcipennis canadensis/dee3c24a563683b900d79be66756b0e6.jpg\",\n  \"110853.jpg\": \"Aves/Falcipennis canadensis/0cbc7da5a9d55c1f9fec487897f678ac.jpg\",\n  \"110855.jpg\": \"Aves/Falcipennis canadensis/cdf5ca563c91de585fd5e9f066c45979.jpg\",\n  \"110857.jpg\": \"Aves/Falcipennis canadensis/34e1141c41185ef87ac32414ec11fefc.jpg\",\n  \"110927.jpg\": \"Aves/Tachycineta bicolor/525a2b20e927301aea7dd23645ea23a6.jpg\",\n  \"110952.jpg\": \"Aves/Tachycineta bicolor/e2be21312c3de36bc0da6be2ff27c560.jpg\",\n  \"110953.jpg\": \"Aves/Tachycineta bicolor/60e17882ab50368baca09b9b20a28a5d.jpg\",\n  \"110964.jpg\": \"Aves/Tachycineta bicolor/77c4767d7007be4ee823f0898581e0c4.jpg\",\n  \"110968.jpg\": \"Aves/Tachycineta bicolor/ea72814b45bad0627ee053b9dd08eb3e.jpg\",\n  \"110995.jpg\": \"Aves/Tachycineta bicolor/c32a287d03ce9588fb1f54c81acf27c8.jpg\",\n  \"111.jpg\": \"Mammalia/Marmota flaviventris/348d485336e348e4726c0af5f778a67b.jpg\",\n  \"11100.jpg\": \"Aves/Rhipidura fuliginosa/457169fdd85e3ad9b8f42266f1c7c2b4.jpg\",\n  \"111002.jpg\": \"Aves/Tachycineta bicolor/9a91d3a2f2cab40af52c78b5850cc8b5.jpg\",\n  \"11101.jpg\": \"Aves/Rhipidura fuliginosa/8d519c6e641b6efd5488b0a8cb49e544.jpg\",\n  \"111016.jpg\": \"Aves/Tachycineta bicolor/23bbef57b3a0683b2363717868559652.jpg\",\n  \"11102.jpg\": \"Aves/Rhipidura fuliginosa/0c7f38dd657658f9753228c36048420d.jpg\",\n  \"11103.jpg\": \"Aves/Rhipidura fuliginosa/e9249bd937b693bc9215773a365af111.jpg\",\n  \"11104.jpg\": \"Aves/Rhipidura fuliginosa/97e782dafade2acc77e88a242592a272.jpg\",\n  \"11105.jpg\": \"Aves/Rhipidura fuliginosa/3547ad5051ed1d06e81d37d7dddbae79.jpg\",\n  \"111067.jpg\": \"Aves/Tachycineta bicolor/74c2a5c7c2393e78ebfbdbdea25ca036.jpg\",\n  \"111084.jpg\": \"Aves/Tachycineta bicolor/499c7c678668db963cf1e0b239a6f303.jpg\",\n  \"111086.jpg\": \"Aves/Tachycineta bicolor/47aeed8b098159b4f42c83a85e359b4b.jpg\",\n  \"11110.jpg\": \"Aves/Rhipidura fuliginosa/3d2eed07a9d644bb8146498969a4651d.jpg\",\n  \"111126.jpg\": \"Aves/Tachycineta bicolor/86a93dd49f5ea60d54c11f81e979f954.jpg\",\n  \"111177.jpg\": \"Aves/Tachycineta bicolor/a9ff077949c30c35105cb19051268af4.jpg\",\n  \"111182.jpg\": \"Aves/Tachycineta bicolor/76575ad7b2355deb170f0b22ede07c9c.jpg\",\n  \"11119.jpg\": \"Aves/Rhipidura fuliginosa/e5537dfbfb0b752d7ad8b048d0f82330.jpg\",\n  \"111190.jpg\": \"Aves/Tachycineta bicolor/1ffdd2996d3cebb9311709222f25d0fd.jpg\",\n  \"111199.jpg\": \"Aves/Tachycineta bicolor/0b10fbe1ae5dfa91434d3b81c617dbf9.jpg\",\n  \"11120.jpg\": \"Aves/Rhipidura fuliginosa/9431d2af1210662f0797330023ecf30b.jpg\",\n  \"11121.jpg\": \"Aves/Rhipidura fuliginosa/5a15d1850166263cc084c86869456764.jpg\",\n  \"111210.jpg\": \"Aves/Tachycineta bicolor/ca2a967ecd5e740b29a2131809125709.jpg\",\n  \"11123.jpg\": \"Aves/Rhipidura fuliginosa/2ab57261794c7ef2d773e495e18e2f68.jpg\",\n  \"111239.jpg\": \"Aves/Tachycineta bicolor/6965106b73491b9588455081d2fed806.jpg\",\n  \"11124.jpg\": \"Aves/Rhipidura fuliginosa/e2ebd16bd9734b4910d6389525138a8f.jpg\",\n  \"11127.jpg\": \"Aves/Rhipidura fuliginosa/1dd064537c654c6338128dc47f21e1d0.jpg\",\n  \"11129.jpg\": \"Aves/Rhipidura fuliginosa/a45ba9063dcbed644cb316f07d9720ce.jpg\",\n  \"111303.jpg\": \"Aves/Tachycineta bicolor/5f73d8fd6e5bbf48733696f75b0be7ce.jpg\",\n  \"11133.jpg\": \"Aves/Rhipidura fuliginosa/688df3f87872a5ff7b939a6a7fad1c4f.jpg\",\n  \"111342.jpg\": \"Insecta/Pyrausta insequalis/0e358bd63ed86c830ce89ed8ca642b0c.jpg\",\n  \"111346.jpg\": \"Insecta/Pyrausta insequalis/2d5b95c0e35f06e3b272aee26a4db62d.jpg\",\n  \"111352.jpg\": \"Insecta/Pyrausta insequalis/7b120a39a5d6c80594a05e2601a332e0.jpg\",\n  \"111353.jpg\": \"Insecta/Pyrausta insequalis/3d19ce8403e71b35a0cdcf079543c588.jpg\",\n  \"111359.jpg\": \"Reptilia/Basiliscus vittatus/e2411b70a7334f62203f50ce2d185849.jpg\",\n  \"11136.jpg\": \"Aves/Rhipidura fuliginosa/639202189bb43e0454a35bed519025a6.jpg\",\n  \"111363.jpg\": \"Reptilia/Basiliscus vittatus/d6db58947a2352c1c9385f0f109d6dbc.jpg\",\n  \"111364.jpg\": \"Reptilia/Basiliscus vittatus/066039a72cef8643489d72306025fd97.jpg\",\n  \"111365.jpg\": \"Reptilia/Basiliscus vittatus/249c0b850498e6fd081bc22784ed56d9.jpg\",\n  \"111368.jpg\": \"Reptilia/Basiliscus vittatus/f52f761a44f01ab8219f39bffb78b777.jpg\",\n  \"11137.jpg\": \"Aves/Rhipidura fuliginosa/02d0dc88411f9c659053d9d0b88074c0.jpg\",\n  \"111370.jpg\": \"Reptilia/Basiliscus vittatus/05524960d77e9e1df1be196c64e5cbce.jpg\",\n  \"111376.jpg\": \"Reptilia/Basiliscus vittatus/3866ecc0d6e8626110165a9425f782ff.jpg\",\n  \"111377.jpg\": \"Reptilia/Basiliscus vittatus/2f63be82b671c9004a029d6f50238d83.jpg\",\n  \"111378.jpg\": \"Reptilia/Basiliscus vittatus/fc8e2e843faf95619298bee396f1d345.jpg\",\n  \"111379.jpg\": \"Reptilia/Basiliscus vittatus/cff53054f0734010ffa34075980f50a5.jpg\",\n  \"11138.jpg\": \"Aves/Rhipidura fuliginosa/e42b8dec79a97670304b6c579512f919.jpg\",\n  \"111380.jpg\": \"Reptilia/Basiliscus vittatus/1994e66a2d1a81a19e355d04deffcd5d.jpg\",\n  \"111383.jpg\": \"Reptilia/Basiliscus vittatus/33f09520d73bfe949fe573fad621e2b9.jpg\",\n  \"111386.jpg\": \"Reptilia/Basiliscus vittatus/9ccd1a488b9bcf913d3963649b48dfb3.jpg\",\n  \"111388.jpg\": \"Reptilia/Basiliscus vittatus/fe8cfa0f541af744368e349ab9eab9a0.jpg\",\n  \"111393.jpg\": \"Reptilia/Basiliscus vittatus/be5f5da46254361773ac4ed9d025f08f.jpg\",\n  \"111397.jpg\": \"Reptilia/Basiliscus vittatus/f480c919222be9ca75758e4ad5dbf128.jpg\",\n  \"11140.jpg\": \"Aves/Rhipidura fuliginosa/c9420fb8a74d2a8369f631d7fe646462.jpg\",\n  \"111402.jpg\": \"Reptilia/Basiliscus vittatus/96ce7672f776384e45e573ad4fc1126a.jpg\",\n  \"111413.jpg\": \"Reptilia/Basiliscus vittatus/dd7cb80e5a8fcc303a83b157b751150e.jpg\",\n  \"111414.jpg\": \"Reptilia/Basiliscus vittatus/90b95f9118912d6617de2070424dc1a1.jpg\",\n  \"111419.jpg\": \"Reptilia/Basiliscus vittatus/ae0bc7fe4b9255440becf3d152e154a9.jpg\",\n  \"111424.jpg\": \"Reptilia/Basiliscus vittatus/2e21fb0d9127b63dd11bff3cc573a623.jpg\",\n  \"111432.jpg\": \"Reptilia/Basiliscus vittatus/6215be094282eecefd130ef756279b56.jpg\",\n  \"111433.jpg\": \"Reptilia/Basiliscus vittatus/034c4dce37990a288514ea7e42bb3b33.jpg\",\n  \"11146.jpg\": \"Amphibia/Pseudacris crucifer/3f9ebcc033d46d24f32e43e413d140f7.jpg\",\n  \"11150.jpg\": \"Amphibia/Pseudacris crucifer/a17bbb5735f2e5d59851db3cd553423c.jpg\",\n  \"111522.jpg\": \"Aves/Parkesia noveboracensis/666ddadb31db06d0f257b56051f86547.jpg\",\n  \"111523.jpg\": \"Aves/Parkesia noveboracensis/cb635448e775b3c58f95210942787984.jpg\",\n  \"111528.jpg\": \"Aves/Parkesia noveboracensis/d422233fc4338fdc5eee373285a1d37d.jpg\",\n  \"111529.jpg\": \"Aves/Parkesia noveboracensis/fe540e3221c4deebbeb8c0c1fd871efc.jpg\",\n  \"11153.jpg\": \"Amphibia/Pseudacris crucifer/b91688e9a310721803877d2de523beef.jpg\",\n  \"111532.jpg\": \"Aves/Parkesia noveboracensis/5fd8bf3e7a2792d2046c229a59d0666a.jpg\",\n  \"111535.jpg\": \"Aves/Parkesia noveboracensis/33490f12d29b921f0b157fa480c39894.jpg\",\n  \"111536.jpg\": \"Aves/Parkesia noveboracensis/2231f58bfbff1bc42fd11739cae19220.jpg\",\n  \"111538.jpg\": \"Aves/Parkesia noveboracensis/f04e5d664a54c74779fc025b4f622eb1.jpg\",\n  \"111539.jpg\": \"Aves/Parkesia noveboracensis/af21dc06462f1856de189765647c152a.jpg\",\n  \"111543.jpg\": \"Aves/Parkesia noveboracensis/c2a09c1d6220481f73b8ada5b11dfd1f.jpg\",\n  \"111544.jpg\": \"Aves/Parkesia noveboracensis/c890a7e88f6718d641bbb97fd0989434.jpg\",\n  \"111547.jpg\": \"Aves/Parkesia noveboracensis/21b054ff4d869699c199c7ee262e3cd8.jpg\",\n  \"111555.jpg\": \"Aves/Parkesia noveboracensis/870e0665166b84d01805e1cb4e09f725.jpg\",\n  \"11156.jpg\": \"Amphibia/Pseudacris crucifer/e0279258c255965d7690d4371fd8081a.jpg\",\n  \"111567.jpg\": \"Aves/Parkesia noveboracensis/cc78547642c7d3ed537b2d387b349dac.jpg\",\n  \"111581.jpg\": \"Aves/Parkesia noveboracensis/9e510b1ea59ebb2d38bf860df56cbbe4.jpg\",\n  \"111582.jpg\": \"Aves/Parkesia noveboracensis/80cb6aa96dfe81959b3d9eb13bdea283.jpg\",\n  \"111583.jpg\": \"Aves/Parkesia noveboracensis/0706b568cb0621e7bb8090bda0fd17fb.jpg\",\n  \"111584.jpg\": \"Aves/Parkesia noveboracensis/9711d6aeb28bbec728451c0046fb4015.jpg\",\n  \"111586.jpg\": \"Aves/Parkesia noveboracensis/d71c1f31acd0b1319f850c54e906fce2.jpg\",\n  \"111587.jpg\": \"Aves/Parkesia noveboracensis/a8fc63dd49fa10fcd1a63345c4d649cd.jpg\",\n  \"111588.jpg\": \"Aves/Parkesia noveboracensis/a4460ca38986344bed8b84b9544a6a13.jpg\",\n  \"111589.jpg\": \"Aves/Parkesia noveboracensis/2ca8c550aaf2739a5255cdbd252ebb41.jpg\",\n  \"111592.jpg\": \"Aves/Parkesia noveboracensis/6ed4dd0f38db8489acca6d0c381153b7.jpg\",\n  \"11164.jpg\": \"Amphibia/Pseudacris crucifer/4c366231c59d363da8196081dfd234ae.jpg\",\n  \"111643.jpg\": \"Insecta/Daphnis nerii/57cfcb067e9597e19106b3aab4302806.jpg\",\n  \"111646.jpg\": \"Insecta/Daphnis nerii/8985c67ba086c70ad67a24762af54982.jpg\",\n  \"111651.jpg\": \"Insecta/Daphnis nerii/c4cc0d5b0c2d0d4842b3ea7d35db7107.jpg\",\n  \"111653.jpg\": \"Insecta/Daphnis nerii/06cab0af8f552640585ac1b6e22883b9.jpg\",\n  \"111654.jpg\": \"Insecta/Daphnis nerii/c3cb046253199aedbc5a8dda7b838574.jpg\",\n  \"111655.jpg\": \"Insecta/Daphnis nerii/51afaee4bbb8157f2a4804d74b1615f6.jpg\",\n  \"111657.jpg\": \"Insecta/Daphnis nerii/f7412a247cec4f2dfded8bcc88be644d.jpg\",\n  \"111662.jpg\": \"Insecta/Daphnis nerii/a138938062a4b7413377ea490e63a9b4.jpg\",\n  \"11173.jpg\": \"Amphibia/Pseudacris crucifer/92fba40ecae424b997d14ff862e12f34.jpg\",\n  \"111733.jpg\": \"Aves/Hydroprogne caspia/10b6604525270575faaba2e473b4a1b2.jpg\",\n  \"111735.jpg\": \"Aves/Hydroprogne caspia/2d4fd4c743597fb9569a2940b5ab0795.jpg\",\n  \"111737.jpg\": \"Aves/Hydroprogne caspia/07e4881c1ae9a1c4aac5fc0572069edd.jpg\",\n  \"111742.jpg\": \"Aves/Hydroprogne caspia/e90e256221d33320836c31cf94f15481.jpg\",\n  \"111763.jpg\": \"Aves/Hydroprogne caspia/f5193f4d75fe948d147b814d324de894.jpg\",\n  \"11178.jpg\": \"Amphibia/Pseudacris crucifer/3cebb14dfed89eb2387d83a90ec8868b.jpg\",\n  \"111810.jpg\": \"Aves/Hydroprogne caspia/0f40ce95ac1bf51efb30994cbb656c3b.jpg\",\n  \"111836.jpg\": \"Aves/Hydroprogne caspia/162b176287d9d379e2c84fdf275e9409.jpg\",\n  \"111846.jpg\": \"Aves/Hydroprogne caspia/b8277fd5d4ebcbd190ab501e12b23839.jpg\",\n  \"111855.jpg\": \"Aves/Hydroprogne caspia/2d3aebddb41e8fd06e7571813ab4ea90.jpg\",\n  \"111858.jpg\": \"Aves/Hydroprogne caspia/b402fa4e477817cae1c753fd74b66408.jpg\",\n  \"111880.jpg\": \"Aves/Hydroprogne caspia/640c425068a04d564ae68e19b41300ed.jpg\",\n  \"111916.jpg\": \"Aves/Hydroprogne caspia/087c05623df7a04da68256c0ed0ade5d.jpg\",\n  \"111931.jpg\": \"Aves/Hydroprogne caspia/5997cdc85441d5534dd790b45aa0a49d.jpg\",\n  \"11195.jpg\": \"Amphibia/Pseudacris crucifer/31578d797b593c881faae69ce58009db.jpg\",\n  \"112003.jpg\": \"Insecta/Leptinotarsa decemlineata/74cb054bede4533e0d63fc12cfc7c730.jpg\",\n  \"112006.jpg\": \"Insecta/Leptinotarsa decemlineata/064a0eca7bbaa573c1a7bfc04b524f60.jpg\",\n  \"112008.jpg\": \"Insecta/Leptinotarsa decemlineata/234fd650cf393e92646b3f0cad6d1063.jpg\",\n  \"112017.jpg\": \"Insecta/Leptinotarsa decemlineata/90f95d6292ecd400a1a91e960d01592b.jpg\",\n  \"112018.jpg\": \"Insecta/Leptinotarsa decemlineata/5b2cecedf93a15e63c3c115476cee6ed.jpg\",\n  \"112025.jpg\": \"Insecta/Leptinotarsa decemlineata/101efc5e12f04419639523fedf72fcb3.jpg\",\n  \"112026.jpg\": \"Insecta/Synchlora aerata/2b1f5dc7b4ec9818950ecb266f8ab142.jpg\",\n  \"112029.jpg\": \"Insecta/Synchlora aerata/fd6cd6ddeefca41b7fd4987fb994db18.jpg\",\n  \"112030.jpg\": \"Insecta/Synchlora aerata/04cb091eca0117934a202f21129e8db2.jpg\",\n  \"112031.jpg\": \"Insecta/Synchlora aerata/0fccc4617a5d510be87a4276611d204f.jpg\",\n  \"112032.jpg\": \"Insecta/Synchlora aerata/9b47c86ff3387d75c7a096fa9347fa46.jpg\",\n  \"112033.jpg\": \"Insecta/Synchlora aerata/8e32f3655f692191057fe0b218e9199c.jpg\",\n  \"112039.jpg\": \"Insecta/Synchlora aerata/0c9ee27f5ddc5a1bf008145bdebf7348.jpg\",\n  \"112040.jpg\": \"Insecta/Synchlora aerata/503f24631bd9e82f27ce556a472512ad.jpg\",\n  \"112041.jpg\": \"Insecta/Synchlora aerata/2dc55ac5c3c8873d837a82c7a7752d54.jpg\",\n  \"112042.jpg\": \"Insecta/Synchlora aerata/3c97466c0df460ee4762b4165cf56fe9.jpg\",\n  \"112043.jpg\": \"Insecta/Synchlora aerata/d1cc2a66dad444bac377a603a63751ba.jpg\",\n  \"112044.jpg\": \"Insecta/Synchlora aerata/b354cc9d696da6bbde4ee27c27f43734.jpg\",\n  \"112046.jpg\": \"Insecta/Synchlora aerata/6b51b0e5bf37dc92fd5832629220a519.jpg\",\n  \"112047.jpg\": \"Insecta/Synchlora aerata/d5db5fdeff56125d87d1d85bc8769303.jpg\",\n  \"112055.jpg\": \"Insecta/Synchlora aerata/b0ce9635118b607aae59afda450fc50c.jpg\",\n  \"112056.jpg\": \"Insecta/Synchlora aerata/7bc3908448b3d10b98f84c39f713ddd3.jpg\",\n  \"112057.jpg\": \"Insecta/Synchlora aerata/e3689a65a0fb70e2d300d8f530c8a24b.jpg\",\n  \"112058.jpg\": \"Insecta/Synchlora aerata/ee75d731dac7c21dc73bf880f253eafe.jpg\",\n  \"112065.jpg\": \"Insecta/Synchlora aerata/59b22f28cc0d9348e71c1bf3c1b98884.jpg\",\n  \"112066.jpg\": \"Insecta/Synchlora aerata/d6d1e398446751d30bb39c4a7e3535ef.jpg\",\n  \"112067.jpg\": \"Insecta/Synchlora aerata/030c310a58347424fea0d10b3fb1f525.jpg\",\n  \"112069.jpg\": \"Insecta/Synchlora aerata/3778f95eed04ded86d38eda1ea3e3cd3.jpg\",\n  \"112074.jpg\": \"Mollusca/Nucella lamellosa/33b63bc29932500d655ef8377d4e8f0e.jpg\",\n  \"112078.jpg\": \"Mollusca/Nucella lamellosa/057dca7760bfd2351874a787fe17e60d.jpg\",\n  \"112079.jpg\": \"Mollusca/Nucella lamellosa/370a5172bfa49f6aa6171351d83c0150.jpg\",\n  \"112083.jpg\": \"Mollusca/Nucella lamellosa/6bc202dd4092c926752e3597ba8dba97.jpg\",\n  \"112085.jpg\": \"Mollusca/Nucella lamellosa/dfcd9322cc5261122e5a31de693cf522.jpg\",\n  \"112086.jpg\": \"Mollusca/Nucella lamellosa/2c1c29265d02d6bf75711f782a0c5f2d.jpg\",\n  \"112094.jpg\": \"Mollusca/Nucella lamellosa/8f7b0e08c34ec5b79bd6db3f9a14628e.jpg\",\n  \"112096.jpg\": \"Mollusca/Nucella lamellosa/406da519b4514be740499a9ee1f106b7.jpg\",\n  \"112097.jpg\": \"Mollusca/Nucella lamellosa/64cfca0014ca84e60db7ba6852c39e75.jpg\",\n  \"112098.jpg\": \"Mollusca/Nucella lamellosa/3f2c1ddd54945133ac952102f7f8dcd4.jpg\",\n  \"112100.jpg\": \"Mollusca/Nucella lamellosa/5d165b54ebb62dfdd2d5907cf80ba5f5.jpg\",\n  \"112105.jpg\": \"Mollusca/Nucella lamellosa/afc41b277d332d26403e54eb6850070f.jpg\",\n  \"112106.jpg\": \"Mollusca/Nucella lamellosa/f0dbe4c7e5afb669148b772ecd35e89f.jpg\",\n  \"112109.jpg\": \"Mollusca/Nucella lamellosa/092a66163c2ee2728cfb8d451b1bc07f.jpg\",\n  \"112110.jpg\": \"Mollusca/Nucella lamellosa/7f977b5faa75ed5b63be3883553ed321.jpg\",\n  \"112111.jpg\": \"Mollusca/Nucella lamellosa/604051399723b89453b8b93496600784.jpg\",\n  \"112112.jpg\": \"Mollusca/Nucella lamellosa/db6dbfea9e67ff2a1acb1b35739b23f3.jpg\",\n  \"112113.jpg\": \"Mollusca/Nucella lamellosa/7d3b586f32f5e2ee394f59429c6a1105.jpg\",\n  \"112116.jpg\": \"Mollusca/Nucella lamellosa/118cf01046fbca6e97b56535e3f99049.jpg\",\n  \"112220.jpg\": \"Reptilia/Pituophis catenifer deserticola/63ac838ff1a995772d4fcde411aa0cac.jpg\",\n  \"112222.jpg\": \"Reptilia/Pituophis catenifer deserticola/a5869e555cc58f5770599f188d2345d2.jpg\",\n  \"112223.jpg\": \"Reptilia/Pituophis catenifer deserticola/0e39dfc2772a306421c94fc447ce3b6e.jpg\",\n  \"112226.jpg\": \"Reptilia/Pituophis catenifer deserticola/9685c09dce297637296fbc72445a2e62.jpg\",\n  \"112231.jpg\": \"Reptilia/Pituophis catenifer deserticola/5b5f8fc5299addde7dc7aecbdc481a00.jpg\",\n  \"112236.jpg\": \"Reptilia/Pituophis catenifer deserticola/c3165fc85443b8fe53ccaeebf59623c3.jpg\",\n  \"11224.jpg\": \"Amphibia/Pseudacris crucifer/547d3df46a41ed995cd913fa02f765ff.jpg\",\n  \"112260.jpg\": \"Insecta/Rivula propinqualis/ae829dc69777dfbc65fa27358aff582f.jpg\",\n  \"112263.jpg\": \"Insecta/Rivula propinqualis/9e66139b9a8e15c6fc4202be2e5da41f.jpg\",\n  \"112264.jpg\": \"Insecta/Rivula propinqualis/c870d892d4b2f8afca73d1f488dfdf13.jpg\",\n  \"112271.jpg\": \"Insecta/Rivula propinqualis/69cca0916297ab6e32b39b6ebb20bfee.jpg\",\n  \"112277.jpg\": \"Insecta/Rivula propinqualis/ff5bba3ca76835d820b023465542a5ab.jpg\",\n  \"112286.jpg\": \"Insecta/Rivula propinqualis/8577443cee9004ece3b82de999d4268a.jpg\",\n  \"112289.jpg\": \"Aves/Serinus serinus/be46c9c1f9b5ef0b892f04efbd10293f.jpg\",\n  \"112295.jpg\": \"Aves/Serinus serinus/ae8a0144e0165f2bb5e3fea220c4a16f.jpg\",\n  \"112296.jpg\": \"Aves/Serinus serinus/30bb954b687a40e0b1ea77bb1bc2418b.jpg\",\n  \"112299.jpg\": \"Aves/Serinus serinus/66be3754e02cc7372dcd4a369f98cdee.jpg\",\n  \"112305.jpg\": \"Aves/Serinus serinus/822031881c97ccb1ea8b59d5baf190f1.jpg\",\n  \"112306.jpg\": \"Aves/Serinus serinus/3f6b942ac665d48b85c22f4593a21c0f.jpg\",\n  \"11231.jpg\": \"Amphibia/Pseudacris crucifer/96208f700ec43af3ff6576acc8596888.jpg\",\n  \"112325.jpg\": \"Aves/Fregata magnificens/5cbb61a71e23dc1e8d8768bb3a7b41b3.jpg\",\n  \"112336.jpg\": \"Aves/Fregata magnificens/8983a5cba20bb2ebf27645353b5f9eb6.jpg\",\n  \"112372.jpg\": \"Aves/Fregata magnificens/fca5dd3258eb6473ec9779e4534d4eaa.jpg\",\n  \"112376.jpg\": \"Aves/Fregata magnificens/e6ef9d95793667c8764f31a864b1f991.jpg\",\n  \"112383.jpg\": \"Aves/Fregata magnificens/6a345e1bcd8557878ecf8503deb83d0f.jpg\",\n  \"112390.jpg\": \"Aves/Fregata magnificens/5ff2ff0c97124449af47f8016d6ee4e8.jpg\",\n  \"112394.jpg\": \"Aves/Fregata magnificens/37a04cc482e3d2af16f0cf208d9e4206.jpg\",\n  \"112397.jpg\": \"Aves/Fregata magnificens/756210707a8d8d30f4a241d172c76223.jpg\",\n  \"112405.jpg\": \"Aves/Fregata magnificens/943b7e306ae366f6d5d6d02023254882.jpg\",\n  \"112411.jpg\": \"Aves/Fregata magnificens/a9732126725d431d2d4bded317d39a06.jpg\",\n  \"112420.jpg\": \"Aves/Fregata magnificens/ff3f761a7fcc84056fe2865522d26184.jpg\",\n  \"112425.jpg\": \"Aves/Fregata magnificens/dade97a22a75915a71244a7158922a0d.jpg\",\n  \"112426.jpg\": \"Aves/Fregata magnificens/6add127361feff32e925fe8a32988975.jpg\",\n  \"112433.jpg\": \"Aves/Fregata magnificens/52a4a6ab68071b53eaf24c0ef9721644.jpg\",\n  \"112449.jpg\": \"Aves/Fregata magnificens/6ffc3e7e6a1b145720913c8d679a19f2.jpg\",\n  \"112455.jpg\": \"Aves/Fregata magnificens/dca84caf51a00f1d67badffb2d53fb8a.jpg\",\n  \"112456.jpg\": \"Aves/Fregata magnificens/05d24e9eb55d63279ee9ffe21a4e1530.jpg\",\n  \"112457.jpg\": \"Aves/Fregata magnificens/2d61b5e4363c9fc6a50c6f89203d3514.jpg\",\n  \"112461.jpg\": \"Aves/Fregata magnificens/c02129e2b318b03800826af46cab0cb1.jpg\",\n  \"112467.jpg\": \"Aves/Fregata magnificens/0dc349224c7fc66c16d95df08ff9a03f.jpg\",\n  \"112471.jpg\": \"Aves/Fregata magnificens/11846397bbfafd3fde082ac3fcf50e3b.jpg\",\n  \"112510.jpg\": \"Aves/Fregata magnificens/bdd76d7a853f52961e2ff4e4b1bf5415.jpg\",\n  \"112518.jpg\": \"Reptilia/Nerodia taxispilota/7d3bc3da2508b94a250bb6be879d98e8.jpg\",\n  \"11252.jpg\": \"Amphibia/Pseudacris crucifer/589d4aa715be6bd834f6bd66e3feb63f.jpg\",\n  \"112520.jpg\": \"Reptilia/Nerodia taxispilota/6fd2617b58cc4b6033ea85fcca208600.jpg\",\n  \"112527.jpg\": \"Reptilia/Nerodia taxispilota/1ddf1c5ac874152799064d28230b0b9f.jpg\",\n  \"112534.jpg\": \"Reptilia/Nerodia taxispilota/51f648f64fc78a7e6248cc7bbfd2032c.jpg\",\n  \"112548.jpg\": \"Animalia/Triakis semifasciata/ee608cf0f7c0f853a880687f69be73f1.jpg\",\n  \"112558.jpg\": \"Aves/Microcarbo melanoleucos brevirostris/b3c176a49ed25021c8821ee5bb75e77d.jpg\",\n  \"112559.jpg\": \"Aves/Microcarbo melanoleucos brevirostris/d0087e89fd8a3165a2ba1de03145e99e.jpg\",\n  \"112560.jpg\": \"Aves/Microcarbo melanoleucos brevirostris/2947dc1fa1d88cb0be57dbb680082611.jpg\",\n  \"112564.jpg\": \"Aves/Microcarbo melanoleucos brevirostris/4ce3444a5487d681b7020542c0f935a9.jpg\",\n  \"112565.jpg\": \"Aves/Microcarbo melanoleucos brevirostris/88262667b62fd803d7ada71f3f7f8cf3.jpg\",\n  \"112573.jpg\": \"Aves/Microcarbo melanoleucos brevirostris/bf974cf60434a42d57747a86b670363d.jpg\",\n  \"112582.jpg\": \"Aves/Microcarbo melanoleucos brevirostris/465ceb57b8b0cb37882daa5488f8c808.jpg\",\n  \"112586.jpg\": \"Aves/Microcarbo melanoleucos brevirostris/297dae6c2c94a9f32aa3a1f47c1c1913.jpg\",\n  \"11259.jpg\": \"Amphibia/Pseudacris crucifer/a1b2856cbb55d36b1e2b8e62c6f661f4.jpg\",\n  \"112591.jpg\": \"Aves/Microcarbo melanoleucos brevirostris/b3d75627ba18aaba02a8b68ad67553b1.jpg\",\n  \"112598.jpg\": \"Aves/Microcarbo melanoleucos brevirostris/169f43150041daeb76b41b67e297e7eb.jpg\",\n  \"11261.jpg\": \"Amphibia/Pseudacris crucifer/5e5ff2d660ab3e23cfb468abf8411fda.jpg\",\n  \"112610.jpg\": \"Aves/Microcarbo melanoleucos brevirostris/5613e787449e55650b9bb09ab34f7eb3.jpg\",\n  \"112617.jpg\": \"Aves/Microcarbo melanoleucos brevirostris/464ac77cc638aef14e70b223ec7aa03c.jpg\",\n  \"112625.jpg\": \"Aves/Microcarbo melanoleucos brevirostris/3a804b75322f67a521569301b4f78682.jpg\",\n  \"112628.jpg\": \"Insecta/Pieris brassicae/17992e113f62e6a5a4e9f9fa53761dab.jpg\",\n  \"112630.jpg\": \"Insecta/Pieris brassicae/a84810a2d3b58cc25d79184afed83fda.jpg\",\n  \"112633.jpg\": \"Insecta/Pieris brassicae/ee96f78dacd722ee7223ebb8f3b88313.jpg\",\n  \"112636.jpg\": \"Insecta/Pieris brassicae/4d65d3acf80fe1316b493df72cd19b35.jpg\",\n  \"112639.jpg\": \"Insecta/Pieris brassicae/4a9c5def113535616dc0e0d8cc452eb2.jpg\",\n  \"112644.jpg\": \"Insecta/Pieris brassicae/449e55c5f78da581af8236849470fae7.jpg\",\n  \"112648.jpg\": \"Insecta/Pieris brassicae/d142db6775aabc9076bdcf8eb0bfb980.jpg\",\n  \"11266.jpg\": \"Amphibia/Pseudacris crucifer/03cb417bc2c5166f29d3bbcae28518d5.jpg\",\n  \"112660.jpg\": \"Insecta/Pieris brassicae/da069366e067bdbc562b5bba7316c2d0.jpg\",\n  \"112714.jpg\": \"Insecta/Naupactus cervinus/949a337dc61c5228db910d1c88816fd2.jpg\",\n  \"112715.jpg\": \"Insecta/Naupactus cervinus/e83248490b5d7cb640b1206fe05116cf.jpg\",\n  \"112716.jpg\": \"Insecta/Naupactus cervinus/350de5b2dcd04c90c0298e2e1059b178.jpg\",\n  \"112718.jpg\": \"Insecta/Naupactus cervinus/e05042734aa5e4b4ed5cdb1f395fda62.jpg\",\n  \"112727.jpg\": \"Actinopterygii/Diodon hystrix/f0437880925d48930ec80c4364eddf6f.jpg\",\n  \"112728.jpg\": \"Actinopterygii/Diodon hystrix/ad906742044048ed8176250dcb15e387.jpg\",\n  \"112729.jpg\": \"Actinopterygii/Diodon hystrix/6dea780b53d58941294807b8957e36c0.jpg\",\n  \"112735.jpg\": \"Actinopterygii/Diodon hystrix/9d7b3438cbd6fdd9a66c9ff398eaab46.jpg\",\n  \"112766.jpg\": \"Insecta/Actias luna/af8da510849a99ec74f0f0dd1e22b987.jpg\",\n  \"112770.jpg\": \"Insecta/Actias luna/02ae0d5b0a357616463dc5144e4f965a.jpg\",\n  \"112781.jpg\": \"Insecta/Actias luna/f6f3eca5c2c87cde14a373a9eacc4b62.jpg\",\n  \"112783.jpg\": \"Insecta/Actias luna/377caea2121f997c4c5b0d20c057727c.jpg\",\n  \"112794.jpg\": \"Insecta/Actias luna/27342650d207b0968310a59c56661306.jpg\",\n  \"11280.jpg\": \"Amphibia/Pseudacris crucifer/946586b79519f2d37e59c7ad9af9d95e.jpg\",\n  \"112822.jpg\": \"Insecta/Actias luna/befe2fef8866217df0f2e1d03c8254e3.jpg\",\n  \"11283.jpg\": \"Amphibia/Pseudacris crucifer/17d8e7eafd6050975f36bdcde63793fa.jpg\",\n  \"11284.jpg\": \"Amphibia/Pseudacris crucifer/f8181f6d2aabfb12e6016f96f20fdd33.jpg\",\n  \"112843.jpg\": \"Insecta/Actias luna/1a5bbd27903a4e6a550700f8da47c333.jpg\",\n  \"112855.jpg\": \"Insecta/Actias luna/dccb858ed8308df572a35c8d2efbc2f3.jpg\",\n  \"112863.jpg\": \"Insecta/Actias luna/5c89bb164018970d981c25cced17de32.jpg\",\n  \"112877.jpg\": \"Insecta/Actias luna/81480ef198011033a4d9027fb202716e.jpg\",\n  \"112908.jpg\": \"Insecta/Actias luna/45b6d36f412e196f8274c2dea909835b.jpg\",\n  \"112909.jpg\": \"Insecta/Actias luna/2984275cc4f4de99047e1fba95e4984c.jpg\",\n  \"112912.jpg\": \"Insecta/Actias luna/b55908eca280910c77c7dd19d6602728.jpg\",\n  \"11292.jpg\": \"Amphibia/Pseudacris crucifer/ba69c2a30be12f2a936615a8e508e1db.jpg\",\n  \"112927.jpg\": \"Insecta/Actias luna/2bb77840f6376d2e93ccd1e975520749.jpg\",\n  \"112929.jpg\": \"Insecta/Actias luna/8ea9dd3b5129a9904b2e4edaa44eed61.jpg\",\n  \"112935.jpg\": \"Insecta/Actias luna/01b00bf0869c3a3efbfc8fa3a3b0a5ed.jpg\",\n  \"112939.jpg\": \"Insecta/Actias luna/076c7f153fccc912aaf329abb2131da3.jpg\",\n  \"112970.jpg\": \"Insecta/Actias luna/cb742a4f9b836f168883c26a26955202.jpg\",\n  \"113052.jpg\": \"Insecta/Antaeotricha schlaegeri/09f344ddf88de040cc60431265095e4f.jpg\",\n  \"113058.jpg\": \"Insecta/Antaeotricha schlaegeri/90b2c5057b4241e132b272e2d5ed6dc3.jpg\",\n  \"113059.jpg\": \"Insecta/Antaeotricha schlaegeri/7937716dbcc7aed1900400674d04c6c4.jpg\",\n  \"113061.jpg\": \"Insecta/Antaeotricha schlaegeri/62c6a43a6c875102ebbc15b2d80049fc.jpg\",\n  \"113062.jpg\": \"Insecta/Antaeotricha schlaegeri/832cd550b2c0787ed91962719538550c.jpg\",\n  \"11311.jpg\": \"Amphibia/Pseudacris crucifer/ef5803e5045eb01fe3b898fb148303d2.jpg\",\n  \"11318.jpg\": \"Amphibia/Pseudacris crucifer/f410754aafa671cc8a2dfb20ed0523b9.jpg\",\n  \"11319.jpg\": \"Amphibia/Pseudacris crucifer/ab96c6c9996b11a0c27698e41a2d515a.jpg\",\n  \"11320.jpg\": \"Aves/Trogon caligatus/bb3c650fef438a5a17ff03ed07ee934c.jpg\",\n  \"11321.jpg\": \"Aves/Trogon caligatus/e0bdab42102bfec830c21f3b04d876c8.jpg\",\n  \"113212.jpg\": \"Mammalia/Tamias striatus/647daacf45a9062ea117bd415bbed9e7.jpg\",\n  \"113217.jpg\": \"Mammalia/Tamias striatus/5e381bb039cae3f532188f91bc12253c.jpg\",\n  \"11322.jpg\": \"Aves/Trogon caligatus/6da1266ad56785e473db77a859653424.jpg\",\n  \"113224.jpg\": \"Mammalia/Tamias striatus/eab5d3006b95dbd6ac46f2b337968d3c.jpg\",\n  \"11324.jpg\": \"Aves/Trogon caligatus/2c84af224c8a7b9bb7b0389684f32896.jpg\",\n  \"113248.jpg\": \"Mammalia/Tamias striatus/951ef98ed884cae2f1cba9a0358b68a5.jpg\",\n  \"113249.jpg\": \"Mammalia/Tamias striatus/9b58a6fc9f13daf1cc64d43e9d4d7305.jpg\",\n  \"113257.jpg\": \"Mammalia/Tamias striatus/cad22585055e4ba319d26abe4352088a.jpg\",\n  \"113276.jpg\": \"Mammalia/Tamias striatus/cef0f56567f1db0ac14845b29603505c.jpg\",\n  \"11328.jpg\": \"Aves/Trogon caligatus/f31a3e3ddfce1f2031e70a455579b4e6.jpg\",\n  \"113287.jpg\": \"Mammalia/Tamias striatus/ef8257d4dd9741500292bd0cb66a4d72.jpg\",\n  \"113295.jpg\": \"Mammalia/Tamias striatus/87e218b6783992df3e0be3283b06df0d.jpg\",\n  \"113310.jpg\": \"Mammalia/Tamias striatus/deab876221bb79053c356fac9258cc50.jpg\",\n  \"11332.jpg\": \"Aves/Trogon caligatus/286998678d97a52b9d11be40d9cbbff2.jpg\",\n  \"113327.jpg\": \"Mammalia/Tamias striatus/054c21bed4c01ebf58cf3f8fc24a0d46.jpg\",\n  \"113337.jpg\": \"Mammalia/Tamias striatus/7d236e0ddbdbf7d89f6600e304250b26.jpg\",\n  \"113339.jpg\": \"Mammalia/Tamias striatus/f711da7a563455014b5c255f7b04e471.jpg\",\n  \"113342.jpg\": \"Mammalia/Tamias striatus/8630e475b29d889432e84fbd323069a0.jpg\",\n  \"11338.jpg\": \"Aves/Trogon caligatus/4d7b9e216be0daf89c758d72c7db7c97.jpg\",\n  \"11339.jpg\": \"Aves/Trogon caligatus/a898f98a9165c7407878d9768e117768.jpg\",\n  \"113403.jpg\": \"Mammalia/Tamias striatus/0f906f64010055032f31909582037834.jpg\",\n  \"113407.jpg\": \"Mammalia/Tamias striatus/93e352da609127a6def460038c44d2f3.jpg\",\n  \"11342.jpg\": \"Aves/Trogon caligatus/1cd7b330f4c9da63fe3bb8fe0be336fa.jpg\",\n  \"113424.jpg\": \"Mammalia/Tamias striatus/afbadc052c4030fc1460ae64f3e04b40.jpg\",\n  \"11343.jpg\": \"Aves/Trogon caligatus/37aa94dba188942a88df5e4ccb9726b9.jpg\",\n  \"113461.jpg\": \"Mammalia/Tamias striatus/f727cfbbe70d667fcf10d4e063f359d6.jpg\",\n  \"11350.jpg\": \"Reptilia/Sonora semiannulata/094a795482ae7334f3b5202ce1c07a6d.jpg\",\n  \"113507.jpg\": \"Mammalia/Tamias striatus/6b6a82602a519b2df5401080c6300d5d.jpg\",\n  \"11351.jpg\": \"Reptilia/Sonora semiannulata/79f82af2ee280d808b9cca346cdb5b37.jpg\",\n  \"113512.jpg\": \"Mammalia/Tamias striatus/26d876ee3e28d12689703523743ba5df.jpg\",\n  \"113521.jpg\": \"Insecta/Diphthera festiva/3d8829a6473eae9ded1515ab95d73270.jpg\",\n  \"113530.jpg\": \"Insecta/Diphthera festiva/4a89e7b7a5d24db83fd02ae14693a8d0.jpg\",\n  \"113533.jpg\": \"Insecta/Diphthera festiva/28e42f7a2092b0e7ec4e4cb50cd19c69.jpg\",\n  \"113544.jpg\": \"Insecta/Diphthera festiva/11e5af570357c3026b911fb394e8bf39.jpg\",\n  \"113545.jpg\": \"Insecta/Diphthera festiva/3fea14c4def559c35d18bf4de371693e.jpg\",\n  \"113565.jpg\": \"Arachnida/Aphonopelma iodius/848ea3af1d49ad6c7c670deb1bcffa8a.jpg\",\n  \"113578.jpg\": \"Arachnida/Aphonopelma iodius/c1eaaabbd754b50a63888b98e13945f4.jpg\",\n  \"11358.jpg\": \"Reptilia/Sonora semiannulata/29068fbbd4479143d6c8a11f78bca2e5.jpg\",\n  \"113580.jpg\": \"Arachnida/Aphonopelma iodius/579f004bf8053f5974490e07ee247610.jpg\",\n  \"113581.jpg\": \"Arachnida/Aphonopelma iodius/47e56d60588ae2448a0f197974c203ec.jpg\",\n  \"113583.jpg\": \"Arachnida/Aphonopelma iodius/6b3a37b614b9b08fcf365826ddff13ab.jpg\",\n  \"11359.jpg\": \"Reptilia/Sonora semiannulata/868a08ce7d16e1634b025818ee9ec61a.jpg\",\n  \"11360.jpg\": \"Reptilia/Sonora semiannulata/c6dc4230c4f9515b056de2c4e9374b75.jpg\",\n  \"11364.jpg\": \"Reptilia/Sonora semiannulata/2e64a669f700a1bb4255539a7f01485d.jpg\",\n  \"11365.jpg\": \"Reptilia/Sonora semiannulata/dafdec9c49f453d6c9665c190bdf8166.jpg\",\n  \"11367.jpg\": \"Reptilia/Sonora semiannulata/8b74c3aeaf52745806b2ba700bf3de64.jpg\",\n  \"11368.jpg\": \"Reptilia/Sonora semiannulata/f21cd2cb0a8d6d5c2ed8c29e1159394f.jpg\",\n  \"11370.jpg\": \"Reptilia/Sonora semiannulata/14de4b49896f7dddb13c5d697b6aedb2.jpg\",\n  \"11371.jpg\": \"Reptilia/Sonora semiannulata/94a63b65d11d1d75a2a8225f22134ce5.jpg\",\n  \"11372.jpg\": \"Reptilia/Sonora semiannulata/4c8627ccab25d47cad2ae6a1cb0b2a37.jpg\",\n  \"11374.jpg\": \"Reptilia/Sonora semiannulata/a975abd61ab10d1d576a5d6a62c11861.jpg\",\n  \"11375.jpg\": \"Reptilia/Sonora semiannulata/bfd94a13435f3dea609172bdde3c9b58.jpg\",\n  \"113756.jpg\": \"Insecta/Ochlodes agricola/3068a52b81e2b57df733b64b1c82b33a.jpg\",\n  \"113757.jpg\": \"Insecta/Ochlodes agricola/1eaba8140fc3a579fefd4ef29d3a8028.jpg\",\n  \"113761.jpg\": \"Insecta/Ochlodes agricola/8c68abdde0219641d8d112de13f9b0fa.jpg\",\n  \"113767.jpg\": \"Insecta/Ochlodes agricola/d011547bf8ee945f76c53adeaf5c8c7b.jpg\",\n  \"11377.jpg\": \"Reptilia/Sonora semiannulata/9daa693ad692c72ebe11a18865723155.jpg\",\n  \"113775.jpg\": \"Insecta/Ochlodes agricola/40cc0bb3d4b45f04817475a4b5dc358f.jpg\",\n  \"113778.jpg\": \"Insecta/Ochlodes agricola/5fd87f3db5eb153e6e998aceb3efa085.jpg\",\n  \"113785.jpg\": \"Insecta/Ochlodes agricola/1aa0ddfbff6ed076f95b0eeb5e5f390e.jpg\",\n  \"113788.jpg\": \"Aves/Mitrephanes phaeocercus/e711f1cdaf22a0b0b1b9b6d39341d5e2.jpg\",\n  \"113794.jpg\": \"Aves/Mitrephanes phaeocercus/a5781f81dcdfd7871a168d97b9fd3c50.jpg\",\n  \"113795.jpg\": \"Aves/Mitrephanes phaeocercus/83b4f425221af91c2fa30c52d4375f40.jpg\",\n  \"113800.jpg\": \"Aves/Mitrephanes phaeocercus/880ce4dc059098392307ab1a7d480b8b.jpg\",\n  \"11381.jpg\": \"Reptilia/Sonora semiannulata/9dc3f6c1625b4c3fb47f9d08fbaeaf8f.jpg\",\n  \"113810.jpg\": \"Arachnida/Phidippus johnsoni/850cbf12314451fbde0ab21d9c55430a.jpg\",\n  \"113814.jpg\": \"Arachnida/Phidippus johnsoni/110b2a0c7a777fec90050c55a8356e86.jpg\",\n  \"11382.jpg\": \"Reptilia/Sonora semiannulata/8f28d6b7e76a8ce1c98475ebd322deb5.jpg\",\n  \"113821.jpg\": \"Arachnida/Phidippus johnsoni/40fe521674a06af997b76469d7fa83c3.jpg\",\n  \"113822.jpg\": \"Arachnida/Phidippus johnsoni/53b78af7f9d91c7492cc4222ec064981.jpg\",\n  \"113824.jpg\": \"Arachnida/Phidippus johnsoni/578bac44937fceeb57b85cdd7a003514.jpg\",\n  \"113825.jpg\": \"Arachnida/Phidippus johnsoni/0409b3546962ee4a3b09d81f5701871c.jpg\",\n  \"113826.jpg\": \"Arachnida/Phidippus johnsoni/e0334144fcdf3ddf98b2aef6281ec8c7.jpg\",\n  \"113830.jpg\": \"Arachnida/Phidippus johnsoni/f65015410218cbb95aedb41ca313c54e.jpg\",\n  \"113833.jpg\": \"Arachnida/Phidippus johnsoni/1c15aa0742baa082ecb990f14d44fb21.jpg\",\n  \"113839.jpg\": \"Arachnida/Phidippus johnsoni/96c58c702393b5003d8380a785cab0d4.jpg\",\n  \"113844.jpg\": \"Arachnida/Phidippus johnsoni/56ebaa4a5ba73c6f2645388155000b5e.jpg\",\n  \"113848.jpg\": \"Arachnida/Phidippus johnsoni/557d1b4c5835db9ec2852ca08d975397.jpg\",\n  \"113849.jpg\": \"Arachnida/Phidippus johnsoni/4a3e9e6cd3cff73408b8e2aa9c00994b.jpg\",\n  \"113850.jpg\": \"Arachnida/Phidippus johnsoni/497c46efd25ea723ebf566b340d11b46.jpg\",\n  \"113851.jpg\": \"Arachnida/Phidippus johnsoni/0b0bd23f61d989ddc89af0feee7ff247.jpg\",\n  \"113852.jpg\": \"Arachnida/Phidippus johnsoni/cac3480a88150f28e0959927f895b402.jpg\",\n  \"113860.jpg\": \"Arachnida/Phidippus johnsoni/ef188b3669509e4520204cb3418a8f68.jpg\",\n  \"113861.jpg\": \"Arachnida/Phidippus johnsoni/7b10080a9073d407521a3bd9b58bdd4b.jpg\",\n  \"113862.jpg\": \"Arachnida/Phidippus johnsoni/eeb163f4b6eba09fd51d9585dfadb836.jpg\",\n  \"113863.jpg\": \"Arachnida/Phidippus johnsoni/391b0d1669972517c094db98385f3203.jpg\",\n  \"11387.jpg\": \"Reptilia/Sonora semiannulata/7c81ee38364272cb4e6f672d24a5c7c1.jpg\",\n  \"113885.jpg\": \"Reptilia/Pseudemys concinna/45fca4b2689e88214cc4fe4064aa908d.jpg\",\n  \"113894.jpg\": \"Reptilia/Pseudemys concinna/239126ca3b6a7f056ffa74857701948d.jpg\",\n  \"113898.jpg\": \"Reptilia/Pseudemys concinna/fb7b145df543753030d879e9c92eb130.jpg\",\n  \"11392.jpg\": \"Reptilia/Sonora semiannulata/7d26d31832b66d2a1bcc0d5a884d5434.jpg\",\n  \"11394.jpg\": \"Reptilia/Sonora semiannulata/3da9ce50495282330d7d91720f584a92.jpg\",\n  \"11395.jpg\": \"Reptilia/Sonora semiannulata/955aacb93deb2039335cf3a3eb42f49f.jpg\",\n  \"113985.jpg\": \"Aves/Egretta rufescens/26832d07ee944e0b425c3e4e3334ba12.jpg\",\n  \"113988.jpg\": \"Aves/Egretta rufescens/2fe077de7bfcd37e320465b77dfbee37.jpg\",\n  \"113993.jpg\": \"Aves/Egretta rufescens/515255c8e12e4a5401e84fa281bde910.jpg\",\n  \"11400.jpg\": \"Insecta/Anax longipes/a997f40a1222045b423ebd11b9ee615a.jpg\",\n  \"114004.jpg\": \"Aves/Egretta rufescens/1d7c9f8e35c935366b928b23d95b5b52.jpg\",\n  \"114016.jpg\": \"Aves/Egretta rufescens/4e70969beae4aa166114e67ae1c68b54.jpg\",\n  \"114025.jpg\": \"Aves/Egretta rufescens/08cce722ce1c00c91001d2025b09660d.jpg\",\n  \"114042.jpg\": \"Aves/Egretta rufescens/9412e0e3bfbec893a9bd4b6d22a2c582.jpg\",\n  \"114047.jpg\": \"Aves/Egretta rufescens/b9409dc87ae89bb49fb3f7efe50d6f13.jpg\",\n  \"114053.jpg\": \"Aves/Egretta rufescens/02bc38ff4c0e7e98ca516647417513ec.jpg\",\n  \"114058.jpg\": \"Aves/Egretta rufescens/bdb0743122deaabdc37a92c8ecdf43c3.jpg\",\n  \"114060.jpg\": \"Aves/Egretta rufescens/1e92b5a2389880eb1b987269015f3905.jpg\",\n  \"114061.jpg\": \"Aves/Egretta rufescens/c17c31fff0a20dc39b4026ab41c8a5f4.jpg\",\n  \"114062.jpg\": \"Aves/Egretta rufescens/d517bd6ef31d5417d3827cff7ac151c2.jpg\",\n  \"114065.jpg\": \"Aves/Egretta rufescens/fd2a3fd14c9414564561de65f1045283.jpg\",\n  \"114074.jpg\": \"Aves/Egretta rufescens/f5fff377e94696738bc6b80a4f181fb3.jpg\",\n  \"114076.jpg\": \"Aves/Egretta rufescens/13b50181312eda2ecff506718d8e5bd8.jpg\",\n  \"114111.jpg\": \"Aves/Egretta rufescens/a1c67bc51521058099a053d5b39351d6.jpg\",\n  \"114119.jpg\": \"Aves/Egretta rufescens/0a634c95a7294dbe6b51f552384b2dd3.jpg\",\n  \"11412.jpg\": \"Insecta/Anax longipes/e0555ba640513de9ab39d6d2177ca256.jpg\",\n  \"114141.jpg\": \"Aves/Egretta rufescens/d45c1d928f948a999b97a67069f0846e.jpg\",\n  \"114143.jpg\": \"Aves/Penelope purpurascens/a64687d21befb24d146d4f19e7258bc9.jpg\",\n  \"114144.jpg\": \"Aves/Penelope purpurascens/5de9dbdba3326db190ef24f477586f32.jpg\",\n  \"114145.jpg\": \"Aves/Penelope purpurascens/3c06c9e30de9907abe0f899741b03b0e.jpg\",\n  \"114152.jpg\": \"Aves/Penelope purpurascens/b08f54ff34d2a5a8718db27351936ce0.jpg\",\n  \"114155.jpg\": \"Aves/Penelope purpurascens/01878d976461052c36b392652694b014.jpg\",\n  \"114160.jpg\": \"Aves/Penelope purpurascens/dbbbfa07431958b8646300053ca72f45.jpg\",\n  \"114166.jpg\": \"Aves/Penelope purpurascens/aece21c1baf73d688d4e096d57fcc81e.jpg\",\n  \"114201.jpg\": \"Insecta/Bombus huntii/d02e8d86b89c94b5bdda2b95d60dacac.jpg\",\n  \"114208.jpg\": \"Insecta/Bombus huntii/275656253f926fc7472afd747dd3e31e.jpg\",\n  \"114214.jpg\": \"Insecta/Bombus huntii/ce4306da424744eb2aeb26fa5952dd38.jpg\",\n  \"11423.jpg\": \"Insecta/Anax longipes/0da3cff15b9e59869e05d8c1e2509223.jpg\",\n  \"11424.jpg\": \"Insecta/Anax longipes/e1e99a5135200f81b92358348eb4989b.jpg\",\n  \"114301.jpg\": \"Mammalia/Oryctolagus cuniculus/3ba86262170e7428b845551a9103b058.jpg\",\n  \"114306.jpg\": \"Mammalia/Oryctolagus cuniculus/64dafb29026b7ba6177c5d914414684d.jpg\",\n  \"114311.jpg\": \"Mammalia/Oryctolagus cuniculus/78dc2ff906a9efa43f0d14770679f166.jpg\",\n  \"114317.jpg\": \"Mammalia/Oryctolagus cuniculus/e6a30aba610958cd0e4978226844f200.jpg\",\n  \"114319.jpg\": \"Mammalia/Oryctolagus cuniculus/87026b095e99d1acaaea2acabfb36f8d.jpg\",\n  \"114321.jpg\": \"Mammalia/Oryctolagus cuniculus/4d50f23d0e8008e60ace6ace6f3a9bed.jpg\",\n  \"114328.jpg\": \"Mammalia/Oryctolagus cuniculus/03648814fc235410d3268964b0a3943e.jpg\",\n  \"114329.jpg\": \"Mammalia/Oryctolagus cuniculus/be8b9dbfc8befa4535027319ffb1af5b.jpg\",\n  \"114342.jpg\": \"Mammalia/Oryctolagus cuniculus/9dc64448c32d257f09b7a642526b6971.jpg\",\n  \"114347.jpg\": \"Mammalia/Oryctolagus cuniculus/5daa50a6c1fb0db078d39da50598e2e4.jpg\",\n  \"114348.jpg\": \"Mammalia/Oryctolagus cuniculus/dc094348a47568e318d8f8259e3eb91d.jpg\",\n  \"114352.jpg\": \"Mammalia/Oryctolagus cuniculus/a8ebd3af9aacd16a2471ae7ffa68736b.jpg\",\n  \"114354.jpg\": \"Mammalia/Oryctolagus cuniculus/df31596719682597bad8da52d1578cac.jpg\",\n  \"114360.jpg\": \"Mammalia/Oryctolagus cuniculus/4c0b38367a511d2e0c09b631b2302848.jpg\",\n  \"114362.jpg\": \"Mammalia/Oryctolagus cuniculus/49a58052ae6a683c8e9fa87962071006.jpg\",\n  \"114372.jpg\": \"Insecta/Nymphalis antiopa/d49860faa1fd46ed6a6952018af0ae04.jpg\",\n  \"114385.jpg\": \"Insecta/Nymphalis antiopa/6e8e176e6d5aa58b12f17d4966e739e5.jpg\",\n  \"114410.jpg\": \"Insecta/Nymphalis antiopa/4f6b6a004c414060d029b8719af463c5.jpg\",\n  \"114411.jpg\": \"Insecta/Nymphalis antiopa/a2bfcee6daa3cfd89afe06d0cec1d284.jpg\",\n  \"114416.jpg\": \"Insecta/Nymphalis antiopa/d832dc25b6708a89647dd9d3a705aac7.jpg\",\n  \"114417.jpg\": \"Insecta/Nymphalis antiopa/a5923c0cefcc253bce30643e6dba3701.jpg\",\n  \"114426.jpg\": \"Insecta/Nymphalis antiopa/1652a53405041bb1e75621fcb4e8545e.jpg\",\n  \"114505.jpg\": \"Insecta/Nymphalis antiopa/e59908227410e02b38731e2f78707235.jpg\",\n  \"114519.jpg\": \"Insecta/Nymphalis antiopa/5cdcf76504da8f294959e2cbe2623572.jpg\",\n  \"114526.jpg\": \"Insecta/Nymphalis antiopa/eaa8b6946adb186b0d1c8c1ef8fa5bad.jpg\",\n  \"114547.jpg\": \"Insecta/Nymphalis antiopa/653534f624492e3d13ee8077ccdb7ecf.jpg\",\n  \"114583.jpg\": \"Insecta/Nymphalis antiopa/ad947077f906181481a37e504b627687.jpg\",\n  \"114592.jpg\": \"Insecta/Nymphalis antiopa/7fe38a0b136e933edd81677a2412ec66.jpg\",\n  \"114599.jpg\": \"Insecta/Nymphalis antiopa/6b0f6056f7b79f49c1709dcb3d2d9e63.jpg\",\n  \"114605.jpg\": \"Insecta/Nymphalis antiopa/7ac03b2eb34e5d645a7bd86aa3afd84f.jpg\",\n  \"114612.jpg\": \"Insecta/Nymphalis antiopa/a988c5fc57f5ab9ac6dbfd9198e6caab.jpg\",\n  \"114628.jpg\": \"Insecta/Nymphalis antiopa/1c6222ed98411a0eb9371853fad12e06.jpg\",\n  \"114696.jpg\": \"Insecta/Nymphalis antiopa/c7b5318491f032ec6df622016a0696cf.jpg\",\n  \"114729.jpg\": \"Insecta/Nymphalis antiopa/b642a12f06827a0f40b54110df387f6d.jpg\",\n  \"114731.jpg\": \"Insecta/Nymphalis antiopa/a0fe4509d49a1a1585b0dfcd691b2249.jpg\",\n  \"114742.jpg\": \"Aves/Grallina cyanoleuca/c8233a775a98626b635c63994c1392bf.jpg\",\n  \"114745.jpg\": \"Aves/Grallina cyanoleuca/87aefbf397d3fe48f9bb9f63c38e0e2e.jpg\",\n  \"114753.jpg\": \"Aves/Grallina cyanoleuca/c9a4f1530c3ecb626927d3170f64e696.jpg\",\n  \"114757.jpg\": \"Aves/Grallina cyanoleuca/5a8325f68f12730a147f165decd8d86f.jpg\",\n  \"114758.jpg\": \"Aves/Grallina cyanoleuca/706d2c762905318ce655ae76a6a345af.jpg\",\n  \"114759.jpg\": \"Aves/Grallina cyanoleuca/d7d8bc1ead110ca2c69e3eb3918f2a37.jpg\",\n  \"114760.jpg\": \"Aves/Grallina cyanoleuca/a0a09a77cf4040f56a20e476e94c73cc.jpg\",\n  \"114761.jpg\": \"Aves/Grallina cyanoleuca/a087f4d47b2839a9ba0652ba295e1765.jpg\",\n  \"114764.jpg\": \"Aves/Grallina cyanoleuca/29a8776105b78774d1928bf13b77b777.jpg\",\n  \"114765.jpg\": \"Aves/Grallina cyanoleuca/c244b4abd64eac485c5aa736e50c704c.jpg\",\n  \"114771.jpg\": \"Aves/Grallina cyanoleuca/81fecf731cb0b1ccb1c96cfef928aa7c.jpg\",\n  \"114772.jpg\": \"Aves/Grallina cyanoleuca/59d8720fdebe1bfb58628ea9958dc760.jpg\",\n  \"114774.jpg\": \"Aves/Grallina cyanoleuca/3d973c72392fbf28e3e7901af8674b92.jpg\",\n  \"114775.jpg\": \"Aves/Grallina cyanoleuca/d47974c90f9d8a60bd82415c3558cf36.jpg\",\n  \"114776.jpg\": \"Aves/Grallina cyanoleuca/4d7ff0e24b928f2e80dd598a6d824b5b.jpg\",\n  \"114779.jpg\": \"Aves/Grallina cyanoleuca/8b34d555f432e5794ac86ac3727b4d99.jpg\",\n  \"114782.jpg\": \"Aves/Grallina cyanoleuca/dc3eeb9e9fe7c8a092338def4dcc8c3e.jpg\",\n  \"114783.jpg\": \"Aves/Grallina cyanoleuca/e07c44d444022e0a04c21531e7869112.jpg\",\n  \"114785.jpg\": \"Aves/Grallina cyanoleuca/0d84a2abf8d0f76c8b1d230f1f307db1.jpg\",\n  \"114788.jpg\": \"Aves/Threskiornis moluccus/d90933d1c08aade923a4c0864be12421.jpg\",\n  \"114792.jpg\": \"Aves/Threskiornis moluccus/f546ef9b27c98af27803c6b8381414cb.jpg\",\n  \"114804.jpg\": \"Aves/Threskiornis moluccus/0c7c5b8f4395646a335b6baf876f84fd.jpg\",\n  \"114805.jpg\": \"Aves/Threskiornis moluccus/34c86bc130fd8b79d4610c2964d603a4.jpg\",\n  \"114806.jpg\": \"Aves/Threskiornis moluccus/97705b3ec848c33f41c5eee550dae21f.jpg\",\n  \"114809.jpg\": \"Aves/Threskiornis moluccus/dcc50f69cc068fe3a550cf0588bb3fad.jpg\",\n  \"114810.jpg\": \"Aves/Threskiornis moluccus/1303f03903f730fff3261afd67493fb8.jpg\",\n  \"114811.jpg\": \"Aves/Threskiornis moluccus/9aa1a6ab02fec76b201fdf6abe5903b5.jpg\",\n  \"114813.jpg\": \"Aves/Threskiornis moluccus/98818eb8bdaacee3ad737834bf81822d.jpg\",\n  \"114818.jpg\": \"Aves/Threskiornis moluccus/0a9a80e41eacfc2e80c55576b203658d.jpg\",\n  \"114820.jpg\": \"Aves/Threskiornis moluccus/edb2ef24a85514029852f199b94e2aa8.jpg\",\n  \"114827.jpg\": \"Aves/Threskiornis moluccus/4579123302afd4517446e041fcb1f1be.jpg\",\n  \"114828.jpg\": \"Aves/Threskiornis moluccus/29b95f1613c630fc8d16629e1f48636f.jpg\",\n  \"115.jpg\": \"Mammalia/Marmota flaviventris/433fcdb5ddfbf494c51b1136167e0c29.jpg\",\n  \"115044.jpg\": \"Reptilia/Pituophis catenifer sayi/cce8832adce947473b386548d7dc4022.jpg\",\n  \"115048.jpg\": \"Reptilia/Pituophis catenifer sayi/7555c944e42cc5b6e5dbd2a084ad7c27.jpg\",\n  \"115052.jpg\": \"Reptilia/Pituophis catenifer sayi/73c802a7a5164fb1fce1bd33ec457f91.jpg\",\n  \"115055.jpg\": \"Reptilia/Pituophis catenifer sayi/83101a6a7e7c66a17d022604db73b80f.jpg\",\n  \"115056.jpg\": \"Reptilia/Pituophis catenifer sayi/3cc4845bed93a576c23090ce73a7a660.jpg\",\n  \"115057.jpg\": \"Reptilia/Pituophis catenifer sayi/fe54dd73e4c4c81ece51c515356ca87b.jpg\",\n  \"115060.jpg\": \"Reptilia/Pituophis catenifer sayi/41b2b37c7fe6a33df07cf262361836f7.jpg\",\n  \"115061.jpg\": \"Reptilia/Pituophis catenifer sayi/37b89dcd1cf65a709a73b17888732409.jpg\",\n  \"115062.jpg\": \"Reptilia/Pituophis catenifer sayi/e7d2cd8cb48ceba77a9a24cf1989d27e.jpg\",\n  \"115063.jpg\": \"Reptilia/Pituophis catenifer sayi/fd8472e7c6bb94cac338bc9d60bebf62.jpg\",\n  \"115068.jpg\": \"Reptilia/Pituophis catenifer sayi/4295650839e88d8dbeba3189d49cb173.jpg\",\n  \"115072.jpg\": \"Reptilia/Pituophis catenifer sayi/cdd5f73e7efcb58d5785e72c6e1a5ae4.jpg\",\n  \"115073.jpg\": \"Reptilia/Pituophis catenifer sayi/5df96aa9d41f9c10bf7941cd36bdb659.jpg\",\n  \"115075.jpg\": \"Reptilia/Pituophis catenifer sayi/85d1283176474f6b0fe48eae634e4d5b.jpg\",\n  \"115076.jpg\": \"Reptilia/Pituophis catenifer sayi/fa7640c3a34017bf335426e6ca1abeb9.jpg\",\n  \"115079.jpg\": \"Reptilia/Pituophis catenifer sayi/b07f6538fe7a7e77ebabda7a0678c607.jpg\",\n  \"115082.jpg\": \"Reptilia/Pituophis catenifer sayi/855c1624a6062b6e3df275bf85803bed.jpg\",\n  \"115086.jpg\": \"Reptilia/Pituophis catenifer sayi/583ec8cc7244953861790c86039af185.jpg\",\n  \"115093.jpg\": \"Reptilia/Pituophis catenifer sayi/e67785a42cb9c8139b3f9d674b25b969.jpg\",\n  \"115108.jpg\": \"Reptilia/Pituophis catenifer sayi/d373d9083efb2031ba755f1a98f474ca.jpg\",\n  \"115112.jpg\": \"Reptilia/Pituophis catenifer sayi/811d9229ba9a8a07f6835eaf3ce5891f.jpg\",\n  \"115120.jpg\": \"Reptilia/Pituophis catenifer sayi/d8938bf59b9b49d401863a22d4ed5fdc.jpg\",\n  \"115122.jpg\": \"Reptilia/Pituophis catenifer sayi/bc3c5ebf338ba71504524e7da838d6ee.jpg\",\n  \"11514.jpg\": \"Aves/Cyanocitta cristata/e60496296c5c2189bed45f43d9e83b11.jpg\",\n  \"115269.jpg\": \"Reptilia/Apalone ferox/e525f8973159947102cd55f4a09d754c.jpg\",\n  \"115274.jpg\": \"Reptilia/Apalone ferox/ebb8da5a9cef8ef8d0529873be828a9c.jpg\",\n  \"115277.jpg\": \"Reptilia/Apalone ferox/eaf45e0546dfd334e594b36641ca05ce.jpg\",\n  \"115284.jpg\": \"Reptilia/Apalone ferox/c6bdd974fbedf2206b0a37a5264dfcde.jpg\",\n  \"115285.jpg\": \"Reptilia/Apalone ferox/daa9388a0577071684f062c6f74785a4.jpg\",\n  \"115287.jpg\": \"Reptilia/Apalone ferox/163e5ea4bd1cd63c3c5ff5e9f120994d.jpg\",\n  \"115290.jpg\": \"Reptilia/Apalone ferox/eb9d79ece027b06b56e42d9e2ab26917.jpg\",\n  \"115294.jpg\": \"Reptilia/Apalone ferox/ea11c366a754ab9d83a92c124e4429ac.jpg\",\n  \"115295.jpg\": \"Reptilia/Apalone ferox/02d48224532506442ee13e4937c9317b.jpg\",\n  \"115301.jpg\": \"Reptilia/Apalone ferox/d52d2c0eca59b74bdd612a9ee73246b4.jpg\",\n  \"115304.jpg\": \"Reptilia/Apalone ferox/d0cdea5d091bd47f0d1e0614ce7a4505.jpg\",\n  \"115306.jpg\": \"Reptilia/Apalone ferox/58e8161b0a01a6b9ce63c889f563ee7f.jpg\",\n  \"11534.jpg\": \"Aves/Cyanocitta cristata/74bff6bdc472213818da59fb48914307.jpg\",\n  \"11535.jpg\": \"Aves/Cyanocitta cristata/b80e604284eea971992ebbba5d60724e.jpg\",\n  \"11549.jpg\": \"Aves/Cyanocitta cristata/2b5bf7dd96360d3fbfa611494b2de3f0.jpg\",\n  \"115570.jpg\": \"Reptilia/Sauromalus ater/05b4f229da37bad3e9dfc9a382fc1869.jpg\",\n  \"115571.jpg\": \"Reptilia/Sauromalus ater/7e7f03c2942aabfb29313fff0287a66c.jpg\",\n  \"115591.jpg\": \"Reptilia/Sauromalus ater/916ccb381acd33a10cf7f084a5dcb528.jpg\",\n  \"115592.jpg\": \"Reptilia/Sauromalus ater/0c2bab09dfe80ded60e46c5234ed8669.jpg\",\n  \"115594.jpg\": \"Reptilia/Sauromalus ater/957ab6084016b78c962b431aeafaa9e8.jpg\",\n  \"115595.jpg\": \"Reptilia/Sauromalus ater/7196264f18cd5c6aafe83080707869d8.jpg\",\n  \"115596.jpg\": \"Reptilia/Sauromalus ater/46be76bf5110048794ac8e9907816218.jpg\",\n  \"115597.jpg\": \"Reptilia/Sauromalus ater/87c2a07d0dba13b7ba5f1728ef7467e4.jpg\",\n  \"115598.jpg\": \"Reptilia/Sauromalus ater/5dbbbd2cfc609f3b00e847aada9b7d47.jpg\",\n  \"115599.jpg\": \"Reptilia/Sauromalus ater/de8529b53208d50f3a186dd8b0f0c27c.jpg\",\n  \"115600.jpg\": \"Reptilia/Sauromalus ater/1aa51c323107a0578d451a5acbf6e206.jpg\",\n  \"115611.jpg\": \"Reptilia/Sauromalus ater/531ba96e9114d7fc3f426949ea5b9b58.jpg\",\n  \"115613.jpg\": \"Reptilia/Sauromalus ater/7aa5c53eac54323d311afe6c4f26f77d.jpg\",\n  \"115616.jpg\": \"Reptilia/Sauromalus ater/d0039460534f8672eedf5f1f95a56f30.jpg\",\n  \"115623.jpg\": \"Reptilia/Sauromalus ater/8d4f0522847121264a6f609bc7b9de56.jpg\",\n  \"115633.jpg\": \"Reptilia/Sauromalus ater/4960f2f6c22c2cf1f473d7090191f2c8.jpg\",\n  \"115637.jpg\": \"Reptilia/Sauromalus ater/ec941ad8cf45801b24de161a2f69f5cf.jpg\",\n  \"115638.jpg\": \"Reptilia/Sauromalus ater/b5579e5427a32c49f0c5528a13d5f35b.jpg\",\n  \"11565.jpg\": \"Aves/Cyanocitta cristata/012f05b34e9335575a466488e8cd266c.jpg\",\n  \"115652.jpg\": \"Reptilia/Sauromalus ater/14662753427ac48f176c9ee172bca979.jpg\",\n  \"115662.jpg\": \"Reptilia/Sauromalus ater/5af1749424844b33c24fec0a7a5fe95c.jpg\",\n  \"115663.jpg\": \"Reptilia/Sauromalus ater/b15576d498d3983d58e59216636bbb11.jpg\",\n  \"115673.jpg\": \"Reptilia/Sauromalus ater/0c8901ec1e1dccadcf05afe8f41e27ae.jpg\",\n  \"115676.jpg\": \"Reptilia/Sauromalus ater/a761189c0f70592eaeea71462b972c3c.jpg\",\n  \"115677.jpg\": \"Animalia/Cancer productus/3099b6d0fcbf8c6680c0d04737488f8f.jpg\",\n  \"115684.jpg\": \"Animalia/Cancer productus/07261f2d14581c911ffc4bd320986150.jpg\",\n  \"115686.jpg\": \"Animalia/Cancer productus/d142f1406a16fb4131e93cd485d0b82f.jpg\",\n  \"115688.jpg\": \"Animalia/Cancer productus/d17397078fc9eebf8e7fdae430026767.jpg\",\n  \"115689.jpg\": \"Animalia/Cancer productus/e4b6fd7b6dbcd594397a032e79b531fd.jpg\",\n  \"115704.jpg\": \"Animalia/Cancer productus/636d4b2dbafc12ebecfbfdade81eed89.jpg\",\n  \"115705.jpg\": \"Animalia/Cancer productus/94b63b6bb36a78f1a1b787ce6e64c553.jpg\",\n  \"115706.jpg\": \"Animalia/Cancer productus/45654d05f3356c1086b646939da86410.jpg\",\n  \"115710.jpg\": \"Animalia/Cancer productus/90e14b190552c32e04d7664c02c4a361.jpg\",\n  \"115714.jpg\": \"Animalia/Cancer productus/9b05dd47c373c7b5da8bfc17bedb0f18.jpg\",\n  \"115722.jpg\": \"Animalia/Cancer productus/eecaac8936722bc45cc55b96ee61aaee.jpg\",\n  \"115728.jpg\": \"Animalia/Cancer productus/c0a58a23d6c87b46560b07e22fe30720.jpg\",\n  \"115729.jpg\": \"Animalia/Cancer productus/7039f653222225caaf300f398cc52436.jpg\",\n  \"115735.jpg\": \"Animalia/Cancer productus/1e248323864caca1dc1de6d910bfbf3c.jpg\",\n  \"115736.jpg\": \"Animalia/Cancer productus/c958f695de1ce2edd86320e89d915edf.jpg\",\n  \"115738.jpg\": \"Animalia/Cancer productus/37e0bbbaedfa5d37daba1ef9fed1284d.jpg\",\n  \"115739.jpg\": \"Animalia/Cancer productus/8e1009a884b3c1c1a7c2e809e19b03b5.jpg\",\n  \"115743.jpg\": \"Animalia/Cancer productus/2baaea99d9277241c2b18188629456c2.jpg\",\n  \"115744.jpg\": \"Animalia/Cancer productus/7721f1e2b3461360c04b190df2174af3.jpg\",\n  \"115745.jpg\": \"Animalia/Cancer productus/f84e06255bdf0460eabe27aa3c7b3869.jpg\",\n  \"115751.jpg\": \"Animalia/Echinothrix calamaris/184fa5af923ac88acdd93fa92aff1b94.jpg\",\n  \"115762.jpg\": \"Animalia/Echinothrix calamaris/50b0dca3a8fd908125c698c65a037ff9.jpg\",\n  \"115768.jpg\": \"Animalia/Emerita analoga/c4dbaab35baa16f07ca9a63c01bb23b5.jpg\",\n  \"115769.jpg\": \"Animalia/Emerita analoga/7070634c0788f0d772a6491d553435a7.jpg\",\n  \"11577.jpg\": \"Aves/Cyanocitta cristata/f95fb1188212174ee42b69894f638eaa.jpg\",\n  \"115775.jpg\": \"Animalia/Emerita analoga/c88ca3d0bb15c8d02e77abe92dd55dc3.jpg\",\n  \"115780.jpg\": \"Animalia/Emerita analoga/8f698b1212f78856bbc81474bd51e701.jpg\",\n  \"115784.jpg\": \"Animalia/Emerita analoga/1002eeac29a86dd3e5f88a1828c9c1ce.jpg\",\n  \"115785.jpg\": \"Animalia/Emerita analoga/7f5c8d687943d7a12e845779eb9a400f.jpg\",\n  \"115787.jpg\": \"Animalia/Emerita analoga/fefaf78300e60fdaa85c50df5eadb93d.jpg\",\n  \"115794.jpg\": \"Animalia/Emerita analoga/1fcec1fb0953360834d6ed96f11c97e9.jpg\",\n  \"115795.jpg\": \"Animalia/Emerita analoga/9e6c72e308917b5b5beb82ff1bc2cd18.jpg\",\n  \"115798.jpg\": \"Animalia/Emerita analoga/7d062d7b360ec437de875b9b1c751998.jpg\",\n  \"115808.jpg\": \"Animalia/Emerita analoga/4a7d7aa22775d33c224eaa04e091b038.jpg\",\n  \"115820.jpg\": \"Animalia/Emerita analoga/98cb03acf17e7b569398119034ccc04a.jpg\",\n  \"115822.jpg\": \"Animalia/Emerita analoga/7623afc06ccbd7b9aeaef9f2e35f0433.jpg\",\n  \"115865.jpg\": \"Reptilia/Aspidoscelis marmorata/6c97e768669cbd9bd0ce3f87d2d37569.jpg\",\n  \"115876.jpg\": \"Reptilia/Aspidoscelis marmorata/8b4908ab2de4ca6b13c5d4e7890f7f02.jpg\",\n  \"115877.jpg\": \"Reptilia/Aspidoscelis marmorata/2ad903642ea4dd738dea715c35816927.jpg\",\n  \"115884.jpg\": \"Reptilia/Aspidoscelis marmorata/032b5e0e8ff17f0bfd4e6507a520a37f.jpg\",\n  \"115956.jpg\": \"Insecta/Hemileuca eglanterina/dcfeb7c75f55b829d068e53d8708c70b.jpg\",\n  \"115969.jpg\": \"Insecta/Hemileuca eglanterina/edf2b97ff4386228f4ebf3d3abb2eebd.jpg\",\n  \"115970.jpg\": \"Insecta/Hemileuca eglanterina/454f5161f1cec1f5db5a428779c4b36b.jpg\",\n  \"115971.jpg\": \"Insecta/Hemileuca eglanterina/db083c2b2bdd60bafb3fe881076ba716.jpg\",\n  \"115996.jpg\": \"Amphibia/Trachycephalus typhonius/2ea5e62078f6ab40917b870bc28c01d0.jpg\",\n  \"115998.jpg\": \"Amphibia/Trachycephalus typhonius/3763f63485f6eccea8def29bb29cef32.jpg\",\n  \"115999.jpg\": \"Amphibia/Trachycephalus typhonius/9bf87a70051bcb8ad6a916f301261cea.jpg\",\n  \"116000.jpg\": \"Amphibia/Trachycephalus typhonius/656deeb3b7248dec734ab3f0a2a5eb24.jpg\",\n  \"116004.jpg\": \"Amphibia/Trachycephalus typhonius/7a4fc2a00c949596255b5e3af1795e5e.jpg\",\n  \"116007.jpg\": \"Amphibia/Trachycephalus typhonius/b322ee83f2bc270334725f0ef7814d68.jpg\",\n  \"116008.jpg\": \"Amphibia/Trachycephalus typhonius/d2b5921fd1d86d58d1d65437d8182a74.jpg\",\n  \"11613.jpg\": \"Aves/Cyanocitta cristata/a5706ade4fe7b93045a3312428788b0e.jpg\",\n  \"116294.jpg\": \"Insecta/Pogonomyrmex barbatus/f4dc1566ccd67bf8942aa62eeac5508e.jpg\",\n  \"116357.jpg\": \"Insecta/Epilachna mexicana/a89e64755f47791efcf05c81060d475c.jpg\",\n  \"116362.jpg\": \"Insecta/Epilachna mexicana/aae4730c9a2091ada819c310e8e22173.jpg\",\n  \"116363.jpg\": \"Insecta/Epilachna mexicana/1d83f80068382b3ae1ade89a22ac2961.jpg\",\n  \"11642.jpg\": \"Aves/Cyanocitta cristata/f1bfb6b7e9d3c3b521f3ddf53b56848b.jpg\",\n  \"116429.jpg\": \"Amphibia/Pseudotriton ruber/f284348a5429d62ba1852cbfcdbc3d67.jpg\",\n  \"116430.jpg\": \"Amphibia/Pseudotriton ruber/1de24c7f707417b4e2438526b9cc0e4f.jpg\",\n  \"116441.jpg\": \"Amphibia/Pseudotriton ruber/873b954ef1246c167f708de041265441.jpg\",\n  \"116442.jpg\": \"Amphibia/Pseudotriton ruber/276259c057b49e6d7a41c6923216f77c.jpg\",\n  \"116446.jpg\": \"Amphibia/Pseudotriton ruber/b02e863ffa101b1a6b29dd68a83d03e2.jpg\",\n  \"116448.jpg\": \"Amphibia/Pseudotriton ruber/c735bd17d866b2f08007342dd3144edc.jpg\",\n  \"116449.jpg\": \"Amphibia/Pseudotriton ruber/d56e0410d40407ab39e8f1cbda1d08ff.jpg\",\n  \"116452.jpg\": \"Insecta/Trichodezia albovittata/e13df2ea6903090ee9bc1caa85ed6d82.jpg\",\n  \"116453.jpg\": \"Insecta/Trichodezia albovittata/fdba7d3ea6154ea33e7c4a3a3f609c57.jpg\",\n  \"116459.jpg\": \"Insecta/Trichodezia albovittata/7cdd3685c9c2ea0357a310d8048720cc.jpg\",\n  \"116467.jpg\": \"Insecta/Trichodezia albovittata/07f16d61f7f750e06b3c9b00e85bbf9f.jpg\",\n  \"116476.jpg\": \"Insecta/Trichodezia albovittata/4db56c2f41c5777453ca6cea986fab6d.jpg\",\n  \"116477.jpg\": \"Insecta/Trichodezia albovittata/a1aca49df22cb0f5dbf8a1d5ebe8cba5.jpg\",\n  \"116478.jpg\": \"Insecta/Trichodezia albovittata/c4d37becdf992fe377b087015ec42b19.jpg\",\n  \"116483.jpg\": \"Insecta/Trichodezia albovittata/25a6d06bafcb982a267f1a1e534a3a6a.jpg\",\n  \"116485.jpg\": \"Insecta/Trichodezia albovittata/30c231acbc2326d067eaa9f2cfb0a306.jpg\",\n  \"116486.jpg\": \"Insecta/Trichodezia albovittata/bf69fc851cab28e8d73c849e8beb6270.jpg\",\n  \"116571.jpg\": \"Insecta/Enallagma geminatum/72edbead6ceb3182fcbb19ed6ff1d37e.jpg\",\n  \"116575.jpg\": \"Insecta/Enallagma geminatum/5550a40b4803683f2499c8453dbb3cb5.jpg\",\n  \"116577.jpg\": \"Insecta/Enallagma geminatum/be4569ef0e116f8d644271f19f972489.jpg\",\n  \"116578.jpg\": \"Insecta/Enallagma geminatum/7623dce1cc02a84b865a79be5ef36acb.jpg\",\n  \"116579.jpg\": \"Insecta/Enallagma geminatum/7cbba47fe05a70dc60ece67ace753a85.jpg\",\n  \"116582.jpg\": \"Insecta/Enallagma geminatum/83d68b52e4181bdb636d0e07755a30e6.jpg\",\n  \"116584.jpg\": \"Insecta/Enallagma geminatum/409b1be5fd08858e4267f92c9e573554.jpg\",\n  \"116586.jpg\": \"Insecta/Enallagma geminatum/8286302903cf5c439a5f16e5ecd10d8c.jpg\",\n  \"116591.jpg\": \"Insecta/Enallagma geminatum/15ba7f8c3820ca5f7a6fb1d05a4e0ca3.jpg\",\n  \"116597.jpg\": \"Insecta/Enallagma geminatum/3061cf9dcc9dbb838ad9b1f0dba6bd3c.jpg\",\n  \"116601.jpg\": \"Insecta/Enallagma geminatum/72e0578a20846e551161b84997046cbf.jpg\",\n  \"116602.jpg\": \"Insecta/Enallagma geminatum/8f5fc8bc6cf67a20155eb2d53ffd308f.jpg\",\n  \"116603.jpg\": \"Insecta/Enallagma geminatum/f1bf2f9b24a64c19934930604535f975.jpg\",\n  \"116605.jpg\": \"Insecta/Enallagma geminatum/502d2907d5b02a3edb7ccfab2af332be.jpg\",\n  \"116606.jpg\": \"Insecta/Enallagma geminatum/4417e67b3343f1a647c8c08311f600f4.jpg\",\n  \"116607.jpg\": \"Insecta/Enallagma geminatum/ed5e2254c2bfb93bd802151049ae43f2.jpg\",\n  \"116608.jpg\": \"Insecta/Enallagma geminatum/e708d711ac21fec52133663871afcfe4.jpg\",\n  \"116612.jpg\": \"Insecta/Enallagma geminatum/86348ba124c4434afd92f0021d92de5a.jpg\",\n  \"116613.jpg\": \"Insecta/Enallagma geminatum/ab74e94f003eb334d6ca08c7c3b5a72e.jpg\",\n  \"116620.jpg\": \"Insecta/Enallagma geminatum/c3ed00c617556002b3742da8f94ab8ac.jpg\",\n  \"11664.jpg\": \"Aves/Cyanocitta cristata/0240b7c95bd9e0f3b18263ac6f560b8c.jpg\",\n  \"116746.jpg\": \"Mammalia/Delphinus delphis/6d14cb2074d04264df9ec86e0ee9186f.jpg\",\n  \"116750.jpg\": \"Mammalia/Delphinus delphis/0f14059e53f44ad8be7d22812071da69.jpg\",\n  \"116758.jpg\": \"Mammalia/Delphinus delphis/da61dec50581a33da72f11684df89bca.jpg\",\n  \"116771.jpg\": \"Aves/Cyanistes caeruleus/bac3766258928ab2e9bacc7e2290ff4e.jpg\",\n  \"116792.jpg\": \"Aves/Cyanistes caeruleus/5142ea2092d43aa6bdbba673d100d15d.jpg\",\n  \"116806.jpg\": \"Aves/Cyanistes caeruleus/8a106069d5736792f59ff9d18e5e3d0b.jpg\",\n  \"116809.jpg\": \"Aves/Cyanistes caeruleus/95f9cbbb243aa7d4fee860a58bfb6f82.jpg\",\n  \"116813.jpg\": \"Aves/Cyanistes caeruleus/13bae0e7cc4fdd45e0477223f30857ea.jpg\",\n  \"116814.jpg\": \"Aves/Cyanistes caeruleus/1c1bd071f71e1b945c77a0f804e308eb.jpg\",\n  \"116815.jpg\": \"Aves/Cyanistes caeruleus/4259f6c1664c8e0c32d23f90e286c4a2.jpg\",\n  \"11683.jpg\": \"Aves/Cyanocitta cristata/71b95a1efcfd766607f806cae49aa70e.jpg\",\n  \"116847.jpg\": \"Aves/Cyanistes caeruleus/1bcb9f8f8f46c2199c92ec0e950e255a.jpg\",\n  \"116851.jpg\": \"Aves/Cyanistes caeruleus/61ee459ef3368bd06284d996aa821283.jpg\",\n  \"116857.jpg\": \"Aves/Cyanistes caeruleus/1a38971d5ebb9d39229837096b6d0c4e.jpg\",\n  \"116858.jpg\": \"Aves/Cyanistes caeruleus/21c26a91c587d2f6ee20fde747874bcb.jpg\",\n  \"116869.jpg\": \"Aves/Cyanistes caeruleus/3bce1016a5dce4444872b3282784e6e8.jpg\",\n  \"116870.jpg\": \"Aves/Cyanistes caeruleus/4330ab37f0003ace6bd6d5317a1d727a.jpg\",\n  \"116873.jpg\": \"Aves/Cyanistes caeruleus/2544f765b88bd4e09cc28906aa21c4f8.jpg\",\n  \"116880.jpg\": \"Aves/Cyanistes caeruleus/adde812f7187234cc2ced10f5da61b56.jpg\",\n  \"116883.jpg\": \"Aves/Cyanistes caeruleus/5efa0dfcc1114faf8ac0fa1768f4b750.jpg\",\n  \"116894.jpg\": \"Aves/Cyanistes caeruleus/a1c7e2c11490095de551ad90a9063ff0.jpg\",\n  \"116901.jpg\": \"Aves/Cyanistes caeruleus/dba4f143c1f953159bf4995eae9a4f6a.jpg\",\n  \"116912.jpg\": \"Aves/Cyanistes caeruleus/c2f12e380ef72c2c77b75150e454a170.jpg\",\n  \"116987.jpg\": \"Aves/Spinus tristis/31b727fd9cba4b5c962275fe189b85e6.jpg\",\n  \"117010.jpg\": \"Aves/Spinus tristis/dbbfdddd71300c5d176ee8425e48fdca.jpg\",\n  \"117055.jpg\": \"Aves/Spinus tristis/172d05aeab0a0341bd5cd4dca03ce06b.jpg\",\n  \"117095.jpg\": \"Aves/Spinus tristis/70ef2fd8ec4009ee2ce18c2f99d56d83.jpg\",\n  \"117099.jpg\": \"Aves/Spinus tristis/d34cfecc4f986c09a85037cde82721af.jpg\",\n  \"117136.jpg\": \"Aves/Spinus tristis/92d6aaf7456a67811d7f7837e6d266ac.jpg\",\n  \"117193.jpg\": \"Aves/Spinus tristis/595a3c65f5611f5187935717de9125d4.jpg\",\n  \"117243.jpg\": \"Aves/Spinus tristis/57eeb53c0b2ba33025c8abb486a10c3e.jpg\",\n  \"117256.jpg\": \"Aves/Spinus tristis/060da92d73f806a9d58a57bdeff38c47.jpg\",\n  \"117291.jpg\": \"Aves/Spinus tristis/33a0586edfd41f796e7512ed377c2552.jpg\",\n  \"117296.jpg\": \"Aves/Spinus tristis/f48e0a83fec25b010ea6ef5163f6560a.jpg\",\n  \"117339.jpg\": \"Aves/Spinus tristis/6658c5d4f648a1932475abb48672568d.jpg\",\n  \"117386.jpg\": \"Aves/Spinus tristis/41f3c47dc369fee2e6c622e8b46b3ed9.jpg\",\n  \"117453.jpg\": \"Aves/Spinus tristis/14cf16bfdf82334e7b5b0a810fa1b5a2.jpg\",\n  \"117653.jpg\": \"Aves/Camptostoma imberbe/0b74059062f8a2acc8720124780ca6e0.jpg\",\n  \"117654.jpg\": \"Aves/Camptostoma imberbe/77d999e8b2e8ec1cbd2deb3d50d76737.jpg\",\n  \"117659.jpg\": \"Aves/Camptostoma imberbe/b5f71c6f04b447a3c746db5732d1ab04.jpg\",\n  \"117662.jpg\": \"Aves/Camptostoma imberbe/44492ee518f11d70bd645a2970d7ea6f.jpg\",\n  \"117670.jpg\": \"Actinopterygii/Naso lituratus/c259f5e4d6b69c273c4b423c569a255c.jpg\",\n  \"117675.jpg\": \"Actinopterygii/Naso lituratus/720f339427ee1f9308ee72aa302fb6cb.jpg\",\n  \"117701.jpg\": \"Insecta/Vespula vulgaris/8d08c8b99398eb1885e3caade1f12a30.jpg\",\n  \"117714.jpg\": \"Insecta/Vespula vulgaris/e989d69732a4c1645f6733d178ea4110.jpg\",\n  \"117728.jpg\": \"Insecta/Vespula vulgaris/038fbb56324ab1e98f4e103d10aaa288.jpg\",\n  \"117729.jpg\": \"Insecta/Vespula vulgaris/d34829021392d202c85f471927eb2708.jpg\",\n  \"117734.jpg\": \"Insecta/Vespula vulgaris/7b9bba61a36c34591e906133f48df939.jpg\",\n  \"11785.jpg\": \"Aves/Cyanocitta cristata/32cc1744600e2f5413936e9e56e79113.jpg\",\n  \"11788.jpg\": \"Aves/Cyanocitta cristata/c8f6b7e1ce6699bc5d6468d962165a02.jpg\",\n  \"11820.jpg\": \"Aves/Cyanocitta cristata/1cadcb74e1f02029e5c367e31c3dd8fc.jpg\",\n  \"118249.jpg\": \"Insecta/Polistes dominula/b9605d113f8a464fc6b6cb4d3ab81bcb.jpg\",\n  \"118263.jpg\": \"Insecta/Polistes dominula/acfa3ad27c99367277f3ede6002d5481.jpg\",\n  \"118264.jpg\": \"Insecta/Polistes dominula/bd0f47d893ad2554974612c245cd7c0c.jpg\",\n  \"118265.jpg\": \"Insecta/Polistes dominula/c2fe8763edb107d0b705a254e8b68038.jpg\",\n  \"118266.jpg\": \"Insecta/Polistes dominula/2c91ec5174f107cde92d377c3fa54a0e.jpg\",\n  \"118267.jpg\": \"Insecta/Polistes dominula/f886b3616dfe35c59c5de19cf053219d.jpg\",\n  \"118269.jpg\": \"Insecta/Polistes dominula/a3cd2a092ab5097a83a00b6f949066b0.jpg\",\n  \"118270.jpg\": \"Insecta/Polistes dominula/c037fd300bf5a97a8b39bcadcc4c7717.jpg\",\n  \"118273.jpg\": \"Insecta/Polistes dominula/a0a8af2afa25f0733053d29f991a5ff4.jpg\",\n  \"118275.jpg\": \"Insecta/Polistes dominula/7df0496c51f79cc9a730a4d46d74a7d7.jpg\",\n  \"118280.jpg\": \"Insecta/Polistes dominula/8442187768337e6257f4592763cb4f38.jpg\",\n  \"118281.jpg\": \"Insecta/Polistes dominula/eabbb49946d1ed23167fe2e063587e3a.jpg\",\n  \"118285.jpg\": \"Insecta/Polistes dominula/203a82bb0e31c71c07677aaeb1f2978e.jpg\",\n  \"118287.jpg\": \"Insecta/Polistes dominula/204e0a13e26551e168430871b384cd8d.jpg\",\n  \"118305.jpg\": \"Insecta/Polistes dominula/7ecbd21d305e19eb9a498b6297c2ea20.jpg\",\n  \"118314.jpg\": \"Insecta/Polistes dominula/42558b3b56bfb1d6f2d572e4b15b730f.jpg\",\n  \"118316.jpg\": \"Insecta/Polistes dominula/831e0623e60252845142e68336183bb3.jpg\",\n  \"118331.jpg\": \"Aves/Anthornis melanura/a9d4e52be77deb0b3fb81db5c08f9b27.jpg\",\n  \"118333.jpg\": \"Aves/Anthornis melanura/cf601b222366af4c84b2ac7a4c9b6223.jpg\",\n  \"118334.jpg\": \"Aves/Anthornis melanura/d55268c2c8f8e237e1c6b1d94ce5ff9d.jpg\",\n  \"118335.jpg\": \"Aves/Anthornis melanura/283b286446a93e49dd4289a36004f412.jpg\",\n  \"118337.jpg\": \"Aves/Anthornis melanura/08772de8e4d896e26e6f9f75436972e7.jpg\",\n  \"118345.jpg\": \"Aves/Anthornis melanura/04212a1ae1f3b34b5c749bec65acef1e.jpg\",\n  \"118346.jpg\": \"Aves/Anthornis melanura/2a2dd9f37328030b9db891b1e0b256dc.jpg\",\n  \"118349.jpg\": \"Aves/Anthornis melanura/88718e429b1def68d6cfdf49c502428d.jpg\",\n  \"118363.jpg\": \"Aves/Anthornis melanura/1e963b938006bc0d5616c10459033268.jpg\",\n  \"118367.jpg\": \"Aves/Anthornis melanura/6c9dce34fd612de32f276384e7e144d9.jpg\",\n  \"118378.jpg\": \"Aves/Anthornis melanura/9624bafc5706443d27e6193bc3c721dd.jpg\",\n  \"118379.jpg\": \"Aves/Anthornis melanura/ce9607927516b6114145e85738225ed5.jpg\",\n  \"118391.jpg\": \"Aves/Anthornis melanura/63e4fd4248f814d1264b23273ae738ce.jpg\",\n  \"118401.jpg\": \"Aves/Anthornis melanura/6eaaa7ddbbf7937ccc7e8de8aaeefd7d.jpg\",\n  \"118402.jpg\": \"Aves/Anthornis melanura/e7ab1d8fcd6846412c418bd478fc0976.jpg\",\n  \"118411.jpg\": \"Aves/Anthornis melanura/e0f70bd0faaf754126e0e64ae1a02899.jpg\",\n  \"118418.jpg\": \"Aves/Anthornis melanura/d2053543165827319e5f41f7ec69308a.jpg\",\n  \"118419.jpg\": \"Aves/Anthornis melanura/e6dbf205f6a9180d2305917af8c05d1d.jpg\",\n  \"118420.jpg\": \"Aves/Anthornis melanura/059a6aef6c97668f21f29ea1e8de6f8c.jpg\",\n  \"118442.jpg\": \"Aves/Anthornis melanura/fc21ca73bbe3cde39ab0d83541355a27.jpg\",\n  \"118445.jpg\": \"Aves/Anthornis melanura/2762a9c51b51d724b3da8c8c75cbe827.jpg\",\n  \"11849.jpg\": \"Aves/Cyanocitta cristata/7320aee4b8a2295ebd5d6a2551e792e1.jpg\",\n  \"118561.jpg\": \"Mammalia/Cervus elaphus nelsoni/c3fcfbcc58d33fe0d060f2ab5168d1ad.jpg\",\n  \"118568.jpg\": \"Mammalia/Cervus elaphus nelsoni/a7ad09d8d4af967d90d3f522a63297e1.jpg\",\n  \"118569.jpg\": \"Mammalia/Cervus elaphus nelsoni/2a6c68c0d2e4ee9cc98697eb2c56d9d0.jpg\",\n  \"118578.jpg\": \"Mammalia/Cervus elaphus nelsoni/ac76f14d12a5af3303b08682ea17d92e.jpg\",\n  \"118585.jpg\": \"Mammalia/Cervus elaphus nelsoni/e67c070966c497efd08439d332ab5d9e.jpg\",\n  \"118587.jpg\": \"Mammalia/Cervus elaphus nelsoni/0d4978ceb911a90279d1ac61ee0a40ad.jpg\",\n  \"118590.jpg\": \"Mammalia/Cervus elaphus nelsoni/c96f468a8d22a8f67b16a5f6f0e381ca.jpg\",\n  \"118606.jpg\": \"Mammalia/Cervus elaphus nelsoni/aed1a70f9937209ba01c2b87daf501cf.jpg\",\n  \"118610.jpg\": \"Mammalia/Cervus elaphus nelsoni/20a1990b15ec64e8657794e46b125676.jpg\",\n  \"118628.jpg\": \"Reptilia/Pantherophis alleghaniensis/43e7abcf06bff8c9d192c2bf4dbe6274.jpg\",\n  \"118632.jpg\": \"Reptilia/Pantherophis alleghaniensis/c5f20116878e3e884fe64b44ada8a615.jpg\",\n  \"118633.jpg\": \"Reptilia/Pantherophis alleghaniensis/0fd8ec76beb3d43e2c0785824b81b5a4.jpg\",\n  \"118645.jpg\": \"Reptilia/Pantherophis alleghaniensis/9188c0b6284a69943fb11bb7ca3a44ee.jpg\",\n  \"118664.jpg\": \"Reptilia/Pantherophis alleghaniensis/ebe39c925610eecd12b8487614e6598f.jpg\",\n  \"118667.jpg\": \"Reptilia/Pantherophis alleghaniensis/015210af99a00f24f28eb4ccb888b2e1.jpg\",\n  \"118673.jpg\": \"Reptilia/Pantherophis alleghaniensis/93a01dc7997a56b9304760f6ecc08a76.jpg\",\n  \"118674.jpg\": \"Reptilia/Pantherophis alleghaniensis/1f46e2fbcf641e93a1a46f734bc806df.jpg\",\n  \"118681.jpg\": \"Reptilia/Pantherophis alleghaniensis/5d4a9e0fd0405f3df46c9c80ee1f450c.jpg\",\n  \"118687.jpg\": \"Reptilia/Pantherophis alleghaniensis/6545eecc9c77718dc3e2258c72091c91.jpg\",\n  \"118693.jpg\": \"Reptilia/Pantherophis alleghaniensis/103ee09670ef6e45a59030d79f0bf26f.jpg\",\n  \"118694.jpg\": \"Reptilia/Pantherophis alleghaniensis/c264a7f387604bdbf38410c0f406cf6d.jpg\",\n  \"118698.jpg\": \"Reptilia/Pantherophis alleghaniensis/62c20f7b48336d164e69162ef39f34a0.jpg\",\n  \"118702.jpg\": \"Reptilia/Pantherophis alleghaniensis/a96b1b0aac19547b91da780b147e605f.jpg\",\n  \"118706.jpg\": \"Reptilia/Pantherophis alleghaniensis/77466e38f25b6e3a3f1d72e42085e79e.jpg\",\n  \"118707.jpg\": \"Reptilia/Pantherophis alleghaniensis/e43400a7fd25c9b017ac333469030761.jpg\",\n  \"118713.jpg\": \"Reptilia/Pantherophis alleghaniensis/96745f86bc430ec700b72543f42a9b0a.jpg\",\n  \"118714.jpg\": \"Reptilia/Pantherophis alleghaniensis/958413811e793b3c49ce8c36a1a00780.jpg\",\n  \"118721.jpg\": \"Reptilia/Pantherophis alleghaniensis/3962c653a1beb287de98827bdb3021e2.jpg\",\n  \"118722.jpg\": \"Reptilia/Pantherophis alleghaniensis/d96251ce7288e84aee743c920ea45ba3.jpg\",\n  \"118737.jpg\": \"Reptilia/Pantherophis alleghaniensis/1c8ccb992aaa79683a1827200a3aab6a.jpg\",\n  \"118751.jpg\": \"Reptilia/Pantherophis alleghaniensis/b947ab6d4b6335daee958d4ff866f394.jpg\",\n  \"118752.jpg\": \"Reptilia/Pantherophis alleghaniensis/649bd1e95bb965c2c35e026ffd8358cf.jpg\",\n  \"11876.jpg\": \"Aves/Cyanocitta cristata/be618379107d79f8d0ef78642d5a2420.jpg\",\n  \"118805.jpg\": \"Insecta/Anacridium aegyptium/d8b00cd39c14f5ea399808b4fe79d0b8.jpg\",\n  \"118806.jpg\": \"Insecta/Anacridium aegyptium/131bf7aabec06802f47adf3622dacd57.jpg\",\n  \"118813.jpg\": \"Insecta/Anacridium aegyptium/ba626e33c80701c012b647422fc51990.jpg\",\n  \"118815.jpg\": \"Insecta/Anacridium aegyptium/c8ae91429273312cd10b6774e1dff07c.jpg\",\n  \"118816.jpg\": \"Insecta/Anacridium aegyptium/79f0990f6eab31a14e48c5071bd7965d.jpg\",\n  \"118854.jpg\": \"Mollusca/Littorina littorea/1caa4dc70362f9a850cbd8679baad072.jpg\",\n  \"118856.jpg\": \"Mollusca/Littorina littorea/6d80db6627eb9839adb5554785c26bee.jpg\",\n  \"118857.jpg\": \"Mollusca/Littorina littorea/da28d336df0cbe93978f3f99a9ff5f8d.jpg\",\n  \"118861.jpg\": \"Mollusca/Littorina littorea/4e8365f49d623b8d8f242a01bacfb67b.jpg\",\n  \"118862.jpg\": \"Mollusca/Littorina littorea/323918be441f9f3d88c6c0a4037ae136.jpg\",\n  \"118971.jpg\": \"Insecta/Pelecinus polyturator/ce47846304483f0b8b59b40e1f4af92b.jpg\",\n  \"118973.jpg\": \"Insecta/Pelecinus polyturator/e1995d9d88ec1f8bad10b1c5abd48e74.jpg\",\n  \"118974.jpg\": \"Insecta/Pelecinus polyturator/e9166b06cbed53b8ff08d840be4e5f06.jpg\",\n  \"118978.jpg\": \"Insecta/Pelecinus polyturator/e9b25ef5f6472668ab3fce1d85e80132.jpg\",\n  \"118982.jpg\": \"Insecta/Pelecinus polyturator/4b5639b7dfc358a889aa8c1becb9142e.jpg\",\n  \"118988.jpg\": \"Insecta/Pelecinus polyturator/3b485afce6d7ce9d3189146125e893fd.jpg\",\n  \"118993.jpg\": \"Insecta/Pelecinus polyturator/7bc7c814410bf26d01f06e994fdb27a3.jpg\",\n  \"118994.jpg\": \"Insecta/Pelecinus polyturator/260abd471d547928c9fa68dc85f350a2.jpg\",\n  \"118995.jpg\": \"Insecta/Pelecinus polyturator/d0a89e5c3ce6a4544900b55445c781a3.jpg\",\n  \"118998.jpg\": \"Insecta/Pelecinus polyturator/b1142af1ee4d9f89a63174fe1fb7d832.jpg\",\n  \"119002.jpg\": \"Aves/Meleagris gallopavo silvestris/50c126e718521ab4fdcadba154c9bae0.jpg\",\n  \"119007.jpg\": \"Aves/Meleagris gallopavo silvestris/65c5ab065fe10ee698c29f5d3612db85.jpg\",\n  \"119017.jpg\": \"Aves/Meleagris gallopavo silvestris/2c6c84cf4e4f40f2aafcc5317baa4628.jpg\",\n  \"119019.jpg\": \"Aves/Meleagris gallopavo silvestris/63b2163853bf081421f7c91be6292ba1.jpg\",\n  \"119020.jpg\": \"Aves/Meleagris gallopavo silvestris/fb10548a3773a6fcb8815276d6f668e8.jpg\",\n  \"119021.jpg\": \"Aves/Meleagris gallopavo silvestris/50996538f3c8c1282d73bb6a3d58e490.jpg\",\n  \"119037.jpg\": \"Aves/Meleagris gallopavo silvestris/74f7a62ee07c8ed30375b1e922723008.jpg\",\n  \"119040.jpg\": \"Aves/Meleagris gallopavo silvestris/cd73537709277c4f2a2f09653620d3b6.jpg\",\n  \"119043.jpg\": \"Aves/Platycercus eximius/aecd05a80c6a5f56fc21ced3c07daf29.jpg\",\n  \"119045.jpg\": \"Aves/Platycercus eximius/742ef1a37b8b681b12ff05ba50c200bd.jpg\",\n  \"119063.jpg\": \"Aves/Platycercus eximius/5868369e01fbb81965ec7704fd15c143.jpg\",\n  \"119065.jpg\": \"Aves/Platycercus eximius/bebc7c1d1edfa319019480d20babb953.jpg\",\n  \"119197.jpg\": \"Arachnida/Phalangium opilio/1bfa93fe5a17f9a214c32a76abafcc7b.jpg\",\n  \"119198.jpg\": \"Arachnida/Phalangium opilio/31c2ff8fce41e29c79bb7d214d07041c.jpg\",\n  \"119208.jpg\": \"Arachnida/Phalangium opilio/fdb2dce881e6bc69ef51abd72dbb83b7.jpg\",\n  \"119213.jpg\": \"Arachnida/Phalangium opilio/17922a9fad4f3841aea6347c8e0de3a7.jpg\",\n  \"119217.jpg\": \"Arachnida/Phalangium opilio/d0ff2050c84f2ef908696e5f32f8ef4f.jpg\",\n  \"119220.jpg\": \"Arachnida/Phalangium opilio/134413666cdcbc201a554442dbac5042.jpg\",\n  \"119239.jpg\": \"Reptilia/Sceloporus jarrovii/fa18a18d274aa1bc739313a838c599eb.jpg\",\n  \"119244.jpg\": \"Reptilia/Sceloporus jarrovii/ee58c57312623457c3f375ee5dfe657a.jpg\",\n  \"119245.jpg\": \"Reptilia/Sceloporus jarrovii/168850544ed1096ccde9c74a5bd1c583.jpg\",\n  \"119246.jpg\": \"Reptilia/Sceloporus jarrovii/0142d7bd49dc4851966b47d71b261e50.jpg\",\n  \"119247.jpg\": \"Reptilia/Sceloporus jarrovii/3263f8fd6bf2eb5c591e61d77b422b79.jpg\",\n  \"119249.jpg\": \"Reptilia/Sceloporus jarrovii/3b6f0c57dbab569cac074b836c654fd4.jpg\",\n  \"11925.jpg\": \"Aves/Cyanocitta cristata/4110d229911b227e8b105eb90fce8288.jpg\",\n  \"119257.jpg\": \"Reptilia/Sceloporus jarrovii/239f810b677e5908d7af472e025ef954.jpg\",\n  \"119301.jpg\": \"Insecta/Monobia quadridens/85a73568204d74f81f56647a6ab7f476.jpg\",\n  \"119302.jpg\": \"Insecta/Monobia quadridens/f30e2f0f1365e7199ec253a322f0d897.jpg\",\n  \"119306.jpg\": \"Insecta/Monobia quadridens/5c267efa766372aebae21c16b59ac91e.jpg\",\n  \"119321.jpg\": \"Insecta/Monobia quadridens/009fee460a4eb2b0dc3f6316f8cb4ad9.jpg\",\n  \"119322.jpg\": \"Insecta/Monobia quadridens/8cf6e77ea373c66efc4cde6b8f9deb34.jpg\",\n  \"119323.jpg\": \"Insecta/Monobia quadridens/b491df7b23e496e784a697db8d42ca98.jpg\",\n  \"119329.jpg\": \"Insecta/Monobia quadridens/0e6f4878e12bc7167a49340349ca7ae4.jpg\",\n  \"11957.jpg\": \"Aves/Cyanocitta cristata/cb36a04ac211ddb3296ba7bc9f72aa0f.jpg\",\n  \"11978.jpg\": \"Aves/Cyanocitta cristata/102befbc7a77d2c85015cdc3021253ec.jpg\",\n  \"119982.jpg\": \"Aves/Melanerpes formicivorus/d1f1766c9c9a01272121babf2971057a.jpg\",\n  \"119999.jpg\": \"Aves/Melanerpes formicivorus/a9a93128133bc43c547b6b082d9f2a54.jpg\",\n  \"120000.jpg\": \"Aves/Melanerpes formicivorus/77c45cf12c301b266cc418f4d8b21a89.jpg\",\n  \"120003.jpg\": \"Aves/Melanerpes formicivorus/ab9f43798e281838f2b1e9b4adda1f72.jpg\",\n  \"120010.jpg\": \"Aves/Melanerpes formicivorus/d36b6b9a8b5d629c92dba189afa1de46.jpg\",\n  \"120026.jpg\": \"Aves/Melanerpes formicivorus/460561368edcd4869c2b7da817ee1e1a.jpg\",\n  \"120051.jpg\": \"Aves/Melanerpes formicivorus/f10898b062eef01bc477013ffed1e149.jpg\",\n  \"120055.jpg\": \"Aves/Melanerpes formicivorus/9bf6326f980aeed195f17eae8b8ff43a.jpg\",\n  \"120083.jpg\": \"Aves/Melanerpes formicivorus/ac011c35bc90aad167b30d2133fb84e0.jpg\",\n  \"120095.jpg\": \"Aves/Melanerpes formicivorus/6282d494fcbba9f017e0bdedc151a084.jpg\",\n  \"120098.jpg\": \"Aves/Melanerpes formicivorus/3ebd5b84de6369367707be5d9f20983e.jpg\",\n  \"120138.jpg\": \"Aves/Melanerpes formicivorus/bdb3f962a978b0ff5b7e483acc71768a.jpg\",\n  \"120145.jpg\": \"Aves/Melanerpes formicivorus/a4543252e0f10b233600062ca8b5f1d5.jpg\",\n  \"120213.jpg\": \"Aves/Melanerpes formicivorus/4227c13a8a21504b3db07d71a30b1e15.jpg\",\n  \"120227.jpg\": \"Aves/Melanerpes formicivorus/7634b6038c5962782a35f7caec7cd1d3.jpg\",\n  \"120228.jpg\": \"Aves/Melanerpes formicivorus/460961db1fe92ddf2e45ce13fad96b49.jpg\",\n  \"120251.jpg\": \"Aves/Melanerpes formicivorus/15df45ec90c0982cc35d22228dadecdf.jpg\",\n  \"120309.jpg\": \"Aves/Melanerpes formicivorus/89bdc02eb97b19e62780f2712ba18a3d.jpg\",\n  \"12031.jpg\": \"Aves/Cyanocitta cristata/9db68839c01f408d44789722c01dc996.jpg\",\n  \"120334.jpg\": \"Aves/Melanerpes formicivorus/2aaaaa6df1cbc31cf52c5376f615db5c.jpg\",\n  \"120335.jpg\": \"Aves/Melanerpes formicivorus/47360bbedb6ed5ec994ec76cda16a380.jpg\",\n  \"120338.jpg\": \"Aves/Melanerpes formicivorus/8eda7112554c5c10f987542ea9cffa80.jpg\",\n  \"120353.jpg\": \"Aves/Melanerpes formicivorus/19622a759943c349122dfec6d8a73859.jpg\",\n  \"120356.jpg\": \"Aves/Melanerpes formicivorus/d1cd81e8f8817a45c1925e94080db314.jpg\",\n  \"120364.jpg\": \"Aves/Melanerpes formicivorus/7a9ac69854b97e302565a6011c295783.jpg\",\n  \"12045.jpg\": \"Aves/Cyanocitta cristata/01665782d3fda0b6434db9240a52e54c.jpg\",\n  \"120534.jpg\": \"Insecta/Spoladea recurvalis/54f237be9347a0e0292419fd88ea422d.jpg\",\n  \"120537.jpg\": \"Insecta/Spoladea recurvalis/5bdd95d27c55041eac1d48f3230487f0.jpg\",\n  \"120543.jpg\": \"Insecta/Spoladea recurvalis/c9625a3df9c944eef52fb58d83f78c20.jpg\",\n  \"120544.jpg\": \"Insecta/Spoladea recurvalis/2d615a6fcefbc713df65a1161588755d.jpg\",\n  \"120549.jpg\": \"Insecta/Spoladea recurvalis/95b64d62d67d0bf702d85f32d7f91399.jpg\",\n  \"120551.jpg\": \"Insecta/Spoladea recurvalis/11511158e371c9bf5aefc40bf5511977.jpg\",\n  \"120557.jpg\": \"Insecta/Spoladea recurvalis/f60f447baefb9c3871bd8528cf414f3e.jpg\",\n  \"120559.jpg\": \"Insecta/Spoladea recurvalis/4c4e8a3285a554678196ffb7ec49434d.jpg\",\n  \"120571.jpg\": \"Insecta/Spoladea recurvalis/f0504cec431d6c3ef9c0e689975b0d4c.jpg\",\n  \"120575.jpg\": \"Insecta/Spoladea recurvalis/1519c4c9b0c7b984e34c5d53a103f79f.jpg\",\n  \"120586.jpg\": \"Insecta/Spoladea recurvalis/1f4f867d5a3793fd419454709e628e8f.jpg\",\n  \"120591.jpg\": \"Insecta/Spoladea recurvalis/1fec363dbdea342d8c2165c39e072a4d.jpg\",\n  \"120592.jpg\": \"Insecta/Spoladea recurvalis/909b75ab10c6649dbd101a7630b8bcd6.jpg\",\n  \"120601.jpg\": \"Insecta/Spoladea recurvalis/4a70b76a85bc8c7ad4f633776cbbcde2.jpg\",\n  \"120604.jpg\": \"Insecta/Spoladea recurvalis/0465c3efb7fd86ae8912f66732b489e8.jpg\",\n  \"120614.jpg\": \"Insecta/Spoladea recurvalis/40ddbd5320fc2a85ee982c873d9fd964.jpg\",\n  \"120615.jpg\": \"Insecta/Spoladea recurvalis/4f6c8536d6038c6bbf6de5311d921aaf.jpg\",\n  \"120627.jpg\": \"Insecta/Spoladea recurvalis/4cc753edcb5f80d8e089f93beacea622.jpg\",\n  \"120629.jpg\": \"Insecta/Spoladea recurvalis/d1274e53155bac3ea5ff54be1595febc.jpg\",\n  \"120631.jpg\": \"Insecta/Spoladea recurvalis/886b480fca2db91795d4266ce733c62d.jpg\",\n  \"120634.jpg\": \"Insecta/Spoladea recurvalis/4d505e82744d3cb423d2029089fdacbd.jpg\",\n  \"120696.jpg\": \"Amphibia/Batrachoseps nigriventris/2aefb947562f8f56eb36733b3c4902c6.jpg\",\n  \"120698.jpg\": \"Amphibia/Batrachoseps nigriventris/ab1c09428d4c2fc56fc34dd5415dce1e.jpg\",\n  \"120701.jpg\": \"Amphibia/Batrachoseps nigriventris/222c6872a8abf04527d6dd7a1f85f49b.jpg\",\n  \"120705.jpg\": \"Amphibia/Batrachoseps nigriventris/70879bae0ce82925631b4d5e7ec9d033.jpg\",\n  \"120706.jpg\": \"Amphibia/Batrachoseps nigriventris/4ce01c481cd66952dfeb4609f2e8b718.jpg\",\n  \"120708.jpg\": \"Amphibia/Batrachoseps nigriventris/9ee904f04dfff4ca48e0881ac5281068.jpg\",\n  \"120711.jpg\": \"Amphibia/Batrachoseps nigriventris/df40c915019a5a745c5eb01659a65093.jpg\",\n  \"120712.jpg\": \"Amphibia/Batrachoseps nigriventris/7c248927423cd7cb1349785b20d0190f.jpg\",\n  \"120713.jpg\": \"Amphibia/Batrachoseps nigriventris/16bf087fe35d6f7b6576ac7a64c14906.jpg\",\n  \"120715.jpg\": \"Amphibia/Batrachoseps nigriventris/a7ce733cf9db97ff27146ff02d47b32e.jpg\",\n  \"120716.jpg\": \"Amphibia/Batrachoseps nigriventris/e1b1332abc54941550843706f4de1695.jpg\",\n  \"120717.jpg\": \"Amphibia/Batrachoseps nigriventris/f6f3a9578f83860fba0cb2d94834d5e7.jpg\",\n  \"120727.jpg\": \"Amphibia/Batrachoseps nigriventris/a4bb4b8097ff487ccfd62d337d989584.jpg\",\n  \"120728.jpg\": \"Amphibia/Batrachoseps nigriventris/30821e52894b2dc30408d3353d5bdcfd.jpg\",\n  \"120729.jpg\": \"Amphibia/Batrachoseps nigriventris/e183a710e1f082b39a73b539270d7120.jpg\",\n  \"120730.jpg\": \"Amphibia/Batrachoseps nigriventris/2b915dcd98cddaa14fde773caa507ce9.jpg\",\n  \"120731.jpg\": \"Amphibia/Batrachoseps nigriventris/498ac4c05752ff1f1e4f9086d47dfdd2.jpg\",\n  \"120737.jpg\": \"Amphibia/Batrachoseps nigriventris/fde7988ce16e9a1e86a7a54a8597c103.jpg\",\n  \"120738.jpg\": \"Amphibia/Batrachoseps nigriventris/ec25a87982b020ffe8ac538afd9cd067.jpg\",\n  \"120739.jpg\": \"Amphibia/Batrachoseps nigriventris/0f25e7fbeafe28aafe9b91f712afbfdd.jpg\",\n  \"121028.jpg\": \"Insecta/Autographa gamma/bba06ab6672b0fcd7b1c28e15f3723f6.jpg\",\n  \"121030.jpg\": \"Insecta/Autographa gamma/892921b5a001502fe29e84da273a5090.jpg\",\n  \"121031.jpg\": \"Insecta/Autographa gamma/cc20d5fb85cc5adbbc9c0a6bdc04618a.jpg\",\n  \"121032.jpg\": \"Insecta/Autographa gamma/388e7036ce446b2d01208fa7176386e5.jpg\",\n  \"121033.jpg\": \"Insecta/Autographa gamma/c0db54f6068615760c2394e9a931948d.jpg\",\n  \"121039.jpg\": \"Insecta/Autographa gamma/aca1139a2ea0fbb6401f06ade7a0ca99.jpg\",\n  \"121040.jpg\": \"Insecta/Autographa gamma/6e06d15ba4b60de4e2f4e27000297f7d.jpg\",\n  \"121044.jpg\": \"Insecta/Autographa gamma/5eb6ea5a5ead74f2f39cbe24d618b007.jpg\",\n  \"121045.jpg\": \"Insecta/Autographa gamma/dd66374ee64f9d1f662e13a42cf1e02b.jpg\",\n  \"121047.jpg\": \"Insecta/Autographa gamma/8c1e043322832e93d700db6501bf1b1f.jpg\",\n  \"121050.jpg\": \"Insecta/Autographa gamma/a99aa52b0b06ab088583d39b88f1d333.jpg\",\n  \"121051.jpg\": \"Insecta/Autographa gamma/fa5ba9527fe9ee216a5d4636be7774e4.jpg\",\n  \"121052.jpg\": \"Insecta/Autographa gamma/c8741aceeecdaae1934fb4605788a717.jpg\",\n  \"121053.jpg\": \"Insecta/Autographa gamma/552d4796cd3d9ef9f20c1b68dc4e428d.jpg\",\n  \"121055.jpg\": \"Insecta/Autographa gamma/0efe08959482fb61b23f6b96453be00b.jpg\",\n  \"121057.jpg\": \"Insecta/Autographa gamma/97ece39fd471776bad37b8cb01efbaa4.jpg\",\n  \"121060.jpg\": \"Insecta/Autographa gamma/bddd707fec85abb3b729c3e64e602ae7.jpg\",\n  \"121062.jpg\": \"Insecta/Autographa gamma/a2fd8e62e06a51cab1b70b44b0808deb.jpg\",\n  \"121063.jpg\": \"Insecta/Autographa gamma/8285286093313c78689f7839cac41b35.jpg\",\n  \"121064.jpg\": \"Insecta/Autographa gamma/70d9752a1b707171709565de57ffb733.jpg\",\n  \"121065.jpg\": \"Insecta/Autographa gamma/4a5a646ee314684ba5bba88fc920c7a9.jpg\",\n  \"121066.jpg\": \"Insecta/Autographa gamma/a1ed9347d5aaf1a7e24d45b5b8856558.jpg\",\n  \"121067.jpg\": \"Insecta/Autographa gamma/cabada1c50311ddb7d113917e08fe4dd.jpg\",\n  \"121068.jpg\": \"Insecta/Autographa gamma/ffb347fdce3ee3a8153db93fbd204d46.jpg\",\n  \"121069.jpg\": \"Insecta/Papilio polyxenes/c50423458252f9d9d9b1d1c4cd900b02.jpg\",\n  \"121080.jpg\": \"Insecta/Papilio polyxenes/3e47ebcf08806b4a77edb2d730aee956.jpg\",\n  \"121087.jpg\": \"Insecta/Papilio polyxenes/a944612e4603d1413550985d98ba4eee.jpg\",\n  \"121106.jpg\": \"Insecta/Papilio polyxenes/6187be26170ffabfd7740e129ebcd800.jpg\",\n  \"121113.jpg\": \"Insecta/Papilio polyxenes/4fd8f2315f776c87b5a6fb0d1d41cd39.jpg\",\n  \"121125.jpg\": \"Insecta/Papilio polyxenes/f44bf45d4b64ee2053f26e97942c2316.jpg\",\n  \"121126.jpg\": \"Insecta/Papilio polyxenes/371fec4b1d66c2caf2bbf40a9e98793e.jpg\",\n  \"121139.jpg\": \"Insecta/Papilio polyxenes/fe455cf47d3d18999b798a65d3fce318.jpg\",\n  \"121151.jpg\": \"Insecta/Papilio polyxenes/a89da767a640e42f65bcafdbe46c524f.jpg\",\n  \"121167.jpg\": \"Insecta/Papilio polyxenes/aeb47a89a6c6a3412956d1285773ba80.jpg\",\n  \"121181.jpg\": \"Insecta/Papilio polyxenes/50fc8e3aa2ef318983df2876d0a0aebb.jpg\",\n  \"121186.jpg\": \"Insecta/Papilio polyxenes/690598322bda27a2e4b925dff1ef5c6e.jpg\",\n  \"121241.jpg\": \"Insecta/Papilio polyxenes/9ea9f1622d69b336505cdc6835edac10.jpg\",\n  \"121242.jpg\": \"Insecta/Papilio polyxenes/6d9321491f5f0c6513fdb847bfa604c1.jpg\",\n  \"121244.jpg\": \"Insecta/Papilio polyxenes/c23ae0ee6ad5a67cf5cb764e25798fc5.jpg\",\n  \"121245.jpg\": \"Insecta/Papilio polyxenes/1d933bc9b2c66ed3bb7104b54ae8c30f.jpg\",\n  \"121255.jpg\": \"Insecta/Papilio polyxenes/b7c68376bf7739b4f98fd156f2add9c1.jpg\",\n  \"121267.jpg\": \"Insecta/Papilio polyxenes/98ec8c943ac498e8cba04dfe09ea7340.jpg\",\n  \"121318.jpg\": \"Insecta/Papilio polyxenes/8345da52fb62ff021cf7abf578ef919c.jpg\",\n  \"121330.jpg\": \"Insecta/Papilio polyxenes/adc9b5f4936c8950581965aa136ac928.jpg\",\n  \"121336.jpg\": \"Insecta/Papilio polyxenes/99199af0f58d161c80cc40eafaed6fbe.jpg\",\n  \"121352.jpg\": \"Insecta/Papilio polyxenes/78afa05c6f445289d2e64a8c8ba44f56.jpg\",\n  \"121356.jpg\": \"Insecta/Papilio polyxenes/924ee77b6adb1e1e4e9ee5a713587aaf.jpg\",\n  \"121383.jpg\": \"Insecta/Papilio polyxenes/0064eb69de7b4cf02eec64da2f3f996b.jpg\",\n  \"121427.jpg\": \"Reptilia/Plestiodon skiltonianus/5665c5252511c68b295b05df914060ab.jpg\",\n  \"121432.jpg\": \"Reptilia/Plestiodon skiltonianus/96bccb2aabd3baeb4b16d1a016090f7b.jpg\",\n  \"121439.jpg\": \"Reptilia/Plestiodon skiltonianus/9557ae964ed003567133f23bc8d4e130.jpg\",\n  \"121443.jpg\": \"Reptilia/Plestiodon skiltonianus/3310b4b0009c58c9e9cdfae56037b2ae.jpg\",\n  \"121444.jpg\": \"Reptilia/Plestiodon skiltonianus/6d21c80cf78c08b10a35272bba6a56c9.jpg\",\n  \"121451.jpg\": \"Reptilia/Plestiodon skiltonianus/8ce8dad1962b7baaf5285778cc3ca4c3.jpg\",\n  \"121452.jpg\": \"Reptilia/Plestiodon skiltonianus/e323ecef6fe57a0aba2b7e5de66d3d1d.jpg\",\n  \"121467.jpg\": \"Reptilia/Plestiodon skiltonianus/251453f006df9db9a9ec0f22bb359339.jpg\",\n  \"121476.jpg\": \"Reptilia/Plestiodon skiltonianus/440bbc0fbc8e779695d7847093b40e7a.jpg\",\n  \"121477.jpg\": \"Reptilia/Plestiodon skiltonianus/983bbcacd4b5d3f3f2e436b1f7b31d66.jpg\",\n  \"121478.jpg\": \"Reptilia/Plestiodon skiltonianus/71bcdaa381217da6f75cd16018846f46.jpg\",\n  \"121483.jpg\": \"Reptilia/Plestiodon skiltonianus/0dd6aca2dea6c15a8818179a931be2b0.jpg\",\n  \"121488.jpg\": \"Reptilia/Plestiodon skiltonianus/088066659ef4bc000eb295f4439914ba.jpg\",\n  \"121494.jpg\": \"Reptilia/Plestiodon skiltonianus/98bb102aa81d64d36255adc4f70e13be.jpg\",\n  \"121509.jpg\": \"Reptilia/Plestiodon skiltonianus/d1584f8fd97184aa91c801c1977ed97c.jpg\",\n  \"121515.jpg\": \"Reptilia/Plestiodon skiltonianus/0f6ab720cb28cb1ce556ef003e4baaa7.jpg\",\n  \"121519.jpg\": \"Reptilia/Plestiodon skiltonianus/4d214ae725a95825d2f47eca209ac0ab.jpg\",\n  \"121526.jpg\": \"Reptilia/Plestiodon skiltonianus/6d15386faf7fe6b001c6d8c28f572d7a.jpg\",\n  \"121596.jpg\": \"Insecta/Myodocha serripes/aff0e31c6555c751182b9683bc4b2770.jpg\",\n  \"121602.jpg\": \"Insecta/Myodocha serripes/830967afc2f02a5aed92cf62fb771c16.jpg\",\n  \"121603.jpg\": \"Insecta/Myodocha serripes/9f7e5c6017b2228bf8199231ae3ac431.jpg\",\n  \"121604.jpg\": \"Insecta/Myodocha serripes/38631f324cba8ccdeb315da98601ad1e.jpg\",\n  \"121612.jpg\": \"Insecta/Myodocha serripes/6688c2f8c36e85a7a8bffb85918fe33d.jpg\",\n  \"121616.jpg\": \"Insecta/Myodocha serripes/b022c54379dfef62081996fd4359ee30.jpg\",\n  \"121617.jpg\": \"Insecta/Myodocha serripes/ad21857991bde4ef6cf0130450641f7d.jpg\",\n  \"121620.jpg\": \"Insecta/Myodocha serripes/b0d23983c07b9e019dc07d1d7e895713.jpg\",\n  \"121878.jpg\": \"Aves/Sialia currucoides/ff84b96fac791fd6a2f8ebaa1c914005.jpg\",\n  \"121880.jpg\": \"Aves/Sialia currucoides/5d1b446bae584d6c459b42c58a37f9a7.jpg\",\n  \"121894.jpg\": \"Aves/Sialia currucoides/86fe6637ddce60fb3d2f5797577577bd.jpg\",\n  \"121900.jpg\": \"Aves/Sialia currucoides/3fb2c5dd8c8c21d73cf3597a56cd34e1.jpg\",\n  \"121923.jpg\": \"Aves/Sialia currucoides/7135a40f5865a3ea53b9b8d124e6d5db.jpg\",\n  \"121931.jpg\": \"Aves/Sialia currucoides/d3157bc9715526ab22084aa6ada4d533.jpg\",\n  \"121934.jpg\": \"Aves/Sialia currucoides/6d3abcfa28341503f3014093b5adb56b.jpg\",\n  \"121935.jpg\": \"Aves/Sialia currucoides/d54ec08a93150ff0bd492e2f6c5ddc0b.jpg\",\n  \"121936.jpg\": \"Aves/Sialia currucoides/84eb71a85866f89db50b92a74b7459ce.jpg\",\n  \"121937.jpg\": \"Aves/Sialia currucoides/7b889da567b327444e858ad255a8ccae.jpg\",\n  \"121941.jpg\": \"Aves/Sialia currucoides/3cd772aa27a90ec3ba21d59f8eab2344.jpg\",\n  \"121955.jpg\": \"Aves/Sialia currucoides/41570d917c470acba212650eee3d777a.jpg\",\n  \"121958.jpg\": \"Aves/Sialia currucoides/e116d7bcfbe6cb41d227417c0feebb58.jpg\",\n  \"121960.jpg\": \"Aves/Sialia currucoides/eb9ca1d88149486abbbe4388239de2f9.jpg\",\n  \"121968.jpg\": \"Aves/Sialia currucoides/3dd7940840d87e002d672ad12622e9c3.jpg\",\n  \"121978.jpg\": \"Aves/Sialia currucoides/e370590cd4ae57e37b137c5c2186d4d2.jpg\",\n  \"121986.jpg\": \"Aves/Sialia currucoides/18306eeee51134a6be2918facefa3198.jpg\",\n  \"121987.jpg\": \"Aves/Sialia currucoides/12c1ac5fe25840a6d9a965c4c99f8fac.jpg\",\n  \"121988.jpg\": \"Aves/Sialia currucoides/f309d955ec10aad1cf9fc39a70b6defd.jpg\",\n  \"122000.jpg\": \"Aves/Sialia currucoides/64fc7fcb40f748f115cff6e53091a567.jpg\",\n  \"122010.jpg\": \"Aves/Sialia currucoides/24e7d3c5fffb1b0144b6617a272d7bd3.jpg\",\n  \"122011.jpg\": \"Aves/Sialia currucoides/339415428bdda9fd38db480297237574.jpg\",\n  \"122013.jpg\": \"Actinopterygii/Pomoxis nigromaculatus/8cc4e560a57aa222d22f5c967f6beafc.jpg\",\n  \"122015.jpg\": \"Actinopterygii/Pomoxis nigromaculatus/16b15614fc9d1ec9dabb564ad60943ba.jpg\",\n  \"122019.jpg\": \"Actinopterygii/Pomoxis nigromaculatus/fca9f5b654a8eec103b98c514e7bfdf6.jpg\",\n  \"122094.jpg\": \"Insecta/Chrysopilus thoracicus/986262e0c08d288060c4a487a44e867b.jpg\",\n  \"122095.jpg\": \"Insecta/Chrysopilus thoracicus/0b475aafdff0e0601757853e94eda24c.jpg\",\n  \"122096.jpg\": \"Insecta/Chrysopilus thoracicus/2eb1301c3155a36d77a1eea7d5cdefcc.jpg\",\n  \"122099.jpg\": \"Insecta/Chrysopilus thoracicus/e20eb409424e9c8a22d1c0c12ac58439.jpg\",\n  \"122100.jpg\": \"Insecta/Chrysopilus thoracicus/2c632890ab93064ae7accae01c5b7907.jpg\",\n  \"122101.jpg\": \"Insecta/Chrysopilus thoracicus/d4e0fab8a4e0230efce6e0de56aa655b.jpg\",\n  \"122102.jpg\": \"Insecta/Chrysopilus thoracicus/aa8da2c9d9460817bb671292678c342e.jpg\",\n  \"122103.jpg\": \"Insecta/Chrysopilus thoracicus/5fa86401a543ce362b677c7149460ea0.jpg\",\n  \"122107.jpg\": \"Insecta/Chrysopilus thoracicus/657ce0077ec825bdc79a8ddc2e4194ee.jpg\",\n  \"122108.jpg\": \"Insecta/Chrysopilus thoracicus/3bd4d58bcc62b71964d07040166c2ae1.jpg\",\n  \"122109.jpg\": \"Insecta/Chrysopilus thoracicus/a5732e4c094af20eb2d283c0b7bb52f4.jpg\",\n  \"122110.jpg\": \"Insecta/Chrysopilus thoracicus/0ad23b5f4f3006aa6ae4df2ee5bb1c1d.jpg\",\n  \"122111.jpg\": \"Insecta/Chrysopilus thoracicus/3f2c87dfc8c9ce2c9fcf076d74b7a4f3.jpg\",\n  \"122114.jpg\": \"Insecta/Chrysopilus thoracicus/efa51424e50026e69e296deb911205cf.jpg\",\n  \"122115.jpg\": \"Insecta/Chrysopilus thoracicus/4b016e4ab2aed47bdf7b88b41a7354d1.jpg\",\n  \"122116.jpg\": \"Insecta/Chrysopilus thoracicus/32ebe5b961bad3b6cb51a3798c8174ef.jpg\",\n  \"122125.jpg\": \"Insecta/Chrysopilus thoracicus/3cf01923fff66fa48016f17ac31832d2.jpg\",\n  \"122129.jpg\": \"Insecta/Chrysopilus thoracicus/d0784db18a0df433061a035f637f196e.jpg\",\n  \"122130.jpg\": \"Insecta/Chrysopilus thoracicus/273b8f4d1f3ce53080d9305c65564873.jpg\",\n  \"122160.jpg\": \"Mammalia/Alouatta palliata/ba8cd1f73ee90ee478e903d22017b9cf.jpg\",\n  \"122161.jpg\": \"Mammalia/Alouatta palliata/e67f64fe5d461936792e1f3eba6d4447.jpg\",\n  \"122164.jpg\": \"Mammalia/Alouatta palliata/caaf301734f721e965194a104fa29670.jpg\",\n  \"122175.jpg\": \"Mammalia/Alouatta palliata/7ceab666c52bb4e87920e2d632b2b553.jpg\",\n  \"122176.jpg\": \"Mammalia/Alouatta palliata/16c9ffd1545510effee45aa159c79b78.jpg\",\n  \"122178.jpg\": \"Mammalia/Alouatta palliata/a4e81103b8cc2629dceb0972989fc0b8.jpg\",\n  \"122179.jpg\": \"Mammalia/Alouatta palliata/a8644d67bfb9133a8cd4e6ece6c7ba55.jpg\",\n  \"122188.jpg\": \"Mammalia/Alouatta palliata/d84171ccdca609946d2f112be1d61cd9.jpg\",\n  \"122189.jpg\": \"Mammalia/Alouatta palliata/0470f26471e7754e25832aa2c3e1937b.jpg\",\n  \"122194.jpg\": \"Insecta/Lucilia sericata/daa8e1d9ea858cd551ff0bb2be67565a.jpg\",\n  \"122196.jpg\": \"Insecta/Lucilia sericata/5a16a586e5c9397316cc125e03869afd.jpg\",\n  \"122201.jpg\": \"Insecta/Lucilia sericata/ca6da6ae0d202f1643cfca08c6e7b78b.jpg\",\n  \"122206.jpg\": \"Insecta/Schinia arcigera/ba3e5ab637f5f8bfbaae8886db77bec9.jpg\",\n  \"122210.jpg\": \"Insecta/Schinia arcigera/05fe43936211ec91847eee35922dc58a.jpg\",\n  \"122217.jpg\": \"Insecta/Schinia arcigera/430d60f468156650a471a2d24596a76f.jpg\",\n  \"122219.jpg\": \"Insecta/Schinia arcigera/579e160170c9cb4c14006a0a1f9d72c9.jpg\",\n  \"122253.jpg\": \"Insecta/Melanitis leda/5c584d6c81a50d2ccfdc643ff4990a8d.jpg\",\n  \"122254.jpg\": \"Insecta/Melanitis leda/5544c1343a70a66f0b73b123ed17526a.jpg\",\n  \"122259.jpg\": \"Insecta/Melanitis leda/06db9a0ddf35671b19103194bea369c6.jpg\",\n  \"122261.jpg\": \"Insecta/Melanitis leda/2cef3367725fb500e87d1b1b6bda6481.jpg\",\n  \"122263.jpg\": \"Insecta/Melanitis leda/ee0cc5ea3ee1e51fe3d6da28bbe7fb7c.jpg\",\n  \"12234.jpg\": \"Aves/Saxicola rubicola/3314c080c99041891917aeae862f9f07.jpg\",\n  \"12235.jpg\": \"Aves/Saxicola rubicola/469dfc421062c908d3fd836a3c2bb5a8.jpg\",\n  \"12236.jpg\": \"Aves/Saxicola rubicola/6fcfa960762cd31fa5bb7cd08f709cad.jpg\",\n  \"12237.jpg\": \"Aves/Saxicola rubicola/51364108d77b97c25a7c34f43e9cedb8.jpg\",\n  \"122379.jpg\": \"Aves/Meleagris ocellata/8da8cc70397f0e62b01e7a281f076bdf.jpg\",\n  \"12238.jpg\": \"Aves/Saxicola rubicola/78b15153adf22f4d5fc6aa16f8c21533.jpg\",\n  \"122380.jpg\": \"Aves/Meleagris ocellata/3a55ede2a17a354da213cdab3e6a2f02.jpg\",\n  \"122381.jpg\": \"Aves/Meleagris ocellata/1baf04a4de6f3403cad8c86784a4b5f0.jpg\",\n  \"122385.jpg\": \"Aves/Meleagris ocellata/f50cdeca07e696208bf7f4e1006b9640.jpg\",\n  \"12239.jpg\": \"Aves/Saxicola rubicola/7e4732cf7b66d33a8e2127cc4aa165a0.jpg\",\n  \"122391.jpg\": \"Aves/Meleagris ocellata/b2514643606e4534cb15c4c3b472e463.jpg\",\n  \"122392.jpg\": \"Mammalia/Ateles geoffroyi/258c090bb5091645a6fe7801f5339e5b.jpg\",\n  \"122393.jpg\": \"Mammalia/Ateles geoffroyi/42f72a6046c641975d1ac65bafe8cd99.jpg\",\n  \"122395.jpg\": \"Mammalia/Ateles geoffroyi/15b4315db1bb19eb650855b2a1221733.jpg\",\n  \"12240.jpg\": \"Aves/Saxicola rubicola/127f8f34bcbea51e39bbf2e2652c91de.jpg\",\n  \"122409.jpg\": \"Mammalia/Ateles geoffroyi/fd29b8ac2fcd651defdf9c35baf078a9.jpg\",\n  \"122410.jpg\": \"Mammalia/Ateles geoffroyi/a0089a4a0cd16baf22d43c47305e42e4.jpg\",\n  \"122412.jpg\": \"Mammalia/Ateles geoffroyi/8c96c4dfbec5786163ded3183fb9a345.jpg\",\n  \"122413.jpg\": \"Mammalia/Ateles geoffroyi/28966e523aa192d6da299d7ff9d96dba.jpg\",\n  \"122414.jpg\": \"Mammalia/Ateles geoffroyi/4abe90ba68e96a9f2b7134c0512540da.jpg\",\n  \"122415.jpg\": \"Mammalia/Ateles geoffroyi/a02c8fbdbf0033c0d578dd9ef4af984c.jpg\",\n  \"122416.jpg\": \"Mammalia/Ateles geoffroyi/425bd37c66f529a6a6bee08e84e0503c.jpg\",\n  \"122417.jpg\": \"Mammalia/Ateles geoffroyi/590d5ee75177ac70c3713eeb0ef6c079.jpg\",\n  \"122424.jpg\": \"Mammalia/Ateles geoffroyi/6db64711e3dce611e1ada8bf9f8ff743.jpg\",\n  \"122426.jpg\": \"Mammalia/Ateles geoffroyi/ea3ea6564a811e4ece511d7411bb07d0.jpg\",\n  \"12243.jpg\": \"Aves/Saxicola rubicola/0ae70c5fa830b080cd0f7676bdd451ef.jpg\",\n  \"122431.jpg\": \"Mammalia/Ateles geoffroyi/1af8191ea6555b9bd90530e8fe14ffcd.jpg\",\n  \"122432.jpg\": \"Mammalia/Ateles geoffroyi/8796e74cf7a337a81a26e5310bccf431.jpg\",\n  \"122433.jpg\": \"Mammalia/Ateles geoffroyi/375bf082de06246fa877e0063e208d17.jpg\",\n  \"122435.jpg\": \"Mammalia/Ateles geoffroyi/1d65981f51681a03cc5dbf1260b65699.jpg\",\n  \"12246.jpg\": \"Aves/Saxicola rubicola/808463d20e81ef0cb71ed50a394a6149.jpg\",\n  \"12247.jpg\": \"Aves/Saxicola rubicola/5d97838b956ef60ddd245f7642a7c92f.jpg\",\n  \"12248.jpg\": \"Aves/Saxicola rubicola/1191b6e7358b8be94c55a36c2f1f35d0.jpg\",\n  \"12249.jpg\": \"Aves/Saxicola rubicola/01f592299159ec5aa64f5f64cee7fce8.jpg\",\n  \"122508.jpg\": \"Insecta/Leucorrhinia hudsonica/c956c6e2d484281054c84448fcbc1baa.jpg\",\n  \"12251.jpg\": \"Aves/Saxicola rubicola/2ad84db38569086f6bf0e74f3e8ea0f6.jpg\",\n  \"122511.jpg\": \"Insecta/Leucorrhinia hudsonica/aaa2dbd1c72bb36545f37dfcad5a201b.jpg\",\n  \"122514.jpg\": \"Insecta/Leucorrhinia hudsonica/e185fd62123d652a6571bc13e27d56e0.jpg\",\n  \"122559.jpg\": \"Insecta/Charaxes jasius/845d209c25d54aca198bf70fcc78e376.jpg\",\n  \"12256.jpg\": \"Aves/Saxicola rubicola/6bef0a7834aa4abe3699067b2cbddf36.jpg\",\n  \"122565.jpg\": \"Insecta/Charaxes jasius/af003eb325f627b89eb00cfe579768ef.jpg\",\n  \"12257.jpg\": \"Aves/Saxicola rubicola/b1f88497d5d101e6517dc3922a341050.jpg\",\n  \"122570.jpg\": \"Insecta/Charaxes jasius/908c4d4cfa6811d3ee74903b2c53df9e.jpg\",\n  \"122571.jpg\": \"Insecta/Charaxes jasius/0cb5d703cc301363e2ed59b5ced3d030.jpg\",\n  \"122573.jpg\": \"Insecta/Cydia latiferreana/2d3fdccb745071c8b14024d691c83f3f.jpg\",\n  \"122576.jpg\": \"Insecta/Cydia latiferreana/6abaeb1bc3ca1f4f3975bca2e6680936.jpg\",\n  \"12258.jpg\": \"Aves/Saxicola rubicola/f4b45439932a044a62035f8b624b94a3.jpg\",\n  \"122581.jpg\": \"Insecta/Cydia latiferreana/501e8254334049db545ab4fcd1db684e.jpg\",\n  \"122589.jpg\": \"Insecta/Cydia latiferreana/ca97b946ab3825b7c4a8f40d5ffded8c.jpg\",\n  \"122598.jpg\": \"Insecta/Cydia latiferreana/0d8f67cc15e92675aa0992d53ada05b7.jpg\",\n  \"122599.jpg\": \"Insecta/Cydia latiferreana/37ca126f265ebd3fd10ff0338c4f1183.jpg\",\n  \"122604.jpg\": \"Insecta/Cydia latiferreana/2f156ee757932a9988ee792ceae74fbe.jpg\",\n  \"12262.jpg\": \"Aves/Saxicola rubicola/f2049f70922f77fb9feae01c8c4e7bf4.jpg\",\n  \"122636.jpg\": \"Insecta/Utetheisa ornatrix/f235cb1d4c4b7ac4a11e8ee5c149c8a0.jpg\",\n  \"12264.jpg\": \"Aves/Saxicola rubicola/a3097802500b621175c08a4703b48db5.jpg\",\n  \"122640.jpg\": \"Insecta/Utetheisa ornatrix/178f56a2761319459bdb4bf2e041ad08.jpg\",\n  \"122644.jpg\": \"Insecta/Utetheisa ornatrix/b2a15879a6c37293065065db5d5cb113.jpg\",\n  \"12265.jpg\": \"Aves/Saxicola rubicola/60dca082d3831b353acf68ee570886ce.jpg\",\n  \"122650.jpg\": \"Insecta/Utetheisa ornatrix/11557ee79fddfd70de293f20e572615e.jpg\",\n  \"122655.jpg\": \"Insecta/Utetheisa ornatrix/c9ce4bc87bdce2c6d694b1cc4a413dad.jpg\",\n  \"122658.jpg\": \"Insecta/Utetheisa ornatrix/fe29e5a8e12d9deced90eab41c715246.jpg\",\n  \"122659.jpg\": \"Insecta/Utetheisa ornatrix/66989067c07c238a69f08483c3bacf6f.jpg\",\n  \"122660.jpg\": \"Insecta/Utetheisa ornatrix/1081aa5449096dae394b615c0babeefa.jpg\",\n  \"122668.jpg\": \"Insecta/Lethe eurydice/48913ca830be0bc2caad0f8e5023ba9a.jpg\",\n  \"122673.jpg\": \"Insecta/Lethe eurydice/49915011439a6b81738b13c2c4d3ce2f.jpg\",\n  \"12268.jpg\": \"Aves/Saxicola rubicola/943f58309ccdb2bd9ebe35136bc5b486.jpg\",\n  \"122681.jpg\": \"Insecta/Lethe eurydice/8ab14460a5ac7742fa5a2e930e83fccf.jpg\",\n  \"122683.jpg\": \"Insecta/Lethe eurydice/3e8f881384201ac91cf1e808a35a0cbf.jpg\",\n  \"122685.jpg\": \"Insecta/Lethe eurydice/40aaad2c44c507e0bb5dd288ac4db0fa.jpg\",\n  \"12269.jpg\": \"Aves/Saxicola rubicola/aaf3d4fc790a66497f1fb5f2892424f4.jpg\",\n  \"122692.jpg\": \"Insecta/Iphiclides podalirius/5afd646332059c092158a7534c56c22a.jpg\",\n  \"122693.jpg\": \"Insecta/Iphiclides podalirius/582418d586ad14f2445befe10779614e.jpg\",\n  \"122696.jpg\": \"Insecta/Iphiclides podalirius/2d6bfbac06c44bc75d2406e25ff53755.jpg\",\n  \"122697.jpg\": \"Insecta/Iphiclides podalirius/3edce543f9873718117a6cabddab0dd3.jpg\",\n  \"122698.jpg\": \"Insecta/Iphiclides podalirius/fe077eed4ad8d3d5dd07d8c87e24a0de.jpg\",\n  \"122700.jpg\": \"Insecta/Iphiclides podalirius/5b4e8a8063aab51ab6163936362d1d52.jpg\",\n  \"122701.jpg\": \"Insecta/Iphiclides podalirius/c29e7a5e0254583e69e7177e4f65ce33.jpg\",\n  \"122702.jpg\": \"Insecta/Iphiclides podalirius/d96fa77bd9604db7968f7361d9f12d16.jpg\",\n  \"122706.jpg\": \"Insecta/Iphiclides podalirius/1c5020554094f2ca00ac76a6b216d8df.jpg\",\n  \"122707.jpg\": \"Insecta/Iphiclides podalirius/20de0829f98806d1529dd3be5222dc58.jpg\",\n  \"122708.jpg\": \"Insecta/Iphiclides podalirius/3cba4b9aa7510b2c5d7ef9d9f58151f5.jpg\",\n  \"122720.jpg\": \"Insecta/Iphiclides podalirius/c6a02c14482d28ede2ff07efceed0f6f.jpg\",\n  \"122722.jpg\": \"Insecta/Iphiclides podalirius/660b2f69f6d13df2ea17894301333e55.jpg\",\n  \"122723.jpg\": \"Insecta/Iphiclides podalirius/f95250c886cdd01c4315a06bd2b06356.jpg\",\n  \"122724.jpg\": \"Insecta/Iphiclides podalirius/c314a5e56f6c2ee984037cd7160d561a.jpg\",\n  \"122725.jpg\": \"Insecta/Iphiclides podalirius/1685ec0f0276ded672d40d2509d3e053.jpg\",\n  \"122726.jpg\": \"Insecta/Iphiclides podalirius/cac69824d6c854166fffce07e6ec99de.jpg\",\n  \"122727.jpg\": \"Insecta/Iphiclides podalirius/7fec5971f5b0439475db72d053cdd383.jpg\",\n  \"122730.jpg\": \"Insecta/Iphiclides podalirius/fd7ed89875d7a7f9d65faf9836bf3e83.jpg\",\n  \"122731.jpg\": \"Insecta/Iphiclides podalirius/6f5906690e2540f5f1ab3315d671051f.jpg\",\n  \"122732.jpg\": \"Insecta/Iphiclides podalirius/3f665b62c8be2ee7fcd6d12d31e04141.jpg\",\n  \"122740.jpg\": \"Insecta/Cupido comyntas/0b87c13212e7308dcc945eb8807d4c1c.jpg\",\n  \"122776.jpg\": \"Insecta/Cupido comyntas/432e1f3ea9e0e98a1e1a4862ee48713a.jpg\",\n  \"12281.jpg\": \"Reptilia/Crotaphytus collaris/072908f32b749ee28721236191418dc3.jpg\",\n  \"122825.jpg\": \"Insecta/Cupido comyntas/efa60fd1cf3ef8f990dfa5ce57193b9d.jpg\",\n  \"122853.jpg\": \"Insecta/Cupido comyntas/2f1759455a4c94c5a1c18df9b20def07.jpg\",\n  \"122855.jpg\": \"Insecta/Cupido comyntas/a1cfcfec4242c1bcdd6aa44ab93f76d0.jpg\",\n  \"122858.jpg\": \"Insecta/Cupido comyntas/a25ec52a05aa50330edb34bd49043898.jpg\",\n  \"122861.jpg\": \"Insecta/Cupido comyntas/4620262fc2464830f6c7c6c9e2e492d3.jpg\",\n  \"122884.jpg\": \"Insecta/Cupido comyntas/85b90571165b91434e5e958210c0b2c5.jpg\",\n  \"122891.jpg\": \"Insecta/Cupido comyntas/51d873de5281e0d0372dc18c511d77f8.jpg\",\n  \"122910.jpg\": \"Insecta/Cupido comyntas/a0f361a348ec8e658c980b0a8fc75bfd.jpg\",\n  \"122921.jpg\": \"Insecta/Cupido comyntas/4cd8723d5cdd0a1c25924c7c6176dbb2.jpg\",\n  \"122939.jpg\": \"Insecta/Cupido comyntas/59f2e90289e05740ba2e8de0abff5270.jpg\",\n  \"12294.jpg\": \"Reptilia/Crotaphytus collaris/4fcdbf1d569be3239d80bc84a7abd0d3.jpg\",\n  \"122941.jpg\": \"Insecta/Cupido comyntas/e37a0b5db563c0b54619d63a61b0c829.jpg\",\n  \"122944.jpg\": \"Insecta/Cupido comyntas/241f5f9f1bf113e36e49e97a88abc50a.jpg\",\n  \"122954.jpg\": \"Insecta/Cupido comyntas/b203da0b89caca9c87dd39a723f9a2ab.jpg\",\n  \"12296.jpg\": \"Reptilia/Crotaphytus collaris/51bce9396f3931ba5e49eaa2878714d9.jpg\",\n  \"12297.jpg\": \"Reptilia/Crotaphytus collaris/085f6d1155bb1cf49498595d3cbad62d.jpg\",\n  \"123.jpg\": \"Mammalia/Marmota flaviventris/3b288dd223448cca95aebb2348a7d999.jpg\",\n  \"123005.jpg\": \"Insecta/Cupido comyntas/8bee4873b0e34189b46c15417a3bce14.jpg\",\n  \"12301.jpg\": \"Reptilia/Crotaphytus collaris/0205eeb97a70497e1456cd178a4ac17e.jpg\",\n  \"123031.jpg\": \"Insecta/Cupido comyntas/7f84d90ff0525311684d268f265a8409.jpg\",\n  \"123032.jpg\": \"Insecta/Cupido comyntas/de53d7551c8b9d842d5834d646a85bc8.jpg\",\n  \"12307.jpg\": \"Reptilia/Crotaphytus collaris/ecb6685b5eb1c7e3e2b3b6b4c18b4771.jpg\",\n  \"123070.jpg\": \"Insecta/Cupido comyntas/6e86d6e194db24d4868ba64453bdd9b6.jpg\",\n  \"123079.jpg\": \"Insecta/Cupido comyntas/78366a9b6c330d2b234c0d4c30562943.jpg\",\n  \"123084.jpg\": \"Insecta/Cupido comyntas/760b84197b51a537a28f7812735ea9a6.jpg\",\n  \"123085.jpg\": \"Insecta/Cupido comyntas/f7598035e4877a9bad35435afe961020.jpg\",\n  \"12309.jpg\": \"Reptilia/Crotaphytus collaris/0e162fab688932a5682d178bdd5557eb.jpg\",\n  \"123103.jpg\": \"Insecta/Cupido comyntas/182932b8ac40ecea1bd33749abd4bdb6.jpg\",\n  \"123105.jpg\": \"Insecta/Cupido comyntas/b04e0d7413ee14ee5326519786e932b1.jpg\",\n  \"12315.jpg\": \"Reptilia/Crotaphytus collaris/836047b894171e6cf35369e89291d4be.jpg\",\n  \"12319.jpg\": \"Reptilia/Crotaphytus collaris/47edfd7e9952bfb848d4e43bc79eb74d.jpg\",\n  \"12320.jpg\": \"Reptilia/Crotaphytus collaris/332a97167f8a7349d1effbe70becf64b.jpg\",\n  \"123206.jpg\": \"Amphibia/Hyla arenicolor/e8ef6e2d7a9f3f44a3e1690ba3e2e075.jpg\",\n  \"123207.jpg\": \"Amphibia/Hyla arenicolor/ed615e71d6b1e4bc268499c037dcfb5d.jpg\",\n  \"123213.jpg\": \"Amphibia/Hyla arenicolor/126c60e93786fa6bd6ea61423401aebd.jpg\",\n  \"123230.jpg\": \"Amphibia/Hyla arenicolor/8f931b0694ec70f4c7751bab9d3c9050.jpg\",\n  \"123242.jpg\": \"Amphibia/Hyla arenicolor/09445733f0fbeda05b86317150c1fdc2.jpg\",\n  \"123244.jpg\": \"Amphibia/Hyla arenicolor/e9a0debc7b62d10f0254777470abb3dc.jpg\",\n  \"123245.jpg\": \"Amphibia/Hyla arenicolor/a9bde18fc0fe570cb442c94eade47861.jpg\",\n  \"123246.jpg\": \"Amphibia/Hyla arenicolor/9293e6f2064c9a4389ee214dde3d25eb.jpg\",\n  \"12325.jpg\": \"Reptilia/Crotaphytus collaris/92a1d109b401c2f673f2c8dad55a64ca.jpg\",\n  \"123251.jpg\": \"Amphibia/Hyla arenicolor/b7f44194210f6687b54918eecde0c25c.jpg\",\n  \"123259.jpg\": \"Amphibia/Hyla arenicolor/190c253f1b203c7a829dc2614d476dae.jpg\",\n  \"123267.jpg\": \"Amphibia/Hyla arenicolor/3071af1feec0ffab8672db1eb0d41810.jpg\",\n  \"123273.jpg\": \"Amphibia/Hyla arenicolor/571d5158927859fe1be5c70980479ed0.jpg\",\n  \"123274.jpg\": \"Amphibia/Hyla arenicolor/f8c9d8f613a69da720759789c5c63e76.jpg\",\n  \"123277.jpg\": \"Amphibia/Hyla arenicolor/0d9eae9af6eb50fa1dc951111ef99122.jpg\",\n  \"123278.jpg\": \"Amphibia/Hyla arenicolor/70895cccc03cfdc15c0b7416e3c421f6.jpg\",\n  \"123279.jpg\": \"Amphibia/Hyla arenicolor/6fbcca7c1124ccee1559481d57b4cd70.jpg\",\n  \"12328.jpg\": \"Reptilia/Crotaphytus collaris/7e298559825f56861e17e4e739896276.jpg\",\n  \"123283.jpg\": \"Amphibia/Hyla arenicolor/8c2ff8ea720b5a6f5419fe0f99611a90.jpg\",\n  \"123289.jpg\": \"Amphibia/Hyla arenicolor/eda84b39afe4fc9e833fa2bd82167710.jpg\",\n  \"123290.jpg\": \"Amphibia/Hyla arenicolor/f7d6a5024471ef902fd425323bf72da8.jpg\",\n  \"123291.jpg\": \"Amphibia/Hyla arenicolor/f6ab9c07572acbb9360d802b0636c18b.jpg\",\n  \"123292.jpg\": \"Amphibia/Hyla arenicolor/46e80a7371ba30ed97d85d809d7d13b0.jpg\",\n  \"123293.jpg\": \"Amphibia/Hyla arenicolor/169fc792f23c4d86e1c1117a0e3b03e6.jpg\",\n  \"123294.jpg\": \"Amphibia/Hyla arenicolor/5d85b44411f5d152bebaad05d3fdb422.jpg\",\n  \"123307.jpg\": \"Amphibia/Hyla arenicolor/d1700c1d84b3ad740bd9c07d9e76ceaa.jpg\",\n  \"12331.jpg\": \"Reptilia/Crotaphytus collaris/4bd0c2eb507d06d0e9ee9b22b86c1a1d.jpg\",\n  \"123320.jpg\": \"Insecta/Malacosoma californicum/945b7551d2c8c4cf483da708864982e9.jpg\",\n  \"12344.jpg\": \"Reptilia/Crotaphytus collaris/f96b0b4eac159dc752433e28d059a8bd.jpg\",\n  \"12349.jpg\": \"Reptilia/Crotaphytus collaris/26caf69de104fbc8b603626f154f1eb7.jpg\",\n  \"12351.jpg\": \"Reptilia/Crotaphytus collaris/0ee327972a9c61c010a853efa8b12d42.jpg\",\n  \"123536.jpg\": \"Amphibia/Hyla cinerea/afcdf3dfca11dd8912a0bfb6f712f15c.jpg\",\n  \"12354.jpg\": \"Reptilia/Crotaphytus collaris/dc6270e0e6a17a5a36e46e56cf1f63cd.jpg\",\n  \"123552.jpg\": \"Amphibia/Hyla cinerea/d5cfbcefce4f88d1efd8abc1a4e456e1.jpg\",\n  \"12356.jpg\": \"Reptilia/Crotaphytus collaris/5e69e8a06cc8692cf507c64d66ed3e69.jpg\",\n  \"123566.jpg\": \"Amphibia/Hyla cinerea/bb0f8788c5c57247bd72dae4b46574f1.jpg\",\n  \"123575.jpg\": \"Amphibia/Hyla cinerea/f67d4ded3e0a1e5ff72b82c4d747bc1f.jpg\",\n  \"12360.jpg\": \"Reptilia/Crotaphytus collaris/45113de17b723d3a2868a7619ce4dc79.jpg\",\n  \"123600.jpg\": \"Amphibia/Hyla cinerea/27cfe1ff3061493979e67d1379cf357b.jpg\",\n  \"123606.jpg\": \"Amphibia/Hyla cinerea/b83c1d26a0c969a20d3ee433024bff70.jpg\",\n  \"123615.jpg\": \"Amphibia/Hyla cinerea/b423bee9141331fb8539dcb2a228f5ab.jpg\",\n  \"123617.jpg\": \"Amphibia/Hyla cinerea/1783ac629f61995716c2c274ac6e7075.jpg\",\n  \"123624.jpg\": \"Amphibia/Hyla cinerea/f757de527f8e2835682a60e301fb013a.jpg\",\n  \"123630.jpg\": \"Amphibia/Hyla cinerea/71fad368762c0495fb8eebce400de595.jpg\",\n  \"123632.jpg\": \"Amphibia/Hyla cinerea/006d43a06f203c7f6c9cef66a9d83ae0.jpg\",\n  \"123671.jpg\": \"Amphibia/Hyla cinerea/95885a3f8537040883a06fe4d4def2c8.jpg\",\n  \"123678.jpg\": \"Amphibia/Hyla cinerea/710f416616b2285e1cee3eb1d86f1235.jpg\",\n  \"123698.jpg\": \"Amphibia/Hyla cinerea/3c2ae3106076a77e4d2caa238ffa1ef4.jpg\",\n  \"123710.jpg\": \"Amphibia/Hyla cinerea/968f7357c33cf41fe939ca116f48043d.jpg\",\n  \"123717.jpg\": \"Amphibia/Hyla cinerea/5e855697d5e83462a8cdd17728c1112f.jpg\",\n  \"123725.jpg\": \"Amphibia/Hyla cinerea/6edbe1e272dae94be56f89274eb8b915.jpg\",\n  \"123726.jpg\": \"Amphibia/Hyla cinerea/79235770e719668ee5d737413ea267e6.jpg\",\n  \"123727.jpg\": \"Amphibia/Hyla cinerea/6b24469f59983418f5674a95438e403a.jpg\",\n  \"12374.jpg\": \"Reptilia/Crotaphytus collaris/3900c7cc2715e03fe5567dfaa63e1b8a.jpg\",\n  \"123746.jpg\": \"Amphibia/Hyla cinerea/80ecda0ca233aba774a4073f2834bf4b.jpg\",\n  \"12375.jpg\": \"Reptilia/Crotaphytus collaris/bc2630f8066f1ef8f808efdc8563e22e.jpg\",\n  \"123773.jpg\": \"Amphibia/Hyla cinerea/4f7adfea353d0bfdab1ba1c7dc1927ab.jpg\",\n  \"123774.jpg\": \"Amphibia/Hyla cinerea/81e70d33fa04daf535687e5e055cc5da.jpg\",\n  \"12378.jpg\": \"Reptilia/Crotaphytus collaris/e65f7170a2e289356601acd874639b49.jpg\",\n  \"123796.jpg\": \"Insecta/Peridea angulosa/cd682e3fffa91d5464b78ab69278bedd.jpg\",\n  \"123798.jpg\": \"Insecta/Peridea angulosa/9f762b28becd1a6bd4a5a53a7de34dc4.jpg\",\n  \"123803.jpg\": \"Insecta/Peridea angulosa/01a282bfca5b1c1f56c592f144490124.jpg\",\n  \"123806.jpg\": \"Insecta/Peridea angulosa/50b9ff0ab7259c3920cac5ad3a284362.jpg\",\n  \"123818.jpg\": \"Insecta/Peridea angulosa/0c3b6b1912c1e1ff62f20bafc03498fb.jpg\",\n  \"12384.jpg\": \"Aves/Plectrophenax nivalis/3f41b832c704f184ccd3f0a81791047e.jpg\",\n  \"123843.jpg\": \"Insecta/Sympetrum madidum/f66b483544262ba3f95df090436c9eab.jpg\",\n  \"123848.jpg\": \"Insecta/Sympetrum madidum/172ca1dcac1579e33115815dec60bdd0.jpg\",\n  \"123854.jpg\": \"Insecta/Sympetrum madidum/99c8371c908b229725f2d0e750b01417.jpg\",\n  \"123867.jpg\": \"Insecta/Schistocerca americana/df38cecca4019b90d97a8528e1bfc416.jpg\",\n  \"123870.jpg\": \"Insecta/Schistocerca americana/7e7bafa576de424c11591dab7c4d34eb.jpg\",\n  \"123871.jpg\": \"Insecta/Schistocerca americana/3de1a4b98b43d0950aa05d20c854cd18.jpg\",\n  \"123877.jpg\": \"Insecta/Schistocerca americana/425b73a371e5fa69865c1f9a6aca62a2.jpg\",\n  \"123878.jpg\": \"Insecta/Schistocerca americana/28a90c614b940ee79d15d64e9a32a737.jpg\",\n  \"123879.jpg\": \"Insecta/Schistocerca americana/14784eb84623ddcfabf50da129d22e89.jpg\",\n  \"123882.jpg\": \"Insecta/Schistocerca americana/a702ec49e2400cf974bfbeaf563bc9c6.jpg\",\n  \"123883.jpg\": \"Insecta/Schistocerca americana/35c904dcfe3993dc36dabd05da4ec0a2.jpg\",\n  \"123884.jpg\": \"Insecta/Schistocerca americana/2cb135332680238eaab394ac645c5511.jpg\",\n  \"123894.jpg\": \"Insecta/Schistocerca americana/d98554af60a48a0248d91ce6f95a88f4.jpg\",\n  \"12390.jpg\": \"Aves/Plectrophenax nivalis/8fa3a8d977cf01ecda4ae8870f3adb37.jpg\",\n  \"123900.jpg\": \"Insecta/Hermetia illucens/74bdc4d4cd97c456192acf71fcf710d2.jpg\",\n  \"123904.jpg\": \"Insecta/Hermetia illucens/207c4e96e31ad358e6ca24ec52f22715.jpg\",\n  \"123905.jpg\": \"Insecta/Hermetia illucens/bf5908da3ea01c6b9dcc390e199faa3e.jpg\",\n  \"123912.jpg\": \"Insecta/Hermetia illucens/f0ea117c195d6ff664df9751c25bf3b9.jpg\",\n  \"123913.jpg\": \"Insecta/Hermetia illucens/f32261fd36b6f07c45a08a74abdeda95.jpg\",\n  \"123928.jpg\": \"Insecta/Hermetia illucens/c3da6fc4ce0a75fb66d27cce48a303d6.jpg\",\n  \"123930.jpg\": \"Insecta/Hermetia illucens/90378ce62d4c849b4ed420c6c13d2ea0.jpg\",\n  \"123931.jpg\": \"Insecta/Hermetia illucens/edb103bbb0301372cd3300d2d4ea4e6d.jpg\",\n  \"123932.jpg\": \"Insecta/Hermetia illucens/3ef5270373fe583a022f49390fb44654.jpg\",\n  \"123938.jpg\": \"Insecta/Otiorhynchus sulcatus/773d771254095bf952bfdc48101c9b27.jpg\",\n  \"123941.jpg\": \"Insecta/Otiorhynchus sulcatus/a324fcc6d36e01e9fbf37ac03e73e927.jpg\",\n  \"123946.jpg\": \"Insecta/Otiorhynchus sulcatus/89847c621ef873a124d1fd03f5699c19.jpg\",\n  \"123948.jpg\": \"Insecta/Otiorhynchus sulcatus/cea26e003e7ba4a6524d43089c7637f5.jpg\",\n  \"123949.jpg\": \"Insecta/Otiorhynchus sulcatus/efe83fb0d18eea051e380db6a68e3d65.jpg\",\n  \"12396.jpg\": \"Aves/Plectrophenax nivalis/2bef998f91d0a53a3d490d7cb827157c.jpg\",\n  \"124.jpg\": \"Mammalia/Marmota flaviventris/df2f4def57148d7e28c4057f7ffc1eb9.jpg\",\n  \"124006.jpg\": \"Insecta/Phoebis sennae/efbe48b352f3393000d2a270ed9a4bcd.jpg\",\n  \"124007.jpg\": \"Insecta/Phoebis sennae/8a2312e26040002ce622566d57a277b5.jpg\",\n  \"124016.jpg\": \"Insecta/Phoebis sennae/9a432aa338fcf52541d643e402c33edd.jpg\",\n  \"124023.jpg\": \"Insecta/Phoebis sennae/d8c829234d4b3c06fff6b4cfb9a2e8e6.jpg\",\n  \"124027.jpg\": \"Insecta/Phoebis sennae/1233e2abea7a0aed86503403afc9f3cc.jpg\",\n  \"124031.jpg\": \"Insecta/Phoebis sennae/e62ab8c0a490a5774c9349f10155a211.jpg\",\n  \"124038.jpg\": \"Insecta/Phoebis sennae/24ad20196043b6633940f44db6eae7f5.jpg\",\n  \"124039.jpg\": \"Insecta/Phoebis sennae/182296c7c0afc05ae03164998ac07501.jpg\",\n  \"124044.jpg\": \"Insecta/Phoebis sennae/fb5f00b889c7d261f877f073e11f41f1.jpg\",\n  \"124045.jpg\": \"Insecta/Phoebis sennae/54f2492e42160290dd33827baa80effb.jpg\",\n  \"124055.jpg\": \"Insecta/Phoebis sennae/c0e021c93ed2f98fdab6292d2e3574c0.jpg\",\n  \"124057.jpg\": \"Insecta/Phoebis sennae/823368da7608c9754581e1507e74c9d4.jpg\",\n  \"124060.jpg\": \"Insecta/Phoebis sennae/4dc9286e537376ebc7c7cc53d9338bcd.jpg\",\n  \"124061.jpg\": \"Insecta/Phoebis sennae/d16ddccb8c0cf5a6100acea8ebb9ed5c.jpg\",\n  \"124073.jpg\": \"Insecta/Phoebis sennae/342daeb5ae38d40e5217e02cf3fc01fc.jpg\",\n  \"124074.jpg\": \"Insecta/Phoebis sennae/c5002bcf50e719fd0b31bfeb748c92b9.jpg\",\n  \"124075.jpg\": \"Insecta/Phoebis sennae/733db531114e0c57122f87502549dd7c.jpg\",\n  \"12409.jpg\": \"Aves/Plectrophenax nivalis/5fa7349d6a089367cb1249110a43968e.jpg\",\n  \"12410.jpg\": \"Aves/Plectrophenax nivalis/d1086659f3cfb4633aa65b77a313bc70.jpg\",\n  \"124119.jpg\": \"Insecta/Phoebis sennae/d3eb4833ed6824353e87c58ae5b851ef.jpg\",\n  \"124129.jpg\": \"Insecta/Phoebis sennae/8b10884dec3cff6155b1e9bb10b8d8b5.jpg\",\n  \"124132.jpg\": \"Insecta/Phoebis sennae/2dee0f936de8e46f7bab83011b0a4f60.jpg\",\n  \"124136.jpg\": \"Insecta/Phoebis sennae/4871fb59743102d75a06f54b959de1e8.jpg\",\n  \"124149.jpg\": \"Insecta/Phoebis sennae/f991799f789f2cb0c7af903f63f8e952.jpg\",\n  \"124243.jpg\": \"Insecta/Vanessa atalanta/d7b1d3206c5f6fea8cc7f2e66c07dfd9.jpg\",\n  \"12426.jpg\": \"Aves/Plectrophenax nivalis/8660e5e894e6db29fce493a9ea0f422c.jpg\",\n  \"12427.jpg\": \"Aves/Plectrophenax nivalis/42b623b72e232093c2af6a06dc4edfb6.jpg\",\n  \"12428.jpg\": \"Aves/Plectrophenax nivalis/c9c00e1c0be280d6963c492c5e0de41b.jpg\",\n  \"124305.jpg\": \"Insecta/Vanessa atalanta/0fd1f14e3ccd597ab26a2f6abb9e52b6.jpg\",\n  \"12432.jpg\": \"Aves/Plectrophenax nivalis/155672ef06e9ede0a384fd770bc0047c.jpg\",\n  \"124333.jpg\": \"Insecta/Vanessa atalanta/71c174331fcf8db4b7827b7b7e5048a0.jpg\",\n  \"124392.jpg\": \"Insecta/Vanessa atalanta/49d50a6a41847dd551b4c5f78cdb6a3e.jpg\",\n  \"12442.jpg\": \"Aves/Plectrophenax nivalis/42787e2e3b2c718c6ea8345efe31f7d1.jpg\",\n  \"12443.jpg\": \"Aves/Plectrophenax nivalis/908a1130c5db33d56151523fca93f9ea.jpg\",\n  \"124432.jpg\": \"Insecta/Vanessa atalanta/a95460ef6da64445177b08b95d679967.jpg\",\n  \"12444.jpg\": \"Aves/Plectrophenax nivalis/457a6e2be04643dc5089cf901fe13cac.jpg\",\n  \"12445.jpg\": \"Aves/Plectrophenax nivalis/8c6cb141681093e4074851fe3e324b25.jpg\",\n  \"124463.jpg\": \"Insecta/Vanessa atalanta/de6813fa64814782d077f6b5b7985585.jpg\",\n  \"124492.jpg\": \"Insecta/Vanessa atalanta/a93f391bf7046f3d1d0fc5c94d1d9a20.jpg\",\n  \"12450.jpg\": \"Insecta/Ischnura elegans/51c47b01784bde77a7e41ac6563d46a6.jpg\",\n  \"12451.jpg\": \"Insecta/Ischnura elegans/6a888a29fbf8b5624a00cc92118cc8a8.jpg\",\n  \"12452.jpg\": \"Insecta/Ischnura elegans/9fc428b313e8b41125676b4eaabff694.jpg\",\n  \"124521.jpg\": \"Insecta/Vanessa atalanta/03f389dea5c8da8c567d798420597326.jpg\",\n  \"12457.jpg\": \"Insecta/Ischnura elegans/0cc20a07a397fc11b7c65f6fc5c4809e.jpg\",\n  \"124581.jpg\": \"Insecta/Vanessa atalanta/965262490faee2bf7c26531e6fa4bff2.jpg\",\n  \"124587.jpg\": \"Insecta/Vanessa atalanta/25f2a01b8a3769b732b835b512d613f3.jpg\",\n  \"12459.jpg\": \"Insecta/Ischnura elegans/7dc6ba3209217aeb558976fbba5f461d.jpg\",\n  \"124596.jpg\": \"Insecta/Vanessa atalanta/59b772dd4a0b816fee343a3cdd25c310.jpg\",\n  \"12460.jpg\": \"Insecta/Ischnura elegans/2c77a396dd2e286599e868a3c944ef5c.jpg\",\n  \"12461.jpg\": \"Insecta/Ischnura elegans/b9481039a6e377e1638d45abe997a8b6.jpg\",\n  \"124610.jpg\": \"Insecta/Vanessa atalanta/ab1d8aabc5c5a03d77bafa1ca81eaa0b.jpg\",\n  \"12462.jpg\": \"Insecta/Ischnura elegans/a5d15df205287f41a6b26cda80abf9f2.jpg\",\n  \"124647.jpg\": \"Insecta/Vanessa atalanta/b3b7bf6d18ea3635854acee2f26be325.jpg\",\n  \"124654.jpg\": \"Insecta/Vanessa atalanta/5ed931b2710b092e91d66eb8745492be.jpg\",\n  \"12466.jpg\": \"Insecta/Ischnura elegans/e3031a64f1e052b6fab9b0de44317ca9.jpg\",\n  \"124669.jpg\": \"Insecta/Vanessa atalanta/68b8fde1d9271b27fa11ab7362f6a77a.jpg\",\n  \"12467.jpg\": \"Insecta/Ischnura elegans/c52b5f492182d738312b97b6b5e58272.jpg\",\n  \"124679.jpg\": \"Insecta/Vanessa atalanta/56071d2e0860a4226b66b175d29b3ced.jpg\",\n  \"124729.jpg\": \"Insecta/Vanessa atalanta/badc42cd7ad5837ded989b8f1be0b305.jpg\",\n  \"12476.jpg\": \"Insecta/Ischnura elegans/20aa5c97e6f4b1bac2bf48631ebe0fd1.jpg\",\n  \"12488.jpg\": \"Insecta/Ischnura elegans/9d6aeeff2049d2f3e5055b5cf21a1816.jpg\",\n  \"124882.jpg\": \"Insecta/Vanessa atalanta/765b2280b8354d822d1644c0071333d9.jpg\",\n  \"12489.jpg\": \"Insecta/Ischnura elegans/1ad0a348615b40287b67a91ba082d362.jpg\",\n  \"12491.jpg\": \"Insecta/Ischnura elegans/5c2b334b37b95278b1823438d672dbed.jpg\",\n  \"12494.jpg\": \"Insecta/Ischnura elegans/3b5c73f01906bd43eab297d26942e75b.jpg\",\n  \"124944.jpg\": \"Insecta/Vanessa atalanta/3a5f3306b1e6bc063a4ccd8548215ddf.jpg\",\n  \"125024.jpg\": \"Insecta/Vanessa atalanta/1a9e533a8e0761e92d36fa6a95d1c035.jpg\",\n  \"125059.jpg\": \"Insecta/Vanessa atalanta/f213e0bd7914a2b701171edc6797376a.jpg\",\n  \"12506.jpg\": \"Insecta/Ischnura elegans/d2832826d09b9418bbef94e32fecb3eb.jpg\",\n  \"12507.jpg\": \"Insecta/Chauliognathus marginatus/581d193010c6ca9b6e64a67bf89282a0.jpg\",\n  \"12508.jpg\": \"Insecta/Chauliognathus marginatus/93eb88f4be63bb1b0b1b0998504aa936.jpg\",\n  \"12511.jpg\": \"Insecta/Chauliognathus marginatus/567b671511694df1575ecb1afdb6edf7.jpg\",\n  \"125114.jpg\": \"Insecta/Apiomerus californicus/794d1eed00b9d233261c87664e4f9a87.jpg\",\n  \"125118.jpg\": \"Insecta/Apiomerus californicus/7fb3231472f0919b91f02e59518056f1.jpg\",\n  \"125119.jpg\": \"Insecta/Apiomerus californicus/541f78f9c16e209840912b05f82d192a.jpg\",\n  \"12512.jpg\": \"Insecta/Chauliognathus marginatus/11a9e27285ba59d24de748abec73bdb3.jpg\",\n  \"12513.jpg\": \"Insecta/Chauliognathus marginatus/70c817cf9fb392256b6cf31e3d87d736.jpg\",\n  \"12514.jpg\": \"Insecta/Chauliognathus marginatus/fbcf7ee0f95dccd554dd1e95745a1c90.jpg\",\n  \"125141.jpg\": \"Amphibia/Scaphiopus couchii/4ada8f4dbe3cadeb7c60ecd5cddac03b.jpg\",\n  \"125142.jpg\": \"Amphibia/Scaphiopus couchii/8d3f06e786b14e586904077b9c599e79.jpg\",\n  \"12515.jpg\": \"Insecta/Chauliognathus marginatus/1c9364a5a75b1c457f1e89c78502e2c2.jpg\",\n  \"125151.jpg\": \"Amphibia/Scaphiopus couchii/f30b9a974fee9506c0fb91e0a2485b21.jpg\",\n  \"125152.jpg\": \"Amphibia/Scaphiopus couchii/81d34ba10bb807541c76c82aabd80fb4.jpg\",\n  \"125157.jpg\": \"Amphibia/Scaphiopus couchii/f1e053b85b4169572549131deda8a4e2.jpg\",\n  \"125161.jpg\": \"Amphibia/Scaphiopus couchii/2c4b2f3760000a1f26196dd4c13aef55.jpg\",\n  \"125162.jpg\": \"Amphibia/Scaphiopus couchii/76f2b2b5bac35a5cbcdfdb286afb68f1.jpg\",\n  \"125168.jpg\": \"Amphibia/Scaphiopus couchii/1976a7a24032194bbfddeead101b6ea9.jpg\",\n  \"125169.jpg\": \"Amphibia/Scaphiopus couchii/3a7642232d554409161a82a5f51331ce.jpg\",\n  \"12518.jpg\": \"Insecta/Chauliognathus marginatus/971b89f12cf4611112a9eca17be9f7e6.jpg\",\n  \"12519.jpg\": \"Insecta/Chauliognathus marginatus/4e116071b8258ef740fe90a794778aef.jpg\",\n  \"125193.jpg\": \"Amphibia/Scaphiopus couchii/cb3a88c9bd745cdb0aa1fc48cca6f76b.jpg\",\n  \"125201.jpg\": \"Amphibia/Scaphiopus couchii/dbb5cbee719e90f3e1d6f3f6751ac906.jpg\",\n  \"12523.jpg\": \"Insecta/Chauliognathus marginatus/d986e55555d1134122628e0ba21a2fa3.jpg\",\n  \"125235.jpg\": \"Amphibia/Scaphiopus couchii/6d56e14e802b195b5c4947dc055ccb0e.jpg\",\n  \"125239.jpg\": \"Amphibia/Scaphiopus couchii/2ea6b6e816b35fc148ecab72bb3c1d67.jpg\",\n  \"12524.jpg\": \"Insecta/Chauliognathus marginatus/7d100572d91678fe8fa44a449431e407.jpg\",\n  \"125246.jpg\": \"Amphibia/Scaphiopus couchii/fe96057d1926f1be26f2407868c21326.jpg\",\n  \"125247.jpg\": \"Amphibia/Scaphiopus couchii/3b8505fdf7151f684057ecd01e1fad50.jpg\",\n  \"125248.jpg\": \"Amphibia/Scaphiopus couchii/1477f725ad953696b6f2fedf0644a805.jpg\",\n  \"125250.jpg\": \"Amphibia/Scaphiopus couchii/1cc95dce3e53c38ea8fc18c9f420d1e9.jpg\",\n  \"125251.jpg\": \"Amphibia/Scaphiopus couchii/3b61ef3b554eafebeee4612274c54b55.jpg\",\n  \"125252.jpg\": \"Amphibia/Scaphiopus couchii/082362d6e04e3ba900a41c99d34125f7.jpg\",\n  \"125254.jpg\": \"Amphibia/Scaphiopus couchii/55f4ee78e484037639336cbe7fda75ae.jpg\",\n  \"12526.jpg\": \"Insecta/Chauliognathus marginatus/f71535dc8286fbab8ca026eeedc9a86c.jpg\",\n  \"125263.jpg\": \"Amphibia/Scaphiopus couchii/bdb0c009d757e2979a8d173987d4b973.jpg\",\n  \"125268.jpg\": \"Amphibia/Scaphiopus couchii/9417f93b9ca3e6e7f4e1598c60c1ab08.jpg\",\n  \"12527.jpg\": \"Insecta/Chauliognathus marginatus/da5f650c3b6951f37a9f619cfd81b8d2.jpg\",\n  \"12528.jpg\": \"Insecta/Chauliognathus marginatus/1d2a9250cbb2499756f18686e7194699.jpg\",\n  \"12529.jpg\": \"Insecta/Chauliognathus marginatus/f8765f78e717b3dbc16a71897ee95db2.jpg\",\n  \"125299.jpg\": \"Insecta/Panthea furcilla/96e9b44778cc59c67e336fa117bc11e6.jpg\",\n  \"125312.jpg\": \"Insecta/Panthea furcilla/07bb797241f603905a5f71e79f34c6ba.jpg\",\n  \"125313.jpg\": \"Insecta/Panthea furcilla/1e37b9a942ff45321895fde8e54c8563.jpg\",\n  \"125315.jpg\": \"Insecta/Panthea furcilla/448b7d18ddd2940bd666a39ed9807c45.jpg\",\n  \"125316.jpg\": \"Insecta/Panthea furcilla/60366c4d64722e472f4a604c095de34c.jpg\",\n  \"125318.jpg\": \"Insecta/Panthea furcilla/7cade40bc62314dffbe5e6c681816e7f.jpg\",\n  \"12542.jpg\": \"Insecta/Chauliognathus marginatus/eba4d7b8ed1e7ad95fb9a9f0a94743bd.jpg\",\n  \"12549.jpg\": \"Aves/Crotophaga sulcirostris/d167d136f01758126a61ee463edd620a.jpg\",\n  \"12580.jpg\": \"Aves/Crotophaga sulcirostris/415e2ed45e59426798dff26fd45ee914.jpg\",\n  \"12583.jpg\": \"Aves/Crotophaga sulcirostris/2a34a61c8ae36113b8796da03d9f68f4.jpg\",\n  \"125895.jpg\": \"Mammalia/Sciurus vulgaris/ed6b48ad5b5039e59a1642e158e7195d.jpg\",\n  \"125896.jpg\": \"Mammalia/Sciurus vulgaris/fbeef0dd751e1ce49aee8b4664e474f9.jpg\",\n  \"125901.jpg\": \"Mammalia/Sciurus vulgaris/ffb195e196e22a9c2bbc146925aac962.jpg\",\n  \"125910.jpg\": \"Mammalia/Sciurus vulgaris/8ee0114d80d9dd9200491089005e0a73.jpg\",\n  \"125911.jpg\": \"Mammalia/Sciurus vulgaris/03f78785e7fb0acc85b5e4f90e02d4e2.jpg\",\n  \"125913.jpg\": \"Mammalia/Sciurus vulgaris/eae7198dace949eab91e42d6cc7b8cce.jpg\",\n  \"125915.jpg\": \"Mammalia/Sciurus vulgaris/1d05982abc173da7723f33f3ba152bee.jpg\",\n  \"125916.jpg\": \"Mammalia/Sciurus vulgaris/335c9964adf62dc5b9f8306e95764f3e.jpg\",\n  \"125919.jpg\": \"Mammalia/Sciurus vulgaris/3c9bdec8c2e410019786a653618ed743.jpg\",\n  \"125932.jpg\": \"Mammalia/Sciurus vulgaris/9b9f1d758131ae06e9a39dd6057c751b.jpg\",\n  \"125935.jpg\": \"Mammalia/Sciurus vulgaris/4494d1c157216f3e63de47f2266cea15.jpg\",\n  \"125940.jpg\": \"Mammalia/Sciurus vulgaris/d4c178c1a987e3ae2b54513bea984c8c.jpg\",\n  \"125942.jpg\": \"Mammalia/Sciurus vulgaris/78d77dbfa538f8bcbd4c484b13bc33c2.jpg\",\n  \"125946.jpg\": \"Mammalia/Sciurus vulgaris/60322afd590ba8f387c102eae7865ee1.jpg\",\n  \"125950.jpg\": \"Mammalia/Sciurus vulgaris/746eaebadad7d8d423332d6d8dbb5eb4.jpg\",\n  \"125960.jpg\": \"Mammalia/Sciurus vulgaris/ba44d343907fbc2f35a93c46b6a20516.jpg\",\n  \"125965.jpg\": \"Mammalia/Sciurus vulgaris/8c3cc6c50856d48856c87fc383689ab0.jpg\",\n  \"125967.jpg\": \"Mammalia/Sciurus vulgaris/a912d0677343b2a43e1d090446bc1aaa.jpg\",\n  \"125969.jpg\": \"Mammalia/Sciurus vulgaris/affdf8f43ecd1578ada43660d07bf94d.jpg\",\n  \"125970.jpg\": \"Mammalia/Sciurus vulgaris/f18ce7e4044c1f3d319b7b888fb7e91c.jpg\",\n  \"125971.jpg\": \"Mammalia/Sciurus vulgaris/17e4ce9c1100ac33b361639dfee4d9d6.jpg\",\n  \"125978.jpg\": \"Mammalia/Sciurus vulgaris/5872160a69b6dc415c5217cc3ad6ec75.jpg\",\n  \"125982.jpg\": \"Mammalia/Sciurus vulgaris/6cc88bc023c96e125c060f536175c978.jpg\",\n  \"125984.jpg\": \"Mammalia/Sciurus vulgaris/18a43137ff9b23a19d04ec7394cc564b.jpg\",\n  \"125997.jpg\": \"Animalia/Dermasterias imbricata/b2553e407048d5f772114343ffc3037e.jpg\",\n  \"125998.jpg\": \"Animalia/Dermasterias imbricata/76e13f5f2ea897a8ca354eea56f1b4d6.jpg\",\n  \"126002.jpg\": \"Animalia/Dermasterias imbricata/e903f17497f1d884c848d42d1405aab9.jpg\",\n  \"126010.jpg\": \"Animalia/Dermasterias imbricata/847541129b6c3395e2e2860bdee4a75c.jpg\",\n  \"126011.jpg\": \"Animalia/Dermasterias imbricata/d1281fbc33418b3e82b179af657e1d45.jpg\",\n  \"126020.jpg\": \"Animalia/Dermasterias imbricata/768f0da4ddd6e9fdeefefa113ab3afef.jpg\",\n  \"126021.jpg\": \"Animalia/Dermasterias imbricata/8d125f5953a656c95b17e43b1b2a2119.jpg\",\n  \"126022.jpg\": \"Animalia/Dermasterias imbricata/7a1b72ad38dce88351ae53c5fdbb6e96.jpg\",\n  \"126028.jpg\": \"Animalia/Dermasterias imbricata/f8f0b32bb361b9ab1e91640fb1ca5791.jpg\",\n  \"126037.jpg\": \"Animalia/Dermasterias imbricata/7b3d15348d9c96fc25fe9b14137b21e4.jpg\",\n  \"126042.jpg\": \"Animalia/Dermasterias imbricata/eac569324c3b9751d35aa12ba0d8fb62.jpg\",\n  \"126053.jpg\": \"Animalia/Dermasterias imbricata/1ef98f0abb5b37f1939b0ed6baa0adec.jpg\",\n  \"126059.jpg\": \"Animalia/Dermasterias imbricata/e59f180538838ba656d87232f11df99d.jpg\",\n  \"126063.jpg\": \"Animalia/Dermasterias imbricata/554f8be3912057f97c571ce82876b808.jpg\",\n  \"126064.jpg\": \"Animalia/Dermasterias imbricata/84a88a8ab745c456296dc93e70a01167.jpg\",\n  \"126065.jpg\": \"Animalia/Dermasterias imbricata/cb9cb7b5daccfe0e5bef5ec07e134e02.jpg\",\n  \"12608.jpg\": \"Aves/Crotophaga sulcirostris/16db2f06875626dfab03088f4aa936cc.jpg\",\n  \"126086.jpg\": \"Animalia/Dermasterias imbricata/a65f4a8f66a4be640dc2668c3a2ffe4e.jpg\",\n  \"12609.jpg\": \"Aves/Crotophaga sulcirostris/aaa68718813eb7d7ed80d4cf84a039f3.jpg\",\n  \"126090.jpg\": \"Animalia/Dermasterias imbricata/a73a3236edeb6908935d8b8c0c94c6a2.jpg\",\n  \"126091.jpg\": \"Animalia/Dermasterias imbricata/44fe75eacfebe4f80dbd911e9493e146.jpg\",\n  \"126099.jpg\": \"Animalia/Dermasterias imbricata/0a57f691730aa5e531d0c24bf9c87d6d.jpg\",\n  \"126101.jpg\": \"Animalia/Dermasterias imbricata/f63f74727a209fd5c857d13564c08c72.jpg\",\n  \"126103.jpg\": \"Animalia/Dermasterias imbricata/9fcda71260ac543ec3368f7a7f7e6f49.jpg\",\n  \"126191.jpg\": \"Insecta/Gonepteryx rhamni/ccfb8805c623eaaea930d914803c3fa0.jpg\",\n  \"126192.jpg\": \"Insecta/Gonepteryx rhamni/79eda09dff63c84e208833781d68ab23.jpg\",\n  \"126194.jpg\": \"Insecta/Gonepteryx rhamni/f68fca058f08e683b8175e5d31caf8aa.jpg\",\n  \"126195.jpg\": \"Insecta/Gonepteryx rhamni/92732481223c2c924e1efa7b0f25c7fb.jpg\",\n  \"126198.jpg\": \"Insecta/Gonepteryx rhamni/3f63a12ccecbd26c660cbd8d48ba61e7.jpg\",\n  \"126203.jpg\": \"Insecta/Gonepteryx rhamni/19a61e24d973b8f05c98374db8bdf297.jpg\",\n  \"126209.jpg\": \"Insecta/Gonepteryx rhamni/e74165f04869a4744ea88997d90727a8.jpg\",\n  \"12621.jpg\": \"Aves/Crotophaga sulcirostris/77abfec0e97e1f42ea91a717b9c07109.jpg\",\n  \"126210.jpg\": \"Insecta/Gonepteryx rhamni/c4822c845a6dd159a21a16944b204ed2.jpg\",\n  \"126212.jpg\": \"Insecta/Gonepteryx rhamni/f80f61a9c62733a764ac010d0c3730d4.jpg\",\n  \"12622.jpg\": \"Aves/Crotophaga sulcirostris/cca52c5f19d9f8133ace1e9f09926397.jpg\",\n  \"126263.jpg\": \"Aves/Bucephala albeola/62d5ae983eefc25a342a6ae434ad01e4.jpg\",\n  \"12629.jpg\": \"Aves/Crotophaga sulcirostris/e90f83f343adb8236ebca786aaac4642.jpg\",\n  \"126330.jpg\": \"Aves/Bucephala albeola/73d6030ddca163adf980dfed13c56de2.jpg\",\n  \"126358.jpg\": \"Aves/Bucephala albeola/fe37804cb7adc02e8f0000da8d405aa9.jpg\",\n  \"12636.jpg\": \"Aves/Crotophaga sulcirostris/2358eaf2133f8e028f3e92a7fcb89ffc.jpg\",\n  \"12643.jpg\": \"Aves/Crotophaga sulcirostris/a98a9aae17655282ff0dcbc6f82ec1a0.jpg\",\n  \"126445.jpg\": \"Aves/Bucephala albeola/c213e7cfbd4978addb800137d0827185.jpg\",\n  \"126472.jpg\": \"Aves/Bucephala albeola/3a5045681163dc1ba2cfb337e1a01cdf.jpg\",\n  \"126487.jpg\": \"Aves/Bucephala albeola/783027600a6db3b66348cc7ba965b438.jpg\",\n  \"126497.jpg\": \"Aves/Bucephala albeola/73ec40f353bc7bc219fa9ad500eca652.jpg\",\n  \"126517.jpg\": \"Aves/Bucephala albeola/194f0a1886bef7cfe67067cd325d9970.jpg\",\n  \"126535.jpg\": \"Aves/Bucephala albeola/5b3a79f3d0eee920fc71f1517d6bc840.jpg\",\n  \"126554.jpg\": \"Aves/Bucephala albeola/385494d23452e9712a639a155cdf05e2.jpg\",\n  \"126567.jpg\": \"Aves/Bucephala albeola/4c604474f8267fc4f3a6dd238ed5db0a.jpg\",\n  \"126622.jpg\": \"Aves/Bucephala albeola/e5635a04389cf4936286a76c6ec97cce.jpg\",\n  \"126661.jpg\": \"Aves/Bucephala albeola/275b92f6e82c3e57de34f5922e0b057f.jpg\",\n  \"12668.jpg\": \"Aves/Crotophaga sulcirostris/b6c5d40207e68324582630db08a1930e.jpg\",\n  \"12674.jpg\": \"Aves/Crotophaga sulcirostris/06581886c8a7d16f069c72ad8d8a45bb.jpg\",\n  \"12676.jpg\": \"Aves/Crotophaga sulcirostris/b9a8313b0618929f3585457e7a235d30.jpg\",\n  \"12680.jpg\": \"Aves/Crotophaga sulcirostris/dea1fe553ec5236cafc660b21f166958.jpg\",\n  \"12687.jpg\": \"Aves/Crotophaga sulcirostris/db106c94fbf7ec7d3a57421248db6d6c.jpg\",\n  \"126903.jpg\": \"Mollusca/Clinocardium nuttallii/8ecf5705b7b2fc1680ef9a77e5eba86d.jpg\",\n  \"12691.jpg\": \"Aves/Crotophaga sulcirostris/6c83a383439abfd8d5a75fde7c670652.jpg\",\n  \"126911.jpg\": \"Mollusca/Clinocardium nuttallii/326d8e65abb7ab67e4d3c0181be9b5bf.jpg\",\n  \"126918.jpg\": \"Mollusca/Clinocardium nuttallii/fa8382239415bdcccd6606332b050e0b.jpg\",\n  \"126919.jpg\": \"Mollusca/Clinocardium nuttallii/1b64da24c001fc4a193455debb64c979.jpg\",\n  \"126924.jpg\": \"Mollusca/Clinocardium nuttallii/e4113c380beab314af7e42d064d4e6e3.jpg\",\n  \"126926.jpg\": \"Mollusca/Clinocardium nuttallii/54fc010a930394408b15128f2d77dee1.jpg\",\n  \"126927.jpg\": \"Mollusca/Clinocardium nuttallii/2593adcd823fd7d1698bf0cca68547d1.jpg\",\n  \"126933.jpg\": \"Mollusca/Clinocardium nuttallii/a01959dc486260c566fdb213a9ab7241.jpg\",\n  \"126934.jpg\": \"Mollusca/Clinocardium nuttallii/96d26282baf5c47ff69f457f493ea49e.jpg\",\n  \"126935.jpg\": \"Mollusca/Clinocardium nuttallii/30196d6a5453a2527d96ac006e862829.jpg\",\n  \"126937.jpg\": \"Mollusca/Clinocardium nuttallii/eea1702a244bfd27e941392f7f20ddbd.jpg\",\n  \"126938.jpg\": \"Mollusca/Clinocardium nuttallii/e9e89c6c3c00e6b1499736e8caf81791.jpg\",\n  \"126939.jpg\": \"Mollusca/Clinocardium nuttallii/f62a1edff4a629ca6a1aa6deeb48b73e.jpg\",\n  \"12694.jpg\": \"Aves/Crotophaga sulcirostris/34f47ca78a3a45d7333e2feba9ae1efc.jpg\",\n  \"126975.jpg\": \"Mollusca/Fissurella volcano/3135dab12c8bca3121c924d210c8e559.jpg\",\n  \"126977.jpg\": \"Mollusca/Fissurella volcano/4f95fe84080c08667207463fb8dffd8f.jpg\",\n  \"12704.jpg\": \"Aves/Crotophaga sulcirostris/cf3f6f79eed505a6c37791638e1003f0.jpg\",\n  \"127044.jpg\": \"Aves/Charadrius nivosus/a6338f71f892d9fb04318de674efd24f.jpg\",\n  \"127049.jpg\": \"Aves/Charadrius nivosus/0210412a4538ade48775888589ad157d.jpg\",\n  \"127054.jpg\": \"Aves/Charadrius nivosus/641923da9e3b3aa7f55734a60fb0eb06.jpg\",\n  \"127077.jpg\": \"Aves/Charadrius nivosus/6af3dcc03f5156627b4523ac8c9fb416.jpg\",\n  \"127081.jpg\": \"Aves/Charadrius nivosus/028f267f71c89a1be77da9fe24885d7c.jpg\",\n  \"127082.jpg\": \"Aves/Charadrius nivosus/75de5908b8fef529e119f28c36a410de.jpg\",\n  \"127086.jpg\": \"Aves/Charadrius nivosus/bc58afaa5447eedbce99a48caa3e96ac.jpg\",\n  \"127102.jpg\": \"Aves/Charadrius nivosus/0411d6da6d0f680942c492c7e8da6ddd.jpg\",\n  \"127103.jpg\": \"Aves/Charadrius nivosus/264a533829b8d0e9ac3ec74c94872531.jpg\",\n  \"127116.jpg\": \"Aves/Charadrius nivosus/cb4feb92c36157a6c320f36129159af1.jpg\",\n  \"127119.jpg\": \"Aves/Charadrius nivosus/53b0c991e9ff45b7f87f80108591c421.jpg\",\n  \"127120.jpg\": \"Aves/Charadrius nivosus/c714f6c7397e0706f8c5b6201c8ce2c7.jpg\",\n  \"127130.jpg\": \"Aves/Charadrius nivosus/be51b1fbfe1742e3f5cda545228c8ae6.jpg\",\n  \"127133.jpg\": \"Aves/Charadrius nivosus/5d477b4803cf80c284c9d3ce1e144bb9.jpg\",\n  \"127148.jpg\": \"Aves/Charadrius nivosus/7b7ed21040fcda19ac0d70725b13302b.jpg\",\n  \"127175.jpg\": \"Aves/Charadrius nivosus/6ffde825d44bdbb9f2e52d35ad7899a3.jpg\",\n  \"127176.jpg\": \"Aves/Charadrius nivosus/88805707bde5cb1631397aead91eebbd.jpg\",\n  \"127192.jpg\": \"Amphibia/Anaxyrus quercicus/d48be634602d14e9a4a58b3518413a7f.jpg\",\n  \"127198.jpg\": \"Amphibia/Anaxyrus quercicus/a354d76aa22a52b963728d96d8b4806a.jpg\",\n  \"127209.jpg\": \"Insecta/Satyrium saepium/f9c1dde7cc9493a760537c21a455af1e.jpg\",\n  \"127214.jpg\": \"Insecta/Satyrium saepium/d4f8ff694329bc9fd8a7c2e7e6af47ec.jpg\",\n  \"127220.jpg\": \"Insecta/Satyrium saepium/11250b521727cf14fe7aed706dd0fc0f.jpg\",\n  \"127221.jpg\": \"Insecta/Satyrium saepium/c3ef51cf93fe9644d8a2f915433200d7.jpg\",\n  \"127222.jpg\": \"Insecta/Satyrium saepium/8f695baffd3b84bb735a7cb3bcf2723e.jpg\",\n  \"127223.jpg\": \"Insecta/Satyrium saepium/cb590a47de746991c4a8b6f123b23fbe.jpg\",\n  \"127225.jpg\": \"Insecta/Satyrium saepium/3ef68d3b1b46a14edb0967c9830570d3.jpg\",\n  \"127226.jpg\": \"Insecta/Satyrium saepium/eb3d27f31b0c0836525ef1999d8f276e.jpg\",\n  \"127227.jpg\": \"Insecta/Satyrium saepium/1dadb1319ec2940785604bcf81e27943.jpg\",\n  \"127228.jpg\": \"Insecta/Satyrium saepium/56731cee0cb7fab388715c8165920473.jpg\",\n  \"127231.jpg\": \"Insecta/Satyrium saepium/319596ecd23945d55c066fb775e6aeca.jpg\",\n  \"127232.jpg\": \"Insecta/Satyrium saepium/3c38dde112b9327dc1173440b29d790e.jpg\",\n  \"127235.jpg\": \"Insecta/Satyrium saepium/b6f8c39ece4de88b03883b9b601ed07e.jpg\",\n  \"127236.jpg\": \"Insecta/Satyrium saepium/9939e1c9c629698ca9826f7737b53182.jpg\",\n  \"127242.jpg\": \"Insecta/Satyrium saepium/c310ab6f5e6479527d734ccab3c61735.jpg\",\n  \"127243.jpg\": \"Insecta/Satyrium saepium/ac17fa0425245a2026c46635b7d05b5b.jpg\",\n  \"127244.jpg\": \"Insecta/Satyrium saepium/14336bc22a05077da25da0fb4039a9c8.jpg\",\n  \"127245.jpg\": \"Insecta/Satyrium saepium/e0adef42ad42f41881622803eec8ee9e.jpg\",\n  \"127251.jpg\": \"Insecta/Pyrausta volupialis/cb44ac77b6fa77e74c1cb8d8633e6338.jpg\",\n  \"127257.jpg\": \"Insecta/Pyrausta volupialis/5c36fdf23fc0c7808165bfec196c9c9f.jpg\",\n  \"127262.jpg\": \"Insecta/Pyrausta volupialis/cf5aad21cd6ba415a6dda29e37f71e88.jpg\",\n  \"127263.jpg\": \"Insecta/Pyrausta volupialis/79a14dbefcd4c4943dc103a5347a513e.jpg\",\n  \"127271.jpg\": \"Insecta/Chalcoela iphitalis/66fa93e12554e78bfa53c4397b5cf981.jpg\",\n  \"127275.jpg\": \"Insecta/Chalcoela iphitalis/c727d787befac6af68740f1d04645d9b.jpg\",\n  \"127286.jpg\": \"Insecta/Chalcoela iphitalis/ae9cff77f271d19bbe143dd08dec9bac.jpg\",\n  \"127292.jpg\": \"Insecta/Chalcoela iphitalis/03ce1eef950c33d628042ac95a1a4c57.jpg\",\n  \"127293.jpg\": \"Insecta/Polites sabuleti/d3edf188073f9a59c27e94b1b71fe486.jpg\",\n  \"127294.jpg\": \"Insecta/Polites sabuleti/3995213718c36731776f1420f7f23f4e.jpg\",\n  \"127301.jpg\": \"Insecta/Polites sabuleti/b63d253488c1f4eb60a90726d146ee97.jpg\",\n  \"127303.jpg\": \"Insecta/Polites sabuleti/7b7f840a7887fa316ddce5c977edd1ec.jpg\",\n  \"127306.jpg\": \"Insecta/Polites sabuleti/84a137ffedf5025bbc5accfd33b0a218.jpg\",\n  \"127307.jpg\": \"Insecta/Polites sabuleti/14cd20e7f5828c2746ed0ef93876d6e9.jpg\",\n  \"127309.jpg\": \"Insecta/Polites sabuleti/e9142e373920c19ed16ae341b07a2f1a.jpg\",\n  \"127316.jpg\": \"Mammalia/Erinaceus europaeus/13d9dc0332b2b61004adde135fcae3bf.jpg\",\n  \"127319.jpg\": \"Mammalia/Erinaceus europaeus/8ec2ebd0458ecd7b029092bf95d50ed3.jpg\",\n  \"127337.jpg\": \"Mammalia/Erinaceus europaeus/5f649a5852b8eac6180d4924eb9137ec.jpg\",\n  \"127340.jpg\": \"Mammalia/Erinaceus europaeus/6cf7d35a7b4c465daf8d7146fce23df4.jpg\",\n  \"127357.jpg\": \"Mammalia/Erinaceus europaeus/b112325beaec568a77fa64290b5e961b.jpg\",\n  \"127363.jpg\": \"Mammalia/Erinaceus europaeus/0138a7bc81e27cb949ad1eea99c2bf08.jpg\",\n  \"127368.jpg\": \"Mammalia/Erinaceus europaeus/c6a512e0d57b2fa09fa411b80739275d.jpg\",\n  \"127379.jpg\": \"Mammalia/Erinaceus europaeus/61beff596a3289787f46aaa27e7dcb5f.jpg\",\n  \"127385.jpg\": \"Mammalia/Erinaceus europaeus/ef18f8c6748f8b8f18bc3b9cc2bd62d5.jpg\",\n  \"127399.jpg\": \"Mammalia/Erinaceus europaeus/9df02c92d1888e9b4216dfda0ef9def8.jpg\",\n  \"127400.jpg\": \"Mammalia/Erinaceus europaeus/f856d9450fb7de7004cf21dd42bd0cdb.jpg\",\n  \"127443.jpg\": \"Aves/Trogon citreolus/6c5639a1b291e875e816175617a7c71a.jpg\",\n  \"127446.jpg\": \"Aves/Trogon citreolus/6b6e70f606f0d3ef479fb6ac19539d90.jpg\",\n  \"127451.jpg\": \"Aves/Trogon citreolus/49bcd9f59da6e14b2f40bc442f02ad34.jpg\",\n  \"127452.jpg\": \"Aves/Trogon citreolus/b686d58cce216cf64ea18952e587128a.jpg\",\n  \"127454.jpg\": \"Aves/Trogon citreolus/6df1f48c1364ab7779b4f09efa6e5e85.jpg\",\n  \"127455.jpg\": \"Aves/Trogon citreolus/9c96d11b104e095a62e11eb2a693a5df.jpg\",\n  \"127459.jpg\": \"Aves/Trogon citreolus/5ab845f6f5223dbb46ffea38febb7e50.jpg\",\n  \"127460.jpg\": \"Aves/Trogon citreolus/7b1509b821f33c0e73196a6c528cca1d.jpg\",\n  \"12747.jpg\": \"Insecta/Nezara viridula/fd097a00dd1ed569f9adf86eabf560cf.jpg\",\n  \"127471.jpg\": \"Aves/Trogon citreolus/72e15ff040f9271ff8ed42bf70c0c020.jpg\",\n  \"127472.jpg\": \"Aves/Trogon citreolus/8bd7666c79287636d0a62f1c48520df7.jpg\",\n  \"127480.jpg\": \"Aves/Trogon citreolus/9a247be3236cc1493b8dd7ac1708314d.jpg\",\n  \"127490.jpg\": \"Aves/Trogon citreolus/9f724b0157ca3bf6ec0076a52e728b87.jpg\",\n  \"127491.jpg\": \"Aves/Trogon citreolus/7296fc9ec3fde0d6e996cb6bfb023976.jpg\",\n  \"127492.jpg\": \"Aves/Trogon citreolus/f43e507361a0a2a5f1f88842422f52cf.jpg\",\n  \"127493.jpg\": \"Aves/Trogon citreolus/39fa8f089226280238694fc8de68d286.jpg\",\n  \"127494.jpg\": \"Aves/Trogon citreolus/0874858939b638c3f457eafec8e315fd.jpg\",\n  \"127498.jpg\": \"Aves/Trogon citreolus/31acdb0deefa505c1168852e72b66bf8.jpg\",\n  \"127499.jpg\": \"Aves/Trogon citreolus/df5b1594e70941f0bc374b55cc3c36d9.jpg\",\n  \"12750.jpg\": \"Insecta/Nezara viridula/c796bb75b1747794c4aee82586be8b92.jpg\",\n  \"127500.jpg\": \"Aves/Trogon citreolus/04b1c50077e1f1471acdcce2b45cddeb.jpg\",\n  \"127504.jpg\": \"Aves/Cuculus canorus/1c72697c1448ca9b1610b689be23cc74.jpg\",\n  \"127508.jpg\": \"Aves/Cuculus canorus/6c87175447ac59fc370f3292b834cb31.jpg\",\n  \"127509.jpg\": \"Aves/Cuculus canorus/2a06eb77c6f71592c16c05db41705e1b.jpg\",\n  \"12751.jpg\": \"Insecta/Nezara viridula/1a2494b945d619957f5a1d19a0259bbb.jpg\",\n  \"127510.jpg\": \"Aves/Cuculus canorus/a765095b2146ae62827b955438c1f112.jpg\",\n  \"127525.jpg\": \"Aves/Cuculus canorus/b13a6458cfb49e9e59c57bd609345403.jpg\",\n  \"12753.jpg\": \"Insecta/Nezara viridula/c29d9f9988004295f7608d19b96b3b6f.jpg\",\n  \"127531.jpg\": \"Aves/Anthus pratensis/0520be12b1d19077390de0b4435ed13a.jpg\",\n  \"127535.jpg\": \"Aves/Anthus pratensis/fb830c6f45d736efba734ee5f4e5b811.jpg\",\n  \"127536.jpg\": \"Aves/Anthus pratensis/e697f01edef2ff78d675cfd3b398fea7.jpg\",\n  \"12755.jpg\": \"Insecta/Nezara viridula/a54e2cdebbac62c4cbba9157c9835bd6.jpg\",\n  \"127553.jpg\": \"Aves/Anthus pratensis/baa299ad7eb66c19c8ee2f4f61f1b343.jpg\",\n  \"127554.jpg\": \"Aves/Anthus pratensis/95a45deba34f04061c978d52dfe45a1f.jpg\",\n  \"12756.jpg\": \"Insecta/Nezara viridula/246d5b9c6d8c6ff5b07ef92c5ef48fac.jpg\",\n  \"127577.jpg\": \"Aves/Struthio camelus/ce0dbd657eda53c279175610bdad6794.jpg\",\n  \"12760.jpg\": \"Insecta/Nezara viridula/b16d653344fec90236dfd2385b3559c2.jpg\",\n  \"127600.jpg\": \"Aves/Struthio camelus/d023cef4d8e4b16be5ca082c3a3d8e6b.jpg\",\n  \"127603.jpg\": \"Aves/Struthio camelus/dd52984fb9e4740bb42cd34c7605d71b.jpg\",\n  \"12761.jpg\": \"Insecta/Nezara viridula/cf7e6ef6277bad2d3010593b8f9d3da8.jpg\",\n  \"127615.jpg\": \"Insecta/Calopteryx splendens/137b64ddce15118d99658e08c7314e55.jpg\",\n  \"127618.jpg\": \"Insecta/Calopteryx splendens/a529ffd02a16c66756b00eae6ece6381.jpg\",\n  \"12762.jpg\": \"Insecta/Nezara viridula/c4b284fe21a51c6073d66cd8f2bf11ad.jpg\",\n  \"127622.jpg\": \"Insecta/Calopteryx splendens/d75e96b1675b5a8b0faba69535d01937.jpg\",\n  \"12763.jpg\": \"Insecta/Nezara viridula/3e68f42a2c0813656f20e24e4135983a.jpg\",\n  \"127634.jpg\": \"Insecta/Calopteryx splendens/9564bfa44061baca4514cb7a594485a3.jpg\",\n  \"127637.jpg\": \"Insecta/Calopteryx splendens/e5ef83a6b57822dcc4b5b3e0835cef6e.jpg\",\n  \"127639.jpg\": \"Insecta/Calopteryx splendens/46fd794934b0f5e893630171bee0748d.jpg\",\n  \"127647.jpg\": \"Insecta/Calopteryx splendens/a2b52aa943b860472c121fbd52b7a882.jpg\",\n  \"12765.jpg\": \"Insecta/Nezara viridula/5ab616491f5b6a1625655cb2f2c62d6c.jpg\",\n  \"12766.jpg\": \"Insecta/Nezara viridula/4613a32a94c0a29b7dcfb60ea55aff25.jpg\",\n  \"127679.jpg\": \"Insecta/Ischnura denticollis/514745642ce2d349f2834ff1a325acf2.jpg\",\n  \"12768.jpg\": \"Insecta/Nezara viridula/cb7d9a9636ba55825994912a3dfa67ee.jpg\",\n  \"127682.jpg\": \"Insecta/Ischnura denticollis/56bdeb26c24bd6d9d623e65bf04f0d40.jpg\",\n  \"127698.jpg\": \"Insecta/Ischnura denticollis/807302cb2ff785940222f34daa4e2440.jpg\",\n  \"12770.jpg\": \"Insecta/Nezara viridula/e66a7d3f51fb4411a9908bc8e5b7657d.jpg\",\n  \"12771.jpg\": \"Insecta/Nezara viridula/c6a3ba67d7a0fac8df7a062c49346013.jpg\",\n  \"127712.jpg\": \"Insecta/Ischnura denticollis/2d8a10027b7c9c2d9fa2e178f2a2d489.jpg\",\n  \"127713.jpg\": \"Insecta/Ischnura denticollis/4bfeb21166cb1e6ab1b7496efb495a3e.jpg\",\n  \"12772.jpg\": \"Insecta/Nezara viridula/dcb7cea129013f06df9bad785207780a.jpg\",\n  \"12777.jpg\": \"Insecta/Nezara viridula/f02986de00e3cab3d148b5116f1aae37.jpg\",\n  \"12778.jpg\": \"Insecta/Nezara viridula/442f337c15235e8a65344be443f80838.jpg\",\n  \"12779.jpg\": \"Insecta/Nezara viridula/16806a05892b0394db99f9120e958b66.jpg\",\n  \"12784.jpg\": \"Insecta/Nezara viridula/fddc9795bf5e591ddb3718a1a70bfbe8.jpg\",\n  \"127878.jpg\": \"Aves/Phainopepla nitens/a3e3910f8e63862bc2d64c3500e38eba.jpg\",\n  \"127879.jpg\": \"Aves/Phainopepla nitens/01a98e445964920e364197ac07d4b0df.jpg\",\n  \"127889.jpg\": \"Aves/Phainopepla nitens/e55143cca56bd42016081a49ece37085.jpg\",\n  \"127892.jpg\": \"Aves/Phainopepla nitens/a74b2a2acdfd76cc4dfb1755c3f0de44.jpg\",\n  \"127903.jpg\": \"Aves/Phainopepla nitens/51b69e3f18a1d39b679c8e70b3480916.jpg\",\n  \"127909.jpg\": \"Aves/Phainopepla nitens/8b838149b9a6ef44118f7479118c4bc2.jpg\",\n  \"127910.jpg\": \"Aves/Phainopepla nitens/4d2f77a0df7dccece377747e9fd764f8.jpg\",\n  \"127913.jpg\": \"Aves/Phainopepla nitens/248ec21fd79b30c5c81a5a28fd6cf80f.jpg\",\n  \"127921.jpg\": \"Aves/Phainopepla nitens/df180397208f7f687b4819e28d8f831b.jpg\",\n  \"127927.jpg\": \"Aves/Phainopepla nitens/9eaf399e56af51758f13948a42d2f59c.jpg\",\n  \"127929.jpg\": \"Aves/Phainopepla nitens/9fdea18706f008f0643c047c067d3c0b.jpg\",\n  \"127930.jpg\": \"Aves/Phainopepla nitens/c14eef64399f6b41bd6836f9d8d1b7cb.jpg\",\n  \"127946.jpg\": \"Aves/Phainopepla nitens/ee5723920674899bd4bf3716f3181ef9.jpg\",\n  \"127947.jpg\": \"Aves/Phainopepla nitens/099511e05c1da3bb36692d7a62b29250.jpg\",\n  \"127955.jpg\": \"Aves/Phainopepla nitens/fa00e25ac7f2e7ecfe8acc697e1841fb.jpg\",\n  \"127957.jpg\": \"Aves/Phainopepla nitens/915cd1e5e849a479fa3f619da0678a26.jpg\",\n  \"127977.jpg\": \"Aves/Phainopepla nitens/db927ef66c5063c5f80b27010d40a9a6.jpg\",\n  \"127980.jpg\": \"Aves/Phainopepla nitens/e9f5a5dba3b59a2e15c33b126c904c2e.jpg\",\n  \"127986.jpg\": \"Aves/Phainopepla nitens/ef7b1700e94458e698d2bc633bf1b3d9.jpg\",\n  \"127991.jpg\": \"Aves/Phainopepla nitens/c4c6f14d7a7a0924d1de52bb3fd07dd4.jpg\",\n  \"128001.jpg\": \"Aves/Phainopepla nitens/74e0248583bf45746d925b32cfdb9922.jpg\",\n  \"128003.jpg\": \"Aves/Phainopepla nitens/0552f990d2aae8b55fd1201e1b0e32c6.jpg\",\n  \"128008.jpg\": \"Aves/Phainopepla nitens/fd9012cdc0cdacff40c914f5c6b2dc43.jpg\",\n  \"128021.jpg\": \"Aves/Phainopepla nitens/6ea49668ecb1c58c406be5f3fc84f6bd.jpg\",\n  \"128027.jpg\": \"Aves/Phainopepla nitens/180433e1f45add75fca6fb81d1b5f173.jpg\",\n  \"128034.jpg\": \"Insecta/Ladona deplanata/d6c537b76a346517d5205435704b9b00.jpg\",\n  \"128037.jpg\": \"Insecta/Ladona deplanata/fae6b443f157713994e1902d697711ed.jpg\",\n  \"128039.jpg\": \"Insecta/Ladona deplanata/d0e3587e434b53c2c221dd67d74642f1.jpg\",\n  \"128040.jpg\": \"Insecta/Ladona deplanata/911a083e4281ee298a6602f4c93ab840.jpg\",\n  \"128051.jpg\": \"Insecta/Ladona deplanata/9bf16b34f0d5fc9c03dd1b0f960a6270.jpg\",\n  \"128052.jpg\": \"Insecta/Ladona deplanata/f61b49992d490136c039419e6c9d7f1b.jpg\",\n  \"128053.jpg\": \"Insecta/Ladona deplanata/b8826c4ce5c05ceca4e0981344d7c4fa.jpg\",\n  \"128054.jpg\": \"Insecta/Ladona deplanata/b638eedcbdcf93b60e395e4b04493212.jpg\",\n  \"128055.jpg\": \"Insecta/Ladona deplanata/9fccf2711cc868b9d6627cddc266f908.jpg\",\n  \"128058.jpg\": \"Insecta/Ladona deplanata/41fe53b2ce353cbbd93317ee20b252df.jpg\",\n  \"128059.jpg\": \"Insecta/Ladona deplanata/681602183aecb9012f707301f58fdd74.jpg\",\n  \"128060.jpg\": \"Insecta/Ladona deplanata/0775b07b27a5d722038bc166c37d4489.jpg\",\n  \"128062.jpg\": \"Insecta/Ladona deplanata/2c468a1e0eb122279d34a26dea00908e.jpg\",\n  \"128063.jpg\": \"Insecta/Ladona deplanata/b89bed3c9a7491b25a8cf794bf1f6d93.jpg\",\n  \"128065.jpg\": \"Insecta/Ladona deplanata/f235eb34f2509ab654fd79adba1485c7.jpg\",\n  \"128066.jpg\": \"Insecta/Ladona deplanata/d483d1a0be12bea4d2bf4e6c4dfc88d1.jpg\",\n  \"128070.jpg\": \"Insecta/Ladona deplanata/1db36498f45a90049d392e819f68ddb3.jpg\",\n  \"128079.jpg\": \"Insecta/Ladona deplanata/b5a58a9a4cedfc6b7ab89e6f9bb41028.jpg\",\n  \"128083.jpg\": \"Insecta/Ladona deplanata/b0a588e047b8dbe21edc1f7d0cf21a96.jpg\",\n  \"128086.jpg\": \"Insecta/Ladona deplanata/6c7c2a0e762a364a3b15474d7c65c9c5.jpg\",\n  \"128087.jpg\": \"Insecta/Ladona deplanata/b54ab8d5adefaa0ace4239006cc4e1a7.jpg\",\n  \"128097.jpg\": \"Insecta/Achalarus lyciades/239df1a1df293968ba175e59be00b9d1.jpg\",\n  \"128105.jpg\": \"Insecta/Achalarus lyciades/3a6f6247c23ce29d476a25c8da869b79.jpg\",\n  \"128110.jpg\": \"Insecta/Achalarus lyciades/54931e538b4b82bdd363525ef391032d.jpg\",\n  \"128112.jpg\": \"Insecta/Achalarus lyciades/c6587b5ef7b6db7c26b5dd61020fb579.jpg\",\n  \"128337.jpg\": \"Insecta/Boloria selene/f2b71257a4ef92ea61d51e95e3664a9f.jpg\",\n  \"128343.jpg\": \"Insecta/Boloria selene/31ac221e3815b804fd368cb4ed39601b.jpg\",\n  \"128345.jpg\": \"Insecta/Boloria selene/065eca5e6a1d45421fe540180e25d774.jpg\",\n  \"128347.jpg\": \"Insecta/Boloria selene/7ef21e7f233139d1e6c4206a2e5ff042.jpg\",\n  \"128350.jpg\": \"Insecta/Boloria selene/a0aea673bcaaf60803a1c3b47ab16bb4.jpg\",\n  \"128538.jpg\": \"Insecta/Bombus impatiens/f71e2f3914a0d62ae5c435d612109912.jpg\",\n  \"128544.jpg\": \"Insecta/Bombus impatiens/0ee1aa5580ae70881afbf84634763fc7.jpg\",\n  \"128550.jpg\": \"Insecta/Bombus impatiens/cf6b2d784f891be222163f64947e3ce5.jpg\",\n  \"128585.jpg\": \"Insecta/Bombus impatiens/e5fad8aa3a5774a4713bb5cab667246f.jpg\",\n  \"128595.jpg\": \"Insecta/Bombus impatiens/5249dfb63606dcab4a803071d1b52af9.jpg\",\n  \"128684.jpg\": \"Insecta/Bombus impatiens/9b781435ea595e1bb02ad239ef11d5db.jpg\",\n  \"128693.jpg\": \"Insecta/Bombus impatiens/e576f4134b7e46ad35d9905ccce2da60.jpg\",\n  \"128695.jpg\": \"Insecta/Bombus impatiens/93834a53cc719ba131ecc549a0b3fdbc.jpg\",\n  \"128704.jpg\": \"Insecta/Bombus impatiens/41af19750309daf4c2c8af80e64e57f0.jpg\",\n  \"128726.jpg\": \"Insecta/Bombus impatiens/4a472adb52a0c34be6980d5f0ee5f7b2.jpg\",\n  \"128736.jpg\": \"Insecta/Bombus impatiens/0fa2bc5f5ba4b7fbedd3f37bbad3db61.jpg\",\n  \"128742.jpg\": \"Insecta/Bombus impatiens/e33d4f54a559905b2d49293ca0bf2754.jpg\",\n  \"128750.jpg\": \"Insecta/Bombus impatiens/53c7fc76d4c9a1b73fe2b5a409f34ebf.jpg\",\n  \"128766.jpg\": \"Insecta/Bombus impatiens/816c9a0ef3b2787743096617915d21cc.jpg\",\n  \"128795.jpg\": \"Insecta/Bombus impatiens/f4dd58384a4d014060058e23b50c4adb.jpg\",\n  \"128802.jpg\": \"Insecta/Bombus impatiens/ff8f80e6de1c0fad20fcc5c1a2f79264.jpg\",\n  \"128804.jpg\": \"Insecta/Bombus impatiens/3ff05ec3b5127017482d7a2e843868a7.jpg\",\n  \"128807.jpg\": \"Insecta/Bombus impatiens/a40a86abab058a4b4ef3bab21082aae9.jpg\",\n  \"128825.jpg\": \"Insecta/Bombus impatiens/07da44a81184d574756857824d6b912d.jpg\",\n  \"128829.jpg\": \"Insecta/Bombus impatiens/cb585b465ee200c0a01984be75d5bc90.jpg\",\n  \"128833.jpg\": \"Insecta/Bombus impatiens/fbef29e251107efc1b72d5a6ad206d21.jpg\",\n  \"128834.jpg\": \"Insecta/Bombus impatiens/6dab2bc7380e36c0cd6f7fe89b1b8709.jpg\",\n  \"12888.jpg\": \"Insecta/Microcrambus elegans/7ee7affa66016d402d7afb9d49927ac0.jpg\",\n  \"128900.jpg\": \"Amphibia/Pelophylax perezi/b41c3e2b96b782e4c5ca09411439a5ae.jpg\",\n  \"12891.jpg\": \"Insecta/Microcrambus elegans/68a9307b49d2806eff5862277dd76321.jpg\",\n  \"128915.jpg\": \"Amphibia/Pelophylax perezi/2e16b56962be6d5156072f725a9a09be.jpg\",\n  \"128916.jpg\": \"Amphibia/Pelophylax perezi/1a57d31df0df4c18014f3ab1b53e1c9e.jpg\",\n  \"128917.jpg\": \"Amphibia/Pelophylax perezi/051d52f4ed608d5dc7d7a8b34346006e.jpg\",\n  \"128919.jpg\": \"Amphibia/Pelophylax perezi/0bfad6ddf8d8388649bed022dd00343b.jpg\",\n  \"128920.jpg\": \"Amphibia/Pelophylax perezi/fae2ebd05faec5057d78f991f4b80e12.jpg\",\n  \"128926.jpg\": \"Amphibia/Pelophylax perezi/9a161c2a9797abe3875beb6e629548d6.jpg\",\n  \"12893.jpg\": \"Insecta/Microcrambus elegans/8ee251968e2cbcc3808643c2145b67a0.jpg\",\n  \"128954.jpg\": \"Animalia/Echinometra mathaei/9068098e3f78a1bf3ac63bc8d28b2064.jpg\",\n  \"128961.jpg\": \"Animalia/Echinometra mathaei/c15345aed86ba882efb9528e955da2cf.jpg\",\n  \"12897.jpg\": \"Insecta/Microcrambus elegans/09826fb8adfff02a699717106529d710.jpg\",\n  \"128975.jpg\": \"Insecta/Arctia villica/34a06b0162ea5fe221e60350442b8ed6.jpg\",\n  \"12898.jpg\": \"Insecta/Microcrambus elegans/a4c087fa4517a91a96441aa4155b189e.jpg\",\n  \"128982.jpg\": \"Insecta/Arctia villica/1fb6814cf62e55cebd934caa57b313e4.jpg\",\n  \"128983.jpg\": \"Insecta/Arctia villica/d016c94d5583651cddeb6f637ff8dd02.jpg\",\n  \"128984.jpg\": \"Insecta/Arctia villica/8e5fc16796ea3ad11640df52638bd387.jpg\",\n  \"12900.jpg\": \"Insecta/Microcrambus elegans/0b057b19370db65d9066023264aaff87.jpg\",\n  \"12904.jpg\": \"Insecta/Microcrambus elegans/9fdaddc4f0f66f4ac894d4b1a71fe49e.jpg\",\n  \"129078.jpg\": \"Aves/Anas americana/c1323b7c841bfc1b42a104b03fd7f72a.jpg\",\n  \"12909.jpg\": \"Insecta/Microcrambus elegans/9dae435402d9d97fdfbb823517f3f14d.jpg\",\n  \"129091.jpg\": \"Aves/Anas americana/84f71284f8b4827af701b68d688fa94d.jpg\",\n  \"129139.jpg\": \"Aves/Anas americana/21afb13a4efa1f70f0cf120eb6aefcc1.jpg\",\n  \"12914.jpg\": \"Insecta/Microcrambus elegans/dc0d6dbd14d64b9cc2b92259bada13ab.jpg\",\n  \"129166.jpg\": \"Aves/Anas americana/baa78052359744f2649e2107d0536be3.jpg\",\n  \"12919.jpg\": \"Insecta/Microcrambus elegans/b569e3199e57257dea5450670bc8f9e5.jpg\",\n  \"129227.jpg\": \"Aves/Anas americana/46778e7c979528f7d82f237853c9715b.jpg\",\n  \"12923.jpg\": \"Insecta/Microcrambus elegans/c3f3e95e9fb26a5ace2e920c2bfd9410.jpg\",\n  \"129257.jpg\": \"Aves/Anas americana/ac4d6750271e86740319e2381469f738.jpg\",\n  \"12928.jpg\": \"Insecta/Microcrambus elegans/d09918b4d8677ddbd013cfc023ca15d7.jpg\",\n  \"129328.jpg\": \"Aves/Anas americana/abb14bcb1d4a5430cefc3e0355931527.jpg\",\n  \"129342.jpg\": \"Aves/Anas americana/a52da58ff93d09239f318c9a33d0b714.jpg\",\n  \"129353.jpg\": \"Aves/Anas americana/3d2feac175a00ec69497a8e1814f69cd.jpg\",\n  \"12936.jpg\": \"Insecta/Microcrambus elegans/4c14768bd2cd1cec4dfb7b07e54ee5c0.jpg\",\n  \"12937.jpg\": \"Insecta/Microcrambus elegans/398adabccb69ba3943a4c9c3e92d31c9.jpg\",\n  \"129373.jpg\": \"Aves/Anas americana/251f63ef35ed12d136f46a2473d33378.jpg\",\n  \"12938.jpg\": \"Insecta/Microcrambus elegans/374f302cdda2b4bedd6af64bd7f35bca.jpg\",\n  \"12941.jpg\": \"Insecta/Microcrambus elegans/4ed9c347090f7ba1c8d9406374121b40.jpg\",\n  \"12942.jpg\": \"Insecta/Microcrambus elegans/425d5a1859f85ad98a49966ed547ffcc.jpg\",\n  \"12943.jpg\": \"Insecta/Microcrambus elegans/f2e46766ca71ae7cb75b3d2539efd952.jpg\",\n  \"129436.jpg\": \"Aves/Anas americana/47638a44a8564144afc368e4041b856e.jpg\",\n  \"12944.jpg\": \"Insecta/Microcrambus elegans/87b1317ca7a6d6c29a19dab300e709a0.jpg\",\n  \"12945.jpg\": \"Aves/Turdus pilaris/89b7eecbc0b309e8ada2fbdaa6a2f994.jpg\",\n  \"129457.jpg\": \"Aves/Anas americana/fa5154be77e2fab5ff13c0e5607731ed.jpg\",\n  \"129467.jpg\": \"Aves/Anas americana/df48600c509e72a3c8d396faecfb65b0.jpg\",\n  \"129474.jpg\": \"Aves/Campylopterus hemileucurus/f489e0a393d038b7cdb2bccb1cc88a50.jpg\",\n  \"129479.jpg\": \"Aves/Campylopterus hemileucurus/4886e2aa60ba6735f4f265097c25a801.jpg\",\n  \"129481.jpg\": \"Aves/Campylopterus hemileucurus/05cce8b7cb9fe7ae5144498ded496709.jpg\",\n  \"129487.jpg\": \"Aves/Campylopterus hemileucurus/a8889e5de5cac9325da883b1d0062965.jpg\",\n  \"129489.jpg\": \"Aves/Campylopterus hemileucurus/60a81a9947c1fc2cbf4a7bc30a4d9da8.jpg\",\n  \"12949.jpg\": \"Aves/Turdus pilaris/becdb617f741f623f5d268121d3651a8.jpg\",\n  \"129494.jpg\": \"Aves/Picoides arcticus/17aa97c9f8c785d5139ed2f872e66e99.jpg\",\n  \"129499.jpg\": \"Aves/Picoides arcticus/85d8a299588e054c23def23d48dfcf72.jpg\",\n  \"129508.jpg\": \"Aves/Picoides arcticus/6606ea2b2b53f6fa0e3f314d1098ca22.jpg\",\n  \"12954.jpg\": \"Aves/Turdus pilaris/975f6726637fb74950be3fe340b99301.jpg\",\n  \"129540.jpg\": \"Aves/Calidris melanotos/8e542eebd6442b66e537971bfe2aeaa0.jpg\",\n  \"129545.jpg\": \"Aves/Calidris melanotos/e1e3b32a87cb6878951ac4bf3100aac6.jpg\",\n  \"129547.jpg\": \"Aves/Calidris melanotos/8d0957ff7e39777b1c86aa5f085305e3.jpg\",\n  \"12955.jpg\": \"Aves/Turdus pilaris/20b612f48a1e8aa38405be7125ab8757.jpg\",\n  \"129554.jpg\": \"Aves/Calidris melanotos/3153b8e0a0fb1532a0ec8e0e3bf02d77.jpg\",\n  \"129557.jpg\": \"Aves/Calidris melanotos/16e708bbeae25c1235af0e788a45e43b.jpg\",\n  \"129562.jpg\": \"Aves/Calidris melanotos/144fd2c442ebe13fafe8715624fd5066.jpg\",\n  \"129568.jpg\": \"Aves/Calidris melanotos/0817a3d435bed6784c8d7ba19ca3a426.jpg\",\n  \"129569.jpg\": \"Aves/Calidris melanotos/b76e260a6de11c89689e9546dcdc8d04.jpg\",\n  \"129578.jpg\": \"Aves/Calidris melanotos/65c5fd44f43313c643c301a546f95253.jpg\",\n  \"129579.jpg\": \"Aves/Calidris melanotos/027ccb92a04a8c6c0172089e5ea0e3e1.jpg\",\n  \"12958.jpg\": \"Aves/Turdus pilaris/c973f8f72214e0eba2c563e3c471c8cd.jpg\",\n  \"129584.jpg\": \"Aves/Calidris melanotos/6527ea1c30f21fbaf13acce3326d67a3.jpg\",\n  \"129587.jpg\": \"Aves/Calidris melanotos/e84eb3a3812aa13bc6f05ef940358464.jpg\",\n  \"129590.jpg\": \"Aves/Calidris melanotos/ebe141eccf156d275f31bfc6ae168c2b.jpg\",\n  \"129608.jpg\": \"Aves/Calidris melanotos/ece9bc1cf39fdf43fac265d70ef87fc9.jpg\",\n  \"129613.jpg\": \"Insecta/Oecophylla smaragdina/883eb901ffd19daaaaab699b697f57ae.jpg\",\n  \"129616.jpg\": \"Insecta/Oecophylla smaragdina/cfeb450d3277770ddceb1456920716aa.jpg\",\n  \"12964.jpg\": \"Aves/Turdus pilaris/1cfb91cc493ed91c2d7c3df7aadd456d.jpg\",\n  \"129671.jpg\": \"Mammalia/Meles meles/6eba134c6dba17d50286385a40cc609c.jpg\",\n  \"129675.jpg\": \"Mammalia/Meles meles/2ae212034fa57f8f56bbfdc9899bb8e4.jpg\",\n  \"129676.jpg\": \"Mammalia/Meles meles/5b639186e5ed171e0dc13f5c3f9dcfd1.jpg\",\n  \"129692.jpg\": \"Mammalia/Meles meles/5cf757311000a2d642507012c99d7b46.jpg\",\n  \"129694.jpg\": \"Mammalia/Meles meles/ac73442121afd464d34b20729eccf01d.jpg\",\n  \"129753.jpg\": \"Aves/Molothrus ater/b99c81f1620c145923f0efdb5be008c3.jpg\",\n  \"129784.jpg\": \"Aves/Molothrus ater/f2e2c474fc43b2309c3020da3ab5c8af.jpg\",\n  \"129800.jpg\": \"Aves/Molothrus ater/08bdfe48d3d10e3b816cbe453dd23cc3.jpg\",\n  \"12981.jpg\": \"Aves/Turdus pilaris/aaabbea09eacae8a839e1a0750b3e482.jpg\",\n  \"129823.jpg\": \"Aves/Molothrus ater/28a720bc1a76c4532106d705503d03b4.jpg\",\n  \"129837.jpg\": \"Aves/Molothrus ater/7a97abc0834d1e65e7ffd4aaea570b93.jpg\",\n  \"129858.jpg\": \"Aves/Molothrus ater/5941bf6d7027a89d1235ec86c8a2ae28.jpg\",\n  \"129861.jpg\": \"Aves/Molothrus ater/fe6c1613018813cf05703ab2f1e07b1d.jpg\",\n  \"129898.jpg\": \"Aves/Molothrus ater/1116cd71eb2fb7f16e8cf2a7483f0622.jpg\",\n  \"129917.jpg\": \"Aves/Molothrus ater/f2f53ffcb149522beef16595fe59d1bb.jpg\",\n  \"129927.jpg\": \"Aves/Molothrus ater/1c2eb7a33243db37e40da41c60579067.jpg\",\n  \"12996.jpg\": \"Aves/Turdus pilaris/fc8ac9716812a9aa18b84d746227474d.jpg\",\n  \"129965.jpg\": \"Aves/Molothrus ater/bac95344b62caa61912a7420b3662159.jpg\",\n  \"12998.jpg\": \"Aves/Turdus pilaris/925c2fe28751572a269b6aa0d53d56cb.jpg\",\n  \"12999.jpg\": \"Aves/Turdus pilaris/6f202d67939f7fcb35c5d61ff6c7b4de.jpg\",\n  \"13000.jpg\": \"Aves/Turdus pilaris/297c2ebe4150d40677afcbd0e88e5579.jpg\",\n  \"13001.jpg\": \"Aves/Turdus pilaris/4402d280c34ec3c5da9eae33817f43f1.jpg\",\n  \"13004.jpg\": \"Aves/Turdus pilaris/631d4ca7b75d50c16cb1149ac7c9c9e9.jpg\",\n  \"130041.jpg\": \"Aves/Molothrus ater/bfd543e2d8225e616cd5ac71cc542aef.jpg\",\n  \"130042.jpg\": \"Aves/Molothrus ater/a309ec3a47571024643e5532884fe752.jpg\",\n  \"130058.jpg\": \"Aves/Molothrus ater/520e065a719bcf457196fe1eea8a6b56.jpg\",\n  \"130077.jpg\": \"Aves/Molothrus ater/b900518ac098d158e62161e4f19c7dd3.jpg\",\n  \"130089.jpg\": \"Aves/Molothrus ater/2852ae85943670faed7eee68b3c26e6b.jpg\",\n  \"130147.jpg\": \"Aves/Molothrus ater/a1eda46c1cb771ef62f71315cfbae8b1.jpg\",\n  \"130153.jpg\": \"Aves/Molothrus ater/b4d8a7ebe847a9682827303305a76d41.jpg\",\n  \"130156.jpg\": \"Mollusca/Ariolimax californicus/d60460c90b5fb289446f851c56571906.jpg\",\n  \"130160.jpg\": \"Mollusca/Ariolimax californicus/40f8eb9a27c84e1adfc9654129f57d0f.jpg\",\n  \"130161.jpg\": \"Mollusca/Ariolimax californicus/1fdf5cd873ac49c0394649a2a5db932c.jpg\",\n  \"130162.jpg\": \"Mollusca/Ariolimax californicus/8d1b1527f780a8fda8168af4424da495.jpg\",\n  \"130175.jpg\": \"Mollusca/Ariolimax californicus/75067b3d3b112b8f1daa11a70171630f.jpg\",\n  \"130241.jpg\": \"Insecta/Melanargia galathea/b62c2ffe0bda255d05a32524e60d2eee.jpg\",\n  \"130244.jpg\": \"Insecta/Melanargia galathea/fd4f8fc1bcb64c46631d2b20225f5c56.jpg\",\n  \"130245.jpg\": \"Insecta/Melanargia galathea/dd0c78c6eea0498ec1797a66963479b0.jpg\",\n  \"130247.jpg\": \"Insecta/Melanargia galathea/e1768da63089de232149dc8c52c97617.jpg\",\n  \"130249.jpg\": \"Insecta/Melanargia galathea/956564baaf3393b3cf74cba390a5d9c9.jpg\",\n  \"130250.jpg\": \"Insecta/Melanargia galathea/0eb8a4fc2356f811025bfda340b96ad4.jpg\",\n  \"130251.jpg\": \"Insecta/Melanargia galathea/4930f940b26114dfdc607c45523d1e7b.jpg\",\n  \"130253.jpg\": \"Insecta/Melanargia galathea/339bc750821b3eac3b6cc75d58f540d7.jpg\",\n  \"130256.jpg\": \"Insecta/Melanargia galathea/23744a892bb96547e57776bc71ec15b6.jpg\",\n  \"130257.jpg\": \"Insecta/Melanargia galathea/b585939cedefd6b6b55f1e6719e22d49.jpg\",\n  \"13026.jpg\": \"Aves/Turdus pilaris/e1f6157510024418296de297b3085e93.jpg\",\n  \"130260.jpg\": \"Insecta/Melanargia galathea/2ab135d2059b8347aabc9d46840717b5.jpg\",\n  \"130264.jpg\": \"Insecta/Melanargia galathea/08ecc4256f68b417474c49f0e4d5b8f8.jpg\",\n  \"130265.jpg\": \"Insecta/Melanargia galathea/55498daeb5bc166922f8c4b0cf83d198.jpg\",\n  \"130266.jpg\": \"Insecta/Melanargia galathea/7b509cd4b860f075e55c457d97252f57.jpg\",\n  \"130269.jpg\": \"Insecta/Melanargia galathea/6889d4750f69d46c387653184e7d469f.jpg\",\n  \"130270.jpg\": \"Insecta/Melanargia galathea/f61369aa82bae988c5cd2b89a5d1e405.jpg\",\n  \"130274.jpg\": \"Insecta/Melanargia galathea/3c07bdbc49b6871fcfde3d8b3d540606.jpg\",\n  \"130275.jpg\": \"Insecta/Melanargia galathea/93c866495190304ac0c250fb8f654fed.jpg\",\n  \"130276.jpg\": \"Insecta/Melanargia galathea/4f9030b7c8af6e01e2cf34d69b33cf89.jpg\",\n  \"130277.jpg\": \"Insecta/Melanargia galathea/5b65202bb22225cf376f02e43db615c5.jpg\",\n  \"130279.jpg\": \"Insecta/Smerinthus cerisyi/068e6c1eabee3e50bf0231561266ab6e.jpg\",\n  \"130280.jpg\": \"Insecta/Smerinthus cerisyi/d21efd201d1a7aaa2293e5b12cf2f528.jpg\",\n  \"130281.jpg\": \"Insecta/Smerinthus cerisyi/efd4f9483fedc3aec2b0eb8718aed597.jpg\",\n  \"130282.jpg\": \"Insecta/Smerinthus cerisyi/2062e4d9b9e97dcfd92d0058f4fbc638.jpg\",\n  \"130287.jpg\": \"Insecta/Smerinthus cerisyi/328eb3e1ddac5b7fc103c9cc52fd12ca.jpg\",\n  \"130289.jpg\": \"Insecta/Smerinthus cerisyi/52d7fd06cf9d9e3d43a7a4ef374caf50.jpg\",\n  \"13029.jpg\": \"Aves/Turdus pilaris/9edba89ef81862bb66d30e8dc58b3d12.jpg\",\n  \"130290.jpg\": \"Insecta/Smerinthus cerisyi/b194d629ca03e8c3551d92dd8cdd1e5c.jpg\",\n  \"130301.jpg\": \"Insecta/Smerinthus cerisyi/4f8359d508e59b9179d9b58f9da44fb2.jpg\",\n  \"130303.jpg\": \"Insecta/Smerinthus cerisyi/95a582905bb415db7475870d3ab3a03e.jpg\",\n  \"130307.jpg\": \"Insecta/Smerinthus cerisyi/380e395cb20313a6eddd2edf8c960cf9.jpg\",\n  \"130308.jpg\": \"Insecta/Smerinthus cerisyi/ea7d47d56f1a63dd7742a919fa7a2769.jpg\",\n  \"130311.jpg\": \"Mollusca/Crepidula fornicata/2c4ba4f0af816e1f546f76136caeb489.jpg\",\n  \"130324.jpg\": \"Mollusca/Crepidula fornicata/46c82ef614a915170f3a0dbb8609b5ef.jpg\",\n  \"130325.jpg\": \"Mollusca/Crepidula fornicata/6e0b25995db1840a426c75eee527c596.jpg\",\n  \"130327.jpg\": \"Mollusca/Crepidula fornicata/b4f735336a282f2ce949306dae802df1.jpg\",\n  \"13034.jpg\": \"Aves/Turdus pilaris/0d2436709c64c6fdc4e7afa11691fb07.jpg\",\n  \"130340.jpg\": \"Mollusca/Crepidula fornicata/7369ec73155f60b6133c2856f9efe3ad.jpg\",\n  \"130343.jpg\": \"Mollusca/Crepidula fornicata/88dfd37ac451661e85c06781661c35c5.jpg\",\n  \"130344.jpg\": \"Mollusca/Crepidula fornicata/27a8730a586036507777a68dbf29d0a9.jpg\",\n  \"13035.jpg\": \"Aves/Turdus pilaris/6bc71a042856d06005bf781f3c1e189a.jpg\",\n  \"130352.jpg\": \"Actinopterygii/Carassius auratus/b90bdfe3df755e4f460ed84ddee1d8c9.jpg\",\n  \"130364.jpg\": \"Reptilia/Aspidoscelis sexlineata/29c20b67f63fced9dc49a10937db6c9e.jpg\",\n  \"130366.jpg\": \"Reptilia/Aspidoscelis sexlineata/3d1c55c157a6bbd3fca28bba18ed201c.jpg\",\n  \"130368.jpg\": \"Reptilia/Aspidoscelis sexlineata/9ede52ce2eca1e3e655571523fbb2952.jpg\",\n  \"13037.jpg\": \"Aves/Turdus pilaris/340bda90101817d8eddff91e7243f232.jpg\",\n  \"130371.jpg\": \"Reptilia/Aspidoscelis sexlineata/1578cfaaf36beaec9fa4175f7d6207d4.jpg\",\n  \"130372.jpg\": \"Reptilia/Aspidoscelis sexlineata/8ba8282f5b69a493ff66ae88d9583e7c.jpg\",\n  \"130373.jpg\": \"Reptilia/Aspidoscelis sexlineata/3e7bd3c6187543b8fb67024eec007545.jpg\",\n  \"130374.jpg\": \"Reptilia/Aspidoscelis sexlineata/f402ade9a82dc728aab983ddea1be6b0.jpg\",\n  \"130375.jpg\": \"Reptilia/Aspidoscelis sexlineata/7d9d5365a804c5a709b659d6ecf632f4.jpg\",\n  \"130381.jpg\": \"Reptilia/Aspidoscelis sexlineata/c74aab8f07646008d63e257cb311d27e.jpg\",\n  \"130382.jpg\": \"Reptilia/Aspidoscelis sexlineata/770a0cc90edd1d8e29a85c37cd64bbe5.jpg\",\n  \"130383.jpg\": \"Reptilia/Aspidoscelis sexlineata/fc8d33f74ad0232944f98ea38dd00652.jpg\",\n  \"130384.jpg\": \"Reptilia/Aspidoscelis sexlineata/65786747de82a22bb7ac0bdae137aa35.jpg\",\n  \"130401.jpg\": \"Reptilia/Aspidoscelis sexlineata/f50dd87549c77c2ada64ef444b39c46d.jpg\",\n  \"130403.jpg\": \"Reptilia/Aspidoscelis sexlineata/563d03723d414d6efbf9ca9903a78534.jpg\",\n  \"130411.jpg\": \"Reptilia/Aspidoscelis sexlineata/802e7e42c5e92eb867c68198bbb4aec3.jpg\",\n  \"130412.jpg\": \"Reptilia/Aspidoscelis sexlineata/d5b0ef42701b2c317e33ef868797ed80.jpg\",\n  \"130422.jpg\": \"Reptilia/Aspidoscelis sexlineata/f742a51709628142a541cb0d38c46dbd.jpg\",\n  \"130423.jpg\": \"Reptilia/Aspidoscelis sexlineata/cff1d72ce7849022ca5536ad83234908.jpg\",\n  \"130427.jpg\": \"Reptilia/Aspidoscelis sexlineata/f8f97665d04a4210f75fdef4486fb57e.jpg\",\n  \"130436.jpg\": \"Arachnida/Loxosceles reclusa/57fce678f004fdf3dfce2a8acf38ca48.jpg\",\n  \"130438.jpg\": \"Arachnida/Loxosceles reclusa/df5f5ccce6eae77b855cb5146d1cb547.jpg\",\n  \"130439.jpg\": \"Arachnida/Loxosceles reclusa/57c22f928f65a3f719b0cef37b0cb935.jpg\",\n  \"130449.jpg\": \"Arachnida/Loxosceles reclusa/26aa4a10e98fd675cbc4e258ba093e6b.jpg\",\n  \"130458.jpg\": \"Reptilia/Heterodon nasicus/90e75986ad25938ef17f423127695b70.jpg\",\n  \"130459.jpg\": \"Reptilia/Heterodon nasicus/34d9961f4915238aa47d3ac635639e69.jpg\",\n  \"13046.jpg\": \"Arachnida/Frontinella communis/f175320feef4f599815c6558eb5f58d3.jpg\",\n  \"130470.jpg\": \"Reptilia/Heterodon nasicus/41f4fdbbab6f4d0e833fb894480dc68e.jpg\",\n  \"130475.jpg\": \"Reptilia/Heterodon nasicus/e7e9cc8beb827d853898505cd1c602ad.jpg\",\n  \"13055.jpg\": \"Arachnida/Frontinella communis/c8b075f4e69226aae489433efd4435c0.jpg\",\n  \"130667.jpg\": \"Animalia/Aetobatus narinari/8d80c128c76625fea2873c0929c48de1.jpg\",\n  \"130671.jpg\": \"Animalia/Aetobatus narinari/b5512e3322b63b67797e2ca83358853c.jpg\",\n  \"130683.jpg\": \"Animalia/Aetobatus narinari/5d3d77e51226f36bea1cc2278ff88ff5.jpg\",\n  \"130684.jpg\": \"Animalia/Aetobatus narinari/7b3bcfdd55253d2b79695610a25374f9.jpg\",\n  \"130688.jpg\": \"Animalia/Aetobatus narinari/eb62475fd510bc6cc8240cd39a04b246.jpg\",\n  \"130785.jpg\": \"Insecta/Lophocampa caryae/b7331485d88509f3c8854c759f617758.jpg\",\n  \"130794.jpg\": \"Insecta/Lophocampa caryae/9c7063de69705242614f947b094399f0.jpg\",\n  \"130796.jpg\": \"Insecta/Lophocampa caryae/f63f2fad3b15a51a53ef6f6066e922da.jpg\",\n  \"130807.jpg\": \"Insecta/Lophocampa caryae/6691d90d770b1561f9ba2c0e82de25d8.jpg\",\n  \"130809.jpg\": \"Insecta/Lophocampa caryae/e7d354902d38814d1c2b231c37428bbe.jpg\",\n  \"130810.jpg\": \"Insecta/Lophocampa caryae/78dcbaf419d32018ad4aa430d3a23b0a.jpg\",\n  \"130811.jpg\": \"Insecta/Lophocampa caryae/e44ee41627d9ba3f0af819fa98936e9f.jpg\",\n  \"130824.jpg\": \"Insecta/Lophocampa caryae/d6dc04d9a9129de2d69fe576221798fe.jpg\",\n  \"130845.jpg\": \"Insecta/Lophocampa caryae/09d4394065580e298abf22d7a7ff4ce0.jpg\",\n  \"130846.jpg\": \"Insecta/Lophocampa caryae/6b462509ebc356754e3627c7c0983f28.jpg\",\n  \"130847.jpg\": \"Insecta/Lophocampa caryae/c6622de57680da6ffff6ce9c97920dac.jpg\",\n  \"130848.jpg\": \"Insecta/Lophocampa caryae/9ac9b63f39317536a425e5cc784975d9.jpg\",\n  \"130850.jpg\": \"Insecta/Lophocampa caryae/aa0752d02f7a305efc65b7483bd294ab.jpg\",\n  \"130856.jpg\": \"Insecta/Lophocampa caryae/23cdf2e77af10366d174a56a5a4d89b6.jpg\",\n  \"130859.jpg\": \"Insecta/Lophocampa caryae/3d0509d2ade794c6481172dd6a1e1686.jpg\",\n  \"130863.jpg\": \"Insecta/Lophocampa caryae/72d879a8c28e1082dc6cece6802ca534.jpg\",\n  \"130865.jpg\": \"Insecta/Lophocampa caryae/5a81f91bd52ae1d24c766e7654db7d4d.jpg\",\n  \"130887.jpg\": \"Insecta/Lophocampa caryae/75acd245ea460797c977907f688fb2bb.jpg\",\n  \"130891.jpg\": \"Insecta/Lophocampa caryae/02fadb7a80b418eb842da10e7592d3be.jpg\",\n  \"130896.jpg\": \"Insecta/Lophocampa caryae/1769abdb7e3d2788400dd01ecf4ad137.jpg\",\n  \"130904.jpg\": \"Insecta/Lophocampa caryae/a68c0a07bf9db7d43e10483bd6cc42d4.jpg\",\n  \"130917.jpg\": \"Insecta/Metcalfa pruinosa/54b4a1aa8f2583882e6cec5dbedfd40d.jpg\",\n  \"130918.jpg\": \"Insecta/Metcalfa pruinosa/6a5465913196c4bfae1fa01f2b18b40e.jpg\",\n  \"130919.jpg\": \"Insecta/Metcalfa pruinosa/d383aad1f71f39ce48235e5e8b62772d.jpg\",\n  \"130923.jpg\": \"Insecta/Metcalfa pruinosa/50ed8dbfb3625426dad34446349b195d.jpg\",\n  \"130925.jpg\": \"Insecta/Metcalfa pruinosa/db89f31666d995c57d910e73b1e8ce3c.jpg\",\n  \"130927.jpg\": \"Insecta/Metcalfa pruinosa/478be4df143cc7767ec5539b4c119c38.jpg\",\n  \"130931.jpg\": \"Insecta/Metcalfa pruinosa/8d940454bf6755ffa969ed7a8bbf647a.jpg\",\n  \"130933.jpg\": \"Insecta/Metcalfa pruinosa/39ecb53c0d174660bf49b71d7c0c4176.jpg\",\n  \"130937.jpg\": \"Insecta/Metcalfa pruinosa/f58782cf08b32ea49b69e22e2cab90fe.jpg\",\n  \"130943.jpg\": \"Insecta/Metcalfa pruinosa/0883d888cd420319c8385fffd47b9057.jpg\",\n  \"130944.jpg\": \"Insecta/Metcalfa pruinosa/eed6f3b82d03a0ce8f66cc3ed1fe8f7c.jpg\",\n  \"130946.jpg\": \"Insecta/Metcalfa pruinosa/bfe1af417620b760bac22e75595c7b07.jpg\",\n  \"130947.jpg\": \"Insecta/Metcalfa pruinosa/151abc8835e3826fa6434448f5e037c1.jpg\",\n  \"130952.jpg\": \"Insecta/Metcalfa pruinosa/e24a4695b5ae337f1a1584f6caf65a8f.jpg\",\n  \"130953.jpg\": \"Insecta/Metcalfa pruinosa/01900aca88f6404245bb9af3e259aa3e.jpg\",\n  \"130957.jpg\": \"Insecta/Metcalfa pruinosa/f036fa622b8c270b77d13acf1af0dbd6.jpg\",\n  \"130960.jpg\": \"Insecta/Metcalfa pruinosa/dae400de68e2203e46946135a7e9afe8.jpg\",\n  \"131.jpg\": \"Aves/Zonotrichia capensis/12ebdfcc270af93554fa0ddf8f225b08.jpg\",\n  \"13106.jpg\": \"Insecta/Hypagyrtis unipunctata/71b2eab6392cc923fc69fb0e1dcdaa5b.jpg\",\n  \"13108.jpg\": \"Insecta/Hypagyrtis unipunctata/5ac59b912ebe2be0c2001d6b0be82a81.jpg\",\n  \"13113.jpg\": \"Insecta/Hypagyrtis unipunctata/034e948a221df6e2fcaf51f16d1c5d4b.jpg\",\n  \"13114.jpg\": \"Insecta/Hypagyrtis unipunctata/2eb1eeb3d7470bb27c91c45cb7c39056.jpg\",\n  \"13115.jpg\": \"Insecta/Hypagyrtis unipunctata/61d5f458bbf448206125cdfac27bcac4.jpg\",\n  \"13120.jpg\": \"Insecta/Hypagyrtis unipunctata/e2f9b4324e4dfbb5d494915596734525.jpg\",\n  \"13121.jpg\": \"Insecta/Hypagyrtis unipunctata/70ea2681f94ebe74582b8dab8f0c9ec5.jpg\",\n  \"131212.jpg\": \"Reptilia/Storeria occipitomaculata/26b7d85f383ae5ab4086b30ddc94c33c.jpg\",\n  \"13122.jpg\": \"Insecta/Hypagyrtis unipunctata/e5a6685784c16b6668fe496fde04553a.jpg\",\n  \"131221.jpg\": \"Reptilia/Storeria occipitomaculata/9bb2617502434a099a9b133eeac9e7a1.jpg\",\n  \"131222.jpg\": \"Reptilia/Storeria occipitomaculata/b312460ccfc4a61135284b4ed74fe12d.jpg\",\n  \"131223.jpg\": \"Reptilia/Storeria occipitomaculata/9ba5b139a27fc6ab0929db4c20ce6b64.jpg\",\n  \"131224.jpg\": \"Reptilia/Storeria occipitomaculata/e04f6545cf33c3b454e8bdfdd1f5094c.jpg\",\n  \"131228.jpg\": \"Reptilia/Storeria occipitomaculata/b0fa17e87514492af4f0df57f7944d80.jpg\",\n  \"131229.jpg\": \"Reptilia/Storeria occipitomaculata/a7131f907fa04b1ec93cba83a6298d86.jpg\",\n  \"131230.jpg\": \"Reptilia/Storeria occipitomaculata/201669a4ad0c31a6fcfec7a675b9af83.jpg\",\n  \"13124.jpg\": \"Mammalia/Odocoileus virginianus clavium/b2445af051d4a042005789795c8fa0e3.jpg\",\n  \"131242.jpg\": \"Reptilia/Storeria occipitomaculata/8195c776b99456956934b11f6ff5cb52.jpg\",\n  \"131243.jpg\": \"Reptilia/Storeria occipitomaculata/d37085c1abddba83d3c20dc5c0598a38.jpg\",\n  \"131246.jpg\": \"Reptilia/Storeria occipitomaculata/4834f6ab814277e14b4837b2f502d300.jpg\",\n  \"131249.jpg\": \"Reptilia/Storeria occipitomaculata/96c4b6b3ef160be8bc840d45d479dc1f.jpg\",\n  \"131250.jpg\": \"Reptilia/Storeria occipitomaculata/1fdebbf717b5c9863a2dffc9071635e5.jpg\",\n  \"131254.jpg\": \"Reptilia/Storeria occipitomaculata/10f0c695054ffc0e31659db0b5e8dbce.jpg\",\n  \"131259.jpg\": \"Reptilia/Storeria occipitomaculata/b578f754fce27766b331887db64fcbb0.jpg\",\n  \"131260.jpg\": \"Reptilia/Storeria occipitomaculata/0e7f9b2a8f4a3f7ad65e5f094cd62c6b.jpg\",\n  \"131261.jpg\": \"Reptilia/Storeria occipitomaculata/254738bb9e3b0c685828b4f948713e0f.jpg\",\n  \"13131.jpg\": \"Mammalia/Odocoileus virginianus clavium/bc16e55f5c2bcee8164412c90dcde2ba.jpg\",\n  \"131322.jpg\": \"Aves/Haliaeetus vocifer/e8bd1e03960c3349ba1f7ea23a87509a.jpg\",\n  \"131328.jpg\": \"Aves/Haliaeetus vocifer/8e4e4f01863981f2aa14acd0cada0e73.jpg\",\n  \"131330.jpg\": \"Aves/Haliaeetus vocifer/bb9f2e5deb93a6040456106398e45b74.jpg\",\n  \"131335.jpg\": \"Aves/Haliaeetus vocifer/9d24357ef6a9bdc3e417be77cc41f8e6.jpg\",\n  \"131341.jpg\": \"Insecta/Epicallima argenticinctella/46aafafc432a52b3eadea69fd8143e54.jpg\",\n  \"131342.jpg\": \"Insecta/Epicallima argenticinctella/d35f2b397de078a54559b9d33551030d.jpg\",\n  \"131344.jpg\": \"Insecta/Epicallima argenticinctella/2ff5fe1b49f3fd10e9b2a3624f10d757.jpg\",\n  \"131345.jpg\": \"Insecta/Epicallima argenticinctella/f1f2dd044f30c4f91a5bebf1faf1515b.jpg\",\n  \"131348.jpg\": \"Reptilia/Tarentola mauritanica/24160f42c9837ed570beeaf3acbe0cf2.jpg\",\n  \"131349.jpg\": \"Reptilia/Tarentola mauritanica/6c69612d1283edac6acbe40c0c3d38d5.jpg\",\n  \"131350.jpg\": \"Reptilia/Tarentola mauritanica/b3e82f5f319c4ea64d2ffd08a65966c5.jpg\",\n  \"131351.jpg\": \"Reptilia/Tarentola mauritanica/b407d0b4cdaa84d9a9b3c6fb07aea25c.jpg\",\n  \"131352.jpg\": \"Reptilia/Tarentola mauritanica/41df8c7537cb07746b70a0666479cc4e.jpg\",\n  \"131353.jpg\": \"Reptilia/Tarentola mauritanica/3e21b0e329c4885173405589d6753de5.jpg\",\n  \"131358.jpg\": \"Reptilia/Tarentola mauritanica/541f117b3a9af187600ecc1246d6d272.jpg\",\n  \"131359.jpg\": \"Reptilia/Tarentola mauritanica/aa1844e898ddb451786ad06f58bdcacc.jpg\",\n  \"131360.jpg\": \"Reptilia/Tarentola mauritanica/37374c933ef280475301e1a47ed4bd01.jpg\",\n  \"131361.jpg\": \"Reptilia/Tarentola mauritanica/82ec310f2ca4a6b146060506b260e4f3.jpg\",\n  \"131365.jpg\": \"Reptilia/Tarentola mauritanica/b4ed69a56c8706f953c557584b1eea73.jpg\",\n  \"131366.jpg\": \"Reptilia/Tarentola mauritanica/8a01107401c473ad178594a89720f8e3.jpg\",\n  \"131368.jpg\": \"Reptilia/Tarentola mauritanica/92310475501fcfd4fdceb1235cb2e453.jpg\",\n  \"131369.jpg\": \"Reptilia/Tarentola mauritanica/7d9331fd98137abf67a34cec70f4901a.jpg\",\n  \"131370.jpg\": \"Reptilia/Tarentola mauritanica/82bdabf636a0cfa990b064434ce71674.jpg\",\n  \"131371.jpg\": \"Reptilia/Tarentola mauritanica/683edb9cad0c5877a8daaa144ea55ffe.jpg\",\n  \"131375.jpg\": \"Reptilia/Tarentola mauritanica/d9272d894278316bae1deef88d738ff0.jpg\",\n  \"131376.jpg\": \"Reptilia/Tarentola mauritanica/603a89c57640a0618a1c5d1e3ebb0ebd.jpg\",\n  \"13138.jpg\": \"Mammalia/Odocoileus virginianus clavium/b0a813ed10fad4b28d6fc0002521ef54.jpg\",\n  \"131380.jpg\": \"Reptilia/Tarentola mauritanica/e7da8c98a197c87864ea9ac3fb62c527.jpg\",\n  \"131381.jpg\": \"Reptilia/Tarentola mauritanica/c6832d66aa97f450dcd930e25f368451.jpg\",\n  \"131385.jpg\": \"Reptilia/Tarentola mauritanica/3a47d4f9f356a2f20d2c52c54678ab52.jpg\",\n  \"131386.jpg\": \"Reptilia/Tarentola mauritanica/adf407ecfa81538e3d275cdf3a5f9895.jpg\",\n  \"131387.jpg\": \"Reptilia/Tarentola mauritanica/33a0064223e2e66dcf51705e4bf238fb.jpg\",\n  \"131390.jpg\": \"Reptilia/Tarentola mauritanica/70e387bf66c433f808496944b0964bf9.jpg\",\n  \"131394.jpg\": \"Reptilia/Tarentola mauritanica/872c368cc910cf5debb6177b96b3b865.jpg\",\n  \"131489.jpg\": \"Insecta/Anisota virginiensis/0ff585f0ffa68d7a193993c3d2a35e2e.jpg\",\n  \"131494.jpg\": \"Insecta/Anisota virginiensis/06faac7ca3145ddd108fd401740615b7.jpg\",\n  \"131503.jpg\": \"Insecta/Anisota virginiensis/e7aeb6357bc4b3f89f007d165f91e9f7.jpg\",\n  \"131504.jpg\": \"Insecta/Anisota virginiensis/c541510625a4b83c39b7f934d1c55cb0.jpg\",\n  \"131506.jpg\": \"Insecta/Anisota virginiensis/40a4737ac208c3b8c5aee5ca5bb45fb4.jpg\",\n  \"131508.jpg\": \"Insecta/Achyra rantalis/cbe94f2063395f86054ab75d88116d20.jpg\",\n  \"131515.jpg\": \"Insecta/Achyra rantalis/0476470902eec9c4528788f45f36c2a8.jpg\",\n  \"131523.jpg\": \"Insecta/Achyra rantalis/0858ab27af9f81dbf5acab20ea38c893.jpg\",\n  \"131524.jpg\": \"Insecta/Achyra rantalis/7fca20f732ae3ac793cd40a19f8f24d9.jpg\",\n  \"131525.jpg\": \"Insecta/Achyra rantalis/754f7baf75a66675d2c7a74f476b559f.jpg\",\n  \"131532.jpg\": \"Insecta/Achyra rantalis/1784afda027f04152acaf8f4712a95a6.jpg\",\n  \"131635.jpg\": \"Aves/Branta sandvicensis/864bf4be8a1cb22b11f0ceb3e04355cf.jpg\",\n  \"131640.jpg\": \"Aves/Branta sandvicensis/8ad238738a31630e033ab8bddec59e7a.jpg\",\n  \"131653.jpg\": \"Aves/Branta sandvicensis/22eeea7aceac3e8a594086e25d98cf8d.jpg\",\n  \"131665.jpg\": \"Aves/Branta sandvicensis/6b081f719d877eaa9b65ae449309edc9.jpg\",\n  \"131669.jpg\": \"Arachnida/Dolomedes triton/e49c6155704b6fa83eeeae3ab14e04be.jpg\",\n  \"131671.jpg\": \"Arachnida/Dolomedes triton/ef9069788adf6da52c6c7165fa77b301.jpg\",\n  \"131675.jpg\": \"Arachnida/Dolomedes triton/c3b426b93f6a9586af1647ec905c54fd.jpg\",\n  \"131676.jpg\": \"Arachnida/Dolomedes triton/e85152693ff75dda7102c61ea186bb90.jpg\",\n  \"131677.jpg\": \"Arachnida/Dolomedes triton/f5044f0fca085be3c115415f3596b176.jpg\",\n  \"131684.jpg\": \"Arachnida/Dolomedes triton/b3c90e9ec2ece78feddfc88f458c458b.jpg\",\n  \"131694.jpg\": \"Arachnida/Dolomedes triton/dd88f71e5ce0f7cc826296644cc45fba.jpg\",\n  \"131695.jpg\": \"Arachnida/Dolomedes triton/17facc897a36c66c83d37222acbb2d52.jpg\",\n  \"131697.jpg\": \"Arachnida/Dolomedes triton/eafcd255ab6a53edbe642f1bf083a970.jpg\",\n  \"131698.jpg\": \"Arachnida/Dolomedes triton/3550c4f46750189718275170ee64072b.jpg\",\n  \"131729.jpg\": \"Insecta/Nannothemis bella/df27d124d92e64ff59fcaa35c3a06368.jpg\",\n  \"131735.jpg\": \"Insecta/Nannothemis bella/547ab2c5fbf52349401c14655636e832.jpg\",\n  \"131739.jpg\": \"Insecta/Nannothemis bella/bf6368ddbfd6900ff669316301c49c4e.jpg\",\n  \"131741.jpg\": \"Insecta/Nannothemis bella/344470cd497fd35b632a5cec8f4bdb5b.jpg\",\n  \"131744.jpg\": \"Insecta/Nannothemis bella/cdeb6215b52db54e4e5d6cbe374ecafa.jpg\",\n  \"131763.jpg\": \"Insecta/Magicicada septendecim/1cb8e4bc138e09442da2a2323a34d41e.jpg\",\n  \"131781.jpg\": \"Insecta/Magicicada septendecim/93a3e6738ce19931f6f6279feccda52c.jpg\",\n  \"131788.jpg\": \"Insecta/Zelus renardii/c92d36a3ba35aa7768e8244f2aacfed9.jpg\",\n  \"131791.jpg\": \"Insecta/Zelus renardii/e356f60f19acd0679a9ee43e697051bb.jpg\",\n  \"131801.jpg\": \"Insecta/Zelus renardii/0ac3ccc279b52d2191a890677a64a4fe.jpg\",\n  \"131808.jpg\": \"Insecta/Zelus renardii/ae006cb187d524d22080b7772dc4cc54.jpg\",\n  \"131812.jpg\": \"Insecta/Zelus renardii/6c1761ed3ae635ec2214f8f26403f0cd.jpg\",\n  \"131813.jpg\": \"Insecta/Zelus renardii/0c96e30351613c3574221f95eb0f6995.jpg\",\n  \"131814.jpg\": \"Insecta/Zelus renardii/7d821cf20b52fadaef48cdcac012ae8c.jpg\",\n  \"131818.jpg\": \"Insecta/Zelus renardii/3c78cc4779e23e52220c1911b9c8c536.jpg\",\n  \"131819.jpg\": \"Insecta/Zelus renardii/4af908c95d6c251a591fba4add66f063.jpg\",\n  \"131827.jpg\": \"Insecta/Zelus renardii/66f2dcd5bb6b1f97d43cbfa19430c399.jpg\",\n  \"131833.jpg\": \"Insecta/Zelus renardii/919c0dd307e5034bd9cd4ecab9fa034d.jpg\",\n  \"131834.jpg\": \"Insecta/Zelus renardii/096018cbf36902716ac9a0532adf8f89.jpg\",\n  \"131835.jpg\": \"Insecta/Zelus renardii/3043baea19bbd80d29a8039a7c5fe7ba.jpg\",\n  \"131840.jpg\": \"Insecta/Zelus renardii/86c2e25a900218e0449cf231ab2d9afd.jpg\",\n  \"131855.jpg\": \"Insecta/Zelus renardii/58c825faaa3d6f6817494372efe9421c.jpg\",\n  \"131856.jpg\": \"Insecta/Zelus renardii/fd3dd5e6ade97f69afb76299e38347ca.jpg\",\n  \"131857.jpg\": \"Insecta/Zelus renardii/e9d731e1af10b5abe4fb7a26ffb8f45d.jpg\",\n  \"131858.jpg\": \"Insecta/Zelus renardii/6b40bbbd8906d5183569f316ba020070.jpg\",\n  \"131859.jpg\": \"Insecta/Zelus renardii/6010ca1996f54f2ebee702e9ffed6920.jpg\",\n  \"131952.jpg\": \"Aves/Pelecanus occidentalis carolinensis/617147d26870917ad9e95f69795d4a30.jpg\",\n  \"131957.jpg\": \"Aves/Pelecanus occidentalis carolinensis/ae7a174398637c91f37b74a5de1bf939.jpg\",\n  \"132093.jpg\": \"Actinopterygii/Lepisosteus oculatus/1e9bf6a1779a5376e25fe46c0b2efe20.jpg\",\n  \"132098.jpg\": \"Actinopterygii/Lepisosteus oculatus/cd16e015c1ac73cf193946ab812426e5.jpg\",\n  \"132100.jpg\": \"Actinopterygii/Lepisosteus oculatus/a6cd97b841a18782cd45e4cffdcc28fb.jpg\",\n  \"132104.jpg\": \"Actinopterygii/Lepisosteus oculatus/00861e2c656c7e1643ff44c2c4d9c96a.jpg\",\n  \"132108.jpg\": \"Actinopterygii/Lepisosteus oculatus/4236bb369970dc2b9bb5241ad0f1ef90.jpg\",\n  \"132158.jpg\": \"Insecta/Zanclognatha jacchusalis/bdc4380e26fb7e10e97024c16259069d.jpg\",\n  \"132163.jpg\": \"Insecta/Zanclognatha jacchusalis/d5646d0c488f6ae41c9b729312182dfc.jpg\",\n  \"132167.jpg\": \"Insecta/Zanclognatha jacchusalis/410c52ac33fa970ef22bdc7e427760c1.jpg\",\n  \"132171.jpg\": \"Aves/Auriparus flaviceps/5c0196d2371a18c8bc155802f3dcd3d6.jpg\",\n  \"132178.jpg\": \"Aves/Auriparus flaviceps/4f80ccfe569d1718b0516ab9cf5ac6a5.jpg\",\n  \"132179.jpg\": \"Aves/Auriparus flaviceps/978936396db5e03d81bcba57a2cb2a13.jpg\",\n  \"132184.jpg\": \"Aves/Auriparus flaviceps/c7baab9a5b6e52cf64be3445802ae95f.jpg\",\n  \"132187.jpg\": \"Aves/Auriparus flaviceps/a17cd84e425be5cb5f2026f12b9883c0.jpg\",\n  \"132192.jpg\": \"Aves/Auriparus flaviceps/52c2f04db20e4fd762049af8255d5d7f.jpg\",\n  \"132194.jpg\": \"Aves/Auriparus flaviceps/a43785140bbb5a3cf5f004458a51d2c7.jpg\",\n  \"132195.jpg\": \"Aves/Auriparus flaviceps/6cf2d29adf56c164b69be5bc55c0dbe7.jpg\",\n  \"132199.jpg\": \"Aves/Auriparus flaviceps/5b6c9fa42fc78cf908514527156d44e5.jpg\",\n  \"132200.jpg\": \"Aves/Auriparus flaviceps/0f12e749b2a35c6fceed5a63e1eab5ab.jpg\",\n  \"132201.jpg\": \"Aves/Auriparus flaviceps/59160c3a451d5aea364e67b33d6f3f88.jpg\",\n  \"132207.jpg\": \"Aves/Auriparus flaviceps/96966ecf6cb94723e3a5e9e3b02c118e.jpg\",\n  \"132222.jpg\": \"Aves/Auriparus flaviceps/f9206b005e36a9f2deb740bcfa1c4907.jpg\",\n  \"132225.jpg\": \"Aves/Auriparus flaviceps/04d23c04a7e08f791cc243c16fb8b88e.jpg\",\n  \"132230.jpg\": \"Aves/Auriparus flaviceps/829d085336be5d41f3e86e4c79b939db.jpg\",\n  \"132232.jpg\": \"Aves/Auriparus flaviceps/50eb56a3dc8b41bb8908f090aa17d3a3.jpg\",\n  \"132236.jpg\": \"Aves/Auriparus flaviceps/086dbe6cb68713887250f1c104d9847f.jpg\",\n  \"132237.jpg\": \"Aves/Auriparus flaviceps/2b7456fc8f230d318a4cfe355aaa8e3c.jpg\",\n  \"132241.jpg\": \"Aves/Auriparus flaviceps/81c8011880aee301b792b7c55ef7e1b6.jpg\",\n  \"132244.jpg\": \"Aves/Auriparus flaviceps/dc383eccdc539f915633efede2968646.jpg\",\n  \"132249.jpg\": \"Aves/Auriparus flaviceps/118bf38079a8cef8b7c3b8b7f90e8fb3.jpg\",\n  \"132252.jpg\": \"Aves/Auriparus flaviceps/1e6d4e0dc15a6b36f15766cd77d324c8.jpg\",\n  \"13231.jpg\": \"Insecta/Lithacodes fasciola/de339c1c24b56ad97d33fcd91abebd78.jpg\",\n  \"132363.jpg\": \"Aves/Mycteria americana/1400c1cb20895851b77572c6d929798e.jpg\",\n  \"132364.jpg\": \"Aves/Mycteria americana/3aa0959fe8c37da81620ab2586fc54c8.jpg\",\n  \"132373.jpg\": \"Aves/Mycteria americana/7d3509c86d0363bbb0d96d04ee805c8a.jpg\",\n  \"132392.jpg\": \"Aves/Mycteria americana/9527bb1520b749b5f94db20399b4f233.jpg\",\n  \"13241.jpg\": \"Insecta/Lithacodes fasciola/678792e8ca520356ea416055c91a6225.jpg\",\n  \"13242.jpg\": \"Insecta/Lithacodes fasciola/fbff775aada2a3fd2ece0ce7928de1be.jpg\",\n  \"132437.jpg\": \"Aves/Mycteria americana/0018f3ff310dec070171d17182221450.jpg\",\n  \"132438.jpg\": \"Aves/Mycteria americana/906b1f6f9ce5b1a3ff9eab90f1c457fe.jpg\",\n  \"132446.jpg\": \"Aves/Mycteria americana/c62e87c022b983f24456953ab0fc5762.jpg\",\n  \"13245.jpg\": \"Insecta/Lithacodes fasciola/54280c8f3a3979f5cc84efa55c3a5959.jpg\",\n  \"132456.jpg\": \"Aves/Mycteria americana/d2524dcacc400be1263e04bb0578e59c.jpg\",\n  \"132489.jpg\": \"Aves/Mycteria americana/110bec94f29e5daa91e1606fca3de436.jpg\",\n  \"132506.jpg\": \"Aves/Mycteria americana/c6d16df0f0121268305ffe4944fa57f2.jpg\",\n  \"132507.jpg\": \"Aves/Mycteria americana/2d3948358c112a3996f91f572da42fd9.jpg\",\n  \"132508.jpg\": \"Aves/Mycteria americana/655696a62231721ea9109a68868eac7e.jpg\",\n  \"132511.jpg\": \"Aves/Mycteria americana/cc5ad581c007e78bd319723bc652f9c9.jpg\",\n  \"132512.jpg\": \"Aves/Mycteria americana/106e9a524fdce76787f08c0f46dd4e05.jpg\",\n  \"132528.jpg\": \"Aves/Mycteria americana/0f156d84584df71576e72089209c9f1d.jpg\",\n  \"132553.jpg\": \"Aves/Mycteria americana/9acbd9baf2179a7ac84ba7856e4427f2.jpg\",\n  \"132556.jpg\": \"Aves/Mycteria americana/346abc189e75804ea95688fc9e3b822a.jpg\",\n  \"13259.jpg\": \"Aves/Cygnus atratus/f4e0ed028b8278607a3e4722d8786192.jpg\",\n  \"13268.jpg\": \"Aves/Cygnus atratus/a13047f2b98ce6264ea329871093139a.jpg\",\n  \"132689.jpg\": \"Insecta/Agrilus planipennis/921b20a3e4577372a3e5d0acacbd64a9.jpg\",\n  \"132690.jpg\": \"Insecta/Agrilus planipennis/1b0167ceb4f0b0d3aaeca67dcfe52007.jpg\",\n  \"132691.jpg\": \"Insecta/Agrilus planipennis/b07aadb4e70bb89e48b64f09c238ac77.jpg\",\n  \"132793.jpg\": \"Reptilia/Stigmochelys pardalis/88f3dd288e9c1827118662c02086c7d2.jpg\",\n  \"132795.jpg\": \"Reptilia/Stigmochelys pardalis/892ce57186bcbe0c080d7107a4246e99.jpg\",\n  \"132796.jpg\": \"Reptilia/Stigmochelys pardalis/890a1678f9e3139aaa9d1b209ddc79fe.jpg\",\n  \"13285.jpg\": \"Aves/Cygnus atratus/0ac2b5442eb013ceba3b9e66ac805f14.jpg\",\n  \"13292.jpg\": \"Aves/Cygnus atratus/fe845ca8ea5532af44d66c5284493d61.jpg\",\n  \"13303.jpg\": \"Aves/Cygnus atratus/231e5ccf3c4545ba7b3daea32dfe961c.jpg\",\n  \"133035.jpg\": \"Insecta/Drepana arcuata/04205583b30a61428ce0bc259952f3e4.jpg\",\n  \"133036.jpg\": \"Insecta/Drepana arcuata/1b87a2c22b885e509a781eac4237a22f.jpg\",\n  \"133037.jpg\": \"Insecta/Drepana arcuata/004c32ce328bac44ba8a49f342d7257c.jpg\",\n  \"133041.jpg\": \"Insecta/Drepana arcuata/e07e730d42a531c175d4c54eb823235b.jpg\",\n  \"133042.jpg\": \"Insecta/Drepana arcuata/48d9db74f838f39178116106eb7585fb.jpg\",\n  \"133046.jpg\": \"Insecta/Drepana arcuata/bd4d2da18c4058b1ac778e00639b9cda.jpg\",\n  \"133047.jpg\": \"Insecta/Drepana arcuata/1d329aa959314334f966ddb986f02833.jpg\",\n  \"133052.jpg\": \"Insecta/Drepana arcuata/dbc3d8088b69079e657a0e3c816c481e.jpg\",\n  \"133054.jpg\": \"Insecta/Drepana arcuata/d5051cf68bdec39f7d4738ec06f0641a.jpg\",\n  \"133058.jpg\": \"Insecta/Drepana arcuata/e380b4429a341f737f4b55c7b89f7471.jpg\",\n  \"133060.jpg\": \"Insecta/Drepana arcuata/30815741f60529fd9bbe20e62756c86a.jpg\",\n  \"133066.jpg\": \"Insecta/Drepana arcuata/e12b4841ff40256c619bf23e310e23a9.jpg\",\n  \"133067.jpg\": \"Insecta/Drepana arcuata/55287c367618cf8d5cd31e5791e17fe8.jpg\",\n  \"133070.jpg\": \"Insecta/Drepana arcuata/b02b25c4dfe52eb1e52134ea3824583b.jpg\",\n  \"133071.jpg\": \"Insecta/Drepana arcuata/5d1dd1bd00aa3e5129f58ed1cc344e86.jpg\",\n  \"133073.jpg\": \"Insecta/Drepana arcuata/108df27de04fdd1fc65fee886c132b08.jpg\",\n  \"133074.jpg\": \"Insecta/Drepana arcuata/7f7fb8ae818dff046efd91bc368929f6.jpg\",\n  \"133077.jpg\": \"Insecta/Drepana arcuata/88cd80662ff41b261647c93efb4e72b0.jpg\",\n  \"133078.jpg\": \"Insecta/Drepana arcuata/d0b9e4e4c7f17964b346d0038168d2d0.jpg\",\n  \"133083.jpg\": \"Insecta/Drepana arcuata/6bb58580acd77c8297dd2edcca3cfe35.jpg\",\n  \"133129.jpg\": \"Aves/Egretta tricolor/61d2d102c4f3990e55ab410fd7919857.jpg\",\n  \"133144.jpg\": \"Aves/Egretta tricolor/16f1cb3f17a86a898d382f7e120fe78c.jpg\",\n  \"133164.jpg\": \"Aves/Egretta tricolor/b8d3d7ef58bff750a5a3a56f1b3da3ae.jpg\",\n  \"13321.jpg\": \"Aves/Cygnus atratus/dccb751f1f53e027ac59909dcbddbe70.jpg\",\n  \"133224.jpg\": \"Aves/Egretta tricolor/328b5507e231ba7f75599695c553a023.jpg\",\n  \"133226.jpg\": \"Aves/Egretta tricolor/7060c0fa7adbe8091bc4631dff3c15a8.jpg\",\n  \"133233.jpg\": \"Aves/Egretta tricolor/39ef318c2063c2719509c0a4c20113e6.jpg\",\n  \"133241.jpg\": \"Aves/Egretta tricolor/1c5febb5a1c0afa973ec36da15fbdf3d.jpg\",\n  \"133254.jpg\": \"Aves/Egretta tricolor/a30790e85c579ca76f6d595fd557df5f.jpg\",\n  \"133256.jpg\": \"Aves/Egretta tricolor/a1d8637eb80f9b1489ff28218e550666.jpg\",\n  \"133269.jpg\": \"Aves/Egretta tricolor/10c001c37506820da69e11d5ac50d49b.jpg\",\n  \"133280.jpg\": \"Aves/Egretta tricolor/3b16591014f5f57e0b2d9eb73d8743f7.jpg\",\n  \"133283.jpg\": \"Aves/Egretta tricolor/00dd26f302a2b435c46e3693d3779627.jpg\",\n  \"133292.jpg\": \"Aves/Egretta tricolor/5eb071c3769a8ddb5e39386db5338cea.jpg\",\n  \"133307.jpg\": \"Aves/Egretta tricolor/0587a93971588b87d67fc5123cb2d03f.jpg\",\n  \"133310.jpg\": \"Aves/Egretta tricolor/35d90bab32ff42e4b7e7a785c006ed66.jpg\",\n  \"133313.jpg\": \"Aves/Egretta tricolor/7d9c8a356b121fad9a0cedfb9782a3bb.jpg\",\n  \"133322.jpg\": \"Aves/Egretta tricolor/2e4f42c0ecb1114ae5eadb6e52e8d813.jpg\",\n  \"133337.jpg\": \"Aves/Egretta tricolor/88a2ac6121501e3d6762f4a70bcab75b.jpg\",\n  \"133366.jpg\": \"Aves/Egretta tricolor/e4d86aec9445aac5ce545b80ed9d393f.jpg\",\n  \"133368.jpg\": \"Aves/Egretta tricolor/4c7b5105be8217e37a94ac81670bd9b0.jpg\",\n  \"133374.jpg\": \"Aves/Egretta tricolor/0987be25110c854e7bb430cdd5e0751f.jpg\",\n  \"13348.jpg\": \"Reptilia/Salvadora hexalepis/639a14c0c88adfea8934253d37b93835.jpg\",\n  \"133486.jpg\": \"Aves/Icterus cucullatus/999d425159cbdf37c173ddf5846653ad.jpg\",\n  \"13349.jpg\": \"Reptilia/Salvadora hexalepis/23d59a4b4a2703e6972cf3c247001566.jpg\",\n  \"133511.jpg\": \"Aves/Icterus cucullatus/e013137a83d46ee24fa649554e1aafca.jpg\",\n  \"133520.jpg\": \"Aves/Icterus cucullatus/a6b9c5972e40fd4a342646c4f2f35acd.jpg\",\n  \"133524.jpg\": \"Aves/Icterus cucullatus/5266f8a3d2d8dac0771243f59f346d25.jpg\",\n  \"133526.jpg\": \"Aves/Icterus cucullatus/60492a9293becf8812140d4aeba89b63.jpg\",\n  \"133533.jpg\": \"Aves/Icterus cucullatus/9b92008bc2b20c0643a84ff41d9d0725.jpg\",\n  \"133550.jpg\": \"Aves/Icterus cucullatus/72c6b0211f01c61482769b8ffa15b2b8.jpg\",\n  \"133552.jpg\": \"Aves/Icterus cucullatus/1b5b723daede55bfdccaee859be2d475.jpg\",\n  \"133559.jpg\": \"Aves/Icterus cucullatus/81d3e7bee1e2dc0a246a42f590a5c2cc.jpg\",\n  \"133562.jpg\": \"Aves/Icterus cucullatus/3183f748aa3f99568977c2c0d2f35622.jpg\",\n  \"133572.jpg\": \"Aves/Icterus cucullatus/edddda110f5be0f70dc787cae6da549b.jpg\",\n  \"133589.jpg\": \"Aves/Icterus cucullatus/56e3306c0a9aec855d5f6f8d0b5acd22.jpg\",\n  \"133590.jpg\": \"Aves/Icterus cucullatus/1045a84eb6a2c6b51a430e410c841b9b.jpg\",\n  \"13360.jpg\": \"Reptilia/Salvadora hexalepis/b8fb5fb4a3b1e6af9573ba1760c42ad5.jpg\",\n  \"133604.jpg\": \"Aves/Icterus cucullatus/03405b010c3b015ae85d7ea34b52df17.jpg\",\n  \"133618.jpg\": \"Aves/Icterus cucullatus/a10394160f5aa67dfc671d01d4cfaff8.jpg\",\n  \"133619.jpg\": \"Aves/Icterus cucullatus/e08147d5f0270dd2a90a68ac596a72b5.jpg\",\n  \"13363.jpg\": \"Reptilia/Salvadora hexalepis/0fb6f72977a5ec2a00cf9771967d3676.jpg\",\n  \"13364.jpg\": \"Reptilia/Salvadora hexalepis/cc10b92647e0d264d3f511fc868eb6b7.jpg\",\n  \"133640.jpg\": \"Aves/Icterus cucullatus/73af9379d946e0ac84e702a740e14c74.jpg\",\n  \"133656.jpg\": \"Aves/Icterus cucullatus/e69d4f1fa6e9048ee3462a030fed12a6.jpg\",\n  \"133677.jpg\": \"Aves/Icterus cucullatus/2f46c8563775c0cceb4b3ec59a21c594.jpg\",\n  \"133688.jpg\": \"Aves/Icterus cucullatus/b660424fc07a78819e6879d4077eaebe.jpg\",\n  \"133694.jpg\": \"Aves/Icterus cucullatus/20647e38a61ca75856bbd9f9cdce86b2.jpg\",\n  \"133708.jpg\": \"Aves/Icterus cucullatus/cd8c44c5af211df243b67c1d4edfe112.jpg\",\n  \"133709.jpg\": \"Aves/Icterus cucullatus/15f203316dc6698f2792489c529fb1e3.jpg\",\n  \"133710.jpg\": \"Insecta/Euphoria basalis/e386b91a47cf35e9cce2456f8ba766c0.jpg\",\n  \"133716.jpg\": \"Insecta/Euphoria basalis/964fe580c547e684f7741db0022d29ba.jpg\",\n  \"133721.jpg\": \"Insecta/Euphoria basalis/1b24fa3631ec7193d93d811394f2561f.jpg\",\n  \"133726.jpg\": \"Insecta/Euphoria basalis/18e629c8e1a91a98540a25dbd073ce20.jpg\",\n  \"133727.jpg\": \"Insecta/Euphoria basalis/08c485710b60f904e27e771ade2e5691.jpg\",\n  \"133963.jpg\": \"Insecta/Dione juno/d98155a07ff19be050746c9a011aa50c.jpg\",\n  \"133964.jpg\": \"Insecta/Dione juno/6f767784c2954d6e11d1960b66ec9296.jpg\",\n  \"133973.jpg\": \"Insecta/Dione juno/9b6c2b44699cc23e1c17a066f1d002cd.jpg\",\n  \"133974.jpg\": \"Insecta/Dione juno/43b9fa06a86a7129ac58543e3179d5bd.jpg\",\n  \"133976.jpg\": \"Insecta/Dione juno/cadcb278f05271d3777f6dd72db63e2b.jpg\",\n  \"133978.jpg\": \"Insecta/Dione juno/806d5e91c6e5b52feee3e8f90ddc4a5c.jpg\",\n  \"13401.jpg\": \"Aves/Circus cyaneus/4dd297f5c78d2676f6d6819adc245859.jpg\",\n  \"13406.jpg\": \"Aves/Circus cyaneus/2e635df7a8e91352fde080ee0c087594.jpg\",\n  \"134162.jpg\": \"Insecta/Calopteryx aequabilis/ca931e535c9908f3f4f73e656753c659.jpg\",\n  \"134163.jpg\": \"Insecta/Calopteryx aequabilis/ef695d7b777d825579970a04ca20b6f8.jpg\",\n  \"134169.jpg\": \"Insecta/Calopteryx aequabilis/1730c52505e799c1482517bff8530a45.jpg\",\n  \"134171.jpg\": \"Insecta/Calopteryx aequabilis/ab31dcf9c1deb02119eb448451094937.jpg\",\n  \"134174.jpg\": \"Insecta/Calopteryx aequabilis/4845c58994cdadba5dd65638f1ec9b3d.jpg\",\n  \"134176.jpg\": \"Insecta/Calopteryx aequabilis/352223aef17c70ec0cf7b5a030fea46c.jpg\",\n  \"134180.jpg\": \"Insecta/Calopteryx aequabilis/77a2b2bf3910d3725e73db50ddc9fc41.jpg\",\n  \"134181.jpg\": \"Insecta/Calopteryx aequabilis/a4c827e8a7953a38f63076be3f653885.jpg\",\n  \"134187.jpg\": \"Insecta/Calopteryx aequabilis/6826b2fd0daf6bde77926a41a63a3608.jpg\",\n  \"134190.jpg\": \"Insecta/Andricus kingi/c1cc8da439018501c3fca5fb30e92d21.jpg\",\n  \"134197.jpg\": \"Insecta/Andricus kingi/883e85a18fc4953fae98570e1d2dd86d.jpg\",\n  \"134243.jpg\": \"Aves/Apus apus/e2531c0fd9eee7d0368bcd33b35b4d20.jpg\",\n  \"134248.jpg\": \"Aves/Apus apus/74c2719ade47159707e4d2921be0e426.jpg\",\n  \"134251.jpg\": \"Aves/Apus apus/f63c29ac5f967f63e9a03281d6835f15.jpg\",\n  \"134254.jpg\": \"Insecta/Cenopis reticulatana/1bd455c64e02c70e1c2ae4d7d2037e46.jpg\",\n  \"134259.jpg\": \"Insecta/Cenopis reticulatana/7a944ff57dcdc9070a3981b9488caf78.jpg\",\n  \"134265.jpg\": \"Insecta/Cenopis reticulatana/2476d4b184f731f4462d93462211ca82.jpg\",\n  \"134271.jpg\": \"Insecta/Cenopis reticulatana/3bb81e477383040767b2d8844d04405d.jpg\",\n  \"134272.jpg\": \"Insecta/Cenopis reticulatana/abd79adf0bc331f872d2bcef07bc4cd6.jpg\",\n  \"134273.jpg\": \"Insecta/Cenopis reticulatana/671942c2cf1b99e773df840c0fb15805.jpg\",\n  \"134274.jpg\": \"Insecta/Cenopis reticulatana/507e25522853662c977c877479044b66.jpg\",\n  \"134303.jpg\": \"Animalia/Pisaster ochraceus/cd33a506c515550c4620647a7902f8ba.jpg\",\n  \"134316.jpg\": \"Animalia/Pisaster ochraceus/6bd3367fda4d13838313e067b46274d7.jpg\",\n  \"134344.jpg\": \"Animalia/Pisaster ochraceus/2fd6f77c0d48c76a1d5d6614d0e12a2e.jpg\",\n  \"134350.jpg\": \"Animalia/Pisaster ochraceus/d180ed954a40b7194380b4806ff9eae8.jpg\",\n  \"134355.jpg\": \"Animalia/Pisaster ochraceus/f5f29f00d90c475809a351295c623a4a.jpg\",\n  \"134360.jpg\": \"Animalia/Pisaster ochraceus/44bba609de6a5ac925b4615ffe45835e.jpg\",\n  \"134363.jpg\": \"Animalia/Pisaster ochraceus/c64e35d2c44d2c5f1c496466dd12e1c9.jpg\",\n  \"134410.jpg\": \"Animalia/Pisaster ochraceus/b632801b3c92dd43617f9b0dfba7a63e.jpg\",\n  \"134498.jpg\": \"Animalia/Pisaster ochraceus/21ce7a0f59a120c5756ab49fc1c53aa7.jpg\",\n  \"13450.jpg\": \"Aves/Circus cyaneus/09963d04b9c46d7d11530d56c9442602.jpg\",\n  \"134529.jpg\": \"Animalia/Pisaster ochraceus/604223914affd85b87d7d85125a3a47e.jpg\",\n  \"134552.jpg\": \"Animalia/Pisaster ochraceus/53bfc5c545bbe4a830ae4f45defd40cf.jpg\",\n  \"134568.jpg\": \"Animalia/Pisaster ochraceus/de6701e2ae8d3528b09c3fc94eb63101.jpg\",\n  \"134583.jpg\": \"Animalia/Pisaster ochraceus/247ffdad1ca7b62f6594997fadb446bc.jpg\",\n  \"134597.jpg\": \"Animalia/Pisaster ochraceus/18e6befbb0442a6773e9318e94683cb7.jpg\",\n  \"134608.jpg\": \"Animalia/Pisaster ochraceus/54fadb2224d9016a9198732b513cccb3.jpg\",\n  \"134630.jpg\": \"Animalia/Pisaster ochraceus/c163cb33bef7bcb9387761caa6978644.jpg\",\n  \"134684.jpg\": \"Animalia/Pisaster ochraceus/769ddac7030dab6b81186fe6c8f81d7b.jpg\",\n  \"13472.jpg\": \"Aves/Circus cyaneus/befc10a6f5771e58de8b0a6d1d9cd507.jpg\",\n  \"13473.jpg\": \"Aves/Circus cyaneus/5e32846f5a18eed3813aeab7877f42d7.jpg\",\n  \"13485.jpg\": \"Aves/Circus cyaneus/ebb12c593479be8b1723a690c1040ada.jpg\",\n  \"13493.jpg\": \"Aves/Circus cyaneus/55c5a519439ca74d066f256abf65bdff.jpg\",\n  \"134939.jpg\": \"Aves/Chloris chloris/674bd7d33a46195d3620fd299e33082d.jpg\",\n  \"134951.jpg\": \"Aves/Chloris chloris/248bcd92d7b1ef7b5548143aa0159033.jpg\",\n  \"134952.jpg\": \"Aves/Chloris chloris/08c00bbb3a68e5b99180076a6b080938.jpg\",\n  \"134955.jpg\": \"Aves/Chloris chloris/b5493ee0b35c4e45cf1594a623951dd5.jpg\",\n  \"134966.jpg\": \"Aves/Chloris chloris/ba937e86211e107537d3c725064ff57d.jpg\",\n  \"134967.jpg\": \"Aves/Chloris chloris/bee8b4489c19323394992d672f9ab31f.jpg\",\n  \"134970.jpg\": \"Aves/Chloris chloris/47d3c6e3ca833f4a89cb1d6b641c047f.jpg\",\n  \"134972.jpg\": \"Aves/Chloris chloris/c01e46bd02f873eb6836e3e975f990f8.jpg\",\n  \"134973.jpg\": \"Aves/Chloris chloris/e933c2474dae93e795782623c448484b.jpg\",\n  \"134996.jpg\": \"Aves/Chloris chloris/9da0f088033f21499b1e2368bfa15946.jpg\",\n  \"134997.jpg\": \"Aves/Chloris chloris/7374b7f376d49a60c0b6cec73c7f6574.jpg\",\n  \"135.jpg\": \"Aves/Zonotrichia capensis/cf859b323bdedb7c91f887b3d97ec6b9.jpg\",\n  \"135004.jpg\": \"Aves/Chloris chloris/ffac3ef28d07da00d532c7e391e2f895.jpg\",\n  \"135021.jpg\": \"Aves/Chloris chloris/ce97bc6ae363573061761464d6ec032e.jpg\",\n  \"135030.jpg\": \"Aves/Chloris chloris/4fa62ff072d485393bfb06cb5ea7afa6.jpg\",\n  \"135033.jpg\": \"Aves/Chloris chloris/96b546e90fbe9604a7f879a8a0cc543e.jpg\",\n  \"135035.jpg\": \"Aves/Chloris chloris/8514d3189162cc1a03cca7cfd086655b.jpg\",\n  \"135064.jpg\": \"Aves/Larus occidentalis/3554dcb86104d10b240d5151a30df522.jpg\",\n  \"135070.jpg\": \"Aves/Larus occidentalis/246b170a122910bd8de6ffc8d0b609c8.jpg\",\n  \"135074.jpg\": \"Aves/Larus occidentalis/fcc2f7a59395786559864597f35a961f.jpg\",\n  \"135112.jpg\": \"Aves/Larus occidentalis/00781fc28310d451194355290f0b15cb.jpg\",\n  \"135156.jpg\": \"Aves/Larus occidentalis/fa9928ba5835c5c495b8057c9df7a5fb.jpg\",\n  \"13516.jpg\": \"Aves/Circus cyaneus/197c40e54b2c34a9b73e799a1704e371.jpg\",\n  \"13517.jpg\": \"Aves/Circus cyaneus/a16b27102d375936e4b20418be937a02.jpg\",\n  \"135222.jpg\": \"Aves/Larus occidentalis/a75a568584286ece7346671ffd663779.jpg\",\n  \"135232.jpg\": \"Aves/Larus occidentalis/6383d7fe9e0c949ede550d7c04c1ed9e.jpg\",\n  \"13524.jpg\": \"Aves/Circus cyaneus/0eb457891daa13436172756b0a164640.jpg\",\n  \"135241.jpg\": \"Aves/Larus occidentalis/fc2ea66aa1ef6b57e0a6deaaab77c6d3.jpg\",\n  \"135297.jpg\": \"Aves/Larus occidentalis/5efc45623d939ca3c2e48b3a7509d07f.jpg\",\n  \"13531.jpg\": \"Aves/Circus cyaneus/74323cb050934b36783e94f1a065c6c5.jpg\",\n  \"135320.jpg\": \"Aves/Larus occidentalis/f97d47483e886ab6a04b40281befe862.jpg\",\n  \"135354.jpg\": \"Aves/Larus occidentalis/b9d3285618ec8a20017467e45269cccd.jpg\",\n  \"13543.jpg\": \"Aves/Circus cyaneus/b20c7dcc8fc51472b0778096f3c0f127.jpg\",\n  \"135445.jpg\": \"Aves/Larus occidentalis/4ab2a82a5a2928cfa500ca18d7678841.jpg\",\n  \"135455.jpg\": \"Aves/Larus occidentalis/b654a1e25c6e86c6bafe749bbb14cda2.jpg\",\n  \"135456.jpg\": \"Aves/Larus occidentalis/37ab0fbded250f0e67161fcbe89a6caa.jpg\",\n  \"135473.jpg\": \"Aves/Larus occidentalis/053b8b5d6c2a4a34339b2c87f157219c.jpg\",\n  \"135515.jpg\": \"Reptilia/Terrapene carolina bauri/468f7e4e9458ef17a834f16c8ccc338c.jpg\",\n  \"135521.jpg\": \"Reptilia/Terrapene carolina bauri/3e30cc9bbbe3253f9606232e3ae8cf98.jpg\",\n  \"135522.jpg\": \"Reptilia/Terrapene carolina bauri/26446bb89a2b8f94cc585b3a80c34178.jpg\",\n  \"135525.jpg\": \"Reptilia/Terrapene carolina bauri/cd0318fd0e4152da7ae4076298c160ee.jpg\",\n  \"135668.jpg\": \"Aves/Vireo gilvus/f1fe6192cf0b847778543d63203e2fc2.jpg\",\n  \"135680.jpg\": \"Aves/Vireo gilvus/c1be3316c65a27fcbd41393af14b4433.jpg\",\n  \"135687.jpg\": \"Aves/Vireo gilvus/9123bd5e7e94a0403b5278200008c5b9.jpg\",\n  \"135691.jpg\": \"Aves/Vireo gilvus/572363a0aa62011badf75afddeb1a7a4.jpg\",\n  \"135697.jpg\": \"Aves/Vireo gilvus/cd9be3b1af3da682afc7631087891c50.jpg\",\n  \"135698.jpg\": \"Aves/Vireo gilvus/e6504315165d25c79a369db5d65be6c8.jpg\",\n  \"135701.jpg\": \"Aves/Vireo gilvus/976cc0ac6300928ddf496547dac58f89.jpg\",\n  \"135708.jpg\": \"Aves/Vireo gilvus/cb002038d743f15537213a9d54cdaf0d.jpg\",\n  \"135709.jpg\": \"Aves/Vireo gilvus/ecc7c0234a86a85914f7403c74304a1a.jpg\",\n  \"135713.jpg\": \"Aves/Vireo gilvus/ab5b298753e840a2d2fff52d50e6e8be.jpg\",\n  \"135723.jpg\": \"Aves/Vireo gilvus/c07d38fb001bcd26615923f5095481f0.jpg\",\n  \"135724.jpg\": \"Aves/Vireo gilvus/0c62e44e1360c8c855a291b31e0331e2.jpg\",\n  \"135726.jpg\": \"Aves/Vireo gilvus/98112e6df7c072732ecafad173a19d06.jpg\",\n  \"135736.jpg\": \"Aves/Vireo gilvus/bdc9c15d5c058ea74a4416bee1375e82.jpg\",\n  \"135740.jpg\": \"Aves/Vireo gilvus/d19bf5c446edbc126f87f907c4bde5f6.jpg\",\n  \"135742.jpg\": \"Aves/Vireo gilvus/109cf118180a842ff8d2532ceecc506b.jpg\",\n  \"135746.jpg\": \"Aves/Vireo gilvus/624826058d338aa75dd2f4189cfcac7c.jpg\",\n  \"135752.jpg\": \"Aves/Vireo gilvus/6cd0677e312a19b90a08bcac319df2bc.jpg\",\n  \"135763.jpg\": \"Aves/Vireo gilvus/f88b23cfe4bd21d3ec943db30338e0c5.jpg\",\n  \"135764.jpg\": \"Aves/Vireo gilvus/de8827c1b38efed90815a9c89deb7bde.jpg\",\n  \"13577.jpg\": \"Aves/Circus cyaneus/f905851c299fd245435345ab1a797aaa.jpg\",\n  \"13578.jpg\": \"Aves/Circus cyaneus/4e52c649ab97d576b4f352a8eaec6289.jpg\",\n  \"135781.jpg\": \"Aves/Vireo gilvus/27342c431b8b9e8fab7ba79e2317fab0.jpg\",\n  \"135788.jpg\": \"Aves/Vireo gilvus/2a3e090d9fb14826ca69c7e84e35f067.jpg\",\n  \"135789.jpg\": \"Aves/Vireo gilvus/13610a889afbe27752e0b7d03ca94732.jpg\",\n  \"135790.jpg\": \"Aves/Vireo gilvus/7e8e62853524d8b5152c43a946414058.jpg\",\n  \"135791.jpg\": \"Aves/Vireo gilvus/f8b9f4b18f0b7f8f6e1389bd975c36d0.jpg\",\n  \"135792.jpg\": \"Aves/Vireo gilvus/60160bbd30e761ac59c0e90fb01e0349.jpg\",\n  \"135803.jpg\": \"Insecta/Archilestes grandis/a8a751fdebffa1195f7338f11e7793c4.jpg\",\n  \"135804.jpg\": \"Insecta/Archilestes grandis/e5666aebfb12dedf1d98b6e9c87f22c5.jpg\",\n  \"135813.jpg\": \"Insecta/Archilestes grandis/b394fd52322d25e3f96d768eb4098ea5.jpg\",\n  \"135823.jpg\": \"Insecta/Archilestes grandis/b8bd3e8954ecd34711ed8d08947f1ba2.jpg\",\n  \"135831.jpg\": \"Insecta/Archilestes grandis/534bf41610434d234b2bd31327ae4cc7.jpg\",\n  \"135835.jpg\": \"Insecta/Archilestes grandis/6e727e190aefb8ca540ceae830989e8b.jpg\",\n  \"135836.jpg\": \"Insecta/Archilestes grandis/b3d97781ac284c938cd87bdd69fa9775.jpg\",\n  \"135837.jpg\": \"Insecta/Archilestes grandis/841069b98dbc7ecbac64f80039ab2127.jpg\",\n  \"135838.jpg\": \"Insecta/Archilestes grandis/bd16812fa4c8451093d409faacca6a58.jpg\",\n  \"135840.jpg\": \"Insecta/Archilestes grandis/8d5fce7d198812540d70f2d987e28164.jpg\",\n  \"135841.jpg\": \"Insecta/Archilestes grandis/ec463b13d4c043d938f0cd3a25fe457a.jpg\",\n  \"135843.jpg\": \"Insecta/Archilestes grandis/9bf932610c220b1e2cefe01f19feba3f.jpg\",\n  \"135853.jpg\": \"Insecta/Archilestes grandis/3fb96b655a8cecbca1be94e5fe769811.jpg\",\n  \"135855.jpg\": \"Insecta/Archilestes grandis/c0f13bb158cd7fb4a312e40decb2742a.jpg\",\n  \"135860.jpg\": \"Insecta/Archilestes grandis/c8c0d7ee989ec63ce96848ad462b9f48.jpg\",\n  \"135861.jpg\": \"Insecta/Archilestes grandis/f7587b9d7e0b4e6189dcaa599b161248.jpg\",\n  \"135869.jpg\": \"Insecta/Archilestes grandis/b8998df5e612a5031ceefc70f19794ee.jpg\",\n  \"135870.jpg\": \"Insecta/Archilestes grandis/bf9a42e7fcc61d9525c3e93bfccb63d6.jpg\",\n  \"135885.jpg\": \"Insecta/Archilestes grandis/976d52dda35f25194603f4f92550fa36.jpg\",\n  \"135888.jpg\": \"Insecta/Archilestes grandis/7939937e1dfb62b8f3a14a74a3fb0908.jpg\",\n  \"135889.jpg\": \"Insecta/Archilestes grandis/8c37bde2b0e71a297f3581f3ae8db9bf.jpg\",\n  \"135891.jpg\": \"Insecta/Archilestes grandis/95c3bfbf3f238e1e4300cd59bafa5216.jpg\",\n  \"135898.jpg\": \"Insecta/Junonia evarete/19ee114148b77b488de7c92f18ec20a1.jpg\",\n  \"135900.jpg\": \"Insecta/Junonia evarete/08e1c78edb7b22feece12bbb4875e9dd.jpg\",\n  \"135910.jpg\": \"Insecta/Junonia evarete/7db707e0840043eb101ecc06d209694b.jpg\",\n  \"135911.jpg\": \"Insecta/Junonia evarete/02bf1e4d4fe6f8437bd3aa60feb06a2c.jpg\",\n  \"135912.jpg\": \"Insecta/Junonia evarete/cc873da088fdfa4d5a04c9c282941df1.jpg\",\n  \"135925.jpg\": \"Aves/Hirundo rustica/f823b3be0be1ba1ce5fe7bba9169dcc4.jpg\",\n  \"135932.jpg\": \"Aves/Hirundo rustica/dee5c794ddb47aa6b51eb918ec7d34a5.jpg\",\n  \"13595.jpg\": \"Aves/Circus cyaneus/359a8525b99b76710e8276edd6fed339.jpg\",\n  \"135962.jpg\": \"Aves/Hirundo rustica/7298b080c7adc74a8d46ca2fc861da49.jpg\",\n  \"135984.jpg\": \"Aves/Hirundo rustica/d07239920ded32556d4fdb70eb915ad1.jpg\",\n  \"136029.jpg\": \"Aves/Hirundo rustica/95e71fc9576d633e353663db75f08d11.jpg\",\n  \"136151.jpg\": \"Aves/Hirundo rustica/c328b4e68bab48659f5627e9e1aa430d.jpg\",\n  \"136178.jpg\": \"Aves/Hirundo rustica/25795c4a10ae7c180def6fb806234129.jpg\",\n  \"136184.jpg\": \"Aves/Hirundo rustica/6497642738b346f85fd9ad26339c07af.jpg\",\n  \"136208.jpg\": \"Aves/Hirundo rustica/5e9b79aa3487b78e6b3438e7e79a3b61.jpg\",\n  \"136219.jpg\": \"Aves/Hirundo rustica/a5ddf5480c33bafd37a7f08abb65ec2e.jpg\",\n  \"13624.jpg\": \"Aves/Circus cyaneus/77731b8322156ef8e10600d3e8c6b5c3.jpg\",\n  \"136311.jpg\": \"Aves/Hirundo rustica/44d98da297fc7b786c005c6f60f0236b.jpg\",\n  \"136325.jpg\": \"Aves/Hirundo rustica/aa75f555a1e38d18acadf1d4b3efac2c.jpg\",\n  \"136400.jpg\": \"Aves/Hirundo rustica/78420afc9339292dae660577eac74139.jpg\",\n  \"136414.jpg\": \"Aves/Hirundo rustica/9575f25e6ce8a1cc0487b1b14238972f.jpg\",\n  \"136475.jpg\": \"Aves/Hirundo rustica/88ee59e2e7c9b8d16ffab90ac9b89cdb.jpg\",\n  \"13652.jpg\": \"Aves/Circus cyaneus/83c8b47db4f253d0d345de48c10d8f37.jpg\",\n  \"136540.jpg\": \"Aves/Hirundo rustica/82ed65df0657a7e9beb393ab4563a373.jpg\",\n  \"13656.jpg\": \"Aves/Circus cyaneus/783a102c1786f6b90c8e8ac0555ca1fa.jpg\",\n  \"136606.jpg\": \"Mammalia/Lontra canadensis/8b2679a7fed5ec97f18a2727abc51273.jpg\",\n  \"136623.jpg\": \"Mammalia/Lontra canadensis/99a6947cd365192200c1dd16166e50c1.jpg\",\n  \"136624.jpg\": \"Mammalia/Lontra canadensis/ccb77b70aee0eaaf6403eb3172f71bf6.jpg\",\n  \"136632.jpg\": \"Mammalia/Lontra canadensis/179a92546f14b3cecf821dfc492b2455.jpg\",\n  \"136643.jpg\": \"Mammalia/Lontra canadensis/14880e6e0328b660fc637409d9dacac4.jpg\",\n  \"136687.jpg\": \"Mammalia/Lontra canadensis/3900a7b686cf9570f0d0d652dce6ea17.jpg\",\n  \"136693.jpg\": \"Mammalia/Lontra canadensis/bf44707018ddc6edff3258d0ed63f0fc.jpg\",\n  \"136704.jpg\": \"Mammalia/Lontra canadensis/7970d337828af2fe16fd8d5f73cb4c46.jpg\",\n  \"136717.jpg\": \"Mammalia/Lontra canadensis/93f4969459649ef38b76f15a6b0e3583.jpg\",\n  \"136743.jpg\": \"Mammalia/Lontra canadensis/7237bc735105ac93485e52244fef89c3.jpg\",\n  \"136770.jpg\": \"Mammalia/Lontra canadensis/07fea8fa822868e292e899444487ac51.jpg\",\n  \"136777.jpg\": \"Mammalia/Lontra canadensis/9c3249e88b69f54339348a5f641dd584.jpg\",\n  \"136793.jpg\": \"Mammalia/Lontra canadensis/c04a5de83bf05dd4bc88b8c1a75036b4.jpg\",\n  \"136800.jpg\": \"Insecta/Lerema accius/03cf7563642601d7bdf1e21004f50034.jpg\",\n  \"136801.jpg\": \"Insecta/Lerema accius/73fef57047683e3d06c21802367b5411.jpg\",\n  \"136804.jpg\": \"Insecta/Lerema accius/9524d526a48231b89b318c5ba05ee7dc.jpg\",\n  \"136808.jpg\": \"Insecta/Lerema accius/b803d6d7c8e4f5ba5bd33248bebbfcb9.jpg\",\n  \"136809.jpg\": \"Insecta/Lerema accius/ab59f271f87eb03d881d839d9cc714e7.jpg\",\n  \"136812.jpg\": \"Insecta/Lerema accius/69f0c87919830370dd7b7299672165cf.jpg\",\n  \"136817.jpg\": \"Insecta/Lerema accius/9300a3a3dbb1414a897b1f3c96ca8959.jpg\",\n  \"136818.jpg\": \"Insecta/Lerema accius/fdcd42380b94fde29db15b7b1ba64a2e.jpg\",\n  \"136822.jpg\": \"Insecta/Lerema accius/b53115b89aed3f7b1b2c298bd47b506b.jpg\",\n  \"136829.jpg\": \"Insecta/Lerema accius/4d3b46a12b283fd134e133a872f72a21.jpg\",\n  \"136839.jpg\": \"Insecta/Lerema accius/2e6179c1d9ad3a8d51e0443e402c4896.jpg\",\n  \"136840.jpg\": \"Insecta/Lerema accius/5f9b4bf9aa659d0a11223d444e122b52.jpg\",\n  \"136841.jpg\": \"Insecta/Lerema accius/34b2fd7c52c7ab93257d21a0a62ab3f6.jpg\",\n  \"136853.jpg\": \"Insecta/Lerema accius/613c6e2450ba60ad5f1b4f23289851e3.jpg\",\n  \"136854.jpg\": \"Insecta/Lerema accius/8f36704870bec90beddb0daf0c561e0c.jpg\",\n  \"136858.jpg\": \"Insecta/Lerema accius/86656c060e818c1effe32d1adfe7f12a.jpg\",\n  \"136860.jpg\": \"Insecta/Lerema accius/a4bf8cbf7533ec1030fcf721b9ee43b5.jpg\",\n  \"136861.jpg\": \"Insecta/Lerema accius/80aea35c69373fe7706ea0f3c531e01a.jpg\",\n  \"136885.jpg\": \"Insecta/Lerema accius/1ad771ac0e942b2f18d2ab0d569447d1.jpg\",\n  \"136908.jpg\": \"Insecta/Lerema accius/095df7d8d28ff7c28efe21508b55ff6c.jpg\",\n  \"136909.jpg\": \"Insecta/Lerema accius/9cb939e70c32a0ee9420e31271e5f50c.jpg\",\n  \"136910.jpg\": \"Insecta/Lerema accius/c13c7b30da47156eafbfa5611bcfed36.jpg\",\n  \"136918.jpg\": \"Insecta/Lerema accius/2efad6301245872ae4f719ec266df826.jpg\",\n  \"136987.jpg\": \"Aves/Corvus albus/9d2ec3b7bd8d7d470c7935fe9bbcfbb9.jpg\",\n  \"136988.jpg\": \"Aves/Corvus albus/5cda2ec4479a0f95a246498646a434a8.jpg\",\n  \"136994.jpg\": \"Aves/Corvus albus/c310bdb7249e5182de1aafe74f66a4c3.jpg\",\n  \"136997.jpg\": \"Aves/Corvus albus/46b59804fdfa9334692cffbd75463127.jpg\",\n  \"137070.jpg\": \"Amphibia/Hyla intermedia/d846530774e7f22657944f3752cbb756.jpg\",\n  \"137071.jpg\": \"Amphibia/Hyla intermedia/c8e795f25a8279ce4a363ff26a81f531.jpg\",\n  \"137086.jpg\": \"Amphibia/Hyla intermedia/29e696ad8acd4115f5b1b3b0e5987d6c.jpg\",\n  \"137087.jpg\": \"Amphibia/Hyla intermedia/8e0b27871b647deca461cc0e9afdddf4.jpg\",\n  \"137088.jpg\": \"Arachnida/Menemerus bivittatus/fd73ffb972de27ed983d5d2e031357d0.jpg\",\n  \"137091.jpg\": \"Arachnida/Menemerus bivittatus/ed940a71e7aec722e3b0554798929fdc.jpg\",\n  \"137092.jpg\": \"Arachnida/Menemerus bivittatus/7d487f522e4beb1a09421149a63a9e19.jpg\",\n  \"137102.jpg\": \"Animalia/Lumbricus terrestris/2dc6726eaa9c1e34aaea905f8a886eba.jpg\",\n  \"137103.jpg\": \"Animalia/Lumbricus terrestris/9801c9960a0a7567f382dfc1f4ccd68f.jpg\",\n  \"137107.jpg\": \"Animalia/Lumbricus terrestris/531667ead972f48be22c43cbcd47fab8.jpg\",\n  \"137114.jpg\": \"Animalia/Lumbricus terrestris/f1e8c3fe8bf4f863a108a24a195a2f09.jpg\",\n  \"137115.jpg\": \"Animalia/Lumbricus terrestris/60e09692d9946cf1b06460f94e26a174.jpg\",\n  \"137116.jpg\": \"Animalia/Lumbricus terrestris/29820a452641044a0e0ae951f01d3718.jpg\",\n  \"137132.jpg\": \"Animalia/Lumbricus terrestris/06c9e50e299566261b73d18b34f7052a.jpg\",\n  \"137143.jpg\": \"Aves/Fregata minor/91f44572a35ed80504b4d1b101ae8576.jpg\",\n  \"137153.jpg\": \"Aves/Fregata minor/2f378faf08554af7917b46fcae52f762.jpg\",\n  \"137154.jpg\": \"Aves/Fregata minor/cefd475a1e094459f8eff8110599ea69.jpg\",\n  \"137163.jpg\": \"Aves/Fregata minor/672bdad513903a2e424ef909ba743d3f.jpg\",\n  \"137164.jpg\": \"Aves/Fregata minor/72a7e38f2992ed8119a7626d9c34da3a.jpg\",\n  \"137223.jpg\": \"Insecta/Euptoieta hegesia/29cddfa5220e76df34c4d8388c534554.jpg\",\n  \"137224.jpg\": \"Insecta/Euptoieta hegesia/1a396ca1340a24c226fd0881796a14c7.jpg\",\n  \"137225.jpg\": \"Insecta/Euptoieta hegesia/1abcd88ee89818cc4033ac1ee44308a7.jpg\",\n  \"137229.jpg\": \"Insecta/Euptoieta hegesia/a4b7b4f19a38479653429259ff3956ec.jpg\",\n  \"137231.jpg\": \"Insecta/Euptoieta hegesia/14db2881a87b5517095204690b005768.jpg\",\n  \"137243.jpg\": \"Insecta/Euptoieta hegesia/321043dccde01b27427131f216406200.jpg\",\n  \"137244.jpg\": \"Insecta/Euptoieta hegesia/d4bc598ee7ff0738ece548fe8e2bfa01.jpg\",\n  \"137249.jpg\": \"Insecta/Euptoieta hegesia/6b73e2336edace917df7ffdaaa696d07.jpg\",\n  \"137261.jpg\": \"Aves/Zenaida auriculata/dc3cd85e11d2c7d242dd86b31f12facf.jpg\",\n  \"137264.jpg\": \"Aves/Zenaida auriculata/22c22fc44ed96b9c5a391377260e04cc.jpg\",\n  \"137265.jpg\": \"Aves/Zenaida auriculata/6969a8de0aa47902fdf46ba9685430a1.jpg\",\n  \"137286.jpg\": \"Mollusca/Caracolus caracolla/52d8a078b145e92491f5cfd55a7f5fd5.jpg\",\n  \"137287.jpg\": \"Mollusca/Caracolus caracolla/3afb89ac11038ef3faf9678c26db8f2e.jpg\",\n  \"137288.jpg\": \"Mollusca/Caracolus caracolla/91a4cefd2c47a103594b581fc6c266f5.jpg\",\n  \"137289.jpg\": \"Mollusca/Caracolus caracolla/35fad61b73b3028073ce96741c96f507.jpg\",\n  \"137290.jpg\": \"Mollusca/Caracolus caracolla/4a321376aad70db1f78e3027267bb93a.jpg\",\n  \"13731.jpg\": \"Aves/Circus cyaneus/b3d4f339c8ab386b7e02dc53d36efa51.jpg\",\n  \"137328.jpg\": \"Mollusca/Caracolus marginella/648ece12bf763d2901eb231b88644e88.jpg\",\n  \"137329.jpg\": \"Mollusca/Caracolus marginella/fb23b0ad1c03e3e7b1a1a8c486914f5e.jpg\",\n  \"137331.jpg\": \"Mollusca/Caracolus marginella/b9d84b39ec3d81c382b7e7b69596411e.jpg\",\n  \"137334.jpg\": \"Mollusca/Caracolus marginella/860930b060eb8c3e4f1cc4378b51fc14.jpg\",\n  \"137335.jpg\": \"Mollusca/Caracolus marginella/c0543fb534f0c7947373f30755e23708.jpg\",\n  \"137371.jpg\": \"Insecta/Acharia stimulea/62eb17851bff6a90b5483a625747d8dd.jpg\",\n  \"137372.jpg\": \"Insecta/Acharia stimulea/1069e8bed2ee1bc4b7f1ac450c03a6d5.jpg\",\n  \"137374.jpg\": \"Insecta/Acharia stimulea/9f3b435d72b107d44b5fdfb153e5cb92.jpg\",\n  \"137375.jpg\": \"Insecta/Acharia stimulea/b901a7480ea95dbbc5db1dd13f20420f.jpg\",\n  \"137376.jpg\": \"Insecta/Acharia stimulea/3044e269f23f4be91c0b4a03aac65374.jpg\",\n  \"137381.jpg\": \"Insecta/Acharia stimulea/f7d3070ce6d80b5ced847bd369bdca2b.jpg\",\n  \"137382.jpg\": \"Insecta/Acharia stimulea/a4a1a14ed2f4c813ade65d704efcdce5.jpg\",\n  \"137384.jpg\": \"Insecta/Acharia stimulea/8bd5a356989a85d6ac22bc797c4a824e.jpg\",\n  \"137391.jpg\": \"Insecta/Acharia stimulea/194968656810c57e917571096b16587b.jpg\",\n  \"137394.jpg\": \"Insecta/Acharia stimulea/0a6c46bad61bffe8b46d5cb2f9313eca.jpg\",\n  \"137398.jpg\": \"Insecta/Acharia stimulea/7949a22c0130e3fa67f8925330744468.jpg\",\n  \"137399.jpg\": \"Insecta/Acharia stimulea/ed52af149c73b8b3bc6f8bf9e10862f8.jpg\",\n  \"137400.jpg\": \"Insecta/Acharia stimulea/2df080aa00b81908e5773f77bcd43fca.jpg\",\n  \"137403.jpg\": \"Insecta/Acharia stimulea/859a08ca8e2d36d4594650e913d783a5.jpg\",\n  \"137405.jpg\": \"Insecta/Acharia stimulea/d9a736247763e5259b5273001f340bc4.jpg\",\n  \"137406.jpg\": \"Insecta/Acharia stimulea/474fa627a634c9bdf718231d41d9cecc.jpg\",\n  \"137407.jpg\": \"Insecta/Acharia stimulea/2585bcbf0fa5e68381a93501bfcf6484.jpg\",\n  \"137437.jpg\": \"Aves/Tringa totanus/fc0b546dc5b977ee80f8599c74c14573.jpg\",\n  \"137440.jpg\": \"Aves/Tringa totanus/9f7fbaeb9999ccdf09409859d0cf15a1.jpg\",\n  \"137445.jpg\": \"Aves/Tringa totanus/f3c4a64b477e99af15decb881c24a07d.jpg\",\n  \"137446.jpg\": \"Aves/Tringa totanus/3f18d4ae9748c7353ccc949c925c3baf.jpg\",\n  \"137447.jpg\": \"Aves/Tringa totanus/3bc62121a581971400ed46ab1783f615.jpg\",\n  \"137448.jpg\": \"Aves/Tringa totanus/a0291b896aef96f591330d560dd4ca85.jpg\",\n  \"137449.jpg\": \"Aves/Tringa totanus/b028bedaca48f200d271e5f7e08c8ec3.jpg\",\n  \"137450.jpg\": \"Aves/Tringa totanus/8073c199119bf8448652822b7c63100d.jpg\",\n  \"137451.jpg\": \"Aves/Tringa totanus/cc728abf0a751e637e1c22aa3e2aa375.jpg\",\n  \"137452.jpg\": \"Aves/Tringa totanus/165f4d8f855903afa62fe0ce3b4679fb.jpg\",\n  \"137455.jpg\": \"Aves/Tringa totanus/ac86746adce50dbc19db72fc8a27bd7e.jpg\",\n  \"137458.jpg\": \"Aves/Tringa totanus/9994a626e499817fc473724dc400f8f1.jpg\",\n  \"137459.jpg\": \"Aves/Tringa totanus/53fbc4471b9e3a562986114f02de08ad.jpg\",\n  \"137460.jpg\": \"Aves/Tringa totanus/584d33163dcdc7c5e748354839468eca.jpg\",\n  \"137461.jpg\": \"Aves/Tringa totanus/8ca291bc6270f0ca8da3d33de8bee6f5.jpg\",\n  \"137462.jpg\": \"Aves/Tringa totanus/30844381ecf59d6b1db2c0850b51f7fc.jpg\",\n  \"137463.jpg\": \"Aves/Tringa totanus/d4fc49890059ac5311e371914eb68c35.jpg\",\n  \"137469.jpg\": \"Aves/Tringa totanus/0813ed717abc84948406cb36b3a31078.jpg\",\n  \"137472.jpg\": \"Aves/Tringa totanus/e6c391f74214ae648f6d134daa2b34e0.jpg\",\n  \"137473.jpg\": \"Aves/Tringa totanus/bb38bbd048c031e4cd0bba5cab93efe8.jpg\",\n  \"137475.jpg\": \"Aves/Tringa totanus/4b2d7c4219780bd97e4f212eb81fc9a0.jpg\",\n  \"137502.jpg\": \"Amphibia/Lithobates clamitans/086066d59c320ba33c17341d38091ff7.jpg\",\n  \"13753.jpg\": \"Aves/Circus cyaneus/a31d5d0e449919ff0e65683fd11da7b3.jpg\",\n  \"137553.jpg\": \"Amphibia/Lithobates clamitans/909445194f388fb84fb8c22d41cbbaf8.jpg\",\n  \"137555.jpg\": \"Amphibia/Lithobates clamitans/abacf8b6c709334c338927850f91c221.jpg\",\n  \"137611.jpg\": \"Amphibia/Lithobates clamitans/c1e1d256927502eb3fe51debf66f65f3.jpg\",\n  \"137652.jpg\": \"Amphibia/Lithobates clamitans/4996b8b2797bcaad5046f62f171fc837.jpg\",\n  \"137660.jpg\": \"Amphibia/Lithobates clamitans/3b331061fce83aafcccfe4f8fd8a54e0.jpg\",\n  \"13768.jpg\": \"Aves/Circus cyaneus/7139d2d7f28866b38184e0bbb5220eeb.jpg\",\n  \"137680.jpg\": \"Amphibia/Lithobates clamitans/2ffdf7d49ca3c0faeb3f636568025185.jpg\",\n  \"13772.jpg\": \"Aves/Circus cyaneus/8851765006247dfe0797fbbc88bb4ad5.jpg\",\n  \"137732.jpg\": \"Amphibia/Lithobates clamitans/9690a5278345b82af0b535607cd7c7a7.jpg\",\n  \"137738.jpg\": \"Amphibia/Lithobates clamitans/5541f0b020b200d36d5d93fa4809558a.jpg\",\n  \"137741.jpg\": \"Amphibia/Lithobates clamitans/aae9a7d33a9b99ff3f48c475099bbc00.jpg\",\n  \"137755.jpg\": \"Amphibia/Lithobates clamitans/4abb9bc80aedb268101a9bb7164f72ca.jpg\",\n  \"137761.jpg\": \"Amphibia/Lithobates clamitans/2e401516f9b40e57ba87b1fa8c49fc81.jpg\",\n  \"137769.jpg\": \"Amphibia/Lithobates clamitans/24557b486d1975057f5a3b036c26b1c2.jpg\",\n  \"137824.jpg\": \"Amphibia/Lithobates clamitans/681f2d57d9d06e77df7067ca2e75560d.jpg\",\n  \"137829.jpg\": \"Amphibia/Lithobates clamitans/b3a48c12983a8a5593e72c0ce41b0f59.jpg\",\n  \"13786.jpg\": \"Aves/Circus cyaneus/b4f618b9ce763503a7c187ad9ec7e5d8.jpg\",\n  \"13792.jpg\": \"Aves/Circus cyaneus/92c1a9ad986aab4ecb3da424bc323829.jpg\",\n  \"137938.jpg\": \"Mammalia/Eidolon helvum/ac30fa532db9dc49a6cdca1b2df4ea47.jpg\",\n  \"137942.jpg\": \"Mammalia/Eidolon helvum/38e9549b5e77752a5d6473ffe62ba1b0.jpg\",\n  \"137943.jpg\": \"Mammalia/Eidolon helvum/812de092ad3e98938438127f7a886b0b.jpg\",\n  \"137944.jpg\": \"Mammalia/Eidolon helvum/c2b7a510f3d166434e0abe2074d8497f.jpg\",\n  \"13795.jpg\": \"Aves/Circus cyaneus/6183f5cabd0da45aa8c0ac063a227190.jpg\",\n  \"138013.jpg\": \"Mammalia/Eidolon helvum/3e8868f60d9b54a41c321774fc12ffda.jpg\",\n  \"138062.jpg\": \"Mammalia/Trichechus manatus/19d81a23f5ead594d76b4b3a3da984f8.jpg\",\n  \"138066.jpg\": \"Mammalia/Trichechus manatus/113879bcff0ab11091cc97509f30e8bf.jpg\",\n  \"138106.jpg\": \"Amphibia/Batrachoseps major/57d6cc6f831d852d2bf88e89a1fad04d.jpg\",\n  \"138109.jpg\": \"Amphibia/Batrachoseps major/e1eaedc9ac3fb77c30f327a665cd1b8f.jpg\",\n  \"138110.jpg\": \"Amphibia/Batrachoseps major/e0ae76f804693f626928e3c539983952.jpg\",\n  \"138113.jpg\": \"Amphibia/Batrachoseps major/a51a9a565aac571409fb46397eaf68bd.jpg\",\n  \"138115.jpg\": \"Amphibia/Batrachoseps major/cea6109f71e46730cffedd1c626467d7.jpg\",\n  \"138116.jpg\": \"Amphibia/Batrachoseps major/ba1ba043174b72995e30e81f802a62b7.jpg\",\n  \"138117.jpg\": \"Amphibia/Batrachoseps major/fc7624a58b4e39d6014f1bb12225d75f.jpg\",\n  \"138118.jpg\": \"Amphibia/Batrachoseps major/a014201d09d9ed15ac9a5a002d386754.jpg\",\n  \"138119.jpg\": \"Amphibia/Batrachoseps major/486986529d1750ce64cf111b945aa960.jpg\",\n  \"138122.jpg\": \"Amphibia/Batrachoseps major/679817ad617d6ce79bf5a327a13710d9.jpg\",\n  \"138135.jpg\": \"Amphibia/Batrachoseps major/11d625ecb53fd92c2c9eac33ce079020.jpg\",\n  \"138136.jpg\": \"Amphibia/Batrachoseps major/07d64011eb3dc13f86ede16168fadb36.jpg\",\n  \"138137.jpg\": \"Amphibia/Batrachoseps major/85cfaad984e14466d7e36168f959380d.jpg\",\n  \"138138.jpg\": \"Amphibia/Batrachoseps major/220640c006544019b993636df8302861.jpg\",\n  \"138142.jpg\": \"Amphibia/Batrachoseps major/7121110c17487c28643271d3c6233dc3.jpg\",\n  \"138143.jpg\": \"Amphibia/Batrachoseps major/f15947f1a479b80f361a85fb6f50d4a3.jpg\",\n  \"138144.jpg\": \"Amphibia/Batrachoseps major/37e5a37f1df26c2275e8329304dce7b5.jpg\",\n  \"138145.jpg\": \"Insecta/Conchylodes ovulalis/84893a2e43e9ff3d24d930a94f685e2b.jpg\",\n  \"138155.jpg\": \"Insecta/Conchylodes ovulalis/e9cfd59c9973e97eed7cca6c0df6a4a5.jpg\",\n  \"138156.jpg\": \"Insecta/Conchylodes ovulalis/528af5f533135414dd54e64bd86accad.jpg\",\n  \"138165.jpg\": \"Insecta/Conchylodes ovulalis/bacc0f9904f5aa4351eb25c74eb423fb.jpg\",\n  \"138167.jpg\": \"Insecta/Conchylodes ovulalis/f510d94fb2ade2c819c3aa2042f8427d.jpg\",\n  \"138168.jpg\": \"Insecta/Conchylodes ovulalis/64d3eb46ecf5f64979766ff6fbfca970.jpg\",\n  \"138170.jpg\": \"Insecta/Conchylodes ovulalis/46db69792e2738cda1a9b798a3f1f2ae.jpg\",\n  \"138210.jpg\": \"Insecta/Polygrammate hebraeicum/833d666d8b9aed01a65f4b707f0a4e08.jpg\",\n  \"138212.jpg\": \"Insecta/Polygrammate hebraeicum/7d49ab2a519d17c021f1396a45355310.jpg\",\n  \"138213.jpg\": \"Insecta/Polygrammate hebraeicum/2a03dde63579727e9e59b76dccb7da3b.jpg\",\n  \"138217.jpg\": \"Insecta/Polygrammate hebraeicum/590fc8849ea9777bc21bcf42a158b076.jpg\",\n  \"138223.jpg\": \"Insecta/Polygrammate hebraeicum/c075b169726a60a07073f601dfa73b87.jpg\",\n  \"138438.jpg\": \"Insecta/Lestes rectangularis/99ff78aad419e7a442ed865f0051ce2b.jpg\",\n  \"138440.jpg\": \"Insecta/Lestes rectangularis/ff39db8c0ebd3defa3a90bf87a507709.jpg\",\n  \"138441.jpg\": \"Insecta/Lestes rectangularis/06be451880c359765c3240452e205412.jpg\",\n  \"138442.jpg\": \"Insecta/Lestes rectangularis/2bb5caade029010843aaeca49b496bd4.jpg\",\n  \"138443.jpg\": \"Insecta/Lestes rectangularis/a6174bb38499729573b0d8c5af6362db.jpg\",\n  \"138446.jpg\": \"Insecta/Lestes rectangularis/2d8d2c8e0df5f382e2ea1d121faf03a5.jpg\",\n  \"138448.jpg\": \"Insecta/Lestes rectangularis/a18c5bb8573b3598e931cd170e8b6ae1.jpg\",\n  \"138449.jpg\": \"Insecta/Lestes rectangularis/c14fa7952be324567607f598fa3af197.jpg\",\n  \"138450.jpg\": \"Insecta/Lestes rectangularis/795961db568983a2730272c58753f52e.jpg\",\n  \"138451.jpg\": \"Insecta/Lestes rectangularis/f2035758c3fbfff085f3dd3fed0a3762.jpg\",\n  \"138456.jpg\": \"Insecta/Lestes rectangularis/f22f63d033c56350e63ae185f77df929.jpg\",\n  \"138459.jpg\": \"Insecta/Lestes rectangularis/02eeba2e2ef705ebfc1662c7bb90992e.jpg\",\n  \"138460.jpg\": \"Insecta/Lestes rectangularis/311ddc9f1441aff24352a55b4ade2760.jpg\",\n  \"138461.jpg\": \"Insecta/Lestes rectangularis/7e5045ef44e3dd47dbcae57bcffea769.jpg\",\n  \"138462.jpg\": \"Insecta/Lestes rectangularis/d8faa83b4914b1665c19a782674da2cb.jpg\",\n  \"138463.jpg\": \"Insecta/Lestes rectangularis/224bcfb568c6511ecdc12e42645d1566.jpg\",\n  \"138464.jpg\": \"Insecta/Lestes rectangularis/6cb486783a07daef7fcbdb649553f722.jpg\",\n  \"138465.jpg\": \"Insecta/Lestes rectangularis/e9750a86824fbebf3927ec9cfdfce703.jpg\",\n  \"138470.jpg\": \"Insecta/Lestes rectangularis/5701bbd147ffe27eb03705008955358d.jpg\",\n  \"138471.jpg\": \"Insecta/Lestes rectangularis/4495071a47d7f8518b8a4d933286acea.jpg\",\n  \"138476.jpg\": \"Insecta/Lestes rectangularis/fa47a60185310f79e45b9582af53179e.jpg\",\n  \"138477.jpg\": \"Insecta/Lestes rectangularis/21e06c89c130769a91b0f4ad27cce85d.jpg\",\n  \"138478.jpg\": \"Insecta/Lestes rectangularis/5b33824d54da8c14cd473c2ef0bb321d.jpg\",\n  \"138479.jpg\": \"Insecta/Lestes rectangularis/d7acf44b78197240dc2604c626375d5d.jpg\",\n  \"138482.jpg\": \"Insecta/Lestes rectangularis/7327d0c07406582fcd79ab6747c17e62.jpg\",\n  \"138483.jpg\": \"Insecta/Thasus neocalifornicus/e0e7f0b969b138ba0dcb6b454a97cc59.jpg\",\n  \"138487.jpg\": \"Insecta/Thasus neocalifornicus/0154957559854864248927c39f367021.jpg\",\n  \"138491.jpg\": \"Insecta/Thasus neocalifornicus/845d031eb1b746a0b1b2619a5f4aa3be.jpg\",\n  \"138494.jpg\": \"Insecta/Thasus neocalifornicus/7d97bae3579c346efd90d77c118468a0.jpg\",\n  \"138495.jpg\": \"Insecta/Thasus neocalifornicus/542a4c38cdef94ea7525ba1dd59c4231.jpg\",\n  \"138500.jpg\": \"Insecta/Thasus neocalifornicus/8841b0558732d3829b4de5fe7864d622.jpg\",\n  \"138501.jpg\": \"Insecta/Anartia fatima fatima/4edd9e48243b075245851be64d1e8fd4.jpg\",\n  \"138504.jpg\": \"Insecta/Anartia fatima fatima/87f79d8eeaadc5d6aa7fd12b7f2b4699.jpg\",\n  \"138512.jpg\": \"Insecta/Anartia fatima fatima/dceec7f2d27ef5d7cd3da8c116072059.jpg\",\n  \"138515.jpg\": \"Insecta/Anartia fatima fatima/2fee999ca9e08486fa77fb97e0ef6edb.jpg\",\n  \"138518.jpg\": \"Insecta/Anartia fatima fatima/e83c2c5ad67a3e64f5c2689925c4d606.jpg\",\n  \"138522.jpg\": \"Insecta/Anartia fatima fatima/4b2c224d9c54b3a271c6b2643c398088.jpg\",\n  \"138528.jpg\": \"Insecta/Anartia fatima fatima/60ff90869b5d67fd1da9dcc61761b407.jpg\",\n  \"138615.jpg\": \"Aves/Aratinga nenday/98a58ec0824b6ee5684d8a67acdf5abc.jpg\",\n  \"138621.jpg\": \"Aves/Aratinga nenday/8770c5edaed16b91c5924d937da79240.jpg\",\n  \"138628.jpg\": \"Aves/Aratinga nenday/30acc0ba549c81c07dd433a440f7098f.jpg\",\n  \"138630.jpg\": \"Insecta/Neotibicen superbus/e0b3b4e9c5f848b0776ea87a2e1582ec.jpg\",\n  \"138634.jpg\": \"Insecta/Neotibicen superbus/3326838d364d52c1581cb1a9d0079d8a.jpg\",\n  \"138635.jpg\": \"Insecta/Neotibicen superbus/013753c0997420582e385092e4c10bad.jpg\",\n  \"138636.jpg\": \"Insecta/Neotibicen superbus/f03ec5bbb0e0cc148e847bdeb2a99f09.jpg\",\n  \"138637.jpg\": \"Insecta/Neotibicen superbus/133907f84a1c8f27715c332df3197ed2.jpg\",\n  \"138638.jpg\": \"Insecta/Neotibicen superbus/c418d7589e781d3410b8dbb1bc0a0f53.jpg\",\n  \"138639.jpg\": \"Insecta/Neotibicen superbus/de0cea6760f5c91ce635384c320a7c16.jpg\",\n  \"138640.jpg\": \"Insecta/Neotibicen superbus/ba406561346b6cb725b8d37e2b1fc950.jpg\",\n  \"138641.jpg\": \"Insecta/Neotibicen superbus/6291e4f9b26f5353ef06d003d0a62767.jpg\",\n  \"138642.jpg\": \"Insecta/Neotibicen superbus/755af3237c6535029d3c4d0acc590f18.jpg\",\n  \"138643.jpg\": \"Insecta/Neotibicen superbus/d90ef78aae134f7edf67b386821ea864.jpg\",\n  \"138644.jpg\": \"Insecta/Neotibicen superbus/714e146b6be9bee944b508bdf6e8ebd3.jpg\",\n  \"138647.jpg\": \"Insecta/Neotibicen superbus/a0b7d6dbbe99a08811d274cf0449b75b.jpg\",\n  \"138652.jpg\": \"Insecta/Neotibicen superbus/2bf98a21f24201f1c410e85f9b797a64.jpg\",\n  \"138653.jpg\": \"Insecta/Neotibicen superbus/5b6b9d35fb712a20c16955e59735a84b.jpg\",\n  \"138659.jpg\": \"Insecta/Neotibicen superbus/b92dd3e5ba13e4925475dadd4836cb46.jpg\",\n  \"138660.jpg\": \"Insecta/Neotibicen superbus/1139ea23a015faac47ce6d89ea894d57.jpg\",\n  \"138661.jpg\": \"Insecta/Neotibicen superbus/0ed9b63b49c618cc40fdbe4430ace453.jpg\",\n  \"138662.jpg\": \"Insecta/Neotibicen superbus/cb3e2e30b23436913bc6e60b766930bb.jpg\",\n  \"138665.jpg\": \"Insecta/Neotibicen superbus/f039a6baaaf765a3bc84001fdd394efd.jpg\",\n  \"138666.jpg\": \"Insecta/Neotibicen superbus/4b7050bfd724c87bce9774b52fce8e9f.jpg\",\n  \"138669.jpg\": \"Insecta/Neotibicen superbus/988989f8dc584d2fd88d522549e058a3.jpg\",\n  \"138710.jpg\": \"Insecta/Melissodes bimaculata/08361e67445daa1dd2ce44b1b234f18b.jpg\",\n  \"138711.jpg\": \"Insecta/Melissodes bimaculata/403f54dbc62c2400be4eb72f44465440.jpg\",\n  \"138712.jpg\": \"Insecta/Melissodes bimaculata/fc37528a4820aed49eb05de26bffb3ff.jpg\",\n  \"138713.jpg\": \"Insecta/Melissodes bimaculata/684f2db6fce094924ab0c7ea0d129a94.jpg\",\n  \"138718.jpg\": \"Insecta/Melissodes bimaculata/43d0511353c28682ce8e4ac38f2e4f26.jpg\",\n  \"138721.jpg\": \"Aves/Cyrtonyx montezumae/4a13aba9b94d0cf4c500057bbf72f8c4.jpg\",\n  \"138725.jpg\": \"Aves/Cyrtonyx montezumae/1e13c2b23efd5c839f540799c4225430.jpg\",\n  \"138732.jpg\": \"Aves/Cyrtonyx montezumae/7fb9e949da820736a5381870df7a967a.jpg\",\n  \"138735.jpg\": \"Aves/Cyrtonyx montezumae/49d98180703a7439a7228fab1481d8ca.jpg\",\n  \"138736.jpg\": \"Aves/Cyrtonyx montezumae/27e56098b2e01cb74150c23d1c4a742a.jpg\",\n  \"138737.jpg\": \"Aves/Cyrtonyx montezumae/1677d40ecedc02663e6d49b415e3b04b.jpg\",\n  \"138742.jpg\": \"Aves/Sturnella magna/3ebfa294f4f5ba6b299a7ffd5a68a7ad.jpg\",\n  \"138745.jpg\": \"Aves/Sturnella magna/bf6b8f24af9d710898bcb38d9ca0f579.jpg\",\n  \"138746.jpg\": \"Aves/Sturnella magna/6bfef2673b7b9176451301bb292251a0.jpg\",\n  \"138747.jpg\": \"Aves/Sturnella magna/e5268cd4e80112f900dccd96c4a9cd56.jpg\",\n  \"138750.jpg\": \"Aves/Sturnella magna/f3ac22f5fac18882908cefaf38f4736d.jpg\",\n  \"138751.jpg\": \"Aves/Sturnella magna/af3d87b256f11eba21af8d7370096584.jpg\",\n  \"138756.jpg\": \"Aves/Sturnella magna/e84e174b3bf4520e3c1a89d87b16cc67.jpg\",\n  \"138762.jpg\": \"Aves/Sturnella magna/a2d49a2a26808ee77ed9af9c0a25cb50.jpg\",\n  \"138768.jpg\": \"Aves/Sturnella magna/16fbf7417ddd17362604fd8859f517e9.jpg\",\n  \"138769.jpg\": \"Aves/Sturnella magna/5901aa4e11d384719cf4cd204f4fcc99.jpg\",\n  \"138772.jpg\": \"Aves/Sturnella magna/ea1856beef77578d9a2dc0048dcac1b9.jpg\",\n  \"138773.jpg\": \"Aves/Sturnella magna/fad3d1ce0d7e2e4c59957875c8ba28a2.jpg\",\n  \"138774.jpg\": \"Aves/Sturnella magna/f31cbef3a3c1cbb3c1ac3aad59023ecd.jpg\",\n  \"138781.jpg\": \"Aves/Sturnella magna/0eefc8a23cc0e6a77ee529d2a13faa59.jpg\",\n  \"138782.jpg\": \"Aves/Sturnella magna/b2517f1251d04e91263cdfd40df712b6.jpg\",\n  \"138785.jpg\": \"Aves/Sturnella magna/f4e4b8661069515270d2896287cb2fd3.jpg\",\n  \"138786.jpg\": \"Aves/Sturnella magna/9c09100a9ae8f1b01d6095c9d665bdf7.jpg\",\n  \"138801.jpg\": \"Aves/Sturnella magna/9c1a93131d28011f9e3a75df589b998a.jpg\",\n  \"138804.jpg\": \"Aves/Sturnella magna/1152f7962a111ca167222e5ae3be4a29.jpg\",\n  \"138805.jpg\": \"Aves/Sturnella magna/2b27867ce9a5c8ee79895dccf2182e44.jpg\",\n  \"138811.jpg\": \"Aves/Sturnella magna/a04b47003d1144f48ab4622e51b59725.jpg\",\n  \"138815.jpg\": \"Aves/Sturnella magna/b48b0ee531e9dc8b89d211c41bb3b8d9.jpg\",\n  \"138816.jpg\": \"Aves/Sturnella magna/ab6dea619257657398d6c197738f3053.jpg\",\n  \"138871.jpg\": \"Aves/Aramides cajaneus/15b248bca3ddbcf1eefba3eb99aabdb8.jpg\",\n  \"138872.jpg\": \"Aves/Aramides cajaneus/b7a726aa0233b0b24db840e194df3edc.jpg\",\n  \"138873.jpg\": \"Aves/Aramides cajaneus/a50a6e1d322686886e5e81bdd1edf817.jpg\",\n  \"138874.jpg\": \"Aves/Aramides cajaneus/9d67a4cf0bedfd8c81d48d64dea94328.jpg\",\n  \"138876.jpg\": \"Aves/Aramides cajaneus/ddb6ff2df79d59e34b561e528cef3bbc.jpg\",\n  \"138877.jpg\": \"Aves/Aramides cajaneus/ff40d1b5b7bc4db9cfadf7f127a0d97c.jpg\",\n  \"138879.jpg\": \"Aves/Aramides cajaneus/c0478851f7cb8d4263fb30a7c87c3276.jpg\",\n  \"138880.jpg\": \"Aves/Aramides cajaneus/1cbd1ad53fbf58d23ef46c2be2d88163.jpg\",\n  \"138885.jpg\": \"Aves/Aramides cajaneus/a32bce178119b90b8ef3418eb1bbcd51.jpg\",\n  \"138887.jpg\": \"Aves/Aramides cajaneus/644b57556267c68f4d0d51ab3d4f2461.jpg\",\n  \"138888.jpg\": \"Aves/Aramides cajaneus/f53296d3563a4519238dce63c6b9f135.jpg\",\n  \"138894.jpg\": \"Aves/Aramides cajaneus/4c357e02eea3bf07a8be4e18978a0b95.jpg\",\n  \"138898.jpg\": \"Insecta/Harrisimemna trisignata/ca711eed9128582412e6243c47677bbc.jpg\",\n  \"138899.jpg\": \"Insecta/Harrisimemna trisignata/428c1720bc6a063bdde13e2e42d9af0b.jpg\",\n  \"138901.jpg\": \"Insecta/Harrisimemna trisignata/719de1f4bcfc6c63e1ff5cc0a30a0267.jpg\",\n  \"138909.jpg\": \"Insecta/Harrisimemna trisignata/2a8dc7adc405a72d72a72cad82c3181e.jpg\",\n  \"138910.jpg\": \"Insecta/Harrisimemna trisignata/805a669356c9724c0ccceb1a6b2fa025.jpg\",\n  \"138915.jpg\": \"Reptilia/Coluber constrictor priapus/9bfb97d3c8a6dca7c68fa1a05e0b5580.jpg\",\n  \"138917.jpg\": \"Reptilia/Coluber constrictor priapus/8f7916208d4ed8da091b44879d378d14.jpg\",\n  \"138919.jpg\": \"Reptilia/Coluber constrictor priapus/110f56e3de68bd889c6f122f9257ebcb.jpg\",\n  \"138920.jpg\": \"Reptilia/Coluber constrictor priapus/ec685e960a91c6b54455efb6f29a9921.jpg\",\n  \"138926.jpg\": \"Reptilia/Coluber constrictor priapus/019fa65635209724d5db0b595df2e49c.jpg\",\n  \"138927.jpg\": \"Reptilia/Coluber constrictor priapus/db2415588b74dad41e6f12af2317e696.jpg\",\n  \"138931.jpg\": \"Reptilia/Coluber constrictor priapus/c22d9342d891bcb0216f54c4b72ec150.jpg\",\n  \"138932.jpg\": \"Reptilia/Coluber constrictor priapus/78888ecf8d8f3a744f7538405c752f65.jpg\",\n  \"138933.jpg\": \"Reptilia/Coluber constrictor priapus/38c7fb505fd14a805435d721da83dff1.jpg\",\n  \"138934.jpg\": \"Reptilia/Coluber constrictor priapus/2e30b9ac5897c3ebe128b44e46b74afd.jpg\",\n  \"138939.jpg\": \"Reptilia/Coluber constrictor priapus/67438892b8212e4080276cf417c3fe0b.jpg\",\n  \"138943.jpg\": \"Reptilia/Coluber constrictor priapus/689d303fa46671a2f3a28df0cd56f0ac.jpg\",\n  \"138944.jpg\": \"Reptilia/Coluber constrictor priapus/84396fa267ae093ee8fa67e1e3bd765b.jpg\",\n  \"138945.jpg\": \"Reptilia/Coluber constrictor priapus/8786c19980c0a87298af8dd13acb4368.jpg\",\n  \"138946.jpg\": \"Reptilia/Coluber constrictor priapus/9998b2da0eb38c6415240e67f76e5ca2.jpg\",\n  \"138951.jpg\": \"Reptilia/Coluber constrictor priapus/02af6d81c109700e9094d92591a2030c.jpg\",\n  \"138953.jpg\": \"Reptilia/Coluber constrictor priapus/4861aeb938c7f9cb4dd8d4f4b38cf776.jpg\",\n  \"138954.jpg\": \"Reptilia/Coluber constrictor priapus/8a64101171437cb427a3cf55a91a82d2.jpg\",\n  \"138955.jpg\": \"Aves/Motacilla flava/06cab7d2dff14a9950a9792446e79293.jpg\",\n  \"138956.jpg\": \"Aves/Motacilla flava/dac4dc38fad116ac8d8689175a2f5803.jpg\",\n  \"138957.jpg\": \"Aves/Motacilla flava/3020afa4506f5470d8632dd1007b547a.jpg\",\n  \"138958.jpg\": \"Aves/Motacilla flava/b4b57db986e696eca1ae206ce9a653cf.jpg\",\n  \"138963.jpg\": \"Aves/Motacilla flava/0368917dec21b721687723c6164aebc8.jpg\",\n  \"138967.jpg\": \"Aves/Motacilla flava/2eef2517ba1fa7fe610e8c704e24a08f.jpg\",\n  \"138968.jpg\": \"Aves/Motacilla flava/59cbcc4f7a7045cdb80e80c3f9e00fcd.jpg\",\n  \"138969.jpg\": \"Aves/Motacilla flava/1b059f4f4efedeeb8b2319c90cb08bf9.jpg\",\n  \"138970.jpg\": \"Aves/Motacilla flava/0ed56e6c6369fa04096f05b74cc73dc6.jpg\",\n  \"138972.jpg\": \"Aves/Motacilla flava/370d4a70e34565f299bee0d7caa11d6b.jpg\",\n  \"138978.jpg\": \"Aves/Motacilla flava/91af345142d4beec699073a9d83ebc33.jpg\",\n  \"138979.jpg\": \"Aves/Motacilla flava/a306af97af01f9ef7322179af2cf77b0.jpg\",\n  \"138980.jpg\": \"Aves/Motacilla flava/eab5c1b5dbb636ae66418d8e8e582cac.jpg\",\n  \"138981.jpg\": \"Aves/Motacilla flava/e0078df707cc4d16b5121eb0ff200447.jpg\",\n  \"138982.jpg\": \"Aves/Motacilla flava/d99f9489fea2792d583c0d6cf471dab4.jpg\",\n  \"138983.jpg\": \"Aves/Motacilla flava/81827cdb23b4f45e4124c13e7520be41.jpg\",\n  \"138984.jpg\": \"Aves/Motacilla flava/6b4f35f2d5a9304303091ce85cf982f2.jpg\",\n  \"138985.jpg\": \"Aves/Motacilla flava/ec9880c09e360e559645dde425f91e2a.jpg\",\n  \"138986.jpg\": \"Aves/Motacilla flava/9520daa613d1f556eb0de857f330d99b.jpg\",\n  \"138987.jpg\": \"Aves/Motacilla flava/f883b4e34ff234565cb1826f38658db1.jpg\",\n  \"138988.jpg\": \"Aves/Motacilla flava/552cbd7ec5371fe30f05b0b5bc62f03c.jpg\",\n  \"138989.jpg\": \"Aves/Motacilla flava/51c1af115c66317cab099ccbca16ccd6.jpg\",\n  \"138994.jpg\": \"Insecta/Enallagma aspersum/70c6990bc95a0e435fab6579e9f5fb38.jpg\",\n  \"138996.jpg\": \"Insecta/Enallagma aspersum/f0d1d9b52a9162561256d74ea818582e.jpg\",\n  \"138999.jpg\": \"Insecta/Enallagma aspersum/962b9ad55cece7740ce48a4c4c78090e.jpg\",\n  \"139.jpg\": \"Aves/Zonotrichia capensis/dc8642b5a8f5ad9a19cfdd4362064ebb.jpg\",\n  \"139003.jpg\": \"Insecta/Enallagma aspersum/742111c20c9f199a544ca744aad334db.jpg\",\n  \"139008.jpg\": \"Insecta/Enallagma aspersum/32c0321618fc7106a33aede80ac158d8.jpg\",\n  \"139033.jpg\": \"Reptilia/Clemmys guttata/e93762e0d3320d30bb2f2328bceaa479.jpg\",\n  \"139034.jpg\": \"Reptilia/Clemmys guttata/3436c68a405e22e764ef8f677c5ed0fd.jpg\",\n  \"139036.jpg\": \"Reptilia/Clemmys guttata/0b61a43e1b7ec20396f07c077880eb6b.jpg\",\n  \"139038.jpg\": \"Reptilia/Clemmys guttata/2c638f401152331cde5b9b8e5246fe6f.jpg\",\n  \"139046.jpg\": \"Reptilia/Clemmys guttata/c0efa5555aed0d077866c9f37b102c82.jpg\",\n  \"139047.jpg\": \"Reptilia/Clemmys guttata/b7b1916fb2fd751d6d2f55d552a56361.jpg\",\n  \"139059.jpg\": \"Reptilia/Clemmys guttata/1b63c3ba65201343715ad57eec3af753.jpg\",\n  \"139062.jpg\": \"Reptilia/Clemmys guttata/523d703e733c3361aa45b2e8d8998cff.jpg\",\n  \"139066.jpg\": \"Mammalia/Vulpes vulpes fulvus/f8da07431c235e22557f5a694acd3df0.jpg\",\n  \"139067.jpg\": \"Mammalia/Vulpes vulpes fulvus/d4b9d317e866805d0771166431923ec1.jpg\",\n  \"139068.jpg\": \"Mammalia/Vulpes vulpes fulvus/1f4a51acfb44a806dab07bbb8824e10a.jpg\",\n  \"139075.jpg\": \"Mammalia/Vulpes vulpes fulvus/8d16c7a7a8baaf0c558ba695a7278f09.jpg\",\n  \"139076.jpg\": \"Mammalia/Vulpes vulpes fulvus/87f11106f899f0e07c326beeec66a000.jpg\",\n  \"139087.jpg\": \"Mammalia/Vulpes vulpes fulvus/816615af3a2f32e91c0bff66f31816a6.jpg\",\n  \"139088.jpg\": \"Mammalia/Vulpes vulpes fulvus/e55e1ef61ede42824cc525b88b3b45c8.jpg\",\n  \"139091.jpg\": \"Mammalia/Vulpes vulpes fulvus/7e0248438160c706d5d59811833884b8.jpg\",\n  \"139092.jpg\": \"Mammalia/Vulpes vulpes fulvus/47fddec1f14d843bebe67670d5d644d2.jpg\",\n  \"139096.jpg\": \"Mammalia/Vulpes vulpes fulvus/7dbb0e0f70feb28c4d0871c8aad92e8e.jpg\",\n  \"139106.jpg\": \"Insecta/Ascalapha odorata/ce5b0c938a7bb05685b0d5e5a28a6793.jpg\",\n  \"139124.jpg\": \"Insecta/Ascalapha odorata/e365c75a1d1244618c1dacca02e4c112.jpg\",\n  \"139127.jpg\": \"Insecta/Ascalapha odorata/11bc58b42bf367d7ac50026802db6ac0.jpg\",\n  \"139140.jpg\": \"Insecta/Ascalapha odorata/3090a853607ba2377ffa09a780c7df5e.jpg\",\n  \"139147.jpg\": \"Insecta/Ascalapha odorata/c0cca7296f62d1e3845ccb6a7f51c3fb.jpg\",\n  \"139148.jpg\": \"Insecta/Ascalapha odorata/d86001a35fc06e5f0250c1627239d2b4.jpg\",\n  \"139155.jpg\": \"Insecta/Ascalapha odorata/498226ede4bd13f90f3e2badee7947fa.jpg\",\n  \"139156.jpg\": \"Insecta/Ascalapha odorata/0c0519acc48611590e2cbda285cd48eb.jpg\",\n  \"139158.jpg\": \"Insecta/Ascalapha odorata/132b165654218f63306f9e01fde00521.jpg\",\n  \"139163.jpg\": \"Insecta/Ascalapha odorata/e19e3b7ae4985d49932aa63c09d72988.jpg\",\n  \"139170.jpg\": \"Insecta/Ascalapha odorata/0512525cd3e25a6d24d0bf8ecf542e95.jpg\",\n  \"139172.jpg\": \"Insecta/Ascalapha odorata/3262f07ea052aeac82c6b26c6571f717.jpg\",\n  \"139175.jpg\": \"Insecta/Ascalapha odorata/c9893d42495d48ac5ace7cd370a89069.jpg\",\n  \"139180.jpg\": \"Insecta/Ascalapha odorata/fa5659ac387c4a473578acc8f32297dd.jpg\",\n  \"139181.jpg\": \"Insecta/Ascalapha odorata/ba37f4b4581d997adc5078e2970f0689.jpg\",\n  \"139187.jpg\": \"Insecta/Ascalapha odorata/ad26223234d9b6202e2edc3fa9128dc6.jpg\",\n  \"139189.jpg\": \"Insecta/Ascalapha odorata/42f74e02fbf25037a14c4d4ef9cf4fbd.jpg\",\n  \"139201.jpg\": \"Insecta/Ascalapha odorata/b2c1ebcda46e16768e1331dcc1cf2f26.jpg\",\n  \"139210.jpg\": \"Insecta/Ascalapha odorata/3512f84e3dce9d00f821e34cd44616b3.jpg\",\n  \"139233.jpg\": \"Insecta/Ascalapha odorata/05464082c1003c93f28259667eb9bf0e.jpg\",\n  \"139237.jpg\": \"Insecta/Ascalapha odorata/6ef9252f6972098e244a26bbbcb022c1.jpg\",\n  \"139239.jpg\": \"Insecta/Ascalapha odorata/693f2f7c43eb06ba0aeb1771ec014b09.jpg\",\n  \"139242.jpg\": \"Insecta/Ascalapha odorata/2d99c050de4161a84049efa7a69b3d77.jpg\",\n  \"139248.jpg\": \"Insecta/Ascalapha odorata/a2b6d1a3eef7c26e449c5ee4a299a1fd.jpg\",\n  \"139257.jpg\": \"Insecta/Epiaeschna heros/d59c1f029079a203cbea0c7a1207183d.jpg\",\n  \"139258.jpg\": \"Insecta/Epiaeschna heros/62b0aaab5cad208c8933fb7697df0315.jpg\",\n  \"139263.jpg\": \"Insecta/Epiaeschna heros/7dc15300e2d9fde6ece709e36c18a460.jpg\",\n  \"139264.jpg\": \"Insecta/Epiaeschna heros/aa0af0248703cb77f095984cb32f1855.jpg\",\n  \"139265.jpg\": \"Insecta/Epiaeschna heros/7736cc270ba76618a9e08ccb127fe9e9.jpg\",\n  \"139266.jpg\": \"Insecta/Epiaeschna heros/e20c7da252102f0ee9c03d76cb595da8.jpg\",\n  \"139267.jpg\": \"Insecta/Epiaeschna heros/c152edbac6a1f76c4b8e4a63b8dd0270.jpg\",\n  \"139270.jpg\": \"Insecta/Epiaeschna heros/0055812560e23abcc96e5efff18bc7b0.jpg\",\n  \"139274.jpg\": \"Insecta/Epiaeschna heros/768becba9953ffcdfe8c5ebc0a027658.jpg\",\n  \"139275.jpg\": \"Insecta/Epiaeschna heros/41ca42ef57561fc67ef15ef674b3b2e4.jpg\",\n  \"139276.jpg\": \"Insecta/Epiaeschna heros/8a5272267f1382658a7cef687cbbe68d.jpg\",\n  \"139277.jpg\": \"Insecta/Epiaeschna heros/f796749c2d1e4edaafca01e2cc7af617.jpg\",\n  \"139278.jpg\": \"Insecta/Epiaeschna heros/df0bbde30d2b4c555a1875f342f069df.jpg\",\n  \"139279.jpg\": \"Insecta/Epiaeschna heros/0f1931e45dc1057def838fc0656b08f9.jpg\",\n  \"139283.jpg\": \"Insecta/Epiaeschna heros/ad22d29cf481dc075c8409d0e3c7de9c.jpg\",\n  \"139285.jpg\": \"Insecta/Epiaeschna heros/f1f519386fbe87ca67816c36ffc80ebe.jpg\",\n  \"139287.jpg\": \"Insecta/Epiaeschna heros/86eac2b4851fe1340d72910b65675dc3.jpg\",\n  \"139291.jpg\": \"Insecta/Epiaeschna heros/2987af721d6e656f9d11ac86b46cc1dd.jpg\",\n  \"139292.jpg\": \"Insecta/Epiaeschna heros/0a7585b52ad6b872dbfc510c4574165f.jpg\",\n  \"139304.jpg\": \"Insecta/Epiaeschna heros/8db247154fecf8a2164c215d2a3aa541.jpg\",\n  \"139306.jpg\": \"Insecta/Gluphisia septentrionis/c09910b06de873d71a1b40dda3038e3d.jpg\",\n  \"139311.jpg\": \"Insecta/Gluphisia septentrionis/3050debfbac56d516983a0faaa40735b.jpg\",\n  \"139315.jpg\": \"Insecta/Gluphisia septentrionis/7a8fcc0c3cb7dddf9f543356dfc4618c.jpg\",\n  \"139319.jpg\": \"Insecta/Gluphisia septentrionis/7a2acf0bf02a82aa0de5ed869e1e3519.jpg\",\n  \"139320.jpg\": \"Insecta/Gluphisia septentrionis/e47a8c6d2f9ac4985b5040cdac2cc643.jpg\",\n  \"139325.jpg\": \"Insecta/Gluphisia septentrionis/0071522a913d681631c24c5bfc5ae9b6.jpg\",\n  \"139341.jpg\": \"Insecta/Libellula incesta/3ccf47e7d93cd21677165296b4546da3.jpg\",\n  \"139342.jpg\": \"Insecta/Libellula incesta/43bfb8762e3e3bc302cc38cc78fb0277.jpg\",\n  \"139343.jpg\": \"Insecta/Libellula incesta/b810687d80ac133bf666123bbb1428cc.jpg\",\n  \"139358.jpg\": \"Insecta/Libellula incesta/25c3b562061e06bffcea2102fa7c16ca.jpg\",\n  \"139365.jpg\": \"Insecta/Libellula incesta/55e3b15719d6a4cc019ea59e9cad3fe7.jpg\",\n  \"139373.jpg\": \"Insecta/Libellula incesta/7be8cd5a76dde90e11bef74aa523cc4e.jpg\",\n  \"139374.jpg\": \"Insecta/Libellula incesta/fbabd1d227688fcd4c707cd6c8756c39.jpg\",\n  \"139384.jpg\": \"Insecta/Libellula incesta/c6199c2df2372abaf9d6a9de00f434ef.jpg\",\n  \"139388.jpg\": \"Insecta/Libellula incesta/9287b37d1dd0f671c426b7905653d086.jpg\",\n  \"139397.jpg\": \"Insecta/Libellula incesta/6f94ffb6c854143bed83c5a89af62338.jpg\",\n  \"139413.jpg\": \"Insecta/Libellula incesta/f1dfd069b3078d44ea927e17ac5268c8.jpg\",\n  \"139414.jpg\": \"Insecta/Libellula incesta/d2c1587e90feeca4c08f338dc4422c66.jpg\",\n  \"139418.jpg\": \"Insecta/Libellula incesta/dae1b57620c76ee5e8aee180483e1a15.jpg\",\n  \"139423.jpg\": \"Insecta/Libellula incesta/c6a639d12c43084ffc18981fe224c4e5.jpg\",\n  \"139424.jpg\": \"Insecta/Libellula incesta/6adf94b99e5d62530b3789c85c156e91.jpg\",\n  \"139427.jpg\": \"Insecta/Libellula incesta/364b009b774ece37063550f77d76153c.jpg\",\n  \"139451.jpg\": \"Insecta/Libellula incesta/495218e2e83604df6fe8bd9e813effe8.jpg\",\n  \"139453.jpg\": \"Insecta/Libellula incesta/6f4f845a527d9f1c25a6249775e3769d.jpg\",\n  \"139468.jpg\": \"Insecta/Libellula incesta/41a61bc2ad53e6e72e292be6fca0b0c5.jpg\",\n  \"139472.jpg\": \"Insecta/Libellula incesta/6be304d08ca18e4382f3651ef566e2aa.jpg\",\n  \"139473.jpg\": \"Insecta/Libellula incesta/6520a5114f3045d2f30908d86600c35c.jpg\",\n  \"139482.jpg\": \"Insecta/Libellula incesta/54a7982b7798934bd97f39a7018589a6.jpg\",\n  \"139485.jpg\": \"Insecta/Libellula incesta/22a41e4998dfb9535a3077f76a042d6a.jpg\",\n  \"139503.jpg\": \"Insecta/Libellula incesta/2ebfd3a09f0d2136b413ae08e7eb12ad.jpg\",\n  \"139523.jpg\": \"Amphibia/Incilius valliceps/875a9161b493641a2ea4a16c4f7b2f79.jpg\",\n  \"139525.jpg\": \"Amphibia/Incilius valliceps/ae3f77d8bc90982ab33b796768e0f569.jpg\",\n  \"139538.jpg\": \"Amphibia/Incilius valliceps/81d31f37c6cf7387030da4a81261010d.jpg\",\n  \"139543.jpg\": \"Amphibia/Incilius valliceps/36c6f60c227dc398f4d2e37a730c935a.jpg\",\n  \"139544.jpg\": \"Amphibia/Incilius valliceps/3efb3f0df305121d522c4b76c1369510.jpg\",\n  \"139545.jpg\": \"Reptilia/Coleonyx elegans/ae7cbbb07d5dcd8fe5963d84d9973d10.jpg\",\n  \"139549.jpg\": \"Reptilia/Coleonyx elegans/ba7f5fe46f835d868e320cd049ca2aef.jpg\",\n  \"139553.jpg\": \"Reptilia/Coleonyx elegans/c71f515e84fedf2cfa93e634b6e5c2a6.jpg\",\n  \"139555.jpg\": \"Reptilia/Coleonyx elegans/05b5f98d8898c0be4194036ac3c5fd8f.jpg\",\n  \"139612.jpg\": \"Insecta/Orthemis discolor/0188ffabd1bc3416f38d97b6117addd1.jpg\",\n  \"139614.jpg\": \"Insecta/Orthemis discolor/533b06eb8952741e94f487777cd34142.jpg\",\n  \"139615.jpg\": \"Insecta/Orthemis discolor/daefc46511c41f4157b2df603c911fb7.jpg\",\n  \"139616.jpg\": \"Insecta/Orthemis discolor/47849a683cf16e2b5fe0d1e0902d20b6.jpg\",\n  \"139617.jpg\": \"Insecta/Orthemis discolor/612884286eda1a475a753ba20736d445.jpg\",\n  \"139618.jpg\": \"Insecta/Orthemis discolor/7240a727bab0715039dc933ac6e7f663.jpg\",\n  \"139625.jpg\": \"Insecta/Orthemis discolor/20df6d9bacadafcb7ae6cafa5a24ce76.jpg\",\n  \"139626.jpg\": \"Insecta/Orthemis discolor/58c32a67cd9f865a1c0a63ccd67430dc.jpg\",\n  \"139627.jpg\": \"Insecta/Orthemis discolor/d9fd8bff4782761aec2263f60543d830.jpg\",\n  \"139629.jpg\": \"Insecta/Orthemis discolor/0a0445745c2c65c1f0df916b21676a3c.jpg\",\n  \"139631.jpg\": \"Insecta/Orthemis discolor/43e761d3cafd1ccd92be95ff90f35286.jpg\",\n  \"139632.jpg\": \"Insecta/Orthemis discolor/644277e4781864aab4e5ab8f15c2e3fb.jpg\",\n  \"139635.jpg\": \"Insecta/Orthemis discolor/5397b1a92b9e61a212dd267d643df307.jpg\",\n  \"139640.jpg\": \"Insecta/Orthemis discolor/31f8d606b3244b780c4594136d5b94a0.jpg\",\n  \"139641.jpg\": \"Insecta/Orthemis discolor/e98da066383cd8ac192fea0ea6ca75f4.jpg\",\n  \"139642.jpg\": \"Insecta/Orthemis discolor/9c31ea2422a382ef32956f18c4f348cd.jpg\",\n  \"139644.jpg\": \"Insecta/Orthemis discolor/3bd8810e71326cab286f5c316a8382fb.jpg\",\n  \"139647.jpg\": \"Insecta/Orthemis discolor/de37fa75278921f12541b6e22a4f2a17.jpg\",\n  \"139648.jpg\": \"Insecta/Orthemis discolor/cbdf6209f3e0a33ca52aaacb05e4817d.jpg\",\n  \"139649.jpg\": \"Insecta/Orthemis discolor/d431cef99bdddf224449546023de085c.jpg\",\n  \"139651.jpg\": \"Insecta/Orthemis discolor/f1f59f6049c685c1cc9d49d15aa2dd38.jpg\",\n  \"139652.jpg\": \"Insecta/Orthemis discolor/5999d2d26d17c524da1d6342ac3b8317.jpg\",\n  \"139653.jpg\": \"Insecta/Orthemis discolor/5c40eb040d6f29804e039a7f358204b1.jpg\",\n  \"139693.jpg\": \"Aves/Setophaga townsendi/589b0a66094c89e9d6b4836b60b81e32.jpg\",\n  \"139695.jpg\": \"Aves/Setophaga townsendi/1e4471bfc81ca946323221fb52f100a0.jpg\",\n  \"139700.jpg\": \"Aves/Setophaga townsendi/15e517b4ffa6a33fb8a730d58f5c13db.jpg\",\n  \"139708.jpg\": \"Aves/Setophaga townsendi/753ca8a8b40ab461ffa5b77bbfaa0d34.jpg\",\n  \"139714.jpg\": \"Aves/Setophaga townsendi/976dd4a65ab21ca735b6a868ed8c7fc9.jpg\",\n  \"139717.jpg\": \"Aves/Setophaga townsendi/7acb838707f4c369b32a30861ff1c467.jpg\",\n  \"139726.jpg\": \"Aves/Setophaga townsendi/623fc67baaf6a53d2aa8476d1d2a8383.jpg\",\n  \"139743.jpg\": \"Aves/Setophaga townsendi/e7a6072976f009edc8f2e32f55c1bd63.jpg\",\n  \"139752.jpg\": \"Aves/Setophaga townsendi/e56eb753a9ca9af2c3732c49984b4493.jpg\",\n  \"139776.jpg\": \"Aves/Setophaga townsendi/3de6b802f4daf2c7d87be49ae0c8f2f9.jpg\",\n  \"139783.jpg\": \"Aves/Setophaga townsendi/1bc88c440846524bb531cb6c25324ef6.jpg\",\n  \"139796.jpg\": \"Aves/Setophaga townsendi/e6e3e7877e7c8a5f28d2329bc91cef42.jpg\",\n  \"139800.jpg\": \"Aves/Setophaga townsendi/575844f4ad5ed40ffedcac8be86b1654.jpg\",\n  \"139803.jpg\": \"Aves/Setophaga townsendi/2a8ec01756ade9baa3eb4744a1add527.jpg\",\n  \"139813.jpg\": \"Aves/Setophaga townsendi/860fa6f5ee341f3f3c8c9ca632267e13.jpg\",\n  \"139823.jpg\": \"Aves/Setophaga townsendi/e2f4c231d1ca60491e21311b0f27eaa9.jpg\",\n  \"139849.jpg\": \"Aves/Setophaga townsendi/cb3ab022cea799abcad6fb325709d92d.jpg\",\n  \"139870.jpg\": \"Aves/Setophaga townsendi/38c5c1fdb20f3048edc40523541746a8.jpg\",\n  \"139883.jpg\": \"Aves/Setophaga townsendi/50efc100bd6e35fbf8f20491e8237bdc.jpg\",\n  \"139892.jpg\": \"Aves/Setophaga townsendi/5d409ea124f049e513dfa757e4b58759.jpg\",\n  \"139893.jpg\": \"Aves/Setophaga townsendi/73d3597cecce5fdff659e3be89c4ebb9.jpg\",\n  \"139900.jpg\": \"Insecta/Propylea quatuordecimpunctata/8808a1c1ba82f1b2c949d4354035fcff.jpg\",\n  \"139903.jpg\": \"Insecta/Propylea quatuordecimpunctata/e6aae7ad1c9a754cddb80cf37de7653b.jpg\",\n  \"139907.jpg\": \"Insecta/Propylea quatuordecimpunctata/b2e1c1aa911198dcec03373762c84275.jpg\",\n  \"139910.jpg\": \"Insecta/Propylea quatuordecimpunctata/bdce88deae793790f5d83669b2cfe8cc.jpg\",\n  \"139911.jpg\": \"Insecta/Propylea quatuordecimpunctata/9934f6a7d21132c397f6dd54f5c153a6.jpg\",\n  \"139915.jpg\": \"Insecta/Propylea quatuordecimpunctata/3dd2360b83b1d6c031653a8768009483.jpg\",\n  \"139916.jpg\": \"Insecta/Propylea quatuordecimpunctata/cc14749b57f6c54c53d666eccb4e6d3c.jpg\",\n  \"139921.jpg\": \"Insecta/Propylea quatuordecimpunctata/31ff045622e70d13878ba04bfd2de85f.jpg\",\n  \"139927.jpg\": \"Insecta/Propylea quatuordecimpunctata/a8837f7cdeb3ea00902e6a9b9f810544.jpg\",\n  \"139938.jpg\": \"Insecta/Propylea quatuordecimpunctata/bd124c1cd97e103442c873856c5f1482.jpg\",\n  \"139992.jpg\": \"Insecta/Sphex ichneumoneus/aea7423c927765035f051287e5883345.jpg\",\n  \"139993.jpg\": \"Insecta/Sphex ichneumoneus/93aecb04d0fbbf4b51738ef8d5b3de23.jpg\",\n  \"139996.jpg\": \"Insecta/Sphex ichneumoneus/4c7e52ec50a35576818f4a5c177aa27f.jpg\",\n  \"140014.jpg\": \"Insecta/Sphex ichneumoneus/d47525006d16dad265a916b2788cb0a2.jpg\",\n  \"140015.jpg\": \"Insecta/Sphex ichneumoneus/4473e690a5d2a31fd2a99fd5e7fce5fc.jpg\",\n  \"140016.jpg\": \"Insecta/Sphex ichneumoneus/c3c9bca4f82bf7e06e81b4c984166e9b.jpg\",\n  \"140017.jpg\": \"Insecta/Sphex ichneumoneus/e775f82e002af533c69c6a517e4a886d.jpg\",\n  \"140018.jpg\": \"Insecta/Sphex ichneumoneus/4c035646a13b6ae76272793e1964a86c.jpg\",\n  \"140026.jpg\": \"Insecta/Sphex ichneumoneus/8dfcc719ba8514c84e96d4432fd0e077.jpg\",\n  \"140032.jpg\": \"Insecta/Sphex ichneumoneus/0a4b809149ccd12c6a1fdcb82b7952e3.jpg\",\n  \"140034.jpg\": \"Insecta/Sphex ichneumoneus/0116e4bdce84b5b6d7aeb2c396b65f32.jpg\",\n  \"140035.jpg\": \"Insecta/Sphex ichneumoneus/6f703f8d590358f79ffc299b69e087a0.jpg\",\n  \"140058.jpg\": \"Insecta/Sphex ichneumoneus/92dc2f1efd4e1fafb8dc98d0dab5c7a4.jpg\",\n  \"140072.jpg\": \"Insecta/Sphex ichneumoneus/45a8664a482f5cb189c1d5670232b4a9.jpg\",\n  \"140075.jpg\": \"Insecta/Sphex ichneumoneus/a33653787b0fea41f2362476d9ba151c.jpg\",\n  \"140076.jpg\": \"Insecta/Sphex ichneumoneus/f6d854b202176a7643f676de9ab5b736.jpg\",\n  \"140081.jpg\": \"Insecta/Sphex ichneumoneus/4d0e4230895544c223678e72a3809ea8.jpg\",\n  \"140082.jpg\": \"Insecta/Sphex ichneumoneus/69433aa3ce2929af12e964cb36d059ba.jpg\",\n  \"140083.jpg\": \"Insecta/Sphex ichneumoneus/94d20f5d8ffb6ef936a4f863a9f2ec2f.jpg\",\n  \"140084.jpg\": \"Aves/Ramphastos ambiguus/459562c71e1c370f087c9c98b709505e.jpg\",\n  \"140085.jpg\": \"Aves/Ramphastos ambiguus/40fcb194dfba819fb6c0ac2b3afd75f3.jpg\",\n  \"140092.jpg\": \"Aves/Ramphastos ambiguus/57e7c19b5fb2cfd94867309ef628c761.jpg\",\n  \"140096.jpg\": \"Aves/Ramphastos ambiguus/c9aac64818cf83a52300d4618f095749.jpg\",\n  \"140098.jpg\": \"Aves/Ramphastos ambiguus/7479bbf8b5590b46c4f01f01d11d6f55.jpg\",\n  \"140099.jpg\": \"Aves/Ramphastos ambiguus/b5b9313e30002671c983993be385246f.jpg\",\n  \"140199.jpg\": \"Amphibia/Smilisca baudinii/c1b55bce9bf5a3f5db7b6514cdaf32e8.jpg\",\n  \"140201.jpg\": \"Amphibia/Smilisca baudinii/57e41025a66d71d3f948ea1173824da6.jpg\",\n  \"140202.jpg\": \"Amphibia/Smilisca baudinii/b9949999ae0a7dbf14e15d6a57e92da0.jpg\",\n  \"140204.jpg\": \"Amphibia/Smilisca baudinii/afa93825bd15acb639ca6ca30f443ad8.jpg\",\n  \"140206.jpg\": \"Amphibia/Smilisca baudinii/fe02a1214e09c79ebac59c30f39f71aa.jpg\",\n  \"140211.jpg\": \"Amphibia/Smilisca baudinii/5138ae6763797c4acb6788f02f370752.jpg\",\n  \"140213.jpg\": \"Amphibia/Smilisca baudinii/35394859373b8f5d92670b5d7c99300f.jpg\",\n  \"140218.jpg\": \"Amphibia/Smilisca baudinii/ee7fa3ad8e02c90eee21d23c5ecca3a6.jpg\",\n  \"140219.jpg\": \"Amphibia/Smilisca baudinii/8d3f70578c36b9a66a342d87863c0cac.jpg\",\n  \"140225.jpg\": \"Amphibia/Smilisca baudinii/1b14f54dae5af60a0eb9ffb9d0168948.jpg\",\n  \"140226.jpg\": \"Amphibia/Smilisca baudinii/fb56618d11abe3eb8c90f529f632e3ad.jpg\",\n  \"140232.jpg\": \"Amphibia/Smilisca baudinii/7e585c8991c9145b9b181249f25c2f14.jpg\",\n  \"140236.jpg\": \"Amphibia/Smilisca baudinii/0ad5e43eb7d2aa5ec46058b9fa15566e.jpg\",\n  \"140241.jpg\": \"Amphibia/Smilisca baudinii/7be583ef71c9051347787d7650154dda.jpg\",\n  \"140242.jpg\": \"Amphibia/Smilisca baudinii/8ed27d05d8560bb4464a99c5e69efdff.jpg\",\n  \"140246.jpg\": \"Amphibia/Smilisca baudinii/17ed5f53d1d0d20f1e6550dbf443077a.jpg\",\n  \"140249.jpg\": \"Amphibia/Smilisca baudinii/8028469aa0266048607ee009da0cca84.jpg\",\n  \"140250.jpg\": \"Amphibia/Smilisca baudinii/fff1f573963df84c5749abe853d3a345.jpg\",\n  \"140252.jpg\": \"Amphibia/Smilisca baudinii/4d18471ec91b43c953b51ce4ae3f8f08.jpg\",\n  \"140300.jpg\": \"Animalia/Strongylocentrotus droebachiensis/97aa6e01a1e37399a3b2fda90a54b003.jpg\",\n  \"140304.jpg\": \"Animalia/Strongylocentrotus droebachiensis/9e34bb6531fde10c04d99ea99a0dc1f4.jpg\",\n  \"140311.jpg\": \"Animalia/Strongylocentrotus droebachiensis/354abbb9fbacc07a2d37993117730cd0.jpg\",\n  \"140316.jpg\": \"Animalia/Strongylocentrotus droebachiensis/3c48e76d8df3e94adad0dfbc09b5281d.jpg\",\n  \"140329.jpg\": \"Insecta/Galasa nigrinodis/f97f18d3c775c32c97643119c0ebd8a1.jpg\",\n  \"140334.jpg\": \"Insecta/Galasa nigrinodis/6f4a38a5843ccf5a6acf6f141ea9d3f9.jpg\",\n  \"140339.jpg\": \"Insecta/Galasa nigrinodis/c65d3c3dbaa0328a5a0f005843ad7ab2.jpg\",\n  \"140340.jpg\": \"Insecta/Galasa nigrinodis/70ca93dc940c87a29721df62a3f29e39.jpg\",\n  \"140341.jpg\": \"Insecta/Galasa nigrinodis/5e27f9d756a89c79aca39d15b5e4844a.jpg\",\n  \"140349.jpg\": \"Insecta/Galasa nigrinodis/94936a719d39e7cbe0c3e7f222404608.jpg\",\n  \"140350.jpg\": \"Insecta/Galasa nigrinodis/1e0f8818c3df91a97d759eceac6ea2a2.jpg\",\n  \"140354.jpg\": \"Insecta/Nicrophorus tomentosus/c5f43a07519deb4a4fe6b1d9338a1e2c.jpg\",\n  \"140359.jpg\": \"Insecta/Nicrophorus tomentosus/f2beb9c9b2ad7ea705b505791d7fa7e2.jpg\",\n  \"140360.jpg\": \"Insecta/Nicrophorus tomentosus/a25f05a076c715214891b50ee7b0b61b.jpg\",\n  \"140361.jpg\": \"Insecta/Nicrophorus tomentosus/44ed2676ea4cba49cfad93bfe5ccbd1c.jpg\",\n  \"140366.jpg\": \"Insecta/Nicrophorus tomentosus/59f7b14bc08ab6f883c0ee62f693f587.jpg\",\n  \"140369.jpg\": \"Reptilia/Plestiodon inexpectatus/90efaff202eda9ec96fb1d0e62c1c5c0.jpg\",\n  \"140370.jpg\": \"Reptilia/Plestiodon inexpectatus/5b6e59923d3fb66206b5d687a71fc8ad.jpg\",\n  \"140372.jpg\": \"Reptilia/Plestiodon inexpectatus/98623e5c1ff47b9210e596e93b157d1b.jpg\",\n  \"140373.jpg\": \"Reptilia/Plestiodon inexpectatus/cef4afc106db86bc8bcba34c2152dfe6.jpg\",\n  \"140380.jpg\": \"Reptilia/Plestiodon inexpectatus/14ed29f6c78fc51a6f2206c7b7a9aa81.jpg\",\n  \"140381.jpg\": \"Reptilia/Crotalus ruber/d3132d5886677deadf999686042e5884.jpg\",\n  \"140384.jpg\": \"Reptilia/Crotalus ruber/1be013a9fce6fdc0cb72fcbe80a642a1.jpg\",\n  \"140388.jpg\": \"Reptilia/Crotalus ruber/d8d4a097ce52d215fd8ac05d405c0308.jpg\",\n  \"140389.jpg\": \"Reptilia/Crotalus ruber/c57527dd31b66ba0694ed4e7140ca5cd.jpg\",\n  \"140390.jpg\": \"Reptilia/Crotalus ruber/d6d71d10a916742d4230db7716984505.jpg\",\n  \"140398.jpg\": \"Reptilia/Crotalus ruber/dd24e962b548e678091948405c610aa1.jpg\",\n  \"140399.jpg\": \"Reptilia/Crotalus ruber/9d0a684a0bb18e37c39ba4cf21d9e881.jpg\",\n  \"140404.jpg\": \"Reptilia/Crotalus ruber/90557e6f66935243ceeca447327eb77f.jpg\",\n  \"140406.jpg\": \"Reptilia/Crotalus ruber/7ee4f849db1e2087266fd4919d066a39.jpg\",\n  \"140409.jpg\": \"Reptilia/Crotalus ruber/7f8184ab34660927e7041eeaa8ffa55b.jpg\",\n  \"140411.jpg\": \"Reptilia/Crotalus ruber/5445237e4a95894c419c291c31eed712.jpg\",\n  \"140412.jpg\": \"Reptilia/Crotalus ruber/e2dca69112d01dcaea276d1b2f6ec9ce.jpg\",\n  \"140413.jpg\": \"Reptilia/Crotalus ruber/3c8634e0e6409e6a4198f9fc39ef452c.jpg\",\n  \"140414.jpg\": \"Reptilia/Crotalus ruber/8de35fabc65f2d97bd5e7bb91adc5b26.jpg\",\n  \"140418.jpg\": \"Reptilia/Crotalus ruber/ab4b78ebf1aecadd4d4e445d90eab496.jpg\",\n  \"140419.jpg\": \"Reptilia/Crotalus ruber/e33240a2749a8da2d1d62b593d302e0e.jpg\",\n  \"140420.jpg\": \"Reptilia/Crotalus ruber/b1ba0e539cd0710b01f3db0ac3c1b290.jpg\",\n  \"140421.jpg\": \"Reptilia/Crotalus ruber/161845829708d458f5339b0c2b3e8a03.jpg\",\n  \"140423.jpg\": \"Reptilia/Crotalus ruber/976dea443998904cea019ebd0f5abc07.jpg\",\n  \"140424.jpg\": \"Reptilia/Crotalus ruber/03679bd3647eb44b1432ea275c52a2b0.jpg\",\n  \"140431.jpg\": \"Reptilia/Crotalus ruber/7015072ccb06c03bdbc4f1272bca03d0.jpg\",\n  \"140432.jpg\": \"Reptilia/Crotalus ruber/f1bd28743cbfb0385fbc5621dd08ee06.jpg\",\n  \"140437.jpg\": \"Reptilia/Crotalus ruber/19ee2972a25a592ef5176627e6b34b9a.jpg\",\n  \"140440.jpg\": \"Insecta/Idaea dimidiata/2c2913ac04fed98d3f98e6ac62ab115b.jpg\",\n  \"140442.jpg\": \"Insecta/Idaea dimidiata/922be6c1da1de773c53975c2696e1df6.jpg\",\n  \"140447.jpg\": \"Insecta/Idaea dimidiata/bc267b358f8751bd9732f5c98efc976f.jpg\",\n  \"140452.jpg\": \"Insecta/Idaea dimidiata/746a1235752cb993aa4f1e40773d59ee.jpg\",\n  \"140457.jpg\": \"Insecta/Idaea dimidiata/b3dca1d3e1d6d9e03cc35961fc7c9285.jpg\",\n  \"140470.jpg\": \"Insecta/Jadera haematoloma/61aac63e21225d536d9d9e650ec45eae.jpg\",\n  \"140471.jpg\": \"Insecta/Jadera haematoloma/99fc134958120f942cac9161f5c904fc.jpg\",\n  \"140478.jpg\": \"Insecta/Jadera haematoloma/1bd7ddab2eadb95c81585e21482f6e0f.jpg\",\n  \"140484.jpg\": \"Insecta/Jadera haematoloma/3f553e20cafb9ebc9ac57beda7cfe352.jpg\",\n  \"140486.jpg\": \"Insecta/Jadera haematoloma/e4248069f4a2f23ae3c1069b59156611.jpg\",\n  \"140502.jpg\": \"Insecta/Jadera haematoloma/4ff1ab2827de53249bf7ec555b318ff5.jpg\",\n  \"140516.jpg\": \"Insecta/Jadera haematoloma/8fd93530a58945bf02a13704c0a78136.jpg\",\n  \"140522.jpg\": \"Insecta/Jadera haematoloma/ca6aa824d8b408c45d8010cc87a1928e.jpg\",\n  \"140531.jpg\": \"Insecta/Jadera haematoloma/4a3e71a7cdc7cfcc2450fee400f199f4.jpg\",\n  \"140541.jpg\": \"Insecta/Jadera haematoloma/fefdd54bcd234229b2651d2228eeb8b0.jpg\",\n  \"140542.jpg\": \"Insecta/Jadera haematoloma/78dd5fcad41eae5166cdff6404125eca.jpg\",\n  \"140543.jpg\": \"Insecta/Jadera haematoloma/fe2fe09bf987e625d37dcb226a03184d.jpg\",\n  \"140942.jpg\": \"Insecta/Euclea delphinii/64306665fbcc2b47512db4563f7fa2b1.jpg\",\n  \"140943.jpg\": \"Insecta/Euclea delphinii/63c5e6940dd2e42e885c3b3a34bac6e7.jpg\",\n  \"140944.jpg\": \"Insecta/Euclea delphinii/ea7e52b3b30badec7822050b1693f52e.jpg\",\n  \"140948.jpg\": \"Insecta/Euclea delphinii/ec4b28d222b8ef0326617d75b39afe99.jpg\",\n  \"140949.jpg\": \"Insecta/Euclea delphinii/339ef4bf1a0be80066c6fa46bed7c709.jpg\",\n  \"140950.jpg\": \"Insecta/Euclea delphinii/0c590ca2b3274159d7e23a8cdeb1ee6b.jpg\",\n  \"140951.jpg\": \"Insecta/Euclea delphinii/120137ec3f8b662f6537612659145dce.jpg\",\n  \"140955.jpg\": \"Insecta/Euclea delphinii/bc5411745702d02e95773b7c2bc77595.jpg\",\n  \"140958.jpg\": \"Insecta/Euclea delphinii/8df828507a2a72c1d3869662e6223a9e.jpg\",\n  \"140963.jpg\": \"Insecta/Euclea delphinii/a77e4cfe513d8b4aa0d39e4955d99fd8.jpg\",\n  \"140965.jpg\": \"Insecta/Euclea delphinii/01e458663c89e8cc76b12aee813c1329.jpg\",\n  \"140966.jpg\": \"Insecta/Euclea delphinii/5c6063c2942e5850a69e5a7b3f99f42a.jpg\",\n  \"140967.jpg\": \"Insecta/Euclea delphinii/56a83ee6446a82fd049bd9572a6645aa.jpg\",\n  \"140970.jpg\": \"Insecta/Euclea delphinii/581e485e16d2cb6297f1e199fa238837.jpg\",\n  \"140971.jpg\": \"Insecta/Euclea delphinii/35c75952a69c498fcd4dc9e178b3f80a.jpg\",\n  \"140978.jpg\": \"Insecta/Euclea delphinii/d40d01219495f27b9ec9771b53f0c461.jpg\",\n  \"140979.jpg\": \"Insecta/Euclea delphinii/bcab6fe193cefe9dcc9e2b4db7d2c18b.jpg\",\n  \"141168.jpg\": \"Aves/Bombycilla cedrorum/fbb52719883bed217842802e5dff6cef.jpg\",\n  \"141179.jpg\": \"Aves/Bombycilla cedrorum/27f13bc5f31238316158a5ac8c540e53.jpg\",\n  \"141194.jpg\": \"Aves/Bombycilla cedrorum/59af06566c88735f2ae83050e0955587.jpg\",\n  \"141216.jpg\": \"Aves/Bombycilla cedrorum/b1a9254645c0252b868ab44802b223a6.jpg\",\n  \"141320.jpg\": \"Aves/Bombycilla cedrorum/f30c9e54e761a6f0be29fad0c0b7c0c6.jpg\",\n  \"141345.jpg\": \"Aves/Bombycilla cedrorum/45fba599a59031b6a7a2988a61be3174.jpg\",\n  \"141367.jpg\": \"Aves/Bombycilla cedrorum/b26b0a950ee4b274577e5445f13e8e9e.jpg\",\n  \"141380.jpg\": \"Aves/Bombycilla cedrorum/e7cee1cfc0b53c0658ee8355a2d6e55f.jpg\",\n  \"141384.jpg\": \"Aves/Bombycilla cedrorum/80f17ab43f7bbcd15ec69ba7ac54b56d.jpg\",\n  \"141393.jpg\": \"Aves/Bombycilla cedrorum/f5b40ed5c116d51ac41b79c936bcb2e3.jpg\",\n  \"141429.jpg\": \"Aves/Bombycilla cedrorum/cdc61a2f898610e6aa96e2a73ccdef14.jpg\",\n  \"141447.jpg\": \"Aves/Bombycilla cedrorum/868f0fba3c25bdd0beb9b108df0699d9.jpg\",\n  \"141451.jpg\": \"Aves/Bombycilla cedrorum/1c4af1b12b183d63e8969979c9b8ad67.jpg\",\n  \"141458.jpg\": \"Aves/Bombycilla cedrorum/bb72b27322a0e4deb780e76709eb0c56.jpg\",\n  \"141469.jpg\": \"Aves/Bombycilla cedrorum/2e73466980a38eaf534f95052cd5469d.jpg\",\n  \"141486.jpg\": \"Aves/Bombycilla cedrorum/4caf31cc39baad3be762fda73fa1a126.jpg\",\n  \"141499.jpg\": \"Aves/Bombycilla cedrorum/049a1ea500c329848d7bd12057e7aa5c.jpg\",\n  \"141566.jpg\": \"Aves/Bombycilla cedrorum/23f354106aa00664611e371082f23aa7.jpg\",\n  \"141605.jpg\": \"Aves/Bombycilla cedrorum/98d7eedaabd33439bd888eab3c3b0541.jpg\",\n  \"141632.jpg\": \"Insecta/Rhagonycha fulva/d47c73b5d004c5a6268c7208287b58d3.jpg\",\n  \"141637.jpg\": \"Insecta/Rhagonycha fulva/709022193367d1e24e39cd0a03dbfd78.jpg\",\n  \"141639.jpg\": \"Insecta/Rhagonycha fulva/fffd6bfd0e1ec1999ea90dd577d57588.jpg\",\n  \"141647.jpg\": \"Insecta/Rhagonycha fulva/d12ec292a43431810d6171c327cf1402.jpg\",\n  \"141648.jpg\": \"Insecta/Rhagonycha fulva/1d47e05b65adad724fe9af6a7b589a72.jpg\",\n  \"141659.jpg\": \"Aves/Coragyps atratus/54144d8fcb83429553254ae47a5f0e5a.jpg\",\n  \"141668.jpg\": \"Aves/Coragyps atratus/bc560900b9c0a86d85f70187bca33274.jpg\",\n  \"141697.jpg\": \"Aves/Coragyps atratus/540f311a99920cec4a46e64e6f2cabbf.jpg\",\n  \"141705.jpg\": \"Aves/Coragyps atratus/c75df9228b6e75e4b96a187e2c41db9e.jpg\",\n  \"141745.jpg\": \"Aves/Coragyps atratus/fba8b91972dd644007446e709636e669.jpg\",\n  \"141757.jpg\": \"Aves/Coragyps atratus/762b49db95ccdc54799126e01ebed2a8.jpg\",\n  \"141782.jpg\": \"Aves/Coragyps atratus/69f79a7b942a29cb39d8faa671b9420f.jpg\",\n  \"141886.jpg\": \"Aves/Coragyps atratus/dc05a8c5d909135326691e92517a16a1.jpg\",\n  \"141947.jpg\": \"Aves/Coragyps atratus/09adac2ec8919939eba1c4b404668961.jpg\",\n  \"141956.jpg\": \"Aves/Coragyps atratus/cc4717b9736a944f5e9681995cd9548c.jpg\",\n  \"142022.jpg\": \"Aves/Coragyps atratus/4d6e536cfb44dd98965230ebff04cee9.jpg\",\n  \"142044.jpg\": \"Aves/Coragyps atratus/94275a775acf5e1bc0d3662768a6e2f9.jpg\",\n  \"142062.jpg\": \"Aves/Coragyps atratus/5820ef514051da9a07480c73237acf89.jpg\",\n  \"142084.jpg\": \"Aves/Coragyps atratus/f088e13e9a5e4e732b7c91e38d75a6f7.jpg\",\n  \"142117.jpg\": \"Reptilia/Agkistrodon piscivorus leucostoma/4e5d9abc558cf6a52f993945bc435ca6.jpg\",\n  \"142118.jpg\": \"Reptilia/Agkistrodon piscivorus leucostoma/bdb7468724ef93ce3e9104b7ca989531.jpg\",\n  \"142119.jpg\": \"Reptilia/Agkistrodon piscivorus leucostoma/09759543c3755c66668be0931d32f3ee.jpg\",\n  \"142135.jpg\": \"Reptilia/Agkistrodon piscivorus leucostoma/b1b18ce398fdb25e749ec0f0c9ddc101.jpg\",\n  \"142136.jpg\": \"Reptilia/Agkistrodon piscivorus leucostoma/9812865fb8e40544dcded66c95da9021.jpg\",\n  \"142144.jpg\": \"Reptilia/Agkistrodon piscivorus leucostoma/1949bdc37f623552a8324fad00b41d56.jpg\",\n  \"142148.jpg\": \"Reptilia/Agkistrodon piscivorus leucostoma/e34e33a3b83f97fc9950d2e75d2c2181.jpg\",\n  \"142157.jpg\": \"Reptilia/Agkistrodon piscivorus leucostoma/45a0a672b14da5940d0737f072a6601b.jpg\",\n  \"142160.jpg\": \"Reptilia/Agkistrodon piscivorus leucostoma/60f5d1fd29969e57f995a137f6effba7.jpg\",\n  \"142167.jpg\": \"Reptilia/Agkistrodon piscivorus leucostoma/a2f4d0eae4672b85f866c183e99efc1a.jpg\",\n  \"142184.jpg\": \"Reptilia/Agkistrodon piscivorus leucostoma/f4511292eabeaf3998781e6134f4bbc8.jpg\",\n  \"142192.jpg\": \"Reptilia/Agkistrodon piscivorus leucostoma/2ca041670a64527b8649542171fd7b02.jpg\",\n  \"142193.jpg\": \"Reptilia/Agkistrodon piscivorus leucostoma/78aec46d5cb576f9685236fbed02b88c.jpg\",\n  \"142194.jpg\": \"Reptilia/Agkistrodon piscivorus leucostoma/498741b3a71f6afd184ddf94ccac8920.jpg\",\n  \"142197.jpg\": \"Reptilia/Agkistrodon piscivorus leucostoma/aa51461243b16f564f6d34c480db908f.jpg\",\n  \"142201.jpg\": \"Reptilia/Agkistrodon piscivorus leucostoma/98e26f4b0bebd9cbbf11b67ca1c7e18a.jpg\",\n  \"142211.jpg\": \"Reptilia/Agkistrodon piscivorus leucostoma/557002c1f86baa510e308fa1fd1fcfc2.jpg\",\n  \"142233.jpg\": \"Reptilia/Agkistrodon piscivorus leucostoma/5fd08f16b764d62e0b2d0ae3e56a5cb7.jpg\",\n  \"142241.jpg\": \"Reptilia/Agkistrodon piscivorus leucostoma/5fbc95bd602a9c51fcd5ac58b1afd5e5.jpg\",\n  \"142250.jpg\": \"Reptilia/Agkistrodon piscivorus leucostoma/4b363e33588a88997910cb36ae892298.jpg\",\n  \"142260.jpg\": \"Reptilia/Agkistrodon piscivorus leucostoma/410dfc2d842822f4e30d38e7bdbde162.jpg\",\n  \"142270.jpg\": \"Reptilia/Agkistrodon piscivorus leucostoma/8c7e7292f09bb76ef38370e1e3e772a8.jpg\",\n  \"142273.jpg\": \"Reptilia/Agkistrodon piscivorus leucostoma/ee43de8aa42660046ed4c630cfccd783.jpg\",\n  \"142280.jpg\": \"Reptilia/Agkistrodon piscivorus leucostoma/beb9218538962abb4bd94d31c0e5fc04.jpg\",\n  \"142281.jpg\": \"Reptilia/Agkistrodon piscivorus leucostoma/6160296bb3a6dfac8dd7805d2bdb87f4.jpg\",\n  \"142282.jpg\": \"Reptilia/Agkistrodon piscivorus leucostoma/db6cf5c3c595abca78a651bef88b80e2.jpg\",\n  \"142401.jpg\": \"Insecta/Dicymolomia julianalis/00329dc8d7955e718b65da287f98a03c.jpg\",\n  \"142402.jpg\": \"Insecta/Dicymolomia julianalis/f62e6ce173dc8eba02eb46ef0716c2db.jpg\",\n  \"142403.jpg\": \"Insecta/Dicymolomia julianalis/ba3cc741b59324f34f5755b2b95beede.jpg\",\n  \"142411.jpg\": \"Insecta/Dicymolomia julianalis/c876188f82c10c92db478dbdee281286.jpg\",\n  \"142412.jpg\": \"Insecta/Dicymolomia julianalis/a4c26093b3ab000a132918710b289306.jpg\",\n  \"142415.jpg\": \"Insecta/Dicymolomia julianalis/826993c36cae2cc72e448f5dc8e877fe.jpg\",\n  \"142422.jpg\": \"Insecta/Dicymolomia julianalis/d1dfad39de1056da36a5dd5653b5c67b.jpg\",\n  \"142425.jpg\": \"Insecta/Nematocampa resistaria/43a743c94d26e96817aa57c9d168e7cc.jpg\",\n  \"142427.jpg\": \"Insecta/Nematocampa resistaria/8c8f6abda579ece8aff0a10e2acc26ec.jpg\",\n  \"142430.jpg\": \"Insecta/Nematocampa resistaria/429879349346d50d7087aec57a3e543c.jpg\",\n  \"142431.jpg\": \"Insecta/Nematocampa resistaria/369226e29f167324b4d474bcab099b64.jpg\",\n  \"142432.jpg\": \"Insecta/Nematocampa resistaria/770a37cda9396f2184c885b8c66fdd5c.jpg\",\n  \"142433.jpg\": \"Insecta/Nematocampa resistaria/20f6a81784f6b230aa598da800f77feb.jpg\",\n  \"142437.jpg\": \"Insecta/Nematocampa resistaria/070fa948984a8f5e070ad9c48f7f483b.jpg\",\n  \"142440.jpg\": \"Insecta/Nematocampa resistaria/e74877fd1c811bf9408604e8bace06e4.jpg\",\n  \"142441.jpg\": \"Insecta/Nematocampa resistaria/aaeebcdd01226ea8b01a93e83e51a2b4.jpg\",\n  \"142445.jpg\": \"Insecta/Nematocampa resistaria/022b9c0da68cfc056ee165ab438828a0.jpg\",\n  \"142446.jpg\": \"Insecta/Nematocampa resistaria/a90ec28b344d7227c4404b4419e7059e.jpg\",\n  \"142450.jpg\": \"Insecta/Nematocampa resistaria/5cfe0ef4b6233a1f8951de8c0cbccc4c.jpg\",\n  \"142454.jpg\": \"Insecta/Nematocampa resistaria/d832095de0a0836cce31fff6d643d8e0.jpg\",\n  \"142455.jpg\": \"Insecta/Nematocampa resistaria/9585347a61229e80409fceb3ca83fdb2.jpg\",\n  \"142456.jpg\": \"Insecta/Nematocampa resistaria/fe512f831059a98fcd00ecf9136e5f5f.jpg\",\n  \"142457.jpg\": \"Insecta/Nematocampa resistaria/393c3a88656007fab214d66d76c4afb7.jpg\",\n  \"142461.jpg\": \"Insecta/Nematocampa resistaria/fb52b647c805ca7a4d1ac665c87677d1.jpg\",\n  \"142462.jpg\": \"Insecta/Nematocampa resistaria/badd4a3d7ee2d7993ed76f2d0f74ce6c.jpg\",\n  \"142467.jpg\": \"Insecta/Nematocampa resistaria/2090eb1a833aa86efa5489715bde2269.jpg\",\n  \"142468.jpg\": \"Insecta/Nematocampa resistaria/d6d4d61d8fdf906dab7d377575c92337.jpg\",\n  \"142492.jpg\": \"Insecta/Limenitis arthemis astyanax/a349bfa992ab8485fad4c517e7ded1f3.jpg\",\n  \"142504.jpg\": \"Insecta/Limenitis arthemis astyanax/c742a6e5a6f8ed74028294bf05b6701a.jpg\",\n  \"142518.jpg\": \"Insecta/Limenitis arthemis astyanax/4f3a42b3db6139cdc674457da8fdd1c1.jpg\",\n  \"142528.jpg\": \"Insecta/Limenitis arthemis astyanax/8049d5b71a56ba8dd11431a3ac89e0be.jpg\",\n  \"142529.jpg\": \"Insecta/Limenitis arthemis astyanax/e29b469f27a598f04cad0a40d049e1a7.jpg\",\n  \"142535.jpg\": \"Insecta/Limenitis arthemis astyanax/666de8a8e1e154f08a6d5543fd3a9291.jpg\",\n  \"142539.jpg\": \"Insecta/Limenitis arthemis astyanax/613092aa5d10c6680dc31c8390a2ac8b.jpg\",\n  \"142541.jpg\": \"Insecta/Limenitis arthemis astyanax/a51990945e67ea0e016e6700e6f4af34.jpg\",\n  \"142561.jpg\": \"Insecta/Limenitis arthemis astyanax/894dff1ab3bf10d3ed3f6fc0f2bddb3d.jpg\",\n  \"142568.jpg\": \"Insecta/Limenitis arthemis astyanax/af21b6c766df45c240b5c1f5ecf6c6f2.jpg\",\n  \"142589.jpg\": \"Insecta/Limenitis arthemis astyanax/962a4f9dc4e46cd9801844410856f5d7.jpg\",\n  \"142614.jpg\": \"Insecta/Limenitis arthemis astyanax/cd717412100534170d420f142d87714b.jpg\",\n  \"142621.jpg\": \"Insecta/Limenitis arthemis astyanax/9b31cfb4779e747b53ac4a0dee38ff48.jpg\",\n  \"142622.jpg\": \"Insecta/Limenitis arthemis astyanax/bb3c553a47fb791a859c01804ff8fbc1.jpg\",\n  \"142624.jpg\": \"Insecta/Limenitis arthemis astyanax/05928351bf13b60f3c2ac83b4e6ad7b6.jpg\",\n  \"142633.jpg\": \"Insecta/Limenitis arthemis astyanax/5c5d4fcb207a710255364a683fff8e3c.jpg\",\n  \"142636.jpg\": \"Insecta/Limenitis arthemis astyanax/48c15ab244d5b551812bbb1b47c8307a.jpg\",\n  \"142637.jpg\": \"Insecta/Limenitis arthemis astyanax/ca5d742d241cf3365ca380e1df522fcb.jpg\",\n  \"142640.jpg\": \"Insecta/Limenitis arthemis astyanax/51739ca20a6ccf3a13bf93e30599e41d.jpg\",\n  \"142653.jpg\": \"Insecta/Limenitis arthemis astyanax/32174dea334c2eb82abca0b0ea1beb85.jpg\",\n  \"142663.jpg\": \"Insecta/Limenitis arthemis astyanax/a14a8dcf64a3a15e21ee21e884d66b94.jpg\",\n  \"142677.jpg\": \"Insecta/Limenitis arthemis astyanax/3ca8f448f015683a48856742d30e50aa.jpg\",\n  \"142683.jpg\": \"Insecta/Limenitis arthemis astyanax/2cf5227c8e1f0697d2182cbf60ae48f7.jpg\",\n  \"142686.jpg\": \"Insecta/Limenitis arthemis astyanax/a5dacacf215c95a2323ab8858e833fbc.jpg\",\n  \"142691.jpg\": \"Insecta/Limenitis arthemis astyanax/1a0525fafa04da8f5d36cd01c109551e.jpg\",\n  \"14288.jpg\": \"Amphibia/Acris crepitans/0411a69c7bbab03eb5fa6a1b5ed2d5f9.jpg\",\n  \"14290.jpg\": \"Amphibia/Acris crepitans/ff2b73faee31454cf9485c58559e6769.jpg\",\n  \"14295.jpg\": \"Amphibia/Acris crepitans/8e4bd3c9cdf26f650f9bc635fbc701a8.jpg\",\n  \"14297.jpg\": \"Amphibia/Acris crepitans/4d76ae5dd119e71dd2b50a662e5c06cc.jpg\",\n  \"14300.jpg\": \"Amphibia/Acris crepitans/b4b3c847169e10983cd969ff28ade481.jpg\",\n  \"14304.jpg\": \"Amphibia/Acris crepitans/8e93cae331575b5b0237628e4cfc3bb0.jpg\",\n  \"14305.jpg\": \"Amphibia/Acris crepitans/0479986ce1a9f7b0d97c7afcd797fb2a.jpg\",\n  \"14306.jpg\": \"Amphibia/Acris crepitans/cac640c7387c8046f101d888a87d9926.jpg\",\n  \"14310.jpg\": \"Amphibia/Acris crepitans/d31208e95e283863e7f347094d8c8579.jpg\",\n  \"143237.jpg\": \"Mammalia/Oreamnos americanus/5ce23ece9fc3d4b56745f993f8caa82f.jpg\",\n  \"143239.jpg\": \"Mammalia/Oreamnos americanus/82353740bd96d6b32711503f880eb8dd.jpg\",\n  \"143241.jpg\": \"Mammalia/Oreamnos americanus/bb9229191f8f667306cfc3400aca5156.jpg\",\n  \"143252.jpg\": \"Mammalia/Oreamnos americanus/f52079addd16e5d968d60b70e43dd400.jpg\",\n  \"143262.jpg\": \"Mammalia/Oreamnos americanus/8e6586345b037bccc4135b286002dfea.jpg\",\n  \"143273.jpg\": \"Mammalia/Oreamnos americanus/d82bce898c895e9e29cbc23d2e064fff.jpg\",\n  \"143278.jpg\": \"Mammalia/Oreamnos americanus/6ce25f5a8b57f01c8e127bdd89d0f795.jpg\",\n  \"143280.jpg\": \"Mammalia/Oreamnos americanus/bf7bbf9dadd361e205d38e99b296dfe8.jpg\",\n  \"143283.jpg\": \"Mammalia/Oreamnos americanus/863d347e133f454166b624b5115ecb78.jpg\",\n  \"143284.jpg\": \"Mammalia/Oreamnos americanus/fc40a81ff7fa5eb6ef4e1947d2ba1fac.jpg\",\n  \"143286.jpg\": \"Mammalia/Oreamnos americanus/ed5aed76ed128c4e49631393c3b227b2.jpg\",\n  \"143292.jpg\": \"Mammalia/Oreamnos americanus/6bf338c950dac2e4acb752fbfef71b7b.jpg\",\n  \"143298.jpg\": \"Insecta/Citheronia regalis/45d6d6b2163be8d411cc66c1019dfd77.jpg\",\n  \"143302.jpg\": \"Insecta/Citheronia regalis/f03c478a44b81170c64be9be62ec091c.jpg\",\n  \"143307.jpg\": \"Insecta/Citheronia regalis/ddac0118a18a92539b040bb238a72dfe.jpg\",\n  \"143308.jpg\": \"Insecta/Citheronia regalis/f4204003595c04f6eebacaf986007175.jpg\",\n  \"143309.jpg\": \"Insecta/Citheronia regalis/1890d9a85db7358516527322f3da1d70.jpg\",\n  \"143310.jpg\": \"Insecta/Citheronia regalis/d9ac0d4d4e1dda46e4ecc75deea444c1.jpg\",\n  \"143311.jpg\": \"Insecta/Citheronia regalis/e7247874d13ccc34e3d810669df1e0fc.jpg\",\n  \"143312.jpg\": \"Insecta/Citheronia regalis/b0c030d9aedd5b475ea6f05a8ce53430.jpg\",\n  \"143313.jpg\": \"Insecta/Citheronia regalis/873eeaee0a7d1702fd9e2660ade4dcd5.jpg\",\n  \"143317.jpg\": \"Insecta/Citheronia regalis/ed6a9caca5676a5aa61d65485f07d220.jpg\",\n  \"143321.jpg\": \"Insecta/Citheronia regalis/84063338379a9e03186ccd7433137af3.jpg\",\n  \"143322.jpg\": \"Insecta/Citheronia regalis/e89aa37b14efd7217d9272b7e943a673.jpg\",\n  \"143323.jpg\": \"Insecta/Citheronia regalis/ab7c925832cc016126e6a540fb1f58b1.jpg\",\n  \"143326.jpg\": \"Mammalia/Cynomys gunnisoni/41a0f43e09de90f04e66ba5f0575fd15.jpg\",\n  \"143334.jpg\": \"Mammalia/Cynomys gunnisoni/9f08592ca68d53e8789678b17c8b41b6.jpg\",\n  \"143338.jpg\": \"Mammalia/Cynomys gunnisoni/0150060d21f50ad09485c8175b2fe99e.jpg\",\n  \"143346.jpg\": \"Insecta/Climaciella brunnea/21ff4c00a10733a8d7625552566a7c58.jpg\",\n  \"143351.jpg\": \"Insecta/Climaciella brunnea/e3e28da9e9a8dcda2e38dc87f08b557a.jpg\",\n  \"143352.jpg\": \"Insecta/Climaciella brunnea/5c1847383df6b6faaf2204fbaa03b594.jpg\",\n  \"143357.jpg\": \"Insecta/Climaciella brunnea/0d16c8e2d5f69ec9848c517d1d8ddf15.jpg\",\n  \"143444.jpg\": \"Mammalia/Hydrochoerus hydrochaeris/e39d7e91d1d008d2b93d4747cda3979a.jpg\",\n  \"143446.jpg\": \"Mammalia/Hydrochoerus hydrochaeris/d9f8d6ff7e5f78410cce9d55ccf06d3e.jpg\",\n  \"143447.jpg\": \"Mammalia/Hydrochoerus hydrochaeris/03a7cbd4b172e6ebc22e8938775540eb.jpg\",\n  \"143469.jpg\": \"Amphibia/Litoria aurea/ee2d6efad15d3647c424769123a87d40.jpg\",\n  \"143473.jpg\": \"Amphibia/Litoria aurea/8e4199dab577f3619c9b0deeb30567b4.jpg\",\n  \"143477.jpg\": \"Amphibia/Litoria aurea/1318adc16467803d20a0a3c3bada3e20.jpg\",\n  \"143509.jpg\": \"Insecta/Eumorpha achemon/b9bd1d55c11da13247fb25fd580516fa.jpg\",\n  \"143512.jpg\": \"Insecta/Eumorpha achemon/608d26c4cdf8db5ade228b2074c0b66d.jpg\",\n  \"143515.jpg\": \"Insecta/Eumorpha achemon/d509450ef5675de56f4253a19fa61d42.jpg\",\n  \"143516.jpg\": \"Insecta/Apatura ilia/f82560f5a8a9f7892841d9d02b8e46ed.jpg\",\n  \"143524.jpg\": \"Insecta/Apatura ilia/92bb60e7c116f3b7a44b283eff8952f7.jpg\",\n  \"143532.jpg\": \"Insecta/Apatura ilia/7098b50e4be128b9d4a530e854885890.jpg\",\n  \"143666.jpg\": \"Insecta/Manduca quinquemaculata/02294e97d1251d463b0dc5af3ee04524.jpg\",\n  \"143668.jpg\": \"Insecta/Manduca quinquemaculata/d455f6c9ab9e6a863005f41aed2deca2.jpg\",\n  \"143680.jpg\": \"Insecta/Manduca quinquemaculata/b0e50003e738418d96c3a242cb82f0f0.jpg\",\n  \"143683.jpg\": \"Insecta/Manduca quinquemaculata/76b305766b8698718a4a90d47e306875.jpg\",\n  \"143691.jpg\": \"Insecta/Polites themistocles/3fec8da92b1631ab0114a5a1e8e756f9.jpg\",\n  \"143692.jpg\": \"Insecta/Polites themistocles/0339bc48677c4563ee21551ee07537bb.jpg\",\n  \"143693.jpg\": \"Insecta/Polites themistocles/ef2de0c420f597c10c12b3b08a50180b.jpg\",\n  \"143700.jpg\": \"Insecta/Polites themistocles/a7737279bb712f858d63c815c75923af.jpg\",\n  \"143704.jpg\": \"Insecta/Polites themistocles/759b74589de3e7a941331297abff33d5.jpg\",\n  \"143713.jpg\": \"Insecta/Gryllodes sigillatus/efe1635b94213ca377aa2852329a4535.jpg\",\n  \"143715.jpg\": \"Insecta/Gryllodes sigillatus/0f5bf9d1f1eea130b868a423b9dabc6c.jpg\",\n  \"143719.jpg\": \"Insecta/Gryllodes sigillatus/fdaa6dc307e51ec72bb9bb18dd5a84d0.jpg\",\n  \"143723.jpg\": \"Insecta/Gryllodes sigillatus/50d7b83874698094c26a5cea40efe15f.jpg\",\n  \"143726.jpg\": \"Insecta/Gryllodes sigillatus/5244454a44ccf214c7021764f2d8a383.jpg\",\n  \"143728.jpg\": \"Insecta/Gryllodes sigillatus/2ff8443a04f06f53e7f80e25ed322060.jpg\",\n  \"143729.jpg\": \"Insecta/Gryllodes sigillatus/85940e61d0873367e7ae057272793bdc.jpg\",\n  \"143731.jpg\": \"Insecta/Gryllodes sigillatus/6f9d27f68f68fc2bd44fc5410b13555d.jpg\",\n  \"143732.jpg\": \"Insecta/Gryllodes sigillatus/74a30a94afdd276f0e5db890b0f3f5ca.jpg\",\n  \"143733.jpg\": \"Insecta/Gryllodes sigillatus/96887d3803c2f55ae511a40da5d2350d.jpg\",\n  \"143774.jpg\": \"Aves/Larus dominicanus/24782201ae29804323b55a786c5323b7.jpg\",\n  \"143786.jpg\": \"Aves/Larus dominicanus/559e411601e90b8bf5ea7d5ad7dbe96f.jpg\",\n  \"143790.jpg\": \"Aves/Larus dominicanus/75eb4cfed71c270e2c532e838dab4045.jpg\",\n  \"143794.jpg\": \"Aves/Larus dominicanus/e7a645284b9d9598ae67c7163f22d3b2.jpg\",\n  \"143805.jpg\": \"Aves/Larus dominicanus/0336aaf2b88bcceaa9b6ac734021713d.jpg\",\n  \"143842.jpg\": \"Aves/Cygnus olor/5ad60723b11672c627f76b0649304084.jpg\",\n  \"143867.jpg\": \"Aves/Cygnus olor/4d4b0f29704525f4acc5cea035bff556.jpg\",\n  \"143893.jpg\": \"Aves/Cygnus olor/22d3d3d7d6efea88327296c9cc429ff3.jpg\",\n  \"143924.jpg\": \"Aves/Cygnus olor/1028967fc88b6e0ec151560ddcc0dfeb.jpg\",\n  \"143962.jpg\": \"Aves/Cygnus olor/bb9705f9f681f8f72331417e77d07020.jpg\",\n  \"143993.jpg\": \"Aves/Cygnus olor/76573b9c19a7bebb8bc48f8636b546db.jpg\",\n  \"144.jpg\": \"Aves/Zonotrichia capensis/e5872d47b0eb367840fba5178698af59.jpg\",\n  \"144000.jpg\": \"Aves/Cygnus olor/28a6673dac4e94c8b0aff5a0edb1a60a.jpg\",\n  \"144011.jpg\": \"Aves/Cygnus olor/364d86798146b5d04727b786a55e71b4.jpg\",\n  \"144059.jpg\": \"Aves/Cygnus olor/5b89163b5a23800168c7c1e4fab991dd.jpg\",\n  \"144062.jpg\": \"Aves/Cygnus olor/c3b66b7f9c8a885be314a963527211ac.jpg\",\n  \"144110.jpg\": \"Aves/Cygnus olor/3a95bf8d3b2b90194d825461bc9b4e02.jpg\",\n  \"144169.jpg\": \"Aves/Cygnus olor/c5ebcb91981c69763fb91fb558c4f0ee.jpg\",\n  \"144194.jpg\": \"Aves/Cygnus olor/1c1019920af3ad6510a1a7bc570dbc84.jpg\",\n  \"144261.jpg\": \"Insecta/Lapara bombycoides/2b492550eb50120a799c854d18618290.jpg\",\n  \"144264.jpg\": \"Insecta/Lapara bombycoides/8e4718f2f46cec369ba15fb8b9fd8b0d.jpg\",\n  \"144265.jpg\": \"Insecta/Lapara bombycoides/973301dda299284e7644db8d08762169.jpg\",\n  \"144269.jpg\": \"Insecta/Lapara bombycoides/8fbb01c34bf6171f9f1fd52cb920a8cc.jpg\",\n  \"144271.jpg\": \"Insecta/Lapara bombycoides/4e92322c8c894884e9220574728112d4.jpg\",\n  \"144298.jpg\": \"Insecta/Paonias myops/6667434b4eacda419843c050aa3a599b.jpg\",\n  \"144299.jpg\": \"Insecta/Paonias myops/af28d9cb054770c665dc104939e0c5f3.jpg\",\n  \"144300.jpg\": \"Insecta/Paonias myops/ae8a6294d9d11a6d563bb77195111786.jpg\",\n  \"144305.jpg\": \"Insecta/Paonias myops/034ef10287fce2fec0f6b195cc34dfd2.jpg\",\n  \"144308.jpg\": \"Insecta/Paonias myops/dfbbdbf9ec009aba200dd7e02c99c362.jpg\",\n  \"144309.jpg\": \"Insecta/Paonias myops/c10866494b599e3ec11352b32b8c16a0.jpg\",\n  \"144310.jpg\": \"Insecta/Paonias myops/b822e3c3e86ec7731f54f6a864e93695.jpg\",\n  \"144311.jpg\": \"Insecta/Paonias myops/91266d11de83d0892094628b79592751.jpg\",\n  \"144313.jpg\": \"Insecta/Paonias myops/2d9b89ec8d27388858af6803c7716e23.jpg\",\n  \"144314.jpg\": \"Insecta/Paonias myops/1573509781b00f423e2197c71148a242.jpg\",\n  \"144316.jpg\": \"Insecta/Paonias myops/c7edfe3f3dde98105a92d06c240c5f02.jpg\",\n  \"144317.jpg\": \"Insecta/Paonias myops/dbcd32b75277e5a113594b644a9f1448.jpg\",\n  \"144319.jpg\": \"Insecta/Paonias myops/68b70068798e6716be50bf227b7e5692.jpg\",\n  \"144320.jpg\": \"Insecta/Paonias myops/0124a7e15b78e3140fdf67c7462796ad.jpg\",\n  \"144322.jpg\": \"Insecta/Paonias myops/b6ae2a946d5180efedef4dfd1584b855.jpg\",\n  \"144323.jpg\": \"Insecta/Paonias myops/2cab7fe4d1204ddb812a33eeea612aa1.jpg\",\n  \"144324.jpg\": \"Insecta/Paonias myops/0d023a1bd3d92bae18dd300cfbb18a69.jpg\",\n  \"144326.jpg\": \"Insecta/Paonias myops/01717757f2bc2463d4144abcf54704ba.jpg\",\n  \"144328.jpg\": \"Insecta/Paonias myops/9ae8843d6a255ff0cd4cb7998e53f4cb.jpg\",\n  \"144331.jpg\": \"Insecta/Paonias myops/179ff41af17c055d749b7b9f8f629283.jpg\",\n  \"144582.jpg\": \"Reptilia/Apalone spinifera emoryi/95b8622c1fe88f42f695380698ea1b73.jpg\",\n  \"144583.jpg\": \"Reptilia/Apalone spinifera emoryi/2fd606d80ac3d67a127cf0e14a4745ae.jpg\",\n  \"144584.jpg\": \"Reptilia/Apalone spinifera emoryi/a91f8d7f6c2ab87d024cfb4a221c8661.jpg\",\n  \"144587.jpg\": \"Reptilia/Apalone spinifera emoryi/1ff264669da8a5b7f48e555be70c90f6.jpg\",\n  \"144588.jpg\": \"Reptilia/Apalone spinifera emoryi/2c8e6a85a4efb7ba5fdb48c823cc1d24.jpg\",\n  \"144589.jpg\": \"Reptilia/Apalone spinifera emoryi/bd837f92423c839ac195388e7397ece9.jpg\",\n  \"144615.jpg\": \"Reptilia/Crocodylus moreletii/834e5ecb08e5a5a33edc1e94ceba6d74.jpg\",\n  \"144624.jpg\": \"Reptilia/Crocodylus moreletii/ae5793f31162dcb76e726f277c6dfc58.jpg\",\n  \"144627.jpg\": \"Reptilia/Crocodylus moreletii/77a27a7f1504ecaa556cfc0211109515.jpg\",\n  \"144634.jpg\": \"Reptilia/Crocodylus moreletii/cd55d730dc87471d864d1b4e91c1077c.jpg\",\n  \"144635.jpg\": \"Reptilia/Crocodylus moreletii/28f0f74bff1a1ae252c9df439d8c6b30.jpg\",\n  \"144642.jpg\": \"Reptilia/Crocodylus moreletii/f01d59a6b974259c41bf986cfc667229.jpg\",\n  \"144648.jpg\": \"Reptilia/Crocodylus moreletii/14d64ecc93c26dd798a0fc277d7b82c3.jpg\",\n  \"14469.jpg\": \"Aves/Falco novaeseelandiae/72c53247c2743b5088f4fb8c697030b9.jpg\",\n  \"14471.jpg\": \"Aves/Falco novaeseelandiae/4fb58ddc3c3d24c74d76306cfa05d8cb.jpg\",\n  \"14474.jpg\": \"Aves/Falco novaeseelandiae/7ffad836e46d61e06727b7ebd5fe15d4.jpg\",\n  \"14475.jpg\": \"Aves/Falco novaeseelandiae/3ed0d91e89c6e06d2c07398f71b31a64.jpg\",\n  \"14479.jpg\": \"Aves/Falco novaeseelandiae/b26444e66079b1c1f0f737270f2d2bbb.jpg\",\n  \"14480.jpg\": \"Aves/Falco novaeseelandiae/88730838abeb8927814ef568f6622051.jpg\",\n  \"14483.jpg\": \"Aves/Falco novaeseelandiae/26df629ae5b331bc83eb51edb5b15cc1.jpg\",\n  \"144857.jpg\": \"Reptilia/Nerodia sipedon sipedon/28b0cc17a6720eb587de8f1e3e3d5ed5.jpg\",\n  \"144858.jpg\": \"Reptilia/Nerodia sipedon sipedon/5bee4bad07c774a6085571150f3e2de1.jpg\",\n  \"144867.jpg\": \"Reptilia/Nerodia sipedon sipedon/8ce817475e05354f77e37cc4e4f50579.jpg\",\n  \"144872.jpg\": \"Reptilia/Nerodia sipedon sipedon/a8365ba25a32ef9d8aedb4cf08971795.jpg\",\n  \"144874.jpg\": \"Reptilia/Nerodia sipedon sipedon/9c3220a351ad6b8f2e2776e21e56ff07.jpg\",\n  \"144875.jpg\": \"Reptilia/Nerodia sipedon sipedon/8598a6e73ec504748d63862fb887bc97.jpg\",\n  \"144876.jpg\": \"Reptilia/Nerodia sipedon sipedon/e5d884482be368a3e5fa64379f16c06b.jpg\",\n  \"144884.jpg\": \"Reptilia/Nerodia sipedon sipedon/b46d2991af8427eb4556bce6e8cdc3ad.jpg\",\n  \"144885.jpg\": \"Reptilia/Nerodia sipedon sipedon/d25defc48a4cdb8f86ddbc314ce01031.jpg\",\n  \"144891.jpg\": \"Reptilia/Nerodia sipedon sipedon/6a98260262bd5ae33c78433867c03169.jpg\",\n  \"144892.jpg\": \"Reptilia/Nerodia sipedon sipedon/86d9754717051f01a52b86e5b123bfef.jpg\",\n  \"144895.jpg\": \"Reptilia/Nerodia sipedon sipedon/6da5ec54074f86dab0da07c162fd3817.jpg\",\n  \"144902.jpg\": \"Reptilia/Nerodia sipedon sipedon/d08746d44bce86f968952c42350d828a.jpg\",\n  \"144909.jpg\": \"Reptilia/Nerodia sipedon sipedon/e0506af713868616a4d16885386aecac.jpg\",\n  \"144910.jpg\": \"Reptilia/Nerodia sipedon sipedon/60984d02564fe78e4d0d5b1678c22883.jpg\",\n  \"144911.jpg\": \"Reptilia/Nerodia sipedon sipedon/bc934014b48f535ab3726c3980e351b9.jpg\",\n  \"144919.jpg\": \"Reptilia/Nerodia sipedon sipedon/6ce9c6ec8c6d55178629f6614761215e.jpg\",\n  \"144920.jpg\": \"Reptilia/Nerodia sipedon sipedon/aa067696d4bafef18368e4dab5b29130.jpg\",\n  \"144921.jpg\": \"Reptilia/Nerodia sipedon sipedon/eb2cd1d75de677feaf8825a062b9ed7c.jpg\",\n  \"144925.jpg\": \"Reptilia/Nerodia sipedon sipedon/1c6597a193afbfe9c638ca0bf72cb37b.jpg\",\n  \"144929.jpg\": \"Reptilia/Nerodia sipedon sipedon/0b668999212a839698b9fbb947de7c34.jpg\",\n  \"144930.jpg\": \"Reptilia/Nerodia sipedon sipedon/8cf34cab20339bc2d6a2e06da6629994.jpg\",\n  \"14495.jpg\": \"Aves/Sitta carolinensis/d85297a232eef93449f22b17f58092a3.jpg\",\n  \"144950.jpg\": \"Reptilia/Nerodia sipedon sipedon/b864e7b77c406b25a288114ec61fea58.jpg\",\n  \"144953.jpg\": \"Reptilia/Nerodia sipedon sipedon/dd1f56d89ed8beb211f02c2b0000c20f.jpg\",\n  \"144955.jpg\": \"Amphibia/Notophthalmus viridescens/e70e78f684d578c1d35fed3c2a338903.jpg\",\n  \"144959.jpg\": \"Amphibia/Notophthalmus viridescens/7a2e1156c2e6f451e26488df520bfd94.jpg\",\n  \"144965.jpg\": \"Amphibia/Notophthalmus viridescens/c1a53278267b34454af834599639173c.jpg\",\n  \"144988.jpg\": \"Amphibia/Notophthalmus viridescens/ad1b5591bb96c9ccef7e7b994ca0fcd0.jpg\",\n  \"14500.jpg\": \"Aves/Sitta carolinensis/29c4f958e23fc3db4cef47416c5245fa.jpg\",\n  \"145000.jpg\": \"Amphibia/Notophthalmus viridescens/1f3cb49892c2de090853636343367365.jpg\",\n  \"145017.jpg\": \"Amphibia/Notophthalmus viridescens/0ca7feca4be20fbe7e810822f9866e70.jpg\",\n  \"145077.jpg\": \"Amphibia/Notophthalmus viridescens/719e68105f5b4ae4731cbc2c6041f2b9.jpg\",\n  \"145087.jpg\": \"Amphibia/Notophthalmus viridescens/1470c156e8e621afc22c3cb40783973e.jpg\",\n  \"145089.jpg\": \"Amphibia/Notophthalmus viridescens/ed38a85c520a59bd361163a3d05c689d.jpg\",\n  \"145101.jpg\": \"Amphibia/Notophthalmus viridescens/6223f6739d0071b18998331bb8657ec2.jpg\",\n  \"145121.jpg\": \"Amphibia/Notophthalmus viridescens/397d35b79b0fb0302f3d948bb393fa3c.jpg\",\n  \"145126.jpg\": \"Amphibia/Notophthalmus viridescens/6d1a1528a2a3217149ce29a0f8f7f15f.jpg\",\n  \"145128.jpg\": \"Amphibia/Notophthalmus viridescens/9c1e6bce52ffc0e81f303242c6e16ca7.jpg\",\n  \"145161.jpg\": \"Amphibia/Notophthalmus viridescens/427e1b1acd2b1250509ceeb048ec7681.jpg\",\n  \"145162.jpg\": \"Amphibia/Notophthalmus viridescens/825cc3b0fdfddd4a8ae44692fe29069e.jpg\",\n  \"145167.jpg\": \"Amphibia/Notophthalmus viridescens/b3b2738ef468568125116a01c1a03c8a.jpg\",\n  \"14518.jpg\": \"Aves/Sitta carolinensis/06c6b3c8f56df75775ca65d62e12cdbb.jpg\",\n  \"145217.jpg\": \"Amphibia/Notophthalmus viridescens/9eec2c48b36bcebb515d8ad4872ed2b9.jpg\",\n  \"145221.jpg\": \"Amphibia/Notophthalmus viridescens/1607a00ce57e6f1963a40ec878c35e54.jpg\",\n  \"145230.jpg\": \"Amphibia/Notophthalmus viridescens/624c5e722ac41c63ab143ac362b14011.jpg\",\n  \"145237.jpg\": \"Amphibia/Notophthalmus viridescens/7998cba3cca85b0f880f7605002f3fb6.jpg\",\n  \"145278.jpg\": \"Reptilia/Sceloporus tristichus/c3f0906e3be483329b7e184f3db57faa.jpg\",\n  \"14528.jpg\": \"Aves/Sitta carolinensis/4e0c3acb3741f0798ba511ec4e264a50.jpg\",\n  \"145281.jpg\": \"Reptilia/Sceloporus tristichus/ca255d7916872cc748732f0074a76726.jpg\",\n  \"145287.jpg\": \"Reptilia/Sceloporus tristichus/a93c4de286dbbfd82f161a423a4bcbaf.jpg\",\n  \"145289.jpg\": \"Reptilia/Sceloporus tristichus/6f29b06ca43b1c622eff6e8b916362f9.jpg\",\n  \"145300.jpg\": \"Reptilia/Sceloporus tristichus/d19ab00dc548be022d2ad4dc4ac144ba.jpg\",\n  \"145304.jpg\": \"Reptilia/Sceloporus tristichus/505693d549e2d0e978dfe0a3b1e01a7e.jpg\",\n  \"145305.jpg\": \"Reptilia/Sceloporus tristichus/af5c741c946fe87caf721b1a121419d7.jpg\",\n  \"145310.jpg\": \"Reptilia/Sceloporus tristichus/c1d0deca45743450a3dc3b70cc6ded01.jpg\",\n  \"145339.jpg\": \"Reptilia/Pituophis deppei/2a6aecef50a8d5608d7ca44cc8e06b48.jpg\",\n  \"14534.jpg\": \"Aves/Sitta carolinensis/350b9695252dfe06c4e11c8ede035b2b.jpg\",\n  \"145340.jpg\": \"Reptilia/Pituophis deppei/2a03b6eccffc3fe97b3cc12d9e1576bf.jpg\",\n  \"145349.jpg\": \"Reptilia/Pituophis deppei/8c98b83ed06b9c2f2fc912daa99eb317.jpg\",\n  \"145357.jpg\": \"Insecta/Catocala maestosa/ff1132101d3a3222fdc7edd3b186998c.jpg\",\n  \"145361.jpg\": \"Insecta/Catocala maestosa/02bf96028a0f695a1de53e0b3184a79f.jpg\",\n  \"145364.jpg\": \"Insecta/Catocala maestosa/bc2e11b7e268cdfc349565d319813ec4.jpg\",\n  \"145371.jpg\": \"Insecta/Catocala maestosa/0d267a02f46540f0202dc1eae5f2f09e.jpg\",\n  \"145376.jpg\": \"Insecta/Homalodisca vitripennis/a94899c0e7f6ea44c6f7f1029d28be31.jpg\",\n  \"145377.jpg\": \"Insecta/Homalodisca vitripennis/67dcf33aa3a6435852578619c0243fe5.jpg\",\n  \"145378.jpg\": \"Insecta/Homalodisca vitripennis/5a180b70174ed40454d52cbc50bf4f4f.jpg\",\n  \"145382.jpg\": \"Insecta/Homalodisca vitripennis/c3c2cb56c2ac9e6740c265c39ae2f6f3.jpg\",\n  \"145383.jpg\": \"Insecta/Homalodisca vitripennis/e3a12ccab04b94804a50537b22f5f56b.jpg\",\n  \"145391.jpg\": \"Insecta/Homalodisca vitripennis/c885693768bbf4dad3ec5532bd656c11.jpg\",\n  \"145394.jpg\": \"Insecta/Homalodisca vitripennis/eeca5a1d42cee2f12765916ea09f59f7.jpg\",\n  \"145399.jpg\": \"Insecta/Homalodisca vitripennis/fe392b3e6e07ddb3432c346b909a183e.jpg\",\n  \"145405.jpg\": \"Insecta/Homalodisca vitripennis/2cbc390ebb557c4db2d3106275656670.jpg\",\n  \"145406.jpg\": \"Insecta/Homalodisca vitripennis/f56fe583c7ae9ca4d92d6b8a3a4c9e29.jpg\",\n  \"145418.jpg\": \"Insecta/Homalodisca vitripennis/e126e0ef5bc83ced7743988c6ecec8af.jpg\",\n  \"145421.jpg\": \"Insecta/Homalodisca vitripennis/aa62ea7c70cea7ca62a05e2ee25fe705.jpg\",\n  \"145425.jpg\": \"Insecta/Homalodisca vitripennis/ac0583771cced57ad1302aaa633e8059.jpg\",\n  \"145442.jpg\": \"Insecta/Asterocampa celtis/03168d1bb15e35c4bb4d5aece7bbf796.jpg\",\n  \"145449.jpg\": \"Insecta/Asterocampa celtis/1d66f66d65dd9cfff613b4f110b1e058.jpg\",\n  \"145465.jpg\": \"Insecta/Asterocampa celtis/3b12d71971802ed82e0af548eb3494c4.jpg\",\n  \"145489.jpg\": \"Insecta/Asterocampa celtis/13a4cac61fbdf4c4dcc474a6fc1c7e42.jpg\",\n  \"145491.jpg\": \"Insecta/Asterocampa celtis/a979cd632c5329c28db253d453427bce.jpg\",\n  \"145492.jpg\": \"Insecta/Asterocampa celtis/0d643471a467d0d5b17e6cd28b363576.jpg\",\n  \"145493.jpg\": \"Insecta/Asterocampa celtis/70e68575c662ca01723566b7b946414d.jpg\",\n  \"145501.jpg\": \"Insecta/Asterocampa celtis/6580e819547da912a1fa9a8469b9cd7e.jpg\",\n  \"145529.jpg\": \"Insecta/Asterocampa celtis/73c9774500bffddd63c9e57920635df9.jpg\",\n  \"145546.jpg\": \"Insecta/Asterocampa celtis/f6c641e046d97020882e4abc52afe115.jpg\",\n  \"145561.jpg\": \"Insecta/Asterocampa celtis/7b45c159226cde27a46236591e83be88.jpg\",\n  \"145562.jpg\": \"Insecta/Asterocampa celtis/45d3d479da7b78d8fd3d76899d99d740.jpg\",\n  \"145577.jpg\": \"Insecta/Asterocampa celtis/36d5e208576d381a25379fb8001dbdc7.jpg\",\n  \"145594.jpg\": \"Insecta/Asterocampa celtis/9798c78f8a25c1a780af2cbc82005903.jpg\",\n  \"145604.jpg\": \"Insecta/Asterocampa celtis/7006816e6dfb548a81288a128b4d426f.jpg\",\n  \"145636.jpg\": \"Insecta/Asterocampa celtis/0c727785e6c0b7254c73d7e2b4e70ecf.jpg\",\n  \"145643.jpg\": \"Insecta/Asterocampa celtis/803116333bff88a9592716150ddb0602.jpg\",\n  \"145648.jpg\": \"Insecta/Asterocampa celtis/9133ec09d506fcd892b4f14370dee56b.jpg\",\n  \"14565.jpg\": \"Aves/Sitta carolinensis/7f877e2a9e65e5878e2de89d621eb122.jpg\",\n  \"145659.jpg\": \"Insecta/Asterocampa celtis/e784dae17251c8d3be10bc470b120f44.jpg\",\n  \"145669.jpg\": \"Insecta/Asterocampa celtis/dfb67c5752cb08850bbf32a8b64eaad7.jpg\",\n  \"145682.jpg\": \"Insecta/Asterocampa celtis/e99e1bd41ef2a38dcf802fc8459278c6.jpg\",\n  \"145688.jpg\": \"Insecta/Asterocampa celtis/6992e1f2c433d3e611d9ea56238b401a.jpg\",\n  \"145868.jpg\": \"Mollusca/Mytilus californianus/18ce00a8b24ef61a8c5530ebb1640506.jpg\",\n  \"145894.jpg\": \"Mollusca/Mytilus californianus/fba8e3c7b4f4c2285278ad742dbd33a0.jpg\",\n  \"145905.jpg\": \"Mollusca/Mytilus californianus/1db6369df6ed7e008819001e48413901.jpg\",\n  \"145944.jpg\": \"Mollusca/Mytilus californianus/a2aef96c3fca6a959e0d02d1c183d92d.jpg\",\n  \"145998.jpg\": \"Insecta/Limenitis reducta/1840cd9d2bbeeb442cb09e925cc2e8c7.jpg\",\n  \"146003.jpg\": \"Insecta/Limenitis reducta/bcf9f4931dce04be69618701731dfea9.jpg\",\n  \"146009.jpg\": \"Insecta/Limenitis reducta/31b52ef807c9c0f75e51b7f33316bd28.jpg\",\n  \"146010.jpg\": \"Insecta/Limenitis reducta/74c874504934206a7241aeab34b722c0.jpg\",\n  \"146015.jpg\": \"Insecta/Limenitis reducta/c3dd8f9872ec1a7a0998363e46092e4e.jpg\",\n  \"146018.jpg\": \"Insecta/Limenitis reducta/5f529ffda99512d99b675bbec83caef1.jpg\",\n  \"146071.jpg\": \"Mammalia/Mustela frenata/86174f90bfe5e6c28f21b9217cfdb4c9.jpg\",\n  \"146078.jpg\": \"Mammalia/Mustela frenata/7708d6dda1428c0e2f11c64ea7553c4f.jpg\",\n  \"146079.jpg\": \"Mammalia/Mustela frenata/6e1c4546d629a890b7c252487d32ca4a.jpg\",\n  \"146088.jpg\": \"Mammalia/Mustela frenata/3db483350b069fc4d04d77c9b6156a05.jpg\",\n  \"146099.jpg\": \"Mammalia/Mustela frenata/51bcad75ca5c549ef7b66764b2767fcb.jpg\",\n  \"146100.jpg\": \"Mammalia/Mustela frenata/401a7c7c25e2fccb1f91e43ccbab5040.jpg\",\n  \"146101.jpg\": \"Mammalia/Mustela frenata/11f895488e326a55ab369429520159bc.jpg\",\n  \"146102.jpg\": \"Mammalia/Mustela frenata/b088a19c9286262476fb47e34a6fa45f.jpg\",\n  \"146108.jpg\": \"Insecta/Bleptina caradrinalis/4a172f607f6b9e2ae1ae41099450c446.jpg\",\n  \"146114.jpg\": \"Insecta/Bleptina caradrinalis/efff4e996e2a06e768e4318729b02186.jpg\",\n  \"146116.jpg\": \"Insecta/Bleptina caradrinalis/fc191994db654c5858d31bd10b98ddc1.jpg\",\n  \"146117.jpg\": \"Insecta/Bleptina caradrinalis/f560668eb2e428b4d77bcdf5b05eea11.jpg\",\n  \"146120.jpg\": \"Insecta/Bleptina caradrinalis/7cf1e6141ce4fe165646e24770bf1ad5.jpg\",\n  \"146126.jpg\": \"Insecta/Chrysochus cobaltinus/8c8478755d05fb09e129fa48e3332287.jpg\",\n  \"146134.jpg\": \"Insecta/Chrysochus cobaltinus/1dad5817e4f81f1d4f7ea5730f6cf3d5.jpg\",\n  \"146137.jpg\": \"Insecta/Chrysochus cobaltinus/4d1b81ef4b4bf44dcb1a87594d16dcc8.jpg\",\n  \"146139.jpg\": \"Insecta/Chrysochus cobaltinus/6595dc58f60f08a8f8f1e8c537a6d880.jpg\",\n  \"146156.jpg\": \"Aves/Charadrius melodus/c57a349b506e238758fd86efeaf629db.jpg\",\n  \"146159.jpg\": \"Aves/Charadrius melodus/93d6902d3f25e6c3204db09d8d17f6fd.jpg\",\n  \"146160.jpg\": \"Aves/Charadrius melodus/cde10b79e90df935caded2503bb21142.jpg\",\n  \"146161.jpg\": \"Aves/Charadrius melodus/de54453f0ae1bd948e2b180ae5126a2e.jpg\",\n  \"146162.jpg\": \"Aves/Charadrius melodus/23497c362bba2530ff400b8c8bf5145f.jpg\",\n  \"146165.jpg\": \"Aves/Charadrius melodus/45c917fe28e6478ca62353d927c044c1.jpg\",\n  \"146169.jpg\": \"Aves/Charadrius melodus/46b4b5416d7bae4de4e456607c58e999.jpg\",\n  \"146172.jpg\": \"Aves/Charadrius melodus/585bd0787151b7a9e9c2061ac4aff916.jpg\",\n  \"146173.jpg\": \"Aves/Charadrius melodus/461f3fd5cc40c6c93cd79c7bc508f678.jpg\",\n  \"146174.jpg\": \"Aves/Charadrius melodus/744dc982e97427b84bef3f37c6fc00d3.jpg\",\n  \"146187.jpg\": \"Aves/Charadrius melodus/1b079a254ae4428351e5ae4bed644b6d.jpg\",\n  \"146188.jpg\": \"Aves/Charadrius melodus/6514aeb2741f63ac187c664dd3fb7eb2.jpg\",\n  \"146190.jpg\": \"Aves/Charadrius melodus/08532d31968a4be00c9415c9a401ff6f.jpg\",\n  \"146194.jpg\": \"Aves/Charadrius melodus/c551a14d0e52c9945ff48dc526f1fac6.jpg\",\n  \"146195.jpg\": \"Aves/Charadrius melodus/c4b08fb4e125a33effff8fe013eef9f6.jpg\",\n  \"146199.jpg\": \"Aves/Charadrius melodus/d018a3f6ca8c4f01ba0203bff3bbd2c5.jpg\",\n  \"146201.jpg\": \"Aves/Charadrius melodus/876e87d8c55b2c719bb029946ae3622b.jpg\",\n  \"146210.jpg\": \"Aves/Charadrius melodus/2e27014c2964450ee6efd858064f4033.jpg\",\n  \"146218.jpg\": \"Aves/Charadrius melodus/71ba3fc91acf54cc332203da06f190c8.jpg\",\n  \"146219.jpg\": \"Aves/Charadrius melodus/28b618e4bcd2ca044d66504fb9860346.jpg\",\n  \"146227.jpg\": \"Aves/Melospiza lincolnii/c709491dff392f3068d248eadf2a931e.jpg\",\n  \"146255.jpg\": \"Aves/Melospiza lincolnii/d2fbcedaf82a02ce23c7e99266deec16.jpg\",\n  \"146256.jpg\": \"Aves/Melospiza lincolnii/3b47a8bb73d611c2d8a28663e19deb2e.jpg\",\n  \"146268.jpg\": \"Aves/Melospiza lincolnii/a146f205498034a359cedb3c55004d9f.jpg\",\n  \"146271.jpg\": \"Aves/Melospiza lincolnii/68a13da65fffd97b32bfec13e8fafeb7.jpg\",\n  \"146288.jpg\": \"Aves/Melospiza lincolnii/ebad794381baa0c7831b04ad7ad42992.jpg\",\n  \"146292.jpg\": \"Aves/Melospiza lincolnii/a5461bf0ca988fe38934e6e0fea65f49.jpg\",\n  \"146294.jpg\": \"Aves/Melospiza lincolnii/58add1d6f5f0c689568af2a033ff4cdb.jpg\",\n  \"146295.jpg\": \"Aves/Melospiza lincolnii/8c5ffcd9f3ba71e49a5f3029d94a8955.jpg\",\n  \"146303.jpg\": \"Aves/Melospiza lincolnii/fd1bf3118990e5e92ad5471e26aaa2bb.jpg\",\n  \"146304.jpg\": \"Aves/Melospiza lincolnii/e970f520c688a81426c6791c1f950fd3.jpg\",\n  \"146305.jpg\": \"Aves/Melospiza lincolnii/cdc144d0e2bd9901d7ac01d94ffc3bd4.jpg\",\n  \"146307.jpg\": \"Aves/Melospiza lincolnii/dacbe9c0b2f40d521b026e7e57c85133.jpg\",\n  \"146322.jpg\": \"Aves/Melospiza lincolnii/38fef434ddc96bddbcb90ac6c28b591d.jpg\",\n  \"146350.jpg\": \"Aves/Melospiza lincolnii/307d88a635eff85fb388c1be10d76314.jpg\",\n  \"146358.jpg\": \"Aves/Melospiza lincolnii/eb79f79a6157150ffd11f5c668b6b0ea.jpg\",\n  \"146359.jpg\": \"Aves/Melospiza lincolnii/74d68e09f787857fc206a73e9c6a80b1.jpg\",\n  \"146370.jpg\": \"Aves/Melospiza lincolnii/098917f075154da0c73e27c5725a87e4.jpg\",\n  \"146378.jpg\": \"Aves/Melospiza lincolnii/4f862e145a43ba2cd4f8820709cfeda5.jpg\",\n  \"146386.jpg\": \"Aves/Melospiza lincolnii/2e099f96dbf24dd7f4109b056254eff1.jpg\",\n  \"146388.jpg\": \"Aves/Melospiza lincolnii/409456176e6e68ed87c4236915b0b046.jpg\",\n  \"146394.jpg\": \"Aves/Melospiza lincolnii/d0eebc48e4fe4f932e17fa93178262e6.jpg\",\n  \"146420.jpg\": \"Aves/Melospiza lincolnii/022f9b649e698d166d5751b3928722fa.jpg\",\n  \"146424.jpg\": \"Aves/Melospiza lincolnii/0bb49ae7a30e9d9cf3e931d430e8df99.jpg\",\n  \"14643.jpg\": \"Aves/Sitta carolinensis/f7aca4a069851d9ae983686c4c9b313d.jpg\",\n  \"146433.jpg\": \"Aves/Melospiza lincolnii/d534d400fd71bc1c484ebd4181b1d8aa.jpg\",\n  \"146436.jpg\": \"Insecta/Vespula germanica/688b7b8b3cb199c8c257ec252d7c448a.jpg\",\n  \"146449.jpg\": \"Insecta/Vespula germanica/55561c774c0ec5932220eada3289fea0.jpg\",\n  \"146453.jpg\": \"Insecta/Vespula germanica/8c2d60bc8a7122bc8446150480a2d06a.jpg\",\n  \"146454.jpg\": \"Insecta/Vespula germanica/c27c75e30b42f78fb4fa1963c442567d.jpg\",\n  \"146455.jpg\": \"Insecta/Limenitis weidemeyerii/ba72e1b3beeef0e3ace9b73bf38b02f6.jpg\",\n  \"146459.jpg\": \"Insecta/Limenitis weidemeyerii/53571cd6d6e24a9385ed3c4fd2c8c588.jpg\",\n  \"146468.jpg\": \"Insecta/Limenitis weidemeyerii/06404d0beb4875be608986a5eb881508.jpg\",\n  \"146470.jpg\": \"Insecta/Limenitis weidemeyerii/29eb5b508fe9b867e72d87caf3b1f76f.jpg\",\n  \"146475.jpg\": \"Insecta/Limenitis weidemeyerii/02e189ccea024db42e0304433670d914.jpg\",\n  \"146676.jpg\": \"Insecta/Dyspteris abortivaria/c71079741061e1253a6604bc5640f517.jpg\",\n  \"146677.jpg\": \"Insecta/Dyspteris abortivaria/a0949bddec2161f07919f32961e13393.jpg\",\n  \"146681.jpg\": \"Insecta/Dyspteris abortivaria/66f98ebc7d130618c3c2ae164477558d.jpg\",\n  \"146684.jpg\": \"Insecta/Dyspteris abortivaria/e522327dbd34b8c944c633c6cbfe1dc1.jpg\",\n  \"146687.jpg\": \"Insecta/Dyspteris abortivaria/e2d36c4e8a92f9ca6383e29b20fa5649.jpg\",\n  \"146689.jpg\": \"Insecta/Dyspteris abortivaria/c2e2c10ec8db86e1ce68fdc5f1d717da.jpg\",\n  \"146690.jpg\": \"Insecta/Dyspteris abortivaria/d483564491e358f4297ac245c120344f.jpg\",\n  \"146712.jpg\": \"Reptilia/Pituophis catenifer/8137ff799286ed6cbea72568615acd96.jpg\",\n  \"146713.jpg\": \"Reptilia/Pituophis catenifer/813923a1666e9016750753fea90ec4cd.jpg\",\n  \"146786.jpg\": \"Reptilia/Pituophis catenifer/71d279e4469e6c5e07eb9321ff081c8d.jpg\",\n  \"146840.jpg\": \"Reptilia/Pituophis catenifer/4b1f1964dfdcb795b373bae226163f6c.jpg\",\n  \"146876.jpg\": \"Reptilia/Pituophis catenifer/8814a92b148e1b6657cf1e931a20ee03.jpg\",\n  \"146903.jpg\": \"Reptilia/Pituophis catenifer/37f0390e60c1c7f084a84543d711193a.jpg\",\n  \"146916.jpg\": \"Reptilia/Pituophis catenifer/855b404cb25683613550466b361ec054.jpg\",\n  \"146918.jpg\": \"Reptilia/Pituophis catenifer/fcf6e34ea911e108db059dbcf6d1e9c0.jpg\",\n  \"146937.jpg\": \"Reptilia/Pituophis catenifer/2d5109da752634b2c06a4992baae2505.jpg\",\n  \"14694.jpg\": \"Aves/Sitta carolinensis/97e982bbdc02efd3251db393e18d5bd9.jpg\",\n  \"146942.jpg\": \"Reptilia/Pituophis catenifer/c755f2e44632b1e9c6df1eaecca73e6d.jpg\",\n  \"146963.jpg\": \"Reptilia/Pituophis catenifer/22ef3875b2c6c5bde9ae01ffc81f1a11.jpg\",\n  \"147011.jpg\": \"Reptilia/Pituophis catenifer/bb356b8a769c23b8f05bd2bc3b448b89.jpg\",\n  \"147021.jpg\": \"Reptilia/Pituophis catenifer/16b00b104eda729dc76841217dff4149.jpg\",\n  \"147029.jpg\": \"Reptilia/Pituophis catenifer/6b4ff24fd4f7048edf40d04343f706d1.jpg\",\n  \"147038.jpg\": \"Reptilia/Pituophis catenifer/6a1cbda4e03e8bf10f55bd76c109c9fd.jpg\",\n  \"147044.jpg\": \"Reptilia/Pituophis catenifer/2d1714b891eb6b75e9f052419a206333.jpg\",\n  \"14707.jpg\": \"Aves/Sitta carolinensis/665afc37b71d0aa45451f03b95a5d37f.jpg\",\n  \"147098.jpg\": \"Aves/Caracara plancus/50306278ccad6f60a5865d6ca4576140.jpg\",\n  \"147102.jpg\": \"Aves/Caracara plancus/5eac267b24e9fbeb504236a0b20554cb.jpg\",\n  \"147103.jpg\": \"Aves/Caracara plancus/928fc803fcc284b8456170ba199452a6.jpg\",\n  \"147182.jpg\": \"Insecta/Polistes metricus/e3b2398297483f49b61dee964de66fcc.jpg\",\n  \"147187.jpg\": \"Insecta/Polistes metricus/d9ba42531f01a006e900443cff093268.jpg\",\n  \"147191.jpg\": \"Insecta/Polistes metricus/a45ef589c63747cacb3bcd6e46fe1961.jpg\",\n  \"147192.jpg\": \"Insecta/Polistes metricus/5687500411bbf6f742b1fe06c2b61a0a.jpg\",\n  \"147197.jpg\": \"Insecta/Polistes metricus/0cee94ee08e709c9a575c728c0cd5915.jpg\",\n  \"147199.jpg\": \"Insecta/Polistes metricus/a11e37fdf12ecea5c40c2ddf337a1084.jpg\",\n  \"147200.jpg\": \"Insecta/Polistes metricus/081371ae9fb43464541c445760fd62d4.jpg\",\n  \"147205.jpg\": \"Insecta/Polistes metricus/fa9cf4eb32c034201dddd2c06075b89c.jpg\",\n  \"147226.jpg\": \"Insecta/Polistes metricus/079088ee7d4999f610bca350c3dca386.jpg\",\n  \"147227.jpg\": \"Insecta/Polistes metricus/7250433a627cf7267cecbde302e23e02.jpg\",\n  \"147228.jpg\": \"Insecta/Polistes metricus/6713791a38834b0dff9c00e4aea00b22.jpg\",\n  \"147239.jpg\": \"Insecta/Polistes metricus/abe202041f1d58cf3f850a7ab60cd7e1.jpg\",\n  \"147244.jpg\": \"Insecta/Polistes metricus/4ac29481817b9e4225ea3b8ec4b22dbc.jpg\",\n  \"147245.jpg\": \"Insecta/Polistes metricus/58871c0713ffb767dfbec38362a17a8a.jpg\",\n  \"147246.jpg\": \"Insecta/Polistes metricus/833e0e4a954f8a7b56b03eeb5edaf24a.jpg\",\n  \"147247.jpg\": \"Insecta/Polistes metricus/4915ed7d78dc51a826d999bc07b2783f.jpg\",\n  \"147249.jpg\": \"Insecta/Polistes metricus/64f83879c96c7194e6cb257423906aa4.jpg\",\n  \"14727.jpg\": \"Aves/Sitta carolinensis/4808ce68a00b2a8c207048fc3d88342c.jpg\",\n  \"14733.jpg\": \"Aves/Sitta carolinensis/e755378ed4f9faf298ffaec04a35dfa4.jpg\",\n  \"147354.jpg\": \"Insecta/Blepharomastix ranalis/e0d8902987b3e4b50c7f525d04d30efb.jpg\",\n  \"147355.jpg\": \"Insecta/Blepharomastix ranalis/fe898be5f117160cbcb51918835d4a1b.jpg\",\n  \"147359.jpg\": \"Insecta/Blepharomastix ranalis/f3c03e4f56896ec0b406302c509d0a0a.jpg\",\n  \"147483.jpg\": \"Insecta/Hyalophora cecropia/bc2f17ee6d41cd7b358374f5abbc6f30.jpg\",\n  \"147484.jpg\": \"Insecta/Hyalophora cecropia/d039f6973fa0a70bc9dfc82dd8de4c8c.jpg\",\n  \"147488.jpg\": \"Insecta/Hyalophora cecropia/39bc9ca0570a017b1ebadf087e1f3b38.jpg\",\n  \"147489.jpg\": \"Insecta/Hyalophora cecropia/d3e6b8ba285db3e8db9ff56d1f4fee16.jpg\",\n  \"147490.jpg\": \"Insecta/Hyalophora cecropia/7463c699a5d0cee130120468b79ab050.jpg\",\n  \"147491.jpg\": \"Insecta/Hyalophora cecropia/737ddd89c28ebcc58096c4122fa2ec8b.jpg\",\n  \"147495.jpg\": \"Insecta/Hyalophora cecropia/d316f44610f9de1a0d1c6e1fe886e35f.jpg\",\n  \"147497.jpg\": \"Insecta/Hyalophora cecropia/51c6c34c59722770e15bee5ae4d878c0.jpg\",\n  \"147498.jpg\": \"Insecta/Hyalophora cecropia/f6342662ade3fa76a0eedfa72aa65998.jpg\",\n  \"147506.jpg\": \"Insecta/Hyalophora cecropia/d2b49e27b529af8bbd6aabf7b2a7806d.jpg\",\n  \"147507.jpg\": \"Insecta/Hyalophora cecropia/bef660cc11393f337de86a60102b4ba0.jpg\",\n  \"14751.jpg\": \"Aves/Sitta carolinensis/fdf7e16fe1e06daa117844c64a984e49.jpg\",\n  \"147511.jpg\": \"Insecta/Hyalophora cecropia/f8680323b202b0dc4dce4454b3c43941.jpg\",\n  \"147513.jpg\": \"Insecta/Hyalophora cecropia/0d265f0be3a5b6acd9bd6126e21ac635.jpg\",\n  \"147516.jpg\": \"Insecta/Hyalophora cecropia/78a63a879fe427bcafefa19b35199f05.jpg\",\n  \"147517.jpg\": \"Insecta/Hyalophora cecropia/eae4248469ababcb250bf2e204ec1066.jpg\",\n  \"147518.jpg\": \"Insecta/Hyalophora cecropia/50eb670b75a9c0ed37e2d88381e08d5f.jpg\",\n  \"147519.jpg\": \"Insecta/Hyalophora cecropia/a1df3ed77f2c124dd8897d7c7650d2a3.jpg\",\n  \"147522.jpg\": \"Insecta/Hyalophora cecropia/29e844924e36a3de8cf1173a621c4570.jpg\",\n  \"147523.jpg\": \"Insecta/Hyalophora cecropia/abe83a4f8144a94dd30c33dec05d2065.jpg\",\n  \"147525.jpg\": \"Insecta/Hyalophora cecropia/5abdd071022280a57ad732d2acd97769.jpg\",\n  \"147528.jpg\": \"Insecta/Hyalophora cecropia/be09854a119e58c1d99866b9ea82e91c.jpg\",\n  \"147531.jpg\": \"Insecta/Hyalophora cecropia/aa1ed567268412e44e02165d908a7ce7.jpg\",\n  \"147532.jpg\": \"Insecta/Hyalophora cecropia/3ce37e8b1289fb07da960504b892855a.jpg\",\n  \"147562.jpg\": \"Reptilia/Charina bottae/4c1f11302e9d864fd6ac21a00f355ed1.jpg\",\n  \"147563.jpg\": \"Reptilia/Charina bottae/04c781f20636e69559b75420dd7ae81d.jpg\",\n  \"147564.jpg\": \"Reptilia/Charina bottae/458bff947c19e90662b839e58bce6f18.jpg\",\n  \"147565.jpg\": \"Reptilia/Charina bottae/1daa8db9b3e5b9523501bebdd8ecfb80.jpg\",\n  \"147566.jpg\": \"Reptilia/Charina bottae/2bdb32cb17d2df260a813633dbd0aa37.jpg\",\n  \"147570.jpg\": \"Reptilia/Charina bottae/6a2e44f1d409dc6f8e2aa0f08d525824.jpg\",\n  \"147571.jpg\": \"Reptilia/Charina bottae/62909791fba830d08d0505107d910a3f.jpg\",\n  \"147573.jpg\": \"Reptilia/Charina bottae/058eadfe4a45993a4349f1271eadee15.jpg\",\n  \"147574.jpg\": \"Reptilia/Charina bottae/ac5ce660f2aa0a2d86ae446161522d87.jpg\",\n  \"147575.jpg\": \"Reptilia/Charina bottae/269b3e3c4a6c73d22a6cfc9d25717bc3.jpg\",\n  \"147576.jpg\": \"Reptilia/Charina bottae/bba4b8f9b04b22e1094a1d8b35491af2.jpg\",\n  \"147581.jpg\": \"Reptilia/Charina bottae/84f850bae948e986a82bccd41c3fac79.jpg\",\n  \"147582.jpg\": \"Reptilia/Charina bottae/30387fce04363aa55b90e3613396a068.jpg\",\n  \"147585.jpg\": \"Reptilia/Charina bottae/2f45cd9ea849a90443d847efe19326a7.jpg\",\n  \"147592.jpg\": \"Reptilia/Charina bottae/f82a8c9a808d4b4fa881b342567b3517.jpg\",\n  \"147593.jpg\": \"Reptilia/Charina bottae/f2ebe9be8891137ece28272eb8272978.jpg\",\n  \"147594.jpg\": \"Reptilia/Charina bottae/523ac3e9472c3486868bfad99cbfe766.jpg\",\n  \"147596.jpg\": \"Reptilia/Charina bottae/3bfa906b4448c5454686109cdea0244b.jpg\",\n  \"147597.jpg\": \"Reptilia/Charina bottae/c42a69e5d5a3dd3c113195a088af22f2.jpg\",\n  \"147598.jpg\": \"Reptilia/Charina bottae/87b6b6ffd9ac30d8a136e5edcd71ff1c.jpg\",\n  \"147599.jpg\": \"Reptilia/Charina bottae/4b3c62fa7d8c19bcee8cdc573748b928.jpg\",\n  \"14765.jpg\": \"Aves/Sitta carolinensis/1eaf37d10075ec3f80e783054ab8402d.jpg\",\n  \"147675.jpg\": \"Aves/Tringa semipalmata/0acfe07c253dc57123b3d8557f7ccde1.jpg\",\n  \"14769.jpg\": \"Aves/Sitta carolinensis/f896b48f983ac6d947a2d6a19ddf8045.jpg\",\n  \"147739.jpg\": \"Aves/Tringa semipalmata/baa8a4edfaade6f9d2720a1ad3b91598.jpg\",\n  \"147765.jpg\": \"Aves/Tringa semipalmata/eb359e16e12f95ae21b795f089acedc9.jpg\",\n  \"147784.jpg\": \"Aves/Tringa semipalmata/400b2a5767554cf2a7c56a52503b56be.jpg\",\n  \"147805.jpg\": \"Aves/Tringa semipalmata/ad49254079dec166f6742c8064181e01.jpg\",\n  \"147806.jpg\": \"Aves/Tringa semipalmata/34f682580c9de4bd74ff88098e41f147.jpg\",\n  \"147819.jpg\": \"Aves/Tringa semipalmata/ce8d9d78995d3d8258961660049acc18.jpg\",\n  \"147826.jpg\": \"Aves/Tringa semipalmata/1cd0b673df496ceab6ffe29d0e692ab4.jpg\",\n  \"147827.jpg\": \"Aves/Tringa semipalmata/acff72b6a3935c2a111cb44c2780f193.jpg\",\n  \"147880.jpg\": \"Aves/Tringa semipalmata/dedb6281040a375efe4e12d36df05bd4.jpg\",\n  \"147885.jpg\": \"Aves/Tringa semipalmata/e78924201880a052d1cff47fab934db6.jpg\",\n  \"147893.jpg\": \"Aves/Tringa semipalmata/d6bacf2728404086a4af77749afea3b5.jpg\",\n  \"147933.jpg\": \"Aves/Tringa semipalmata/0e9e5bcf26905c7ed63f20cb8864e466.jpg\",\n  \"147935.jpg\": \"Aves/Tringa semipalmata/4c62b34b0f767216422920b49952cad0.jpg\",\n  \"147943.jpg\": \"Aves/Tringa semipalmata/3ab7f4e9f324918b591fcbadb21f524e.jpg\",\n  \"147970.jpg\": \"Aves/Tringa semipalmata/d2ebb4fef9ed4b5cc7d979b9585428b7.jpg\",\n  \"147985.jpg\": \"Aves/Tringa semipalmata/e3beaff83466cab7560a3d36d9e1bceb.jpg\",\n  \"148005.jpg\": \"Aves/Tringa semipalmata/8b10b780567e7558c529d3610c412196.jpg\",\n  \"148090.jpg\": \"Aves/Tringa semipalmata/9fd814650f059005d7ddb27978a243c6.jpg\",\n  \"148109.jpg\": \"Aves/Tringa semipalmata/b99b5591d2a3dee24ab0e0c5a1053620.jpg\",\n  \"148137.jpg\": \"Aves/Tringa semipalmata/b709a46be1c75fde792204e7d0fb7eb4.jpg\",\n  \"148153.jpg\": \"Aves/Tringa semipalmata/2742e65cb18d2cb4027d3c9867541ca4.jpg\",\n  \"148170.jpg\": \"Aves/Tringa semipalmata/1f1ff3f39cefbbc840c2fd47ccde9641.jpg\",\n  \"14822.jpg\": \"Aves/Sitta carolinensis/3e879da7fa534d7f4053a59970608df3.jpg\",\n  \"148226.jpg\": \"Aves/Falco peregrinus/a856e6c6a26d7cebb2b529184a51c23a.jpg\",\n  \"148231.jpg\": \"Aves/Falco peregrinus/1daa4c343d4495346f59310e12cc6d0b.jpg\",\n  \"148240.jpg\": \"Aves/Falco peregrinus/a91aa170522c1546437b9beb9ecdfece.jpg\",\n  \"148253.jpg\": \"Aves/Falco peregrinus/0b7ee9eba4f0cc157d47724982400804.jpg\",\n  \"148264.jpg\": \"Aves/Falco peregrinus/68347a85a528def3569bc3e326e8686a.jpg\",\n  \"14828.jpg\": \"Aves/Sitta carolinensis/578e79ff7de03702f2a9f7c397136d2e.jpg\",\n  \"148281.jpg\": \"Aves/Falco peregrinus/b131ce09a2a480548365279ee10d3716.jpg\",\n  \"148301.jpg\": \"Aves/Falco peregrinus/3f829bfcee5e052a6c2b3b266a9805fc.jpg\",\n  \"148346.jpg\": \"Aves/Falco peregrinus/d977835e6e868249026027fe17bd2bae.jpg\",\n  \"148386.jpg\": \"Aves/Falco peregrinus/8c7e89e1d6c22ef0d84f40bf501dc08a.jpg\",\n  \"148388.jpg\": \"Aves/Falco peregrinus/d1e537f646ae2d195238f4b549d6e566.jpg\",\n  \"148399.jpg\": \"Aves/Falco peregrinus/dc18249c8700b61db99bbecf58462220.jpg\",\n  \"148422.jpg\": \"Aves/Falco peregrinus/a8d550e0b7ccb3405786684b2b35e7f3.jpg\",\n  \"148423.jpg\": \"Aves/Falco peregrinus/13226f2db45368f1ae56985ccd76835c.jpg\",\n  \"148432.jpg\": \"Aves/Falco peregrinus/067acaefbb199717912cb3d772ebc3df.jpg\",\n  \"148435.jpg\": \"Aves/Falco peregrinus/ae980ec0c60012fbc0fccd56e84ee055.jpg\",\n  \"148448.jpg\": \"Aves/Falco peregrinus/ad466fee781c45a30df583e861af6d7a.jpg\",\n  \"148471.jpg\": \"Aves/Falco peregrinus/31d414385b4a62e1b3a1f4c238c0a30c.jpg\",\n  \"148489.jpg\": \"Aves/Falco peregrinus/23ef7d0154ac95d6478d49a282b34d2e.jpg\",\n  \"148499.jpg\": \"Aves/Falco peregrinus/4f384904fc86ddff026919911fb091a7.jpg\",\n  \"148587.jpg\": \"Aves/Melanitta fusca/a3ac1ae679749a5f626a0b32d2aaa270.jpg\",\n  \"148597.jpg\": \"Aves/Melanitta fusca/4e3fa136b917a75c866566279a4d8738.jpg\",\n  \"148600.jpg\": \"Aves/Melanitta fusca/1e616f1a7a64b09db54930280920ae36.jpg\",\n  \"148601.jpg\": \"Aves/Melanitta fusca/fbab0d0081ceb7a9bef4b35e6e96b14a.jpg\",\n  \"148602.jpg\": \"Aves/Melanitta fusca/32691adb7fa503b7d8bcac57f5febc42.jpg\",\n  \"148605.jpg\": \"Aves/Melanitta fusca/488f18b09e3d9fb137a778c063995a6c.jpg\",\n  \"148609.jpg\": \"Aves/Melanitta fusca/6aa8809c93e9c75490043c773d0f39cb.jpg\",\n  \"148610.jpg\": \"Aves/Melanitta fusca/47cf9d3581d6f91ec2e29476b82c5fca.jpg\",\n  \"148615.jpg\": \"Aves/Melanitta fusca/c403ab30445a1dedd7ecdfef41c9b556.jpg\",\n  \"148618.jpg\": \"Aves/Melanitta fusca/d3c70bb6e2029c490ef62ed0edec2684.jpg\",\n  \"148620.jpg\": \"Aves/Melanitta fusca/f269b2f16befa7fa1b4bc0b72a55b0df.jpg\",\n  \"148623.jpg\": \"Aves/Melanitta fusca/75657a3da34aa8bedb855da4fb11c1c7.jpg\",\n  \"148625.jpg\": \"Aves/Melanitta fusca/5fa827ac9b3fd734c9c9dabe4f3a979b.jpg\",\n  \"148626.jpg\": \"Aves/Melanitta fusca/a7ea672d8bd1e47de7b6c856cbf78693.jpg\",\n  \"148653.jpg\": \"Aves/Regulus satrapa/e09abc05385a205fda661ccceb3c86fe.jpg\",\n  \"148663.jpg\": \"Aves/Regulus satrapa/896eb0e5324b9811fdc1ef900ec9881c.jpg\",\n  \"148673.jpg\": \"Aves/Regulus satrapa/81b79c4a51202a8c416f8d5d42b0e555.jpg\",\n  \"148676.jpg\": \"Aves/Regulus satrapa/e42ce4bd892d686fd8947e9c37fee71b.jpg\",\n  \"148679.jpg\": \"Aves/Regulus satrapa/2f2ba84e1867b1af21c63b533d2b1914.jpg\",\n  \"148684.jpg\": \"Aves/Regulus satrapa/49ec2e88d77af25a48edafb5b5034c62.jpg\",\n  \"148698.jpg\": \"Aves/Regulus satrapa/b4f56f8a82d3ec882e47bba4153fb51a.jpg\",\n  \"148710.jpg\": \"Aves/Regulus satrapa/b63439b2fdd2c659803f847c22e5690f.jpg\",\n  \"148714.jpg\": \"Aves/Regulus satrapa/87dcfb89cb2cc3f6a3531cf79a1c4b37.jpg\",\n  \"148716.jpg\": \"Aves/Regulus satrapa/a2a4c9f81e951834a5c35ccd0fa1b867.jpg\",\n  \"148726.jpg\": \"Aves/Regulus satrapa/1e9496c0a83be8619fadbe79cf7bbfdf.jpg\",\n  \"148727.jpg\": \"Aves/Regulus satrapa/e9034b084aa6de0e956b72d5c53a99a2.jpg\",\n  \"148738.jpg\": \"Aves/Regulus satrapa/652378edc2a22ee8c1abfa514eeed0f5.jpg\",\n  \"148746.jpg\": \"Aves/Regulus satrapa/9a8906dbd42af9fd594e875871d592d7.jpg\",\n  \"148762.jpg\": \"Aves/Regulus satrapa/006fd4cede9247ec305c0c56442f2de7.jpg\",\n  \"14878.jpg\": \"Aves/Sitta carolinensis/7b06b4627eb443b47a68f59d75a0a386.jpg\",\n  \"148782.jpg\": \"Aves/Regulus satrapa/44ef69ef384b30ab0d92424d10884ba0.jpg\",\n  \"148783.jpg\": \"Aves/Regulus satrapa/b85354fbb81490d361db6fdd4b9989f3.jpg\",\n  \"148797.jpg\": \"Aves/Regulus satrapa/533c74d39dc903cd79e099b9f09b3053.jpg\",\n  \"148800.jpg\": \"Aves/Regulus satrapa/8699601874da33db07026e7269b12791.jpg\",\n  \"148809.jpg\": \"Aves/Regulus satrapa/c8c8cb0416d867141a74f14f8b6544f7.jpg\",\n  \"148812.jpg\": \"Reptilia/Thamnophis sirtalis/66c80f29e871c7689fa46c29182cabd8.jpg\",\n  \"148819.jpg\": \"Reptilia/Thamnophis sirtalis/1917ec4d5c489b1375dda70ac4b08875.jpg\",\n  \"148834.jpg\": \"Reptilia/Thamnophis sirtalis/c9947a37a5884e5f53d2cdcdcd0a9329.jpg\",\n  \"148841.jpg\": \"Reptilia/Thamnophis sirtalis/b56f6f871f9396bc567d004183c2efed.jpg\",\n  \"148859.jpg\": \"Reptilia/Thamnophis sirtalis/a3e66e5b39fb60dd127e7c92d5796231.jpg\",\n  \"148875.jpg\": \"Reptilia/Thamnophis sirtalis/17fb6762768ec1c3db80ede288d444ac.jpg\",\n  \"148880.jpg\": \"Reptilia/Thamnophis sirtalis/537cfc2d4fc28e40500cb038cbd1434c.jpg\",\n  \"148882.jpg\": \"Reptilia/Thamnophis sirtalis/6f1558899b502b0f898836771fa7ab33.jpg\",\n  \"148883.jpg\": \"Reptilia/Thamnophis sirtalis/edb265ac7952631a77e417a29c2b85fb.jpg\",\n  \"148884.jpg\": \"Reptilia/Thamnophis sirtalis/e54da943c2a3b1642bcf1caf51a02a38.jpg\",\n  \"148896.jpg\": \"Reptilia/Thamnophis sirtalis/729983ed6ec2518956bc96e1cd739788.jpg\",\n  \"148911.jpg\": \"Reptilia/Thamnophis sirtalis/c3d37d08cad2725093fd4b34cff211ab.jpg\",\n  \"148912.jpg\": \"Reptilia/Thamnophis sirtalis/392536fd292408408a40fa0faf4b86d1.jpg\",\n  \"148920.jpg\": \"Reptilia/Thamnophis sirtalis/0114d05abe8ce9f026eabad1cb5468c3.jpg\",\n  \"148921.jpg\": \"Reptilia/Thamnophis sirtalis/372b58aab684fad46bfd49575bfcbebd.jpg\",\n  \"148927.jpg\": \"Reptilia/Thamnophis sirtalis/d43eeee3b9c14a130e278063402f5fc1.jpg\",\n  \"148938.jpg\": \"Reptilia/Thamnophis sirtalis/4a5833b6152a8c51d4a5a5025c326e93.jpg\",\n  \"148947.jpg\": \"Reptilia/Thamnophis sirtalis/346dd7e68ae847910be4128ba3b1c4a3.jpg\",\n  \"148953.jpg\": \"Reptilia/Thamnophis sirtalis/9f3efc265f93659e59c0ca154e713b22.jpg\",\n  \"148954.jpg\": \"Reptilia/Thamnophis sirtalis/05fc234aced198085b5d8bf3603351d0.jpg\",\n  \"148973.jpg\": \"Reptilia/Thamnophis sirtalis/a70935efe37bd9cd6649f73dab922a62.jpg\",\n  \"148979.jpg\": \"Reptilia/Thamnophis sirtalis/3f35bda411e0910226a0b16c9a4cae4b.jpg\",\n  \"148992.jpg\": \"Reptilia/Thamnophis sirtalis/33c8cdb9a040f303e31af478cc6aa50c.jpg\",\n  \"148994.jpg\": \"Reptilia/Thamnophis sirtalis/a88cac6325a574dc56b991294900d283.jpg\",\n  \"14909.jpg\": \"Aves/Sitta carolinensis/d0dd2284a4e0a002633aaf7559ff2f13.jpg\",\n  \"149143.jpg\": \"Aves/Anas crecca/c0877defd4e6ff2f5455ad5e79d39edd.jpg\",\n  \"149199.jpg\": \"Aves/Anas crecca/5daf0d258bef3dd302cdb09a23b930ff.jpg\",\n  \"149206.jpg\": \"Aves/Anas crecca/0749c900ef392c71b69d48aaaf451631.jpg\",\n  \"149223.jpg\": \"Aves/Anas crecca/8cb7dba1834ca7c0bd0f8e37c09cee73.jpg\",\n  \"149224.jpg\": \"Aves/Anas crecca/49e293d50818dc1e0edf9c5fd3461f9b.jpg\",\n  \"149226.jpg\": \"Aves/Anas crecca/75a8f7e0479c269a855c6a921b7dcdb6.jpg\",\n  \"149229.jpg\": \"Aves/Anas crecca/997fedd8327892fad8527e0bf29ae11d.jpg\",\n  \"149250.jpg\": \"Aves/Anas crecca/d7166de9549df73524f192520635ed1f.jpg\",\n  \"149265.jpg\": \"Aves/Anas crecca/7f4f122e2ccdace545f136c913e28c81.jpg\",\n  \"14927.jpg\": \"Aves/Sitta carolinensis/f0168a72fda72b79f210ad11efb8ec3c.jpg\",\n  \"149280.jpg\": \"Aves/Anas crecca/674b15eed5d2a9ec8ceded9ec0b5708b.jpg\",\n  \"149298.jpg\": \"Aves/Anas crecca/01e03812ec81aabf4b1ef8aa2d4a73e6.jpg\",\n  \"149306.jpg\": \"Aves/Anas crecca/04cbff5d5d3677dedb866c827420e074.jpg\",\n  \"149309.jpg\": \"Aves/Anas crecca/ca84510a96b1229625e003d233aa1436.jpg\",\n  \"149310.jpg\": \"Aves/Anas crecca/bc74580d1d594009cbced79f517bf05c.jpg\",\n  \"149314.jpg\": \"Aves/Anas crecca/f526eb12e31889f51d4f3f5d081e7057.jpg\",\n  \"149318.jpg\": \"Aves/Anas crecca/be78c250a0dd55cbe5d3f71a556137cc.jpg\",\n  \"149327.jpg\": \"Aves/Anas crecca/6b2f51070e279510b67d454f4f1cfbc6.jpg\",\n  \"149350.jpg\": \"Aves/Pteroglossus torquatus/e5225785591e490a08af596c13dd4e8a.jpg\",\n  \"149352.jpg\": \"Aves/Pteroglossus torquatus/9ca40f38e3051738f98ee31551a927d4.jpg\",\n  \"149357.jpg\": \"Aves/Pteroglossus torquatus/56242d3ed84682a66a2567633e61594a.jpg\",\n  \"149364.jpg\": \"Aves/Pteroglossus torquatus/77b8669a179cf8dce69a5a77b7a9cf32.jpg\",\n  \"149365.jpg\": \"Aves/Pteroglossus torquatus/978ceace0bcfefd9a460f7ee9532e691.jpg\",\n  \"149366.jpg\": \"Aves/Pteroglossus torquatus/0fda3bc90501da9cfdcd76c99d04db2f.jpg\",\n  \"149367.jpg\": \"Aves/Pteroglossus torquatus/b4117629e2c6feb12337eef75377759d.jpg\",\n  \"149368.jpg\": \"Aves/Pteroglossus torquatus/7ded16133f16b24ce3df2b31d3469827.jpg\",\n  \"149373.jpg\": \"Aves/Pteroglossus torquatus/31a3f3fe33531eea20820d58365204b6.jpg\",\n  \"149374.jpg\": \"Aves/Pteroglossus torquatus/16c22a68c09915b881ac6f6b25352003.jpg\",\n  \"149375.jpg\": \"Aves/Pteroglossus torquatus/fd3c471019d8f301a0c5ed2b01f33dd6.jpg\",\n  \"149376.jpg\": \"Aves/Pteroglossus torquatus/35a57b85512bdf6081592ac0d4826caf.jpg\",\n  \"149377.jpg\": \"Aves/Pteroglossus torquatus/afac8998043bf513af46d158170529b7.jpg\",\n  \"149379.jpg\": \"Aves/Pteroglossus torquatus/40bfeae0aa3ac4bf742e5283e0c55a02.jpg\",\n  \"149381.jpg\": \"Aves/Pteroglossus torquatus/fdcad0d390a676c4190a1801f9327091.jpg\",\n  \"149382.jpg\": \"Aves/Pteroglossus torquatus/c73087aea3a7031a9795fc88006098fb.jpg\",\n  \"149386.jpg\": \"Aves/Pteroglossus torquatus/512097d2094437a2768e178fb4f2c90d.jpg\",\n  \"149418.jpg\": \"Insecta/Vespula squamosa/0aa09da0352b16dd1e4ef3382297583c.jpg\",\n  \"149421.jpg\": \"Insecta/Vespula squamosa/efaa2fd6889ce40fcc57cb6a3fe3407d.jpg\",\n  \"149422.jpg\": \"Insecta/Vespula squamosa/7d2be65956495804ba18acd81f3107fd.jpg\",\n  \"149427.jpg\": \"Insecta/Vespula squamosa/080aeb94048494a8803e4a44bd227dc6.jpg\",\n  \"149439.jpg\": \"Insecta/Vespula squamosa/b969f2021fcf5dcb6e9a2d18f3198df6.jpg\",\n  \"14947.jpg\": \"Aves/Sitta carolinensis/379a0ca801dde4b3ef51bb020eb5c6ba.jpg\",\n  \"14953.jpg\": \"Aves/Sitta carolinensis/32f5d224675bda9da877e3418a73cfa7.jpg\",\n  \"149538.jpg\": \"Aves/Junco hyemalis caniceps/d3b4b5004a44e7ddf368c378267d66d4.jpg\",\n  \"149539.jpg\": \"Aves/Junco hyemalis caniceps/507483c72915acdefc95b8ce04ce6700.jpg\",\n  \"149542.jpg\": \"Aves/Junco hyemalis caniceps/7d5845c2410e5d4fa2b6c2a84f5c3f94.jpg\",\n  \"149547.jpg\": \"Aves/Junco hyemalis caniceps/94332dc5dffe2e45888f169335a2e0bb.jpg\",\n  \"149550.jpg\": \"Aves/Junco hyemalis caniceps/88008c401efa69dbbb65af140b6e03ab.jpg\",\n  \"149554.jpg\": \"Aves/Junco hyemalis caniceps/b3b0aa82d0162b0a29639a76936548a2.jpg\",\n  \"149564.jpg\": \"Mammalia/Tamandua mexicana/2fe6dfa981871bc04773ce36ae159f78.jpg\",\n  \"14957.jpg\": \"Aves/Sitta carolinensis/3836d9bce0cea1328c428d20b492003a.jpg\",\n  \"149571.jpg\": \"Mammalia/Tamandua mexicana/4b4f384fbfab32bf409c6c112715bdcd.jpg\",\n  \"149576.jpg\": \"Mammalia/Tamandua mexicana/1d66083789ca9982215bf5d0729682c8.jpg\",\n  \"149583.jpg\": \"Mammalia/Tamandua mexicana/eca94c930b7b8a8bcd04bfdf433c41de.jpg\",\n  \"149584.jpg\": \"Aves/Chloroceryle aenea/d2e4d5ca8b4e7bf3c4f3bced2e6cc7fe.jpg\",\n  \"149587.jpg\": \"Aves/Chloroceryle aenea/76fd3e92327d5c5cc7e115564f20c6ef.jpg\",\n  \"149590.jpg\": \"Aves/Chloroceryle aenea/adaa558d303f49db61b9e76a6f165ab4.jpg\",\n  \"149593.jpg\": \"Aves/Chloroceryle aenea/50dae671b94fa44eafd91cb5440796b5.jpg\",\n  \"149595.jpg\": \"Aves/Chloroceryle aenea/aa8cb21aa929bba71afcabbcdbd9703e.jpg\",\n  \"149598.jpg\": \"Aves/Chloroceryle aenea/286df0f47714cd91536266a374cc3ee0.jpg\",\n  \"149599.jpg\": \"Aves/Chloroceryle aenea/495de552b0f24b5a232d1d285f7df7b0.jpg\",\n  \"149719.jpg\": \"Insecta/Pasiphila rectangulata/691e46504bfad79416c99bb6dce9b0f3.jpg\",\n  \"149722.jpg\": \"Insecta/Pasiphila rectangulata/105413da0965c0a25875dc8b5947efd8.jpg\",\n  \"149726.jpg\": \"Insecta/Pasiphila rectangulata/a070c78de2b0132e2222b9286c778d33.jpg\",\n  \"149729.jpg\": \"Insecta/Pasiphila rectangulata/ab50b095ea5d5b383d7f28549d245738.jpg\",\n  \"149735.jpg\": \"Insecta/Pasiphila rectangulata/633513b625d6f175b7c58f8de878384b.jpg\",\n  \"149736.jpg\": \"Insecta/Pasiphila rectangulata/08a7f8624792b7db9dfcff01ab956dcc.jpg\",\n  \"149747.jpg\": \"Insecta/Pasiphila rectangulata/85fdf2c0005633be716f71d26b35d51e.jpg\",\n  \"149821.jpg\": \"Insecta/Vespa crabro/4584b975a5c7f38c83a47edb873b46d4.jpg\",\n  \"149823.jpg\": \"Insecta/Vespa crabro/77458a2c34028b30d00e9bc4dd7a6dde.jpg\",\n  \"149827.jpg\": \"Insecta/Vespa crabro/dc3ae0f1ea31532d90e11ce2dcde8573.jpg\",\n  \"149845.jpg\": \"Insecta/Vespa crabro/d544b5191873f05706fc94f97b569837.jpg\",\n  \"149846.jpg\": \"Insecta/Vespa crabro/19cfd2375036de3cefd13b9338d2b0fd.jpg\",\n  \"149847.jpg\": \"Insecta/Vespa crabro/4a0488e75ff6154a4d6daa758bf8fe4f.jpg\",\n  \"149848.jpg\": \"Insecta/Vespa crabro/3ce81e8273a0d98adb224e7b78ed5ef7.jpg\",\n  \"149852.jpg\": \"Insecta/Vespa crabro/1874dae51c104dc32c6731eb03181ed6.jpg\",\n  \"149855.jpg\": \"Insecta/Vespa crabro/2e0aee22391740ec326e42a30377241a.jpg\",\n  \"149856.jpg\": \"Insecta/Vespa crabro/838801c754129e92b327bd7c9d9ce88c.jpg\",\n  \"149857.jpg\": \"Insecta/Vespa crabro/cc9633d0584b0642571c2ab2e87a1919.jpg\",\n  \"149858.jpg\": \"Insecta/Vespa crabro/987fd18bea7ed99081029e0a322c25f4.jpg\",\n  \"149860.jpg\": \"Insecta/Vespa crabro/b2d0912d7f20b8b51e983ca4764c66a9.jpg\",\n  \"149861.jpg\": \"Insecta/Vespa crabro/28f050d0aef3f4675d61d57b055df523.jpg\",\n  \"149867.jpg\": \"Insecta/Vespa crabro/d160c15fb64a784260a4c6f252e99359.jpg\",\n  \"149868.jpg\": \"Insecta/Vespa crabro/a564fe738af48a7c13fa0fe311fa833c.jpg\",\n  \"149869.jpg\": \"Insecta/Vespa crabro/2840ed04242c6900f0c913f676a259da.jpg\",\n  \"149875.jpg\": \"Insecta/Vespa crabro/c87ef44c1ca6aeabadcff860d5092358.jpg\",\n  \"149876.jpg\": \"Insecta/Vespa crabro/8f41641b3dff6a9db01c48572e71612a.jpg\",\n  \"149894.jpg\": \"Reptilia/Storeria occipitomaculata occipitomaculata/a79cf4fa77c64b9bca4043acff0649f1.jpg\",\n  \"149901.jpg\": \"Reptilia/Storeria occipitomaculata occipitomaculata/dd885e2305816a81cf710d16ea4e21e8.jpg\",\n  \"149905.jpg\": \"Reptilia/Storeria occipitomaculata occipitomaculata/62338578b18ee3f51bc3614bfc57fc74.jpg\",\n  \"149906.jpg\": \"Reptilia/Storeria occipitomaculata occipitomaculata/e312404e1c73bc8420bd657c77990fdd.jpg\",\n  \"149909.jpg\": \"Reptilia/Storeria occipitomaculata occipitomaculata/e8469d2ca9c16efdb51c479ff989ba93.jpg\",\n  \"149910.jpg\": \"Reptilia/Storeria occipitomaculata occipitomaculata/b10694b302add103724dfb0553d88ad3.jpg\",\n  \"149911.jpg\": \"Reptilia/Storeria occipitomaculata occipitomaculata/7840df6aed99a9c7a44009625a28700f.jpg\",\n  \"149914.jpg\": \"Aves/Egretta sacra/a1e76059959074b09f4e1bfce8e093a1.jpg\",\n  \"149918.jpg\": \"Aves/Egretta sacra/a4096088ce26200e17322c85c761c031.jpg\",\n  \"149925.jpg\": \"Aves/Egretta sacra/0baf772d9cb9c074ef120f1b1b17d1f3.jpg\",\n  \"149929.jpg\": \"Aves/Egretta sacra/2fd527d09d542d6cc0e9b2f7ce643b66.jpg\",\n  \"149930.jpg\": \"Aves/Egretta sacra/1b0527d3ac3fc704bbffed4f3ff9d61e.jpg\",\n  \"149933.jpg\": \"Aves/Egretta sacra/3aa7472ef24bb2ef38c5533378456bcd.jpg\",\n  \"149953.jpg\": \"Aves/Trogon melanocephalus/6b505e18e958519bc678d582bb81d992.jpg\",\n  \"149955.jpg\": \"Aves/Trogon melanocephalus/4c4ebeb40481cf6c09545703689be596.jpg\",\n  \"149957.jpg\": \"Aves/Trogon melanocephalus/82d8ffafa9523dcaabc698ca469d04ca.jpg\",\n  \"149968.jpg\": \"Aves/Trogon melanocephalus/c27da6fcafdd0b90513fc437d8874d05.jpg\",\n  \"149977.jpg\": \"Aves/Trogon melanocephalus/d4a3f2038a7d1ef9e53e11b8322938c4.jpg\",\n  \"149981.jpg\": \"Aves/Trogon melanocephalus/40cd824a3aa6b4e701503da8655b7f7c.jpg\",\n  \"149982.jpg\": \"Aves/Trogon melanocephalus/4408a559dfdd92c1dac0f28314fc813c.jpg\",\n  \"149984.jpg\": \"Aves/Trogon melanocephalus/a16323b80ae073bfe84a6af3a9650a85.jpg\",\n  \"149985.jpg\": \"Aves/Trogon melanocephalus/bb77dca9b79a37dcfe5fe686e3880dd4.jpg\",\n  \"149986.jpg\": \"Aves/Trogon melanocephalus/8acdc93168c6eb59b6d6cdd591c5e660.jpg\",\n  \"149992.jpg\": \"Animalia/Scolopendra heros/b5feb9f9b7a8092b33680abafcc6abd4.jpg\",\n  \"149996.jpg\": \"Animalia/Scolopendra heros/48afb655714673c63865daeb7b7a7577.jpg\",\n  \"149997.jpg\": \"Animalia/Scolopendra heros/a52c871f6f0bb7a12f6b98a772a8c1f7.jpg\",\n  \"150.jpg\": \"Aves/Zonotrichia capensis/3125ec6484df7c96e965942484a1a169.jpg\",\n  \"150010.jpg\": \"Animalia/Scolopendra heros/7210d80abd64cda69db22298f9176d4f.jpg\",\n  \"150012.jpg\": \"Animalia/Scolopendra heros/5b01669f7208830de19dd217274f0592.jpg\",\n  \"150013.jpg\": \"Animalia/Scolopendra heros/613fb8ab4adf3db1653f4175f9f98a88.jpg\",\n  \"150018.jpg\": \"Insecta/Pantographa limata/f6019bc43100c97e09fd24529c8ce976.jpg\",\n  \"150020.jpg\": \"Insecta/Pantographa limata/f1213784eb39708fafa21040ddd985d2.jpg\",\n  \"150022.jpg\": \"Insecta/Pantographa limata/55805cd52feab1301e833565a5e5f911.jpg\",\n  \"150027.jpg\": \"Insecta/Pantographa limata/ef10d1a8d5b95f415515afbbdecff576.jpg\",\n  \"150032.jpg\": \"Insecta/Pantographa limata/030993ca4d42657fb669492900612efb.jpg\",\n  \"150036.jpg\": \"Insecta/Pantographa limata/978ad613fdecafd9acef2e423fb3236a.jpg\",\n  \"150038.jpg\": \"Aves/Gallinula chloropus/b9f2d1c88c6812cff75f66fae9af90f2.jpg\",\n  \"150039.jpg\": \"Aves/Gallinula chloropus/b9fa073b2773919743f696bd33c1eb57.jpg\",\n  \"150041.jpg\": \"Aves/Gallinula chloropus/fa5d364c00bdbcb86eb63cd694eea1ab.jpg\",\n  \"150061.jpg\": \"Aves/Gallinula chloropus/8eab18752969160d1172d4b5f5b05954.jpg\",\n  \"150065.jpg\": \"Aves/Gallinula chloropus/dd47a3c9f39ba10c6e2642997eaa8206.jpg\",\n  \"150066.jpg\": \"Aves/Gallinula chloropus/c8e3a5afa30488336ed6f0086f456e4d.jpg\",\n  \"150075.jpg\": \"Aves/Gallinula chloropus/6afb8afe9c889b47b804056077ae82b9.jpg\",\n  \"150080.jpg\": \"Aves/Gallinula chloropus/937d47b014cb9388042cf978ce91f3ae.jpg\",\n  \"150094.jpg\": \"Aves/Gallinula chloropus/9e25132028156875d851183f3cd94aad.jpg\",\n  \"150095.jpg\": \"Aves/Gallinula chloropus/427288a1c4c45ee249261d2a95004b02.jpg\",\n  \"15010.jpg\": \"Reptilia/Sistrurus miliarius/4281a63544e7ccbe890c1a0ee82390e8.jpg\",\n  \"150100.jpg\": \"Aves/Gallinula chloropus/39fce4ae3bc298ac97d61d826616ceaf.jpg\",\n  \"150103.jpg\": \"Aves/Gallinula chloropus/72150b97cb4d2422c95e3aceb85e5f09.jpg\",\n  \"150109.jpg\": \"Aves/Gallinula chloropus/eb76d25949696f4bb6f41c667b7e4fcd.jpg\",\n  \"15011.jpg\": \"Reptilia/Sistrurus miliarius/b4797c180c655d8a8f049d5c2a34bb04.jpg\",\n  \"150110.jpg\": \"Aves/Gallinula chloropus/0f919a29e279435089fa130c1eb7ba06.jpg\",\n  \"150112.jpg\": \"Aves/Gallinula chloropus/0e5fef37c8705981020123b3fb6acd48.jpg\",\n  \"150124.jpg\": \"Aves/Gallinula chloropus/ed192c35e3e41e3b2cc74719323fb063.jpg\",\n  \"150127.jpg\": \"Aves/Gallinula chloropus/dbbf40977c514bde781cf72b52f53f80.jpg\",\n  \"150130.jpg\": \"Aves/Gallinula chloropus/66788bcbfdf06cdfad748772ac3e7dc7.jpg\",\n  \"150138.jpg\": \"Aves/Gallinula chloropus/2dd5e7d75ddbc9dc086d506262e99914.jpg\",\n  \"150140.jpg\": \"Aves/Gallinula chloropus/865a9cb155603baaacd9584a4ea05004.jpg\",\n  \"15015.jpg\": \"Reptilia/Sistrurus miliarius/28f44ca824fad0b71afd8ed687c2c488.jpg\",\n  \"15016.jpg\": \"Reptilia/Sistrurus miliarius/8bfce7af34b72c64010e3aaab816b736.jpg\",\n  \"15017.jpg\": \"Reptilia/Sistrurus miliarius/d4e80f1ecbc3e2fbfd5af04eca82aa82.jpg\",\n  \"150227.jpg\": \"Aves/Falco sparverius/1a6d69a0ee007c178beffa86ada0bb1d.jpg\",\n  \"150231.jpg\": \"Aves/Falco sparverius/0a08f9e3f57e0fce82fe134eb2e47551.jpg\",\n  \"150236.jpg\": \"Aves/Falco sparverius/40e7f149a117426f4b59533b9c7775c4.jpg\",\n  \"150250.jpg\": \"Aves/Falco sparverius/5b55d37704296f96b24c0968c8ed0900.jpg\",\n  \"150283.jpg\": \"Aves/Falco sparverius/a8e17a6cb644174f891e64acc22f8c57.jpg\",\n  \"150305.jpg\": \"Aves/Falco sparverius/d4ba2e1711381370889729724ebecd7e.jpg\",\n  \"150364.jpg\": \"Aves/Falco sparverius/9b29c3618c1414756a38f449139e418a.jpg\",\n  \"150367.jpg\": \"Aves/Falco sparverius/d1fc8dfec981139476de3d146b77092f.jpg\",\n  \"150405.jpg\": \"Aves/Falco sparverius/b72f88f2fc54e9e02b5db21a6d297027.jpg\",\n  \"150421.jpg\": \"Aves/Falco sparverius/34e5c82a8f244f2010fffd33b734778c.jpg\",\n  \"150462.jpg\": \"Aves/Falco sparverius/d3c6e3d7a3ae998a725e6da936be5313.jpg\",\n  \"150478.jpg\": \"Aves/Falco sparverius/a4f4ad76e1dc50bfc66102c1df65f50b.jpg\",\n  \"150544.jpg\": \"Aves/Falco sparverius/6d23d28b3addb0a3e632054925caa2f6.jpg\",\n  \"150578.jpg\": \"Aves/Falco sparverius/6f0d79be66653fdfb1ecca6a1a153073.jpg\",\n  \"150639.jpg\": \"Aves/Falco sparverius/7cf330ea2631a932ea3b327536fafebc.jpg\",\n  \"150644.jpg\": \"Aves/Falco sparverius/1f07fbb98d2f53885ac1dfe82d89a29d.jpg\",\n  \"150713.jpg\": \"Aves/Falco sparverius/08e2903e0e8d8b678e7209fb1ba338df.jpg\",\n  \"150724.jpg\": \"Aves/Falco sparverius/bd0d043512907422bbdd278808bf5310.jpg\",\n  \"150761.jpg\": \"Aves/Falco sparverius/04fab6f71ea41528ccccf2694ec54957.jpg\",\n  \"150793.jpg\": \"Aves/Falco sparverius/ef5bb27c0227e2c705b86072bb28f08b.jpg\",\n  \"150813.jpg\": \"Aves/Falco sparverius/674984911471c380ec4787d2447ed803.jpg\",\n  \"150844.jpg\": \"Aves/Falco sparverius/c847d37e0ddb70b86d97ea46594df46e.jpg\",\n  \"150868.jpg\": \"Aves/Falco sparverius/6f858f197b996e300b79aa0e2d9010e2.jpg\",\n  \"150877.jpg\": \"Aves/Picoides albolarvatus/7f2918b662c7f5fd50ad27f1227c3905.jpg\",\n  \"150878.jpg\": \"Aves/Picoides albolarvatus/ccb70dddbc8ab7e3a324909d91d86416.jpg\",\n  \"150879.jpg\": \"Aves/Picoides albolarvatus/8baa45ded29b6ebe210557960ecf96bf.jpg\",\n  \"150881.jpg\": \"Aves/Picoides albolarvatus/80c641f27a0550368bc2d834affc66f3.jpg\",\n  \"150882.jpg\": \"Aves/Picoides albolarvatus/507570248b52abf46e08cb21b7569621.jpg\",\n  \"150883.jpg\": \"Aves/Picoides albolarvatus/b6e65565d149fd731e33a528c12746e0.jpg\",\n  \"150886.jpg\": \"Aves/Picoides albolarvatus/d4cfbed8f16943d8044403ba7205e6cb.jpg\",\n  \"150890.jpg\": \"Aves/Picoides albolarvatus/439007bebcaca1ffc4a91dc9291dde87.jpg\",\n  \"150896.jpg\": \"Aves/Picoides albolarvatus/b7ef2c5b32f7bdf959993b14e3557ece.jpg\",\n  \"150899.jpg\": \"Aves/Picoides albolarvatus/aa04cf7c28755d0800635a1b3700a555.jpg\",\n  \"150900.jpg\": \"Aves/Picoides albolarvatus/cdc9cc3f58545bd2abf8c5acf892b306.jpg\",\n  \"150901.jpg\": \"Aves/Picoides albolarvatus/4deed05ee1f5e064858e918f6bec42fb.jpg\",\n  \"150902.jpg\": \"Aves/Picoides albolarvatus/bb1dae6c2332ca0130a3c72f720e180c.jpg\",\n  \"150903.jpg\": \"Aves/Picoides albolarvatus/19fbee2f74ee00a9666b113370bd7f4f.jpg\",\n  \"150907.jpg\": \"Aves/Picoides albolarvatus/c5a75b3ccd330c795a83f5a4a5696163.jpg\",\n  \"150908.jpg\": \"Aves/Picoides albolarvatus/ef18777536c2b74b9a274d90738ef8b4.jpg\",\n  \"150909.jpg\": \"Aves/Picoides albolarvatus/0545d00dd237a3c2dbae4d00b47abd62.jpg\",\n  \"150917.jpg\": \"Aves/Picoides albolarvatus/75969e7c200a9dc0e09de7050612125f.jpg\",\n  \"150919.jpg\": \"Insecta/Anax imperator/d6b7f7aaaa29439c6e9ba51b2f8c5416.jpg\",\n  \"150921.jpg\": \"Insecta/Anax imperator/2fae807af12e446c7917105e22f68e7d.jpg\",\n  \"150922.jpg\": \"Insecta/Anax imperator/0e7ac835f89e1c3cd481dabe89d5a616.jpg\",\n  \"150925.jpg\": \"Insecta/Anax imperator/43bbc8c8a9901e2809141e5366bf5c72.jpg\",\n  \"150926.jpg\": \"Insecta/Anax imperator/8e8ae4f278a2ec4ef364a5db86109cc8.jpg\",\n  \"150972.jpg\": \"Aves/Setophaga nigrescens/48e7df40654c1cfb480d3d2134069910.jpg\",\n  \"150973.jpg\": \"Aves/Setophaga nigrescens/f8c31cdb206d3291193d4a4a8047d21f.jpg\",\n  \"150974.jpg\": \"Aves/Setophaga nigrescens/9e396993dc0b3de91956aab3a87728d5.jpg\",\n  \"150975.jpg\": \"Aves/Setophaga nigrescens/efc8a7906ef5d5a4db39ca80cde0116f.jpg\",\n  \"150987.jpg\": \"Aves/Setophaga nigrescens/5c339c9887008db0bccbf9375f42fcc4.jpg\",\n  \"150992.jpg\": \"Aves/Setophaga nigrescens/a7076b875ea16beee41710dd068592f6.jpg\",\n  \"150993.jpg\": \"Aves/Setophaga nigrescens/77480620eabd75782fd03e33547cb106.jpg\",\n  \"150999.jpg\": \"Aves/Setophaga nigrescens/8184dc0eec9400cbece9e37e9634b894.jpg\",\n  \"151001.jpg\": \"Aves/Setophaga nigrescens/8a3a69449811d79d9e2b7d6b6d2c99fb.jpg\",\n  \"151003.jpg\": \"Aves/Setophaga nigrescens/7314e173c67103a59dfa85856517179f.jpg\",\n  \"151010.jpg\": \"Aves/Setophaga nigrescens/560d1d470ab6d4a23b5ebff3ec657cab.jpg\",\n  \"151013.jpg\": \"Aves/Setophaga nigrescens/d272c8dba874f6ef3eabcfbb6b03c1d8.jpg\",\n  \"151014.jpg\": \"Aves/Setophaga nigrescens/ba9c5589b1767b20aacd237d10daba0f.jpg\",\n  \"151016.jpg\": \"Aves/Setophaga nigrescens/42e14ca1c921457e30ae1ce0eb217a44.jpg\",\n  \"151017.jpg\": \"Aves/Setophaga nigrescens/bfb6980d0713266fd75c8722d48495db.jpg\",\n  \"151039.jpg\": \"Aves/Setophaga nigrescens/48cdedf4929433eb74733b1cb0b23cf4.jpg\",\n  \"151040.jpg\": \"Aves/Setophaga nigrescens/7b6c7d39f3ff69c067166a054f0c17ae.jpg\",\n  \"151042.jpg\": \"Aves/Setophaga nigrescens/e251bf5e5096df5db066df5c142087bc.jpg\",\n  \"151043.jpg\": \"Aves/Setophaga nigrescens/26cefcb86316990910166c7ae2de1eb6.jpg\",\n  \"151047.jpg\": \"Aves/Setophaga nigrescens/8290babf3c625ca17ff16de44038eb35.jpg\",\n  \"151054.jpg\": \"Aves/Setophaga nigrescens/0a7571e76d4e33bdc06dc8ef73b5e8ac.jpg\",\n  \"151073.jpg\": \"Aves/Corvus corone/c0267b1622ad9d2a02de479c4daf0621.jpg\",\n  \"151080.jpg\": \"Aves/Corvus corone/d7e7ece52d5e10b3b9cc6e8d176cc78c.jpg\",\n  \"151104.jpg\": \"Aves/Corvus corone/aabcdc9479fc4f8c7f4f0ca28524731e.jpg\",\n  \"151109.jpg\": \"Aves/Corvus corone/8789af3b033a0d680702793f91b2cd09.jpg\",\n  \"151119.jpg\": \"Aves/Corvus corone/90db28c668ca57b86e5f5caf79ed25b4.jpg\",\n  \"151120.jpg\": \"Aves/Corvus corone/47661830d3d4584e8fcdb8ce9deca59b.jpg\",\n  \"151121.jpg\": \"Aves/Corvus corone/394b2c041415d7c92e14f6839c301232.jpg\",\n  \"151123.jpg\": \"Aves/Corvus corone/62105f48122dc5f8241e15f4fc1dac4d.jpg\",\n  \"151135.jpg\": \"Aves/Corvus corone/d7ed1b05729249f2fe3c16280c5283c3.jpg\",\n  \"151138.jpg\": \"Aves/Corvus corone/3a4cc39a6971adebd00b80a8adb7df32.jpg\",\n  \"151139.jpg\": \"Aves/Corvus corone/900a8a1ba4fa17eab212fe7f6363bdff.jpg\",\n  \"151148.jpg\": \"Aves/Corvus corone/70b97e0332671904fad92b5f2fda5e3e.jpg\",\n  \"151155.jpg\": \"Aves/Corvus corone/f08f42e0b545db4f06c754c74c450426.jpg\",\n  \"151157.jpg\": \"Aves/Corvus corone/0f22da00452fb1b6a86f821ef1acbb17.jpg\",\n  \"151164.jpg\": \"Aves/Corvus corone/a436418215bc3a2dcf5e5339de48565e.jpg\",\n  \"151165.jpg\": \"Aves/Corvus corone/8a66a34c162b9dc9076c13587955d19d.jpg\",\n  \"151171.jpg\": \"Aves/Corvus corone/81db1f620f6ce1d27779f09535e68b84.jpg\",\n  \"151197.jpg\": \"Aves/Troglodytes pacificus/7c7bb58dd5a9e881fe2c3f58713857af.jpg\",\n  \"151198.jpg\": \"Aves/Troglodytes pacificus/76ce252b0630f57fe83be6d20cb67bc8.jpg\",\n  \"151200.jpg\": \"Aves/Troglodytes pacificus/25526b44f40e3402c11dc3df77db5d1d.jpg\",\n  \"151201.jpg\": \"Aves/Troglodytes pacificus/706b0f9f9e7d5460923ce3836952e246.jpg\",\n  \"151209.jpg\": \"Aves/Troglodytes pacificus/c5d6920ba79662a0fc3924aa0e4d5ac4.jpg\",\n  \"151210.jpg\": \"Aves/Troglodytes pacificus/2891e26c9a0367fce1d272e6e4430224.jpg\",\n  \"151211.jpg\": \"Aves/Troglodytes pacificus/c4934abc8edb54e3c91e704f7f994982.jpg\",\n  \"151216.jpg\": \"Aves/Troglodytes pacificus/618b87442bf6720171c296785a5861b0.jpg\",\n  \"151217.jpg\": \"Aves/Troglodytes pacificus/d8752919dbfe6a31acbb9f3a8006d1e1.jpg\",\n  \"151218.jpg\": \"Aves/Troglodytes pacificus/2da7dca3825b26f09de0b8d95122f041.jpg\",\n  \"151222.jpg\": \"Aves/Troglodytes pacificus/da7c148f7c8bcc8eb2fd5b4e22246301.jpg\",\n  \"151223.jpg\": \"Aves/Troglodytes pacificus/38077706fbe5432c8ed866726bc86f27.jpg\",\n  \"151229.jpg\": \"Aves/Troglodytes pacificus/b1049602b6ec05e8cfb612916bbcf09a.jpg\",\n  \"151230.jpg\": \"Aves/Troglodytes pacificus/b92df972e930a25cb674c5cbf0293949.jpg\",\n  \"151232.jpg\": \"Aves/Troglodytes pacificus/9c8706485639746e4ceae411bc4c323c.jpg\",\n  \"151233.jpg\": \"Aves/Troglodytes pacificus/a9512787a704e4f776ab879d53106395.jpg\",\n  \"151244.jpg\": \"Aves/Troglodytes pacificus/267331bbcf6a104ce9557b94e220bad3.jpg\",\n  \"151248.jpg\": \"Aves/Troglodytes pacificus/83100684f230ba57240dae933d3996e5.jpg\",\n  \"151293.jpg\": \"Aves/Icterus abeillei/a4569d7371ef480b4fc332c5e1db32bd.jpg\",\n  \"151299.jpg\": \"Aves/Icterus abeillei/dbd14a5077bd919f89af09016aecfe32.jpg\",\n  \"151300.jpg\": \"Aves/Icterus abeillei/db9e910b99ffcabd021fcaa60a512aa9.jpg\",\n  \"151301.jpg\": \"Aves/Icterus abeillei/a4f8a73630c91a9374f46fbec3393f83.jpg\",\n  \"151309.jpg\": \"Aves/Icterus abeillei/e3d72f8561d094797b9b4e0de604ca5f.jpg\",\n  \"151325.jpg\": \"Aves/Icterus abeillei/29de7f57e36be49554c20ac3dd214861.jpg\",\n  \"151326.jpg\": \"Aves/Icterus abeillei/89213cc507863567b1d5f1673283852c.jpg\",\n  \"151327.jpg\": \"Aves/Icterus abeillei/f4cc18cbaf62fc99266bb4480dd9d1c5.jpg\",\n  \"151331.jpg\": \"Aves/Icterus abeillei/3633e6cd067fdfeea2da539801d04f17.jpg\",\n  \"151338.jpg\": \"Aves/Icterus abeillei/d8bcf8ce2c1d621f4f8f834068a2e688.jpg\",\n  \"151346.jpg\": \"Aves/Icterus abeillei/03edde12094e0140091c01dc4ec9498a.jpg\",\n  \"151355.jpg\": \"Aves/Icterus abeillei/53e196d201cc1f5a08fe91f675265b0c.jpg\",\n  \"151356.jpg\": \"Aves/Icterus abeillei/2ef82a02ba3f1df36e2e61957f7b3173.jpg\",\n  \"151357.jpg\": \"Aves/Icterus abeillei/f65f85394901f03870b375f324c872cb.jpg\",\n  \"151364.jpg\": \"Aves/Icterus abeillei/9978d672d24383dbffc601492031d419.jpg\",\n  \"151365.jpg\": \"Aves/Icterus abeillei/bbd53903ada021d91103c4dd3922282c.jpg\",\n  \"151366.jpg\": \"Aves/Icterus abeillei/2a7db764157c264e971ff3cba9d505a4.jpg\",\n  \"151367.jpg\": \"Aves/Icterus abeillei/28bbed9453fb7aba6d3c6c5b11f56d82.jpg\",\n  \"151368.jpg\": \"Aves/Icterus abeillei/077961cfde479e67fb6aa79805ee6df1.jpg\",\n  \"151410.jpg\": \"Aves/Anas superciliosa  platyrhynchos/eff0569aceb1136ff8defa657a4077fc.jpg\",\n  \"151426.jpg\": \"Aves/Anas superciliosa  platyrhynchos/de3a44c015ec684060a947a220e4cfe7.jpg\",\n  \"151427.jpg\": \"Aves/Anas superciliosa  platyrhynchos/181464e8df06cfa5f1bb9c07d035c06c.jpg\",\n  \"151430.jpg\": \"Aves/Anas superciliosa  platyrhynchos/7754fdba2dd4e876f6d69815b6850a2f.jpg\",\n  \"151445.jpg\": \"Aves/Sphyrapicus thyroideus/fc158259caf8acdbc96564fea61dfbf8.jpg\",\n  \"151446.jpg\": \"Aves/Sphyrapicus thyroideus/9e10fb3a268035bc694ca4011969655b.jpg\",\n  \"151448.jpg\": \"Aves/Sphyrapicus thyroideus/beddd5abfe2967bd1867f0cde25243f7.jpg\",\n  \"151449.jpg\": \"Aves/Sphyrapicus thyroideus/44a10a0f5eb7b2654681f13fb682bd69.jpg\",\n  \"151457.jpg\": \"Aves/Sphyrapicus thyroideus/1e6751a85707f4e5f442b9e9861e80b7.jpg\",\n  \"151464.jpg\": \"Aves/Sphyrapicus thyroideus/5f08d7218aae79db0ee88e30d149b9b3.jpg\",\n  \"151469.jpg\": \"Aves/Sphyrapicus thyroideus/6425a8cee8505b2e36b29af78879afc2.jpg\",\n  \"151473.jpg\": \"Insecta/Cycloneda polita/95a42f3b96ee1240e294c07b400be098.jpg\",\n  \"151476.jpg\": \"Insecta/Cycloneda polita/c7f606b5bcecf4269ddf708d7b7bad0d.jpg\",\n  \"151479.jpg\": \"Insecta/Cycloneda polita/ce1262aaf9257701dedaa260a76fd64b.jpg\",\n  \"151481.jpg\": \"Insecta/Cycloneda polita/911adba560ddd17d77b4fc25ef9434d4.jpg\",\n  \"151482.jpg\": \"Insecta/Cycloneda polita/309823eb818262deb3980950e1e89af9.jpg\",\n  \"151483.jpg\": \"Insecta/Cycloneda polita/eaa9f9d093f64cd36ca39a19e53bdd62.jpg\",\n  \"151558.jpg\": \"Insecta/Heliomata cycladata/c6b403c3f75ae351a5d52164b206ce63.jpg\",\n  \"151565.jpg\": \"Insecta/Heliomata cycladata/952d4a85e3b6074b232ab926bde47983.jpg\",\n  \"151567.jpg\": \"Insecta/Heliomata cycladata/b07c9134249a9afc193d532c8a2d122b.jpg\",\n  \"151568.jpg\": \"Insecta/Heliomata cycladata/d59de638795ce706e5cb429deee6f7fe.jpg\",\n  \"151570.jpg\": \"Insecta/Heliomata cycladata/911fa9de205bb3589adb107a4a2e1a49.jpg\",\n  \"151609.jpg\": \"Aves/Anser cygnoides domesticus/f925c0add12cde44c3761f8277110526.jpg\",\n  \"151614.jpg\": \"Aves/Anser cygnoides domesticus/a968f6ee4185b1896811bdecc8c3d5f7.jpg\",\n  \"151619.jpg\": \"Aves/Anser cygnoides domesticus/f463f6b490f510b6166660c463a5e661.jpg\",\n  \"151620.jpg\": \"Aves/Anser cygnoides domesticus/35839f1047f186d50c4eab64f7e93112.jpg\",\n  \"151621.jpg\": \"Aves/Anser cygnoides domesticus/c711769689c0d17ebc3d17199f42689d.jpg\",\n  \"151627.jpg\": \"Aves/Anser cygnoides domesticus/5eec913a3d08f39aba4a53ab35901b44.jpg\",\n  \"151631.jpg\": \"Aves/Anser cygnoides domesticus/e777df8f5e5ecdeff53d4dd683eb84de.jpg\",\n  \"151635.jpg\": \"Aves/Anser cygnoides domesticus/05461ae556c61b77e4a46ae1f144403f.jpg\",\n  \"151636.jpg\": \"Aves/Anser cygnoides domesticus/7d7806764bee165c0b1712d437607ac7.jpg\",\n  \"151637.jpg\": \"Aves/Anser cygnoides domesticus/ce71a4071ec9f2df531e684c19053bf0.jpg\",\n  \"151669.jpg\": \"Reptilia/Lampropeltis splendida/103774e73663ca3dc5708776e3703509.jpg\",\n  \"151675.jpg\": \"Reptilia/Lampropeltis splendida/5fd2dbe8b0b600770f841b8c1c4afdc9.jpg\",\n  \"151679.jpg\": \"Reptilia/Lampropeltis splendida/36f252f9021f885bb5d944fed8d67fe5.jpg\",\n  \"151689.jpg\": \"Reptilia/Lampropeltis splendida/8d1f667776bfa4c52372aac6aaae948d.jpg\",\n  \"151699.jpg\": \"Reptilia/Lampropeltis splendida/61a59f1ad3c0e35ea97303e188240a78.jpg\",\n  \"151700.jpg\": \"Reptilia/Lampropeltis splendida/8fb892fde12cfa4d9cc2a74aecb1b1f6.jpg\",\n  \"151703.jpg\": \"Reptilia/Lampropeltis splendida/09d7a57e8737e528c8db266b489d1e59.jpg\",\n  \"151709.jpg\": \"Insecta/Chauliognathus pensylvanicus/4cc6dbcd14d4aacdf5e812135cd50232.jpg\",\n  \"151710.jpg\": \"Insecta/Chauliognathus pensylvanicus/fbb2c081998793af033c1c8f983a7339.jpg\",\n  \"151713.jpg\": \"Insecta/Chauliognathus pensylvanicus/a6c52a8ce78e559e483c6a1fd1041457.jpg\",\n  \"151715.jpg\": \"Insecta/Chauliognathus pensylvanicus/b774492a346eabfbfcc4326d17e348cb.jpg\",\n  \"151723.jpg\": \"Insecta/Chauliognathus pensylvanicus/b098009ca906caf66d5936a89575e0f9.jpg\",\n  \"151724.jpg\": \"Insecta/Chauliognathus pensylvanicus/0065d8048bfc24b7949c8b0f53dc644c.jpg\",\n  \"151727.jpg\": \"Insecta/Chauliognathus pensylvanicus/56369ab25f052173380849f32d29ed83.jpg\",\n  \"151731.jpg\": \"Insecta/Chauliognathus pensylvanicus/1282def8b78029ec3ae8f1a7ef13ff74.jpg\",\n  \"151734.jpg\": \"Insecta/Chauliognathus pensylvanicus/3a8e64fa221e9409af243ee6bb89525e.jpg\",\n  \"151735.jpg\": \"Insecta/Chauliognathus pensylvanicus/c87f061b28efb9a5bedf5bd3d01f5cc1.jpg\",\n  \"151736.jpg\": \"Insecta/Chauliognathus pensylvanicus/0f19dfc6a887a796a4719e92c082c27b.jpg\",\n  \"151739.jpg\": \"Insecta/Chauliognathus pensylvanicus/c87f13149038012abefa09ef2d73ef9f.jpg\",\n  \"151744.jpg\": \"Insecta/Chauliognathus pensylvanicus/ba8c182ac39f09da3e4ba6a2ba729fbe.jpg\",\n  \"151750.jpg\": \"Insecta/Chauliognathus pensylvanicus/26efc37ce2e093bc3f32910e41f4e1f5.jpg\",\n  \"151753.jpg\": \"Insecta/Chauliognathus pensylvanicus/75d6a4a962ca649967347d004f95c3a9.jpg\",\n  \"151764.jpg\": \"Insecta/Chauliognathus pensylvanicus/22c80c6c5aab92114a1ba7efd497b64f.jpg\",\n  \"151772.jpg\": \"Insecta/Chauliognathus pensylvanicus/dd9d76a0a15eee9bf9c75b0b8b47b3de.jpg\",\n  \"151781.jpg\": \"Insecta/Chauliognathus pensylvanicus/2bce6a33e65a3ebebfaff93292746704.jpg\",\n  \"151802.jpg\": \"Insecta/Ascia monuste/9d7fa31819b89e74ea3a9d6bde6fbf99.jpg\",\n  \"151807.jpg\": \"Insecta/Ascia monuste/080c7cf83ba158da73afbb5f2ce8c560.jpg\",\n  \"151811.jpg\": \"Insecta/Ascia monuste/8f1f9a3682f273caf15b7d867858a044.jpg\",\n  \"151812.jpg\": \"Insecta/Ascia monuste/12a7a862d1d9ab1dd51173930364778f.jpg\",\n  \"151813.jpg\": \"Insecta/Ascia monuste/bce8dba9d9e309c517555464e5378b98.jpg\",\n  \"151826.jpg\": \"Insecta/Ascia monuste/5f835760dded1db302c6cd1435dbc8de.jpg\",\n  \"151831.jpg\": \"Insecta/Ascia monuste/dadbe5b4e30554082a183f189ce43b39.jpg\",\n  \"151832.jpg\": \"Insecta/Ascia monuste/8ac66b5958c13081c2c4328d9388a971.jpg\",\n  \"151836.jpg\": \"Insecta/Ascia monuste/ca632bc72b5c7bc67e9274d04e8958c4.jpg\",\n  \"151837.jpg\": \"Insecta/Ascia monuste/43eed50bc008cf78d66ea9a627df3658.jpg\",\n  \"151840.jpg\": \"Insecta/Ascia monuste/f853fdba8560a74fbd6eb59e11ed19b4.jpg\",\n  \"151844.jpg\": \"Insecta/Ascia monuste/465ee7b339afdef08d826e067a9c4ca1.jpg\",\n  \"151847.jpg\": \"Insecta/Ascia monuste/8b950adae30913a0271bb0d06ad84588.jpg\",\n  \"151854.jpg\": \"Insecta/Ascia monuste/f1a42b8506ff1c129567782e1bd786c3.jpg\",\n  \"151857.jpg\": \"Insecta/Ascia monuste/8789f281edcab9cb80cf69d2a0a30d9b.jpg\",\n  \"151861.jpg\": \"Insecta/Ascia monuste/497432bb8047f4bca62e4940fcb1809e.jpg\",\n  \"151862.jpg\": \"Insecta/Ascia monuste/4b0c38657ce9c116c8fc65d509c47d59.jpg\",\n  \"151866.jpg\": \"Insecta/Ascia monuste/00e8a2221915582e14d85d26cc0ac803.jpg\",\n  \"151877.jpg\": \"Insecta/Ascia monuste/f20c5af26e2c713e3cfc0ac3d35a6fd9.jpg\",\n  \"151879.jpg\": \"Insecta/Ascia monuste/a06899063a82c0f98bf1245e9de99790.jpg\",\n  \"152097.jpg\": \"Aves/Mycteria ibis/74372dc303c29187fa32d8844d6c0bbd.jpg\",\n  \"152103.jpg\": \"Aves/Mycteria ibis/2a7cfe1cfe0e6ae47e7c9a807182fdab.jpg\",\n  \"152105.jpg\": \"Aves/Mycteria ibis/d82097a47ab33d6b75dc9819f5ba8a01.jpg\",\n  \"152124.jpg\": \"Insecta/Iridopsis defectaria/c8decf853abf400b21c9637a841c88da.jpg\",\n  \"152126.jpg\": \"Insecta/Iridopsis defectaria/8b5cb7be0af9ad04b573fc440bff4451.jpg\",\n  \"152137.jpg\": \"Insecta/Iridopsis defectaria/d0bef6f9150707785faa39732e9067a5.jpg\",\n  \"152140.jpg\": \"Insecta/Iridopsis defectaria/557604cc8cbef47e5bf86ae67cd54b8a.jpg\",\n  \"152165.jpg\": \"Aves/Larus canus/e9553468c7495b3b7356ae3c73671f9e.jpg\",\n  \"152167.jpg\": \"Aves/Larus canus/ac1b3c542ac67949a678ffa1352536ba.jpg\",\n  \"152171.jpg\": \"Aves/Larus canus/542c1020cfcc9aa40770f4dcb0b53377.jpg\",\n  \"152173.jpg\": \"Aves/Larus canus/32ebf61f27daa04c5415dec79b177a7b.jpg\",\n  \"152176.jpg\": \"Aves/Larus canus/c2ea6678474c0f858cfd24e2649c205f.jpg\",\n  \"152188.jpg\": \"Aves/Larus canus/03f06ef6e04fd8871f2e9a243118e7f1.jpg\",\n  \"152189.jpg\": \"Aves/Larus canus/fbd5a6dc6b7bb340829984c02069a3f2.jpg\",\n  \"152221.jpg\": \"Aves/Larus canus/81d4f61d8fceb9248f650b4a52dc9f46.jpg\",\n  \"152252.jpg\": \"Aves/Larus canus/789888d17e73355a46565bccd6335d0a.jpg\",\n  \"152254.jpg\": \"Aves/Larus canus/8996df191f7a685c2e932f5ed64a1a2e.jpg\",\n  \"152260.jpg\": \"Aves/Larus canus/f1445f768bbdd706c06bf36fdb463207.jpg\",\n  \"152265.jpg\": \"Aves/Larus canus/29da2a06a7768016c675f150e822089d.jpg\",\n  \"152266.jpg\": \"Aves/Larus canus/429c0c6802a78d5f7f5f3782d3fed2d1.jpg\",\n  \"152274.jpg\": \"Aves/Larus canus/30d93e2d0348564ceb452a23ba9da02c.jpg\",\n  \"152275.jpg\": \"Aves/Larus canus/a77f5c15a0b3b4d80aaf3822f5a13b3f.jpg\",\n  \"152284.jpg\": \"Mollusca/Nuttallina californica/22e5e213fc3ad9dbf4a202c1dcff1ff6.jpg\",\n  \"152290.jpg\": \"Mollusca/Nuttallina californica/52530e3ce852b6ccd1f26beb91b0d847.jpg\",\n  \"152303.jpg\": \"Mollusca/Nuttallina californica/67b9d603b9bdfccf967ecfdd863dd070.jpg\",\n  \"152304.jpg\": \"Mollusca/Nuttallina californica/3937f24c02f70f2caf40658b7d45c629.jpg\",\n  \"152309.jpg\": \"Aves/Merops apiaster/ec6757d9201e7f88b167f515f410b7a9.jpg\",\n  \"152310.jpg\": \"Aves/Merops apiaster/5020abb6635bfa994902eb9c2d64f71a.jpg\",\n  \"152318.jpg\": \"Aves/Merops apiaster/091133a42dcf13179523d24be2586f51.jpg\",\n  \"152319.jpg\": \"Aves/Merops apiaster/5067e37c07a247d81eed607b724c7105.jpg\",\n  \"152331.jpg\": \"Aves/Merops apiaster/48e5b6d77333817e2879d7f435d3efac.jpg\",\n  \"152332.jpg\": \"Aves/Merops apiaster/4043ec974af9cc3d84b0d10ced40d216.jpg\",\n  \"152334.jpg\": \"Aves/Merops apiaster/120e2bbc92e735108a6044f20c9cf4b5.jpg\",\n  \"152340.jpg\": \"Aves/Merops apiaster/2e60eb231979ac0e9483fbc29b09ad87.jpg\",\n  \"152341.jpg\": \"Aves/Merops apiaster/4259111e4c33e7fc68fe5fa1200ef78c.jpg\",\n  \"152343.jpg\": \"Aves/Merops apiaster/c44e345a31011428d5bb1c564ab5aa7c.jpg\",\n  \"152345.jpg\": \"Aves/Merops apiaster/d2136dac0c10377017796efee5248bdc.jpg\",\n  \"152347.jpg\": \"Aves/Merops apiaster/134f648525619a1bc7f94b6985f00901.jpg\",\n  \"152406.jpg\": \"Insecta/Haploa confusa/de3e17989d25256a32406caef226e5cf.jpg\",\n  \"152407.jpg\": \"Insecta/Haploa confusa/093216f1cd85bfd94b43d49a1985184d.jpg\",\n  \"152408.jpg\": \"Insecta/Haploa confusa/0da5f963017c1776104422108158e9a4.jpg\",\n  \"152411.jpg\": \"Insecta/Haploa confusa/14e1e39b7b8fce2bcb10f0f1dfa08bc8.jpg\",\n  \"152412.jpg\": \"Insecta/Haploa confusa/e789d902bded646646a15109aceee976.jpg\",\n  \"152416.jpg\": \"Insecta/Haploa confusa/bd6df2ba8dfddff317aec96ce4c90f86.jpg\",\n  \"152419.jpg\": \"Insecta/Haploa confusa/ab43a979460f745dc2a84c202365b0a4.jpg\",\n  \"152424.jpg\": \"Aves/Sula granti/aeba8d3fa76396d55f95b278ea89bfe0.jpg\",\n  \"152428.jpg\": \"Aves/Sula granti/9b715e9cb56390c579037643ffff8134.jpg\",\n  \"152432.jpg\": \"Aves/Sula granti/2ce3026a10106e18e620943eb9de64e1.jpg\",\n  \"152433.jpg\": \"Aves/Sula granti/49b8db04c04231f91ed53e913b113d63.jpg\",\n  \"152755.jpg\": \"Amphibia/Eurycea lucifuga/49b929d2ad76ad10963449430684f15c.jpg\",\n  \"152761.jpg\": \"Amphibia/Eurycea lucifuga/b392db4cff308f12e3a788f438688e09.jpg\",\n  \"152763.jpg\": \"Amphibia/Eurycea lucifuga/48651bf54d078b5da5d88b7b0cca2a41.jpg\",\n  \"152764.jpg\": \"Amphibia/Eurycea lucifuga/8debb9392851110032dd5837d125e683.jpg\",\n  \"152766.jpg\": \"Amphibia/Eurycea lucifuga/f5db239a1a4e56389de7332431407a21.jpg\",\n  \"152811.jpg\": \"Insecta/Chelinidea vittiger/50161d2d620fea2b66ba371c7f388434.jpg\",\n  \"152815.jpg\": \"Insecta/Chelinidea vittiger/01cb2bb9ee83fb5b9674e43bd76e0dfb.jpg\",\n  \"152940.jpg\": \"Insecta/Rhionaeschna californica/65deeda0a20d90f7e30d33d34a2bf769.jpg\",\n  \"152942.jpg\": \"Insecta/Rhionaeschna californica/a8af393dff70ff4a2b57564d144fe1fd.jpg\",\n  \"152943.jpg\": \"Insecta/Rhionaeschna californica/9b1c162cd0ae04ad74859d9b71bfb6d4.jpg\",\n  \"152945.jpg\": \"Insecta/Rhionaeschna californica/e390840469c1183e44c0bff7f35f5ce1.jpg\",\n  \"152946.jpg\": \"Insecta/Rhionaeschna californica/d432f778a8d2ee69836fbaca56b9cafc.jpg\",\n  \"152954.jpg\": \"Insecta/Rhionaeschna californica/8eae1d99a05185483d1dd0ffd8a9e75f.jpg\",\n  \"153084.jpg\": \"Mammalia/Tachyglossus aculeatus/820661fcc3dfc891a93acd0a26fc5bcd.jpg\",\n  \"153087.jpg\": \"Mammalia/Tachyglossus aculeatus/48ab5f383c53d4f7c9a2cd7b1e48897d.jpg\",\n  \"153091.jpg\": \"Mammalia/Tachyglossus aculeatus/954113bab9f41bf3415eeb3bec44ca33.jpg\",\n  \"153096.jpg\": \"Mammalia/Tachyglossus aculeatus/085d57f61826d3dcac156a63ef9841f9.jpg\",\n  \"153097.jpg\": \"Mammalia/Tachyglossus aculeatus/4c5309e6d979e56c6b92199b173f3e9f.jpg\",\n  \"153100.jpg\": \"Mammalia/Tachyglossus aculeatus/393a711e77671d7c2f9d720c1d7f360f.jpg\",\n  \"153192.jpg\": \"Aves/Streptopelia decaocto/8b9f9b35395071492881e2b041b325e4.jpg\",\n  \"153216.jpg\": \"Aves/Streptopelia decaocto/c20d1f8f8f6254696466f55c9bd074b5.jpg\",\n  \"153255.jpg\": \"Aves/Streptopelia decaocto/b0d113abe6279ed6229856ac2d4d7b15.jpg\",\n  \"153291.jpg\": \"Aves/Streptopelia decaocto/9b04acbd52d07fcabb075b7fdef3f4f2.jpg\",\n  \"153292.jpg\": \"Aves/Streptopelia decaocto/e6eefabc467cce7ecbea072a852eaf18.jpg\",\n  \"153308.jpg\": \"Aves/Streptopelia decaocto/e956031818c07608425288c0a529dbb1.jpg\",\n  \"153314.jpg\": \"Aves/Streptopelia decaocto/9ccf1bd1ba06f88cadd42511fea2915f.jpg\",\n  \"153315.jpg\": \"Aves/Streptopelia decaocto/72003a3b50d82392bcd794f6508a5d81.jpg\",\n  \"153341.jpg\": \"Aves/Streptopelia decaocto/e9829f21063ebc52a385722dc35cc8bc.jpg\",\n  \"153344.jpg\": \"Aves/Streptopelia decaocto/4946c603f89b728f7902f8f3a0412538.jpg\",\n  \"153380.jpg\": \"Aves/Streptopelia decaocto/2f85d130d4f277327b34fe63b27b8c51.jpg\",\n  \"153385.jpg\": \"Aves/Streptopelia decaocto/be4e4b63130ec40c5c36b432e353a6fa.jpg\",\n  \"153412.jpg\": \"Aves/Streptopelia decaocto/fa418290d99fc04d5257e3aa5c980b9a.jpg\",\n  \"153418.jpg\": \"Aves/Streptopelia decaocto/83465c386b4f5eaa2296d5ed45499e3e.jpg\",\n  \"153443.jpg\": \"Aves/Streptopelia decaocto/40fc9889982450b2ef0ce26c60382e1d.jpg\",\n  \"153518.jpg\": \"Aves/Streptopelia decaocto/a5bc49b34a6aeaa54ea483a992619836.jpg\",\n  \"153537.jpg\": \"Aves/Streptopelia decaocto/07284867e5e5f6e3a72a5460f6c933ae.jpg\",\n  \"153559.jpg\": \"Aves/Streptopelia decaocto/884f84b9242d313511db4eba10af72c4.jpg\",\n  \"153563.jpg\": \"Aves/Streptopelia decaocto/8378a185f67daed81be7452d0a6a96d6.jpg\",\n  \"153572.jpg\": \"Aves/Streptopelia decaocto/6da3226fb5e5d6097f182dbc7b11fa5b.jpg\",\n  \"153621.jpg\": \"Insecta/Polyommatus icarus/a37dcd8e118b357e9027acc7dacf0542.jpg\",\n  \"153622.jpg\": \"Insecta/Polyommatus icarus/8538b44061411a804b01be1d3c4dca53.jpg\",\n  \"153623.jpg\": \"Insecta/Polyommatus icarus/1f331b30e950a008bced124e18b65a06.jpg\",\n  \"153628.jpg\": \"Insecta/Polyommatus icarus/98da0449bb6e2e588e445c8df9f25c58.jpg\",\n  \"153629.jpg\": \"Insecta/Polyommatus icarus/ba4cca7ec4e39b5350a60db2fa77892b.jpg\",\n  \"153634.jpg\": \"Insecta/Polyommatus icarus/0282f6f5c71705f527f5571bbe440ed6.jpg\",\n  \"153635.jpg\": \"Insecta/Polyommatus icarus/309ec8e955a6546c816933efb79791b5.jpg\",\n  \"153636.jpg\": \"Insecta/Polyommatus icarus/5fef211ecb3612a1f2c8c2282646a610.jpg\",\n  \"153637.jpg\": \"Insecta/Polyommatus icarus/028fabe89e1698b479e4643e7d132df5.jpg\",\n  \"153638.jpg\": \"Insecta/Polyommatus icarus/d4452d650fec76f2263c9ac25ce1422a.jpg\",\n  \"153641.jpg\": \"Insecta/Polyommatus icarus/167ea333b3bd1d8cd07f44041a0844c5.jpg\",\n  \"153660.jpg\": \"Insecta/Polyommatus icarus/b8afb8e189808253b35288ff1fad9b97.jpg\",\n  \"153661.jpg\": \"Insecta/Polyommatus icarus/b4d18407b1d6477fadf881635224aac0.jpg\",\n  \"153662.jpg\": \"Insecta/Polyommatus icarus/d43fc7fd8c3fdb3461e541f61f29ff3e.jpg\",\n  \"153663.jpg\": \"Insecta/Polyommatus icarus/04ad56b065f6e7c2d95c447fbd28dd4b.jpg\",\n  \"153664.jpg\": \"Insecta/Polyommatus icarus/3e947e5f75d7a188f3a66252b4446df5.jpg\",\n  \"153669.jpg\": \"Insecta/Polyommatus icarus/9a3000204992b3c9a264036c20d168dc.jpg\",\n  \"153670.jpg\": \"Insecta/Polyommatus icarus/60610c938c3f76b0ed1a8f75c95b8ead.jpg\",\n  \"153671.jpg\": \"Insecta/Polyommatus icarus/d6017e4ad3e27d0053efcc18e284214f.jpg\",\n  \"153988.jpg\": \"Reptilia/Diadophis punctatus edwardsii/eb90b158f75ba20baf5d687fdbbff476.jpg\",\n  \"153990.jpg\": \"Reptilia/Diadophis punctatus edwardsii/594f3ed46c26e5d6da8829c30fce2c33.jpg\",\n  \"153991.jpg\": \"Reptilia/Diadophis punctatus edwardsii/79ccd98bbb9973be4e3b1feaa7288ad7.jpg\",\n  \"153999.jpg\": \"Reptilia/Diadophis punctatus edwardsii/4cc5195f62fcc678ad3583fff0b1dd1e.jpg\",\n  \"154002.jpg\": \"Reptilia/Diadophis punctatus edwardsii/56d01bdcb0d329a975a452369f91302f.jpg\",\n  \"154006.jpg\": \"Reptilia/Diadophis punctatus edwardsii/7fb01a81e3efc55e4fb6e6ffcc6ddb35.jpg\",\n  \"154010.jpg\": \"Reptilia/Diadophis punctatus edwardsii/792c3e3582f59e8fc380ab3faf82dcbf.jpg\",\n  \"154011.jpg\": \"Reptilia/Diadophis punctatus edwardsii/97c4123d7f0b53071a2af9e544af1d34.jpg\",\n  \"154013.jpg\": \"Reptilia/Diadophis punctatus edwardsii/9c98b13caacbd07a0af552500cafeed5.jpg\",\n  \"154123.jpg\": \"Insecta/Cycloneda sanguinea/43449a281254efcbab4be1b979e29777.jpg\",\n  \"154126.jpg\": \"Insecta/Cycloneda sanguinea/08da675b61805896bb0708d7a1cd8725.jpg\",\n  \"154137.jpg\": \"Insecta/Cycloneda sanguinea/494e163857d83735a8b235860c460d28.jpg\",\n  \"154138.jpg\": \"Insecta/Cycloneda sanguinea/39624d5e4332b42168ee0f528b3b15ff.jpg\",\n  \"154143.jpg\": \"Insecta/Cycloneda sanguinea/14ea9304f4131b1d0fb9633cab5956d9.jpg\",\n  \"154146.jpg\": \"Insecta/Cycloneda sanguinea/0be1f7749e32b396b356a26f4c82bcf7.jpg\",\n  \"154147.jpg\": \"Insecta/Cycloneda sanguinea/8cfa60b3e538e19388bf4523ddecb682.jpg\",\n  \"154167.jpg\": \"Insecta/Cycloneda sanguinea/6d70f0c9467296baf3c51828f135f652.jpg\",\n  \"154170.jpg\": \"Insecta/Cycloneda sanguinea/f8e05355f1780caf202581fe57db63d3.jpg\",\n  \"154172.jpg\": \"Insecta/Cycloneda sanguinea/2fa08971086fb6af9b35da460397e926.jpg\",\n  \"154181.jpg\": \"Insecta/Cycloneda sanguinea/a1804e7d002f2792900185789f9f4252.jpg\",\n  \"154190.jpg\": \"Insecta/Cycloneda sanguinea/bb0f07edb3b763fd985290be17b838fe.jpg\",\n  \"154202.jpg\": \"Insecta/Cycloneda sanguinea/c39a415857bb66b6e9242a50aab0b6eb.jpg\",\n  \"154203.jpg\": \"Insecta/Cycloneda sanguinea/0621f3dc5ec9b71f5c4234cd7d728867.jpg\",\n  \"154207.jpg\": \"Insecta/Cycloneda sanguinea/f274ddef8f8c01495da3901503f757e0.jpg\",\n  \"154218.jpg\": \"Insecta/Cycloneda sanguinea/22beff510a76f83103489a9d6e89fb67.jpg\",\n  \"154230.jpg\": \"Insecta/Cycloneda sanguinea/17670c0613959479eff83de4652f07aa.jpg\",\n  \"154231.jpg\": \"Insecta/Cycloneda sanguinea/7c43e5faee9fff0f6af31ede25f30405.jpg\",\n  \"154235.jpg\": \"Insecta/Cycloneda sanguinea/96386b9ebe98a26072f682afbbe20e20.jpg\",\n  \"154251.jpg\": \"Reptilia/Trachemys scripta scripta/88756bff52077a9a14bd305ec7cf79e8.jpg\",\n  \"154254.jpg\": \"Reptilia/Trachemys scripta scripta/e8cf60a7f2c2dc4ba44d510137888cdf.jpg\",\n  \"154255.jpg\": \"Reptilia/Trachemys scripta scripta/de3a7b90ca981aa788a055e943df8b0e.jpg\",\n  \"154256.jpg\": \"Reptilia/Trachemys scripta scripta/50663bc036192e1e652cc959b6b54ded.jpg\",\n  \"154260.jpg\": \"Reptilia/Trachemys scripta scripta/a2495740819f37945ea6098145ad9d47.jpg\",\n  \"154261.jpg\": \"Reptilia/Trachemys scripta scripta/23773ac610d9a7220002e63c30ea40af.jpg\",\n  \"154262.jpg\": \"Reptilia/Trachemys scripta scripta/622567ac864f711387a9dddb5d208091.jpg\",\n  \"154263.jpg\": \"Reptilia/Trachemys scripta scripta/a2e1931e77218b38fdfc21c9c644cfad.jpg\",\n  \"154264.jpg\": \"Reptilia/Trachemys scripta scripta/cd1b472d8d69ffdb3c41589a74fdc94c.jpg\",\n  \"154265.jpg\": \"Reptilia/Trachemys scripta scripta/d2bb59e27a8873926c3dadbfa20a5dc4.jpg\",\n  \"154266.jpg\": \"Reptilia/Trachemys scripta scripta/c0383a2dbf601276b7be80e5e3b593e7.jpg\",\n  \"154269.jpg\": \"Reptilia/Trachemys scripta scripta/9a830499954789237f55b09c6a2ddf80.jpg\",\n  \"154270.jpg\": \"Reptilia/Trachemys scripta scripta/03d28e5a20a6664c4383ef3417cb915b.jpg\",\n  \"154272.jpg\": \"Reptilia/Trachemys scripta scripta/ede00cb827faa916101a6503a52e9092.jpg\",\n  \"154273.jpg\": \"Reptilia/Trachemys scripta scripta/ec941b8e3847b59f97c014ecb483a63b.jpg\",\n  \"154274.jpg\": \"Reptilia/Trachemys scripta scripta/c20e631a48fd328c923de007d078d141.jpg\",\n  \"154275.jpg\": \"Reptilia/Trachemys scripta scripta/5190a649cc6a4f1804d88d81abd2d570.jpg\",\n  \"154276.jpg\": \"Reptilia/Trachemys scripta scripta/0223df2f132511db6fb58a614fc72681.jpg\",\n  \"154281.jpg\": \"Reptilia/Trachemys scripta scripta/30ce903f72eafdf18c6f5d01409da164.jpg\",\n  \"154320.jpg\": \"Insecta/Ecliptopera silaceata/7377f80ab85836d1ea34a6d8ed247b6e.jpg\",\n  \"154324.jpg\": \"Insecta/Ecliptopera silaceata/de882957f87936786479aba73db7588e.jpg\",\n  \"154326.jpg\": \"Insecta/Ecliptopera silaceata/f5774438caaa9c06122307728ccf4fc6.jpg\",\n  \"154331.jpg\": \"Insecta/Ecliptopera silaceata/50f5f67442217cbf0b8a8094b92e1b83.jpg\",\n  \"154338.jpg\": \"Insecta/Ecliptopera silaceata/0f34577496b80b9483d3cc5e2f642e1c.jpg\",\n  \"154347.jpg\": \"Arachnida/Oxyopes salticus/d669d78d50de0e0f800d79d7c064b063.jpg\",\n  \"154348.jpg\": \"Arachnida/Oxyopes salticus/b81aabc328af936b02a422b62cbdd2c9.jpg\",\n  \"154355.jpg\": \"Arachnida/Oxyopes salticus/a40ff3a329b5c5346a30f647862e8189.jpg\",\n  \"154362.jpg\": \"Arachnida/Oxyopes salticus/27526c9ce1aa186acd3657f2ee361e22.jpg\",\n  \"154367.jpg\": \"Arachnida/Oxyopes salticus/751420a7f64831b4dd7fb13dbd16b051.jpg\",\n  \"154368.jpg\": \"Arachnida/Oxyopes salticus/bf7d3673f82481c5f5b9bdc799919ccd.jpg\",\n  \"154372.jpg\": \"Arachnida/Oxyopes salticus/a97af79a218ef0b57157c8b08eb18beb.jpg\",\n  \"154373.jpg\": \"Arachnida/Oxyopes salticus/b1ffecd46e4bc303a8fc95ed9871778b.jpg\",\n  \"154382.jpg\": \"Reptilia/Terrapene carolina triunguis/762fdd4cf25525f4c9405acc9318af04.jpg\",\n  \"154390.jpg\": \"Reptilia/Terrapene carolina triunguis/16f9910e2bdf273f8d6ccbeb04c3e93d.jpg\",\n  \"154391.jpg\": \"Reptilia/Terrapene carolina triunguis/f9290ef4225bd8feb06460458acce6e5.jpg\",\n  \"154394.jpg\": \"Reptilia/Terrapene carolina triunguis/4f407d8082b10cce59275eb9a63b83bd.jpg\",\n  \"154397.jpg\": \"Reptilia/Terrapene carolina triunguis/9ac5573ed7f6771f60ddd257d2c9155c.jpg\",\n  \"154405.jpg\": \"Reptilia/Terrapene carolina triunguis/841664c45a49f318537ca51a9d363ede.jpg\",\n  \"154414.jpg\": \"Reptilia/Terrapene carolina triunguis/aa4dfbc081d19ee02bfb6804f68b76c4.jpg\",\n  \"154430.jpg\": \"Reptilia/Terrapene carolina triunguis/8c44631542bc64c2213ec747c029d29d.jpg\",\n  \"154441.jpg\": \"Reptilia/Terrapene carolina triunguis/33590b11f3bb16da4a71178eb972b8f9.jpg\",\n  \"154444.jpg\": \"Reptilia/Terrapene carolina triunguis/5af091b2900811148849b8fa1a4ff723.jpg\",\n  \"154455.jpg\": \"Reptilia/Terrapene carolina triunguis/bfb8c35af3af2d1b81a83417b1c8fbc5.jpg\",\n  \"154473.jpg\": \"Reptilia/Terrapene carolina triunguis/cdd865a5fc8eaf05de57121e0112c6fd.jpg\",\n  \"154493.jpg\": \"Reptilia/Terrapene carolina triunguis/a2ee045e7008e49da749ace85d4a15d5.jpg\",\n  \"154498.jpg\": \"Reptilia/Terrapene carolina triunguis/68a59abf9860b81bb22cf258d8640adb.jpg\",\n  \"154499.jpg\": \"Reptilia/Terrapene carolina triunguis/c5b0e452557943c069f9d007ce412fbe.jpg\",\n  \"154501.jpg\": \"Reptilia/Terrapene carolina triunguis/f1b8525e7a9c5f73d47d45890de8c3ae.jpg\",\n  \"154506.jpg\": \"Reptilia/Terrapene carolina triunguis/d86926e355171080496759ddc980a9f0.jpg\",\n  \"154507.jpg\": \"Reptilia/Terrapene carolina triunguis/7ca7e6de80a40c7d57c0d10d2aba88b4.jpg\",\n  \"154508.jpg\": \"Reptilia/Terrapene carolina triunguis/4e98cc5c1bc5513dee5a19fa77d39922.jpg\",\n  \"154512.jpg\": \"Reptilia/Terrapene carolina triunguis/a824e0a7179071bd98a56c039ed4788e.jpg\",\n  \"154515.jpg\": \"Reptilia/Terrapene carolina triunguis/fa4e17710b13d3382bc5c89eaa6c6d5c.jpg\",\n  \"154535.jpg\": \"Reptilia/Terrapene carolina triunguis/3611a66020001104bc37206e3c0ae751.jpg\",\n  \"154559.jpg\": \"Insecta/Ancistrocerus gazella/9a83311c713d4e4b5ec3759428cb33bb.jpg\",\n  \"154566.jpg\": \"Insecta/Ancistrocerus gazella/36a3ce578d6b6f0dfe73eebbfd8270d4.jpg\",\n  \"154567.jpg\": \"Insecta/Ancistrocerus gazella/67df4c5ede85bd341c0f0fe936619748.jpg\",\n  \"154576.jpg\": \"Insecta/Ancistrocerus gazella/7bb67ffa78675e4c29e2e2f03f483012.jpg\",\n  \"154679.jpg\": \"Insecta/Libellula quadrimaculata/2e8d122f8af55635866237519d54e613.jpg\",\n  \"154697.jpg\": \"Insecta/Libellula quadrimaculata/2b8d842b86457a997c4f14b38265d8ef.jpg\",\n  \"154704.jpg\": \"Insecta/Libellula quadrimaculata/545e66592d6ba35abfe9fbd689ceeb61.jpg\",\n  \"154705.jpg\": \"Insecta/Libellula quadrimaculata/aa9095ad0f6a8af7cac9e4f54fbb8df0.jpg\",\n  \"154708.jpg\": \"Insecta/Libellula quadrimaculata/45d7e341b933cc504b853ef5feeb7a93.jpg\",\n  \"154709.jpg\": \"Insecta/Libellula quadrimaculata/839d2077748e01897e8ae210640e54a9.jpg\",\n  \"154710.jpg\": \"Insecta/Libellula quadrimaculata/349b3b3f41286cf22272370ebe6e7b50.jpg\",\n  \"154713.jpg\": \"Insecta/Libellula quadrimaculata/4b3a889fd8bb818b87579d80a7e48df2.jpg\",\n  \"154722.jpg\": \"Insecta/Libellula quadrimaculata/ad5e0889a085bb6ee3de4ade45f625e8.jpg\",\n  \"154724.jpg\": \"Insecta/Libellula quadrimaculata/bcfbc20c69e1b4a1d96afed2221a79fe.jpg\",\n  \"154728.jpg\": \"Insecta/Libellula quadrimaculata/65a6ffe5fb56a3366623381798557ce4.jpg\",\n  \"154729.jpg\": \"Insecta/Libellula quadrimaculata/21224a48338bc6b271c284b9c3832f58.jpg\",\n  \"154730.jpg\": \"Insecta/Libellula quadrimaculata/65d4d21e66f4b0ae93c0966fecd2dc78.jpg\",\n  \"154732.jpg\": \"Insecta/Libellula quadrimaculata/4f5f2f7f1a428b21104a3f8c807a6d45.jpg\",\n  \"154757.jpg\": \"Insecta/Libellula quadrimaculata/eb2b4f4e3161139332ec343109b0f1ab.jpg\",\n  \"154759.jpg\": \"Insecta/Libellula quadrimaculata/429e297bcdf3504c254ecf38e5d00cd6.jpg\",\n  \"154765.jpg\": \"Insecta/Libellula quadrimaculata/43e676ad0bad5bf8391f19a1841dbedc.jpg\",\n  \"154768.jpg\": \"Insecta/Libellula quadrimaculata/c079b34510521cc67f020c39ae515e3f.jpg\",\n  \"154769.jpg\": \"Insecta/Libellula quadrimaculata/75aac577a22176bf9eeab90c640cb7e6.jpg\",\n  \"154771.jpg\": \"Insecta/Libellula quadrimaculata/e11048970dfd2f72f5bc5263689ffa24.jpg\",\n  \"154773.jpg\": \"Insecta/Libellula quadrimaculata/fabf36e76b92b6605e79941b4d2a6e51.jpg\",\n  \"154775.jpg\": \"Insecta/Libellula quadrimaculata/3822aa83906a12b967c410f8f186d197.jpg\",\n  \"154777.jpg\": \"Insecta/Libellula quadrimaculata/9d354dd622e4aede202a44275aa242f1.jpg\",\n  \"154778.jpg\": \"Insecta/Libellula quadrimaculata/41c21b5a5b73174bd7a31af59868bcbe.jpg\",\n  \"154787.jpg\": \"Aves/Nucifraga columbiana/7af5753fc43a2acddef6088f8e2210fc.jpg\",\n  \"154793.jpg\": \"Aves/Nucifraga columbiana/08e6d37098ee02741ed17d43410fac9d.jpg\",\n  \"154794.jpg\": \"Aves/Nucifraga columbiana/56d2ef054f98dc63f7d7b3a91f82a464.jpg\",\n  \"154798.jpg\": \"Aves/Nucifraga columbiana/6016926dd6f73fb096a7704ab43b5ca5.jpg\",\n  \"154803.jpg\": \"Aves/Nucifraga columbiana/0254912c03e3237b4836b546b04e26f6.jpg\",\n  \"154806.jpg\": \"Aves/Nucifraga columbiana/60c6b00ab3e3858d0dbf1921c09c7906.jpg\",\n  \"154816.jpg\": \"Aves/Nucifraga columbiana/e02fd7c84e28e5c2d0179255a88f3326.jpg\",\n  \"154817.jpg\": \"Aves/Nucifraga columbiana/722e75b2652056ddb2d80535530042fd.jpg\",\n  \"154818.jpg\": \"Aves/Nucifraga columbiana/3eb7dc40f40d6375f4bb516b20719bf9.jpg\",\n  \"154823.jpg\": \"Aves/Nucifraga columbiana/fb9afa6480901f2e2777f375ba5807e3.jpg\",\n  \"154824.jpg\": \"Aves/Nucifraga columbiana/a8eb648fa553a6d36fd43187a0fde7d7.jpg\",\n  \"154825.jpg\": \"Aves/Nucifraga columbiana/5c2308a9f71b4deab3ab8f53e35c2981.jpg\",\n  \"154829.jpg\": \"Aves/Nucifraga columbiana/7b10ba35e9589b6579004e8b069f794f.jpg\",\n  \"154832.jpg\": \"Aves/Nucifraga columbiana/f16d02fa7316257aa5003fbcf9fc7e10.jpg\",\n  \"154839.jpg\": \"Aves/Nucifraga columbiana/f6dc128f9720ffc95d4c55d34373c049.jpg\",\n  \"154847.jpg\": \"Aves/Nucifraga columbiana/d50ef6642e3305fd082ee751ffcb8a53.jpg\",\n  \"154849.jpg\": \"Aves/Nucifraga columbiana/cec4c4666309f890462c123b4717bc09.jpg\",\n  \"154852.jpg\": \"Aves/Nucifraga columbiana/704c2986c9f748589ec564e7387dfd9c.jpg\",\n  \"154856.jpg\": \"Aves/Nucifraga columbiana/80a2cb4eae6258fc6505a6179774eec3.jpg\",\n  \"154862.jpg\": \"Aves/Nucifraga columbiana/92ffea9426bbdd1255e07a87b0ace7f0.jpg\",\n  \"154865.jpg\": \"Aves/Nucifraga columbiana/74ecfd8e99a1b6b1f107283c710a24ed.jpg\",\n  \"154870.jpg\": \"Insecta/Cactophagus spinolae/57100fba3260a46fe22f7a5cd907c3b9.jpg\",\n  \"154873.jpg\": \"Insecta/Cactophagus spinolae/1884845b27a894b3867b0e8764f149f8.jpg\",\n  \"154943.jpg\": \"Insecta/Simyra insularis/6ca9e80cd3d5b1512cdccf5384dd23b1.jpg\",\n  \"154964.jpg\": \"Reptilia/Pantherophis guttatus/fd4973232b777b7e7ed09f7d2f2a2430.jpg\",\n  \"15497.jpg\": \"Aves/Myiodynastes luteiventris/f69cdfb43c847fcdfe5a4e04a3db8949.jpg\",\n  \"154971.jpg\": \"Reptilia/Pantherophis guttatus/a4b12078971e0ae508d40ea4747a236e.jpg\",\n  \"154975.jpg\": \"Reptilia/Pantherophis guttatus/2dffd1e76c9cf73b36797a9bb34cee37.jpg\",\n  \"154979.jpg\": \"Reptilia/Pantherophis guttatus/be3410440167a90d7475fe4bdc5be079.jpg\",\n  \"15498.jpg\": \"Aves/Myiodynastes luteiventris/3607e137e7d1cf85064801d40faf3522.jpg\",\n  \"155.jpg\": \"Aves/Zonotrichia capensis/5292d93c0c06d214f6158b1bb977e70a.jpg\",\n  \"15500.jpg\": \"Aves/Myiodynastes luteiventris/d417d019a0e8018944767b4a183a2068.jpg\",\n  \"15504.jpg\": \"Aves/Myiodynastes luteiventris/a21f0a5bd8f8182dca9581ea6c3ca2fb.jpg\",\n  \"15507.jpg\": \"Aves/Myiodynastes luteiventris/35e6d23cd2f4d2fedb3d551aefaf70ff.jpg\",\n  \"15510.jpg\": \"Aves/Myiodynastes luteiventris/2cd8f2825cc80c4a3c8b167b720c8373.jpg\",\n  \"15511.jpg\": \"Aves/Myiodynastes luteiventris/d7c9e3d4f8612c61ee5792d8e710b47e.jpg\",\n  \"15512.jpg\": \"Aves/Myiodynastes luteiventris/2c9ef496a529b65fb98258d5b009f9bf.jpg\",\n  \"15515.jpg\": \"Aves/Myiodynastes luteiventris/798bbee1460229d54cd41fed40691213.jpg\",\n  \"15516.jpg\": \"Aves/Myiodynastes luteiventris/d361afdf534bf55f2d43cece221c015a.jpg\",\n  \"155226.jpg\": \"Insecta/Argynnis paphia/8f7a4ff8c94af3df893bbcb5a19409b1.jpg\",\n  \"155228.jpg\": \"Insecta/Argynnis paphia/83543098a1dc88fee1ff545bb3fb9b4d.jpg\",\n  \"155229.jpg\": \"Insecta/Argynnis paphia/01f9c40a50cf3cca61a839cd15e521e1.jpg\",\n  \"155231.jpg\": \"Insecta/Argynnis paphia/2605b222172b3312ff42afb82a2ce495.jpg\",\n  \"155232.jpg\": \"Insecta/Argynnis paphia/34758ef570fe6570a109b80d3bfcf4cd.jpg\",\n  \"155236.jpg\": \"Insecta/Argynnis paphia/e35337053ad40d315eeb03713ae288ca.jpg\",\n  \"155237.jpg\": \"Insecta/Argynnis paphia/f5064d41e223b3223bef52b63fc75a6b.jpg\",\n  \"155242.jpg\": \"Insecta/Argynnis paphia/9371032d0ad50420790dfc6c8554d807.jpg\",\n  \"155243.jpg\": \"Insecta/Argynnis paphia/14c3c7a78cb37462ecd7a7ebcb427398.jpg\",\n  \"155249.jpg\": \"Insecta/Argynnis paphia/3f7ba6aa59183acd2fe83dbaca2736e9.jpg\",\n  \"155256.jpg\": \"Insecta/Argynnis paphia/dc123bbae838619fbef39e605b43aed2.jpg\",\n  \"155260.jpg\": \"Insecta/Argynnis paphia/ca031f1b1b8f161ac503efbb7afd3c10.jpg\",\n  \"155265.jpg\": \"Insecta/Argynnis paphia/9d6086ec54502a731f448449f47cf206.jpg\",\n  \"155267.jpg\": \"Insecta/Argynnis paphia/07d0d68c42a4e2698a638a4a6a5900ad.jpg\",\n  \"155268.jpg\": \"Insecta/Argynnis paphia/09d8c895a053da9a2c8e8240584f91bb.jpg\",\n  \"15527.jpg\": \"Aves/Pheucticus melanocephalus/4dd6d71762b99f335a0ad9bd7825a6fe.jpg\",\n  \"155273.jpg\": \"Insecta/Argynnis paphia/fb14edc42d91ebe46651e0f42cba6559.jpg\",\n  \"155276.jpg\": \"Insecta/Argynnis paphia/10de63739090fed6aad217ac80e79b2b.jpg\",\n  \"155279.jpg\": \"Insecta/Argynnis paphia/5f6085506d9766b7333f65a76a28bf6c.jpg\",\n  \"155339.jpg\": \"Aves/Cardellina pusilla/e43b35f21d7b442584cad69d6bba55dc.jpg\",\n  \"155345.jpg\": \"Aves/Cardellina pusilla/79da3de3094deea8fd603fa16c1fa9c3.jpg\",\n  \"155351.jpg\": \"Aves/Cardellina pusilla/eae0fc16c74365cd67e54996094586d5.jpg\",\n  \"15537.jpg\": \"Aves/Pheucticus melanocephalus/724fb59f118aa647975b3165a8bd3f66.jpg\",\n  \"155370.jpg\": \"Aves/Cardellina pusilla/427e005ad8e444799098710446fb0195.jpg\",\n  \"155379.jpg\": \"Aves/Cardellina pusilla/a039f38f17192a5919028614b86586fe.jpg\",\n  \"155388.jpg\": \"Aves/Cardellina pusilla/f4e51d113d3180e57d683f128944987d.jpg\",\n  \"155405.jpg\": \"Aves/Cardellina pusilla/4a178aa0ed351b41fbde1d771e24cfe6.jpg\",\n  \"155410.jpg\": \"Aves/Cardellina pusilla/d58e1a24da8a70a9d43ac44105a45a6d.jpg\",\n  \"155442.jpg\": \"Aves/Cardellina pusilla/5f130fdc39f9311b6f5de8dd7059fcf7.jpg\",\n  \"155456.jpg\": \"Aves/Cardellina pusilla/972508776b7512f817739008b5f6b432.jpg\",\n  \"15546.jpg\": \"Aves/Pheucticus melanocephalus/9839289b2a99235789039e207489b34c.jpg\",\n  \"155489.jpg\": \"Aves/Cardellina pusilla/879a4ec389afadf97581d7bf076efb03.jpg\",\n  \"155530.jpg\": \"Aves/Cardellina pusilla/a11c50fc08e33a8660200a3c8963f918.jpg\",\n  \"155535.jpg\": \"Aves/Cardellina pusilla/04ed8a53987574207c09eddc9dfcb264.jpg\",\n  \"155539.jpg\": \"Aves/Cardellina pusilla/f31d8a6a6dd2f645dfc03fe362d2965b.jpg\",\n  \"155550.jpg\": \"Aves/Cardellina pusilla/de6828ce1c862ccf4119d8011deb28e8.jpg\",\n  \"155551.jpg\": \"Aves/Cardellina pusilla/ed821205373d5cd04ff6eaa1b207cc55.jpg\",\n  \"15556.jpg\": \"Aves/Pheucticus melanocephalus/6fb4146a36c8d7e9d5d1b92b79779459.jpg\",\n  \"155573.jpg\": \"Aves/Cardellina pusilla/4dbc5628cf8b85a35b02802ee276dd92.jpg\",\n  \"155583.jpg\": \"Aves/Cardellina pusilla/39784a2cfbfb8ba370928a3db8ebec49.jpg\",\n  \"155587.jpg\": \"Aves/Cardellina pusilla/e79b858d523cfde201b01c4a9ed2f4f5.jpg\",\n  \"155656.jpg\": \"Aves/Cardellina pusilla/55ce4582a1acac149d2b6674f5568d92.jpg\",\n  \"155668.jpg\": \"Aves/Cardellina pusilla/b556439eb76b65ead62fbdc890fbd8d1.jpg\",\n  \"155686.jpg\": \"Aves/Larus glaucescens/7ec630fd671202221a9effa013da0f13.jpg\",\n  \"155694.jpg\": \"Aves/Larus glaucescens/2bd6854d73606661204523ebbcd8a06a.jpg\",\n  \"155701.jpg\": \"Aves/Larus glaucescens/8d93ec306b9ca8217911eaefcd0037ff.jpg\",\n  \"155714.jpg\": \"Aves/Larus glaucescens/76c868214c2de639a65b07645c0b1de8.jpg\",\n  \"155718.jpg\": \"Aves/Larus glaucescens/3e496bc17980db893323aeec493b2914.jpg\",\n  \"155727.jpg\": \"Aves/Larus glaucescens/162ce7254579806cbb986228b83b98c3.jpg\",\n  \"155731.jpg\": \"Aves/Larus glaucescens/05db8b44245c53c1fccf4345f2a7114a.jpg\",\n  \"155732.jpg\": \"Aves/Larus glaucescens/4eadf51dc751ab74e5cd0987440c7cb8.jpg\",\n  \"155741.jpg\": \"Aves/Larus glaucescens/291fa8939248f527f7a1cc10beee6842.jpg\",\n  \"155746.jpg\": \"Aves/Larus glaucescens/862abece77a90a29a9d2a9045b752f46.jpg\",\n  \"155758.jpg\": \"Aves/Larus glaucescens/e9ed8f102e1dfb7dccd02fdd88c225ca.jpg\",\n  \"155759.jpg\": \"Aves/Larus glaucescens/4ad823f058446c89bbba117f1094c302.jpg\",\n  \"155770.jpg\": \"Aves/Larus glaucescens/001ffffd78973cc0b74cc2734847463d.jpg\",\n  \"155771.jpg\": \"Aves/Larus glaucescens/7507435359f9223aa4948165810a0764.jpg\",\n  \"155774.jpg\": \"Aves/Larus glaucescens/68703f137be8901e9ffaedc7583a526b.jpg\",\n  \"155776.jpg\": \"Aves/Larus glaucescens/4b9c66743a5ab48e8905695a9fb488b4.jpg\",\n  \"155777.jpg\": \"Aves/Larus glaucescens/cbbc1a6bee3e77de8913bfb1107ef4a8.jpg\",\n  \"155778.jpg\": \"Aves/Larus glaucescens/4560224c9bcd3a0ef3f432f16d7715a6.jpg\",\n  \"155779.jpg\": \"Aves/Larus glaucescens/de6353bef14cb5a02f06cabecc649ec9.jpg\",\n  \"15579.jpg\": \"Aves/Pheucticus melanocephalus/4bfa2ed6aeffafb21aede1f5859c9e98.jpg\",\n  \"15584.jpg\": \"Aves/Pheucticus melanocephalus/8659c6593d556d730fa9899f5110c837.jpg\",\n  \"15592.jpg\": \"Aves/Pheucticus melanocephalus/7e5b08fabd8c3d659681cafa88bb7369.jpg\",\n  \"155938.jpg\": \"Mollusca/Tegula funebralis/f9084b19a84da35df013b497244f1244.jpg\",\n  \"155939.jpg\": \"Mollusca/Tegula funebralis/2295eb6c7c0b60893200c63ed5c5b692.jpg\",\n  \"155947.jpg\": \"Mollusca/Tegula funebralis/aa05dd09c3d68704f64488f330ef13e9.jpg\",\n  \"15595.jpg\": \"Aves/Pheucticus melanocephalus/eabaa85425ad37210749e77e16edeb34.jpg\",\n  \"155984.jpg\": \"Mollusca/Tegula funebralis/f841d98e9aebcb2023cd2777c2434d16.jpg\",\n  \"156021.jpg\": \"Mollusca/Tegula funebralis/039172ff9b7451acef1c9576b22123b1.jpg\",\n  \"156024.jpg\": \"Mollusca/Tegula funebralis/4f72e1c7b6abd6629f1af4fe04ee30ad.jpg\",\n  \"15606.jpg\": \"Aves/Pheucticus melanocephalus/88fa0c1615ea0caa5ed0361cddc8a806.jpg\",\n  \"156069.jpg\": \"Insecta/Acalymma vittatum/0806814f5d397dae5af8345a17d7451d.jpg\",\n  \"156075.jpg\": \"Insecta/Acalymma vittatum/2b8d42d5eb06520f0af2fd77873584ec.jpg\",\n  \"156076.jpg\": \"Insecta/Acalymma vittatum/4591a5d38100502dd3ea9a77a324b1cc.jpg\",\n  \"156079.jpg\": \"Insecta/Acalymma vittatum/6e369bdc8ca176ec3b95e059975438f5.jpg\",\n  \"15611.jpg\": \"Aves/Pheucticus melanocephalus/182adb00d9f88282362904c6fb8450a2.jpg\",\n  \"156141.jpg\": \"Reptilia/Aspidoscelis tigris/12681582b12acd09be23c2a782a086ea.jpg\",\n  \"156159.jpg\": \"Reptilia/Aspidoscelis tigris/29d59388ec31920cda9fe7591468961a.jpg\",\n  \"156160.jpg\": \"Reptilia/Aspidoscelis tigris/bd2aad1684f9262686c62f95ff23c816.jpg\",\n  \"156163.jpg\": \"Reptilia/Aspidoscelis tigris/1d8b1b43547a705a52c3b5b0525d9a7e.jpg\",\n  \"156176.jpg\": \"Reptilia/Aspidoscelis tigris/c837b8e7f8b02abc336bebe82df2297e.jpg\",\n  \"156177.jpg\": \"Reptilia/Aspidoscelis tigris/55a8536c52948c835522ae5a98dd4ca6.jpg\",\n  \"156181.jpg\": \"Reptilia/Aspidoscelis tigris/93345ce28567fb7e88ee36639a597d88.jpg\",\n  \"156200.jpg\": \"Reptilia/Aspidoscelis tigris/54061dd10ecf53d827974850d8a2ada3.jpg\",\n  \"156202.jpg\": \"Reptilia/Aspidoscelis tigris/b7d80476fcdc0888a327d4149dd350e6.jpg\",\n  \"156204.jpg\": \"Reptilia/Aspidoscelis tigris/1d8067f6fe833f28d57885df898561bb.jpg\",\n  \"156208.jpg\": \"Reptilia/Aspidoscelis tigris/1d0ff19c9d1c435c094ba2019a4ce939.jpg\",\n  \"156226.jpg\": \"Reptilia/Aspidoscelis tigris/1744c6225b6d4ef2c7d018ceb1643a6a.jpg\",\n  \"156227.jpg\": \"Reptilia/Aspidoscelis tigris/d8a7ac833b5c6b8f8b9db63cfed47658.jpg\",\n  \"156231.jpg\": \"Reptilia/Aspidoscelis tigris/caa27dfaa25f96d0d28a9cabe2d458db.jpg\",\n  \"156232.jpg\": \"Reptilia/Aspidoscelis tigris/393d3b99a22c0e3931d3acf8f8ef741b.jpg\",\n  \"156234.jpg\": \"Reptilia/Aspidoscelis tigris/3267f90a08cbce3b36424da93262e269.jpg\",\n  \"156235.jpg\": \"Reptilia/Aspidoscelis tigris/6fec4d9a625f3a83d672c3df5768701d.jpg\",\n  \"156243.jpg\": \"Reptilia/Aspidoscelis tigris/d7097bf5f440381ee330edd5ef92f1b1.jpg\",\n  \"15625.jpg\": \"Aves/Pheucticus melanocephalus/842903ffcbd245fca4a643ca378aa52e.jpg\",\n  \"156256.jpg\": \"Reptilia/Aspidoscelis tigris/4341ee45348cb60afa784e626b36bbd8.jpg\",\n  \"156271.jpg\": \"Reptilia/Aspidoscelis tigris/7e5da9da6fb655f4c2f9ab0d36a38943.jpg\",\n  \"156278.jpg\": \"Reptilia/Aspidoscelis tigris/0aa8f21579aec31c98de420658071732.jpg\",\n  \"156279.jpg\": \"Reptilia/Aspidoscelis tigris/318b83a7c281cf815d2e27a1c0555f52.jpg\",\n  \"15628.jpg\": \"Aves/Pheucticus melanocephalus/e7f1a68aec55876edd8bd98edeb192d9.jpg\",\n  \"156282.jpg\": \"Reptilia/Aspidoscelis tigris/1cd31e91627b2f417cdee9276982d888.jpg\",\n  \"156284.jpg\": \"Insecta/Psychomorpha epimenis/caefe83acb696d6fb445f77df3b4e095.jpg\",\n  \"156292.jpg\": \"Insecta/Psychomorpha epimenis/45c87ae7fdad941fec99b3349e9f59c3.jpg\",\n  \"156300.jpg\": \"Insecta/Psychomorpha epimenis/2fef41694050a0209f8076bb403913fd.jpg\",\n  \"156306.jpg\": \"Insecta/Psychomorpha epimenis/ce856bf5b8156411b6a458b9df8bebc9.jpg\",\n  \"156307.jpg\": \"Insecta/Psychomorpha epimenis/54e3bac4cf717ba25cd78c1fce565251.jpg\",\n  \"156308.jpg\": \"Insecta/Psychomorpha epimenis/59fbea1080871e4e41ad2a8cfdeea5ef.jpg\",\n  \"156309.jpg\": \"Insecta/Aeshna palmata/014b1437cd8d84a02305f8e5e76b8d58.jpg\",\n  \"15632.jpg\": \"Aves/Pheucticus melanocephalus/173b9cab9ab4d6c3a2ca034e39a46651.jpg\",\n  \"156323.jpg\": \"Insecta/Aeshna palmata/ea74248a997b095c10d29fa3f06d5eee.jpg\",\n  \"156324.jpg\": \"Insecta/Aeshna palmata/06c01e3a07322d7ed6b53cb0ea5e514f.jpg\",\n  \"156330.jpg\": \"Insecta/Aeshna palmata/487b169b0740d8523d9c258a44fa9589.jpg\",\n  \"156331.jpg\": \"Insecta/Aeshna palmata/d24a0859c005e1822cac49a5e31005e6.jpg\",\n  \"156338.jpg\": \"Aves/Limosa fedoa/94336cb773d5bd3bf21359a8192eba87.jpg\",\n  \"156359.jpg\": \"Aves/Limosa fedoa/38dbfd168379192826e7d980aae51c7c.jpg\",\n  \"156396.jpg\": \"Aves/Limosa fedoa/f6e29a3f5e0dd4cb2d3df59e95ba03d5.jpg\",\n  \"156413.jpg\": \"Aves/Limosa fedoa/df79703df5b6672390155165b982f4fb.jpg\",\n  \"15642.jpg\": \"Aves/Pheucticus melanocephalus/36404c8d3d07c0fbd9bd904959f572bd.jpg\",\n  \"156428.jpg\": \"Aves/Limosa fedoa/04c49cf01694f89f19c67df8477ce995.jpg\",\n  \"156434.jpg\": \"Aves/Limosa fedoa/6d5486146c6cd5eaa901f09b53575a27.jpg\",\n  \"156471.jpg\": \"Aves/Limosa fedoa/83b52b5b0fb0079b82644cabc97fd608.jpg\",\n  \"156490.jpg\": \"Aves/Limosa fedoa/b4045a07490515b9d0fccc00eceeccff.jpg\",\n  \"156491.jpg\": \"Aves/Limosa fedoa/f2f86a9b76862d92f06c970783d4db1e.jpg\",\n  \"156608.jpg\": \"Aves/Limosa fedoa/7095515b3982544bc52fdd0955f760b1.jpg\",\n  \"156611.jpg\": \"Reptilia/Phelsuma laticauda/97bbf078217965f5a9aca6ffde344f8c.jpg\",\n  \"156612.jpg\": \"Reptilia/Phelsuma laticauda/fe6e51b5d2a60f29affaf321288d12af.jpg\",\n  \"156614.jpg\": \"Reptilia/Phelsuma laticauda/822ee413b37d1a2e9b54b18c9b959ac4.jpg\",\n  \"156618.jpg\": \"Reptilia/Phelsuma laticauda/a48b6499b04a366f18819257bd38c2f0.jpg\",\n  \"156619.jpg\": \"Reptilia/Phelsuma laticauda/8db5d9d1385b384f9c808d45513e6dc4.jpg\",\n  \"156623.jpg\": \"Reptilia/Phelsuma laticauda/1b67bf216740767c91b1f86613641389.jpg\",\n  \"156624.jpg\": \"Reptilia/Phelsuma laticauda/900eeaab6e12a55981eeadb2911920d9.jpg\",\n  \"156625.jpg\": \"Reptilia/Phelsuma laticauda/bd971c1cd18c282c4c269354ecc9cbc8.jpg\",\n  \"156627.jpg\": \"Reptilia/Phelsuma laticauda/342f6d4ccb9ad0e4f7b6b102511add18.jpg\",\n  \"156628.jpg\": \"Reptilia/Phelsuma laticauda/6e4ce106c865b54ea015f75375befa66.jpg\",\n  \"156629.jpg\": \"Reptilia/Phelsuma laticauda/97f2be90cb7c251473ddbea0302acf08.jpg\",\n  \"156630.jpg\": \"Reptilia/Phelsuma laticauda/14e07cce48a8a2433c755af983ae4965.jpg\",\n  \"156632.jpg\": \"Reptilia/Phelsuma laticauda/a1894c3def1d2eac52629dde6784b737.jpg\",\n  \"156637.jpg\": \"Reptilia/Phelsuma laticauda/53b0d0f6660c90535880b4a700fb9e87.jpg\",\n  \"156638.jpg\": \"Reptilia/Phelsuma laticauda/750a9daf0eaa7d82f8544b7c2d3ced5f.jpg\",\n  \"156647.jpg\": \"Reptilia/Phelsuma laticauda/16904d26ae8e1050154263a15292144e.jpg\",\n  \"156650.jpg\": \"Reptilia/Phelsuma laticauda/806b0e53cb0bc12a50a160d721fa618c.jpg\",\n  \"156651.jpg\": \"Reptilia/Phelsuma laticauda/4e1470f282ac89335b2431d7a211e023.jpg\",\n  \"156779.jpg\": \"Aves/Petrochelidon fulva/66f6bb045ed0019eca47eb9d08d8c4a4.jpg\",\n  \"156781.jpg\": \"Aves/Petrochelidon fulva/aab8768bb81e7901674ca47eb220e411.jpg\",\n  \"15683.jpg\": \"Aves/Pheucticus melanocephalus/64fd244f2fbac8f4becfd5668333ebcb.jpg\",\n  \"15694.jpg\": \"Aves/Pheucticus melanocephalus/b99f61f1edf13eb6a2dc94333e9e26ed.jpg\",\n  \"156997.jpg\": \"Amphibia/Taricha granulosa/de8d4511199c5f3479513eda88ebe432.jpg\",\n  \"157008.jpg\": \"Amphibia/Taricha granulosa/972b10c34860a8584c05779c0164e9f4.jpg\",\n  \"157016.jpg\": \"Amphibia/Taricha granulosa/2bb6fded068759aa92678d5b22dd97cb.jpg\",\n  \"157018.jpg\": \"Amphibia/Taricha granulosa/9f06e4a4a2c389060626a2ef8a37fdc9.jpg\",\n  \"157045.jpg\": \"Amphibia/Taricha granulosa/949069da722d4617fae64e25ccda0e3b.jpg\",\n  \"157046.jpg\": \"Amphibia/Taricha granulosa/9d42150254c3936c5ae6e4c420e5b3d4.jpg\",\n  \"157054.jpg\": \"Amphibia/Taricha granulosa/f5699c13057efcb2e1aa5ee9d85e1a98.jpg\",\n  \"157055.jpg\": \"Amphibia/Taricha granulosa/4fba2f381ce37555162f439c43276e27.jpg\",\n  \"157065.jpg\": \"Amphibia/Taricha granulosa/bcb0769fbb047057349212d5e7a5bbe3.jpg\",\n  \"157069.jpg\": \"Amphibia/Taricha granulosa/4c88af4ca81c03c46e11fe21a7140f4c.jpg\",\n  \"157084.jpg\": \"Amphibia/Taricha granulosa/778e79ebfdf1943d2e6ad4675236c35e.jpg\",\n  \"157099.jpg\": \"Amphibia/Taricha granulosa/d5a74eefc7558f3ceaa066b342ea5dcb.jpg\",\n  \"15710.jpg\": \"Aves/Pheucticus melanocephalus/be60a3c811f0c0b425a234dcb1cafd22.jpg\",\n  \"157101.jpg\": \"Amphibia/Taricha granulosa/477d96809139d57e73f024f86ce6131c.jpg\",\n  \"157105.jpg\": \"Amphibia/Taricha granulosa/127f6008a89d77f496e60d9d577ad836.jpg\",\n  \"157106.jpg\": \"Amphibia/Taricha granulosa/a66bf45d5a4ba8244e53f67108eb4537.jpg\",\n  \"157130.jpg\": \"Amphibia/Taricha granulosa/3b58189f195ded66b411a00fc2f47b5d.jpg\",\n  \"157141.jpg\": \"Amphibia/Taricha granulosa/d9eaf3578056b661d9b2457203d3acf1.jpg\",\n  \"157164.jpg\": \"Amphibia/Taricha granulosa/6c28be8cfac96383a0ca1e423f2d0b38.jpg\",\n  \"157167.jpg\": \"Amphibia/Taricha granulosa/1d05b2fa790f1e21e59fad3b2baa0947.jpg\",\n  \"157173.jpg\": \"Amphibia/Taricha granulosa/098451a6ebe051b28ebbb0fb6e383c7d.jpg\",\n  \"157188.jpg\": \"Amphibia/Taricha granulosa/9626b83454ae24dac8aff5c8f36e49a0.jpg\",\n  \"157198.jpg\": \"Amphibia/Taricha granulosa/eb0b20fa9f29681bd26e16e56a88776c.jpg\",\n  \"157250.jpg\": \"Reptilia/Pantherophis obsoletus/a1e1fded1b2fa63cd8d05e941a5681dc.jpg\",\n  \"157255.jpg\": \"Reptilia/Pantherophis obsoletus/118fec73611768aaeaa3e594f9239f94.jpg\",\n  \"157256.jpg\": \"Reptilia/Pantherophis obsoletus/8b515a5b34cf2e9629ec9a4fc8d62e9c.jpg\",\n  \"157263.jpg\": \"Reptilia/Pantherophis obsoletus/c286aee5ed2e66d0fffcd8f470da98e4.jpg\",\n  \"157275.jpg\": \"Reptilia/Pantherophis obsoletus/9035b0111b3a57baa119005422dc1532.jpg\",\n  \"157299.jpg\": \"Reptilia/Pantherophis obsoletus/ae76036398f9f9e4e5e3982f0e531b9d.jpg\",\n  \"157308.jpg\": \"Reptilia/Pantherophis obsoletus/f5613313f7d535650c8579e9f512c660.jpg\",\n  \"157320.jpg\": \"Reptilia/Pantherophis obsoletus/67f455555e28ad79eb417a75caf28658.jpg\",\n  \"157336.jpg\": \"Reptilia/Pantherophis obsoletus/adfaeb057610173b414c2948ae82f8bd.jpg\",\n  \"157365.jpg\": \"Reptilia/Pantherophis obsoletus/f62397202e3f0109fabc629043383667.jpg\",\n  \"157388.jpg\": \"Reptilia/Pantherophis obsoletus/bedff675e65cd43b632dd2b57a77abf0.jpg\",\n  \"157389.jpg\": \"Reptilia/Pantherophis obsoletus/62102d191f07e4ac16c288b83cc78d43.jpg\",\n  \"157390.jpg\": \"Reptilia/Pantherophis obsoletus/7a4dc05b3be7adcc719c16e1f0a6bcae.jpg\",\n  \"15740.jpg\": \"Aves/Pheucticus melanocephalus/d01c3646a6f5b86e42fdc5cc1e3c1056.jpg\",\n  \"157440.jpg\": \"Reptilia/Pantherophis obsoletus/fe4431ed06cb07a30341b9ec8f174cdf.jpg\",\n  \"157441.jpg\": \"Reptilia/Pantherophis obsoletus/3429cd5bc2e6fae951e06d91c7b54090.jpg\",\n  \"157458.jpg\": \"Reptilia/Pantherophis obsoletus/3685abff2a3534237f8e7832f80aa710.jpg\",\n  \"157470.jpg\": \"Reptilia/Pantherophis obsoletus/58987a7ed88aba198e8f410b841b7dd6.jpg\",\n  \"157471.jpg\": \"Reptilia/Pantherophis obsoletus/2f0ac53a04db0572903f3c8ec166519a.jpg\",\n  \"157472.jpg\": \"Reptilia/Pantherophis obsoletus/e3a0b749d53929010c95b6d4284c43d5.jpg\",\n  \"157485.jpg\": \"Reptilia/Pantherophis obsoletus/1b5db3ca688b2e1ff29e19e6bd40f137.jpg\",\n  \"157506.jpg\": \"Reptilia/Pantherophis obsoletus/e2ac05126693f1b759c1f8bec9745955.jpg\",\n  \"157513.jpg\": \"Insecta/Anicla infecta/08006b377aa7832a72250d9871b430d6.jpg\",\n  \"157518.jpg\": \"Insecta/Anicla infecta/fa12397e3f6718083f3b98cfc0d34de2.jpg\",\n  \"157519.jpg\": \"Insecta/Anicla infecta/22a6073a90da5fbdaad83b4af19bf340.jpg\",\n  \"15752.jpg\": \"Aves/Haematopus finschi/d65b196147187cf774b94018d113742b.jpg\",\n  \"157520.jpg\": \"Insecta/Anicla infecta/b4c7b42cbee0c37d25f567461637993f.jpg\",\n  \"157521.jpg\": \"Insecta/Anicla infecta/e0139940eb3944c83f3d7290a229fb7b.jpg\",\n  \"157522.jpg\": \"Insecta/Anicla infecta/532ddeb945fbce84e6a8bae69c7506e9.jpg\",\n  \"157523.jpg\": \"Insecta/Anicla infecta/7b3694367356945c7f66f3a0e630ed54.jpg\",\n  \"157524.jpg\": \"Insecta/Anicla infecta/97946f4f6c13f119fd81070b00c52725.jpg\",\n  \"157532.jpg\": \"Insecta/Anicla infecta/130d2a926127a1b43b1331c49e62112b.jpg\",\n  \"157533.jpg\": \"Insecta/Anicla infecta/2952633a8dbc59b9b0997cd73cba8f0b.jpg\",\n  \"157535.jpg\": \"Insecta/Anicla infecta/5f7ffcaaef4d20a809444997982892a1.jpg\",\n  \"157536.jpg\": \"Insecta/Anicla infecta/8ee470b6f713a85bbe3e68f8b66a5d59.jpg\",\n  \"157537.jpg\": \"Insecta/Anicla infecta/d50004a60d7fc4f6000fe944ac131b75.jpg\",\n  \"157539.jpg\": \"Insecta/Anicla infecta/2a5b9beed175cb860a7f4e8b3c9cd06d.jpg\",\n  \"15754.jpg\": \"Aves/Haematopus finschi/b7c60dbc1ab62bea411da96168f8f70a.jpg\",\n  \"157540.jpg\": \"Insecta/Anicla infecta/636782ec3e4606807ec68550c7b6dd3b.jpg\",\n  \"157541.jpg\": \"Insecta/Anicla infecta/373a2b7dc387fc50a81cc881d8f9dee8.jpg\",\n  \"157545.jpg\": \"Insecta/Anicla infecta/ae756865ed145dd9b4d1434b722d11ff.jpg\",\n  \"157558.jpg\": \"Insecta/Anicla infecta/25c974d664d7207e791da02924e3d565.jpg\",\n  \"157559.jpg\": \"Insecta/Anicla infecta/d878a0c9beb21b4ac6937c0a1820be66.jpg\",\n  \"157576.jpg\": \"Insecta/Plecia nearctica/9f0a0047eff33ca2f40d2cb8df07d237.jpg\",\n  \"157578.jpg\": \"Insecta/Plecia nearctica/a6ac106997cb99c6629ba818453daceb.jpg\",\n  \"157602.jpg\": \"Insecta/Lucanus capreolus/7a4d4edad42441c4d85fa0fa1f5b9349.jpg\",\n  \"157603.jpg\": \"Insecta/Lucanus capreolus/71a42fb1019445c52a6639309d808321.jpg\",\n  \"157604.jpg\": \"Insecta/Lucanus capreolus/6f849203b01feafa9bbf9fa2667d3fed.jpg\",\n  \"157606.jpg\": \"Insecta/Lucanus capreolus/f54cbaf4e122dad992218617c89cdb5e.jpg\",\n  \"157611.jpg\": \"Insecta/Lucanus capreolus/7dc2c3589fdb4e6ee3e9306c471d6a91.jpg\",\n  \"157612.jpg\": \"Insecta/Lucanus capreolus/7cf547c7c2a8bed054b0f1f6fd49a621.jpg\",\n  \"157615.jpg\": \"Insecta/Lucanus capreolus/8a4bf28c8fb29be5049a891fcf6a5b1c.jpg\",\n  \"157616.jpg\": \"Insecta/Lucanus capreolus/2bef9f2ea2987c17308c9da5ffa9c2d5.jpg\",\n  \"157618.jpg\": \"Insecta/Lucanus capreolus/d915ab9ca5c5f11d8e70492193bc36eb.jpg\",\n  \"157619.jpg\": \"Insecta/Lucanus capreolus/74c7953dcc9b33398e0f3cd02706136c.jpg\",\n  \"157631.jpg\": \"Amphibia/Rana draytonii/9cf0f9f4ce5a26c9e1d715c6101aa750.jpg\",\n  \"157641.jpg\": \"Amphibia/Rana draytonii/e678d14a67a60aff9cad5696ec7c8bb2.jpg\",\n  \"157642.jpg\": \"Amphibia/Rana draytonii/1a800a65d71c838e766758cf1fefdbf8.jpg\",\n  \"157647.jpg\": \"Amphibia/Rana draytonii/d51749230bfd4e8c0927d25ea2a81a01.jpg\",\n  \"157648.jpg\": \"Amphibia/Rana draytonii/c4b0154dba6d64b021d9bf5484a5590a.jpg\",\n  \"157649.jpg\": \"Amphibia/Rana draytonii/8ea471a9ad4d05fe24653d4bc4bc3607.jpg\",\n  \"157653.jpg\": \"Amphibia/Rana draytonii/81f40bf53094373e0fb9bf67763758d2.jpg\",\n  \"157654.jpg\": \"Amphibia/Rana draytonii/f976d59035a1180fdabc3e8404db734a.jpg\",\n  \"157655.jpg\": \"Amphibia/Rana draytonii/c15b9d071ec499b00b26d12679d4e239.jpg\",\n  \"157656.jpg\": \"Amphibia/Rana draytonii/f913783eebb6f25f177b32da988ad196.jpg\",\n  \"157657.jpg\": \"Amphibia/Rana draytonii/8e10fea4449f6d28d0077b452ccc857d.jpg\",\n  \"157658.jpg\": \"Amphibia/Rana draytonii/86d863a63a5bf6caead3dfae36db1eba.jpg\",\n  \"157660.jpg\": \"Amphibia/Rana draytonii/669bbb3417687026ceb35778b55c5b5c.jpg\",\n  \"157663.jpg\": \"Amphibia/Rana draytonii/81fc744c793e04c819e7097747af7db2.jpg\",\n  \"157669.jpg\": \"Amphibia/Rana draytonii/131628fbb72a928df6664dea3e131eac.jpg\",\n  \"157673.jpg\": \"Amphibia/Rana draytonii/6f9bb26fb92d6a4d8e1b8a1c1ec8ed6b.jpg\",\n  \"157677.jpg\": \"Amphibia/Rana draytonii/77e2528944d04a955019c023827d267d.jpg\",\n  \"157680.jpg\": \"Amphibia/Rana draytonii/90c9b542c7b3bd0a847f36f43f26877e.jpg\",\n  \"157681.jpg\": \"Amphibia/Rana draytonii/6ff0d13d36e7235dc43a12e9ee3bf281.jpg\",\n  \"157683.jpg\": \"Amphibia/Rana draytonii/b9d57711597be406336b804773b6efa4.jpg\",\n  \"157684.jpg\": \"Amphibia/Rana draytonii/fcaf1612761c6278d4bb77e667ea818f.jpg\",\n  \"157685.jpg\": \"Amphibia/Rana draytonii/e20beedd23e34fb82f787e742426c14b.jpg\",\n  \"157707.jpg\": \"Insecta/Sympetrum illotum/f8ec3ad1a8bc826b3166231713fefacb.jpg\",\n  \"157714.jpg\": \"Insecta/Sympetrum illotum/6a56680cd771545611ddfae0a89021ed.jpg\",\n  \"157715.jpg\": \"Insecta/Sympetrum illotum/a4e657013e09661890d05e42633ae5d4.jpg\",\n  \"157721.jpg\": \"Insecta/Sympetrum illotum/86ce4fe18e35cd33bfade8cf00433314.jpg\",\n  \"157753.jpg\": \"Insecta/Sympetrum illotum/fb1eb317deefc7b6ac85f2cdaf30e7ba.jpg\",\n  \"157757.jpg\": \"Insecta/Sympetrum illotum/9e732995c19426de4d911ab9e9a229f0.jpg\",\n  \"157759.jpg\": \"Insecta/Sympetrum illotum/b884077a35ca633e0e38731297ce7f73.jpg\",\n  \"15776.jpg\": \"Aves/Haematopus finschi/cc20c2931bbcc317ac5cd2dc5a8c792b.jpg\",\n  \"157768.jpg\": \"Insecta/Sympetrum illotum/47c0ff68b505e17fe834452091166652.jpg\",\n  \"157770.jpg\": \"Insecta/Sympetrum illotum/941badf06db561d832b8b460dd517687.jpg\",\n  \"157777.jpg\": \"Insecta/Sympetrum illotum/ab954e8ece4a46c59486ea110e6f42d7.jpg\",\n  \"157778.jpg\": \"Insecta/Sympetrum illotum/92a6a36503414d6d731ee4281b2e4851.jpg\",\n  \"157779.jpg\": \"Insecta/Sympetrum illotum/7c3f996e1fbe36ca1aae70cd700740fa.jpg\",\n  \"157780.jpg\": \"Insecta/Sympetrum illotum/9514c1ec1cfd041458cca1da8b79a6cf.jpg\",\n  \"157784.jpg\": \"Insecta/Sympetrum illotum/63cf008faece1d14aa2337209a6b4e63.jpg\",\n  \"157785.jpg\": \"Insecta/Sympetrum illotum/dcbfbce4722026b9b3bc1300dccabe04.jpg\",\n  \"157790.jpg\": \"Insecta/Sympetrum illotum/c9106ff0570a0cdb2052a16bf7a456db.jpg\",\n  \"157791.jpg\": \"Insecta/Sympetrum illotum/830b03b55b29083ec684fab4f7cd337a.jpg\",\n  \"157793.jpg\": \"Insecta/Sympetrum illotum/d65be16a62da2abf714888751086e727.jpg\",\n  \"157794.jpg\": \"Insecta/Sympetrum illotum/affe1fab24182b8f87a45569f12bd201.jpg\",\n  \"157807.jpg\": \"Insecta/Sympetrum illotum/63c42d1c520e06e0717022c2d15637e0.jpg\",\n  \"157808.jpg\": \"Insecta/Sympetrum illotum/4d92ad306580ed6600fec4edeedfc36b.jpg\",\n  \"157815.jpg\": \"Insecta/Sympetrum illotum/c440d1d4d7fddfb634ae96c150324d15.jpg\",\n  \"157817.jpg\": \"Insecta/Sympetrum illotum/35941e8373b2116e4c7eea64a3640fe0.jpg\",\n  \"157822.jpg\": \"Insecta/Cordulegaster diastatops/f7e0498cfd501f25b02e72b6c832a6c0.jpg\",\n  \"157826.jpg\": \"Insecta/Cordulegaster diastatops/ad14515b8c60cfc7fb51205f1ca1b5ce.jpg\",\n  \"157827.jpg\": \"Insecta/Cordulegaster diastatops/7f626689ffcc625902498db4fa86a1ff.jpg\",\n  \"157835.jpg\": \"Insecta/Cordulegaster diastatops/040a4183f0fd860712592b0ad2a97f59.jpg\",\n  \"157841.jpg\": \"Insecta/Cordulegaster diastatops/b2250a7eddaa47be755547d22182f586.jpg\",\n  \"157843.jpg\": \"Insecta/Psamatodes abydata/5ab1549223762fd288f05d1ccb464a13.jpg\",\n  \"157844.jpg\": \"Insecta/Psamatodes abydata/96778021c699c040fedee6150ececbad.jpg\",\n  \"157846.jpg\": \"Insecta/Psamatodes abydata/8ebef015112b7a1b75c6c5c77296dbf7.jpg\",\n  \"157857.jpg\": \"Insecta/Psamatodes abydata/e883e51ffa6bff65210dbca95649858f.jpg\",\n  \"157858.jpg\": \"Insecta/Psamatodes abydata/70f268ecc9580d01e8aebf3dc933cdfe.jpg\",\n  \"157859.jpg\": \"Insecta/Psamatodes abydata/cf3bd18c15c01d6c0de278e3234a7bf7.jpg\",\n  \"157868.jpg\": \"Insecta/Psamatodes abydata/bb652c42f102d9c1ad3778541f232dd3.jpg\",\n  \"157869.jpg\": \"Insecta/Psamatodes abydata/a1e31104d40c82dd3a1ba23052691eb9.jpg\",\n  \"157870.jpg\": \"Insecta/Psamatodes abydata/8f06fd2f9957cb57ce4eac4f9f57f505.jpg\",\n  \"157871.jpg\": \"Insecta/Psamatodes abydata/bb9fbb2af9a16ce7538543b78668d862.jpg\",\n  \"157872.jpg\": \"Insecta/Psamatodes abydata/e539eff82c66b0b26a64152cb6cd87fc.jpg\",\n  \"157873.jpg\": \"Insecta/Psamatodes abydata/72ec3f8231a417f8e574f445ca2a64a9.jpg\",\n  \"157875.jpg\": \"Insecta/Psamatodes abydata/a8c258229f3d31b31516588f3189ddb8.jpg\",\n  \"157876.jpg\": \"Insecta/Psamatodes abydata/5476dd61921bbb0fef5693a7b9faf952.jpg\",\n  \"157877.jpg\": \"Insecta/Psamatodes abydata/55a9bfca1c4a4ad7da641fcbab39dba9.jpg\",\n  \"157878.jpg\": \"Insecta/Psamatodes abydata/0e335f464f3604d508d34bd7a18b2b53.jpg\",\n  \"157879.jpg\": \"Insecta/Psamatodes abydata/089b953ea65d7ded56a9b540e2774140.jpg\",\n  \"157883.jpg\": \"Insecta/Psamatodes abydata/16d6ce37109b394cd9d4f77789e5a724.jpg\",\n  \"157888.jpg\": \"Insecta/Psamatodes abydata/dea63a867950da9f53947860f5955db9.jpg\",\n  \"157889.jpg\": \"Insecta/Psamatodes abydata/8e82d63e43bf6e5bf2d771a262f3822a.jpg\",\n  \"157890.jpg\": \"Insecta/Psamatodes abydata/290f2006eb9b0e44ad01d90226587a98.jpg\",\n  \"157891.jpg\": \"Insecta/Psamatodes abydata/30b7b39b1ac6511d9b1d40637410f5d7.jpg\",\n  \"157892.jpg\": \"Insecta/Psamatodes abydata/4df0720e85d74f2e58b448a034a6802b.jpg\",\n  \"157907.jpg\": \"Insecta/Gomphus militaris/a22acab8f2c5616a709c00452fc3cdc8.jpg\",\n  \"157908.jpg\": \"Insecta/Gomphus militaris/1f18bfed5ce44dfccf3af53a40782b7c.jpg\",\n  \"157909.jpg\": \"Insecta/Gomphus militaris/86877ef7c9a7b1047ed973d2967d4e30.jpg\",\n  \"157911.jpg\": \"Insecta/Gomphus militaris/e5cd9c59df37767e925696b755efa036.jpg\",\n  \"157912.jpg\": \"Insecta/Gomphus militaris/20f1f9c0fbd1327172bfcf24c80960fd.jpg\",\n  \"157915.jpg\": \"Insecta/Gomphus militaris/2958b2ffa75fc79de6e418fea8b3e23d.jpg\",\n  \"157916.jpg\": \"Insecta/Gomphus militaris/f92b27f15e80d3582a479efe56e57235.jpg\",\n  \"157917.jpg\": \"Insecta/Gomphus militaris/966c2093d916f1b871cca56685258740.jpg\",\n  \"157926.jpg\": \"Insecta/Gomphus militaris/82b21786305e698b83316636d9a7ab40.jpg\",\n  \"157927.jpg\": \"Insecta/Gomphus militaris/19578421f3492d8542da4b5635413bd0.jpg\",\n  \"157928.jpg\": \"Insecta/Gomphus militaris/dfd05ce0c97972f4af9ebc93a9ed4375.jpg\",\n  \"157933.jpg\": \"Insecta/Gomphus militaris/43d546f436dfb590034cc87e5b7b551b.jpg\",\n  \"157934.jpg\": \"Insecta/Gomphus militaris/da3b56ab7d0f4e8997e78699d6c98bd8.jpg\",\n  \"157935.jpg\": \"Insecta/Gomphus militaris/c88ba8a43a8425038ae2ecb0622d95b2.jpg\",\n  \"157943.jpg\": \"Insecta/Gomphus militaris/1b8d13cb174cea0437d2ca95bd09d4e8.jpg\",\n  \"157944.jpg\": \"Insecta/Gomphus militaris/b0db91256222a80b3abeed9810332c98.jpg\",\n  \"157949.jpg\": \"Insecta/Gomphus militaris/193a529d8ed4deed507dddca16f591c3.jpg\",\n  \"157950.jpg\": \"Insecta/Gomphus militaris/534f42a385db16bdba5554a717f0aec9.jpg\",\n  \"157961.jpg\": \"Insecta/Gomphus militaris/b81a0c2b2fbc277135d774412b08ade6.jpg\",\n  \"157962.jpg\": \"Insecta/Gomphus militaris/a43bbb691210e32f802fd29b4c9ad00d.jpg\",\n  \"157963.jpg\": \"Insecta/Gomphus militaris/d20aae46834aeabd4b37446dfe1609d0.jpg\",\n  \"157964.jpg\": \"Insecta/Gomphus militaris/2b97cd9c2cbc2b5ccf2d86a05328d507.jpg\",\n  \"157965.jpg\": \"Insecta/Gomphus militaris/c6b85e1a851a7c6b866be6b3169a3941.jpg\",\n  \"158151.jpg\": \"Reptilia/Gerrhonotus infernalis/a12553b56f93d97f544d3b57d11ee0ca.jpg\",\n  \"158156.jpg\": \"Reptilia/Gerrhonotus infernalis/7164f53cac7f5758565c102a158f0a3f.jpg\",\n  \"158157.jpg\": \"Reptilia/Gerrhonotus infernalis/55549f2c14c6d4b82c7bf3e844ca99e4.jpg\",\n  \"158158.jpg\": \"Reptilia/Gerrhonotus infernalis/cbd925a0130b5d517ff6b7f24b2f9038.jpg\",\n  \"158159.jpg\": \"Reptilia/Gerrhonotus infernalis/eafb029eecf47c1e75d0e054e2a46515.jpg\",\n  \"158160.jpg\": \"Reptilia/Gerrhonotus infernalis/a33939d0b0bb94dfd037a476662984a5.jpg\",\n  \"158178.jpg\": \"Reptilia/Gerrhonotus infernalis/01056ec80ef71065e91b61fbce302287.jpg\",\n  \"158221.jpg\": \"Aves/Passerina leclancherii/de608451dae10e3e61fa69f5ce0f87a9.jpg\",\n  \"158223.jpg\": \"Aves/Passerina leclancherii/b3c6b689600c483484ac2c787cbfb80b.jpg\",\n  \"158228.jpg\": \"Aves/Passerina leclancherii/330b1c0ee891fe980da7810c378f4678.jpg\",\n  \"158232.jpg\": \"Aves/Passerina leclancherii/37dae453a45106a16542a3ca84229afe.jpg\",\n  \"158238.jpg\": \"Aves/Passerina leclancherii/9827fcb461a489be1675a3fadf24a9c2.jpg\",\n  \"158279.jpg\": \"Mollusca/Discus rotundatus/b237bc90d7d040c61975a4836a5ce7c5.jpg\",\n  \"158312.jpg\": \"Reptilia/Thamnophis sirtalis fitchi/384cff66d8284f45ebe9695b750e1be9.jpg\",\n  \"158313.jpg\": \"Reptilia/Thamnophis sirtalis fitchi/f7feffb78ea5dc3e4f122d4abdcd0a4f.jpg\",\n  \"158319.jpg\": \"Reptilia/Thamnophis sirtalis fitchi/551e5c71383bd60f099f292386518c2b.jpg\",\n  \"158322.jpg\": \"Reptilia/Thamnophis sirtalis fitchi/e2ef1816261629f034f11ca126a7eb18.jpg\",\n  \"158327.jpg\": \"Reptilia/Thamnophis sirtalis fitchi/1744a5e3802ab6e9dd237b07462aad90.jpg\",\n  \"158328.jpg\": \"Reptilia/Thamnophis sirtalis fitchi/521c7c45aad553dc231305a6caaf8459.jpg\",\n  \"158333.jpg\": \"Reptilia/Thamnophis sirtalis fitchi/56fa2a88bf972521bb64b21fa67952d4.jpg\",\n  \"158447.jpg\": \"Aves/Parus major/e9b676401c4327043c08755515899c01.jpg\",\n  \"158448.jpg\": \"Aves/Parus major/7eb9fa2af1925706ff4cbe7270c82d6d.jpg\",\n  \"158467.jpg\": \"Aves/Parus major/d537e434f4d84b0a4d15d4eecb4de424.jpg\",\n  \"158468.jpg\": \"Aves/Parus major/b2766292cae148228858b8c6cee67193.jpg\",\n  \"158473.jpg\": \"Aves/Parus major/fc3a919e96ee8a9bd1f418266411e370.jpg\",\n  \"158474.jpg\": \"Aves/Parus major/582ce71764e7695d7362b2e8c51dc7ed.jpg\",\n  \"158479.jpg\": \"Aves/Parus major/a7e6d57140bed54c267af7fd34f3a547.jpg\",\n  \"158501.jpg\": \"Aves/Parus major/d1c6557f29372fac84423c1bb76e8118.jpg\",\n  \"158502.jpg\": \"Aves/Parus major/e7f0e5b3e1eeffc7f2e07d95683e1816.jpg\",\n  \"158532.jpg\": \"Aves/Parus major/324a73ffd52c8555a042901fa99acd7f.jpg\",\n  \"158535.jpg\": \"Aves/Parus major/adb19a0a15ed40eeb4498d2bc504d028.jpg\",\n  \"158537.jpg\": \"Aves/Parus major/351adfc91e64cdcadf1422acfd6027d1.jpg\",\n  \"158542.jpg\": \"Aves/Parus major/f6ff21a27931a5f921e89a945ff5f09a.jpg\",\n  \"158551.jpg\": \"Aves/Parus major/14fcfe325d06c4ec759e305518c20f90.jpg\",\n  \"158558.jpg\": \"Aves/Parus major/cea0abad395b48cbcd761d1c6569f37b.jpg\",\n  \"158563.jpg\": \"Aves/Parus major/6762e3cc53f73798d7a8c9671ec3ef99.jpg\",\n  \"158564.jpg\": \"Aves/Parus major/9c8fe84e18ced7a583de660b30300c50.jpg\",\n  \"158575.jpg\": \"Aves/Parus major/cb534a659aa3b7bfd6f09e60eac577ce.jpg\",\n  \"158583.jpg\": \"Aves/Parus major/313495c17610fa1f0bfcbb675d9caf51.jpg\",\n  \"158594.jpg\": \"Insecta/Chromagrion conditum/c882afd3b1893cc471ada571bd55c8d1.jpg\",\n  \"158601.jpg\": \"Insecta/Chromagrion conditum/5148eedd93af429be9581d8b764ad613.jpg\",\n  \"158603.jpg\": \"Insecta/Chromagrion conditum/3aad5bce581143cafe66e3f19219b108.jpg\",\n  \"158609.jpg\": \"Insecta/Chromagrion conditum/de0eea0c6c9dd65a1e448ac572689713.jpg\",\n  \"158610.jpg\": \"Insecta/Chromagrion conditum/d296985eda60d795c151c3d1988b5c6f.jpg\",\n  \"158613.jpg\": \"Insecta/Chromagrion conditum/eababe566aed8926c67c6872c0e975a1.jpg\",\n  \"158614.jpg\": \"Insecta/Chromagrion conditum/1a88c0fbd3118f9869d039e36dd6c0ee.jpg\",\n  \"158652.jpg\": \"Aves/Ciconia ciconia/649b4c68a9dca3c874ff094160a79230.jpg\",\n  \"158675.jpg\": \"Aves/Ciconia ciconia/2d8da178f98c7f349b5b6b4617a363a4.jpg\",\n  \"158684.jpg\": \"Aves/Ciconia ciconia/c7611306343ed2888bf8d058d339a2c3.jpg\",\n  \"158685.jpg\": \"Aves/Ciconia ciconia/9beaf3d02feffe8111332ec2d1fcf71c.jpg\",\n  \"158686.jpg\": \"Aves/Ciconia ciconia/ce792fd45db7a3528375ec685dd38b75.jpg\",\n  \"158687.jpg\": \"Aves/Ciconia ciconia/57df9d4621a2d0c9fc154599cab75ea4.jpg\",\n  \"158688.jpg\": \"Aves/Ciconia ciconia/a69bbd75adf24e1802da4c1e28c4df8e.jpg\",\n  \"158696.jpg\": \"Aves/Ciconia ciconia/083c78a3a591ccd2289851dafbfb7e0e.jpg\",\n  \"158697.jpg\": \"Aves/Ciconia ciconia/328d3a6bbacce90f9a493fc393741bed.jpg\",\n  \"158709.jpg\": \"Aves/Ciconia ciconia/3990211e4ef692286a38b745d5f386b2.jpg\",\n  \"158716.jpg\": \"Aves/Ciconia ciconia/4d517c9ba1b70db1a2d974baeda06bc9.jpg\",\n  \"158723.jpg\": \"Aves/Ciconia ciconia/d7ea710e1ac6422ee0050b172c6595e4.jpg\",\n  \"158736.jpg\": \"Aves/Ciconia ciconia/dccd16840b1707229baaa2fa0a9d88df.jpg\",\n  \"158738.jpg\": \"Aves/Ciconia ciconia/989fa99587f7e4b776523ad65fed2a15.jpg\",\n  \"158763.jpg\": \"Insecta/Anaea aidea/43b0bc7ab77e6bc4916427319a3112fe.jpg\",\n  \"158764.jpg\": \"Insecta/Anaea aidea/82b2303b243f29a06510788f14cdbb77.jpg\",\n  \"158768.jpg\": \"Insecta/Anaea aidea/9cc053635e124652e8b997a08410485d.jpg\",\n  \"158771.jpg\": \"Insecta/Anaea aidea/4ac273876c51954f3fee4175ac70dcc6.jpg\",\n  \"158772.jpg\": \"Insecta/Anaea aidea/85e49d143603004a97e1e1a87be55d0d.jpg\",\n  \"158774.jpg\": \"Insecta/Anaea aidea/eeb6277038e776fd93576f57dc878dbf.jpg\",\n  \"158777.jpg\": \"Aves/Larus thayeri/d70bca281817065be55ecfa46959060f.jpg\",\n  \"158782.jpg\": \"Aves/Larus thayeri/ab63a3bea92d2525a7af21ac10a60c62.jpg\",\n  \"158789.jpg\": \"Aves/Larus thayeri/c671eae5161aeb99fad82eb78c19a50b.jpg\",\n  \"158795.jpg\": \"Aves/Selasphorus platycercus/d08e5b3785eaca6b1842bf9ceb94e8f0.jpg\",\n  \"158799.jpg\": \"Aves/Selasphorus platycercus/a17bf0f120476567ece753933e80dc3f.jpg\",\n  \"158800.jpg\": \"Aves/Selasphorus platycercus/68d49137e89928beee9e41bb59c84a84.jpg\",\n  \"158801.jpg\": \"Aves/Selasphorus platycercus/e0aef5c7ac9930775fb1f9bf1c508720.jpg\",\n  \"158803.jpg\": \"Aves/Selasphorus platycercus/582e2bd337af8f23b35c193c8895eede.jpg\",\n  \"158804.jpg\": \"Aves/Selasphorus platycercus/30940e4b930984b6621d1721e401bfc0.jpg\",\n  \"158808.jpg\": \"Aves/Selasphorus platycercus/677496bd1d610d3dbe87f24ba755203b.jpg\",\n  \"158812.jpg\": \"Aves/Selasphorus platycercus/4f861f90a08485178c939a31d3555681.jpg\",\n  \"158820.jpg\": \"Aves/Selasphorus platycercus/f4f78216a93f5f42d911456dcdcdd6fc.jpg\",\n  \"158821.jpg\": \"Aves/Selasphorus platycercus/d6c26b916138e5c6d712c937cb17d75d.jpg\",\n  \"158822.jpg\": \"Aves/Selasphorus platycercus/6ea85d6e4b9b6b65d5226f1e498b67aa.jpg\",\n  \"158823.jpg\": \"Aves/Selasphorus platycercus/63da8e4f2331690d9f4e2ec1c260dd5d.jpg\",\n  \"158824.jpg\": \"Aves/Selasphorus platycercus/0ac19f09044e45eac7e09c3466ac4d0f.jpg\",\n  \"158831.jpg\": \"Aves/Selasphorus platycercus/dc05b1a433a235315c3f78dd46147596.jpg\",\n  \"158834.jpg\": \"Aves/Selasphorus platycercus/5c8b62501a4a83d5f5ecf0a1a0a9aed0.jpg\",\n  \"158837.jpg\": \"Aves/Selasphorus platycercus/0a41a8033b95cc4d9c9c2fc7a0e6a2cc.jpg\",\n  \"158839.jpg\": \"Aves/Selasphorus platycercus/59a6389e03e0b2456fa5692a7beec149.jpg\",\n  \"158840.jpg\": \"Aves/Selasphorus platycercus/874e26ca955faf847a9b8b87e340263e.jpg\",\n  \"158842.jpg\": \"Aves/Selasphorus platycercus/56746cc0a8e2fc33784cf2db67c9225a.jpg\",\n  \"158844.jpg\": \"Aves/Selasphorus platycercus/9900db76121b13ca9b02edd27418a45d.jpg\",\n  \"158848.jpg\": \"Aves/Selasphorus platycercus/357e520248c6bb4dabede13a11859a5c.jpg\",\n  \"158857.jpg\": \"Aves/Anas platyrhynchos diazi/496ca20d9dc97112ce2fa2dcd6998a57.jpg\",\n  \"158858.jpg\": \"Aves/Anas platyrhynchos diazi/4f671a0a55ef6d6975c5556cc41fdefe.jpg\",\n  \"158859.jpg\": \"Aves/Anas platyrhynchos diazi/20e94b5fbe4c17b9f84687c968695641.jpg\",\n  \"158871.jpg\": \"Aves/Anas platyrhynchos diazi/755ae6823a862b1917ed4d08802a2f33.jpg\",\n  \"158874.jpg\": \"Aves/Anas platyrhynchos diazi/cb1dfc189899b86f9cddfc0ab5c3af4a.jpg\",\n  \"158875.jpg\": \"Aves/Anas platyrhynchos diazi/31919e2b7d652834118106221ebebf8b.jpg\",\n  \"158877.jpg\": \"Aves/Anas platyrhynchos diazi/70d5c027a60d3cde8bb8ae68699b1d86.jpg\",\n  \"158881.jpg\": \"Aves/Anas platyrhynchos diazi/ff54d9e32e8f85f353493241317d4613.jpg\",\n  \"158894.jpg\": \"Aves/Anas platyrhynchos diazi/37a86a2137330cdb0c43bc3ffe06f9a4.jpg\",\n  \"158895.jpg\": \"Aves/Anas platyrhynchos diazi/0eeae682983e857772d02eb6b808f40f.jpg\",\n  \"158897.jpg\": \"Aves/Anas platyrhynchos diazi/c19a0b5145c0b3110b0ab9f67e292787.jpg\",\n  \"158899.jpg\": \"Aves/Anas platyrhynchos diazi/851c940b9c168bdf9d747f2d30fa6cd0.jpg\",\n  \"158918.jpg\": \"Mollusca/Bradybaena similaris/c2591e810ad0c5063cd71a7332098e24.jpg\",\n  \"158926.jpg\": \"Mollusca/Bradybaena similaris/b9bfcbd71328e9d8f84a2818a226ea02.jpg\",\n  \"158927.jpg\": \"Mollusca/Bradybaena similaris/405f87244f58312bce701959cf2091d8.jpg\",\n  \"158932.jpg\": \"Mollusca/Bradybaena similaris/cd92868efd4870b99dd6fc17bba8f659.jpg\",\n  \"158933.jpg\": \"Mollusca/Bradybaena similaris/da6aba8bcbe3ce2d019dfc7155e3cbd6.jpg\",\n  \"158934.jpg\": \"Mollusca/Bradybaena similaris/f801d633adadcb7e103b8becc6afe20d.jpg\",\n  \"158938.jpg\": \"Mollusca/Bradybaena similaris/6e1fe000364994adc72a2c850ea033f0.jpg\",\n  \"158939.jpg\": \"Mollusca/Bradybaena similaris/ae6e0a2306852f46fc3d4f7906feec6c.jpg\",\n  \"159001.jpg\": \"Aves/Pandion haliaetus/317d543233b4a1fc48e88e4c067c79b9.jpg\",\n  \"159050.jpg\": \"Aves/Pandion haliaetus/e15db2d54ac1e4f15654b9312fd95e02.jpg\",\n  \"159104.jpg\": \"Aves/Pandion haliaetus/4c7f81c87d4fd4838879b780ab430510.jpg\",\n  \"159145.jpg\": \"Aves/Pandion haliaetus/26854c9f27520871bfda7e03eab662f5.jpg\",\n  \"159179.jpg\": \"Aves/Pandion haliaetus/34b8d70345dc5cecad4ac232724e64d2.jpg\",\n  \"159180.jpg\": \"Aves/Pandion haliaetus/f87d7ffb46a0c229a147c63035a2e07f.jpg\",\n  \"159196.jpg\": \"Aves/Pandion haliaetus/7f7addb0360fb02d0f420552359bd6ac.jpg\",\n  \"159210.jpg\": \"Aves/Pandion haliaetus/3f9479217107e5c1497a399ddcc0d50c.jpg\",\n  \"159259.jpg\": \"Aves/Pandion haliaetus/d9a35c892c4f73b3ecb753b0acc21922.jpg\",\n  \"159307.jpg\": \"Aves/Pandion haliaetus/b5dac1078618bc25923fe3af0b8d0682.jpg\",\n  \"159309.jpg\": \"Aves/Pandion haliaetus/b67166f2fbe161f72639039c65ab6b9e.jpg\",\n  \"159314.jpg\": \"Aves/Pandion haliaetus/65205f35ec7b0f9f6975389f72265aa3.jpg\",\n  \"15933.jpg\": \"Aves/Buteo jamaicensis/5625426727f2512a04df68fb9eda5d3e.jpg\",\n  \"159551.jpg\": \"Aves/Pandion haliaetus/6382ea61ce240d0c969cb8f0d9fc089e.jpg\",\n  \"159608.jpg\": \"Aves/Pandion haliaetus/fc2d88f041640ef2b0e70ece9eaea3ba.jpg\",\n  \"159636.jpg\": \"Aves/Pandion haliaetus/eadb6acf5ab41f16c5a8e030b5a7388d.jpg\",\n  \"159685.jpg\": \"Aves/Pandion haliaetus/a6078361a7db447a3c906daa66c881e3.jpg\",\n  \"159784.jpg\": \"Aves/Pandion haliaetus/57757b45e332c5de69415823863ad894.jpg\",\n  \"159790.jpg\": \"Aves/Pandion haliaetus/4a23968408f129bc7e9232bed0353dde.jpg\",\n  \"159809.jpg\": \"Aves/Pandion haliaetus/ebc461c858278c5f465ec5c72454728a.jpg\",\n  \"159823.jpg\": \"Aves/Pandion haliaetus/b3aea8d93c75e267674703e80655182d.jpg\",\n  \"159862.jpg\": \"Aves/Pandion haliaetus/aa93e49c4cea76af2ee8c4dcd0d3e78a.jpg\",\n  \"159903.jpg\": \"Aves/Pandion haliaetus/92b9f263fd6b9e53b8fa2dd8936bea73.jpg\",\n  \"160037.jpg\": \"Aves/Pandion haliaetus/18d5f577ec1898a6d262f64813b28639.jpg\",\n  \"160038.jpg\": \"Aves/Pandion haliaetus/78fbf165e69fde3a88338bd26bf3e9f3.jpg\",\n  \"160049.jpg\": \"Aves/Pandion haliaetus/82d5da8f0e32ac38a954ec49c2f40a6a.jpg\",\n  \"160054.jpg\": \"Insecta/Ischnura cervula/90a18f2e5eb2a940edfa3d9235793ccf.jpg\",\n  \"160069.jpg\": \"Insecta/Ischnura cervula/fb99fa5b6a697dbc0f291f11c0360d5e.jpg\",\n  \"160074.jpg\": \"Insecta/Ischnura cervula/72a7991391367cb028af68b186c16e1e.jpg\",\n  \"160076.jpg\": \"Insecta/Ischnura cervula/823083d019fb5abc251ecacb7a480dae.jpg\",\n  \"160082.jpg\": \"Insecta/Ischnura cervula/854864d60d0f29fb277872bf7f65fc81.jpg\",\n  \"160083.jpg\": \"Insecta/Ischnura cervula/144ccf462f98789960b3d2d3374f55d5.jpg\",\n  \"160085.jpg\": \"Insecta/Ischnura cervula/20eadfe6d1dcfaa9d023a8430eceeaeb.jpg\",\n  \"160088.jpg\": \"Insecta/Ischnura cervula/21b6ca31947bb66e641abea99ad5ec03.jpg\",\n  \"160093.jpg\": \"Insecta/Ischnura cervula/02146f278d647347d7f859d4baad0f54.jpg\",\n  \"160111.jpg\": \"Insecta/Ischnura cervula/65c7a2be0b8163b994800d794e299b5d.jpg\",\n  \"160113.jpg\": \"Insecta/Ischnura cervula/e0212eb144ecea0fdb0b52352e89a756.jpg\",\n  \"160114.jpg\": \"Insecta/Ischnura cervula/87714eae4f82af384800bba1ba441f26.jpg\",\n  \"160122.jpg\": \"Insecta/Ischnura cervula/2ab4a8fb75e975208b6a61a53736c05c.jpg\",\n  \"160135.jpg\": \"Insecta/Ischnura cervula/46504ab69773e70f5250335f45d225e4.jpg\",\n  \"160146.jpg\": \"Insecta/Ischnura cervula/d75225aa029bdbbfb7b8ddeeebeaa54e.jpg\",\n  \"160171.jpg\": \"Insecta/Ischnura cervula/840fae61f15f07e610c2821def2cd808.jpg\",\n  \"160184.jpg\": \"Insecta/Ischnura cervula/a3c359843c3f6d8916e1e5bf9e02380b.jpg\",\n  \"160189.jpg\": \"Insecta/Ischnura cervula/2f11ac8cc399316d2da2ef23ee3b66ff.jpg\",\n  \"160196.jpg\": \"Insecta/Ischnura cervula/d003384a3f92f48532b5c5ca7eadad63.jpg\",\n  \"160198.jpg\": \"Insecta/Ischnura cervula/3dffcb72c6cec7377e80fef690ef3515.jpg\",\n  \"160215.jpg\": \"Insecta/Ischnura cervula/d64d3a88e8d17ac3333de6842287b944.jpg\",\n  \"160223.jpg\": \"Insecta/Ischnura cervula/d28cd43e087b9988c8de6f0d2b63304b.jpg\",\n  \"160238.jpg\": \"Aves/Buteo plagiatus/87be48c0376a415e0579e6a566ead592.jpg\",\n  \"160248.jpg\": \"Aves/Buteo plagiatus/199b262434a00db686eb970964934c78.jpg\",\n  \"160255.jpg\": \"Aves/Buteo plagiatus/43df81dd1e56bf3cc16defc49e08c11c.jpg\",\n  \"160258.jpg\": \"Aves/Buteo plagiatus/cb5c9b24897aa5bc2884dd66ae84b184.jpg\",\n  \"160259.jpg\": \"Aves/Buteo plagiatus/6729266ab55911e56ec42b73e5f51b08.jpg\",\n  \"160262.jpg\": \"Aves/Buteo plagiatus/2eae8dee106c4f6f006775dc52158726.jpg\",\n  \"160263.jpg\": \"Aves/Buteo plagiatus/993c82dafaebea449ba1de813170daf2.jpg\",\n  \"160268.jpg\": \"Aves/Buteo plagiatus/c46392099036b0d86f0de8152bfce83f.jpg\",\n  \"160271.jpg\": \"Aves/Buteo plagiatus/26a29fdc821df19adac2ac825d3a50fa.jpg\",\n  \"160275.jpg\": \"Aves/Buteo plagiatus/be2483a8c13673de4aeb8b10b9298d5d.jpg\",\n  \"160278.jpg\": \"Aves/Buteo plagiatus/68ecea9cf6ba51fd0092e04a9484f7f7.jpg\",\n  \"16029.jpg\": \"Aves/Buteo jamaicensis/96321a74f538f304cc131afbbb41f55e.jpg\",\n  \"160294.jpg\": \"Aves/Buteo plagiatus/7317c2018f514a4cc6ab9a5ef271570b.jpg\",\n  \"160300.jpg\": \"Aves/Buteo plagiatus/1729f297c60af2caf1f56c6521b7660f.jpg\",\n  \"160301.jpg\": \"Aves/Buteo plagiatus/a0b957a400050f2c8f2dd23726b44b91.jpg\",\n  \"160302.jpg\": \"Aves/Buteo plagiatus/fd72511f2ec65823dcff844f2b670366.jpg\",\n  \"160310.jpg\": \"Aves/Buteo plagiatus/c460a2cf0ef8888e06629c49034eef58.jpg\",\n  \"160316.jpg\": \"Aves/Buteo plagiatus/6900c62b912e1dc8afe7ad06508c9166.jpg\",\n  \"160317.jpg\": \"Aves/Buteo plagiatus/0ee39c8a806c2145f242648d52b624d1.jpg\",\n  \"160321.jpg\": \"Aves/Buteo plagiatus/c96a8f69de29453bb5afd39fb2d4d7d9.jpg\",\n  \"160328.jpg\": \"Aves/Buteo plagiatus/72cfe83889c846d750caa8fedfc8dd67.jpg\",\n  \"160333.jpg\": \"Aves/Buteo plagiatus/7fba015ef7d44dc4665c47d69784f6e3.jpg\",\n  \"160334.jpg\": \"Aves/Buteo plagiatus/e95d8f0fea7b477f7e4cd215d5a063ba.jpg\",\n  \"160335.jpg\": \"Aves/Buteo plagiatus/b94ec22d070a85ef6df80267811b9992.jpg\",\n  \"160347.jpg\": \"Insecta/Pantala flavescens/689b7cc142f472bdd203cb09b4c15ba3.jpg\",\n  \"160356.jpg\": \"Insecta/Pantala flavescens/e0047931a0088445a8123b31aadb9797.jpg\",\n  \"160370.jpg\": \"Insecta/Pantala flavescens/68a0c893349d9f279adca09f5ee255c7.jpg\",\n  \"160371.jpg\": \"Insecta/Pantala flavescens/354273f9f48b1afa3f1a3ddb6937a6cc.jpg\",\n  \"160372.jpg\": \"Insecta/Pantala flavescens/8f4d8a4318724fe21721b31479e02ebf.jpg\",\n  \"160382.jpg\": \"Insecta/Pantala flavescens/fbfd6865b84d6dae2cc137ee8aecbec0.jpg\",\n  \"160388.jpg\": \"Insecta/Pantala flavescens/977c2b24736f7ff7629ba7a3f87e7a4a.jpg\",\n  \"160389.jpg\": \"Insecta/Pantala flavescens/bb0dc96a61770edb325f35d69ac41c4c.jpg\",\n  \"160391.jpg\": \"Insecta/Pantala flavescens/3d880b3c90f3ffbbea87fce17c7f172b.jpg\",\n  \"160412.jpg\": \"Insecta/Pantala flavescens/8fc8483a93c73a2c42710a559bdd153d.jpg\",\n  \"160430.jpg\": \"Insecta/Pantala flavescens/ce2ba32fc38e855b729524cb47a10de1.jpg\",\n  \"160431.jpg\": \"Insecta/Pantala flavescens/8dd9ff75dba5a5235ab094d6694e0c2f.jpg\",\n  \"160443.jpg\": \"Insecta/Pantala flavescens/f613b85cc91fc5fd8d1c4299a85cf6cf.jpg\",\n  \"160454.jpg\": \"Insecta/Pantala flavescens/feb54bf5bf283609a4d7ad1b19a3f775.jpg\",\n  \"160467.jpg\": \"Insecta/Pantala flavescens/d949835ba3aa7ebf39b051498796d1a7.jpg\",\n  \"160468.jpg\": \"Insecta/Pantala flavescens/dcea0518fae3e70da4a274e1bd666c50.jpg\",\n  \"160476.jpg\": \"Insecta/Pantala flavescens/bc350e75b73520ae2df026245d15f36e.jpg\",\n  \"160486.jpg\": \"Insecta/Pantala flavescens/92b779fd8f15b53743eb159daa4a0e10.jpg\",\n  \"160488.jpg\": \"Insecta/Pantala flavescens/9ffceca811a31afa20ba9ed98064def9.jpg\",\n  \"160519.jpg\": \"Aves/Rhipidura leucophrys/9bdda50ac11ab98ce67933031d972480.jpg\",\n  \"160524.jpg\": \"Aves/Rhipidura leucophrys/ebff5f7fe9802e28e171683409b0d47b.jpg\",\n  \"160525.jpg\": \"Aves/Rhipidura leucophrys/01ab2aeb24d3596ead2ab564ad81e85f.jpg\",\n  \"160529.jpg\": \"Aves/Rhipidura leucophrys/29d0e208cc7481265d78a92c571255fa.jpg\",\n  \"160538.jpg\": \"Aves/Rhipidura leucophrys/0419c9a99a7565addb4702bdc9a01f65.jpg\",\n  \"160539.jpg\": \"Aves/Rhipidura leucophrys/0944cb5ddf08e9fe79255ba89bf8fc7e.jpg\",\n  \"160560.jpg\": \"Insecta/Hyles lineata/28590d323d140e6df1c66d6736b3b1c9.jpg\",\n  \"160568.jpg\": \"Insecta/Hyles lineata/5260943bd09fcc90c87c6fed17637226.jpg\",\n  \"160585.jpg\": \"Insecta/Hyles lineata/d97793a04872b74d560bcc9ab4aefd44.jpg\",\n  \"160590.jpg\": \"Insecta/Hyles lineata/edc05b835124c15d914bd879b26ea41a.jpg\",\n  \"160597.jpg\": \"Insecta/Hyles lineata/c2bbb4de353a2a6c7ef07fbff2cdc446.jpg\",\n  \"160627.jpg\": \"Insecta/Hyles lineata/dbc6a34bb0710d6ed9b55b45409b0210.jpg\",\n  \"160651.jpg\": \"Insecta/Hyles lineata/67afe976ef5485a0e97f11377bf36918.jpg\",\n  \"160653.jpg\": \"Insecta/Hyles lineata/9839e4a89fbac12b3bc70792ab572170.jpg\",\n  \"160684.jpg\": \"Insecta/Hyles lineata/06b3f00a835440ac12cd3afe182909a4.jpg\",\n  \"160702.jpg\": \"Insecta/Hyles lineata/b56bcce6536312c67f01a2670a49727a.jpg\",\n  \"160732.jpg\": \"Insecta/Hyles lineata/2cc6cdb07e743752dbb842871a2f9830.jpg\",\n  \"160748.jpg\": \"Insecta/Hyles lineata/09b7b416bedd1fb41230ba70e06fac5c.jpg\",\n  \"160749.jpg\": \"Insecta/Hyles lineata/a150bd94f406824d5dc609a3bac6e675.jpg\",\n  \"160776.jpg\": \"Insecta/Hyles lineata/b0214248bcacf576a014b53141a12506.jpg\",\n  \"160781.jpg\": \"Insecta/Hyles lineata/52f738196002d1c775fbe510828f3053.jpg\",\n  \"160790.jpg\": \"Insecta/Hyles lineata/294e4fb21ff85c17194298c3c7575793.jpg\",\n  \"160796.jpg\": \"Insecta/Hyles lineata/e820a7331c6da2469f466c73800780b7.jpg\",\n  \"160802.jpg\": \"Insecta/Hyles lineata/bebda310d07f73e51e9ff59639aea222.jpg\",\n  \"160809.jpg\": \"Insecta/Hyles lineata/2f3f76b4df158a13ed3f606135072b1f.jpg\",\n  \"160812.jpg\": \"Insecta/Hyles lineata/133cfa1d3842e34ba969a3b3bd020c4d.jpg\",\n  \"160825.jpg\": \"Insecta/Hyles lineata/a82e3b52e8481f8826422d1f3d15591f.jpg\",\n  \"160878.jpg\": \"Aves/Setophaga discolor/b54bbc52db39989db32a0831b872ee0d.jpg\",\n  \"160880.jpg\": \"Aves/Setophaga discolor/a541e9de8ece976eef73b00a627ae7a4.jpg\",\n  \"160886.jpg\": \"Aves/Setophaga discolor/e2a388c547ba5e1bcf2a7c7457894e70.jpg\",\n  \"160889.jpg\": \"Aves/Setophaga discolor/5019064cb48979daaf2a8d3ffc7a06c2.jpg\",\n  \"160894.jpg\": \"Aves/Setophaga discolor/92f24b1ce9a898487f0e50f584b36fe9.jpg\",\n  \"160895.jpg\": \"Aves/Setophaga discolor/2fbd57190b04e2be7c0db17b7137b0fe.jpg\",\n  \"160896.jpg\": \"Aves/Setophaga discolor/85843bde9155f9368c2ff8fd2a0075b4.jpg\",\n  \"160900.jpg\": \"Aves/Setophaga discolor/3d4348ce48fcbaf1dd9fda300f3f4c4b.jpg\",\n  \"160901.jpg\": \"Aves/Setophaga discolor/0f853ade2843f4801e32fbfd04c57854.jpg\",\n  \"160905.jpg\": \"Aves/Setophaga discolor/f94b782510d2dd4f261b0fa4a3734f44.jpg\",\n  \"160906.jpg\": \"Aves/Setophaga discolor/449f22088b60163f0094a9da2a616b9b.jpg\",\n  \"160907.jpg\": \"Aves/Setophaga discolor/79f1926f354f65681b1131dde85f41ed.jpg\",\n  \"160908.jpg\": \"Aves/Setophaga discolor/65b9c927cf2f1ce8b246657e9dc2e6ab.jpg\",\n  \"160910.jpg\": \"Aves/Setophaga discolor/52603f29742caa66442f58ae671355d8.jpg\",\n  \"160911.jpg\": \"Aves/Setophaga discolor/f3078afaeaba0ae45bc4a1fab944ed70.jpg\",\n  \"160912.jpg\": \"Aves/Setophaga discolor/e31685248ca4c4b8101be88e17ef5cc3.jpg\",\n  \"160917.jpg\": \"Aves/Setophaga discolor/793d7798c84b2c02ecccf7765614fc7b.jpg\",\n  \"160920.jpg\": \"Aves/Setophaga discolor/0f5aadd18bcfaa4c8c63d0e6a7609a43.jpg\",\n  \"160922.jpg\": \"Aves/Setophaga discolor/67e89c38b5756f92512a339094a6a075.jpg\",\n  \"160925.jpg\": \"Aves/Setophaga discolor/a31f81fa18154b1e097f38bb0d5783fe.jpg\",\n  \"160926.jpg\": \"Aves/Setophaga discolor/e8efd492f75da1910b3a45e31fb8901f.jpg\",\n  \"161125.jpg\": \"Aves/Branta bernicla/49d644ebde499cf3d39787bd2b81f5ad.jpg\",\n  \"161140.jpg\": \"Aves/Branta bernicla/54928263f8f520ac71f1cb7772515a6d.jpg\",\n  \"161141.jpg\": \"Aves/Branta bernicla/749edbd3e9821ef543db56cac6d251de.jpg\",\n  \"161155.jpg\": \"Aves/Branta bernicla/c95cf9c275cf4fd1b3d054421cbc0a86.jpg\",\n  \"161169.jpg\": \"Aves/Branta bernicla/767da509ed674e61316c890aee4605d6.jpg\",\n  \"161171.jpg\": \"Aves/Branta bernicla/6ee97142b1710ced883440bb72d045a4.jpg\",\n  \"16119.jpg\": \"Aves/Buteo jamaicensis/785c7ecf0e1b3426ccd5f270c11fe027.jpg\",\n  \"161231.jpg\": \"Insecta/Parallelia bistriaris/6c71806dcebb0de0e82fa2f1cfb2d3f6.jpg\",\n  \"161234.jpg\": \"Insecta/Parallelia bistriaris/ad41d84b8cc0dc1119bf4aefe92e3d3f.jpg\",\n  \"161235.jpg\": \"Insecta/Parallelia bistriaris/b40dbe80887165b1003b2eb97121a19c.jpg\",\n  \"161236.jpg\": \"Insecta/Parallelia bistriaris/81418ef4797a5ba43c94f187163acf94.jpg\",\n  \"161240.jpg\": \"Insecta/Parallelia bistriaris/64db754591ba6c60d52955a5c898c39f.jpg\",\n  \"161244.jpg\": \"Insecta/Parallelia bistriaris/368ac77a812548178af1376652b88495.jpg\",\n  \"161245.jpg\": \"Insecta/Parallelia bistriaris/8d2150e7062efc90b1ecc4892d27cd3c.jpg\",\n  \"161248.jpg\": \"Insecta/Parallelia bistriaris/7a77a36096c81956fccb8d07599e6e30.jpg\",\n  \"161250.jpg\": \"Insecta/Parallelia bistriaris/f5f1c180ab9f0fda6b5d3610f735b791.jpg\",\n  \"161307.jpg\": \"Insecta/Clogmia albipunctata/c1df3ecf57799feb5244f4d259a53770.jpg\",\n  \"161308.jpg\": \"Insecta/Clogmia albipunctata/c5ed3edff3fc792d34b72f8465952b59.jpg\",\n  \"161312.jpg\": \"Insecta/Clogmia albipunctata/150986c7643039fd9558a6d9979fa059.jpg\",\n  \"161314.jpg\": \"Insecta/Clogmia albipunctata/0c487f091796a1daa939aa1143e90878.jpg\",\n  \"161315.jpg\": \"Insecta/Clogmia albipunctata/da35b12c3dd9281f85f99bef8a95c2f6.jpg\",\n  \"161324.jpg\": \"Insecta/Clogmia albipunctata/1b84bd6d624ad86adec8fc2aec8e52dc.jpg\",\n  \"161325.jpg\": \"Insecta/Clogmia albipunctata/0c8bcceceb5cf869ade89d807ae67e85.jpg\",\n  \"161330.jpg\": \"Insecta/Clogmia albipunctata/3c316c7dea92fd30310ab2b07c11d60b.jpg\",\n  \"161332.jpg\": \"Insecta/Clogmia albipunctata/804dddcd5b648ac29c2e5dea51897311.jpg\",\n  \"161333.jpg\": \"Insecta/Labidomera clivicollis/5830d83a3058d625f21c0265cdd1ec15.jpg\",\n  \"161335.jpg\": \"Insecta/Labidomera clivicollis/27c09fb72cfd3caa1d4f28d6b0679461.jpg\",\n  \"161337.jpg\": \"Insecta/Labidomera clivicollis/d0781b0eb70aa3e652ca5fa649b9bdd8.jpg\",\n  \"161339.jpg\": \"Insecta/Labidomera clivicollis/dd1b63d0e0528486b3a1f50ba4b61d4b.jpg\",\n  \"161340.jpg\": \"Insecta/Labidomera clivicollis/8e84f0af8c54b71aa03587e718c3c8d4.jpg\",\n  \"161344.jpg\": \"Insecta/Labidomera clivicollis/03dc5ee0ad08bab60d0e6850c7a13598.jpg\",\n  \"161353.jpg\": \"Insecta/Labidomera clivicollis/a1074423ed179c9ba7ff2dec76ed3cc2.jpg\",\n  \"161357.jpg\": \"Insecta/Labidomera clivicollis/cb4eaadff8c641d410c56ad53c0436e4.jpg\",\n  \"161359.jpg\": \"Insecta/Labidomera clivicollis/17ad73448260121f429459ea99d375f6.jpg\",\n  \"161361.jpg\": \"Insecta/Labidomera clivicollis/2b17c8dca962462c395a4159abbffa4d.jpg\",\n  \"161362.jpg\": \"Insecta/Labidomera clivicollis/fca57be5a1aa53c95de538ff6e1f08f6.jpg\",\n  \"161367.jpg\": \"Insecta/Labidomera clivicollis/d51d8d01c62634686bb568e1eb6e3b30.jpg\",\n  \"161383.jpg\": \"Insecta/Labidomera clivicollis/f6d75fa6e561f005b91c7315ad186a4a.jpg\",\n  \"161384.jpg\": \"Insecta/Labidomera clivicollis/7e2954ac3dba08308d20eda306e313c5.jpg\",\n  \"161386.jpg\": \"Insecta/Labidomera clivicollis/803d3028ce41f6d5b88659c27882c998.jpg\",\n  \"161392.jpg\": \"Insecta/Labidomera clivicollis/28aadfaf7924ad79ac13f5c79c59b79c.jpg\",\n  \"161400.jpg\": \"Insecta/Labidomera clivicollis/86acf4a57802d995ee2043938720bcbe.jpg\",\n  \"161401.jpg\": \"Insecta/Labidomera clivicollis/3c0787bf90709f74898ff7224058da3c.jpg\",\n  \"161408.jpg\": \"Insecta/Labidomera clivicollis/5331c7e6e214575d2ba72ea78a3b94f8.jpg\",\n  \"161414.jpg\": \"Insecta/Labidomera clivicollis/bbad3c9c869a1a862008840281142c12.jpg\",\n  \"161471.jpg\": \"Insecta/Wallengrenia otho/f892ba2cb870386da7105e139e429885.jpg\",\n  \"161472.jpg\": \"Insecta/Wallengrenia otho/7eb5461b0f4f7c6251a023743ee8a663.jpg\",\n  \"161476.jpg\": \"Insecta/Wallengrenia otho/47a57058de14f52ff14d243e2b390f46.jpg\",\n  \"161477.jpg\": \"Insecta/Wallengrenia otho/e2cb1a2183121b4648856033a69cc302.jpg\",\n  \"161480.jpg\": \"Insecta/Wallengrenia otho/533d07c052a83f6ca8ac8d0046d0e320.jpg\",\n  \"161486.jpg\": \"Insecta/Wallengrenia otho/0c162afaa915c24f62b282ce0b161a92.jpg\",\n  \"161495.jpg\": \"Insecta/Wallengrenia otho/cc215180163832e4bfe6e2a3e2951613.jpg\",\n  \"161500.jpg\": \"Insecta/Romalea microptera/a4e16cbe827138a78e25f3734a49ce0c.jpg\",\n  \"161503.jpg\": \"Insecta/Romalea microptera/c51cba724d711050577de026de72780e.jpg\",\n  \"161504.jpg\": \"Insecta/Romalea microptera/3c802c487430bbdd1b3e6b84255f6b80.jpg\",\n  \"161505.jpg\": \"Insecta/Romalea microptera/82ee0e37925d960460e46299abbb6da9.jpg\",\n  \"161506.jpg\": \"Insecta/Romalea microptera/220311fb9828b9ed5ad2c3d89af190b4.jpg\",\n  \"161507.jpg\": \"Insecta/Romalea microptera/04a5cbad6788c96071030be597a43e62.jpg\",\n  \"161511.jpg\": \"Insecta/Romalea microptera/df4127a42cf60f106672d497b6a4dc10.jpg\",\n  \"161512.jpg\": \"Insecta/Romalea microptera/2f03cc0ce60016a2adb5a3d3f26bf99a.jpg\",\n  \"161515.jpg\": \"Insecta/Romalea microptera/72fc919e381aa841e0001aec8b3510e2.jpg\",\n  \"161516.jpg\": \"Insecta/Romalea microptera/babe36dd0dbb673c3e2d4f4c4c354e51.jpg\",\n  \"161520.jpg\": \"Insecta/Romalea microptera/e765ea78bd315a4814b3d376a4faf963.jpg\",\n  \"161526.jpg\": \"Insecta/Romalea microptera/85da9097498aaef35bc4fccb92d272e8.jpg\",\n  \"161531.jpg\": \"Insecta/Romalea microptera/fbc16139c5d7128d8f6d45007963786c.jpg\",\n  \"161532.jpg\": \"Insecta/Romalea microptera/953269b44867417122839aac41e77a98.jpg\",\n  \"161533.jpg\": \"Insecta/Romalea microptera/7d8be3087f394c8b3b3379f9af6fda92.jpg\",\n  \"161534.jpg\": \"Insecta/Romalea microptera/de9b7481ac45ca22c2e910fe290a674c.jpg\",\n  \"161535.jpg\": \"Insecta/Romalea microptera/6379fa009250e81f9bfb4d2a30ec3034.jpg\",\n  \"161536.jpg\": \"Insecta/Romalea microptera/365cdd69ca57af9d650f77702cdbbe68.jpg\",\n  \"161537.jpg\": \"Insecta/Romalea microptera/948972916f434eeaf41dacd55cba7a31.jpg\",\n  \"161540.jpg\": \"Insecta/Romalea microptera/e539464895fefee4a58391c98c9f8730.jpg\",\n  \"161541.jpg\": \"Insecta/Romalea microptera/f66f021c8950d0434e716f25b60f3f74.jpg\",\n  \"161558.jpg\": \"Mollusca/Kelletia kelletii/60703778cc091368cf843c5b8ebcd17b.jpg\",\n  \"161565.jpg\": \"Mollusca/Kelletia kelletii/d48588c1b694067b2269f97d79db80f4.jpg\",\n  \"161618.jpg\": \"Insecta/Adelpha eulalia/606daf05dfa1fb9c8e5608aa3ea9aeb4.jpg\",\n  \"161624.jpg\": \"Insecta/Adelpha eulalia/a39a2f1c2206e9c49b5c0cca88c5feed.jpg\",\n  \"161626.jpg\": \"Insecta/Adelpha eulalia/fb8f51bd2a803362a525e4ed00f79dfb.jpg\",\n  \"161627.jpg\": \"Insecta/Adelpha eulalia/f465e8eb64e7efb72c8b468173af8fbf.jpg\",\n  \"161628.jpg\": \"Insecta/Adelpha eulalia/95de9ce0b926684756a26b5d0a808784.jpg\",\n  \"161629.jpg\": \"Insecta/Adelpha eulalia/e6204bd9e1e58a7010477cc75455ea06.jpg\",\n  \"161631.jpg\": \"Insecta/Adelpha eulalia/4c85e4e6dda312e62be4bfdb0b90cb39.jpg\",\n  \"161638.jpg\": \"Insecta/Adelpha eulalia/434cc09c15771ac9bf5954ecc468f922.jpg\",\n  \"161641.jpg\": \"Insecta/Adelpha eulalia/3923283cdc439f6abbd55eb565597cb9.jpg\",\n  \"161644.jpg\": \"Insecta/Adelpha eulalia/81747d12e51b448e90c840b7976c3c2b.jpg\",\n  \"161651.jpg\": \"Insecta/Adelpha eulalia/46f97bca056520bccae377e1886c6f8f.jpg\",\n  \"161692.jpg\": \"Mammalia/Odocoileus hemionus hemionus/6fb642934570481398b131e3889b1cf9.jpg\",\n  \"161693.jpg\": \"Mammalia/Odocoileus hemionus hemionus/94c09961e0cd880d57cf131ef77c2d7e.jpg\",\n  \"161698.jpg\": \"Mammalia/Odocoileus hemionus hemionus/21cbdcbfbf5a60400b54e9501e2ee84d.jpg\",\n  \"161699.jpg\": \"Mammalia/Odocoileus hemionus hemionus/75d8e60f261b8d7fca5b7f1e0bfca87e.jpg\",\n  \"161717.jpg\": \"Insecta/Brachygastra mellifica/443f41445a94918e9b82aa69464b8533.jpg\",\n  \"161778.jpg\": \"Insecta/Hagenius brevistylus/e2dbab328848358a5e512b7bb90b08d8.jpg\",\n  \"161780.jpg\": \"Insecta/Hagenius brevistylus/328a427cc0cae20bb32e9a856bcc1fb7.jpg\",\n  \"161781.jpg\": \"Insecta/Hagenius brevistylus/66d3b143ba148518ad284863e6dc0026.jpg\",\n  \"161782.jpg\": \"Insecta/Hagenius brevistylus/b95855e6738217becb05f04a38a499f4.jpg\",\n  \"161787.jpg\": \"Insecta/Hagenius brevistylus/976fe9f2ddec2a969eaeac0b4623320d.jpg\",\n  \"161789.jpg\": \"Insecta/Hagenius brevistylus/b577052508fdea413e33e067b75104fb.jpg\",\n  \"161791.jpg\": \"Insecta/Hagenius brevistylus/090dc44734462772dfc444d78add9f87.jpg\",\n  \"161792.jpg\": \"Insecta/Hagenius brevistylus/c4fcb27492058b0e50f072d4f4c43a60.jpg\",\n  \"161793.jpg\": \"Insecta/Hagenius brevistylus/0f34fd7feeb256c17edc15715ad2ac36.jpg\",\n  \"161798.jpg\": \"Insecta/Hagenius brevistylus/cf286b27e8479a527a6473a7f8771c33.jpg\",\n  \"161799.jpg\": \"Insecta/Hagenius brevistylus/6068e24f1b92e075cac40431ea370406.jpg\",\n  \"161800.jpg\": \"Insecta/Hagenius brevistylus/2a076e46ecd4b65642453f4046717247.jpg\",\n  \"161803.jpg\": \"Insecta/Hagenius brevistylus/232834ec3f6acf7c6a607b82394ddc1c.jpg\",\n  \"161804.jpg\": \"Insecta/Hagenius brevistylus/f2c8c6a534640211cc4e92285714d644.jpg\",\n  \"161805.jpg\": \"Insecta/Hagenius brevistylus/37e082fdfd9c0e367b7a3d2d350c154d.jpg\",\n  \"161806.jpg\": \"Insecta/Hagenius brevistylus/5007585c08fa6bfd61d14240f46d856b.jpg\",\n  \"161807.jpg\": \"Insecta/Hagenius brevistylus/6463b7a70767850e84f0ae0650c1e6ea.jpg\",\n  \"161809.jpg\": \"Insecta/Hagenius brevistylus/99432e1d3503df381694e8a5c7b8e662.jpg\",\n  \"161810.jpg\": \"Insecta/Hagenius brevistylus/d499cdd65a68e16707261513bcb8da46.jpg\",\n  \"161816.jpg\": \"Insecta/Hagenius brevistylus/a2d1ed7fc228b0a247f8fafbf5c29f5a.jpg\",\n  \"161817.jpg\": \"Insecta/Hagenius brevistylus/876127ca2357599422fac2fc9c37c385.jpg\",\n  \"161818.jpg\": \"Insecta/Hagenius brevistylus/b4ce193b2ba7530da4711e0da2a4b68e.jpg\",\n  \"161819.jpg\": \"Insecta/Hagenius brevistylus/340d916585d7408b9e7beab00d18abdb.jpg\",\n  \"161821.jpg\": \"Insecta/Hagenius brevistylus/e8a01386be0eb83d2d346f7b11028adb.jpg\",\n  \"161825.jpg\": \"Aves/Anthus rubescens/ea7220999ca38fb902f4935b9b874f06.jpg\",\n  \"161838.jpg\": \"Aves/Anthus rubescens/88e31baa5a4cbaadae8da09dd872803b.jpg\",\n  \"161844.jpg\": \"Aves/Anthus rubescens/4b35cd9bff8e33d960daf9af4b5bd638.jpg\",\n  \"161845.jpg\": \"Aves/Anthus rubescens/3e0a26585581b83f63666d2fe0e0dfc3.jpg\",\n  \"161846.jpg\": \"Aves/Anthus rubescens/e523432ce4ab80e03e2e182bdd32dc26.jpg\",\n  \"161848.jpg\": \"Aves/Anthus rubescens/6636bf956f9606dc1cdd1931d179099f.jpg\",\n  \"161851.jpg\": \"Aves/Anthus rubescens/fed5a4f3901c19399eda4399565b7310.jpg\",\n  \"161855.jpg\": \"Aves/Anthus rubescens/643f4866acb7b221e396078aa937dc5a.jpg\",\n  \"161859.jpg\": \"Aves/Anthus rubescens/e3ec419ac96b59391e46573944f7d085.jpg\",\n  \"161867.jpg\": \"Aves/Anthus rubescens/c5181f7b7289f28c638d388319d870b4.jpg\",\n  \"161868.jpg\": \"Aves/Anthus rubescens/69d68d47a4c82ce503ad45e6ea7c06d0.jpg\",\n  \"161869.jpg\": \"Aves/Anthus rubescens/abdf7a2df22e4de4ab8d684958348350.jpg\",\n  \"161886.jpg\": \"Aves/Anthus rubescens/24b116fa786b9837ce4aec9274e054e8.jpg\",\n  \"161897.jpg\": \"Aves/Anthus rubescens/f40bcf99530c0f54ccc0e3dfa11738fa.jpg\",\n  \"161913.jpg\": \"Aves/Anthus rubescens/82b4013c7101d062b53331abf4fdfaf1.jpg\",\n  \"161914.jpg\": \"Aves/Anthus rubescens/8b1d528b5daa727fdd0c07e59c85eca1.jpg\",\n  \"161923.jpg\": \"Aves/Anthus rubescens/7ed6accae4dec35959fe80777db0e500.jpg\",\n  \"161928.jpg\": \"Aves/Anthus rubescens/4d7df8da56a3fa89c6dabed41edc7a8b.jpg\",\n  \"161932.jpg\": \"Aves/Anthus rubescens/12801c093ce0086b686d7299e9b19600.jpg\",\n  \"161943.jpg\": \"Aves/Anthus rubescens/c411cd230f7731ebb6fdd07d63777a24.jpg\",\n  \"161945.jpg\": \"Aves/Anthus rubescens/1e5785b9f8bfe9adc2d3b9e04d915da8.jpg\",\n  \"161949.jpg\": \"Aves/Anthus rubescens/3b48a8358e7cec43b61afbd312afe19a.jpg\",\n  \"161950.jpg\": \"Aves/Anthus rubescens/7f0a085fcd5a09b9432ad4ea8279ab33.jpg\",\n  \"161957.jpg\": \"Aves/Anthus rubescens/2d9fab97adc1dea8d7282a97004c6afe.jpg\",\n  \"161966.jpg\": \"Aves/Anthus rubescens/d8623a4e36b74bc4aa8edba5a6f20f57.jpg\",\n  \"161976.jpg\": \"Aves/Anthus rubescens/8a51ed09198e36ab203c4fd9e8e93893.jpg\",\n  \"162.jpg\": \"Aves/Zonotrichia capensis/d49c2d67463cd894a65a6bba882dc088.jpg\",\n  \"162123.jpg\": \"Reptilia/Aspidoscelis exsanguis/a8a2afc88c28d14636ac04da927a63c9.jpg\",\n  \"162127.jpg\": \"Reptilia/Aspidoscelis exsanguis/963133e16d582ed3261dbae1e46030e2.jpg\",\n  \"162128.jpg\": \"Reptilia/Aspidoscelis exsanguis/01ee7825af45ce97b8aad632857fc8d0.jpg\",\n  \"162129.jpg\": \"Reptilia/Aspidoscelis exsanguis/060a758ca69b9768e4011e8bfe8a92ee.jpg\",\n  \"162134.jpg\": \"Reptilia/Aspidoscelis exsanguis/508f847132c303ad070876a732321e9d.jpg\",\n  \"162138.jpg\": \"Reptilia/Aspidoscelis exsanguis/d6174b7c694ffe2e6162fba395496aea.jpg\",\n  \"162142.jpg\": \"Reptilia/Aspidoscelis exsanguis/44f278ddc7bb28e561da1aab8fa1ed25.jpg\",\n  \"162143.jpg\": \"Reptilia/Aspidoscelis exsanguis/a8b724d5e366d4e29b3a739ce15ecf36.jpg\",\n  \"162144.jpg\": \"Reptilia/Aspidoscelis exsanguis/d0f6406967a033ef966d15233d1e01a8.jpg\",\n  \"162145.jpg\": \"Reptilia/Aspidoscelis exsanguis/9f2f302ccc22160b619d94eaeb172ca0.jpg\",\n  \"162150.jpg\": \"Reptilia/Aspidoscelis exsanguis/08ec204bccb767797a6356575f4ddd5e.jpg\",\n  \"162151.jpg\": \"Reptilia/Aspidoscelis exsanguis/4234837fc378685809163b21fcc88d5e.jpg\",\n  \"162152.jpg\": \"Reptilia/Aspidoscelis exsanguis/9a4070be8f88fdbafb8d8f9d12d454b5.jpg\",\n  \"162160.jpg\": \"Reptilia/Aspidoscelis exsanguis/c6eeff2c503504436ae27428c3551160.jpg\",\n  \"162161.jpg\": \"Reptilia/Aspidoscelis exsanguis/16c4e345c0a320fa62c526e87c127cd0.jpg\",\n  \"162166.jpg\": \"Reptilia/Aspidoscelis exsanguis/eb945f2b3e1aaa191fc3ec8a4a1124c8.jpg\",\n  \"162167.jpg\": \"Reptilia/Aspidoscelis exsanguis/90e206ea26ddbb95acb9bda6155782dd.jpg\",\n  \"162168.jpg\": \"Reptilia/Aspidoscelis exsanguis/5995385f54266d248fa82c3764d464ed.jpg\",\n  \"162169.jpg\": \"Reptilia/Aspidoscelis exsanguis/33c338e42178d075e5c9c986b561ea20.jpg\",\n  \"162176.jpg\": \"Aves/Cardinalis sinuatus/cb0f68e72409ce1280dbc25ac25fccc1.jpg\",\n  \"162180.jpg\": \"Aves/Cardinalis sinuatus/1e0d6dae448b76bc8b1c4f86f123bf46.jpg\",\n  \"162183.jpg\": \"Aves/Cardinalis sinuatus/814c20db3448bf2c2e8695a780cdfa27.jpg\",\n  \"162184.jpg\": \"Aves/Cardinalis sinuatus/80e74d18454237906269a5b59d6b6980.jpg\",\n  \"162185.jpg\": \"Aves/Cardinalis sinuatus/d78a83fb5ddc483cf0f492d70f014152.jpg\",\n  \"162187.jpg\": \"Aves/Cardinalis sinuatus/d7ab5e420a35b1f7992e136f50372e62.jpg\",\n  \"162190.jpg\": \"Aves/Cardinalis sinuatus/01774a5890da8f258dc02e6977f09d22.jpg\",\n  \"162194.jpg\": \"Aves/Cardinalis sinuatus/3a0a07ce5600ed498a1042b96d9d3e07.jpg\",\n  \"162195.jpg\": \"Aves/Cardinalis sinuatus/30c0684e32918dcdee48711cbca2520d.jpg\",\n  \"162196.jpg\": \"Aves/Cardinalis sinuatus/a95987d8a25a729bbbc863e002f2222b.jpg\",\n  \"162198.jpg\": \"Aves/Cardinalis sinuatus/56d558f7b35a4de8e00bfe72380b100b.jpg\",\n  \"162204.jpg\": \"Aves/Cardinalis sinuatus/36ef1a43fdbfdf54f4d9f3f1cf959c37.jpg\",\n  \"162205.jpg\": \"Aves/Cardinalis sinuatus/d152cd5a3f803c42f01b7b14a263efdd.jpg\",\n  \"162207.jpg\": \"Aves/Cardinalis sinuatus/a452efae5d3ccd3aa0d72703c645afc2.jpg\",\n  \"162209.jpg\": \"Aves/Cardinalis sinuatus/1537d4a9b671016a84cb1a8ea278e925.jpg\",\n  \"162223.jpg\": \"Aves/Cardinalis sinuatus/e214a2bb19949d6f8c04f01a56b8cb09.jpg\",\n  \"162226.jpg\": \"Aves/Cardinalis sinuatus/54ae5933ffa3356b00736b3c86622837.jpg\",\n  \"16223.jpg\": \"Aves/Buteo jamaicensis/8757b2cbeac467ef9812023485a5682d.jpg\",\n  \"162235.jpg\": \"Aves/Cardinalis sinuatus/6d2a94fe5f40ae189691e19946646019.jpg\",\n  \"162241.jpg\": \"Aves/Cardinalis sinuatus/1a8b2aa74d7e6cfbd76a8b3da4eaad42.jpg\",\n  \"162244.jpg\": \"Aves/Cardinalis sinuatus/bb7740f220f62c1aa5fb6126bc2e09e7.jpg\",\n  \"162247.jpg\": \"Aves/Cardinalis sinuatus/526e1342464d71e0b97b71508d971057.jpg\",\n  \"162248.jpg\": \"Aves/Cardinalis sinuatus/6bd7f0189251f82e5546851f52aa2051.jpg\",\n  \"162250.jpg\": \"Insecta/Polygonia gracilis/0f030fa7ee7e630cd669b3fa88714deb.jpg\",\n  \"162262.jpg\": \"Insecta/Polygonia gracilis/106824a312decbc223a3161a5881cb43.jpg\",\n  \"162263.jpg\": \"Insecta/Polygonia gracilis/f8c0e8b3af1748ad73457061bf01d440.jpg\",\n  \"162268.jpg\": \"Insecta/Polygonia gracilis/20aa03a18b0728778076b7bf71ee649a.jpg\",\n  \"162271.jpg\": \"Insecta/Polygonia gracilis/3c0a00227df0f967f32ed9720ba43a23.jpg\",\n  \"162289.jpg\": \"Insecta/Aglais urticae/fc43c5e32669656384c9e97bcd29fef2.jpg\",\n  \"162297.jpg\": \"Insecta/Aglais urticae/ae3440e636364076b0c381eccfcbcf1d.jpg\",\n  \"162299.jpg\": \"Insecta/Aglais urticae/1164e7feb7ddc68a8ff3a1f0ceafcf6b.jpg\",\n  \"162307.jpg\": \"Insecta/Aglais urticae/9bbeb5ab8ee57068266f0e87aa69ab45.jpg\",\n  \"162308.jpg\": \"Insecta/Aglais urticae/00388895f92f3158b492ad436bfa9823.jpg\",\n  \"162309.jpg\": \"Insecta/Aglais urticae/ad163f32b0b721c41dddf9bf3e730a3a.jpg\",\n  \"162310.jpg\": \"Insecta/Aglais urticae/94ffe72a45baac07bc200b11cb8a7f89.jpg\",\n  \"162314.jpg\": \"Insecta/Aglais urticae/c409502ba0ed71140624fc067ffa2142.jpg\",\n  \"162315.jpg\": \"Insecta/Aglais urticae/35de5a4614cbb49ffba7ef1a87c17a45.jpg\",\n  \"162317.jpg\": \"Insecta/Aglais urticae/4061f83c981f5a8a81ad2efb934e4a60.jpg\",\n  \"162323.jpg\": \"Insecta/Aglais urticae/645aa9b669054ae12ac990604c5a143f.jpg\",\n  \"162330.jpg\": \"Insecta/Aglais urticae/255736257af45263b2c828d0adc998bb.jpg\",\n  \"162332.jpg\": \"Insecta/Aglais urticae/cb3155aa372eaeb45f9334a307f0148b.jpg\",\n  \"162334.jpg\": \"Insecta/Aglais urticae/9e38061e0f9879ccb2b54365a4b70eef.jpg\",\n  \"162337.jpg\": \"Insecta/Aglais urticae/db6d40818064ad4db37013dc1d10d170.jpg\",\n  \"162340.jpg\": \"Insecta/Aglais urticae/c7bc476f3fff15a8d7d2f8661a864fcf.jpg\",\n  \"162341.jpg\": \"Insecta/Aglais urticae/217a0aa7d04aa760f9b8dd91c2f8bdb1.jpg\",\n  \"162348.jpg\": \"Insecta/Aglais urticae/ab46d9fefc39e9844ee160f23eb3b670.jpg\",\n  \"162350.jpg\": \"Insecta/Aglais urticae/8a08f751b27f0bb71f8da20db714ea5e.jpg\",\n  \"162352.jpg\": \"Insecta/Aglais urticae/7142e8778fa379371048ff4d5b307ec2.jpg\",\n  \"162357.jpg\": \"Insecta/Aglais urticae/4494c7940cb439ff2ca5787a77962fc1.jpg\",\n  \"162369.jpg\": \"Aves/Salpinctes obsoletus/25d6199e322cca7412b0e842c5099d26.jpg\",\n  \"162387.jpg\": \"Aves/Salpinctes obsoletus/24c58312251825e916c53af00601553c.jpg\",\n  \"162389.jpg\": \"Aves/Salpinctes obsoletus/f7e1b286d362d34d3071d6f8c859752c.jpg\",\n  \"162390.jpg\": \"Aves/Salpinctes obsoletus/4b75a137be8d1d690c3818c6612e0b00.jpg\",\n  \"162396.jpg\": \"Aves/Salpinctes obsoletus/9cb3723c68bc8055fbb6fef452169ca0.jpg\",\n  \"162404.jpg\": \"Aves/Salpinctes obsoletus/5aa97bb93091e658c8a42dbc587d9be2.jpg\",\n  \"162407.jpg\": \"Aves/Salpinctes obsoletus/c91afe6649067f0d7ef5d81f13e24590.jpg\",\n  \"162441.jpg\": \"Aves/Salpinctes obsoletus/246d7519a085b9d45ea8a959d9e6abd0.jpg\",\n  \"162443.jpg\": \"Aves/Salpinctes obsoletus/128047bee4ee20ccc7183fbb4b79699b.jpg\",\n  \"162448.jpg\": \"Aves/Salpinctes obsoletus/e4c4d437a40f2e2c15057e93abb6578c.jpg\",\n  \"162454.jpg\": \"Aves/Salpinctes obsoletus/1fa6378f61c44eb98f67c3c6b3047334.jpg\",\n  \"162460.jpg\": \"Aves/Salpinctes obsoletus/78b3b09fc8936fc188a0edbf4cb7d980.jpg\",\n  \"162463.jpg\": \"Aves/Salpinctes obsoletus/7355676c36435a82169a0700b0c587bc.jpg\",\n  \"162471.jpg\": \"Aves/Salpinctes obsoletus/aea54b0a08a1c419a3f36942de769fff.jpg\",\n  \"162478.jpg\": \"Aves/Salpinctes obsoletus/b74dfd1fec4edd098fd2f8768e52739f.jpg\",\n  \"162483.jpg\": \"Aves/Salpinctes obsoletus/577cc7daec9658d5d47aeef5934bbc14.jpg\",\n  \"162484.jpg\": \"Aves/Salpinctes obsoletus/2e2c99a739d11ebcd3a4118ce75eba55.jpg\",\n  \"162489.jpg\": \"Aves/Salpinctes obsoletus/b069afe6159b353cb9b20b75479933cf.jpg\",\n  \"162493.jpg\": \"Aves/Salpinctes obsoletus/399e736841529c1a24a950d2a4ee0219.jpg\",\n  \"162497.jpg\": \"Aves/Salpinctes obsoletus/f846f837e9335ea70720f3272929edfa.jpg\",\n  \"162501.jpg\": \"Aves/Salpinctes obsoletus/be6ae4c0699cca6d742a0e61c04183b7.jpg\",\n  \"162502.jpg\": \"Aves/Salpinctes obsoletus/2096dddd4475524ae23e932b6d3f36a2.jpg\",\n  \"162506.jpg\": \"Aves/Salpinctes obsoletus/3d1f1a2fb94fec4a75bac8cad15ac660.jpg\",\n  \"162522.jpg\": \"Aves/Limnodromus griseus/3e386679e9adacdcad8d41781919857b.jpg\",\n  \"162524.jpg\": \"Aves/Limnodromus griseus/cf0575bf644f14f894329e5226516d3d.jpg\",\n  \"162525.jpg\": \"Aves/Limnodromus griseus/26b86bf4aff72eecd44bdce52517241b.jpg\",\n  \"162526.jpg\": \"Aves/Limnodromus griseus/c7f8532d0a08e61c70e8b72f205dc7f7.jpg\",\n  \"162529.jpg\": \"Aves/Limnodromus griseus/54f8fae3df44251605686972df4742eb.jpg\",\n  \"162542.jpg\": \"Aves/Limnodromus griseus/b55e0245ab7f88e1b63f5dcc4219b201.jpg\",\n  \"162555.jpg\": \"Aves/Limnodromus griseus/24343100329d17aaf9c8a4c442e32cbb.jpg\",\n  \"162560.jpg\": \"Aves/Limnodromus griseus/35766fef3034c3d735fbf7e58fcb793d.jpg\",\n  \"162565.jpg\": \"Aves/Limnodromus griseus/9c2458eeff00d6458a4695847031b462.jpg\",\n  \"162573.jpg\": \"Aves/Limnodromus griseus/61aa4f4290920db1594a844076c14c94.jpg\",\n  \"162582.jpg\": \"Aves/Limnodromus griseus/e91637e63e8ca02f33f8ffaea05ff544.jpg\",\n  \"162585.jpg\": \"Aves/Limnodromus griseus/b45f0fe13b8cd8c0f37a2be9422db7c6.jpg\",\n  \"162586.jpg\": \"Insecta/Calosoma scrutator/ca8943f0ea88495fadf1f5a5ce38e159.jpg\",\n  \"162587.jpg\": \"Insecta/Calosoma scrutator/9f749fedaedca0b03d79bc2f66fd7ae3.jpg\",\n  \"162588.jpg\": \"Insecta/Calosoma scrutator/033bcb2572a7fe5c350f34fc60e196bc.jpg\",\n  \"162589.jpg\": \"Insecta/Calosoma scrutator/9ba85a85430c2cffc60e266d8e145e0c.jpg\",\n  \"162590.jpg\": \"Insecta/Calosoma scrutator/85b207672b9fe80a4c80a4fd7c15afbb.jpg\",\n  \"162591.jpg\": \"Insecta/Calosoma scrutator/2e04f05ae5c5a9190d97e29168640822.jpg\",\n  \"162592.jpg\": \"Insecta/Calosoma scrutator/e87dd802e756f8ce6780431682f58426.jpg\",\n  \"162597.jpg\": \"Insecta/Calosoma scrutator/f830e6b90534773da4b3e481b241711b.jpg\",\n  \"162598.jpg\": \"Insecta/Calosoma scrutator/e178386f316d4e7282eaf7e01a013782.jpg\",\n  \"162599.jpg\": \"Insecta/Calosoma scrutator/ebc0a4a1136f19f9fba8493972ebeaf0.jpg\",\n  \"1626.jpg\": \"Amphibia/Agalychnis dacnicolor/0424b2d48a5b495c9ef6793d2479f453.jpg\",\n  \"162600.jpg\": \"Insecta/Calosoma scrutator/50b580ce9a3e206cedf3570ef3021e74.jpg\",\n  \"162601.jpg\": \"Insecta/Calosoma scrutator/0d38d8dc7df535f34f79aa4bd22457c5.jpg\",\n  \"162602.jpg\": \"Insecta/Calosoma scrutator/74a22db0e1829dc993f89e8aeb123eef.jpg\",\n  \"162606.jpg\": \"Insecta/Calosoma scrutator/9ae1beadc056b965700c1806c97c45fe.jpg\",\n  \"162607.jpg\": \"Insecta/Calosoma scrutator/2d151addb51b974f83cf9660454e02e8.jpg\",\n  \"162608.jpg\": \"Insecta/Calosoma scrutator/1efcbbc0b71f6d535645cc0c4af53806.jpg\",\n  \"162609.jpg\": \"Insecta/Calosoma scrutator/87220923218b205c473fa2394eb1424f.jpg\",\n  \"162613.jpg\": \"Insecta/Calosoma scrutator/310740b6e55f0fe7f883c53fa340224e.jpg\",\n  \"162615.jpg\": \"Insecta/Calosoma scrutator/6256012422eed9d62fd79ed7c453bb9e.jpg\",\n  \"162616.jpg\": \"Insecta/Calosoma scrutator/7d44142e593594529001be84d8b2171c.jpg\",\n  \"162626.jpg\": \"Insecta/Calosoma scrutator/fa36e77116524a7b8cea2a7f96da55c5.jpg\",\n  \"162627.jpg\": \"Insecta/Calosoma scrutator/e0ac6f8ea9120881614a7f10ecf50636.jpg\",\n  \"162631.jpg\": \"Insecta/Calosoma scrutator/0cc4cfaeb4891561cb19037d95ed31d0.jpg\",\n  \"162632.jpg\": \"Reptilia/Aspidoscelis tesselata/4726c8b04afac06724c6b3b4218e06c2.jpg\",\n  \"162633.jpg\": \"Reptilia/Aspidoscelis tesselata/cbf0e83ec26aeb830dd6022f7edc2d4a.jpg\",\n  \"162639.jpg\": \"Reptilia/Aspidoscelis tesselata/0b1e18a256cff8c36e50cda8f0ea726b.jpg\",\n  \"162640.jpg\": \"Reptilia/Aspidoscelis tesselata/4fc27b33cf00159af1b991b439d4e81d.jpg\",\n  \"162643.jpg\": \"Reptilia/Aspidoscelis tesselata/0a4ff0e557293e196cae48d771b3c439.jpg\",\n  \"162646.jpg\": \"Reptilia/Aspidoscelis tesselata/3b8db5577c194bd1cfd5ff0975fad98c.jpg\",\n  \"162648.jpg\": \"Reptilia/Aspidoscelis tesselata/3578bd857d1c9ec73e96adfa90ee3fd5.jpg\",\n  \"162654.jpg\": \"Reptilia/Aspidoscelis tesselata/cfdcc84327e3e1cf2deae600be063201.jpg\",\n  \"162656.jpg\": \"Reptilia/Diadophis punctatus/589625a2e1a31144619dad013e33a362.jpg\",\n  \"162674.jpg\": \"Reptilia/Diadophis punctatus/0a26209ba443ecb6940faa5d43252dad.jpg\",\n  \"162719.jpg\": \"Reptilia/Diadophis punctatus/8a645a6238312b9e178fa56a6f78bb02.jpg\",\n  \"162720.jpg\": \"Reptilia/Diadophis punctatus/44e63666c6b7350c1a02604e6e43ec11.jpg\",\n  \"162740.jpg\": \"Reptilia/Diadophis punctatus/5868a93696fca529a800e9b7e15aa399.jpg\",\n  \"162747.jpg\": \"Reptilia/Diadophis punctatus/d076931587f13d9a54dba5c6c0578820.jpg\",\n  \"162771.jpg\": \"Reptilia/Diadophis punctatus/30211d1cedf03170eb954986957271ea.jpg\",\n  \"162775.jpg\": \"Reptilia/Diadophis punctatus/25a09b5a03ce1614780c9382916ffd81.jpg\",\n  \"162776.jpg\": \"Reptilia/Diadophis punctatus/38c07814c0702676eb2a2377e61d5125.jpg\",\n  \"162777.jpg\": \"Reptilia/Diadophis punctatus/cfc3c78c4a9d5026901fc8a3d32d1a9d.jpg\",\n  \"162793.jpg\": \"Reptilia/Diadophis punctatus/85496bb88cd8ede1d8fe42f4208a7276.jpg\",\n  \"162795.jpg\": \"Reptilia/Diadophis punctatus/c782d68c2aca6c74855a4b73e65dce13.jpg\",\n  \"162804.jpg\": \"Reptilia/Diadophis punctatus/706d64646a6b7ed9f01c6ac79cda65bd.jpg\",\n  \"162811.jpg\": \"Reptilia/Diadophis punctatus/8f9d3c6b18537bcbb668d00de547edee.jpg\",\n  \"162813.jpg\": \"Reptilia/Diadophis punctatus/3464f0d7badc81e87df54140056cada0.jpg\",\n  \"162863.jpg\": \"Reptilia/Diadophis punctatus/d4a32cc84c7f12c2ab8404c7980e1404.jpg\",\n  \"162875.jpg\": \"Reptilia/Diadophis punctatus/e2327ec89a4965704ec49bb8999b1206.jpg\",\n  \"162886.jpg\": \"Reptilia/Diadophis punctatus/2419861f9700ccb9b03361e150980f89.jpg\",\n  \"162887.jpg\": \"Reptilia/Diadophis punctatus/08aa9572dff63c3bf84ef3744b055151.jpg\",\n  \"163227.jpg\": \"Insecta/Plagodis phlogosaria/7590af3f616c7da978c29e4415a743ab.jpg\",\n  \"163229.jpg\": \"Insecta/Plagodis phlogosaria/dcc7177de60a4d93bc54a4bfc577c192.jpg\",\n  \"163233.jpg\": \"Insecta/Plagodis phlogosaria/9e539e88919341c498a6958de110f851.jpg\",\n  \"163236.jpg\": \"Insecta/Plagodis phlogosaria/60d7320749f1622bba0993fa9e1913c5.jpg\",\n  \"163248.jpg\": \"Insecta/Ctenucha virginica/03a2e0fc397f95e7c47b5794174a3d6a.jpg\",\n  \"163254.jpg\": \"Insecta/Ctenucha virginica/6dc8bab7262cb95d13e4ba9a0f23c5a3.jpg\",\n  \"163256.jpg\": \"Insecta/Ctenucha virginica/f34f236997d81c0728c4b9c26226a56d.jpg\",\n  \"163261.jpg\": \"Insecta/Ctenucha virginica/2ae9afb3775563059f6eafd40a5fc14f.jpg\",\n  \"163264.jpg\": \"Insecta/Ctenucha virginica/38d860d2de85d01dcb638f4bdf621f83.jpg\",\n  \"163265.jpg\": \"Insecta/Ctenucha virginica/4c9cd602b0bc1dbe414feaaf6b3da3a5.jpg\",\n  \"163268.jpg\": \"Insecta/Ctenucha virginica/7fd42d18ab55621f11850d4abb14e362.jpg\",\n  \"163272.jpg\": \"Insecta/Ctenucha virginica/37f0220e503fecd5baa8c83c4e087c7a.jpg\",\n  \"163273.jpg\": \"Insecta/Ctenucha virginica/4c2035d8e72a8ed88f768c54003345fd.jpg\",\n  \"163275.jpg\": \"Insecta/Ctenucha virginica/78e35cdf212ae90afab5c4e0d1347f1e.jpg\",\n  \"163276.jpg\": \"Insecta/Ctenucha virginica/4addc3a468cc4bc932b2da900b40375f.jpg\",\n  \"163278.jpg\": \"Insecta/Ctenucha virginica/348093091639728446b6b220d440c6b1.jpg\",\n  \"163287.jpg\": \"Insecta/Ctenucha virginica/560eead099e22985e043ce6e6d7044f6.jpg\",\n  \"163288.jpg\": \"Insecta/Ctenucha virginica/75f962c7a8afc46935dcf28df468471f.jpg\",\n  \"163293.jpg\": \"Insecta/Ctenucha virginica/c6747c5cdd067075a478d6a5712e0bc6.jpg\",\n  \"163295.jpg\": \"Insecta/Ctenucha virginica/454ad00d5559857d68db210c548470a6.jpg\",\n  \"163298.jpg\": \"Insecta/Ctenucha virginica/f6e7ba121c99f7cdb773d5a070d24ce7.jpg\",\n  \"163300.jpg\": \"Insecta/Ctenucha virginica/ff301e44ed4d1cc1842b6814931b8af3.jpg\",\n  \"163311.jpg\": \"Insecta/Ctenucha virginica/0323d15de512d494d63ba468a4d9e380.jpg\",\n  \"163321.jpg\": \"Insecta/Ctenucha virginica/864b654bbc0bc3035a79324996e75d79.jpg\",\n  \"163325.jpg\": \"Insecta/Ctenucha virginica/b5a8816a7769fa7039b1492020832ab3.jpg\",\n  \"163326.jpg\": \"Insecta/Ctenucha virginica/e94010e49156563caa2ded60fffac4d4.jpg\",\n  \"163327.jpg\": \"Insecta/Ctenucha virginica/e6130ff1a67d7a9fe4f95221f6c85885.jpg\",\n  \"163337.jpg\": \"Insecta/Ctenucha virginica/32ec832a3867ea5af75ccbdd61ce01a1.jpg\",\n  \"163339.jpg\": \"Insecta/Ctenucha virginica/c74976010b283b83795d344fe8f6d2cf.jpg\",\n  \"16341.jpg\": \"Aves/Buteo jamaicensis/b532216fad359794cf7438f6c3e51813.jpg\",\n  \"163484.jpg\": \"Insecta/Erynnis propertius/262b6357c83c70b6734efec642117ef4.jpg\",\n  \"163497.jpg\": \"Insecta/Erynnis propertius/9b74228f988d8ba2d2f0e2d435a52a09.jpg\",\n  \"1635.jpg\": \"Amphibia/Agalychnis dacnicolor/cac24b30ea3b806ac99804729fd37c74.jpg\",\n  \"163502.jpg\": \"Insecta/Erynnis propertius/06fade4bf57daaa13f608f2769476600.jpg\",\n  \"163509.jpg\": \"Insecta/Erynnis propertius/3586049a3f8c5cf3239ae82bfbfd25fe.jpg\",\n  \"16360.jpg\": \"Aves/Buteo jamaicensis/8134c80dcd7ce242e8a461fb2b3faaa4.jpg\",\n  \"163614.jpg\": \"Insecta/Satyrium sylvinus/8df38733c67e38a630c9e1c9c5bfac67.jpg\",\n  \"163616.jpg\": \"Insecta/Satyrium sylvinus/48d9d2b5a113003b1e91d250bca766c3.jpg\",\n  \"163617.jpg\": \"Insecta/Satyrium sylvinus/09dd302a92e9b35e3fc82f03f0577603.jpg\",\n  \"163621.jpg\": \"Insecta/Satyrium sylvinus/4724fcc19528eac0d62aa63cff00db27.jpg\",\n  \"163622.jpg\": \"Insecta/Satyrium sylvinus/8ff580ae5b886a081ad26d56bd7fc484.jpg\",\n  \"163628.jpg\": \"Aves/Notiomystis cincta/5e9137ce87ca490407ade1362c101ca5.jpg\",\n  \"163629.jpg\": \"Aves/Notiomystis cincta/a57fe9f9d9bb684138f2622a1bb9a5da.jpg\",\n  \"163632.jpg\": \"Aves/Notiomystis cincta/a1a3ae585450f26bad1e851ca08a9218.jpg\",\n  \"163639.jpg\": \"Aves/Notiomystis cincta/9ceabe91382e5b9d19d1fe2385e39267.jpg\",\n  \"163641.jpg\": \"Aves/Notiomystis cincta/a6177838c88192218166c697dce3f0a0.jpg\",\n  \"163662.jpg\": \"Mollusca/Ilyanassa obsoleta/3407b3582a60e1949f6339a28a682076.jpg\",\n  \"163663.jpg\": \"Mollusca/Ilyanassa obsoleta/d82ea957d5e22c86d08bff605038477f.jpg\",\n  \"16367.jpg\": \"Aves/Buteo jamaicensis/8afe8fd3799a137a5251089d4b3102bb.jpg\",\n  \"163735.jpg\": \"Mammalia/Antilocapra americana/8cee3bb5c92af85b0337acf01fd286c2.jpg\",\n  \"163737.jpg\": \"Mammalia/Antilocapra americana/6701420385c4089ae6c9e07a081cc14f.jpg\",\n  \"163746.jpg\": \"Mammalia/Antilocapra americana/1161037d0afb53a663eaf76a95dd1198.jpg\",\n  \"163764.jpg\": \"Mammalia/Antilocapra americana/93220d18656d0464921f1f4dd62bbfa4.jpg\",\n  \"163773.jpg\": \"Mammalia/Antilocapra americana/71a6631e4d28fe048d8966a7265c7fd1.jpg\",\n  \"163793.jpg\": \"Mammalia/Antilocapra americana/b21885e46d2bb268ed3ccebf11d922f6.jpg\",\n  \"163794.jpg\": \"Mammalia/Antilocapra americana/3eaafcfd59da2aa1983eadd0c8a9c572.jpg\",\n  \"163795.jpg\": \"Mammalia/Antilocapra americana/6fc753a6d4a1b2a7e8e9cc03b2ec4325.jpg\",\n  \"163846.jpg\": \"Mammalia/Antilocapra americana/15f875ce20959e54b8f2f86950cefa44.jpg\",\n  \"163860.jpg\": \"Mammalia/Antilocapra americana/998c1afdc02e7dcc0c0598d98ce60c5e.jpg\",\n  \"163889.jpg\": \"Insecta/Calycopis cecrops/06f0115b236d590f4c98f4199d00286e.jpg\",\n  \"163890.jpg\": \"Insecta/Calycopis cecrops/f52322e6612cb85228d0ca388161d6b1.jpg\",\n  \"163891.jpg\": \"Insecta/Calycopis cecrops/1a86005b9b224ee4b9b91b06b9d64aa0.jpg\",\n  \"163892.jpg\": \"Insecta/Calycopis cecrops/106cde222df96fe65b9943835e9cd69a.jpg\",\n  \"163896.jpg\": \"Insecta/Calycopis cecrops/3a8ebfc7cd9c93ad2cd352b4710589ca.jpg\",\n  \"163899.jpg\": \"Insecta/Calycopis cecrops/5ad95e714e6b64ffcc1aa9100a2dc057.jpg\",\n  \"163901.jpg\": \"Insecta/Calycopis cecrops/3fa2e1c3845d440c3cf681efebbe75cc.jpg\",\n  \"163903.jpg\": \"Insecta/Calycopis cecrops/13095a411bc0a2717e685d99b95ab8f9.jpg\",\n  \"163912.jpg\": \"Insecta/Calycopis cecrops/c57c01f95826a19d34715bf8e8d539d9.jpg\",\n  \"163913.jpg\": \"Insecta/Calycopis cecrops/366d61e0e3cbe03f4d4e5261ec4162a2.jpg\",\n  \"163914.jpg\": \"Insecta/Calycopis cecrops/d1eb22a3f793ddc943dbf523e1f13c34.jpg\",\n  \"163915.jpg\": \"Insecta/Calycopis cecrops/84cbc58a4aff137275e207e2eacdf3f7.jpg\",\n  \"16392.jpg\": \"Aves/Buteo jamaicensis/c8236f375156251bcdc996e4c022e759.jpg\",\n  \"163920.jpg\": \"Insecta/Calycopis cecrops/c0844472aad699d04a1e668209405e2b.jpg\",\n  \"163921.jpg\": \"Insecta/Calycopis cecrops/dcb6d485f0802470567fc7ab016d7ded.jpg\",\n  \"163923.jpg\": \"Insecta/Calycopis cecrops/d946b7924588f1ad8aad4200d08e46f4.jpg\",\n  \"163928.jpg\": \"Insecta/Calycopis cecrops/1b2ab82bafb6aa5f8dfd8d1ee814d92d.jpg\",\n  \"163929.jpg\": \"Insecta/Calycopis cecrops/08dafbb35d1f3c6c7b90b9560e0e9eab.jpg\",\n  \"163931.jpg\": \"Insecta/Calycopis cecrops/942f71a30bbc6613e1757cb98a60a36b.jpg\",\n  \"163934.jpg\": \"Insecta/Calycopis cecrops/edfddfa61b0e0d5c9f9884c41ba0b967.jpg\",\n  \"163935.jpg\": \"Insecta/Calycopis cecrops/74a216987f32844b9e78ba329ec388f3.jpg\",\n  \"163936.jpg\": \"Insecta/Calycopis cecrops/e649d1a4ba9bb4c85cbb79bbf179957d.jpg\",\n  \"163938.jpg\": \"Insecta/Calycopis cecrops/46ab66d204a58772ee7bf4044ed39038.jpg\",\n  \"163939.jpg\": \"Insecta/Calycopis cecrops/34bd533f865815c87552c3899eac2ace.jpg\",\n  \"164019.jpg\": \"Aves/Setophaga fusca/e5444920cfdced651323940bb47068e7.jpg\",\n  \"164035.jpg\": \"Aves/Setophaga fusca/4ded6e29749fd774c705cd4640fedd4e.jpg\",\n  \"164037.jpg\": \"Aves/Setophaga fusca/c37fccfe59caaece972152464d7f0475.jpg\",\n  \"164043.jpg\": \"Aves/Setophaga fusca/d2bd43cb7922b5ebc5e15adec797ae2e.jpg\",\n  \"164046.jpg\": \"Aves/Setophaga fusca/e08b1f6dee6110e69846e0f12b389680.jpg\",\n  \"164047.jpg\": \"Aves/Setophaga fusca/9f90d3812e91521b397575afcef7e443.jpg\",\n  \"164049.jpg\": \"Aves/Setophaga fusca/4eca74e696333af9c7c8401917e2d899.jpg\",\n  \"164050.jpg\": \"Aves/Setophaga fusca/27dc82e5c683768dfb2facf641ddc353.jpg\",\n  \"164051.jpg\": \"Aves/Setophaga fusca/d7062f54a72d18d08f9f1429c60108e8.jpg\",\n  \"164055.jpg\": \"Aves/Setophaga fusca/32d045deb809a9ea7cdc5cd1f16e7cc9.jpg\",\n  \"164060.jpg\": \"Aves/Setophaga fusca/cfdcc44fb60685c358fcaeb6a3c82622.jpg\",\n  \"164067.jpg\": \"Aves/Setophaga fusca/7cf1cdeb2159d4e5fc0af8f1d37b9ba5.jpg\",\n  \"164074.jpg\": \"Aves/Setophaga fusca/99305d22e5a6fad72e75f4f249ab24d4.jpg\",\n  \"164075.jpg\": \"Aves/Setophaga fusca/5c2bde58e7f93ded1371842d738bfa11.jpg\",\n  \"164077.jpg\": \"Aves/Setophaga fusca/12986caf45e2c200c2cdb7621ce2a373.jpg\",\n  \"164078.jpg\": \"Aves/Setophaga fusca/e3d29d535702281c7b6c5baf3fe9b7ca.jpg\",\n  \"164081.jpg\": \"Aves/Setophaga fusca/6eb36b5c92770eaf797807e72e629ba7.jpg\",\n  \"164091.jpg\": \"Aves/Setophaga fusca/2da5a5dd2e2747144991bf8adc2f0ae7.jpg\",\n  \"164092.jpg\": \"Aves/Setophaga fusca/02a53146c417b8fa327dee38ac259023.jpg\",\n  \"164093.jpg\": \"Aves/Setophaga fusca/8f2acfc710d18cc0192a0eb0cda16423.jpg\",\n  \"164094.jpg\": \"Aves/Setophaga fusca/571d1e485316de7f871c135d6aa959b7.jpg\",\n  \"164102.jpg\": \"Insecta/Anartia jatrophae/de49c6993e88c4a15ae524486f0fc185.jpg\",\n  \"164108.jpg\": \"Insecta/Anartia jatrophae/e42d874db0ba104ac38bcbf3a195542b.jpg\",\n  \"164110.jpg\": \"Insecta/Anartia jatrophae/b03c2de82fbd4c0f27a603a9c010bc08.jpg\",\n  \"164115.jpg\": \"Insecta/Anartia jatrophae/dbc41d7f0c789f04ca0c5c3f718a329f.jpg\",\n  \"164118.jpg\": \"Insecta/Anartia jatrophae/aa89706b6e8c7c4ece75c2c593b9efce.jpg\",\n  \"164132.jpg\": \"Insecta/Anartia jatrophae/25db003b4d301fef6adb034b79f13124.jpg\",\n  \"164134.jpg\": \"Insecta/Anartia jatrophae/457b24fd20f6365856349ac7de53f421.jpg\",\n  \"164156.jpg\": \"Insecta/Anartia jatrophae/36573a440df467113c25a61d834a9730.jpg\",\n  \"164157.jpg\": \"Insecta/Anartia jatrophae/5f12d53cd2528aff19033a82a6264508.jpg\",\n  \"164159.jpg\": \"Insecta/Anartia jatrophae/08a1735dbcc0270909e7be6355d0a216.jpg\",\n  \"164161.jpg\": \"Insecta/Anartia jatrophae/67301b9e4aaa6150adb42ebb9f1a2f07.jpg\",\n  \"164165.jpg\": \"Insecta/Anartia jatrophae/579ea88da8c7a1fb6c384f0a901fa671.jpg\",\n  \"164171.jpg\": \"Insecta/Anartia jatrophae/c6f72930b9ddc2aa27acf73c824e02a6.jpg\",\n  \"164175.jpg\": \"Insecta/Anartia jatrophae/3093f51354964d3d51ca5442876c3110.jpg\",\n  \"164181.jpg\": \"Insecta/Anartia jatrophae/ed4cc197cfb549305bb162fa02fecb4c.jpg\",\n  \"164187.jpg\": \"Insecta/Anartia jatrophae/b414d727a85af1ab350c5aa459a93009.jpg\",\n  \"164192.jpg\": \"Insecta/Anartia jatrophae/20dcff2a1ebaf1fca383799f8647c4cc.jpg\",\n  \"164196.jpg\": \"Insecta/Anartia jatrophae/8a915d94b641c4fd139eef274fee0a49.jpg\",\n  \"1642.jpg\": \"Amphibia/Agalychnis dacnicolor/305480fe9ca63375b56b92561b2f5a29.jpg\",\n  \"164203.jpg\": \"Insecta/Anartia jatrophae/6bf091ade626d8dae9afd621d813fcd0.jpg\",\n  \"164223.jpg\": \"Insecta/Anartia jatrophae/d66146df6ca0d68fc3f7c774878229e1.jpg\",\n  \"164224.jpg\": \"Insecta/Anartia jatrophae/43572bc60dde81fedeb0c7fdc3fb7f52.jpg\",\n  \"164245.jpg\": \"Aves/Passerina versicolor/629832cd93ebc01458c42e4dc39ccd0c.jpg\",\n  \"164248.jpg\": \"Aves/Passerina versicolor/9cfb14761e6232bba2bf92543998a15d.jpg\",\n  \"164249.jpg\": \"Aves/Passerina versicolor/fdee0aaac09d02f84f9ad1af97064673.jpg\",\n  \"164250.jpg\": \"Aves/Passerina versicolor/f4a41e52a043eb1cb1d9723c87920bd7.jpg\",\n  \"164258.jpg\": \"Aves/Passerina versicolor/94151979808ee162e01a4b381ffb6398.jpg\",\n  \"164260.jpg\": \"Aves/Passerina versicolor/a6cc5eaccc1e7d0308bea01eacd029ed.jpg\",\n  \"164261.jpg\": \"Aves/Passerina versicolor/62d5347c9f00ef5c03b3879eb04d328c.jpg\",\n  \"164262.jpg\": \"Aves/Passerina versicolor/5cfd253409922e95d8289bd4eb4651ea.jpg\",\n  \"164263.jpg\": \"Aves/Passerina versicolor/6617562c6f63c5cd6c51efd6dba183aa.jpg\",\n  \"164264.jpg\": \"Aves/Passerina versicolor/251642b56291bfb08dad89b318586360.jpg\",\n  \"164265.jpg\": \"Aves/Passerina versicolor/8cda02879122f81f94dce2270439aae4.jpg\",\n  \"164267.jpg\": \"Aves/Passerina versicolor/4b07cf8d148f21576f54e87326f08007.jpg\",\n  \"164270.jpg\": \"Aves/Passerina versicolor/ac18b771c484b4a924e7959a59dd28b6.jpg\",\n  \"164271.jpg\": \"Aves/Passerina versicolor/e3e2acd6fd11eb9d6a4ed28663dc7f2a.jpg\",\n  \"164275.jpg\": \"Aves/Passerina versicolor/f408e16dca23197fd714f3f59727bea9.jpg\",\n  \"164276.jpg\": \"Aves/Passerina versicolor/2569f419a650424947176f4feafe6a6b.jpg\",\n  \"164277.jpg\": \"Aves/Passerina versicolor/89ba76da73b0e3d4d6bed77ab05b7cb9.jpg\",\n  \"164278.jpg\": \"Aves/Passerina versicolor/b84a3b06b7d24a6f598548bc83de56ad.jpg\",\n  \"164283.jpg\": \"Aves/Passerina versicolor/a79b138ac2d69049353755835b1b0326.jpg\",\n  \"164299.jpg\": \"Insecta/Zanclognatha pedipilalis/4bb8e8b635fc1e6172967cc8ecb817ec.jpg\",\n  \"1643.jpg\": \"Amphibia/Agalychnis dacnicolor/0058d6a2725b4166833e65e028928567.jpg\",\n  \"164303.jpg\": \"Insecta/Zanclognatha pedipilalis/d535a566807ee94119247604a3cc383c.jpg\",\n  \"164305.jpg\": \"Insecta/Zanclognatha pedipilalis/15c28c65f169543f83b81a3b2673b33e.jpg\",\n  \"164307.jpg\": \"Insecta/Zanclognatha pedipilalis/136480df175b031387d8a401fcfa134a.jpg\",\n  \"164316.jpg\": \"Aves/Corvus cornix/1b3f8a03c8d69681116fa834a1cb6cdb.jpg\",\n  \"164320.jpg\": \"Aves/Corvus cornix/25001187fae4c613f48449648b27bacb.jpg\",\n  \"164327.jpg\": \"Aves/Corvus cornix/9018c980702cd2d0cf7eb4bba9808a7b.jpg\",\n  \"164328.jpg\": \"Aves/Corvus cornix/3b6dcbce6155b11f8d9c8d177f082f95.jpg\",\n  \"164336.jpg\": \"Aves/Corvus cornix/141ad101c1cb628000e7bb859c7862f8.jpg\",\n  \"164338.jpg\": \"Aves/Corvus cornix/3f54ab068bbe64146de4e7b96ac8bbd3.jpg\",\n  \"164342.jpg\": \"Aves/Corvus cornix/63adadc8662866976cf125416b919834.jpg\",\n  \"164345.jpg\": \"Aves/Corvus cornix/6600a812beabf570e2573fe0c866d60c.jpg\",\n  \"164346.jpg\": \"Aves/Corvus cornix/19dca33b8204804431e9cc14fa3af1e7.jpg\",\n  \"164347.jpg\": \"Aves/Corvus cornix/566bd59291d6a23b4c596b031d421436.jpg\",\n  \"164363.jpg\": \"Aves/Corvus cornix/200005b9c77a5dcea2dec31fd9a21f9e.jpg\",\n  \"164381.jpg\": \"Aves/Corvus cornix/629a189e5c1e163e0386804f532c5f7c.jpg\",\n  \"164388.jpg\": \"Aves/Corvus cornix/44e5ac9505c48ce549f150c3066fae6a.jpg\",\n  \"164389.jpg\": \"Aves/Corvus cornix/fc1c5406793a18cb9c3ed9ed69dddff9.jpg\",\n  \"164390.jpg\": \"Aves/Corvus cornix/fab4f7b8653c8c4eb9507d3c61e5b262.jpg\",\n  \"1644.jpg\": \"Amphibia/Agalychnis dacnicolor/6a4c2652194a87806db425b986ac7d2a.jpg\",\n  \"164400.jpg\": \"Aves/Corvus cornix/cf749cda9c2eb06224ba5d1ded45d200.jpg\",\n  \"164401.jpg\": \"Aves/Corvus cornix/3b4d7eb60973cf24771d78c299f6e327.jpg\",\n  \"164407.jpg\": \"Aves/Corvus cornix/5cacfac1efbc53cbff15345067871fbd.jpg\",\n  \"164423.jpg\": \"Aves/Agelaius phoeniceus/19aa51bb725e2b4ba45ce45994f92f2a.jpg\",\n  \"164471.jpg\": \"Aves/Agelaius phoeniceus/7bae68cd13e09e9218872b5916cb523d.jpg\",\n  \"164576.jpg\": \"Aves/Agelaius phoeniceus/6ffe67a908a9d3e44fbda382c04ae803.jpg\",\n  \"164585.jpg\": \"Aves/Agelaius phoeniceus/50c2b3a105ef36eb659282651882da78.jpg\",\n  \"164595.jpg\": \"Aves/Agelaius phoeniceus/f68432390a0863c47a6becf1358b14d5.jpg\",\n  \"1646.jpg\": \"Amphibia/Agalychnis dacnicolor/53cfb850f6c06356377e26b43352924b.jpg\",\n  \"164755.jpg\": \"Aves/Agelaius phoeniceus/8701dc09b05db045b4b3c5f2fc61dc29.jpg\",\n  \"164887.jpg\": \"Aves/Agelaius phoeniceus/898507e10611dd9f68da75bd1a32725f.jpg\",\n  \"164891.jpg\": \"Aves/Agelaius phoeniceus/1680f7791aee8b05a084bd68004b5e7b.jpg\",\n  \"164896.jpg\": \"Aves/Agelaius phoeniceus/194f4394561fa547f06e5478264c7c0f.jpg\",\n  \"164935.jpg\": \"Aves/Agelaius phoeniceus/a0bdf87800163f870b4055cbdc7fde47.jpg\",\n  \"164943.jpg\": \"Aves/Agelaius phoeniceus/77e604fd9905b2e174e5111e13ba446a.jpg\",\n  \"164944.jpg\": \"Aves/Agelaius phoeniceus/82b7227dfe030754bc487f3705983e95.jpg\",\n  \"165005.jpg\": \"Aves/Agelaius phoeniceus/766158b5a333da8c39de8102402e58a5.jpg\",\n  \"165048.jpg\": \"Aves/Agelaius phoeniceus/30c987b44cb05802c400b06128f79945.jpg\",\n  \"165079.jpg\": \"Aves/Agelaius phoeniceus/644f2df9f42cc91cf9c73678c71ba198.jpg\",\n  \"165094.jpg\": \"Aves/Agelaius phoeniceus/42861903eb4bee8fce34c94b7ceb15e8.jpg\",\n  \"165120.jpg\": \"Aves/Agelaius phoeniceus/77e99f807c0c02608601531b07df76b5.jpg\",\n  \"165277.jpg\": \"Aves/Agelaius phoeniceus/8882c7b38b575fb7370b2902d61792e7.jpg\",\n  \"16532.jpg\": \"Aves/Buteo jamaicensis/f666bd86446ffbf4b1400ec43c338c31.jpg\",\n  \"165397.jpg\": \"Aves/Agelaius phoeniceus/78a333c2f015d842b35ada94c5e7853a.jpg\",\n  \"165426.jpg\": \"Aves/Agelaius phoeniceus/f60b78d6f30e9e4b1b3a374253015d6e.jpg\",\n  \"165596.jpg\": \"Aves/Calocitta formosa/a39f11265754e51ed6b23be98e3adc3a.jpg\",\n  \"165597.jpg\": \"Aves/Calocitta formosa/936014734e6cc2fe9c0bb2d65a9f0007.jpg\",\n  \"165600.jpg\": \"Aves/Calocitta formosa/1f97f455131862145afa51a2b9e32654.jpg\",\n  \"165609.jpg\": \"Aves/Calocitta formosa/28675cf00aa1abc8707f6b36bfdf9c84.jpg\",\n  \"165610.jpg\": \"Aves/Calocitta formosa/47e1fa8118aaa4ada37da0a53dfc5dc4.jpg\",\n  \"165611.jpg\": \"Aves/Calocitta formosa/e277d5fdff24ca32942aa1f5e01fa51a.jpg\",\n  \"165623.jpg\": \"Aves/Calocitta formosa/799e68289a80efb2a2121b03874d6713.jpg\",\n  \"165624.jpg\": \"Aves/Calocitta formosa/5ad754216748b2ee7a1189c04f5ed465.jpg\",\n  \"165625.jpg\": \"Aves/Calocitta formosa/59ed53de7d9559a31672a7491a47d91c.jpg\",\n  \"165627.jpg\": \"Aves/Calocitta formosa/7184103842356bb42203f1f665e46f67.jpg\",\n  \"165633.jpg\": \"Reptilia/Plestiodon tetragrammus/909cd4fb122ddfe8517e7c19a65e001f.jpg\",\n  \"165635.jpg\": \"Reptilia/Plestiodon tetragrammus/0766ae50eb4992b8185aeccd77c0d43c.jpg\",\n  \"165636.jpg\": \"Reptilia/Plestiodon tetragrammus/14447ca48948293c828fe3924fd09c16.jpg\",\n  \"165647.jpg\": \"Reptilia/Plestiodon tetragrammus/344b85a66e8987a1df849f256480d502.jpg\",\n  \"165651.jpg\": \"Reptilia/Plestiodon tetragrammus/d4c733ade0b6534e72ff2c1012ade89d.jpg\",\n  \"165662.jpg\": \"Insecta/Apiomerus spissipes/31dfae97e6fccb817792713ab4defaf5.jpg\",\n  \"165668.jpg\": \"Insecta/Apiomerus spissipes/167c7461c0f0246905515a8e8d816f72.jpg\",\n  \"165669.jpg\": \"Insecta/Apiomerus spissipes/b100e9c094aa820c50161c38389c27c5.jpg\",\n  \"165673.jpg\": \"Insecta/Apiomerus spissipes/2f28a95825e6cdfec81c1c7c70510997.jpg\",\n  \"165674.jpg\": \"Insecta/Apiomerus spissipes/be5732b62c634ea9d8ce25cea4759ab0.jpg\",\n  \"165676.jpg\": \"Insecta/Apiomerus spissipes/de79096a11c6d92cc6b81980308e133f.jpg\",\n  \"165681.jpg\": \"Insecta/Tabanus atratus/cb7a54d47ba95471c9ec1b1a15082688.jpg\",\n  \"165690.jpg\": \"Insecta/Tabanus atratus/f435120527a03b28ab9f281c7242ab99.jpg\",\n  \"165691.jpg\": \"Insecta/Tabanus atratus/7b496fe3ed372b3b0fbd3fcd41b4b03f.jpg\",\n  \"165696.jpg\": \"Insecta/Tabanus atratus/38771bc13aa944675b4133e7bce9d4a1.jpg\",\n  \"165702.jpg\": \"Insecta/Tabanus atratus/5f44c2d23f2faafc8fa59643e0430a4b.jpg\",\n  \"165703.jpg\": \"Insecta/Tabanus atratus/f3355755d70240ae057b31d691649334.jpg\",\n  \"165704.jpg\": \"Insecta/Tabanus atratus/c1e03629b926d0d2e055dfb07269ac78.jpg\",\n  \"165705.jpg\": \"Insecta/Tabanus atratus/f716bf58757fb44660518a0ad12c25e6.jpg\",\n  \"165706.jpg\": \"Insecta/Tabanus atratus/6f363c21bfc730e5e73daa57829e9aa8.jpg\",\n  \"165708.jpg\": \"Insecta/Tabanus atratus/73f953eb1db764036ac47669b6a10dd0.jpg\",\n  \"165738.jpg\": \"Aves/Tringa melanoleuca/8d6b40211d892020ece05ed75c94e419.jpg\",\n  \"165745.jpg\": \"Aves/Tringa melanoleuca/f63a26521a8c682f7c33a057f63faf09.jpg\",\n  \"165751.jpg\": \"Aves/Tringa melanoleuca/4d1ae6489d60b70249f31a0b8836bf93.jpg\",\n  \"165772.jpg\": \"Aves/Tringa melanoleuca/a3e87c75a3d253ef656ec49f3adf2f0c.jpg\",\n  \"165773.jpg\": \"Aves/Tringa melanoleuca/d2cda13b34f04ce22eca57e38d0fd08d.jpg\",\n  \"165783.jpg\": \"Aves/Tringa melanoleuca/b79cc81bb92799d3f6fef171e9664e25.jpg\",\n  \"165806.jpg\": \"Aves/Tringa melanoleuca/8f3689b318b20d2fd0e88481ea8be359.jpg\",\n  \"165827.jpg\": \"Aves/Tringa melanoleuca/e6669a06c8550c4dae677ffd76a4e9ef.jpg\",\n  \"165831.jpg\": \"Aves/Tringa melanoleuca/954c06249b6e06a19381f87c7f642e28.jpg\",\n  \"165861.jpg\": \"Aves/Tringa melanoleuca/325bc5a2f910e4091b6dd6c517efc1cb.jpg\",\n  \"165868.jpg\": \"Aves/Tringa melanoleuca/6652e3e8c37887f30c7daece5d4ada9c.jpg\",\n  \"165869.jpg\": \"Aves/Tringa melanoleuca/a69778220b77098b318aae0cb108f0ae.jpg\",\n  \"165871.jpg\": \"Aves/Tringa melanoleuca/603960c6c04a5f3fe19325d9cb46c735.jpg\",\n  \"165881.jpg\": \"Aves/Tringa melanoleuca/515d31273307b79bc739f1e5d9e55f56.jpg\",\n  \"165923.jpg\": \"Aves/Tringa melanoleuca/4843c8904352fa312d82984ba7bf962f.jpg\",\n  \"165926.jpg\": \"Aves/Tringa melanoleuca/a6061cd5db20e3f17f2b7807cdb97a25.jpg\",\n  \"165927.jpg\": \"Aves/Tringa melanoleuca/21c9f3a0edb3aa14c017a25f1e85ae88.jpg\",\n  \"166011.jpg\": \"Aves/Tringa melanoleuca/72afe582664912f41e03680d0e84feda.jpg\",\n  \"166064.jpg\": \"Aves/Tringa melanoleuca/177d8de09c03b64df73ffe29dd184066.jpg\",\n  \"166078.jpg\": \"Aves/Tringa melanoleuca/9c5c798b0b0e6b5fcd2dd360bc8ad1b9.jpg\",\n  \"166190.jpg\": \"Aves/Vanellus vanellus/639853179e087614c7eed0e3b445c390.jpg\",\n  \"166191.jpg\": \"Aves/Vanellus vanellus/3434a08fe96c8cdbb8ea897575d1f739.jpg\",\n  \"166192.jpg\": \"Aves/Vanellus vanellus/e8d53bb940d34cfdae11f974076c94a8.jpg\",\n  \"166198.jpg\": \"Aves/Vanellus vanellus/8d568a6296da7b97e263b0e5065d85bf.jpg\",\n  \"166205.jpg\": \"Aves/Vanellus vanellus/f4d4b230dc30bc08dcd75ee5481ca7ec.jpg\",\n  \"166211.jpg\": \"Aves/Vanellus vanellus/7fb6c6b99c88228aa38a2e2973ca5b5b.jpg\",\n  \"166219.jpg\": \"Aves/Vanellus vanellus/dc96b67097b43e36623ca13fd358d24f.jpg\",\n  \"166220.jpg\": \"Aves/Vanellus vanellus/f162d45842dd46c51674600772adef67.jpg\",\n  \"166228.jpg\": \"Aves/Vanellus vanellus/ce9ec16aa865fc106d3fcbe980ded7ef.jpg\",\n  \"166231.jpg\": \"Aves/Vanellus vanellus/c56cd9c02f08130b1c5bf745c030f2b9.jpg\",\n  \"166232.jpg\": \"Aves/Vanellus vanellus/e4adce5c9a1cdd8bdd1a19da1b8c3774.jpg\",\n  \"166233.jpg\": \"Aves/Vanellus vanellus/975483a25c20df71c7f5ad5ce5e8baf8.jpg\",\n  \"166241.jpg\": \"Aves/Vanellus vanellus/ee6f39100bd29cda7342143a981c7a23.jpg\",\n  \"166247.jpg\": \"Aves/Vanellus vanellus/af00c8fb01495d5e1e5190ba23317164.jpg\",\n  \"166252.jpg\": \"Aves/Vanellus vanellus/eb594b7a62973ec1f99bc6ff25dc8a81.jpg\",\n  \"16626.jpg\": \"Aves/Buteo jamaicensis/455a3758378c5954d929e999951d171d.jpg\",\n  \"166266.jpg\": \"Aves/Vanellus vanellus/2e9b7a5d19b2a6c38bf15775daf22dda.jpg\",\n  \"166269.jpg\": \"Animalia/Dendraster excentricus/5e4342bd4eca43db3353200410d4e0a4.jpg\",\n  \"166274.jpg\": \"Animalia/Dendraster excentricus/4580d0f1743499ba47e3dfa5f3dd2435.jpg\",\n  \"166275.jpg\": \"Animalia/Dendraster excentricus/53aa209ca372965f113299a1932d42b4.jpg\",\n  \"166276.jpg\": \"Animalia/Dendraster excentricus/fb25aa78c1de0360a3cf203fbad129c5.jpg\",\n  \"166277.jpg\": \"Animalia/Dendraster excentricus/88955b58b6ec386451ab213d37dcd490.jpg\",\n  \"166278.jpg\": \"Animalia/Dendraster excentricus/58778160f1a1c817d0c2f8e7b609b239.jpg\",\n  \"166279.jpg\": \"Animalia/Dendraster excentricus/901f9ca46a6ff6fb61b8c093833e7517.jpg\",\n  \"166280.jpg\": \"Animalia/Dendraster excentricus/4e9e69cfd28f5536fe4ee8af3efd9bea.jpg\",\n  \"166286.jpg\": \"Animalia/Dendraster excentricus/0bae552561b10ce0a3a49549eeb8472b.jpg\",\n  \"166287.jpg\": \"Animalia/Dendraster excentricus/cba54df35f2acb8f0de79c39bec632af.jpg\",\n  \"166304.jpg\": \"Animalia/Dendraster excentricus/12d23a0a9b062da5eef919c8184e8f85.jpg\",\n  \"166307.jpg\": \"Animalia/Dendraster excentricus/d780d3c8e944b829a7fe511807d1ec34.jpg\",\n  \"166322.jpg\": \"Reptilia/Sceloporus grammicus/22a879888d0b17196591be297e903d22.jpg\",\n  \"166328.jpg\": \"Reptilia/Sceloporus grammicus/10160eb9dcecd8c3a8cd938ec43ea42a.jpg\",\n  \"166336.jpg\": \"Reptilia/Sceloporus grammicus/877f03df3782ec88c1bb3b41b5eba127.jpg\",\n  \"166340.jpg\": \"Reptilia/Sceloporus grammicus/3df6bec1abb9d992a351a620cbd345bb.jpg\",\n  \"166344.jpg\": \"Reptilia/Sceloporus grammicus/21431e73bd9c3d3c335fa8ff338f2894.jpg\",\n  \"166346.jpg\": \"Reptilia/Sceloporus grammicus/dd094d381bb61d00af5688f4e54027e8.jpg\",\n  \"166347.jpg\": \"Reptilia/Sceloporus grammicus/e0891090a9d69cd265f41a257c03688f.jpg\",\n  \"166350.jpg\": \"Reptilia/Sceloporus grammicus/76483fc10a671d6a6331ee68e32d5deb.jpg\",\n  \"166352.jpg\": \"Reptilia/Sceloporus grammicus/f413412f9529c1ecf541a234985f0d5c.jpg\",\n  \"166479.jpg\": \"Aves/Piaya cayana/6b71e5e6bd41dc86e2a20f3ac4258141.jpg\",\n  \"166494.jpg\": \"Aves/Piaya cayana/062304247f63706ce98d10148a7b9b2c.jpg\",\n  \"166495.jpg\": \"Aves/Piaya cayana/444acbd5d64ae2cf0d94a73251bae9de.jpg\",\n  \"166504.jpg\": \"Aves/Piaya cayana/2bf890c41351569cafce6bd2daa79156.jpg\",\n  \"166505.jpg\": \"Aves/Piaya cayana/569ca0cfd6b6dd584ee1f70eff27d5ca.jpg\",\n  \"166506.jpg\": \"Aves/Piaya cayana/10add3325f9e05fe1da6d79879dcf7f8.jpg\",\n  \"166507.jpg\": \"Aves/Piaya cayana/ad424d8be871ff11f5427748c995b384.jpg\",\n  \"166509.jpg\": \"Aves/Piaya cayana/8043c1e9b1fb64c33e018b7907da7141.jpg\",\n  \"166513.jpg\": \"Aves/Piaya cayana/27d22b734fb641a44fecbcfa76a37419.jpg\",\n  \"166514.jpg\": \"Aves/Piaya cayana/c068b9ebdc643b4cd96f95b94a026753.jpg\",\n  \"166518.jpg\": \"Aves/Piaya cayana/0cf2483402ffbaf72e7bc31493e524c3.jpg\",\n  \"166522.jpg\": \"Aves/Piaya cayana/e94423d55f9040bc9def9f62a1e9de77.jpg\",\n  \"166532.jpg\": \"Aves/Piaya cayana/68a3f5261be18df51e821c7eeeda9474.jpg\",\n  \"166533.jpg\": \"Aves/Piaya cayana/1899c1f6cb59b3ed174999ff6d871626.jpg\",\n  \"166534.jpg\": \"Aves/Piaya cayana/92bf546a9c748f0a2b21f37d972b1efb.jpg\",\n  \"166539.jpg\": \"Aves/Piaya cayana/9cbf19e92cf5d5bef2c0405812bac227.jpg\",\n  \"166542.jpg\": \"Aves/Piaya cayana/83d3076b23ff263c6e2616f36201a7e2.jpg\",\n  \"166545.jpg\": \"Aves/Piaya cayana/0ac3de76047aba12ff88a7a986528121.jpg\",\n  \"166546.jpg\": \"Aves/Piaya cayana/006c8cb242bf36022ac0727b6d650421.jpg\",\n  \"166547.jpg\": \"Aves/Piaya cayana/f51c3db84b2be28b95c1012474483460.jpg\",\n  \"166553.jpg\": \"Aves/Piaya cayana/e3570b40b5c45c310d10833afa55c8e0.jpg\",\n  \"166554.jpg\": \"Aves/Piaya cayana/dfec8ec6a61258ebf0bb27be724f3211.jpg\",\n  \"166557.jpg\": \"Aves/Piaya cayana/ea0d3d74f87365691f63a53839c5308c.jpg\",\n  \"166625.jpg\": \"Aves/Oceanites oceanicus/98acd07a5ed3a99a0c3f31d00d6c12ff.jpg\",\n  \"166626.jpg\": \"Aves/Oceanites oceanicus/8654d910e257d36a6170cd79bd157af3.jpg\",\n  \"166628.jpg\": \"Aves/Oceanites oceanicus/e797dc74701085adbe55d7548ed3267d.jpg\",\n  \"166664.jpg\": \"Insecta/Poecilocapsus lineatus/57b7adf747734f59812844d4f3e0d928.jpg\",\n  \"166668.jpg\": \"Insecta/Poecilocapsus lineatus/a1ebf20a0813ed8fd9a6f26c2178f8ea.jpg\",\n  \"166670.jpg\": \"Insecta/Poecilocapsus lineatus/60815817a7ae0ece754f534913e78ccd.jpg\",\n  \"166672.jpg\": \"Insecta/Poecilocapsus lineatus/076559395d9020d3ac9f5cd219974961.jpg\",\n  \"166673.jpg\": \"Insecta/Poecilocapsus lineatus/94d57f3415c17774f9cc127081f557b4.jpg\",\n  \"166678.jpg\": \"Insecta/Poecilocapsus lineatus/d043adf293193e8f5cc4697188a9e1c7.jpg\",\n  \"166683.jpg\": \"Insecta/Poecilocapsus lineatus/a710bb187a9615c78bfd8c4fcce14048.jpg\",\n  \"166688.jpg\": \"Mammalia/Canis latrans/51b8c7b9203cd6d7a1f05193222b0685.jpg\",\n  \"166690.jpg\": \"Mammalia/Canis latrans/030fdef47af3f449948fccc8d92f010c.jpg\",\n  \"166780.jpg\": \"Mammalia/Canis latrans/6941ce36b50204d1ebc953ba42de5d61.jpg\",\n  \"166803.jpg\": \"Mammalia/Canis latrans/efbe7f0c7c08bbda0ccaa3b85b1fa4ab.jpg\",\n  \"166883.jpg\": \"Mammalia/Canis latrans/49a96d280f8be5976965d23c16522e1b.jpg\",\n  \"166913.jpg\": \"Mammalia/Canis latrans/2f174d1adb1b08edeb90f8310972d8b8.jpg\",\n  \"166946.jpg\": \"Mammalia/Canis latrans/320a4f6d20094929e8736f6d81964c03.jpg\",\n  \"166953.jpg\": \"Mammalia/Canis latrans/d71a5af57b45f570ff926b65cb258a25.jpg\",\n  \"166966.jpg\": \"Mammalia/Canis latrans/e7da2ef7631269477edc0eed43f71c65.jpg\",\n  \"166975.jpg\": \"Mammalia/Canis latrans/c7be4267aed23c148fe7fb0752acbc6f.jpg\",\n  \"166994.jpg\": \"Mammalia/Canis latrans/6d813070fca063faab694c0351ab31f7.jpg\",\n  \"167.jpg\": \"Aves/Zonotrichia capensis/62172d03757eb052051e42d77c2f1bcc.jpg\",\n  \"167010.jpg\": \"Mammalia/Canis latrans/f73be3494568178d55784d69ebfa4d2b.jpg\",\n  \"167077.jpg\": \"Mammalia/Canis latrans/fb6444eae43447f6aed9fc0060d1fe6a.jpg\",\n  \"167114.jpg\": \"Mammalia/Canis latrans/58d5ac7ad12dd5db3f0c30c923cb5665.jpg\",\n  \"167148.jpg\": \"Mammalia/Canis latrans/e8cc1123f6735c3fa817ec724b4f2af0.jpg\",\n  \"167258.jpg\": \"Mammalia/Canis latrans/316a6584c28df9ef312ed23807bc42ee.jpg\",\n  \"167299.jpg\": \"Mammalia/Canis latrans/02c1de9aa7a46645b4f9f96eb9ae4547.jpg\",\n  \"167312.jpg\": \"Mammalia/Canis latrans/99c8a52e2eb128ab34f8c7e2d0ca8fad.jpg\",\n  \"167324.jpg\": \"Mammalia/Canis latrans/43991d485d3050ba1b493d916568b345.jpg\",\n  \"167344.jpg\": \"Mammalia/Canis latrans/d40e1af5949112492fb8a6711f43ae14.jpg\",\n  \"167471.jpg\": \"Mammalia/Canis latrans/7380e36562470f1b8fe3b0dbf9e86429.jpg\",\n  \"167476.jpg\": \"Mammalia/Canis latrans/21ce9d142a2327bda90be96900050a3c.jpg\",\n  \"167486.jpg\": \"Mammalia/Canis latrans/d5437e6992a08aab9321cdb16afd7e1c.jpg\",\n  \"167495.jpg\": \"Mammalia/Canis latrans/326d40ac6eed66a2ba9a3b2966a6adf0.jpg\",\n  \"167497.jpg\": \"Aves/Upupa epops/50eff536b1d6e83fed32b2a73d4861a6.jpg\",\n  \"167501.jpg\": \"Aves/Upupa epops/e20bbe415a051a6739e147cd67e456e7.jpg\",\n  \"167502.jpg\": \"Aves/Upupa epops/ed14a053b572ae237466bcd789ca0a51.jpg\",\n  \"167511.jpg\": \"Aves/Upupa epops/dec96d033358269705c9e44c7c875e4f.jpg\",\n  \"167520.jpg\": \"Aves/Upupa epops/4000f59b6ad0f5953c35478f20b62d53.jpg\",\n  \"167523.jpg\": \"Aves/Upupa epops/16d39ab2dedf588ffaf53e3f05e59d84.jpg\",\n  \"167524.jpg\": \"Aves/Upupa epops/48664ee775bd4794cb9473fda0679af6.jpg\",\n  \"167525.jpg\": \"Aves/Upupa epops/4cafdcbf8238f62add255c58d89e2d4b.jpg\",\n  \"167543.jpg\": \"Aves/Upupa epops/0458964107dac64a774b9b23e61fe4f7.jpg\",\n  \"167544.jpg\": \"Aves/Upupa epops/cf48740cc183f4395a05a350261675e4.jpg\",\n  \"167545.jpg\": \"Aves/Upupa epops/10faf130fc9925158a3bb5c6b9c308a6.jpg\",\n  \"167548.jpg\": \"Aves/Upupa epops/63e0e43f3874cc81c0ce5aa35d707aeb.jpg\",\n  \"167549.jpg\": \"Aves/Upupa epops/3280554cd738a0b4cc8671060579f8ea.jpg\",\n  \"167552.jpg\": \"Aves/Upupa epops/718feaa02de2b7ef6b7cc7945ed1ed53.jpg\",\n  \"167585.jpg\": \"Actinopterygii/Micropterus dolomieu/352b43c629d3e6f583c1fb4e0b998541.jpg\",\n  \"167590.jpg\": \"Actinopterygii/Micropterus dolomieu/8c0547e98a01a2bf4ccb3ca1ce382103.jpg\",\n  \"167591.jpg\": \"Actinopterygii/Micropterus dolomieu/32c87048df4d015e277c74bce17213af.jpg\",\n  \"167595.jpg\": \"Actinopterygii/Micropterus dolomieu/375c314cdbaf3c5ff92d642228dfe537.jpg\",\n  \"167598.jpg\": \"Actinopterygii/Micropterus dolomieu/fd583d00c0fd14d447750019800709ca.jpg\",\n  \"167599.jpg\": \"Actinopterygii/Micropterus dolomieu/07b213dfe8276ddfeeb870e684e3e7fa.jpg\",\n  \"167601.jpg\": \"Aves/Geranoaetus albicaudatus/2d63729a7bbc34f02b837665b387db54.jpg\",\n  \"167602.jpg\": \"Aves/Geranoaetus albicaudatus/3fb90e39902beb1f2646096cd15677e1.jpg\",\n  \"167603.jpg\": \"Aves/Geranoaetus albicaudatus/7cada6227660160e255a9e310f5fbcad.jpg\",\n  \"167608.jpg\": \"Aves/Geranoaetus albicaudatus/4c7a3cbef8d4603250e1cb6d9b6b80a5.jpg\",\n  \"167609.jpg\": \"Aves/Geranoaetus albicaudatus/b362fdee0313314d1b064fd71fad3c84.jpg\",\n  \"167610.jpg\": \"Aves/Geranoaetus albicaudatus/e09ef7c7589ba485af7d5deb543696f4.jpg\",\n  \"167611.jpg\": \"Aves/Geranoaetus albicaudatus/aba2b84afb62ee15fdc7c2c1587fcca7.jpg\",\n  \"167612.jpg\": \"Aves/Geranoaetus albicaudatus/d9225a06f9095a6d0f1a05a6abcf5c04.jpg\",\n  \"167617.jpg\": \"Aves/Geranoaetus albicaudatus/7cc1dab2cd96614dbf49c466a89f39ba.jpg\",\n  \"167619.jpg\": \"Aves/Geranoaetus albicaudatus/061458dd4f0582e12c7136e2e0530816.jpg\",\n  \"167622.jpg\": \"Aves/Geranoaetus albicaudatus/9b151e300e34c1c5afcb304fa5e161f7.jpg\",\n  \"167632.jpg\": \"Aves/Geranoaetus albicaudatus/222fb30440021b7f43c19329388db72c.jpg\",\n  \"167637.jpg\": \"Aves/Geranoaetus albicaudatus/047571a8533d8e2034746dacf9aeaa35.jpg\",\n  \"167639.jpg\": \"Aves/Geranoaetus albicaudatus/1b5d75e147f48308e367a5faab89673d.jpg\",\n  \"167641.jpg\": \"Aves/Geranoaetus albicaudatus/e4ac316f3bc60d5e3d82d5bcc3211da9.jpg\",\n  \"167645.jpg\": \"Aves/Geranoaetus albicaudatus/86edce17f05e23dd5274e5a3c7c2176f.jpg\",\n  \"167662.jpg\": \"Aves/Geranoaetus albicaudatus/3f4bdede1245b8d0b299aed16f583b15.jpg\",\n  \"16779.jpg\": \"Aves/Buteo jamaicensis/568118dd2b8394a5e454b7a182dc9fb6.jpg\",\n  \"167797.jpg\": \"Insecta/Taeniopoda eques/8632bfa9544f2571cc9fedf00450e082.jpg\",\n  \"167798.jpg\": \"Insecta/Taeniopoda eques/8cd3f07d8dad9f3ef58e221eff8d1914.jpg\",\n  \"167805.jpg\": \"Insecta/Taeniopoda eques/ed982cfd1a0176d47ce0c15ded229ff9.jpg\",\n  \"167806.jpg\": \"Insecta/Taeniopoda eques/11e2022e6d09a64cf701f49753062ea6.jpg\",\n  \"167807.jpg\": \"Insecta/Taeniopoda eques/18d0d36833f02f3e137693abfbdcbfdf.jpg\",\n  \"167808.jpg\": \"Insecta/Taeniopoda eques/a0414d198d6aa22915975f92253c407e.jpg\",\n  \"167810.jpg\": \"Insecta/Taeniopoda eques/a72d153f17c7b6f373c3e9feea6393b6.jpg\",\n  \"167811.jpg\": \"Insecta/Taeniopoda eques/21a24b7e8e88d853b43361105095343e.jpg\",\n  \"167813.jpg\": \"Insecta/Taeniopoda eques/2705c065ed3832721232ddd09d01c955.jpg\",\n  \"167818.jpg\": \"Insecta/Taeniopoda eques/8084b087e4aa5dc75b5021ca99c4761a.jpg\",\n  \"167819.jpg\": \"Insecta/Taeniopoda eques/eda6c892841be78b92b60e30d854e153.jpg\",\n  \"167820.jpg\": \"Insecta/Taeniopoda eques/157d2385c5b685962404ea2ee1cf8f4f.jpg\",\n  \"167822.jpg\": \"Insecta/Taeniopoda eques/4cf9dee4ca876c3a8ba15ec9817371bd.jpg\",\n  \"167823.jpg\": \"Insecta/Taeniopoda eques/fdef5443816429d5247f2352eb40e7a6.jpg\",\n  \"167824.jpg\": \"Insecta/Taeniopoda eques/f0227a8242ab7ea4f4c2d150c0db7dee.jpg\",\n  \"167826.jpg\": \"Insecta/Taeniopoda eques/89f118ea05293758f8e8765a000db8a6.jpg\",\n  \"167827.jpg\": \"Insecta/Taeniopoda eques/5934ec960fca24b7da1b631bb12b91e2.jpg\",\n  \"167828.jpg\": \"Insecta/Taeniopoda eques/b6d3f99d30e5b272ed7217989d8d09c6.jpg\",\n  \"167829.jpg\": \"Insecta/Taeniopoda eques/bfeeb0e6d6ea295a607eddb272f5a177.jpg\",\n  \"167830.jpg\": \"Insecta/Taeniopoda eques/c4d600d687828ab2465fdaa26ad0143f.jpg\",\n  \"167833.jpg\": \"Insecta/Taeniopoda eques/5922773444b1a9c2383bb7618cd10f49.jpg\",\n  \"167834.jpg\": \"Insecta/Taeniopoda eques/48a3ab5d9868d42144f163d1f0130a15.jpg\",\n  \"167942.jpg\": \"Insecta/Pseudomops septentrionalis/dd3b5929cdf45b05bc6e45379c4ae0ac.jpg\",\n  \"167943.jpg\": \"Insecta/Pseudomops septentrionalis/4a09223b0d42a14f89d6adf44ac0dc12.jpg\",\n  \"167951.jpg\": \"Insecta/Pseudomops septentrionalis/8de8e62c10013eed313b08dbe8c5b13b.jpg\",\n  \"168.jpg\": \"Aves/Zonotrichia capensis/3ddac55ace88981c2e552ede0101928a.jpg\",\n  \"16806.jpg\": \"Aves/Buteo jamaicensis/72989c50f1e4f9827b9ca3c523138d0d.jpg\",\n  \"168178.jpg\": \"Insecta/Satyrium californica/933cac784ad27503e18f5d34fd58a7eb.jpg\",\n  \"168179.jpg\": \"Insecta/Satyrium californica/208663d080fa0672ab35b89231eb15ad.jpg\",\n  \"168182.jpg\": \"Insecta/Satyrium californica/7bd7ed4a717b407c46bba31ef897b782.jpg\",\n  \"168188.jpg\": \"Insecta/Satyrium californica/0a88d88e11c49a9c465604d51d479b8f.jpg\",\n  \"168189.jpg\": \"Insecta/Satyrium californica/7b6758e9998fec82b056c9b7e4426519.jpg\",\n  \"168193.jpg\": \"Reptilia/Tantilla nigriceps/8ecf4893c5429f194b6fb220d76dd6bc.jpg\",\n  \"168197.jpg\": \"Reptilia/Tantilla nigriceps/9165e4da5ad3a20d8a77386ced42609b.jpg\",\n  \"168222.jpg\": \"Insecta/Phyllodesma americana/114605e1225be721ecb02453e3b0ee9e.jpg\",\n  \"168223.jpg\": \"Insecta/Phyllodesma americana/804f487ddae1fb3f4aee88385c3426dc.jpg\",\n  \"168224.jpg\": \"Insecta/Phyllodesma americana/f51ffb1ab8684f5b93f72fe34fbce7bb.jpg\",\n  \"168225.jpg\": \"Insecta/Phyllodesma americana/01d8c43115b8d550fdd93dd7d04a4f08.jpg\",\n  \"168226.jpg\": \"Insecta/Phyllodesma americana/1ed1c8d9a3641586a60201bb809868c2.jpg\",\n  \"168229.jpg\": \"Insecta/Phyllodesma americana/a77e6058e2611c2ae01b14bb8fb323e2.jpg\",\n  \"168231.jpg\": \"Insecta/Microtia elva/e1ab76ebc6591d955c8f309a20ba1ce3.jpg\",\n  \"168232.jpg\": \"Insecta/Microtia elva/4b03e2f98a10cbb6bc74f0309dba32d9.jpg\",\n  \"168233.jpg\": \"Insecta/Microtia elva/7c979792fc06934e682c1269e9f12083.jpg\",\n  \"168234.jpg\": \"Insecta/Microtia elva/54950714bc5d7d35fa9c7574b96b089e.jpg\",\n  \"168235.jpg\": \"Insecta/Microtia elva/177e09be60d3c1b85fb87e39384b4b77.jpg\",\n  \"168236.jpg\": \"Insecta/Microtia elva/e70af3edbde0c05aa15ee309c733740b.jpg\",\n  \"168243.jpg\": \"Insecta/Microtia elva/e3670501b69019df8c74428ceb9083e4.jpg\",\n  \"168245.jpg\": \"Insecta/Microtia elva/b8b2bf6760f13b2ceddd0402349f78f3.jpg\",\n  \"168246.jpg\": \"Insecta/Microtia elva/3205d024af44c0025adf5a543d054eed.jpg\",\n  \"168247.jpg\": \"Insecta/Microtia elva/cbdb95b15de112542a61c696b21a1ef0.jpg\",\n  \"168248.jpg\": \"Insecta/Microtia elva/337bc35071289bb848b25fc39ded6948.jpg\",\n  \"168249.jpg\": \"Insecta/Microtia elva/b651d7aa2492dce4d1a35b2427f51713.jpg\",\n  \"168250.jpg\": \"Insecta/Microtia elva/e4fba385dad6a85d57c74e4ecbdee027.jpg\",\n  \"168251.jpg\": \"Insecta/Microtia elva/fb2ff441eed5abce5b56203fca355123.jpg\",\n  \"168252.jpg\": \"Insecta/Microtia elva/ec81a9c665b78f19252e2f9965943b51.jpg\",\n  \"168253.jpg\": \"Insecta/Microtia elva/b5214b5b5bfdd1f63e4013d2c741c95f.jpg\",\n  \"168254.jpg\": \"Insecta/Microtia elva/60de8af8917b78712c9f47abb8df0086.jpg\",\n  \"168255.jpg\": \"Insecta/Microtia elva/8880333a73c1c709ca0011a605678c65.jpg\",\n  \"168257.jpg\": \"Insecta/Microtia elva/c55368923513b39e3e639e1087fc0152.jpg\",\n  \"168258.jpg\": \"Insecta/Microtia elva/baa68a526db09bfc6497c09587f7d3f9.jpg\",\n  \"168259.jpg\": \"Insecta/Microtia elva/8ad08e6b5c7d24e3b16f070f9f688916.jpg\",\n  \"168260.jpg\": \"Insecta/Microtia elva/996f082dc7aa19f458ea08c9a488fc94.jpg\",\n  \"168263.jpg\": \"Insecta/Microtia elva/a105d4c4474d87bf555db3004e861376.jpg\",\n  \"168264.jpg\": \"Insecta/Microtia elva/3529047e868cb1363aa9fa67bc1e6922.jpg\",\n  \"168276.jpg\": \"Aves/Rallus limicola/a79b57ce4a2a37080868d9df85c041d3.jpg\",\n  \"168277.jpg\": \"Aves/Rallus limicola/250b6ecb774f64995b03e4f7ac5ec0d6.jpg\",\n  \"168279.jpg\": \"Aves/Rallus limicola/8a11e2e086f1662b9af01944ced62021.jpg\",\n  \"168280.jpg\": \"Aves/Rallus limicola/cea0bc62285ddd16ad9e92623acbbee7.jpg\",\n  \"168284.jpg\": \"Aves/Rallus limicola/7937fb2aef35b49274b40f8041b3111a.jpg\",\n  \"168285.jpg\": \"Aves/Rallus limicola/34c52d549031055deba407ec539798ce.jpg\",\n  \"168288.jpg\": \"Aves/Rallus limicola/af4e62116425081a9ec1ab33d8ca8cd0.jpg\",\n  \"168291.jpg\": \"Aves/Rallus limicola/0e1c41557fc54b1c26da36e96a6d4ddd.jpg\",\n  \"168292.jpg\": \"Aves/Rallus limicola/22763942f591de1935d64e936929f05e.jpg\",\n  \"168294.jpg\": \"Aves/Rallus limicola/30010e73d4936e4b58818467b5207ff7.jpg\",\n  \"168295.jpg\": \"Aves/Rallus limicola/20009996c201d7590bb843a3e60d86f1.jpg\",\n  \"168296.jpg\": \"Aves/Rallus limicola/47f6ee9f9a3b3e5077be652e833e6a76.jpg\",\n  \"168297.jpg\": \"Aves/Rallus limicola/8fdae0837f11baee4eebbf25135e1072.jpg\",\n  \"168304.jpg\": \"Aves/Rallus limicola/495d86dfc1af00bb4a9a47cbc0c38382.jpg\",\n  \"168315.jpg\": \"Aves/Rallus limicola/2762c9803db20399f202eb2a6014a92b.jpg\",\n  \"168316.jpg\": \"Aves/Rallus limicola/9fc4f356a79d429c8a3359cd9a608e37.jpg\",\n  \"168317.jpg\": \"Aves/Rallus limicola/949297f9c4e8a65728e3d5afaf24e6bd.jpg\",\n  \"168321.jpg\": \"Aves/Rallus limicola/de9b1aa511eb867d58c93ad2bdc0a753.jpg\",\n  \"168322.jpg\": \"Aves/Rallus limicola/eb0f9ee7edf4450bbbdafb67b9e00978.jpg\",\n  \"168326.jpg\": \"Aves/Rallus limicola/0b5ae0599dd08bede5bd26b890292312.jpg\",\n  \"168328.jpg\": \"Aves/Rallus limicola/3741625659672a5cbd99cc2d7b7b99ff.jpg\",\n  \"168332.jpg\": \"Aves/Rallus limicola/3e01f317b622a7bd8b6f532f1b851b87.jpg\",\n  \"16862.jpg\": \"Aves/Buteo jamaicensis/5caf5632eb15321770b34f014015e941.jpg\",\n  \"168662.jpg\": \"Animalia/Porpita porpita/cce585298505c6ad4ba7a5a4832dfd9e.jpg\",\n  \"168666.jpg\": \"Animalia/Porpita porpita/7b248f31a1990d7fbce53927c1488334.jpg\",\n  \"168667.jpg\": \"Animalia/Porpita porpita/5cc0f7dbdc1c6bac07728c9271164440.jpg\",\n  \"168669.jpg\": \"Animalia/Porpita porpita/86715b94c686b610cd9dbb6a3e445e1f.jpg\",\n  \"168687.jpg\": \"Mammalia/Procyon lotor lotor/5a245bc87ef904812d79a813e8449d96.jpg\",\n  \"168689.jpg\": \"Mammalia/Procyon lotor lotor/daac1cd888c43419ef04c4172c9b4f69.jpg\",\n  \"168690.jpg\": \"Reptilia/Vipera berus/4f3c4c7b82cb0290a44c812184f9b919.jpg\",\n  \"168692.jpg\": \"Reptilia/Vipera berus/99e8257d26519382e5160b30aaf90160.jpg\",\n  \"168693.jpg\": \"Reptilia/Vipera berus/f9f77766e89bdddb81bc41df3bec3165.jpg\",\n  \"168701.jpg\": \"Reptilia/Vipera berus/625bfbbaa4f4c7bfb0b05af13abcf2bf.jpg\",\n  \"168710.jpg\": \"Aves/Empidonax hammondii/1003150113419b61c875137af3436368.jpg\",\n  \"168715.jpg\": \"Aves/Empidonax hammondii/810b9ec81c62968ce75cb2f2cb87974c.jpg\",\n  \"168719.jpg\": \"Aves/Empidonax hammondii/30a2bf5fd31539f39ccd569b76b5edfd.jpg\",\n  \"168727.jpg\": \"Insecta/Chytolita morbidalis/96c397a7dbfebc54cf24cb09f3c1b357.jpg\",\n  \"168728.jpg\": \"Insecta/Chytolita morbidalis/7f04d50bc2b39efff2103faa65154542.jpg\",\n  \"168730.jpg\": \"Insecta/Chytolita morbidalis/3540504d7d76bd708715b42d822f7f83.jpg\",\n  \"168731.jpg\": \"Insecta/Chytolita morbidalis/f18c77feb89c842f986244dd28044771.jpg\",\n  \"168735.jpg\": \"Insecta/Chytolita morbidalis/055f8ffa7b37013c0ab760c9ae15414c.jpg\",\n  \"168748.jpg\": \"Insecta/Papilio troilus/8d117021154b31d39fc8f2ef66823c73.jpg\",\n  \"168757.jpg\": \"Insecta/Papilio troilus/4f143ebf29b863a6abaab19f0747eb8e.jpg\",\n  \"168771.jpg\": \"Insecta/Papilio troilus/6d58d67f36f77cddc2dd782a3a7aa98f.jpg\",\n  \"168776.jpg\": \"Insecta/Papilio troilus/6ccab8e8e7c490a18245866af1ddf101.jpg\",\n  \"168779.jpg\": \"Insecta/Papilio troilus/267c7a3c615fad2f3ff19b63676804f9.jpg\",\n  \"168807.jpg\": \"Insecta/Papilio troilus/0eec946ff21bc9a812f2ad7a75561beb.jpg\",\n  \"168815.jpg\": \"Insecta/Papilio troilus/b7398783fbf4e978fb3c0b4243b3590e.jpg\",\n  \"168820.jpg\": \"Insecta/Papilio troilus/4d6f916218e66333e79881e2db974498.jpg\",\n  \"168832.jpg\": \"Insecta/Papilio troilus/be6c41fe97cdeb2eab142a5f3c24dd60.jpg\",\n  \"168833.jpg\": \"Insecta/Papilio troilus/b2801394e1e8c1d5446590685c0822cf.jpg\",\n  \"168835.jpg\": \"Insecta/Papilio troilus/0bf311499dfd3c8c9fd73308cf9fd403.jpg\",\n  \"168840.jpg\": \"Insecta/Papilio troilus/b3d0cca6f76939fbc76805a07cc1648a.jpg\",\n  \"168850.jpg\": \"Insecta/Papilio troilus/b336b0eb2ec7281f92d6c6987cf34e02.jpg\",\n  \"168859.jpg\": \"Insecta/Papilio troilus/3da110bb67024c9ab174aec3b666ca22.jpg\",\n  \"168867.jpg\": \"Insecta/Papilio troilus/b2c64560505075bd195975d16a0880e9.jpg\",\n  \"168873.jpg\": \"Insecta/Papilio troilus/03903325d41d9f4fba4baa763a3ec491.jpg\",\n  \"168920.jpg\": \"Insecta/Papilio troilus/d78458862de52e1fa5021c3b40002dab.jpg\",\n  \"168925.jpg\": \"Insecta/Papilio troilus/a4e92f13aaa20a0104dd9102f43bb3c9.jpg\",\n  \"168960.jpg\": \"Aves/Recurvirostra avosetta/9681bd87f55d7ce93f4b6ff6b411b4b9.jpg\",\n  \"168965.jpg\": \"Aves/Recurvirostra avosetta/69c469652736569785e615db55bf1d41.jpg\",\n  \"168970.jpg\": \"Aves/Recurvirostra avosetta/93da83b31179f7837ef0968871298e93.jpg\",\n  \"168976.jpg\": \"Aves/Recurvirostra avosetta/60e7aaa79428b9642a9485bbc51a7b3e.jpg\",\n  \"169035.jpg\": \"Aves/Ardea herodias occidentalis/6f808f86682912d3b27a4c6af36457e5.jpg\",\n  \"169040.jpg\": \"Aves/Ardea herodias occidentalis/08b8db1e6ffb7bdee7f502cbf0dc26ec.jpg\",\n  \"169045.jpg\": \"Aves/Ardea herodias occidentalis/3fa962662751a1316bdaf008c4269c61.jpg\",\n  \"169050.jpg\": \"Insecta/Cyclophora pendulinaria/1249576bef7fd590e9c2aa811ab3e121.jpg\",\n  \"169057.jpg\": \"Insecta/Cyclophora pendulinaria/d642cdae6b2d51fa1924bbc7eba88b58.jpg\",\n  \"169059.jpg\": \"Insecta/Cyclophora pendulinaria/aa05749959f86719356e09c605bfd49d.jpg\",\n  \"169066.jpg\": \"Insecta/Cyclophora pendulinaria/2366c367d01b95d8e0675363ff4fcabd.jpg\",\n  \"169089.jpg\": \"Aves/Ficedula hypoleuca/ac18c4f3bda70822892d957f0278414c.jpg\",\n  \"169094.jpg\": \"Aves/Ficedula hypoleuca/a1cda436237168a20d0e8d50e708ed14.jpg\",\n  \"169099.jpg\": \"Aves/Ficedula hypoleuca/41aec582ec02eded200e6aa0632b3b66.jpg\",\n  \"169104.jpg\": \"Aves/Ficedula hypoleuca/87a534ee5b7a7cf981c09e12f1a0fdb9.jpg\",\n  \"169105.jpg\": \"Aves/Ficedula hypoleuca/2f6e880c91dd0584fa402bbcae724f2f.jpg\",\n  \"169109.jpg\": \"Aves/Turdus grayi/f713c7b1e99e1f768a82e635c3f48c09.jpg\",\n  \"169122.jpg\": \"Aves/Turdus grayi/914f7e8ee4245420bf4763eb0ce59474.jpg\",\n  \"169123.jpg\": \"Aves/Turdus grayi/30fc63fedae15f5457a25c44a05d34ad.jpg\",\n  \"169125.jpg\": \"Aves/Turdus grayi/ca34898d9a3c4e4b396568aa0239a0fb.jpg\",\n  \"169151.jpg\": \"Aves/Turdus grayi/fd7d034c16f00afb05953466fbfb9588.jpg\",\n  \"169157.jpg\": \"Aves/Turdus grayi/1c304e73871bd13ff3de3c1a3cb10ce9.jpg\",\n  \"169166.jpg\": \"Aves/Turdus grayi/0df13ef181427a0194b5fbef9d83a482.jpg\",\n  \"169169.jpg\": \"Aves/Turdus grayi/205640fcc4f7d5a91c1f8bd670fe91e9.jpg\",\n  \"169171.jpg\": \"Aves/Turdus grayi/15088208a3fa8ad08ef8e3e4d97bf938.jpg\",\n  \"169174.jpg\": \"Aves/Turdus grayi/773aa1495288c1ebd00fb6bb4fd7377d.jpg\",\n  \"169176.jpg\": \"Aves/Turdus grayi/1c1bfd8f907909aefe7792ac8d70fe83.jpg\",\n  \"169178.jpg\": \"Aves/Turdus grayi/39dabb79ad24eeb52502b9cf3567e6cb.jpg\",\n  \"169180.jpg\": \"Aves/Turdus grayi/c7cbf036d721d4119ac67646e9b98aae.jpg\",\n  \"169197.jpg\": \"Aves/Turdus grayi/f99ecb96175f004d56ff2d9ca3f2eda7.jpg\",\n  \"169199.jpg\": \"Aves/Turdus grayi/df425b85ace70b5c00f4beed05551616.jpg\",\n  \"169205.jpg\": \"Aves/Turdus grayi/a5bc70cdd99868d0500d125604b6db77.jpg\",\n  \"169209.jpg\": \"Aves/Turdus grayi/577e2a38f7b88890f714233cf52441c7.jpg\",\n  \"169212.jpg\": \"Aves/Turdus grayi/cb08cda092741a4db4ac6fbb0b236925.jpg\",\n  \"169220.jpg\": \"Aves/Turdus grayi/cc9507b460c3033cd76a07d74314375c.jpg\",\n  \"169221.jpg\": \"Aves/Turdus grayi/9f1f5ed4c0b4eb30d60c9dadf1611d3c.jpg\",\n  \"169229.jpg\": \"Aves/Turdus grayi/c2ff3d3346c7894f92da7572f5f6d816.jpg\",\n  \"169233.jpg\": \"Reptilia/Hemidactylus mabouia/87315b0cbad0724dbbe78896e4069bea.jpg\",\n  \"169237.jpg\": \"Reptilia/Hemidactylus mabouia/152aa817450b972e3c634e73d5625c53.jpg\",\n  \"169239.jpg\": \"Reptilia/Hemidactylus mabouia/32adbf9e374672e6ae6babe21a1d7751.jpg\",\n  \"169241.jpg\": \"Reptilia/Hemidactylus mabouia/69577b2b9b823d0c034d77a672056fc0.jpg\",\n  \"169250.jpg\": \"Reptilia/Hemidactylus mabouia/bafd8b4e8bc26a60c3c51608ddad7060.jpg\",\n  \"169457.jpg\": \"Aves/Hirundo neoxena/1d3b50b46cdc520b019b9054e6b761ff.jpg\",\n  \"169462.jpg\": \"Aves/Hirundo neoxena/dc512b8bfeeb61a5a45589faa8a35574.jpg\",\n  \"169463.jpg\": \"Aves/Hirundo neoxena/8b05b960b6459e6d21093bc83ab6b50e.jpg\",\n  \"169468.jpg\": \"Aves/Hirundo neoxena/1c58832a6bb627398eebb7c574a1fdf2.jpg\",\n  \"169469.jpg\": \"Aves/Hirundo neoxena/2586fb1226e97669a763f675a3534da7.jpg\",\n  \"169470.jpg\": \"Aves/Hirundo neoxena/dc096f45004273e00139bdb8814b9dd6.jpg\",\n  \"169475.jpg\": \"Aves/Hirundo neoxena/0c8e3e4ef3065b33fd8f348b3cc2001b.jpg\",\n  \"169478.jpg\": \"Aves/Hirundo neoxena/3c64595e2c21288ba4e54fc47cd6424d.jpg\",\n  \"169481.jpg\": \"Aves/Hirundo neoxena/53cb31b5bebec9d53f87f8f7db1c0cbb.jpg\",\n  \"169484.jpg\": \"Aves/Hirundo neoxena/4f9f417ee2262740281972bc1fb55caa.jpg\",\n  \"169485.jpg\": \"Aves/Hirundo neoxena/da7d42f9f5bf90e4a676e6e301f98127.jpg\",\n  \"169487.jpg\": \"Aves/Hirundo neoxena/05896b4565392a7e2bd856d346b35d73.jpg\",\n  \"169488.jpg\": \"Aves/Hirundo neoxena/ea0d99462d4d74721c94a139e10c9dce.jpg\",\n  \"169491.jpg\": \"Aves/Hirundo neoxena/15b253ac3895aabfdf47342cfc2a423b.jpg\",\n  \"169494.jpg\": \"Aves/Hirundo neoxena/a466d3afc5939f3fa367e1fe33ad3600.jpg\",\n  \"169496.jpg\": \"Aves/Hirundo neoxena/ef2153c565af78a747e441f3007bf6d2.jpg\",\n  \"169498.jpg\": \"Reptilia/Coleonyx brevis/8980b0f33b694dda68f46c6b899b7570.jpg\",\n  \"169499.jpg\": \"Reptilia/Coleonyx brevis/ad0b588866d7dd61501a426a0f606bc6.jpg\",\n  \"169503.jpg\": \"Reptilia/Coleonyx brevis/c64d4a22b1297d1e8bfe9db266431bff.jpg\",\n  \"169504.jpg\": \"Reptilia/Coleonyx brevis/42cb6a5f0b067bf2e43b5b3bc2dc8e5e.jpg\",\n  \"169505.jpg\": \"Reptilia/Coleonyx brevis/7546f594662152e00d564fdd72b0e58c.jpg\",\n  \"169506.jpg\": \"Reptilia/Coleonyx brevis/9d10207386d06d36c04b2fd6728760cc.jpg\",\n  \"169507.jpg\": \"Reptilia/Coleonyx brevis/3c84938379db3069d0a892700a2f7c58.jpg\",\n  \"169512.jpg\": \"Reptilia/Coleonyx brevis/89a693a3bf82c388baa1863850fd99c9.jpg\",\n  \"169516.jpg\": \"Reptilia/Coleonyx brevis/f19fc2a63b195d42a74f0b3bc80ee05b.jpg\",\n  \"169518.jpg\": \"Reptilia/Coleonyx brevis/fbd5ec08317e968ac494f3c6c713c6da.jpg\",\n  \"169519.jpg\": \"Reptilia/Coleonyx brevis/f65224ad658fbbea8b12d0102f6d0f71.jpg\",\n  \"169520.jpg\": \"Reptilia/Coleonyx brevis/439e56fa865655f5e9dcb5948372728d.jpg\",\n  \"169524.jpg\": \"Reptilia/Coleonyx brevis/6978b1b1cf14c56911305fa92b75b4b0.jpg\",\n  \"169525.jpg\": \"Reptilia/Coleonyx brevis/34d25a0ca79cd4b2e523876a12686ac4.jpg\",\n  \"169526.jpg\": \"Reptilia/Coleonyx brevis/445a48ddce4ac813f82c0a4902c27227.jpg\",\n  \"169527.jpg\": \"Reptilia/Coleonyx brevis/36d6e51452654938a25779894321bd94.jpg\",\n  \"169528.jpg\": \"Reptilia/Coleonyx brevis/43ac5b84fb551282afa1db3b8342aae8.jpg\",\n  \"169531.jpg\": \"Reptilia/Coleonyx brevis/5bd0ed0f4f101789d7ddaf5dddb143d0.jpg\",\n  \"169532.jpg\": \"Reptilia/Coleonyx brevis/5c5d2c39b0662a978ac7ea1861a8036e.jpg\",\n  \"169542.jpg\": \"Reptilia/Coleonyx brevis/6f491181dd20147dfb89dbae97a9ca68.jpg\",\n  \"169543.jpg\": \"Reptilia/Coleonyx brevis/1345765adec5c5865c62eb270c46293d.jpg\",\n  \"169546.jpg\": \"Reptilia/Coleonyx brevis/80546e435db41968342540f1d32806fe.jpg\",\n  \"169547.jpg\": \"Reptilia/Coleonyx brevis/039522fb60f5c747416f578d5e9a2a7e.jpg\",\n  \"169553.jpg\": \"Reptilia/Coleonyx brevis/5ce198e9ce38648afcf5ef12fa735545.jpg\",\n  \"169558.jpg\": \"Insecta/Cordulia shurtleffii/58cbb15bf42c3719748d0ca1c16a5a31.jpg\",\n  \"169559.jpg\": \"Insecta/Cordulia shurtleffii/ca63f570cc661fde667002cb0ec433b8.jpg\",\n  \"169560.jpg\": \"Insecta/Cordulia shurtleffii/aabda994802f4b18d588b0ee9ab24225.jpg\",\n  \"169566.jpg\": \"Insecta/Cordulia shurtleffii/4f8a7285183cf23a12f9806b709525d4.jpg\",\n  \"169569.jpg\": \"Insecta/Cordulia shurtleffii/699eab6618833d7a56bb1232320d404e.jpg\",\n  \"169570.jpg\": \"Insecta/Cordulia shurtleffii/1873bf0193815ed00a377592411b12a8.jpg\",\n  \"169586.jpg\": \"Insecta/Celithemis eponina/09cb727947ce9cc40b7f057bbad5fbf3.jpg\",\n  \"16961.jpg\": \"Aves/Buteo jamaicensis/d784cedf8b113aa7a6d55074a99c1994.jpg\",\n  \"169626.jpg\": \"Insecta/Celithemis eponina/1790ac8e164ff97961f18640890d22e3.jpg\",\n  \"169631.jpg\": \"Insecta/Celithemis eponina/58d1bf77297e1595df1b3d2f96010e83.jpg\",\n  \"169650.jpg\": \"Insecta/Celithemis eponina/0f941b9a03b04f5de8fb38a9eff9ec7b.jpg\",\n  \"169664.jpg\": \"Insecta/Celithemis eponina/6e907f0e83553526aa1052dd87e771b0.jpg\",\n  \"169675.jpg\": \"Insecta/Celithemis eponina/a1496ba0b643516b240cff7c2e3675a8.jpg\",\n  \"169680.jpg\": \"Insecta/Celithemis eponina/4798177e6797a11b6952b2c3cdeac1c0.jpg\",\n  \"169690.jpg\": \"Insecta/Celithemis eponina/a63a185da4f732e6cf14e9a83eea4620.jpg\",\n  \"169694.jpg\": \"Insecta/Celithemis eponina/caffc79db9e8f99f70379a40820e7fd8.jpg\",\n  \"169738.jpg\": \"Insecta/Celithemis eponina/ecb50aeb5830be5dd919e3398b934745.jpg\",\n  \"169744.jpg\": \"Insecta/Celithemis eponina/fd7fc13d093a66838a9c1da0bad67854.jpg\",\n  \"169746.jpg\": \"Insecta/Celithemis eponina/f6aa490860c2760be02e4b5d96b5b0c3.jpg\",\n  \"169751.jpg\": \"Insecta/Celithemis eponina/afe1c2d48b22182acb1459d40b6a0365.jpg\",\n  \"169753.jpg\": \"Insecta/Celithemis eponina/5734999fbcc8a3306dde939f18884f85.jpg\",\n  \"169762.jpg\": \"Insecta/Celithemis eponina/631e4984fa935e79e2e774270fff36b1.jpg\",\n  \"169769.jpg\": \"Insecta/Celithemis eponina/46136ac812db0ed6ed4248ac8a6405cc.jpg\",\n  \"169771.jpg\": \"Insecta/Celithemis eponina/7517ea20ebe154ebe7f464e1e87681d7.jpg\",\n  \"169782.jpg\": \"Insecta/Celithemis eponina/26596bdaf1b686ce93c7b54df03edacf.jpg\",\n  \"169787.jpg\": \"Insecta/Celithemis eponina/6fcf3c2f35b7c64b09c3dc2f1200e23c.jpg\",\n  \"169813.jpg\": \"Insecta/Celithemis eponina/1f1651342a83cb29f7ee5960ff74b7f9.jpg\",\n  \"169814.jpg\": \"Insecta/Celithemis eponina/d258197359e8703e8fc1fff1a9c6afa1.jpg\",\n  \"169817.jpg\": \"Aves/Cinnyris jugularis/6ef744eeab50d97420e4f59bac6c232e.jpg\",\n  \"169825.jpg\": \"Aves/Cinnyris jugularis/7e4ac84295132f60069060f7ead268f6.jpg\",\n  \"169834.jpg\": \"Aves/Cinnyris jugularis/f4139734916aa1de0948d8e8ce9401af.jpg\",\n  \"169835.jpg\": \"Aves/Cinnyris jugularis/be8f9f6eb78be2245a86326ebcda01b9.jpg\",\n  \"169836.jpg\": \"Aves/Cinnyris jugularis/bf2c85ce72b85a34a45c6b75894af1a0.jpg\",\n  \"169839.jpg\": \"Aves/Cinnyris jugularis/6cdf489212630e03ed51a09f1d30d6c0.jpg\",\n  \"169840.jpg\": \"Insecta/Leptoglossus oppositus/1df91e0eebe7aab15aea0e2dc71f8429.jpg\",\n  \"169842.jpg\": \"Insecta/Leptoglossus oppositus/70ca3af73761e7daa6cde5cde2482b07.jpg\",\n  \"169852.jpg\": \"Insecta/Leptoglossus oppositus/aa94f2754736f5f1d9d26cf382a6b061.jpg\",\n  \"169853.jpg\": \"Insecta/Leptoglossus oppositus/8de71101c139dc8a571819bb8234ea9b.jpg\",\n  \"169854.jpg\": \"Insecta/Leptoglossus oppositus/00ccb04bc421b191e1f8693b8b6547cb.jpg\",\n  \"169861.jpg\": \"Insecta/Polistes chinensis/e40bd5097e655537b5e42ff68d039f4d.jpg\",\n  \"169877.jpg\": \"Insecta/Polistes chinensis/b904627a4e914b271a0680215edd6689.jpg\",\n  \"169886.jpg\": \"Insecta/Hemipenthes sinuosa/caf3df6d21ee5e6ce26865c63d12bbcb.jpg\",\n  \"169887.jpg\": \"Insecta/Hemipenthes sinuosa/419ba842bcdf11987b6ec97b46493dde.jpg\",\n  \"169891.jpg\": \"Insecta/Hemipenthes sinuosa/0d2d89b3310d5d164a5cb0b99d47ef26.jpg\",\n  \"169894.jpg\": \"Insecta/Hemipenthes sinuosa/7beda5e002b4413c025e63c757ae41c5.jpg\",\n  \"169895.jpg\": \"Insecta/Hemipenthes sinuosa/e6d86857023f5009bfc4a5dee8c96f21.jpg\",\n  \"169901.jpg\": \"Insecta/Hemipenthes sinuosa/669f0b1be284647832fba0f2f2542932.jpg\",\n  \"169904.jpg\": \"Insecta/Hemipenthes sinuosa/509bd50b29c4e21a87d832b6f50115a3.jpg\",\n  \"169905.jpg\": \"Insecta/Hemipenthes sinuosa/676f382feecd4389672546f45255f1a7.jpg\",\n  \"169906.jpg\": \"Insecta/Hemipenthes sinuosa/5c3f2dd9f80ae180eb0b198c93dd38d0.jpg\",\n  \"169913.jpg\": \"Mollusca/Aeolidia papillosa/fd9bcba58eefd676133a60dd1a87e4f8.jpg\",\n  \"169949.jpg\": \"Mollusca/Aeolidia papillosa/913ecf19d02019018f181237b8e40735.jpg\",\n  \"169950.jpg\": \"Mollusca/Aeolidia papillosa/ef29ca731d3b8644b9bf4a5b7ae02ffc.jpg\",\n  \"169953.jpg\": \"Mollusca/Aeolidia papillosa/1a4c6124d9c02cfdce96250630121af5.jpg\",\n  \"169972.jpg\": \"Mollusca/Aeolidia papillosa/e49fd5ca1d03fbfaa025631289e1ae5c.jpg\",\n  \"169977.jpg\": \"Mollusca/Aeolidia papillosa/881cbf0650998898fa183561fc883d34.jpg\",\n  \"169983.jpg\": \"Mollusca/Aeolidia papillosa/9df927ae22a31af72b10232378223fb1.jpg\",\n  \"169986.jpg\": \"Mollusca/Aeolidia papillosa/5804ffd7baf824578363904c8000ee3f.jpg\",\n  \"169987.jpg\": \"Mollusca/Aeolidia papillosa/14c4fc06e90d72b5f3ac26b09ac03af2.jpg\",\n  \"170.jpg\": \"Aves/Zonotrichia capensis/030135edb0aef1049068af977a970479.jpg\",\n  \"1700.jpg\": \"Reptilia/Sceloporus orcutti/a35ca40d47b5ef96e7a631057ebc5c67.jpg\",\n  \"170005.jpg\": \"Mollusca/Aeolidia papillosa/58788c1a3f6202d59803ea97a2e3252f.jpg\",\n  \"170006.jpg\": \"Mollusca/Aeolidia papillosa/1659d45fed313a9b44f126963b55c0b7.jpg\",\n  \"170013.jpg\": \"Mollusca/Aeolidia papillosa/fda7a7d6032eebcdc3cbe29195e09e28.jpg\",\n  \"170014.jpg\": \"Mollusca/Aeolidia papillosa/3dc168d39a78b2112ef377a939a5feae.jpg\",\n  \"170015.jpg\": \"Mollusca/Aeolidia papillosa/eafcaf363f7d29d34b618aa92a179a4f.jpg\",\n  \"170018.jpg\": \"Insecta/Psyllobora vigintimaculata/e18a5fda4e2bb6371aef5e8b38a27260.jpg\",\n  \"170021.jpg\": \"Insecta/Psyllobora vigintimaculata/56a3e6f93316af96b11cd212133fc8d7.jpg\",\n  \"170023.jpg\": \"Insecta/Psyllobora vigintimaculata/bf16a20d15ef8bd6ce3eeb590bdda90c.jpg\",\n  \"170027.jpg\": \"Insecta/Psyllobora vigintimaculata/074dd04d1c796b7cb9a7abe2bcf9e78f.jpg\",\n  \"170041.jpg\": \"Insecta/Psyllobora vigintimaculata/57e142081291719e424b49ee7b687285.jpg\",\n  \"170047.jpg\": \"Insecta/Psyllobora vigintimaculata/616c7e74e0b485353a902c6bc86f8ee5.jpg\",\n  \"170054.jpg\": \"Insecta/Palthis angulalis/6d49039a36456335de0e06d6f28fea06.jpg\",\n  \"170056.jpg\": \"Insecta/Palthis angulalis/dbee4cb78e2adb9cbb80d431a48de862.jpg\",\n  \"170057.jpg\": \"Insecta/Palthis angulalis/ecde481f412ee50d4480a2590eb3945e.jpg\",\n  \"170058.jpg\": \"Insecta/Palthis angulalis/b8e33ca1e3f99f06e4eae040f1a3119c.jpg\",\n  \"170059.jpg\": \"Insecta/Palthis angulalis/650801f40c318b574b2433b7d20edcca.jpg\",\n  \"170063.jpg\": \"Insecta/Palthis angulalis/6996f8e115fb97d550b86cc4f9c1db0d.jpg\",\n  \"170064.jpg\": \"Insecta/Palthis angulalis/56f44e0b81feef4a7d6f26cf3743385f.jpg\",\n  \"170069.jpg\": \"Insecta/Palthis angulalis/15dc54cfbedc95fce2a95b9e12dbee8a.jpg\",\n  \"170070.jpg\": \"Insecta/Palthis angulalis/9733159c3fb3e3763189841844fd3b2b.jpg\",\n  \"170072.jpg\": \"Insecta/Palthis angulalis/94abab73bb818b01ff41f39add76383a.jpg\",\n  \"170076.jpg\": \"Insecta/Palthis angulalis/0e4c0e3304f38cf289ae69316808775b.jpg\",\n  \"170078.jpg\": \"Insecta/Palthis angulalis/97e5f01d9b43b586fe83af64f1e67811.jpg\",\n  \"170079.jpg\": \"Insecta/Palthis angulalis/3e3e49b445c9355e1d4f580e06c91c8d.jpg\",\n  \"170082.jpg\": \"Insecta/Palthis angulalis/0a76db8eb1b02835aa9f402ecf615227.jpg\",\n  \"170084.jpg\": \"Insecta/Palthis angulalis/f77aa2176db1ea83d3b315998c78d3ea.jpg\",\n  \"170085.jpg\": \"Insecta/Palthis angulalis/1c9316e349e6d83f66f9976df9c4bf51.jpg\",\n  \"170086.jpg\": \"Insecta/Palthis angulalis/ed51532525650387c7872ae280d9f546.jpg\",\n  \"170087.jpg\": \"Insecta/Palthis angulalis/f3fe24b5e3530377bc498ad474ee7a99.jpg\",\n  \"170090.jpg\": \"Insecta/Palthis angulalis/666f013f4109fd68552ab48e57c44122.jpg\",\n  \"170091.jpg\": \"Insecta/Palthis angulalis/d7b964c04be926a16b91bbb88123b4a2.jpg\",\n  \"170176.jpg\": \"Mammalia/Otospermophilus beecheyi/0d56f5284bcdf3b74be1def910aff3d0.jpg\",\n  \"170180.jpg\": \"Mammalia/Otospermophilus beecheyi/5de945d14198270b69518d89074ef02d.jpg\",\n  \"170182.jpg\": \"Mammalia/Otospermophilus beecheyi/416b849b4a3db4e089ad485ee8c0a781.jpg\",\n  \"170207.jpg\": \"Mammalia/Otospermophilus beecheyi/7d242ef7fc4cc1b2bceeb53f3af2b54f.jpg\",\n  \"170229.jpg\": \"Mammalia/Otospermophilus beecheyi/f5253121282490a5a2c16c2e851ea8bb.jpg\",\n  \"170240.jpg\": \"Mammalia/Otospermophilus beecheyi/ef81759c1a547aa72d16b51ed1fcd228.jpg\",\n  \"170301.jpg\": \"Mammalia/Otospermophilus beecheyi/3a8bd954c5956b93f2e2cafdeaf1bdec.jpg\",\n  \"170316.jpg\": \"Mammalia/Otospermophilus beecheyi/06ffe0c5f9c80a478ed251cb15c33c34.jpg\",\n  \"170322.jpg\": \"Mammalia/Otospermophilus beecheyi/e15c9cd20842745643e65a54d1f35d92.jpg\",\n  \"170332.jpg\": \"Mammalia/Otospermophilus beecheyi/e237e728beef30859045f62c2352e72f.jpg\",\n  \"17039.jpg\": \"Aves/Buteo jamaicensis/df57d026ad3b0c931aa08fbfb8e40fa1.jpg\",\n  \"170431.jpg\": \"Mammalia/Otospermophilus beecheyi/93f611e8220878c7226fe00d8e732147.jpg\",\n  \"170435.jpg\": \"Mammalia/Otospermophilus beecheyi/03b087219e6bc943a0298a26dab1ab1c.jpg\",\n  \"170467.jpg\": \"Mammalia/Otospermophilus beecheyi/685f41447971cebd0583058c830490b9.jpg\",\n  \"17048.jpg\": \"Aves/Buteo jamaicensis/004065771db529aab05f110fcbeb2af4.jpg\",\n  \"170482.jpg\": \"Mammalia/Otospermophilus beecheyi/b62b9a91ddfb00c6b424fc47cb468d0c.jpg\",\n  \"170490.jpg\": \"Mammalia/Otospermophilus beecheyi/ee70546336346ad92b29b2a74f1c9bd2.jpg\",\n  \"1705.jpg\": \"Reptilia/Sceloporus orcutti/f749715239394030cff405cb1131364c.jpg\",\n  \"170508.jpg\": \"Mammalia/Otospermophilus beecheyi/6f74e6ccf1e24402f53437c7544aa7f6.jpg\",\n  \"170541.jpg\": \"Mammalia/Otospermophilus beecheyi/98a658261d61e373fb98242d792aedc9.jpg\",\n  \"17062.jpg\": \"Aves/Buteo jamaicensis/83a759688e5829d05bbe97d239a1e7cc.jpg\",\n  \"170686.jpg\": \"Aves/Empidonax occidentalis/dbf5d892a7c7f66e4f123beaf04f0b28.jpg\",\n  \"170693.jpg\": \"Aves/Empidonax occidentalis/19b90dc43236c44f96a29cf2ae2568cc.jpg\",\n  \"170697.jpg\": \"Aves/Empidonax occidentalis/0948ce43c762f6a9b8675244cd854fcc.jpg\",\n  \"170698.jpg\": \"Aves/Empidonax occidentalis/44aec88ecae37483f40020f575836768.jpg\",\n  \"170701.jpg\": \"Aves/Empidonax occidentalis/cea4029662dd402c0bfe8a1728eac516.jpg\",\n  \"170704.jpg\": \"Aves/Gymnorhina tibicen/0bd7dd1a61dcd8ee4945286d00e19691.jpg\",\n  \"170706.jpg\": \"Aves/Gymnorhina tibicen/0f5a2797001dc58defb55c47c616a427.jpg\",\n  \"170709.jpg\": \"Aves/Gymnorhina tibicen/7b4d50c723b6d1e2e88936bfc4347254.jpg\",\n  \"170712.jpg\": \"Aves/Gymnorhina tibicen/1eb5a86d60f2408422b15bbddfb974b6.jpg\",\n  \"170713.jpg\": \"Aves/Gymnorhina tibicen/988c57075917b3cfba0cbc2bd0b426b0.jpg\",\n  \"170720.jpg\": \"Aves/Gymnorhina tibicen/7672d2d876e72ef3261f9b9d474eb317.jpg\",\n  \"170723.jpg\": \"Aves/Gymnorhina tibicen/ae0600d0732e5fcb8fd6007ef0855abc.jpg\",\n  \"170724.jpg\": \"Aves/Gymnorhina tibicen/b9bd266920da1a0764bd3a486a8af91c.jpg\",\n  \"170728.jpg\": \"Aves/Gymnorhina tibicen/37d41e949d399230e3a209f1c29d7016.jpg\",\n  \"170729.jpg\": \"Aves/Gymnorhina tibicen/bf44140905dd08bd4b0520d4d1238e37.jpg\",\n  \"170730.jpg\": \"Aves/Gymnorhina tibicen/01a94a21f17177f5870e3bdc5520ef4a.jpg\",\n  \"170732.jpg\": \"Aves/Gymnorhina tibicen/2cfce71410dac6bbfabce9a790565158.jpg\",\n  \"170735.jpg\": \"Aves/Gymnorhina tibicen/0078ca9d9f17394137236107f0266d24.jpg\",\n  \"170739.jpg\": \"Aves/Gymnorhina tibicen/0357f56156cee251caf05438a82ea01c.jpg\",\n  \"170741.jpg\": \"Aves/Gymnorhina tibicen/53bba6b22c019797d2cd194cf55c9354.jpg\",\n  \"170771.jpg\": \"Aves/Gymnorhina tibicen/7ff2a3764829d2bcbbaf543e49ea4c79.jpg\",\n  \"170780.jpg\": \"Aves/Gymnorhina tibicen/433b2b235df744cd991fcdce92f406f1.jpg\",\n  \"170782.jpg\": \"Aves/Gymnorhina tibicen/4099a3879ad282104e2503e9afcf2ec5.jpg\",\n  \"170783.jpg\": \"Aves/Gymnorhina tibicen/382bfbf866687720bb28b90fb9c4fbce.jpg\",\n  \"170786.jpg\": \"Aves/Gymnorhina tibicen/1727ca8f46f30d14106e86eed06aadd1.jpg\",\n  \"170788.jpg\": \"Aves/Gymnorhina tibicen/5df5930235ebd0879b566ad67bede1b9.jpg\",\n  \"170792.jpg\": \"Aves/Gymnorhina tibicen/98e567c94a785d954fc06806b94ff849.jpg\",\n  \"170803.jpg\": \"Insecta/Musca domestica/59f7937f2c108cf189f920277e8b658e.jpg\",\n  \"170804.jpg\": \"Insecta/Musca domestica/df773138212c515a24a4aee691274d5a.jpg\",\n  \"170806.jpg\": \"Insecta/Musca domestica/fd387ae09150a40a16b65066fde4ea55.jpg\",\n  \"170811.jpg\": \"Insecta/Musca domestica/94017ad94c390e6a2941347215a62cd8.jpg\",\n  \"170812.jpg\": \"Insecta/Musca domestica/93fbc9c5c172b7dec629870d9fde0b6d.jpg\",\n  \"170813.jpg\": \"Insecta/Musca domestica/b064b254994e27788d8641a1c02b98b7.jpg\",\n  \"170815.jpg\": \"Insecta/Musca domestica/fcdff67f8790f5cb58e0e2b20308b6c8.jpg\",\n  \"170817.jpg\": \"Insecta/Musca domestica/ddd3aa8ffcbde3bfee7c89d223112733.jpg\",\n  \"170818.jpg\": \"Reptilia/Xantusia vigilis/592adf3144271bc37a59192240202c86.jpg\",\n  \"170823.jpg\": \"Reptilia/Xantusia vigilis/a32eeabfcb3f40808486d5cc78576b17.jpg\",\n  \"170824.jpg\": \"Reptilia/Xantusia vigilis/fc065398ea0f73d748fa79722d2e72e9.jpg\",\n  \"170830.jpg\": \"Reptilia/Xantusia vigilis/af5857ebcf66125957ede97c8f7ecba1.jpg\",\n  \"170831.jpg\": \"Reptilia/Xantusia vigilis/971dfc3663fa0ccf1aefe7dcfc516e25.jpg\",\n  \"170838.jpg\": \"Reptilia/Xantusia vigilis/d0895ead78f724663193a81dd2f18eb2.jpg\",\n  \"170842.jpg\": \"Mollusca/Lobatus gigas/4cb78e2409aee7ddebca82027349d53e.jpg\",\n  \"170846.jpg\": \"Mollusca/Lobatus gigas/4665054473631f6bb38f1d68d3966980.jpg\",\n  \"170858.jpg\": \"Mollusca/Lobatus gigas/a933bda82ba6013b2989617fb787e428.jpg\",\n  \"170931.jpg\": \"Insecta/Hypsopygia olinalis/d4d1f433eb045abe5efedb6dd1c12781.jpg\",\n  \"170932.jpg\": \"Insecta/Hypsopygia olinalis/4be44910d7c13ea42added96bb14cdf2.jpg\",\n  \"170933.jpg\": \"Insecta/Hypsopygia olinalis/6e06719cead75a057a8eb7a87747ac07.jpg\",\n  \"170937.jpg\": \"Insecta/Hypsopygia olinalis/1ae3309cca3166e8c8a74ead8088c592.jpg\",\n  \"170938.jpg\": \"Insecta/Hypsopygia olinalis/abd04b3eee046022643456141862b049.jpg\",\n  \"170939.jpg\": \"Insecta/Hypsopygia olinalis/b5ab809aada0ce252d2f83ed10559e8f.jpg\",\n  \"170945.jpg\": \"Insecta/Hypsopygia olinalis/9deb380eb457e62b4f598baacaf605dd.jpg\",\n  \"170947.jpg\": \"Insecta/Hypsopygia olinalis/ede950c6e1619b01e2f8ff0a0ed3dc02.jpg\",\n  \"170950.jpg\": \"Insecta/Hypsopygia olinalis/66770995751b8853dc0390c2f4402a34.jpg\",\n  \"170952.jpg\": \"Insecta/Hypsopygia olinalis/bc112d3555fe1c5cc59b77da6904accd.jpg\",\n  \"170956.jpg\": \"Insecta/Hypsopygia olinalis/2a5fc257d2d284c6fb55d5f2de0a1118.jpg\",\n  \"170957.jpg\": \"Insecta/Hypsopygia olinalis/03a4a384daa234e31da8eafd9b7426d2.jpg\",\n  \"170960.jpg\": \"Insecta/Hypsopygia olinalis/14f87abf015ed910b719ee48c5749fbc.jpg\",\n  \"170964.jpg\": \"Insecta/Hypsopygia olinalis/d5ec5fa6407ec8b6b0f290fc0f362bcf.jpg\",\n  \"170966.jpg\": \"Insecta/Hypsopygia olinalis/309bc5255247345f279708fa0d5fce19.jpg\",\n  \"170967.jpg\": \"Insecta/Hypsopygia olinalis/9283a7f5f0be841b9987a34cf31cfc95.jpg\",\n  \"170968.jpg\": \"Insecta/Hypsopygia olinalis/a058a80543b6b8a0c0325641111865b1.jpg\",\n  \"170971.jpg\": \"Insecta/Hypsopygia olinalis/9c9781c3129c1cda3320f13ed28d96ba.jpg\",\n  \"170975.jpg\": \"Insecta/Hypsopygia olinalis/9ad17f61b747a525f6f2b226df972d2c.jpg\",\n  \"171.jpg\": \"Aves/Zonotrichia capensis/0b062261c64631be00ba4686e40cb234.jpg\",\n  \"171007.jpg\": \"Animalia/Cardisoma guanhumi/4ce36e02dcfc7aad9e3057e90d10ec9f.jpg\",\n  \"171008.jpg\": \"Animalia/Cardisoma guanhumi/cf3540bed7a97f660776f4f5fd0d5a8b.jpg\",\n  \"171014.jpg\": \"Animalia/Cardisoma guanhumi/e83de945f1a0f0e080c293759b40bebb.jpg\",\n  \"171015.jpg\": \"Animalia/Cardisoma guanhumi/46efc01e4e365f4f416b846e84b6f7e6.jpg\",\n  \"171017.jpg\": \"Animalia/Cardisoma guanhumi/f62a107d77bf3f2b220430599850c4b2.jpg\",\n  \"1711.jpg\": \"Reptilia/Sceloporus orcutti/28b10b486d836d4a07b78768acfa3137.jpg\",\n  \"171144.jpg\": \"Mammalia/Phacochoerus africanus/f4101bc7baff9c84e5470295c09d597b.jpg\",\n  \"171145.jpg\": \"Mammalia/Phacochoerus africanus/7b159fbe3d05e67c4b512df6666a8101.jpg\",\n  \"171148.jpg\": \"Mammalia/Phacochoerus africanus/47fd69317d40a2e2c65177f98504e5cb.jpg\",\n  \"171158.jpg\": \"Mammalia/Phacochoerus africanus/b5ce152570c48542d6f293cc78a7212d.jpg\",\n  \"171159.jpg\": \"Mammalia/Phacochoerus africanus/27219acf9765364ba19d9c4e0defe898.jpg\",\n  \"171160.jpg\": \"Mammalia/Phacochoerus africanus/3d9587ab5f3eeec32520b7bf8a09f186.jpg\",\n  \"171162.jpg\": \"Mammalia/Phacochoerus africanus/68eedf3bbd6839c3b926d85666fa53ce.jpg\",\n  \"171163.jpg\": \"Mammalia/Phacochoerus africanus/7d4827a0d72e631cba885b9197fd8f56.jpg\",\n  \"171167.jpg\": \"Mammalia/Phacochoerus africanus/9ee504047916583e5f20371422d6f5e0.jpg\",\n  \"171170.jpg\": \"Mammalia/Phacochoerus africanus/6dfcf520abe23ebe313aa9d236558035.jpg\",\n  \"171177.jpg\": \"Mammalia/Phacochoerus africanus/a07337724979d0513264fd91b647f644.jpg\",\n  \"171179.jpg\": \"Mammalia/Phacochoerus africanus/564416dee495facab279922f8e472bb1.jpg\",\n  \"171180.jpg\": \"Mammalia/Phacochoerus africanus/c6203f0010de96ffcc53d532ea610d60.jpg\",\n  \"1712.jpg\": \"Reptilia/Sceloporus orcutti/651664e54c46320accb5e689a3582e26.jpg\",\n  \"1713.jpg\": \"Reptilia/Sceloporus orcutti/dee9b6c3fd2b919c177687f20e6499af.jpg\",\n  \"1715.jpg\": \"Reptilia/Sceloporus orcutti/8284e6e42d04fd34049595d7eac0b81e.jpg\",\n  \"171508.jpg\": \"Reptilia/Thamnophis atratus/1cd09bece25bb5a88871a406926368ad.jpg\",\n  \"171511.jpg\": \"Reptilia/Thamnophis atratus/92dee8485d2705d9014246594ba2e633.jpg\",\n  \"171524.jpg\": \"Reptilia/Thamnophis atratus/f6cde63ccf3ec158181ed8143fadb5a3.jpg\",\n  \"171529.jpg\": \"Reptilia/Thamnophis atratus/1ed720f1811b2bf1d72f2bcd3f17c139.jpg\",\n  \"171530.jpg\": \"Reptilia/Thamnophis atratus/c6de4082046ac2c20ad70405535beec3.jpg\",\n  \"171539.jpg\": \"Insecta/Lambdina fiscellaria/aa91a04f81d42d08fa1a07874916bfbb.jpg\",\n  \"171544.jpg\": \"Insecta/Lambdina fiscellaria/3ebded3aae892be12084ebf7994dc92e.jpg\",\n  \"171547.jpg\": \"Insecta/Lambdina fiscellaria/93ca6e882cf613602f1708b61bbe50cb.jpg\",\n  \"171556.jpg\": \"Insecta/Lambdina fiscellaria/14d2067129a28f30ab6fe9f9aa2871d6.jpg\",\n  \"171560.jpg\": \"Insecta/Lambdina fiscellaria/2b9063e26a50e71a3a33aceb7487c08f.jpg\",\n  \"171578.jpg\": \"Reptilia/Gopherus berlandieri/a6309a60758f7f30358e187d6d108f36.jpg\",\n  \"171579.jpg\": \"Reptilia/Gopherus berlandieri/0e876fee4c6ba0cbcebbcb90f0c5e03d.jpg\",\n  \"171582.jpg\": \"Reptilia/Gopherus berlandieri/d9b748f9f3eb508c9c33845b53adaa97.jpg\",\n  \"171585.jpg\": \"Reptilia/Gopherus berlandieri/12c6fac1ad46e7f442d3d285efae2112.jpg\",\n  \"171589.jpg\": \"Reptilia/Gopherus berlandieri/e2810e3b982a906cb8e717f87933cad8.jpg\",\n  \"171590.jpg\": \"Reptilia/Gopherus berlandieri/3babeb3aba11b734978ebc8361c6b408.jpg\",\n  \"1716.jpg\": \"Reptilia/Sceloporus orcutti/b24b7e3861f4fee7a46399810a4a3481.jpg\",\n  \"171603.jpg\": \"Reptilia/Gopherus berlandieri/9184737efd426bd1a7ef6d0caf9514d2.jpg\",\n  \"171604.jpg\": \"Reptilia/Gopherus berlandieri/dfbfc8249a664361dbdeca0a26d3e746.jpg\",\n  \"171618.jpg\": \"Reptilia/Gopherus berlandieri/54aba5c20ae99c9f11094b243c84a788.jpg\",\n  \"171629.jpg\": \"Reptilia/Gopherus berlandieri/162cd2a347618539ef5dd463c8fdbf55.jpg\",\n  \"171631.jpg\": \"Reptilia/Gopherus berlandieri/f29b4c0b90aa03b7d183cb5e23cb4fe6.jpg\",\n  \"171638.jpg\": \"Reptilia/Gopherus berlandieri/378841c6731cda3b901129d5b9f17d7a.jpg\",\n  \"171642.jpg\": \"Reptilia/Gopherus berlandieri/f15ed9986065312a26d896d80b23e93b.jpg\",\n  \"171644.jpg\": \"Reptilia/Gopherus berlandieri/c2c244aed0aecde95de03b129bcf3423.jpg\",\n  \"171648.jpg\": \"Reptilia/Gopherus berlandieri/360570f197eeaf67a4cb38a18ce7c8ac.jpg\",\n  \"171653.jpg\": \"Reptilia/Gopherus berlandieri/95291de61cf23760e6d7f0c536c81e37.jpg\",\n  \"171659.jpg\": \"Reptilia/Gopherus berlandieri/46e40b794e0a6c0a1aa62a15b7040c95.jpg\",\n  \"171662.jpg\": \"Reptilia/Gopherus berlandieri/e77e2e53abd70dc2ff3f7b90bac5a52f.jpg\",\n  \"171663.jpg\": \"Reptilia/Gopherus berlandieri/1f0a72e018d8511b1c2893c003741530.jpg\",\n  \"171668.jpg\": \"Reptilia/Gopherus berlandieri/e7e988ea87cc3064ca00576d7c4c9509.jpg\",\n  \"171672.jpg\": \"Reptilia/Gopherus berlandieri/f3388adfb15ec0fe71b14f54ca32490c.jpg\",\n  \"17170.jpg\": \"Aves/Buteo jamaicensis/4caf176d33e3b10b4143320c09492e6f.jpg\",\n  \"171818.jpg\": \"Arachnida/Micrathena sagittata/4f3dbdaa9f099de5623cf93d7367dc1e.jpg\",\n  \"171823.jpg\": \"Arachnida/Micrathena sagittata/80cd20fe485b1da4ad727d930c89e70f.jpg\",\n  \"171831.jpg\": \"Arachnida/Micrathena sagittata/6a4d54f1522427d527cf59214acb920a.jpg\",\n  \"171832.jpg\": \"Arachnida/Micrathena sagittata/50bc862aa0749128003e68c2c0d970e3.jpg\",\n  \"171833.jpg\": \"Arachnida/Micrathena sagittata/88211888c02ae47bbac11b9800a10d0c.jpg\",\n  \"171834.jpg\": \"Arachnida/Micrathena sagittata/a46491f39347d7981aa344f75f9619e8.jpg\",\n  \"171885.jpg\": \"Insecta/Paltothemis lineatipes/6a7b2aaf299bff10775409ac51f25c86.jpg\",\n  \"171887.jpg\": \"Insecta/Paltothemis lineatipes/5e3f5163324e95fc6f1a44f52a75b758.jpg\",\n  \"171889.jpg\": \"Insecta/Paltothemis lineatipes/11126ba0b69f3e15fe72fe46e3cdb89c.jpg\",\n  \"171890.jpg\": \"Insecta/Paltothemis lineatipes/808a1bb8164199ab409b2f9ae127e8d5.jpg\",\n  \"171891.jpg\": \"Insecta/Paltothemis lineatipes/f1688d9c07b9c5f9900f1e1e87f593a0.jpg\",\n  \"171892.jpg\": \"Insecta/Paltothemis lineatipes/cf92c8713620de4bd1842b2f9affd61e.jpg\",\n  \"171893.jpg\": \"Insecta/Paltothemis lineatipes/c578968eb0b26b7fcf1c9b581a3a6854.jpg\",\n  \"171894.jpg\": \"Insecta/Paltothemis lineatipes/313c284b328ac8544a151d2c3c02918b.jpg\",\n  \"171895.jpg\": \"Insecta/Paltothemis lineatipes/8f4db4cf5c161d98e7f0b9a9d3165724.jpg\",\n  \"171896.jpg\": \"Insecta/Paltothemis lineatipes/e7c422542f0e54d40988daa9c15ad441.jpg\",\n  \"171898.jpg\": \"Insecta/Paltothemis lineatipes/30275b6c5bcf45de1b46222bc174a01e.jpg\",\n  \"171899.jpg\": \"Insecta/Paltothemis lineatipes/c031f9cfbfca603b2ce5796510f83862.jpg\",\n  \"1719.jpg\": \"Reptilia/Sceloporus orcutti/b864f27cd3dbe9b83ea2beccefc28de1.jpg\",\n  \"171900.jpg\": \"Insecta/Paltothemis lineatipes/b86ceb6113b58b2755909f0955045ad6.jpg\",\n  \"171901.jpg\": \"Insecta/Paltothemis lineatipes/0390b3d524053f958dbf51f4ee6be3d9.jpg\",\n  \"171903.jpg\": \"Insecta/Paltothemis lineatipes/e0608553cef7798d73eef52c9c57bb06.jpg\",\n  \"171904.jpg\": \"Insecta/Paltothemis lineatipes/42de4ec8f4817fbd5ab5cd3977b1c448.jpg\",\n  \"171906.jpg\": \"Insecta/Paltothemis lineatipes/8a7bff80dd699b4eab1535152d89325d.jpg\",\n  \"171908.jpg\": \"Insecta/Paltothemis lineatipes/55950cdebe2331ae6ff1bc1cc0bc0bc7.jpg\",\n  \"171916.jpg\": \"Insecta/Paltothemis lineatipes/249f9444ba669943f0bd6d35813f75ef.jpg\",\n  \"171917.jpg\": \"Insecta/Paltothemis lineatipes/7ea1230347333e3c24ce489ec338ccce.jpg\",\n  \"171919.jpg\": \"Insecta/Paltothemis lineatipes/3cfaa15b0cea12db3c4b6799db2c5eae.jpg\",\n  \"171920.jpg\": \"Insecta/Paltothemis lineatipes/782e9ca63082f79a0fa464f5ddb22c27.jpg\",\n  \"171922.jpg\": \"Insecta/Paltothemis lineatipes/f8ea3d992254aca0ec465493cd1c8680.jpg\",\n  \"171926.jpg\": \"Insecta/Paltothemis lineatipes/3eff32ea77f8623bfa61bf8055949bd4.jpg\",\n  \"171941.jpg\": \"Insecta/Spodoptera litura/ed16342e1ab2c223a18f78e91589ed02.jpg\",\n  \"171947.jpg\": \"Insecta/Spodoptera litura/8cffa25a352ba555f880eb39fe5d8fa7.jpg\",\n  \"171953.jpg\": \"Insecta/Spodoptera litura/1e180f80e2f16f510e622513f457e356.jpg\",\n  \"171954.jpg\": \"Insecta/Spodoptera litura/0eb30f6e6884f47f128c390f85d54523.jpg\",\n  \"171975.jpg\": \"Aves/Vanellus chilensis/2e4d483edb533445667606c569ddb933.jpg\",\n  \"171977.jpg\": \"Aves/Vanellus chilensis/55783fbca7539c42492f86fa97f93e52.jpg\",\n  \"172024.jpg\": \"Aves/Larus californicus/dbc7d746bb6dba8703cab3ddac9039db.jpg\",\n  \"172026.jpg\": \"Aves/Larus californicus/a9696dc129c4234c098ab402d796b91a.jpg\",\n  \"172034.jpg\": \"Aves/Larus californicus/a505b37bc1446b9c1f88bc292130bade.jpg\",\n  \"172048.jpg\": \"Aves/Larus californicus/50163121f5e2f5dea3b2f7fdf200e99a.jpg\",\n  \"172068.jpg\": \"Aves/Larus californicus/70c0b2c72f21ac6a07b6136076b98a1e.jpg\",\n  \"172073.jpg\": \"Aves/Larus californicus/e7337809180cd22455f54f327647736b.jpg\",\n  \"172081.jpg\": \"Aves/Larus californicus/1b2b836f778c6e10d16949445e677ddd.jpg\",\n  \"172105.jpg\": \"Aves/Larus californicus/ffcfc2c6425b3ca690316ca630c71808.jpg\",\n  \"172109.jpg\": \"Aves/Larus californicus/6c7e6ca3452f4fa409009dc7e6fce214.jpg\",\n  \"172113.jpg\": \"Aves/Larus californicus/3d25658fb8dfc8ad2bd631104b7f8f95.jpg\",\n  \"172128.jpg\": \"Aves/Larus californicus/c65c5df444ae8945c6b2c1e977d69a12.jpg\",\n  \"172133.jpg\": \"Aves/Larus californicus/d9aa0145f2bbe95f1ced9dfc711df3c2.jpg\",\n  \"172135.jpg\": \"Aves/Larus californicus/ced5d33b1fbca6b79e3cc609e67ca799.jpg\",\n  \"172146.jpg\": \"Aves/Larus californicus/35a64c672394cc10f35d861780d5c253.jpg\",\n  \"172172.jpg\": \"Aves/Larus californicus/2e2a32e228d8407a595e69e97b0f72a2.jpg\",\n  \"172195.jpg\": \"Aves/Larus californicus/5b0cf7879b248ea711184de64e4c2942.jpg\",\n  \"172366.jpg\": \"Reptilia/Opheodrys vernalis/40e0adda5fd404d0ecb0c420ab3e3fa3.jpg\",\n  \"172369.jpg\": \"Reptilia/Opheodrys vernalis/097badd0296ed278691ad95d9b57c070.jpg\",\n  \"172370.jpg\": \"Reptilia/Opheodrys vernalis/b156d0faa01349fb9a1295cf0e1467a1.jpg\",\n  \"172372.jpg\": \"Reptilia/Opheodrys vernalis/fdf9059c1ccef42d1419d2a5d8dfe557.jpg\",\n  \"172373.jpg\": \"Reptilia/Opheodrys vernalis/ae37006334f1aa0f6cef2dc93b7be8fb.jpg\",\n  \"172374.jpg\": \"Reptilia/Opheodrys vernalis/bad3247aee1a67b833765161b776026d.jpg\",\n  \"172378.jpg\": \"Reptilia/Opheodrys vernalis/51665382017bc551713c1cdfe5213b4e.jpg\",\n  \"172380.jpg\": \"Reptilia/Opheodrys vernalis/e70f07c5b0d0f764d63c91e854da5bb8.jpg\",\n  \"172383.jpg\": \"Reptilia/Opheodrys vernalis/93c5f1b22cc0a5b51a2d6a38288164f6.jpg\",\n  \"172391.jpg\": \"Reptilia/Sternotherus odoratus/62b4de11a31c13d955804c0b3eb0b534.jpg\",\n  \"172392.jpg\": \"Reptilia/Sternotherus odoratus/02bfdd00e26f77259e36c76ba3764b3f.jpg\",\n  \"172393.jpg\": \"Reptilia/Sternotherus odoratus/354a80e52714491434234dd95f4cf2cd.jpg\",\n  \"172394.jpg\": \"Reptilia/Sternotherus odoratus/1b5690b32b1fe0c01665cf72e01705e1.jpg\",\n  \"172395.jpg\": \"Reptilia/Sternotherus odoratus/8e7febec225e0c984103f40d74f44eea.jpg\",\n  \"172399.jpg\": \"Reptilia/Sternotherus odoratus/c53a2418e789254f039a82f209c65d5d.jpg\",\n  \"172400.jpg\": \"Reptilia/Sternotherus odoratus/d14b63ad7039747b4285cbf7c7d2f68d.jpg\",\n  \"172401.jpg\": \"Reptilia/Sternotherus odoratus/ef4bb1b1f14fa42ccb8a3299cdfe05c4.jpg\",\n  \"172408.jpg\": \"Reptilia/Sternotherus odoratus/b95a4b2cdf8df794c1034775c02bc689.jpg\",\n  \"172409.jpg\": \"Reptilia/Sternotherus odoratus/46c1e6d1e8bc76dcf530ecc5d4ed5e3a.jpg\",\n  \"172413.jpg\": \"Reptilia/Sternotherus odoratus/6c66921525036b6a89b6e000957461b4.jpg\",\n  \"172416.jpg\": \"Reptilia/Sternotherus odoratus/d3aace84f42ad632031d1b431c7739dc.jpg\",\n  \"172417.jpg\": \"Reptilia/Sternotherus odoratus/878c9398b2264bbcf5bf7e8873ad1c16.jpg\",\n  \"172425.jpg\": \"Reptilia/Sternotherus odoratus/1989f696ed2ead9577d841d5efec4af2.jpg\",\n  \"172426.jpg\": \"Reptilia/Sternotherus odoratus/b8b91f7fafc18cd9caad8671dcad9887.jpg\",\n  \"17243.jpg\": \"Aves/Buteo jamaicensis/534ba49c994af50dc0b08085e3543876.jpg\",\n  \"172443.jpg\": \"Reptilia/Sternotherus odoratus/9a273d544fd6bb25e958b98b68f9ebc5.jpg\",\n  \"172444.jpg\": \"Reptilia/Sternotherus odoratus/654d9ed11935532966cd2f56b710b457.jpg\",\n  \"172448.jpg\": \"Reptilia/Sternotherus odoratus/f0bfe0ab1474f3e1d7f26143ce088220.jpg\",\n  \"172449.jpg\": \"Reptilia/Sternotherus odoratus/458ef02cd095c5fcfe7c486d324a062b.jpg\",\n  \"172450.jpg\": \"Reptilia/Sternotherus odoratus/fb7cc871f680c40db4d89536cf91830e.jpg\",\n  \"172454.jpg\": \"Aves/Aythya ferina/13c5e2e315160ebe9fb33977ac38907b.jpg\",\n  \"172459.jpg\": \"Aves/Aythya ferina/d46f9a9fd5bf8d66545bd3ef36dbc30e.jpg\",\n  \"172467.jpg\": \"Aves/Aythya ferina/fd31ae898677a7d912ec52a056e5fc66.jpg\",\n  \"172470.jpg\": \"Aves/Aythya ferina/c3d7e4941cc5b13f4bf13c0c8476c750.jpg\",\n  \"172484.jpg\": \"Aves/Aythya ferina/b7b281d6ee1798d5722b0e94f84981f8.jpg\",\n  \"172485.jpg\": \"Aves/Aythya ferina/028b3d0114377f88f95c4a96d3546077.jpg\",\n  \"172490.jpg\": \"Mammalia/Neovison vison/ffaa152ba7ad54303fd65bc3c019b10d.jpg\",\n  \"172496.jpg\": \"Mammalia/Neovison vison/3db4c15cbcbde9bed2951f813cf2e0d8.jpg\",\n  \"172497.jpg\": \"Mammalia/Neovison vison/97a91b538249d4e71df8228ef9724f58.jpg\",\n  \"172498.jpg\": \"Mammalia/Neovison vison/e497cc0b15636ccab77148b61fff5f88.jpg\",\n  \"172506.jpg\": \"Mammalia/Neovison vison/e29838c0dcc2630173931af4d981705c.jpg\",\n  \"172510.jpg\": \"Mammalia/Neovison vison/259de2608d0205694337c626ea7302b3.jpg\",\n  \"172520.jpg\": \"Mammalia/Neovison vison/7d14a05c222b0f3b52484c3140b0c5dd.jpg\",\n  \"172521.jpg\": \"Mammalia/Neovison vison/cf6b93043f181d14de20664d58cec0ee.jpg\",\n  \"172525.jpg\": \"Mammalia/Neovison vison/e0d41aa555f2a6c3b8ff2a3457997a42.jpg\",\n  \"172539.jpg\": \"Mammalia/Neovison vison/c941727e8e7785fb15a219667776ee12.jpg\",\n  \"172549.jpg\": \"Mammalia/Neovison vison/1eebbef2ed1ae6e162ba090e029469c1.jpg\",\n  \"172564.jpg\": \"Mammalia/Neovison vison/cf473c47c7226acd2eb01a9a22964306.jpg\",\n  \"172568.jpg\": \"Mammalia/Neovison vison/86cfe6299c334a24eeb76373ae1e5b93.jpg\",\n  \"172570.jpg\": \"Mammalia/Neovison vison/6c4c73ce89b74f3b653d754d3b8bfe3c.jpg\",\n  \"172586.jpg\": \"Mammalia/Neovison vison/632d27d1ef311118f05894adcdb2da48.jpg\",\n  \"172587.jpg\": \"Mammalia/Neovison vison/aaededc8fa659e3c323a3b67b472064a.jpg\",\n  \"172588.jpg\": \"Mammalia/Neovison vison/8d8386ace6c08d3731e4b2fd4ba78b03.jpg\",\n  \"172589.jpg\": \"Mammalia/Neovison vison/47d52703ce2baff1497bea2527af53a5.jpg\",\n  \"172592.jpg\": \"Mammalia/Neovison vison/d3f8ad1d919028045527e2a5fb938718.jpg\",\n  \"172595.jpg\": \"Mammalia/Neovison vison/f240ff4a30dafd1288a792f5c60b4234.jpg\",\n  \"172838.jpg\": \"Aves/Passerculus sandwichensis/6fa625c79bac2ac3cd465c6a80b8b96b.jpg\",\n  \"172854.jpg\": \"Aves/Passerculus sandwichensis/96debdec1c8230a1fa7f55766920cb0c.jpg\",\n  \"172878.jpg\": \"Aves/Passerculus sandwichensis/d03a886aa5d71d94e1a252cb689885dc.jpg\",\n  \"172882.jpg\": \"Aves/Passerculus sandwichensis/ea336bf3d97b65cd1b877f8c3aa5dcc3.jpg\",\n  \"172900.jpg\": \"Aves/Passerculus sandwichensis/0ca782c2e480fa5fb1b6fe1bd048e29f.jpg\",\n  \"172903.jpg\": \"Aves/Passerculus sandwichensis/05b7c8e99b93861f9156f0f6f95d6530.jpg\",\n  \"172934.jpg\": \"Aves/Passerculus sandwichensis/1467e6b69415faab8f0dea2cc515a3f2.jpg\",\n  \"172937.jpg\": \"Aves/Passerculus sandwichensis/4c42d95b3c57739563b4c6759eac5f5e.jpg\",\n  \"172949.jpg\": \"Aves/Passerculus sandwichensis/70e7b1a90967ba37a63fbe9cf37bbcde.jpg\",\n  \"172969.jpg\": \"Aves/Passerculus sandwichensis/d50469487fcf3645dfa4bc2109938ec7.jpg\",\n  \"172971.jpg\": \"Aves/Passerculus sandwichensis/b64fb7b999c9b92ce9d8388f7e4e1656.jpg\",\n  \"173025.jpg\": \"Aves/Passerculus sandwichensis/1b06bb6770b3fb4fc7b5db534f0079a3.jpg\",\n  \"173030.jpg\": \"Aves/Passerculus sandwichensis/0e40377de66acc2dc49abc90c6902606.jpg\",\n  \"173042.jpg\": \"Aves/Passerculus sandwichensis/cb387d98f902d889c8361dad0ef858a2.jpg\",\n  \"173046.jpg\": \"Aves/Passerculus sandwichensis/5ff7acf3293fe497926fc38dc6f6ed50.jpg\",\n  \"173047.jpg\": \"Aves/Passerculus sandwichensis/bee8f1697da198db90385b94fb551bdf.jpg\",\n  \"173049.jpg\": \"Aves/Passerculus sandwichensis/e40281674f93136b61a7de817b70db1c.jpg\",\n  \"173053.jpg\": \"Aves/Passerculus sandwichensis/e22ecc024fe1abc514d236f914ea6652.jpg\",\n  \"173056.jpg\": \"Aves/Passerculus sandwichensis/53a9881f0ca201ba91ca756155309b86.jpg\",\n  \"173071.jpg\": \"Aves/Passerculus sandwichensis/ea4282237a7c3b10e89d11f805f5683c.jpg\",\n  \"173097.jpg\": \"Aves/Passerculus sandwichensis/9a0cc84468b4cbc3c86efea6115fd30c.jpg\",\n  \"1731.jpg\": \"Reptilia/Sceloporus orcutti/bac1356671dcd25f0f1ce4a5c5d8af85.jpg\",\n  \"173131.jpg\": \"Aves/Passerculus sandwichensis/92a48e8877891e655dfcc1c3e1c0425e.jpg\",\n  \"173165.jpg\": \"Aves/Passerculus sandwichensis/c1f0ab0cca50aebb29317284addb023d.jpg\",\n  \"1732.jpg\": \"Reptilia/Sceloporus orcutti/4a4308f2176169ba01dc291f6287a064.jpg\",\n  \"173208.jpg\": \"Aves/Passerculus sandwichensis/d3b00b5bdd3ae094c05be9c6494e0b09.jpg\",\n  \"173212.jpg\": \"Aves/Passerculus sandwichensis/9142f1572cf2778b7dcc4258eea0dd82.jpg\",\n  \"173219.jpg\": \"Aves/Passerculus sandwichensis/3209c3eb00c9bf7712a1a6809788c409.jpg\",\n  \"173259.jpg\": \"Arachnida/Argiope bruennichi/9faab4551d841e9ee318590dd6447918.jpg\",\n  \"173262.jpg\": \"Arachnida/Argiope bruennichi/4d9e603841dcd1d999f73195fe4ae8bb.jpg\",\n  \"173263.jpg\": \"Arachnida/Argiope bruennichi/9524836ab408670c721662c081988b29.jpg\",\n  \"173266.jpg\": \"Arachnida/Argiope bruennichi/426e901c9bd1433af0c188fce92fc94f.jpg\",\n  \"173267.jpg\": \"Arachnida/Argiope bruennichi/266f805a9a32164bd6fcf6ca7bda687b.jpg\",\n  \"173274.jpg\": \"Arachnida/Argiope bruennichi/2a8fe118e61c9b6550414380d6d95a16.jpg\",\n  \"173275.jpg\": \"Arachnida/Argiope bruennichi/7249386e66b8ec22f3e91f60866d2be6.jpg\",\n  \"173276.jpg\": \"Arachnida/Argiope bruennichi/ad60ff7f90ffcfc0a36a592966c944b9.jpg\",\n  \"173289.jpg\": \"Arachnida/Argiope bruennichi/63fe05efec76bfae7b5742eeb6350a7e.jpg\",\n  \"173291.jpg\": \"Arachnida/Argiope bruennichi/d1a79206d82bd0032c24287ea0bc0204.jpg\",\n  \"173292.jpg\": \"Arachnida/Argiope bruennichi/2fba57555736b948817af3e73e4a1625.jpg\",\n  \"173294.jpg\": \"Arachnida/Argiope bruennichi/cf85c561106e30ba5766a56aa13cd49a.jpg\",\n  \"173304.jpg\": \"Aves/Dumetella carolinensis/bae7f8795da4c55181936444efd632a6.jpg\",\n  \"173306.jpg\": \"Aves/Dumetella carolinensis/4fd772347ced204d92c34d463df7451a.jpg\",\n  \"173309.jpg\": \"Aves/Dumetella carolinensis/54011ada08ad493de1894f7262256c4a.jpg\",\n  \"173317.jpg\": \"Aves/Dumetella carolinensis/64d257ac13fc606b573f21bd2468206a.jpg\",\n  \"173344.jpg\": \"Aves/Dumetella carolinensis/6fe86aa0196472a790ba0ee8ff076629.jpg\",\n  \"173364.jpg\": \"Aves/Dumetella carolinensis/5226c0d5ecd46aaffaaf31f5485bd799.jpg\",\n  \"173365.jpg\": \"Aves/Dumetella carolinensis/aeade35fc2d5689ad0e0b0d1b80ceb05.jpg\",\n  \"173367.jpg\": \"Aves/Dumetella carolinensis/d9d381a036f395baac578679f5c9cdb3.jpg\",\n  \"173374.jpg\": \"Aves/Dumetella carolinensis/6fe1998823d00a90c2ae09d84c8b8326.jpg\",\n  \"173400.jpg\": \"Aves/Dumetella carolinensis/7cb2b1f1517801a21d413913a4c55d45.jpg\",\n  \"173413.jpg\": \"Aves/Dumetella carolinensis/fb2f55f0faf11497250b813da17d1cd1.jpg\",\n  \"173426.jpg\": \"Aves/Dumetella carolinensis/adeab60e2447d26b58ae89bad40f5e5a.jpg\",\n  \"173436.jpg\": \"Aves/Dumetella carolinensis/5f26fe37d6a7e6c59fdefce409d5eff8.jpg\",\n  \"173447.jpg\": \"Aves/Dumetella carolinensis/185818a92781747a3100fdd5c0f6f50b.jpg\",\n  \"173452.jpg\": \"Aves/Dumetella carolinensis/3c520ab70e8109ae65f14d693e51c677.jpg\",\n  \"173464.jpg\": \"Aves/Dumetella carolinensis/20a10a5bbe429511142ee74b11d0a9e7.jpg\",\n  \"173473.jpg\": \"Aves/Dumetella carolinensis/296f3095e197399940e757942bc1f96e.jpg\",\n  \"173505.jpg\": \"Aves/Dumetella carolinensis/cb46a215f77b91757428965be57334bd.jpg\",\n  \"173523.jpg\": \"Aves/Dumetella carolinensis/6634475cdc82b2411f75fdcc44cc0604.jpg\",\n  \"173539.jpg\": \"Aves/Dumetella carolinensis/66afaf8966bf017c309eab46ec1a5f54.jpg\",\n  \"173547.jpg\": \"Aves/Dumetella carolinensis/0a8e9a1b811d0a67742a2c84e6e53bcf.jpg\",\n  \"173559.jpg\": \"Aves/Dumetella carolinensis/e6cb7d949200ebb51fd89c760d659419.jpg\",\n  \"173564.jpg\": \"Aves/Dumetella carolinensis/583e88b816bf28476e3be7894586d406.jpg\",\n  \"173581.jpg\": \"Aves/Dumetella carolinensis/904c27c12e5fdecf5237dc775864db09.jpg\",\n  \"1736.jpg\": \"Reptilia/Sceloporus orcutti/de494c8d81f1e919beb036b84a539965.jpg\",\n  \"173637.jpg\": \"Aves/Dumetella carolinensis/556df87c81221ac3c463aa7c6f95abb4.jpg\",\n  \"1737.jpg\": \"Reptilia/Sceloporus orcutti/e3de907846ca0e92b22aa5a59ab403d0.jpg\",\n  \"1738.jpg\": \"Reptilia/Sceloporus orcutti/931026fc47a4aec65def0bc6aedc549e.jpg\",\n  \"17381.jpg\": \"Aves/Buteo jamaicensis/19bd07d84ae9dd8199661c1822945649.jpg\",\n  \"173902.jpg\": \"Mollusca/Ariolimax columbianus/00937dfeef0fea3b6185d68f89d27b18.jpg\",\n  \"173903.jpg\": \"Mollusca/Ariolimax columbianus/4251ce5d6a4ba70791af1b90a8d1bed9.jpg\",\n  \"173905.jpg\": \"Mollusca/Ariolimax columbianus/8bd89bc310887609f2665fcccd18965c.jpg\",\n  \"173906.jpg\": \"Mollusca/Ariolimax columbianus/ea5b384cc0abcd654ef44249ac632beb.jpg\",\n  \"173907.jpg\": \"Mollusca/Ariolimax columbianus/5217e3fc32748fa1087975887677de6c.jpg\",\n  \"173923.jpg\": \"Mollusca/Ariolimax columbianus/d71e7999bcc7e98f42e2b815aab3004b.jpg\",\n  \"173932.jpg\": \"Mollusca/Ariolimax columbianus/4327d6e705b159d12a4c2a290c6e0ce9.jpg\",\n  \"173940.jpg\": \"Mollusca/Ariolimax columbianus/03802e05d54e220d5c27ebc211283d5d.jpg\",\n  \"173942.jpg\": \"Mollusca/Ariolimax columbianus/2791b5712b399be8b486963f639ca7af.jpg\",\n  \"173957.jpg\": \"Mollusca/Ariolimax columbianus/7ebfc35205589e960d7c5403601882ab.jpg\",\n  \"173958.jpg\": \"Mollusca/Ariolimax columbianus/3571650fd81fe0603a99e91b591a005c.jpg\",\n  \"173963.jpg\": \"Mollusca/Ariolimax columbianus/05e1a523f0a8d23bc258bd92b6bd567b.jpg\",\n  \"173971.jpg\": \"Mollusca/Ariolimax columbianus/0bf9129d1add2709f2d7e646039a82b3.jpg\",\n  \"173979.jpg\": \"Mollusca/Ariolimax columbianus/d20af1193c100516f23b8769cddd97ff.jpg\",\n  \"173988.jpg\": \"Mollusca/Ariolimax columbianus/8cb4c3a6f67b2da4dd44ff89fdc07dd1.jpg\",\n  \"173991.jpg\": \"Mollusca/Ariolimax columbianus/afb3aa0af5859a259999e7a26fd3fb13.jpg\",\n  \"173995.jpg\": \"Mollusca/Ariolimax columbianus/b002f5d6db2a99658905a3a5ed10fb8c.jpg\",\n  \"173996.jpg\": \"Mollusca/Ariolimax columbianus/8d755bb1bc34afc09bf7e074ca267e4e.jpg\",\n  \"174002.jpg\": \"Mollusca/Ariolimax columbianus/6bf97c8ed3c28a1cb10916d1c1026e41.jpg\",\n  \"17402.jpg\": \"Aves/Buteo jamaicensis/7d74c659db09ab4b7563887e2e3b3ece.jpg\",\n  \"174056.jpg\": \"Mammalia/Marmota monax/e49361a93c372d1beb65c7cfad9c5ea4.jpg\",\n  \"174058.jpg\": \"Mammalia/Marmota monax/e03091f1e683c15f4b495b5a93e2351b.jpg\",\n  \"174067.jpg\": \"Mammalia/Marmota monax/f8d0a7c7c75079a0a0d8fde4eac6d768.jpg\",\n  \"174069.jpg\": \"Mammalia/Marmota monax/cff4438eb087a1b470ddd8513178c756.jpg\",\n  \"174075.jpg\": \"Mammalia/Marmota monax/0bbadcb5857454b1cff3284b5c2bdf09.jpg\",\n  \"174077.jpg\": \"Mammalia/Marmota monax/994b131bb4590ba3b88b8b5200128428.jpg\",\n  \"174079.jpg\": \"Mammalia/Marmota monax/7a3d64a913db3f47cfb5c0b030e6dabe.jpg\",\n  \"174093.jpg\": \"Mammalia/Marmota monax/61d4e70646b07f795326543566d3443b.jpg\",\n  \"174114.jpg\": \"Mammalia/Marmota monax/804c33ed81afff8728d0fdef13a504fa.jpg\",\n  \"174116.jpg\": \"Mammalia/Marmota monax/bdc5984f6b89473e1a0b63f686a02dcc.jpg\",\n  \"174125.jpg\": \"Mammalia/Marmota monax/fdad69405abb06db9d72a67d2dd7c88d.jpg\",\n  \"174162.jpg\": \"Mammalia/Marmota monax/d850a2893932edb7473be001e40ed079.jpg\",\n  \"174175.jpg\": \"Mammalia/Marmota monax/ad39b35146be53e466c6eec1bbaedf36.jpg\",\n  \"174184.jpg\": \"Mammalia/Marmota monax/03b128644f5723fe6c3fa92110dc2bfa.jpg\",\n  \"174210.jpg\": \"Mammalia/Marmota monax/2a720e2d440ade8c7aeab3333da875f4.jpg\",\n  \"174231.jpg\": \"Mammalia/Marmota monax/b00bff0c468f75b2bfb3dce1c22c1b08.jpg\",\n  \"174242.jpg\": \"Mammalia/Marmota monax/64e321423d9853a43293ec9ac629f46d.jpg\",\n  \"174248.jpg\": \"Mammalia/Marmota monax/08ff1335b8de0d70f14140266abc09a1.jpg\",\n  \"174258.jpg\": \"Insecta/Biston betularia/7896709a28ef920be0b8f945e3a467e5.jpg\",\n  \"174260.jpg\": \"Insecta/Biston betularia/ef1c485220d0c395aec4eedc67e5aa04.jpg\",\n  \"174261.jpg\": \"Insecta/Biston betularia/c7c2e51b1e9c6d79fdb77253251f679c.jpg\",\n  \"174262.jpg\": \"Insecta/Biston betularia/38952707a8d3261bc3949326d7f6888e.jpg\",\n  \"174265.jpg\": \"Insecta/Biston betularia/e4256476331fd97804d02a7d4f5655a8.jpg\",\n  \"174270.jpg\": \"Insecta/Biston betularia/32b2381f2ff12f6c75b5f6de8c8c5e22.jpg\",\n  \"174271.jpg\": \"Insecta/Biston betularia/06e08f6be01d2c1efe4d791b7ec55859.jpg\",\n  \"174281.jpg\": \"Insecta/Biston betularia/282a2a0da460d839e4531adad7dacb9e.jpg\",\n  \"174285.jpg\": \"Reptilia/Phrynosoma hernandesi/12c6da93fd6a2fafad9899dd99724e40.jpg\",\n  \"174291.jpg\": \"Reptilia/Phrynosoma hernandesi/daad2ddb4d0d2df9a39f698f41b09f84.jpg\",\n  \"174292.jpg\": \"Reptilia/Phrynosoma hernandesi/261cd6fcfc5c152f9567a61cc4cebeb9.jpg\",\n  \"1743.jpg\": \"Reptilia/Sceloporus orcutti/6806aad0f237ceaf3e8fc81fad5e60e1.jpg\",\n  \"174302.jpg\": \"Reptilia/Phrynosoma hernandesi/4611a235bc5760792e5e15ee555cdf17.jpg\",\n  \"174303.jpg\": \"Reptilia/Phrynosoma hernandesi/24753dd4dde63df783854fa0ade40963.jpg\",\n  \"174304.jpg\": \"Reptilia/Phrynosoma hernandesi/d98e0ad87f5ae9975570b75f52a6905e.jpg\",\n  \"174312.jpg\": \"Reptilia/Phrynosoma hernandesi/15a1fed59d244e3ba7c876d8a618bbe2.jpg\",\n  \"174314.jpg\": \"Reptilia/Phrynosoma hernandesi/d530d00f469fae15dfeb03ec910ce30a.jpg\",\n  \"174316.jpg\": \"Reptilia/Phrynosoma hernandesi/4427f5f65b828600a998a177843aae7e.jpg\",\n  \"174317.jpg\": \"Reptilia/Phrynosoma hernandesi/aa5e5a33d1747e828b8abb9c6636d7b9.jpg\",\n  \"174322.jpg\": \"Reptilia/Phrynosoma hernandesi/d5527d37c04938b6ba8cd387c9ffa217.jpg\",\n  \"174323.jpg\": \"Reptilia/Phrynosoma hernandesi/f706fc262ae836de875efaf50e4d0465.jpg\",\n  \"174334.jpg\": \"Reptilia/Phrynosoma hernandesi/6ccc3eb986a5ac81d439a273ac39124e.jpg\",\n  \"174335.jpg\": \"Reptilia/Phrynosoma hernandesi/ff0fd6ac9e392ce29fdfdd213adaa68e.jpg\",\n  \"174341.jpg\": \"Reptilia/Phrynosoma hernandesi/b58041608f5278396a3d048ae476143c.jpg\",\n  \"174342.jpg\": \"Reptilia/Phrynosoma hernandesi/3722abed6a97a7dab8b527c6b484ad00.jpg\",\n  \"174344.jpg\": \"Reptilia/Phrynosoma hernandesi/89b14024ed4df5f4193e780c3af9f23c.jpg\",\n  \"174349.jpg\": \"Reptilia/Phrynosoma hernandesi/9e117d3256517806fb6d23b5527cb8b7.jpg\",\n  \"174357.jpg\": \"Reptilia/Phrynosoma hernandesi/9db4aaac7ff45e63df1945efab230b9a.jpg\",\n  \"174367.jpg\": \"Reptilia/Phrynosoma hernandesi/f12634db79f04f6822894b33c7d829a6.jpg\",\n  \"174613.jpg\": \"Insecta/Schinia florida/de228a3ce7713f0ba266b17fbb18d1fe.jpg\",\n  \"174614.jpg\": \"Insecta/Schinia florida/bc14f54c7b89c76c7bef2474cb4aa280.jpg\",\n  \"174616.jpg\": \"Insecta/Schinia florida/9fde5e8a636910fe68a842928881447f.jpg\",\n  \"174619.jpg\": \"Insecta/Schinia florida/dd6dc039454da27e24dba076bfd67dff.jpg\",\n  \"174622.jpg\": \"Insecta/Schinia florida/1fce0be31e861f3ebc813266aa811439.jpg\",\n  \"174634.jpg\": \"Aves/Phoenicurus phoenicurus/29d120fcf2306f888203cd05f01bea3c.jpg\",\n  \"174635.jpg\": \"Aves/Phoenicurus phoenicurus/dc3f2a60cbd5170007510e9e57151e55.jpg\",\n  \"174637.jpg\": \"Aves/Phoenicurus phoenicurus/b9aa7bca5985230d032c0e922c33386f.jpg\",\n  \"174638.jpg\": \"Aves/Phoenicurus phoenicurus/d19a84086b433cf3b6d34e2dae0cad11.jpg\",\n  \"174652.jpg\": \"Aves/Phoenicurus phoenicurus/4dbfde620e2117939c67a2bb4745d64d.jpg\",\n  \"174655.jpg\": \"Aves/Phoenicurus phoenicurus/d45d36c8282085891ebb91a650471f08.jpg\",\n  \"174663.jpg\": \"Aves/Oenanthe oenanthe/ac37f21c38da77666b6bb726e71cb28f.jpg\",\n  \"174671.jpg\": \"Aves/Oenanthe oenanthe/c28e08da4706d636b3fa4db043c555e5.jpg\",\n  \"174677.jpg\": \"Aves/Oenanthe oenanthe/b96d581a2a614f7d4bffed18c9687fbe.jpg\",\n  \"174685.jpg\": \"Aves/Oenanthe oenanthe/3532e116a4d27699841f5ee740a5388b.jpg\",\n  \"174689.jpg\": \"Aves/Oenanthe oenanthe/3bc540d77ab6853cbdd97233bdd81457.jpg\",\n  \"174690.jpg\": \"Aves/Oenanthe oenanthe/1b0ecad3d34e7b49e1789026cd867008.jpg\",\n  \"174692.jpg\": \"Aves/Oenanthe oenanthe/9032c690e5254fd8cd36b73d0f61a9c0.jpg\",\n  \"174695.jpg\": \"Aves/Oenanthe oenanthe/c638ad4b4f3754a944bd831ad25c7bc5.jpg\",\n  \"174696.jpg\": \"Aves/Oenanthe oenanthe/16fca8c50a8e847400ee940d1d035c5e.jpg\",\n  \"174702.jpg\": \"Aves/Oenanthe oenanthe/08f3aaaff84a9b96e7635a77c5aff474.jpg\",\n  \"174703.jpg\": \"Aves/Oenanthe oenanthe/af231c7cb2cbb98a1d139d25e9e4450d.jpg\",\n  \"174704.jpg\": \"Aves/Oenanthe oenanthe/5a898752f6efd989508379f909a7269d.jpg\",\n  \"174707.jpg\": \"Aves/Oenanthe oenanthe/06592e1f81e19db9858c61f764d72ecb.jpg\",\n  \"174708.jpg\": \"Aves/Oenanthe oenanthe/771fca6fff2b527448a7f5a65fb4b398.jpg\",\n  \"174709.jpg\": \"Aves/Oenanthe oenanthe/b4661e69e741dd3943f48509eb8f8610.jpg\",\n  \"174711.jpg\": \"Aves/Oenanthe oenanthe/cc4202af34c03391be167107cdbe2c6c.jpg\",\n  \"174714.jpg\": \"Aves/Oenanthe oenanthe/ed9d806281b895ac8ec6051a1c121a8b.jpg\",\n  \"174718.jpg\": \"Aves/Oenanthe oenanthe/c07cb0db6416c0714fc783a7cc81df44.jpg\",\n  \"174724.jpg\": \"Aves/Oenanthe oenanthe/7c25c68768c15d29f0c3ef63d22bec5a.jpg\",\n  \"174725.jpg\": \"Aves/Oenanthe oenanthe/c409bb2d492b3f6ef2f3be1d228e3f9a.jpg\",\n  \"174726.jpg\": \"Aves/Oenanthe oenanthe/ec854f054ee4148cd72b907ed6249675.jpg\",\n  \"174727.jpg\": \"Aves/Oenanthe oenanthe/d0fa6a9e923d763965b2985ea3201063.jpg\",\n  \"174731.jpg\": \"Aves/Oenanthe oenanthe/5fefeb0ad99e412fc191fa6a065bdbdb.jpg\",\n  \"174773.jpg\": \"Aves/Terathopius ecaudatus/10309e39750138bafa5e777c1ce2630d.jpg\",\n  \"174775.jpg\": \"Aves/Terathopius ecaudatus/ab1c3a7c7f4b0578ec971241b165ed31.jpg\",\n  \"174777.jpg\": \"Aves/Terathopius ecaudatus/39e90e8c7b6192358bc956dc66acb2ae.jpg\",\n  \"174787.jpg\": \"Amphibia/Anaxyrus terrestris/7afb14d51a8ddb749c5bbd40866c8644.jpg\",\n  \"174791.jpg\": \"Amphibia/Anaxyrus terrestris/c7d1b275f1b008ed91bee7ce83c87309.jpg\",\n  \"174792.jpg\": \"Amphibia/Anaxyrus terrestris/bc5d5dbb8ba09455dda471a98d6abb2a.jpg\",\n  \"174807.jpg\": \"Amphibia/Anaxyrus terrestris/8c48112917a34047a5e22f3f5bfc1fc5.jpg\",\n  \"174808.jpg\": \"Amphibia/Anaxyrus terrestris/5b5344b42ef667f97b96e30e4c8e9dca.jpg\",\n  \"174810.jpg\": \"Amphibia/Anaxyrus terrestris/c98556d4e5bd2259a0758e5ff9ba0aa8.jpg\",\n  \"174812.jpg\": \"Amphibia/Anaxyrus terrestris/78a11d5b9ed2bfda271a373aa42aa1c5.jpg\",\n  \"174817.jpg\": \"Amphibia/Anaxyrus terrestris/b56a717f16c0da50b11178676eab30f8.jpg\",\n  \"174818.jpg\": \"Amphibia/Anaxyrus terrestris/f0d6cd884f5ec94e3f7f70f7f0f11406.jpg\",\n  \"174819.jpg\": \"Amphibia/Anaxyrus terrestris/5a114cd83c822f0043656cc190c2ff66.jpg\",\n  \"174842.jpg\": \"Amphibia/Batrachoseps attenuatus/d10cb3785aef8b7eecd4cbe020865e5e.jpg\",\n  \"174843.jpg\": \"Amphibia/Batrachoseps attenuatus/768d8300bddfffef811feeb4cf7c5d82.jpg\",\n  \"174857.jpg\": \"Amphibia/Batrachoseps attenuatus/8933589c024e9c19af230d0a34adc145.jpg\",\n  \"174867.jpg\": \"Amphibia/Batrachoseps attenuatus/e1a0741223243094bcefa3fe5fc49a46.jpg\",\n  \"1749.jpg\": \"Reptilia/Sceloporus orcutti/408907ed790f7f8dd4c2cb1684216632.jpg\",\n  \"174916.jpg\": \"Amphibia/Batrachoseps attenuatus/222ec4cd730e0c8e2ea2e3954d3758a0.jpg\",\n  \"174936.jpg\": \"Amphibia/Batrachoseps attenuatus/0127d1cc7a3b924f1b50a6a134e191fb.jpg\",\n  \"174939.jpg\": \"Amphibia/Batrachoseps attenuatus/61f02920603e94f0ac6181393126339f.jpg\",\n  \"174959.jpg\": \"Amphibia/Batrachoseps attenuatus/c08a75c69ae2e6c99268f5d4a109d1dd.jpg\",\n  \"174979.jpg\": \"Amphibia/Batrachoseps attenuatus/f6b2765021cc61622393df340e06f947.jpg\",\n  \"1750.jpg\": \"Reptilia/Sceloporus orcutti/8f153ff4ed9bc5db209d36f822a9a735.jpg\",\n  \"175021.jpg\": \"Amphibia/Batrachoseps attenuatus/4ebd71b9874fe0884bccc6f0dab71f01.jpg\",\n  \"175023.jpg\": \"Amphibia/Batrachoseps attenuatus/a5404b3f9f1d2c47042af16aad2a8911.jpg\",\n  \"175067.jpg\": \"Amphibia/Batrachoseps attenuatus/6ed018355f460f4226f4f22d2e99887f.jpg\",\n  \"175074.jpg\": \"Amphibia/Batrachoseps attenuatus/68f64aa2af6c0bce6022df7694898482.jpg\",\n  \"175086.jpg\": \"Amphibia/Batrachoseps attenuatus/61c03f313bfdd1d81852b84aa9fc3e84.jpg\",\n  \"175092.jpg\": \"Amphibia/Batrachoseps attenuatus/a7e13a35b06c7fb20dfee9c3555e3f19.jpg\",\n  \"1751.jpg\": \"Reptilia/Sceloporus orcutti/61bfd01b822ff23a4fb4d6ab4201ce86.jpg\",\n  \"175111.jpg\": \"Amphibia/Batrachoseps attenuatus/be9ebda5d12e5c5cbca1ee579f6b0c5e.jpg\",\n  \"175129.jpg\": \"Amphibia/Batrachoseps attenuatus/18fb4177c10a112aaaaba4be07ff5fe0.jpg\",\n  \"175148.jpg\": \"Amphibia/Batrachoseps attenuatus/de641ab3aab39e4f980910d3fbf3dd15.jpg\",\n  \"175161.jpg\": \"Amphibia/Batrachoseps attenuatus/dd012450dc8d8071b614b494df1b8ac7.jpg\",\n  \"175187.jpg\": \"Amphibia/Batrachoseps attenuatus/d34485cbc5fe3c15f554098169c2bb4f.jpg\",\n  \"1752.jpg\": \"Reptilia/Sceloporus orcutti/b5539b2abef150cda2e16dc5ed24329e.jpg\",\n  \"175228.jpg\": \"Amphibia/Batrachoseps attenuatus/b3dfad64223c4778d4c33c1a445fb450.jpg\",\n  \"175268.jpg\": \"Reptilia/Nerodia sipedon/4df1e07b4bcf29791e6a09cd5d384a71.jpg\",\n  \"175270.jpg\": \"Reptilia/Nerodia sipedon/2a75f6b0e160959a5139a8dd70082d65.jpg\",\n  \"175272.jpg\": \"Reptilia/Nerodia sipedon/fc8114394fa5c699fa3ee6050be9695d.jpg\",\n  \"175276.jpg\": \"Reptilia/Nerodia sipedon/3b6412b16babcf3e7ea5e0072fe243e6.jpg\",\n  \"175286.jpg\": \"Reptilia/Nerodia sipedon/1161089aab4a9ab60400f4849f9ee174.jpg\",\n  \"175294.jpg\": \"Reptilia/Nerodia sipedon/7f0f2a8fce512d42b925145cd2ee194d.jpg\",\n  \"175296.jpg\": \"Reptilia/Nerodia sipedon/d460e30cbb184950dd29c1fb79e5acc6.jpg\",\n  \"175298.jpg\": \"Reptilia/Nerodia sipedon/d64b20974ac5969e02a5c21460e7b8c8.jpg\",\n  \"1753.jpg\": \"Reptilia/Sceloporus orcutti/8e08e31327250d069fc5ce87f0f79aab.jpg\",\n  \"175302.jpg\": \"Reptilia/Nerodia sipedon/41f007bfde93a1f464cab4e621e2d2ca.jpg\",\n  \"175312.jpg\": \"Reptilia/Nerodia sipedon/dd5aab3bd47fdf9e82a80e0bc7695410.jpg\",\n  \"175315.jpg\": \"Reptilia/Nerodia sipedon/6484f2f503cd6350edb9f5997d55f1cd.jpg\",\n  \"175317.jpg\": \"Reptilia/Nerodia sipedon/2d79fdca1fea05c09f2547377d6e5e53.jpg\",\n  \"175323.jpg\": \"Reptilia/Nerodia sipedon/9787701d2ed74026fc8b1e319fda77c8.jpg\",\n  \"175324.jpg\": \"Reptilia/Nerodia sipedon/974e1048bdafd109f72e33b7676f8dcb.jpg\",\n  \"175336.jpg\": \"Reptilia/Nerodia sipedon/018200d60e7de7bda8653713d67bc057.jpg\",\n  \"175343.jpg\": \"Reptilia/Nerodia sipedon/17d7deeb33ee5e2d4896deb78e23befa.jpg\",\n  \"175355.jpg\": \"Reptilia/Nerodia sipedon/4ceacacab81d6f46ea9b0d4154a9f91f.jpg\",\n  \"175378.jpg\": \"Reptilia/Nerodia sipedon/5f86845610ded24c44c5adfa52223418.jpg\",\n  \"1754.jpg\": \"Reptilia/Sceloporus orcutti/182dca5637aff3e188f3779e3584c3e7.jpg\",\n  \"175402.jpg\": \"Reptilia/Nerodia sipedon/773e29b5aae68a95e311f4feacf96e6b.jpg\",\n  \"175408.jpg\": \"Insecta/Microcentrum rhombifolium/8658ade9e7b01b4b7b93cc7b9346b239.jpg\",\n  \"175412.jpg\": \"Insecta/Microcentrum rhombifolium/288b4a8f5fbc00faf24079cda36feb41.jpg\",\n  \"175414.jpg\": \"Insecta/Microcentrum rhombifolium/e9063195c7cb2798b99e81347713e9cb.jpg\",\n  \"175485.jpg\": \"Insecta/Lycomorpha pholus/00119b5ce2386fd1f2cf455eae047884.jpg\",\n  \"175491.jpg\": \"Insecta/Lycomorpha pholus/32c88ac47f4afae8028b27e8b282e245.jpg\",\n  \"175494.jpg\": \"Insecta/Lycomorpha pholus/b6b0d68ba3264ee7dd4dba9c1b2ade34.jpg\",\n  \"175496.jpg\": \"Insecta/Lycomorpha pholus/ad8bc737949cad8668ccf5cf2b8f23df.jpg\",\n  \"175522.jpg\": \"Insecta/Argia sedula/96685e26f094d5ab0162eb6af3169f1a.jpg\",\n  \"175528.jpg\": \"Insecta/Argia sedula/8c9ba99c82a60436f9bc5960115cfbb7.jpg\",\n  \"175529.jpg\": \"Insecta/Argia sedula/c4dc16812c87ee4e7354a312b3c2d752.jpg\",\n  \"175534.jpg\": \"Insecta/Argia sedula/d3ff98be60af6be24f9f68e9bbfa208b.jpg\",\n  \"175557.jpg\": \"Insecta/Argia sedula/043b4556881294162404baff1a114c48.jpg\",\n  \"175573.jpg\": \"Insecta/Argia sedula/e9c77cddd7139eaf199cd6982c14917c.jpg\",\n  \"175575.jpg\": \"Insecta/Argia sedula/5f5dd3c07b9d1d12b6a7cb8d2196a714.jpg\",\n  \"175576.jpg\": \"Insecta/Argia sedula/f0e4415d387b1e48512d8cb1f6b18a14.jpg\",\n  \"175583.jpg\": \"Insecta/Argia sedula/91fb9662e25c036b2751b67e5fa96b83.jpg\",\n  \"175596.jpg\": \"Insecta/Argia sedula/6a7cd68aac7fd85c8475be5e0db7e37c.jpg\",\n  \"175597.jpg\": \"Insecta/Argia sedula/b9362bb9a6ee64f3a92193bbc5607cdc.jpg\",\n  \"175613.jpg\": \"Insecta/Argia sedula/179da5673a26d85e106957152ac38d37.jpg\",\n  \"175619.jpg\": \"Insecta/Argia sedula/5437daa5f2a6d5e5eddc4728c76aca99.jpg\",\n  \"175628.jpg\": \"Insecta/Argia sedula/3189b984c5c944e5be9769b65b4cbfd4.jpg\",\n  \"175634.jpg\": \"Insecta/Argia sedula/f5284b0d94e3ab9dc1af27bba93a7306.jpg\",\n  \"175645.jpg\": \"Insecta/Argia sedula/5010b939ca74a23e2cedb83d49519123.jpg\",\n  \"175652.jpg\": \"Insecta/Argia sedula/2a25b8e6618b4f6daf34e1af27ba24e8.jpg\",\n  \"175658.jpg\": \"Insecta/Argia sedula/baef36549f46b3df97beab9d9b12e45e.jpg\",\n  \"175659.jpg\": \"Insecta/Argia sedula/4ddf5eba5e11d5caf357a89497d8387b.jpg\",\n  \"175660.jpg\": \"Insecta/Argia sedula/b85d869870f7c7d8816044fe26f43602.jpg\",\n  \"175661.jpg\": \"Insecta/Argia sedula/27965cbefe4a70ae9d2533312b660831.jpg\",\n  \"175665.jpg\": \"Insecta/Argia sedula/3035b90b6de7b1e39143d34c18a13738.jpg\",\n  \"175667.jpg\": \"Insecta/Argia sedula/bfe93c0013f97b81ab05ab9301a94384.jpg\",\n  \"17567.jpg\": \"Aves/Buteo jamaicensis/d6d4b1df67fe54cf1ed69594fd4ccfaf.jpg\",\n  \"175670.jpg\": \"Insecta/Argia sedula/c8775a63c97ab2c9cf320c590bc35bc9.jpg\",\n  \"175742.jpg\": \"Insecta/Achlyodes pallida/00e97764cf4a054681c347d70ef322c4.jpg\",\n  \"175744.jpg\": \"Insecta/Achlyodes pallida/d83a164be75846af5d0da46eef716823.jpg\",\n  \"175746.jpg\": \"Insecta/Achlyodes pallida/a9f8882b046d86e07072808481d5587a.jpg\",\n  \"175747.jpg\": \"Insecta/Achlyodes pallida/94e3e49463aa89fe2cff4820f921de21.jpg\",\n  \"175750.jpg\": \"Insecta/Achlyodes pallida/a98de76f586f526296c81c1bdd054773.jpg\",\n  \"175757.jpg\": \"Insecta/Achlyodes pallida/0ebdf384d6cfcb6e47220bf2646ac37f.jpg\",\n  \"175758.jpg\": \"Insecta/Achlyodes pallida/e9726d8c5005a4f0385ab729747b36b4.jpg\",\n  \"1758.jpg\": \"Insecta/Trimerotropis pallidipennis/180d5747ebe28594342836e0a0348db2.jpg\",\n  \"175812.jpg\": \"Reptilia/Aspidoscelis hyperythra/66d0d98138ad3b1fc7f7517f9d04fb1d.jpg\",\n  \"175815.jpg\": \"Reptilia/Aspidoscelis hyperythra/185ef370b7055c0a3b3734551bc62529.jpg\",\n  \"175818.jpg\": \"Reptilia/Aspidoscelis hyperythra/820fa0bc28b06c4445a20f17a42ee67e.jpg\",\n  \"175819.jpg\": \"Reptilia/Aspidoscelis hyperythra/2e35fb65b7907e0aa321e93fa18956dd.jpg\",\n  \"175826.jpg\": \"Reptilia/Aspidoscelis hyperythra/7659c4125b4f15eff653904d62cc76de.jpg\",\n  \"175968.jpg\": \"Insecta/Papilio multicaudata/e44e9954d8af2815386ba3ded4eee8e2.jpg\",\n  \"175969.jpg\": \"Insecta/Papilio multicaudata/99b5c8a411dbe17d7751b1e29964fadc.jpg\",\n  \"175970.jpg\": \"Insecta/Papilio multicaudata/f79a78e91f1a2f2b097705a01cde1e83.jpg\",\n  \"175974.jpg\": \"Insecta/Papilio multicaudata/9547ecc3fef9d2a642f5a449a25ca905.jpg\",\n  \"175975.jpg\": \"Insecta/Papilio multicaudata/0b06bf0fbd28802b1ccbfb47abed5e4f.jpg\",\n  \"175984.jpg\": \"Insecta/Papilio multicaudata/79d09a5cc5a0474718ed01c9cc314a78.jpg\",\n  \"175989.jpg\": \"Insecta/Papilio multicaudata/af73fcea234e9d178084f2af5a60d6c2.jpg\",\n  \"175992.jpg\": \"Insecta/Papilio multicaudata/3cf42e6086bc64caeea096009cfe3960.jpg\",\n  \"175993.jpg\": \"Insecta/Papilio multicaudata/aaab9dcaf753f9bc1cae82bca36c34a8.jpg\",\n  \"176.jpg\": \"Aves/Zonotrichia capensis/cd7201ddb1094e7e4cf374e35b276dce.jpg\",\n  \"176005.jpg\": \"Insecta/Papilio multicaudata/53ec8140f751902adc734e254b702def.jpg\",\n  \"176006.jpg\": \"Insecta/Papilio multicaudata/b034a5e0d2bb2bc03022bcfbbe5f68a1.jpg\",\n  \"176008.jpg\": \"Insecta/Papilio multicaudata/3ea8014070fc94d08fc0dc5b688494ea.jpg\",\n  \"176009.jpg\": \"Insecta/Papilio multicaudata/92bf904bcf66c5c8f263fd7f6459cc1f.jpg\",\n  \"176010.jpg\": \"Insecta/Papilio multicaudata/abf939d2394faa09e12cbe4745f12058.jpg\",\n  \"176030.jpg\": \"Insecta/Papilio multicaudata/f04c349e2b05cd801be0c7ec1a0197a3.jpg\",\n  \"176034.jpg\": \"Insecta/Papilio multicaudata/12034c1e4500f3e382c1905a4856d457.jpg\",\n  \"176037.jpg\": \"Insecta/Papilio multicaudata/ec923bf7a4f2a8470cd31875be79ad44.jpg\",\n  \"176043.jpg\": \"Insecta/Papilio multicaudata/b8a2610c5562247d7934382a3cdc751a.jpg\",\n  \"176045.jpg\": \"Insecta/Papilio multicaudata/81775bd1207429ae08a069f8b25c3f46.jpg\",\n  \"176052.jpg\": \"Insecta/Papilio multicaudata/36f0b487ca839a5d4e4c3ffcd87cf5ce.jpg\",\n  \"176055.jpg\": \"Insecta/Campaea perlata/8a4fe12834e9f452a5643a850eae229f.jpg\",\n  \"176059.jpg\": \"Insecta/Campaea perlata/5853b9519e25a32530d0eed5e4b9c8f4.jpg\",\n  \"176067.jpg\": \"Insecta/Campaea perlata/f1fdb7fe868a5b561970c5538a5a9efd.jpg\",\n  \"176075.jpg\": \"Insecta/Campaea perlata/4f9906217f9078a4515c67372d69533a.jpg\",\n  \"176081.jpg\": \"Insecta/Campaea perlata/b153b3c79d106fc97ae63d92dbf5c244.jpg\",\n  \"176091.jpg\": \"Insecta/Campaea perlata/2ba988ac2c3930308944dbb722932204.jpg\",\n  \"176092.jpg\": \"Insecta/Campaea perlata/972f491938aec945ce138b20a9eb98eb.jpg\",\n  \"176095.jpg\": \"Insecta/Campaea perlata/5fbdb23c8abbab67ca10fd5e7e612b78.jpg\",\n  \"176097.jpg\": \"Insecta/Campaea perlata/bf043c13cf1fec929ff446b38717fe0d.jpg\",\n  \"176104.jpg\": \"Insecta/Campaea perlata/1c4d4f18a6956fc15335adf0fe89aece.jpg\",\n  \"176105.jpg\": \"Insecta/Campaea perlata/0b266eda47a6b0b9e2458431b58503d7.jpg\",\n  \"176111.jpg\": \"Insecta/Campaea perlata/a52ef144d25a5448887eba43de9e6628.jpg\",\n  \"176113.jpg\": \"Insecta/Campaea perlata/01004d6744b52b5cce431f10ba23a959.jpg\",\n  \"176119.jpg\": \"Insecta/Campaea perlata/8a52cfdb2847955e32f5ac8fc883c04c.jpg\",\n  \"176122.jpg\": \"Insecta/Campaea perlata/23e12e4a67f963e1ed52d56e24b6d395.jpg\",\n  \"176128.jpg\": \"Insecta/Campaea perlata/75f5e2b367ba3420999597372580883d.jpg\",\n  \"176142.jpg\": \"Insecta/Campaea perlata/ee934acf22de30dc74eaf2f5e984702d.jpg\",\n  \"176150.jpg\": \"Insecta/Campaea perlata/27fb877dc14745a6051f630288524daf.jpg\",\n  \"176151.jpg\": \"Insecta/Campaea perlata/df9671e8a832e8a5e6c4f6fe6041f839.jpg\",\n  \"176153.jpg\": \"Insecta/Campaea perlata/4653b5bb2dfc94f475d0d17b77bdd5dc.jpg\",\n  \"176161.jpg\": \"Amphibia/Anaxyrus debilis/45baabcb5a1c76dc6dbcdcb22d0fc763.jpg\",\n  \"176169.jpg\": \"Amphibia/Anaxyrus debilis/873308c22f44fb4ee745bb43ce7f7723.jpg\",\n  \"176170.jpg\": \"Amphibia/Anaxyrus debilis/9b720657f8926795b3e4a6fda2b9e618.jpg\",\n  \"176177.jpg\": \"Amphibia/Anaxyrus debilis/c21dd7145d6a673352642d8b76542ab1.jpg\",\n  \"176181.jpg\": \"Amphibia/Anaxyrus debilis/8c904f5adc4f41397679ebd3b268b460.jpg\",\n  \"176182.jpg\": \"Amphibia/Anaxyrus debilis/ca1e8b37627fcb5e6c3eabdd8a6894d0.jpg\",\n  \"176258.jpg\": \"Insecta/Grammia ornata/5ae4d3aa9fbe3261d89f2d1cc98d6595.jpg\",\n  \"176260.jpg\": \"Insecta/Grammia ornata/4128dd6440f0c2986e3890aa634af67d.jpg\",\n  \"176266.jpg\": \"Insecta/Grammia ornata/f480228234b64f0196652c12a0380b0d.jpg\",\n  \"176268.jpg\": \"Insecta/Grammia ornata/54be29115be4bd7f8c9e5c3f3e14d524.jpg\",\n  \"176274.jpg\": \"Insecta/Grammia ornata/6e8ac625d7bb515b4792d3fd1ff98e63.jpg\",\n  \"176279.jpg\": \"Insecta/Grammia ornata/c5e68a658c67b982ed7daa910dbd1800.jpg\",\n  \"176282.jpg\": \"Insecta/Marpesia petreus/d27cd9e56f42ce0d7f1f31df8ffb2416.jpg\",\n  \"176289.jpg\": \"Insecta/Marpesia petreus/0622848097c8a6a604bc9948de4fb6b2.jpg\",\n  \"176290.jpg\": \"Insecta/Marpesia petreus/aad61f656a6d7b1d9ccad26c285cb46c.jpg\",\n  \"176296.jpg\": \"Insecta/Marpesia petreus/3672d8ea9ca1e6fad199b16deeb92ef5.jpg\",\n  \"176302.jpg\": \"Insecta/Marpesia petreus/e1584490038f0c72fc9de4717714e6ee.jpg\",\n  \"176303.jpg\": \"Insecta/Marpesia petreus/1d1c84de992e8d65a72a4325dcd2f0aa.jpg\",\n  \"176304.jpg\": \"Insecta/Marpesia petreus/4117c11ff1df4f61e7b45fcea3cde499.jpg\",\n  \"176308.jpg\": \"Insecta/Marpesia petreus/ad1cd0629a91c88d004a83e7c011b72e.jpg\",\n  \"176309.jpg\": \"Insecta/Marpesia petreus/d1ac151c87ff419516dc99be96f641e7.jpg\",\n  \"176317.jpg\": \"Insecta/Marpesia petreus/c5c4bc93ccdb3c55837696f2b4452ac2.jpg\",\n  \"176319.jpg\": \"Insecta/Marpesia petreus/61f6ffca3ae03e9b2e2bf3ee93cdb61b.jpg\",\n  \"176325.jpg\": \"Insecta/Marpesia petreus/60d2e612fb6d8f86dcd9ee0e6099cd3e.jpg\",\n  \"176329.jpg\": \"Insecta/Marpesia petreus/74d1955cfc33cfa8417e9fd50b4d64b5.jpg\",\n  \"176335.jpg\": \"Insecta/Marpesia petreus/d4f6d9441daf2f992413101bb4a7b590.jpg\",\n  \"176336.jpg\": \"Insecta/Marpesia petreus/769756267b079133ad0a30bc2af4a7d1.jpg\",\n  \"176337.jpg\": \"Insecta/Marpesia petreus/10c92c5686abe8e018f0768d48efc05c.jpg\",\n  \"176342.jpg\": \"Insecta/Marpesia petreus/50c40524506a489a3a2d9cd7d3a7b502.jpg\",\n  \"176343.jpg\": \"Insecta/Marpesia petreus/e85a201c718d57a62e438f668f94851b.jpg\",\n  \"176351.jpg\": \"Insecta/Marpesia petreus/7bcc7d1b9ea8fbc6d0d85e0e210b74e8.jpg\",\n  \"176357.jpg\": \"Insecta/Marpesia petreus/a7708f1a1a21d001ea847ed19c46413c.jpg\",\n  \"176395.jpg\": \"Aves/Contopus pertinax/82e4832ebfe1b38cc401e6591e3fc8eb.jpg\",\n  \"176398.jpg\": \"Aves/Contopus pertinax/a3a83e62efdd11567c3e7409fb8f41af.jpg\",\n  \"176401.jpg\": \"Aves/Contopus pertinax/251c09766245e15c3b0d31e5b7ca7f28.jpg\",\n  \"176414.jpg\": \"Aves/Contopus pertinax/191c12a28aee59d9807e1ef019cd2ff5.jpg\",\n  \"176415.jpg\": \"Aves/Contopus pertinax/ef160093fad800ef771b54bfd1e522f6.jpg\",\n  \"176417.jpg\": \"Insecta/Leptoglossus clypealis/7649fcee9c6fdbba2afeaa988c12f519.jpg\",\n  \"176419.jpg\": \"Insecta/Leptoglossus clypealis/fd20134a9cba7750e3ceb3135d380c0b.jpg\",\n  \"176426.jpg\": \"Insecta/Leptoglossus clypealis/a52c9488d5f9c7ef178198b31ac6bca4.jpg\",\n  \"176429.jpg\": \"Insecta/Leptoglossus clypealis/add2fc36dc282ce07463be63854ac69a.jpg\",\n  \"176433.jpg\": \"Insecta/Leptoglossus clypealis/d6e14af0aa1dbb22de56c8175ac87f48.jpg\",\n  \"176438.jpg\": \"Aves/Patagioenas fasciata/392ed8b397aafd242cbf457865b2cf5e.jpg\",\n  \"176441.jpg\": \"Aves/Patagioenas fasciata/9b509b0b2c0b60199e40393f4e820f6f.jpg\",\n  \"176442.jpg\": \"Aves/Patagioenas fasciata/fe8506f652b8899a3b45113edb9b51d5.jpg\",\n  \"176443.jpg\": \"Aves/Patagioenas fasciata/dd00b799a6cfe833b33146919a6ef818.jpg\",\n  \"176444.jpg\": \"Aves/Patagioenas fasciata/f247a75bb69ee18fe73b8a1256cc52f5.jpg\",\n  \"176445.jpg\": \"Aves/Patagioenas fasciata/e4ab00033c61ce96bbd2021b1970242a.jpg\",\n  \"176446.jpg\": \"Aves/Patagioenas fasciata/8427b353e36f0e03e6161dbc96c0a95c.jpg\",\n  \"176452.jpg\": \"Aves/Patagioenas fasciata/62ffa83dd16510a397d9a64325bf5156.jpg\",\n  \"176471.jpg\": \"Aves/Patagioenas fasciata/4da9f96439a6a703afe1b304aaff0514.jpg\",\n  \"176475.jpg\": \"Aves/Patagioenas fasciata/7513111a307324ace05176b525e33efa.jpg\",\n  \"176478.jpg\": \"Aves/Patagioenas fasciata/15e6f7f42f1753bf7508191f42cacd69.jpg\",\n  \"176492.jpg\": \"Aves/Patagioenas fasciata/eca63bb51853b0c7ea3fe6bf469b458d.jpg\",\n  \"176495.jpg\": \"Aves/Patagioenas fasciata/6c246719a5a55c2ab120a1e1fb5c72e5.jpg\",\n  \"176496.jpg\": \"Aves/Patagioenas fasciata/dbc031de2f456a2ef3dd76747dc8568a.jpg\",\n  \"176501.jpg\": \"Aves/Patagioenas fasciata/c982884a64568fe453c6fc3f5eb4f2ac.jpg\",\n  \"176509.jpg\": \"Aves/Patagioenas fasciata/95485b71a8e203036b372b4d4055934c.jpg\",\n  \"176513.jpg\": \"Aves/Patagioenas fasciata/ea89f1a2eed9b878076be695a5ba0d7b.jpg\",\n  \"176527.jpg\": \"Aves/Patagioenas fasciata/9842e6d492578e052d5e105f95827cfd.jpg\",\n  \"176543.jpg\": \"Aves/Patagioenas fasciata/7897e50414eb103e8d3accbc6438817d.jpg\",\n  \"176544.jpg\": \"Aves/Patagioenas fasciata/7cd0a626d60916414ce9c9cac0e7b2b6.jpg\",\n  \"176559.jpg\": \"Aves/Patagioenas fasciata/b1bc510b14a7f7096f79cc0dcff6ecd6.jpg\",\n  \"176566.jpg\": \"Aves/Cyanocorax yucatanicus/0b922727ef7fd227d01382aeb2a59863.jpg\",\n  \"176567.jpg\": \"Aves/Cyanocorax yucatanicus/7e1eb6d33dad8e588b399ecfa4b92c88.jpg\",\n  \"176572.jpg\": \"Aves/Cyanocorax yucatanicus/dd72887e5926d64da2a00c969ee6bfe4.jpg\",\n  \"176575.jpg\": \"Aves/Cyanocorax yucatanicus/26f5e65136e90ba0f2eb3e3db647efc9.jpg\",\n  \"176576.jpg\": \"Aves/Cyanocorax yucatanicus/bc6a9e506218367a8b1a8d781fff01cb.jpg\",\n  \"176577.jpg\": \"Aves/Cyanocorax yucatanicus/73dd1f8d1928260d50d7fbb57a1680ab.jpg\",\n  \"176578.jpg\": \"Aves/Cyanocorax yucatanicus/529e1a902d6fd250e1a40ea9f660aa9b.jpg\",\n  \"176579.jpg\": \"Aves/Cyanocorax yucatanicus/d4703416386ee274e5732487b854a931.jpg\",\n  \"176591.jpg\": \"Aves/Cyanocorax yucatanicus/58857b2e9a8b2be87a98c7cd4f2062d5.jpg\",\n  \"1766.jpg\": \"Insecta/Trimerotropis pallidipennis/3708646a4acaf97e83c250bf1d174383.jpg\",\n  \"176647.jpg\": \"Insecta/Echinargus isola/d5c847494a2d35fdb3a9f1549b1bf479.jpg\",\n  \"176650.jpg\": \"Insecta/Echinargus isola/1963a0d0bd4fd5e7032f856f059f7137.jpg\",\n  \"176658.jpg\": \"Insecta/Echinargus isola/cb6d9daa18607667b04ef8f31899a9a3.jpg\",\n  \"176660.jpg\": \"Insecta/Echinargus isola/76ba1dc976d5d4dfd2c4afd7ef3b14cc.jpg\",\n  \"176665.jpg\": \"Insecta/Echinargus isola/1d588f3d8332f9d28e26565c87465ba6.jpg\",\n  \"176673.jpg\": \"Insecta/Echinargus isola/552f6fd4d7eeaee42fc176053cef98b9.jpg\",\n  \"176684.jpg\": \"Insecta/Echinargus isola/7e6ef416318bd77255d9edfb0c39441f.jpg\",\n  \"176689.jpg\": \"Insecta/Echinargus isola/3cb40e605f8fcac8b1d3b41212431972.jpg\",\n  \"176692.jpg\": \"Insecta/Echinargus isola/ad484e680a704f93b8da8e24d9c3fe83.jpg\",\n  \"176693.jpg\": \"Insecta/Echinargus isola/29c4bba9cc050fa5b29ac15652a7f2a9.jpg\",\n  \"176698.jpg\": \"Insecta/Echinargus isola/0598791e10db80eecff6dca0c14ac4cd.jpg\",\n  \"176717.jpg\": \"Insecta/Echinargus isola/701530c50aef5b54e827a2be4c0b40a8.jpg\",\n  \"176725.jpg\": \"Insecta/Echinargus isola/5b9f961e746b79328a8239eb04055606.jpg\",\n  \"176734.jpg\": \"Insecta/Echinargus isola/328c04775317dde1fdea4da4ddf28355.jpg\",\n  \"176737.jpg\": \"Insecta/Echinargus isola/0e0aaa80a3999d91e9e92af96993a40c.jpg\",\n  \"176750.jpg\": \"Insecta/Echinargus isola/4c78e13c879566d163693ac2bb837d07.jpg\",\n  \"176751.jpg\": \"Insecta/Echinargus isola/6fab7ae355927ed9b77f2ed55677b82b.jpg\",\n  \"176755.jpg\": \"Insecta/Echinargus isola/df3e6821ad10e21b5426676b3ce69931.jpg\",\n  \"176763.jpg\": \"Insecta/Echinargus isola/824743d0acc5df2efd8f808d4a2d6ae0.jpg\",\n  \"176764.jpg\": \"Insecta/Echinargus isola/78550cafc2d880494c2853136d3a19b1.jpg\",\n  \"176768.jpg\": \"Insecta/Echinargus isola/3bf49d75789659e18eae441652b1bc7f.jpg\",\n  \"176780.jpg\": \"Insecta/Echinargus isola/6b667218c4e44955d0aa30338368a97c.jpg\",\n  \"176785.jpg\": \"Insecta/Echinargus isola/18293ebf507d67ada198641ccc7fffaa.jpg\",\n  \"176803.jpg\": \"Animalia/Ligia occidentalis/07108963c2f3f051b34c7c8214e63fda.jpg\",\n  \"176811.jpg\": \"Animalia/Ligia occidentalis/46c2d8f56858d8b051562f0927872176.jpg\",\n  \"176812.jpg\": \"Animalia/Ligia occidentalis/9cc9835e817439020d82c3c28b73bc4f.jpg\",\n  \"176813.jpg\": \"Animalia/Ligia occidentalis/d1cef3b38fc7a1b05cceb65ce59cf955.jpg\",\n  \"176815.jpg\": \"Animalia/Ligia occidentalis/7965dfdec4c28c89ab0077976545839e.jpg\",\n  \"176816.jpg\": \"Animalia/Ligia occidentalis/0b37dc9ce7ee4c3631b2b8ad3fae38c1.jpg\",\n  \"176818.jpg\": \"Animalia/Ligia occidentalis/56a0fcd961c73ba9fedbb3d305d4b93d.jpg\",\n  \"176819.jpg\": \"Animalia/Ligia occidentalis/f9f44735fa4bdeaa2ea0512c3e47e80e.jpg\",\n  \"176820.jpg\": \"Animalia/Ligia occidentalis/b836e6f82086608c97d79945ab5def02.jpg\",\n  \"176825.jpg\": \"Animalia/Ligia occidentalis/22fa103b30587dc47f5017751448cbfb.jpg\",\n  \"176827.jpg\": \"Animalia/Ligia occidentalis/8dd4c5491c6a722ed75aa583411fcf6e.jpg\",\n  \"176828.jpg\": \"Animalia/Ligia occidentalis/f6521ed94b3857ad0e396f21dd53c6ee.jpg\",\n  \"176829.jpg\": \"Animalia/Ligia occidentalis/4a5d20de2e9458e3c65b4ceb22d27742.jpg\",\n  \"17683.jpg\": \"Aves/Buteo jamaicensis/569609f9d6a5bc645b82d40b2e8dd492.jpg\",\n  \"176832.jpg\": \"Animalia/Ligia occidentalis/abfbdd9c6f54bf7a3d5347b8a592c537.jpg\",\n  \"176834.jpg\": \"Animalia/Ligia occidentalis/2a86eb9f166c28926b79a66355925cb2.jpg\",\n  \"176845.jpg\": \"Aves/Pica hudsonia/8cf5b1e34af499ccb63628772d4d84cc.jpg\",\n  \"176846.jpg\": \"Aves/Pica hudsonia/b1a445dada6b60f5e10f1639da1281fc.jpg\",\n  \"176849.jpg\": \"Aves/Pica hudsonia/0a3a5922aed65765006b35365fcc2823.jpg\",\n  \"176850.jpg\": \"Aves/Pica hudsonia/a9aa4472341341465b5eb56f2f4c55b4.jpg\",\n  \"176860.jpg\": \"Aves/Pica hudsonia/302502c4a8663b890510b26facba886e.jpg\",\n  \"176861.jpg\": \"Aves/Pica hudsonia/8b03c0a2c2fce3c18fd09833e771a271.jpg\",\n  \"176863.jpg\": \"Aves/Pica hudsonia/b533a96d9cca29a4f7c425955086ff76.jpg\",\n  \"176869.jpg\": \"Aves/Pica hudsonia/b4813874fc76f52ed355a8276b42484b.jpg\",\n  \"176876.jpg\": \"Aves/Pica hudsonia/74b204423c96c0d0feb2c7e0b9232fbd.jpg\",\n  \"176881.jpg\": \"Aves/Pica hudsonia/0ba5e2bc472dfacb78d685f644ec0e4b.jpg\",\n  \"176885.jpg\": \"Aves/Pica hudsonia/a69cf933f77ad38d2ef37897442ed5ab.jpg\",\n  \"176900.jpg\": \"Aves/Pica hudsonia/3b0ae2c20416a965e6c34d6dd27ec3fa.jpg\",\n  \"176913.jpg\": \"Aves/Pica hudsonia/254087b495130a66be648549a089cade.jpg\",\n  \"176926.jpg\": \"Aves/Pica hudsonia/21fb7c181fbdc511258b211da64210de.jpg\",\n  \"176928.jpg\": \"Aves/Pica hudsonia/05383b9f667fe511432e55b1aad6f4ed.jpg\",\n  \"176940.jpg\": \"Aves/Pica hudsonia/007c5fa674beb503d7f0a981b3fc9a4d.jpg\",\n  \"176944.jpg\": \"Aves/Pica hudsonia/11d666fc0dd2ea2b997b0556c73f253f.jpg\",\n  \"176949.jpg\": \"Aves/Pica hudsonia/9b6b32ec613cf3bfbf13191e7e8a916a.jpg\",\n  \"176955.jpg\": \"Insecta/Hamadryas februa/636900a3d4656ada9f98745eb4e41c2d.jpg\",\n  \"176963.jpg\": \"Insecta/Hamadryas februa/da9555fe1a5898735480d1578af9448a.jpg\",\n  \"176964.jpg\": \"Insecta/Hamadryas februa/f4181e312e865bc5cdb85224e668f44d.jpg\",\n  \"176965.jpg\": \"Insecta/Hamadryas februa/7555a5e0fd477dcedf4ad3fd8a154630.jpg\",\n  \"176971.jpg\": \"Insecta/Hamadryas februa/db9f62af38116f0f30ecf4dc545df124.jpg\",\n  \"176974.jpg\": \"Insecta/Hamadryas februa/efc1708ca6ef5643b8e6f81b902f3dc3.jpg\",\n  \"176990.jpg\": \"Amphibia/Lithobates sylvaticus/c2892136c86c8c1e62f26abd8db32f46.jpg\",\n  \"1770.jpg\": \"Insecta/Trimerotropis pallidipennis/47616aca4c26f9fbd4f2848823f0c8a2.jpg\",\n  \"177000.jpg\": \"Amphibia/Lithobates sylvaticus/8a7552ea2487432fba17e1a00c7c7123.jpg\",\n  \"177008.jpg\": \"Amphibia/Lithobates sylvaticus/5bfaecdd15e7e39ce19cd83080c2bfb8.jpg\",\n  \"177024.jpg\": \"Amphibia/Lithobates sylvaticus/38e3042934de3d0a9c9f4e8a99561c4c.jpg\",\n  \"177051.jpg\": \"Amphibia/Lithobates sylvaticus/3139a0921d2de749690e24f389b02127.jpg\",\n  \"177055.jpg\": \"Amphibia/Lithobates sylvaticus/4f09b866b79bb80266dab9248451aba3.jpg\",\n  \"177057.jpg\": \"Amphibia/Lithobates sylvaticus/d53f208130e7c63dca9640c5ec9293ac.jpg\",\n  \"177058.jpg\": \"Amphibia/Lithobates sylvaticus/7940416b053b811a7da57b50f39cf2e3.jpg\",\n  \"177061.jpg\": \"Amphibia/Lithobates sylvaticus/7839151b1df50b575bf26c914ce5c520.jpg\",\n  \"177074.jpg\": \"Amphibia/Lithobates sylvaticus/1d9ab49de5d9d706d0f6da7b39dbf51c.jpg\",\n  \"177078.jpg\": \"Amphibia/Lithobates sylvaticus/65e499ce87a2f7ade3c313f84b1a61db.jpg\",\n  \"177084.jpg\": \"Amphibia/Lithobates sylvaticus/6ee5e391ef20e2c9206c10746ed96414.jpg\",\n  \"177090.jpg\": \"Amphibia/Lithobates sylvaticus/61118f90d4a38b9558f1b40413c8ca4b.jpg\",\n  \"177092.jpg\": \"Amphibia/Lithobates sylvaticus/8041333459ba6b3dba0dcfbedec74359.jpg\",\n  \"1771.jpg\": \"Insecta/Trimerotropis pallidipennis/de7456918611f83d6cf3f0b032c82cb3.jpg\",\n  \"177108.jpg\": \"Amphibia/Lithobates sylvaticus/72ca272498007ee9adb32ac09b193505.jpg\",\n  \"177110.jpg\": \"Amphibia/Lithobates sylvaticus/9e1c2920d3eb8b3d67b1778806f39643.jpg\",\n  \"177112.jpg\": \"Amphibia/Lithobates sylvaticus/f89a4c9c96b7f4fe6c62122e6860de56.jpg\",\n  \"177136.jpg\": \"Amphibia/Lithobates sylvaticus/7c0cf982a22475f7cec3018bbfdf9b1e.jpg\",\n  \"177148.jpg\": \"Amphibia/Lithobates sylvaticus/eea0d4169ade8472d7ae39f6d108ede1.jpg\",\n  \"177149.jpg\": \"Amphibia/Lithobates sylvaticus/eeceb818a3dd545be94dd9f835549db2.jpg\",\n  \"177171.jpg\": \"Amphibia/Lithobates sylvaticus/73fb6ff313c9b92927cbd8e79029366c.jpg\",\n  \"177172.jpg\": \"Amphibia/Lithobates sylvaticus/7da6cfff020a0aca49feadbf69d3b48f.jpg\",\n  \"177174.jpg\": \"Amphibia/Lithobates sylvaticus/3dddae3c805e3bd04fd8333d97ed3ea0.jpg\",\n  \"177178.jpg\": \"Amphibia/Lithobates sylvaticus/b28f9e0292623361846c2cd388e081f1.jpg\",\n  \"177189.jpg\": \"Mollusca/Corbicula fluminea/4b609b1feda5490cc3f10b76050b9c3b.jpg\",\n  \"177199.jpg\": \"Mollusca/Corbicula fluminea/432665f1342fab8c4abcbb7bdd27a33d.jpg\",\n  \"1772.jpg\": \"Insecta/Trimerotropis pallidipennis/8bda453e75b5c4d64acd60e53036db9d.jpg\",\n  \"177212.jpg\": \"Mollusca/Corbicula fluminea/fe786b32de2848b81ee22dcde6c901ff.jpg\",\n  \"177217.jpg\": \"Mollusca/Corbicula fluminea/f8753e57108580ecd24e6cc80f5d3fb0.jpg\",\n  \"177218.jpg\": \"Mollusca/Corbicula fluminea/20a4e73a17c17862cba570c34260e0d5.jpg\",\n  \"177230.jpg\": \"Mollusca/Corbicula fluminea/5ab71e7887cc37a22f9ea2da038bb29b.jpg\",\n  \"177234.jpg\": \"Mollusca/Corbicula fluminea/c3943e2a279d065069e6f3c5758f182d.jpg\",\n  \"177240.jpg\": \"Mollusca/Corbicula fluminea/05924eb1845dbb93a82e066882b03817.jpg\",\n  \"177242.jpg\": \"Mollusca/Corbicula fluminea/6ff0f98f262ec508dbef3fbe7a99f7ae.jpg\",\n  \"177258.jpg\": \"Mollusca/Corbicula fluminea/a2dd6405256e4df97441149220a19b33.jpg\",\n  \"177266.jpg\": \"Mollusca/Corbicula fluminea/93e8a5140ad56436dea27c264b9f8931.jpg\",\n  \"177268.jpg\": \"Mollusca/Corbicula fluminea/dc66fc1120bf0b8575e655e6574b5387.jpg\",\n  \"177269.jpg\": \"Mollusca/Corbicula fluminea/380f1ccba87efd1c1a453c042f3b9fa5.jpg\",\n  \"177272.jpg\": \"Insecta/Euborellia annulipes/2e0eee7111f070dee0840c37b8230518.jpg\",\n  \"177279.jpg\": \"Insecta/Euborellia annulipes/8e3f140b6e9afb49cc1f550344a13235.jpg\",\n  \"177286.jpg\": \"Insecta/Phyciodes mylitta/110b51bfa82ccbd1beceb6bc1ca258ca.jpg\",\n  \"177288.jpg\": \"Insecta/Phyciodes mylitta/1a4627c8c47a867992b9885bb98fd3b7.jpg\",\n  \"177290.jpg\": \"Insecta/Phyciodes mylitta/bf3771777d84926eedde1e51f3f5197c.jpg\",\n  \"177296.jpg\": \"Insecta/Phyciodes mylitta/a7faeb2da121a5cdd79d4eff9028e893.jpg\",\n  \"177312.jpg\": \"Insecta/Phyciodes mylitta/58f25ce764eaf038b18216a3bf8d244d.jpg\",\n  \"177321.jpg\": \"Insecta/Phyciodes mylitta/206ba273c099b9f8cb179f141360f435.jpg\",\n  \"177338.jpg\": \"Insecta/Phyciodes mylitta/a4cb1c1c72fd49f532c3a20a544d5271.jpg\",\n  \"177344.jpg\": \"Insecta/Phyciodes mylitta/f05101aca548de385d123ec9c0594bdb.jpg\",\n  \"177346.jpg\": \"Insecta/Phyciodes mylitta/1f236f63815775180115cb9af458bfaa.jpg\",\n  \"177350.jpg\": \"Insecta/Phyciodes mylitta/ebce23c78c59448a51c315d28aa4feec.jpg\",\n  \"177351.jpg\": \"Insecta/Phyciodes mylitta/fe2f292fc319f5386cb874bfff95ba8d.jpg\",\n  \"177355.jpg\": \"Insecta/Phyciodes mylitta/ee4ffc592d8df7ecfc2a4409b32f7334.jpg\",\n  \"177359.jpg\": \"Insecta/Phyciodes mylitta/c757a07986ed93c6764c68215cea7b7d.jpg\",\n  \"177363.jpg\": \"Insecta/Phyciodes mylitta/86bdd3b02c92ffcf8c4cdf410b08255d.jpg\",\n  \"177368.jpg\": \"Insecta/Phyciodes mylitta/4b610ac9c407fb0c77a323dc8be1aee5.jpg\",\n  \"177379.jpg\": \"Insecta/Phyciodes mylitta/44d8bcb5474b7acd5b6f6841af6c366e.jpg\",\n  \"177383.jpg\": \"Insecta/Phyciodes mylitta/1063b5beb7b469d58499bfe768a93923.jpg\",\n  \"177391.jpg\": \"Insecta/Phyciodes mylitta/28d9ac753e792a3313c5637a554daac9.jpg\",\n  \"177393.jpg\": \"Insecta/Phyciodes mylitta/0e626957ffaf5eeaf468f6662c05a8b5.jpg\",\n  \"177402.jpg\": \"Insecta/Phyciodes mylitta/e9058b2e7905105944cbc9e29b24e628.jpg\",\n  \"177412.jpg\": \"Insecta/Phyciodes mylitta/29dccc42ddf1946576079d877fdc60c4.jpg\",\n  \"177444.jpg\": \"Insecta/Anisota senatoria/28944c4846efa3fe2ffeda4b520b4762.jpg\",\n  \"177447.jpg\": \"Insecta/Anisota senatoria/c23c3611d0f3e10a987ca6045186a872.jpg\",\n  \"177448.jpg\": \"Insecta/Anisota senatoria/03ac748860916f66dcca0ddaa4a1504a.jpg\",\n  \"177452.jpg\": \"Insecta/Anisota senatoria/55bf4fb1c3d77230eaa8b8bf7cb094f3.jpg\",\n  \"177455.jpg\": \"Insecta/Anisota senatoria/67e018e21c324da0ab45343d7e156bcb.jpg\",\n  \"177456.jpg\": \"Insecta/Anisota senatoria/9fea73cd8ca10a13ddce71788e5040ec.jpg\",\n  \"177461.jpg\": \"Insecta/Anisota senatoria/c1920fd1ad9e7194c3ca31ce66b2a84a.jpg\",\n  \"177538.jpg\": \"Mollusca/Deroceras reticulatum/fcae44250cfba7acc586c5e63f620010.jpg\",\n  \"177540.jpg\": \"Mollusca/Deroceras reticulatum/2cd862750146d064e312af440f2eb4fb.jpg\",\n  \"177541.jpg\": \"Mollusca/Deroceras reticulatum/5c6d420df27b3a43fae4a8ced448cb71.jpg\",\n  \"177544.jpg\": \"Mollusca/Deroceras reticulatum/04593370b1498f5daa20bd0c5905d35f.jpg\",\n  \"177545.jpg\": \"Mollusca/Deroceras reticulatum/8001c7de422efba86fd9da399fe3cf36.jpg\",\n  \"177546.jpg\": \"Mollusca/Deroceras reticulatum/39038c1f3242c20db772cc651fe0f27a.jpg\",\n  \"177553.jpg\": \"Mollusca/Deroceras reticulatum/273a420d17d0c76f80f5e79251e5d42a.jpg\",\n  \"177556.jpg\": \"Mollusca/Deroceras reticulatum/f32e866068431fc61e4d4e600dbcd74e.jpg\",\n  \"177557.jpg\": \"Mollusca/Deroceras reticulatum/80146ff211951dc34237a2e7d07becfd.jpg\",\n  \"177559.jpg\": \"Mollusca/Deroceras reticulatum/05100d9bcd1ac81053a0ccdf3956a238.jpg\",\n  \"177564.jpg\": \"Mollusca/Deroceras reticulatum/a6fea1f4f1a8d0aa40a3cde3bbef4f07.jpg\",\n  \"177565.jpg\": \"Mollusca/Deroceras reticulatum/1010db6b42121b0d82605f6e1b8ab7d4.jpg\",\n  \"177566.jpg\": \"Mollusca/Deroceras reticulatum/a06b0eac62b8f26f73b4a4b5cf8c3208.jpg\",\n  \"177580.jpg\": \"Mollusca/Deroceras reticulatum/66a67cb0bd1e3e1d9efd7071ffdb4960.jpg\",\n  \"177581.jpg\": \"Mollusca/Deroceras reticulatum/e08d4e78e58720043e66da96bcdaa7dc.jpg\",\n  \"177585.jpg\": \"Mollusca/Deroceras reticulatum/d671cf162ed33881bd9590b4023fc720.jpg\",\n  \"177586.jpg\": \"Mollusca/Deroceras reticulatum/c2db1352f8a95353f122dbff210fec00.jpg\",\n  \"177591.jpg\": \"Mollusca/Deroceras reticulatum/10967d8e67d783ae443d47413cb49028.jpg\",\n  \"177602.jpg\": \"Insecta/Antheraea polyphemus/041b0cc4bab8754b470ff1be9eac535e.jpg\",\n  \"177606.jpg\": \"Insecta/Antheraea polyphemus/b459e41cb65ca93173c23b8b01b17130.jpg\",\n  \"177608.jpg\": \"Insecta/Antheraea polyphemus/dcad0c694208ff9ddd6e8641796ac768.jpg\",\n  \"177609.jpg\": \"Insecta/Antheraea polyphemus/7f7155f91eaee23fd6c9e99fe14e4823.jpg\",\n  \"177617.jpg\": \"Insecta/Antheraea polyphemus/48c642d186122fc35d0935067535eabe.jpg\",\n  \"177618.jpg\": \"Insecta/Antheraea polyphemus/46497616124bb50be97a59b642965c65.jpg\",\n  \"177621.jpg\": \"Insecta/Antheraea polyphemus/93c37394c25a1d5380652d158c143642.jpg\",\n  \"177668.jpg\": \"Insecta/Antheraea polyphemus/79fc59fa3ef997d916baacb621d6f619.jpg\",\n  \"177671.jpg\": \"Insecta/Antheraea polyphemus/86a535e3cc7a35cf6db0c5ba2ea61f20.jpg\",\n  \"177672.jpg\": \"Insecta/Antheraea polyphemus/7fdf82548ab3c9da03e745329fd9d3c4.jpg\",\n  \"177684.jpg\": \"Insecta/Antheraea polyphemus/c01989d3055e1d30307b36ffa7b448f4.jpg\",\n  \"177688.jpg\": \"Insecta/Antheraea polyphemus/ae6da6530fde942229a957538b9630b5.jpg\",\n  \"177702.jpg\": \"Insecta/Antheraea polyphemus/0b31044148ae472cb652adfe86354610.jpg\",\n  \"177708.jpg\": \"Insecta/Antheraea polyphemus/27e1b9f911fa675b797cf97abfaf18f1.jpg\",\n  \"177738.jpg\": \"Insecta/Antheraea polyphemus/261674bbe58937284fedc5a123b2ede7.jpg\",\n  \"177739.jpg\": \"Insecta/Antheraea polyphemus/6f5a0bfa8feab54b62e8f654b05683a4.jpg\",\n  \"177745.jpg\": \"Insecta/Antheraea polyphemus/cab8bed3368330261ff3fb0b82775719.jpg\",\n  \"177746.jpg\": \"Insecta/Antheraea polyphemus/ae0c54b030b631cba99632f3e2414686.jpg\",\n  \"177754.jpg\": \"Insecta/Antheraea polyphemus/6c7238304552f3d53df0bbe727419501.jpg\",\n  \"177768.jpg\": \"Insecta/Antheraea polyphemus/7e274549309f1f0a433dd8d197bb8678.jpg\",\n  \"177780.jpg\": \"Insecta/Isa textula/a1b7bb829830f71e0c76255778ec2f0a.jpg\",\n  \"177782.jpg\": \"Insecta/Isa textula/48871b580de9d70aca2fe9247f7aaa68.jpg\",\n  \"177783.jpg\": \"Insecta/Isa textula/3c476ec19effdd24c7ef82bc3b2b5aa2.jpg\",\n  \"177791.jpg\": \"Insecta/Isa textula/96afb7513cb3f0eced6b8764a8cf2e4e.jpg\",\n  \"177796.jpg\": \"Mammalia/Cynomys ludovicianus/587ad903f5d042131e5799cea347462a.jpg\",\n  \"177808.jpg\": \"Mammalia/Cynomys ludovicianus/ef6d494159e79f8d47312980bc151fc7.jpg\",\n  \"177809.jpg\": \"Mammalia/Cynomys ludovicianus/5c3c4c0a16b716c8f94905c72deb8f9d.jpg\",\n  \"177822.jpg\": \"Mammalia/Cynomys ludovicianus/372f274e3d329e3c9270377e9b8a4b21.jpg\",\n  \"177833.jpg\": \"Mammalia/Cynomys ludovicianus/1ddb82e3d3413c5c939fa0a8d79e683b.jpg\",\n  \"177843.jpg\": \"Mammalia/Cynomys ludovicianus/aa07e6338de5df92bab4e4a080f7d299.jpg\",\n  \"177846.jpg\": \"Mammalia/Cynomys ludovicianus/8e033365d324486bdb03ed4e957452f8.jpg\",\n  \"177850.jpg\": \"Mammalia/Cynomys ludovicianus/20d67c3abf836e61721312fb1f51e13f.jpg\",\n  \"177860.jpg\": \"Mammalia/Cynomys ludovicianus/65ff984c276ed3325ecdb0027de52a24.jpg\",\n  \"177870.jpg\": \"Mammalia/Cynomys ludovicianus/e024804df6a0075853814e652806fb90.jpg\",\n  \"177882.jpg\": \"Mammalia/Cynomys ludovicianus/a82187e87b37f1e8d1e0e4538d919e01.jpg\",\n  \"177884.jpg\": \"Mammalia/Cynomys ludovicianus/e51d025a12e8050d7f2249bcc6177fa6.jpg\",\n  \"1779.jpg\": \"Insecta/Trimerotropis pallidipennis/a419f9c86013fa43abbc24ba451f94e4.jpg\",\n  \"177903.jpg\": \"Mammalia/Cynomys ludovicianus/a6aaf709aa1f27ed49cbc3db27cdc0d6.jpg\",\n  \"177908.jpg\": \"Mammalia/Cynomys ludovicianus/425676b531664487555f162f65128228.jpg\",\n  \"177912.jpg\": \"Mammalia/Cynomys ludovicianus/59047eaa30aa36b2d5f7f8f95160c6cb.jpg\",\n  \"177917.jpg\": \"Mammalia/Cynomys ludovicianus/23aadb9a38fa82cda5f0170d7d3a48fa.jpg\",\n  \"177933.jpg\": \"Reptilia/Bothriechis schlegelii/4647ce9d5d09aeff6a03f6804850aa48.jpg\",\n  \"177934.jpg\": \"Reptilia/Bothriechis schlegelii/88ab3fbabf16aad4a1784dc98d0a2cc3.jpg\",\n  \"177939.jpg\": \"Reptilia/Bothriechis schlegelii/ce8a33097def02e3716d96692fc133ea.jpg\",\n  \"177941.jpg\": \"Reptilia/Bothriechis schlegelii/846851c5906fa20d6c9632a7f114bb33.jpg\",\n  \"177953.jpg\": \"Aves/Crax rubra/fd899e7bad4d730de56edbe10e8e38c0.jpg\",\n  \"177970.jpg\": \"Aves/Crax rubra/9428895b9208ad3cc03a341c6ba46d39.jpg\",\n  \"177980.jpg\": \"Aves/Crax rubra/4252587df8d463f7ab2706d3d15f329f.jpg\",\n  \"177981.jpg\": \"Aves/Crax rubra/66c686eabf4df38628de7b581c005d0c.jpg\",\n  \"177983.jpg\": \"Aves/Crax rubra/9afd0507570cfb6ca2519395bea02d8e.jpg\",\n  \"177984.jpg\": \"Aves/Crax rubra/640ca9fa01a4214b1512f901036fb70e.jpg\",\n  \"177985.jpg\": \"Aves/Ramphocelus passerinii/5326510072461ec76c31b319af88617c.jpg\",\n  \"177986.jpg\": \"Aves/Ramphocelus passerinii/310c09ea5532fbc3ed8688e74f40f20b.jpg\",\n  \"177987.jpg\": \"Aves/Ramphocelus passerinii/de5139412af4f9dffff9390218a8d1ea.jpg\",\n  \"177988.jpg\": \"Aves/Ramphocelus passerinii/86884a465344eff5e230cc13e6b4ce06.jpg\",\n  \"177996.jpg\": \"Aves/Ramphocelus passerinii/a4f620ddbe7cc452fc6c6eb221312a6f.jpg\",\n  \"178026.jpg\": \"Aves/Todiramphus chloris/fa126479f233910b7f11ff6d3de0531f.jpg\",\n  \"178028.jpg\": \"Aves/Todiramphus chloris/b93adba5e050d0e8e1e3ed99720a19a6.jpg\",\n  \"178034.jpg\": \"Aves/Todiramphus chloris/8404641e31c3e025951722cb2cd3bf5d.jpg\",\n  \"178145.jpg\": \"Insecta/Liometopum occidentale/6b8f206cd3aadc386e31e6b14ca90812.jpg\",\n  \"178159.jpg\": \"Reptilia/Urosaurus graciosus/007147af6ffaf37ef3d36ad520e5b3a5.jpg\",\n  \"178165.jpg\": \"Reptilia/Urosaurus graciosus/574e9bc6968bdc2a86b115b6ada774fc.jpg\",\n  \"178168.jpg\": \"Reptilia/Urosaurus graciosus/a56a6272d01ae158314debdb4e5d0faf.jpg\",\n  \"178284.jpg\": \"Mammalia/Phocoena phocoena/834d567c8c10881ff0e4d031120f3934.jpg\",\n  \"178292.jpg\": \"Mammalia/Phocoena phocoena/008b9310a1c52b0b957a709d9a6e8dc6.jpg\",\n  \"178293.jpg\": \"Mammalia/Phocoena phocoena/b5bdd64d4ab563b054228d35d1221263.jpg\",\n  \"178294.jpg\": \"Mammalia/Phocoena phocoena/d02209ea2c7fdaed23491eab54d4a644.jpg\",\n  \"178300.jpg\": \"Mammalia/Phocoena phocoena/c660ef2949e0d9472e9a7ae453be6bb5.jpg\",\n  \"178368.jpg\": \"Aves/Junco hyemalis hyemalis/210ec39cbb7da1d1c1ecdde72bc35845.jpg\",\n  \"178371.jpg\": \"Aves/Junco hyemalis hyemalis/7d074b783da251de37883121df64ce5e.jpg\",\n  \"178374.jpg\": \"Aves/Junco hyemalis hyemalis/843a816abc866b940dc148a0eeb38dfc.jpg\",\n  \"178375.jpg\": \"Aves/Junco hyemalis hyemalis/3c18268b910db14c04fd9399e6e22787.jpg\",\n  \"178376.jpg\": \"Aves/Junco hyemalis hyemalis/137d4fe0941d24433debebdb8123efd6.jpg\",\n  \"178377.jpg\": \"Aves/Junco hyemalis hyemalis/4936c4bbac2b8ec36fb6d785caa71d57.jpg\",\n  \"178378.jpg\": \"Aves/Junco hyemalis hyemalis/1dcea9313218022ef2af67533a1419d2.jpg\",\n  \"178379.jpg\": \"Aves/Junco hyemalis hyemalis/17df8bb33c042cec1af2b0a38d8208df.jpg\",\n  \"178380.jpg\": \"Aves/Junco hyemalis hyemalis/71981deced9625d0380c5efddabc68bb.jpg\",\n  \"178381.jpg\": \"Aves/Junco hyemalis hyemalis/b0153765411d123f30a10c97debad9de.jpg\",\n  \"178384.jpg\": \"Aves/Junco hyemalis hyemalis/da4885a97009db57b225349ddf25da88.jpg\",\n  \"178385.jpg\": \"Aves/Junco hyemalis hyemalis/93a0ac3f159cb9de027b122176784032.jpg\",\n  \"178392.jpg\": \"Aves/Junco hyemalis hyemalis/6a2060cbfdcb11fad08eac9c5744a237.jpg\",\n  \"178397.jpg\": \"Aves/Junco hyemalis hyemalis/2c50f716375d9c785f093cb8bc6136e3.jpg\",\n  \"178398.jpg\": \"Aves/Junco hyemalis hyemalis/01f18218b380bb2883dcfbc454154457.jpg\",\n  \"1784.jpg\": \"Insecta/Trimerotropis pallidipennis/a3acdd77d4a87b17c00e194c388f2edb.jpg\",\n  \"178400.jpg\": \"Aves/Junco hyemalis hyemalis/9a8823a65a57c4de7b667d0e10a5c367.jpg\",\n  \"178403.jpg\": \"Aves/Junco hyemalis hyemalis/cb2c775a87b5c9d61cd0a68bcae8766e.jpg\",\n  \"178406.jpg\": \"Aves/Junco hyemalis hyemalis/56d5ed7d6546988b93844d967ce53b81.jpg\",\n  \"178410.jpg\": \"Animalia/Balanus glandula/7954975314c4065229cab47692c322b7.jpg\",\n  \"178415.jpg\": \"Animalia/Balanus glandula/7ea3156290c8e3dd763856448abf7e9e.jpg\",\n  \"1786.jpg\": \"Insecta/Trimerotropis pallidipennis/1c4627be0161b892985b2bfd46a7ae5f.jpg\",\n  \"178639.jpg\": \"Mollusca/Theba pisana/9052487defecba5bc4a49f41d83495ac.jpg\",\n  \"178653.jpg\": \"Insecta/Chrysolina bankii/ed3d8883dc117bf4fa96ba1a043251b1.jpg\",\n  \"178654.jpg\": \"Insecta/Chrysolina bankii/39c9461c98e06a461b3b8a95f49e9030.jpg\",\n  \"178659.jpg\": \"Insecta/Chrysolina bankii/02c7d39de2a10414119dcd28ed47c4de.jpg\",\n  \"178661.jpg\": \"Insecta/Chrysolina bankii/a102d0f652e3d650344ec9a50bedc735.jpg\",\n  \"178662.jpg\": \"Insecta/Chrysolina bankii/a9e203c7f79685cdcf887b662f13c4ca.jpg\",\n  \"178663.jpg\": \"Insecta/Chrysolina bankii/fc334c64b6cea46bb3fcaa984a453204.jpg\",\n  \"178680.jpg\": \"Aves/Emberiza citrinella/2e7dedbf2fb44bf5ded69aa98570b189.jpg\",\n  \"178692.jpg\": \"Aves/Emberiza citrinella/f16189b2f3733a022927c2a937456b98.jpg\",\n  \"178695.jpg\": \"Aves/Emberiza citrinella/7a77abce2b564a347012d2d598e4dbef.jpg\",\n  \"178703.jpg\": \"Aves/Emberiza citrinella/0ce9dcc5a7f48fe15783bc31f50caffd.jpg\",\n  \"178704.jpg\": \"Aves/Emberiza citrinella/089552ad71eeb06fd56a693554178000.jpg\",\n  \"178712.jpg\": \"Aves/Emberiza citrinella/f0abd6627bb2c91f6a22ca7a2068c8bf.jpg\",\n  \"178713.jpg\": \"Aves/Emberiza citrinella/ff792c1c77a4dcefa3885d5efabc950a.jpg\",\n  \"178717.jpg\": \"Aves/Emberiza citrinella/8fcabe8e9ec613316acea93428f57b39.jpg\",\n  \"178718.jpg\": \"Aves/Emberiza citrinella/38a81e0dcd2698b0c9cd5954fdabb5c9.jpg\",\n  \"178728.jpg\": \"Aves/Emberiza citrinella/1a1bc19b85e34e69670e5c22b2e52ee8.jpg\",\n  \"178730.jpg\": \"Aves/Emberiza citrinella/99c8704c8c3c42b3ef89aed1c6d454bc.jpg\",\n  \"178731.jpg\": \"Aves/Emberiza citrinella/ff97e2682788e193769070cb7da48612.jpg\",\n  \"178747.jpg\": \"Aves/Emberiza citrinella/cb86cf1852aa2c2fe06f844a59f13b13.jpg\",\n  \"178756.jpg\": \"Aves/Emberiza citrinella/74cddd64a527c2f0225092aa6140ec18.jpg\",\n  \"178758.jpg\": \"Aves/Emberiza citrinella/9ec769ad5fae75ed5064ade988ef6820.jpg\",\n  \"178763.jpg\": \"Aves/Emberiza citrinella/2f5a9e2aefc4f3de594307ec9751784d.jpg\",\n  \"178777.jpg\": \"Aves/Emberiza citrinella/5ada8f6f754ca63a9d953dd240f3f283.jpg\",\n  \"178785.jpg\": \"Aves/Emberiza citrinella/171f5cfe3b1a90e0c6bb5fd083e61b97.jpg\",\n  \"178796.jpg\": \"Aves/Emberiza citrinella/3f93a7d6e16e47e7b2d4d0bfdd418569.jpg\",\n  \"178807.jpg\": \"Aves/Emberiza citrinella/857446ecdf0d9c7ca423db656acec3a7.jpg\",\n  \"178808.jpg\": \"Aves/Emberiza citrinella/b4e6ef5c2e78bb6540d93e4f4d0c8267.jpg\",\n  \"178814.jpg\": \"Insecta/Scantius aegyptius/d37da79b5ade3721fd16daedfb973b4e.jpg\",\n  \"178834.jpg\": \"Insecta/Scantius aegyptius/6d8a90d5d15d4a3786288c2b194fface.jpg\",\n  \"178837.jpg\": \"Insecta/Scantius aegyptius/0c11cf6edc760051d8cd5145619cac6c.jpg\",\n  \"178838.jpg\": \"Insecta/Scantius aegyptius/f243514e5a5d8967c1b2cdacf4623491.jpg\",\n  \"178846.jpg\": \"Insecta/Prosapia bicincta/e351dfd33d3be440e59af7d8394d3841.jpg\",\n  \"178848.jpg\": \"Insecta/Prosapia bicincta/3d1d34cf50d54e0551c58088387d3757.jpg\",\n  \"178850.jpg\": \"Insecta/Prosapia bicincta/b22db2cec26e3d841ec4a5889e86ca17.jpg\",\n  \"178851.jpg\": \"Insecta/Prosapia bicincta/6bc918e502de9042d22e4f380dca0000.jpg\",\n  \"178852.jpg\": \"Insecta/Prosapia bicincta/debf0c7d458090339b90c6e45a87984f.jpg\",\n  \"178853.jpg\": \"Insecta/Prosapia bicincta/c191d4dad92f98acb7a62766ca9e76e8.jpg\",\n  \"178854.jpg\": \"Insecta/Prosapia bicincta/b905fd3b6fb3bf6874e23c1fc9fc5a28.jpg\",\n  \"178858.jpg\": \"Insecta/Prosapia bicincta/57a88efce06c3da642576b6d681ec430.jpg\",\n  \"178864.jpg\": \"Insecta/Prosapia bicincta/c76f0862abe3995b472155a989633c8c.jpg\",\n  \"178872.jpg\": \"Insecta/Prosapia bicincta/774b5c851429d10fdab5960e05817031.jpg\",\n  \"178873.jpg\": \"Insecta/Prosapia bicincta/58d2ea04adcc76671d862c2e7f23577d.jpg\",\n  \"178874.jpg\": \"Insecta/Prosapia bicincta/8048795cc617ba1f67a2bab6f4241bd1.jpg\",\n  \"178875.jpg\": \"Insecta/Prosapia bicincta/30f3fbc051269cac04871eef5792fc67.jpg\",\n  \"178877.jpg\": \"Insecta/Prosapia bicincta/eb74ead6f533cbbfda47631d92f77ad5.jpg\",\n  \"178878.jpg\": \"Insecta/Prosapia bicincta/ad91db9d1fae68580689eb7ca2439890.jpg\",\n  \"178880.jpg\": \"Insecta/Prosapia bicincta/a28b8b60b050667cebea98ef6f76090e.jpg\",\n  \"178881.jpg\": \"Insecta/Prosapia bicincta/e0e1e1eddc9a78b3df4f582f0a5e2d19.jpg\",\n  \"178882.jpg\": \"Insecta/Prosapia bicincta/07290ae19ae9a28cc3af9d6b20b6b6c5.jpg\",\n  \"178885.jpg\": \"Insecta/Prosapia bicincta/cef4f45fe0ea5967fcd68597a2a11497.jpg\",\n  \"178891.jpg\": \"Insecta/Prosapia bicincta/6bd67178df1b223492d92eac98ade53b.jpg\",\n  \"178896.jpg\": \"Insecta/Prosapia bicincta/089dcb5df203e35af3080c1b100f59e5.jpg\",\n  \"178901.jpg\": \"Insecta/Desmia funeralis/1c4d375c2573d7595485e60866bc722d.jpg\",\n  \"178903.jpg\": \"Insecta/Desmia funeralis/eb5a6e17de3b8379cd4bcdf000f12c20.jpg\",\n  \"178906.jpg\": \"Insecta/Desmia funeralis/26fa4d7b30b5c455ff5b9fee75ec8d84.jpg\",\n  \"178908.jpg\": \"Insecta/Desmia funeralis/c6ee13ccbf0310bf63f910e80ca75d9e.jpg\",\n  \"178912.jpg\": \"Insecta/Desmia funeralis/1e1b225d11e7043a32a4a52d3609cc43.jpg\",\n  \"179031.jpg\": \"Aves/Corvus brachyrhynchos/8941f943bc3e87dc7525bf3285a1e6c7.jpg\",\n  \"179067.jpg\": \"Aves/Corvus brachyrhynchos/43f47373cf432f377dae6251a334c230.jpg\",\n  \"179074.jpg\": \"Aves/Corvus brachyrhynchos/dd3dc353f0bf31d7566ce9b3f740b324.jpg\",\n  \"179079.jpg\": \"Aves/Corvus brachyrhynchos/020f74b30884eda162f218501279e0ff.jpg\",\n  \"179230.jpg\": \"Aves/Corvus brachyrhynchos/3e5feb44db109caa63a74f6378a0b942.jpg\",\n  \"179262.jpg\": \"Aves/Corvus brachyrhynchos/79ff6a0f6ecdff5883d6ab1ac8f7d2db.jpg\",\n  \"179285.jpg\": \"Aves/Corvus brachyrhynchos/2977e55cc26a31fa39ab7a2c8912877b.jpg\",\n  \"1793.jpg\": \"Insecta/Trimerotropis pallidipennis/eb6d3b8ec4b44a72e908ee8ad31703c6.jpg\",\n  \"179307.jpg\": \"Aves/Corvus brachyrhynchos/09cfca3c689038980ed0b3569a9cad71.jpg\",\n  \"179364.jpg\": \"Aves/Corvus brachyrhynchos/9db667686c0a4b80dd0884272741225d.jpg\",\n  \"17937.jpg\": \"Actinopterygii/Lepomis macrochirus/6d51602a1e9549b57cfd77bcc097564d.jpg\",\n  \"179391.jpg\": \"Aves/Corvus brachyrhynchos/95ede4808bf5d9a053274950726af001.jpg\",\n  \"17940.jpg\": \"Actinopterygii/Lepomis macrochirus/58df2e2c87642e510a001006290b5ab9.jpg\",\n  \"17942.jpg\": \"Actinopterygii/Lepomis macrochirus/d4434975cafd5cd20f464f533dcec113.jpg\",\n  \"179425.jpg\": \"Aves/Corvus brachyrhynchos/73db7f1dc9923561f29b5936614b4efb.jpg\",\n  \"179426.jpg\": \"Aves/Corvus brachyrhynchos/ebd580183d8ef4141f71be72efa91d41.jpg\",\n  \"179472.jpg\": \"Aves/Corvus brachyrhynchos/a1e9cae84a013799f58c66b9c9f004d6.jpg\",\n  \"17949.jpg\": \"Actinopterygii/Lepomis macrochirus/9cf95bed974bbb24813e4c2154424d09.jpg\",\n  \"179497.jpg\": \"Aves/Corvus brachyrhynchos/1f5b2403ef452b231bc6fc9c08225a22.jpg\",\n  \"179505.jpg\": \"Aves/Corvus brachyrhynchos/4c8c2c8ec220b3a8632fa2a59bf034a8.jpg\",\n  \"17953.jpg\": \"Actinopterygii/Lepomis macrochirus/ccffcad251444d00afc9daf49b75c9f0.jpg\",\n  \"179545.jpg\": \"Aves/Corvus brachyrhynchos/73e2964bda993eddd6371c2133fa6171.jpg\",\n  \"179595.jpg\": \"Aves/Corvus brachyrhynchos/e08e70750a68648e178d7974c7167068.jpg\",\n  \"179610.jpg\": \"Aves/Corvus brachyrhynchos/fc8ee36553256efa7ebd928387e05c00.jpg\",\n  \"179618.jpg\": \"Aves/Corvus brachyrhynchos/276a0e7758712d0e06b7e2aebd972978.jpg\",\n  \"17975.jpg\": \"Actinopterygii/Lepomis macrochirus/74621f18ce50125f618bc17423312cb9.jpg\",\n  \"17977.jpg\": \"Actinopterygii/Lepomis macrochirus/3c87729e31718eafd347782ef04062c4.jpg\",\n  \"179801.jpg\": \"Aves/Charadrius wilsonia/e2112e7cce3f55bca959042c5254c459.jpg\",\n  \"179809.jpg\": \"Aves/Charadrius wilsonia/39cfdd5147ae19ebc270b1bb46927663.jpg\",\n  \"17981.jpg\": \"Actinopterygii/Lepomis macrochirus/613279fcb492117c93e32c5de566d9d8.jpg\",\n  \"179811.jpg\": \"Aves/Charadrius wilsonia/ef21bd388cd743ab16249b2aca2d1198.jpg\",\n  \"179812.jpg\": \"Aves/Charadrius wilsonia/d49cd47d4cd6b0bdf32e87887fa20646.jpg\",\n  \"179813.jpg\": \"Aves/Charadrius wilsonia/b55ebee462ab05f8ff1178b64a11b35b.jpg\",\n  \"179814.jpg\": \"Aves/Charadrius wilsonia/ac835f1921a12996ab968cd2ed9fcc06.jpg\",\n  \"179817.jpg\": \"Aves/Charadrius wilsonia/03506251f3a2c1ce5fb1c93580f3eb17.jpg\",\n  \"179818.jpg\": \"Aves/Charadrius wilsonia/48657b8c6e9488ab9f86ce7f7bcd2b8b.jpg\",\n  \"179819.jpg\": \"Aves/Charadrius wilsonia/72848542fb0eba87b19b9562ef3514b5.jpg\",\n  \"179820.jpg\": \"Aves/Charadrius wilsonia/f0a2f9d283da8ecc300f0a331c418de6.jpg\",\n  \"179824.jpg\": \"Aves/Charadrius wilsonia/4f241e6c38893487dd13579c0de0bb3e.jpg\",\n  \"179825.jpg\": \"Aves/Charadrius wilsonia/88622cdc434de4a9a5c1fdbac9c15b33.jpg\",\n  \"179826.jpg\": \"Aves/Charadrius wilsonia/efed923acc8a4ba7ed8a293c0b7a722d.jpg\",\n  \"179827.jpg\": \"Aves/Charadrius wilsonia/b1cbdca2316597b5dfbd969eea9d86f2.jpg\",\n  \"179828.jpg\": \"Aves/Charadrius wilsonia/2b8509965eeb1bbebd3768f792fd05d9.jpg\",\n  \"179829.jpg\": \"Aves/Charadrius wilsonia/37dbc138a8a98c075221af19d6d59c6b.jpg\",\n  \"179830.jpg\": \"Aves/Charadrius wilsonia/13b7140ecc2c585bfe9a32a5f7cd4deb.jpg\",\n  \"179839.jpg\": \"Aves/Charadrius wilsonia/431c9b1ff43ec40b93d679277e39561a.jpg\",\n  \"179840.jpg\": \"Aves/Charadrius wilsonia/f768fa60c73a73d7266ad513db4517ac.jpg\",\n  \"179845.jpg\": \"Aves/Charadrius wilsonia/ae48e3b41a52fe2ac4fad706e9190b22.jpg\",\n  \"179846.jpg\": \"Insecta/Euphydryas phaeton/fc1be50df9bc58678358d4521bf91358.jpg\",\n  \"179847.jpg\": \"Insecta/Euphydryas phaeton/bc024ce17f1d0579c32fda9482eea143.jpg\",\n  \"179850.jpg\": \"Insecta/Euphydryas phaeton/10f225bab85d66b8c5c6cd314559583c.jpg\",\n  \"179859.jpg\": \"Insecta/Euphydryas phaeton/514cb442fbd926b808eb84912c4df68c.jpg\",\n  \"179860.jpg\": \"Insecta/Euphydryas phaeton/84e2b8b8cfce8edaec7f665a5ba3e831.jpg\",\n  \"179877.jpg\": \"Insecta/Euphydryas phaeton/df875fed07cd454436c5411c7f139923.jpg\",\n  \"17988.jpg\": \"Actinopterygii/Lepomis macrochirus/2a966d4d1834f60f49f4e68171fc0ef1.jpg\",\n  \"179886.jpg\": \"Insecta/Euphydryas phaeton/f32aa455f7b08f5430fcec21fb16e47e.jpg\",\n  \"179889.jpg\": \"Insecta/Euphydryas phaeton/de54500abb3e99ae67f9ebc7a9d706f5.jpg\",\n  \"179890.jpg\": \"Insecta/Euphydryas phaeton/5e4c1514e14f7c72339ab2f3f6c35f3a.jpg\",\n  \"179891.jpg\": \"Insecta/Euphydryas phaeton/0835010a31c40ab3287e62e5c9eceadc.jpg\",\n  \"179895.jpg\": \"Insecta/Euphydryas phaeton/85fea6d1b7c8bb160bcbcd8a5eff55e7.jpg\",\n  \"179896.jpg\": \"Insecta/Euphydryas phaeton/3adbdb2e13011b8aed886fe8547e0235.jpg\",\n  \"179897.jpg\": \"Insecta/Euphydryas phaeton/957097c5f8e46755434b256d018e4e4f.jpg\",\n  \"179899.jpg\": \"Insecta/Euphydryas phaeton/004eb70db24b979f72ca04c43e8a297f.jpg\",\n  \"1799.jpg\": \"Insecta/Trimerotropis pallidipennis/37ce28e77e538aec8464e4d41236e744.jpg\",\n  \"179900.jpg\": \"Insecta/Euphydryas phaeton/1862fab77977573cdc2399cfe191bcf6.jpg\",\n  \"179901.jpg\": \"Insecta/Euphydryas phaeton/72e18f21d9dd72e406c7b11a73bd67df.jpg\",\n  \"179905.jpg\": \"Insecta/Euphydryas phaeton/853928399ed823092b07bb63b29cb518.jpg\",\n  \"179935.jpg\": \"Insecta/Idia americalis/4c57af16d5594f701dca45c6e5b25b34.jpg\",\n  \"179937.jpg\": \"Insecta/Idia americalis/d089d37ba855d1d5c8a8a087b5c99196.jpg\",\n  \"179941.jpg\": \"Insecta/Idia americalis/ac5b905c5bec2ebfa6bbbc1a39d984f6.jpg\",\n  \"179945.jpg\": \"Insecta/Idia americalis/fedc8cc5c0356982765e0565aece72ba.jpg\",\n  \"179946.jpg\": \"Insecta/Idia americalis/b5a7a783664049e5125f7afeca7ccbc2.jpg\",\n  \"179947.jpg\": \"Insecta/Idia americalis/8c823a50ddecc01213aa1fc4cd358f9d.jpg\",\n  \"179949.jpg\": \"Insecta/Idia americalis/64d65b128c49bceb79eb1a5ba46cc910.jpg\",\n  \"17995.jpg\": \"Actinopterygii/Lepomis macrochirus/a3271520aa281099175458b3dcb18063.jpg\",\n  \"179963.jpg\": \"Insecta/Idia americalis/687165785a8ef90ad426ff4c4f6e6579.jpg\",\n  \"179964.jpg\": \"Insecta/Idia americalis/e3e2e52460b785f028f8f82b76b22ba1.jpg\",\n  \"179965.jpg\": \"Insecta/Idia americalis/71c109cc00467cfbb99b411a259b2fbb.jpg\",\n  \"179968.jpg\": \"Insecta/Idia americalis/e61c234e6dd473b66f903ad685898e10.jpg\",\n  \"179970.jpg\": \"Insecta/Idia americalis/14853e5e846e0227a215ec651ed5bce2.jpg\",\n  \"179971.jpg\": \"Insecta/Idia americalis/c8e0541899c23700c914252f4e066143.jpg\",\n  \"179983.jpg\": \"Insecta/Idia americalis/dec56d7dc6e30e3300cc0d5f2beb0d41.jpg\",\n  \"179985.jpg\": \"Insecta/Idia americalis/9d65e12ec8509f5aaf75866efb4772a8.jpg\",\n  \"179986.jpg\": \"Insecta/Idia americalis/74adbcd8856a1043f5a45f44f7951af8.jpg\",\n  \"179988.jpg\": \"Insecta/Idia americalis/e0fb6beee38e2453f657fdd8f800104e.jpg\",\n  \"179989.jpg\": \"Insecta/Idia americalis/9a6e33d0eeb43eb90ed2e07346c641a2.jpg\",\n  \"18.jpg\": \"Mammalia/Marmota flaviventris/488272f1fec42b2029f3dd78780a57f8.jpg\",\n  \"180.jpg\": \"Aves/Zonotrichia capensis/edbc578245a1d11a34d0ebb834e4ee1f.jpg\",\n  \"18003.jpg\": \"Actinopterygii/Lepomis macrochirus/421c3903c7747b7e655a7cebc6239adc.jpg\",\n  \"18008.jpg\": \"Actinopterygii/Lepomis macrochirus/7210031f2ac59267c9f987aea9f9f6d1.jpg\",\n  \"18009.jpg\": \"Actinopterygii/Lepomis macrochirus/6f8eceeed9e18743627a52242c6a4ef2.jpg\",\n  \"18014.jpg\": \"Actinopterygii/Lepomis macrochirus/ba920128de2e221df1c0feaedd53fbd2.jpg\",\n  \"180144.jpg\": \"Aves/Gallinago delicata/5fce129cd7d2327fc0ee05742ee237a4.jpg\",\n  \"18015.jpg\": \"Actinopterygii/Lepomis macrochirus/20286e0f5b62e3789e96124d6441db5b.jpg\",\n  \"180150.jpg\": \"Aves/Gallinago delicata/dfce64767982dc6ac4c3eaeaef67ad10.jpg\",\n  \"180152.jpg\": \"Aves/Gallinago delicata/be24b04be40b9f65c9fd74cd6e097ac3.jpg\",\n  \"180153.jpg\": \"Aves/Gallinago delicata/c7a3706331ef26efff6ce09b73544e44.jpg\",\n  \"180162.jpg\": \"Aves/Gallinago delicata/5c1fb1a8f715078643a0ce94772b80e4.jpg\",\n  \"180166.jpg\": \"Aves/Gallinago delicata/4761e498b60ce425c44b15703e3f2f3d.jpg\",\n  \"180167.jpg\": \"Aves/Gallinago delicata/66e61153d95dcfb3e754dfd985f07855.jpg\",\n  \"18017.jpg\": \"Actinopterygii/Lepomis macrochirus/156cd14dfb090810f9278a3fd546ecd6.jpg\",\n  \"180177.jpg\": \"Aves/Gallinago delicata/21340a9aef0b3b8b11f3d0a5432b1c62.jpg\",\n  \"180180.jpg\": \"Aves/Gallinago delicata/3a021acfee3d5dbe092874795523fe9e.jpg\",\n  \"1802.jpg\": \"Insecta/Trimerotropis pallidipennis/f96e068834b734a1920fc2c645d080b5.jpg\",\n  \"180214.jpg\": \"Aves/Gallinago delicata/2de51acf9c8a9cd690216149139ba390.jpg\",\n  \"180222.jpg\": \"Aves/Gallinago delicata/b5cd6b6736c5c440f49c572e73c0be32.jpg\",\n  \"180228.jpg\": \"Aves/Gallinago delicata/ad3e1c88b38d542620c6717ac9fbb8c3.jpg\",\n  \"180230.jpg\": \"Aves/Gallinago delicata/78f59dba2da74a3aad05a983d41f0187.jpg\",\n  \"180234.jpg\": \"Aves/Gallinago delicata/d8b0075e65fc3055de449dd24105d244.jpg\",\n  \"180242.jpg\": \"Aves/Gallinago delicata/b7a15305fccff6f45bb8017fbae3f86e.jpg\",\n  \"180244.jpg\": \"Aves/Gallinago delicata/041e2a6d0a252c9c315e50705c8e1a94.jpg\",\n  \"180249.jpg\": \"Aves/Gallinago delicata/1b39ef4c4f46000f29d7efc08d35fb47.jpg\",\n  \"18025.jpg\": \"Reptilia/Lampropeltis californiae/a22a8dc845ec0649d6b58ca3a95ec18b.jpg\",\n  \"180252.jpg\": \"Aves/Gallinago delicata/fb479b9578f155e04ec638554f1b9839.jpg\",\n  \"180253.jpg\": \"Aves/Gallinago delicata/565b44e31112f08ca63311bb1c50f1a7.jpg\",\n  \"180261.jpg\": \"Reptilia/Elgaria kingii/dae71cdab4932ad86edac8ce69e6ba54.jpg\",\n  \"180263.jpg\": \"Reptilia/Elgaria kingii/a930b51817303ed5ee7f28265c9a25a1.jpg\",\n  \"180266.jpg\": \"Reptilia/Elgaria kingii/d0375393e1fcbc0921cee440b2b1432f.jpg\",\n  \"180268.jpg\": \"Reptilia/Elgaria kingii/b54cc340d4bacd43edf8d31ff36e183b.jpg\",\n  \"180269.jpg\": \"Reptilia/Elgaria kingii/af54f6a594062f7b9c6d43c444fc8a51.jpg\",\n  \"180276.jpg\": \"Reptilia/Elgaria kingii/0a56e94600a499460b0db834db430de0.jpg\",\n  \"180278.jpg\": \"Insecta/Celithemis fasciata/1bdbf2dbbdbf56251d2c5c46d5bac52d.jpg\",\n  \"180283.jpg\": \"Insecta/Celithemis fasciata/85ac0216bd742e38f5f1d2e7bc01acfb.jpg\",\n  \"180288.jpg\": \"Insecta/Celithemis fasciata/48c7470cf8a2223ed1b1e2a2b21c23f4.jpg\",\n  \"180292.jpg\": \"Insecta/Celithemis fasciata/6b1dc6f570e69ebd5692884d3ed3c58b.jpg\",\n  \"180294.jpg\": \"Insecta/Celithemis fasciata/7a574a64edcdcec5bddbda1137ddc345.jpg\",\n  \"180295.jpg\": \"Insecta/Celithemis fasciata/0d1b9c93d9ea99e99742b06db53a4704.jpg\",\n  \"180296.jpg\": \"Insecta/Celithemis fasciata/d1421de8c7bb4b9cee98d2393546c518.jpg\",\n  \"180297.jpg\": \"Insecta/Celithemis fasciata/6d59dfb11b61c0432f191fbba45e771f.jpg\",\n  \"180298.jpg\": \"Insecta/Celithemis fasciata/58510b084bfcd7484072cdb4d4aa259e.jpg\",\n  \"180299.jpg\": \"Insecta/Celithemis fasciata/9580f277978b3088147e1bbfc8cc48c8.jpg\",\n  \"1803.jpg\": \"Insecta/Trimerotropis pallidipennis/9f0a75a015a9a31b26f166482711bd58.jpg\",\n  \"180300.jpg\": \"Insecta/Celithemis fasciata/d50edf8d4ab8379bcff2ffd7f29a77f1.jpg\",\n  \"180306.jpg\": \"Insecta/Celithemis fasciata/77fcd3caab39df84d847b2eed4ec1d8f.jpg\",\n  \"180307.jpg\": \"Insecta/Celithemis fasciata/a434ebad2caae422085fc8138f4def76.jpg\",\n  \"180309.jpg\": \"Insecta/Celithemis fasciata/dc1d7ab54eddc2183de353fe184f6369.jpg\",\n  \"180310.jpg\": \"Insecta/Celithemis fasciata/3eefd64e4e5cdc5c55be2628566686ef.jpg\",\n  \"180311.jpg\": \"Insecta/Celithemis fasciata/87f3d833a5e4c4ff17001e49a5eb5a44.jpg\",\n  \"180314.jpg\": \"Insecta/Celithemis fasciata/7efb66550c744144461d94583610094f.jpg\",\n  \"180319.jpg\": \"Insecta/Celithemis fasciata/b89a976316ca4763204db31d00dc1090.jpg\",\n  \"180325.jpg\": \"Aves/Petroica australis longipes/cc1667a6ac4c305bdc456b178fd4b057.jpg\",\n  \"180326.jpg\": \"Aves/Petroica australis longipes/054bddcf5a492b00def11ec95fd46872.jpg\",\n  \"180331.jpg\": \"Aves/Petroica australis longipes/722513219b3cbc06700f99b8046254fc.jpg\",\n  \"180333.jpg\": \"Aves/Petroica australis longipes/4a3b2a884074d9d867545dce3361cbfe.jpg\",\n  \"180338.jpg\": \"Aves/Petroica australis longipes/44567904ae2732bb126a22b262a3c0ab.jpg\",\n  \"180339.jpg\": \"Aves/Petroica australis longipes/9262542a5bc697834d5f31b031f82265.jpg\",\n  \"180344.jpg\": \"Aves/Petroica australis longipes/64ea964fe2134bb43e06a91f82f73dda.jpg\",\n  \"180346.jpg\": \"Aves/Petroica australis longipes/10c86fd2cc2ad7d3c999a29084967c50.jpg\",\n  \"180353.jpg\": \"Aves/Petroica australis longipes/12a4ed6b223e17033cfffe848cdeee5e.jpg\",\n  \"180354.jpg\": \"Aves/Petroica australis longipes/56c700749e91fba28a48fad4ec481f1d.jpg\",\n  \"180355.jpg\": \"Aves/Petroica australis longipes/609e54253fba965a68284801c760b30d.jpg\",\n  \"180357.jpg\": \"Aves/Petroica australis longipes/500ec83be1a96e0fe85d7b84f5f2a656.jpg\",\n  \"180358.jpg\": \"Aves/Petroica australis longipes/b26fae2c9755e3812d26f3ebee86daa0.jpg\",\n  \"18036.jpg\": \"Reptilia/Lampropeltis californiae/b254b7b84dc5648d1f797583000c3691.jpg\",\n  \"180362.jpg\": \"Aves/Petroica australis longipes/bc3d97cd49ea058f2969205dac356666.jpg\",\n  \"180363.jpg\": \"Aves/Petroica australis longipes/407bb9e082c9b1928e9ac3b2b0854880.jpg\",\n  \"180364.jpg\": \"Aves/Petroica australis longipes/119dc1551b5a0737a901c675977f62b8.jpg\",\n  \"180368.jpg\": \"Aves/Petroica australis longipes/87fb8283226462a180c114f54ff72820.jpg\",\n  \"180370.jpg\": \"Aves/Petroica australis longipes/6e885167cbafca999933f02326262898.jpg\",\n  \"180372.jpg\": \"Aves/Petroica australis longipes/7cdc9810e57d4eb829645e49fe90bfe6.jpg\",\n  \"180373.jpg\": \"Aves/Petroica australis longipes/c4d1b5ea34ad0ce9215d7bf94120001f.jpg\",\n  \"180375.jpg\": \"Aves/Petroica australis longipes/e7809d88415453b7d0c5be5983261593.jpg\",\n  \"180378.jpg\": \"Aves/Petroica australis longipes/84e594edcfba8863372e9d9b89021576.jpg\",\n  \"180379.jpg\": \"Aves/Petroica australis longipes/46c0bd22a5f5801d9add78338834dfd9.jpg\",\n  \"180384.jpg\": \"Insecta/Sympetrum internum/c8d65a7c06300b6a321a8eeb7c42148f.jpg\",\n  \"1804.jpg\": \"Insecta/Trimerotropis pallidipennis/98b38f689fe130095227ec2b2d6477bf.jpg\",\n  \"180401.jpg\": \"Insecta/Sympetrum internum/e61bd9fcc472e87ed013a2b8cb2481bb.jpg\",\n  \"180406.jpg\": \"Insecta/Sympetrum internum/634bee2ffcffee40e09cc6c7ee6d7aa9.jpg\",\n  \"180407.jpg\": \"Insecta/Sympetrum internum/87998b6203c83ab7ba83834039c579a4.jpg\",\n  \"18041.jpg\": \"Reptilia/Lampropeltis californiae/55cdd19bdfff31c45c0f477d460f93fa.jpg\",\n  \"180411.jpg\": \"Reptilia/Anolis equestris/bc0c24d14df5e9c1c381f4f06251714b.jpg\",\n  \"180412.jpg\": \"Reptilia/Anolis equestris/0da5e53497663ec7733ce35539d1de9d.jpg\",\n  \"180413.jpg\": \"Reptilia/Anolis equestris/9894d7dc78b2d20195e46939e6a8ae89.jpg\",\n  \"18042.jpg\": \"Reptilia/Lampropeltis californiae/c25d573eb9fc4e7b185dbf76a5f2403b.jpg\",\n  \"180427.jpg\": \"Insecta/Forficula auricularia/ab64cee869153e334a01f158d1c19427.jpg\",\n  \"180429.jpg\": \"Insecta/Forficula auricularia/fa7eead9495091d4f82d8d9be7d46ef6.jpg\",\n  \"180435.jpg\": \"Insecta/Forficula auricularia/368b14dec8d8756c3fed4ceba8f3d4b6.jpg\",\n  \"18044.jpg\": \"Reptilia/Lampropeltis californiae/80ef74dfb852f6a47f7009fdebeecbd5.jpg\",\n  \"180445.jpg\": \"Insecta/Forficula auricularia/dd013f10e54b1059e08e8d3e1ff1cf57.jpg\",\n  \"180446.jpg\": \"Insecta/Forficula auricularia/89d473c96453add522e3d185920644eb.jpg\",\n  \"180457.jpg\": \"Insecta/Forficula auricularia/5c5985aaa0e2d47d047f5f5f8f2d1f00.jpg\",\n  \"180460.jpg\": \"Insecta/Forficula auricularia/5248bd19779ded7ee98201dc60de378a.jpg\",\n  \"180470.jpg\": \"Insecta/Forficula auricularia/65afb207f03f1d49c1f963cb317266d8.jpg\",\n  \"18048.jpg\": \"Reptilia/Lampropeltis californiae/7d09a6b090a4cd3d7304da740cf901de.jpg\",\n  \"180482.jpg\": \"Insecta/Forficula auricularia/c5499b6ad97803c00280b4d863e533f7.jpg\",\n  \"180487.jpg\": \"Insecta/Forficula auricularia/d594f31cdd0b9708798a2e673a584da6.jpg\",\n  \"180490.jpg\": \"Insecta/Forficula auricularia/a7d967b0a539b5e9895f50413bed32ab.jpg\",\n  \"180491.jpg\": \"Insecta/Forficula auricularia/0e38109ef638e7453af51d9638afb19e.jpg\",\n  \"180494.jpg\": \"Insecta/Forficula auricularia/cd47f6d6b85ee40dca09fedcc301d8c7.jpg\",\n  \"180495.jpg\": \"Insecta/Forficula auricularia/3ba6cb288011462f5db861dad91215d6.jpg\",\n  \"180496.jpg\": \"Insecta/Forficula auricularia/9d2e197e26c41e1268051ab40647f3d5.jpg\",\n  \"18050.jpg\": \"Reptilia/Lampropeltis californiae/07c8c8f2882fd64836ffb560fde98496.jpg\",\n  \"180501.jpg\": \"Insecta/Forficula auricularia/2ab9003099b66ee1c79da33732db8c80.jpg\",\n  \"180502.jpg\": \"Insecta/Forficula auricularia/b27ef5aeb5b57badae20f135f5f4ff2b.jpg\",\n  \"180507.jpg\": \"Insecta/Forficula auricularia/eb413169a4874f1ba8918375b342d759.jpg\",\n  \"180508.jpg\": \"Insecta/Forficula auricularia/02312280928d71603d32d2d258e28ef3.jpg\",\n  \"180513.jpg\": \"Insecta/Forficula auricularia/786356741bedfdd367209b0bcdfdb2d4.jpg\",\n  \"180599.jpg\": \"Mammalia/Rupicapra rupicapra/28ce9bc7e23fb10a768d4158ef86abf4.jpg\",\n  \"180606.jpg\": \"Mammalia/Rupicapra rupicapra/f2bc3204b81195e440cc17938ae5e9be.jpg\",\n  \"180609.jpg\": \"Mammalia/Rupicapra rupicapra/3e1a1ebe7dc0b4f3eea9154158c0d212.jpg\",\n  \"180630.jpg\": \"Aves/Melanitta americana/025f4ac252c05270c26fbecc707df627.jpg\",\n  \"180636.jpg\": \"Aves/Melanitta americana/36dd8da98dac1b6140e735be3ffe4404.jpg\",\n  \"18064.jpg\": \"Reptilia/Lampropeltis californiae/2b192ff3de3e0bb0f70f54bf61850491.jpg\",\n  \"180641.jpg\": \"Aves/Melanitta americana/c4b3aac34163fa209a4b68714ef87c6f.jpg\",\n  \"180644.jpg\": \"Aves/Melanitta americana/a479cdfde8fed7bf294179a6776dbed1.jpg\",\n  \"18065.jpg\": \"Reptilia/Lampropeltis californiae/802ede8d584c3a20f8df42c882a50d97.jpg\",\n  \"180669.jpg\": \"Aves/Arenaria interpres/d7080fcbbe94de3a52a7fbabf2e7b201.jpg\",\n  \"180673.jpg\": \"Aves/Arenaria interpres/61ec77b2af36933613997aa78a495ff8.jpg\",\n  \"18068.jpg\": \"Reptilia/Lampropeltis californiae/fa0c6d3a6187f616bf9ea00c128160e9.jpg\",\n  \"180688.jpg\": \"Aves/Arenaria interpres/8a1b4b18aad06be2e634b251b6feb3a4.jpg\",\n  \"180698.jpg\": \"Aves/Arenaria interpres/9339aec8bf5b48596f5d5d278a83fdb0.jpg\",\n  \"1807.jpg\": \"Insecta/Trimerotropis pallidipennis/69a376efb64bec187a16b159ae088a2b.jpg\",\n  \"180725.jpg\": \"Aves/Arenaria interpres/1fbf158e404859e851dd9732a7ede018.jpg\",\n  \"180731.jpg\": \"Aves/Arenaria interpres/87885fd203d0e5fa69827a3cd8ab1bd4.jpg\",\n  \"180741.jpg\": \"Aves/Arenaria interpres/1a530afbc11e9674d283bc011552fed8.jpg\",\n  \"1808.jpg\": \"Insecta/Trimerotropis pallidipennis/7f108cbfed04ccda7c94ffe9a49f0ef7.jpg\",\n  \"180807.jpg\": \"Aves/Arenaria interpres/5a6927dca842a9eb6944934885fe1d66.jpg\",\n  \"180811.jpg\": \"Aves/Arenaria interpres/1a6a81a1c6a9e4632f62304d57162287.jpg\",\n  \"180818.jpg\": \"Aves/Arenaria interpres/441ddb226941e7e17f4f55041ffd1076.jpg\",\n  \"180824.jpg\": \"Aves/Arenaria interpres/521724392435e846cf6ac59bff394537.jpg\",\n  \"180826.jpg\": \"Aves/Arenaria interpres/7d41f6a04c832165c6e8c79f5471352f.jpg\",\n  \"180846.jpg\": \"Aves/Arenaria interpres/aa746149190cae29787b9c4d179076d4.jpg\",\n  \"18089.jpg\": \"Reptilia/Lampropeltis californiae/3e21fa55606b9de8c7252070fa923598.jpg\",\n  \"18090.jpg\": \"Reptilia/Lampropeltis californiae/75be5d95a8a22980ad1e757ce83cbc65.jpg\",\n  \"180900.jpg\": \"Insecta/Coccinella californica/e5c5f432d19902094911498bdeca441a.jpg\",\n  \"180901.jpg\": \"Insecta/Coccinella californica/571b0a0ff7e4143c040ce5bd7075e6db.jpg\",\n  \"180902.jpg\": \"Insecta/Coccinella californica/4421a10cff601f824e315ae123b7cac4.jpg\",\n  \"180903.jpg\": \"Insecta/Coccinella californica/115ffe39c807e42473d3a38070915189.jpg\",\n  \"180906.jpg\": \"Insecta/Coccinella californica/674b39642d1a1fe079fef7f46c6f4f4f.jpg\",\n  \"180907.jpg\": \"Insecta/Coccinella californica/47d6f71f04a33dacca7427278f7aba01.jpg\",\n  \"180911.jpg\": \"Insecta/Coccinella californica/a90e76fbbec908435d81c207161c3a26.jpg\",\n  \"180914.jpg\": \"Insecta/Coccinella californica/716e9791060fcff5225a387ed4e85792.jpg\",\n  \"180920.jpg\": \"Insecta/Coccinella californica/a8932ac8db583473307995dd4876a932.jpg\",\n  \"180922.jpg\": \"Insecta/Coccinella californica/db22b44daf3839c751b00c410da567d4.jpg\",\n  \"180923.jpg\": \"Insecta/Coccinella californica/58b9de468d1dd16b564c9c1900a89fe7.jpg\",\n  \"180924.jpg\": \"Insecta/Coccinella californica/67c1a4fcf794cbae351cf530c8cda3c0.jpg\",\n  \"180934.jpg\": \"Insecta/Coccinella californica/1aefbde0a6c22a55bf1bcc7fb122a42d.jpg\",\n  \"180936.jpg\": \"Insecta/Coccinella californica/d7b100dd1d615bc9c7ecc8f10ac60d69.jpg\",\n  \"180937.jpg\": \"Insecta/Coccinella californica/af08376ae1230420dcae1707b06a2fec.jpg\",\n  \"180938.jpg\": \"Insecta/Coccinella californica/d302ba8937af826d9cdcb7f7e52fff1b.jpg\",\n  \"180940.jpg\": \"Insecta/Coccinella californica/c9887d24c802c7894bf39c6e8ce73c12.jpg\",\n  \"180941.jpg\": \"Insecta/Coccinella californica/05b5705c162198228df1628ede977ad8.jpg\",\n  \"180942.jpg\": \"Insecta/Coccinella californica/e0ac197b602480d7989bf5557c01b473.jpg\",\n  \"180943.jpg\": \"Insecta/Coccinella californica/9fe3219ead481087ed466e44ff9db5ee.jpg\",\n  \"18095.jpg\": \"Reptilia/Lampropeltis californiae/66bfca166f2c75c4d6b70f908efb6ead.jpg\",\n  \"18101.jpg\": \"Reptilia/Lampropeltis californiae/dffee8ee7ef4034bbd0479dc3fcde9a3.jpg\",\n  \"181141.jpg\": \"Insecta/Diaethria anna/b015966551251b134d04411aea00cf7e.jpg\",\n  \"181142.jpg\": \"Insecta/Diaethria anna/a378cf3c59d4d71fd90449e7c58e1e4f.jpg\",\n  \"181148.jpg\": \"Insecta/Diaethria anna/f9445fc8c0b37770080c4fcfc02f60f5.jpg\",\n  \"181153.jpg\": \"Insecta/Diaethria anna/294488bcdac0c9e9f34bc765ebb69d27.jpg\",\n  \"18116.jpg\": \"Reptilia/Lampropeltis californiae/f25d8d89e403bc9cbfa6a12c0531136e.jpg\",\n  \"181161.jpg\": \"Insecta/Diaethria anna/cc9347ace23c5bc0667eda4681f64c75.jpg\",\n  \"1812.jpg\": \"Insecta/Trimerotropis pallidipennis/c971384332386b883f3747f1789d9c3f.jpg\",\n  \"18125.jpg\": \"Reptilia/Lampropeltis californiae/e8ae87e49a1dbb03555e0a841a29082b.jpg\",\n  \"18129.jpg\": \"Reptilia/Lampropeltis californiae/eab5aeaa7e8c11964c93a5150590456f.jpg\",\n  \"1813.jpg\": \"Insecta/Trimerotropis pallidipennis/72b3f94adda957b583c56052222db57f.jpg\",\n  \"181335.jpg\": \"Aves/Hemiphaga novaeseelandiae/d0fab11b12e9738a60b5ecbccb073e3a.jpg\",\n  \"181337.jpg\": \"Aves/Hemiphaga novaeseelandiae/5db85825e388676631537ce45326598c.jpg\",\n  \"181341.jpg\": \"Aves/Hemiphaga novaeseelandiae/5faca462d247ea21e6f830bdb635c952.jpg\",\n  \"181386.jpg\": \"Aves/Hemiphaga novaeseelandiae/728f7990d7c5ab1349cd40424f5b8acf.jpg\",\n  \"181412.jpg\": \"Aves/Hemiphaga novaeseelandiae/ee8f332ffccd38fed235bdc8f82a6f9b.jpg\",\n  \"181415.jpg\": \"Aves/Hemiphaga novaeseelandiae/ee43ee04cdd75d746a06bc36e446c9f2.jpg\",\n  \"181500.jpg\": \"Aves/Hemiphaga novaeseelandiae/7dd8636dae745bc7cab55592f8d974b2.jpg\",\n  \"181510.jpg\": \"Aves/Hemiphaga novaeseelandiae/96fd8c5c31d842eef89c908200f0de62.jpg\",\n  \"18153.jpg\": \"Reptilia/Lampropeltis californiae/c033b63a0acaea73ccad3fe51a148eff.jpg\",\n  \"18158.jpg\": \"Reptilia/Lampropeltis californiae/23278fe6b5bb5941c0121c98fe0ac7bd.jpg\",\n  \"18169.jpg\": \"Reptilia/Lampropeltis californiae/8a3454d5bf0a6d7ca0c3939fdec71d0f.jpg\",\n  \"1817.jpg\": \"Insecta/Trimerotropis pallidipennis/05aca6bdcda83791067633d024d3df6e.jpg\",\n  \"181700.jpg\": \"Aves/Hemiphaga novaeseelandiae/5eeeeed72c08684fdd7148e981886437.jpg\",\n  \"181703.jpg\": \"Aves/Hemiphaga novaeseelandiae/ea52099d8657d13b5c74ceac64f8ac9c.jpg\",\n  \"181725.jpg\": \"Aves/Hemiphaga novaeseelandiae/098f7adcd57acbc0eb4eaa0240e5c4ee.jpg\",\n  \"18173.jpg\": \"Reptilia/Lampropeltis californiae/034e22d392051c20e88591e57d014631.jpg\",\n  \"181737.jpg\": \"Aves/Hemiphaga novaeseelandiae/5f9e26414856be67eddf589783b8e198.jpg\",\n  \"181776.jpg\": \"Aves/Hemiphaga novaeseelandiae/6b1b5682db26b6b6aaa1224190e8ded5.jpg\",\n  \"181778.jpg\": \"Aves/Hemiphaga novaeseelandiae/f53f87d31776481f01ea0696d2010d0e.jpg\",\n  \"181835.jpg\": \"Aves/Hemiphaga novaeseelandiae/e15cbc17eb2a3c887004514362f75ef9.jpg\",\n  \"181853.jpg\": \"Aves/Hemiphaga novaeseelandiae/85a8e1cb3e93d272840e2232528929e1.jpg\",\n  \"181857.jpg\": \"Aves/Paroaria capitata/200eba79c4fe77874d79ab5706f4eace.jpg\",\n  \"18190.jpg\": \"Insecta/Typocerus velutinus/94f4df24e50a07100a30e8e88b832227.jpg\",\n  \"18191.jpg\": \"Insecta/Typocerus velutinus/9051f17179841d01ba632665aa1faf85.jpg\",\n  \"18193.jpg\": \"Insecta/Typocerus velutinus/8b2243e215689f313cc2491905d343f0.jpg\",\n  \"18195.jpg\": \"Insecta/Typocerus velutinus/d4d4a4a42a0c83d964d704a9ebe43798.jpg\",\n  \"18196.jpg\": \"Insecta/Typocerus velutinus/711a0b481239d1e49919bb4b0e22037b.jpg\",\n  \"181987.jpg\": \"Aves/Melanerpes uropygialis/54a0779829c1e8530d76506901871527.jpg\",\n  \"181991.jpg\": \"Aves/Melanerpes uropygialis/b10ec43edaad7d768c004683e5f249d1.jpg\",\n  \"182011.jpg\": \"Aves/Melanerpes uropygialis/afd856f44919309d09abcbd49dc4d1c2.jpg\",\n  \"182012.jpg\": \"Aves/Melanerpes uropygialis/d1487fd116de8987d1e7513cf284df89.jpg\",\n  \"182013.jpg\": \"Aves/Melanerpes uropygialis/cf6d9fb8cec501c74d55ab8f9fc32f89.jpg\",\n  \"182014.jpg\": \"Aves/Melanerpes uropygialis/dfd7ddaebae4dd0917a456975bad076c.jpg\",\n  \"182016.jpg\": \"Aves/Melanerpes uropygialis/d584c5a7f974eef11648a84ceb6cb567.jpg\",\n  \"182017.jpg\": \"Aves/Melanerpes uropygialis/2c94b116b34bcfeb982d307b94111b8d.jpg\",\n  \"182018.jpg\": \"Aves/Melanerpes uropygialis/4f6653dfa160e9a53e5b28f71b7fca94.jpg\",\n  \"182025.jpg\": \"Aves/Melanerpes uropygialis/fbbe650705779b64046d27d5c1396d32.jpg\",\n  \"182030.jpg\": \"Aves/Melanerpes uropygialis/03ba2fb544e9711a959e599c2ca08d7a.jpg\",\n  \"182032.jpg\": \"Aves/Melanerpes uropygialis/50c9a3f677c9880cbd8c79fbb92b9707.jpg\",\n  \"182037.jpg\": \"Aves/Melanerpes uropygialis/df5a1c07d5ef992e5047ac89c31a1871.jpg\",\n  \"18204.jpg\": \"Insecta/Typocerus velutinus/b52e8904bb5e4f45b1fec4e0552d64c1.jpg\",\n  \"182041.jpg\": \"Aves/Melanerpes uropygialis/2dc1faa1a2d5f0ae517596f8fb65452e.jpg\",\n  \"182043.jpg\": \"Aves/Melanerpes uropygialis/a125756aa4055fc5a3ce275728e4cfa7.jpg\",\n  \"182044.jpg\": \"Aves/Melanerpes uropygialis/26d5a3d9798c22d11a770d828e27f939.jpg\",\n  \"182045.jpg\": \"Aves/Melanerpes uropygialis/1fb6fd0ba8de91863578d10cd763ad1e.jpg\",\n  \"182048.jpg\": \"Aves/Melanerpes uropygialis/a3960c3be44e7f95097f258db18dd98e.jpg\",\n  \"182051.jpg\": \"Aves/Melanerpes uropygialis/0538cee1351c7f8459d6e3cc19d95c50.jpg\",\n  \"18206.jpg\": \"Insecta/Typocerus velutinus/6356a677fe187ce18833618e31028314.jpg\",\n  \"182072.jpg\": \"Aves/Dendrocygna viduata/143028ab90021c2da85d083494b61cc9.jpg\",\n  \"18210.jpg\": \"Insecta/Typocerus velutinus/62ea33ccd11b17475b5aa3a2dad3da33.jpg\",\n  \"18214.jpg\": \"Insecta/Typocerus velutinus/0ae1c5323ed4088d575a541a70573c92.jpg\",\n  \"182153.jpg\": \"Aves/Passerina caerulea/c8f3a45da8d78c58303652bfcbdb1328.jpg\",\n  \"18217.jpg\": \"Mollusca/Lottia gigantea/670e4e845e5cf9a3b97bbe45c541c0b4.jpg\",\n  \"18218.jpg\": \"Mollusca/Lottia gigantea/b450921b1dcbfd85eebed2569fc13783.jpg\",\n  \"182180.jpg\": \"Aves/Passerina caerulea/06b54ec19cde282e57643b9b69782cc3.jpg\",\n  \"182181.jpg\": \"Aves/Passerina caerulea/0e7035033014fefd600268f66619642c.jpg\",\n  \"18219.jpg\": \"Mollusca/Lottia gigantea/057025c99a1d694d4a4126baf4f3ccaf.jpg\",\n  \"182194.jpg\": \"Aves/Passerina caerulea/ccdc985ea36a353c86aeef300c2ad18e.jpg\",\n  \"182196.jpg\": \"Aves/Passerina caerulea/bec7dbe14c25ac23fcbc64e03815d817.jpg\",\n  \"182198.jpg\": \"Aves/Passerina caerulea/970e5f5462913802113bff37e52fdd34.jpg\",\n  \"182199.jpg\": \"Aves/Passerina caerulea/7efe68992e2be706172e9187711fabe3.jpg\",\n  \"1822.jpg\": \"Insecta/Trimerotropis pallidipennis/28233e5af8fad231a61c3600d61d9b8e.jpg\",\n  \"18220.jpg\": \"Mollusca/Lottia gigantea/4b7f964fb33992895b0fc77e6a856deb.jpg\",\n  \"182210.jpg\": \"Aves/Passerina caerulea/81753cb4fe07d40ebf09074147c834cb.jpg\",\n  \"182225.jpg\": \"Aves/Passerina caerulea/97b6b2012bbe5f30e471d7d1ab199675.jpg\",\n  \"18223.jpg\": \"Mollusca/Lottia gigantea/d4464c40f7f63aaa9da500e663d0a928.jpg\",\n  \"182232.jpg\": \"Aves/Passerina caerulea/806465772042d8c74601c9dc2c33ce43.jpg\",\n  \"18224.jpg\": \"Mollusca/Lottia gigantea/2b342c35a3323dd14ed17a5b78bf1613.jpg\",\n  \"182240.jpg\": \"Aves/Passerina caerulea/6d257f9f6e89ec6cba8cfe3e3d8bfe60.jpg\",\n  \"182241.jpg\": \"Aves/Passerina caerulea/eb1987469dacd769894fbe6f2101051e.jpg\",\n  \"182246.jpg\": \"Aves/Passerina caerulea/aea29ac24042615d8c95ff76830ec3b7.jpg\",\n  \"18225.jpg\": \"Mollusca/Lottia gigantea/a6f95e84636ef0c0244f29f703b0687b.jpg\",\n  \"182253.jpg\": \"Aves/Passerina caerulea/41ea39bf8c622679ab9aeeb1da9cbc12.jpg\",\n  \"18230.jpg\": \"Mollusca/Lottia gigantea/9e0de85db9a8378c393d9498ff5d5fcf.jpg\",\n  \"182307.jpg\": \"Aves/Passerina caerulea/5d285689a78cec0f400a85d67cd5296a.jpg\",\n  \"182309.jpg\": \"Aves/Passerina caerulea/29aa42d4a9561d11d189f80be02bec24.jpg\",\n  \"182321.jpg\": \"Aves/Passerina caerulea/569b4089a2fa6680f36204626043debd.jpg\",\n  \"182327.jpg\": \"Aves/Passerina caerulea/e4b2043a68475c10b8efad6467d302dd.jpg\",\n  \"182332.jpg\": \"Aves/Passerina caerulea/d7dd099df62b27901db3493309a0c792.jpg\",\n  \"182333.jpg\": \"Aves/Passerina caerulea/1173c1f7a8cd301f64f8195db42558aa.jpg\",\n  \"182352.jpg\": \"Aves/Passerina caerulea/9f6db45106d0faeac7cf1ab5249bdfda.jpg\",\n  \"182409.jpg\": \"Aves/Amazilia violiceps/d9c52fc81c14e80642546ed521b24c84.jpg\",\n  \"182410.jpg\": \"Aves/Amazilia violiceps/40e160b03e64a3be85000540d46f93a3.jpg\",\n  \"182411.jpg\": \"Aves/Amazilia violiceps/adc9a9b400420ec3a9a7834b515b54f9.jpg\",\n  \"182412.jpg\": \"Aves/Amazilia violiceps/df29cc952101cad2a72b1a2364f5fef0.jpg\",\n  \"182413.jpg\": \"Aves/Amazilia violiceps/518e366357b8384f2712ab1e62417222.jpg\",\n  \"182414.jpg\": \"Aves/Amazilia violiceps/918e2b2e3c39bc980060af61ac523cc2.jpg\",\n  \"182417.jpg\": \"Aves/Amazilia violiceps/da7b0ac7cdfa29d0d847a9d2d03072a1.jpg\",\n  \"18242.jpg\": \"Mollusca/Lottia gigantea/407acd22abffe9dfd33561eb49e6655a.jpg\",\n  \"182422.jpg\": \"Aves/Amazilia violiceps/c96d2ed6395e087bb9fae75364227387.jpg\",\n  \"182424.jpg\": \"Aves/Amazilia violiceps/4218d729da6ece9ae93fa79b5a2384c8.jpg\",\n  \"182428.jpg\": \"Aves/Amazilia violiceps/45f9dad246bfdb41a3c897fb8f9cd8a7.jpg\",\n  \"182429.jpg\": \"Aves/Amazilia violiceps/eb4d12caf5ed9645ffa86f81a30cf161.jpg\",\n  \"18243.jpg\": \"Mollusca/Lottia gigantea/de7fcf53b62ef44c48cf161e3a804b58.jpg\",\n  \"182446.jpg\": \"Aves/Amazilia violiceps/930aae0e1a6d00780b31bc0d7fa08cbd.jpg\",\n  \"18245.jpg\": \"Mollusca/Lottia gigantea/a29c1238bc3bfa14ebbf8b3238e74b45.jpg\",\n  \"182458.jpg\": \"Aves/Amazilia violiceps/588f0e3e85f7fc65b476ac15f3b207cb.jpg\",\n  \"182460.jpg\": \"Aves/Amazilia violiceps/871d84454d15e0731c6eb939d1d1d935.jpg\",\n  \"182461.jpg\": \"Aves/Amazilia violiceps/106b1fc25329852dfcab42f4e666eae0.jpg\",\n  \"182465.jpg\": \"Aves/Amazilia violiceps/aaaf44bd29e5772471f1bd2fc885e761.jpg\",\n  \"182467.jpg\": \"Aves/Amazilia violiceps/9d452cb8b8ea5647ebf0070752623a5f.jpg\",\n  \"18247.jpg\": \"Mollusca/Lottia gigantea/ed13d6567fb83d8aae743f642cc6b277.jpg\",\n  \"182471.jpg\": \"Aves/Amazilia violiceps/ef15825c3dc568be4a3425457fd71778.jpg\",\n  \"182472.jpg\": \"Aves/Amazilia violiceps/b3d9164b45ecfb44cb3062cf2c3dbda4.jpg\",\n  \"182474.jpg\": \"Aves/Amazilia violiceps/774b1cb82328e43aed13ccbf62f4a0a7.jpg\",\n  \"18248.jpg\": \"Mollusca/Lottia gigantea/83c6e122170729307b8abafb672f5233.jpg\",\n  \"182498.jpg\": \"Insecta/Libellula vibrans/4498f9c272645de33348c00beb61c3a8.jpg\",\n  \"1825.jpg\": \"Aves/Sagittarius serpentarius/c918ae79fb1453a172c347bda97968a8.jpg\",\n  \"182503.jpg\": \"Insecta/Libellula vibrans/be7adb5ea5fe6e845555194ad478e3f1.jpg\",\n  \"182518.jpg\": \"Insecta/Libellula vibrans/cfe724de3c03eeec45976df0727945be.jpg\",\n  \"18252.jpg\": \"Mollusca/Lottia gigantea/3e8475ec9c3ce9776ac44360ac9b0d42.jpg\",\n  \"182525.jpg\": \"Insecta/Libellula vibrans/eb61eaaf554110f1b5a4c2d7bffeff2f.jpg\",\n  \"182529.jpg\": \"Insecta/Libellula vibrans/942abc81a1dc34cf24f0b983d4ee7161.jpg\",\n  \"182531.jpg\": \"Insecta/Libellula vibrans/f279320245c0a159c74461f18263b14c.jpg\",\n  \"182536.jpg\": \"Insecta/Libellula vibrans/39e71f4dab4207a7ea069cdb71815d8e.jpg\",\n  \"182543.jpg\": \"Insecta/Libellula vibrans/fbd9750074023f683faa5ac0bad3c7cd.jpg\",\n  \"182547.jpg\": \"Insecta/Libellula vibrans/edb1e230c08c0de901bc96845bab8238.jpg\",\n  \"182556.jpg\": \"Insecta/Libellula vibrans/0f8d868dde2237cf035f51a7cd8fdc5c.jpg\",\n  \"182559.jpg\": \"Insecta/Libellula vibrans/9833aa3b1978ff09126b63547a72a46a.jpg\",\n  \"182560.jpg\": \"Insecta/Libellula vibrans/ea684bb3cd941eaa8219f50560313f08.jpg\",\n  \"182569.jpg\": \"Insecta/Libellula vibrans/52e0595b777f89c41075b80848eeef81.jpg\",\n  \"182570.jpg\": \"Insecta/Libellula vibrans/b827ba90748fe24f4411d86db59e7580.jpg\",\n  \"182573.jpg\": \"Insecta/Libellula vibrans/02a755f6c40ffac16d8c9a49f6b3840b.jpg\",\n  \"182583.jpg\": \"Insecta/Libellula vibrans/98d6739b1bd6f0278227e3112b75ba33.jpg\",\n  \"182585.jpg\": \"Insecta/Libellula vibrans/10e73ce79888a74bf687894f38de56d7.jpg\",\n  \"182587.jpg\": \"Insecta/Libellula vibrans/4fa5113a977cb0f4ce4db7f848dcf285.jpg\",\n  \"182590.jpg\": \"Insecta/Libellula vibrans/3f669b2b5f105d5b9c88a3261d54ebfd.jpg\",\n  \"18261.jpg\": \"Mollusca/Lottia gigantea/a4b9e43e82baf5e0ea45595b975b888d.jpg\",\n  \"182611.jpg\": \"Insecta/Libellula vibrans/74905bff9544de18b6c949045116fc19.jpg\",\n  \"18262.jpg\": \"Mollusca/Lottia gigantea/8d273624c3a7a9bc627998ada714acc0.jpg\",\n  \"182621.jpg\": \"Insecta/Palpita quadristigmalis/9c7a22b965e6f100e947d24de3fe6735.jpg\",\n  \"182625.jpg\": \"Insecta/Palpita quadristigmalis/d01112fed399e04e0fcece1a2f17a308.jpg\",\n  \"182626.jpg\": \"Insecta/Palpita quadristigmalis/0c7ae0c9f0f54ec73bd51b6e4e102b8a.jpg\",\n  \"182636.jpg\": \"Insecta/Palpita quadristigmalis/88e12ad105219333ff94e47b4c442d23.jpg\",\n  \"182637.jpg\": \"Insecta/Palpita quadristigmalis/597eb95d591317b89e4b5c9a07d889b7.jpg\",\n  \"182638.jpg\": \"Insecta/Palpita quadristigmalis/d8f657feff0d776c5a2a7d6e05ce5df7.jpg\",\n  \"182641.jpg\": \"Insecta/Palpita quadristigmalis/5eb105d7733e316173b958f88e02eba1.jpg\",\n  \"182671.jpg\": \"Insecta/Libellula cyanea/b3491b14b462e441ed142eda4697a116.jpg\",\n  \"182672.jpg\": \"Insecta/Libellula cyanea/0a4072fcb280070733ae7bd126b3049d.jpg\",\n  \"182679.jpg\": \"Insecta/Libellula cyanea/66e39b0ff82f250a276de3b7ff2d9a8f.jpg\",\n  \"182680.jpg\": \"Insecta/Libellula cyanea/6cf84926a3f655ff8a65430a6e435bd7.jpg\",\n  \"182681.jpg\": \"Insecta/Libellula cyanea/5ec8560c81fea752db61c618c83e25a8.jpg\",\n  \"182682.jpg\": \"Insecta/Libellula cyanea/c3dc62120818fbd3661f559bc52d2b31.jpg\",\n  \"182684.jpg\": \"Insecta/Libellula cyanea/c3b81ccea819ae2e52bc185c0e60907c.jpg\",\n  \"182685.jpg\": \"Insecta/Libellula cyanea/63f18198e77628664f6bbe5e1d760708.jpg\",\n  \"182687.jpg\": \"Insecta/Libellula cyanea/63d8b868554008c7370a38fed1fd38fb.jpg\",\n  \"182690.jpg\": \"Insecta/Libellula cyanea/e09cd3145d93625f954cbe6141f965cd.jpg\",\n  \"182692.jpg\": \"Insecta/Libellula cyanea/d1e073beee18bfd74a78ec94e0ecf08b.jpg\",\n  \"182708.jpg\": \"Insecta/Libellula cyanea/0a1b4770cb771c52fb72eebfcb541ece.jpg\",\n  \"182714.jpg\": \"Insecta/Libellula cyanea/7ab0002917f8bc4adc033dd087ea2c52.jpg\",\n  \"182715.jpg\": \"Insecta/Libellula cyanea/94d5147fb0730cabcbb989ceed194a1f.jpg\",\n  \"182717.jpg\": \"Insecta/Libellula cyanea/747adaba893c9f3ed900de11f18ad514.jpg\",\n  \"182718.jpg\": \"Insecta/Libellula cyanea/3a68be4c34756cf0e81d74d7f9c85047.jpg\",\n  \"182719.jpg\": \"Insecta/Libellula cyanea/1086ae1cf8b0c85904a37e48f6adbb33.jpg\",\n  \"182720.jpg\": \"Insecta/Libellula cyanea/160bbb89a85727466b7fe8cbf0e9efc6.jpg\",\n  \"182721.jpg\": \"Insecta/Libellula cyanea/5062a63229e858e8b552a9f14da74131.jpg\",\n  \"182722.jpg\": \"Insecta/Libellula cyanea/88d6f36912355a9d4854a2db0a6147ed.jpg\",\n  \"182727.jpg\": \"Insecta/Libellula cyanea/331a201e006d388f7f297d8587fe51d1.jpg\",\n  \"182999.jpg\": \"Amphibia/Pseudacris streckeri/8e409ee1d1fb2d95a6844396a941f9ca.jpg\",\n  \"183.jpg\": \"Aves/Zonotrichia capensis/ae6ad4ddb7c7bc8e7ccdb44db8d87435.jpg\",\n  \"183000.jpg\": \"Amphibia/Pseudacris streckeri/b0f644f4e0e07983cce2820d47fb7ef3.jpg\",\n  \"183003.jpg\": \"Amphibia/Pseudacris streckeri/27c26a81423a726e893676fe8e644161.jpg\",\n  \"183006.jpg\": \"Amphibia/Pseudacris streckeri/e5515dfe1319f96a26c109d3667b76a1.jpg\",\n  \"183009.jpg\": \"Amphibia/Pseudacris streckeri/891f44a2cfd937131427fbf56475e719.jpg\",\n  \"183014.jpg\": \"Amphibia/Pseudacris streckeri/934d6f099d1556204f7c8b2d792b47fb.jpg\",\n  \"183018.jpg\": \"Amphibia/Pseudacris streckeri/4df59401cbb7d1ee63223f79b9fdcf7d.jpg\",\n  \"1831.jpg\": \"Aves/Sagittarius serpentarius/c8d4066b79864937606ef8d6936fee64.jpg\",\n  \"183120.jpg\": \"Animalia/Myliobatis californica/9ee1ddd2ba71a100a4c7536be83a6e52.jpg\",\n  \"183123.jpg\": \"Animalia/Myliobatis californica/d1c7df14a8db68bebbf52309dbadc719.jpg\",\n  \"183124.jpg\": \"Animalia/Myliobatis californica/daf8e82de1bf7b6c6eca48dce8008a05.jpg\",\n  \"183125.jpg\": \"Animalia/Myliobatis californica/79e9e5446c948181c07b3fbc85fe3ddd.jpg\",\n  \"183132.jpg\": \"Animalia/Myliobatis californica/be4983a1955db5079d0fa9b7f00a8463.jpg\",\n  \"183135.jpg\": \"Animalia/Myliobatis californica/bad398c97c93823b474db76039cc0d9d.jpg\",\n  \"183211.jpg\": \"Insecta/Vanessa virginiensis/da2bd04c93644f4b4b39035c3c2c8d54.jpg\",\n  \"183248.jpg\": \"Insecta/Vanessa virginiensis/8101be2434c6bf99549732f9a63864b9.jpg\",\n  \"183254.jpg\": \"Insecta/Vanessa virginiensis/d44a9e5695b7cfaec072ed50a8d41372.jpg\",\n  \"183263.jpg\": \"Insecta/Vanessa virginiensis/53c86ff6b5a374acbc10a376f634ee93.jpg\",\n  \"183279.jpg\": \"Insecta/Vanessa virginiensis/8debf73affe9995598c5dc720f936370.jpg\",\n  \"183290.jpg\": \"Insecta/Vanessa virginiensis/860598c5f1f8b6b8cdc346e0904fffcc.jpg\",\n  \"183332.jpg\": \"Insecta/Vanessa virginiensis/2ebe75a397522b255d0eb0a0ee87ecea.jpg\",\n  \"183342.jpg\": \"Insecta/Vanessa virginiensis/201c65571f2d1ae671e582aed56264f1.jpg\",\n  \"183343.jpg\": \"Insecta/Vanessa virginiensis/0de644658451669d531bccb7d5363c4f.jpg\",\n  \"183360.jpg\": \"Insecta/Vanessa virginiensis/66860121f6b3c088a5d805db7f853840.jpg\",\n  \"183378.jpg\": \"Insecta/Vanessa virginiensis/ed167fab00204174a8410c46073df1d5.jpg\",\n  \"1834.jpg\": \"Aves/Sagittarius serpentarius/4865af5c0d8df5033135b6d39187dd44.jpg\",\n  \"183441.jpg\": \"Insecta/Vanessa virginiensis/eeb613b389bddf5ad1441943f82d912f.jpg\",\n  \"183442.jpg\": \"Insecta/Vanessa virginiensis/887699d5ba51e4a35c0dea30739a7cf1.jpg\",\n  \"183569.jpg\": \"Insecta/Vanessa virginiensis/30db2b6baa31189dd1070a9a387c2204.jpg\",\n  \"183590.jpg\": \"Insecta/Vanessa virginiensis/4dc234127b9961ea09d9ed8543a98d76.jpg\",\n  \"183611.jpg\": \"Insecta/Vanessa virginiensis/abb9f3338350cf7dd76f3634f67d771e.jpg\",\n  \"183621.jpg\": \"Insecta/Vanessa virginiensis/b09bbcf8940267145c34c9632eda1686.jpg\",\n  \"183625.jpg\": \"Insecta/Vanessa virginiensis/21e382228f4a21e62b9fdb8d72693258.jpg\",\n  \"183633.jpg\": \"Insecta/Vanessa virginiensis/1962329adbe50212aa0757edb46f613b.jpg\",\n  \"183683.jpg\": \"Insecta/Calycopis isobeon/bde8f07b85ade150de08862663520c8f.jpg\",\n  \"183684.jpg\": \"Insecta/Calycopis isobeon/ceb843513880a53d29d3a824d7a40be5.jpg\",\n  \"183687.jpg\": \"Insecta/Calycopis isobeon/055ab26566db2989cfad55afdd20ab57.jpg\",\n  \"183688.jpg\": \"Insecta/Calycopis isobeon/c2213e1b03128006cec67f934d210858.jpg\",\n  \"183698.jpg\": \"Insecta/Calycopis isobeon/ceb7b10d45015b30b621fa2f8de13e65.jpg\",\n  \"1837.jpg\": \"Aves/Sagittarius serpentarius/c68ed04bbba7394c2600364644bc8f80.jpg\",\n  \"183700.jpg\": \"Insecta/Calycopis isobeon/d4ee6b29eddb2841c800a2173c46bb72.jpg\",\n  \"183709.jpg\": \"Insecta/Calycopis isobeon/136dc4500fef160f1ededdb82e2f1086.jpg\",\n  \"183710.jpg\": \"Insecta/Calycopis isobeon/74afadb85346b0a47d78f2f625f6d201.jpg\",\n  \"183711.jpg\": \"Insecta/Calycopis isobeon/912ca833033e5a11cd1c89b2552547e1.jpg\",\n  \"183714.jpg\": \"Insecta/Calycopis isobeon/d1ae5a9b4d35447e0b8a525610fd2e37.jpg\",\n  \"183715.jpg\": \"Insecta/Calycopis isobeon/cc327629ff3651aaf46af5ffcbb16602.jpg\",\n  \"183716.jpg\": \"Insecta/Calycopis isobeon/87544c2f6e152096d022e2b233d8119a.jpg\",\n  \"183717.jpg\": \"Insecta/Calycopis isobeon/2166a772d1349973b144e9dd12e9ed02.jpg\",\n  \"183718.jpg\": \"Insecta/Calycopis isobeon/4e3d045eb0ad2ecbfff0691f65062c92.jpg\",\n  \"183721.jpg\": \"Insecta/Calycopis isobeon/cc1b89ab975e89a710eb6249d356555a.jpg\",\n  \"183726.jpg\": \"Insecta/Calycopis isobeon/0e6e479d967732e004f9a46238c94046.jpg\",\n  \"183727.jpg\": \"Insecta/Calycopis isobeon/29a5794347fdc6085302d5fa8676305c.jpg\",\n  \"183728.jpg\": \"Insecta/Calycopis isobeon/c8d9b8cfc5b89d30edf28ff5cfc4f801.jpg\",\n  \"183729.jpg\": \"Insecta/Calycopis isobeon/898e7f4b677f009aabdfe2b0f0ccef54.jpg\",\n  \"183734.jpg\": \"Insecta/Leptoglossus occidentalis/af3eed8fc16c8865c26c1cf8c1c0cc27.jpg\",\n  \"183735.jpg\": \"Insecta/Leptoglossus occidentalis/6544c6a6b911800a27011fe6ab644d69.jpg\",\n  \"183736.jpg\": \"Insecta/Leptoglossus occidentalis/26ba1b1b92b2c3a4b09735e81556b321.jpg\",\n  \"183740.jpg\": \"Insecta/Leptoglossus occidentalis/ad3e629a9d7008c9e4aaa28523abd0ca.jpg\",\n  \"183741.jpg\": \"Insecta/Leptoglossus occidentalis/1f653458a6927671ba99b9771c8f786f.jpg\",\n  \"183743.jpg\": \"Insecta/Leptoglossus occidentalis/9c4beb52ef0ec8e277a260cbacd6b889.jpg\",\n  \"183745.jpg\": \"Insecta/Leptoglossus occidentalis/faf1a1a1aab6cda0ed2086a9929e6bf0.jpg\",\n  \"183747.jpg\": \"Insecta/Leptoglossus occidentalis/2c1224d17dfc24008c61a9a234e176d6.jpg\",\n  \"183750.jpg\": \"Insecta/Leptoglossus occidentalis/32f1e99f26674ec757911c42a2d9ffdb.jpg\",\n  \"183759.jpg\": \"Insecta/Leptoglossus occidentalis/b4bbc2727fdaf2437a7e1779a25eb888.jpg\",\n  \"183763.jpg\": \"Insecta/Leptoglossus occidentalis/935d4edf0bdb4366f0a56680c50f9789.jpg\",\n  \"183765.jpg\": \"Insecta/Leptoglossus occidentalis/2811372eaf5b46f37203ca79ce1c91f5.jpg\",\n  \"183768.jpg\": \"Insecta/Leptoglossus occidentalis/f4287ce1f68f6bd13db68b560fa56444.jpg\",\n  \"183777.jpg\": \"Insecta/Leptoglossus occidentalis/3133e21b9113141833438ac8e23d9826.jpg\",\n  \"183778.jpg\": \"Insecta/Leptoglossus occidentalis/0cb16ea1cf276d1b47b00211261bf40a.jpg\",\n  \"183779.jpg\": \"Insecta/Leptoglossus occidentalis/2599ddd17b992f5b42d6bf404533faf3.jpg\",\n  \"183782.jpg\": \"Insecta/Leptoglossus occidentalis/04b72bcf966d2e1a8b0440c60feb30d4.jpg\",\n  \"183788.jpg\": \"Insecta/Leptoglossus occidentalis/370fe304faca161268df577a81fc7a78.jpg\",\n  \"183789.jpg\": \"Insecta/Leptoglossus occidentalis/026117b155fdb89622ebd14f25c3eebd.jpg\",\n  \"183792.jpg\": \"Mollusca/Lottia scabra/0f7b9c0976f762ca2869c7e6fee4d701.jpg\",\n  \"183798.jpg\": \"Mollusca/Lottia scabra/41968fa9e849b628bb39a9bad459f1d1.jpg\",\n  \"1838.jpg\": \"Aves/Sagittarius serpentarius/bda6b3abc05bee0259d8e4a11af88d50.jpg\",\n  \"183801.jpg\": \"Mollusca/Lottia scabra/8ab95bb6b18e0c3a80fbba7e63335854.jpg\",\n  \"183803.jpg\": \"Mollusca/Lottia scabra/8f2eafe9e09ee700f352377df8ece816.jpg\",\n  \"183804.jpg\": \"Mollusca/Lottia scabra/4cc142537d9001f1e6aeed9dba0a2acd.jpg\",\n  \"183809.jpg\": \"Mollusca/Lottia scabra/e5ecefbe6c8b4ff0a3b7602a448d5fc9.jpg\",\n  \"183812.jpg\": \"Mollusca/Lottia scabra/dee13fd58a167e330571034a7c4eae70.jpg\",\n  \"183819.jpg\": \"Mollusca/Lottia scabra/fa0a6d6651389f0cd1fe8b56b8357806.jpg\",\n  \"183820.jpg\": \"Mollusca/Lottia scabra/d51de63edc4e3bc674eaaab36bc79a83.jpg\",\n  \"183821.jpg\": \"Mollusca/Lottia scabra/88d304ad021975c8cafd5eabf3673051.jpg\",\n  \"183823.jpg\": \"Mollusca/Lottia scabra/f14103c9f0f44581f884fdda5baf694b.jpg\",\n  \"183827.jpg\": \"Mollusca/Lottia scabra/323e59d7711b00d8505c2bb892739c1c.jpg\",\n  \"183832.jpg\": \"Mollusca/Lottia scabra/3476099cd80ce01fddd4131249e24198.jpg\",\n  \"183834.jpg\": \"Mollusca/Lottia scabra/257b61e4c451e1979f1880760eb17ed3.jpg\",\n  \"183954.jpg\": \"Aves/Fulica atra/2dfda4eed92220ad397ea38db53026af.jpg\",\n  \"183960.jpg\": \"Aves/Fulica atra/a77600a2db4a0276232d534b9aa892b7.jpg\",\n  \"183988.jpg\": \"Aves/Fulica atra/c3b00eed9a7cb9dac57054c9b667fa1c.jpg\",\n  \"183990.jpg\": \"Aves/Fulica atra/9be1d8911da958fe7d0cec827d307f09.jpg\",\n  \"184.jpg\": \"Aves/Zonotrichia capensis/0387136aac22e809977178dbf5ce5a08.jpg\",\n  \"184000.jpg\": \"Aves/Fulica atra/f00fe90a156bc9bcb37281e28b0cd2b6.jpg\",\n  \"184007.jpg\": \"Aves/Fulica atra/feca63fbcf0f8681a9ae8d2522835175.jpg\",\n  \"184010.jpg\": \"Aves/Fulica atra/c31a0ba9e42e2d7e22a330eac632df85.jpg\",\n  \"184027.jpg\": \"Aves/Fulica atra/530b2a35a62ed559c20df019d6f6b62e.jpg\",\n  \"184032.jpg\": \"Aves/Fulica atra/e82ed7bf379c3def9679135124f96db4.jpg\",\n  \"184034.jpg\": \"Aves/Fulica atra/8d17ee4b993c300a93d7436647d25c39.jpg\",\n  \"184067.jpg\": \"Aves/Fulica atra/847a9635f371e4fe835cf6b338c21b6e.jpg\",\n  \"184078.jpg\": \"Aves/Fulica atra/0d7af16d9a8403037de624f27e180e79.jpg\",\n  \"184097.jpg\": \"Aves/Fulica atra/2dadc33a03c1af5849c8fe652b2ca12c.jpg\",\n  \"184098.jpg\": \"Aves/Fulica atra/130b710ec1e451793f770853adfc0d4f.jpg\",\n  \"184110.jpg\": \"Aves/Fulica atra/dcb13dc2c8061d004358abce876686ef.jpg\",\n  \"184113.jpg\": \"Aves/Fulica atra/450accb67569ccda5cf37ad3e2bbfcaa.jpg\",\n  \"184118.jpg\": \"Aves/Fulica atra/c44712ef5129102a0d4d014a26181520.jpg\",\n  \"184124.jpg\": \"Aves/Fulica atra/625a82b18dd336fed193c6e5786cb308.jpg\",\n  \"184132.jpg\": \"Aves/Fulica atra/3b701bce71e665307285d49c7a39a60d.jpg\",\n  \"184137.jpg\": \"Aves/Fulica atra/f4cef6d2e9b0c602993c9858c5781b1d.jpg\",\n  \"184255.jpg\": \"Insecta/Callophrys augustinus/36a28d68af97bb0dd0712f752a6c622e.jpg\",\n  \"184258.jpg\": \"Insecta/Callophrys augustinus/efe5987a30b814d2b4b8f59b42c11565.jpg\",\n  \"184260.jpg\": \"Insecta/Callophrys augustinus/aa0555d4b331fd06cee6cf0235212c5e.jpg\",\n  \"184261.jpg\": \"Insecta/Callophrys augustinus/2703d6999e81d45523ea5cfd1ed95874.jpg\",\n  \"184262.jpg\": \"Insecta/Callophrys augustinus/2c3074c1ccacd0e7ee172a1e2bdd20a3.jpg\",\n  \"184264.jpg\": \"Insecta/Callophrys augustinus/a2fcbaa24ccc92e36ac0bbd28e08b084.jpg\",\n  \"184267.jpg\": \"Insecta/Callophrys augustinus/24e55688484ee21548e0d295fdd072bf.jpg\",\n  \"184268.jpg\": \"Insecta/Callophrys augustinus/c8e27f9b65a4516133b96ab8bce374ac.jpg\",\n  \"184270.jpg\": \"Insecta/Callophrys augustinus/2aa747770aebd5dfd0a8c13332575826.jpg\",\n  \"184271.jpg\": \"Insecta/Callophrys augustinus/a65764aa2439e243315c7ecbaa6b92c3.jpg\",\n  \"184272.jpg\": \"Insecta/Callophrys augustinus/39df341369e58cc0b26138e77b08c665.jpg\",\n  \"184273.jpg\": \"Insecta/Callophrys augustinus/54545c05ea3e1be21f45851904a45a26.jpg\",\n  \"184277.jpg\": \"Insecta/Callophrys augustinus/22feba25d14e11705937a0401014e464.jpg\",\n  \"184280.jpg\": \"Insecta/Callophrys augustinus/e4e600cc756b7feac7c1c5181ef1ff2a.jpg\",\n  \"184281.jpg\": \"Insecta/Callophrys augustinus/5270679d8d2c84c1c1d006acba95cff4.jpg\",\n  \"184282.jpg\": \"Insecta/Callophrys augustinus/264e731ab2a4c3ce00dd6562948c028f.jpg\",\n  \"184283.jpg\": \"Insecta/Callophrys augustinus/c520047e5c4be77f011b13a1f86e48e2.jpg\",\n  \"184284.jpg\": \"Insecta/Callophrys augustinus/ac59eb73044283c22b16c635f93fe8dd.jpg\",\n  \"184286.jpg\": \"Insecta/Callophrys augustinus/314d110a4baed766c5db2f10604afbd1.jpg\",\n  \"184287.jpg\": \"Insecta/Callophrys augustinus/a66ddb7a89a8297ac4021d08db2b11e0.jpg\",\n  \"184290.jpg\": \"Insecta/Callophrys augustinus/c227386f80fb9e79aad8c60e106cabb5.jpg\",\n  \"184291.jpg\": \"Insecta/Callophrys augustinus/e7c26743ad70028e6121515dc0b2c9fc.jpg\",\n  \"184316.jpg\": \"Insecta/Celastrina ladon/792b86e5c6ed500c73887a6a8f14286d.jpg\",\n  \"184324.jpg\": \"Insecta/Celastrina ladon/e19de93635ce48ff5ecd9a5eb98f784f.jpg\",\n  \"184325.jpg\": \"Insecta/Celastrina ladon/6e21650b31a9a85cdfba35a1fa4bd342.jpg\",\n  \"184326.jpg\": \"Insecta/Celastrina ladon/69f25ad208c35c8c888a880c49e719ba.jpg\",\n  \"184327.jpg\": \"Insecta/Celastrina ladon/b7fb491da6348d281a302d9d3f26b35a.jpg\",\n  \"184328.jpg\": \"Insecta/Celastrina ladon/2af7d907d8ec8684bfe8229d993ab065.jpg\",\n  \"184329.jpg\": \"Insecta/Celastrina ladon/9e6d927302c111d9afbd3e0027e9edd4.jpg\",\n  \"184330.jpg\": \"Insecta/Celastrina ladon/0c649b80e06d6a398494a650e0445963.jpg\",\n  \"184331.jpg\": \"Insecta/Celastrina ladon/e7116021b92945ad9e571be9f03fabb8.jpg\",\n  \"184337.jpg\": \"Insecta/Celastrina ladon/3510ab19c2112227550eb05ced8341b4.jpg\",\n  \"184338.jpg\": \"Insecta/Celastrina ladon/4337bfe491969d6a3eb9337ddd35bfb4.jpg\",\n  \"184344.jpg\": \"Insecta/Celastrina ladon/2ca7d8f1a4054641949af881c5746a8f.jpg\",\n  \"184346.jpg\": \"Insecta/Celastrina ladon/ee00ffb1fa6c9672277977969fc407c2.jpg\",\n  \"184347.jpg\": \"Insecta/Celastrina ladon/17957bcf0db983aadf80fb954bc1972b.jpg\",\n  \"184351.jpg\": \"Insecta/Celastrina ladon/c1af098c22279d6a6321eb1346293072.jpg\",\n  \"184354.jpg\": \"Insecta/Celastrina ladon/d881dff6dc7c195198e36ad5132ae72a.jpg\",\n  \"184355.jpg\": \"Insecta/Celastrina ladon/72512a99a9f2cf992762740bb13b4344.jpg\",\n  \"184356.jpg\": \"Insecta/Celastrina ladon/dda9846481abc7f08e1b001508b0cf18.jpg\",\n  \"184357.jpg\": \"Insecta/Celastrina ladon/14fac601b4a8e8592f721373414bf181.jpg\",\n  \"184358.jpg\": \"Insecta/Celastrina ladon/a3e423421c64b5b2a43b0ae01e8d45e7.jpg\",\n  \"184367.jpg\": \"Insecta/Melitaea didyma/bedd2335f41495df98a3a55b899092ee.jpg\",\n  \"184368.jpg\": \"Insecta/Melitaea didyma/22e5a8bf0727d1d69ae4b4a2f30f568f.jpg\",\n  \"184371.jpg\": \"Insecta/Melitaea didyma/11a0972ee97ecf8b1eeed59f3a86ac1f.jpg\",\n  \"184375.jpg\": \"Insecta/Melitaea didyma/a3b70c9d91c4fc4f34cc6cbda07dfda6.jpg\",\n  \"184570.jpg\": \"Mollusca/Oxychilus draparnaudi/ae4f2d57fe22ccec6b37fc3d520ca734.jpg\",\n  \"184571.jpg\": \"Mollusca/Oxychilus draparnaudi/8727337a8bf6f2e86ddf5248297e6453.jpg\",\n  \"184573.jpg\": \"Mollusca/Oxychilus draparnaudi/d70c39b642ef8522911a733920cf8b98.jpg\",\n  \"184574.jpg\": \"Mollusca/Oxychilus draparnaudi/8d15b64f99c9f9ec9db995554c6476ff.jpg\",\n  \"184579.jpg\": \"Mollusca/Oxychilus draparnaudi/3cbd4a5cb9e1a163d1b8cb8af9b1142b.jpg\",\n  \"184580.jpg\": \"Mollusca/Oxychilus draparnaudi/43fe12e5338b45563bd847fff7c546f4.jpg\",\n  \"184585.jpg\": \"Mollusca/Oxychilus draparnaudi/6bb601162cf3ff53768e5e93d6715136.jpg\",\n  \"184586.jpg\": \"Mollusca/Oxychilus draparnaudi/45c6486892ebf72da090f18691899934.jpg\",\n  \"184587.jpg\": \"Mollusca/Oxychilus draparnaudi/c06f873bd3a58a7865645bbf28b02cda.jpg\",\n  \"184591.jpg\": \"Mollusca/Oxychilus draparnaudi/e588acc63401de5983674c63b474f0c4.jpg\",\n  \"184599.jpg\": \"Mollusca/Oxychilus draparnaudi/33a26b55f707992680acf12ba8173443.jpg\",\n  \"184600.jpg\": \"Mollusca/Oxychilus draparnaudi/8e16273c23717c3c4d597c450ec91348.jpg\",\n  \"184604.jpg\": \"Mollusca/Oxychilus draparnaudi/a90554d1823ac959feed32db3863372c.jpg\",\n  \"184605.jpg\": \"Mollusca/Oxychilus draparnaudi/484e3445e53dadc018f8b1d5ab5a497b.jpg\",\n  \"184606.jpg\": \"Mollusca/Oxychilus draparnaudi/83a6fc2ebd6f305cb5b01db338fb451c.jpg\",\n  \"184607.jpg\": \"Mollusca/Oxychilus draparnaudi/a1a16fa621e2de09707770ca7ba7c2a0.jpg\",\n  \"184608.jpg\": \"Mollusca/Oxychilus draparnaudi/4fdf2dca7781d0364feba3991edbfc9b.jpg\",\n  \"184610.jpg\": \"Mollusca/Oxychilus draparnaudi/acc5a4489ca5d5a8743d14efe13b9e3a.jpg\",\n  \"184611.jpg\": \"Mollusca/Oxychilus draparnaudi/eca338a61d3059a6600b26ca1b5a0cef.jpg\",\n  \"184653.jpg\": \"Mollusca/Flabellina iodinea/4b9b93814f97adec48aa45d5e295eb1c.jpg\",\n  \"184659.jpg\": \"Mollusca/Flabellina iodinea/3cc61043ee8c5a262b3e1747ce42b2a9.jpg\",\n  \"184660.jpg\": \"Mollusca/Flabellina iodinea/4a6a25b79a57d5506709a269c585c6fc.jpg\",\n  \"184661.jpg\": \"Mollusca/Flabellina iodinea/8b54e56c2656de27e4e7fdf821e0fb50.jpg\",\n  \"184663.jpg\": \"Mollusca/Flabellina iodinea/67e46fbb179aeac6edc910220bb6b545.jpg\",\n  \"184664.jpg\": \"Mollusca/Flabellina iodinea/245d7e6d2df19bee31079af8468eabaa.jpg\",\n  \"184668.jpg\": \"Mollusca/Flabellina iodinea/9ea853b9683bc21ed0dc43134707eee5.jpg\",\n  \"184670.jpg\": \"Mollusca/Flabellina iodinea/1ad53ca6104069c34d5db6db495d069f.jpg\",\n  \"184671.jpg\": \"Mollusca/Flabellina iodinea/d6aee0094c6638a6e83bbe4a934cc50d.jpg\",\n  \"184675.jpg\": \"Mollusca/Flabellina iodinea/203bcf6074e3b368024c3685c437c71c.jpg\",\n  \"184677.jpg\": \"Mollusca/Flabellina iodinea/a1e699696d26fb4fab8a4ebe8615c26a.jpg\",\n  \"184684.jpg\": \"Mollusca/Flabellina iodinea/206fcad03c13c01714dd322ea39ee534.jpg\",\n  \"184685.jpg\": \"Mollusca/Flabellina iodinea/476d3fa69d7e864c4a9709f008e114e0.jpg\",\n  \"184686.jpg\": \"Mollusca/Flabellina iodinea/bf9f8d46d13eab6849090501f201a589.jpg\",\n  \"184687.jpg\": \"Mollusca/Flabellina iodinea/6fa76364cde4dc18e02ed6a8d04d0c19.jpg\",\n  \"184688.jpg\": \"Mollusca/Flabellina iodinea/82c0500c8574092c5731bf56dc8f5057.jpg\",\n  \"184690.jpg\": \"Mollusca/Flabellina iodinea/36fd6df4789619d97f517be07092fb0a.jpg\",\n  \"184773.jpg\": \"Aves/Galbula ruficauda/cfdc1d17da66f3deac316d2887db8775.jpg\",\n  \"184778.jpg\": \"Aves/Galbula ruficauda/10f9688ec9d47d255536cc9705b3bcb6.jpg\",\n  \"184779.jpg\": \"Aves/Galbula ruficauda/e611686210ff1879586cf62f0c835495.jpg\",\n  \"184781.jpg\": \"Aves/Galbula ruficauda/fda97761b1538db0d5f0603c67d0378f.jpg\",\n  \"184787.jpg\": \"Aves/Myioborus miniatus/3312509af166f5477e2bfba8ffb0cceb.jpg\",\n  \"184796.jpg\": \"Aves/Myioborus miniatus/4b45daee7afa90c5e1c658e3114426a6.jpg\",\n  \"184798.jpg\": \"Aves/Myioborus miniatus/e80ef62d7a464b0f8aed03a26899a68c.jpg\",\n  \"184802.jpg\": \"Aves/Myioborus miniatus/686d39000afc6907e987e0ab34a813ab.jpg\",\n  \"184807.jpg\": \"Aves/Myioborus miniatus/fa45121c9ade1ce51091819301d6956a.jpg\",\n  \"184813.jpg\": \"Aves/Myioborus miniatus/9e18a9593717418b7651e05189dd5ea0.jpg\",\n  \"184816.jpg\": \"Aves/Myioborus miniatus/f41bb5d7b3dc136ab0b4fb8d910fe7f7.jpg\",\n  \"184866.jpg\": \"Aves/Podiceps nigricollis/c0a8f4445e3bf9f5eb858a3faa6fc880.jpg\",\n  \"184878.jpg\": \"Aves/Podiceps nigricollis/89985372f3732302a92ebb2cfd8bf366.jpg\",\n  \"184892.jpg\": \"Aves/Podiceps nigricollis/5d22b5d649bf5b3c1473fc8c768842c6.jpg\",\n  \"184916.jpg\": \"Aves/Podiceps nigricollis/0ffc7e36a9faebe85541bd66e9e6eadf.jpg\",\n  \"184926.jpg\": \"Aves/Podiceps nigricollis/7d4a8e69f499a18c058644fef1326631.jpg\",\n  \"184939.jpg\": \"Aves/Podiceps nigricollis/8e36b611eabbcec66859f660edfe267d.jpg\",\n  \"184952.jpg\": \"Aves/Podiceps nigricollis/e983e6c761ff6e064bc187ffbab613f1.jpg\",\n  \"184955.jpg\": \"Aves/Podiceps nigricollis/e2e5182e6fff0d28476b1d0ace980b1b.jpg\",\n  \"184993.jpg\": \"Aves/Podiceps nigricollis/4fb0b8faf96dcc455febf32c9d983fb2.jpg\",\n  \"185.jpg\": \"Aves/Zonotrichia capensis/d23c30e5ebc535252de47f7347a1bc02.jpg\",\n  \"185001.jpg\": \"Aves/Podiceps nigricollis/dd8d2ada1de4ef109810d1660200d0ea.jpg\",\n  \"185033.jpg\": \"Aves/Podiceps nigricollis/9d3e856337992f8c908d0c5f7b22ad9b.jpg\",\n  \"185045.jpg\": \"Aves/Podiceps nigricollis/5a2c3b29c091294a18159a328b21b773.jpg\",\n  \"185046.jpg\": \"Aves/Podiceps nigricollis/1508fffb8922e94555ba755b7981e647.jpg\",\n  \"185048.jpg\": \"Aves/Podiceps nigricollis/d4e11a73ff7f381f4edb6028a8d6b566.jpg\",\n  \"185052.jpg\": \"Aves/Podiceps nigricollis/36be547ed773146558ffdec086165cee.jpg\",\n  \"185087.jpg\": \"Aves/Podiceps nigricollis/e251ff67cbdc8ca4bdf098b2cec068fc.jpg\",\n  \"185131.jpg\": \"Aves/Podiceps nigricollis/963051798c7ca4be1a240ea0d94cc959.jpg\",\n  \"185138.jpg\": \"Aves/Podiceps nigricollis/f28c8d0383e05e6c7625b1a2c95fc271.jpg\",\n  \"185150.jpg\": \"Insecta/Microstylum morosum/eb47d08bda658709bdcc8e4592d7237a.jpg\",\n  \"185158.jpg\": \"Insecta/Microstylum morosum/f7581ff1a4d462d67ae2f71de1818ff3.jpg\",\n  \"185159.jpg\": \"Insecta/Microstylum morosum/8a56692d743506b049280485a6d4fa3a.jpg\",\n  \"185178.jpg\": \"Insecta/Microstylum morosum/489eddde45574e3e00f6dc8c9436c07d.jpg\",\n  \"185218.jpg\": \"Aves/Numenius americanus/82f906a0d24777cc3000c41150a3a5f3.jpg\",\n  \"185247.jpg\": \"Aves/Numenius americanus/acd75670a2997ae6e3413997ada6035d.jpg\",\n  \"185253.jpg\": \"Aves/Numenius americanus/e95ae465e4e535fa581fdf546e2b9601.jpg\",\n  \"185256.jpg\": \"Aves/Numenius americanus/166a08fe994412a1f3446a64c3c1a10b.jpg\",\n  \"185261.jpg\": \"Aves/Numenius americanus/4ced64d409bd072a1282928af001a494.jpg\",\n  \"185317.jpg\": \"Aves/Numenius americanus/884d46457f4c5a7e33981004c0a1d9a7.jpg\",\n  \"185321.jpg\": \"Aves/Numenius americanus/2ebf36128beeeccaeefa9396b83b7fff.jpg\",\n  \"185323.jpg\": \"Aves/Numenius americanus/7b55e5a31ae23ff6cafaaf7da73b6a7f.jpg\",\n  \"185324.jpg\": \"Aves/Numenius americanus/440f679bdb2b2dd7b4111ff5c27b900d.jpg\",\n  \"185325.jpg\": \"Aves/Numenius americanus/542a76b5bfb25c99c02bdcbb238f4730.jpg\",\n  \"185338.jpg\": \"Aves/Numenius americanus/6098843c62a2fe7eae2aea7da95ee310.jpg\",\n  \"185343.jpg\": \"Aves/Numenius americanus/2f3745a57bfec689355a4455752dc2bd.jpg\",\n  \"185354.jpg\": \"Aves/Numenius americanus/a7991569fe9eb7970d5eb24bb4233eda.jpg\",\n  \"185365.jpg\": \"Aves/Numenius americanus/ef4fc27159f934e893990a1e551380cf.jpg\",\n  \"185394.jpg\": \"Aves/Numenius americanus/42d589e19bce68aeb76c8b2a60c664ce.jpg\",\n  \"185403.jpg\": \"Aves/Numenius americanus/d2cfef7dfc68fa0cf81108a12ba08127.jpg\",\n  \"185413.jpg\": \"Aves/Numenius americanus/f2b8c69e425749be159bf62282bb98c2.jpg\",\n  \"185417.jpg\": \"Aves/Numenius americanus/b958b5a0b50b47a25d3518339da77a1c.jpg\",\n  \"185453.jpg\": \"Aves/Chroicocephalus novaehollandiae/ed9d04edfb0cdecbeb82a34e512e5174.jpg\",\n  \"185454.jpg\": \"Aves/Chroicocephalus novaehollandiae/fcabb03583e0a36d394947a6e4dc4e29.jpg\",\n  \"185457.jpg\": \"Aves/Chroicocephalus novaehollandiae/e386476555dcc6cdfab70f4154e6279b.jpg\",\n  \"185460.jpg\": \"Aves/Chroicocephalus novaehollandiae/6a598d021740a30899e6f5b72a90c347.jpg\",\n  \"185467.jpg\": \"Aves/Chroicocephalus novaehollandiae/f371fae8eb5ba8a124eca38c17bc0548.jpg\",\n  \"185469.jpg\": \"Aves/Chroicocephalus novaehollandiae/7dbdcfd6bfdf5020c24911c3c9fabfa4.jpg\",\n  \"185474.jpg\": \"Aves/Chroicocephalus novaehollandiae/5be65d75c0a21847e26e653c3d1f8fed.jpg\",\n  \"185486.jpg\": \"Aves/Chroicocephalus novaehollandiae/57d2e71bc82f315bc1ad0dcabe18f037.jpg\",\n  \"185487.jpg\": \"Aves/Chroicocephalus novaehollandiae/e3f5df68f18f2d8788f7bb4b18da44e7.jpg\",\n  \"185488.jpg\": \"Aves/Chroicocephalus novaehollandiae/4e275b443f63444583d9fd9fcd04c4a6.jpg\",\n  \"185489.jpg\": \"Aves/Chroicocephalus novaehollandiae/15d5343ec579c18023d7b58c50464af7.jpg\",\n  \"185493.jpg\": \"Aves/Chroicocephalus novaehollandiae/9fec00a7f606dc45b96f5cdaf64eb42c.jpg\",\n  \"185506.jpg\": \"Insecta/Phyllopalpus pulchellus/16805234b0c24165ff4e53881b64f390.jpg\",\n  \"185517.jpg\": \"Insecta/Phyllopalpus pulchellus/586d958d924132042bebe0915fa710d0.jpg\",\n  \"185519.jpg\": \"Insecta/Phyllopalpus pulchellus/9e6be3bd9c7e19ce368b3d3bb8fe5b29.jpg\",\n  \"185523.jpg\": \"Insecta/Phyllopalpus pulchellus/f0bfff80c85843fcd3198b22ecc0aa16.jpg\",\n  \"185524.jpg\": \"Insecta/Phyllopalpus pulchellus/da8150d73f6199c8587f405dc3084027.jpg\",\n  \"185527.jpg\": \"Insecta/Phyllopalpus pulchellus/a563119d847e14cce1d784e42581f548.jpg\",\n  \"185664.jpg\": \"Aves/Coccyzus erythropthalmus/fa59b7888d5d6d900c888bd600f8d128.jpg\",\n  \"185675.jpg\": \"Aves/Coccyzus erythropthalmus/08aa1f4ff2f77b1e88d3899f2c2fc6cb.jpg\",\n  \"185676.jpg\": \"Aves/Coccyzus erythropthalmus/66fcb782dcc61e271e106fba733cea1e.jpg\",\n  \"185677.jpg\": \"Aves/Coccyzus erythropthalmus/d3924875fc7aecee4fa0d5db4f5278d2.jpg\",\n  \"185682.jpg\": \"Aves/Coccyzus erythropthalmus/bccf46e0b4f0de26737adfa38d630a26.jpg\",\n  \"185683.jpg\": \"Aves/Coccyzus erythropthalmus/e952134e25fd2178913728be2b03288b.jpg\",\n  \"185684.jpg\": \"Aves/Coccyzus erythropthalmus/5854c4d67c7ecc72d0001f1045462b41.jpg\",\n  \"185687.jpg\": \"Aves/Coccyzus erythropthalmus/d757d66f52bfdf62f6ad36f6f9441ec9.jpg\",\n  \"185704.jpg\": \"Mollusca/Planorbella trivolvis/f054e35c3b8c61bd4f271aff79e4f50e.jpg\",\n  \"185705.jpg\": \"Mollusca/Planorbella trivolvis/1cc1f2afa6cc38c5686ff4025dd2bb34.jpg\",\n  \"185709.jpg\": \"Mollusca/Planorbella trivolvis/f5f6a5fa785ed419688e10d89ef07447.jpg\",\n  \"185855.jpg\": \"Insecta/Poanes hobomok/72d9c6b543b5ff249c572fc91900fa0e.jpg\",\n  \"185856.jpg\": \"Insecta/Poanes hobomok/6f5e39fad0ee2fbb75816d5f4040a723.jpg\",\n  \"185857.jpg\": \"Insecta/Poanes hobomok/8214f1aa57b53f748e67e11cb02158bf.jpg\",\n  \"185858.jpg\": \"Insecta/Poanes hobomok/4e7354c53922eaad0c095af502c4c0a1.jpg\",\n  \"185868.jpg\": \"Insecta/Poanes hobomok/cc78c0769d34ffd6888bed23d1f281a1.jpg\",\n  \"185869.jpg\": \"Insecta/Poanes hobomok/94ddc9af885aac1b4be0a8480ed0a6c2.jpg\",\n  \"185875.jpg\": \"Insecta/Poanes hobomok/560de5e063264389cb7daa41e84df152.jpg\",\n  \"185888.jpg\": \"Insecta/Poanes hobomok/02c2378cc4b78acf4f44e4b6cc0f0650.jpg\",\n  \"185900.jpg\": \"Insecta/Poanes hobomok/ccd842825eccc7262889de16f23f28dc.jpg\",\n  \"185913.jpg\": \"Insecta/Poanes hobomok/402efda68841981ebfda5e9e2bbc79ac.jpg\",\n  \"185915.jpg\": \"Insecta/Poanes hobomok/5c615ac81ffd69f323df07fecd5e580d.jpg\",\n  \"185919.jpg\": \"Insecta/Poanes hobomok/782361307bd2e28bc37c128343c181be.jpg\",\n  \"185928.jpg\": \"Insecta/Poanes hobomok/6a6b4bdc48c1d7eec1ae2653a3980870.jpg\",\n  \"185931.jpg\": \"Insecta/Poanes hobomok/67602a4e10cbfa4cf08e1a9e6e4abb07.jpg\",\n  \"185932.jpg\": \"Insecta/Poanes hobomok/a60b74d30f7f51d866711d1a2806cbd4.jpg\",\n  \"185933.jpg\": \"Insecta/Poanes hobomok/066f36b648c5fbf34ff28445d5bbe070.jpg\",\n  \"185950.jpg\": \"Insecta/Poanes hobomok/a7228e837ca3bebca30e574ab25eddd5.jpg\",\n  \"185970.jpg\": \"Insecta/Aidemona azteca/5bb98bd6ac2112c18a4d1a91d41e51a9.jpg\",\n  \"185973.jpg\": \"Insecta/Aidemona azteca/5870386e6e5be6b84d1e490866c6112c.jpg\",\n  \"185974.jpg\": \"Insecta/Aidemona azteca/c50777bfee6a29134c6ac5865e9e48d1.jpg\",\n  \"185975.jpg\": \"Insecta/Aidemona azteca/4360db96c09d617c434761c6684ee76d.jpg\",\n  \"186049.jpg\": \"Mammalia/Thomomys bottae/73458b16663fe368d4788d3ba3d9e21c.jpg\",\n  \"186050.jpg\": \"Mammalia/Thomomys bottae/e40987cde5d4e32ff752ff6ca2bb33da.jpg\",\n  \"186057.jpg\": \"Mammalia/Thomomys bottae/577313bea9a09dcc0b60c3382e9a563a.jpg\",\n  \"186065.jpg\": \"Mammalia/Thomomys bottae/ec5f5f9ff72c934ec9b6e6d795190faf.jpg\",\n  \"186067.jpg\": \"Mammalia/Thomomys bottae/b317e71669c19efdf34879ac20ab6678.jpg\",\n  \"186077.jpg\": \"Mammalia/Thomomys bottae/0142807bf782f962edd6a65dab34c21e.jpg\",\n  \"186082.jpg\": \"Mammalia/Thomomys bottae/c54ba2c6ae8ea1c12fd5a3c452820c66.jpg\",\n  \"186089.jpg\": \"Mammalia/Thomomys bottae/f48e36be5ded843a7bb8c18452d18702.jpg\",\n  \"186090.jpg\": \"Mammalia/Thomomys bottae/0ecffb5914228ff287fdd3478ac2ffb2.jpg\",\n  \"186094.jpg\": \"Mammalia/Thomomys bottae/d9d7ca0f82315afb446c5c9db23a486c.jpg\",\n  \"186099.jpg\": \"Mammalia/Thomomys bottae/895a5f9e44392ca3b04ee65d2e345615.jpg\",\n  \"186100.jpg\": \"Mammalia/Thomomys bottae/ab96c539facb6533ef792f982052ba92.jpg\",\n  \"186112.jpg\": \"Mammalia/Thomomys bottae/3c173da1c5a4873e622325b4d536e8b6.jpg\",\n  \"186116.jpg\": \"Mammalia/Thomomys bottae/4ac35a1d0cd16fe89d264594cd5e07e9.jpg\",\n  \"186125.jpg\": \"Mammalia/Thomomys bottae/8f38ee92279dbb4a75242c8d5b9e4111.jpg\",\n  \"186136.jpg\": \"Mammalia/Thomomys bottae/bed900bcc0469d1b71824cd5fb87951f.jpg\",\n  \"186137.jpg\": \"Mammalia/Thomomys bottae/3db53c3dbd51fa8386dc1bcdf39cbd1f.jpg\",\n  \"186146.jpg\": \"Mammalia/Thomomys bottae/16689fd1ceed13f7e7ab2e0877967762.jpg\",\n  \"186147.jpg\": \"Mammalia/Thomomys bottae/4a36a472684275ea090e9ac2415947f1.jpg\",\n  \"186153.jpg\": \"Mammalia/Thomomys bottae/0dec4e096dd602fd35b2515232f65121.jpg\",\n  \"186164.jpg\": \"Aves/Tringa nebularia/222adcdcb814a52b07aeabe1f7c41da0.jpg\",\n  \"186165.jpg\": \"Aves/Tringa nebularia/d854adf258d2055b6290b169c5fbf688.jpg\",\n  \"186176.jpg\": \"Aves/Tringa nebularia/17e85201671cd8fb99b100dc608088aa.jpg\",\n  \"186177.jpg\": \"Aves/Tringa nebularia/3b68e6c2e0e9441acff8e5adf14be544.jpg\",\n  \"186178.jpg\": \"Aves/Tringa nebularia/e1b724d3cce45ecd0da29355cca76f1e.jpg\",\n  \"186185.jpg\": \"Aves/Tringa nebularia/5ed2266aa23525c9a4914835266d1da8.jpg\",\n  \"186186.jpg\": \"Aves/Tringa nebularia/c0ad934dd38b347649e6af6700f4324d.jpg\",\n  \"186191.jpg\": \"Insecta/Sphinx kalmiae/23996341fae7d20723a5985b17c7180c.jpg\",\n  \"186198.jpg\": \"Insecta/Sphinx kalmiae/828f14f34787fa2aba4a28ffd77dfbb4.jpg\",\n  \"186202.jpg\": \"Insecta/Sphinx kalmiae/8f84e1106d69a0e65b568a0e25d31447.jpg\",\n  \"186204.jpg\": \"Insecta/Sphinx kalmiae/becb60816e414e26d27c9cd61312ec85.jpg\",\n  \"186209.jpg\": \"Insecta/Sphinx kalmiae/07b1043b6ec7a52eaa78a546283a3914.jpg\",\n  \"186210.jpg\": \"Insecta/Sphinx kalmiae/acb74d6b2e6055b4ed1f34244f51153f.jpg\",\n  \"186213.jpg\": \"Insecta/Sphinx kalmiae/fb8a6161fdd7eb84f5ccdbf3e0bda0ff.jpg\",\n  \"186221.jpg\": \"Insecta/Sphinx kalmiae/e6de9610b654403fbdadc0d58f6d5eaf.jpg\",\n  \"186222.jpg\": \"Insecta/Sphinx kalmiae/b05606f5bfe197629349ea25f6b35f3b.jpg\",\n  \"186235.jpg\": \"Aves/Passer italiae/e6e25c44f7c1782cbc03f205d6e8676c.jpg\",\n  \"186238.jpg\": \"Aves/Passer italiae/a3a9989a7ff42cb51b0502f4dcf69251.jpg\",\n  \"186240.jpg\": \"Aves/Passer italiae/0760ad6b01046996664902c8f1ff4162.jpg\",\n  \"186247.jpg\": \"Animalia/Limulus polyphemus/020e53cf836d0f25261b1d80994ae008.jpg\",\n  \"186248.jpg\": \"Animalia/Limulus polyphemus/4533bd841ab6e77dad74fb7b21bb1cac.jpg\",\n  \"186253.jpg\": \"Animalia/Limulus polyphemus/d5faa8d3cee098f8085144a664af43fa.jpg\",\n  \"186257.jpg\": \"Animalia/Limulus polyphemus/f8e23a4a454e2cfb41c73cbee04de2e7.jpg\",\n  \"186266.jpg\": \"Animalia/Limulus polyphemus/d35f11a295e02787930c5b27d0bc172b.jpg\",\n  \"186267.jpg\": \"Animalia/Limulus polyphemus/5979ee4fb40a6c96e19ebb3e194337a8.jpg\",\n  \"186268.jpg\": \"Animalia/Limulus polyphemus/5bbfc2b74a1d5cdc3b8f8d4fdf9fa500.jpg\",\n  \"186270.jpg\": \"Animalia/Limulus polyphemus/d0b88ad2cd480a9b7b9dbd1c34b96bc4.jpg\",\n  \"186271.jpg\": \"Animalia/Limulus polyphemus/171db206306b797989ade048b44be082.jpg\",\n  \"186275.jpg\": \"Animalia/Limulus polyphemus/0e29deda24b16e83a0c5d7c58b1c325c.jpg\",\n  \"186279.jpg\": \"Animalia/Limulus polyphemus/f39b1e599e9f5270f078f8a873bc787b.jpg\",\n  \"186282.jpg\": \"Animalia/Limulus polyphemus/746050919ec7e0706f855c4c5c410d8e.jpg\",\n  \"186289.jpg\": \"Animalia/Limulus polyphemus/47ced4515b318b02469bf13252ce2d4e.jpg\",\n  \"186300.jpg\": \"Animalia/Limulus polyphemus/fc0c9653e48134f82243577edc1c8686.jpg\",\n  \"186316.jpg\": \"Animalia/Limulus polyphemus/5096aab1a455a4e58512cd092df0e025.jpg\",\n  \"186326.jpg\": \"Animalia/Limulus polyphemus/c506fa5670a61493c9ae3be2c683df96.jpg\",\n  \"186327.jpg\": \"Animalia/Limulus polyphemus/e3efa7d6348bf824b72243f5fdc2320b.jpg\",\n  \"186337.jpg\": \"Reptilia/Anolis nebulosus/2bc913e8f10f87950363c1f4ce65b20c.jpg\",\n  \"186338.jpg\": \"Reptilia/Anolis nebulosus/a124499b2c320bb23432a34dcf070425.jpg\",\n  \"186339.jpg\": \"Reptilia/Anolis nebulosus/464f8f37f34d4c99d03fbfee99e1c42e.jpg\",\n  \"186340.jpg\": \"Reptilia/Anolis nebulosus/818b91d152ff6cf07d5e82adc25f3e62.jpg\",\n  \"186341.jpg\": \"Reptilia/Anolis nebulosus/83a871da9d552589603659838459f3bf.jpg\",\n  \"186342.jpg\": \"Reptilia/Anolis nebulosus/6f0274d77123c90c2ebbafe91b73cbd4.jpg\",\n  \"186343.jpg\": \"Reptilia/Anolis nebulosus/32f8df910dcb04a5cf013bc9c9bfc305.jpg\",\n  \"186347.jpg\": \"Reptilia/Anolis nebulosus/2ec250ea3c769fbcbbad3fd740086a31.jpg\",\n  \"186358.jpg\": \"Reptilia/Anolis nebulosus/1c31f0c90c2c7782c8967231f3d03108.jpg\",\n  \"186362.jpg\": \"Reptilia/Anolis nebulosus/88cfa434af130c1fdd36c09f11c28503.jpg\",\n  \"186369.jpg\": \"Reptilia/Anolis nebulosus/88e9644494fd8afde215325e702c43a8.jpg\",\n  \"186373.jpg\": \"Reptilia/Anolis nebulosus/7f0611f48c42be2f19722019d6a63ed2.jpg\",\n  \"186374.jpg\": \"Reptilia/Anolis nebulosus/bf5f056899ddb4404c45dff9cb7bf620.jpg\",\n  \"186375.jpg\": \"Reptilia/Anolis nebulosus/cc1b1d1844b2fa2947c78f1133855e49.jpg\",\n  \"186379.jpg\": \"Aves/Callipepla californica/9020b7893fc8dcf45071720bc6bc5148.jpg\",\n  \"186380.jpg\": \"Aves/Callipepla californica/6c7e7ef54c0cc066f98dfa95e7eda46f.jpg\",\n  \"186390.jpg\": \"Aves/Callipepla californica/c027e0ad0951e2ae6c1e8a00f0c10ff1.jpg\",\n  \"186391.jpg\": \"Aves/Callipepla californica/5612fae074e6f02ecd4d3b96b70d7a0f.jpg\",\n  \"186396.jpg\": \"Aves/Callipepla californica/e6944e9750adbcbab08337eb0b4fa79b.jpg\",\n  \"186405.jpg\": \"Aves/Callipepla californica/9d10899cf58205fc642bca242b371e43.jpg\",\n  \"186416.jpg\": \"Aves/Callipepla californica/8ffc25e032b1a44ba593e065da89f6ff.jpg\",\n  \"186432.jpg\": \"Aves/Callipepla californica/c17a2913919e11555234bdd815a60b99.jpg\",\n  \"186440.jpg\": \"Aves/Callipepla californica/f7447fa778781b0c5f2c316fc1cdb42e.jpg\",\n  \"186455.jpg\": \"Aves/Callipepla californica/34edcf3ea021bbd8cd144b47e258ae79.jpg\",\n  \"186464.jpg\": \"Aves/Callipepla californica/276267d85b888b30ebeee8b01a9a3ab3.jpg\",\n  \"186485.jpg\": \"Aves/Callipepla californica/470035593c6bf8fdfe8e6ea034da5b9f.jpg\",\n  \"186508.jpg\": \"Aves/Callipepla californica/6e11c67030b2ff278a9d02ea8e8aa5a1.jpg\",\n  \"186511.jpg\": \"Aves/Callipepla californica/b35e5a3d3c54fe21cbcf65ee404d757a.jpg\",\n  \"186569.jpg\": \"Aves/Callipepla californica/d0bb277b8e0e6492c52f19b5b1e713a2.jpg\",\n  \"186589.jpg\": \"Aves/Callipepla californica/5ea122eceaf7ed870005548f453491cb.jpg\",\n  \"186597.jpg\": \"Aves/Callipepla californica/d09b55a6fafe08399b1f8c002262b8a1.jpg\",\n  \"186607.jpg\": \"Aves/Callipepla californica/d07b28e3498f1bb60962fcea89bbb2a2.jpg\",\n  \"186623.jpg\": \"Aves/Callipepla californica/82b5cb838d7a1f7febcb8217342f162d.jpg\",\n  \"186677.jpg\": \"Insecta/Ischnura perparva/1c1716504982efb78b06027a61c82481.jpg\",\n  \"186679.jpg\": \"Insecta/Ischnura perparva/a6ccc2a44c07c4043fbac4dbdf0b9071.jpg\",\n  \"186683.jpg\": \"Insecta/Ischnura perparva/b5fed3a98bd1e13553f71a63a6a9fe33.jpg\",\n  \"186685.jpg\": \"Insecta/Ischnura perparva/ee0d39d435c1530e94a041424b621b76.jpg\",\n  \"186697.jpg\": \"Insecta/Ischnura perparva/4694b81649161f07f9d7310c72195f45.jpg\",\n  \"186701.jpg\": \"Insecta/Ischnura perparva/f86d87c9c68b69e4a1a43c015b355982.jpg\",\n  \"186708.jpg\": \"Insecta/Ischnura perparva/49777471eae2892b7970816b2a3782d4.jpg\",\n  \"186782.jpg\": \"Mammalia/Macaca mulatta/6429fb2986d589b8d51a63e6a2288bb1.jpg\",\n  \"186789.jpg\": \"Mammalia/Macaca mulatta/5cb557a353d2a0cb06815e79a5d95b78.jpg\",\n  \"18679.jpg\": \"Aves/Corvus corax/bf694b2285bedcde3755174529a72919.jpg\",\n  \"186790.jpg\": \"Mammalia/Macaca mulatta/e419c52d8b34858d89de7924d4e8a7d0.jpg\",\n  \"186797.jpg\": \"Insecta/Polites vibex/01dac1733d6f4a7d8a0e896e240f9872.jpg\",\n  \"186799.jpg\": \"Insecta/Polites vibex/1bc8d314646dc98b79a60f78fd467ddb.jpg\",\n  \"186806.jpg\": \"Insecta/Polites vibex/a638c4915cf427e3942ef2405a9bf7df.jpg\",\n  \"186809.jpg\": \"Insecta/Polites vibex/9dfcd997687b23e85ca09cc6cc5baeff.jpg\",\n  \"186815.jpg\": \"Insecta/Phyciodes graphica/3b8899ac51a00c7a5fc3ad5e07b8c2bb.jpg\",\n  \"186817.jpg\": \"Insecta/Phyciodes graphica/95cdbd706e4412f1fdc5dcb1ae431171.jpg\",\n  \"186819.jpg\": \"Insecta/Phyciodes graphica/434bd8195d73fee7613812242dc89a5d.jpg\",\n  \"186824.jpg\": \"Insecta/Phyciodes graphica/df264989fb0260b131bf495408af6c73.jpg\",\n  \"18683.jpg\": \"Aves/Corvus corax/855c358cb678294c8bff8cd8072cb117.jpg\",\n  \"18699.jpg\": \"Aves/Corvus corax/2e959bc56caf44b14d079202f1db0e2f.jpg\",\n  \"1870.jpg\": \"Aves/Junco hyemalis oreganus/bfb665faac3b4f974a972d9befe36956.jpg\",\n  \"187023.jpg\": \"Mammalia/Canis mesomelas/dfb3991d5f4c7cfe4ad092e3d220fcdf.jpg\",\n  \"187026.jpg\": \"Mammalia/Canis mesomelas/25e6548a2b6b5b3bd457c1bb876685af.jpg\",\n  \"187038.jpg\": \"Mammalia/Canis mesomelas/5129a4ba26168f41e3dbf80e994fac5f.jpg\",\n  \"187039.jpg\": \"Mammalia/Canis mesomelas/026efe54ecfa1a613a6931a1e02a2815.jpg\",\n  \"187042.jpg\": \"Aves/Tiaris olivaceus/54fe0a53fe94685790dee31924a0f4db.jpg\",\n  \"187044.jpg\": \"Aves/Tiaris olivaceus/5c3e4a102374ef7bbce3515e87116171.jpg\",\n  \"187047.jpg\": \"Aves/Tiaris olivaceus/3a072ae04e68faaabe82e6a8dae68fae.jpg\",\n  \"187054.jpg\": \"Aves/Tiaris olivaceus/66ec2fb47949626d8cf6eb4b6cfc4e45.jpg\",\n  \"187057.jpg\": \"Aves/Tiaris olivaceus/22242984a7562ee1c84fe5b6da00922a.jpg\",\n  \"187060.jpg\": \"Aves/Tiaris olivaceus/7f334e2b2ebed6248874639ddf2a234b.jpg\",\n  \"187065.jpg\": \"Aves/Tiaris olivaceus/a07b599b2fb213216b4f91d5cb21c00b.jpg\",\n  \"187111.jpg\": \"Reptilia/Chrysemys picta/a6e302105360e4f0b0977e9df93dc18a.jpg\",\n  \"187132.jpg\": \"Reptilia/Chrysemys picta/7b8cb14298d6d1f9c2dbfb395852f318.jpg\",\n  \"187135.jpg\": \"Reptilia/Chrysemys picta/cee54cc101ebdb745d9a9244be91b1b1.jpg\",\n  \"187140.jpg\": \"Reptilia/Chrysemys picta/282ed738e64476af4c4ade8822c395b5.jpg\",\n  \"187155.jpg\": \"Reptilia/Chrysemys picta/e07e49f778f778ea6b9ca0ad38a9bc51.jpg\",\n  \"1872.jpg\": \"Aves/Junco hyemalis oreganus/3476c73927303c9b448a8aef0591f4be.jpg\",\n  \"187244.jpg\": \"Reptilia/Chrysemys picta/b295a7355e23f6baab2bf560e92ebced.jpg\",\n  \"187262.jpg\": \"Reptilia/Chrysemys picta/1af0e810379fe8bb64db23fd8bd7c917.jpg\",\n  \"187268.jpg\": \"Reptilia/Chrysemys picta/e7b8081fd96778e52fa5459afb2b5d85.jpg\",\n  \"187320.jpg\": \"Reptilia/Chrysemys picta/23b8cea54be25b70b046afebc706e6ce.jpg\",\n  \"187331.jpg\": \"Reptilia/Chrysemys picta/a9aa20b7215296f21c282520f6f6b91b.jpg\",\n  \"187346.jpg\": \"Reptilia/Chrysemys picta/0cfb902f191c2d955b78ef2179a76a98.jpg\",\n  \"187355.jpg\": \"Reptilia/Chrysemys picta/7b6f257512acece11926909cb395d71a.jpg\",\n  \"187359.jpg\": \"Insecta/Syntomoides imaon/192ff1fefe00de61c356111a976dc998.jpg\",\n  \"187364.jpg\": \"Insecta/Syntomoides imaon/5fae29fed697a7f0563e3e0230df3f08.jpg\",\n  \"187372.jpg\": \"Insecta/Syntomoides imaon/ab42782918277906080ed2f31c284338.jpg\",\n  \"187382.jpg\": \"Insecta/Epargyreus clarus/433d22e4eedb5cd9f3f16207e11b5c1e.jpg\",\n  \"187383.jpg\": \"Insecta/Epargyreus clarus/9f6fb535aa2cdbf2a174758ee17d909a.jpg\",\n  \"187407.jpg\": \"Insecta/Epargyreus clarus/70ec2640c30df8a3012c48810ca790b7.jpg\",\n  \"187418.jpg\": \"Insecta/Epargyreus clarus/8dabe7b9d73ab65d4ebb31965ba80f17.jpg\",\n  \"187451.jpg\": \"Insecta/Epargyreus clarus/56e15c6352dbad07085e067494382010.jpg\",\n  \"187470.jpg\": \"Insecta/Epargyreus clarus/5d3532d1cbcec201b9fd819a167ca6d1.jpg\",\n  \"187472.jpg\": \"Insecta/Epargyreus clarus/cd7993f373f5566291bfbe14b7b1c679.jpg\",\n  \"187484.jpg\": \"Insecta/Epargyreus clarus/c1e2d3b6d30089507fce0c58be1c8d2a.jpg\",\n  \"187487.jpg\": \"Insecta/Epargyreus clarus/8d088b4628ad69d0350dbaafa909d4ed.jpg\",\n  \"187494.jpg\": \"Insecta/Epargyreus clarus/403338ba4817b7147e71511b80a09e1f.jpg\",\n  \"187498.jpg\": \"Insecta/Epargyreus clarus/b21c8ac532318597aed14b80046500d1.jpg\",\n  \"187514.jpg\": \"Insecta/Epargyreus clarus/65a5feaa89c92b672344458298889d82.jpg\",\n  \"187539.jpg\": \"Insecta/Epargyreus clarus/8c02fbae06f3ab9705d6e6e9ecd121a3.jpg\",\n  \"18756.jpg\": \"Aves/Corvus corax/ef00b1775a92b1435a0c6e6c132efcdd.jpg\",\n  \"187560.jpg\": \"Insecta/Epargyreus clarus/5a8e03bcfcc880c99e568f3eb09cb827.jpg\",\n  \"187630.jpg\": \"Insecta/Epargyreus clarus/36481f4b255a95d666eb79b073c8488a.jpg\",\n  \"187637.jpg\": \"Insecta/Epargyreus clarus/d84e1b15fe18045147a33a12a1ea9b28.jpg\",\n  \"187655.jpg\": \"Insecta/Epargyreus clarus/5ad3431ea0174a94dc8b96c534de965a.jpg\",\n  \"187671.jpg\": \"Insecta/Epargyreus clarus/8e52efc777b80b1238a54abc10a89bc3.jpg\",\n  \"187722.jpg\": \"Insecta/Epargyreus clarus/051dd1a7d90c9f8f0e808d947b598582.jpg\",\n  \"187795.jpg\": \"Insecta/Biblis hyperia/6e78695968b5202f7861e570fe99e57e.jpg\",\n  \"187797.jpg\": \"Insecta/Biblis hyperia/f61e9c9bf0f2567bb0bc573f42828199.jpg\",\n  \"187799.jpg\": \"Insecta/Biblis hyperia/ee7a987986a16585d9e50cd8011fe2a9.jpg\",\n  \"1878.jpg\": \"Aves/Junco hyemalis oreganus/9c24d700a8f6e32b153c3b5d5db94e0e.jpg\",\n  \"187800.jpg\": \"Insecta/Biblis hyperia/a590095301d34c793e060817f50fce53.jpg\",\n  \"187809.jpg\": \"Insecta/Biblis hyperia/85682eb4e5cc4c6c6b0769ae0b55e6cb.jpg\",\n  \"187812.jpg\": \"Mammalia/Capra hircus/074a57f603158e6436af8ab539d8ef82.jpg\",\n  \"187824.jpg\": \"Mammalia/Capra hircus/0e90756b7f4a5a5e5d2a51583d3197ef.jpg\",\n  \"187831.jpg\": \"Mammalia/Capra hircus/e5805d37644fa60c1a5771c50ee5cfa9.jpg\",\n  \"187876.jpg\": \"Insecta/Agrius convolvuli/f43858f67fc9e6b680006b52039f0021.jpg\",\n  \"187877.jpg\": \"Insecta/Agrius convolvuli/70b9c64b58109e90e784d469e0f00a48.jpg\",\n  \"187878.jpg\": \"Insecta/Agrius convolvuli/e286dc96748aa339d0b89421ddd320c4.jpg\",\n  \"187879.jpg\": \"Insecta/Agrius convolvuli/a284372e006dad9a39e9929901705e01.jpg\",\n  \"187886.jpg\": \"Insecta/Agrius convolvuli/32c7c4758a12f5b19d2d5d866ca662c7.jpg\",\n  \"187893.jpg\": \"Insecta/Agrius convolvuli/4a948abed85928997556b93c261b3518.jpg\",\n  \"187896.jpg\": \"Insecta/Agrius convolvuli/8e5906bf7c9e32df223581a26b519913.jpg\",\n  \"187900.jpg\": \"Insecta/Agrius convolvuli/7b76d365b8613a1ab8a086cfd6fb9456.jpg\",\n  \"187907.jpg\": \"Insecta/Agrius convolvuli/a4461ccaa94beb6be925616a2a3e9f53.jpg\",\n  \"187919.jpg\": \"Insecta/Aeshna constricta/6919e719a6d4fb952fceb16d7ecd81c6.jpg\",\n  \"187928.jpg\": \"Insecta/Lithacodia musta/a606be9119891cdeacc386d204942c4a.jpg\",\n  \"187929.jpg\": \"Insecta/Lithacodia musta/d608dfafbeef3a9e297391aa21b1150c.jpg\",\n  \"187930.jpg\": \"Insecta/Lithacodia musta/d23c4743bb0c99209d23906c919310da.jpg\",\n  \"187935.jpg\": \"Insecta/Lithacodia musta/00646f19d78f14ec1bd65965999e8c80.jpg\",\n  \"187941.jpg\": \"Insecta/Lithacodia musta/5129c94951a92f0056b79fbf02e028a8.jpg\",\n  \"187952.jpg\": \"Aves/Amazona autumnalis/5fabc07819e492feff9dfa87b527d898.jpg\",\n  \"187953.jpg\": \"Aves/Amazona autumnalis/37181a4310f36bb73cf903339bccbfb8.jpg\",\n  \"187962.jpg\": \"Aves/Amazona autumnalis/d9558832d537350bf9e74aa1ce644c9a.jpg\",\n  \"187965.jpg\": \"Aves/Amazona autumnalis/748a5b047fdebdebb314b4073afcc765.jpg\",\n  \"187969.jpg\": \"Aves/Amazona autumnalis/5e44d4010a157091e0ddd1689e0a30fd.jpg\",\n  \"1880.jpg\": \"Aves/Junco hyemalis oreganus/0f17bbecb15d712c0c322fbbe1eeec38.jpg\",\n  \"188005.jpg\": \"Insecta/Poanes melane/0a5e996aa350da4f57d8fb65e41b7876.jpg\",\n  \"188006.jpg\": \"Insecta/Poanes melane/84ef4a322d4507a8ae4c7a811c23d1cf.jpg\",\n  \"188010.jpg\": \"Insecta/Poanes melane/bee69bb48129cde13ada3d1a6aef68e5.jpg\",\n  \"188025.jpg\": \"Insecta/Poanes melane/fafb33bd0b163007dc1258810a71059c.jpg\",\n  \"188048.jpg\": \"Insecta/Poanes melane/3dd4e63ac9756eb7168f479d7108219c.jpg\",\n  \"188052.jpg\": \"Insecta/Poanes melane/23b0c196d411bdb200263e42ebed877b.jpg\",\n  \"188087.jpg\": \"Insecta/Poanes melane/e9cb31cb1a34fe5333b5ce6f17af0cc8.jpg\",\n  \"188095.jpg\": \"Insecta/Poanes melane/0757fdc5ef8bb03be304757bf3479b97.jpg\",\n  \"1881.jpg\": \"Aves/Junco hyemalis oreganus/e48375e6116af5e3b141df869a6f9c4e.jpg\",\n  \"188102.jpg\": \"Insecta/Poanes melane/cc4a4fef23da2b8d4f89bff08e9acbc2.jpg\",\n  \"188125.jpg\": \"Insecta/Poanes melane/f46a83de81effb4ff0919520795e756f.jpg\",\n  \"188149.jpg\": \"Insecta/Poanes melane/f71e9ced847c049db40fe4efcbf1de85.jpg\",\n  \"188152.jpg\": \"Insecta/Poanes melane/fdd4605467b6de1810d406613a35395a.jpg\",\n  \"188160.jpg\": \"Insecta/Poanes melane/fed29f3212f9e0b2beb45670f02aedc7.jpg\",\n  \"18817.jpg\": \"Aves/Corvus corax/5c8a45b04264f2ee6ff7e81320b915e0.jpg\",\n  \"188178.jpg\": \"Insecta/Poanes melane/7b99abeae314ae80bab07ede5624c767.jpg\",\n  \"188180.jpg\": \"Insecta/Poanes melane/22b70876139e2f041a39013eea12f77f.jpg\",\n  \"188183.jpg\": \"Insecta/Poanes melane/3d388d6e8e3b8be7b303c49af936d99a.jpg\",\n  \"188187.jpg\": \"Insecta/Poanes melane/3307b51ddbdf172a4588f3cbf6ba7723.jpg\",\n  \"188193.jpg\": \"Insecta/Poanes melane/793421da1d79f8ef818a3b5e6a02efd1.jpg\",\n  \"188200.jpg\": \"Insecta/Poanes melane/a6d00c2c99b9976807952efe08c77ed2.jpg\",\n  \"188208.jpg\": \"Insecta/Poanes melane/bcf3deaead08ae3be987d0a211fed7a2.jpg\",\n  \"188232.jpg\": \"Amphibia/Eurycea bislineata/5071e60eee1994f9c4199edb1b4953e6.jpg\",\n  \"188233.jpg\": \"Amphibia/Eurycea bislineata/5184ded79d7ad3145c390910b3a5d99e.jpg\",\n  \"188237.jpg\": \"Amphibia/Eurycea bislineata/0c23094c22c8e5788054ff57469d858c.jpg\",\n  \"188239.jpg\": \"Amphibia/Eurycea bislineata/89908476a772a0ef9726d36629166158.jpg\",\n  \"18824.jpg\": \"Aves/Corvus corax/949dc4de3872d0610443b1c47a1ece68.jpg\",\n  \"188245.jpg\": \"Amphibia/Eurycea bislineata/96b9b618e8bcdf6813ae890412a33f67.jpg\",\n  \"188246.jpg\": \"Amphibia/Eurycea bislineata/4c0081e4db77943aad70c8fd4e5784ed.jpg\",\n  \"188247.jpg\": \"Amphibia/Eurycea bislineata/c9dc7cc2c79d39ab02a77220df40ed73.jpg\",\n  \"188250.jpg\": \"Amphibia/Eurycea bislineata/b614800594f03dd55f278155c778d7ae.jpg\",\n  \"188255.jpg\": \"Amphibia/Eurycea bislineata/2641b0bd177de8ba6fb294f38bebb7bf.jpg\",\n  \"188260.jpg\": \"Amphibia/Eurycea bislineata/03c2a962aa0f3b5aa20e905c817d2df0.jpg\",\n  \"188262.jpg\": \"Amphibia/Eurycea bislineata/0e95a5fd3dbea3bf64c047e3597d530b.jpg\",\n  \"188263.jpg\": \"Amphibia/Eurycea bislineata/b666d20a9652430e5c5533d0e9773403.jpg\",\n  \"188264.jpg\": \"Amphibia/Eurycea bislineata/457b6cc79007f1788822383abad8bceb.jpg\",\n  \"188265.jpg\": \"Amphibia/Eurycea bislineata/d4d788f91bed879dd82167a63b5a7fb1.jpg\",\n  \"188266.jpg\": \"Amphibia/Eurycea bislineata/a88e3f82251f3e2f7f09c8dc26e9c94b.jpg\",\n  \"188270.jpg\": \"Amphibia/Eurycea bislineata/459d4a0a2f006ac8b9010ac9f45664a1.jpg\",\n  \"188272.jpg\": \"Amphibia/Eurycea bislineata/3d466b16df3b1a95737fd6ce61f66cdc.jpg\",\n  \"188275.jpg\": \"Amphibia/Eurycea bislineata/3a8e238dcf954fa0669d53dc6886634c.jpg\",\n  \"188281.jpg\": \"Amphibia/Eurycea bislineata/c61686e1d93007ff35fc34d581ace505.jpg\",\n  \"188282.jpg\": \"Amphibia/Eurycea bislineata/ca7b2432cebdf09d6a19dd29a2c89991.jpg\",\n  \"188288.jpg\": \"Insecta/Urbanus procne/9913f954ef7a3c1693fee52c2bd44006.jpg\",\n  \"188290.jpg\": \"Insecta/Urbanus procne/530fcbecc112b43b4628d6cf301c2f51.jpg\",\n  \"188300.jpg\": \"Insecta/Urbanus procne/070fec53bdae56bec54da179d9724412.jpg\",\n  \"188311.jpg\": \"Insecta/Urbanus procne/fede28a0a4767fb796999d8cc0b17145.jpg\",\n  \"188319.jpg\": \"Insecta/Urbanus procne/2955ee8cf8abcd2a6ef4671847c85a33.jpg\",\n  \"188322.jpg\": \"Insecta/Urbanus procne/21db265ef564b6aa28c15e9160e67638.jpg\",\n  \"18855.jpg\": \"Aves/Corvus corax/27672e31216a33811b121e24fd947256.jpg\",\n  \"188560.jpg\": \"Insecta/Kricogonia lyside/cf9edd09c5a41d6e04d7e994f05ca69c.jpg\",\n  \"188565.jpg\": \"Insecta/Kricogonia lyside/9002c0cfb7586cb7b35defd2d2628f31.jpg\",\n  \"188571.jpg\": \"Insecta/Kricogonia lyside/30dd19f728b6c5e936777bda8a4421e8.jpg\",\n  \"188576.jpg\": \"Insecta/Kricogonia lyside/622e93e89a72254d2de3cb0fe52c678a.jpg\",\n  \"188578.jpg\": \"Insecta/Kricogonia lyside/ba08e0f81ceef73d89675be05f0b76c6.jpg\",\n  \"188580.jpg\": \"Insecta/Kricogonia lyside/b9149636459cf2a7beb7b4385d3a974e.jpg\",\n  \"188589.jpg\": \"Insecta/Kricogonia lyside/6102c357f39001e5be10267e22e35f86.jpg\",\n  \"1886.jpg\": \"Aves/Junco hyemalis oreganus/735d71bd988e9296bc50e23f6769839f.jpg\",\n  \"18877.jpg\": \"Aves/Corvus corax/37e4b64534ed4e973b81daa8e4ff6228.jpg\",\n  \"188773.jpg\": \"Insecta/Scoliopteryx libatrix/1dd90ba0dfef0e7beade78af6888db72.jpg\",\n  \"188786.jpg\": \"Insecta/Scoliopteryx libatrix/597cae79b3c3378fbaec41389602d5bd.jpg\",\n  \"188789.jpg\": \"Insecta/Scoliopteryx libatrix/4dc2c5334d21d69e98a3143b03ed98eb.jpg\",\n  \"188792.jpg\": \"Aves/Vireo cassinii/8902a4e48ad7815eb87d29ef67b62835.jpg\",\n  \"188797.jpg\": \"Aves/Vireo cassinii/1e93ec6dd47a9f8dee2112c5b1260fa2.jpg\",\n  \"188801.jpg\": \"Aves/Vireo cassinii/a98e841fdacfa68b4f2a0839a4842403.jpg\",\n  \"188802.jpg\": \"Aves/Vireo cassinii/a2599941a44ef6971bfe5532901c81d4.jpg\",\n  \"188805.jpg\": \"Aves/Vireo cassinii/e4ba2286fc81adccac496c2fc1b44724.jpg\",\n  \"188808.jpg\": \"Insecta/Battus philenor/d5bbc298826ce7ff471594e4bf05f64c.jpg\",\n  \"188809.jpg\": \"Insecta/Battus philenor/9e4fac9df5681ad376ea5b22557911ce.jpg\",\n  \"188827.jpg\": \"Insecta/Battus philenor/8192bf920ade3b26fc7677b1c9ca1687.jpg\",\n  \"188847.jpg\": \"Insecta/Battus philenor/afc8a8ad3e8e556f61c002ae22884884.jpg\",\n  \"188871.jpg\": \"Insecta/Battus philenor/c66b2510be0c4ef697c4007fc2fc3739.jpg\",\n  \"188919.jpg\": \"Insecta/Battus philenor/b5e9d0ba93bb96f475af3e395fe13a36.jpg\",\n  \"188934.jpg\": \"Insecta/Battus philenor/205cfa367693f00daff972eaa07a6dba.jpg\",\n  \"188955.jpg\": \"Insecta/Battus philenor/4d622162a4a076887384eb683b6b24e9.jpg\",\n  \"188957.jpg\": \"Insecta/Battus philenor/983cfb3be94f978fb7d8e389a97925c1.jpg\",\n  \"188986.jpg\": \"Insecta/Battus philenor/51839c74170d30e8b63b6f5b06ddf962.jpg\",\n  \"188990.jpg\": \"Insecta/Battus philenor/0d550ec1a9032ad544603a4387ae0b44.jpg\",\n  \"188999.jpg\": \"Insecta/Battus philenor/0cdc18b004c62a55e62344c6267cc8c5.jpg\",\n  \"18903.jpg\": \"Aves/Corvus corax/3d8fb86de6e29bdc189ba1bfa0309cbd.jpg\",\n  \"189051.jpg\": \"Insecta/Battus philenor/c16ad8e70720ed0430f9e292db4076aa.jpg\",\n  \"189078.jpg\": \"Insecta/Battus philenor/18e948a694b47dae98b192945c7754fa.jpg\",\n  \"189108.jpg\": \"Insecta/Battus philenor/74424322b68082a1fe88fe9a0b730e5b.jpg\",\n  \"189109.jpg\": \"Insecta/Battus philenor/bfa43325437037b6af1365248933dd94.jpg\",\n  \"189111.jpg\": \"Insecta/Battus philenor/0be27d90d8125f5f574301c4239369e7.jpg\",\n  \"189134.jpg\": \"Insecta/Battus philenor/c1e2a3a65ccce4fda9836aebf1f2ce05.jpg\",\n  \"189169.jpg\": \"Insecta/Battus philenor/cf5de3c83c2cec64c39f76d73e7f7e81.jpg\",\n  \"189184.jpg\": \"Insecta/Battus philenor/9d82bc6a4a51f11bdf3cfbadd54b40e7.jpg\",\n  \"189247.jpg\": \"Aves/Ara militaris/50633422031d9cc2ae998f2ff0697254.jpg\",\n  \"189249.jpg\": \"Aves/Ara militaris/084306f1fb1622f88ac1af071162c31f.jpg\",\n  \"189258.jpg\": \"Insecta/Anaea andria/4e636e943310103ee917145d57e8fe92.jpg\",\n  \"189271.jpg\": \"Insecta/Anaea andria/50059776b0176c9e425b9b7606ecf7e0.jpg\",\n  \"189275.jpg\": \"Insecta/Anaea andria/7ca77d0d906090d7e974372b25131ef1.jpg\",\n  \"189279.jpg\": \"Insecta/Anaea andria/fd848d6e1c191fdb92df828c980052e3.jpg\",\n  \"189280.jpg\": \"Insecta/Anaea andria/23e7af24492e4d87b629b3bbdb9de8cb.jpg\",\n  \"189281.jpg\": \"Insecta/Anaea andria/433e37e016abc3edca7465bf27079cd9.jpg\",\n  \"189282.jpg\": \"Insecta/Anaea andria/d32c671da2799ef23c65969570936847.jpg\",\n  \"189286.jpg\": \"Insecta/Anaea andria/6d2ebbf9accc23b2b2747ff1f87f7524.jpg\",\n  \"189287.jpg\": \"Insecta/Anaea andria/eeb33f0c403a58baab334729af37ff5e.jpg\",\n  \"189288.jpg\": \"Insecta/Anaea andria/9849aadf41abf3a64f0055f86d514291.jpg\",\n  \"189289.jpg\": \"Insecta/Anaea andria/553d0aeeae027d997b05c54f58574ab6.jpg\",\n  \"189290.jpg\": \"Insecta/Anaea andria/28304d88e1841c6d51f4b83f394bf6ce.jpg\",\n  \"189300.jpg\": \"Insecta/Anaea andria/45bdf4b6e34f09a54d5295a9be83514b.jpg\",\n  \"189303.jpg\": \"Insecta/Anaea andria/21ba11f3f8cf30a2bf8303febf4cc61b.jpg\",\n  \"189307.jpg\": \"Insecta/Anaea andria/0447202f9dcf9a54ac70d39f1dad387b.jpg\",\n  \"189308.jpg\": \"Insecta/Anaea andria/780c3b3ac2a389f3fb3ba86c702eab3c.jpg\",\n  \"189309.jpg\": \"Insecta/Anaea andria/1b0e54bf1030f2dc4d52f3ddf77f7439.jpg\",\n  \"189311.jpg\": \"Insecta/Anaea andria/a00dd874f3f294db39c40470a3a78335.jpg\",\n  \"189314.jpg\": \"Insecta/Anaea andria/2af78628d996f4ee33e78922910d7674.jpg\",\n  \"189335.jpg\": \"Aves/Icterus parisorum/a477d48f526904cf1dd3e803aa441c72.jpg\",\n  \"189336.jpg\": \"Aves/Icterus parisorum/c751412c767cd90cc5702e553383354e.jpg\",\n  \"189337.jpg\": \"Aves/Icterus parisorum/3b159a90a213a5b1cb6cbe51f9fe878e.jpg\",\n  \"189338.jpg\": \"Aves/Icterus parisorum/b48f204aa9656abd246884584fdf91c0.jpg\",\n  \"189342.jpg\": \"Aves/Icterus parisorum/addefe63cb9c298d043590e6b6587afe.jpg\",\n  \"189343.jpg\": \"Aves/Icterus parisorum/7d3ca10877cc8c05a2a3ebcbcd0e6c94.jpg\",\n  \"189346.jpg\": \"Aves/Icterus parisorum/fead68bc27e797abbdbecabc7c8cfe3c.jpg\",\n  \"189348.jpg\": \"Aves/Icterus parisorum/2f544969d21192787df66084795afcc8.jpg\",\n  \"189349.jpg\": \"Aves/Icterus parisorum/3636d535288dec48e3861e7b7968fdb6.jpg\",\n  \"189350.jpg\": \"Aves/Icterus parisorum/788d6979d275f48f9801df89fc7170d1.jpg\",\n  \"189351.jpg\": \"Aves/Icterus parisorum/f2ec1bb61a16f9a55330ba0305a00c34.jpg\",\n  \"189353.jpg\": \"Aves/Icterus parisorum/bedb0e0a4dc9135a350353d39b5a4cd9.jpg\",\n  \"189354.jpg\": \"Aves/Icterus parisorum/6ec35b2982a0a556c3d9912068aed129.jpg\",\n  \"189356.jpg\": \"Aves/Icterus parisorum/f6500689d842c2c6d69404744e8ba856.jpg\",\n  \"189357.jpg\": \"Aves/Icterus parisorum/2193d5f638a8a33f0ab3dc861665b01b.jpg\",\n  \"189358.jpg\": \"Aves/Icterus parisorum/0a36f1fa1712bee2bea6f9a861650159.jpg\",\n  \"189361.jpg\": \"Aves/Icterus parisorum/b6cdd0270330114774bea1d4086a1a8d.jpg\",\n  \"189362.jpg\": \"Aves/Icterus parisorum/8a72b972a3d07ff5a88b46a3f7a25cfa.jpg\",\n  \"189363.jpg\": \"Aves/Icterus parisorum/5f4c1de4c48a74044f2511f82d806875.jpg\",\n  \"189364.jpg\": \"Aves/Icterus parisorum/c40cc1c4ec6409455ee8b40ceecd695c.jpg\",\n  \"189366.jpg\": \"Aves/Icterus parisorum/9d9adb3b720ff91740d6cfc8d2e1832e.jpg\",\n  \"189371.jpg\": \"Aves/Milvus milvus/10dd932ca793e4ff14b3d1574eb489eb.jpg\",\n  \"189389.jpg\": \"Aves/Milvus milvus/389729b8a528014ad3027c72eb35546c.jpg\",\n  \"189390.jpg\": \"Aves/Milvus milvus/cb33f4025aac3e415a186c770271ba26.jpg\",\n  \"189395.jpg\": \"Aves/Milvus milvus/37b0ee97fa52c47ca9a8dee91a53f665.jpg\",\n  \"189398.jpg\": \"Aves/Milvus milvus/9957dd01f06be6ddf6db07ea69cecf56.jpg\",\n  \"189399.jpg\": \"Aves/Milvus milvus/bc83d407a5d400570801f1206c73e64a.jpg\",\n  \"18940.jpg\": \"Aves/Corvus corax/5c3559e3991c369279e80dcb5e54563a.jpg\",\n  \"189410.jpg\": \"Aves/Milvus milvus/11e11cb796b6b46967533b7f5ac6797c.jpg\",\n  \"189426.jpg\": \"Aves/Milvus milvus/3c2495d46f60a0905459401fc9d9954e.jpg\",\n  \"189431.jpg\": \"Aves/Milvus milvus/be2d1ded89f3fd26697b4c77356f5fa2.jpg\",\n  \"189444.jpg\": \"Aves/Milvus milvus/ee464ea01558016f0e99aea564eed092.jpg\",\n  \"189446.jpg\": \"Aves/Milvus milvus/ef7ea4fad5ecb9d165813afefae4d06f.jpg\",\n  \"189447.jpg\": \"Aves/Milvus milvus/139421cfca823ebaf6b1d6ca14d5b585.jpg\",\n  \"189448.jpg\": \"Aves/Milvus milvus/ee59062e4c85003b2347f31b5329b37e.jpg\",\n  \"189458.jpg\": \"Aves/Milvus milvus/7b03d06b43c3612ec6312a6700bbcfdb.jpg\",\n  \"189459.jpg\": \"Aves/Milvus milvus/0e5b7bb2293d70882f634af325ddcc83.jpg\",\n  \"189470.jpg\": \"Aves/Milvus milvus/cf93a78b2fe28f7708fe585491bf4c7d.jpg\",\n  \"189481.jpg\": \"Aves/Milvus milvus/9a47d87e8f388af7b3fc5e9e26f0c09d.jpg\",\n  \"189493.jpg\": \"Aves/Milvus milvus/7cd9f18123c6112723a30b2a17c6d666.jpg\",\n  \"189495.jpg\": \"Aves/Milvus milvus/2a636cbd3cff4796fca5a4aa285b76e6.jpg\",\n  \"189522.jpg\": \"Aves/Milvus milvus/b7cc153ade59d197844632fb84798094.jpg\",\n  \"189534.jpg\": \"Aves/Milvus milvus/c26d37f9a509eca7601c435def78e03b.jpg\",\n  \"189573.jpg\": \"Aves/Thalasseus sandvicensis/cd24bb7c9366700de8af6cbbe136f5f8.jpg\",\n  \"189574.jpg\": \"Aves/Thalasseus sandvicensis/b9fa6b04c23d0da208bce4b3c5f41031.jpg\",\n  \"189615.jpg\": \"Aves/Thalasseus sandvicensis/c59d687edbc0750502453645e7aa0da5.jpg\",\n  \"189616.jpg\": \"Aves/Thalasseus sandvicensis/314df09ff1b7e0ec7059f630a0b1cf71.jpg\",\n  \"189617.jpg\": \"Aves/Thalasseus sandvicensis/7b43af60d1fe1a37314d6d5aed9743d1.jpg\",\n  \"189618.jpg\": \"Aves/Thalasseus sandvicensis/e63267f1b8593ad782fcec3bf7ae67a6.jpg\",\n  \"189623.jpg\": \"Aves/Thalasseus sandvicensis/fcd2c4ab9a35cf5ae7849799878d5ebf.jpg\",\n  \"189625.jpg\": \"Aves/Thalasseus sandvicensis/f5ba69009a0391377e70f26003f252e9.jpg\",\n  \"189635.jpg\": \"Aves/Thalasseus sandvicensis/0b12c8569dcca81249e1778ae54f9f80.jpg\",\n  \"189636.jpg\": \"Aves/Thalasseus sandvicensis/59e387fe7b6506d5112818d4ad02dcfb.jpg\",\n  \"189648.jpg\": \"Insecta/Trichopoda pennipes/e59108931a124bc1f609da35751d5df3.jpg\",\n  \"189649.jpg\": \"Insecta/Trichopoda pennipes/62fd5b0ac20664d76f38e319e40c19a3.jpg\",\n  \"189653.jpg\": \"Insecta/Trichopoda pennipes/6ffd7ee2b353c16fceec341797dbfbb6.jpg\",\n  \"189654.jpg\": \"Insecta/Trichopoda pennipes/d9af935c35d0149a43b9a5b5e83c1ae7.jpg\",\n  \"189661.jpg\": \"Insecta/Trichopoda pennipes/6d95d599e72c1e7099219787d3b7bdeb.jpg\",\n  \"1897.jpg\": \"Aves/Junco hyemalis oreganus/4e396b52848935b67f50ca7f718f346a.jpg\",\n  \"189716.jpg\": \"Insecta/Scopula rubraria/003832a9bc05ddb21c8d1bc38cd204aa.jpg\",\n  \"189726.jpg\": \"Insecta/Scopula rubraria/60c0cbec9f0972554370551fb9b6a15d.jpg\",\n  \"189735.jpg\": \"Mammalia/Equus quagga/7e640ffb2938833a9c3836bcaf9841ec.jpg\",\n  \"189738.jpg\": \"Mammalia/Equus quagga/5dc75b40206fd657bb210a294c2cfc72.jpg\",\n  \"189764.jpg\": \"Mammalia/Equus quagga/ef07ab6504a564b375be2d109c39a1fd.jpg\",\n  \"18977.jpg\": \"Aves/Corvus corax/5af006633296f36e4786152836be6032.jpg\",\n  \"189774.jpg\": \"Mammalia/Equus quagga/6afa02d24cf6faf981f212122bf27ef0.jpg\",\n  \"189780.jpg\": \"Mammalia/Equus quagga/27cdf5bdcf45242535e3d8f837e63820.jpg\",\n  \"189782.jpg\": \"Mammalia/Equus quagga/d9ffb70831e2816dbe6fbd7afacc64bd.jpg\",\n  \"189791.jpg\": \"Mammalia/Equus quagga/c12bb73bf3cb57798f9358f5f441b67c.jpg\",\n  \"189815.jpg\": \"Insecta/Enallagma civile/b6cce4531b304f9c2588536409ae40da.jpg\",\n  \"189820.jpg\": \"Insecta/Enallagma civile/bc824e2ba455bf0dcf0044a15a903dc8.jpg\",\n  \"189845.jpg\": \"Insecta/Enallagma civile/afc0490486f2c7d7bfa610f044ad4809.jpg\",\n  \"189846.jpg\": \"Insecta/Enallagma civile/d282b25ce9d4d21fa464db1632dfe0c6.jpg\",\n  \"189849.jpg\": \"Insecta/Enallagma civile/d13fb5efdc68755e1c807e39df2674d0.jpg\",\n  \"18985.jpg\": \"Aves/Corvus corax/85d755ef8fa66c47f0099aaa8dfb222f.jpg\",\n  \"189854.jpg\": \"Insecta/Enallagma civile/23673c4bef1ea7b94e130c579ab95122.jpg\",\n  \"189855.jpg\": \"Insecta/Enallagma civile/a6f4834b06990b485b6bed800b5eb744.jpg\",\n  \"189856.jpg\": \"Insecta/Enallagma civile/fe839789d58ffc660522cec732778aa3.jpg\",\n  \"189877.jpg\": \"Insecta/Enallagma civile/ffef3f89171acb4c9f6c3e747b5a18be.jpg\",\n  \"189886.jpg\": \"Insecta/Enallagma civile/65615b144c5370050f5421b66f61a01d.jpg\",\n  \"189917.jpg\": \"Insecta/Enallagma civile/a2a926134a2613a9ce07a8a8448481cc.jpg\",\n  \"189935.jpg\": \"Insecta/Enallagma civile/b6727a80d387a3d16f65b14e87afcdbe.jpg\",\n  \"189954.jpg\": \"Insecta/Enallagma civile/67700733100fe133a4e173548a00f3d6.jpg\",\n  \"189955.jpg\": \"Insecta/Enallagma civile/9295193dfd363139142fc6a5814f359d.jpg\",\n  \"189970.jpg\": \"Insecta/Enallagma civile/cde8add52792b20dc954d16a082115ed.jpg\",\n  \"189988.jpg\": \"Insecta/Enallagma civile/d729d460f44593b3a5ec9e3f5933f1a7.jpg\",\n  \"189999.jpg\": \"Insecta/Enallagma civile/2b5643111c27c943853c02e3b6796302.jpg\",\n  \"190.jpg\": \"Aves/Zonotrichia capensis/4ec920db1310dfd054b6cda6d27bb53a.jpg\",\n  \"190000.jpg\": \"Insecta/Enallagma civile/88350b5551fd7eb45ae6c08bda7a7298.jpg\",\n  \"190001.jpg\": \"Insecta/Enallagma civile/51ac6f1470275c0b0c31de57242e0336.jpg\",\n  \"190015.jpg\": \"Insecta/Enallagma civile/0bfbabd6dbcc54cda4c3c339cd633f5e.jpg\",\n  \"190027.jpg\": \"Insecta/Enallagma civile/e5d705e2260804a4c46009f3d40abf81.jpg\",\n  \"190030.jpg\": \"Insecta/Enallagma civile/2beec3e3171072a1bbf6cf9455d221e7.jpg\",\n  \"190034.jpg\": \"Insecta/Enallagma civile/1d134a96db51a3497a48fdd0391cab32.jpg\",\n  \"190036.jpg\": \"Insecta/Enallagma civile/c62f06cad89628547f9f34ba4ea24bac.jpg\",\n  \"190042.jpg\": \"Insecta/Enallagma civile/62ae82893fdcc739c8244ed9a5f01b54.jpg\",\n  \"190117.jpg\": \"Aves/Actitis macularius/aa1aa3372e4ad978df6e565870c5b2de.jpg\",\n  \"190124.jpg\": \"Aves/Actitis macularius/7a16968e6a99c6c785422eebb004884b.jpg\",\n  \"190150.jpg\": \"Aves/Actitis macularius/7af5a3dadd7dfcd171bc954ac0d5ff28.jpg\",\n  \"19016.jpg\": \"Aves/Corvus corax/dc4eb13ca210915919eb93ad8355c69a.jpg\",\n  \"19019.jpg\": \"Aves/Corvus corax/78bc89c4035e064747eb82a05f16b37a.jpg\",\n  \"190231.jpg\": \"Aves/Actitis macularius/9e08d0980020405807d7ea810e024d3e.jpg\",\n  \"190241.jpg\": \"Aves/Actitis macularius/76bddf858fe6baca99d1a121d38dfc2f.jpg\",\n  \"190243.jpg\": \"Aves/Actitis macularius/5c15cdfa47c97afb4e3f85ac1bfc6e7f.jpg\",\n  \"190244.jpg\": \"Aves/Actitis macularius/f7e9ebb520f06cb2a55478b42f7f32aa.jpg\",\n  \"190263.jpg\": \"Aves/Actitis macularius/5799ce9d43f8850345b72228679f3d20.jpg\",\n  \"19028.jpg\": \"Aves/Corvus corax/60e71196f3096ce914ee7897c879d5bc.jpg\",\n  \"190304.jpg\": \"Aves/Actitis macularius/4e211c4885030fbdc1d0d0acf093f148.jpg\",\n  \"190305.jpg\": \"Aves/Actitis macularius/08cbddf19c2fb51bf9450e88768e3d71.jpg\",\n  \"190342.jpg\": \"Aves/Actitis macularius/b7e8135f4debf2d9d035a19b56d2761b.jpg\",\n  \"190350.jpg\": \"Aves/Actitis macularius/3c8b2efcce65c699f8a207208cf86c4e.jpg\",\n  \"190358.jpg\": \"Aves/Actitis macularius/90b1cd6db86c4a0c1a76d643a026827a.jpg\",\n  \"190440.jpg\": \"Aves/Actitis macularius/df912245aecdfed952b3d9a2179c6070.jpg\",\n  \"190459.jpg\": \"Aves/Actitis macularius/10f544806f54573b1cf5bb54e5ef0dca.jpg\",\n  \"190486.jpg\": \"Aves/Actitis macularius/2e00f93029d1e095ed6804755636784b.jpg\",\n  \"190525.jpg\": \"Aves/Actitis macularius/341b078627fa5de51864d2f482671037.jpg\",\n  \"190526.jpg\": \"Aves/Actitis macularius/bc0aeabfd4c0785c9e268fc550700ac5.jpg\",\n  \"19056.jpg\": \"Aves/Corvus corax/750fb6eb8766dde55a7f6c3325ba0246.jpg\",\n  \"190587.jpg\": \"Aves/Actitis macularius/17c09176adb23db74032f8ed99df59b2.jpg\",\n  \"190594.jpg\": \"Insecta/Crocothemis servilia/4ffd819c87da00cfcbf2c9849f1a3a0b.jpg\",\n  \"190602.jpg\": \"Insecta/Crocothemis servilia/7e4700960f72d16182ec9013cbb51eea.jpg\",\n  \"190603.jpg\": \"Insecta/Crocothemis servilia/7f31300ad3ffdfad40d594b0d9b12b5b.jpg\",\n  \"190604.jpg\": \"Insecta/Crocothemis servilia/04af6b88fcb4a0468d28402a67677f5d.jpg\",\n  \"19061.jpg\": \"Aves/Corvus corax/42626702b52044c44be26440a1b014ff.jpg\",\n  \"190614.jpg\": \"Insecta/Crocothemis servilia/36370eed4d05e8eb73e39a56bbed9906.jpg\",\n  \"190615.jpg\": \"Insecta/Crocothemis servilia/67819441b3d363f6e498cf5dfa69e09b.jpg\",\n  \"190620.jpg\": \"Insecta/Crocothemis servilia/39af16f78b28abae58195d587554dff4.jpg\",\n  \"190621.jpg\": \"Insecta/Crocothemis servilia/69838071f40a155bf5cadf5c4d0164c9.jpg\",\n  \"190629.jpg\": \"Insecta/Crocothemis servilia/8aae128e500ad4c4347b4a5fe0c36a00.jpg\",\n  \"19065.jpg\": \"Aves/Corvus corax/98819c2ed082af7b06c18ac11ac2fdfe.jpg\",\n  \"19081.jpg\": \"Aves/Corvus corax/f5ea47c57e13a1042d544f27d8df5ccb.jpg\",\n  \"190812.jpg\": \"Animalia/Parastichopus californicus/bd89fd1bc464702565d4aab2af61e7de.jpg\",\n  \"190820.jpg\": \"Animalia/Parastichopus californicus/b9967877230e4e184c436cb87c879428.jpg\",\n  \"190900.jpg\": \"Insecta/Lethe anthedon/d34f20dab208aa85cc162ddb0cd89625.jpg\",\n  \"190901.jpg\": \"Insecta/Lethe anthedon/54840d5bd8f8e32f4146ae2ac9ca5dc2.jpg\",\n  \"190906.jpg\": \"Insecta/Lethe anthedon/d24a1146bdd8bf40ba9c05d4beccab51.jpg\",\n  \"190911.jpg\": \"Insecta/Lethe anthedon/cf44520f29b13673123391291a2a500f.jpg\",\n  \"190923.jpg\": \"Insecta/Lethe anthedon/c230c02ef7968fcd02aac29ba4d847ed.jpg\",\n  \"190927.jpg\": \"Insecta/Lethe anthedon/4aeb7495a027f837a2eb911ebdf2106d.jpg\",\n  \"190933.jpg\": \"Insecta/Lethe anthedon/619a9a758b58cd50112e9f3479699216.jpg\",\n  \"190934.jpg\": \"Insecta/Lethe anthedon/0312f70cdf146e003c23bafa36508ae0.jpg\",\n  \"190936.jpg\": \"Insecta/Lethe anthedon/cf5a02982bc2846c8c9d1575f0d7cd04.jpg\",\n  \"190940.jpg\": \"Insecta/Lethe anthedon/0272d52b98750671a5468dc9d87a9b42.jpg\",\n  \"190946.jpg\": \"Insecta/Lethe anthedon/99f760208c1d872b32b9f68adb449ec8.jpg\",\n  \"190952.jpg\": \"Insecta/Lethe anthedon/0b677a594a8f83e58dbfaeaa272e5f7f.jpg\",\n  \"190953.jpg\": \"Insecta/Lethe anthedon/c8fcf416333cfb014884e117e8d2f7bd.jpg\",\n  \"190954.jpg\": \"Insecta/Lethe anthedon/cc419acecc26b712a35ea0c5a03178ef.jpg\",\n  \"190955.jpg\": \"Insecta/Lethe anthedon/a4fb2e14481a1a3d32af92b6439a659c.jpg\",\n  \"190956.jpg\": \"Insecta/Lethe anthedon/321d75fd982bfe66cdc4d4fdbbe82e62.jpg\",\n  \"190961.jpg\": \"Insecta/Lethe anthedon/bd847439b1e9596968ce265b516724c3.jpg\",\n  \"190964.jpg\": \"Insecta/Lethe anthedon/1435d8d1e2b3b0e5809a318644f84c7b.jpg\",\n  \"190967.jpg\": \"Insecta/Lethe anthedon/ed3f6ef1c315010b3bd336c9ca6af43d.jpg\",\n  \"190972.jpg\": \"Insecta/Lethe anthedon/a4d122900fc0be31dff96c8ed77b0219.jpg\",\n  \"190973.jpg\": \"Insecta/Lethe anthedon/83464e71291e468f8aa7d2346f2a99ef.jpg\",\n  \"19098.jpg\": \"Aves/Corvus corax/a98c73ed0fa94d095f76a85412d53207.jpg\",\n  \"190981.jpg\": \"Insecta/Lethe anthedon/3b8b5502a41073ac8018b8a9d8226668.jpg\",\n  \"191135.jpg\": \"Aves/Sitta europaea/f153f7e3c04897dfade87644df29c0eb.jpg\",\n  \"191141.jpg\": \"Aves/Sitta europaea/46d45724396fdb8714eb2f061acf7246.jpg\",\n  \"191142.jpg\": \"Aves/Sitta europaea/b004327237b4d56ad5ddad1be20187f0.jpg\",\n  \"191144.jpg\": \"Aves/Sitta europaea/f53b213f9555abb6cdb151854114beba.jpg\",\n  \"191149.jpg\": \"Aves/Sitta europaea/d04916c717ad00c97fa57884ffc950f2.jpg\",\n  \"19115.jpg\": \"Aves/Accipiter nisus/76662460a66387c17ff606a7fd2ba23d.jpg\",\n  \"191150.jpg\": \"Aves/Sitta europaea/5e25206343755279636d7aa3d3b08035.jpg\",\n  \"19116.jpg\": \"Aves/Accipiter nisus/22bb5427b4c5ce1d7531e6a8eb2b2291.jpg\",\n  \"19117.jpg\": \"Aves/Accipiter nisus/6bd61f4641a408882a9de01a971f6683.jpg\",\n  \"191170.jpg\": \"Aves/Sitta europaea/a274dc7d100a43a57717fb1f8f0cc480.jpg\",\n  \"19118.jpg\": \"Aves/Accipiter nisus/3200928d4926c61c4cc3e4ac15948c7a.jpg\",\n  \"191186.jpg\": \"Aves/Sitta europaea/199706db65ebf76836c270648eddd12c.jpg\",\n  \"191189.jpg\": \"Aves/Sitta europaea/9f59188c3d20dbd58cab18d841699627.jpg\",\n  \"1912.jpg\": \"Aves/Junco hyemalis oreganus/1fe1c49cd44e33dc9bd87a41293bb2a2.jpg\",\n  \"191204.jpg\": \"Aves/Sitta europaea/8e5c1cbfcd48e9a2fb4c3c0ca36b801c.jpg\",\n  \"191205.jpg\": \"Aves/Sitta europaea/4076da3d802ddb681b8721a3634de553.jpg\",\n  \"191206.jpg\": \"Aves/Sitta europaea/34cd22ea6cf8330105350b21e5f42b13.jpg\",\n  \"191207.jpg\": \"Aves/Sitta europaea/77d437aca1cf0fedd5f7b64aafb5647a.jpg\",\n  \"191216.jpg\": \"Aves/Sitta europaea/fafca0757665d4f5fcd59c836f0f5a93.jpg\",\n  \"19122.jpg\": \"Aves/Accipiter nisus/0b23d7d90b3f26c402a37b364dd5fdfa.jpg\",\n  \"191221.jpg\": \"Aves/Sitta europaea/960d240c8aa8d1cc4d55f419bf0ad98f.jpg\",\n  \"191224.jpg\": \"Aves/Sitta europaea/b21eba90a68441d2f8e3c5573cd71e20.jpg\",\n  \"191229.jpg\": \"Aves/Sitta europaea/75f45880706e5196ba2840b1bd866759.jpg\",\n  \"19123.jpg\": \"Aves/Accipiter nisus/a1a96b4f18bbaee75f79db06ac177beb.jpg\",\n  \"19124.jpg\": \"Aves/Accipiter nisus/05ca3ae795211e15a79493794942264c.jpg\",\n  \"19125.jpg\": \"Aves/Accipiter nisus/8803f8332724abe60bfbfb8f7ef7ee0d.jpg\",\n  \"19126.jpg\": \"Aves/Accipiter nisus/8e0b3c36692cf792b66eacb4e820dce6.jpg\",\n  \"19127.jpg\": \"Aves/Accipiter nisus/29652a93621d1bf344c1e7542da382e5.jpg\",\n  \"19128.jpg\": \"Aves/Accipiter nisus/5848b8d224ce03955309c582e88f475a.jpg\",\n  \"19129.jpg\": \"Aves/Accipiter nisus/f67f7811afbc364abbbd979d2bcd534f.jpg\",\n  \"19130.jpg\": \"Aves/Accipiter nisus/9c7644e1e221db01846fed46bb26ae78.jpg\",\n  \"19136.jpg\": \"Aves/Accipiter nisus/0264bf36e891e6b2dd1267d3c9711cb8.jpg\",\n  \"19144.jpg\": \"Aves/Accipiter nisus/49bb3143fcff35cf427ff2ec8588c7e2.jpg\",\n  \"191445.jpg\": \"Mammalia/Rattus rattus/db090f5fd8b2ecdef045b24878a57afc.jpg\",\n  \"191452.jpg\": \"Mammalia/Rattus rattus/2117d13cb444a0a313638876643d2a31.jpg\",\n  \"191455.jpg\": \"Mammalia/Rattus rattus/cfce694559dccf5dec98be5956567aca.jpg\",\n  \"191456.jpg\": \"Mammalia/Rattus rattus/0aae2f2f9c6c6ac2f85ae09a3efa9ce1.jpg\",\n  \"191457.jpg\": \"Mammalia/Rattus rattus/bb35baed460f39901141036d77540480.jpg\",\n  \"191460.jpg\": \"Mammalia/Rattus rattus/3297209d9898f363a240dd3cea7cab95.jpg\",\n  \"191461.jpg\": \"Mammalia/Rattus rattus/0ab53fd46b2e01927f935ab175e24007.jpg\",\n  \"191468.jpg\": \"Mammalia/Rattus rattus/ab9c54adc9c78d9108d1945644d81f96.jpg\",\n  \"191469.jpg\": \"Mammalia/Rattus rattus/25ed3521d7e1f5494260a93d3e906775.jpg\",\n  \"19147.jpg\": \"Aves/Accipiter nisus/98d87b7e5bffe601599da9c7b45a8868.jpg\",\n  \"191470.jpg\": \"Mammalia/Rattus rattus/9fec60e40f18eb4345ba796427401149.jpg\",\n  \"191471.jpg\": \"Mammalia/Rattus rattus/f368ddcc44649cdf5a2b65544e772f1c.jpg\",\n  \"191472.jpg\": \"Mammalia/Rattus rattus/b893a2a20474621f88311314fc34a45d.jpg\",\n  \"191473.jpg\": \"Mammalia/Rattus rattus/e720b6c9c517c2865de9469412d0cf06.jpg\",\n  \"191474.jpg\": \"Mammalia/Rattus rattus/866925e46ec1578c25adc080f9cbc172.jpg\",\n  \"191482.jpg\": \"Mammalia/Rattus rattus/06c6683db5090642324bde3e9388ec96.jpg\",\n  \"191484.jpg\": \"Mammalia/Rattus rattus/d0a9d815d004ca4d4a7c27e023ea7a11.jpg\",\n  \"191494.jpg\": \"Insecta/Chalybion californicum/f3487dfad56a32800d4564d3f8e1b563.jpg\",\n  \"191500.jpg\": \"Insecta/Chalybion californicum/620f99f7ad7525185ef827a4a24f1806.jpg\",\n  \"191501.jpg\": \"Insecta/Chalybion californicum/b103cf996ac1e5cf674576733ff509b0.jpg\",\n  \"191505.jpg\": \"Insecta/Chalybion californicum/215c31066fefb6fe9affebed72e0e8f0.jpg\",\n  \"191512.jpg\": \"Insecta/Chalybion californicum/f1c2ad56aa6686739d4c0a9a468517cd.jpg\",\n  \"191513.jpg\": \"Insecta/Chalybion californicum/202ea55358996f91e5bab3517b11b738.jpg\",\n  \"191520.jpg\": \"Insecta/Eumorpha vitis/8e1a2b8a34c23fa00013e8d608715604.jpg\",\n  \"191521.jpg\": \"Insecta/Eumorpha vitis/66b29fd7877d09b5ee22ab0cb1fa95d6.jpg\",\n  \"191523.jpg\": \"Insecta/Eumorpha vitis/5adf95960c1342a9d3894a8c1254fed8.jpg\",\n  \"191524.jpg\": \"Insecta/Eumorpha vitis/172ab5df62b70758968e367b5042b427.jpg\",\n  \"191532.jpg\": \"Insecta/Eumorpha vitis/e163a9a166e147b7626dcf56729bae82.jpg\",\n  \"191539.jpg\": \"Insecta/Eumorpha vitis/16e8122077a0ff7134d6802b5717f91d.jpg\",\n  \"19154.jpg\": \"Aves/Accipiter nisus/90a54c7a54442183a4f6be2484cdbe91.jpg\",\n  \"191542.jpg\": \"Insecta/Eumorpha vitis/9cf52a79db3fbceed3d9ead14e4ea888.jpg\",\n  \"19155.jpg\": \"Aves/Accipiter nisus/fb4e0647cf8a1d69284c240b799b3227.jpg\",\n  \"191550.jpg\": \"Insecta/Pyrausta laticlavia/eec39bd44888b6773d0e0736070e9c3d.jpg\",\n  \"191556.jpg\": \"Insecta/Pyrausta laticlavia/afc55d404933d576d01bfc015efa60bc.jpg\",\n  \"19156.jpg\": \"Aves/Accipiter nisus/b7272563943166d739a65057f043be99.jpg\",\n  \"191563.jpg\": \"Insecta/Pyrausta laticlavia/871f7c239d3e23c1c1eb245fd6313442.jpg\",\n  \"191564.jpg\": \"Insecta/Pyrausta laticlavia/1e09421cd3a036eb14fb21fd90193700.jpg\",\n  \"191571.jpg\": \"Insecta/Pyrausta laticlavia/96209a32cc02ac4aef3fcb1ef3152d6a.jpg\",\n  \"191583.jpg\": \"Aves/Lanius collurio/ef0617158261a07b52d418b1691d710e.jpg\",\n  \"191585.jpg\": \"Aves/Lanius collurio/0e598e57df478c97d9c7dd80316785fa.jpg\",\n  \"191587.jpg\": \"Aves/Lanius collurio/e46bbc01e002a696834dbaa3dda2201b.jpg\",\n  \"191588.jpg\": \"Aves/Lanius collurio/59a2bd5bab245accde4f333139417e4f.jpg\",\n  \"191590.jpg\": \"Aves/Lanius collurio/632dfef7aa207f1c86684e6b509652fd.jpg\",\n  \"191591.jpg\": \"Aves/Lanius collurio/6f9d13652bb5afb5a764e7c5b4b570ba.jpg\",\n  \"191599.jpg\": \"Aves/Lanius collurio/11b300e862a009751a865a9e723b9dd3.jpg\",\n  \"19160.jpg\": \"Aves/Accipiter nisus/4f34674c90f4258a47cd3ecc8adced90.jpg\",\n  \"191603.jpg\": \"Aves/Lanius collurio/d404887fb0b4cd7c986025221836cfb0.jpg\",\n  \"191615.jpg\": \"Aves/Lanius collurio/48b0d7cbe231364d6a79511eb432e7f4.jpg\",\n  \"191616.jpg\": \"Aves/Lanius collurio/3ba970d4961a925f547f574388f59aef.jpg\",\n  \"191617.jpg\": \"Aves/Lanius collurio/9a8b9b0a2fda32472e7feeadc462ec18.jpg\",\n  \"191618.jpg\": \"Aves/Lanius collurio/dda2ddaf67eff07af9703c026151e122.jpg\",\n  \"191622.jpg\": \"Aves/Lanius collurio/4a17452d87085b742dea2fd35e336110.jpg\",\n  \"191625.jpg\": \"Aves/Lanius collurio/4f82708e62bbc021fe0a225dccb00588.jpg\",\n  \"191627.jpg\": \"Aves/Lanius collurio/ef69c6988582cabdcb9525755ed626b1.jpg\",\n  \"191628.jpg\": \"Aves/Lanius collurio/c7063a39f12a7d4f97c862c6361a2c96.jpg\",\n  \"191630.jpg\": \"Aves/Lanius collurio/91bef2b7f060cc4818851876a4189492.jpg\",\n  \"191642.jpg\": \"Aves/Lanius collurio/b5d1ce3d46e83619a7749688bc82304a.jpg\",\n  \"191645.jpg\": \"Aves/Lanius collurio/82fc58e6df2c44409cace2eafb0698ec.jpg\",\n  \"191651.jpg\": \"Reptilia/Cophosaurus texanus texanus/2a6f933dae8825e38840ae021611baa0.jpg\",\n  \"191652.jpg\": \"Reptilia/Cophosaurus texanus texanus/01ea25a20b2bf0c17a5308b33be239a3.jpg\",\n  \"191658.jpg\": \"Reptilia/Cophosaurus texanus texanus/5897be050fb12a52f42149698077ba73.jpg\",\n  \"191659.jpg\": \"Reptilia/Cophosaurus texanus texanus/ceeb529c900c042fcbeab1840c64aef9.jpg\",\n  \"191661.jpg\": \"Reptilia/Cophosaurus texanus texanus/7de767559322f2787793969affba030c.jpg\",\n  \"191662.jpg\": \"Reptilia/Cophosaurus texanus texanus/e1a4610c084ee7d4cc6cb84b4ad2f718.jpg\",\n  \"191663.jpg\": \"Reptilia/Cophosaurus texanus texanus/261e2cb28993e89ebc5635594a579b6f.jpg\",\n  \"191664.jpg\": \"Reptilia/Cophosaurus texanus texanus/1ef63384023b50be43b290d64981c674.jpg\",\n  \"191670.jpg\": \"Amphibia/Rana aurora/ebc07eb3ef2341e313f9fe47892cb774.jpg\",\n  \"191671.jpg\": \"Amphibia/Rana aurora/f62c27b972b822a73d1a1a17fd80438a.jpg\",\n  \"191672.jpg\": \"Amphibia/Rana aurora/093d92cd64d166cdc4151f4edef11f86.jpg\",\n  \"191673.jpg\": \"Amphibia/Rana aurora/76eab12e4d767f1af0ad410b23af0eb5.jpg\",\n  \"19168.jpg\": \"Aves/Chen caerulescens/ae81fdb043603014413abf31f238a19e.jpg\",\n  \"191684.jpg\": \"Amphibia/Rana aurora/075e6d74b4c41f26b5188b11b434e70b.jpg\",\n  \"191692.jpg\": \"Amphibia/Rana aurora/516abea7e5ebeb88f120034949794ffe.jpg\",\n  \"191699.jpg\": \"Amphibia/Rana aurora/a6c1717802fbe221a83230546af2eb3f.jpg\",\n  \"1917.jpg\": \"Aves/Junco hyemalis oreganus/44344e1b68769b858b08ba6800cb5514.jpg\",\n  \"191700.jpg\": \"Amphibia/Rana aurora/d9e384e37d955c1d7e92cf9d7d248a92.jpg\",\n  \"191703.jpg\": \"Amphibia/Rana aurora/12a193f9d194da74c3c7df6967fec4b3.jpg\",\n  \"191708.jpg\": \"Amphibia/Rana aurora/ae7fe46dfadda998ef8bdb9cf03ea990.jpg\",\n  \"191709.jpg\": \"Amphibia/Rana aurora/802af26f99fd6123b131152998d4e6ce.jpg\",\n  \"191711.jpg\": \"Amphibia/Rana aurora/aa1996381336bdcbe56805564c1ca974.jpg\",\n  \"191712.jpg\": \"Amphibia/Rana aurora/b3fe312964d7c82ed049bdf1cc2222fe.jpg\",\n  \"191713.jpg\": \"Amphibia/Rana aurora/26845819fb6f545cbac87bcf9622e7bb.jpg\",\n  \"191714.jpg\": \"Amphibia/Rana aurora/b99a775a0aab3da57ac2599892abe3d1.jpg\",\n  \"191715.jpg\": \"Amphibia/Rana aurora/827e6e6d10ce77f567b7a2504b3d748f.jpg\",\n  \"191716.jpg\": \"Amphibia/Rana aurora/3784d4558740495cd67c86223124edb7.jpg\",\n  \"191717.jpg\": \"Amphibia/Rana aurora/4d3e8f052ecfbe0674921ad0568fd079.jpg\",\n  \"191730.jpg\": \"Insecta/Phaeoura quernaria/83f23e5bf7ea89997678c22ca2aa80c2.jpg\",\n  \"191734.jpg\": \"Insecta/Ischnura posita/cb0ec5b4327479593d8a3820d1fe4632.jpg\",\n  \"191737.jpg\": \"Insecta/Ischnura posita/d19ee1d7f4d2347dd45d61c538183b51.jpg\",\n  \"191755.jpg\": \"Insecta/Ischnura posita/94bbaed0c7e27f4311c7ffd6ec44ee16.jpg\",\n  \"191759.jpg\": \"Insecta/Ischnura posita/4d703dc207f6107492aedd3963356e77.jpg\",\n  \"191779.jpg\": \"Insecta/Ischnura posita/d9786d0763e97a4878ad72898fa20c21.jpg\",\n  \"191794.jpg\": \"Insecta/Ischnura posita/6be4658f63804b30e993308759e311ca.jpg\",\n  \"191809.jpg\": \"Insecta/Ischnura posita/07be2632b13de6546cafbc2b8b376bf7.jpg\",\n  \"191812.jpg\": \"Insecta/Ischnura posita/c97c88891b81925ff08bbe215ddef9dd.jpg\",\n  \"191829.jpg\": \"Insecta/Ischnura posita/36001e05b3cd2b9f8b29c9acbe66fed4.jpg\",\n  \"191830.jpg\": \"Insecta/Ischnura posita/bc5ea2dbab4284dc81325484f8d7e12b.jpg\",\n  \"191835.jpg\": \"Insecta/Ischnura posita/258f95b4ac1317a6a23bc08f6be0d359.jpg\",\n  \"191836.jpg\": \"Insecta/Ischnura posita/cc9bdffcc530db9efd48b90e0dc5c7c5.jpg\",\n  \"191842.jpg\": \"Insecta/Ischnura posita/7cea38410038f9d97a6a1e04e3c4a051.jpg\",\n  \"191850.jpg\": \"Insecta/Ischnura posita/d574f127e3f3bee927ac826afd18f425.jpg\",\n  \"191875.jpg\": \"Insecta/Ischnura posita/2cc4b9df052932cef1413b6a33adeab4.jpg\",\n  \"191877.jpg\": \"Insecta/Ischnura posita/5d46d94025b717884f7405c3b8f2db8f.jpg\",\n  \"191890.jpg\": \"Insecta/Ischnura posita/b527bdcbf40f901ab68ed1bb5a7269f9.jpg\",\n  \"191893.jpg\": \"Insecta/Ischnura posita/cc9ad3d67ad7c1005bbd831ed0e3a375.jpg\",\n  \"191896.jpg\": \"Insecta/Ischnura posita/f42b9dc964e146ff97892b5e39017104.jpg\",\n  \"1919.jpg\": \"Aves/Junco hyemalis oreganus/3bc041d244b167bf1d9ef20f669cce9f.jpg\",\n  \"191902.jpg\": \"Insecta/Ischnura posita/61da72ed21330038699d9ae8f971577a.jpg\",\n  \"191912.jpg\": \"Insecta/Ischnura posita/7de1798c3fb148a7996cb0fc3c45aa08.jpg\",\n  \"191925.jpg\": \"Insecta/Ischnura posita/dd2c0ca6c5f012f57372fa76a0e437b8.jpg\",\n  \"191930.jpg\": \"Insecta/Ischnura posita/c61fcb56ecb9233f741868a5a4d107cd.jpg\",\n  \"191966.jpg\": \"Insecta/Ischnura posita/cbc6c543b026ef0c4997904e09ab8200.jpg\",\n  \"192.jpg\": \"Aves/Zonotrichia capensis/afe1691a5b3b582820aafde2c67b3aef.jpg\",\n  \"192020.jpg\": \"Reptilia/Apalone spinifera/2ab32b82b10f1f7b36223bd71a7d01ae.jpg\",\n  \"192021.jpg\": \"Reptilia/Apalone spinifera/e82b698e7c1c9d0ab4b84716c56e847c.jpg\",\n  \"192026.jpg\": \"Reptilia/Apalone spinifera/893b7b39de857a2c060c3727e364a8df.jpg\",\n  \"192027.jpg\": \"Reptilia/Apalone spinifera/c633347d6bcaf319ff4e1cf47180f032.jpg\",\n  \"192028.jpg\": \"Reptilia/Apalone spinifera/0e590de303c70cfcc7f90573ed18624a.jpg\",\n  \"192040.jpg\": \"Reptilia/Apalone spinifera/95e897e19f44b5bc37f61f6af8e2b62b.jpg\",\n  \"192046.jpg\": \"Reptilia/Apalone spinifera/0da675a8d08e67a3b588227932541c77.jpg\",\n  \"192049.jpg\": \"Reptilia/Apalone spinifera/1983ea9f1c46e1284af22caec59c8713.jpg\",\n  \"19205.jpg\": \"Aves/Chen caerulescens/a3a804c6032177f8f7082bd6978297bc.jpg\",\n  \"192053.jpg\": \"Reptilia/Apalone spinifera/a9e3fc9b69cb23add2395c988585565e.jpg\",\n  \"192058.jpg\": \"Reptilia/Apalone spinifera/48b8457685639a36de3120048bce7bc6.jpg\",\n  \"192063.jpg\": \"Reptilia/Apalone spinifera/ab633769a03cec7eb65e1d719002f190.jpg\",\n  \"192066.jpg\": \"Reptilia/Apalone spinifera/d84fb2dcc9477ec8e16a95b1f19cdc9a.jpg\",\n  \"192072.jpg\": \"Reptilia/Apalone spinifera/0f84fc85efea4c9553f5886de43cee32.jpg\",\n  \"192076.jpg\": \"Reptilia/Apalone spinifera/68c0586c6c99d6735c1da5604f7e7e2f.jpg\",\n  \"192136.jpg\": \"Reptilia/Apalone spinifera/fb9ef42a3a4e5f33a92bb673fd14094d.jpg\",\n  \"192148.jpg\": \"Reptilia/Apalone spinifera/48af531edbabac3a39b997859dfd79a4.jpg\",\n  \"192153.jpg\": \"Reptilia/Apalone spinifera/69682b5c30a9745738bdbf298805eebc.jpg\",\n  \"192164.jpg\": \"Reptilia/Apalone spinifera/7163ef10b82bb04b0ddad476ed6e4cd5.jpg\",\n  \"192166.jpg\": \"Reptilia/Apalone spinifera/e059f3ee70386bbbd0e158f6675d2a6c.jpg\",\n  \"192171.jpg\": \"Reptilia/Apalone spinifera/4baa4c2a63ab2f44921bb4679bb5082c.jpg\",\n  \"192174.jpg\": \"Reptilia/Apalone spinifera/10ff1560fa44c86fb2229a121d8e97a9.jpg\",\n  \"192176.jpg\": \"Insecta/Astraptes fulgerator/f8d5a0f4064575591ff2dba9404210f4.jpg\",\n  \"192177.jpg\": \"Insecta/Astraptes fulgerator/5f54864babb157d2f61be7ecbbd7e6d0.jpg\",\n  \"192183.jpg\": \"Insecta/Astraptes fulgerator/623b37b9021c26ab0cce5796b33651b2.jpg\",\n  \"192192.jpg\": \"Insecta/Astraptes fulgerator/db0cd145da18a7eb231aea1c3a11b9ed.jpg\",\n  \"1922.jpg\": \"Aves/Junco hyemalis oreganus/778707b6ea500b5cb35276bc8851a36d.jpg\",\n  \"192203.jpg\": \"Insecta/Astraptes fulgerator/3469df57515f57a48dd0299310d3e7d4.jpg\",\n  \"192207.jpg\": \"Insecta/Astraptes fulgerator/929787056d0fa6a877193cb03708b159.jpg\",\n  \"192211.jpg\": \"Insecta/Dryas iulia moderata/4f1ed55ebfddc72577c883cfa75bea07.jpg\",\n  \"192215.jpg\": \"Insecta/Dryas iulia moderata/d205ad58d54fb39b5a599aaa0c776379.jpg\",\n  \"192221.jpg\": \"Insecta/Dryas iulia moderata/b99a373830e0344f32fa41849680e946.jpg\",\n  \"192237.jpg\": \"Amphibia/Hyla plicata/f39fc64012fe2e808ee2f163f3ede232.jpg\",\n  \"192245.jpg\": \"Amphibia/Hyla plicata/e9ef9597317e71211b66fc6bb2b9467f.jpg\",\n  \"192252.jpg\": \"Amphibia/Hyla plicata/ef5319ec8f93f2b0a5c9613bb1824877.jpg\",\n  \"192254.jpg\": \"Amphibia/Hyla plicata/7ecfee24a843a1bf463dfcdc568c0f5b.jpg\",\n  \"192259.jpg\": \"Arachnida/Latrodectus geometricus/1a84ca31b447e72bf8293d37e6fb6149.jpg\",\n  \"192274.jpg\": \"Arachnida/Latrodectus geometricus/8c81fe6c26996afb77387545777730d1.jpg\",\n  \"192278.jpg\": \"Arachnida/Latrodectus geometricus/6209926bd78e47e9dab61adaf6f5f8fa.jpg\",\n  \"192301.jpg\": \"Arachnida/Latrodectus geometricus/ee246f670d40b7ce5219869094aed4d6.jpg\",\n  \"192307.jpg\": \"Arachnida/Latrodectus geometricus/afd999c9ce3303a6b084e6b365e3576c.jpg\",\n  \"192313.jpg\": \"Arachnida/Latrodectus geometricus/410667d347be8d51887117ec0fb56bb0.jpg\",\n  \"192315.jpg\": \"Arachnida/Latrodectus geometricus/99b7a743119c107d195232f79ecace92.jpg\",\n  \"192320.jpg\": \"Arachnida/Latrodectus geometricus/576e6a796e3509875db640707099a694.jpg\",\n  \"192324.jpg\": \"Arachnida/Latrodectus geometricus/14ec435fd01fa4e2fba5358bae635397.jpg\",\n  \"192325.jpg\": \"Arachnida/Latrodectus geometricus/72c6dd147cf0bb392f79460ac5f586ae.jpg\",\n  \"192333.jpg\": \"Arachnida/Latrodectus geometricus/a9a6bb1a70f554a36de33194d1a23c60.jpg\",\n  \"192339.jpg\": \"Arachnida/Latrodectus geometricus/d556f2ef85b4ce08446578d954f44254.jpg\",\n  \"192340.jpg\": \"Arachnida/Latrodectus geometricus/ea196e487e5ad36d027bad5f1989b56b.jpg\",\n  \"192341.jpg\": \"Arachnida/Latrodectus geometricus/e81ea7e324fb20c7f8ca79661d41d1a7.jpg\",\n  \"192343.jpg\": \"Arachnida/Latrodectus geometricus/d4a943bf3c95c08a2cdb0beedb4196ff.jpg\",\n  \"192345.jpg\": \"Arachnida/Latrodectus geometricus/81dd5b0a1e0169179f0fdc83ed06779b.jpg\",\n  \"192351.jpg\": \"Arachnida/Latrodectus geometricus/2a1a7ef56b3fbbbbe3f58473c1d4f98b.jpg\",\n  \"192355.jpg\": \"Arachnida/Latrodectus geometricus/4d7c00156d987d9e30a8f532d22637e4.jpg\",\n  \"192408.jpg\": \"Aves/Carduelis carduelis/6a7f3e4b02b09392eb8798c7aa72c8c0.jpg\",\n  \"192409.jpg\": \"Aves/Carduelis carduelis/21c36af9b29504a2b9b3647ec6b0b668.jpg\",\n  \"192415.jpg\": \"Aves/Carduelis carduelis/b72ca1990359fb795d480c70e671a368.jpg\",\n  \"192418.jpg\": \"Aves/Carduelis carduelis/6a2a2749ba9c4dd1ba05b25993e74583.jpg\",\n  \"192430.jpg\": \"Aves/Carduelis carduelis/06727f58b43611ed140901afe5b6c76f.jpg\",\n  \"192434.jpg\": \"Aves/Carduelis carduelis/a4072691e7c3204bf95e9a9291fe1371.jpg\",\n  \"192438.jpg\": \"Aves/Carduelis carduelis/94a2434bd544f08027bed2b085ea90ef.jpg\",\n  \"192442.jpg\": \"Aves/Carduelis carduelis/6823876da921ed9e46ce3d35f2d9a04b.jpg\",\n  \"192444.jpg\": \"Aves/Carduelis carduelis/5dff57034628bfc47a1bcbe821785e1d.jpg\",\n  \"192452.jpg\": \"Aves/Carduelis carduelis/b973ea9c5769b855ee105631588fc64f.jpg\",\n  \"192468.jpg\": \"Aves/Carduelis carduelis/366134a26c99377c43863c5efa05c9a1.jpg\",\n  \"192473.jpg\": \"Aves/Carduelis carduelis/26e05d264031d576710d0b8fca1629ca.jpg\",\n  \"192477.jpg\": \"Aves/Carduelis carduelis/279d2f4e4a9f605150828537a10ce1a0.jpg\",\n  \"192478.jpg\": \"Aves/Carduelis carduelis/fab8ab9f1dd8385ec999948f01a07ace.jpg\",\n  \"192485.jpg\": \"Aves/Carduelis carduelis/266a7e8e35268a6bea0488515182d479.jpg\",\n  \"192495.jpg\": \"Aves/Carduelis carduelis/2dc888adb498c343f7f0cf02d6217936.jpg\",\n  \"192507.jpg\": \"Aves/Carduelis carduelis/30d35d7e5b9dde307e78270b155efdda.jpg\",\n  \"19255.jpg\": \"Aves/Chen caerulescens/062fcb385cf35aeab69db5b64c07232c.jpg\",\n  \"192565.jpg\": \"Insecta/Hetaerina americana/6d80857e0fcc47412b2e65edaab1f2ff.jpg\",\n  \"192582.jpg\": \"Insecta/Hetaerina americana/76e4d60684a11db897787a8749620af8.jpg\",\n  \"192593.jpg\": \"Insecta/Hetaerina americana/69c8b64bf0e98892f30c80141a6aefd1.jpg\",\n  \"192609.jpg\": \"Insecta/Hetaerina americana/cf3e87e53cdf0a32bbe8f78d5ba3e285.jpg\",\n  \"192619.jpg\": \"Insecta/Hetaerina americana/be4db5a53e93bd27e2388a29fdc7ea2b.jpg\",\n  \"192624.jpg\": \"Insecta/Hetaerina americana/333e0f11fad2ac1611ca8e0f1466f6a6.jpg\",\n  \"192626.jpg\": \"Insecta/Hetaerina americana/aa5583fd56f70810ad5b2589b9ba3928.jpg\",\n  \"192632.jpg\": \"Insecta/Hetaerina americana/5e8d94b706ef1ebb67f2573a6cbc768e.jpg\",\n  \"192638.jpg\": \"Insecta/Hetaerina americana/89ed60e17633d57eb46deb8b8c113bf6.jpg\",\n  \"192639.jpg\": \"Insecta/Hetaerina americana/b27ac8f705e207d8118a347fa62bd3d1.jpg\",\n  \"192640.jpg\": \"Insecta/Hetaerina americana/730b6507de3ffc565c59b8cd6c94228c.jpg\",\n  \"192641.jpg\": \"Insecta/Hetaerina americana/bb9bd83614a0a524e61e9c9c20fa47ca.jpg\",\n  \"192648.jpg\": \"Insecta/Hetaerina americana/4bf10c16c51d0309a3891ab3cdda76f9.jpg\",\n  \"192656.jpg\": \"Insecta/Hetaerina americana/ed13114c94c96cfc56fc848fa5d1b94a.jpg\",\n  \"192663.jpg\": \"Insecta/Hetaerina americana/4f6b5c9b4ab79b3d892370bf8a1ce26b.jpg\",\n  \"192685.jpg\": \"Insecta/Hetaerina americana/07aabe6ca0df643d65936ccba7ff5095.jpg\",\n  \"192690.jpg\": \"Insecta/Hetaerina americana/e9b035f383d9f713e74899c5586f08f6.jpg\",\n  \"192719.jpg\": \"Insecta/Hetaerina americana/f9b3c009662827bed886a8b12dd9c2a0.jpg\",\n  \"192720.jpg\": \"Insecta/Hetaerina americana/afc7cb9f358a8b5c1d931ed420894b6d.jpg\",\n  \"192727.jpg\": \"Insecta/Hetaerina americana/3cee6e68dcd24df4acecbf69b2a15b12.jpg\",\n  \"192731.jpg\": \"Insecta/Hetaerina americana/ee49a3fc5fcb768e11b37f5ba6ed3cf4.jpg\",\n  \"192741.jpg\": \"Insecta/Hetaerina americana/77322087b646e0ff2bc3636162436b3a.jpg\",\n  \"192743.jpg\": \"Insecta/Hetaerina americana/f8a7d7d12166cf80236572b7a2a95d3a.jpg\",\n  \"192749.jpg\": \"Mammalia/Sylvilagus palustris/991a2c29c7ed18ed99870354b211a503.jpg\",\n  \"192750.jpg\": \"Mammalia/Sylvilagus palustris/522615e12bb6b4fc05d68e78a2a162cc.jpg\",\n  \"192751.jpg\": \"Mammalia/Sylvilagus palustris/ed310732b92f541f93783744a5ea7e6e.jpg\",\n  \"192757.jpg\": \"Mammalia/Sylvilagus palustris/5fdf3d456e551d49bcd63b49833dbe33.jpg\",\n  \"192759.jpg\": \"Mammalia/Sylvilagus palustris/55c36254f87ed49785d3b071136b3b6e.jpg\",\n  \"192760.jpg\": \"Mammalia/Sylvilagus palustris/77dad3d51d82239d7ad4f139e6495af2.jpg\",\n  \"192780.jpg\": \"Mammalia/Erethizon dorsatum/02e00bb48a4f211971bf5741640dd943.jpg\",\n  \"192801.jpg\": \"Mammalia/Erethizon dorsatum/d6c2d4bad6e3425252badb8c7cb23652.jpg\",\n  \"192813.jpg\": \"Mammalia/Erethizon dorsatum/20519d1588123bbbe7a1c0b96800ba31.jpg\",\n  \"192817.jpg\": \"Mammalia/Erethizon dorsatum/5489b04089414ab1077d31a95512a5bf.jpg\",\n  \"192820.jpg\": \"Mammalia/Erethizon dorsatum/2f10ae23e28959e980673ba80f434f1c.jpg\",\n  \"192829.jpg\": \"Mammalia/Erethizon dorsatum/36ee61d1ca338bbc6f6b391d8053dd4b.jpg\",\n  \"192840.jpg\": \"Mammalia/Erethizon dorsatum/26e2bfdb0c09feb34dc53e957f2bcba1.jpg\",\n  \"192861.jpg\": \"Mammalia/Erethizon dorsatum/9829871f94d161becb969813fcc733ac.jpg\",\n  \"192864.jpg\": \"Mammalia/Erethizon dorsatum/a6705c8db68c0fe04443f4459b666e91.jpg\",\n  \"192875.jpg\": \"Mammalia/Erethizon dorsatum/793f9d0cd353ca3ffcbac9be42426905.jpg\",\n  \"192879.jpg\": \"Mammalia/Erethizon dorsatum/296bd0a19cf6fc2d27314849d1da3648.jpg\",\n  \"192880.jpg\": \"Mammalia/Erethizon dorsatum/8a9d76f10ec528f008376dcbd9ed3c88.jpg\",\n  \"192892.jpg\": \"Mammalia/Erethizon dorsatum/4c4c713e47d4030cf0480ebb9338183d.jpg\",\n  \"1929.jpg\": \"Aves/Junco hyemalis oreganus/c4638436d2dcbcb7ffb1dafcdc386aec.jpg\",\n  \"192900.jpg\": \"Mammalia/Erethizon dorsatum/dfd5243e5acae14690314491c06faffd.jpg\",\n  \"192907.jpg\": \"Mammalia/Erethizon dorsatum/84e92e50a33b3385b7a4c4cf67979ab1.jpg\",\n  \"192909.jpg\": \"Mammalia/Erethizon dorsatum/5ee285dd55e10d503068238e9fa7645e.jpg\",\n  \"192912.jpg\": \"Mammalia/Erethizon dorsatum/a10e09a7f1a06c1aceed045205c68417.jpg\",\n  \"192920.jpg\": \"Mammalia/Erethizon dorsatum/bacaf74adb70a5b6f06681988d787768.jpg\",\n  \"192948.jpg\": \"Animalia/Romaleon antennarium/f2e691bd6df08e4384447c682b762230.jpg\",\n  \"192949.jpg\": \"Animalia/Romaleon antennarium/34fee3ad992ff740d8eb40ed54d56ba2.jpg\",\n  \"192958.jpg\": \"Animalia/Romaleon antennarium/468c7ead44915313ae45c570bc5f87e7.jpg\",\n  \"19296.jpg\": \"Aves/Chen caerulescens/130491969cafe678f490467444a094c8.jpg\",\n  \"192967.jpg\": \"Animalia/Romaleon antennarium/fb97e4a9050f88745f1b3185b6b8a812.jpg\",\n  \"192979.jpg\": \"Animalia/Romaleon antennarium/57e443489d466754a596ec9f65d8d2ea.jpg\",\n  \"192996.jpg\": \"Mollusca/Geukensia demissa/62629144372defd04500b259030971bc.jpg\",\n  \"193.jpg\": \"Aves/Zonotrichia capensis/9bba60e4598838cdb26b97d3c449b9de.jpg\",\n  \"193000.jpg\": \"Mollusca/Geukensia demissa/98d6c4730b35c1392bdb935404fc05d6.jpg\",\n  \"193011.jpg\": \"Mollusca/Geukensia demissa/311bff79d4d8dfeaca81afb53554f30c.jpg\",\n  \"193014.jpg\": \"Insecta/Anartia fatima/046c99f813a1868a4b2ccaa30f87ce3a.jpg\",\n  \"193016.jpg\": \"Insecta/Anartia fatima/83423cd7e848b50a385c12e3698ccb7d.jpg\",\n  \"193017.jpg\": \"Insecta/Anartia fatima/926bef50884872d2b0cd2a61c237b47b.jpg\",\n  \"193025.jpg\": \"Insecta/Anartia fatima/cae4e1cc14848829fd711fc98e2138b7.jpg\",\n  \"193026.jpg\": \"Insecta/Anartia fatima/1d15a5bae3dcc20f9f529b56b769ac5b.jpg\",\n  \"193027.jpg\": \"Insecta/Anartia fatima/01f7043af6b3f9cdfdbf598d26c715b1.jpg\",\n  \"193028.jpg\": \"Insecta/Anartia fatima/e46c8ade113324e15ee12a16bb3d5dd5.jpg\",\n  \"193031.jpg\": \"Insecta/Anartia fatima/89f12400a777bb05eab05f3651a9685f.jpg\",\n  \"193032.jpg\": \"Insecta/Anartia fatima/e9158990aaeb0b3e40173cd9af202fef.jpg\",\n  \"193035.jpg\": \"Insecta/Anartia fatima/c287b1541ef112b9ca3c52fc52d635f2.jpg\",\n  \"193042.jpg\": \"Insecta/Anartia fatima/a53a7f0916b62e72a4324de2008b85bf.jpg\",\n  \"193043.jpg\": \"Insecta/Anartia fatima/13e6d2cac007d8b8c5ea57b8454812f3.jpg\",\n  \"193047.jpg\": \"Insecta/Anartia fatima/072050d034936f8883bb3294a5153a3f.jpg\",\n  \"193050.jpg\": \"Insecta/Anartia fatima/d4a41b322f0afcf701691a1b6d533272.jpg\",\n  \"193055.jpg\": \"Insecta/Anartia fatima/2695fae61566e9c0b2749bfa2cd94c92.jpg\",\n  \"193056.jpg\": \"Insecta/Anartia fatima/68d084c80ed8595a8a95454439ed6b49.jpg\",\n  \"193057.jpg\": \"Insecta/Anartia fatima/ba7bc7f85e5b8b6bb29346b61e2e2cfb.jpg\",\n  \"193058.jpg\": \"Insecta/Anartia fatima/0730a1378a31043f9515baa28ecddc53.jpg\",\n  \"193060.jpg\": \"Insecta/Anartia fatima/6de8bd6bdcaed6c830be54a43dbd8019.jpg\",\n  \"193171.jpg\": \"Aves/Riparia riparia/e1aae086e02736630a4b3790805b7418.jpg\",\n  \"19318.jpg\": \"Aves/Chen caerulescens/3dd11497c14da0e73a5d99a2735458dc.jpg\",\n  \"193184.jpg\": \"Aves/Riparia riparia/4f9246c1f64a64ea501006f6e5b8b039.jpg\",\n  \"193188.jpg\": \"Aves/Riparia riparia/971d8a27df973fd09797ee4140f61b1d.jpg\",\n  \"193189.jpg\": \"Aves/Riparia riparia/4efbf537cbe6a4880f9d22b6a820a77b.jpg\",\n  \"193190.jpg\": \"Aves/Riparia riparia/18757298ff3372086492d6448fd2df08.jpg\",\n  \"193191.jpg\": \"Aves/Riparia riparia/8a2473c50695e8fd0223f46b42a5037d.jpg\",\n  \"193197.jpg\": \"Aves/Riparia riparia/e2898ffe41aaef73f3f56a3529552fd0.jpg\",\n  \"193198.jpg\": \"Aves/Riparia riparia/5c01a2f55b31317944da7341f9e6b4da.jpg\",\n  \"1932.jpg\": \"Aves/Junco hyemalis oreganus/143148a1ee2deda44b7286a9144ff013.jpg\",\n  \"193200.jpg\": \"Aves/Riparia riparia/168dc89cc37b548b8a20ee3ceb37b9a7.jpg\",\n  \"193203.jpg\": \"Aves/Riparia riparia/8b5e6e541c1d07bfe0873ae06b7a800e.jpg\",\n  \"193204.jpg\": \"Aves/Riparia riparia/21dd749205693ddf10828c12df85b60f.jpg\",\n  \"193205.jpg\": \"Aves/Riparia riparia/f4826aa295449048fa9a7c89596a6f1d.jpg\",\n  \"193206.jpg\": \"Aves/Riparia riparia/abd6bd91ca17f48c9d26fa30f2742e61.jpg\",\n  \"193213.jpg\": \"Aves/Riparia riparia/8c0fedb034ad9a8dec223aed68d82886.jpg\",\n  \"19322.jpg\": \"Aves/Chen caerulescens/dce9b67132942facdbf62e2f51d3f26c.jpg\",\n  \"193221.jpg\": \"Aves/Riparia riparia/806b14b04c1fe2fb3a93e9610f7881ac.jpg\",\n  \"193223.jpg\": \"Aves/Riparia riparia/05e336b007fb2ecce838dea60ba32901.jpg\",\n  \"193224.jpg\": \"Aves/Riparia riparia/e24a59ce56607c11d29cdbdac58953df.jpg\",\n  \"193229.jpg\": \"Aves/Scolopax minor/8323e415cf4d4af4b09903281a2954f3.jpg\",\n  \"193230.jpg\": \"Aves/Scolopax minor/2551369a9c1e89dd8b2aec95a02aca59.jpg\",\n  \"193235.jpg\": \"Aves/Scolopax minor/947d20411ae1a8dba80e8f2df9854c47.jpg\",\n  \"193236.jpg\": \"Aves/Scolopax minor/2520c2d0057336ef71a01f17adf91cde.jpg\",\n  \"193238.jpg\": \"Aves/Scolopax minor/e43f2b2bd6ca7a7f0d2fb8d9b2187548.jpg\",\n  \"193240.jpg\": \"Aves/Scolopax minor/8968c3d6e18574090bbe9204a3eb3077.jpg\",\n  \"193246.jpg\": \"Aves/Scolopax minor/9a491f2f2428a908a813d8c4ebb48628.jpg\",\n  \"193248.jpg\": \"Aves/Scolopax minor/762c5c44f6dd4a354c18abece75a3367.jpg\",\n  \"193249.jpg\": \"Aves/Scolopax minor/63376c4f64d2c55bf5189b58c0e6be56.jpg\",\n  \"193253.jpg\": \"Aves/Scolopax minor/99d8f0796de7daf31d3a7203b467ad38.jpg\",\n  \"193255.jpg\": \"Aves/Scolopax minor/12bf06ade7d12187c545d0d965796e1a.jpg\",\n  \"193258.jpg\": \"Aves/Scolopax minor/0568ba0d4521fa367a2c83b4828ac7e1.jpg\",\n  \"193259.jpg\": \"Aves/Scolopax minor/eebf25f9ce7f3b832ae0a5cd6c5b0207.jpg\",\n  \"193264.jpg\": \"Aves/Scolopax minor/d0c469d14c991670b5b695c10f9c8747.jpg\",\n  \"193265.jpg\": \"Aves/Scolopax minor/1e12bf9154ba893f37dfaf05d78d543c.jpg\",\n  \"193267.jpg\": \"Aves/Scolopax minor/308939f1bee339f78100f32da028a770.jpg\",\n  \"193269.jpg\": \"Aves/Scolopax minor/bb11d0b1d721145f96b430885011fe69.jpg\",\n  \"193319.jpg\": \"Insecta/Haploa lecontei/78b82aade278041df3f156e29b531958.jpg\",\n  \"193325.jpg\": \"Insecta/Haploa lecontei/c5077a5ff498b9c878ea1331d48f494d.jpg\",\n  \"193326.jpg\": \"Insecta/Haploa lecontei/f35d37110c476ca4b7a3adcd9318313a.jpg\",\n  \"193327.jpg\": \"Insecta/Haploa lecontei/bee5174a70d8b1565dad934984e2f739.jpg\",\n  \"193335.jpg\": \"Insecta/Haploa lecontei/c98c241109f48d225dc41a1aafa5c0cd.jpg\",\n  \"193338.jpg\": \"Insecta/Haploa lecontei/fba0f59aa1d1279ac0de1090bb1d2cef.jpg\",\n  \"193339.jpg\": \"Insecta/Haploa lecontei/b249c722796b708d58b6ffc45d0f3943.jpg\",\n  \"193340.jpg\": \"Insecta/Haploa lecontei/8ea2bdd353baf363e6d0b31376a5ea15.jpg\",\n  \"193342.jpg\": \"Insecta/Haploa lecontei/5bbdd59b7ca0c527031462505998ef37.jpg\",\n  \"193347.jpg\": \"Insecta/Haploa lecontei/7c32f91b9af4bba44a3b93107ebc3b99.jpg\",\n  \"19343.jpg\": \"Insecta/Acanthocephala declivis/a0e6a29e6f0109818c52bec2b58f8950.jpg\",\n  \"19344.jpg\": \"Insecta/Acanthocephala declivis/2a76cfd650b2d434d3665dea777e2af4.jpg\",\n  \"193474.jpg\": \"Aves/Lonchura oryzivora/ba0d611fcb00ca2b3662d5e16e244964.jpg\",\n  \"193475.jpg\": \"Aves/Lonchura oryzivora/c7355d28d744938ec2df1ac87122fc66.jpg\",\n  \"193476.jpg\": \"Aves/Lonchura oryzivora/17eef15def17385ef63fc798c8641c3b.jpg\",\n  \"193480.jpg\": \"Aves/Lonchura oryzivora/0282d9c81b46d137a4d6b5ebc28d923e.jpg\",\n  \"193481.jpg\": \"Aves/Lonchura oryzivora/85c2b13d4933602f08439677e9662eca.jpg\",\n  \"193487.jpg\": \"Insecta/Chlosyne janais/f17221e8d1d301f1778d989e0bafcadf.jpg\",\n  \"193488.jpg\": \"Insecta/Chlosyne janais/10b1c879a160da83ab75e1a18e19d86f.jpg\",\n  \"19349.jpg\": \"Insecta/Acanthocephala declivis/956419d7248851503c259dfb6161af23.jpg\",\n  \"193490.jpg\": \"Insecta/Chlosyne janais/651d5f37464c524479d6b09783fd7fbc.jpg\",\n  \"193491.jpg\": \"Insecta/Chlosyne janais/bc3253de88972006ead421f27afeb86b.jpg\",\n  \"193493.jpg\": \"Insecta/Chlosyne janais/d812c70ac353002f638b2c53bc709c32.jpg\",\n  \"193494.jpg\": \"Insecta/Chlosyne janais/b39c6180d1bbf36644c4d7e8104e639f.jpg\",\n  \"193496.jpg\": \"Insecta/Chlosyne janais/9e03a9ee450eddc6790266131e3e9405.jpg\",\n  \"193497.jpg\": \"Insecta/Chlosyne janais/a87fcc66b6a47b2e6d3aaec5e55ee1e8.jpg\",\n  \"19350.jpg\": \"Insecta/Acanthocephala declivis/cd07c6125640b400ec82ea9801ddf054.jpg\",\n  \"193505.jpg\": \"Insecta/Chlosyne janais/5cd537c4ff7de80e86dfd6163c37f345.jpg\",\n  \"193509.jpg\": \"Insecta/Chlosyne janais/432cdff3e4cab7429bae422178d54329.jpg\",\n  \"19351.jpg\": \"Insecta/Acanthocephala declivis/aa5d849ebd9eb2718a3549ee81f7414d.jpg\",\n  \"193511.jpg\": \"Insecta/Chlosyne janais/61016722ab176dc92728ea29572cdd53.jpg\",\n  \"193512.jpg\": \"Insecta/Chlosyne janais/825227f340ed8bdbec540c558eb57bb6.jpg\",\n  \"193514.jpg\": \"Insecta/Chlosyne janais/782148fae7f0b2a1d7a6c1df8dad5918.jpg\",\n  \"193521.jpg\": \"Insecta/Chlosyne janais/7bf3f3cdb6c2ba0f88201c76279045e0.jpg\",\n  \"193524.jpg\": \"Insecta/Chlosyne janais/247ed6bd8736b21382f93eaf733a324c.jpg\",\n  \"193525.jpg\": \"Insecta/Chlosyne janais/de02f31c00d8db80b8311063c1bacd83.jpg\",\n  \"193526.jpg\": \"Insecta/Chlosyne janais/bc8bd5f1f8049bedf10dbc61eb4f05c1.jpg\",\n  \"193527.jpg\": \"Insecta/Chlosyne janais/8df8c0b320165463e49906c8aac5c9ff.jpg\",\n  \"193528.jpg\": \"Insecta/Chlosyne janais/4e526cf3d4016c9c2636fc465b3406da.jpg\",\n  \"193529.jpg\": \"Insecta/Chlosyne janais/df4fa9cb3976233cec35079b3df2e4ac.jpg\",\n  \"19355.jpg\": \"Insecta/Acanthocephala declivis/fd1c6de34fa7dc8fe8f11323824441c1.jpg\",\n  \"19356.jpg\": \"Insecta/Acanthocephala declivis/c8c9f3072137c0759c00b52c6bd68256.jpg\",\n  \"193569.jpg\": \"Mollusca/Phidiana hiltoni/4a3112089efa2e5a889688d4b7bc9d27.jpg\",\n  \"19357.jpg\": \"Insecta/Acanthocephala declivis/d8f9c9653c057a9f6a9935d5dd2cf432.jpg\",\n  \"193570.jpg\": \"Mollusca/Phidiana hiltoni/9d652752693ff908aec1e57a6939907a.jpg\",\n  \"193572.jpg\": \"Mollusca/Phidiana hiltoni/901f4160cf14937a8b9824cc0cf38d68.jpg\",\n  \"193573.jpg\": \"Mollusca/Phidiana hiltoni/c0a199d06970b03efc935ee93e0e83df.jpg\",\n  \"19358.jpg\": \"Insecta/Acanthocephala declivis/dec5acf57f355f17301629a221727b2d.jpg\",\n  \"193583.jpg\": \"Mollusca/Phidiana hiltoni/8d9476c5f0dad6b47c5004994b3fb843.jpg\",\n  \"193589.jpg\": \"Mollusca/Phidiana hiltoni/92b841ec8ef95bb741c26e3494860439.jpg\",\n  \"193600.jpg\": \"Mollusca/Phidiana hiltoni/f514e5af612b2c6a4541eec2a3ed2646.jpg\",\n  \"193607.jpg\": \"Mollusca/Phidiana hiltoni/74cb3b1b91304b34c411ae30e3148b6c.jpg\",\n  \"193608.jpg\": \"Mollusca/Phidiana hiltoni/6a8583a3870f20930a612580f243298c.jpg\",\n  \"19361.jpg\": \"Insecta/Acanthocephala declivis/1e4a323df1ce4f7cc1073caa86f1caa7.jpg\",\n  \"19362.jpg\": \"Insecta/Acanthocephala declivis/7c0053f741107c185383f7afdac40a2f.jpg\",\n  \"193644.jpg\": \"Mollusca/Phidiana hiltoni/20eb6f1ac3dfbf539b9abfccb963d453.jpg\",\n  \"19365.jpg\": \"Insecta/Acanthocephala declivis/c02a0e9a6e9ddcc4c4bec8d9f6f43678.jpg\",\n  \"193653.jpg\": \"Mollusca/Phidiana hiltoni/980a828364e8ead29e2c9066831537d0.jpg\",\n  \"193656.jpg\": \"Mollusca/Phidiana hiltoni/10d056f22b29d5c8f8ad84e302aa7826.jpg\",\n  \"19366.jpg\": \"Insecta/Acanthocephala declivis/102683131f23f311657bcfc203aec8cf.jpg\",\n  \"193662.jpg\": \"Mollusca/Phidiana hiltoni/4100cf632bf52741e0bef9f87de3e5b3.jpg\",\n  \"193665.jpg\": \"Mollusca/Phidiana hiltoni/aabc5a04e5bacd93b44cbfed3a61e543.jpg\",\n  \"193674.jpg\": \"Mollusca/Phidiana hiltoni/cc42db3aacbafd6c0f1e045b8fadf1eb.jpg\",\n  \"19368.jpg\": \"Insecta/Acanthocephala declivis/1bb89e564ba088a4ee758b06ada184b5.jpg\",\n  \"193680.jpg\": \"Actinopterygii/Sphyraena barracuda/589644a6bcad0391ade5cd45bab6fa44.jpg\",\n  \"193684.jpg\": \"Actinopterygii/Sphyraena barracuda/d5be3ab58a7cf34d0cc69e1bbbe90869.jpg\",\n  \"193685.jpg\": \"Actinopterygii/Sphyraena barracuda/9bb590089880970c854565d1f54a2a57.jpg\",\n  \"19369.jpg\": \"Insecta/Acanthocephala declivis/720eebc92afe6901ab07aa3be0276935.jpg\",\n  \"193698.jpg\": \"Actinopterygii/Sphyraena barracuda/f9f4427104556a31c6ff296ca5526c8d.jpg\",\n  \"193699.jpg\": \"Actinopterygii/Sphyraena barracuda/2c61b079ba298c0cf9522c2472c3b4a8.jpg\",\n  \"1937.jpg\": \"Aves/Junco hyemalis oreganus/3711d33bd923437372ed4bdc352868ea.jpg\",\n  \"193707.jpg\": \"Actinopterygii/Sphyraena barracuda/37be4cc98e1d969cf2a7f8a481f4b01d.jpg\",\n  \"193708.jpg\": \"Actinopterygii/Sphyraena barracuda/c9973d1ad58497d2b9be0d6a3402359a.jpg\",\n  \"193709.jpg\": \"Actinopterygii/Sphyraena barracuda/4b09d747a2be9f6533db6accefa7c5c7.jpg\",\n  \"193715.jpg\": \"Insecta/Delphinia picta/f2e0a1e15bcce670cf5754ce58eee843.jpg\",\n  \"193716.jpg\": \"Insecta/Delphinia picta/51ed77896a521c47b3795efe5746feb2.jpg\",\n  \"193717.jpg\": \"Insecta/Delphinia picta/3e6872d369570f9bbe418ca264ed0bc3.jpg\",\n  \"193719.jpg\": \"Insecta/Delphinia picta/77ac5ebf55e3378fec26c5c2b76b5663.jpg\",\n  \"193720.jpg\": \"Insecta/Delphinia picta/9e9e9ca876f054eed0fbab9b53a73b75.jpg\",\n  \"193721.jpg\": \"Insecta/Delphinia picta/58c6d77463be0aa03b64119335032d38.jpg\",\n  \"193724.jpg\": \"Insecta/Delphinia picta/e9349f196e616d1f6b5732aa489488b1.jpg\",\n  \"193727.jpg\": \"Insecta/Delphinia picta/753b53f867320068eb8abd62669b78b9.jpg\",\n  \"19373.jpg\": \"Insecta/Acanthocephala declivis/6e342a4b7831549c0f2c80c6f4808832.jpg\",\n  \"193730.jpg\": \"Insecta/Delphinia picta/68cb4ea8a7fb3b6a9b9c0a3a8c40dfe5.jpg\",\n  \"19374.jpg\": \"Insecta/Acanthocephala declivis/6eb3682a63edbd0bd6a2f6d707d58f88.jpg\",\n  \"193752.jpg\": \"Aves/Spizella pallida/0fa51858acb021d8561af663e43168c8.jpg\",\n  \"193753.jpg\": \"Aves/Spizella pallida/cf309d2085b636c780640d49e08c78a6.jpg\",\n  \"193763.jpg\": \"Aves/Spizella pallida/49f78204e30cbaf80409607e8c4c9fb4.jpg\",\n  \"193767.jpg\": \"Aves/Spizella pallida/f678226bfaa671c602407f2b606e7522.jpg\",\n  \"193770.jpg\": \"Aves/Spizella pallida/d4273dbb82fefb1f1b6695ce1490844a.jpg\",\n  \"193771.jpg\": \"Aves/Spizella pallida/96a7d9b744bffaa31fd0eefc5a09afd3.jpg\",\n  \"193773.jpg\": \"Aves/Spizella pallida/913e90a2e895430f77f0e68a447d2af8.jpg\",\n  \"193774.jpg\": \"Aves/Spizella pallida/04849ad4b8ae975c0d4b53edae0a7e26.jpg\",\n  \"193776.jpg\": \"Aves/Spizella pallida/f3466b99bd5898e642f965a54f08f4fd.jpg\",\n  \"193781.jpg\": \"Aves/Spizella pallida/cc964569da4964059f221200a9583fe8.jpg\",\n  \"19379.jpg\": \"Insecta/Acanthocephala declivis/2d76e0a07c76ed6ff82974023de3f0fc.jpg\",\n  \"193791.jpg\": \"Aves/Spizella pallida/840533c3b7a0de69de44d601377e9bdb.jpg\",\n  \"1938.jpg\": \"Aves/Junco hyemalis oreganus/453ade66d7edaccba65a7a64387076c9.jpg\",\n  \"193800.jpg\": \"Aves/Spizella pallida/af2383348341087dde93774601f66581.jpg\",\n  \"193805.jpg\": \"Aves/Spizella pallida/5685596a27a476fcd7d23c929a0cbed2.jpg\",\n  \"193813.jpg\": \"Aves/Spizella pallida/e545af1a82bbc666aa43c80425fad149.jpg\",\n  \"193816.jpg\": \"Aves/Spizella pallida/6b51a9c6613325e57a87839e253700c2.jpg\",\n  \"193818.jpg\": \"Aves/Spizella pallida/814dd08374c2dfb9b9052dff1c8113ff.jpg\",\n  \"193819.jpg\": \"Aves/Spizella pallida/c87b64e427dec4f34d197b8b0f88a8c1.jpg\",\n  \"193820.jpg\": \"Aves/Spizella pallida/b86ca6045f07732d382a3d8ebb4e6ef9.jpg\",\n  \"193824.jpg\": \"Aves/Spizella pallida/a3d2005e0301294e6053fc8436501d32.jpg\",\n  \"193831.jpg\": \"Aves/Spizella pallida/5308126202459798f2fa3251aa407b16.jpg\",\n  \"193832.jpg\": \"Aves/Spizella pallida/f410b029ceb2fabc142c03f00edbd8d2.jpg\",\n  \"19384.jpg\": \"Insecta/Acanthocephala declivis/175a6fe565e77b1093f7e56a1aba3941.jpg\",\n  \"19385.jpg\": \"Insecta/Acanthocephala declivis/7723d2b2ca8c6c18cc2fdd46b4123a23.jpg\",\n  \"19389.jpg\": \"Insecta/Acanthocephala declivis/3453adf91b317289cc1ebead635cb6ce.jpg\",\n  \"193902.jpg\": \"Aves/Sayornis phoebe/899ea81b35a145435aebc9da4f7837fa.jpg\",\n  \"19392.jpg\": \"Insecta/Panopoda rufimargo/2540cf629e6ae8b6d4c4e3a414987260.jpg\",\n  \"193923.jpg\": \"Aves/Sayornis phoebe/cd1ba8034fd775b4eb78f892e31463a6.jpg\",\n  \"19395.jpg\": \"Insecta/Panopoda rufimargo/9f1e1e2b0e3f33c8f1aedca81a901d91.jpg\",\n  \"193955.jpg\": \"Aves/Sayornis phoebe/152232e592061f4eca6684528ce7f853.jpg\",\n  \"19396.jpg\": \"Insecta/Panopoda rufimargo/0a44a960ddf7d66714b4420d502b5cf1.jpg\",\n  \"19398.jpg\": \"Insecta/Panopoda rufimargo/b0c4a444b3e28a3133fda8e574005319.jpg\",\n  \"1940.jpg\": \"Aves/Junco hyemalis oreganus/de3b57407aa6b746902600675b8e3aab.jpg\",\n  \"194005.jpg\": \"Aves/Sayornis phoebe/f759514ab411f599dfe36f1ee004118d.jpg\",\n  \"194027.jpg\": \"Aves/Sayornis phoebe/09071f7fcca24378af2940d399152596.jpg\",\n  \"19408.jpg\": \"Insecta/Panopoda rufimargo/d19502171486e015faeedff7761cf7cb.jpg\",\n  \"194082.jpg\": \"Aves/Sayornis phoebe/6a8f11538d20bd793087f31e9f3b0d9f.jpg\",\n  \"194090.jpg\": \"Aves/Sayornis phoebe/c2e714492f460788ff055d6060208776.jpg\",\n  \"194096.jpg\": \"Aves/Sayornis phoebe/79da01c0dcc831be4f2039ef004e86d7.jpg\",\n  \"1941.jpg\": \"Aves/Junco hyemalis oreganus/552a8267361f0d61c2ef137498965f45.jpg\",\n  \"194127.jpg\": \"Aves/Sayornis phoebe/3172c524a34ac669b8a4ac93fd35df46.jpg\",\n  \"19413.jpg\": \"Insecta/Panopoda rufimargo/41c15afc3d51e8f2725aca774b6591e2.jpg\",\n  \"19414.jpg\": \"Insecta/Junonia genoveva/5ef1baba6f84f0140a5734b4d339cb08.jpg\",\n  \"194172.jpg\": \"Aves/Sayornis phoebe/8892c72e5b68f77622f1cb62b93f2264.jpg\",\n  \"1942.jpg\": \"Aves/Junco hyemalis oreganus/5f737425a1ded4eed3a1b901bfa951dc.jpg\",\n  \"19420.jpg\": \"Insecta/Junonia genoveva/8de59602b98a3ac6524461dd039a2d73.jpg\",\n  \"194215.jpg\": \"Aves/Sayornis phoebe/967b7e5316c702af44b3b581f296acd3.jpg\",\n  \"19424.jpg\": \"Insecta/Junonia genoveva/b2bfb4e86e3250ec207f82979fbee1df.jpg\",\n  \"194261.jpg\": \"Aves/Sayornis phoebe/fc6c5ee6c9367cd384bb2be7dd52289f.jpg\",\n  \"194295.jpg\": \"Aves/Sayornis phoebe/67e4d729b9832b173e3ef0e067f2a190.jpg\",\n  \"194297.jpg\": \"Aves/Sayornis phoebe/9e96b1e743f115f40f375a08faa43580.jpg\",\n  \"194355.jpg\": \"Aves/Sayornis phoebe/ac102a67584ccc7bf6c0da3c6b8a3013.jpg\",\n  \"194369.jpg\": \"Aves/Sayornis phoebe/8a4048413ecf40e221604ebc786528aa.jpg\",\n  \"194370.jpg\": \"Aves/Sayornis phoebe/062913bc84eb19495a5141e43bd4e836.jpg\",\n  \"194378.jpg\": \"Aves/Sayornis phoebe/aa0ddad4fa2c85d629e49f25ae9b774e.jpg\",\n  \"194398.jpg\": \"Aves/Melanotis caerulescens/d183f7053f4d012cf7f7cd4a62054f2f.jpg\",\n  \"194401.jpg\": \"Aves/Melanotis caerulescens/04811ea417f9cb584ccad22caa9979b1.jpg\",\n  \"194402.jpg\": \"Aves/Melanotis caerulescens/8385663e31aa05242866ef707ee169e1.jpg\",\n  \"194403.jpg\": \"Aves/Melanotis caerulescens/8babe02f1e4aab348eda250798cbe3f8.jpg\",\n  \"194416.jpg\": \"Aves/Melanotis caerulescens/77c20ec1815835df94db60beb53460b1.jpg\",\n  \"194421.jpg\": \"Aves/Melanotis caerulescens/10869376d932dc8fc4dbdd746c88a201.jpg\",\n  \"194429.jpg\": \"Aves/Melanotis caerulescens/719df10dea20631d46cf29a188c9f9e1.jpg\",\n  \"194430.jpg\": \"Aves/Melanotis caerulescens/7002a333814ac4dd610abd9ba0641f2d.jpg\",\n  \"194433.jpg\": \"Reptilia/Caiman crocodilus/44a339d85ac5bf71576560b4952ac119.jpg\",\n  \"194438.jpg\": \"Reptilia/Caiman crocodilus/d4e8dab0ce210d42da89f1ca939e3ee5.jpg\",\n  \"194441.jpg\": \"Reptilia/Caiman crocodilus/db77d3d0fe51633bfece1e97c229361e.jpg\",\n  \"194445.jpg\": \"Reptilia/Caiman crocodilus/9e3ccdd734aa912b003feccf9e46df04.jpg\",\n  \"194446.jpg\": \"Reptilia/Caiman crocodilus/34bfe999b8631d265e5e326b0bae8da5.jpg\",\n  \"194447.jpg\": \"Reptilia/Caiman crocodilus/a23a5c9aa9de90a4cee87d929cec677f.jpg\",\n  \"194518.jpg\": \"Aves/Egretta gularis/1f00b7cc76105c2ce77103461d0a9172.jpg\",\n  \"194521.jpg\": \"Aves/Egretta gularis/81d126ad1880159d26e49d17128f0a12.jpg\",\n  \"194524.jpg\": \"Aves/Egretta gularis/68fd29edf051936395dc0a6dbc53406a.jpg\",\n  \"194525.jpg\": \"Aves/Egretta gularis/7785e3cf0e1f233c773f1433f05e9a0e.jpg\",\n  \"194533.jpg\": \"Insecta/Sympetrum sanguineum/2b03e5ddd345620345ed6aff26a3d245.jpg\",\n  \"194534.jpg\": \"Insecta/Sympetrum sanguineum/a8bb740a678a9c3002074764c965b12e.jpg\",\n  \"194536.jpg\": \"Insecta/Sympetrum sanguineum/afaabfc19710fdd31d29d80edcd42ac7.jpg\",\n  \"194542.jpg\": \"Insecta/Sympetrum sanguineum/26b4fb06409feea59e5d912d2c9bdaaf.jpg\",\n  \"194551.jpg\": \"Insecta/Sympetrum sanguineum/fda4ca5461d15909acd1a0589e8d13c5.jpg\",\n  \"194552.jpg\": \"Insecta/Sympetrum sanguineum/8684feed717d1cb99bae121b072c45d0.jpg\",\n  \"194553.jpg\": \"Insecta/Sympetrum sanguineum/734ba7e59ed086bcdc5a42a0f059ed5d.jpg\",\n  \"19456.jpg\": \"Aves/Cyanocorax yncas/e81d1b3d81b4dd1eceda5453d02c2315.jpg\",\n  \"19459.jpg\": \"Aves/Cyanocorax yncas/53c24b2279be07fed9bfe1925dca1f13.jpg\",\n  \"19460.jpg\": \"Aves/Cyanocorax yncas/1f8ecbe20ee5b24ab613bdfa07126a6e.jpg\",\n  \"19472.jpg\": \"Aves/Cyanocorax yncas/a9278c20b19a11da6f667bd18d6e200c.jpg\",\n  \"19473.jpg\": \"Aves/Cyanocorax yncas/77240917a2e5025e13797b96024b067d.jpg\",\n  \"19474.jpg\": \"Aves/Cyanocorax yncas/9b665f783a2b3325e6cf55b3c8691999.jpg\",\n  \"194771.jpg\": \"Insecta/Diplacodes trivialis/de97fc081754dc0f0a6ba4dc67656585.jpg\",\n  \"194772.jpg\": \"Insecta/Diplacodes trivialis/0221b474a135f95eb2b0fb42e0af1436.jpg\",\n  \"194776.jpg\": \"Insecta/Diplacodes trivialis/e1ad72af9ab8219b685710a161b9ab37.jpg\",\n  \"194780.jpg\": \"Insecta/Diplacodes trivialis/8ce71433a6c527650c73877c56ddfff6.jpg\",\n  \"194784.jpg\": \"Insecta/Diplacodes trivialis/eff63835f1c9ae1324b6094da58e4e35.jpg\",\n  \"1948.jpg\": \"Mollusca/Neobernaya spadicea/031f9b48886e84b6853c154ce0ec2e01.jpg\",\n  \"19480.jpg\": \"Aves/Cyanocorax yncas/5f464f798faefb2ace0700529534d7fa.jpg\",\n  \"19484.jpg\": \"Aves/Cyanocorax yncas/cc7303a49fd596136b93090d1893d41c.jpg\",\n  \"19486.jpg\": \"Aves/Cyanocorax yncas/7d70e8fcdbba326cbe738eab3485aa15.jpg\",\n  \"194872.jpg\": \"Amphibia/Oophaga pumilio/4799546ef5c639a1c34fcc6bac9f8d75.jpg\",\n  \"194876.jpg\": \"Amphibia/Oophaga pumilio/c999dad7fd31e774f466e6152d265500.jpg\",\n  \"194877.jpg\": \"Amphibia/Oophaga pumilio/42493fb3f3aaaf8f926859456b756800.jpg\",\n  \"194881.jpg\": \"Amphibia/Oophaga pumilio/641dea9cd6963ccdb053f49301a146c2.jpg\",\n  \"194889.jpg\": \"Amphibia/Oophaga pumilio/47cf7552432a02f220bb93f4b3fbc4de.jpg\",\n  \"194891.jpg\": \"Amphibia/Oophaga pumilio/0e6724be9ca3b58114eb105c4bedae41.jpg\",\n  \"194898.jpg\": \"Amphibia/Oophaga pumilio/5c9a61d09e77fa82b68026d2f7d2161f.jpg\",\n  \"1949.jpg\": \"Mollusca/Neobernaya spadicea/9f98166a037cd5ce35712c736aa37c26.jpg\",\n  \"19490.jpg\": \"Aves/Cyanocorax yncas/ffc61ac89833ddd1801a82c9c8be7c4d.jpg\",\n  \"194900.jpg\": \"Amphibia/Oophaga pumilio/2190c2040eccb26cc532b9de46000dd6.jpg\",\n  \"194902.jpg\": \"Amphibia/Oophaga pumilio/27e9bff670caf967c1899c4d45e66eb3.jpg\",\n  \"194906.jpg\": \"Mammalia/Alouatta pigra/0614b97bbfc6f0750f747e7145ff4f15.jpg\",\n  \"194908.jpg\": \"Mammalia/Alouatta pigra/8852c38c45bcc61b929c693b0deb946f.jpg\",\n  \"194914.jpg\": \"Mammalia/Alouatta pigra/cfd4138fa537504b2c7b0164465202a0.jpg\",\n  \"194923.jpg\": \"Mammalia/Alouatta pigra/256a3af7bea47cca49d47dc43da484fe.jpg\",\n  \"194924.jpg\": \"Mammalia/Alouatta pigra/3f7989d415639118532ba1d9eca839ec.jpg\",\n  \"194925.jpg\": \"Mammalia/Alouatta pigra/a1b7c363f621795e0ad30da3089f76c4.jpg\",\n  \"194979.jpg\": \"Insecta/Brephidium exilis/e4bdc47727c78ae57b95a3ab8ecf806c.jpg\",\n  \"194981.jpg\": \"Insecta/Brephidium exilis/35c0957f275b3df1a2fa045519e77aa6.jpg\",\n  \"194982.jpg\": \"Insecta/Brephidium exilis/db568b4f82c3c9ef9628dc3c53f9add5.jpg\",\n  \"19499.jpg\": \"Aves/Cyanocorax yncas/223da3f41492fec5b061af111921f91a.jpg\",\n  \"194996.jpg\": \"Insecta/Brephidium exilis/3b52a0fb0754e0b766d82f2638197afb.jpg\",\n  \"194997.jpg\": \"Insecta/Brephidium exilis/fc26cdcb9bd3205418953c38775fd239.jpg\",\n  \"19500.jpg\": \"Aves/Cyanocorax yncas/e698e668fecb30ea2efdf6b26a1ab50e.jpg\",\n  \"195005.jpg\": \"Insecta/Brephidium exilis/cefee046e68734921086a3c8734d6b60.jpg\",\n  \"195015.jpg\": \"Insecta/Brephidium exilis/4cb2a96a43558091df512c385a7023fe.jpg\",\n  \"195016.jpg\": \"Insecta/Brephidium exilis/994470dcf7fb72130921515afdf549c3.jpg\",\n  \"195017.jpg\": \"Insecta/Brephidium exilis/173c3b02b63d43eb0403af78909c29ef.jpg\",\n  \"195018.jpg\": \"Insecta/Brephidium exilis/527eea10e2a47536ee10dfe06a90e32a.jpg\",\n  \"195023.jpg\": \"Insecta/Brephidium exilis/cede8e4ee734f521dcdb71ebe33b0889.jpg\",\n  \"195036.jpg\": \"Insecta/Brephidium exilis/16d60077e9143897f7c1e3c9ae991657.jpg\",\n  \"195048.jpg\": \"Insecta/Brephidium exilis/ddb6edad627591c8b9789d034581dcf5.jpg\",\n  \"195066.jpg\": \"Insecta/Brephidium exilis/e5a55521e7ca6569a03240c423a3bcce.jpg\",\n  \"195068.jpg\": \"Insecta/Brephidium exilis/e8dfaed26ccc6ac44f6745869da6ddd4.jpg\",\n  \"19507.jpg\": \"Aves/Cyanocorax yncas/c8e742962082d85b7b1b793570b36aaf.jpg\",\n  \"195079.jpg\": \"Insecta/Brephidium exilis/6583e432fa12dc98d37a9095e348c1a9.jpg\",\n  \"19508.jpg\": \"Aves/Cyanocorax yncas/1023a720d2c49d91fdcf34d43b7fdc57.jpg\",\n  \"195080.jpg\": \"Insecta/Brephidium exilis/b1c82157eb8f80946a87e08dc0411572.jpg\",\n  \"195087.jpg\": \"Insecta/Brephidium exilis/1b004e528b71e55d6567ae857f96f1e1.jpg\",\n  \"195093.jpg\": \"Insecta/Brephidium exilis/e5ad1956df50c9a72131a5216a08db22.jpg\",\n  \"195094.jpg\": \"Insecta/Brephidium exilis/4ae4529ef2039d9b948178a839f0a0e0.jpg\",\n  \"195099.jpg\": \"Insecta/Brephidium exilis/f649711a65f43bdcd1b181380b1fe56c.jpg\",\n  \"1951.jpg\": \"Mollusca/Neobernaya spadicea/4f3dca39eefc7b0f8217641a973dbc8d.jpg\",\n  \"195110.jpg\": \"Insecta/Brephidium exilis/aec54103703739492bb3473b4cc7c397.jpg\",\n  \"195120.jpg\": \"Insecta/Brephidium exilis/d61433c580db6045fa26e8d4feab8574.jpg\",\n  \"195124.jpg\": \"Insecta/Brephidium exilis/bebe47846122a2fa32a566fa0c01cd9f.jpg\",\n  \"19514.jpg\": \"Aves/Cyanocorax yncas/2faf1c345b3423f6f9db253ff20414c1.jpg\",\n  \"19517.jpg\": \"Aves/Cyanocorax yncas/1c0a34de9998cd0e8afb7431d43a7c35.jpg\",\n  \"19520.jpg\": \"Aves/Cyanocorax yncas/1fa02dbe68e52c8b18550d0f90e154d0.jpg\",\n  \"195210.jpg\": \"Amphibia/Hyla eximia/5996e4707ab5058d39b430f548490db3.jpg\",\n  \"195212.jpg\": \"Amphibia/Hyla eximia/237b6e6a4f0c43527747cb8159f782e1.jpg\",\n  \"195213.jpg\": \"Amphibia/Hyla eximia/7fc0740e89d8b5cc05fdd03b6be20324.jpg\",\n  \"195229.jpg\": \"Amphibia/Hyla eximia/4223439d813ef9e95ba94098f2b78fad.jpg\",\n  \"19523.jpg\": \"Aves/Cyanocorax yncas/780fdf1fc04f51c272c5802e72fba94a.jpg\",\n  \"195231.jpg\": \"Amphibia/Hyla eximia/e4a6b52958ffc6f25e225a7a82b8f4fb.jpg\",\n  \"19524.jpg\": \"Aves/Cyanocorax yncas/d9aaf39de8cc9d2047dbb540c9ff53cd.jpg\",\n  \"195240.jpg\": \"Amphibia/Hyla eximia/384e80cf128461d9e013ddb09283c138.jpg\",\n  \"195241.jpg\": \"Amphibia/Hyla eximia/79819ce1cccab1f844287daa445198fe.jpg\",\n  \"195242.jpg\": \"Amphibia/Hyla eximia/a79d43ed287a1162cf3b2432aca37fe1.jpg\",\n  \"195245.jpg\": \"Amphibia/Hyla eximia/2a7c931211f305fd2e1cd44789fd86ae.jpg\",\n  \"19525.jpg\": \"Aves/Cyanocorax yncas/a3e9e080d350b84ad493e3716570df7b.jpg\",\n  \"19527.jpg\": \"Aves/Cyanocorax yncas/a6dc09eb79338570188d30afa398443d.jpg\",\n  \"195273.jpg\": \"Arachnida/Araneus marmoreus/71b920a56a7836c33862ad23d851de01.jpg\",\n  \"195275.jpg\": \"Arachnida/Araneus marmoreus/a5d927ca0ad0d9e77b2b2b39d939bbde.jpg\",\n  \"195276.jpg\": \"Arachnida/Araneus marmoreus/d99fe6ab434f182189a7a8cbe359f4ad.jpg\",\n  \"195278.jpg\": \"Arachnida/Araneus marmoreus/1f1b1410b180c0cbf407e1798e6a66b5.jpg\",\n  \"195283.jpg\": \"Arachnida/Araneus marmoreus/940f0aafa74c16c4dfdd821e1636db5e.jpg\",\n  \"195287.jpg\": \"Arachnida/Araneus marmoreus/c1cf38b6cb5df732ef9d942cb6f8b4c5.jpg\",\n  \"195289.jpg\": \"Arachnida/Araneus marmoreus/fb4ac8a798bc9408f19fb7306af1e9db.jpg\",\n  \"195299.jpg\": \"Reptilia/Phrynosoma orbiculare/e647ac8c75ed39de3a7cae2a07bdd614.jpg\",\n  \"19530.jpg\": \"Aves/Cyanocorax yncas/14bda103db22cdbb17df8358189d0103.jpg\",\n  \"195300.jpg\": \"Reptilia/Phrynosoma orbiculare/2e51885be0620edc99cb4718ac9e5460.jpg\",\n  \"195302.jpg\": \"Reptilia/Phrynosoma orbiculare/c6dc656d2417aa8e409f50e8f5dba7f6.jpg\",\n  \"195303.jpg\": \"Reptilia/Phrynosoma orbiculare/5b9517baca771b01da113c043ea42f94.jpg\",\n  \"195304.jpg\": \"Reptilia/Phrynosoma orbiculare/28e3a66a8e206e661755376a22c3badc.jpg\",\n  \"195318.jpg\": \"Reptilia/Phrynosoma orbiculare/4ff8bad27abc87b4503c858dd98f965c.jpg\",\n  \"195322.jpg\": \"Reptilia/Phrynosoma orbiculare/81feb207187ea7a47e4ce80b6bc9b9eb.jpg\",\n  \"195323.jpg\": \"Reptilia/Phrynosoma orbiculare/be753f467ed72033da4521d01f8fb32f.jpg\",\n  \"195324.jpg\": \"Reptilia/Phrynosoma orbiculare/3d0ba44dc563eac7601756db16e29c69.jpg\",\n  \"19547.jpg\": \"Aves/Branta hutchinsii/18d9de620f084a446e77f5e69d0ae886.jpg\",\n  \"1955.jpg\": \"Mollusca/Neobernaya spadicea/e5414402f60dda4ab59789e1809141f3.jpg\",\n  \"195601.jpg\": \"Insecta/Junonia almana/1dff90b4e6b2f0ec5f29f5d21d52f8ad.jpg\",\n  \"195605.jpg\": \"Insecta/Junonia almana/381634e1e04639ae2c9bda9b0157f1f2.jpg\",\n  \"195606.jpg\": \"Insecta/Junonia almana/c67689cfa7cd9aba9d6a607705a09cad.jpg\",\n  \"195627.jpg\": \"Reptilia/Hierophis viridiflavus/8352a48661d1fe9090bebdf1b0bfd84d.jpg\",\n  \"195628.jpg\": \"Reptilia/Hierophis viridiflavus/f01a50d18f6805eec350e1ac2a29ef99.jpg\",\n  \"195630.jpg\": \"Reptilia/Hierophis viridiflavus/dd0078bda7bfbeb639252dbb96f2db1e.jpg\",\n  \"195631.jpg\": \"Reptilia/Hierophis viridiflavus/fcee97db82065120464d4eb74bb78ba5.jpg\",\n  \"195632.jpg\": \"Reptilia/Hierophis viridiflavus/a5bd2ccccb5ddedc07a1e141e2ef99b9.jpg\",\n  \"195639.jpg\": \"Reptilia/Hierophis viridiflavus/12d62e4cd1773fab6dc5175666c38cec.jpg\",\n  \"195640.jpg\": \"Reptilia/Hierophis viridiflavus/0194b1ffb57cf5df0bffa890d0cec0e1.jpg\",\n  \"195646.jpg\": \"Reptilia/Hierophis viridiflavus/ace89bbcc45b4d03bcc10cab680c849a.jpg\",\n  \"195648.jpg\": \"Reptilia/Hierophis viridiflavus/e107e9c4fabc413f32ab8cdb638d4ded.jpg\",\n  \"19567.jpg\": \"Aves/Branta hutchinsii/fa63054a1320a2a664a1bf814e98acf5.jpg\",\n  \"195683.jpg\": \"Aves/Empidonax difficilis/163d4173912b082da5d65ec7176f3465.jpg\",\n  \"195685.jpg\": \"Aves/Empidonax difficilis/8e44efb65c6f8328a3e2999ef52d75ed.jpg\",\n  \"195686.jpg\": \"Aves/Empidonax difficilis/bdc4288ad7c0eaa5c04a328399c7c6e3.jpg\",\n  \"195691.jpg\": \"Aves/Empidonax difficilis/beccceac860adb59dc9680b0a0334a2a.jpg\",\n  \"195697.jpg\": \"Aves/Empidonax difficilis/169709094b7fdd99edaae7a9b4791f72.jpg\",\n  \"195698.jpg\": \"Aves/Empidonax difficilis/e78975f438fc3b1894c74a86c0fbe99b.jpg\",\n  \"195699.jpg\": \"Aves/Empidonax difficilis/172afa7d3573bfe902d6841b46ddc37f.jpg\",\n  \"195701.jpg\": \"Aves/Empidonax difficilis/959088376b8fc91906cb178a5a9a2451.jpg\",\n  \"195706.jpg\": \"Aves/Empidonax difficilis/b13ba157d643bdf862329f8f277ea040.jpg\",\n  \"19571.jpg\": \"Aves/Branta hutchinsii/44c93997562d263cf52cd61683c46500.jpg\",\n  \"195721.jpg\": \"Aves/Empidonax difficilis/3c3e2c27461147b7477f714b1a8e8b56.jpg\",\n  \"195722.jpg\": \"Aves/Empidonax difficilis/61d4f65d1efe1a161d7cc2f5f9b1d303.jpg\",\n  \"195723.jpg\": \"Aves/Empidonax difficilis/27b6ba502470def4ecdbeb261b3bef6f.jpg\",\n  \"195738.jpg\": \"Aves/Empidonax difficilis/5cc26df11470fb23ebf6eca85a9a4dce.jpg\",\n  \"195740.jpg\": \"Aves/Empidonax difficilis/7454495cfb36a650bdf3039cabff313a.jpg\",\n  \"195745.jpg\": \"Aves/Empidonax difficilis/0df6920b38393b7c6edc32d46cf63b1f.jpg\",\n  \"195752.jpg\": \"Aves/Empidonax difficilis/bdfcce21871b1fa82f44753a42d21dc2.jpg\",\n  \"195761.jpg\": \"Aves/Empidonax difficilis/6e3b3fedf6120731ad8c295dc3bf3195.jpg\",\n  \"195763.jpg\": \"Aves/Basileuterus rufifrons/c16f5bfe6f7907811275d37e4bc31a0f.jpg\",\n  \"195764.jpg\": \"Aves/Basileuterus rufifrons/e5e87bd49b591c807a6372835e466dd8.jpg\",\n  \"195767.jpg\": \"Aves/Basileuterus rufifrons/c8fb07481047471ba511092cfbf67302.jpg\",\n  \"195768.jpg\": \"Aves/Basileuterus rufifrons/601fcbec8e74631f0bf5ab47b218aec6.jpg\",\n  \"195769.jpg\": \"Aves/Basileuterus rufifrons/5271cb2b7c80783662cfc662d11f092d.jpg\",\n  \"195771.jpg\": \"Aves/Basileuterus rufifrons/066761ef82e22351f6c9111d7bb6ab85.jpg\",\n  \"195773.jpg\": \"Aves/Basileuterus rufifrons/768fdff2b49aee6309f5b32a0440558c.jpg\",\n  \"195774.jpg\": \"Aves/Basileuterus rufifrons/f3a340138ec853bee1cde60545566703.jpg\",\n  \"195775.jpg\": \"Aves/Basileuterus rufifrons/d7c332caf4d65d992eb8336fc5cad20c.jpg\",\n  \"19578.jpg\": \"Aves/Branta hutchinsii/63a62c350f4f89428bec8fe8b59f163e.jpg\",\n  \"195780.jpg\": \"Aves/Basileuterus rufifrons/8afa7a8722dba0934520d8acccac17c6.jpg\",\n  \"195781.jpg\": \"Aves/Basileuterus rufifrons/c3b3726085bdc9aca4afad155376def3.jpg\",\n  \"195782.jpg\": \"Aves/Basileuterus rufifrons/85a427f7addfd7a170a43e0d72096e06.jpg\",\n  \"195783.jpg\": \"Aves/Basileuterus rufifrons/5e7098e73dea7a75677435decca8019c.jpg\",\n  \"195787.jpg\": \"Aves/Basileuterus rufifrons/1c55ab3d4b1201310caa36478f076d39.jpg\",\n  \"195789.jpg\": \"Aves/Basileuterus rufifrons/4ef0427743c570955f3604a73cb9176b.jpg\",\n  \"195790.jpg\": \"Aves/Basileuterus rufifrons/366bab9b733e4139a89c1c64e8441aae.jpg\",\n  \"195791.jpg\": \"Aves/Basileuterus rufifrons/3dd835ebe8f96933443f9922556c2c6b.jpg\",\n  \"195792.jpg\": \"Aves/Basileuterus rufifrons/57f077d50223e9d12b2dcd6ff1b27d48.jpg\",\n  \"195795.jpg\": \"Aves/Basileuterus rufifrons/1b5ed6bf14acf023aa69e706fe09ea73.jpg\",\n  \"195796.jpg\": \"Aves/Basileuterus rufifrons/9d6cf9a902563d65818db900a9fa1049.jpg\",\n  \"195799.jpg\": \"Aves/Basileuterus rufifrons/6acaaca477f9efb11a2b339581ba442e.jpg\",\n  \"19581.jpg\": \"Aves/Branta hutchinsii/2a6226d5f6704263975db1f2ef3a9548.jpg\",\n  \"1959.jpg\": \"Mollusca/Neobernaya spadicea/ed0ddff8206c1f2e0e5b7b4fcc2d2395.jpg\",\n  \"195901.jpg\": \"Insecta/Anatrytone logan/feea1acac98e946635202c886189f7bf.jpg\",\n  \"195904.jpg\": \"Insecta/Anatrytone logan/ac68f366a739d49c44d5747502a061cb.jpg\",\n  \"195906.jpg\": \"Insecta/Anatrytone logan/2267379efcbe4024ad72a48d81e3aef0.jpg\",\n  \"195907.jpg\": \"Insecta/Anatrytone logan/2a5ea3488d010945b76e722e8e716650.jpg\",\n  \"195908.jpg\": \"Insecta/Anatrytone logan/64d005322e71701c349fe859ad301514.jpg\",\n  \"195909.jpg\": \"Insecta/Anatrytone logan/a48195cefd0b37e93fe0b9f8423343e7.jpg\",\n  \"195911.jpg\": \"Insecta/Anatrytone logan/c33dcf2e0c35ccb297a4efca9138f897.jpg\",\n  \"195912.jpg\": \"Insecta/Anatrytone logan/f475cc66ca1ff8fbcd2e68ae78c187b5.jpg\",\n  \"195913.jpg\": \"Insecta/Anatrytone logan/4bc01817a528f65ebcf063136bb3caae.jpg\",\n  \"195914.jpg\": \"Insecta/Anatrytone logan/06c4f1a2b3528de5581fe40ff1541400.jpg\",\n  \"195915.jpg\": \"Insecta/Anatrytone logan/6d883d140cdb88d67920e4af4c5367a9.jpg\",\n  \"195916.jpg\": \"Insecta/Anatrytone logan/2e318293bd4b063c22f06a23b461a766.jpg\",\n  \"195921.jpg\": \"Insecta/Anatrytone logan/89b6c91b69858b568c20d64d6c6cc2d4.jpg\",\n  \"195931.jpg\": \"Insecta/Anatrytone logan/170c94b33f6e551624137eda38ffff60.jpg\",\n  \"195941.jpg\": \"Insecta/Anatrytone logan/58c73f58ad189acb3575e83689dfd607.jpg\",\n  \"195942.jpg\": \"Insecta/Anatrytone logan/d5b4b34f7d7228a38a8e0eaf50676ff6.jpg\",\n  \"195944.jpg\": \"Insecta/Anatrytone logan/573dedaaa8f9905acb4d97e073f91841.jpg\",\n  \"195945.jpg\": \"Insecta/Anatrytone logan/edd24b7400986c70092844b55cf69191.jpg\",\n  \"19604.jpg\": \"Aves/Branta hutchinsii/5099de314c0b3287fb0d4664af65f000.jpg\",\n  \"196192.jpg\": \"Insecta/Catocala ilia/f0b47c18494806e766902b5a461ab5d2.jpg\",\n  \"196195.jpg\": \"Insecta/Catocala ilia/651067b520c2719ac04137c52b3fa558.jpg\",\n  \"196196.jpg\": \"Insecta/Catocala ilia/fb9d7542623d6ba9dd7e560bb57805cf.jpg\",\n  \"196197.jpg\": \"Insecta/Catocala ilia/94d5b40117eb338bbbb77a1dec84084d.jpg\",\n  \"196198.jpg\": \"Insecta/Catocala ilia/7090fa4342f51914fb8e680cc96518a5.jpg\",\n  \"196205.jpg\": \"Insecta/Catocala ilia/b6d0f13ec818ba7e6b9744279430a141.jpg\",\n  \"196213.jpg\": \"Insecta/Eusarca confusaria/c985ce5bf0ff4419b799e601fbbc3d8f.jpg\",\n  \"196216.jpg\": \"Insecta/Eusarca confusaria/5d0df518ef8ca75bf0bef87b496d4a13.jpg\",\n  \"196218.jpg\": \"Insecta/Eusarca confusaria/173f9f299718e99b4e00c3dec4c5b8cb.jpg\",\n  \"196219.jpg\": \"Insecta/Eusarca confusaria/d827a30ece78f67ea6b18bb2c70340ef.jpg\",\n  \"196220.jpg\": \"Insecta/Eusarca confusaria/cb02a6fce24e8828dc592f303127deb2.jpg\",\n  \"196223.jpg\": \"Insecta/Eusarca confusaria/68ad300d840645ace2582ddd2ac73d65.jpg\",\n  \"196227.jpg\": \"Insecta/Eusarca confusaria/2295df0f32f7dd227a0dcd4d6a6eb7b9.jpg\",\n  \"196228.jpg\": \"Insecta/Eusarca confusaria/c077f22e116bfb057abd8dd82e8e7470.jpg\",\n  \"196229.jpg\": \"Insecta/Eusarca confusaria/6dc9ef4ea7b93df809c63bba768c0563.jpg\",\n  \"196230.jpg\": \"Insecta/Eusarca confusaria/56f83be0f310cc99aae3b3ff5a332a2c.jpg\",\n  \"196232.jpg\": \"Insecta/Eusarca confusaria/9ad15127a7cad359234787f3bd9c517c.jpg\",\n  \"196233.jpg\": \"Insecta/Eusarca confusaria/0845c8c1147b76e5e6f944df6832f9a4.jpg\",\n  \"196237.jpg\": \"Insecta/Eusarca confusaria/37c5d56a14a89a088bac3a6f47e55b49.jpg\",\n  \"196238.jpg\": \"Insecta/Eusarca confusaria/d6197e709c283fa38fe6e9d3578499bd.jpg\",\n  \"196240.jpg\": \"Insecta/Eusarca confusaria/7ad478a23384e027fbe6e959d097baf7.jpg\",\n  \"196243.jpg\": \"Insecta/Eusarca confusaria/2dc3e575641c562325586926ab11dc6f.jpg\",\n  \"196250.jpg\": \"Insecta/Eusarca confusaria/d6896e948019b0430bb08b58cc00f018.jpg\",\n  \"196254.jpg\": \"Insecta/Eusarca confusaria/5026263b41505971e7507e5a60c97e51.jpg\",\n  \"1963.jpg\": \"Mollusca/Neobernaya spadicea/42822684ff8995b1635d1d323f91bcad.jpg\",\n  \"1964.jpg\": \"Mollusca/Neobernaya spadicea/7fab9ce071c417102a8767014cfb4b1a.jpg\",\n  \"196542.jpg\": \"Insecta/Coryphista meadii/a1bc7e922c3820870fa2fb32ab18eda0.jpg\",\n  \"196546.jpg\": \"Insecta/Coryphista meadii/4ac0a4cdb0f0f14b4e64c215e3f3d3c8.jpg\",\n  \"196552.jpg\": \"Insecta/Coryphista meadii/07284b9cb11bc0da5886e5a2486095a3.jpg\",\n  \"196554.jpg\": \"Insecta/Coryphista meadii/0d4bd54428c942ed080d3b1c22465676.jpg\",\n  \"196555.jpg\": \"Insecta/Coryphista meadii/04c5b610602fd42314ee582986170412.jpg\",\n  \"196570.jpg\": \"Reptilia/Crotalus molossus/faee2e7e321fab11764ad1c5f5cff9f0.jpg\",\n  \"196574.jpg\": \"Reptilia/Crotalus molossus/b4c4ea8e71eacb9a668f7b4338759635.jpg\",\n  \"196578.jpg\": \"Reptilia/Crotalus molossus/cb8af75799985114a78a08e036b8ea83.jpg\",\n  \"196580.jpg\": \"Reptilia/Crotalus molossus/5538704d727cf2462633bddab4f2dd5a.jpg\",\n  \"196582.jpg\": \"Reptilia/Crotalus molossus/b1833bae4ef15bea2785fb528b02597a.jpg\",\n  \"196591.jpg\": \"Reptilia/Crotalus molossus/aa12b19a65e4272b05f7f11642e2d8c5.jpg\",\n  \"196595.jpg\": \"Reptilia/Crotalus molossus/048938d80398a375b0cc3f3cabab05a9.jpg\",\n  \"196597.jpg\": \"Reptilia/Crotalus molossus/5ad9d1bab14399fda2e3dbac41b6110f.jpg\",\n  \"196602.jpg\": \"Reptilia/Crotalus molossus/98cf8c0f7599116bd94cbe1405a134cc.jpg\",\n  \"196603.jpg\": \"Reptilia/Crotalus molossus/7a93b17c145a992c155a2860686a16e6.jpg\",\n  \"196611.jpg\": \"Reptilia/Crotalus molossus/b24b2e372b33a522ecc94854199236bb.jpg\",\n  \"196628.jpg\": \"Reptilia/Crotalus molossus/df62bbd425e19dbe88273be326c331f2.jpg\",\n  \"196629.jpg\": \"Reptilia/Crotalus molossus/ca6b12574d75c23509dc7b3e370ae20a.jpg\",\n  \"196634.jpg\": \"Reptilia/Crotalus molossus/c3afcf890b620e75420236ef44ff8cab.jpg\",\n  \"196647.jpg\": \"Reptilia/Crotalus molossus/c1f7857256c52c571260f533b96e2182.jpg\",\n  \"196654.jpg\": \"Reptilia/Crotalus molossus/05209b5d2612708365dbe4c95aef88ba.jpg\",\n  \"196655.jpg\": \"Reptilia/Crotalus molossus/663da6da76d2a14bcc3a79223b118dba.jpg\",\n  \"196658.jpg\": \"Reptilia/Crotalus molossus/69f40211d9bd5efdadddec4c024a5228.jpg\",\n  \"196681.jpg\": \"Animalia/Armadillidium vulgare/d83899f05430a463e688ae005a898d91.jpg\",\n  \"196686.jpg\": \"Animalia/Armadillidium vulgare/ea7540a0905a2e784fb87bc353bd3d74.jpg\",\n  \"196688.jpg\": \"Animalia/Armadillidium vulgare/5357e752c2892c58f94cb872db396066.jpg\",\n  \"196716.jpg\": \"Animalia/Armadillidium vulgare/f2710242797b51de738450878f12d8a8.jpg\",\n  \"196719.jpg\": \"Animalia/Armadillidium vulgare/f007da024bf2d71e121001524a7ea9ac.jpg\",\n  \"196761.jpg\": \"Animalia/Armadillidium vulgare/ad92de8edcb3bb05fa236f082ddad00e.jpg\",\n  \"196766.jpg\": \"Animalia/Armadillidium vulgare/ad8a465ef420b97029ab0aecb6a8463c.jpg\",\n  \"196783.jpg\": \"Animalia/Armadillidium vulgare/9c96c14a047fc0707ac3be9c32524166.jpg\",\n  \"196808.jpg\": \"Animalia/Armadillidium vulgare/9dd96559c6f2f4068ed0104b8844feaa.jpg\",\n  \"196809.jpg\": \"Animalia/Armadillidium vulgare/82b9ab447791077b93c50e43b4e8e41f.jpg\",\n  \"196823.jpg\": \"Animalia/Armadillidium vulgare/0533b3d01ea4d80ed4d7c1d91a61ab41.jpg\",\n  \"196824.jpg\": \"Animalia/Armadillidium vulgare/b2fcce08181f153238102b977a44b15a.jpg\",\n  \"196847.jpg\": \"Animalia/Armadillidium vulgare/4bd0db82c56239063148fbec23408af8.jpg\",\n  \"196853.jpg\": \"Animalia/Armadillidium vulgare/ef3a39585ef079b11920363df78e5f83.jpg\",\n  \"196865.jpg\": \"Animalia/Armadillidium vulgare/d5a5638abb200ef6085217ae0be86533.jpg\",\n  \"196877.jpg\": \"Animalia/Armadillidium vulgare/4a890b93616d9276f841780f580b599c.jpg\",\n  \"196937.jpg\": \"Insecta/Phigalia titea/8a2913700dd53026a433a414bd1e4f7b.jpg\",\n  \"196945.jpg\": \"Insecta/Phigalia titea/34c64c762afdee9a26fbba16dba66446.jpg\",\n  \"196951.jpg\": \"Insecta/Phigalia titea/e19e06a42543bb8eb36f91d5551264c3.jpg\",\n  \"196952.jpg\": \"Insecta/Phigalia titea/577f08bb9b23762dfbf64fa3b621f532.jpg\",\n  \"196953.jpg\": \"Reptilia/Crotalus triseriatus/bd0a9948adb916797339fcb5ca5ad46c.jpg\",\n  \"196955.jpg\": \"Reptilia/Crotalus triseriatus/8199aee17d6dc2e20be48fe88e29f5d7.jpg\",\n  \"196958.jpg\": \"Reptilia/Crotalus triseriatus/caded62fe5c25553cf116a678bb1ef72.jpg\",\n  \"196964.jpg\": \"Reptilia/Crotalus triseriatus/284c834a2b3a43a26f4a4f412bc73981.jpg\",\n  \"196970.jpg\": \"Insecta/Schistocerca nitens/361381a40d8416219262081f3b3f0742.jpg\",\n  \"196971.jpg\": \"Insecta/Schistocerca nitens/834383e11194d91091867d362ce195ec.jpg\",\n  \"196979.jpg\": \"Insecta/Schistocerca nitens/c9a3233e8f6d74a82d1798dbd3c86ffb.jpg\",\n  \"196980.jpg\": \"Insecta/Schistocerca nitens/6389edc7fc6e17c492b829d838c121bb.jpg\",\n  \"196981.jpg\": \"Insecta/Schistocerca nitens/0fe17b65e9a9a46031b3d73ac7bfa22f.jpg\",\n  \"196982.jpg\": \"Insecta/Schistocerca nitens/6e1576c2b705aa6b282e4a94d02f4814.jpg\",\n  \"196984.jpg\": \"Insecta/Schistocerca nitens/7b823975785b293c0fa9038690c75c2b.jpg\",\n  \"196993.jpg\": \"Insecta/Schistocerca nitens/897de7c7c977ec877a47343397ba8a21.jpg\",\n  \"196995.jpg\": \"Insecta/Schistocerca nitens/fc201968524e171278095864bf08454b.jpg\",\n  \"196996.jpg\": \"Insecta/Schistocerca nitens/471285f70151840cf35e79ac0e5779c2.jpg\",\n  \"196999.jpg\": \"Insecta/Schistocerca nitens/9f147a8dd3228f86136cb3613e6b4320.jpg\",\n  \"197002.jpg\": \"Insecta/Schistocerca nitens/8abfa35c7b1b451127fb0b58565114fd.jpg\",\n  \"197003.jpg\": \"Insecta/Schistocerca nitens/c42248017dc57ae4ebfe08642fe682d5.jpg\",\n  \"197006.jpg\": \"Insecta/Schistocerca nitens/9204e0c3f107177b9200cc37f9caf489.jpg\",\n  \"197015.jpg\": \"Insecta/Schistocerca nitens/9b160617634fafb11be3e5254d9e8158.jpg\",\n  \"197016.jpg\": \"Insecta/Schistocerca nitens/3b16cbd2dd52b7ac07077dfd76dbd0ba.jpg\",\n  \"197025.jpg\": \"Insecta/Schistocerca nitens/a2bf0a9642898ab30ceca8a688e0288e.jpg\",\n  \"197026.jpg\": \"Insecta/Schistocerca nitens/df70d1f9711a3418c19ff5702a56ee39.jpg\",\n  \"197040.jpg\": \"Insecta/Schistocerca nitens/48e4a09bdb4dc0c8ea97996244e23828.jpg\",\n  \"197056.jpg\": \"Insecta/Schistocerca nitens/8c85dc1c31bbbf623619ea521115b569.jpg\",\n  \"197072.jpg\": \"Amphibia/Dicamptodon tenebrosus/ba15e3be650244c111ba88575fd1b24c.jpg\",\n  \"197073.jpg\": \"Amphibia/Dicamptodon tenebrosus/a00d0bfbde01bb208bf3537d9755a8a3.jpg\",\n  \"197074.jpg\": \"Amphibia/Dicamptodon tenebrosus/353349cf28c13997c97c9ca8e4d43a45.jpg\",\n  \"197084.jpg\": \"Amphibia/Dicamptodon tenebrosus/98b820de6f00be13f40edab875ee8dc7.jpg\",\n  \"197096.jpg\": \"Mammalia/Eptesicus fuscus/a6db5cccb8e0402108a3c3cd36dcbf9f.jpg\",\n  \"1971.jpg\": \"Mollusca/Neobernaya spadicea/ecb450dbc4f73f57f5a0e6faf5abe2f4.jpg\",\n  \"197103.jpg\": \"Mammalia/Eptesicus fuscus/793b8b819b95c4fdadac344712f7e63e.jpg\",\n  \"197104.jpg\": \"Mammalia/Eptesicus fuscus/74a3ae3310df7f1a4b6718abf98e0318.jpg\",\n  \"197105.jpg\": \"Mammalia/Eptesicus fuscus/006541484934b5b47408813d99d831f5.jpg\",\n  \"197117.jpg\": \"Mammalia/Eptesicus fuscus/0700e2f9284fe491d6a392a291c3f913.jpg\",\n  \"197121.jpg\": \"Insecta/Megisto cymela/cefa99c27f2c177586aa44756a08e4eb.jpg\",\n  \"197124.jpg\": \"Insecta/Megisto cymela/0319ad36780a8ba00524824d9a285aaf.jpg\",\n  \"197131.jpg\": \"Insecta/Megisto cymela/de61da8eddca6294ffc029ed6dda6e12.jpg\",\n  \"197134.jpg\": \"Insecta/Megisto cymela/61ff2fa9bbe30e408c086764503690dc.jpg\",\n  \"197135.jpg\": \"Insecta/Megisto cymela/bb8802d705fbc0d1a5d50f14b3fa3cb7.jpg\",\n  \"197140.jpg\": \"Insecta/Megisto cymela/4da7200c12c94baaacf715316f3970a6.jpg\",\n  \"197158.jpg\": \"Insecta/Megisto cymela/ba0b7fa906e9b0a52387bc32b4f72916.jpg\",\n  \"197164.jpg\": \"Insecta/Megisto cymela/affd64dbe5645c6600ccf70dcb7de741.jpg\",\n  \"197165.jpg\": \"Insecta/Megisto cymela/87344377a713ff3c530c5a65e7086646.jpg\",\n  \"197168.jpg\": \"Insecta/Megisto cymela/5dab5a41902b626b65bdf597853d658d.jpg\",\n  \"197190.jpg\": \"Insecta/Megisto cymela/5228d9e9e47de55543a271fcb0d7555c.jpg\",\n  \"197192.jpg\": \"Insecta/Megisto cymela/4e8779509e4e7d2828255cd7d288a8e9.jpg\",\n  \"197199.jpg\": \"Insecta/Megisto cymela/06ae6d04a0b880dc5bcdb9be8871c102.jpg\",\n  \"1972.jpg\": \"Mollusca/Neobernaya spadicea/0cda10c0a623a0aedce9a61658ed9b38.jpg\",\n  \"197202.jpg\": \"Insecta/Megisto cymela/74520251d2ddd102a64a05ba73c8a42d.jpg\",\n  \"197203.jpg\": \"Insecta/Megisto cymela/ff914b25469643aeb76a64a877cb3e20.jpg\",\n  \"197215.jpg\": \"Insecta/Megisto cymela/d15746eeed385d2850e86532c0994c93.jpg\",\n  \"197216.jpg\": \"Insecta/Megisto cymela/c1343d577df4a31c6e5408e505a2ceac.jpg\",\n  \"197233.jpg\": \"Insecta/Megisto cymela/9268f4676dfa0f28114e3beecc0a8eac.jpg\",\n  \"197236.jpg\": \"Insecta/Megisto cymela/4c3fd50996b70e958c052eea2b9856d8.jpg\",\n  \"197246.jpg\": \"Insecta/Megisto cymela/b11add1d65e659ca8a255810f8e818bf.jpg\",\n  \"197249.jpg\": \"Insecta/Megisto cymela/d5dd89234b537424b861f0ecf15811c7.jpg\",\n  \"197250.jpg\": \"Insecta/Megisto cymela/bed48806b99ab91ff113c99da76692ef.jpg\",\n  \"197258.jpg\": \"Insecta/Megisto cymela/c50124aa1deb7ef0908f52fcf490fe92.jpg\",\n  \"19735.jpg\": \"Reptilia/Thamnophis hammondii/d8fdf0116845a9eca7d1ae37151379ed.jpg\",\n  \"19736.jpg\": \"Reptilia/Thamnophis hammondii/848839b0ef851f192f186c8620d06fcb.jpg\",\n  \"197386.jpg\": \"Reptilia/Agkistrodon piscivorus/e36f662e1c90a6b3c12331581d12b8e8.jpg\",\n  \"197394.jpg\": \"Reptilia/Agkistrodon piscivorus/0fbc33f133678ec8a5ff35f3e850f77e.jpg\",\n  \"19740.jpg\": \"Reptilia/Thamnophis hammondii/95ec60ca601b7bb95cad369ba9d95daf.jpg\",\n  \"19741.jpg\": \"Reptilia/Thamnophis hammondii/4ae5ebd20a3929f04fdfd7fb7ef72042.jpg\",\n  \"19742.jpg\": \"Reptilia/Thamnophis hammondii/a4ef63d55082358c0a091d2df02b269f.jpg\",\n  \"197420.jpg\": \"Reptilia/Agkistrodon piscivorus/8be80a92e7fd0a74406bbb7488225db1.jpg\",\n  \"197430.jpg\": \"Reptilia/Agkistrodon piscivorus/552c43aca14035c4ae491bfc2cb8937c.jpg\",\n  \"197433.jpg\": \"Reptilia/Agkistrodon piscivorus/78e159f04180b4ccf5aaae3689e1b98a.jpg\",\n  \"197442.jpg\": \"Reptilia/Agkistrodon piscivorus/c58a1c45e854e5b2d4acb5ef2ae5abdb.jpg\",\n  \"197445.jpg\": \"Reptilia/Agkistrodon piscivorus/7b61b93cf603d1e9c4450fa2173740ca.jpg\",\n  \"197448.jpg\": \"Reptilia/Agkistrodon piscivorus/a0be698788c62abc2d7660a79bf72555.jpg\",\n  \"197464.jpg\": \"Reptilia/Agkistrodon piscivorus/4050cd2a3499968c8234bbc11a8373fd.jpg\",\n  \"197477.jpg\": \"Reptilia/Agkistrodon piscivorus/b340ab8fb794fb9f7070fcd5ba39a3bd.jpg\",\n  \"197479.jpg\": \"Reptilia/Agkistrodon piscivorus/51cc8b0a674f5a2f827fb020e4217db5.jpg\",\n  \"197481.jpg\": \"Reptilia/Agkistrodon piscivorus/6e2b04fd252bed24a68e4cce357c244d.jpg\",\n  \"197485.jpg\": \"Reptilia/Agkistrodon piscivorus/d4eabed43f058282df2c1c676717c719.jpg\",\n  \"197499.jpg\": \"Reptilia/Agkistrodon piscivorus/65888d546d7bfe0b4eb0904455043f24.jpg\",\n  \"19750.jpg\": \"Reptilia/Thamnophis hammondii/5ad741ec7bced15207edd71186935972.jpg\",\n  \"197508.jpg\": \"Reptilia/Agkistrodon piscivorus/b67eafe38b49cf2a2e02ce19ea35a039.jpg\",\n  \"19751.jpg\": \"Reptilia/Thamnophis hammondii/a1d369e6fa5d3b01e05f7e56214a8faf.jpg\",\n  \"19752.jpg\": \"Reptilia/Thamnophis hammondii/bfec2c396c2f0a4f1645bb4fe310560b.jpg\",\n  \"19753.jpg\": \"Reptilia/Thamnophis hammondii/6d2cac9c1a092647412655c06edd1c3d.jpg\",\n  \"197533.jpg\": \"Reptilia/Agkistrodon piscivorus/43d9a105a89939eb0179b3fbfcd7332b.jpg\",\n  \"197549.jpg\": \"Reptilia/Agkistrodon piscivorus/94dfc8e24bfdee0231f0f7e61d3cb635.jpg\",\n  \"197550.jpg\": \"Reptilia/Agkistrodon piscivorus/184c0f0cdea4dca2850647b4e52e4cef.jpg\",\n  \"197552.jpg\": \"Aves/Porphyrio poliocephalus/82a3b8fceac3cf5a40ec5f7776bac54a.jpg\",\n  \"197565.jpg\": \"Aves/Porphyrio poliocephalus/2b1e608fb793460e9749305ead80048f.jpg\",\n  \"197570.jpg\": \"Aves/Megarynchus pitangua/3df1c09e3e0b4167d5a67f3321390a62.jpg\",\n  \"197571.jpg\": \"Aves/Megarynchus pitangua/2d1a35537150236e33d69e44734a6b70.jpg\",\n  \"197572.jpg\": \"Aves/Megarynchus pitangua/49921c30c901b9d356828fa3ea8c28fb.jpg\",\n  \"197574.jpg\": \"Aves/Megarynchus pitangua/0492fd3a439208ef8e6ed75ee958a541.jpg\",\n  \"197579.jpg\": \"Aves/Megarynchus pitangua/da2781e1fed5a20001105d9c0183f487.jpg\",\n  \"19758.jpg\": \"Reptilia/Thamnophis hammondii/a052332fb61c1085037889637c959775.jpg\",\n  \"19759.jpg\": \"Reptilia/Thamnophis hammondii/7ad7a624b275d5786efb15abc15b3561.jpg\",\n  \"197602.jpg\": \"Reptilia/Storeria dekayi texana/ce21c5bb0f574dc237c19646be05b0aa.jpg\",\n  \"197606.jpg\": \"Reptilia/Storeria dekayi texana/565df3f81d856b4411ae6d9c43bf7f4c.jpg\",\n  \"197607.jpg\": \"Reptilia/Storeria dekayi texana/c2caae2511007163531063d461cc3fc0.jpg\",\n  \"197610.jpg\": \"Reptilia/Storeria dekayi texana/57a044b56886106a4d80d2099e7d5760.jpg\",\n  \"197617.jpg\": \"Reptilia/Storeria dekayi texana/b4614d04d78e26a340c1e63c6aa57eec.jpg\",\n  \"197618.jpg\": \"Reptilia/Storeria dekayi texana/c928345918815b2eaad7723d1cf0292d.jpg\",\n  \"197619.jpg\": \"Reptilia/Storeria dekayi texana/82f86b8de5e98a0d9725e5332e43fb8b.jpg\",\n  \"19763.jpg\": \"Reptilia/Thamnophis hammondii/4d793a4924548fd5f11de7231f5b4406.jpg\",\n  \"197630.jpg\": \"Reptilia/Storeria dekayi texana/3419815f23d392c5eb8cc398f448e21a.jpg\",\n  \"197631.jpg\": \"Reptilia/Storeria dekayi texana/6b232fb603a26adf08b3061d3d368b2f.jpg\",\n  \"197632.jpg\": \"Reptilia/Storeria dekayi texana/c00a4f191bf7a19aafcfe40084e080db.jpg\",\n  \"197634.jpg\": \"Reptilia/Storeria dekayi texana/bddaf536e26cebfd14a7973e2e296c0b.jpg\",\n  \"197638.jpg\": \"Reptilia/Storeria dekayi texana/73db375e87255d6fc49df4efc52f606b.jpg\",\n  \"197639.jpg\": \"Reptilia/Storeria dekayi texana/21024e6673c8ae9e6782fa17adc40c83.jpg\",\n  \"19764.jpg\": \"Reptilia/Thamnophis hammondii/c4267e1fcabcdb4d570d62de1cb4d065.jpg\",\n  \"197640.jpg\": \"Reptilia/Storeria dekayi texana/5dd83b915d71d16acdfc1ca15b75e3f5.jpg\",\n  \"197646.jpg\": \"Reptilia/Storeria dekayi texana/9e63606e5d6a0c5fbebf4f8f190a4fc7.jpg\",\n  \"197647.jpg\": \"Reptilia/Storeria dekayi texana/4da36fc7ae4e0444a9d67c7f2dc9745c.jpg\",\n  \"197648.jpg\": \"Reptilia/Storeria dekayi texana/8ee323e1ac408d6e9c7a08a4cd1fa528.jpg\",\n  \"19765.jpg\": \"Reptilia/Thamnophis hammondii/2aca4121823eb87da54c12f525ca504e.jpg\",\n  \"197652.jpg\": \"Reptilia/Storeria dekayi texana/8ae84859065f46f7a508c7e2ce8f1f16.jpg\",\n  \"197653.jpg\": \"Reptilia/Storeria dekayi texana/94c43ce1e663cb82b66dca87518d9cfc.jpg\",\n  \"197661.jpg\": \"Reptilia/Storeria dekayi texana/ab28e9740386c13b6720bb28b3faab1c.jpg\",\n  \"197667.jpg\": \"Aves/Pluvialis fulva/78379632b2b580b8243ee38cdf6aace1.jpg\",\n  \"197668.jpg\": \"Aves/Pluvialis fulva/dde0e97fd304efd773b952a58f8f6402.jpg\",\n  \"19767.jpg\": \"Reptilia/Thamnophis hammondii/db13bfbc31faae8a9ad646267dad8a30.jpg\",\n  \"197675.jpg\": \"Aves/Pluvialis fulva/2d6238a0613aedd2176f563835c3b21f.jpg\",\n  \"197676.jpg\": \"Aves/Pluvialis fulva/7f39836364611d4120b5b02e748cc9d2.jpg\",\n  \"197677.jpg\": \"Aves/Pluvialis fulva/9c44657f9307fd1e0d5c0e29d7e1a214.jpg\",\n  \"19768.jpg\": \"Reptilia/Thamnophis hammondii/6b9ffb645b90baa8a532ac55ebdcac31.jpg\",\n  \"197680.jpg\": \"Aves/Pluvialis fulva/f0baf197be150561ff9906b4a26b9d16.jpg\",\n  \"197681.jpg\": \"Aves/Pluvialis fulva/cfee7fcc9d5aff30f962353a4ed51cce.jpg\",\n  \"197682.jpg\": \"Aves/Pluvialis fulva/8e753f3650f0014e1aed34b1c7208cbf.jpg\",\n  \"197685.jpg\": \"Aves/Pluvialis fulva/bb7d25e2fcd23dad2ba734893898be0e.jpg\",\n  \"197686.jpg\": \"Aves/Pluvialis fulva/925702759f931fb2252ed9a232b28088.jpg\",\n  \"197687.jpg\": \"Aves/Pluvialis fulva/b9f7b380fea10bfc8460ae5b6e012389.jpg\",\n  \"19769.jpg\": \"Reptilia/Thamnophis hammondii/fb15d6895461a01ebc79216c0adcdfaa.jpg\",\n  \"197697.jpg\": \"Aves/Pluvialis fulva/ab18c36b114d0b09c7a0171d02f48d8e.jpg\",\n  \"19770.jpg\": \"Reptilia/Thamnophis hammondii/a5b9bbaf8a01dd431659b9f78b61bb22.jpg\",\n  \"197703.jpg\": \"Aves/Pluvialis fulva/92d3cc664fbc7f2d8faed1df12d016c1.jpg\",\n  \"197705.jpg\": \"Aves/Pluvialis fulva/fb38347e836fa1d8f4ad6eeb222f1bfd.jpg\",\n  \"197707.jpg\": \"Aves/Pluvialis fulva/28b08e20b70a840f1540c2843daff65b.jpg\",\n  \"197709.jpg\": \"Aves/Pluvialis fulva/9ab9b7fb95bebf3e971e4717155b0d24.jpg\",\n  \"19771.jpg\": \"Reptilia/Thamnophis hammondii/bcc850c04e68922cbebea32dad0e8f04.jpg\",\n  \"197710.jpg\": \"Aves/Pluvialis fulva/7fcb2336562582019f9dc3040a90a757.jpg\",\n  \"19772.jpg\": \"Reptilia/Thamnophis hammondii/693a7d76bf79ac811748ce6d5fe27bbe.jpg\",\n  \"19773.jpg\": \"Reptilia/Thamnophis hammondii/92e9bfa8119da102f947614b13d248c1.jpg\",\n  \"19780.jpg\": \"Reptilia/Thamnophis hammondii/93087217d35a297593ca9230f06f920b.jpg\",\n  \"197822.jpg\": \"Aves/Anser anser/073627db145f0aaf9a6b216b3db7389f.jpg\",\n  \"197824.jpg\": \"Aves/Anser anser/65daf6955bd137c15b056ff00c70da38.jpg\",\n  \"197829.jpg\": \"Aves/Anser anser/d2c1b7b8bfbcb1162f6ae5df9a1d384a.jpg\",\n  \"197842.jpg\": \"Aves/Anser anser/58c3aef58f818b1375d0a34d2eaa67ea.jpg\",\n  \"197862.jpg\": \"Aves/Anser anser/2fa0fa552ca23f7cb1c00f4eeb6a3ad9.jpg\",\n  \"197892.jpg\": \"Aves/Anser anser/d14eec908703b66ff2fa96e7881967e7.jpg\",\n  \"197917.jpg\": \"Aves/Anser anser/4d51da45fc99808b1835babe39605eff.jpg\",\n  \"197919.jpg\": \"Aves/Anser anser/08f5aa1b98d149aee9286d2aa449aef3.jpg\",\n  \"19792.jpg\": \"Insecta/Polygonia c-album/d6d7e3204bf8ddb7efe231512cc27c11.jpg\",\n  \"19799.jpg\": \"Insecta/Polygonia c-album/befe395fead4c38c37f1d1513738b5ed.jpg\",\n  \"19800.jpg\": \"Insecta/Polygonia c-album/447869919867e52fee52c370aaa8a9b5.jpg\",\n  \"19801.jpg\": \"Insecta/Polygonia c-album/744b49f6915e9eaa7f86520d24ed61c3.jpg\",\n  \"19802.jpg\": \"Insecta/Polygonia c-album/d7c2d221e8b3aaa3efd38546155c3608.jpg\",\n  \"19810.jpg\": \"Insecta/Polygonia c-album/5ee1f2b6d39556af09ffcddadd594a1e.jpg\",\n  \"19811.jpg\": \"Insecta/Polygonia c-album/b55220d98183b1850f33e20935effaaf.jpg\",\n  \"19812.jpg\": \"Insecta/Polygonia c-album/a19bb6f3bb5e9ed7f6bf9c36dbf3802b.jpg\",\n  \"19815.jpg\": \"Insecta/Polygonia c-album/d7f73669ecb97dcb3afa149b6dc3b02b.jpg\",\n  \"198189.jpg\": \"Aves/Melanerpes aurifrons/69025bf5264da8fb0cc3c5ce9ebee992.jpg\",\n  \"198190.jpg\": \"Aves/Melanerpes aurifrons/76aefd5fea757229a8ff76897977601d.jpg\",\n  \"198199.jpg\": \"Aves/Melanerpes aurifrons/c3cffc1141a518e7e3195af895f30cd7.jpg\",\n  \"19820.jpg\": \"Insecta/Polygonia c-album/a9200fa832c6e3036089de15f430e21c.jpg\",\n  \"19821.jpg\": \"Insecta/Polygonia c-album/dac6a9af07548d7087af47217234d5a1.jpg\",\n  \"19822.jpg\": \"Insecta/Polygonia c-album/2f6345623790d499f6614885f78b6d6d.jpg\",\n  \"198225.jpg\": \"Aves/Melanerpes aurifrons/a735943cce992138b45a0b84155011f2.jpg\",\n  \"19823.jpg\": \"Insecta/Polygonia c-album/7a4fcd70c41847f8c6528878c4cdf85b.jpg\",\n  \"198233.jpg\": \"Aves/Melanerpes aurifrons/da1b3ffd6443da5c5ed85b778dc9e941.jpg\",\n  \"19824.jpg\": \"Insecta/Polygonia c-album/4f00850fbb7485553f233fc673d2b04d.jpg\",\n  \"19825.jpg\": \"Insecta/Polygonia c-album/2d6d49d917a6b27e2d4139699d179161.jpg\",\n  \"198274.jpg\": \"Aves/Melanerpes aurifrons/93872c55039ef7e3ed7ab11dcaac1097.jpg\",\n  \"198275.jpg\": \"Aves/Melanerpes aurifrons/9c95c4cd02584d07dee0a30217c13146.jpg\",\n  \"198282.jpg\": \"Aves/Melanerpes aurifrons/d7115cfef796966c8919f10b222c296a.jpg\",\n  \"198293.jpg\": \"Aves/Melanerpes aurifrons/ed2251c2183f22fe722f017b00e0846d.jpg\",\n  \"19831.jpg\": \"Insecta/Polygonia c-album/25c4331541b03e7a57c9601bb444d444.jpg\",\n  \"198327.jpg\": \"Aves/Melanerpes aurifrons/bdd3aeaa3d35cbb6d6e12497856e38ce.jpg\",\n  \"198331.jpg\": \"Aves/Melanerpes aurifrons/2a05c9824b0032ffe870262b53794c92.jpg\",\n  \"198332.jpg\": \"Aves/Melanerpes aurifrons/b931271ed14e92b150be43ea1a8eb074.jpg\",\n  \"19835.jpg\": \"Insecta/Polygonia c-album/0134fa4fea665002b9853efd0bab237d.jpg\",\n  \"198353.jpg\": \"Aves/Melanerpes aurifrons/f290633f357fe2e6485bddd42b783a7c.jpg\",\n  \"19836.jpg\": \"Insecta/Polygonia c-album/5705311b6344edb2b4b0e97e886371dc.jpg\",\n  \"19837.jpg\": \"Insecta/Polygonia c-album/2d9ae0e1a7380ad7372c9bf7ce96a263.jpg\",\n  \"198372.jpg\": \"Aves/Melanerpes aurifrons/f6d305f75972742b3a5271ca8ea5d4f3.jpg\",\n  \"198373.jpg\": \"Aves/Melanerpes aurifrons/a24ea300c1b3d4c785e4023abf88a1e7.jpg\",\n  \"198377.jpg\": \"Aves/Melanerpes aurifrons/4a25e860aabc9b9d727f9836469af664.jpg\",\n  \"198390.jpg\": \"Aves/Melanerpes aurifrons/ea9d3f2f36bd62772d1117f4f85a1e9c.jpg\",\n  \"198400.jpg\": \"Aves/Melanerpes aurifrons/f348c7a58e39acbd06abfa40c66a7d74.jpg\",\n  \"198427.jpg\": \"Aves/Melanerpes aurifrons/d82265c680ed75275c58f9bb12147679.jpg\",\n  \"19843.jpg\": \"Insecta/Polygonia c-album/be754c584e96dd9b2459469770cfe103.jpg\",\n  \"198431.jpg\": \"Aves/Melanerpes aurifrons/2617d2bbfa0af550222fc1c9a9424381.jpg\",\n  \"19844.jpg\": \"Insecta/Polygonia c-album/3928b20902ece7d007afdec4b7e4a9cd.jpg\",\n  \"198443.jpg\": \"Aves/Melanerpes aurifrons/65b9de4bc738816e130867a57faa99f0.jpg\",\n  \"198481.jpg\": \"Reptilia/Lepidochelys olivacea/50d6c50b6988a0a657a6e30beaa5dce3.jpg\",\n  \"198485.jpg\": \"Reptilia/Lepidochelys olivacea/11617324b780412632f2fa9380ff8826.jpg\",\n  \"198489.jpg\": \"Reptilia/Lepidochelys olivacea/06e601b9edadaad74a1654145cc7b7f1.jpg\",\n  \"198491.jpg\": \"Reptilia/Lepidochelys olivacea/3623b1013b43077104e99aa02ce15484.jpg\",\n  \"198492.jpg\": \"Reptilia/Lepidochelys olivacea/a61a6247b74ef24f5d12026c3091399f.jpg\",\n  \"198497.jpg\": \"Reptilia/Lepidochelys olivacea/a2209f5af0c65032ad85a009635a09f9.jpg\",\n  \"198499.jpg\": \"Arachnida/Salticus scenicus/5ae520d8fcbd30cdfade3209ad89f36b.jpg\",\n  \"198505.jpg\": \"Arachnida/Salticus scenicus/9b1e0fa8abf74576c3eb118b79b7e7d6.jpg\",\n  \"198514.jpg\": \"Arachnida/Salticus scenicus/676c3ef3cd7a8e9a6fe4d1137bddf9a3.jpg\",\n  \"198515.jpg\": \"Arachnida/Salticus scenicus/d8475d950de1d4855c87a45f59ed8606.jpg\",\n  \"198520.jpg\": \"Arachnida/Salticus scenicus/f79227caf31c00fd9157f0873d7a084d.jpg\",\n  \"198521.jpg\": \"Arachnida/Salticus scenicus/7de125434fd24aa5a6a44968c9fdb8f1.jpg\",\n  \"198522.jpg\": \"Arachnida/Salticus scenicus/baa0b83a2e0b57467a33c6d2bdc334ac.jpg\",\n  \"198525.jpg\": \"Arachnida/Salticus scenicus/dfc1c96d00a2f9665471f865d1971e4e.jpg\",\n  \"198526.jpg\": \"Arachnida/Salticus scenicus/eb57abcafaf8d9704d5d30aab3facb22.jpg\",\n  \"198530.jpg\": \"Arachnida/Salticus scenicus/5e4cbf33cac4474c3e7570eab1771903.jpg\",\n  \"198531.jpg\": \"Arachnida/Salticus scenicus/094ea32f5465ce404b089318ead96ec7.jpg\",\n  \"198534.jpg\": \"Arachnida/Salticus scenicus/8972872554e7f70e045999eae5cfb1f2.jpg\",\n  \"198538.jpg\": \"Arachnida/Salticus scenicus/301c9d56435bc980b7e7abf749fc7f13.jpg\",\n  \"198539.jpg\": \"Arachnida/Salticus scenicus/b02436b7b7f450a2c21fb0cca56eb662.jpg\",\n  \"198540.jpg\": \"Arachnida/Salticus scenicus/d56c43bc12d2b05b083b4663b6ed72c9.jpg\",\n  \"198543.jpg\": \"Arachnida/Salticus scenicus/e23fe7b16f40075ac127aab522a3df01.jpg\",\n  \"198544.jpg\": \"Arachnida/Salticus scenicus/8adecc24a3e57c2d4915ba1919b2fec3.jpg\",\n  \"198545.jpg\": \"Arachnida/Salticus scenicus/4a654af7bca2f2da13fbbc507f68b952.jpg\",\n  \"198546.jpg\": \"Arachnida/Salticus scenicus/6b6e848dbfd2871239af318f027aa6db.jpg\",\n  \"198547.jpg\": \"Arachnida/Salticus scenicus/6c6935a6b6daf7e20b5f70ee3ec5cb09.jpg\",\n  \"198550.jpg\": \"Arachnida/Salticus scenicus/ab1b484889ddbc5d7e48277963caa721.jpg\",\n  \"198557.jpg\": \"Insecta/Heliconius erato/5609732b65c72796f0f0069d357a8583.jpg\",\n  \"198560.jpg\": \"Insecta/Heliconius erato/cc529ed0cccd7f20ca335e2f9612df57.jpg\",\n  \"198561.jpg\": \"Insecta/Heliconius erato/26aecc64bc2b0682a4c0f5b8a24411b3.jpg\",\n  \"198567.jpg\": \"Insecta/Heliconius erato/bd314aa831cec4fda8c28b4fa3b40431.jpg\",\n  \"198571.jpg\": \"Insecta/Heliconius erato/5024036bc33b94a22b37e69db2c9746f.jpg\",\n  \"198572.jpg\": \"Insecta/Heliconius erato/55737c36c32066cd73f629548da3e067.jpg\",\n  \"198577.jpg\": \"Insecta/Heliconius erato/e3791efb8a6cec8faa49b10a3630119f.jpg\",\n  \"198585.jpg\": \"Insecta/Heliconius erato/4daeeb3c57434b84179ec6ed29bf69b4.jpg\",\n  \"198638.jpg\": \"Reptilia/Ctenosaura pectinata/e59b08e03ac2f837d64a7fdbbaa7b25e.jpg\",\n  \"198642.jpg\": \"Reptilia/Ctenosaura pectinata/d1a694630bd8273ddc6a7e361c26be0d.jpg\",\n  \"198643.jpg\": \"Reptilia/Ctenosaura pectinata/bb985476ea32863b091482f710c56417.jpg\",\n  \"198647.jpg\": \"Reptilia/Ctenosaura pectinata/9905008becc30b931e7d0cc32100d408.jpg\",\n  \"198649.jpg\": \"Reptilia/Ctenosaura pectinata/d4cebfcd321ec5a0920aa17435bf4509.jpg\",\n  \"198651.jpg\": \"Reptilia/Ctenosaura pectinata/1179812cfa51480553136c8176c22df1.jpg\",\n  \"198652.jpg\": \"Reptilia/Ctenosaura pectinata/2ef771f591ffb1f6d7b178db522c3e9b.jpg\",\n  \"198653.jpg\": \"Reptilia/Ctenosaura pectinata/ee3a0ccc15d9813c9edf352ec7909f5a.jpg\",\n  \"198656.jpg\": \"Reptilia/Ctenosaura pectinata/29c12c34e7726857118aa37c10c4fa72.jpg\",\n  \"198657.jpg\": \"Reptilia/Ctenosaura pectinata/580df81fd31da69a23a58b396155d8a3.jpg\",\n  \"198659.jpg\": \"Reptilia/Ctenosaura pectinata/174a29dd3b0144d628afa6bc7b6fea3a.jpg\",\n  \"198665.jpg\": \"Reptilia/Ctenosaura pectinata/1dbc0f6f6ad361fa7468ead272481c64.jpg\",\n  \"198673.jpg\": \"Reptilia/Ctenosaura pectinata/d0302f469f6cd97cc93ea2bc5bac9783.jpg\",\n  \"198675.jpg\": \"Reptilia/Ctenosaura pectinata/06b89cf154b666176483998c113b3eb5.jpg\",\n  \"198676.jpg\": \"Reptilia/Ctenosaura pectinata/a010471eac2a09963b515adb952f75e8.jpg\",\n  \"198677.jpg\": \"Reptilia/Ctenosaura pectinata/d348e19f496bbef197db6d037bd750e4.jpg\",\n  \"198678.jpg\": \"Reptilia/Ctenosaura pectinata/4e9f6409b45e338b94c58ed92792ed3d.jpg\",\n  \"198680.jpg\": \"Reptilia/Ctenosaura pectinata/e183622eded1f6d072c57c373cd78439.jpg\",\n  \"198686.jpg\": \"Reptilia/Ctenosaura pectinata/3aa64a03c1abfa55883f8bc586e5e52d.jpg\",\n  \"198687.jpg\": \"Reptilia/Ctenosaura pectinata/a08c9de6f39284323f040d0115c05937.jpg\",\n  \"198696.jpg\": \"Reptilia/Ctenosaura pectinata/8d8207cd257393dccac924cbadfeedf0.jpg\",\n  \"19880.jpg\": \"Aves/Pipilo maculatus/a3be62e693b81b7fc27e9d3b75e7b9ea.jpg\",\n  \"198917.jpg\": \"Reptilia/Podarcis sicula/acc246bea64dec7515295e53eefffa0f.jpg\",\n  \"198919.jpg\": \"Reptilia/Podarcis sicula/ca705315e79980e8b05e1d1b50e5077f.jpg\",\n  \"198928.jpg\": \"Reptilia/Podarcis sicula/0a058432bdf10550d2ea8c637f705e9d.jpg\",\n  \"198939.jpg\": \"Reptilia/Podarcis sicula/2a1ae6faa99e6352d2649050dfcfad04.jpg\",\n  \"198940.jpg\": \"Reptilia/Podarcis sicula/4516aa3b15820705600893dafb3cfaaf.jpg\",\n  \"198941.jpg\": \"Reptilia/Podarcis sicula/b2e7a83b4c3063d182c5c564a0a9b291.jpg\",\n  \"198948.jpg\": \"Reptilia/Podarcis sicula/99db6b12ba84c1d8884d865b789c7df9.jpg\",\n  \"19895.jpg\": \"Aves/Pipilo maculatus/39ae883ec4cde054c89492d4d97d52e6.jpg\",\n  \"198950.jpg\": \"Reptilia/Podarcis sicula/b2dbf2c25bda2879082a1f67da2d48b9.jpg\",\n  \"198951.jpg\": \"Reptilia/Podarcis sicula/63800e4f5bc5ddecbd15480157a1070e.jpg\",\n  \"198952.jpg\": \"Reptilia/Podarcis sicula/15c68c0ddbf99e141efe42677b9e8b98.jpg\",\n  \"198953.jpg\": \"Reptilia/Podarcis sicula/3b1212177fc4de3c16d3f626b81e8288.jpg\",\n  \"198961.jpg\": \"Reptilia/Podarcis sicula/2e28cca985f7694fc75f7aef8f245a3a.jpg\",\n  \"198968.jpg\": \"Reptilia/Podarcis sicula/e50831f9677d195258a308f63c02e6cb.jpg\",\n  \"198970.jpg\": \"Reptilia/Podarcis sicula/9f7892920ff94feb25bcd11893768098.jpg\",\n  \"198973.jpg\": \"Reptilia/Podarcis sicula/124ea086e22d92a6e9c2a6749d8ebc09.jpg\",\n  \"198978.jpg\": \"Reptilia/Podarcis sicula/6435f465ca5c017b17e339e8c4a39e59.jpg\",\n  \"198979.jpg\": \"Reptilia/Podarcis sicula/e7725e8af0256352016a58841b0aab2d.jpg\",\n  \"198982.jpg\": \"Reptilia/Podarcis sicula/69f53f7430a01fafc4f75fa7c589dfa7.jpg\",\n  \"198983.jpg\": \"Reptilia/Podarcis sicula/85b2fb30f34e9c7f12da996e9c3787bd.jpg\",\n  \"19913.jpg\": \"Aves/Pipilo maculatus/cd89a4451cdd906b7545bff56def4fc5.jpg\",\n  \"199170.jpg\": \"Insecta/Papilio canadensis/acf0857e80a6be2f4f62e110b398c405.jpg\",\n  \"199171.jpg\": \"Insecta/Papilio canadensis/a131826226b37794fd5b8d2e51785e04.jpg\",\n  \"199172.jpg\": \"Insecta/Papilio canadensis/4039a51db103835847447c67a7707eda.jpg\",\n  \"199177.jpg\": \"Insecta/Papilio canadensis/089c6be3c1c186038e69afec5cb1ba8d.jpg\",\n  \"199190.jpg\": \"Insecta/Papilio canadensis/91ebf6f2a95f785ffa4f049ddf803d82.jpg\",\n  \"199191.jpg\": \"Insecta/Papilio canadensis/a1adb2eb1c6baad0bf340e1dce74d273.jpg\",\n  \"199192.jpg\": \"Insecta/Papilio canadensis/557ced8c4dff570b6d91eab43e69d181.jpg\",\n  \"199193.jpg\": \"Insecta/Papilio canadensis/a38854b97e3be0a37257326495e6583b.jpg\",\n  \"199198.jpg\": \"Insecta/Papilio canadensis/ed2f6c291b8ac7d80c9f6038396489a0.jpg\",\n  \"199199.jpg\": \"Insecta/Papilio canadensis/ecf07193c595a71308ac776ec88ebebc.jpg\",\n  \"199201.jpg\": \"Insecta/Papilio canadensis/b5f8cb2e515675773bbec7e3f63ff2bc.jpg\",\n  \"199207.jpg\": \"Insecta/Papilio canadensis/bde31ab9465ae2e1b5a8f4a3cecd70c4.jpg\",\n  \"199208.jpg\": \"Insecta/Papilio canadensis/68401507278e2c7773c2a60dab45e49d.jpg\",\n  \"199209.jpg\": \"Insecta/Papilio canadensis/33137dcb6ad7dce277abb583f2b6610e.jpg\",\n  \"199210.jpg\": \"Insecta/Papilio canadensis/8f67925ee1e9e26a7f97ee616ec084b5.jpg\",\n  \"199211.jpg\": \"Insecta/Papilio canadensis/30b3158290f5ae0219ce832a9da40fe3.jpg\",\n  \"199212.jpg\": \"Insecta/Papilio canadensis/131c23c81f6f029740924a3b2d96152f.jpg\",\n  \"199216.jpg\": \"Insecta/Papilio canadensis/696bb3ad6405ab19dde5cfdb683ea99e.jpg\",\n  \"199223.jpg\": \"Insecta/Papilio canadensis/3b0b689d9ff4f1efe0ce97a622cae958.jpg\",\n  \"199225.jpg\": \"Insecta/Papilio canadensis/5981bfd35aa87ff0baeb3cff10435617.jpg\",\n  \"199228.jpg\": \"Insecta/Cacyreus marshalli/d707b33c34bed5888ea6059750696339.jpg\",\n  \"199234.jpg\": \"Insecta/Cacyreus marshalli/3b94938fa32efcaf48e3527907c99c8e.jpg\",\n  \"199235.jpg\": \"Insecta/Cacyreus marshalli/9968e1a5ec151b4f92b9099c1bda62a0.jpg\",\n  \"199236.jpg\": \"Insecta/Cacyreus marshalli/c755adc533626eb47835ed9190f4b40e.jpg\",\n  \"199238.jpg\": \"Insecta/Cacyreus marshalli/0a5b0b507223fb073f7ed8b6da5d09e6.jpg\",\n  \"199242.jpg\": \"Insecta/Cacyreus marshalli/26324a619ebe4d5f22e974a34b61ffb6.jpg\",\n  \"199243.jpg\": \"Insecta/Cacyreus marshalli/fc88607b0c1a4ceacdce431e52b0ea59.jpg\",\n  \"199244.jpg\": \"Insecta/Cacyreus marshalli/9167bc82e5d35a563645b295310cb73c.jpg\",\n  \"199249.jpg\": \"Insecta/Habrosyne scripta/405c02e80cbf30376e4e9f10960b64dd.jpg\",\n  \"199261.jpg\": \"Insecta/Habrosyne scripta/b19927d03f6c3c9088d0c79cf93169bf.jpg\",\n  \"199266.jpg\": \"Insecta/Habrosyne scripta/5b171c0eaf406ee01efe3445fe56707a.jpg\",\n  \"199268.jpg\": \"Insecta/Habrosyne scripta/bbcaadeeac1deb5d56e80d9b38419466.jpg\",\n  \"199276.jpg\": \"Insecta/Habrosyne scripta/20e272991257800075d96578f06da53b.jpg\",\n  \"199279.jpg\": \"Insecta/Habrosyne scripta/75d4d1605743dd80abe700e7e4dc676c.jpg\",\n  \"199374.jpg\": \"Aves/Phalacrocorax brasilianus/eb3385a4496ba3d71ca475112bebc8e2.jpg\",\n  \"199375.jpg\": \"Aves/Phalacrocorax brasilianus/2b687ce8f3efdb1d5795470e681b0032.jpg\",\n  \"199385.jpg\": \"Aves/Phalacrocorax brasilianus/af77c7fa687ccc24cb893621c3607a92.jpg\",\n  \"199428.jpg\": \"Aves/Phalacrocorax brasilianus/7712ad8ff602d4ac3a5be54225653109.jpg\",\n  \"199442.jpg\": \"Aves/Phalacrocorax brasilianus/0a310da6bb895c14de6d1247e08d1dcc.jpg\",\n  \"199446.jpg\": \"Aves/Phalacrocorax brasilianus/1ebff73ab6410d04c7f4757999112d49.jpg\",\n  \"199485.jpg\": \"Aves/Phalacrocorax brasilianus/548e16ad2c9fce6b1a2f67c14fa8c89d.jpg\",\n  \"199548.jpg\": \"Aves/Phalacrocorax brasilianus/c630b9204258c22e02f0828acaeb2bf9.jpg\",\n  \"199556.jpg\": \"Aves/Phalacrocorax brasilianus/63a890c02777e840068e832af3a13c21.jpg\",\n  \"199568.jpg\": \"Aves/Phalacrocorax brasilianus/6727720ca86f51803525f1b601842d1b.jpg\",\n  \"199606.jpg\": \"Aves/Phalacrocorax brasilianus/f7aa5edbc8a04ded1edcf09c157c0f4c.jpg\",\n  \"19961.jpg\": \"Aves/Pipilo maculatus/c7bc7209a2da3825db92097f04f1c97a.jpg\",\n  \"199614.jpg\": \"Aves/Phalacrocorax brasilianus/69313288ec690ed051e9c906e6c760e4.jpg\",\n  \"199643.jpg\": \"Aves/Phalacrocorax brasilianus/8648e0c9d2508453bd631ca54fa058bd.jpg\",\n  \"199652.jpg\": \"Aves/Lonchura punctulata/a550ef3d5aa29a4efc19cdde7fe59afe.jpg\",\n  \"199655.jpg\": \"Aves/Lonchura punctulata/6bc8e3dc810229c41a80e19a7703d1ff.jpg\",\n  \"199656.jpg\": \"Aves/Lonchura punctulata/888db276ccbcec23c5670d81bd7663ac.jpg\",\n  \"199659.jpg\": \"Aves/Lonchura punctulata/cb59f7031eb48497178b62934c73149a.jpg\",\n  \"199660.jpg\": \"Aves/Lonchura punctulata/bdd46962f81501890be639600da6a725.jpg\",\n  \"199663.jpg\": \"Aves/Lonchura punctulata/a4f9bbfe79f004ebc684c13b6b4ec640.jpg\",\n  \"199667.jpg\": \"Aves/Lonchura punctulata/67d2ddccb42419f9682c2702b229ab78.jpg\",\n  \"199672.jpg\": \"Aves/Lonchura punctulata/ed984de68cf630783ad96b7809abcf7c.jpg\",\n  \"199675.jpg\": \"Aves/Lonchura punctulata/fe84f0654f43eca9583c3276366512d9.jpg\",\n  \"199676.jpg\": \"Aves/Lonchura punctulata/557e13dc61f391ae24522b3cfacb0c42.jpg\",\n  \"199680.jpg\": \"Aves/Lonchura punctulata/91b58719668bd43b1f166ed60026e0d1.jpg\",\n  \"199688.jpg\": \"Aves/Lonchura punctulata/5842c38cb69df4e5e966c2e0b3e7392e.jpg\",\n  \"199690.jpg\": \"Aves/Lonchura punctulata/38361264a1e0452c2640df3ede7d43c1.jpg\",\n  \"199693.jpg\": \"Aves/Lonchura punctulata/b92e75ecbc47be8aee2b18615eb07cec.jpg\",\n  \"199694.jpg\": \"Aves/Lonchura punctulata/dc6b34c5ff09731a1eaa4be4a183c3c2.jpg\",\n  \"199700.jpg\": \"Aves/Lonchura punctulata/0d29f7d5ebcc5494111447328bee1c59.jpg\",\n  \"199751.jpg\": \"Mammalia/Procyon lotor/c826ace3a83aa9a676af0d7bc7440355.jpg\",\n  \"19981.jpg\": \"Aves/Pipilo maculatus/ffca8c7024dde0d35ecdae92d8a48286.jpg\",\n  \"199812.jpg\": \"Mammalia/Procyon lotor/d1edd0ed61351d5bddf0eba9db35f01a.jpg\",\n  \"199869.jpg\": \"Mammalia/Procyon lotor/8db861bb38acbeb272281b88449b6cb9.jpg\",\n  \"199916.jpg\": \"Mammalia/Procyon lotor/3dfcc1bf9271fc2fd4fdb95cd1f20541.jpg\",\n  \"199932.jpg\": \"Mammalia/Procyon lotor/2f35f2373acb5f26f388d32fdba289c3.jpg\",\n  \"199948.jpg\": \"Mammalia/Procyon lotor/466786810e90f866d0a2a86422d93f5d.jpg\",\n  \"20006.jpg\": \"Aves/Pipilo maculatus/5cf2bcd4a54b7b5633ace5c94f04d368.jpg\",\n  \"200099.jpg\": \"Mammalia/Procyon lotor/011e74f076710d654bcb62d144e8247e.jpg\",\n  \"200150.jpg\": \"Mammalia/Procyon lotor/e5eb11dcee15aab8833e3eada6e46e8f.jpg\",\n  \"20016.jpg\": \"Aves/Pipilo maculatus/c320e029e64e2b9bb895ba004ebadef8.jpg\",\n  \"200207.jpg\": \"Mammalia/Procyon lotor/b6571816786b3747724944cc92cfef6f.jpg\",\n  \"200224.jpg\": \"Mammalia/Procyon lotor/9e5beceb10c062381a2b2901c9badaba.jpg\",\n  \"200264.jpg\": \"Mammalia/Procyon lotor/a942b69179e4d52806938e86310a4cd7.jpg\",\n  \"200267.jpg\": \"Mammalia/Procyon lotor/766fd21563be88cca610d20bca6ee429.jpg\",\n  \"200343.jpg\": \"Mammalia/Procyon lotor/8c36d6e192db89e5af83f13ec62b71a8.jpg\",\n  \"20040.jpg\": \"Aves/Pipilo maculatus/c379b235f2a32d00966f44fd5ff819c3.jpg\",\n  \"20042.jpg\": \"Aves/Pipilo maculatus/8aeb23cac7ac4ef5fbfe4562b40dfa77.jpg\",\n  \"200493.jpg\": \"Mammalia/Procyon lotor/eb105e148c8ae55f9beb5362c7cc187a.jpg\",\n  \"200549.jpg\": \"Mammalia/Procyon lotor/fd585f23339dfd1f1da77c6cb2b3eb2c.jpg\",\n  \"200561.jpg\": \"Mammalia/Procyon lotor/f3ac0d94aef4f2d8a410a25107573e13.jpg\",\n  \"200621.jpg\": \"Aves/Chloroceryle americana/7d767b8b872c49be544b6b4da7b7f920.jpg\",\n  \"200624.jpg\": \"Aves/Chloroceryle americana/307b9b37b79ca0a4069c5c55eb92922b.jpg\",\n  \"200625.jpg\": \"Aves/Chloroceryle americana/73ad65c413544145bee502b6146974db.jpg\",\n  \"200631.jpg\": \"Aves/Chloroceryle americana/680a45908c3ba7cc0d8a2180db0ad14e.jpg\",\n  \"200633.jpg\": \"Aves/Chloroceryle americana/c3ab19b14f009ba34801c16e4041c314.jpg\",\n  \"200635.jpg\": \"Aves/Chloroceryle americana/704914a316c525aec3c328e7ef312adf.jpg\",\n  \"200642.jpg\": \"Aves/Chloroceryle americana/88d96dc375f619a22ffdb2b2246f3466.jpg\",\n  \"200643.jpg\": \"Aves/Chloroceryle americana/a1a574e8d6bf4f005a0c0b2d13f25835.jpg\",\n  \"200650.jpg\": \"Aves/Chloroceryle americana/09438e9b001c50df2702987c1b06c672.jpg\",\n  \"200677.jpg\": \"Aves/Chloroceryle americana/4066c1fedc407403b0b6c1c77dc109a2.jpg\",\n  \"200683.jpg\": \"Aves/Chloroceryle americana/a45e6d8c9f58752ea9689b919c114eac.jpg\",\n  \"200684.jpg\": \"Aves/Chloroceryle americana/5eef90a0ee846af07a169c035fde59a9.jpg\",\n  \"200693.jpg\": \"Aves/Chloroceryle americana/739ee6df6be669ffb1319908cf492413.jpg\",\n  \"200698.jpg\": \"Aves/Chloroceryle americana/3745f47585a1285e0bcbdb4ef2b74e05.jpg\",\n  \"200710.jpg\": \"Aves/Chloroceryle americana/01b5728dc23dad4837ba56069c3c9782.jpg\",\n  \"200711.jpg\": \"Aves/Chloroceryle americana/8505e851f7a78a9ebc1f86159db70598.jpg\",\n  \"200721.jpg\": \"Aves/Chloroceryle americana/d96ab0f77e9171dd29d5343d83000abe.jpg\",\n  \"200723.jpg\": \"Aves/Chloroceryle americana/4e6b6bf7ed50266b6a0c9d13c8bf382b.jpg\",\n  \"200724.jpg\": \"Aves/Chloroceryle americana/c2a43ee0685717d2c38418ba40d07826.jpg\",\n  \"200725.jpg\": \"Aves/Chloroceryle americana/ba0a3430acaf56353c9ff1fb5468c71d.jpg\",\n  \"200735.jpg\": \"Aves/Chloroceryle americana/6e627c04d07d9db9c6b92ad705061cb3.jpg\",\n  \"200773.jpg\": \"Mammalia/Sigmodon hispidus/fd456092bff290868f41b97013118c04.jpg\",\n  \"200781.jpg\": \"Mammalia/Sigmodon hispidus/eeb1f69c5c470a235ad8a4cdb88e0c95.jpg\",\n  \"200784.jpg\": \"Mammalia/Sigmodon hispidus/e25cabc8260e5417e84a1d31072b56a6.jpg\",\n  \"200787.jpg\": \"Mammalia/Sigmodon hispidus/5a57259d5cf38b0bd1180421a29c5c09.jpg\",\n  \"200801.jpg\": \"Mammalia/Sigmodon hispidus/93eda7a762b39211b8528988dfb56683.jpg\",\n  \"200806.jpg\": \"Mammalia/Sigmodon hispidus/07256564b2926eb7d4e8486f5f87e598.jpg\",\n  \"200808.jpg\": \"Mammalia/Sigmodon hispidus/235b360798f63126c6afca4528b1a71f.jpg\",\n  \"200809.jpg\": \"Mammalia/Sigmodon hispidus/1dd64d6042d4873db2220f97afa40a87.jpg\",\n  \"200816.jpg\": \"Mammalia/Sigmodon hispidus/6dc57ccccf4a68c9ffe6d52c447411bf.jpg\",\n  \"200817.jpg\": \"Mammalia/Sigmodon hispidus/d027709eb1c1dd95c42f353ad55a6e7e.jpg\",\n  \"200818.jpg\": \"Mammalia/Sigmodon hispidus/5845535950b6bd2f7f2e7eb9e04eab06.jpg\",\n  \"200819.jpg\": \"Mammalia/Sigmodon hispidus/985b6a3c63ffc4334fd03bcc662c071f.jpg\",\n  \"200861.jpg\": \"Insecta/Speyeria cybele/e89023c563a54ada0040f5ba53537727.jpg\",\n  \"200868.jpg\": \"Insecta/Speyeria cybele/12b76da03852efd1d8f751d7a79c9e77.jpg\",\n  \"200878.jpg\": \"Insecta/Speyeria cybele/999a5b2609840853de7b781c0b38c00a.jpg\",\n  \"200915.jpg\": \"Insecta/Speyeria cybele/2edcc5d303ae99c97264c0b38da548c6.jpg\",\n  \"200921.jpg\": \"Insecta/Speyeria cybele/bd83ca4e697fb186bfaba64af32ad2a8.jpg\",\n  \"200922.jpg\": \"Insecta/Speyeria cybele/3d92a0e2697f47007aaba0cd4ce7491b.jpg\",\n  \"200925.jpg\": \"Insecta/Speyeria cybele/085a20c70407fcc980064f1b4e922984.jpg\",\n  \"200937.jpg\": \"Insecta/Speyeria cybele/5f5c803ebd0a2d6569d3297e18bfb1ed.jpg\",\n  \"200951.jpg\": \"Insecta/Speyeria cybele/889077fa9bfdf6f9caeef06b67cf0a0d.jpg\",\n  \"200956.jpg\": \"Insecta/Speyeria cybele/21e12eaaa4a7e417fb106053b544b1b4.jpg\",\n  \"200959.jpg\": \"Insecta/Speyeria cybele/ec9450ce0f4c8705223b6a203ca16e8c.jpg\",\n  \"200982.jpg\": \"Insecta/Speyeria cybele/de69319318bc99b4e53410d711a66aff.jpg\",\n  \"200984.jpg\": \"Insecta/Speyeria cybele/4eceab5695578bfc1426a697c6bfa921.jpg\",\n  \"200987.jpg\": \"Insecta/Speyeria cybele/88699a8d6ec9c3fffa5f303449195f03.jpg\",\n  \"200990.jpg\": \"Insecta/Speyeria cybele/8b82f2db0c77d3b52f71270889463dc6.jpg\",\n  \"201014.jpg\": \"Insecta/Speyeria cybele/1ff7c37381fd5cb5ed35af0f40aa27fc.jpg\",\n  \"201019.jpg\": \"Insecta/Speyeria cybele/30edb51dc5d048a736130ea01150fef2.jpg\",\n  \"201024.jpg\": \"Insecta/Speyeria cybele/d8dfbe5840b6319bbe30489517e0072f.jpg\",\n  \"20103.jpg\": \"Aves/Pipilo maculatus/c6bf180fed824a8aa78715cf6ba82925.jpg\",\n  \"201042.jpg\": \"Insecta/Speyeria cybele/1fd38b7fedb286a02257b051363c1b0e.jpg\",\n  \"201043.jpg\": \"Insecta/Speyeria cybele/542dca25206f90f9fe960f47302bea87.jpg\",\n  \"20105.jpg\": \"Aves/Pipilo maculatus/b3e42c06b46324f0f764870182d60390.jpg\",\n  \"201087.jpg\": \"Insecta/Speyeria cybele/e7ef9f02cf5bfa1df8f28c9da8383f01.jpg\",\n  \"201093.jpg\": \"Insecta/Speyeria cybele/40c112c042ca8626a286100aad6a7596.jpg\",\n  \"201131.jpg\": \"Mammalia/Lasiurus borealis/2c7aadf105d31fb27452fc8671d41941.jpg\",\n  \"201132.jpg\": \"Mammalia/Lasiurus borealis/e489b23ead136a36bf056b970c408124.jpg\",\n  \"201135.jpg\": \"Mammalia/Lasiurus borealis/6fec925d4736a358916cd2c7a57ff10c.jpg\",\n  \"201139.jpg\": \"Mammalia/Lasiurus borealis/da5da9e478880fa57d9e22fdfdf71573.jpg\",\n  \"201140.jpg\": \"Mammalia/Lasiurus borealis/452eda99b534d3a7cba21138a62164c0.jpg\",\n  \"20118.jpg\": \"Aves/Pipilo maculatus/66d28265111cff7c1731e91756053f2c.jpg\",\n  \"20119.jpg\": \"Aves/Pipilo maculatus/ab9e0d9f3626ec4c9fb907be38c04027.jpg\",\n  \"201209.jpg\": \"Insecta/Platyprepia virginalis/88d9890d9852ce9ab2f1de3341910be5.jpg\",\n  \"201213.jpg\": \"Insecta/Platyprepia virginalis/7c9587a0c42424bdd4499a781b02f6a9.jpg\",\n  \"201214.jpg\": \"Insecta/Platyprepia virginalis/f712e2f3531eb310bf232226466d8cce.jpg\",\n  \"201216.jpg\": \"Insecta/Platyprepia virginalis/05c72f4a30c129e6bcdf860d7d03a858.jpg\",\n  \"201228.jpg\": \"Reptilia/Anolis carolinensis/194a405eaad276e779acaceaf345e984.jpg\",\n  \"201229.jpg\": \"Reptilia/Anolis carolinensis/661dac44cd2fc3a585ae5c7a5d14b5a0.jpg\",\n  \"201238.jpg\": \"Reptilia/Anolis carolinensis/fa33065697faaf539e82107eb29a23f0.jpg\",\n  \"20135.jpg\": \"Aves/Pipilo maculatus/a80497ab49ea9b57415c6d2caf3d503b.jpg\",\n  \"201393.jpg\": \"Reptilia/Anolis carolinensis/8918682f458bfc27736d7a6d24d74619.jpg\",\n  \"201399.jpg\": \"Reptilia/Anolis carolinensis/577460e72b13c9a2e00f15089179a2e4.jpg\",\n  \"201401.jpg\": \"Reptilia/Anolis carolinensis/57d181271c4f1967c895911ac38e36ff.jpg\",\n  \"201417.jpg\": \"Reptilia/Anolis carolinensis/eb9a347473230e62ff7bec3c4ae783d5.jpg\",\n  \"201481.jpg\": \"Reptilia/Anolis carolinensis/a21271179f8c30feadfce1a286b40c03.jpg\",\n  \"201686.jpg\": \"Reptilia/Anolis carolinensis/16a4aad3fae811ab096972e14e2777ce.jpg\",\n  \"201711.jpg\": \"Reptilia/Anolis carolinensis/b66f0c5b0cf2df634efda3b208c7c143.jpg\",\n  \"201763.jpg\": \"Reptilia/Anolis carolinensis/2a4894d7ef3926d7c46523309bd586f4.jpg\",\n  \"201771.jpg\": \"Reptilia/Anolis carolinensis/283b142a271f93d623da0dc19a40f245.jpg\",\n  \"201812.jpg\": \"Reptilia/Anolis carolinensis/ca272f5d5d45d6e333d3b64e34002fca.jpg\",\n  \"201844.jpg\": \"Reptilia/Anolis carolinensis/2efd21dbe413bc2a56213059a3319c1a.jpg\",\n  \"201869.jpg\": \"Reptilia/Anolis carolinensis/2979e3d9434420f396dc315e24764112.jpg\",\n  \"201876.jpg\": \"Reptilia/Anolis carolinensis/727b3fff822ceaac7e28482e2595e70a.jpg\",\n  \"201939.jpg\": \"Reptilia/Anolis carolinensis/b46ef2bc91eeccf0c1c628b2f2a1f59a.jpg\",\n  \"201944.jpg\": \"Reptilia/Anolis carolinensis/7cb83489d8f7b59faaf76dfe790581eb.jpg\",\n  \"201955.jpg\": \"Reptilia/Anolis carolinensis/22de068dd77ae920e41268cf26f188ad.jpg\",\n  \"20198.jpg\": \"Aves/Pipilo maculatus/521155e1148d453373d36a81d99160cb.jpg\",\n  \"201990.jpg\": \"Insecta/Sympetrum striolatum/f466d69ef156dd232a233ba0f4602a3b.jpg\",\n  \"201992.jpg\": \"Insecta/Sympetrum striolatum/fb86dd7fa0d6970790a38a499e9afdcb.jpg\",\n  \"201993.jpg\": \"Insecta/Sympetrum striolatum/10fe869cbdf035ec5a1c532c021c0d8f.jpg\",\n  \"201999.jpg\": \"Insecta/Sympetrum striolatum/e201131d76a49fe57fa85d7f74081fbb.jpg\",\n  \"202000.jpg\": \"Insecta/Sympetrum striolatum/89a4a288feb3f6cbf23642cd233b9276.jpg\",\n  \"202001.jpg\": \"Insecta/Sympetrum striolatum/86c2ed91329c82fdd6d78ae39ae7bfb5.jpg\",\n  \"202002.jpg\": \"Insecta/Sympetrum striolatum/5d7bdadf09cf60c6673b38bdaa8b0555.jpg\",\n  \"202003.jpg\": \"Insecta/Sympetrum striolatum/eeb0f97e7b3256f396a9c2aa7499209e.jpg\",\n  \"202004.jpg\": \"Insecta/Sympetrum striolatum/fe0cb228abc32eb9d00c919df911283a.jpg\",\n  \"202005.jpg\": \"Insecta/Sympetrum striolatum/27dc7e3f979c717306c6977cdeda3fa1.jpg\",\n  \"202008.jpg\": \"Insecta/Sympetrum striolatum/33aed5daac71dcf3e605865b20c86ed3.jpg\",\n  \"202009.jpg\": \"Insecta/Sympetrum striolatum/a6df484c492d9f82ce971c5d1a77cbc4.jpg\",\n  \"20201.jpg\": \"Aves/Pipilo maculatus/7508d6fcbc344fee744d4eac440554af.jpg\",\n  \"202012.jpg\": \"Insecta/Sympetrum striolatum/a9c2dd009633dc34f2b8fc4af6e77a66.jpg\",\n  \"202013.jpg\": \"Insecta/Sympetrum striolatum/8ccc1f30717043991dbca7dc02ad5c9d.jpg\",\n  \"202018.jpg\": \"Insecta/Sympetrum striolatum/538566a376530551c762788ecb0b5943.jpg\",\n  \"202022.jpg\": \"Insecta/Sympetrum striolatum/b85018cf81106bc3a7c86d852eef1204.jpg\",\n  \"202025.jpg\": \"Insecta/Sympetrum striolatum/402a878927555eade37e8df4a9e491d3.jpg\",\n  \"202029.jpg\": \"Insecta/Chlosyne nycteis/9e679662e6b6d571362e4396b1560b6d.jpg\",\n  \"202030.jpg\": \"Insecta/Chlosyne nycteis/b369cb2e5d74a5fb23377ac7d0f1444d.jpg\",\n  \"202031.jpg\": \"Insecta/Chlosyne nycteis/0d4c5de21f3af3fc2b9673466817ce6c.jpg\",\n  \"202032.jpg\": \"Insecta/Chlosyne nycteis/d609632e450015fe18e1d1f1f6678992.jpg\",\n  \"202045.jpg\": \"Insecta/Chlosyne nycteis/aa2761a74d08badecab9b79619f4d09a.jpg\",\n  \"202046.jpg\": \"Insecta/Chlosyne nycteis/abb84720ca41ecb68ae68346ba92fd35.jpg\",\n  \"202049.jpg\": \"Insecta/Chlosyne nycteis/b47b7a0a3de2a25295fffdae6d323319.jpg\",\n  \"202050.jpg\": \"Insecta/Chlosyne nycteis/39a1c3cf79eeaae00cd00dd32d83172c.jpg\",\n  \"202051.jpg\": \"Insecta/Chlosyne nycteis/1970062704b3025c8e22061559e0ac6c.jpg\",\n  \"202053.jpg\": \"Insecta/Chlosyne nycteis/93d8d39dab1c43627245130b014cec51.jpg\",\n  \"202054.jpg\": \"Insecta/Chlosyne nycteis/8564fc8ccc6cf9cdae68829cf15fed44.jpg\",\n  \"202055.jpg\": \"Insecta/Chlosyne nycteis/40814ad704d9344d43421838f7990fa4.jpg\",\n  \"202057.jpg\": \"Insecta/Chlosyne nycteis/edf14ab6e3dfda4b23c5e229516f644c.jpg\",\n  \"202058.jpg\": \"Insecta/Chlosyne nycteis/ae0cfbd0e0cadc92764c95c54474e063.jpg\",\n  \"202061.jpg\": \"Insecta/Chlosyne nycteis/dc3266796d7b30bc895aa4c06122cc82.jpg\",\n  \"202062.jpg\": \"Insecta/Chlosyne nycteis/e96eb54b6e6a6e6e2154c52c124ffaea.jpg\",\n  \"202063.jpg\": \"Insecta/Chlosyne nycteis/1a72388ffa9243ecdb699807748e46e9.jpg\",\n  \"202064.jpg\": \"Insecta/Chlosyne nycteis/bc9f3aa60c274a063ac8c45cb7a0db66.jpg\",\n  \"202068.jpg\": \"Insecta/Chlosyne nycteis/7a3f3ceb240733da48429dd70fe30a64.jpg\",\n  \"202076.jpg\": \"Insecta/Chlosyne nycteis/66873d14f876a682d15a18ca154819ad.jpg\",\n  \"202077.jpg\": \"Insecta/Chlosyne nycteis/ce64eb93b59787f673653f4c2d383013.jpg\",\n  \"202079.jpg\": \"Insecta/Chlosyne nycteis/6cb73682cb073ed9d4657a9f3ca2d111.jpg\",\n  \"202081.jpg\": \"Insecta/Chlosyne nycteis/abc0e42d94e63ff57668699c5f73b356.jpg\",\n  \"202085.jpg\": \"Insecta/Chlosyne nycteis/c37726908361119467677d0d1763b7b7.jpg\",\n  \"202088.jpg\": \"Aves/Buteo regalis/9ae06c208431aee987a4e59843f592dd.jpg\",\n  \"202090.jpg\": \"Aves/Buteo regalis/90897be1dcecfbc556ad1e93da155ce3.jpg\",\n  \"202094.jpg\": \"Aves/Buteo regalis/3d02ff6b5090944b9934a4270b1f9614.jpg\",\n  \"202096.jpg\": \"Aves/Buteo regalis/38e600d45272e64990892332b11a251e.jpg\",\n  \"202100.jpg\": \"Aves/Buteo regalis/7ff84959087c210a259227408e4ab39b.jpg\",\n  \"202101.jpg\": \"Aves/Buteo regalis/9435d377b350fba7f52fdb0f4cb78abf.jpg\",\n  \"202102.jpg\": \"Aves/Buteo regalis/4bd75867f8cdfa6d7f00ce63ab37bbb6.jpg\",\n  \"202108.jpg\": \"Aves/Buteo regalis/862afaa7853e80d117336e4c74b79651.jpg\",\n  \"202109.jpg\": \"Aves/Buteo regalis/922d3d519807aaeadaa9411a5ed42928.jpg\",\n  \"202110.jpg\": \"Aves/Buteo regalis/f226756c576ba580fd42983b3e914ef5.jpg\",\n  \"202114.jpg\": \"Aves/Buteo regalis/358369730679f243a17b4652c6282e87.jpg\",\n  \"202123.jpg\": \"Aves/Buteo regalis/e5a85d85b861f3e294344d70c1b17f0b.jpg\",\n  \"202124.jpg\": \"Aves/Buteo regalis/f0f32acd885789670b96ee50170cce70.jpg\",\n  \"202125.jpg\": \"Aves/Buteo regalis/04aea7ac961f411e03e726e8ab78e281.jpg\",\n  \"202126.jpg\": \"Aves/Buteo regalis/9766fcff7c851becbe746981a6f060f1.jpg\",\n  \"202127.jpg\": \"Aves/Buteo regalis/0564e89e0516ddd9679e029a57d92888.jpg\",\n  \"202131.jpg\": \"Aves/Buteo regalis/cafe31b8075d0edd55d254a09fafb038.jpg\",\n  \"202133.jpg\": \"Aves/Buteo regalis/ec636ff5f75d23014d7bd518aacf41b0.jpg\",\n  \"202136.jpg\": \"Aves/Buteo regalis/7b7bd213e5f3a34c413e22ca744f0547.jpg\",\n  \"202137.jpg\": \"Aves/Buteo regalis/34a513f05614f826b67abc65592697a0.jpg\",\n  \"202138.jpg\": \"Aves/Buteo regalis/da7a835356a457e421d46f8a19e82231.jpg\",\n  \"202139.jpg\": \"Aves/Buteo regalis/ae3d5c60ef83a5515870495e07dba16a.jpg\",\n  \"202141.jpg\": \"Aves/Buteo regalis/9020a8668b08fd221d48299dfd1aebe0.jpg\",\n  \"202142.jpg\": \"Aves/Buteo regalis/a56795167da355527eaad3246532542e.jpg\",\n  \"202143.jpg\": \"Insecta/Erpetogomphus designatus/9acea2da4bb418bf09357d72c3e5e22b.jpg\",\n  \"202144.jpg\": \"Insecta/Erpetogomphus designatus/49e8eec89e608156d0882633b2d82927.jpg\",\n  \"202145.jpg\": \"Insecta/Erpetogomphus designatus/3a6be5657bd5e645c897ab6b201869f1.jpg\",\n  \"202146.jpg\": \"Insecta/Erpetogomphus designatus/de5017d9a13f7caaa83894d1a244f5e1.jpg\",\n  \"202147.jpg\": \"Insecta/Erpetogomphus designatus/cd1e1dba84d4e990518ef6521819389c.jpg\",\n  \"202151.jpg\": \"Insecta/Erpetogomphus designatus/73c9b17eaa332e670dbcc72720a1aa8a.jpg\",\n  \"202152.jpg\": \"Insecta/Erpetogomphus designatus/5e7bc1e9f835417fba151189ca69b306.jpg\",\n  \"202154.jpg\": \"Insecta/Erpetogomphus designatus/e6f5196e28fb5604da9f4ce939971d67.jpg\",\n  \"202156.jpg\": \"Insecta/Erpetogomphus designatus/320de5d5ba06f596e0ee60b2dceb0c70.jpg\",\n  \"202157.jpg\": \"Insecta/Erpetogomphus designatus/6d648cc610e4506e22ae102a1add544a.jpg\",\n  \"202158.jpg\": \"Insecta/Erpetogomphus designatus/49d6fa4cdf8d2c0f3818ec4b5c4430c4.jpg\",\n  \"202161.jpg\": \"Insecta/Erpetogomphus designatus/22e797a20fe54b7725c7cbc654442913.jpg\",\n  \"202162.jpg\": \"Insecta/Erpetogomphus designatus/b62a06f28a036d8847aa370378cfa8d2.jpg\",\n  \"202163.jpg\": \"Insecta/Erpetogomphus designatus/e129b1a881f295a0c1f372e0b1349ed5.jpg\",\n  \"202164.jpg\": \"Insecta/Erpetogomphus designatus/80fd3737b9c1ad1ab34e6d9e01f40b4a.jpg\",\n  \"202165.jpg\": \"Insecta/Erpetogomphus designatus/a610b2acc34a5fe91a431feb513f360c.jpg\",\n  \"202166.jpg\": \"Insecta/Erpetogomphus designatus/229017e66a03bcd0cd8671206425316e.jpg\",\n  \"202175.jpg\": \"Insecta/Erpetogomphus designatus/5dbe44b0e76f0caf956dda5dd6722738.jpg\",\n  \"202178.jpg\": \"Insecta/Erpetogomphus designatus/34754d2bc8e65ceac1f07063ad1eaf8e.jpg\",\n  \"202180.jpg\": \"Insecta/Erpetogomphus designatus/c2bb2ecf5a2de785fee05658a23236f7.jpg\",\n  \"202181.jpg\": \"Insecta/Erpetogomphus designatus/8c48dc071f4869761827138d8bf18601.jpg\",\n  \"202184.jpg\": \"Insecta/Erpetogomphus designatus/27868ffd71987f03324800a0ba59a7ba.jpg\",\n  \"202187.jpg\": \"Insecta/Erpetogomphus designatus/1a87a85fdcdb8c758ab481920b5235ac.jpg\",\n  \"202188.jpg\": \"Insecta/Erpetogomphus designatus/eb651c76524020765c383073c1c089d8.jpg\",\n  \"202191.jpg\": \"Insecta/Erpetogomphus designatus/5f09bbc28ace8acbf271fbb3125cb322.jpg\",\n  \"202192.jpg\": \"Insecta/Erpetogomphus designatus/0fa4575342e192d122ae4bca7df1cf54.jpg\",\n  \"202193.jpg\": \"Insecta/Erpetogomphus designatus/46a8c0397e5c4a8a0be078114bbfdee8.jpg\",\n  \"202194.jpg\": \"Mammalia/Dasyprocta punctata/1888adc9b3c156cea2b06be67465cf2c.jpg\",\n  \"202197.jpg\": \"Mammalia/Dasyprocta punctata/32f1fa543f6e24db1e65b1c69bccb21c.jpg\",\n  \"202204.jpg\": \"Mammalia/Dasyprocta punctata/f0bc6a05c10ba8cbc4bcf66c7516513f.jpg\",\n  \"202207.jpg\": \"Mammalia/Dasyprocta punctata/6955020d37a4d1adba8fb133516c2b9f.jpg\",\n  \"202210.jpg\": \"Mammalia/Dasyprocta punctata/99147af7cdf4cb74df2774a960c822e0.jpg\",\n  \"202216.jpg\": \"Mammalia/Dasyprocta punctata/5baf3aeff28bc13961029571b52e5946.jpg\",\n  \"202217.jpg\": \"Mammalia/Dasyprocta punctata/6954eecfa7c86794608c19aec0a678a3.jpg\",\n  \"202219.jpg\": \"Mammalia/Dasyprocta punctata/ec35fd08f66fac861c55bf300889c91e.jpg\",\n  \"202220.jpg\": \"Mammalia/Dasyprocta punctata/a64fd5d85b300b9b6215dd3b10cc66d1.jpg\",\n  \"202225.jpg\": \"Mammalia/Dasyprocta punctata/ed759140283c4a66eb4253636e5d9b40.jpg\",\n  \"20224.jpg\": \"Aves/Pipilo maculatus/fab727da6c0feb2deacf6b82e65a99d8.jpg\",\n  \"20225.jpg\": \"Aves/Pipilo maculatus/51afee14235d38db1283d98a67899577.jpg\",\n  \"202283.jpg\": \"Aves/Fulmarus glacialis/2505bda4241b9e4ad2aac2e4682c6731.jpg\",\n  \"202286.jpg\": \"Aves/Fulmarus glacialis/d620656ca32452ac453a1038cbb9508c.jpg\",\n  \"202289.jpg\": \"Aves/Fulmarus glacialis/728147ea0c5609555ab2469b1bad1ed8.jpg\",\n  \"20229.jpg\": \"Aves/Pipilo maculatus/d6c79b213a425f7658062d708c98aa14.jpg\",\n  \"202292.jpg\": \"Aves/Fulmarus glacialis/35d17e602d465f5ead8ff94175bf9c2d.jpg\",\n  \"202294.jpg\": \"Aves/Fulmarus glacialis/fea2fdceb2d427224d4799f651b56fdc.jpg\",\n  \"202301.jpg\": \"Aves/Fulmarus glacialis/5d838e4cdece67b1d85811ad2e9fda3e.jpg\",\n  \"202309.jpg\": \"Aves/Fulmarus glacialis/dec456197ec380b8a4d0b417ebd72722.jpg\",\n  \"202310.jpg\": \"Aves/Fulmarus glacialis/5e7c4f5dbae17e012e8f799a6e46ddc8.jpg\",\n  \"202311.jpg\": \"Aves/Fulmarus glacialis/d12ff99f0e167666271af65e614cf41c.jpg\",\n  \"202315.jpg\": \"Aves/Fulmarus glacialis/462534bef85904f5b39a8075741e42aa.jpg\",\n  \"202320.jpg\": \"Aves/Fulmarus glacialis/c816ec60c76839c3314dcc5e7b4a29e8.jpg\",\n  \"202322.jpg\": \"Aves/Fulmarus glacialis/e50849f5f11722657c3b387bb2ae7480.jpg\",\n  \"202325.jpg\": \"Aves/Fulmarus glacialis/6130d2fba9a450c0ad83399500f83979.jpg\",\n  \"202326.jpg\": \"Aves/Fulmarus glacialis/2f946b49ab34d59118742e19e1c35c91.jpg\",\n  \"202327.jpg\": \"Aves/Fulmarus glacialis/3600680827c3ecf407ab2f5ca1215df0.jpg\",\n  \"202330.jpg\": \"Aves/Fulmarus glacialis/f98c97f3f0df7da3959f93ec8330f27c.jpg\",\n  \"202333.jpg\": \"Aves/Fulmarus glacialis/ae2bab6afeac3b875621b1382d750e54.jpg\",\n  \"20234.jpg\": \"Aves/Pipilo maculatus/f17facd8856e923d568a870f931c3394.jpg\",\n  \"202573.jpg\": \"Aves/Anas acuta/cece77b80c980e29ec49601a9a1bcbf8.jpg\",\n  \"202599.jpg\": \"Aves/Anas acuta/f201eac763c421bd5f4e244d6bec5173.jpg\",\n  \"20263.jpg\": \"Aves/Pipilo maculatus/961cc7236a1f3233c9ee11b57e2b941a.jpg\",\n  \"202649.jpg\": \"Aves/Anas acuta/a4621b25886102b56785e8e03505ae36.jpg\",\n  \"202677.jpg\": \"Aves/Anas acuta/6a8a9755002e483cc0c7da9a3f6b8791.jpg\",\n  \"202696.jpg\": \"Aves/Anas acuta/ba102626392e05c11e81d46fcd9bde16.jpg\",\n  \"20270.jpg\": \"Reptilia/Uta stansburiana/d452dc6ce095f3d73627e366141eadf4.jpg\",\n  \"202715.jpg\": \"Aves/Anas acuta/6deb23dcb05f41f58c18305f86e5009a.jpg\",\n  \"202722.jpg\": \"Aves/Anas acuta/82d59db1d3041d92a6b91dd2442ccb73.jpg\",\n  \"202729.jpg\": \"Aves/Anas acuta/cfe65a520b5de2b4463352c292d84cef.jpg\",\n  \"202740.jpg\": \"Aves/Anas acuta/cdedf1d29bb1095b7f3c66ef8af4864f.jpg\",\n  \"202741.jpg\": \"Aves/Anas acuta/91dcbe95e2f9d49ffa35efb3809c31fe.jpg\",\n  \"20276.jpg\": \"Reptilia/Uta stansburiana/c857a50eb0b8205fab58a36488524ac5.jpg\",\n  \"202762.jpg\": \"Aves/Anas acuta/c9cdb5e60b76f7b006f0b603453fbc99.jpg\",\n  \"202765.jpg\": \"Aves/Anas acuta/ef9e1167ca2ba8aed12afbd55d91cdea.jpg\",\n  \"202778.jpg\": \"Aves/Anas acuta/f64e1dbe6c6de9ad1c46a6d9c9ac618f.jpg\",\n  \"202783.jpg\": \"Aves/Anas acuta/78d18d31d8641ad0f2f88d802527527e.jpg\",\n  \"20279.jpg\": \"Reptilia/Uta stansburiana/85dbceb42338e7ca597e935a0ddbc654.jpg\",\n  \"202799.jpg\": \"Aves/Anas acuta/0ecdb7bb95ec1e9b241b2ac02df85202.jpg\",\n  \"202803.jpg\": \"Aves/Anas acuta/b166314e3bb438543aa481f0367f15bb.jpg\",\n  \"20292.jpg\": \"Reptilia/Uta stansburiana/d70f757b766e5b8c35ab89c798930ad2.jpg\",\n  \"20298.jpg\": \"Reptilia/Uta stansburiana/bba993daadfffebe159cfe6d28a636a7.jpg\",\n  \"20306.jpg\": \"Reptilia/Uta stansburiana/ea049975d9e3855acb7f0d863e41912c.jpg\",\n  \"203092.jpg\": \"Insecta/Toxomerus marginatus/aef3e1f17e15d181c5ea0d79f2bc325f.jpg\",\n  \"203098.jpg\": \"Insecta/Toxomerus marginatus/c940d55e8fb9c3351c1d5ca543abcc6f.jpg\",\n  \"203104.jpg\": \"Insecta/Toxomerus marginatus/52826e84684d0238fcfb38e62fd29932.jpg\",\n  \"203112.jpg\": \"Insecta/Toxomerus marginatus/8d1dae9f7aa045c4662577b9fac71e99.jpg\",\n  \"203113.jpg\": \"Insecta/Toxomerus marginatus/57bfbe2c5812849deb9929d48bcdd3bb.jpg\",\n  \"203117.jpg\": \"Insecta/Toxomerus marginatus/6bf78a3057508bee45dbffcc19828842.jpg\",\n  \"203118.jpg\": \"Insecta/Toxomerus marginatus/8bea352e01415ef1e78dcdd588c2b583.jpg\",\n  \"20312.jpg\": \"Reptilia/Uta stansburiana/6556889610ab915f02c582c3ac051e06.jpg\",\n  \"203127.jpg\": \"Insecta/Toxomerus marginatus/a8b897c7e1717e2a634cbc5e36bccf44.jpg\",\n  \"203134.jpg\": \"Insecta/Toxomerus marginatus/190ef3f35aae618b8564d900492fdd0e.jpg\",\n  \"203145.jpg\": \"Insecta/Toxomerus marginatus/41fe18785f35cf9328cc228259eb3432.jpg\",\n  \"203146.jpg\": \"Insecta/Toxomerus marginatus/7a3f08341956b8d908718e9acfc36209.jpg\",\n  \"203154.jpg\": \"Insecta/Toxomerus marginatus/3b9edb3418e27bdec338460fd83d65d0.jpg\",\n  \"203155.jpg\": \"Insecta/Toxomerus marginatus/0dec85b9b0549e4f5b7429f7b43bdbef.jpg\",\n  \"203156.jpg\": \"Insecta/Toxomerus marginatus/f5a0b605977705f33ce17afdb7ef5c20.jpg\",\n  \"203158.jpg\": \"Insecta/Toxomerus marginatus/7664d7eb4536100dbc3071d6cfab4c6a.jpg\",\n  \"203160.jpg\": \"Insecta/Toxomerus marginatus/b4803b2f76355ab62594d8b618274c17.jpg\",\n  \"203163.jpg\": \"Insecta/Toxomerus marginatus/0d48c4b333fc7691907330654e140c70.jpg\",\n  \"203164.jpg\": \"Insecta/Toxomerus marginatus/c6f58fbde1900d7071741597a73b6814.jpg\",\n  \"203176.jpg\": \"Insecta/Toxomerus marginatus/f9efca9070e26826f2cd5b2992ef41f5.jpg\",\n  \"203198.jpg\": \"Mollusca/Okenia rosacea/2323a8367b4a008757a5f7a4caa4a430.jpg\",\n  \"203202.jpg\": \"Mollusca/Okenia rosacea/00fc807f4d9c5a8efc511c43c2dd6aa4.jpg\",\n  \"203217.jpg\": \"Mollusca/Okenia rosacea/4884a9f6dffb6cf195c2672759874a99.jpg\",\n  \"203220.jpg\": \"Mollusca/Okenia rosacea/5564fde00c8d2e509de582b055de5e53.jpg\",\n  \"203231.jpg\": \"Mollusca/Okenia rosacea/60ab93031892653dd5772fbe4da49ecb.jpg\",\n  \"203269.jpg\": \"Mollusca/Okenia rosacea/b4ecc1e3f50874f37bb85c808e8ef9ea.jpg\",\n  \"203272.jpg\": \"Mollusca/Okenia rosacea/b4b4fc9fa5923e649d38b3d05ca4dbd6.jpg\",\n  \"203278.jpg\": \"Mollusca/Okenia rosacea/2f19ae994277582c16891d402fbb8b79.jpg\",\n  \"203284.jpg\": \"Mollusca/Okenia rosacea/6440f05c9ddabf3fe2e85511e8f78923.jpg\",\n  \"203320.jpg\": \"Mollusca/Okenia rosacea/8e2df7905926c9b2a773ad8f9ec2be58.jpg\",\n  \"203346.jpg\": \"Mollusca/Okenia rosacea/c0fb76cc0c449101026780eb5bf4002f.jpg\",\n  \"203352.jpg\": \"Mollusca/Okenia rosacea/be2aebf873bf822880a61c9bb0e26493.jpg\",\n  \"203357.jpg\": \"Mollusca/Okenia rosacea/700af596174e7ac37608f6851229cc90.jpg\",\n  \"203382.jpg\": \"Mollusca/Okenia rosacea/22cddf0feb04b676d06642df6de4d83e.jpg\",\n  \"203408.jpg\": \"Mollusca/Okenia rosacea/2369f907142180c26ada85135298ea9d.jpg\",\n  \"203410.jpg\": \"Mollusca/Okenia rosacea/5fc3b65e336e7c9f7b6b5f5308eece48.jpg\",\n  \"203493.jpg\": \"Insecta/Aporia crataegi/e9087d4ff2376f580556c9ecc14d2e05.jpg\",\n  \"203498.jpg\": \"Insecta/Aporia crataegi/c76d9e53016ae903c679c6aeb8ff37ba.jpg\",\n  \"203502.jpg\": \"Insecta/Aporia crataegi/82ab083d42db7c153d8242aed3daa3a4.jpg\",\n  \"203505.jpg\": \"Insecta/Aporia crataegi/e10a25bd8fd6c3fc8adb994db5079f21.jpg\",\n  \"203507.jpg\": \"Insecta/Clepsis peritana/fbbeee3a170e28808c917929ca5432a7.jpg\",\n  \"203508.jpg\": \"Insecta/Clepsis peritana/320c2f1fb82992574a269f860a942250.jpg\",\n  \"203512.jpg\": \"Insecta/Clepsis peritana/fba3cac42544fd20c88920ff74e75aef.jpg\",\n  \"203515.jpg\": \"Insecta/Clepsis peritana/8f6dd0b1cb59cf869cd79784d8c3e6b9.jpg\",\n  \"203517.jpg\": \"Insecta/Clepsis peritana/509dad8435ab624ef023434d6c659742.jpg\",\n  \"203522.jpg\": \"Insecta/Clepsis peritana/dc3dc1510f6bed6851d931eaf1dfee29.jpg\",\n  \"203523.jpg\": \"Insecta/Clepsis peritana/7a4dfe7c9eed3d2fdd0a6976abdeafca.jpg\",\n  \"203528.jpg\": \"Insecta/Clepsis peritana/09b80f39b2288c6a3d247bf00c562df8.jpg\",\n  \"203532.jpg\": \"Insecta/Clepsis peritana/9bcab69bbe19a83b37050026423f53ea.jpg\",\n  \"203534.jpg\": \"Insecta/Clepsis peritana/f20689e1ebcfdc99edca35e9d30c83e3.jpg\",\n  \"203537.jpg\": \"Insecta/Clepsis peritana/384f9c6b9be00a045fdaf5f174c912b9.jpg\",\n  \"203538.jpg\": \"Insecta/Clepsis peritana/da893640b5a9115fad712c1ec6bac0c3.jpg\",\n  \"203540.jpg\": \"Insecta/Clepsis peritana/ffaee917e626c3e6865fadc077dc1c49.jpg\",\n  \"203544.jpg\": \"Insecta/Clepsis peritana/d1240a52f5156ec2556ebec15203f30a.jpg\",\n  \"203547.jpg\": \"Insecta/Clepsis peritana/90a9616126a78ec03ee38c8acd70dc56.jpg\",\n  \"203551.jpg\": \"Insecta/Clepsis peritana/bccf6a25e402ccb5b51c3699361e6452.jpg\",\n  \"203560.jpg\": \"Insecta/Clepsis peritana/a15604eeb2c7dd8299bf0d7caf700449.jpg\",\n  \"203561.jpg\": \"Insecta/Clepsis peritana/4ba805b72249d524a15b5020907840fe.jpg\",\n  \"203562.jpg\": \"Insecta/Clepsis peritana/d397e1005b73d3f5b7bf885063f460b5.jpg\",\n  \"20361.jpg\": \"Reptilia/Uta stansburiana/f2c2f2e8a54bbde3d4002aa4d6f051ae.jpg\",\n  \"20379.jpg\": \"Reptilia/Uta stansburiana/d7bf3e2c702c2851a7e454e857fa30cd.jpg\",\n  \"203796.jpg\": \"Aves/Cairina moschata/688957dc06c6981e35e9a62350224395.jpg\",\n  \"203883.jpg\": \"Insecta/Phlogophora meticulosa/c020d924fe03bcd439793d92a77cb1f8.jpg\",\n  \"203887.jpg\": \"Insecta/Phlogophora meticulosa/2d9abf5ec9f0f86213ebb4e05e5dfb54.jpg\",\n  \"203888.jpg\": \"Insecta/Phlogophora meticulosa/46b696a524c5d3f7766d2bd2d92dee28.jpg\",\n  \"203894.jpg\": \"Insecta/Phlogophora meticulosa/d8c58de6bcc1fbfd204228fe6b50f6a6.jpg\",\n  \"203897.jpg\": \"Insecta/Phlogophora meticulosa/d394916cfa5e1c5893ad9428d5eccbc6.jpg\",\n  \"204027.jpg\": \"Aves/Limnodromus scolopaceus/4e4a237be6adb7f0a89299d1c351e959.jpg\",\n  \"204029.jpg\": \"Aves/Limnodromus scolopaceus/8c6c3124a05809beeaaa4409b0e9a5d0.jpg\",\n  \"20403.jpg\": \"Reptilia/Uta stansburiana/3fc738dd715e5188304b442d0cdb4217.jpg\",\n  \"204030.jpg\": \"Aves/Limnodromus scolopaceus/11619358d47375b0e1922faa88dabfc6.jpg\",\n  \"204031.jpg\": \"Aves/Limnodromus scolopaceus/efe98b5f8b779d0bfb85f23e5dc204c7.jpg\",\n  \"204040.jpg\": \"Aves/Limnodromus scolopaceus/0952cf526c90c7d3b21dbdc03dc98a8b.jpg\",\n  \"204043.jpg\": \"Aves/Limnodromus scolopaceus/8411336a8f27fa711e95aa15d8469dd2.jpg\",\n  \"204049.jpg\": \"Aves/Limnodromus scolopaceus/70fc6914ec6e2cdf39fdaf17a1bc4f87.jpg\",\n  \"204050.jpg\": \"Aves/Limnodromus scolopaceus/7f3da425aec8dbfe9a9874077902d860.jpg\",\n  \"204052.jpg\": \"Aves/Limnodromus scolopaceus/3846c9e331bc17ac76b1d60e6a8233c8.jpg\",\n  \"204054.jpg\": \"Aves/Limnodromus scolopaceus/213a0a7a3c25ab20b1428302677cfaed.jpg\",\n  \"204056.jpg\": \"Aves/Limnodromus scolopaceus/33843b3df7fd5105cd3d089ea865b775.jpg\",\n  \"204064.jpg\": \"Aves/Limnodromus scolopaceus/326acf4149a4a6d5f3cf58af0d1d1020.jpg\",\n  \"204068.jpg\": \"Reptilia/Nerodia erythrogaster/f5f7a2b3462930f1dcb84a367f4e0c19.jpg\",\n  \"20407.jpg\": \"Reptilia/Uta stansburiana/db2fabdb6e6217d804dba012083463d1.jpg\",\n  \"204073.jpg\": \"Reptilia/Nerodia erythrogaster/6f452a82003b9f03dacac2362143fa37.jpg\",\n  \"204089.jpg\": \"Reptilia/Nerodia erythrogaster/4504f395571042173ab4bc06264a243f.jpg\",\n  \"204095.jpg\": \"Reptilia/Nerodia erythrogaster/0c2ae896a012d7545a6996ffd5670f61.jpg\",\n  \"204100.jpg\": \"Reptilia/Nerodia erythrogaster/37a44c94e7b49ef747ab5973832be5c1.jpg\",\n  \"204104.jpg\": \"Reptilia/Nerodia erythrogaster/5d557aa690c5e6cbd3dfe81a910751e1.jpg\",\n  \"204117.jpg\": \"Reptilia/Nerodia erythrogaster/464fb130713de4bcf9f950d76ed49413.jpg\",\n  \"204118.jpg\": \"Reptilia/Nerodia erythrogaster/527ee49ea5c3144e6676b98807d05ff4.jpg\",\n  \"204119.jpg\": \"Reptilia/Nerodia erythrogaster/747f00062167fb30e6e7761bb487a413.jpg\",\n  \"20412.jpg\": \"Reptilia/Uta stansburiana/52ebdabac4ed9e5ae7396944b63ad793.jpg\",\n  \"204120.jpg\": \"Reptilia/Nerodia erythrogaster/e43c6f1163bae97a1120a3ab302d21f1.jpg\",\n  \"204129.jpg\": \"Reptilia/Nerodia erythrogaster/f9282a815da8bd791b7272de789c526a.jpg\",\n  \"204130.jpg\": \"Reptilia/Nerodia erythrogaster/a1fb33ffff58e890dd6c0c9d69d59ed3.jpg\",\n  \"204133.jpg\": \"Reptilia/Nerodia erythrogaster/a21c4fd109495daf1e27f088dbb3930d.jpg\",\n  \"204135.jpg\": \"Reptilia/Nerodia erythrogaster/ca59cce38c63d031be8b8f3ba7f34ccc.jpg\",\n  \"204141.jpg\": \"Reptilia/Nerodia erythrogaster/a2dbe4fc8634dcfc4e3d0348cb38d708.jpg\",\n  \"204177.jpg\": \"Reptilia/Nerodia erythrogaster/bd247f5f31bcc8e914b22e0aef1a7d1e.jpg\",\n  \"204178.jpg\": \"Reptilia/Nerodia erythrogaster/6d8173ec66becc9e55f657838a2e0dd6.jpg\",\n  \"204183.jpg\": \"Reptilia/Nerodia erythrogaster/16718244fdc54208dbac4e904c18acf5.jpg\",\n  \"204184.jpg\": \"Reptilia/Nerodia erythrogaster/5dc805a9651a884a55fe7e3f04467d82.jpg\",\n  \"204189.jpg\": \"Reptilia/Nerodia erythrogaster/de5c84f6ad6ab80f3d7b08538ef93f0b.jpg\",\n  \"204193.jpg\": \"Aves/Ardea cocoi/b94c3404c129c83a7d4817af475efdea.jpg\",\n  \"204196.jpg\": \"Aves/Ardea cocoi/ddb6bdddbc6d1fb0d20bb7d225e46027.jpg\",\n  \"204198.jpg\": \"Aves/Ardea cocoi/7b972da197dcea87a7fb37f9d653350a.jpg\",\n  \"204202.jpg\": \"Aves/Ardea cocoi/2cb86a72d78fe07ed68292d4d4a65d53.jpg\",\n  \"204254.jpg\": \"Animalia/Metacarcinus magister/d6ce86c07144f7818ca2acfc86821317.jpg\",\n  \"204256.jpg\": \"Animalia/Metacarcinus magister/afc32eb1221a85ded819d5e71b65a616.jpg\",\n  \"204258.jpg\": \"Animalia/Metacarcinus magister/75cd8235247c3b8b4f36bf0eff5e4a98.jpg\",\n  \"204259.jpg\": \"Animalia/Metacarcinus magister/6cc8893bf085cae4677b23fca564f097.jpg\",\n  \"204260.jpg\": \"Animalia/Metacarcinus magister/d47dab5f82656423d0a671360f259d76.jpg\",\n  \"204264.jpg\": \"Animalia/Metacarcinus magister/ec259f1b82aa6e9ecd60c0eeed587c68.jpg\",\n  \"204267.jpg\": \"Animalia/Metacarcinus magister/6931c81e78a38dde1d6896bbfa1f1cba.jpg\",\n  \"204268.jpg\": \"Animalia/Metacarcinus magister/f89b52f88461e74a88f38aab37d45999.jpg\",\n  \"204271.jpg\": \"Animalia/Metacarcinus magister/ff2a6e4a928ef26bb884da6d4ab698d2.jpg\",\n  \"204272.jpg\": \"Animalia/Metacarcinus magister/ea771233f9f030ffb57ec6d6c52c6d2c.jpg\",\n  \"204280.jpg\": \"Animalia/Metacarcinus magister/990e5998cbba8e3e3b41655d61547f24.jpg\",\n  \"204281.jpg\": \"Animalia/Metacarcinus magister/8499b5c426f9b475ea79e842ef7d397e.jpg\",\n  \"204286.jpg\": \"Animalia/Metacarcinus magister/24d0ec49b8f27129f2c71e9ebe04903f.jpg\",\n  \"204287.jpg\": \"Animalia/Metacarcinus magister/3ccf1c5d1c4d29518c892fa947d68319.jpg\",\n  \"204293.jpg\": \"Animalia/Metacarcinus magister/0b3c6a46d611359289abdfd44d01002f.jpg\",\n  \"204295.jpg\": \"Animalia/Metacarcinus magister/b9767e622504925b8b8c3be913f29d5d.jpg\",\n  \"204296.jpg\": \"Animalia/Metacarcinus magister/7d8fb9122b7c41c13f4ed94375810747.jpg\",\n  \"204525.jpg\": \"Aves/Aeronautes saxatalis/c39ddca6581b701e8db656c38d28b2af.jpg\",\n  \"204527.jpg\": \"Aves/Aeronautes saxatalis/1774fd1e8e636e5b0a29c990ea55f5e7.jpg\",\n  \"204529.jpg\": \"Aves/Aeronautes saxatalis/f738f461430062811f8866e73a570b53.jpg\",\n  \"204546.jpg\": \"Aves/Aeronautes saxatalis/8fdefe52286514509f20c96411c3a976.jpg\",\n  \"204548.jpg\": \"Aves/Aeronautes saxatalis/f3f53fdef708a9be1996b6b351e78bb9.jpg\",\n  \"204557.jpg\": \"Aves/Aeronautes saxatalis/8444fb9ff4cbbe39b994eb600d449748.jpg\",\n  \"204627.jpg\": \"Insecta/Atta texana/3ba8db4bd22ea7408953c605b74de8eb.jpg\",\n  \"204638.jpg\": \"Insecta/Atta texana/6701695fae69a15034aad020e71a6d8f.jpg\",\n  \"204659.jpg\": \"Insecta/Callophrys henrici/2931d954b2d0f5012613e9a3236a73f4.jpg\",\n  \"204664.jpg\": \"Insecta/Callophrys henrici/bdbb655256df1524b3bc607fd941be0a.jpg\",\n  \"204665.jpg\": \"Insecta/Callophrys henrici/e86b1cafa28f731a8134d328ca774f84.jpg\",\n  \"204673.jpg\": \"Insecta/Copaeodes minima/228849ff682f434877ff259224c3a496.jpg\",\n  \"204676.jpg\": \"Insecta/Copaeodes minima/abe9cb2b4590c5b43a4920e9c0af3ef1.jpg\",\n  \"204682.jpg\": \"Insecta/Copaeodes minima/1f106730d53543b87848e0aca3edbee4.jpg\",\n  \"204684.jpg\": \"Insecta/Copaeodes minima/4537b2f6504c0ce4fd4aa2c1e98dd0eb.jpg\",\n  \"204686.jpg\": \"Insecta/Copaeodes minima/f8b00d2f4ecf8ce371d986f5563d9a75.jpg\",\n  \"204692.jpg\": \"Insecta/Copaeodes minima/87bf251c9b2264eaa481ff88316559fe.jpg\",\n  \"204694.jpg\": \"Insecta/Copaeodes minima/8632783ca834b5c6f864192292196772.jpg\",\n  \"204695.jpg\": \"Insecta/Copaeodes minima/aa2044923922d17a4ddbc0615c919b46.jpg\",\n  \"204746.jpg\": \"Aves/Setophaga chrysoparia/9acb1605dd4339701540870befe079b6.jpg\",\n  \"204747.jpg\": \"Aves/Setophaga chrysoparia/25cdb0f682af713da99a8d0cce709c24.jpg\",\n  \"204750.jpg\": \"Aves/Setophaga chrysoparia/7f5d4a3095cc33e32da684dd366f1a83.jpg\",\n  \"204757.jpg\": \"Aves/Setophaga chrysoparia/6e7860aa6504e759c3655cfd75433f28.jpg\",\n  \"204759.jpg\": \"Aves/Setophaga chrysoparia/4c471e56ac77a3a813f7d976a92a8bf8.jpg\",\n  \"204760.jpg\": \"Aves/Setophaga chrysoparia/c84302d9d6b917dc1bab49b619c4735c.jpg\",\n  \"204769.jpg\": \"Insecta/Erynnis funeralis/1d4707f29359f391f0ca5ca25d5cc490.jpg\",\n  \"204772.jpg\": \"Insecta/Erynnis funeralis/7605e3becae550e7f9b3ff738cafb2b1.jpg\",\n  \"204774.jpg\": \"Insecta/Erynnis funeralis/5c836a9306fd8c2e871c519aff232cbe.jpg\",\n  \"204775.jpg\": \"Insecta/Erynnis funeralis/017022533fcae043a1c564e15f7e4b4f.jpg\",\n  \"204784.jpg\": \"Insecta/Erynnis funeralis/421d36e2c63629b943e88d77f1703123.jpg\",\n  \"204785.jpg\": \"Insecta/Erynnis funeralis/7bffe04f7346b2a8ce5bccc4b5b6c012.jpg\",\n  \"204786.jpg\": \"Insecta/Erynnis funeralis/067ffdd734eb307abb573fb6f38f7682.jpg\",\n  \"204787.jpg\": \"Insecta/Erynnis funeralis/8bd1e28793e33603888678eea9b8c267.jpg\",\n  \"20480.jpg\": \"Reptilia/Uta stansburiana/940652d99973042840bd2b3b0bc3af2d.jpg\",\n  \"204800.jpg\": \"Insecta/Erynnis funeralis/91b383bea8fb655e8c703abad66f0d25.jpg\",\n  \"204803.jpg\": \"Insecta/Erynnis funeralis/f6e6f15a15bed4d400eeefd28d9ded0f.jpg\",\n  \"204804.jpg\": \"Insecta/Erynnis funeralis/12bfb6ad4cc29ac91987b23941292abc.jpg\",\n  \"204808.jpg\": \"Insecta/Erynnis funeralis/f65d9cc6bc1555d36913d5d96b78f844.jpg\",\n  \"204813.jpg\": \"Insecta/Erynnis funeralis/0cccaea2379c0d68cf856242e3c5e537.jpg\",\n  \"204814.jpg\": \"Insecta/Erynnis funeralis/7218d297c724015099734e43a2819ab7.jpg\",\n  \"204818.jpg\": \"Insecta/Erynnis funeralis/2ea399600d7749bc185e759c3ed9da79.jpg\",\n  \"204826.jpg\": \"Insecta/Erynnis funeralis/3a8a3e0da3316656d18d78d0d04cbb88.jpg\",\n  \"204831.jpg\": \"Insecta/Erynnis funeralis/60f7977cfc0b6096179b7379fb721d16.jpg\",\n  \"204836.jpg\": \"Insecta/Erynnis funeralis/11c30c3fe68c019ef3b3b988b58a2006.jpg\",\n  \"204838.jpg\": \"Insecta/Erynnis funeralis/50c363996624bfb438468d4a4aff988b.jpg\",\n  \"204840.jpg\": \"Insecta/Erynnis funeralis/e2648101b18e43f8b1fb0ca58613805a.jpg\",\n  \"204845.jpg\": \"Insecta/Erynnis funeralis/7df3a7a34164edd4aa183d3824f77674.jpg\",\n  \"204849.jpg\": \"Aves/Setophaga tigrina/e8e3d27d6fa7d80108fe3317b2b8d262.jpg\",\n  \"204850.jpg\": \"Aves/Setophaga tigrina/76a0af1687566a1e94b6a2233cc23873.jpg\",\n  \"204851.jpg\": \"Aves/Setophaga tigrina/f5198d2dbdf305fa755bc00f6259504d.jpg\",\n  \"204852.jpg\": \"Aves/Setophaga tigrina/5014c027d53bd8d1774c30234972d1c5.jpg\",\n  \"204853.jpg\": \"Aves/Setophaga tigrina/cf564d9fbe2510f679dfb4b3000fa458.jpg\",\n  \"204854.jpg\": \"Aves/Setophaga tigrina/c994c15480022b53f2a2df4191922926.jpg\",\n  \"204855.jpg\": \"Aves/Setophaga tigrina/8a8976e09ba5b46de5ff844ede0a9da6.jpg\",\n  \"204856.jpg\": \"Aves/Setophaga tigrina/4832734fc7bb42ebfbbf9c58e3ac5bd6.jpg\",\n  \"204857.jpg\": \"Aves/Setophaga tigrina/f43c9d67c33a466cac0fec3d7e638a4f.jpg\",\n  \"204861.jpg\": \"Aves/Setophaga tigrina/c5ca0c6c16c6443aed99b68afa3c9989.jpg\",\n  \"204862.jpg\": \"Aves/Setophaga tigrina/f35a26ef1bf4e4cc67ffa6bb50db988e.jpg\",\n  \"204863.jpg\": \"Aves/Setophaga tigrina/f3950841622284147fccc1af6b4fe2bb.jpg\",\n  \"204868.jpg\": \"Aves/Setophaga tigrina/6692f67145c6f48583c14b261ef71d06.jpg\",\n  \"204869.jpg\": \"Aves/Setophaga tigrina/99454677adb9cf56c62890d5d78985a2.jpg\",\n  \"204872.jpg\": \"Aves/Setophaga tigrina/896cc448b0a193e8f1ec5553a9345e5d.jpg\",\n  \"204874.jpg\": \"Aves/Setophaga tigrina/894b85b4afbd093a75f1642845b4aa4f.jpg\",\n  \"204880.jpg\": \"Aves/Setophaga tigrina/68833fbc97af3609c48928519ab19099.jpg\",\n  \"204881.jpg\": \"Aves/Setophaga tigrina/16896318accb89b7be01d85eafb8d9a0.jpg\",\n  \"204882.jpg\": \"Aves/Setophaga tigrina/e7c98b3782c883da6782e9110c9c3bb8.jpg\",\n  \"204883.jpg\": \"Aves/Setophaga tigrina/c89138084bb663c4814df16f428e0ee6.jpg\",\n  \"204884.jpg\": \"Aves/Setophaga tigrina/9fec529f00caf3dea9350f2c0f646cf9.jpg\",\n  \"204886.jpg\": \"Aves/Setophaga tigrina/17b30f0e733566316fba472a872fa1f3.jpg\",\n  \"204890.jpg\": \"Insecta/Charadra deridens/380262f5a4c01318bb64975f7b462af9.jpg\",\n  \"204893.jpg\": \"Insecta/Charadra deridens/ff53cd1ed5089498f34e18a5d474423b.jpg\",\n  \"204895.jpg\": \"Insecta/Charadra deridens/2e1858a07e341db811a55301d7c06a63.jpg\",\n  \"204896.jpg\": \"Insecta/Charadra deridens/1e7a99c31d086b4202ffb6233adde42a.jpg\",\n  \"204901.jpg\": \"Reptilia/Natrix tessellata/dba6f67d7cded140682cc4661756f386.jpg\",\n  \"204911.jpg\": \"Reptilia/Natrix tessellata/68a16fbf2dff74e1bf45e761b52d5d7d.jpg\",\n  \"204915.jpg\": \"Reptilia/Natrix tessellata/9a9b364218b0f5981a02eff9d4f3fa77.jpg\",\n  \"204916.jpg\": \"Reptilia/Natrix tessellata/029dbe2e4c8bebe1faaab1875f8ed21e.jpg\",\n  \"204918.jpg\": \"Reptilia/Natrix tessellata/5ef760e55e74ad07a9a9ed10edd21d77.jpg\",\n  \"204924.jpg\": \"Reptilia/Natrix tessellata/bbf1a9ef72f8eba82ee6162ceaf89d9d.jpg\",\n  \"204930.jpg\": \"Aves/Mimus gilvus/c41058396a0555ab7950bfe74b4dcdc7.jpg\",\n  \"204933.jpg\": \"Aves/Mimus gilvus/744bd271b98c0d3852614cf346e43b4b.jpg\",\n  \"204935.jpg\": \"Aves/Mimus gilvus/a153e2f893aadeef0a3cc4f273acf3a7.jpg\",\n  \"204936.jpg\": \"Aves/Mimus gilvus/2f8a7c5d838771992c45e3f85d8d1717.jpg\",\n  \"204938.jpg\": \"Aves/Mimus gilvus/46704f041599fec5f12c788feb46124c.jpg\",\n  \"204945.jpg\": \"Aves/Mimus gilvus/301bf946d51ccd6c271261cf08e3dc38.jpg\",\n  \"204955.jpg\": \"Aves/Mimus gilvus/3a572d611c8adfdfbe1fbf1bdad2d9a5.jpg\",\n  \"204959.jpg\": \"Aves/Mimus gilvus/58a96255bd1960ed25796ee71da3aa21.jpg\",\n  \"204961.jpg\": \"Aves/Mimus gilvus/52c34153298985412f97962b77dbae97.jpg\",\n  \"204963.jpg\": \"Aves/Mimus gilvus/b6e7dbe1ad67d427359f7b897f5d04ae.jpg\",\n  \"204964.jpg\": \"Aves/Mimus gilvus/41794ef99212f692bcb0aeee84c67f89.jpg\",\n  \"204969.jpg\": \"Aves/Mimus gilvus/43cef5f961c98e156f79a5b4532cf274.jpg\",\n  \"204971.jpg\": \"Aves/Mimus gilvus/ae7387428520405bc26a93fc3638ea14.jpg\",\n  \"204975.jpg\": \"Aves/Mimus gilvus/2f0288bd3b37a8db7c827fa9844833f5.jpg\",\n  \"204976.jpg\": \"Aves/Mimus gilvus/b8663b66920a1398026ed794d7c48dc6.jpg\",\n  \"204984.jpg\": \"Aves/Mimus gilvus/0a4256a28ded3ffcd86284a2e7555f18.jpg\",\n  \"204985.jpg\": \"Aves/Mimus gilvus/6ccb52f1af065c8ffd241c8071e5d245.jpg\",\n  \"204992.jpg\": \"Aves/Mimus gilvus/92281ad0fc08acc7f8ff9794d3b0ad1e.jpg\",\n  \"204995.jpg\": \"Aves/Mimus gilvus/7da799aa4fc454881052b12997908f61.jpg\",\n  \"204997.jpg\": \"Aves/Mimus gilvus/7b492361ab38be8e321d28509e948439.jpg\",\n  \"204998.jpg\": \"Aves/Mimus gilvus/cc397b3c52cc59a2ce4538197c8243ca.jpg\",\n  \"204999.jpg\": \"Aves/Mimus gilvus/3089f02daef2c40168bb87c53b801521.jpg\",\n  \"205002.jpg\": \"Aves/Mimus gilvus/4aa811efe0a04f245f165c45fae055da.jpg\",\n  \"205003.jpg\": \"Aves/Mimus gilvus/e454782c2bb76d3dc1a7e4fc5f0333d7.jpg\",\n  \"205008.jpg\": \"Aves/Mimus gilvus/dee4f1dc2d482454ae27598f792988eb.jpg\",\n  \"205043.jpg\": \"Reptilia/Coluber lateralis lateralis/83d7c71b286fb3438c778458f2f409bd.jpg\",\n  \"205048.jpg\": \"Reptilia/Coluber lateralis lateralis/cae9c913e1a85af0ad6e980c2dbcee1d.jpg\",\n  \"205051.jpg\": \"Reptilia/Coluber lateralis lateralis/4f6dfd41f3401917a57855bd42696804.jpg\",\n  \"205052.jpg\": \"Reptilia/Coluber lateralis lateralis/eb003deed588661bd58194222ed944ed.jpg\",\n  \"205059.jpg\": \"Reptilia/Coluber lateralis lateralis/cc7624de0f22a8d17713d2d9c5e1fe24.jpg\",\n  \"205062.jpg\": \"Reptilia/Coluber lateralis lateralis/822578217aa335d83febd86e55250599.jpg\",\n  \"205063.jpg\": \"Reptilia/Coluber lateralis lateralis/efe7af88e5550016016807580480a3a0.jpg\",\n  \"205070.jpg\": \"Reptilia/Coluber lateralis lateralis/e2304d51243f28d53961972e08e56d46.jpg\",\n  \"205080.jpg\": \"Reptilia/Coluber lateralis lateralis/8c0fac6ce5e285d17f81404242077971.jpg\",\n  \"205084.jpg\": \"Reptilia/Coluber lateralis lateralis/5f2f55324b9f26c34569de0068f5656f.jpg\",\n  \"205096.jpg\": \"Reptilia/Coluber lateralis lateralis/e78a9fb796e5a21717f5b90900bec6b5.jpg\",\n  \"205097.jpg\": \"Reptilia/Coluber lateralis lateralis/328da4d6298dce91b049d0f34beab157.jpg\",\n  \"205100.jpg\": \"Reptilia/Coluber lateralis lateralis/b7fdc80f060068e49eae0c6348dfa85b.jpg\",\n  \"205101.jpg\": \"Reptilia/Coluber lateralis lateralis/98c611ac3f31508ce2715984b781e2bc.jpg\",\n  \"205106.jpg\": \"Animalia/Cyanea capillata/4a9f5fad477e625fcb71be476131ac19.jpg\",\n  \"205109.jpg\": \"Animalia/Cyanea capillata/675a038b9f3c05a63f8fc5c156ff6c5d.jpg\",\n  \"205126.jpg\": \"Animalia/Cyanea capillata/03170f50f8470b1d695f5349d45de6c3.jpg\",\n  \"205131.jpg\": \"Animalia/Cyanea capillata/5429d6d8586d01ab4f6ff64d2238f7d1.jpg\",\n  \"205140.jpg\": \"Mollusca/Pomacea canaliculata/746186128d1fffbf413f5892ad9ca915.jpg\",\n  \"205151.jpg\": \"Mollusca/Pomacea canaliculata/2a57b33c2689a8f87731df7aedf54372.jpg\",\n  \"205155.jpg\": \"Mollusca/Pomacea canaliculata/575b29f2a65783de31ac6bd21a97942d.jpg\",\n  \"205159.jpg\": \"Mollusca/Pomacea canaliculata/73d16076c0f4973b5ddc2da9a40c9082.jpg\",\n  \"205160.jpg\": \"Mollusca/Pomacea canaliculata/a45073ddfdb9c87fde65849df1bfd2c8.jpg\",\n  \"205161.jpg\": \"Mollusca/Pomacea canaliculata/bbfe2e8326e58dbadec0f7d571c697da.jpg\",\n  \"205168.jpg\": \"Aves/Setophaga citrina/45050557c983034ebfbff0a91dd51d66.jpg\",\n  \"205170.jpg\": \"Aves/Setophaga citrina/1f8361dab435f3f94fa3d49e0fd59bd1.jpg\",\n  \"205171.jpg\": \"Aves/Setophaga citrina/1c6a781ce33dbb005773e99aed4ad58b.jpg\",\n  \"205179.jpg\": \"Aves/Setophaga citrina/72684b040270b24ab56d19984941479e.jpg\",\n  \"205184.jpg\": \"Aves/Setophaga citrina/ebc1bb71a79d65d3e83eb11259366d87.jpg\",\n  \"205187.jpg\": \"Aves/Setophaga citrina/d3ae2edef0da096636ee6da4dba25383.jpg\",\n  \"205193.jpg\": \"Aves/Setophaga citrina/0c15bb9bf1affd8426a3d1ec8799fa81.jpg\",\n  \"205194.jpg\": \"Aves/Setophaga citrina/482d5375fd33b73eb552d501b90149f9.jpg\",\n  \"205195.jpg\": \"Aves/Setophaga citrina/3eb25fd13950fe6022c2554b8ff5aef0.jpg\",\n  \"20520.jpg\": \"Reptilia/Uta stansburiana/990f9aa92f2f9686d80009b53d39bdfa.jpg\",\n  \"205203.jpg\": \"Aves/Setophaga citrina/0a06990c548ae8b6dd6c73cd4cd116c8.jpg\",\n  \"205204.jpg\": \"Aves/Setophaga citrina/6683b399705f1a8e5c1ce70b959b5d4e.jpg\",\n  \"205209.jpg\": \"Aves/Setophaga citrina/785b60646fec1e8fa8696d99dc5c1eb0.jpg\",\n  \"205210.jpg\": \"Aves/Setophaga citrina/bf3d2444fcd800f81c110a7b2e4f9cb3.jpg\",\n  \"205213.jpg\": \"Aves/Setophaga citrina/08d77a15a2f189b19a0cb598897ba953.jpg\",\n  \"205214.jpg\": \"Aves/Setophaga citrina/dbb34288afc26ac96b038695847b3893.jpg\",\n  \"205222.jpg\": \"Aves/Setophaga citrina/1688b669ec9c43e40b8db2bca2ebc67b.jpg\",\n  \"205223.jpg\": \"Aves/Setophaga citrina/eaf9aeb1c195417c1aa2696c37044848.jpg\",\n  \"205224.jpg\": \"Aves/Setophaga citrina/782bf53a16b60beba3083e9c63e60515.jpg\",\n  \"205227.jpg\": \"Aves/Setophaga citrina/5e99ef619485f40c0f0665b8c7ec2dfc.jpg\",\n  \"205230.jpg\": \"Aves/Ardea alba modesta/d8b0150c5f92c18d209637cb41a2436a.jpg\",\n  \"205232.jpg\": \"Aves/Ardea alba modesta/697d80fb755bb638bf154eee01936182.jpg\",\n  \"205250.jpg\": \"Aves/Ardea alba modesta/a08ff0b7f2c16bc79b144f67bf8395f9.jpg\",\n  \"205263.jpg\": \"Aves/Vireo bellii/df0189418c2fa1228888104cb70c92c9.jpg\",\n  \"205264.jpg\": \"Aves/Vireo bellii/6aa871c8f1d536ec0462ffcda6c6d07d.jpg\",\n  \"205265.jpg\": \"Aves/Vireo bellii/07581ceffc6e564bce8eeeb906ff6662.jpg\",\n  \"205267.jpg\": \"Aves/Vireo bellii/8b129014bdf2080bdb69c74e8d4e1446.jpg\",\n  \"205274.jpg\": \"Aves/Vireo bellii/a84d11e6bf48df485928b34634cb41cc.jpg\",\n  \"20533.jpg\": \"Reptilia/Uta stansburiana/9cb48b7a997974703b708d3ba67c57c3.jpg\",\n  \"20534.jpg\": \"Reptilia/Uta stansburiana/9866e88129ad6d02d92c73640057ff5c.jpg\",\n  \"20539.jpg\": \"Reptilia/Uta stansburiana/cf0b5c2c89d39bd199cd2bc2a7d12b8d.jpg\",\n  \"205448.jpg\": \"Aves/Larus livens/94b922e6b2040aa91502c0ae9d524c75.jpg\",\n  \"205452.jpg\": \"Aves/Larus livens/25b228975af58fbeebd8cf8a58d596d7.jpg\",\n  \"205460.jpg\": \"Aves/Larus livens/33f052b2ef1ec703ef59657681c96e21.jpg\",\n  \"205461.jpg\": \"Aves/Larus livens/4d492ed284d32b5b279ab2548f385b63.jpg\",\n  \"205465.jpg\": \"Aves/Larus livens/9efd338cd1a716d8d3b75dd9b6effa79.jpg\",\n  \"205478.jpg\": \"Insecta/Largus californicus/d754d994ec663463fcc6e24ad1336db6.jpg\",\n  \"205482.jpg\": \"Insecta/Largus californicus/2c9f0a84874f92b913030102c4c1aa51.jpg\",\n  \"205487.jpg\": \"Insecta/Largus californicus/9f6f90cd3e2d77c67ba1da64aab64c7e.jpg\",\n  \"20549.jpg\": \"Reptilia/Uta stansburiana/590635be24c462f0eeb04872068dd3c5.jpg\",\n  \"205497.jpg\": \"Insecta/Largus californicus/4e0f00579a4f48f5fc917af26dd17dab.jpg\",\n  \"205498.jpg\": \"Insecta/Largus californicus/fc0cf948d108e9e63d85eec20652bddf.jpg\",\n  \"205499.jpg\": \"Insecta/Largus californicus/789b3ebeae68b46f273fcab103b11c96.jpg\",\n  \"205500.jpg\": \"Insecta/Largus californicus/955225ff8f0c5de68585b5e9d9f1fe92.jpg\",\n  \"205505.jpg\": \"Insecta/Largus californicus/07cccdbc5ee07c4a1000f22ed1b8ca03.jpg\",\n  \"205506.jpg\": \"Insecta/Largus californicus/6cdea0fc93aa9e683e11f5c742158576.jpg\",\n  \"205513.jpg\": \"Insecta/Largus californicus/2ab0dd9620fa8bf26703c2a680a9ce22.jpg\",\n  \"205514.jpg\": \"Insecta/Largus californicus/6529ddfe91159b3e057ed7087efb4b7e.jpg\",\n  \"205515.jpg\": \"Insecta/Largus californicus/6686b9afcd3a7232ba0e32c70c9a64b2.jpg\",\n  \"205516.jpg\": \"Insecta/Largus californicus/2da476ab1f46314898b8554c18503dc4.jpg\",\n  \"205517.jpg\": \"Insecta/Largus californicus/2610ca9317e4486a63a99631df954bff.jpg\",\n  \"205519.jpg\": \"Insecta/Largus californicus/d529cc2cef5b780a96fc68776bb4b250.jpg\",\n  \"205522.jpg\": \"Insecta/Largus californicus/63c0d60c1f566ad2ae4c647b9789e5d4.jpg\",\n  \"205526.jpg\": \"Insecta/Largus californicus/36980b57ac1bf48aea82d9e1c8303f44.jpg\",\n  \"205529.jpg\": \"Insecta/Largus californicus/8dad13b8fc72a212c5bff08636533351.jpg\",\n  \"205531.jpg\": \"Insecta/Largus californicus/a4e45d041ac823c538a0532e9dc9cd0a.jpg\",\n  \"205532.jpg\": \"Insecta/Largus californicus/a65f4a88dad7c791b5a9d3b21a8ef469.jpg\",\n  \"205533.jpg\": \"Insecta/Largus californicus/fadd7a2002dbbfeab16d38e1afec1153.jpg\",\n  \"205535.jpg\": \"Insecta/Largus californicus/a52555b5aabb575d5d36940a8c06653c.jpg\",\n  \"205566.jpg\": \"Aves/Zenaida macroura/9322ff68d07189a17497d785babd2da7.jpg\",\n  \"20559.jpg\": \"Reptilia/Uta stansburiana/f587365e150fce42a58d37b2acf9a26a.jpg\",\n  \"205602.jpg\": \"Aves/Zenaida macroura/a48f4eee724039591dac80719dfc2bdf.jpg\",\n  \"20570.jpg\": \"Reptilia/Uta stansburiana/53a17a0b9f70788928bdfd34b1257abf.jpg\",\n  \"205842.jpg\": \"Aves/Zenaida macroura/a4327e01be8107728ff5264d06f36f86.jpg\",\n  \"205843.jpg\": \"Aves/Zenaida macroura/b5a381a6f120a1074e9733192ff526b7.jpg\",\n  \"20587.jpg\": \"Reptilia/Uta stansburiana/d0be43512232a982b091b1c3c60c717f.jpg\",\n  \"206008.jpg\": \"Aves/Zenaida macroura/c1cdbd761d939dd2ff63583b246b00ec.jpg\",\n  \"20603.jpg\": \"Reptilia/Uta stansburiana/69f8c42ebabce5203891d4658c5a3067.jpg\",\n  \"20607.jpg\": \"Reptilia/Uta stansburiana/0eb05c3662b1e6e530dcc350ebe16aac.jpg\",\n  \"20609.jpg\": \"Reptilia/Uta stansburiana/f86b14749980c07eabbc14a5224dd419.jpg\",\n  \"20613.jpg\": \"Reptilia/Uta stansburiana/caa57ad15f3e0418231ebd75f8c01446.jpg\",\n  \"206211.jpg\": \"Aves/Zenaida macroura/d6a69a932bd9bc9441e1f40353f3fe78.jpg\",\n  \"206217.jpg\": \"Aves/Zenaida macroura/4475b1daade3d4bfce6098882956b392.jpg\",\n  \"206246.jpg\": \"Aves/Zenaida macroura/7032fad37cbd16e7dc52650cadd2968f.jpg\",\n  \"206281.jpg\": \"Aves/Zenaida macroura/dd035e8115927418699743109a7ea04a.jpg\",\n  \"20629.jpg\": \"Insecta/Hymenia perspectalis/0233c24b9ad7c480a0353d1a85ad088d.jpg\",\n  \"20630.jpg\": \"Insecta/Hymenia perspectalis/a018db5cafdfbd85ccaa2a02a72e89bc.jpg\",\n  \"206301.jpg\": \"Aves/Zenaida macroura/ff507ee635cefe8666fa547c9bf63a04.jpg\",\n  \"20631.jpg\": \"Insecta/Hymenia perspectalis/8ce66326bb442478ae2e5f867c96fd2a.jpg\",\n  \"206327.jpg\": \"Aves/Zenaida macroura/d5ebe9be96792b60fe042236f287d858.jpg\",\n  \"206337.jpg\": \"Aves/Zenaida macroura/aaf2908eedb88243a0916138dea962d6.jpg\",\n  \"206348.jpg\": \"Aves/Zenaida macroura/b218ed3fe08658d33b008d78c45834c3.jpg\",\n  \"206352.jpg\": \"Aves/Zenaida macroura/61395de6dcbc5a5f29b91bed7ce63bac.jpg\",\n  \"206365.jpg\": \"Aves/Zenaida macroura/cc371f61c53f1dc95376166b7565e032.jpg\",\n  \"20637.jpg\": \"Insecta/Hymenia perspectalis/7e673834102f329130cabad5f479653d.jpg\",\n  \"20638.jpg\": \"Insecta/Hymenia perspectalis/b9f436cd684b219944ead8bf4f5ded28.jpg\",\n  \"20639.jpg\": \"Insecta/Hymenia perspectalis/4727ff7abd5494ae4b23b0edb698b643.jpg\",\n  \"206396.jpg\": \"Aves/Zenaida macroura/48dc49aa01b652d21510906c2eba8326.jpg\",\n  \"20640.jpg\": \"Insecta/Hymenia perspectalis/6f0224cddbfc4a1e5c2f9d22c33fb181.jpg\",\n  \"20641.jpg\": \"Insecta/Hymenia perspectalis/4608b1ce89f781df99c17fc41dda0a9c.jpg\",\n  \"20644.jpg\": \"Insecta/Hymenia perspectalis/c19f61c0fa26b54c327dfd3d27b06987.jpg\",\n  \"20645.jpg\": \"Insecta/Hymenia perspectalis/9d2d3ea786aa0b8dbe3ce3f5299222b3.jpg\",\n  \"20646.jpg\": \"Insecta/Hymenia perspectalis/c3470b617c640d409258c8eef95bdc08.jpg\",\n  \"206473.jpg\": \"Aves/Zenaida macroura/dbefabd20c35d2e31c6c798f1e756c8e.jpg\",\n  \"20649.jpg\": \"Insecta/Hymenia perspectalis/b9a285c08bfa00b5e7169a0a8b158673.jpg\",\n  \"206502.jpg\": \"Aves/Zenaida macroura/5946159379eda9218ebd382b6b124a83.jpg\",\n  \"20656.jpg\": \"Insecta/Hymenia perspectalis/50578c5649df1568304a3a35e982cd37.jpg\",\n  \"20657.jpg\": \"Insecta/Hymenia perspectalis/e3060da0e72a205b26cf5da230f15e9d.jpg\",\n  \"20658.jpg\": \"Insecta/Hymenia perspectalis/c53fa23a7ec1732ea72c4a7244795aa1.jpg\",\n  \"20665.jpg\": \"Insecta/Hymenia perspectalis/f36d9a5a76cc4206a6f342f73e3e1841.jpg\",\n  \"206653.jpg\": \"Actinopterygii/Perca flavescens/a0a38635bcf63db1522c5960a995e06e.jpg\",\n  \"20666.jpg\": \"Insecta/Hymenia perspectalis/38b23e87c64a9256585e3a53cd32f2dc.jpg\",\n  \"206662.jpg\": \"Actinopterygii/Perca flavescens/e918d1e8b0d9c1e79832fceaeaf727fd.jpg\",\n  \"206665.jpg\": \"Actinopterygii/Perca flavescens/03a4ed4e3742d766312dc007fc3afe18.jpg\",\n  \"206668.jpg\": \"Actinopterygii/Perca flavescens/fffc1483f70f332817e7f73df0926b06.jpg\",\n  \"206669.jpg\": \"Actinopterygii/Perca flavescens/b8a054c27dd2c675ccc48f0d0cc346ee.jpg\",\n  \"206673.jpg\": \"Actinopterygii/Perca flavescens/1a5c5d7c839cf5cb22fa1e61499692ba.jpg\",\n  \"206674.jpg\": \"Insecta/Callosamia promethea/34e245c9ff4e9d1c210b7c223fe7f5db.jpg\",\n  \"206678.jpg\": \"Insecta/Callosamia promethea/11630a897cf4e183e8458186db7415a7.jpg\",\n  \"206691.jpg\": \"Insecta/Callosamia promethea/20c63674b98f648b637a70a9fe3cd39a.jpg\",\n  \"20670.jpg\": \"Insecta/Hymenia perspectalis/4a287d9db9c40b23cbd4bf912d707146.jpg\",\n  \"20673.jpg\": \"Insecta/Hymenia perspectalis/a4e291dc4d297597ece76d89537098c6.jpg\",\n  \"20674.jpg\": \"Insecta/Hymenia perspectalis/e4dee3b9f48c35e73a2130a8b81f17e3.jpg\",\n  \"206772.jpg\": \"Mammalia/Phocarctos hookeri/2dde2c768c5245bb9199771ba50c018e.jpg\",\n  \"206783.jpg\": \"Mammalia/Phocarctos hookeri/77631d3b9c86b60c57656488d7ac2d4b.jpg\",\n  \"206784.jpg\": \"Mammalia/Phocarctos hookeri/6fe4d786555a54ee14ef2180871f7eff.jpg\",\n  \"206797.jpg\": \"Mammalia/Phocarctos hookeri/716ac4b32e53d707fe0fc3f25598db29.jpg\",\n  \"206798.jpg\": \"Mammalia/Phocarctos hookeri/ec7a4836f91e477b7102e9e7648aef50.jpg\",\n  \"206799.jpg\": \"Mammalia/Phocarctos hookeri/7f327beb2dcc511416bdac42ebf06fc0.jpg\",\n  \"206800.jpg\": \"Mammalia/Phocarctos hookeri/05f045a6fc1f9acb6d01c732dbc26946.jpg\",\n  \"206804.jpg\": \"Mammalia/Phocarctos hookeri/755f0698b9427940ced8a85ad4d205b9.jpg\",\n  \"206807.jpg\": \"Mammalia/Phocarctos hookeri/a9bc789668d56fc3aa9231f9c8491c20.jpg\",\n  \"206808.jpg\": \"Mammalia/Phocarctos hookeri/4218f2ec5362c0abb5c28208c5f68d32.jpg\",\n  \"206817.jpg\": \"Aves/Zonotrichia leucophrys/bfb192b88662adee2be1ea022a92bee2.jpg\",\n  \"206875.jpg\": \"Aves/Zonotrichia leucophrys/f13da4fc5d8333dad7dd118d974d02e0.jpg\",\n  \"20689.jpg\": \"Mammalia/Tamias merriami/430d2b2079c5dac929cb48a42ab5b5fe.jpg\",\n  \"20690.jpg\": \"Mammalia/Tamias merriami/9d140faa2b61172b06d5f4a0b0cbd824.jpg\",\n  \"20697.jpg\": \"Mammalia/Tamias merriami/48d0613b789675fc6879293ef5355fe0.jpg\",\n  \"206973.jpg\": \"Aves/Zonotrichia leucophrys/207cdabe96174f659a93a50613d3983e.jpg\",\n  \"206987.jpg\": \"Aves/Zonotrichia leucophrys/de93be6e29919e2da977d09b395c3862.jpg\",\n  \"20701.jpg\": \"Mammalia/Tamias merriami/beefc33dc71d4786d54891fc4a11875a.jpg\",\n  \"20704.jpg\": \"Mammalia/Tamias merriami/d5762a7f2625fc67e7d3db7cee695bd5.jpg\",\n  \"207069.jpg\": \"Aves/Zonotrichia leucophrys/140f19be6c29b82ec2e2eed74fd1042f.jpg\",\n  \"20714.jpg\": \"Mammalia/Tamias merriami/9b0c21d9840f9ba0797a6bc574debc02.jpg\",\n  \"207167.jpg\": \"Aves/Zonotrichia leucophrys/84ae33a71c2bc0e08648bd1d83dab426.jpg\",\n  \"207192.jpg\": \"Aves/Zonotrichia leucophrys/8c6e069d882e3743d5c09f1041272286.jpg\",\n  \"207270.jpg\": \"Aves/Zonotrichia leucophrys/11116620010e89f8fbfa13643c9382b0.jpg\",\n  \"207294.jpg\": \"Aves/Zonotrichia leucophrys/c2d000282a1335bc833417a7f0a27529.jpg\",\n  \"207336.jpg\": \"Aves/Zonotrichia leucophrys/2c5a151437fea1bc413cf0c4dd659212.jpg\",\n  \"207350.jpg\": \"Aves/Zonotrichia leucophrys/85f630d471ac2939e834c9033191c838.jpg\",\n  \"207360.jpg\": \"Aves/Zonotrichia leucophrys/df721ddc4c70ae24d265402afe367194.jpg\",\n  \"207361.jpg\": \"Aves/Zonotrichia leucophrys/6ddf8c0b259f60b9df6ff0a5b56211a6.jpg\",\n  \"207366.jpg\": \"Aves/Zonotrichia leucophrys/b76af0b6269ff67e7812b95b8b3418d3.jpg\",\n  \"207376.jpg\": \"Aves/Zonotrichia leucophrys/8b3b7235232c65129ea2388de1127e0f.jpg\",\n  \"207446.jpg\": \"Aves/Zonotrichia leucophrys/c5968f35d6512b0d1cf5a7761dfeb707.jpg\",\n  \"207466.jpg\": \"Aves/Zonotrichia leucophrys/d1d638403e9b0ad9fb2f696c9ece371b.jpg\",\n  \"207471.jpg\": \"Aves/Zonotrichia leucophrys/36af9abadeffa07378b544bfa2a2c4e3.jpg\",\n  \"207495.jpg\": \"Aves/Zonotrichia leucophrys/7887d7f78a014e4fe945664157170c47.jpg\",\n  \"207514.jpg\": \"Aves/Zonotrichia leucophrys/c8328a71bfbedc0189db2156089dbe9e.jpg\",\n  \"207529.jpg\": \"Reptilia/Gopherus agassizii/31f3a8636cf7c50cdd64ad37c9988c23.jpg\",\n  \"207531.jpg\": \"Reptilia/Gopherus agassizii/9867100fbcaa5f68a61841704aef9892.jpg\",\n  \"207535.jpg\": \"Reptilia/Gopherus agassizii/5d7bbfa9f272b2dbc1fc27d7d8fb4003.jpg\",\n  \"207542.jpg\": \"Reptilia/Gopherus agassizii/064a082db636ad998b223932270b0a21.jpg\",\n  \"207545.jpg\": \"Reptilia/Gopherus agassizii/a52624de43f80d14ad9758d4a87eaf27.jpg\",\n  \"207546.jpg\": \"Reptilia/Gopherus agassizii/fedc56bb18b2c7eea05f4b9546bb7efb.jpg\",\n  \"207547.jpg\": \"Reptilia/Gopherus agassizii/3aaa2cfd2e6f484cfe72db8b56a5acae.jpg\",\n  \"207551.jpg\": \"Reptilia/Gopherus agassizii/695afbf4403a807adcb59dd860d0a41e.jpg\",\n  \"207552.jpg\": \"Reptilia/Gopherus agassizii/b220af877218d8a8b24ff0380754faad.jpg\",\n  \"207553.jpg\": \"Reptilia/Gopherus agassizii/0d1a0eb72290be225bfee3716cc8e001.jpg\",\n  \"207554.jpg\": \"Reptilia/Gopherus agassizii/f41c18c3e489f3579cf8e31c48513b3e.jpg\",\n  \"207556.jpg\": \"Reptilia/Gopherus agassizii/9f75f178e1a493110e7fcb61ade5eeb7.jpg\",\n  \"207560.jpg\": \"Reptilia/Gopherus agassizii/40f33fa0a3985e35c15f0886c94e95ce.jpg\",\n  \"207564.jpg\": \"Reptilia/Gopherus agassizii/37ae63811a4e7bf1c865828ce7e1d169.jpg\",\n  \"207567.jpg\": \"Reptilia/Gopherus agassizii/e96a5ab357f37cecfb263ee052d8fdc6.jpg\",\n  \"207572.jpg\": \"Reptilia/Gopherus agassizii/fdb0fe166184de7f13981ec1854407b7.jpg\",\n  \"207575.jpg\": \"Reptilia/Gopherus agassizii/810ef25c0d27c1075b6917669b6f37b0.jpg\",\n  \"207578.jpg\": \"Reptilia/Gopherus agassizii/e3fa081837ad1b0a8f805d589018245a.jpg\",\n  \"207581.jpg\": \"Reptilia/Gopherus agassizii/438365f90d1ecc02a718046d1a945f15.jpg\",\n  \"207582.jpg\": \"Reptilia/Gopherus agassizii/92cee38dbed08538c83450c59015efd7.jpg\",\n  \"207583.jpg\": \"Reptilia/Gopherus agassizii/58cc06318cac7d9327d53dcf31c33a75.jpg\",\n  \"207584.jpg\": \"Animalia/Pisaster brevispinus/ff1e036fb193a23676be7814505f874b.jpg\",\n  \"207597.jpg\": \"Animalia/Pisaster brevispinus/568a5bfaad146765e6655c9a87c8737b.jpg\",\n  \"207716.jpg\": \"Insecta/Leucorrhinia proxima/0c252cd136f4f7b09e73ec116ea8c924.jpg\",\n  \"207719.jpg\": \"Insecta/Leucorrhinia proxima/58061fb0b54ac9e67192da895ebc22b9.jpg\",\n  \"207726.jpg\": \"Insecta/Leucorrhinia proxima/6e8558ce83d281c7c415f806b0aed3b4.jpg\",\n  \"207728.jpg\": \"Insecta/Leucorrhinia proxima/7af39a722586c3651fbed68753c8368a.jpg\",\n  \"207729.jpg\": \"Insecta/Heterocampa biundata/36a184fcb71f652256d9bf7f67710961.jpg\",\n  \"207733.jpg\": \"Insecta/Heterocampa biundata/e5144f339955d8a0e8a2a8af1618618d.jpg\",\n  \"207737.jpg\": \"Insecta/Heterocampa biundata/5f46ee1a13ea97a0722eb7798cc6921d.jpg\",\n  \"207743.jpg\": \"Insecta/Heterocampa biundata/e2fd7e3de9cf70dc96b39f60e0153729.jpg\",\n  \"207749.jpg\": \"Aves/Amazilia yucatanensis/d782a43bc1bf0b305026a19ae3aa6756.jpg\",\n  \"207752.jpg\": \"Aves/Amazilia yucatanensis/59f0e20baa2dc7aa35dd8e5ee4b5caad.jpg\",\n  \"207755.jpg\": \"Aves/Amazilia yucatanensis/eae326bba9d32a01ae98da1aa13b75bd.jpg\",\n  \"207760.jpg\": \"Aves/Amazilia yucatanensis/60d4e97ad17a575313bb2263da99861c.jpg\",\n  \"207761.jpg\": \"Aves/Amazilia yucatanensis/15c661954b99046de277c45cfe52c50c.jpg\",\n  \"207769.jpg\": \"Aves/Amazilia yucatanensis/a75940bcdfd79a99b81417f49e8c1a3c.jpg\",\n  \"207770.jpg\": \"Aves/Amazilia yucatanensis/23ed3aa8651d5c0e8f3961ceaeb628a6.jpg\",\n  \"207776.jpg\": \"Aves/Amazilia yucatanensis/53eafea412a05c6b76a3b5cc966a2ed3.jpg\",\n  \"207783.jpg\": \"Aves/Amazilia yucatanensis/af7d4377c449b2e19f59dcd618463467.jpg\",\n  \"207785.jpg\": \"Aves/Amazilia yucatanensis/d9c4caf4dd3b8cd5047a56b52ef0bf20.jpg\",\n  \"207793.jpg\": \"Mammalia/Bassariscus astutus/76758c15ef1e739765cb21b4e73f9fdc.jpg\",\n  \"207796.jpg\": \"Mammalia/Bassariscus astutus/386d1fa8f6c77320a7deaa5c06cd4bab.jpg\",\n  \"207800.jpg\": \"Mammalia/Bassariscus astutus/89e726148fff34d7448aa86e2c4e09f6.jpg\",\n  \"207801.jpg\": \"Mammalia/Bassariscus astutus/c052028f88420b9a3f2b254aeae83588.jpg\",\n  \"207802.jpg\": \"Mammalia/Bassariscus astutus/f65be5256b309fe28733cb7a6ebb7704.jpg\",\n  \"207805.jpg\": \"Mammalia/Bassariscus astutus/53a115a9594dc3c47bbb011143a1e518.jpg\",\n  \"207813.jpg\": \"Mammalia/Bassariscus astutus/5264eaa99bdefce39d0cb6f94be4a0dd.jpg\",\n  \"207815.jpg\": \"Mammalia/Bassariscus astutus/53fba6af7624f4cfdf5191fde28ee266.jpg\",\n  \"207817.jpg\": \"Mammalia/Bassariscus astutus/6ca89ad9cf2bec39323b156edfa92454.jpg\",\n  \"207819.jpg\": \"Mammalia/Bassariscus astutus/5c3f24b3c768d173557d9e88b777856f.jpg\",\n  \"207821.jpg\": \"Mammalia/Bassariscus astutus/6434aa5213f6dd7a47b953fc11379e5c.jpg\",\n  \"207823.jpg\": \"Mammalia/Bassariscus astutus/95283697bd8c63f5692f6e2b4f077fc1.jpg\",\n  \"207828.jpg\": \"Mammalia/Bassariscus astutus/d27f7216c45714293bf9ac0870e8c6f0.jpg\",\n  \"207829.jpg\": \"Mammalia/Bassariscus astutus/e698c939e78cd465d1f383216c5dd8ba.jpg\",\n  \"207834.jpg\": \"Mammalia/Bassariscus astutus/ae21fc5dec6d0bb01454061a743d7f70.jpg\",\n  \"207835.jpg\": \"Mammalia/Bassariscus astutus/e870a90059704891f7e0d396d0761e4f.jpg\",\n  \"207841.jpg\": \"Aves/Aix galericulata/18302d347fcfec0928453cd727116284.jpg\",\n  \"207842.jpg\": \"Aves/Aix galericulata/f8ce3f6df7625557315059c173d84399.jpg\",\n  \"207846.jpg\": \"Aves/Aix galericulata/b3631651ddbc530842baf56558c26856.jpg\",\n  \"207856.jpg\": \"Aves/Aix galericulata/bc49378b084e95152f3089612cb0f0ca.jpg\",\n  \"207860.jpg\": \"Aves/Aix galericulata/c4c609f32a18ad31007bdec5b73a1ed0.jpg\",\n  \"207861.jpg\": \"Aves/Aix galericulata/5ca4024ad76a04e6506b2bd04a30d33d.jpg\",\n  \"207862.jpg\": \"Aves/Aix galericulata/f93827f0fbef94abe36cba78e0ac9ca6.jpg\",\n  \"207863.jpg\": \"Aves/Aix galericulata/b32c17ff31f2a624ca3d9003ab36bf13.jpg\",\n  \"207864.jpg\": \"Aves/Aix galericulata/0701decce196cea76097b596b698e945.jpg\",\n  \"207874.jpg\": \"Aves/Aix galericulata/b1bb08db5d45c629966a2d98694c46ec.jpg\",\n  \"207875.jpg\": \"Aves/Aix galericulata/6f6f03270b848cd5f054ff81f69d8886.jpg\",\n  \"207876.jpg\": \"Aves/Aix galericulata/b60b3691779e5ae9add7dc8533ce6cae.jpg\",\n  \"207877.jpg\": \"Aves/Aix galericulata/126ea4363497741e3802fbf595314aa3.jpg\",\n  \"207878.jpg\": \"Aves/Aix galericulata/224fe076ef38e0f97414611770d84a4a.jpg\",\n  \"207888.jpg\": \"Aves/Aix galericulata/e5e494d850327cc835729bf669746f89.jpg\",\n  \"207889.jpg\": \"Aves/Aix galericulata/881f412c90d1fc4fd2163fa8f53f95e6.jpg\",\n  \"207890.jpg\": \"Aves/Aix galericulata/7b5189b8450ebac4a890ec8798c025e4.jpg\",\n  \"207894.jpg\": \"Aves/Alca torda/bf438b54110f6e39b07ae2b6bc1a6d0d.jpg\",\n  \"207895.jpg\": \"Aves/Alca torda/053b03596448cf0aa00219ed4e496936.jpg\",\n  \"207898.jpg\": \"Aves/Alca torda/00a40a67212a5e850c74632fd9624c96.jpg\",\n  \"208028.jpg\": \"Reptilia/Ophisaurus attenuatus attenuatus/389f11a35f38aa4371659b1075ad0581.jpg\",\n  \"208043.jpg\": \"Reptilia/Ophisaurus attenuatus attenuatus/f5b4eb5e3fce3024e9da1702f22e7ce2.jpg\",\n  \"208047.jpg\": \"Reptilia/Ophisaurus attenuatus attenuatus/9c516468dfb613ad9c99a99292c693dc.jpg\",\n  \"208049.jpg\": \"Reptilia/Ophisaurus attenuatus attenuatus/ad9fa59f0e1abf2446b6787ea3c604ee.jpg\",\n  \"208050.jpg\": \"Aves/Patagioenas leucocephala/090fba2beb8e292915bfb30d5eb04456.jpg\",\n  \"208051.jpg\": \"Aves/Patagioenas leucocephala/a7dd05db5bcc03d77aa7a460d1ef69a1.jpg\",\n  \"208059.jpg\": \"Aves/Patagioenas leucocephala/e0a0eef12c2c8cdd71b2241679f1e284.jpg\",\n  \"208105.jpg\": \"Aves/Lanius ludovicianus/cd7956994b01de12c5ea4a13e6c0129c.jpg\",\n  \"208110.jpg\": \"Aves/Lanius ludovicianus/6498b326d3c7efc50c48ab33558282ae.jpg\",\n  \"208123.jpg\": \"Aves/Lanius ludovicianus/abdec85917b0c76e403cfbaacb037761.jpg\",\n  \"208125.jpg\": \"Aves/Lanius ludovicianus/221d690637f357d4baa5c35ab89cada7.jpg\",\n  \"208126.jpg\": \"Aves/Lanius ludovicianus/13af1fd5b312901671a63cc722c5651b.jpg\",\n  \"208131.jpg\": \"Aves/Lanius ludovicianus/1319b2284a1a6beffaa5fcec6bab63f8.jpg\",\n  \"208133.jpg\": \"Aves/Lanius ludovicianus/01bda95e91ac412413d24777c4f87458.jpg\",\n  \"208137.jpg\": \"Aves/Lanius ludovicianus/14a6040fe0e1c166a96a5fbe66060d52.jpg\",\n  \"208146.jpg\": \"Aves/Lanius ludovicianus/88f24c17dc898171c07aa21a351e7369.jpg\",\n  \"208149.jpg\": \"Aves/Lanius ludovicianus/1ed076c217d2ffa9453c8dd184cb4c1e.jpg\",\n  \"208174.jpg\": \"Aves/Lanius ludovicianus/a24d6a7790296038f1d0f202814b5d47.jpg\",\n  \"208176.jpg\": \"Aves/Lanius ludovicianus/77b7340b27dc1ee853750e36ff8ff9b1.jpg\",\n  \"208199.jpg\": \"Aves/Lanius ludovicianus/8d6ebb710e31e866227214af4e7b6ece.jpg\",\n  \"208205.jpg\": \"Aves/Lanius ludovicianus/f1aad012d5adae8966ed674a7e1da3cf.jpg\",\n  \"208211.jpg\": \"Aves/Lanius ludovicianus/fc5993f7f589277f22b22caf74c479e6.jpg\",\n  \"208223.jpg\": \"Aves/Lanius ludovicianus/347d12d973a29b85899aaea2787140b9.jpg\",\n  \"208254.jpg\": \"Aves/Lanius ludovicianus/297a32408fc7f9c990a4ff8f79358e46.jpg\",\n  \"208265.jpg\": \"Aves/Lanius ludovicianus/309399a0adc069b751e2dc897ebe2dd5.jpg\",\n  \"208301.jpg\": \"Aves/Lanius ludovicianus/abb3366702000e3c4314da53d831cc2f.jpg\",\n  \"208316.jpg\": \"Aves/Lanius ludovicianus/a98beb320d5617505e95f6284296ca62.jpg\",\n  \"208318.jpg\": \"Aves/Lanius ludovicianus/b3991b92aec791b91d564bc2e4a203e4.jpg\",\n  \"208320.jpg\": \"Aves/Lanius ludovicianus/6be9fcedde2c3ce59e697288947e058b.jpg\",\n  \"208333.jpg\": \"Aves/Lanius ludovicianus/826b5b8a277d333158a0c4970eda5ff3.jpg\",\n  \"208350.jpg\": \"Aves/Lanius ludovicianus/7aacfb7fd8f637ac1b7912456b8fc0d2.jpg\",\n  \"208367.jpg\": \"Mammalia/Ovis canadensis canadensis/8e4729ca2f5b6bbeac385d95cc729298.jpg\",\n  \"208375.jpg\": \"Mammalia/Ovis canadensis canadensis/566df73aa5e22f4ee869ca09baf3c922.jpg\",\n  \"208376.jpg\": \"Mammalia/Ovis canadensis canadensis/86a28cfde50a4b7f3d4521198ba9c5a5.jpg\",\n  \"208386.jpg\": \"Insecta/Anagrapha falcifera/38af7b556124d8abc4c697a21bffb8d4.jpg\",\n  \"208390.jpg\": \"Insecta/Anagrapha falcifera/d03c5f8594f89bf7d2b9cf48d9e60ae8.jpg\",\n  \"208395.jpg\": \"Insecta/Anagrapha falcifera/362344083f37b32408c91147472f6be5.jpg\",\n  \"208396.jpg\": \"Insecta/Anagrapha falcifera/510f52820495842fd169571a33d4b29d.jpg\",\n  \"208401.jpg\": \"Insecta/Anagrapha falcifera/ecb98bdd28383301df12e4f28a755fea.jpg\",\n  \"208402.jpg\": \"Aves/Gallinula tenebrosa/e0f1062e610f16717fd85b83e72cd468.jpg\",\n  \"208407.jpg\": \"Aves/Gallinula tenebrosa/8d0f96756e3e23acb8e4c9b49335d5e4.jpg\",\n  \"208414.jpg\": \"Aves/Gallinula tenebrosa/2663111355c0ecad84c00f41635d33f1.jpg\",\n  \"208420.jpg\": \"Aves/Gallinula tenebrosa/f331f616566b39fd027163de8531c152.jpg\",\n  \"208425.jpg\": \"Aves/Gallinula tenebrosa/733157de606e025f06c6c08a7291b945.jpg\",\n  \"208434.jpg\": \"Aves/Gallinula tenebrosa/94bad0e2a6152634b8310ed4c35fb1f1.jpg\",\n  \"208435.jpg\": \"Aves/Gallinula tenebrosa/4a3dfc92592196db6eca9254fa9104d6.jpg\",\n  \"20844.jpg\": \"Mammalia/Herpestes javanicus/4dba6bef3614587a46fcc4c9d1d0b763.jpg\",\n  \"208455.jpg\": \"Insecta/Bagrada hilaris/e04f528b95460aef8bc49527bdd83f31.jpg\",\n  \"20846.jpg\": \"Mammalia/Herpestes javanicus/4213d021759e63b7a71d755d346d0bd2.jpg\",\n  \"208462.jpg\": \"Insecta/Bagrada hilaris/d885d8214fa224e2c96cdf785be72db3.jpg\",\n  \"208466.jpg\": \"Insecta/Bagrada hilaris/17600c3a1597d47746be1e8f536d14ae.jpg\",\n  \"208469.jpg\": \"Insecta/Bagrada hilaris/cd215e0989909f2dcb3af64198224295.jpg\",\n  \"208477.jpg\": \"Insecta/Bagrada hilaris/8caf0d44dc231e3afc33974ed091f17b.jpg\",\n  \"208479.jpg\": \"Insecta/Bagrada hilaris/ac9fa9387a5d85747c42c9a2a6a726ee.jpg\",\n  \"208480.jpg\": \"Insecta/Bagrada hilaris/adde7ce4e5a0192606c4c4d3155063d0.jpg\",\n  \"208482.jpg\": \"Insecta/Bagrada hilaris/4d291d3e9867ba186c3e13de175323cc.jpg\",\n  \"208484.jpg\": \"Insecta/Bagrada hilaris/840e0ed1de7852465bc94760cd3b7860.jpg\",\n  \"208488.jpg\": \"Insecta/Bagrada hilaris/3d7ecf93705ba66ae53d177c79f29cc0.jpg\",\n  \"208489.jpg\": \"Insecta/Bagrada hilaris/dc8d14f2033d280bb279be4d8c536d6b.jpg\",\n  \"208490.jpg\": \"Insecta/Bagrada hilaris/63b361927924512dbebc558368b8e143.jpg\",\n  \"208498.jpg\": \"Aves/Ammodramus maritimus/2947d09c5fc6a36d690d73af5bc15938.jpg\",\n  \"20850.jpg\": \"Mammalia/Herpestes javanicus/d300b0041cd36254583465460a93189d.jpg\",\n  \"208501.jpg\": \"Aves/Ammodramus maritimus/4c58c51c947b7e10cf72e11e881be585.jpg\",\n  \"208505.jpg\": \"Aves/Ammodramus maritimus/b71b0bc54b58c3656be9af4a9dffd948.jpg\",\n  \"208506.jpg\": \"Aves/Ammodramus maritimus/b0cb810c63242b47700f14518701ad62.jpg\",\n  \"208513.jpg\": \"Aves/Ammodramus maritimus/e8058921a679446e6c03b532d953a564.jpg\",\n  \"208517.jpg\": \"Mammalia/Ictidomys parvidens/28f2fccf89a4353a2777ef40275a893e.jpg\",\n  \"208518.jpg\": \"Mammalia/Ictidomys parvidens/702b378fb3e7c4b051e2727bb0cca25f.jpg\",\n  \"208522.jpg\": \"Mammalia/Ictidomys parvidens/c6ddbc20721828196a6e49a5dba33fe3.jpg\",\n  \"208523.jpg\": \"Mammalia/Ictidomys parvidens/b5c9e0859b49768ae6ecdf91b11d9212.jpg\",\n  \"208524.jpg\": \"Mammalia/Ictidomys parvidens/4a3a9503b32fa708c4141505dead17dc.jpg\",\n  \"20853.jpg\": \"Mammalia/Herpestes javanicus/63b0cf770f6f59d19bf9904ecf516b57.jpg\",\n  \"208530.jpg\": \"Mammalia/Ictidomys parvidens/d0857cecc09b660ad2496c678211a647.jpg\",\n  \"20854.jpg\": \"Mammalia/Herpestes javanicus/c000438a5eba584a4c3ed3597cccda2d.jpg\",\n  \"20858.jpg\": \"Mammalia/Herpestes javanicus/bd751c8d213c6ae09e2d37cda8e4acfd.jpg\",\n  \"208581.jpg\": \"Mammalia/Alces americanus/99c4e6dd3dabcb22174c193604babd4e.jpg\",\n  \"20859.jpg\": \"Aves/Cistothorus palustris/306feb03e2cd6ea6d2fdc9a58df08f4b.jpg\",\n  \"208599.jpg\": \"Mammalia/Alces americanus/ef9313322e1819d855853563a805a355.jpg\",\n  \"208600.jpg\": \"Mammalia/Alces americanus/26389f7143a63ee3f7b16b1c7cf924e6.jpg\",\n  \"208603.jpg\": \"Mammalia/Alces americanus/c2a55c4047ce226ce3b47317c5c92e28.jpg\",\n  \"20861.jpg\": \"Aves/Cistothorus palustris/456a6d2c6e77e719ef6a6f123b0ee438.jpg\",\n  \"208643.jpg\": \"Mammalia/Alces americanus/9cbe3222148068d149786b39b23e59fa.jpg\",\n  \"20867.jpg\": \"Aves/Cistothorus palustris/ab641512cb6ac1927a731a482bc21191.jpg\",\n  \"20872.jpg\": \"Aves/Cistothorus palustris/b49f8afd831ce43904d6c6af1f483876.jpg\",\n  \"208738.jpg\": \"Mammalia/Alces americanus/1485988fdaac721fccc22279d42ecf91.jpg\",\n  \"208745.jpg\": \"Mammalia/Alces americanus/3385ccae0421f36b2599d4b6e53f1a2e.jpg\",\n  \"208754.jpg\": \"Mammalia/Alces americanus/5bbb4eb866ce7d34d7aee1efc54f22c2.jpg\",\n  \"208761.jpg\": \"Mammalia/Alces americanus/29441471179b7c44ec1c4a7564503b72.jpg\",\n  \"208767.jpg\": \"Mammalia/Alces americanus/c29d0b652f499135d6612d30aed75b98.jpg\",\n  \"20880.jpg\": \"Aves/Cistothorus palustris/f9e528b7404be1d75695d192b138eda7.jpg\",\n  \"208823.jpg\": \"Aves/Stercorarius parasiticus/473fc536a970e0a9bbd33c4b07095ed3.jpg\",\n  \"208824.jpg\": \"Aves/Stercorarius parasiticus/c7545603ac42d6d25c15015e338523c1.jpg\",\n  \"208829.jpg\": \"Aves/Stercorarius parasiticus/168e49f4f50107ba92ea095abe5eed08.jpg\",\n  \"208830.jpg\": \"Aves/Stercorarius parasiticus/ccd77b25da55923757eb611001db50c5.jpg\",\n  \"20884.jpg\": \"Aves/Cistothorus palustris/16d9bce6b5ef25001820493b6e11fbd7.jpg\",\n  \"20891.jpg\": \"Aves/Cistothorus palustris/055839bf04f13690f6c0086899cec0bc.jpg\",\n  \"208926.jpg\": \"Insecta/Erythrodiplax minuscula/d2e07d638bf5b3dbe0e5973d2f149695.jpg\",\n  \"208927.jpg\": \"Insecta/Erythrodiplax minuscula/36b0af8526eef0b544f60ffb28214091.jpg\",\n  \"208933.jpg\": \"Insecta/Erythrodiplax minuscula/d4504e1d0de036574db9175ae59544f1.jpg\",\n  \"208935.jpg\": \"Insecta/Erythrodiplax minuscula/2ddf5955c4459e3810172736c0354f9e.jpg\",\n  \"208936.jpg\": \"Insecta/Erythrodiplax minuscula/3223bd94f52e72cadb6a0a61f18a37b1.jpg\",\n  \"208949.jpg\": \"Insecta/Erythrodiplax minuscula/2c7c70e5870de4ccefbee8dbb561214c.jpg\",\n  \"208950.jpg\": \"Insecta/Erythrodiplax minuscula/4cf3c52b0346ef9a8316c897d38f7ebc.jpg\",\n  \"208954.jpg\": \"Aves/Eugenes fulgens/e7f5a33524c71de7c832c91b416ddce6.jpg\",\n  \"208955.jpg\": \"Aves/Eugenes fulgens/3b8c1281759a94d17609ef4cd736b185.jpg\",\n  \"208966.jpg\": \"Aves/Eugenes fulgens/7fba9e7aa72273edc5f72f9663ad932b.jpg\",\n  \"208969.jpg\": \"Aves/Eugenes fulgens/e2eb265aa2d88973ae7ae07ca3610955.jpg\",\n  \"208970.jpg\": \"Aves/Eugenes fulgens/537c76026dbe9e8b0b8ad2aed8981ef0.jpg\",\n  \"208972.jpg\": \"Aves/Eugenes fulgens/9a5a9c7880c01867545abf54aa12f439.jpg\",\n  \"208985.jpg\": \"Aves/Eugenes fulgens/33cc48082907fcf29f6577ac1fa31410.jpg\",\n  \"2090.jpg\": \"Aves/Loxia curvirostra/7c5f26fa303a9e4860b83abb0adfce96.jpg\",\n  \"209019.jpg\": \"Reptilia/Crotalus oreganus helleri/a79d318010a750fa8ce1234f9c3b1b1b.jpg\",\n  \"20902.jpg\": \"Aves/Cistothorus palustris/ba82e8f6dd87374e744e50ca1727973f.jpg\",\n  \"209024.jpg\": \"Reptilia/Crotalus oreganus helleri/77dc7a2fea3be07a414f7d850f360f7a.jpg\",\n  \"209029.jpg\": \"Reptilia/Crotalus oreganus helleri/1247f259b821992b1f9d102827eda165.jpg\",\n  \"209038.jpg\": \"Reptilia/Crotalus oreganus helleri/4691d625d5a25bff01ef9f4abbb6ba83.jpg\",\n  \"209046.jpg\": \"Reptilia/Crotalus oreganus helleri/dcb4ef49532380710c9fac1f19046997.jpg\",\n  \"209057.jpg\": \"Reptilia/Crotalus oreganus helleri/155428ebf9a6ec12bc35ad624445a11b.jpg\",\n  \"209058.jpg\": \"Reptilia/Crotalus oreganus helleri/46978a0ad79b717576166e02555136f7.jpg\",\n  \"20907.jpg\": \"Aves/Cistothorus palustris/225cd4a1b99066f307ffa47b406333f7.jpg\",\n  \"209071.jpg\": \"Reptilia/Crotalus oreganus helleri/f366fcc2489296e1e277b443daa60f0a.jpg\",\n  \"20908.jpg\": \"Aves/Cistothorus palustris/cb2cbdd726195253382a41e0178f2b09.jpg\",\n  \"209083.jpg\": \"Reptilia/Crotalus oreganus helleri/407b4557765cee91c1d55f227c8226d6.jpg\",\n  \"20909.jpg\": \"Aves/Cistothorus palustris/a2c7fdd5a9188c7d63106f09ec09b716.jpg\",\n  \"209096.jpg\": \"Reptilia/Crotalus oreganus helleri/afb4737c0736ee00d0dada220583d593.jpg\",\n  \"209105.jpg\": \"Reptilia/Crotalus oreganus helleri/9ef8966a397b7e6ec31677a1f0018422.jpg\",\n  \"209112.jpg\": \"Reptilia/Crotalus oreganus helleri/3d992880c5548c173ef6f5fecd99ed9d.jpg\",\n  \"209120.jpg\": \"Reptilia/Crotalus oreganus helleri/50df5da3013a1993466c995c98124764.jpg\",\n  \"209126.jpg\": \"Reptilia/Crotalus oreganus helleri/3b865dfe3263c8ac7394787da258a74d.jpg\",\n  \"209146.jpg\": \"Reptilia/Crotalus oreganus helleri/0184ba22f5199b9721b0cfa598338426.jpg\",\n  \"20916.jpg\": \"Aves/Cistothorus palustris/e0d47d35eb56277c99db1b2ad2ec8c59.jpg\",\n  \"209162.jpg\": \"Reptilia/Crotalus oreganus helleri/8521c853a27409d22d68a2a5cebce186.jpg\",\n  \"209169.jpg\": \"Reptilia/Crotalus oreganus helleri/0cf42778f46111cccea48a75cd81d8fc.jpg\",\n  \"20917.jpg\": \"Aves/Cistothorus palustris/cf6b707cda3a9e0374a3604ade960a07.jpg\",\n  \"209189.jpg\": \"Reptilia/Crotalus oreganus helleri/791f7e851b7331b850b2abd5a22d9669.jpg\",\n  \"209191.jpg\": \"Reptilia/Crotalus oreganus helleri/650c77b7001946b17238f5e1ef31e205.jpg\",\n  \"209197.jpg\": \"Reptilia/Crotalus oreganus helleri/1f2ccd0da1d721e857ccf481570b3f83.jpg\",\n  \"209198.jpg\": \"Reptilia/Crotalus oreganus helleri/899a64670e5185d048801fe28ed81d66.jpg\",\n  \"2092.jpg\": \"Aves/Loxia curvirostra/51d4fdc81bdff3625ed033c3a6ff286e.jpg\",\n  \"209211.jpg\": \"Amphibia/Lithobates catesbeianus/edf3cbaad1444139bef2f1334456f960.jpg\",\n  \"209215.jpg\": \"Amphibia/Lithobates catesbeianus/48fda8f14454d2f002a592c13ad8f67d.jpg\",\n  \"20922.jpg\": \"Aves/Cistothorus palustris/e3ca32c5251695ce6947cd73ff6f0e07.jpg\",\n  \"209268.jpg\": \"Amphibia/Lithobates catesbeianus/d83742d02d8c3a440ecddd59b8c39e57.jpg\",\n  \"20927.jpg\": \"Aves/Cistothorus palustris/11e920de090715fbe052e582e9491662.jpg\",\n  \"20928.jpg\": \"Aves/Cistothorus palustris/a6c253f570cc8b597704573554eb50d6.jpg\",\n  \"20929.jpg\": \"Aves/Cistothorus palustris/a2e43215fdb7dccd761459f1b321d4d5.jpg\",\n  \"2093.jpg\": \"Aves/Loxia curvirostra/afa77f956dc420ef013f92cdeae7f683.jpg\",\n  \"209357.jpg\": \"Amphibia/Lithobates catesbeianus/347e1dff1285f654af6c43b1206c2472.jpg\",\n  \"209358.jpg\": \"Amphibia/Lithobates catesbeianus/c009dc084aa2dc92ed3b56f6733340eb.jpg\",\n  \"209375.jpg\": \"Amphibia/Lithobates catesbeianus/1c4bb59766765d605fc8bd8e1f2e15ee.jpg\",\n  \"20938.jpg\": \"Aves/Cistothorus palustris/eddda1bf4867d6082a61e3cbb281cc29.jpg\",\n  \"209401.jpg\": \"Amphibia/Lithobates catesbeianus/7d227c0589bf88a0bac79cfa736a5d94.jpg\",\n  \"209439.jpg\": \"Amphibia/Lithobates catesbeianus/ce355e7114875ac733ed19aa8c036def.jpg\",\n  \"20945.jpg\": \"Aves/Cistothorus palustris/68488c6dd7da31733ca1a21e12553f1a.jpg\",\n  \"209470.jpg\": \"Amphibia/Lithobates catesbeianus/0a5825a9023f0bf3166f65dcaec7a5d6.jpg\",\n  \"209500.jpg\": \"Amphibia/Lithobates catesbeianus/99548288329e681569b80194459036d7.jpg\",\n  \"209508.jpg\": \"Amphibia/Lithobates catesbeianus/35efacbdd50145e8e2f8dbd6575d6281.jpg\",\n  \"20951.jpg\": \"Aves/Cistothorus palustris/9497ab907f9de3eee7899e39b9f5b553.jpg\",\n  \"209563.jpg\": \"Amphibia/Lithobates catesbeianus/a7859b40aaa709bcf0b49d331dc0c888.jpg\",\n  \"209582.jpg\": \"Amphibia/Lithobates catesbeianus/296e988b76b113efb956dc464c21d52d.jpg\",\n  \"209606.jpg\": \"Amphibia/Lithobates catesbeianus/fe3dcbfeddb00d2c06d8642435524728.jpg\",\n  \"209610.jpg\": \"Amphibia/Lithobates catesbeianus/3364d0a6e399f9f650575fab1ff497f8.jpg\",\n  \"209664.jpg\": \"Amphibia/Lithobates catesbeianus/d68392dadeb103c15038acf1d7772f0f.jpg\",\n  \"209666.jpg\": \"Amphibia/Lithobates catesbeianus/abdaa32271a0464ac8eaeb8072fcb323.jpg\",\n  \"20971.jpg\": \"Aves/Cistothorus palustris/bd3d3396e595539993caa6907faae1a4.jpg\",\n  \"20975.jpg\": \"Aves/Cistothorus palustris/cda021f23b5f63fd500c2fa3875ebfa1.jpg\",\n  \"209769.jpg\": \"Amphibia/Lithobates catesbeianus/c5a7bff34236507d7d6f56f1a3547308.jpg\",\n  \"209815.jpg\": \"Amphibia/Lithobates catesbeianus/031953d8e434dab5a0dfb100d0bb3763.jpg\",\n  \"209831.jpg\": \"Amphibia/Lithobates catesbeianus/90323aec0ae0e95ad69fe7a397c296ad.jpg\",\n  \"20985.jpg\": \"Reptilia/Terrapene ornata ornata/21e0f33e418e7b301adb2760249cfabb.jpg\",\n  \"209853.jpg\": \"Amphibia/Lithobates catesbeianus/86e8baf74fc654d249999328508d4a0e.jpg\",\n  \"209854.jpg\": \"Insecta/Ellychnia corrusca/3803d8b51249a7b38fb15f3af5ae91f4.jpg\",\n  \"209855.jpg\": \"Insecta/Ellychnia corrusca/ceabee353dd28917c9d3b707c28dae2b.jpg\",\n  \"209863.jpg\": \"Insecta/Ellychnia corrusca/817fb97cfb1225f05e20bfb9e6f9d8a9.jpg\",\n  \"209865.jpg\": \"Insecta/Ellychnia corrusca/796a0b484863cbfd4061277d7b8ad1da.jpg\",\n  \"209866.jpg\": \"Insecta/Ellychnia corrusca/a58795adba8f6bd83bd8dcda06e8d478.jpg\",\n  \"209867.jpg\": \"Insecta/Ellychnia corrusca/e6b32339c6a51d35816b3522bc451c4c.jpg\",\n  \"209871.jpg\": \"Insecta/Ellychnia corrusca/126d1af70f31e56afc6e40b56e4542f0.jpg\",\n  \"209872.jpg\": \"Insecta/Ellychnia corrusca/8f5b33f9fcdba6aebcfbd56eba785fdf.jpg\",\n  \"209878.jpg\": \"Insecta/Ellychnia corrusca/8bdf3a791f1fa814fd7a76a51d6313c6.jpg\",\n  \"209883.jpg\": \"Aves/Turdus rufopalliatus/b26245474f37ec531a0dd058428c35f7.jpg\",\n  \"209884.jpg\": \"Aves/Turdus rufopalliatus/32098c9bd4af17248b044d913edfb401.jpg\",\n  \"209885.jpg\": \"Aves/Turdus rufopalliatus/c0beed7251721e51f4480ba8ae7f4f2d.jpg\",\n  \"209897.jpg\": \"Aves/Turdus rufopalliatus/66364b6092ae3a0ffa19970cbdd03b57.jpg\",\n  \"209899.jpg\": \"Aves/Turdus rufopalliatus/beb2374230a8cce060e1dfcf8f3c6568.jpg\",\n  \"2099.jpg\": \"Aves/Loxia curvirostra/386543857def62de8c9610266e6bfb9e.jpg\",\n  \"20990.jpg\": \"Reptilia/Terrapene ornata ornata/10d7284017d799561d5a7cda1dbdd523.jpg\",\n  \"209902.jpg\": \"Aves/Turdus rufopalliatus/a902248abd8cf0a6dd53148a22eb7dcd.jpg\",\n  \"209903.jpg\": \"Aves/Turdus rufopalliatus/233105161c42b5aedf2010398e795849.jpg\",\n  \"209905.jpg\": \"Aves/Turdus rufopalliatus/e409ee9758d266c7efd2ab628b0112ad.jpg\",\n  \"209907.jpg\": \"Aves/Turdus rufopalliatus/2ac1f2bf2104c116e4dd59b2dedcfa42.jpg\",\n  \"209908.jpg\": \"Aves/Turdus rufopalliatus/3488e15476b0f5f48a97917edb0e1742.jpg\",\n  \"209909.jpg\": \"Aves/Turdus rufopalliatus/4facbaa76afd5931eb4057d260043801.jpg\",\n  \"209926.jpg\": \"Aves/Turdus rufopalliatus/0eb357a24e5151d4721765e4ce38fa78.jpg\",\n  \"209927.jpg\": \"Aves/Turdus rufopalliatus/b185b7c3879f178e730e38e419424a37.jpg\",\n  \"209929.jpg\": \"Aves/Turdus rufopalliatus/4a026b783e283f4d5aa758c0e693cc02.jpg\",\n  \"20993.jpg\": \"Reptilia/Terrapene ornata ornata/3a91d039ce150a67a6c3e664a7ba4060.jpg\",\n  \"209932.jpg\": \"Aves/Turdus rufopalliatus/00eb9f0c0040de4bc3c2538b5e1ad426.jpg\",\n  \"209934.jpg\": \"Aves/Turdus rufopalliatus/d5b97e656ab3a186647111671d3adb90.jpg\",\n  \"209940.jpg\": \"Aves/Turdus rufopalliatus/488a2aed07b2f556cad34a4e0686803c.jpg\",\n  \"209941.jpg\": \"Aves/Turdus rufopalliatus/f447e1730292a24890414a5a387029ae.jpg\",\n  \"209950.jpg\": \"Aves/Turdus rufopalliatus/cd31782dae37b834f3446c734bbe0d0f.jpg\",\n  \"209959.jpg\": \"Aves/Turdus rufopalliatus/5ab18b1899f76bbc46e3fba44c535194.jpg\",\n  \"209962.jpg\": \"Aves/Turdus rufopalliatus/b4d706734ce26755f2456263b9e6b3ea.jpg\",\n  \"209972.jpg\": \"Aves/Turdus rufopalliatus/57317f76d73d4a9621f5ed71153158f3.jpg\",\n  \"209978.jpg\": \"Aves/Copsychus saularis/dafe4d4e05197905acf35e5217195ed7.jpg\",\n  \"209979.jpg\": \"Aves/Copsychus saularis/f1f8a2e857081b5767f09b8423181087.jpg\",\n  \"209993.jpg\": \"Aves/Copsychus saularis/9c2c924af5c967d8a3a68618a842490c.jpg\",\n  \"209996.jpg\": \"Aves/Copsychus saularis/e8106f5fc735fdf2b208c25c0648083d.jpg\",\n  \"209997.jpg\": \"Aves/Copsychus saularis/d4853ac4fed879837207e0664049d5b4.jpg\",\n  \"209999.jpg\": \"Aves/Copsychus saularis/02543497164cd45c5de2872772f3c382.jpg\",\n  \"21.jpg\": \"Mammalia/Marmota flaviventris/b2692ce861115c0405b162181cfe578a.jpg\",\n  \"210097.jpg\": \"Aves/Ardeola ralloides/2fce87358a4618db8f6e9e2195cd5356.jpg\",\n  \"2101.jpg\": \"Aves/Loxia curvirostra/e010a85a2085e2071c83ec39f6b0b0fd.jpg\",\n  \"210110.jpg\": \"Aves/Ardeola ralloides/b0e273af6e632e462b24c52dfe392136.jpg\",\n  \"210111.jpg\": \"Aves/Ardeola ralloides/90d11164270993b3b17edeeb7639b7e1.jpg\",\n  \"210115.jpg\": \"Aves/Ardeola ralloides/fc81ec1267f14d1efae72a89b1a18a74.jpg\",\n  \"210122.jpg\": \"Aves/Ardeola ralloides/cce0c0fa94ee1c255b3f4af8b1859168.jpg\",\n  \"210127.jpg\": \"Aves/Ardeola ralloides/b3944188497cba62dc1fbe46bb77139c.jpg\",\n  \"210128.jpg\": \"Aves/Ardeola ralloides/42121bb7c10193dfac720f6227bf5ee7.jpg\",\n  \"210129.jpg\": \"Aves/Ardeola ralloides/2748efdb9fe8b1dbbd9d816b307cedcf.jpg\",\n  \"210130.jpg\": \"Animalia/Pycnopodia helianthoides/76d8309f33ca0d45f7a30c7ef7854b98.jpg\",\n  \"210140.jpg\": \"Animalia/Pycnopodia helianthoides/5a9d06058ff97c91e2e6e36fedb41954.jpg\",\n  \"210146.jpg\": \"Animalia/Pycnopodia helianthoides/82b979304a375e4bee392fa12799c107.jpg\",\n  \"210148.jpg\": \"Animalia/Pycnopodia helianthoides/6052c60021610f1a1959ba5f8ecd261c.jpg\",\n  \"210150.jpg\": \"Animalia/Pycnopodia helianthoides/114b27e7451cc0fdb1d40796246707de.jpg\",\n  \"210160.jpg\": \"Animalia/Pycnopodia helianthoides/60ef5ba5d68a7e6c0af26b844ed0300a.jpg\",\n  \"210189.jpg\": \"Amphibia/Lithobates sphenocephalus/90b5eb1b087f70de477d8d1c14893084.jpg\",\n  \"210194.jpg\": \"Amphibia/Lithobates sphenocephalus/8052137c6e018b4dd36cd8caa30e74a6.jpg\",\n  \"210199.jpg\": \"Amphibia/Lithobates sphenocephalus/ce1e374abdc31b391df73bd9979f71d5.jpg\",\n  \"2102.jpg\": \"Aves/Loxia curvirostra/ebebe8f6094c3439d1bec90f4eb9ac14.jpg\",\n  \"210200.jpg\": \"Amphibia/Lithobates sphenocephalus/f89dac4a605d930c96b8982684ff4a25.jpg\",\n  \"210201.jpg\": \"Amphibia/Lithobates sphenocephalus/214ef55ea68e27e3711493b4d810ceef.jpg\",\n  \"210211.jpg\": \"Amphibia/Lithobates sphenocephalus/cc07e0c756f6ebf4f95a150de7e6e8dc.jpg\",\n  \"210220.jpg\": \"Amphibia/Lithobates sphenocephalus/84a6ef45894b367cdd60e1dec8509fa8.jpg\",\n  \"210246.jpg\": \"Amphibia/Lithobates sphenocephalus/42807349ddfea481b7ecc3fd57f2cc54.jpg\",\n  \"210254.jpg\": \"Amphibia/Lithobates sphenocephalus/035f32ac49ce3a5a8e2b8d8aa5e52045.jpg\",\n  \"210271.jpg\": \"Amphibia/Lithobates sphenocephalus/5f8fe3dc7bdb55c44dd2703bb3abd7bf.jpg\",\n  \"210277.jpg\": \"Amphibia/Lithobates sphenocephalus/34547b30ac49cd2da6e004a3d58fb522.jpg\",\n  \"210279.jpg\": \"Amphibia/Lithobates sphenocephalus/ba053faf0d8d8bfd1b09cdd791ec0069.jpg\",\n  \"210287.jpg\": \"Amphibia/Lithobates sphenocephalus/bc75d97bbd5b4831995bed04b73e61ef.jpg\",\n  \"210299.jpg\": \"Amphibia/Lithobates sphenocephalus/20306a77dcd9771f36185130380df847.jpg\",\n  \"210304.jpg\": \"Amphibia/Lithobates sphenocephalus/e8c789d0728e3c0fcdb31eff3ce689e2.jpg\",\n  \"210308.jpg\": \"Amphibia/Lithobates sphenocephalus/de753888f1962421906bbbe24ce399e9.jpg\",\n  \"210310.jpg\": \"Amphibia/Lithobates sphenocephalus/8974af073caf10a3a3d56af3af688fa0.jpg\",\n  \"210351.jpg\": \"Amphibia/Lithobates sphenocephalus/9e3cd282019879096aa1b830d30dce72.jpg\",\n  \"210353.jpg\": \"Amphibia/Lithobates sphenocephalus/8c5b047aef2f5cde9c44cce705f04af1.jpg\",\n  \"210366.jpg\": \"Insecta/Pseudoleon superbus/48b1956c664e0c83b5abf7a6bf5b12cc.jpg\",\n  \"210370.jpg\": \"Insecta/Pseudoleon superbus/7ef0dc598c3235e2793b119c539cfbd4.jpg\",\n  \"210380.jpg\": \"Insecta/Pseudoleon superbus/201b9129b5537e21b49fdb1b018ed197.jpg\",\n  \"210381.jpg\": \"Insecta/Pseudoleon superbus/517370be7b02f11f7b02052162f1217b.jpg\",\n  \"210382.jpg\": \"Insecta/Pseudoleon superbus/0892ae5b2018393dd2541aa59697706a.jpg\",\n  \"210383.jpg\": \"Insecta/Pseudoleon superbus/446ea74590ca3055da99c769138a81a2.jpg\",\n  \"210389.jpg\": \"Insecta/Pseudoleon superbus/9c9013e56dfd61374d40644b10f2594f.jpg\",\n  \"210393.jpg\": \"Insecta/Pseudoleon superbus/cb305a434e3e81694d8802a195fb68b5.jpg\",\n  \"210399.jpg\": \"Insecta/Pseudoleon superbus/a0d034751d56c11c5504ab1b577b0b73.jpg\",\n  \"210402.jpg\": \"Mollusca/Arion ater/16e63406822c16fcd2e042f130bbf9f4.jpg\",\n  \"210403.jpg\": \"Mollusca/Arion ater/8b12658bfd4ea4f82efedc4d58506398.jpg\",\n  \"210404.jpg\": \"Mollusca/Arion ater/452ee075d5916c4e1f95fa1abe58a771.jpg\",\n  \"210405.jpg\": \"Mollusca/Arion ater/a1dd0a0965d646b06e316b13f090afb7.jpg\",\n  \"210406.jpg\": \"Mollusca/Arion ater/f6a97be5c0dc60e3d96e89ba587d9215.jpg\",\n  \"210411.jpg\": \"Mollusca/Arion ater/8b7644f9a53c5adfe00f4784b697fcf5.jpg\",\n  \"210412.jpg\": \"Mollusca/Arion ater/d723210ad497ebb1e487dfad46017150.jpg\",\n  \"210414.jpg\": \"Mollusca/Arion ater/ce0c9322335e575cf3876d930b651cf1.jpg\",\n  \"210415.jpg\": \"Mollusca/Arion ater/97d4d83171d5259b64a6a207468868e0.jpg\",\n  \"210416.jpg\": \"Mollusca/Arion ater/8541badea5e095c12a0253b0d6bd0738.jpg\",\n  \"210417.jpg\": \"Mollusca/Arion ater/43e8414dff29b4428f4c2802e38bf7b6.jpg\",\n  \"210418.jpg\": \"Mollusca/Arion ater/643f48ee3b10beceefa46083f62b185e.jpg\",\n  \"210419.jpg\": \"Mollusca/Arion ater/c20a420b92d405ac4da05c4f46e698f4.jpg\",\n  \"210420.jpg\": \"Mollusca/Arion ater/0dc491c0ca704280c7d4ad0bf217615d.jpg\",\n  \"210431.jpg\": \"Mollusca/Arion ater/fbae4f9928c6aabc90675d7d0b1b58b1.jpg\",\n  \"210440.jpg\": \"Mollusca/Arion ater/4512c62f96fe824e4bf14e5993a9228a.jpg\",\n  \"210443.jpg\": \"Mollusca/Arion ater/13f4c69458928f100c9889891b357771.jpg\",\n  \"210445.jpg\": \"Mollusca/Arion ater/6c8c62dda188bfc595057920bbbb65eb.jpg\",\n  \"210446.jpg\": \"Mollusca/Arion ater/9c7eb06bf4fba233a228525a1522f867.jpg\",\n  \"210449.jpg\": \"Insecta/Erythrodiplax berenice/ae2f7448cd27acb7ca2d5664520de8b5.jpg\",\n  \"210450.jpg\": \"Insecta/Erythrodiplax berenice/edee2d3432c81e19ef862707945a00ab.jpg\",\n  \"210462.jpg\": \"Insecta/Erythrodiplax berenice/c5a97067c6d81acfc389113fa287601e.jpg\",\n  \"210464.jpg\": \"Insecta/Erythrodiplax berenice/09b5de7e31fb8733137111337b29d4b2.jpg\",\n  \"210466.jpg\": \"Insecta/Erythrodiplax berenice/13eb6d81a5394f414bd32e66a968f074.jpg\",\n  \"210476.jpg\": \"Insecta/Erythrodiplax berenice/1d4af73e361ec35b3ffa4f2a2510cf50.jpg\",\n  \"210477.jpg\": \"Insecta/Erythrodiplax berenice/37249b03f41d656a3937cd69d0232a48.jpg\",\n  \"210478.jpg\": \"Insecta/Erythrodiplax berenice/68fe941344e21358bc56b2cc028e6a86.jpg\",\n  \"210479.jpg\": \"Insecta/Erythrodiplax berenice/55af4da702d31b97f95922a8d543ce92.jpg\",\n  \"210482.jpg\": \"Insecta/Erythrodiplax berenice/46699421130b0ab2c548d26d34bdacab.jpg\",\n  \"210483.jpg\": \"Insecta/Erythrodiplax berenice/f5f92a6c6226082a7a9d4f4a4ebe72e1.jpg\",\n  \"210484.jpg\": \"Insecta/Erythrodiplax berenice/8f1f269d4e05a91a63fc7251a9925485.jpg\",\n  \"210493.jpg\": \"Insecta/Erythrodiplax berenice/499e108ecb9a7a26eda457d43054ddb8.jpg\",\n  \"210499.jpg\": \"Insecta/Erythrodiplax berenice/0fa59fb8bd117745b2ae20a82389197a.jpg\",\n  \"210500.jpg\": \"Insecta/Erythrodiplax berenice/7dae99898516b5c325f81359c2a19bc2.jpg\",\n  \"210509.jpg\": \"Insecta/Erythrodiplax berenice/1ce0f9f2b78b2a631abdcae0254d1bb8.jpg\",\n  \"210516.jpg\": \"Insecta/Erythrodiplax berenice/ae1bf9a44b720f3e1c067c0dd2483659.jpg\",\n  \"210517.jpg\": \"Insecta/Erythrodiplax berenice/b99e9d3e55f8f85304ef32e9391244e6.jpg\",\n  \"210518.jpg\": \"Insecta/Erythrodiplax berenice/870b0bd630ffb993270ca9e041d952d9.jpg\",\n  \"210520.jpg\": \"Insecta/Erythrodiplax berenice/4f03c863a4d882d2b10a4e6323456820.jpg\",\n  \"210524.jpg\": \"Insecta/Erythrodiplax berenice/f378da0e0a04d40e25a56d3b0e453127.jpg\",\n  \"210543.jpg\": \"Insecta/Erythrodiplax berenice/c9b9073ae2247fc32b3df48723d9668f.jpg\",\n  \"210935.jpg\": \"Reptilia/Sceloporus clarkii/9eb731a96b5c20a4b8ccb84c79abffbc.jpg\",\n  \"210941.jpg\": \"Reptilia/Sceloporus clarkii/6cb98eb1134f50d3e1327699f59cb1b5.jpg\",\n  \"210945.jpg\": \"Reptilia/Sceloporus clarkii/5ac1b25059da915e0c1658897567c9e3.jpg\",\n  \"210946.jpg\": \"Reptilia/Sceloporus clarkii/0d2409af924b9826730e9010f6f9958c.jpg\",\n  \"210950.jpg\": \"Reptilia/Sceloporus clarkii/dd8783c91001f004a2b46e5901d7a7ac.jpg\",\n  \"211180.jpg\": \"Insecta/Pieris napi/c79554084ac6a7d475ecbab6835f36aa.jpg\",\n  \"211181.jpg\": \"Insecta/Pieris napi/e9d1d9b683d3801d66c19a89abac2966.jpg\",\n  \"211188.jpg\": \"Insecta/Pieris napi/76f43bd56bba4e5e5665bf51882d035e.jpg\",\n  \"211189.jpg\": \"Insecta/Pieris napi/255b43e1725b791d0f665a61a3e64ea7.jpg\",\n  \"211190.jpg\": \"Insecta/Pieris napi/3ee93920a2f4209e38c9cdc222c0aa5b.jpg\",\n  \"211200.jpg\": \"Insecta/Pieris napi/aa686caf92818d040a0830a2404f8b14.jpg\",\n  \"211201.jpg\": \"Insecta/Pieris napi/4eaf799211498d8313efaa4278001975.jpg\",\n  \"211204.jpg\": \"Insecta/Pieris napi/079ce27932355d6d038a89b74fbc935f.jpg\",\n  \"211205.jpg\": \"Insecta/Pieris napi/bdcf017214a7ee58399795fb3fc60687.jpg\",\n  \"211210.jpg\": \"Insecta/Pieris napi/08903831f788fbb157e109511da88cf9.jpg\",\n  \"211211.jpg\": \"Insecta/Pieris napi/df2fba03bce85d82d79648c3d4abc837.jpg\",\n  \"211223.jpg\": \"Insecta/Pieris napi/544fb751f74b7b0ab52a77df4f0f9d8c.jpg\",\n  \"211224.jpg\": \"Insecta/Pieris napi/9c28371876413501c04279abf990af0b.jpg\",\n  \"211227.jpg\": \"Insecta/Pieris napi/8dac32570d6bc8994cbdf3a8a1656cd0.jpg\",\n  \"211233.jpg\": \"Insecta/Pieris napi/3e7001ffc3f68738958f6bec665467e0.jpg\",\n  \"211241.jpg\": \"Insecta/Pieris napi/aea8a3c74bc059b2d8a82550bd6d13ab.jpg\",\n  \"211242.jpg\": \"Insecta/Pieris napi/b03ced835b399deb3b64b787f806be93.jpg\",\n  \"211245.jpg\": \"Insecta/Pieris napi/7a95d8da14a919adaeae96908079a75e.jpg\",\n  \"211246.jpg\": \"Insecta/Pieris napi/347e53f58978e7ddcca18818df66bacd.jpg\",\n  \"211277.jpg\": \"Insecta/Agraulis vanillae/4b371f53ea364ecb1cd1ee3e6d42a9b0.jpg\",\n  \"211295.jpg\": \"Insecta/Agraulis vanillae/88cd0610d3623693d58a5a00d85c6abf.jpg\",\n  \"211310.jpg\": \"Insecta/Agraulis vanillae/fb46344bb990745934a917c477165383.jpg\",\n  \"211354.jpg\": \"Insecta/Agraulis vanillae/1413fd80abb709857f2b414ec3136d4a.jpg\",\n  \"211366.jpg\": \"Insecta/Agraulis vanillae/cd3cae573670c7ae8e0776c124b87dc4.jpg\",\n  \"211406.jpg\": \"Insecta/Agraulis vanillae/38bb06bef51d5014db211c180ca08508.jpg\",\n  \"211427.jpg\": \"Insecta/Agraulis vanillae/b60498a44975dc5a35229f508f00cba4.jpg\",\n  \"211433.jpg\": \"Insecta/Agraulis vanillae/f55c32028d694f9980ee4905d070330e.jpg\",\n  \"211447.jpg\": \"Insecta/Agraulis vanillae/0a1a43f4c2a7c6e3a9040d9a2135b83a.jpg\",\n  \"211500.jpg\": \"Insecta/Agraulis vanillae/19196dd208e4787a7a6690ed1cccbc3a.jpg\",\n  \"211532.jpg\": \"Insecta/Agraulis vanillae/d6f5d1b64038cfd10c92435c6a28f709.jpg\",\n  \"211663.jpg\": \"Insecta/Agraulis vanillae/0dd445d43b3d59c90d48afdc39c446f9.jpg\",\n  \"211711.jpg\": \"Insecta/Agraulis vanillae/6e1255bb786f3b6b94adb676cb271ff0.jpg\",\n  \"211729.jpg\": \"Insecta/Agraulis vanillae/1d68c9fcad393b1b5c59f5225bbe56e4.jpg\",\n  \"211730.jpg\": \"Insecta/Agraulis vanillae/537182d8765b41f76983891b64497b48.jpg\",\n  \"211741.jpg\": \"Insecta/Agraulis vanillae/3b0749f06e363e548df5d3c9177a40cf.jpg\",\n  \"211771.jpg\": \"Insecta/Agraulis vanillae/ffc49a8fad72e12f5e327e9c4ba4e67e.jpg\",\n  \"211843.jpg\": \"Insecta/Agraulis vanillae/14ec6e92d8964eca12cf5660ca66b273.jpg\",\n  \"211876.jpg\": \"Insecta/Agraulis vanillae/f8ebff0a97c695668757d38922b5a778.jpg\",\n  \"211889.jpg\": \"Insecta/Agraulis vanillae/003d5a6724db83ebfb50bc7c79ff2c49.jpg\",\n  \"211974.jpg\": \"Insecta/Agraulis vanillae/ef2f11212cd00af310378410c9523b13.jpg\",\n  \"212009.jpg\": \"Mammalia/Ictidomys tridecemlineatus/64ee8b7a176562f6a79aea6e03394df2.jpg\",\n  \"212014.jpg\": \"Mammalia/Ictidomys tridecemlineatus/1f4cd5dd92543449dbd26fe4766d4120.jpg\",\n  \"212023.jpg\": \"Mammalia/Ictidomys tridecemlineatus/9d1c650499b1c4f49f2ba3da13ca97a4.jpg\",\n  \"212025.jpg\": \"Mammalia/Ictidomys tridecemlineatus/c229d454b905b18981c9d5bc1407e9d1.jpg\",\n  \"212027.jpg\": \"Mammalia/Ictidomys tridecemlineatus/bd79f93d742740b6b7936ad01be9fb50.jpg\",\n  \"212033.jpg\": \"Mammalia/Ictidomys tridecemlineatus/44e81b6af0fdb264e286a072c395df60.jpg\",\n  \"212034.jpg\": \"Mammalia/Ictidomys tridecemlineatus/687280f795bb4617cd6a4f62481f8864.jpg\",\n  \"212035.jpg\": \"Mammalia/Ictidomys tridecemlineatus/74a068b8e48a696a5a102d6cd38c5d37.jpg\",\n  \"212090.jpg\": \"Arachnida/Latrodectus hesperus/cbd0ab0407bffc42b0115c88e4e3137c.jpg\",\n  \"212095.jpg\": \"Arachnida/Latrodectus hesperus/a43d242eb4bbf8d16df1816e87dcbfc9.jpg\",\n  \"212096.jpg\": \"Arachnida/Latrodectus hesperus/faad28173a14642b9e13f6cde5e6ff69.jpg\",\n  \"2121.jpg\": \"Aves/Loxia curvirostra/88c0ac99a4cd8b2aa467f3408351dbb9.jpg\",\n  \"212100.jpg\": \"Arachnida/Latrodectus hesperus/4daaa20a9d12c63670b880bc09cf68ca.jpg\",\n  \"212102.jpg\": \"Arachnida/Latrodectus hesperus/42b64ef576fefbf9fb20ba319945b1bb.jpg\",\n  \"212103.jpg\": \"Arachnida/Latrodectus hesperus/2c16bbae59a4f90ee4efb9b5682fc4d6.jpg\",\n  \"212104.jpg\": \"Arachnida/Latrodectus hesperus/f162960e6f55e2b0356b6a256551fef1.jpg\",\n  \"212107.jpg\": \"Arachnida/Latrodectus hesperus/3bc96662125856147f443f5bc8e9cdcf.jpg\",\n  \"212110.jpg\": \"Arachnida/Latrodectus hesperus/2771c23904a23a191c65ae2f54cc694f.jpg\",\n  \"212125.jpg\": \"Arachnida/Latrodectus hesperus/d8d45ddd1e4cf81d5652cc94fbe40ac8.jpg\",\n  \"212127.jpg\": \"Arachnida/Latrodectus hesperus/5608ad0f52c16da0f3bc674ee84199ca.jpg\",\n  \"212135.jpg\": \"Arachnida/Latrodectus hesperus/aebcfb8f02c59f1c7ee1f870288f6418.jpg\",\n  \"212138.jpg\": \"Arachnida/Latrodectus hesperus/cfca9e5800ee2637a0790847f791e664.jpg\",\n  \"212140.jpg\": \"Arachnida/Latrodectus hesperus/e9dc58042d68f03fbcdff9b0131c7ecd.jpg\",\n  \"212141.jpg\": \"Arachnida/Latrodectus hesperus/8ad7834497a8409fdec40bf45f58915a.jpg\",\n  \"212144.jpg\": \"Arachnida/Latrodectus hesperus/317d88f42f734fa9476db8c0a10cc196.jpg\",\n  \"212145.jpg\": \"Arachnida/Latrodectus hesperus/f649f3f7a5033971c32151c609286b0b.jpg\",\n  \"212146.jpg\": \"Arachnida/Latrodectus hesperus/da8138aefa8e28ffcb917fe800934449.jpg\",\n  \"212150.jpg\": \"Arachnida/Latrodectus hesperus/75fe5acf90fd42b3e7dea5a5fb687630.jpg\",\n  \"212161.jpg\": \"Arachnida/Latrodectus hesperus/66be77f326e6c0c236e9d7a598cee7dc.jpg\",\n  \"212162.jpg\": \"Arachnida/Latrodectus hesperus/4e432c9d6e6eaf9d6978bf50c4ac1758.jpg\",\n  \"212167.jpg\": \"Aves/Aimophila ruficeps/f3c9a61b230bf7fec46528e42071e4a9.jpg\",\n  \"212170.jpg\": \"Aves/Aimophila ruficeps/9ebe57fdb589585609dbbd04f3272b82.jpg\",\n  \"212176.jpg\": \"Aves/Aimophila ruficeps/bc1458778859a53b48b8b2d3e64dcbbf.jpg\",\n  \"212177.jpg\": \"Aves/Aimophila ruficeps/f89e62353180b49eb39d3d53bbf2f785.jpg\",\n  \"212180.jpg\": \"Aves/Aimophila ruficeps/f932d77d688ebe8a6be3902f05bbf032.jpg\",\n  \"212184.jpg\": \"Aves/Aimophila ruficeps/7a1ab54dc5c0e4bb668a0d1b293f79cf.jpg\",\n  \"212191.jpg\": \"Aves/Aimophila ruficeps/e756f589440f55763a6d100a247103a5.jpg\",\n  \"212192.jpg\": \"Aves/Aimophila ruficeps/fc363e7de43641d3e9e8d5726832ab85.jpg\",\n  \"212193.jpg\": \"Aves/Aimophila ruficeps/70189d6020aad4afe95ac0eb4c3ff94e.jpg\",\n  \"212194.jpg\": \"Aves/Aimophila ruficeps/e1b37094ebc5a9c7849d7bdf34e3f784.jpg\",\n  \"212195.jpg\": \"Aves/Aimophila ruficeps/0adf0d3680d47b73024a56cfbac88172.jpg\",\n  \"212196.jpg\": \"Aves/Aimophila ruficeps/20f1723c177ffa64f22da4259c869164.jpg\",\n  \"212199.jpg\": \"Aves/Aimophila ruficeps/e756f7b4ac0c87c5606123124c6bc33a.jpg\",\n  \"212202.jpg\": \"Aves/Aimophila ruficeps/b00e45245695fb712aa579a7e0fc3ad4.jpg\",\n  \"212203.jpg\": \"Aves/Aimophila ruficeps/e4dc86817e8339c5c2dc8716404e4914.jpg\",\n  \"212204.jpg\": \"Aves/Aimophila ruficeps/93ef0140baa668c0fa6e981fdd39d1c6.jpg\",\n  \"212209.jpg\": \"Aves/Aimophila ruficeps/003095d45dabfeb43d027177a5a9bec6.jpg\",\n  \"212228.jpg\": \"Aves/Aimophila ruficeps/39d222dcfdf5dbf57fc4df21e78c61bb.jpg\",\n  \"212229.jpg\": \"Aves/Aimophila ruficeps/0b000a576d5632f6533efd8c7a702656.jpg\",\n  \"212232.jpg\": \"Aves/Aimophila ruficeps/3f213ef591dd5f4f073423ee7557ffd7.jpg\",\n  \"212233.jpg\": \"Aves/Aimophila ruficeps/a444c57a435a5900d7545fcc013763d5.jpg\",\n  \"212235.jpg\": \"Aves/Aimophila ruficeps/ce6f756f087fd3db58988661dc907c26.jpg\",\n  \"212236.jpg\": \"Aves/Aimophila ruficeps/e6f1e6626f587d8d03fc8db4a4de4c10.jpg\",\n  \"212239.jpg\": \"Aves/Aimophila ruficeps/ba0ea48ddcf21e9d02c32db599ce3774.jpg\",\n  \"212243.jpg\": \"Insecta/Lestes alacer/3ade1da4e233e87a31b5044333c7e2e5.jpg\",\n  \"212246.jpg\": \"Insecta/Lestes alacer/3c698ad8765c3ba23f57c55ad3c80696.jpg\",\n  \"212265.jpg\": \"Insecta/Lestes alacer/3bc6436d5d6e9b25382506dc876450ce.jpg\",\n  \"212266.jpg\": \"Insecta/Lestes alacer/348063ec35b2f873f8a8a046d82d0b0e.jpg\",\n  \"212282.jpg\": \"Mammalia/Dasypus novemcinctus/b6263e739e20f3d9023f55d349429bb5.jpg\",\n  \"212311.jpg\": \"Mammalia/Dasypus novemcinctus/5383c6f53ac3472828a44911876bb603.jpg\",\n  \"212322.jpg\": \"Mammalia/Dasypus novemcinctus/c0b5b19391de5bc21d497e1bff260b04.jpg\",\n  \"212332.jpg\": \"Mammalia/Dasypus novemcinctus/84f0667eeb13669ff93d25040f636e4b.jpg\",\n  \"212344.jpg\": \"Mammalia/Dasypus novemcinctus/197a04a8267c9194c60daa6a5f4c1725.jpg\",\n  \"212355.jpg\": \"Mammalia/Dasypus novemcinctus/187e3075decc1ced63091645ff9df797.jpg\",\n  \"212413.jpg\": \"Mammalia/Dasypus novemcinctus/8d9c2a590efd9b54bfc8c7c59d1493a2.jpg\",\n  \"212414.jpg\": \"Mammalia/Dasypus novemcinctus/f97c8daf024218bc847f30231d933fc2.jpg\",\n  \"212434.jpg\": \"Mammalia/Dasypus novemcinctus/553a7f6d4b2d0ed6652c05db378acea7.jpg\",\n  \"212438.jpg\": \"Mammalia/Dasypus novemcinctus/75f00ce4191501bc4c65b7b70495b231.jpg\",\n  \"212443.jpg\": \"Mammalia/Dasypus novemcinctus/0ee16e9363e14603fab4f3f03b00c622.jpg\",\n  \"212461.jpg\": \"Mammalia/Dasypus novemcinctus/7959836ae22c7a86baa73d2709fa271e.jpg\",\n  \"212480.jpg\": \"Mammalia/Dasypus novemcinctus/c5bf50d40dafce6f346ba8a4db91e4c8.jpg\",\n  \"212497.jpg\": \"Mammalia/Dasypus novemcinctus/d0fe4ae33aacceb5a5279a4531db9fd9.jpg\",\n  \"212499.jpg\": \"Mammalia/Dasypus novemcinctus/e5dcd5d3b89333823042d1265762b5ef.jpg\",\n  \"212504.jpg\": \"Mammalia/Dasypus novemcinctus/6f037d6d569fd9c1859e3eb2bb3c85fd.jpg\",\n  \"213070.jpg\": \"Aves/Bubulcus ibis/c473ac3258a07181dff3cda2a39599bc.jpg\",\n  \"213099.jpg\": \"Aves/Bubulcus ibis/ac895205e5f1e4812b68d535ec885bc4.jpg\",\n  \"213111.jpg\": \"Aves/Bubulcus ibis/b5bdfd8f005d8800f459bdcca987e41d.jpg\",\n  \"213134.jpg\": \"Aves/Bubulcus ibis/09e848755243974e8596516d9c70c179.jpg\",\n  \"213142.jpg\": \"Aves/Bubulcus ibis/58ac5eb59057044bac9ac4c1a792281d.jpg\",\n  \"213225.jpg\": \"Aves/Bubulcus ibis/301f12312c007e48bb7d4e09432e35e3.jpg\",\n  \"213275.jpg\": \"Aves/Bubulcus ibis/41f4a2ed0bd0a5915802376b6d55be0e.jpg\",\n  \"213276.jpg\": \"Aves/Bubulcus ibis/8cc1de3921f4b20b01172cee9799b84b.jpg\",\n  \"213277.jpg\": \"Aves/Bubulcus ibis/79276561786c2bf712a6f68dbbfb9956.jpg\",\n  \"213293.jpg\": \"Aves/Bubulcus ibis/f34fcf768c5102183cd814b85c143778.jpg\",\n  \"213313.jpg\": \"Aves/Bubulcus ibis/f726df586cb85a3293c946d8837d664d.jpg\",\n  \"213333.jpg\": \"Aves/Bubulcus ibis/759d765d91a396585d73adbe94b93680.jpg\",\n  \"213399.jpg\": \"Aves/Bubulcus ibis/43886d5e32279cde53cc68fb2b8c63a5.jpg\",\n  \"213687.jpg\": \"Insecta/Amphipsalta zelandica/cec6d14b4aa67fdc0c56fb2fe1de2f27.jpg\",\n  \"213692.jpg\": \"Insecta/Amphipsalta zelandica/6ad1315a9d69b56feabd6e2fc57bf458.jpg\",\n  \"213696.jpg\": \"Insecta/Amphipsalta zelandica/76132df450a70a5294b76ae69837a004.jpg\",\n  \"213697.jpg\": \"Insecta/Amphipsalta zelandica/c779cbc15ebae6c62e3441b6ff138c92.jpg\",\n  \"2138.jpg\": \"Aves/Loxia curvirostra/5b6cbb77c97b8835a3df14314ca17061.jpg\",\n  \"213877.jpg\": \"Insecta/Cicindela punctulata/7f51196090937f9fce7a29acaadb9248.jpg\",\n  \"21389.jpg\": \"Aves/Archilochus colubris/31d4bd20bd554174114be86f1d2daa05.jpg\",\n  \"213895.jpg\": \"Insecta/Cicindela punctulata/3c6f0fa225992aa7473be19932ed42a1.jpg\",\n  \"213896.jpg\": \"Insecta/Cicindela punctulata/f88b7a28b8e66e50ef27bb983742a922.jpg\",\n  \"213897.jpg\": \"Insecta/Cicindela punctulata/47c28ef7ac8f83122581c3363f9fe6f6.jpg\",\n  \"213902.jpg\": \"Animalia/Procambarus clarkii/71bff87c38429f959ba8ce34089106c3.jpg\",\n  \"213906.jpg\": \"Animalia/Procambarus clarkii/d519c710059e06e442c07401887864dd.jpg\",\n  \"21392.jpg\": \"Aves/Archilochus colubris/4410462cd0aed7e0044421a52adeffd4.jpg\",\n  \"213925.jpg\": \"Animalia/Procambarus clarkii/fd63cad1866b154936144fcaf45c7a0c.jpg\",\n  \"213942.jpg\": \"Animalia/Procambarus clarkii/1a9d0d25cf80ee2f20221f4fb7fe33ac.jpg\",\n  \"213943.jpg\": \"Animalia/Procambarus clarkii/dd6a88367176e512867b80f39b557d95.jpg\",\n  \"213961.jpg\": \"Animalia/Procambarus clarkii/1dbc3742f1cf9dc1f903c52f07b1c194.jpg\",\n  \"213970.jpg\": \"Animalia/Procambarus clarkii/92c707f82f81c9d12edcb636c168455e.jpg\",\n  \"213978.jpg\": \"Animalia/Procambarus clarkii/ab6f0df9b0067e004f8ec4375afe4f94.jpg\",\n  \"213998.jpg\": \"Animalia/Procambarus clarkii/703bd83f61d00795c27085aa2864d1c2.jpg\",\n  \"213999.jpg\": \"Animalia/Procambarus clarkii/85d8b9bb16aa86934ecbd9e5f114a8b0.jpg\",\n  \"214011.jpg\": \"Animalia/Procambarus clarkii/5bd427bec3fd1eaf6527fc30a5fbc908.jpg\",\n  \"21402.jpg\": \"Aves/Archilochus colubris/9fdbb14bc74dafc0f3d8f0614c0f3b64.jpg\",\n  \"214020.jpg\": \"Animalia/Procambarus clarkii/a1e90c73b6df1f3deb8f73efd65ba1b7.jpg\",\n  \"214033.jpg\": \"Animalia/Procambarus clarkii/ee06e037ea0e4a18a61295e835a38362.jpg\",\n  \"214045.jpg\": \"Animalia/Procambarus clarkii/61a4cb95c7cd91f7e15ccedfa2ae9aff.jpg\",\n  \"214046.jpg\": \"Animalia/Procambarus clarkii/8dd39d333bbaa1329c5dbc295beca897.jpg\",\n  \"214055.jpg\": \"Animalia/Procambarus clarkii/6e210115b8e54bd3dc7459ddfefe3e6b.jpg\",\n  \"214056.jpg\": \"Insecta/Eurema mexicana/7117712405bf737bbd1780fb7630b991.jpg\",\n  \"214062.jpg\": \"Insecta/Eurema mexicana/78fe9cf821b2841a0e00fc960b046bc6.jpg\",\n  \"214066.jpg\": \"Insecta/Eurema mexicana/ebbebda6bcfd9bcd7273a4b57ff0d680.jpg\",\n  \"214067.jpg\": \"Insecta/Eurema mexicana/3d171a9e88273825a07c98fa24f723d4.jpg\",\n  \"214081.jpg\": \"Aves/Gygis alba/5f89de41f08f34677a9fef3122885475.jpg\",\n  \"214086.jpg\": \"Aves/Gygis alba/cf551a153c571d4358f58ede53972cae.jpg\",\n  \"214088.jpg\": \"Aves/Gygis alba/bb0368f01050e24d9c3f1d1027319fd8.jpg\",\n  \"214089.jpg\": \"Aves/Gygis alba/894bfd5b4ae1390c42e7b2b61530bf2d.jpg\",\n  \"214108.jpg\": \"Aves/Myiarchus tuberculifer/bb0deb1d55d4444aa54b9c257416c0f4.jpg\",\n  \"214110.jpg\": \"Aves/Myiarchus tuberculifer/847c9e83e1f20006828693e4dc5b5173.jpg\",\n  \"214115.jpg\": \"Aves/Myiarchus tuberculifer/f922c7c63c76c04c78b89ee24f7ad45f.jpg\",\n  \"214152.jpg\": \"Insecta/Papilio zelicaon/d5a780adf04a682d104f369c3a0bf484.jpg\",\n  \"21416.jpg\": \"Aves/Archilochus colubris/468396105330bdcc03062065231c040d.jpg\",\n  \"214171.jpg\": \"Insecta/Papilio zelicaon/439632ad6b071c1bf7b937d6f0fb962d.jpg\",\n  \"214172.jpg\": \"Insecta/Papilio zelicaon/2fdd8539a26fb4fdfaa130d25b51b700.jpg\",\n  \"214173.jpg\": \"Insecta/Papilio zelicaon/0c22a7ed2970fe3c85bd46b92c3ff49a.jpg\",\n  \"214178.jpg\": \"Insecta/Papilio zelicaon/db1c57f596e892d70715c0ae83845ce9.jpg\",\n  \"214179.jpg\": \"Insecta/Papilio zelicaon/b33e834fedfafa15bffe127b5abb19e6.jpg\",\n  \"214181.jpg\": \"Insecta/Papilio zelicaon/a022af35b3b981be1be8a5a1ad46f4d6.jpg\",\n  \"2142.jpg\": \"Aves/Loxia curvirostra/3a262cd1d4ac3e94ef491cf92f9468f6.jpg\",\n  \"214200.jpg\": \"Insecta/Papilio zelicaon/a7525f71aa650dc8acf8505aad97a947.jpg\",\n  \"214201.jpg\": \"Insecta/Papilio zelicaon/a2b19179174f3a69cd07a4c621c9c390.jpg\",\n  \"214205.jpg\": \"Insecta/Papilio zelicaon/3d5d4e91c7deb82bfc770edb6de6984b.jpg\",\n  \"214219.jpg\": \"Insecta/Papilio zelicaon/c7febedf18a69047c845e291d199f4c6.jpg\",\n  \"214226.jpg\": \"Insecta/Papilio zelicaon/43fba1ec5564688ee0fd566d0d2cc05f.jpg\",\n  \"214243.jpg\": \"Insecta/Papilio zelicaon/7ed5f41e46299e188920af43e4498dc0.jpg\",\n  \"214254.jpg\": \"Insecta/Papilio zelicaon/f8e62ba61486d9a89aff4ba052db7874.jpg\",\n  \"214277.jpg\": \"Insecta/Papilio zelicaon/3565ad2e2a8a704673368ce93b98d61c.jpg\",\n  \"21428.jpg\": \"Aves/Archilochus colubris/c08f60940a61610f14031be34d89f106.jpg\",\n  \"214286.jpg\": \"Insecta/Papilio zelicaon/41ca05abbe187f0186a8db5da7486787.jpg\",\n  \"214289.jpg\": \"Insecta/Papilio zelicaon/3328d89a2c56544443313dd5137d8b27.jpg\",\n  \"214304.jpg\": \"Insecta/Papilio zelicaon/276be84c03592408f18c442d8fb195b6.jpg\",\n  \"214317.jpg\": \"Insecta/Papilio zelicaon/1387da75953dd56b422d858410a7e3d9.jpg\",\n  \"214319.jpg\": \"Insecta/Papilio zelicaon/dbb7c067fe0957fe9b2163dde3916b17.jpg\",\n  \"214320.jpg\": \"Insecta/Papilio zelicaon/7ea9343893296324b610b8d717d920a2.jpg\",\n  \"214324.jpg\": \"Insecta/Papilio zelicaon/f1237411fbe04d5501fe2f2f874ff0a8.jpg\",\n  \"214325.jpg\": \"Insecta/Papilio zelicaon/fef9e616200ce10eeb0c3ef71c6ab3ed.jpg\",\n  \"214332.jpg\": \"Insecta/Papilio zelicaon/04b5b401266b561595b2c384e81aaf53.jpg\",\n  \"214357.jpg\": \"Insecta/Papilio zelicaon/8069828e80c96db6dd0d9f96bd5c1fc0.jpg\",\n  \"21436.jpg\": \"Aves/Archilochus colubris/674a510ea57baee10217a1af22986f0b.jpg\",\n  \"214380.jpg\": \"Mammalia/Ovis canadensis nelsoni/19f7088c31e9aca6a421f371c4252429.jpg\",\n  \"214404.jpg\": \"Mammalia/Ovis canadensis nelsoni/2290ff50da0fbb815d16c29885b83bea.jpg\",\n  \"214412.jpg\": \"Insecta/Pelidnota punctata/947a72df3945af151f2611317e6c7e5a.jpg\",\n  \"214420.jpg\": \"Insecta/Pelidnota punctata/58dd01a4b0b91c1ffe88bede6b76a50d.jpg\",\n  \"214421.jpg\": \"Insecta/Pelidnota punctata/2b908d235c14bdde5365032de4a98ba4.jpg\",\n  \"214422.jpg\": \"Insecta/Pelidnota punctata/808342965033e2ae2bfd9ed3fde82bff.jpg\",\n  \"214423.jpg\": \"Insecta/Pelidnota punctata/f80004777d88ee54e83f19229ce1a4cc.jpg\",\n  \"214425.jpg\": \"Insecta/Pelidnota punctata/13689e2b8393872e1bcadb2f1da58852.jpg\",\n  \"214429.jpg\": \"Insecta/Pelidnota punctata/ddf8af3ac4668d2532e0362b37193e71.jpg\",\n  \"214438.jpg\": \"Insecta/Pelidnota punctata/fbd76e52a22923336a04efc0f8ae4600.jpg\",\n  \"214439.jpg\": \"Insecta/Pelidnota punctata/36f87d0d4dd37005bb90dca2f0b0faa4.jpg\",\n  \"214441.jpg\": \"Insecta/Pelidnota punctata/7a8a3f52cfee60c9eee3cdc0c5a1b5ac.jpg\",\n  \"214442.jpg\": \"Insecta/Pelidnota punctata/eeeb345c9073524689a6e2814bca060a.jpg\",\n  \"214443.jpg\": \"Insecta/Pelidnota punctata/b4379294842af85466f48c189b1f600a.jpg\",\n  \"214444.jpg\": \"Insecta/Pelidnota punctata/488a6ade32b7be4a121d142d832359fb.jpg\",\n  \"214445.jpg\": \"Insecta/Pelidnota punctata/51f2d406879a6333d94cdbef32b8b1f7.jpg\",\n  \"214446.jpg\": \"Insecta/Pelidnota punctata/c2a0a20d34e9382c0dc31699863ce760.jpg\",\n  \"214447.jpg\": \"Insecta/Pelidnota punctata/c4ca698b6cafbaf2a32b049dc0413572.jpg\",\n  \"214448.jpg\": \"Insecta/Pelidnota punctata/c2b125414a1cf19b832ea26df6e767e5.jpg\",\n  \"214452.jpg\": \"Insecta/Pelidnota punctata/60c908344bea510eaa9a4ae2f3c59879.jpg\",\n  \"214455.jpg\": \"Insecta/Pelidnota punctata/cbdc73fc6564ba1df04f12e7af5978df.jpg\",\n  \"214465.jpg\": \"Insecta/Pelidnota punctata/136292a7bd82e95a63e1c07c4d76f47a.jpg\",\n  \"214466.jpg\": \"Insecta/Pelidnota punctata/4800cefa2ff853e605a562aa18c030c3.jpg\",\n  \"21458.jpg\": \"Aves/Archilochus colubris/584b6be9de37281309a364869c0fadc0.jpg\",\n  \"214673.jpg\": \"Insecta/Dromogomphus spoliatus/b992e5b5e18352c5db15b7c86375552b.jpg\",\n  \"214674.jpg\": \"Insecta/Dromogomphus spoliatus/dd3b6981720246b29910bb7cf0ec06fb.jpg\",\n  \"214676.jpg\": \"Insecta/Dromogomphus spoliatus/b68c924c369f9882d8d65c5371d76b28.jpg\",\n  \"214679.jpg\": \"Insecta/Dromogomphus spoliatus/36bd6a9d7dba6eea5ff0a2ef6686091f.jpg\",\n  \"214680.jpg\": \"Insecta/Dromogomphus spoliatus/0e529952c1bb62d72ea711861360504c.jpg\",\n  \"214681.jpg\": \"Insecta/Dromogomphus spoliatus/d1fd3d3797bc6b64aa690a03774956a8.jpg\",\n  \"214682.jpg\": \"Insecta/Dromogomphus spoliatus/ce9bdbcf045898988dc0eeae87288276.jpg\",\n  \"214684.jpg\": \"Insecta/Dromogomphus spoliatus/cf911308f30cfcb65bc235f2a21e6173.jpg\",\n  \"214685.jpg\": \"Insecta/Dromogomphus spoliatus/02d2ca6b5ca537fdbfc339aabf48cb13.jpg\",\n  \"214692.jpg\": \"Insecta/Dromogomphus spoliatus/31deaf49b1112347393c4c9f6a2254cb.jpg\",\n  \"214695.jpg\": \"Insecta/Dromogomphus spoliatus/a29c1bcedd2ca61bbf551208c09f7eb1.jpg\",\n  \"21470.jpg\": \"Aves/Archilochus colubris/0659c86ae4fb3073245681be7d7f50ec.jpg\",\n  \"214703.jpg\": \"Insecta/Dromogomphus spoliatus/1f8801ab267f31eca8ec3cec51866bcd.jpg\",\n  \"214708.jpg\": \"Insecta/Dromogomphus spoliatus/5985a96d4a6e94fbdd9050c087358abe.jpg\",\n  \"214709.jpg\": \"Insecta/Dromogomphus spoliatus/cae8a1893d65121e1ccd3c747e7be07b.jpg\",\n  \"214711.jpg\": \"Insecta/Dromogomphus spoliatus/92cca92290186099b8df171383c605f9.jpg\",\n  \"214718.jpg\": \"Insecta/Dromogomphus spoliatus/f0c215041e9f76b19d25c467f7c27f61.jpg\",\n  \"214719.jpg\": \"Insecta/Dromogomphus spoliatus/ba44fd4a0fefca4760c244871d43c921.jpg\",\n  \"214845.jpg\": \"Mammalia/Odocoileus virginianus/2d88682b281b1d5ab90e8afcdcd68a21.jpg\",\n  \"214885.jpg\": \"Mammalia/Odocoileus virginianus/4983a9d87e4bf77c476bc272bd90cad3.jpg\",\n  \"215134.jpg\": \"Mammalia/Odocoileus virginianus/7c1f287e1c348f805c0e287327e1bdda.jpg\",\n  \"2152.jpg\": \"Aves/Loxia curvirostra/ae54b23a2640e642e8d8ddd6617838ea.jpg\",\n  \"215309.jpg\": \"Mammalia/Odocoileus virginianus/781a78729ad2a32ef8dbdaad0becac86.jpg\",\n  \"215339.jpg\": \"Mammalia/Odocoileus virginianus/f85f8b87376ee8e629787139eabd8743.jpg\",\n  \"215426.jpg\": \"Mammalia/Odocoileus virginianus/a233a4215d4f0984310faaf7714de673.jpg\",\n  \"215624.jpg\": \"Mammalia/Odocoileus virginianus/e12fd4db3f8a495106dc88e293efe3f9.jpg\",\n  \"21571.jpg\": \"Aves/Archilochus colubris/8ef35cd58158068046f92fd57e8400f3.jpg\",\n  \"215730.jpg\": \"Mammalia/Odocoileus virginianus/f86996104b0c75c43389208889be4532.jpg\",\n  \"215967.jpg\": \"Aves/Icterus bullockii/7f01dced5fef8dc8eb2c03df00f2b32c.jpg\",\n  \"215968.jpg\": \"Aves/Icterus bullockii/53ea8118a05e17fd434b7c4e2b73744c.jpg\",\n  \"215971.jpg\": \"Aves/Icterus bullockii/d1138c9f9fc6158dfc621313ccab1588.jpg\",\n  \"215974.jpg\": \"Aves/Icterus bullockii/eca11f0494e2f794cc8b156d8e4d3341.jpg\",\n  \"215975.jpg\": \"Aves/Icterus bullockii/3508c7b503a2e0382115f6efdd4dae59.jpg\",\n  \"215978.jpg\": \"Aves/Icterus bullockii/d041cc190c18ae9e29a5fbeb52b819cc.jpg\",\n  \"215979.jpg\": \"Aves/Icterus bullockii/4ed2ca2ac0004f9745271694e7ff27cf.jpg\",\n  \"215986.jpg\": \"Aves/Icterus bullockii/220f59fb7c75599ea30517a4a1fb6240.jpg\",\n  \"215987.jpg\": \"Aves/Icterus bullockii/1d4046056b7266d8c2bfee025aceae24.jpg\",\n  \"215991.jpg\": \"Aves/Icterus bullockii/818bb4a21254f7d7c5f62a3807596558.jpg\",\n  \"215993.jpg\": \"Aves/Icterus bullockii/bd88e93aa027e47aeb3d2f4586aae91d.jpg\",\n  \"216011.jpg\": \"Aves/Icterus bullockii/ed4d19e88b3a41b2927d49afdcb0a689.jpg\",\n  \"216016.jpg\": \"Aves/Icterus bullockii/01aedc765b697b968c0ebc27c0cdba5c.jpg\",\n  \"216017.jpg\": \"Aves/Icterus bullockii/d2c47d3635ad8bb57e65fbc7c1d8e4ff.jpg\",\n  \"216023.jpg\": \"Aves/Icterus bullockii/050d55ad429946f1774cb378d3d0ec84.jpg\",\n  \"216053.jpg\": \"Aves/Icterus bullockii/c188fddf3ddf0add1bf848195967d437.jpg\",\n  \"21606.jpg\": \"Aves/Archilochus colubris/a7c0b42aa77a001ec1fa315a747ff006.jpg\",\n  \"216064.jpg\": \"Aves/Icterus bullockii/d3b01f9851c84b0c444b32208cbc004b.jpg\",\n  \"216081.jpg\": \"Aves/Icterus bullockii/8fe246f2f1d1ca773d644bac81740df7.jpg\",\n  \"216097.jpg\": \"Aves/Icterus bullockii/0bd163bdaf2abcc01e7cbde4a6e7263b.jpg\",\n  \"216111.jpg\": \"Insecta/Thasus gigas/5375ef170ae829c2fb3f2278b5641b77.jpg\",\n  \"216113.jpg\": \"Insecta/Thasus gigas/9ed31064807bb99532ec615cec82ca70.jpg\",\n  \"216115.jpg\": \"Insecta/Thasus gigas/50fdb3487c769e9db93f7da94e691b77.jpg\",\n  \"216121.jpg\": \"Insecta/Thasus gigas/26b384a9ef4768644c5ca6ef4fa23033.jpg\",\n  \"21625.jpg\": \"Aves/Archilochus colubris/a837b4913362b6b268736ee593b0ef02.jpg\",\n  \"2163.jpg\": \"Aves/Loxia curvirostra/49503ee7fe253ccf388a0d419f25a9fc.jpg\",\n  \"216309.jpg\": \"Insecta/Panchlora nivea/bbb5bd02150e5135733a109f9c139e39.jpg\",\n  \"216313.jpg\": \"Insecta/Panchlora nivea/d40f0f8b4c38b6898a9be66189d5d57c.jpg\",\n  \"216314.jpg\": \"Insecta/Panchlora nivea/8cb9598bd93005a7db15a58d7c871ade.jpg\",\n  \"216320.jpg\": \"Insecta/Panchlora nivea/24ba1fd1d5d669e2a1846b7d02bc8e4b.jpg\",\n  \"216325.jpg\": \"Insecta/Libellula comanche/6770abe6b6fe4a13f01b1c942eda985d.jpg\",\n  \"216327.jpg\": \"Insecta/Libellula comanche/2ca998d5a34a207df851c29f4f6eaf78.jpg\",\n  \"216332.jpg\": \"Insecta/Libellula comanche/4cbbbc2fdecc999f92adbf5aba4c4293.jpg\",\n  \"216335.jpg\": \"Insecta/Libellula comanche/693b93867830963b7c6f23470b9cc740.jpg\",\n  \"216336.jpg\": \"Insecta/Libellula comanche/c915e9a416a67da8753c0f57dda6514c.jpg\",\n  \"216337.jpg\": \"Insecta/Libellula comanche/bbd0fad24483f6f8faa88be898cb740d.jpg\",\n  \"216338.jpg\": \"Insecta/Libellula comanche/0202698cff1311db3c232f4cb4aa485c.jpg\",\n  \"216343.jpg\": \"Insecta/Libellula comanche/271126d9ec456072c22f6158db135ea1.jpg\",\n  \"216352.jpg\": \"Insecta/Libellula comanche/c1a7ce38a50edf1339f404a545f5c01d.jpg\",\n  \"216354.jpg\": \"Insecta/Libellula comanche/e29fcd29fc1e188f06c4c1de03c74722.jpg\",\n  \"216355.jpg\": \"Insecta/Libellula comanche/81b4412231e8f109dd90dc76fb133f52.jpg\",\n  \"216356.jpg\": \"Insecta/Libellula comanche/d3ffba5bf8f9407e4b3575b42316cbad.jpg\",\n  \"216360.jpg\": \"Insecta/Libellula comanche/c337922a975e9d392cb9074288d19250.jpg\",\n  \"216361.jpg\": \"Insecta/Libellula comanche/a8141bbf6242d42d30dcd785de1213d5.jpg\",\n  \"216362.jpg\": \"Insecta/Libellula comanche/a25b9a38fc9b8f18794a9bdbb75409de.jpg\",\n  \"216363.jpg\": \"Insecta/Libellula comanche/bb607ec74d904494688b965c3dc6285f.jpg\",\n  \"216364.jpg\": \"Insecta/Libellula comanche/f48e1d643ce51b60f1cdea56125f21f0.jpg\",\n  \"216365.jpg\": \"Insecta/Libellula comanche/dec6a13eabd8f5d23aaf92972141b930.jpg\",\n  \"216366.jpg\": \"Insecta/Libellula comanche/b360337f1d0c012deb3c07bffb517a20.jpg\",\n  \"216367.jpg\": \"Insecta/Libellula comanche/6e990d151b8b92e0844112e15d0399d1.jpg\",\n  \"216372.jpg\": \"Insecta/Libellula comanche/c00f63f1feab9c8f39883eb9f9240e9e.jpg\",\n  \"21638.jpg\": \"Aves/Archilochus colubris/6b848149b8a609ce5f8f24dd20b76ba9.jpg\",\n  \"216461.jpg\": \"Insecta/Panorpa nuptialis/317e86107456769a1233e375ea80837c.jpg\",\n  \"216468.jpg\": \"Insecta/Panorpa nuptialis/12d0a638331043bc9b7aa5561da02654.jpg\",\n  \"216469.jpg\": \"Insecta/Panorpa nuptialis/1791eddc268cf728e7a4432aba29aa61.jpg\",\n  \"216470.jpg\": \"Insecta/Panorpa nuptialis/385c7723ab1a86c914f0f6399aca918d.jpg\",\n  \"216479.jpg\": \"Insecta/Panorpa nuptialis/83df5aefa3e074a9b6e561744bb7c394.jpg\",\n  \"216485.jpg\": \"Insecta/Boloria bellona/d22cc004d61eda554917613453d5952e.jpg\",\n  \"216486.jpg\": \"Insecta/Boloria bellona/d0047f63b71975b2099647e2aa255f3f.jpg\",\n  \"216487.jpg\": \"Insecta/Boloria bellona/eeb5ab8d1258cc8ec907c10431283421.jpg\",\n  \"21649.jpg\": \"Aves/Archilochus colubris/3caab107fb37ac902dd1f309d171ad2c.jpg\",\n  \"216499.jpg\": \"Insecta/Boloria bellona/81d52138fef150a4d0d20f3e3daa040a.jpg\",\n  \"216504.jpg\": \"Insecta/Boloria bellona/4dd31a5b0d0acf3848c3b04138658a7f.jpg\",\n  \"216505.jpg\": \"Insecta/Boloria bellona/d3358a19cae6b956d2ed8bcec2c9f8b8.jpg\",\n  \"216506.jpg\": \"Insecta/Boloria bellona/876b509ac6d39a028f19c6dd492254ff.jpg\",\n  \"216518.jpg\": \"Insecta/Boloria bellona/5bd8a62ab660190fbe9b433e01f41910.jpg\",\n  \"216539.jpg\": \"Aves/Piranga flava/446a73e74d8cff1ed9f03d9caffa352f.jpg\",\n  \"216542.jpg\": \"Aves/Piranga flava/89b29af63450f4fb2ed62fc6db77827d.jpg\",\n  \"216543.jpg\": \"Aves/Piranga flava/99f45fc4a7459b56fd48b505a3ed7b73.jpg\",\n  \"216544.jpg\": \"Aves/Piranga flava/6390f64dfd1eae3fc22e764a04a99a2f.jpg\",\n  \"216546.jpg\": \"Aves/Piranga flava/7d162b0f9a7a16e9752e257ae5d858cb.jpg\",\n  \"216547.jpg\": \"Aves/Piranga flava/4cc0827e9b1b32b4f3234dfdb3ee8439.jpg\",\n  \"216548.jpg\": \"Aves/Piranga flava/29e27db17d31b43e688ecf41ad2dfeb7.jpg\",\n  \"216549.jpg\": \"Aves/Piranga flava/5ee2e1cf976d65bbda4e8e8c95f074f5.jpg\",\n  \"216550.jpg\": \"Aves/Piranga flava/5fae0b45b35f60c7c9aba32d3c26c86b.jpg\",\n  \"216551.jpg\": \"Aves/Piranga flava/3abf49975b56b73d9ece0c9aff6aede2.jpg\",\n  \"216552.jpg\": \"Aves/Piranga flava/9aba27a692822bba8ac38114b9f081d9.jpg\",\n  \"216554.jpg\": \"Aves/Piranga flava/fb0fbee7f8acd076534b7ba54e057c12.jpg\",\n  \"216555.jpg\": \"Aves/Piranga flava/cb1f90dac4fd5ac25f6216be2b5b64e6.jpg\",\n  \"216557.jpg\": \"Aves/Piranga flava/b1997dc332e0d5b63c01d6200daa90b9.jpg\",\n  \"216558.jpg\": \"Aves/Piranga flava/a9674037a4c0fa39a746abab19fd7b51.jpg\",\n  \"216559.jpg\": \"Aves/Piranga flava/65c784c5a470fe1752899b167b3a52b4.jpg\",\n  \"216560.jpg\": \"Aves/Piranga flava/cd287a5fda125f3745f486dde33b003c.jpg\",\n  \"216563.jpg\": \"Aves/Piranga flava/1e05ce8e169862f244faecfcf5f594d0.jpg\",\n  \"216568.jpg\": \"Aves/Piranga flava/d796d0253f923523ad10a4d40a9e5fec.jpg\",\n  \"216569.jpg\": \"Aves/Piranga flava/7b3e9437de89ffbc939353871922bfe8.jpg\",\n  \"216570.jpg\": \"Aves/Piranga flava/83289a0f833473a0339781be822d636f.jpg\",\n  \"216577.jpg\": \"Aves/Piranga flava/9478f01e0248a10f899cd1cc773aa7bc.jpg\",\n  \"216584.jpg\": \"Aves/Callipepla squamata/f17f524a8c3488d555e3cdf46f099079.jpg\",\n  \"216589.jpg\": \"Aves/Callipepla squamata/5ce05cef4629d23cdc64e12bc33e9472.jpg\",\n  \"216591.jpg\": \"Aves/Callipepla squamata/6e5e5d1ce9d1642e7e47410a7ffd7eea.jpg\",\n  \"216593.jpg\": \"Aves/Callipepla squamata/72e913001987f1a2fdd4cc6631b91110.jpg\",\n  \"216594.jpg\": \"Aves/Callipepla squamata/5f00b17026e1836336b35ce359bd0659.jpg\",\n  \"216595.jpg\": \"Aves/Callipepla squamata/2acf1a18b016435afef7291a1bc7890f.jpg\",\n  \"216597.jpg\": \"Aves/Callipepla squamata/a8ffed3aa86aaa889f2fa7b5884c1f24.jpg\",\n  \"216602.jpg\": \"Aves/Callipepla squamata/9292dbfc05b248eddaefe2ada3cdc6dd.jpg\",\n  \"216604.jpg\": \"Aves/Callipepla squamata/643612156d4fb2617bb745d41d3d8f79.jpg\",\n  \"216608.jpg\": \"Aves/Callipepla squamata/81a0e7eee2c58798ab82aa6111d2c76e.jpg\",\n  \"216614.jpg\": \"Aves/Callipepla squamata/cb9a2b93f9cb6682869b9f4eeb718b3e.jpg\",\n  \"216615.jpg\": \"Aves/Callipepla squamata/59485072b700c98e9945e285f65665d2.jpg\",\n  \"216616.jpg\": \"Aves/Callipepla squamata/67b346835447874fbfe430dd8623c620.jpg\",\n  \"216622.jpg\": \"Aves/Callipepla squamata/c97c664db9d9f21979e047f19304c0a2.jpg\",\n  \"216623.jpg\": \"Aves/Callipepla squamata/39fd91b76686fdb3cdf4ba24eb3c835f.jpg\",\n  \"216624.jpg\": \"Aves/Callipepla squamata/90fecb6c23ccab6aaf1bcd9ebb035e09.jpg\",\n  \"216625.jpg\": \"Aves/Callipepla squamata/76bda76518bf2247252665ff743e8c23.jpg\",\n  \"216628.jpg\": \"Aves/Callipepla squamata/56cc170a62f046872966a9d974e2016a.jpg\",\n  \"216629.jpg\": \"Aves/Callipepla squamata/45ac24976d89d04f0351237c9419caf5.jpg\",\n  \"216630.jpg\": \"Aves/Callipepla squamata/21788edb6c49dfcfe9f5c73f9136546f.jpg\",\n  \"216631.jpg\": \"Aves/Callipepla squamata/d476ca885dac3d51d05ff2cfd15982b6.jpg\",\n  \"216633.jpg\": \"Insecta/Hypena baltimoralis/8ae7a65de8228e9c8391a3366091cfda.jpg\",\n  \"216635.jpg\": \"Insecta/Hypena baltimoralis/87080aecf2adcb109092e64026007e1c.jpg\",\n  \"216639.jpg\": \"Insecta/Hypena baltimoralis/0328c0199c5351584087e793e1deea36.jpg\",\n  \"216640.jpg\": \"Insecta/Hypena baltimoralis/7166ceda5fdec1e71eec8fe80c99f653.jpg\",\n  \"216641.jpg\": \"Insecta/Hypena baltimoralis/f8a5b5d8a4570a122e99a6100fad6f1d.jpg\",\n  \"216643.jpg\": \"Insecta/Hypena baltimoralis/18858e7542c2c07cf0059806884b246f.jpg\",\n  \"216644.jpg\": \"Insecta/Hypena baltimoralis/0c40b66d61a095bde4bdd5ad49a3484d.jpg\",\n  \"216645.jpg\": \"Insecta/Hypena baltimoralis/2deb1737b59a27592abcf2959c2c35d2.jpg\",\n  \"216647.jpg\": \"Insecta/Hypena baltimoralis/106a9d7519e7bf6a6bbbe98c7bc5ffb8.jpg\",\n  \"216648.jpg\": \"Insecta/Hypena baltimoralis/f61379b8b3d4e315b9edc9d743923adf.jpg\",\n  \"216649.jpg\": \"Insecta/Hypena baltimoralis/ca582cd3fc29062526cc156cd259f9f2.jpg\",\n  \"21665.jpg\": \"Aves/Archilochus colubris/d34e2c148ca1fe8a5671cdab8f88b7fc.jpg\",\n  \"216650.jpg\": \"Insecta/Hypena baltimoralis/a9c1b70bc3d2b4fc2c7495e509f3b709.jpg\",\n  \"216651.jpg\": \"Insecta/Hypena baltimoralis/c6a7198948a1d7b689c15dab4de381ab.jpg\",\n  \"216654.jpg\": \"Insecta/Hypena baltimoralis/7e5255cfe46652412a9dfbcab3effde3.jpg\",\n  \"216655.jpg\": \"Insecta/Hypena baltimoralis/79c97eb767c30da335e35b53d075eac5.jpg\",\n  \"216657.jpg\": \"Insecta/Hypena baltimoralis/fd1b925c375b777f32d2ed0c8bd36187.jpg\",\n  \"216659.jpg\": \"Insecta/Hypena baltimoralis/b32de67b1f3b30c8945e74d4b8dc56a1.jpg\",\n  \"216660.jpg\": \"Insecta/Hypena baltimoralis/40c16b40910756b02d041878f612f596.jpg\",\n  \"216666.jpg\": \"Insecta/Hypena baltimoralis/6401b7be51aa234cda89a6fc8e6136f0.jpg\",\n  \"216670.jpg\": \"Insecta/Hypena baltimoralis/96d97228a8ba6bceeefe54575f0b55be.jpg\",\n  \"216672.jpg\": \"Aves/Vireo flavifrons/49402f44660db8ac106d3fca96375420.jpg\",\n  \"216673.jpg\": \"Aves/Vireo flavifrons/7ef41a49d716610ffd07b0e6dbc8d6e8.jpg\",\n  \"216676.jpg\": \"Aves/Vireo flavifrons/e99ae812c18b0c1185e171a170ca5efe.jpg\",\n  \"21668.jpg\": \"Aves/Archilochus colubris/20461bdbfb994c984839c8282bdea71f.jpg\",\n  \"216687.jpg\": \"Aves/Vireo flavifrons/376fa238b419e0790fbb42296a96aab2.jpg\",\n  \"216688.jpg\": \"Aves/Vireo flavifrons/0d59283a7679bd653469fec80e646e2b.jpg\",\n  \"216696.jpg\": \"Aves/Vireo flavifrons/1c762e27f9c286b4dbf95c0620886979.jpg\",\n  \"216699.jpg\": \"Aves/Vireo flavifrons/68d3726b0babf9190195c86b0471479e.jpg\",\n  \"21670.jpg\": \"Aves/Archilochus colubris/6630f9b38994cec90634a4e6e15f9f5c.jpg\",\n  \"216705.jpg\": \"Aves/Vireo flavifrons/7ec04b6b9e1f0391233a16bc977cdaad.jpg\",\n  \"216709.jpg\": \"Insecta/Lestes disjunctus/dcb188c1e783873a5e496bf56739fc56.jpg\",\n  \"216714.jpg\": \"Insecta/Lestes disjunctus/facafe4df9dd541136ee219e8ee876f8.jpg\",\n  \"216715.jpg\": \"Insecta/Lestes disjunctus/7dc44ed8bf9182b8017738ebe35abce7.jpg\",\n  \"216723.jpg\": \"Insecta/Lestes disjunctus/77ce5968c68716fcea09c5f7355e7da5.jpg\",\n  \"216724.jpg\": \"Aves/Contopus virens/7c1d5f0549707368b298a2dc75a1f0fb.jpg\",\n  \"216725.jpg\": \"Aves/Contopus virens/285783b8f898b1f2cda8f2be93d8bb5c.jpg\",\n  \"216726.jpg\": \"Aves/Contopus virens/5c3a4b9048fa47cca187afee4e05be0d.jpg\",\n  \"216731.jpg\": \"Aves/Contopus virens/a35b0e7a496c68392f605707b6aa0aeb.jpg\",\n  \"216732.jpg\": \"Aves/Contopus virens/43ad25aad87573e05db1f2b68770d385.jpg\",\n  \"216733.jpg\": \"Aves/Contopus virens/8621ee9072f042f3378bd93bee52c750.jpg\",\n  \"216734.jpg\": \"Aves/Contopus virens/059d6e4722ed276998bf5d3ab229f91e.jpg\",\n  \"216737.jpg\": \"Aves/Contopus virens/5eccb901aca679a8e77ad5549a11acf9.jpg\",\n  \"216738.jpg\": \"Aves/Contopus virens/d04cea0e0f4a3ecc7b11c6fcc4584566.jpg\",\n  \"216744.jpg\": \"Aves/Contopus virens/0b8b8a15180f4d27f2e42132e9b42cf0.jpg\",\n  \"216746.jpg\": \"Aves/Contopus virens/b43a266c4d0a37325de03aa5702ee2fb.jpg\",\n  \"216757.jpg\": \"Aves/Contopus virens/7ef148256b0f1c022ad033c85aeac157.jpg\",\n  \"216758.jpg\": \"Aves/Contopus virens/eed46244f3469301c9a4f177f4ff5f9c.jpg\",\n  \"216762.jpg\": \"Aves/Contopus virens/c2cbab864727bdc84a6aed3579d1e9b1.jpg\",\n  \"216768.jpg\": \"Aves/Contopus virens/cc6e84d1bd6a59a6f5524a0fed8f01f4.jpg\",\n  \"216779.jpg\": \"Aves/Contopus virens/76b5758ebb858f3f524dad0e9ec48dbb.jpg\",\n  \"21678.jpg\": \"Aves/Archilochus colubris/1a2df76c9cd55cc7d72d6a8533199eac.jpg\",\n  \"216780.jpg\": \"Aves/Contopus virens/8f76c5f3af0f50ea6b0b7e1c1048b6c0.jpg\",\n  \"216781.jpg\": \"Aves/Contopus virens/c39ba2a882b9b0e551aa175fa679f952.jpg\",\n  \"216782.jpg\": \"Aves/Contopus virens/97e28c0c5b599249845742fd7c43a8e9.jpg\",\n  \"216790.jpg\": \"Aves/Contopus virens/668a228237c3f82b55f9fc27d3960bef.jpg\",\n  \"216791.jpg\": \"Aves/Contopus virens/d924c2deedb54e679eacc0a53951da59.jpg\",\n  \"216792.jpg\": \"Aves/Contopus virens/e96b9610a2767fdcc5b3a2c1daed8d76.jpg\",\n  \"216793.jpg\": \"Aves/Contopus virens/8da78b6fdceef8ab42b2d7d16451aded.jpg\",\n  \"216813.jpg\": \"Reptilia/Coluber flagellum/1f73cb63a176e21a03385cb9d855fe72.jpg\",\n  \"216817.jpg\": \"Reptilia/Coluber flagellum/4970aa8c7563252b789bf2647d7851c4.jpg\",\n  \"216819.jpg\": \"Reptilia/Coluber flagellum/74f1881da91fdfe17f6dd2fc072a11aa.jpg\",\n  \"216829.jpg\": \"Reptilia/Coluber flagellum/8d941157e998a911d1c2b8b74203c8d0.jpg\",\n  \"216839.jpg\": \"Reptilia/Coluber flagellum/a4954e1dfcad4aa54374e8d766f9de28.jpg\",\n  \"216848.jpg\": \"Reptilia/Coluber flagellum/8824425e42b3246489895212c7f07c36.jpg\",\n  \"216851.jpg\": \"Reptilia/Coluber flagellum/7e36d25c09fe9a691d7957ecd364a918.jpg\",\n  \"216854.jpg\": \"Reptilia/Coluber flagellum/0a6cfa10b4a1212e34d1c7471b90e890.jpg\",\n  \"216855.jpg\": \"Reptilia/Coluber flagellum/d27a4fba8c37dc3e8e15210a12f82f2b.jpg\",\n  \"216867.jpg\": \"Reptilia/Coluber flagellum/9312f43c804f665f38dd602af9245aff.jpg\",\n  \"216870.jpg\": \"Reptilia/Coluber flagellum/cd6a02bb6c855bf3a35d112c313d49bc.jpg\",\n  \"216871.jpg\": \"Reptilia/Coluber flagellum/4541dc8178bc947d4ce60480819f1a5e.jpg\",\n  \"216872.jpg\": \"Reptilia/Coluber flagellum/345ede6eabf168bd9c955b744feccab1.jpg\",\n  \"216881.jpg\": \"Reptilia/Coluber flagellum/e76788e2dea193e604de920cf3f4a206.jpg\",\n  \"216885.jpg\": \"Reptilia/Coluber flagellum/34987dc91c7599553ba33f92237953d7.jpg\",\n  \"216897.jpg\": \"Reptilia/Coluber flagellum/1d378853674ed2678b710bba1178296c.jpg\",\n  \"216907.jpg\": \"Reptilia/Coluber flagellum/0e535e71d40e7af96cadd876d3cff3fe.jpg\",\n  \"216908.jpg\": \"Reptilia/Coluber flagellum/05b37ce8cc2e4ee9e47e26dcf82a9180.jpg\",\n  \"216920.jpg\": \"Reptilia/Coluber flagellum/12e099f0e044393be3771bba93ad5d9a.jpg\",\n  \"216921.jpg\": \"Reptilia/Coluber flagellum/3fcc10f7a968af8a110ed54b293b92f1.jpg\",\n  \"216923.jpg\": \"Aves/Tringa incana/aa711f7f5200f47c47aec8b00ab6bacb.jpg\",\n  \"216929.jpg\": \"Aves/Tringa incana/d401f6c6bf7c44a9d742b45620e69868.jpg\",\n  \"21693.jpg\": \"Aves/Archilochus colubris/9dbf6b1b5ee50020ac431754dcc792de.jpg\",\n  \"216930.jpg\": \"Aves/Tringa incana/32ccab1e147214d7b98bfab8e8bc2d1a.jpg\",\n  \"216931.jpg\": \"Aves/Tringa incana/9d1b070d936921ac63d50760eba604b0.jpg\",\n  \"216932.jpg\": \"Aves/Tringa incana/c4af4443b01dfd098c07c6f1fe32caae.jpg\",\n  \"216933.jpg\": \"Aves/Tringa incana/60100c3b930ca707e3d05b03f04a50aa.jpg\",\n  \"216934.jpg\": \"Aves/Tringa incana/04cd9b3b806ddabe3c5627e5cda85e04.jpg\",\n  \"216938.jpg\": \"Aves/Tringa incana/cf4b85a086851a21e039498ab0de4409.jpg\",\n  \"216944.jpg\": \"Aves/Tringa incana/9993b36825d8b7ef8b1dad550817d0bf.jpg\",\n  \"216945.jpg\": \"Aves/Tringa incana/e9691fa74ff1c30682d956ae9e6de80d.jpg\",\n  \"216950.jpg\": \"Aves/Tringa incana/36c70c54520d009acde48704e3ff1876.jpg\",\n  \"216954.jpg\": \"Aves/Tringa incana/3fcec37496c787d80545f402f952d7f7.jpg\",\n  \"216963.jpg\": \"Aves/Tringa incana/93d719fdcb329031d8e4efea50cd8ef5.jpg\",\n  \"216964.jpg\": \"Aves/Tringa incana/d206ff397c5401e9b3c9778a39869622.jpg\",\n  \"216965.jpg\": \"Aves/Tringa incana/1da7a932231cdcc9b91687c1eb1bdea2.jpg\",\n  \"216966.jpg\": \"Aves/Tringa incana/fe3da1212f04f26f4b8b70a0f36334d3.jpg\",\n  \"216969.jpg\": \"Aves/Tringa incana/65d7fd6b7c7833f73149cc751280a05f.jpg\",\n  \"216970.jpg\": \"Aves/Tringa incana/28839be95d8b7a60eb0f6cd6fafe4828.jpg\",\n  \"21698.jpg\": \"Aves/Archilochus colubris/26f8c0b0f3155ef1595764f928cc03fa.jpg\",\n  \"217000.jpg\": \"Reptilia/Amblyrhynchus cristatus/63c5102139322bc4d15e15cbb84d6fac.jpg\",\n  \"217009.jpg\": \"Reptilia/Amblyrhynchus cristatus/83fa494772084a2e4e6cfea984a7f331.jpg\",\n  \"217012.jpg\": \"Reptilia/Amblyrhynchus cristatus/7b47092ab34b96171fdbe39a1e5e8f63.jpg\",\n  \"217103.jpg\": \"Mollusca/Crassostrea virginica/edd46bbf0e703e707b2c201af83bd7e1.jpg\",\n  \"217115.jpg\": \"Mollusca/Crassostrea virginica/5b28098275dd16538a7c88c63027720d.jpg\",\n  \"217137.jpg\": \"Aves/Dives dives/040b6c0a3bce8a3824185751efb5696a.jpg\",\n  \"217138.jpg\": \"Aves/Dives dives/5fc4f76e9ed83a9133b61266b97e894e.jpg\",\n  \"217139.jpg\": \"Aves/Dives dives/f591acfc83d045f3c2c4224f58f9005c.jpg\",\n  \"217145.jpg\": \"Aves/Dives dives/384c7db85fae3ba40224ce837fced5ac.jpg\",\n  \"217154.jpg\": \"Aves/Dives dives/be772657a7104674921fa4751005ed71.jpg\",\n  \"217155.jpg\": \"Aves/Dives dives/e0fac75dcc90b3fbd049800fd1b760e2.jpg\",\n  \"217157.jpg\": \"Aves/Dives dives/b760ac4210a1fc12ddc828e42ffbf7a9.jpg\",\n  \"21725.jpg\": \"Aves/Archilochus colubris/fe46424cdb10acd4ed17da38bb0a5331.jpg\",\n  \"21727.jpg\": \"Aves/Setophaga pinus/c7b4b15764c3bda466645aaef0ad1e7c.jpg\",\n  \"21735.jpg\": \"Aves/Setophaga pinus/10bdc24363cf60c6a55befc05eb6aa0a.jpg\",\n  \"217356.jpg\": \"Aves/Ardea alba/96604558a07cbf2a8796d516cf4e0bc2.jpg\",\n  \"21737.jpg\": \"Aves/Setophaga pinus/1d927f7407b85ca1a2c7652d78ba256e.jpg\",\n  \"217408.jpg\": \"Aves/Ardea alba/02dde95665930e336ff189d0c322a551.jpg\",\n  \"217476.jpg\": \"Aves/Ardea alba/72e34e9ed8355c82fbb7fdd9b7fa39d6.jpg\",\n  \"21758.jpg\": \"Aves/Setophaga pinus/14e82cbbeb9faa69e4b4b1fb4e93d10f.jpg\",\n  \"21761.jpg\": \"Aves/Setophaga pinus/f9986946849c2431917c719446ea0496.jpg\",\n  \"217709.jpg\": \"Aves/Ardea alba/48d801a0d169019bbf60a1c01be2f3a7.jpg\",\n  \"21771.jpg\": \"Aves/Setophaga pinus/0cf23b04a4c741ed885c6a63ca6b7539.jpg\",\n  \"21773.jpg\": \"Aves/Setophaga pinus/d58229ffb4b24268279f1277e25de7ea.jpg\",\n  \"21774.jpg\": \"Aves/Setophaga pinus/09d7d76a049f47d03daf710f8f7a633e.jpg\",\n  \"21779.jpg\": \"Aves/Setophaga pinus/4f2c949939d0a57e53a982945c32547f.jpg\",\n  \"21783.jpg\": \"Aves/Setophaga pinus/800b78d236b78af584322356ecad9156.jpg\",\n  \"21786.jpg\": \"Aves/Setophaga pinus/e66d783dfb679e895975d2684477de8f.jpg\",\n  \"217882.jpg\": \"Aves/Ardea alba/d12eb8ab5c1b4465bad7eca2aa14a76c.jpg\",\n  \"217885.jpg\": \"Aves/Ardea alba/a841ae37818a2e33dd42158c03148fa0.jpg\",\n  \"217965.jpg\": \"Aves/Ardea alba/4753e8dcf1194cf64b2282ef31614f8f.jpg\",\n  \"218003.jpg\": \"Aves/Ardea alba/67815d169b0aefbf85d066f757e163ac.jpg\",\n  \"218045.jpg\": \"Aves/Ardea alba/249bb02c2308981841ab5ad882451684.jpg\",\n  \"218060.jpg\": \"Aves/Ardea alba/bff59c775594b38155eaf74c8fb6ffe3.jpg\",\n  \"21828.jpg\": \"Aves/Setophaga pinus/0eb2583852e85ac1321b55942525b63b.jpg\",\n  \"21833.jpg\": \"Aves/Setophaga pinus/41e64c8517454f52b0f5fd0a506dbdc2.jpg\",\n  \"218338.jpg\": \"Aves/Ardea alba/225d74e4e34f3e8d1706bfb8f3c7868d.jpg\",\n  \"21834.jpg\": \"Aves/Setophaga pinus/a583dbc8530022542686070983dd1276.jpg\",\n  \"218341.jpg\": \"Aves/Ardea alba/9778cddd4471e667bd076dbf5f6b8a7c.jpg\",\n  \"21835.jpg\": \"Aves/Setophaga pinus/8dd1e3950a17969165acbcee790883bd.jpg\",\n  \"21836.jpg\": \"Aves/Setophaga pinus/ce64096d0db63e37fab6670adb86a1ad.jpg\",\n  \"21842.jpg\": \"Aves/Setophaga pinus/44c958c35a1610db7b4560e702fe0085.jpg\",\n  \"21843.jpg\": \"Aves/Setophaga pinus/1a95a3323929cec21e78ec456d6a9bba.jpg\",\n  \"21844.jpg\": \"Aves/Setophaga pinus/cf3167bbb1805f70e68e3c248ed7575a.jpg\",\n  \"21845.jpg\": \"Aves/Setophaga pinus/b300af66dae39a1d37bdb475ea4bcb97.jpg\",\n  \"21851.jpg\": \"Amphibia/Dendrobates auratus/6ccc223b3ff9aa3e668619e49ecd86d2.jpg\",\n  \"21857.jpg\": \"Amphibia/Dendrobates auratus/429e4051acf1d1e7635d8a3e5f5dccfa.jpg\",\n  \"218578.jpg\": \"Aves/Ardea alba/94abfcb9a12365741f5de3dec19b0750.jpg\",\n  \"21861.jpg\": \"Amphibia/Dendrobates auratus/fb63500f3ea6fe728c1c50b3bdbee9b3.jpg\",\n  \"218613.jpg\": \"Aves/Ardea alba/4b574cbd9d001a07edf9c5b7390057e8.jpg\",\n  \"21862.jpg\": \"Amphibia/Dendrobates auratus/96bcd9373b81ae0ceb41fa4f1fa431a7.jpg\",\n  \"218642.jpg\": \"Aves/Ardea alba/ec7937be07e3800b28319d3fbdc1e80b.jpg\",\n  \"21867.jpg\": \"Amphibia/Dendrobates auratus/7080eebe2c8f4b32945041291ee6dfaf.jpg\",\n  \"21870.jpg\": \"Amphibia/Dendrobates auratus/980f9bbe0453d3d1f5b151596575f2b2.jpg\",\n  \"21875.jpg\": \"Actinopterygii/Salmo trutta/65ca8afa792ad74f7ef4d553b6195591.jpg\",\n  \"218891.jpg\": \"Aves/Ardea alba/1ac788416670a7f1c7618fe4dacf4951.jpg\",\n  \"218892.jpg\": \"Aves/Ardea alba/5585db1b6d8d1134cf2c875866c3a5fd.jpg\",\n  \"21891.jpg\": \"Actinopterygii/Salmo trutta/1520bcd9947a5b648737fa84473e204e.jpg\",\n  \"21892.jpg\": \"Actinopterygii/Salmo trutta/e5a81ce7c6e892a0255428a942fda515.jpg\",\n  \"21893.jpg\": \"Actinopterygii/Salmo trutta/8c42b231dbf3e2ffce767af902e4b29b.jpg\",\n  \"218930.jpg\": \"Aves/Ardea alba/236d1085f0a7408642355885a856bf1e.jpg\",\n  \"218944.jpg\": \"Aves/Ardea alba/e474690eb6909ad65ebf231c717a0c57.jpg\",\n  \"218961.jpg\": \"Aves/Ardea alba/c8ed29ac89c2def6227839a9d8e3c868.jpg\",\n  \"21897.jpg\": \"Actinopterygii/Salmo trutta/dd00a6b61ccad3111e62ca3ab210cf76.jpg\",\n  \"21908.jpg\": \"Actinopterygii/Salmo trutta/f1d38e556455e233ff328973fd4416ec.jpg\",\n  \"21910.jpg\": \"Actinopterygii/Salmo trutta/2ebb1297620503cdec9c637dbf737cad.jpg\",\n  \"21919.jpg\": \"Insecta/Scolypopa australis/4c943e06fd2c6d62285c78c2cfb46b7e.jpg\",\n  \"21923.jpg\": \"Insecta/Scolypopa australis/08e8ff20249930f59387459b4e00e016.jpg\",\n  \"21932.jpg\": \"Insecta/Scolypopa australis/432018fd5c2176f915eec972236ffbc7.jpg\",\n  \"21938.jpg\": \"Insecta/Scolypopa australis/1c9c2574cf701da9cafdba80bd806684.jpg\",\n  \"21939.jpg\": \"Aves/Galerida cristata/a78b608c56df24079be6b42c332d45b2.jpg\",\n  \"219433.jpg\": \"Mammalia/Oryctolagus cuniculus domesticus/828b0d35a3f18b0a420705f8c6826821.jpg\",\n  \"219434.jpg\": \"Mammalia/Oryctolagus cuniculus domesticus/14f450a159e00bb8bdc0a8dfc8c0f371.jpg\",\n  \"219435.jpg\": \"Mammalia/Oryctolagus cuniculus domesticus/b9791562811936e8c2a3866f407273f6.jpg\",\n  \"219438.jpg\": \"Aves/Manorina melanocephala/a7a2f9b1c5f4e64cb85981880e35a759.jpg\",\n  \"219445.jpg\": \"Aves/Manorina melanocephala/3b11861a6ff9336b98cb87e7f3f9cb13.jpg\",\n  \"219446.jpg\": \"Aves/Manorina melanocephala/1b1566e94cee5d66b8b3d0037cdefa04.jpg\",\n  \"219447.jpg\": \"Aves/Manorina melanocephala/13535fbc593dfddbf804057118eedd4b.jpg\",\n  \"219448.jpg\": \"Aves/Manorina melanocephala/7a01086ad32b6da0c1b2f6bf4076a806.jpg\",\n  \"219449.jpg\": \"Aves/Manorina melanocephala/9c652a6bab652ce790506c92ac85fcec.jpg\",\n  \"219455.jpg\": \"Aves/Manorina melanocephala/a876001106a89272cb44dea57e2a2aeb.jpg\",\n  \"219458.jpg\": \"Aves/Manorina melanocephala/0d3d468bb85ab2b840143f442d3cb52c.jpg\",\n  \"219460.jpg\": \"Aves/Manorina melanocephala/8657cc1e1b155940abc5f4e3fd99b3d9.jpg\",\n  \"219462.jpg\": \"Aves/Manorina melanocephala/9f2d5a5f29ddd73de823055ce9cd14a8.jpg\",\n  \"219463.jpg\": \"Insecta/Mestra amymone/fd95a542610ab2fbd0762b998f2b980e.jpg\",\n  \"219465.jpg\": \"Insecta/Mestra amymone/482674a0dfebd268ce87889b48394713.jpg\",\n  \"219469.jpg\": \"Insecta/Mestra amymone/626bd4bbbbc4940125459fec63d28b3d.jpg\",\n  \"21947.jpg\": \"Aves/Galerida cristata/eea7fcc7807107cf650d00d835951f89.jpg\",\n  \"219472.jpg\": \"Insecta/Mestra amymone/d628d72ece2378fd67616e59820018a9.jpg\",\n  \"219477.jpg\": \"Insecta/Mestra amymone/0184bd66a69ba48847498e1a9084c6d0.jpg\",\n  \"219483.jpg\": \"Insecta/Mestra amymone/226ee57a72623872670175424e340532.jpg\",\n  \"219484.jpg\": \"Insecta/Mestra amymone/87053f6050fea28da329b514b1b78bbf.jpg\",\n  \"219487.jpg\": \"Insecta/Mestra amymone/aeb8a0bab48005a27394bc2e501dcda6.jpg\",\n  \"219497.jpg\": \"Insecta/Mestra amymone/e0f358f95125238c4c18134bf646c264.jpg\",\n  \"219498.jpg\": \"Insecta/Mestra amymone/c473f7150df46b2cd98177db1b97d244.jpg\",\n  \"219499.jpg\": \"Insecta/Mestra amymone/6b646fac787883a323d0fe1f372c9e45.jpg\",\n  \"219501.jpg\": \"Insecta/Mestra amymone/d6c610bcd2a519d67413913dd7e53cd1.jpg\",\n  \"219502.jpg\": \"Insecta/Mestra amymone/8cd6d36da8af7bd4e5ea25a3ddc43508.jpg\",\n  \"219528.jpg\": \"Insecta/Mestra amymone/b9baa0898b64dd2b9b51a6e9f92a440b.jpg\",\n  \"219529.jpg\": \"Insecta/Mestra amymone/1115744166914f28748b795ed175733e.jpg\",\n  \"219533.jpg\": \"Insecta/Mestra amymone/95ab228523596df30174d9204530956b.jpg\",\n  \"219534.jpg\": \"Insecta/Mestra amymone/4bb944cfb48266f661154eddb1f8a8a4.jpg\",\n  \"219535.jpg\": \"Insecta/Mestra amymone/2aa39f62c0ec3bbaee16e98f77c4abcf.jpg\",\n  \"219538.jpg\": \"Insecta/Mestra amymone/66a4645721ea3d601b5d3c2380b79068.jpg\",\n  \"219543.jpg\": \"Insecta/Mestra amymone/9d3c2fa2916b6fab6a7fc05836a5db66.jpg\",\n  \"219544.jpg\": \"Insecta/Mestra amymone/8b0ef57ebbf8bad9d7fcfdfa467c32ce.jpg\",\n  \"219549.jpg\": \"Insecta/Mestra amymone/d182817374e458acfa996e6751fa3e7d.jpg\",\n  \"219564.jpg\": \"Reptilia/Agkistrodon contortrix laticinctus/95e9ee964c64b5dadfb49c0c9a858611.jpg\",\n  \"219565.jpg\": \"Reptilia/Agkistrodon contortrix laticinctus/05adcfbeb8e96b84f701cf8bcb04ac57.jpg\",\n  \"219567.jpg\": \"Reptilia/Agkistrodon contortrix laticinctus/9bb73a38918f0d1bf0e1171925c7802a.jpg\",\n  \"219568.jpg\": \"Reptilia/Agkistrodon contortrix laticinctus/48947ecb1a893083ab1937a675eba2f2.jpg\",\n  \"219570.jpg\": \"Reptilia/Agkistrodon contortrix laticinctus/34711b126e9b0981837fa412c503f101.jpg\",\n  \"219571.jpg\": \"Reptilia/Agkistrodon contortrix laticinctus/0165b3367100c452ba971eaa09c5b69d.jpg\",\n  \"219572.jpg\": \"Reptilia/Agkistrodon contortrix laticinctus/a78fe1dfd176763846a6132ba2ccb947.jpg\",\n  \"219579.jpg\": \"Reptilia/Agkistrodon contortrix laticinctus/c57a0244829ee6fd2f2e2ac0423250b3.jpg\",\n  \"219580.jpg\": \"Reptilia/Agkistrodon contortrix laticinctus/1ac4d0d5f54c2861bdd769e6509f9150.jpg\",\n  \"219581.jpg\": \"Reptilia/Agkistrodon contortrix laticinctus/322dc8729ea733260128c5d3bd9bfa4a.jpg\",\n  \"219582.jpg\": \"Reptilia/Agkistrodon contortrix laticinctus/828ece6afb88640562c8c438a298619e.jpg\",\n  \"219584.jpg\": \"Reptilia/Agkistrodon contortrix laticinctus/f121ac03a05f78f794a187f77b80295b.jpg\",\n  \"219589.jpg\": \"Reptilia/Agkistrodon contortrix laticinctus/b6e52024aed91575fe53060165a8d0fc.jpg\",\n  \"219590.jpg\": \"Reptilia/Agkistrodon contortrix laticinctus/07aadeb205033b9f13b9782cb84c5887.jpg\",\n  \"219591.jpg\": \"Reptilia/Agkistrodon contortrix laticinctus/0a7e8e8ed55de7c92d45061aa5c3e8c2.jpg\",\n  \"219593.jpg\": \"Reptilia/Agkistrodon contortrix laticinctus/0ba5cd96a910c51554ed0e08f7eaa25c.jpg\",\n  \"219594.jpg\": \"Reptilia/Agkistrodon contortrix laticinctus/0af010dabacfe5c520341b4b9d9fed35.jpg\",\n  \"219596.jpg\": \"Reptilia/Agkistrodon contortrix laticinctus/958378a02a0f77ae07048e1dc5d10177.jpg\",\n  \"219597.jpg\": \"Reptilia/Agkistrodon contortrix laticinctus/d1f1e84302f395bb26d1a19873e529c2.jpg\",\n  \"219603.jpg\": \"Reptilia/Agkistrodon contortrix laticinctus/e64d6400fd6ddf61f745c40db30da4be.jpg\",\n  \"21964.jpg\": \"Aves/Galerida cristata/1c3d10bae23133ca129b1700b4a8b5e8.jpg\",\n  \"219678.jpg\": \"Aves/Petrochelidon pyrrhonota/a552e8615d4721eefb75d7cc2bdfc5f3.jpg\",\n  \"219680.jpg\": \"Aves/Petrochelidon pyrrhonota/3e66004f9e6bb8021497f1cb63e9b0fb.jpg\",\n  \"219706.jpg\": \"Aves/Petrochelidon pyrrhonota/78e8b76e023824600431719f6d02bc68.jpg\",\n  \"219714.jpg\": \"Aves/Petrochelidon pyrrhonota/89e37552f97bf3d7dcbdb74c2d7d58f8.jpg\",\n  \"21974.jpg\": \"Aves/Galerida cristata/a74288f992b778b175dfdbe51cc882ee.jpg\",\n  \"219752.jpg\": \"Aves/Petrochelidon pyrrhonota/578e1289248a6307ee1129f5236e83dd.jpg\",\n  \"219780.jpg\": \"Aves/Petrochelidon pyrrhonota/49ed01aead9a991a871ec8ce2a266a24.jpg\",\n  \"219796.jpg\": \"Aves/Petrochelidon pyrrhonota/0f516fa8c7b8c0ed1b7a3aae43b6b2a0.jpg\",\n  \"21981.jpg\": \"Aves/Galerida cristata/58653e1dfcee8fb9b94ab9f02fb9d9f8.jpg\",\n  \"219835.jpg\": \"Aves/Petrochelidon pyrrhonota/eaf79883c3e406a293618b4eb0c55d34.jpg\",\n  \"219847.jpg\": \"Aves/Petrochelidon pyrrhonota/09c1df59cb56d4a724a34fec8f4139b8.jpg\",\n  \"21985.jpg\": \"Aves/Galerida cristata/1daecebc3e5ee0a684ca829423ffd320.jpg\",\n  \"219865.jpg\": \"Insecta/Pararge aegeria/41f0a9f45cb997382b94a14d62645e01.jpg\",\n  \"219866.jpg\": \"Insecta/Pararge aegeria/de27671173be4cbf955a6fbddf887368.jpg\",\n  \"219867.jpg\": \"Insecta/Pararge aegeria/b63e9b1d30d0febd900558b57e13c5f3.jpg\",\n  \"219870.jpg\": \"Insecta/Pararge aegeria/5cc5a034be9dec052265ba1f41ce255e.jpg\",\n  \"219872.jpg\": \"Insecta/Pararge aegeria/348d7e028e6c417820b15ab9384137f2.jpg\",\n  \"219881.jpg\": \"Insecta/Pararge aegeria/7c3fe55ae9ca0620cc3b49a1450feff8.jpg\",\n  \"219900.jpg\": \"Insecta/Pararge aegeria/04a8c0ef69752d07a9461cfec75e96ef.jpg\",\n  \"219914.jpg\": \"Insecta/Pararge aegeria/8695008194ac3c6ccdd27bd4da89eb00.jpg\",\n  \"219915.jpg\": \"Insecta/Pararge aegeria/22520fff109bae3794aba8342e8b83e9.jpg\",\n  \"219916.jpg\": \"Insecta/Pararge aegeria/7455f12a4368d8072c3f8e169c19876d.jpg\",\n  \"219921.jpg\": \"Insecta/Pararge aegeria/3fda459e0f85b6af497eb732d3aeb7de.jpg\",\n  \"219924.jpg\": \"Insecta/Pararge aegeria/307f0c8535f21054d5fd5ffbfe11e84a.jpg\",\n  \"219925.jpg\": \"Insecta/Pararge aegeria/6c7c55b338630799757582ff856ee475.jpg\",\n  \"219926.jpg\": \"Insecta/Pararge aegeria/6906e2c841f33ef2e31abe962704c079.jpg\",\n  \"219948.jpg\": \"Insecta/Pararge aegeria/4cc6f91e1d48227ecd3d25388b5498a0.jpg\",\n  \"219956.jpg\": \"Insecta/Pararge aegeria/10184e987458bd2d7da77e31ddea1714.jpg\",\n  \"219961.jpg\": \"Insecta/Pararge aegeria/625d6a0bf37b864a23985097d9c67c80.jpg\",\n  \"219963.jpg\": \"Insecta/Pararge aegeria/e34cb8eea80e46caac2aa8ff45d4f95c.jpg\",\n  \"219967.jpg\": \"Insecta/Pararge aegeria/146127af48ce3cd4623c5acad9e350e9.jpg\",\n  \"219970.jpg\": \"Insecta/Pararge aegeria/57a5dbe6cb52e0934b5d2f062699a0a4.jpg\",\n  \"219971.jpg\": \"Insecta/Pararge aegeria/fa5adf8b9dc5f7b3f144ccb11c93fe52.jpg\",\n  \"219972.jpg\": \"Insecta/Pararge aegeria/f4734e12991d7bec7d27266baeaa1c6c.jpg\",\n  \"219973.jpg\": \"Insecta/Pararge aegeria/871ebd1a002ae2227cb49a53b0cc5172.jpg\",\n  \"220000.jpg\": \"Aves/Nyctidromus albicollis/02e43063a0a5efdd11f11bbe3035e984.jpg\",\n  \"220002.jpg\": \"Aves/Nyctidromus albicollis/e9989e453ba518a5b072117e0645cab4.jpg\",\n  \"220023.jpg\": \"Aves/Nyctidromus albicollis/33208860cda4abc967feede6f303374a.jpg\",\n  \"220024.jpg\": \"Aves/Nyctidromus albicollis/b1d55a33dd01333d8deeb5e23bd84210.jpg\",\n  \"220027.jpg\": \"Aves/Nyctidromus albicollis/9c7623a852e964338b4e6afc800711b5.jpg\",\n  \"220028.jpg\": \"Aves/Nyctidromus albicollis/303b176b1b8204fa5f408c862d3dbc76.jpg\",\n  \"220029.jpg\": \"Aves/Nyctidromus albicollis/bb011a2d714bc66469de4dd8fd02e4d0.jpg\",\n  \"220031.jpg\": \"Mammalia/Orcinus orca/43d153228252a48fb3a479a6db980384.jpg\",\n  \"220032.jpg\": \"Mammalia/Orcinus orca/f047f6a5299e9e0ae5f850dfd56bed5a.jpg\",\n  \"220033.jpg\": \"Mammalia/Orcinus orca/b0d72990292addc4ff41dba8f12f9ee8.jpg\",\n  \"220038.jpg\": \"Mammalia/Orcinus orca/1dac8e7d8c245ce3fc86b3ace69349e7.jpg\",\n  \"220041.jpg\": \"Mammalia/Orcinus orca/00ceb59e5944f9679db30887dfe09a06.jpg\",\n  \"220042.jpg\": \"Mammalia/Orcinus orca/afc55dc08c5e414e169b6222942fd79a.jpg\",\n  \"220047.jpg\": \"Mammalia/Orcinus orca/1016c649f652fbf44f16c4c4f35d7719.jpg\",\n  \"220053.jpg\": \"Mammalia/Orcinus orca/dca04f2790fdce361a63eba58a3af00a.jpg\",\n  \"220054.jpg\": \"Mammalia/Orcinus orca/be1ae778fb0ec4033a5fcdb6c04a1968.jpg\",\n  \"220062.jpg\": \"Mammalia/Orcinus orca/cc35f6304933f8b9e27a1b4b8f105666.jpg\",\n  \"220069.jpg\": \"Mammalia/Orcinus orca/e69b1b7735a20cc343e68775dae17605.jpg\",\n  \"220070.jpg\": \"Mammalia/Orcinus orca/671f454f0ecb2488a118ead8ac8cffba.jpg\",\n  \"220078.jpg\": \"Mammalia/Orcinus orca/b2845d97577ecc07d52e9c708699826b.jpg\",\n  \"220081.jpg\": \"Mammalia/Orcinus orca/047a68a9f45c2891514751bbb58b8e13.jpg\",\n  \"220087.jpg\": \"Mammalia/Orcinus orca/5b02dc859168574d25848746cda3cb0b.jpg\",\n  \"220088.jpg\": \"Mammalia/Orcinus orca/0658d4b0d87611f9a27fd0204713b143.jpg\",\n  \"220101.jpg\": \"Mammalia/Aepyceros melampus/b933b74c0df41eb48c74c0bfdcb0fe63.jpg\",\n  \"220102.jpg\": \"Mammalia/Aepyceros melampus/65768fabb2fbc7a273417fd561ef4d6e.jpg\",\n  \"220106.jpg\": \"Mammalia/Aepyceros melampus/07df709c1faa8ce99dfa6e956f7d5ee8.jpg\",\n  \"220108.jpg\": \"Mammalia/Aepyceros melampus/30db3bb479ad12a4980fc2c4a4e11fe4.jpg\",\n  \"220121.jpg\": \"Mammalia/Aepyceros melampus/7ae8c20d493c1a1bbec9e40d97c48502.jpg\",\n  \"220123.jpg\": \"Mammalia/Aepyceros melampus/5955f1565b8e3932ba63e435b063ac29.jpg\",\n  \"220124.jpg\": \"Mammalia/Aepyceros melampus/d2da247773b0f66d97fcd6a870bb62d8.jpg\",\n  \"220125.jpg\": \"Mammalia/Aepyceros melampus/d86ab92d941aa02725386f1af565e73d.jpg\",\n  \"220126.jpg\": \"Mammalia/Aepyceros melampus/884cf2cbc316f6c80fdfe30743a39f24.jpg\",\n  \"220128.jpg\": \"Mammalia/Aepyceros melampus/82ac35f52616152124f77f82f7a6fa94.jpg\",\n  \"220130.jpg\": \"Mammalia/Aepyceros melampus/ad23b2135f66dcf13a58df5eaf0cb448.jpg\",\n  \"220131.jpg\": \"Mammalia/Aepyceros melampus/a0cce9cfcf914211f1fa52cb156dfa34.jpg\",\n  \"220133.jpg\": \"Mammalia/Aepyceros melampus/6a97b41c44eb6d4b45e25bc173ae1aea.jpg\",\n  \"220379.jpg\": \"Aves/Netta rufina/9cf0211281b53d8c23865e18ffcf4011.jpg\",\n  \"220387.jpg\": \"Aves/Netta rufina/67abcfc4ab9c45d8a76993056fcc237e.jpg\",\n  \"220399.jpg\": \"Mollusca/Ambigolimax valentianus/16ca80e729b571e899b4fe3eb90e179f.jpg\",\n  \"220411.jpg\": \"Mollusca/Ambigolimax valentianus/1fbe05385faec942db8a651e129f1490.jpg\",\n  \"220417.jpg\": \"Mollusca/Ambigolimax valentianus/6a6564c68cde4e4c091ba1e6e43a744d.jpg\",\n  \"220418.jpg\": \"Mollusca/Ambigolimax valentianus/aefff5cff0ed7f7df3de1b570a557bf3.jpg\",\n  \"220421.jpg\": \"Mollusca/Ambigolimax valentianus/1abe0fcffedcf769e5e957ea73a17cd0.jpg\",\n  \"220426.jpg\": \"Mollusca/Ambigolimax valentianus/d62e2933c9f4c25062d83a225135c800.jpg\",\n  \"220427.jpg\": \"Mollusca/Ambigolimax valentianus/6972bfb49f6d920da143274d25b71fec.jpg\",\n  \"220429.jpg\": \"Mollusca/Ambigolimax valentianus/30c3894110eaeb06a616824e688b5589.jpg\",\n  \"220431.jpg\": \"Mollusca/Ambigolimax valentianus/014c8781cab3d2ea8e5c0a92e6d117a4.jpg\",\n  \"220433.jpg\": \"Mollusca/Ambigolimax valentianus/8517825d4f2c9bf80b5e1a994a4164c2.jpg\",\n  \"220437.jpg\": \"Mollusca/Ambigolimax valentianus/62a43c7bccb7be825b5675c930743603.jpg\",\n  \"220438.jpg\": \"Mollusca/Ambigolimax valentianus/2b5bf4f4eff10fb50e51a54912043d5c.jpg\",\n  \"220441.jpg\": \"Mollusca/Ambigolimax valentianus/3d4c7cf6606263d2c1d92fba19b319bd.jpg\",\n  \"220450.jpg\": \"Mollusca/Ambigolimax valentianus/7a55c458e7d29ae16565da8dd3edda48.jpg\",\n  \"220452.jpg\": \"Mollusca/Ambigolimax valentianus/0592b4a960604b5fc2c46f71af7a553c.jpg\",\n  \"220453.jpg\": \"Mollusca/Ambigolimax valentianus/ee0fb23fa18b2e185ccf26248134f9a8.jpg\",\n  \"220454.jpg\": \"Mollusca/Ambigolimax valentianus/aad106085d1dcc0428775d422f961afe.jpg\",\n  \"220455.jpg\": \"Mollusca/Ambigolimax valentianus/d695a7292aec7517950e55a112ce4f87.jpg\",\n  \"220457.jpg\": \"Mollusca/Ambigolimax valentianus/10dad8ebec5ce426ae86e90d85d52e54.jpg\",\n  \"220458.jpg\": \"Mollusca/Ambigolimax valentianus/61290918f570c6add2b7a85236420630.jpg\",\n  \"220468.jpg\": \"Aves/Himantopus mexicanus/cdd35ea68579e07fcdf190e5127c88f8.jpg\",\n  \"220485.jpg\": \"Aves/Himantopus mexicanus/f0c609ce3cab4f662a04bb6bb6c9fa71.jpg\",\n  \"220539.jpg\": \"Aves/Himantopus mexicanus/77a3520fd4d2d532a553152395d8032d.jpg\",\n  \"220541.jpg\": \"Aves/Himantopus mexicanus/7962db21116968a17059f7e6729d91bd.jpg\",\n  \"220546.jpg\": \"Aves/Himantopus mexicanus/386a95389c2742dc920844caad187bbe.jpg\",\n  \"220724.jpg\": \"Aves/Himantopus mexicanus/37df8343de98db52487d9b2e94f88cfb.jpg\",\n  \"220725.jpg\": \"Aves/Himantopus mexicanus/632088dd2b297774555b27b0c6e7154f.jpg\",\n  \"220740.jpg\": \"Aves/Himantopus mexicanus/9512eac758e87d988654b594193183ad.jpg\",\n  \"220743.jpg\": \"Aves/Himantopus mexicanus/e325b45388b6368a7ab18c620a6444bb.jpg\",\n  \"220765.jpg\": \"Aves/Himantopus mexicanus/2d7568aea4493d3281a03a27faa3c67b.jpg\",\n  \"220824.jpg\": \"Aves/Himantopus mexicanus/c722e0863d7356bff6cc8220fb7d1ce5.jpg\",\n  \"220854.jpg\": \"Aves/Himantopus mexicanus/a6f7f9fe34aa121b6a61eb3f48215de0.jpg\",\n  \"221031.jpg\": \"Mollusca/Megapallifera mutabilis/de0291596f9ad9d5e0e73713a6a66915.jpg\",\n  \"221032.jpg\": \"Mollusca/Megapallifera mutabilis/5ec969b8cd2a83861aef1d21e3897692.jpg\",\n  \"221041.jpg\": \"Mollusca/Megapallifera mutabilis/5972c647254079076ef75d08353a50a6.jpg\",\n  \"221047.jpg\": \"Mollusca/Megapallifera mutabilis/0e70c932bf4a2b711397bea175a5c809.jpg\",\n  \"221049.jpg\": \"Insecta/Euphoria kernii/ce30146cdc90fd0ca91f3216f57781f0.jpg\",\n  \"221054.jpg\": \"Insecta/Euphoria kernii/d2c924250487d795f3534ef1fc860b2f.jpg\",\n  \"221055.jpg\": \"Insecta/Euphoria kernii/6054dcb3d515c185596c9d3a2826926e.jpg\",\n  \"221059.jpg\": \"Insecta/Euphoria kernii/38934957763a2d8a27220bc0723b9c37.jpg\",\n  \"221072.jpg\": \"Insecta/Euphoria kernii/1521215e42631695b919d5ceef9f720f.jpg\",\n  \"221073.jpg\": \"Insecta/Euphoria kernii/7c9c9f61c8a858b84a32e3b041b8670a.jpg\",\n  \"221078.jpg\": \"Insecta/Dactylotum bicolor/1f680d728925c399f77cc1f7718ee64a.jpg\",\n  \"221081.jpg\": \"Insecta/Dactylotum bicolor/c6497bcfc259e57daabf1aa31051d031.jpg\",\n  \"221086.jpg\": \"Insecta/Dactylotum bicolor/46717f3d4c404aab533f46b4e17f904f.jpg\",\n  \"221087.jpg\": \"Insecta/Dactylotum bicolor/d4b8695e218928c7d3c060755f8cc216.jpg\",\n  \"221092.jpg\": \"Insecta/Dactylotum bicolor/5a0aeeab22b8f122898e794f87d6911b.jpg\",\n  \"221099.jpg\": \"Insecta/Dactylotum bicolor/8723aad1258e59b21e17d3503e42616f.jpg\",\n  \"221164.jpg\": \"Aves/Lagopus lagopus/95936e4f03424cc1a6aa0638f4782f77.jpg\",\n  \"221167.jpg\": \"Aves/Lagopus lagopus/305bf170007d17f05ae0fef5b5660881.jpg\",\n  \"221176.jpg\": \"Aves/Lagopus lagopus/14ed36da2b5cd79f27619f4156a73e77.jpg\",\n  \"221180.jpg\": \"Insecta/Anthidium manicatum/e47019b3fa03c3b5063b50734326412b.jpg\",\n  \"221181.jpg\": \"Insecta/Anthidium manicatum/e23df684fa5d6c97c142ec8cd06f9f41.jpg\",\n  \"221184.jpg\": \"Insecta/Anthidium manicatum/88b2ebbff4e2b5664734483eeb1bf068.jpg\",\n  \"221185.jpg\": \"Insecta/Anthidium manicatum/ca90fd235efe54def7f663c8dc86fde1.jpg\",\n  \"221186.jpg\": \"Insecta/Anthidium manicatum/0d5f16b18b949d603d31e1ea452cb535.jpg\",\n  \"221187.jpg\": \"Insecta/Anthidium manicatum/5e91b09684298256c18e6a91e2f5aee6.jpg\",\n  \"221191.jpg\": \"Insecta/Anthidium manicatum/7bfdf87923dbf42d11cc274f95fe6c47.jpg\",\n  \"221192.jpg\": \"Insecta/Anthidium manicatum/19d7d4d9090fdc53118f68f7f5f590fd.jpg\",\n  \"221193.jpg\": \"Insecta/Anthidium manicatum/b14f86d1064eb9c144d7d950d5562373.jpg\",\n  \"221194.jpg\": \"Insecta/Anthidium manicatum/3507628dfe90be3edd7adebe86cdeb3a.jpg\",\n  \"221196.jpg\": \"Insecta/Anthidium manicatum/db283a9bb2f51bd3028d48b7b43ea92e.jpg\",\n  \"221197.jpg\": \"Insecta/Anthidium manicatum/07433e7c97208e88beb3e24defe7f49e.jpg\",\n  \"221198.jpg\": \"Insecta/Anthidium manicatum/d59d2b0158faf091dce413ce28dba4e7.jpg\",\n  \"221204.jpg\": \"Insecta/Anthidium manicatum/e57689652fba7ec5cb8ebd039e4a758d.jpg\",\n  \"221205.jpg\": \"Insecta/Anthidium manicatum/7806ab38b21fa330c0c202404d4a5687.jpg\",\n  \"221206.jpg\": \"Insecta/Anthidium manicatum/fb124ae2e0744c257d7349c90e97ca13.jpg\",\n  \"221207.jpg\": \"Insecta/Anthidium manicatum/196538899ddccc5bd646bfaeab7099c7.jpg\",\n  \"221213.jpg\": \"Insecta/Anthidium manicatum/937ef607ee3139d20d0a75b141a92202.jpg\",\n  \"221214.jpg\": \"Insecta/Anthidium manicatum/5fde71b266e0ba9852f7ff82de7ef095.jpg\",\n  \"221215.jpg\": \"Insecta/Anthidium manicatum/4397b00964bab392feff77cec7feddbf.jpg\",\n  \"221216.jpg\": \"Insecta/Anthidium manicatum/9a431c86d7d2836e7f65a773826f6836.jpg\",\n  \"221225.jpg\": \"Aves/Polioptila melanura/c159504d031da68b2bd803c63402fe1b.jpg\",\n  \"221231.jpg\": \"Aves/Polioptila melanura/6f12e50aac69e30fbe18ce996fa4ad40.jpg\",\n  \"221250.jpg\": \"Aves/Polioptila melanura/6692fd2507884df7f9920c19497c2986.jpg\",\n  \"221253.jpg\": \"Aves/Polioptila melanura/eb76102219a2987db0092f6e00e44a1d.jpg\",\n  \"221271.jpg\": \"Reptilia/Chelonia mydas/21cb129961233df0cda3a0e18cbc9790.jpg\",\n  \"221284.jpg\": \"Reptilia/Chelonia mydas/d89851dc077b5d0d4b83ed3d67a16546.jpg\",\n  \"221285.jpg\": \"Reptilia/Chelonia mydas/bdc0b1698c98acd19cf6ac2c2fef0bb0.jpg\",\n  \"221297.jpg\": \"Reptilia/Chelonia mydas/df94583212b430b22798d35131b7ade4.jpg\",\n  \"221298.jpg\": \"Reptilia/Chelonia mydas/e3d6f74b10cb4ad65ad4fff9f79e3903.jpg\",\n  \"221303.jpg\": \"Reptilia/Chelonia mydas/1c7fa65dfbdb0917b511a62f02b98ccc.jpg\",\n  \"221313.jpg\": \"Reptilia/Chelonia mydas/35b9151c9ab3a173f9153ad7222259eb.jpg\",\n  \"221321.jpg\": \"Reptilia/Chelonia mydas/e58e140be08181bc46e43bc5e02548d0.jpg\",\n  \"221323.jpg\": \"Reptilia/Chelonia mydas/2fcfdb71420603381b675889b8590d1f.jpg\",\n  \"221346.jpg\": \"Reptilia/Chelonia mydas/e3a2d849cb11f1e74a82ea7143c4d06c.jpg\",\n  \"221368.jpg\": \"Reptilia/Chelonia mydas/11aeffb2222e16adc03254ab8d28fe25.jpg\",\n  \"221375.jpg\": \"Reptilia/Chelonia mydas/65cf34ed90228c6671c868a14ee1aeee.jpg\",\n  \"221380.jpg\": \"Reptilia/Chelonia mydas/1927e55075bd24211ddadcd1f3a14856.jpg\",\n  \"221381.jpg\": \"Reptilia/Chelonia mydas/2f5e96a61b0682bc7c26a727430f1fdf.jpg\",\n  \"221402.jpg\": \"Reptilia/Chelonia mydas/87de153482d8beab5b8e2a4625246d1a.jpg\",\n  \"221526.jpg\": \"Aves/Selasphorus calliope/acca7d0eff1ea36c86857f5daa46b1cf.jpg\",\n  \"221527.jpg\": \"Aves/Selasphorus calliope/bde40ee5d82164d5ca9d2ac5f0e350d7.jpg\",\n  \"221528.jpg\": \"Aves/Selasphorus calliope/1776227453958bc00019b8ce75ee91e7.jpg\",\n  \"221531.jpg\": \"Aves/Selasphorus calliope/41ee2e50c5ab5e49d3c7fb01654e93cd.jpg\",\n  \"221542.jpg\": \"Aves/Selasphorus calliope/45fc5e849693a4f83e0b3f864298fa4d.jpg\",\n  \"221543.jpg\": \"Aves/Selasphorus calliope/550b500c37c2397681228b63f91acbd5.jpg\",\n  \"221554.jpg\": \"Insecta/Pyrgus oileus/49e1c38b600b7a799b3067385d3b89ca.jpg\",\n  \"221557.jpg\": \"Insecta/Pyrgus oileus/15dc846ba1c97e340a9ff18beef14634.jpg\",\n  \"221577.jpg\": \"Insecta/Pyrgus oileus/60c92acb9cbdcd0cc6799c76c150fdd8.jpg\",\n  \"221586.jpg\": \"Insecta/Pyrgus oileus/f56dd3c877049e21ffcd53c36244bb75.jpg\",\n  \"221587.jpg\": \"Insecta/Pyrgus oileus/049c3b9bc33e604e56373b7e73846c0d.jpg\",\n  \"221593.jpg\": \"Insecta/Pyrgus oileus/d91ee0a590b447b2301905112be2a8d3.jpg\",\n  \"221595.jpg\": \"Insecta/Pyrgus oileus/06dcbb6cd46fe2222b49ed3dbb501330.jpg\",\n  \"221597.jpg\": \"Insecta/Pyrgus oileus/6e5ff8ff9599ea6d05d3e10ffca1d364.jpg\",\n  \"221601.jpg\": \"Insecta/Pyrgus oileus/3312f527b76b1ff80a3f73e3cdd8a7a3.jpg\",\n  \"221610.jpg\": \"Insecta/Pyrgus oileus/9baf33f604b79e5b52d715b21d392e72.jpg\",\n  \"221617.jpg\": \"Insecta/Pyrgus oileus/a8e51beb46e1f6cde3d47ee328645b41.jpg\",\n  \"221618.jpg\": \"Insecta/Pyrgus oileus/60ad7b638345c1047f637de46f0afc54.jpg\",\n  \"221623.jpg\": \"Insecta/Pyrgus oileus/cd6ba6e5b23eaf40a4e0bffec52f1837.jpg\",\n  \"221624.jpg\": \"Insecta/Pyrgus oileus/c3a891f4a07ec7da9fde315d345ccaed.jpg\",\n  \"221625.jpg\": \"Insecta/Pyrgus oileus/6da525385676faf29943c13d720707aa.jpg\",\n  \"221627.jpg\": \"Insecta/Pyrgus oileus/05dcd508cefde805a68d9a8333bdd8ea.jpg\",\n  \"221632.jpg\": \"Insecta/Pyrgus oileus/b9f10ab69089bb91242eb2cf53761b63.jpg\",\n  \"221633.jpg\": \"Insecta/Pyrgus oileus/ad7e659d48e14aec6782f9c57c0e6959.jpg\",\n  \"221634.jpg\": \"Insecta/Pyrgus oileus/89d4d5011476e430b1ec4aaa000dcf18.jpg\",\n  \"221642.jpg\": \"Aves/Streptopelia turtur/02847ffd976967fe454f19a258be8811.jpg\",\n  \"221643.jpg\": \"Aves/Streptopelia turtur/8e6b54d205bd3cf0d166eb96d443f32b.jpg\",\n  \"221644.jpg\": \"Aves/Streptopelia turtur/5b07b58131ec9abb9f5e61b296511d85.jpg\",\n  \"221645.jpg\": \"Aves/Streptopelia turtur/a0f43f1aed516e53731958aa9d1b0b3c.jpg\",\n  \"221657.jpg\": \"Aves/Streptopelia turtur/ebe7cfacfcad02ca65014d4c08710a03.jpg\",\n  \"221658.jpg\": \"Aves/Setophaga coronata auduboni/f11acf111a97a4331a348e0459bffcaa.jpg\",\n  \"221659.jpg\": \"Aves/Setophaga coronata auduboni/c2fa0ac4642768f5442697521f8e47e9.jpg\",\n  \"221670.jpg\": \"Aves/Setophaga coronata auduboni/de189097d62f1ac7e58c80fed859f56c.jpg\",\n  \"221675.jpg\": \"Aves/Setophaga coronata auduboni/90dd433e1548d1feaade821bdcbdd58e.jpg\",\n  \"221690.jpg\": \"Aves/Setophaga coronata auduboni/91f6a9fa4534d206b91b2521469963c0.jpg\",\n  \"221699.jpg\": \"Aves/Setophaga coronata auduboni/0e877e589ba7e0522378aad31b1a4af9.jpg\",\n  \"221706.jpg\": \"Aves/Setophaga coronata auduboni/da92302e975c1a53d5ef6229bee71256.jpg\",\n  \"221711.jpg\": \"Aves/Setophaga coronata auduboni/c2408ad077af00386992f517c8b04f88.jpg\",\n  \"221725.jpg\": \"Aves/Setophaga coronata auduboni/7f4784da11030fafed8765cd5b1abf90.jpg\",\n  \"221731.jpg\": \"Aves/Setophaga coronata auduboni/e926c26414e8e495bc113899f3698137.jpg\",\n  \"221742.jpg\": \"Aves/Setophaga coronata auduboni/c8ba1951bd6e89191123a06a880d8240.jpg\",\n  \"221750.jpg\": \"Aves/Setophaga coronata auduboni/6daab9b1e1e012eb09a9740b54adfcf6.jpg\",\n  \"221773.jpg\": \"Aves/Setophaga coronata auduboni/539a38ca18e47d7f384e0ca672ed4564.jpg\",\n  \"221774.jpg\": \"Aves/Setophaga coronata auduboni/efeb7c1c2131b5cdb60cdb3cf8217dc1.jpg\",\n  \"221782.jpg\": \"Aves/Setophaga coronata auduboni/c4b456f33de24c2ffce18479286d8b2b.jpg\",\n  \"221792.jpg\": \"Aves/Setophaga coronata auduboni/d0dcd68f5b96234ef2a299626c112b1e.jpg\",\n  \"221794.jpg\": \"Aves/Setophaga coronata auduboni/bf029872751dfaa4b6fafae160059ea2.jpg\",\n  \"221795.jpg\": \"Aves/Setophaga coronata auduboni/bf4a41bf6a1bb90c0f4e7a138a421055.jpg\",\n  \"221796.jpg\": \"Aves/Setophaga coronata auduboni/028cd0e2d3cbecf11c3a32fd72ca0858.jpg\",\n  \"221801.jpg\": \"Aves/Setophaga coronata auduboni/7e6d2f08429aeb6983b50cfe5769d07f.jpg\",\n  \"221813.jpg\": \"Aves/Setophaga coronata auduboni/668e4c13c7db8c3640ab96b23e833208.jpg\",\n  \"221826.jpg\": \"Aves/Setophaga coronata auduboni/3a29faca34c437af555af9e4d9372936.jpg\",\n  \"221827.jpg\": \"Aves/Setophaga coronata auduboni/41b9fd055090f426d740db2285f6dfe5.jpg\",\n  \"221841.jpg\": \"Insecta/Plutella xylostella/e44255ee584dd58d24b9c88e1eff98b6.jpg\",\n  \"221845.jpg\": \"Insecta/Plutella xylostella/b4372d8b383a9971b597c635be3535e0.jpg\",\n  \"221847.jpg\": \"Insecta/Plutella xylostella/723a88c9a14f9245e7170122e850c8a5.jpg\",\n  \"221848.jpg\": \"Insecta/Plutella xylostella/d42dd9e6c5ee0cbb4237a12f4dd7cdc4.jpg\",\n  \"221854.jpg\": \"Insecta/Plutella xylostella/fea918d3c7ef7173b6e983c1610376f4.jpg\",\n  \"221860.jpg\": \"Insecta/Plutella xylostella/ddfef8fa20eef455db92aec672b39946.jpg\",\n  \"221865.jpg\": \"Insecta/Plutella xylostella/0bcffe4d043f9947c416f518909f668e.jpg\",\n  \"221866.jpg\": \"Insecta/Plutella xylostella/2a9e40af818bf00874f6d0cc9f1ed9ab.jpg\",\n  \"221876.jpg\": \"Insecta/Plutella xylostella/50df0e4701fe77610803b59677617b63.jpg\",\n  \"221877.jpg\": \"Insecta/Plutella xylostella/d556928850ea4b024791045c5cfe3283.jpg\",\n  \"221878.jpg\": \"Insecta/Plutella xylostella/3f193447b89997814ca93f608ef48f42.jpg\",\n  \"221879.jpg\": \"Insecta/Plutella xylostella/5d11c622a9923d9034ea550748158be4.jpg\",\n  \"221882.jpg\": \"Insecta/Plutella xylostella/cf535ede68137462c03bba7b7d270924.jpg\",\n  \"221883.jpg\": \"Insecta/Plutella xylostella/c2d09b12958892dcc5b44a46567d2393.jpg\",\n  \"221884.jpg\": \"Insecta/Plutella xylostella/3d6eb8771979828cf9748cdcbe61888b.jpg\",\n  \"221888.jpg\": \"Insecta/Plutella xylostella/0c5bf30015fd6019bf3198e8f7182a43.jpg\",\n  \"221889.jpg\": \"Insecta/Plutella xylostella/de467860c59b3c11eaa39fd062c2a9fd.jpg\",\n  \"221893.jpg\": \"Insecta/Plutella xylostella/f69b8c564d4007fc2531ce46bffca3d8.jpg\",\n  \"221895.jpg\": \"Insecta/Plutella xylostella/bd86af0556ec7f5c91b0d3fcdfca26af.jpg\",\n  \"221899.jpg\": \"Insecta/Plutella xylostella/1d3ac72cdb93ffd704ca1fb6767316b3.jpg\",\n  \"2219.jpg\": \"Aves/Picoides pubescens/397f120a46d6f7ddf570267a82e83929.jpg\",\n  \"221908.jpg\": \"Insecta/Pieris rapae/04a1b8a621f18f4b2fb95c9ac4448502.jpg\",\n  \"221941.jpg\": \"Insecta/Pieris rapae/a6fd69e0c810cf33aa697b4aadfee6b9.jpg\",\n  \"221997.jpg\": \"Insecta/Pieris rapae/df18c1e16cc421327589f7161813942c.jpg\",\n  \"222059.jpg\": \"Insecta/Pieris rapae/cb50f765c6562be52f27d4548c92fa30.jpg\",\n  \"222081.jpg\": \"Insecta/Pieris rapae/fc945f19cfcf5b6e04d2e0fd2c2e684d.jpg\",\n  \"222189.jpg\": \"Insecta/Pieris rapae/005847e612b13376ade00ff6fa49ec60.jpg\",\n  \"222279.jpg\": \"Insecta/Pieris rapae/04a88130a12e3aacf76fe0d4f966efd5.jpg\",\n  \"222367.jpg\": \"Insecta/Pieris rapae/e503a885f26dbee86b3c102354c43775.jpg\",\n  \"222374.jpg\": \"Insecta/Pieris rapae/41058b220b977a6e1a9d745f1f439603.jpg\",\n  \"222439.jpg\": \"Insecta/Pieris rapae/237a3bcc44efbeb9c5cba8f443b7fc41.jpg\",\n  \"222443.jpg\": \"Insecta/Pieris rapae/81ed2d5aaa195159cfbb56f8a52856c5.jpg\",\n  \"222518.jpg\": \"Insecta/Pieris rapae/02e03b222a3712aad0c1cf0361ac0b7b.jpg\",\n  \"222540.jpg\": \"Insecta/Pieris rapae/5acafe09a4f61e5b86530dced9c32ef4.jpg\",\n  \"222567.jpg\": \"Insecta/Pieris rapae/09bada2e2f7c7c29f0936bcf75cab0d3.jpg\",\n  \"222572.jpg\": \"Insecta/Pieris rapae/5ee0ed1b0d02a6d54cb260e3376e6850.jpg\",\n  \"2226.jpg\": \"Aves/Picoides pubescens/6eb505bc4bf7c49fb1451c89e52a87b4.jpg\",\n  \"222646.jpg\": \"Insecta/Pieris rapae/ab038b28ab664ddc7a669db6fffd72ed.jpg\",\n  \"222708.jpg\": \"Reptilia/Barisia imbricata/b2dd2499fdc77d09c057c2b40daaabd2.jpg\",\n  \"222715.jpg\": \"Reptilia/Barisia imbricata/7e1372b54e41eee9f0204905df591365.jpg\",\n  \"222720.jpg\": \"Reptilia/Barisia imbricata/b4e239923cba3a1d026638709e04fb3b.jpg\",\n  \"222721.jpg\": \"Reptilia/Barisia imbricata/cf4f6eed5cc8bdfb4067e79f52b26ced.jpg\",\n  \"222722.jpg\": \"Reptilia/Barisia imbricata/1cbbabc597910317ab3bb5d700c183ac.jpg\",\n  \"222723.jpg\": \"Reptilia/Barisia imbricata/ee01cfe499a39bbe586ae9e5840f8fd3.jpg\",\n  \"222730.jpg\": \"Reptilia/Barisia imbricata/a9c5f2efd07c3e5dda87efda554321c8.jpg\",\n  \"222740.jpg\": \"Reptilia/Caretta caretta/090b32b1e1d67523f76c233a8444b5d5.jpg\",\n  \"222741.jpg\": \"Reptilia/Caretta caretta/321a9cd9c495af96c612525dd60b3ae3.jpg\",\n  \"222748.jpg\": \"Reptilia/Caretta caretta/ce728b17d32691c4f92994d8b6b2be58.jpg\",\n  \"222754.jpg\": \"Reptilia/Caretta caretta/97d6ed7ed5266160446d9436a4e3c759.jpg\",\n  \"222756.jpg\": \"Reptilia/Caretta caretta/59ba939c8983b4ff0fc77be7e0587971.jpg\",\n  \"222759.jpg\": \"Aves/Larus glaucoides/5f1ef75c00b52c2ef723f0f8222c19a1.jpg\",\n  \"222760.jpg\": \"Aves/Larus glaucoides/3392d9edd71d4ba297adc8e9de74e499.jpg\",\n  \"222796.jpg\": \"Aves/Chenonetta jubata/e77c34f766196b4a593d589097820de0.jpg\",\n  \"222803.jpg\": \"Aves/Chenonetta jubata/c59a0ef0465c6379c834b78ff7f0e3d3.jpg\",\n  \"222976.jpg\": \"Mollusca/Hermissenda crassicornis/47dc912ace6bb381e0e64a022ba44aba.jpg\",\n  \"222983.jpg\": \"Mollusca/Hermissenda crassicornis/4f263275ece2379580e6bfebd71576dc.jpg\",\n  \"222984.jpg\": \"Mollusca/Hermissenda crassicornis/74a745f56a38f1ad7a0689d2ec77399d.jpg\",\n  \"222987.jpg\": \"Mollusca/Hermissenda crassicornis/f8499b5495ee0e919fa5fae64de10e85.jpg\",\n  \"222993.jpg\": \"Mollusca/Hermissenda crassicornis/69c4213e618aba65cee4fc7ee3413a14.jpg\",\n  \"222994.jpg\": \"Mollusca/Hermissenda crassicornis/c4f9a735c5718ef92bb880c290c1efe5.jpg\",\n  \"222998.jpg\": \"Mollusca/Hermissenda crassicornis/2bd6eae2d13ba3568c747be27f14e7b6.jpg\",\n  \"223009.jpg\": \"Mollusca/Hermissenda crassicornis/cdfbab4844e0c26061af1dd0343125f6.jpg\",\n  \"223011.jpg\": \"Mollusca/Hermissenda crassicornis/94820bd27db3e882331d1cdf87839554.jpg\",\n  \"223014.jpg\": \"Mollusca/Hermissenda crassicornis/063b6f168a53b40bdb343a611fb43d9a.jpg\",\n  \"223027.jpg\": \"Mollusca/Hermissenda crassicornis/527c937d6c3fd2810344989cc3682198.jpg\",\n  \"223031.jpg\": \"Mollusca/Hermissenda crassicornis/22dcf0c3cdd8d9dd39dfbd2c79ec91e6.jpg\",\n  \"223053.jpg\": \"Mollusca/Hermissenda crassicornis/387704fbea46751d2b6a21e054ca1d44.jpg\",\n  \"223061.jpg\": \"Mollusca/Hermissenda crassicornis/ace5ca9630b0b81e23dacf1e3f2013d6.jpg\",\n  \"223062.jpg\": \"Mollusca/Hermissenda crassicornis/d95ef1feb76a2bff2f88261ce8b38f05.jpg\",\n  \"223067.jpg\": \"Mollusca/Hermissenda crassicornis/16b9ad9121a77b731ba32f9fd17a1e43.jpg\",\n  \"223068.jpg\": \"Mollusca/Hermissenda crassicornis/bbc6e963f052d9718a058b86bc0bdce0.jpg\",\n  \"223076.jpg\": \"Mollusca/Hermissenda crassicornis/664e81b0c387ba6b63222bb928765306.jpg\",\n  \"223077.jpg\": \"Mollusca/Hermissenda crassicornis/f8ac40cdd86e0fc1ebcc4188087bf818.jpg\",\n  \"223078.jpg\": \"Mollusca/Hermissenda crassicornis/435932db7d9075de24a028be0cd36feb.jpg\",\n  \"223088.jpg\": \"Insecta/Andricus crystallinus/3b1ef31b0b814b2a2fb2242ae2799814.jpg\",\n  \"223092.jpg\": \"Insecta/Andricus crystallinus/8c09735d66c024bf8ef69d348ad669f6.jpg\",\n  \"223110.jpg\": \"Aves/Calothorax lucifer/f9b4ab8533b368af5161a237976e857a.jpg\",\n  \"223115.jpg\": \"Aves/Calothorax lucifer/dbe8b82b8ab0bccfa544822655b53f6f.jpg\",\n  \"223119.jpg\": \"Aves/Calothorax lucifer/3f87749b4fd1e42627c1d07fdacecde0.jpg\",\n  \"223284.jpg\": \"Aves/Hylocharis leucotis/75e27d0161613fc6ceb9b25496cf31e7.jpg\",\n  \"223286.jpg\": \"Aves/Hylocharis leucotis/75e34897b1778994c911bf75dce9c807.jpg\",\n  \"223289.jpg\": \"Aves/Hylocharis leucotis/e3cd31cbdff1ae9a2ec84425557780bf.jpg\",\n  \"223290.jpg\": \"Aves/Hylocharis leucotis/8496c8b1b26e5916fcd4f2f18bcba55c.jpg\",\n  \"223291.jpg\": \"Aves/Hylocharis leucotis/7f78c358f7de6ed62a723eebe5582f2b.jpg\",\n  \"223293.jpg\": \"Aves/Hylocharis leucotis/eb3ddb01835721127a165d1dd40335fa.jpg\",\n  \"223294.jpg\": \"Aves/Hylocharis leucotis/a5325ad061f195aadf6a5fd79bfcaa0d.jpg\",\n  \"223301.jpg\": \"Aves/Troglodytes hiemalis/61d3ed93eeaf5e585e966e4656c98f3f.jpg\",\n  \"223302.jpg\": \"Aves/Troglodytes hiemalis/8ebdb37d363c2dc6872c699728ade0e4.jpg\",\n  \"223305.jpg\": \"Aves/Troglodytes hiemalis/62151e0d5da0cfa2427f22e18430d5fc.jpg\",\n  \"223306.jpg\": \"Aves/Troglodytes hiemalis/92d9d43c9b92cd6bef2d0c95d40d045e.jpg\",\n  \"223307.jpg\": \"Aves/Troglodytes hiemalis/714e79fdd5e04640c7da7609f2067ccb.jpg\",\n  \"223316.jpg\": \"Aves/Troglodytes hiemalis/b976da5fb4b6dad556f71b079d51a8e0.jpg\",\n  \"223317.jpg\": \"Aves/Troglodytes hiemalis/b8a842e8d7ce649263031fd39c4e36c8.jpg\",\n  \"223318.jpg\": \"Aves/Troglodytes hiemalis/5664ace3ab4416f56382edbd1a28c48e.jpg\",\n  \"223320.jpg\": \"Aves/Troglodytes hiemalis/0a229d5e97c6221198579c177d0c6a9d.jpg\",\n  \"223321.jpg\": \"Aves/Troglodytes hiemalis/88a0a60bce407a37dfecf4822ff041f6.jpg\",\n  \"223322.jpg\": \"Aves/Troglodytes hiemalis/e1143f3270172c90a90caf3d294cf1e1.jpg\",\n  \"223323.jpg\": \"Aves/Troglodytes hiemalis/8fecaf2949b989f25cd2b15f0ef340ed.jpg\",\n  \"223324.jpg\": \"Aves/Troglodytes hiemalis/a1b12f1a5e45a38420819e08972d879e.jpg\",\n  \"223325.jpg\": \"Aves/Troglodytes hiemalis/ed098fd43221c71ffbb36ccf9939a660.jpg\",\n  \"223327.jpg\": \"Aves/Troglodytes hiemalis/859765297bdba16024a7ead373e26de1.jpg\",\n  \"223328.jpg\": \"Aves/Troglodytes hiemalis/35dd6f3832d5424112c1aeecc9c19097.jpg\",\n  \"223334.jpg\": \"Aves/Troglodytes hiemalis/87d9bd5ffed5cc1715400a9383255b41.jpg\",\n  \"223335.jpg\": \"Aves/Troglodytes hiemalis/c97325df30b315e6186fa737da77b2ed.jpg\",\n  \"223336.jpg\": \"Aves/Troglodytes hiemalis/e9ba6df3e82d1a944c93e066fc389bff.jpg\",\n  \"223345.jpg\": \"Aves/Buteo albonotatus/8b844a494e3cd33ac1422bb53c0e5cc6.jpg\",\n  \"223346.jpg\": \"Aves/Buteo albonotatus/b622a02b4767b21cb3075d2188389471.jpg\",\n  \"223347.jpg\": \"Aves/Buteo albonotatus/7c8d0db7dff542f8e2775755d867a3cf.jpg\",\n  \"223350.jpg\": \"Aves/Buteo albonotatus/3464bfe4475865e4fe44b1d1c2453bcc.jpg\",\n  \"223353.jpg\": \"Aves/Buteo albonotatus/d62bca0f36c5d0cd9ac1a49ef50f9e17.jpg\",\n  \"223354.jpg\": \"Aves/Buteo albonotatus/806e869afdb229b470e59d9660178db5.jpg\",\n  \"223355.jpg\": \"Aves/Buteo albonotatus/55642f86e2fc70e463bb1fd7266f7653.jpg\",\n  \"223360.jpg\": \"Aves/Buteo albonotatus/046b383772129e18252acca221de41db.jpg\",\n  \"223364.jpg\": \"Aves/Buteo albonotatus/392092b7447896b1e0e1780cfaf839fa.jpg\",\n  \"223365.jpg\": \"Aves/Buteo albonotatus/f34ff54406d3cad8336a43737da28346.jpg\",\n  \"223366.jpg\": \"Aves/Buteo albonotatus/ce0b2953415a8720d3fd08fb640f87d3.jpg\",\n  \"223367.jpg\": \"Aves/Buteo albonotatus/fd73642ee19d59510a0ebde2a6f0e02d.jpg\",\n  \"223368.jpg\": \"Aves/Buteo albonotatus/feaf8400f40720f52452aeffb2bc5c58.jpg\",\n  \"223370.jpg\": \"Aves/Buteo albonotatus/c465a2692a99d9b4349896b50cbe434f.jpg\",\n  \"223371.jpg\": \"Aves/Buteo albonotatus/4ad7737a20210ff769f7a1c3f5ce767c.jpg\",\n  \"223372.jpg\": \"Aves/Buteo albonotatus/bce5256a9372f5ff207870208957d0f4.jpg\",\n  \"223373.jpg\": \"Aves/Buteo albonotatus/8a88483bba12ddc2f95a9de3d0db135a.jpg\",\n  \"223377.jpg\": \"Aves/Buteo albonotatus/4031f22f1f0dc347639391cde857556a.jpg\",\n  \"223378.jpg\": \"Aves/Buteo albonotatus/23d5f623096afe0cf675031be52816be.jpg\",\n  \"223384.jpg\": \"Aves/Buteo albonotatus/fea5ff645c77da14f9f1e464102ab191.jpg\",\n  \"223387.jpg\": \"Aves/Buteo albonotatus/d7fcd2e28db9ffc5dec52b46ec4ed550.jpg\",\n  \"223390.jpg\": \"Aves/Buteo albonotatus/6f593a7af326cf86d0712785e6695539.jpg\",\n  \"223391.jpg\": \"Aves/Buteo albonotatus/a7fb5b48f0780813838fb40612f7859d.jpg\",\n  \"223453.jpg\": \"Reptilia/Tantilla gracilis/0c65253bc487f63e638df8840bf05238.jpg\",\n  \"223454.jpg\": \"Reptilia/Tantilla gracilis/b9c756e89d2cf6b6fa23611f6ee061de.jpg\",\n  \"223463.jpg\": \"Reptilia/Tantilla gracilis/203097bb8eaa4f8a6202f30d38a78e9c.jpg\",\n  \"223464.jpg\": \"Reptilia/Tantilla gracilis/58230d4a9b388f39429cca90d93dfc5b.jpg\",\n  \"223472.jpg\": \"Reptilia/Tantilla gracilis/f5a22626f755f826be07d3503ce106e3.jpg\",\n  \"223473.jpg\": \"Reptilia/Tantilla gracilis/d8ae3067dc9a3531b4ced65231470fb9.jpg\",\n  \"223474.jpg\": \"Reptilia/Tantilla gracilis/1bd3fd4ed881024532db9a90154ffd93.jpg\",\n  \"223475.jpg\": \"Reptilia/Tantilla gracilis/32e672a058c220bb01feec61f3f8ae41.jpg\",\n  \"223476.jpg\": \"Reptilia/Tantilla gracilis/2c23f5a2d76a19da34698993321dc8d4.jpg\",\n  \"223480.jpg\": \"Reptilia/Tantilla gracilis/06ac1a1b6e470284c05f7b120471e7e2.jpg\",\n  \"223481.jpg\": \"Reptilia/Tantilla gracilis/0eec2c71750a5137803f7cac6581f6db.jpg\",\n  \"223485.jpg\": \"Reptilia/Tantilla gracilis/f11149f2678cc9c12706f56d47be9cfb.jpg\",\n  \"223486.jpg\": \"Reptilia/Tantilla gracilis/2cdcf20154bbc489e40810e251237394.jpg\",\n  \"223488.jpg\": \"Reptilia/Tantilla gracilis/ee45dc60eebb0c56eef47709430697ee.jpg\",\n  \"223489.jpg\": \"Reptilia/Tantilla gracilis/01526221954627df24baec0e9f6f152b.jpg\",\n  \"223499.jpg\": \"Reptilia/Tantilla gracilis/8dd531e60234ee9014a308d87335b24e.jpg\",\n  \"223500.jpg\": \"Reptilia/Tantilla gracilis/269d0da5f01c935e5e9af65c78709633.jpg\",\n  \"223501.jpg\": \"Reptilia/Tantilla gracilis/f550b7bfc4b86d16736ab96fd5c89d7b.jpg\",\n  \"223502.jpg\": \"Reptilia/Tantilla gracilis/d83a55c10a4e669452bacebc6a129261.jpg\",\n  \"223503.jpg\": \"Reptilia/Tantilla gracilis/0129855bc1e723b1daafcfaf281d5219.jpg\",\n  \"223504.jpg\": \"Reptilia/Tantilla gracilis/dfaa3feaca89b5e2fe94bcbfd32d3310.jpg\",\n  \"223512.jpg\": \"Reptilia/Tantilla gracilis/c23068b7978fa439f1437d08e6a1bc05.jpg\",\n  \"223513.jpg\": \"Reptilia/Tantilla gracilis/30bfda826f2b5546c2985cf11d378641.jpg\",\n  \"223529.jpg\": \"Reptilia/Pantherophis emoryi/07d67cde1dced6a43e14dff6bf4c6c3f.jpg\",\n  \"223540.jpg\": \"Reptilia/Pantherophis emoryi/efba1e314cd718df182657bedd9cbb9f.jpg\",\n  \"223545.jpg\": \"Reptilia/Pantherophis emoryi/43ac5355099f31304119f2492e8aa833.jpg\",\n  \"223547.jpg\": \"Reptilia/Pantherophis emoryi/6acb7a45fb16a75778d78d7a544a8f29.jpg\",\n  \"223549.jpg\": \"Reptilia/Pantherophis emoryi/3f10fd74c2ecebd13260340add06fb52.jpg\",\n  \"223553.jpg\": \"Reptilia/Pantherophis emoryi/c1c30744a0104311c5b61b6ea3a713b6.jpg\",\n  \"223556.jpg\": \"Reptilia/Pantherophis emoryi/433bc535704663040c1bfe463bff1ea2.jpg\",\n  \"223562.jpg\": \"Reptilia/Pantherophis emoryi/6d686771309e02734455315de33de516.jpg\",\n  \"223564.jpg\": \"Reptilia/Pantherophis emoryi/afc0c1d795300bebb8b3b853c9ebfaab.jpg\",\n  \"223565.jpg\": \"Reptilia/Pantherophis emoryi/af0a6dc1a5c2c8562859acb7b21f00e7.jpg\",\n  \"223566.jpg\": \"Reptilia/Pantherophis emoryi/6c626ffe097b37beefd8bda56041114f.jpg\",\n  \"223575.jpg\": \"Reptilia/Pantherophis emoryi/10563be8c85a1fd977da552c7ab8ca5b.jpg\",\n  \"223581.jpg\": \"Reptilia/Pantherophis emoryi/07659565231fd18777919a676915ee8d.jpg\",\n  \"223583.jpg\": \"Reptilia/Pantherophis emoryi/953f4f1207c9c84b9eea59b3b6681d4e.jpg\",\n  \"223589.jpg\": \"Reptilia/Pantherophis emoryi/0ceefa2aa098c1ae802c8203b7083f63.jpg\",\n  \"223593.jpg\": \"Reptilia/Pantherophis emoryi/4326dfc005316cae90ebec1fa1a17b7c.jpg\",\n  \"223597.jpg\": \"Reptilia/Pantherophis emoryi/7381b3fe10dccf09c8593648d75bab69.jpg\",\n  \"223602.jpg\": \"Reptilia/Pantherophis emoryi/d37c964828dfeab4ca6fcb0928abfc57.jpg\",\n  \"223617.jpg\": \"Reptilia/Pantherophis emoryi/9684056b734ec63e8ffba0336d818d8c.jpg\",\n  \"223629.jpg\": \"Reptilia/Pantherophis emoryi/75ea91053632ace2c94c64f6ef9ec6f3.jpg\",\n  \"223630.jpg\": \"Reptilia/Pantherophis emoryi/8bc3894bb6ac78c27d816b60a0bb46d3.jpg\",\n  \"223639.jpg\": \"Reptilia/Pantherophis emoryi/921931991466bbccd2b9bb0e38094471.jpg\",\n  \"223656.jpg\": \"Aves/Estrilda astrild/63ca7365fa82b91b4254917a0c2e5857.jpg\",\n  \"223668.jpg\": \"Aves/Estrilda astrild/56d5a8fddd7c081ef88a918b9d85cb16.jpg\",\n  \"223669.jpg\": \"Aves/Icterus graduacauda/7d117e08e54746336d55689403777235.jpg\",\n  \"223670.jpg\": \"Aves/Icterus graduacauda/82172917980d9d57a0fe29cd6f4ad31e.jpg\",\n  \"223679.jpg\": \"Aves/Icterus graduacauda/040523683ca9d67b5a119d4b88a0be15.jpg\",\n  \"223680.jpg\": \"Aves/Icterus graduacauda/24206dadefca549bd05a8a9a5233ae13.jpg\",\n  \"223682.jpg\": \"Aves/Icterus graduacauda/9845c719e4120d9956cc84a15db5fb14.jpg\",\n  \"223696.jpg\": \"Aves/Icterus graduacauda/a8a3e7e37e5f1df4a94936a3ed3ba85e.jpg\",\n  \"223698.jpg\": \"Reptilia/Lampropeltis holbrooki/a16886f58d1a0fdfac557b5e6c0f325a.jpg\",\n  \"223705.jpg\": \"Reptilia/Lampropeltis holbrooki/18d190d5c94b309847e0148ac8e546f8.jpg\",\n  \"223706.jpg\": \"Reptilia/Lampropeltis holbrooki/c44c0cdc0617fffb2dd272bb48cfd098.jpg\",\n  \"223712.jpg\": \"Reptilia/Lampropeltis holbrooki/b7192e91173b2ce18b3e4fdc06b8c1a0.jpg\",\n  \"223720.jpg\": \"Reptilia/Lampropeltis holbrooki/b0002e4d27e3a64b993f5c8f6c4d3294.jpg\",\n  \"223724.jpg\": \"Reptilia/Lampropeltis holbrooki/6db20cabeb5ce3e43239c37586e5c230.jpg\",\n  \"223725.jpg\": \"Reptilia/Lampropeltis holbrooki/3f62f696ffc8014a236cd098718156b3.jpg\",\n  \"223731.jpg\": \"Reptilia/Lampropeltis holbrooki/6574b6e5eab63a9995610a23724fcbca.jpg\",\n  \"223732.jpg\": \"Reptilia/Lampropeltis holbrooki/b269b074e57a00961f216bbacf4747f3.jpg\",\n  \"223733.jpg\": \"Reptilia/Lampropeltis holbrooki/9238e9d1b2cb3f786282527e7db6b540.jpg\",\n  \"223735.jpg\": \"Reptilia/Lampropeltis holbrooki/6b3b8e544db55085b78c3eb4188dd286.jpg\",\n  \"223736.jpg\": \"Reptilia/Lampropeltis holbrooki/62a494ed3df95af7b6bdee6c97feb35b.jpg\",\n  \"223737.jpg\": \"Reptilia/Lampropeltis holbrooki/2ff458d6c9334627354a13894e4b0bca.jpg\",\n  \"223740.jpg\": \"Reptilia/Lampropeltis holbrooki/b222cf7a4460e7f45b0d7f95b894084b.jpg\",\n  \"223744.jpg\": \"Reptilia/Lampropeltis holbrooki/d1d40bddfcafd3252c3649a6278b4083.jpg\",\n  \"223753.jpg\": \"Reptilia/Lampropeltis holbrooki/b782a62e884889a01f904335c91a3eb4.jpg\",\n  \"223761.jpg\": \"Reptilia/Lampropeltis holbrooki/53aedf8ecdd628f0f5b96bbb2d00d90a.jpg\",\n  \"223778.jpg\": \"Aves/Toxostoma curvirostre/9705fbab75f6ea08e6ca754ca692404d.jpg\",\n  \"223779.jpg\": \"Aves/Toxostoma curvirostre/9e1de9756ba01ec794975d56575ab135.jpg\",\n  \"223805.jpg\": \"Aves/Toxostoma curvirostre/935e94a2d98c1cba10e676fff483f9ce.jpg\",\n  \"223806.jpg\": \"Aves/Toxostoma curvirostre/4615831a7df27737482c143a6fac9880.jpg\",\n  \"223834.jpg\": \"Aves/Toxostoma curvirostre/0399a184ac06c88363c232f77296ad81.jpg\",\n  \"223844.jpg\": \"Aves/Toxostoma curvirostre/7141de628d0a2390e34d8684309acb9a.jpg\",\n  \"223880.jpg\": \"Aves/Toxostoma curvirostre/a7909d05da4071946d0f768b51dd60a3.jpg\",\n  \"223886.jpg\": \"Aves/Toxostoma curvirostre/f177d7577a972946e6e11ac22dfdd978.jpg\",\n  \"223890.jpg\": \"Aves/Toxostoma curvirostre/3020ea83922ecd024c6c0e9172d05ab3.jpg\",\n  \"223899.jpg\": \"Aves/Toxostoma curvirostre/b709f830a72cfb8b76f298a2d0eb2b6a.jpg\",\n  \"223906.jpg\": \"Aves/Toxostoma curvirostre/f59ccbd5fbd1fdfbf346ce897c56a14d.jpg\",\n  \"223935.jpg\": \"Aves/Toxostoma curvirostre/ab8d19573464711e41eb7d3160fe2960.jpg\",\n  \"22394.jpg\": \"Aves/Sialia sialis/24a071233330ab204d568c5867296b13.jpg\",\n  \"223976.jpg\": \"Aves/Toxostoma curvirostre/29e7ee3841078757aceb8e344fdb9153.jpg\",\n  \"223981.jpg\": \"Aves/Toxostoma curvirostre/621c0e1de929362d4a7eda3fa09e3119.jpg\",\n  \"223993.jpg\": \"Aves/Toxostoma curvirostre/4a05b33c821fcb8315eb5e0c0ea4eabc.jpg\",\n  \"224022.jpg\": \"Aves/Toxostoma curvirostre/24c2cabcd81b5b152b16d0bde4b0a901.jpg\",\n  \"224023.jpg\": \"Aves/Toxostoma curvirostre/36e5937c0b9cd7e028a5102a6bf67da4.jpg\",\n  \"224028.jpg\": \"Aves/Toxostoma curvirostre/b951ac41553684445935bb942982d3bf.jpg\",\n  \"224029.jpg\": \"Aves/Toxostoma curvirostre/e004d969dba11d0bed3e37f03d342b0a.jpg\",\n  \"224033.jpg\": \"Aves/Toxostoma curvirostre/60ff0472d448e36ab733bf48d5b3be09.jpg\",\n  \"224052.jpg\": \"Aves/Toxostoma curvirostre/56523a894312307f2016c47dc636d537.jpg\",\n  \"224095.jpg\": \"Aves/Toxostoma curvirostre/953325427e77d28dcba9fadf344dfc0b.jpg\",\n  \"224098.jpg\": \"Aves/Toxostoma curvirostre/ffda12d8de521a9bd5e55f35b57c5674.jpg\",\n  \"224108.jpg\": \"Aves/Buteo lagopus/78eea5cf97fc57cefc9d64fbba06c197.jpg\",\n  \"224118.jpg\": \"Aves/Buteo lagopus/595a87ef2ef8d808eac3ec93c3fd7eed.jpg\",\n  \"224119.jpg\": \"Aves/Buteo lagopus/dd94313958af8a93722339b1c98e85bd.jpg\",\n  \"224125.jpg\": \"Aves/Buteo lagopus/572501ebbef87c9380d86779c1ed44b5.jpg\",\n  \"224126.jpg\": \"Aves/Buteo lagopus/bfbc7028e21cab0f911e67d8d0597d6a.jpg\",\n  \"224129.jpg\": \"Aves/Buteo lagopus/5442b3613f2af290aed1588b06de8729.jpg\",\n  \"224130.jpg\": \"Aves/Buteo lagopus/b014eef3f8c06f830260648b2533f4db.jpg\",\n  \"224135.jpg\": \"Aves/Buteo lagopus/59d9f5d6d792bec3b6919fe47ef923ae.jpg\",\n  \"22415.jpg\": \"Aves/Sialia sialis/b71b305802aa84ba5b7b76daae06f858.jpg\",\n  \"224154.jpg\": \"Aves/Buteo lagopus/50c6a2ef95ac908dc95102171d31e965.jpg\",\n  \"224155.jpg\": \"Aves/Buteo lagopus/f961bd575856add429f79aa9e7072aac.jpg\",\n  \"224157.jpg\": \"Aves/Buteo lagopus/b60781f0152b5347ad41c78cd7e7ee86.jpg\",\n  \"224158.jpg\": \"Aves/Buteo lagopus/3535f0cd0b41bc3d414a708fcc8b50c0.jpg\",\n  \"224164.jpg\": \"Aves/Buteo lagopus/1cc3251413b925e476de282129b0c450.jpg\",\n  \"224167.jpg\": \"Aves/Buteo lagopus/17937fd196757a46dc3e8ea6b011eb1a.jpg\",\n  \"224169.jpg\": \"Aves/Buteo lagopus/9b6f984696f4406d039e5f45c574beec.jpg\",\n  \"224170.jpg\": \"Aves/Buteo lagopus/681b5a8ad4f2a7cdddf6a70d0b9eb20a.jpg\",\n  \"224176.jpg\": \"Aves/Buteo lagopus/d36d1b450c3dd831ca4f5b39929b8db9.jpg\",\n  \"224177.jpg\": \"Aves/Buteo lagopus/4a0d27104581cf7b6ff5f93633a1b378.jpg\",\n  \"224178.jpg\": \"Aves/Buteo lagopus/dbb1360f10a772dd847502d40522c9fb.jpg\",\n  \"224184.jpg\": \"Aves/Buteo lagopus/a8f3c15fe59f6ebeca93e49633bb5114.jpg\",\n  \"224202.jpg\": \"Aves/Buteo lagopus/0da6f02c18fbce838f15448926e5c985.jpg\",\n  \"224210.jpg\": \"Reptilia/Lampropeltis calligaster calligaster/ad254279c06e31b76c2220d0a5fbcdbe.jpg\",\n  \"224211.jpg\": \"Reptilia/Lampropeltis calligaster calligaster/58a16c485c458b0e918b7521936a5558.jpg\",\n  \"224212.jpg\": \"Reptilia/Lampropeltis calligaster calligaster/9108ff2f13f0df03a979d66dd15639f9.jpg\",\n  \"224214.jpg\": \"Reptilia/Lampropeltis calligaster calligaster/b94b3f04008d73d057d45b80fa1c4749.jpg\",\n  \"224218.jpg\": \"Reptilia/Lampropeltis calligaster calligaster/04654f236347cdbf29fec35e38ef8dda.jpg\",\n  \"224219.jpg\": \"Reptilia/Lampropeltis calligaster calligaster/b1d12ba7103cfccf67a7f7c0530ada34.jpg\",\n  \"224223.jpg\": \"Reptilia/Lampropeltis calligaster calligaster/55b9772f93d94f5738d705ae754eef22.jpg\",\n  \"224225.jpg\": \"Reptilia/Lampropeltis calligaster calligaster/005d8ca4e74e75f166c159cb3ab3d637.jpg\",\n  \"224238.jpg\": \"Reptilia/Lampropeltis calligaster calligaster/1b98e7a37489a990fffe37e13014ac4d.jpg\",\n  \"224239.jpg\": \"Reptilia/Lampropeltis calligaster calligaster/61dd63836a216d179a38c7af92ff6e1e.jpg\",\n  \"224523.jpg\": \"Aves/Ictinia mississippiensis/6125866dffdc1b451c0e90a7712eda2f.jpg\",\n  \"224527.jpg\": \"Aves/Ictinia mississippiensis/8cf2313c9791676a06f16f0d5a1865f3.jpg\",\n  \"224532.jpg\": \"Aves/Ictinia mississippiensis/c4d28ca4fd90f7e070ef1829c5e80b77.jpg\",\n  \"224559.jpg\": \"Aves/Ictinia mississippiensis/dc70f89bdbfac86734bf32d736911013.jpg\",\n  \"224562.jpg\": \"Aves/Ictinia mississippiensis/c05c3f1550f0eb7954ea4e572e29f33b.jpg\",\n  \"224568.jpg\": \"Aves/Ictinia mississippiensis/0ef82e43549d8ee8b74ceeee13dfaabd.jpg\",\n  \"224579.jpg\": \"Aves/Ictinia mississippiensis/e57b05ea72d2a623f86490846fca07ed.jpg\",\n  \"224580.jpg\": \"Aves/Ictinia mississippiensis/ff27e5e777f2b50531949fc18f35dcab.jpg\",\n  \"224590.jpg\": \"Aves/Ictinia mississippiensis/452d885da40724760bf15af60d8f13e4.jpg\",\n  \"224591.jpg\": \"Aves/Ictinia mississippiensis/3deb556e57aa8d4d8ece5878afa44e35.jpg\",\n  \"224596.jpg\": \"Aves/Ictinia mississippiensis/433fa0771761e4c206b92d6421bac523.jpg\",\n  \"224597.jpg\": \"Aves/Ictinia mississippiensis/3acaa3a1d5652ad9d72d9388e066e659.jpg\",\n  \"224606.jpg\": \"Aves/Ictinia mississippiensis/c4777dac0247b080df78d1bad7e1de07.jpg\",\n  \"224616.jpg\": \"Aves/Ictinia mississippiensis/0cf282196e7c5d4e46eebc7f7e5abe08.jpg\",\n  \"224617.jpg\": \"Aves/Ictinia mississippiensis/8c23e6ace9eb9c56b5fb3be6daee69bf.jpg\",\n  \"224622.jpg\": \"Aves/Ictinia mississippiensis/215d2741ef08afd85ca6d0b1080ef941.jpg\",\n  \"224624.jpg\": \"Aves/Ictinia mississippiensis/8ed8a0c8e92b4c858c59a4cb362b9111.jpg\",\n  \"224626.jpg\": \"Aves/Ictinia mississippiensis/98283e701535527b9b36f8708d5dfa3c.jpg\",\n  \"224635.jpg\": \"Aves/Ictinia mississippiensis/3f6a2e0fad86c46d58fd5f46e7af413e.jpg\",\n  \"224637.jpg\": \"Reptilia/Pantherophis bairdi/38f332368c1808482417217cd0f50d07.jpg\",\n  \"224644.jpg\": \"Reptilia/Pantherophis bairdi/2ce73597346877d0e06188efc2970bee.jpg\",\n  \"224650.jpg\": \"Reptilia/Pantherophis bairdi/4e610756d40618dacad0e5c56fd93db7.jpg\",\n  \"224659.jpg\": \"Reptilia/Pantherophis bairdi/52089a3828dacd53e3460545284ba5d7.jpg\",\n  \"22466.jpg\": \"Aves/Sialia sialis/6e66b43cf4861a6af3dabcc218b86cbb.jpg\",\n  \"224660.jpg\": \"Reptilia/Pantherophis bairdi/2dd98219f6cccee702b26efdba7ac26a.jpg\",\n  \"224684.jpg\": \"Insecta/Callophrys dumetorum/2a20e59a754dc45462547e0c43a71979.jpg\",\n  \"224688.jpg\": \"Insecta/Callophrys dumetorum/5af458c9e4d1bf468558d220abdb20d1.jpg\",\n  \"224691.jpg\": \"Insecta/Callophrys dumetorum/808c292783ee2a057f92063bd52b7130.jpg\",\n  \"224694.jpg\": \"Insecta/Callophrys dumetorum/825440df31ddf3beec9d114f08c1ea67.jpg\",\n  \"224699.jpg\": \"Reptilia/Sceloporus cyanogenys/ada76d6aa07ac4cb1da0707eaae655f4.jpg\",\n  \"224702.jpg\": \"Reptilia/Sceloporus cyanogenys/636f50329c4ef98bdc31e59b12ddf8b5.jpg\",\n  \"224706.jpg\": \"Reptilia/Sceloporus cyanogenys/9f5375769df0e35baa81c3ee8c94c0a5.jpg\",\n  \"224707.jpg\": \"Reptilia/Sceloporus cyanogenys/19b72a01505ac99e370933f08fd69216.jpg\",\n  \"224712.jpg\": \"Reptilia/Sceloporus cyanogenys/658a851e2833105ff6d23b919b81fe3e.jpg\",\n  \"224714.jpg\": \"Reptilia/Sceloporus cyanogenys/3aa8214d8c6ef59c731dce88ad243425.jpg\",\n  \"224716.jpg\": \"Reptilia/Sceloporus cyanogenys/2f243fa396ea46f7cac55cfc0194b48e.jpg\",\n  \"224719.jpg\": \"Reptilia/Sceloporus cyanogenys/dc310afca97ff08ea173ea6fae133199.jpg\",\n  \"224721.jpg\": \"Insecta/Cyllopsis gemma/90ca00757f6b4d58c3acdd3280b2ef9b.jpg\",\n  \"224722.jpg\": \"Insecta/Cyllopsis gemma/c33e5f74358f0162e7e3ec0e7ba8e403.jpg\",\n  \"224726.jpg\": \"Insecta/Cyllopsis gemma/6bd8fa268b539b2112f4fc8dd5065937.jpg\",\n  \"224727.jpg\": \"Insecta/Cyllopsis gemma/4fd2f575c8e8e1da4147a726f2af41c6.jpg\",\n  \"224728.jpg\": \"Insecta/Cyllopsis gemma/e5dde2ba75daeb813629fb9ab0675b40.jpg\",\n  \"224732.jpg\": \"Insecta/Cyllopsis gemma/1c163fd93bc287bd3190afea164854c7.jpg\",\n  \"224736.jpg\": \"Insecta/Cyllopsis gemma/76eb69b94f3a9b3d79c8bd37f4eb3f05.jpg\",\n  \"224739.jpg\": \"Insecta/Cyllopsis gemma/3240b1349958c7d35cdc65dce763faa6.jpg\",\n  \"224740.jpg\": \"Insecta/Cyllopsis gemma/532c1a6ea675a8d48fef5634abfe113d.jpg\",\n  \"224745.jpg\": \"Insecta/Calledapteryx dryopterata/5ab16375fdbde04cca7999b16b9e3a42.jpg\",\n  \"224750.jpg\": \"Insecta/Calledapteryx dryopterata/f94471739933298abea8b286fe525751.jpg\",\n  \"224753.jpg\": \"Insecta/Calledapteryx dryopterata/66469665b8ea4fbfd75724ee3c6a1cd8.jpg\",\n  \"224755.jpg\": \"Insecta/Calledapteryx dryopterata/dbeae3ab1093a438caaed964addfddab.jpg\",\n  \"224770.jpg\": \"Aves/Vireo philadelphicus/4738eb62b8bb2efecfbc46afccf2f68f.jpg\",\n  \"224771.jpg\": \"Aves/Vireo philadelphicus/5b9400714ff8d593925eaa40ea516393.jpg\",\n  \"224775.jpg\": \"Aves/Vireo philadelphicus/d0ded207fcbf9379fa63150f8c525d08.jpg\",\n  \"224783.jpg\": \"Aves/Vireo philadelphicus/a20e6116b39996b82d31109b9a24bc5a.jpg\",\n  \"224785.jpg\": \"Aves/Vireo philadelphicus/7e9330e89bbb66a69e123d623adf8f5b.jpg\",\n  \"224790.jpg\": \"Amphibia/Eleutherodactylus planirostris/8b5459d2fcc9ae35914d715a84216995.jpg\",\n  \"224792.jpg\": \"Amphibia/Eleutherodactylus planirostris/5029de1b9e84c84412c4d50b7119e4a9.jpg\",\n  \"224795.jpg\": \"Amphibia/Eleutherodactylus planirostris/9c352251d293a32f20eec7ef25a4612c.jpg\",\n  \"224799.jpg\": \"Amphibia/Eleutherodactylus planirostris/5e06704aeeecc64925f77aff89ab157d.jpg\",\n  \"224801.jpg\": \"Animalia/Panulirus interruptus/dd387acc1792a0c7bdd218ba0702ba9c.jpg\",\n  \"224802.jpg\": \"Animalia/Panulirus interruptus/d7acb6cb3a466a2b72e2ded471862969.jpg\",\n  \"224834.jpg\": \"Animalia/Panulirus interruptus/7759186ad773f005c6f143647313484f.jpg\",\n  \"22497.jpg\": \"Aves/Sialia sialis/afec543ccc11e5b82aa5e89625b976ba.jpg\",\n  \"22498.jpg\": \"Aves/Sialia sialis/717a1722858dae49cd057acfa5c19134.jpg\",\n  \"225028.jpg\": \"Reptilia/Plestiodon obsoletus/293a4ede75bf8a926b7b8ed7917745fc.jpg\",\n  \"225029.jpg\": \"Reptilia/Plestiodon obsoletus/5eacdec04c94ecc91ecd49f92b03dd9d.jpg\",\n  \"225030.jpg\": \"Reptilia/Plestiodon obsoletus/025dacaa3ba32fbe3cde4c69094e824f.jpg\",\n  \"225034.jpg\": \"Reptilia/Plestiodon obsoletus/3bcb04793df4f133f1b7cdb2b70a5dd6.jpg\",\n  \"225036.jpg\": \"Reptilia/Plestiodon obsoletus/a7458bf7ff9e424e72faa196a5b669c8.jpg\",\n  \"225037.jpg\": \"Reptilia/Plestiodon obsoletus/cd421bdf2575013c28671209315b7bbf.jpg\",\n  \"225041.jpg\": \"Reptilia/Plestiodon obsoletus/e26ea5d33721d53c63bd1023aabb8ca5.jpg\",\n  \"225062.jpg\": \"Reptilia/Plestiodon obsoletus/bac642029070a3052fdd3475e97f94e1.jpg\",\n  \"225064.jpg\": \"Reptilia/Plestiodon obsoletus/8c4696e6ce3033dfa4aaff516740f84a.jpg\",\n  \"225067.jpg\": \"Insecta/Myscelia ethusa/3fbfb20d58f52c0226cd342104395dd3.jpg\",\n  \"225071.jpg\": \"Insecta/Myscelia ethusa/c3759741c46d98db5bd3cec041c286ae.jpg\",\n  \"225073.jpg\": \"Insecta/Myscelia ethusa/932d609ce0fdffac1d5efc4e421c065b.jpg\",\n  \"225085.jpg\": \"Insecta/Myscelia ethusa/bc7643ea59527cc8410677ddc024ad21.jpg\",\n  \"225087.jpg\": \"Insecta/Myscelia ethusa/2e6ab8374f8283183669be9a650b0088.jpg\",\n  \"225089.jpg\": \"Insecta/Myscelia ethusa/b557b52a85a0719ff56b40651eb28f43.jpg\",\n  \"225101.jpg\": \"Insecta/Myscelia ethusa/0afbc6e42e26765c2d7d40c60d2abd34.jpg\",\n  \"225102.jpg\": \"Insecta/Myscelia ethusa/9dc6faa3d011e1bf2af857ed075f060b.jpg\",\n  \"225103.jpg\": \"Insecta/Myscelia ethusa/c55ec8bf9b509d0490c5ebaced2cf9b2.jpg\",\n  \"22514.jpg\": \"Aves/Sialia sialis/3dfc01b5962f62db2767842612b2d7b5.jpg\",\n  \"225242.jpg\": \"Aves/Streptopelia chinensis/f3a910afb1176aa0de5c5978f9c2842b.jpg\",\n  \"225244.jpg\": \"Aves/Streptopelia chinensis/37987fc643da214d8767f66be5c04c66.jpg\",\n  \"225247.jpg\": \"Aves/Streptopelia chinensis/f686607b2c856e3d3dd2647f29c31dad.jpg\",\n  \"225249.jpg\": \"Aves/Streptopelia chinensis/bf7579a8e35bfb6a3b47ef0eea60b974.jpg\",\n  \"225250.jpg\": \"Aves/Streptopelia chinensis/e8ff28efa234f27d5c58484762c2f29b.jpg\",\n  \"225255.jpg\": \"Aves/Streptopelia chinensis/e1a19cdbbba8441d7acd676c10bf5672.jpg\",\n  \"225259.jpg\": \"Aves/Streptopelia chinensis/8c54f9de95cdab1437d796598ed61465.jpg\",\n  \"225260.jpg\": \"Aves/Streptopelia chinensis/6cff5f7a0227be714ba2db9f81c4ac99.jpg\",\n  \"225264.jpg\": \"Aves/Streptopelia chinensis/ee999ca44cc46648aacf9f59fced84c1.jpg\",\n  \"225268.jpg\": \"Aves/Streptopelia chinensis/07846c0f05cb08a422b722484b94db57.jpg\",\n  \"225270.jpg\": \"Aves/Streptopelia chinensis/2896532e7ad55b67cf7ef2287c47040c.jpg\",\n  \"225271.jpg\": \"Aves/Streptopelia chinensis/f48d0e519fbd519ec9135793f8b66c97.jpg\",\n  \"225276.jpg\": \"Aves/Streptopelia chinensis/f6dd374541c319d8bf2d3350325e5b63.jpg\",\n  \"225281.jpg\": \"Aves/Streptopelia chinensis/2fa7c5e1d97dcb144f33bd609ffc56d2.jpg\",\n  \"225282.jpg\": \"Aves/Streptopelia chinensis/0fcdf171a9763b69bac531234c915cfd.jpg\",\n  \"225291.jpg\": \"Aves/Streptopelia chinensis/8aaa38623a72b9cebac14237fb8922d2.jpg\",\n  \"225298.jpg\": \"Aves/Streptopelia chinensis/a9696376a6801e66a77863af7061ade8.jpg\",\n  \"225305.jpg\": \"Aves/Streptopelia chinensis/2ea91106a95774a13f8ae8236265efcc.jpg\",\n  \"225306.jpg\": \"Aves/Streptopelia chinensis/7daadcd83895d2e06b4b0dc0e425f2c9.jpg\",\n  \"225336.jpg\": \"Aves/Delichon urbicum/66b0861c0da3e6fe15dcbed509d62ed2.jpg\",\n  \"225338.jpg\": \"Aves/Delichon urbicum/7d74657a834eea910094fe80b64488ff.jpg\",\n  \"225346.jpg\": \"Aves/Delichon urbicum/339e471471ec31ccd2de4b055d682db4.jpg\",\n  \"225348.jpg\": \"Aves/Delichon urbicum/f932ff79b15f2a3df9011750d153abc1.jpg\",\n  \"225349.jpg\": \"Aves/Delichon urbicum/a23e900218bae22f7052959a2b1fde20.jpg\",\n  \"225355.jpg\": \"Aves/Delichon urbicum/ced2443a454511291f7dbadb8be78ab0.jpg\",\n  \"225357.jpg\": \"Aves/Delichon urbicum/902f73bd3d52ac361335972e8ddb9987.jpg\",\n  \"225358.jpg\": \"Aves/Delichon urbicum/154a7bf100883448425508471c898a57.jpg\",\n  \"225400.jpg\": \"Actinopterygii/Syngnathus leptorhynchus/af93e2dc5d1006d421fedab0da2825d8.jpg\",\n  \"22546.jpg\": \"Aves/Sialia sialis/1bf7dc36600693f575a480ee439dba28.jpg\",\n  \"225529.jpg\": \"Reptilia/Pseudemys gorzugi/55981be3d4eeea0a44d806cae0a46326.jpg\",\n  \"22553.jpg\": \"Aves/Sialia sialis/35d46405518cfa67bcc5a8f34587e455.jpg\",\n  \"225531.jpg\": \"Reptilia/Pseudemys gorzugi/3a09ead1ab39ff621b43831232a7ae5f.jpg\",\n  \"225550.jpg\": \"Aves/Psarocolius montezuma/77c4755fd34d683924e82dcdcb9636ce.jpg\",\n  \"225551.jpg\": \"Aves/Psarocolius montezuma/2b36dbe5163e193a5a8f6affb686e1ab.jpg\",\n  \"225552.jpg\": \"Aves/Psarocolius montezuma/253e9a3260252ea6c0317dbec72e90c1.jpg\",\n  \"225555.jpg\": \"Aves/Psarocolius montezuma/416e8130bf40fe130a2166c4be5302d7.jpg\",\n  \"225556.jpg\": \"Aves/Psarocolius montezuma/823b826c4a96fffb4c962a54af086aa4.jpg\",\n  \"225565.jpg\": \"Aves/Psarocolius montezuma/981cee6a28b35ca8620ad197d6513907.jpg\",\n  \"225567.jpg\": \"Aves/Psarocolius montezuma/1bf277782764923f3a6993c5f2bf8676.jpg\",\n  \"225568.jpg\": \"Aves/Psarocolius montezuma/3503b461f38925d69f079c5e424a1431.jpg\",\n  \"225571.jpg\": \"Aves/Psarocolius montezuma/f8e0296adda3edfd99d1479e3f884f96.jpg\",\n  \"225572.jpg\": \"Aves/Psarocolius montezuma/aa358ac502630b3cd26f016b3eb732d2.jpg\",\n  \"225573.jpg\": \"Aves/Psarocolius montezuma/bf2b65e891acddd1f12da5088f306624.jpg\",\n  \"225574.jpg\": \"Aves/Psarocolius montezuma/11779ed5e5e75e810669f4449e2bd709.jpg\",\n  \"225577.jpg\": \"Aves/Psarocolius montezuma/4f5839e66030ed66358d67f5426b4bdf.jpg\",\n  \"225578.jpg\": \"Aves/Psarocolius montezuma/ce8257baf8b6967187d2eac326e7b287.jpg\",\n  \"225579.jpg\": \"Aves/Psarocolius montezuma/852e05561137c92bcf39015c77ad0a12.jpg\",\n  \"225581.jpg\": \"Aves/Psarocolius montezuma/420da32904321c9857134f8f67ab2698.jpg\",\n  \"225588.jpg\": \"Aves/Psarocolius montezuma/87da9f106eaeb2cad95cb692089827a2.jpg\",\n  \"225590.jpg\": \"Aves/Psarocolius montezuma/ee8f31448a9778b50ce4980edef1e05b.jpg\",\n  \"225591.jpg\": \"Aves/Psarocolius montezuma/5b0d9b8cedb4270a1b1fc5c5d0871831.jpg\",\n  \"225596.jpg\": \"Aves/Psarocolius montezuma/ab794b487252789afa6f2a762bcc37ac.jpg\",\n  \"22569.jpg\": \"Aves/Sialia sialis/4a825a4a5acf7cba8e80b9b46e7088d9.jpg\",\n  \"225702.jpg\": \"Insecta/Euptoieta claudia/4f3735116ecd475e31a99c4a97581f3c.jpg\",\n  \"225704.jpg\": \"Insecta/Euptoieta claudia/15f4d20a98f16715a407ef4bf1030d2c.jpg\",\n  \"225716.jpg\": \"Insecta/Euptoieta claudia/0d3db93637a0031fcb3b703de915da8d.jpg\",\n  \"225723.jpg\": \"Insecta/Euptoieta claudia/7dd832d3c023edf2ab3bdd64a018ff4f.jpg\",\n  \"225740.jpg\": \"Insecta/Euptoieta claudia/aa8cf2a34b4ce508d2215d00b5bd3e63.jpg\",\n  \"225750.jpg\": \"Insecta/Euptoieta claudia/cdefb615632828a68ebbf7322e6b3277.jpg\",\n  \"225783.jpg\": \"Insecta/Euptoieta claudia/7121b7394e44664d4038b860898abe02.jpg\",\n  \"225786.jpg\": \"Insecta/Euptoieta claudia/3bdc6108cc84fda9a1dd1b8a8a6ac07a.jpg\",\n  \"225804.jpg\": \"Insecta/Euptoieta claudia/3fdfd5d950a0e099658af45d5c36ebfe.jpg\",\n  \"225814.jpg\": \"Insecta/Euptoieta claudia/a60b5819641030c3e08bb1125d5eca19.jpg\",\n  \"225819.jpg\": \"Insecta/Euptoieta claudia/792cefeb4f3e1fbd59e315e4603ba52e.jpg\",\n  \"225825.jpg\": \"Insecta/Euptoieta claudia/3d5b84582b9080ddc6bbf0ce2926475b.jpg\",\n  \"225835.jpg\": \"Insecta/Euptoieta claudia/b86f7a2de826d551b7d66e1da5096f70.jpg\",\n  \"225867.jpg\": \"Insecta/Euptoieta claudia/775972cb89bebdbb00ecd5a7d9bffb09.jpg\",\n  \"225871.jpg\": \"Insecta/Euptoieta claudia/0deaba7e39599a28b387578e0be5f92f.jpg\",\n  \"225885.jpg\": \"Insecta/Euptoieta claudia/162de6e890bd3b8508f14da96c0f6343.jpg\",\n  \"225889.jpg\": \"Insecta/Euptoieta claudia/a54e2303c392e27602e6a428846f99fd.jpg\",\n  \"225894.jpg\": \"Insecta/Euptoieta claudia/29d39e8293deace01ddb19313958be57.jpg\",\n  \"225907.jpg\": \"Insecta/Euptoieta claudia/4ffe27dced9eec85e9a67900924cf51c.jpg\",\n  \"225932.jpg\": \"Insecta/Euptoieta claudia/ff2519668e4f12721cf9a7bdb70588d4.jpg\",\n  \"225968.jpg\": \"Insecta/Tramea lacerata/ef8de2f62f35ad0776bc24475c3a8e09.jpg\",\n  \"225969.jpg\": \"Insecta/Tramea lacerata/a26627faa2a19dc46371d18d98556655.jpg\",\n  \"225976.jpg\": \"Insecta/Tramea lacerata/eff28a09586664c92973ca08b56a9639.jpg\",\n  \"225989.jpg\": \"Insecta/Tramea lacerata/e6d8e69ee9e863c327232146ac950686.jpg\",\n  \"226008.jpg\": \"Insecta/Tramea lacerata/364ac3a82235125b9f96922970d140da.jpg\",\n  \"226029.jpg\": \"Insecta/Tramea lacerata/fb8816e4517367d19628cf99f5d72889.jpg\",\n  \"226035.jpg\": \"Insecta/Tramea lacerata/ab47ef11278f1d25c11864a28038762f.jpg\",\n  \"226041.jpg\": \"Insecta/Tramea lacerata/c7e4eec104ef2aa90fdbf22c0e7a00c9.jpg\",\n  \"226048.jpg\": \"Insecta/Tramea lacerata/a895d361a07ca79d95cc15de4760e70c.jpg\",\n  \"226053.jpg\": \"Insecta/Tramea lacerata/bf9565e2a9dc84a5272ad2982bf28a64.jpg\",\n  \"226058.jpg\": \"Insecta/Tramea lacerata/8d804da469b47fdc1f382982dfea569b.jpg\",\n  \"226060.jpg\": \"Insecta/Tramea lacerata/a172d4d4f2043220e20fd9b159ac1083.jpg\",\n  \"226066.jpg\": \"Insecta/Tramea lacerata/5688922b13d0d4bbed0705be14a4527d.jpg\",\n  \"226068.jpg\": \"Insecta/Tramea lacerata/f527c3ee4875b56613c8efdff927b8dc.jpg\",\n  \"226086.jpg\": \"Insecta/Tramea lacerata/52125d47cda4a6ad566ffc3c94fd6735.jpg\",\n  \"226088.jpg\": \"Insecta/Tramea lacerata/334f6061d98e4edbd07f679fef251720.jpg\",\n  \"226090.jpg\": \"Insecta/Tramea lacerata/f0253d916475b77eb14afc277e35275d.jpg\",\n  \"226091.jpg\": \"Insecta/Tramea lacerata/24ca84f912bf4070cd5b9194249631a0.jpg\",\n  \"226095.jpg\": \"Insecta/Tramea lacerata/4639b3cbc5cabc4b2ae51e3aacd56aec.jpg\",\n  \"226104.jpg\": \"Insecta/Tramea lacerata/1ee40989f55c79325bd8561ada5228ae.jpg\",\n  \"226117.jpg\": \"Insecta/Tramea lacerata/0a0cf6a6aa06986c222066037c291678.jpg\",\n  \"226118.jpg\": \"Insecta/Tramea lacerata/8ebbeee5c7722a33a816935524d51c4f.jpg\",\n  \"226125.jpg\": \"Insecta/Tramea lacerata/d9436cbee57a6bc8577a353bbc037a5d.jpg\",\n  \"226129.jpg\": \"Insecta/Tramea lacerata/fe72854606d78bdd28676f63d23d4257.jpg\",\n  \"226133.jpg\": \"Amphibia/Lithobates blairi/7ee6d79eb127dc7bc2b7d96585f75cb1.jpg\",\n  \"226134.jpg\": \"Amphibia/Lithobates blairi/705412153d0dadd0335a4e531b06eb09.jpg\",\n  \"226138.jpg\": \"Amphibia/Lithobates blairi/c8d6f750ef0fcd7644876770fd748b9e.jpg\",\n  \"226143.jpg\": \"Amphibia/Lithobates blairi/02c015c44e040b7037d808ce43db88a7.jpg\",\n  \"226144.jpg\": \"Amphibia/Lithobates blairi/655dbdec6b7d517cbc4b26b4ae76292c.jpg\",\n  \"226145.jpg\": \"Amphibia/Lithobates blairi/2a2ec759df6c4f09e2fe968fce1ee345.jpg\",\n  \"226148.jpg\": \"Amphibia/Lithobates blairi/3a74006a7b827abe9f671baf4a1e7b27.jpg\",\n  \"226149.jpg\": \"Amphibia/Lithobates blairi/1fba3a15fccf3b277c6f4bf05066f2f3.jpg\",\n  \"226150.jpg\": \"Amphibia/Lithobates blairi/585f9f2de8f624b679d34b7d3e330a19.jpg\",\n  \"226152.jpg\": \"Amphibia/Lithobates blairi/ad66b70be3f94f44d456d843d3a1576f.jpg\",\n  \"226155.jpg\": \"Amphibia/Lithobates blairi/8c94b96a65873f2cdb4f5c7f1eaab434.jpg\",\n  \"226165.jpg\": \"Amphibia/Lithobates blairi/9aa0c47d29854ffc3f2b7c6893f763d7.jpg\",\n  \"226166.jpg\": \"Amphibia/Lithobates blairi/25a93ed77946415ada11cf8d1cce3c4c.jpg\",\n  \"226167.jpg\": \"Amphibia/Lithobates blairi/dad7009b225adc3ca7c43dd6efedd722.jpg\",\n  \"226168.jpg\": \"Amphibia/Lithobates blairi/f413369387597069b0893d01e07f6695.jpg\",\n  \"226169.jpg\": \"Amphibia/Lithobates blairi/626fac65b7817d0940b07c3152ce0bc8.jpg\",\n  \"226170.jpg\": \"Amphibia/Lithobates blairi/66ddb3c986f8899bcaa75f3e46a056cd.jpg\",\n  \"226172.jpg\": \"Amphibia/Lithobates blairi/58fcc6283b88b8ada81856068e8c8644.jpg\",\n  \"226173.jpg\": \"Amphibia/Lithobates blairi/09e6a261a5912e3e2c887158c940d7fd.jpg\",\n  \"226174.jpg\": \"Amphibia/Lithobates blairi/f7b5c3fdfe0d3c38d8955726b8144b81.jpg\",\n  \"226178.jpg\": \"Amphibia/Lithobates blairi/23b5326f1fd8c442fb95e16708f0a90f.jpg\",\n  \"22623.jpg\": \"Aves/Sialia sialis/4992f6e2721c3c77934de181aebbb1b0.jpg\",\n  \"226230.jpg\": \"Amphibia/Gastrophryne carolinensis/316c510bf09813b258504ceb706be604.jpg\",\n  \"226231.jpg\": \"Amphibia/Gastrophryne carolinensis/7b61317f9d7a8728189394d1838d4e71.jpg\",\n  \"226234.jpg\": \"Amphibia/Gastrophryne carolinensis/d525570c3144d703c15ba126307c5bc1.jpg\",\n  \"226238.jpg\": \"Amphibia/Gastrophryne carolinensis/06c42216de0f5be4a6a99dd4477cf22f.jpg\",\n  \"226239.jpg\": \"Amphibia/Gastrophryne carolinensis/1f53e2c06b4dee5523b2ed2509c010a3.jpg\",\n  \"226245.jpg\": \"Amphibia/Gastrophryne carolinensis/413d7aecbd34039e7760a5b5acd23eea.jpg\",\n  \"226253.jpg\": \"Amphibia/Gastrophryne carolinensis/82489e70b5ffb208b9ca49b0b772647c.jpg\",\n  \"226257.jpg\": \"Amphibia/Gastrophryne carolinensis/dbb9b09894b948c187d8c865b59b2424.jpg\",\n  \"226258.jpg\": \"Amphibia/Gastrophryne carolinensis/d2149665c8b784f8fc6ec492e6632cc3.jpg\",\n  \"226262.jpg\": \"Amphibia/Gastrophryne carolinensis/ae4c633cb8be8ba90444cbac1a7d47ef.jpg\",\n  \"226266.jpg\": \"Amphibia/Gastrophryne carolinensis/d96a51781ac2d5ccf5ec575e2b947250.jpg\",\n  \"226269.jpg\": \"Amphibia/Gastrophryne carolinensis/d57f0c6e736ffa200c3d0694aceb9014.jpg\",\n  \"226270.jpg\": \"Amphibia/Gastrophryne carolinensis/4da1a5345eea1b98891bb3916dd7cb5a.jpg\",\n  \"226272.jpg\": \"Amphibia/Gastrophryne carolinensis/12848dde69ab131f63edb26905969d01.jpg\",\n  \"226273.jpg\": \"Amphibia/Gastrophryne carolinensis/e9d17ffde948cdc135f51e31f8624ad7.jpg\",\n  \"226275.jpg\": \"Amphibia/Gastrophryne carolinensis/5c081a9c4f69b013b0e14c0cf4fc6457.jpg\",\n  \"226279.jpg\": \"Amphibia/Gastrophryne carolinensis/9c80e8cf815269b0ed2d811adff87b0f.jpg\",\n  \"226280.jpg\": \"Amphibia/Gastrophryne carolinensis/b66246f55a34da1536e3f4c4a7be8b1b.jpg\",\n  \"226281.jpg\": \"Amphibia/Gastrophryne carolinensis/955a6fa0a7249b7fc5aac687a23abe8b.jpg\",\n  \"226283.jpg\": \"Amphibia/Gastrophryne carolinensis/9c9f15112f14badab33cba48277c80db.jpg\",\n  \"226291.jpg\": \"Reptilia/Nerodia erythrogaster flavigaster/4096c1cbf4825a2fd17f9c7daa0bc218.jpg\",\n  \"226292.jpg\": \"Reptilia/Nerodia erythrogaster flavigaster/920fd22b3c4f450fd092fda606eb1c91.jpg\",\n  \"226293.jpg\": \"Reptilia/Nerodia erythrogaster flavigaster/9d780534c7b7f49c109b475c002de6b2.jpg\",\n  \"226296.jpg\": \"Reptilia/Nerodia erythrogaster flavigaster/1b25011a89ad22a4862c18640dfe287a.jpg\",\n  \"226310.jpg\": \"Reptilia/Nerodia erythrogaster flavigaster/a6c88ba7a1dfb59d96e02ad13699fe77.jpg\",\n  \"226313.jpg\": \"Reptilia/Nerodia erythrogaster flavigaster/afb70a53b4ee689318f8c2963525cc62.jpg\",\n  \"226314.jpg\": \"Reptilia/Nerodia erythrogaster flavigaster/2b1213bc0169e18b3eb8bcef39c4e009.jpg\",\n  \"226316.jpg\": \"Reptilia/Nerodia erythrogaster flavigaster/d337e7164fbea2a10153073b8eccecab.jpg\",\n  \"226320.jpg\": \"Reptilia/Nerodia erythrogaster flavigaster/502f66985bae3fb432d4ff181faa4d83.jpg\",\n  \"226325.jpg\": \"Insecta/Orthetrum cancellatum/f89dc6e1c2d8a174f9ba932f2b7c6dc2.jpg\",\n  \"226329.jpg\": \"Insecta/Orthetrum cancellatum/7a3a6a75e389e69cc63647a552adba6b.jpg\",\n  \"226335.jpg\": \"Insecta/Orthetrum cancellatum/7b959391580485ee7e3634b13b30f274.jpg\",\n  \"226337.jpg\": \"Insecta/Orthetrum cancellatum/66a7ca2a78fd68f6785bd23660253420.jpg\",\n  \"226338.jpg\": \"Insecta/Orthetrum cancellatum/9b6cfbdf636c98fa19bce4a71ba615a2.jpg\",\n  \"226339.jpg\": \"Insecta/Orthetrum cancellatum/a5cb5d1765f6aae6225ad4a8d64954e3.jpg\",\n  \"226340.jpg\": \"Insecta/Orthetrum cancellatum/05dedde763b35b14a6f5140c80feac44.jpg\",\n  \"226341.jpg\": \"Insecta/Orthetrum cancellatum/61b99c3ebcc8785f711c011ecd4b41f3.jpg\",\n  \"226342.jpg\": \"Insecta/Orthetrum cancellatum/99f37e5716b400c5859f25e18983ff09.jpg\",\n  \"226343.jpg\": \"Insecta/Orthetrum cancellatum/5058eb15ce3232c874baded5c2549785.jpg\",\n  \"226345.jpg\": \"Insecta/Orthetrum cancellatum/8f4469c9525e7828e529a7a734218834.jpg\",\n  \"226346.jpg\": \"Insecta/Orthetrum cancellatum/f770945788e7c6ddd9b52db1902528bd.jpg\",\n  \"226348.jpg\": \"Insecta/Orthetrum cancellatum/d99826c5a52ba41b2a4523929eff05dc.jpg\",\n  \"226351.jpg\": \"Insecta/Orthetrum cancellatum/49f64ab65743f3a6770eb8e3507b54a5.jpg\",\n  \"226352.jpg\": \"Insecta/Orthetrum cancellatum/8d3239821ba6b5704bcf920139f3bec5.jpg\",\n  \"226353.jpg\": \"Insecta/Orthetrum cancellatum/1bf02bfdec51ad45f784451ec98d0a86.jpg\",\n  \"226354.jpg\": \"Insecta/Orthetrum cancellatum/32cdb0ccf1d74fe0ed3b9a272e565994.jpg\",\n  \"226355.jpg\": \"Insecta/Orthetrum cancellatum/4b801a86b86b9cc966eb75f51bd74252.jpg\",\n  \"226361.jpg\": \"Aves/Volatinia jacarina/d9cd79009b7107bf8ee47748970fd18c.jpg\",\n  \"226362.jpg\": \"Aves/Volatinia jacarina/141786dca51d16a29f4d03a17a39a581.jpg\",\n  \"226364.jpg\": \"Aves/Volatinia jacarina/af7705d8fe219eb62a51a556a6eec48d.jpg\",\n  \"226365.jpg\": \"Aves/Volatinia jacarina/1b0661439d74ee6f63fc9b0938086d93.jpg\",\n  \"226366.jpg\": \"Aves/Volatinia jacarina/9b99047d6d9f7c579bb93eabf9591330.jpg\",\n  \"226367.jpg\": \"Aves/Volatinia jacarina/9a0d0e1d3406edcdf57422167c60e631.jpg\",\n  \"226371.jpg\": \"Aves/Volatinia jacarina/daa59a64994d7286cf05e0330f57c849.jpg\",\n  \"226375.jpg\": \"Aves/Volatinia jacarina/eb7b6c7222b1f1520fa1f7e81432c18a.jpg\",\n  \"226381.jpg\": \"Aves/Volatinia jacarina/2f9be5e9be3a2cd01c12d089bca63033.jpg\",\n  \"226383.jpg\": \"Aves/Volatinia jacarina/c6618aaef97cb6c87d421e1aa4db72a6.jpg\",\n  \"226385.jpg\": \"Aves/Volatinia jacarina/6f3faf297904b4e15d2c292ed84a8645.jpg\",\n  \"226387.jpg\": \"Aves/Volatinia jacarina/95d7050e6a3d9fbd1e08de618b2a0bca.jpg\",\n  \"226388.jpg\": \"Aves/Volatinia jacarina/9f113fe325220bcbae6abd208686f301.jpg\",\n  \"226389.jpg\": \"Aves/Volatinia jacarina/55fb27107d58d6ebdd9db5abd3fe90af.jpg\",\n  \"22639.jpg\": \"Aves/Sialia sialis/eeb9907412f419596157acb1e340afbb.jpg\",\n  \"226390.jpg\": \"Aves/Volatinia jacarina/e3a8db6186411f93441364b7dc29cd7d.jpg\",\n  \"226407.jpg\": \"Aves/Volatinia jacarina/748a6aa0aec4219672070de3ad964ad8.jpg\",\n  \"226425.jpg\": \"Aves/Volatinia jacarina/a48bc123201dbbef3861e9e73da961a8.jpg\",\n  \"226426.jpg\": \"Aves/Volatinia jacarina/352d08a9136d8d53a3c2519520746a02.jpg\",\n  \"226427.jpg\": \"Aves/Volatinia jacarina/c7760465634f48458c70af3d983ae3ba.jpg\",\n  \"226429.jpg\": \"Aves/Volatinia jacarina/835118a0111b130f7f0d9d45cd15dda7.jpg\",\n  \"226431.jpg\": \"Aves/Volatinia jacarina/2fc355bcfe439a7f734c3be1c1dc65b4.jpg\",\n  \"226442.jpg\": \"Insecta/Pontania californica/b0cb3c16805b11a9c5104522317ad06c.jpg\",\n  \"226446.jpg\": \"Insecta/Pontania californica/21d0efe1c138ebd5cf04a38ff78f8090.jpg\",\n  \"226453.jpg\": \"Insecta/Pontania californica/bfebf4677bbe1cf0c429d270de4863d8.jpg\",\n  \"226459.jpg\": \"Amphibia/Anaxyrus cognatus/bfb04c515e38637017d30e79ee8c7baa.jpg\",\n  \"226460.jpg\": \"Amphibia/Anaxyrus cognatus/3200df935e7bdf8488530976af57db17.jpg\",\n  \"226463.jpg\": \"Amphibia/Anaxyrus cognatus/053089ee21cd05333c160b7fae7ee403.jpg\",\n  \"226464.jpg\": \"Amphibia/Anaxyrus cognatus/5bf10193449b3fab9b25031eb09c71b3.jpg\",\n  \"226468.jpg\": \"Amphibia/Anaxyrus cognatus/9d5a89756443cadc4caf4acafe335b8b.jpg\",\n  \"226470.jpg\": \"Amphibia/Anaxyrus cognatus/289e354afc274a1023d879d22fd63c45.jpg\",\n  \"226473.jpg\": \"Amphibia/Anaxyrus cognatus/103789d1cd246c92d0723638aeb6eb23.jpg\",\n  \"226476.jpg\": \"Amphibia/Anaxyrus cognatus/8b1c1efec69f18ddf4ae2b925ee6d855.jpg\",\n  \"226477.jpg\": \"Amphibia/Anaxyrus cognatus/103d97de030e6244f64264fbe6fb69d5.jpg\",\n  \"226478.jpg\": \"Amphibia/Anaxyrus cognatus/51a9c2484727cab135bd724af9cfab0a.jpg\",\n  \"226480.jpg\": \"Amphibia/Anaxyrus cognatus/14f45e95b6c9f2967267f8357d114db3.jpg\",\n  \"226481.jpg\": \"Amphibia/Anaxyrus cognatus/216ca7714764fe93ba38816704723de0.jpg\",\n  \"226482.jpg\": \"Amphibia/Anaxyrus cognatus/ddde000a42001f193cf19a3962814e52.jpg\",\n  \"226485.jpg\": \"Amphibia/Anaxyrus cognatus/4d0338772006382e6f88946feff97198.jpg\",\n  \"226493.jpg\": \"Amphibia/Anaxyrus cognatus/7da846038e7d740e2fb1a64a8b624de7.jpg\",\n  \"226495.jpg\": \"Amphibia/Anaxyrus cognatus/c6726ea157a31a62168ce2a32dd9616d.jpg\",\n  \"226496.jpg\": \"Amphibia/Anaxyrus cognatus/a35edd7a62e2c1e2b26b6f3a1bf03c3b.jpg\",\n  \"226497.jpg\": \"Amphibia/Anaxyrus cognatus/e3d7fda01ebebd600b391b45c411c7c1.jpg\",\n  \"226498.jpg\": \"Amphibia/Anaxyrus cognatus/39af22f18b6dc1c8843e9897d5144d05.jpg\",\n  \"226501.jpg\": \"Amphibia/Anaxyrus cognatus/99c852e1f646b3a55dd9dc8ea7a8303f.jpg\",\n  \"226503.jpg\": \"Amphibia/Craugastor augusti/30e215aef4ce340adf57334a5f8900d0.jpg\",\n  \"226513.jpg\": \"Amphibia/Craugastor augusti/4688b0622b4441f1f908d8db6f29fa04.jpg\",\n  \"226514.jpg\": \"Amphibia/Craugastor augusti/fd7843ce5d6a92230142b330b05175be.jpg\",\n  \"226525.jpg\": \"Aves/Helmitheros vermivorum/99849f23c276b332a3714569780f863e.jpg\",\n  \"226528.jpg\": \"Aves/Helmitheros vermivorum/828a6ca304b6f6f400aca80dfacbf740.jpg\",\n  \"226529.jpg\": \"Aves/Helmitheros vermivorum/7e9a91b224248880c662c92942533e42.jpg\",\n  \"22653.jpg\": \"Aves/Sialia sialis/8b961c86d00302e6710013f430ee1df5.jpg\",\n  \"226532.jpg\": \"Aves/Helmitheros vermivorum/21296306992cb5934701e743bae0e440.jpg\",\n  \"226533.jpg\": \"Aves/Helmitheros vermivorum/6e973f99e611e133891de5230fbb2284.jpg\",\n  \"226544.jpg\": \"Aves/Helmitheros vermivorum/b3e0c7ac77b0821db09abb37348b0b49.jpg\",\n  \"226569.jpg\": \"Insecta/Probole amicaria/d87ca055af89a503a2023bc6ecc0d0f0.jpg\",\n  \"226570.jpg\": \"Insecta/Probole amicaria/4d7626a65d70210eac2d4a9732fe6371.jpg\",\n  \"226576.jpg\": \"Insecta/Probole amicaria/f0c7d72a38b0d166ed7020d7822d8d44.jpg\",\n  \"22658.jpg\": \"Aves/Sialia sialis/21dca32723c981e031ba653c34ba1c9e.jpg\",\n  \"226587.jpg\": \"Amphibia/Eleutherodactylus cystignathoides/19cc6420658d330933b32a0997cf6cd6.jpg\",\n  \"226588.jpg\": \"Amphibia/Eleutherodactylus cystignathoides/b64772266e8df13e8a790af9835256cd.jpg\",\n  \"226589.jpg\": \"Amphibia/Eleutherodactylus cystignathoides/cc8077498920ddc00b79a25a5cd176c6.jpg\",\n  \"226590.jpg\": \"Amphibia/Eleutherodactylus cystignathoides/d79b262fc9c2456f3d147a947f662e7c.jpg\",\n  \"226591.jpg\": \"Amphibia/Eleutherodactylus cystignathoides/9c235a4bab57faa90bc6ff493a3a7c7d.jpg\",\n  \"226592.jpg\": \"Amphibia/Eleutherodactylus cystignathoides/fb0c34b50a775e9fded6b5e146f3e694.jpg\",\n  \"226596.jpg\": \"Amphibia/Eleutherodactylus cystignathoides/1764079230d99df9571910c0102ce4c7.jpg\",\n  \"226597.jpg\": \"Amphibia/Eleutherodactylus cystignathoides/f4915fb7c77c1a64aa643306263e27cf.jpg\",\n  \"226598.jpg\": \"Amphibia/Eleutherodactylus cystignathoides/73f15ee995bef76a6a4f2bcbf8138b77.jpg\",\n  \"226599.jpg\": \"Amphibia/Eleutherodactylus cystignathoides/0721518848fbd2bcc50df920705f35eb.jpg\",\n  \"226601.jpg\": \"Amphibia/Eleutherodactylus cystignathoides/e1ed45c9927fbb5d316b6384bb120e2c.jpg\",\n  \"226602.jpg\": \"Amphibia/Eleutherodactylus cystignathoides/3ee0451fe3379a21f25852b34f5c388b.jpg\",\n  \"226607.jpg\": \"Amphibia/Eleutherodactylus cystignathoides/20fe73a29d1791da708c516d9811c4f5.jpg\",\n  \"226608.jpg\": \"Amphibia/Eleutherodactylus cystignathoides/d46195536985ccbfce9f6fee5f451729.jpg\",\n  \"226609.jpg\": \"Amphibia/Eleutherodactylus cystignathoides/4e885b6c1447aaf3cb4296d8d19b7195.jpg\",\n  \"226610.jpg\": \"Amphibia/Eleutherodactylus cystignathoides/4280bb30236d7747101495c56a9f7052.jpg\",\n  \"226611.jpg\": \"Amphibia/Eleutherodactylus cystignathoides/f04d28003333832cb86f03961c8abf95.jpg\",\n  \"226614.jpg\": \"Amphibia/Eleutherodactylus cystignathoides/73de1543522ad7c15802b502f21b11ed.jpg\",\n  \"226617.jpg\": \"Amphibia/Eleutherodactylus cystignathoides/e004cfa93fcd410b18c69fa929ee895b.jpg\",\n  \"226620.jpg\": \"Amphibia/Eleutherodactylus cystignathoides/e7bd765eedce22fa0c6c9c3965885903.jpg\",\n  \"226621.jpg\": \"Amphibia/Eleutherodactylus cystignathoides/57d38c709a913aecd42ec0a00345d62d.jpg\",\n  \"226622.jpg\": \"Amphibia/Eleutherodactylus cystignathoides/9a835d0cde08b48c292fe6ed46b9eb72.jpg\",\n  \"226664.jpg\": \"Insecta/Lampides boeticus/7d82ef433e8608b81ad05945336eb3c8.jpg\",\n  \"226665.jpg\": \"Insecta/Lampides boeticus/b3a0b5449bc8af927bc2c6ec8b9600ee.jpg\",\n  \"226669.jpg\": \"Insecta/Lampides boeticus/5e0268c06c9b10aeff3d9beb2b2f6c46.jpg\",\n  \"226670.jpg\": \"Insecta/Lampides boeticus/a17a8d4efccdeb4968de095313925a2c.jpg\",\n  \"226671.jpg\": \"Insecta/Lampides boeticus/90dff14ceced9c41d6365c401c752e93.jpg\",\n  \"226674.jpg\": \"Insecta/Lampides boeticus/f77490d7bdbe1dc8fd9110846c53498b.jpg\",\n  \"226675.jpg\": \"Insecta/Lampides boeticus/b9452f103fcd7407bc7de484d342ab89.jpg\",\n  \"226677.jpg\": \"Insecta/Lampides boeticus/82fb7db2d6d1ed201f8b1f1422edc0e1.jpg\",\n  \"226682.jpg\": \"Insecta/Lampides boeticus/3674f1245a037fa3d8e84da3225e552a.jpg\",\n  \"226683.jpg\": \"Insecta/Lampides boeticus/2bdba834d53d90da63b8e797eddeab71.jpg\",\n  \"226684.jpg\": \"Insecta/Lampides boeticus/b2e2aa12ee015576c037c7dacf71c425.jpg\",\n  \"226686.jpg\": \"Insecta/Lampides boeticus/927181615ada26df07402f404637855b.jpg\",\n  \"226687.jpg\": \"Insecta/Lampides boeticus/d531f797652284530cbaeb2b1138d0c6.jpg\",\n  \"226688.jpg\": \"Insecta/Lampides boeticus/e09006360f3852bdbce733bd341a3da1.jpg\",\n  \"226689.jpg\": \"Insecta/Lampides boeticus/b83c877a7df90e4d1a9e7db536ee19ef.jpg\",\n  \"226690.jpg\": \"Insecta/Lampides boeticus/0dc4ff4e342bd2dab9e8292508c3b2a0.jpg\",\n  \"226691.jpg\": \"Insecta/Lampides boeticus/6d4d40d863549a20a1f334ae30cf93c5.jpg\",\n  \"226692.jpg\": \"Insecta/Lampides boeticus/ea52f8d4f2a7b90aa07a715b10d1e48e.jpg\",\n  \"226693.jpg\": \"Insecta/Lampides boeticus/7cdb6bfc07e773460e4ff79ff40e1051.jpg\",\n  \"226702.jpg\": \"Insecta/Lampides boeticus/25e8600235a23921c9feb1227e2d10e0.jpg\",\n  \"226769.jpg\": \"Insecta/Datana ministra/428a7d9a7c62199139b49e6a8df2281c.jpg\",\n  \"226804.jpg\": \"Reptilia/Crotalus atrox/179b6965239f89cd943ab253c2af6059.jpg\",\n  \"226806.jpg\": \"Reptilia/Crotalus atrox/d2ad2712bdde356f65ced30150a2ca33.jpg\",\n  \"226829.jpg\": \"Reptilia/Crotalus atrox/af602edf365e421f74ab8b6674bd5084.jpg\",\n  \"226838.jpg\": \"Reptilia/Crotalus atrox/e96555afa576def4a1158dd86263af8c.jpg\",\n  \"226844.jpg\": \"Reptilia/Crotalus atrox/412b37dc1e1965c8faf9883711bfb6d3.jpg\",\n  \"226847.jpg\": \"Reptilia/Crotalus atrox/dbd16bdec1251f547b09dde6a1ca0428.jpg\",\n  \"226867.jpg\": \"Reptilia/Crotalus atrox/f7baa5bf671530c2a9b7fb877376cf38.jpg\",\n  \"226880.jpg\": \"Reptilia/Crotalus atrox/91ce2a4c301c788bc152d2a0fc3f269b.jpg\",\n  \"226903.jpg\": \"Reptilia/Crotalus atrox/895a7e68bff091275711843fd88a0fc9.jpg\",\n  \"226910.jpg\": \"Reptilia/Crotalus atrox/470f7aa22f6e6cd14a6c06bec371f219.jpg\",\n  \"226946.jpg\": \"Reptilia/Crotalus atrox/d59d75998fe02e6e8458b9ba607a7ea4.jpg\",\n  \"226969.jpg\": \"Reptilia/Crotalus atrox/a9e9f6cfed73fffaa6ef2268a838604b.jpg\",\n  \"226991.jpg\": \"Reptilia/Crotalus atrox/6504723d1826b9431b0fa29b438a8727.jpg\",\n  \"227004.jpg\": \"Reptilia/Crotalus atrox/5e4b04bec87975a5b9668956d20e3a85.jpg\",\n  \"227057.jpg\": \"Reptilia/Crotalus atrox/1701afcab5011d03b22db2764a7e1043.jpg\",\n  \"227066.jpg\": \"Reptilia/Crotalus atrox/209fc2d7299a9126e77a63152d6dfe82.jpg\",\n  \"227132.jpg\": \"Reptilia/Crotalus atrox/12f6d87682b92db68bf7f222fb373c50.jpg\",\n  \"227207.jpg\": \"Reptilia/Crotalus atrox/5ccbf57043085684b8222bc3b90a90f1.jpg\",\n  \"227226.jpg\": \"Reptilia/Crotalus atrox/bccf6fc64031f5f2aee6632b2f79ee40.jpg\",\n  \"227241.jpg\": \"Reptilia/Crotalus atrox/06debf9b59b79af9b87b3b7ec5220137.jpg\",\n  \"227285.jpg\": \"Aves/Dolichonyx oryzivorus/419ba9f4d51c0c7a234b5c91e72982cd.jpg\",\n  \"227292.jpg\": \"Aves/Dolichonyx oryzivorus/ba58d0fd7d0d9049378bcb001f49c70f.jpg\",\n  \"227293.jpg\": \"Aves/Dolichonyx oryzivorus/6ef3fab4eb676a274de55fd9685f094f.jpg\",\n  \"227308.jpg\": \"Aves/Dolichonyx oryzivorus/5769f39f97862031a3aeba4fbe3ad056.jpg\",\n  \"227309.jpg\": \"Aves/Dolichonyx oryzivorus/ca3c579479266a64cf8aaf30da97abf1.jpg\",\n  \"22731.jpg\": \"Aves/Sialia sialis/1dbc3863630225eeaf9b944d4e0b3b0b.jpg\",\n  \"227312.jpg\": \"Aves/Dolichonyx oryzivorus/6c52605937048f8416cee0453c42b0cf.jpg\",\n  \"227316.jpg\": \"Aves/Dolichonyx oryzivorus/0ee766fe38af16097f453d784477cb72.jpg\",\n  \"227317.jpg\": \"Aves/Dolichonyx oryzivorus/7a620643e60d33867ce997a82c764920.jpg\",\n  \"227318.jpg\": \"Aves/Dolichonyx oryzivorus/2af66de5da45b95b267b17e47d0d4093.jpg\",\n  \"227320.jpg\": \"Aves/Dolichonyx oryzivorus/84852a8a4f25e748e38a3a4c42b088a4.jpg\",\n  \"227326.jpg\": \"Aves/Dolichonyx oryzivorus/e832078d58f6d249bb61348a5ec25d0f.jpg\",\n  \"227338.jpg\": \"Aves/Dolichonyx oryzivorus/cf74f7ec5eae9c243b28b00b2d4ccc9d.jpg\",\n  \"227342.jpg\": \"Aves/Dolichonyx oryzivorus/6e9fd7fe27b8242edeb64a1cd8b9d2a3.jpg\",\n  \"227347.jpg\": \"Aves/Dolichonyx oryzivorus/db195dc35cf59e081da428ae01e75030.jpg\",\n  \"227361.jpg\": \"Aves/Dolichonyx oryzivorus/3d8b44c6c1e40150d26eb7e90559a287.jpg\",\n  \"227364.jpg\": \"Aves/Dolichonyx oryzivorus/ae26ad0dc6ad3bd774980f069ef4a2f1.jpg\",\n  \"227368.jpg\": \"Aves/Dolichonyx oryzivorus/11ffc1776529264ff00d45d1b9f2a4d7.jpg\",\n  \"227379.jpg\": \"Aves/Dolichonyx oryzivorus/1bdb3ca901a815075c8cff79e1b12b9e.jpg\",\n  \"227381.jpg\": \"Aves/Dolichonyx oryzivorus/def75816f1ff37cfefaf80b97efc2700.jpg\",\n  \"227382.jpg\": \"Insecta/Ennomos magnaria/bffac8fcc0374d0ae744dee53ab4b9ad.jpg\",\n  \"22739.jpg\": \"Aves/Sialia sialis/2f1c6f6369f138ec9f2df721b3f55f81.jpg\",\n  \"227392.jpg\": \"Insecta/Ennomos magnaria/326f52a9d11378cf3421ada6d29f875a.jpg\",\n  \"227393.jpg\": \"Insecta/Ennomos magnaria/aa88198c5701411e0768a8dbff567414.jpg\",\n  \"227394.jpg\": \"Insecta/Ennomos magnaria/d871b72eb289ce41d3d79186e58ebc70.jpg\",\n  \"227398.jpg\": \"Insecta/Ennomos magnaria/e73ce171abe9ed39f82f3ef3f8792c71.jpg\",\n  \"227402.jpg\": \"Insecta/Ennomos magnaria/ff74f2b370531d36c19f5a3a920b8747.jpg\",\n  \"227408.jpg\": \"Insecta/Ennomos magnaria/f4f8b4b8e8993b4a40a39e2e849c9e5e.jpg\",\n  \"227410.jpg\": \"Insecta/Ennomos magnaria/cbd489e2a32f2d23315e8c4cb16889cf.jpg\",\n  \"227513.jpg\": \"Insecta/Gibbifer californicus/9aa71162f28d4334fff71139691e3bc7.jpg\",\n  \"227519.jpg\": \"Insecta/Gibbifer californicus/6d498af324824fc58646b032cb9390db.jpg\",\n  \"227520.jpg\": \"Insecta/Gibbifer californicus/b9ffb48e49f4f320df13176101b8cd1b.jpg\",\n  \"227522.jpg\": \"Insecta/Gibbifer californicus/2adc3c94f12a59414c93bc5ee66c88b8.jpg\",\n  \"227523.jpg\": \"Insecta/Gibbifer californicus/e562e75442bdfd1014fb11bef3a5162f.jpg\",\n  \"227524.jpg\": \"Insecta/Gibbifer californicus/ec35a1229671c6fafae69716804e45ec.jpg\",\n  \"227526.jpg\": \"Insecta/Gibbifer californicus/9cad00fa8f7f1cb462ed26c94040cd7a.jpg\",\n  \"227556.jpg\": \"Arachnida/Micrathena gracilis/aa16897ecf0441321e0f8bb96a0502d3.jpg\",\n  \"227557.jpg\": \"Arachnida/Micrathena gracilis/66844b4629bfecf4d94b0ccb02f4b98c.jpg\",\n  \"227559.jpg\": \"Arachnida/Micrathena gracilis/db86ec89ef587b22232794410eb113bd.jpg\",\n  \"227560.jpg\": \"Arachnida/Micrathena gracilis/dd3953d7c09b2d4ffe038407fea259a7.jpg\",\n  \"227561.jpg\": \"Arachnida/Micrathena gracilis/0f52ab9c162a801735c078105ad4b501.jpg\",\n  \"227585.jpg\": \"Arachnida/Micrathena gracilis/6516f9e7cf4c22ec55386cbd5343965b.jpg\",\n  \"227589.jpg\": \"Arachnida/Micrathena gracilis/6ed7ee4ec2eb6b045c4c77cd7d01a42a.jpg\",\n  \"227590.jpg\": \"Arachnida/Micrathena gracilis/f47da22f414b4e0f3473314404151e67.jpg\",\n  \"227591.jpg\": \"Arachnida/Micrathena gracilis/9c605d814020fd6bc604b494ca061224.jpg\",\n  \"227596.jpg\": \"Arachnida/Micrathena gracilis/8036314fb500353735cd4abb4f72fc75.jpg\",\n  \"227599.jpg\": \"Arachnida/Micrathena gracilis/08de30cd9844886df99df1949ee973ac.jpg\",\n  \"22760.jpg\": \"Aves/Sialia sialis/19d10c45a25d05078c74675db35178d6.jpg\",\n  \"227603.jpg\": \"Arachnida/Micrathena gracilis/32f1d1b040bbfe66eafc7b23426e8775.jpg\",\n  \"227608.jpg\": \"Arachnida/Micrathena gracilis/6e764b207d239a23b7436d11ec006fb6.jpg\",\n  \"227609.jpg\": \"Arachnida/Micrathena gracilis/b58e84eaeab878e488d3940d400aee6b.jpg\",\n  \"227610.jpg\": \"Arachnida/Micrathena gracilis/c7d42868998973a9c747ddd22d2680ec.jpg\",\n  \"227611.jpg\": \"Arachnida/Micrathena gracilis/8aaaffa4ffba695b7ee8f6fe90dc0ce7.jpg\",\n  \"227657.jpg\": \"Insecta/Sunira bicolorago/cbbe5c0a2fd4d5d22b8c8b60c5b8bc19.jpg\",\n  \"227659.jpg\": \"Insecta/Sunira bicolorago/4ff7e046ea7476defe18d914cfcd1d78.jpg\",\n  \"227660.jpg\": \"Insecta/Sunira bicolorago/64615f22199144c22c78c6c03759696b.jpg\",\n  \"22776.jpg\": \"Aves/Sialia sialis/5101ea621b05b0809ab1a69ef6909afb.jpg\",\n  \"227840.jpg\": \"Reptilia/Callisaurus draconoides/851fb83883e3a5a7db672ea8beac9012.jpg\",\n  \"227841.jpg\": \"Reptilia/Callisaurus draconoides/1c15931e28a3aff91c8c2fb229da1a94.jpg\",\n  \"227845.jpg\": \"Reptilia/Callisaurus draconoides/ed856c7652e26dfcf9ef8a5c9ae08e9d.jpg\",\n  \"227852.jpg\": \"Reptilia/Callisaurus draconoides/78b86937eb198da2b10907feeed45df3.jpg\",\n  \"227854.jpg\": \"Reptilia/Callisaurus draconoides/6f58e72f841312c6673070f3904b19e3.jpg\",\n  \"227864.jpg\": \"Reptilia/Callisaurus draconoides/10c16983ec4bd61e72198352395c03d7.jpg\",\n  \"227865.jpg\": \"Reptilia/Callisaurus draconoides/d70109a661fa169e34cfb810e7ad8fba.jpg\",\n  \"227866.jpg\": \"Reptilia/Callisaurus draconoides/2c7c6f3e97c85c8809604b040f29ae46.jpg\",\n  \"227868.jpg\": \"Reptilia/Callisaurus draconoides/dfd775e25d61e55bc357e274caeceb31.jpg\",\n  \"227869.jpg\": \"Reptilia/Callisaurus draconoides/b250bcb234f94d1e6c731dfce6c39bf6.jpg\",\n  \"227870.jpg\": \"Reptilia/Callisaurus draconoides/0bfab14f0ed7b142d379fd7f0e4a5516.jpg\",\n  \"227876.jpg\": \"Reptilia/Callisaurus draconoides/dd33658c37f61d8e9bdd81b9ce452812.jpg\",\n  \"227877.jpg\": \"Reptilia/Callisaurus draconoides/db3ecea449fcfbf0e7c0e38725212f1d.jpg\",\n  \"227880.jpg\": \"Reptilia/Callisaurus draconoides/ce3f80e087311f566601b043d8c76c75.jpg\",\n  \"227885.jpg\": \"Reptilia/Callisaurus draconoides/33497e961fe21a513edb9d72819cbfa2.jpg\",\n  \"227886.jpg\": \"Reptilia/Callisaurus draconoides/eff86baad544f7a0284e0099d8d5d2a1.jpg\",\n  \"227889.jpg\": \"Reptilia/Callisaurus draconoides/0ad4b3d059c52b4924172366b2e5361b.jpg\",\n  \"227893.jpg\": \"Reptilia/Callisaurus draconoides/b18958af258dcd5c106135fd7f70565e.jpg\",\n  \"227896.jpg\": \"Reptilia/Callisaurus draconoides/18129dc400559d976622d3476ae5c26b.jpg\",\n  \"227900.jpg\": \"Reptilia/Callisaurus draconoides/d933137b24c1fccca6b21d021b9bbbc6.jpg\",\n  \"227901.jpg\": \"Reptilia/Callisaurus draconoides/aea826882ed17bd513d24a1c6d7fcf61.jpg\",\n  \"227925.jpg\": \"Arachnida/Aphonopelma hentzi/0c2a31dc937d3e69c920c36d7d5e65ef.jpg\",\n  \"227926.jpg\": \"Arachnida/Aphonopelma hentzi/2bb665ad00eaa981d033393f220bdb45.jpg\",\n  \"227930.jpg\": \"Arachnida/Aphonopelma hentzi/ff0ca39e0e65f95c73d6ee1c534b3e2e.jpg\",\n  \"227936.jpg\": \"Arachnida/Aphonopelma hentzi/3f93318bdaf571cb2ebe47c25741077d.jpg\",\n  \"227937.jpg\": \"Arachnida/Aphonopelma hentzi/4b6ae4154a712ba95354ba28c78809a8.jpg\",\n  \"227942.jpg\": \"Arachnida/Aphonopelma hentzi/9f80c743ee3e86346aa633e3e055cc8a.jpg\",\n  \"227943.jpg\": \"Arachnida/Aphonopelma hentzi/b5cd2d1c58692194ea80f63eb35246af.jpg\",\n  \"227944.jpg\": \"Arachnida/Aphonopelma hentzi/409108bfc9ebce6705d2c66c86fb5b28.jpg\",\n  \"227947.jpg\": \"Arachnida/Aphonopelma hentzi/617ec439cc9c75ca0bafd58813af1149.jpg\",\n  \"227948.jpg\": \"Arachnida/Aphonopelma hentzi/2f00e806ce6625e4e8640e0e0bb290a8.jpg\",\n  \"227954.jpg\": \"Arachnida/Aphonopelma hentzi/e66231ee24d8e4327f74962926f2853f.jpg\",\n  \"227955.jpg\": \"Arachnida/Aphonopelma hentzi/d550de07d8c7870731dc45db75651dff.jpg\",\n  \"227958.jpg\": \"Arachnida/Aphonopelma hentzi/d84b8a133254224b83a5e52266cd0c22.jpg\",\n  \"227964.jpg\": \"Arachnida/Aphonopelma hentzi/bd1d9c7f541a8c89dcd3cef587e0686c.jpg\",\n  \"227965.jpg\": \"Arachnida/Aphonopelma hentzi/332cea541fa885682994d4691df6a28e.jpg\",\n  \"227967.jpg\": \"Arachnida/Aphonopelma hentzi/a264ad2d81da005e9810e9f0c0cf1b02.jpg\",\n  \"227968.jpg\": \"Arachnida/Aphonopelma hentzi/abb2cb3d86bc6be4080bed4c1581c994.jpg\",\n  \"227969.jpg\": \"Arachnida/Aphonopelma hentzi/691594401befb737b819723b363b5b59.jpg\",\n  \"227970.jpg\": \"Arachnida/Aphonopelma hentzi/e5ad2448222977bd4b023fdd897ae104.jpg\",\n  \"227971.jpg\": \"Arachnida/Aphonopelma hentzi/13d2775fd2c1c12f0ca8d6a49cdbd21d.jpg\",\n  \"227972.jpg\": \"Arachnida/Aphonopelma hentzi/e958a80e3b56cc91fce9617da6621784.jpg\",\n  \"228010.jpg\": \"Aves/Oxyura jamaicensis/3c494d389af7a62c212b2a1ac7fc6105.jpg\",\n  \"228060.jpg\": \"Aves/Oxyura jamaicensis/b9c14b9cc09ab1977a8c4d41c043aa5b.jpg\",\n  \"228086.jpg\": \"Aves/Oxyura jamaicensis/49cae3a807b8c103c1da45565256ffd8.jpg\",\n  \"228088.jpg\": \"Aves/Oxyura jamaicensis/83b70f655b61088450f8f279c3920ac4.jpg\",\n  \"228109.jpg\": \"Aves/Oxyura jamaicensis/4b92e54c1c300617cd659ce60f124ebc.jpg\",\n  \"228111.jpg\": \"Aves/Oxyura jamaicensis/ccf93ddbf1af550ad6feb386d0d5c32b.jpg\",\n  \"228117.jpg\": \"Aves/Oxyura jamaicensis/cfff17e8289ca3d1d80519cf6e9f6025.jpg\",\n  \"228121.jpg\": \"Aves/Oxyura jamaicensis/5549f31147f8c79a48e635ac079820a5.jpg\",\n  \"228124.jpg\": \"Aves/Oxyura jamaicensis/5f223156ba4d27a05090dd865b076dcf.jpg\",\n  \"228161.jpg\": \"Aves/Oxyura jamaicensis/0df850cc6f6b72b34f5de102a91807a9.jpg\",\n  \"228168.jpg\": \"Aves/Oxyura jamaicensis/6ee9345c76c079bda9e719b2e9eee5c9.jpg\",\n  \"228172.jpg\": \"Aves/Oxyura jamaicensis/149f79b27e820581eee286748222ea4c.jpg\",\n  \"228211.jpg\": \"Aves/Oxyura jamaicensis/5d686c1d494860e49ef0cdf110f951e6.jpg\",\n  \"228212.jpg\": \"Aves/Oxyura jamaicensis/e9fcff5c8affc85ff02a927a2bff883f.jpg\",\n  \"228219.jpg\": \"Aves/Oxyura jamaicensis/3aa009eb6406f2b5fddd14daef98c321.jpg\",\n  \"228252.jpg\": \"Aves/Oxyura jamaicensis/a54936025e9d17d2a976c726f2d09a09.jpg\",\n  \"228259.jpg\": \"Aves/Oxyura jamaicensis/6ffa2bf022082445f788e94f31aefd84.jpg\",\n  \"228263.jpg\": \"Aves/Oxyura jamaicensis/fc6a02cd8bee61c53b7094d85b5adaf7.jpg\",\n  \"228272.jpg\": \"Aves/Oxyura jamaicensis/f5800b3e319c1caa658ce21301d2ccf2.jpg\",\n  \"228293.jpg\": \"Aves/Oxyura jamaicensis/fc6b5e95982af6c1b219632ccfc89cf4.jpg\",\n  \"228297.jpg\": \"Aves/Oxyura jamaicensis/28753555199438ebdd758ce19c9e9cb5.jpg\",\n  \"228336.jpg\": \"Aves/Oxyura jamaicensis/69ff3e7a69da2dadb2f8936d57c8ef76.jpg\",\n  \"228371.jpg\": \"Aves/Branta leucopsis/eaa8f53166d86d240295a19a9ed4f972.jpg\",\n  \"228421.jpg\": \"Mammalia/Callospermophilus lateralis/927c9151b07f635d67279e0c91776eae.jpg\",\n  \"228428.jpg\": \"Mammalia/Callospermophilus lateralis/af1d44cab26fbc1bf4c9322893f98a64.jpg\",\n  \"228433.jpg\": \"Mammalia/Callospermophilus lateralis/d44764bbc8a8e05c4517e1729d794267.jpg\",\n  \"228451.jpg\": \"Mammalia/Callospermophilus lateralis/f83399bea27d2af796caca56e79fd50a.jpg\",\n  \"228460.jpg\": \"Mammalia/Callospermophilus lateralis/98f9f653f429294a1a82872a9509edee.jpg\",\n  \"228461.jpg\": \"Mammalia/Callospermophilus lateralis/7448b31212f5c87b7b7c3535844491f3.jpg\",\n  \"228473.jpg\": \"Mammalia/Callospermophilus lateralis/dae98677af063ec6e9ca4a2cd374c5ed.jpg\",\n  \"228474.jpg\": \"Mammalia/Callospermophilus lateralis/8424b4b67dae51016aaaca86c7ef9ecf.jpg\",\n  \"228475.jpg\": \"Mammalia/Callospermophilus lateralis/bf30620b4bce4dc090e25a4c2e16005b.jpg\",\n  \"228476.jpg\": \"Mammalia/Callospermophilus lateralis/6aca0e7d8de0326ce7eb58f1aa4c164a.jpg\",\n  \"228490.jpg\": \"Mammalia/Callospermophilus lateralis/f1f5e66be1cf4e98bb25f2540983dac9.jpg\",\n  \"228493.jpg\": \"Mammalia/Callospermophilus lateralis/522cc84959ddacb71895b25bcf938def.jpg\",\n  \"228495.jpg\": \"Mammalia/Callospermophilus lateralis/3586ecb0a64fd526edb5646f6ba49c05.jpg\",\n  \"228515.jpg\": \"Mammalia/Callospermophilus lateralis/c01693bf1788939989526400884eb50b.jpg\",\n  \"22852.jpg\": \"Aves/Sialia sialis/380b22c9f4392437a8dbbedcebd1d7c0.jpg\",\n  \"228523.jpg\": \"Mammalia/Callospermophilus lateralis/f06ad33eb23840cc2f18aa5904b58044.jpg\",\n  \"228530.jpg\": \"Mammalia/Callospermophilus lateralis/5f1eb80176a6dd0a994715abe48174e7.jpg\",\n  \"228543.jpg\": \"Mammalia/Callospermophilus lateralis/67a721f7bae15a1d6cb3acd5cb8408af.jpg\",\n  \"228544.jpg\": \"Mammalia/Callospermophilus lateralis/1020ecaa14cee51869635643cd8df4bf.jpg\",\n  \"228546.jpg\": \"Mammalia/Callospermophilus lateralis/cbedbd94bf44736a75bd18ba2f0ba6f4.jpg\",\n  \"228549.jpg\": \"Mammalia/Callospermophilus lateralis/0e94f345e81e1db897b332c46a30ffa6.jpg\",\n  \"228554.jpg\": \"Mammalia/Callospermophilus lateralis/515b874b93fe3dfd896da6d08098c8d6.jpg\",\n  \"228557.jpg\": \"Insecta/Dasymutilla occidentalis/4989d17b5de174d073a4b6063452ffd6.jpg\",\n  \"228560.jpg\": \"Insecta/Dasymutilla occidentalis/4686196205e9dc33151e0dce01f0284e.jpg\",\n  \"228561.jpg\": \"Insecta/Dasymutilla occidentalis/1caa0ba241982e9509aac58298fd9114.jpg\",\n  \"228568.jpg\": \"Insecta/Dasymutilla occidentalis/9a586f5e7e9d1ac0cc0b2c4c9b20439b.jpg\",\n  \"228569.jpg\": \"Insecta/Dasymutilla occidentalis/8d26e36004bd4797baf8b4ff8f6da306.jpg\",\n  \"228571.jpg\": \"Insecta/Dasymutilla occidentalis/a2ee48dcdd07ba16ad8c1f4c3f3a6a81.jpg\",\n  \"228572.jpg\": \"Insecta/Dasymutilla occidentalis/b3f659d44e489112b520aa5551b302c7.jpg\",\n  \"228574.jpg\": \"Insecta/Dasymutilla occidentalis/4e8291a465ff06929f0cac2430115275.jpg\",\n  \"228576.jpg\": \"Insecta/Dasymutilla occidentalis/30a74c1986df8fca264e2d7e01b6d724.jpg\",\n  \"228579.jpg\": \"Insecta/Dasymutilla occidentalis/03f40f5579f23a98b5cf47b34156260a.jpg\",\n  \"228580.jpg\": \"Insecta/Dasymutilla occidentalis/b49d2626c8825c2616d4ca9259a0a7f6.jpg\",\n  \"228581.jpg\": \"Insecta/Dasymutilla occidentalis/5b70fe7ea13c4c0481d81729097264a9.jpg\",\n  \"228586.jpg\": \"Insecta/Dasymutilla occidentalis/d864e774f482366db90bdc671a060981.jpg\",\n  \"228592.jpg\": \"Insecta/Scathophaga stercoraria/cf1382e96e00d7cf1930f3e4ee210cbd.jpg\",\n  \"228597.jpg\": \"Insecta/Scathophaga stercoraria/6acff6fd5911f013e34149f8d3180004.jpg\",\n  \"228600.jpg\": \"Insecta/Scathophaga stercoraria/810703cd4580df6d14f8369e2bdaeda0.jpg\",\n  \"228622.jpg\": \"Insecta/Scathophaga stercoraria/c58943fc7de2cdcc435f485309a38a91.jpg\",\n  \"228630.jpg\": \"Actinopterygii/Zanclus cornutus/c6a380f3c19b2275430669745eecde8b.jpg\",\n  \"228640.jpg\": \"Actinopterygii/Zanclus cornutus/9b73ac4f92cb3b32e08c459d6afd7c24.jpg\",\n  \"228641.jpg\": \"Actinopterygii/Zanclus cornutus/f3f76672a243beee77c1cc7905cf99bb.jpg\",\n  \"228642.jpg\": \"Actinopterygii/Zanclus cornutus/7e37dcc485f7ffe82b066e22a382f8f7.jpg\",\n  \"228648.jpg\": \"Actinopterygii/Zanclus cornutus/9a10c1929b07aaed8285c71fc5891396.jpg\",\n  \"228651.jpg\": \"Actinopterygii/Zanclus cornutus/e1ce867efcfeec1e41a8006dd73e8902.jpg\",\n  \"228668.jpg\": \"Actinopterygii/Zanclus cornutus/d83a67a380d03cd257c215949f228796.jpg\",\n  \"228669.jpg\": \"Actinopterygii/Zanclus cornutus/064c0ef1f044fce3ea4f16e22eb7e60c.jpg\",\n  \"228707.jpg\": \"Aves/Quiscalus niger/4d0ddbd12ba670e1d30dd1d8c3be0eb0.jpg\",\n  \"228708.jpg\": \"Aves/Quiscalus niger/dd4252e2d53c9c29e128391e55d63692.jpg\",\n  \"228717.jpg\": \"Aves/Quiscalus niger/de7cc05e589b46241f9c4fd45481b639.jpg\",\n  \"228720.jpg\": \"Aves/Quiscalus niger/597db8d8b0db5515e05dc12152c7656f.jpg\",\n  \"228722.jpg\": \"Aves/Quiscalus niger/73f4f394dd2e1f425e76767173a17032.jpg\",\n  \"228725.jpg\": \"Aves/Quiscalus niger/992a862bcf5158ae3ed003ac26deaab8.jpg\",\n  \"228726.jpg\": \"Aves/Quiscalus niger/d446c71a9bab12133fbda2110446bd33.jpg\",\n  \"228727.jpg\": \"Aves/Quiscalus niger/8c6e55c088ddd9ec7d85361095d4f7f5.jpg\",\n  \"228728.jpg\": \"Aves/Quiscalus niger/bb3a8aaaee3f505cb6ce88d6ee6275f3.jpg\",\n  \"228729.jpg\": \"Aves/Quiscalus niger/b95594425416f1032399ac2f872c5961.jpg\",\n  \"228734.jpg\": \"Reptilia/Glyptemys insculpta/f28147c0002935696d05510587e17658.jpg\",\n  \"228735.jpg\": \"Reptilia/Glyptemys insculpta/aa1bd2588f2ab61b240bacf84fbf6f2d.jpg\",\n  \"228750.jpg\": \"Reptilia/Glyptemys insculpta/a0fbd545d8bb6ea47c51fc8f1ed9554c.jpg\",\n  \"228751.jpg\": \"Reptilia/Glyptemys insculpta/9b2981045f2dc5c9297a9a558df1bd3a.jpg\",\n  \"228754.jpg\": \"Reptilia/Glyptemys insculpta/939226b4742c59d905ce193c42533e21.jpg\",\n  \"228756.jpg\": \"Reptilia/Glyptemys insculpta/5cd8bb7f051aa7aa659bc7e027c91855.jpg\",\n  \"228757.jpg\": \"Reptilia/Glyptemys insculpta/7712347b5f7f785542021fab6cbd6966.jpg\",\n  \"228758.jpg\": \"Reptilia/Glyptemys insculpta/209cfae0e1c5a243d127cf8b90e0a6c5.jpg\",\n  \"228759.jpg\": \"Reptilia/Glyptemys insculpta/8a232f0c4a638db1dd1560f3872cb558.jpg\",\n  \"228764.jpg\": \"Insecta/Lasiommata megera/6d9be957ab7e0a3f3bff0a5026a2f47e.jpg\",\n  \"228765.jpg\": \"Insecta/Lasiommata megera/ad64005ff89c89ee075da793a7be942e.jpg\",\n  \"228770.jpg\": \"Insecta/Lasiommata megera/1207d99375ec66cf53213fc997dddbe8.jpg\",\n  \"228772.jpg\": \"Insecta/Lasiommata megera/bea87a2004030b150401f466492c7dff.jpg\",\n  \"228782.jpg\": \"Insecta/Lasiommata megera/be54f66617d92150f4c2acdb902454b6.jpg\",\n  \"228786.jpg\": \"Insecta/Lasiommata megera/690675826f7194d99ce756e45031a817.jpg\",\n  \"228797.jpg\": \"Insecta/Lasiommata megera/14a340f8b8f1202a45afcb4c7f9ed571.jpg\",\n  \"228798.jpg\": \"Insecta/Lasiommata megera/2b389064fa11e1e1c5b4a2ddc0f22294.jpg\",\n  \"228804.jpg\": \"Insecta/Lasiommata megera/940718dff6e70b4ad8ca0386cbe6b58c.jpg\",\n  \"228805.jpg\": \"Insecta/Lasiommata megera/1e7e8083ee932da2b515a9f4757bbbe0.jpg\",\n  \"228806.jpg\": \"Insecta/Lasiommata megera/f85794c5f8cd0a71c019f73700bf8818.jpg\",\n  \"228807.jpg\": \"Insecta/Lasiommata megera/9dbc6b3edff957bc3f4c4ee2771680dd.jpg\",\n  \"228814.jpg\": \"Insecta/Lasiommata megera/a8056d0e094ea7de0a9d0a2e3a674f95.jpg\",\n  \"228815.jpg\": \"Insecta/Lasiommata megera/f9008f5b6e916d9261308d3cf845933d.jpg\",\n  \"228817.jpg\": \"Insecta/Lasiommata megera/3b8f1adc85fd56b25798f10b966c253a.jpg\",\n  \"228818.jpg\": \"Insecta/Lasiommata megera/09fb6ec02bf471b0d116b64bb7034498.jpg\",\n  \"228819.jpg\": \"Insecta/Lasiommata megera/4a3b851962d11130db809477d1868d02.jpg\",\n  \"228820.jpg\": \"Insecta/Lasiommata megera/d7e32b31e78c7235eae3300c9e3f380d.jpg\",\n  \"228822.jpg\": \"Insecta/Lasiommata megera/7e6eff35f6e253e74413e15aa0419797.jpg\",\n  \"228823.jpg\": \"Insecta/Lasiommata megera/2e95ec8e4f43d422cebfb8013ab4f25a.jpg\",\n  \"228824.jpg\": \"Insecta/Lasiommata megera/ef7cb8d179bf063f39b54700fedc286b.jpg\",\n  \"228868.jpg\": \"Aves/Cathartes burrovianus/66c19a5cc50cc2faf2724c05312d8036.jpg\",\n  \"228869.jpg\": \"Aves/Cathartes burrovianus/649928d8a3e392911cfc4dfcabfcc86e.jpg\",\n  \"228870.jpg\": \"Aves/Cathartes burrovianus/d6a6bd57a6ee4107ac0b56dcb4018b01.jpg\",\n  \"228872.jpg\": \"Aves/Cathartes burrovianus/ee4f72005cbb30aaeb393ac8cdb8d4f2.jpg\",\n  \"229035.jpg\": \"Aves/Picoides scalaris/b25be279f81086ff2eaf97ce17e65b8a.jpg\",\n  \"229041.jpg\": \"Aves/Picoides scalaris/073a49f2f828e3affd1bbca058d792f8.jpg\",\n  \"229054.jpg\": \"Aves/Picoides scalaris/4bf72d021894e85e99bde67be7b0dce0.jpg\",\n  \"229059.jpg\": \"Aves/Picoides scalaris/427bad3102c59309f62e34ab377cfa5a.jpg\",\n  \"229072.jpg\": \"Aves/Picoides scalaris/470d0b365f4c0fbc4988ece7bceac15e.jpg\",\n  \"229083.jpg\": \"Aves/Picoides scalaris/455b27204655a3755f877d2a8f0868ce.jpg\",\n  \"229084.jpg\": \"Aves/Picoides scalaris/fca5fe4b13b9621c2b37b4ad0b14e500.jpg\",\n  \"229108.jpg\": \"Aves/Picoides scalaris/56d26f428e691426bf23626a649659f6.jpg\",\n  \"229115.jpg\": \"Aves/Picoides scalaris/f427c604bbb3ad47bfc1dab0cdf4a38d.jpg\",\n  \"229130.jpg\": \"Aves/Picoides scalaris/b19ebd98622554eb94f81d433e8962d7.jpg\",\n  \"229133.jpg\": \"Aves/Picoides scalaris/567fb3e4bd350f2b8928ad3687faa9df.jpg\",\n  \"229150.jpg\": \"Aves/Picoides scalaris/39dd644ffe776c1b548f20e69614c81a.jpg\",\n  \"229160.jpg\": \"Aves/Picoides scalaris/2fbe07dc88805c8f78c6f53958ee1e4e.jpg\",\n  \"229177.jpg\": \"Aves/Picoides scalaris/b083b001c56325b28211ceec340a397d.jpg\",\n  \"229178.jpg\": \"Aves/Picoides scalaris/9a4f9fd779897e8e81a4fbd288d71faf.jpg\",\n  \"229179.jpg\": \"Aves/Picoides scalaris/7c8b916de79ae31b9f5ee8c864cacc32.jpg\",\n  \"229197.jpg\": \"Aves/Picoides scalaris/08ca9bb69f28d0824916da7dea967254.jpg\",\n  \"229198.jpg\": \"Aves/Picoides scalaris/6ee1a6aed97c15f182dfbdb0e94e4357.jpg\",\n  \"229201.jpg\": \"Aves/Picoides scalaris/0492a440aabba37c0482b236229a1c4c.jpg\",\n  \"229217.jpg\": \"Aves/Picoides scalaris/48ec161010d22d786431e45d60dd1087.jpg\",\n  \"229226.jpg\": \"Aves/Picoides scalaris/f7f0fff6bbbfc5af3da5c87e3171a999.jpg\",\n  \"229228.jpg\": \"Reptilia/Holbrookia propinqua/707ac1c637968711b6a6f2645cc64a76.jpg\",\n  \"229229.jpg\": \"Reptilia/Holbrookia propinqua/5c8a2c5c02b3660cc84abd3c7e684a21.jpg\",\n  \"229235.jpg\": \"Reptilia/Holbrookia propinqua/e39dcb9b8e692c298326b763e920642a.jpg\",\n  \"229237.jpg\": \"Reptilia/Holbrookia propinqua/26daa0d5ae4f81339e8cd2194048609a.jpg\",\n  \"229238.jpg\": \"Reptilia/Holbrookia propinqua/3d134f01d9037d32c9297b275355b899.jpg\",\n  \"229240.jpg\": \"Reptilia/Holbrookia propinqua/df598285ae12a8e044654fd10d4f2d14.jpg\",\n  \"229288.jpg\": \"Aves/Emberiza calandra/315b93ec76bf2108bbdb1e173b56b917.jpg\",\n  \"229289.jpg\": \"Aves/Emberiza calandra/199009a087c292b2528e83b3fcf95105.jpg\",\n  \"229304.jpg\": \"Aves/Emberiza calandra/2aa19ce8b8e2f32927a367fc52c4176a.jpg\",\n  \"22944.jpg\": \"Aves/Sialia sialis/1c480e094d022e3ffb06606b895972c7.jpg\",\n  \"229559.jpg\": \"Reptilia/Sceloporus consobrinus/480ea77230787c364b34efbf8a4fb27f.jpg\",\n  \"229566.jpg\": \"Reptilia/Sceloporus consobrinus/8e79816bfeee746b85a585ae499dd9a5.jpg\",\n  \"229571.jpg\": \"Reptilia/Sceloporus consobrinus/3bd580d19044f6422cf5c7d3b25bbbce.jpg\",\n  \"229579.jpg\": \"Reptilia/Sceloporus consobrinus/1ba79606c81c0d21713ceba0dbfdd3c4.jpg\",\n  \"229588.jpg\": \"Reptilia/Sceloporus consobrinus/006a77b96b25041ce710fa040dbe4327.jpg\",\n  \"229590.jpg\": \"Reptilia/Sceloporus consobrinus/9a613414c4243351c0097c280ff654fa.jpg\",\n  \"229592.jpg\": \"Reptilia/Sceloporus consobrinus/1fe0fcc3502baaba37545d2e69c01b4a.jpg\",\n  \"229594.jpg\": \"Reptilia/Sceloporus consobrinus/56c12bd094f96f44be1256767047778d.jpg\",\n  \"229629.jpg\": \"Reptilia/Sceloporus consobrinus/5d3f3ff0490ff131b8183b7bffd1120f.jpg\",\n  \"229630.jpg\": \"Reptilia/Sceloporus consobrinus/13729b504845664f7655e7e9eb048d07.jpg\",\n  \"229631.jpg\": \"Reptilia/Sceloporus consobrinus/cc580a98382a7be59705b8c7251fd512.jpg\",\n  \"229632.jpg\": \"Reptilia/Sceloporus consobrinus/483b070f63d869f44ec867e27a92d2a7.jpg\",\n  \"229635.jpg\": \"Reptilia/Sceloporus consobrinus/e39526d4d479ef319eb75735cf1ec62b.jpg\",\n  \"229639.jpg\": \"Reptilia/Sceloporus consobrinus/2eac22ab1afb1969cb68d67ca8e882f1.jpg\",\n  \"229641.jpg\": \"Reptilia/Sceloporus consobrinus/d888628c3f8a20a476f321df842df1a7.jpg\",\n  \"229642.jpg\": \"Reptilia/Sceloporus consobrinus/489791596da458b4fb5e7e2a0ca4b8f3.jpg\",\n  \"229646.jpg\": \"Reptilia/Sceloporus consobrinus/7e481a1457a10095656f450fc7904deb.jpg\",\n  \"229650.jpg\": \"Reptilia/Sceloporus consobrinus/368849cabd014905be082324496c7657.jpg\",\n  \"229671.jpg\": \"Reptilia/Nerodia fasciata confluens/231285a7e3717494622c1d41a4ac3616.jpg\",\n  \"229676.jpg\": \"Reptilia/Nerodia fasciata confluens/5bd9b431de85efd48aa0f61c31b9df7e.jpg\",\n  \"229679.jpg\": \"Reptilia/Nerodia fasciata confluens/90649d3970210ddf35efadac27431a0b.jpg\",\n  \"229681.jpg\": \"Reptilia/Nerodia fasciata confluens/3f434a3f397d010031672a7b1508955a.jpg\",\n  \"229684.jpg\": \"Reptilia/Nerodia fasciata confluens/4619b7654e4ff5ce47a82c502d12432d.jpg\",\n  \"229686.jpg\": \"Reptilia/Nerodia fasciata confluens/e7876b3cf9ae96f151056902c2f75ad6.jpg\",\n  \"229692.jpg\": \"Reptilia/Nerodia fasciata confluens/824f6e78f751fbcd4265583cb8fd4b18.jpg\",\n  \"229696.jpg\": \"Reptilia/Nerodia fasciata confluens/53ebd275032e4e5ea048c3ccd6ed39ae.jpg\",\n  \"229700.jpg\": \"Reptilia/Nerodia fasciata confluens/9cec819e5f5ce5aa33db906a2469afbb.jpg\",\n  \"229703.jpg\": \"Reptilia/Nerodia fasciata confluens/4b4a04e7f02822c05558e1c574045635.jpg\",\n  \"229706.jpg\": \"Reptilia/Nerodia fasciata confluens/b8ab1289f41b67b1319dee8ce47ae6f6.jpg\",\n  \"229708.jpg\": \"Reptilia/Nerodia fasciata confluens/a16989377c8a49183fa7d2f1f928ea2d.jpg\",\n  \"229716.jpg\": \"Reptilia/Nerodia fasciata confluens/a4558bb6bdee05c3c4cfcdbeeb88e96f.jpg\",\n  \"229717.jpg\": \"Reptilia/Nerodia fasciata confluens/d7354745beef67e875b2fee2f7f0860a.jpg\",\n  \"229718.jpg\": \"Reptilia/Nerodia fasciata confluens/aebfb8d3b0847955fdff6104dcdede8f.jpg\",\n  \"229719.jpg\": \"Reptilia/Nerodia fasciata confluens/94886f74b5e4cf570ed2faadb7d9dbf4.jpg\",\n  \"229720.jpg\": \"Reptilia/Nerodia fasciata confluens/f9d78da6a273a59c2e5a0b2852974922.jpg\",\n  \"229728.jpg\": \"Reptilia/Nerodia fasciata confluens/1af9746b50acab62652643b4522f64a2.jpg\",\n  \"229737.jpg\": \"Reptilia/Nerodia fasciata confluens/9c59be994a371e7220d66713419b6f61.jpg\",\n  \"229740.jpg\": \"Reptilia/Nerodia fasciata confluens/a29fc96645ba40d6b624729ec1f05900.jpg\",\n  \"229746.jpg\": \"Reptilia/Cophosaurus texanus/b128e853e3b49f914fe9f3bd37591b93.jpg\",\n  \"229749.jpg\": \"Reptilia/Cophosaurus texanus/5ee37d0378885bac69a1f5225c2d9946.jpg\",\n  \"229754.jpg\": \"Reptilia/Cophosaurus texanus/9bb31d234a25654492df75d46cb8bcd7.jpg\",\n  \"229755.jpg\": \"Reptilia/Cophosaurus texanus/570b76d6ee98fe60454707b08f9496a7.jpg\",\n  \"229762.jpg\": \"Reptilia/Cophosaurus texanus/ff82636177a9db1f169c5139b23f52e2.jpg\",\n  \"229771.jpg\": \"Reptilia/Cophosaurus texanus/f6a551a657d39ac4ffcf0eb38b375c6d.jpg\",\n  \"229778.jpg\": \"Reptilia/Cophosaurus texanus/532343eaa59b7adf3ec090e939100698.jpg\",\n  \"229779.jpg\": \"Reptilia/Cophosaurus texanus/996b1b0f0da46f595c48d90fa2438a18.jpg\",\n  \"229781.jpg\": \"Reptilia/Cophosaurus texanus/c0640dd0887cbf5fe8b41b7215eb6ef0.jpg\",\n  \"229783.jpg\": \"Reptilia/Cophosaurus texanus/d1dea41a26a300cad47a745d113c6af8.jpg\",\n  \"229790.jpg\": \"Reptilia/Cophosaurus texanus/88c6884e793b04bdd1ce11704ff30631.jpg\",\n  \"229803.jpg\": \"Reptilia/Cophosaurus texanus/62d9bacc2b044c70c9678c3b5f071b62.jpg\",\n  \"229804.jpg\": \"Reptilia/Cophosaurus texanus/59c213f304e743fed7d6731bb1ef477c.jpg\",\n  \"229807.jpg\": \"Reptilia/Cophosaurus texanus/0e636d23ee0c65f2c67b25e4c51433ee.jpg\",\n  \"229809.jpg\": \"Reptilia/Cophosaurus texanus/221ce66b84040524a9663ac6309286c1.jpg\",\n  \"229810.jpg\": \"Reptilia/Cophosaurus texanus/c7d6fd9b6d4d8cd3a5253146a38dd679.jpg\",\n  \"229812.jpg\": \"Reptilia/Cophosaurus texanus/04e699c45f18f4fca97e9bb749167519.jpg\",\n  \"229832.jpg\": \"Reptilia/Cophosaurus texanus/c7a5588506f19e4e762703cde9cd67ac.jpg\",\n  \"229835.jpg\": \"Reptilia/Cophosaurus texanus/a4f3139acfb4e1c5176d1466377722d2.jpg\",\n  \"229837.jpg\": \"Reptilia/Cophosaurus texanus/92aa17480816051dfef7eeb63ab13e55.jpg\",\n  \"229838.jpg\": \"Reptilia/Cophosaurus texanus/792e5f1c9f274d710c183ade1b318add.jpg\",\n  \"229842.jpg\": \"Reptilia/Cophosaurus texanus/b0b4452442dc28c7a8d487a55d1e7a8b.jpg\",\n  \"229844.jpg\": \"Reptilia/Crotalus lepidus/e5c477c7b3a886c9144ccdb37a10a64f.jpg\",\n  \"229845.jpg\": \"Reptilia/Crotalus lepidus/95f145186b9b8b7803ff009f4af54082.jpg\",\n  \"229846.jpg\": \"Reptilia/Crotalus lepidus/8513ab59d43ed242814ffa72231c7c1c.jpg\",\n  \"229847.jpg\": \"Reptilia/Crotalus lepidus/d4eb538ad325a62446cc919f7202ad68.jpg\",\n  \"229848.jpg\": \"Reptilia/Crotalus lepidus/a0ed1a890f57d2972874276ce1a454d2.jpg\",\n  \"229855.jpg\": \"Reptilia/Crotalus lepidus/2e2a97d56ef7fdb50326de7056e606b8.jpg\",\n  \"229856.jpg\": \"Reptilia/Crotalus lepidus/710a37e4e00cee64c4082e24eab54b3e.jpg\",\n  \"229860.jpg\": \"Reptilia/Crotalus lepidus/8ecab1d3852ce2f128034d14ec02aa47.jpg\",\n  \"2302.jpg\": \"Aves/Picoides pubescens/44d6fd60485bcb2e579955242bdbe3cc.jpg\",\n  \"230201.jpg\": \"Reptilia/Phrynosoma cornutum/5b87b3dfe0a27a1433c09e44ab02254b.jpg\",\n  \"230206.jpg\": \"Reptilia/Phrynosoma cornutum/7d62bce62753c2e89e221b7520251199.jpg\",\n  \"230240.jpg\": \"Reptilia/Phrynosoma cornutum/9379d13eef6220c7ba74eabc042b251b.jpg\",\n  \"230257.jpg\": \"Reptilia/Phrynosoma cornutum/4875478c35a9290f588aada478136ac8.jpg\",\n  \"230269.jpg\": \"Reptilia/Phrynosoma cornutum/1fcd2be14abdb6029a06128347c8801b.jpg\",\n  \"230271.jpg\": \"Reptilia/Phrynosoma cornutum/26179daf7b7d86a9986af275204b8ac3.jpg\",\n  \"230285.jpg\": \"Reptilia/Phrynosoma cornutum/deed12f3189235a28ed7dab4c472a7ca.jpg\",\n  \"230286.jpg\": \"Reptilia/Phrynosoma cornutum/19fe96630961e64760b58a39fbf56ed0.jpg\",\n  \"230287.jpg\": \"Reptilia/Phrynosoma cornutum/1ed512c515bf3bce2aac1a10ebcabd90.jpg\",\n  \"230290.jpg\": \"Reptilia/Phrynosoma cornutum/d1c41ca0ce7e3cb3123359721b2ebc30.jpg\",\n  \"230293.jpg\": \"Reptilia/Phrynosoma cornutum/6585f63d20856d358fb4a251e0a46818.jpg\",\n  \"230298.jpg\": \"Reptilia/Phrynosoma cornutum/5cfb862b72e3b3db2bd38d175c0553ea.jpg\",\n  \"230306.jpg\": \"Reptilia/Phrynosoma cornutum/bbc246dfc232979d6d9a53f605244f50.jpg\",\n  \"230307.jpg\": \"Reptilia/Phrynosoma cornutum/8fa942ed13e045e7f33cae69c95aa265.jpg\",\n  \"230328.jpg\": \"Reptilia/Phrynosoma cornutum/2485148fc86c172504dd2dbf181aec9e.jpg\",\n  \"230339.jpg\": \"Reptilia/Phrynosoma cornutum/3ca41482d1e5588e9f7ef195b3255a32.jpg\",\n  \"230352.jpg\": \"Reptilia/Phrynosoma cornutum/a191587f030405406f445fef398b10b0.jpg\",\n  \"230353.jpg\": \"Reptilia/Phrynosoma cornutum/298c8bfc059804273ab21094f4cc769e.jpg\",\n  \"230354.jpg\": \"Reptilia/Phrynosoma cornutum/e207892213939b0e5ad4210d507b5fd5.jpg\",\n  \"230356.jpg\": \"Reptilia/Phrynosoma cornutum/a2b7956331e60d820e32ffb5fd8edff3.jpg\",\n  \"230358.jpg\": \"Reptilia/Phrynosoma cornutum/bce5a1894fc700fb98560f5a895077af.jpg\",\n  \"230386.jpg\": \"Reptilia/Phrynosoma cornutum/6eb39d0cd9d34b2739b119d04eaf8109.jpg\",\n  \"230389.jpg\": \"Reptilia/Phrynosoma cornutum/8d4e53ebc722c0a9aa0a88eadb599ab5.jpg\",\n  \"230393.jpg\": \"Reptilia/Phrynosoma cornutum/8a2b433606e45e5f91fd5a11cd921ec9.jpg\",\n  \"230476.jpg\": \"Amphibia/Spea bombifrons/e0e6e1ac412d932c32170665ef5d194f.jpg\",\n  \"230477.jpg\": \"Amphibia/Spea bombifrons/7e3041779d1848b284ecb11b16b92f82.jpg\",\n  \"230484.jpg\": \"Amphibia/Spea bombifrons/6175b37d641d4c809cf3bc6fb12a340a.jpg\",\n  \"230485.jpg\": \"Amphibia/Spea bombifrons/2f6dc4c2a0f45ef04e3c6ddbd5d63bc1.jpg\",\n  \"230486.jpg\": \"Amphibia/Spea bombifrons/c3786780323ee56d36266eae7885da37.jpg\",\n  \"230495.jpg\": \"Amphibia/Spea bombifrons/e595fecc4d3d8b7baa20a8b73e3f4537.jpg\",\n  \"230497.jpg\": \"Aves/Muscicapa striata/d407b46d2d61278768e631df24ee0f51.jpg\",\n  \"230498.jpg\": \"Aves/Muscicapa striata/b3f392400224b6e1ca91a2799801a0b9.jpg\",\n  \"230500.jpg\": \"Aves/Muscicapa striata/e1fe0b9230992ef20230bb28d9b9d053.jpg\",\n  \"230501.jpg\": \"Aves/Muscicapa striata/1c4bdc806c2ee966bfdc54860bd51387.jpg\",\n  \"230508.jpg\": \"Aves/Muscicapa striata/16573fc751a02ccd3fd27f94bd3996ac.jpg\",\n  \"230509.jpg\": \"Aves/Muscicapa striata/8da135a52a0cab3fc77f626c695888b3.jpg\",\n  \"230510.jpg\": \"Aves/Muscicapa striata/328fce0404e9981a190a196a4078a4b0.jpg\",\n  \"230511.jpg\": \"Aves/Muscicapa striata/d9203273d922c8d70c964a339fffca05.jpg\",\n  \"230512.jpg\": \"Aves/Muscicapa striata/864a169072cef3a6ce3138a5cddb0750.jpg\",\n  \"230513.jpg\": \"Aves/Muscicapa striata/ddd9874d0593b43691a7e75d6525d0d8.jpg\",\n  \"230517.jpg\": \"Aves/Muscicapa striata/f5c8fb50ac73477951f9a2580b956f4c.jpg\",\n  \"230518.jpg\": \"Aves/Muscicapa striata/bb5b9800a3630f5878a91611d775cb02.jpg\",\n  \"230520.jpg\": \"Aves/Muscicapa striata/e90bfcdcb867570270fc9af841df2f5d.jpg\",\n  \"230521.jpg\": \"Aves/Muscicapa striata/56b20ee515f01c81abff1d16fba6ffdb.jpg\",\n  \"230522.jpg\": \"Aves/Muscicapa striata/b0c685792c8e650881c66528218bc68b.jpg\",\n  \"230523.jpg\": \"Aves/Muscicapa striata/a121333d1ef4198e66ccc77ebc3d6f95.jpg\",\n  \"230524.jpg\": \"Aves/Muscicapa striata/adfbc95eb69fef271ff9236aa177e46f.jpg\",\n  \"230528.jpg\": \"Aves/Muscicapa striata/65760aa3817b821a877c7b30d9765065.jpg\",\n  \"230529.jpg\": \"Aves/Muscicapa striata/a434888cd54902cf334f30738a4902c3.jpg\",\n  \"230531.jpg\": \"Aves/Muscicapa striata/664b9f6935bfb48ea6681b1ff2f1b559.jpg\",\n  \"230532.jpg\": \"Aves/Muscicapa striata/fa1f503060ef8cb73d0c3a2ac9adcddc.jpg\",\n  \"230534.jpg\": \"Aves/Muscicapa striata/659f96ef493f028cffd5eb4eae53337d.jpg\",\n  \"230607.jpg\": \"Aves/Calidris himantopus/32ccb5751576b418f9a8aca11bbf3698.jpg\",\n  \"230608.jpg\": \"Aves/Calidris himantopus/7a848f69b4e1a30d50de8159273fcd0c.jpg\",\n  \"230617.jpg\": \"Aves/Calidris himantopus/5b41a2b4c1c10ab5ac04a54138f23781.jpg\",\n  \"230618.jpg\": \"Aves/Calidris himantopus/dcd1203d84dedf7cd07ffb27b98a5b61.jpg\",\n  \"230619.jpg\": \"Aves/Calidris himantopus/0d51407f72fe8def4d208fae6017417f.jpg\",\n  \"230622.jpg\": \"Aves/Calidris himantopus/a86b27e67b30fdfee75aff85a29f2f26.jpg\",\n  \"230625.jpg\": \"Aves/Calidris himantopus/485924a70816393a585a58b5980701f7.jpg\",\n  \"230626.jpg\": \"Aves/Calidris himantopus/afe9cf718c8957a72959ce8cf098e51b.jpg\",\n  \"230702.jpg\": \"Aves/Haliaeetus leucocephalus/33b6ec9f0c8ae506b4c4c52230d5edc4.jpg\",\n  \"230712.jpg\": \"Aves/Haliaeetus leucocephalus/ab6f8bf97c87b3ced5bebdf80a0efba0.jpg\",\n  \"230722.jpg\": \"Aves/Haliaeetus leucocephalus/e68f019db916fed79108926a8d3d356a.jpg\",\n  \"230723.jpg\": \"Aves/Haliaeetus leucocephalus/8615231c96be46380d04ab258417d542.jpg\",\n  \"230774.jpg\": \"Aves/Haliaeetus leucocephalus/7326d2e91ca6056e2e7b04f177cbe810.jpg\",\n  \"230821.jpg\": \"Aves/Haliaeetus leucocephalus/ad0e541bd8c7fac1506774d8356c1064.jpg\",\n  \"230849.jpg\": \"Aves/Haliaeetus leucocephalus/0ed8a66eea66a57851f9f969c2d08673.jpg\",\n  \"230942.jpg\": \"Aves/Haliaeetus leucocephalus/0583165199af52b4e64f88b3f43f7276.jpg\",\n  \"230957.jpg\": \"Aves/Haliaeetus leucocephalus/741d635111a30e41783a5d23586c6d7c.jpg\",\n  \"231023.jpg\": \"Aves/Haliaeetus leucocephalus/2bf1e90c385b0d51eb593730137fa2a4.jpg\",\n  \"231040.jpg\": \"Aves/Haliaeetus leucocephalus/82fd41f759cd6aa1e4ef3d1a1d580840.jpg\",\n  \"231070.jpg\": \"Aves/Haliaeetus leucocephalus/6f643e9ff74621e3030a87b5a520cd37.jpg\",\n  \"231132.jpg\": \"Aves/Haliaeetus leucocephalus/3e6bfa027746bfac0197c5f7ea01d174.jpg\",\n  \"231146.jpg\": \"Aves/Haliaeetus leucocephalus/dee14c90f5fec1a4d5de51280f65cbc8.jpg\",\n  \"231147.jpg\": \"Aves/Haliaeetus leucocephalus/2eb2e76dafc4f805f05a90be0c054dff.jpg\",\n  \"231206.jpg\": \"Aves/Haliaeetus leucocephalus/360b9807242d433d79f4584f10dfa0cf.jpg\",\n  \"231246.jpg\": \"Aves/Haliaeetus leucocephalus/72e8c1f8408f9831a45599b58d08cc2d.jpg\",\n  \"231338.jpg\": \"Aves/Haliaeetus leucocephalus/2926e56c376facb86edf90a15de403da.jpg\",\n  \"231345.jpg\": \"Aves/Haliaeetus leucocephalus/4866fb86c87c4b39032eecf861693b85.jpg\",\n  \"231374.jpg\": \"Insecta/Apatelodes torrefacta/03b2265c3563ae4d3e1ec352fde64e59.jpg\",\n  \"231377.jpg\": \"Insecta/Apatelodes torrefacta/e7e7d33edfb0ef3db7266d6e3a16364b.jpg\",\n  \"231384.jpg\": \"Insecta/Apatelodes torrefacta/c26d2372e1e59acdc30bcf16fad6c815.jpg\",\n  \"231385.jpg\": \"Insecta/Apatelodes torrefacta/242b19dc562083de3f2a94bfb4fe0da4.jpg\",\n  \"231389.jpg\": \"Insecta/Apatelodes torrefacta/75bde5cbf8fd94d73935994232ac8741.jpg\",\n  \"231390.jpg\": \"Aves/Corvus frugilegus/2a85f8889a57d757c40232435792c782.jpg\",\n  \"231395.jpg\": \"Aves/Corvus frugilegus/eaa29e1fcbe94d9e650be22df4b50330.jpg\",\n  \"231397.jpg\": \"Aves/Corvus frugilegus/0f92c4eb01e4acd82f5889f4cca141b1.jpg\",\n  \"231400.jpg\": \"Aves/Corvus frugilegus/20c884bb3a3ee1f0b768fc2af34f72af.jpg\",\n  \"231413.jpg\": \"Aves/Corvus frugilegus/3b88e85d846bd873e56e1f6e7dc477a6.jpg\",\n  \"231425.jpg\": \"Aves/Corvus frugilegus/960a6966d6a70f7b66e84b588172f02f.jpg\",\n  \"231426.jpg\": \"Aves/Porzana carolina/e6fab6c6fe16854107445cf77755d1e9.jpg\",\n  \"231431.jpg\": \"Aves/Porzana carolina/6844c7da8c1d8cdbe63789bcff9a4c6a.jpg\",\n  \"231441.jpg\": \"Aves/Porzana carolina/2ad587a59462843839323480c8bfef6e.jpg\",\n  \"231445.jpg\": \"Aves/Porzana carolina/3f45c90750e4b6347986cd6b02a5e971.jpg\",\n  \"231464.jpg\": \"Aves/Porzana carolina/04b8c82a23598eaf1e5d854030fd4a54.jpg\",\n  \"231465.jpg\": \"Aves/Porzana carolina/d17b8b1c64e072b16fc0e3965d365a08.jpg\",\n  \"231471.jpg\": \"Aves/Porzana carolina/f02f6515dfbbd242876c110f4be2f741.jpg\",\n  \"231477.jpg\": \"Aves/Porzana carolina/20ad848ab11c894c177eff9726009b26.jpg\",\n  \"231484.jpg\": \"Aves/Porzana carolina/978ab3bb2228a809eb683d7fd5292ece.jpg\",\n  \"231485.jpg\": \"Aves/Porzana carolina/23c97c2fe77a22fd898276efa0dfe469.jpg\",\n  \"231489.jpg\": \"Aves/Porzana carolina/57d5d7607e5c72b730537c7fd15b9d7c.jpg\",\n  \"231490.jpg\": \"Aves/Porzana carolina/e486068cada0e4b8e4ad35479ceef63c.jpg\",\n  \"231494.jpg\": \"Aves/Porzana carolina/ad7804f5717902db53aa8210c6789ef3.jpg\",\n  \"231499.jpg\": \"Aves/Porzana carolina/baad82ea17d34916725afdc0b6b1d5b0.jpg\",\n  \"231501.jpg\": \"Aves/Porzana carolina/18c3ab15a401e3448c08d73d3576995e.jpg\",\n  \"231503.jpg\": \"Aves/Porzana carolina/04b089c83339bb5f549466322d87edcd.jpg\",\n  \"231504.jpg\": \"Aves/Porzana carolina/a66b7b8bf124d2ce694d5fcb45f527f0.jpg\",\n  \"231508.jpg\": \"Aves/Porzana carolina/cbf008a9720f73dd800d122ddfffcb9f.jpg\",\n  \"231514.jpg\": \"Aves/Porzana carolina/cae9058b800a4c70c8033b8dce4ed664.jpg\",\n  \"231519.jpg\": \"Aves/Porzana carolina/f21709f411791e0917cb6bcfe847389e.jpg\",\n  \"231529.jpg\": \"Aves/Porzana carolina/61abae428d8bb29d9ac40791ca5fb9fd.jpg\",\n  \"231530.jpg\": \"Aves/Porzana carolina/8a54537304e23b5c01abcefdb7cbc378.jpg\",\n  \"231532.jpg\": \"Aves/Porzana carolina/60c725e1780ff814acad1a01721c1e19.jpg\",\n  \"231638.jpg\": \"Animalia/Carcinus maenas/739869910aded01301b7d6b9f9181860.jpg\",\n  \"231641.jpg\": \"Animalia/Carcinus maenas/9fca9aebabf942f5ddfedf257c214cf7.jpg\",\n  \"231652.jpg\": \"Animalia/Carcinus maenas/e52a2ef90aa6cc96fc23de01470840fa.jpg\",\n  \"231655.jpg\": \"Animalia/Carcinus maenas/9ff1be9549109c679ab6fad0435fa101.jpg\",\n  \"231656.jpg\": \"Animalia/Carcinus maenas/bf696b32063296e2cb628cc099fb497e.jpg\",\n  \"231660.jpg\": \"Animalia/Carcinus maenas/7a76be3dda74a3f60cf5a0634e2303df.jpg\",\n  \"231661.jpg\": \"Animalia/Carcinus maenas/e6cffa6bde47aaf68181b296cd51ed56.jpg\",\n  \"231670.jpg\": \"Aves/Melanerpes carolinus/28a3927fabcb747895989b9c0de13916.jpg\",\n  \"231690.jpg\": \"Aves/Melanerpes carolinus/7b9abdc7c4c67adf8206ffe8800f8c5f.jpg\",\n  \"231723.jpg\": \"Aves/Melanerpes carolinus/8536329e3507d0a574491f0272fd02ce.jpg\",\n  \"231740.jpg\": \"Aves/Melanerpes carolinus/6ffb2853fb3cd7fe9bd6c57bf7d08171.jpg\",\n  \"231757.jpg\": \"Aves/Melanerpes carolinus/db49f12cfd2698c3df8676fd42a976c2.jpg\",\n  \"231768.jpg\": \"Aves/Melanerpes carolinus/03889535f757eafc7ff492067df0b557.jpg\",\n  \"231793.jpg\": \"Aves/Melanerpes carolinus/e1ddcb8c4a27bed1e7fab3f29463878f.jpg\",\n  \"231794.jpg\": \"Aves/Melanerpes carolinus/88038fb9057871f5ed54def9f489e619.jpg\",\n  \"231803.jpg\": \"Aves/Melanerpes carolinus/dfb8ccd121a30652121c7867f1c4ad50.jpg\",\n  \"231825.jpg\": \"Aves/Melanerpes carolinus/d7221475cec133aaadeea2968e12d758.jpg\",\n  \"231842.jpg\": \"Aves/Melanerpes carolinus/31f1c3671137e5edc49fcc62123e34c8.jpg\",\n  \"231844.jpg\": \"Aves/Melanerpes carolinus/5b338c21de4a6bc866ee6de27cfd3a3e.jpg\",\n  \"231868.jpg\": \"Aves/Melanerpes carolinus/11ceccb6388c4d32ce9acd4237e0bb6b.jpg\",\n  \"231945.jpg\": \"Aves/Melanerpes carolinus/6ce8221726b9df2224a5d431bdd17d75.jpg\",\n  \"231968.jpg\": \"Aves/Melanerpes carolinus/ec7fb4c00d5ed7d7346897a98a200a62.jpg\",\n  \"232041.jpg\": \"Aves/Melanerpes carolinus/e866068b72886b201d46ccd4526186d7.jpg\",\n  \"232058.jpg\": \"Aves/Melanerpes carolinus/191538411bb04085df9d2b72d0372759.jpg\",\n  \"232084.jpg\": \"Aves/Melanerpes carolinus/995fb3e590985d72bafc1e43e57e55fd.jpg\",\n  \"232110.jpg\": \"Aves/Melanerpes carolinus/c32df4d24bca2ea4688a66d66129b00c.jpg\",\n  \"232177.jpg\": \"Aves/Melanerpes carolinus/7b997920a65fe86ed50a0128f2ef417b.jpg\",\n  \"232181.jpg\": \"Aves/Melanerpes carolinus/d97f799aeef28cc0fde83b7fda8a4d92.jpg\",\n  \"232203.jpg\": \"Aves/Melanerpes carolinus/852c05800c1d74bd2afad56be899706a.jpg\",\n  \"232222.jpg\": \"Aves/Melanerpes carolinus/f1de718f59062f87da33d97452b15a10.jpg\",\n  \"232223.jpg\": \"Aves/Melanerpes carolinus/9c989271427d32c80bb4e319a01dade4.jpg\",\n  \"232225.jpg\": \"Aves/Melanerpes carolinus/c97ede34224ec9e0f2a991ec2195099f.jpg\",\n  \"232270.jpg\": \"Aves/Todiramphus sanctus/e5ddbbc2055cf1380769f18f4114d0a6.jpg\",\n  \"232272.jpg\": \"Aves/Todiramphus sanctus/9efb9a6a139c826b2651d3a2d94b70e1.jpg\",\n  \"232276.jpg\": \"Aves/Todiramphus sanctus/3c866f22d6b1db83ae20b2a245a7a576.jpg\",\n  \"232282.jpg\": \"Aves/Todiramphus sanctus/d3df806b813132a3c31e72acdd223750.jpg\",\n  \"232283.jpg\": \"Aves/Todiramphus sanctus/deb037f923fa87c31da50d10ebe4dd37.jpg\",\n  \"232293.jpg\": \"Insecta/Lycaena hyllus/a5139a897ed4064ad5fcaa2f84c6bcaa.jpg\",\n  \"232295.jpg\": \"Insecta/Lycaena hyllus/6e2d7c7afa67b9bdea3febb5bfd30e2a.jpg\",\n  \"232296.jpg\": \"Insecta/Lycaena hyllus/b8cdc90c0579f1e91be9a30d9122a183.jpg\",\n  \"232301.jpg\": \"Insecta/Lycaena hyllus/09c298f4faeb7f71847fe338240dfc6d.jpg\",\n  \"232304.jpg\": \"Insecta/Lycaena hyllus/5129cfb0a311da6d545493f2a7c6147f.jpg\",\n  \"232305.jpg\": \"Insecta/Lycaena hyllus/c316d71995ae196f94cc04aeec4a46c5.jpg\",\n  \"232306.jpg\": \"Insecta/Lycaena hyllus/db85bd13f48bc1e4e3bfaf927b75f889.jpg\",\n  \"232307.jpg\": \"Insecta/Lycaena hyllus/0abcc92dcff292b6384645f99ec94522.jpg\",\n  \"232312.jpg\": \"Insecta/Lycaena hyllus/cb0b15d4725d8f118cf29f1599ef1e4c.jpg\",\n  \"232315.jpg\": \"Insecta/Lycaena hyllus/f1a5eb6aa6dcb21645d6d44732d449b8.jpg\",\n  \"232316.jpg\": \"Insecta/Lycaena hyllus/2b8c426d48bad2ece5169f9f9a285510.jpg\",\n  \"232317.jpg\": \"Insecta/Lycaena hyllus/38669e5f4ca85bb58a2e67a5883f7176.jpg\",\n  \"232318.jpg\": \"Insecta/Lycaena hyllus/6756031e2e826cb3a0eea824b1934640.jpg\",\n  \"232319.jpg\": \"Insecta/Lycaena hyllus/0443078c4b9f5bff9e4a662dfddd2efe.jpg\",\n  \"232321.jpg\": \"Insecta/Lycaena hyllus/4dfa957f0a2042d87a088a0c1d6f98d4.jpg\",\n  \"232322.jpg\": \"Insecta/Lycaena hyllus/04fac3dba098d8bd0042eb28edcbdf8b.jpg\",\n  \"232323.jpg\": \"Insecta/Lycaena hyllus/6729c7ca2435b49caf64803b7bc8b045.jpg\",\n  \"232327.jpg\": \"Insecta/Lycaena hyllus/016a8565761001464383ec04483132d2.jpg\",\n  \"232331.jpg\": \"Insecta/Lycaena hyllus/847ac129bb95479402dc08a4e9b53324.jpg\",\n  \"232332.jpg\": \"Insecta/Lycaena hyllus/c07fdaddf54e056d97baa42711fb48ff.jpg\",\n  \"232335.jpg\": \"Insecta/Lycaena hyllus/16b5a701390cea9036205b0c2e71dbde.jpg\",\n  \"232336.jpg\": \"Insecta/Lycaena hyllus/9cd0273ea8a76eae9394217f4582cd9a.jpg\",\n  \"232337.jpg\": \"Insecta/Lycaena hyllus/665f6b50b5aaad1338a618ae483e7496.jpg\",\n  \"232361.jpg\": \"Insecta/Papilio eurymedon/34b6e5e5363510c70f83cc25b0b66ef3.jpg\",\n  \"232363.jpg\": \"Insecta/Papilio eurymedon/6afa46e19a533365e263ef3056db0cef.jpg\",\n  \"232366.jpg\": \"Insecta/Papilio eurymedon/cb9e519e785bdc6872584775f5f1f53b.jpg\",\n  \"232369.jpg\": \"Insecta/Papilio eurymedon/a42d5949ba36753003804dc65a9364df.jpg\",\n  \"232371.jpg\": \"Insecta/Papilio eurymedon/f5506cc9e11f7e57113bd2225c1bca0f.jpg\",\n  \"232373.jpg\": \"Insecta/Papilio eurymedon/84778408807af290639c856344778b11.jpg\",\n  \"232385.jpg\": \"Insecta/Papilio eurymedon/827973b37a2be7a2ed1ad5c2dfbf0e70.jpg\",\n  \"232390.jpg\": \"Insecta/Papilio eurymedon/5da921809c2a8cbbc7b953d24f7c6b4b.jpg\",\n  \"232409.jpg\": \"Insecta/Papilio eurymedon/7bcd74cb82b6eff51ee65440fbcdc6e6.jpg\",\n  \"232410.jpg\": \"Insecta/Papilio eurymedon/d6c89f4450c4206226de4bb8a3f75cb6.jpg\",\n  \"232417.jpg\": \"Insecta/Papilio eurymedon/a4e2c2d9446775e86f5534993b15aff3.jpg\",\n  \"232418.jpg\": \"Insecta/Papilio eurymedon/d550b9767da305e6221b206f08540ead.jpg\",\n  \"232419.jpg\": \"Insecta/Papilio eurymedon/46f6cec5418d90bfad4fde20223c94c4.jpg\",\n  \"232431.jpg\": \"Insecta/Papilio eurymedon/3d112c9bc48a46027a5d6a1c0b398642.jpg\",\n  \"232444.jpg\": \"Insecta/Papilio eurymedon/6a4f70db6aad5ee5f08957b6c836d702.jpg\",\n  \"232452.jpg\": \"Insecta/Papilio eurymedon/6416bea60ddb48ecd85bbd0e77261d83.jpg\",\n  \"232454.jpg\": \"Insecta/Papilio eurymedon/de26bc93546be9bfd107e0cd5687c4b8.jpg\",\n  \"232459.jpg\": \"Insecta/Papilio eurymedon/beffc699dfd3e1f0cb6f77c7eb32c3bd.jpg\",\n  \"232470.jpg\": \"Insecta/Papilio eurymedon/4daab7c83a18ca6d9366aa4caa7b701c.jpg\",\n  \"232479.jpg\": \"Insecta/Papilio eurymedon/e188c365867bdcc16f7255756f6b3f66.jpg\",\n  \"232503.jpg\": \"Mammalia/Mus musculus/91ac0499b8664655ad84093146a5254f.jpg\",\n  \"232506.jpg\": \"Mammalia/Mus musculus/cef91c8ae44c57defa3203ab7b55117d.jpg\",\n  \"232530.jpg\": \"Mammalia/Mus musculus/3db978c56eebf51fd7fefd08e9edcecc.jpg\",\n  \"232547.jpg\": \"Mammalia/Mus musculus/0807af805ddaccc8ec05499e55c19039.jpg\",\n  \"232548.jpg\": \"Mammalia/Mus musculus/e24c3cdacacae3c3ea170d508c086331.jpg\",\n  \"232556.jpg\": \"Mammalia/Mus musculus/5f49796b5100ef68ca9b62264d025d01.jpg\",\n  \"232562.jpg\": \"Mammalia/Mus musculus/27c323e4264a1720d94bc21957bbd80b.jpg\",\n  \"232584.jpg\": \"Mammalia/Mus musculus/bb77ecd7fac06e2855e972a660a0457d.jpg\",\n  \"232599.jpg\": \"Mammalia/Mus musculus/59a1983ac933370500dd7786d14715b8.jpg\",\n  \"232604.jpg\": \"Mammalia/Mus musculus/ad4d196a09b26cbbd2d31c693b5dce16.jpg\",\n  \"232614.jpg\": \"Amphibia/Rana dalmatina/2b242a99b0ea81d5041630e7bd9b50db.jpg\",\n  \"232629.jpg\": \"Amphibia/Rana dalmatina/d91210008d05482a692fa20743a376da.jpg\",\n  \"232630.jpg\": \"Amphibia/Rana dalmatina/068cdc6beb94b939ca397f449c77c983.jpg\",\n  \"232634.jpg\": \"Amphibia/Rana dalmatina/3a128d147dd07d075b95eb52774ffb8f.jpg\",\n  \"232637.jpg\": \"Amphibia/Rana dalmatina/7e7df1f4d81d61381f248ac11d4afa30.jpg\",\n  \"232645.jpg\": \"Amphibia/Rana dalmatina/97c71b3dcbc48cf8b233671793bcb852.jpg\",\n  \"232652.jpg\": \"Amphibia/Rana dalmatina/4eab1abd3e8724892a115c5faf5c95bc.jpg\",\n  \"232656.jpg\": \"Amphibia/Rana dalmatina/257ca71e21edd233bb4e9166fa80ba4c.jpg\",\n  \"232657.jpg\": \"Amphibia/Rana dalmatina/6e463c4de02595877a8768fa14752d9e.jpg\",\n  \"232658.jpg\": \"Amphibia/Rana dalmatina/2682ac645bf0324797fed42e73c9a304.jpg\",\n  \"232659.jpg\": \"Amphibia/Rana dalmatina/866ddcd8c44c779b310102f65c409fc1.jpg\",\n  \"232662.jpg\": \"Amphibia/Rana dalmatina/276011391234129ed5d09cdb0d56bbad.jpg\",\n  \"232664.jpg\": \"Amphibia/Rana dalmatina/fdf52ebad366cd1e7f10dffdb99415c8.jpg\",\n  \"232665.jpg\": \"Amphibia/Rana dalmatina/568a8467761a1f1c8273c9bd102ef72a.jpg\",\n  \"232666.jpg\": \"Amphibia/Rana dalmatina/78c6e8a32f90d3c237fbfc7bbe9b8e17.jpg\",\n  \"232667.jpg\": \"Amphibia/Rana dalmatina/b1116499951d4a70c0dbb1a74de3152c.jpg\",\n  \"232668.jpg\": \"Amphibia/Rana dalmatina/9df2db7ff6c92fc5a88ecdffa4f2752c.jpg\",\n  \"232675.jpg\": \"Insecta/Helicoverpa zea/4aaaa11d330a4249191f24b5cf8ca0e4.jpg\",\n  \"232680.jpg\": \"Insecta/Helicoverpa zea/78980763d911edb7ae40052eea465c1d.jpg\",\n  \"232691.jpg\": \"Insecta/Helicoverpa zea/21d92dd1deea33e5eadb93c475fd291f.jpg\",\n  \"232692.jpg\": \"Insecta/Helicoverpa zea/18e925d0bb091c516a3822baec8e8654.jpg\",\n  \"232700.jpg\": \"Insecta/Helicoverpa zea/8848f6db1040d17f7c0d6937280fb661.jpg\",\n  \"232701.jpg\": \"Insecta/Helicoverpa zea/7c8f6feccf46fc671f27258de2feb0ef.jpg\",\n  \"232705.jpg\": \"Insecta/Helicoverpa zea/2c026d8996e4b421c1c5812c258b1dbc.jpg\",\n  \"232708.jpg\": \"Insecta/Helicoverpa zea/321b4e777cd7b95e5adbb489f03d8d47.jpg\",\n  \"232741.jpg\": \"Animalia/Phragmatopoma californica/b6c94467c5ae92ae3b3f74288ffcd077.jpg\",\n  \"232748.jpg\": \"Insecta/Nephelodes minians/49f381106b6fe44f3dc9b9cc7f3f63bc.jpg\",\n  \"232751.jpg\": \"Insecta/Nephelodes minians/ad885b8935d3f64a284d192ef5f166c3.jpg\",\n  \"232757.jpg\": \"Insecta/Nephelodes minians/16e88dd1200aa3e70e2629031b21f15f.jpg\",\n  \"232760.jpg\": \"Insecta/Nephelodes minians/8fe0f317fdcd313dac5fa55e874342bf.jpg\",\n  \"232765.jpg\": \"Insecta/Nephelodes minians/92dc4aa4d5ebb76254a76a921c34f31a.jpg\",\n  \"232771.jpg\": \"Amphibia/Anaxyrus speciosus/bc4997256def542a3644b35d632e4e95.jpg\",\n  \"232772.jpg\": \"Amphibia/Anaxyrus speciosus/31af4280baca7ff1389185f26ad00580.jpg\",\n  \"232776.jpg\": \"Amphibia/Anaxyrus speciosus/0fda925b30227bb15ce2703a43f68dcb.jpg\",\n  \"232777.jpg\": \"Amphibia/Anaxyrus speciosus/91390df365aa96cb122c7bbbbb0cb023.jpg\",\n  \"232784.jpg\": \"Amphibia/Anaxyrus speciosus/e657791e905c2ad34fe365c75f93a3d0.jpg\",\n  \"232792.jpg\": \"Amphibia/Anaxyrus speciosus/840bd00b59cf6e5e1b246fce5704e0ce.jpg\",\n  \"232796.jpg\": \"Amphibia/Anaxyrus speciosus/5af5d829a09c1e167fd09d04429b77ea.jpg\",\n  \"232797.jpg\": \"Amphibia/Anaxyrus speciosus/f6c83a03550ce7cfff7dbb02e91a05bb.jpg\",\n  \"232798.jpg\": \"Amphibia/Anaxyrus speciosus/d2b264b8c030c79b905e49cecad71e91.jpg\",\n  \"232799.jpg\": \"Amphibia/Anaxyrus speciosus/e61e4dfe015540c890b6aa3c7feb71d5.jpg\",\n  \"232821.jpg\": \"Amphibia/Anaxyrus speciosus/b986d00950582feaf10d48d53cfb1bae.jpg\",\n  \"232824.jpg\": \"Amphibia/Anaxyrus speciosus/17c05ed75e234989fe80536e93bb29a0.jpg\",\n  \"232838.jpg\": \"Amphibia/Anaxyrus speciosus/68f6ca1d80ea7296d1a34d5786cc5f7e.jpg\",\n  \"232839.jpg\": \"Amphibia/Anaxyrus speciosus/994d755bc062212d562de2dc28867b8c.jpg\",\n  \"232852.jpg\": \"Amphibia/Anaxyrus speciosus/11e7fc4faab12926d308ca4aac3f10e4.jpg\",\n  \"232854.jpg\": \"Amphibia/Anaxyrus speciosus/86583638654244f608843b48f43ed58b.jpg\",\n  \"232858.jpg\": \"Amphibia/Anaxyrus speciosus/80772e5a180f4d4ff9fbcc3c25c8bcc7.jpg\",\n  \"232874.jpg\": \"Amphibia/Anaxyrus speciosus/7f127661e3cde95756af855b9bcaed19.jpg\",\n  \"232876.jpg\": \"Amphibia/Anaxyrus speciosus/eaf8609cec5be8b4cd1538ee25bf5066.jpg\",\n  \"232877.jpg\": \"Amphibia/Anaxyrus speciosus/557da824d627f76b7aae2c00fbce7391.jpg\",\n  \"232880.jpg\": \"Amphibia/Anaxyrus speciosus/8c3fb5a06d2c17e9d5afaaad4a37e4c1.jpg\",\n  \"232891.jpg\": \"Insecta/Bombus fervidus/6d209a5b13eaaad0680fb3e71045528a.jpg\",\n  \"232898.jpg\": \"Insecta/Bombus fervidus/1653b52f2e4a1bea36412514afba557d.jpg\",\n  \"232899.jpg\": \"Insecta/Bombus fervidus/8318da66ff3d34ad9fc404bceb04d59c.jpg\",\n  \"232901.jpg\": \"Insecta/Bombus fervidus/5ccd5be52eeac9bbb519c73d3262b9ae.jpg\",\n  \"232902.jpg\": \"Insecta/Bombus fervidus/c59fed2b865addfadc1a2dfb9b334c02.jpg\",\n  \"232932.jpg\": \"Reptilia/Rhinocheilus lecontei/77f276dc9c7a34e4de534d394b43fc67.jpg\",\n  \"232940.jpg\": \"Reptilia/Rhinocheilus lecontei/0efdb2ee7cbb524bdccc41fd7d311613.jpg\",\n  \"232941.jpg\": \"Reptilia/Rhinocheilus lecontei/2a8aa7ef1ca337d5200a72f9dd8c46ee.jpg\",\n  \"232942.jpg\": \"Reptilia/Rhinocheilus lecontei/4da60fb12cb5275046ff09addfdabdb2.jpg\",\n  \"232958.jpg\": \"Reptilia/Rhinocheilus lecontei/ccd2a5e02f7f45bf233c1f8865e5c12d.jpg\",\n  \"232961.jpg\": \"Reptilia/Rhinocheilus lecontei/e778d86e24d86daebdb67dec760f1d50.jpg\",\n  \"232962.jpg\": \"Reptilia/Rhinocheilus lecontei/eb283399b0d7a082f8f3bfc2e6fccede.jpg\",\n  \"232972.jpg\": \"Reptilia/Rhinocheilus lecontei/6e42fcddea0ba8d28a5c33f5827df164.jpg\",\n  \"232973.jpg\": \"Reptilia/Rhinocheilus lecontei/946b18a7aacf193bc7d1a1344f6ad238.jpg\",\n  \"232984.jpg\": \"Reptilia/Rhinocheilus lecontei/f4d7423727ac597269d3ef7d70541def.jpg\",\n  \"232985.jpg\": \"Reptilia/Rhinocheilus lecontei/cc9828ab26471dd38ac6e516488d58bf.jpg\",\n  \"233002.jpg\": \"Reptilia/Rhinocheilus lecontei/864a8580352661e9acc162ab73c7e874.jpg\",\n  \"233003.jpg\": \"Reptilia/Rhinocheilus lecontei/2df2238a210b33492592874c6c087215.jpg\",\n  \"233004.jpg\": \"Reptilia/Rhinocheilus lecontei/fe8c73fb2ad5057c93e94ed0e5d6d84b.jpg\",\n  \"233005.jpg\": \"Reptilia/Rhinocheilus lecontei/0a3759d6f6168b690405bcba6358d856.jpg\",\n  \"233014.jpg\": \"Reptilia/Rhinocheilus lecontei/9c3df7c31583f91f33fa66bcacd1ba2d.jpg\",\n  \"233021.jpg\": \"Reptilia/Rhinocheilus lecontei/c528f41c8dd47afd396a514b10f2d085.jpg\",\n  \"233038.jpg\": \"Reptilia/Rhinocheilus lecontei/eeffdbb6db4c362a2ba112554096c376.jpg\",\n  \"233042.jpg\": \"Reptilia/Rhinocheilus lecontei/1ca43645f7fbb9334af8bb44a6a7b606.jpg\",\n  \"233043.jpg\": \"Reptilia/Rhinocheilus lecontei/468726cf1d50038f42a60a036c1f2f23.jpg\",\n  \"233055.jpg\": \"Aves/Baeolophus inornatus/cbe927c6aade540ad143d65e30a30117.jpg\",\n  \"233060.jpg\": \"Aves/Baeolophus inornatus/aff673e0f993ced28017b9a0fe4cc1a5.jpg\",\n  \"233061.jpg\": \"Aves/Baeolophus inornatus/9696afa493799531765a73410c49d676.jpg\",\n  \"233063.jpg\": \"Aves/Baeolophus inornatus/6b8cafb464ff3f2aa7e6d54975a03705.jpg\",\n  \"233070.jpg\": \"Aves/Baeolophus inornatus/97ff1fa6f96c6a211da6690c4f14aaf1.jpg\",\n  \"233072.jpg\": \"Aves/Baeolophus inornatus/948fed48fc4462d7e05f32d1663bf064.jpg\",\n  \"233075.jpg\": \"Aves/Baeolophus inornatus/21a4d440a892de72658cf9027f352da8.jpg\",\n  \"233085.jpg\": \"Aves/Baeolophus inornatus/7759fbd2121b643811f3febd5ccaaa8e.jpg\",\n  \"233089.jpg\": \"Aves/Baeolophus inornatus/a0a0ccdca4c65269d7af590ed7faf026.jpg\",\n  \"233091.jpg\": \"Aves/Baeolophus inornatus/508474b9d26ee19dbd488c05aad66b1c.jpg\",\n  \"233094.jpg\": \"Aves/Baeolophus inornatus/0e29c0a4e9dc8eb42b34d085c72d25e2.jpg\",\n  \"233111.jpg\": \"Aves/Baeolophus inornatus/182b4fc4d3edf78bf03c072a31870cda.jpg\",\n  \"233121.jpg\": \"Aves/Baeolophus inornatus/ad75c8f1963364fffa40521e57c7827f.jpg\",\n  \"233125.jpg\": \"Aves/Baeolophus inornatus/ee7ef401146c0ccc400ebea2a76984e6.jpg\",\n  \"233147.jpg\": \"Aves/Baeolophus inornatus/975f79f2d7f091bb5a860241ece7564a.jpg\",\n  \"233149.jpg\": \"Aves/Baeolophus inornatus/3d2194e64840dee1130762b43c41174c.jpg\",\n  \"233150.jpg\": \"Aves/Baeolophus inornatus/5c8dba65e2ef99faae491510e9ed1042.jpg\",\n  \"233156.jpg\": \"Aves/Baeolophus inornatus/a6f52eeab85c7cb9ade6f077b3b5fbea.jpg\",\n  \"233160.jpg\": \"Aves/Baeolophus inornatus/86ae29f98d2e64875affda22651161fe.jpg\",\n  \"233170.jpg\": \"Aves/Baeolophus inornatus/f042046fc9b5eb311bc0ec86061d9dad.jpg\",\n  \"233175.jpg\": \"Aves/Baeolophus inornatus/8887044f58d0fbe4b64ea95d23b6817e.jpg\",\n  \"233179.jpg\": \"Aves/Baeolophus inornatus/652c694b31b06f7e7437cf35a32ce9bc.jpg\",\n  \"233182.jpg\": \"Aves/Baeolophus inornatus/df2e5ad5b3457a0aa518b1426015fba8.jpg\",\n  \"233185.jpg\": \"Reptilia/Heterodon platirhinos/2fb10ec0c7769b12d54ad4cadea42648.jpg\",\n  \"233186.jpg\": \"Reptilia/Heterodon platirhinos/0826100e9ed861bebc67b118f93581d9.jpg\",\n  \"233192.jpg\": \"Reptilia/Heterodon platirhinos/08d62a0401817355766d4568d0820748.jpg\",\n  \"233196.jpg\": \"Reptilia/Heterodon platirhinos/f32307700feab838890b0fee2ac40787.jpg\",\n  \"233198.jpg\": \"Reptilia/Heterodon platirhinos/e7880f2ed78155e822997ce25a921fbe.jpg\",\n  \"233211.jpg\": \"Reptilia/Heterodon platirhinos/acb5b5f3ca9fd1594787feb19b4d5bde.jpg\",\n  \"233217.jpg\": \"Reptilia/Heterodon platirhinos/22fe7bae72feb899bd37ad2936057a37.jpg\",\n  \"233228.jpg\": \"Reptilia/Heterodon platirhinos/a0aa14b06a927e887c2ddeba655dbc45.jpg\",\n  \"233229.jpg\": \"Reptilia/Heterodon platirhinos/905b95d2c650fe01ae2f02fc19e7b250.jpg\",\n  \"233230.jpg\": \"Reptilia/Heterodon platirhinos/eed75885e6681ebd473bee487776d7c8.jpg\",\n  \"233239.jpg\": \"Reptilia/Heterodon platirhinos/d0d4212d03ee4dbd35b5066042cd3ce4.jpg\",\n  \"233242.jpg\": \"Reptilia/Heterodon platirhinos/09db04afa698aec9f4653af4be1b1ab9.jpg\",\n  \"233245.jpg\": \"Reptilia/Heterodon platirhinos/5723bd5f4744db1c469550b1dd6da622.jpg\",\n  \"233251.jpg\": \"Reptilia/Heterodon platirhinos/c1c5fb4aa2761e952d8562bda12b9f14.jpg\",\n  \"233257.jpg\": \"Reptilia/Heterodon platirhinos/75df001bab75bdcd0d4c9187f2ea8593.jpg\",\n  \"233258.jpg\": \"Reptilia/Heterodon platirhinos/fedd38ab955995d49fac63166c744d72.jpg\",\n  \"233265.jpg\": \"Reptilia/Heterodon platirhinos/0737fab6ca2cd653bf0aa0de33c76d96.jpg\",\n  \"233268.jpg\": \"Reptilia/Heterodon platirhinos/39b57c036e02577c20a3e520a8808775.jpg\",\n  \"233274.jpg\": \"Reptilia/Heterodon platirhinos/019e6c8298b7f6136d3c3ebaa97f5ae5.jpg\",\n  \"233278.jpg\": \"Reptilia/Heterodon platirhinos/409c6243b177a618ed69878a67ea17b4.jpg\",\n  \"233281.jpg\": \"Reptilia/Heterodon platirhinos/66af93cc1b20046f6651cc33d83d42a2.jpg\",\n  \"233285.jpg\": \"Reptilia/Heterodon platirhinos/8a306ad6d0209a8a14fab279ce3988ba.jpg\",\n  \"233296.jpg\": \"Reptilia/Heterodon platirhinos/966d57e05ddfee90de3d6c3c7a4720f0.jpg\",\n  \"233300.jpg\": \"Insecta/Feltia jaculifera/7191a66260217427cdde37497e7ccdd8.jpg\",\n  \"233304.jpg\": \"Insecta/Feltia jaculifera/fd23bd18e547436dee0d1e46ecb5d33f.jpg\",\n  \"233305.jpg\": \"Insecta/Feltia jaculifera/f6590f52d97f4039afae18e935c7c54b.jpg\",\n  \"233308.jpg\": \"Insecta/Feltia jaculifera/c3860a3289d1ee0e262b607d69bea766.jpg\",\n  \"233311.jpg\": \"Insecta/Feltia jaculifera/97a78f16b54c90d5bf86489d3246013a.jpg\",\n  \"233312.jpg\": \"Insecta/Anageshna primordialis/71d43fc6c97e6df0d587388100e58b27.jpg\",\n  \"233314.jpg\": \"Insecta/Anageshna primordialis/c7487ae579cd0d26978809f02935e6b6.jpg\",\n  \"233319.jpg\": \"Insecta/Anageshna primordialis/22e07bd47f2ef00d051655c21be304f0.jpg\",\n  \"233321.jpg\": \"Insecta/Anageshna primordialis/8ad086920959914a7fa61e17fe65af24.jpg\",\n  \"233322.jpg\": \"Insecta/Anageshna primordialis/87c09f2b53216602aaac1284871870ad.jpg\",\n  \"233327.jpg\": \"Insecta/Anageshna primordialis/1e1b31e0c71b974b5acbc923557db2f3.jpg\",\n  \"233430.jpg\": \"Aves/Sialia mexicana/7a706104e705a00d7f346d0a659a5c67.jpg\",\n  \"233432.jpg\": \"Aves/Sialia mexicana/7b0803391236d593c13120f9a4724158.jpg\",\n  \"233437.jpg\": \"Aves/Sialia mexicana/2265c09c78994c803e1bf79b81a2b76f.jpg\",\n  \"233440.jpg\": \"Aves/Sialia mexicana/345fcee1e1198ffd11ca549ae87af9c0.jpg\",\n  \"233483.jpg\": \"Aves/Sialia mexicana/605a96d38b42586503b1ccc16b66ea13.jpg\",\n  \"233484.jpg\": \"Aves/Sialia mexicana/438a085e114843a04fe93ebdcdd0997d.jpg\",\n  \"233523.jpg\": \"Aves/Sialia mexicana/aeeaab2da82c60880da7a20e89f4246c.jpg\",\n  \"233542.jpg\": \"Aves/Sialia mexicana/a1418f9564819ed534723e064c03efaf.jpg\",\n  \"233647.jpg\": \"Aves/Sialia mexicana/c8cc1ae0cbc578576302edb311c39d94.jpg\",\n  \"233650.jpg\": \"Aves/Sialia mexicana/8eb30200a7477bd4567bbad9ff7a4600.jpg\",\n  \"233651.jpg\": \"Aves/Sialia mexicana/7fce5fddbc7a549c2dad599b9e583cca.jpg\",\n  \"233653.jpg\": \"Aves/Sialia mexicana/1054869fc3a2bceca2204177389c3558.jpg\",\n  \"233702.jpg\": \"Aves/Sialia mexicana/c00d2fa12518c6f9ff1fb98ce8218e47.jpg\",\n  \"233703.jpg\": \"Aves/Sialia mexicana/a6fef41507b60fac16d80178571153ef.jpg\",\n  \"233712.jpg\": \"Aves/Sialia mexicana/00ff189820543e87403cf031b1d62cee.jpg\",\n  \"233716.jpg\": \"Aves/Sialia mexicana/d505a43f45697fa4794b1ff712888929.jpg\",\n  \"233720.jpg\": \"Aves/Sialia mexicana/0f174c28dfdd1b9b13751d6bc2fcc14a.jpg\",\n  \"233740.jpg\": \"Aves/Sialia mexicana/17c6cbe7fb1ab24b1fd98da43ce6745c.jpg\",\n  \"233741.jpg\": \"Aves/Sialia mexicana/ff67166634d13035604438ee12361172.jpg\",\n  \"233749.jpg\": \"Aves/Sialia mexicana/43a31a04857a60aae02930abb8c85676.jpg\",\n  \"233751.jpg\": \"Aves/Sialia mexicana/fd975fe814689c2d1e2ddb5819890473.jpg\",\n  \"233776.jpg\": \"Aves/Sialia mexicana/abfcc92db43b2210088094754a7843be.jpg\",\n  \"233787.jpg\": \"Aves/Sialia mexicana/0f411cbf49d570b12f628044cd042736.jpg\",\n  \"234030.jpg\": \"Aves/Setophaga pensylvanica/b81a720fd26f543fc6846a3e47e3916c.jpg\",\n  \"234034.jpg\": \"Aves/Setophaga pensylvanica/04033a076f9c4582bb0d24d02c09cebe.jpg\",\n  \"234036.jpg\": \"Aves/Setophaga pensylvanica/bfd1d9a2a1030f6e1da70e9bf560753a.jpg\",\n  \"234055.jpg\": \"Aves/Setophaga pensylvanica/daed81b2b7124f22769cf1c9e948fa23.jpg\",\n  \"234056.jpg\": \"Aves/Setophaga pensylvanica/a95451f768ca2bb9261f1e24674f0b83.jpg\",\n  \"234059.jpg\": \"Aves/Setophaga pensylvanica/36b9291a963db0810ced6ee7df37122f.jpg\",\n  \"234060.jpg\": \"Aves/Setophaga pensylvanica/bf8d78a5bd9ec2dbf34ace124989faa8.jpg\",\n  \"234061.jpg\": \"Aves/Setophaga pensylvanica/d3457c9c8eeb5f67edc951f794eeb232.jpg\",\n  \"234066.jpg\": \"Aves/Setophaga pensylvanica/10d4a150c78cac522fc88287a23a1bb3.jpg\",\n  \"234080.jpg\": \"Aves/Setophaga pensylvanica/d23713afa16a7d296ccd82baf1e63b4b.jpg\",\n  \"234081.jpg\": \"Aves/Setophaga pensylvanica/8c15e7560a99a481168f6807b7390720.jpg\",\n  \"234082.jpg\": \"Aves/Setophaga pensylvanica/196d38a11cef57ee705800cf3a08cec2.jpg\",\n  \"234092.jpg\": \"Aves/Setophaga pensylvanica/78d8f1d6ae937967915a3378d47dc0cf.jpg\",\n  \"234100.jpg\": \"Aves/Setophaga pensylvanica/ca209a59a51c92cc8c90fcd6ec09a9cb.jpg\",\n  \"234101.jpg\": \"Aves/Setophaga pensylvanica/a7b920b03bb106a5b37084f1be1feed0.jpg\",\n  \"234103.jpg\": \"Aves/Setophaga pensylvanica/cb0cc1fef7a5a5883794f860751cfe12.jpg\",\n  \"234112.jpg\": \"Aves/Setophaga pensylvanica/95366dc12ca9c1358f6bab7aa997c0b4.jpg\",\n  \"234118.jpg\": \"Aves/Setophaga pensylvanica/4f7801e6628b51c336294a702ec978cb.jpg\",\n  \"234120.jpg\": \"Aves/Setophaga pensylvanica/48152a2ff9230d49eb7af02b438ca310.jpg\",\n  \"234122.jpg\": \"Aves/Setophaga pensylvanica/13086c994615c4189a4585bc37819cf6.jpg\",\n  \"234123.jpg\": \"Aves/Setophaga pensylvanica/8e00be301f0e5a1612816bf4e7bba822.jpg\",\n  \"234130.jpg\": \"Aves/Setophaga pensylvanica/c492cd156e6201417b1fd27339d50b94.jpg\",\n  \"234131.jpg\": \"Aves/Setophaga pensylvanica/95e5aa9a7ccceb5cf9a8e59f63325100.jpg\",\n  \"234141.jpg\": \"Insecta/Progomphus obscurus/204426a72a8959eba5f714673f06c171.jpg\",\n  \"234145.jpg\": \"Insecta/Progomphus obscurus/2cbfd462a4a04833c4993afc91879f52.jpg\",\n  \"234149.jpg\": \"Insecta/Progomphus obscurus/72fc5942959bfb85aa538a0ef44b1153.jpg\",\n  \"234160.jpg\": \"Insecta/Progomphus obscurus/1ab70c472bdcfcca03b3a813d2427924.jpg\",\n  \"234169.jpg\": \"Aves/Aythya novaeseelandiae/20c13e8d7533b0c4a5d7d5e801d2b3e1.jpg\",\n  \"234172.jpg\": \"Aves/Aythya novaeseelandiae/17445f1612a40f1ac9d96ce4354eddec.jpg\",\n  \"234176.jpg\": \"Aves/Aythya novaeseelandiae/98c4bf81a1ba61b0b7f02ffbbd93f29a.jpg\",\n  \"234177.jpg\": \"Aves/Aythya novaeseelandiae/6906dcf6d864d0e3d298dea584977e6d.jpg\",\n  \"234183.jpg\": \"Aves/Aythya novaeseelandiae/69b3cdbd9ab968b34246ef140a81235d.jpg\",\n  \"234188.jpg\": \"Aves/Aythya novaeseelandiae/5ad3d8c3b569d2afdc16462b690a9151.jpg\",\n  \"234197.jpg\": \"Aves/Aythya novaeseelandiae/89af7ffe6df916687fa7935e3e8a8bb4.jpg\",\n  \"234201.jpg\": \"Aves/Aythya novaeseelandiae/2725506d0d01c5aebb8ed43a5a6d1bca.jpg\",\n  \"234205.jpg\": \"Aves/Aythya novaeseelandiae/598099368a16a53e8d4bc209ff2c90a9.jpg\",\n  \"234217.jpg\": \"Reptilia/Crotalus lepidus lepidus/af3bd3600111186a7e16a0a1040fa4a0.jpg\",\n  \"234219.jpg\": \"Reptilia/Crotalus lepidus lepidus/c9d8d121ef777943ab14fe2d13858c2d.jpg\",\n  \"234222.jpg\": \"Reptilia/Crotalus lepidus lepidus/4ecded009e0a56c40074a01ae01d8e17.jpg\",\n  \"234223.jpg\": \"Reptilia/Crotalus lepidus lepidus/9a191a2604908e898a22d035030fda57.jpg\",\n  \"234226.jpg\": \"Aves/Tringa glareola/db19603bb3baff5ce6bc9e25e7e27dc5.jpg\",\n  \"234236.jpg\": \"Aves/Tringa glareola/23b449076fbee026032fc8df5b9b2763.jpg\",\n  \"234244.jpg\": \"Aves/Tringa glareola/81729838e2d28031252cf3cf08050282.jpg\",\n  \"234249.jpg\": \"Aves/Tringa glareola/e93453b19800eff30df79185f060a31b.jpg\",\n  \"234395.jpg\": \"Aves/Psittacula krameri/4cb079628ad9c5b9ac1327a6b1c92ce3.jpg\",\n  \"234397.jpg\": \"Aves/Psittacula krameri/75616733d60f583886d83e883066c455.jpg\",\n  \"234403.jpg\": \"Aves/Psittacula krameri/eb2405c38074ced7abe582774f724cc4.jpg\",\n  \"234408.jpg\": \"Aves/Psittacula krameri/8ce20e775a6cd5bb903ae1013e6989e0.jpg\",\n  \"234415.jpg\": \"Aves/Psittacula krameri/d097d7ac8a3080b999ac0040d37396bb.jpg\",\n  \"234421.jpg\": \"Aves/Psittacula krameri/2740801614f657d3d69a0769d46ea01d.jpg\",\n  \"234422.jpg\": \"Aves/Psittacula krameri/453664ea3d1d91f46862ecaba49f3ba3.jpg\",\n  \"234430.jpg\": \"Aves/Psittacula krameri/3f8c2dd675966040551c73c7b0602a48.jpg\",\n  \"234431.jpg\": \"Aves/Psittacula krameri/1a3ce0aff06b6dfbe2374d9b57af675b.jpg\",\n  \"234432.jpg\": \"Aves/Psittacula krameri/3d79f88b82064727e6239e79f0f9f62c.jpg\",\n  \"234439.jpg\": \"Aves/Psittacula krameri/649b90f44689d6237b44aad86a4f275d.jpg\",\n  \"234441.jpg\": \"Aves/Psittacula krameri/ce9ce47cb545278ac556766983cfe899.jpg\",\n  \"234443.jpg\": \"Aves/Psittacula krameri/43b5ff9a29fbf1886c5e63da043d1088.jpg\",\n  \"234452.jpg\": \"Aves/Psittacula krameri/addf6e798ceda6794df63108db216c42.jpg\",\n  \"234465.jpg\": \"Reptilia/Nerodia rhombifer/a033188014608e1cb23b5ca386e34545.jpg\",\n  \"234469.jpg\": \"Reptilia/Nerodia rhombifer/5835e044b5e588bec7f15ff940d23ebc.jpg\",\n  \"234489.jpg\": \"Reptilia/Nerodia rhombifer/7ec228db1f52dd20cdf0767a3d28a3f2.jpg\",\n  \"234500.jpg\": \"Reptilia/Nerodia rhombifer/738ab628cb4548117fb7263f06062453.jpg\",\n  \"234505.jpg\": \"Reptilia/Nerodia rhombifer/b2cba68dc8fdebf7cc4aa202b96abe63.jpg\",\n  \"234506.jpg\": \"Reptilia/Nerodia rhombifer/d23a952daee8fea8d7c13817e2098fae.jpg\",\n  \"234508.jpg\": \"Reptilia/Nerodia rhombifer/8ce31c9efb0110f5f9b9776e02d7aa37.jpg\",\n  \"234522.jpg\": \"Reptilia/Nerodia rhombifer/91fecd46634e4f9677eb342850519fe3.jpg\",\n  \"234524.jpg\": \"Reptilia/Nerodia rhombifer/35cd8c881a7adb8f9ee2e3ec578f54e7.jpg\",\n  \"234533.jpg\": \"Reptilia/Nerodia rhombifer/f09202ac0305cab47d3c687a17e7201c.jpg\",\n  \"234545.jpg\": \"Reptilia/Nerodia rhombifer/3fe430f95a167979e191899e8f98f311.jpg\",\n  \"234550.jpg\": \"Reptilia/Nerodia rhombifer/9074a538c76823db6208979ca70f4091.jpg\",\n  \"234551.jpg\": \"Reptilia/Nerodia rhombifer/30622fb2e5751ed0a05e877c1095dae6.jpg\",\n  \"234552.jpg\": \"Reptilia/Nerodia rhombifer/5217fa223539b855206300c022fbdb82.jpg\",\n  \"234569.jpg\": \"Reptilia/Nerodia rhombifer/4b5f9961520ef204bc0a07f65511db4e.jpg\",\n  \"23457.jpg\": \"Insecta/Syrbula admirabilis/2d5e7c4ab4e697f364d4a1bbced4a44c.jpg\",\n  \"234587.jpg\": \"Reptilia/Nerodia rhombifer/25fba8f294aa36189216e61340d2601a.jpg\",\n  \"23460.jpg\": \"Insecta/Syrbula admirabilis/661c81bc8dd02fe862031f0665a04ce7.jpg\",\n  \"234602.jpg\": \"Reptilia/Nerodia rhombifer/3be1af577a709aa3d19553a625ccd7d4.jpg\",\n  \"234608.jpg\": \"Reptilia/Nerodia rhombifer/7a9b8d7125b3c58edc21109b0878fdd5.jpg\",\n  \"234616.jpg\": \"Reptilia/Nerodia rhombifer/8467cfe3f622f75bc61d53fe4c93bc4f.jpg\",\n  \"23462.jpg\": \"Insecta/Syrbula admirabilis/16ad26878d93fad7c0fb57b8022e81e6.jpg\",\n  \"234620.jpg\": \"Reptilia/Nerodia rhombifer/53d632f917f34b2c53f4beca24bd4c22.jpg\",\n  \"234621.jpg\": \"Reptilia/Nerodia rhombifer/a04c4ade6552197e122e50b26574e19f.jpg\",\n  \"23463.jpg\": \"Insecta/Syrbula admirabilis/57ec50f1e0b8803d73af32b4eaf0eeff.jpg\",\n  \"23464.jpg\": \"Insecta/Syrbula admirabilis/716fdaa21af1d356769d1c692339f4d0.jpg\",\n  \"23468.jpg\": \"Insecta/Syrbula admirabilis/b844341f866286fd2b2e59d8837f0aa1.jpg\",\n  \"23472.jpg\": \"Insecta/Syrbula admirabilis/935d47d37b1e8aabc14e84cfbc6a993f.jpg\",\n  \"23473.jpg\": \"Insecta/Syrbula admirabilis/834737bd671ccf88dd86df14e18c2d1c.jpg\",\n  \"234762.jpg\": \"Insecta/Pyrrharctia isabella/f0e023e5f05a5e5b902f12ca91da38d6.jpg\",\n  \"234784.jpg\": \"Insecta/Pyrrharctia isabella/8f980ac7dbcb4325211968ccd1b410e5.jpg\",\n  \"234790.jpg\": \"Insecta/Pyrrharctia isabella/506a63a27673b86f8c71a33280b048b1.jpg\",\n  \"234796.jpg\": \"Insecta/Pyrrharctia isabella/b0252e62ef142aaae0c4051de21b79ac.jpg\",\n  \"234799.jpg\": \"Insecta/Pyrrharctia isabella/e9e8f36068ea7faf1e356e2c839542bc.jpg\",\n  \"234807.jpg\": \"Insecta/Pyrrharctia isabella/c4ad4e4eb12e182fac36148c09654022.jpg\",\n  \"234811.jpg\": \"Insecta/Pyrrharctia isabella/bd5350736bc671ffa1c9049729ac9c80.jpg\",\n  \"234823.jpg\": \"Insecta/Pyrrharctia isabella/1cec747ba10978375768c93b01cbff02.jpg\",\n  \"234826.jpg\": \"Insecta/Pyrrharctia isabella/b4ac8303fd2ff1a9ddb9ac27bd8229db.jpg\",\n  \"234833.jpg\": \"Insecta/Pyrrharctia isabella/c089d4d1ee363d737e34887e366d11b7.jpg\",\n  \"234834.jpg\": \"Insecta/Pyrrharctia isabella/b4cdb33071fad56c21c989001f08d5e5.jpg\",\n  \"234837.jpg\": \"Insecta/Pyrrharctia isabella/276bacf5e1aacc80b30ef4ade221023f.jpg\",\n  \"23486.jpg\": \"Insecta/Syrbula admirabilis/096923a1db9a5b2c6f78cb5ed41b298f.jpg\",\n  \"234883.jpg\": \"Insecta/Pyrrharctia isabella/5e7ff783fc911a5d02a62cb7f53d6ce7.jpg\",\n  \"234889.jpg\": \"Insecta/Pyrrharctia isabella/79718b82370840577b2d80217a357fb8.jpg\",\n  \"234898.jpg\": \"Insecta/Pyrrharctia isabella/09333f2f217f84be47eb8c6e3c33c0e3.jpg\",\n  \"234899.jpg\": \"Insecta/Pyrrharctia isabella/aef938c6c88d6404a5d7791389f3fdb8.jpg\",\n  \"2349.jpg\": \"Aves/Picoides pubescens/f741d3136459a68886b13bdd3e082d4d.jpg\",\n  \"234909.jpg\": \"Insecta/Pyrrharctia isabella/40d633bd908bea53480fc0802ffac620.jpg\",\n  \"234910.jpg\": \"Insecta/Pyrrharctia isabella/f118a65755363fd5c98df9d17a79f734.jpg\",\n  \"234916.jpg\": \"Insecta/Pyrrharctia isabella/e79d9ee64ed08870d77b26776ee470df.jpg\",\n  \"234931.jpg\": \"Insecta/Pyrrharctia isabella/22702e487c072eb4dd8b4468cf0f7303.jpg\",\n  \"234939.jpg\": \"Insecta/Pyrrharctia isabella/2c20f933e633e8f3461c7e55e81eabbf.jpg\",\n  \"234956.jpg\": \"Insecta/Pyrrharctia isabella/084388058bf86e17523e57d848caaf31.jpg\",\n  \"235008.jpg\": \"Mammalia/Taxidea taxus/9df15a96d24ee16f43a109056db68938.jpg\",\n  \"235009.jpg\": \"Mammalia/Taxidea taxus/78c9fbc6297a8687722f4a60dd65cb61.jpg\",\n  \"235013.jpg\": \"Mammalia/Taxidea taxus/e00e1ee22e0fad3410dc6e67b35c91ba.jpg\",\n  \"235014.jpg\": \"Mammalia/Taxidea taxus/0fbe5018cff27ed2a86451b251822835.jpg\",\n  \"235015.jpg\": \"Mammalia/Taxidea taxus/9e2084bc9dffd2a4defdce65a4a40048.jpg\",\n  \"23502.jpg\": \"Amphibia/Lithobates pipiens/cf333cf68ec474dd4df65e65a7f44620.jpg\",\n  \"235021.jpg\": \"Mammalia/Taxidea taxus/2ae59f4d198255561443e1801da9aa91.jpg\",\n  \"235022.jpg\": \"Mammalia/Taxidea taxus/144b2d8aba86fb7dfba2c793df04e031.jpg\",\n  \"235023.jpg\": \"Mammalia/Taxidea taxus/f836a476798408d8999eb25ad6165f00.jpg\",\n  \"235038.jpg\": \"Mammalia/Taxidea taxus/ffd53645cfa93ccc66cea616149b7566.jpg\",\n  \"235040.jpg\": \"Mammalia/Taxidea taxus/7f9b9dc718ee05e10ac2b9a8225237c4.jpg\",\n  \"235041.jpg\": \"Mammalia/Taxidea taxus/6f44cd1c62c72c68668b940687d5262a.jpg\",\n  \"235054.jpg\": \"Mammalia/Taxidea taxus/b48683a8ca0fdbe3625a08b74133bebd.jpg\",\n  \"235057.jpg\": \"Mammalia/Taxidea taxus/68e7d72bf6b9bb862e613bfd2edabf4e.jpg\",\n  \"235060.jpg\": \"Mammalia/Taxidea taxus/3ac4f42c570a7aeb44fb3150c6b6b5ed.jpg\",\n  \"235061.jpg\": \"Mammalia/Taxidea taxus/378536ce77ae078ee4b80013f9fab595.jpg\",\n  \"235062.jpg\": \"Mammalia/Taxidea taxus/e286f948be0ab3df6e9a74ef045feaca.jpg\",\n  \"235067.jpg\": \"Aves/Calamospiza melanocorys/cf7b74810628141250360a1db4f08c3b.jpg\",\n  \"235068.jpg\": \"Aves/Calamospiza melanocorys/8df091c9ec3f7b5e83471b7791ca7755.jpg\",\n  \"235069.jpg\": \"Aves/Calamospiza melanocorys/8562c45168cc78b766b7b3ddc03a8147.jpg\",\n  \"235070.jpg\": \"Aves/Calamospiza melanocorys/d1d948537618584be3286e7bebf2eeda.jpg\",\n  \"235071.jpg\": \"Aves/Calamospiza melanocorys/83050002a149cb11848a67a6d3a44fdd.jpg\",\n  \"235072.jpg\": \"Aves/Calamospiza melanocorys/a49b3cfb56396ce0e6e30765143e98a2.jpg\",\n  \"235073.jpg\": \"Aves/Calamospiza melanocorys/bd414e382d1d9070942121a0f09164d4.jpg\",\n  \"235074.jpg\": \"Aves/Calamospiza melanocorys/45ba2c1314f1255d0215ead448ce441b.jpg\",\n  \"235076.jpg\": \"Aves/Calamospiza melanocorys/12fb155080bba779d7e66f8fc871d685.jpg\",\n  \"235077.jpg\": \"Aves/Calamospiza melanocorys/06127deaab146b3efa8e8751a80fa9e0.jpg\",\n  \"235079.jpg\": \"Aves/Calamospiza melanocorys/2cb57ba5200e22520af8dd84fc372e7f.jpg\",\n  \"235080.jpg\": \"Aves/Calamospiza melanocorys/2e99486fe4d308825bc054df9f5a5c99.jpg\",\n  \"235085.jpg\": \"Aves/Calamospiza melanocorys/d01bc0c5fbe41dbf542ff81674147626.jpg\",\n  \"235086.jpg\": \"Aves/Calamospiza melanocorys/b8394da2edbcb7c1abd8d8cb0565af85.jpg\",\n  \"235089.jpg\": \"Aves/Calamospiza melanocorys/d8e22808afe4a5e9c310684f9262d132.jpg\",\n  \"235094.jpg\": \"Aves/Calamospiza melanocorys/eff913d98d5623eb133c60d10e29e395.jpg\",\n  \"235097.jpg\": \"Aves/Calamospiza melanocorys/432f663f194be082ba7973fb9d0e848f.jpg\",\n  \"235102.jpg\": \"Aves/Calamospiza melanocorys/94fa10ca7a7e22df5b96fe9b8e3f4412.jpg\",\n  \"235103.jpg\": \"Aves/Calamospiza melanocorys/50e1de7c2e3aab3700abeb6491ac68f0.jpg\",\n  \"235104.jpg\": \"Aves/Calamospiza melanocorys/b886f5abd5225302e1ada1fc0f0b0c87.jpg\",\n  \"235105.jpg\": \"Aves/Calamospiza melanocorys/25b9eef324d5f8f2530c94274b46af3b.jpg\",\n  \"23520.jpg\": \"Amphibia/Lithobates pipiens/a02e631c3c972b0e4f6cd397a4232076.jpg\",\n  \"235201.jpg\": \"Insecta/Mantis religiosa/1319ebcebfd5227d1125ac9e51fd0a2a.jpg\",\n  \"235206.jpg\": \"Insecta/Mantis religiosa/868a606804b4ec62652501e97a1a5f0a.jpg\",\n  \"235235.jpg\": \"Insecta/Mantis religiosa/0d16c0dbcdce16e44fddd088d62e6403.jpg\",\n  \"235239.jpg\": \"Insecta/Mantis religiosa/7a92e8d93713e7d2d4b82dee0e46999f.jpg\",\n  \"235245.jpg\": \"Insecta/Mantis religiosa/18b630edadffb9ce6de2b59309e2bfb6.jpg\",\n  \"235254.jpg\": \"Insecta/Mantis religiosa/02fb27183f3b649254c3838629f9a06b.jpg\",\n  \"235255.jpg\": \"Insecta/Mantis religiosa/35d563b1044ea86b241cec0223237109.jpg\",\n  \"235260.jpg\": \"Insecta/Mantis religiosa/f54efb4f3c04e118c3fafbdc9aa8adce.jpg\",\n  \"235268.jpg\": \"Insecta/Mantis religiosa/fbaffd415eadf12316a0040f74659864.jpg\",\n  \"235278.jpg\": \"Insecta/Mantis religiosa/2ba5ea891f16a11f351c2ef9c4f3cc30.jpg\",\n  \"235282.jpg\": \"Insecta/Mantis religiosa/555cf726ddb3a91620b7e517ffd97bba.jpg\",\n  \"235287.jpg\": \"Insecta/Mantis religiosa/2f56832191bc1050cc1377fe1164ec05.jpg\",\n  \"235328.jpg\": \"Insecta/Mantis religiosa/5c59926c661d3843c75840c39010182f.jpg\",\n  \"235331.jpg\": \"Insecta/Mantis religiosa/20fb5f17452b9a2eaf49dce5801b1ff8.jpg\",\n  \"235336.jpg\": \"Insecta/Mantis religiosa/71c649a1645f4edd2e87d4273356e986.jpg\",\n  \"235339.jpg\": \"Insecta/Mantis religiosa/3d3f3eecb1445fe43abc254885661c21.jpg\",\n  \"235342.jpg\": \"Insecta/Mantis religiosa/d0ca9c2c7718a46aa9456cefed06861a.jpg\",\n  \"235344.jpg\": \"Insecta/Mantis religiosa/2e883c13f2cf55539d6ebee409bef9f9.jpg\",\n  \"235351.jpg\": \"Insecta/Mantis religiosa/e0634d5b78b0bf47021653579858b688.jpg\",\n  \"235355.jpg\": \"Insecta/Mantis religiosa/543368bb3431775219cc69c4b22642f0.jpg\",\n  \"235362.jpg\": \"Insecta/Mantis religiosa/863e0329a95131d44c838c7e724b1155.jpg\",\n  \"235369.jpg\": \"Insecta/Marimatha nigrofimbria/3fd90417e72556e6348f4494f705a9ad.jpg\",\n  \"235377.jpg\": \"Insecta/Marimatha nigrofimbria/31e125a89b89b28b4f94ea8315ce0cf4.jpg\",\n  \"235381.jpg\": \"Insecta/Marimatha nigrofimbria/22a4554315858d249f35e0452825e3b5.jpg\",\n  \"235384.jpg\": \"Insecta/Marimatha nigrofimbria/97fc9f3f8371c79e6113a6d8315af3a5.jpg\",\n  \"235386.jpg\": \"Insecta/Marimatha nigrofimbria/d9f2c07312be59f9d9b37e930f417d65.jpg\",\n  \"235387.jpg\": \"Insecta/Marimatha nigrofimbria/319d72f7cd8a3c2de81b824272fa54b6.jpg\",\n  \"235389.jpg\": \"Insecta/Marimatha nigrofimbria/4a6d6e59acf4f45e8317e11277a98dd1.jpg\",\n  \"235391.jpg\": \"Insecta/Marimatha nigrofimbria/21d5ff65cd184f61a97a13d357f8b94b.jpg\",\n  \"235392.jpg\": \"Insecta/Marimatha nigrofimbria/da7d0774d810f7f5e9ef215447cc6599.jpg\",\n  \"235393.jpg\": \"Insecta/Marimatha nigrofimbria/bc159d660ff22ac538c084a3be7b69a0.jpg\",\n  \"235394.jpg\": \"Insecta/Marimatha nigrofimbria/cdbe98c5292ba184b4854cd61103de78.jpg\",\n  \"235397.jpg\": \"Insecta/Marimatha nigrofimbria/c554dd5179714d524f3bbcf587c00654.jpg\",\n  \"235400.jpg\": \"Insecta/Marimatha nigrofimbria/cd1e69afd622eba6c82a64a8dd20e131.jpg\",\n  \"235402.jpg\": \"Insecta/Marimatha nigrofimbria/9fb9d78f85e560fb12be19374908f3f9.jpg\",\n  \"235407.jpg\": \"Insecta/Marimatha nigrofimbria/e142609557abfbc2d51586d4ff0499f0.jpg\",\n  \"235408.jpg\": \"Insecta/Marimatha nigrofimbria/0a4134cf8c1b85cb79704cc0c630574d.jpg\",\n  \"235410.jpg\": \"Insecta/Marimatha nigrofimbria/b974689098e456051196ad574be61e38.jpg\",\n  \"235432.jpg\": \"Insecta/Aeshna canadensis/11d12fb124f3607356f89d0c835b6851.jpg\",\n  \"235433.jpg\": \"Insecta/Aeshna canadensis/a731e89424a631d76bc6443d036dc704.jpg\",\n  \"235437.jpg\": \"Insecta/Aeshna canadensis/c3775c043ea55fdd35e0316e6253a60f.jpg\",\n  \"235438.jpg\": \"Insecta/Aeshna canadensis/ade188b779f109d9501a594980652fcc.jpg\",\n  \"23544.jpg\": \"Amphibia/Lithobates pipiens/b874394b905543690154824da6e74019.jpg\",\n  \"235441.jpg\": \"Insecta/Aeshna canadensis/c527335b5de568a040ba355ecfa2a645.jpg\",\n  \"235446.jpg\": \"Insecta/Aeshna canadensis/1422db0992312eefb3acebcb2b601dbd.jpg\",\n  \"235450.jpg\": \"Insecta/Aeshna canadensis/52bbbfb7784be63b049317434bf63a6d.jpg\",\n  \"235453.jpg\": \"Insecta/Aeshna canadensis/4e7d1ed494d5b6d5ba34e50244d104eb.jpg\",\n  \"235458.jpg\": \"Insecta/Aeshna canadensis/fa776a9e0de65e45eb6a4ebd78b99250.jpg\",\n  \"23551.jpg\": \"Amphibia/Lithobates pipiens/0f360a98e5f134c9beda6c90130703e3.jpg\",\n  \"23553.jpg\": \"Amphibia/Lithobates pipiens/5f4a7104feaf9d3c6b75e297aeefeea3.jpg\",\n  \"23555.jpg\": \"Amphibia/Lithobates pipiens/960aaa014ed664545f0896c8454e8e9d.jpg\",\n  \"235610.jpg\": \"Reptilia/Sceloporus poinsettii/a1e45cb92e7492c301e4e667409f7836.jpg\",\n  \"235611.jpg\": \"Reptilia/Sceloporus poinsettii/f5e2691f8e0e6cad6f80607c3defe820.jpg\",\n  \"235613.jpg\": \"Reptilia/Sceloporus poinsettii/3c7eac94e1aee302e33bdf4a133de52b.jpg\",\n  \"235616.jpg\": \"Reptilia/Sceloporus poinsettii/adf1df019558cbe6df06b390f08e3f6a.jpg\",\n  \"235621.jpg\": \"Reptilia/Sceloporus poinsettii/a7b41cf0fc655f72787a9b2e6916ff19.jpg\",\n  \"235622.jpg\": \"Reptilia/Sceloporus poinsettii/3bc63b340140c3308925198f4e21612e.jpg\",\n  \"235629.jpg\": \"Reptilia/Sceloporus poinsettii/fa81164f36441c6dfb1e50990cd4d67d.jpg\",\n  \"235635.jpg\": \"Reptilia/Sceloporus poinsettii/e689cc2867f6c84aef528316bc1aee2f.jpg\",\n  \"235637.jpg\": \"Reptilia/Sceloporus poinsettii/9903420d822cf21dca9fd90c0714cc3c.jpg\",\n  \"235640.jpg\": \"Reptilia/Sceloporus poinsettii/143014ba50bd0c294784198b7a4853d0.jpg\",\n  \"235641.jpg\": \"Reptilia/Sceloporus poinsettii/2d5257676ff975f60b8b9b96233fe801.jpg\",\n  \"235642.jpg\": \"Reptilia/Sceloporus poinsettii/3518a4e77503b31f7efe8134a93c4b06.jpg\",\n  \"23565.jpg\": \"Amphibia/Lithobates pipiens/217fab393285359e022f17510f40d468.jpg\",\n  \"235652.jpg\": \"Reptilia/Sceloporus poinsettii/cb19f11e9ad5375a307a956e049d877d.jpg\",\n  \"235653.jpg\": \"Reptilia/Sceloporus poinsettii/fc682932b59a34530508673d91165e62.jpg\",\n  \"235654.jpg\": \"Reptilia/Sceloporus poinsettii/3186c5726b6098753b1a9bb976b3b174.jpg\",\n  \"235655.jpg\": \"Reptilia/Sceloporus poinsettii/f2785b8abaf83c06abd114b101359e0c.jpg\",\n  \"235656.jpg\": \"Reptilia/Sceloporus poinsettii/d9141651de5d89f7dd3d6711cdd1e6d7.jpg\",\n  \"235658.jpg\": \"Reptilia/Sceloporus poinsettii/c04bb32bfb2b41ea4dd91eb3be324acb.jpg\",\n  \"235659.jpg\": \"Reptilia/Sceloporus poinsettii/89c30eef03ec907b59c5685b3146648e.jpg\",\n  \"235663.jpg\": \"Reptilia/Sceloporus poinsettii/5d1b5314c57e2590f6a49e6a1240007d.jpg\",\n  \"235664.jpg\": \"Reptilia/Sceloporus poinsettii/db1cef4b170e7d8c0086e1ecaa0bd8fc.jpg\",\n  \"235666.jpg\": \"Reptilia/Sceloporus poinsettii/6870998d92a8b44afc19850ac80dc149.jpg\",\n  \"235673.jpg\": \"Reptilia/Sceloporus poinsettii/3b71d6ddc29c085830d62966d7e059c6.jpg\",\n  \"235675.jpg\": \"Reptilia/Sceloporus poinsettii/dbfc408a587b8967bc88ebf6417c70c9.jpg\",\n  \"235731.jpg\": \"Insecta/Clemensia albata/41cd744c2c1f6309bb2783d255b48eb0.jpg\",\n  \"235733.jpg\": \"Insecta/Clemensia albata/f00b47f293aa293333225903e4bf952b.jpg\",\n  \"235734.jpg\": \"Insecta/Clemensia albata/a1f37e012b0ec7f0f31408de6aa6700e.jpg\",\n  \"235743.jpg\": \"Insecta/Clemensia albata/d7b6eff08f3a7c6ded5a2da4830a6494.jpg\",\n  \"235745.jpg\": \"Insecta/Clemensia albata/480e5b172f7aa4be400aac0fec3fc09b.jpg\",\n  \"235746.jpg\": \"Insecta/Clemensia albata/3d2cba6f793f2647530ae33e5ef0f9b8.jpg\",\n  \"235748.jpg\": \"Insecta/Clemensia albata/13fac1e0039246d75fba14f3bb877457.jpg\",\n  \"235749.jpg\": \"Insecta/Clemensia albata/87a6336fc9dc37a5183cbeeacc00bce3.jpg\",\n  \"23575.jpg\": \"Amphibia/Lithobates pipiens/def72b995da4eea54d8981ff2c89f883.jpg\",\n  \"23577.jpg\": \"Amphibia/Lithobates pipiens/2d58c91af10caab52f72acfae949fd21.jpg\",\n  \"235916.jpg\": \"Aves/Vanellus armatus/62bf6d1f16f07a79644437a99307d51b.jpg\",\n  \"235917.jpg\": \"Aves/Vanellus armatus/da9d7cb5c48ee1ef27e42e5269411ac3.jpg\",\n  \"235918.jpg\": \"Aves/Vanellus armatus/1157f7247b9685057ca77d1e83f146e0.jpg\",\n  \"235919.jpg\": \"Aves/Vanellus armatus/bb12e8e59f6b6857472ba49b20d2c51a.jpg\",\n  \"23592.jpg\": \"Amphibia/Lithobates pipiens/52cf78d8bba073f018b73cc794bab36c.jpg\",\n  \"235927.jpg\": \"Reptilia/Aspidoscelis gularis/906a91c473ab55c78a364da9a66485c9.jpg\",\n  \"235936.jpg\": \"Reptilia/Aspidoscelis gularis/b99c7fe6518c5fa859b5de1ab9cde099.jpg\",\n  \"235944.jpg\": \"Reptilia/Aspidoscelis gularis/19c4b618eb5aa66e73d6a082e0e72165.jpg\",\n  \"235945.jpg\": \"Reptilia/Aspidoscelis gularis/70ac526f277c96ee80d833a24381a599.jpg\",\n  \"235946.jpg\": \"Reptilia/Aspidoscelis gularis/7ad1f436b3e1dc8370f6145e3dfd6109.jpg\",\n  \"235953.jpg\": \"Reptilia/Aspidoscelis gularis/ceefdeb1f9c109dce7ec3b8d0d55a4b5.jpg\",\n  \"235969.jpg\": \"Reptilia/Aspidoscelis gularis/0a5c15474738ecb8bf1d6b77a56ee7fa.jpg\",\n  \"235975.jpg\": \"Reptilia/Aspidoscelis gularis/2317f184c7501fa22f8a863c9d6fa6bf.jpg\",\n  \"23599.jpg\": \"Amphibia/Lithobates pipiens/64673905c37927a082d93fa3a4e72528.jpg\",\n  \"235990.jpg\": \"Reptilia/Aspidoscelis gularis/17e3658cb2a66327c853ab52d381b8e5.jpg\",\n  \"235991.jpg\": \"Reptilia/Aspidoscelis gularis/35680c86236a8f00e6283b4ff871e39c.jpg\",\n  \"235992.jpg\": \"Reptilia/Aspidoscelis gularis/78a2a7ed1f02c7eb4c9b57dda16d73f0.jpg\",\n  \"236009.jpg\": \"Reptilia/Aspidoscelis gularis/b8eac644eb2fd3cad86b68f65fcc951b.jpg\",\n  \"236010.jpg\": \"Reptilia/Aspidoscelis gularis/d37d3581c3c947f776c404c3232fe388.jpg\",\n  \"236037.jpg\": \"Reptilia/Aspidoscelis gularis/1f4a013f31fb01a9d87840542b2a4f6a.jpg\",\n  \"236040.jpg\": \"Reptilia/Aspidoscelis gularis/fa3e4aec2fcf7bcf573d080a39614490.jpg\",\n  \"23605.jpg\": \"Amphibia/Lithobates pipiens/6664bb4bf753e84781e0b6aae5a60193.jpg\",\n  \"236053.jpg\": \"Reptilia/Aspidoscelis gularis/a39491702ad207604120f5ba497cb887.jpg\",\n  \"236054.jpg\": \"Reptilia/Aspidoscelis gularis/362d7769628cf70f8ecb410e8992889d.jpg\",\n  \"236058.jpg\": \"Reptilia/Aspidoscelis gularis/9e5dabe3a9161f672d28b41942348511.jpg\",\n  \"236071.jpg\": \"Reptilia/Aspidoscelis gularis/4d9ef432f69f3180d635549b31011149.jpg\",\n  \"236075.jpg\": \"Reptilia/Aspidoscelis gularis/f1d72ad85164771863b78b93b1600eef.jpg\",\n  \"236086.jpg\": \"Reptilia/Aspidoscelis gularis/f98bcf12ebcb8dd030f62160f490f82d.jpg\",\n  \"236087.jpg\": \"Reptilia/Aspidoscelis gularis/1f6797032b75698b72ca2d0417f2d2a6.jpg\",\n  \"236095.jpg\": \"Reptilia/Aspidoscelis gularis/6247e47386b847d626d82305f52de932.jpg\",\n  \"236103.jpg\": \"Insecta/Polygonia faunus/a028334504d853d8009569eddf9b8606.jpg\",\n  \"236105.jpg\": \"Insecta/Polygonia faunus/e52dda5173d86652de16a0f476f35b1b.jpg\",\n  \"236108.jpg\": \"Insecta/Polygonia faunus/c9ca64d6743ed6eeda039a0b896cf3e3.jpg\",\n  \"236109.jpg\": \"Insecta/Polygonia faunus/4cbdbe9781b4bfa1fa6a9038594ba558.jpg\",\n  \"236110.jpg\": \"Insecta/Polygonia faunus/ff4ff5787ce6795eed62c2b358d3d3ab.jpg\",\n  \"236114.jpg\": \"Insecta/Phlogophora periculosa/8bf00eb869c168220a2db0421092cfd9.jpg\",\n  \"236115.jpg\": \"Insecta/Phlogophora periculosa/29794c97996cec1977ef4a1b14c588d8.jpg\",\n  \"236118.jpg\": \"Insecta/Phlogophora periculosa/ace269ab201aa4ec65c18f77a8b30c5d.jpg\",\n  \"236124.jpg\": \"Insecta/Phlogophora periculosa/5d6698bafebae19412ca329b10e42e00.jpg\",\n  \"236128.jpg\": \"Aves/Emberiza schoeniclus/18da64ef3be6691fc027cf7f46a23b98.jpg\",\n  \"236136.jpg\": \"Aves/Emberiza schoeniclus/f087d9e33f1633780eacf46dcbef3057.jpg\",\n  \"236137.jpg\": \"Aves/Emberiza schoeniclus/cdc40d1d55598f842f0f4fbd2780d467.jpg\",\n  \"236149.jpg\": \"Aves/Emberiza schoeniclus/69aa8586ecccaf2ca70a5c8aad74f875.jpg\",\n  \"236152.jpg\": \"Aves/Emberiza schoeniclus/4c8cf9f04d1be1fb5c40a934220f93bd.jpg\",\n  \"236154.jpg\": \"Aves/Emberiza schoeniclus/fcedbe6c0e6edcaad9054ed6af943012.jpg\",\n  \"23617.jpg\": \"Amphibia/Lithobates pipiens/2fb65aa45ecc49741466a74cf5ab0f9a.jpg\",\n  \"236200.jpg\": \"Aves/Buteo platypterus/1b73411ae540645adf965b226e648930.jpg\",\n  \"236201.jpg\": \"Aves/Buteo platypterus/050c9a5f645b4ce9c22b1795f2893128.jpg\",\n  \"236202.jpg\": \"Aves/Buteo platypterus/676ccbd440dea0bfdd1641b2a0a92bdf.jpg\",\n  \"236206.jpg\": \"Aves/Buteo platypterus/075212b52c81056790b8a21faa927603.jpg\",\n  \"236210.jpg\": \"Aves/Buteo platypterus/96c0e4c2f0bac7b74d0a887e5997bb22.jpg\",\n  \"236213.jpg\": \"Aves/Buteo platypterus/61fecef3b98ce08836cc84ee34288256.jpg\",\n  \"236217.jpg\": \"Aves/Buteo platypterus/8172f4822296224dc9ea2f6f5b5b8e06.jpg\",\n  \"236218.jpg\": \"Aves/Buteo platypterus/fa948351724283a360e6a9d5cfa1d1ff.jpg\",\n  \"23622.jpg\": \"Amphibia/Lithobates pipiens/4522d1f93d5145e618f1204a3a2509b5.jpg\",\n  \"236229.jpg\": \"Aves/Buteo platypterus/39bd92484d52a261b14ea6482829f2b9.jpg\",\n  \"236237.jpg\": \"Aves/Buteo platypterus/3705a11eb2b4a8a8188d322f9752ca87.jpg\",\n  \"236259.jpg\": \"Aves/Buteo platypterus/7d4ee5d49ea2044ee36b3421b08d5a85.jpg\",\n  \"236264.jpg\": \"Aves/Buteo platypterus/7fc0f074a825304c447821312147249e.jpg\",\n  \"236265.jpg\": \"Aves/Buteo platypterus/8df78de4ee4fe557c21475b562023355.jpg\",\n  \"236266.jpg\": \"Aves/Buteo platypterus/614366b67a6015aaca25eb2f43359f03.jpg\",\n  \"236267.jpg\": \"Aves/Buteo platypterus/0890259a139f3f7912391cd4ddc4f3bf.jpg\",\n  \"236268.jpg\": \"Aves/Buteo platypterus/b6ade28ca825eb095d0f29c6553d6f9b.jpg\",\n  \"236269.jpg\": \"Aves/Buteo platypterus/83ce45c521e9136d3d92dd1532a3a6ab.jpg\",\n  \"236281.jpg\": \"Aves/Buteo platypterus/f2aa3fac37e1cebf6e00e00778931581.jpg\",\n  \"236290.jpg\": \"Aves/Buteo platypterus/24b76193eb70e19b1b0a753543d66166.jpg\",\n  \"236292.jpg\": \"Aves/Buteo platypterus/a923bf6fa891667fb5dd32f0b3b22056.jpg\",\n  \"236293.jpg\": \"Aves/Buteo platypterus/031c2d477a58d90ecb9b34a3efeb52f5.jpg\",\n  \"236303.jpg\": \"Aves/Buteo platypterus/bc2302c438e096ab75fda9648b85ac9c.jpg\",\n  \"236311.jpg\": \"Arachnida/Verrucosa arenata/d95e26c6e5fe4126aa9fca6689538e9d.jpg\",\n  \"236316.jpg\": \"Arachnida/Verrucosa arenata/0e2b9c7ee8968de4be0acda8d135a1db.jpg\",\n  \"236326.jpg\": \"Arachnida/Verrucosa arenata/72221bf7d66f96b15e45b0490c81e324.jpg\",\n  \"236327.jpg\": \"Arachnida/Verrucosa arenata/e339886ac5afbfa944810eabb5a8c932.jpg\",\n  \"236328.jpg\": \"Arachnida/Verrucosa arenata/8599230a7bb464522035f4380f384a29.jpg\",\n  \"236329.jpg\": \"Arachnida/Verrucosa arenata/99fa6ace3dea0a7fdac2ea6a00648877.jpg\",\n  \"236330.jpg\": \"Arachnida/Verrucosa arenata/ebe91f527375cca65aae851e2e26570e.jpg\",\n  \"236333.jpg\": \"Arachnida/Verrucosa arenata/5a43a0592c7716aa9a4439fd14bdbad8.jpg\",\n  \"236336.jpg\": \"Arachnida/Verrucosa arenata/411e11786ecd755fa5a277194002df4d.jpg\",\n  \"236337.jpg\": \"Arachnida/Verrucosa arenata/96a0c9e369a2613317e9cb6a3d52b91f.jpg\",\n  \"236340.jpg\": \"Arachnida/Verrucosa arenata/24de69cb54b9ef9832f446c2a5898950.jpg\",\n  \"236345.jpg\": \"Arachnida/Verrucosa arenata/c6546ac2cd3bd180e7fac0bae9198663.jpg\",\n  \"236346.jpg\": \"Arachnida/Verrucosa arenata/7270819f7ddbcd00b9877fb1e1d025ae.jpg\",\n  \"236347.jpg\": \"Arachnida/Verrucosa arenata/6463e49051d5af581f2eac17a8a2dc8b.jpg\",\n  \"236357.jpg\": \"Arachnida/Verrucosa arenata/d20c2296448019c4a9fa1a7a8f34122a.jpg\",\n  \"236359.jpg\": \"Arachnida/Verrucosa arenata/84e804fa5df86d7ffa66353d2cadd842.jpg\",\n  \"236360.jpg\": \"Arachnida/Verrucosa arenata/a18a45a62b3c7a12f5fc51416f128451.jpg\",\n  \"236361.jpg\": \"Arachnida/Verrucosa arenata/376e00d32f979c8c5e05017f0744d3d1.jpg\",\n  \"23646.jpg\": \"Amphibia/Lithobates pipiens/0a61a80d0f5330e53f5441ebf0227794.jpg\",\n  \"23647.jpg\": \"Amphibia/Lithobates pipiens/8d137d6c3f092a442d1e325214f73bf5.jpg\",\n  \"236528.jpg\": \"Aves/Ammodramus savannarum/e1c853c0a07ecaf66728a24d4324499f.jpg\",\n  \"236532.jpg\": \"Aves/Ammodramus savannarum/df49fb5bb1720cb9b0c6469fb141d99e.jpg\",\n  \"236537.jpg\": \"Aves/Ammodramus savannarum/6b7ec6a2f350a4bca58b38040068e1e0.jpg\",\n  \"23654.jpg\": \"Amphibia/Lithobates pipiens/f68735a2f58ce2e92e19e65a62df1f5c.jpg\",\n  \"236545.jpg\": \"Aves/Ammodramus savannarum/dc83d31169d987febafd4ed9a61f13fb.jpg\",\n  \"236548.jpg\": \"Aves/Ammodramus savannarum/3c61c6abd713ba47245c143d253011a0.jpg\",\n  \"23655.jpg\": \"Amphibia/Lithobates pipiens/da38b74c31b4250e7a94552f6dbbcdcb.jpg\",\n  \"236550.jpg\": \"Aves/Ammodramus savannarum/f6ec002e483dc9dab59a1efe4a5ada11.jpg\",\n  \"236551.jpg\": \"Aves/Ammodramus savannarum/56c55921d29dc2d22bf16a9094f708b1.jpg\",\n  \"236555.jpg\": \"Aves/Ammodramus savannarum/e5a131883bdc308d41b24836b6139b25.jpg\",\n  \"236556.jpg\": \"Aves/Ammodramus savannarum/e435e13b6dbbe8d94b8998310919a8ca.jpg\",\n  \"236563.jpg\": \"Aves/Ammodramus savannarum/905a5f996ab9f46251796a034e1f1211.jpg\",\n  \"236564.jpg\": \"Aves/Ammodramus savannarum/c8d6e53efcc7070c587da13d60feeb37.jpg\",\n  \"236570.jpg\": \"Aves/Ammodramus savannarum/a58b094b0b7999fd5ab3f339ebef1aca.jpg\",\n  \"236571.jpg\": \"Aves/Ammodramus savannarum/eeb7fa53f421e1cac679bb96ab6c9175.jpg\",\n  \"236572.jpg\": \"Aves/Ammodramus savannarum/59e327a9e4d91226a7621682e86c2297.jpg\",\n  \"236575.jpg\": \"Aves/Ammodramus savannarum/869a6f8e21129e297bd14653f0c8b2ec.jpg\",\n  \"236576.jpg\": \"Aves/Ammodramus savannarum/c810ae08c488c9b3d14c1aadbee309ef.jpg\",\n  \"236580.jpg\": \"Aves/Ammodramus savannarum/81da3f6aa349785b5c65995dc0c47959.jpg\",\n  \"236581.jpg\": \"Aves/Ammodramus savannarum/d3df2509988f9501917073755c47cf87.jpg\",\n  \"236586.jpg\": \"Aves/Ammodramus savannarum/0e293e0bfb84f6d724ed5d49f604408c.jpg\",\n  \"236587.jpg\": \"Aves/Ammodramus savannarum/a27b75a34e11ad31231c5daeae5bc445.jpg\",\n  \"23663.jpg\": \"Amphibia/Lithobates pipiens/b6773ce583d4ef2a5526bf839b6f569b.jpg\",\n  \"23664.jpg\": \"Amphibia/Lithobates pipiens/de238a7b760a760cee036e583a25f3ff.jpg\",\n  \"2367.jpg\": \"Aves/Picoides pubescens/46a16bb5a757fb215691bbb9f9e171ff.jpg\",\n  \"23676.jpg\": \"Amphibia/Lithobates pipiens/46730c3a40912d959060043266b294e0.jpg\",\n  \"236774.jpg\": \"Insecta/Apis mellifera/806b85e9f451e4eb7e4b1284bed3479e.jpg\",\n  \"23693.jpg\": \"Amphibia/Lithobates pipiens/dcfd79e2225402d3b977dbd2b6775e4a.jpg\",\n  \"23695.jpg\": \"Amphibia/Lithobates pipiens/754a086e97fc5b2674bca7e10680618c.jpg\",\n  \"236983.jpg\": \"Insecta/Apis mellifera/9e642a3f097fe2a80e7f606203456646.jpg\",\n  \"23700.jpg\": \"Amphibia/Lithobates pipiens/472819361aebb4de377270258822c5cc.jpg\",\n  \"237018.jpg\": \"Insecta/Apis mellifera/0cd2c4e06b755ed773f6ee609a12ad51.jpg\",\n  \"237149.jpg\": \"Insecta/Apis mellifera/9fdc9410e5859051d51a762704f276b2.jpg\",\n  \"23716.jpg\": \"Amphibia/Lithobates pipiens/72f93c25aa2ba4e02b44774f87530bb8.jpg\",\n  \"237195.jpg\": \"Insecta/Apis mellifera/cac5e56ae0128fdfe89060ab74334ce0.jpg\",\n  \"237220.jpg\": \"Insecta/Apis mellifera/65ca01c452a2ed14fb5b8bb63d49836c.jpg\",\n  \"23728.jpg\": \"Actinopterygii/Haemulon sciurus/8ecb4e71fe99eba0972f4e75d566516b.jpg\",\n  \"23735.jpg\": \"Actinopterygii/Haemulon sciurus/bc0790faa32c8468235fb14da98072a1.jpg\",\n  \"23736.jpg\": \"Actinopterygii/Haemulon sciurus/21c133eac86e3ca59b04f6e4e74179ec.jpg\",\n  \"23745.jpg\": \"Insecta/Uresiphita reversalis/e2c05d98de1a6480bb9d6db74e343600.jpg\",\n  \"23746.jpg\": \"Insecta/Uresiphita reversalis/e6f84a93e51491e0fe330b94e5d74d97.jpg\",\n  \"23747.jpg\": \"Insecta/Uresiphita reversalis/804925b1f64f08e5657b3ecda5207271.jpg\",\n  \"23748.jpg\": \"Insecta/Uresiphita reversalis/512fc8aec1fb5fbf0ff85f41a785ff35.jpg\",\n  \"23750.jpg\": \"Insecta/Uresiphita reversalis/3fa99c4a3f39cc3993b1d13ccd90511c.jpg\",\n  \"23751.jpg\": \"Insecta/Uresiphita reversalis/d994d7e3d0094c145f9df875d752f895.jpg\",\n  \"237516.jpg\": \"Insecta/Apis mellifera/f4e20b5cd2ab092c02e7804c92e40553.jpg\",\n  \"23752.jpg\": \"Insecta/Uresiphita reversalis/a79cf8f582f063de4835c6b84ab35ea2.jpg\",\n  \"23753.jpg\": \"Insecta/Uresiphita reversalis/85dd4173ebb20ad6b0a6bfafed9aed81.jpg\",\n  \"237541.jpg\": \"Insecta/Apis mellifera/2e8a92bdcfe87bbd613017f30e5c9744.jpg\",\n  \"23755.jpg\": \"Insecta/Uresiphita reversalis/f5841a45f680b4b5c1d1a79d4d8e1d12.jpg\",\n  \"23759.jpg\": \"Insecta/Uresiphita reversalis/5304bc7c750d8bea90a3ee1bde49c6c3.jpg\",\n  \"23763.jpg\": \"Insecta/Uresiphita reversalis/20e88fe7faec57492923875d3338c6a2.jpg\",\n  \"237632.jpg\": \"Insecta/Apis mellifera/e67da676199c09d35e6fa2cd054cc325.jpg\",\n  \"237693.jpg\": \"Insecta/Apis mellifera/f267d9872d16c37f7d5f1a3ccdbcae69.jpg\",\n  \"23771.jpg\": \"Insecta/Uresiphita reversalis/20396a132d31b220fb267e257c18f5ea.jpg\",\n  \"23773.jpg\": \"Insecta/Uresiphita reversalis/dbf4430288a7639a052665643b26697e.jpg\",\n  \"23774.jpg\": \"Insecta/Uresiphita reversalis/395e3f92588039e580dd4423768adf5d.jpg\",\n  \"23775.jpg\": \"Insecta/Uresiphita reversalis/4f72bfe505773ff649c63b1e9555dea2.jpg\",\n  \"23776.jpg\": \"Insecta/Uresiphita reversalis/51a8eeabc44b4edd0202bfa3a3d05f77.jpg\",\n  \"23778.jpg\": \"Insecta/Uresiphita reversalis/92f8ebee967f97fa0d3284e85a73fefd.jpg\",\n  \"23779.jpg\": \"Insecta/Uresiphita reversalis/ce9d73f8b15f4e2dc98c29766ef81559.jpg\",\n  \"23781.jpg\": \"Insecta/Uresiphita reversalis/8b1a33f3ca3062aa1afc7f742d5908ab.jpg\",\n  \"237909.jpg\": \"Insecta/Apis mellifera/85787c150fd883ff9e7c264a316d7807.jpg\",\n  \"23810.jpg\": \"Mammalia/Glaucomys volans/95c237f8364c61652d992e1ae3ff78ba.jpg\",\n  \"238102.jpg\": \"Insecta/Apis mellifera/902b6f73ddcbda62f329e68b74fe508c.jpg\",\n  \"23811.jpg\": \"Mammalia/Glaucomys volans/dfaaea908cecf54d803ae27bcee3b1a4.jpg\",\n  \"23812.jpg\": \"Mammalia/Glaucomys volans/f49f77a49073c3736a828f7872e3a3c5.jpg\",\n  \"23815.jpg\": \"Mammalia/Glaucomys volans/d453e5ec482a0996cdd6edcbc992a098.jpg\",\n  \"23821.jpg\": \"Mammalia/Glaucomys volans/a535c078f416422bfd7ff297294ee509.jpg\",\n  \"23823.jpg\": \"Mammalia/Glaucomys volans/7d98bc6ffe09f1949e343a64f1ce8d2b.jpg\",\n  \"23826.jpg\": \"Insecta/Basiaeschna janata/7a92f192a386a9cc70f6d60fe54e4bb3.jpg\",\n  \"23827.jpg\": \"Insecta/Basiaeschna janata/c8a8c32fe4ed64d9d807327de0aa9841.jpg\",\n  \"23835.jpg\": \"Insecta/Basiaeschna janata/b663c09225bbd0d5626e8b2f1f953784.jpg\",\n  \"23841.jpg\": \"Insecta/Basiaeschna janata/cee28bc9fe3807dfc54226d0730f590b.jpg\",\n  \"238538.jpg\": \"Insecta/Apis mellifera/862e43adcddfe0b01cd1df55b63c19c8.jpg\",\n  \"238577.jpg\": \"Insecta/Plebejus lupini/0b5ebf3b4a57ecad85d9acb2fd122a30.jpg\",\n  \"238586.jpg\": \"Insecta/Plebejus lupini/0904dbf453a85cdb0f809bf6132496cb.jpg\",\n  \"238656.jpg\": \"Insecta/Pleuroprucha insulsaria/9c5bb53c4c557d6abd7bb413fce04068.jpg\",\n  \"238657.jpg\": \"Insecta/Pleuroprucha insulsaria/8b7ced1352896691a9be29014dfd7483.jpg\",\n  \"238659.jpg\": \"Insecta/Pleuroprucha insulsaria/3eb8bfaaf9ca599ef1910202ba260a09.jpg\",\n  \"238661.jpg\": \"Insecta/Pleuroprucha insulsaria/95887eb5e51e93dc48cfa89023c8b5d4.jpg\",\n  \"238662.jpg\": \"Insecta/Pleuroprucha insulsaria/ee4a3b803dd71a27706e26a899a59c96.jpg\",\n  \"238663.jpg\": \"Insecta/Pleuroprucha insulsaria/5dcb4174f29d46559dfa071a37a94c4a.jpg\",\n  \"238664.jpg\": \"Insecta/Pleuroprucha insulsaria/151addac339c9d39d1ceb1301b3e8412.jpg\",\n  \"238665.jpg\": \"Insecta/Pleuroprucha insulsaria/7ba26eaad9a43a60356dc8608563ec00.jpg\",\n  \"238667.jpg\": \"Insecta/Pleuroprucha insulsaria/63719c10c8713a394a47ee2d852eff6b.jpg\",\n  \"238670.jpg\": \"Insecta/Pleuroprucha insulsaria/a9f8aa54a9053e465b2deafdbeb84ee6.jpg\",\n  \"238671.jpg\": \"Insecta/Pleuroprucha insulsaria/2f598a74b72ee9fd4e5e5d7a3009f3a7.jpg\",\n  \"238675.jpg\": \"Insecta/Pleuroprucha insulsaria/6ed61c126afd7b28ffa4c52fa9ae475c.jpg\",\n  \"238678.jpg\": \"Insecta/Pleuroprucha insulsaria/eb3f2cf87655d6bfd641cc87a7f8b731.jpg\",\n  \"238681.jpg\": \"Insecta/Pleuroprucha insulsaria/d68fb9bcd8a67ec020699f07983435db.jpg\",\n  \"238682.jpg\": \"Insecta/Pleuroprucha insulsaria/25c70b34bab28bf4fdc584631e1e5b52.jpg\",\n  \"238685.jpg\": \"Insecta/Pleuroprucha insulsaria/c055fc12f3aee57ef59c6ffc9d5a4ebb.jpg\",\n  \"238686.jpg\": \"Insecta/Pleuroprucha insulsaria/01daf70c5032ee953a7af2efbb16f8c9.jpg\",\n  \"238690.jpg\": \"Insecta/Pleuroprucha insulsaria/82e3886eb19d3e66f1b500b672a046fc.jpg\",\n  \"238693.jpg\": \"Insecta/Pleuroprucha insulsaria/b2a89afd9299cbe50376ec3a32894e78.jpg\",\n  \"238703.jpg\": \"Insecta/Aeshna mixta/a25466cdda4eb2ee1b3bfe1d31f240f2.jpg\",\n  \"238704.jpg\": \"Insecta/Aeshna mixta/9c977ef3ec7ff803ef3081908d7aefd3.jpg\",\n  \"238705.jpg\": \"Insecta/Aeshna mixta/643b2e3ca6e0601f569f456385df2685.jpg\",\n  \"238717.jpg\": \"Insecta/Aeshna mixta/1ac82cba5c162bc1882d2db179f18f72.jpg\",\n  \"238722.jpg\": \"Insecta/Aeshna mixta/cb84f64da542066fed5140cf40dcc757.jpg\",\n  \"238758.jpg\": \"Insecta/Battus polydamas/974a99c4253fa8b8f35e780e3fcf5845.jpg\",\n  \"238761.jpg\": \"Insecta/Battus polydamas/e4416bfb3ffffe6e3398af10ef173ad8.jpg\",\n  \"238766.jpg\": \"Insecta/Battus polydamas/7a44ca6c4374a6b4920e0473df2c78a2.jpg\",\n  \"238769.jpg\": \"Insecta/Battus polydamas/8a9e1ebab717b9756fdd9844812c2cc4.jpg\",\n  \"238770.jpg\": \"Insecta/Battus polydamas/7705ed08f6bd0f429af6b47496d44c0a.jpg\",\n  \"238781.jpg\": \"Reptilia/Bogertophis subocularis/4653e6d4ade9b8a8c7c221fd43da07b5.jpg\",\n  \"238782.jpg\": \"Reptilia/Bogertophis subocularis/09a780668ce6e1fbb7af4ab092acc352.jpg\",\n  \"238785.jpg\": \"Reptilia/Bogertophis subocularis/16816c2d1a34ca48e6894a8b7b1c284f.jpg\",\n  \"238786.jpg\": \"Reptilia/Bogertophis subocularis/9e3caed2ed3429d6f84b6bb6800475c3.jpg\",\n  \"238794.jpg\": \"Reptilia/Bogertophis subocularis/a98b7d6bd396a8320af7c840901244f9.jpg\",\n  \"238798.jpg\": \"Reptilia/Bogertophis subocularis/2879fe4ec58724693dbf21696abd1ddf.jpg\",\n  \"238799.jpg\": \"Reptilia/Bogertophis subocularis/b5f2dc14194d9fa24f60a2c3bca6cb22.jpg\",\n  \"238800.jpg\": \"Reptilia/Bogertophis subocularis/bef8811668400e4a34fc38db47d6c781.jpg\",\n  \"238801.jpg\": \"Reptilia/Bogertophis subocularis/a50326056b405f1ffa77d0bf65362ff8.jpg\",\n  \"238809.jpg\": \"Reptilia/Bogertophis subocularis/cd5a5032322c1db62bf8954c02760126.jpg\",\n  \"238810.jpg\": \"Reptilia/Bogertophis subocularis/f0818d81c8e4abf5e8e1f547333b91c2.jpg\",\n  \"238813.jpg\": \"Reptilia/Bogertophis subocularis/dafaba0d2b9000515d177e105d6b4a03.jpg\",\n  \"238814.jpg\": \"Reptilia/Bogertophis subocularis/bd65b5ef999bea13a5076ba9507588c2.jpg\",\n  \"238815.jpg\": \"Reptilia/Bogertophis subocularis/93415bc381dd18a31e0e239fba5eb176.jpg\",\n  \"238816.jpg\": \"Reptilia/Bogertophis subocularis/98c78e3493e694b96a808ef2cba8b890.jpg\",\n  \"238817.jpg\": \"Reptilia/Bogertophis subocularis/19d89d15001cb02391368b006fed0be2.jpg\",\n  \"238818.jpg\": \"Reptilia/Bogertophis subocularis/03fb0a4c0ba91985cfaae7e7b59e4e46.jpg\",\n  \"238819.jpg\": \"Reptilia/Bogertophis subocularis/11610fcc4cbc6dd7d573db1ae7b24d8c.jpg\",\n  \"238820.jpg\": \"Reptilia/Bogertophis subocularis/7ee4f9a10933fc4186ce9df672f958e7.jpg\",\n  \"238821.jpg\": \"Reptilia/Bogertophis subocularis/8c2f728ecd79e5f33d02d82de32915e3.jpg\",\n  \"238824.jpg\": \"Aves/Chloroceryle amazona/3e96f0477b0c2fe3f4ecf97a381d729f.jpg\",\n  \"238827.jpg\": \"Aves/Chloroceryle amazona/8436410e4fcc170f0207ca7057d558c7.jpg\",\n  \"238835.jpg\": \"Aves/Chloroceryle amazona/163655fc526cf08ade3c295755abd4c8.jpg\",\n  \"238844.jpg\": \"Aves/Chloroceryle amazona/4213145d511d5e672a5f648ea4db4d2a.jpg\",\n  \"238845.jpg\": \"Aves/Chloroceryle amazona/8691c6754605f040ed26cf87c21d8d39.jpg\",\n  \"238846.jpg\": \"Aves/Chloroceryle amazona/aa4e02b267b430f7f059b0b6945532e4.jpg\",\n  \"238847.jpg\": \"Aves/Chloroceryle amazona/8b5c9aebcef447a830c01b1390fd1488.jpg\",\n  \"238848.jpg\": \"Aves/Chloroceryle amazona/15f6477cd0cc241f85f508044c65937a.jpg\",\n  \"238849.jpg\": \"Aves/Chloroceryle amazona/e2bcf8d19e4659fc5fbb5f0ade16d9ea.jpg\",\n  \"238852.jpg\": \"Aves/Chloroceryle amazona/cc6786de6e34599b666a77cb65b1b16e.jpg\",\n  \"238854.jpg\": \"Reptilia/Terrapene ornata luteola/0dc10b3348dba2ebd9aabf3f761b0df5.jpg\",\n  \"238861.jpg\": \"Reptilia/Terrapene ornata luteola/18da1785fafd3b961c12e57285011119.jpg\",\n  \"238863.jpg\": \"Reptilia/Terrapene ornata luteola/477da29690aa959a725ad79acda2b978.jpg\",\n  \"238864.jpg\": \"Reptilia/Terrapene ornata luteola/f5c95902a7dd88180f3b246e54b259aa.jpg\",\n  \"238879.jpg\": \"Reptilia/Terrapene ornata luteola/82448aa851924840eb202e03bb1e86cd.jpg\",\n  \"238880.jpg\": \"Reptilia/Terrapene ornata luteola/3de49a70503838b267cb3c54386a8c32.jpg\",\n  \"238885.jpg\": \"Aves/Cardinalis cardinalis/b0a4cc2f6aec87c5aef68a57eebb1c57.jpg\",\n  \"238919.jpg\": \"Aves/Cardinalis cardinalis/b56bcbb29d06a81a51ea83c490d3f17e.jpg\",\n  \"238942.jpg\": \"Aves/Cardinalis cardinalis/ffeecc587ecd5303541a578e8878af9c.jpg\",\n  \"239002.jpg\": \"Aves/Cardinalis cardinalis/642fd101b7b2f7ce53a8d05d8757d4e3.jpg\",\n  \"239013.jpg\": \"Aves/Cardinalis cardinalis/5b25d608a3ea7247cbc3db9e96a7a568.jpg\",\n  \"239029.jpg\": \"Aves/Cardinalis cardinalis/00659160c8beb260e4583ba8bd6ec4e5.jpg\",\n  \"239041.jpg\": \"Aves/Cardinalis cardinalis/795b98208bc8f8d400bfe7aab5dbb2a9.jpg\",\n  \"239069.jpg\": \"Aves/Cardinalis cardinalis/3b0f0d6e7fa373401e28fb1a04cc532e.jpg\",\n  \"239070.jpg\": \"Aves/Cardinalis cardinalis/1b6c648eb273c1a8e8b9f075c95cb246.jpg\",\n  \"239159.jpg\": \"Aves/Cardinalis cardinalis/fd12e3bfa782d6189ff5a8b206a4723a.jpg\",\n  \"239192.jpg\": \"Aves/Cardinalis cardinalis/99f5ab6459bb6d8320cf40ff6ffab9ad.jpg\",\n  \"239229.jpg\": \"Aves/Cardinalis cardinalis/391069ae3cf44a4124e6c3e548d06ca7.jpg\",\n  \"239267.jpg\": \"Aves/Cardinalis cardinalis/eb4c6c4e598867dc9582d67fac1a63a0.jpg\",\n  \"239319.jpg\": \"Aves/Cardinalis cardinalis/873766919c2f5d308e9969789ebb5bb4.jpg\",\n  \"239327.jpg\": \"Aves/Cardinalis cardinalis/0959a38d1e777086583eb52b20b7f0e5.jpg\",\n  \"239577.jpg\": \"Aves/Cardinalis cardinalis/0d3db8daabd6f941ca60cf51d24ea796.jpg\",\n  \"239609.jpg\": \"Aves/Cardinalis cardinalis/a3fdd8ac6aefb5a8c61940321e53a13f.jpg\",\n  \"239743.jpg\": \"Aves/Cardinalis cardinalis/ec209231a11e82f0afdc023834d9d2d5.jpg\",\n  \"239771.jpg\": \"Aves/Cardinalis cardinalis/9cc55637fcee22c3d3f9e324a0953eb6.jpg\",\n  \"239832.jpg\": \"Aves/Cardinalis cardinalis/9f0574876ffc6d66e0e9224ff465eb8f.jpg\",\n  \"239863.jpg\": \"Aves/Cardinalis cardinalis/62cfe9d9ecbabad82617abdbceeafff0.jpg\",\n  \"23988.jpg\": \"Insecta/Thaumetopoea pityocampa/25c1480a3b2c011b5799f5760326b001.jpg\",\n  \"239926.jpg\": \"Aves/Cardinalis cardinalis/8e62cca5f6dbab00805e4fb35a8015ad.jpg\",\n  \"239942.jpg\": \"Aves/Cardinalis cardinalis/36e1aa7ffd709eb2c43f39f4fb36c93a.jpg\",\n  \"239943.jpg\": \"Aves/Cardinalis cardinalis/92d562d4297904176d21d045f0679f0c.jpg\",\n  \"239957.jpg\": \"Aves/Cardinalis cardinalis/bb3a65c3b0bbc5b1b289503323db9fe7.jpg\",\n  \"23998.jpg\": \"Mammalia/Tamias dorsalis/b5df8f0a33c1756908c0a83169610746.jpg\",\n  \"239982.jpg\": \"Aves/Cardinalis cardinalis/051f4a99cfbe827d6cb9b137f6a48730.jpg\",\n  \"23999.jpg\": \"Mammalia/Tamias dorsalis/807c92504421f6d150b6a6936b8aebb9.jpg\",\n  \"24002.jpg\": \"Mammalia/Tamias dorsalis/e3c0945011d7e8d54b3d328b80ffa20f.jpg\",\n  \"24005.jpg\": \"Mammalia/Tamias dorsalis/660e6daf9d8543a97faebdcfbe71acdb.jpg\",\n  \"24006.jpg\": \"Mammalia/Tamias dorsalis/cd4972763b3f5f3dea630b68e1efc1c4.jpg\",\n  \"24008.jpg\": \"Mammalia/Tamias dorsalis/1ba016b86b23bea89c86cf55494c8f56.jpg\",\n  \"24012.jpg\": \"Aves/Tyrannus vociferans/af56c923bfd4c53d84567951c8aabbe4.jpg\",\n  \"24013.jpg\": \"Aves/Tyrannus vociferans/e6c869aaee53b00968c9856e9f60007f.jpg\",\n  \"24015.jpg\": \"Aves/Tyrannus vociferans/530092383f3563f2ff70917cbce79558.jpg\",\n  \"24019.jpg\": \"Aves/Tyrannus vociferans/62e2db46456dc924f1fc9212af8ca5fb.jpg\",\n  \"240231.jpg\": \"Reptilia/Terrapene ornata/e6c1f694709b7fe9b17a14a677cd6abd.jpg\",\n  \"240240.jpg\": \"Reptilia/Terrapene ornata/a37991a0d376a85b6c6cda8e33066f31.jpg\",\n  \"240264.jpg\": \"Reptilia/Terrapene ornata/2dee62caac3b882e98739192cfd79027.jpg\",\n  \"240278.jpg\": \"Reptilia/Terrapene ornata/fb6cbe5187d13df3c47414e5658a4c74.jpg\",\n  \"240279.jpg\": \"Reptilia/Terrapene ornata/5d275740b6195a51f19e7405c75e90e6.jpg\",\n  \"240290.jpg\": \"Reptilia/Terrapene ornata/b1ca0c5b650edb763ee97b8227ce076b.jpg\",\n  \"240300.jpg\": \"Reptilia/Terrapene ornata/b7aa3d75e55b4815d4ea24da50372002.jpg\",\n  \"240301.jpg\": \"Reptilia/Terrapene ornata/36dad9b5829da1a464e76cb532e41098.jpg\",\n  \"240305.jpg\": \"Reptilia/Terrapene ornata/cb1b66df4d578641e155b15a5bf01121.jpg\",\n  \"240314.jpg\": \"Reptilia/Terrapene ornata/ce2a0a2e82d32dbc5da4377203e5cce6.jpg\",\n  \"240315.jpg\": \"Reptilia/Terrapene ornata/336f27014965b18a08ec97f5ccc9ee80.jpg\",\n  \"240327.jpg\": \"Reptilia/Terrapene ornata/e227967802cf7b754b1f8e92e0dce8a9.jpg\",\n  \"240334.jpg\": \"Reptilia/Terrapene ornata/a89134905c7a37c043198395f7db15a2.jpg\",\n  \"240335.jpg\": \"Reptilia/Terrapene ornata/f65474f08818ee81b9f4092a667dc21b.jpg\",\n  \"240336.jpg\": \"Reptilia/Terrapene ornata/fe81e1bd7f33ce7d6c3af90a5215f7f4.jpg\",\n  \"240338.jpg\": \"Reptilia/Terrapene ornata/1f4816447bd2dd60466fce4fef79eaf9.jpg\",\n  \"240339.jpg\": \"Reptilia/Terrapene ornata/ace39911eb9a71e2fea82756ed4fdd8a.jpg\",\n  \"240340.jpg\": \"Reptilia/Terrapene ornata/fcb6b271a43d268d77ef849ba9612352.jpg\",\n  \"240344.jpg\": \"Reptilia/Terrapene ornata/930dcc57747b4a016550177835aec076.jpg\",\n  \"240345.jpg\": \"Reptilia/Terrapene ornata/134390e078652bf732f768fae12c1c70.jpg\",\n  \"240359.jpg\": \"Reptilia/Terrapene ornata/ff99cfdfc719defe1181ae20dbae8665.jpg\",\n  \"240375.jpg\": \"Insecta/Hemideina thoracica/b250b98b8c36a0a9a66b94cf5550f0c4.jpg\",\n  \"240390.jpg\": \"Insecta/Hemideina thoracica/ea23f6f37dbf10a6fbd6d5550b3f0524.jpg\",\n  \"240396.jpg\": \"Insecta/Hemideina thoracica/5d2ef335bb922d7e564bd9779f3ac8aa.jpg\",\n  \"240399.jpg\": \"Insecta/Hemideina thoracica/c7e1f376d658cca2ff7b2fefea75debc.jpg\",\n  \"240403.jpg\": \"Actinopterygii/Amia calva/0da119238279cb8bb7b4a97691aef013.jpg\",\n  \"240408.jpg\": \"Actinopterygii/Amia calva/66aefc750f0df88540bb51200c34a687.jpg\",\n  \"240411.jpg\": \"Actinopterygii/Amia calva/75a3ce7ee6e11f5042c3dbd70be74fe3.jpg\",\n  \"240424.jpg\": \"Insecta/Hordnia atropunctata/9c750ffdbf141a3a0469a6ea2c88031e.jpg\",\n  \"240431.jpg\": \"Insecta/Hordnia atropunctata/30b5af46efd29feffaad0adc61eca436.jpg\",\n  \"240436.jpg\": \"Insecta/Hordnia atropunctata/ce2359ce644eec0d37d1cc0fc91aae43.jpg\",\n  \"240437.jpg\": \"Insecta/Hordnia atropunctata/dcf650ba83a5733adad6f409d87d6fa4.jpg\",\n  \"240605.jpg\": \"Insecta/Lascoria ambigualis/b40cf67bb3967eb684fd13dfc7aaa837.jpg\",\n  \"240606.jpg\": \"Insecta/Lascoria ambigualis/3188370c45d0113832b169b99e71461b.jpg\",\n  \"240620.jpg\": \"Insecta/Lascoria ambigualis/1fa8817ae38bfb0ef4fbdec264632cc6.jpg\",\n  \"240623.jpg\": \"Insecta/Lascoria ambigualis/f08d85179f0e32714ef4bba40b766f73.jpg\",\n  \"240624.jpg\": \"Insecta/Lascoria ambigualis/305a389e77b3800cf2c0d76d10081edf.jpg\",\n  \"240677.jpg\": \"Aves/Aix sponsa/847a498c947c4b264e583d63c057437a.jpg\",\n  \"24070.jpg\": \"Aves/Tyrannus vociferans/37c3ec3e40071bf03eddd1427bbe44b4.jpg\",\n  \"240706.jpg\": \"Aves/Aix sponsa/8331d3cef57e2b3727694161ccd069d3.jpg\",\n  \"24071.jpg\": \"Aves/Tyrannus vociferans/75e1b647957df3c335bd07c68665dfb5.jpg\",\n  \"240742.jpg\": \"Aves/Aix sponsa/ad036623379d41ab8795780b9f3c2d6c.jpg\",\n  \"240749.jpg\": \"Aves/Aix sponsa/5f6e9a860457558a983cbae079e1dc9d.jpg\",\n  \"240750.jpg\": \"Aves/Aix sponsa/c51e85a5c44f3e1dac23dea91984f0c0.jpg\",\n  \"24076.jpg\": \"Aves/Tyrannus vociferans/a4abb667b07688d2664af79e7da6bf6a.jpg\",\n  \"240760.jpg\": \"Aves/Aix sponsa/bdcd472b997e3d8d2941d46a98d8332a.jpg\",\n  \"24084.jpg\": \"Aves/Tyrannus vociferans/5d0542065175ae70c1bfbbc14cfd1101.jpg\",\n  \"240873.jpg\": \"Aves/Aix sponsa/bea8dd506a69fd00f53298bc8ac8afa8.jpg\",\n  \"24091.jpg\": \"Aves/Tyrannus vociferans/e8dac5d18b42bb7cf30cc6e7f0efd33c.jpg\",\n  \"24092.jpg\": \"Aves/Tyrannus vociferans/82b22cd44b9012b9dfd945a6df7a0dc9.jpg\",\n  \"240921.jpg\": \"Aves/Aix sponsa/521e87f37fdc236a1ce630172d0e5e81.jpg\",\n  \"24093.jpg\": \"Aves/Tyrannus vociferans/3767637a62052c0c6f9d5345cc59b2f8.jpg\",\n  \"24094.jpg\": \"Aves/Tyrannus vociferans/605c04f7e1d8f1d915a26b2cee29e4c4.jpg\",\n  \"240954.jpg\": \"Aves/Aix sponsa/dcf7a4069ed52131beb133416f7ee17f.jpg\",\n  \"24096.jpg\": \"Aves/Tyrannus vociferans/c4500f59c18b7ddab9d916e36a48aa21.jpg\",\n  \"241002.jpg\": \"Aves/Aix sponsa/929f0eed19f76993761221025ef5dd7b.jpg\",\n  \"241015.jpg\": \"Aves/Aix sponsa/72d5f5c2090195b09df1af8c3314e343.jpg\",\n  \"24105.jpg\": \"Aves/Tyrannus vociferans/f410db457182efa5fdc853e8210aeb6b.jpg\",\n  \"24106.jpg\": \"Aves/Tyrannus vociferans/cf4c94eff1c5232baaaa4a34c52a16bc.jpg\",\n  \"241062.jpg\": \"Aves/Aix sponsa/1be7689863640ee9ffa081c132cd0d73.jpg\",\n  \"241068.jpg\": \"Aves/Aix sponsa/5a259238b3e90a5277c656af371fd76f.jpg\",\n  \"241085.jpg\": \"Aves/Aix sponsa/9b489dbafd325a6603881fb79e2a77f3.jpg\",\n  \"241090.jpg\": \"Aves/Aix sponsa/1736025f09e81becbd9709dd3a4785a2.jpg\",\n  \"241164.jpg\": \"Mammalia/Equus caballus/48593ed7564a41ecff36adfd0ef868ca.jpg\",\n  \"241179.jpg\": \"Mammalia/Equus caballus/a4c4b131ea1e2d3fedc40d1a4892f70d.jpg\",\n  \"241180.jpg\": \"Mammalia/Equus caballus/26ec07a27e170902a80ce24414d8c3bd.jpg\",\n  \"241182.jpg\": \"Mammalia/Equus caballus/cd36304914d1933baeff3a72f14b95d2.jpg\",\n  \"241189.jpg\": \"Mammalia/Equus caballus/b02efbb2e5eb06dfd81fbd6d2cf7456c.jpg\",\n  \"241191.jpg\": \"Mammalia/Equus caballus/1a7e7efb21238f207933361a430535e0.jpg\",\n  \"241192.jpg\": \"Mammalia/Equus caballus/78b2238d6b6ecb54419464abe1d50bef.jpg\",\n  \"241202.jpg\": \"Mammalia/Equus caballus/eea1477f3a2cc1aec296b1363cbb9ea2.jpg\",\n  \"241269.jpg\": \"Insecta/Cucullia convexipennis/c3b106cfd67929e673c4991732896f34.jpg\",\n  \"241271.jpg\": \"Insecta/Cucullia convexipennis/854e1bc143d833cdb943ec8c92aab9f5.jpg\",\n  \"241274.jpg\": \"Insecta/Cucullia convexipennis/114a7716c3637e44d4570ac0ab4e9be3.jpg\",\n  \"241276.jpg\": \"Insecta/Cucullia convexipennis/54c806a4be5909493fda4b4c2f08f156.jpg\",\n  \"241290.jpg\": \"Insecta/Cucullia convexipennis/62ea8600746b4cf2acdaf6cb2d966dcb.jpg\",\n  \"241293.jpg\": \"Insecta/Cucullia convexipennis/1f112054328e40bc55c532bd0fc9f0b0.jpg\",\n  \"241298.jpg\": \"Insecta/Cucullia convexipennis/8920e45f7843b6ee34c0ef4978d6c027.jpg\",\n  \"241301.jpg\": \"Insecta/Herpetogramma pertextalis/d547c49385950fef3d2f815cddb3ba41.jpg\",\n  \"241302.jpg\": \"Insecta/Herpetogramma pertextalis/a700e10490d367ee9c7b6a3cd0f251e9.jpg\",\n  \"241322.jpg\": \"Insecta/Platynota idaeusalis/3c3d93f1d206a2ccddaa578453b8e55e.jpg\",\n  \"241323.jpg\": \"Insecta/Platynota idaeusalis/9bbb6ce13a534d67d0f851b735bcdb5a.jpg\",\n  \"24133.jpg\": \"Aves/Tyrannus vociferans/6321ba29151c13ce3a26a90effa8164f.jpg\",\n  \"24135.jpg\": \"Aves/Tyrannus vociferans/038e45695960c17f841bc64f332f4f9a.jpg\",\n  \"241506.jpg\": \"Aves/Podiceps cristatus/5e141eff8dcb91aa484971ff36d94a24.jpg\",\n  \"241507.jpg\": \"Aves/Podiceps cristatus/f3a8fbd1f2130f49e16f564f0e7c49ef.jpg\",\n  \"241508.jpg\": \"Aves/Podiceps cristatus/cde340b08d5e9d6a38f5f8227796c0a5.jpg\",\n  \"241523.jpg\": \"Aves/Podiceps cristatus/a71e2c940e68731a54b32275a8f23866.jpg\",\n  \"24153.jpg\": \"Aves/Tyrannus vociferans/76cff188081edd5d5f31c85e2dd7ec6c.jpg\",\n  \"241534.jpg\": \"Aves/Podiceps cristatus/2e3711077ea299586674bec267b1488a.jpg\",\n  \"241543.jpg\": \"Aves/Podiceps cristatus/8b315800807409c6c9351b7507968384.jpg\",\n  \"241545.jpg\": \"Aves/Podiceps cristatus/2cf026bcefaaea3bc068ba501153ec9e.jpg\",\n  \"241546.jpg\": \"Aves/Podiceps cristatus/92bc6c02508e733ea30222ddb5793461.jpg\",\n  \"241549.jpg\": \"Aves/Podiceps cristatus/23be4274603d693b7a6cc232c32f9d9d.jpg\",\n  \"241551.jpg\": \"Aves/Podiceps cristatus/437ace4c9fd7535ca8af1ea988db6f13.jpg\",\n  \"241557.jpg\": \"Aves/Podiceps cristatus/5b4e1cbb14f5e8deba67b290c8d880f6.jpg\",\n  \"241560.jpg\": \"Aves/Podiceps cristatus/7e48ad14a728d0b1f116b7b7c23599e5.jpg\",\n  \"241581.jpg\": \"Aves/Podiceps cristatus/abd9cb341b40310080690e0cddc77f10.jpg\",\n  \"241582.jpg\": \"Aves/Podiceps cristatus/ed0d7bdba82818ffa3c8f5872126df46.jpg\",\n  \"241583.jpg\": \"Aves/Podiceps cristatus/fe2f8a1290f86f6791823c947aa6f552.jpg\",\n  \"241587.jpg\": \"Aves/Podiceps cristatus/d57eef190231d3c853cfa603cea57e21.jpg\",\n  \"2416.jpg\": \"Aves/Picoides pubescens/6a5112f908706d81fe917893886e7868.jpg\",\n  \"241604.jpg\": \"Aves/Podiceps cristatus/f05f1b8e0434717853cbcabd23a86b05.jpg\",\n  \"241605.jpg\": \"Aves/Podiceps cristatus/d5df0f1781055b50e51b8303cd7b3a39.jpg\",\n  \"241625.jpg\": \"Aves/Podiceps cristatus/1571e1508daa78418e9a8119ee0aa40c.jpg\",\n  \"241626.jpg\": \"Aves/Podiceps cristatus/50aea8c3a26d8c58ac904b41f83ed926.jpg\",\n  \"241725.jpg\": \"Insecta/Conocephalus brevipennis/de80d6bf455249f7c66062cfe736d6db.jpg\",\n  \"241726.jpg\": \"Insecta/Conocephalus brevipennis/6713265434f90d4b8240ec65f8c9fb04.jpg\",\n  \"241727.jpg\": \"Insecta/Conocephalus brevipennis/c561b0f5c68451b3257e2436c5e29b8e.jpg\",\n  \"241728.jpg\": \"Insecta/Conocephalus brevipennis/22955c2ce84df75b05e2848926141aca.jpg\",\n  \"241731.jpg\": \"Insecta/Conocephalus brevipennis/eeb9e5dc4b1cfc0802a3c5a00724ae13.jpg\",\n  \"241732.jpg\": \"Insecta/Conocephalus brevipennis/d54f6299c4a82ed49f23bedd23dace20.jpg\",\n  \"241742.jpg\": \"Insecta/Conocephalus brevipennis/c40c724222b3d1497abaefe4ac14d382.jpg\",\n  \"241752.jpg\": \"Aves/Coccyzus minor/d2cda0999838b51b5845842c6a9b4277.jpg\",\n  \"241753.jpg\": \"Aves/Coccyzus minor/e66f93811745e4514e5e97c40c01d8a1.jpg\",\n  \"241755.jpg\": \"Aves/Coccyzus minor/2a206cd263e11c0bf588152ee2e1c1f4.jpg\",\n  \"241763.jpg\": \"Aves/Coccyzus minor/8a0a09994d7a2a947233cd3b83f2fca7.jpg\",\n  \"241764.jpg\": \"Aves/Coccyzus minor/b94592eea18c2fbc26a7ac256ca22858.jpg\",\n  \"241769.jpg\": \"Aves/Coccyzus minor/54c5ad86bde2adf7b21fbd227bc74404.jpg\",\n  \"24185.jpg\": \"Aves/Tyrannus vociferans/7dcb08a5ff0c0818ed64e7418afa78d3.jpg\",\n  \"24186.jpg\": \"Aves/Tyrannus vociferans/54d740626f5547231d8638d991b22192.jpg\",\n  \"24188.jpg\": \"Aves/Tyrannus vociferans/7b36f11f76f0c12da6ea83becc35b453.jpg\",\n  \"24189.jpg\": \"Aves/Tyrannus vociferans/39070f44d02af8ec2746697f916d88b3.jpg\",\n  \"241931.jpg\": \"Aves/Fringilla montifringilla/52465669f0a535e2416067aff6173f0c.jpg\",\n  \"24194.jpg\": \"Aves/Tyrannus vociferans/61d817f3b56579d520a799ae3601c010.jpg\",\n  \"241942.jpg\": \"Aves/Fringilla montifringilla/db2e30124c1ed21f5a0e06952a9af101.jpg\",\n  \"241943.jpg\": \"Aves/Fringilla montifringilla/1583ced876fe596eafaa36f6b6b723f6.jpg\",\n  \"241944.jpg\": \"Aves/Fringilla montifringilla/21ac188982b4f83e02650c43f55cd09c.jpg\",\n  \"241945.jpg\": \"Aves/Fringilla montifringilla/52fecfabe1f00143917cbc81d78e636a.jpg\",\n  \"241946.jpg\": \"Aves/Fringilla montifringilla/86075163ba15ecb903ef5c14ff920602.jpg\",\n  \"241947.jpg\": \"Aves/Fringilla montifringilla/5ccafaa0f751398af5c59f049e24ffdc.jpg\",\n  \"241948.jpg\": \"Aves/Fringilla montifringilla/97724c63047dd55cd07723075292ce75.jpg\",\n  \"241949.jpg\": \"Aves/Fringilla montifringilla/6c72da93ebb9a4d2166d9e6ceed7a098.jpg\",\n  \"241952.jpg\": \"Aves/Fringilla montifringilla/38944c1f31030e270ce4abf7fb669456.jpg\",\n  \"241953.jpg\": \"Aves/Fringilla montifringilla/30257533c7fb8d52d53b19377188af03.jpg\",\n  \"241954.jpg\": \"Aves/Fringilla montifringilla/b1d1620e2c54c68d7b85f7d83f7b3e1e.jpg\",\n  \"241955.jpg\": \"Aves/Fringilla montifringilla/3b588bbecc00ac7c21b166f6ea3c163f.jpg\",\n  \"241957.jpg\": \"Aves/Fringilla montifringilla/f9417dfddd920f3ed11fdb16a55edfea.jpg\",\n  \"241958.jpg\": \"Aves/Fringilla montifringilla/8e4014bd885ba7287be89cf7f3225fc1.jpg\",\n  \"241963.jpg\": \"Aves/Fringilla montifringilla/3c4a2020595e1a44ad9eae1f6fdb3a19.jpg\",\n  \"241969.jpg\": \"Aves/Fringilla montifringilla/bcaeeb3904f607b0b8221a76b38b6475.jpg\",\n  \"241970.jpg\": \"Aves/Fringilla montifringilla/7f763061411a29051ae3e3447c1f16e4.jpg\",\n  \"241971.jpg\": \"Aves/Fringilla montifringilla/03de10bb9fe2b1b35f46af36a3af991b.jpg\",\n  \"241974.jpg\": \"Aves/Fringilla montifringilla/00b976e92e14d183d2bb29415eda2ff9.jpg\",\n  \"242003.jpg\": \"Aves/Geococcyx californianus/1954bf0654257b8d6dd70b089d9cf2ea.jpg\",\n  \"242015.jpg\": \"Aves/Geococcyx californianus/f5ed720b7afb445ffb9f5173ef48b177.jpg\",\n  \"242019.jpg\": \"Aves/Geococcyx californianus/3f82d489f3e0c9ef3fe872383389d256.jpg\",\n  \"242024.jpg\": \"Aves/Geococcyx californianus/655775cd80b6ddf0fee8dfb6c5bc4f64.jpg\",\n  \"242031.jpg\": \"Aves/Geococcyx californianus/c46ccc98d4c51d96a556ea0a6a4aa932.jpg\",\n  \"242039.jpg\": \"Aves/Geococcyx californianus/ea44cd444c512ab7f2f7ad53da32fe94.jpg\",\n  \"242059.jpg\": \"Aves/Geococcyx californianus/1ac4c0a44fc77307d28f72889b7eb1b9.jpg\",\n  \"242095.jpg\": \"Aves/Geococcyx californianus/8aa9eede5558022bfa6903aa56b5a1b0.jpg\",\n  \"242124.jpg\": \"Aves/Geococcyx californianus/ce29c548376979ff3965cf13e89345dc.jpg\",\n  \"242132.jpg\": \"Aves/Geococcyx californianus/1ca5f57c8d1aef9ee1b61d8a43d99577.jpg\",\n  \"242136.jpg\": \"Aves/Geococcyx californianus/6fa12be8d2fd53670b8c3e8a45592c37.jpg\",\n  \"242138.jpg\": \"Aves/Geococcyx californianus/3d66d6ac47e91ba3b2976bf88af3d60a.jpg\",\n  \"242140.jpg\": \"Aves/Geococcyx californianus/7f47d775fda3e2fdcbc704771461cdaf.jpg\",\n  \"242145.jpg\": \"Aves/Geococcyx californianus/158e78d05e0164560b76a8d5a53868f4.jpg\",\n  \"242177.jpg\": \"Aves/Geococcyx californianus/44d3fc2570637b8b8bcf51e0baee7605.jpg\",\n  \"242186.jpg\": \"Aves/Geococcyx californianus/f227a5ccdf06733c515e96882c7d98b1.jpg\",\n  \"242224.jpg\": \"Aves/Geococcyx californianus/29587ee145ae72a4eb566c7baff860ed.jpg\",\n  \"242286.jpg\": \"Aves/Geococcyx californianus/a76b99e2d8119bc0e358876c6f06adf5.jpg\",\n  \"242287.jpg\": \"Aves/Geococcyx californianus/54dd68b66b0efa1fdc6f1b3f7189fa41.jpg\",\n  \"242300.jpg\": \"Aves/Geococcyx californianus/412e898ed30410410e0e97f6a6703d43.jpg\",\n  \"242315.jpg\": \"Aves/Geococcyx californianus/f429c3660da071f79c508b7b07c23e90.jpg\",\n  \"242365.jpg\": \"Aves/Thalasseus elegans/6b098464f053f5d1fba506ae024c8406.jpg\",\n  \"242368.jpg\": \"Aves/Thalasseus elegans/224b8b71188d1fda6adf6252c6d146cd.jpg\",\n  \"242369.jpg\": \"Aves/Thalasseus elegans/9b6747dbedc9a50ef8c15d66e9885d0c.jpg\",\n  \"242376.jpg\": \"Aves/Thalasseus elegans/6311dfe1fa660b19ea90a23c9c83a650.jpg\",\n  \"242378.jpg\": \"Aves/Thalasseus elegans/d939acafb61b7745cec464ce76de6d78.jpg\",\n  \"242380.jpg\": \"Aves/Thalasseus elegans/b6733e8eec33845a2a6e216b4d3d3433.jpg\",\n  \"24240.jpg\": \"Actinopterygii/Cyprinus carpio haematopterus/35226f8babf57d7037a98998a09022fb.jpg\",\n  \"242400.jpg\": \"Aves/Thalasseus elegans/b401e394416bbfce95638217ba2f9e27.jpg\",\n  \"242414.jpg\": \"Aves/Thalasseus elegans/362f7a3af19c14a0f898f9885fd9199c.jpg\",\n  \"242457.jpg\": \"Aves/Thalasseus elegans/bafd5d059e59e3a90f636121bcef7c02.jpg\",\n  \"242480.jpg\": \"Aves/Gallus gallus domesticus/9cc7186ccc6337073c82e15804643153.jpg\",\n  \"242481.jpg\": \"Aves/Gallus gallus domesticus/0e8e3f75483a0e1712275e9b95ab1623.jpg\",\n  \"242482.jpg\": \"Aves/Gallus gallus domesticus/dbdb40c4bda084a280181cc820611477.jpg\",\n  \"242493.jpg\": \"Aves/Gallus gallus domesticus/b590b226e651e7127e7199375f8920ae.jpg\",\n  \"24250.jpg\": \"Actinopterygii/Cyprinus carpio haematopterus/c4e87f52e92bdcd41ad6d5d4d04302e5.jpg\",\n  \"242502.jpg\": \"Aves/Gallus gallus domesticus/1b19e5cb18f4a496975d26e82ed34c18.jpg\",\n  \"242503.jpg\": \"Aves/Gallus gallus domesticus/bdfa6c6aece8e41f9681aeddad11d35b.jpg\",\n  \"242519.jpg\": \"Aves/Gallus gallus domesticus/da16aa8c0eb3de286d585f77d81fb4aa.jpg\",\n  \"242532.jpg\": \"Aves/Gallus gallus domesticus/59ec4dced3f691bf35553e66532d117e.jpg\",\n  \"242535.jpg\": \"Aves/Gallus gallus domesticus/6b3eeb644b7cfa99eb0ad17b56115249.jpg\",\n  \"242537.jpg\": \"Aves/Gallus gallus domesticus/331c3e6b1f9b8ac660cf1a57bebe7e99.jpg\",\n  \"242576.jpg\": \"Insecta/Hetaerina titia/e6f6fe74a9cc353d386840a3fbf0ae2b.jpg\",\n  \"242577.jpg\": \"Insecta/Hetaerina titia/127d92d268065f86a6733c1251b61725.jpg\",\n  \"242588.jpg\": \"Insecta/Hetaerina titia/7fdfe22c3670597b22a420a731a9c988.jpg\",\n  \"242589.jpg\": \"Insecta/Hetaerina titia/bbe930bf72c601cab1d8f275ed2f820f.jpg\",\n  \"242601.jpg\": \"Insecta/Hetaerina titia/4cd850b9aa02e14d3b5ebb8cd4e8a3db.jpg\",\n  \"242610.jpg\": \"Insecta/Hetaerina titia/23f4ffaa780f89712e149ba3d06ee68b.jpg\",\n  \"242611.jpg\": \"Insecta/Hetaerina titia/bb139f01007cc45d26e9028fd274f6d9.jpg\",\n  \"242612.jpg\": \"Insecta/Hetaerina titia/931f4b98ed8c23f50c21fd290d65bf0d.jpg\",\n  \"242613.jpg\": \"Insecta/Hetaerina titia/7791f2862c0448d64802002c9db6c862.jpg\",\n  \"242614.jpg\": \"Insecta/Hetaerina titia/4bbbc23a268f1b0f4779c40fe79f7200.jpg\",\n  \"242618.jpg\": \"Insecta/Hetaerina titia/216ba7dd218cc5103b0bd1275c9284ba.jpg\",\n  \"242623.jpg\": \"Insecta/Hetaerina titia/f7babc9b17bbeda96db66e743a63a604.jpg\",\n  \"242624.jpg\": \"Insecta/Hetaerina titia/9c575001f1e89d51af3217cfa2dd1a3a.jpg\",\n  \"242625.jpg\": \"Insecta/Hetaerina titia/fd18d956d70a441223828f6b463f286d.jpg\",\n  \"242627.jpg\": \"Insecta/Hetaerina titia/64e82fb019bec4951c940048a830ebf7.jpg\",\n  \"242628.jpg\": \"Insecta/Hetaerina titia/b206a44557383bcec7d022e7efe68eb6.jpg\",\n  \"242629.jpg\": \"Insecta/Hetaerina titia/168562925af9322cadb26457ad1b10da.jpg\",\n  \"242633.jpg\": \"Insecta/Xylocopa micans/77f6d7267a0a394b4e26ab5eac11ddb2.jpg\",\n  \"242634.jpg\": \"Insecta/Xylocopa micans/80f4eb28c607eb7f93e9f3f7151b0993.jpg\",\n  \"242637.jpg\": \"Insecta/Xylocopa micans/f4c49c0b883ecc7607530564677675e1.jpg\",\n  \"242638.jpg\": \"Insecta/Xylocopa micans/1b9ff4915feba006345898851b46eaa7.jpg\",\n  \"242641.jpg\": \"Insecta/Xylocopa micans/21323e78bcbaca12c794efdfc1748597.jpg\",\n  \"242643.jpg\": \"Insecta/Xylocopa micans/604890525e6bf3af842db545fc7b7069.jpg\",\n  \"242654.jpg\": \"Insecta/Xylocopa micans/f98ea8dc3e2108a06332f0c6ac70e20d.jpg\",\n  \"242658.jpg\": \"Insecta/Xylocopa micans/ce0f0575c349893cc04d463b82ccb30f.jpg\",\n  \"242659.jpg\": \"Insecta/Xylocopa micans/80c6c36da989be8b0c0e14eab76bbe00.jpg\",\n  \"242660.jpg\": \"Insecta/Xylocopa micans/8c67959db44e64b0f23c7b730eec78c6.jpg\",\n  \"242666.jpg\": \"Insecta/Xylocopa micans/c61a20c29c8fca3bb11a03022e817a92.jpg\",\n  \"242667.jpg\": \"Insecta/Xylocopa micans/6765915d4fad09d9b1ba24c75ec6b697.jpg\",\n  \"242671.jpg\": \"Insecta/Xylocopa micans/92566892683b1c980739774e7939d7fe.jpg\",\n  \"242687.jpg\": \"Insecta/Xylocopa micans/f8b3fa539320fe7025bc5192d80056b4.jpg\",\n  \"242688.jpg\": \"Insecta/Xylocopa micans/ef5dccc437d6286574895afe2516f9aa.jpg\",\n  \"242689.jpg\": \"Insecta/Xylocopa micans/3bff6c42ad21825a805caa5d320908b6.jpg\",\n  \"242697.jpg\": \"Insecta/Xylocopa micans/e781609d7beb38e90ad0514df648ca75.jpg\",\n  \"242698.jpg\": \"Insecta/Xylocopa micans/20f69d6bfbcc75e3b75891b717759003.jpg\",\n  \"24270.jpg\": \"Insecta/Heliopetes laviana/9f1cc2d5163098382b502d183bac62e2.jpg\",\n  \"242700.jpg\": \"Insecta/Xylocopa micans/258a9196d604bc4dee51fbe2a322da96.jpg\",\n  \"242701.jpg\": \"Insecta/Xylocopa micans/5ad51bd94bc35f494baba7b194dbfa6a.jpg\",\n  \"242702.jpg\": \"Insecta/Xylocopa micans/0e6504d3b0583e69bf8e8b35dcb5a632.jpg\",\n  \"24271.jpg\": \"Insecta/Heliopetes laviana/8f4f15ddd96298aa15afe48ff5f3b7f0.jpg\",\n  \"24273.jpg\": \"Insecta/Heliopetes laviana/934871acb722a2bb5953e2a7c01e70b0.jpg\",\n  \"24274.jpg\": \"Insecta/Heliopetes laviana/8fc1748b4f616975579aad3d6ae71ab3.jpg\",\n  \"242766.jpg\": \"Insecta/Brachystola magna/06f4a0dfc850896955fd9c714d94c27a.jpg\",\n  \"242770.jpg\": \"Insecta/Brachystola magna/9093c7876206be94e85ff9d3e8c1df61.jpg\",\n  \"242771.jpg\": \"Insecta/Brachystola magna/9bf37749a72ce80506f9b47ae2ab3372.jpg\",\n  \"242772.jpg\": \"Insecta/Brachystola magna/20a105a1ddb9cb7fd919d1331556f3a8.jpg\",\n  \"24278.jpg\": \"Insecta/Heliopetes laviana/6410b97252fbe0d4f67035398b778209.jpg\",\n  \"24279.jpg\": \"Insecta/Heliopetes laviana/2fd90ec5fe57fd6631596389559619da.jpg\",\n  \"24280.jpg\": \"Insecta/Heliopetes laviana/345b08ff87af66d1adc8c5a96109748c.jpg\",\n  \"24281.jpg\": \"Insecta/Heliopetes laviana/4b43e29e82ff7fb29342ee1f03cb8e70.jpg\",\n  \"24282.jpg\": \"Insecta/Heliopetes laviana/b65f3f271e9202a37995dbb83bf926a9.jpg\",\n  \"24283.jpg\": \"Insecta/Heliopetes laviana/b9d18e2708d596d283123de1a6e176d3.jpg\",\n  \"24284.jpg\": \"Insecta/Heliopetes laviana/417543a4aa89850c754d5c393ab5f834.jpg\",\n  \"24285.jpg\": \"Insecta/Heliopetes laviana/64b2fefd16842bc73be34f2a1b44170f.jpg\",\n  \"24286.jpg\": \"Insecta/Heliopetes laviana/64ef433fc8be2fa08c3a3906c5e4e70b.jpg\",\n  \"24290.jpg\": \"Insecta/Heliopetes laviana/1f1e6f9270d177627aed2e13f0711f95.jpg\",\n  \"24291.jpg\": \"Insecta/Heliopetes laviana/45b8e3e47a61c009adad0246a0fe81bd.jpg\",\n  \"24295.jpg\": \"Insecta/Heliopetes laviana/eda8688864dac1a79083a368e8823ef7.jpg\",\n  \"242982.jpg\": \"Insecta/Chlosyne theona/938caba75a3b4eed37a2522c798f06fa.jpg\",\n  \"242983.jpg\": \"Insecta/Chlosyne theona/9b4458f0378b894406c48816bdbd3db2.jpg\",\n  \"242984.jpg\": \"Insecta/Chlosyne theona/5363c5c441272c682b9c4a678fdc2a1a.jpg\",\n  \"242989.jpg\": \"Insecta/Chlosyne theona/e0ef3b04c58b82aabe88f35458b3b73b.jpg\",\n  \"242990.jpg\": \"Insecta/Chlosyne theona/d5b04e5c238db772e78c56eb336f47dc.jpg\",\n  \"242991.jpg\": \"Insecta/Chlosyne theona/3238afc1207f8b82dc2589093f13f4e7.jpg\",\n  \"242992.jpg\": \"Insecta/Chlosyne theona/f30704d3a8a5a36411d4c2199ea2a90c.jpg\",\n  \"242994.jpg\": \"Insecta/Chlosyne theona/1da40e2492f6ee585d6eba69db851df3.jpg\",\n  \"242995.jpg\": \"Insecta/Chlosyne theona/c7de12aadc9d58b629167972752d4703.jpg\",\n  \"243000.jpg\": \"Insecta/Chlosyne theona/9f6d552a930ff6066488e568a077edfe.jpg\",\n  \"243005.jpg\": \"Insecta/Chlosyne theona/08a89fb78f07830afcd5b21224af1a03.jpg\",\n  \"243006.jpg\": \"Insecta/Chlosyne theona/abeaa137fea00c5fbede1f4dc4a2e7cb.jpg\",\n  \"243007.jpg\": \"Insecta/Chlosyne theona/f965d6639ce894f3a043f92b12dd1285.jpg\",\n  \"243008.jpg\": \"Insecta/Chlosyne theona/348195f6334ac43eb1cf2e0586ba2eb8.jpg\",\n  \"243009.jpg\": \"Insecta/Chlosyne theona/d510f3c7367a96ed9a14b05735e5c0ee.jpg\",\n  \"243010.jpg\": \"Insecta/Chlosyne theona/4b88fb5be1a5b84fa31fe75f9e7b80c4.jpg\",\n  \"243013.jpg\": \"Insecta/Chlosyne theona/4b0162a62146ac498f193c5229b99537.jpg\",\n  \"243016.jpg\": \"Insecta/Chlosyne theona/6174695fe3da0429655756dca3f97697.jpg\",\n  \"243036.jpg\": \"Actinopterygii/Morone saxatilis/63d1b9f2d0c0ac2617912850bb7b7789.jpg\",\n  \"243042.jpg\": \"Actinopterygii/Morone saxatilis/0b791d4b0bcdde003eb88fa8f95863cd.jpg\",\n  \"243046.jpg\": \"Actinopterygii/Morone saxatilis/db2a7a71c5a53e8ff11b812c938fb1ea.jpg\",\n  \"243047.jpg\": \"Actinopterygii/Morone saxatilis/506668c551431dfe4bac4a78e72fa5c3.jpg\",\n  \"243048.jpg\": \"Actinopterygii/Morone saxatilis/df0f83f007e9c7b51f1bd5e3ec7ff9d5.jpg\",\n  \"243050.jpg\": \"Actinopterygii/Morone saxatilis/d9b4fcb74f807d58890c4aef8cae32de.jpg\",\n  \"243053.jpg\": \"Actinopterygii/Morone saxatilis/20321d8f554fcd3ca8fe289faef085db.jpg\",\n  \"243058.jpg\": \"Actinopterygii/Morone saxatilis/88cccd4baa89d472ebc4f98606c4120c.jpg\",\n  \"243059.jpg\": \"Actinopterygii/Morone saxatilis/0ba617532910ada23d24c91c6262ef8b.jpg\",\n  \"243064.jpg\": \"Insecta/Siphanta acuta/3276567583ae1b3a79107e9ca3acf3c1.jpg\",\n  \"243069.jpg\": \"Insecta/Siphanta acuta/f3da32fc11ff4dd1d7f094603ae1b341.jpg\",\n  \"243079.jpg\": \"Insecta/Siphanta acuta/ce7d581331f25d085636e62e819d7169.jpg\",\n  \"243087.jpg\": \"Insecta/Siphanta acuta/47f1bb818aeaa7fc9def2ba334fbd44e.jpg\",\n  \"243088.jpg\": \"Insecta/Siphanta acuta/c4a8e5fc6a03281caf8dadd3ad8da297.jpg\",\n  \"24309.jpg\": \"Insecta/Heliopetes laviana/cf0bfb3708ecc76ef1d67134c6836c9b.jpg\",\n  \"24310.jpg\": \"Insecta/Heliopetes laviana/fc23c68999bd191b0e16721039ac26bd.jpg\",\n  \"243131.jpg\": \"Insecta/Sabulodes aegrotata/c6a06f481382429dc288d091418c0b45.jpg\",\n  \"243141.jpg\": \"Insecta/Sabulodes aegrotata/7dad2599a1bdb3d69010a8559bd1ce2a.jpg\",\n  \"243146.jpg\": \"Insecta/Sabulodes aegrotata/9015f2ef48933f78b05924f0f5c42057.jpg\",\n  \"243148.jpg\": \"Insecta/Sabulodes aegrotata/177f463ee3aa7982b4334a96d0191bba.jpg\",\n  \"24316.jpg\": \"Aves/Setophaga occidentalis/bec6e3e0e2addc8aa2d1ca578da91f52.jpg\",\n  \"24317.jpg\": \"Aves/Setophaga occidentalis/eb982ccb52abff11e4e305137bddbfa1.jpg\",\n  \"243173.jpg\": \"Aves/Sula leucogaster/e6cbd0e444087ccdd28922a7ed24a18d.jpg\",\n  \"243175.jpg\": \"Aves/Sula leucogaster/6b382cb6e57251d6b87325d623c55202.jpg\",\n  \"243183.jpg\": \"Aves/Sula leucogaster/bb2010395829eab1f315a28cacbc13b4.jpg\",\n  \"243184.jpg\": \"Aves/Sula leucogaster/fa53831b7ed9767143fb45078e583d3a.jpg\",\n  \"243193.jpg\": \"Aves/Sula leucogaster/ee8c955e96eba72aae1c9c170c4019c0.jpg\",\n  \"243198.jpg\": \"Aves/Sula leucogaster/d2d6e173dbd0e54f58ebb3cd6a1b5e4a.jpg\",\n  \"243209.jpg\": \"Aves/Sula leucogaster/0f310735864e42eb0e1328f13e616838.jpg\",\n  \"243216.jpg\": \"Aves/Sula leucogaster/1544b3308fb17d485318355931b571d1.jpg\",\n  \"243218.jpg\": \"Aves/Sula leucogaster/71a158b53138d751acd0458d46ca8b62.jpg\",\n  \"243224.jpg\": \"Aves/Sula leucogaster/995fcd7093c0cbdb248bc59726716c60.jpg\",\n  \"243229.jpg\": \"Aves/Sula leucogaster/4af5553288075860b68ddecb6f6bd743.jpg\",\n  \"243230.jpg\": \"Aves/Sula leucogaster/1a06124912685c4ca3488ff6ca81c087.jpg\",\n  \"243231.jpg\": \"Aves/Sula leucogaster/6e95f9f1106771a7275ceb2908a08636.jpg\",\n  \"243236.jpg\": \"Aves/Sula leucogaster/f07ccf6a3ca6aa3b6188db8d87b90690.jpg\",\n  \"243237.jpg\": \"Aves/Sula leucogaster/67239fd7d6f7a73461fc2f8f96ec7755.jpg\",\n  \"24325.jpg\": \"Aves/Setophaga occidentalis/cba41d7bebadf0b0bec2241f98a891ee.jpg\",\n  \"24328.jpg\": \"Aves/Setophaga occidentalis/7101162a60fc7220860ef0049b00a6ab.jpg\",\n  \"24331.jpg\": \"Aves/Setophaga occidentalis/09c7c6f5738fa3673a99788ba2ebe04e.jpg\",\n  \"243318.jpg\": \"Insecta/Bombus bimaculatus/b74cb76816fd928196d2c7779bd0a852.jpg\",\n  \"243319.jpg\": \"Insecta/Bombus bimaculatus/63db02d31bc08b4867597b71956ce3fa.jpg\",\n  \"243320.jpg\": \"Insecta/Bombus bimaculatus/065a1b5195ca411cdaa0349d915c8cf0.jpg\",\n  \"243321.jpg\": \"Insecta/Bombus bimaculatus/36026e3af36bdfe650b6a4a412717a95.jpg\",\n  \"243322.jpg\": \"Insecta/Bombus bimaculatus/c67ee9f0cc0e044b3a38c3477eab2b12.jpg\",\n  \"243323.jpg\": \"Insecta/Bombus bimaculatus/1e5224fd8d286eef22dbfc9cca0976e1.jpg\",\n  \"243324.jpg\": \"Insecta/Bombus bimaculatus/cfc5add1b810a99ba773166089b83190.jpg\",\n  \"243326.jpg\": \"Insecta/Bombus bimaculatus/001292548d4871531ab883a6cb96b22e.jpg\",\n  \"243327.jpg\": \"Insecta/Bombus bimaculatus/f0f038b843213eda06692a26b39dd40f.jpg\",\n  \"243328.jpg\": \"Insecta/Bombus bimaculatus/0026a39b18059b0256608b81d39459be.jpg\",\n  \"243329.jpg\": \"Insecta/Bombus bimaculatus/1ed3838220941b50b7f9fa33a8ede598.jpg\",\n  \"243336.jpg\": \"Insecta/Bombus bimaculatus/b4bfb54ae6f58feab6f61a0344c179ee.jpg\",\n  \"243337.jpg\": \"Insecta/Bombus bimaculatus/bf0f6f4e28a6b2d27b359ab3d01165cc.jpg\",\n  \"24334.jpg\": \"Aves/Setophaga occidentalis/52720d0086192515b430561f20c7dfba.jpg\",\n  \"243342.jpg\": \"Insecta/Bombus bimaculatus/e32b85f5ddbaf5f937fd82af63ddbf55.jpg\",\n  \"243343.jpg\": \"Insecta/Bombus bimaculatus/35ece1bbb0ef7d8091d225446465489b.jpg\",\n  \"243347.jpg\": \"Insecta/Bombus bimaculatus/ad7c40d404376e7a5776b297805e2474.jpg\",\n  \"243348.jpg\": \"Insecta/Bombus bimaculatus/ce8c0fb0444d836b4abfa989e874a4b8.jpg\",\n  \"243349.jpg\": \"Insecta/Bombus bimaculatus/588eeedc2b1db246a6993a26ee83bc10.jpg\",\n  \"243352.jpg\": \"Insecta/Bombus bimaculatus/9cf0126f3a9a94e72c9461fbe774bb02.jpg\",\n  \"243353.jpg\": \"Insecta/Bombus bimaculatus/1ea96007a6db0da9fc2722c04086217e.jpg\",\n  \"243354.jpg\": \"Insecta/Bombus bimaculatus/9a08646b71912de767b193a166b736e0.jpg\",\n  \"243364.jpg\": \"Insecta/Mallodon dasystomus/6cc81032d58e97e657856c6632cf2519.jpg\",\n  \"243369.jpg\": \"Insecta/Mallodon dasystomus/9d53cdc4011d2976418fdee7c8381b80.jpg\",\n  \"243371.jpg\": \"Insecta/Mallodon dasystomus/bd9a01dbc9ba00967e75806e9edb5ac2.jpg\",\n  \"24346.jpg\": \"Aves/Setophaga occidentalis/c1a4477d30f1e85446725854c36e5c18.jpg\",\n  \"243464.jpg\": \"Reptilia/Crotalus cerastes/3c6de749b437bcf846a344078441789f.jpg\",\n  \"243467.jpg\": \"Reptilia/Crotalus cerastes/0bde155d22c875b8cefc563ef88846f7.jpg\",\n  \"243470.jpg\": \"Reptilia/Crotalus cerastes/41f3218f6b3690ec4328df4e5fb400e1.jpg\",\n  \"243474.jpg\": \"Reptilia/Crotalus cerastes/8b2a35bff46cdb12eda4fbde379718b9.jpg\",\n  \"243477.jpg\": \"Reptilia/Crotalus cerastes/85e187cd9499174b2b6f0e8acab8fd5d.jpg\",\n  \"243479.jpg\": \"Reptilia/Crotalus cerastes/2802e7f72afbcd2b8995c6c937f36d86.jpg\",\n  \"243583.jpg\": \"Mollusca/Mytilus edulis/7f3ef39b0cb66637ef6c36be88322d34.jpg\",\n  \"243585.jpg\": \"Mollusca/Mytilus edulis/d2969ba21090bcd40431a1c6e54f0d61.jpg\",\n  \"243586.jpg\": \"Mollusca/Mytilus edulis/4a7d1c4bfdf2670bd3b72439cc5d4e78.jpg\",\n  \"243588.jpg\": \"Mollusca/Mytilus edulis/d08051adaa27b3764995bb63996fc3b2.jpg\",\n  \"243591.jpg\": \"Mollusca/Mytilus edulis/c0d1ff1cf23357e3b14aa44578e3f726.jpg\",\n  \"243592.jpg\": \"Mollusca/Mytilus edulis/50c071551cee5a752533c9f3adb92760.jpg\",\n  \"243593.jpg\": \"Mollusca/Mytilus edulis/23b6970ce8be0631e09d63b721854412.jpg\",\n  \"243596.jpg\": \"Mollusca/Mytilus edulis/c2ef191b96b5c2f5be3aa16f62ce1e3e.jpg\",\n  \"243597.jpg\": \"Mollusca/Mytilus edulis/ebcbe2a4f5f0c0bfb1b51685de963e2a.jpg\",\n  \"243601.jpg\": \"Mollusca/Mytilus edulis/8f4ac2725b6b0f227b392521ef12d269.jpg\",\n  \"243605.jpg\": \"Mollusca/Mytilus edulis/e00e499c5321689840398789f22f3ac6.jpg\",\n  \"243613.jpg\": \"Aves/Spizella pusilla/caf12f38c0bde4dc96b10edb2b640765.jpg\",\n  \"243620.jpg\": \"Aves/Spizella pusilla/c7f75a24d27cf4d13397d762956d2db9.jpg\",\n  \"243627.jpg\": \"Aves/Spizella pusilla/bf179cc62e0209dde6853d418dd1167f.jpg\",\n  \"243628.jpg\": \"Aves/Spizella pusilla/24133acd011d0b8862835bdc5e438e86.jpg\",\n  \"243629.jpg\": \"Aves/Spizella pusilla/af55f6812b81b273b2631eb1a64ba21b.jpg\",\n  \"243654.jpg\": \"Aves/Spizella pusilla/f965830bd6a670b29967005049e8540d.jpg\",\n  \"243656.jpg\": \"Aves/Spizella pusilla/a955379cb0c3ac89eb0a934e8ed6cf2d.jpg\",\n  \"243657.jpg\": \"Aves/Spizella pusilla/25c5ae022d596c44be1e866981cc954c.jpg\",\n  \"243658.jpg\": \"Aves/Spizella pusilla/5544e33d3523320ef1aef8475f40c21a.jpg\",\n  \"243659.jpg\": \"Aves/Spizella pusilla/1c4397a004cc39d76318c4b0e44a1074.jpg\",\n  \"243663.jpg\": \"Aves/Spizella pusilla/848bee04a0acd4684bca7e39b8d8feef.jpg\",\n  \"243666.jpg\": \"Aves/Spizella pusilla/6f9cb780a51d8b3335988e8aa1cfe3fd.jpg\",\n  \"243667.jpg\": \"Aves/Spizella pusilla/45cf5b3f6fd7dc755dae21eae07652d3.jpg\",\n  \"243668.jpg\": \"Aves/Spizella pusilla/b17e95c35019ff67cb9481cc7a87b73e.jpg\",\n  \"243673.jpg\": \"Aves/Spizella pusilla/b14dd39d160b3e6f1a122f9de4bf1946.jpg\",\n  \"243689.jpg\": \"Aves/Spizella pusilla/1ea61f41c27da8d96eed010cbf9429f1.jpg\",\n  \"243696.jpg\": \"Aves/Spizella pusilla/4f7ce844dff1d6b83eeab2300176b34e.jpg\",\n  \"243699.jpg\": \"Aves/Spizella pusilla/1a4d9c1af31de83da0da9eded2b67f19.jpg\",\n  \"243711.jpg\": \"Aves/Spizella pusilla/c37ac534a16b29152ae3eda4c4b83533.jpg\",\n  \"243718.jpg\": \"Aves/Spizella pusilla/f225afc63024d6f78fc5b6670e4da8b3.jpg\",\n  \"243726.jpg\": \"Aves/Spizella pusilla/c282684e8e55bea22bcb5272e1224fbb.jpg\",\n  \"243729.jpg\": \"Reptilia/Gekko gecko/9a259b54d4cf0aa3189859ac2e274c48.jpg\",\n  \"243730.jpg\": \"Reptilia/Gekko gecko/9c2489e5567d512e8818b82972dd6b50.jpg\",\n  \"243733.jpg\": \"Reptilia/Gekko gecko/dc485dcdc4e9493fcfa8600942ef49cb.jpg\",\n  \"24376.jpg\": \"Insecta/Bombus griseocollis/0931abdaaef2984536ff5c8808f25fd2.jpg\",\n  \"24383.jpg\": \"Insecta/Bombus griseocollis/33d555985fdeb70008e0effa74ee8f7f.jpg\",\n  \"24384.jpg\": \"Insecta/Bombus griseocollis/8ae4eae7e49db9243e498d0d00ace8be.jpg\",\n  \"24385.jpg\": \"Insecta/Bombus griseocollis/dd7ebff0b73caaf3c19658651292e91e.jpg\",\n  \"24386.jpg\": \"Insecta/Bombus griseocollis/e7eda397a92c26fd9aacd3ca4885a8b4.jpg\",\n  \"24387.jpg\": \"Insecta/Bombus griseocollis/eb4bf5db054374776829e254ef2bd971.jpg\",\n  \"24388.jpg\": \"Insecta/Bombus griseocollis/3471fbda75c158f274f7b380d396976d.jpg\",\n  \"24389.jpg\": \"Insecta/Bombus griseocollis/d79eae21fab6db892896b714e6d2b132.jpg\",\n  \"24390.jpg\": \"Insecta/Bombus griseocollis/d70b75a8f5ad6f8b7378fff5e5b978ca.jpg\",\n  \"24393.jpg\": \"Insecta/Bombus griseocollis/cf29a1bae07d2fc81a54485e0d0ecbc0.jpg\",\n  \"243945.jpg\": \"Insecta/Hippodamia variegata/80487328b61a9295cd4b2e69107d276a.jpg\",\n  \"243948.jpg\": \"Insecta/Hippodamia variegata/97eb6df52c96ba75d7dfbf9c6b820374.jpg\",\n  \"24395.jpg\": \"Insecta/Bombus griseocollis/2878c90d964558516a83b98e5f9cf838.jpg\",\n  \"243951.jpg\": \"Insecta/Hippodamia variegata/9a2bafabb0d2139369defe34bb6113ac.jpg\",\n  \"243956.jpg\": \"Insecta/Hippodamia variegata/0c6f4947eb72c9b3be091afa93ca0e3a.jpg\",\n  \"24396.jpg\": \"Insecta/Bombus griseocollis/505716bc5505a4a917871bbc3abf47c7.jpg\",\n  \"243966.jpg\": \"Aves/Chordeiles acutipennis/12f0f9eafb9244a8580c941093a09752.jpg\",\n  \"243975.jpg\": \"Aves/Chordeiles acutipennis/17f71efe05ec253c28f023ff3f6982d3.jpg\",\n  \"243983.jpg\": \"Aves/Chordeiles acutipennis/e45cfaec9d046b8aaa71ea4b4cf764c5.jpg\",\n  \"243987.jpg\": \"Aves/Chordeiles acutipennis/87ec30f56bf8591a57aeea239d0413bf.jpg\",\n  \"243988.jpg\": \"Aves/Anhinga novaehollandiae/91ca0495789f309dc843bd22706b3bc0.jpg\",\n  \"243992.jpg\": \"Aves/Anhinga novaehollandiae/9b4341d8fabdbbd1940dfbb8240253e9.jpg\",\n  \"243994.jpg\": \"Aves/Anhinga novaehollandiae/b043eebf7518bf88a5e1cd54e6d99d4a.jpg\",\n  \"243998.jpg\": \"Aves/Anhinga novaehollandiae/f83025495bff0dd6217e810e759a0385.jpg\",\n  \"244001.jpg\": \"Aves/Anhinga novaehollandiae/9554c27f6163c2df0892aff7b82df953.jpg\",\n  \"244005.jpg\": \"Aves/Anhinga novaehollandiae/b292bda762e48fa7fc81429b1ac7135e.jpg\",\n  \"244010.jpg\": \"Insecta/Hypena madefactalis/2afc765a90769e08d14f4858516235e6.jpg\",\n  \"244011.jpg\": \"Insecta/Hypena madefactalis/bf9a32a362365c661030ebd2892135c5.jpg\",\n  \"244012.jpg\": \"Insecta/Hypena madefactalis/807e52b99a7709cb6677e030e813f9dc.jpg\",\n  \"244021.jpg\": \"Aves/Falco femoralis/748af2b00d0327f8e6484727e7a5a9a7.jpg\",\n  \"244022.jpg\": \"Aves/Falco femoralis/c39e950fa031945371c99b213d987e42.jpg\",\n  \"244027.jpg\": \"Aves/Falco femoralis/8ebd46f3b05b9a788aa7f49939dfd16d.jpg\",\n  \"244036.jpg\": \"Aves/Falco femoralis/3cc6035880f8303c013eb228be31457f.jpg\",\n  \"24404.jpg\": \"Insecta/Bombus griseocollis/d957db672189e88c84371ff3365ba797.jpg\",\n  \"244045.jpg\": \"Insecta/Elophila obliteralis/0800d4f68efba1516b59a96b171b2588.jpg\",\n  \"244047.jpg\": \"Insecta/Elophila obliteralis/28f750d1e25afd189256d14b0b85557b.jpg\",\n  \"24405.jpg\": \"Insecta/Bombus griseocollis/51971dc212386f69390130e7eff3fa8c.jpg\",\n  \"244059.jpg\": \"Insecta/Elophila obliteralis/4e3ca1cfc68240ca7ae85bb5a496b36e.jpg\",\n  \"244061.jpg\": \"Insecta/Elophila obliteralis/884041d404588124ecf4ae0700cbb802.jpg\",\n  \"244064.jpg\": \"Insecta/Elophila obliteralis/daa95c4b352736d459739402836ab037.jpg\",\n  \"244065.jpg\": \"Insecta/Elophila obliteralis/24dd89afb1ef60c207cda2457b14c6c5.jpg\",\n  \"244086.jpg\": \"Insecta/Cisseps fulvicollis/0af946e2b084f80121c138fb1979e012.jpg\",\n  \"244090.jpg\": \"Insecta/Cisseps fulvicollis/13ff1c4af00d6102180e3ed8816eacf9.jpg\",\n  \"244094.jpg\": \"Insecta/Cisseps fulvicollis/f781af12da8d0bb627d3103788b46f83.jpg\",\n  \"244096.jpg\": \"Insecta/Cisseps fulvicollis/2f2eb8df8cc0faca42b27cc82b6217c5.jpg\",\n  \"244098.jpg\": \"Insecta/Cisseps fulvicollis/26780592856be4d4d83af7c3b74e0809.jpg\",\n  \"24410.jpg\": \"Insecta/Bombus griseocollis/34e9b940c563ff9f15a97d3a26e39d0e.jpg\",\n  \"24411.jpg\": \"Insecta/Bombus griseocollis/6c9e4db4f277a87fe3bcd2dd119782d3.jpg\",\n  \"244110.jpg\": \"Insecta/Cisseps fulvicollis/c3e2818f5b6861c1c0e59c40db9ac8f3.jpg\",\n  \"244112.jpg\": \"Insecta/Cisseps fulvicollis/3c746a7e8473f02e8efade217d97004a.jpg\",\n  \"244122.jpg\": \"Insecta/Cisseps fulvicollis/c2c18f7d3601579791eb218ec48abd6e.jpg\",\n  \"244124.jpg\": \"Insecta/Cisseps fulvicollis/3cc0faca3090a1a1b13017bd105a40f9.jpg\",\n  \"244125.jpg\": \"Insecta/Cisseps fulvicollis/616283ab442e03937425ea423ee289ac.jpg\",\n  \"24414.jpg\": \"Insecta/Bombus griseocollis/155cc6abb734b61437892ab89cb55177.jpg\",\n  \"244144.jpg\": \"Insecta/Cisseps fulvicollis/aa0c4cb5e3bfeb8123d6b3f9e542e71e.jpg\",\n  \"244149.jpg\": \"Insecta/Cisseps fulvicollis/62028cca5bcf49e3277d99bdc4d19604.jpg\",\n  \"24415.jpg\": \"Insecta/Bombus griseocollis/d0984c59776abd9d180cee9d2e8f4810.jpg\",\n  \"244153.jpg\": \"Insecta/Cisseps fulvicollis/f8d4e4eb846e06c9117d157532b0859f.jpg\",\n  \"244155.jpg\": \"Insecta/Cisseps fulvicollis/a30334c9379c9436dc7713fbb428a595.jpg\",\n  \"24416.jpg\": \"Insecta/Bombus griseocollis/478439eeaacc7b7cacf8e68cdd9090f2.jpg\",\n  \"244166.jpg\": \"Insecta/Cisseps fulvicollis/73188c2f27aa6b87c3a1d58cdc0d2b4a.jpg\",\n  \"244167.jpg\": \"Insecta/Cisseps fulvicollis/99ec1358850483f817c4d3d547186c6d.jpg\",\n  \"24417.jpg\": \"Insecta/Bombus griseocollis/253066b7f357738c78b332c67ed917a8.jpg\",\n  \"244173.jpg\": \"Insecta/Cisseps fulvicollis/0fb8d02e80b614058ef73a699ce6d1aa.jpg\",\n  \"244176.jpg\": \"Insecta/Cisseps fulvicollis/72607862ff78e5b3e5c9fa210f30c653.jpg\",\n  \"24418.jpg\": \"Insecta/Bombus griseocollis/ff1ac68a5e402c58df912001d2cc5685.jpg\",\n  \"244181.jpg\": \"Insecta/Cisseps fulvicollis/7782b8cf5fd320dc54a72cab5ed28e68.jpg\",\n  \"244187.jpg\": \"Insecta/Cisseps fulvicollis/88da84053645e57d2a1d30a04f387fe6.jpg\",\n  \"244188.jpg\": \"Insecta/Cisseps fulvicollis/ec7a99db8bf49c357b1dba4375b9509b.jpg\",\n  \"244189.jpg\": \"Insecta/Cisseps fulvicollis/3952c7b35ef238b6944c986a90fe3bb7.jpg\",\n  \"24419.jpg\": \"Insecta/Bombus griseocollis/6892a486e6f61b149b25f41f27e5bce8.jpg\",\n  \"244193.jpg\": \"Insecta/Cisseps fulvicollis/cd2fe4ac87c31e0702d18c41b29f3900.jpg\",\n  \"244194.jpg\": \"Aves/Vireo plumbeus/0735b228c20ea101acfb92c94be1445e.jpg\",\n  \"244198.jpg\": \"Aves/Vireo plumbeus/30e6167df6d9439c5125795e9fd1997c.jpg\",\n  \"24420.jpg\": \"Insecta/Bombus griseocollis/d48903cd1c8c1dc8d7c1e2fa188af23c.jpg\",\n  \"244202.jpg\": \"Aves/Vireo plumbeus/6b8a514b7ee78769e0842cd6ec68af39.jpg\",\n  \"244280.jpg\": \"Aves/Columbina inca/b3a6f78b1934864e5bcf4499b533266a.jpg\",\n  \"24430.jpg\": \"Insecta/Bombus griseocollis/b44467913c24afb813c37a249ce24a7f.jpg\",\n  \"24434.jpg\": \"Insecta/Manduca rustica/3af22e72f011e7243bf50c8351e2a233.jpg\",\n  \"244341.jpg\": \"Aves/Columbina inca/fcb89707eee64cf475092292e42afba6.jpg\",\n  \"244371.jpg\": \"Aves/Columbina inca/1369661c59aa9531188615445068efd2.jpg\",\n  \"244405.jpg\": \"Aves/Columbina inca/a91b291c679c27f48690cac0bcc402d5.jpg\",\n  \"244424.jpg\": \"Aves/Columbina inca/f8751569f98d355776ba28625c6566b7.jpg\",\n  \"24444.jpg\": \"Insecta/Manduca rustica/f31056c2b1f99f27d3ba199dca16dd9f.jpg\",\n  \"244443.jpg\": \"Aves/Columbina inca/c3449533fd25dbbaaf7ca88322be2005.jpg\",\n  \"244468.jpg\": \"Aves/Columbina inca/4e06f79e18d938d3f4acd4cd92ff359a.jpg\",\n  \"24447.jpg\": \"Insecta/Manduca rustica/638f60d16671517995b3d1dcb8b2f4bd.jpg\",\n  \"244475.jpg\": \"Aves/Columbina inca/e83edd02292610815c77a4943cbc7799.jpg\",\n  \"24450.jpg\": \"Insecta/Manduca rustica/f77add5492d2013d9443b3b3c5aaf51c.jpg\",\n  \"24451.jpg\": \"Insecta/Manduca rustica/639530e44769ca37bc6e684ee754c745.jpg\",\n  \"244521.jpg\": \"Aves/Columbina inca/95444c72351a73943be5675646f06806.jpg\",\n  \"244522.jpg\": \"Aves/Columbina inca/1acfa5c424639bea197dd5a0a0248200.jpg\",\n  \"244540.jpg\": \"Aves/Columbina inca/077072c84a58eed4126a2ea056ca8e43.jpg\",\n  \"244542.jpg\": \"Aves/Columbina inca/342e54339425350a191131e07b0f00b2.jpg\",\n  \"24456.jpg\": \"Arachnida/Dolomedes tenebrosus/1e0b6fdefbd7288e56422e0f52010ae8.jpg\",\n  \"24457.jpg\": \"Arachnida/Dolomedes tenebrosus/b3ee69e902771573a7ebb61e9d072abf.jpg\",\n  \"244576.jpg\": \"Aves/Columbina inca/acc76ebd02f322a7393b4c1c8ec5b31a.jpg\",\n  \"24458.jpg\": \"Arachnida/Dolomedes tenebrosus/b5287eee53eba2264ee097f6e1a4c532.jpg\",\n  \"244607.jpg\": \"Aves/Columbina inca/3b698eaa62d8c15dac60a5f8bd833326.jpg\",\n  \"24462.jpg\": \"Arachnida/Dolomedes tenebrosus/96e0dd932bca225684488a7fce40b1ee.jpg\",\n  \"244625.jpg\": \"Aves/Columbina inca/9b564b9a8e782f38427871f682c55baa.jpg\",\n  \"244661.jpg\": \"Aves/Cardellina rubra/ac1395a6be2a3c9a601a207b49217fce.jpg\",\n  \"244662.jpg\": \"Aves/Cardellina rubra/1bc3eb20f9bcc48982935261df5de4c3.jpg\",\n  \"244663.jpg\": \"Aves/Cardellina rubra/495074f13427c628ca4d31a4aa9543f0.jpg\",\n  \"244666.jpg\": \"Aves/Cardellina rubra/28058980162fd09d3594380ad9dda609.jpg\",\n  \"244676.jpg\": \"Actinopterygii/Salvelinus fontinalis/5aa3df4dcf67648100c3557acc37d07d.jpg\",\n  \"244677.jpg\": \"Actinopterygii/Salvelinus fontinalis/042c80b2d3f573de4c854d89c3d8cd8b.jpg\",\n  \"244680.jpg\": \"Actinopterygii/Salvelinus fontinalis/95d18b106d37674084e4c469e34ad56f.jpg\",\n  \"244685.jpg\": \"Actinopterygii/Salvelinus fontinalis/b31850c6e1ccd7fc8b5a70174e886ee0.jpg\",\n  \"244692.jpg\": \"Actinopterygii/Salvelinus fontinalis/519d037c86cd4588dffa00279855b0c1.jpg\",\n  \"244693.jpg\": \"Actinopterygii/Salvelinus fontinalis/b04bec10c1f5f9069c385216ff504423.jpg\",\n  \"244705.jpg\": \"Actinopterygii/Salvelinus fontinalis/d08025574b054fa354d3c520cfa7f394.jpg\",\n  \"244707.jpg\": \"Actinopterygii/Salvelinus fontinalis/f2281bbefd4d0f4cb301de24ddc91c8f.jpg\",\n  \"244709.jpg\": \"Actinopterygii/Salvelinus fontinalis/39f8ba45b52878f3cac9f9e0673181e0.jpg\",\n  \"244715.jpg\": \"Actinopterygii/Salvelinus fontinalis/906511f5705883c4442e525fd2d4f08b.jpg\",\n  \"244719.jpg\": \"Actinopterygii/Salvelinus fontinalis/402537db54cd197490962f1275bc5696.jpg\",\n  \"244722.jpg\": \"Actinopterygii/Salvelinus fontinalis/f7cec9059109a4766b62ba105cb49cfe.jpg\",\n  \"244723.jpg\": \"Actinopterygii/Salvelinus fontinalis/25680fa579d51915f079d66eebb10ff9.jpg\",\n  \"244725.jpg\": \"Actinopterygii/Salvelinus fontinalis/dcbde4bf44d1d7e087339e1da89b871c.jpg\",\n  \"244726.jpg\": \"Actinopterygii/Salvelinus fontinalis/1d4b5ea4782fa31f59b27be7bb621d47.jpg\",\n  \"244727.jpg\": \"Actinopterygii/Salvelinus fontinalis/6d2303f0d1014d070238fa1088f3b6ca.jpg\",\n  \"244728.jpg\": \"Actinopterygii/Salvelinus fontinalis/a7ed1cac712840e2bd759672e0b30799.jpg\",\n  \"244734.jpg\": \"Amphibia/Lithobates berlandieri/14fc6406b5f3fa9e955a9823db1ad612.jpg\",\n  \"244735.jpg\": \"Amphibia/Lithobates berlandieri/6ef76ca7247ef2942c94be3df6ac2a49.jpg\",\n  \"244751.jpg\": \"Amphibia/Lithobates berlandieri/c9e756381aa55b589cce2d9c3b857cc8.jpg\",\n  \"24476.jpg\": \"Arachnida/Dolomedes tenebrosus/06aaba9a5965b9d35baa7cd011d76dc0.jpg\",\n  \"244760.jpg\": \"Amphibia/Lithobates berlandieri/f01a5829ad50ef44d4e11a0928ebfcd6.jpg\",\n  \"244761.jpg\": \"Amphibia/Lithobates berlandieri/5531b186ffd091c63410bc0abe0fab1f.jpg\",\n  \"244762.jpg\": \"Amphibia/Lithobates berlandieri/0f9f59ef83f3d1117b79c431c04cc700.jpg\",\n  \"244763.jpg\": \"Amphibia/Lithobates berlandieri/20bcd1c8151884340b8321744938b108.jpg\",\n  \"24478.jpg\": \"Arachnida/Dolomedes tenebrosus/bc49feea79367626a0f01a3d31e0ea18.jpg\",\n  \"244781.jpg\": \"Amphibia/Lithobates berlandieri/2a7c949a8525265fd05e3093900ae452.jpg\",\n  \"244784.jpg\": \"Amphibia/Lithobates berlandieri/f92cc659856d0b554e51662efaa5aac2.jpg\",\n  \"244785.jpg\": \"Amphibia/Lithobates berlandieri/1b09ba7f94dd19d8209135085dc51cf1.jpg\",\n  \"244793.jpg\": \"Amphibia/Lithobates berlandieri/35e41325381235fdbb75cfcc4e76835d.jpg\",\n  \"244794.jpg\": \"Amphibia/Lithobates berlandieri/51593e221cac71a6662e7c2d8d001851.jpg\",\n  \"244797.jpg\": \"Amphibia/Lithobates berlandieri/5fd7c052b1fe71f09631ae64d94b67b7.jpg\",\n  \"244812.jpg\": \"Amphibia/Lithobates berlandieri/ebb9dc222baa527264666d76641a0600.jpg\",\n  \"24483.jpg\": \"Arachnida/Dolomedes tenebrosus/ab09804cf7219c48fe96f74fceedc790.jpg\",\n  \"244836.jpg\": \"Amphibia/Lithobates berlandieri/e58b7b30f2d212fa3065c93322c5341e.jpg\",\n  \"24484.jpg\": \"Insecta/Colias philodice/2d3630c01f9c20ebe403f1f36f65c7ca.jpg\",\n  \"244851.jpg\": \"Amphibia/Lithobates berlandieri/9a86ec8dae71e8c226bf58f5c83ccc5e.jpg\",\n  \"244869.jpg\": \"Amphibia/Lithobates berlandieri/213e1dc4f35ef5e8ca720f13b68e5fe9.jpg\",\n  \"244875.jpg\": \"Amphibia/Lithobates berlandieri/aeaa4da24c2c4f3d5eed440bd651ceb2.jpg\",\n  \"244880.jpg\": \"Amphibia/Lithobates berlandieri/be527fd55b828dbc891f0f4d7641e173.jpg\",\n  \"244891.jpg\": \"Amphibia/Lithobates berlandieri/b08a43a1a48387b991dcb3ab77ab086e.jpg\",\n  \"244893.jpg\": \"Amphibia/Lithobates berlandieri/53fcfd8d0e27066162f2b85dcafdc27b.jpg\",\n  \"24495.jpg\": \"Insecta/Colias philodice/c644d03121fbc05dd0ef8165a6bc09dc.jpg\",\n  \"244965.jpg\": \"Aves/Ciccaba virgata/2d843fda1f93060faffa42f0733664e2.jpg\",\n  \"244968.jpg\": \"Aves/Ciccaba virgata/1b6df1af3d6101e0c954dd6f5cd336bf.jpg\",\n  \"244970.jpg\": \"Aves/Ciccaba virgata/cdf8773c6acc5688d5f365bf121b6bd3.jpg\",\n  \"244974.jpg\": \"Insecta/Idia lubricalis/2bdeec688c241bfe3437c857fecb69dc.jpg\",\n  \"244975.jpg\": \"Insecta/Idia lubricalis/0db2ad8a2c27df316bbe1381a9abaf42.jpg\",\n  \"244980.jpg\": \"Insecta/Idia lubricalis/3c420cf3fab73b7e73d5756efe60b3f9.jpg\",\n  \"244982.jpg\": \"Insecta/Idia lubricalis/79d6d908035cb026eea1a0f9abdd9963.jpg\",\n  \"244987.jpg\": \"Insecta/Idia lubricalis/e6219f301b6448e46813a8b079cf4c7a.jpg\",\n  \"24499.jpg\": \"Insecta/Colias philodice/7cb01b1ac98bdd94463a554091db975a.jpg\",\n  \"244991.jpg\": \"Insecta/Idia lubricalis/c6b401d55a168271ef346269f1385117.jpg\",\n  \"244998.jpg\": \"Insecta/Asterocampa leilia/4fc4958b588a1e0e5c16a1bf02abd710.jpg\",\n  \"245001.jpg\": \"Insecta/Asterocampa leilia/b527e44683098afe88b08646b1d7df13.jpg\",\n  \"245003.jpg\": \"Insecta/Asterocampa leilia/509092d0d06abd2e32f1bc188948bf78.jpg\",\n  \"24501.jpg\": \"Insecta/Colias philodice/08e174232ba3ae41f494f641d0936d05.jpg\",\n  \"245010.jpg\": \"Insecta/Asterocampa leilia/5e329294c37a5d9552fa498bd5695331.jpg\",\n  \"245011.jpg\": \"Insecta/Asterocampa leilia/404ac4b5b2f05e37d914d4f6218acc53.jpg\",\n  \"245015.jpg\": \"Insecta/Asterocampa leilia/050d4c20eb08b9f212ecc5ead2e806b7.jpg\",\n  \"245016.jpg\": \"Insecta/Asterocampa leilia/83d4ff44a0943319e944201264530788.jpg\",\n  \"245017.jpg\": \"Insecta/Asterocampa leilia/f3733c9bcb3d5a9d3a96a5594f4696f3.jpg\",\n  \"24502.jpg\": \"Insecta/Colias philodice/05aa96524942b661965df5f55bfb436a.jpg\",\n  \"245021.jpg\": \"Insecta/Asterocampa leilia/ce98c1cf63bdd3c1c9e963894afacac6.jpg\",\n  \"245022.jpg\": \"Insecta/Asterocampa leilia/d2a31385d040a035832ab4f2d7b0078b.jpg\",\n  \"245035.jpg\": \"Aves/Columba livia domestica/d6d5d51010029cc0279dffdc6ab5abee.jpg\",\n  \"245061.jpg\": \"Aves/Columba livia domestica/3d60edfe08e30b89c3fee9920195e184.jpg\",\n  \"245077.jpg\": \"Aves/Columba livia domestica/a2f6509ff7f03b5d0515da7f79b5917f.jpg\",\n  \"24508.jpg\": \"Insecta/Colias philodice/8c42001334e0f50fe0da49a7783e2203.jpg\",\n  \"245089.jpg\": \"Aves/Columba livia domestica/0be383b1c393bf86e1386fe2cda3bcb3.jpg\",\n  \"24509.jpg\": \"Insecta/Colias philodice/c9ef77fd441d96a48813f2635f77a145.jpg\",\n  \"245093.jpg\": \"Aves/Columba livia domestica/76d3ed0609bc11f1174411b14f022e7f.jpg\",\n  \"24510.jpg\": \"Insecta/Colias philodice/ba858d88a073fafec3a6da7e07768907.jpg\",\n  \"245104.jpg\": \"Aves/Columba livia domestica/934adb27ffdfac024936b2f7c29b2f91.jpg\",\n  \"245125.jpg\": \"Aves/Columba livia domestica/407bae90e55f03892beeec6c02a2ec74.jpg\",\n  \"245136.jpg\": \"Aves/Columba livia domestica/ce30157bb2a479f725f9b48630843038.jpg\",\n  \"245142.jpg\": \"Aves/Columba livia domestica/d3cecc2314186a48a8d91a0b92e97bb2.jpg\",\n  \"245168.jpg\": \"Aves/Columba livia domestica/ecac1c8d2de81a43f5e4a76ec48f97a8.jpg\",\n  \"245169.jpg\": \"Aves/Columba livia domestica/ec52f9a132162213f2801fa3a43dec8a.jpg\",\n  \"245186.jpg\": \"Aves/Columba livia domestica/a610660a7f6893055bf8a4e21c0b87bd.jpg\",\n  \"245191.jpg\": \"Aves/Columba livia domestica/f42fcec11a11033f301a4d859d45c1c5.jpg\",\n  \"245222.jpg\": \"Insecta/Darapsa myron/a5e014301853913d2a837432ff20d335.jpg\",\n  \"245224.jpg\": \"Insecta/Darapsa myron/965d7bc7bfef5ae5e6d9738441b8661e.jpg\",\n  \"245225.jpg\": \"Insecta/Darapsa myron/4716ce372601d87f68713b99d96f7bfc.jpg\",\n  \"245228.jpg\": \"Insecta/Darapsa myron/35098f73428ff93321c6dbf80cfd5479.jpg\",\n  \"245232.jpg\": \"Insecta/Darapsa myron/d9bfd13efb817fecc2b28d87f9c2ec67.jpg\",\n  \"245234.jpg\": \"Insecta/Darapsa myron/2c249e175bb27acb94fe4364f674598d.jpg\",\n  \"245235.jpg\": \"Insecta/Darapsa myron/8a8463d392c1db8628725188d37bfad2.jpg\",\n  \"245237.jpg\": \"Insecta/Darapsa myron/6ed6ad395d5430c0cbf0c099789d2c78.jpg\",\n  \"24524.jpg\": \"Insecta/Colias philodice/56913ba0994996dcfa2457cb8898cc3d.jpg\",\n  \"245240.jpg\": \"Insecta/Darapsa myron/5776d7904e548f53f9a3bac5e549f6c0.jpg\",\n  \"245241.jpg\": \"Insecta/Darapsa myron/ac9d12c8382ce05180c66966dc443ec6.jpg\",\n  \"245242.jpg\": \"Insecta/Darapsa myron/3662577717d4c86723a18e2df71bfca1.jpg\",\n  \"245246.jpg\": \"Insecta/Darapsa myron/03c55e7e5134135b56303596b5ac24e7.jpg\",\n  \"24525.jpg\": \"Insecta/Colias philodice/9661bf9bdf43a326456df67d34d6fec0.jpg\",\n  \"245251.jpg\": \"Insecta/Darapsa myron/3bc1ccb3c59fa77ab46982397f8ac5ae.jpg\",\n  \"245253.jpg\": \"Insecta/Darapsa myron/88e1f913e0f8ff942fb951bfdefdf016.jpg\",\n  \"245254.jpg\": \"Insecta/Darapsa myron/fa4183106a364e6ec69e90853b04c88e.jpg\",\n  \"245256.jpg\": \"Insecta/Darapsa myron/76df547aeb1521bb1396d5f6d8992ceb.jpg\",\n  \"245261.jpg\": \"Insecta/Darapsa myron/d6b826c5be6a27b4668c984cf6ba866e.jpg\",\n  \"245262.jpg\": \"Insecta/Darapsa myron/ced526747da03b91cceb47fd0367c9bb.jpg\",\n  \"245269.jpg\": \"Insecta/Darapsa myron/96c8a7e488b16a87849702d7aea741f5.jpg\",\n  \"24545.jpg\": \"Insecta/Colias philodice/272344690fa5e853e5f126d41de05787.jpg\",\n  \"24546.jpg\": \"Insecta/Colias philodice/c3a26df50c7b98da5a5786a5a3ff13f3.jpg\",\n  \"24548.jpg\": \"Insecta/Colias philodice/2cdf054c59a8066af56689a051d90153.jpg\",\n  \"245495.jpg\": \"Mammalia/Felis catus/c842984b5033e9ff3a10d942e6a3bb94.jpg\",\n  \"245498.jpg\": \"Mammalia/Felis catus/c30c2a9d2a345e67d0bafea45e614a1e.jpg\",\n  \"245499.jpg\": \"Mammalia/Felis catus/fce99f31652f476d668ee94607ecd098.jpg\",\n  \"245505.jpg\": \"Mammalia/Felis catus/4c960c13983464679039f55ec49e7c1c.jpg\",\n  \"245509.jpg\": \"Mammalia/Felis catus/54018c12bd340693ec79da0eba7e7073.jpg\",\n  \"245513.jpg\": \"Mammalia/Felis catus/bd7550973828dc81fb3fcd500f86f810.jpg\",\n  \"245519.jpg\": \"Mammalia/Felis catus/85e22085cd37d95f974445dd0d14a78c.jpg\",\n  \"245524.jpg\": \"Mammalia/Felis catus/af7968bb2f40c2b93ed5a92c36f54259.jpg\",\n  \"245530.jpg\": \"Mammalia/Felis catus/376a57feb8553470283bfaf896c04063.jpg\",\n  \"245538.jpg\": \"Mammalia/Felis catus/d4faaa0d9874a9392db4b1d7cb609fa7.jpg\",\n  \"245543.jpg\": \"Mammalia/Felis catus/d6cf9a4a28b2e54cecf6f912fcf06806.jpg\",\n  \"245551.jpg\": \"Mammalia/Felis catus/b56609bdab6d8584b9714ee31882ddf7.jpg\",\n  \"245566.jpg\": \"Mammalia/Felis catus/641b4f427fccb4e19424887fccaffdd4.jpg\",\n  \"245567.jpg\": \"Mammalia/Felis catus/cf9fa546d5247a67e6cc1c3f540d828d.jpg\",\n  \"245568.jpg\": \"Mammalia/Felis catus/1b453e7fa42d229afa7a8def19e1317d.jpg\",\n  \"245573.jpg\": \"Mammalia/Felis catus/c3ba4933a2ee95c8f750a6a2600584df.jpg\",\n  \"24558.jpg\": \"Insecta/Colias philodice/9398f23e91492cd409d9ba178c2cf850.jpg\",\n  \"245617.jpg\": \"Insecta/Polites peckius/ec8e0d089576891cf37681a9f53b4f39.jpg\",\n  \"245620.jpg\": \"Insecta/Polites peckius/21f6fd4277976847fb59a62923f561c4.jpg\",\n  \"245621.jpg\": \"Insecta/Polites peckius/2007730a4dd910bc8846d4c9c6fe3941.jpg\",\n  \"245628.jpg\": \"Insecta/Polites peckius/58ed1f17da9ef7c3d2a5f0a32c6dd9ac.jpg\",\n  \"245636.jpg\": \"Insecta/Polites peckius/ae016c4dee2e8ba4137b088538aa9e35.jpg\",\n  \"245640.jpg\": \"Insecta/Polites peckius/6e8b6a1188e5f9dcec519fe5350dd581.jpg\",\n  \"245647.jpg\": \"Insecta/Polites peckius/12e310d3569403494430c1a581a4e6cd.jpg\",\n  \"245650.jpg\": \"Insecta/Polites peckius/e915b70c759cc4884ed44369a458c299.jpg\",\n  \"245651.jpg\": \"Insecta/Polites peckius/b6600e661d5120f44c56b5366bd6fd70.jpg\",\n  \"245666.jpg\": \"Insecta/Polites peckius/2f3223075834a97ad27fbc6a38ccb717.jpg\",\n  \"245687.jpg\": \"Insecta/Polites peckius/4857ee6d54e070b9280de9627bdf74f0.jpg\",\n  \"245688.jpg\": \"Insecta/Polites peckius/51fc5bcc1362066dfbda9c3d149f7035.jpg\",\n  \"24570.jpg\": \"Insecta/Colias philodice/eabe9e5d1d33601feddb20882c2e462c.jpg\",\n  \"245700.jpg\": \"Insecta/Polites peckius/4c388e2c2c2d8a08b7f1e5fc0991a0e8.jpg\",\n  \"245702.jpg\": \"Insecta/Polites peckius/061680bf44d581af91447422e870d28c.jpg\",\n  \"245708.jpg\": \"Insecta/Polites peckius/ee6a91822161d40b9ae5f7f3c67869c4.jpg\",\n  \"245709.jpg\": \"Insecta/Polites peckius/28387860bc896c215cf663856fa78705.jpg\",\n  \"245713.jpg\": \"Insecta/Polites peckius/b06ec010b2d8ace0dd798120e4820d3d.jpg\",\n  \"245722.jpg\": \"Insecta/Polites peckius/1b15d387b0c08d4d96d56bcbc967398c.jpg\",\n  \"245723.jpg\": \"Insecta/Polites peckius/b631165a829d8e9b7f8963a5b8d6c5b9.jpg\",\n  \"245729.jpg\": \"Insecta/Polites peckius/c6a092be977c6283852b5c0f9485f1ad.jpg\",\n  \"24573.jpg\": \"Insecta/Colias philodice/e927f8e263477eb6c06991d77ab2cd9b.jpg\",\n  \"245733.jpg\": \"Insecta/Polites peckius/c7577a1643b5158597ccc16fb975e60c.jpg\",\n  \"24576.jpg\": \"Insecta/Colias philodice/6670f387723cfc9c91cd351e8bf351de.jpg\",\n  \"245779.jpg\": \"Insecta/Argia immunda/fc11bbab7c2588c5d1588a36114bf3cb.jpg\",\n  \"245780.jpg\": \"Insecta/Argia immunda/8595e0ae7bbcf233b3dfec1927ebbf51.jpg\",\n  \"245789.jpg\": \"Insecta/Argia immunda/8a1ac05b5f012812919f4b047f896649.jpg\",\n  \"245830.jpg\": \"Insecta/Argia immunda/bcd5756ed6089e4104987910ca6b0518.jpg\",\n  \"245831.jpg\": \"Insecta/Argia immunda/15064303805485de32166dce678d51e0.jpg\",\n  \"245836.jpg\": \"Insecta/Argia immunda/b2eb6653c99ef5e91ab75f16fe52757f.jpg\",\n  \"245842.jpg\": \"Insecta/Argia immunda/a679cd3f9b0344c707a2f13049f39f4f.jpg\",\n  \"245845.jpg\": \"Insecta/Argia immunda/a846f77a89ab7039cf48c8f769a6fed7.jpg\",\n  \"245846.jpg\": \"Insecta/Argia immunda/56d1f0cd323a95d5f4ed0c9cccf084d8.jpg\",\n  \"24585.jpg\": \"Insecta/Colias philodice/1107ffcd37b3f491071160af4604b59b.jpg\",\n  \"245853.jpg\": \"Insecta/Argia immunda/8f662e4ec6ea3ce72d1e9d15a40fd53f.jpg\",\n  \"245861.jpg\": \"Insecta/Argia immunda/8ce4aff4429c1003600dbec2318a2af0.jpg\",\n  \"245862.jpg\": \"Insecta/Argia immunda/18edcd45bf15c73f17cd54e355c214e2.jpg\",\n  \"245868.jpg\": \"Insecta/Argia immunda/6e762163500308a2d9b94163c0efc382.jpg\",\n  \"245901.jpg\": \"Insecta/Argia immunda/b2bcbe15c94391fd1b3e98af319f44f0.jpg\",\n  \"245902.jpg\": \"Insecta/Argia immunda/f4119fa2ae763e6007307d0601c6bdee.jpg\",\n  \"245903.jpg\": \"Insecta/Argia immunda/dd68ac8c32df62459360c0c99eaef3b9.jpg\",\n  \"24592.jpg\": \"Insecta/Colias philodice/098b764cf48838a46cf7085fd33f5b7e.jpg\",\n  \"245920.jpg\": \"Insecta/Argia immunda/6eecf488aafc73657453b8ed32a81236.jpg\",\n  \"245921.jpg\": \"Insecta/Argia immunda/02d738b24ce408563c4c982fec0ce3da.jpg\",\n  \"245949.jpg\": \"Amphibia/Plethodon albagula/a73cb6654032bc42085c45de6fccd310.jpg\",\n  \"245950.jpg\": \"Amphibia/Plethodon albagula/eb42123f6fe84a32481accc4ca69737f.jpg\",\n  \"245951.jpg\": \"Amphibia/Plethodon albagula/811546c248ea77dbe82aff89b5738873.jpg\",\n  \"245952.jpg\": \"Amphibia/Plethodon albagula/fc9ef9f188f5adc6d9e4d810e3d51b86.jpg\",\n  \"245955.jpg\": \"Amphibia/Plethodon albagula/c41e166f08102984bd5b7c5e7d90ef50.jpg\",\n  \"245956.jpg\": \"Amphibia/Plethodon albagula/2c1f4310e7a3027441138f2bb684c41e.jpg\",\n  \"245961.jpg\": \"Amphibia/Plethodon albagula/dfd75679350714b264231aae62ed7053.jpg\",\n  \"245968.jpg\": \"Amphibia/Plethodon albagula/a2a636ffadda886d4f7f3193e35d089e.jpg\",\n  \"245969.jpg\": \"Amphibia/Plethodon albagula/77c6d603a57b67f1b3485b382f06cb09.jpg\",\n  \"245972.jpg\": \"Amphibia/Plethodon albagula/be2acaea44c72fa7191325f6f7561675.jpg\",\n  \"245975.jpg\": \"Amphibia/Plethodon albagula/59d950f6bc13e641d85264b71e517634.jpg\",\n  \"24598.jpg\": \"Mammalia/Myocastor coypus/3950f8e4bd2af868be489e65902d2ab1.jpg\",\n  \"245983.jpg\": \"Amphibia/Plethodon albagula/153ef101ead61ad51f8cf654a00b075e.jpg\",\n  \"245984.jpg\": \"Amphibia/Plethodon albagula/aa8c95949d5facc2ea8e1d96dc48ea0d.jpg\",\n  \"245985.jpg\": \"Amphibia/Plethodon albagula/d655e7009e42dfa91ed76c156e3177c8.jpg\",\n  \"245986.jpg\": \"Amphibia/Plethodon albagula/08a46847ef2ec6096670a79601e7c1b2.jpg\",\n  \"245988.jpg\": \"Amphibia/Plethodon albagula/144c860476e8b2e5e309178c6965b2e0.jpg\",\n  \"245990.jpg\": \"Amphibia/Plethodon albagula/ed1544c110e4d104e71cb18607aff0f3.jpg\",\n  \"245995.jpg\": \"Amphibia/Plethodon albagula/2175ef5409482e6bba656bcc12f77ae7.jpg\",\n  \"245996.jpg\": \"Amphibia/Plethodon albagula/e6375aa5f8061f230457e9de2cdf8ea2.jpg\",\n  \"245997.jpg\": \"Amphibia/Plethodon albagula/4352145ae74db363107c6c21514c8397.jpg\",\n  \"246006.jpg\": \"Aves/Pyrrhocorax graculus/2e22267c7c6d103e638dbe1c18837e5e.jpg\",\n  \"246017.jpg\": \"Aves/Pyrrhocorax graculus/15c1de88f8f1fddb66d9ea4d4e03a6f1.jpg\",\n  \"246023.jpg\": \"Aves/Pyrrhocorax graculus/9b4d6061af24ca6af52dcab3a8a2dd91.jpg\",\n  \"24605.jpg\": \"Mammalia/Myocastor coypus/506ecebe5e7a1b135d56fe4ba41893ea.jpg\",\n  \"246080.jpg\": \"Insecta/Bittacomorpha clavipes/a21c5cf26df1ba640cf791422a5ea14d.jpg\",\n  \"246081.jpg\": \"Insecta/Bittacomorpha clavipes/64732ff6e384ec6bafd44369b2fc1aaa.jpg\",\n  \"246082.jpg\": \"Insecta/Bittacomorpha clavipes/78c08bc4e479a82b82b4c5286e7a2b6f.jpg\",\n  \"246085.jpg\": \"Insecta/Bittacomorpha clavipes/f6c0b1891e62f09a642076adec4a73a0.jpg\",\n  \"246086.jpg\": \"Insecta/Bittacomorpha clavipes/08a98fa0ebb980f511a5ca68bf88ec73.jpg\",\n  \"246087.jpg\": \"Insecta/Bittacomorpha clavipes/58d22ef7e25c00f3c9a21d9dd68349f8.jpg\",\n  \"246091.jpg\": \"Insecta/Bittacomorpha clavipes/c7534eb9d1b3644a206b3e517d05a789.jpg\",\n  \"24610.jpg\": \"Mammalia/Myocastor coypus/7bb14d999908b4e32be15ee66f53f651.jpg\",\n  \"246151.jpg\": \"Aves/Pavo cristatus/022b8b92082a2529df6149da2ff8f22e.jpg\",\n  \"246152.jpg\": \"Aves/Pavo cristatus/0f30d853275880510fb75f5f21df2932.jpg\",\n  \"246153.jpg\": \"Aves/Pavo cristatus/f6feea61bb75367c24ec3ae42a883985.jpg\",\n  \"246158.jpg\": \"Aves/Pavo cristatus/2b6c09c0cac35631b173ebe1afcff5aa.jpg\",\n  \"246164.jpg\": \"Aves/Pavo cristatus/eecce0ff9b3daf5990997368b5ff9b70.jpg\",\n  \"246165.jpg\": \"Aves/Pavo cristatus/c576827907d87d7d1030c666193a824a.jpg\",\n  \"246166.jpg\": \"Aves/Pavo cristatus/d815950a6be1b6bc56e1853c90e3fcf3.jpg\",\n  \"246167.jpg\": \"Aves/Pavo cristatus/6f166f0aa40fbf738cdc32b02d092c3b.jpg\",\n  \"246171.jpg\": \"Aves/Pavo cristatus/1eddca5ac99f5a7051c9a13b7c9bd214.jpg\",\n  \"246172.jpg\": \"Aves/Pavo cristatus/131601c649243d6c5ad507e2c24a92d1.jpg\",\n  \"246173.jpg\": \"Aves/Pavo cristatus/91586ccf04d634c813c4a8704d51b962.jpg\",\n  \"246177.jpg\": \"Aves/Pavo cristatus/3abc8896a303328337651eff220e34fc.jpg\",\n  \"246181.jpg\": \"Aves/Pavo cristatus/63793007866cb72627ab2cc59b90c88c.jpg\",\n  \"246187.jpg\": \"Aves/Pavo cristatus/aabb3fb565a7dbfcb9a4095928d6cb63.jpg\",\n  \"246194.jpg\": \"Aves/Pavo cristatus/65d4766e788ff71944eb0a2cd7922f6c.jpg\",\n  \"246198.jpg\": \"Aves/Pavo cristatus/82b33c042b92f5a46a94573f9bca996c.jpg\",\n  \"246200.jpg\": \"Aves/Pavo cristatus/479bf857d6831048302119ba9251ddc0.jpg\",\n  \"246201.jpg\": \"Aves/Pavo cristatus/e955301272677b774182cd8ecbd1280a.jpg\",\n  \"246210.jpg\": \"Aves/Pavo cristatus/dcb6abbacdaf49026331bfba1f74d452.jpg\",\n  \"246218.jpg\": \"Aves/Pavo cristatus/fe34257aef6d764f16e3176a98d68add.jpg\",\n  \"24622.jpg\": \"Mammalia/Myocastor coypus/0f5cb0511f03bd5ab262a67dc5569da6.jpg\",\n  \"24628.jpg\": \"Mammalia/Myocastor coypus/c3992d3f523c83a4bac91612cfad7199.jpg\",\n  \"24636.jpg\": \"Mammalia/Myocastor coypus/282b0e6f5a666485df510160348cda9b.jpg\",\n  \"246381.jpg\": \"Aves/Xema sabini/9643af5d82b584743409d883720731ad.jpg\",\n  \"246385.jpg\": \"Aves/Xema sabini/126b08e3cdf7d6698d5693503703239c.jpg\",\n  \"246389.jpg\": \"Aves/Xema sabini/6a98fb0d3082983ce124e6d993b20e0b.jpg\",\n  \"246402.jpg\": \"Aves/Xema sabini/af1353e749db2d10295e297d99643122.jpg\",\n  \"246403.jpg\": \"Aves/Xema sabini/f431ea7a4901f56a78cd5da11e017381.jpg\",\n  \"246467.jpg\": \"Reptilia/Coluber constrictor/070c1936d425e8f6923038e912ad17b1.jpg\",\n  \"246472.jpg\": \"Reptilia/Coluber constrictor/1e2f230bef84773d651da483df3cf2ba.jpg\",\n  \"246481.jpg\": \"Reptilia/Coluber constrictor/76cdf41e11f500edd520bce39b49bc18.jpg\",\n  \"246482.jpg\": \"Reptilia/Coluber constrictor/cca73d967ae25fe47a8b8ee59ccd843c.jpg\",\n  \"246486.jpg\": \"Reptilia/Coluber constrictor/15e64cefc9f233ba3e8681d0bb833ef2.jpg\",\n  \"246487.jpg\": \"Reptilia/Coluber constrictor/607e0b5cc1979090781e243d2e283bf1.jpg\",\n  \"246488.jpg\": \"Reptilia/Coluber constrictor/4f34cb24d5b48cc3a91d170a225fa2ac.jpg\",\n  \"246505.jpg\": \"Reptilia/Coluber constrictor/f430bf3692c89dcd6b6f60b7abe27cba.jpg\",\n  \"246509.jpg\": \"Reptilia/Coluber constrictor/e1bc49e40ae4aa63ac69019f27992796.jpg\",\n  \"246511.jpg\": \"Reptilia/Coluber constrictor/f167b756e56f37522c7ab8cd7f25efa3.jpg\",\n  \"246512.jpg\": \"Reptilia/Coluber constrictor/3637939a83dac9003b49b7441552679c.jpg\",\n  \"246513.jpg\": \"Reptilia/Coluber constrictor/cbd7529390313f7467af652daf918093.jpg\",\n  \"246514.jpg\": \"Reptilia/Coluber constrictor/0f83354cbedff7a11792f3b49da77c03.jpg\",\n  \"246518.jpg\": \"Reptilia/Coluber constrictor/0a17153aee4fe9bde2f3aa6e8c2ae760.jpg\",\n  \"246519.jpg\": \"Reptilia/Coluber constrictor/ededcaab24f93e03943d5a87cf1992ad.jpg\",\n  \"246522.jpg\": \"Reptilia/Coluber constrictor/3e0dd60062ceb89a13781db539a5f84f.jpg\",\n  \"246523.jpg\": \"Reptilia/Coluber constrictor/9a66a91cf0d26189fe1cf6fdf1e4b208.jpg\",\n  \"246533.jpg\": \"Reptilia/Coluber constrictor/b01025dcc993f852b574ad5dd156f623.jpg\",\n  \"246534.jpg\": \"Reptilia/Coluber constrictor/ce238d4ea7f8dd4b02bd05c884281959.jpg\",\n  \"246535.jpg\": \"Reptilia/Coluber constrictor/d3e17ceac9c40f61c620c1492dc8a811.jpg\",\n  \"246543.jpg\": \"Reptilia/Coluber constrictor/3816ddab3371a050fcd8ad09e24914ba.jpg\",\n  \"246586.jpg\": \"Reptilia/Heloderma suspectum/dbbbdbda4ee38a651a16397263caa2ef.jpg\",\n  \"246587.jpg\": \"Reptilia/Heloderma suspectum/249e9d2bb2a7e4a49e1bea86a9bbccc5.jpg\",\n  \"246588.jpg\": \"Reptilia/Heloderma suspectum/348f08e37682e6b1e057bc69b57f3e01.jpg\",\n  \"246598.jpg\": \"Reptilia/Heloderma suspectum/3180ac17af1bc3545754a5a07685f9a8.jpg\",\n  \"246610.jpg\": \"Reptilia/Heloderma suspectum/63dfbbb6327331eda81e68d8afd77db9.jpg\",\n  \"246616.jpg\": \"Reptilia/Heloderma suspectum/31cd0c36e4d5c21f6e7591ccd0a91f67.jpg\",\n  \"246617.jpg\": \"Reptilia/Heloderma suspectum/ba04b20409916dda3ee79a55b59e70bb.jpg\",\n  \"246618.jpg\": \"Reptilia/Heloderma suspectum/fffc3346368915f852150e6a6658cb30.jpg\",\n  \"246619.jpg\": \"Reptilia/Heloderma suspectum/45409f716d516f3e8e02770ec0aeb372.jpg\",\n  \"246621.jpg\": \"Reptilia/Heloderma suspectum/37233bfff9669a3df01cebc0d39a216d.jpg\",\n  \"246622.jpg\": \"Reptilia/Heloderma suspectum/322f568aa514f41b49e7e5cec6d73b95.jpg\",\n  \"246623.jpg\": \"Reptilia/Heloderma suspectum/97c8ee5dbfc3623213ced5e89fac8bd3.jpg\",\n  \"246624.jpg\": \"Reptilia/Heloderma suspectum/94c4cd2a227f14837b2e949d8d1fdbeb.jpg\",\n  \"246625.jpg\": \"Reptilia/Heloderma suspectum/46a3155b496f953df2badaff8430269b.jpg\",\n  \"246626.jpg\": \"Reptilia/Heloderma suspectum/2507977a71ff466099ff40e6b1964349.jpg\",\n  \"246631.jpg\": \"Reptilia/Heloderma suspectum/fb96bd015562b9ac8e5cf2d41b092f37.jpg\",\n  \"246632.jpg\": \"Reptilia/Heloderma suspectum/921d1ba0baa91b0dfa4df56a2fd01542.jpg\",\n  \"246633.jpg\": \"Reptilia/Heloderma suspectum/f4073ae29777a2db8fe0415da7e2ac54.jpg\",\n  \"246637.jpg\": \"Reptilia/Heloderma suspectum/ab22abcb352c48771a0652ce0bd9412f.jpg\",\n  \"246642.jpg\": \"Reptilia/Heloderma suspectum/15bd4f1e081bd9d4ddd918a1cbbfaa27.jpg\",\n  \"246717.jpg\": \"Insecta/Idia aemula/d2c025905317b0e3de7dd92764e5475a.jpg\",\n  \"246729.jpg\": \"Insecta/Idia aemula/578e4fb8e006b1c80cb15d68c618bd1f.jpg\",\n  \"246731.jpg\": \"Insecta/Idia aemula/bcfa986e021aa2a1381bfc66d2257bea.jpg\",\n  \"246732.jpg\": \"Insecta/Idia aemula/6587d30564a223b3dceb3699c7eb49f9.jpg\",\n  \"246735.jpg\": \"Insecta/Idia aemula/05b9eb363f7ac85694ea686aa2691806.jpg\",\n  \"246736.jpg\": \"Insecta/Idia aemula/8250ee426c5c86d06184d3fc7a617625.jpg\",\n  \"246738.jpg\": \"Insecta/Idia aemula/f8f8d8c6973483c5b59757b746f9ce50.jpg\",\n  \"246739.jpg\": \"Insecta/Idia aemula/152ff9f1103c66224ba189272b4dfa65.jpg\",\n  \"246740.jpg\": \"Insecta/Idia aemula/715a3ad93ca968df13dd97080ef88d67.jpg\",\n  \"246741.jpg\": \"Insecta/Idia aemula/fda7c7033393ceefadf17aeb90d9d998.jpg\",\n  \"246744.jpg\": \"Insecta/Idia aemula/3f846666f4e49e21362202fda4068847.jpg\",\n  \"246746.jpg\": \"Insecta/Idia aemula/aa5fd3051267a1b4d5bde152a374e391.jpg\",\n  \"246750.jpg\": \"Insecta/Idia aemula/186c6e81c8b1eb1d228a5902e1a96a1f.jpg\",\n  \"246752.jpg\": \"Insecta/Idia aemula/666526b0cd4207d4c13ed6beef1e1b0d.jpg\",\n  \"246754.jpg\": \"Insecta/Idia aemula/46f295529f748d5133263f3197389711.jpg\",\n  \"246756.jpg\": \"Insecta/Idia aemula/7d15668f6c7934892d1dee2e33d64279.jpg\",\n  \"246757.jpg\": \"Insecta/Idia aemula/1289c4d661662648c9a96541ebbd973c.jpg\",\n  \"246758.jpg\": \"Insecta/Idia aemula/94e59c3cccb56208d50b2edfbdb2043d.jpg\",\n  \"246767.jpg\": \"Insecta/Idia aemula/5bf94c133b662e1eba9acbdc16e73e08.jpg\",\n  \"246769.jpg\": \"Insecta/Idia aemula/c8d78792beafd37e1af6adce76c9f690.jpg\",\n  \"246770.jpg\": \"Insecta/Idia aemula/16c830fa82f31e6ed7673307939813e0.jpg\",\n  \"246779.jpg\": \"Insecta/Ochropleura implecta/03bee67a3ddcae20576535066a17ae87.jpg\",\n  \"246782.jpg\": \"Insecta/Ochropleura implecta/bb0d3875df5ddce0bc7cef8e1c8966a5.jpg\",\n  \"246790.jpg\": \"Insecta/Ochropleura implecta/2baf80f052efe4f7edc4721bc653a374.jpg\",\n  \"24680.jpg\": \"Mammalia/Myocastor coypus/647e21019133e76ba9bc31b95fe1f2c1.jpg\",\n  \"246803.jpg\": \"Insecta/Galgula partita/12877d9becd511fe9f0b12434b3d5a19.jpg\",\n  \"246808.jpg\": \"Insecta/Galgula partita/c0f0d16b1227dd116027beac3af9babf.jpg\",\n  \"246809.jpg\": \"Insecta/Galgula partita/60839414f23a812e05bea6909d5b6325.jpg\",\n  \"246811.jpg\": \"Insecta/Galgula partita/5a755c1a19f51be136c39512a14bae6a.jpg\",\n  \"246812.jpg\": \"Insecta/Galgula partita/4875c62fe73793f1db483ab5b82519f5.jpg\",\n  \"246816.jpg\": \"Insecta/Galgula partita/7020c5704b31753a194a9ed8492f58a2.jpg\",\n  \"246826.jpg\": \"Insecta/Galgula partita/ec70abd30347579e146dce410975135b.jpg\",\n  \"246832.jpg\": \"Insecta/Galgula partita/bc7d28414f88ae57c961ae4aa7832aa6.jpg\",\n  \"246833.jpg\": \"Insecta/Galgula partita/50bd5fb47d2f7df838f3392e112eb932.jpg\",\n  \"246834.jpg\": \"Insecta/Galgula partita/e5d0ba3b69a152ee47102d7b6da285b6.jpg\",\n  \"246837.jpg\": \"Insecta/Galgula partita/3fb7eac968cd403f02e977213297098a.jpg\",\n  \"246843.jpg\": \"Insecta/Galgula partita/50d6b6bab447c29841f44b0ac545d2f0.jpg\",\n  \"246852.jpg\": \"Insecta/Galgula partita/cb3345c1ece57968b4a91ed793cd7c53.jpg\",\n  \"246853.jpg\": \"Insecta/Galgula partita/f5663b64d806f53f64b1da9d401739a0.jpg\",\n  \"246855.jpg\": \"Insecta/Galgula partita/14e1f4bd82d7b679857a75f42f08d8d4.jpg\",\n  \"246857.jpg\": \"Insecta/Galgula partita/6c576de3516645140275c0efd520132a.jpg\",\n  \"246859.jpg\": \"Insecta/Galgula partita/958cec831b6adc5ff02a88255e37f374.jpg\",\n  \"246864.jpg\": \"Insecta/Galgula partita/ce26c00a6213c37f4c70d315da490970.jpg\",\n  \"246865.jpg\": \"Insecta/Galgula partita/15784aa4f36348c5d3d13274a19ced6f.jpg\",\n  \"246867.jpg\": \"Insecta/Galgula partita/c920559d37239790264c6fec8101fa5d.jpg\",\n  \"246868.jpg\": \"Insecta/Galgula partita/13ce7e20afbf1eadbef3dbb7a0d8f27e.jpg\",\n  \"246870.jpg\": \"Insecta/Galgula partita/b3653a77c459ee07239dd6e09d9d9d33.jpg\",\n  \"246874.jpg\": \"Insecta/Macaria aemulataria/eb9b1b764516c88362a9162e46266972.jpg\",\n  \"246876.jpg\": \"Insecta/Macaria aemulataria/14479e3fdc2aeebf9d0cb21eaa5d5242.jpg\",\n  \"246877.jpg\": \"Insecta/Macaria aemulataria/250a772bce719df0a0be9d679882dfb8.jpg\",\n  \"246880.jpg\": \"Insecta/Macaria aemulataria/88779bb71760e35e26649cfbe599dc8c.jpg\",\n  \"246881.jpg\": \"Insecta/Macaria aemulataria/43e2554b0d826c545249addcd5d8978d.jpg\",\n  \"246883.jpg\": \"Insecta/Macaria aemulataria/f82295d01fb59a17a2cfae483fa81dd7.jpg\",\n  \"246884.jpg\": \"Insecta/Macaria aemulataria/f40013b4d6dc70a5e085e9849dda30f9.jpg\",\n  \"246912.jpg\": \"Insecta/Satyrium titus/5ea1a7e0f87392783731fbf4701f981e.jpg\",\n  \"246917.jpg\": \"Insecta/Satyrium titus/697ed67388573966352f418a8e27b0e3.jpg\",\n  \"246918.jpg\": \"Insecta/Satyrium titus/c8a4686a1c094e6d473d4c15dec79332.jpg\",\n  \"24692.jpg\": \"Mammalia/Myocastor coypus/e5f1ec50fb065ad823f45d20a8e07277.jpg\",\n  \"246921.jpg\": \"Insecta/Satyrium titus/7099a91071814291fae502643402f4c1.jpg\",\n  \"246923.jpg\": \"Insecta/Satyrium titus/e881e99b4e14ab05e380f32b8206b3cc.jpg\",\n  \"246924.jpg\": \"Insecta/Satyrium titus/9d13e4084e9611bfaf43ad0338661c96.jpg\",\n  \"24705.jpg\": \"Mammalia/Myocastor coypus/290159172485fa6d6be76abbc7a56841.jpg\",\n  \"24713.jpg\": \"Mammalia/Myocastor coypus/3a4d6e1c5e20b6c12faf8697840b221e.jpg\",\n  \"24721.jpg\": \"Mammalia/Myocastor coypus/0d784d720c1402baa0bc260c4adbb8e1.jpg\",\n  \"247232.jpg\": \"Aves/Zosterops japonicus/c721c08ee78e7bfeb4698c67bb69fb24.jpg\",\n  \"247234.jpg\": \"Aves/Zosterops japonicus/5d280526cda3f7136a74215d97bf24ff.jpg\",\n  \"247235.jpg\": \"Aves/Zosterops japonicus/de519d33dba20f65ef77cfb4c5b489b8.jpg\",\n  \"247248.jpg\": \"Aves/Zosterops japonicus/fe6b6a08e3f255eb1ee2c98264c2bc83.jpg\",\n  \"247249.jpg\": \"Aves/Zosterops japonicus/c4b9f698cffebe642182e344a403b60a.jpg\",\n  \"247252.jpg\": \"Aves/Zosterops japonicus/05f922acd02b95033ecfa741881ded7d.jpg\",\n  \"247276.jpg\": \"Aves/Surnia ulula/f76d90ddaf5fd10debdc2bb28a2c1e5a.jpg\",\n  \"247280.jpg\": \"Aves/Surnia ulula/5c3a1175e36409b4c87b72f6b8637b43.jpg\",\n  \"247281.jpg\": \"Aves/Surnia ulula/0e1acb4e1c18395fe17e34af7f21a31a.jpg\",\n  \"247282.jpg\": \"Aves/Surnia ulula/a7394c8697b4f06bb5eea3a3187517d9.jpg\",\n  \"247286.jpg\": \"Insecta/Orthonama obstipata/6f81163010414581e68860d22709c3a0.jpg\",\n  \"247289.jpg\": \"Insecta/Orthonama obstipata/fd6ad15578880d67e7bb33c737f576cc.jpg\",\n  \"247290.jpg\": \"Insecta/Orthonama obstipata/b1d29aea74a6ffae9d07d81662314663.jpg\",\n  \"247293.jpg\": \"Insecta/Orthonama obstipata/180480a993f007936fcf2dfa9c24dcac.jpg\",\n  \"24730.jpg\": \"Mammalia/Myocastor coypus/daf6daee16290c81ecedc84ce1c2bdeb.jpg\",\n  \"247300.jpg\": \"Insecta/Orthonama obstipata/d5969345a205946fb6a1fea6a4146c55.jpg\",\n  \"247301.jpg\": \"Insecta/Orthonama obstipata/611452c2916e6388241ae0c332a869f1.jpg\",\n  \"247302.jpg\": \"Insecta/Orthonama obstipata/e88c92cd8280ca22ad8f56c82262c5f2.jpg\",\n  \"247304.jpg\": \"Insecta/Orthonama obstipata/c190b9b610d98a44ef996dbbfb6c30ca.jpg\",\n  \"247307.jpg\": \"Insecta/Orthonama obstipata/07eb4434a66d526eb99026c96a8fe300.jpg\",\n  \"247314.jpg\": \"Insecta/Orthonama obstipata/8d87914850d047f8da1089472f1756a6.jpg\",\n  \"247316.jpg\": \"Insecta/Orthonama obstipata/cec7e7d7fcd8ddb8cdf36b80d861bb6c.jpg\",\n  \"247317.jpg\": \"Insecta/Orthonama obstipata/dc8cd9a12d408bb7fd261ca89faa0a32.jpg\",\n  \"247319.jpg\": \"Insecta/Orthonama obstipata/f67d7f9b3662030c29b6fe1a216fb41b.jpg\",\n  \"247321.jpg\": \"Insecta/Orthonama obstipata/ef5a4fda57a964915f0556586d1f3331.jpg\",\n  \"247322.jpg\": \"Insecta/Orthonama obstipata/d5c678af14908dbfda3478ef2b2f563d.jpg\",\n  \"247323.jpg\": \"Insecta/Orthonama obstipata/1f176095b5db8af1b578ccd624655f3c.jpg\",\n  \"247324.jpg\": \"Insecta/Orthonama obstipata/e892c90707287900da0e1dd397151110.jpg\",\n  \"247382.jpg\": \"Aves/Nestor meridionalis/383d562eb8d4b3c424afc8bfa88d5a56.jpg\",\n  \"247392.jpg\": \"Aves/Nestor meridionalis/7d5f45791c8f326e0756f9492e0b4d51.jpg\",\n  \"247395.jpg\": \"Aves/Nestor meridionalis/53f5315bcb64ced5585c572d2088cd1b.jpg\",\n  \"247405.jpg\": \"Aves/Nestor meridionalis/5b0f14426b0f78ab32d0f3c7a60bc7e1.jpg\",\n  \"247412.jpg\": \"Aves/Nestor meridionalis/5637fc989e2c6c4eb55670fa7cce488d.jpg\",\n  \"247420.jpg\": \"Aves/Nestor meridionalis/46bedceee284b1e14207c74643ba5e3a.jpg\",\n  \"247422.jpg\": \"Aves/Nestor meridionalis/413f91ac1b9d863d3308f2b7dc8fd6fc.jpg\",\n  \"247423.jpg\": \"Aves/Nestor meridionalis/58d667d6d9f7039be3f85a1682d275de.jpg\",\n  \"247424.jpg\": \"Aves/Nestor meridionalis/750fa2dfec177e74a9d9c0d7a37bfd9c.jpg\",\n  \"247425.jpg\": \"Aves/Nestor meridionalis/06d4c3422146839914eee5694fd9a21c.jpg\",\n  \"247429.jpg\": \"Aves/Nestor meridionalis/54ad4f4863ed1c85f024577c0169d3fe.jpg\",\n  \"247432.jpg\": \"Aves/Nestor meridionalis/c955d2cd6236116015ec0d51566648cc.jpg\",\n  \"247435.jpg\": \"Aves/Nestor meridionalis/78567767716fd26315cca455666dbfb9.jpg\",\n  \"247438.jpg\": \"Aves/Nestor meridionalis/c03bda062c2a32dfd436e66de0b9a5c7.jpg\",\n  \"247454.jpg\": \"Aves/Nestor meridionalis/7362e120c0baa4852f79b6aeb52bc32b.jpg\",\n  \"247457.jpg\": \"Aves/Nestor meridionalis/69a79bc16f53e88e5fada00bc9d44919.jpg\",\n  \"247461.jpg\": \"Aves/Nestor meridionalis/fe1fe6532f8a2916261077747a631d0b.jpg\",\n  \"247552.jpg\": \"Insecta/Adalia bipunctata/12b72f682aaf79cb6fed082ad089118a.jpg\",\n  \"247555.jpg\": \"Insecta/Adalia bipunctata/46a9226737b10c7a4a605929f37656e6.jpg\",\n  \"247556.jpg\": \"Insecta/Adalia bipunctata/81662645387e95405894f4917b5eeb69.jpg\",\n  \"247565.jpg\": \"Insecta/Adalia bipunctata/61e9d5d8448b80e359c439126cb52b7e.jpg\",\n  \"247566.jpg\": \"Insecta/Adalia bipunctata/c0a19762f83cfd88063c7f23bbd70a10.jpg\",\n  \"247567.jpg\": \"Insecta/Adalia bipunctata/2db1bbf3b7ec821d307199f895d040b5.jpg\",\n  \"247571.jpg\": \"Insecta/Adalia bipunctata/9277abc93894377080adec2946fca7e0.jpg\",\n  \"247572.jpg\": \"Insecta/Adalia bipunctata/1a9fae511c4f3eabe360f6e6a95c572b.jpg\",\n  \"247573.jpg\": \"Insecta/Adalia bipunctata/51dd2588e2762190b4d6df1fd9f5331e.jpg\",\n  \"247574.jpg\": \"Insecta/Adalia bipunctata/7a52cf7518240c0753c5948db0b0897d.jpg\",\n  \"247577.jpg\": \"Insecta/Adalia bipunctata/982389908fbe68b4260f1d7a64931569.jpg\",\n  \"247582.jpg\": \"Insecta/Adalia bipunctata/e4ab27ddf304a56fd836b31fc69ba216.jpg\",\n  \"247583.jpg\": \"Insecta/Adalia bipunctata/fb0544d010197fb011b7b596adf084b3.jpg\",\n  \"24759.jpg\": \"Mammalia/Myocastor coypus/ff55d4a7a8e198c0203b9e5a2ab5260a.jpg\",\n  \"247591.jpg\": \"Insecta/Adalia bipunctata/3fae694941641c251321456b4d396e04.jpg\",\n  \"247595.jpg\": \"Insecta/Adalia bipunctata/0d3153d87abcbf5343f899f7941e57b7.jpg\",\n  \"247596.jpg\": \"Insecta/Adalia bipunctata/1c13dfa549e80d7346b2478e32ac8688.jpg\",\n  \"247597.jpg\": \"Insecta/Adalia bipunctata/28d4e820e9dc86e444bec76984c5fda8.jpg\",\n  \"247598.jpg\": \"Insecta/Adalia bipunctata/9f9807709d5988927ab485fad9b221dd.jpg\",\n  \"247599.jpg\": \"Insecta/Adalia bipunctata/3e2a4d4598111d997d94867179741a91.jpg\",\n  \"247600.jpg\": \"Insecta/Adalia bipunctata/6a2c43f9718a4f302fc0e7d4536b17cb.jpg\",\n  \"247601.jpg\": \"Insecta/Adalia bipunctata/844d2a3360a25ccd2b7d8400c6ef466e.jpg\",\n  \"247602.jpg\": \"Aves/Setophaga caerulescens/8d12e283a3fdc1872b7cd3c1168a9482.jpg\",\n  \"247606.jpg\": \"Aves/Setophaga caerulescens/f438d1cc57e17b5a6de8d3135edd15e2.jpg\",\n  \"247607.jpg\": \"Aves/Setophaga caerulescens/79b11183ef05e8ab49e1b7ee2d3edf63.jpg\",\n  \"247609.jpg\": \"Aves/Setophaga caerulescens/121c619a6df96933c92d347bc5e77f49.jpg\",\n  \"247611.jpg\": \"Aves/Setophaga caerulescens/1ad0449c0986f231110fbd57562307d3.jpg\",\n  \"247616.jpg\": \"Aves/Setophaga caerulescens/602a5062ca59667cd9b8383d9256c74c.jpg\",\n  \"247618.jpg\": \"Aves/Setophaga caerulescens/2c3f80c4bac0bc8723e60e9fa73814e6.jpg\",\n  \"247620.jpg\": \"Aves/Setophaga caerulescens/48663477ae4aae6aa11ea1064bc1ecdd.jpg\",\n  \"247622.jpg\": \"Aves/Setophaga caerulescens/b3ae2b2d0cb6bac817d0e800820f0bff.jpg\",\n  \"247627.jpg\": \"Aves/Setophaga caerulescens/f01510eb9d28fde8b048e54e8f07c30e.jpg\",\n  \"247630.jpg\": \"Aves/Setophaga caerulescens/e95e370bfba715638a2efd9c6b3bd4fe.jpg\",\n  \"247634.jpg\": \"Aves/Setophaga caerulescens/34c0b42f3e16eeac6605796e568b6bbf.jpg\",\n  \"247638.jpg\": \"Aves/Setophaga caerulescens/a1c68be7235523dee617961617b039ac.jpg\",\n  \"247641.jpg\": \"Aves/Setophaga caerulescens/c227719e5a539bc756abe4bb36160dd6.jpg\",\n  \"247642.jpg\": \"Aves/Setophaga caerulescens/2eeab0e55f4571d96e35c351da6528c6.jpg\",\n  \"247643.jpg\": \"Aves/Setophaga caerulescens/833c01965896accad04ae39360c460a1.jpg\",\n  \"247645.jpg\": \"Aves/Setophaga caerulescens/496d14e4acf9d34783703eea3f97b3b4.jpg\",\n  \"247652.jpg\": \"Aves/Setophaga caerulescens/e0b4d667febbf9fbe4766adc36d35720.jpg\",\n  \"247667.jpg\": \"Aves/Setophaga caerulescens/2e606be2b9f0bdb833d67089e8165f7f.jpg\",\n  \"247668.jpg\": \"Aves/Setophaga caerulescens/426bab001129ed0a1ce8e79fd6308102.jpg\",\n  \"247676.jpg\": \"Aves/Setophaga caerulescens/6795750754002b625dc3c4eadaa02605.jpg\",\n  \"247677.jpg\": \"Aves/Setophaga caerulescens/5e4f92eeafea9514f6d5fcbd25db32d7.jpg\",\n  \"247681.jpg\": \"Aves/Setophaga caerulescens/7f2aa12dfcdda16667f6cd9e9d2c8e4e.jpg\",\n  \"247685.jpg\": \"Insecta/Atlides halesus/de088e02850e19d0997a3e2383018eaa.jpg\",\n  \"247688.jpg\": \"Insecta/Atlides halesus/3bee5d88a38536fd316dacf44d450fff.jpg\",\n  \"24769.jpg\": \"Mammalia/Myocastor coypus/b998868d8c72070933e4878c8c8e8931.jpg\",\n  \"247701.jpg\": \"Insecta/Atlides halesus/5300f96083656754fd72a1426a994ba3.jpg\",\n  \"247703.jpg\": \"Insecta/Atlides halesus/2e38a49c3ab649f64838cfdbb58a1c29.jpg\",\n  \"247707.jpg\": \"Insecta/Atlides halesus/17bf24016698478be5236fcc8c4de10d.jpg\",\n  \"247708.jpg\": \"Insecta/Atlides halesus/86f8cd44a66e2adffe827a4ea79ccde0.jpg\",\n  \"247725.jpg\": \"Insecta/Atlides halesus/7e13a7410dcb8d8f93fc7a888b17bdf3.jpg\",\n  \"24773.jpg\": \"Mammalia/Myocastor coypus/476498debadddf3228cbeaf59df5d7ff.jpg\",\n  \"247735.jpg\": \"Insecta/Atlides halesus/2c3a1dfb7821cc9649aa6d24cbc71ffb.jpg\",\n  \"247736.jpg\": \"Insecta/Atlides halesus/f6c64c0f5d69099c94130bb2bfa146fd.jpg\",\n  \"24774.jpg\": \"Insecta/Dioprosopa clavata/cfc8434a16fa9719f14fa4746b6f2234.jpg\",\n  \"247742.jpg\": \"Insecta/Atlides halesus/63c5b6b577b46b8fb1914e94b222a2a2.jpg\",\n  \"247743.jpg\": \"Insecta/Atlides halesus/2cd2747c513724d53b36c281dad2ef29.jpg\",\n  \"247748.jpg\": \"Insecta/Atlides halesus/f67372c3acbd458d39a5b880d91be33a.jpg\",\n  \"24775.jpg\": \"Insecta/Dioprosopa clavata/d58705d0b288a795ab631b9f88d3bc20.jpg\",\n  \"247750.jpg\": \"Insecta/Atlides halesus/efbcb7e93fe12f33efc7247fe54ed87c.jpg\",\n  \"247753.jpg\": \"Insecta/Atlides halesus/77d57ce81f5282612747e1a50f73a803.jpg\",\n  \"247756.jpg\": \"Insecta/Atlides halesus/f659b700d5cacf311a1e379899c05a74.jpg\",\n  \"247757.jpg\": \"Insecta/Atlides halesus/b2239b7dd4a23d86500dd2cc7532876b.jpg\",\n  \"247759.jpg\": \"Insecta/Argia nahuana/3556f2c4a890d9056c392fa03bb86ef5.jpg\",\n  \"247761.jpg\": \"Insecta/Argia nahuana/5eed236eea6c7de3142d3518da801e16.jpg\",\n  \"247762.jpg\": \"Insecta/Argia nahuana/b150006a914f82891f268324f1a06451.jpg\",\n  \"247763.jpg\": \"Insecta/Argia nahuana/0f195b7a929032f25f95c80f2c2560dc.jpg\",\n  \"247764.jpg\": \"Insecta/Argia nahuana/2260c42b2d19232e0969734033bc0031.jpg\",\n  \"247770.jpg\": \"Insecta/Argia nahuana/34a5157a1d12a1dcd991384ebf94b61f.jpg\",\n  \"247771.jpg\": \"Insecta/Argia nahuana/90d6ef92a388d35f399a6059faaece5c.jpg\",\n  \"247779.jpg\": \"Insecta/Argia nahuana/8cb90f8a654cee28ae78df370424fd80.jpg\",\n  \"247785.jpg\": \"Insecta/Argia nahuana/5cbc0546c7620634e5ca340c38c42c4f.jpg\",\n  \"247786.jpg\": \"Insecta/Argia nahuana/432b6e35186c708ed9d038980e6183d5.jpg\",\n  \"247790.jpg\": \"Insecta/Argia nahuana/ca0c876373f0394130cf51e9710a303a.jpg\",\n  \"247795.jpg\": \"Insecta/Argia nahuana/b610318518cdaf92d1bd1a87a283dfad.jpg\",\n  \"247796.jpg\": \"Insecta/Argia nahuana/bcc6654f5157985c3bfdc3bf895144bb.jpg\",\n  \"247797.jpg\": \"Insecta/Argia nahuana/45af4b61bfd00215a274b49ed948ff64.jpg\",\n  \"24780.jpg\": \"Insecta/Dioprosopa clavata/184e8a46827f95d1da2ccb9e9c97fd22.jpg\",\n  \"247803.jpg\": \"Insecta/Argia nahuana/7e706a5b3331b8618702a9c01003b672.jpg\",\n  \"247804.jpg\": \"Insecta/Argia nahuana/cb31d1805a6726889f6e9c96ae6c6c63.jpg\",\n  \"24781.jpg\": \"Insecta/Dioprosopa clavata/f4031dc76c19590f3afabc2b332fbe17.jpg\",\n  \"247810.jpg\": \"Insecta/Argia nahuana/9065b01992a6e3b7f5e8bfe31e39b5d2.jpg\",\n  \"247817.jpg\": \"Insecta/Argia nahuana/3abc62fe6f09faf5f81cc379aea87077.jpg\",\n  \"247818.jpg\": \"Insecta/Argia nahuana/b4d4d87da287dd6c140853a6cb0109db.jpg\",\n  \"247826.jpg\": \"Insecta/Eupithecia miserulata/a9a6e4e90afc3e03a98b52095c8331c5.jpg\",\n  \"24784.jpg\": \"Insecta/Dioprosopa clavata/25eda7941144c5459c25bc8744b101ca.jpg\",\n  \"247841.jpg\": \"Insecta/Eupithecia miserulata/183a3af12788833cd62f42063d76067a.jpg\",\n  \"247842.jpg\": \"Insecta/Eupithecia miserulata/f70d9970e9cdbec27114517bcf0115bb.jpg\",\n  \"247844.jpg\": \"Insecta/Eupithecia miserulata/93be9e7a96629ca882506706779173aa.jpg\",\n  \"24785.jpg\": \"Insecta/Dioprosopa clavata/5834bd688b65345bad2c041d5f6100f1.jpg\",\n  \"24786.jpg\": \"Insecta/Dioprosopa clavata/1d1b0920613d708714c9aab87193280e.jpg\",\n  \"24789.jpg\": \"Insecta/Dioprosopa clavata/9678e0b3e44415d808362465cba3e257.jpg\",\n  \"24790.jpg\": \"Insecta/Dioprosopa clavata/5d82ee635e7af39bd8baa61adcf070d9.jpg\",\n  \"24791.jpg\": \"Insecta/Dioprosopa clavata/58b2d579692df4d49536db8c5ef1d1c8.jpg\",\n  \"24796.jpg\": \"Insecta/Dioprosopa clavata/624ba04c67936e836d01248055039b23.jpg\",\n  \"24797.jpg\": \"Insecta/Dioprosopa clavata/f6f176a632826bc065eb7388ee52e631.jpg\",\n  \"24798.jpg\": \"Insecta/Dioprosopa clavata/bf26b122066ca2b3e24f662b9a5351a9.jpg\",\n  \"24799.jpg\": \"Insecta/Dioprosopa clavata/8450ebad81705281ba98f1d314a03081.jpg\",\n  \"24800.jpg\": \"Insecta/Dioprosopa clavata/a237b58d43e40cea69e17b1a28126957.jpg\",\n  \"24801.jpg\": \"Insecta/Dioprosopa clavata/2e5da4a81a192d2c849c4702de933477.jpg\",\n  \"24802.jpg\": \"Insecta/Dioprosopa clavata/f322f6cc23c562a2daf300cd8c8323d0.jpg\",\n  \"24807.jpg\": \"Insecta/Dioprosopa clavata/302df7024d4c87311f3355129d48d366.jpg\",\n  \"24808.jpg\": \"Insecta/Dioprosopa clavata/0f1566462d7a229a14c882f718cf4a7c.jpg\",\n  \"24809.jpg\": \"Insecta/Dioprosopa clavata/ae62924ed1812f9b88763146aa29a8ad.jpg\",\n  \"248206.jpg\": \"Insecta/Haematopis grataria/190709fcbaf7d4bb5ede44f8b25c8742.jpg\",\n  \"248207.jpg\": \"Insecta/Haematopis grataria/5de77c96433a77f9dd6c11f4bb3ca900.jpg\",\n  \"248208.jpg\": \"Insecta/Haematopis grataria/1a29127903a35c1b0e2bce7dd55a3707.jpg\",\n  \"248210.jpg\": \"Insecta/Haematopis grataria/abd87405360d0c44a60da9dda36d6f5b.jpg\",\n  \"248211.jpg\": \"Insecta/Haematopis grataria/efd16685557f4968236a05d7ba39b528.jpg\",\n  \"248219.jpg\": \"Insecta/Haematopis grataria/0311fef9b92dee06d18551e530e208f5.jpg\",\n  \"24822.jpg\": \"Insecta/Dioprosopa clavata/d0f66964d48f73c9ce8ef1a4de4b461e.jpg\",\n  \"248224.jpg\": \"Insecta/Haematopis grataria/f3cf482d5a9714f76fed10ed62bb71be.jpg\",\n  \"248227.jpg\": \"Insecta/Haematopis grataria/0f3339f3b9e0fdab4fab5684ff0d3c53.jpg\",\n  \"248229.jpg\": \"Insecta/Haematopis grataria/684371263ee1c2df7aa4a50ec53007c8.jpg\",\n  \"24823.jpg\": \"Insecta/Dioprosopa clavata/d5c0a61a40db3e619ede4b7f238befc6.jpg\",\n  \"248234.jpg\": \"Insecta/Haematopis grataria/dc9d0d14bcda22c73ae9f1c2bff9d0be.jpg\",\n  \"248236.jpg\": \"Insecta/Haematopis grataria/028a4f60f0133e914b9f92f9dcbca8e3.jpg\",\n  \"248237.jpg\": \"Insecta/Haematopis grataria/8ccf90197d2b74513da8f39dd676ee16.jpg\",\n  \"24824.jpg\": \"Insecta/Dioprosopa clavata/f38a565e0c2ecaf1f7d63ab78c462bf7.jpg\",\n  \"248245.jpg\": \"Insecta/Haematopis grataria/f9b9ddfad603bfeb8de21fa3462187fb.jpg\",\n  \"248249.jpg\": \"Insecta/Haematopis grataria/aa32b8bb65c706fbc63750d1e1ab61c1.jpg\",\n  \"24825.jpg\": \"Insecta/Dioprosopa clavata/c669413e88e2f24479862cd1f8401d73.jpg\",\n  \"248254.jpg\": \"Insecta/Haematopis grataria/11df50f945c793f87b67c12f18d9503d.jpg\",\n  \"24826.jpg\": \"Insecta/Dioprosopa clavata/8498c095061d57d5b01542f37337b58c.jpg\",\n  \"248260.jpg\": \"Insecta/Haematopis grataria/43e068ee0ba73ddbc9cbba83374f2e58.jpg\",\n  \"248262.jpg\": \"Insecta/Haematopis grataria/07205a4359a20641abc419d101d38291.jpg\",\n  \"248264.jpg\": \"Insecta/Haematopis grataria/0f6f8df72d3d8109ffaf48b94986587b.jpg\",\n  \"248267.jpg\": \"Insecta/Haematopis grataria/7e3102192c9cd5edc9211d73ee4debea.jpg\",\n  \"248268.jpg\": \"Insecta/Haematopis grataria/95de202c952b10ebafcc9cecc1b10a70.jpg\",\n  \"24827.jpg\": \"Insecta/Dioprosopa clavata/d1c3de540f34b3d3ff51ec1a5b996699.jpg\",\n  \"24828.jpg\": \"Insecta/Agriphila vulgivagella/685405b38a166523dbd9d0859d33048c.jpg\",\n  \"24831.jpg\": \"Insecta/Agriphila vulgivagella/3b7915a62122d3a18ef2425e1a6b9e33.jpg\",\n  \"24832.jpg\": \"Insecta/Agriphila vulgivagella/bda06ed8f543a526d3c7d4ea236860fa.jpg\",\n  \"24838.jpg\": \"Insecta/Agriphila vulgivagella/48f483efee39104497d62eee3722d13d.jpg\",\n  \"24839.jpg\": \"Insecta/Agriphila vulgivagella/d7b6bedcdfb3f01387eed04ead786a28.jpg\",\n  \"24843.jpg\": \"Insecta/Agriphila vulgivagella/3b3154d32e5b6a603c50d6bdb865be2d.jpg\",\n  \"248499.jpg\": \"Mammalia/Arctocephalus forsteri/2d67704175afce7459b72fc0ad9658f3.jpg\",\n  \"248501.jpg\": \"Mammalia/Arctocephalus forsteri/56c2302b7effd5dc5b069801424994b8.jpg\",\n  \"248502.jpg\": \"Mammalia/Arctocephalus forsteri/c3a31613c528047b0c98cc9005336177.jpg\",\n  \"248520.jpg\": \"Mammalia/Arctocephalus forsteri/72100b561ae540504abdf4c111351f06.jpg\",\n  \"248521.jpg\": \"Mammalia/Arctocephalus forsteri/78cec8881a5bf702b5b55a36b65b4d8f.jpg\",\n  \"248522.jpg\": \"Mammalia/Arctocephalus forsteri/791cfb92d3ca91d6bf0faf36a0e95861.jpg\",\n  \"248532.jpg\": \"Mammalia/Arctocephalus forsteri/6ccbc063748ba6f0872bf7f5e0d5b565.jpg\",\n  \"248534.jpg\": \"Mammalia/Arctocephalus forsteri/6a7dcdb9b7aabed40783069969f7cb9c.jpg\",\n  \"248538.jpg\": \"Mammalia/Arctocephalus forsteri/7274c76a8bf49c975dec6a60f02d6313.jpg\",\n  \"248540.jpg\": \"Mammalia/Arctocephalus forsteri/8490c46fad4a262ae8e224ee57c53249.jpg\",\n  \"248572.jpg\": \"Mammalia/Arctocephalus forsteri/5dd0cb2aa06faf026e7088e8cb900f6e.jpg\",\n  \"248576.jpg\": \"Mammalia/Arctocephalus forsteri/6756abd84c2a4de20c31c089150be6bb.jpg\",\n  \"248591.jpg\": \"Mammalia/Arctocephalus forsteri/9f8dacb73dfe06e4afac0a00176a569a.jpg\",\n  \"248592.jpg\": \"Mammalia/Arctocephalus forsteri/35f0a933a0919068aa2d019ceafc432f.jpg\",\n  \"248762.jpg\": \"Aves/Platalea regia/41015dc9abe42281bb8d5681f5fb8082.jpg\",\n  \"248763.jpg\": \"Aves/Platalea regia/d0d082a16098366b9dc0b16a7e4f5f08.jpg\",\n  \"248769.jpg\": \"Aves/Platalea regia/4a5b03ff1afe355899b55259b9c44234.jpg\",\n  \"248774.jpg\": \"Aves/Platalea regia/d0856bcce815ce3b9e1801056e44298a.jpg\",\n  \"248776.jpg\": \"Aves/Platalea regia/98bed07c4fb5adb978c06d47da9b09fd.jpg\",\n  \"248777.jpg\": \"Aves/Platalea regia/85296091db3dccdd3cdcb8178f14ec9b.jpg\",\n  \"248779.jpg\": \"Aves/Platalea regia/652aa9aa6309dddda6f0434990b8fdbf.jpg\",\n  \"248800.jpg\": \"Actinopterygii/Cyprinus carpio/455b67fa0c81738d1ec78b97dca53583.jpg\",\n  \"248812.jpg\": \"Actinopterygii/Cyprinus carpio/99a961fec1c57f5cdd34ffc442bdf012.jpg\",\n  \"248816.jpg\": \"Actinopterygii/Cyprinus carpio/2bb30b44511ba7bc475d64a3bfbfe05c.jpg\",\n  \"248825.jpg\": \"Actinopterygii/Cyprinus carpio/89bd0d26af553da81259f3e50b811ec4.jpg\",\n  \"248844.jpg\": \"Actinopterygii/Cyprinus carpio/f6a8b628d74bcb713c14369396781869.jpg\",\n  \"248851.jpg\": \"Actinopterygii/Cyprinus carpio/8836cad0f012c1aaf67966f78eaf7663.jpg\",\n  \"248859.jpg\": \"Actinopterygii/Cyprinus carpio/2c7ea3bde742f4d9600daa729e30b6d2.jpg\",\n  \"248861.jpg\": \"Actinopterygii/Cyprinus carpio/5a6790d4ca5bad15b8ae5c834ed3657a.jpg\",\n  \"248872.jpg\": \"Actinopterygii/Cyprinus carpio/ac982e5c2aabb33951fa130d6467ad81.jpg\",\n  \"248875.jpg\": \"Actinopterygii/Cyprinus carpio/563e7458fa5384af246c177e30fe48ad.jpg\",\n  \"248876.jpg\": \"Actinopterygii/Cyprinus carpio/455118be99522620f81993f4428ca8b8.jpg\",\n  \"248878.jpg\": \"Actinopterygii/Cyprinus carpio/dbed06113eac08e68297d9180ce4bf40.jpg\",\n  \"248880.jpg\": \"Actinopterygii/Cyprinus carpio/72fe1431d41c02b6a98e9b75e0a8c711.jpg\",\n  \"248889.jpg\": \"Actinopterygii/Cyprinus carpio/7c18f35018d99dbc660e4baa8ab57fef.jpg\",\n  \"24889.jpg\": \"Insecta/Lycaena helloides/d544ef1f550a42d8c825650ee8ea8d42.jpg\",\n  \"248901.jpg\": \"Insecta/Lygus lineolaris/70ed662a7ef219d3843d959a53595bda.jpg\",\n  \"248908.jpg\": \"Insecta/Lygus lineolaris/52ec6789689d0a4aa45e01cc0e1ce57d.jpg\",\n  \"248909.jpg\": \"Insecta/Lygus lineolaris/7e5a76500b85a1c4eb1efc1d16011434.jpg\",\n  \"248915.jpg\": \"Insecta/Lygus lineolaris/33532b74179e157867cd7af7689538fe.jpg\",\n  \"248920.jpg\": \"Actinopterygii/Lepomis gibbosus/16bdc28ff321fba2ae73933cc3ea1ea7.jpg\",\n  \"248921.jpg\": \"Actinopterygii/Lepomis gibbosus/e4e641a68a22b652e94a28b6b6c1689d.jpg\",\n  \"248923.jpg\": \"Actinopterygii/Lepomis gibbosus/6c3523a1f74dd875187c4695b7d4ad7a.jpg\",\n  \"248925.jpg\": \"Actinopterygii/Lepomis gibbosus/7ae15aaf0a6444596b029e437f2d0e87.jpg\",\n  \"248926.jpg\": \"Actinopterygii/Lepomis gibbosus/b372d1b25446d77139b91380d3602ea8.jpg\",\n  \"248936.jpg\": \"Actinopterygii/Lepomis gibbosus/49bf3e10b4160b4e5252d316f2e054cd.jpg\",\n  \"248937.jpg\": \"Actinopterygii/Lepomis gibbosus/12c43f8c3513d82489ee8b1bfa403c60.jpg\",\n  \"248938.jpg\": \"Actinopterygii/Lepomis gibbosus/f42496710f63a839718f1956e0c25665.jpg\",\n  \"248942.jpg\": \"Actinopterygii/Lepomis gibbosus/395b301626666c944738c3d2b4f60dda.jpg\",\n  \"248943.jpg\": \"Actinopterygii/Lepomis gibbosus/8c84eaba5da0f44b108a82a022d5d2c9.jpg\",\n  \"248944.jpg\": \"Actinopterygii/Lepomis gibbosus/98a689355cb6e3a824b6fecbc53c565e.jpg\",\n  \"248945.jpg\": \"Actinopterygii/Lepomis gibbosus/432895c9ccebc8f2306cfa3f18efa393.jpg\",\n  \"248947.jpg\": \"Actinopterygii/Lepomis gibbosus/b1cb02ca7572e957086e37f2de6c1a3c.jpg\",\n  \"248948.jpg\": \"Actinopterygii/Lepomis gibbosus/d64c6366e307cfec1c279d07b0c3051e.jpg\",\n  \"248952.jpg\": \"Actinopterygii/Lepomis gibbosus/67145cb425000f5421d860c419037e7b.jpg\",\n  \"248955.jpg\": \"Actinopterygii/Lepomis gibbosus/3e0116ddaa1942d6398b0b6e3c4438d3.jpg\",\n  \"248956.jpg\": \"Actinopterygii/Lepomis gibbosus/5738de5e52c746f41bee1792fc9e387d.jpg\",\n  \"248957.jpg\": \"Actinopterygii/Lepomis gibbosus/3750084061aa2416de5d48215ee34f61.jpg\",\n  \"24896.jpg\": \"Insecta/Lycaena helloides/c9a03dd258a33ebfa4805d71bd1cf909.jpg\",\n  \"24897.jpg\": \"Insecta/Lycaena helloides/90de2cae3482f9cb70614d3e7ac73a55.jpg\",\n  \"24898.jpg\": \"Insecta/Lycaena helloides/449c9f18100698761068ec7aee800825.jpg\",\n  \"24900.jpg\": \"Insecta/Lycaena helloides/732187aa3521eedf3652785862d57581.jpg\",\n  \"24901.jpg\": \"Insecta/Lycaena helloides/47c086d6c0e161b969e69ece130486c8.jpg\",\n  \"24905.jpg\": \"Insecta/Lycaena helloides/7fd69d29ff2b9844959ad3fc7faf167b.jpg\",\n  \"24906.jpg\": \"Insecta/Lycaena helloides/90912b490d795c802d86b15e7c413979.jpg\",\n  \"249070.jpg\": \"Amphibia/Anaxyrus boreas halophilus/93ddce167796e455482340741d834725.jpg\",\n  \"249071.jpg\": \"Amphibia/Anaxyrus boreas halophilus/b5387d94c5d5e1d26ccc5c5efbf72eec.jpg\",\n  \"249072.jpg\": \"Amphibia/Anaxyrus boreas halophilus/21c2d8cbb175d71c06632e25b8bc4ad9.jpg\",\n  \"249090.jpg\": \"Amphibia/Anaxyrus boreas halophilus/15ba19f506d3dbb8aa4ff08b0159fc8e.jpg\",\n  \"249097.jpg\": \"Amphibia/Anaxyrus boreas halophilus/a300bdc782a69ad3336e895d21e161fe.jpg\",\n  \"249104.jpg\": \"Amphibia/Anaxyrus boreas halophilus/6e3f982b5161bb7a9525456662e8f875.jpg\",\n  \"249105.jpg\": \"Amphibia/Anaxyrus boreas halophilus/1ba3e515b4fb4a9878639016de4844d7.jpg\",\n  \"249106.jpg\": \"Amphibia/Anaxyrus boreas halophilus/aea1b74b8d5128d598d1ad7393626ba6.jpg\",\n  \"249107.jpg\": \"Amphibia/Anaxyrus boreas halophilus/16f587d69e5668e5d8aa659edbfe5d97.jpg\",\n  \"249110.jpg\": \"Amphibia/Anaxyrus boreas halophilus/7b5b1a7e887aebd14e35478cc840cbf8.jpg\",\n  \"249111.jpg\": \"Amphibia/Anaxyrus boreas halophilus/f96220ab8923e004b5ed95176e3bf4aa.jpg\",\n  \"249112.jpg\": \"Amphibia/Anaxyrus boreas halophilus/fd132f4b368febf263406cfc8a862f42.jpg\",\n  \"249113.jpg\": \"Amphibia/Anaxyrus boreas halophilus/9337309b7e9e39b5aed2e6aeade9ede5.jpg\",\n  \"249121.jpg\": \"Amphibia/Anaxyrus boreas halophilus/a1992e63812d507b37867a4da6e75280.jpg\",\n  \"249122.jpg\": \"Amphibia/Anaxyrus boreas halophilus/1a0b3e59146654f5d2088bcc8a17bfaf.jpg\",\n  \"249133.jpg\": \"Amphibia/Anaxyrus boreas halophilus/8d148789567a8cf439f4dfe7d798227d.jpg\",\n  \"249134.jpg\": \"Amphibia/Anaxyrus boreas halophilus/32fb9a7b2895e9d607707809bd14e31e.jpg\",\n  \"249137.jpg\": \"Amphibia/Anaxyrus boreas halophilus/eb850a10ffb07069ebd00be3aee22c00.jpg\",\n  \"249138.jpg\": \"Amphibia/Anaxyrus boreas halophilus/bbd36ef5742d242de7f960b02a987948.jpg\",\n  \"249139.jpg\": \"Amphibia/Anaxyrus boreas halophilus/be883269c0b21aa2eb65b19c2724d360.jpg\",\n  \"249144.jpg\": \"Arachnida/Larinioides cornutus/3802c3aff3929ddf29454db49aea6efc.jpg\",\n  \"249150.jpg\": \"Arachnida/Larinioides cornutus/5bb9fed80c3ddb557927f23e197c8373.jpg\",\n  \"249154.jpg\": \"Arachnida/Larinioides cornutus/0e05c7f8254385a2db2aba50af99c67c.jpg\",\n  \"249155.jpg\": \"Amphibia/Spea hammondii/478cd8e75d85b687956a6f9dbaa6c8ea.jpg\",\n  \"249158.jpg\": \"Amphibia/Spea hammondii/55481c195ab3d5cbfc8a186d26611a91.jpg\",\n  \"249159.jpg\": \"Amphibia/Spea hammondii/92baf566a5b6aa8080e3524adc9d9a1b.jpg\",\n  \"249171.jpg\": \"Amphibia/Eleutherodactylus marnockii/ab893b0ec751a4e509ebf0208083f76f.jpg\",\n  \"249172.jpg\": \"Amphibia/Eleutherodactylus marnockii/01dfbe21f82ec6d34eb8b1141911fd9a.jpg\",\n  \"249173.jpg\": \"Amphibia/Eleutherodactylus marnockii/a7af21020347cda823b250c71e6417f3.jpg\",\n  \"249176.jpg\": \"Amphibia/Eleutherodactylus marnockii/694a4a325393f928be53dd1907667390.jpg\",\n  \"249177.jpg\": \"Amphibia/Eleutherodactylus marnockii/a247ea5630be917b4a2dbbb7b832fd10.jpg\",\n  \"249179.jpg\": \"Amphibia/Eleutherodactylus marnockii/64528feabb5def9a1e65bb38cae89ca8.jpg\",\n  \"249180.jpg\": \"Amphibia/Eleutherodactylus marnockii/ac8adbd31d23dd9cde15621776252554.jpg\",\n  \"249181.jpg\": \"Amphibia/Eleutherodactylus marnockii/d329712ee1c26479f41d767f27b19f02.jpg\",\n  \"249182.jpg\": \"Amphibia/Eleutherodactylus marnockii/64527ff1793737429236749b3efcc28f.jpg\",\n  \"249185.jpg\": \"Amphibia/Eleutherodactylus marnockii/1080ffcd00a0088ada57649bbe12a87f.jpg\",\n  \"249187.jpg\": \"Amphibia/Eleutherodactylus marnockii/f0fd4c052105c25b775db8f538ab1958.jpg\",\n  \"249189.jpg\": \"Amphibia/Eleutherodactylus marnockii/52841b28751063673efe888f4bd98b04.jpg\",\n  \"249193.jpg\": \"Amphibia/Eleutherodactylus marnockii/4b6d6eff02c89ab7f28cd69ac3443a36.jpg\",\n  \"249195.jpg\": \"Amphibia/Eleutherodactylus marnockii/4bbc59cb8dc21bd8aeaf8702d9d248f0.jpg\",\n  \"249196.jpg\": \"Amphibia/Eleutherodactylus marnockii/fc211c0b188fee2246a4751f65645c15.jpg\",\n  \"249197.jpg\": \"Amphibia/Eleutherodactylus marnockii/f5072cb9d0b3f19e6b927646e0e6b75d.jpg\",\n  \"249198.jpg\": \"Amphibia/Eleutherodactylus marnockii/3e8213121fa76c5714f1e1dbf190342d.jpg\",\n  \"249203.jpg\": \"Amphibia/Eleutherodactylus marnockii/fcd8088f3abc2de410cef6839a99f2e8.jpg\",\n  \"249204.jpg\": \"Amphibia/Eleutherodactylus marnockii/057a102d55ec556cc92602d23013356d.jpg\",\n  \"249274.jpg\": \"Reptilia/Agkistrodon contortrix mokasen/4872e30a407797378d8d3ba2590cd6b3.jpg\",\n  \"249280.jpg\": \"Reptilia/Agkistrodon contortrix mokasen/1ba57e56be756dddd5fb501d9fcd2c26.jpg\",\n  \"249285.jpg\": \"Reptilia/Agkistrodon contortrix mokasen/036b3edce7195e13c0c2a4431e731b32.jpg\",\n  \"249290.jpg\": \"Reptilia/Agkistrodon contortrix mokasen/27f4f2423b57921556e479818020fc68.jpg\",\n  \"249293.jpg\": \"Reptilia/Agkistrodon contortrix mokasen/5813a90f26ae8ad53b7e048814d14d23.jpg\",\n  \"249567.jpg\": \"Insecta/Aglais io/daade5f3a9cc27af3c608d7967c7408f.jpg\",\n  \"249568.jpg\": \"Insecta/Aglais io/9bb0f97868611b09285b61d10cc9284a.jpg\",\n  \"249569.jpg\": \"Insecta/Aglais io/d2ec2c3119c4ae8d22efef34d9b79f00.jpg\",\n  \"249574.jpg\": \"Insecta/Aglais io/098dd4dbcf78aed2dc728ce45ec46fb6.jpg\",\n  \"249580.jpg\": \"Insecta/Aglais io/0b675a7ae6a3374d9b064696676f7594.jpg\",\n  \"249587.jpg\": \"Insecta/Aglais io/cd5ea67634946598ac058dc7c4225423.jpg\",\n  \"249594.jpg\": \"Insecta/Aglais io/f20f8eac4d86e67bc02e56541ad216c1.jpg\",\n  \"249595.jpg\": \"Insecta/Aglais io/5a6d1d07f1ae571110eb191d01096f28.jpg\",\n  \"249596.jpg\": \"Insecta/Aglais io/707a79159fd56c8e7dc5bf26b969f61d.jpg\",\n  \"249608.jpg\": \"Insecta/Aglais io/7159f78a9fa82bda15bda0c7377da6ee.jpg\",\n  \"249609.jpg\": \"Insecta/Aglais io/0d859c1c6399bcf25f3f696420ad4482.jpg\",\n  \"249612.jpg\": \"Insecta/Aglais io/57728ed80a5513bad383b757b7525e15.jpg\",\n  \"249613.jpg\": \"Insecta/Aglais io/b5addd757e85fd978d0e1d97a0d6d786.jpg\",\n  \"249614.jpg\": \"Insecta/Aglais io/c46cb0ed456f34f8733df86e4d893b3f.jpg\",\n  \"249622.jpg\": \"Insecta/Aglais io/f2a0ae25456f3f5b254c89a17fa5f3d4.jpg\",\n  \"249624.jpg\": \"Insecta/Aglais io/6ef8fb3097e1da2f23f6cc9332ae42ed.jpg\",\n  \"249630.jpg\": \"Insecta/Aglais io/65666073c5aa0bc656ac0daef9a18a71.jpg\",\n  \"249633.jpg\": \"Insecta/Aglais io/4435c95bb83c742297d5f1bc9311703a.jpg\",\n  \"249634.jpg\": \"Insecta/Aglais io/208bed20e93d1909049f50d768d7a9c0.jpg\",\n  \"249645.jpg\": \"Insecta/Aglais io/6d7ebbfad089ab69f64122debd50ca7e.jpg\",\n  \"249646.jpg\": \"Insecta/Aglais io/2b39559f4a262ae94f53e2a2bb90a242.jpg\",\n  \"249659.jpg\": \"Insecta/Aglais io/b91555b6eb85e20f697de8960afd5470.jpg\",\n  \"249703.jpg\": \"Aves/Ortalis vetula/48beffa728e9f75f9a1aded90ce26246.jpg\",\n  \"249704.jpg\": \"Aves/Ortalis vetula/f1588e4f44c19a4386857d6bda4e533b.jpg\",\n  \"249708.jpg\": \"Aves/Ortalis vetula/a4500c8406adcdf05e0efacbcee772cc.jpg\",\n  \"249713.jpg\": \"Aves/Ortalis vetula/462032cf51d300120c0aad57d2ad970e.jpg\",\n  \"249715.jpg\": \"Aves/Ortalis vetula/d311e01684be29dd3db0cd9883d12bcb.jpg\",\n  \"249716.jpg\": \"Aves/Ortalis vetula/7758b34b8436121fb82d2133bc9a985b.jpg\",\n  \"249717.jpg\": \"Aves/Ortalis vetula/a5bdd3a758bbced9def2e4b976a99c0a.jpg\",\n  \"249718.jpg\": \"Aves/Ortalis vetula/d68066e403122a178ea9566834d2b087.jpg\",\n  \"249719.jpg\": \"Aves/Ortalis vetula/36669f38f94d6e955b3acb97e6d33186.jpg\",\n  \"249732.jpg\": \"Aves/Ortalis vetula/47b67f1271062f0a3a8792f4253d0291.jpg\",\n  \"249735.jpg\": \"Aves/Ortalis vetula/51347cf0062a70500861173707b205fc.jpg\",\n  \"249739.jpg\": \"Aves/Ortalis vetula/d685a9a9e2a6aaeb8f1feaf26b444305.jpg\",\n  \"249741.jpg\": \"Aves/Ortalis vetula/95ac3888f94d1ebe971cd24ed1d5833c.jpg\",\n  \"249746.jpg\": \"Aves/Ortalis vetula/94fd6fb235274f98f5bff108fb8dd507.jpg\",\n  \"249751.jpg\": \"Aves/Ortalis vetula/ae42664bdfeb55c048c584f2656acb71.jpg\",\n  \"249752.jpg\": \"Aves/Ortalis vetula/8384699fda5068c515f4a3196c6ec0a7.jpg\",\n  \"249767.jpg\": \"Aves/Ortalis vetula/f3869d0de33334f07919ebc21df58e2c.jpg\",\n  \"249782.jpg\": \"Aves/Ortalis vetula/231016365af3a5ceb349fde3a3e0ec92.jpg\",\n  \"249785.jpg\": \"Aves/Ortalis vetula/54766bd3f53f222ead19d92523447111.jpg\",\n  \"249790.jpg\": \"Insecta/Eantis tamenund/a44c557ea395b47ffc63f3a4dff98cfa.jpg\",\n  \"249791.jpg\": \"Insecta/Eantis tamenund/81eb62dc21ae5f8f505f8ce8b9457d7c.jpg\",\n  \"249793.jpg\": \"Insecta/Eantis tamenund/07a834da1bb866dbc0855728b2d0a0e2.jpg\",\n  \"249794.jpg\": \"Insecta/Eantis tamenund/ad45e4c012b96762a77874f57ed5a139.jpg\",\n  \"249795.jpg\": \"Insecta/Eantis tamenund/ddc3ae319b2cdb8c38eba86952e96b93.jpg\",\n  \"249798.jpg\": \"Insecta/Eantis tamenund/d903c07bd60bc86c738fd26b38b358e8.jpg\",\n  \"249799.jpg\": \"Insecta/Eantis tamenund/84dff34c7dd5602925b40d987c7e9b71.jpg\",\n  \"249801.jpg\": \"Insecta/Eantis tamenund/11375ae224435d04b87d3a3b700b0dcf.jpg\",\n  \"249803.jpg\": \"Insecta/Eantis tamenund/3b3ad37f7211b8062aa0a9716e04b682.jpg\",\n  \"249805.jpg\": \"Insecta/Eantis tamenund/57575afdc4643af3bd516d4b52e4b00d.jpg\",\n  \"249809.jpg\": \"Insecta/Eantis tamenund/85263c11f1190815c6324a73bb0222e6.jpg\",\n  \"249810.jpg\": \"Insecta/Eantis tamenund/d7d0525003a2d5b8430ebd874992a9ac.jpg\",\n  \"249817.jpg\": \"Insecta/Eantis tamenund/91ee2267e68824d4ff1afbfdf6e2b5b6.jpg\",\n  \"249819.jpg\": \"Insecta/Eantis tamenund/19e449a148a8aa222d243dcb321e1125.jpg\",\n  \"249820.jpg\": \"Insecta/Eantis tamenund/2e2182d70566f7e3831ff0d1220c35c2.jpg\",\n  \"249823.jpg\": \"Insecta/Eantis tamenund/dcd77d8a86bf5a7da460990c87923b30.jpg\",\n  \"249824.jpg\": \"Insecta/Eantis tamenund/0e7b28187352cc2357898b1a323e20b7.jpg\",\n  \"249826.jpg\": \"Insecta/Diabrotica undecimpunctata undecimpunctata/f6bf1fa283196ba8875d556f84c7b998.jpg\",\n  \"249834.jpg\": \"Insecta/Diabrotica undecimpunctata undecimpunctata/82d6a94abe58b12847b588a06ce25407.jpg\",\n  \"249835.jpg\": \"Insecta/Diabrotica undecimpunctata undecimpunctata/e870983cfbaf00374db009a7cd441c3b.jpg\",\n  \"249841.jpg\": \"Insecta/Polistes fuscatus/b1893bde15a954c7408e5543dc0ef533.jpg\",\n  \"249842.jpg\": \"Insecta/Polistes fuscatus/3bbdc2a583f39c4ce04ce9f94d01f136.jpg\",\n  \"249844.jpg\": \"Insecta/Polistes fuscatus/a6f709efa67b25e3b7fb6e798409259b.jpg\",\n  \"249846.jpg\": \"Insecta/Polistes fuscatus/0bdcef8de011c622b9fd1e6d3a8334cd.jpg\",\n  \"249848.jpg\": \"Insecta/Polistes fuscatus/024b7b2b8881470270fec02cb13e9e21.jpg\",\n  \"249849.jpg\": \"Insecta/Polistes fuscatus/e1ce949bcb454ff07820e978483f2686.jpg\",\n  \"249850.jpg\": \"Insecta/Polistes fuscatus/0df8bd907d649519e98bf715bf10f1e7.jpg\",\n  \"249851.jpg\": \"Insecta/Polistes fuscatus/1bb0f2e1b37e0b4842d387a7d54b9691.jpg\",\n  \"249853.jpg\": \"Insecta/Polistes fuscatus/67f1e8ac48f1726b80f3ee3404795dde.jpg\",\n  \"249854.jpg\": \"Insecta/Polistes fuscatus/19fb76106410df9d934f919afb12bb29.jpg\",\n  \"249858.jpg\": \"Insecta/Polistes fuscatus/ebf744242b44718f5c75ccf6696effc7.jpg\",\n  \"249862.jpg\": \"Insecta/Polistes fuscatus/9df8f7407257295719ddf8138616b0cb.jpg\",\n  \"249868.jpg\": \"Insecta/Polistes fuscatus/bc7c2e4cc6d267d68d98a034ee88091c.jpg\",\n  \"249876.jpg\": \"Insecta/Polistes fuscatus/1773906f176fca6626dc6afcc5dca8ce.jpg\",\n  \"249877.jpg\": \"Insecta/Polistes fuscatus/ccb1defe7b080bdd5cff2ce59e72bb48.jpg\",\n  \"249881.jpg\": \"Insecta/Polistes fuscatus/50f559c5bceebcedf759651a17e48465.jpg\",\n  \"249882.jpg\": \"Insecta/Polistes fuscatus/a3cfbfe6c3a4aeac80491b55ec2a1e8c.jpg\",\n  \"249883.jpg\": \"Insecta/Polistes fuscatus/5d1c3f2f7311149ba7765d77e22229ae.jpg\",\n  \"249884.jpg\": \"Insecta/Polistes fuscatus/b10a83da6c1e35502e73db157433c538.jpg\",\n  \"249885.jpg\": \"Insecta/Polistes fuscatus/2af48f1fe1695265ef65536091140589.jpg\",\n  \"249886.jpg\": \"Insecta/Polistes fuscatus/5b939955ca3c41be853f6b7faead4b79.jpg\",\n  \"249887.jpg\": \"Insecta/Polistes fuscatus/4fade047610a5c3aaac7ebde60276846.jpg\",\n  \"249894.jpg\": \"Insecta/Sphecius speciosus/ff5130d26ab8b72a3065a0708a4c15b3.jpg\",\n  \"249895.jpg\": \"Insecta/Sphecius speciosus/3bf82098ffc31431faa8f8efd18541ba.jpg\",\n  \"249897.jpg\": \"Insecta/Sphecius speciosus/d4971e831345e3dcdc275171726d7213.jpg\",\n  \"249901.jpg\": \"Insecta/Sphecius speciosus/4c4162cbd706e82def87290524e75a3b.jpg\",\n  \"249905.jpg\": \"Insecta/Sphecius speciosus/3959d5f217931cb44db61d770e4477bf.jpg\",\n  \"249911.jpg\": \"Insecta/Sphecius speciosus/f7e59b9f34c300bf4f7320686f99d5a0.jpg\",\n  \"249918.jpg\": \"Insecta/Sphecius speciosus/8cbd3da28587cd838e0a07eaaab9d99b.jpg\",\n  \"249922.jpg\": \"Insecta/Sphecius speciosus/3e354d75ad37109d6b497251c022c952.jpg\",\n  \"249928.jpg\": \"Insecta/Sphecius speciosus/7fd74e52ab3cc64b30652763a31b12bd.jpg\",\n  \"249929.jpg\": \"Insecta/Sphecius speciosus/9caa8e0b67f698d0dcf39cabdc99082c.jpg\",\n  \"249936.jpg\": \"Insecta/Sphecius speciosus/a9b6787e24c75aaf2166a3e7650ee534.jpg\",\n  \"249944.jpg\": \"Insecta/Sphecius speciosus/81ad57e9fbdaacd4389b6d65abcd8791.jpg\",\n  \"249945.jpg\": \"Insecta/Sphecius speciosus/7fbde4ea847cc24ecedacc4d62a24a85.jpg\",\n  \"249946.jpg\": \"Insecta/Sphecius speciosus/61ac08befd5250ff251227c2f5358460.jpg\",\n  \"249948.jpg\": \"Insecta/Sphecius speciosus/766aee8f389cefca4b031f61cdc9af2c.jpg\",\n  \"249951.jpg\": \"Insecta/Sphecius speciosus/a0f75e296784732a5aae2ce10f8354c8.jpg\",\n  \"250042.jpg\": \"Insecta/Hypoprepia fucosa/2073434a5bc331831d8f6f44f39432fd.jpg\",\n  \"250046.jpg\": \"Insecta/Hypoprepia fucosa/f52bf991450218d0b1f149b8c54b8ec9.jpg\",\n  \"250050.jpg\": \"Insecta/Hypoprepia fucosa/80bc076693bd5132ea959b92ad8f3301.jpg\",\n  \"250051.jpg\": \"Insecta/Hypoprepia fucosa/db2c3301a8d1b06e3662870b5ba02742.jpg\",\n  \"250052.jpg\": \"Insecta/Hypoprepia fucosa/1a330a10a73cae9ce5aefeae4a85b916.jpg\",\n  \"250055.jpg\": \"Insecta/Hypoprepia fucosa/ccf2e1444a330e0c4340eadc80ba0123.jpg\",\n  \"250060.jpg\": \"Insecta/Hypoprepia fucosa/ba9a35465d30d30aaaa7b841de4852ab.jpg\",\n  \"250064.jpg\": \"Insecta/Hypoprepia fucosa/2f7b3774e447837781c64753832c3071.jpg\",\n  \"250066.jpg\": \"Insecta/Hypoprepia fucosa/85c7c4cd3855d2058acc69c96043a066.jpg\",\n  \"250073.jpg\": \"Insecta/Hypoprepia fucosa/8017a31a38b21f0075d2ea33acab0b8a.jpg\",\n  \"250081.jpg\": \"Insecta/Hypoprepia fucosa/42aa5abc0046517b9e6d5d342a667e9b.jpg\",\n  \"250082.jpg\": \"Insecta/Hypoprepia fucosa/130880596862967ac3cc5761ac884184.jpg\",\n  \"250083.jpg\": \"Insecta/Hypoprepia fucosa/454276a6bf9b5ccb00624b33f88f304f.jpg\",\n  \"250088.jpg\": \"Insecta/Hypoprepia fucosa/a2cba20262decbf71b303be518d17551.jpg\",\n  \"250095.jpg\": \"Insecta/Hypoprepia fucosa/4bc9d05862b323fc4fbc690ceebf608e.jpg\",\n  \"250098.jpg\": \"Insecta/Hypoprepia fucosa/76d0ef5764362ff81b0237cffdc6257b.jpg\",\n  \"250101.jpg\": \"Insecta/Hypoprepia fucosa/b554693b4b861fe932a95ec4df5cb49d.jpg\",\n  \"250108.jpg\": \"Insecta/Hypoprepia fucosa/1a13062568ad4827de3486f7d155b10f.jpg\",\n  \"250109.jpg\": \"Insecta/Hypoprepia fucosa/679e5f6b5d2274b9274d1b7f4f5dee1a.jpg\",\n  \"250110.jpg\": \"Insecta/Hypoprepia fucosa/7ebf4901540d40314101b73b1352941d.jpg\",\n  \"250144.jpg\": \"Mammalia/Sciurus carolinensis/832409efe8282ee99f8b5a2be48847b4.jpg\",\n  \"250179.jpg\": \"Mammalia/Sciurus carolinensis/b2411a7179ed3d5d494c1b0f6ac89637.jpg\",\n  \"250182.jpg\": \"Mammalia/Sciurus carolinensis/335eb99201b3aafdcd9a47ff133e75f2.jpg\",\n  \"250184.jpg\": \"Mammalia/Sciurus carolinensis/c16ee640ffe18451900048d28e6bd069.jpg\",\n  \"250185.jpg\": \"Mammalia/Sciurus carolinensis/1d2cfcc030c4d43c3260297b512446ef.jpg\",\n  \"2502.jpg\": \"Aves/Picoides pubescens/79544a78ceba589b46ce8cda2aee4346.jpg\",\n  \"250290.jpg\": \"Mammalia/Sciurus carolinensis/8a04dc231c15c9bf950afa23d3d3820b.jpg\",\n  \"250304.jpg\": \"Mammalia/Sciurus carolinensis/ce7078b3681b4ef6054809e856ec702a.jpg\",\n  \"250333.jpg\": \"Mammalia/Sciurus carolinensis/89bfc196fe026142b2af3a85d46e51c1.jpg\",\n  \"250345.jpg\": \"Mammalia/Sciurus carolinensis/f2e91515f9b5cf7652c7e6a7b50910d1.jpg\",\n  \"250372.jpg\": \"Mammalia/Sciurus carolinensis/5798fc89c31296806d525efbf639df45.jpg\",\n  \"250420.jpg\": \"Mammalia/Sciurus carolinensis/7848b6b13defce5a0b4c2dcbbd12216d.jpg\",\n  \"250457.jpg\": \"Mammalia/Sciurus carolinensis/ad5e93fea106a83a38c9af01e49bbab5.jpg\",\n  \"250540.jpg\": \"Mammalia/Sciurus carolinensis/bbe1dc26b8a1e5bbbe836070e10c9db5.jpg\",\n  \"250726.jpg\": \"Mammalia/Sciurus carolinensis/931f8caa3438c65162d1986451e08c58.jpg\",\n  \"250773.jpg\": \"Mammalia/Sciurus carolinensis/728c058cdd6fd8deca0c066b9176d6c7.jpg\",\n  \"250821.jpg\": \"Mammalia/Sciurus carolinensis/8fbf16a8bd7aa7631ac94847f11afc59.jpg\",\n  \"250852.jpg\": \"Mammalia/Sciurus carolinensis/f1c5ac82caa65b564ee460509dfbc2ad.jpg\",\n  \"250958.jpg\": \"Mammalia/Sciurus carolinensis/7e5c90acbd3efa429684c56ceae672cb.jpg\",\n  \"251017.jpg\": \"Mammalia/Sciurus carolinensis/ba0012510b8d69915b015dddac2aa3fc.jpg\",\n  \"251046.jpg\": \"Mammalia/Sciurus carolinensis/9c9da3f236090e7cbefe43f0c2b2e505.jpg\",\n  \"251135.jpg\": \"Aves/Busarellus nigricollis/294f9e37c942abed7fa5e7652e8d1dcb.jpg\",\n  \"251138.jpg\": \"Aves/Busarellus nigricollis/bb870d956c0ff08e02ca62def08eedfc.jpg\",\n  \"251141.jpg\": \"Aves/Busarellus nigricollis/76e35b357487cc1089009aac75df5010.jpg\",\n  \"251143.jpg\": \"Aves/Busarellus nigricollis/159569d436e1460916ea53f568efd888.jpg\",\n  \"251144.jpg\": \"Aves/Busarellus nigricollis/04b40c4d081f087e314228a52bf3e0c2.jpg\",\n  \"251206.jpg\": \"Insecta/Libellula luctuosa/4d6a9bf3e10d5117a0956e805647078f.jpg\",\n  \"251216.jpg\": \"Insecta/Libellula luctuosa/c107ef088f10f4233dac6be42065440a.jpg\",\n  \"251249.jpg\": \"Insecta/Libellula luctuosa/c6c17daa14cd7f61f46406e81dd27dfa.jpg\",\n  \"251273.jpg\": \"Insecta/Libellula luctuosa/975a624dbebf6237c094bd7feec5bb37.jpg\",\n  \"251291.jpg\": \"Insecta/Libellula luctuosa/8fac59151314f98a1cd50acf68e66ce4.jpg\",\n  \"251312.jpg\": \"Insecta/Libellula luctuosa/45cf1d9697551bb70c6f9d9892b1b5e8.jpg\",\n  \"251335.jpg\": \"Insecta/Libellula luctuosa/b4aa66400b375673f68727b92c5041f5.jpg\",\n  \"251338.jpg\": \"Insecta/Libellula luctuosa/0af658a8e21924e99523046142b7693c.jpg\",\n  \"251371.jpg\": \"Insecta/Libellula luctuosa/f076ebdeb7aef0fee3c74896d501ddb2.jpg\",\n  \"251385.jpg\": \"Insecta/Libellula luctuosa/a5220a99d564d230cb346365173468d6.jpg\",\n  \"251389.jpg\": \"Insecta/Libellula luctuosa/3c899fda73b9be2bce29835b3e817ceb.jpg\",\n  \"251405.jpg\": \"Insecta/Libellula luctuosa/11396eb86d44c3ab6874e6627df53a59.jpg\",\n  \"251440.jpg\": \"Insecta/Libellula luctuosa/6fb1b1abbc4e5e9c62d1630c7cc86a75.jpg\",\n  \"251453.jpg\": \"Insecta/Libellula luctuosa/42ab0b80fd85845ea25c2daadd03764e.jpg\",\n  \"251465.jpg\": \"Insecta/Libellula luctuosa/51218425bd247ad36f2109830bb34a4e.jpg\",\n  \"251491.jpg\": \"Insecta/Libellula luctuosa/1b8ee676c710cb3b83a8744ce177be95.jpg\",\n  \"251496.jpg\": \"Insecta/Libellula luctuosa/3477155e724cf7a7f7f01b2f9c5a7685.jpg\",\n  \"251506.jpg\": \"Insecta/Libellula luctuosa/a07cefcf9f7be8cbc86fcc9f5ad1c398.jpg\",\n  \"251512.jpg\": \"Insecta/Libellula luctuosa/8063d524cb60e44285ac0e391bd19dcd.jpg\",\n  \"251529.jpg\": \"Insecta/Libellula luctuosa/b6e1f4620156f68ed992e53497b14bd8.jpg\",\n  \"251543.jpg\": \"Insecta/Libellula luctuosa/02d10aec4b4a16b6c1d8cadbd45acaa7.jpg\",\n  \"251557.jpg\": \"Insecta/Libellula luctuosa/c7bc8d3254d6151193206aaf4af55717.jpg\",\n  \"251581.jpg\": \"Insecta/Libellula luctuosa/adc3fa4f8f94dc9b6d126f979db88b21.jpg\",\n  \"251588.jpg\": \"Insecta/Libellula luctuosa/c220968c5500774bdc894417a7b0840f.jpg\",\n  \"251604.jpg\": \"Insecta/Libellula luctuosa/0b247ee5513f34170c78ea02b5aebdb8.jpg\",\n  \"251683.jpg\": \"Insecta/Libellula luctuosa/adac4e7efff73370572c2ca536ab102c.jpg\",\n  \"251790.jpg\": \"Mammalia/Enhydra lutris nereis/683594e0599f133ed4e10729b14688b7.jpg\",\n  \"251802.jpg\": \"Mammalia/Enhydra lutris nereis/b206d3e822c822805927ac7b8479bf7d.jpg\",\n  \"251812.jpg\": \"Mammalia/Enhydra lutris nereis/f375e085e50a7424ca45128e18342170.jpg\",\n  \"251820.jpg\": \"Mammalia/Enhydra lutris nereis/03552062adc835b189961773b58b31bf.jpg\",\n  \"251839.jpg\": \"Mammalia/Enhydra lutris nereis/3b99d519c3f0c3c679b0ff20fb3365c9.jpg\",\n  \"251851.jpg\": \"Mammalia/Enhydra lutris nereis/46b8676707d82192a2729f969d95d8a0.jpg\",\n  \"251856.jpg\": \"Mammalia/Enhydra lutris nereis/4a6ec58dc582d60305ac8dcaa76d8115.jpg\",\n  \"251857.jpg\": \"Mammalia/Enhydra lutris nereis/f3cacda9cc427e3050b273f51ab55160.jpg\",\n  \"251858.jpg\": \"Mammalia/Enhydra lutris nereis/3829c5badc6105016061a4a9a9cc1fe0.jpg\",\n  \"251867.jpg\": \"Mammalia/Enhydra lutris nereis/80bdade6132f0f1afbf0fbd886582eee.jpg\",\n  \"251869.jpg\": \"Mammalia/Enhydra lutris nereis/372c5d9a595045b0d33eb26c99eff364.jpg\",\n  \"251873.jpg\": \"Arachnida/Dermacentor variabilis/6180634bb3851fa2196f02a836c3b6ab.jpg\",\n  \"251874.jpg\": \"Arachnida/Dermacentor variabilis/b628de3e2d5d0b172ff73b2479cff059.jpg\",\n  \"251875.jpg\": \"Arachnida/Dermacentor variabilis/bb6b7f88a9ffcdec36b6147cda4f92ec.jpg\",\n  \"251880.jpg\": \"Arachnida/Dermacentor variabilis/021b523ae057385f260f8867eba4c5f9.jpg\",\n  \"251886.jpg\": \"Arachnida/Dermacentor variabilis/19e3b87266532c8c2c637a3b39bee9e7.jpg\",\n  \"251888.jpg\": \"Arachnida/Dermacentor variabilis/dcf8c8c7a76aeeec08181485c22fa686.jpg\",\n  \"251891.jpg\": \"Arachnida/Dermacentor variabilis/7f0e6333d8f5444927ddd6e10a5734c2.jpg\",\n  \"251897.jpg\": \"Arachnida/Dermacentor variabilis/338fd078d1e82680b7a36b638f380db6.jpg\",\n  \"251900.jpg\": \"Arachnida/Dermacentor variabilis/2dbded7fd435889d3fd6619dd77443b0.jpg\",\n  \"251907.jpg\": \"Arachnida/Dermacentor variabilis/5166c6db3585f831bf8a974f9c6d4251.jpg\",\n  \"251908.jpg\": \"Arachnida/Dermacentor variabilis/8094016322fb7a84fa92eb9e82eb1297.jpg\",\n  \"251909.jpg\": \"Arachnida/Dermacentor variabilis/d2a3532be0edeb90fd44e770adf5a10a.jpg\",\n  \"251912.jpg\": \"Arachnida/Dermacentor variabilis/04f775c9c3676982509b6be6a215a641.jpg\",\n  \"251919.jpg\": \"Arachnida/Dermacentor variabilis/42cf8f6f611c614dfc3abb2ceb9f7a22.jpg\",\n  \"251920.jpg\": \"Arachnida/Dermacentor variabilis/7d6a6d367523630f80ec8bf3370deff4.jpg\",\n  \"251921.jpg\": \"Arachnida/Dermacentor variabilis/cd089aaa45c5117f805b608d721e562f.jpg\",\n  \"251926.jpg\": \"Arachnida/Dermacentor variabilis/614fa56e535c1c3f9af504500c560a09.jpg\",\n  \"251928.jpg\": \"Insecta/Epitheca princeps/3d8d30a9e842d55a61d8f24fbc3d6ba1.jpg\",\n  \"251936.jpg\": \"Insecta/Epitheca princeps/274801f770e83ae9dba5be90d8d18409.jpg\",\n  \"251937.jpg\": \"Insecta/Epitheca princeps/10e290efb45918803a33c8d51cd6512d.jpg\",\n  \"251938.jpg\": \"Insecta/Epitheca princeps/2e6d5a2f14f9142b12bb96a1f1adccb7.jpg\",\n  \"251940.jpg\": \"Insecta/Epitheca princeps/8d53185e8ee9bb11cbe70d1c587f5c55.jpg\",\n  \"251943.jpg\": \"Insecta/Epitheca princeps/c00998e96514c30f3ffcd5beb1fbff37.jpg\",\n  \"251946.jpg\": \"Insecta/Epitheca princeps/6a0b435756784016a177430d97649d59.jpg\",\n  \"251947.jpg\": \"Insecta/Epitheca princeps/00a481e948949e2e17b6e454b844f6f4.jpg\",\n  \"251948.jpg\": \"Insecta/Epitheca princeps/8508e489d0712bb8f61433ea4fad436c.jpg\",\n  \"251950.jpg\": \"Insecta/Epitheca princeps/c43b8a90d132e79bc89092c0f0c901cf.jpg\",\n  \"251951.jpg\": \"Insecta/Epitheca princeps/11b4643edf71d03d16c3b7667ee6408a.jpg\",\n  \"251957.jpg\": \"Insecta/Epitheca princeps/a6ca9bbdf64e35277a2d4471113f0d72.jpg\",\n  \"251959.jpg\": \"Insecta/Epitheca princeps/7c306735b7609408647d8490463c22ba.jpg\",\n  \"251962.jpg\": \"Insecta/Epitheca princeps/abc45ee36ecf1951cf7bf50607de0618.jpg\",\n  \"251963.jpg\": \"Insecta/Epitheca princeps/ba69f659f7f4152b2be7f0dd2a3ee46d.jpg\",\n  \"251965.jpg\": \"Insecta/Epitheca princeps/0dcfbab7b79f893e3995bf714a7c6e73.jpg\",\n  \"251966.jpg\": \"Insecta/Epitheca princeps/802d96a3ade3db975f60aa316e7a8182.jpg\",\n  \"251967.jpg\": \"Insecta/Epitheca princeps/ca3800a2412b1e5d2f3d888d05e61862.jpg\",\n  \"251971.jpg\": \"Insecta/Epitheca princeps/b229a8bc39ffe5e12ce84eed8239fdc8.jpg\",\n  \"252004.jpg\": \"Aves/Tyrannus forficatus/0a0953ef338a7eb3fbda64c0379a5d07.jpg\",\n  \"252012.jpg\": \"Aves/Tyrannus forficatus/da2853032a51dbd4490089661f4bb4a7.jpg\",\n  \"252014.jpg\": \"Aves/Tyrannus forficatus/b8c620c6c613be8ff62a73c2d46d942d.jpg\",\n  \"252028.jpg\": \"Aves/Tyrannus forficatus/198c9ee40b6ab84a7e0e95520154bd9c.jpg\",\n  \"252035.jpg\": \"Aves/Tyrannus forficatus/f9fff30ef9098eef297077c4487ed027.jpg\",\n  \"252048.jpg\": \"Aves/Tyrannus forficatus/c24924997a2c07596373bbb13dc3bc4c.jpg\",\n  \"252049.jpg\": \"Aves/Tyrannus forficatus/279f09dbbb9ffe162cc87857a41735fa.jpg\",\n  \"252098.jpg\": \"Aves/Tyrannus forficatus/f319648a8e3bbed0de65be7579ae0e78.jpg\",\n  \"252109.jpg\": \"Aves/Tyrannus forficatus/c91c94fc050c5da3493380611b5b9511.jpg\",\n  \"252116.jpg\": \"Aves/Tyrannus forficatus/5470b63ae55a178759d1a3e861de1b26.jpg\",\n  \"252143.jpg\": \"Aves/Tyrannus forficatus/c3d4203b27489d3e7e8164983613a09e.jpg\",\n  \"252165.jpg\": \"Aves/Tyrannus forficatus/ff75e1e59a07115265a6cbc1b2e09dec.jpg\",\n  \"252181.jpg\": \"Aves/Tyrannus forficatus/5f1b021d4e197ddf917d14b0b30fd5b1.jpg\",\n  \"252196.jpg\": \"Aves/Tyrannus forficatus/69c81dffd954732362755465a891954e.jpg\",\n  \"252219.jpg\": \"Aves/Tyrannus forficatus/f502ca1bca0541672a1b90a71dd9c6b5.jpg\",\n  \"252233.jpg\": \"Aves/Tyrannus forficatus/4097602e0c4a7316ec351ef1aec9abc3.jpg\",\n  \"252239.jpg\": \"Aves/Tyrannus forficatus/339745f4e4dbcac7de7a3b50c74ce1ea.jpg\",\n  \"252245.jpg\": \"Aves/Tyrannus forficatus/c1c5da8672be92ca7a8c1faf45bf9e0d.jpg\",\n  \"252263.jpg\": \"Aves/Tyrannus forficatus/b86cb4554f2b580ee5390eb8ef0a5219.jpg\",\n  \"252293.jpg\": \"Aves/Tyrannus forficatus/28dae797d38a4c275d7242788bb559fa.jpg\",\n  \"252297.jpg\": \"Amphibia/Notophthalmus viridescens viridescens/ace3f8a9159a5166240bf72684bf0e38.jpg\",\n  \"252301.jpg\": \"Amphibia/Notophthalmus viridescens viridescens/7720c3a376b4f17651a9f6536f51bd5c.jpg\",\n  \"252302.jpg\": \"Amphibia/Notophthalmus viridescens viridescens/ee64ffaabc8ab8192ed2f6ea01bfd291.jpg\",\n  \"252303.jpg\": \"Amphibia/Notophthalmus viridescens viridescens/fe9a02ba5326cec4cc10a24b718cf7ed.jpg\",\n  \"252311.jpg\": \"Amphibia/Notophthalmus viridescens viridescens/7a5902eb13ab5f476bca676359628213.jpg\",\n  \"252312.jpg\": \"Amphibia/Notophthalmus viridescens viridescens/eafdf0ef2774af0eb500daf8c9a3311e.jpg\",\n  \"252319.jpg\": \"Amphibia/Notophthalmus viridescens viridescens/7d794d351252bdf77e7dbeb5be0dcebe.jpg\",\n  \"252322.jpg\": \"Amphibia/Notophthalmus viridescens viridescens/91f60cf7f577b326bd733b9d3b12ad4f.jpg\",\n  \"252337.jpg\": \"Amphibia/Notophthalmus viridescens viridescens/99a174eb94ecef89c87c6dc9002bd6c4.jpg\",\n  \"252341.jpg\": \"Amphibia/Notophthalmus viridescens viridescens/818bd185c28414916884484681b05194.jpg\",\n  \"252346.jpg\": \"Amphibia/Notophthalmus viridescens viridescens/1896fb6d98eab974eb491334ce711d66.jpg\",\n  \"252347.jpg\": \"Amphibia/Notophthalmus viridescens viridescens/a3e50125d9e4c42aef8149a303626c78.jpg\",\n  \"252355.jpg\": \"Amphibia/Notophthalmus viridescens viridescens/75a4c9b41f8f08927b6fe42c0149f371.jpg\",\n  \"252356.jpg\": \"Amphibia/Notophthalmus viridescens viridescens/0695d9ac304d4adefc7d2be2175f296b.jpg\",\n  \"252359.jpg\": \"Amphibia/Notophthalmus viridescens viridescens/92b75d8799ac4d6fccd947e80325ef95.jpg\",\n  \"252362.jpg\": \"Amphibia/Notophthalmus viridescens viridescens/c20b3ba0ce7bf567938828aa9b15aff4.jpg\",\n  \"252366.jpg\": \"Amphibia/Notophthalmus viridescens viridescens/0e799970792ceefe4ccdd75526a63fb7.jpg\",\n  \"252368.jpg\": \"Amphibia/Notophthalmus viridescens viridescens/14f26bd726d30fd6f107d661f780d083.jpg\",\n  \"252380.jpg\": \"Actinopterygii/Scarus rubroviolaceus/3a9440a48e3b93726e0175e102ef8919.jpg\",\n  \"252386.jpg\": \"Actinopterygii/Scarus rubroviolaceus/cbcd42dc66583b200d3788091dd49be3.jpg\",\n  \"252395.jpg\": \"Aves/Pachyramphus aglaiae/d09848b1b775e75d0eb66e008eda09d8.jpg\",\n  \"252399.jpg\": \"Aves/Pachyramphus aglaiae/d110b95a7fe0bce2ffac9d017fe76375.jpg\",\n  \"252404.jpg\": \"Aves/Pachyramphus aglaiae/21bc3b652fe5d0ff75ef6be3a7ae5a80.jpg\",\n  \"252406.jpg\": \"Aves/Pachyramphus aglaiae/df9e4437730be8cfd573e71faac5d4da.jpg\",\n  \"252416.jpg\": \"Aves/Pachyramphus aglaiae/833a1ed92f4a07e95cb52815aa475de1.jpg\",\n  \"252425.jpg\": \"Aves/Pachyramphus aglaiae/a1cc71ea314b2453920f1ff667557abf.jpg\",\n  \"252432.jpg\": \"Aves/Pachyramphus aglaiae/9a49887697a47d585afc0e17db4e8616.jpg\",\n  \"2525.jpg\": \"Aves/Picoides pubescens/51e08c1d0ccddb78b79ac3d7016ae589.jpg\",\n  \"252619.jpg\": \"Insecta/Phyciodes cocyta/c671606673a4bde948bb4c3e180104e5.jpg\",\n  \"252620.jpg\": \"Insecta/Phyciodes cocyta/3961e35c574bd1b0febf97530e77f9ee.jpg\",\n  \"252621.jpg\": \"Insecta/Phyciodes cocyta/ed63aba70e9bd66c3ce97b84f218afb5.jpg\",\n  \"252622.jpg\": \"Insecta/Phyciodes cocyta/fa4a1a791a9e368fb45b50663b52d892.jpg\",\n  \"252623.jpg\": \"Insecta/Phyciodes cocyta/37cc3d2f5aae42499fe126f02b57d2e7.jpg\",\n  \"252625.jpg\": \"Insecta/Phyciodes cocyta/c0c59e70ff9b9051167b0f292cec4ed0.jpg\",\n  \"252628.jpg\": \"Insecta/Phyciodes cocyta/cac3f70381b6f691a44fb5e58021cc00.jpg\",\n  \"252631.jpg\": \"Insecta/Phyciodes cocyta/764015215718d4b289bcb05b535ccca8.jpg\",\n  \"252632.jpg\": \"Insecta/Phyciodes cocyta/4280a706f97f5b15fffe19e8a4f1e964.jpg\",\n  \"252635.jpg\": \"Insecta/Phyciodes cocyta/ec26b056a481719a85e02d94e5b40fdd.jpg\",\n  \"252639.jpg\": \"Insecta/Phyciodes cocyta/c763c311b9b09af2954e968abca138fc.jpg\",\n  \"252643.jpg\": \"Insecta/Phyciodes cocyta/ea36b82d2fd57c93b7e7a6dfa9c604e5.jpg\",\n  \"252646.jpg\": \"Insecta/Phyciodes cocyta/9b06e90fce136d54c4f9e2d85732269c.jpg\",\n  \"252647.jpg\": \"Insecta/Phyciodes cocyta/2a74608943b5f873cb9124cd3995db3f.jpg\",\n  \"252648.jpg\": \"Insecta/Phyciodes cocyta/83e2b4d553308ed9615f99fb8ef597f3.jpg\",\n  \"252649.jpg\": \"Insecta/Phyciodes cocyta/f313e71d22b41a4cb89db85d4b03242f.jpg\",\n  \"252650.jpg\": \"Insecta/Phyciodes cocyta/cb46ade8e962d46b5f057b7b54919ccf.jpg\",\n  \"252651.jpg\": \"Insecta/Phyciodes cocyta/51db08c5cef1e50d234264c96a6a2848.jpg\",\n  \"252655.jpg\": \"Insecta/Phyciodes cocyta/1f0bfa0285f5fc32f2ad214fb7daba13.jpg\",\n  \"252656.jpg\": \"Insecta/Phyciodes cocyta/ee1245586040e332d51aaac87c9763c7.jpg\",\n  \"252658.jpg\": \"Insecta/Phyciodes cocyta/5e2adda31f249bb95407a0337581bca2.jpg\",\n  \"252659.jpg\": \"Insecta/Phyciodes cocyta/f3cb422de1a66319abf743ae45f85a96.jpg\",\n  \"252679.jpg\": \"Mammalia/Lynx rufus/a736e37997aff0d0bdaad881bf597777.jpg\",\n  \"252735.jpg\": \"Mammalia/Lynx rufus/c793fb8d528a5efda12e88ff2ce223e5.jpg\",\n  \"252743.jpg\": \"Mammalia/Lynx rufus/165d6b013ef624f7b845ce9a28f42cc9.jpg\",\n  \"252746.jpg\": \"Mammalia/Lynx rufus/c21359d851604b0cf095216b2c43781f.jpg\",\n  \"252775.jpg\": \"Mammalia/Lynx rufus/a3e3c14bfda3f5cb6f761cdfdb7a765e.jpg\",\n  \"252799.jpg\": \"Mammalia/Lynx rufus/31f408e32bd06e84480b4b5e494999d5.jpg\",\n  \"252811.jpg\": \"Mammalia/Lynx rufus/3898cbac62cb59cb5bbfdae8e99de9dd.jpg\",\n  \"252839.jpg\": \"Mammalia/Lynx rufus/19ea60b471c3059cf07fd7403adcf1b1.jpg\",\n  \"252890.jpg\": \"Mammalia/Lynx rufus/702d30124640775ac77361260163e395.jpg\",\n  \"252897.jpg\": \"Mammalia/Lynx rufus/57aef0530c55ca54c845309252814a7e.jpg\",\n  \"252939.jpg\": \"Mammalia/Lynx rufus/89a1aa6b8ebaa65e2e9d5657fea6a828.jpg\",\n  \"252941.jpg\": \"Mammalia/Lynx rufus/60d78c95ba40191f88d4534550693f70.jpg\",\n  \"252946.jpg\": \"Mammalia/Lynx rufus/cf543aac5d5ebc6dde2295f3e129003c.jpg\",\n  \"252970.jpg\": \"Mammalia/Lynx rufus/9b7048b78c3c146d2d1734c3b476acc9.jpg\",\n  \"253004.jpg\": \"Mammalia/Lynx rufus/9c71157904191f689459a7784b088ba6.jpg\",\n  \"253018.jpg\": \"Mammalia/Lynx rufus/2dfebe059c9b5b3bdc37f2e6ae47aec4.jpg\",\n  \"253028.jpg\": \"Mammalia/Lynx rufus/a01788ffc5561cb393407c3a7f5ee70d.jpg\",\n  \"253036.jpg\": \"Mammalia/Lynx rufus/a16f077fd1d6225106b18955868d3a7b.jpg\",\n  \"253040.jpg\": \"Mammalia/Lynx rufus/53d6dcbb3a3ea09b89e21f8cff3fb3c4.jpg\",\n  \"253089.jpg\": \"Insecta/Agrotis ipsilon/8119d7def7610a7f31eea3e9788cd028.jpg\",\n  \"253090.jpg\": \"Insecta/Agrotis ipsilon/d5cbf6f5904ca3f37914343386c53c29.jpg\",\n  \"253091.jpg\": \"Insecta/Agrotis ipsilon/8ec1feddd0fb7c1671aa91d4c51f4f12.jpg\",\n  \"253096.jpg\": \"Insecta/Agrotis ipsilon/316a5fe0e1e068d421ad8ebb3d72dcf5.jpg\",\n  \"253099.jpg\": \"Insecta/Agrotis ipsilon/f47d698e482d8a30980dbc67d61e3904.jpg\",\n  \"2531.jpg\": \"Aves/Picoides pubescens/adcdf578101127dac62cc4adeee5e5e5.jpg\",\n  \"253101.jpg\": \"Insecta/Agrotis ipsilon/4f6b6f69c20adb1ec52d3e973cb7a77d.jpg\",\n  \"253105.jpg\": \"Insecta/Agrotis ipsilon/8792ac4d787b6478ee81eecf015e9e50.jpg\",\n  \"253106.jpg\": \"Insecta/Agrotis ipsilon/8b9db8d8a6e4931acacae2a4ac83102d.jpg\",\n  \"253107.jpg\": \"Insecta/Agrotis ipsilon/5d3211fc23cfbd0ae01a1073546a171b.jpg\",\n  \"253108.jpg\": \"Insecta/Agrotis ipsilon/a8c4c42a9aa1118b9cd0a4bea5067829.jpg\",\n  \"253110.jpg\": \"Insecta/Agrotis ipsilon/8b09dc5000a58c0ac7fbe54a422943ae.jpg\",\n  \"253111.jpg\": \"Insecta/Agrotis ipsilon/958f6deb31021f8f8e82d2ec89703ffe.jpg\",\n  \"253112.jpg\": \"Insecta/Agrotis ipsilon/e7e246b4fdab0b00123d45feb082a124.jpg\",\n  \"253113.jpg\": \"Insecta/Agrotis ipsilon/be00cc601de8ba5913a1627bd3a9b0bc.jpg\",\n  \"253121.jpg\": \"Insecta/Agrotis ipsilon/bb8032bc9b5f374842446c6a1f92d128.jpg\",\n  \"253122.jpg\": \"Insecta/Agrotis ipsilon/82c9159e797221ad7a2851d344ca86bc.jpg\",\n  \"253128.jpg\": \"Insecta/Agrotis ipsilon/c01911373af479809cce8243dcb6cbd3.jpg\",\n  \"253129.jpg\": \"Insecta/Agrotis ipsilon/ce0ae2ebc332ab39c15c9487bbbd9e03.jpg\",\n  \"253130.jpg\": \"Insecta/Agrotis ipsilon/88515a2d6c6902077553d7bb3dedc3a9.jpg\",\n  \"253133.jpg\": \"Insecta/Agrotis ipsilon/8e620c078ce914c51fc64cd80b878f70.jpg\",\n  \"253573.jpg\": \"Reptilia/Natrix natrix/23d433f3097fd23e7158da9f8e4f1dc3.jpg\",\n  \"253582.jpg\": \"Reptilia/Natrix natrix/083a930d8071798f5d5df4f6b3800e05.jpg\",\n  \"253583.jpg\": \"Reptilia/Natrix natrix/60287b25c475b7d199f7d82d2ccdee6e.jpg\",\n  \"253585.jpg\": \"Reptilia/Natrix natrix/6762fa8e803f979ac0a68597c3989981.jpg\",\n  \"253586.jpg\": \"Reptilia/Natrix natrix/67725ac499a95c7eb2ba3dac60324361.jpg\",\n  \"253588.jpg\": \"Reptilia/Natrix natrix/56e969839ad9719b5df3d416a3bfdaa1.jpg\",\n  \"253589.jpg\": \"Reptilia/Natrix natrix/773dae8a28ea479163f15517d7223b54.jpg\",\n  \"253590.jpg\": \"Reptilia/Natrix natrix/8ede6113bde312c248e381197944fcf2.jpg\",\n  \"253591.jpg\": \"Reptilia/Natrix natrix/dc8948c648d9e81848d3fc7a357a5fcd.jpg\",\n  \"253592.jpg\": \"Reptilia/Natrix natrix/b77e30d3a943a46ef568e143e6dc2660.jpg\",\n  \"253598.jpg\": \"Reptilia/Natrix natrix/a2396cb86e175d2a075bdfae73b11a03.jpg\",\n  \"253599.jpg\": \"Reptilia/Natrix natrix/90dd2d4459a6513fdc3ecc5ff03bffcd.jpg\",\n  \"253601.jpg\": \"Reptilia/Natrix natrix/e8fcc258b7e14d48f7340efcfb85c15d.jpg\",\n  \"253606.jpg\": \"Reptilia/Natrix natrix/91d1fbc2e9808a446dcd9ba69b998348.jpg\",\n  \"253607.jpg\": \"Reptilia/Natrix natrix/6acbfd0c0c634887ac8e350ce0c332fe.jpg\",\n  \"253611.jpg\": \"Reptilia/Natrix natrix/0383daa845800978d9efb9b4dc030035.jpg\",\n  \"253613.jpg\": \"Reptilia/Natrix natrix/fddd7447c8958bde633abb27608f2420.jpg\",\n  \"253615.jpg\": \"Reptilia/Natrix natrix/9a5c7da35be10d941f6078ef28cc1e89.jpg\",\n  \"253616.jpg\": \"Reptilia/Natrix natrix/8fa194e26ffc841201ad87656d284a11.jpg\",\n  \"253617.jpg\": \"Reptilia/Natrix natrix/7c097c2b44833e40d38ae1612d32abe7.jpg\",\n  \"253618.jpg\": \"Reptilia/Natrix natrix/fb1eff89199128f0c27879983fdccdc3.jpg\",\n  \"253622.jpg\": \"Reptilia/Natrix natrix/17bd324273bbeb9369960bf48e656e24.jpg\",\n  \"253628.jpg\": \"Reptilia/Natrix natrix/81a87546852963bcae7c640ed19da84c.jpg\",\n  \"253629.jpg\": \"Reptilia/Natrix natrix/85a5d5d4fc6a4911bf90bc36127f699a.jpg\",\n  \"253630.jpg\": \"Reptilia/Natrix natrix/d6327cb8457219c247f7183125b2440d.jpg\",\n  \"253633.jpg\": \"Reptilia/Natrix natrix/7722a871107dbcb497e5ad0a2ae54684.jpg\",\n  \"253659.jpg\": \"Insecta/Oryctes nasicornis/07c6e06b4188353801f4a7f00297e05c.jpg\",\n  \"253661.jpg\": \"Insecta/Oryctes nasicornis/98c4961019b6a8e95523befa14adce63.jpg\",\n  \"253664.jpg\": \"Insecta/Oryctes nasicornis/6be784c0dd535573af449e16e3687063.jpg\",\n  \"253668.jpg\": \"Insecta/Oryctes nasicornis/704afc8d6cc78b5f07e96e2d98de67ba.jpg\",\n  \"253673.jpg\": \"Insecta/Oryctes nasicornis/601375977595cf78350f3fa273dcba33.jpg\",\n  \"253674.jpg\": \"Insecta/Oryctes nasicornis/bd1743159c27785e8b6465a8db554907.jpg\",\n  \"253675.jpg\": \"Arachnida/Latrodectus mactans/2c113d3a1d083e41c7ab3ef44464f39b.jpg\",\n  \"253681.jpg\": \"Arachnida/Latrodectus mactans/2d577b05f99421bb2750fca90679a4a8.jpg\",\n  \"253682.jpg\": \"Arachnida/Latrodectus mactans/b9ce4710d6d7a0c8fc697c8af464ed6f.jpg\",\n  \"253686.jpg\": \"Arachnida/Latrodectus mactans/3b57cb61c7fef15528c2e8cad4d40a35.jpg\",\n  \"253689.jpg\": \"Arachnida/Latrodectus mactans/e5e2c0759c7c4702ffbca815ae96a572.jpg\",\n  \"253691.jpg\": \"Arachnida/Latrodectus mactans/9e01c4dfc5626c895fcae0d6680f36c1.jpg\",\n  \"253694.jpg\": \"Arachnida/Latrodectus mactans/34813f19624b29d19f415e54780387a8.jpg\",\n  \"253695.jpg\": \"Arachnida/Latrodectus mactans/54f93cb416dee7eff18cf9a03fa5c380.jpg\",\n  \"253701.jpg\": \"Arachnida/Latrodectus mactans/611257db7cd564c2527d312d62c10fe0.jpg\",\n  \"253702.jpg\": \"Arachnida/Latrodectus mactans/57786527b8a38ddda722a1cf3597c8fb.jpg\",\n  \"253704.jpg\": \"Arachnida/Latrodectus mactans/699bec25bea7a70e5d7e84ed2c9345ec.jpg\",\n  \"253705.jpg\": \"Arachnida/Latrodectus mactans/efcb6dc76765f5d2e04c7ca2d35b37a5.jpg\",\n  \"253710.jpg\": \"Arachnida/Latrodectus mactans/b973b9e1b4fb371462b0fb010e177f30.jpg\",\n  \"253712.jpg\": \"Arachnida/Latrodectus mactans/7068f9df9c78be6c87cecb8c6a1a0b73.jpg\",\n  \"253714.jpg\": \"Arachnida/Latrodectus mactans/0cf7e5d8bde5d8329c23ee38d70d1e85.jpg\",\n  \"253721.jpg\": \"Arachnida/Latrodectus mactans/4ba689e81a4b0c2a6334cf84930c0b93.jpg\",\n  \"253722.jpg\": \"Arachnida/Latrodectus mactans/ca59c795a0fd4896c466b0f1f938b019.jpg\",\n  \"253727.jpg\": \"Arachnida/Latrodectus mactans/c358c435380464defb2d44d56f2485b9.jpg\",\n  \"253728.jpg\": \"Arachnida/Latrodectus mactans/35bc9f9fbe64c5c09a53c6a552136433.jpg\",\n  \"253729.jpg\": \"Arachnida/Latrodectus mactans/ab3d92a815b21279c40b9b6df3c27d5c.jpg\",\n  \"253730.jpg\": \"Arachnida/Latrodectus mactans/6c0beff8380b7d00fc51630d5bf9b577.jpg\",\n  \"253731.jpg\": \"Insecta/Cerma cerintha/2d59b8c70a66c4bff4ea4c8952bf81ad.jpg\",\n  \"253732.jpg\": \"Insecta/Cerma cerintha/54cd8709cad9b1d48b4ac477479507c1.jpg\",\n  \"253733.jpg\": \"Insecta/Cerma cerintha/ad376192fd9a5f732329cfc991901de3.jpg\",\n  \"253745.jpg\": \"Insecta/Cerma cerintha/9fab1dc84e829b36ef2189df618a1a7f.jpg\",\n  \"253750.jpg\": \"Animalia/Scutigera coleoptrata/b302a58ecfd6241a06cd8abaaa112ed0.jpg\",\n  \"253752.jpg\": \"Animalia/Scutigera coleoptrata/07d8cb803aff06830dbc62418459ebab.jpg\",\n  \"253754.jpg\": \"Animalia/Scutigera coleoptrata/5e418e04c691c72bf5d46400619845b3.jpg\",\n  \"253758.jpg\": \"Animalia/Scutigera coleoptrata/7759a7c7487f1385444a3f6ce79d712b.jpg\",\n  \"253760.jpg\": \"Animalia/Scutigera coleoptrata/8548629fe82780b895d63714e766625c.jpg\",\n  \"253767.jpg\": \"Animalia/Scutigera coleoptrata/2b81ffed52eef00355c5f66ed536ae65.jpg\",\n  \"253770.jpg\": \"Animalia/Scutigera coleoptrata/2927836d2ecfcf8874550a2ed400f738.jpg\",\n  \"253777.jpg\": \"Animalia/Scutigera coleoptrata/2104da08dc7401a89571b6094a7fcb16.jpg\",\n  \"253778.jpg\": \"Animalia/Scutigera coleoptrata/b9766a987a649461e0dd20fffc399163.jpg\",\n  \"253779.jpg\": \"Animalia/Scutigera coleoptrata/4e3ac19dc389d54d50bfa77ea388d2c5.jpg\",\n  \"253780.jpg\": \"Animalia/Scutigera coleoptrata/bd08ea7bbe01e8fa0b3b77463eee0428.jpg\",\n  \"253789.jpg\": \"Animalia/Scutigera coleoptrata/3406b098004aa364c8a98dac19631f00.jpg\",\n  \"253794.jpg\": \"Animalia/Scutigera coleoptrata/e024677f845bb6d8c7bda57947b4a3cc.jpg\",\n  \"253798.jpg\": \"Animalia/Scutigera coleoptrata/e1a643e50fb3a32f6508ff5f01146d99.jpg\",\n  \"253813.jpg\": \"Reptilia/Plestiodon fasciatus/30739541eca9fcf635ad3660e53d5196.jpg\",\n  \"253816.jpg\": \"Reptilia/Plestiodon fasciatus/b499f9231814a99bab5eae82a81875c1.jpg\",\n  \"253839.jpg\": \"Reptilia/Plestiodon fasciatus/945526c09389ebacb4b9b38fbd22f8c9.jpg\",\n  \"253857.jpg\": \"Reptilia/Plestiodon fasciatus/a9c08157cf025ffc36473ccfc5325c48.jpg\",\n  \"253862.jpg\": \"Reptilia/Plestiodon fasciatus/caa754de980a28c0d8b194c51a282df8.jpg\",\n  \"253869.jpg\": \"Reptilia/Plestiodon fasciatus/86a481caebd3617b6019883885903a48.jpg\",\n  \"253876.jpg\": \"Reptilia/Plestiodon fasciatus/4e24bef9b96f18f0fa9c20cf9036b625.jpg\",\n  \"253877.jpg\": \"Reptilia/Plestiodon fasciatus/567a53fca27fc57d0c4e8f47ed41c577.jpg\",\n  \"253880.jpg\": \"Reptilia/Plestiodon fasciatus/a124f907b325c06883c1f43e7a8c5222.jpg\",\n  \"253883.jpg\": \"Reptilia/Plestiodon fasciatus/1025791741614f829942b2d57437ed5b.jpg\",\n  \"253896.jpg\": \"Reptilia/Plestiodon fasciatus/66eb4c4de2a4df8bb7542974a6d346c8.jpg\",\n  \"253898.jpg\": \"Reptilia/Plestiodon fasciatus/9fdd8f9eb3fd31d2cf3d564cffd0abdb.jpg\",\n  \"253908.jpg\": \"Reptilia/Plestiodon fasciatus/468719c4005e68768f1b37125e030992.jpg\",\n  \"253914.jpg\": \"Reptilia/Plestiodon fasciatus/d5447f57f4316fb1afab2a3b8b968c0f.jpg\",\n  \"253918.jpg\": \"Reptilia/Plestiodon fasciatus/c87713960e88929e1d9abf9501e70783.jpg\",\n  \"253927.jpg\": \"Reptilia/Plestiodon fasciatus/820b86391f5324c73a1c7d0797d4b83e.jpg\",\n  \"253931.jpg\": \"Reptilia/Plestiodon fasciatus/f184e5f8e8c1106cabb9343eb40508ed.jpg\",\n  \"253946.jpg\": \"Reptilia/Plestiodon fasciatus/2ccc63665f98a479e899595af9872f3f.jpg\",\n  \"253958.jpg\": \"Reptilia/Plestiodon fasciatus/0049f8dd01f9127ef6c6f2770577cf93.jpg\",\n  \"253971.jpg\": \"Reptilia/Plestiodon fasciatus/8b857a815f555c603ce61d5d5e84fe57.jpg\",\n  \"253980.jpg\": \"Reptilia/Plestiodon fasciatus/29b3b6427aa31b5012538ff45c42c41c.jpg\",\n  \"253990.jpg\": \"Reptilia/Plestiodon fasciatus/d5369a2037b162d56213c26eb4e0c54e.jpg\",\n  \"253993.jpg\": \"Reptilia/Plestiodon fasciatus/c5e864718b32d401f9e8ae757fb9571a.jpg\",\n  \"253999.jpg\": \"Reptilia/Plestiodon fasciatus/b45be3b55849ebec6bcaf2c33c8a9053.jpg\",\n  \"254002.jpg\": \"Reptilia/Plestiodon fasciatus/1df94d4a92f63be816bf9ffd34671b45.jpg\",\n  \"254006.jpg\": \"Reptilia/Plestiodon fasciatus/f665df9e78f714497fc53d88859c328b.jpg\",\n  \"254034.jpg\": \"Insecta/Anax junius/a0952df0930e9770cd3fd5a66a2dd466.jpg\",\n  \"254083.jpg\": \"Insecta/Anax junius/980a8ba3359cb3dca2532227a54fe8bd.jpg\",\n  \"254161.jpg\": \"Insecta/Anax junius/0fc5eb22bcd88be14d9e7372bcc85094.jpg\",\n  \"254166.jpg\": \"Insecta/Anax junius/609e0612fe1192466a59fbfd914fa74a.jpg\",\n  \"254167.jpg\": \"Insecta/Anax junius/5e1984d827e5cc89416ae5c736af4afb.jpg\",\n  \"254168.jpg\": \"Insecta/Anax junius/96fdc5d2e1aa7c5c3e9fb915dc608122.jpg\",\n  \"254169.jpg\": \"Insecta/Anax junius/3be2c2752a26ebb8bbe0facca5f4c5f7.jpg\",\n  \"254170.jpg\": \"Insecta/Anax junius/26e166e77272c8ae6ecd7f609d51dd38.jpg\",\n  \"254266.jpg\": \"Insecta/Anax junius/06a7664e268c5c78d30de589713ce08a.jpg\",\n  \"254271.jpg\": \"Insecta/Anax junius/db1b833b8e2ee3d424458b2d1450708d.jpg\",\n  \"254295.jpg\": \"Insecta/Anax junius/a6ebaf7079fd7b90a6b2b750191f1769.jpg\",\n  \"254301.jpg\": \"Insecta/Anax junius/bf42a9b026445c95b0212a524925554b.jpg\",\n  \"254310.jpg\": \"Insecta/Anax junius/1d244a8ff8581798856f6a4214b4b4eb.jpg\",\n  \"254329.jpg\": \"Insecta/Anax junius/c20d76f3dd037aa53fa7f33b49d0d42d.jpg\",\n  \"254343.jpg\": \"Insecta/Anax junius/e38554e4c65000754d44c5d5860bdd1a.jpg\",\n  \"254344.jpg\": \"Insecta/Anax junius/4646bc96e4513519fa2b4868827a1863.jpg\",\n  \"254351.jpg\": \"Insecta/Anax junius/64c629d94d28a12245097d13e9c22338.jpg\",\n  \"254382.jpg\": \"Insecta/Anax junius/d3a2e21324851674b71709cfc4e0c5d2.jpg\",\n  \"254384.jpg\": \"Insecta/Anax junius/3fa2dc30514f0b4790694784fc27dc33.jpg\",\n  \"254404.jpg\": \"Insecta/Anax junius/313724ac461155723debc247031fe92f.jpg\",\n  \"254413.jpg\": \"Insecta/Anax junius/30f26aa2c5888f3cc067afdd574d2b42.jpg\",\n  \"254423.jpg\": \"Insecta/Anax junius/2e07972121bfebc95d303cd61763620f.jpg\",\n  \"254448.jpg\": \"Insecta/Anax junius/8856737b62e937c2631eb3ed7f16326e.jpg\",\n  \"254459.jpg\": \"Reptilia/Kinosternon subrubrum/9b2fb86008c9fe708c2ab2388180d082.jpg\",\n  \"254460.jpg\": \"Reptilia/Kinosternon subrubrum/309323e28a0de5be30cc559b2a1b1e33.jpg\",\n  \"254467.jpg\": \"Reptilia/Kinosternon subrubrum/7113124f11db0274887ea665ce9b959f.jpg\",\n  \"254468.jpg\": \"Reptilia/Kinosternon subrubrum/3e6a011f3098a7b2851875de87b94a74.jpg\",\n  \"254469.jpg\": \"Reptilia/Kinosternon subrubrum/d7d8ed834394643aab8c166f34ac76f1.jpg\",\n  \"254485.jpg\": \"Insecta/Pseudomyrmex gracilis/38bb522d0262da66f68cf99dcf17b258.jpg\",\n  \"254489.jpg\": \"Insecta/Pseudomyrmex gracilis/e57e4c4adc7847b64a3018bb9f2e246a.jpg\",\n  \"254494.jpg\": \"Insecta/Pseudomyrmex gracilis/495410e05656234076e6f173f08e181c.jpg\",\n  \"254495.jpg\": \"Insecta/Pseudomyrmex gracilis/dedd4bbc5bf28a4ef75f8b1aaab280d5.jpg\",\n  \"254496.jpg\": \"Insecta/Pseudomyrmex gracilis/008e6fb98528bbff4c2d2e743be138f4.jpg\",\n  \"254584.jpg\": \"Reptilia/Pituophis catenifer affinis/dee1026151d51815401c7f59838a64aa.jpg\",\n  \"254589.jpg\": \"Reptilia/Pituophis catenifer affinis/a4c955b32fb01d927cd44afe1742185e.jpg\",\n  \"254596.jpg\": \"Reptilia/Pituophis catenifer affinis/627aae873899b9035a20e0bdbc2a283a.jpg\",\n  \"254601.jpg\": \"Reptilia/Pituophis catenifer affinis/f1fbac6bb8db8eca5ea7f08a312fd57f.jpg\",\n  \"254602.jpg\": \"Reptilia/Pituophis catenifer affinis/e4a224d8df05abf2ea91fdabc8bee3bd.jpg\",\n  \"254604.jpg\": \"Reptilia/Pituophis catenifer affinis/114bc9c6b8f665d3931276f0744d15cf.jpg\",\n  \"254630.jpg\": \"Insecta/Melanoplus bivittatus/bb97f1b4431606fb2144f327c059fc7f.jpg\",\n  \"254631.jpg\": \"Insecta/Melanoplus bivittatus/7818cda242ae5252581435de58f09018.jpg\",\n  \"254633.jpg\": \"Insecta/Melanoplus bivittatus/428a3d9c9d76affdb25df86f9ff21275.jpg\",\n  \"254641.jpg\": \"Insecta/Melanoplus bivittatus/87fef453d9ce46fd1a95b4d5b7bbbdae.jpg\",\n  \"254642.jpg\": \"Insecta/Melanoplus bivittatus/a2d7dba668e348d3337561c1defb2fbe.jpg\",\n  \"254646.jpg\": \"Insecta/Melanoplus bivittatus/a8f87542d90f2984325de781b256849c.jpg\",\n  \"254651.jpg\": \"Insecta/Melanoplus bivittatus/75c08238fcdf9abb3f6983b94188640f.jpg\",\n  \"254652.jpg\": \"Insecta/Melanoplus bivittatus/bda8315adc531c5cf82521133524af41.jpg\",\n  \"254656.jpg\": \"Insecta/Melanoplus bivittatus/5c10d141436dac450a4cca26841a8298.jpg\",\n  \"254657.jpg\": \"Insecta/Melanoplus bivittatus/e9a5c427abdbdc2ddc02823195d65972.jpg\",\n  \"254658.jpg\": \"Insecta/Melanoplus bivittatus/d6813b07f35f7a3e6ee55138d10b15b7.jpg\",\n  \"254659.jpg\": \"Insecta/Melanoplus bivittatus/e06fb0bec62237695b11e5e7f64896ec.jpg\",\n  \"254660.jpg\": \"Insecta/Melanoplus bivittatus/00bbf828b7d45e357841e2472ea01b08.jpg\",\n  \"254669.jpg\": \"Insecta/Melanoplus bivittatus/79c3c2dfd2649e23727364c28d7d9ecf.jpg\",\n  \"254671.jpg\": \"Insecta/Melanoplus bivittatus/a51d2b856d2523bd0c2bb58ec7cd5883.jpg\",\n  \"254675.jpg\": \"Insecta/Melanoplus bivittatus/eb5a15c34918aaa37e0ebecc88b07f90.jpg\",\n  \"254682.jpg\": \"Insecta/Melanoplus bivittatus/120438c9625ef7c567025ba0b17a6143.jpg\",\n  \"254686.jpg\": \"Insecta/Melanoplus bivittatus/13b978040159206d4f4f6398150a5cbe.jpg\",\n  \"254692.jpg\": \"Insecta/Melanoplus bivittatus/7b589c48822f32a6deb0b3283433247f.jpg\",\n  \"254693.jpg\": \"Insecta/Melanoplus bivittatus/3976c19c783e21668404bc20bdd25419.jpg\",\n  \"254696.jpg\": \"Insecta/Melanoplus bivittatus/54fa754cc3d3d65cb69fa0e3546b4c45.jpg\",\n  \"254776.jpg\": \"Aves/Coracias garrulus/99cef1b57fdf53e1d50af1ea5e43b8e5.jpg\",\n  \"254778.jpg\": \"Aves/Coracias garrulus/ba30cbdcff4f98f364a44e9f9d198dfe.jpg\",\n  \"254781.jpg\": \"Aves/Coracias garrulus/49f08ec3ad98e2d8a0b683d9f9d3c39f.jpg\",\n  \"254782.jpg\": \"Aves/Coracias garrulus/e8c07733f153c5f42fd23fd93452904b.jpg\",\n  \"254787.jpg\": \"Aves/Coracias garrulus/c08f23a8da16b757cf459a2362740258.jpg\",\n  \"254853.jpg\": \"Mammalia/Antrozous pallidus/0b4cf5f1dd5b6361d04f76185305ad47.jpg\",\n  \"254859.jpg\": \"Mammalia/Antrozous pallidus/76b63389aabbb1434af4f5f2a7498bfe.jpg\",\n  \"254868.jpg\": \"Mammalia/Antrozous pallidus/17567c9827ac59eb9ff486c06a4d5e30.jpg\",\n  \"254876.jpg\": \"Mammalia/Antrozous pallidus/b765a91eb28254f0b10994c22c1dc47c.jpg\",\n  \"254977.jpg\": \"Aves/Haematopus unicolor/ee7bbff32c165d6209932527d78325a3.jpg\",\n  \"254982.jpg\": \"Aves/Haematopus unicolor/25ae53c102bbc2df99659ff7e2868a6d.jpg\",\n  \"254989.jpg\": \"Aves/Haematopus unicolor/002fc862b79d66306e6914b7e9a7ee3f.jpg\",\n  \"254990.jpg\": \"Aves/Haematopus unicolor/8f8d49fc1d6f66fd0213828d99e979df.jpg\",\n  \"254993.jpg\": \"Aves/Haematopus unicolor/9febccd86cfbcd4cebe36b0724b1563c.jpg\",\n  \"255008.jpg\": \"Aves/Haematopus unicolor/66bdd70aee2ccc2ceac4d0051b184d11.jpg\",\n  \"255009.jpg\": \"Aves/Haematopus unicolor/655517fcb15fe5aec1029bb730cdb59e.jpg\",\n  \"255012.jpg\": \"Aves/Haematopus unicolor/cd9f4ded25f6cb163ce6a8f1de8ee1f0.jpg\",\n  \"255020.jpg\": \"Aves/Haematopus unicolor/c5d2ad0113bd15351a771c1d95a42f7f.jpg\",\n  \"255042.jpg\": \"Aves/Haematopus unicolor/201824036f44794b546e5fda3dad789f.jpg\",\n  \"255045.jpg\": \"Aves/Haematopus unicolor/ee4dcef7e45180f78ef11cc14e1ff3e0.jpg\",\n  \"255046.jpg\": \"Aves/Haematopus unicolor/9c2545a81c789b82824913034b5c35f6.jpg\",\n  \"255047.jpg\": \"Aves/Haematopus unicolor/946baf66603c2809b71e55713f6f581d.jpg\",\n  \"25510.jpg\": \"Aves/Turdus viscivorus/69403f8770d08ea1346d455c6e63748e.jpg\",\n  \"255107.jpg\": \"Insecta/Celastrina argiolus/dc41d764c7dc8a3e5cc61767210b297f.jpg\",\n  \"255119.jpg\": \"Insecta/Celastrina argiolus/aaf0e90c7ed40dc5c79128cff146c5a7.jpg\",\n  \"255120.jpg\": \"Insecta/Celastrina argiolus/d480b3ba2a44c0a2c823bbd5314b5862.jpg\",\n  \"255132.jpg\": \"Reptilia/Pseudemys texana/50a6f21132b0b4777d285454fe3f4b6a.jpg\",\n  \"255134.jpg\": \"Reptilia/Pseudemys texana/66475bb7768e077005d571884540328a.jpg\",\n  \"255149.jpg\": \"Reptilia/Pseudemys texana/e37c3c5ab9e9dc6d93a18a5d962d033a.jpg\",\n  \"255159.jpg\": \"Reptilia/Pseudemys texana/93191de9a999945e7d7d1b9039d669ae.jpg\",\n  \"25516.jpg\": \"Aves/Turdus viscivorus/9d48d56db60f72d0cc4f4260f1c578da.jpg\",\n  \"255160.jpg\": \"Reptilia/Pseudemys texana/5258785e52f65bfa521f6850b9640454.jpg\",\n  \"255161.jpg\": \"Reptilia/Pseudemys texana/33b9def43f06a14a1bce81266486c3a9.jpg\",\n  \"255168.jpg\": \"Reptilia/Pseudemys texana/ac7b60f9fa48d882aeaacdb795d31ea1.jpg\",\n  \"255170.jpg\": \"Reptilia/Pseudemys texana/f4381dc0e6fb49f4d17c22c30a22c9cc.jpg\",\n  \"255175.jpg\": \"Reptilia/Pseudemys texana/9535c1b656e9c82f6684c306d100fdb3.jpg\",\n  \"255176.jpg\": \"Reptilia/Pseudemys texana/9681862ae70ecc24f11882942c70a46b.jpg\",\n  \"255204.jpg\": \"Reptilia/Pseudemys texana/8d372e1a43c36dd47c8dd4f44dd92db8.jpg\",\n  \"255205.jpg\": \"Reptilia/Pseudemys texana/0a59d245a101e14ea9ba858cb665915c.jpg\",\n  \"255209.jpg\": \"Reptilia/Pseudemys texana/abf48c84e3728dbe1153d41bff5eb908.jpg\",\n  \"255213.jpg\": \"Reptilia/Pseudemys texana/187070cea387bd993a5e90517faf65ca.jpg\",\n  \"255214.jpg\": \"Reptilia/Pseudemys texana/6b135f077b98d57f991a3c982f88e1b2.jpg\",\n  \"255384.jpg\": \"Aves/Alauda arvensis/fb74d85e34ea630f8b86838b77cf7121.jpg\",\n  \"25539.jpg\": \"Aves/Turdus viscivorus/a4674a2da2ba4d482ae097df6d5b4f93.jpg\",\n  \"255391.jpg\": \"Aves/Alauda arvensis/a754411d295c99380def7ad6cbe50126.jpg\",\n  \"255406.jpg\": \"Aves/Alauda arvensis/9e5adbf3630ab554484591bb7af67670.jpg\",\n  \"255422.jpg\": \"Aves/Alauda arvensis/f2dc640654551507de60e0b512172b3b.jpg\",\n  \"255436.jpg\": \"Aves/Alauda arvensis/b62d15d984b338ed2a486dc04797bcdb.jpg\",\n  \"25558.jpg\": \"Insecta/Gomphus exilis/672e8c00bf02c105fe1367c6ffd83003.jpg\",\n  \"255600.jpg\": \"Reptilia/Aspidoscelis tigris tigris/df12264beaf42d8ec1e7e135f6b1f484.jpg\",\n  \"255605.jpg\": \"Reptilia/Aspidoscelis tigris tigris/38f1c39b82113a13850feec89c5258dd.jpg\",\n  \"255608.jpg\": \"Reptilia/Aspidoscelis tigris tigris/ac48d30ff758092ba9198b05447bfd76.jpg\",\n  \"255613.jpg\": \"Reptilia/Aspidoscelis tigris tigris/25890a22e6ec67c1a6ed610f3f394f49.jpg\",\n  \"255618.jpg\": \"Reptilia/Aspidoscelis tigris tigris/26cbcb73d5cb870a3ce2cd18f340c0a3.jpg\",\n  \"255621.jpg\": \"Reptilia/Aspidoscelis tigris tigris/62d2764f3fd9f5f949f0275567a8ac0f.jpg\",\n  \"255626.jpg\": \"Insecta/Eumorpha fasciatus/2e63caedcdea65eb4fe35ff656363fdf.jpg\",\n  \"255628.jpg\": \"Insecta/Eumorpha fasciatus/a9a8c2c50709f44407ffa975ffd7f6ac.jpg\",\n  \"255633.jpg\": \"Insecta/Eumorpha fasciatus/5af88644b7c9beacc72638aa49a80f28.jpg\",\n  \"255639.jpg\": \"Insecta/Eumorpha fasciatus/ed73eea3bbc685fe06d3136ae841e2da.jpg\",\n  \"255640.jpg\": \"Insecta/Eumorpha fasciatus/acb5e2b6706de9c0d89fc381f9e6bfb4.jpg\",\n  \"255641.jpg\": \"Insecta/Eumorpha fasciatus/0ce89b745b6b5d1d98dad484567ec568.jpg\",\n  \"255643.jpg\": \"Insecta/Eumorpha fasciatus/5f24499e309fd0be63251b0397be099b.jpg\",\n  \"255645.jpg\": \"Insecta/Abaeis nicippe/9e4b8a759ed4d123c976c3958029e8a3.jpg\",\n  \"255650.jpg\": \"Insecta/Abaeis nicippe/bc33e06d35144fde034630eb4c8afb86.jpg\",\n  \"255666.jpg\": \"Insecta/Abaeis nicippe/41b3147b1d41f7cf1cd4dc4a73d1670b.jpg\",\n  \"255669.jpg\": \"Insecta/Abaeis nicippe/ec64e7e5b7baaa9cb3af5dac5a02ee1b.jpg\",\n  \"255671.jpg\": \"Insecta/Abaeis nicippe/b4046bb1f0c71ff5a24f6de318d90921.jpg\",\n  \"255678.jpg\": \"Insecta/Abaeis nicippe/c8ada09f9491cf2bfcc51e74ab4f092f.jpg\",\n  \"255679.jpg\": \"Insecta/Abaeis nicippe/faf6628eecbb4884b0956a948ada9485.jpg\",\n  \"255680.jpg\": \"Insecta/Abaeis nicippe/48adf50bfef8a0e62464d63aa6684a54.jpg\",\n  \"25569.jpg\": \"Insecta/Gomphus exilis/07e6104ccf44024fb44f02e856ff1f6a.jpg\",\n  \"255694.jpg\": \"Insecta/Abaeis nicippe/0e72d9ec67436cdb02f3e758c9b4ea20.jpg\",\n  \"255695.jpg\": \"Insecta/Abaeis nicippe/27107bdb916ac48b827554d00ee136cd.jpg\",\n  \"255701.jpg\": \"Insecta/Abaeis nicippe/5eb290b449d36973d3f87f6356584fb6.jpg\",\n  \"255708.jpg\": \"Insecta/Abaeis nicippe/bb3135392f542b1d46b98bbeabafdca6.jpg\",\n  \"255719.jpg\": \"Insecta/Abaeis nicippe/da4be7c22bec9c8735c143d0563ae49e.jpg\",\n  \"25573.jpg\": \"Insecta/Gomphus exilis/0c5299ee5f45d6e8a3b8d9fc3ff40301.jpg\",\n  \"255733.jpg\": \"Insecta/Abaeis nicippe/50730b906273cd0e424f9ef209fa815c.jpg\",\n  \"255734.jpg\": \"Insecta/Abaeis nicippe/225156a22ec237383516aadb64a528a4.jpg\",\n  \"255736.jpg\": \"Insecta/Abaeis nicippe/fb869fa1e72f45f7868347c3d178432e.jpg\",\n  \"255737.jpg\": \"Insecta/Abaeis nicippe/3abeb87ec49db4f775139a74025e2e0b.jpg\",\n  \"255743.jpg\": \"Insecta/Abaeis nicippe/e718e26305d284a5e062e37953fdf991.jpg\",\n  \"255745.jpg\": \"Insecta/Abaeis nicippe/a31e21ccd6bad7e36888e33ee9df183b.jpg\",\n  \"255755.jpg\": \"Aves/Asio otus/d9972d018ac55f593f0bd239e416cb67.jpg\",\n  \"255762.jpg\": \"Aves/Asio otus/3dcb82ef6c12fcbb000621be310f7516.jpg\",\n  \"255768.jpg\": \"Aves/Asio otus/3713ea581fee0a13473cfb61b66e6bd4.jpg\",\n  \"255778.jpg\": \"Aves/Asio otus/11d37db59b98792644868eeaecc823e4.jpg\",\n  \"255780.jpg\": \"Aves/Asio otus/8ad55a436085bb15f9b37cff7bdf7d32.jpg\",\n  \"255782.jpg\": \"Aves/Asio otus/29b4fa9767c7222c4f28c2631398a887.jpg\",\n  \"255786.jpg\": \"Aves/Asio otus/f654bca7fc81345248c7328f517f20c7.jpg\",\n  \"255787.jpg\": \"Aves/Asio otus/f73bbf90091a8edae998b43ce6a2c677.jpg\",\n  \"255795.jpg\": \"Aves/Asio otus/17f04845c766ba8f1e01ee5481dadb9c.jpg\",\n  \"255796.jpg\": \"Aves/Asio otus/45021ec116093cf3aeff7509a7cf3fe4.jpg\",\n  \"255797.jpg\": \"Aves/Asio otus/acb567452797e06ce59eabfaba05e2ef.jpg\",\n  \"255798.jpg\": \"Aves/Asio otus/4868ad339e6950203ccc183032993975.jpg\",\n  \"25580.jpg\": \"Insecta/Gomphus exilis/cd94204935a3fb933f592432fcfc8233.jpg\",\n  \"255801.jpg\": \"Aves/Asio otus/e0f231ebcfc56a5881583f7e55e5b21b.jpg\",\n  \"255802.jpg\": \"Aves/Asio otus/746233918acbc146c3abf16a34d2c3d1.jpg\",\n  \"255804.jpg\": \"Aves/Asio otus/1cf8a3251d51b9152f05f676603f3803.jpg\",\n  \"255805.jpg\": \"Aves/Asio otus/7faf96f43bf5a7692562044dc844d152.jpg\",\n  \"255806.jpg\": \"Aves/Asio otus/a6108ed8fc176f5d800b78bf43f66e51.jpg\",\n  \"255807.jpg\": \"Aves/Asio otus/483b194c47a608b76239c8374e255a50.jpg\",\n  \"255808.jpg\": \"Aves/Asio otus/cafc41617a3d1dac2172fb5b4d3298a8.jpg\",\n  \"255809.jpg\": \"Aves/Asio otus/1af0df7ca62cc6b06932a0bfcaa583ca.jpg\",\n  \"25582.jpg\": \"Insecta/Gomphus exilis/a28252b78ed5cd00c9945c31deea5471.jpg\",\n  \"25583.jpg\": \"Insecta/Gomphus exilis/387caff157e7a6a6e6d49b5137a56696.jpg\",\n  \"25587.jpg\": \"Insecta/Gomphus exilis/0afef9ad51ed2e4c4994f0c9909cd212.jpg\",\n  \"256040.jpg\": \"Aves/Aphelocoma californica/9ca2dae637881e14d5ff78a419a2b2aa.jpg\",\n  \"256045.jpg\": \"Aves/Aphelocoma californica/2bea6593712795f2ae197baa4611606b.jpg\",\n  \"256073.jpg\": \"Aves/Aphelocoma californica/a6cd98ee8d0897a6c4aeb401758a08ef.jpg\",\n  \"256096.jpg\": \"Aves/Aphelocoma californica/9eac473f87dccbd63d8d52bb1a528614.jpg\",\n  \"256179.jpg\": \"Aves/Aphelocoma californica/ba25b02abe067db6b7b0daaaa9fa29a5.jpg\",\n  \"256184.jpg\": \"Aves/Aphelocoma californica/d7975eaeeb970fb00f65794abfb604e2.jpg\",\n  \"256230.jpg\": \"Aves/Aphelocoma californica/a55443096099d68efbb87786c3b2df4e.jpg\",\n  \"256235.jpg\": \"Aves/Aphelocoma californica/a6709212eba4dcb2be3e8ffc1a9b0a64.jpg\",\n  \"256238.jpg\": \"Aves/Aphelocoma californica/aee291be3682df9465b65dc399acc610.jpg\",\n  \"256257.jpg\": \"Aves/Aphelocoma californica/d9ea4cb7fdd8f954394478dced3e5640.jpg\",\n  \"256295.jpg\": \"Aves/Aphelocoma californica/f51202e75e943eb8f890405a980d60de.jpg\",\n  \"256313.jpg\": \"Aves/Aphelocoma californica/4e416e5d26b100e6d1c0f6e95699ac12.jpg\",\n  \"256325.jpg\": \"Aves/Aphelocoma californica/f67a220bcef531acd2d47b4985c6603e.jpg\",\n  \"256329.jpg\": \"Aves/Aphelocoma californica/ef7ee3f93b249a37bb921682c4a024c2.jpg\",\n  \"256347.jpg\": \"Aves/Aphelocoma californica/07b14c2871bfabdf297e8869c777b422.jpg\",\n  \"25635.jpg\": \"Insecta/Chrysodeixis includens/8a3e3fbc83c7451dc9da21db9a917dc1.jpg\",\n  \"256355.jpg\": \"Aves/Aphelocoma californica/61bc81b1bdc4d4cf98e053060ba73b08.jpg\",\n  \"25636.jpg\": \"Insecta/Chrysodeixis includens/06dae277ec361a54ea099d949257005c.jpg\",\n  \"256366.jpg\": \"Aves/Aphelocoma californica/0dfb907f45014c8c8ed63a6455d7f89d.jpg\",\n  \"25637.jpg\": \"Insecta/Chrysodeixis includens/23a7e84dcb5025a5f3c476ac0811c7cd.jpg\",\n  \"25638.jpg\": \"Insecta/Chrysodeixis includens/46e689da0a0e0eddf96b5c03cab09b2a.jpg\",\n  \"256411.jpg\": \"Aves/Aphelocoma californica/f843cfef7230418218122c065253cfc7.jpg\",\n  \"256440.jpg\": \"Aves/Aphelocoma californica/97a839480c0344db840c1f59913f0a24.jpg\",\n  \"256447.jpg\": \"Aves/Aphelocoma californica/d80cdaabe9bd76d4a7afa8ad952ab452.jpg\",\n  \"256454.jpg\": \"Aves/Aphelocoma californica/3919aaf857a39b1afb8d2802838a2114.jpg\",\n  \"256458.jpg\": \"Aves/Aphelocoma californica/bfd74ec8e5d60998c4e5deece5502ebc.jpg\",\n  \"25647.jpg\": \"Insecta/Chrysodeixis includens/c7971d137e2de5631ea7e3e63d327867.jpg\",\n  \"256489.jpg\": \"Aves/Leucophaeus atricilla/c32af879d9a229d71c81a5a703f86be0.jpg\",\n  \"256497.jpg\": \"Aves/Leucophaeus atricilla/2716893d2fcb9fbc87594689b19df8da.jpg\",\n  \"25650.jpg\": \"Insecta/Chrysodeixis includens/80ea1a8da9edb97afc105b2f54d78754.jpg\",\n  \"256513.jpg\": \"Aves/Leucophaeus atricilla/954991b97b5c825d21c29214212a12ae.jpg\",\n  \"256519.jpg\": \"Aves/Leucophaeus atricilla/49f41bb02a8569b26c525208c58b5128.jpg\",\n  \"256523.jpg\": \"Aves/Leucophaeus atricilla/5498599c68878bff6398d1acdae8ba24.jpg\",\n  \"256550.jpg\": \"Aves/Leucophaeus atricilla/456e274fb8eaa58643459e1196698529.jpg\",\n  \"256582.jpg\": \"Aves/Leucophaeus atricilla/51e16cffcaf85b961854bfd2f471e85c.jpg\",\n  \"256733.jpg\": \"Aves/Leucophaeus atricilla/2b9e69d62a4525ec8b4d0ccafd2a0ccd.jpg\",\n  \"256745.jpg\": \"Aves/Leucophaeus atricilla/3935c2363d975d0a0b6cff7b5e1eac78.jpg\",\n  \"256758.jpg\": \"Aves/Leucophaeus atricilla/686bb09d76e8f4efe4ce88b7039f4c80.jpg\",\n  \"256780.jpg\": \"Aves/Leucophaeus atricilla/6429a120fd932bbdbf4680d53e6f7f47.jpg\",\n  \"256852.jpg\": \"Aves/Leucophaeus atricilla/5b3804deeaa0055e21e4bb66de00f0cd.jpg\",\n  \"256869.jpg\": \"Aves/Leucophaeus atricilla/b5e16da585c524079e5bc16569126831.jpg\",\n  \"256900.jpg\": \"Aves/Pelecanus occidentalis/1b4b893854f3a754dd0669adbdf52a35.jpg\",\n  \"256997.jpg\": \"Aves/Pelecanus occidentalis/e0fa76acd1505d6b2fbe6cf89a935169.jpg\",\n  \"257023.jpg\": \"Aves/Pelecanus occidentalis/d892474f397859252825e371fcf99979.jpg\",\n  \"257025.jpg\": \"Aves/Pelecanus occidentalis/32c6850b7631c04926affe5a453d5ba2.jpg\",\n  \"257085.jpg\": \"Aves/Pelecanus occidentalis/e6235dd2e8dc40de285ff794c33ffdb4.jpg\",\n  \"257173.jpg\": \"Aves/Pelecanus occidentalis/c7e38845761c8df682ddd75d867b836e.jpg\",\n  \"257176.jpg\": \"Aves/Pelecanus occidentalis/2ca9a4fa4309ebcef43c59fb36392eec.jpg\",\n  \"257189.jpg\": \"Aves/Pelecanus occidentalis/0d4cb03d138dfb880280fdd98e49f3a8.jpg\",\n  \"257211.jpg\": \"Aves/Pelecanus occidentalis/36ed50c7c79c25351f9cf7619c2c506d.jpg\",\n  \"257218.jpg\": \"Aves/Pelecanus occidentalis/7ca8d11a63c63e65da7a19ef910f6dce.jpg\",\n  \"257228.jpg\": \"Aves/Pelecanus occidentalis/a24f7d5f3fafa91ebfc4a4fe9d79dcca.jpg\",\n  \"257297.jpg\": \"Aves/Pelecanus occidentalis/e73cace6152bbd4888bd5659a78f32f5.jpg\",\n  \"257386.jpg\": \"Aves/Pelecanus occidentalis/f203536e120e5132fc693e3788c03ecd.jpg\",\n  \"257438.jpg\": \"Aves/Pelecanus occidentalis/0d36a5fcffc57d9732b5b1fedb7686a8.jpg\",\n  \"257698.jpg\": \"Insecta/Prionus californicus/fdc7d20246fa02d0020dd7f283cb0b81.jpg\",\n  \"257705.jpg\": \"Insecta/Prionus californicus/d8cd2fb1252c187062d24907ccf04b90.jpg\",\n  \"257706.jpg\": \"Insecta/Prionus californicus/8085f161dff883b219dd2ed633385785.jpg\",\n  \"257715.jpg\": \"Insecta/Prionus californicus/2ec99df6a5d20cd98c611a91b42766e3.jpg\",\n  \"257719.jpg\": \"Insecta/Prionus californicus/b05867af9b19b3e6408690fb476a1ae6.jpg\",\n  \"257720.jpg\": \"Insecta/Prionus californicus/59a8b25c8eae4e9fe1ab53cc149f1641.jpg\",\n  \"257756.jpg\": \"Actinopterygii/Ameiurus melas/ff6813dd69e89de08acf214a99afec9e.jpg\",\n  \"257762.jpg\": \"Actinopterygii/Ameiurus melas/ada508afe7888dd2cf802617038afdc0.jpg\",\n  \"257770.jpg\": \"Actinopterygii/Ameiurus melas/cef49bdba5960bb52173f00eb767c788.jpg\",\n  \"257773.jpg\": \"Insecta/Dromogomphus spinosus/41e1b97334d7f850a8f12325ad70dc8e.jpg\",\n  \"257774.jpg\": \"Insecta/Dromogomphus spinosus/5c3da909b93bc202195e175192306cc9.jpg\",\n  \"257778.jpg\": \"Insecta/Dromogomphus spinosus/2df2f82192c271b1f693b1c4660b3e4b.jpg\",\n  \"257779.jpg\": \"Insecta/Dromogomphus spinosus/a3ed1d49a6b12d40f11b380c35631c17.jpg\",\n  \"257780.jpg\": \"Insecta/Dromogomphus spinosus/f804b536b9ad69568f948a672458b4a2.jpg\",\n  \"257782.jpg\": \"Insecta/Dromogomphus spinosus/99459feec1ad6f1040ed9d9a35b5ef70.jpg\",\n  \"257783.jpg\": \"Insecta/Dromogomphus spinosus/70ffba22c9d82700db7beac1c3e4d291.jpg\",\n  \"257790.jpg\": \"Insecta/Dromogomphus spinosus/1d3fdf5be393f0ef60f55c2c3ee3f830.jpg\",\n  \"257793.jpg\": \"Insecta/Dromogomphus spinosus/088f77478c85b361ecb350f29934e701.jpg\",\n  \"257802.jpg\": \"Insecta/Dromogomphus spinosus/0a043b5fee5fe904888bcbfa5af7a493.jpg\",\n  \"257803.jpg\": \"Insecta/Dromogomphus spinosus/a10ba679b80303bfc66217285fdf075d.jpg\",\n  \"257805.jpg\": \"Insecta/Dromogomphus spinosus/c56ea0a6fe918f347cc45e8340cf9244.jpg\",\n  \"257806.jpg\": \"Insecta/Dromogomphus spinosus/f16dc52b486c1542cd9fb9721297e359.jpg\",\n  \"257807.jpg\": \"Insecta/Dromogomphus spinosus/a74728b59c67846512e743ed292a717d.jpg\",\n  \"257810.jpg\": \"Insecta/Dromogomphus spinosus/3ec695ae849a8f5937975ddfca130d6b.jpg\",\n  \"257814.jpg\": \"Insecta/Dromogomphus spinosus/d098298ed865c9b95a76f521bb87b7b8.jpg\",\n  \"257815.jpg\": \"Insecta/Dromogomphus spinosus/eecad1dc32e7f75b37327f603f52c5cb.jpg\",\n  \"257820.jpg\": \"Insecta/Dromogomphus spinosus/af23fabb982a62a7f6f2c2abd2a29884.jpg\",\n  \"257821.jpg\": \"Insecta/Dromogomphus spinosus/441fd5f79ee63685dfcd77dadf1e0eb3.jpg\",\n  \"257822.jpg\": \"Insecta/Dromogomphus spinosus/e8c555d7899653f752d95d159c00e5a5.jpg\",\n  \"257823.jpg\": \"Insecta/Dromogomphus spinosus/ac5c885ea5db0bb2c1c534d3cc75b4ac.jpg\",\n  \"257824.jpg\": \"Insecta/Dromogomphus spinosus/32cff8558b06efc55ad9180ebcbcd132.jpg\",\n  \"257825.jpg\": \"Insecta/Dromogomphus spinosus/e28f4d0fe96028317e8bf865af27a1f7.jpg\",\n  \"257843.jpg\": \"Insecta/Brechmorhoga mendax/dcdd356e6ced664607082df04095a8a0.jpg\",\n  \"257846.jpg\": \"Insecta/Brechmorhoga mendax/38b21103472853dc1c6b9cda872d299b.jpg\",\n  \"257847.jpg\": \"Insecta/Brechmorhoga mendax/f1ed531aba91de7d0368fc2ea4991885.jpg\",\n  \"257848.jpg\": \"Insecta/Brechmorhoga mendax/b1645cd408222ead0bc08d1ab4ecb147.jpg\",\n  \"257849.jpg\": \"Insecta/Brechmorhoga mendax/cee85b76f479107e5fc484349217e2f0.jpg\",\n  \"257853.jpg\": \"Insecta/Brechmorhoga mendax/da138244082efc6bddbd410bdf60f8d5.jpg\",\n  \"257854.jpg\": \"Insecta/Brechmorhoga mendax/34d1b6b5a7177bb89b719aa60bfb07e4.jpg\",\n  \"257855.jpg\": \"Insecta/Brechmorhoga mendax/8773654c79249d2203527ad7bf72b111.jpg\",\n  \"257856.jpg\": \"Insecta/Brechmorhoga mendax/a019c9075bae3cce50a11cb5c97f14c6.jpg\",\n  \"257857.jpg\": \"Insecta/Brechmorhoga mendax/bf6947a0546987fd8d614bad9cb80bf4.jpg\",\n  \"257858.jpg\": \"Insecta/Brechmorhoga mendax/6388d8e7fabff38a80492e90c158c0f3.jpg\",\n  \"257859.jpg\": \"Insecta/Brechmorhoga mendax/1c74f609a73091d42938dfeab5834c40.jpg\",\n  \"257862.jpg\": \"Insecta/Brechmorhoga mendax/e2e508f9a3227ee3c13148c051348ed1.jpg\",\n  \"257867.jpg\": \"Insecta/Brechmorhoga mendax/ea8d58a1a000ff64e960920fd2c9c1ba.jpg\",\n  \"257869.jpg\": \"Insecta/Brechmorhoga mendax/35326c4ceff0cfc940d12d0ab59a2d48.jpg\",\n  \"257870.jpg\": \"Insecta/Brechmorhoga mendax/3feb01f5d42eefc66138c2efb08f2a0e.jpg\",\n  \"257871.jpg\": \"Insecta/Brechmorhoga mendax/a43491cd060d464d06e2fc3b29061742.jpg\",\n  \"257872.jpg\": \"Insecta/Brechmorhoga mendax/239c08976d2c06fa71cb4cf609a9f95f.jpg\",\n  \"257873.jpg\": \"Insecta/Brechmorhoga mendax/d0f20a1f9b9503517b6fe84b896adaf7.jpg\",\n  \"257880.jpg\": \"Insecta/Brechmorhoga mendax/0a0e9ac271fc472b2a54951802174f96.jpg\",\n  \"257885.jpg\": \"Insecta/Brechmorhoga mendax/3638bb09395e3d1cdd484c2a83ece078.jpg\",\n  \"257886.jpg\": \"Insecta/Brechmorhoga mendax/3e6db982f5507210bfacb95a9a0cc5c3.jpg\",\n  \"257891.jpg\": \"Aves/Pipilo chlorurus/00e034a6e722959884fb1a06dd3311de.jpg\",\n  \"257892.jpg\": \"Aves/Pipilo chlorurus/c98f9c6ce57c0f70ea203a78dfbb81ff.jpg\",\n  \"257893.jpg\": \"Aves/Pipilo chlorurus/e72c80c0453c3fd165c20cd8c557a491.jpg\",\n  \"257899.jpg\": \"Aves/Pipilo chlorurus/0558f193e552a2296008ecca5dd293d6.jpg\",\n  \"257900.jpg\": \"Aves/Pipilo chlorurus/6502c719ae6f5185c2a673e9b1b8580a.jpg\",\n  \"257902.jpg\": \"Aves/Pipilo chlorurus/3ec49cad5cbc5a1522c6b270804041e0.jpg\",\n  \"257903.jpg\": \"Aves/Pipilo chlorurus/90ca7fc73a0283346bc95f85d78bcfb8.jpg\",\n  \"257904.jpg\": \"Aves/Pipilo chlorurus/f0093c5a74eb78cd52686e085ce8201e.jpg\",\n  \"257905.jpg\": \"Aves/Pipilo chlorurus/5223bc0cf2c1c8bde0d6e0c709c9fa0a.jpg\",\n  \"257906.jpg\": \"Aves/Pipilo chlorurus/9b5dc2735ed27af314d09213b1ae778c.jpg\",\n  \"257907.jpg\": \"Aves/Pipilo chlorurus/d544998b9dc36db20e946f1800224d17.jpg\",\n  \"257915.jpg\": \"Aves/Pipilo chlorurus/64f7e5c4ce8976f7285bb3cfb624dc76.jpg\",\n  \"257922.jpg\": \"Aves/Pipilo chlorurus/6f8b913496c1f5c1ef448726c21ea1e5.jpg\",\n  \"257923.jpg\": \"Aves/Pipilo chlorurus/a341c812abee8d75ea09c22877493625.jpg\",\n  \"257925.jpg\": \"Aves/Pipilo chlorurus/bc1957d500545bf7e3834fb9db9fe147.jpg\",\n  \"257926.jpg\": \"Aves/Pipilo chlorurus/e491a47aace14bc5611d923fd645cd99.jpg\",\n  \"257927.jpg\": \"Aves/Pipilo chlorurus/f017d62f12421718a2a75b9490aaada4.jpg\",\n  \"257930.jpg\": \"Aves/Pipilo chlorurus/407a12c071834fa5c8447e8cb017439e.jpg\",\n  \"257931.jpg\": \"Aves/Pipilo chlorurus/82313aebe12122bafa1194e4ad578dca.jpg\",\n  \"257932.jpg\": \"Aves/Pipilo chlorurus/deb708a1dc0f548da5c057caa25b1a58.jpg\",\n  \"257943.jpg\": \"Aves/Pipilo chlorurus/733be65fc51e30f312b84004f74f3c51.jpg\",\n  \"257946.jpg\": \"Insecta/Arigomphus submedianus/517f3480a858c15ce1ee294facb09f0c.jpg\",\n  \"257947.jpg\": \"Insecta/Arigomphus submedianus/6778e16903074aa96c3e5fb693d55077.jpg\",\n  \"257960.jpg\": \"Insecta/Arigomphus submedianus/19ab9e6226379ae60eff19834011e823.jpg\",\n  \"257961.jpg\": \"Insecta/Arigomphus submedianus/f96ab8c7aad0d51d045898ee402ee0ea.jpg\",\n  \"257966.jpg\": \"Insecta/Arigomphus submedianus/c40975d7837fa809d70a8014a2e0b3bf.jpg\",\n  \"257984.jpg\": \"Insecta/Spodoptera ornithogalli/ca7a59c99df6cf8d6a57d27edc22bb0a.jpg\",\n  \"257987.jpg\": \"Insecta/Spodoptera ornithogalli/ed0993edc362630dd7efdc2cfcc37a8a.jpg\",\n  \"257995.jpg\": \"Insecta/Spodoptera ornithogalli/455d0c389e437d07a99ff882f82fa6f6.jpg\",\n  \"257996.jpg\": \"Insecta/Spodoptera ornithogalli/a92779d6028a2a1ef5b7f901a0067f67.jpg\",\n  \"257997.jpg\": \"Insecta/Spodoptera ornithogalli/037901a69d90bb6dd551799bebddcacf.jpg\",\n  \"258007.jpg\": \"Insecta/Spodoptera ornithogalli/2e73516863ae2fb92564ae9f7b6aec01.jpg\",\n  \"258009.jpg\": \"Insecta/Spodoptera ornithogalli/739245cec856b726cee6b31056bf8cee.jpg\",\n  \"258010.jpg\": \"Insecta/Spodoptera ornithogalli/de3125ffbd3c0d35b31e2aa2152719ac.jpg\",\n  \"258011.jpg\": \"Insecta/Spodoptera ornithogalli/b0ca8afca825423ebd74d5df77891e8b.jpg\",\n  \"258012.jpg\": \"Insecta/Spodoptera ornithogalli/cc4598efb2a00fb2334194b8ee1b89cf.jpg\",\n  \"258013.jpg\": \"Insecta/Spodoptera ornithogalli/9bc71b0c0aa74fb1693c6d41b369a533.jpg\",\n  \"258014.jpg\": \"Insecta/Spodoptera ornithogalli/5212175b0627c2416f8fe6eeac3619cd.jpg\",\n  \"258019.jpg\": \"Insecta/Spodoptera ornithogalli/bb039e29d9fdd6cc0cd4f134826ce5cc.jpg\",\n  \"258020.jpg\": \"Insecta/Spodoptera ornithogalli/77f7d554bfa57c1cfb7b39bfb79b774a.jpg\",\n  \"258021.jpg\": \"Insecta/Spodoptera ornithogalli/da88ed228bbafa62383780fb71073c76.jpg\",\n  \"258025.jpg\": \"Insecta/Spodoptera ornithogalli/c422e3c4eb3a8268e98e1eb16556a61f.jpg\",\n  \"258026.jpg\": \"Insecta/Spodoptera ornithogalli/32946f6c054d4a54213771690ec86dfa.jpg\",\n  \"258028.jpg\": \"Insecta/Spodoptera ornithogalli/79facf67dfcd73a41a8fbcec05614d4b.jpg\",\n  \"258029.jpg\": \"Insecta/Spodoptera ornithogalli/5ec8884e8f8cc43159996c016bf6c3d9.jpg\",\n  \"258031.jpg\": \"Insecta/Spodoptera ornithogalli/dec55508ca5c871aa830783954e8bd46.jpg\",\n  \"258040.jpg\": \"Insecta/Spodoptera ornithogalli/0f53abfb3f6c76eb79a2344969c558a0.jpg\",\n  \"258042.jpg\": \"Insecta/Spodoptera ornithogalli/90dfbb53405f1c324d8f9fd49538b572.jpg\",\n  \"258043.jpg\": \"Insecta/Spodoptera ornithogalli/6c8152d6dabe5a9da1341aa60b7c309f.jpg\",\n  \"258166.jpg\": \"Insecta/Polistes exclamans/266d30c2cd91b17ebf5b34d9b06dc340.jpg\",\n  \"258168.jpg\": \"Insecta/Polistes exclamans/8c1f210ab45972e3864474ed3695ce6f.jpg\",\n  \"258169.jpg\": \"Insecta/Polistes exclamans/a3f1538c50d295f8f1fdd220b4985eaf.jpg\",\n  \"258178.jpg\": \"Insecta/Polistes exclamans/447a2ca1f7d3831089fdaac2c708337d.jpg\",\n  \"258179.jpg\": \"Insecta/Polistes exclamans/51a4347566f9c91aaeb6e28040e574b8.jpg\",\n  \"258186.jpg\": \"Insecta/Polistes exclamans/3dcc04797c90029c860d65eeb5a65649.jpg\",\n  \"258187.jpg\": \"Insecta/Polistes exclamans/f530179e6ac49a64b790f9208624729d.jpg\",\n  \"258195.jpg\": \"Insecta/Polistes exclamans/737bfc01bb2ab971cd3b11e2242086e1.jpg\",\n  \"258206.jpg\": \"Insecta/Polistes exclamans/d6fd6eb7b93f29b672a47111f8d77017.jpg\",\n  \"258207.jpg\": \"Insecta/Polistes exclamans/08b9b58b26e704213b31937360294500.jpg\",\n  \"258212.jpg\": \"Insecta/Polistes exclamans/0781d70a850eb0b994244b5b957aaa05.jpg\",\n  \"258215.jpg\": \"Insecta/Polistes exclamans/05ef3b41cee8abd258d675e085562ad2.jpg\",\n  \"258226.jpg\": \"Insecta/Polistes exclamans/2f1704d5aaff77b7636883ef71e850c8.jpg\",\n  \"258244.jpg\": \"Insecta/Polistes exclamans/eb8cb239921585440119a52f3aedb587.jpg\",\n  \"258248.jpg\": \"Mammalia/Mirounga leonina/b1e982e55a98d6cb84e104a1915c6a04.jpg\",\n  \"258256.jpg\": \"Mammalia/Mirounga leonina/7cc34229a38aacbe962202e307bfe289.jpg\",\n  \"258267.jpg\": \"Mammalia/Mirounga leonina/e28a6ad3486da02b06833a186a14e24c.jpg\",\n  \"258274.jpg\": \"Insecta/Cimbex americana/269d6d626db4ad0897c9ca4cab5928dd.jpg\",\n  \"258275.jpg\": \"Insecta/Cimbex americana/16afdc6281cbe7cc51f237a7b3efec6e.jpg\",\n  \"258278.jpg\": \"Insecta/Cimbex americana/76ba423f096d895aec3ea606bf0b9870.jpg\",\n  \"258283.jpg\": \"Insecta/Cimbex americana/2f08ee7215811387a7e0098dbcdcefb2.jpg\",\n  \"258284.jpg\": \"Insecta/Cimbex americana/16d8b0a3ab4dc419bda7b0ca9308a5c7.jpg\",\n  \"258286.jpg\": \"Insecta/Hyalophora euryalus/58e6840f98665a2a37a0677d9467d89a.jpg\",\n  \"258295.jpg\": \"Insecta/Hyalophora euryalus/8066b259894f24ef820d652845e2bd14.jpg\",\n  \"258296.jpg\": \"Insecta/Hyalophora euryalus/e76ee926e0479a3a7b854d6fb5932f08.jpg\",\n  \"258297.jpg\": \"Insecta/Hyalophora euryalus/892c98cf00e73a7e3638c349ebbf53ff.jpg\",\n  \"258299.jpg\": \"Insecta/Hyalophora euryalus/758e484a8e24636dcc3102d7b92c4e81.jpg\",\n  \"258301.jpg\": \"Insecta/Hyalophora euryalus/4855602bff527d25b5b24d5b45ebcdd3.jpg\",\n  \"258305.jpg\": \"Insecta/Hyalophora euryalus/4ac551240e2e7a6dd5ef9e810ed5bd38.jpg\",\n  \"258306.jpg\": \"Insecta/Hyalophora euryalus/9e74a1008e709345c0eda53c494c05e2.jpg\",\n  \"258307.jpg\": \"Insecta/Hyalophora euryalus/674dff9feb323cb6837e4fd2d753be8d.jpg\",\n  \"25833.jpg\": \"Insecta/Urola nivalis/a1af8a65e1a5fdfabc8b545939a867cb.jpg\",\n  \"258332.jpg\": \"Aves/Anous stolidus/da5d6a0fb4f8c3df53b5abc8b99bc6dc.jpg\",\n  \"25835.jpg\": \"Insecta/Urola nivalis/72630daac9b67c32dac0780f91ca4473.jpg\",\n  \"25840.jpg\": \"Insecta/Urola nivalis/a8751a90ad7028a46b326d8d59e53341.jpg\",\n  \"25842.jpg\": \"Insecta/Urola nivalis/53fc5fc24c9349d89c5b579685739628.jpg\",\n  \"258429.jpg\": \"Aves/Anas querquedula/4afdc6417697e3ac249b9fe718e5b5c6.jpg\",\n  \"258434.jpg\": \"Aves/Anas querquedula/531d4ef9aa7fb905865e1863a4bddf95.jpg\",\n  \"258435.jpg\": \"Aves/Anas querquedula/29da24545e6da956dbc4a860c6fa751b.jpg\",\n  \"258440.jpg\": \"Aves/Anas querquedula/3b21b7917383df870fb42da394e31dc2.jpg\",\n  \"258444.jpg\": \"Aves/Anas querquedula/8567a3869bafeeebb9b5a2b6b2d66882.jpg\",\n  \"258451.jpg\": \"Insecta/Erynnis tristis/3a2128ccde4c3bb30654a545e0fb69b4.jpg\",\n  \"258452.jpg\": \"Insecta/Erynnis tristis/86728bec8cedbde075d9be815a7da280.jpg\",\n  \"258455.jpg\": \"Insecta/Erynnis tristis/5318c65d83ff4190e86852aac38eccfd.jpg\",\n  \"25846.jpg\": \"Insecta/Urola nivalis/95ad71f02bad1fdb9aa9bfc1568741a4.jpg\",\n  \"258464.jpg\": \"Insecta/Erynnis tristis/36352a26a46086f1af876e3e5feb037f.jpg\",\n  \"258465.jpg\": \"Insecta/Erynnis tristis/3c14acda5a829b690d0e30d0ea0a027b.jpg\",\n  \"258467.jpg\": \"Insecta/Erynnis tristis/6e7829db8c2aa04d3b454285b34cfa32.jpg\",\n  \"258468.jpg\": \"Insecta/Erynnis tristis/b50ca288dcc3a5edf71b040be837b952.jpg\",\n  \"258469.jpg\": \"Insecta/Erynnis tristis/36b4a61006b8298a96624afeb9e3f181.jpg\",\n  \"25847.jpg\": \"Insecta/Urola nivalis/c0ff27b94cd1d2a36360e0d815224e61.jpg\",\n  \"258470.jpg\": \"Insecta/Erynnis tristis/11145fcf551913e4a98a9338eeab5ecf.jpg\",\n  \"258472.jpg\": \"Insecta/Erynnis tristis/520d5b4b6585262805217da1b80602c9.jpg\",\n  \"258473.jpg\": \"Insecta/Erynnis tristis/a1acdad7cfd1a36e6c71364b8ef42bee.jpg\",\n  \"258474.jpg\": \"Insecta/Erynnis tristis/79f28dada1657efdb03e7a6249530288.jpg\",\n  \"258477.jpg\": \"Insecta/Erynnis tristis/21425d6b187cd16557155c91dcd232c6.jpg\",\n  \"25848.jpg\": \"Insecta/Urola nivalis/da3ec282dcd1ca3197406aaf3951b083.jpg\",\n  \"258482.jpg\": \"Insecta/Erynnis tristis/e2f06c050ccbe9f22883c7eb9119636e.jpg\",\n  \"258483.jpg\": \"Insecta/Erynnis tristis/2f97932a28edc8164a3f1a7f21cbc1e7.jpg\",\n  \"258484.jpg\": \"Insecta/Erynnis tristis/e6aada47759b78fc0b9bde8a9143a3b7.jpg\",\n  \"25849.jpg\": \"Insecta/Urola nivalis/530fd8f0149e96793fb6efd73e2205e7.jpg\",\n  \"258498.jpg\": \"Insecta/Erynnis tristis/fe04d29f87004e7f95a0df748ce17a4a.jpg\",\n  \"258499.jpg\": \"Insecta/Erynnis tristis/e979924446a685bde340837b511f1b43.jpg\",\n  \"258500.jpg\": \"Insecta/Erynnis tristis/8276e7a2227ea24471dcf1ba3cb8ebe7.jpg\",\n  \"258502.jpg\": \"Insecta/Erynnis tristis/f33e9daa772c1738f2c5adc23775bea5.jpg\",\n  \"258509.jpg\": \"Insecta/Erynnis tristis/763610d36e7791d2e8dc62d40498c4a6.jpg\",\n  \"258514.jpg\": \"Insecta/Erynnis tristis/b007f3817ef4d5927085d97161b1b606.jpg\",\n  \"258515.jpg\": \"Insecta/Erynnis tristis/14826eb76babdfcd8e8164367bfb37d8.jpg\",\n  \"25853.jpg\": \"Insecta/Urola nivalis/3cc076ccd4dda722ec098126299f318d.jpg\",\n  \"258535.jpg\": \"Aves/Poecile carolinensis/4520566a0c74cd19c1004891965d5914.jpg\",\n  \"25854.jpg\": \"Insecta/Urola nivalis/25e082e624a0e469443bb9fb7d9c2019.jpg\",\n  \"25855.jpg\": \"Insecta/Urola nivalis/af58c87bc89788e1c13e45205425268b.jpg\",\n  \"25856.jpg\": \"Insecta/Urola nivalis/513a4c4f4fe1d8ece0dab11f69d50802.jpg\",\n  \"258563.jpg\": \"Aves/Poecile carolinensis/072b0592845621c88559cc9b74ef5c9e.jpg\",\n  \"258564.jpg\": \"Aves/Poecile carolinensis/03914fef6dd08d7ad9c973915d62a1d1.jpg\",\n  \"25857.jpg\": \"Insecta/Urola nivalis/23f64ae99f4d2abc0942e2ba02705786.jpg\",\n  \"258575.jpg\": \"Aves/Poecile carolinensis/6b1f0a1bb7b28c6d32f55aba8e57eb8a.jpg\",\n  \"258576.jpg\": \"Aves/Poecile carolinensis/8d9865671b3ee0c310d587ba9bcf4eda.jpg\",\n  \"258587.jpg\": \"Aves/Poecile carolinensis/7ebfa82ac9ce584a2501a1c64514aa40.jpg\",\n  \"258592.jpg\": \"Aves/Poecile carolinensis/91df78f1f7cb23ac5d2df2c56e17913f.jpg\",\n  \"258602.jpg\": \"Aves/Poecile carolinensis/8f4b53288dca2dba09daa567d922fe42.jpg\",\n  \"258603.jpg\": \"Aves/Poecile carolinensis/fb18f5798f866841652d99e4b5f49a6c.jpg\",\n  \"25861.jpg\": \"Insecta/Urola nivalis/9d0ceff968ffc4056a6aca6f413b7359.jpg\",\n  \"258616.jpg\": \"Aves/Poecile carolinensis/c981e4b0230b98abf3cf16634a968b14.jpg\",\n  \"258617.jpg\": \"Aves/Poecile carolinensis/0ebd9a592f9b205ae2c626a9a909613c.jpg\",\n  \"25862.jpg\": \"Insecta/Urola nivalis/90d3450546aa1ab6b8fefe32ac63f136.jpg\",\n  \"258620.jpg\": \"Aves/Poecile carolinensis/a0bb078ed5ec20fd4f4ab42691a13bee.jpg\",\n  \"258625.jpg\": \"Aves/Poecile carolinensis/1c41addb071487de5f05e8312d7e0ae4.jpg\",\n  \"258637.jpg\": \"Aves/Poecile carolinensis/eb41bbe8605bfa57a516e7fa46570251.jpg\",\n  \"258681.jpg\": \"Aves/Poecile carolinensis/504ef08a5a0cb88ac1cbe04c414c67a1.jpg\",\n  \"258682.jpg\": \"Aves/Poecile carolinensis/20f9d416ffdfbd8ce87e1651b5d532af.jpg\",\n  \"258688.jpg\": \"Aves/Poecile carolinensis/383ce08e43eb8ceea2732f45f8e1bfe2.jpg\",\n  \"258715.jpg\": \"Aves/Poecile carolinensis/8db5b452e0aeb3a4b3352331e0c13814.jpg\",\n  \"258725.jpg\": \"Aves/Poecile carolinensis/d9750f0c034a01c6f1c30908baef8906.jpg\",\n  \"258727.jpg\": \"Aves/Poecile carolinensis/fb4d3b7b1ce7429abb7c7efb2dab8129.jpg\",\n  \"25873.jpg\": \"Insecta/Urola nivalis/b493a1c4b1fa13348c44dde5fb886d83.jpg\",\n  \"25876.jpg\": \"Insecta/Urola nivalis/fe4c8c428875f8d9df7636271fec0549.jpg\",\n  \"25877.jpg\": \"Insecta/Urola nivalis/51e609ee57dd8ccb43d0f841b2642440.jpg\",\n  \"25878.jpg\": \"Insecta/Urola nivalis/d1fab2d5e197cb3fa4ecba2070cc81bf.jpg\",\n  \"25879.jpg\": \"Insecta/Urola nivalis/0183e0ca4aec6aa8b08d76f16c840eba.jpg\",\n  \"25880.jpg\": \"Insecta/Urola nivalis/abe98f632edd0cd44638fec9f214ef7b.jpg\",\n  \"258809.jpg\": \"Aves/Poecile carolinensis/9ab4450d3aba8f9a4071274c4903a702.jpg\",\n  \"25881.jpg\": \"Insecta/Urola nivalis/55c69178c1d48e40928f328d2cd50575.jpg\",\n  \"258811.jpg\": \"Aves/Poecile carolinensis/1e9ef3c6b45e181bab28ba82ef101d4d.jpg\",\n  \"258816.jpg\": \"Mammalia/Scalopus aquaticus/849afdce8ba99f8f1326395c3346bcf4.jpg\",\n  \"258817.jpg\": \"Mammalia/Scalopus aquaticus/2c8e62c2ab8d1be6219a30cdc4a169ad.jpg\",\n  \"25882.jpg\": \"Insecta/Urola nivalis/61f2cca8dd3c7862ea875c3cf55d20fa.jpg\",\n  \"258825.jpg\": \"Mammalia/Scalopus aquaticus/f4ee40989a08ec58e7a51b8b805140cf.jpg\",\n  \"258826.jpg\": \"Mammalia/Scalopus aquaticus/164c4bbac86c613d9e80ba96a1b948fb.jpg\",\n  \"258827.jpg\": \"Mammalia/Scalopus aquaticus/8b531ddc4626329cff1a56b53d26434f.jpg\",\n  \"25883.jpg\": \"Insecta/Urola nivalis/f1f21d791a2fd9772d8ff3e2286f00b3.jpg\",\n  \"258831.jpg\": \"Mammalia/Scalopus aquaticus/99bf319a6aabb61b93503575dd9b9e20.jpg\",\n  \"258833.jpg\": \"Reptilia/Ctenosaura similis/ae3625bdf4fb1d4e6379be26ae39da69.jpg\",\n  \"258834.jpg\": \"Reptilia/Ctenosaura similis/784b73614ec28a83dc9c29e748b9be05.jpg\",\n  \"258835.jpg\": \"Reptilia/Ctenosaura similis/0c43112098b96af5a9a28118fdc95caa.jpg\",\n  \"258852.jpg\": \"Reptilia/Ctenosaura similis/022c351b776db67e3972c1c4edb93bb4.jpg\",\n  \"258871.jpg\": \"Reptilia/Ctenosaura similis/08fd068e5854ca2b224c6a328a1aa704.jpg\",\n  \"258877.jpg\": \"Reptilia/Ctenosaura similis/dc44c8ba312de9af6304a470a3e100d5.jpg\",\n  \"258878.jpg\": \"Reptilia/Ctenosaura similis/5165c98c0689a2b1f2f661162f95fb4d.jpg\",\n  \"258881.jpg\": \"Reptilia/Ctenosaura similis/bf86286c1491cb16106685561f4d1e5c.jpg\",\n  \"258889.jpg\": \"Reptilia/Ctenosaura similis/dba4450de43c9535c64672d51df1120a.jpg\",\n  \"258890.jpg\": \"Reptilia/Ctenosaura similis/2e72dd27e147cbca66a8e253bac4619e.jpg\",\n  \"258901.jpg\": \"Reptilia/Ctenosaura similis/8c33345c8229c95a072692101f0392f8.jpg\",\n  \"258906.jpg\": \"Reptilia/Ctenosaura similis/839419f6359079779b69a7d95697afc9.jpg\",\n  \"258910.jpg\": \"Reptilia/Ctenosaura similis/82e024c5c16e686b72ae9adbbc482909.jpg\",\n  \"258924.jpg\": \"Reptilia/Ctenosaura similis/92b677c4088a4d6b2fe410180ea79ff9.jpg\",\n  \"258930.jpg\": \"Reptilia/Ctenosaura similis/c2369cc9390045bc9e5049156aa05482.jpg\",\n  \"258939.jpg\": \"Reptilia/Ctenosaura similis/61ab56e53e0599822d4120c8d39f9522.jpg\",\n  \"258940.jpg\": \"Reptilia/Ctenosaura similis/5dfbc185212352337addad76b6c0708c.jpg\",\n  \"258941.jpg\": \"Reptilia/Ctenosaura similis/82d7709c4fec767899295bea2ac98a80.jpg\",\n  \"258948.jpg\": \"Reptilia/Ctenosaura similis/bf8c2eabdaff5ffcfe8964330ff751f4.jpg\",\n  \"258956.jpg\": \"Reptilia/Ctenosaura similis/d8a06680d5296a752df8e17a860486b8.jpg\",\n  \"258961.jpg\": \"Reptilia/Ctenosaura similis/3eae5e6d0ab64c0e58a7ce84297e468c.jpg\",\n  \"258994.jpg\": \"Actinopterygii/Mola mola/4fa7fe7627f31fc62c7665e222b88271.jpg\",\n  \"258995.jpg\": \"Actinopterygii/Mola mola/eba2ee2ea1f87eeed1ed50cffc96a143.jpg\",\n  \"259000.jpg\": \"Actinopterygii/Mola mola/d0c4bf0604462d3f3f8eacc450bd6c8c.jpg\",\n  \"259001.jpg\": \"Actinopterygii/Mola mola/8f43d5340742cf8e00a178908b28226d.jpg\",\n  \"259006.jpg\": \"Actinopterygii/Mola mola/55373ef2fe7192c22f4833ffa689768b.jpg\",\n  \"259009.jpg\": \"Actinopterygii/Mola mola/46bed102294ed475ed3dae6a64a49040.jpg\",\n  \"259013.jpg\": \"Actinopterygii/Mola mola/6953abff6522fcafa5ff64c5c95e6291.jpg\",\n  \"259014.jpg\": \"Actinopterygii/Mola mola/6808a7b7b0268f1d717dd6a2eea90771.jpg\",\n  \"259021.jpg\": \"Actinopterygii/Mola mola/ae640dbc63dcd084b192b38df878140a.jpg\",\n  \"259030.jpg\": \"Actinopterygii/Mola mola/2a74884a2c76becc13958786dfff1004.jpg\",\n  \"259032.jpg\": \"Actinopterygii/Mola mola/0db459956c58faef932f732fb51241a8.jpg\",\n  \"259035.jpg\": \"Reptilia/Virginia striatula/ef7194d7218023e289894f7d2593c4a5.jpg\",\n  \"259040.jpg\": \"Reptilia/Virginia striatula/b728e312ea585f5b7fa9987243d7f4c3.jpg\",\n  \"259047.jpg\": \"Reptilia/Virginia striatula/0f0b8c9df1437f9024596fde6402ecd1.jpg\",\n  \"259069.jpg\": \"Reptilia/Virginia striatula/99a824b90878f073d221cf3e5759ac74.jpg\",\n  \"259071.jpg\": \"Reptilia/Virginia striatula/83534d535b713f9ebfb38a96eab7c147.jpg\",\n  \"259072.jpg\": \"Reptilia/Virginia striatula/c77474e8b83db30ca9ed670ea9968575.jpg\",\n  \"259074.jpg\": \"Reptilia/Virginia striatula/939d25b2cf52b13ef4fa3f2c407ac8f6.jpg\",\n  \"259078.jpg\": \"Reptilia/Virginia striatula/51d6041e9c642c3a58067f120c23cc80.jpg\",\n  \"259088.jpg\": \"Reptilia/Virginia striatula/99eeac7ddc88a2fd2423d443e4b56f5b.jpg\",\n  \"259099.jpg\": \"Reptilia/Virginia striatula/9ea02115df30cace67c4de151fa9a58a.jpg\",\n  \"259104.jpg\": \"Reptilia/Virginia striatula/c1ead45d7767e645bebbb6e59633717d.jpg\",\n  \"259106.jpg\": \"Reptilia/Virginia striatula/6b5ebe232d0c2922ec8303b28d69541b.jpg\",\n  \"259110.jpg\": \"Reptilia/Virginia striatula/794382b8cf9b1e9bdaea13b75fffa1c7.jpg\",\n  \"259111.jpg\": \"Reptilia/Virginia striatula/ffaeba7a54e710e6c1c728c4966dc0ec.jpg\",\n  \"259117.jpg\": \"Reptilia/Virginia striatula/80d17b4cadcf2d80ae85ee1815920e6d.jpg\",\n  \"259125.jpg\": \"Reptilia/Virginia striatula/7d23870deaab8bcebb51fd6ad8d94b92.jpg\",\n  \"259126.jpg\": \"Reptilia/Virginia striatula/5608bb58bcaca41d9c104adb2fe01e61.jpg\",\n  \"259138.jpg\": \"Reptilia/Virginia striatula/81f2821e9166749a7762f0ad6f0accb5.jpg\",\n  \"259140.jpg\": \"Reptilia/Virginia striatula/1da3746a998cfc976b6717732d392fff.jpg\",\n  \"259141.jpg\": \"Reptilia/Virginia striatula/dcc401658e44dd073e844dc6a75477eb.jpg\",\n  \"259143.jpg\": \"Reptilia/Virginia striatula/85f9a5155657af5fef810127ab33de09.jpg\",\n  \"259156.jpg\": \"Reptilia/Virginia striatula/cbdc645d1f6642a7029fc725e3f3ccfd.jpg\",\n  \"259160.jpg\": \"Reptilia/Aspidoscelis velox/70fdaaa7e258b68dc487e883638b13f7.jpg\",\n  \"259164.jpg\": \"Reptilia/Aspidoscelis velox/7f1b4af5763432239899b0f2d677e746.jpg\",\n  \"259166.jpg\": \"Reptilia/Aspidoscelis velox/7387cd838bacb7ceafb977389dc0cb05.jpg\",\n  \"259170.jpg\": \"Reptilia/Aspidoscelis velox/3eef0a3ea6bde4ecd57080b620c5c88b.jpg\",\n  \"259249.jpg\": \"Insecta/Hemiargus ceraunus/6dde23372b3701a39d73ce4954f73833.jpg\",\n  \"259250.jpg\": \"Insecta/Hemiargus ceraunus/82ad08ff8f4079eda4e12880ddd217e1.jpg\",\n  \"259252.jpg\": \"Insecta/Hemiargus ceraunus/c8c512a9e158e5cee410fc1c2cbb2c71.jpg\",\n  \"259258.jpg\": \"Insecta/Hemiargus ceraunus/dd840db5c5ce9689648b9efcf428e3fc.jpg\",\n  \"259259.jpg\": \"Insecta/Hemiargus ceraunus/46c7094bbce42427dc16933d73e13a6a.jpg\",\n  \"259276.jpg\": \"Insecta/Hemiargus ceraunus/4947587ca2b6c39712139cfb40b0c67a.jpg\",\n  \"259277.jpg\": \"Insecta/Hemiargus ceraunus/ca225a9d544e91cc12ac97d7e7ae2d98.jpg\",\n  \"259286.jpg\": \"Insecta/Hemiargus ceraunus/959311ab55e5ff1bcaf1a6ae690c976e.jpg\",\n  \"259309.jpg\": \"Insecta/Hemiargus ceraunus/09dfce4018eb6aa8b5701a23f673506c.jpg\",\n  \"259311.jpg\": \"Insecta/Hemiargus ceraunus/6b51c22f838916679ca528c9170de24a.jpg\",\n  \"259317.jpg\": \"Insecta/Hemiargus ceraunus/1ed6ae2040bd9585a8422f9e61e74cbf.jpg\",\n  \"259318.jpg\": \"Insecta/Hemiargus ceraunus/8ee977fb498be6b768421a4842225ce1.jpg\",\n  \"259322.jpg\": \"Insecta/Hemiargus ceraunus/957c2ea4086e33d23edea81259e41851.jpg\",\n  \"259328.jpg\": \"Insecta/Hemiargus ceraunus/d864adaf923d05d73495957d44cea67b.jpg\",\n  \"259337.jpg\": \"Insecta/Hemiargus ceraunus/b66192d11dc8d6531726c13595640116.jpg\",\n  \"259353.jpg\": \"Insecta/Hemiargus ceraunus/9e588809cc1437e13a6f3d4dfdf2c072.jpg\",\n  \"259357.jpg\": \"Insecta/Hemiargus ceraunus/b27503e2d90a82c7564b33b5347e4aaf.jpg\",\n  \"259359.jpg\": \"Insecta/Hemiargus ceraunus/ac7341b2f596bf299f22302f6751c64c.jpg\",\n  \"259364.jpg\": \"Insecta/Hemiargus ceraunus/2d6bc8304468bf7d0d0c395bc5692d75.jpg\",\n  \"259366.jpg\": \"Insecta/Hemiargus ceraunus/c8e7c66c9fce53556ba592c42900ccc5.jpg\",\n  \"259370.jpg\": \"Insecta/Hemaris thysbe/b62ebfaa53e4791dc22e51b58d7698d1.jpg\",\n  \"259372.jpg\": \"Insecta/Hemaris thysbe/e70d1327f94c23cc8356608c6b4ea275.jpg\",\n  \"259374.jpg\": \"Insecta/Hemaris thysbe/c338bc1feae436a52bf9bddc977a0519.jpg\",\n  \"259379.jpg\": \"Insecta/Hemaris thysbe/11b48da88292da39c9dd7ad8fed1a82a.jpg\",\n  \"259385.jpg\": \"Insecta/Hemaris thysbe/0d35b27bfa7cc0bb1110e59e8b236b07.jpg\",\n  \"259390.jpg\": \"Insecta/Hemaris thysbe/a045fe16ad1ae26b0c2de62518853025.jpg\",\n  \"259392.jpg\": \"Insecta/Hemaris thysbe/ad610d0003078a37c8f9b637ac690b54.jpg\",\n  \"259393.jpg\": \"Insecta/Hemaris thysbe/5752bc7297a906cc6f2c8e58b4d07ff1.jpg\",\n  \"259397.jpg\": \"Insecta/Hemaris thysbe/0c4ee68ca1410ad9f241cba4bbf32577.jpg\",\n  \"259398.jpg\": \"Insecta/Hemaris thysbe/63297a6e965827e864b09a98f23cc971.jpg\",\n  \"259399.jpg\": \"Insecta/Hemaris thysbe/37ed443b070be57431ec8638d686274d.jpg\",\n  \"259400.jpg\": \"Insecta/Hemaris thysbe/02cdf468101d5e7c3e5b79250688bba7.jpg\",\n  \"259405.jpg\": \"Insecta/Hemaris thysbe/7cd9a2e13d9acc46ffe1b466b1ba7ae4.jpg\",\n  \"259406.jpg\": \"Insecta/Hemaris thysbe/6b89001e6eb51e03e89958ee6f47ce52.jpg\",\n  \"259412.jpg\": \"Insecta/Hemaris thysbe/8718aec7e5ce2646228633bccde5a030.jpg\",\n  \"259426.jpg\": \"Insecta/Hemaris thysbe/b09f13de96327065bdfa455db37092e1.jpg\",\n  \"259431.jpg\": \"Insecta/Hemaris thysbe/588b75ef01e1ba855de86dcc4c39de60.jpg\",\n  \"259432.jpg\": \"Insecta/Hemaris thysbe/f4454096ca5d7ce30814cf92c0e8f284.jpg\",\n  \"259444.jpg\": \"Insecta/Hemaris thysbe/4ca6faf4754a104117f591fddeb0640e.jpg\",\n  \"259445.jpg\": \"Insecta/Hemaris thysbe/4fd5f39a4bc6fdee47b2d42e6fff6143.jpg\",\n  \"259446.jpg\": \"Insecta/Hemaris thysbe/2ac7f201aa6dcbf83a6f1e723f1a526f.jpg\",\n  \"259459.jpg\": \"Insecta/Hemaris thysbe/f80dac19f88b76db9930b91590ddca47.jpg\",\n  \"259481.jpg\": \"Insecta/Feniseca tarquinius/5c28ab054bb595a87a8629836ce86e1c.jpg\",\n  \"259482.jpg\": \"Insecta/Feniseca tarquinius/e8c06b0aaaa345200fba6683b84c9889.jpg\",\n  \"259484.jpg\": \"Insecta/Feniseca tarquinius/d95d09276aaff4c6d4d4add69aaeac05.jpg\",\n  \"259485.jpg\": \"Insecta/Feniseca tarquinius/2b961a90be034694f7f8e87c55cc860f.jpg\",\n  \"259489.jpg\": \"Insecta/Feniseca tarquinius/6a1209204d9506785f88daed24381a50.jpg\",\n  \"259490.jpg\": \"Insecta/Feniseca tarquinius/421c392002e667a80caa41e1b1ec7f26.jpg\",\n  \"259495.jpg\": \"Insecta/Feniseca tarquinius/e9195f565a784d1132d730c60e2dd9d4.jpg\",\n  \"259527.jpg\": \"Insecta/Melittia cucurbitae/55a2d93f14876bb4d19bf1c6a9c0dc7b.jpg\",\n  \"259530.jpg\": \"Insecta/Melittia cucurbitae/a3546c727e88dfd230e2117a44996dd2.jpg\",\n  \"259533.jpg\": \"Insecta/Melittia cucurbitae/9a860a6e59979eb299f96c8925f2ded0.jpg\",\n  \"259538.jpg\": \"Insecta/Melittia cucurbitae/2100b55245761e4dd24237e287b601a9.jpg\",\n  \"259539.jpg\": \"Insecta/Melittia cucurbitae/169b8107d1ee6a90d478cbcac99d2aaa.jpg\",\n  \"259717.jpg\": \"Insecta/Macromia illinoiensis/291a70e4e9f4f006bc641941c41bc97b.jpg\",\n  \"259718.jpg\": \"Insecta/Macromia illinoiensis/1a5a2a72da77c95c2bd5641690cc4c4b.jpg\",\n  \"259732.jpg\": \"Insecta/Macromia illinoiensis/113064cbce9d5e1fae2ccf329f773d03.jpg\",\n  \"259733.jpg\": \"Insecta/Macromia illinoiensis/96f26ac2c8baccd04b51e3b2ac0cf334.jpg\",\n  \"259735.jpg\": \"Insecta/Macromia illinoiensis/a7c9e224d7a18ea18cf4b4acd178ec46.jpg\",\n  \"259744.jpg\": \"Insecta/Macromia illinoiensis/0ecf95b0075c215eea74848441cee416.jpg\",\n  \"259745.jpg\": \"Insecta/Macromia illinoiensis/60cab120797817dfcb00159f8b1de084.jpg\",\n  \"259746.jpg\": \"Insecta/Macromia illinoiensis/1e5c926190c978ecaf02f7a2348a26db.jpg\",\n  \"259748.jpg\": \"Insecta/Macromia illinoiensis/3b57d446100b49623750a292c28b4858.jpg\",\n  \"259749.jpg\": \"Insecta/Macromia illinoiensis/955a94aefbcf6702d5a04c53b44bb9c0.jpg\",\n  \"259750.jpg\": \"Insecta/Macromia illinoiensis/3d6f7f182d06ed35eb3b00be40c49fd3.jpg\",\n  \"259759.jpg\": \"Insecta/Macromia illinoiensis/79e9abcaa840d6479c517fd24ce33067.jpg\",\n  \"259762.jpg\": \"Insecta/Macromia illinoiensis/6ea1ea826960ffb36943264215da72fd.jpg\",\n  \"259765.jpg\": \"Insecta/Macromia illinoiensis/b91c001207c420d0200c44559e1d7b86.jpg\",\n  \"259766.jpg\": \"Insecta/Macromia illinoiensis/f9b331477b442020c465410d69ddbba6.jpg\",\n  \"259767.jpg\": \"Insecta/Macromia illinoiensis/69943bb1b863a88342114095fb5271f3.jpg\",\n  \"259771.jpg\": \"Insecta/Macromia illinoiensis/2790d5200ed14e09ebf2a071bcbb1935.jpg\",\n  \"259772.jpg\": \"Insecta/Macromia illinoiensis/6817c0c48fd89f2a6ec36ad46ee37dce.jpg\",\n  \"259773.jpg\": \"Insecta/Macromia illinoiensis/7299fd10a91fc9e7b7e31d1439e39c98.jpg\",\n  \"259777.jpg\": \"Insecta/Macromia illinoiensis/d5ae846ea2a279cbf29103e9cf3e086a.jpg\",\n  \"259780.jpg\": \"Insecta/Macromia illinoiensis/03e569402e16b6990730f9262b7787c5.jpg\",\n  \"259781.jpg\": \"Insecta/Macromia illinoiensis/5a9d6bce06d9e840cd56814a0bfcd212.jpg\",\n  \"259846.jpg\": \"Mammalia/Chlorocebus pygerythrus/d5312e942e537682d7c43d109e609cf5.jpg\",\n  \"259847.jpg\": \"Mammalia/Chlorocebus pygerythrus/6e559d6850cea0a9ed5017580f55c947.jpg\",\n  \"259855.jpg\": \"Mammalia/Chlorocebus pygerythrus/9661e33c04b5be163844a571b32caf60.jpg\",\n  \"259865.jpg\": \"Mammalia/Chlorocebus pygerythrus/42b0e378e963f609cb4e05d50232d571.jpg\",\n  \"259867.jpg\": \"Mammalia/Chlorocebus pygerythrus/3b61b42036f62b7b4fb2ba3ef43b8df4.jpg\",\n  \"259871.jpg\": \"Mammalia/Chlorocebus pygerythrus/650de6878a2bc04323a6dba0277063cc.jpg\",\n  \"259875.jpg\": \"Mammalia/Chlorocebus pygerythrus/eaf8c4fd845d9e39d5dd691b3dbaaa1f.jpg\",\n  \"259876.jpg\": \"Mammalia/Chlorocebus pygerythrus/d011c9da696825dc8dfb51edc4719875.jpg\",\n  \"260126.jpg\": \"Reptilia/Crocodylus niloticus/c7ad46805e5873a8acf9f9dda65c8c9e.jpg\",\n  \"260130.jpg\": \"Reptilia/Crocodylus niloticus/c8414f281d5b3f1a5aafa7d0a7545180.jpg\",\n  \"260136.jpg\": \"Reptilia/Crocodylus niloticus/5b705dd3fd7e3c2daae307eb49cca38b.jpg\",\n  \"260137.jpg\": \"Reptilia/Crocodylus niloticus/0b22ab95d5255b03cd727bde9d094bba.jpg\",\n  \"260140.jpg\": \"Reptilia/Crocodylus niloticus/e80074dbb7f7b691c6dfcff28d33d6ac.jpg\",\n  \"260142.jpg\": \"Reptilia/Crocodylus niloticus/df7f807ffa384d67e685fd4dcdcb5a41.jpg\",\n  \"260323.jpg\": \"Aves/Mniotilta varia/bd64a9872848cb3b0f8ad3089dd402e2.jpg\",\n  \"260328.jpg\": \"Aves/Mniotilta varia/56022abc2018f65731ca771743875d51.jpg\",\n  \"260333.jpg\": \"Aves/Mniotilta varia/1a3b8625e35ef183b19e95d05019dcb0.jpg\",\n  \"260338.jpg\": \"Aves/Mniotilta varia/2716341dc9c0ebf47fe2bba7b746ec96.jpg\",\n  \"260344.jpg\": \"Aves/Mniotilta varia/7754c6525fd9d63af0723c5aeec6b71b.jpg\",\n  \"26035.jpg\": \"Insecta/Cercyonis pegala/88584ed49fa9ab8f38f0b4b4be1c1f0f.jpg\",\n  \"260352.jpg\": \"Aves/Mniotilta varia/fc3b6bbed717b277c0559a592bb42aec.jpg\",\n  \"260360.jpg\": \"Aves/Mniotilta varia/22eb9d1cc5ee94d1d5afc6465d59f9c4.jpg\",\n  \"260385.jpg\": \"Aves/Mniotilta varia/2e5d663400e2458cd4e1fb0d413ae05e.jpg\",\n  \"260387.jpg\": \"Aves/Mniotilta varia/0d41b7033f87431e3dfcd26c72a7ebe3.jpg\",\n  \"260405.jpg\": \"Aves/Mniotilta varia/f97279f108f03ae77b0c5431cc62a044.jpg\",\n  \"260409.jpg\": \"Aves/Mniotilta varia/92d7743845a0e5508039ef4fb9dde37c.jpg\",\n  \"260413.jpg\": \"Aves/Mniotilta varia/109a3f299154908e9292f63dc66b7f1a.jpg\",\n  \"260415.jpg\": \"Aves/Mniotilta varia/c4db610a9d2679f861a1090ec2fa4068.jpg\",\n  \"260417.jpg\": \"Aves/Mniotilta varia/ef92cbb4aec4cc97981b1bc4d15858a7.jpg\",\n  \"260418.jpg\": \"Aves/Mniotilta varia/d1901f0f3d03e776a1c11966fec76e6d.jpg\",\n  \"260423.jpg\": \"Aves/Mniotilta varia/58dd4974e74a10c9401bbfee98bd3446.jpg\",\n  \"260429.jpg\": \"Aves/Mniotilta varia/9c2b4c2c246eb7892615d66e2458798c.jpg\",\n  \"260447.jpg\": \"Aves/Mniotilta varia/0c3dfaa9d2126815e199a8acbe2b14e8.jpg\",\n  \"260457.jpg\": \"Aves/Mniotilta varia/0a0ca3778500d161c1a878230ba1c708.jpg\",\n  \"260473.jpg\": \"Aves/Mniotilta varia/0f9cec3cba3f180a61baa3281d1ad909.jpg\",\n  \"26048.jpg\": \"Insecta/Cercyonis pegala/1b680247a224f0c1812995d952225b7a.jpg\",\n  \"260483.jpg\": \"Aves/Mniotilta varia/6606b59045f203078e92a1567cf74c21.jpg\",\n  \"260487.jpg\": \"Aves/Mniotilta varia/59cf312a0fe8e1bd808b2fe6cce3aaa6.jpg\",\n  \"260507.jpg\": \"Aves/Mniotilta varia/d868b05ab06349f4912893f371383706.jpg\",\n  \"260510.jpg\": \"Aves/Mniotilta varia/d3abdcae3ec80552d33cc76c54910d2e.jpg\",\n  \"26053.jpg\": \"Insecta/Cercyonis pegala/bcb181866d20d9155ee90611750535a6.jpg\",\n  \"260598.jpg\": \"Insecta/Eristalis tenax/ae003d4dfab601a0e2a05dda948e2e41.jpg\",\n  \"260600.jpg\": \"Insecta/Eristalis tenax/251c5aaf1563ddde5e71fbb1ecbcf1b6.jpg\",\n  \"260601.jpg\": \"Insecta/Eristalis tenax/bf85bda51286dc47935c4dc2fd79cd2c.jpg\",\n  \"260602.jpg\": \"Insecta/Eristalis tenax/7bb87ff6a397a5368bfa8c8e250ab704.jpg\",\n  \"260603.jpg\": \"Insecta/Eristalis tenax/b1299aa49f64cf8e8e30ed5bc0004df0.jpg\",\n  \"260608.jpg\": \"Insecta/Eristalis tenax/bcbbffbc9c3a73c5228c3fb58cf24fb6.jpg\",\n  \"260609.jpg\": \"Insecta/Eristalis tenax/9a3a79dd81d34491b10a4df19d920819.jpg\",\n  \"260615.jpg\": \"Insecta/Eristalis tenax/6fc92a75e73ba4d5735fbd321a018a3c.jpg\",\n  \"260618.jpg\": \"Insecta/Eristalis tenax/cc582620555f77a65586761914e98ff9.jpg\",\n  \"260619.jpg\": \"Insecta/Eristalis tenax/8f3946fe410bf88851f459b28d145829.jpg\",\n  \"260620.jpg\": \"Insecta/Eristalis tenax/4e47b78e20efd11419a9ecd530f62146.jpg\",\n  \"260629.jpg\": \"Insecta/Eristalis tenax/1c15a31f22edd04e898e1969ca709cae.jpg\",\n  \"26063.jpg\": \"Insecta/Cercyonis pegala/7644bb44ca0d95e524a582a4e92c98ec.jpg\",\n  \"260637.jpg\": \"Insecta/Eristalis tenax/46df1eb5ab71549d9541707442e74b4d.jpg\",\n  \"260638.jpg\": \"Insecta/Eristalis tenax/c833c133130be0c4491528d35a81dea5.jpg\",\n  \"260639.jpg\": \"Insecta/Eristalis tenax/08a20d4c03db8eed077d428ac0dcf145.jpg\",\n  \"26064.jpg\": \"Insecta/Cercyonis pegala/f7b4574f1b4d1d4f599efd39c19b4863.jpg\",\n  \"260643.jpg\": \"Insecta/Eristalis tenax/ad43bdaf1796a1fdd3d64f239058387f.jpg\",\n  \"260651.jpg\": \"Insecta/Eristalis tenax/68f32f58850ca8f0ffa544c7469da462.jpg\",\n  \"260652.jpg\": \"Insecta/Eristalis tenax/747f5203dbbfdde68f50ae44164589d4.jpg\",\n  \"260653.jpg\": \"Insecta/Eristalis tenax/2a26770766570d2c1d6c5256cf51361d.jpg\",\n  \"26072.jpg\": \"Insecta/Cercyonis pegala/627f8483e7721b8686aaa56ea270fbf4.jpg\",\n  \"260835.jpg\": \"Insecta/Syritta pipiens/6253f29d8bad26195038b1414c770a2d.jpg\",\n  \"260845.jpg\": \"Insecta/Syritta pipiens/4bc07899f1cb6b7f27e36f6bab97d0c3.jpg\",\n  \"260846.jpg\": \"Insecta/Syritta pipiens/230638ed02e8db124191a5e6df02142a.jpg\",\n  \"26085.jpg\": \"Insecta/Cercyonis pegala/32884c9e37705eb6618c6b927d25b738.jpg\",\n  \"260852.jpg\": \"Insecta/Boisea trivittata/6b581bd4c564dea53f7fcbfa48ff6ecd.jpg\",\n  \"260858.jpg\": \"Insecta/Boisea trivittata/f561fd8d67a1a16ff7e4460cdff6eac6.jpg\",\n  \"26086.jpg\": \"Insecta/Cercyonis pegala/19f335d3856870a9b690320456371cde.jpg\",\n  \"260863.jpg\": \"Insecta/Boisea trivittata/a6433026cfa5fb31abc66203bdec2e6b.jpg\",\n  \"260866.jpg\": \"Insecta/Boisea trivittata/0c708d4f8072d565ae9df27e2643dc54.jpg\",\n  \"260868.jpg\": \"Insecta/Boisea trivittata/fe09571817737b4f91526fec52639761.jpg\",\n  \"260872.jpg\": \"Insecta/Boisea trivittata/795aaec5a8677bfe3b2e2c8a4753f098.jpg\",\n  \"260874.jpg\": \"Insecta/Boisea trivittata/8c4c13f702216dba4a498a420a54421f.jpg\",\n  \"260878.jpg\": \"Insecta/Boisea trivittata/6dd883553c45bbc4df982e2d4e3dc92b.jpg\",\n  \"260898.jpg\": \"Insecta/Boisea trivittata/db64656fffbe1d9def6af10322f58691.jpg\",\n  \"260902.jpg\": \"Insecta/Boisea trivittata/40a196af87362bd2e494a1ef600c9b94.jpg\",\n  \"260908.jpg\": \"Insecta/Dythemis velox/03ef2a8b54e0d9fec083f04c46287114.jpg\",\n  \"260909.jpg\": \"Insecta/Dythemis velox/e818a1a4b6d49d538f6725e427786aba.jpg\",\n  \"260917.jpg\": \"Insecta/Dythemis velox/356566bba46743867c6b52e18d0f4e33.jpg\",\n  \"260932.jpg\": \"Insecta/Dythemis velox/2c63e7835b98faffc98324fef203b615.jpg\",\n  \"260949.jpg\": \"Insecta/Dythemis velox/f0fbda4391e2dbd437b85b71643eb236.jpg\",\n  \"260950.jpg\": \"Insecta/Dythemis velox/a535c188f09449d7d654508ceb6ce2da.jpg\",\n  \"260951.jpg\": \"Insecta/Dythemis velox/7683a58f040a88fd68917fab1e5b0c95.jpg\",\n  \"260957.jpg\": \"Insecta/Dythemis velox/00154e6477444be3bdee3a882e86a819.jpg\",\n  \"260984.jpg\": \"Insecta/Dythemis velox/2692542547ffa39a0828aa55a6e288cb.jpg\",\n  \"260985.jpg\": \"Insecta/Dythemis velox/e277abf79fae72208433c7623b0187c2.jpg\",\n  \"260992.jpg\": \"Insecta/Dythemis velox/6accca8954ac9e26f1920574f76e2053.jpg\",\n  \"260998.jpg\": \"Insecta/Dythemis velox/b8eacc0e043285a2c6c5e8a712539b87.jpg\",\n  \"260999.jpg\": \"Insecta/Dythemis velox/dc50d464b3ee0c65246bd1538b00843f.jpg\",\n  \"261001.jpg\": \"Insecta/Dythemis velox/0f28c51b1acf353b300b3fe2712dfb04.jpg\",\n  \"26102.jpg\": \"Insecta/Cercyonis pegala/dbda2b75f9234c37b647d9aee14a2bb4.jpg\",\n  \"261025.jpg\": \"Insecta/Dythemis velox/ff9dd952a4beb73699f71727354a6d33.jpg\",\n  \"261026.jpg\": \"Insecta/Dythemis velox/90ec231ff092357aeb6b5b2282660f6b.jpg\",\n  \"261028.jpg\": \"Insecta/Dythemis velox/5ecb30502f64fcb7f0b3d1cfdbb59c10.jpg\",\n  \"261037.jpg\": \"Insecta/Dythemis velox/a0eafb9105659ead532a4de2de45106f.jpg\",\n  \"261040.jpg\": \"Insecta/Dythemis velox/880fbf52ec64053aac3ad414e654a72d.jpg\",\n  \"261041.jpg\": \"Insecta/Dythemis velox/e9ba9f47dc0d0f4e96c7fa164bc1d3ba.jpg\",\n  \"261149.jpg\": \"Mammalia/Cebus capucinus/b794877c0939ef8bc96de6108ff11a98.jpg\",\n  \"26115.jpg\": \"Insecta/Cercyonis pegala/1fbdc2e6fa194c3e3015268d5dfe96f4.jpg\",\n  \"261150.jpg\": \"Mammalia/Cebus capucinus/33b2119b295a082840ab2c73f06a6da1.jpg\",\n  \"261152.jpg\": \"Mammalia/Cebus capucinus/a00dc0277d7822263c9df539f38499d6.jpg\",\n  \"261154.jpg\": \"Mammalia/Cebus capucinus/0bd501f8702829e2e347553d82743fa7.jpg\",\n  \"261160.jpg\": \"Mammalia/Cebus capucinus/6b1869ad724c7c015f860ee6ff686b8e.jpg\",\n  \"26117.jpg\": \"Insecta/Cercyonis pegala/9897023d99d876cebc43caa7c7f66543.jpg\",\n  \"26119.jpg\": \"Insecta/Cercyonis pegala/8c592a8736be5f3f7b54b9c56d9ee2e7.jpg\",\n  \"26123.jpg\": \"Insecta/Cercyonis pegala/94da090f4b4058a82674d752748cc0f7.jpg\",\n  \"261287.jpg\": \"Aves/Bonasa umbellus/c65bc4f27ac371a9b67c6c1543b2b825.jpg\",\n  \"261290.jpg\": \"Aves/Bonasa umbellus/74af01829953955312499920b941f29f.jpg\",\n  \"261292.jpg\": \"Aves/Bonasa umbellus/ec2d9e4818034ce7d2d11ccc4718a7bb.jpg\",\n  \"261300.jpg\": \"Aves/Bonasa umbellus/0c67e5e8bd12afc034e5e9963f54bca8.jpg\",\n  \"261303.jpg\": \"Aves/Bonasa umbellus/65cf0b5a5ebb9c309eaa7db9ed89fd78.jpg\",\n  \"261304.jpg\": \"Aves/Bonasa umbellus/43c4971e2d65216dbad52efcdde8e696.jpg\",\n  \"261307.jpg\": \"Aves/Bonasa umbellus/ddc8b9ce983c72b50d37c75d2d913f14.jpg\",\n  \"261311.jpg\": \"Aves/Bonasa umbellus/2f51f8adb05b48bbaafb45bc4d3e1c3c.jpg\",\n  \"261312.jpg\": \"Aves/Bonasa umbellus/65f6d6f23eb88518a06f38524326eea4.jpg\",\n  \"261316.jpg\": \"Aves/Bonasa umbellus/b95e50588e1e3b501934afe3e2f23565.jpg\",\n  \"261321.jpg\": \"Aves/Bonasa umbellus/8a2d78706f432038ee96728c363612f7.jpg\",\n  \"261325.jpg\": \"Aves/Bonasa umbellus/231a95a0ecbbffa1aa5b1d67752349c2.jpg\",\n  \"261326.jpg\": \"Aves/Bonasa umbellus/52ff1f69cba09712da564e8ebb252845.jpg\",\n  \"261327.jpg\": \"Aves/Bonasa umbellus/c8731ae3393f9443ccf995253e889243.jpg\",\n  \"26133.jpg\": \"Insecta/Cercyonis pegala/6f1fc1a2d18b06536712862dfd3eb752.jpg\",\n  \"261332.jpg\": \"Aves/Bonasa umbellus/71ad39ba6a7aefc0a6c4d11e8b6bfe4a.jpg\",\n  \"261340.jpg\": \"Aves/Bonasa umbellus/2fadd57535306337048839076f4e6e01.jpg\",\n  \"261341.jpg\": \"Aves/Bonasa umbellus/52ed9b15e2c4e4f57a63de02df7d3484.jpg\",\n  \"261342.jpg\": \"Aves/Bonasa umbellus/364d550ca4fa3927db82e3027cb9edc4.jpg\",\n  \"261343.jpg\": \"Aves/Bonasa umbellus/3419899e376aa5c0d1fc9473a93121bc.jpg\",\n  \"261346.jpg\": \"Aves/Bonasa umbellus/3f501676e31d1c8d20d0cd0464360eec.jpg\",\n  \"261348.jpg\": \"Aves/Bonasa umbellus/a19d6318bb7d9e749401420f47fdce79.jpg\",\n  \"261350.jpg\": \"Aves/Bonasa umbellus/4c02fb6f6464c3a0e591917d3774b96e.jpg\",\n  \"261354.jpg\": \"Aves/Bonasa umbellus/f4de1831126f992819b33dfc8f88a33c.jpg\",\n  \"261355.jpg\": \"Insecta/Leucorrhinia intacta/6003a0b5c22daa53899642d13563ad38.jpg\",\n  \"261361.jpg\": \"Insecta/Leucorrhinia intacta/a44264fd48ae7845af814e3f404df49c.jpg\",\n  \"261363.jpg\": \"Insecta/Leucorrhinia intacta/1d3b73a904cff47d5aaf846a0d24d28f.jpg\",\n  \"261366.jpg\": \"Insecta/Leucorrhinia intacta/4bc24f68cce15be3fe43c211d9607101.jpg\",\n  \"261369.jpg\": \"Insecta/Leucorrhinia intacta/1a02d2957d7bba5aa8f61c547e3cf785.jpg\",\n  \"261377.jpg\": \"Insecta/Leucorrhinia intacta/a74dfb57becae4689a8edc929e97a422.jpg\",\n  \"261378.jpg\": \"Insecta/Leucorrhinia intacta/47a22ee5fae2cdcb289daaa837950b56.jpg\",\n  \"261393.jpg\": \"Insecta/Leucorrhinia intacta/4a015ef2d0d6a93d0d63d2727bc3987b.jpg\",\n  \"261394.jpg\": \"Insecta/Leucorrhinia intacta/7bed8098a2a964a02f6001b65167f244.jpg\",\n  \"261398.jpg\": \"Insecta/Leucorrhinia intacta/7b86bc9b787fda4a4cc2fc529b424228.jpg\",\n  \"261400.jpg\": \"Insecta/Leucorrhinia intacta/490c5ea8702cb79197283dcfdbb1b511.jpg\",\n  \"261401.jpg\": \"Insecta/Leucorrhinia intacta/939264fabd5958f92d5e8a20dd526e8e.jpg\",\n  \"261402.jpg\": \"Insecta/Leucorrhinia intacta/f4b233f5b1a93895fa52d82a41fbb161.jpg\",\n  \"26141.jpg\": \"Insecta/Cercyonis pegala/d97bf8c10ac719430c9953ef56dbc6ce.jpg\",\n  \"261410.jpg\": \"Insecta/Leucorrhinia intacta/68ae1ac017007494784cf9fb8f438570.jpg\",\n  \"261415.jpg\": \"Insecta/Leucorrhinia intacta/166f79759246dee8a12ca6148b23a2fe.jpg\",\n  \"26142.jpg\": \"Insecta/Cercyonis pegala/f435bd80c6088d3f9d00d689f9be9df0.jpg\",\n  \"261421.jpg\": \"Insecta/Leucorrhinia intacta/87aa0071be2ace68f269a31275382449.jpg\",\n  \"261423.jpg\": \"Insecta/Leucorrhinia intacta/4a8718bb6da609c953396280a0bec1b9.jpg\",\n  \"261424.jpg\": \"Insecta/Leucorrhinia intacta/f68d35b19d772077ef0ddcd2cefa6d4e.jpg\",\n  \"261426.jpg\": \"Insecta/Leucorrhinia intacta/7ac90669fc7a2b23b0302689f617fa55.jpg\",\n  \"26143.jpg\": \"Insecta/Cercyonis pegala/d1a21dea93f7256682d0352df1258a1f.jpg\",\n  \"261431.jpg\": \"Insecta/Leucorrhinia intacta/5a16269f32344b51c22990c66bbb75ed.jpg\",\n  \"261444.jpg\": \"Insecta/Leucorrhinia intacta/3a6d698d9901bb92849bb64715558b9b.jpg\",\n  \"261448.jpg\": \"Insecta/Leucorrhinia intacta/2fdbd11550d6eebb33c796b6b13cf020.jpg\",\n  \"261513.jpg\": \"Insecta/Exomala orientalis/3bb5ab1557a3e5fa8758b60eb7b29446.jpg\",\n  \"261514.jpg\": \"Insecta/Exomala orientalis/b8bbfb1cdb3a406327043560bc930659.jpg\",\n  \"261515.jpg\": \"Insecta/Exomala orientalis/244a0dfe7caced9d3f6e93706bfa17d5.jpg\",\n  \"261522.jpg\": \"Insecta/Exomala orientalis/885953c4de8e693c55452f625fc21993.jpg\",\n  \"261523.jpg\": \"Insecta/Exomala orientalis/4e701458344dc1215544f8fedf755cde.jpg\",\n  \"261524.jpg\": \"Insecta/Exomala orientalis/27208b19b027f4f913d8e3e1a9e58d34.jpg\",\n  \"261526.jpg\": \"Insecta/Exomala orientalis/c462cd91cab2d3877aaa7f67b08d1daf.jpg\",\n  \"261528.jpg\": \"Insecta/Exomala orientalis/517ee4fc58b5dbd246a0177b8a61881b.jpg\",\n  \"261529.jpg\": \"Insecta/Exomala orientalis/bc2f344126c7f92313b6c01bc42aec07.jpg\",\n  \"261535.jpg\": \"Insecta/Exomala orientalis/d4887aedfc96dd6240c30b3767a9639e.jpg\",\n  \"261536.jpg\": \"Insecta/Exomala orientalis/d83e65cdb68f9059975aeb8b4c14517e.jpg\",\n  \"261537.jpg\": \"Insecta/Exomala orientalis/89181aa72fe1cf3ea5cfa57ffcfb4e63.jpg\",\n  \"26154.jpg\": \"Insecta/Cercyonis pegala/eb36f4b424de511b798c9ec077a6e412.jpg\",\n  \"261541.jpg\": \"Arachnida/Amblyomma americanum/66dfd2869ae996aa2fd23faf385424bc.jpg\",\n  \"261544.jpg\": \"Arachnida/Amblyomma americanum/b31767684d86bf534c44dbe209e76679.jpg\",\n  \"261550.jpg\": \"Arachnida/Amblyomma americanum/ffbcbdf27a5ec08076492cfc5a145965.jpg\",\n  \"261553.jpg\": \"Reptilia/Aspidoscelis tigris mundus/6193618b6143325da1768c7ed9eb0784.jpg\",\n  \"261555.jpg\": \"Reptilia/Aspidoscelis tigris mundus/72e0d396646ef72be50275b07f47a62d.jpg\",\n  \"261558.jpg\": \"Reptilia/Aspidoscelis tigris mundus/18d032604fcd460c7a970e97751825cf.jpg\",\n  \"261561.jpg\": \"Reptilia/Aspidoscelis tigris mundus/de6e9245cb514d09c4f9ae3a9074a3be.jpg\",\n  \"261569.jpg\": \"Reptilia/Aspidoscelis tigris mundus/b86b72b8d6ed0ac9ab251c68c16c5225.jpg\",\n  \"261573.jpg\": \"Reptilia/Aspidoscelis tigris mundus/5f3f17a40819fd8e946de966e941c38c.jpg\",\n  \"261578.jpg\": \"Reptilia/Aspidoscelis tigris mundus/5fc4ec316b1ed0aa2bfb45e432fff1dc.jpg\",\n  \"261582.jpg\": \"Mollusca/Helix pomatia/80154317c302835da4d224d64c212698.jpg\",\n  \"261584.jpg\": \"Mollusca/Helix pomatia/5288aaff691529edfd2d9ab5361f0686.jpg\",\n  \"261585.jpg\": \"Mollusca/Helix pomatia/bf98d244e4be3fc565906efa590f4054.jpg\",\n  \"261586.jpg\": \"Mollusca/Helix pomatia/5b8c8b38772cd971a1c0899b5a361da3.jpg\",\n  \"261587.jpg\": \"Mollusca/Helix pomatia/8da6888d99d64a236419c1d04ca1de23.jpg\",\n  \"261589.jpg\": \"Mollusca/Helix pomatia/1a11b381347f230bb3d31aa6005706dc.jpg\",\n  \"261590.jpg\": \"Mollusca/Helix pomatia/98137d4ec9d5d6b6235adff1524b1dc4.jpg\",\n  \"261592.jpg\": \"Mollusca/Helix pomatia/fdce70c0c1bd486e770556d932d6f13e.jpg\",\n  \"261594.jpg\": \"Mollusca/Helix pomatia/7139210254364460a46887fd1e3092cf.jpg\",\n  \"261597.jpg\": \"Mollusca/Helix pomatia/c8b9dc7d3922d1d0fc295b8f82734624.jpg\",\n  \"261598.jpg\": \"Mollusca/Helix pomatia/3f407a38c2baa9614b358e72e6724288.jpg\",\n  \"261599.jpg\": \"Mollusca/Helix pomatia/df91eb6ef488beb38fe9c17a138167e8.jpg\",\n  \"261604.jpg\": \"Mollusca/Helix pomatia/5f565fc13267d074e0efc4e77491d1c9.jpg\",\n  \"261605.jpg\": \"Mollusca/Helix pomatia/e5853c82d50829aa37cdbdbeb8584467.jpg\",\n  \"261614.jpg\": \"Mollusca/Helix pomatia/d9474873b6f5cc9aace06389b5d85d46.jpg\",\n  \"261615.jpg\": \"Mollusca/Helix pomatia/342ac85da1bfe84477e2bfcf02f03b9c.jpg\",\n  \"261618.jpg\": \"Mollusca/Helix pomatia/167519113cde8e00e2e7359f07f25a98.jpg\",\n  \"26172.jpg\": \"Insecta/Cercyonis pegala/a2a7335ede9de92ec892706424549b57.jpg\",\n  \"26181.jpg\": \"Insecta/Cercyonis pegala/2277c99af9d9f9281d25c4b16ccc7f7b.jpg\",\n  \"26190.jpg\": \"Insecta/Cercyonis pegala/36b2d0c209e9b49bc20f01141a227b7c.jpg\",\n  \"26193.jpg\": \"Insecta/Cercyonis pegala/6f574312a9c711a84f106841eac1be54.jpg\",\n  \"26195.jpg\": \"Insecta/Cercyonis pegala/e674d526752ee82566920fe18e0985f2.jpg\",\n  \"262297.jpg\": \"Insecta/Siproeta stelenes/92477e2ed756e911781a9396d077205e.jpg\",\n  \"262304.jpg\": \"Insecta/Siproeta stelenes/908f6d038a47c77dae7c6c18352b1455.jpg\",\n  \"262309.jpg\": \"Insecta/Siproeta stelenes/b611f003adda34e124b10442337c0e06.jpg\",\n  \"262310.jpg\": \"Insecta/Siproeta stelenes/2a0aeaf853749beda37f4f4f8a40dcda.jpg\",\n  \"262311.jpg\": \"Insecta/Siproeta stelenes/e4da1b05a8ad94b0673b2f3de4da2b9c.jpg\",\n  \"262319.jpg\": \"Insecta/Siproeta stelenes/9eb4ca6f7aa9a4e391ef2d19ada8c3c6.jpg\",\n  \"262321.jpg\": \"Insecta/Siproeta stelenes/c61c9bc33d650606f1b3607f2b02b30c.jpg\",\n  \"262329.jpg\": \"Insecta/Siproeta stelenes/a96f95a20953c4ad80c1d5c39727eccd.jpg\",\n  \"262331.jpg\": \"Insecta/Siproeta stelenes/2b93af5d621cabc3d3c18cf1bfc89fd9.jpg\",\n  \"262335.jpg\": \"Insecta/Siproeta stelenes/5368809639d9cca6f2ca8122819b6b32.jpg\",\n  \"262336.jpg\": \"Insecta/Siproeta stelenes/5a10df1e5a85f738d75d4ab54f026b7f.jpg\",\n  \"262337.jpg\": \"Insecta/Siproeta stelenes/45ab2c7978f4c25c1b2a59a1985af998.jpg\",\n  \"262340.jpg\": \"Insecta/Siproeta stelenes/8a8b15ca877e6eee5a960633a06a9cbf.jpg\",\n  \"262343.jpg\": \"Insecta/Siproeta stelenes/76b634ce10cc1f37d119269b1da1e457.jpg\",\n  \"262344.jpg\": \"Insecta/Siproeta stelenes/6690021b276471252b85bc747229e74f.jpg\",\n  \"262351.jpg\": \"Insecta/Siproeta stelenes/ea6502b5c43d7040cdef6faa511bad5f.jpg\",\n  \"262356.jpg\": \"Insecta/Siproeta stelenes/8864100d2c836c7831bf6d74d2a2026b.jpg\",\n  \"262358.jpg\": \"Insecta/Siproeta stelenes/4e108a4befb5af2b08e6570d9daaa8a4.jpg\",\n  \"262363.jpg\": \"Insecta/Siproeta stelenes/ee2815ed7f9a81e769ef8a48f8284c42.jpg\",\n  \"262364.jpg\": \"Insecta/Siproeta stelenes/d22e7659761e182fe3e8e7f8df7090f2.jpg\",\n  \"262372.jpg\": \"Insecta/Siproeta stelenes/c8f77f743fb8fd1d6d437b93e639fb17.jpg\",\n  \"262417.jpg\": \"Animalia/Oxidus gracilis/d3ff9630ff387a5538585f267dd4adb3.jpg\",\n  \"262418.jpg\": \"Animalia/Oxidus gracilis/31cd1d95e1db79fe7690a7bde1593a43.jpg\",\n  \"262422.jpg\": \"Animalia/Oxidus gracilis/367f5d031f627308ce519a587b9186bf.jpg\",\n  \"262423.jpg\": \"Animalia/Oxidus gracilis/6c040c73c1c6159fa17aa1dc992309e9.jpg\",\n  \"262435.jpg\": \"Aves/Amazilia tzacatl/66c502c50fae260cff167bbdc3549e8d.jpg\",\n  \"262436.jpg\": \"Aves/Amazilia tzacatl/c952e7981c9ca3512c0850b7217d3d61.jpg\",\n  \"262437.jpg\": \"Aves/Amazilia tzacatl/6a6d80d4b4a06690aed929a74775b60b.jpg\",\n  \"262438.jpg\": \"Aves/Amazilia tzacatl/8782b328d2ea94b61c018e0bc32b7085.jpg\",\n  \"262442.jpg\": \"Aves/Amazilia tzacatl/f4c11cb1b206dcb2149338004af55308.jpg\",\n  \"262443.jpg\": \"Aves/Amazilia tzacatl/032c8b77f0af860e93df3f0c34ccc479.jpg\",\n  \"262444.jpg\": \"Aves/Amazilia tzacatl/8cc77d4e3e1eb198a9eb34db73c69036.jpg\",\n  \"262446.jpg\": \"Aves/Amazilia tzacatl/f9e28743c5885a8c96dc3dd39fc3f19c.jpg\",\n  \"262454.jpg\": \"Insecta/Dynastes tityus/c910be7ca55ea7fd320a523f4db864a2.jpg\",\n  \"262458.jpg\": \"Insecta/Dynastes tityus/6d2e3614e8d8bec5ace899f3acda4c59.jpg\",\n  \"262459.jpg\": \"Insecta/Dynastes tityus/9441c095201eaf5d10ec191f671c9fd0.jpg\",\n  \"262460.jpg\": \"Insecta/Dynastes tityus/0581f16dae98965fbe70c6dacdda3a2f.jpg\",\n  \"262461.jpg\": \"Insecta/Dynastes tityus/4475678a4022de5a32968bb841cac908.jpg\",\n  \"262467.jpg\": \"Insecta/Dynastes tityus/d3a98b4f07f05910a020f2fb03edb319.jpg\",\n  \"262470.jpg\": \"Insecta/Dynastes tityus/caa7dff0289b7a9f6eebefe48302f13f.jpg\",\n  \"262473.jpg\": \"Insecta/Dynastes tityus/91d843b62606f049dcf5f801a120679e.jpg\",\n  \"262550.jpg\": \"Aves/Passer domesticus/887e1d06f7e83b82ceb34d20ab39e7ce.jpg\",\n  \"262629.jpg\": \"Aves/Passer domesticus/252b99c9aa394eb3a8c59791d08d71b6.jpg\",\n  \"262669.jpg\": \"Aves/Passer domesticus/bec7b125556d22608f435e9712c144cd.jpg\",\n  \"262844.jpg\": \"Aves/Passer domesticus/bab4ef600e9ae687ad10b40295c3421b.jpg\",\n  \"262874.jpg\": \"Aves/Passer domesticus/091b702db73d43050307ead7a9102695.jpg\",\n  \"262899.jpg\": \"Aves/Passer domesticus/db96a80386f8ac13aaa4c0833875d25f.jpg\",\n  \"26293.jpg\": \"Aves/Calidris virgata/40447a3542d9b2001ebad26ab16ba23f.jpg\",\n  \"263002.jpg\": \"Aves/Passer domesticus/b514454b2f5f0f0a4d2e640048d6bc96.jpg\",\n  \"26302.jpg\": \"Aves/Calidris virgata/d982cbde2d3203f6e94e6c01f72358ee.jpg\",\n  \"26307.jpg\": \"Aves/Calidris virgata/163f8007de08c3ed137d50b162c65533.jpg\",\n  \"263105.jpg\": \"Aves/Passer domesticus/6ee80dd7791ede6292994fdaedfa2371.jpg\",\n  \"26312.jpg\": \"Aves/Calidris virgata/e4b9c2f01c527307c1ad23fb5e37c7a0.jpg\",\n  \"263132.jpg\": \"Aves/Passer domesticus/8895df1e3a627c55ed3336b6ebfd46ed.jpg\",\n  \"263165.jpg\": \"Aves/Passer domesticus/c61d6201ecbfcbabff0f9cc226c4e7e1.jpg\",\n  \"263195.jpg\": \"Aves/Passer domesticus/33e6b2e88998bb0dd3c246da167e34cb.jpg\",\n  \"263307.jpg\": \"Aves/Passer domesticus/0191ce2975b1d5111a508147e1861642.jpg\",\n  \"263415.jpg\": \"Aves/Passer domesticus/c9c52b5f9adfbc1e30e040b208cd3a08.jpg\",\n  \"263436.jpg\": \"Aves/Passer domesticus/06834876015c61f413e616e3d59b2057.jpg\",\n  \"26349.jpg\": \"Aves/Calidris virgata/088679f9e6bfae72a3b74c9d88c10a3a.jpg\",\n  \"263581.jpg\": \"Aves/Passer domesticus/a3c30d1dab41f4996db77518b49fae74.jpg\",\n  \"263636.jpg\": \"Aves/Passer domesticus/204cd72fb45979a4c82189762cb38c77.jpg\",\n  \"263692.jpg\": \"Aves/Passer domesticus/b96439d6a0e17233b6c4a342173b3f35.jpg\",\n  \"26373.jpg\": \"Aves/Calidris virgata/2a72898214366b480e9848e446faef81.jpg\",\n  \"263765.jpg\": \"Aves/Passer domesticus/193ce3b36d6f4da7a23e2142a89e8aad.jpg\",\n  \"26378.jpg\": \"Aves/Calidris virgata/31f0b5781ffa422580116fefe869d843.jpg\",\n  \"263786.jpg\": \"Insecta/Eremnophila aureonotata/6df0416cd305f33dca68a2532b70877e.jpg\",\n  \"263788.jpg\": \"Insecta/Eremnophila aureonotata/f34a066f9fac83426bebad2faf9f3303.jpg\",\n  \"263792.jpg\": \"Insecta/Eremnophila aureonotata/1906e84ff75a135e1512ad1d93e33dab.jpg\",\n  \"263793.jpg\": \"Insecta/Eremnophila aureonotata/b894e96acd2ae132bf0d0764267128d2.jpg\",\n  \"263831.jpg\": \"Amphibia/Osteopilus septentrionalis/d1f9de7987c59ca50c344cd24185a315.jpg\",\n  \"263833.jpg\": \"Amphibia/Osteopilus septentrionalis/d670288aeae0fe8d7d8a7032e2e1b705.jpg\",\n  \"263834.jpg\": \"Amphibia/Osteopilus septentrionalis/e0963f66ab568a42ff499fe06d04a143.jpg\",\n  \"263837.jpg\": \"Amphibia/Osteopilus septentrionalis/381583de62530012e1c5477a1826185c.jpg\",\n  \"263838.jpg\": \"Amphibia/Osteopilus septentrionalis/d4eee1fb8a3592f92939ec4c1683d9ae.jpg\",\n  \"263847.jpg\": \"Amphibia/Osteopilus septentrionalis/c42f949af8aae3f9fb2d1c6b910f629a.jpg\",\n  \"263848.jpg\": \"Amphibia/Osteopilus septentrionalis/6046ca8d06def98a6cb331358b759c3e.jpg\",\n  \"263849.jpg\": \"Amphibia/Osteopilus septentrionalis/faf2abf6fc269fce8413bbca52ed0ea4.jpg\",\n  \"263856.jpg\": \"Amphibia/Osteopilus septentrionalis/121153c07ada084b0204bc7b6d7c4a43.jpg\",\n  \"263857.jpg\": \"Amphibia/Osteopilus septentrionalis/8463a291a010c4dd39449268c6bc267f.jpg\",\n  \"263858.jpg\": \"Amphibia/Osteopilus septentrionalis/fb6ea94b923662149f2c3828fdbb8f2b.jpg\",\n  \"263859.jpg\": \"Amphibia/Osteopilus septentrionalis/f4f5ed7ff973bb4706cfa6a5f1ac2298.jpg\",\n  \"263860.jpg\": \"Amphibia/Osteopilus septentrionalis/57fe503bcc76a387fbcbe26918fd0c71.jpg\",\n  \"263865.jpg\": \"Amphibia/Osteopilus septentrionalis/08bc4dbf9de595a2813930dbe8f6e224.jpg\",\n  \"263867.jpg\": \"Amphibia/Osteopilus septentrionalis/65d598c88a96256b4d47416f6c70e0dd.jpg\",\n  \"263870.jpg\": \"Amphibia/Osteopilus septentrionalis/deb7eddc851ae50cb4bd3cd47e1e6044.jpg\",\n  \"263871.jpg\": \"Amphibia/Osteopilus septentrionalis/99d4fddd9339019db64bf1868652b0fd.jpg\",\n  \"263875.jpg\": \"Amphibia/Osteopilus septentrionalis/663e40cb33aac54677378e2f6a7697f6.jpg\",\n  \"263880.jpg\": \"Amphibia/Osteopilus septentrionalis/b802b5823304235cd0104c78a0502043.jpg\",\n  \"263886.jpg\": \"Amphibia/Osteopilus septentrionalis/e7cb472ecce053793c5d306c88c707e6.jpg\",\n  \"263887.jpg\": \"Amphibia/Osteopilus septentrionalis/dbd23ef955479a1638a5c9aa006e5c4d.jpg\",\n  \"263888.jpg\": \"Amphibia/Osteopilus septentrionalis/94f87166e913c8226572ca35b1401f39.jpg\",\n  \"263889.jpg\": \"Aves/Hylocichla mustelina/9d56c7809a8ad5c756b7b201cc0f82bc.jpg\",\n  \"263890.jpg\": \"Aves/Hylocichla mustelina/d0bc10efdb13699fb48e017d04858c62.jpg\",\n  \"263891.jpg\": \"Aves/Hylocichla mustelina/ca23cca4174416471228dead3347e03a.jpg\",\n  \"263894.jpg\": \"Aves/Hylocichla mustelina/d206b775cf4cca630e3380be08ea055f.jpg\",\n  \"263895.jpg\": \"Aves/Hylocichla mustelina/6186821fb73bfc0fdd986f8f1966e37b.jpg\",\n  \"263896.jpg\": \"Aves/Hylocichla mustelina/50541b2c2fecc5f6e870e8904dab917f.jpg\",\n  \"263897.jpg\": \"Aves/Hylocichla mustelina/22b400754dacc6890b4a3c669f97af19.jpg\",\n  \"263898.jpg\": \"Aves/Hylocichla mustelina/02546a8d91cb1ebe7766ceb3e191d7a6.jpg\",\n  \"263901.jpg\": \"Aves/Hylocichla mustelina/56e345f59deb9b5bfeb528a8cbb025cb.jpg\",\n  \"263902.jpg\": \"Aves/Hylocichla mustelina/ff090f1ead365da2b243ec6a3ff63618.jpg\",\n  \"263903.jpg\": \"Aves/Hylocichla mustelina/d84f2a6abf1238fa6bfe93c8bf664451.jpg\",\n  \"263923.jpg\": \"Aves/Hylocichla mustelina/cf59430f0d3bec84e1da399fd4564a6a.jpg\",\n  \"263925.jpg\": \"Aves/Hylocichla mustelina/903187f3e4425bd05e049045fce729b1.jpg\",\n  \"263926.jpg\": \"Aves/Hylocichla mustelina/a66c50ff13617db5fc79bc1f10b6c4b1.jpg\",\n  \"263934.jpg\": \"Aves/Hylocichla mustelina/b2d1a7ff2185783b3b252d9f272aceb6.jpg\",\n  \"263937.jpg\": \"Aves/Hylocichla mustelina/5920538c9871c1f6a0f47116a135ec7f.jpg\",\n  \"263938.jpg\": \"Aves/Hylocichla mustelina/ba10da18fecd0a0bd170b8535ccddc03.jpg\",\n  \"263943.jpg\": \"Aves/Hylocichla mustelina/3a8bfde8d213da4dd83195a5c2e25ede.jpg\",\n  \"263947.jpg\": \"Aves/Hylocichla mustelina/4de2277ef5149b11b4ef00daae0232d5.jpg\",\n  \"263956.jpg\": \"Aves/Hylocichla mustelina/368a6380f10ae5c3b652f84bbe86c8b9.jpg\",\n  \"263959.jpg\": \"Aves/Hylocichla mustelina/1429120518bb38aa1ff536bf0357b7f1.jpg\",\n  \"264027.jpg\": \"Aves/Turdus merula/a2221f5479ec6657090b465320e7931a.jpg\",\n  \"264031.jpg\": \"Aves/Turdus merula/0a6723c5209b5a2d647d7ae602982fff.jpg\",\n  \"264044.jpg\": \"Aves/Turdus merula/0e403e971c8a3745548ec12b0c29a8a4.jpg\",\n  \"264045.jpg\": \"Aves/Turdus merula/27ec13922cacf6868726e0e986be5ab2.jpg\",\n  \"264065.jpg\": \"Aves/Turdus merula/f62f86e6f5f42ed7ea9225a8698c27c5.jpg\",\n  \"264085.jpg\": \"Aves/Turdus merula/a50af47862f1f3a071d6d9913123f5a9.jpg\",\n  \"264118.jpg\": \"Aves/Turdus merula/83fc9adff7537a2f28c0eebe2d004993.jpg\",\n  \"264122.jpg\": \"Aves/Turdus merula/1f7be1b3f7c02ce05e143f4000163822.jpg\",\n  \"264152.jpg\": \"Aves/Turdus merula/ce81bfa4bea1669b9439e1beb994e581.jpg\",\n  \"264194.jpg\": \"Aves/Turdus merula/9bb78a7f3af2a1a7b09292cc208812bd.jpg\",\n  \"264195.jpg\": \"Aves/Turdus merula/7693e9bafa6b0203bc5627cbaddf8f0e.jpg\",\n  \"264201.jpg\": \"Aves/Turdus merula/b82f74e74ddc7eb815bcfca0d620b9af.jpg\",\n  \"264223.jpg\": \"Aves/Turdus merula/375f9e0c561090e96743c189aff3429c.jpg\",\n  \"264230.jpg\": \"Aves/Turdus merula/392d543c66fcc021b789842f01ed9d70.jpg\",\n  \"264298.jpg\": \"Aves/Turdus merula/d8778621450b7ec040dc5f46ef684826.jpg\",\n  \"264303.jpg\": \"Aves/Turdus merula/8c80fce94cb5132116c63e167f0b4ccb.jpg\",\n  \"264322.jpg\": \"Aves/Turdus merula/653efdbe302fae39b822e1cf4d251341.jpg\",\n  \"264326.jpg\": \"Aves/Turdus merula/1bc51ac039f19acacb4ffe7fde140cbb.jpg\",\n  \"264333.jpg\": \"Aves/Turdus merula/c79a8e2f97650115f613a6811d225697.jpg\",\n  \"264351.jpg\": \"Aves/Cepphus grylle/daa9117b3c9bde9bb03bff0e1d6b1215.jpg\",\n  \"264352.jpg\": \"Aves/Cepphus grylle/54c6feeafc228c62d0b7a81480b22ea8.jpg\",\n  \"264356.jpg\": \"Aves/Cepphus grylle/c01dcb33ffaf4d01e4297413c7fc346a.jpg\",\n  \"264359.jpg\": \"Aves/Cepphus grylle/466b9c2c69da52e6260af111ec922784.jpg\",\n  \"264360.jpg\": \"Aves/Cepphus grylle/2889ac85a38af1c9c44bf5ab9058e8ba.jpg\",\n  \"264361.jpg\": \"Aves/Cepphus grylle/ea8dfb574574861194db82ee16ab5783.jpg\",\n  \"264365.jpg\": \"Insecta/Satyrium calanus/ac5e04ce80e45eb2ca7ab96837c5c689.jpg\",\n  \"264366.jpg\": \"Insecta/Satyrium calanus/d52babacceb0c8317364d3f463af9f2d.jpg\",\n  \"264367.jpg\": \"Insecta/Satyrium calanus/3be3f5e2b5cc05e33a469f4d7ee7a9dc.jpg\",\n  \"264370.jpg\": \"Insecta/Satyrium calanus/e2cffa195e5ba69b7433f096837f6902.jpg\",\n  \"264371.jpg\": \"Insecta/Satyrium calanus/54268ecf892c511f4152079b5fffe5e5.jpg\",\n  \"264375.jpg\": \"Insecta/Satyrium calanus/e0d98d5083edb3422e78179d21b857ea.jpg\",\n  \"264377.jpg\": \"Insecta/Satyrium calanus/68ec51024031dcf6291bf98847539de7.jpg\",\n  \"264387.jpg\": \"Insecta/Satyrium calanus/160dcb0d078db0bf67d1a88a8df647c4.jpg\",\n  \"264389.jpg\": \"Insecta/Satyrium calanus/2d792bf48a2b902083d52562b9472a34.jpg\",\n  \"264390.jpg\": \"Insecta/Satyrium calanus/40d201cfee4b44c74462149ae8045893.jpg\",\n  \"264391.jpg\": \"Insecta/Satyrium calanus/aca0fe32c473ac1e688982fc5286121b.jpg\",\n  \"264392.jpg\": \"Insecta/Satyrium calanus/c8d62ce68f14361a64f12c1ef6cad468.jpg\",\n  \"264398.jpg\": \"Insecta/Satyrium calanus/99a5fb7077bb3ee394bb6970bd3b10e6.jpg\",\n  \"264400.jpg\": \"Insecta/Satyrium calanus/3c5808450868e3f808434aa7391f7105.jpg\",\n  \"264404.jpg\": \"Insecta/Satyrium calanus/5d1f6ee8e1549404db11298a5459fe5c.jpg\",\n  \"264406.jpg\": \"Insecta/Satyrium calanus/fde1aa7c6a88b889e77c215e8202e83b.jpg\",\n  \"264408.jpg\": \"Insecta/Satyrium calanus/9e9f8ec1a393dc9de9fc689e3b9b6bda.jpg\",\n  \"264411.jpg\": \"Insecta/Satyrium calanus/c3880c1982c20a3c80956d713e306913.jpg\",\n  \"264415.jpg\": \"Insecta/Satyrium calanus/bef9be6e30420997a8ab4e0e6a12184f.jpg\",\n  \"264416.jpg\": \"Insecta/Satyrium calanus/ac91af2f979c37fc93273daf517700a2.jpg\",\n  \"264420.jpg\": \"Insecta/Satyrium calanus/dde3ccf61c2f0e249a2ae645db252416.jpg\",\n  \"264660.jpg\": \"Aves/Cochlearius cochlearius/4d8c3f18feaefcf81078f562c9a40478.jpg\",\n  \"264668.jpg\": \"Aves/Cochlearius cochlearius/e2b8be3c8c885e0077410c93f619398a.jpg\",\n  \"264670.jpg\": \"Aves/Cochlearius cochlearius/f2b4b4618fda1145780fb388c712422d.jpg\",\n  \"264671.jpg\": \"Aves/Cochlearius cochlearius/42a1ba87d2c7483266c330efb0004fa5.jpg\",\n  \"264672.jpg\": \"Aves/Cochlearius cochlearius/5bc14c9835e836807dcb040b6e4ca780.jpg\",\n  \"264674.jpg\": \"Aves/Cochlearius cochlearius/07098efa8169033bc1e0280bfd8356ce.jpg\",\n  \"264678.jpg\": \"Aves/Cochlearius cochlearius/5c6a6ab13bc3b07b4395ff7aa6bd7963.jpg\",\n  \"264679.jpg\": \"Aves/Cochlearius cochlearius/3ba97ad25ab49aa86e78eabc2edda2fa.jpg\",\n  \"264680.jpg\": \"Aves/Cochlearius cochlearius/74e908a77dd89fafe30812ad13fb62a1.jpg\",\n  \"264683.jpg\": \"Aves/Cochlearius cochlearius/3e7233731dc6eae28558798341aa370a.jpg\",\n  \"264686.jpg\": \"Aves/Cochlearius cochlearius/c6a675120545caba462e26053cedb7e5.jpg\",\n  \"264718.jpg\": \"Aves/Todiramphus sanctus vagans/583630cb8a7620cc7f6d631038a8f581.jpg\",\n  \"264719.jpg\": \"Aves/Todiramphus sanctus vagans/909f5e9777029a7faa688407ea286b30.jpg\",\n  \"264720.jpg\": \"Aves/Todiramphus sanctus vagans/2c14563fa3d187df39275b9fa31a7058.jpg\",\n  \"264726.jpg\": \"Aves/Todiramphus sanctus vagans/06ef59bd4ccde155f99add8fe39055b1.jpg\",\n  \"264727.jpg\": \"Aves/Todiramphus sanctus vagans/f6633ebc78d5ce26de017b43161ca13b.jpg\",\n  \"264728.jpg\": \"Aves/Todiramphus sanctus vagans/23b8e4fc04993f266828e725ca70dfdb.jpg\",\n  \"264729.jpg\": \"Aves/Todiramphus sanctus vagans/89e733860eddfcaf5684347acf5768be.jpg\",\n  \"264739.jpg\": \"Aves/Todiramphus sanctus vagans/d833f6fb1adc508d078da31fdc912217.jpg\",\n  \"264740.jpg\": \"Aves/Todiramphus sanctus vagans/4f10706f2ce3994407c8be25ff8b31d3.jpg\",\n  \"264744.jpg\": \"Aves/Todiramphus sanctus vagans/15e638aa58c924e37a56bcd1c0de1ee9.jpg\",\n  \"264746.jpg\": \"Aves/Todiramphus sanctus vagans/dbf9fe5f538bcbc5782ddcc5fdca7e83.jpg\",\n  \"264747.jpg\": \"Aves/Todiramphus sanctus vagans/64fba5791e0409e1ae93ca4d14c2f46a.jpg\",\n  \"264751.jpg\": \"Aves/Todiramphus sanctus vagans/6fdd52b1dec34b400853718752ced1b6.jpg\",\n  \"264757.jpg\": \"Aves/Todiramphus sanctus vagans/544d97af17ac325b66233db7ebfe724a.jpg\",\n  \"264764.jpg\": \"Aves/Todiramphus sanctus vagans/fac89ee7a0831fc6596871a34e44a608.jpg\",\n  \"264812.jpg\": \"Aves/Fratercula cirrhata/468a5a629e336507fd5ef68b9f741024.jpg\",\n  \"264817.jpg\": \"Aves/Fratercula cirrhata/d1ef28ec8b6a0e1b37e0c81475fe28ec.jpg\",\n  \"264818.jpg\": \"Aves/Fratercula cirrhata/318ad77b19d7be76407678410322b804.jpg\",\n  \"264822.jpg\": \"Aves/Fratercula cirrhata/cc1946a1910e330014a4712171274968.jpg\",\n  \"264823.jpg\": \"Aves/Fratercula cirrhata/8789805603fa6c660c34af8c9f33a6aa.jpg\",\n  \"264831.jpg\": \"Aves/Larus argentatus smithsonianus/df5c3e1967e49d3e9a3dbd182a24e40e.jpg\",\n  \"264833.jpg\": \"Aves/Larus argentatus smithsonianus/edba1b01d64b0cebe2af4dfd3dfce306.jpg\",\n  \"264834.jpg\": \"Aves/Larus argentatus smithsonianus/6d15948a8c1197b9fe7736f104847c1f.jpg\",\n  \"264835.jpg\": \"Aves/Larus argentatus smithsonianus/b441e5e96cc435335f70bed9e4c2234d.jpg\",\n  \"264839.jpg\": \"Aves/Larus argentatus smithsonianus/d3b501aad0981c3b3c3ad5945c4c4308.jpg\",\n  \"264840.jpg\": \"Aves/Larus argentatus smithsonianus/d59edfa3ecad3eda303048f00aede6d9.jpg\",\n  \"264853.jpg\": \"Aves/Larus argentatus smithsonianus/54f46d47ed2c907b975b33f536149523.jpg\",\n  \"264858.jpg\": \"Aves/Larus argentatus smithsonianus/e6e1e31ccc8c5a5c3ba0472142c6e8b7.jpg\",\n  \"264861.jpg\": \"Aves/Larus argentatus smithsonianus/7cfa2900c6c5e84043ca4bda61320e5b.jpg\",\n  \"265103.jpg\": \"Aves/Cathartes aura/ed0a682656f3443d35757c9eb9df6d88.jpg\",\n  \"265145.jpg\": \"Aves/Cathartes aura/df1da23ae9f4a7e9648b28d898af4cfc.jpg\",\n  \"265196.jpg\": \"Aves/Cathartes aura/afa739438ae5c3c39fcbbe9ebfcb4a6e.jpg\",\n  \"265204.jpg\": \"Aves/Cathartes aura/c4e0599ed2e20d9d734c7af5dba4f2ca.jpg\",\n  \"265207.jpg\": \"Aves/Cathartes aura/c11f12fd686d03a0400025b6f0eaa508.jpg\",\n  \"265246.jpg\": \"Aves/Cathartes aura/86b0efff65dc37c7a8c63fe7602d5843.jpg\",\n  \"2653.jpg\": \"Aves/Picoides pubescens/d77b7bbfe5c31eed20ca9d09aeeb1917.jpg\",\n  \"265335.jpg\": \"Aves/Cathartes aura/a4918ddef3092e5ed0c9bd8a82bf4465.jpg\",\n  \"265355.jpg\": \"Aves/Cathartes aura/95691b3149b39678c043a7459cb31575.jpg\",\n  \"265429.jpg\": \"Aves/Cathartes aura/dd7f517a51be7048a3ede0deda526840.jpg\",\n  \"265599.jpg\": \"Aves/Cathartes aura/4e900f8c160aa34cd1489f48af24c041.jpg\",\n  \"265649.jpg\": \"Aves/Cathartes aura/fe398fbdf3c7ac29d550e8b6514a588e.jpg\",\n  \"265696.jpg\": \"Aves/Cathartes aura/5641ae6cea68234b9e6477851336353e.jpg\",\n  \"265734.jpg\": \"Aves/Cathartes aura/34b4642437344139e9bef70cf64f5f9c.jpg\",\n  \"265802.jpg\": \"Aves/Cathartes aura/c8b689074eeb414f934037341bf63a61.jpg\",\n  \"265828.jpg\": \"Aves/Cathartes aura/568d2aa57b115288c197bb379b904872.jpg\",\n  \"26587.jpg\": \"Aves/Charadrius semipalmatus/4a5b9b75d421b2464e2a3c8e0cfee8d2.jpg\",\n  \"26592.jpg\": \"Aves/Charadrius semipalmatus/31789f8a4790000319909d5d5b6919c8.jpg\",\n  \"265976.jpg\": \"Aves/Cathartes aura/b72be33beb8408dd58bd03a52f50ff32.jpg\",\n  \"26607.jpg\": \"Aves/Charadrius semipalmatus/6e91629e2c137f08f95c498b5af5ad9f.jpg\",\n  \"266077.jpg\": \"Aves/Cathartes aura/ef851ad8e34775a96601256ddd769684.jpg\",\n  \"266081.jpg\": \"Aves/Cathartes aura/ce54c5bd2646dd5e185744c345a6019c.jpg\",\n  \"266145.jpg\": \"Aves/Cathartes aura/492809de483068e592be8f00ebf0b024.jpg\",\n  \"266223.jpg\": \"Aves/Actophilornis africanus/9afa85f9e44135de1535776bd2c0752c.jpg\",\n  \"266224.jpg\": \"Aves/Actophilornis africanus/65b1350cce103336b42cec3c3d771b72.jpg\",\n  \"266231.jpg\": \"Aves/Actophilornis africanus/7385d0f5e18cebf12fa2cc46019ebf5f.jpg\",\n  \"266233.jpg\": \"Aves/Actophilornis africanus/368762fd9b9439fd7fb15c15e9bb0295.jpg\",\n  \"266234.jpg\": \"Aves/Actophilornis africanus/e493eeacc5d471e5544a150fe55b0475.jpg\",\n  \"266238.jpg\": \"Aves/Actophilornis africanus/df82fd2564a25a5980678321eccf533e.jpg\",\n  \"26628.jpg\": \"Aves/Charadrius semipalmatus/271435ab63937c626bb62d40f53bc8f7.jpg\",\n  \"266283.jpg\": \"Aves/Haliastur indus/f7d9adbb536f59839f3bf1e9b82274f6.jpg\",\n  \"266285.jpg\": \"Aves/Haliastur indus/7ab45ec68d791bdfeb9fdac17ac2dcb8.jpg\",\n  \"266286.jpg\": \"Aves/Haliastur indus/54aa28780ec92ce158d355d410af111f.jpg\",\n  \"266292.jpg\": \"Aves/Haliastur indus/e7ead6ccb617b3916bb99c570b429d78.jpg\",\n  \"266294.jpg\": \"Aves/Haliastur indus/138f7c9a716267d7584ee74c0e16d112.jpg\",\n  \"26635.jpg\": \"Aves/Charadrius semipalmatus/5f5444307633050bceae830f12c89d66.jpg\",\n  \"26640.jpg\": \"Aves/Charadrius semipalmatus/a01edc9b2281944991722463d3b536cf.jpg\",\n  \"266418.jpg\": \"Reptilia/Plestiodon skiltonianus skiltonianus/ac31b0c762e8a9399544d8c8b403048b.jpg\",\n  \"266419.jpg\": \"Reptilia/Plestiodon skiltonianus skiltonianus/91e06556cf4c32a9a6c7ea9083e85e2f.jpg\",\n  \"26642.jpg\": \"Aves/Charadrius semipalmatus/903c16c0854b69495744a466eef067ce.jpg\",\n  \"266434.jpg\": \"Reptilia/Plestiodon skiltonianus skiltonianus/15bc42198a3491ac594b9eab1ce52596.jpg\",\n  \"266436.jpg\": \"Reptilia/Plestiodon skiltonianus skiltonianus/fde11f17cea41b2d21da54f654682f76.jpg\",\n  \"266437.jpg\": \"Reptilia/Plestiodon skiltonianus skiltonianus/2bfe639c3c68afbcd957b9e968224b1d.jpg\",\n  \"26647.jpg\": \"Aves/Charadrius semipalmatus/6ca4b9dd66c02ab9a4f86a2df79f0aa7.jpg\",\n  \"26650.jpg\": \"Aves/Charadrius semipalmatus/f529532937c9b24a266bb71726fcc99e.jpg\",\n  \"266525.jpg\": \"Reptilia/Thamnophis sauritus/8ea8877cfad2d9355de15ae3daa1fb19.jpg\",\n  \"266528.jpg\": \"Reptilia/Thamnophis sauritus/f14697014a262fd2be52536e6fa6ae95.jpg\",\n  \"266530.jpg\": \"Reptilia/Thamnophis sauritus/a84ed9867e6e7c2910755166a0779ee5.jpg\",\n  \"266536.jpg\": \"Reptilia/Thamnophis sauritus/7312379f3a162897a63dfca9a212b263.jpg\",\n  \"266537.jpg\": \"Reptilia/Thamnophis sauritus/2be1e42258985daa75ea87208d518221.jpg\",\n  \"266538.jpg\": \"Reptilia/Thamnophis sauritus/20ca245eb9c39345279a35f5b8acd0c0.jpg\",\n  \"266543.jpg\": \"Insecta/Enallagma cyathigerum/d43540eead6b2a14ddc49b06d2948844.jpg\",\n  \"266545.jpg\": \"Insecta/Enallagma cyathigerum/2db843a1dada93d8658c33c0c718a234.jpg\",\n  \"266550.jpg\": \"Insecta/Enallagma cyathigerum/61eb8642e92e243bc23eb7d79ca785af.jpg\",\n  \"266558.jpg\": \"Insecta/Enallagma cyathigerum/9402a42d86b374076413109dbd318117.jpg\",\n  \"266560.jpg\": \"Insecta/Enallagma cyathigerum/e2508aa665dde379bcc67626f166dfcf.jpg\",\n  \"266561.jpg\": \"Insecta/Enallagma cyathigerum/493f76d71d93c381ee62b96d44f7be97.jpg\",\n  \"266562.jpg\": \"Insecta/Enallagma cyathigerum/44148993b9df54756c11a3407094df0c.jpg\",\n  \"266564.jpg\": \"Aves/Colinus virginianus/027477ef89238c1534d45b956669d8e5.jpg\",\n  \"266565.jpg\": \"Aves/Colinus virginianus/e531c7f611b335a6529ec18f9d6efad7.jpg\",\n  \"266566.jpg\": \"Aves/Colinus virginianus/d3fb659eb0213a46587af39449286f0e.jpg\",\n  \"266573.jpg\": \"Aves/Colinus virginianus/6889ee2b9e803dd9cfc25fc0cb36325f.jpg\",\n  \"266576.jpg\": \"Aves/Colinus virginianus/761deb3aacc58174e7df2fef4fc3664e.jpg\",\n  \"266577.jpg\": \"Aves/Colinus virginianus/e853d5c02b1171fff8107260217c63bb.jpg\",\n  \"266578.jpg\": \"Aves/Colinus virginianus/a5cff421c5383733d3ad232ecbe3f14e.jpg\",\n  \"266587.jpg\": \"Aves/Colinus virginianus/7f14c10f7ad993be3ff8edf5096c33cc.jpg\",\n  \"266609.jpg\": \"Aves/Colinus virginianus/1cd3d24740d756261166f24981fe2676.jpg\",\n  \"266616.jpg\": \"Aves/Colinus virginianus/23bfa069bab6da3ab3eea8e41f845fe8.jpg\",\n  \"266619.jpg\": \"Aves/Colinus virginianus/66a30dbb84d0b2f3f12104430f9f3c9a.jpg\",\n  \"266626.jpg\": \"Aves/Colinus virginianus/06b307ea6712b2ab847185ddd789d295.jpg\",\n  \"266632.jpg\": \"Aves/Colinus virginianus/adaca09cdfd4a7e1a4376c6321de3374.jpg\",\n  \"266633.jpg\": \"Aves/Colinus virginianus/b31048f4c3b741ac50dfd7fc355756ab.jpg\",\n  \"266640.jpg\": \"Aves/Colinus virginianus/f372f47c07aa88289735930e26ea04e6.jpg\",\n  \"266642.jpg\": \"Aves/Colinus virginianus/ecb0ef518d5fbe697b1506e29a905de7.jpg\",\n  \"266648.jpg\": \"Aves/Colinus virginianus/755245c33c32ca7e6a5d0d3c3b511c4c.jpg\",\n  \"266649.jpg\": \"Aves/Colinus virginianus/0f54e5f9f8af6dba0114b74c2a83583f.jpg\",\n  \"266653.jpg\": \"Aves/Colinus virginianus/9d235aece4c9e9ffb688e1f936db1aee.jpg\",\n  \"266683.jpg\": \"Insecta/Lerodea eufala/e199215a9e03eb98498f08e178885526.jpg\",\n  \"266697.jpg\": \"Insecta/Lerodea eufala/3de4775f1c15ca568766bf68433ec9c0.jpg\",\n  \"266698.jpg\": \"Insecta/Lerodea eufala/4c1f05b3efbe477e61af6ecfa9734a92.jpg\",\n  \"266701.jpg\": \"Insecta/Lerodea eufala/07f4689c8881b952c928778aa3f92c25.jpg\",\n  \"266702.jpg\": \"Insecta/Lerodea eufala/786cc766840ad56bc1b1416e478481af.jpg\",\n  \"266703.jpg\": \"Insecta/Lerodea eufala/aa795fd51cb904067579e1aa204ded77.jpg\",\n  \"266710.jpg\": \"Insecta/Lerodea eufala/bbae1ba37fdae9e8eb187d06ea6b8ca6.jpg\",\n  \"266714.jpg\": \"Insecta/Lerodea eufala/a6f2a4732752114bb747099dd4f8c123.jpg\",\n  \"266715.jpg\": \"Insecta/Lerodea eufala/0cef632f5bf8207ddf3aa02353ff8eda.jpg\",\n  \"266719.jpg\": \"Insecta/Lerodea eufala/fae5d277e810a9ac06cea704051c9f02.jpg\",\n  \"266720.jpg\": \"Insecta/Lerodea eufala/7597789bbb6ea2ce7f65a2afd4b675bb.jpg\",\n  \"266726.jpg\": \"Insecta/Lerodea eufala/fe6c5263ffbe87a4eecfb9d1dddf12db.jpg\",\n  \"266727.jpg\": \"Insecta/Lerodea eufala/606ffa7cbf82c3147efbe047544a9721.jpg\",\n  \"266738.jpg\": \"Insecta/Lerodea eufala/4f197843365f8a184d06675f55ff5241.jpg\",\n  \"26674.jpg\": \"Aves/Charadrius semipalmatus/0180e449f6ea4a5d607854b5d58a8a24.jpg\",\n  \"266745.jpg\": \"Insecta/Lerodea eufala/f85ff536a805859f19a25c2b3acc3f1b.jpg\",\n  \"266746.jpg\": \"Insecta/Lerodea eufala/5d2f242d1186fc44694fca99102f868a.jpg\",\n  \"266747.jpg\": \"Insecta/Lerodea eufala/e829e78807db3b683af57ab5402b77e9.jpg\",\n  \"266750.jpg\": \"Insecta/Lerodea eufala/c9773c693785811f5ccef299833f8c54.jpg\",\n  \"266756.jpg\": \"Insecta/Lerodea eufala/8979c0c026a243103dbe1291a8a7396b.jpg\",\n  \"266768.jpg\": \"Insecta/Lerodea eufala/b6b3dd87546957b6591848410882c847.jpg\",\n  \"266791.jpg\": \"Arachnida/Araneus diadematus/0d1a7c85fae4bbbfd8543f370a4a983f.jpg\",\n  \"266796.jpg\": \"Arachnida/Araneus diadematus/8492f52e79bf84cc544bab0a23651b03.jpg\",\n  \"266798.jpg\": \"Arachnida/Araneus diadematus/2dc03d36835577f0bead4ae70288aa86.jpg\",\n  \"2668.jpg\": \"Aves/Picoides pubescens/3070c0a28d9ac5ecc939383909f341e4.jpg\",\n  \"266806.jpg\": \"Arachnida/Araneus diadematus/cf1601a3c6841812aa6001693993b42a.jpg\",\n  \"266817.jpg\": \"Arachnida/Araneus diadematus/f37237119f670956aee049acaf11e2b6.jpg\",\n  \"26683.jpg\": \"Aves/Charadrius semipalmatus/36e7e8dbc95f4e2f3e9190cad7806547.jpg\",\n  \"266844.jpg\": \"Arachnida/Araneus diadematus/cddfc00cfc9f9fe77fe0ae6ad9fe2a07.jpg\",\n  \"266845.jpg\": \"Arachnida/Araneus diadematus/22eed824a22adf756c1a0535727bf1cb.jpg\",\n  \"266849.jpg\": \"Arachnida/Araneus diadematus/175fc6499f314ecdb6ad8ce0518f78ef.jpg\",\n  \"266850.jpg\": \"Arachnida/Araneus diadematus/0ef39e41903a5142b3e6ece6965bf18d.jpg\",\n  \"266859.jpg\": \"Arachnida/Araneus diadematus/8e32a66d4605e9fac0b8888e5a04eab2.jpg\",\n  \"266873.jpg\": \"Arachnida/Araneus diadematus/222c6174fc61f990a8c1f9200dbe9a51.jpg\",\n  \"266874.jpg\": \"Arachnida/Araneus diadematus/e264dce7fbf80c43d28ce4c38bcb6528.jpg\",\n  \"266875.jpg\": \"Arachnida/Araneus diadematus/1e9322f3e8ca7a996fa7b088083f0028.jpg\",\n  \"266900.jpg\": \"Arachnida/Araneus diadematus/5f1345c2db60a7e2754923223eefc968.jpg\",\n  \"266906.jpg\": \"Arachnida/Araneus diadematus/87076ea43a92dfe4255704f06cd627f7.jpg\",\n  \"266918.jpg\": \"Arachnida/Araneus diadematus/a975819169dca4902a883edf4a7216f7.jpg\",\n  \"266919.jpg\": \"Arachnida/Araneus diadematus/23680db0553250040393a161625204cb.jpg\",\n  \"266922.jpg\": \"Arachnida/Araneus diadematus/394fa3fc3ec380221bb4fdff7a2d8b3a.jpg\",\n  \"266927.jpg\": \"Arachnida/Araneus diadematus/3b5dc2a2315f1567ac74cc9edeacbf90.jpg\",\n  \"266934.jpg\": \"Arachnida/Araneus diadematus/d289e06797b17d1e38855f9e63136e14.jpg\",\n  \"266942.jpg\": \"Arachnida/Araneus diadematus/dd7e90212d86eb4e39a1889cdb489ad3.jpg\",\n  \"266948.jpg\": \"Arachnida/Araneus diadematus/46fcc12e51103a474ddbd4cf33426b94.jpg\",\n  \"266967.jpg\": \"Aves/Alcedo atthis/80c89c437c99bf6c5c7608dd4e5c3891.jpg\",\n  \"266968.jpg\": \"Aves/Alcedo atthis/5a448741722cd83f9db6f43119c13541.jpg\",\n  \"266972.jpg\": \"Aves/Alcedo atthis/4dd45f05e5854393ecf65d7225198831.jpg\",\n  \"266974.jpg\": \"Aves/Alcedo atthis/626de0781d7b816443392e9d385f4562.jpg\",\n  \"266975.jpg\": \"Aves/Alcedo atthis/a19ef54adb90116022a5001a863e49e3.jpg\",\n  \"266976.jpg\": \"Aves/Alcedo atthis/2360cdaf6563f8795d6647d3330569c8.jpg\",\n  \"266978.jpg\": \"Aves/Alcedo atthis/6c9d15f0dfc28db9dc22c43afe73a39c.jpg\",\n  \"266980.jpg\": \"Aves/Alcedo atthis/b18e369c8ed94b6286585dd7d0d60183.jpg\",\n  \"266984.jpg\": \"Aves/Alcedo atthis/d11be983afbf11f5921f74f04022ad03.jpg\",\n  \"266986.jpg\": \"Aves/Alcedo atthis/522fecd697b67e11579673d76e9b37b1.jpg\",\n  \"266988.jpg\": \"Aves/Alcedo atthis/4a99a90a5ce3f13efa1bae4ae2096dcb.jpg\",\n  \"266994.jpg\": \"Aves/Alcedo atthis/eba9c0b27a9f299f6e45b0572901e8ab.jpg\",\n  \"266996.jpg\": \"Aves/Alcedo atthis/c034bf8d36e46ecdacd36b5264bbc760.jpg\",\n  \"26701.jpg\": \"Aves/Charadrius semipalmatus/c0874f24d5c1adf36b81a6c11cbdb4b9.jpg\",\n  \"267013.jpg\": \"Aves/Alcedo atthis/c63a327fa950e1bd62feddfd3bae4cb6.jpg\",\n  \"267014.jpg\": \"Aves/Alcedo atthis/4863575ab7df7378637ef50d90a07c42.jpg\",\n  \"267016.jpg\": \"Aves/Alcedo atthis/65cf4652a9c62f8ecc11fbdd29965f51.jpg\",\n  \"267018.jpg\": \"Aves/Alcedo atthis/d8b49e70a2fe269585081d7e7684948f.jpg\",\n  \"26706.jpg\": \"Aves/Charadrius semipalmatus/e9d75fc1d44948f99961692c1108d92f.jpg\",\n  \"267069.jpg\": \"Aves/Aythya valisineria/11d14a260490e24dfe7e74b8bd9ab6f7.jpg\",\n  \"267076.jpg\": \"Aves/Aythya valisineria/667a6f669f4516eef720e79869989854.jpg\",\n  \"267106.jpg\": \"Aves/Aythya valisineria/201bf3634d3da305c756071ac1711c26.jpg\",\n  \"267115.jpg\": \"Aves/Aythya valisineria/32272eabe56762e951c03351679e7bf3.jpg\",\n  \"267129.jpg\": \"Aves/Aythya valisineria/531ce0f7ab3e329bdb5ee4e96c5d67aa.jpg\",\n  \"267137.jpg\": \"Aves/Aythya valisineria/c8d677c3ecab2ef21dac635d05d97c0e.jpg\",\n  \"267138.jpg\": \"Aves/Aythya valisineria/ec98960d3a707733fbae33a223e280ec.jpg\",\n  \"267176.jpg\": \"Aves/Aythya valisineria/25fedb71a9a61846c94371133848502d.jpg\",\n  \"267188.jpg\": \"Aves/Aythya valisineria/fe23b547eb2cab2c3a47644ceab6d88f.jpg\",\n  \"267194.jpg\": \"Insecta/Pachysphinx modesta/3ee171a6cc7f69165aa71f5a1083db39.jpg\",\n  \"267198.jpg\": \"Insecta/Pachysphinx modesta/aac01d1fa85121ccf71b60131b02a22e.jpg\",\n  \"267202.jpg\": \"Insecta/Pachysphinx modesta/c7d5149807ab2344c9de1cd793e15f17.jpg\",\n  \"267211.jpg\": \"Insecta/Pachysphinx modesta/94cbec18836e52193f31e53ca6d76cb1.jpg\",\n  \"267219.jpg\": \"Aves/Haematopus palliatus/a7035f2d2134787068fcaa08c41f3729.jpg\",\n  \"267221.jpg\": \"Aves/Haematopus palliatus/2dea75bc657921328eceb595d4fb1b9e.jpg\",\n  \"267237.jpg\": \"Aves/Haematopus palliatus/ed6aa95ce3a41db6d55d89ad367a4fb0.jpg\",\n  \"26724.jpg\": \"Aves/Charadrius semipalmatus/dd6254c2b2da3c776f5ce02754231427.jpg\",\n  \"267245.jpg\": \"Aves/Haematopus palliatus/59866e26e3b2dbc73c2629a836a87d9b.jpg\",\n  \"267249.jpg\": \"Aves/Haematopus palliatus/70bf111b35c2e911b02548eb392946bd.jpg\",\n  \"267265.jpg\": \"Aves/Haematopus palliatus/3cfab5e6138fecebcb2b18e5155eb09f.jpg\",\n  \"267276.jpg\": \"Aves/Haematopus palliatus/610ab4ae2d901b94ed86c1746e014648.jpg\",\n  \"267284.jpg\": \"Aves/Haematopus palliatus/06ca010d56f75b1ab38597b9e6e5cec1.jpg\",\n  \"267286.jpg\": \"Aves/Haematopus palliatus/dc2af47f3345946aae90272e4fedc266.jpg\",\n  \"26730.jpg\": \"Aves/Charadrius semipalmatus/b48c14bfbbb1c974bf6ce4d0e773dc5f.jpg\",\n  \"267300.jpg\": \"Aves/Haematopus palliatus/19caa4793211eac4fed075ad53002e61.jpg\",\n  \"267313.jpg\": \"Aves/Haematopus palliatus/01c38edf959b2a1339398df1b2b2388f.jpg\",\n  \"267318.jpg\": \"Aves/Haematopus palliatus/f1b6b9db8579881220c190d36f436530.jpg\",\n  \"267331.jpg\": \"Aves/Haematopus palliatus/22045f9ae6ba556bb8d33863da9ba702.jpg\",\n  \"267344.jpg\": \"Aves/Haematopus palliatus/b8fc7c0a8ea2046db1192cef2066256e.jpg\",\n  \"26736.jpg\": \"Aves/Charadrius semipalmatus/e7846dd7ae464c54ac16c5ada793c002.jpg\",\n  \"267385.jpg\": \"Aves/Morus serrator/9aa140752f42c6d7b09ca2798ef9346b.jpg\",\n  \"267393.jpg\": \"Aves/Morus serrator/3b030644555122b4d2481b82e5d631bc.jpg\",\n  \"267396.jpg\": \"Aves/Morus serrator/377b5e7633a322fb5c1dbe54f7c6ae7a.jpg\",\n  \"26747.jpg\": \"Aves/Charadrius semipalmatus/3b16fe3a109a78cd8c9a2eff06728670.jpg\",\n  \"267512.jpg\": \"Aves/Charadrius dubius/53e7e87a366748460dfca5691c6f2990.jpg\",\n  \"267517.jpg\": \"Aves/Charadrius dubius/747117e18d8ffa7198b15b9b67afa01d.jpg\",\n  \"267518.jpg\": \"Aves/Charadrius dubius/fafea05c2dd60b09cdf1dd5af7361b7d.jpg\",\n  \"267519.jpg\": \"Aves/Charadrius dubius/347615dbe0836a7254a183c78ff08a7d.jpg\",\n  \"267521.jpg\": \"Aves/Charadrius dubius/45233ad5269491bf384d30821c8fa4fb.jpg\",\n  \"26753.jpg\": \"Aves/Charadrius semipalmatus/c682a3c7dac5791787072f6ebbaf6c9c.jpg\",\n  \"267531.jpg\": \"Aves/Charadrius dubius/1cc57e181f8ac6fdd4d41b1aaeeb9845.jpg\",\n  \"267534.jpg\": \"Aves/Charadrius dubius/693a17704bcea2b5703b9a01a873df7b.jpg\",\n  \"26754.jpg\": \"Aves/Charadrius semipalmatus/14cc83d9a6b90fbec3467bd92aa197b9.jpg\",\n  \"267581.jpg\": \"Aves/Caracara cheriway/1e15f571bb2574199eb04f90420d943e.jpg\",\n  \"267598.jpg\": \"Aves/Caracara cheriway/6c8992c1eb92e0c86fe661beb2cb7000.jpg\",\n  \"267660.jpg\": \"Aves/Caracara cheriway/3b5defb1bc114a5e1f7d926437f1af3c.jpg\",\n  \"267664.jpg\": \"Aves/Caracara cheriway/2a715deebdbf787b1dddcc72fd3fcf1c.jpg\",\n  \"267671.jpg\": \"Aves/Caracara cheriway/b35c3e890654188e2c8d20bb22c73a26.jpg\",\n  \"267675.jpg\": \"Aves/Caracara cheriway/aa5fda9921655516df9267d29dd9d4fd.jpg\",\n  \"267704.jpg\": \"Aves/Caracara cheriway/2f9ab5103b25991edeb4936793df5073.jpg\",\n  \"267711.jpg\": \"Aves/Caracara cheriway/cbfd0730bede0e3bc73f61f1939bf753.jpg\",\n  \"267718.jpg\": \"Aves/Caracara cheriway/5abdf8608bf9165258ee66225226ded7.jpg\",\n  \"267741.jpg\": \"Aves/Caracara cheriway/cb9f9523239e12d0dbca389342e1c065.jpg\",\n  \"267770.jpg\": \"Aves/Caracara cheriway/e744c416351163e4606d2ff6e3f7cd90.jpg\",\n  \"267771.jpg\": \"Aves/Caracara cheriway/a9c3fce354783bf0a58a2787b4c20647.jpg\",\n  \"267785.jpg\": \"Aves/Caracara cheriway/feed77c6439d9bb33b228a136a953563.jpg\",\n  \"267789.jpg\": \"Aves/Caracara cheriway/a532299d41d645fe337b21c09d80cc8e.jpg\",\n  \"267796.jpg\": \"Aves/Caracara cheriway/54c83ea787cb7bb981e34ed5840df02c.jpg\",\n  \"267807.jpg\": \"Aves/Caracara cheriway/f00c20ffe1e4582b86fd77a1203106a5.jpg\",\n  \"267832.jpg\": \"Aves/Caracara cheriway/9e89570cbb7900975db496fb59162d25.jpg\",\n  \"267841.jpg\": \"Aves/Alectoris rufa/625294dae7fa5e4de1ab1cbc243679d8.jpg\",\n  \"267842.jpg\": \"Aves/Alectoris rufa/676cd98261bb128037048e13077e1241.jpg\",\n  \"267852.jpg\": \"Aves/Alectoris rufa/68e99617866be10d669967ffd4f8d241.jpg\",\n  \"267854.jpg\": \"Aves/Alectoris rufa/68814037e4438227e6ce3fe269a34d51.jpg\",\n  \"267855.jpg\": \"Aves/Falco rufigularis/01c7f15ab0d2a115cba390e9af81becd.jpg\",\n  \"267866.jpg\": \"Aves/Falco rufigularis/63f7cf07f72fd442ed310e5fc9771202.jpg\",\n  \"267868.jpg\": \"Aves/Falco rufigularis/f5b66911ef9e4746d8c3ccaac29d51d0.jpg\",\n  \"267869.jpg\": \"Aves/Falco rufigularis/610a64f031c8a59a94ddd22895f5d2d1.jpg\",\n  \"267879.jpg\": \"Aves/Falco rufigularis/dd2ce1ef037824520df845c1d143bcd6.jpg\",\n  \"267882.jpg\": \"Aves/Falco rufigularis/d94d2c0b392e0324d77955f15dc63a80.jpg\",\n  \"267891.jpg\": \"Aves/Buteo buteo/147a721153fed5ef1405302b87bf8bc9.jpg\",\n  \"267900.jpg\": \"Aves/Buteo buteo/f1f6b2cf4960a3e37f2747e00322bc11.jpg\",\n  \"267926.jpg\": \"Aves/Buteo buteo/01cfb7dfc468ee7cf4cb72c5d193995d.jpg\",\n  \"267927.jpg\": \"Aves/Buteo buteo/65018e94b70df7dc8d754195a82f0c9e.jpg\",\n  \"267928.jpg\": \"Aves/Buteo buteo/065b88987d4712e491aa7e9fdfb7c193.jpg\",\n  \"267929.jpg\": \"Aves/Buteo buteo/a6a3742cedb258d6cfe553355372ddf9.jpg\",\n  \"267930.jpg\": \"Aves/Buteo buteo/11137977afc151436acae605c29cc351.jpg\",\n  \"267992.jpg\": \"Aves/Buteo buteo/3b6933d93f23d4e5a9a11ad4baff9dda.jpg\",\n  \"267995.jpg\": \"Aves/Buteo buteo/f502bfb2dceb3e0f018a264b8428b0bc.jpg\",\n  \"267996.jpg\": \"Aves/Buteo buteo/48fecfd20d1eefd1b33ab2b76fc7ec7c.jpg\",\n  \"268057.jpg\": \"Aves/Buteo buteo/fde1e2a0a2aabeaefce03e5482f2f120.jpg\",\n  \"268071.jpg\": \"Aves/Buteo buteo/5f5247a3a9d537f06b1d9ba2f318dc5c.jpg\",\n  \"2681.jpg\": \"Aves/Picoides pubescens/918bbcca22109ca7a52f09e060225d8d.jpg\",\n  \"268105.jpg\": \"Aves/Buteo buteo/bf43e7328747e6da55bd69d0942fc218.jpg\",\n  \"268116.jpg\": \"Aves/Buteo buteo/97f57816a2370e71b08a71a24b8f61eb.jpg\",\n  \"26812.jpg\": \"Actinopterygii/Diodon holocanthus/ecbfb8a50626ae1b786d5103b5c0b700.jpg\",\n  \"268121.jpg\": \"Aves/Buteo buteo/2b3abad3d680380a4a3b03d1c0262ab7.jpg\",\n  \"268122.jpg\": \"Aves/Buteo buteo/d05805b97c14a02e746421166e017e33.jpg\",\n  \"268123.jpg\": \"Aves/Buteo buteo/3d233aa4a21665fb5ad437d8b849032a.jpg\",\n  \"268128.jpg\": \"Aves/Buteo buteo/9d456abb1bddb3c063ecd9658f8d398b.jpg\",\n  \"26813.jpg\": \"Actinopterygii/Diodon holocanthus/2c4c365b3af80120bb167fb8ea447253.jpg\",\n  \"26815.jpg\": \"Aves/Buteo swainsoni/2c3a8f8d84b525ea897ef08f00e21e8e.jpg\",\n  \"268156.jpg\": \"Aves/Buteo buteo/19b083785b515c5eafacee88e6b559cf.jpg\",\n  \"268219.jpg\": \"Aves/Buteo buteo/158d5684feefff5e53447414c20485b4.jpg\",\n  \"268271.jpg\": \"Aves/Buteo buteo/93ef88b25da805ba62cf7d0fca9c96d3.jpg\",\n  \"268278.jpg\": \"Aves/Buteo buteo/55fe4f0ef81c5b962bd25b0c7750a429.jpg\",\n  \"268312.jpg\": \"Aves/Buteo buteo/67f80e9d18a091007009df037f186d42.jpg\",\n  \"268338.jpg\": \"Aves/Buteo buteo/84494705245256b7195a46810db9ebb9.jpg\",\n  \"2685.jpg\": \"Aves/Picoides pubescens/2d897b7e01cff5db4fc874085cad9d07.jpg\",\n  \"268521.jpg\": \"Aves/Ramphastos sulfuratus/57f04529f20ae1f4436d756fbe3afb84.jpg\",\n  \"268522.jpg\": \"Aves/Ramphastos sulfuratus/2808008395ae024cf86b202d876b0efb.jpg\",\n  \"268526.jpg\": \"Aves/Ramphastos sulfuratus/04435a9799925c268a523c65f5d8f94b.jpg\",\n  \"268527.jpg\": \"Aves/Ramphastos sulfuratus/0eccb324df1e2449c81853ed555cad60.jpg\",\n  \"268528.jpg\": \"Aves/Ramphastos sulfuratus/0dddf7ada7958e562a75ed1e5a0a0f9d.jpg\",\n  \"268529.jpg\": \"Aves/Ramphastos sulfuratus/ea3c36662d6d931e83e16a04e88ad197.jpg\",\n  \"268530.jpg\": \"Aves/Ramphastos sulfuratus/4076ac37e02e95bb3516b347d2bfdf88.jpg\",\n  \"268531.jpg\": \"Aves/Ramphastos sulfuratus/26bc7033b92ad66666cec1457c82f5a7.jpg\",\n  \"268533.jpg\": \"Aves/Ramphastos sulfuratus/aaa4c8dd061a7b5f3b26fe450686588f.jpg\",\n  \"268535.jpg\": \"Aves/Ramphastos sulfuratus/10f9df8ebdf1da286861c341a5d7e520.jpg\",\n  \"268541.jpg\": \"Aves/Ramphastos sulfuratus/59ae0c949a9f2c479fc0203f65c614aa.jpg\",\n  \"268542.jpg\": \"Aves/Ramphastos sulfuratus/3ff155efc25f8ba63e6e1b50c24c6aaf.jpg\",\n  \"268543.jpg\": \"Aves/Ramphastos sulfuratus/fb536e1720e26bcd8ecd24856ddfc73d.jpg\",\n  \"268544.jpg\": \"Aves/Ramphastos sulfuratus/f9f7da4ce58d44c7b4bea20b7b6fdc7b.jpg\",\n  \"268547.jpg\": \"Aves/Ramphastos sulfuratus/5c72e1b73edb6c1f1981bc1d1eb62d79.jpg\",\n  \"268548.jpg\": \"Aves/Ramphastos sulfuratus/1162751750bbbc0880a1193dfb170cac.jpg\",\n  \"268549.jpg\": \"Aves/Ramphastos sulfuratus/2a6b080b96bc586950cfde4cd0f748f0.jpg\",\n  \"268551.jpg\": \"Aves/Ramphastos sulfuratus/621c27fe6aa038ece2ae1f115d8b796c.jpg\",\n  \"268557.jpg\": \"Aves/Ramphastos sulfuratus/29c68ac06f73dc4890b8c4eb760641f0.jpg\",\n  \"268558.jpg\": \"Aves/Ramphastos sulfuratus/01464a4b0400502ebcddfad8b7952f18.jpg\",\n  \"26856.jpg\": \"Aves/Buteo swainsoni/4a332e0f4e3d6abc6fd48b4793706900.jpg\",\n  \"26861.jpg\": \"Aves/Buteo swainsoni/a942f1455bd7b13aa1c90cf892652b01.jpg\",\n  \"268726.jpg\": \"Insecta/Libellula pulchella/b773d077a67b955a5ec24d845da06241.jpg\",\n  \"268733.jpg\": \"Insecta/Libellula pulchella/c1d738727bc054b80aadce4f868b629b.jpg\",\n  \"268739.jpg\": \"Insecta/Libellula pulchella/ebd79e2796b3b5b35186353a5a4fb87f.jpg\",\n  \"268744.jpg\": \"Insecta/Libellula pulchella/29113591db92dabd6950a31c495c6349.jpg\",\n  \"268765.jpg\": \"Insecta/Libellula pulchella/5114bd92bd19d927948201736c3da7b2.jpg\",\n  \"268774.jpg\": \"Insecta/Libellula pulchella/63d951c24f61f33ba2eaa0c3b57e4865.jpg\",\n  \"26878.jpg\": \"Aves/Buteo swainsoni/aa645906c677be52159ac3172f79bc16.jpg\",\n  \"26879.jpg\": \"Aves/Buteo swainsoni/fe94b1ffbdca5ea6a8bc55c6eed75a0a.jpg\",\n  \"268798.jpg\": \"Insecta/Libellula pulchella/0ee192849ff43430827d7ecb65e490b4.jpg\",\n  \"26880.jpg\": \"Aves/Buteo swainsoni/9fb475e57d62b9baa932bb3a3edab12c.jpg\",\n  \"268801.jpg\": \"Insecta/Libellula pulchella/85e1016a9ecfebce93e2a25a5bef59f9.jpg\",\n  \"268814.jpg\": \"Insecta/Libellula pulchella/428e4e48a9ba651bb97206ece4a14e3f.jpg\",\n  \"268828.jpg\": \"Insecta/Libellula pulchella/5c822959ee6364c79094ebf0cc52efb0.jpg\",\n  \"268837.jpg\": \"Insecta/Libellula pulchella/46cc4b3baf44ca62e4ccc727bf72dc54.jpg\",\n  \"268847.jpg\": \"Insecta/Libellula pulchella/9780480eee750d300a302d7233f556cc.jpg\",\n  \"26885.jpg\": \"Aves/Buteo swainsoni/958b6479b2a5808ff51e26cb03f71c87.jpg\",\n  \"26886.jpg\": \"Aves/Buteo swainsoni/28a58188b2c1283480f7d7aa83fd9218.jpg\",\n  \"268861.jpg\": \"Insecta/Libellula pulchella/d8c31d19f4cfb9b185da483553b5e16a.jpg\",\n  \"268864.jpg\": \"Insecta/Libellula pulchella/87d8b66c6b9815dacf47e97ab48e6f90.jpg\",\n  \"268872.jpg\": \"Insecta/Libellula pulchella/cd497c200f5a9b35dfeb609e124d88e2.jpg\",\n  \"268886.jpg\": \"Insecta/Libellula pulchella/5a07a189c08f077125f90fd267e34ec1.jpg\",\n  \"268888.jpg\": \"Insecta/Libellula pulchella/0d2f242173f287b9a49a7ddaf99057c6.jpg\",\n  \"268894.jpg\": \"Insecta/Libellula pulchella/cb46018791b2d57eaa353d07acd231da.jpg\",\n  \"268895.jpg\": \"Insecta/Libellula pulchella/b6147be6847eb43eb97097d153766466.jpg\",\n  \"268905.jpg\": \"Insecta/Libellula pulchella/7a71297648492aeeb6b67b37747f3366.jpg\",\n  \"268912.jpg\": \"Insecta/Libellula pulchella/3b11b5a2b8df422924b57ac8cffd2824.jpg\",\n  \"268915.jpg\": \"Insecta/Libellula pulchella/9f8c07f0a8e38dec7bfd2428759cfde6.jpg\",\n  \"268917.jpg\": \"Insecta/Libellula pulchella/ac5c06a7de544eb75c1cd90308f18651.jpg\",\n  \"268928.jpg\": \"Insecta/Libellula pulchella/eae943910bfffcf6152726bda48b3a66.jpg\",\n  \"268938.jpg\": \"Insecta/Nicrophorus orbicollis/73d03d774b1fb86a97dd221ee70566d1.jpg\",\n  \"268939.jpg\": \"Insecta/Nicrophorus orbicollis/9b09691d4c434e31cd72e7cb5f458c5a.jpg\",\n  \"268940.jpg\": \"Insecta/Nicrophorus orbicollis/410f37ef0e11db7b1946e37c58a7b414.jpg\",\n  \"268941.jpg\": \"Insecta/Nicrophorus orbicollis/2851a8edb4074005b1910197cf137cca.jpg\",\n  \"268950.jpg\": \"Amphibia/Incilius nebulifer/a794b2e4373df7a82e2967f9ddbe6782.jpg\",\n  \"268957.jpg\": \"Amphibia/Incilius nebulifer/c64a0763128cc026968b35bce2fc31a2.jpg\",\n  \"268967.jpg\": \"Amphibia/Incilius nebulifer/3523daebbe3255ade3147f45da25ff10.jpg\",\n  \"26897.jpg\": \"Aves/Buteo swainsoni/fdd5a3640d87ca94494311f25cb045d8.jpg\",\n  \"26901.jpg\": \"Aves/Buteo swainsoni/18cbf4163b619e54b16631a8c80bb7ad.jpg\",\n  \"269022.jpg\": \"Amphibia/Incilius nebulifer/641b12ff0d31df212d120d746afe53a8.jpg\",\n  \"269023.jpg\": \"Amphibia/Incilius nebulifer/2055ab7c7219411e715a6f88aa4b22da.jpg\",\n  \"269034.jpg\": \"Amphibia/Incilius nebulifer/d779f62ebd6bc40f55e790b7876f9fec.jpg\",\n  \"269058.jpg\": \"Amphibia/Incilius nebulifer/32aeb484b651ecb0d9ea053faa631975.jpg\",\n  \"26907.jpg\": \"Aves/Buteo swainsoni/f555c6ac93d1cf54e47b4e114490f1c7.jpg\",\n  \"269070.jpg\": \"Amphibia/Incilius nebulifer/4caf9c483ef3f831078289ea61f2e4f2.jpg\",\n  \"269078.jpg\": \"Amphibia/Incilius nebulifer/7b8117c89cbc6101c5a29d050e22cdef.jpg\",\n  \"269126.jpg\": \"Amphibia/Incilius nebulifer/b26c836033a055e9da338886f45e222d.jpg\",\n  \"269137.jpg\": \"Amphibia/Incilius nebulifer/ba82a443cf826de7249571cb86be274f.jpg\",\n  \"269153.jpg\": \"Amphibia/Incilius nebulifer/54b55511d1c418e53e8e96a32a5c8b48.jpg\",\n  \"269154.jpg\": \"Amphibia/Incilius nebulifer/e603a8626a5635ee314c12fa5db6ec36.jpg\",\n  \"26916.jpg\": \"Aves/Buteo swainsoni/adf4c22e221358cd29534cfd4cf9726b.jpg\",\n  \"26918.jpg\": \"Aves/Buteo swainsoni/8caf71c69626e1b453e9c7aa32cf2f29.jpg\",\n  \"269190.jpg\": \"Amphibia/Incilius nebulifer/257c72bced119ad9d6b6e1b7fa74f5c4.jpg\",\n  \"269191.jpg\": \"Amphibia/Incilius nebulifer/8f6ca67eb4581ec980d58eb436e2646f.jpg\",\n  \"269239.jpg\": \"Amphibia/Incilius nebulifer/e1bc6b86847334c9ecbbfaf1e724445e.jpg\",\n  \"26926.jpg\": \"Aves/Buteo swainsoni/221b0dfec28fe8bcf4247740b22ff3a6.jpg\",\n  \"26927.jpg\": \"Aves/Buteo swainsoni/28d4de3b6a4a58118150e467a74bad4e.jpg\",\n  \"269300.jpg\": \"Amphibia/Incilius nebulifer/8645bd02f2ed45a1f835f8a2dc349407.jpg\",\n  \"269307.jpg\": \"Amphibia/Incilius nebulifer/825cdf6b801a82d5987d16e95e8d1b64.jpg\",\n  \"26931.jpg\": \"Aves/Buteo swainsoni/c7323e48348d11633fa6b5037b3cd44b.jpg\",\n  \"269310.jpg\": \"Amphibia/Incilius nebulifer/0996ac78a1adf6b9af014744b0414e5c.jpg\",\n  \"269316.jpg\": \"Amphibia/Incilius nebulifer/2eb48d19ca116038132dc293fea010eb.jpg\",\n  \"269323.jpg\": \"Amphibia/Incilius nebulifer/2dfc887dcde6457c1589b33d7247f44e.jpg\",\n  \"269334.jpg\": \"Amphibia/Incilius nebulifer/67c065a09bb3eb4321ab5b1fab72fcbc.jpg\",\n  \"269353.jpg\": \"Amphibia/Incilius nebulifer/2d1e71cdb065dd6aa75b3787e41a8771.jpg\",\n  \"26938.jpg\": \"Aves/Buteo swainsoni/1cf1f7cf2a2be0db81ca140b45d55bc8.jpg\",\n  \"269386.jpg\": \"Amphibia/Incilius nebulifer/f851066603c4a9efa8c6a26bd88e240b.jpg\",\n  \"269407.jpg\": \"Amphibia/Incilius nebulifer/ce7f4b6601977d89bdb86e23d6fd1c9b.jpg\",\n  \"269438.jpg\": \"Amphibia/Incilius nebulifer/1979e57cfc1548a91fed4ab056d90cb3.jpg\",\n  \"269446.jpg\": \"Amphibia/Incilius nebulifer/a0ea1381ad33f6f629181394fe1598ed.jpg\",\n  \"269495.jpg\": \"Amphibia/Incilius nebulifer/affcb0a02b5428f11f5bd196607bb84f.jpg\",\n  \"269499.jpg\": \"Insecta/Sympetrum danae/c7595bed23163164715354344a319ad3.jpg\",\n  \"269500.jpg\": \"Insecta/Sympetrum danae/84328421215a6fe12cc2a7a203c7d0b0.jpg\",\n  \"269501.jpg\": \"Insecta/Sympetrum danae/076e98266c8e20bc337812350c1c7007.jpg\",\n  \"269503.jpg\": \"Insecta/Sympetrum danae/9cc0b6e2ae1a36c5f6f236bdb658da99.jpg\",\n  \"269506.jpg\": \"Insecta/Sympetrum danae/4ccfc24a5ea95bf16767d95b6de24a00.jpg\",\n  \"269507.jpg\": \"Insecta/Sympetrum danae/4a42345f95d844b4a93272b3be2bca11.jpg\",\n  \"269509.jpg\": \"Insecta/Sympetrum danae/80f0dfab5f39a4c4a83c74ab7943ff38.jpg\",\n  \"269510.jpg\": \"Insecta/Sympetrum danae/7c268fa74f8c7aaf0836031c100a87a5.jpg\",\n  \"269511.jpg\": \"Insecta/Sympetrum danae/a50a9b3b9ea99278b26576ebedf17ec2.jpg\",\n  \"269514.jpg\": \"Insecta/Sympetrum danae/2187c9ed82d8b610164c4147779cad2d.jpg\",\n  \"269516.jpg\": \"Insecta/Sympetrum danae/98aa270dce80c5962e910b62d492c9d6.jpg\",\n  \"269517.jpg\": \"Insecta/Sympetrum danae/207a28fdfdabe9aba4d698b3264af0bd.jpg\",\n  \"269518.jpg\": \"Insecta/Sympetrum danae/52b00a66d1e7812f0ea7d2ce6ead6abc.jpg\",\n  \"269519.jpg\": \"Insecta/Sympetrum danae/f677b0c13216fbad744624998f9ad9c1.jpg\",\n  \"269520.jpg\": \"Insecta/Sympetrum danae/94f28b9791cd14fe1837ee705d9b2050.jpg\",\n  \"269521.jpg\": \"Insecta/Sympetrum danae/b110a31c0fdabdc95b35bc46aa0b27f6.jpg\",\n  \"269522.jpg\": \"Insecta/Sympetrum danae/319e3bd7a455d325ef0851539ddc22b2.jpg\",\n  \"269526.jpg\": \"Insecta/Sympetrum danae/f1fcc8192d624f045e69315a4222d9f8.jpg\",\n  \"269527.jpg\": \"Insecta/Sympetrum danae/db8b194c64fcd2b57d5ad3a0af0ec4f3.jpg\",\n  \"269528.jpg\": \"Insecta/Sympetrum danae/764dfe60b994127053eb1727fc2cf449.jpg\",\n  \"269530.jpg\": \"Insecta/Sympetrum danae/e7953063cb6c205a65329ab96ad85666.jpg\",\n  \"269531.jpg\": \"Insecta/Sympetrum danae/8ab6de4107714754eca3f3048d08e54b.jpg\",\n  \"269532.jpg\": \"Insecta/Sympetrum danae/5d23369f284f30d37edd65f2e64bd84a.jpg\",\n  \"269533.jpg\": \"Insecta/Sympetrum danae/b84acd775e983b239e66e429c3de25b1.jpg\",\n  \"269534.jpg\": \"Insecta/Sympetrum danae/8333f52398cd2ff69aa2f01cc142ac57.jpg\",\n  \"269535.jpg\": \"Insecta/Sympetrum danae/248d2aa596e0583e9b1a6862130f092c.jpg\",\n  \"269536.jpg\": \"Insecta/Sympetrum danae/374717b060557d7492c93c408c4adf89.jpg\",\n  \"269601.jpg\": \"Insecta/Emmelina monodactyla/1a31c78b442cb0c03c87ecfb7952b3ad.jpg\",\n  \"269602.jpg\": \"Insecta/Emmelina monodactyla/eaa1f214216ec77d84e961b410a31a86.jpg\",\n  \"269603.jpg\": \"Insecta/Emmelina monodactyla/7ca2e51e53e5b9b58b5a2fa1438a9b35.jpg\",\n  \"269604.jpg\": \"Insecta/Emmelina monodactyla/0384d4d09669fccc4e1be5fd7dd82f3a.jpg\",\n  \"269606.jpg\": \"Insecta/Emmelina monodactyla/10b93d87a4acccc83f77299cfcfbf9ae.jpg\",\n  \"269609.jpg\": \"Insecta/Emmelina monodactyla/f5af7c0861096c55818938d43179c3b5.jpg\",\n  \"269613.jpg\": \"Insecta/Emmelina monodactyla/ccbddfc0c43c387bef9057e73a310ac4.jpg\",\n  \"269614.jpg\": \"Insecta/Emmelina monodactyla/3c2ebc389a953cf228ea186b030f55a0.jpg\",\n  \"269619.jpg\": \"Insecta/Emmelina monodactyla/5728b1dc784b60df993dda0821b3880f.jpg\",\n  \"269626.jpg\": \"Insecta/Emmelina monodactyla/7e20b62989771763226e4bb80cba7633.jpg\",\n  \"269630.jpg\": \"Insecta/Emmelina monodactyla/78b1baa70a61b252f474c4ad582e189d.jpg\",\n  \"269632.jpg\": \"Insecta/Emmelina monodactyla/d3ac532807be45cf99a933fbfc924de3.jpg\",\n  \"269646.jpg\": \"Insecta/Ocypus olens/6d6eae309c39fcaf2c595eaddb8cf61f.jpg\",\n  \"269649.jpg\": \"Insecta/Ocypus olens/7c35429d1201b358e1c60f58861e4b1f.jpg\",\n  \"269650.jpg\": \"Insecta/Ocypus olens/4870ede7fbeb48e60e3a2c6eae6b1cb8.jpg\",\n  \"269651.jpg\": \"Insecta/Ocypus olens/4b2125133d3e0ade6b56e418f206ace5.jpg\",\n  \"269656.jpg\": \"Insecta/Ocypus olens/fbe14ea6d12ebb9aabfbe128d8b741cc.jpg\",\n  \"26970.jpg\": \"Aves/Buteo swainsoni/4b5cb765fac4b3bc0df4e7d5eac45217.jpg\",\n  \"26971.jpg\": \"Aves/Buteo swainsoni/9ba12b3381e5fc95f67c7beb3083b248.jpg\",\n  \"269810.jpg\": \"Insecta/Platycnemis pennipes/918933597a703a734b12c9d9ca4188f4.jpg\",\n  \"269821.jpg\": \"Insecta/Platycnemis pennipes/4c7bbe58ec82d30d27bf64a7c85f843d.jpg\",\n  \"269830.jpg\": \"Insecta/Platycnemis pennipes/38d928b5be496fdfdf75b3f752d7ff26.jpg\",\n  \"269834.jpg\": \"Insecta/Platycnemis pennipes/d38070d29cef51e1e8169fcbaaefb166.jpg\",\n  \"269839.jpg\": \"Aves/Larus michahellis/b4fe1866685b6dbf670eb19fe3f148b2.jpg\",\n  \"269840.jpg\": \"Aves/Larus michahellis/778eab3d4036311cac7af75247e7c739.jpg\",\n  \"269844.jpg\": \"Aves/Larus michahellis/7fb89f7cd2abdd47f9abe008007cb232.jpg\",\n  \"26985.jpg\": \"Aves/Buteo swainsoni/070176256a59f2563f1a05bcbbb74d39.jpg\",\n  \"269855.jpg\": \"Aves/Larus michahellis/034fef3bed6abee09cd09baf4d2451bc.jpg\",\n  \"269860.jpg\": \"Aves/Larus michahellis/8749966f0ec512663afaa226e4920c99.jpg\",\n  \"269861.jpg\": \"Aves/Larus michahellis/ca1692ec97ed5d1455d2f41c84a00cda.jpg\",\n  \"269864.jpg\": \"Aves/Larus michahellis/5316cbc8e726df42dd24361b58815837.jpg\",\n  \"269880.jpg\": \"Aves/Larus michahellis/bb7cc02445d459cd25ba60a4ea0ac7a7.jpg\",\n  \"269882.jpg\": \"Aves/Larus michahellis/de09aace377850d9033f98062c81e6f2.jpg\",\n  \"269883.jpg\": \"Aves/Larus michahellis/1b0892d11469cf42421560c216aced78.jpg\",\n  \"269887.jpg\": \"Aves/Larus michahellis/60cc56790f0d64d64dec08b199decff0.jpg\",\n  \"269891.jpg\": \"Aves/Larus michahellis/c799b92f141d2796a221fc27d158d8dd.jpg\",\n  \"269892.jpg\": \"Aves/Larus michahellis/524270df1ac6bfc630094422850d2b67.jpg\",\n  \"269894.jpg\": \"Aves/Larus michahellis/3bf031141295bb3e58bf11e78f3fba9f.jpg\",\n  \"270139.jpg\": \"Mammalia/Tragelaphus strepsiceros/0703e3b9ebdc1db7d0132f898e256ce9.jpg\",\n  \"270143.jpg\": \"Mammalia/Tragelaphus strepsiceros/660b2ea0c78d4f144ab0162e1f926429.jpg\",\n  \"270147.jpg\": \"Mammalia/Tragelaphus strepsiceros/4ec6b39ca62e39db4a1190415571ca47.jpg\",\n  \"270151.jpg\": \"Mammalia/Tragelaphus strepsiceros/177efc9b319b4c4743ce09f572fe78ba.jpg\",\n  \"270160.jpg\": \"Aves/Hymenolaimus malacorhynchos/7c52027248833376a104df041af5ddae.jpg\",\n  \"270168.jpg\": \"Aves/Hymenolaimus malacorhynchos/66e43278b6057f5c6bafe899342b6c2f.jpg\",\n  \"270174.jpg\": \"Aves/Hymenolaimus malacorhynchos/5f7774cc604a8c21dc1026b3d265e680.jpg\",\n  \"270188.jpg\": \"Actinopterygii/Ostracion meleagris/00beadd5459cc0b3318df3da006345b7.jpg\",\n  \"270194.jpg\": \"Actinopterygii/Ostracion meleagris/053b71f835ef748328a33bc9945ec056.jpg\",\n  \"270206.jpg\": \"Actinopterygii/Ostracion meleagris/5e9d6e93c28a48f97c9864ebae3a00d2.jpg\",\n  \"270213.jpg\": \"Actinopterygii/Ostracion meleagris/7461e99bca4236204f19df084b4c7a41.jpg\",\n  \"270220.jpg\": \"Actinopterygii/Ostracion meleagris/9a94572133a4e81dcde58ced3c1dbde3.jpg\",\n  \"27026.jpg\": \"Aves/Buteo swainsoni/50186a0a3cf71284a27fd88780dd55e6.jpg\",\n  \"270297.jpg\": \"Reptilia/Varanus varius/bef16a6e177095dab000f9c01fe57809.jpg\",\n  \"270305.jpg\": \"Reptilia/Varanus varius/8eb946ecfdd781f01582e58715b3dbb9.jpg\",\n  \"270307.jpg\": \"Reptilia/Varanus varius/b8a44146e0225096468e7412d8e59a0b.jpg\",\n  \"270330.jpg\": \"Insecta/Chortophaga viridifasciata/01c572243224dd06cac8fc819759c396.jpg\",\n  \"270333.jpg\": \"Insecta/Chortophaga viridifasciata/d8f2660f3fa0c6e7334702fa316b6fd7.jpg\",\n  \"270334.jpg\": \"Insecta/Chortophaga viridifasciata/467a43e96b2e42816c6e92fef7e36a47.jpg\",\n  \"270335.jpg\": \"Insecta/Chortophaga viridifasciata/de27fa5a16724693ff35fcc491395475.jpg\",\n  \"270338.jpg\": \"Insecta/Chortophaga viridifasciata/343cf301a31f5c447f7cf078dccf9646.jpg\",\n  \"270346.jpg\": \"Insecta/Chortophaga viridifasciata/58f2252cbcbd6138123bcd62088867ea.jpg\",\n  \"270351.jpg\": \"Insecta/Chortophaga viridifasciata/0ad35b676752eda5f0ddebb8c57a0812.jpg\",\n  \"270355.jpg\": \"Insecta/Chortophaga viridifasciata/7e4a7e0bd05e4a5cb48e8e55d2e83fe8.jpg\",\n  \"270358.jpg\": \"Insecta/Chortophaga viridifasciata/880ab992d0af9924e6ca0f539af9302f.jpg\",\n  \"270370.jpg\": \"Aves/Sterna forsteri/5ac793a26c7a1bfca8a7cebd64dbbe51.jpg\",\n  \"270386.jpg\": \"Aves/Sterna forsteri/53f64f2f50916d1bf523d09f292e8501.jpg\",\n  \"27039.jpg\": \"Aves/Buteo swainsoni/a93f22d8de909ecd2d951f1d83483bb2.jpg\",\n  \"270403.jpg\": \"Aves/Sterna forsteri/253eeaf6a88ef3bca0c3e2742db41c70.jpg\",\n  \"270405.jpg\": \"Aves/Sterna forsteri/c75767ed516e7eff0fb62317d8284c25.jpg\",\n  \"270406.jpg\": \"Aves/Sterna forsteri/2d108f3b3cab4efef1c676356f8021e6.jpg\",\n  \"270416.jpg\": \"Aves/Sterna forsteri/bb9fd8acb6ac29f37cac3bf5201c048c.jpg\",\n  \"270427.jpg\": \"Aves/Sterna forsteri/1cb614e40038ba13bebc3de738d32b25.jpg\",\n  \"270429.jpg\": \"Aves/Sterna forsteri/7a31e9af48aab28a6381dc2a03dc916c.jpg\",\n  \"270457.jpg\": \"Aves/Sterna forsteri/d8b506228972efabc2067598c4baad32.jpg\",\n  \"27048.jpg\": \"Aves/Buteo swainsoni/e54eee5dc20da55feffb00e718ea009f.jpg\",\n  \"27049.jpg\": \"Aves/Buteo swainsoni/63b69979fd5c594b9279f4321e340700.jpg\",\n  \"270501.jpg\": \"Aves/Sterna forsteri/ef3c1f5e9006d03d0ec113c7404fb688.jpg\",\n  \"270510.jpg\": \"Aves/Sterna forsteri/a78e9e8c55198cc3bb2ae1870891e5b6.jpg\",\n  \"270527.jpg\": \"Aves/Sterna forsteri/c6faddec04ed0a0e77558f631b5f3279.jpg\",\n  \"270528.jpg\": \"Aves/Sterna forsteri/ab17bed3e52b5d34be5b3450361c09a2.jpg\",\n  \"270530.jpg\": \"Aves/Sterna forsteri/349a202d0dcc0a5f86c4fd3c470da4ea.jpg\",\n  \"270536.jpg\": \"Aves/Sterna forsteri/b5e0104897b889dcbdc8bbb7ad1a240c.jpg\",\n  \"270567.jpg\": \"Aves/Sterna forsteri/785f0dfcda585541f626aeac918d96ca.jpg\",\n  \"270578.jpg\": \"Aves/Sterna forsteri/ac736b28edfcea36583949f20fb77d7c.jpg\",\n  \"270580.jpg\": \"Aves/Sterna forsteri/63b5a24db9ed6afd1905edcae4497e92.jpg\",\n  \"270583.jpg\": \"Aves/Sterna forsteri/271763f926420f8b19881489e5a6cd9d.jpg\",\n  \"270584.jpg\": \"Aves/Sterna forsteri/f8c1a4b07c2131ace206c364b8b73fd5.jpg\",\n  \"27065.jpg\": \"Mammalia/Xerospermophilus spilosoma/b8ec5d3b2d3d6f7b52cd46d2e153a397.jpg\",\n  \"27069.jpg\": \"Mammalia/Xerospermophilus spilosoma/e762b94331f642993f888394c1e37de4.jpg\",\n  \"27074.jpg\": \"Mammalia/Xerospermophilus spilosoma/c08d0f783ede27c8b33cdea879a01405.jpg\",\n  \"27075.jpg\": \"Mammalia/Xerospermophilus spilosoma/a5403523c6931e28264ff7345120bfab.jpg\",\n  \"270823.jpg\": \"Amphibia/Desmognathus ochrophaeus/12f0be20637cfc9f4437971f7eeab68d.jpg\",\n  \"270824.jpg\": \"Amphibia/Desmognathus ochrophaeus/f830f86391c7a098f5d51f8e3e93fd2a.jpg\",\n  \"270836.jpg\": \"Aves/Limosa lapponica/a45de9b3eaf33fa100b0a27aeedeeccb.jpg\",\n  \"270838.jpg\": \"Aves/Limosa lapponica/dc00d9f85fa878afeb5ad1737e4d0ee0.jpg\",\n  \"270849.jpg\": \"Aves/Limosa lapponica/80c878f0a9e62356d8cf22c2c8a04f61.jpg\",\n  \"270857.jpg\": \"Aves/Limosa lapponica/09004fbe56435b8806f583f8dbe93151.jpg\",\n  \"270858.jpg\": \"Aves/Limosa lapponica/01c3c3431461f2cefaa16e37f69e1e3c.jpg\",\n  \"271034.jpg\": \"Aves/Coracias caudatus/ef523856a1a95d1eb2ae1e76a1ff38b3.jpg\",\n  \"271037.jpg\": \"Aves/Coracias caudatus/0527594e25be6e185420ae77855382cd.jpg\",\n  \"271038.jpg\": \"Aves/Coracias caudatus/7adf3c2340f34894683e7ee9973e2944.jpg\",\n  \"271046.jpg\": \"Aves/Coracias caudatus/94f3911c8324c8825cffeaec9d983c4f.jpg\",\n  \"271047.jpg\": \"Aves/Coracias caudatus/5bd4464c7fd2e6eb851a860ec8588a3d.jpg\",\n  \"271123.jpg\": \"Insecta/Calopteron reticulatum/342231aa777bc6e1ac364d98027bbf4d.jpg\",\n  \"271129.jpg\": \"Insecta/Calopteron reticulatum/d8047f9d1a39fa6da5af98f028a1e3da.jpg\",\n  \"271136.jpg\": \"Insecta/Calopteron reticulatum/b930d4ad41ac2ade14cace8129f47343.jpg\",\n  \"271140.jpg\": \"Insecta/Calopteron reticulatum/4aa3fa8cc0011466d5707eff6e760a81.jpg\",\n  \"271142.jpg\": \"Insecta/Calopteron reticulatum/11770e3f720f87246f0f90892bbac8f1.jpg\",\n  \"271144.jpg\": \"Insecta/Calopteron reticulatum/448ed80ac05cd34ed0f32269ee8f791e.jpg\",\n  \"271150.jpg\": \"Insecta/Calopteron reticulatum/6d8dce9c73f5fae3488296a05d9a1a65.jpg\",\n  \"271153.jpg\": \"Insecta/Calopteron reticulatum/3e99fb7a8a4dfe82ffcb6bd3da88e73a.jpg\",\n  \"271158.jpg\": \"Aves/Vireo griseus/2b6e142c4a466319515ed05823f58df9.jpg\",\n  \"271163.jpg\": \"Aves/Vireo griseus/197e65a0a2955d21ef45b386bd054904.jpg\",\n  \"271168.jpg\": \"Aves/Vireo griseus/12659de080824d177ea48a916a59edef.jpg\",\n  \"271175.jpg\": \"Aves/Vireo griseus/7c962355ecaf97bfc7f0d50f043a758e.jpg\",\n  \"271176.jpg\": \"Aves/Vireo griseus/9b336bfd9f7055ade26680d08368fa64.jpg\",\n  \"271178.jpg\": \"Aves/Vireo griseus/2ecf2afe0522ed5cbb658f5dcea1d1d7.jpg\",\n  \"271179.jpg\": \"Aves/Vireo griseus/7b1a51c6d64c11558177541bf2c52a3e.jpg\",\n  \"271180.jpg\": \"Aves/Vireo griseus/c8d5882f33bb0a3637b2301e54018fa0.jpg\",\n  \"271181.jpg\": \"Aves/Vireo griseus/eadc544181fa23e1e7e553ada2fae3a8.jpg\",\n  \"271182.jpg\": \"Aves/Vireo griseus/57c6fb658954c449cf96b5944f9b36ee.jpg\",\n  \"271184.jpg\": \"Aves/Vireo griseus/0dfd74f27a2fa394117ad616ea402f2d.jpg\",\n  \"271192.jpg\": \"Aves/Vireo griseus/2bca791f57763d39115ceb417971ac57.jpg\",\n  \"271200.jpg\": \"Aves/Vireo griseus/d85a82a608ab15c96a132b946ad16432.jpg\",\n  \"271208.jpg\": \"Aves/Vireo griseus/ffe3d8856868a22375e6b4924b7fd472.jpg\",\n  \"271210.jpg\": \"Aves/Vireo griseus/788f4d209599ffacafe05b132bb1affb.jpg\",\n  \"271211.jpg\": \"Aves/Vireo griseus/84fe79bd54db4af388af768b29540e17.jpg\",\n  \"271214.jpg\": \"Aves/Vireo griseus/4b1eb400fcc4dffa21c75a17cf0f7409.jpg\",\n  \"271215.jpg\": \"Aves/Vireo griseus/15bd011eaf4d1ac93598c0ce87ccb110.jpg\",\n  \"271229.jpg\": \"Aves/Vireo griseus/a61bf438fb4cabf3e68fae5a78dbca2e.jpg\",\n  \"271230.jpg\": \"Aves/Vireo griseus/ced7ee2896aea757066c254774f4fe6d.jpg\",\n  \"271234.jpg\": \"Aves/Vireo griseus/a473d377631ceed157bef38cb274d1ae.jpg\",\n  \"271237.jpg\": \"Aves/Vireo griseus/db49bd95443659b6529dc6acfdd1b4c0.jpg\",\n  \"271238.jpg\": \"Aves/Vireo griseus/a0080ebd1b9d0a084eb864a30804a9cb.jpg\",\n  \"271249.jpg\": \"Aves/Vireo griseus/af75e4d3227679f264010d334580105c.jpg\",\n  \"271264.jpg\": \"Reptilia/Nerodia fasciata/acec5bd2eb5adc80fffa7c6685e1227e.jpg\",\n  \"271266.jpg\": \"Reptilia/Nerodia fasciata/a91bbe59396c7c46d1c9328f8936c32c.jpg\",\n  \"271269.jpg\": \"Reptilia/Nerodia fasciata/c28fe68e83566ed78bb6856d13cf29a0.jpg\",\n  \"271278.jpg\": \"Reptilia/Nerodia fasciata/695e4132dc4735ba5608aae2857821c6.jpg\",\n  \"271283.jpg\": \"Reptilia/Nerodia fasciata/5183f390fcd2aea54d43a89004326cc2.jpg\",\n  \"271284.jpg\": \"Reptilia/Nerodia fasciata/09370f3aef27943401ff3d569b15558e.jpg\",\n  \"271287.jpg\": \"Reptilia/Nerodia fasciata/b5cb9b2d3e7231f558ae92552b82bbf9.jpg\",\n  \"271304.jpg\": \"Aves/Limosa limosa/ee0932815ccee74453fed10a44329767.jpg\",\n  \"271316.jpg\": \"Aves/Limosa limosa/83c9825bc1130b4a9754499e85ec59a8.jpg\",\n  \"271464.jpg\": \"Aves/Quiscalus major/d1f1a552d903f18bfe1f0c2f9ddb253e.jpg\",\n  \"271467.jpg\": \"Aves/Quiscalus major/b37fe14b5e182cb935c1e7a2a8c5b485.jpg\",\n  \"271474.jpg\": \"Aves/Quiscalus major/8aed4eef1ecc0ed68f92548569d9e293.jpg\",\n  \"271480.jpg\": \"Aves/Quiscalus major/713687adf4eea7fb6f34591a037c9325.jpg\",\n  \"271501.jpg\": \"Aves/Quiscalus major/3452a3631489dcb391f5e0ab7d653962.jpg\",\n  \"271506.jpg\": \"Aves/Quiscalus major/2682c7ce44d21d88db1cec19639b18b7.jpg\",\n  \"271513.jpg\": \"Aves/Quiscalus major/f9ad319935c3c8f9d6d6f4221a808a53.jpg\",\n  \"271514.jpg\": \"Aves/Quiscalus major/39838f9af15ee41f3c6ea56c8159ce58.jpg\",\n  \"271515.jpg\": \"Aves/Quiscalus major/cb64e7c93c680d0f556f1b03c5c7e0af.jpg\",\n  \"271517.jpg\": \"Aves/Quiscalus major/7f6b3dacdbb324112493f9d3bc575f3f.jpg\",\n  \"271531.jpg\": \"Aves/Quiscalus major/d8094ad6fc6758e64215160238c645e7.jpg\",\n  \"271539.jpg\": \"Aves/Quiscalus major/aa4cb24d06d11da6960935e681b40a1c.jpg\",\n  \"271546.jpg\": \"Aves/Quiscalus major/c202deb8581d3268fe84fb9efbc98c01.jpg\",\n  \"271547.jpg\": \"Aves/Quiscalus major/2fd3c23d0ee313671f0a90395d0e6657.jpg\",\n  \"271549.jpg\": \"Aves/Quiscalus major/48f052f35f77e6776aeade5d2c52e20e.jpg\",\n  \"271562.jpg\": \"Aves/Quiscalus major/1c9b675ae07c58ad0410edac39a5f0b1.jpg\",\n  \"271565.jpg\": \"Aves/Quiscalus major/1c33f1627fd2057027427e4d9264ec69.jpg\",\n  \"271566.jpg\": \"Aves/Quiscalus major/0028a75758e9ce7f9747b0ae561dd4ca.jpg\",\n  \"271567.jpg\": \"Aves/Quiscalus major/f3e6c1febd28ae7d0fd6b6decfe4c363.jpg\",\n  \"271568.jpg\": \"Aves/Quiscalus major/bc2e7ccfef2d3a2b2f2c8f070fc2c207.jpg\",\n  \"271572.jpg\": \"Aves/Sula nebouxii/0f42b12fb1385002bdb8eae134c8929e.jpg\",\n  \"271573.jpg\": \"Aves/Sula nebouxii/977406cbb7070cf9863e09491c70ed9d.jpg\",\n  \"271587.jpg\": \"Aves/Sula nebouxii/b8a1b2a2a75ec0d0028034ba37d650b1.jpg\",\n  \"271588.jpg\": \"Aves/Sula nebouxii/04337bcc47f459dc3b60f146a0c6ee59.jpg\",\n  \"271591.jpg\": \"Aves/Sula nebouxii/a51e3508c352a468ac0bcd5702ca5b81.jpg\",\n  \"271599.jpg\": \"Aves/Sula nebouxii/b1ceb9d850d54c29bbda34066b34024f.jpg\",\n  \"271602.jpg\": \"Aves/Sula nebouxii/eec1acee80e48715d62a371fc1a9af95.jpg\",\n  \"271603.jpg\": \"Aves/Sula nebouxii/ae4717b9b3d4602d3aad25666e291ce1.jpg\",\n  \"271606.jpg\": \"Aves/Sula nebouxii/6b3690624778aadbcbdac41fe1ea9bc7.jpg\",\n  \"271607.jpg\": \"Aves/Sula nebouxii/b13aeae98f44a0d396afd40e65db9d0c.jpg\",\n  \"27161.jpg\": \"Insecta/Anisomorpha buprestoides/0f3fd955cbecfc9b3d3bd490a8b8287e.jpg\",\n  \"271614.jpg\": \"Aves/Sula nebouxii/3bd56e63bc476cb45e564a5fec80e4cf.jpg\",\n  \"271616.jpg\": \"Aves/Sula nebouxii/51ee897a8555defbde0eca8dd66b2250.jpg\",\n  \"271619.jpg\": \"Aves/Sula nebouxii/39308f2eaafb6f706d8c3070dee20a5f.jpg\",\n  \"271626.jpg\": \"Aves/Sula nebouxii/51fd347506536cb18b526dd9d3602db7.jpg\",\n  \"271629.jpg\": \"Aves/Sula nebouxii/8640f7bd1d2a232ed0109264ae66bd50.jpg\",\n  \"27163.jpg\": \"Insecta/Anisomorpha buprestoides/579c0d32e971944b233d158ff827de59.jpg\",\n  \"271630.jpg\": \"Aves/Sula nebouxii/3bccaecb97e4fcc37ebaf36b707f8459.jpg\",\n  \"27165.jpg\": \"Insecta/Anisomorpha buprestoides/34443dad8c7f65e05400ef9ba3e50229.jpg\",\n  \"27172.jpg\": \"Insecta/Anisomorpha buprestoides/3e360f0265d04688df665da74c74d03d.jpg\",\n  \"27173.jpg\": \"Insecta/Anisomorpha buprestoides/5c00053e46534e1b644572cf2a094f77.jpg\",\n  \"27181.jpg\": \"Insecta/Anisomorpha buprestoides/5ca4a0c7b028735bc472a645b2150168.jpg\",\n  \"271824.jpg\": \"Aves/Egretta garzetta/125ec92c8c94be3adbb2aa6c8708a98b.jpg\",\n  \"271846.jpg\": \"Aves/Egretta garzetta/4d76eb44b688e980ed9601ea3119b07d.jpg\",\n  \"27185.jpg\": \"Insecta/Nemoria bistriaria/8426940c08f1f4ac8fdc1f861b7dcf59.jpg\",\n  \"271854.jpg\": \"Aves/Egretta garzetta/7b2dac83f630fddd822a44186afc615e.jpg\",\n  \"271865.jpg\": \"Aves/Egretta garzetta/86c8a59ec7bdf39574e8c1a1225068d1.jpg\",\n  \"271880.jpg\": \"Aves/Egretta garzetta/2419bec8df6fd4584c7b96401f22eb9b.jpg\",\n  \"271881.jpg\": \"Aves/Egretta garzetta/231ecd555b3107bc226b607ff3b8d498.jpg\",\n  \"27189.jpg\": \"Insecta/Nemoria bistriaria/b8dd8c56b34f47351dc44eab42de2b30.jpg\",\n  \"271890.jpg\": \"Aves/Egretta garzetta/410093dc765ebc61d9d491485b5da74d.jpg\",\n  \"271897.jpg\": \"Aves/Egretta garzetta/a39a8960b69f5d2aa1e7a0fb1420a8cf.jpg\",\n  \"271899.jpg\": \"Aves/Egretta garzetta/6a52e8f99902412ec4a59501d612a07c.jpg\",\n  \"271904.jpg\": \"Aves/Egretta garzetta/c7aa9f066132ee752fa944f9f95b33fd.jpg\",\n  \"271906.jpg\": \"Aves/Egretta garzetta/ec601285f45f81f8fe7b6752d61dc1c3.jpg\",\n  \"271909.jpg\": \"Aves/Egretta garzetta/db01e05891f9bc141a81419b6d8a16fe.jpg\",\n  \"27191.jpg\": \"Insecta/Nemoria bistriaria/a1ba3eaa0f258706aa248b26d22a5b4e.jpg\",\n  \"271916.jpg\": \"Aves/Egretta garzetta/a1f8599b3a903428e0a94915ad83cab0.jpg\",\n  \"271917.jpg\": \"Aves/Egretta garzetta/689d0cc9087888003a74d419e1723be5.jpg\",\n  \"271921.jpg\": \"Aves/Egretta garzetta/ab170009e54c484895fc7ff4e7268a26.jpg\",\n  \"271932.jpg\": \"Aves/Egretta garzetta/70218f6fef9d44c2275ec3aeb09be098.jpg\",\n  \"271938.jpg\": \"Aves/Egretta garzetta/db7bef8761d780ce5f5b87d167638308.jpg\",\n  \"27197.jpg\": \"Insecta/Nemoria bistriaria/2b69a127ad85fc72484bdb6a46649a3d.jpg\",\n  \"2720.jpg\": \"Aves/Picoides pubescens/994aefba26c4ab1c2920a4e2b802530e.jpg\",\n  \"27202.jpg\": \"Insecta/Nemoria bistriaria/2f0d33cf3063b82ba68cdba79c242679.jpg\",\n  \"272118.jpg\": \"Aves/Geothlypis formosa/13929cee22208681141d9cb1da01fc37.jpg\",\n  \"272127.jpg\": \"Aves/Geothlypis formosa/f5e76dfc23f758ccbfc537a86ab0f825.jpg\",\n  \"272128.jpg\": \"Aves/Geothlypis formosa/56140426525da7e40d8edd95109e6930.jpg\",\n  \"272131.jpg\": \"Aves/Geothlypis philadelphia/ab8a0c9680cb7a20c0dd4c0e727103e2.jpg\",\n  \"272134.jpg\": \"Aves/Geothlypis philadelphia/7fcc3d7594f596a1afeeffac6bcd04fb.jpg\",\n  \"272135.jpg\": \"Aves/Geothlypis philadelphia/228e77bec741cb7bb2cbb6f0915460d5.jpg\",\n  \"272152.jpg\": \"Aves/Geothlypis philadelphia/bad9dbdfec6a3ee81a3426cc32cca944.jpg\",\n  \"272155.jpg\": \"Aves/Geothlypis philadelphia/7ffb2963492b620f89c09d49e024956c.jpg\",\n  \"272160.jpg\": \"Aves/Geothlypis philadelphia/db1fdb72c17e2e45f3d150a73fda7565.jpg\",\n  \"27223.jpg\": \"Actinopterygii/Gambusia affinis/6886d98bb55c2e9ecb61722c9d673387.jpg\",\n  \"27225.jpg\": \"Actinopterygii/Gambusia affinis/bb008a55884fe6b8749e9c02d25918db.jpg\",\n  \"27227.jpg\": \"Actinopterygii/Gambusia affinis/15e57be8567ae2847f8a6f411d5575e1.jpg\",\n  \"27228.jpg\": \"Actinopterygii/Gambusia affinis/9e91eefa9b137821ae8d194a9101a548.jpg\",\n  \"272308.jpg\": \"Mollusca/Mopalia lignosa/70e230fe234db61b16e3249fbad7e853.jpg\",\n  \"27234.jpg\": \"Actinopterygii/Gambusia affinis/1cfacd60d883d6151d29d8cb04d361f0.jpg\",\n  \"272340.jpg\": \"Aves/Regulus calendula/b740e2cf90ab604568c02e0d64b612fe.jpg\",\n  \"272364.jpg\": \"Aves/Regulus calendula/b5d92d2a4885f1e961b8d3a0c805a6f3.jpg\",\n  \"272386.jpg\": \"Aves/Regulus calendula/0a90f2aeffbb5b3c8cee653406073f16.jpg\",\n  \"272407.jpg\": \"Aves/Regulus calendula/45846bed641a802a1632bf5317b7e4f8.jpg\",\n  \"27241.jpg\": \"Actinopterygii/Gambusia affinis/4e2eb2bca0be4eaf8cef7990bc0ae16d.jpg\",\n  \"272421.jpg\": \"Aves/Regulus calendula/a9f00c875c5179ed3a467b3936e1cde6.jpg\",\n  \"272534.jpg\": \"Aves/Regulus calendula/f271265e6dad81210c05093a221793c3.jpg\",\n  \"272541.jpg\": \"Aves/Regulus calendula/f4f39362b7542d542de148137c360e65.jpg\",\n  \"272545.jpg\": \"Aves/Regulus calendula/eedf7031ceacf19c1d4f640152116ba5.jpg\",\n  \"272573.jpg\": \"Aves/Regulus calendula/4a9ee84c578730efdf159a474e0427f1.jpg\",\n  \"272580.jpg\": \"Aves/Regulus calendula/4af9d88c411a1b7d8f49374946420d5f.jpg\",\n  \"272585.jpg\": \"Aves/Regulus calendula/d204a17eb960f4fdccabe79a43121410.jpg\",\n  \"272591.jpg\": \"Aves/Regulus calendula/de8a4c869eff784422c3a1767086a77a.jpg\",\n  \"272599.jpg\": \"Aves/Regulus calendula/054a73b0a662538c9ec5583fdcce8ee0.jpg\",\n  \"272603.jpg\": \"Aves/Regulus calendula/2e094b5feb96c7ba04688643e24af6a6.jpg\",\n  \"272623.jpg\": \"Aves/Regulus calendula/43bb6da3780d55b1e852c09e1bf454a1.jpg\",\n  \"27263.jpg\": \"Actinopterygii/Gambusia affinis/03caf156331d7bfe82d860d689159396.jpg\",\n  \"272645.jpg\": \"Aves/Regulus calendula/155f706b15c379b4aab43ab3616da970.jpg\",\n  \"272677.jpg\": \"Aves/Regulus calendula/f0c83337f1d3ba8cbeb86870b5e9bd75.jpg\",\n  \"272678.jpg\": \"Aves/Regulus calendula/1dc48bb78d58ca825b58382e32912769.jpg\",\n  \"27268.jpg\": \"Actinopterygii/Gambusia affinis/c4763459df950b416a133acd93ab6489.jpg\",\n  \"272686.jpg\": \"Aves/Regulus calendula/b89cda4e790fe3c0343206454109613f.jpg\",\n  \"272732.jpg\": \"Aves/Regulus calendula/c0d97316f0a578886c97f09af6e4ee65.jpg\",\n  \"272739.jpg\": \"Aves/Regulus calendula/3472ec984edab52f6eb52488d6022f2f.jpg\",\n  \"272756.jpg\": \"Aves/Regulus calendula/6baebb8a1bcdca2797f3573639382a26.jpg\",\n  \"272768.jpg\": \"Aves/Regulus calendula/6e464819e27a4f09baa8b8d304dde7f2.jpg\",\n  \"272774.jpg\": \"Aves/Regulus calendula/cf3c2102459ec60268d73d34cab2e5ee.jpg\",\n  \"272913.jpg\": \"Mammalia/Puma concolor/40062057cfe3c478d08b23d1e31725bc.jpg\",\n  \"272919.jpg\": \"Mammalia/Puma concolor/bb5e70031f26faa235175cc86db20754.jpg\",\n  \"272925.jpg\": \"Mammalia/Puma concolor/5081bcbb82f5220b497803d920ba977a.jpg\",\n  \"272929.jpg\": \"Mammalia/Puma concolor/c4c7e31a816fda71f06adba8cf1eeefc.jpg\",\n  \"272930.jpg\": \"Mammalia/Puma concolor/6e91f125a068ef203b591ad223039f86.jpg\",\n  \"272932.jpg\": \"Mammalia/Puma concolor/68ccf37071fe559dd13bbbcdb2456a61.jpg\",\n  \"272947.jpg\": \"Mammalia/Puma concolor/100f51f63c30abf9bb1475c153432496.jpg\",\n  \"272953.jpg\": \"Mammalia/Puma concolor/1a0f9fd998d6792c69ef48b3abf57c77.jpg\",\n  \"272962.jpg\": \"Mammalia/Puma concolor/5ffc6afa16263b20b9aea196a15ed545.jpg\",\n  \"272965.jpg\": \"Mammalia/Puma concolor/6d81748cdb8c8cb2cae89c2aae3d9ec0.jpg\",\n  \"272967.jpg\": \"Mammalia/Puma concolor/e935e4f4991ccf59998ff1918f826d3d.jpg\",\n  \"272968.jpg\": \"Mammalia/Puma concolor/ae236d700d359d945501cc037247e349.jpg\",\n  \"272969.jpg\": \"Mammalia/Puma concolor/a03707394d1f81819590a0e20ff71c61.jpg\",\n  \"273010.jpg\": \"Insecta/Erythemis collocata/46c83d8bd9f427e2498bf3620efc558c.jpg\",\n  \"273011.jpg\": \"Insecta/Erythemis collocata/bfa334523aaa621817442401a962baab.jpg\",\n  \"273016.jpg\": \"Insecta/Erythemis collocata/ebe0b775eee4ba5e54bff58a8316bbd0.jpg\",\n  \"273018.jpg\": \"Insecta/Erythemis collocata/180374d93a9b1d148c8fb49670acb9be.jpg\",\n  \"273021.jpg\": \"Insecta/Erythemis collocata/26fa68901bfe766bddccfc28f1fa5a3e.jpg\",\n  \"273025.jpg\": \"Insecta/Erythemis collocata/cb354d5eef70d860d4c893f4509c058e.jpg\",\n  \"273031.jpg\": \"Insecta/Erythemis collocata/37f651c47b52fe0eae0781e8a80954a1.jpg\",\n  \"273032.jpg\": \"Insecta/Erythemis collocata/c3cbd606f60f1b73dccfe786100c01f8.jpg\",\n  \"273034.jpg\": \"Insecta/Erythemis collocata/4710a483d953b4e68fd56406ff4a2296.jpg\",\n  \"273035.jpg\": \"Insecta/Erythemis collocata/3cf596e2c0f6ac3f6468bb5d5995abfb.jpg\",\n  \"273036.jpg\": \"Insecta/Erythemis collocata/f1544fdd43e1289ffcc8d21f5d333897.jpg\",\n  \"273037.jpg\": \"Insecta/Erythemis collocata/706ea676503c36b01d4d934eded677a0.jpg\",\n  \"273047.jpg\": \"Insecta/Erythemis collocata/6b16c09db155fa871c388adb432fcf78.jpg\",\n  \"273048.jpg\": \"Insecta/Erythemis collocata/2684f3708589f1541453df782259d384.jpg\",\n  \"273066.jpg\": \"Insecta/Erythemis collocata/9cf6d49faadf9da48add596b3c75131b.jpg\",\n  \"273070.jpg\": \"Insecta/Erythemis collocata/7754f4697dad5d9cd17bd6dfa293504e.jpg\",\n  \"273072.jpg\": \"Insecta/Erythemis collocata/f7624ed832749cd10b235420d217552a.jpg\",\n  \"273073.jpg\": \"Insecta/Erythemis collocata/24979b7254729ef853c65a4f64ab1626.jpg\",\n  \"273075.jpg\": \"Insecta/Erythemis collocata/641e33df5a8670e39fdeb7be4a7400af.jpg\",\n  \"273076.jpg\": \"Insecta/Erythemis collocata/aa3b76743622bdf8b17dfc4356c58c9a.jpg\",\n  \"273077.jpg\": \"Insecta/Erythemis collocata/2bcf140027832244414d581c82e0a3b9.jpg\",\n  \"273078.jpg\": \"Mollusca/Arion rufus/4f00e3e646c45b03164380e8eb410143.jpg\",\n  \"273079.jpg\": \"Mollusca/Arion rufus/2a8584e2930d260901f6c5547caa1182.jpg\",\n  \"273081.jpg\": \"Mollusca/Arion rufus/f8c5567dc834b8156763307b5b3c3b37.jpg\",\n  \"273082.jpg\": \"Mollusca/Arion rufus/07ffb43a4d7ccfc7bf67251f9f98c42e.jpg\",\n  \"273083.jpg\": \"Mollusca/Arion rufus/b598253d3a6604f777ba6c1313b2def8.jpg\",\n  \"273084.jpg\": \"Mollusca/Arion rufus/95f85718c24d36453beec79e53e08bd3.jpg\",\n  \"273085.jpg\": \"Mollusca/Arion rufus/22a71e564419fbcf69578d5f9844cee0.jpg\",\n  \"273086.jpg\": \"Mollusca/Arion rufus/d600ce0fb69a955b6b0e614c7fd6ecc3.jpg\",\n  \"273089.jpg\": \"Mollusca/Arion rufus/92c9ccd7334a28ab7f4256c9c78b6456.jpg\",\n  \"273090.jpg\": \"Mollusca/Arion rufus/745fff2f98ed2f31611c8b1a0416034c.jpg\",\n  \"273096.jpg\": \"Mollusca/Arion rufus/be328d77fdd5e3b7eeab45caeb0541b1.jpg\",\n  \"273102.jpg\": \"Mollusca/Arion rufus/868996a86d8fd6db47bb92479696a658.jpg\",\n  \"273104.jpg\": \"Mollusca/Arion rufus/fb18b21423e69f4a942c116925e6a2d8.jpg\",\n  \"273106.jpg\": \"Mollusca/Arion rufus/30ade5da3feda30b8d5e4557886c1cc6.jpg\",\n  \"273109.jpg\": \"Mollusca/Arion rufus/84c7623bafa8fc698759b6033f4fde9e.jpg\",\n  \"273110.jpg\": \"Mollusca/Arion rufus/1fd17f7e59ae586d4d799d6572c0f7f2.jpg\",\n  \"273111.jpg\": \"Mollusca/Arion rufus/6ec3979c8ea3b2d14f5b1ecd3ce5cd07.jpg\",\n  \"273112.jpg\": \"Mollusca/Arion rufus/f1d5b0509c6ae0d5559873d5ffc634e9.jpg\",\n  \"273113.jpg\": \"Mollusca/Arion rufus/7ea1cbd6dd994b83d74b94828b3ef331.jpg\",\n  \"273114.jpg\": \"Mollusca/Arion rufus/923315eaa3f57b0405005844cbdcc1ef.jpg\",\n  \"2732.jpg\": \"Aves/Picoides pubescens/adaaf653ee9e3c43c99ae5eefe9f8fda.jpg\",\n  \"273205.jpg\": \"Aves/Tyrannus tyrannus/a93b71e8ceaf5430cd9d65dc19da6c08.jpg\",\n  \"273208.jpg\": \"Aves/Tyrannus tyrannus/2e21cfccb82ce584dd4048e8d1041dd4.jpg\",\n  \"273210.jpg\": \"Aves/Tyrannus tyrannus/7f9c258797a00a4ccf2e9ec3916a994f.jpg\",\n  \"273232.jpg\": \"Aves/Tyrannus tyrannus/7f1640539b5751963d74c3de5ffd0596.jpg\",\n  \"273263.jpg\": \"Aves/Tyrannus tyrannus/c365197dd65a3df457516ce7796c37be.jpg\",\n  \"273264.jpg\": \"Aves/Tyrannus tyrannus/673ae3b0261e7cf350b50409289b12c4.jpg\",\n  \"273267.jpg\": \"Aves/Tyrannus tyrannus/098fdb228ffc3c745fb57bf80a19f036.jpg\",\n  \"273273.jpg\": \"Aves/Tyrannus tyrannus/cca84106f15c85199c87644efa1cdea2.jpg\",\n  \"273312.jpg\": \"Aves/Tyrannus tyrannus/d24a3067fee349afc18bb0a981502625.jpg\",\n  \"273327.jpg\": \"Aves/Tyrannus tyrannus/6ec1a2021ce6fc8b8516e3437d1a4a6d.jpg\",\n  \"273335.jpg\": \"Aves/Tyrannus tyrannus/08fad517fdb0e19abccf36b56177f3ae.jpg\",\n  \"273343.jpg\": \"Aves/Tyrannus tyrannus/f85889caf8938fc616cfdf1b73c0551e.jpg\",\n  \"273348.jpg\": \"Aves/Tyrannus tyrannus/155fe32bcb058d0ef09c2b358d4b1413.jpg\",\n  \"273351.jpg\": \"Aves/Tyrannus tyrannus/33bb4906cbf22051591d42a9f15ea114.jpg\",\n  \"273352.jpg\": \"Aves/Tyrannus tyrannus/be7789bd4bebe7d3f2667ec7f2e1201a.jpg\",\n  \"273358.jpg\": \"Aves/Tyrannus tyrannus/b9675e61490fb4512469f1b3906bfa3f.jpg\",\n  \"273374.jpg\": \"Aves/Tyrannus tyrannus/7bc824ca1fb50dd286b6c7c921a5be0d.jpg\",\n  \"273376.jpg\": \"Aves/Tyrannus tyrannus/e0339008aaa4cba8f3fe63d1e9b5c5f6.jpg\",\n  \"273407.jpg\": \"Aves/Tyrannus tyrannus/e8940bfadfbcce1cacba4dff5e26b843.jpg\",\n  \"273439.jpg\": \"Aves/Tyrannus tyrannus/50256f46e11edc649af99262b2da165d.jpg\",\n  \"273444.jpg\": \"Aves/Tyrannus tyrannus/64d6b4c45d25bfe4a382dbfae4340663.jpg\",\n  \"273468.jpg\": \"Aves/Tyrannus tyrannus/f189029855388c5c6e6cbe3a700491db.jpg\",\n  \"273486.jpg\": \"Aves/Tyrannus tyrannus/36f8078755362371ea4e20e2057d03a0.jpg\",\n  \"273497.jpg\": \"Aves/Tyrannus tyrannus/720029fb2bf749884036b2d8d20292e8.jpg\",\n  \"273671.jpg\": \"Aves/Tachybaptus ruficollis/c6d3d311477b79285aa55e56178f9ab1.jpg\",\n  \"273672.jpg\": \"Aves/Tachybaptus ruficollis/40b5bf247c32ce4177aef57c1733f6e8.jpg\",\n  \"273673.jpg\": \"Aves/Tachybaptus ruficollis/f1f832cc6e9b44c76df48ae0413814bc.jpg\",\n  \"273677.jpg\": \"Aves/Tachybaptus ruficollis/cfae59d7909fb8407c0dd4ac7d54dd6c.jpg\",\n  \"273685.jpg\": \"Aves/Tachybaptus ruficollis/f2ad7d8e3a0e8bb643f483425434f077.jpg\",\n  \"273687.jpg\": \"Aves/Tachybaptus ruficollis/714a077619d1fcfc69c99c90652100ac.jpg\",\n  \"273691.jpg\": \"Aves/Tachybaptus ruficollis/eaf16093c9a10fc93fa0c8dff3c774ef.jpg\",\n  \"273697.jpg\": \"Aves/Tachybaptus ruficollis/b8439b3cfe014ad70f8d08204c856083.jpg\",\n  \"273698.jpg\": \"Aves/Tachybaptus ruficollis/8794eaacafc7625cc68bfd1c14a47568.jpg\",\n  \"273705.jpg\": \"Aves/Tachybaptus ruficollis/4f37ea09e622e34630d4800c6b69b24a.jpg\",\n  \"273727.jpg\": \"Aves/Tachybaptus ruficollis/d7ba637e81a7590c3b2a856d1a165f5f.jpg\",\n  \"273731.jpg\": \"Aves/Tachybaptus ruficollis/128d92609783a73d515bf0e78b27e784.jpg\",\n  \"273745.jpg\": \"Aves/Tachybaptus ruficollis/018acf6a94306427bfefe32fd46350e8.jpg\",\n  \"273746.jpg\": \"Aves/Tachybaptus ruficollis/1859e73ac1ec53d6b05e3b4eae45c915.jpg\",\n  \"273748.jpg\": \"Aves/Tachybaptus ruficollis/7ebe48b305ca1c2c88628c9fe43ad048.jpg\",\n  \"273878.jpg\": \"Insecta/Copestylum mexicanum/a13d011f1fbe88dafb6bca86c36422c9.jpg\",\n  \"273885.jpg\": \"Insecta/Copestylum mexicanum/df4c0a72ac7d557e64e63eeec25c42e6.jpg\",\n  \"273886.jpg\": \"Insecta/Copestylum mexicanum/0c115d583cac5984112ad5610039a1d9.jpg\",\n  \"273887.jpg\": \"Insecta/Copestylum mexicanum/9f156db96b9ee02f3b50bb79ea090089.jpg\",\n  \"273888.jpg\": \"Insecta/Copestylum mexicanum/fc9f8a8e3813ac997060867cd02d48a3.jpg\",\n  \"273892.jpg\": \"Insecta/Copestylum mexicanum/e85e7380b1b69453ca9188f02217e69b.jpg\",\n  \"273913.jpg\": \"Insecta/Copestylum mexicanum/44c2628b0b51df900087cbe30044f8f7.jpg\",\n  \"273919.jpg\": \"Aves/Dendrocopos major/eab1aca623fe370c62c4bb5cb7357c89.jpg\",\n  \"273930.jpg\": \"Aves/Dendrocopos major/874e6c0782989b6bc7d3ef99660d0386.jpg\",\n  \"273934.jpg\": \"Aves/Dendrocopos major/e76690e581070689adbf41d94432ea31.jpg\",\n  \"273941.jpg\": \"Aves/Dendrocopos major/267d8fd17fc83c43068ce480d7e4397f.jpg\",\n  \"273942.jpg\": \"Aves/Dendrocopos major/f2e87046d8e6a7b07d64c300f32c860f.jpg\",\n  \"273947.jpg\": \"Aves/Dendrocopos major/3bb6efa42d92dd1ebd1fafff70a73bfd.jpg\",\n  \"273952.jpg\": \"Aves/Dendrocopos major/84edd3288084d697c9526cb9cbca488f.jpg\",\n  \"273956.jpg\": \"Aves/Dendrocopos major/ce24068162d20e3631342b859b3b4323.jpg\",\n  \"273960.jpg\": \"Aves/Dendrocopos major/ab07d149e1465165d13cf76279251a8d.jpg\",\n  \"273984.jpg\": \"Aves/Dendrocopos major/86120236e9766ff3f83f1c484f7a3ef1.jpg\",\n  \"273986.jpg\": \"Aves/Dendrocopos major/8746cb156f7a98748822d549a1c316df.jpg\",\n  \"273990.jpg\": \"Aves/Dendrocopos major/069b734de252d9f790994e04b13764c3.jpg\",\n  \"273995.jpg\": \"Aves/Dendrocopos major/e8be3ca9c5e511b2057a4af2f91be027.jpg\",\n  \"274003.jpg\": \"Aves/Dendrocopos major/a1d98821bee8ac9bae8a2a72eed6c83e.jpg\",\n  \"274016.jpg\": \"Aves/Dendrocopos major/dbde12ca1c8e69d8afe7d8d77f103ee5.jpg\",\n  \"274017.jpg\": \"Aves/Dendrocopos major/46631d217590c8bcb798c0ef5f8aa5f8.jpg\",\n  \"274031.jpg\": \"Aves/Dendrocopos major/9145f9e8f4ec414a096f1bebe426e17c.jpg\",\n  \"274032.jpg\": \"Aves/Dendrocopos major/642bcaa99271282690bfbfec28f9da83.jpg\",\n  \"274041.jpg\": \"Aves/Dendrocopos major/e0968029acc24dd33ae244e8f9d029d0.jpg\",\n  \"274042.jpg\": \"Aves/Dendrocopos major/3cdb7e7ed165bc311682bcc43a76f9dc.jpg\",\n  \"2742.jpg\": \"Aves/Picoides pubescens/63d149bcb9fa68e159e88da7506ec105.jpg\",\n  \"274274.jpg\": \"Amphibia/Plethodon vehiculum/3cd717a4ed1190b57437e0a587052291.jpg\",\n  \"274278.jpg\": \"Amphibia/Plethodon vehiculum/f6e9b699a528d4f5dfcf359da2dd4cd0.jpg\",\n  \"274288.jpg\": \"Amphibia/Plethodon vehiculum/a8b58c5d09575ed0b7d7a96c0baf30ad.jpg\",\n  \"274309.jpg\": \"Aves/Haemorhous cassinii/c5f28fe155732dcce48b278e3850cf8c.jpg\",\n  \"274310.jpg\": \"Aves/Haemorhous cassinii/210082f86da21ecfafc6b75e8486ae74.jpg\",\n  \"274318.jpg\": \"Aves/Haemorhous cassinii/4bfff35097e64c189f6b7a387d90e3e9.jpg\",\n  \"274322.jpg\": \"Aves/Haemorhous cassinii/792b11aaa9fd65f607fda5581296609c.jpg\",\n  \"274323.jpg\": \"Aves/Haemorhous cassinii/a380d01d343d8c59115ed3f7ec6e2a13.jpg\",\n  \"274325.jpg\": \"Aves/Haemorhous cassinii/3d30dcba14ad3236e39bf64bc6d28678.jpg\",\n  \"274326.jpg\": \"Aves/Haemorhous cassinii/50014cce042fa4fcfd88d5c991b71b9c.jpg\",\n  \"274328.jpg\": \"Aves/Haemorhous cassinii/9b8c68bd821a6c81612b1e767058ac37.jpg\",\n  \"274329.jpg\": \"Aves/Haemorhous cassinii/2f66e9f577ef7b88ee2467c3fe08c573.jpg\",\n  \"274333.jpg\": \"Aves/Haemorhous cassinii/36224e838c53d2f3298f8bb4628e5464.jpg\",\n  \"274334.jpg\": \"Aves/Haemorhous cassinii/0d2f10d08a345f219f606080657e2fba.jpg\",\n  \"274337.jpg\": \"Aves/Haemorhous cassinii/9d7fbb2c0d3a3a5330c44a5c28b5f84a.jpg\",\n  \"274338.jpg\": \"Aves/Haemorhous cassinii/ae4d99acee0d631c41986c05a2430cbe.jpg\",\n  \"274340.jpg\": \"Aves/Haemorhous cassinii/6db54650f7287bbe8a9ed08aa3db52e5.jpg\",\n  \"274341.jpg\": \"Aves/Haemorhous cassinii/52201e1512855c60d2138140aeee92d1.jpg\",\n  \"274342.jpg\": \"Aves/Haemorhous cassinii/041b90df09fb61c680d693e15f930800.jpg\",\n  \"274359.jpg\": \"Aves/Catharus ustulatus/a3848994017de11a1be3c2db77b7a5c1.jpg\",\n  \"274360.jpg\": \"Aves/Catharus ustulatus/f6d046f73b19ecda7505049cca0560ab.jpg\",\n  \"274377.jpg\": \"Aves/Catharus ustulatus/60049dfa8ac8404dc5a062c964845e2f.jpg\",\n  \"274378.jpg\": \"Aves/Catharus ustulatus/113d1a67bc4bdc365ee68acfc25c354e.jpg\",\n  \"274384.jpg\": \"Aves/Catharus ustulatus/d2df4377e2b6c9830315d401c690e453.jpg\",\n  \"274386.jpg\": \"Aves/Catharus ustulatus/0d3201cfa633a13ab671f79eb5ed3bc3.jpg\",\n  \"274387.jpg\": \"Aves/Catharus ustulatus/340581f854b7cf3a02a7f63b28e965e6.jpg\",\n  \"274394.jpg\": \"Aves/Catharus ustulatus/5b13fbc16f64c441901d04f21200e959.jpg\",\n  \"274400.jpg\": \"Aves/Catharus ustulatus/605a4591d9b068ad1146e1e96201186c.jpg\",\n  \"274402.jpg\": \"Aves/Catharus ustulatus/b5b783d2577e39cb297fccf599c6bebc.jpg\",\n  \"274406.jpg\": \"Aves/Catharus ustulatus/701a56d3e20dedebc903e8fec4d4b2fa.jpg\",\n  \"274407.jpg\": \"Aves/Catharus ustulatus/3467628295811acb6878ce7847331334.jpg\",\n  \"274408.jpg\": \"Aves/Catharus ustulatus/be683c3c903ab8f91379732c115f389c.jpg\",\n  \"274409.jpg\": \"Aves/Catharus ustulatus/f1ba656161f832fbaef8608dc07f6fe1.jpg\",\n  \"274411.jpg\": \"Aves/Catharus ustulatus/743c80aecc3670bfb2d3faefea8932fb.jpg\",\n  \"274413.jpg\": \"Aves/Catharus ustulatus/edaa2d890e232cfc09be459e8bb3b38a.jpg\",\n  \"274414.jpg\": \"Aves/Catharus ustulatus/75f96569d369673276951cf5ee17fabd.jpg\",\n  \"274427.jpg\": \"Aves/Catharus ustulatus/eed3ba4bf28ad507df48d8a67fc07ef9.jpg\",\n  \"27443.jpg\": \"Insecta/Coreus marginatus/342e886006a5e8a662ddb5726712c279.jpg\",\n  \"274434.jpg\": \"Aves/Catharus ustulatus/b8794d49522843c2974bf46b1da178d5.jpg\",\n  \"274437.jpg\": \"Aves/Catharus ustulatus/4b3b776c25211eb8b93a0cc39bb75b29.jpg\",\n  \"274440.jpg\": \"Aves/Catharus ustulatus/06124e372dcfcb9f4312e5c5822b538f.jpg\",\n  \"274442.jpg\": \"Aves/Catharus ustulatus/8f0ad6e7fb99fc5447c504762a60ebac.jpg\",\n  \"27447.jpg\": \"Insecta/Coreus marginatus/8b10d2a33f15dbf8dee9b15080ce1224.jpg\",\n  \"27448.jpg\": \"Insecta/Coreus marginatus/b0fe41bda25fac1e3ea452e583df1362.jpg\",\n  \"27452.jpg\": \"Insecta/Coreus marginatus/4935c6fe2bbd8f46e9b01da4732489e6.jpg\",\n  \"274544.jpg\": \"Amphibia/Eurycea cirrigera/450173cb955f8ad721db182871ee176c.jpg\",\n  \"274547.jpg\": \"Amphibia/Eurycea cirrigera/df4dd28552b910e2830f6b8e799fc17f.jpg\",\n  \"274552.jpg\": \"Amphibia/Eurycea cirrigera/27d41804da4c7f9f70c5584bc6835cd7.jpg\",\n  \"274560.jpg\": \"Amphibia/Eurycea cirrigera/0ec9bb6314e5e331634012cf4541238d.jpg\",\n  \"274562.jpg\": \"Amphibia/Eurycea cirrigera/27e51a0b8a7fe8e227392d8bf8963037.jpg\",\n  \"274663.jpg\": \"Insecta/Mydas clavatus/7bf2ac70875772f77eaa1369bd1a988e.jpg\",\n  \"274666.jpg\": \"Insecta/Mydas clavatus/ea60d377987b2a466cd18f155bcba2ca.jpg\",\n  \"274667.jpg\": \"Insecta/Mydas clavatus/bc56dc3a089320b2bdcf1ff864a1adfc.jpg\",\n  \"274668.jpg\": \"Insecta/Mydas clavatus/9b0612626e77c89b84696187d23a0662.jpg\",\n  \"274671.jpg\": \"Insecta/Mydas clavatus/ca9cdc31eefcdaa156d1156bf2d65ac1.jpg\",\n  \"274751.jpg\": \"Reptilia/Kinosternon flavescens/9232a32492d4b3a7271c0bd70143ec08.jpg\",\n  \"274754.jpg\": \"Reptilia/Kinosternon flavescens/e9d693b3b53f5f0e31864b6761a319fe.jpg\",\n  \"274755.jpg\": \"Reptilia/Kinosternon flavescens/4294c011a52b683266b356d72be6cb91.jpg\",\n  \"274756.jpg\": \"Reptilia/Kinosternon flavescens/5b2c9111802ed9630b79fdbbafcd4637.jpg\",\n  \"274757.jpg\": \"Reptilia/Kinosternon flavescens/2b0fb894124bc04efe2475c5677d667c.jpg\",\n  \"274758.jpg\": \"Reptilia/Kinosternon flavescens/e77efef76b8c9a048569eb3dc6580db0.jpg\",\n  \"274761.jpg\": \"Reptilia/Kinosternon flavescens/0c6f1b9371f067b7c44a0ebacd0e9c27.jpg\",\n  \"274765.jpg\": \"Reptilia/Kinosternon flavescens/e29c85e082398fa247a2e05a2f17da9b.jpg\",\n  \"274766.jpg\": \"Reptilia/Kinosternon flavescens/c6f0753e1695456c47b6e3ccc9de0da5.jpg\",\n  \"274770.jpg\": \"Reptilia/Kinosternon flavescens/40552bc765c512a77a4f466efb7c0651.jpg\",\n  \"274771.jpg\": \"Reptilia/Kinosternon flavescens/83056bf4beacba73abacaa71b3c97b61.jpg\",\n  \"274772.jpg\": \"Reptilia/Kinosternon flavescens/0c1e1580ec71780d89cabe6acd148f60.jpg\",\n  \"274773.jpg\": \"Reptilia/Kinosternon flavescens/648efa94e397e9b0cfc45923d5974577.jpg\",\n  \"274774.jpg\": \"Reptilia/Kinosternon flavescens/83d9723b2dccf5c32c47da3e0d305d91.jpg\",\n  \"274781.jpg\": \"Reptilia/Kinosternon flavescens/8cfb3d1bd4c5ef21b9ad80b6fadebb6e.jpg\",\n  \"274782.jpg\": \"Reptilia/Kinosternon flavescens/f6a4120d35c2dbce0989174eac370ac8.jpg\",\n  \"274785.jpg\": \"Reptilia/Kinosternon flavescens/5d0e3854ee081045161e6ba84526a155.jpg\",\n  \"274790.jpg\": \"Reptilia/Kinosternon flavescens/fa7d634f159fe05201aaf96e69dd2b65.jpg\",\n  \"274791.jpg\": \"Reptilia/Kinosternon flavescens/93976c33db00aa54c67dea07abf08f60.jpg\",\n  \"274839.jpg\": \"Aves/Milvus migrans/86a4a26b2e07622a391a45a4c6c5f7d4.jpg\",\n  \"274842.jpg\": \"Aves/Milvus migrans/10fad71995190e2f99dfef630f688db4.jpg\",\n  \"274847.jpg\": \"Aves/Milvus migrans/a3a19be16a5ed1cab3568bf7ff3c5941.jpg\",\n  \"274849.jpg\": \"Aves/Milvus migrans/b4dea30eb42795fc9fb89f12c4483d7d.jpg\",\n  \"274864.jpg\": \"Aves/Milvus migrans/0505ead6c51dea58c9a9839f413e36f0.jpg\",\n  \"274865.jpg\": \"Aves/Milvus migrans/a4666e7e608e79578bfb10e51428916c.jpg\",\n  \"274866.jpg\": \"Aves/Milvus migrans/ec19738f422bc44f60ce073dabd53f33.jpg\",\n  \"274872.jpg\": \"Aves/Milvus migrans/1e415fc88fb65273dc9b40e7db1ba857.jpg\",\n  \"274876.jpg\": \"Aves/Milvus migrans/a2e8f15cea5ca173c8ecc9e73f4f0b3b.jpg\",\n  \"274877.jpg\": \"Aves/Milvus migrans/0b3139c93904a4f6f9f5db1789d0a974.jpg\",\n  \"274878.jpg\": \"Aves/Milvus migrans/3b49b3b61a9b620f4a547919992c117d.jpg\",\n  \"274879.jpg\": \"Aves/Milvus migrans/bc9ed5a416583e5e0418ecc71826ceeb.jpg\",\n  \"274882.jpg\": \"Aves/Milvus migrans/6ce59f740224001fa32b7073ce0bb128.jpg\",\n  \"274886.jpg\": \"Aves/Milvus migrans/8c8ecda8f187c7f7745380dbe1f44b08.jpg\",\n  \"274887.jpg\": \"Aves/Milvus migrans/2e028a3aba9c867e7f358be5389be822.jpg\",\n  \"274888.jpg\": \"Aves/Milvus migrans/ffef2eef542b0912014564f86b8503e6.jpg\",\n  \"274895.jpg\": \"Aves/Milvus migrans/ccee8c25946939c9400b6aa5682e662c.jpg\",\n  \"274897.jpg\": \"Aves/Milvus migrans/fea2246a6fbcd473ba237bc9d30421ab.jpg\",\n  \"274898.jpg\": \"Aves/Milvus migrans/8aa1735cbfd9fda00e6f5a5112a067b9.jpg\",\n  \"274900.jpg\": \"Aves/Milvus migrans/1f7d82fd4b2dd85cd812444ac9105b36.jpg\",\n  \"27491.jpg\": \"Aves/Melanitta perspicillata/dd364013e30681b03dfe736a58f554b4.jpg\",\n  \"274911.jpg\": \"Reptilia/Anguis fragilis/954897c63d1b91a030315e09b3867701.jpg\",\n  \"274917.jpg\": \"Reptilia/Anguis fragilis/8cbeba1f2c1e901a8476f82b17a6431c.jpg\",\n  \"274918.jpg\": \"Reptilia/Anguis fragilis/eb613cbe759dfa9b7b94c76a5494292f.jpg\",\n  \"274932.jpg\": \"Reptilia/Anguis fragilis/3bdea06bc632e20493f67dd857982d57.jpg\",\n  \"274933.jpg\": \"Reptilia/Anguis fragilis/14b7faddd308e00e6b2277e96bd96300.jpg\",\n  \"274935.jpg\": \"Reptilia/Anguis fragilis/0e0d7c906544415def9dddf68a136ae4.jpg\",\n  \"274936.jpg\": \"Reptilia/Anguis fragilis/0332feece9503f2aabb4fbfc0e2203ee.jpg\",\n  \"274937.jpg\": \"Reptilia/Anguis fragilis/c589e7f11d3e55aac5dc7d0812adcd78.jpg\",\n  \"274938.jpg\": \"Reptilia/Anguis fragilis/4ff11d344846300c9ed25f8b32038c3c.jpg\",\n  \"274939.jpg\": \"Reptilia/Anguis fragilis/807feb5001a26b72c84736eb31b5e029.jpg\",\n  \"274945.jpg\": \"Reptilia/Anguis fragilis/a3a4ca473ce15d2b57731f0979bcdddf.jpg\",\n  \"274949.jpg\": \"Reptilia/Anguis fragilis/d004a26bbedaf9eb9dbcee72359a7200.jpg\",\n  \"27495.jpg\": \"Aves/Melanitta perspicillata/1a0c0dedcf7da16e05985ff9b77db425.jpg\",\n  \"274955.jpg\": \"Reptilia/Anguis fragilis/8acb88de29815aa4622a296d362715d0.jpg\",\n  \"274961.jpg\": \"Reptilia/Anguis fragilis/b9aa56c9f948b856d9835cd5e983ea85.jpg\",\n  \"274964.jpg\": \"Reptilia/Anguis fragilis/b1a2d899f249505dc7769a6b5e978b50.jpg\",\n  \"275211.jpg\": \"Insecta/Junonia coenia/2cc55386d7fff0ba7e01ffa18ed99458.jpg\",\n  \"275248.jpg\": \"Insecta/Junonia coenia/6b7459733ec178377469e51d5dc89f77.jpg\",\n  \"275250.jpg\": \"Insecta/Junonia coenia/b2a75c009ab841766ca17471b1fcaad4.jpg\",\n  \"275381.jpg\": \"Insecta/Junonia coenia/ce213d015418c371c1b11645b0e91dfa.jpg\",\n  \"27542.jpg\": \"Aves/Melanitta perspicillata/62d7e61a99be7a09089d1fe76d01e556.jpg\",\n  \"275438.jpg\": \"Insecta/Junonia coenia/46d1f192b12b32fab793c447f61c9def.jpg\",\n  \"27544.jpg\": \"Aves/Melanitta perspicillata/6f5e23c4bdc629c6b875edd8515e61be.jpg\",\n  \"27550.jpg\": \"Aves/Melanitta perspicillata/1d37f9bd30b326bed1baa9898f3bd837.jpg\",\n  \"27552.jpg\": \"Aves/Melanitta perspicillata/d423d5be437abef0fd5b3922149ff33f.jpg\",\n  \"275529.jpg\": \"Insecta/Junonia coenia/efb34227b7968dac476e02ca8a178d6d.jpg\",\n  \"275532.jpg\": \"Insecta/Junonia coenia/8d97697a5e8bd399e4153fa66ad314bd.jpg\",\n  \"275578.jpg\": \"Insecta/Junonia coenia/0c846b1e8b24a68cc65e1a51ba9e3882.jpg\",\n  \"275579.jpg\": \"Insecta/Junonia coenia/a23fe874f45a6d19bed0786fcd8899bc.jpg\",\n  \"275584.jpg\": \"Insecta/Junonia coenia/ff35a881f78f91b745af86c506966410.jpg\",\n  \"275605.jpg\": \"Insecta/Junonia coenia/31308ccf90696fbb50b3eb78d2c23d75.jpg\",\n  \"275613.jpg\": \"Insecta/Junonia coenia/c47d46da95099861a4924f51d2775558.jpg\",\n  \"275662.jpg\": \"Insecta/Junonia coenia/a85a6c2c54f646887eae976ecb6b21a1.jpg\",\n  \"275667.jpg\": \"Insecta/Junonia coenia/ea7fd95181844f9a3328aaec368352ae.jpg\",\n  \"275744.jpg\": \"Insecta/Junonia coenia/914e8c1bd79bb0de610778a305ba41ff.jpg\",\n  \"275807.jpg\": \"Insecta/Junonia coenia/ea940b6013322cac614115de35caf828.jpg\",\n  \"275821.jpg\": \"Insecta/Junonia coenia/64eb7d1b71e61965eedfb3ac62f3c3a4.jpg\",\n  \"27594.jpg\": \"Aves/Melanitta perspicillata/9fd627684420e7e2d2ee4db3e4ed97a2.jpg\",\n  \"27595.jpg\": \"Aves/Melanitta perspicillata/4b36159dc7e60e7a5ac361ab1de6bca7.jpg\",\n  \"275970.jpg\": \"Insecta/Junonia coenia/4a1f503a2fb345913a941a2a33e8046b.jpg\",\n  \"276048.jpg\": \"Insecta/Junonia coenia/eb944dacf22facc4d148cf9c541b1b40.jpg\",\n  \"27606.jpg\": \"Aves/Melanitta perspicillata/586de83e2d2b430687f429c56fe0e896.jpg\",\n  \"276070.jpg\": \"Insecta/Perithemis intensa/5daca9e1183730a55b1d20cf6bcdd923.jpg\",\n  \"276083.jpg\": \"Insecta/Perithemis intensa/56f47d6aae947021de09d20ea64fad7c.jpg\",\n  \"276090.jpg\": \"Insecta/Perithemis intensa/491930940c5e78e45918b66c121e290f.jpg\",\n  \"276094.jpg\": \"Insecta/Perithemis intensa/b88b94f49142702d622d4e3002245930.jpg\",\n  \"276110.jpg\": \"Aves/Aechmophorus occidentalis/c08a97d82d5fdef0c36ac544d494c558.jpg\",\n  \"276114.jpg\": \"Aves/Aechmophorus occidentalis/a38f9f316555e37302e6b0668bc3c78b.jpg\",\n  \"276115.jpg\": \"Aves/Aechmophorus occidentalis/0a6f25ebccf757d48bec03718cd2a372.jpg\",\n  \"276124.jpg\": \"Aves/Aechmophorus occidentalis/db388e0fc5577c590ad2cf7fed34983b.jpg\",\n  \"276127.jpg\": \"Aves/Aechmophorus occidentalis/c6455fa3067d46f852fad617c13e7521.jpg\",\n  \"276139.jpg\": \"Aves/Aechmophorus occidentalis/3f897f3a99b086440095744cd48bb19d.jpg\",\n  \"276144.jpg\": \"Aves/Aechmophorus occidentalis/7ea05274305987a6bb5842ac5da08536.jpg\",\n  \"276154.jpg\": \"Aves/Aechmophorus occidentalis/21ef76daa3cf1fa349695130176238a5.jpg\",\n  \"276162.jpg\": \"Aves/Aechmophorus occidentalis/db7112c9fb31e0dd85a26815e73c9daa.jpg\",\n  \"276176.jpg\": \"Aves/Aechmophorus occidentalis/6f14d6ee09aa3083e2890fff2c9081cf.jpg\",\n  \"276186.jpg\": \"Aves/Aechmophorus occidentalis/45cdf397d3bf7aa236b8810a744abf7e.jpg\",\n  \"276193.jpg\": \"Aves/Aechmophorus occidentalis/daaf75f1f4089eb27f68f149dc2602f8.jpg\",\n  \"276232.jpg\": \"Aves/Aechmophorus occidentalis/500f7b580ca45f0d0fd5de523518a7a8.jpg\",\n  \"276237.jpg\": \"Aves/Aechmophorus occidentalis/8fa84f8b6aad0357969a3bf03df3ce90.jpg\",\n  \"276240.jpg\": \"Aves/Aechmophorus occidentalis/c49034aa286d9d44518b37d1cd962ea7.jpg\",\n  \"276264.jpg\": \"Aves/Aechmophorus occidentalis/67b233bd083ac1d2222e5974f727d04e.jpg\",\n  \"27627.jpg\": \"Aves/Melanitta perspicillata/0fbe0def07416c64daadb22620af1373.jpg\",\n  \"276271.jpg\": \"Aves/Aechmophorus occidentalis/411fdcfa7768ede9469bddba06f7eda3.jpg\",\n  \"276276.jpg\": \"Aves/Aechmophorus occidentalis/9dd3bab31793c1540145a4de1eaf210b.jpg\",\n  \"276287.jpg\": \"Aves/Aechmophorus occidentalis/b96d8fe7c3668379690d91bea28c71e7.jpg\",\n  \"27633.jpg\": \"Aves/Melanitta perspicillata/6dd269a488dfcd8f1e3fbd559dba76be.jpg\",\n  \"27651.jpg\": \"Aves/Melanitta perspicillata/50cfc9e8ae9d7ccf896abbc2f9a9cb05.jpg\",\n  \"27652.jpg\": \"Aves/Melanitta perspicillata/d33f29012f8fc01815790f8fd428679b.jpg\",\n  \"276608.jpg\": \"Insecta/Olla v-nigrum/904d842e993cd5a18830a7fa225ac850.jpg\",\n  \"276609.jpg\": \"Insecta/Olla v-nigrum/14361724182bdb3355e0cbd60d9deb7b.jpg\",\n  \"276612.jpg\": \"Insecta/Olla v-nigrum/cb3c276fd3cbe3657ee4ea06eed8c733.jpg\",\n  \"276613.jpg\": \"Insecta/Olla v-nigrum/ba97b67ac62a97b4d81c5becd9194a2b.jpg\",\n  \"276614.jpg\": \"Insecta/Olla v-nigrum/8cbe725b469cca51bec1551524a589f5.jpg\",\n  \"276619.jpg\": \"Insecta/Olla v-nigrum/b543e9cbaad95356d51ff1cf33b811c2.jpg\",\n  \"276624.jpg\": \"Insecta/Olla v-nigrum/1b27a402b45c5a31a095a4e29295cb41.jpg\",\n  \"276627.jpg\": \"Insecta/Olla v-nigrum/d07275c6f31b81fd807115a63e2f3518.jpg\",\n  \"276628.jpg\": \"Insecta/Olla v-nigrum/8a57cbe8b1021ea8dc161feb386986fe.jpg\",\n  \"276630.jpg\": \"Insecta/Olla v-nigrum/f40b30eeaa903b7a54bd488c7938c806.jpg\",\n  \"276631.jpg\": \"Insecta/Olla v-nigrum/88dede0d76625083802a67bbee57666f.jpg\",\n  \"276639.jpg\": \"Insecta/Olla v-nigrum/69681698d10e1d601cfc26611471bc2c.jpg\",\n  \"276640.jpg\": \"Insecta/Olla v-nigrum/4ddd94594c21fec5360b719e5b3e007e.jpg\",\n  \"276645.jpg\": \"Insecta/Olla v-nigrum/87e01964f8b58ac7f25493a638b31deb.jpg\",\n  \"276646.jpg\": \"Insecta/Olla v-nigrum/3309d71e9aefc697813d2b60188942d8.jpg\",\n  \"276647.jpg\": \"Insecta/Olla v-nigrum/d8ed5e7aabbb7050567eb080d6536a47.jpg\",\n  \"276648.jpg\": \"Insecta/Olla v-nigrum/8ebd88bfebd7f911dbdc2481b35cd051.jpg\",\n  \"276650.jpg\": \"Insecta/Olla v-nigrum/07f895a856c9226f89e2b70aeb351c25.jpg\",\n  \"276651.jpg\": \"Insecta/Olla v-nigrum/85413dee6ca6a31d17ba396b10d20528.jpg\",\n  \"276653.jpg\": \"Insecta/Olla v-nigrum/64540cf16aff25f8294852cf67195a02.jpg\",\n  \"276665.jpg\": \"Animalia/Anthopleura elegantissima/c975221dbf4faa64fb8ad56a51f9b259.jpg\",\n  \"276667.jpg\": \"Animalia/Anthopleura elegantissima/816a44bcdc47cdf64ac44398ab36e6e5.jpg\",\n  \"27668.jpg\": \"Aves/Melanitta perspicillata/50e90224b40853d3b9310663ce06b6de.jpg\",\n  \"27669.jpg\": \"Aves/Melanitta perspicillata/9b829153d14c10e1b9df8c4d750ba519.jpg\",\n  \"276781.jpg\": \"Aves/Phalacrocorax carbo/5424a7f7c8ea537dbb9fbb18560a34db.jpg\",\n  \"27679.jpg\": \"Aves/Melanitta perspicillata/e8126bc5894d4b761c977035c21e6cb0.jpg\",\n  \"276794.jpg\": \"Aves/Phalacrocorax carbo/bca0a453b4aaceb5ad3a8e615553249a.jpg\",\n  \"276814.jpg\": \"Aves/Phalacrocorax carbo/19f5c876f758ffe92f3a18f908a4dc2d.jpg\",\n  \"276823.jpg\": \"Aves/Phalacrocorax carbo/935706c89bd89e99915117c6031ae0e7.jpg\",\n  \"276825.jpg\": \"Aves/Phalacrocorax carbo/4352a008d379d1a29a5bca581f5d9cf7.jpg\",\n  \"276845.jpg\": \"Aves/Phalacrocorax carbo/296b038252bf8e29f973fb1211809dab.jpg\",\n  \"276847.jpg\": \"Aves/Phalacrocorax carbo/5c1752439db7a4723db073f7563c9b8d.jpg\",\n  \"276866.jpg\": \"Aves/Phalacrocorax carbo/60571848bf513a955f3fe8dc6bc3191c.jpg\",\n  \"27687.jpg\": \"Aves/Melanitta perspicillata/2662f629350b091041beb08ed6188f39.jpg\",\n  \"276877.jpg\": \"Aves/Phalacrocorax carbo/c4d8fe83f14708bbc5e3bb382d40ff92.jpg\",\n  \"276883.jpg\": \"Aves/Phalacrocorax carbo/a41676780dac901e1b9d9ebc6e4e98a6.jpg\",\n  \"276903.jpg\": \"Aves/Phalacrocorax carbo/0c6781e68c205ea5cd80f9a78f882bf3.jpg\",\n  \"276911.jpg\": \"Aves/Phalacrocorax carbo/c3554cc118728a2cdddaec38b1a62ba7.jpg\",\n  \"276957.jpg\": \"Reptilia/Gambelia wislizenii/2efd3849315fcb4d22201ecd133f91fc.jpg\",\n  \"276958.jpg\": \"Reptilia/Gambelia wislizenii/889de2515792e63e69a468d507de8a79.jpg\",\n  \"276959.jpg\": \"Reptilia/Gambelia wislizenii/4229280c83b16aabf6c4f45e21480f6d.jpg\",\n  \"276960.jpg\": \"Reptilia/Gambelia wislizenii/e8931a453ab97bac17a5e273b813e7ae.jpg\",\n  \"276962.jpg\": \"Reptilia/Gambelia wislizenii/03a7c9e541011e4c025e2b3cccdc5e30.jpg\",\n  \"276963.jpg\": \"Reptilia/Gambelia wislizenii/94d7a09fae00fd8f6bb30b9f7c32d685.jpg\",\n  \"276967.jpg\": \"Reptilia/Gambelia wislizenii/6046f168b79614ac7293e78de78e9d65.jpg\",\n  \"276968.jpg\": \"Reptilia/Gambelia wislizenii/3907105b2c9a8196145cffdb69ee8631.jpg\",\n  \"276973.jpg\": \"Reptilia/Gambelia wislizenii/d17ea549ca86b5275acaaf789d11ce2d.jpg\",\n  \"276974.jpg\": \"Reptilia/Gambelia wislizenii/d160c02df9be88e0c78f2dd4c31cc95c.jpg\",\n  \"276978.jpg\": \"Reptilia/Gambelia wislizenii/a0c5f4368bdb9a51c75b253dcce8f323.jpg\",\n  \"276983.jpg\": \"Reptilia/Gambelia wislizenii/0b1030f5b28f360e6d1a8f77933abf37.jpg\",\n  \"276984.jpg\": \"Reptilia/Gambelia wislizenii/c89d77d7681b558c4de825ffb82f1c4e.jpg\",\n  \"277.jpg\": \"Mollusca/Olivella biplicata/ffbbe5ba5152a493a8216e3aa26bf0f1.jpg\",\n  \"277046.jpg\": \"Insecta/Lethe appalachia/4b3363b9d4d79d74d9d2eaa2d98d5302.jpg\",\n  \"277048.jpg\": \"Insecta/Lethe appalachia/d2240fd5ce985ef2b2775f9c8633f7ea.jpg\",\n  \"277050.jpg\": \"Insecta/Lethe appalachia/0cdb4a8c7df50f1b3afe276004cacbab.jpg\",\n  \"277051.jpg\": \"Insecta/Lethe appalachia/4cf3fe6147ce25fb53cb904471fb6571.jpg\",\n  \"277058.jpg\": \"Insecta/Lethe appalachia/4f09bfc44ad71853cf3e5bd492e7ca06.jpg\",\n  \"277059.jpg\": \"Insecta/Lethe appalachia/2d95d9bc1d88064fc1e601e79ad48fb9.jpg\",\n  \"277062.jpg\": \"Insecta/Lethe appalachia/b38a0cef3915dc3d21e029eb77a97b41.jpg\",\n  \"277067.jpg\": \"Insecta/Lethe appalachia/d911b54edf800046c5ada7e40599e503.jpg\",\n  \"277074.jpg\": \"Insecta/Celastrina lucia/2a61daf77079edf6f35e33e2b7589fa4.jpg\",\n  \"277080.jpg\": \"Insecta/Celastrina lucia/dfd069fba934c0637584e91b6a36022c.jpg\",\n  \"277084.jpg\": \"Insecta/Celastrina lucia/5ac3c56b0daaa3f5fe1f1a9097368622.jpg\",\n  \"277087.jpg\": \"Mollusca/Octopus rubescens/1a141539089f7a599f9a42f5c339ee77.jpg\",\n  \"277094.jpg\": \"Mollusca/Octopus rubescens/c81bcbd30142be201bcf123e83b2c432.jpg\",\n  \"277095.jpg\": \"Mollusca/Octopus rubescens/3ed1712cca69537321442d5e41bea804.jpg\",\n  \"277096.jpg\": \"Mollusca/Octopus rubescens/c79c3eeab57e772f0ae6d471c559d9af.jpg\",\n  \"277101.jpg\": \"Mollusca/Octopus rubescens/a5fdcdf98be4eb472fffc283f098fe20.jpg\",\n  \"277109.jpg\": \"Mollusca/Octopus rubescens/1004a73bfd7f4edfd623909fcac936a7.jpg\",\n  \"277110.jpg\": \"Mollusca/Octopus rubescens/0932ab200475f654f6112ec3c019ae79.jpg\",\n  \"277111.jpg\": \"Mollusca/Octopus rubescens/fc3a51a740d55d3ebeab29cda9326f75.jpg\",\n  \"277115.jpg\": \"Mollusca/Octopus rubescens/06d5610010bb5355de8f129233de7b17.jpg\",\n  \"277120.jpg\": \"Mollusca/Octopus rubescens/6f3aed4eb74c67e59efc4b22eddd2a1c.jpg\",\n  \"277122.jpg\": \"Mollusca/Octopus rubescens/44e12467cec08a14eb3881bdffb92359.jpg\",\n  \"277123.jpg\": \"Mollusca/Octopus rubescens/8ffe5df65670cc52bbbd37a290bbf750.jpg\",\n  \"277126.jpg\": \"Mollusca/Octopus rubescens/68b3847b9dbb64efa25620c30ae57fe8.jpg\",\n  \"277127.jpg\": \"Mollusca/Octopus rubescens/7602e1351b7aef851b73cac453bd87e2.jpg\",\n  \"277130.jpg\": \"Mollusca/Octopus rubescens/a53128472e22abbf5131e82ce9390f80.jpg\",\n  \"277141.jpg\": \"Mollusca/Octopus rubescens/c6bcbe675287c2cd3d99758d9b124193.jpg\",\n  \"277142.jpg\": \"Mollusca/Octopus rubescens/a64500063aa4123e967915e14f042bc9.jpg\",\n  \"277156.jpg\": \"Aves/Myiarchus tyrannulus/645ae6f4ca383f422f09095f5d4b9ce0.jpg\",\n  \"277157.jpg\": \"Aves/Myiarchus tyrannulus/8b8bc03c1dc4d53c1d9be4385925e37d.jpg\",\n  \"277159.jpg\": \"Aves/Myiarchus tyrannulus/c8ca1eb335fbda8df92905ea1e4784ec.jpg\",\n  \"277160.jpg\": \"Aves/Myiarchus tyrannulus/08794f8b6baea5a1045efc3b91ec2fef.jpg\",\n  \"277173.jpg\": \"Aves/Myiarchus tyrannulus/2e3260a24b2897f44987a229dee081f4.jpg\",\n  \"277175.jpg\": \"Aves/Myiarchus tyrannulus/e3d73ea37ddb8866810b1a71dff99900.jpg\",\n  \"277181.jpg\": \"Insecta/Melanoplus differentialis/71ae49afb1924e95cbffef7dc3d7eb0c.jpg\",\n  \"277182.jpg\": \"Insecta/Melanoplus differentialis/07463a4567f56282f4a8ebafc02765cb.jpg\",\n  \"277189.jpg\": \"Insecta/Melanoplus differentialis/a433910f59d89003e5b90cea4786c0d3.jpg\",\n  \"277192.jpg\": \"Insecta/Melanoplus differentialis/e398b3e3ca7fbf44a9e92862c020204a.jpg\",\n  \"277194.jpg\": \"Insecta/Melanoplus differentialis/7de2b88e6e44e0650ff81c0dba4bcf35.jpg\",\n  \"277196.jpg\": \"Insecta/Melanoplus differentialis/ccddca5bfc07beef79910d7048afecff.jpg\",\n  \"277197.jpg\": \"Insecta/Melanoplus differentialis/d826bbbeabeb2f618d706152f9b1ad31.jpg\",\n  \"277199.jpg\": \"Insecta/Melanoplus differentialis/3a65a09ff5078a9f07aae076feeabcc1.jpg\",\n  \"277219.jpg\": \"Insecta/Melanoplus differentialis/e33307a3ddfcbec8c0e03ba947e3a43b.jpg\",\n  \"277220.jpg\": \"Insecta/Melanoplus differentialis/410cc070b36ddb520f71a0ea333d0faa.jpg\",\n  \"277221.jpg\": \"Insecta/Melanoplus differentialis/dde92053d6fe822571e6273b9b0ebc5b.jpg\",\n  \"277226.jpg\": \"Insecta/Melanoplus differentialis/7d12d287e14322c8878efef5c2b5c189.jpg\",\n  \"277252.jpg\": \"Insecta/Melanoplus differentialis/03690eb3d3b4363c3e6bfcbd4e42894d.jpg\",\n  \"277254.jpg\": \"Insecta/Melanoplus differentialis/c421795f60d50d3be61777531fec1b0d.jpg\",\n  \"277255.jpg\": \"Insecta/Melanoplus differentialis/effa8d5f2866cdec49cfae09e22c25ff.jpg\",\n  \"277271.jpg\": \"Insecta/Melanoplus differentialis/6c4599b85c770c9d0dbff723247eafde.jpg\",\n  \"277272.jpg\": \"Insecta/Melanoplus differentialis/ab547c63d3531a10e0b6865265e3a069.jpg\",\n  \"277273.jpg\": \"Insecta/Melanoplus differentialis/e9b1f6c4a89ab62925ad6184e6c6939a.jpg\",\n  \"277275.jpg\": \"Insecta/Melanoplus differentialis/db66a78ddc6def47407daec7e4222f2e.jpg\",\n  \"277306.jpg\": \"Insecta/Melanoplus differentialis/8d465ca8360bf11698efdf8953040895.jpg\",\n  \"277309.jpg\": \"Insecta/Melanoplus differentialis/590fe1ca27e0833c924214ef20c3b055.jpg\",\n  \"277312.jpg\": \"Insecta/Melanoplus differentialis/5c749dd470ff16ff484282a455843fd8.jpg\",\n  \"277313.jpg\": \"Insecta/Autochton cellus/f30d00ed964e001ba603707d739dacad.jpg\",\n  \"277321.jpg\": \"Insecta/Autochton cellus/ba294e9bbbf5dbe57d60016639f2e353.jpg\",\n  \"277322.jpg\": \"Insecta/Autochton cellus/7e7a1f948f63bacd29c8a66a00121e51.jpg\",\n  \"277323.jpg\": \"Insecta/Autochton cellus/74c1d43b4139efaa177e2f94a3f4f71b.jpg\",\n  \"277324.jpg\": \"Insecta/Autochton cellus/72b50756ba22c3cce17381953e05bce7.jpg\",\n  \"277332.jpg\": \"Insecta/Autochton cellus/e0a5ea1bf6dc95b5a9be3f1111744c63.jpg\",\n  \"277431.jpg\": \"Insecta/Acanalonia conica/bfe66fb13c01600f2b24fa2d226911dc.jpg\",\n  \"277432.jpg\": \"Insecta/Acanalonia conica/ca3a9f4e86c82c454174e4bb8c9467cc.jpg\",\n  \"277437.jpg\": \"Insecta/Acanalonia conica/bcbc1b3f40999d06d56aaa986cf8377c.jpg\",\n  \"277438.jpg\": \"Insecta/Acanalonia conica/6b62a3fadc365aba158778ddb796281b.jpg\",\n  \"277442.jpg\": \"Insecta/Acanalonia conica/eed733bc5a3f460d25b3c1d6b23cc1c0.jpg\",\n  \"277443.jpg\": \"Insecta/Acanalonia conica/31d2cea378f9ba891dae4d5f6afa13cd.jpg\",\n  \"277501.jpg\": \"Insecta/Libellula forensis/151de92f223fd02342e2a489fa18ac4e.jpg\",\n  \"277502.jpg\": \"Insecta/Libellula forensis/ac7b76b3cd8471d911cc4a3cf1b57d7b.jpg\",\n  \"277503.jpg\": \"Insecta/Libellula forensis/a7fda857088c64054554b37e85d82757.jpg\",\n  \"277504.jpg\": \"Insecta/Libellula forensis/c09b151e3748546e30dafdfe1d2720e5.jpg\",\n  \"277511.jpg\": \"Insecta/Libellula forensis/2007e135f355c89bb8b8dd71f80c20e2.jpg\",\n  \"277512.jpg\": \"Insecta/Libellula forensis/727442fa0bbc0c17f2d25a6d0986a41c.jpg\",\n  \"277518.jpg\": \"Insecta/Libellula forensis/4c07e726a7c515c6a5e1869c7b43671b.jpg\",\n  \"277519.jpg\": \"Insecta/Libellula forensis/bf22f419edc856194c086eda2ee2c46c.jpg\",\n  \"277521.jpg\": \"Insecta/Libellula forensis/043b7c90bc0d6ab463c925c8222b860a.jpg\",\n  \"277522.jpg\": \"Insecta/Libellula forensis/442d8d975fdbb7db858be7bb4f783920.jpg\",\n  \"277523.jpg\": \"Insecta/Libellula forensis/f9b3724d3544fb8a165cead04447454e.jpg\",\n  \"277526.jpg\": \"Insecta/Libellula forensis/2f7e5b5f09a2f2de659a65d7c9386ee1.jpg\",\n  \"277527.jpg\": \"Insecta/Libellula forensis/feeefac19c38d4cc54bc2e2b6baeeea0.jpg\",\n  \"277532.jpg\": \"Insecta/Libellula forensis/95c7446a7adf3a4fbfd831f263cfee00.jpg\",\n  \"277535.jpg\": \"Insecta/Libellula forensis/498fda2a1fd23ac0c98d08642815b7fc.jpg\",\n  \"277537.jpg\": \"Insecta/Libellula forensis/1e7676417f76582a6b7d40c4ce2b9e58.jpg\",\n  \"277541.jpg\": \"Insecta/Libellula forensis/7e8b38b7031fadf7ae0ccacbc3b1d788.jpg\",\n  \"277545.jpg\": \"Insecta/Libellula forensis/ad35846e66ba32d8876b1a08dc23f416.jpg\",\n  \"277555.jpg\": \"Insecta/Libellula forensis/dfd408f5b50033c5ebfc108087b166f0.jpg\",\n  \"277558.jpg\": \"Insecta/Libellula forensis/b4adce2490275510d3a657b8afe84738.jpg\",\n  \"277562.jpg\": \"Insecta/Libellula forensis/9642de84797efff27fe8e01d1a421b3b.jpg\",\n  \"277566.jpg\": \"Insecta/Libellula forensis/c576b7e946b3127afa62ba372458a5c0.jpg\",\n  \"277567.jpg\": \"Insecta/Libellula forensis/b83aa14b97c03d6874d07f7a69278f33.jpg\",\n  \"277569.jpg\": \"Insecta/Libellula forensis/f3761e6e54e4df2a2915e53b5ef2f81b.jpg\",\n  \"27757.jpg\": \"Aves/Setophaga virens/f16c745006ae531b6e7d45a57da6dfe7.jpg\",\n  \"277580.jpg\": \"Aves/Thamnophilus doliatus/891113d4d16d3ea985db39b8f5096cea.jpg\",\n  \"277584.jpg\": \"Aves/Thamnophilus doliatus/604e48c3ace168e65bf382885eeff480.jpg\",\n  \"277587.jpg\": \"Aves/Thamnophilus doliatus/9b56ba4531c0c501d89fbbefb342170d.jpg\",\n  \"277588.jpg\": \"Aves/Thamnophilus doliatus/32f4535b1c0bc7a9de0a4d54b26b68fa.jpg\",\n  \"277589.jpg\": \"Aves/Thamnophilus doliatus/fb48b2a11a36671f10fbb372440977e2.jpg\",\n  \"27761.jpg\": \"Aves/Setophaga virens/306de72d680e0268451b77eb9514df6e.jpg\",\n  \"27766.jpg\": \"Aves/Setophaga virens/75ca00694140681f9c97430a7deeba1d.jpg\",\n  \"27768.jpg\": \"Aves/Setophaga virens/31d0bf16545db4d43d6ea4b0c47f5750.jpg\",\n  \"277713.jpg\": \"Insecta/Argia apicalis/d5021e9c6f2c82cab56f3f6b6c7fd5a5.jpg\",\n  \"277714.jpg\": \"Insecta/Argia apicalis/d9b2fcacf213636bd0e3b5448369cd7b.jpg\",\n  \"277725.jpg\": \"Insecta/Argia apicalis/576bc9141677e3f5bf727c0c3e98e5cc.jpg\",\n  \"277731.jpg\": \"Insecta/Argia apicalis/f7dfebd62ee1c214f722f24f24c66590.jpg\",\n  \"277733.jpg\": \"Insecta/Argia apicalis/bb2fe950ff081168a441faf0d3f3133a.jpg\",\n  \"277741.jpg\": \"Insecta/Argia apicalis/bde2464c174214fd6e7403b3c1b8b4cc.jpg\",\n  \"277750.jpg\": \"Insecta/Argia apicalis/493b23f8a7a1cf5eb867118f4e39a4ea.jpg\",\n  \"277751.jpg\": \"Insecta/Argia apicalis/6668ce70885dcc1ed18178b90495584a.jpg\",\n  \"277753.jpg\": \"Insecta/Argia apicalis/21df3790b7522449bcd703fb80bb521a.jpg\",\n  \"277760.jpg\": \"Insecta/Argia apicalis/04479c9679befd2ce5a29f92ac4ee438.jpg\",\n  \"27777.jpg\": \"Aves/Setophaga virens/f4a583752ccc23ef790066ec95325154.jpg\",\n  \"277779.jpg\": \"Insecta/Argia apicalis/6579503c9b1354561e452534ffcdd5a4.jpg\",\n  \"277781.jpg\": \"Insecta/Argia apicalis/d87227fbd7172acbf7ce8a2830855f79.jpg\",\n  \"277789.jpg\": \"Insecta/Argia apicalis/b509fe13cebe86419357767d583200b3.jpg\",\n  \"277792.jpg\": \"Insecta/Argia apicalis/399ccdecff3d39c55c49284afb3af616.jpg\",\n  \"277793.jpg\": \"Insecta/Argia apicalis/a2b10c229a7174454e93d447d722a8d3.jpg\",\n  \"277797.jpg\": \"Insecta/Argia apicalis/895f0ca1ff4c413e30d1bd6a5545b684.jpg\",\n  \"277798.jpg\": \"Insecta/Argia apicalis/06545db32de08c7a0a8e22bd977dcebe.jpg\",\n  \"27780.jpg\": \"Aves/Setophaga virens/e08eb1e8c936c571ccf793848c12d0dd.jpg\",\n  \"277803.jpg\": \"Insecta/Argia apicalis/6486cda4adc87f567642c6964ec7381c.jpg\",\n  \"277807.jpg\": \"Insecta/Argia apicalis/7a93a4535b156be2ebba3fe9587e08c9.jpg\",\n  \"27781.jpg\": \"Aves/Setophaga virens/ccfdecdca60f0420813620140b12cca2.jpg\",\n  \"27782.jpg\": \"Aves/Setophaga virens/97b11e3fac9a465974ee8abc1f38f2af.jpg\",\n  \"277821.jpg\": \"Insecta/Argia apicalis/04bf62155f6d741174bdf02a686edf60.jpg\",\n  \"277822.jpg\": \"Insecta/Argia apicalis/f6cfb98c68fd315838713bc8fbf4919a.jpg\",\n  \"277826.jpg\": \"Insecta/Argia apicalis/54474db98a9e259f6e50736817c7704f.jpg\",\n  \"27791.jpg\": \"Aves/Setophaga virens/9e0d3fe28ac04812e5181782dd11b7d2.jpg\",\n  \"278.jpg\": \"Mollusca/Olivella biplicata/f3fea08eea877b0e07f12ccac8b93bee.jpg\",\n  \"27803.jpg\": \"Aves/Setophaga virens/713c902e6ede1ed5ea51c2ea6f219bd6.jpg\",\n  \"27807.jpg\": \"Aves/Setophaga virens/50a4878199a37c2c6636c69c3fd3e26d.jpg\",\n  \"27808.jpg\": \"Aves/Setophaga virens/5f7d03ed478cc7bd8c70409c01379aea.jpg\",\n  \"27812.jpg\": \"Aves/Setophaga virens/e4b3766b2affb538675b5e2307ea7dec.jpg\",\n  \"27813.jpg\": \"Aves/Setophaga virens/7c254ccc94efa73689c970dc0be30a57.jpg\",\n  \"27818.jpg\": \"Aves/Setophaga virens/79af407b88a4e4342c7b160bc1abe353.jpg\",\n  \"278473.jpg\": \"Mammalia/Otospermophilus variegatus/16e8c613b719086a376ccf3bd69bd2d9.jpg\",\n  \"278482.jpg\": \"Mammalia/Otospermophilus variegatus/f4253615fd83fe3c2a4bc4bbcb73f1f6.jpg\",\n  \"27849.jpg\": \"Aves/Setophaga virens/def40edf81be79170d71579a8727f420.jpg\",\n  \"278493.jpg\": \"Mammalia/Otospermophilus variegatus/457889ba3891c0d0f5cf41eda6852818.jpg\",\n  \"278506.jpg\": \"Mammalia/Otospermophilus variegatus/c51b9815c2d9a630ae98f91cdeb75971.jpg\",\n  \"278521.jpg\": \"Mammalia/Otospermophilus variegatus/b11e7d52d2d853134c8941141bb92351.jpg\",\n  \"27853.jpg\": \"Aves/Setophaga virens/eff71ce23bc01fe3f9d017a5057ec15f.jpg\",\n  \"278533.jpg\": \"Mammalia/Otospermophilus variegatus/2b30d34244978103c9736429f32172b5.jpg\",\n  \"278538.jpg\": \"Mammalia/Otospermophilus variegatus/2c4bd07b854ffc6f7fd06057b83aaf10.jpg\",\n  \"278542.jpg\": \"Mammalia/Otospermophilus variegatus/af9b892804c37ae80305c9538cda03be.jpg\",\n  \"278543.jpg\": \"Mammalia/Otospermophilus variegatus/145aab518b61e7004d945852517441ed.jpg\",\n  \"27855.jpg\": \"Aves/Setophaga virens/13dbdf3b4de84684879c88ab2a6b8a1f.jpg\",\n  \"278558.jpg\": \"Mammalia/Otospermophilus variegatus/a8ba5da5dd74fffdb655765240d57623.jpg\",\n  \"278564.jpg\": \"Mammalia/Otospermophilus variegatus/fd94d85e7d8a851979f9a042ed27e5c3.jpg\",\n  \"278566.jpg\": \"Mammalia/Otospermophilus variegatus/88409116c81a27fff3d555a69e66d1a6.jpg\",\n  \"278571.jpg\": \"Mammalia/Otospermophilus variegatus/039964dbfa1a68d9de5526cf108a74dc.jpg\",\n  \"278577.jpg\": \"Mammalia/Otospermophilus variegatus/6f73143e777acb9cec5e19054781729d.jpg\",\n  \"278585.jpg\": \"Mammalia/Otospermophilus variegatus/2f495e4cfe4ccc54c6e947c7d70bf50d.jpg\",\n  \"278587.jpg\": \"Mammalia/Otospermophilus variegatus/c09dc78236b1f3084c357d35b84fedf3.jpg\",\n  \"278589.jpg\": \"Mammalia/Otospermophilus variegatus/ca4f67e1578046d9f9ba656e772aa181.jpg\",\n  \"278607.jpg\": \"Mammalia/Otospermophilus variegatus/15a4196a3e107098a4f41c91d5401520.jpg\",\n  \"278613.jpg\": \"Mammalia/Otospermophilus variegatus/ab40a6b80e991a5491ba04af523c40a3.jpg\",\n  \"278627.jpg\": \"Mammalia/Otospermophilus variegatus/e3e02780fec1db6ea1d9a6e4f7416ffc.jpg\",\n  \"278634.jpg\": \"Mammalia/Otospermophilus variegatus/dc154db07ce64e2a60130014e7454929.jpg\",\n  \"27865.jpg\": \"Aves/Setophaga virens/128741bc184e6590f0efe5b5cc3f818a.jpg\",\n  \"278675.jpg\": \"Mammalia/Otospermophilus variegatus/7f1084b2a04e2c184ecb9e37c45975b8.jpg\",\n  \"27870.jpg\": \"Aves/Setophaga virens/91f85807382d547a7b2b05e7ac6e6aa7.jpg\",\n  \"27872.jpg\": \"Aves/Setophaga virens/8acec3903abf4a139c12ff4a58fa0435.jpg\",\n  \"27875.jpg\": \"Aves/Setophaga virens/73eb27359b4ec2031c87f9d516628dfd.jpg\",\n  \"278782.jpg\": \"Amphibia/Ensatina eschscholtzii oregonensis/7f2be0ca5e1a641011d149f7cdd55aa6.jpg\",\n  \"278784.jpg\": \"Amphibia/Ensatina eschscholtzii oregonensis/a51b32675c4e69395568faa923d1da20.jpg\",\n  \"278795.jpg\": \"Amphibia/Ensatina eschscholtzii oregonensis/c4ea0a5036a1b69f43b1215174386ee5.jpg\",\n  \"278796.jpg\": \"Amphibia/Ensatina eschscholtzii oregonensis/477c116cb6298b15dac9d5fcd1f1a916.jpg\",\n  \"278797.jpg\": \"Amphibia/Ensatina eschscholtzii oregonensis/f745aa78869fb6c02965410a0436aaa1.jpg\",\n  \"278798.jpg\": \"Amphibia/Ensatina eschscholtzii oregonensis/957cb5baa4b586647b43797fdb8e3baf.jpg\",\n  \"278801.jpg\": \"Amphibia/Ensatina eschscholtzii oregonensis/34445d2bef8025c5f60e1a941d87f855.jpg\",\n  \"278802.jpg\": \"Amphibia/Ensatina eschscholtzii oregonensis/1dd8a6fba889c453c74f5057b17ddf78.jpg\",\n  \"278803.jpg\": \"Amphibia/Ensatina eschscholtzii oregonensis/682e0069b57337a9e195d254d53b3bbc.jpg\",\n  \"278804.jpg\": \"Amphibia/Ensatina eschscholtzii oregonensis/84d2aff0d27c2b787538edaa4e4ec8ff.jpg\",\n  \"278805.jpg\": \"Amphibia/Ensatina eschscholtzii oregonensis/b5dd943d3f2a876abda17f5d515e8cf4.jpg\",\n  \"278806.jpg\": \"Amphibia/Ensatina eschscholtzii oregonensis/912378db3921d91c3e5e9efe6fa67f61.jpg\",\n  \"278807.jpg\": \"Amphibia/Ensatina eschscholtzii oregonensis/b948b94aab3659cab402696566690c2c.jpg\",\n  \"278808.jpg\": \"Amphibia/Ensatina eschscholtzii oregonensis/fcb9abbae251cb916240f976eaa87372.jpg\",\n  \"278809.jpg\": \"Amphibia/Ensatina eschscholtzii oregonensis/96e8e9d834722e3b36ef084fdb0cf2c2.jpg\",\n  \"278810.jpg\": \"Amphibia/Ensatina eschscholtzii oregonensis/31b5043688e0561ca61ff71464e84c14.jpg\",\n  \"278813.jpg\": \"Insecta/Pyronia tithonus/db0480ba9778e77d0e56f905c296f9da.jpg\",\n  \"278814.jpg\": \"Insecta/Pyronia tithonus/e4e4d553f1321ee5a6fdc7b0c72e1a7d.jpg\",\n  \"278819.jpg\": \"Insecta/Pyronia tithonus/a4bae407e0a257b47125d9636d522756.jpg\",\n  \"27882.jpg\": \"Aves/Setophaga virens/95128e1a81f856579113280b5553e389.jpg\",\n  \"278821.jpg\": \"Insecta/Pyronia tithonus/081747c4ff7426c8c46acbc94eebc654.jpg\",\n  \"278829.jpg\": \"Insecta/Pyronia tithonus/82783d60b8eec559170b1a7366760f8f.jpg\",\n  \"278844.jpg\": \"Insecta/Trigonopeltastes delta/f618033e396133fba27b90565ca745ad.jpg\",\n  \"278846.jpg\": \"Insecta/Trigonopeltastes delta/3ff1af26e8a409988d9634b66ad655f2.jpg\",\n  \"278847.jpg\": \"Insecta/Trigonopeltastes delta/37df8cfe829575d1936b6cb5804e3c93.jpg\",\n  \"27885.jpg\": \"Aves/Setophaga virens/57cd3541d62ab7e5bb04bda309976957.jpg\",\n  \"27886.jpg\": \"Aves/Setophaga virens/f0d698cd1f4c1bb526aa048c2a5cb0ab.jpg\",\n  \"278943.jpg\": \"Aves/Vermivora chrysoptera/903bfee5bab9ddc018cef359d67c8c9a.jpg\",\n  \"278947.jpg\": \"Aves/Vermivora chrysoptera/5125fe92ef4016f3844f205b2837117b.jpg\",\n  \"278948.jpg\": \"Aves/Vermivora chrysoptera/bd5d116691571434aa7e3fa87343464d.jpg\",\n  \"278949.jpg\": \"Aves/Vermivora chrysoptera/46cbc1d7a8d39a78eaa736bc16f52ed6.jpg\",\n  \"278953.jpg\": \"Aves/Vermivora chrysoptera/584fade5f5d41bcf36bdb76f2bd04fde.jpg\",\n  \"278954.jpg\": \"Aves/Vermivora chrysoptera/1409fb083a844834c6b164e1a7a424c7.jpg\",\n  \"278957.jpg\": \"Aves/Vermivora chrysoptera/f9dfdc3a18c8504c3deae3d60ff4e4da.jpg\",\n  \"278982.jpg\": \"Aves/Dacelo novaeguineae/ea1e8bdb45bceb235a23fd2d77e9f812.jpg\",\n  \"278983.jpg\": \"Aves/Dacelo novaeguineae/9702b9288914d2884494b45212543f1f.jpg\",\n  \"278984.jpg\": \"Aves/Dacelo novaeguineae/ad4067a19ee6b622b808e4ce175d3ed1.jpg\",\n  \"278986.jpg\": \"Aves/Dacelo novaeguineae/adbc68793f440173a836941a521d74f1.jpg\",\n  \"278987.jpg\": \"Aves/Dacelo novaeguineae/fa4c46af684bb1561da729a26de05318.jpg\",\n  \"278989.jpg\": \"Aves/Dacelo novaeguineae/31a96ff238a1b17a26181f976439bd87.jpg\",\n  \"27899.jpg\": \"Aves/Setophaga virens/672abd86a38cd1549b38a37c18ff5914.jpg\",\n  \"278990.jpg\": \"Aves/Dacelo novaeguineae/fab8a98984b6b0e20557cfbcc9aee080.jpg\",\n  \"278993.jpg\": \"Aves/Dacelo novaeguineae/5ecc2a6ddf53f7c4975efd2e653e09f3.jpg\",\n  \"278996.jpg\": \"Aves/Dacelo novaeguineae/0aefd6415cf88293d5d059cb28fd82e9.jpg\",\n  \"279001.jpg\": \"Aves/Dacelo novaeguineae/f366588bd175920c09768b8b698661f2.jpg\",\n  \"279003.jpg\": \"Aves/Dacelo novaeguineae/ee50b573cd398e90c495eac29426677f.jpg\",\n  \"279014.jpg\": \"Aves/Dacelo novaeguineae/70207ff8a3b3fe4158e8965b1ae38fe1.jpg\",\n  \"279015.jpg\": \"Aves/Dacelo novaeguineae/752d5e3eb4cc9aecaa7faf02536ab029.jpg\",\n  \"279016.jpg\": \"Aves/Dacelo novaeguineae/94e650e27e5b8ef359efc3fa27dfc4c8.jpg\",\n  \"279017.jpg\": \"Aves/Dacelo novaeguineae/b3e9e1e210587d009ab26a3e56384a56.jpg\",\n  \"279018.jpg\": \"Aves/Dacelo novaeguineae/caa9836422538fbee0be1750046bb6cf.jpg\",\n  \"279024.jpg\": \"Aves/Dacelo novaeguineae/13a092285bca19d881fcae911f46a688.jpg\",\n  \"279025.jpg\": \"Aves/Dacelo novaeguineae/e6198ea45ca2b013e28b87402729ac0f.jpg\",\n  \"279027.jpg\": \"Aves/Dacelo novaeguineae/a8019aec694ea944b12895feb6a41c3b.jpg\",\n  \"279028.jpg\": \"Aves/Dacelo novaeguineae/5708d5e73e7c8826faaf8c7fde57866a.jpg\",\n  \"279029.jpg\": \"Aves/Dacelo novaeguineae/5b82fdee2dc3aaed18577bba0040e032.jpg\",\n  \"279030.jpg\": \"Aves/Dacelo novaeguineae/6025f6f7387d8f00694b35eadabc0022.jpg\",\n  \"2793.jpg\": \"Aves/Picoides pubescens/0d02f5c3c0e49b06e974a8c00d8a3ab5.jpg\",\n  \"279396.jpg\": \"Insecta/Plectrodera scalator/bc948c5aaed292d7aeb776dbaf07aef0.jpg\",\n  \"279397.jpg\": \"Insecta/Plectrodera scalator/063489db732ac9466f48541bede78982.jpg\",\n  \"279409.jpg\": \"Insecta/Plectrodera scalator/a70993fb1f989428a84c2ec20eb02d24.jpg\",\n  \"279411.jpg\": \"Insecta/Plectrodera scalator/e0fb563e1defa95746f24fed958677e5.jpg\",\n  \"27948.jpg\": \"Mollusca/Leukoma staminea/9a26d7c1c8c53d6edeba88c1ddb2b1f7.jpg\",\n  \"27952.jpg\": \"Mollusca/Leukoma staminea/47be21d8b5ef81ccb3eea8c35ccdd6f0.jpg\",\n  \"27953.jpg\": \"Mollusca/Leukoma staminea/581b3375f889a2443b8cc0f2ae86f832.jpg\",\n  \"27970.jpg\": \"Mollusca/Leukoma staminea/a9b9885d062df47b942b2f1f7f1ab0d6.jpg\",\n  \"27974.jpg\": \"Mollusca/Leukoma staminea/d905ae09c71b2721991268803e3cda90.jpg\",\n  \"27975.jpg\": \"Mollusca/Leukoma staminea/4b243b12c282b4cf3859262e0b5495ab.jpg\",\n  \"27976.jpg\": \"Mollusca/Leukoma staminea/63c8e2d82ef1113fc9ed30f2140b471c.jpg\",\n  \"27978.jpg\": \"Mollusca/Leukoma staminea/92c57e10876cf28879e6b1376d481859.jpg\",\n  \"27979.jpg\": \"Mollusca/Leukoma staminea/19df4fb8ce52ee8c8cbb6e0972e7e91a.jpg\",\n  \"27982.jpg\": \"Mollusca/Leukoma staminea/deac4be55cadd31c69a8d6563996572a.jpg\",\n  \"27985.jpg\": \"Mollusca/Leukoma staminea/ce8cad7217ff4d1f78df7e1795b3b90c.jpg\",\n  \"27986.jpg\": \"Mollusca/Leukoma staminea/f69ce8db3d26a2e80f253c012186a497.jpg\",\n  \"27987.jpg\": \"Mollusca/Leukoma staminea/d5e69c6c336b3b1d1e43023664f62195.jpg\",\n  \"27988.jpg\": \"Mollusca/Leukoma staminea/409b282121a2d23e66bca1a3d449ca12.jpg\",\n  \"27992.jpg\": \"Mollusca/Leukoma staminea/ef50df68db47287ceb3d6c37c06beef5.jpg\",\n  \"27993.jpg\": \"Mollusca/Leukoma staminea/144e26e31399b2e04a043049505ce301.jpg\",\n  \"27994.jpg\": \"Mollusca/Leukoma staminea/453c73da621aea0310061d33aff5d9a9.jpg\",\n  \"28001.jpg\": \"Aves/Tringa flavipes/ac63eea9518abea099cbf096aefcfa7d.jpg\",\n  \"28006.jpg\": \"Aves/Tringa flavipes/365dc5df9753405d2af591efbeafe928.jpg\",\n  \"28007.jpg\": \"Aves/Tringa flavipes/9dc9aaa4456397bbd9fc77862a167c4c.jpg\",\n  \"28010.jpg\": \"Aves/Tringa flavipes/e81e71a05101d043a5ecff8a9ede5b5a.jpg\",\n  \"280155.jpg\": \"Aves/Bartramia longicauda/00637621ea0b1f47af8bf00ea0308633.jpg\",\n  \"280156.jpg\": \"Aves/Bartramia longicauda/5448d0c76f25877c2b777e30de9293a2.jpg\",\n  \"280157.jpg\": \"Aves/Bartramia longicauda/55f7ce2d60d2ad860c48987695707b43.jpg\",\n  \"280159.jpg\": \"Aves/Bartramia longicauda/91fb58426b0bda48c5f4692995b91635.jpg\",\n  \"280162.jpg\": \"Aves/Bartramia longicauda/5f2659ace15e4afd398e0faf60c3efde.jpg\",\n  \"280163.jpg\": \"Aves/Bartramia longicauda/68b0694bf50bfb5c683da1465da5d79c.jpg\",\n  \"280166.jpg\": \"Aves/Bartramia longicauda/a86d3c91d09b37c7f3754a89de7e8d6c.jpg\",\n  \"280167.jpg\": \"Aves/Bartramia longicauda/310d07064b89271aee81ba74d6370bde.jpg\",\n  \"280169.jpg\": \"Aves/Bartramia longicauda/2358e52e214700d98aa1a02d2e3e40fb.jpg\",\n  \"280170.jpg\": \"Aves/Bartramia longicauda/1ba1ebe94831d7882cec5672ee9601e8.jpg\",\n  \"280171.jpg\": \"Aves/Bartramia longicauda/8b7e641d93c3f3d454132618c0e3d6ee.jpg\",\n  \"280172.jpg\": \"Aves/Bartramia longicauda/fb99fa5012251be8f1cc835373164972.jpg\",\n  \"280173.jpg\": \"Aves/Bartramia longicauda/4a5bb9dfec42599279f4e1c5c1f687e2.jpg\",\n  \"280174.jpg\": \"Aves/Bartramia longicauda/48e79a1d413ea896178f67fb60aa7b34.jpg\",\n  \"280178.jpg\": \"Aves/Bartramia longicauda/d9673a636ed19a575ec7bde1c0863eb8.jpg\",\n  \"280179.jpg\": \"Aves/Bartramia longicauda/db1651467a94562aac111ba98eb62bdf.jpg\",\n  \"280180.jpg\": \"Aves/Bartramia longicauda/2ebc3baee29af5cbba01e6d1ea67ccfa.jpg\",\n  \"280185.jpg\": \"Aves/Bartramia longicauda/6588dd3ef2c9e302dbe095a9dc3829e9.jpg\",\n  \"280186.jpg\": \"Aves/Bartramia longicauda/bad0aadcf3bb6a41e2537fa312a851dc.jpg\",\n  \"280188.jpg\": \"Aves/Bartramia longicauda/55c89de4857e4404bb7612b8323c36d6.jpg\",\n  \"280191.jpg\": \"Aves/Bartramia longicauda/37f2c66b24e0603c0dcbf8eb036b9b8b.jpg\",\n  \"280192.jpg\": \"Aves/Bartramia longicauda/c2401acfb8bd944e36233b7d04b8120f.jpg\",\n  \"28021.jpg\": \"Aves/Tringa flavipes/b425e13f09dad2029cd935b6cf902f4b.jpg\",\n  \"28026.jpg\": \"Aves/Tringa flavipes/f8cfda57d821dc503f81c21feb2f7a9a.jpg\",\n  \"28028.jpg\": \"Aves/Tringa flavipes/5046d952612fcbabf3368a819a6ae383.jpg\",\n  \"280281.jpg\": \"Insecta/Poanes zabulon/968a17d6685ac05376e214da6219e310.jpg\",\n  \"280283.jpg\": \"Insecta/Poanes zabulon/0120af294d05e395a1eeb8afa817e1ba.jpg\",\n  \"280294.jpg\": \"Insecta/Poanes zabulon/2ef47aebe2aa35198719615e3f53e444.jpg\",\n  \"280298.jpg\": \"Insecta/Poanes zabulon/f5a5293cab972ec2fbc2fb6445bc98f1.jpg\",\n  \"280299.jpg\": \"Insecta/Poanes zabulon/115fd92df410a38ce6033f4c7220d6b8.jpg\",\n  \"280300.jpg\": \"Insecta/Poanes zabulon/2dd34c314fbef08368fe7cdbc4b751bf.jpg\",\n  \"280305.jpg\": \"Insecta/Poanes zabulon/b734e63683fc614495efc420778c3859.jpg\",\n  \"280309.jpg\": \"Insecta/Poanes zabulon/3b81cb0f2eb4c6283cb785488827f1a6.jpg\",\n  \"280310.jpg\": \"Insecta/Poanes zabulon/c179fb06b71ca0c339dc03aa054399ee.jpg\",\n  \"280317.jpg\": \"Insecta/Poanes zabulon/b7a92c24b1b25e03506e95f8dac2c6a0.jpg\",\n  \"280319.jpg\": \"Insecta/Poanes zabulon/15840d2a150ddfb48c3752d2f15c6719.jpg\",\n  \"280324.jpg\": \"Insecta/Poanes zabulon/0987cefc422bd5aed256b00e506aa562.jpg\",\n  \"280336.jpg\": \"Insecta/Poanes zabulon/0c7b7b5a9312aa49f4c2f7429cf40239.jpg\",\n  \"280337.jpg\": \"Insecta/Poanes zabulon/f3c50fa281b8507f61363718802857ca.jpg\",\n  \"280339.jpg\": \"Insecta/Poanes zabulon/8b031c3a42cf9fe0b44bc4aebefcd2ac.jpg\",\n  \"280340.jpg\": \"Insecta/Poanes zabulon/54a8850f58d518567584b6929ca4c807.jpg\",\n  \"280342.jpg\": \"Insecta/Poanes zabulon/1105c08849c16dfdef782646656a9712.jpg\",\n  \"280344.jpg\": \"Insecta/Poanes zabulon/826a2286b3757eb65ad6fcf4cb6a6a24.jpg\",\n  \"280347.jpg\": \"Insecta/Poanes zabulon/8709b89ef5e5102ed247b837f32f019e.jpg\",\n  \"280351.jpg\": \"Insecta/Poanes zabulon/99479b7c1d8eb057ccea56bf6f335698.jpg\",\n  \"28042.jpg\": \"Aves/Tringa flavipes/29946a97cfafb1317bfe15ee29ffcf76.jpg\",\n  \"28048.jpg\": \"Aves/Tringa flavipes/c65a41641b26411b9eb1fcb6f46099f3.jpg\",\n  \"28050.jpg\": \"Aves/Tringa flavipes/5ddbdc320c0d362a21e7d6ef3946cf3d.jpg\",\n  \"28051.jpg\": \"Aves/Tringa flavipes/47d86bf94be6a86ebc2594bfc17dfb15.jpg\",\n  \"28058.jpg\": \"Aves/Tringa flavipes/5899f2da443fbb137e7b4f2d2c04e4a7.jpg\",\n  \"28068.jpg\": \"Aves/Tringa flavipes/cb3783cbff6adcbdf606758038eccd00.jpg\",\n  \"28077.jpg\": \"Aves/Tringa flavipes/a82cb287d3d0f07d397da6838c184eac.jpg\",\n  \"28083.jpg\": \"Aves/Tringa flavipes/67fb6c1523903d6b17d5b03147dc69e5.jpg\",\n  \"280910.jpg\": \"Aves/Uria aalge/96c92ff582b9d8b9e9b083552b215f05.jpg\",\n  \"280911.jpg\": \"Aves/Uria aalge/53ae20b63f4cecfe4e19f339c2f342da.jpg\",\n  \"280918.jpg\": \"Aves/Uria aalge/10331e12b29947649d69f06cd4a1ea24.jpg\",\n  \"280931.jpg\": \"Aves/Uria aalge/9a0bda64d535bea84f215074144b2eb4.jpg\",\n  \"280936.jpg\": \"Aves/Uria aalge/1822438670c4d56f8fb8170326ec5b18.jpg\",\n  \"280937.jpg\": \"Aves/Uria aalge/601bcfbc84df936c4f3c15b178453b50.jpg\",\n  \"280982.jpg\": \"Aves/Uria aalge/9080ca329b57a090924d95b360fe18cb.jpg\",\n  \"280988.jpg\": \"Aves/Uria aalge/cde7f47ebf1bfbfcb89fecf07876fe3f.jpg\",\n  \"281019.jpg\": \"Aves/Uria aalge/ddbc95d7288ee1038846ff92b2b3cf35.jpg\",\n  \"281021.jpg\": \"Aves/Uria aalge/7d0885bd8b827b484ca5fb23f76887a5.jpg\",\n  \"281023.jpg\": \"Aves/Uria aalge/7cf43f853ac3ebc10329a6d6a54f3af1.jpg\",\n  \"28104.jpg\": \"Aves/Tringa flavipes/6817df21c65e990720077e5a95e7b0f5.jpg\",\n  \"281043.jpg\": \"Aves/Uria aalge/bbfe7432c47c5fe50a9949d29ad3d4b8.jpg\",\n  \"281046.jpg\": \"Aves/Uria aalge/714a0eeb75582b8765e7d891dfe177e5.jpg\",\n  \"281057.jpg\": \"Mammalia/Ursus arctos horribilis/ca77315baa97f41b833a39ef241d5743.jpg\",\n  \"281061.jpg\": \"Mammalia/Ursus arctos horribilis/c926995e8099afe3722644f7e2503d28.jpg\",\n  \"281075.jpg\": \"Mammalia/Ursus arctos horribilis/5226c6e1a4de216f232665f1cd28fbd2.jpg\",\n  \"281086.jpg\": \"Mammalia/Ursus arctos horribilis/a47b4be9aa9bab4ae1feb1f8f0c92c37.jpg\",\n  \"281087.jpg\": \"Mammalia/Ursus arctos horribilis/3d09effaad21db97ef3e42d0eb423cc0.jpg\",\n  \"281124.jpg\": \"Amphibia/Ensatina eschscholtzii xanthoptica/c18804f630f59e9636af3cc9af28a691.jpg\",\n  \"281125.jpg\": \"Amphibia/Ensatina eschscholtzii xanthoptica/ff14f74597119ccb1df23d230c3f3036.jpg\",\n  \"281126.jpg\": \"Amphibia/Ensatina eschscholtzii xanthoptica/51d78bda6e5e09fea7a45911f4871f3e.jpg\",\n  \"281128.jpg\": \"Amphibia/Ensatina eschscholtzii xanthoptica/64150a621aa7798e11b8d4a4f274526c.jpg\",\n  \"28113.jpg\": \"Aves/Tringa flavipes/21abc416e3fa78cd0bfc4671b0e89749.jpg\",\n  \"281143.jpg\": \"Amphibia/Ensatina eschscholtzii xanthoptica/65893e46baf93f25b521840f757bebc1.jpg\",\n  \"281144.jpg\": \"Amphibia/Ensatina eschscholtzii xanthoptica/24b90f5edce523994f75769f5bba76b4.jpg\",\n  \"281146.jpg\": \"Amphibia/Ensatina eschscholtzii xanthoptica/2a9ae5350f74a7a5f379aa4fd93f1320.jpg\",\n  \"281147.jpg\": \"Amphibia/Ensatina eschscholtzii xanthoptica/a57a5e8de11d0631244c1a8e60381a01.jpg\",\n  \"281150.jpg\": \"Amphibia/Ensatina eschscholtzii xanthoptica/39c0e99e68c3c1a803dffdf381e9088f.jpg\",\n  \"281151.jpg\": \"Amphibia/Ensatina eschscholtzii xanthoptica/9c4a95eb7055220d75016ab7e3b00f45.jpg\",\n  \"281152.jpg\": \"Amphibia/Ensatina eschscholtzii xanthoptica/e5782ef53fab468bcd2942da76aed574.jpg\",\n  \"281158.jpg\": \"Amphibia/Ensatina eschscholtzii xanthoptica/b14967d2a0bf4cca49eba5884c64773a.jpg\",\n  \"281159.jpg\": \"Amphibia/Ensatina eschscholtzii xanthoptica/f7a02db09a4143cb5b21ad79464d9474.jpg\",\n  \"281160.jpg\": \"Amphibia/Ensatina eschscholtzii xanthoptica/e5c867fc9611400dd4d1d75cdb78402c.jpg\",\n  \"281161.jpg\": \"Amphibia/Ensatina eschscholtzii xanthoptica/5d75aff78bdf435c62ae57b23eca12aa.jpg\",\n  \"281170.jpg\": \"Amphibia/Ensatina eschscholtzii xanthoptica/de8b684d1135e4e34df84181db99456a.jpg\",\n  \"281171.jpg\": \"Amphibia/Ensatina eschscholtzii xanthoptica/9d96c09025b78a3a8503067b37b4783e.jpg\",\n  \"281172.jpg\": \"Amphibia/Ensatina eschscholtzii xanthoptica/0b96aa5295d21a54050cd49543ded357.jpg\",\n  \"281176.jpg\": \"Amphibia/Ensatina eschscholtzii xanthoptica/a06f88316f14680ef3b0b0a21b29d863.jpg\",\n  \"281177.jpg\": \"Amphibia/Ensatina eschscholtzii xanthoptica/d56895ca6d9a9e21d75885650ccb9082.jpg\",\n  \"281178.jpg\": \"Amphibia/Ensatina eschscholtzii xanthoptica/2abc564b01e61f5b50595fc07c41aa45.jpg\",\n  \"281179.jpg\": \"Amphibia/Ensatina eschscholtzii xanthoptica/4ac6abd949ec499786cdbd8a6803ca06.jpg\",\n  \"281188.jpg\": \"Amphibia/Ensatina eschscholtzii xanthoptica/eaa3823fcd133036d9cec44d41db666e.jpg\",\n  \"281189.jpg\": \"Amphibia/Ensatina eschscholtzii xanthoptica/d0a152ba3f6130eba8da9d144d40be49.jpg\",\n  \"281192.jpg\": \"Amphibia/Ensatina eschscholtzii xanthoptica/7764b30c110ea8b6e0fe50b51a5dc0d3.jpg\",\n  \"28121.jpg\": \"Aves/Tringa flavipes/a0070d17bb01d7d4fc0d97b6ef127b39.jpg\",\n  \"281218.jpg\": \"Aves/Columbina talpacoti/4ed102bf4eee19ead1982e00e4554f3d.jpg\",\n  \"28122.jpg\": \"Aves/Tringa flavipes/e47501ec907df99ca67ea50fec715ac8.jpg\",\n  \"281229.jpg\": \"Aves/Columbina talpacoti/3f8376442d7c5660ee9390e1e6f5f318.jpg\",\n  \"281230.jpg\": \"Aves/Columbina talpacoti/411f54c1f0d1a5ac5efd8b881afbb60d.jpg\",\n  \"281231.jpg\": \"Aves/Columbina talpacoti/5b058718b4890dcb5c8f8835c70aa233.jpg\",\n  \"281232.jpg\": \"Aves/Columbina talpacoti/c16fc81d4897018067244e4508cfc422.jpg\",\n  \"281233.jpg\": \"Aves/Columbina talpacoti/0c0cc1a2a518481189681c202f798905.jpg\",\n  \"281237.jpg\": \"Aves/Columbina talpacoti/1dacd2b454c41cdcbc756568022daff9.jpg\",\n  \"281238.jpg\": \"Aves/Columbina talpacoti/c4fde701d28d30d2de7513e6fd0dd902.jpg\",\n  \"281247.jpg\": \"Aves/Columbina talpacoti/b269ed4e4d8b020adae2cfc58ded6ee3.jpg\",\n  \"281249.jpg\": \"Aves/Columbina talpacoti/f4ed93af057080de68e5ee4f859ee255.jpg\",\n  \"281259.jpg\": \"Aves/Columbina talpacoti/85e83f2a40962f9d56e83435ad31b0b3.jpg\",\n  \"281263.jpg\": \"Aves/Columbina talpacoti/dd74f6d8a10fee49486153912d42c281.jpg\",\n  \"281269.jpg\": \"Aves/Columbina talpacoti/f1af286fb1dd173700479fede03fff4a.jpg\",\n  \"281270.jpg\": \"Aves/Columbina talpacoti/9e8a9f4db225835a63552ff000324442.jpg\",\n  \"281271.jpg\": \"Aves/Columbina talpacoti/be39e64b2aecb8a436ec9bbb43824c1b.jpg\",\n  \"281272.jpg\": \"Aves/Columbina talpacoti/fe247cf65f99131108765263e16d772d.jpg\",\n  \"281274.jpg\": \"Aves/Columbina talpacoti/8c7e449481a0bb93401b48a9bb420c95.jpg\",\n  \"281284.jpg\": \"Aves/Aegithalos caudatus/ac192bc87cc3b54a34bb61bc1b29583e.jpg\",\n  \"281285.jpg\": \"Aves/Aegithalos caudatus/298d0fb69879c12be79ced498d495665.jpg\",\n  \"281286.jpg\": \"Aves/Aegithalos caudatus/d612562c7844775f8d827b94e6d0e759.jpg\",\n  \"281287.jpg\": \"Aves/Aegithalos caudatus/daf8c9e61fd37039afb7770e12f07077.jpg\",\n  \"281288.jpg\": \"Aves/Aegithalos caudatus/145883d5aa4e320be5a504183ab02459.jpg\",\n  \"281291.jpg\": \"Aves/Aegithalos caudatus/ea36938bb84e8dfeb240c35a6305242f.jpg\",\n  \"281296.jpg\": \"Aves/Aegithalos caudatus/4260adf4093fcbf18d70acdd49ccf077.jpg\",\n  \"281298.jpg\": \"Aves/Aegithalos caudatus/cd7806ab4a5694253191c18fe9629069.jpg\",\n  \"281299.jpg\": \"Aves/Aegithalos caudatus/5f60abb48ac6f3da3e7fc95f16d80998.jpg\",\n  \"281301.jpg\": \"Aves/Aegithalos caudatus/02e9234543874cf3c7afb0550151d983.jpg\",\n  \"281302.jpg\": \"Aves/Aegithalos caudatus/b3d277710f3ccc1d071bf19266168d31.jpg\",\n  \"281303.jpg\": \"Aves/Aegithalos caudatus/63a142bfe2b56fccd57ed8785f1a6484.jpg\",\n  \"281304.jpg\": \"Aves/Aegithalos caudatus/380a55885a641c33b1412d73f9e93d34.jpg\",\n  \"281306.jpg\": \"Aves/Aegithalos caudatus/6c011637c707bc9b38048cec31c99db4.jpg\",\n  \"281313.jpg\": \"Aves/Aegithalos caudatus/19897bb336fc1bf5752d3c4baea0d3f4.jpg\",\n  \"281316.jpg\": \"Aves/Aegithalos caudatus/a39c81a4af6e7963f1f64f6293bb272a.jpg\",\n  \"281317.jpg\": \"Aves/Aegithalos caudatus/4433089dd7e769606de4d589d3bb6a19.jpg\",\n  \"281323.jpg\": \"Aves/Aegithalos caudatus/64d92e0e3beeca7820ff95230f3e59e4.jpg\",\n  \"28133.jpg\": \"Reptilia/Thamnophis elegans vagrans/1e68d5489f2792e5d52a1c0999412213.jpg\",\n  \"281330.jpg\": \"Aves/Aegithalos caudatus/a4d4824e4b194a3a1f8f447f5723cd1a.jpg\",\n  \"281331.jpg\": \"Aves/Aegithalos caudatus/84743c7ca5a400ee8d5b0f683ff3a7ae.jpg\",\n  \"28135.jpg\": \"Reptilia/Thamnophis elegans vagrans/629b5c62af118008c6c7a91cbdfcd31f.jpg\",\n  \"28138.jpg\": \"Reptilia/Thamnophis elegans vagrans/97b80a30459c891ea9e4497a1d23e7ca.jpg\",\n  \"281401.jpg\": \"Mammalia/Kobus ellipsiprymnus/3320da1e8b9ba80344a883bc6a69b353.jpg\",\n  \"281404.jpg\": \"Mammalia/Kobus ellipsiprymnus/33265435226dab5cabd45e584c92f73f.jpg\",\n  \"28143.jpg\": \"Reptilia/Thamnophis elegans vagrans/21a264283114392148e1e7b6bcd1dbd9.jpg\",\n  \"281435.jpg\": \"Aves/Anas platyrhynchos/91c6ee70dc19a5ec5025aee289be2ca6.jpg\",\n  \"28147.jpg\": \"Reptilia/Thamnophis elegans vagrans/f8d56935a5f5ecee1241040dcada6af4.jpg\",\n  \"28148.jpg\": \"Reptilia/Thamnophis elegans vagrans/27da7f89d4be8ad25caee651f2333212.jpg\",\n  \"28149.jpg\": \"Reptilia/Thamnophis elegans vagrans/0c6a28c304b21bdf91b4243f1f42b58a.jpg\",\n  \"281529.jpg\": \"Aves/Anas platyrhynchos/6f0f29e1f8b465b5a17dce0431fec2d8.jpg\",\n  \"28164.jpg\": \"Reptilia/Thamnophis elegans vagrans/792297b452a6e8630493c34a1c92905d.jpg\",\n  \"28166.jpg\": \"Aves/Circus cyaneus hudsonius/ae945ce77a118acfeac38f9414b8eede.jpg\",\n  \"28168.jpg\": \"Aves/Circus cyaneus hudsonius/6d0cd68139051cf34390304d27d0a168.jpg\",\n  \"28169.jpg\": \"Aves/Circus cyaneus hudsonius/5b89c43c463eb06762d8acd98278d107.jpg\",\n  \"281886.jpg\": \"Aves/Anas platyrhynchos/d0965d3da36650059f617f9232d41ce3.jpg\",\n  \"2819.jpg\": \"Aves/Picoides pubescens/b8ac16db1b449b0d6a629009274500b1.jpg\",\n  \"282759.jpg\": \"Aves/Anas platyrhynchos/4f75c2955b1bd1263eb65fc252d72c3a.jpg\",\n  \"282761.jpg\": \"Aves/Anas platyrhynchos/11a86910864aef676e9f2e4373b58155.jpg\",\n  \"282880.jpg\": \"Aves/Anas platyrhynchos/b6004efc0c9e605dcf9ff11eee59ac41.jpg\",\n  \"282891.jpg\": \"Aves/Anas platyrhynchos/65c22d2b104cc540f6eb2f5a06bf7f64.jpg\",\n  \"283018.jpg\": \"Aves/Anas platyrhynchos/982aa7f74dd43637039a41e88c597489.jpg\",\n  \"283148.jpg\": \"Aves/Anas platyrhynchos/734667aac081b4a1a18d54d6205b7db2.jpg\",\n  \"2833.jpg\": \"Aves/Picoides pubescens/5d1d64a368f461294a45ee07b8cba0cf.jpg\",\n  \"28330.jpg\": \"Mammalia/Lasionycteris noctivagans/44cfb7b704dc0929bc53c52c2a776bd0.jpg\",\n  \"28335.jpg\": \"Mammalia/Lasionycteris noctivagans/7bed0b232c87fc8f8aeb0033820e4d79.jpg\",\n  \"283360.jpg\": \"Aves/Anas platyrhynchos/0a6dbbe804961b2859555f58887434ff.jpg\",\n  \"28341.jpg\": \"Mammalia/Lasionycteris noctivagans/c2aab22ae1b7561c614371c5f6111322.jpg\",\n  \"28347.jpg\": \"Mammalia/Lasionycteris noctivagans/d69ab14674e3609037923715f8186417.jpg\",\n  \"28350.jpg\": \"Reptilia/Elgaria multicarinata webbii/3aafa4e83566e9e4e6d843d2445efdb5.jpg\",\n  \"28351.jpg\": \"Reptilia/Elgaria multicarinata webbii/f319be5eab8d70d63fe8c1650fbdb4c3.jpg\",\n  \"283597.jpg\": \"Aves/Ardenna gravis/2621a84ee7435b73156ab3855f6a6c68.jpg\",\n  \"28360.jpg\": \"Reptilia/Elgaria multicarinata webbii/0abcc6080db2960641ad3db2ec207ec8.jpg\",\n  \"283600.jpg\": \"Aves/Ardenna gravis/15ceb42c876d45305cc79ad8195f0557.jpg\",\n  \"283601.jpg\": \"Aves/Ardenna gravis/dfed9c19fe871597eb631a139d4bf89e.jpg\",\n  \"283602.jpg\": \"Aves/Ardenna gravis/fa6b024a60a74f99f950772bdc970241.jpg\",\n  \"283607.jpg\": \"Aves/Ardenna gravis/78c38a203c46315299a73df288d5ce10.jpg\",\n  \"28365.jpg\": \"Reptilia/Elgaria multicarinata webbii/9353353d31f99490f90925c1c5a98673.jpg\",\n  \"283658.jpg\": \"Aves/Zenaida asiatica/5c062911b03ef1ad1fbdc462787ef625.jpg\",\n  \"283660.jpg\": \"Aves/Zenaida asiatica/d8fc05586346b29a91938d1520ce48d1.jpg\",\n  \"283680.jpg\": \"Aves/Zenaida asiatica/bf9573b700c0bdee48920c53e8358cce.jpg\",\n  \"283713.jpg\": \"Aves/Zenaida asiatica/3cc623f02cef837205df4ed56a877ac0.jpg\",\n  \"28374.jpg\": \"Reptilia/Elgaria multicarinata webbii/953c971fb2a46e87bfa5f0100e880221.jpg\",\n  \"28376.jpg\": \"Reptilia/Elgaria multicarinata webbii/4fc09abcd1e03a095016ea80f0bedb75.jpg\",\n  \"283763.jpg\": \"Aves/Zenaida asiatica/aea9b209621adc7520c5b52be3303eea.jpg\",\n  \"283767.jpg\": \"Aves/Zenaida asiatica/9bf9a366870116c2eff318858f77daef.jpg\",\n  \"283769.jpg\": \"Aves/Zenaida asiatica/736581f9706a8be858cd5edfc1ebe866.jpg\",\n  \"28378.jpg\": \"Reptilia/Elgaria multicarinata webbii/7fafc041fe7734dd48f1f2abbf9b17e7.jpg\",\n  \"283810.jpg\": \"Aves/Zenaida asiatica/a07ea35cac8267ef9490da8eba9f889b.jpg\",\n  \"283820.jpg\": \"Aves/Zenaida asiatica/970a864408521b1e0446f2b2c31bcde5.jpg\",\n  \"283864.jpg\": \"Aves/Zenaida asiatica/ee1257d4d35c42a458bff19238cc6c28.jpg\",\n  \"283865.jpg\": \"Aves/Zenaida asiatica/a315e3b7570fca806cf52799d12b03ac.jpg\",\n  \"283871.jpg\": \"Aves/Zenaida asiatica/1b469e602d0a72e2aa42420e778b0fd6.jpg\",\n  \"283884.jpg\": \"Aves/Zenaida asiatica/d5bcdbdc6f510aa5c084e433cfbba7c1.jpg\",\n  \"283892.jpg\": \"Aves/Zenaida asiatica/85c4cce633a2ce112808a960c2706c1b.jpg\",\n  \"283898.jpg\": \"Aves/Zenaida asiatica/33987e8298229684b47fd91fb7ad22b1.jpg\",\n  \"283942.jpg\": \"Aves/Zenaida asiatica/f35b2edbaf795b9b4f801279fabd9f24.jpg\",\n  \"283947.jpg\": \"Aves/Zenaida asiatica/880c16604717c4d24dd822cc5907aeb1.jpg\",\n  \"283949.jpg\": \"Aves/Zenaida asiatica/61e07d8fb80811130fe2d9856b2c15c9.jpg\",\n  \"283980.jpg\": \"Aves/Zenaida asiatica/a5789a34d7b38cc00ed1ba0b2df430e3.jpg\",\n  \"283998.jpg\": \"Aves/Zenaida asiatica/4e3ae8b2c55bdaea97f7a6cef47c1018.jpg\",\n  \"284.jpg\": \"Mollusca/Olivella biplicata/67c3626d806016ac556eeda3adbc6880.jpg\",\n  \"284031.jpg\": \"Aves/Tachycineta thalassina/7f062e813ffa54c62ded101ca8903dc0.jpg\",\n  \"284032.jpg\": \"Aves/Tachycineta thalassina/01d55c206faa927b26c6bc8fa89954e7.jpg\",\n  \"284035.jpg\": \"Aves/Tachycineta thalassina/a89efad4341ab86c596d6a5f7ec81e11.jpg\",\n  \"284046.jpg\": \"Aves/Tachycineta thalassina/a7cafbd64d927cac4d471d24d35df35a.jpg\",\n  \"284049.jpg\": \"Aves/Tachycineta thalassina/78e4e43d0d7ccc866679cc8f427a18f7.jpg\",\n  \"284055.jpg\": \"Aves/Tachycineta thalassina/90a3119cae77ec3a29880197772093c2.jpg\",\n  \"284069.jpg\": \"Aves/Tachycineta thalassina/8e5a0264deaf701a259aa4558c80b148.jpg\",\n  \"284085.jpg\": \"Aves/Tachycineta thalassina/a7fc9d452fa2b270a755ed4fa14d85cc.jpg\",\n  \"284092.jpg\": \"Aves/Tachycineta thalassina/1d620022ce06605cb39eeb31a7eb47de.jpg\",\n  \"284103.jpg\": \"Aves/Tachycineta thalassina/e153b3f048834aa904360b505c2968bb.jpg\",\n  \"284110.jpg\": \"Aves/Tachycineta thalassina/0ebea072484b59d548fd1476ab622401.jpg\",\n  \"284111.jpg\": \"Aves/Tachycineta thalassina/e67e17ec262278c31a456d276610f2a4.jpg\",\n  \"284120.jpg\": \"Aves/Tachycineta thalassina/15af7526aa94bbeb7faffd6403a338ac.jpg\",\n  \"284121.jpg\": \"Aves/Tachycineta thalassina/38fa7fb0bc80d1349e8d956f2ecc2ed3.jpg\",\n  \"284122.jpg\": \"Aves/Tachycineta thalassina/63206369fbe86bc0c37569e81e9b21f5.jpg\",\n  \"284125.jpg\": \"Aves/Tachycineta thalassina/8c6a3f9a6618b592a71d2147c38b1a50.jpg\",\n  \"284126.jpg\": \"Aves/Tachycineta thalassina/07354ba5a83c3ad9c8a744fc4aaec394.jpg\",\n  \"284130.jpg\": \"Aves/Tachycineta thalassina/38f8f02f2fb2dbefc4bad49611c31414.jpg\",\n  \"284133.jpg\": \"Aves/Tachycineta thalassina/edf71f6708eebf69238109acbf952ab4.jpg\",\n  \"284138.jpg\": \"Mammalia/Giraffa camelopardalis/587ad052b3209976fa3bfb632013a3c5.jpg\",\n  \"284139.jpg\": \"Mammalia/Giraffa camelopardalis/195ef77d6bbef0c9ddc3db9a9e74edb1.jpg\",\n  \"284142.jpg\": \"Mammalia/Giraffa camelopardalis/bdb360ad1cf7de7263b9f4f29bce8837.jpg\",\n  \"284155.jpg\": \"Mammalia/Giraffa camelopardalis/17f3116573d3f5b91f6d00f69cc7fcdf.jpg\",\n  \"284162.jpg\": \"Mammalia/Giraffa camelopardalis/8cd663569d05e0f79b84dd9b90b79401.jpg\",\n  \"284165.jpg\": \"Mammalia/Giraffa camelopardalis/c09fe0ffc675f7c2b76e12a1f7445e29.jpg\",\n  \"28418.jpg\": \"Reptilia/Elgaria multicarinata webbii/5d7e793145b77e39474469c097493638.jpg\",\n  \"28419.jpg\": \"Reptilia/Elgaria multicarinata webbii/61c5f566d5b0200685701e5aed989948.jpg\",\n  \"28420.jpg\": \"Reptilia/Elgaria multicarinata webbii/63a6a8d42cc9c29822dffcfa48bd60f0.jpg\",\n  \"284295.jpg\": \"Insecta/Sceliphron caementarium/416148b8f903c26b15bde23e9e775cf1.jpg\",\n  \"284302.jpg\": \"Insecta/Sceliphron caementarium/aed4bdcf6608c6a4d0162dd2ab2df2a9.jpg\",\n  \"284310.jpg\": \"Insecta/Sceliphron caementarium/19a066b6522980cac67cb7ad221deb45.jpg\",\n  \"284316.jpg\": \"Insecta/Sceliphron caementarium/3b756398c0a9865c113914311977449e.jpg\",\n  \"284317.jpg\": \"Insecta/Sceliphron caementarium/3a725ca6e3dd71b3a7ad6b5f309d0e04.jpg\",\n  \"284322.jpg\": \"Insecta/Sceliphron caementarium/39e7023bdc7a7c480fdd8482cf3fd683.jpg\",\n  \"284325.jpg\": \"Insecta/Sceliphron caementarium/f90d74b35efa8d5dad49509088d700a4.jpg\",\n  \"28433.jpg\": \"Reptilia/Elgaria multicarinata webbii/ced58f9bea1689f8d162b94748a9577f.jpg\",\n  \"284339.jpg\": \"Insecta/Sceliphron caementarium/e9ebbbc48c67dbe4f23e23ccb4dc2cd2.jpg\",\n  \"284363.jpg\": \"Insecta/Sceliphron caementarium/1199ef4eba9f4cd1fe30adbb1214d830.jpg\",\n  \"284366.jpg\": \"Insecta/Sceliphron caementarium/8e92ab009e3756059fecfaba3603599d.jpg\",\n  \"284369.jpg\": \"Insecta/Sceliphron caementarium/ed2eb2695341699a0c350977282f5152.jpg\",\n  \"284370.jpg\": \"Insecta/Sceliphron caementarium/cee494b2d5ebb910dbde7ad1daff7240.jpg\",\n  \"284400.jpg\": \"Insecta/Sceliphron caementarium/da8e0cfeb1009dfaf6c1e79da7e960f8.jpg\",\n  \"284403.jpg\": \"Insecta/Sceliphron caementarium/d62d07fe049406dc8026bf2baf422b9c.jpg\",\n  \"284409.jpg\": \"Insecta/Sceliphron caementarium/ad06fcb962abe33f8ff85fbc95d3844d.jpg\",\n  \"284421.jpg\": \"Insecta/Sceliphron caementarium/5acd81132dbaa744ab3a20afcca956af.jpg\",\n  \"284429.jpg\": \"Insecta/Sceliphron caementarium/bd6840d7e3d2bcdc082cb3082742d98b.jpg\",\n  \"284436.jpg\": \"Insecta/Sceliphron caementarium/96e27f52d6e6293234f6d8d4a8d47aac.jpg\",\n  \"284488.jpg\": \"Mollusca/Crepidula adunca/00e71f61f8366d23639bc1067e8eb7bc.jpg\",\n  \"284489.jpg\": \"Mollusca/Crepidula adunca/c5a537811f90bc5eb42b3afedb74eac7.jpg\",\n  \"284508.jpg\": \"Mollusca/Crepidula adunca/71cbdcc2b7f900405a9e3e6c21635599.jpg\",\n  \"284514.jpg\": \"Aves/Podiceps auritus/fb22705d25317e9d3778d13585797d5c.jpg\",\n  \"284524.jpg\": \"Aves/Podiceps auritus/7e5d8cbab140c21a429a573d39676ee8.jpg\",\n  \"284527.jpg\": \"Aves/Podiceps auritus/f0f5a4a4deff61315d6444e80f748200.jpg\",\n  \"284539.jpg\": \"Aves/Podiceps auritus/864f135d7dcfe9dd1d076ae7b3981747.jpg\",\n  \"28455.jpg\": \"Reptilia/Elgaria multicarinata webbii/dfb03c06293aee2065ae2fdd6f08719c.jpg\",\n  \"284552.jpg\": \"Aves/Podiceps auritus/72cc1406d388d2253dae097f860ac928.jpg\",\n  \"284582.jpg\": \"Aves/Podiceps auritus/dbd5e23690edebe3f03ec736c831d7b5.jpg\",\n  \"284601.jpg\": \"Aves/Podiceps auritus/36bbbade772e9f83bec82e32ac440aa1.jpg\",\n  \"284605.jpg\": \"Aves/Podiceps auritus/5bb86ed41d06a2cb561320294bcadda3.jpg\",\n  \"28462.jpg\": \"Reptilia/Elgaria multicarinata webbii/31a2d7c622055f6ce2b3bdc22509bace.jpg\",\n  \"284627.jpg\": \"Aves/Podiceps auritus/dddd0e2f98e81c4f10e273dc3d69faa6.jpg\",\n  \"284633.jpg\": \"Aves/Podiceps auritus/a426cb00c3fb2df865a3dc6246b8d48d.jpg\",\n  \"284642.jpg\": \"Aves/Podiceps auritus/822a0041c5b7f30208b2608c6e8e4bf8.jpg\",\n  \"28465.jpg\": \"Reptilia/Elgaria multicarinata webbii/8024f0c2a2865fe01547ca570bb869d1.jpg\",\n  \"284650.jpg\": \"Aves/Podiceps auritus/7406a61bd8b6408fc39742cd4ef42282.jpg\",\n  \"284666.jpg\": \"Aves/Podiceps auritus/ccd91c316f7dd21646f29dc536341b85.jpg\",\n  \"284667.jpg\": \"Aves/Podiceps auritus/b3c52f697bbe812bc311c72eaf5b0357.jpg\",\n  \"28467.jpg\": \"Reptilia/Elgaria multicarinata webbii/32a95f9a94dc6647630f229ce63f2161.jpg\",\n  \"284672.jpg\": \"Aves/Podiceps auritus/eaac610b477a48a7ab59cc5ef7982211.jpg\",\n  \"284676.jpg\": \"Aves/Podiceps auritus/7b6012d02668af049e99abe5b74fe995.jpg\",\n  \"284678.jpg\": \"Aves/Podiceps auritus/1201a0087f3b78f9c6d08fecbe952e02.jpg\",\n  \"284681.jpg\": \"Aves/Podiceps auritus/b5ea14388238fc8d776947aaaa8f4214.jpg\",\n  \"284682.jpg\": \"Aves/Podiceps auritus/17362e8bca25c9ac25ab19e53054ad2c.jpg\",\n  \"284689.jpg\": \"Aves/Podiceps auritus/d0c17744dea4a9190c43a1dbfea2fc27.jpg\",\n  \"284690.jpg\": \"Aves/Podiceps auritus/203ad23b8040d5d08f3cf18616050b95.jpg\",\n  \"284691.jpg\": \"Aves/Podiceps auritus/98e355cc3192ed25dc37373a97ba4938.jpg\",\n  \"284707.jpg\": \"Insecta/Megaphasma denticrus/6d75e6b50ce249422f6885ba2d1ecdbb.jpg\",\n  \"284708.jpg\": \"Insecta/Megaphasma denticrus/43654bbc60511a975db1f1f030902e33.jpg\",\n  \"284715.jpg\": \"Insecta/Megaphasma denticrus/2eba1c3768a6321f02e3ba46e2a99472.jpg\",\n  \"284716.jpg\": \"Insecta/Megaphasma denticrus/dba645e79f2ee3f296d4e28775960dc1.jpg\",\n  \"284717.jpg\": \"Insecta/Megaphasma denticrus/76f79818500ff796aad2467962248d0e.jpg\",\n  \"28472.jpg\": \"Reptilia/Elgaria multicarinata webbii/156d9fea887f4eff08db3588b8cfca9c.jpg\",\n  \"284721.jpg\": \"Insecta/Megaphasma denticrus/5cf602efa33c8c6f3a58a4f311d8f0d5.jpg\",\n  \"284813.jpg\": \"Aves/Anas bahamensis/9eb56d9fa10cc7b136489aad7a8e7c9e.jpg\",\n  \"28482.jpg\": \"Reptilia/Elgaria multicarinata webbii/fba9c2b54a869fd60a54147096f90c02.jpg\",\n  \"284820.jpg\": \"Mollusca/Hermissenda opalescens/115bb142ab6deba8a6c04034e5221db7.jpg\",\n  \"284821.jpg\": \"Mollusca/Hermissenda opalescens/6371c7a69acf293aa22856a60705252a.jpg\",\n  \"284852.jpg\": \"Mollusca/Hermissenda opalescens/816c3012795342773d5b656f8ec75d10.jpg\",\n  \"284853.jpg\": \"Mollusca/Hermissenda opalescens/ad55a35e0081985cf1073800fdd3944e.jpg\",\n  \"284854.jpg\": \"Mollusca/Hermissenda opalescens/78eba611958535b93dd80b0546f41f62.jpg\",\n  \"28488.jpg\": \"Reptilia/Elgaria multicarinata webbii/afc5771efbf6b95026927fcad7eab066.jpg\",\n  \"284886.jpg\": \"Mollusca/Hermissenda opalescens/d8b944be717c668ba2a9faec4ac2bbd4.jpg\",\n  \"284888.jpg\": \"Mollusca/Hermissenda opalescens/6271d0dcecbb3eb6ecfe94eb31d3970f.jpg\",\n  \"284889.jpg\": \"Mollusca/Hermissenda opalescens/a7959f00ae483bc03a34fd0e25eef7eb.jpg\",\n  \"284890.jpg\": \"Mollusca/Hermissenda opalescens/578bce246b3522d8b13a6d86c6a8b142.jpg\",\n  \"284893.jpg\": \"Mollusca/Hermissenda opalescens/d91e8fb0488c9606a22e33a79514cfea.jpg\",\n  \"284900.jpg\": \"Mollusca/Hermissenda opalescens/e0503261cb50007e7db01ff022d52ac8.jpg\",\n  \"284901.jpg\": \"Mollusca/Hermissenda opalescens/f6b40f991e4e0ec2aa09fcd21899d135.jpg\",\n  \"284902.jpg\": \"Mollusca/Hermissenda opalescens/99f06bf32db6323ea46e5cd181f6a767.jpg\",\n  \"284903.jpg\": \"Mollusca/Hermissenda opalescens/5bef102791de91dd4add1d30174c85a3.jpg\",\n  \"284910.jpg\": \"Mollusca/Hermissenda opalescens/8a37c43cf3453946f334782bca2ff71e.jpg\",\n  \"284912.jpg\": \"Mollusca/Hermissenda opalescens/db4aa6f5e28f9da912c3a3907caa5c4f.jpg\",\n  \"284967.jpg\": \"Insecta/Manduca sexta/5d830ce3201f14cc7771d8a4ff911845.jpg\",\n  \"284972.jpg\": \"Insecta/Manduca sexta/35076780712d3dbd751e4792437b55cb.jpg\",\n  \"28498.jpg\": \"Reptilia/Elgaria multicarinata webbii/68bb4f7fd4a2880ad860a745d91cf378.jpg\",\n  \"284981.jpg\": \"Insecta/Manduca sexta/17d2413438517baa58a1dacf60bce334.jpg\",\n  \"284987.jpg\": \"Insecta/Manduca sexta/336610ba97996d19e2501d9136eed224.jpg\",\n  \"284999.jpg\": \"Insecta/Manduca sexta/2704a33cc4f5dc9dbaff414bb7c8afe3.jpg\",\n  \"285.jpg\": \"Mollusca/Olivella biplicata/5f2367d172ecb0c65d5492f9b9151426.jpg\",\n  \"285004.jpg\": \"Insecta/Manduca sexta/435922b1ff37b4bf7988be59a2608d9f.jpg\",\n  \"285011.jpg\": \"Insecta/Manduca sexta/a2dd4cdbbea669f0608b1beef573a231.jpg\",\n  \"285012.jpg\": \"Insecta/Manduca sexta/34b5b2ff341f5f963ae7cf3ccd9b04d0.jpg\",\n  \"285013.jpg\": \"Insecta/Manduca sexta/9877b9bb58ea051bc1fb75467464712e.jpg\",\n  \"285020.jpg\": \"Insecta/Manduca sexta/15bbcc3316d026c8258c0c0f3aa30621.jpg\",\n  \"285023.jpg\": \"Insecta/Manduca sexta/8232dfcf85d6fa814c8e9ea32c03e0cb.jpg\",\n  \"285024.jpg\": \"Insecta/Manduca sexta/878797055479750560816940aace36ea.jpg\",\n  \"285025.jpg\": \"Insecta/Manduca sexta/d0407bd0dee509dc120443cf5ac8510b.jpg\",\n  \"285032.jpg\": \"Insecta/Manduca sexta/b91ccf84207f9f7dda485c1cf9b62323.jpg\",\n  \"285039.jpg\": \"Insecta/Manduca sexta/c43f7f1d4b297ff1c3108d3ae89d1324.jpg\",\n  \"285048.jpg\": \"Insecta/Manduca sexta/94f061a06785540eaccf092b19f3ea08.jpg\",\n  \"285062.jpg\": \"Insecta/Manduca sexta/12916d069eed1c2d45bfa09d06413919.jpg\",\n  \"285064.jpg\": \"Insecta/Tetracis cachexiata/7b0c33fc5a08faceec22b981eec2cb1a.jpg\",\n  \"285067.jpg\": \"Insecta/Tetracis cachexiata/32804d27b91d222e3738fba7d3c1039a.jpg\",\n  \"285071.jpg\": \"Insecta/Tetracis cachexiata/c7ad131e4bd845caf70390e39cb57188.jpg\",\n  \"285074.jpg\": \"Insecta/Tetracis cachexiata/577491f3d0bfb90732075626afde5b34.jpg\",\n  \"285076.jpg\": \"Insecta/Tetracis cachexiata/f9235894635a935f29e29312d919743a.jpg\",\n  \"285081.jpg\": \"Insecta/Tetracis cachexiata/5be9511d8416998f51472dd852a7f346.jpg\",\n  \"285084.jpg\": \"Mammalia/Ammospermophilus leucurus/2af9aae71e1055cbf885ba52bb9209a3.jpg\",\n  \"285087.jpg\": \"Mammalia/Ammospermophilus leucurus/4482743b911fb2d8fa4937370e2f63ee.jpg\",\n  \"285089.jpg\": \"Mammalia/Ammospermophilus leucurus/70b325f56ceff85aa755884a19a6f979.jpg\",\n  \"285091.jpg\": \"Mammalia/Ammospermophilus leucurus/972c7d465297900c6f9b8ff69010cdf3.jpg\",\n  \"285093.jpg\": \"Mammalia/Ammospermophilus leucurus/3fa67dd2c04aab708f78f47959bddffc.jpg\",\n  \"285094.jpg\": \"Mammalia/Ammospermophilus leucurus/264bdb544e63b67a11dd85eeac54f800.jpg\",\n  \"285095.jpg\": \"Mammalia/Ammospermophilus leucurus/9e7268d00fd724565078d9f1e1470914.jpg\",\n  \"285096.jpg\": \"Mammalia/Ammospermophilus leucurus/7b9138dd059043e8f9e49b03d1e70649.jpg\",\n  \"285104.jpg\": \"Mammalia/Ammospermophilus leucurus/d6a1574e885cd65069c861ac83030348.jpg\",\n  \"285105.jpg\": \"Mammalia/Ammospermophilus leucurus/5cf444a126a987d79b1a26090b5a1a0e.jpg\",\n  \"285108.jpg\": \"Mammalia/Ammospermophilus leucurus/59bd1b2e0cfc32ffbeae9a95e235b7c4.jpg\",\n  \"285111.jpg\": \"Mammalia/Ammospermophilus leucurus/6bc65aecbc97318c5b663bb3712573c5.jpg\",\n  \"285112.jpg\": \"Mammalia/Ammospermophilus leucurus/9023d54ddc62647d76734db90d1db0e2.jpg\",\n  \"285113.jpg\": \"Mammalia/Ammospermophilus leucurus/17d13456288b0b1bdd252498a4447f82.jpg\",\n  \"285115.jpg\": \"Mammalia/Ammospermophilus leucurus/a7063ba00bcf8af637e9ed1db6584b90.jpg\",\n  \"285116.jpg\": \"Mammalia/Ammospermophilus leucurus/4290b2ce88266b8cba5beed0e42f70a1.jpg\",\n  \"285117.jpg\": \"Mammalia/Ammospermophilus leucurus/8bb9a3a9cd467d5866597be6e133fcf9.jpg\",\n  \"285119.jpg\": \"Mammalia/Ammospermophilus leucurus/4daa42fe19fa6950cce17f9591781e47.jpg\",\n  \"285122.jpg\": \"Mammalia/Ammospermophilus leucurus/fc15ffcd26ab552a8272bdf412ab91a7.jpg\",\n  \"285123.jpg\": \"Mammalia/Ammospermophilus leucurus/653b400cc020aca0f06915f6f46aefb4.jpg\",\n  \"285154.jpg\": \"Insecta/Erynnis baptisiae/27eae9483fc4f0e62646046b134bcda6.jpg\",\n  \"285155.jpg\": \"Insecta/Erynnis baptisiae/797a01dcfaa37905a8a8febf1f440824.jpg\",\n  \"285160.jpg\": \"Insecta/Erynnis baptisiae/419b4406034aee5952f2cff7b1aabd0c.jpg\",\n  \"285161.jpg\": \"Insecta/Erynnis baptisiae/010cf2b30260514ef8db4d15a44ef33c.jpg\",\n  \"285162.jpg\": \"Insecta/Erynnis baptisiae/1c1e549e5363cc2eb5d8224ebfd7dbfd.jpg\",\n  \"285163.jpg\": \"Insecta/Erynnis baptisiae/c0985076a2a9f7021616665e485b5fe6.jpg\",\n  \"285173.jpg\": \"Insecta/Erynnis baptisiae/45a51bc0880ac7750922a87662da68b5.jpg\",\n  \"285175.jpg\": \"Insecta/Erynnis baptisiae/f3ddb39d9d5602730447aa63330a9033.jpg\",\n  \"285176.jpg\": \"Insecta/Erynnis baptisiae/4996f6126e10155af3e2a230ca708f40.jpg\",\n  \"285177.jpg\": \"Insecta/Erynnis baptisiae/29b1fe72ce95259bb375606d8f5949d3.jpg\",\n  \"285179.jpg\": \"Insecta/Erynnis baptisiae/94dd47408f3a56a1c6b114a5dc72cce8.jpg\",\n  \"285180.jpg\": \"Insecta/Erynnis baptisiae/f8f16affc5265577e41dc8ee264a2f63.jpg\",\n  \"285182.jpg\": \"Insecta/Erynnis baptisiae/a63c19c2ffa72846410ee9f3d7316a61.jpg\",\n  \"285183.jpg\": \"Insecta/Erynnis baptisiae/1f9f03b53b23e5708c7cd6074b3f26ef.jpg\",\n  \"285185.jpg\": \"Insecta/Erynnis baptisiae/9449c6f752b54201768c4175a0d595c3.jpg\",\n  \"285186.jpg\": \"Insecta/Erynnis baptisiae/357bc5eed4b8e422a047e849192be7fc.jpg\",\n  \"285187.jpg\": \"Insecta/Erynnis baptisiae/ab92c5c20a463a08c09549386bbfff13.jpg\",\n  \"285189.jpg\": \"Insecta/Erynnis baptisiae/b293735185f6961d66d5227838ea50ca.jpg\",\n  \"285192.jpg\": \"Insecta/Erynnis baptisiae/935ea448df56ce96fde6da4702ac7be6.jpg\",\n  \"285195.jpg\": \"Insecta/Erynnis baptisiae/b1d74697f0cb721b5bfaa344395c5304.jpg\",\n  \"285196.jpg\": \"Insecta/Erynnis baptisiae/41628fbf17a944432c4a56b668670943.jpg\",\n  \"285201.jpg\": \"Insecta/Erynnis baptisiae/d619e8f2b0cb866a3c4a10992b62d214.jpg\",\n  \"28521.jpg\": \"Insecta/Nehalennia irene/63c76e5091fe8ede683246a2646c7093.jpg\",\n  \"28523.jpg\": \"Insecta/Nehalennia irene/4b9f3013a0f0e4e0dda83a4c0074e716.jpg\",\n  \"28529.jpg\": \"Insecta/Nehalennia irene/078e6fa8270492b00a206844a588ed60.jpg\",\n  \"285307.jpg\": \"Aves/Aphelocoma wollweberi/ddf164db5bc2e80c800213ea568b68c8.jpg\",\n  \"285308.jpg\": \"Aves/Aphelocoma wollweberi/af309a101853eed29a1711de7b905864.jpg\",\n  \"285315.jpg\": \"Aves/Aphelocoma wollweberi/a90668f530ff9e7000d2181596efc967.jpg\",\n  \"285316.jpg\": \"Aves/Aphelocoma wollweberi/7256b6223bbe3ac7bdf5cfa321b1a92a.jpg\",\n  \"285319.jpg\": \"Aves/Aphelocoma wollweberi/d6b14f9f54d9e9f22420d5687cadf984.jpg\",\n  \"285320.jpg\": \"Aves/Aphelocoma wollweberi/fcc6805b14e79dbdcc493e11e8fe6fb5.jpg\",\n  \"285328.jpg\": \"Aves/Aphelocoma wollweberi/9ebf756c7069e13936f7783b55ce2888.jpg\",\n  \"285329.jpg\": \"Aves/Aphelocoma wollweberi/de0e0f4e1c07cf6e18da37680521ff85.jpg\",\n  \"285331.jpg\": \"Aves/Aphelocoma wollweberi/7586092e00432470b936f659ab4a4a08.jpg\",\n  \"285332.jpg\": \"Aves/Aphelocoma wollweberi/694f1379e6793c7d61ef989e4eb2a38c.jpg\",\n  \"285339.jpg\": \"Aves/Aphelocoma wollweberi/0ac183cfed699748d818682aaf8e878d.jpg\",\n  \"285341.jpg\": \"Aves/Aphelocoma wollweberi/ea7343e27fb309c1d18abf334dcd60d2.jpg\",\n  \"285342.jpg\": \"Aves/Aphelocoma wollweberi/4f6c07dbb42fa30bbea0f60ac3b2aea9.jpg\",\n  \"285344.jpg\": \"Aves/Aphelocoma wollweberi/71dbc6e5d9d355b89c7176d2d3a20b13.jpg\",\n  \"285345.jpg\": \"Aves/Aphelocoma wollweberi/7aa8febc8f4893348d62540d3f6470fe.jpg\",\n  \"285346.jpg\": \"Aves/Aphelocoma wollweberi/13e2eaf7b286a8aac4ac62c9c5c84e97.jpg\",\n  \"285347.jpg\": \"Aves/Aphelocoma wollweberi/2550ecaeb52072d03300bbbf896304e1.jpg\",\n  \"285348.jpg\": \"Aves/Aphelocoma wollweberi/fffc8726f1ea36dfb30fbb5d5f023118.jpg\",\n  \"285352.jpg\": \"Aves/Aphelocoma wollweberi/e5efe7908026c2f71a0617d2ee75a026.jpg\",\n  \"285354.jpg\": \"Aves/Aphelocoma wollweberi/3b9f352d36d7382288a4ad7d8cb34890.jpg\",\n  \"285356.jpg\": \"Aves/Aphelocoma wollweberi/623f6405a6a4ea64c5f29e2b6f2ca0eb.jpg\",\n  \"285357.jpg\": \"Aves/Aphelocoma wollweberi/0b42b6ccd14232bb548f958565115b56.jpg\",\n  \"285358.jpg\": \"Insecta/Stenopelmatus fuscus/8641e7e2d434e87d8209976c6102e953.jpg\",\n  \"285359.jpg\": \"Insecta/Stenopelmatus fuscus/06f43efdf6c4ed941e39d52da54f4bac.jpg\",\n  \"285363.jpg\": \"Insecta/Stenopelmatus fuscus/0b16f75f7444b6e12fe8b46638e9e62c.jpg\",\n  \"285364.jpg\": \"Insecta/Stenopelmatus fuscus/700ea170518017b4f70943e9322933a2.jpg\",\n  \"285374.jpg\": \"Insecta/Stenopelmatus fuscus/9adfa6a1995e14d9be3d63b0b49dd750.jpg\",\n  \"285380.jpg\": \"Insecta/Stenopelmatus fuscus/e463e4894330582deba35783ea9438dc.jpg\",\n  \"285381.jpg\": \"Insecta/Stenopelmatus fuscus/0701c3b04288faa870297981bae5c912.jpg\",\n  \"285383.jpg\": \"Insecta/Stenopelmatus fuscus/d9ad07e795b56aee4a5fc3e6b320c269.jpg\",\n  \"285384.jpg\": \"Insecta/Stenopelmatus fuscus/5614018f48e7f297aa9c21506ac4556b.jpg\",\n  \"285394.jpg\": \"Mammalia/Pecari tajacu/ff0b51563fdf6498358d0dafd74bdaaf.jpg\",\n  \"285396.jpg\": \"Mammalia/Pecari tajacu/02d6555393f71c6d91433f6089d0ba34.jpg\",\n  \"285397.jpg\": \"Mammalia/Pecari tajacu/eec4c9af77ecc3a9c99104d2ee551780.jpg\",\n  \"285408.jpg\": \"Mammalia/Pecari tajacu/8e9f7c88a223498ea09c966f82dd8a3b.jpg\",\n  \"28541.jpg\": \"Insecta/Nehalennia irene/6a448d72e77328303fa04168753c5c37.jpg\",\n  \"285425.jpg\": \"Mammalia/Pecari tajacu/4e307a03d0c7cc82d9557e120b903391.jpg\",\n  \"285426.jpg\": \"Mammalia/Pecari tajacu/9e83affd03ecfdebef77182f238d09d9.jpg\",\n  \"285428.jpg\": \"Mammalia/Pecari tajacu/f2c34a6946d4bd0726d88d15f2fec403.jpg\",\n  \"285453.jpg\": \"Mammalia/Pecari tajacu/a31b35e2a0a63aa05381dd874b81a6d8.jpg\",\n  \"285469.jpg\": \"Mammalia/Pecari tajacu/39c8fbe3df0816b949109a6011dcb7c8.jpg\",\n  \"28547.jpg\": \"Insecta/Nehalennia irene/718a9f2bc3f14daeddda63f18fcd94bc.jpg\",\n  \"285471.jpg\": \"Mammalia/Pecari tajacu/77c56b02f4745e669619354740233528.jpg\",\n  \"285476.jpg\": \"Mammalia/Pecari tajacu/4d834a0ce2c2761963d7536364c9cd83.jpg\",\n  \"285478.jpg\": \"Mammalia/Pecari tajacu/54815f1e416f6e0659a62b13ff3d400d.jpg\",\n  \"285486.jpg\": \"Mammalia/Pecari tajacu/2964b21d55f2cb9e7276c0ba3d5392ae.jpg\",\n  \"285497.jpg\": \"Mammalia/Pecari tajacu/c58f1a129c9dec8b43e08cce7bf0a9eb.jpg\",\n  \"28551.jpg\": \"Insecta/Cydalima perspectalis/38347e81ae1c636433a06a65e897fc53.jpg\",\n  \"28556.jpg\": \"Insecta/Cydalima perspectalis/6d0962f0dce502ee1c288004ac25a379.jpg\",\n  \"28557.jpg\": \"Insecta/Cydalima perspectalis/11188dc2bee17ac7f33f16e5e9e87856.jpg\",\n  \"28561.jpg\": \"Insecta/Cydalima perspectalis/4935c010339a0516f392f713f9eb4261.jpg\",\n  \"28565.jpg\": \"Insecta/Cydalima perspectalis/ee937aa75440b93c990467e7bb7fa8d7.jpg\",\n  \"28573.jpg\": \"Insecta/Crocothemis erythraea/3342eb19b3a815d9b562072ead97765a.jpg\",\n  \"28574.jpg\": \"Insecta/Crocothemis erythraea/03e2abdefc460f823da1e2aeb3007d3f.jpg\",\n  \"28575.jpg\": \"Insecta/Crocothemis erythraea/001b7401177a0bb7deea3df6edd7ee33.jpg\",\n  \"28576.jpg\": \"Insecta/Crocothemis erythraea/9dee6f8be70be7d60721e35788101120.jpg\",\n  \"285767.jpg\": \"Insecta/Alypia octomaculata/04d65ea41147eea1ea56d968734451ae.jpg\",\n  \"285769.jpg\": \"Insecta/Alypia octomaculata/08e10ea7277616b958347bc855ae0009.jpg\",\n  \"28577.jpg\": \"Insecta/Crocothemis erythraea/71a3cbe2214daf05348ce5b3604c5baf.jpg\",\n  \"285772.jpg\": \"Insecta/Alypia octomaculata/866f6a2f8e7c8bb99f04fceb2dc4b443.jpg\",\n  \"285774.jpg\": \"Insecta/Alypia octomaculata/f2ceb5ca67841fa17dbb89e9ab0a7599.jpg\",\n  \"285777.jpg\": \"Insecta/Alypia octomaculata/4e8883057128e9438e7064b1663a0cf7.jpg\",\n  \"285778.jpg\": \"Insecta/Alypia octomaculata/8b6ab9b0c97037b85d3a591ef14f4129.jpg\",\n  \"285779.jpg\": \"Insecta/Alypia octomaculata/b8493e1fe6433be83e179b763aff173d.jpg\",\n  \"285780.jpg\": \"Insecta/Alypia octomaculata/cce865314282c4d672e585ba4161d3a8.jpg\",\n  \"285781.jpg\": \"Insecta/Alypia octomaculata/aa1d1390cef8f2bf4e961ddf031b667e.jpg\",\n  \"285782.jpg\": \"Insecta/Alypia octomaculata/8ec6901692189eeb5d02337d3f16fad4.jpg\",\n  \"285785.jpg\": \"Insecta/Alypia octomaculata/346788afd899bb3ba699033858dc9bb8.jpg\",\n  \"285786.jpg\": \"Insecta/Alypia octomaculata/ba0b3b9e865fb1f10deb707316d3babf.jpg\",\n  \"285789.jpg\": \"Insecta/Alypia octomaculata/f31fff8fa5a3cc607d33eaee17bd067d.jpg\",\n  \"28579.jpg\": \"Insecta/Crocothemis erythraea/3896fa4f7699a2c5d29611f804c08cbb.jpg\",\n  \"285797.jpg\": \"Insecta/Alypia octomaculata/c83785312c90574a3552a84edf15da56.jpg\",\n  \"285799.jpg\": \"Insecta/Alypia octomaculata/e5d2a35bf6e624cb357db1d24d942595.jpg\",\n  \"285800.jpg\": \"Insecta/Alypia octomaculata/78cc33dc418bd4f5652aeb7ff97b69fe.jpg\",\n  \"285807.jpg\": \"Insecta/Alypia octomaculata/c5ab92935bb3d15a91ce2625462817d6.jpg\",\n  \"285818.jpg\": \"Insecta/Alypia octomaculata/13067c0cf3e6a22e897420f79c28ef98.jpg\",\n  \"285821.jpg\": \"Reptilia/Iguana iguana/a2e1eee27a940d0af7b81dc0504a73d3.jpg\",\n  \"285823.jpg\": \"Reptilia/Iguana iguana/3aafb11a04ad70feba63657aeb11c734.jpg\",\n  \"28583.jpg\": \"Insecta/Crocothemis erythraea/32a762143bdaf101f631e363e2348ef4.jpg\",\n  \"28585.jpg\": \"Insecta/Crocothemis erythraea/bee8f5aabdd661e7b1a94f62d20433a8.jpg\",\n  \"285857.jpg\": \"Reptilia/Iguana iguana/dffe99029d1479938bdc47bcfea81746.jpg\",\n  \"28588.jpg\": \"Insecta/Crocothemis erythraea/3d30c78be3c7ddf35f32666d556c5c49.jpg\",\n  \"285881.jpg\": \"Reptilia/Iguana iguana/5e62f85bf33569af6a808565b096a517.jpg\",\n  \"28589.jpg\": \"Insecta/Crocothemis erythraea/b54259b329ba0bc55493d47ad0854ecd.jpg\",\n  \"285891.jpg\": \"Reptilia/Iguana iguana/8663a059f5bc6b3e7b4c6cae50c54ca9.jpg\",\n  \"28590.jpg\": \"Insecta/Crocothemis erythraea/1432e6fe9fe30e4233d445991e344302.jpg\",\n  \"285903.jpg\": \"Reptilia/Iguana iguana/50e864cdf231ffc01ac977906e538abf.jpg\",\n  \"285914.jpg\": \"Reptilia/Iguana iguana/78ce0f2abad50546226fb3a46031e69d.jpg\",\n  \"285915.jpg\": \"Reptilia/Iguana iguana/b1d92b15b09696484cddb9f0589ce188.jpg\",\n  \"285925.jpg\": \"Reptilia/Iguana iguana/c5e474cd584cf27e40f1c4135ee6136a.jpg\",\n  \"285933.jpg\": \"Reptilia/Iguana iguana/a192cb361770a552b142cd7c48f38714.jpg\",\n  \"285937.jpg\": \"Reptilia/Iguana iguana/1aabf952e4c79951e0e5ca37f47be487.jpg\",\n  \"285939.jpg\": \"Reptilia/Iguana iguana/67e3aa05ae4ff928357bf39e3b0351c5.jpg\",\n  \"285973.jpg\": \"Reptilia/Iguana iguana/a9972de329a9669b6936c4bf3507b7c2.jpg\",\n  \"285975.jpg\": \"Reptilia/Iguana iguana/3ef02ac788844ef5e2965fa01724aa92.jpg\",\n  \"285986.jpg\": \"Reptilia/Iguana iguana/06c65aeccdd9197c1871dc407147389a.jpg\",\n  \"286.jpg\": \"Mollusca/Olivella biplicata/12a204720bee5aba3d0cac42a8be6dea.jpg\",\n  \"28600.jpg\": \"Insecta/Crocothemis erythraea/9aa081530b30b4b697993787a68e3def.jpg\",\n  \"28601.jpg\": \"Insecta/Crocothemis erythraea/4f0015fbb55c6cc788db50ae5dea3b2c.jpg\",\n  \"286022.jpg\": \"Reptilia/Iguana iguana/896bfb6ce5006aca6c74f68ec0087734.jpg\",\n  \"286024.jpg\": \"Reptilia/Iguana iguana/436bc24d0751ed075930982eed7aee5a.jpg\",\n  \"28603.jpg\": \"Insecta/Crocothemis erythraea/c4551987e44351c79c4dbde7fd072e89.jpg\",\n  \"28607.jpg\": \"Insecta/Crocothemis erythraea/8c98217e776434b3cbae39de923aa6f3.jpg\",\n  \"286081.jpg\": \"Insecta/Spragueia leo/4866363c7b68aefa162bc03ff23306b0.jpg\",\n  \"286086.jpg\": \"Insecta/Spragueia leo/c469e6ef36b78b3358a420bb51c720e8.jpg\",\n  \"286089.jpg\": \"Insecta/Spragueia leo/893a5a32c7752c0c5e99276a95f7cd98.jpg\",\n  \"286090.jpg\": \"Insecta/Spragueia leo/cbd84e25cc232dd82d6abf4f750f8968.jpg\",\n  \"286106.jpg\": \"Aves/Polioptila caerulea/f891d080cfc4ba1e2400dfceb51fa5e8.jpg\",\n  \"28612.jpg\": \"Insecta/Crocothemis erythraea/82726edc0861e8c7cf79f402be6a4283.jpg\",\n  \"286121.jpg\": \"Aves/Polioptila caerulea/004504430114640c93b71eaa6aba0380.jpg\",\n  \"286125.jpg\": \"Aves/Polioptila caerulea/1a5ad7fd762b19a13ccaa3cdd0ff4f71.jpg\",\n  \"286127.jpg\": \"Aves/Polioptila caerulea/3924bd3d420f5d951563fa7a943c91fe.jpg\",\n  \"286131.jpg\": \"Aves/Polioptila caerulea/adbf55744c996f120c55f989738395e9.jpg\",\n  \"286163.jpg\": \"Aves/Polioptila caerulea/cf455a99401669287a5635f864d2010e.jpg\",\n  \"286173.jpg\": \"Aves/Polioptila caerulea/78ae772bbd63e0c7211f2d045d0445d5.jpg\",\n  \"286178.jpg\": \"Aves/Polioptila caerulea/4065266188ba5056ec5f4fce4705c38d.jpg\",\n  \"286184.jpg\": \"Aves/Polioptila caerulea/766fd4484dcdf43f1f241ce0d64b2023.jpg\",\n  \"28619.jpg\": \"Aves/Aulacorhynchus prasinus/680da99759ca222075aea0b448acb44b.jpg\",\n  \"286191.jpg\": \"Aves/Polioptila caerulea/e8a7c717947123cfd1e6916b2c76c51b.jpg\",\n  \"286195.jpg\": \"Aves/Polioptila caerulea/7be6bfb8ceaa1763b5f97a33dbb9f31e.jpg\",\n  \"286212.jpg\": \"Aves/Polioptila caerulea/f01b64aa62da9b41f6a4d8c80bd78013.jpg\",\n  \"286215.jpg\": \"Aves/Polioptila caerulea/b497e6b2451972aabb2899e0a3d5b84b.jpg\",\n  \"28623.jpg\": \"Aves/Aulacorhynchus prasinus/885997c3191c2733dee4fde836bf77d1.jpg\",\n  \"28624.jpg\": \"Aves/Aulacorhynchus prasinus/c900dd65435f383bddc3eba2c2149fde.jpg\",\n  \"286280.jpg\": \"Aves/Polioptila caerulea/11cc6b9be2cdc4566e28855d5cecde73.jpg\",\n  \"286289.jpg\": \"Aves/Polioptila caerulea/b289f452b21f72b026d0ab1152cc792c.jpg\",\n  \"286303.jpg\": \"Aves/Polioptila caerulea/705421ed7ab13bfcdda3c379bfe49c6f.jpg\",\n  \"286339.jpg\": \"Aves/Polioptila caerulea/2e0bba4c1d8505e2d3bc27a0da44ab7f.jpg\",\n  \"28634.jpg\": \"Aves/Aulacorhynchus prasinus/364976ac1e4023216fbfeecdbd2ed9bb.jpg\",\n  \"286350.jpg\": \"Aves/Polioptila caerulea/819cc81c9aeb715e7116cdf2b04c7192.jpg\",\n  \"286352.jpg\": \"Aves/Polioptila caerulea/7dc0ccf0684c6106809f1a2e0e77eadd.jpg\",\n  \"28637.jpg\": \"Mollusca/Dreissena polymorpha/73e29c7339a7b66c5e7de04d7823b193.jpg\",\n  \"286392.jpg\": \"Aves/Polioptila caerulea/c6c1ef047fb1e4645f9131b676aac3ae.jpg\",\n  \"286397.jpg\": \"Aves/Polioptila caerulea/f1f48cc11be89b41584941019f206db7.jpg\",\n  \"286418.jpg\": \"Aves/Polioptila caerulea/f1df89a6c38b2f4ecd78c597b66f5c81.jpg\",\n  \"286471.jpg\": \"Aves/Polioptila caerulea/e33143e41889ef66c992514d33cd50cf.jpg\",\n  \"28653.jpg\": \"Mollusca/Dreissena polymorpha/9dc7b3aa901a3f4feb20b7a8eeffe933.jpg\",\n  \"28655.jpg\": \"Mollusca/Dreissena polymorpha/d7ed7ac2736506d8856aebf492c4ccb7.jpg\",\n  \"286711.jpg\": \"Insecta/Ceratomia catalpae/0632f7a3f241c8bb9c512982c3aa44c6.jpg\",\n  \"286712.jpg\": \"Insecta/Ceratomia catalpae/7c4c5bcd3bdb3d0be3fcb49cbdfdca12.jpg\",\n  \"286718.jpg\": \"Insecta/Ceratomia catalpae/4146cfcbebba8814bc83e0ebcb3f9f99.jpg\",\n  \"286721.jpg\": \"Insecta/Ceratomia catalpae/a4142a17d7420d0e80d89beb65510759.jpg\",\n  \"286722.jpg\": \"Insecta/Ceratomia catalpae/b6599f386be00a3fa1b2090b239ea5eb.jpg\",\n  \"286723.jpg\": \"Insecta/Ceratomia catalpae/f914c891aa103993fc9d63ec6e1b7960.jpg\",\n  \"286729.jpg\": \"Mollusca/Megastraea undosa/884835c40c6bab4993fb380e911d3eeb.jpg\",\n  \"286735.jpg\": \"Mollusca/Megastraea undosa/6c1bdfabf1a4baf4148b5c4d4945347d.jpg\",\n  \"286741.jpg\": \"Mollusca/Megastraea undosa/1808b6f35092ac6a2188d9eeef9d278b.jpg\",\n  \"286742.jpg\": \"Mollusca/Megastraea undosa/89e5d8190458931059ec1d95cd8cd6f1.jpg\",\n  \"286753.jpg\": \"Aves/Tyrannus verticalis/4d40ff5a1754746fd3d8adeb8880685e.jpg\",\n  \"286778.jpg\": \"Aves/Tyrannus verticalis/ad1def17d78094761587f6be2212aad4.jpg\",\n  \"286786.jpg\": \"Aves/Tyrannus verticalis/05a34bc133c08c5080bc93568ef7d50e.jpg\",\n  \"286812.jpg\": \"Aves/Tyrannus verticalis/22c36214209a0147fd22cbe092ed8aa5.jpg\",\n  \"286828.jpg\": \"Aves/Tyrannus verticalis/9a4f8734c1a722c1cb474294dd956eeb.jpg\",\n  \"286831.jpg\": \"Aves/Tyrannus verticalis/76000fc66b0f5006696e060e10e3a4b6.jpg\",\n  \"286835.jpg\": \"Aves/Tyrannus verticalis/b6b55f0d544c77b6ae9aa7a33b8ec95e.jpg\",\n  \"286854.jpg\": \"Aves/Tyrannus verticalis/e62df3fec9f9f4c30cbf51fcf11e02ad.jpg\",\n  \"286878.jpg\": \"Aves/Tyrannus verticalis/5da134da12075e848b72212451fccc23.jpg\",\n  \"286886.jpg\": \"Aves/Tyrannus verticalis/636c1930dd8200117519a6939a513068.jpg\",\n  \"286887.jpg\": \"Aves/Tyrannus verticalis/c696263ddf06f5db844acfce6f0f744c.jpg\",\n  \"286903.jpg\": \"Aves/Tyrannus verticalis/70d915a68f5a435adb6c40fba1595d70.jpg\",\n  \"286955.jpg\": \"Aves/Tyrannus verticalis/1494fcd3b270e2a389480c31a0d364fa.jpg\",\n  \"286974.jpg\": \"Aves/Tyrannus verticalis/d5fb096cfa4620b4ba2cc3d15935b9f2.jpg\",\n  \"286982.jpg\": \"Aves/Tyrannus verticalis/1e32dde68bfbf865c72aac297870ad2f.jpg\",\n  \"286983.jpg\": \"Aves/Tyrannus verticalis/1f758400dd67cc6c0614baf2045e285a.jpg\",\n  \"286987.jpg\": \"Aves/Tyrannus verticalis/873f98817f5f5e6bfb5841dd2712bf00.jpg\",\n  \"286993.jpg\": \"Aves/Tyrannus verticalis/4b74fe7b8c9c0241fa0dc21a4ff9adad.jpg\",\n  \"287.jpg\": \"Mollusca/Olivella biplicata/76f49ab2e330ee398313a452c18cf94c.jpg\",\n  \"287016.jpg\": \"Aves/Tyrannus verticalis/472036f7fc21c8365d6fb95ee40120ea.jpg\",\n  \"287019.jpg\": \"Aves/Tyrannus verticalis/82a39edabdb31e7f0cc1679fa1d3910a.jpg\",\n  \"287023.jpg\": \"Aves/Tyrannus verticalis/bdaff9f1bcbea59220e817c8eb4c5397.jpg\",\n  \"287032.jpg\": \"Aves/Tyrannus verticalis/977c28171b999d5541d5aea391af1866.jpg\",\n  \"287096.jpg\": \"Aves/Serinus mozambicus/4d6674615ea002aa9dd2604799345cb7.jpg\",\n  \"287098.jpg\": \"Aves/Serinus mozambicus/8bf12a123720d396a0c79af4d0ea2c07.jpg\",\n  \"287110.jpg\": \"Aves/Serinus mozambicus/f6875ef32fc3a47532edc4c6aaaec8ef.jpg\",\n  \"287254.jpg\": \"Mollusca/Arion subfuscus/aaae7854957d8225a1d8de1cfbd228c3.jpg\",\n  \"287256.jpg\": \"Mollusca/Arion subfuscus/c69176f4d7ad26b8ecfe0ef1eea51e8b.jpg\",\n  \"287260.jpg\": \"Mollusca/Arion subfuscus/ce15171768147e1f43a13e2b4a9a89df.jpg\",\n  \"287261.jpg\": \"Mollusca/Arion subfuscus/12f15dc08c06865099d8b5b6cdef742f.jpg\",\n  \"287262.jpg\": \"Mollusca/Arion subfuscus/e893238603f9105e400b5a21cf348bb4.jpg\",\n  \"287263.jpg\": \"Mollusca/Arion subfuscus/7ef9b60ce5a563f4faa532d9c984fbc2.jpg\",\n  \"287271.jpg\": \"Mollusca/Arion subfuscus/f662e9a5ff78a6709def1b1a0114d3a9.jpg\",\n  \"287272.jpg\": \"Mollusca/Arion subfuscus/058e2657c9ba39e7e9a6dc974050b5c5.jpg\",\n  \"287275.jpg\": \"Mollusca/Arion subfuscus/8fc15a5d7a4236a3c3fd95601331f96e.jpg\",\n  \"287280.jpg\": \"Arachnida/Neoscona crucifera/0830d59883a2b50d8736e38e8c008aff.jpg\",\n  \"287281.jpg\": \"Arachnida/Neoscona crucifera/0dfb2335864d8a92ad4696f1ecf6e719.jpg\",\n  \"287289.jpg\": \"Arachnida/Neoscona crucifera/9dcffaa0b37df81eb66d859e1e39ea0d.jpg\",\n  \"287290.jpg\": \"Arachnida/Neoscona crucifera/24d0af75e05f433c0421fcf395a0c57e.jpg\",\n  \"287294.jpg\": \"Arachnida/Neoscona crucifera/7bd6715700b1b5487a208a1414bf68ff.jpg\",\n  \"287295.jpg\": \"Arachnida/Neoscona crucifera/5bf6807b3b493f82a17e63d3b365201e.jpg\",\n  \"287301.jpg\": \"Arachnida/Neoscona crucifera/876d34f8fa3ab43d56f637d5cc3b65f1.jpg\",\n  \"287302.jpg\": \"Arachnida/Neoscona crucifera/f88e0f5e30a422214cec968fc2a336cc.jpg\",\n  \"287312.jpg\": \"Arachnida/Neoscona crucifera/747ec527858e94fcef759fb60d3e98b7.jpg\",\n  \"287313.jpg\": \"Arachnida/Neoscona crucifera/01ed9d23afa065c2073648bb9be2b686.jpg\",\n  \"287314.jpg\": \"Arachnida/Neoscona crucifera/3820f036d51e86033ab968c3ca8d73ae.jpg\",\n  \"287315.jpg\": \"Arachnida/Neoscona crucifera/48285fc172b953348f83c195b77be967.jpg\",\n  \"287316.jpg\": \"Arachnida/Neoscona crucifera/7a1713279b1d8380e3693637310d0a95.jpg\",\n  \"287317.jpg\": \"Arachnida/Neoscona crucifera/fe50d67541d41c7b61840f64c48056d3.jpg\",\n  \"287320.jpg\": \"Arachnida/Neoscona crucifera/3c9d39b3cc07f319fd3998252e7ad920.jpg\",\n  \"287322.jpg\": \"Arachnida/Neoscona crucifera/6c3eacff494f6c4c4576a3125cb00d71.jpg\",\n  \"287323.jpg\": \"Arachnida/Neoscona crucifera/9860fe5e5c24ea6cc0b5c94df8f49689.jpg\",\n  \"287324.jpg\": \"Arachnida/Neoscona crucifera/91d64b9d7ec11f4f156808317ada7bfc.jpg\",\n  \"287329.jpg\": \"Arachnida/Neoscona crucifera/2bf0f54f0407351ea0cad797283aecc1.jpg\",\n  \"287330.jpg\": \"Arachnida/Neoscona crucifera/bfafb802e8a936a52ecd60a9c701f08f.jpg\",\n  \"287340.jpg\": \"Aves/Myiozetetes similis/ba47c9772a3a27c3271b5d31ce952eaa.jpg\",\n  \"287347.jpg\": \"Aves/Myiozetetes similis/4976c5f4f7e19714f6b9a491619bda75.jpg\",\n  \"287352.jpg\": \"Aves/Myiozetetes similis/4f8ee61e94a1b1c5ded7d9ea921c229a.jpg\",\n  \"287366.jpg\": \"Aves/Myiozetetes similis/f65de53eea01bf6db166e441f25fbb11.jpg\",\n  \"287377.jpg\": \"Aves/Myiozetetes similis/6d48755484436d46f00d6cc669b31634.jpg\",\n  \"287378.jpg\": \"Aves/Myiozetetes similis/aa3d323039e82a312583acb0932a85a7.jpg\",\n  \"287381.jpg\": \"Aves/Myiozetetes similis/78140214ae8d8e0d4addfe337a0da499.jpg\",\n  \"287387.jpg\": \"Aves/Myiozetetes similis/41623fa17558a3cefb418ffeeef9d2e9.jpg\",\n  \"287389.jpg\": \"Aves/Myiozetetes similis/2b97d08a2bb3cc77ecc76c0c89f5c6af.jpg\",\n  \"287401.jpg\": \"Aves/Myiozetetes similis/7bbc852ac24ec0af352cd8cfb8851f96.jpg\",\n  \"287405.jpg\": \"Aves/Myiozetetes similis/45318c733b5cc43d45a36e40acdd81e6.jpg\",\n  \"287406.jpg\": \"Aves/Myiozetetes similis/2acc12006865ff21061c40d836c486da.jpg\",\n  \"287417.jpg\": \"Aves/Myiozetetes similis/6b33d3de499ed5627debb223277d4c11.jpg\",\n  \"287426.jpg\": \"Aves/Myiozetetes similis/e35e36f9587b6b2b71532a97b283a4aa.jpg\",\n  \"287431.jpg\": \"Aves/Myiozetetes similis/efa9e3079b3aed9f9f76d8914de63727.jpg\",\n  \"287438.jpg\": \"Aves/Myiozetetes similis/6dae2cd47f362e059f09c1fd8d3cd850.jpg\",\n  \"287440.jpg\": \"Aves/Myiozetetes similis/522e7163b520580783a1c4be5244d3ca.jpg\",\n  \"287443.jpg\": \"Aves/Myiozetetes similis/e298a6a42d4ae6d15ad62a482fa4f867.jpg\",\n  \"287445.jpg\": \"Aves/Myiozetetes similis/d12891c621232285a33746426f622f76.jpg\",\n  \"287451.jpg\": \"Aves/Myiozetetes similis/eebb356fb00c3578d0b9a08964b44490.jpg\",\n  \"287461.jpg\": \"Aves/Myiozetetes similis/6218ad9973005eed7b1e2cb62d659cac.jpg\",\n  \"28751.jpg\": \"Insecta/Popillia japonica/85896fbb5396eb0e3a2673ad2ca4eee9.jpg\",\n  \"287660.jpg\": \"Insecta/Grammia virgo/7f5209fa18d10d8f59c75b0a706181cf.jpg\",\n  \"287665.jpg\": \"Insecta/Grammia virgo/83e6ab6d99ca51a81d22c17aa64f1f4b.jpg\",\n  \"287666.jpg\": \"Insecta/Grammia virgo/9adbb6082f42a1efefdf323fce734ae6.jpg\",\n  \"287669.jpg\": \"Insecta/Grammia virgo/bd6f6a8f1aa88a30e98cf60b12ff54fa.jpg\",\n  \"287670.jpg\": \"Insecta/Grammia virgo/03818509d908763bbc60df21b28fe0b5.jpg\",\n  \"287671.jpg\": \"Insecta/Grammia virgo/5ab1a6f3b8ffd8353d56e270444bbc30.jpg\",\n  \"287674.jpg\": \"Insecta/Grammia virgo/b7d782c4d033cec2bcc73ffcdeceab9a.jpg\",\n  \"287680.jpg\": \"Insecta/Grammia virgo/a88d301be2fb5ece51979ff8c84036c4.jpg\",\n  \"287688.jpg\": \"Insecta/Papilio machaon/bf397b7cb12dc34a2e4393110df0480c.jpg\",\n  \"28769.jpg\": \"Insecta/Popillia japonica/f6337ece5c6aff951052ff01af2f77fe.jpg\",\n  \"287692.jpg\": \"Insecta/Papilio machaon/3813e93efdbc0071458655f8330e9913.jpg\",\n  \"287693.jpg\": \"Insecta/Papilio machaon/1f19b6779878e9582a1c1c46401b89d7.jpg\",\n  \"28771.jpg\": \"Insecta/Popillia japonica/44800c21825f651c3689ffa95fd5d9e5.jpg\",\n  \"287710.jpg\": \"Insecta/Papilio machaon/60489ac4505aa21d90fc31aebea1b311.jpg\",\n  \"287715.jpg\": \"Insecta/Papilio machaon/45cf45d5919803d04c76e6aebb02ab97.jpg\",\n  \"287716.jpg\": \"Insecta/Papilio machaon/3864ce9ddefd1e1a0ae425b88dc1fd31.jpg\",\n  \"287717.jpg\": \"Insecta/Papilio machaon/46414a139b3eb70fecbeaae17de5e4f1.jpg\",\n  \"287722.jpg\": \"Insecta/Papilio machaon/4a161118309d15b108107e2162f22757.jpg\",\n  \"287733.jpg\": \"Insecta/Papilio machaon/6ae34b6acd96ecb34d6f0885949c1bdb.jpg\",\n  \"287735.jpg\": \"Insecta/Papilio machaon/c585cf594f6bdbbf681d0645ff0ba5b4.jpg\",\n  \"287736.jpg\": \"Insecta/Papilio machaon/2206e544e432324c8006b17a9a91c9c8.jpg\",\n  \"287739.jpg\": \"Insecta/Papilio machaon/1b32a386c16af5d9af7cebf1be2b87fd.jpg\",\n  \"287742.jpg\": \"Insecta/Papilio machaon/cae0a847f20d24860fe7254dc14db834.jpg\",\n  \"287743.jpg\": \"Insecta/Papilio machaon/8d76d69f370c9ff9fdcbf6c69375e4dc.jpg\",\n  \"287749.jpg\": \"Insecta/Papilio machaon/a63b1954834c17e5d59ccfd9e2b642b9.jpg\",\n  \"287750.jpg\": \"Insecta/Papilio machaon/0869e9a98ebcdcfceda90d5d03744c00.jpg\",\n  \"287751.jpg\": \"Insecta/Papilio machaon/509de6d75610dd9b2d1f09bc33760471.jpg\",\n  \"287754.jpg\": \"Insecta/Papilio machaon/1fdb2da5a25161bf1b273851bbaa8770.jpg\",\n  \"287755.jpg\": \"Insecta/Papilio machaon/ece67f1d5756e2c2d1c5e51fc8a15822.jpg\",\n  \"287758.jpg\": \"Insecta/Papilio machaon/1a56d40a258aea4a17d2d707559657e1.jpg\",\n  \"287759.jpg\": \"Insecta/Papilio machaon/50d39bfd6810c4771f01fb93aaf42108.jpg\",\n  \"287760.jpg\": \"Insecta/Papilio machaon/1451f81f8362b9d7fbfa4188b7d6895c.jpg\",\n  \"287770.jpg\": \"Aves/Pyrrhula pyrrhula/006f1dfd23bca8714cb98f3f60694c7d.jpg\",\n  \"287771.jpg\": \"Aves/Pyrrhula pyrrhula/acb4720d53ff124a90ecedf203be364a.jpg\",\n  \"287772.jpg\": \"Aves/Pyrrhula pyrrhula/756f4cd34c802bc202e816afd372941a.jpg\",\n  \"287777.jpg\": \"Aves/Pyrrhula pyrrhula/114d6387349ec80de1eb5234ec50023f.jpg\",\n  \"28778.jpg\": \"Insecta/Popillia japonica/82cbdc4a5b7e2e50b1fc957dd1282f47.jpg\",\n  \"287787.jpg\": \"Aves/Pyrrhula pyrrhula/d2e3990b09a2c3d6dcb7f9474f73efd4.jpg\",\n  \"287788.jpg\": \"Aves/Pyrrhula pyrrhula/b6250f2084c9d40e498822c8bc419a74.jpg\",\n  \"287790.jpg\": \"Aves/Pyrrhula pyrrhula/c2691c4aa42589ade12c29d5ec419a82.jpg\",\n  \"287794.jpg\": \"Aves/Pyrrhula pyrrhula/3d33077387e3e219b3e1796b1c356ee8.jpg\",\n  \"287800.jpg\": \"Aves/Pyrrhula pyrrhula/adb30f84e1d48d9dac916bdbbc3ec11d.jpg\",\n  \"287801.jpg\": \"Aves/Pyrrhula pyrrhula/9632846914d287da3e49ba0c70d824eb.jpg\",\n  \"287803.jpg\": \"Aves/Pyrrhula pyrrhula/1ed2d1b7f2bc87c5c887d803052586a0.jpg\",\n  \"287809.jpg\": \"Aves/Pyrrhula pyrrhula/2541629c78508c4c0b03cae9203cbc6c.jpg\",\n  \"287817.jpg\": \"Aves/Pyrrhula pyrrhula/fe10f31ee0f9b09900ba4141f1d11e29.jpg\",\n  \"287819.jpg\": \"Aves/Pyrrhula pyrrhula/465bf453c8f1a905eeeadc81c1c3b5b0.jpg\",\n  \"287823.jpg\": \"Aves/Pyrrhula pyrrhula/05225cdf88158d38e29003f6ea5a8089.jpg\",\n  \"287825.jpg\": \"Insecta/Halyomorpha halys/68683e111fd32675e08a923d01128b69.jpg\",\n  \"287831.jpg\": \"Insecta/Halyomorpha halys/9916db6f8b51acf0d362b269afdb04d3.jpg\",\n  \"287837.jpg\": \"Insecta/Halyomorpha halys/d0551172727c92b1c5bcb9f3014c4d18.jpg\",\n  \"287846.jpg\": \"Insecta/Halyomorpha halys/72c977c8cce7fcb14ac792e008d6382f.jpg\",\n  \"287847.jpg\": \"Insecta/Halyomorpha halys/c66f4a3dd776a6f859814e690cd22b54.jpg\",\n  \"287848.jpg\": \"Insecta/Halyomorpha halys/0cf31068fd76561be9ae119e65c9e815.jpg\",\n  \"287854.jpg\": \"Insecta/Halyomorpha halys/d521f27a083e7e26a601c84042f69caa.jpg\",\n  \"287860.jpg\": \"Insecta/Halyomorpha halys/aa4d8f949e29dffdb32443a06575d8c0.jpg\",\n  \"287864.jpg\": \"Insecta/Halyomorpha halys/3055db9c948db2365b2b026de8b33c1b.jpg\",\n  \"287866.jpg\": \"Insecta/Halyomorpha halys/8efc12d9778970f73507a01bf27af8ce.jpg\",\n  \"287870.jpg\": \"Insecta/Halyomorpha halys/72a5bf00cd026e3456e41cb39ea622db.jpg\",\n  \"287873.jpg\": \"Insecta/Halyomorpha halys/435ede4d118aef8407a917252edfb366.jpg\",\n  \"287875.jpg\": \"Insecta/Halyomorpha halys/9cfa1fcb221a48e9054f64752ee81695.jpg\",\n  \"287879.jpg\": \"Insecta/Halyomorpha halys/561b850e54eea6517694e354ab5d09f3.jpg\",\n  \"287889.jpg\": \"Insecta/Halyomorpha halys/80bb3372a7e8a2c5c052b029359c0579.jpg\",\n  \"287890.jpg\": \"Insecta/Halyomorpha halys/3f39506fd97af9e7ab28c35dd5cedd1c.jpg\",\n  \"287892.jpg\": \"Insecta/Halyomorpha halys/913149a5e9828ab80067412ca4a14001.jpg\",\n  \"287898.jpg\": \"Insecta/Halyomorpha halys/0e53e2b67563be14b03b1803234d71b1.jpg\",\n  \"287903.jpg\": \"Insecta/Halyomorpha halys/81b6dfdbe487693211b76fc430754415.jpg\",\n  \"287904.jpg\": \"Insecta/Halyomorpha halys/cd4e85c99087411fcf7aa0e4cac7b650.jpg\",\n  \"287908.jpg\": \"Arachnida/Paraphidippus aurantius/58ef46f0e23c8eef6179300405f49ce3.jpg\",\n  \"287909.jpg\": \"Arachnida/Paraphidippus aurantius/3147ae7ad630dc1b48eae8d0a9bb3dc4.jpg\",\n  \"287910.jpg\": \"Arachnida/Paraphidippus aurantius/23c06840c87c97b6d1fe518c3d777c4e.jpg\",\n  \"287911.jpg\": \"Arachnida/Paraphidippus aurantius/cedbc4e0ad35f016262eb13579150d75.jpg\",\n  \"287912.jpg\": \"Arachnida/Paraphidippus aurantius/6e2dac45353e1e2e57d127913f8e813d.jpg\",\n  \"287915.jpg\": \"Arachnida/Paraphidippus aurantius/e4f94b63fed6fa6432beac5d8977e47f.jpg\",\n  \"287920.jpg\": \"Arachnida/Paraphidippus aurantius/364c703b69e276a075e52dcadffcf4f9.jpg\",\n  \"287924.jpg\": \"Arachnida/Paraphidippus aurantius/36c052a46d4140808d2cc50c46a2dea0.jpg\",\n  \"287925.jpg\": \"Arachnida/Paraphidippus aurantius/cb23ba5e50bb3b510601d98935bc987a.jpg\",\n  \"287926.jpg\": \"Arachnida/Paraphidippus aurantius/c2649875ae0a4d4c363347f759b014d6.jpg\",\n  \"287933.jpg\": \"Arachnida/Paraphidippus aurantius/e1e8b980c5b1d7a1b78922e8156d7d29.jpg\",\n  \"287934.jpg\": \"Arachnida/Paraphidippus aurantius/c7d6fe42028c90029231513deb414a63.jpg\",\n  \"287938.jpg\": \"Arachnida/Paraphidippus aurantius/ef0c990bcec970582be3f461bd443326.jpg\",\n  \"287951.jpg\": \"Arachnida/Paraphidippus aurantius/d5469c1214d87a8965727f4d19f539d9.jpg\",\n  \"287957.jpg\": \"Arachnida/Paraphidippus aurantius/295db4ad397769c079aa29b62b444fab.jpg\",\n  \"287958.jpg\": \"Arachnida/Paraphidippus aurantius/71e28484c9a2e4214c9184283ac35ece.jpg\",\n  \"287959.jpg\": \"Arachnida/Paraphidippus aurantius/3295db97e21885bca47eede3faeb6bdd.jpg\",\n  \"287994.jpg\": \"Insecta/Orthemis ferruginea/55ab73e9088319c66399fc991889af97.jpg\",\n  \"287996.jpg\": \"Insecta/Orthemis ferruginea/4a6467625b1e1f15d69283aefae249f8.jpg\",\n  \"288008.jpg\": \"Insecta/Orthemis ferruginea/3ed1adb7330be9a61820339eb60bde69.jpg\",\n  \"288053.jpg\": \"Insecta/Orthemis ferruginea/3f353c83b28880a20250e1949b5696a9.jpg\",\n  \"288056.jpg\": \"Insecta/Orthemis ferruginea/0e27cfcb38e7d50adcdb52a07023c256.jpg\",\n  \"288062.jpg\": \"Insecta/Orthemis ferruginea/c5e9d791ee5961b2c7abe06b4b2a0ba3.jpg\",\n  \"288067.jpg\": \"Insecta/Orthemis ferruginea/0c8d90aa8f6a55121e2f52b34c4b9644.jpg\",\n  \"288074.jpg\": \"Insecta/Orthemis ferruginea/9cff484a7b850deee601fc9d16ef4d7c.jpg\",\n  \"288081.jpg\": \"Insecta/Orthemis ferruginea/42a21e09b5fbff1cd3b43034c5d04ccf.jpg\",\n  \"288085.jpg\": \"Insecta/Orthemis ferruginea/37c78dca49adb0fe116c82a05036e461.jpg\",\n  \"288103.jpg\": \"Insecta/Orthemis ferruginea/237b49681438e914b9d5894dd1256763.jpg\",\n  \"28811.jpg\": \"Insecta/Popillia japonica/5894f84262a39a5b9de5bbfed98d6f27.jpg\",\n  \"288119.jpg\": \"Insecta/Orthemis ferruginea/2f304ffb6b914ff43cecdd7cac401ec0.jpg\",\n  \"288136.jpg\": \"Insecta/Orthemis ferruginea/5e978bca510b3c51e8ceeaf429a2eb70.jpg\",\n  \"288140.jpg\": \"Insecta/Orthemis ferruginea/b95ef95bae43c348614b842d89ff43a7.jpg\",\n  \"288141.jpg\": \"Insecta/Orthemis ferruginea/dc211fe86f113a410a5993d2d091b0ad.jpg\",\n  \"288142.jpg\": \"Insecta/Orthemis ferruginea/6ddb5a04ec2f0314e7c6f59ef38691d7.jpg\",\n  \"288157.jpg\": \"Insecta/Orthemis ferruginea/ecaab8879ad8090e3281d6dc240008af.jpg\",\n  \"288160.jpg\": \"Insecta/Orthemis ferruginea/ffb65d280443004c7ded659d9ce29658.jpg\",\n  \"288161.jpg\": \"Insecta/Orthemis ferruginea/9d4fe133ebfb4a8cbf62999a3dd9a97c.jpg\",\n  \"288189.jpg\": \"Insecta/Orthemis ferruginea/0db2b56511dcbf47b9a194121158075e.jpg\",\n  \"288193.jpg\": \"Insecta/Orthemis ferruginea/a8902be3ebda2e976e032b2809c693bf.jpg\",\n  \"288219.jpg\": \"Insecta/Orthemis ferruginea/4df28d48385d82e7ccca8f6a913efa16.jpg\",\n  \"288222.jpg\": \"Insecta/Orthemis ferruginea/5c4da6c2de05631bd866b13ddb67db09.jpg\",\n  \"288223.jpg\": \"Insecta/Orthemis ferruginea/75182eca5f4d7948b8be2664260c8eed.jpg\",\n  \"288226.jpg\": \"Insecta/Orthemis ferruginea/0c31ff44ab0bf6dff8943b096d53f383.jpg\",\n  \"288229.jpg\": \"Insecta/Orthemis ferruginea/fca71577dd5d180619dea6d48e3e0a00.jpg\",\n  \"288232.jpg\": \"Aves/Strix nebulosa/14390e1ab318e78c68e3a3a1d9d67b28.jpg\",\n  \"288233.jpg\": \"Aves/Strix nebulosa/be8fa868423dd6de4ce3a51a776e2ad4.jpg\",\n  \"288237.jpg\": \"Aves/Strix nebulosa/588053d78ac4c809e389ab1139f269fd.jpg\",\n  \"288238.jpg\": \"Aves/Strix nebulosa/08b9e6377362296d5239a9a0f205c83a.jpg\",\n  \"288247.jpg\": \"Aves/Strix nebulosa/e34214548ce9379d09665c9cad122078.jpg\",\n  \"288262.jpg\": \"Aves/Florisuga mellivora/0504624e129620778e3dbcd54f5665a8.jpg\",\n  \"288264.jpg\": \"Aves/Florisuga mellivora/344fea397b351512e06e2357e231e399.jpg\",\n  \"288266.jpg\": \"Aves/Florisuga mellivora/4b0a049cd30a7acb045cf41db76f6aea.jpg\",\n  \"288267.jpg\": \"Aves/Florisuga mellivora/43f474fa486ab86888e15dd21fda6e5d.jpg\",\n  \"288271.jpg\": \"Aves/Florisuga mellivora/35448e73744d7823d7b6a56d606f6a5e.jpg\",\n  \"288277.jpg\": \"Aves/Florisuga mellivora/bfabe30df34edddc88e0dbda651bdffb.jpg\",\n  \"288283.jpg\": \"Mammalia/Choloepus hoffmanni/ac715b5973d3a1934f96f44aaded3f93.jpg\",\n  \"288290.jpg\": \"Mammalia/Choloepus hoffmanni/7aceb61e0cd59deb2e6603fd267aade7.jpg\",\n  \"288306.jpg\": \"Insecta/Papilio rutulus/7ca2ade4b9adba95854863b92d468ddf.jpg\",\n  \"288319.jpg\": \"Insecta/Papilio rutulus/c904a74777edf888e3fc82c821084300.jpg\",\n  \"288323.jpg\": \"Insecta/Papilio rutulus/e1409e3fdd343d4b94dd9a12c33b030f.jpg\",\n  \"288356.jpg\": \"Insecta/Papilio rutulus/6e145cade9761a9f81cfd2c71d81ef87.jpg\",\n  \"288365.jpg\": \"Insecta/Papilio rutulus/e891d491882c70e678836a54b5fb9ba9.jpg\",\n  \"288367.jpg\": \"Insecta/Papilio rutulus/d3bf35fcc9968bd2b04ea98f5e774cbc.jpg\",\n  \"288380.jpg\": \"Insecta/Papilio rutulus/bf5df76f454dc59a17f6da489c34fa99.jpg\",\n  \"288391.jpg\": \"Insecta/Papilio rutulus/c328e49ece13ab58d36536d1f0a6cf4c.jpg\",\n  \"288392.jpg\": \"Insecta/Papilio rutulus/74361375d07f651cfe743a4be81d2ad4.jpg\",\n  \"288420.jpg\": \"Insecta/Papilio rutulus/cfd797c8c10ab060904c8f86bebabae3.jpg\",\n  \"288439.jpg\": \"Insecta/Papilio rutulus/42cf6e2083a9683e51bb223493f5d8a8.jpg\",\n  \"288455.jpg\": \"Insecta/Papilio rutulus/922c56a7fc97178f0928876bc0d578c1.jpg\",\n  \"28846.jpg\": \"Insecta/Popillia japonica/109e91dd25cef4536e5aee58f655f726.jpg\",\n  \"288471.jpg\": \"Insecta/Papilio rutulus/c023c7e66ee2acd833c92a920a409197.jpg\",\n  \"288473.jpg\": \"Insecta/Papilio rutulus/7de5535563b3531e1a056a61fb3da0ba.jpg\",\n  \"288474.jpg\": \"Insecta/Papilio rutulus/d45f94cc3a226d3f79207c0de1f66fd3.jpg\",\n  \"288476.jpg\": \"Insecta/Papilio rutulus/b6e9f5ea93609b10167caf259eb248cb.jpg\",\n  \"28848.jpg\": \"Insecta/Popillia japonica/4b8ded649502ce328b5d85ba17ed9f97.jpg\",\n  \"288482.jpg\": \"Insecta/Papilio rutulus/a7a9dc3bb2049f26f2851bf551f0e652.jpg\",\n  \"288487.jpg\": \"Insecta/Papilio rutulus/0eb4e15f53ac69c6ecd508998d07d484.jpg\",\n  \"288491.jpg\": \"Insecta/Papilio rutulus/5a16cdb47795aca0197106f960bf8b27.jpg\",\n  \"288496.jpg\": \"Insecta/Papilio rutulus/ec0d704a324bb118145aeb36d6244edd.jpg\",\n  \"288498.jpg\": \"Insecta/Papilio rutulus/df19f1f39e7ff0b5070f47de6cb18f7a.jpg\",\n  \"288522.jpg\": \"Reptilia/Phrynosoma blainvillii/e56988e37ecc289957b27a0c82166206.jpg\",\n  \"288523.jpg\": \"Reptilia/Phrynosoma blainvillii/2f103911342e896b3cb53f3b68e0e987.jpg\",\n  \"288535.jpg\": \"Reptilia/Phrynosoma blainvillii/e2ac6d703c5ca73a9d91198ffde1b643.jpg\",\n  \"288536.jpg\": \"Reptilia/Phrynosoma blainvillii/e980808eaf63a05ab08efec7ddc6e272.jpg\",\n  \"288547.jpg\": \"Reptilia/Phrynosoma blainvillii/c11729f60c40f759dd2e40393cf56777.jpg\",\n  \"288553.jpg\": \"Reptilia/Phrynosoma blainvillii/896bfa7c25e64936bdeacad2b9986ded.jpg\",\n  \"288555.jpg\": \"Reptilia/Phrynosoma blainvillii/cfce6b85065637bf9d29c972332bf396.jpg\",\n  \"288556.jpg\": \"Reptilia/Phrynosoma blainvillii/672141981460d8b2e6cc03f52cbafc5d.jpg\",\n  \"288559.jpg\": \"Reptilia/Phrynosoma blainvillii/0b8865d63f0f4778ebc69df346decfd4.jpg\",\n  \"288560.jpg\": \"Reptilia/Phrynosoma blainvillii/44c7255e28ac1ece7a082292d28dd3a4.jpg\",\n  \"28857.jpg\": \"Insecta/Popillia japonica/b35b9c826ec383af4e199f23d24ccf66.jpg\",\n  \"288570.jpg\": \"Reptilia/Phrynosoma blainvillii/d18ae9ab169e5f20fc10d18bcfb9b6a0.jpg\",\n  \"288579.jpg\": \"Reptilia/Phrynosoma blainvillii/75cc7c40fd216d8c17b89cc28f9caf36.jpg\",\n  \"288580.jpg\": \"Reptilia/Phrynosoma blainvillii/ee7e0b5a3a793dd4ee5dd83f1762d436.jpg\",\n  \"288581.jpg\": \"Reptilia/Phrynosoma blainvillii/72920b5cf8331552ea7d16370b1bb641.jpg\",\n  \"28859.jpg\": \"Insecta/Popillia japonica/2a3cd8459a6f8be45a1d798a0629cf9a.jpg\",\n  \"288603.jpg\": \"Reptilia/Phrynosoma blainvillii/7e98b3ead6a17f2da7196c90167815a6.jpg\",\n  \"288609.jpg\": \"Reptilia/Phrynosoma blainvillii/2c2facce89113fc93bef5d7ad5e0b94b.jpg\",\n  \"288616.jpg\": \"Reptilia/Phrynosoma blainvillii/573152603d657ebb860c7002faa5ca46.jpg\",\n  \"288622.jpg\": \"Reptilia/Phrynosoma blainvillii/7af90ead5db68cb6aa5772b9b2cdac8b.jpg\",\n  \"288639.jpg\": \"Reptilia/Phrynosoma blainvillii/a2f029156a7a78498593bf248f3c5d0c.jpg\",\n  \"288652.jpg\": \"Aves/Elanus leucurus/7a830dda2c9b53fe4de19d0a668bd2d7.jpg\",\n  \"288654.jpg\": \"Aves/Elanus leucurus/862f7c88a9d025d5c7a588efb1ba5c74.jpg\",\n  \"288684.jpg\": \"Aves/Elanus leucurus/0e1573e21a275255bd93e76c1efd4390.jpg\",\n  \"288688.jpg\": \"Aves/Elanus leucurus/c8f95e45e7257399ab5c94bae568a324.jpg\",\n  \"288694.jpg\": \"Aves/Elanus leucurus/0de352522b832b2db1e21a28cc522979.jpg\",\n  \"288705.jpg\": \"Aves/Elanus leucurus/5cfb875f76d9e465f5ff8b27835e07ff.jpg\",\n  \"288720.jpg\": \"Aves/Elanus leucurus/04ef9aac1a80712d9e40634a16cdc0ad.jpg\",\n  \"28874.jpg\": \"Insecta/Popillia japonica/71b3f172926c8f9dc611e0fb310eb494.jpg\",\n  \"288758.jpg\": \"Aves/Elanus leucurus/12fe82711f9b501febebb750dc6c5467.jpg\",\n  \"288766.jpg\": \"Aves/Elanus leucurus/a841501843b0b7bc983f2a95d4ab0845.jpg\",\n  \"288772.jpg\": \"Aves/Elanus leucurus/d2bff2e4967fefcea5eb7efbf45b0e98.jpg\",\n  \"288775.jpg\": \"Aves/Elanus leucurus/b7ccc66d3010f2e845782e5c0dce683b.jpg\",\n  \"288781.jpg\": \"Aves/Elanus leucurus/0b205d314d291731d65f26bfd54af076.jpg\",\n  \"288794.jpg\": \"Aves/Elanus leucurus/00828eb13e39f051f303066b98701472.jpg\",\n  \"288815.jpg\": \"Aves/Elanus leucurus/1093faaf2bd38adcabd14da970c3ce8e.jpg\",\n  \"288824.jpg\": \"Aves/Elanus leucurus/bd74e51f0184be2811392c437f54f57c.jpg\",\n  \"288828.jpg\": \"Aves/Elanus leucurus/af0815ed5c2fd6635013c35432de8cd6.jpg\",\n  \"288837.jpg\": \"Aves/Elanus leucurus/0f704359739e1f328ccca1ac868ce7b2.jpg\",\n  \"288859.jpg\": \"Aves/Elanus leucurus/182b514de77ffb56b4350e71521451ee.jpg\",\n  \"288877.jpg\": \"Aves/Elanus leucurus/8fc7d54965bb4eb10c3146cb5190eeae.jpg\",\n  \"288889.jpg\": \"Aves/Elanus leucurus/50d320b341a800a5185d5a8d5225d110.jpg\",\n  \"288893.jpg\": \"Aves/Elanus leucurus/aa29fc3afd3cc880b91c7c0878629082.jpg\",\n  \"288894.jpg\": \"Aves/Elanus leucurus/c6e271521e51c662e23b06deb0e0dfb0.jpg\",\n  \"28890.jpg\": \"Insecta/Popillia japonica/1fda29eba33681e19b0488e7e3244e64.jpg\",\n  \"288930.jpg\": \"Aves/Oreothlypis ruficapilla/1b55123569f03cff5497f6f4aa2946b5.jpg\",\n  \"288959.jpg\": \"Aves/Oreothlypis ruficapilla/d6bf07e33edaf756cdfc077e51651009.jpg\",\n  \"288968.jpg\": \"Aves/Oreothlypis ruficapilla/106738d5b228b8d007eaf81e60b28ef5.jpg\",\n  \"288975.jpg\": \"Aves/Oreothlypis ruficapilla/8ee62035a0f33b20bcd506347779d334.jpg\",\n  \"288978.jpg\": \"Aves/Oreothlypis ruficapilla/e56cfa7bd3c1ce1be3fbace314277574.jpg\",\n  \"289.jpg\": \"Mollusca/Olivella biplicata/95c8a7108f57f5f012af4c0155a6d216.jpg\",\n  \"28900.jpg\": \"Insecta/Popillia japonica/7a3573fc6d30fd7c45916e1dee7c6783.jpg\",\n  \"289027.jpg\": \"Aves/Oreothlypis ruficapilla/f228a5672bd362863771798b5b06bd6e.jpg\",\n  \"289028.jpg\": \"Aves/Oreothlypis ruficapilla/13c9d5724c1e5f6d192d6776c3197066.jpg\",\n  \"289031.jpg\": \"Aves/Oreothlypis ruficapilla/704fe453aa54146f70ca57be4ac2fabb.jpg\",\n  \"289032.jpg\": \"Aves/Oreothlypis ruficapilla/d3c03f80b39732dab81f57be88e3f8e9.jpg\",\n  \"289034.jpg\": \"Aves/Oreothlypis ruficapilla/914f4f5a2dd5589448b1e0c22a8887b3.jpg\",\n  \"289039.jpg\": \"Aves/Oreothlypis ruficapilla/da062b0e71e2d8857c45998e8d08bce6.jpg\",\n  \"289041.jpg\": \"Aves/Oreothlypis ruficapilla/edef03bbc4046a495f17c69df61bc9a3.jpg\",\n  \"289047.jpg\": \"Aves/Oreothlypis ruficapilla/537886c4d84b9637640ca86cdc9186a4.jpg\",\n  \"289048.jpg\": \"Aves/Oreothlypis ruficapilla/79323a9454409324d5eff196469c43b6.jpg\",\n  \"289055.jpg\": \"Aves/Oreothlypis ruficapilla/8beff1811373ee25cde888d5976f713d.jpg\",\n  \"289063.jpg\": \"Aves/Oreothlypis ruficapilla/b58fd390a863aea027de2ba4a72c1ba3.jpg\",\n  \"289064.jpg\": \"Aves/Oreothlypis ruficapilla/19967749f3df7af755fe130421f00832.jpg\",\n  \"289072.jpg\": \"Aves/Oreothlypis ruficapilla/79ffe18a4b95f444f81a774a64c75192.jpg\",\n  \"289077.jpg\": \"Aves/Oreothlypis ruficapilla/0d777b2aabad16bd9791e62c277cdd06.jpg\",\n  \"289080.jpg\": \"Aves/Oreothlypis ruficapilla/6679faca530f19d95f7d7d9a7c7cbd47.jpg\",\n  \"289086.jpg\": \"Aves/Oreothlypis ruficapilla/f8e423af687c25f6d6133fe123e5323e.jpg\",\n  \"289087.jpg\": \"Aves/Oreothlypis ruficapilla/8ef67229efe18a6901cf657011bd0d6a.jpg\",\n  \"289101.jpg\": \"Insecta/Leptoglossus phyllopus/95f495f6e530705f5761316982ea5b1a.jpg\",\n  \"289103.jpg\": \"Insecta/Leptoglossus phyllopus/7a08031be886690c0805a07eec6b3185.jpg\",\n  \"289105.jpg\": \"Insecta/Leptoglossus phyllopus/6ad6f464869d0d01ce6fb550900b7b09.jpg\",\n  \"289107.jpg\": \"Insecta/Leptoglossus phyllopus/d43edfd9755ee3e014514c2dfb54b02d.jpg\",\n  \"289108.jpg\": \"Insecta/Leptoglossus phyllopus/f0916d89ce3e7d3b7f5044e53fdd036d.jpg\",\n  \"289113.jpg\": \"Insecta/Leptoglossus phyllopus/424552e7e77b13e12f56f4674c85fd82.jpg\",\n  \"289114.jpg\": \"Insecta/Leptoglossus phyllopus/f8a774d5208c32c04759ee0f65283189.jpg\",\n  \"289116.jpg\": \"Insecta/Leptoglossus phyllopus/632eeb2e700dc344fba0e0046ec37c7e.jpg\",\n  \"289121.jpg\": \"Insecta/Leptoglossus phyllopus/d89c1c60224bf8a733df652c7fa5636c.jpg\",\n  \"289122.jpg\": \"Insecta/Leptoglossus phyllopus/b32392fa86b6d3f83c53ce5623b24ce2.jpg\",\n  \"289124.jpg\": \"Insecta/Leptoglossus phyllopus/6762a8967f0005cade95ff88ec6a4cd2.jpg\",\n  \"289133.jpg\": \"Insecta/Leptoglossus phyllopus/457d067e502c8651ad4a016e7ebb5e2d.jpg\",\n  \"289146.jpg\": \"Insecta/Leptoglossus phyllopus/d6e27338a7cad0c4b4d34196b4c5b59d.jpg\",\n  \"289148.jpg\": \"Insecta/Leptoglossus phyllopus/695b10c958ade49da3ac2937ab2618e7.jpg\",\n  \"289151.jpg\": \"Insecta/Leptoglossus phyllopus/33fcfbd4d95e6894dddd1eba7b1ef6d4.jpg\",\n  \"289152.jpg\": \"Insecta/Leptoglossus phyllopus/1e23615826eaf5aa8bf62c10b75a26a9.jpg\",\n  \"289156.jpg\": \"Insecta/Leptoglossus phyllopus/85e84d0543b7f59b829848c61c9472db.jpg\",\n  \"289170.jpg\": \"Aves/Petroica australis australis/afdda9d34bfdf183f82071ec04bbc89c.jpg\",\n  \"289177.jpg\": \"Aves/Petroica australis australis/1023a975fb4062c1eaa773f50132d0c4.jpg\",\n  \"289178.jpg\": \"Aves/Petroica australis australis/c494b69fc91d86c733a32a316ab6bb4a.jpg\",\n  \"289182.jpg\": \"Aves/Petroica australis australis/3917f2b66f547392fe395d3f1c863e5d.jpg\",\n  \"289184.jpg\": \"Aves/Petroica australis australis/f8b91d7987f88ad3febce69873bf5f03.jpg\",\n  \"289186.jpg\": \"Aves/Sylvia atricapilla/9f46a80e4e7989f8a36cb65d050c1857.jpg\",\n  \"289187.jpg\": \"Aves/Sylvia atricapilla/ee73d3cd9d152744417c4ac0823afc24.jpg\",\n  \"289190.jpg\": \"Aves/Sylvia atricapilla/73b8c8f2ac58a404a6871e9b37f25ffe.jpg\",\n  \"289191.jpg\": \"Aves/Sylvia atricapilla/63f284908641d8360097f0b5efd5b0c3.jpg\",\n  \"289199.jpg\": \"Aves/Sylvia atricapilla/1bcd6cf97528ff342f6fe50e9a94798b.jpg\",\n  \"289201.jpg\": \"Aves/Sylvia atricapilla/85bcfdf270aeb56c8b6f33fab22be519.jpg\",\n  \"289202.jpg\": \"Aves/Sylvia atricapilla/ba28a717ba70eb0874528b0bff245c83.jpg\",\n  \"289204.jpg\": \"Aves/Sylvia atricapilla/890de7cd4d63780042b1c1723f3eff82.jpg\",\n  \"289207.jpg\": \"Aves/Sylvia atricapilla/34089b74fd386d64cea737c80585d2e6.jpg\",\n  \"289208.jpg\": \"Aves/Sylvia atricapilla/0687d2d2e9191f75d7f80d2075935cc5.jpg\",\n  \"289209.jpg\": \"Aves/Sylvia atricapilla/80c9d6ca15f94967ffbada7967b31449.jpg\",\n  \"289210.jpg\": \"Aves/Sylvia atricapilla/4053eb4fa40502d4bb76bd4340c736f7.jpg\",\n  \"289211.jpg\": \"Aves/Sylvia atricapilla/9c36cb3b90c680cd3ef14624c473cdeb.jpg\",\n  \"289214.jpg\": \"Aves/Sylvia atricapilla/cb4ec4dd481188a0292eab5adacaaf90.jpg\",\n  \"289215.jpg\": \"Aves/Sylvia atricapilla/6adf483c53804df957baf31c2e101e3f.jpg\",\n  \"289218.jpg\": \"Aves/Sylvia atricapilla/38a1a293c1cd071c70d1a7416d8dcdea.jpg\",\n  \"289221.jpg\": \"Aves/Sylvia atricapilla/5ea85c18e8655f477930474c7925b314.jpg\",\n  \"289223.jpg\": \"Aves/Sylvia atricapilla/88ade2b3958c27d2bb97a1d1fd8cf2e6.jpg\",\n  \"289224.jpg\": \"Aves/Sylvia atricapilla/35c4dd4b93a188e4f675c0e6b1ab0e89.jpg\",\n  \"289228.jpg\": \"Aves/Sylvia atricapilla/a71f403c9d184a1c6bbb942997f15833.jpg\",\n  \"289296.jpg\": \"Insecta/Perithemis tenera/cf742d60740f4137e8479417bca91095.jpg\",\n  \"289319.jpg\": \"Insecta/Perithemis tenera/a1fadb733a90ccc9c9ec75c014067837.jpg\",\n  \"289341.jpg\": \"Insecta/Perithemis tenera/243929bfdbb45b12facfc312cc9fa03e.jpg\",\n  \"289367.jpg\": \"Insecta/Perithemis tenera/38e3fb72a2cd8d1cd7de14995ba8a617.jpg\",\n  \"289373.jpg\": \"Insecta/Perithemis tenera/f0c389d5a0b6f4085a247fe77becefd0.jpg\",\n  \"289381.jpg\": \"Insecta/Perithemis tenera/732c7a90d41ca00c62cdc01ca8dd9e5a.jpg\",\n  \"289394.jpg\": \"Insecta/Perithemis tenera/c4aed26d8f3dcb5ff618ae818830c54d.jpg\",\n  \"289404.jpg\": \"Insecta/Perithemis tenera/709ae9c35e261079a41c99837ee6403a.jpg\",\n  \"289413.jpg\": \"Insecta/Perithemis tenera/9104f2df4a28f6dc90dd5f98cf39f562.jpg\",\n  \"289414.jpg\": \"Insecta/Perithemis tenera/a2ba7baa343c657c80501344be481e17.jpg\",\n  \"289428.jpg\": \"Insecta/Perithemis tenera/cbef288d1aa49c20d021c7032eadc2d1.jpg\",\n  \"289441.jpg\": \"Insecta/Perithemis tenera/b1151f4684dd60055ab0fd87515bd100.jpg\",\n  \"289450.jpg\": \"Insecta/Perithemis tenera/8a5a781faff7a4026e854ab7714463a8.jpg\",\n  \"289453.jpg\": \"Insecta/Perithemis tenera/f3b68a409ea9b012f37008e46ceba220.jpg\",\n  \"289463.jpg\": \"Insecta/Perithemis tenera/f102296b71eee906727ab172b0c06144.jpg\",\n  \"289483.jpg\": \"Insecta/Perithemis tenera/6ec4f5505befabc0aabc8db279ffcb57.jpg\",\n  \"289488.jpg\": \"Insecta/Perithemis tenera/7ae982e35ee568c5e5668a97d9a43b1f.jpg\",\n  \"289505.jpg\": \"Insecta/Perithemis tenera/f38c144ebd6e80608687fbedf20f11da.jpg\",\n  \"289522.jpg\": \"Insecta/Perithemis tenera/66e95fd9b6f37967434996e02cf93a95.jpg\",\n  \"289557.jpg\": \"Insecta/Perithemis tenera/69ffcd85a515122665901e459da6a29b.jpg\",\n  \"289560.jpg\": \"Insecta/Perithemis tenera/8483a2174841c5c33c14d87e55b61466.jpg\",\n  \"289626.jpg\": \"Arachnida/Centruroides vittatus/2d4586d18b237f9995cbd68d99dec0bc.jpg\",\n  \"289635.jpg\": \"Arachnida/Centruroides vittatus/783245920ac95834fb4dd0359d9b24cd.jpg\",\n  \"289636.jpg\": \"Arachnida/Centruroides vittatus/3faa9dfb01be23ae90c1b2700ee199e0.jpg\",\n  \"289639.jpg\": \"Arachnida/Centruroides vittatus/90ea4c58ff9333f0799f5771837fc7b4.jpg\",\n  \"289640.jpg\": \"Arachnida/Centruroides vittatus/68be88fa0bfed692b141af1a0e92956b.jpg\",\n  \"289643.jpg\": \"Arachnida/Centruroides vittatus/3b6e10c04925f9ffcfc29ae8af2211b6.jpg\",\n  \"289644.jpg\": \"Arachnida/Centruroides vittatus/b08f8c6766e001df8cd62f5eb1bec5c2.jpg\",\n  \"289645.jpg\": \"Arachnida/Centruroides vittatus/c716f2f56c4d4dffbbeb1de6c49fa34f.jpg\",\n  \"289646.jpg\": \"Arachnida/Centruroides vittatus/7ec9bd49f12a684c02cf995fa87cf5bb.jpg\",\n  \"289647.jpg\": \"Arachnida/Centruroides vittatus/b370ef8ca8e448bef8e6fbc6baef9495.jpg\",\n  \"289650.jpg\": \"Arachnida/Centruroides vittatus/5071a5b7ec7d1ab0fde3c6e2d93de6e3.jpg\",\n  \"289651.jpg\": \"Arachnida/Centruroides vittatus/46605b942dd7dd6280f16d3957ffcbd9.jpg\",\n  \"289660.jpg\": \"Arachnida/Centruroides vittatus/19d94521e68ef839904148433190c67f.jpg\",\n  \"289661.jpg\": \"Arachnida/Centruroides vittatus/cdc1d0d5f2f19364ed1ba7fb46d0b1ea.jpg\",\n  \"289667.jpg\": \"Arachnida/Centruroides vittatus/8e2cac22f60e7ac97a477bd774ef36e0.jpg\",\n  \"289669.jpg\": \"Arachnida/Centruroides vittatus/6cafdff97074f1612fb5482ae4e62c83.jpg\",\n  \"289673.jpg\": \"Arachnida/Centruroides vittatus/d442d18aa76b45a9db030a68fc330115.jpg\",\n  \"289674.jpg\": \"Arachnida/Centruroides vittatus/0be953356339542db52bf4d64d99fb09.jpg\",\n  \"289679.jpg\": \"Arachnida/Centruroides vittatus/f1a484267e8f62a5940f99783a065e89.jpg\",\n  \"289683.jpg\": \"Arachnida/Centruroides vittatus/2ad70eb398628f90fae45f46311a977d.jpg\",\n  \"289686.jpg\": \"Arachnida/Centruroides vittatus/cb1c5f97a1a1b70041a78ae1e36e2701.jpg\",\n  \"289688.jpg\": \"Arachnida/Centruroides vittatus/ed0b934bac6d1b036bdfb2533aedf139.jpg\",\n  \"289705.jpg\": \"Arachnida/Centruroides vittatus/95d1c66c82e4942473d2b2647d65dbd9.jpg\",\n  \"289714.jpg\": \"Amphibia/Ichthyosaura alpestris/f73429cf22ecc670663ed9bda0179ea4.jpg\",\n  \"289729.jpg\": \"Amphibia/Ichthyosaura alpestris/7ed24039418ae49d2c6a850b24336ece.jpg\",\n  \"289731.jpg\": \"Amphibia/Ichthyosaura alpestris/a45dd86664bf06f7a589383deef6b2a1.jpg\",\n  \"289732.jpg\": \"Amphibia/Ichthyosaura alpestris/c59de6fe6333c47fe5e7d85590ef9daa.jpg\",\n  \"289735.jpg\": \"Amphibia/Ichthyosaura alpestris/9ff5c7c77538df293ded1fef4597c0ea.jpg\",\n  \"289740.jpg\": \"Insecta/Protoboarmia porcelaria/a52052a7c45eaba2d8c96359a6518335.jpg\",\n  \"289743.jpg\": \"Insecta/Protoboarmia porcelaria/48af74cd4f73793006ce8ab9bce6b3f4.jpg\",\n  \"289747.jpg\": \"Insecta/Protoboarmia porcelaria/a80f91ba97771d8f77b09dfc03996c43.jpg\",\n  \"289748.jpg\": \"Insecta/Protoboarmia porcelaria/043c78ed9ad2a7e8c03801ae84cca44e.jpg\",\n  \"289751.jpg\": \"Insecta/Protoboarmia porcelaria/62caa6035da60ee5572602965c70843a.jpg\",\n  \"289758.jpg\": \"Insecta/Protoboarmia porcelaria/cf16c0ed8f2b049101caaf5048bbf94d.jpg\",\n  \"289759.jpg\": \"Insecta/Stagmomantis carolina/27d3f6d5d398a9f10a4aaba12dde0b3b.jpg\",\n  \"289762.jpg\": \"Insecta/Stagmomantis carolina/3de56089735749c3cf86ed230dbac7bf.jpg\",\n  \"289766.jpg\": \"Insecta/Stagmomantis carolina/04f2ab69bde2f9cc4ffc8dba91a15fd9.jpg\",\n  \"289772.jpg\": \"Insecta/Stagmomantis carolina/aab4c4fcaaa4348a5e31b7c9123d0c95.jpg\",\n  \"289781.jpg\": \"Insecta/Stagmomantis carolina/1b5eb147533bc110993fe1f7a5198e1b.jpg\",\n  \"289783.jpg\": \"Insecta/Stagmomantis carolina/d823f488ca82d5dd713b20c03d369390.jpg\",\n  \"289784.jpg\": \"Insecta/Stagmomantis carolina/811ee5580618e34cdd4b2bb7d204668d.jpg\",\n  \"289785.jpg\": \"Insecta/Stagmomantis carolina/f5a47627ec57d2bc882e4c88394db586.jpg\",\n  \"289790.jpg\": \"Insecta/Stagmomantis carolina/015517227b002d86b5a850dbc3a3ea8f.jpg\",\n  \"289791.jpg\": \"Insecta/Stagmomantis carolina/aeb2882ffac5391a64eb0c418dbd2735.jpg\",\n  \"289796.jpg\": \"Insecta/Stagmomantis carolina/34cfc2990e92efea8ef32468011e68ba.jpg\",\n  \"289801.jpg\": \"Insecta/Stagmomantis carolina/aa75c9bec39a7d6e897d90860d82e80a.jpg\",\n  \"289805.jpg\": \"Insecta/Stagmomantis carolina/ca168747d0042fbe7b563517be59e819.jpg\",\n  \"289824.jpg\": \"Insecta/Stagmomantis carolina/3fd765202ea8f58e26b392588156a0ac.jpg\",\n  \"289834.jpg\": \"Insecta/Stagmomantis carolina/d09ca8a1fa04b5cc38a210b7d98300e0.jpg\",\n  \"289836.jpg\": \"Insecta/Stagmomantis carolina/556bb7506d3448edc39a2671f483a7fd.jpg\",\n  \"289837.jpg\": \"Insecta/Stagmomantis carolina/88c3066b2c170443e12174dfab93355c.jpg\",\n  \"289838.jpg\": \"Insecta/Stagmomantis carolina/1675bfdaa13108386c64484ecc50a4a7.jpg\",\n  \"289848.jpg\": \"Insecta/Stagmomantis carolina/9b9a19e553662b974a6b210b5084757f.jpg\",\n  \"289855.jpg\": \"Insecta/Stagmomantis carolina/2dca26b27abd7b77b4b431b19f722992.jpg\",\n  \"289856.jpg\": \"Insecta/Stagmomantis carolina/eb20a2e6686dfc2de1d1f75a5db0334f.jpg\",\n  \"289860.jpg\": \"Aves/Anhinga rufa/5dded772c1563cd50eed98cdae914339.jpg\",\n  \"289867.jpg\": \"Aves/Anhinga rufa/b01d101f36ef7426ed1375b52dbf5ee7.jpg\",\n  \"289871.jpg\": \"Aves/Anhinga rufa/2c46a3153d62604c6a9b5b777eb23283.jpg\",\n  \"289881.jpg\": \"Aves/Chroicocephalus ridibundus/0bd8a83cf3abe2372a5d245af883cf5f.jpg\",\n  \"289882.jpg\": \"Aves/Chroicocephalus ridibundus/742f4fb5484d369e58cdce904c910f24.jpg\",\n  \"289885.jpg\": \"Aves/Chroicocephalus ridibundus/917067be026924f15a5ce048fe3539b3.jpg\",\n  \"289899.jpg\": \"Aves/Chroicocephalus ridibundus/c6da6fc55227a98275150b91236f1799.jpg\",\n  \"289900.jpg\": \"Aves/Chroicocephalus ridibundus/182fba81db3c641db03137625c7d698c.jpg\",\n  \"289909.jpg\": \"Aves/Chroicocephalus ridibundus/15486785576b3dca752e3d618fc13715.jpg\",\n  \"289917.jpg\": \"Aves/Chroicocephalus ridibundus/3284262d5f65fbec7f92acae5048460c.jpg\",\n  \"289918.jpg\": \"Aves/Chroicocephalus ridibundus/a1a1485906dcac7517ba939c0fc2930b.jpg\",\n  \"289928.jpg\": \"Aves/Chroicocephalus ridibundus/43cf6195fe37667d47fd99ddbadba408.jpg\",\n  \"289929.jpg\": \"Aves/Chroicocephalus ridibundus/606e387c60a3c9c4e40ed7b6fafae221.jpg\",\n  \"289933.jpg\": \"Aves/Chroicocephalus ridibundus/65f596a7c2fb79dfb7e5ecbf995a1490.jpg\",\n  \"289934.jpg\": \"Aves/Chroicocephalus ridibundus/1f68e54f7d5d8cc1b7076270ac2d068a.jpg\",\n  \"28994.jpg\": \"Insecta/Diabrotica undecimpunctata/08bfefa8db7d2f02749fce432e5b2a6a.jpg\",\n  \"289942.jpg\": \"Aves/Chroicocephalus ridibundus/6edc3f0b38bbfa1e1d574d1d7c334936.jpg\",\n  \"289960.jpg\": \"Aves/Chroicocephalus ridibundus/0ac9675e79ddbaa48af1adfeec0960a2.jpg\",\n  \"289997.jpg\": \"Aves/Chroicocephalus ridibundus/0a1160d0a8266a59d26f5078308afb48.jpg\",\n  \"289998.jpg\": \"Aves/Chroicocephalus ridibundus/4a261d505f86c798f27f7b9561d3f1c9.jpg\",\n  \"290.jpg\": \"Mollusca/Olivella biplicata/31894479da498a32f678fe64e51851c6.jpg\",\n  \"290000.jpg\": \"Aves/Chroicocephalus ridibundus/0dfed142e60bd0ca66f1e8fb47408aa4.jpg\",\n  \"290002.jpg\": \"Aves/Chroicocephalus ridibundus/bcacb98858687014748f834b44c85464.jpg\",\n  \"290015.jpg\": \"Aves/Chroicocephalus ridibundus/fced07ee606497870b93c8773ed4c78b.jpg\",\n  \"290024.jpg\": \"Aves/Chroicocephalus ridibundus/ab7a3930146175b78147301ccc07a1cf.jpg\",\n  \"290026.jpg\": \"Aves/Chroicocephalus ridibundus/72ef5acb2294f3efb6d4e05de26e327f.jpg\",\n  \"29003.jpg\": \"Insecta/Diabrotica undecimpunctata/765747412be42bff0d4f0d2713ea04d4.jpg\",\n  \"290051.jpg\": \"Aves/Cairina moschata domestica/c80efbea6d82331c3fa0cec6713eda90.jpg\",\n  \"290056.jpg\": \"Aves/Cairina moschata domestica/a26e35404e4a60ac3f74d473223229a9.jpg\",\n  \"29007.jpg\": \"Insecta/Diabrotica undecimpunctata/41d2f94547f2b6aaef0ecdc6bf590370.jpg\",\n  \"290074.jpg\": \"Aves/Cairina moschata domestica/3a5920cd2645f30a8e84f0af54d96bdb.jpg\",\n  \"290078.jpg\": \"Aves/Cairina moschata domestica/3f5f5367e83e354a82aacd30429dc82c.jpg\",\n  \"290079.jpg\": \"Aves/Cairina moschata domestica/63081537928497eb35a757cf85441657.jpg\",\n  \"290108.jpg\": \"Aves/Cairina moschata domestica/f31b8dab0dbb5c87395fa852455b5b52.jpg\",\n  \"290115.jpg\": \"Aves/Cairina moschata domestica/af3af4b6999a1e7ec04e47fce3a1c596.jpg\",\n  \"290135.jpg\": \"Aves/Cairina moschata domestica/027835430bea516ad4f2f316dd150e19.jpg\",\n  \"290160.jpg\": \"Aves/Cairina moschata domestica/7b97381699b09d1850b078b6f976a200.jpg\",\n  \"290161.jpg\": \"Aves/Cairina moschata domestica/d8838979d63ba7c5a700acfc167908b2.jpg\",\n  \"290165.jpg\": \"Aves/Cairina moschata domestica/74a047be67dc86af3060ff0ec688a931.jpg\",\n  \"290176.jpg\": \"Aves/Cairina moschata domestica/01a0134af3712d4f5ae1c4d1b8251bc7.jpg\",\n  \"290199.jpg\": \"Aves/Cairina moschata domestica/08dd6af026f7b52a386c94d4dc4533b9.jpg\",\n  \"29022.jpg\": \"Insecta/Diabrotica undecimpunctata/af74d137dffa0b07860cb73e04041fae.jpg\",\n  \"290230.jpg\": \"Aves/Cairina moschata domestica/8b403ab1b1875acf7e2465b090b1f878.jpg\",\n  \"29035.jpg\": \"Insecta/Diabrotica undecimpunctata/b0191b527248f0369ae78a9b1a0197e6.jpg\",\n  \"29036.jpg\": \"Insecta/Diabrotica undecimpunctata/4aba6bbc67d1e1a68b5ebc132fde8591.jpg\",\n  \"29037.jpg\": \"Insecta/Diabrotica undecimpunctata/0553a6f081c463f4fdcd170e610be613.jpg\",\n  \"290371.jpg\": \"Aves/Aechmophorus clarkii/604b9c50e388835a9a8fa8992385287d.jpg\",\n  \"290372.jpg\": \"Aves/Aechmophorus clarkii/950849ae3123eab3d98fbece3839c1a5.jpg\",\n  \"290374.jpg\": \"Aves/Aechmophorus clarkii/c94f0623db973e13f4100ac0956cc641.jpg\",\n  \"290375.jpg\": \"Aves/Aechmophorus clarkii/a77d4943721c5595d6cf6b35b3b4b127.jpg\",\n  \"290376.jpg\": \"Aves/Aechmophorus clarkii/8dc6b2b30b425078a06b74f8e5b3a894.jpg\",\n  \"290377.jpg\": \"Aves/Aechmophorus clarkii/5b6c07f76e21f871d9461f296b8cc314.jpg\",\n  \"29038.jpg\": \"Insecta/Diabrotica undecimpunctata/08776d08392c3da65692e87a397ec84c.jpg\",\n  \"290380.jpg\": \"Aves/Aechmophorus clarkii/2c2290d058d52f7a2fd269565d047faa.jpg\",\n  \"290397.jpg\": \"Aves/Aechmophorus clarkii/25e34572001ceb34cb6b45e1d740c15a.jpg\",\n  \"290400.jpg\": \"Aves/Aechmophorus clarkii/82896a53d76659317f41f88526d01450.jpg\",\n  \"290406.jpg\": \"Aves/Aechmophorus clarkii/29a241db1845480c6ffb6f1707f9dd71.jpg\",\n  \"290411.jpg\": \"Aves/Aechmophorus clarkii/1db5c50ef12b1b23f0e6f4cd460657f0.jpg\",\n  \"290412.jpg\": \"Aves/Aechmophorus clarkii/8e84793c1a8067887da2f9d2afc9918b.jpg\",\n  \"290413.jpg\": \"Aves/Aechmophorus clarkii/eeb8915df4b91a39f01a8729f420d411.jpg\",\n  \"290428.jpg\": \"Aves/Aechmophorus clarkii/2dcb926b7528cfe6531b1267a5ec7830.jpg\",\n  \"290429.jpg\": \"Aves/Aechmophorus clarkii/6f4e34f933d84359c2b0d25a9cdbce1b.jpg\",\n  \"290434.jpg\": \"Aves/Aechmophorus clarkii/03388ecfd58b8d9608c81b7c499abdc5.jpg\",\n  \"290439.jpg\": \"Aves/Aechmophorus clarkii/231243b179bb8f1723a7a8631cba6d2a.jpg\",\n  \"290442.jpg\": \"Aves/Aechmophorus clarkii/2304611fddfa621cfd27e6f35472af5d.jpg\",\n  \"290445.jpg\": \"Aves/Aechmophorus clarkii/4348ccc459f430bf8411ea0dc67cde57.jpg\",\n  \"290447.jpg\": \"Insecta/Caenurgina erechtea/d17f08e4dbfcd9398a8ec7ed3d5d33e6.jpg\",\n  \"290448.jpg\": \"Insecta/Caenurgina erechtea/f6a2747a75bdac3fd5ae6673d6321111.jpg\",\n  \"290449.jpg\": \"Insecta/Caenurgina erechtea/e97d61779ef03a09f4d9e52c61ff9ad8.jpg\",\n  \"290455.jpg\": \"Insecta/Caenurgina erechtea/5a788dcb743d2be63572e7f2f928b528.jpg\",\n  \"290456.jpg\": \"Insecta/Caenurgina erechtea/5f664d1039a84290fb87ff7b18fed27a.jpg\",\n  \"290462.jpg\": \"Insecta/Caenurgina erechtea/d94c34329f4ed6ceddfcd3ed8b612391.jpg\",\n  \"290463.jpg\": \"Insecta/Caenurgina erechtea/3777eff2b3ca7e605e9d49fbb42b67f5.jpg\",\n  \"290464.jpg\": \"Insecta/Caenurgina erechtea/e5be7cde53b15cf6e286d852a031bfeb.jpg\",\n  \"290466.jpg\": \"Insecta/Caenurgina erechtea/64fb268ea963d86ac1d98811f4931762.jpg\",\n  \"290470.jpg\": \"Insecta/Caenurgina erechtea/d5dc391a2389a29767070255b9d2c4e5.jpg\",\n  \"290471.jpg\": \"Insecta/Caenurgina erechtea/b70e3c7e1cd5ae948a9d7a354772d323.jpg\",\n  \"290472.jpg\": \"Insecta/Caenurgina erechtea/06421fee7da8d591c0c6bc0c75ab3715.jpg\",\n  \"290474.jpg\": \"Insecta/Caenurgina erechtea/7dca503e726b64c9eb0434ff022b20dd.jpg\",\n  \"290478.jpg\": \"Insecta/Caenurgina erechtea/65a2a5e18ffd52c43f117bd6121f0dec.jpg\",\n  \"290484.jpg\": \"Insecta/Caenurgina erechtea/e6390d60a3e66be2b0dfc7abd378d173.jpg\",\n  \"290487.jpg\": \"Insecta/Caenurgina erechtea/774de316fa9561093ce6885ffa31dafd.jpg\",\n  \"290489.jpg\": \"Insecta/Caenurgina erechtea/7d5cf765d2e356f4b56ff2fff3b9388d.jpg\",\n  \"29049.jpg\": \"Insecta/Diabrotica undecimpunctata/9d63e7bb7b74e3c7413e5cabd4ed681f.jpg\",\n  \"290490.jpg\": \"Insecta/Caenurgina erechtea/8305f4a9e8e51a22087fb46ec404ce7b.jpg\",\n  \"290494.jpg\": \"Insecta/Caenurgina erechtea/283bcc65faa421fb2185f3e0aa037c9d.jpg\",\n  \"290495.jpg\": \"Insecta/Caenurgina erechtea/02840cb221b07d441bead86130b0b720.jpg\",\n  \"290496.jpg\": \"Insecta/Caenurgina erechtea/5049a2539b92e626883530f00d71b389.jpg\",\n  \"290498.jpg\": \"Insecta/Caenurgina erechtea/b3e458ab276853a2317d7b338ce05602.jpg\",\n  \"290499.jpg\": \"Insecta/Caenurgina erechtea/ff54a1c40207c76ceb5fe9c1d5bdeee1.jpg\",\n  \"290500.jpg\": \"Insecta/Caenurgina erechtea/30f6ccf635f1b66fa1daec5c46a0a669.jpg\",\n  \"290501.jpg\": \"Aves/Phylloscopus collybita/e15e55bd4eec753af1260c6d05e68ac7.jpg\",\n  \"290502.jpg\": \"Aves/Phylloscopus collybita/ab8dc2ac860495422b4fe7adee549acf.jpg\",\n  \"290503.jpg\": \"Aves/Phylloscopus collybita/5ab0e87b2364ed400cd07cfbc85c7090.jpg\",\n  \"290504.jpg\": \"Aves/Phylloscopus collybita/3e9bf629926854e2148869ee16b1e80e.jpg\",\n  \"290511.jpg\": \"Aves/Phylloscopus collybita/a1b1b99f0f8f98c119ba44bb3a35435b.jpg\",\n  \"290513.jpg\": \"Aves/Phylloscopus collybita/6d82a1c25b74a43939c2575736120215.jpg\",\n  \"290518.jpg\": \"Aves/Phylloscopus collybita/39acabe1b6bd2e983b9ae848f50c2bd2.jpg\",\n  \"290519.jpg\": \"Aves/Phylloscopus collybita/fea62e26229140e591364d774294dd24.jpg\",\n  \"290522.jpg\": \"Aves/Phylloscopus collybita/8f7111bf4737ae9aca7f0780a88da04c.jpg\",\n  \"290524.jpg\": \"Aves/Phylloscopus collybita/7b98126445946d3b24c2581254a0fbe3.jpg\",\n  \"290528.jpg\": \"Aves/Phylloscopus collybita/0352f0a3f21930886d366cd4e1e9bdee.jpg\",\n  \"290530.jpg\": \"Aves/Phylloscopus collybita/e4d57af3e2002b6b3c9c9076d10cb41b.jpg\",\n  \"290531.jpg\": \"Aves/Phylloscopus collybita/d179ed7e516fdc598da69f2e12fafae0.jpg\",\n  \"290532.jpg\": \"Aves/Phylloscopus collybita/5043b35e78e0864791be1328e7755f4d.jpg\",\n  \"290533.jpg\": \"Aves/Phylloscopus collybita/92f5e07158359da1b64e8e5ed077d95d.jpg\",\n  \"290534.jpg\": \"Aves/Phylloscopus collybita/aeb7397a5c027281556fecf2318bd9ea.jpg\",\n  \"290535.jpg\": \"Aves/Phylloscopus collybita/2c96a170942555809e0753dee75781df.jpg\",\n  \"290536.jpg\": \"Aves/Phylloscopus collybita/6d2f31f82d0c7149c1b3e98c382a8576.jpg\",\n  \"290537.jpg\": \"Aves/Phylloscopus collybita/038305f4f303d3c09992809f5f252ed4.jpg\",\n  \"290544.jpg\": \"Aves/Phylloscopus collybita/0066a345ff4e7dabd91bf88b67bb90c1.jpg\",\n  \"290551.jpg\": \"Amphibia/Gyrinophilus porphyriticus/b292df7634f8289b9bbc4e06eace0e36.jpg\",\n  \"290562.jpg\": \"Amphibia/Gyrinophilus porphyriticus/f282df573cd2e89c15cf31690c0a628e.jpg\",\n  \"290563.jpg\": \"Amphibia/Gyrinophilus porphyriticus/6172dfaf28b338c8064158717a7d7a8d.jpg\",\n  \"290576.jpg\": \"Insecta/Polistes apachus/052d63ec9a65d9387ae2d53d28f0c7b2.jpg\",\n  \"29058.jpg\": \"Insecta/Diabrotica undecimpunctata/5a51ee3a205b25a6eeeeaaac8ef5d922.jpg\",\n  \"290583.jpg\": \"Insecta/Polistes apachus/d7f6fae346ae6af59e1bb832fa6a0ee1.jpg\",\n  \"290604.jpg\": \"Insecta/Polistes apachus/c37dd8335122c32aefac18d240805ab8.jpg\",\n  \"290614.jpg\": \"Insecta/Polistes apachus/94cc325c075a4a46a813abd096b75df1.jpg\",\n  \"290615.jpg\": \"Aves/Momotus coeruliceps/205406d8655238dc1be55cfed9735121.jpg\",\n  \"290619.jpg\": \"Aves/Momotus coeruliceps/1d537b1a0b8d89eaa50af961670fa428.jpg\",\n  \"290625.jpg\": \"Aves/Momotus coeruliceps/2b3bc693b14e3ff4105c811ed24a47ab.jpg\",\n  \"290628.jpg\": \"Aves/Periparus ater/517f0daf6e807d2704f0b8f60e112a9c.jpg\",\n  \"29063.jpg\": \"Insecta/Diabrotica undecimpunctata/68f01a9ede2eaa87a5eaf60bf04c7034.jpg\",\n  \"290630.jpg\": \"Aves/Periparus ater/86d677b8f77fb471c8cb9250545b3adb.jpg\",\n  \"290636.jpg\": \"Aves/Periparus ater/f324857edb05f6f8f96d8b3a5f675cfc.jpg\",\n  \"29064.jpg\": \"Insecta/Diabrotica undecimpunctata/49be3ec9da5133079e94322b6fd57159.jpg\",\n  \"290643.jpg\": \"Aves/Periparus ater/9aa6820a98dbc24d2d6084d677650955.jpg\",\n  \"290655.jpg\": \"Aves/Periparus ater/6de2a7a2c0df00efaf8d73409dc827a7.jpg\",\n  \"290663.jpg\": \"Aves/Periparus ater/2ea52aaf88ae0b15a1c721629d2fe68c.jpg\",\n  \"290762.jpg\": \"Arachnida/Steatoda triangulosa/3c55533ecc22d46d71919e7ddeacd71c.jpg\",\n  \"290763.jpg\": \"Arachnida/Steatoda triangulosa/3ae6e0a1f5d2a433f7bbcfd335bf85f3.jpg\",\n  \"290764.jpg\": \"Arachnida/Steatoda triangulosa/41dba452629b0d4fd652e72b11d8d3b9.jpg\",\n  \"290767.jpg\": \"Arachnida/Steatoda triangulosa/68025403324c054e0004830ac609ac2a.jpg\",\n  \"290772.jpg\": \"Aves/Phoebastria immutabilis/04dea1cb3d5f68207d828a5898717503.jpg\",\n  \"290774.jpg\": \"Aves/Phoebastria immutabilis/8e7bb82624bd48ff5feca2126a7986f3.jpg\",\n  \"29081.jpg\": \"Insecta/Diabrotica undecimpunctata/3304007273c0edb38316b01181672daa.jpg\",\n  \"29083.jpg\": \"Insecta/Diabrotica undecimpunctata/eea9647e5ad2258f4e72c6fe161336ba.jpg\",\n  \"291.jpg\": \"Mollusca/Olivella biplicata/a07fb2e34f7ff3aa7eb153400128c47c.jpg\",\n  \"29105.jpg\": \"Insecta/Diabrotica undecimpunctata/d49cf9297238ea8563e22367801c9535.jpg\",\n  \"291252.jpg\": \"Aves/Xiphorhynchus flavigaster/5730eab630879f2b4b6c9ad5b966a9b3.jpg\",\n  \"29126.jpg\": \"Insecta/Diabrotica undecimpunctata/4308591db6068abb75d70231a17a2c5d.jpg\",\n  \"291261.jpg\": \"Aves/Xiphorhynchus flavigaster/a44d9a6e30079dcafd18886e8f7054e5.jpg\",\n  \"291267.jpg\": \"Aves/Xiphorhynchus flavigaster/2b76df8561b94004ac1fd4c7ca656a08.jpg\",\n  \"291268.jpg\": \"Aves/Xiphorhynchus flavigaster/4767a23cc9259138cb5ca4a1468395dd.jpg\",\n  \"291269.jpg\": \"Aves/Xiphorhynchus flavigaster/ce1efe8c031d51688711629198eb9741.jpg\",\n  \"291292.jpg\": \"Insecta/Phryganidia californica/cd34d437de8d0d1a571e89baee86c61d.jpg\",\n  \"291295.jpg\": \"Insecta/Phryganidia californica/b8c036b7e16c76e7e885426681df377b.jpg\",\n  \"291302.jpg\": \"Insecta/Phryganidia californica/8ddae5fcf89ec4318452934b68bb6082.jpg\",\n  \"291306.jpg\": \"Insecta/Phryganidia californica/53c05b3e0eb472d5a26cea79fc1ee4b5.jpg\",\n  \"291312.jpg\": \"Reptilia/Podarcis muralis/bd5ff40fd3074bfc9c58aa9f70819467.jpg\",\n  \"291314.jpg\": \"Reptilia/Podarcis muralis/62fe51824c12a158fb5c72cb3af3f966.jpg\",\n  \"291322.jpg\": \"Reptilia/Podarcis muralis/b3186eff84860ee610f58e760f747476.jpg\",\n  \"291338.jpg\": \"Reptilia/Podarcis muralis/f4700ba0d85cd088397fd0c30863b1d4.jpg\",\n  \"29134.jpg\": \"Insecta/Diabrotica undecimpunctata/b0dc974b195ad6cf8e219f5771a84a61.jpg\",\n  \"291343.jpg\": \"Reptilia/Podarcis muralis/1cf62ec10117913f941ff6dc50c54916.jpg\",\n  \"291349.jpg\": \"Reptilia/Podarcis muralis/d442500751f00150fbcd4824efbe2eec.jpg\",\n  \"291352.jpg\": \"Reptilia/Podarcis muralis/d8d610bc021c222f9bcab5a036fad9f9.jpg\",\n  \"291364.jpg\": \"Reptilia/Podarcis muralis/c9ecf07fedfddf3a7891859d16a7f066.jpg\",\n  \"291366.jpg\": \"Reptilia/Podarcis muralis/24935a0e394813ce361665ee4d556af4.jpg\",\n  \"291379.jpg\": \"Reptilia/Podarcis muralis/c4bc8c2182f15c0ca200bc926e7c7bcf.jpg\",\n  \"291389.jpg\": \"Reptilia/Podarcis muralis/400be9b6938dce542958799195dc5af3.jpg\",\n  \"291395.jpg\": \"Reptilia/Podarcis muralis/aab7ca573a402bb559afef6fc22cebc1.jpg\",\n  \"291397.jpg\": \"Reptilia/Podarcis muralis/839442a612cd702e4a249c65a8cccfa1.jpg\",\n  \"291398.jpg\": \"Reptilia/Podarcis muralis/b7f4ca13e140d27ba779916147233358.jpg\",\n  \"291399.jpg\": \"Reptilia/Podarcis muralis/c381e6ba42901120087758e8678522ef.jpg\",\n  \"291404.jpg\": \"Reptilia/Podarcis muralis/641ed9aec72872909b1f00d0aee049b1.jpg\",\n  \"291405.jpg\": \"Reptilia/Podarcis muralis/1317028067092050235e53e8cc053192.jpg\",\n  \"291416.jpg\": \"Reptilia/Podarcis muralis/decd482c22c8834f0de3701d73efaa53.jpg\",\n  \"291417.jpg\": \"Reptilia/Podarcis muralis/ab1479e9e325bcd00abae7a0dfe78bf6.jpg\",\n  \"29142.jpg\": \"Insecta/Diabrotica undecimpunctata/14ed7022a655eb5f888064d03b7e98f0.jpg\",\n  \"291429.jpg\": \"Reptilia/Podarcis muralis/63cd2eeb3b4a30471b96bf98756c65fa.jpg\",\n  \"291430.jpg\": \"Reptilia/Podarcis muralis/49b67f003af09b0d6baafc5d31d7448c.jpg\",\n  \"291438.jpg\": \"Reptilia/Podarcis muralis/fc8d81e0b636f82f3cf56fc4de2b0e5c.jpg\",\n  \"291445.jpg\": \"Reptilia/Podarcis muralis/a28e418154426265e9e41324059c9e08.jpg\",\n  \"291447.jpg\": \"Reptilia/Podarcis muralis/238ca308fecd072c7946f613554799df.jpg\",\n  \"29145.jpg\": \"Insecta/Diabrotica undecimpunctata/4ee0b7b37c553631b24a988064d7c587.jpg\",\n  \"291452.jpg\": \"Insecta/Orgyia postica/0ede49cf2b5d9e28ce720c521f883ccb.jpg\",\n  \"291461.jpg\": \"Insecta/Orgyia postica/dbeea075c02edd33c6b2684eebdbe2ce.jpg\",\n  \"291464.jpg\": \"Insecta/Orgyia postica/0e584be3efe93583b4ed753abeddca74.jpg\",\n  \"291484.jpg\": \"Insecta/Thyris sepulchralis/064eddf191ae95d14932131fc3298637.jpg\",\n  \"291486.jpg\": \"Insecta/Thyris sepulchralis/1fd5b06f80fb32b6d4da7683bdb6cbf1.jpg\",\n  \"291487.jpg\": \"Insecta/Thyris sepulchralis/a0010fb7fbf4e47793f81596427f05b3.jpg\",\n  \"291502.jpg\": \"Insecta/Thyris sepulchralis/f58e746b0b322efd536072786b54c7ac.jpg\",\n  \"291503.jpg\": \"Insecta/Thyris sepulchralis/a8d8fa3e8c4cca281f644575559c7dbd.jpg\",\n  \"29154.jpg\": \"Insecta/Diabrotica undecimpunctata/575eb0a48299e4ce5a48113379c218dc.jpg\",\n  \"29161.jpg\": \"Insecta/Diabrotica undecimpunctata/2c63878dabbefaae0ca917e34abf949a.jpg\",\n  \"29164.jpg\": \"Aves/Amazilia beryllina/f81e39a02066acf50ffdc5cd25f4f30e.jpg\",\n  \"29165.jpg\": \"Aves/Amazilia beryllina/c4ff16625f463e054e93f33beddc341e.jpg\",\n  \"291658.jpg\": \"Aves/Sphyrapicus ruber/40426aa0ea16a1e3070d25c52d1845f0.jpg\",\n  \"291666.jpg\": \"Aves/Sphyrapicus ruber/11a1d3df8bb6efe546e54adbd6bbc7ab.jpg\",\n  \"291667.jpg\": \"Aves/Sphyrapicus ruber/3c866dd1354bf57fd11e07b8bf1ebcaf.jpg\",\n  \"291668.jpg\": \"Aves/Sphyrapicus ruber/9fbfbb242ea77ab95faa73789c4b05a5.jpg\",\n  \"291669.jpg\": \"Aves/Sphyrapicus ruber/51bb6024264c75f70fefbb906f88657d.jpg\",\n  \"291670.jpg\": \"Aves/Sphyrapicus ruber/35fe1bc87d4a881eb488b44dce20d1db.jpg\",\n  \"291673.jpg\": \"Aves/Sphyrapicus ruber/c8456f4c1ce64662dba0eca6d82f1d91.jpg\",\n  \"291674.jpg\": \"Aves/Sphyrapicus ruber/624b9f64bcb0f7aaa7f7cf92204331dd.jpg\",\n  \"291675.jpg\": \"Aves/Sphyrapicus ruber/ccd7f9df53cb630676f3dbbe25906d3e.jpg\",\n  \"291684.jpg\": \"Aves/Sphyrapicus ruber/89e58b7a3a36d8bd2555962d4ed9fbc8.jpg\",\n  \"291685.jpg\": \"Aves/Sphyrapicus ruber/53459e8a89002b728759a7d72baecfdd.jpg\",\n  \"291688.jpg\": \"Aves/Sphyrapicus ruber/dde0d6e4f0a8e9f4ce844f466d04281a.jpg\",\n  \"291692.jpg\": \"Aves/Sphyrapicus ruber/67ae82e085cd344ff376d3640ea7cefc.jpg\",\n  \"291709.jpg\": \"Aves/Sphyrapicus ruber/c40dafc14a107616a5832f4bc2c3b44d.jpg\",\n  \"291710.jpg\": \"Aves/Sphyrapicus ruber/1175aeee59d9dae9a11cb72ac3eae29f.jpg\",\n  \"291715.jpg\": \"Aves/Sphyrapicus ruber/929226ebc5106a53b588ffd2db420f42.jpg\",\n  \"291717.jpg\": \"Aves/Sphyrapicus ruber/b97fdaed441b9b9e2567a60fe4cdcf45.jpg\",\n  \"291718.jpg\": \"Aves/Sphyrapicus ruber/0f86b32470a98510742fc19b5a0471ce.jpg\",\n  \"291723.jpg\": \"Aves/Sphyrapicus ruber/87e34a27063b6e178350b4f2dbde9770.jpg\",\n  \"291724.jpg\": \"Aves/Sphyrapicus ruber/b777d5aa779ffbef76e237c15624d3ea.jpg\",\n  \"291726.jpg\": \"Aves/Sphyrapicus ruber/3aea47f4f96926d42aa6fa9d64ea5f40.jpg\",\n  \"291729.jpg\": \"Aves/Sphyrapicus ruber/541cadb43e7f174a709508c757870d1f.jpg\",\n  \"29173.jpg\": \"Aves/Amazilia beryllina/252e6e55fda06672511e85a9c7147ff3.jpg\",\n  \"291731.jpg\": \"Aves/Sphyrapicus ruber/bf7c378bedb43da7414742fed8e9bba3.jpg\",\n  \"291732.jpg\": \"Aves/Sphyrapicus ruber/813c5e3b71520c632a76fc4bf64f656b.jpg\",\n  \"29175.jpg\": \"Aves/Amazilia beryllina/90cc655df4422f25b9d5afd6033b6026.jpg\",\n  \"291752.jpg\": \"Aves/Sphyrapicus ruber/08367fda96e96e34e992a47048b4a968.jpg\",\n  \"291753.jpg\": \"Aves/Sphyrapicus ruber/662d4c608188939f592513ed497b9ab9.jpg\",\n  \"291765.jpg\": \"Reptilia/Coluber flagellum flagellum/11fac5030a0d4211a57dc24e591a7b4d.jpg\",\n  \"29177.jpg\": \"Aves/Amazilia beryllina/e2ba4bf55f23d2528e1709c7f9fa30a1.jpg\",\n  \"291775.jpg\": \"Reptilia/Coluber flagellum flagellum/6d0949118ff76ec7fc03f351d693e8c2.jpg\",\n  \"291776.jpg\": \"Reptilia/Coluber flagellum flagellum/47d783bdd8eb606360f02b17df068baa.jpg\",\n  \"291783.jpg\": \"Reptilia/Coluber flagellum flagellum/fc1f8a47887a26c5dda860ac04d22e28.jpg\",\n  \"291790.jpg\": \"Arachnida/Uroctonus mordax/49de19bafdca8ef850974dbe0205ce00.jpg\",\n  \"291791.jpg\": \"Arachnida/Uroctonus mordax/b6799bb6bb8635222f8a1ec2d82e3c86.jpg\",\n  \"291792.jpg\": \"Arachnida/Uroctonus mordax/b647cb033cfe8d01529c3b957ef192f5.jpg\",\n  \"291811.jpg\": \"Arachnida/Uroctonus mordax/fa8f0082e2a1ffb05e6577790e411292.jpg\",\n  \"291812.jpg\": \"Arachnida/Uroctonus mordax/353389155b3cd74fcdbf75be77136bc9.jpg\",\n  \"291815.jpg\": \"Arachnida/Uroctonus mordax/40e0659292cbc4ccb1b03e9093b4157f.jpg\",\n  \"291816.jpg\": \"Arachnida/Uroctonus mordax/cab841d64f7fc443e283c2295bc56a9e.jpg\",\n  \"291824.jpg\": \"Arachnida/Uroctonus mordax/9c761fe9bcbdffede5f8a18c8b6f923e.jpg\",\n  \"291830.jpg\": \"Arachnida/Uroctonus mordax/0f3a106eb2565836a5e9cd9ffa367767.jpg\",\n  \"291831.jpg\": \"Arachnida/Uroctonus mordax/1036697822bca2bad9a5902ac6d75f35.jpg\",\n  \"291832.jpg\": \"Arachnida/Uroctonus mordax/bd3327c4520e4cb3e6bb2b2d1652b2a1.jpg\",\n  \"291835.jpg\": \"Arachnida/Uroctonus mordax/f3792ef1847746be16294a363369d1c4.jpg\",\n  \"291836.jpg\": \"Arachnida/Uroctonus mordax/c8bb43dd3152089dc3d55252a5d8d2ee.jpg\",\n  \"291837.jpg\": \"Arachnida/Uroctonus mordax/93f90d022e5e4731cb9b57fd3a1fcfe3.jpg\",\n  \"291858.jpg\": \"Arachnida/Uroctonus mordax/988e8b476d31d7f1ffe55ee1c4e0ea96.jpg\",\n  \"29186.jpg\": \"Aves/Amazilia beryllina/1d38825cf95f888f7e984688034abf0e.jpg\",\n  \"291862.jpg\": \"Arachnida/Uroctonus mordax/9c02d07846ae24e12883fd23d59b0509.jpg\",\n  \"291871.jpg\": \"Arachnida/Uroctonus mordax/6a7f0b917d12ca6ecd0cb360ff8413fb.jpg\",\n  \"291873.jpg\": \"Arachnida/Uroctonus mordax/f6b7402e3980ab5ea4ddae1849b60230.jpg\",\n  \"291877.jpg\": \"Arachnida/Uroctonus mordax/a8016623bc85497ca9344826e8a63ee8.jpg\",\n  \"291878.jpg\": \"Arachnida/Ixodes scapularis/8de76ad6a9b1b1b653fd548a804542f9.jpg\",\n  \"291882.jpg\": \"Arachnida/Ixodes scapularis/8964a328150257f85ab085ea514ceadc.jpg\",\n  \"291892.jpg\": \"Arachnida/Ixodes scapularis/a74f4e4a3e3aa2b7d5f6cc494fc22722.jpg\",\n  \"291894.jpg\": \"Arachnida/Ixodes scapularis/8143c99d603857181a4e8774adc56f9e.jpg\",\n  \"29193.jpg\": \"Aves/Amazilia beryllina/efb00e45ca577c99fda30d3aede0bdc1.jpg\",\n  \"29195.jpg\": \"Insecta/Phalaenophana pyramusalis/7c7d182aca0fcf242c67a000ef54aa65.jpg\",\n  \"29201.jpg\": \"Insecta/Phalaenophana pyramusalis/17ded34c652a106822e1bae20044d35b.jpg\",\n  \"29202.jpg\": \"Insecta/Phalaenophana pyramusalis/6561c23e79808f877264dcc189f035f4.jpg\",\n  \"29204.jpg\": \"Insecta/Phalaenophana pyramusalis/aaafc9a6cdcff26f774082a5e3295921.jpg\",\n  \"29206.jpg\": \"Insecta/Phalaenophana pyramusalis/71f95f2a91e7c6fa9ce385c738ea35dc.jpg\",\n  \"292098.jpg\": \"Insecta/Pyrisitia proterpia/2aa9d8214b5128cc49493c94b02239c6.jpg\",\n  \"292102.jpg\": \"Insecta/Pyrisitia proterpia/6d6dbbe9aaca84a58d39f61d011fd0e3.jpg\",\n  \"292107.jpg\": \"Insecta/Pyrisitia proterpia/0bdc815f8d5de4b9a51cb3e3d0b5616e.jpg\",\n  \"292108.jpg\": \"Insecta/Pyrisitia proterpia/741d306e2fde3712f68df34d7367b91e.jpg\",\n  \"292109.jpg\": \"Insecta/Pyrisitia proterpia/fd76589c5b1101f25805ef9ad91fe98d.jpg\",\n  \"29211.jpg\": \"Insecta/Phalaenophana pyramusalis/e3e65ca02ab6024989d25adc1ead4a94.jpg\",\n  \"292111.jpg\": \"Insecta/Pyrisitia proterpia/7d2adc419577e899143b1ddfd2c62125.jpg\",\n  \"292114.jpg\": \"Insecta/Pyrisitia proterpia/3ec5aad48a6968ccfed0dbec32b4c1bb.jpg\",\n  \"29213.jpg\": \"Insecta/Lygaeus kalmii/c4ed25f3f3f63c9244e76782c4bf64e6.jpg\",\n  \"29215.jpg\": \"Insecta/Lygaeus kalmii/97254a6fe72c2ec8f065e51800ad405d.jpg\",\n  \"292163.jpg\": \"Aves/Glaucidium brasilianum/5dd5c7bf1a635d0a51156d8662130d75.jpg\",\n  \"292170.jpg\": \"Aves/Glaucidium brasilianum/055c9e4a7da90c0e7a732f39fbf12147.jpg\",\n  \"292171.jpg\": \"Aves/Glaucidium brasilianum/c5ed725f8c8e5f951243fa29489af44d.jpg\",\n  \"292172.jpg\": \"Aves/Glaucidium brasilianum/936d48eed13f8c540cc353cb10dc2243.jpg\",\n  \"292177.jpg\": \"Aves/Glaucidium brasilianum/e624f53221757e44b7f971631fa8a726.jpg\",\n  \"292178.jpg\": \"Aves/Glaucidium brasilianum/38e0f77708690eb9cae04165fe6f4074.jpg\",\n  \"292179.jpg\": \"Aves/Glaucidium brasilianum/4fc0bdacc87e0062b11225a0e2c64cc7.jpg\",\n  \"292183.jpg\": \"Aves/Glaucidium brasilianum/043dc1df8d7cb4cb26ea4fc734ff5807.jpg\",\n  \"292184.jpg\": \"Aves/Glaucidium brasilianum/308ba286ed07e03eb4c0ba30eae8a40b.jpg\",\n  \"292185.jpg\": \"Aves/Glaucidium brasilianum/64bc47e7916b16a0cd525db6e526d9a5.jpg\",\n  \"292195.jpg\": \"Aves/Glaucidium brasilianum/f294e75800f7a7fca7f93930b9501461.jpg\",\n  \"292196.jpg\": \"Aves/Glaucidium brasilianum/7d548a765cd7925bd2fab06810cfcced.jpg\",\n  \"29220.jpg\": \"Insecta/Lygaeus kalmii/5239d03e5fc959bbe1bc5af0d9a917dc.jpg\",\n  \"292201.jpg\": \"Aves/Glaucidium brasilianum/6d373f2790db73a2d43a3816e0d495d8.jpg\",\n  \"29221.jpg\": \"Insecta/Lygaeus kalmii/ad81b2801476ed12d6482defaefa739c.jpg\",\n  \"29223.jpg\": \"Insecta/Lygaeus kalmii/f43b7999047671b88b11f39a973db7aa.jpg\",\n  \"292233.jpg\": \"Aves/Glaucidium brasilianum/b7565f35bfe1601f7305546d7c1ff9c0.jpg\",\n  \"292238.jpg\": \"Aves/Glaucidium brasilianum/872c62cbfab6a44af9ba5450dd2fce6a.jpg\",\n  \"292241.jpg\": \"Aves/Glaucidium brasilianum/842d53ded465de819798e337991aefca.jpg\",\n  \"292242.jpg\": \"Aves/Glaucidium brasilianum/018bae703b308e455d448373b350df32.jpg\",\n  \"292243.jpg\": \"Aves/Glaucidium brasilianum/6eabb7b17017e4438424fe880666968f.jpg\",\n  \"292244.jpg\": \"Aves/Glaucidium brasilianum/4e4a7f778736ddf5746f6adbdee1f555.jpg\",\n  \"292249.jpg\": \"Aves/Glaucidium brasilianum/96ec1549d794deb0d803550ab1517b94.jpg\",\n  \"292250.jpg\": \"Aves/Glaucidium brasilianum/62213ac1c96565314fc34f68a80af07e.jpg\",\n  \"292253.jpg\": \"Aves/Glaucidium brasilianum/010fb3b3c61b2c52f41e80511bdefa6f.jpg\",\n  \"292270.jpg\": \"Aves/Platalea leucorodia/9a94d6e29a2b5ceef572fcc24d290d1c.jpg\",\n  \"292283.jpg\": \"Aves/Platalea leucorodia/63ccf7a452e1f5fa818ee1750fdfcae8.jpg\",\n  \"29229.jpg\": \"Insecta/Lygaeus kalmii/ea7908ceda7db15f7cd9de5a50cadd9c.jpg\",\n  \"29230.jpg\": \"Insecta/Lygaeus kalmii/0044bc5dde72af059c3c9c4c2adf8545.jpg\",\n  \"292414.jpg\": \"Insecta/Marpesia chiron/b097de908d690589fb2f8808c13fe2a4.jpg\",\n  \"292415.jpg\": \"Insecta/Marpesia chiron/88a2816b2dd0ab2ac92b1e39ce1c2b26.jpg\",\n  \"292417.jpg\": \"Insecta/Marpesia chiron/95db0d89249f5b1cfcc97295e753613d.jpg\",\n  \"29242.jpg\": \"Insecta/Lygaeus kalmii/93e21e6be89f1648a1d666e4cdb12161.jpg\",\n  \"292421.jpg\": \"Insecta/Marpesia chiron/0f69e0ac0029ea3d79e441635d7f5ac4.jpg\",\n  \"292427.jpg\": \"Insecta/Marpesia chiron/8b183be85bdfd8ab6f6d96dc1415e97a.jpg\",\n  \"292431.jpg\": \"Insecta/Marpesia chiron/f59d589d17bf2ee4e378b325db117bfa.jpg\",\n  \"29245.jpg\": \"Insecta/Lygaeus kalmii/3b7d8a55d24fa85e4292ea1978e18d27.jpg\",\n  \"29256.jpg\": \"Insecta/Lygaeus kalmii/8d77fb1d43e9c893774a35f15b66fd04.jpg\",\n  \"292576.jpg\": \"Aves/Turdus migratorius/2207f6f708671c6d8be891baa6389983.jpg\",\n  \"29260.jpg\": \"Insecta/Lygaeus kalmii/da9ec402fc3e0c5d7616d8fad9f28995.jpg\",\n  \"29261.jpg\": \"Insecta/Lygaeus kalmii/211d3f9fc4e667a97439bd31ced8021e.jpg\",\n  \"29262.jpg\": \"Insecta/Lygaeus kalmii/67f612a7f64c31537bafee130e27d3b1.jpg\",\n  \"29266.jpg\": \"Insecta/Lygaeus kalmii/04f62687144d666e36a02e6b6cbdcfd7.jpg\",\n  \"292665.jpg\": \"Aves/Turdus migratorius/072f14eee4d182a9ea185bacd022b213.jpg\",\n  \"29271.jpg\": \"Insecta/Lygaeus kalmii/288729aab3c967b6868c5b322115df5f.jpg\",\n  \"29276.jpg\": \"Insecta/Lygaeus kalmii/3d82cfe12c8f255ef948c2e776fd3f09.jpg\",\n  \"292768.jpg\": \"Aves/Turdus migratorius/783342e7bd38b95e96adf25727abb604.jpg\",\n  \"29277.jpg\": \"Insecta/Lygaeus kalmii/e5c2aeeb0ceed576a4d7aaa05a3f22b2.jpg\",\n  \"292798.jpg\": \"Aves/Turdus migratorius/a90951dff753a50d9446a5ce350aaa35.jpg\",\n  \"292958.jpg\": \"Aves/Turdus migratorius/5334f16218886d1ccc82bfabaf694ccc.jpg\",\n  \"29306.jpg\": \"Insecta/Lygaeus kalmii/f9d993af2a928039e2b67b6b188d667f.jpg\",\n  \"293072.jpg\": \"Aves/Turdus migratorius/4313f6a763166cbae9713d33dfa1a497.jpg\",\n  \"293074.jpg\": \"Aves/Turdus migratorius/cec466d6b04bff068f9b7f4f99500388.jpg\",\n  \"29316.jpg\": \"Insecta/Lygaeus kalmii/cc00ef1f2963332cd1d44e762bb2eaef.jpg\",\n  \"293182.jpg\": \"Aves/Turdus migratorius/9af46629dfb1238f5c5a7cd7c883b407.jpg\",\n  \"2932.jpg\": \"Aves/Picoides pubescens/46a7d2ec59065ae40f5ef5545b622f9c.jpg\",\n  \"293287.jpg\": \"Aves/Turdus migratorius/60528b05e69b00eacc79455c3896c2fb.jpg\",\n  \"29330.jpg\": \"Insecta/Ancyloxypha numitor/3e484f043a0bf52fa35184ccf96fcd1a.jpg\",\n  \"293387.jpg\": \"Aves/Turdus migratorius/88ebdcee714baafd63372b4aeab9903f.jpg\",\n  \"293415.jpg\": \"Aves/Turdus migratorius/223f0fb435e9e3198720f2977785929b.jpg\",\n  \"293438.jpg\": \"Aves/Turdus migratorius/87b3e27392c875670e3751e3b467b28d.jpg\",\n  \"29344.jpg\": \"Insecta/Ancyloxypha numitor/65a73be6b6861eff5940d276a7bd5c98.jpg\",\n  \"29345.jpg\": \"Insecta/Ancyloxypha numitor/1b9db415105654909835c297fd4ede21.jpg\",\n  \"293489.jpg\": \"Aves/Turdus migratorius/4106e3a4d20a472f08cc70745bb4c689.jpg\",\n  \"293500.jpg\": \"Aves/Turdus migratorius/b6f674d0c0bf7d93f4f4334b17af903f.jpg\",\n  \"29351.jpg\": \"Insecta/Ancyloxypha numitor/c0e2871f6c5901bcc70c4d98ffe8f229.jpg\",\n  \"29354.jpg\": \"Insecta/Ancyloxypha numitor/307458df811e71adace54298e6bc6230.jpg\",\n  \"29355.jpg\": \"Insecta/Ancyloxypha numitor/6e2a596720aa1edd6739fc123d34c2d7.jpg\",\n  \"293558.jpg\": \"Aves/Turdus migratorius/0a1efbe1ff26930d47497e04337e8c39.jpg\",\n  \"29357.jpg\": \"Insecta/Ancyloxypha numitor/40d6a49696f4eaccf5ef7b9bf8f15e1f.jpg\",\n  \"29365.jpg\": \"Insecta/Ancyloxypha numitor/5670854aa1c4d98fd80771ede1ec2395.jpg\",\n  \"293693.jpg\": \"Aves/Turdus migratorius/adeac4bc89f5be6e7cbf4f87791f9a97.jpg\",\n  \"293699.jpg\": \"Aves/Turdus migratorius/e4c389762a6e68c6ff16b1173c24b211.jpg\",\n  \"293731.jpg\": \"Aves/Turdus migratorius/2a6eec847c9a07809bd64d4ddd73c17c.jpg\",\n  \"29374.jpg\": \"Insecta/Ancyloxypha numitor/3ccab137b414a8de2838844883d4d6b1.jpg\",\n  \"293786.jpg\": \"Aves/Turdus migratorius/156f6b15badb07ed05bb0198d744c6f9.jpg\",\n  \"29379.jpg\": \"Insecta/Ancyloxypha numitor/c09e5809cd2b4f98d4aa1657251873a4.jpg\",\n  \"29381.jpg\": \"Insecta/Ancyloxypha numitor/103a12279b82d3e0206d58aa7ba1295a.jpg\",\n  \"29383.jpg\": \"Insecta/Ancyloxypha numitor/da1ed840f23468417678dc0fadc01ef8.jpg\",\n  \"29385.jpg\": \"Insecta/Ancyloxypha numitor/c4e1f734274400193758c61c0943d6ba.jpg\",\n  \"293867.jpg\": \"Aves/Turdus migratorius/ca6567092e5cd67958dbab184466ac80.jpg\",\n  \"294002.jpg\": \"Mammalia/Mirounga angustirostris/5991e24f7b2750fdba82641b56f3bb0a.jpg\",\n  \"294036.jpg\": \"Mammalia/Mirounga angustirostris/f81367257e2511c05c9f0342d0010cf8.jpg\",\n  \"294039.jpg\": \"Mammalia/Mirounga angustirostris/81783e035c9a486371789161fa2702b2.jpg\",\n  \"294040.jpg\": \"Mammalia/Mirounga angustirostris/fd64484b3dc4178f05c22eb1af0463b1.jpg\",\n  \"294054.jpg\": \"Mammalia/Mirounga angustirostris/193fbf2d35f9210aa770051b80f8ba95.jpg\",\n  \"29406.jpg\": \"Insecta/Ancyloxypha numitor/f45d599a2f0796e2422a19bd2c3a938f.jpg\",\n  \"294070.jpg\": \"Mammalia/Mirounga angustirostris/b29585fcc91b89ded4ea70f593bd08b4.jpg\",\n  \"294071.jpg\": \"Mammalia/Mirounga angustirostris/cdbf35ba3443c0f46664ae2d1f898c0e.jpg\",\n  \"294079.jpg\": \"Mammalia/Mirounga angustirostris/56e37dd2186ba676b8b2a49e5b326735.jpg\",\n  \"294099.jpg\": \"Mammalia/Mirounga angustirostris/c5bfa160245e3f8e18187aed86b3ae40.jpg\",\n  \"29411.jpg\": \"Insecta/Ancyloxypha numitor/2ad84142bf846c4fd07ddfe36def2702.jpg\",\n  \"294112.jpg\": \"Mammalia/Mirounga angustirostris/efc209949e2daaf9bd84830dee7dd0e5.jpg\",\n  \"294113.jpg\": \"Mammalia/Mirounga angustirostris/b3aeebaa091a787dc18f6f764d19080c.jpg\",\n  \"294115.jpg\": \"Mammalia/Mirounga angustirostris/8746388e18ed5d1776833574910f6ab0.jpg\",\n  \"294116.jpg\": \"Mammalia/Bradypus variegatus/d6ab81635442bf23bbb23e9b87cff6b2.jpg\",\n  \"294117.jpg\": \"Mammalia/Bradypus variegatus/d53bf2c587ec390bb388e8b79385dcab.jpg\",\n  \"294120.jpg\": \"Mammalia/Bradypus variegatus/27c46ed51775dc7ca06fa12424d8e071.jpg\",\n  \"294121.jpg\": \"Mammalia/Bradypus variegatus/00e14c5887c754cdcb6db0c548edc402.jpg\",\n  \"294146.jpg\": \"Insecta/Boyeria vinosa/21de9854e274609f22f83b9ad4450544.jpg\",\n  \"294147.jpg\": \"Insecta/Boyeria vinosa/0275d1df50803b1fa262c83a056782c8.jpg\",\n  \"294151.jpg\": \"Insecta/Boyeria vinosa/59a7007d6cfc92ed9b9b3aae4c93c706.jpg\",\n  \"294155.jpg\": \"Aves/Turdus plumbeus/1968bc192cd7b01e3c560570a678d427.jpg\",\n  \"294156.jpg\": \"Aves/Turdus plumbeus/6c2fb1d2b9e67068188fe1845cf416a9.jpg\",\n  \"294159.jpg\": \"Aves/Turdus plumbeus/5f95971ebf616b16aa8408b482eb4956.jpg\",\n  \"29416.jpg\": \"Insecta/Ancyloxypha numitor/4108c95f9fc6cb99029bd965ff88d194.jpg\",\n  \"294161.jpg\": \"Aves/Turdus plumbeus/1ffa7b8436ce505584fa4bf6809321c4.jpg\",\n  \"294169.jpg\": \"Aves/Zenaida aurita/19487c14afda71bb8f03b3d64fad20f3.jpg\",\n  \"294170.jpg\": \"Aves/Zenaida aurita/bc862157b90548e8ad85adf699cd4df7.jpg\",\n  \"294172.jpg\": \"Aves/Zenaida aurita/702afd97619e51e3adcc8d178458a598.jpg\",\n  \"294180.jpg\": \"Aves/Zenaida aurita/3661b00e6064fad00805a60e83059685.jpg\",\n  \"294182.jpg\": \"Aves/Zenaida aurita/7d118938d87b3e1911b15771260605b3.jpg\",\n  \"294183.jpg\": \"Aves/Zenaida aurita/f42060e0c427c9980994ff09ce6163a3.jpg\",\n  \"294186.jpg\": \"Aves/Zenaida aurita/7a44fad6a1ebf6bfff8b20a8654c033f.jpg\",\n  \"294187.jpg\": \"Aves/Zenaida aurita/44899529d330b4e4ef8acc4844669002.jpg\",\n  \"294188.jpg\": \"Aves/Zenaida aurita/50c18c0ed780a1bf11b208309d8fa77c.jpg\",\n  \"294192.jpg\": \"Aves/Zenaida aurita/3f52cd5b1c7c9ebd7cc48dce9b7806b0.jpg\",\n  \"29422.jpg\": \"Insecta/Ancyloxypha numitor/da9d8687e68afedeafea98fb264d127d.jpg\",\n  \"294226.jpg\": \"Mammalia/Neotoma fuscipes/b6a6346fe16c6d50d573a8d2c36d6153.jpg\",\n  \"29424.jpg\": \"Insecta/Ancyloxypha numitor/e26c9500ff1417bedae952031d57cfd0.jpg\",\n  \"294246.jpg\": \"Aves/Amaurornis phoenicurus/6ea60da8c31c346fe8b3ef0798ebac9d.jpg\",\n  \"29425.jpg\": \"Insecta/Ancyloxypha numitor/20d0e46dca7f9baaee0d25716619e766.jpg\",\n  \"294251.jpg\": \"Aves/Amaurornis phoenicurus/30b151c0bcc873f0b11940f0a9c611a6.jpg\",\n  \"294257.jpg\": \"Aves/Amaurornis phoenicurus/767a06f8d755321fb54c5104759a9369.jpg\",\n  \"294260.jpg\": \"Aves/Amaurornis phoenicurus/2541fc54b2259d42b00fd9c728a89538.jpg\",\n  \"294261.jpg\": \"Aves/Amaurornis phoenicurus/b457436ef92465fa33ef462946599647.jpg\",\n  \"294262.jpg\": \"Arachnida/Mastigoproctus giganteus/4a971ebdf3f6b55f15c47328e68cc156.jpg\",\n  \"294269.jpg\": \"Arachnida/Mastigoproctus giganteus/13f455293159791a2f92efe3e59b86d9.jpg\",\n  \"29427.jpg\": \"Insecta/Ancyloxypha numitor/4297d690e7f8d4f0c16aac0c3a24e3a4.jpg\",\n  \"294271.jpg\": \"Arachnida/Mastigoproctus giganteus/97b90c90b082c01dfbec8dda94b8d0d5.jpg\",\n  \"294275.jpg\": \"Arachnida/Mastigoproctus giganteus/c86c3538ebd9750de9584a3130a36840.jpg\",\n  \"294278.jpg\": \"Arachnida/Mastigoproctus giganteus/180f194b16ed97b5b3457e6cda9f0a3d.jpg\",\n  \"294279.jpg\": \"Arachnida/Mastigoproctus giganteus/7b3405b61c13f120ee6fa5d511024d87.jpg\",\n  \"29428.jpg\": \"Insecta/Ancyloxypha numitor/5f55f6194f80fea039e277955588c022.jpg\",\n  \"294283.jpg\": \"Arachnida/Mastigoproctus giganteus/889428a2f3e7475e5184d4cbf4089076.jpg\",\n  \"294288.jpg\": \"Arachnida/Mastigoproctus giganteus/a38d2b82ea426b6d666b135177270fd8.jpg\",\n  \"294290.jpg\": \"Arachnida/Mastigoproctus giganteus/76c379a8e30350b9fc2326b16b81b884.jpg\",\n  \"29430.jpg\": \"Insecta/Ancyloxypha numitor/d86d717d93f15ad6ad1b4e7b36fbacc5.jpg\",\n  \"29431.jpg\": \"Insecta/Ancyloxypha numitor/fa4c51c20eb2568e05cf9c0577320618.jpg\",\n  \"29432.jpg\": \"Insecta/Ancyloxypha numitor/358f217d465ef71693cc204e8720f4c0.jpg\",\n  \"29436.jpg\": \"Insecta/Ancyloxypha numitor/2a0026ce9aca57fde04a676937a4c130.jpg\",\n  \"294470.jpg\": \"Aves/Colaptes rubiginosus/a2234a65f4c36a391222b2896f814009.jpg\",\n  \"294471.jpg\": \"Aves/Colaptes rubiginosus/c6336c693bf492d2b84288bc15361b2d.jpg\",\n  \"294472.jpg\": \"Aves/Colaptes rubiginosus/e53d958b73d65f6996d30ca0ca47a931.jpg\",\n  \"294478.jpg\": \"Aves/Colaptes rubiginosus/3cd8837ab8c49963a1a90feb525afafe.jpg\",\n  \"294479.jpg\": \"Aves/Colaptes rubiginosus/86d447f8ae5103d376c3f5a84d728238.jpg\",\n  \"294771.jpg\": \"Aves/Limosa haemastica/6ba65072d9111eff2af5d0d3bba5d1f8.jpg\",\n  \"294776.jpg\": \"Aves/Limosa haemastica/23463973c8c281b56a875b4fd84f7d75.jpg\",\n  \"294777.jpg\": \"Aves/Limosa haemastica/16f1b9673129fb2a2ec7f567db669a9b.jpg\",\n  \"294778.jpg\": \"Aves/Limosa haemastica/c35199e62db05d6f2b4894024dfc5de6.jpg\",\n  \"294789.jpg\": \"Aves/Myadestes townsendi/6fcd4b5da5d98f953bdeb7b33b9f2430.jpg\",\n  \"294793.jpg\": \"Aves/Myadestes townsendi/815e67617f01f2b5f79d47048e9dd3b0.jpg\",\n  \"294794.jpg\": \"Aves/Myadestes townsendi/0be4f67713bd2332140094458b0e68ef.jpg\",\n  \"294805.jpg\": \"Aves/Myadestes townsendi/979d408babb073051d4bc4da3873e948.jpg\",\n  \"294812.jpg\": \"Aves/Myadestes townsendi/197035393d356f7f51faf4d3a8c79bc3.jpg\",\n  \"294814.jpg\": \"Aves/Myadestes townsendi/9e93ed740f15f3108b7443d0fdfe45e2.jpg\",\n  \"294817.jpg\": \"Aves/Myadestes townsendi/3f485195ff21d62a91e9165091cd946a.jpg\",\n  \"294818.jpg\": \"Aves/Myadestes townsendi/73238d4c2cb725a9aa87cfc0d8d3dddb.jpg\",\n  \"294825.jpg\": \"Aves/Myadestes townsendi/07b32c6e7d3e5f839cee1560bd30d841.jpg\",\n  \"294826.jpg\": \"Aves/Myadestes townsendi/abeda4adb645c1633ec5abc144027f82.jpg\",\n  \"294831.jpg\": \"Aves/Myadestes townsendi/5def1fac01bcbcb736dff6662f3572a0.jpg\",\n  \"294832.jpg\": \"Aves/Myadestes townsendi/350365429abf94504966784b64fda94a.jpg\",\n  \"294866.jpg\": \"Aves/Tringa solitaria/6ae27a34d4e62aadb47bf9d93f0b2a40.jpg\",\n  \"294873.jpg\": \"Aves/Tringa solitaria/9bf589a6e8809dcb3d981a2039105078.jpg\",\n  \"294880.jpg\": \"Aves/Tringa solitaria/e478f762214cbcfae292926a894fd174.jpg\",\n  \"294888.jpg\": \"Aves/Tringa solitaria/61250a9b7796e946699aa513da04ac5d.jpg\",\n  \"294913.jpg\": \"Aves/Tringa solitaria/49b0aac417fa29f8002ef82455bf23b6.jpg\",\n  \"294921.jpg\": \"Aves/Tringa solitaria/3c365ae84dab4894958ac40be4e6ec2d.jpg\",\n  \"294923.jpg\": \"Aves/Tringa solitaria/670d1db97f3266d8f07cceb933c28afe.jpg\",\n  \"294927.jpg\": \"Aves/Tringa solitaria/64a12f53ec6f522d47f1f6c169375dab.jpg\",\n  \"294930.jpg\": \"Aves/Tringa solitaria/b0fa0d146416ec25702b54d81e3eaa9a.jpg\",\n  \"294937.jpg\": \"Aves/Tringa solitaria/96818b1342d21b70dec90fdd02f7d7ca.jpg\",\n  \"294946.jpg\": \"Aves/Tringa solitaria/feb2ac8a0f0bbca6cdeba85fed8053f9.jpg\",\n  \"294947.jpg\": \"Aves/Tringa solitaria/48c91a627a7ba75b2badf8f4928e7840.jpg\",\n  \"294950.jpg\": \"Aves/Tringa solitaria/468392268f8ab6527d0fb4ceebb394a2.jpg\",\n  \"294953.jpg\": \"Aves/Tringa solitaria/36665e792d29cbb96174504ba8f805c1.jpg\",\n  \"294961.jpg\": \"Aves/Tringa solitaria/1f3037d8f6af5dbe0411f72c9834d7ad.jpg\",\n  \"294970.jpg\": \"Aves/Tringa solitaria/6d19bb6309c23fb0c49f89e44e4af0ae.jpg\",\n  \"294972.jpg\": \"Aves/Tringa solitaria/566fa0776f75519bf59b46ece0f1bcfc.jpg\",\n  \"294979.jpg\": \"Aves/Tringa solitaria/74e27290d0ae775bd1be1c02935e2c7d.jpg\",\n  \"294987.jpg\": \"Aves/Tringa solitaria/74fddb1b6350e9fa23645a9622334e9e.jpg\",\n  \"295004.jpg\": \"Aves/Tringa solitaria/80cecfc5a75fd0dcb108db3000d13323.jpg\",\n  \"295005.jpg\": \"Aves/Tringa solitaria/56519bfc6f00932849cd4b98b78ea468.jpg\",\n  \"295006.jpg\": \"Aves/Tringa solitaria/095ec4cf1a0e68112b1180dc44bd9e27.jpg\",\n  \"295016.jpg\": \"Aves/Callipepla gambelii/6fffe0cf7d6d69a6205679ebd4f2c4d7.jpg\",\n  \"295020.jpg\": \"Aves/Callipepla gambelii/2a458cb490712e70cb860f383a57a767.jpg\",\n  \"295022.jpg\": \"Aves/Callipepla gambelii/c2ded44fd147603e6983bc5478baddef.jpg\",\n  \"295023.jpg\": \"Aves/Callipepla gambelii/c258c4ac0f92706202706452e1c30fd9.jpg\",\n  \"295030.jpg\": \"Aves/Callipepla gambelii/47f19f028393c78b6805f46e8f894f87.jpg\",\n  \"295037.jpg\": \"Aves/Callipepla gambelii/5aa3b0361d67631807ef326221c58cdd.jpg\",\n  \"295038.jpg\": \"Aves/Callipepla gambelii/c8e88a0b7980c152c04a22c0c63d6ab6.jpg\",\n  \"295042.jpg\": \"Aves/Callipepla gambelii/e136a75f850723e01fb526c7030eccc4.jpg\",\n  \"295049.jpg\": \"Aves/Callipepla gambelii/313ea8648d3e6efd8e217e59af426338.jpg\",\n  \"295051.jpg\": \"Aves/Callipepla gambelii/82bbfabc4ecfb49d0661f66e415ea356.jpg\",\n  \"295082.jpg\": \"Aves/Callipepla gambelii/919e4a1c08607b8337f6d67ead57aa55.jpg\",\n  \"295087.jpg\": \"Aves/Callipepla gambelii/bd056f79f1483dcb21f1445e6ef1ca53.jpg\",\n  \"295088.jpg\": \"Aves/Callipepla gambelii/1b1845d0540f4a136fd7bca14a8827cd.jpg\",\n  \"295093.jpg\": \"Aves/Callipepla gambelii/af0c61fc96a70fb8ab007e5f72f43ccf.jpg\",\n  \"295097.jpg\": \"Aves/Callipepla gambelii/2dbac8823192128aff6a8266195621a1.jpg\",\n  \"295100.jpg\": \"Insecta/Enallagma exsulans/2586da3c763e45ae8deae9fa3d8cc704.jpg\",\n  \"295101.jpg\": \"Insecta/Enallagma exsulans/be595f28033dcb13b73d5618260329f1.jpg\",\n  \"295102.jpg\": \"Insecta/Enallagma exsulans/a94af94aac35606e2e03a54a792dfbf6.jpg\",\n  \"295103.jpg\": \"Insecta/Enallagma exsulans/3afe4c89150eb9123ec722f013528976.jpg\",\n  \"295105.jpg\": \"Insecta/Enallagma exsulans/f2d022ed1d581ee691f81bf6b44a54cd.jpg\",\n  \"295108.jpg\": \"Insecta/Enallagma exsulans/1cbdb221917d31c9970942218d74082c.jpg\",\n  \"295109.jpg\": \"Insecta/Enallagma exsulans/68edf5becaeb191601038786b082d032.jpg\",\n  \"295110.jpg\": \"Insecta/Enallagma exsulans/dda76db8b9f92a3a4a4521a7420683c2.jpg\",\n  \"295112.jpg\": \"Insecta/Enallagma exsulans/0d56743c9bbf554b3726ef5397ed2400.jpg\",\n  \"295113.jpg\": \"Insecta/Enallagma exsulans/5edb11b9bfee7a1cd4649f15c393bf99.jpg\",\n  \"295120.jpg\": \"Insecta/Enallagma exsulans/7fa3780db1ff9014f53a6dbbcb4d9aa7.jpg\",\n  \"295137.jpg\": \"Insecta/Enallagma exsulans/9b7342f385d0f0ad8a2526346d4e25d3.jpg\",\n  \"295138.jpg\": \"Insecta/Enallagma exsulans/d96b3f8a97e4fc21340d9b83abc1bbcb.jpg\",\n  \"295139.jpg\": \"Insecta/Enallagma exsulans/ea207d4732cfe5d845fa2f07e60f7d88.jpg\",\n  \"295143.jpg\": \"Insecta/Enallagma exsulans/1cc1382bbee9197fc182390eb7f66885.jpg\",\n  \"295145.jpg\": \"Insecta/Enallagma exsulans/6d8cf6180073ea06c1e31a7e3fdfa109.jpg\",\n  \"295148.jpg\": \"Insecta/Enallagma exsulans/d696c4129d850b43d3738a5f60734722.jpg\",\n  \"295157.jpg\": \"Insecta/Enallagma exsulans/a35c7c0a4df70c190a79fc7fc34b3e81.jpg\",\n  \"295158.jpg\": \"Insecta/Enallagma exsulans/90fc5add832dd471a3ec26fc8fca342a.jpg\",\n  \"295161.jpg\": \"Insecta/Enallagma exsulans/fe70f53194b1db6f48cb445096252e15.jpg\",\n  \"295162.jpg\": \"Insecta/Enallagma exsulans/ce1b636c93ef32ba1d3883e75c1da661.jpg\",\n  \"295167.jpg\": \"Insecta/Libellula semifasciata/859cf56d7a0a5349c1aee0b06daad73e.jpg\",\n  \"295175.jpg\": \"Insecta/Libellula semifasciata/878c4f4be400e6b3817b726b7d88dc56.jpg\",\n  \"295178.jpg\": \"Insecta/Libellula semifasciata/6542189968b4734c4d06af3c258c5f0f.jpg\",\n  \"295188.jpg\": \"Insecta/Libellula semifasciata/cce9f39d1e42c6f2e621d774ac21c319.jpg\",\n  \"295190.jpg\": \"Insecta/Libellula semifasciata/b737e1943d3d7f6a5bff9a9085b761a8.jpg\",\n  \"295195.jpg\": \"Insecta/Libellula semifasciata/7f4d35e625f5e2dabf3b5d64fa54b43b.jpg\",\n  \"295200.jpg\": \"Insecta/Libellula semifasciata/4e918f0207cf7fdf6b5ed93e2bb074c9.jpg\",\n  \"295216.jpg\": \"Actinopterygii/Chaetodon lunula/21c85559c5ca830889beb3b8eeb909f7.jpg\",\n  \"295231.jpg\": \"Actinopterygii/Chaetodon lunula/eb0c8ce44fe6d51afae5289d78aff123.jpg\",\n  \"295251.jpg\": \"Aves/Calidris alba/1490ed792f6f53b2260741178e03aab2.jpg\",\n  \"295278.jpg\": \"Aves/Calidris alba/c46f495cd3580c0a54972611aa055c39.jpg\",\n  \"295284.jpg\": \"Aves/Calidris alba/a302c5daa933eed3c83fd0389afe4f97.jpg\",\n  \"295299.jpg\": \"Aves/Calidris alba/d747fc70ce7252ce303ede7068b9cd63.jpg\",\n  \"295341.jpg\": \"Aves/Calidris alba/c3e2afa357526fc29fa5fae34efaf579.jpg\",\n  \"295345.jpg\": \"Aves/Calidris alba/a927404548a76a131c7d67f1a25dd4e9.jpg\",\n  \"295390.jpg\": \"Aves/Calidris alba/b007bd4884ec7a8f319931410c2f859c.jpg\",\n  \"295400.jpg\": \"Aves/Calidris alba/af284f9b6ac053ff276d7201ba0b49e7.jpg\",\n  \"295418.jpg\": \"Aves/Calidris alba/526fa18731415458c0b793b635262c89.jpg\",\n  \"295432.jpg\": \"Aves/Calidris alba/cbf555d372a73c9a3314d28f84a0ef61.jpg\",\n  \"295450.jpg\": \"Aves/Calidris alba/c898e79ba341f15ca6ed67fb6a4baa16.jpg\",\n  \"295541.jpg\": \"Aves/Calidris alba/e500bc4a231d8cc3b3bcb49208fceec7.jpg\",\n  \"295542.jpg\": \"Aves/Calidris alba/138e9bb2cd515d18a397c534ca4f0f91.jpg\",\n  \"295580.jpg\": \"Aves/Anas rubripes/a2456531b4b780860484f8b4ea4baa1a.jpg\",\n  \"295587.jpg\": \"Aves/Anas rubripes/b0f01bde7835cd77605fdafd8093543b.jpg\",\n  \"295626.jpg\": \"Aves/Anas rubripes/6874e58d7857aecf9623c64844887988.jpg\",\n  \"295629.jpg\": \"Aves/Anas rubripes/dfc64570a44853b205fbbd8c9e2a1501.jpg\",\n  \"295630.jpg\": \"Aves/Anas rubripes/55a26a8fe556238dfae071e685396c99.jpg\",\n  \"295640.jpg\": \"Aves/Anas rubripes/283e9481b1bdceac9164b109aea89a55.jpg\",\n  \"295641.jpg\": \"Aves/Anas rubripes/2f2a0b548059c8f8ed1a227bca78da8f.jpg\",\n  \"295659.jpg\": \"Insecta/Sympetrum vicinum/72c5a6878aa80bdcfbd7ed484d4251a2.jpg\",\n  \"295661.jpg\": \"Insecta/Sympetrum vicinum/fe166f2b4d83e2ed36cfa5154af4bfb6.jpg\",\n  \"295680.jpg\": \"Insecta/Sympetrum vicinum/ef78a3a6cb82fdb46eb73705fb577b92.jpg\",\n  \"295689.jpg\": \"Insecta/Sympetrum vicinum/a4f89bc5b31d0eb7f3b8e3cdf099e3b7.jpg\",\n  \"295696.jpg\": \"Insecta/Sympetrum vicinum/82e064bbb6b4729e5185d16066e7dd77.jpg\",\n  \"295698.jpg\": \"Insecta/Sympetrum vicinum/54cc212cb276ce243464107e3011278d.jpg\",\n  \"295713.jpg\": \"Insecta/Sympetrum vicinum/4b495d89297f8e97ba54b6b33e6526d1.jpg\",\n  \"295715.jpg\": \"Insecta/Sympetrum vicinum/9d22d136891e428377e6997c2dbfaa92.jpg\",\n  \"295731.jpg\": \"Insecta/Sympetrum vicinum/e4c2033c9f8c213dc1deb1c8dfbdf89e.jpg\",\n  \"295741.jpg\": \"Insecta/Sympetrum vicinum/9e3b0f8911a2fc17c7f4559dfe81a4b5.jpg\",\n  \"295751.jpg\": \"Insecta/Sympetrum vicinum/1be500536528e22676621f3633794fad.jpg\",\n  \"295762.jpg\": \"Insecta/Sympetrum vicinum/f68f9e3eef7515970b9d1635afb8f94a.jpg\",\n  \"29577.jpg\": \"Aves/Alectura lathami/d2509b7e83a757af0a62858cf0be281e.jpg\",\n  \"29578.jpg\": \"Aves/Alectura lathami/e1bf9e28505a8c20906575a2aa0605d5.jpg\",\n  \"295784.jpg\": \"Insecta/Sympetrum vicinum/de61494f84c9f0837bf47e8d600e28b5.jpg\",\n  \"295785.jpg\": \"Insecta/Sympetrum vicinum/f90cfa3051e0017fd0ea7702754c4889.jpg\",\n  \"295790.jpg\": \"Insecta/Sympetrum vicinum/2633fe075349e2946a31937813f9cb37.jpg\",\n  \"295791.jpg\": \"Insecta/Sympetrum vicinum/c32bf29be478b4e7ef3c88456f432d7e.jpg\",\n  \"2958.jpg\": \"Aves/Picoides pubescens/f25bf6a517ac48a9577984014423f111.jpg\",\n  \"295820.jpg\": \"Insecta/Sympetrum vicinum/dff8e0ea6f4a7b7b31e6d469f46eefd4.jpg\",\n  \"295824.jpg\": \"Insecta/Sympetrum vicinum/e5782eb530815f2c6fa4ad8aeda23d7f.jpg\",\n  \"295842.jpg\": \"Insecta/Sympetrum vicinum/1ccc4679d15fecb254c713059118a5db.jpg\",\n  \"295846.jpg\": \"Insecta/Sympetrum vicinum/e46bd464b405aa865a43bffa38fbeae5.jpg\",\n  \"295855.jpg\": \"Insecta/Sympetrum vicinum/1ec0eea01cc143ba728a3c4bb341a371.jpg\",\n  \"295873.jpg\": \"Insecta/Sympetrum vicinum/276d77f157597f2ed9af0096f4f66548.jpg\",\n  \"295886.jpg\": \"Insecta/Sympetrum vicinum/abff3c1c38855cc15e864b3018c6bf66.jpg\",\n  \"295897.jpg\": \"Insecta/Sympetrum vicinum/957b483e045c9f29b2ed0445103c0368.jpg\",\n  \"29593.jpg\": \"Aves/Alectura lathami/0284d33ea2e35bd7ee34a74559d51881.jpg\",\n  \"29594.jpg\": \"Aves/Alectura lathami/921d3c68ec10faa720b550219cb4620f.jpg\",\n  \"29595.jpg\": \"Aves/Alectura lathami/3cd8e696a2fdac8bb2df2b5d9ffdcbfc.jpg\",\n  \"29596.jpg\": \"Aves/Alectura lathami/22816e842742ab36245de0c58190e351.jpg\",\n  \"29599.jpg\": \"Aves/Alectura lathami/6d6f09b24cc49810dbc6dedb0f17ef80.jpg\",\n  \"295994.jpg\": \"Insecta/Eurytides marcellus/d79187e4c390bce53ca533c0a17c311e.jpg\",\n  \"295998.jpg\": \"Insecta/Eurytides marcellus/eaf5b33298cece6c70fcb90da67faf23.jpg\",\n  \"295999.jpg\": \"Insecta/Eurytides marcellus/daa56765a351a656459c2c5746aaa366.jpg\",\n  \"296001.jpg\": \"Insecta/Eurytides marcellus/8fe83b4197d3c82484f01c2388c9ae26.jpg\",\n  \"296003.jpg\": \"Insecta/Eurytides marcellus/03bf1357e2bece34e621a99047520f60.jpg\",\n  \"296006.jpg\": \"Insecta/Eurytides marcellus/52074157ba5cdbb4158fd48d859183e0.jpg\",\n  \"296008.jpg\": \"Insecta/Eurytides marcellus/4dd53ae918e3bf756cb704c16df8c482.jpg\",\n  \"296011.jpg\": \"Insecta/Eurytides marcellus/7d42e3bbec57741a6fc71713c37fdb17.jpg\",\n  \"296013.jpg\": \"Insecta/Eurytides marcellus/d3adc133894612022cdbc84a6a4bb38b.jpg\",\n  \"296019.jpg\": \"Insecta/Eurytides marcellus/ba8d5fa8997410e847ab739e66046310.jpg\",\n  \"296024.jpg\": \"Insecta/Eurytides marcellus/00266e7f73393bedc95d10929089e19c.jpg\",\n  \"296026.jpg\": \"Insecta/Eurytides marcellus/b44284981e2221bd1d1466a00f5c354b.jpg\",\n  \"296030.jpg\": \"Insecta/Eurytides marcellus/4cbfd435d919c8fe29d2cd65b974ef77.jpg\",\n  \"296031.jpg\": \"Insecta/Eurytides marcellus/e6836c02b25bc18ab5b89d76328cfa21.jpg\",\n  \"296034.jpg\": \"Insecta/Eurytides marcellus/1b85ef9e0a825bc4de959dcd656f50ff.jpg\",\n  \"296039.jpg\": \"Insecta/Eurytides marcellus/eb0ea5ddaad14aeb2ca865b722037efb.jpg\",\n  \"296041.jpg\": \"Insecta/Eurytides marcellus/7b5ea4fd7d44d62432d11754577e2f0e.jpg\",\n  \"296046.jpg\": \"Insecta/Eurytides marcellus/46dac198e7c7f4e87f33de4415b50e91.jpg\",\n  \"296047.jpg\": \"Amphibia/Hyla squirella/6e801510c640015f8e1d7a6fa1827c57.jpg\",\n  \"296048.jpg\": \"Amphibia/Hyla squirella/3e58085b34eae488fcfd07a32dc2abe3.jpg\",\n  \"296058.jpg\": \"Amphibia/Hyla squirella/a9ff19e57e2082df011800ea769c9d34.jpg\",\n  \"296059.jpg\": \"Amphibia/Hyla squirella/f7ce97ecb033addf460ee780bb54abf7.jpg\",\n  \"29606.jpg\": \"Aves/Himantopus leucocephalus/33f9926b8fd0acaf0290ee4481a15e02.jpg\",\n  \"296063.jpg\": \"Amphibia/Hyla squirella/a626b1e1118573c90743f57ae288c1ef.jpg\",\n  \"296064.jpg\": \"Amphibia/Hyla squirella/01eaea1e90b03efadde263a32f91db3d.jpg\",\n  \"296065.jpg\": \"Amphibia/Hyla squirella/65c731c362beaffb30e3f199cb28cd93.jpg\",\n  \"29607.jpg\": \"Aves/Himantopus leucocephalus/7b4c0cbd4eee44f16eda6475ec267fba.jpg\",\n  \"296070.jpg\": \"Amphibia/Hyla squirella/35c63c6401cb5b77642f7473c9871f80.jpg\",\n  \"296071.jpg\": \"Amphibia/Hyla squirella/0abfe3676db0cbc47847f84b94d0c907.jpg\",\n  \"296072.jpg\": \"Amphibia/Hyla squirella/a7464bdf21d3bfb589d8687a0f908fe6.jpg\",\n  \"296073.jpg\": \"Amphibia/Hyla squirella/34fa0371f617d9956a7ae14ebf64d186.jpg\",\n  \"296074.jpg\": \"Amphibia/Hyla squirella/6dc977a33db8696ef9800123ff036499.jpg\",\n  \"29608.jpg\": \"Aves/Himantopus leucocephalus/4b8ec42ba979b2c75e6754472c856201.jpg\",\n  \"296080.jpg\": \"Amphibia/Hyla squirella/4a568da0318082af8029ad2e58f70389.jpg\",\n  \"296082.jpg\": \"Amphibia/Hyla squirella/d3936ef114e5948e71a558032b4be4ad.jpg\",\n  \"296083.jpg\": \"Amphibia/Hyla squirella/c15bc63a3c7cb1c0d2543fd67106d1fe.jpg\",\n  \"296084.jpg\": \"Amphibia/Hyla squirella/675b314dac0e22861a329a8a50eb47db.jpg\",\n  \"296089.jpg\": \"Amphibia/Hyla squirella/4ec4cdb9a9633b1d81bc554242a0da21.jpg\",\n  \"296091.jpg\": \"Amphibia/Hyla squirella/9fe2f72642f9e200280ba4788da3d20a.jpg\",\n  \"296183.jpg\": \"Insecta/Polygonia progne/be13f18529cfd240e8739fb18cae7909.jpg\",\n  \"296193.jpg\": \"Insecta/Polygonia progne/46f30e47c5d784768fa69c5df49315d4.jpg\",\n  \"296195.jpg\": \"Insecta/Polygonia progne/33a3915f47e24a32584bb5b29487f212.jpg\",\n  \"29620.jpg\": \"Aves/Himantopus leucocephalus/e6c93be38bea8efa73daacbb07d7f147.jpg\",\n  \"296203.jpg\": \"Insecta/Polygonia progne/a1ba3d6eb7978e31525a39a4817f64dc.jpg\",\n  \"29621.jpg\": \"Aves/Himantopus leucocephalus/01a1d3ee157cbb306cb57cf3b95942f4.jpg\",\n  \"296217.jpg\": \"Insecta/Speyeria aphrodite/b90bb69188e343badd7458719067bb52.jpg\",\n  \"296219.jpg\": \"Insecta/Speyeria aphrodite/688d95a58eeb3513194bc210a0d9e632.jpg\",\n  \"296220.jpg\": \"Insecta/Speyeria aphrodite/72d1bbc53fadc740107f49029df6f92c.jpg\",\n  \"296221.jpg\": \"Insecta/Speyeria aphrodite/50b02b5929c1e2238792abb836e99b18.jpg\",\n  \"296225.jpg\": \"Mammalia/Mustela nivalis/d623818d917af57d75b812754e189b40.jpg\",\n  \"296228.jpg\": \"Mammalia/Mustela nivalis/7a2f5c30100afcef4030836b23298734.jpg\",\n  \"296229.jpg\": \"Mammalia/Mustela nivalis/28bcef82530351ddd365ab271a82b24a.jpg\",\n  \"296236.jpg\": \"Mammalia/Mustela nivalis/5485463dc0a717390ab269887b318b34.jpg\",\n  \"296238.jpg\": \"Mammalia/Mustela nivalis/c70fbd1221ce5cc8f2b9eb5a07434880.jpg\",\n  \"29628.jpg\": \"Aves/Himantopus leucocephalus/c289f65fd103d42b84f9d2251d5f2c3f.jpg\",\n  \"29629.jpg\": \"Aves/Himantopus leucocephalus/bf3de6a89906b25e5cccd4e281f784e8.jpg\",\n  \"296304.jpg\": \"Mammalia/Peromyscus maniculatus/2fac6ea5b6f627e995af30dd30d61cf5.jpg\",\n  \"296305.jpg\": \"Mammalia/Peromyscus maniculatus/32fbe18c374e183c916885a9078c04c6.jpg\",\n  \"296306.jpg\": \"Mammalia/Peromyscus maniculatus/544ddc0c079e250b8bea691afd603f89.jpg\",\n  \"296307.jpg\": \"Mammalia/Peromyscus maniculatus/7a15d5be799e5935c05114cb07009e39.jpg\",\n  \"296308.jpg\": \"Mammalia/Peromyscus maniculatus/bdbc786c35901b60bdb9511818fe59f1.jpg\",\n  \"296315.jpg\": \"Mammalia/Peromyscus maniculatus/1101ec91b5884798254f8c5fe280a1a8.jpg\",\n  \"296323.jpg\": \"Mammalia/Peromyscus maniculatus/8e9d5c0495ffadf69b4c2857143f655f.jpg\",\n  \"296342.jpg\": \"Insecta/Pholisora catullus/fcfab942c56c21dad7e9cdf74b297abd.jpg\",\n  \"296343.jpg\": \"Insecta/Pholisora catullus/757a0a2e2a26c44dadc567cf842f9ef2.jpg\",\n  \"296344.jpg\": \"Insecta/Pholisora catullus/76d7d01357e64786a6b541a75b48f25e.jpg\",\n  \"296345.jpg\": \"Insecta/Pholisora catullus/54c23685e95a81f3075c1d76ea960548.jpg\",\n  \"296346.jpg\": \"Insecta/Pholisora catullus/541b4845f08eccf3e89f06f1c6b95e2d.jpg\",\n  \"296347.jpg\": \"Insecta/Pholisora catullus/4ffcf41a9f6379bec195e2c39e2f8984.jpg\",\n  \"296348.jpg\": \"Insecta/Pholisora catullus/caa6826b9678d19159a83c0a1f7aa993.jpg\",\n  \"296352.jpg\": \"Insecta/Pholisora catullus/895ad9361b707d9fcf5dddf6baff6a82.jpg\",\n  \"296353.jpg\": \"Insecta/Pholisora catullus/8afb45d7b8e0227b464d074853867a25.jpg\",\n  \"296355.jpg\": \"Insecta/Pholisora catullus/8d19fee691197f628181550342d5e7e4.jpg\",\n  \"296356.jpg\": \"Insecta/Pholisora catullus/71505a32f641a208b53c88d82d83c6b9.jpg\",\n  \"296359.jpg\": \"Insecta/Pholisora catullus/183c2e9a68777f303afedfabf82568c9.jpg\",\n  \"296360.jpg\": \"Insecta/Pholisora catullus/ccb8168dfb32c493db108bf4b0748b3c.jpg\",\n  \"296364.jpg\": \"Insecta/Pholisora catullus/a2538418ce0f4f9b16e62704f49ff826.jpg\",\n  \"296365.jpg\": \"Insecta/Pholisora catullus/25cd132241e84613291ca3203feecae3.jpg\",\n  \"296367.jpg\": \"Insecta/Pholisora catullus/6f1d586f5420715446efdb845eca430a.jpg\",\n  \"296368.jpg\": \"Insecta/Pholisora catullus/a417e5d446a1a2640a440d2272cb037b.jpg\",\n  \"296375.jpg\": \"Insecta/Pholisora catullus/cc40bc9b5f85be16e3fb5a0abb392b96.jpg\",\n  \"296376.jpg\": \"Insecta/Pholisora catullus/b8f9c8e1f14eb708f8b712cc781382a4.jpg\",\n  \"296377.jpg\": \"Insecta/Pholisora catullus/d385b37f3e9b1e10efb815a12aea495c.jpg\",\n  \"296378.jpg\": \"Insecta/Pholisora catullus/958e5af027caf0ccdb01991eb33ee3bb.jpg\",\n  \"296380.jpg\": \"Aves/Butorides striata/3c65258fb6fc52f8e9771489389bcd52.jpg\",\n  \"296382.jpg\": \"Aves/Butorides striata/14ca85c73105aaa977d66572d8a75a9c.jpg\",\n  \"296387.jpg\": \"Aves/Butorides striata/a078e4273fde715d18a7ab942b39ce1a.jpg\",\n  \"296389.jpg\": \"Aves/Butorides striata/3491ce1324e2d0e69d5d2a0061828a3e.jpg\",\n  \"296390.jpg\": \"Aves/Butorides striata/8977bf7a24df079fa229481c0c9d34a9.jpg\",\n  \"296397.jpg\": \"Aves/Butorides striata/b5052c6286be090117f23727b0e0c695.jpg\",\n  \"296400.jpg\": \"Aves/Butorides striata/aebc381f260ff1f7c8a6dab0d7c48cd9.jpg\",\n  \"296401.jpg\": \"Aves/Butorides striata/df697784c0d26bd0e385eb4c8a039eb6.jpg\",\n  \"296404.jpg\": \"Aves/Butorides striata/d0f3a497d5db8a87ceb01e5b263a614b.jpg\",\n  \"296405.jpg\": \"Aves/Butorides striata/db4a5296e08ef02f220b582a30980304.jpg\",\n  \"296406.jpg\": \"Aves/Butorides striata/ea786c456c0b8d053fe8f70355bfa8f0.jpg\",\n  \"296408.jpg\": \"Aves/Butorides striata/d3e3bb714c0f8d746093da3b85832aab.jpg\",\n  \"29641.jpg\": \"Aves/Himantopus leucocephalus/d81f859c1b6557b48eba53efffa51ef6.jpg\",\n  \"296411.jpg\": \"Aves/Butorides striata/b0bae7287498efcfb6435f275fd89f83.jpg\",\n  \"296412.jpg\": \"Aves/Butorides striata/5a84087924aa133cb63e555f3c630806.jpg\",\n  \"296421.jpg\": \"Aves/Butorides striata/7fb2946f3ead74ccc53ed5ab930af502.jpg\",\n  \"296425.jpg\": \"Aves/Butorides striata/00233fd4de6b5c045a609d63c4bf941c.jpg\",\n  \"296430.jpg\": \"Aves/Butorides striata/dba3c7a21e1defa332b6c10ef27ca7f7.jpg\",\n  \"296432.jpg\": \"Aves/Butorides striata/03404161c3800e63fa1c3cce3e1ee5aa.jpg\",\n  \"296433.jpg\": \"Aves/Butorides striata/46bbae588a73a141fd7ea995a28b2653.jpg\",\n  \"296439.jpg\": \"Insecta/Papilio demodocus/9bffe4a44024fa282f6b342df955a32b.jpg\",\n  \"29644.jpg\": \"Aves/Himantopus leucocephalus/74d93736883ae2096d183315a2cc5b92.jpg\",\n  \"296444.jpg\": \"Insecta/Papilio demodocus/e18bb8cbc229bc348ce53ef7547085c3.jpg\",\n  \"296449.jpg\": \"Insecta/Papilio demodocus/5847800a2b00509166a22f1b67f0d5dc.jpg\",\n  \"29645.jpg\": \"Aves/Himantopus leucocephalus/49c8e212464e31c460f9ccfe059a8d46.jpg\",\n  \"296456.jpg\": \"Amphibia/Ambystoma texanum/989ee74e4a79a7e34a489e05df7b2cc2.jpg\",\n  \"296463.jpg\": \"Amphibia/Ambystoma texanum/271aaa0afd889a20d6d9b8be41f2c959.jpg\",\n  \"296468.jpg\": \"Amphibia/Ambystoma texanum/0975f393c277d2345fa4be392ec5b1fa.jpg\",\n  \"296475.jpg\": \"Amphibia/Ambystoma texanum/41b9880a0380c1b16a69e937bd0ea699.jpg\",\n  \"296482.jpg\": \"Amphibia/Ambystoma texanum/cebb3db6816a04d71694df09ea6a8fb1.jpg\",\n  \"296489.jpg\": \"Amphibia/Ambystoma texanum/8b634b430799874ebda3114f3db74cc8.jpg\",\n  \"296490.jpg\": \"Amphibia/Ambystoma texanum/187a1e23b11cf0ce538d7bee64ae8920.jpg\",\n  \"296491.jpg\": \"Amphibia/Ambystoma texanum/1a87eaf9c1e97e6b5e8a71282cf6adad.jpg\",\n  \"296492.jpg\": \"Mammalia/Hippopotamus amphibius/3af3dc72755457b002f519644027eefa.jpg\",\n  \"296500.jpg\": \"Mammalia/Hippopotamus amphibius/4e534cd3c2c07382280c1790059f2ce0.jpg\",\n  \"296505.jpg\": \"Mammalia/Hippopotamus amphibius/5eb4b30afaa67a8951d0afa313d309aa.jpg\",\n  \"296510.jpg\": \"Mammalia/Hippopotamus amphibius/defd932f1348f97196649968ac0f08d6.jpg\",\n  \"296516.jpg\": \"Mammalia/Hippopotamus amphibius/ec88b0b97f9d306e7fead8d6227209dc.jpg\",\n  \"296519.jpg\": \"Mammalia/Hippopotamus amphibius/2ec724c3680c0ae591c690eb269cffba.jpg\",\n  \"29652.jpg\": \"Aves/Himantopus leucocephalus/d4f8bc509d09ee27e43bd0e0abd24be7.jpg\",\n  \"296530.jpg\": \"Mammalia/Hippopotamus amphibius/3f270ff00307fd96d8fa1fe09951a13c.jpg\",\n  \"296532.jpg\": \"Mammalia/Hippopotamus amphibius/1d882ae0c8726511605f6542151a1896.jpg\",\n  \"296535.jpg\": \"Mammalia/Hippopotamus amphibius/1db054d0826f70462d9003979cea7d68.jpg\",\n  \"296537.jpg\": \"Mammalia/Hippopotamus amphibius/e1e2d929b35f2e9d1aa678669d1a93d6.jpg\",\n  \"296539.jpg\": \"Mammalia/Hippopotamus amphibius/3612173b517cc749203631b4bd4de573.jpg\",\n  \"296545.jpg\": \"Mammalia/Hippopotamus amphibius/aea16087212f4a58a9e0c60b38f95767.jpg\",\n  \"296546.jpg\": \"Aves/Herpetotheres cachinnans/8e9841f2a059cdfc2b924a4b9da1e351.jpg\",\n  \"296548.jpg\": \"Aves/Herpetotheres cachinnans/001f3a604deed62afbd5e8b694b4f5eb.jpg\",\n  \"296549.jpg\": \"Aves/Herpetotheres cachinnans/4e0c241c874497458ba644aae0b035a0.jpg\",\n  \"296555.jpg\": \"Aves/Herpetotheres cachinnans/e80d8a35b67221490ffd561b2989101f.jpg\",\n  \"296557.jpg\": \"Aves/Herpetotheres cachinnans/f1821145429d31d5ccfac2173a50ffb3.jpg\",\n  \"296560.jpg\": \"Aves/Herpetotheres cachinnans/ab80956eefd46df3b838856f11574de7.jpg\",\n  \"296563.jpg\": \"Aves/Herpetotheres cachinnans/9a6984ec8bce7547201d30b47a8e1456.jpg\",\n  \"296565.jpg\": \"Aves/Herpetotheres cachinnans/7ef7db78cb6cdec47ffecca730ce9fb1.jpg\",\n  \"296566.jpg\": \"Aves/Herpetotheres cachinnans/17964e0956c0d6a321ee3ee3561903cb.jpg\",\n  \"296567.jpg\": \"Aves/Herpetotheres cachinnans/fd4ed218c08726b57792ca7ca2e9797a.jpg\",\n  \"29657.jpg\": \"Reptilia/Thamnophis cyrtopsis/02aea5a13983d279a5b3de356fe123c5.jpg\",\n  \"296576.jpg\": \"Aves/Herpetotheres cachinnans/0c8418546812204edab5d28674fdc35d.jpg\",\n  \"296577.jpg\": \"Aves/Herpetotheres cachinnans/13248cef7928d5ce40c9fc8465b69f49.jpg\",\n  \"296578.jpg\": \"Aves/Herpetotheres cachinnans/ef0399cb0e76892a58981b7549fca9de.jpg\",\n  \"296581.jpg\": \"Aves/Herpetotheres cachinnans/c748bb6cadc4de8ae4e84be8396c4664.jpg\",\n  \"296584.jpg\": \"Aves/Herpetotheres cachinnans/cbff62675208a283d091f6a06e012179.jpg\",\n  \"296585.jpg\": \"Aves/Herpetotheres cachinnans/6bbfd20f86cc4fb8397cddf7ff5adcd5.jpg\",\n  \"296586.jpg\": \"Aves/Herpetotheres cachinnans/8e461d8316107207c75f54b7130cec08.jpg\",\n  \"296587.jpg\": \"Aves/Herpetotheres cachinnans/03810441ff1e186d9b901ecdb12e0c41.jpg\",\n  \"296588.jpg\": \"Aves/Herpetotheres cachinnans/fd036e5e7e3f0b4794e6e5e84d484df8.jpg\",\n  \"296589.jpg\": \"Aves/Herpetotheres cachinnans/c427e3ed4bf8a639a337e597152882e0.jpg\",\n  \"296590.jpg\": \"Aves/Herpetotheres cachinnans/472b11137228ab6680175259d25b9495.jpg\",\n  \"296591.jpg\": \"Aves/Herpetotheres cachinnans/5adbdd354ab54019551e4f32fc09369b.jpg\",\n  \"29663.jpg\": \"Reptilia/Thamnophis cyrtopsis/5350141e587290196f08195586666097.jpg\",\n  \"29664.jpg\": \"Reptilia/Thamnophis cyrtopsis/3a5ec52ef7170b9ad0fd33501d645c14.jpg\",\n  \"29665.jpg\": \"Reptilia/Thamnophis cyrtopsis/d7dd1d9d3e54cb7ebe791d30efccedcf.jpg\",\n  \"29666.jpg\": \"Reptilia/Thamnophis cyrtopsis/e221b49d9f7b8e03d57d33b5e612aee2.jpg\",\n  \"29670.jpg\": \"Reptilia/Thamnophis cyrtopsis/5e642d1aa961cb013223ca2390a4b9a9.jpg\",\n  \"29671.jpg\": \"Reptilia/Thamnophis cyrtopsis/bd5bf133db0297092586ab43d2020bb9.jpg\",\n  \"29674.jpg\": \"Reptilia/Thamnophis cyrtopsis/5347bab7a92ba0d2d78bb23cb4a60860.jpg\",\n  \"29675.jpg\": \"Reptilia/Thamnophis cyrtopsis/89f18c4ce308606d48cff488f4183f38.jpg\",\n  \"29676.jpg\": \"Reptilia/Thamnophis cyrtopsis/3a76ddabfb18ed15d15dd02dabd5b6ad.jpg\",\n  \"29677.jpg\": \"Reptilia/Thamnophis cyrtopsis/d85a6af0a644183d83a53a287c9de251.jpg\",\n  \"296771.jpg\": \"Aves/Xanthocephalus xanthocephalus/9a679908c8761340e170ef68f75122d1.jpg\",\n  \"296782.jpg\": \"Aves/Xanthocephalus xanthocephalus/46aac147e181bdbab1019f625fb5e10a.jpg\",\n  \"296785.jpg\": \"Aves/Xanthocephalus xanthocephalus/f8e6f48e5d9b9ec14e40d3525f9dea84.jpg\",\n  \"296792.jpg\": \"Aves/Xanthocephalus xanthocephalus/eb201a407f0e3279497c4fc84e93c485.jpg\",\n  \"296794.jpg\": \"Aves/Xanthocephalus xanthocephalus/f75cfd0a5ca6526d563429f8fbf96eea.jpg\",\n  \"296799.jpg\": \"Aves/Xanthocephalus xanthocephalus/45fb2823ba09d67c330d3a212718c431.jpg\",\n  \"296804.jpg\": \"Aves/Xanthocephalus xanthocephalus/99c346ad7000ff4e67ce59e39f6aab21.jpg\",\n  \"296810.jpg\": \"Aves/Xanthocephalus xanthocephalus/90bab498a1e291c192e7c9cb271666ab.jpg\",\n  \"296811.jpg\": \"Aves/Xanthocephalus xanthocephalus/e9ce8a51ec7a935a9e022dc9d8e0de9a.jpg\",\n  \"29682.jpg\": \"Reptilia/Thamnophis cyrtopsis/dfb01d91dcf780543a97247b0467077e.jpg\",\n  \"296834.jpg\": \"Aves/Xanthocephalus xanthocephalus/a2a123626c8ed8ff55401e080af177b0.jpg\",\n  \"296851.jpg\": \"Aves/Xanthocephalus xanthocephalus/447ae516561798a75977307570e2ba88.jpg\",\n  \"296855.jpg\": \"Aves/Xanthocephalus xanthocephalus/bac47d340f0e1c953d1790788b1692d7.jpg\",\n  \"296859.jpg\": \"Aves/Xanthocephalus xanthocephalus/b611d9dd196976b29ae0bf768822377f.jpg\",\n  \"296867.jpg\": \"Aves/Xanthocephalus xanthocephalus/92a653d8bc6073a3ee2cfe21969e0d10.jpg\",\n  \"296871.jpg\": \"Aves/Xanthocephalus xanthocephalus/7698ed2b387c427703c0e1fced0141fe.jpg\",\n  \"29688.jpg\": \"Reptilia/Thamnophis cyrtopsis/b65c2c7ae7d1c044b3be299890d5c0be.jpg\",\n  \"29689.jpg\": \"Reptilia/Thamnophis cyrtopsis/9d8338821c72c19b29739d2665051395.jpg\",\n  \"29690.jpg\": \"Reptilia/Thamnophis cyrtopsis/360e605eee3b2a1dcec4be9b1a095ad4.jpg\",\n  \"29691.jpg\": \"Reptilia/Thamnophis cyrtopsis/6c0fdb136ca7e7f95af391eaee19c79d.jpg\",\n  \"296930.jpg\": \"Aves/Passerina cyanea/4c3991ccc40549e67636961ab4e21975.jpg\",\n  \"296931.jpg\": \"Aves/Passerina cyanea/c9a92200b1094ac586f8df23e5614056.jpg\",\n  \"296932.jpg\": \"Aves/Passerina cyanea/1cdaa921ff28abe91426d0fc120ebcc1.jpg\",\n  \"29694.jpg\": \"Reptilia/Thamnophis cyrtopsis/ae5d988617226e6d8af84b1ff59578f9.jpg\",\n  \"296940.jpg\": \"Aves/Passerina cyanea/7d64f427af18ac92b0cac871b5c02ec3.jpg\",\n  \"296948.jpg\": \"Aves/Passerina cyanea/6838768af2b69aae07d69d78dfeadb91.jpg\",\n  \"29695.jpg\": \"Reptilia/Thamnophis cyrtopsis/c620f538f1b8c7b87bac1c1085e821e6.jpg\",\n  \"296975.jpg\": \"Aves/Passerina cyanea/f2723be0fba0027c966bb0b0bad5cc1c.jpg\",\n  \"29698.jpg\": \"Reptilia/Thamnophis cyrtopsis/a4b5d2347b8e771c27dafa24a574895d.jpg\",\n  \"296980.jpg\": \"Aves/Passerina cyanea/1cc9461d54e7cbe918952c8d5c5c33db.jpg\",\n  \"296985.jpg\": \"Aves/Passerina cyanea/e5fe050c39400bcf2a7a8a364fc251e9.jpg\",\n  \"296986.jpg\": \"Aves/Passerina cyanea/112b22187db32e393a2279d084cebc8d.jpg\",\n  \"296989.jpg\": \"Aves/Passerina cyanea/114c5af08093df5ecc9daf7e355b7a42.jpg\",\n  \"29699.jpg\": \"Reptilia/Thamnophis cyrtopsis/078696e258d1aa4c5d4c2c7861382567.jpg\",\n  \"297005.jpg\": \"Aves/Passerina cyanea/229b5a02ddd6a686f82a27ec7a475462.jpg\",\n  \"297017.jpg\": \"Aves/Passerina cyanea/2c94c0fd5dac5180074afc1099817deb.jpg\",\n  \"29702.jpg\": \"Reptilia/Thamnophis cyrtopsis/61d4ae879db432b17fd480f08af7f373.jpg\",\n  \"297020.jpg\": \"Aves/Passerina cyanea/eda84a23d5ccfe50f252fe2516016925.jpg\",\n  \"297027.jpg\": \"Aves/Passerina cyanea/fea21abc63ab9a9cfef9ee0b760f1a3c.jpg\",\n  \"29703.jpg\": \"Reptilia/Thamnophis cyrtopsis/c2b06530bd8990e7a2ef3895ff66b244.jpg\",\n  \"297078.jpg\": \"Aves/Passerina cyanea/f7059c663bb509b4c69ee464ec127f65.jpg\",\n  \"297094.jpg\": \"Aves/Passerina cyanea/89eaeb342dc34f23f357de52cab418a3.jpg\",\n  \"297105.jpg\": \"Aves/Passerina cyanea/44a91b13f76cb2a5cf9a187a30794018.jpg\",\n  \"297146.jpg\": \"Aves/Passerina cyanea/599c732f1acb199dede88253ba9b1cb6.jpg\",\n  \"297169.jpg\": \"Aves/Passerina cyanea/1ba0ef158bff8b2c37efa28df098f80c.jpg\",\n  \"297170.jpg\": \"Aves/Passerina cyanea/9e35b5435174ec5851a1734917be9eca.jpg\",\n  \"297171.jpg\": \"Aves/Passerina cyanea/3e1b7fa42603914f7d5901cc6973e367.jpg\",\n  \"297181.jpg\": \"Aves/Passerina cyanea/dacbdb71aa9a81f17247e7ae9243c988.jpg\",\n  \"297189.jpg\": \"Aves/Passerina cyanea/c318b12fe8f1153f8d9c6a1c05bad62a.jpg\",\n  \"297192.jpg\": \"Aves/Passerina cyanea/6271bea785a4ba9528dd467a7374d078.jpg\",\n  \"297228.jpg\": \"Mammalia/Cervus nippon/356725d7e79f6d8c377062e1d2cfcf91.jpg\",\n  \"297277.jpg\": \"Insecta/Micrathyria hagenii/df6d37e0d33025a3226695d5bcd4c5e4.jpg\",\n  \"297281.jpg\": \"Insecta/Micrathyria hagenii/764d940bba5062176e86052c9ab3f6d9.jpg\",\n  \"297283.jpg\": \"Insecta/Micrathyria hagenii/3dfe7b4ab814edf3be700fa794252f22.jpg\",\n  \"297284.jpg\": \"Insecta/Micrathyria hagenii/274ba38bb2f33e8a4bd6308bc1d1ff9d.jpg\",\n  \"297285.jpg\": \"Insecta/Micrathyria hagenii/40db6a0b36ece800aeff14721635bc98.jpg\",\n  \"297286.jpg\": \"Insecta/Micrathyria hagenii/dbfdefdd8d1d88f0c6e3ecdce4440036.jpg\",\n  \"297287.jpg\": \"Insecta/Micrathyria hagenii/9dc35a43c8d52083761841dd77108763.jpg\",\n  \"297288.jpg\": \"Insecta/Micrathyria hagenii/4081e2c9a95383fcff39451f8d5f3c61.jpg\",\n  \"297290.jpg\": \"Insecta/Micrathyria hagenii/d7800215db8a2739ca7e0bb2e49dc533.jpg\",\n  \"297300.jpg\": \"Insecta/Micrathyria hagenii/1003108a48c46dbd6e2ec5dd3d369355.jpg\",\n  \"297301.jpg\": \"Insecta/Micrathyria hagenii/7ff78cea997dfc40fb46348f80d10547.jpg\",\n  \"297306.jpg\": \"Insecta/Micrathyria hagenii/521978bb66ca6d696eea66d3c6a1e794.jpg\",\n  \"297310.jpg\": \"Insecta/Micrathyria hagenii/a405f64832a2aeca24109a2b6daae920.jpg\",\n  \"297311.jpg\": \"Insecta/Micrathyria hagenii/847fef3451e3805d0fa21d8b690d9fa0.jpg\",\n  \"297324.jpg\": \"Insecta/Micrathyria hagenii/ede6c1aac230e0f43ba53163b5af1fe7.jpg\",\n  \"297325.jpg\": \"Insecta/Micrathyria hagenii/fc0b4974ef75ace5eea20ceca0197aef.jpg\",\n  \"297326.jpg\": \"Insecta/Micrathyria hagenii/fe98a5c139920b8856641a6ee4a19756.jpg\",\n  \"297329.jpg\": \"Insecta/Micrathyria hagenii/ce1625be93f3e5c89a589ef9f2af4d81.jpg\",\n  \"297334.jpg\": \"Aves/Arremonops rufivirgatus/312d2e6de0387b00ef06989c39e256b9.jpg\",\n  \"297336.jpg\": \"Aves/Arremonops rufivirgatus/1d2e3a60d7b9be5cc8904c281f07b4a0.jpg\",\n  \"297337.jpg\": \"Aves/Arremonops rufivirgatus/8baf32db24fa8149c22f381794cac65e.jpg\",\n  \"297340.jpg\": \"Aves/Arremonops rufivirgatus/12b143e9ad89f377a34795d36cfddc24.jpg\",\n  \"297352.jpg\": \"Aves/Arremonops rufivirgatus/3244255ec6b3e35a026ca0cd89747c61.jpg\",\n  \"297358.jpg\": \"Aves/Arremonops rufivirgatus/968a3ca367bfb1def77b71c2719bfeb7.jpg\",\n  \"29736.jpg\": \"Aves/Circus aeruginosus/d684e098174f9cbe97a0aa3baadca8f2.jpg\",\n  \"297361.jpg\": \"Aves/Arremonops rufivirgatus/a19e3608a3638b46e4d31f73ba54425e.jpg\",\n  \"29737.jpg\": \"Aves/Circus aeruginosus/003f57afbe3e4cd1f6547d514810a59e.jpg\",\n  \"29738.jpg\": \"Aves/Circus aeruginosus/7551939cbfeaa359963900184af4288b.jpg\",\n  \"29742.jpg\": \"Aves/Circus aeruginosus/11f84543a5c05ab963841b46e89fca7a.jpg\",\n  \"29743.jpg\": \"Aves/Circus aeruginosus/b5c177d1adefd2eb0eff7f1dabf66782.jpg\",\n  \"29750.jpg\": \"Aves/Circus aeruginosus/238e4b6959bd0d4a3ff0fd27217b11e4.jpg\",\n  \"29751.jpg\": \"Aves/Circus aeruginosus/e31053f80102c38a95b6fcc0796cf766.jpg\",\n  \"29752.jpg\": \"Aves/Circus aeruginosus/fdbf9af28dcc342be5ab98f8fba6c2d4.jpg\",\n  \"29756.jpg\": \"Aves/Circus aeruginosus/4712552b88f417b1717dcd4aaa026ef8.jpg\",\n  \"29757.jpg\": \"Aves/Circus aeruginosus/f89b87ae83c8535240bdc70ca32401d7.jpg\",\n  \"29761.jpg\": \"Aves/Circus aeruginosus/2971f3caa14c810ad155566c0d9910c4.jpg\",\n  \"29762.jpg\": \"Aves/Circus aeruginosus/cfa29b7cda22b6feaf7fd36b5c0eacdc.jpg\",\n  \"29763.jpg\": \"Aves/Circus aeruginosus/c81cc0f973cc1c547dd914914a440e1d.jpg\",\n  \"29769.jpg\": \"Aves/Circus aeruginosus/550dd38447b10866209a4384c1f5111d.jpg\",\n  \"29770.jpg\": \"Aves/Circus aeruginosus/8493cffa3162bf7191422c53f02ec15f.jpg\",\n  \"29771.jpg\": \"Aves/Circus aeruginosus/8baf783eb4cbaf2fbac33797377d05f0.jpg\",\n  \"29772.jpg\": \"Aves/Circus aeruginosus/7606c3f30a73e4faf8955e73e6c2981c.jpg\",\n  \"29773.jpg\": \"Aves/Circus aeruginosus/5c595b8f4cd71c35a614d6e3abdbd296.jpg\",\n  \"29774.jpg\": \"Aves/Circus aeruginosus/6dbd8a48e802adb55b8fd00e6943f0d3.jpg\",\n  \"29779.jpg\": \"Aves/Circus aeruginosus/c2d88d0abab6d35d7a929c764fb56a13.jpg\",\n  \"29781.jpg\": \"Aves/Circus aeruginosus/19086abfb8d2aeb1820f033bf90836c9.jpg\",\n  \"29784.jpg\": \"Aves/Circus aeruginosus/da340b9412db6d08130230baa93d3840.jpg\",\n  \"29786.jpg\": \"Insecta/Anania funebris/e3936a197d71002e3e21f3f1b9ee68f4.jpg\",\n  \"29787.jpg\": \"Insecta/Anania funebris/b78bbf87704c8f47e514bfd9e3195139.jpg\",\n  \"29789.jpg\": \"Insecta/Anania funebris/b303e73eed5d5d851ec2d9c05af6b978.jpg\",\n  \"29790.jpg\": \"Insecta/Anania funebris/217b3e2f31485b4a8cd8379205faefe8.jpg\",\n  \"29798.jpg\": \"Insecta/Anania funebris/35589e2c9baafdb9e356d902e85c8ba2.jpg\",\n  \"29802.jpg\": \"Insecta/Anania funebris/898198e6bef018076c1a577203c4f0b7.jpg\",\n  \"298457.jpg\": \"Aves/Protonotaria citrea/edc1718f503d63ebc279583b59aad71a.jpg\",\n  \"298458.jpg\": \"Aves/Protonotaria citrea/b367f80149daa77705407ac2cb133005.jpg\",\n  \"298459.jpg\": \"Aves/Protonotaria citrea/85812352e4ae93ec24edff41146fe133.jpg\",\n  \"298463.jpg\": \"Aves/Protonotaria citrea/b289d79543d2d3ab0322695d483368ae.jpg\",\n  \"298464.jpg\": \"Aves/Protonotaria citrea/3b45cd5d97d18e8742ddf2a32020c91b.jpg\",\n  \"298468.jpg\": \"Aves/Protonotaria citrea/4806589e2a417d67afb2af76102f468d.jpg\",\n  \"298473.jpg\": \"Aves/Protonotaria citrea/1d1b8a28d946a4474bcd390c2def196c.jpg\",\n  \"298480.jpg\": \"Aves/Protonotaria citrea/261df77bfd1be6f8a2892c4a1e3e153f.jpg\",\n  \"298484.jpg\": \"Aves/Protonotaria citrea/362eb839520f2c9ef3c66f6fdb11ca1f.jpg\",\n  \"298487.jpg\": \"Aves/Protonotaria citrea/5bcaffcdd566dc20527b960e18d0ccba.jpg\",\n  \"298488.jpg\": \"Aves/Protonotaria citrea/2abea848c1f34b6f9f663178ed04beeb.jpg\",\n  \"298495.jpg\": \"Aves/Protonotaria citrea/96c6025dc7f9cf87835467fef32a9420.jpg\",\n  \"298496.jpg\": \"Aves/Protonotaria citrea/5a6e06cf25eddb269d4b3abc56eef0c4.jpg\",\n  \"298504.jpg\": \"Aves/Protonotaria citrea/5443e16756d943a1e8d04e5eb7fbccbd.jpg\",\n  \"298505.jpg\": \"Aves/Protonotaria citrea/5150966ba38e48f85513233e625ebccc.jpg\",\n  \"298521.jpg\": \"Aves/Protonotaria citrea/5864c698cd697580a247a58b4922cd3e.jpg\",\n  \"298532.jpg\": \"Aves/Protonotaria citrea/dbcbe3b88ab0576f3d325c6fe2e72dbb.jpg\",\n  \"298534.jpg\": \"Aves/Protonotaria citrea/708b292d114f02ea34f4999262965fe6.jpg\",\n  \"298535.jpg\": \"Aves/Protonotaria citrea/7b48266e0f01766ce39289927fa30039.jpg\",\n  \"298536.jpg\": \"Aves/Protonotaria citrea/6799e2dff6cd5842bc2dfaae83e7e280.jpg\",\n  \"298539.jpg\": \"Aves/Protonotaria citrea/3b99c24acde37713ca15af914d96d395.jpg\",\n  \"298540.jpg\": \"Aves/Protonotaria citrea/5ddb7a860907790f554917af8e8d216b.jpg\",\n  \"298858.jpg\": \"Aves/Eolophus roseicapilla/7629f39a70c512d621164b1e1464a59b.jpg\",\n  \"298865.jpg\": \"Aves/Eolophus roseicapilla/076aebcb5c74c5aefd8176accee95597.jpg\",\n  \"298867.jpg\": \"Aves/Eolophus roseicapilla/2cb433d848aeea91c87b062f3ddf26e6.jpg\",\n  \"298878.jpg\": \"Aves/Sula dactylatra/9b7d032c261461a1caa22420337fb32f.jpg\",\n  \"298881.jpg\": \"Aves/Sula dactylatra/bfd376e052cd50a09443f8509ddd9df6.jpg\",\n  \"298886.jpg\": \"Aves/Sula dactylatra/63dd1c59d90f1926db52bb749025e0dd.jpg\",\n  \"298888.jpg\": \"Aves/Sula dactylatra/6bdd493fbb9a3dd766e459b32b88d01b.jpg\",\n  \"298891.jpg\": \"Aves/Sula dactylatra/b7653181d89899aabbcad679ea9368c1.jpg\",\n  \"298892.jpg\": \"Aves/Sula dactylatra/dd6ddeced60d943ab4c912b36521628b.jpg\",\n  \"29890.jpg\": \"Insecta/Celithemis elisa/6114d297bb6a67765ae16ffb06b89b35.jpg\",\n  \"298919.jpg\": \"Mammalia/Syncerus caffer/8cbc029f1dc66e03dc31ff36918c8103.jpg\",\n  \"298922.jpg\": \"Mammalia/Syncerus caffer/eb8f54583200c834fec95b33be4c6c40.jpg\",\n  \"298930.jpg\": \"Mammalia/Syncerus caffer/d5b16f33f14619dff39d0422ae9666b3.jpg\",\n  \"298932.jpg\": \"Mammalia/Syncerus caffer/b43ceee480fe13368d2d50cc7611ae7b.jpg\",\n  \"298936.jpg\": \"Mammalia/Syncerus caffer/1eb51e30a7f9edff06746991d965f226.jpg\",\n  \"298939.jpg\": \"Mammalia/Syncerus caffer/bb7810261b0dd8255f806d63bc848e38.jpg\",\n  \"298942.jpg\": \"Mammalia/Syncerus caffer/2d10f2fe71fab36a02143d7f93d7b132.jpg\",\n  \"298955.jpg\": \"Mammalia/Syncerus caffer/777488d18593ea3a17103e96de0995f3.jpg\",\n  \"298958.jpg\": \"Mammalia/Syncerus caffer/c5760bfbb65897cde0e4a087e5820640.jpg\",\n  \"298959.jpg\": \"Mammalia/Syncerus caffer/3bc361870c5e4cd9cd1b67261e838ea9.jpg\",\n  \"298977.jpg\": \"Insecta/Anthocharis sara/0dc3fffffebaf6674d70d58b2c505894.jpg\",\n  \"298978.jpg\": \"Insecta/Anthocharis sara/996009de76eea85881c9cee7ff710579.jpg\",\n  \"29898.jpg\": \"Insecta/Celithemis elisa/0278eb16d5758b43585ff2caa9ed8d91.jpg\",\n  \"298982.jpg\": \"Insecta/Anthocharis sara/878222e1538e8d57270d2a6f1ce99ee8.jpg\",\n  \"298988.jpg\": \"Insecta/Anthocharis sara/4c658f3d8e120c623e266ab04473f3be.jpg\",\n  \"298989.jpg\": \"Insecta/Anthocharis sara/75e8c6100c94b28dff15c85c93e11c27.jpg\",\n  \"298994.jpg\": \"Insecta/Anthocharis sara/9ee1f28bec5177d4031b62c9d8d9de78.jpg\",\n  \"299000.jpg\": \"Insecta/Anthocharis sara/b0bff14ce4f611151b41f24e824ba178.jpg\",\n  \"29901.jpg\": \"Insecta/Celithemis elisa/0bbebe3eadf10ad52758d6b745ef48d3.jpg\",\n  \"299015.jpg\": \"Insecta/Anthocharis sara/4488bacb2aeeab0b85fd90a5f1e18424.jpg\",\n  \"299017.jpg\": \"Insecta/Anthocharis sara/eb7177ea8a167936611b61914c05be51.jpg\",\n  \"299018.jpg\": \"Insecta/Anthocharis sara/f74571487a7b8c863eca5a829f4fdb17.jpg\",\n  \"299024.jpg\": \"Insecta/Anthocharis sara/212612afe8c55ce0503f4be361e3b4fd.jpg\",\n  \"299029.jpg\": \"Insecta/Anthocharis sara/c8a20613055d6be54548efdda93e4f33.jpg\",\n  \"299033.jpg\": \"Insecta/Anthocharis sara/22455f6a7e1221582ebfff12a9a20e1a.jpg\",\n  \"299034.jpg\": \"Insecta/Anthocharis sara/9659cfef27c04db7a3923d133a1a9169.jpg\",\n  \"299036.jpg\": \"Insecta/Anthocharis sara/a65d83534033e35b2282ed499fc26a6e.jpg\",\n  \"299037.jpg\": \"Insecta/Anthocharis sara/a18b32193ea8788b43ba47ed0051a3e8.jpg\",\n  \"299038.jpg\": \"Insecta/Anthocharis sara/285ffd204910e1a6c3794d479bb7bc80.jpg\",\n  \"299039.jpg\": \"Insecta/Anthocharis sara/d5ea0bc9b1f276b19550b05074035bd2.jpg\",\n  \"29904.jpg\": \"Insecta/Celithemis elisa/14b60cd37417236b877d1f503ae45fed.jpg\",\n  \"299054.jpg\": \"Insecta/Anthocharis sara/c30363c6177b6b22161643742a6ffc77.jpg\",\n  \"299055.jpg\": \"Insecta/Anthocharis sara/0238aa160c0d5d13a5a0d633b21a8a63.jpg\",\n  \"299056.jpg\": \"Insecta/Anthocharis sara/4532712f3262724506c415d82659b93c.jpg\",\n  \"299057.jpg\": \"Insecta/Anthocharis sara/0169d6f0ae95291e9c04d2ce223a4e81.jpg\",\n  \"299066.jpg\": \"Insecta/Polygonia comma/91ed4821dbf06cb38308b1685bb0ac4e.jpg\",\n  \"299073.jpg\": \"Insecta/Polygonia comma/d4e3a7a032a985df8acfe6819d636309.jpg\",\n  \"299087.jpg\": \"Insecta/Polygonia comma/6f96f43e1e2c25c863be656b8ae1da6b.jpg\",\n  \"299088.jpg\": \"Insecta/Polygonia comma/224ea542d8c39dd55263d1e3fa3f956e.jpg\",\n  \"299095.jpg\": \"Insecta/Polygonia comma/ec9b40d588022b391e95f7d5a2a502e1.jpg\",\n  \"299117.jpg\": \"Insecta/Polygonia comma/e0e27483ef97f7713940dc0b0714684f.jpg\",\n  \"29915.jpg\": \"Insecta/Celithemis elisa/19820bd6840a4ba5659cbd4f6853501b.jpg\",\n  \"299152.jpg\": \"Insecta/Polygonia comma/3c1406578259cdd8be52fba388e989ba.jpg\",\n  \"299156.jpg\": \"Insecta/Polygonia comma/956b615ef567088d5ab727eb28b487ef.jpg\",\n  \"299157.jpg\": \"Insecta/Polygonia comma/b5162710c77462c34cdaf94734086f1c.jpg\",\n  \"29916.jpg\": \"Insecta/Celithemis elisa/1d1c573ed25a05d9d27e4f42d3007145.jpg\",\n  \"299169.jpg\": \"Insecta/Polygonia comma/15d9c2ae06492cfef586e0857e29678b.jpg\",\n  \"299175.jpg\": \"Insecta/Polygonia comma/a166efc1180df05d026fef64809e5a9e.jpg\",\n  \"299180.jpg\": \"Insecta/Polygonia comma/82a8c2f0f45bab044897a8802a0d8d6f.jpg\",\n  \"299182.jpg\": \"Insecta/Polygonia comma/f059eb0199124cc5497566558437a589.jpg\",\n  \"299190.jpg\": \"Insecta/Polygonia comma/0adbecc7b1665c52d211d8137219173a.jpg\",\n  \"299202.jpg\": \"Insecta/Polygonia comma/8e92a0998bd81009350ba15a2c93ca12.jpg\",\n  \"299211.jpg\": \"Insecta/Polygonia comma/758c01d34da1be3d7c6f0817cb6e81a3.jpg\",\n  \"299226.jpg\": \"Insecta/Polygonia comma/0ee5f2c13e0647607baa4dbb63a7e87f.jpg\",\n  \"29923.jpg\": \"Insecta/Celithemis elisa/4dded7ce5d489032de7da1a301da364c.jpg\",\n  \"299239.jpg\": \"Insecta/Polygonia comma/4517acb2bd2b1ed6ca23f489695d8940.jpg\",\n  \"299246.jpg\": \"Insecta/Polygonia comma/754db46c3977a0febdb7a7063eebe0c4.jpg\",\n  \"299249.jpg\": \"Insecta/Polygonia comma/1a717455d748ae63f6841c3947736084.jpg\",\n  \"29925.jpg\": \"Insecta/Celithemis elisa/6ef54b18bad86557afe5da8698cfd4f5.jpg\",\n  \"29927.jpg\": \"Insecta/Celithemis elisa/f2bc3f5d69fe9df49360b7c9f663f119.jpg\",\n  \"299280.jpg\": \"Aves/Athene cunicularia/cd69458f1b1d9445aba785e168e272ce.jpg\",\n  \"299281.jpg\": \"Aves/Athene cunicularia/16140226222b53d86d83380d0159215d.jpg\",\n  \"299303.jpg\": \"Aves/Athene cunicularia/594a3fe618f6183cc8fd0ce513e6da49.jpg\",\n  \"299304.jpg\": \"Aves/Athene cunicularia/9eb62c9b7133816e4945c9388f29376a.jpg\",\n  \"299305.jpg\": \"Aves/Athene cunicularia/78b6d8aaac4f503c6699e7a99127951b.jpg\",\n  \"299315.jpg\": \"Aves/Athene cunicularia/8e8130517944f261a6cf471feab94c69.jpg\",\n  \"299323.jpg\": \"Aves/Athene cunicularia/d4f6a2749372fe295ebeef018ca78a93.jpg\",\n  \"299333.jpg\": \"Aves/Athene cunicularia/1fe17cf4a5eb6008ef2c82c56b725532.jpg\",\n  \"299334.jpg\": \"Aves/Athene cunicularia/d256c6b6253ace10f83bcd6a56d7fc18.jpg\",\n  \"29934.jpg\": \"Insecta/Celithemis elisa/1ade81f42b60f0bca3d6daade54a50e5.jpg\",\n  \"299340.jpg\": \"Aves/Athene cunicularia/4dc792895d84baa2e0ba51021332c1c4.jpg\",\n  \"299344.jpg\": \"Aves/Athene cunicularia/79f7b76cfbae3848a13d15372fb1ea0e.jpg\",\n  \"29935.jpg\": \"Insecta/Celithemis elisa/7175239fface6b71cdef6ff3211c95e4.jpg\",\n  \"299350.jpg\": \"Aves/Athene cunicularia/262f7ee5f766a2f299cfc1d747c57e43.jpg\",\n  \"299353.jpg\": \"Aves/Athene cunicularia/0676e4440e792dd5b09e6378200535a3.jpg\",\n  \"29936.jpg\": \"Insecta/Celithemis elisa/68be74fd7080a2d198b5b53f3d010c78.jpg\",\n  \"299366.jpg\": \"Aves/Athene cunicularia/017fa9d785668ceb882b0e56d177d732.jpg\",\n  \"299371.jpg\": \"Aves/Athene cunicularia/b27b11bb878703709cc9727807f733cb.jpg\",\n  \"299380.jpg\": \"Aves/Athene cunicularia/7a9ea2e6988e69a7efb9443b984465f7.jpg\",\n  \"299384.jpg\": \"Aves/Athene cunicularia/a2c52fa4b9d31fc683f528dd5fb5f6c2.jpg\",\n  \"299403.jpg\": \"Aves/Athene cunicularia/12ce3f3e30687837ea1dccce742d34cb.jpg\",\n  \"299408.jpg\": \"Aves/Athene cunicularia/c742274fbf680861ade9051acc4db203.jpg\",\n  \"29941.jpg\": \"Insecta/Celithemis elisa/8694aa0e0656e6f48c78b4ba760d41a1.jpg\",\n  \"29942.jpg\": \"Insecta/Celithemis elisa/503880cc3e898c11fcc3ef5b2b93bd7d.jpg\",\n  \"299429.jpg\": \"Aves/Athene cunicularia/272991e13676565d7fd6dfcbd9b85b39.jpg\",\n  \"29943.jpg\": \"Insecta/Celithemis elisa/4274f4864562226fdff5f4dec3655f5b.jpg\",\n  \"299433.jpg\": \"Aves/Athene cunicularia/44e30c22584e58470b87f837e7246c1b.jpg\",\n  \"299435.jpg\": \"Aves/Megascops asio/2195868c51d10cc8ceb7b7acf0e495e2.jpg\",\n  \"299436.jpg\": \"Aves/Megascops asio/7c1a89a0da06b2e6d58d1ec77de1b62f.jpg\",\n  \"299437.jpg\": \"Aves/Megascops asio/6d1627c3dfdabce941413daed223cdc8.jpg\",\n  \"299439.jpg\": \"Aves/Megascops asio/ff72e25db9fc57ffbd2c3b46f91970bb.jpg\",\n  \"299452.jpg\": \"Aves/Megascops asio/6c92b4b983c278c2b27f28e27d3a056c.jpg\",\n  \"299454.jpg\": \"Aves/Megascops asio/368b3d0f251bf9cf4b8129745cc3686e.jpg\",\n  \"299455.jpg\": \"Aves/Megascops asio/1cbfa7edb477c1df604438b8cdf976ae.jpg\",\n  \"299457.jpg\": \"Aves/Megascops asio/5ac23bf08362f037ec38b248105a830b.jpg\",\n  \"299458.jpg\": \"Aves/Megascops asio/f9979636a1055f47d01c852bb6d40eab.jpg\",\n  \"29946.jpg\": \"Insecta/Celithemis elisa/73389ea4bedefdfdad00747f50b074e3.jpg\",\n  \"299466.jpg\": \"Aves/Megascops asio/678a14313e1944b80d5348cc9fa9de30.jpg\",\n  \"299467.jpg\": \"Aves/Megascops asio/ec331565cb79800217b9d6e7ec06724a.jpg\",\n  \"299468.jpg\": \"Aves/Megascops asio/c42da400d4cd47f3f7df26380ab26b3f.jpg\",\n  \"299470.jpg\": \"Aves/Megascops asio/98e0b434513ea82e9000ab83d08f3816.jpg\",\n  \"299482.jpg\": \"Aves/Megascops asio/04671155070bb08a8b8f7fe72988e8f7.jpg\",\n  \"299486.jpg\": \"Aves/Megascops asio/8641b4311d49df25c7aa7ab1bfe97d09.jpg\",\n  \"299495.jpg\": \"Aves/Megascops asio/0cdf9ddc502a7d2608b7947e67c3945a.jpg\",\n  \"299498.jpg\": \"Aves/Megascops asio/35bd21b19fae40676fb85903c8cde3f8.jpg\",\n  \"299500.jpg\": \"Aves/Megascops asio/096d2d8fe0848acaac3451ffa3e2b792.jpg\",\n  \"299501.jpg\": \"Aves/Megascops asio/b2856a59848ed85d1db82ef2f9038879.jpg\",\n  \"299509.jpg\": \"Aves/Megascops asio/60b936fcba26469d5e45f94ad859c26b.jpg\",\n  \"299527.jpg\": \"Insecta/Periplaneta americana/8fea616fd08d97582d16d79bb4bb4037.jpg\",\n  \"299538.jpg\": \"Insecta/Periplaneta americana/6c031f93b316126dd7e2a9032b30e4a0.jpg\",\n  \"299541.jpg\": \"Insecta/Periplaneta americana/5ab03a65c4611525e8f094110ba85981.jpg\",\n  \"299546.jpg\": \"Insecta/Periplaneta americana/49873c71a2788c1ea2fb30d66b7e42c8.jpg\",\n  \"299547.jpg\": \"Insecta/Periplaneta americana/02fad9c818b5fe08a079cbf1b2714863.jpg\",\n  \"299548.jpg\": \"Insecta/Periplaneta americana/385d3d36a964c33dee32a6dfc5bbe928.jpg\",\n  \"299553.jpg\": \"Insecta/Periplaneta americana/5f2e34d495325e65ebed6f11e868fdec.jpg\",\n  \"299554.jpg\": \"Insecta/Periplaneta americana/1ea88ff81da34edb425d3c26ede27e91.jpg\",\n  \"299555.jpg\": \"Insecta/Periplaneta americana/b0a5dd202774fb040b9989d15cc2651c.jpg\",\n  \"299559.jpg\": \"Insecta/Periplaneta americana/ff40dceaf7cbf1f2a65608176107a440.jpg\",\n  \"299561.jpg\": \"Insecta/Periplaneta americana/4b84b6a081e4c57d8637a298ab2573b4.jpg\",\n  \"299562.jpg\": \"Aves/Perisoreus canadensis/eeed8cb48acf18dcf81bf95f794cd5c8.jpg\",\n  \"299569.jpg\": \"Aves/Perisoreus canadensis/12a5b5b03f7258a83dc3be0d2bf15444.jpg\",\n  \"299570.jpg\": \"Aves/Perisoreus canadensis/70cde80a8b8a7a1fe4ec6413cacc294c.jpg\",\n  \"299571.jpg\": \"Aves/Perisoreus canadensis/1a793b94aade05392fac03138894737b.jpg\",\n  \"299576.jpg\": \"Aves/Perisoreus canadensis/83ff93dd9cdd87a1de99c47c21d7c8b6.jpg\",\n  \"299577.jpg\": \"Aves/Perisoreus canadensis/6c331199b2e617c9f5cbef6305538767.jpg\",\n  \"299578.jpg\": \"Aves/Perisoreus canadensis/d2c2c68eee001809626cdbd728a66652.jpg\",\n  \"299579.jpg\": \"Aves/Perisoreus canadensis/ce1c8609fe55bc4a87fc21762074c500.jpg\",\n  \"299593.jpg\": \"Aves/Perisoreus canadensis/198ff018cefcf75fe1b198eb3b704220.jpg\",\n  \"299594.jpg\": \"Aves/Perisoreus canadensis/51b012b022ced3f3c09f14fc4ccfb3bd.jpg\",\n  \"299596.jpg\": \"Aves/Perisoreus canadensis/2c65828e8320d6d30771bd6ae174a49b.jpg\",\n  \"299598.jpg\": \"Aves/Perisoreus canadensis/0a1ec9ba4f210e1f166463f9db1eed84.jpg\",\n  \"299600.jpg\": \"Aves/Perisoreus canadensis/5e8abf1a79ccf82f773f2831ecd307b0.jpg\",\n  \"299607.jpg\": \"Aves/Perisoreus canadensis/1b0c8a9fee2b52eab780187c8712a1bb.jpg\",\n  \"299613.jpg\": \"Aves/Perisoreus canadensis/eae211e39e666612e14c7bda2000b950.jpg\",\n  \"29962.jpg\": \"Insecta/Celithemis elisa/1d9d81e49815b023e1e0db0347acca25.jpg\",\n  \"299620.jpg\": \"Aves/Perisoreus canadensis/0702be907fabe72ddc4a0c8cb0a307ce.jpg\",\n  \"299621.jpg\": \"Aves/Perisoreus canadensis/2b60d6129c5074ac844f76c6a6ae4a1e.jpg\",\n  \"299623.jpg\": \"Aves/Perisoreus canadensis/6efcb03709a546e9f19a71061c7310f0.jpg\",\n  \"299628.jpg\": \"Aves/Perisoreus canadensis/bca82e643fcfa18b0b21d9e9e684f3ce.jpg\",\n  \"29963.jpg\": \"Insecta/Celithemis elisa/e1ae9d156b97d0219d50e6d895e28aa3.jpg\",\n  \"299630.jpg\": \"Aves/Perisoreus canadensis/a6548e61fa6cc9d2409dfd521cc03186.jpg\",\n  \"299638.jpg\": \"Aves/Perisoreus canadensis/762daa516fb81a70af8983de2c779b63.jpg\",\n  \"29964.jpg\": \"Insecta/Celithemis elisa/55cf978c5fdaf94fa086a156863514f3.jpg\",\n  \"299640.jpg\": \"Aves/Perisoreus canadensis/87aa471dbfd4ba2f23382c3f8ef42cce.jpg\",\n  \"29966.jpg\": \"Insecta/Celithemis elisa/5fc233d8411b8447609e72ac31779f7d.jpg\",\n  \"29968.jpg\": \"Insecta/Celithemis elisa/725370fdcc2562d9734f8f9321530338.jpg\",\n  \"29969.jpg\": \"Insecta/Celithemis elisa/f5961649670296b94e0f49b7ccdabe68.jpg\",\n  \"29980.jpg\": \"Insecta/Celithemis elisa/0464be26ef66ffff81389301efb809ff.jpg\",\n  \"299806.jpg\": \"Insecta/Phyciodes phaon/2559c7af845c2aa5070833e12af244ee.jpg\",\n  \"299809.jpg\": \"Insecta/Phyciodes phaon/d1eb342176e2b7df8f18a4efbbbeded3.jpg\",\n  \"299816.jpg\": \"Insecta/Phyciodes phaon/9fa9eab4515c93c8969a59a8e1f9fb6f.jpg\",\n  \"299822.jpg\": \"Insecta/Phyciodes phaon/d836a55bf8fd7572b47214ce388219bb.jpg\",\n  \"299837.jpg\": \"Insecta/Phyciodes phaon/90c4ae2b012d39d8da96d97d8d6e58e5.jpg\",\n  \"299838.jpg\": \"Insecta/Phyciodes phaon/8055b1ebc1aefb79f6bb69051df6d24b.jpg\",\n  \"299849.jpg\": \"Insecta/Phyciodes phaon/450097535a23d11fcb6c9f30ab4e97cc.jpg\",\n  \"29985.jpg\": \"Aves/Euphonia hirundinacea/8da6af78705c134fa58180f4a5c28fa1.jpg\",\n  \"299852.jpg\": \"Insecta/Phyciodes phaon/10b2e8c779499ad843792b8c924334b0.jpg\",\n  \"299859.jpg\": \"Insecta/Phyciodes phaon/4037d2b282c7402e9862e6a096008bba.jpg\",\n  \"29986.jpg\": \"Aves/Euphonia hirundinacea/5da855d0c499525adf857ef3e0803c64.jpg\",\n  \"299862.jpg\": \"Insecta/Phyciodes phaon/633fa589db1b09a377d8729d2bb6aaf6.jpg\",\n  \"299863.jpg\": \"Insecta/Phyciodes phaon/3f615b1e5bc1de09163e5dfb2c055199.jpg\",\n  \"299864.jpg\": \"Insecta/Phyciodes phaon/49d74ab89ab1d2c0bc46e0e9235766f5.jpg\",\n  \"299874.jpg\": \"Insecta/Phyciodes phaon/0b9570e61375be56c63bfdd4e659c10f.jpg\",\n  \"299881.jpg\": \"Insecta/Phyciodes phaon/537210410f62f83e77a19712b45d7d82.jpg\",\n  \"299882.jpg\": \"Insecta/Phyciodes phaon/3a9d975d24d62f0d331f27c8813a91ef.jpg\",\n  \"299900.jpg\": \"Insecta/Phyciodes phaon/9460ca1845baa417e0842e6f90201ee0.jpg\",\n  \"299908.jpg\": \"Insecta/Phyciodes phaon/5330b7a3fd97e05c77336ae403b2aaf7.jpg\",\n  \"299916.jpg\": \"Insecta/Phyciodes phaon/5d191a61a7cf23b863448ade0f5e2c29.jpg\",\n  \"299921.jpg\": \"Insecta/Phyciodes phaon/9bbbfb33fb449c86a1b7f7a6f4d4ebe8.jpg\",\n  \"299929.jpg\": \"Insecta/Phyciodes phaon/8c6e3d6026856470b30f4ab5a63ff73f.jpg\",\n  \"299930.jpg\": \"Insecta/Phyciodes phaon/09447328fa98aee44fdd4ab1848299ae.jpg\",\n  \"299955.jpg\": \"Insecta/Phyciodes phaon/8d7071add51675b752aee1a75d1664ee.jpg\",\n  \"299976.jpg\": \"Insecta/Phyciodes phaon/fa36f37986032771555be39f9813ccf8.jpg\",\n  \"299977.jpg\": \"Insecta/Phyciodes phaon/bd7ec13ef74c7dedad68ac6218809bc0.jpg\",\n  \"29998.jpg\": \"Aves/Euphonia hirundinacea/841aa4e0f17275f7c399d2453c58b696.jpg\",\n  \"299986.jpg\": \"Arachnida/Neoscona oaxacensis/229b3dba7ff1ef5505bf6a6dd92ea474.jpg\",\n  \"299987.jpg\": \"Arachnida/Neoscona oaxacensis/28070b21b4f1b6dc0a3082d9381fddb2.jpg\",\n  \"299995.jpg\": \"Arachnida/Neoscona oaxacensis/9c0501d9ca5f9cdfb7539a5edf28b8d4.jpg\",\n  \"299998.jpg\": \"Arachnida/Neoscona oaxacensis/b8ff0d9406960cbfa51e8d16a392406b.jpg\",\n  \"300006.jpg\": \"Arachnida/Neoscona oaxacensis/9337067d26173e476bd833b037c7009c.jpg\",\n  \"300011.jpg\": \"Arachnida/Neoscona oaxacensis/cebbb84a5a40d6c7b89dd4a3dffc289b.jpg\",\n  \"300018.jpg\": \"Arachnida/Neoscona oaxacensis/abb2c5413a17ef33ece06237279b57d1.jpg\",\n  \"300022.jpg\": \"Arachnida/Neoscona oaxacensis/6155b9337decff4c3a4585c19b297608.jpg\",\n  \"300023.jpg\": \"Arachnida/Neoscona oaxacensis/154d17faa94720c8778c73452116c2d7.jpg\",\n  \"300032.jpg\": \"Arachnida/Neoscona oaxacensis/05fa9aed0124843059e1b6b3476d23c2.jpg\",\n  \"300038.jpg\": \"Arachnida/Neoscona oaxacensis/c6a791f8b782e565f2c64db52584416a.jpg\",\n  \"300040.jpg\": \"Arachnida/Neoscona oaxacensis/14d6a52d37bc035fe27d38cf3f239b89.jpg\",\n  \"300041.jpg\": \"Arachnida/Neoscona oaxacensis/35b0a24b113f117e03f37d14501afca8.jpg\",\n  \"300043.jpg\": \"Arachnida/Neoscona oaxacensis/118eebb1e5cce05e93a59d75d17f5cdc.jpg\",\n  \"300057.jpg\": \"Arachnida/Neoscona oaxacensis/f740efdedee44901fce0c1332cf64b60.jpg\",\n  \"300058.jpg\": \"Arachnida/Neoscona oaxacensis/01e6db1f642454009047e684be9946ff.jpg\",\n  \"300059.jpg\": \"Arachnida/Neoscona oaxacensis/e651ca24aa52b03329c27744a71d4d18.jpg\",\n  \"300060.jpg\": \"Arachnida/Neoscona oaxacensis/c5ad40342f619f529a8e59d76c42042b.jpg\",\n  \"300062.jpg\": \"Arachnida/Neoscona oaxacensis/dfd735e3ab47d41616f6d6e064e87054.jpg\",\n  \"300068.jpg\": \"Arachnida/Neoscona oaxacensis/c54d222c1b3f38469a26fe02ba0b68fb.jpg\",\n  \"300074.jpg\": \"Arachnida/Neoscona oaxacensis/00c34a21f78dedf0b600f147101d3dc6.jpg\",\n  \"30014.jpg\": \"Aves/Euphonia hirundinacea/f577c36d143a311e1fb8f6d28b7fd5cf.jpg\",\n  \"300158.jpg\": \"Reptilia/Coronella austriaca/146ea2d8d6642d4a45403894468c2fa2.jpg\",\n  \"300165.jpg\": \"Reptilia/Coronella austriaca/75d5b9ab129c0ec82bd9518bac860cf7.jpg\",\n  \"300166.jpg\": \"Reptilia/Coronella austriaca/fb5c2a7c0cb9e3a8e502bc33e55772f7.jpg\",\n  \"30017.jpg\": \"Aves/Euphonia hirundinacea/bfb9b6bdaf1dacee726751ad7923d463.jpg\",\n  \"300170.jpg\": \"Reptilia/Coronella austriaca/66c9a02dec82ba5bf4dcdae2baab5c01.jpg\",\n  \"300172.jpg\": \"Reptilia/Coronella austriaca/94c3fa4a68a0f5ad7a9edb4ae55ef486.jpg\",\n  \"300173.jpg\": \"Reptilia/Coronella austriaca/c887760d0acbc20969b79c92f54c7cc8.jpg\",\n  \"30018.jpg\": \"Aves/Euphonia hirundinacea/eb8c93254d16313136fb15e981435820.jpg\",\n  \"30022.jpg\": \"Mollusca/Triopha catalinae/0f78b0706ef6e44e3b79b3d1b7d3c572.jpg\",\n  \"30034.jpg\": \"Mollusca/Triopha catalinae/df747d93bf65813f004d10475900ec9f.jpg\",\n  \"30047.jpg\": \"Mollusca/Triopha catalinae/5e5b9f79e6c15cd9148c75e68241f745.jpg\",\n  \"300517.jpg\": \"Insecta/Aphantopus hyperantus/f6635a97d57edf2ea25804e1cda04a6e.jpg\",\n  \"300519.jpg\": \"Insecta/Aphantopus hyperantus/e308974a5de57b8853abc212c840b4d5.jpg\",\n  \"300520.jpg\": \"Insecta/Aphantopus hyperantus/1aa436532c73e4c26f616ea14a4b6b8d.jpg\",\n  \"300522.jpg\": \"Insecta/Aphantopus hyperantus/3d5c379ec4077f0d823f2ec569dc803b.jpg\",\n  \"300526.jpg\": \"Insecta/Aphantopus hyperantus/ee1694b4a8d08391c6e46acc7fdf013c.jpg\",\n  \"300527.jpg\": \"Insecta/Aphantopus hyperantus/92cf1da45c994609a81fb1e3746bcb1e.jpg\",\n  \"300528.jpg\": \"Insecta/Aphantopus hyperantus/da9bf901683bdc1749ee41ad2d4dfa97.jpg\",\n  \"300529.jpg\": \"Insecta/Aphantopus hyperantus/651c994dcd748529c36af742d180714c.jpg\",\n  \"300530.jpg\": \"Insecta/Aphantopus hyperantus/8037c0eec2511283fd68a25dc5c0c050.jpg\",\n  \"300532.jpg\": \"Insecta/Aphantopus hyperantus/dfdb087660737eddfae2b13b0befa852.jpg\",\n  \"300533.jpg\": \"Insecta/Aphantopus hyperantus/49ae958dbe1a34c45fb1ec328263c8c6.jpg\",\n  \"300534.jpg\": \"Insecta/Aphantopus hyperantus/254950551087b0b1b8d58579a318d46e.jpg\",\n  \"300535.jpg\": \"Insecta/Aphantopus hyperantus/088e884a9ace25005d49371b70cabdae.jpg\",\n  \"300537.jpg\": \"Insecta/Aphantopus hyperantus/3e1214c2f8b8bb04565f44f2d7653c05.jpg\",\n  \"300549.jpg\": \"Insecta/Aphantopus hyperantus/0f2de02988f482b46c04b47b4f8a8627.jpg\",\n  \"300551.jpg\": \"Insecta/Aphantopus hyperantus/e45f3a35707819fe4d8cf2cb4d691ea7.jpg\",\n  \"300552.jpg\": \"Insecta/Aphantopus hyperantus/3ee41ebba8848186d67f59b533342732.jpg\",\n  \"300556.jpg\": \"Insecta/Hemideina crassidens/161e753db5eeb5059f3eafe6f39268e0.jpg\",\n  \"300557.jpg\": \"Insecta/Hemideina crassidens/821da88a29fa9641e93c706080f6f4b8.jpg\",\n  \"300558.jpg\": \"Insecta/Hemideina crassidens/8a882ccbe31b03f3d1914a62e2669b78.jpg\",\n  \"300561.jpg\": \"Insecta/Hemideina crassidens/968e6e274b7d01ca7246e68045f1935c.jpg\",\n  \"300563.jpg\": \"Insecta/Hemideina crassidens/7ad3795d6b3af3fef23de238743d0670.jpg\",\n  \"300570.jpg\": \"Insecta/Hemideina crassidens/ab69760ff7533ae2f1c575e1aa00b021.jpg\",\n  \"300572.jpg\": \"Insecta/Hemideina crassidens/92cbd19222aca2a9fe87071b7588df9f.jpg\",\n  \"300573.jpg\": \"Insecta/Hemideina crassidens/8c1bcf5c77e45a4286efa3280e371d9a.jpg\",\n  \"300574.jpg\": \"Insecta/Hemideina crassidens/3098d9bf7a8ee10933d4e86fdce5dca4.jpg\",\n  \"300575.jpg\": \"Insecta/Hemideina crassidens/aca3a6b7498b92b23e22aa50f6919a55.jpg\",\n  \"300576.jpg\": \"Insecta/Hemideina crassidens/710ebbf542139a809236458d0e1d8855.jpg\",\n  \"300577.jpg\": \"Insecta/Hemideina crassidens/59868f3b736848b54d5a8228f8f1637e.jpg\",\n  \"300578.jpg\": \"Insecta/Hemideina crassidens/70fd5b2bc64df934e41518c6009b39e7.jpg\",\n  \"300579.jpg\": \"Insecta/Hemideina crassidens/8c4a4b4649fa1a6f2a8dbd61da99aefc.jpg\",\n  \"300585.jpg\": \"Insecta/Hemideina crassidens/a6e10cf4797ddaf4848acbc23e548e84.jpg\",\n  \"300588.jpg\": \"Insecta/Hemideina crassidens/dd67c9156e7ec5b3faa716f1ced55471.jpg\",\n  \"300590.jpg\": \"Insecta/Hemideina crassidens/fc5e5ef8ae125712714fff9682fa4bcc.jpg\",\n  \"300591.jpg\": \"Insecta/Hemideina crassidens/ef85c84022a59f475c43500d7e55b339.jpg\",\n  \"300592.jpg\": \"Insecta/Hemideina crassidens/24ce36a07e4db1d80ae4bdf6a320a838.jpg\",\n  \"300594.jpg\": \"Insecta/Hemideina crassidens/2aa32239455d8e5ed42bc28906614070.jpg\",\n  \"30060.jpg\": \"Mollusca/Triopha catalinae/add6d3ca96819222f497d8ba72ac5051.jpg\",\n  \"300656.jpg\": \"Aves/Momotus mexicanus/9802ee03599d9359e8406d8849199089.jpg\",\n  \"300660.jpg\": \"Aves/Momotus mexicanus/ee64e3300d59bbadd00835f460e2febb.jpg\",\n  \"300664.jpg\": \"Aves/Momotus mexicanus/014baf8ac8aceb5605b565e696f04d94.jpg\",\n  \"300667.jpg\": \"Aves/Momotus mexicanus/edf313050322557bf61bf30283ee4405.jpg\",\n  \"300668.jpg\": \"Aves/Momotus mexicanus/d6396523e59dcef17c54f4cf01dd2ea0.jpg\",\n  \"300669.jpg\": \"Aves/Momotus mexicanus/cbe7a15bd92d904e04cac98a8c22edc4.jpg\",\n  \"300670.jpg\": \"Aves/Momotus mexicanus/ce07c9e2e1f0499793b5bd521dd4f0bf.jpg\",\n  \"300671.jpg\": \"Aves/Momotus mexicanus/d4925a2682d0da95ac4d83ecbb57dcef.jpg\",\n  \"300672.jpg\": \"Aves/Momotus mexicanus/c8dc8aacaa88ff9efbb018eb7a9adc43.jpg\",\n  \"300673.jpg\": \"Aves/Momotus mexicanus/db7ab1d5375e17926a08bcdc336f3e28.jpg\",\n  \"300675.jpg\": \"Aves/Momotus mexicanus/0f91e7d8bba993b45fabb3df737c4432.jpg\",\n  \"300680.jpg\": \"Aves/Momotus mexicanus/64240a20f0124836694293d0b5439db0.jpg\",\n  \"300682.jpg\": \"Aves/Momotus mexicanus/7a6111f2686afe0db7522ef31b29fd2c.jpg\",\n  \"300683.jpg\": \"Aves/Momotus mexicanus/2d279f0552616f03e31cb79b4ea01344.jpg\",\n  \"300684.jpg\": \"Aves/Momotus mexicanus/2d2d124da81495836d5a51d11081d608.jpg\",\n  \"300686.jpg\": \"Aves/Momotus mexicanus/7af738b9b292d4bca25164b2c928390d.jpg\",\n  \"300688.jpg\": \"Aves/Momotus mexicanus/6ec2ab9c0c93636721e8094d57819124.jpg\",\n  \"300689.jpg\": \"Aves/Momotus mexicanus/179b97e5791dc9f2b9192ade3a8cc58b.jpg\",\n  \"300690.jpg\": \"Aves/Momotus mexicanus/22f8a1f2a9da648e2334e88d214465d2.jpg\",\n  \"300692.jpg\": \"Aves/Momotus mexicanus/4b00b05e1a2c5d045dd9d30e85fb9545.jpg\",\n  \"300693.jpg\": \"Aves/Momotus mexicanus/c51bd8ca39a1984572ccc6f608f6e2b9.jpg\",\n  \"300694.jpg\": \"Aves/Momotus mexicanus/97384ba7624483d5f50a3d3a24ec5ee9.jpg\",\n  \"300695.jpg\": \"Aves/Momotus mexicanus/77600e55faf2903c3a6b840c03a21bb0.jpg\",\n  \"300696.jpg\": \"Aves/Momotus mexicanus/0a73158378e7d3c1bb4601693568e39b.jpg\",\n  \"300697.jpg\": \"Aves/Momotus mexicanus/532e0f288afe699e2074b62d2959bf25.jpg\",\n  \"300699.jpg\": \"Aves/Momotus mexicanus/60e2c50e79bde0046c5b8027986f3448.jpg\",\n  \"30071.jpg\": \"Mollusca/Triopha catalinae/c3816c2d6a0686b9fe9c530892887671.jpg\",\n  \"30072.jpg\": \"Mollusca/Triopha catalinae/54928283b0e372e6754aaac85f5495f5.jpg\",\n  \"300732.jpg\": \"Aves/Sturnella neglecta/1e2ccc2cc861f34389c5f900e1b406ff.jpg\",\n  \"300734.jpg\": \"Aves/Sturnella neglecta/86316b04c6ca492edc384d537f8fd97c.jpg\",\n  \"300748.jpg\": \"Aves/Sturnella neglecta/0d9d71de2a643a99ea95190026187d22.jpg\",\n  \"300765.jpg\": \"Aves/Sturnella neglecta/ee0fbcfadb0c1fd18f8b7882dd6e4c24.jpg\",\n  \"300766.jpg\": \"Aves/Sturnella neglecta/4ce85e27d35cce349bf37a25e8e6ba87.jpg\",\n  \"300776.jpg\": \"Aves/Sturnella neglecta/f6e289271b23d06a744ee609fd7586c5.jpg\",\n  \"300778.jpg\": \"Aves/Sturnella neglecta/29f395f8e556dcce1097914799f448c7.jpg\",\n  \"300785.jpg\": \"Aves/Sturnella neglecta/30273b99150cae6b2f3bd5f4801f2d63.jpg\",\n  \"300788.jpg\": \"Aves/Sturnella neglecta/e12c2dbe5a7205ae06d87ae283261eb8.jpg\",\n  \"300826.jpg\": \"Aves/Sturnella neglecta/1307f29d683dcc049240b2aa3adfcd4a.jpg\",\n  \"300839.jpg\": \"Aves/Sturnella neglecta/27cd9bd1a50c66b3b9cefcb61c4cd099.jpg\",\n  \"300841.jpg\": \"Aves/Sturnella neglecta/c04a2f9d7a0f0f9ee80839394e280f43.jpg\",\n  \"300856.jpg\": \"Aves/Sturnella neglecta/41539766e2f214d0fb3906882aa47698.jpg\",\n  \"300874.jpg\": \"Aves/Sturnella neglecta/8de8e534664802b55b0131fbf6164eef.jpg\",\n  \"300900.jpg\": \"Aves/Sturnella neglecta/8a5a75c824f75b3f3e1532e9ff6e893c.jpg\",\n  \"300902.jpg\": \"Aves/Sturnella neglecta/ea8fbd47eee51ff91c94056d4976b66f.jpg\",\n  \"300912.jpg\": \"Aves/Sturnella neglecta/3fa44e8ee907c8257996a8a923715984.jpg\",\n  \"300914.jpg\": \"Aves/Sturnella neglecta/260962f2c67c030da627db8b6f169067.jpg\",\n  \"300925.jpg\": \"Aves/Sturnella neglecta/ca8bc28d49d0fb0955113df0f0af337a.jpg\",\n  \"300933.jpg\": \"Aves/Calidris maritima/720fee3348072a3400245d2179ee183c.jpg\",\n  \"300934.jpg\": \"Aves/Calidris maritima/910940a196afda36c818563b26545a47.jpg\",\n  \"30094.jpg\": \"Mollusca/Triopha catalinae/605bc949967dc7e02df36a49509b8a6d.jpg\",\n  \"300941.jpg\": \"Aves/Calidris maritima/382921d5b8eb16b4ed8e5dc0bc9b98eb.jpg\",\n  \"300952.jpg\": \"Aves/Calidris maritima/e45b965019878067dadba5823e6c463d.jpg\",\n  \"300958.jpg\": \"Mollusca/Cerithideopsis californica/f72e64b1ce3773c167006f35dd16c667.jpg\",\n  \"300965.jpg\": \"Mollusca/Cerithideopsis californica/72a7f095dfa4d73b2be8623ddbe6f54c.jpg\",\n  \"30099.jpg\": \"Mollusca/Triopha catalinae/3f8b8398bf46115806b4d9bda1de78a1.jpg\",\n  \"301.jpg\": \"Mollusca/Olivella biplicata/564f6c3e28610aacafb3cdedc80c15a7.jpg\",\n  \"30103.jpg\": \"Mollusca/Triopha catalinae/aff8d8bbd888ca223ce90264ba830273.jpg\",\n  \"30117.jpg\": \"Mollusca/Triopha catalinae/441ba3d41313eefd2feb0e951e5c9fd5.jpg\",\n  \"301192.jpg\": \"Insecta/Plusiodonta compressipalpis/d1a2f8b35249316257e5bf328dc6d369.jpg\",\n  \"301198.jpg\": \"Insecta/Plusiodonta compressipalpis/faf148b8b08a0f47dea8a43ab8bb6640.jpg\",\n  \"301199.jpg\": \"Insecta/Plusiodonta compressipalpis/087bbed213c2691d5b77ee867a9272a7.jpg\",\n  \"301203.jpg\": \"Insecta/Plusiodonta compressipalpis/de12d8efdb6e7683c7ce516a734881a2.jpg\",\n  \"301204.jpg\": \"Insecta/Plusiodonta compressipalpis/0a7815713e3da88814b73474a86d1d38.jpg\",\n  \"301207.jpg\": \"Insecta/Plusiodonta compressipalpis/5558dd2cac185e13cb4325919122e346.jpg\",\n  \"301208.jpg\": \"Insecta/Plusiodonta compressipalpis/3e6240416f566fbcbefa0029bf19ea63.jpg\",\n  \"301212.jpg\": \"Insecta/Plusiodonta compressipalpis/73f570146373369d1be795b45930ffb0.jpg\",\n  \"301213.jpg\": \"Insecta/Plusiodonta compressipalpis/ac91852258d5e39547e0ecfb8cafffe8.jpg\",\n  \"301214.jpg\": \"Insecta/Plusiodonta compressipalpis/834c39dd8eb89f2d98aa09f689f22a6c.jpg\",\n  \"301223.jpg\": \"Insecta/Plusiodonta compressipalpis/48abc90118816cfd827178eade7b37d9.jpg\",\n  \"301224.jpg\": \"Insecta/Plusiodonta compressipalpis/21c0239671cdcbb3538698d6c521170e.jpg\",\n  \"301228.jpg\": \"Insecta/Plusiodonta compressipalpis/88bcc3268c3056355a88b59c3cf755a6.jpg\",\n  \"301229.jpg\": \"Insecta/Plusiodonta compressipalpis/7c4ef82758354e98983c090ea8845c0c.jpg\",\n  \"301237.jpg\": \"Insecta/Caedicia simplex/76480f7dd13420acd9d595030ee64270.jpg\",\n  \"301246.jpg\": \"Insecta/Caedicia simplex/3592406ba5a0d159b13ad9af47072a75.jpg\",\n  \"301250.jpg\": \"Insecta/Caedicia simplex/a416174486b05ac19d7098c7e719e78c.jpg\",\n  \"301255.jpg\": \"Insecta/Caedicia simplex/668d8ef1181cdabe24680e5949f29d20.jpg\",\n  \"301259.jpg\": \"Insecta/Caedicia simplex/fbb05904e4382b6429fb3955be7f3702.jpg\",\n  \"301270.jpg\": \"Insecta/Caedicia simplex/d799d6be2f692779d45f63f8387dea85.jpg\",\n  \"301278.jpg\": \"Aves/Larus hyperboreus/58fe3c04d789319a8c9eba0ba61dfa2f.jpg\",\n  \"301279.jpg\": \"Aves/Larus hyperboreus/6863539106ba00d6c130cefe712250c0.jpg\",\n  \"301280.jpg\": \"Aves/Larus hyperboreus/c74be50aaa19770090376bb64fe90801.jpg\",\n  \"301285.jpg\": \"Aves/Larus hyperboreus/d37497bce08b93cf2ea4c5c110a6942e.jpg\",\n  \"301290.jpg\": \"Aves/Larus hyperboreus/ac2515e99fa503f5c974cef0e11fe5fc.jpg\",\n  \"301292.jpg\": \"Aves/Larus hyperboreus/01c718120ef3e45f38fd2739f8b2151b.jpg\",\n  \"301308.jpg\": \"Aves/Larus hyperboreus/98f218e6bfd150792745bef64ea1b7de.jpg\",\n  \"301320.jpg\": \"Aves/Larus hyperboreus/57ce194e4b88634e3e23fc787c2e8bc5.jpg\",\n  \"301323.jpg\": \"Aves/Larus hyperboreus/2e79888f135f2f11c1d10dc1a88eadfe.jpg\",\n  \"301325.jpg\": \"Aves/Larus hyperboreus/e2acd87cf979bed95c7401db57853f15.jpg\",\n  \"301331.jpg\": \"Aves/Larus hyperboreus/d6f7671fa9ccedd3d33a3ea8b05c0604.jpg\",\n  \"301333.jpg\": \"Aves/Larus hyperboreus/b5891d24d4f9cf724655de59c988481d.jpg\",\n  \"30136.jpg\": \"Mollusca/Triopha catalinae/d077f0b95a33e706b989cc788c4ea06c.jpg\",\n  \"301366.jpg\": \"Aves/Pelecanus erythrorhynchos/884a447e3ed93348d429a1772b12f649.jpg\",\n  \"301385.jpg\": \"Aves/Pelecanus erythrorhynchos/bb546657372b2b9ec5c2cb7aab5e6673.jpg\",\n  \"30142.jpg\": \"Mollusca/Triopha catalinae/f456a6923fe16c6f57a17d1bd4d7fc05.jpg\",\n  \"301438.jpg\": \"Aves/Pelecanus erythrorhynchos/56d651ef5858975dae3740aba468703c.jpg\",\n  \"301520.jpg\": \"Aves/Pelecanus erythrorhynchos/0b0d24e56e0267f9840c0956c4e042ea.jpg\",\n  \"30158.jpg\": \"Mollusca/Triopha catalinae/79c4eb915e154ae79eac864d695122b7.jpg\",\n  \"30162.jpg\": \"Mollusca/Triopha catalinae/e464f38943edc118e6df8c84df1e961b.jpg\",\n  \"30173.jpg\": \"Mollusca/Triopha catalinae/88cdc09c56dd3e573df96919e7b7c7f5.jpg\",\n  \"301746.jpg\": \"Aves/Pelecanus erythrorhynchos/36cd432ff06e3d3de8d07d07b7de2ca7.jpg\",\n  \"301809.jpg\": \"Aves/Pelecanus erythrorhynchos/e6b5b58381fbb8ea1adca23dd47e21b3.jpg\",\n  \"30183.jpg\": \"Insecta/Vanessa itea/eef91e1f5960a5e5bf1abfdb0d4b7f5f.jpg\",\n  \"30190.jpg\": \"Insecta/Vanessa itea/8269a8cd80f747cb562cda27aac7fbda.jpg\",\n  \"301918.jpg\": \"Reptilia/Sceloporus torquatus/795f71f0c47cf28a29be7706f7beeb24.jpg\",\n  \"301921.jpg\": \"Reptilia/Sceloporus torquatus/e223303994c1152e8cec319f767a41e3.jpg\",\n  \"301924.jpg\": \"Reptilia/Sceloporus torquatus/2cff47262ebcd7e737961426ff269f91.jpg\",\n  \"301928.jpg\": \"Reptilia/Sceloporus torquatus/98b80b28d4e7b29529c1a3054bf5d778.jpg\",\n  \"301929.jpg\": \"Reptilia/Sceloporus torquatus/394288886062911e6afe394a694e3ca2.jpg\",\n  \"301930.jpg\": \"Reptilia/Sceloporus torquatus/2ce81fb7a12f151e7cfcb042d9db9a54.jpg\",\n  \"301932.jpg\": \"Reptilia/Sceloporus torquatus/9d927153a30c3883f4a21bc9a03ec86c.jpg\",\n  \"301933.jpg\": \"Reptilia/Sceloporus torquatus/1399d82e26d55da2bd2791d6ecf1a37d.jpg\",\n  \"301939.jpg\": \"Reptilia/Sceloporus torquatus/f439cd01e44bffa335790f1f94c3aa7b.jpg\",\n  \"30194.jpg\": \"Insecta/Vanessa itea/2e9fe7ccec4b21b1f63f4b09c01c7e1b.jpg\",\n  \"301940.jpg\": \"Reptilia/Sceloporus torquatus/ee484776db717e37e243a213e1efe393.jpg\",\n  \"301941.jpg\": \"Reptilia/Sceloporus torquatus/544452d559dbf1eb14a810168e54f6eb.jpg\",\n  \"301943.jpg\": \"Reptilia/Sceloporus torquatus/726b21b0a07d4f9dcfa2623ed581f71f.jpg\",\n  \"301944.jpg\": \"Reptilia/Sceloporus torquatus/b077f6e6def7ca0c3b404f951fdd77a3.jpg\",\n  \"301945.jpg\": \"Reptilia/Sceloporus torquatus/a3ed2aabe5f9b525423e088789da5426.jpg\",\n  \"301946.jpg\": \"Reptilia/Sceloporus torquatus/96ea57b77626923711ee4a05ec012a5d.jpg\",\n  \"301948.jpg\": \"Reptilia/Sceloporus torquatus/0d817ecca26e7c99cdd5c5d53b0f4b21.jpg\",\n  \"301951.jpg\": \"Reptilia/Sceloporus torquatus/ea54bc97b02ae58bc1b77ca32a5904be.jpg\",\n  \"301953.jpg\": \"Reptilia/Sceloporus torquatus/13528eedb085b6ebd2f710c7464a8a7d.jpg\",\n  \"301960.jpg\": \"Reptilia/Sceloporus torquatus/fdffaedd2dde9cc5079b6c3948bb4b9a.jpg\",\n  \"301963.jpg\": \"Aves/Eremophila alpestris/9c6b17bd663d4ade98585ee65ee9c663.jpg\",\n  \"301965.jpg\": \"Aves/Eremophila alpestris/d396f6d7103e587d6e1c8c3268375b55.jpg\",\n  \"301975.jpg\": \"Aves/Eremophila alpestris/57da6c2a4a47f651e72f9e4527e271ed.jpg\",\n  \"302.jpg\": \"Mollusca/Olivella biplicata/1c505a6cb57b123daa5ae52caea9a1cf.jpg\",\n  \"302000.jpg\": \"Aves/Eremophila alpestris/902781f2fa4d538b522523b23ac2be0e.jpg\",\n  \"30201.jpg\": \"Insecta/Vanessa itea/f8fc2007f895c453b7426c907b4bd296.jpg\",\n  \"302015.jpg\": \"Aves/Eremophila alpestris/21b6548ef798f3485497ce0bd25431a2.jpg\",\n  \"302016.jpg\": \"Aves/Eremophila alpestris/5b942e818c2e7c1bc0763ce693d5e2fe.jpg\",\n  \"302021.jpg\": \"Aves/Eremophila alpestris/a8099cb1324745801bde010595a432c3.jpg\",\n  \"302023.jpg\": \"Aves/Eremophila alpestris/fab599001015076f0859217ce5eb1ef5.jpg\",\n  \"302026.jpg\": \"Aves/Eremophila alpestris/9db8d3cc144bb602fc2dc728609e890a.jpg\",\n  \"30204.jpg\": \"Insecta/Vanessa itea/afd8c7b80cced0d1368c70ca8d8e304d.jpg\",\n  \"302046.jpg\": \"Aves/Eremophila alpestris/97970d15d27eae7746351403e82be59b.jpg\",\n  \"302049.jpg\": \"Aves/Eremophila alpestris/821e82f80459e499c2839b64ad39f62e.jpg\",\n  \"302063.jpg\": \"Aves/Eremophila alpestris/f02916a50069b298cabf75bed46cb357.jpg\",\n  \"302068.jpg\": \"Aves/Eremophila alpestris/6072e07141ce36fb0ea7367fc3e1c0b3.jpg\",\n  \"302069.jpg\": \"Aves/Eremophila alpestris/0a2f77618fb57df95b778b9831564c0b.jpg\",\n  \"302077.jpg\": \"Aves/Eremophila alpestris/dacc63af3bc5b0b3e6427c799f909feb.jpg\",\n  \"302080.jpg\": \"Aves/Eremophila alpestris/7d7034f7d935b69c0a8bcf99be1b3566.jpg\",\n  \"302091.jpg\": \"Aves/Eremophila alpestris/9b2dc1322bb30f81af8332e02b85db19.jpg\",\n  \"302093.jpg\": \"Aves/Eremophila alpestris/ec379271b306862f84f851641c69aabc.jpg\",\n  \"302100.jpg\": \"Aves/Eremophila alpestris/ccbc3c4f97d250a58c08f6a8aa67556f.jpg\",\n  \"302112.jpg\": \"Aves/Eremophila alpestris/e613431fd875b13ea37fa7d9a441543c.jpg\",\n  \"302113.jpg\": \"Aves/Eremophila alpestris/c18ec62e6436c09014bbb0000c7b795d.jpg\",\n  \"302116.jpg\": \"Aves/Eremophila alpestris/14e1cb166e9c49ead9917c919c9944ec.jpg\",\n  \"302117.jpg\": \"Aves/Eremophila alpestris/dabc02c46392cbdce77413df7468ccf0.jpg\",\n  \"302133.jpg\": \"Animalia/Ginglymostoma cirratum/d22afd2e152190ef12187326d00d5544.jpg\",\n  \"302136.jpg\": \"Animalia/Ginglymostoma cirratum/9e5a742fd789c49095b3a9dfc53a42b6.jpg\",\n  \"302142.jpg\": \"Animalia/Ginglymostoma cirratum/ac49993ba87fa0014ee5e221f56a4817.jpg\",\n  \"302143.jpg\": \"Mammalia/Lepus europaeus/ff85fb707468ded72bee644abd074955.jpg\",\n  \"302148.jpg\": \"Mammalia/Lepus europaeus/4dbcf829c2efee893aaf67859b5234f5.jpg\",\n  \"302149.jpg\": \"Mammalia/Lepus europaeus/f8c94b5bdf412e0a923bec95bad3c265.jpg\",\n  \"30215.jpg\": \"Insecta/Vanessa itea/9828f49096009f9d21f5c7f22638ab50.jpg\",\n  \"302156.jpg\": \"Mammalia/Lepus europaeus/240c62fdf594e9d088f00299c7e376e6.jpg\",\n  \"302161.jpg\": \"Mammalia/Lepus europaeus/3fdb18112e55ad118d5a56f7c4a1cc7e.jpg\",\n  \"302164.jpg\": \"Mammalia/Lepus europaeus/8026bdc7de7cb72e13fe08b35c2efde5.jpg\",\n  \"302166.jpg\": \"Mammalia/Lepus europaeus/75f94a1e30868c9da5ee40a853a94fe4.jpg\",\n  \"302171.jpg\": \"Mammalia/Lepus europaeus/77a9881675adfefa00e0ad9959e18d14.jpg\",\n  \"302182.jpg\": \"Mammalia/Lepus europaeus/79fd25f17afccbc9d3ccaecb1a3dfbce.jpg\",\n  \"302183.jpg\": \"Mammalia/Lepus europaeus/2ed903d7db0cd86a48973435acc343dd.jpg\",\n  \"302185.jpg\": \"Mammalia/Lepus europaeus/19e0a1235f32b9a5c275963392e73312.jpg\",\n  \"302196.jpg\": \"Mammalia/Lepus europaeus/06e9ab11e17c7c698d37f82eabf89047.jpg\",\n  \"302207.jpg\": \"Mammalia/Lepus europaeus/5a5fe9b31d36103c33f505e0937147ef.jpg\",\n  \"302210.jpg\": \"Mammalia/Lepus europaeus/4b0810916092a14cbb0091336ef217d0.jpg\",\n  \"302213.jpg\": \"Mammalia/Lepus europaeus/393d27e7c0f3565efcbd4d33be425b58.jpg\",\n  \"302222.jpg\": \"Mammalia/Lepus europaeus/941d762d45bdb1fd520972f16d8a620f.jpg\",\n  \"302247.jpg\": \"Aves/Loxia leucoptera/d7fdcab61e80952321e5d352188fcc0a.jpg\",\n  \"302259.jpg\": \"Aves/Loxia leucoptera/ff20886c71c396656ccbb446139f5122.jpg\",\n  \"30226.jpg\": \"Insecta/Vanessa itea/baa3b425365a88ba80a24543031bd41a.jpg\",\n  \"302262.jpg\": \"Aves/Loxia leucoptera/ac37284d326ee596659d0a9fc8d85a74.jpg\",\n  \"30227.jpg\": \"Insecta/Vanessa itea/ac2896d3b55092e1e259aa722c58b685.jpg\",\n  \"302271.jpg\": \"Aves/Loxia leucoptera/9fc9b257a75fd680a75798cfca7b4175.jpg\",\n  \"3023.jpg\": \"Aves/Picoides nuttallii/c40a743c7af0b44d7126b9daca139c97.jpg\",\n  \"30235.jpg\": \"Insecta/Vanessa itea/607998cd8428dd9ef0cef1fab76c3f20.jpg\",\n  \"302361.jpg\": \"Mammalia/Tadarida brasiliensis/c1bdfec7c1e548a431d12e1baed0bddb.jpg\",\n  \"302365.jpg\": \"Mammalia/Tadarida brasiliensis/70c5a29d1e6cb63aed12065b600e2a7f.jpg\",\n  \"302371.jpg\": \"Mammalia/Tadarida brasiliensis/d84baabec32b46abd68cae6494133752.jpg\",\n  \"302372.jpg\": \"Mammalia/Tadarida brasiliensis/de62da2cbc3d7d23faa6a635b507803c.jpg\",\n  \"302378.jpg\": \"Mammalia/Tadarida brasiliensis/37e75c57553ee0e7141df2e1a2f101bd.jpg\",\n  \"302379.jpg\": \"Mammalia/Tadarida brasiliensis/b56f56488addd5b851d4679dd8681972.jpg\",\n  \"302390.jpg\": \"Mammalia/Tadarida brasiliensis/64158f91988624b3e487669d26cfd110.jpg\",\n  \"302391.jpg\": \"Reptilia/Salvadora grahamiae/07c6861393f34176b881c1b35117e6d3.jpg\",\n  \"302393.jpg\": \"Reptilia/Salvadora grahamiae/df5b3ebc68023d8cb753255b32bf4ed3.jpg\",\n  \"302399.jpg\": \"Reptilia/Salvadora grahamiae/c09d5938060563e98a511e4771340d57.jpg\",\n  \"302411.jpg\": \"Reptilia/Salvadora grahamiae/8f032d1b174060f16d3b11c9d9928558.jpg\",\n  \"302412.jpg\": \"Reptilia/Salvadora grahamiae/1d2dd686f8fc1b598aa802443ebd6edd.jpg\",\n  \"302413.jpg\": \"Reptilia/Salvadora grahamiae/a17becba32ea4536bcf71ded4a508cde.jpg\",\n  \"302430.jpg\": \"Aves/Chen rossii/bad37cdfe7f18195035e94d82c1cd44e.jpg\",\n  \"302447.jpg\": \"Aves/Chen rossii/20ca740b58574875e3c415cdd75468f0.jpg\",\n  \"30246.jpg\": \"Insecta/Vanessa itea/285372596db790d4b76e0f05fc30944b.jpg\",\n  \"302469.jpg\": \"Aves/Chen rossii/9f03af37826fa2fd8bef81886a4b1fde.jpg\",\n  \"302475.jpg\": \"Aves/Chen rossii/e1c24e5824b96a1f144e809dceadc4e9.jpg\",\n  \"302596.jpg\": \"Reptilia/Emydoidea blandingii/9f3e6055f7d4005ace0f9d728f1f72be.jpg\",\n  \"302600.jpg\": \"Reptilia/Emydoidea blandingii/707b4db9dcd53aae8edfc7be21a0043c.jpg\",\n  \"302601.jpg\": \"Reptilia/Emydoidea blandingii/469dff0a0f0be6a3f1dbd95408acfd76.jpg\",\n  \"302602.jpg\": \"Reptilia/Emydoidea blandingii/8fdedd183e3175c72e8ec29f9d8f1f27.jpg\",\n  \"302612.jpg\": \"Reptilia/Emydoidea blandingii/5b71a2f1970565016f9f329da1e1abb9.jpg\",\n  \"302613.jpg\": \"Reptilia/Emydoidea blandingii/5ff1b50b8ed1c1dbc71a5d1c777ef097.jpg\",\n  \"302614.jpg\": \"Reptilia/Emydoidea blandingii/f34a598b6a80f1f31b837da60d7fbeed.jpg\",\n  \"302615.jpg\": \"Reptilia/Emydoidea blandingii/b03c6d882ef4b7904614200d222c825f.jpg\",\n  \"302617.jpg\": \"Reptilia/Emydoidea blandingii/d43eac815348d1d3397868daeb6f35e8.jpg\",\n  \"302618.jpg\": \"Reptilia/Emydoidea blandingii/2e8df0a5d7c740207bad33da0d12c75a.jpg\",\n  \"302619.jpg\": \"Reptilia/Emydoidea blandingii/3e1f016161df463f2ee7b557d42d13c6.jpg\",\n  \"302620.jpg\": \"Reptilia/Emydoidea blandingii/7580cc6a52eaadfaeff46cc007f96c17.jpg\",\n  \"302622.jpg\": \"Reptilia/Emydoidea blandingii/45c03092e0b1796ef0a078d1a6e1efdb.jpg\",\n  \"302625.jpg\": \"Reptilia/Emydoidea blandingii/d9aac0c746dd0cd302d46c2ab0b75ba5.jpg\",\n  \"302627.jpg\": \"Reptilia/Emydoidea blandingii/74e3404dd83ce262da78ff4dc5c2415a.jpg\",\n  \"302628.jpg\": \"Reptilia/Emydoidea blandingii/5528b4f80d0c0d8e8425e908f7506e34.jpg\",\n  \"302630.jpg\": \"Reptilia/Emydoidea blandingii/2e71846962ff35579ae61446c7f4700b.jpg\",\n  \"302631.jpg\": \"Reptilia/Emydoidea blandingii/1761d221aea6af41c1f071012058d44c.jpg\",\n  \"302632.jpg\": \"Reptilia/Emydoidea blandingii/ce2c318f7869e602af6b8ef90b795def.jpg\",\n  \"30264.jpg\": \"Insecta/Vanessa itea/b61aee52e9afc7b4d84514503133bd69.jpg\",\n  \"302640.jpg\": \"Insecta/Coenonympha arcania/fce1920464fa7737aeb57cde66001164.jpg\",\n  \"302643.jpg\": \"Insecta/Coenonympha arcania/586939b84222294dce2574c5f76e7637.jpg\",\n  \"302648.jpg\": \"Insecta/Coenonympha arcania/f7a435ed362f7175a10d1469d68c9cd7.jpg\",\n  \"302649.jpg\": \"Insecta/Coenonympha arcania/d691824337b492f7759936f06dc3adba.jpg\",\n  \"30278.jpg\": \"Insecta/Vanessa itea/17481f71ecae874049e3c12d1bc50087.jpg\",\n  \"30280.jpg\": \"Insecta/Vanessa itea/5528d9d42decd02b931674080fa96b60.jpg\",\n  \"30288.jpg\": \"Insecta/Vanessa itea/829f6351eba45063580589c4f88741b0.jpg\",\n  \"302889.jpg\": \"Aves/Calidris minutilla/5f7a03088efe2b2425f7c6d122fd79db.jpg\",\n  \"302892.jpg\": \"Aves/Calidris minutilla/2093377e6d7293792bbee5a0962646e1.jpg\",\n  \"30290.jpg\": \"Insecta/Vanessa itea/3178e36793b7a16a90386a075c98326c.jpg\",\n  \"302907.jpg\": \"Aves/Calidris minutilla/983cde8780f316d32f5530dc32868a94.jpg\",\n  \"302911.jpg\": \"Aves/Calidris minutilla/7f6e1dcbe75957c6e52e196e3bcdc601.jpg\",\n  \"302912.jpg\": \"Aves/Calidris minutilla/5a5abca10c3ff93dddec6037797ef41d.jpg\",\n  \"302936.jpg\": \"Aves/Calidris minutilla/270fe607343473c334278f7539a1a188.jpg\",\n  \"30294.jpg\": \"Insecta/Vanessa itea/196516cc2df62e867924545861385986.jpg\",\n  \"302964.jpg\": \"Aves/Calidris minutilla/79772862d828812b7871b42db3f933a7.jpg\",\n  \"302976.jpg\": \"Aves/Calidris minutilla/a85189277f60e81d53d187b9fd8cd493.jpg\",\n  \"30298.jpg\": \"Insecta/Vanessa itea/736ef19cca9cc5d6d395325bdd137dc9.jpg\",\n  \"302996.jpg\": \"Aves/Calidris minutilla/8952d6ae64a5fd269af71ea6f260d30b.jpg\",\n  \"303040.jpg\": \"Aves/Calidris minutilla/989612d311764ee3aa9a47a67a32f156.jpg\",\n  \"303083.jpg\": \"Aves/Calidris minutilla/42b644de6a3016e6154c3e4715e2d36b.jpg\",\n  \"303084.jpg\": \"Aves/Calidris minutilla/64a4608bc779c7375caead4acb352606.jpg\",\n  \"303167.jpg\": \"Aves/Calidris minutilla/1aaba0904f1c7961080410b2e1198766.jpg\",\n  \"303191.jpg\": \"Aves/Calidris minutilla/87c3b982f2fe176291853aef2e7c9a25.jpg\",\n  \"3032.jpg\": \"Aves/Picoides nuttallii/acfad8f4e146921f4f84d4536b1d1f6a.jpg\",\n  \"3033.jpg\": \"Aves/Picoides nuttallii/67eb472fd557318e296391497d224222.jpg\",\n  \"303418.jpg\": \"Reptilia/Aspidoscelis sexlineata sexlineata/4c241a02b5bda8e6c2bb1eff163f8934.jpg\",\n  \"303426.jpg\": \"Reptilia/Aspidoscelis sexlineata sexlineata/baed9a6acc5a46ac2fd714dec1248a7a.jpg\",\n  \"303427.jpg\": \"Reptilia/Aspidoscelis sexlineata sexlineata/a90de3a6a8f4d5bc6bd7c3b3fca44d97.jpg\",\n  \"303428.jpg\": \"Reptilia/Aspidoscelis sexlineata sexlineata/19b7d9e14bef12b236f0ea1222e18587.jpg\",\n  \"303430.jpg\": \"Aves/Phalacrocorax penicillatus/c61051e1c836d940892bb6e48b92de54.jpg\",\n  \"303435.jpg\": \"Aves/Phalacrocorax penicillatus/ba0b21fd46ed063b5259a2406b4fbd8b.jpg\",\n  \"303436.jpg\": \"Aves/Phalacrocorax penicillatus/81fefa5a13dbed1f8c7f324027e9177b.jpg\",\n  \"303437.jpg\": \"Aves/Phalacrocorax penicillatus/c77ec1a6c1d186e35c0ec3d6e0068cbe.jpg\",\n  \"303439.jpg\": \"Aves/Phalacrocorax penicillatus/078df24b0f072337c4196b431ecb2135.jpg\",\n  \"303447.jpg\": \"Aves/Phalacrocorax penicillatus/a7b4e12e0c33dd5912be9c566ff82a70.jpg\",\n  \"303449.jpg\": \"Aves/Phalacrocorax penicillatus/3dfee15bed84185d575e6fb4b36f889b.jpg\",\n  \"303453.jpg\": \"Aves/Phalacrocorax penicillatus/6156883b5604e73609a490d5364bbdc7.jpg\",\n  \"303454.jpg\": \"Aves/Phalacrocorax penicillatus/2b045954bbbd37686296c8959e8333a4.jpg\",\n  \"303461.jpg\": \"Aves/Phalacrocorax penicillatus/da8e9281aa645ecfe7c37347707f6198.jpg\",\n  \"303467.jpg\": \"Aves/Phalacrocorax penicillatus/133442a70805c2da9290f83817e816dc.jpg\",\n  \"303514.jpg\": \"Aves/Phalacrocorax penicillatus/9ad161ae934355a8ab5c4ae2076e3000.jpg\",\n  \"30352.jpg\": \"Aves/Ninox novaeseelandiae novaeseelandiae/0ebf0f0ce1f1ce41a18c414a8537327b.jpg\",\n  \"303524.jpg\": \"Aves/Phalacrocorax penicillatus/eb346e0bf187f9f7559cce4313fcc330.jpg\",\n  \"303547.jpg\": \"Aves/Phalacrocorax penicillatus/c76f108b59c5c7185bb7a1f3c5cb6168.jpg\",\n  \"303549.jpg\": \"Aves/Phalacrocorax penicillatus/9624269126dac0269824f25b8b9474f9.jpg\",\n  \"303551.jpg\": \"Aves/Phalacrocorax penicillatus/fdcb77aaff4aa73bc4bb8b3a46742058.jpg\",\n  \"303574.jpg\": \"Aves/Vireo olivaceus/d4579ee133b317f163762c985348512a.jpg\",\n  \"30358.jpg\": \"Aves/Ninox novaeseelandiae novaeseelandiae/c284eae513ce8f323088b00d8e4192e6.jpg\",\n  \"303581.jpg\": \"Aves/Vireo olivaceus/21d4ad95e2d2aeb544eb21cd67c08dfa.jpg\",\n  \"303584.jpg\": \"Aves/Vireo olivaceus/f51e2d631830975f616e6ce57eb6b859.jpg\",\n  \"303585.jpg\": \"Aves/Vireo olivaceus/6df0bd0bb28b1589c5cf3a6698a890a7.jpg\",\n  \"303592.jpg\": \"Aves/Vireo olivaceus/29f4efe8d1597c7759e272c8bcffc49f.jpg\",\n  \"303600.jpg\": \"Aves/Vireo olivaceus/64bd19ba76dc625219bdb95f7e30f60d.jpg\",\n  \"303621.jpg\": \"Aves/Vireo olivaceus/90ac9de647d8f1c773864f4c0380a76e.jpg\",\n  \"303622.jpg\": \"Aves/Vireo olivaceus/c2e920d3ce0748613f7f625fb9eb75a4.jpg\",\n  \"303625.jpg\": \"Aves/Vireo olivaceus/7a2ea2feb482d53db86d5373dea2e434.jpg\",\n  \"303633.jpg\": \"Aves/Vireo olivaceus/9323bb685703db89b4e7099b17c125ee.jpg\",\n  \"303635.jpg\": \"Aves/Vireo olivaceus/d03adc02b285379d8cbaaf2d02d43773.jpg\",\n  \"303636.jpg\": \"Aves/Vireo olivaceus/bc6794e0cd35e63684d8a2f4aa028b6a.jpg\",\n  \"303638.jpg\": \"Aves/Vireo olivaceus/7dfebaa9783e125275d7094aecca5393.jpg\",\n  \"30365.jpg\": \"Aves/Ninox novaeseelandiae novaeseelandiae/2f64fbca2fb1bc510e75f3dc87fcef16.jpg\",\n  \"303658.jpg\": \"Aves/Vireo olivaceus/b9da673d5d1d185bd5bd7e32d271c024.jpg\",\n  \"303659.jpg\": \"Aves/Vireo olivaceus/ee4a036a3a78371ed6fa01121090b9f1.jpg\",\n  \"303660.jpg\": \"Aves/Vireo olivaceus/0f8c1a256a7a0e95ad764b7c044d8f48.jpg\",\n  \"30367.jpg\": \"Aves/Ninox novaeseelandiae novaeseelandiae/eb146ae562eb43ff25f9dbc93b9d169a.jpg\",\n  \"303677.jpg\": \"Aves/Vireo olivaceus/82b6a505dc28c6dc8a7ccc8cc5a83dde.jpg\",\n  \"303699.jpg\": \"Aves/Vireo olivaceus/104d437338ce5b267deee9e23a331c11.jpg\",\n  \"303700.jpg\": \"Aves/Vireo olivaceus/63baed24df7a763a8ebd93d1b1671993.jpg\",\n  \"303707.jpg\": \"Aves/Vireo olivaceus/3fd8bfcba3a54137f3dce8cd7ce56bf4.jpg\",\n  \"303832.jpg\": \"Aves/Icteria virens/d220916bbdf654f0903d5c2a5c6df789.jpg\",\n  \"303833.jpg\": \"Aves/Icteria virens/56ea8b444101eef4735679b7c526f03e.jpg\",\n  \"303835.jpg\": \"Aves/Icteria virens/ab6e4e35bad233fe9d4412d4d503ef46.jpg\",\n  \"303838.jpg\": \"Aves/Icteria virens/7723e6124fb368b86e9f4be028c36e42.jpg\",\n  \"303842.jpg\": \"Aves/Icteria virens/c4297c646cdfd5cd0af390ca1ddfc1c3.jpg\",\n  \"303843.jpg\": \"Aves/Icteria virens/9d0dbb6c4124a8d7e2a19811ddb997a1.jpg\",\n  \"303847.jpg\": \"Aves/Icteria virens/0c8a2b4c988ff5a4b73de742fff2f79f.jpg\",\n  \"303853.jpg\": \"Aves/Icteria virens/faebf1ecd15c1c7a22fb30cc2a3b84d4.jpg\",\n  \"303857.jpg\": \"Aves/Icteria virens/67056f11a9983a73e375f1d3ae60bdee.jpg\",\n  \"303863.jpg\": \"Aves/Icteria virens/5bd8f55ffcc702e119179993092faee4.jpg\",\n  \"303868.jpg\": \"Aves/Icteria virens/f0b3260fdb1aa94305b993e7b3dd013e.jpg\",\n  \"303869.jpg\": \"Aves/Icteria virens/4d6d02607a82b9a89c65f87c401c8117.jpg\",\n  \"303881.jpg\": \"Aves/Icteria virens/1c3ffb8531ae0cb0933201f3f421dc7d.jpg\",\n  \"303884.jpg\": \"Aves/Icteria virens/f7a3c3af9e89837243938c9ec492e655.jpg\",\n  \"303898.jpg\": \"Aves/Icteria virens/9364271bc4fd856f4f9575ef313e83d0.jpg\",\n  \"303900.jpg\": \"Aves/Icteria virens/adda03c0b76e56c67f6a280989014118.jpg\",\n  \"303903.jpg\": \"Aves/Icteria virens/bfc8e20f205ce12210a73d1b4ffae28c.jpg\",\n  \"303904.jpg\": \"Aves/Icteria virens/d521c5ceeea47c7dd9dd072f63f4df88.jpg\",\n  \"303905.jpg\": \"Aves/Icteria virens/74a970a26c64c8b2b04a14a74563412c.jpg\",\n  \"3040.jpg\": \"Aves/Picoides nuttallii/710870105260339da98f697ba23c8817.jpg\",\n  \"304218.jpg\": \"Mammalia/Nasua narica/c554ae67a73a968b160b2434da110b98.jpg\",\n  \"304220.jpg\": \"Mammalia/Nasua narica/a3fc5f98e8b709877ac2609467a85b47.jpg\",\n  \"304235.jpg\": \"Mammalia/Nasua narica/ce41b1ee687b91d8f130abb9811dc3fe.jpg\",\n  \"304243.jpg\": \"Mammalia/Nasua narica/d90d2cc36214d2616b8fbc7fa5d07272.jpg\",\n  \"304256.jpg\": \"Mammalia/Nasua narica/2484d13d0891e669845f5242c8f6c526.jpg\",\n  \"304262.jpg\": \"Mammalia/Nasua narica/fbd9ac936149cc1d21f8f88edb9faa2f.jpg\",\n  \"304276.jpg\": \"Mammalia/Nasua narica/f4a1793e816e2e8baefccb8c4d7f74aa.jpg\",\n  \"304277.jpg\": \"Mammalia/Nasua narica/68ad835dfc366e3e3ee0a97b8efcc674.jpg\",\n  \"304279.jpg\": \"Mammalia/Nasua narica/c94eb303ed99ed9763c048c2b9ad11fd.jpg\",\n  \"304302.jpg\": \"Mammalia/Nasua narica/c5b1ba8cc8b8f071c58e4111b102ca36.jpg\",\n  \"304308.jpg\": \"Mammalia/Nasua narica/ac9cf6d65f5ac5f3c3cc8bcca98edf42.jpg\",\n  \"304311.jpg\": \"Mammalia/Nasua narica/b9ca6ec371cd7868ac675147d8b8045d.jpg\",\n  \"304319.jpg\": \"Mammalia/Nasua narica/0de842d29e617a89f7a9ea6253a882b4.jpg\",\n  \"304326.jpg\": \"Mammalia/Nasua narica/8233a99f675bc6d36cf57c4d51ea1f88.jpg\",\n  \"304346.jpg\": \"Mammalia/Nasua narica/45435cf30de50096560b9e8027141606.jpg\",\n  \"304347.jpg\": \"Mammalia/Nasua narica/518deaaca71409c17e2cd2d98cccc952.jpg\",\n  \"304349.jpg\": \"Mammalia/Nasua narica/e92e190780b7ff9d069c76b4d94c30d9.jpg\",\n  \"304358.jpg\": \"Aves/Accipiter gentilis/9d128bfa6267f21f4f94b03a7286271a.jpg\",\n  \"304363.jpg\": \"Aves/Accipiter gentilis/cec1d9b4cdaf8142eb480064d27105a1.jpg\",\n  \"304375.jpg\": \"Aves/Accipiter gentilis/edce9b328c4f47fde7c931c8083c65f7.jpg\",\n  \"304378.jpg\": \"Aves/Accipiter gentilis/0b9a8f017cfb895e32b813ece9ac92bc.jpg\",\n  \"304384.jpg\": \"Aves/Accipiter gentilis/de18c80b017f2391529201a3acfef659.jpg\",\n  \"304387.jpg\": \"Aves/Vermivora cyanoptera/313e3b55e40520e8b092c81bccc004c7.jpg\",\n  \"304389.jpg\": \"Aves/Vermivora cyanoptera/336b5ab0a356cc627f268cd4c68545f1.jpg\",\n  \"304390.jpg\": \"Aves/Vermivora cyanoptera/a860e57b3fdbae90182f18c33e8dad7e.jpg\",\n  \"304391.jpg\": \"Aves/Vermivora cyanoptera/223fe418347dbfc0f61dba4605e8325e.jpg\",\n  \"304393.jpg\": \"Aves/Vermivora cyanoptera/36a51a2497dfd6c5e2d82cbd93f90c80.jpg\",\n  \"304397.jpg\": \"Aves/Vermivora cyanoptera/542823128ab731ecf07d894adb2cd045.jpg\",\n  \"304398.jpg\": \"Aves/Vermivora cyanoptera/c694b06bb287cb541945c82e3192822e.jpg\",\n  \"304399.jpg\": \"Aves/Vermivora cyanoptera/fd57357f2aea5ee3bff362f419a641d7.jpg\",\n  \"304400.jpg\": \"Aves/Vermivora cyanoptera/bbba7d16ad1473635ab432e689fc80c9.jpg\",\n  \"304406.jpg\": \"Aves/Vermivora cyanoptera/d88e7d5ff4e182a7ea5125a566c92f37.jpg\",\n  \"304407.jpg\": \"Aves/Vermivora cyanoptera/ea2a5a0b9790c59d4983b1a37b286dab.jpg\",\n  \"304408.jpg\": \"Aves/Vermivora cyanoptera/206493ab294e3ded4c7d385ebf079fe6.jpg\",\n  \"304409.jpg\": \"Aves/Vermivora cyanoptera/9ff316b4ebac190ec74490f05bb84937.jpg\",\n  \"304410.jpg\": \"Aves/Vermivora cyanoptera/0442be03ddc6b718bdc9f49856144aee.jpg\",\n  \"304411.jpg\": \"Aves/Vermivora cyanoptera/47728717cccbff2b5b6ce17e481c49a6.jpg\",\n  \"304412.jpg\": \"Aves/Vermivora cyanoptera/75e7939f88037cc78cd9af1d1b9750e9.jpg\",\n  \"304416.jpg\": \"Aves/Vermivora cyanoptera/746e3c276ed3e072d3d39df8524e7db3.jpg\",\n  \"304417.jpg\": \"Aves/Vermivora cyanoptera/823eda6b23ded61b73a51af9a73c7f75.jpg\",\n  \"304418.jpg\": \"Aves/Vermivora cyanoptera/cd47645869f91eb37d3bfec361e2fb47.jpg\",\n  \"304419.jpg\": \"Aves/Vermivora cyanoptera/e1cd1d27a598c0459395a4ccce66a2ec.jpg\",\n  \"304420.jpg\": \"Aves/Vermivora cyanoptera/c7b5ea63bb2fa623ed3d6abb9d1096b0.jpg\",\n  \"304421.jpg\": \"Aves/Vermivora cyanoptera/0ac0ea40ec7049b88d293d3feb9eacf7.jpg\",\n  \"304426.jpg\": \"Aves/Setophaga striata/c67c23fac4bd2a295c1a23772e3556a9.jpg\",\n  \"304428.jpg\": \"Aves/Setophaga striata/81583500684d936c39a22f19d4d6fd09.jpg\",\n  \"304429.jpg\": \"Aves/Setophaga striata/8c6b25e03ba38acc3b9f30dc6230be9e.jpg\",\n  \"304430.jpg\": \"Aves/Setophaga striata/71d53f0fe3880b77bdb16232c3ecef72.jpg\",\n  \"304431.jpg\": \"Aves/Setophaga striata/929351ea0aefc779e9e4c2f5686b1603.jpg\",\n  \"304432.jpg\": \"Aves/Setophaga striata/c9dd260e3d27cf400a1c4cefe349a5de.jpg\",\n  \"304433.jpg\": \"Aves/Setophaga striata/a8aa43d0286d39f8427475e26d263520.jpg\",\n  \"304434.jpg\": \"Aves/Setophaga striata/b369defc71ed4d2bd1b88993fbd2c252.jpg\",\n  \"304437.jpg\": \"Aves/Setophaga striata/1ba9e8384e6e042f3d1693856c7893a1.jpg\",\n  \"304438.jpg\": \"Aves/Setophaga striata/5eaaa24e6c570b6cb4a2a71667aea128.jpg\",\n  \"304439.jpg\": \"Aves/Setophaga striata/2fbded02e635594e188cda54ce80999d.jpg\",\n  \"304440.jpg\": \"Aves/Setophaga striata/e9f919c42749766679ed241253b808b7.jpg\",\n  \"304444.jpg\": \"Aves/Setophaga striata/53145c5ffad9d2b7d3c03ae418ba5e34.jpg\",\n  \"304445.jpg\": \"Aves/Setophaga striata/f9941634fe0903521bd9f5bf8938e140.jpg\",\n  \"304453.jpg\": \"Aves/Setophaga striata/6178897df389ac381ba751e7760cf389.jpg\",\n  \"304459.jpg\": \"Aves/Setophaga striata/bfc9bf1b9b0abc78e5d9e82f4d30982f.jpg\",\n  \"304460.jpg\": \"Aves/Setophaga striata/1d4b46510d0da0dc84ac2120acf2eedf.jpg\",\n  \"304461.jpg\": \"Aves/Setophaga striata/afa890d2db4d828e4b7cef60eb9712fc.jpg\",\n  \"304469.jpg\": \"Aves/Setophaga striata/32e99b6d1e37a047b5cf5fd469633989.jpg\",\n  \"304484.jpg\": \"Aves/Setophaga striata/1f279ea352b27f03a853ddfc8ae46f56.jpg\",\n  \"304503.jpg\": \"Mollusca/Otala lactea/e652faa3d1685e9333af589b89df136b.jpg\",\n  \"304504.jpg\": \"Mollusca/Otala lactea/157db1132f2fb430fa299f51e0005917.jpg\",\n  \"304508.jpg\": \"Mollusca/Otala lactea/058325b900834a6231c69f04f574a874.jpg\",\n  \"304527.jpg\": \"Mollusca/Otala lactea/a515d4f9499adaf68f0e40668b07adac.jpg\",\n  \"304528.jpg\": \"Mollusca/Otala lactea/9e6db36f90e1f5abc2da4d7d6e4b55b6.jpg\",\n  \"304568.jpg\": \"Mollusca/Otala lactea/9b235c2b7e6af6bf12913b36af2c67dc.jpg\",\n  \"304574.jpg\": \"Mollusca/Otala lactea/0c13f99efba7a9ba63525b4d0657624b.jpg\",\n  \"304577.jpg\": \"Mollusca/Otala lactea/f8cdf12733ba0620fc618d7b6ff7da8d.jpg\",\n  \"304581.jpg\": \"Mollusca/Otala lactea/02daa20f2ea6a0e66a25a7dba406ce57.jpg\",\n  \"304584.jpg\": \"Mollusca/Otala lactea/4672696b36b55b13b16065c0cb44a049.jpg\",\n  \"304585.jpg\": \"Mollusca/Otala lactea/593b9748a959c8345ec31f9b2ad51ac3.jpg\",\n  \"304586.jpg\": \"Mollusca/Otala lactea/027c73bdd1fd8b30f932f45904a5c86e.jpg\",\n  \"304593.jpg\": \"Mollusca/Otala lactea/b9fea0775701a163e3cdafa8f2ce61d1.jpg\",\n  \"304606.jpg\": \"Mollusca/Otala lactea/fb26e8c3a792f6f55f1818d75f9176d3.jpg\",\n  \"304611.jpg\": \"Mollusca/Otala lactea/801cfc38ff8ebaf8d58dd0f0abfdca19.jpg\",\n  \"304616.jpg\": \"Mollusca/Otala lactea/07f1fde68494695521de1ed8c6d74cdc.jpg\",\n  \"304620.jpg\": \"Mollusca/Otala lactea/12f095825ebae48b99899df9ab28266d.jpg\",\n  \"304621.jpg\": \"Mollusca/Otala lactea/24be1eb66a43eda84e6cd0302b916080.jpg\",\n  \"304628.jpg\": \"Mollusca/Otala lactea/342aaf79097d6dfb701296d2b07dd420.jpg\",\n  \"304661.jpg\": \"Mollusca/Otala lactea/85845da34f46b69643d547c242916ae2.jpg\",\n  \"304665.jpg\": \"Mollusca/Otala lactea/e0bd97ae38ce89b5842176e5644d679f.jpg\",\n  \"304674.jpg\": \"Insecta/Patalene olyzonaria/624fa945f54767c67cb0b81bb4dc253f.jpg\",\n  \"304680.jpg\": \"Insecta/Patalene olyzonaria/7d69686e6e9f97c77f4428e1725ca561.jpg\",\n  \"304681.jpg\": \"Insecta/Patalene olyzonaria/7b874c3283fbc0a5bf8f4e75c26a116f.jpg\",\n  \"304686.jpg\": \"Insecta/Patalene olyzonaria/7b6c65ff1915e3c5a64f5bfac58b2f19.jpg\",\n  \"304690.jpg\": \"Insecta/Patalene olyzonaria/93e063314be18b859c426711eb92a114.jpg\",\n  \"304691.jpg\": \"Insecta/Patalene olyzonaria/afa95633dd5298ff86435c72251963cb.jpg\",\n  \"304692.jpg\": \"Insecta/Patalene olyzonaria/d9121475cb64d21db4fa81420f8af49a.jpg\",\n  \"304699.jpg\": \"Aves/Haemorhous purpureus/fa01cd426501e601b87543f54e33b32c.jpg\",\n  \"304717.jpg\": \"Aves/Haemorhous purpureus/a1a89f3b9c6ea7e7dfdc06fbde04adad.jpg\",\n  \"304719.jpg\": \"Aves/Haemorhous purpureus/def7747b1b79fd1896a8623eaea4562f.jpg\",\n  \"304726.jpg\": \"Aves/Haemorhous purpureus/a22e352d1c5ce2e862fd6cc724f006d3.jpg\",\n  \"304732.jpg\": \"Aves/Haemorhous purpureus/8439df2f0f06868914d6e8deb17a62a0.jpg\",\n  \"304737.jpg\": \"Aves/Haemorhous purpureus/451af6b4e5814307950437f0e91b9ec8.jpg\",\n  \"304740.jpg\": \"Aves/Haemorhous purpureus/3bb2d7cf44fee317ab1ee4f0684d53da.jpg\",\n  \"304741.jpg\": \"Aves/Haemorhous purpureus/74478c3c9ab0433b5802671372640a02.jpg\",\n  \"304745.jpg\": \"Aves/Haemorhous purpureus/4d8c7a4e3e49371edd4fbeb1ffa53361.jpg\",\n  \"304767.jpg\": \"Aves/Haemorhous purpureus/1547d4da316b051e176f048680da6fe5.jpg\",\n  \"304772.jpg\": \"Aves/Haemorhous purpureus/1439ee0c7747141257ae935a047a20f9.jpg\",\n  \"304803.jpg\": \"Aves/Haemorhous purpureus/e3e60aa74f575ee9d0c9100571e233ad.jpg\",\n  \"304805.jpg\": \"Aves/Haemorhous purpureus/a3933d7519e6817f55746650ed912574.jpg\",\n  \"304814.jpg\": \"Aves/Haemorhous purpureus/3ea1a1f4284ef3d3bac799f1781d3936.jpg\",\n  \"304815.jpg\": \"Aves/Haemorhous purpureus/992226deff002a65165f8d8f1561cda6.jpg\",\n  \"304816.jpg\": \"Aves/Haemorhous purpureus/27215e58dd9150cc1d6de2c42ca8520e.jpg\",\n  \"304820.jpg\": \"Aves/Haemorhous purpureus/e65beea4c70d7acd072ed6b34e74653a.jpg\",\n  \"304841.jpg\": \"Aves/Haemorhous purpureus/45e238d65dd7b721344ae2fc6444b8ab.jpg\",\n  \"304843.jpg\": \"Aves/Haemorhous purpureus/0b1f79629ddd8fe1bc32c71eea6c9ced.jpg\",\n  \"304898.jpg\": \"Actinopterygii/Abudefduf sordidus/6a7577fa2a555ac7b8f7fdae8ddce431.jpg\",\n  \"304903.jpg\": \"Actinopterygii/Abudefduf sordidus/106223c4dc2ce874a0382e6f54265bb9.jpg\",\n  \"304938.jpg\": \"Aves/Tyrannus couchii/cadbb3bec16cf1764b93dffbea45544b.jpg\",\n  \"304951.jpg\": \"Aves/Tyrannus couchii/ec406d9178736b04d5fa827bb5954367.jpg\",\n  \"304952.jpg\": \"Aves/Tyrannus couchii/2aac1ef418eaebbdc4ab0d0ff63cbad9.jpg\",\n  \"304955.jpg\": \"Aves/Tyrannus couchii/6ddc1d7111dc4985c2eda12eed3e30e0.jpg\",\n  \"304956.jpg\": \"Aves/Tyrannus couchii/6b1b46c3dc81ce3c58fd0dfb804dd642.jpg\",\n  \"304957.jpg\": \"Aves/Tyrannus couchii/84216d105a4c5864a9cacde5366c7c74.jpg\",\n  \"304963.jpg\": \"Aves/Tyrannus couchii/722e2d2b74c60c5042569b0b34f45bdc.jpg\",\n  \"304964.jpg\": \"Aves/Tyrannus couchii/c54f179c7dd4f5c0b75f70fa7989193c.jpg\",\n  \"304965.jpg\": \"Aves/Tyrannus couchii/d2eef4fcb47daa365cdfab8d9ff9135f.jpg\",\n  \"305083.jpg\": \"Reptilia/Emys orbicularis/383ae896c1a1b12fac287b87fdc2c879.jpg\",\n  \"305086.jpg\": \"Reptilia/Emys orbicularis/742ce58bbeeabf3f7dc317910daee3d0.jpg\",\n  \"305094.jpg\": \"Mammalia/Blarina brevicauda/51ab6aa5089c2c7c1a9a5466c12246fa.jpg\",\n  \"305106.jpg\": \"Mammalia/Blarina brevicauda/0d3790de965d6b17810d8c3a0596d645.jpg\",\n  \"305160.jpg\": \"Insecta/Boisea rubrolineata/e75e81d6ef70a2ffc3fc8e8f9dfe3607.jpg\",\n  \"305161.jpg\": \"Insecta/Boisea rubrolineata/7fc727f9db487d232c74f36c86d5f8d5.jpg\",\n  \"305164.jpg\": \"Insecta/Boisea rubrolineata/b1af334cf2fb78cca9b0ec6d77f7f3c0.jpg\",\n  \"305165.jpg\": \"Insecta/Boisea rubrolineata/1ac175f8d5aa1b0cb58b2788c2d53916.jpg\",\n  \"305167.jpg\": \"Insecta/Boisea rubrolineata/b702ad1662728c554be9085fe7e7b799.jpg\",\n  \"305171.jpg\": \"Insecta/Boisea rubrolineata/678a940a248c6de040ccf07618d44fde.jpg\",\n  \"305173.jpg\": \"Insecta/Boisea rubrolineata/5b65e2606e8935e91691f2716e1ce690.jpg\",\n  \"305175.jpg\": \"Insecta/Boisea rubrolineata/dd9c2c8e8c5918330e1569bf0613f499.jpg\",\n  \"305182.jpg\": \"Insecta/Boisea rubrolineata/eb8c2cdbddeaf5a676ea65d65938ac1b.jpg\",\n  \"305185.jpg\": \"Insecta/Boisea rubrolineata/df8dfe3be8512311b24bbeead17133e0.jpg\",\n  \"305186.jpg\": \"Insecta/Boisea rubrolineata/b393599a3d89d4b8949b5887b15832f8.jpg\",\n  \"305188.jpg\": \"Insecta/Boisea rubrolineata/746deef173487e2dc9db5c5a3cf8fa96.jpg\",\n  \"305189.jpg\": \"Insecta/Boisea rubrolineata/4c9f8d96d1dcd006687e28611280090d.jpg\",\n  \"305192.jpg\": \"Insecta/Boisea rubrolineata/e4172dcc518708d865aed072b1fe040d.jpg\",\n  \"305197.jpg\": \"Insecta/Boisea rubrolineata/cbe10dd925eafef5bbc27e1d47a17e39.jpg\",\n  \"305198.jpg\": \"Insecta/Boisea rubrolineata/4bd168dc30c7219991004ed72969bbec.jpg\",\n  \"305199.jpg\": \"Insecta/Boisea rubrolineata/8a55766572c8667f3542dd69473941d8.jpg\",\n  \"305202.jpg\": \"Insecta/Boisea rubrolineata/13f6de008b8f8145a0f86ba0aad08236.jpg\",\n  \"305203.jpg\": \"Insecta/Boisea rubrolineata/9ef55ba00b7b6e5a25cfe319386ffb23.jpg\",\n  \"305222.jpg\": \"Aves/Sitta pusilla/ebeddab243d217d9cd8afacd82a541f3.jpg\",\n  \"305235.jpg\": \"Aves/Sitta pusilla/f57f9261cdfbdbfc79e4b6c6db6196d2.jpg\",\n  \"305236.jpg\": \"Aves/Sitta pusilla/8721bbd298665e3f80db7de4a97bf5c2.jpg\",\n  \"305239.jpg\": \"Aves/Sitta pusilla/f88b108ffa5b11fde6115dc1fd823d4a.jpg\",\n  \"305247.jpg\": \"Aves/Sitta pusilla/04457f11bfe2bd99da031cde86103007.jpg\",\n  \"305249.jpg\": \"Aves/Sitta pusilla/677a2031801f39383d3deadb6a3f81e5.jpg\",\n  \"305333.jpg\": \"Reptilia/Elgaria multicarinata/93dee837b36cecdba95f3fd178d61013.jpg\",\n  \"305385.jpg\": \"Reptilia/Elgaria multicarinata/976f9451bb2aaa7440614172432b056d.jpg\",\n  \"305397.jpg\": \"Reptilia/Elgaria multicarinata/aa4343f59d828017342e368f9486b540.jpg\",\n  \"305410.jpg\": \"Reptilia/Elgaria multicarinata/de1ce42c73e293cbb568cdfe8ad52286.jpg\",\n  \"305432.jpg\": \"Reptilia/Elgaria multicarinata/e40174c11def3e79d2824ae1fe5c69af.jpg\",\n  \"305446.jpg\": \"Reptilia/Elgaria multicarinata/3f2e4f4ca1e58e01e9f399b623a340d9.jpg\",\n  \"305462.jpg\": \"Reptilia/Elgaria multicarinata/5466d498fad5d1fdba25c7bd38bea64b.jpg\",\n  \"305465.jpg\": \"Reptilia/Elgaria multicarinata/db7289ffd4f042ed0528d14cb643bebe.jpg\",\n  \"305484.jpg\": \"Reptilia/Elgaria multicarinata/3cb8e56517410c9e8420d18fcbb9da1a.jpg\",\n  \"30550.jpg\": \"Insecta/Hypolimnas bolina/6acd76b71e8568dd8bdd40e7f8b0489a.jpg\",\n  \"305503.jpg\": \"Reptilia/Elgaria multicarinata/4a1823f204ffa9ef8dec85d9939f6868.jpg\",\n  \"305507.jpg\": \"Reptilia/Elgaria multicarinata/2d09bc72628a41629a0fa65d0c08e3cd.jpg\",\n  \"30551.jpg\": \"Insecta/Hypolimnas bolina/60d4803b0fd8818e59f316f5e7d73c91.jpg\",\n  \"305513.jpg\": \"Reptilia/Elgaria multicarinata/da7e0f8467c3c61c5f48240075c66bf0.jpg\",\n  \"305516.jpg\": \"Reptilia/Elgaria multicarinata/7b6d2c0b861ca5b1e2a3fc7cc5ba27b7.jpg\",\n  \"305538.jpg\": \"Reptilia/Elgaria multicarinata/2aeb8a7fba8e4a0613124b9ad63f6b46.jpg\",\n  \"305548.jpg\": \"Reptilia/Elgaria multicarinata/c1b96b315edaa20281a71ba5ef7af3e2.jpg\",\n  \"305572.jpg\": \"Reptilia/Elgaria multicarinata/8322aefb40de58c1c2d0ef2c1dafbf57.jpg\",\n  \"305580.jpg\": \"Reptilia/Elgaria multicarinata/c87707fdb3d666b62635fce9e4fcd475.jpg\",\n  \"305581.jpg\": \"Reptilia/Elgaria multicarinata/bfbd7aa4c1b349748d10d99024218933.jpg\",\n  \"305603.jpg\": \"Reptilia/Elgaria multicarinata/5af682e072fd172c6e135ac40934af32.jpg\",\n  \"305624.jpg\": \"Reptilia/Elgaria multicarinata/7b43e661b81fc8037da379a3447ccfe6.jpg\",\n  \"30563.jpg\": \"Insecta/Hypolimnas bolina/9f3b5fac209ea386bad706a61f4c92b4.jpg\",\n  \"305632.jpg\": \"Reptilia/Elgaria multicarinata/800265eb93d224c5a9c7115f53b8dc51.jpg\",\n  \"305633.jpg\": \"Reptilia/Elgaria multicarinata/72cd5c89c2db411a950fb29ea702ae1b.jpg\",\n  \"30565.jpg\": \"Insecta/Hypolimnas bolina/98aeb9f4fb9ba21e3799bc35aa9750f4.jpg\",\n  \"30567.jpg\": \"Insecta/Hypolimnas bolina/45ca1c66df9133f642c8cbdd93ace661.jpg\",\n  \"30568.jpg\": \"Insecta/Hypolimnas bolina/16ba9628e68ef633e1fded608f2c7e83.jpg\",\n  \"305708.jpg\": \"Mammalia/Capreolus capreolus/98e64cb440e52d154b5c9ef7f56d95dd.jpg\",\n  \"30571.jpg\": \"Aves/Megaceryle alcyon/11c29cf6378b2d4731cebeb492257918.jpg\",\n  \"305714.jpg\": \"Mammalia/Capreolus capreolus/d9d279163cc3f7e4f0a4db7dad03313f.jpg\",\n  \"30572.jpg\": \"Aves/Megaceryle alcyon/f455638cb7ec6799082758214c05cace.jpg\",\n  \"305733.jpg\": \"Mammalia/Capreolus capreolus/d5fec29ceafbeac8325cbe6d367bd574.jpg\",\n  \"305734.jpg\": \"Mammalia/Capreolus capreolus/271c3aeced1b9b618bc62f7659b27a16.jpg\",\n  \"305750.jpg\": \"Mammalia/Capreolus capreolus/af24bd59f633c16f2b91c480726fe634.jpg\",\n  \"305765.jpg\": \"Mammalia/Capreolus capreolus/b6877724dc954e97f748250c5c34aa90.jpg\",\n  \"305769.jpg\": \"Mammalia/Capreolus capreolus/ece0986a6ef671d66a6e40486a777f41.jpg\",\n  \"305789.jpg\": \"Mammalia/Capreolus capreolus/d6c346de38f0b930236ac2ff1288ffd7.jpg\",\n  \"305790.jpg\": \"Mammalia/Capreolus capreolus/588740ba987a3615ce4557c1de6f2e8f.jpg\",\n  \"305801.jpg\": \"Mammalia/Capreolus capreolus/a7f4f9ba68738af829d9f05da5502b2c.jpg\",\n  \"305803.jpg\": \"Mammalia/Capreolus capreolus/d21975276b2994f8fd8958e5463f8372.jpg\",\n  \"305809.jpg\": \"Mammalia/Capreolus capreolus/ef6aa7a48c5786e98e61b6372a2af351.jpg\",\n  \"305810.jpg\": \"Mammalia/Capreolus capreolus/b3db1aff82d8bfc4866595d9cc4cd5b0.jpg\",\n  \"305818.jpg\": \"Mammalia/Capreolus capreolus/bdc9f1b6731ecbe301898c05841f7d2e.jpg\",\n  \"305819.jpg\": \"Mammalia/Capreolus capreolus/c9efaabca6acdd8d598c58c887c7d9ec.jpg\",\n  \"305820.jpg\": \"Mammalia/Capreolus capreolus/00d65a65e8be9861cebcb5345de0ba66.jpg\",\n  \"305837.jpg\": \"Mammalia/Capreolus capreolus/95d73bdf2530f0bc595fe25745035a38.jpg\",\n  \"305839.jpg\": \"Mammalia/Capreolus capreolus/ab703b7206d68a1887f89540177c5341.jpg\",\n  \"30593.jpg\": \"Aves/Megaceryle alcyon/fc2db07b48f38e097d20289f9093ee5b.jpg\",\n  \"306010.jpg\": \"Mammalia/Lepus californicus/6fa5c46a380a7706de8e923955f370b9.jpg\",\n  \"306031.jpg\": \"Mammalia/Lepus californicus/c75bdd7e466259d1a3b17052c79064dc.jpg\",\n  \"306035.jpg\": \"Mammalia/Lepus californicus/67a5c8a0fc7560b07440cc5cebe94d11.jpg\",\n  \"306037.jpg\": \"Mammalia/Lepus californicus/3bcc0b9b9fab9ee5f4fbaa0e97d8dd48.jpg\",\n  \"306044.jpg\": \"Mammalia/Lepus californicus/c322d1d406ecf2711a9924209cb6e515.jpg\",\n  \"306049.jpg\": \"Mammalia/Lepus californicus/98faae8547def3c756fe4786761f705b.jpg\",\n  \"306053.jpg\": \"Mammalia/Lepus californicus/66fe6d9000e2ba96ca6b88088b00b7a7.jpg\",\n  \"306058.jpg\": \"Mammalia/Lepus californicus/f6fa3f715738720e9f948dca5ab7c6f1.jpg\",\n  \"306074.jpg\": \"Mammalia/Lepus californicus/d98cd4daac2c6476b3c4c7f338e351a8.jpg\",\n  \"306107.jpg\": \"Mammalia/Lepus californicus/1c1e540c32ca77226b18c86694a9b0be.jpg\",\n  \"306118.jpg\": \"Mammalia/Lepus californicus/4d557655899c476180598eed451c51f7.jpg\",\n  \"306129.jpg\": \"Mammalia/Lepus californicus/d22b535732753c53bd4fb58e0172bf84.jpg\",\n  \"306140.jpg\": \"Mammalia/Lepus californicus/7faafde70dd5da8336dfd5735a7e4682.jpg\",\n  \"306143.jpg\": \"Mammalia/Lepus californicus/2709d25d8b865c79eeb1f6e9c45cb59c.jpg\",\n  \"306148.jpg\": \"Mammalia/Lepus californicus/da8d6301aa70de246b7611420eb63eaa.jpg\",\n  \"306165.jpg\": \"Mammalia/Lepus californicus/5522c189b632d1a9d057078d1f6ac3ab.jpg\",\n  \"306173.jpg\": \"Mammalia/Lepus californicus/f67020cfda018d3a5dc706627e0765a0.jpg\",\n  \"306178.jpg\": \"Mammalia/Lepus californicus/882ad02fa8e0d591090754a284ad2152.jpg\",\n  \"306179.jpg\": \"Mammalia/Lepus californicus/7a69a3d043c3da28c0cd7597cab50afd.jpg\",\n  \"306185.jpg\": \"Mammalia/Lepus californicus/876cb6d2f327599517dc5583f2de5c88.jpg\",\n  \"306187.jpg\": \"Mammalia/Lepus californicus/82970fe4a6de3ae003006747fd4f6a3d.jpg\",\n  \"306219.jpg\": \"Mammalia/Lepus californicus/2504bc7396cf0fb131bccc23b632f737.jpg\",\n  \"306222.jpg\": \"Mammalia/Lepus californicus/33e6a2bc70e9d1652e168a14417dd040.jpg\",\n  \"306233.jpg\": \"Insecta/Libytheana carinenta/625c00b27323e1c1e654f857c0cdb99e.jpg\",\n  \"306236.jpg\": \"Insecta/Libytheana carinenta/8b825d7e85675739365dc1b2f930607b.jpg\",\n  \"306246.jpg\": \"Insecta/Libytheana carinenta/4562e93590b7640bd9232c6fe044d4a2.jpg\",\n  \"306264.jpg\": \"Insecta/Libytheana carinenta/fb0ca3fd2a6ed2bfc2b4dd0798aced32.jpg\",\n  \"306266.jpg\": \"Insecta/Libytheana carinenta/1f8716a76d0d2f5696ea675d49753353.jpg\",\n  \"306278.jpg\": \"Insecta/Libytheana carinenta/148fdc84dbe6f7a5302759983f22f7b9.jpg\",\n  \"306286.jpg\": \"Insecta/Libytheana carinenta/b4e99f90b94e3097bbe94a8102e21a0b.jpg\",\n  \"306318.jpg\": \"Insecta/Libytheana carinenta/de0878765cdfb6499f923fe8935c1928.jpg\",\n  \"306340.jpg\": \"Insecta/Libytheana carinenta/3c4f5f33fbb7850c0216ab1f0ef15432.jpg\",\n  \"306342.jpg\": \"Insecta/Libytheana carinenta/b47f0cc08a6da1c7658136b504ff0f2b.jpg\",\n  \"306343.jpg\": \"Insecta/Libytheana carinenta/b24f960af7dff2be0f2e9f415ae34bac.jpg\",\n  \"306345.jpg\": \"Insecta/Libytheana carinenta/76f6d8accc477bd1c7e6a316cf4ce906.jpg\",\n  \"306356.jpg\": \"Insecta/Libytheana carinenta/d72b0786214cac5617f87c75774c549b.jpg\",\n  \"306364.jpg\": \"Insecta/Libytheana carinenta/fb0dc31642f27478a9f0e48c2f315660.jpg\",\n  \"306374.jpg\": \"Insecta/Libytheana carinenta/192c79eced327886605dfdb8814a9ffd.jpg\",\n  \"306376.jpg\": \"Insecta/Libytheana carinenta/86adac3cf32a67eb968b92993f0abac5.jpg\",\n  \"306397.jpg\": \"Insecta/Libytheana carinenta/e478a827dbf406773e06920dc5221f9c.jpg\",\n  \"306399.jpg\": \"Insecta/Libytheana carinenta/031f2679da5abaab470b61893ac14ea2.jpg\",\n  \"306401.jpg\": \"Insecta/Libytheana carinenta/662ac06080ad456316a87eddad0c0af0.jpg\",\n  \"306409.jpg\": \"Insecta/Libytheana carinenta/4a66f24ad7c77436b0c848eb459400ff.jpg\",\n  \"30643.jpg\": \"Aves/Megaceryle alcyon/035317d3cf2a8df23da32b6e71c3f769.jpg\",\n  \"306540.jpg\": \"Aves/Anas gracilis/de89143b7095ec0b1af3499a983f227e.jpg\",\n  \"306565.jpg\": \"Mollusca/Crassadoma gigantea/c8fa1817e2f2f1d2975b93d3f57a6fbe.jpg\",\n  \"306567.jpg\": \"Mollusca/Crassadoma gigantea/7f74c08aef3be89a633cba11c3b7a84c.jpg\",\n  \"306568.jpg\": \"Mollusca/Crassadoma gigantea/25602cac9b9440e93325826be6f3cdd4.jpg\",\n  \"306569.jpg\": \"Mollusca/Crassadoma gigantea/7a68e38e308bf5518c614caf74021dca.jpg\",\n  \"306570.jpg\": \"Mollusca/Crassadoma gigantea/03a8a28f1ab96940919a23987ac28c97.jpg\",\n  \"306572.jpg\": \"Mollusca/Crassadoma gigantea/2f394f666eaf22f12fb4f5f2f7280879.jpg\",\n  \"306574.jpg\": \"Mollusca/Crassadoma gigantea/17e6942d3039f6f2164feb285135f221.jpg\",\n  \"306588.jpg\": \"Mollusca/Crassadoma gigantea/d261a9e28a7a1440ee6c00dea46ce2dd.jpg\",\n  \"306589.jpg\": \"Mollusca/Crassadoma gigantea/f1b8911f5368714ea6f628dd31d4bfc1.jpg\",\n  \"306597.jpg\": \"Mollusca/Crassadoma gigantea/fcf9e2156507051a46460fa768fa7938.jpg\",\n  \"306598.jpg\": \"Mollusca/Crassadoma gigantea/16b038986034a48c131bacaed9c4c7ef.jpg\",\n  \"306600.jpg\": \"Mollusca/Crassadoma gigantea/eeecb4eb20feb9fa68fd8028cd06dab1.jpg\",\n  \"306615.jpg\": \"Mollusca/Crassadoma gigantea/85deecaac58722e96d5c738c3eb7ff2c.jpg\",\n  \"306616.jpg\": \"Mollusca/Crassadoma gigantea/414345ecacda3c773e6408d5c37952cd.jpg\",\n  \"306621.jpg\": \"Insecta/Ponometia erastrioides/ee7eba37e93adf781939d4050f9b0468.jpg\",\n  \"306622.jpg\": \"Insecta/Ponometia erastrioides/736785b6a8fbd3c0043913e873dc4352.jpg\",\n  \"306626.jpg\": \"Insecta/Ponometia erastrioides/d0461bbf2093467602bbb7cdb1c44883.jpg\",\n  \"306629.jpg\": \"Insecta/Ponometia erastrioides/f39ad1455982eb8a7a99f2d1170d836b.jpg\",\n  \"306630.jpg\": \"Insecta/Ponometia erastrioides/529bd9c09059d3fb6c8ba3d7b667c756.jpg\",\n  \"306633.jpg\": \"Insecta/Ponometia erastrioides/cba9cd2d090cfe5ac3fdfc7fa11c1f6d.jpg\",\n  \"306639.jpg\": \"Insecta/Ponometia erastrioides/a4acbe710f23723aac088dac567f49a5.jpg\",\n  \"306650.jpg\": \"Aves/Sayornis saya/f6d63dd6ac1b6a3606f047dea887cb91.jpg\",\n  \"306662.jpg\": \"Aves/Sayornis saya/ebeeb0160323b12dbf264d1444dfe461.jpg\",\n  \"306694.jpg\": \"Aves/Sayornis saya/b490d0432219337510c84afd6da2f189.jpg\",\n  \"306697.jpg\": \"Aves/Sayornis saya/804667f7f4e40a31b811d2bf940880cd.jpg\",\n  \"30671.jpg\": \"Aves/Megaceryle alcyon/62eb11c6a196bb7e7fcc59ea1b71c8f1.jpg\",\n  \"306717.jpg\": \"Aves/Sayornis saya/512e6ae31b963a85c8227415aa258270.jpg\",\n  \"306733.jpg\": \"Aves/Sayornis saya/7776547533ecb86adcf1c0c47d1a0827.jpg\",\n  \"306738.jpg\": \"Aves/Sayornis saya/c28775a56905047b63df900bf94d8e68.jpg\",\n  \"306757.jpg\": \"Aves/Sayornis saya/86f36372b4f16655569db71b956e7127.jpg\",\n  \"30676.jpg\": \"Aves/Megaceryle alcyon/927c486715aa3867f0ef283ac1936e74.jpg\",\n  \"306763.jpg\": \"Aves/Sayornis saya/257f892a0fc1958b96326fff470dd129.jpg\",\n  \"306794.jpg\": \"Aves/Sayornis saya/7a75767177d34843bb75f9bb5c19f8aa.jpg\",\n  \"306812.jpg\": \"Aves/Sayornis saya/c0aa86dfd3853a4c0764accd9364773e.jpg\",\n  \"306838.jpg\": \"Aves/Sayornis saya/f6196db1df48ce8a5348ae82f23b6e43.jpg\",\n  \"306853.jpg\": \"Aves/Sayornis saya/af32f72b798dbff395cae323b292a8d8.jpg\",\n  \"306854.jpg\": \"Aves/Sayornis saya/d0514459513151b17cdb481a0bbbd8d7.jpg\",\n  \"306857.jpg\": \"Aves/Sayornis saya/ca51a86bd602cb92bdf08580844e0e2a.jpg\",\n  \"30687.jpg\": \"Aves/Megaceryle alcyon/e506f50b758a9487af6f911d45ea25fd.jpg\",\n  \"306872.jpg\": \"Aves/Sayornis saya/6357f668f1ba50b0a1acff0db9775c5b.jpg\",\n  \"306875.jpg\": \"Aves/Sayornis saya/dc8bbfe8f36328c7f091f969e924d708.jpg\",\n  \"306876.jpg\": \"Aves/Sayornis saya/162f6bbcb2fa7d59fab2ac51e931ec4c.jpg\",\n  \"306881.jpg\": \"Aves/Sayornis saya/b5bdaa51c2045d12736822cde16f97c4.jpg\",\n  \"306883.jpg\": \"Aves/Sayornis saya/723cd8ca483a3e03e1d85e0a73fadb17.jpg\",\n  \"306895.jpg\": \"Aves/Sayornis saya/79d3a1af3b30f836b4292283721ac07d.jpg\",\n  \"306896.jpg\": \"Aves/Sayornis saya/48874211ea3c347c3a6b14f8884c469d.jpg\",\n  \"306925.jpg\": \"Aves/Sayornis saya/72d7ed37ef5c10a1573c1486ac148e82.jpg\",\n  \"306938.jpg\": \"Aves/Sayornis saya/23b8cb7abc34735980ffca22b35e48d2.jpg\",\n  \"306978.jpg\": \"Insecta/Dryas iulia/436706dc2e126afef35623c7ed38e1d0.jpg\",\n  \"306979.jpg\": \"Insecta/Dryas iulia/6769830b5d2db66e91b28caf9ca39228.jpg\",\n  \"306980.jpg\": \"Insecta/Dryas iulia/9cedd1c40cc9aa70442af5dbdeae20c9.jpg\",\n  \"306985.jpg\": \"Insecta/Dryas iulia/aee7345fc9b502d4ed611db3b72d8953.jpg\",\n  \"306986.jpg\": \"Insecta/Dryas iulia/b83a27bc8bd4866595c5171c7ed67e34.jpg\",\n  \"306987.jpg\": \"Insecta/Dryas iulia/485075e43c1481dbc125f48d207a021e.jpg\",\n  \"306988.jpg\": \"Insecta/Dryas iulia/b96261d7b03f72d0f2cc2648b0b98a7f.jpg\",\n  \"306994.jpg\": \"Insecta/Dryas iulia/eb8475eb58e0ca9064569731937c06ff.jpg\",\n  \"306995.jpg\": \"Insecta/Dryas iulia/5368afa59fa22f24538430ad9dee2b9e.jpg\",\n  \"306996.jpg\": \"Insecta/Dryas iulia/1a258c1c99c97e126da0c351aa4736c3.jpg\",\n  \"307000.jpg\": \"Insecta/Dryas iulia/c9000f0f26141cce0f7ecbf6b8ff7bca.jpg\",\n  \"307010.jpg\": \"Insecta/Dryas iulia/c6bfd3a8090747c3d73acc0edae0f445.jpg\",\n  \"307011.jpg\": \"Insecta/Dryas iulia/68df1a0cd5cb1d73595cbe195401ab26.jpg\",\n  \"307013.jpg\": \"Insecta/Dryas iulia/b1878be2063b9f0803ea94d881b5ab3a.jpg\",\n  \"307017.jpg\": \"Insecta/Dryas iulia/537ec41ad08ca85d0950e50309a58138.jpg\",\n  \"307028.jpg\": \"Insecta/Dryas iulia/39e5344a91c34b4c0ec505b89f149754.jpg\",\n  \"307033.jpg\": \"Insecta/Dryas iulia/882bf982cbd5e1a84dd0bef9f0bc30bb.jpg\",\n  \"307055.jpg\": \"Insecta/Dryas iulia/b8eef4f7d4c77c88dc2b8e391c26c19a.jpg\",\n  \"307057.jpg\": \"Insecta/Dryas iulia/a4b71bcd3b78860b8feb37b2110cfe3a.jpg\",\n  \"307060.jpg\": \"Insecta/Dryas iulia/403aa8194a300333cccd2973afa33348.jpg\",\n  \"307061.jpg\": \"Insecta/Dryas iulia/27af523616076e405154b0e6a4dd1cee.jpg\",\n  \"307066.jpg\": \"Insecta/Leuconycta diphteroides/53959062b8d9abf36752010306911469.jpg\",\n  \"307076.jpg\": \"Insecta/Leuconycta diphteroides/dcdfd206453ca596d58f796a93527757.jpg\",\n  \"307077.jpg\": \"Insecta/Leuconycta diphteroides/2c2b2b8feb3d11bd8acf953f50b9dc51.jpg\",\n  \"307082.jpg\": \"Insecta/Leuconycta diphteroides/4b267e064b8a321b85fa1e67241a7b44.jpg\",\n  \"307085.jpg\": \"Insecta/Leuconycta diphteroides/7a426f1d829f99b2bb4c937a85cd1493.jpg\",\n  \"307087.jpg\": \"Insecta/Leuconycta diphteroides/51079c3547cc46c205dc786e4d397880.jpg\",\n  \"307089.jpg\": \"Insecta/Leuconycta diphteroides/54cce74c84b776e1ea31fe39059d4b00.jpg\",\n  \"307094.jpg\": \"Insecta/Leuconycta diphteroides/07d33e65b5e98fbf1d26882ae6088408.jpg\",\n  \"307162.jpg\": \"Mammalia/Procavia capensis/41f5192d531597bc5651221a6afbde9a.jpg\",\n  \"307166.jpg\": \"Mammalia/Procavia capensis/14c96e07d9f708d0719eec9fbc1bc16c.jpg\",\n  \"307175.jpg\": \"Mammalia/Procavia capensis/4cadc1ba0e00e2071d853dc7c907fdef.jpg\",\n  \"307181.jpg\": \"Mammalia/Procavia capensis/aacd589b532b888fa7319f723dafe150.jpg\",\n  \"307182.jpg\": \"Mammalia/Procavia capensis/699e5c937b8675aaea6e53bc63ba50e1.jpg\",\n  \"307194.jpg\": \"Amphibia/Anaxyrus americanus americanus/bd906f98b2f569cc5f9de2e272a98bf9.jpg\",\n  \"307198.jpg\": \"Amphibia/Anaxyrus americanus americanus/6866f354b3238290191bb020c6e474a9.jpg\",\n  \"307204.jpg\": \"Amphibia/Anaxyrus americanus americanus/21294f1966c1b5922e18f44113c7c348.jpg\",\n  \"307205.jpg\": \"Amphibia/Anaxyrus americanus americanus/af3e7616b679b32ba2db10c4c668e13b.jpg\",\n  \"307214.jpg\": \"Amphibia/Anaxyrus americanus americanus/11faa895755283f68c627131344dec68.jpg\",\n  \"307215.jpg\": \"Amphibia/Anaxyrus americanus americanus/d26c8cbb6f9cbb439bcdc8078a6f42a6.jpg\",\n  \"307217.jpg\": \"Arachnida/Platycryptus undatus/ef5b144d9b7d864794e0da1e1ebceb7f.jpg\",\n  \"307218.jpg\": \"Arachnida/Platycryptus undatus/cebcbee81f682001785da3ac1d53c100.jpg\",\n  \"307227.jpg\": \"Arachnida/Platycryptus undatus/b576ba2538c9669e674a4761c182123c.jpg\",\n  \"307231.jpg\": \"Arachnida/Platycryptus undatus/3dd5e3ff6a386d69ab2eedad7ee236db.jpg\",\n  \"307233.jpg\": \"Arachnida/Platycryptus undatus/8550c2d44af41b540fba93dba3a22fb3.jpg\",\n  \"307234.jpg\": \"Arachnida/Platycryptus undatus/a723921a611c16bcb0649ead22d1d63e.jpg\",\n  \"307247.jpg\": \"Arachnida/Platycryptus undatus/d5d4dc82fafd00928bc65280f35e4a13.jpg\",\n  \"307252.jpg\": \"Arachnida/Platycryptus undatus/edbc4ae331b83e8e5170b1c6afffaab4.jpg\",\n  \"30731.jpg\": \"Aves/Megaceryle alcyon/ee0906e54da4bf93d50c231bdb6f877a.jpg\",\n  \"307397.jpg\": \"Aves/Chamaea fasciata/362c440a7f7548257a819471f10e050f.jpg\",\n  \"307400.jpg\": \"Aves/Chamaea fasciata/92960415138b2ec7010525f11dc3f841.jpg\",\n  \"307409.jpg\": \"Aves/Chamaea fasciata/09667262b32ebc1b8feb8668c9330558.jpg\",\n  \"307410.jpg\": \"Aves/Chamaea fasciata/6ca0215b42bb02e80855e3815687595e.jpg\",\n  \"307411.jpg\": \"Aves/Chamaea fasciata/633146b81bd726501b28e7b292422045.jpg\",\n  \"307415.jpg\": \"Aves/Chamaea fasciata/8002c3f18423031ab9b8027e756af72e.jpg\",\n  \"307421.jpg\": \"Aves/Chamaea fasciata/5d7358469864fb5015f2511190864ace.jpg\",\n  \"307426.jpg\": \"Aves/Chamaea fasciata/1de9329f1cb3d123dd28f8df387691cb.jpg\",\n  \"307437.jpg\": \"Aves/Chamaea fasciata/72d79e60bbec1762d08c4198abbe35e5.jpg\",\n  \"307438.jpg\": \"Aves/Chamaea fasciata/b8dc129c38b359d7eb63434c14c29377.jpg\",\n  \"307440.jpg\": \"Aves/Chamaea fasciata/d0490a47960f1cc51ffe3833b17ae580.jpg\",\n  \"307443.jpg\": \"Aves/Chamaea fasciata/6a1be93bc8a8003513a64547e8d324f5.jpg\",\n  \"307444.jpg\": \"Aves/Chamaea fasciata/295d479e1cf38a245cb0cf333de502b5.jpg\",\n  \"307445.jpg\": \"Aves/Chamaea fasciata/9f5300dd324a79ab80060a20df64a11b.jpg\",\n  \"307450.jpg\": \"Aves/Chamaea fasciata/4da88cb0158366b97f5bf2516f173d75.jpg\",\n  \"307453.jpg\": \"Aves/Chamaea fasciata/e042a3d0f8314066ec239c25be057089.jpg\",\n  \"307454.jpg\": \"Aves/Chamaea fasciata/ec164baf488f8cc0150dd0b2e711d31e.jpg\",\n  \"307455.jpg\": \"Aves/Chamaea fasciata/c3b20a6146613dbae97cf9dd76f7e940.jpg\",\n  \"307456.jpg\": \"Aves/Chamaea fasciata/5613911bcb3a1b1efb985a402517ad0e.jpg\",\n  \"307459.jpg\": \"Aves/Chamaea fasciata/d8c654423b08e56c35a903bcfe071df0.jpg\",\n  \"307463.jpg\": \"Aves/Chamaea fasciata/4f6053967e887d91b7eadb9675a296fe.jpg\",\n  \"307478.jpg\": \"Reptilia/Terrapene carolina carolina/6425d6bac29603fe041480127773c8a4.jpg\",\n  \"307491.jpg\": \"Reptilia/Terrapene carolina carolina/49cd6c8b9b16d2cc53f55894ed08bf78.jpg\",\n  \"307496.jpg\": \"Reptilia/Terrapene carolina carolina/908ad3aee5b173558489b06849a7014c.jpg\",\n  \"307506.jpg\": \"Reptilia/Terrapene carolina carolina/c34378afe22ffc8302f1626478237524.jpg\",\n  \"30753.jpg\": \"Aves/Megaceryle alcyon/adaf245f07c660c5b2cf46c442a765e3.jpg\",\n  \"307536.jpg\": \"Reptilia/Terrapene carolina carolina/3ae0cd5fd4cf1e85476c1ecaca6d1549.jpg\",\n  \"307546.jpg\": \"Reptilia/Terrapene carolina carolina/7882aff16e1a4857790b280a535bf20f.jpg\",\n  \"307555.jpg\": \"Reptilia/Terrapene carolina carolina/51f9cb34a97dfea7a4f769cc3decae94.jpg\",\n  \"307586.jpg\": \"Reptilia/Terrapene carolina carolina/568a21d18d4ecffdd4b8593fadd06a46.jpg\",\n  \"307588.jpg\": \"Reptilia/Terrapene carolina carolina/e93a29e93292a5f3855792492d9c6d45.jpg\",\n  \"307597.jpg\": \"Reptilia/Terrapene carolina carolina/81bb0d5d956b765f141c4442cd55cddb.jpg\",\n  \"3076.jpg\": \"Aves/Picoides nuttallii/41cfcb5023270faff9144b9e7847d332.jpg\",\n  \"307604.jpg\": \"Reptilia/Terrapene carolina carolina/1a6f2d5e3efeaf509bae9cb2ca8ed3a6.jpg\",\n  \"307608.jpg\": \"Reptilia/Terrapene carolina carolina/44b2fe3025af4cd6100bf9d3b8798040.jpg\",\n  \"307619.jpg\": \"Reptilia/Terrapene carolina carolina/4841d2f4d01785b48ce1305d59fd2249.jpg\",\n  \"307635.jpg\": \"Reptilia/Terrapene carolina carolina/ad9f5a891656f9e6fb0345b9837ed00d.jpg\",\n  \"307636.jpg\": \"Reptilia/Terrapene carolina carolina/de926f7048c07b6370fadeddb6eb7522.jpg\",\n  \"307653.jpg\": \"Reptilia/Terrapene carolina carolina/f171a7292b16a2ca06a6095f86ee1b20.jpg\",\n  \"307658.jpg\": \"Reptilia/Terrapene carolina carolina/6c1aa9755e2e0323ad1b18c0569a1648.jpg\",\n  \"307669.jpg\": \"Reptilia/Terrapene carolina carolina/6a67c6a35a29bb59c3b470fb63f8aeb8.jpg\",\n  \"307680.jpg\": \"Reptilia/Terrapene carolina carolina/807a2c7a22ffcd8ef0cbec118af92417.jpg\",\n  \"307694.jpg\": \"Aves/Recurvirostra americana/d46255592ea8d1d0fcecfe864e90f539.jpg\",\n  \"3077.jpg\": \"Aves/Picoides nuttallii/52f16d2e2d48cc30eea8a147b310ea7e.jpg\",\n  \"307726.jpg\": \"Aves/Recurvirostra americana/1209d32af34f90d815e287e67c3345b5.jpg\",\n  \"307802.jpg\": \"Aves/Recurvirostra americana/42853a80d219c26740ad2e2b928d55c9.jpg\",\n  \"307804.jpg\": \"Aves/Recurvirostra americana/99916dd85cce86ba76dbd4be32ff50c5.jpg\",\n  \"307831.jpg\": \"Aves/Recurvirostra americana/08e9bd2251a6faf2c1b7586d12aad3fb.jpg\",\n  \"307836.jpg\": \"Aves/Recurvirostra americana/534043a1dfa5e8cd0afa8e970a8392a1.jpg\",\n  \"307838.jpg\": \"Aves/Recurvirostra americana/6974760f7df83f760164c0ad6896e3c2.jpg\",\n  \"307877.jpg\": \"Aves/Recurvirostra americana/ee12d7665cd99f9abcd1ca063dbe37a5.jpg\",\n  \"307894.jpg\": \"Aves/Recurvirostra americana/9b91f8d7ff33675c9b28f997cb3bd684.jpg\",\n  \"307943.jpg\": \"Aves/Recurvirostra americana/d255b3b15e07df753fd67a95fe5fb105.jpg\",\n  \"307962.jpg\": \"Aves/Recurvirostra americana/aba939f097b09a63e906de493fd13493.jpg\",\n  \"307966.jpg\": \"Aves/Recurvirostra americana/7cd2fa8f80dc7502c8d8955c8e167167.jpg\",\n  \"307968.jpg\": \"Aves/Recurvirostra americana/c01071aad2f36b9818b670b2f36e1751.jpg\",\n  \"308.jpg\": \"Mollusca/Olivella biplicata/99a0b5ddc20c22ae16e07150047cd6d3.jpg\",\n  \"3080.jpg\": \"Aves/Picoides nuttallii/52d44a07608d3a02e4cffad371eac234.jpg\",\n  \"308031.jpg\": \"Aves/Recurvirostra americana/42426abcae8a091b330d71869846edb6.jpg\",\n  \"308033.jpg\": \"Insecta/Erythemis vesiculosa/d4d7f550fb109d31d78d8f8d5a3c5edb.jpg\",\n  \"308034.jpg\": \"Insecta/Erythemis vesiculosa/67c039600bf713d73b54234e77ded84e.jpg\",\n  \"308035.jpg\": \"Insecta/Erythemis vesiculosa/90bf52660ac1ba8525091f74a2d5a61e.jpg\",\n  \"308037.jpg\": \"Insecta/Erythemis vesiculosa/2a444081e7adc3074c401b16fbbe51d7.jpg\",\n  \"308038.jpg\": \"Insecta/Erythemis vesiculosa/7b7a62d2200821795859bc59f8ced41f.jpg\",\n  \"308039.jpg\": \"Insecta/Erythemis vesiculosa/3ae764066f76d80505dc6c8ff4fef131.jpg\",\n  \"308041.jpg\": \"Insecta/Erythemis vesiculosa/9f1116f80563ffb43279393c46a3de5e.jpg\",\n  \"308044.jpg\": \"Insecta/Erythemis vesiculosa/37c04a69c6cbd44a15546a4acdc5f952.jpg\",\n  \"308045.jpg\": \"Insecta/Erythemis vesiculosa/de281a6cb26521fc3ae49916b84c64d9.jpg\",\n  \"308047.jpg\": \"Insecta/Erythemis vesiculosa/763de8c7e88b223e4ec26a107e86334d.jpg\",\n  \"308056.jpg\": \"Insecta/Erythemis vesiculosa/9b2934ccf18e24cad36529b20764cff0.jpg\",\n  \"308059.jpg\": \"Insecta/Erythemis vesiculosa/730ec78f441eff1e485d97aa59cc757b.jpg\",\n  \"308060.jpg\": \"Insecta/Erythemis vesiculosa/417051dc11e9694da7e23d1b6b53140b.jpg\",\n  \"308062.jpg\": \"Insecta/Erythemis vesiculosa/2788ffd4d6576fc84145e580a8ebf010.jpg\",\n  \"308063.jpg\": \"Insecta/Erythemis vesiculosa/bc460b10e4b44959317e7352411b3ec0.jpg\",\n  \"308068.jpg\": \"Insecta/Erythemis vesiculosa/887a77bb6a40157b548669eb0576ceda.jpg\",\n  \"308069.jpg\": \"Insecta/Erythemis vesiculosa/840282b55a1987294e4a9090bb15ad33.jpg\",\n  \"308070.jpg\": \"Insecta/Erythemis vesiculosa/eb62990d39558f28e2b2f91f8d986f9e.jpg\",\n  \"308071.jpg\": \"Insecta/Erythemis vesiculosa/7ec8b256143623dcc38017c7d1686396.jpg\",\n  \"308072.jpg\": \"Insecta/Erythemis vesiculosa/37cd79975968b1a1f4ff876f670cb280.jpg\",\n  \"308075.jpg\": \"Insecta/Erythemis vesiculosa/0e2451c6f18624b1d5ae7f39df80b850.jpg\",\n  \"308078.jpg\": \"Insecta/Erythemis vesiculosa/078a094c388d97195e0536a96ceb1b73.jpg\",\n  \"308085.jpg\": \"Insecta/Erynnis horatius/208660c1f8d915ab5242476eef78403b.jpg\",\n  \"308095.jpg\": \"Insecta/Erynnis horatius/60c907cddb7eb990d2a0624c70174a22.jpg\",\n  \"308105.jpg\": \"Insecta/Erynnis horatius/cf1fd570f8a3c2059d3e6fc0e2d51546.jpg\",\n  \"308106.jpg\": \"Insecta/Erynnis horatius/a49c35bd10a2cceda67b5292ffdba935.jpg\",\n  \"308108.jpg\": \"Insecta/Erynnis horatius/88727ea553a420e51a5fb02f4293a849.jpg\",\n  \"308109.jpg\": \"Insecta/Erynnis horatius/511c9354d6d5817b0ec8eae00d039718.jpg\",\n  \"308114.jpg\": \"Insecta/Erynnis horatius/c7a36df3d5b0ce9c2d9dc4798dd0d256.jpg\",\n  \"308115.jpg\": \"Insecta/Erynnis horatius/d59e01aba7e7374b02b72d7b130081cf.jpg\",\n  \"308116.jpg\": \"Insecta/Erynnis horatius/0a348879561386f14b1d7add5db8cf69.jpg\",\n  \"308118.jpg\": \"Insecta/Erynnis horatius/bb4b5af0e51e06e8338422301f0d0075.jpg\",\n  \"308123.jpg\": \"Insecta/Erynnis horatius/f50df31a2dcd96622d7f39445f68079d.jpg\",\n  \"308124.jpg\": \"Insecta/Erynnis horatius/24896d30c96970a45247e40a588100cf.jpg\",\n  \"308125.jpg\": \"Insecta/Erynnis horatius/9d9baf248c968db33c7d89f98b7206a2.jpg\",\n  \"308129.jpg\": \"Insecta/Erynnis horatius/37f3f8a2a856c10b92b16351c8a0993f.jpg\",\n  \"308134.jpg\": \"Insecta/Erynnis horatius/f590099825fa37126c6efe0a4b1d1e3e.jpg\",\n  \"308152.jpg\": \"Insecta/Erynnis horatius/1700fabc79cf7629666da95931c2ce5f.jpg\",\n  \"308155.jpg\": \"Insecta/Erynnis horatius/5381ba9e26302d87a941230babff0012.jpg\",\n  \"308157.jpg\": \"Insecta/Erynnis horatius/273c0a5acfb027ef0da06d83afdc950d.jpg\",\n  \"308159.jpg\": \"Insecta/Erynnis horatius/f30fa07bb9808565cc9a67ffeae81428.jpg\",\n  \"308160.jpg\": \"Insecta/Erynnis horatius/c2b6be5835c0dc456241e0c8ca5896b7.jpg\",\n  \"308166.jpg\": \"Insecta/Erynnis horatius/b5ec0ef8bb571ab924f6285da787148d.jpg\",\n  \"308176.jpg\": \"Insecta/Erynnis horatius/dee5de893777740efc018200e84224c4.jpg\",\n  \"308182.jpg\": \"Insecta/Erynnis horatius/fa0d542405fd1e61df4afc00892ef3e4.jpg\",\n  \"308183.jpg\": \"Aves/Setophaga dominica/2bfe45371cf475398bf348b82f93482f.jpg\",\n  \"308198.jpg\": \"Aves/Setophaga dominica/0c04f206d26b2c57f438adb2908665d9.jpg\",\n  \"308201.jpg\": \"Aves/Setophaga dominica/7c824b21e3b26bc19dde50912edba599.jpg\",\n  \"308203.jpg\": \"Aves/Setophaga dominica/977492367cd8f3d3a21a43a0418ae03f.jpg\",\n  \"308205.jpg\": \"Aves/Setophaga dominica/666f147b4122ee2e2b6359614231649a.jpg\",\n  \"308207.jpg\": \"Aves/Setophaga dominica/752bc89a029bf891ea90400abca2172b.jpg\",\n  \"308208.jpg\": \"Aves/Setophaga dominica/e34b1b5d92ee8955481acda798d8514c.jpg\",\n  \"308209.jpg\": \"Aves/Setophaga dominica/300f5bf933a6ea3ac2cb149be3907f8c.jpg\",\n  \"308210.jpg\": \"Aves/Setophaga dominica/e6f351fb7d5c473c0a25629eddbdaf50.jpg\",\n  \"308212.jpg\": \"Aves/Setophaga dominica/64775a3510c69d56b54b3c3efa431026.jpg\",\n  \"308219.jpg\": \"Aves/Setophaga dominica/a3ae91d4ea5244aacb5260e087620d95.jpg\",\n  \"308223.jpg\": \"Aves/Setophaga dominica/970da54abd598817760d81559f7b6cb0.jpg\",\n  \"308224.jpg\": \"Aves/Setophaga dominica/64db2b83d77e2d91d369e427a004bfb0.jpg\",\n  \"308229.jpg\": \"Aves/Setophaga dominica/5f6af95e7ec1836be95b749986de0c55.jpg\",\n  \"308232.jpg\": \"Aves/Setophaga dominica/d2769c124eac90b80787d0ad3f25e939.jpg\",\n  \"308235.jpg\": \"Aves/Setophaga dominica/9aee3abd34fc4c0a06981c1c02f9f5e7.jpg\",\n  \"308236.jpg\": \"Aves/Setophaga dominica/5a8ebbdcf95c02fc24f2cd40efc73249.jpg\",\n  \"308237.jpg\": \"Aves/Setophaga dominica/590515af19e204de1eb4d96aeb765c01.jpg\",\n  \"308240.jpg\": \"Aves/Setophaga dominica/d0fcda863c62400d77129b4f32601fff.jpg\",\n  \"308241.jpg\": \"Aves/Setophaga dominica/086266a01ba8a6d96ad9cfb827a01ade.jpg\",\n  \"308247.jpg\": \"Aves/Setophaga dominica/ce018e7e5f4eb22ca849f8921d89cdb4.jpg\",\n  \"308248.jpg\": \"Aves/Parkesia motacilla/c9ee93dc21827b4122213b533cafbc3b.jpg\",\n  \"30825.jpg\": \"Aves/Megaceryle alcyon/f5d63c7c07d51ec09ce2153758ee19ef.jpg\",\n  \"308250.jpg\": \"Aves/Parkesia motacilla/e47e8cc3705bc6d9ce1db1635cc6fb6f.jpg\",\n  \"308251.jpg\": \"Aves/Parkesia motacilla/3740c94b09a07269c56784760591c86d.jpg\",\n  \"308262.jpg\": \"Aves/Parkesia motacilla/b6e825d7ce0a4e0fc329e9d3a3d2848e.jpg\",\n  \"308263.jpg\": \"Aves/Parkesia motacilla/0e831b0af04b05aec040be6cd5cbef97.jpg\",\n  \"308273.jpg\": \"Aves/Parkesia motacilla/fcd972bdaca7dce4d2f32343f4af1410.jpg\",\n  \"308274.jpg\": \"Aves/Parkesia motacilla/0fb22572f1b930a5a359bfd8aaddc141.jpg\",\n  \"308277.jpg\": \"Aves/Parkesia motacilla/6020e5b7a35e489c49215c6bdbc78dcf.jpg\",\n  \"308286.jpg\": \"Aves/Parkesia motacilla/b91f3ba74e413b7ac6d52422e07f02cc.jpg\",\n  \"308293.jpg\": \"Aves/Parkesia motacilla/bb9a063009a3b6e94f8c4f23d8c13693.jpg\",\n  \"308294.jpg\": \"Aves/Parkesia motacilla/f290c7009f4d047e338bff6485afbe2e.jpg\",\n  \"308295.jpg\": \"Aves/Parkesia motacilla/31a53f5b2bf91f9bbe1944239ea159a2.jpg\",\n  \"308297.jpg\": \"Aves/Parkesia motacilla/ec13b3f157133ede4addb51be4c7dfc7.jpg\",\n  \"308298.jpg\": \"Aves/Parkesia motacilla/78b488a4b7c094bb4510c0ce64c1f9cf.jpg\",\n  \"308302.jpg\": \"Aves/Parkesia motacilla/fd12f3de9661c2ffb331661dc1cc4076.jpg\",\n  \"308306.jpg\": \"Aves/Parkesia motacilla/055719af85c3d525842349bb42f335c3.jpg\",\n  \"308310.jpg\": \"Aves/Parkesia motacilla/24d5161dee6c8e0f816e0e0f99035f7f.jpg\",\n  \"30837.jpg\": \"Aves/Megaceryle alcyon/bdf4e23dfc08d25f5381b8c2c3935475.jpg\",\n  \"308403.jpg\": \"Insecta/Danaus gilippus/81cd71dfccdd78c1834257cc2966a803.jpg\",\n  \"308424.jpg\": \"Insecta/Danaus gilippus/3fbb9b35d740298f5852715088c6db9b.jpg\",\n  \"308434.jpg\": \"Insecta/Danaus gilippus/bd5e281236d66e34fa39dba47f216512.jpg\",\n  \"308444.jpg\": \"Insecta/Danaus gilippus/dadd9606eeb7dbbe8771109a4ae9a02a.jpg\",\n  \"308454.jpg\": \"Insecta/Danaus gilippus/696a693cf89b95dd92c0b49e4d84d61e.jpg\",\n  \"308457.jpg\": \"Insecta/Danaus gilippus/3ead14a595737413af0511667975bbee.jpg\",\n  \"308476.jpg\": \"Insecta/Danaus gilippus/053e5426bb3854ec13ff3008beaf3f64.jpg\",\n  \"308489.jpg\": \"Insecta/Danaus gilippus/122e1247c5f991d7a9b75eece5cf84e6.jpg\",\n  \"308522.jpg\": \"Insecta/Danaus gilippus/ac8633090d41c245f69084c980ce1fa4.jpg\",\n  \"308553.jpg\": \"Insecta/Danaus gilippus/7b1361f967aa0c14e43c0f45cd48c01e.jpg\",\n  \"308554.jpg\": \"Insecta/Danaus gilippus/b7cb62cbc6c71a87c1e3f2bb9a12b05a.jpg\",\n  \"308565.jpg\": \"Insecta/Danaus gilippus/259c490304ddeaeca81420991deab0f9.jpg\",\n  \"308574.jpg\": \"Insecta/Danaus gilippus/b257855c53de0e98cd4393d295f86bcd.jpg\",\n  \"308586.jpg\": \"Insecta/Danaus gilippus/84309b6a52607a3956ccd9f11e81a8d6.jpg\",\n  \"308608.jpg\": \"Insecta/Danaus gilippus/240b11efdf08165a2e2b9dc172ba1c19.jpg\",\n  \"308614.jpg\": \"Insecta/Danaus gilippus/8b52d23eebd483adf97029c3e08550d6.jpg\",\n  \"308635.jpg\": \"Insecta/Danaus gilippus/f8f6f94f48f7aa2cc1b044c90dda80e1.jpg\",\n  \"308733.jpg\": \"Insecta/Danaus gilippus/6f95b948fa16d69224b67f52f784a95c.jpg\",\n  \"308745.jpg\": \"Insecta/Danaus gilippus/dc335004f5fedce168fa94a0bd29c528.jpg\",\n  \"308748.jpg\": \"Insecta/Danaus gilippus/036aa717b901d87ce31e4593c75fd4ce.jpg\",\n  \"308768.jpg\": \"Insecta/Danaus gilippus/0329c46c4ecaf7fb393415b469184a2e.jpg\",\n  \"308790.jpg\": \"Insecta/Harmonia axyridis/eaae670c2f4504d1a966847c762c5e2e.jpg\",\n  \"308847.jpg\": \"Insecta/Harmonia axyridis/26a7e04ce8dd5d50c8f79f3cc010de8c.jpg\",\n  \"308981.jpg\": \"Insecta/Harmonia axyridis/009d218f63bb78d4e0ebdbf6d659f4b7.jpg\",\n  \"308987.jpg\": \"Insecta/Harmonia axyridis/2dedb511f9f885a523f3b1a2eef64efc.jpg\",\n  \"309.jpg\": \"Mollusca/Olivella biplicata/cf9986b54d0dd98a91b8620c7e850602.jpg\",\n  \"309001.jpg\": \"Insecta/Harmonia axyridis/0c5619fa699d5de60543208ae69eb056.jpg\",\n  \"309007.jpg\": \"Insecta/Harmonia axyridis/658d8f5bbac062f595b3b17dc5f149b0.jpg\",\n  \"309053.jpg\": \"Insecta/Harmonia axyridis/6dc069f825cfd447fa1f1569ea5bcb14.jpg\",\n  \"3091.jpg\": \"Aves/Picoides nuttallii/fddddf2b729a65809b72e8b98b9ae321.jpg\",\n  \"30917.jpg\": \"Aves/Megaceryle alcyon/cc4fbcc4b68279af476b9d0950580544.jpg\",\n  \"3092.jpg\": \"Aves/Picoides nuttallii/2e5d3e499116735b349f8b43c1d64f7d.jpg\",\n  \"309200.jpg\": \"Insecta/Harmonia axyridis/2c4bbedd4629024f26e2a415dfc1280d.jpg\",\n  \"309226.jpg\": \"Insecta/Harmonia axyridis/a08006c9962ab9b7c57811ec0ef419b2.jpg\",\n  \"309298.jpg\": \"Insecta/Harmonia axyridis/aa1935df93cbb6c9219eb7c86746ca42.jpg\",\n  \"309325.jpg\": \"Insecta/Harmonia axyridis/5fa54c55ebd9269351c6d0c380df1a1a.jpg\",\n  \"30933.jpg\": \"Aves/Megaceryle alcyon/13e7abaa9e9e07cd8991b0ef193101be.jpg\",\n  \"309358.jpg\": \"Insecta/Harmonia axyridis/4724d7c086005b1576141de6a2571699.jpg\",\n  \"309378.jpg\": \"Insecta/Harmonia axyridis/1ccad4be721925a153224a3a616b3b85.jpg\",\n  \"309430.jpg\": \"Insecta/Harmonia axyridis/b2c33f70201430ffcf41b8f4de95bd62.jpg\",\n  \"309483.jpg\": \"Insecta/Harmonia axyridis/22707e61b742bd7eade3ace101489607.jpg\",\n  \"30952.jpg\": \"Aves/Megaceryle alcyon/d2360f3a09e43dbce951b9e129a7922c.jpg\",\n  \"30954.jpg\": \"Aves/Megaceryle alcyon/588e0816183f374c7304ab29caeae3e1.jpg\",\n  \"309546.jpg\": \"Insecta/Harmonia axyridis/69990e9c26d922f8d2c77b405484afa0.jpg\",\n  \"30956.jpg\": \"Aves/Megaceryle alcyon/18a6f1c392e63e57580a90a9dc57e881.jpg\",\n  \"309584.jpg\": \"Animalia/Scolopendra polymorpha/a646fabee241006be7957c609479e4e7.jpg\",\n  \"309585.jpg\": \"Animalia/Scolopendra polymorpha/d29a5b2678518e436af6d5c57051797e.jpg\",\n  \"309589.jpg\": \"Animalia/Scolopendra polymorpha/cf74053f29549830856f18ab4278422d.jpg\",\n  \"309593.jpg\": \"Animalia/Scolopendra polymorpha/87d69b5830efe819d031ca5258a6f015.jpg\",\n  \"309605.jpg\": \"Animalia/Scolopendra polymorpha/ba3969a8a38e86d209eb318c10cae4b4.jpg\",\n  \"309606.jpg\": \"Animalia/Scolopendra polymorpha/9c7f8f981b6bc3c7150830d9c8489758.jpg\",\n  \"309609.jpg\": \"Animalia/Scolopendra polymorpha/101593b1eaea2f891acc73d1e276612d.jpg\",\n  \"309614.jpg\": \"Animalia/Scolopendra polymorpha/ff70fe5acdbf404ace1ac412c9478514.jpg\",\n  \"309615.jpg\": \"Animalia/Scolopendra polymorpha/89a3a35aaaec23e1597ac81c3b54b4c0.jpg\",\n  \"309619.jpg\": \"Animalia/Scolopendra polymorpha/c12458e61042147a59862f1282cf7e87.jpg\",\n  \"309620.jpg\": \"Animalia/Scolopendra polymorpha/97e4a5680526c37a731e0cb036da639a.jpg\",\n  \"309628.jpg\": \"Animalia/Scolopendra polymorpha/6401f0caa5a8e4c1cb00b51eeb2d4951.jpg\",\n  \"309629.jpg\": \"Animalia/Scolopendra polymorpha/85bcf1a07b5550cebb96c27f00622744.jpg\",\n  \"309631.jpg\": \"Animalia/Scolopendra polymorpha/049655c16e26db97dd429fdc95564069.jpg\",\n  \"309632.jpg\": \"Animalia/Scolopendra polymorpha/e16fe9c311dbf936c51e911774cd921e.jpg\",\n  \"309638.jpg\": \"Insecta/Cycnia tenera/a92202dca598c5b77bd92238901bdaa7.jpg\",\n  \"309640.jpg\": \"Insecta/Cycnia tenera/6f73e62aae3f4443af7eb359dd3c424e.jpg\",\n  \"309645.jpg\": \"Insecta/Cycnia tenera/f574a86713d3197c29b38c135fd4a4f3.jpg\",\n  \"30965.jpg\": \"Aves/Megaceryle alcyon/f3ec8a1d183c4ce8b04b0468485cde37.jpg\",\n  \"309652.jpg\": \"Insecta/Cycnia tenera/9632c21f631321afc0380569fdccedfe.jpg\",\n  \"309658.jpg\": \"Insecta/Cycnia tenera/9caef5ef281f8e4a2d32774e5682f214.jpg\",\n  \"309660.jpg\": \"Insecta/Cycnia tenera/887fda9653c3770dd0f7484bc9789738.jpg\",\n  \"30975.jpg\": \"Aves/Megaceryle alcyon/b3fd98455a79dbea8721391c10380d7b.jpg\",\n  \"309886.jpg\": \"Reptilia/Dipsosaurus dorsalis/dcab928341275935d9ab3e6e407b0af5.jpg\",\n  \"309887.jpg\": \"Reptilia/Dipsosaurus dorsalis/4638999982b759d386b45cd2085a65ea.jpg\",\n  \"309895.jpg\": \"Reptilia/Dipsosaurus dorsalis/6553bc4d8744b151c6e7e9ffbe42c535.jpg\",\n  \"309897.jpg\": \"Reptilia/Dipsosaurus dorsalis/47f5e6b2f548709d8ea7f3760c1a9d36.jpg\",\n  \"309902.jpg\": \"Reptilia/Dipsosaurus dorsalis/ff3ba0f8e2fa77310f3b50dc6ac22edb.jpg\",\n  \"309904.jpg\": \"Reptilia/Dipsosaurus dorsalis/6f914b28bb71ee8a101f825ea11522d4.jpg\",\n  \"309908.jpg\": \"Reptilia/Dipsosaurus dorsalis/3cadc19621e78a22fdee3b49bed276a9.jpg\",\n  \"309911.jpg\": \"Reptilia/Dipsosaurus dorsalis/9facbc13d16cb4057e9d07de86dc745c.jpg\",\n  \"309912.jpg\": \"Reptilia/Dipsosaurus dorsalis/98d4d2f009e6549b7f9ef43e7772a3cf.jpg\",\n  \"309913.jpg\": \"Reptilia/Dipsosaurus dorsalis/af555ab41c4eb71edd444b22160bac78.jpg\",\n  \"309916.jpg\": \"Reptilia/Dipsosaurus dorsalis/ac2f24d327d38d84e67480beaf8b8179.jpg\",\n  \"309921.jpg\": \"Reptilia/Dipsosaurus dorsalis/77183c49483877cdd0ec80b5d988d3fa.jpg\",\n  \"309923.jpg\": \"Reptilia/Dipsosaurus dorsalis/4eb20b28bbf224db32382aa1ee6c5f4c.jpg\",\n  \"309925.jpg\": \"Reptilia/Dipsosaurus dorsalis/86bb66d419a9389bad38c1cc11506e11.jpg\",\n  \"309927.jpg\": \"Reptilia/Dipsosaurus dorsalis/9f38959892f59f813ccee1cf9a5d7f50.jpg\",\n  \"309928.jpg\": \"Reptilia/Dipsosaurus dorsalis/c9145fcf782d8284a1f666d6db1cecd0.jpg\",\n  \"309929.jpg\": \"Reptilia/Dipsosaurus dorsalis/2958f2ba697758f0b9479302274a95e3.jpg\",\n  \"309931.jpg\": \"Reptilia/Dipsosaurus dorsalis/6c1b8df83d818966d5e88a946f5997d9.jpg\",\n  \"309936.jpg\": \"Reptilia/Dipsosaurus dorsalis/0b02d328ff0271285353a2eda3c031c9.jpg\",\n  \"309946.jpg\": \"Reptilia/Dipsosaurus dorsalis/60d5c9c61b26161fdae457b8c06d237d.jpg\",\n  \"309948.jpg\": \"Reptilia/Dipsosaurus dorsalis/7c553b5010a053a4b09469283dfd03e2.jpg\",\n  \"309949.jpg\": \"Reptilia/Dipsosaurus dorsalis/99dd87113ccc0c87f5ae4f6a1031efd4.jpg\",\n  \"309956.jpg\": \"Aves/Piranga rubra/338041f6e19e325063dba0e4f097b742.jpg\",\n  \"309960.jpg\": \"Aves/Piranga rubra/dfb2de0be91f9c9269a1fbef152adf9a.jpg\",\n  \"309963.jpg\": \"Aves/Piranga rubra/1b93b65ff703f288c3e7102c08606275.jpg\",\n  \"309966.jpg\": \"Aves/Piranga rubra/49fe5bf983e83d3113c8dbf53f84f444.jpg\",\n  \"309972.jpg\": \"Aves/Piranga rubra/1b6bab2aa601be97e6d1b2c88c81b798.jpg\",\n  \"309994.jpg\": \"Aves/Piranga rubra/f89efa00b462d6335aa86a3050d852b6.jpg\",\n  \"310023.jpg\": \"Aves/Piranga rubra/13e9a4eac7efc10ad08e809e0e1c3832.jpg\",\n  \"310035.jpg\": \"Aves/Piranga rubra/db24ea310600870601d0c53b2422fdfb.jpg\",\n  \"310057.jpg\": \"Aves/Piranga rubra/a011119efeb6ef1181b91d38f7a44fa1.jpg\",\n  \"310066.jpg\": \"Aves/Piranga rubra/08034762ef546bba44469fd79480e5a8.jpg\",\n  \"310071.jpg\": \"Aves/Piranga rubra/480df7349571b292df19415eec9444af.jpg\",\n  \"310103.jpg\": \"Aves/Piranga rubra/23cfc1ed880ffda795e78196b24ffd48.jpg\",\n  \"310134.jpg\": \"Aves/Piranga rubra/f973cccf400c043d8f930905bef70494.jpg\",\n  \"310141.jpg\": \"Aves/Piranga rubra/466819fa7c61a626a3cbf0f17e7f4126.jpg\",\n  \"310153.jpg\": \"Aves/Piranga rubra/13226e102be043014fb360bc042c86d6.jpg\",\n  \"310194.jpg\": \"Aves/Piranga rubra/1aba73c06b0dcc45a2517e770a687bf4.jpg\",\n  \"310195.jpg\": \"Aves/Piranga rubra/237ca39c3caf0a339dc7c6887f0e9379.jpg\",\n  \"310201.jpg\": \"Aves/Piranga rubra/b0a2bfccc8a43ffbc91d3e7a1d27e3f9.jpg\",\n  \"310204.jpg\": \"Aves/Piranga rubra/a5f1d138acce38f3322f06682723cc83.jpg\",\n  \"310216.jpg\": \"Insecta/Hyphantria cunea/de806cb9630e2aa0965818ec230cdb39.jpg\",\n  \"310217.jpg\": \"Insecta/Hyphantria cunea/6f11edefb3afc3b499f1079ef2cdfaa0.jpg\",\n  \"310223.jpg\": \"Insecta/Hyphantria cunea/49a2d013235f90c34fb62a6ff73a2e15.jpg\",\n  \"310231.jpg\": \"Insecta/Hyphantria cunea/3f8ec41095ad22c700550c72b57475d7.jpg\",\n  \"310232.jpg\": \"Insecta/Hyphantria cunea/0edfa243211345d93a8fa2c118b1a57f.jpg\",\n  \"310233.jpg\": \"Insecta/Hyphantria cunea/cff60501af3d51c9e11e0c04b798b9c3.jpg\",\n  \"310235.jpg\": \"Insecta/Hyphantria cunea/3a7131b1951a709707e7c0b87d352394.jpg\",\n  \"310236.jpg\": \"Insecta/Hyphantria cunea/833f668b1cc1e97732d1a0cc5a11e69c.jpg\",\n  \"310237.jpg\": \"Insecta/Hyphantria cunea/02119d3c6a4cd3dd504014792a30db4c.jpg\",\n  \"310238.jpg\": \"Insecta/Hyphantria cunea/b896eb9895228a3bdf6a16ee9bdd352f.jpg\",\n  \"310241.jpg\": \"Insecta/Hyphantria cunea/73e09f302839ae1851ed684d54cc9ba2.jpg\",\n  \"310248.jpg\": \"Insecta/Hyphantria cunea/8a919c3a9475929e1acad442df92098f.jpg\",\n  \"310250.jpg\": \"Insecta/Hyphantria cunea/fd931b8f18fa20b55a519d97a9b1820f.jpg\",\n  \"310251.jpg\": \"Insecta/Hyphantria cunea/36e1d69b3316ff379bcb643741597c31.jpg\",\n  \"310252.jpg\": \"Insecta/Hyphantria cunea/ee742c8c491d7f8e1718416325a2cc07.jpg\",\n  \"310253.jpg\": \"Insecta/Hyphantria cunea/95e723914c3bab297ef58a8deb079664.jpg\",\n  \"310255.jpg\": \"Insecta/Hyphantria cunea/f9c25d32fc1a755ce64d95a0ef6b2045.jpg\",\n  \"310261.jpg\": \"Insecta/Hyphantria cunea/bf0aa7fd546f13b09d8dbdabc83d6dfd.jpg\",\n  \"310262.jpg\": \"Insecta/Hyphantria cunea/ee6134d08c9cbe1483dc9e2ad655adc6.jpg\",\n  \"310263.jpg\": \"Insecta/Hyphantria cunea/0d2521b800d9d815e74ff8c4d2f04122.jpg\",\n  \"3104.jpg\": \"Aves/Picoides nuttallii/ca659718bd0320483f7143ec856ff24a.jpg\",\n  \"310428.jpg\": \"Aves/Tyrannus melancholicus/0c51bb34f906f03b7c4924a146564b46.jpg\",\n  \"310433.jpg\": \"Aves/Tyrannus melancholicus/39e71802938a35d411d80522ab064c9d.jpg\",\n  \"310437.jpg\": \"Aves/Tyrannus melancholicus/fdab6373b9f17e2b533d81e6932b4658.jpg\",\n  \"310444.jpg\": \"Aves/Tyrannus melancholicus/849fef7bee750b5d296f3830568f3f62.jpg\",\n  \"310454.jpg\": \"Aves/Tyrannus melancholicus/b5aa6f0e58b1ec1a10b03744688bf11c.jpg\",\n  \"310471.jpg\": \"Aves/Tyrannus melancholicus/fe9dcbf72fd5ed630ccbee637523f94f.jpg\",\n  \"310472.jpg\": \"Aves/Tyrannus melancholicus/ea7d3d7c7c208f615093526ac458ee73.jpg\",\n  \"310489.jpg\": \"Aves/Tyrannus melancholicus/86ec3c132d15b87d5984c9fbedc40fe9.jpg\",\n  \"310490.jpg\": \"Aves/Tyrannus melancholicus/ee01844bb92419bfb5d5f6f8f311ba23.jpg\",\n  \"310492.jpg\": \"Aves/Tyrannus melancholicus/3e8fbe605e8b2407c4b4fdb778bebd70.jpg\",\n  \"310500.jpg\": \"Aves/Tyrannus melancholicus/d83048e51ccc0151151ce84a59aaf61c.jpg\",\n  \"310519.jpg\": \"Aves/Tyrannus melancholicus/9aa9e80efcd5d306bd069b30390bee13.jpg\",\n  \"310530.jpg\": \"Aves/Tyrannus melancholicus/18d2deea90b88d242d07cb3ebf485760.jpg\",\n  \"310534.jpg\": \"Aves/Tyrannus melancholicus/373f5ec5fb869a806d72d29c63a88f89.jpg\",\n  \"310535.jpg\": \"Aves/Tyrannus melancholicus/c3eb83fcebfe4b38bb7fd506759b39fd.jpg\",\n  \"310536.jpg\": \"Aves/Tyrannus melancholicus/be783316216aca5e97d6df47b4f77abd.jpg\",\n  \"310541.jpg\": \"Aves/Tyrannus melancholicus/4f9ea7a7714297324a9921342fcf5eb4.jpg\",\n  \"310545.jpg\": \"Aves/Tyrannus melancholicus/42fc22cc33cc1c692dde7355376624e9.jpg\",\n  \"310550.jpg\": \"Aves/Tyrannus melancholicus/1a2dc791b83528032fe0130107f26401.jpg\",\n  \"310559.jpg\": \"Aves/Cyanerpes cyaneus/badddc81dbc089db4cc8aef24f58c12d.jpg\",\n  \"310562.jpg\": \"Aves/Cyanerpes cyaneus/4711a1b66e1cef81d9f1522ce5a618fb.jpg\",\n  \"310566.jpg\": \"Aves/Cyanerpes cyaneus/14d6bda1f6816cc95b17d33d03c1001a.jpg\",\n  \"310568.jpg\": \"Aves/Cyanerpes cyaneus/ea470a77321a78e6b069d1bb3d75c105.jpg\",\n  \"310573.jpg\": \"Aves/Cyanerpes cyaneus/83f36a6e2d057c7a038aecf1601f0ba4.jpg\",\n  \"310575.jpg\": \"Aves/Cyanerpes cyaneus/00b12cb486216ed7590527bb73b2baf6.jpg\",\n  \"310578.jpg\": \"Aves/Cyanerpes cyaneus/bdc7476ac721c5563448ac1abac1bc7d.jpg\",\n  \"310581.jpg\": \"Aves/Dendragapus fuliginosus/0a931b348a9f8f0d4f2e67622bede732.jpg\",\n  \"310590.jpg\": \"Aves/Dendragapus fuliginosus/73e56aac642130a129726b7e7a5c73d1.jpg\",\n  \"310593.jpg\": \"Aves/Dendragapus fuliginosus/e8eeabf508d058fe54c0f464750218bb.jpg\",\n  \"310594.jpg\": \"Aves/Dendragapus fuliginosus/3af3e6257cd4560c084eaa89d8f5cdd9.jpg\",\n  \"310604.jpg\": \"Aves/Dendragapus fuliginosus/a3bdd1cd531bc1fa425fcf8b40e401ac.jpg\",\n  \"310612.jpg\": \"Aves/Dendragapus fuliginosus/98f1d2d073c56d54b72173a9b8ea3ef3.jpg\",\n  \"310613.jpg\": \"Aves/Dendragapus fuliginosus/fb19d48e09903ce6c9aa52c97d4a988b.jpg\",\n  \"310615.jpg\": \"Aves/Dendragapus fuliginosus/6a7b21f7370f02e01a14c4b2e006e641.jpg\",\n  \"310616.jpg\": \"Aves/Dendragapus fuliginosus/887d949d47707b3b62b7a5e311e36d11.jpg\",\n  \"310639.jpg\": \"Insecta/Orthetrum sabina/a26303d50a4253ca24b2a849347df350.jpg\",\n  \"310641.jpg\": \"Insecta/Orthetrum sabina/25752d10c456f26586bdde6c09dc23fa.jpg\",\n  \"310642.jpg\": \"Insecta/Orthetrum sabina/4cd780233551853aabcee37803ef37e7.jpg\",\n  \"310643.jpg\": \"Insecta/Orthetrum sabina/7d85b81e729eb5c18a4f535a98753427.jpg\",\n  \"310665.jpg\": \"Insecta/Argia lugens/eb06fceb5d87e149e0c594b79bdc593c.jpg\",\n  \"310674.jpg\": \"Insecta/Argia lugens/9a681e0d9feb60995f88235342fc5b40.jpg\",\n  \"310695.jpg\": \"Insecta/Argia lugens/421893d68489ec6e754d4f27fbe37e46.jpg\",\n  \"310699.jpg\": \"Insecta/Argia lugens/90b0d22933afec505fe304d5866e9c8a.jpg\",\n  \"31070.jpg\": \"Aves/Megaceryle alcyon/48a6916c027a102ecc630e5a19bc2773.jpg\",\n  \"310700.jpg\": \"Insecta/Argia lugens/29deec866df99464b721f751f846da2f.jpg\",\n  \"310707.jpg\": \"Mammalia/Tursiops truncatus/65e1894cd75785acfa518664d368455d.jpg\",\n  \"310712.jpg\": \"Mammalia/Tursiops truncatus/cb9ad795544b387efcf2ede83c09ab5a.jpg\",\n  \"310728.jpg\": \"Mammalia/Tursiops truncatus/4896f4de52783635d9b828d5127ba8a0.jpg\",\n  \"310747.jpg\": \"Mammalia/Tursiops truncatus/6a5772b2de6581e0fa56e47a28b28277.jpg\",\n  \"310758.jpg\": \"Mammalia/Tursiops truncatus/727bdb544b9beb45b50af1bca07a9d2e.jpg\",\n  \"310762.jpg\": \"Mammalia/Tursiops truncatus/fcebef8a586a73e496dc91fc6a5d137a.jpg\",\n  \"310772.jpg\": \"Mammalia/Tursiops truncatus/91ea1579994d6eb49f3ee9f026960e98.jpg\",\n  \"310776.jpg\": \"Mammalia/Tursiops truncatus/bac0bec9ade498474b711ffd333a59bf.jpg\",\n  \"310777.jpg\": \"Mammalia/Tursiops truncatus/f06749a7bcb65b7d762058ed3b4c06ea.jpg\",\n  \"310778.jpg\": \"Mammalia/Tursiops truncatus/59fbdafcf8f0d25c95394b0997134507.jpg\",\n  \"310781.jpg\": \"Mammalia/Tursiops truncatus/c9210aabd7b4fa3c1fa57ee56588d4a4.jpg\",\n  \"310785.jpg\": \"Mammalia/Tursiops truncatus/c8e44144a1e14c205216b9238dbeece6.jpg\",\n  \"310808.jpg\": \"Mammalia/Tursiops truncatus/4ffe1dff751e779e7ef9d60e76cb8e91.jpg\",\n  \"310812.jpg\": \"Mammalia/Tursiops truncatus/6c5561454d347284fb3b1850c41ed4ba.jpg\",\n  \"310814.jpg\": \"Aves/Pooecetes gramineus/b96fc7b6c698ba70339a82244ebcdcdd.jpg\",\n  \"310815.jpg\": \"Aves/Pooecetes gramineus/5381f7317c4892d8beeead623869a6b5.jpg\",\n  \"310818.jpg\": \"Aves/Pooecetes gramineus/a5dac5ffc607cbc8383e0e8ca51d4c5c.jpg\",\n  \"310819.jpg\": \"Aves/Pooecetes gramineus/1f77ea1bd5c8acb110912e61a05252bc.jpg\",\n  \"310829.jpg\": \"Aves/Pooecetes gramineus/e80fbd72b001fa835f9854ba21594118.jpg\",\n  \"310837.jpg\": \"Aves/Pooecetes gramineus/5cc5cbdef3aa5bf4fb33cba4108242eb.jpg\",\n  \"310840.jpg\": \"Aves/Pooecetes gramineus/7a0a9b53569c28cd6c6fe82754cebaf5.jpg\",\n  \"310848.jpg\": \"Aves/Pooecetes gramineus/1561ee224f1535999fa201c9d97c80c9.jpg\",\n  \"310851.jpg\": \"Aves/Pooecetes gramineus/f5751e099f5fdebedc1f27192e2350ec.jpg\",\n  \"310852.jpg\": \"Aves/Pooecetes gramineus/5854d64eb91570164abdfb229fc37ecd.jpg\",\n  \"310853.jpg\": \"Aves/Pooecetes gramineus/6928ba86ecc6f8c4ec15d5510787416f.jpg\",\n  \"310857.jpg\": \"Aves/Pooecetes gramineus/c835d4d5971f06bb17e6eb8535c85589.jpg\",\n  \"310869.jpg\": \"Aves/Pooecetes gramineus/758114a092f628b2a0802b82f5926ea2.jpg\",\n  \"310871.jpg\": \"Aves/Pooecetes gramineus/a230a99ad3bbd0529e0cdfd770f8157f.jpg\",\n  \"310872.jpg\": \"Aves/Pooecetes gramineus/c8b97516b83eb4e86f547528265a28f7.jpg\",\n  \"310874.jpg\": \"Aves/Pooecetes gramineus/c1851d04a49d90e4df2bd8543f3288a0.jpg\",\n  \"310875.jpg\": \"Aves/Pooecetes gramineus/ad11582e448478accafbff009510fad1.jpg\",\n  \"310882.jpg\": \"Aves/Pooecetes gramineus/c0f915576b172cb63786140d3e5afc54.jpg\",\n  \"310883.jpg\": \"Aves/Pooecetes gramineus/2e9d25d0705600b542918f2b23e73e6f.jpg\",\n  \"310888.jpg\": \"Aves/Pooecetes gramineus/c4c4804c55977b3c7025d5d204b1ec50.jpg\",\n  \"310889.jpg\": \"Aves/Pooecetes gramineus/e8f70ed7ca869df248dd722ac9634e69.jpg\",\n  \"31089.jpg\": \"Aves/Megaceryle alcyon/d84929f4ed93452b6e75e592af2d0d85.jpg\",\n  \"310892.jpg\": \"Aves/Pooecetes gramineus/20fc48e6a85bc85fc6cdfe06137cdbe8.jpg\",\n  \"310898.jpg\": \"Aves/Pooecetes gramineus/d906b2dd1e09e9cf288020302638665d.jpg\",\n  \"310899.jpg\": \"Aves/Pooecetes gramineus/5ec97dece0b5412c1b3e136f181697b6.jpg\",\n  \"3109.jpg\": \"Aves/Picoides nuttallii/acd42884b5e65ad6037aa61bc59975d6.jpg\",\n  \"310902.jpg\": \"Aves/Pooecetes gramineus/2bf592730b8080a94591ba9d9db60f02.jpg\",\n  \"310903.jpg\": \"Aves/Pooecetes gramineus/81498061d6a6e9c7c5e3af110684d857.jpg\",\n  \"310908.jpg\": \"Aves/Pooecetes gramineus/df80266e094ddb2f83d77671e425ed44.jpg\",\n  \"31093.jpg\": \"Reptilia/Bothrops asper/6b51b706bb24830f87b7a9f43f1ee26a.jpg\",\n  \"31094.jpg\": \"Reptilia/Bothrops asper/4ece3b6f1ae1d44d8dff9b696ec5b394.jpg\",\n  \"31095.jpg\": \"Reptilia/Bothrops asper/8caa064aa5a2b171c9cef5f55f7f99ca.jpg\",\n  \"31096.jpg\": \"Reptilia/Bothrops asper/530099d49ae14d74fef682b62c3059ee.jpg\",\n  \"31098.jpg\": \"Reptilia/Bothrops asper/924e002415e1b84b59186091533c348d.jpg\",\n  \"31099.jpg\": \"Reptilia/Bothrops asper/a3a8b24878723dbb75e103c2071c333a.jpg\",\n  \"311035.jpg\": \"Animalia/Pagurus samuelis/e5ad753b96a737e73ed7b4e243cab333.jpg\",\n  \"311039.jpg\": \"Animalia/Pagurus samuelis/31f83d7b686c6f6d486b5f237a9986b4.jpg\",\n  \"311040.jpg\": \"Animalia/Pagurus samuelis/0a827bf5739f27e06d51c2be590ca66a.jpg\",\n  \"31105.jpg\": \"Reptilia/Bothrops asper/9f1af4664919042b4cf4e85bea331a8c.jpg\",\n  \"311051.jpg\": \"Animalia/Pagurus samuelis/941c00c804442088827edf37ad8f3b71.jpg\",\n  \"311061.jpg\": \"Animalia/Pagurus samuelis/a776c84cbe5e0dd01bed16cb501c26b8.jpg\",\n  \"311062.jpg\": \"Animalia/Pagurus samuelis/7e060ff208163fd155d9eabd5dbc6fda.jpg\",\n  \"311063.jpg\": \"Animalia/Pagurus samuelis/61a135d9934e7c43a0cff9519e3e56ab.jpg\",\n  \"31110.jpg\": \"Aves/Actitis hypoleucos/178fd1938726d554496d21519d5a342b.jpg\",\n  \"31112.jpg\": \"Aves/Actitis hypoleucos/f39d8c7e14c691492b12687cc4b54634.jpg\",\n  \"31121.jpg\": \"Aves/Actitis hypoleucos/4186f2a6e42c3f439af4b546020e7c1c.jpg\",\n  \"31123.jpg\": \"Aves/Actitis hypoleucos/957d4494f4c76ed25a0271fd19ee5169.jpg\",\n  \"31124.jpg\": \"Aves/Actitis hypoleucos/7d2bd842646143b15cbfca6f2b671f42.jpg\",\n  \"31125.jpg\": \"Aves/Actitis hypoleucos/87eeb18b561f983c72bca59943cb7046.jpg\",\n  \"31127.jpg\": \"Aves/Actitis hypoleucos/4bef642dff3d8fd8a97ba5d91b069616.jpg\",\n  \"31128.jpg\": \"Aves/Actitis hypoleucos/b9baf9c5f3d9a71f7391aead9fb861bc.jpg\",\n  \"31141.jpg\": \"Aves/Actitis hypoleucos/c8f1e6377c4629b9699ccb905f57cf95.jpg\",\n  \"311438.jpg\": \"Amphibia/Ambystoma laterale/7f4be12f56d19332cb809a4397f1eeea.jpg\",\n  \"311439.jpg\": \"Amphibia/Ambystoma laterale/2997bbe4709479a1cd5426cf9c23be47.jpg\",\n  \"311443.jpg\": \"Amphibia/Ambystoma laterale/5228b7cd0df793d8b5bc433b0f51838f.jpg\",\n  \"311448.jpg\": \"Amphibia/Ambystoma laterale/d311ea74f9ea5a6c03072c9f3ea035cc.jpg\",\n  \"311450.jpg\": \"Amphibia/Ambystoma laterale/d9a7fc12e63a6fd9861fb8924469392b.jpg\",\n  \"311451.jpg\": \"Amphibia/Ambystoma laterale/7add18ed5a0075cdbbba620e476f767b.jpg\",\n  \"311452.jpg\": \"Amphibia/Ambystoma laterale/d0690e86c55e746ab9d99468345d9000.jpg\",\n  \"311456.jpg\": \"Amphibia/Ambystoma laterale/b8659ed312c6eddc5d6935eb4aba7e74.jpg\",\n  \"311457.jpg\": \"Amphibia/Ambystoma laterale/e4604d9c68ab944204d2730393ec4b7e.jpg\",\n  \"311462.jpg\": \"Amphibia/Ambystoma laterale/f660700f2dba254128b63ad4b96a5835.jpg\",\n  \"311463.jpg\": \"Amphibia/Ambystoma laterale/af80898ae4c398fa382900cd7c0943a5.jpg\",\n  \"311469.jpg\": \"Amphibia/Ambystoma laterale/61e6922a1203d8a8e1464a4fd7c8c2af.jpg\",\n  \"311476.jpg\": \"Amphibia/Ambystoma laterale/72c6e4537d4fe2ddd4cf61b1a6645bfc.jpg\",\n  \"311477.jpg\": \"Amphibia/Ambystoma laterale/6b7678fc7ef2fb1102fa7cbc8c00e8b4.jpg\",\n  \"311481.jpg\": \"Amphibia/Ambystoma laterale/6a54c72213d7e6d137e3f1ec8c176856.jpg\",\n  \"311485.jpg\": \"Amphibia/Ambystoma laterale/50ed5592ac106aa9d553cbc5de7da33b.jpg\",\n  \"311487.jpg\": \"Animalia/Patiriella regularis/b797fab925e325b060f7c2e16a1345e6.jpg\",\n  \"31149.jpg\": \"Aves/Actitis hypoleucos/add828e4197dc6a13a0cd683f1b1b5b8.jpg\",\n  \"31150.jpg\": \"Aves/Actitis hypoleucos/5b2de53e5f1f24effc4257e828fb5bb3.jpg\",\n  \"311500.jpg\": \"Animalia/Patiriella regularis/69d966a103f2e9066105a1a8231511bd.jpg\",\n  \"311508.jpg\": \"Animalia/Patiriella regularis/864d95c55eb5b3d531fc428557b0c31f.jpg\",\n  \"31151.jpg\": \"Aves/Actitis hypoleucos/eb7983a3a368ccacd7f6f120274032bc.jpg\",\n  \"311512.jpg\": \"Animalia/Patiriella regularis/a66c16ffc68315e392ec17de0d68fdef.jpg\",\n  \"31152.jpg\": \"Aves/Actitis hypoleucos/a745315dc343696186e5ec651e69f966.jpg\",\n  \"311529.jpg\": \"Animalia/Patiriella regularis/c1f5116492e1b0772e20ebedb5bc78e6.jpg\",\n  \"311542.jpg\": \"Insecta/Phyciodes tharos/a2b3b5c365a672fe6a00acdc07d895ff.jpg\",\n  \"311543.jpg\": \"Insecta/Phyciodes tharos/6b93c493f2900f1170444642c4c66032.jpg\",\n  \"311546.jpg\": \"Insecta/Phyciodes tharos/6cf001f0fc40ddb075353d6761139090.jpg\",\n  \"311582.jpg\": \"Insecta/Phyciodes tharos/0b3c3fb5c3f69df02762dfc9470bc0d7.jpg\",\n  \"311583.jpg\": \"Insecta/Phyciodes tharos/6ad4cfab463df0e4a95e410dcb863ce4.jpg\",\n  \"31159.jpg\": \"Aves/Actitis hypoleucos/beb69c102a0f92c82c5c7aa83c0226a7.jpg\",\n  \"311613.jpg\": \"Insecta/Phyciodes tharos/efcf4b238b0f71ea3ea5de980d9d827c.jpg\",\n  \"31162.jpg\": \"Aves/Actitis hypoleucos/ba16fad51a8c9bebe4b614b9d9640ae1.jpg\",\n  \"311629.jpg\": \"Insecta/Phyciodes tharos/940dd6bfaa709acc7c0017b27b235c56.jpg\",\n  \"31164.jpg\": \"Aves/Actitis hypoleucos/34715f011f02a08b86f1e0f34650e496.jpg\",\n  \"31166.jpg\": \"Aves/Actitis hypoleucos/ca4b75a401444828d48791021f9ddacb.jpg\",\n  \"311679.jpg\": \"Insecta/Phyciodes tharos/e21d94c8149fa57effe028da2d01e331.jpg\",\n  \"3117.jpg\": \"Aves/Picoides nuttallii/ba7fcbacdec034809c33bcfa4943d09f.jpg\",\n  \"311708.jpg\": \"Insecta/Phyciodes tharos/92708fd05a4e06090c338cb877790ab1.jpg\",\n  \"311736.jpg\": \"Insecta/Phyciodes tharos/c58d3ff3bafc90f634832564f035ae25.jpg\",\n  \"311770.jpg\": \"Insecta/Phyciodes tharos/8da991ae834c81536dfbd62bec85a146.jpg\",\n  \"311773.jpg\": \"Insecta/Phyciodes tharos/168ec68c2c0e0cdadbf395f543140021.jpg\",\n  \"311809.jpg\": \"Insecta/Phyciodes tharos/28bd6b6b268f8d5ffc0fb943cfe407d3.jpg\",\n  \"311819.jpg\": \"Insecta/Phyciodes tharos/5bf9cd196d5ed1adae378178894df711.jpg\",\n  \"311821.jpg\": \"Insecta/Phyciodes tharos/d284287f6b1f03663c5a5b7ea79989a7.jpg\",\n  \"311855.jpg\": \"Insecta/Phyciodes tharos/aa3ddea19e180cead351df2d9b037250.jpg\",\n  \"311858.jpg\": \"Insecta/Phyciodes tharos/34041c6b8dbe7e2df1ab0d915d713fda.jpg\",\n  \"31189.jpg\": \"Aves/Geopelia striata/39982f3b39b00ce0fce25e824558eb69.jpg\",\n  \"31190.jpg\": \"Aves/Geopelia striata/4828fb9c325864b7619f36360d061f03.jpg\",\n  \"311909.jpg\": \"Insecta/Phyciodes tharos/a894e2eb1a6f488d41055447a2521552.jpg\",\n  \"31194.jpg\": \"Aves/Geopelia striata/926a4bd1355a766873856b32d2105fb1.jpg\",\n  \"311945.jpg\": \"Insecta/Phyciodes tharos/03e2872aeef69722e3ec25e590685062.jpg\",\n  \"311946.jpg\": \"Insecta/Phyciodes tharos/090c9ede54056710ae6e3ca579506719.jpg\",\n  \"31195.jpg\": \"Aves/Geopelia striata/eccead0b56ccdda29b70b0fd2c32de1d.jpg\",\n  \"31196.jpg\": \"Aves/Geopelia striata/a9e018690102b0335e3a5210433068b4.jpg\",\n  \"311972.jpg\": \"Insecta/Phyciodes tharos/891666cd2f5fbfa82c36651f517f9cc3.jpg\",\n  \"311975.jpg\": \"Insecta/Phyciodes tharos/1d178adf57a4a665fd14a4aff9d36ec4.jpg\",\n  \"311977.jpg\": \"Insecta/Phyciodes tharos/cf6cc7abf1265698d3df8e4024a11aeb.jpg\",\n  \"311992.jpg\": \"Insecta/Phyciodes tharos/04147fde7e5a4f3e7e79103ce6d59811.jpg\",\n  \"312000.jpg\": \"Insecta/Phyciodes tharos/fbb7815ca1593069ee73c1446c8e6d2f.jpg\",\n  \"312017.jpg\": \"Insecta/Phyciodes tharos/c7bfccc32a7c3596a16eb389f99ea49e.jpg\",\n  \"31203.jpg\": \"Aves/Geopelia striata/5e9379e5b528b124c25107a32660883f.jpg\",\n  \"31204.jpg\": \"Aves/Geopelia striata/eda1d64bbc130e6c5660e8ce00615e57.jpg\",\n  \"31207.jpg\": \"Aves/Geopelia striata/f0a2e15e5db9b52786d6ba82599fe99a.jpg\",\n  \"31210.jpg\": \"Aves/Geopelia striata/ae4943d07a805416515787bff86a90b8.jpg\",\n  \"31211.jpg\": \"Aves/Geopelia striata/ae6bf41f6eab998f83f044ec29d92386.jpg\",\n  \"312148.jpg\": \"Aves/Gallinago gallinago/97b34dcf6c19cb25ff84e24fe3ba230d.jpg\",\n  \"312151.jpg\": \"Aves/Gallinago gallinago/78fda000ed1ce5784913648a0b3cbad7.jpg\",\n  \"312162.jpg\": \"Aves/Gallinago gallinago/e1b1989b6a1f86d129f57016f04bdd31.jpg\",\n  \"312163.jpg\": \"Aves/Gallinago gallinago/3d7856717a044cd0b58108664c5b837b.jpg\",\n  \"312169.jpg\": \"Insecta/Leptotes marina/7a3caa568b154f6860e059f558a16e95.jpg\",\n  \"312172.jpg\": \"Insecta/Leptotes marina/f0115277358c56db22dba93e8c741fab.jpg\",\n  \"312173.jpg\": \"Insecta/Leptotes marina/629c417c84e0e46e1967cd3a641e40cb.jpg\",\n  \"312176.jpg\": \"Insecta/Leptotes marina/f2389825c9a2fbfdb16377a81fb29f33.jpg\",\n  \"312177.jpg\": \"Insecta/Leptotes marina/25fd04ea0ee32cf5b9071d72cd51c5f8.jpg\",\n  \"31218.jpg\": \"Aves/Geopelia striata/8f4664e85fcf443300a659cf8eeb51d5.jpg\",\n  \"312191.jpg\": \"Insecta/Leptotes marina/9d6eac006b170e629e7946bf8cffda89.jpg\",\n  \"312195.jpg\": \"Insecta/Leptotes marina/332878d51d6009a61e397dcc38f53e0c.jpg\",\n  \"312204.jpg\": \"Insecta/Leptotes marina/39b2f2d1e5d4ba91b95ae26fdf439494.jpg\",\n  \"312210.jpg\": \"Insecta/Leptotes marina/63b497fa24495006fd34fea86948009e.jpg\",\n  \"31222.jpg\": \"Aves/Geopelia striata/fea3d0b3ecbfdc4d1505097d73172a22.jpg\",\n  \"312224.jpg\": \"Insecta/Leptotes marina/c97fe8045e17e8a81ed1583e4d11748e.jpg\",\n  \"312237.jpg\": \"Insecta/Leptotes marina/196294234a79216d97f8a1aa338df71d.jpg\",\n  \"31224.jpg\": \"Aves/Geopelia striata/9edcac56eef1088140f75c78c560344a.jpg\",\n  \"312246.jpg\": \"Insecta/Leptotes marina/9bbac1dea5e88bbb93653c0226710435.jpg\",\n  \"31225.jpg\": \"Aves/Geopelia striata/bbd7e800f81b775ad14b72eb5d1f0223.jpg\",\n  \"312264.jpg\": \"Insecta/Leptotes marina/d42016a56b36733229daf9c3abcf4c1f.jpg\",\n  \"312265.jpg\": \"Insecta/Leptotes marina/a6fdd3fa1f10201c651287307bb37903.jpg\",\n  \"31227.jpg\": \"Aves/Geopelia striata/354b27e975cfecde9acfdc824e146562.jpg\",\n  \"312271.jpg\": \"Insecta/Leptotes marina/4819c8e2538241b13bf8d6da00b2a018.jpg\",\n  \"312275.jpg\": \"Insecta/Leptotes marina/91757f7a66d93c71ad4f20e3092edfc6.jpg\",\n  \"312278.jpg\": \"Insecta/Leptotes marina/8e8dd07be89066872e2ce545d66dd591.jpg\",\n  \"31228.jpg\": \"Aves/Geopelia striata/bd6d8b1864d4e117bda46029826838c4.jpg\",\n  \"312284.jpg\": \"Insecta/Leptotes marina/456638dbba62106da32bfb1b8ff0d09c.jpg\",\n  \"31229.jpg\": \"Aves/Geopelia striata/e612fa069fa9d439e059ffc451609c31.jpg\",\n  \"312293.jpg\": \"Insecta/Leptotes marina/bd389f69c4c6c3277c1cd81109d3a647.jpg\",\n  \"31230.jpg\": \"Aves/Geopelia striata/379e8fc044ecb12cb9f2c46ec947dad7.jpg\",\n  \"31233.jpg\": \"Aves/Geopelia striata/5fd6d1e7bc11c85412233885b0ae9828.jpg\",\n  \"31235.jpg\": \"Aves/Geopelia striata/b2f1c2d06406f772f6394d0cf8b75c7b.jpg\",\n  \"3124.jpg\": \"Aves/Picoides nuttallii/72060e02fe12ad9407df7dc847682a39.jpg\",\n  \"31250.jpg\": \"Insecta/Argia vivida/a42e3e2b362a317099d41e3b7b40295d.jpg\",\n  \"31263.jpg\": \"Insecta/Argia vivida/fbf8cd6a2d887c1ecc15bcab1cf2fa73.jpg\",\n  \"31275.jpg\": \"Insecta/Argia vivida/567157ef064177f024c2091dd2cac130.jpg\",\n  \"312849.jpg\": \"Aves/Oreothlypis celata/2f366c055747ed145562e4390a6f904f.jpg\",\n  \"312879.jpg\": \"Aves/Oreothlypis celata/eb28aae858ff8c3775b9649bd72ae815.jpg\",\n  \"312888.jpg\": \"Aves/Oreothlypis celata/3e9b059045d486d2c65c59c68438c405.jpg\",\n  \"312901.jpg\": \"Aves/Oreothlypis celata/9697d24fa4d98b1cb6639dd8145fb223.jpg\",\n  \"31291.jpg\": \"Insecta/Argia vivida/ef95d5d1ef447d2f63fcface1b92cf5e.jpg\",\n  \"312930.jpg\": \"Aves/Oreothlypis celata/71358b80b44091465642e8c7b53d4551.jpg\",\n  \"312937.jpg\": \"Aves/Oreothlypis celata/0834ed5c3dd132de00f58d324a93496f.jpg\",\n  \"312970.jpg\": \"Aves/Oreothlypis celata/35e9fc64c17e8eb9330a97aa6feb7931.jpg\",\n  \"312991.jpg\": \"Aves/Oreothlypis celata/4e85724fbfc7d355d653cc675a06bf24.jpg\",\n  \"312992.jpg\": \"Aves/Oreothlypis celata/5f2b43b01e9d75fba5adbab6b5469e26.jpg\",\n  \"313027.jpg\": \"Aves/Oreothlypis celata/10468592a476cb7e4adff89c5feb9a4f.jpg\",\n  \"313033.jpg\": \"Aves/Oreothlypis celata/d929ebbc6b771eba37cd34aa38651ceb.jpg\",\n  \"313058.jpg\": \"Aves/Oreothlypis celata/2587776ed0dae0e07c6b48f6ecdf29bb.jpg\",\n  \"313063.jpg\": \"Aves/Oreothlypis celata/9035b97735b8fd4b4637f5e2f2ddf161.jpg\",\n  \"313066.jpg\": \"Aves/Oreothlypis celata/6896583b0eb6d6ec2e1185a7ae496a7f.jpg\",\n  \"313069.jpg\": \"Aves/Oreothlypis celata/3d13d153a85dc0a01dd38166f6e8855b.jpg\",\n  \"313075.jpg\": \"Aves/Oreothlypis celata/51bca59d99322ba93d7f80659c96f6d4.jpg\",\n  \"313093.jpg\": \"Aves/Oreothlypis celata/40a6b3ee5a2b17cf025408d95a7c1bc4.jpg\",\n  \"313094.jpg\": \"Aves/Oreothlypis celata/13696445b455782380527842ee0c59ad.jpg\",\n  \"313104.jpg\": \"Aves/Oreothlypis celata/d2cdb3b41df4a5eee8bed779703ba394.jpg\",\n  \"313109.jpg\": \"Aves/Oreothlypis celata/b4b34d44e3ad16fe3498ebc5192691d3.jpg\",\n  \"31312.jpg\": \"Insecta/Argia vivida/37470afbe63fb2051f7612d11aaaaa7b.jpg\",\n  \"313125.jpg\": \"Aves/Oreothlypis celata/2933c9bebd12940a869d4561892b90a3.jpg\",\n  \"313126.jpg\": \"Aves/Oreothlypis celata/817aaa94a7b1f1f28af02c502fbaa5d7.jpg\",\n  \"31313.jpg\": \"Insecta/Argia vivida/f537dfde50ae452f183ec58a0f77f1c2.jpg\",\n  \"31314.jpg\": \"Insecta/Argia vivida/73260ebae3016d3898d7a0dd02520dfe.jpg\",\n  \"313159.jpg\": \"Aves/Oreothlypis celata/7cb1c47e3154e449e72ea4879752dc79.jpg\",\n  \"313180.jpg\": \"Aves/Oreothlypis celata/47425484ae62bc985a860759269371c3.jpg\",\n  \"313182.jpg\": \"Insecta/Corydalus cornutus/aea7bd7aae1a2bdaeb16baae5407a82d.jpg\",\n  \"313185.jpg\": \"Insecta/Corydalus cornutus/e2d2594c5a0d0473d472ad4fbd4701de.jpg\",\n  \"313188.jpg\": \"Insecta/Corydalus cornutus/8dfbbe71062cc16307652ac899112e4b.jpg\",\n  \"313191.jpg\": \"Insecta/Corydalus cornutus/3e5be1b4e69f9d8e2d67fc802a06911f.jpg\",\n  \"313197.jpg\": \"Insecta/Corydalus cornutus/70d01eebd44107545046ebda6ce1ebf1.jpg\",\n  \"313198.jpg\": \"Insecta/Corydalus cornutus/5188e210493f9bb57b1577f0666e724c.jpg\",\n  \"313199.jpg\": \"Insecta/Corydalus cornutus/f31ef94720009732ebb542c181c7aacd.jpg\",\n  \"313201.jpg\": \"Insecta/Corydalus cornutus/a2519c32dc4efe8613e86d2bc1f73c38.jpg\",\n  \"313202.jpg\": \"Insecta/Corydalus cornutus/28358a9a82b8ced0d9ddbe9e7e84dcb5.jpg\",\n  \"313208.jpg\": \"Insecta/Corydalus cornutus/a4240f25c4a027f22148260e133f1f96.jpg\",\n  \"313209.jpg\": \"Insecta/Corydalus cornutus/25664104d8583aa813710f0e729c4ed6.jpg\",\n  \"313220.jpg\": \"Insecta/Corydalus cornutus/5e455b1a728c30287e05b030587c0d12.jpg\",\n  \"313238.jpg\": \"Aves/Buteo brachyurus/c6e5487f732ff24c222010b705ce9714.jpg\",\n  \"313239.jpg\": \"Aves/Buteo brachyurus/b34e9cbe0729b10aea68368db4a9caed.jpg\",\n  \"313241.jpg\": \"Aves/Buteo brachyurus/5e0a33634ca2742d0d350f726c61a029.jpg\",\n  \"313249.jpg\": \"Aves/Buteo brachyurus/6d9614b02737e2991c8204c0009f1e81.jpg\",\n  \"31325.jpg\": \"Insecta/Argia vivida/9e381900f1916145cf53a670d0d34e46.jpg\",\n  \"313250.jpg\": \"Aves/Buteo brachyurus/048b34ed2f8cc4522814da401a2b8397.jpg\",\n  \"313251.jpg\": \"Aves/Buteo brachyurus/c8221c2d589f2fd597c8efb268507095.jpg\",\n  \"313252.jpg\": \"Aves/Buteo brachyurus/cdec9193d184b1530cadf94148d5ac9f.jpg\",\n  \"313258.jpg\": \"Aves/Buteo brachyurus/05bf05de54c7bb7b475961961a653d54.jpg\",\n  \"313263.jpg\": \"Insecta/Cycloneda munda/0d0081d6ac005f6dfdbb6e16df1b4e84.jpg\",\n  \"313264.jpg\": \"Insecta/Cycloneda munda/ff2fe41457048261299e9cdb8e06da0d.jpg\",\n  \"313265.jpg\": \"Insecta/Cycloneda munda/91b60d423fd0a7427da760af64bc8ae8.jpg\",\n  \"313266.jpg\": \"Insecta/Cycloneda munda/1886743f81d8b3d72f26886faa909ac3.jpg\",\n  \"313267.jpg\": \"Insecta/Cycloneda munda/5baeb47d657996f5c450e3b34743c0dd.jpg\",\n  \"313270.jpg\": \"Insecta/Cycloneda munda/851b4594b4d6235ddd1a8f0b139bf6b4.jpg\",\n  \"313271.jpg\": \"Insecta/Cycloneda munda/2391a2c85728e0ce233f3c0ec9a4fad5.jpg\",\n  \"313272.jpg\": \"Insecta/Cycloneda munda/5aaf46917450e523ceb77fd21452b3e4.jpg\",\n  \"313274.jpg\": \"Insecta/Cycloneda munda/00b01f5030ed4d2a1161071f14cb1403.jpg\",\n  \"313278.jpg\": \"Insecta/Cycloneda munda/cb7545e10ce35e7fc11c86c43984bb98.jpg\",\n  \"313279.jpg\": \"Insecta/Cycloneda munda/9d9e0f60551b5ff4434cd6185d906af2.jpg\",\n  \"313286.jpg\": \"Insecta/Cycloneda munda/df7d95e5c8960a2dcd681b86fdcdcbbf.jpg\",\n  \"313288.jpg\": \"Insecta/Cycloneda munda/d93fdd74f135fe0af4739322f991d733.jpg\",\n  \"31329.jpg\": \"Insecta/Argia vivida/720725f5b73c7bef06fb52ff80ec4c77.jpg\",\n  \"313290.jpg\": \"Insecta/Cycloneda munda/de65cf1517f42c89d8945c5726b68f9e.jpg\",\n  \"313299.jpg\": \"Insecta/Cycloneda munda/45698a103abdf436e3f644db9cfd648c.jpg\",\n  \"313300.jpg\": \"Insecta/Cycloneda munda/075c7cdbb57eb1144553a78c986e9139.jpg\",\n  \"313301.jpg\": \"Insecta/Cycloneda munda/c6906564aec00b2922b6b67851c7f321.jpg\",\n  \"313302.jpg\": \"Insecta/Cycloneda munda/962e8f5bc8b6ef06022116625d6583b2.jpg\",\n  \"313303.jpg\": \"Insecta/Cycloneda munda/992e19f31f3c3a9cd08a096a45c0b4b4.jpg\",\n  \"313428.jpg\": \"Insecta/Rhionaeschna multicolor/50da5907ed448794aa1fc5dc09dcdfbb.jpg\",\n  \"313431.jpg\": \"Insecta/Rhionaeschna multicolor/e98a6c15816d1d593d81c1d6142c60c2.jpg\",\n  \"313437.jpg\": \"Insecta/Rhionaeschna multicolor/edbbd4b3ecda08d7948b642f00c37fca.jpg\",\n  \"313449.jpg\": \"Insecta/Rhionaeschna multicolor/9d6306f3e479454b8d033f3a6acbc436.jpg\",\n  \"313459.jpg\": \"Insecta/Rhionaeschna multicolor/a564d1e99cddaf372e16c9af42af0474.jpg\",\n  \"313466.jpg\": \"Insecta/Rhionaeschna multicolor/f228f990e691bc8e6909238e5dcb8e1a.jpg\",\n  \"313467.jpg\": \"Insecta/Rhionaeschna multicolor/2ae7a975fe888a9f94d5b1d1dba056b9.jpg\",\n  \"313475.jpg\": \"Insecta/Rhionaeschna multicolor/18be948944ba8881f1084f4dbc969518.jpg\",\n  \"313478.jpg\": \"Insecta/Rhionaeschna multicolor/2a7b207ff812249bd8f986db9fe5eec4.jpg\",\n  \"313496.jpg\": \"Insecta/Rhionaeschna multicolor/d4d9316ab67bdc72374a68269a964158.jpg\",\n  \"31351.jpg\": \"Insecta/Argia vivida/76d4531e356eea2060f82ac22898d6ac.jpg\",\n  \"313510.jpg\": \"Insecta/Rhionaeschna multicolor/d1ba7aa03242248087da898d1eaf9a6b.jpg\",\n  \"313529.jpg\": \"Insecta/Rhionaeschna multicolor/5f718dd10bd5ced99358eec50a4acc51.jpg\",\n  \"313530.jpg\": \"Insecta/Rhionaeschna multicolor/f4b406cb8ebe2744b452e982b98201d6.jpg\",\n  \"313531.jpg\": \"Insecta/Rhionaeschna multicolor/f45756020432b8a76fe65079f7de839f.jpg\",\n  \"313532.jpg\": \"Insecta/Rhionaeschna multicolor/511cde524b060a4e88ba3834864e7cfa.jpg\",\n  \"313541.jpg\": \"Insecta/Rhionaeschna multicolor/ab1df909a3ef3be100cbea32703f6c89.jpg\",\n  \"313552.jpg\": \"Insecta/Rhionaeschna multicolor/8ad80894b5e9ccbae4a7428f5ad81f19.jpg\",\n  \"313553.jpg\": \"Insecta/Rhionaeschna multicolor/6c1af7a2b2603d93c5f0df9f4e219eee.jpg\",\n  \"313554.jpg\": \"Insecta/Rhionaeschna multicolor/f9f925e0c89775bdb5f514fd5c2a0e96.jpg\",\n  \"313571.jpg\": \"Insecta/Rhionaeschna multicolor/83a0cf3fb8b0ed39d7f70837b0d10777.jpg\",\n  \"313574.jpg\": \"Insecta/Rhionaeschna multicolor/7b9d785638d93b35bd8aafe7e332ffb5.jpg\",\n  \"313576.jpg\": \"Aves/Phalaropus tricolor/34fc6b399c66f582cde6ad9263df7a76.jpg\",\n  \"313595.jpg\": \"Aves/Phalaropus tricolor/9c86040a19146c0d814eebf3b7803762.jpg\",\n  \"313596.jpg\": \"Aves/Phalaropus tricolor/9878924f6a44c9e844928a96be2e543c.jpg\",\n  \"313605.jpg\": \"Aves/Phalaropus tricolor/920ab067a26ff5133fac13747fef3b3c.jpg\",\n  \"313606.jpg\": \"Aves/Phalaropus tricolor/641b7e820f8430d3ccb5c1cfcb4d1fc8.jpg\",\n  \"313607.jpg\": \"Aves/Phalaropus tricolor/ed803827a350473e29961ee7903c97a0.jpg\",\n  \"313608.jpg\": \"Aves/Phalaropus tricolor/7cf3438886863b2efd7fe623bd21c077.jpg\",\n  \"31361.jpg\": \"Insecta/Argia vivida/3d7db42cad7360f767ce3c52a7b2dcf1.jpg\",\n  \"313620.jpg\": \"Aves/Phalaropus tricolor/442729b6fed514e311051a7d0a2aa030.jpg\",\n  \"313645.jpg\": \"Aves/Phalaropus tricolor/2eb1e20fe26e45d15e6a5ccb9b7f6f4a.jpg\",\n  \"313649.jpg\": \"Aves/Phalaropus tricolor/4db2b8abc887dbc729c8066f131127cc.jpg\",\n  \"313658.jpg\": \"Aves/Phalaropus tricolor/de90b5aed0dd1395b3d4b84117b148e2.jpg\",\n  \"313660.jpg\": \"Aves/Phalaropus tricolor/e462a260f7cc733b138fd399a6077045.jpg\",\n  \"31368.jpg\": \"Insecta/Argia vivida/26bf67d25560acf99a9776d8780af757.jpg\",\n  \"313774.jpg\": \"Aves/Setophaga ruticilla/409010eb00e81e0b39465e86eaa8f383.jpg\",\n  \"31378.jpg\": \"Insecta/Argia vivida/4e2e20deeeb552e968504682afd5d868.jpg\",\n  \"313780.jpg\": \"Aves/Setophaga ruticilla/c7fc3009cf91cfb44a3502ad75a6925e.jpg\",\n  \"313797.jpg\": \"Aves/Setophaga ruticilla/8357dae5948ea44121cc71bce899d44d.jpg\",\n  \"313799.jpg\": \"Aves/Setophaga ruticilla/14f0e411794b0caaa52e1b806729977e.jpg\",\n  \"313810.jpg\": \"Aves/Setophaga ruticilla/9171719f41f1954b58abfee05fbe18db.jpg\",\n  \"313811.jpg\": \"Aves/Setophaga ruticilla/4b180bd49ba7a1e395326f134a143f27.jpg\",\n  \"313854.jpg\": \"Aves/Setophaga ruticilla/113893f5c417dd0601a2517ba626e5b7.jpg\",\n  \"313857.jpg\": \"Aves/Setophaga ruticilla/f0629702c71c9da4c5976eb8641e7a68.jpg\",\n  \"313858.jpg\": \"Aves/Setophaga ruticilla/93a8e05d99277e74ba2c503eedd3f906.jpg\",\n  \"313869.jpg\": \"Aves/Setophaga ruticilla/cc4a48318cc27387e35fe73f9eaf761a.jpg\",\n  \"313877.jpg\": \"Aves/Setophaga ruticilla/93097840f36b565fdef060a1ff3ca856.jpg\",\n  \"313886.jpg\": \"Aves/Setophaga ruticilla/c371815c84c6de3102bc9c6827c8c14b.jpg\",\n  \"313893.jpg\": \"Aves/Setophaga ruticilla/77b6ae1c85492b77f064877dde494bbb.jpg\",\n  \"313895.jpg\": \"Aves/Setophaga ruticilla/d3363d342e4806aa7495148217934b48.jpg\",\n  \"313905.jpg\": \"Aves/Setophaga ruticilla/d8251f90e6164f93b6aa56fc94f68f10.jpg\",\n  \"313909.jpg\": \"Aves/Setophaga ruticilla/6cc5935298f8d5b9b5c49fb20866502c.jpg\",\n  \"313912.jpg\": \"Aves/Setophaga ruticilla/a8d753e7aabecbae0b0bd995f5a59267.jpg\",\n  \"313916.jpg\": \"Aves/Setophaga ruticilla/412a9c000c7e6c5830bcc9ee7b72d01f.jpg\",\n  \"313922.jpg\": \"Aves/Setophaga ruticilla/25d6566f977f01f04257a048d2902d58.jpg\",\n  \"313926.jpg\": \"Aves/Setophaga ruticilla/0c5adab913933cc12558d2b4e5b9f2f9.jpg\",\n  \"313927.jpg\": \"Aves/Setophaga ruticilla/9a7422dd8a776f7cf1827feadee70352.jpg\",\n  \"313962.jpg\": \"Aves/Setophaga ruticilla/b023c0961e23831aefc332ce4609ed6f.jpg\",\n  \"313963.jpg\": \"Aves/Setophaga ruticilla/405352fcf641b595bbe0c2c44b0a5dcc.jpg\",\n  \"313966.jpg\": \"Aves/Setophaga ruticilla/a4b3b475fbfdd22b430e9410dede0a99.jpg\",\n  \"31397.jpg\": \"Insecta/Argia vivida/a8fd889b88443dad761bf4e474acdf55.jpg\",\n  \"313970.jpg\": \"Aves/Setophaga ruticilla/3376322fc7b63f55855368186d7369df.jpg\",\n  \"313982.jpg\": \"Aves/Setophaga ruticilla/aa4f2c8294e7936ad135713aae3ef725.jpg\",\n  \"31409.jpg\": \"Insecta/Argia vivida/255e21037ef29cb28051bec74945fccd.jpg\",\n  \"314096.jpg\": \"Aves/Antigone canadensis/d4edc6e0c9ce2dd7eb904951ae96c414.jpg\",\n  \"314129.jpg\": \"Aves/Antigone canadensis/b63bf66cca497c23520d566df03e9c97.jpg\",\n  \"31414.jpg\": \"Insecta/Argia vivida/c9390aa46b959975af60f0bdc51171b7.jpg\",\n  \"31415.jpg\": \"Insecta/Argia vivida/a4e7752a570e81824a0ea177cef8086e.jpg\",\n  \"3142.jpg\": \"Aves/Picoides nuttallii/7b9a933ed32475545e99412b38b7f6f1.jpg\",\n  \"314204.jpg\": \"Aves/Antigone canadensis/15ba6e1006250536f690cb0cc7c9d588.jpg\",\n  \"314230.jpg\": \"Aves/Antigone canadensis/ee1d53c5cda53965e291ecd1ab7bdae5.jpg\",\n  \"31424.jpg\": \"Insecta/Argia vivida/b62f56403bbcbccff018379fa957d035.jpg\",\n  \"3143.jpg\": \"Aves/Picoides nuttallii/84eb6f5c71dc5ad0b01e4ff1925856e1.jpg\",\n  \"314486.jpg\": \"Insecta/Elaphria grata/f5485bf78fc57c672c9445dc7b08ca69.jpg\",\n  \"314488.jpg\": \"Insecta/Elaphria grata/5b7e81cea92f433b7716e56a1c16c59b.jpg\",\n  \"314654.jpg\": \"Reptilia/Phrynosoma modestum/6f6a60bd604b22b7be71400fdcd3cfba.jpg\",\n  \"314655.jpg\": \"Reptilia/Phrynosoma modestum/1960e5f67bd0631fceafca6b00c25865.jpg\",\n  \"314656.jpg\": \"Reptilia/Phrynosoma modestum/c158efa4203e848222c7e180c2a61896.jpg\",\n  \"314666.jpg\": \"Reptilia/Phrynosoma modestum/e057178a06777c9dcac09997e014f30c.jpg\",\n  \"314675.jpg\": \"Reptilia/Phrynosoma modestum/057b8c590a834bbcdbccdb9b4158dad8.jpg\",\n  \"314680.jpg\": \"Reptilia/Phrynosoma modestum/125ff8bfee037f0c69190a48fa826433.jpg\",\n  \"314683.jpg\": \"Reptilia/Phrynosoma modestum/5604b70f23f09a3b8e3fba7a099e4227.jpg\",\n  \"314684.jpg\": \"Reptilia/Phrynosoma modestum/e59a95c7d3950627f271c67c4c20f4a2.jpg\",\n  \"314687.jpg\": \"Reptilia/Phrynosoma modestum/728ad0466f1a9434a3074d9895e28801.jpg\",\n  \"314688.jpg\": \"Reptilia/Phrynosoma modestum/73100c2c81c0466c143596c8bc4b8f09.jpg\",\n  \"314689.jpg\": \"Reptilia/Phrynosoma modestum/140abb411afe165f7db94f58ab398e0a.jpg\",\n  \"314690.jpg\": \"Reptilia/Phrynosoma modestum/a05dc50daee956ff3e3ba3c77ad432f6.jpg\",\n  \"314693.jpg\": \"Reptilia/Phrynosoma modestum/43eadc169e8c940fd48bb22051c39958.jpg\",\n  \"314694.jpg\": \"Reptilia/Phrynosoma modestum/ba5536a47db11d47190e9ac18f030b58.jpg\",\n  \"314698.jpg\": \"Reptilia/Phrynosoma modestum/4865fd329eb14815d2b847ae731cbd3d.jpg\",\n  \"314699.jpg\": \"Reptilia/Phrynosoma modestum/80e1d363ce088b4e04a148e5b59e08cb.jpg\",\n  \"314707.jpg\": \"Reptilia/Phrynosoma modestum/bd3b751317044b729723d3de41578f0c.jpg\",\n  \"314797.jpg\": \"Insecta/Pyrgus albescens/cf10ad53cf7f779f2440804ca7f3c40d.jpg\",\n  \"314802.jpg\": \"Insecta/Pyrgus albescens/e406b36e882ac687c16f6cc33a021a61.jpg\",\n  \"314803.jpg\": \"Insecta/Pyrgus albescens/950f72531d9ed322e5d42e7932807e69.jpg\",\n  \"314809.jpg\": \"Insecta/Pyrgus albescens/7dd0c207375f40f207feb9a0bc2a9559.jpg\",\n  \"314812.jpg\": \"Insecta/Pyrgus albescens/3870f9a3733a2dc6c049c25cb6bf6e6b.jpg\",\n  \"314817.jpg\": \"Insecta/Pyrgus albescens/be6ea9011ec3d101bdab000bc6f21b58.jpg\",\n  \"314821.jpg\": \"Aves/Campephilus guatemalensis/5f3e4dc8895cc8a3f3b3b90445773410.jpg\",\n  \"314829.jpg\": \"Aves/Campephilus guatemalensis/bd7299d73921ef807ead3f63398861bb.jpg\",\n  \"314830.jpg\": \"Aves/Campephilus guatemalensis/688f709083bee5c18d5b9b2b063108ec.jpg\",\n  \"314831.jpg\": \"Aves/Campephilus guatemalensis/0f923ac625328f547aa1ddb7c5e709c1.jpg\",\n  \"314836.jpg\": \"Aves/Campephilus guatemalensis/9e7dd1a00fa753f447501edf1dc59da7.jpg\",\n  \"314837.jpg\": \"Aves/Campephilus guatemalensis/60cf5b078a2f58aa9d5fa706568983dc.jpg\",\n  \"314843.jpg\": \"Aves/Campephilus guatemalensis/a60b9081fa7de792ff0935e720cd47bb.jpg\",\n  \"314846.jpg\": \"Aves/Campephilus guatemalensis/451792c781972e125bec5cd5829929a1.jpg\",\n  \"314847.jpg\": \"Aves/Campephilus guatemalensis/9d8f64f71b73a1a9a7177528ff69c268.jpg\",\n  \"314848.jpg\": \"Aves/Campephilus guatemalensis/652ff3b8873b2b63460fba88c43c7389.jpg\",\n  \"314849.jpg\": \"Aves/Campephilus guatemalensis/2e694c850c5f6350db0006c711c091fe.jpg\",\n  \"314853.jpg\": \"Aves/Campephilus guatemalensis/eafab194d0ef37e7f3d35a654e89aed8.jpg\",\n  \"314857.jpg\": \"Aves/Campephilus guatemalensis/2009b4b444e2a10af9cda38c58220a9d.jpg\",\n  \"314861.jpg\": \"Aves/Campephilus guatemalensis/8f553467cf906c475872c0a3767aa122.jpg\",\n  \"314862.jpg\": \"Aves/Campephilus guatemalensis/e6efdfa062883fbe1d0f32cc37858e24.jpg\",\n  \"314863.jpg\": \"Aves/Campephilus guatemalensis/9af864aac31d9d5bf6c13ed2e94705ec.jpg\",\n  \"314864.jpg\": \"Aves/Campephilus guatemalensis/3f168f9fedc56a03863cf3195c5deae8.jpg\",\n  \"314865.jpg\": \"Aves/Campephilus guatemalensis/aaa5800dac36efec387a12f724a95023.jpg\",\n  \"314866.jpg\": \"Aves/Campephilus guatemalensis/d63fc4584a0fb5eb1f653c5ec9a17c0c.jpg\",\n  \"314871.jpg\": \"Aves/Campephilus guatemalensis/8a52b000122b9996126df4dfedeb4724.jpg\",\n  \"314872.jpg\": \"Aves/Campephilus guatemalensis/682047dd041c1baa91afcaea7c0d9ae5.jpg\",\n  \"314876.jpg\": \"Aves/Campephilus guatemalensis/fbe4f2cea67e94b65a46997fd6ec37cb.jpg\",\n  \"314877.jpg\": \"Aves/Campephilus guatemalensis/5bf1ce1f2262a8037ffd0138ab58a67d.jpg\",\n  \"314883.jpg\": \"Aves/Campephilus guatemalensis/482694a820734c86d3b5f98eb5f2c972.jpg\",\n  \"315003.jpg\": \"Aves/Haematopus ostralegus/97c144b67827b492c02663893f1dcb4d.jpg\",\n  \"315007.jpg\": \"Aves/Haematopus ostralegus/074688bcd954523b3e45dc19e1bc6ffa.jpg\",\n  \"315008.jpg\": \"Aves/Haematopus ostralegus/0972f86d8ab10ea7b849c350ce84239c.jpg\",\n  \"315012.jpg\": \"Aves/Haematopus ostralegus/19d7ec426eee1b77d18109d3311be40f.jpg\",\n  \"315020.jpg\": \"Aves/Haematopus ostralegus/c4ef2570ae579699a7fe8d4d38c4dce3.jpg\",\n  \"315022.jpg\": \"Aves/Haematopus ostralegus/c0ef7003bc2a787b29f478fd8b0a965b.jpg\",\n  \"315025.jpg\": \"Aves/Haematopus ostralegus/ee1794f2c874f0264f969a690d2c431f.jpg\",\n  \"315026.jpg\": \"Aves/Haematopus ostralegus/4dda996da2fec9f413600e28ceef9553.jpg\",\n  \"315027.jpg\": \"Aves/Haematopus ostralegus/923a36c57309a939d3867fe9afad63d4.jpg\",\n  \"315038.jpg\": \"Aves/Haematopus ostralegus/9b7b3efb7100424a85c08dc5bb21c9d3.jpg\",\n  \"315045.jpg\": \"Aves/Haematopus ostralegus/8495ccca90ca581e5e861a0968e9bba3.jpg\",\n  \"315049.jpg\": \"Amphibia/Ambystoma opacum/6d0b8c29933de1164d95379d96e3e5ad.jpg\",\n  \"315053.jpg\": \"Amphibia/Ambystoma opacum/8a6168d2f1bd536adf8795d8b42b241f.jpg\",\n  \"315069.jpg\": \"Amphibia/Ambystoma opacum/8e14521cedfc3be1776385eddc311553.jpg\",\n  \"315070.jpg\": \"Amphibia/Ambystoma opacum/c0ec0116383d292fb483824c86775dc9.jpg\",\n  \"315071.jpg\": \"Amphibia/Ambystoma opacum/f5d488b50ac1b4ca0ea1f3b736de3fea.jpg\",\n  \"315073.jpg\": \"Amphibia/Ambystoma opacum/adc6232b617d8a481640c35872c97f0c.jpg\",\n  \"315076.jpg\": \"Amphibia/Ambystoma opacum/362ed335decf72c69bb73f6c7c7ead3b.jpg\",\n  \"315087.jpg\": \"Amphibia/Ambystoma opacum/379b9f814b5a65becd9626d0ec866c9f.jpg\",\n  \"315089.jpg\": \"Amphibia/Ambystoma opacum/c3d86c78095aad92e4e88f9559a2ace9.jpg\",\n  \"31509.jpg\": \"Aves/Ephippiorhynchus senegalensis/9d792875848f3d160b16357990c38198.jpg\",\n  \"315090.jpg\": \"Amphibia/Ambystoma opacum/7e708994995f77960b0f331eb607fa9d.jpg\",\n  \"315091.jpg\": \"Amphibia/Ambystoma opacum/574b2bad21366d071cb0eef4ea330970.jpg\",\n  \"315092.jpg\": \"Amphibia/Ambystoma opacum/06cfe24e2256dd2826025f8755d3e03d.jpg\",\n  \"315093.jpg\": \"Amphibia/Ambystoma opacum/058c27cf7fe96367e841f8ff940a7d58.jpg\",\n  \"315096.jpg\": \"Amphibia/Ambystoma opacum/f870af8dc152c003d415d481b6414360.jpg\",\n  \"315097.jpg\": \"Amphibia/Ambystoma opacum/eccd2c1b2df6410ab38b7ba87bb1d18f.jpg\",\n  \"3151.jpg\": \"Aves/Picoides nuttallii/3757812897eed6679f7efa4653012848.jpg\",\n  \"315100.jpg\": \"Amphibia/Ambystoma opacum/a428246539f6739350380ce06f0033e3.jpg\",\n  \"315102.jpg\": \"Amphibia/Ambystoma opacum/f5bcf230b094087cbdd4c0d7b01acd44.jpg\",\n  \"315104.jpg\": \"Insecta/Lacinipolia renigera/4a1b96eead0ca753103372f9d3aebb57.jpg\",\n  \"315106.jpg\": \"Insecta/Lacinipolia renigera/cbd35d77b7c003cf2d69bc79af8b467d.jpg\",\n  \"315107.jpg\": \"Insecta/Lacinipolia renigera/cae852586fb68379d003453831430b9f.jpg\",\n  \"315108.jpg\": \"Insecta/Lacinipolia renigera/afa308580346955df396ab9f844d8433.jpg\",\n  \"31511.jpg\": \"Aves/Ephippiorhynchus senegalensis/19dc3feb3f834fbefb3904932a309446.jpg\",\n  \"315111.jpg\": \"Insecta/Lacinipolia renigera/1581dd12338423939b5046591761cfba.jpg\",\n  \"315112.jpg\": \"Insecta/Lacinipolia renigera/e9f6c563c79eec43a07f85cd3b43ffe5.jpg\",\n  \"315114.jpg\": \"Insecta/Lacinipolia renigera/30c297535b50c73400d2b976a74b4987.jpg\",\n  \"315115.jpg\": \"Insecta/Lacinipolia renigera/e8cbe495c94bd4ad45f3b2d1fb6fe680.jpg\",\n  \"315117.jpg\": \"Insecta/Lacinipolia renigera/bcfe4ef0aaf1c99791b06eb04d1f00e0.jpg\",\n  \"315118.jpg\": \"Insecta/Lacinipolia renigera/6402406909d46fabb2f269b8e8f142da.jpg\",\n  \"315120.jpg\": \"Insecta/Lacinipolia renigera/decdd55147cbcb70f3cafd73c3e7b587.jpg\",\n  \"315123.jpg\": \"Insecta/Lacinipolia renigera/8f2b6bcbf765c8047208321e10cdae98.jpg\",\n  \"315124.jpg\": \"Insecta/Lacinipolia renigera/8150d67ae83979206bda55eb026936d7.jpg\",\n  \"31513.jpg\": \"Aves/Ephippiorhynchus senegalensis/0c82670e704c10fb53cb483c0457f544.jpg\",\n  \"315130.jpg\": \"Insecta/Lacinipolia renigera/184087c657d70e8719911e7e36d08d96.jpg\",\n  \"315132.jpg\": \"Insecta/Lacinipolia renigera/6770d439e10009b55f4098c3b50a271d.jpg\",\n  \"315140.jpg\": \"Insecta/Lacinipolia renigera/a0b5c78db69880b1c60cfb144a6b39ee.jpg\",\n  \"315142.jpg\": \"Insecta/Lacinipolia renigera/3b546722ca2d015bed7ca6c1601b3aec.jpg\",\n  \"315143.jpg\": \"Insecta/Lacinipolia renigera/479d09c1da538fe376b514133b639567.jpg\",\n  \"315144.jpg\": \"Insecta/Lacinipolia renigera/89af7defd4e95914fafd94ebcfdf97ba.jpg\",\n  \"315145.jpg\": \"Insecta/Lacinipolia renigera/4ea0dd1f8e7c09d5eb864298d0cef6d1.jpg\",\n  \"315147.jpg\": \"Insecta/Lacinipolia renigera/fed09f30980e6a62b2f279f380417320.jpg\",\n  \"315148.jpg\": \"Insecta/Lacinipolia renigera/3ae6018ee8079e2baaf0d4b278f9ce29.jpg\",\n  \"315156.jpg\": \"Mollusca/Megathura crenulata/2eca14ae28af1dd84d37da97f15f7680.jpg\",\n  \"315157.jpg\": \"Mollusca/Megathura crenulata/679bf82c1e10ca5ede57c7bc400f02c8.jpg\",\n  \"315158.jpg\": \"Mollusca/Megathura crenulata/c3eb41cf50e7ab6c14a672f87264b918.jpg\",\n  \"315165.jpg\": \"Mollusca/Megathura crenulata/a4b3235027db2e522a83d2898fd9c7b7.jpg\",\n  \"315168.jpg\": \"Mollusca/Megathura crenulata/a907c5655432e439db6fe7b85c388c7e.jpg\",\n  \"3152.jpg\": \"Aves/Picoides nuttallii/dd886980f766244f6b8e38e3d63da5df.jpg\",\n  \"315210.jpg\": \"Aves/Megadyptes antipodes/e5d433d84266d5334fbf6fae98958c1d.jpg\",\n  \"315214.jpg\": \"Aves/Megadyptes antipodes/abe5043f6c22be69cdf55573b2a2e14b.jpg\",\n  \"315215.jpg\": \"Aves/Megadyptes antipodes/24fd44069cdebdd07fbd9660e493c7e8.jpg\",\n  \"31522.jpg\": \"Aves/Ephippiorhynchus senegalensis/f92127f2e56bc2040fa5be84f9556334.jpg\",\n  \"315235.jpg\": \"Reptilia/Actinemys marmorata/6cd7738d136dff3d445615726cece2ee.jpg\",\n  \"315243.jpg\": \"Reptilia/Actinemys marmorata/eb9e988cc9be6137b3e053d53d9680ec.jpg\",\n  \"315246.jpg\": \"Reptilia/Actinemys marmorata/e3ad0830814561f91eb44b9aebe67cf2.jpg\",\n  \"315247.jpg\": \"Reptilia/Actinemys marmorata/b7a59907b9bc1f7fc3ecdabf9bd14570.jpg\",\n  \"315255.jpg\": \"Reptilia/Actinemys marmorata/9e4cc9acad4a4f457f6441e1d69cbe28.jpg\",\n  \"315261.jpg\": \"Reptilia/Actinemys marmorata/1fac6c0acefa7cebed2dfb00a03d7a05.jpg\",\n  \"315262.jpg\": \"Reptilia/Actinemys marmorata/cc0e66e19c878e07ef312570d5664bfd.jpg\",\n  \"315263.jpg\": \"Reptilia/Actinemys marmorata/7962622bab64a4d5eca9026ef44a6574.jpg\",\n  \"315264.jpg\": \"Reptilia/Actinemys marmorata/959468b63d0af74a0d07da6f7fbfc647.jpg\",\n  \"315265.jpg\": \"Reptilia/Actinemys marmorata/f6f23d2acaddcaaf4eed121771de6d4f.jpg\",\n  \"315282.jpg\": \"Reptilia/Actinemys marmorata/473ed4f86c8e14fce69797b4cf84a59a.jpg\",\n  \"31529.jpg\": \"Animalia/Linckia laevigata/2e7fbec6df4ba919649c4b788d9a31bf.jpg\",\n  \"315292.jpg\": \"Reptilia/Actinemys marmorata/0b17e0037e60ce0322ed75906d3b4390.jpg\",\n  \"315308.jpg\": \"Reptilia/Actinemys marmorata/851ed9a81df7460f3ce1fba2e4567ba1.jpg\",\n  \"315309.jpg\": \"Reptilia/Actinemys marmorata/1e2565781cf044522994ef8b093378c7.jpg\",\n  \"31531.jpg\": \"Animalia/Linckia laevigata/8b115558817f9b7d6ed8897772ff83c2.jpg\",\n  \"31532.jpg\": \"Animalia/Linckia laevigata/953af2b811b4888181f272bb11aee27c.jpg\",\n  \"31533.jpg\": \"Animalia/Linckia laevigata/2572cb720fc96110bc24d02f701f9271.jpg\",\n  \"315335.jpg\": \"Reptilia/Thamnophis cyrtopsis ocellatus/2cfcda9025eb7a78ac2976ab3341c2a0.jpg\",\n  \"315337.jpg\": \"Reptilia/Thamnophis cyrtopsis ocellatus/d48c2919c2803ae8b631247d55a4b1ee.jpg\",\n  \"315338.jpg\": \"Reptilia/Thamnophis cyrtopsis ocellatus/6b9dd87171363ec3804b6fad02f58c2d.jpg\",\n  \"315339.jpg\": \"Reptilia/Thamnophis cyrtopsis ocellatus/932f8486ae4f71ab631a5a72f3fb4fc5.jpg\",\n  \"315342.jpg\": \"Insecta/Oncometopia orbona/f46224465ba4d7923c0f83918c15b945.jpg\",\n  \"315343.jpg\": \"Insecta/Oncometopia orbona/3b1f418ec693fb3da2f26285cf2509d0.jpg\",\n  \"315344.jpg\": \"Insecta/Oncometopia orbona/6b1199cf88775273fbfc04f0278ba118.jpg\",\n  \"315345.jpg\": \"Insecta/Oncometopia orbona/6342c7debfc4a8a084a0d02b33318f6f.jpg\",\n  \"315348.jpg\": \"Insecta/Oncometopia orbona/4ff7db7c341095c9a2a28bd32f7c23d8.jpg\",\n  \"315403.jpg\": \"Arachnida/Steatoda grossa/119d724a5c8fa64c9ad7baac7c36bc0e.jpg\",\n  \"315410.jpg\": \"Arachnida/Steatoda grossa/8b2d37ef672d5889d7724826e22ae1cf.jpg\",\n  \"315414.jpg\": \"Arachnida/Steatoda grossa/c9ae105130e60d4ccd7816807fe33c29.jpg\",\n  \"315419.jpg\": \"Arachnida/Steatoda grossa/28b76f50c5f651c4d695daad13ea2ee2.jpg\",\n  \"315421.jpg\": \"Arachnida/Steatoda grossa/04a6c19578b514393ad9554f134d571e.jpg\",\n  \"315424.jpg\": \"Arachnida/Steatoda grossa/c02a195f55f96df043d8941c6ecc5bc3.jpg\",\n  \"315542.jpg\": \"Aves/Anas penelope/86fe149d0d52fce18caaec41fd467c5c.jpg\",\n  \"315575.jpg\": \"Aves/Anas penelope/22f2e662105636a416b588eb0260774d.jpg\",\n  \"315576.jpg\": \"Aves/Anas penelope/9914bf44067a5da3fc7af9cea6117945.jpg\",\n  \"315582.jpg\": \"Aves/Anas penelope/4cd3fce2481f21c407f1d7baf18ffc33.jpg\",\n  \"315583.jpg\": \"Aves/Anas penelope/5c63131ecdd24f433137a259164c5042.jpg\",\n  \"315590.jpg\": \"Aves/Anas penelope/6f13debdcc7c0695cac1ee2e0c623b4f.jpg\",\n  \"315591.jpg\": \"Aves/Anas penelope/47896bff83c5801d4bb53efabfb0f6c9.jpg\",\n  \"3156.jpg\": \"Aves/Picoides nuttallii/edb0dc2f3d5ea95c4d9e7d87b77bb409.jpg\",\n  \"315600.jpg\": \"Aves/Anas penelope/b8a4d6a47b6bfbbba047dc30a8f967d3.jpg\",\n  \"315611.jpg\": \"Aves/Anas penelope/18e520d14fb09ced9766ee96d933eaaa.jpg\",\n  \"31579.jpg\": \"Reptilia/Crotalus molossus nigrescens/ce123030846ff67c942209fc0ff6e833.jpg\",\n  \"315799.jpg\": \"Insecta/Pyrisitia lisa/cad6ea6c585bc2ee97d46891f2596180.jpg\",\n  \"315802.jpg\": \"Insecta/Pyrisitia lisa/d18dd28a9657b9b1b144104931e3d640.jpg\",\n  \"315815.jpg\": \"Insecta/Pyrisitia lisa/a6b28a21356656d93e4263ec6efd09c3.jpg\",\n  \"315817.jpg\": \"Insecta/Pyrisitia lisa/e74cb3e2ae4e2619f7a9196b5a8aab4a.jpg\",\n  \"315827.jpg\": \"Insecta/Pyrisitia lisa/4a4cf7f5fd78842749c975ea9f3cd67d.jpg\",\n  \"315828.jpg\": \"Insecta/Pyrisitia lisa/1bc30c6d3dedc129abc0b79f54074926.jpg\",\n  \"315829.jpg\": \"Insecta/Pyrisitia lisa/2fb53b9aa8e4055a7970bbb799876d91.jpg\",\n  \"315832.jpg\": \"Insecta/Pyrisitia lisa/d59e2508585d16cbf0632b5f4f424077.jpg\",\n  \"315833.jpg\": \"Insecta/Pyrisitia lisa/e103c37ee28183f8075c0260906e57ff.jpg\",\n  \"315846.jpg\": \"Insecta/Pyrisitia lisa/348b7f339f174ca33364927972531514.jpg\",\n  \"315847.jpg\": \"Insecta/Pyrisitia lisa/43b66473b89108379f21263cce72f163.jpg\",\n  \"315864.jpg\": \"Insecta/Pyrisitia lisa/096c2bd7303dd4d1e90237d1f186ddb8.jpg\",\n  \"315865.jpg\": \"Insecta/Pyrisitia lisa/2819412ac5f72257971034cfa0552f3c.jpg\",\n  \"31587.jpg\": \"Reptilia/Crotalus molossus nigrescens/3dbbe93584ff0f3f1a3275d13771882a.jpg\",\n  \"315878.jpg\": \"Insecta/Pyrisitia lisa/bb9f2af02c529b08a77036b68504d973.jpg\",\n  \"315883.jpg\": \"Insecta/Pyrisitia lisa/bec133a763e763efc66bba3a090fc4e2.jpg\",\n  \"315885.jpg\": \"Insecta/Pyrisitia lisa/396998f1f47092a21b7b64b11e3307c8.jpg\",\n  \"315886.jpg\": \"Insecta/Pyrisitia lisa/fcfe689f3eef173d0b272276c15abe4f.jpg\",\n  \"315891.jpg\": \"Insecta/Pyrisitia lisa/fb0a2cf620b895bffde2d427f6202545.jpg\",\n  \"315908.jpg\": \"Insecta/Pyrisitia lisa/aaf9428f4b9c9d2298526e1767bf01de.jpg\",\n  \"315915.jpg\": \"Insecta/Pyrisitia lisa/f1b537e73c9ff4453da95a50f80cfbb4.jpg\",\n  \"315932.jpg\": \"Insecta/Pyrisitia lisa/684273d5d2d7cba6d28c1307eab432ed.jpg\",\n  \"315934.jpg\": \"Insecta/Pyrisitia lisa/94ab087240d30a26a87f608333cb734f.jpg\",\n  \"315945.jpg\": \"Insecta/Pyrisitia lisa/4818225d113289b6bee8368ca6241c9b.jpg\",\n  \"315955.jpg\": \"Insecta/Sympetrum fonscolombii/9424820d1f02a4463309f4c250183060.jpg\",\n  \"315962.jpg\": \"Insecta/Sympetrum fonscolombii/861060c494d19847e6bd7ee5fd89a9a0.jpg\",\n  \"315963.jpg\": \"Insecta/Sympetrum fonscolombii/2d73f71e76de480dc79cbe1e9ebdb9fa.jpg\",\n  \"315966.jpg\": \"Insecta/Sympetrum fonscolombii/4d09b72cae3cc4b9590fde17a51f48d5.jpg\",\n  \"315981.jpg\": \"Insecta/Sympetrum fonscolombii/14a88dd6b16188c2908bf490731529b5.jpg\",\n  \"315986.jpg\": \"Insecta/Sympetrum fonscolombii/f0c15c0a4f961cbadb74730fa861c83c.jpg\",\n  \"315988.jpg\": \"Insecta/Sympetrum fonscolombii/1e82f184a7717cda0c84f5bc52831aaf.jpg\",\n  \"316003.jpg\": \"Insecta/Vanessa cardui/3a8a905d86e75e3cdf444760631ab12d.jpg\",\n  \"316027.jpg\": \"Insecta/Vanessa cardui/8ed92d9b6b305757a130a4a1175134f3.jpg\",\n  \"316078.jpg\": \"Insecta/Vanessa cardui/d057cecf749845cf393469d04b9b0f3b.jpg\",\n  \"316080.jpg\": \"Insecta/Vanessa cardui/eeb712a0015bf75a693e64111589af06.jpg\",\n  \"316099.jpg\": \"Insecta/Vanessa cardui/83286d7a2ced67897ecd4bd9ca2d5ef7.jpg\",\n  \"316102.jpg\": \"Insecta/Vanessa cardui/097e23403849fe43cf77cd2ee051658c.jpg\",\n  \"316143.jpg\": \"Insecta/Vanessa cardui/59cce4ff17dbdbda11061c8a981fac31.jpg\",\n  \"316144.jpg\": \"Insecta/Vanessa cardui/76090fd54d6138498da75d603d737c40.jpg\",\n  \"316181.jpg\": \"Insecta/Vanessa cardui/c514edb8bd4adaf889486de4f300408e.jpg\",\n  \"316183.jpg\": \"Insecta/Vanessa cardui/5971894abf6b4d8c9267163e473894e7.jpg\",\n  \"316195.jpg\": \"Insecta/Vanessa cardui/2c94245fd97d07bb2da19fdb32fc6a2d.jpg\",\n  \"316205.jpg\": \"Insecta/Vanessa cardui/b9bed9ecaf8b08952baeaba70202faae.jpg\",\n  \"316221.jpg\": \"Insecta/Vanessa cardui/d18c423cf9f49eb8411a8ecf0bfec747.jpg\",\n  \"316251.jpg\": \"Insecta/Vanessa cardui/7808cabc8f8f57c87f4a0b24b1c30d44.jpg\",\n  \"316281.jpg\": \"Insecta/Vanessa cardui/5325bf444b4c5964f3c8e5d6618ceaad.jpg\",\n  \"316288.jpg\": \"Insecta/Vanessa cardui/0368fae90e1ecc0f121434da8084279c.jpg\",\n  \"316309.jpg\": \"Insecta/Vanessa cardui/340a6c6c564176243a950cdc1fbf145c.jpg\",\n  \"316365.jpg\": \"Insecta/Vanessa cardui/85a000798005268e6678da445b52fc1c.jpg\",\n  \"316384.jpg\": \"Insecta/Vanessa cardui/a863462b73cc1134213e3377b7634db3.jpg\",\n  \"316399.jpg\": \"Insecta/Vanessa cardui/a796e946c64752964ad05592c9cb5dd4.jpg\",\n  \"316475.jpg\": \"Insecta/Vanessa cardui/c4db869ebe3d30191e2829d14b5e1769.jpg\",\n  \"316478.jpg\": \"Insecta/Cotinis nitida/1ef23684867aec783ee6d2a8802f3484.jpg\",\n  \"316480.jpg\": \"Insecta/Cotinis nitida/8a4fa46881348ca21ed77ccaabf5438c.jpg\",\n  \"316485.jpg\": \"Insecta/Cotinis nitida/ab622223f5a367bfaeea676744bdc3a5.jpg\",\n  \"316492.jpg\": \"Insecta/Cotinis nitida/dd0997d03c38251e2ef4cfa75dcf2edc.jpg\",\n  \"316493.jpg\": \"Insecta/Cotinis nitida/69763ae7d1b55c3596225b129a3f6bcd.jpg\",\n  \"316495.jpg\": \"Insecta/Cotinis nitida/eaa872d7723bbe2acf84e13b033700ae.jpg\",\n  \"316498.jpg\": \"Insecta/Cotinis nitida/6b861d7cb272f08b701b123b7154c70e.jpg\",\n  \"316499.jpg\": \"Insecta/Cotinis nitida/1600c7067b7c21d1184144fcae5034a2.jpg\",\n  \"316502.jpg\": \"Insecta/Cotinis nitida/d1132198261857ae0fb2dc8509be2b79.jpg\",\n  \"316507.jpg\": \"Insecta/Cotinis nitida/afd6aa251c36c187527fb14a1c693a62.jpg\",\n  \"316508.jpg\": \"Insecta/Cotinis nitida/16a83f50af3d8b2e8ce3bf8e0bffe577.jpg\",\n  \"316511.jpg\": \"Insecta/Cotinis nitida/1295b2b951414d46754863fb4f127709.jpg\",\n  \"316514.jpg\": \"Insecta/Cotinis nitida/418f73b794959588cfd6cc466f5680fb.jpg\",\n  \"316518.jpg\": \"Insecta/Cotinis nitida/e6915c323b441eafa6a5065b3de5ecad.jpg\",\n  \"316526.jpg\": \"Insecta/Cotinis nitida/29693bf2b69a296d967c3051a1d71806.jpg\",\n  \"316531.jpg\": \"Insecta/Cotinis nitida/66f26bc406c847ab03ea533fbb26fccb.jpg\",\n  \"316537.jpg\": \"Insecta/Cotinis nitida/658efe2a89acbfcaccae53a8f296485d.jpg\",\n  \"316540.jpg\": \"Mammalia/Dama dama/5f2e6cfad4b9978ca04886b53dca964d.jpg\",\n  \"316552.jpg\": \"Mammalia/Dama dama/b990a6a21c7a42025c26fce0e4034123.jpg\",\n  \"316559.jpg\": \"Insecta/Episyrphus balteatus/8b24d7bf2fedf164d2a06e186e181c50.jpg\",\n  \"316563.jpg\": \"Insecta/Episyrphus balteatus/5a7298eb1def8aa69ea7c6a7e8d123ff.jpg\",\n  \"316565.jpg\": \"Insecta/Episyrphus balteatus/09e5cd20f540f74c1040df0ff9de3aed.jpg\",\n  \"316566.jpg\": \"Insecta/Episyrphus balteatus/a7d7b8e03918aec69b276d101c51c9f7.jpg\",\n  \"316575.jpg\": \"Insecta/Episyrphus balteatus/a6851ea170bdcb6bbdaa3cb3c1a0b921.jpg\",\n  \"316580.jpg\": \"Insecta/Episyrphus balteatus/d50e3102a1a3959bb41065ad7bc27e5f.jpg\",\n  \"316583.jpg\": \"Insecta/Episyrphus balteatus/6898f7bc0af6c65fb7ec244170b40f96.jpg\",\n  \"316586.jpg\": \"Insecta/Episyrphus balteatus/5260f93199a8b6c41a3a2e3dc1b3f63e.jpg\",\n  \"316587.jpg\": \"Insecta/Episyrphus balteatus/78e27dd7a173836023b162f563000790.jpg\",\n  \"316588.jpg\": \"Insecta/Episyrphus balteatus/76da174d0632fb865dade2f039723f21.jpg\",\n  \"316597.jpg\": \"Insecta/Episyrphus balteatus/105b5e6898f41ef71aea98cef8fb6d9b.jpg\",\n  \"316602.jpg\": \"Insecta/Episyrphus balteatus/e4c990f65ebad8fb2723be0d6f1895f1.jpg\",\n  \"316603.jpg\": \"Insecta/Episyrphus balteatus/13b7dd4ce888cadda90a32f0ff67cc24.jpg\",\n  \"316604.jpg\": \"Insecta/Episyrphus balteatus/98418a604284f42ba2f514429175a431.jpg\",\n  \"316605.jpg\": \"Insecta/Episyrphus balteatus/d4cbe44487e0fafa4d30db6aed2d7f55.jpg\",\n  \"316606.jpg\": \"Insecta/Episyrphus balteatus/c6887e2b6479d98dff511ed825c8271f.jpg\",\n  \"316607.jpg\": \"Insecta/Episyrphus balteatus/85ea6c210104e71dec816b8d7ee1e547.jpg\",\n  \"316608.jpg\": \"Insecta/Episyrphus balteatus/831be0a2099143c90abc3d95e9048fe3.jpg\",\n  \"316609.jpg\": \"Insecta/Episyrphus balteatus/fcfed8627fd8eb7df1b709ae4b817753.jpg\",\n  \"316687.jpg\": \"Aves/Quiscalus quiscula/770c802df4e255db36dff3fc4a0a13e5.jpg\",\n  \"316698.jpg\": \"Aves/Quiscalus quiscula/7195f2733d7ee92359778bc3cd6444ba.jpg\",\n  \"316705.jpg\": \"Aves/Quiscalus quiscula/2ea35130df9ed15f679268f102b0a5a3.jpg\",\n  \"316728.jpg\": \"Aves/Quiscalus quiscula/bc8d0834ff7d6332f1b1821773a7b148.jpg\",\n  \"316731.jpg\": \"Aves/Quiscalus quiscula/bc44d0f8d70e891ac082d43f9185cd64.jpg\",\n  \"31674.jpg\": \"Insecta/Hippodamia convergens/29ce536e8da2177509ece393dc7a67d2.jpg\",\n  \"316750.jpg\": \"Aves/Quiscalus quiscula/39e695f201658cc67881b174b74b8f45.jpg\",\n  \"316762.jpg\": \"Aves/Quiscalus quiscula/6e48711fcc0652d385bac5bfad972201.jpg\",\n  \"316765.jpg\": \"Aves/Quiscalus quiscula/06026d5c85419bead5eb51226d5dfe8e.jpg\",\n  \"31678.jpg\": \"Insecta/Hippodamia convergens/452e59129439362c1b99a9ef410a4bb7.jpg\",\n  \"31680.jpg\": \"Insecta/Hippodamia convergens/89c2219ede1434c6b00df85dda0dc729.jpg\",\n  \"316804.jpg\": \"Aves/Quiscalus quiscula/4f6784ecc7659cbd7a7b4513de828bc3.jpg\",\n  \"316825.jpg\": \"Aves/Quiscalus quiscula/b84d4de33a0e0ca62ddef5e693f5dfaf.jpg\",\n  \"316832.jpg\": \"Aves/Quiscalus quiscula/95e808c5a16a8a6629fa4db13ee95487.jpg\",\n  \"316895.jpg\": \"Aves/Quiscalus quiscula/ca903018153710e774fa79a9a4bf8ee1.jpg\",\n  \"316902.jpg\": \"Aves/Quiscalus quiscula/262b9cffc5b642a7471abcb6657cb136.jpg\",\n  \"316926.jpg\": \"Aves/Quiscalus quiscula/f9f72a1f652f70fc823e37b2f4bc4af8.jpg\",\n  \"31693.jpg\": \"Insecta/Hippodamia convergens/d9debecbb04b167b3d7af4f5dd52c247.jpg\",\n  \"316936.jpg\": \"Aves/Quiscalus quiscula/52af36fd0a386b901b6cad3e431b8355.jpg\",\n  \"316950.jpg\": \"Aves/Quiscalus quiscula/08a95c10bfa5edd8fe2c52b1a7779a62.jpg\",\n  \"317128.jpg\": \"Animalia/Pacifastacus leniusculus/85290496814409dd7094304ba37bb7f2.jpg\",\n  \"317129.jpg\": \"Animalia/Pacifastacus leniusculus/0b7986e32185eebfd459fe4a2e1e6835.jpg\",\n  \"317130.jpg\": \"Animalia/Pacifastacus leniusculus/7f1b08de7d2e2c042e6e9d1331d36198.jpg\",\n  \"317131.jpg\": \"Animalia/Pacifastacus leniusculus/5866aea82f85f5ef7f0223736b9e117c.jpg\",\n  \"317135.jpg\": \"Animalia/Pacifastacus leniusculus/8b158038246b1ac32fe672d35a03407f.jpg\",\n  \"317139.jpg\": \"Animalia/Pacifastacus leniusculus/da2aa435da0656c8c542bbfe23c91c02.jpg\",\n  \"31714.jpg\": \"Insecta/Hippodamia convergens/25785b55d5cc263c084cc58f8853ba6b.jpg\",\n  \"317141.jpg\": \"Animalia/Pacifastacus leniusculus/310aa44e83242fca124f5604e486ecd3.jpg\",\n  \"317142.jpg\": \"Animalia/Pacifastacus leniusculus/00a9adfd0443c56e8cd19c27f1f49e7b.jpg\",\n  \"317143.jpg\": \"Animalia/Pacifastacus leniusculus/c7cc50fe25dfa7b572a67542c88258f1.jpg\",\n  \"317144.jpg\": \"Animalia/Pacifastacus leniusculus/9d09e63974f31b3cdba6c7debc608100.jpg\",\n  \"317154.jpg\": \"Animalia/Pacifastacus leniusculus/310bf32cfde4c96843b7cf64389562b7.jpg\",\n  \"317155.jpg\": \"Animalia/Pacifastacus leniusculus/f88826bea98b1d3f9c105db4427c20f1.jpg\",\n  \"317156.jpg\": \"Animalia/Pacifastacus leniusculus/3bd169c0bd9a6945adef994d2cf8d2c7.jpg\",\n  \"317160.jpg\": \"Animalia/Pacifastacus leniusculus/dd96c8d2e9d6ff9b04f579cb5052bc45.jpg\",\n  \"317161.jpg\": \"Animalia/Pacifastacus leniusculus/fde3cc3748620a6a8a371c4c705f2e7e.jpg\",\n  \"317165.jpg\": \"Animalia/Pacifastacus leniusculus/23ab9b370e75de36ab381f8c4832436b.jpg\",\n  \"317168.jpg\": \"Animalia/Pacifastacus leniusculus/6eb876c7d220a68aa6fd1281e2aeb8ba.jpg\",\n  \"317171.jpg\": \"Aves/Tigrisoma mexicanum/f9c6e03c673754eba1b53ebbf755a594.jpg\",\n  \"317177.jpg\": \"Aves/Tigrisoma mexicanum/0a5fba38ba340747e95e7b7238a49141.jpg\",\n  \"317178.jpg\": \"Aves/Tigrisoma mexicanum/0d192c5193e95384ae3dce2c3f794efb.jpg\",\n  \"317179.jpg\": \"Aves/Tigrisoma mexicanum/3d185ff33c1f3bfc6d1b4ee98b44e6b7.jpg\",\n  \"317184.jpg\": \"Aves/Tigrisoma mexicanum/e280741a05c8f53e728d36d847ee7ca1.jpg\",\n  \"317185.jpg\": \"Aves/Tigrisoma mexicanum/e4a77626cbdfc31c14e7e822e2bf2dd2.jpg\",\n  \"317192.jpg\": \"Aves/Tigrisoma mexicanum/a1541355b97ce0e552ee7d95e23fb1a8.jpg\",\n  \"317193.jpg\": \"Aves/Tigrisoma mexicanum/cbb9ab52daa33c62c82ed029fe0c993e.jpg\",\n  \"317196.jpg\": \"Aves/Tigrisoma mexicanum/299f7e359be7a9102cb1341fa09e5899.jpg\",\n  \"317198.jpg\": \"Aves/Tigrisoma mexicanum/adb229fb56c6034c5fabae246a1c116f.jpg\",\n  \"317203.jpg\": \"Aves/Tigrisoma mexicanum/ec1815f34685b372efda9b9b7b6e6ba0.jpg\",\n  \"317204.jpg\": \"Aves/Tigrisoma mexicanum/a7479a2bf0976e5eaa93315f2032b0c0.jpg\",\n  \"317206.jpg\": \"Aves/Tigrisoma mexicanum/d55072249be7621868a3e62cae31ac29.jpg\",\n  \"317211.jpg\": \"Aves/Tigrisoma mexicanum/ab7ed3f875417d8a946bb4cf8aa0454e.jpg\",\n  \"317221.jpg\": \"Aves/Tigrisoma mexicanum/470cd266910e22a88556357c4e8665c1.jpg\",\n  \"317227.jpg\": \"Aves/Tigrisoma mexicanum/e69113909f3f89e39fd3624fefdd361a.jpg\",\n  \"317229.jpg\": \"Aves/Tigrisoma mexicanum/60b7a82205ebbd9a820f42f59a0cf360.jpg\",\n  \"317230.jpg\": \"Aves/Tigrisoma mexicanum/0237eac8cca7c7c75d80a71a87c7f165.jpg\",\n  \"317234.jpg\": \"Aves/Tigrisoma mexicanum/cf3e9beafe5e2de585565f8bf5b024b5.jpg\",\n  \"317235.jpg\": \"Aves/Tigrisoma mexicanum/cd066aa69fa073dc1c8b30038f72ba67.jpg\",\n  \"317236.jpg\": \"Aves/Tigrisoma mexicanum/12c5cba7981bf4abe9f3797c18e5fb10.jpg\",\n  \"317240.jpg\": \"Aves/Tigrisoma mexicanum/ad5761855c1e4c15cdb29ba5c1605eaa.jpg\",\n  \"317243.jpg\": \"Animalia/Anthopleura artemisia/3cc3e1b8ef6c4acf38e7491fc46cbd18.jpg\",\n  \"317248.jpg\": \"Animalia/Anthopleura artemisia/aad1dc8d4d593f179289f75d92574292.jpg\",\n  \"317252.jpg\": \"Animalia/Anthopleura artemisia/6c732d039656f5a0691dba4d2a42d789.jpg\",\n  \"317253.jpg\": \"Animalia/Anthopleura artemisia/a9dc306ba30706267df79b1d945dcad7.jpg\",\n  \"317257.jpg\": \"Animalia/Anthopleura artemisia/c46119ce23b423d71baf72c34a6c4a49.jpg\",\n  \"317258.jpg\": \"Animalia/Anthopleura artemisia/e045fbf39da1ad1bb82593ddd515a643.jpg\",\n  \"317261.jpg\": \"Animalia/Anthopleura artemisia/e4e1ed6f88a496df50fbf24968377f50.jpg\",\n  \"317263.jpg\": \"Animalia/Anthopleura artemisia/752b86f59badd477b48133a4c615b0dc.jpg\",\n  \"317268.jpg\": \"Animalia/Anthopleura artemisia/42dacf1e05860db5f1995a4bdb078530.jpg\",\n  \"317269.jpg\": \"Animalia/Anthopleura artemisia/3f2c641a89a2286ce2a2a0378d6653fb.jpg\",\n  \"31727.jpg\": \"Insecta/Hippodamia convergens/83acff033d1b6fa3dccd91a19debb450.jpg\",\n  \"317280.jpg\": \"Animalia/Anthopleura artemisia/167bd63fc827a51d37221d6feb888a1b.jpg\",\n  \"317282.jpg\": \"Animalia/Anthopleura artemisia/ac451399db388389699ae4a01e2d6510.jpg\",\n  \"317286.jpg\": \"Animalia/Anthopleura artemisia/43bd94f48a4e19d01c6739a1fe147e6c.jpg\",\n  \"317287.jpg\": \"Aves/Sphyrapicus nuchalis/b418df6fcdfdcb09ea734d905080ff82.jpg\",\n  \"317288.jpg\": \"Aves/Sphyrapicus nuchalis/7446659d703fde5df9b5c2f821f8ff2a.jpg\",\n  \"317290.jpg\": \"Aves/Sphyrapicus nuchalis/ed83c0669f5497e4578c4e3d4d901293.jpg\",\n  \"317291.jpg\": \"Aves/Sphyrapicus nuchalis/7ff4252e7de657315993ae3c9c224ebb.jpg\",\n  \"317292.jpg\": \"Aves/Sphyrapicus nuchalis/e186e4002d2fa71fb23f1f344a69a1bd.jpg\",\n  \"317293.jpg\": \"Aves/Sphyrapicus nuchalis/b52405f08eb87c16032035ece58dc542.jpg\",\n  \"317297.jpg\": \"Aves/Sphyrapicus nuchalis/eb6016c250ae49db782fc0e078b4347a.jpg\",\n  \"317302.jpg\": \"Aves/Sphyrapicus nuchalis/7f2d0803072fdd9efa681eea13f6dd5c.jpg\",\n  \"317303.jpg\": \"Aves/Sphyrapicus nuchalis/0540cb70771a0a6ef27247d5026b8e0d.jpg\",\n  \"317304.jpg\": \"Aves/Sphyrapicus nuchalis/83d09d0b3eccc9d68a617558f61ed730.jpg\",\n  \"317305.jpg\": \"Aves/Sphyrapicus nuchalis/2925756b024ac1ea344733a115949df5.jpg\",\n  \"317306.jpg\": \"Aves/Sphyrapicus nuchalis/3af2e0e7ceb0c5500b7cfaf05b99d78d.jpg\",\n  \"317307.jpg\": \"Aves/Sphyrapicus nuchalis/651589d30548e7192610c4c239eb398b.jpg\",\n  \"317308.jpg\": \"Aves/Sphyrapicus nuchalis/6850de1813742b7fa6b5eab071ab3782.jpg\",\n  \"317313.jpg\": \"Aves/Sphyrapicus nuchalis/dffefec677a4177de1adb01f4507aedf.jpg\",\n  \"317314.jpg\": \"Aves/Sphyrapicus nuchalis/9e4a169d03f4c851b31d408b6949d8f7.jpg\",\n  \"317315.jpg\": \"Aves/Sphyrapicus nuchalis/efc82e4b5310536f7c6f3da9fb2f06ea.jpg\",\n  \"317316.jpg\": \"Aves/Sphyrapicus nuchalis/305e0124a75a69afab86a6af5e737b25.jpg\",\n  \"317318.jpg\": \"Aves/Sphyrapicus nuchalis/03dc9a665a871d4ae10a768b61d1a964.jpg\",\n  \"317320.jpg\": \"Aves/Sphyrapicus nuchalis/9499ddcb7bc44581e75b23c8af43c8f2.jpg\",\n  \"317325.jpg\": \"Aves/Sphyrapicus nuchalis/2a035ce53895319cc799e495a67dd9a0.jpg\",\n  \"317404.jpg\": \"Amphibia/Pseudacris cadaverina/b6dc23efbab6405da66436edb9d5ef77.jpg\",\n  \"317406.jpg\": \"Amphibia/Pseudacris cadaverina/d55a122c72f0f4a3f24cb8c3e0a357dc.jpg\",\n  \"317407.jpg\": \"Amphibia/Pseudacris cadaverina/21c59257e6558854c304d7236c88411f.jpg\",\n  \"317408.jpg\": \"Amphibia/Pseudacris cadaverina/3fa70c3b64f37c019bcc0bffac1ca1e2.jpg\",\n  \"317409.jpg\": \"Amphibia/Pseudacris cadaverina/71d362d088bb975c9c5b5f5645ac7443.jpg\",\n  \"317411.jpg\": \"Amphibia/Pseudacris cadaverina/0521b7a613b7b9530ab054cb28181e23.jpg\",\n  \"317422.jpg\": \"Amphibia/Pseudacris cadaverina/315238ffeeb7738cc7f5066d25b94d89.jpg\",\n  \"317428.jpg\": \"Amphibia/Pseudacris cadaverina/c14bdce2c55dc24ab32b7731cae3c3b5.jpg\",\n  \"31743.jpg\": \"Insecta/Hippodamia convergens/3f64bd64229d83fe87974f74b99717ce.jpg\",\n  \"317430.jpg\": \"Amphibia/Pseudacris cadaverina/52437110c5f500dd38196f0b9772e275.jpg\",\n  \"317450.jpg\": \"Amphibia/Pseudacris cadaverina/e4ec77495defdb95cd4e0957c6bd867b.jpg\",\n  \"317454.jpg\": \"Amphibia/Pseudacris cadaverina/487adabb7540b97b484c77c2f0cca7b2.jpg\",\n  \"317455.jpg\": \"Amphibia/Pseudacris cadaverina/32276514eb18ba8f582973b9c155e9f1.jpg\",\n  \"317457.jpg\": \"Amphibia/Pseudacris cadaverina/8d6873ee28dd96b46c55d1048a09cab4.jpg\",\n  \"317461.jpg\": \"Amphibia/Pseudacris cadaverina/cff2db1cb45764031420cb4d4bf5f4e8.jpg\",\n  \"317468.jpg\": \"Amphibia/Pseudacris cadaverina/937ee707184f6fdab9875d74a8e4b82e.jpg\",\n  \"317471.jpg\": \"Amphibia/Pseudacris cadaverina/50f82d68cc6e487e675f7eab070a1f4c.jpg\",\n  \"317475.jpg\": \"Amphibia/Pseudacris cadaverina/186f7499b28fd85dfc1485c461cd5037.jpg\",\n  \"317477.jpg\": \"Amphibia/Pseudacris cadaverina/fcde8921c977a21bad72faa3e6055669.jpg\",\n  \"317486.jpg\": \"Amphibia/Pseudacris cadaverina/9d32c48379188c42119d6156b1b6b040.jpg\",\n  \"317494.jpg\": \"Insecta/Prenolepis imparis/3f1f1d1d52198753265d5ee203230c07.jpg\",\n  \"317496.jpg\": \"Insecta/Prenolepis imparis/15d145820f7ac44a630586fe5f0e3940.jpg\",\n  \"3175.jpg\": \"Aves/Picoides nuttallii/b8357081776a31e3ad585fcc4f20f2fc.jpg\",\n  \"317505.jpg\": \"Insecta/Prenolepis imparis/9dfd821261a58e7ed6bce29bccf744c6.jpg\",\n  \"317509.jpg\": \"Insecta/Prenolepis imparis/9e6689fdec399f014338eb4f074b9770.jpg\",\n  \"317511.jpg\": \"Insecta/Prenolepis imparis/b5b11dea20e19a51c57b0ab9c9a4b39e.jpg\",\n  \"317515.jpg\": \"Insecta/Prenolepis imparis/09d184ece8eb0cd7dab3e5ccad3fb33c.jpg\",\n  \"317516.jpg\": \"Insecta/Prenolepis imparis/dcea7fcbfb2e6c449b4997223e07bba9.jpg\",\n  \"31765.jpg\": \"Insecta/Hippodamia convergens/bd71fcc043fe9727b5902243c0b066b2.jpg\",\n  \"31771.jpg\": \"Insecta/Hippodamia convergens/97c5f9b547e3fbfafc90edce6cf568ad.jpg\",\n  \"317763.jpg\": \"Mammalia/Ammospermophilus harrisii/8074871e655cfaf0fd6d3ee5e14dd46b.jpg\",\n  \"317768.jpg\": \"Mammalia/Ammospermophilus harrisii/e285d24a5aa4d4c6add81339b8d3d0b2.jpg\",\n  \"317769.jpg\": \"Mammalia/Ammospermophilus harrisii/dd5a132fb90f35f515c4cc375dc8e1a8.jpg\",\n  \"317772.jpg\": \"Mammalia/Ammospermophilus harrisii/7feac6299c014729a97c0ce3d8ed65c7.jpg\",\n  \"317799.jpg\": \"Aves/Numenius arquata/c21967ce5d29f1da32660f84fec0a86f.jpg\",\n  \"317800.jpg\": \"Aves/Numenius arquata/51536fc400f2782f7919509b4f934080.jpg\",\n  \"317805.jpg\": \"Aves/Numenius arquata/f8b274d7d966bbb7686b46b79b39bdf8.jpg\",\n  \"317806.jpg\": \"Aves/Numenius arquata/91467587a561a322200661c5da9a0aa8.jpg\",\n  \"317841.jpg\": \"Aves/Catherpes mexicanus/1a9e254b31fddfb9fb3b6c0d0e291477.jpg\",\n  \"317843.jpg\": \"Aves/Catherpes mexicanus/23571279b7dccf97fe7de179b227a44c.jpg\",\n  \"317844.jpg\": \"Aves/Catherpes mexicanus/ba211a972422055a7532808503087083.jpg\",\n  \"317853.jpg\": \"Aves/Catherpes mexicanus/9eea229ae7cf24426d1e91b7816e28ff.jpg\",\n  \"317860.jpg\": \"Aves/Catherpes mexicanus/4778496bb6df4970db173e6bfcebf9b6.jpg\",\n  \"317864.jpg\": \"Aves/Catherpes mexicanus/34447af0d0d5f2125b08eb6e3be5bfa6.jpg\",\n  \"317867.jpg\": \"Aves/Catherpes mexicanus/3cad6b40978bfd76572518e5d54fd627.jpg\",\n  \"317881.jpg\": \"Aves/Catherpes mexicanus/0cd17109813ade85a75d84937543ad3e.jpg\",\n  \"317882.jpg\": \"Aves/Catherpes mexicanus/846ab6f5b8dbf0efd1822949ca2f0320.jpg\",\n  \"317883.jpg\": \"Aves/Catherpes mexicanus/01d6d964bd77bc549f4f45100d298bd2.jpg\",\n  \"317885.jpg\": \"Aves/Catherpes mexicanus/e4f116555305aeb214c6e6bfa96678ff.jpg\",\n  \"317888.jpg\": \"Aves/Catherpes mexicanus/b63b33a26e5e090896c42bf796bb9196.jpg\",\n  \"317889.jpg\": \"Aves/Catherpes mexicanus/508343c355e8c244693014fa3fe715a7.jpg\",\n  \"317890.jpg\": \"Aves/Catherpes mexicanus/ad197cbe4529fa3260487982d86cb702.jpg\",\n  \"317893.jpg\": \"Aves/Catherpes mexicanus/8402abc115e41c5294e793b5149e31be.jpg\",\n  \"317895.jpg\": \"Aves/Catherpes mexicanus/8494b4619b08f22d347a77d1eef19b01.jpg\",\n  \"317899.jpg\": \"Aves/Catherpes mexicanus/cc8d7f8049edcd39e292df35a9e11623.jpg\",\n  \"317901.jpg\": \"Aves/Catherpes mexicanus/26e3f70c6714ffaf3ba4faaf1c6b6ec5.jpg\",\n  \"317902.jpg\": \"Aves/Catherpes mexicanus/ec9473128bb8e8df065300b0c3722d2f.jpg\",\n  \"317903.jpg\": \"Aves/Catherpes mexicanus/944d58eb52948aca49f6defbfc989ce2.jpg\",\n  \"317911.jpg\": \"Aves/Catherpes mexicanus/38fec98503f056aaa9caa86be1dcfb52.jpg\",\n  \"317920.jpg\": \"Amphibia/Rana boylii/c8d7b99058085cf0bfa170e2790c99d4.jpg\",\n  \"317921.jpg\": \"Amphibia/Rana boylii/92a450b24f3547673f5bd02a5058510b.jpg\",\n  \"317922.jpg\": \"Amphibia/Rana boylii/34898e3c0f834b07cc519dcb6f40c6f0.jpg\",\n  \"317923.jpg\": \"Amphibia/Rana boylii/dd5338094bb28b1e046dd0029956cbd8.jpg\",\n  \"317924.jpg\": \"Amphibia/Rana boylii/adeb2fc9257207c9164ef88a6a663a1f.jpg\",\n  \"317925.jpg\": \"Amphibia/Rana boylii/d1a878fc33d33845374e6d60e9c4c6bd.jpg\",\n  \"317928.jpg\": \"Amphibia/Rana boylii/eeb60746eb0d038418552ab1ecfce3ba.jpg\",\n  \"317929.jpg\": \"Amphibia/Rana boylii/91ead8b507f3c35aceb9b1adfdbe2875.jpg\",\n  \"317930.jpg\": \"Amphibia/Rana boylii/650b944b35fa68139eaa8def9d4e204e.jpg\",\n  \"317937.jpg\": \"Amphibia/Rana boylii/0b9073de64ece077f2fc28f532a7b104.jpg\",\n  \"317944.jpg\": \"Amphibia/Rana boylii/825d3360575b58d4ea4d3bd0d6546ab5.jpg\",\n  \"317945.jpg\": \"Amphibia/Rana boylii/f9996f24d0e4f3c0e436c6ef72a55c51.jpg\",\n  \"317948.jpg\": \"Amphibia/Rana boylii/1d47cd336eceae7394980c0f3a126c9d.jpg\",\n  \"317951.jpg\": \"Amphibia/Rana boylii/41ef750e951850bb1219bab3b412dfa7.jpg\",\n  \"317952.jpg\": \"Insecta/Argia tibialis/dcbe4f37044629255389c2d9d9abfcda.jpg\",\n  \"317959.jpg\": \"Insecta/Argia tibialis/96b57389c3c40f0d194f2deef257d94e.jpg\",\n  \"317966.jpg\": \"Insecta/Argia tibialis/60d7fd695064d3162fabb0e88265cf2e.jpg\",\n  \"317967.jpg\": \"Insecta/Argia tibialis/b77d7d193a660109aad77003aafe86a1.jpg\",\n  \"317968.jpg\": \"Insecta/Argia tibialis/2c298161cfafaa8c922dee675a15725e.jpg\",\n  \"317970.jpg\": \"Insecta/Argia tibialis/f22ab2bb8ec5c3dd26477f18e2adf04a.jpg\",\n  \"317971.jpg\": \"Insecta/Argia tibialis/100c762ccf17977da0e65acddb98d44c.jpg\",\n  \"317972.jpg\": \"Insecta/Argia tibialis/5467940e6ebc76a2229e13ee7969018f.jpg\",\n  \"317973.jpg\": \"Insecta/Argia tibialis/cdc739514249a936046ff49f9e5b36cf.jpg\",\n  \"317975.jpg\": \"Insecta/Argia tibialis/2d1e5cd148bda8c353c5111d66285949.jpg\",\n  \"317976.jpg\": \"Insecta/Argia tibialis/956a5d5cd377acf9219d56bf98d1c4eb.jpg\",\n  \"317977.jpg\": \"Insecta/Argia tibialis/e52e5d2101729853577c83797e4c76a6.jpg\",\n  \"317981.jpg\": \"Insecta/Argia tibialis/1ea27f2063add9e19b6079c3a28c0700.jpg\",\n  \"317984.jpg\": \"Insecta/Argia tibialis/72c836039abcf38edaf853b64a2e0d29.jpg\",\n  \"317991.jpg\": \"Insecta/Argia tibialis/1958348af26df53da7fc660f71cd2d7c.jpg\",\n  \"317992.jpg\": \"Insecta/Argia tibialis/71fa3acc56ef0b5681d5195e415ac88d.jpg\",\n  \"317995.jpg\": \"Insecta/Argia tibialis/422f8fd815954f12fbbf08e6e7aae821.jpg\",\n  \"317996.jpg\": \"Insecta/Argia tibialis/f1d467df5d5db235460dce6a9db01a2a.jpg\",\n  \"3180.jpg\": \"Aves/Picoides nuttallii/4fa937e9d2fd07088094117c5e16047c.jpg\",\n  \"318001.jpg\": \"Insecta/Argia tibialis/1477bb2c351868ef31d6a673523dd3b2.jpg\",\n  \"318004.jpg\": \"Insecta/Automeris io/435bc1f7547c87447f8c8d44b18d977c.jpg\",\n  \"318005.jpg\": \"Insecta/Automeris io/ec12661960b112da516de933685a7f4b.jpg\",\n  \"318015.jpg\": \"Insecta/Automeris io/626de830f54681bf73d1aa0128f05272.jpg\",\n  \"318020.jpg\": \"Insecta/Automeris io/1106f0537257934dee58faea1ab4cf88.jpg\",\n  \"318027.jpg\": \"Insecta/Automeris io/7667ba02d025d84c1b6330867915bacd.jpg\",\n  \"318030.jpg\": \"Insecta/Automeris io/1a117681af42f9cf076892ae631aa9a6.jpg\",\n  \"318033.jpg\": \"Insecta/Automeris io/2fe01cb73b0f387c349e741a409cf9c6.jpg\",\n  \"318036.jpg\": \"Insecta/Automeris io/8386496fa49dbc3a6c8bc91eeb7be707.jpg\",\n  \"318037.jpg\": \"Insecta/Automeris io/9eb83801d913c358521701f031ebb742.jpg\",\n  \"318050.jpg\": \"Insecta/Automeris io/432ff86e329870555963e971df68aa94.jpg\",\n  \"318053.jpg\": \"Insecta/Automeris io/605f8a22891e7d382ffda690a45f5264.jpg\",\n  \"318061.jpg\": \"Insecta/Automeris io/dabc0f8e872e2c383987fbe01f686192.jpg\",\n  \"318065.jpg\": \"Insecta/Automeris io/8f1927d97c8076fe1ea3d57c46f5d457.jpg\",\n  \"318081.jpg\": \"Insecta/Automeris io/1b0b652c32ce9ec54e884ff9e7e07110.jpg\",\n  \"318082.jpg\": \"Insecta/Automeris io/c30372983c04429f9e024102c167f0ff.jpg\",\n  \"318083.jpg\": \"Insecta/Automeris io/d65fadf9f0cda63aa91bd8e1067f20df.jpg\",\n  \"318084.jpg\": \"Insecta/Automeris io/40f9092a93ecc47555ed8c4a898237b5.jpg\",\n  \"318085.jpg\": \"Insecta/Automeris io/9599e773e3b641e22291a132bf64b71e.jpg\",\n  \"318086.jpg\": \"Insecta/Automeris io/f42b33dec03e46adf44b2cfe25246643.jpg\",\n  \"318089.jpg\": \"Insecta/Automeris io/3eff18a8ceeb14bf4b0702532e34c434.jpg\",\n  \"318100.jpg\": \"Insecta/Automeris io/d477b0a7ec1e07cafd7e9135a2b868a7.jpg\",\n  \"318112.jpg\": \"Insecta/Zelus longipes/55f47c532d6f364604d6099a340bd95f.jpg\",\n  \"318113.jpg\": \"Insecta/Zelus longipes/c310218794e008c4be2ba0a2dc3bc0e6.jpg\",\n  \"318114.jpg\": \"Insecta/Zelus longipes/d3358718eced3b9f4408b7d32ef0338d.jpg\",\n  \"318115.jpg\": \"Insecta/Zelus longipes/a1216813a4682a70b547b8d582d0e103.jpg\",\n  \"318123.jpg\": \"Insecta/Zelus longipes/9ec7d5156a715f934daca062acbcd711.jpg\",\n  \"318127.jpg\": \"Insecta/Zelus longipes/7494c198a806dc29bd1521d9469e80fe.jpg\",\n  \"318128.jpg\": \"Insecta/Zelus longipes/9033d51013d6705123a08932f87aa2f8.jpg\",\n  \"318129.jpg\": \"Insecta/Zelus longipes/fbeacd3b3fb691fca771dc41e3fd5044.jpg\",\n  \"318130.jpg\": \"Insecta/Zelus longipes/3208c6662f85fd31873bef15d9c2e58f.jpg\",\n  \"318136.jpg\": \"Insecta/Zelus longipes/c6c3a791e6855abccb7e216eed774801.jpg\",\n  \"318141.jpg\": \"Insecta/Zelus longipes/8f5ad5590384c92bbde62d52614d0690.jpg\",\n  \"318142.jpg\": \"Insecta/Zelus longipes/5409d7f45fac92246209bb09a6a7fa47.jpg\",\n  \"318143.jpg\": \"Insecta/Zelus longipes/aa40e6c82b49d7638a1788c12db17992.jpg\",\n  \"318144.jpg\": \"Insecta/Zelus longipes/5c24793de16f0f1cf4813a578c126ff7.jpg\",\n  \"318145.jpg\": \"Insecta/Zelus longipes/b4e90f8f55f26bbd4c2cf56ac53173ad.jpg\",\n  \"318148.jpg\": \"Insecta/Zelus longipes/ff79506c76f20d074eb1cb579d4f681d.jpg\",\n  \"318153.jpg\": \"Insecta/Zelus longipes/83e07fa7d12de03eb5e7aba4ac0c76bd.jpg\",\n  \"318159.jpg\": \"Insecta/Zelus longipes/a579b3e2eab924f3447d8445126ea4cf.jpg\",\n  \"318169.jpg\": \"Insecta/Zelus longipes/a8a961ff8013237d95c49771700bb26b.jpg\",\n  \"318173.jpg\": \"Insecta/Zelus longipes/94a6a89cc79d7584cce8341ba0dbfb0d.jpg\",\n  \"318178.jpg\": \"Aves/Phalaenoptilus nuttallii/c8503913cc8a86b2a3bf91ccf379427c.jpg\",\n  \"318182.jpg\": \"Aves/Phalaenoptilus nuttallii/f72653b27b99db161eb46bc23b487e7a.jpg\",\n  \"318187.jpg\": \"Aves/Phalaenoptilus nuttallii/87ac6152ef1495642c4e75f48e4c9dc4.jpg\",\n  \"318200.jpg\": \"Aves/Phalaenoptilus nuttallii/53a3dafc1b86d3b004fa8a432c5581e6.jpg\",\n  \"318201.jpg\": \"Aves/Phalaenoptilus nuttallii/bd9a7e4fd80e9ec174f16261a402fbc8.jpg\",\n  \"318202.jpg\": \"Aves/Phalaenoptilus nuttallii/b7c4432fff29430f7958665c08284eca.jpg\",\n  \"31824.jpg\": \"Insecta/Hippodamia convergens/6b79b42857d446fdb249f4ae71d3cb55.jpg\",\n  \"318258.jpg\": \"Reptilia/Sceloporus olivaceus/e03e1d54f2347092ca75107ef8f6d707.jpg\",\n  \"318264.jpg\": \"Reptilia/Sceloporus olivaceus/2a4430253974234df6f8ce4f24890f33.jpg\",\n  \"318265.jpg\": \"Reptilia/Sceloporus olivaceus/98f5e5a9c77a0fd452082dcc021ebfdb.jpg\",\n  \"318272.jpg\": \"Reptilia/Sceloporus olivaceus/51de830f881a749379274e1d7e4835f3.jpg\",\n  \"318276.jpg\": \"Reptilia/Sceloporus olivaceus/9997456808662f2dec02eac86c3f0552.jpg\",\n  \"318304.jpg\": \"Reptilia/Sceloporus olivaceus/d1a1f4673cdecc7a725d78737034c819.jpg\",\n  \"318310.jpg\": \"Reptilia/Sceloporus olivaceus/480855c6a89d3e372591678300058a5e.jpg\",\n  \"318341.jpg\": \"Reptilia/Sceloporus olivaceus/a8f6ccabc1245c2efd1a057e5a747019.jpg\",\n  \"318344.jpg\": \"Reptilia/Sceloporus olivaceus/624602f4a053db24c5f14308f5c7df49.jpg\",\n  \"318349.jpg\": \"Reptilia/Sceloporus olivaceus/00db595db2d15f1f6fa271bf4a5f5235.jpg\",\n  \"318351.jpg\": \"Reptilia/Sceloporus olivaceus/03c47d2ca895b2d1a3c573a410d8d761.jpg\",\n  \"318393.jpg\": \"Reptilia/Sceloporus olivaceus/113d1ab93eb2547aedbf544290298929.jpg\",\n  \"3184.jpg\": \"Aves/Picoides nuttallii/5b1882fdce63453f54b3395203dd2eaa.jpg\",\n  \"318410.jpg\": \"Reptilia/Sceloporus olivaceus/82615df6e18fdaf152782b51788e0135.jpg\",\n  \"318416.jpg\": \"Reptilia/Sceloporus olivaceus/1c7ded02331ce6c8f7028dec651c2cb1.jpg\",\n  \"318458.jpg\": \"Reptilia/Sceloporus olivaceus/fe8de2de0048fd79252429c74381ebbb.jpg\",\n  \"31849.jpg\": \"Insecta/Hippodamia convergens/df176c0f5bd034cdd55804476763573d.jpg\",\n  \"318520.jpg\": \"Reptilia/Sceloporus olivaceus/aeaa22220ba7b3b290855489740f38b0.jpg\",\n  \"318526.jpg\": \"Reptilia/Sceloporus olivaceus/6f8bc3ca7321d94d5e3efafaec6d009d.jpg\",\n  \"318527.jpg\": \"Reptilia/Sceloporus olivaceus/f14575139c822bebff49c0a7bb638e41.jpg\",\n  \"318552.jpg\": \"Reptilia/Sceloporus olivaceus/e11bdb4f66892be2e48dcc4e996fd3d6.jpg\",\n  \"318555.jpg\": \"Reptilia/Sceloporus olivaceus/add9a3539dbdc5fd5488faa8e432a393.jpg\",\n  \"31856.jpg\": \"Insecta/Hippodamia convergens/d80ace9761a01ae3b21b194c1cb68bad.jpg\",\n  \"318563.jpg\": \"Reptilia/Sceloporus olivaceus/3551f4efb30df2ce0a465d1494ad5c84.jpg\",\n  \"318576.jpg\": \"Reptilia/Sceloporus olivaceus/b22a0fe822b40328d25612e9ecbe43e8.jpg\",\n  \"318585.jpg\": \"Aves/Passer montanus/ea8f4b3c56e0284419a555b586b5fd24.jpg\",\n  \"318586.jpg\": \"Aves/Passer montanus/b40d55bc3e1e112c460246d1c75295e0.jpg\",\n  \"318593.jpg\": \"Aves/Passer montanus/b24fe6114dc251e7557a41fd8adb0b24.jpg\",\n  \"318599.jpg\": \"Aves/Passer montanus/cb999c7b4cbb2cc03cc0fe4c3da21cb8.jpg\",\n  \"318610.jpg\": \"Aves/Passer montanus/d7f02843215a05895cc167ac9afb220f.jpg\",\n  \"318616.jpg\": \"Aves/Passer montanus/305eb316f18f00f98bcda395a75ba638.jpg\",\n  \"318617.jpg\": \"Aves/Passer montanus/ed0bd1074dc5e4c33b7afc7449064fbc.jpg\",\n  \"318618.jpg\": \"Aves/Passer montanus/1b50701ef386bf8ab66373b2badc287e.jpg\",\n  \"318619.jpg\": \"Aves/Passer montanus/6951e6d7564b698aaffeb4144ec187d1.jpg\",\n  \"318622.jpg\": \"Aves/Passer montanus/1d885d6b3e7dcf5ed7941e4b7324dfcd.jpg\",\n  \"318625.jpg\": \"Aves/Passer montanus/f0d69f0fd56b4eba346cc3f9a5764a8e.jpg\",\n  \"318629.jpg\": \"Aves/Passer montanus/57eba5b6ace16f709af1798146117456.jpg\",\n  \"318630.jpg\": \"Aves/Passer montanus/aeb4b22ed17882a1ac97d9ba401a5e96.jpg\",\n  \"318640.jpg\": \"Aves/Passer montanus/ee09d5bb55c3aa6b90d504c5ea7382c0.jpg\",\n  \"318651.jpg\": \"Aves/Passer montanus/92f1f5b9f91b0ebad06258dd1c8f51a3.jpg\",\n  \"318652.jpg\": \"Aves/Passer montanus/e2739dcb81ccd2c67dad0d25d45d3522.jpg\",\n  \"318660.jpg\": \"Aves/Passer montanus/00c3eaa0f62d32781af2f1b5e2481a2c.jpg\",\n  \"318669.jpg\": \"Aves/Passer montanus/b5a76c1e806166fd3137142bc9a9936c.jpg\",\n  \"318690.jpg\": \"Insecta/Feltia herilis/51b0b5df68ffee852745d14304d91e2b.jpg\",\n  \"318694.jpg\": \"Insecta/Feltia herilis/4c07fabfc126981503f8fc051eefeaf0.jpg\",\n  \"3187.jpg\": \"Aves/Picoides nuttallii/f07a755c22996fa9edb47ff81097c649.jpg\",\n  \"31870.jpg\": \"Insecta/Hippodamia convergens/b3df038a99449d79205236cb2b8fb143.jpg\",\n  \"318702.jpg\": \"Insecta/Feltia herilis/99e5476856abbbe652712de332cfe456.jpg\",\n  \"318705.jpg\": \"Insecta/Feltia herilis/cf72fb7372ea6059e9fce281b72f3547.jpg\",\n  \"318707.jpg\": \"Insecta/Feltia herilis/81a196df62a13340d40a366580fa975d.jpg\",\n  \"318710.jpg\": \"Aves/Gallirallus australis/1e12ac00c6bd98226e8dcfda1f92b9a7.jpg\",\n  \"318714.jpg\": \"Aves/Gallirallus australis/fdce39a0211d5286a2d47ea9372d5df2.jpg\",\n  \"318719.jpg\": \"Aves/Gallirallus australis/96f8a266d951179e25e5fb7eec1312ab.jpg\",\n  \"318721.jpg\": \"Aves/Gallirallus australis/a388e4ace81875292429c7fd4f833af1.jpg\",\n  \"318728.jpg\": \"Aves/Gallirallus australis/36f09dfcc9b4dab7cdb27e70a04d4c85.jpg\",\n  \"318729.jpg\": \"Aves/Gallirallus australis/11a8e45d4ee29d1e19a5d9bff77b89d4.jpg\",\n  \"318742.jpg\": \"Aves/Gallirallus australis/97d3ee6ada5c97f484637e9f29e6b46d.jpg\",\n  \"318743.jpg\": \"Reptilia/Pantherophis spiloides/49d5a7a8c22e61cf1605ef243de5926a.jpg\",\n  \"318752.jpg\": \"Reptilia/Pantherophis spiloides/840ca22420b27c97997a37141b0260c2.jpg\",\n  \"318753.jpg\": \"Reptilia/Pantherophis spiloides/6813fa631f0df8bcd2c84422a512ee04.jpg\",\n  \"318756.jpg\": \"Reptilia/Pantherophis spiloides/17c5be62ab84acafe71a69c4b9fd30c9.jpg\",\n  \"318761.jpg\": \"Reptilia/Pantherophis spiloides/091cae86aa7bb0dff88e5f3892aab30e.jpg\",\n  \"318762.jpg\": \"Reptilia/Pantherophis spiloides/30dce5c87064bb89f70fd0d1532caac2.jpg\",\n  \"318765.jpg\": \"Reptilia/Pantherophis spiloides/dc75b91375ba90e7a42a7d18241f0633.jpg\",\n  \"318766.jpg\": \"Reptilia/Pantherophis spiloides/2559017244e8f877129632f639b0b442.jpg\",\n  \"318770.jpg\": \"Reptilia/Pantherophis spiloides/3b5323270083a3b8bad8931f31ede431.jpg\",\n  \"318771.jpg\": \"Reptilia/Pantherophis spiloides/a74ae8ac017c6f71328b7d97c4b9f505.jpg\",\n  \"318772.jpg\": \"Reptilia/Pantherophis spiloides/06476d6af0b878e99a8e557471292b17.jpg\",\n  \"318775.jpg\": \"Reptilia/Pantherophis spiloides/0121ec51901677623aaf4206e128bd7d.jpg\",\n  \"318780.jpg\": \"Reptilia/Pantherophis spiloides/a29df740c67a84a0336e4b61597a0158.jpg\",\n  \"318781.jpg\": \"Reptilia/Pantherophis spiloides/453cf66c7bea10500885fcb103f0194a.jpg\",\n  \"318784.jpg\": \"Reptilia/Pantherophis spiloides/66ed2ec1cf3b2d903ec07e13bde749ab.jpg\",\n  \"318785.jpg\": \"Reptilia/Pantherophis spiloides/e557ee074114a0eb8b85f94d7378a130.jpg\",\n  \"318789.jpg\": \"Reptilia/Pantherophis spiloides/e3ca4a51e0317fd7806a888a05e1aedb.jpg\",\n  \"318790.jpg\": \"Reptilia/Pantherophis spiloides/506c9927d0ddcf46e1ed23baa1927504.jpg\",\n  \"318791.jpg\": \"Reptilia/Pantherophis spiloides/84e9a63f0266ca8dfeebf437df5d3101.jpg\",\n  \"318793.jpg\": \"Mollusca/Haliotis rufescens/a02951167e940ceebe4a8ae9fe8dbcc7.jpg\",\n  \"318838.jpg\": \"Insecta/Autographa californica/8f44ff4f4f819e9b8781cfa8a788a2f5.jpg\",\n  \"318839.jpg\": \"Insecta/Autographa californica/d78405d62e5f7ab3128e38dfff6f535c.jpg\",\n  \"318842.jpg\": \"Insecta/Autographa californica/dde15fd52171dc65578912bb4d2ce8d2.jpg\",\n  \"318855.jpg\": \"Insecta/Autographa californica/39f784ccf37fd7b1aa9fcf032a208928.jpg\",\n  \"3190.jpg\": \"Insecta/Trichodes ornatus/384ce527ce9a192aedf662c42715f4c1.jpg\",\n  \"3191.jpg\": \"Insecta/Trichodes ornatus/9e9e7d9c7b6032e2df731469d0e90bd4.jpg\",\n  \"3192.jpg\": \"Insecta/Trichodes ornatus/c6611b076b22c7887c78560871f51146.jpg\",\n  \"319271.jpg\": \"Aves/Larus dominicanus dominicanus/dd4839e89fdd01802fbbe850084bb224.jpg\",\n  \"319272.jpg\": \"Aves/Larus dominicanus dominicanus/1f201047b6bff572673eb137f2df5b55.jpg\",\n  \"319291.jpg\": \"Aves/Larus dominicanus dominicanus/474213c9ac4882e03326eb6217ce62dd.jpg\",\n  \"319292.jpg\": \"Aves/Larus dominicanus dominicanus/416684d168d0e6325d72c2328c473186.jpg\",\n  \"3193.jpg\": \"Insecta/Trichodes ornatus/27429a646c7b1ecf6ba5fc21a247f868.jpg\",\n  \"31930.jpg\": \"Insecta/Hippodamia convergens/3f8b0cc267de38974c1ce55d72bc2b37.jpg\",\n  \"319306.jpg\": \"Aves/Larus dominicanus dominicanus/fa0e048a148473709f9bcb952837efdd.jpg\",\n  \"319307.jpg\": \"Aves/Larus dominicanus dominicanus/11d37746f8d1867abb5b7ee4a49cf0c0.jpg\",\n  \"319310.jpg\": \"Aves/Larus dominicanus dominicanus/221e76bb630becaaef50e2aca45ea8c4.jpg\",\n  \"319320.jpg\": \"Aves/Larus dominicanus dominicanus/6a28437ec1b0c8f588a1e0e2f2e37c51.jpg\",\n  \"319321.jpg\": \"Aves/Larus dominicanus dominicanus/4dc81a326b01bc0fa29c9157013c3ece.jpg\",\n  \"319330.jpg\": \"Aves/Larus dominicanus dominicanus/f61fe094c9b0000919351a8fd3ac5b10.jpg\",\n  \"319332.jpg\": \"Aves/Larus dominicanus dominicanus/76456567fccb973d7a60427bd04bb4f8.jpg\",\n  \"319333.jpg\": \"Aves/Larus dominicanus dominicanus/17c0519e5e63aefd91d70b3a58e59f2c.jpg\",\n  \"31936.jpg\": \"Insecta/Hippodamia convergens/f12490caee4c959a816976e9831eeeb9.jpg\",\n  \"319363.jpg\": \"Reptilia/Pseudemys nelsoni/df213c05a4149a2626fe6fe5aacf9b0f.jpg\",\n  \"319364.jpg\": \"Reptilia/Pseudemys nelsoni/ff8517e17d1e7bdaeb9743f785406e06.jpg\",\n  \"319375.jpg\": \"Reptilia/Pseudemys nelsoni/25c698666647666e9967fcdbd39bd0a6.jpg\",\n  \"319376.jpg\": \"Reptilia/Pseudemys nelsoni/54351e4f11ce07689842b172038afe4b.jpg\",\n  \"319377.jpg\": \"Reptilia/Pseudemys nelsoni/8477d4bc641c02d73d1499b736c38de7.jpg\",\n  \"319395.jpg\": \"Arachnida/Dolomedes minor/9e48bc3be6f866d4114780fe25d00a6e.jpg\",\n  \"3194.jpg\": \"Insecta/Trichodes ornatus/2b62325412e291af34e5beecabcd5472.jpg\",\n  \"319400.jpg\": \"Arachnida/Dolomedes minor/adca2d6233e17732921b14d1f8078587.jpg\",\n  \"319403.jpg\": \"Arachnida/Dolomedes minor/4963f353a79d5c8eb697197abb902e20.jpg\",\n  \"319404.jpg\": \"Arachnida/Dolomedes minor/29dcdc66d1901fd97621d381aa03c440.jpg\",\n  \"319406.jpg\": \"Arachnida/Dolomedes minor/0a244581043b96d449feba2312feaa2e.jpg\",\n  \"319412.jpg\": \"Arachnida/Dolomedes minor/5cffb7b6f572874e39e410cf7c4656d2.jpg\",\n  \"319417.jpg\": \"Arachnida/Dolomedes minor/884437d09e7d1f86dd8903d0f4d42046.jpg\",\n  \"319420.jpg\": \"Arachnida/Dolomedes minor/75232d098225e30f619d237a2dff7c14.jpg\",\n  \"319421.jpg\": \"Arachnida/Dolomedes minor/527fc937969214c6bd1f1e05f462e245.jpg\",\n  \"319424.jpg\": \"Arachnida/Dolomedes minor/b0e9a78a54cc4fe1530d82f4ad032282.jpg\",\n  \"319428.jpg\": \"Arachnida/Dolomedes minor/6a310b415d3abb76f02e2f15564a9654.jpg\",\n  \"31943.jpg\": \"Insecta/Hippodamia convergens/e83555d32fb45e1c43115cb57339b2db.jpg\",\n  \"319435.jpg\": \"Arachnida/Dolomedes minor/7c2a72fd23a4b48ae7caf07d3add01f4.jpg\",\n  \"319437.jpg\": \"Arachnida/Dolomedes minor/8cda449bdaf2c1e9d84b985c4afc7e67.jpg\",\n  \"319438.jpg\": \"Arachnida/Dolomedes minor/396b4ce84d0bfa2c264ec95ea1311b84.jpg\",\n  \"319441.jpg\": \"Arachnida/Dolomedes minor/dcd31f6848077099d1b501bc430eb08f.jpg\",\n  \"319475.jpg\": \"Aves/Anas chlorotis/5d713814e20fc7853c168cdac869a16c.jpg\",\n  \"319476.jpg\": \"Aves/Anas chlorotis/3befa7d0951958942bdace3260d41ba0.jpg\",\n  \"319485.jpg\": \"Aves/Anas chlorotis/fa31594f5d82a9199e4b8bb27a77d98f.jpg\",\n  \"3195.jpg\": \"Insecta/Trichodes ornatus/53002a4a7507dd1b777df47901a4ac69.jpg\",\n  \"319501.jpg\": \"Insecta/Palomena prasina/00964f4308510a12bc3d303dc4ac34fd.jpg\",\n  \"319504.jpg\": \"Insecta/Palomena prasina/dd4fbbe38f86155ee64b3cc1c9452917.jpg\",\n  \"319505.jpg\": \"Insecta/Palomena prasina/0f5da71c871daba8b496e09ec0b81911.jpg\",\n  \"319508.jpg\": \"Insecta/Poanes viator/c85c39cc77f2459daa5047497dc032fd.jpg\",\n  \"319513.jpg\": \"Insecta/Poanes viator/5fd1d9e94955f56d18f6ace9da434e39.jpg\",\n  \"319522.jpg\": \"Insecta/Poanes viator/4eba7cd2934f11b578447e7833e179c6.jpg\",\n  \"319524.jpg\": \"Insecta/Poanes viator/4421bec3a86daa2a7cfa3c2caa714b2b.jpg\",\n  \"319526.jpg\": \"Mollusca/Tegula brunnea/aa12a49e48278398a632c710ed1046bd.jpg\",\n  \"319533.jpg\": \"Mollusca/Tegula brunnea/2238f830455ca4aea4cb78149c45b2bc.jpg\",\n  \"319534.jpg\": \"Mollusca/Tegula brunnea/6b5ad4ebcc7b944cea26b608db66a958.jpg\",\n  \"319535.jpg\": \"Mollusca/Tegula brunnea/3fad2cc81bbe358793e579f6e7da823e.jpg\",\n  \"319536.jpg\": \"Mollusca/Tegula brunnea/7166f48c9d0857428ebb0814c531d1e1.jpg\",\n  \"319537.jpg\": \"Mollusca/Tegula brunnea/fa459bb7a1d65223f837f9f2d79c9cbd.jpg\",\n  \"319538.jpg\": \"Mollusca/Tegula brunnea/8576f8dbdc0ed0f9afce4c16f5ca0171.jpg\",\n  \"319539.jpg\": \"Mollusca/Tegula brunnea/65e6159bd5144b341f2d9f964e4dd1de.jpg\",\n  \"319541.jpg\": \"Mollusca/Tegula brunnea/94510cab03be43dd422d08c78953d065.jpg\",\n  \"319542.jpg\": \"Mollusca/Tegula brunnea/21d3ef0185d22984c184c3eb950747d9.jpg\",\n  \"319547.jpg\": \"Mollusca/Tegula brunnea/00d61b109f6c9125b9e42caa31d8a2e3.jpg\",\n  \"319548.jpg\": \"Mollusca/Tegula brunnea/c9e0f881b87c7d0d5b66fd9a4f4587f7.jpg\",\n  \"319549.jpg\": \"Mollusca/Tegula brunnea/d753b662e25a3f15f278c62836ca65f8.jpg\",\n  \"319550.jpg\": \"Mollusca/Tegula brunnea/8258ee8b2ff502ff65bdb67bf0b43cc2.jpg\",\n  \"319551.jpg\": \"Mollusca/Tegula brunnea/537974fea4e9e2fcd014c813a6847994.jpg\",\n  \"319555.jpg\": \"Mollusca/Tegula brunnea/0e1725ad6b102bfd2107dc56080d78af.jpg\",\n  \"319556.jpg\": \"Mollusca/Tegula brunnea/b1c58b37ea45f91eb54af6efd421e550.jpg\",\n  \"319562.jpg\": \"Mollusca/Tegula brunnea/3eefc0db30c1de0042eeb2894a76ee77.jpg\",\n  \"319595.jpg\": \"Aves/Progne subis/75cbb4404ea78ab3d96e157fe32a29f8.jpg\",\n  \"319599.jpg\": \"Aves/Progne subis/bd4076125d81e82341c32f7c026c6feb.jpg\",\n  \"319605.jpg\": \"Aves/Progne subis/0e58fe6566cccf1fa75c64c5cba5e361.jpg\",\n  \"319630.jpg\": \"Aves/Progne subis/d76585ee1a4b19adfb493f0719f5e98d.jpg\",\n  \"31964.jpg\": \"Insecta/Hippodamia convergens/2a4cfb0014913e71b982abb9831835a1.jpg\",\n  \"319666.jpg\": \"Aves/Progne subis/4d4bcc6fd41dcf27cee5114d4bb1b3de.jpg\",\n  \"319672.jpg\": \"Aves/Progne subis/517bb27cb4a33bd1ac23ca0f886766ef.jpg\",\n  \"319675.jpg\": \"Aves/Progne subis/14229e73f6a547c1753a1a6f27a8e977.jpg\",\n  \"319701.jpg\": \"Aves/Progne subis/63e6854b5f8b02c8dafa81f78cb736b9.jpg\",\n  \"319719.jpg\": \"Aves/Progne subis/3c27d9e10611534b6b2b02c027523a0f.jpg\",\n  \"319724.jpg\": \"Insecta/Bombus vosnesenskii/1d5a1eee1003a472744542e06c954103.jpg\",\n  \"319730.jpg\": \"Insecta/Bombus vosnesenskii/e38b341f61dd330f746ce51ba45a21d6.jpg\",\n  \"319738.jpg\": \"Insecta/Bombus vosnesenskii/ffa0cbccf2300b5683c29c50a1391d9f.jpg\",\n  \"319742.jpg\": \"Insecta/Bombus vosnesenskii/4737136721730b9cf2d96be721c12a36.jpg\",\n  \"319743.jpg\": \"Insecta/Bombus vosnesenskii/1853d61c3102496cf117bd123325b2b1.jpg\",\n  \"319744.jpg\": \"Insecta/Bombus vosnesenskii/86be9d5e852d14111fe8dd0771d1e382.jpg\",\n  \"319745.jpg\": \"Insecta/Bombus vosnesenskii/a42eb05e577b288cd113fafc5abcab3f.jpg\",\n  \"319751.jpg\": \"Insecta/Bombus vosnesenskii/f4b8eac395b5f63816dc33d187865d7d.jpg\",\n  \"319755.jpg\": \"Insecta/Bombus vosnesenskii/ce07403122cc7fa6a6152ad4b9257c67.jpg\",\n  \"319759.jpg\": \"Insecta/Bombus vosnesenskii/d7ae08fc61fff8d77235a8363067254a.jpg\",\n  \"319760.jpg\": \"Insecta/Bombus vosnesenskii/d4ee1b0f1d37b2590221bbb1598c3664.jpg\",\n  \"319763.jpg\": \"Insecta/Bombus vosnesenskii/85785a448ee3534e074dbefe190b35a2.jpg\",\n  \"319770.jpg\": \"Insecta/Bombus vosnesenskii/7238064bc07c18f2f654e41ae39f4e5a.jpg\",\n  \"319774.jpg\": \"Insecta/Bombus vosnesenskii/3bca875199ac618733c9abcdf629477c.jpg\",\n  \"319775.jpg\": \"Insecta/Bombus vosnesenskii/84292ccbe5fce7d3a3858ad357a24e29.jpg\",\n  \"319778.jpg\": \"Insecta/Bombus vosnesenskii/07f59a1b333159444b82ba7d36903f86.jpg\",\n  \"319783.jpg\": \"Insecta/Bombus vosnesenskii/415e6617a7e7c60e0642bb7e6b6e5122.jpg\",\n  \"319784.jpg\": \"Insecta/Bombus vosnesenskii/1397197f30e7b68b264956120b394c8e.jpg\",\n  \"319789.jpg\": \"Insecta/Bombus vosnesenskii/ad0b0c4a55841d863db777be22aa9ac9.jpg\",\n  \"319797.jpg\": \"Insecta/Bombus vosnesenskii/6723e023da0a650cba899690487b0fea.jpg\",\n  \"319799.jpg\": \"Insecta/Bombus vosnesenskii/71d56bb027c590c78cbab3e60ec482d7.jpg\",\n  \"319808.jpg\": \"Insecta/Bombus vosnesenskii/268081dd5189063450642e38db880b31.jpg\",\n  \"319811.jpg\": \"Insecta/Bombus vosnesenskii/5fe3af54d9302061e2ed3e1b71f722cf.jpg\",\n  \"319891.jpg\": \"Animalia/Tetraclita rubescens/b2dc3bed8e2abd656434fc649ad02796.jpg\",\n  \"319940.jpg\": \"Insecta/Oreta rosea/f48d1dcde0d1a46d9e2478345a803744.jpg\",\n  \"319943.jpg\": \"Insecta/Oreta rosea/e3dba71e1b681b3b7753ea7689209ff5.jpg\",\n  \"319944.jpg\": \"Insecta/Oreta rosea/d93e15483053bdd2ebc2ada2703d4dde.jpg\",\n  \"319945.jpg\": \"Insecta/Oreta rosea/1a4b9bd909bf8dbece731a840fc9f380.jpg\",\n  \"319948.jpg\": \"Insecta/Oreta rosea/db78211eb852149d5c3c021d9bcc5b3d.jpg\",\n  \"319950.jpg\": \"Insecta/Oreta rosea/3d30467df6fe0c15df366b03766099c7.jpg\",\n  \"319951.jpg\": \"Insecta/Oreta rosea/dc871dea3669c9e5b2e35979e8468514.jpg\",\n  \"319953.jpg\": \"Insecta/Oreta rosea/81885f2c5e7bc42f56330d4c60b22cbe.jpg\",\n  \"319964.jpg\": \"Actinopterygii/Fistularia commersonii/3f14d4a413975af28a1a1c7ddaf54874.jpg\",\n  \"319993.jpg\": \"Animalia/Stenopus hispidus/02bfe48b6761d23f0689f0d1f7f81b39.jpg\",\n  \"320000.jpg\": \"Animalia/Stenopus hispidus/d1c366edfbc8e15589a991cc274f6fc5.jpg\",\n  \"320034.jpg\": \"Aves/Nyctanassa violacea/6e109eb495ceeca22d21d9477bec7446.jpg\",\n  \"320051.jpg\": \"Aves/Nyctanassa violacea/051fa54c84cde36a3479bb556f82bd3f.jpg\",\n  \"320066.jpg\": \"Aves/Nyctanassa violacea/20f8b7d7143e672f9b801762655fa4a2.jpg\",\n  \"320076.jpg\": \"Aves/Nyctanassa violacea/dfdf1eef3535da43ed4346a984f198a3.jpg\",\n  \"320085.jpg\": \"Aves/Nyctanassa violacea/fc93158c128cf65df110cb2217dd87df.jpg\",\n  \"320109.jpg\": \"Aves/Nyctanassa violacea/cd6566dbae22fc9137165158206a57a4.jpg\",\n  \"320157.jpg\": \"Aves/Nyctanassa violacea/9b4c9c043cf571498c917fa885e2ec9f.jpg\",\n  \"320180.jpg\": \"Aves/Nyctanassa violacea/8de0ab5d6598faa565f51849fe93b305.jpg\",\n  \"320181.jpg\": \"Aves/Nyctanassa violacea/2a369d2016870c6f9bb6676e615045cf.jpg\",\n  \"320182.jpg\": \"Aves/Nyctanassa violacea/79155c16da2d1b2bc73c9b9e89fddcc9.jpg\",\n  \"320185.jpg\": \"Aves/Nyctanassa violacea/ec15ee7da15130e41b02e2a84d001399.jpg\",\n  \"320204.jpg\": \"Aves/Nyctanassa violacea/3e3b92c1ae98bae908743dfa47cac2b1.jpg\",\n  \"320217.jpg\": \"Aves/Nyctanassa violacea/79deb3c79381d39c97e6bb7ccc8ee7f3.jpg\",\n  \"320273.jpg\": \"Aves/Nyctanassa violacea/de7b0f53c229899442ce6ea1f0a53c9c.jpg\",\n  \"320304.jpg\": \"Aves/Nyctanassa violacea/462bdbf086de12588050b28b0e6f0866.jpg\",\n  \"320317.jpg\": \"Aves/Nyctanassa violacea/9c33b2880205a94e55afe285bebcdcc9.jpg\",\n  \"320337.jpg\": \"Aves/Nyctanassa violacea/d2a1151e4d3f9c45b98b3037a4b1b234.jpg\",\n  \"320344.jpg\": \"Aves/Nyctanassa violacea/67f58947e1be0c1b51bb479a50cd8de5.jpg\",\n  \"320349.jpg\": \"Aves/Nyctanassa violacea/a18d4c17d5c31006abf8391946637444.jpg\",\n  \"320385.jpg\": \"Aves/Ixoreus naevius/2a72c1db2761f22ea0819fdc4d160ac3.jpg\",\n  \"320386.jpg\": \"Aves/Ixoreus naevius/2d9d0ae0b35c268ac7d9c9ed933df653.jpg\",\n  \"320387.jpg\": \"Aves/Ixoreus naevius/bc6028278d1f7d6d8e3f67ec1c666c08.jpg\",\n  \"320397.jpg\": \"Aves/Ixoreus naevius/41b55afc8d169a117872679ac5266fe3.jpg\",\n  \"320404.jpg\": \"Aves/Ixoreus naevius/4f8112131ce30937c844f6c9b7fe8a75.jpg\",\n  \"320413.jpg\": \"Aves/Ixoreus naevius/894c39eed99c59b45e5bfc02e3cb80e6.jpg\",\n  \"320415.jpg\": \"Aves/Ixoreus naevius/759a3562db3e0c50bdf66c8953c96d72.jpg\",\n  \"320423.jpg\": \"Aves/Ixoreus naevius/e56828497418a2ac95b5b8114d69b7a8.jpg\",\n  \"320432.jpg\": \"Aves/Ixoreus naevius/2d49f68b21ca21fd21cfaad31b597e66.jpg\",\n  \"320434.jpg\": \"Aves/Ixoreus naevius/eab7353e1a5ed2aa00a873d38f96377a.jpg\",\n  \"320439.jpg\": \"Aves/Ixoreus naevius/63e6f175358ed71253438e90828111f2.jpg\",\n  \"320445.jpg\": \"Aves/Ixoreus naevius/89335789f087ccc3c58a25f6c4fd20f8.jpg\",\n  \"320451.jpg\": \"Aves/Ixoreus naevius/573e9f58d123830122395f8811f16f43.jpg\",\n  \"320452.jpg\": \"Aves/Ixoreus naevius/7789ba94b8907abba7428aca08bf012f.jpg\",\n  \"320454.jpg\": \"Aves/Ixoreus naevius/cc536a86d6b3d6a0a56e0cc06fdd54c0.jpg\",\n  \"320456.jpg\": \"Aves/Ixoreus naevius/c037b1d053d096352da68e0af1ce34e8.jpg\",\n  \"320464.jpg\": \"Aves/Ixoreus naevius/ad0b40f39a6efa31cd4ef6c55479040b.jpg\",\n  \"320465.jpg\": \"Aves/Ixoreus naevius/fe57eccfbcd54431b1cbf0d529ceba0d.jpg\",\n  \"320466.jpg\": \"Aves/Ixoreus naevius/796a8c8df158329ae7bad1ff67760983.jpg\",\n  \"320469.jpg\": \"Aves/Ixoreus naevius/ef8b02befcab454a306fc885ef5b105f.jpg\",\n  \"320536.jpg\": \"Aves/Calidris fuscicollis/cf967601fcdb6a134ded97cc14ec0f95.jpg\",\n  \"320537.jpg\": \"Aves/Calidris fuscicollis/a76e8ed19c2213b26e61eeeca4f1ff9e.jpg\",\n  \"320538.jpg\": \"Aves/Calidris fuscicollis/0352d0d5a9f85bdf24d64c3817b24bba.jpg\",\n  \"320544.jpg\": \"Insecta/Erynnis icelus/eba0c6eeaa9a2e3e1a0d29e91068370f.jpg\",\n  \"320545.jpg\": \"Insecta/Erynnis icelus/f90ab18b8a768d9b1cc299377a19d60d.jpg\",\n  \"320548.jpg\": \"Insecta/Erynnis icelus/386ef9750bd1c91340f3d1c9372c12d3.jpg\",\n  \"320550.jpg\": \"Insecta/Erynnis icelus/f4d028bea23113fd0ad320c5c2ee9313.jpg\",\n  \"320551.jpg\": \"Insecta/Erynnis icelus/39f8743eaee50fdf2729aaafd7b1bdf9.jpg\",\n  \"320559.jpg\": \"Insecta/Brachymesia gravida/ddec1f37a7d866304c7b435a579b76a3.jpg\",\n  \"320564.jpg\": \"Insecta/Brachymesia gravida/77948e44a49d20574d4614c7a5c660d8.jpg\",\n  \"320566.jpg\": \"Insecta/Brachymesia gravida/3037f61afd2bf14640a45c53c2859b0b.jpg\",\n  \"320573.jpg\": \"Insecta/Brachymesia gravida/257d49edf85e352c1ec2bfd67a081a98.jpg\",\n  \"320575.jpg\": \"Insecta/Brachymesia gravida/115720024267db1d28655c08f7eafec1.jpg\",\n  \"320578.jpg\": \"Insecta/Brachymesia gravida/aceac801a58d62b968d4a38a1b31620a.jpg\",\n  \"320582.jpg\": \"Insecta/Brachymesia gravida/6103b91a4195c7de395da9e6d3ce9b4b.jpg\",\n  \"320588.jpg\": \"Insecta/Brachymesia gravida/4abacec2244501dfe20d6d0035b1c88f.jpg\",\n  \"320589.jpg\": \"Insecta/Brachymesia gravida/56c9d4a5c96a0651f0a5d61a7e215230.jpg\",\n  \"320590.jpg\": \"Insecta/Brachymesia gravida/90d9e01614adfa406dea91c3cd79de2c.jpg\",\n  \"320600.jpg\": \"Insecta/Brachymesia gravida/30dab02ca5acabbd642354801f5b470d.jpg\",\n  \"320604.jpg\": \"Insecta/Brachymesia gravida/6cbad30da03a76bfb9412e8dd2319b6b.jpg\",\n  \"320606.jpg\": \"Insecta/Brachymesia gravida/2e62d2251db271d8afa4223b21e83181.jpg\",\n  \"320607.jpg\": \"Insecta/Brachymesia gravida/8e5fbbbbe2699c74403ca6691b5c78ec.jpg\",\n  \"320620.jpg\": \"Insecta/Brachymesia gravida/94e68b5ef658eb0d1565f8a338512d29.jpg\",\n  \"320628.jpg\": \"Insecta/Brachymesia gravida/ea818d764adb278ff8fabf1bfbf313c3.jpg\",\n  \"320637.jpg\": \"Insecta/Brachymesia gravida/8051329e49cbf17b8ecdde76cb158df1.jpg\",\n  \"320645.jpg\": \"Insecta/Brachymesia gravida/a0880f805982a999be123f1b6e7cfbe3.jpg\",\n  \"320646.jpg\": \"Insecta/Brachymesia gravida/7e37ba7a7f7e2479ecd99bd62147dbe6.jpg\",\n  \"320657.jpg\": \"Insecta/Brachymesia gravida/71e39af48a93530933199e54b975634d.jpg\",\n  \"320661.jpg\": \"Insecta/Brachymesia gravida/0e868ce3bb295ec2f1108fa91384c118.jpg\",\n  \"320663.jpg\": \"Insecta/Brachymesia gravida/23e61c5ccc5a169dce1434ad1a7c8b46.jpg\",\n  \"320667.jpg\": \"Insecta/Brachymesia gravida/cdcc2702ab3d0971ca1071646c310321.jpg\",\n  \"320681.jpg\": \"Animalia/Acropora palmata/acf79f10785602f4b972b483c86beb28.jpg\",\n  \"320683.jpg\": \"Insecta/Melanchroia chephise/aea974ca36874a5c1cf5f060912d2988.jpg\",\n  \"320684.jpg\": \"Insecta/Melanchroia chephise/2c033e426db6910f7ff3a56f32fed17d.jpg\",\n  \"320690.jpg\": \"Insecta/Melanchroia chephise/38e23d12ba04d545cfc902b080859ed1.jpg\",\n  \"320691.jpg\": \"Insecta/Melanchroia chephise/3fe980e307567ff6be885a6c03c78672.jpg\",\n  \"320692.jpg\": \"Insecta/Melanchroia chephise/f79caff867f3d3452759dbcc07d8f166.jpg\",\n  \"320696.jpg\": \"Insecta/Melanchroia chephise/31cf895a0c734f444e7530582dca3c1d.jpg\",\n  \"320697.jpg\": \"Insecta/Melanchroia chephise/f8a421660f2142106975a318fd0cfa3a.jpg\",\n  \"320699.jpg\": \"Insecta/Melanchroia chephise/08f66ed47b681e0638a602bee039ad48.jpg\",\n  \"320700.jpg\": \"Insecta/Melanchroia chephise/0bb2458065409cada3b09694e037a50a.jpg\",\n  \"320701.jpg\": \"Insecta/Melanchroia chephise/1fc70dfa89135a636322c936321d75cb.jpg\",\n  \"320705.jpg\": \"Insecta/Melanchroia chephise/384293792589126f02c8d0405a36c43b.jpg\",\n  \"320709.jpg\": \"Insecta/Melanchroia chephise/652babe3221ea741c08ca7f0c53ee768.jpg\",\n  \"320710.jpg\": \"Insecta/Melanchroia chephise/e43b82b5e16108e828740134a1a28595.jpg\",\n  \"320711.jpg\": \"Insecta/Melanchroia chephise/1a031a87bb87b3b6aefdfe3a4abe4f3c.jpg\",\n  \"320712.jpg\": \"Insecta/Melanchroia chephise/d203c35f6bc017cfda29c59a5db0a817.jpg\",\n  \"320714.jpg\": \"Insecta/Melanchroia chephise/c3042c147cf6708a8edcd1abb1240379.jpg\",\n  \"320715.jpg\": \"Insecta/Melanchroia chephise/42680137161bbfa5526825a2bea1a384.jpg\",\n  \"320722.jpg\": \"Insecta/Melanchroia chephise/c94541db64ce298ffc6524a0abe685b5.jpg\",\n  \"320724.jpg\": \"Insecta/Melanchroia chephise/318001049ac246534c3206b96b320757.jpg\",\n  \"320725.jpg\": \"Insecta/Melanchroia chephise/c4c61f33452ffd1e41b3a7ffb499ff63.jpg\",\n  \"32073.jpg\": \"Insecta/Photinus pyralis/a0e68fd2ee593d3cd33354e767bbc807.jpg\",\n  \"32074.jpg\": \"Insecta/Photinus pyralis/9522b3942bed0d18aacd68156cab56a9.jpg\",\n  \"32075.jpg\": \"Insecta/Photinus pyralis/9769ef3f15d6677cf9ef59a799d3a410.jpg\",\n  \"32076.jpg\": \"Insecta/Photinus pyralis/96e9107f8d0fa8e32dda9f04674fddd5.jpg\",\n  \"32077.jpg\": \"Insecta/Photinus pyralis/f04200050f991520226392565782b3e9.jpg\",\n  \"32078.jpg\": \"Insecta/Photinus pyralis/0ebb17eca222460672bb4f86cc0d66e2.jpg\",\n  \"320795.jpg\": \"Aves/Sterna striata/45e23f8710e0a251491a39d0dde43529.jpg\",\n  \"320801.jpg\": \"Aves/Sterna striata/5069b3190c23c1787fb2c1f48b6abc2e.jpg\",\n  \"320802.jpg\": \"Aves/Sterna striata/7f22d9497f461f39feada2fed3bb4858.jpg\",\n  \"32082.jpg\": \"Insecta/Photinus pyralis/bbaa769254458fc3cb837635dca5949c.jpg\",\n  \"320824.jpg\": \"Aves/Sterna striata/623a646352b85655830b40e1dd108b5b.jpg\",\n  \"320827.jpg\": \"Aves/Sterna striata/19d4e780cfb5a54345d451fd366c7569.jpg\",\n  \"32083.jpg\": \"Insecta/Photinus pyralis/0084097d7c96123992bc7712e2fd53e7.jpg\",\n  \"320838.jpg\": \"Aves/Sterna striata/7c578ce42dbaa19329b0cb5a2f8b0c24.jpg\",\n  \"320841.jpg\": \"Aves/Sterna striata/3612b17d7cc497f189175284d31eb5d0.jpg\",\n  \"320846.jpg\": \"Aves/Alisterus scapularis/7f9540935867a0f1ffcb4790e913f0cf.jpg\",\n  \"320852.jpg\": \"Aves/Alisterus scapularis/7750bd84c2bfc9089a29644c18851bd4.jpg\",\n  \"320854.jpg\": \"Aves/Alisterus scapularis/9c091a348b48028dcaeecc4f21d1f7d9.jpg\",\n  \"320861.jpg\": \"Reptilia/Storeria dekayi/5c1630bf48d4c352c3b80c3e8c11a389.jpg\",\n  \"320866.jpg\": \"Reptilia/Storeria dekayi/02f1d7e22777d5d19e90afce3805015d.jpg\",\n  \"320876.jpg\": \"Reptilia/Storeria dekayi/eebc158c7459310d9a303977ee6fab9e.jpg\",\n  \"320888.jpg\": \"Reptilia/Storeria dekayi/f5231e7164196d5e35385c54cb009d6a.jpg\",\n  \"320890.jpg\": \"Reptilia/Storeria dekayi/38068a4ae1fc80d4fa29e8c8b90a104e.jpg\",\n  \"320900.jpg\": \"Reptilia/Storeria dekayi/4a20c7660a9081475e18f9b3cf97281c.jpg\",\n  \"320902.jpg\": \"Reptilia/Storeria dekayi/23b7f7edc964971a0c35972df146a435.jpg\",\n  \"320905.jpg\": \"Reptilia/Storeria dekayi/9915aa209fb32723205da0acef50b197.jpg\",\n  \"320906.jpg\": \"Reptilia/Storeria dekayi/7eeae03257662ecf37a5a5d3ca97969e.jpg\",\n  \"320915.jpg\": \"Reptilia/Storeria dekayi/84f17250f7153c047cc947a677ad80db.jpg\",\n  \"32092.jpg\": \"Insecta/Photinus pyralis/fbf25c84aebfb5de56dfbda273731de9.jpg\",\n  \"32093.jpg\": \"Insecta/Photinus pyralis/eefe603abc6bc2554c766fd4c5448be3.jpg\",\n  \"320930.jpg\": \"Reptilia/Storeria dekayi/1c607a779413da627cdae0e831fc1352.jpg\",\n  \"320934.jpg\": \"Reptilia/Storeria dekayi/36e8061ca0dee580971d15e3e1de274a.jpg\",\n  \"320935.jpg\": \"Reptilia/Storeria dekayi/35fdc5fc07e5ee43c2a5114e778ae994.jpg\",\n  \"320937.jpg\": \"Reptilia/Storeria dekayi/0d1a897a1b9cb4baf0239067074de715.jpg\",\n  \"32094.jpg\": \"Insecta/Photinus pyralis/0100a8e448789524526cffd2e84273ad.jpg\",\n  \"32095.jpg\": \"Insecta/Photinus pyralis/807619a53509f41005ba38ff632d2dc4.jpg\",\n  \"320954.jpg\": \"Reptilia/Storeria dekayi/e4f9e5cc3bcea8236bc5a72ca610ef50.jpg\",\n  \"320958.jpg\": \"Reptilia/Storeria dekayi/e0c6aff6e006d61918c1ae3d7dcb8685.jpg\",\n  \"320959.jpg\": \"Reptilia/Storeria dekayi/b18a31c4b2c9cb1c301dc2463386b5ce.jpg\",\n  \"32096.jpg\": \"Insecta/Photinus pyralis/5c012e7f6517c4160f1886bbfb49fe3f.jpg\",\n  \"320962.jpg\": \"Reptilia/Storeria dekayi/16eeebaca5975db4b448290fd7df2d09.jpg\",\n  \"32100.jpg\": \"Insecta/Photinus pyralis/d66b10f6ec27e46101666165a0a6f2b1.jpg\",\n  \"32101.jpg\": \"Insecta/Photinus pyralis/72d1369660783fe0af651375f86cdd72.jpg\",\n  \"32104.jpg\": \"Insecta/Photinus pyralis/752d14a3f21befca6c575a6c7f883c66.jpg\",\n  \"321069.jpg\": \"Mammalia/Elephas maximus/702973e21732804cb3ff8a7f0285367f.jpg\",\n  \"321077.jpg\": \"Mammalia/Elephas maximus/cc5f0942a03594d69be348ea89e0aad0.jpg\",\n  \"32113.jpg\": \"Insecta/Photinus pyralis/32657785e79a803d447125a3db0206d8.jpg\",\n  \"32114.jpg\": \"Insecta/Photinus pyralis/f6907e540e807ce5d439e2de5ef5c755.jpg\",\n  \"321282.jpg\": \"Insecta/Colias eurytheme/5ff01ff5837da46f5287638000ca13f0.jpg\",\n  \"321297.jpg\": \"Insecta/Colias eurytheme/d4884d0d2c753b189a4ac0286500760a.jpg\",\n  \"321337.jpg\": \"Insecta/Colias eurytheme/c7c942f82182cf8110ddf78c8cfbbc0d.jpg\",\n  \"321338.jpg\": \"Insecta/Colias eurytheme/c3454193c0f2b3a27d6179bc237b0ec1.jpg\",\n  \"321343.jpg\": \"Insecta/Colias eurytheme/279e7e02052287901d871dfe2de74143.jpg\",\n  \"321353.jpg\": \"Insecta/Colias eurytheme/76d1b0b8b37ded363c53bac8d07acf0d.jpg\",\n  \"321364.jpg\": \"Insecta/Colias eurytheme/337282ed62207f16afcf829d97e2cc0e.jpg\",\n  \"321384.jpg\": \"Insecta/Colias eurytheme/c790efb87f45b18f618337a020a82952.jpg\",\n  \"321390.jpg\": \"Insecta/Colias eurytheme/fdce4dc158c0a048ec4e1dd7251d830a.jpg\",\n  \"321452.jpg\": \"Insecta/Colias eurytheme/c74972d40d395dab332aa2f32a3c43a7.jpg\",\n  \"321461.jpg\": \"Insecta/Colias eurytheme/49a6f9fe934ae41368ff8a3b778914f1.jpg\",\n  \"321468.jpg\": \"Insecta/Colias eurytheme/39f51fdfbfe2bfd7a27db3d5d4eaea30.jpg\",\n  \"32148.jpg\": \"Insecta/Papilio glaucus/1eed8c09af5db657ec15adbd1245dbf6.jpg\",\n  \"3215.jpg\": \"Insecta/Trichodes ornatus/bb1a07efc36313c58dfd5447ba67dc8c.jpg\",\n  \"321542.jpg\": \"Insecta/Colias eurytheme/338140381438b8271b5fa32b5d44b53f.jpg\",\n  \"321560.jpg\": \"Insecta/Colias eurytheme/bece3adbdb367931ac1dc4a4669bb0e5.jpg\",\n  \"321572.jpg\": \"Insecta/Colias eurytheme/599bdf119725d9d949e8839e2c79a4a1.jpg\",\n  \"321584.jpg\": \"Insecta/Colias eurytheme/35d1b86567e9e4050901e9fb05dab246.jpg\",\n  \"321585.jpg\": \"Insecta/Colias eurytheme/2887618540111804f1dd690082db6feb.jpg\",\n  \"321601.jpg\": \"Insecta/Colias eurytheme/cc85487aafb58fda424fc6f65d898abd.jpg\",\n  \"321605.jpg\": \"Insecta/Colias eurytheme/8b6a6254316bb93a1920d111094909fa.jpg\",\n  \"321610.jpg\": \"Insecta/Colias eurytheme/7c4c86b288f2636e02d52de498a24711.jpg\",\n  \"321627.jpg\": \"Insecta/Colias eurytheme/8460b604687e583d4c5958f4d622010d.jpg\",\n  \"321632.jpg\": \"Insecta/Colias eurytheme/f6515be9549de42326be3bdca6553992.jpg\",\n  \"321643.jpg\": \"Aves/Melanerpes erythrocephalus/729aad86897c8abd77ac1e05ee6c7e2e.jpg\",\n  \"321645.jpg\": \"Aves/Melanerpes erythrocephalus/53eebf4b43cc590abdeeee1b59f1ce1f.jpg\",\n  \"321646.jpg\": \"Aves/Melanerpes erythrocephalus/264a334c6d0a451d5b7f01a5e44f3e60.jpg\",\n  \"321652.jpg\": \"Aves/Melanerpes erythrocephalus/d9a3e43b932155a1ef7fd4b496e8e5a5.jpg\",\n  \"321660.jpg\": \"Aves/Melanerpes erythrocephalus/e019bd12339e84fe535b34a1ef2bae2c.jpg\",\n  \"321662.jpg\": \"Aves/Melanerpes erythrocephalus/a9d116b6feec54b03e9b7459b240eca6.jpg\",\n  \"321665.jpg\": \"Aves/Melanerpes erythrocephalus/c7c1200e27ae43d6fa14a16c4ba49258.jpg\",\n  \"321666.jpg\": \"Aves/Melanerpes erythrocephalus/781821c7e7f4d11995771070862390b0.jpg\",\n  \"321672.jpg\": \"Aves/Melanerpes erythrocephalus/1f696168953b381485baf53d18049a8c.jpg\",\n  \"321682.jpg\": \"Aves/Melanerpes erythrocephalus/bc2119f8b45371e938f632f33fe56de5.jpg\",\n  \"321687.jpg\": \"Aves/Melanerpes erythrocephalus/a7fed0b1273e094c033af9e226e16bc0.jpg\",\n  \"321697.jpg\": \"Aves/Melanerpes erythrocephalus/ce7b8aaa05cd2fc727baa20a6a6c41ec.jpg\",\n  \"321703.jpg\": \"Aves/Melanerpes erythrocephalus/48dcadd73d7a3ea1dae95dada698dd63.jpg\",\n  \"321704.jpg\": \"Aves/Melanerpes erythrocephalus/62596de4cd5aa306bfb293b33ab1bc64.jpg\",\n  \"321705.jpg\": \"Aves/Melanerpes erythrocephalus/913954d80f611ce73f6644e02bd1cfa6.jpg\",\n  \"321710.jpg\": \"Aves/Melanerpes erythrocephalus/82e1b798f287fdff83d1a04f50cb693b.jpg\",\n  \"321716.jpg\": \"Aves/Melanerpes erythrocephalus/91f89754068cbb1fa11ac25de33546fe.jpg\",\n  \"321723.jpg\": \"Aves/Melanerpes erythrocephalus/0baef4440d3d7314c986798b48487537.jpg\",\n  \"321738.jpg\": \"Aves/Melanerpes erythrocephalus/2700e05b0b7f2907fc586e77ba80c8af.jpg\",\n  \"321750.jpg\": \"Aves/Melanerpes erythrocephalus/6e8cfa573014119b09e6aebac57fb5c7.jpg\",\n  \"321754.jpg\": \"Aves/Melanerpes erythrocephalus/d1ab69dcdd42493f6261722fcb3d597c.jpg\",\n  \"321775.jpg\": \"Aves/Melanerpes erythrocephalus/a59c7efef91529e7d07692c4e63817ad.jpg\",\n  \"321776.jpg\": \"Aves/Melanerpes erythrocephalus/839840b632c0381120ab2a52b2509795.jpg\",\n  \"321822.jpg\": \"Aves/Athene noctua/3249c3ce85c898af0de4cf953b6fc05c.jpg\",\n  \"321823.jpg\": \"Aves/Athene noctua/0fc78b4e1404ae7ec0979a81e62120c1.jpg\",\n  \"321829.jpg\": \"Aves/Athene noctua/f4520d319ea52a00cdfce50b8f06c0a4.jpg\",\n  \"321830.jpg\": \"Aves/Athene noctua/fa5577bc17d0923c1dbbfcbce374dc41.jpg\",\n  \"321835.jpg\": \"Aves/Athene noctua/27a5bcf469c902bbf41bce7a47dcbecb.jpg\",\n  \"321996.jpg\": \"Aves/Branta canadensis/660084a12316f957d0721d7e0fb1da9b.jpg\",\n  \"322022.jpg\": \"Aves/Branta canadensis/e95dde1b1025f9311a4c38acf6376130.jpg\",\n  \"322028.jpg\": \"Aves/Branta canadensis/cce2ddf3385835849d613a1452274ede.jpg\",\n  \"322152.jpg\": \"Aves/Branta canadensis/4e021630d1bf381ddccb112ae3544148.jpg\",\n  \"32217.jpg\": \"Insecta/Papilio glaucus/e5b8ae4f4598bf7e13b887a54afd8b3d.jpg\",\n  \"32219.jpg\": \"Insecta/Papilio glaucus/5e217a13dcd216280625032e6e860b3b.jpg\",\n  \"32250.jpg\": \"Insecta/Papilio glaucus/d54b718d798d7126152b3fa7df4f82e2.jpg\",\n  \"32252.jpg\": \"Insecta/Papilio glaucus/6c4cb505a8ce981c8683c0ae108f1c65.jpg\",\n  \"32254.jpg\": \"Insecta/Papilio glaucus/2cc8cc6cc7923915121b016dcfee8854.jpg\",\n  \"322585.jpg\": \"Aves/Branta canadensis/4c9eb10dfa5b745a726760caa5e75501.jpg\",\n  \"322611.jpg\": \"Aves/Branta canadensis/9376681663d5466f0c377b136c90efc9.jpg\",\n  \"322672.jpg\": \"Aves/Branta canadensis/7837902a4a7212212c560dd4e27af962.jpg\",\n  \"322784.jpg\": \"Aves/Branta canadensis/26c9a383207e2f60a4e4e389b3724cc4.jpg\",\n  \"323110.jpg\": \"Aves/Branta canadensis/3aeba77d65d87e561701b33a8af5ca32.jpg\",\n  \"32345.jpg\": \"Insecta/Papilio glaucus/1e115eb57b7650a7eb113e7bdfd3f82d.jpg\",\n  \"323464.jpg\": \"Aves/Spizella passerina/94d42d6204b2ae5a7d1a6e8c4975e030.jpg\",\n  \"323473.jpg\": \"Aves/Spizella passerina/a9ada3795120948e2171dea5d88ed682.jpg\",\n  \"323480.jpg\": \"Aves/Spizella passerina/0666c4455548f5279dac155a05e4a2aa.jpg\",\n  \"3235.jpg\": \"Insecta/Coccinella septempunctata/f281e50871f693489f25f107f5ab3995.jpg\",\n  \"323500.jpg\": \"Aves/Spizella passerina/0bf931d82ebcaa4624dc61e07596e16b.jpg\",\n  \"323510.jpg\": \"Aves/Spizella passerina/97e93c14c9e8412d3a9542e404000137.jpg\",\n  \"323513.jpg\": \"Aves/Spizella passerina/e7c4adcd15cddd1841d2d950d2d06260.jpg\",\n  \"323523.jpg\": \"Aves/Spizella passerina/a39d1526fd190dbe5a4410f2bd6f50a0.jpg\",\n  \"323550.jpg\": \"Aves/Spizella passerina/2d778a38ba7635ccec677b78b5d0bec4.jpg\",\n  \"323570.jpg\": \"Aves/Spizella passerina/900cd6bd45c771ab014bb21d2558d3e3.jpg\",\n  \"323583.jpg\": \"Aves/Spizella passerina/7a6d46cbebe1941ef43d5c7d495b5ec9.jpg\",\n  \"323596.jpg\": \"Aves/Spizella passerina/4c21f0325407a090d3b8cb3ec5d012e7.jpg\",\n  \"323598.jpg\": \"Aves/Spizella passerina/4c9f7dffcfd9f565d4047a69dae10254.jpg\",\n  \"323659.jpg\": \"Aves/Spizella passerina/55c7db36fe9605c007db1b7f76308542.jpg\",\n  \"323660.jpg\": \"Aves/Spizella passerina/821fa58658536c381a89c8a6bfb61586.jpg\",\n  \"323695.jpg\": \"Aves/Spizella passerina/7b0bd1e5b895c82aad9d80dd1c908a6b.jpg\",\n  \"323748.jpg\": \"Aves/Spizella passerina/37ae32a6ce2ec4d7b88b338b59ad7b2d.jpg\",\n  \"323794.jpg\": \"Aves/Spizella passerina/043f40ca68ba7b02f7b7c4f132cf419a.jpg\",\n  \"323798.jpg\": \"Aves/Spizella passerina/17ddfc7089f8993b9a19f3aec1bdf1a1.jpg\",\n  \"32381.jpg\": \"Insecta/Papilio glaucus/4d4c7e1d964ac59b9d3e1645d2ae5dcb.jpg\",\n  \"323842.jpg\": \"Aves/Spizella passerina/4e5852f45b9d8c1622addb745bd9a84d.jpg\",\n  \"323854.jpg\": \"Aves/Spizella passerina/4b55ecf8166123c8eb2b1914858dee87.jpg\",\n  \"323878.jpg\": \"Aves/Spizella passerina/35b47b6f1a0535f47d394c5c3e39d050.jpg\",\n  \"323880.jpg\": \"Aves/Spizella passerina/21a67eb0972502af86a0d65c8bba2156.jpg\",\n  \"323915.jpg\": \"Amphibia/Lithobates clamitans melanota/6d57b75163231cde20c4c3b8e28ce22c.jpg\",\n  \"323922.jpg\": \"Amphibia/Lithobates clamitans melanota/338572c3c48307bc64a7ed6f9f0be5dc.jpg\",\n  \"323924.jpg\": \"Amphibia/Lithobates clamitans melanota/8331ad13f0777aa390cd5ff464221141.jpg\",\n  \"323925.jpg\": \"Amphibia/Lithobates clamitans melanota/450e8f0179c5917c9ee69eb24374d46c.jpg\",\n  \"323929.jpg\": \"Amphibia/Lithobates clamitans melanota/a00c82506438a75cc96a8adf23cba9e1.jpg\",\n  \"323930.jpg\": \"Amphibia/Lithobates clamitans melanota/e8af09565d272b9d32c4bc357b234c5a.jpg\",\n  \"323939.jpg\": \"Amphibia/Lithobates clamitans melanota/7951a9618349d4911ea945d669908e60.jpg\",\n  \"323952.jpg\": \"Insecta/Lymantria dispar/4ec4d4f3f93016de94565d11dd65d3ac.jpg\",\n  \"323954.jpg\": \"Insecta/Lymantria dispar/605948948c0b45d07418ad8fc248dedc.jpg\",\n  \"323955.jpg\": \"Insecta/Lymantria dispar/fbb3af45144acceb2f54762b9d86ddcf.jpg\",\n  \"323957.jpg\": \"Insecta/Lymantria dispar/1ed2d1fec57ecb04487bb1bea9f25ffc.jpg\",\n  \"323958.jpg\": \"Insecta/Lymantria dispar/ca747f0652337058eae2d12ee1266ce1.jpg\",\n  \"32396.jpg\": \"Insecta/Papilio glaucus/fd8880adae1ed4b8e3ffa98c2e1ee6a3.jpg\",\n  \"323985.jpg\": \"Insecta/Lymantria dispar/e572aa2b5d1ee213f0adfcb589225ec8.jpg\",\n  \"323986.jpg\": \"Insecta/Lymantria dispar/6262425f584a413a21f48e73fae475b9.jpg\",\n  \"323988.jpg\": \"Insecta/Lymantria dispar/85c1205243ea475ca18be8caba9a6682.jpg\",\n  \"323994.jpg\": \"Insecta/Lymantria dispar/621f9723387f615dad2bb5b9f204a69e.jpg\",\n  \"323996.jpg\": \"Insecta/Lymantria dispar/a32a40e282f5404c65e29cad232adde0.jpg\",\n  \"324001.jpg\": \"Insecta/Lymantria dispar/7a202b1c7ea9de2774b6956f5925edce.jpg\",\n  \"324003.jpg\": \"Insecta/Lymantria dispar/cc76c8887e3ebaf60eedf3b201254019.jpg\",\n  \"324017.jpg\": \"Insecta/Lymantria dispar/b4d99bd92dbee0c3d633c91fb8b03082.jpg\",\n  \"324022.jpg\": \"Mollusca/Rabdotus dealbatus/fc4182c68960a35bc2a7d4525d96f618.jpg\",\n  \"324030.jpg\": \"Mollusca/Rabdotus dealbatus/07a5583ce70c65ac5d3cfb42b0ed6c19.jpg\",\n  \"324031.jpg\": \"Mollusca/Rabdotus dealbatus/5fb5a9327e39034b72453721bc37372b.jpg\",\n  \"324038.jpg\": \"Mollusca/Rabdotus dealbatus/256d0a8e036b63c01b6dd0f4e2afa6ab.jpg\",\n  \"324039.jpg\": \"Mollusca/Rabdotus dealbatus/8e7c9f28786277eed72fd53f66dccb08.jpg\",\n  \"32405.jpg\": \"Insecta/Papilio glaucus/29ed8874cf28bc33c308129d8af741bb.jpg\",\n  \"324052.jpg\": \"Mollusca/Rabdotus dealbatus/1d495a69832be11b2b7c34f34351888f.jpg\",\n  \"324090.jpg\": \"Aves/Melozone crissalis/250ead54c115eadf2fa69d642a45fa3f.jpg\",\n  \"324107.jpg\": \"Aves/Melozone crissalis/c657c8b82624f9d622d9126358d4c44f.jpg\",\n  \"324119.jpg\": \"Aves/Melozone crissalis/f492f91f051044fd4c746fb6e8defe9d.jpg\",\n  \"324161.jpg\": \"Aves/Melozone crissalis/9627d77e922146d853bc8c2919daffdb.jpg\",\n  \"324175.jpg\": \"Aves/Melozone crissalis/fc829d49e7aca4f7548e172570149dd4.jpg\",\n  \"324216.jpg\": \"Aves/Melozone crissalis/d28442016dd5b3e3af29835af1f6898b.jpg\",\n  \"324221.jpg\": \"Aves/Melozone crissalis/cc2ff2854759bca66dc6a9f3859d6b48.jpg\",\n  \"324237.jpg\": \"Aves/Melozone crissalis/092ce32cc68ced489e5f4a55e696e15b.jpg\",\n  \"324278.jpg\": \"Aves/Melozone crissalis/4079aab28d7ec0824484be58a9b690eb.jpg\",\n  \"324325.jpg\": \"Aves/Melozone crissalis/391cdc1e3ecddc92ea46b818f8e97b62.jpg\",\n  \"324327.jpg\": \"Aves/Melozone crissalis/874763618c7a990b51277865a2f0e680.jpg\",\n  \"324351.jpg\": \"Aves/Melozone crissalis/dfd9942746e89810a513d0881c5cc592.jpg\",\n  \"324352.jpg\": \"Aves/Melozone crissalis/9d18e6116cbaa00be3a30c6482f904b3.jpg\",\n  \"324382.jpg\": \"Aves/Melozone crissalis/b30c09718a946125265a30a6a6a7d2b4.jpg\",\n  \"324385.jpg\": \"Aves/Melozone crissalis/cd965040c58a72c3dc6163f2290bac5d.jpg\",\n  \"324396.jpg\": \"Aves/Melozone crissalis/a2d739f80ee898c427656bff2d4fa4d0.jpg\",\n  \"324402.jpg\": \"Aves/Melozone crissalis/03b2a3186ae20e1b274361cb675ad5ac.jpg\",\n  \"324425.jpg\": \"Aves/Melozone crissalis/bc839519443d8b5b7c6e650927cc9550.jpg\",\n  \"324427.jpg\": \"Aves/Melozone crissalis/d2c5b281256a56590fdd6021e1b023fc.jpg\",\n  \"324430.jpg\": \"Aves/Melozone crissalis/748cca6b19093990f4f3216bdcea0a9e.jpg\",\n  \"324445.jpg\": \"Aves/Melozone crissalis/f677d1bf9c4348f007476bc049ffd372.jpg\",\n  \"324447.jpg\": \"Aves/Melozone crissalis/a49be1f011e9ddd29c9308c33bbc78fa.jpg\",\n  \"32446.jpg\": \"Insecta/Papilio glaucus/eccc0fa5024d3b613b3e41c87fc5783f.jpg\",\n  \"324483.jpg\": \"Insecta/Lestes dryas/c9df12b0b3c99b26f6fde3e25ab1969d.jpg\",\n  \"324484.jpg\": \"Insecta/Lestes dryas/2d2b47bc5706be92258a3135f6caf26b.jpg\",\n  \"324485.jpg\": \"Insecta/Lestes dryas/bad2d03750d1e4ae73b9b4928f380e22.jpg\",\n  \"324490.jpg\": \"Insecta/Lestes dryas/c30b1c4ad37e534d425a00b9319d9afe.jpg\",\n  \"324544.jpg\": \"Insecta/Anthanassa tulcis/63f29ef3cecc712964568aa0560b46c5.jpg\",\n  \"324548.jpg\": \"Insecta/Anthanassa tulcis/b3c2414e21a3a2c70605fa5edf1175b8.jpg\",\n  \"324549.jpg\": \"Insecta/Anthanassa tulcis/a3d8fd295ac659ba443bf8544f5ba5d8.jpg\",\n  \"324550.jpg\": \"Insecta/Anthanassa tulcis/e055afad4d04533d2ed40ad3d16fe16d.jpg\",\n  \"324551.jpg\": \"Insecta/Anthanassa tulcis/1bdc93988023072537223950e6a01e4c.jpg\",\n  \"324552.jpg\": \"Insecta/Anthanassa tulcis/93833840e51b750dd9c3e1097039ed86.jpg\",\n  \"324563.jpg\": \"Insecta/Anthanassa tulcis/4c139eb98df76aada0147e33d760931e.jpg\",\n  \"324565.jpg\": \"Insecta/Anthanassa tulcis/8c5348f9b3377b902c2f55950d8bbcd6.jpg\",\n  \"324567.jpg\": \"Insecta/Anthanassa tulcis/47336b2084952e26f98cd85902ccb910.jpg\",\n  \"324573.jpg\": \"Insecta/Anthanassa tulcis/f1a0eaa039ea03897448250cfc9590c4.jpg\",\n  \"324574.jpg\": \"Insecta/Anthanassa tulcis/b86a62a40e45356576a22e864b9437fe.jpg\",\n  \"324577.jpg\": \"Insecta/Anthanassa tulcis/ee29cffd463835997a135e65ec204fbb.jpg\",\n  \"324578.jpg\": \"Insecta/Anthanassa tulcis/bf5d1a1f0b7c666fd6304bd1ff3ae597.jpg\",\n  \"324579.jpg\": \"Insecta/Anthanassa tulcis/5613d07644a73a10d50bb4254642892a.jpg\",\n  \"324580.jpg\": \"Insecta/Anthanassa tulcis/71f426ed5f380c05bd3ca01ac102f207.jpg\",\n  \"324590.jpg\": \"Insecta/Anthanassa tulcis/b14df1416d992a6e0661cc7c969c29b1.jpg\",\n  \"324600.jpg\": \"Insecta/Anthanassa tulcis/a96fb6a5c3b0daab2dc0f2cca3afb971.jpg\",\n  \"324608.jpg\": \"Insecta/Anthanassa tulcis/dd71f72ec8a43bb047e4df45c8997f70.jpg\",\n  \"324609.jpg\": \"Insecta/Anthanassa tulcis/edf394172665ce7c9724f83184ece779.jpg\",\n  \"324610.jpg\": \"Insecta/Anthanassa tulcis/a2701507b3428216f7a5c774a195bfcf.jpg\",\n  \"324705.jpg\": \"Aves/Toxostoma rufum/e00f691b3b924581c69af69f5d99abdb.jpg\",\n  \"324714.jpg\": \"Aves/Toxostoma rufum/0abaaad3370f9a82f3a7167d80cb2805.jpg\",\n  \"324717.jpg\": \"Aves/Toxostoma rufum/ebc234832f5f7774512080f587c7df00.jpg\",\n  \"324719.jpg\": \"Aves/Toxostoma rufum/534efe6365f34bd6ce852c8ae3282db4.jpg\",\n  \"324728.jpg\": \"Aves/Toxostoma rufum/66487b765f98e40fa3f0d1726282f8b6.jpg\",\n  \"324730.jpg\": \"Aves/Toxostoma rufum/d58f957b34ba7dccf8d9eced2656ebdd.jpg\",\n  \"324732.jpg\": \"Aves/Toxostoma rufum/dd66c1a0657443d760ec4a886a612046.jpg\",\n  \"324739.jpg\": \"Aves/Toxostoma rufum/044a556be475a66f543abc76502d81a1.jpg\",\n  \"324740.jpg\": \"Aves/Toxostoma rufum/ad93adf72d1f14c16f86ef649f7d8337.jpg\",\n  \"324743.jpg\": \"Aves/Toxostoma rufum/1e424235a39a7dac02d35b536a64804b.jpg\",\n  \"324756.jpg\": \"Aves/Toxostoma rufum/1a67b76f747df5c0ee1c0b25f6e65ec9.jpg\",\n  \"324764.jpg\": \"Aves/Toxostoma rufum/b871914ff2551a0dfef016d5d5ff65d1.jpg\",\n  \"32477.jpg\": \"Insecta/Papilio glaucus/25009ee484db11c90331e8ae885a466f.jpg\",\n  \"324771.jpg\": \"Aves/Toxostoma rufum/1be04f410f6d4735f1676f4f900509cc.jpg\",\n  \"324811.jpg\": \"Aves/Toxostoma rufum/63b987acf09ad9a85fddb5da87bc9528.jpg\",\n  \"324822.jpg\": \"Aves/Toxostoma rufum/bac1de785d90ae534f7783d98d6ada49.jpg\",\n  \"324831.jpg\": \"Aves/Toxostoma rufum/6566a8825976c6219cd8defe8a66975f.jpg\",\n  \"324836.jpg\": \"Aves/Toxostoma rufum/a7e9c01c82c4729346462f68e9f64935.jpg\",\n  \"324852.jpg\": \"Aves/Toxostoma rufum/0dee99e74065752cde9c3d8f1b3cd95f.jpg\",\n  \"324865.jpg\": \"Aves/Toxostoma rufum/131fb729c8161f2b7ca9e3d6ec798be9.jpg\",\n  \"324866.jpg\": \"Aves/Toxostoma rufum/708aadb060675fe1da942b1e35de4000.jpg\",\n  \"324873.jpg\": \"Aves/Toxostoma rufum/7ba4d818fb24b19bf8a1bec6f55bd97f.jpg\",\n  \"324881.jpg\": \"Aves/Toxostoma rufum/81ad1f03d36411dcba6049ff1621c106.jpg\",\n  \"324895.jpg\": \"Aves/Toxostoma rufum/f72771746c0ae046b86669befdb4152d.jpg\",\n  \"324899.jpg\": \"Reptilia/Hemidactylus turcicus/1add1eb91fdfdcfd4949b46ae91e75b0.jpg\",\n  \"324906.jpg\": \"Reptilia/Hemidactylus turcicus/1149c43301655091d942185fbc23cbd2.jpg\",\n  \"324926.jpg\": \"Reptilia/Hemidactylus turcicus/b8265c2dc8776c450d7339eac2857bb7.jpg\",\n  \"324938.jpg\": \"Reptilia/Hemidactylus turcicus/3bec5b0109c2358c6427b67cedbd26f1.jpg\",\n  \"324942.jpg\": \"Reptilia/Hemidactylus turcicus/2426909657d0fbc4f2a753f7345865fe.jpg\",\n  \"324949.jpg\": \"Reptilia/Hemidactylus turcicus/047e0eee752feaaeb0bc8fbe3675d698.jpg\",\n  \"324970.jpg\": \"Reptilia/Hemidactylus turcicus/a250f5b3cfa3fad771f36739fb441d91.jpg\",\n  \"324981.jpg\": \"Reptilia/Hemidactylus turcicus/a343b8692d8ac80df80405a2cc806a7e.jpg\",\n  \"324982.jpg\": \"Reptilia/Hemidactylus turcicus/1938eb52e42434711dc9fb01bf8912e0.jpg\",\n  \"324992.jpg\": \"Reptilia/Hemidactylus turcicus/f7d0c9b45263d693a9eccece6367b7ad.jpg\",\n  \"324994.jpg\": \"Reptilia/Hemidactylus turcicus/6ebe60e6ddc4becab7ae5656edaacd1f.jpg\",\n  \"325019.jpg\": \"Reptilia/Hemidactylus turcicus/58023f92045d508cb7e74715aaff4411.jpg\",\n  \"325021.jpg\": \"Reptilia/Hemidactylus turcicus/12e017a5f8d627f4c9544825591324e5.jpg\",\n  \"325025.jpg\": \"Reptilia/Hemidactylus turcicus/60217a43c8af284e743028df8a703fc6.jpg\",\n  \"325033.jpg\": \"Reptilia/Hemidactylus turcicus/2942b8414895dbb55bfb1eaa2d9e701b.jpg\",\n  \"325052.jpg\": \"Reptilia/Hemidactylus turcicus/19e8ad016147d56677fdecad6e6eaad0.jpg\",\n  \"325061.jpg\": \"Reptilia/Hemidactylus turcicus/15570b79e68a30445d475e7de73257ab.jpg\",\n  \"325071.jpg\": \"Reptilia/Hemidactylus turcicus/aa3a2d2e7bb2042df630202bd2468b53.jpg\",\n  \"325092.jpg\": \"Reptilia/Hemidactylus turcicus/9c48ae42ef40aba403f4b9272e2adfd8.jpg\",\n  \"325105.jpg\": \"Reptilia/Hemidactylus turcicus/7130bd57c3c5c99665b50e9502962bb9.jpg\",\n  \"325111.jpg\": \"Reptilia/Hemidactylus turcicus/e3aceae04c71e069d5a9e59509b56887.jpg\",\n  \"325124.jpg\": \"Reptilia/Hemidactylus turcicus/a0fada53670ad232cc8be3fdc441c435.jpg\",\n  \"325132.jpg\": \"Reptilia/Hemidactylus turcicus/b525bf23c6fe1cdbf86086d9f8fba081.jpg\",\n  \"32514.jpg\": \"Insecta/Papilio glaucus/64d501d978d789eca7bfca717e2ff355.jpg\",\n  \"325149.jpg\": \"Reptilia/Hemidactylus turcicus/c2f996952db97369a6bb8abb3ee5bb89.jpg\",\n  \"325153.jpg\": \"Reptilia/Hemidactylus turcicus/f75dea20205dd87c66c9ffb173bb4870.jpg\",\n  \"325154.jpg\": \"Reptilia/Hemidactylus turcicus/9f68173c6e8d3c23acdb766e5aab8b1f.jpg\",\n  \"325199.jpg\": \"Reptilia/Hemidactylus turcicus/82fabc416ca4d95196164d45388b6030.jpg\",\n  \"325207.jpg\": \"Aves/Rynchops niger/5987f7a3e50cb17ce79932b4c0341c7a.jpg\",\n  \"325226.jpg\": \"Aves/Rynchops niger/988ebcf7c9708622f34aee732b7fe2ea.jpg\",\n  \"32531.jpg\": \"Insecta/Papilio glaucus/335c93303137f915bbf3197d3ce5f73f.jpg\",\n  \"325317.jpg\": \"Aves/Rynchops niger/61425808af2157f1666af90fb1d17cb1.jpg\",\n  \"325371.jpg\": \"Aves/Rynchops niger/7e11c603fc21ee7f51ee8bf198d22a62.jpg\",\n  \"325395.jpg\": \"Mammalia/Ovis aries/455bc036cf80efcaab9bd38f728db515.jpg\",\n  \"325400.jpg\": \"Mammalia/Ovis aries/3299249b5002c6bdfbb91b4dfb29f5d7.jpg\",\n  \"325402.jpg\": \"Insecta/Megachile sculpturalis/d3693c49182bb72d0e4b60ecae26890a.jpg\",\n  \"325403.jpg\": \"Insecta/Megachile sculpturalis/ff7aa07e8b22b3c8eea5f8c46af7b3fe.jpg\",\n  \"32544.jpg\": \"Insecta/Papilio glaucus/d43309ffbab31c101c0ea4d774f44e7e.jpg\",\n  \"32559.jpg\": \"Insecta/Papilio glaucus/1b56d52c8aec37dc87890590a4c559ac.jpg\",\n  \"325685.jpg\": \"Animalia/Narceus americanus/851922e223daaa64c37569ec11a1c610.jpg\",\n  \"325691.jpg\": \"Animalia/Narceus americanus/16a09e55b5092cc4a4a7785ac3ff8e92.jpg\",\n  \"325697.jpg\": \"Animalia/Narceus americanus/5bb6ae9619b77ad48e5a0bfed6fcda34.jpg\",\n  \"325705.jpg\": \"Animalia/Narceus americanus/1ef633142225b5673e66c4ae9c97c35a.jpg\",\n  \"325706.jpg\": \"Animalia/Narceus americanus/936b0532179e1eb1ee6fd284056b6c3a.jpg\",\n  \"325719.jpg\": \"Animalia/Narceus americanus/a542309d655ca90d5aff92be0ba83f3c.jpg\",\n  \"325720.jpg\": \"Animalia/Narceus americanus/60501ca1a4d2d73ed2b0b3a72b8f7392.jpg\",\n  \"325731.jpg\": \"Reptilia/Thamnophis sirtalis sirtalis/3ca7e47deb45f498b1b9dd612298db42.jpg\",\n  \"325773.jpg\": \"Reptilia/Thamnophis sirtalis sirtalis/419879c36256ffef8214fb33a24b2866.jpg\",\n  \"325780.jpg\": \"Reptilia/Thamnophis sirtalis sirtalis/628eb6b88a172310e90d1c8591889989.jpg\",\n  \"325807.jpg\": \"Reptilia/Thamnophis sirtalis sirtalis/2ca374ed9a316dc410e7e53faf447a79.jpg\",\n  \"325815.jpg\": \"Reptilia/Thamnophis sirtalis sirtalis/f2832efdcf0a229c2d1073c3e6d0f064.jpg\",\n  \"325858.jpg\": \"Reptilia/Thamnophis sirtalis sirtalis/701bde188b1b44f760250619e92a8468.jpg\",\n  \"325870.jpg\": \"Reptilia/Thamnophis sirtalis sirtalis/ea93e1cea112c5937aac17189e63375a.jpg\",\n  \"325891.jpg\": \"Reptilia/Thamnophis sirtalis sirtalis/059f3953c655f702c9516a8da3456de8.jpg\",\n  \"325903.jpg\": \"Reptilia/Thamnophis sirtalis sirtalis/754f45c90a1bd75f8cdf7055e348c049.jpg\",\n  \"325912.jpg\": \"Reptilia/Thamnophis sirtalis sirtalis/4270feb7fe9fabc6bb69ae33bd27e95d.jpg\",\n  \"325914.jpg\": \"Reptilia/Thamnophis sirtalis sirtalis/75d6a215fdbcbc82a14000bc47d8e3c3.jpg\",\n  \"325960.jpg\": \"Reptilia/Thamnophis sirtalis sirtalis/fefe83ecb708d352b18f47f16897f60c.jpg\",\n  \"325963.jpg\": \"Reptilia/Thamnophis sirtalis sirtalis/5bf39569b091f686b08bbb0d8463f295.jpg\",\n  \"325981.jpg\": \"Reptilia/Thamnophis sirtalis sirtalis/fdf539ddcd470cc4f58b94b255679f08.jpg\",\n  \"326020.jpg\": \"Reptilia/Thamnophis sirtalis sirtalis/0b7e533657a69227f3f92136efb2565c.jpg\",\n  \"326026.jpg\": \"Reptilia/Thamnophis sirtalis sirtalis/2c999d6be24449cf82a82f688b003f50.jpg\",\n  \"326028.jpg\": \"Reptilia/Thamnophis sirtalis sirtalis/3012befbe52bbc9809ef0a826a13f0ca.jpg\",\n  \"326045.jpg\": \"Reptilia/Thamnophis sirtalis sirtalis/c0292660fc34871a73978796f47402b5.jpg\",\n  \"326060.jpg\": \"Reptilia/Thamnophis sirtalis sirtalis/fea12346fe09207f9949ad0782dc5df5.jpg\",\n  \"326096.jpg\": \"Insecta/Coenonympha tullia california/403801fb735b92c513005b4528e44248.jpg\",\n  \"326103.jpg\": \"Insecta/Coenonympha tullia california/ea546141e2031cb3969ab54ce2991fc9.jpg\",\n  \"326117.jpg\": \"Insecta/Coenonympha tullia california/3e30295da8ff3586f8038e8078447c7c.jpg\",\n  \"326118.jpg\": \"Insecta/Coenonympha tullia california/9feac60ac8a4a5c4f34871ac8e5431c9.jpg\",\n  \"326119.jpg\": \"Insecta/Coenonympha tullia california/a7c13406078a56e030780c57eb4b9adc.jpg\",\n  \"326122.jpg\": \"Insecta/Coenonympha tullia california/dc3f6ce1103c349ccd40fe9326ccf844.jpg\",\n  \"326126.jpg\": \"Insecta/Coenonympha tullia california/7934ff79d98049c42068b6bd1c70e1fa.jpg\",\n  \"326135.jpg\": \"Insecta/Coenonympha tullia california/7c1384a2e495eb5a23180157a0a83f4b.jpg\",\n  \"326137.jpg\": \"Insecta/Coenonympha tullia california/d7c2362023e3a202c3ef35166a46892a.jpg\",\n  \"326142.jpg\": \"Insecta/Coenonympha tullia california/cf8a3f89d7f9bd6bf6b4cc9202c9df02.jpg\",\n  \"326144.jpg\": \"Insecta/Coenonympha tullia california/21f01d84b3bf03f1b9ec2aac49571137.jpg\",\n  \"326147.jpg\": \"Insecta/Coenonympha tullia california/53922f2d1dc2cb95f1a7d03ff9bf4b25.jpg\",\n  \"326160.jpg\": \"Insecta/Coenonympha tullia california/d4f8cb98b424d4d5e5ca94b711e5aa20.jpg\",\n  \"326161.jpg\": \"Insecta/Coenonympha tullia california/a0079f382cb3c7d507145a5fcf1c7994.jpg\",\n  \"326165.jpg\": \"Insecta/Coenonympha tullia california/0a5f88aae30f9a24722dd63f4848e917.jpg\",\n  \"326174.jpg\": \"Insecta/Coenonympha tullia california/d01f68abfbed833c8da29aafb80746d3.jpg\",\n  \"326184.jpg\": \"Insecta/Coenonympha tullia california/20fc6e6b4e303f2169209896193da1b8.jpg\",\n  \"326190.jpg\": \"Insecta/Coenonympha tullia california/c12ec5ed698f8b831ad54a467704bdbb.jpg\",\n  \"326191.jpg\": \"Insecta/Coenonympha tullia california/436ce35afd7aae6c3b5a8db15ee90167.jpg\",\n  \"326202.jpg\": \"Insecta/Coenonympha tullia california/338ae760c13164c776b873c2ea3eb2df.jpg\",\n  \"326210.jpg\": \"Insecta/Coenonympha tullia california/6f7e07bd5b32434c1f9cfef71791eef8.jpg\",\n  \"326226.jpg\": \"Insecta/Coenonympha tullia california/92564b36a49a422c4ddd40b8303d3f72.jpg\",\n  \"32623.jpg\": \"Insecta/Papilio glaucus/8880562a012412e1393119275f9e49df.jpg\",\n  \"326233.jpg\": \"Insecta/Coenonympha tullia california/e4ee4546232e26e17ef63a7a1a44d813.jpg\",\n  \"326255.jpg\": \"Insecta/Coenonympha tullia california/b042eca4eaf1222b620b5ce72cb3ba6e.jpg\",\n  \"326275.jpg\": \"Insecta/Diacme elealis/02941d49280e3cba98dddd9d00713681.jpg\",\n  \"326281.jpg\": \"Insecta/Diacme elealis/b01061d9735e3d225818d96f1edcd3d2.jpg\",\n  \"326283.jpg\": \"Insecta/Diacme elealis/f097210866902b1247d0cafe0bde56e7.jpg\",\n  \"326340.jpg\": \"Aves/Pelecanus onocrotalus/6c6ab4ab7818afcee050ea214113896a.jpg\",\n  \"326419.jpg\": \"Aves/Corvus caurinus/89bb7c2c06598d26c03629012255d89c.jpg\",\n  \"326425.jpg\": \"Aves/Corvus caurinus/d4fe44c1891518f15fa74e97296b4914.jpg\",\n  \"326429.jpg\": \"Insecta/Octogomphus specularis/c22b275c4c1542d29619c99d10c620e9.jpg\",\n  \"326432.jpg\": \"Insecta/Octogomphus specularis/7117c03f836722240768a418bde176d9.jpg\",\n  \"326441.jpg\": \"Insecta/Octogomphus specularis/dbbc7e278188270fa6a72990254eea2c.jpg\",\n  \"326555.jpg\": \"Animalia/Pisaster giganteus/75ad754f1387405889a61fd005d768e6.jpg\",\n  \"326569.jpg\": \"Animalia/Pisaster giganteus/c90ed70252e38decb4c59edd4e99d6e6.jpg\",\n  \"326571.jpg\": \"Animalia/Pisaster giganteus/667cec0fe7b9cbf65201d7e517338ffe.jpg\",\n  \"326574.jpg\": \"Animalia/Pisaster giganteus/9a6ef66fc4d90734553bca7c0e7387dd.jpg\",\n  \"326578.jpg\": \"Animalia/Pisaster giganteus/8330f74c8717f069d2377a901aea64f9.jpg\",\n  \"326602.jpg\": \"Animalia/Hemigrapsus oregonensis/a51e5709f8e50ff52f3939db64da294d.jpg\",\n  \"326603.jpg\": \"Animalia/Hemigrapsus oregonensis/64ad056acde1e46f956dc8cc90804469.jpg\",\n  \"326604.jpg\": \"Animalia/Hemigrapsus oregonensis/509c0a2f0173ac9964a17378105e7ca8.jpg\",\n  \"326605.jpg\": \"Animalia/Hemigrapsus oregonensis/da82f087fe488d8e5d12dd3036c89986.jpg\",\n  \"326606.jpg\": \"Animalia/Hemigrapsus oregonensis/592142e70be6628cbffafb5954550fa7.jpg\",\n  \"326607.jpg\": \"Animalia/Hemigrapsus oregonensis/55e14eb945edca7b4e3d5f13d9c04694.jpg\",\n  \"326614.jpg\": \"Animalia/Hemigrapsus oregonensis/5ab9da4b42af4314d3808c0baaf4917f.jpg\",\n  \"326615.jpg\": \"Animalia/Hemigrapsus oregonensis/59529b170ddb2d7f7f57ebd7e7cff68a.jpg\",\n  \"326616.jpg\": \"Animalia/Hemigrapsus oregonensis/d8933edd2f21d5b7b28ab53d4bfaf3df.jpg\",\n  \"326617.jpg\": \"Animalia/Hemigrapsus oregonensis/4960f1e9e7b6d8f2ece04ce493a41cb5.jpg\",\n  \"326618.jpg\": \"Animalia/Hemigrapsus oregonensis/6023854999c8d5e31ff1f8a72993b34f.jpg\",\n  \"326619.jpg\": \"Animalia/Hemigrapsus oregonensis/566daad67aba57a9317f8dde3d70399b.jpg\",\n  \"326620.jpg\": \"Animalia/Hemigrapsus oregonensis/58c56741251429bda2c0c38274457618.jpg\",\n  \"326621.jpg\": \"Animalia/Hemigrapsus oregonensis/23baba3e8bbcda0c3f0d0b1d161985b3.jpg\",\n  \"326624.jpg\": \"Animalia/Hemigrapsus oregonensis/942efdd7b8ab32ae5da5c3bb3e6d1ff5.jpg\",\n  \"326626.jpg\": \"Animalia/Hemigrapsus oregonensis/1fe2abce7bb529a0c615c738481f211d.jpg\",\n  \"326629.jpg\": \"Animalia/Hemigrapsus oregonensis/b3e333e9bc6bedf460fd414f3eca11e7.jpg\",\n  \"326630.jpg\": \"Animalia/Hemigrapsus oregonensis/76fdab1b34a297707785d49cc3183c75.jpg\",\n  \"326641.jpg\": \"Animalia/Hemigrapsus oregonensis/fad471f44893acc626439100b8a162c5.jpg\",\n  \"326650.jpg\": \"Animalia/Hemigrapsus oregonensis/ebf6189a2fbf1e3511c9fefb21c774bb.jpg\",\n  \"326735.jpg\": \"Aves/Gallus gallus/12b150708faccabcfe9754b662265266.jpg\",\n  \"326741.jpg\": \"Aves/Gallus gallus/554645b45715bdc1c445d10f02928725.jpg\",\n  \"326745.jpg\": \"Aves/Gallus gallus/e67d8e7ba7f4b5379ae4158ef21df6e7.jpg\",\n  \"326757.jpg\": \"Aves/Gallus gallus/11491b0977d07e9c516e169ae6a939eb.jpg\",\n  \"326759.jpg\": \"Aves/Euphagus carolinus/9f11be23e5ad5394d88f5b278098c233.jpg\",\n  \"326767.jpg\": \"Aves/Euphagus carolinus/20887704421b9b33cbabf020656d4564.jpg\",\n  \"326772.jpg\": \"Aves/Euphagus carolinus/22c44e2c0531f5339eefce48a5447a1f.jpg\",\n  \"326784.jpg\": \"Aves/Euphagus carolinus/d926de107279d34e3d9e571db743f03c.jpg\",\n  \"326785.jpg\": \"Aves/Euphagus carolinus/e721b36420d83174699b30ec3e0f6406.jpg\",\n  \"326789.jpg\": \"Aves/Euphagus carolinus/137cae2d6ae9266e419d0af9532f2122.jpg\",\n  \"326793.jpg\": \"Aves/Euphagus carolinus/5d5a5bc2076c5c509e8419fd30f3ee7a.jpg\",\n  \"326794.jpg\": \"Aves/Euphagus carolinus/acec50703c9497cdb4f322830e9bc455.jpg\",\n  \"326795.jpg\": \"Aves/Euphagus carolinus/97357cb542bcaff75ab8689678c2c372.jpg\",\n  \"326802.jpg\": \"Aves/Euphagus carolinus/5e321e82bcad9eaef6f62e2cbc0d37cc.jpg\",\n  \"326808.jpg\": \"Aves/Euphagus carolinus/a33e18b2f06228e86ddb81425c2c3fa1.jpg\",\n  \"326812.jpg\": \"Aves/Euphagus carolinus/f384b60b8e554900d4b2a00a7776a8c9.jpg\",\n  \"326815.jpg\": \"Aves/Euphagus carolinus/29daba4c885a442afa54829ca47875f0.jpg\",\n  \"326820.jpg\": \"Aves/Euphagus carolinus/4c4cd9a980ec19d7c8fcdeab506b327b.jpg\",\n  \"326821.jpg\": \"Aves/Euphagus carolinus/d15264e55592451e5e9ea4a642b9b839.jpg\",\n  \"326822.jpg\": \"Aves/Euphagus carolinus/7909052ad17dbc3e9a9d8847a1985298.jpg\",\n  \"326823.jpg\": \"Aves/Euphagus carolinus/23b9874aa843587b253b23b60a77a435.jpg\",\n  \"326824.jpg\": \"Aves/Platycercus elegans/2f6386232e5b00f9f1812aeadd3a29ea.jpg\",\n  \"326833.jpg\": \"Aves/Platycercus elegans/4ffce9dcc3c9773b667ea4dced5a094a.jpg\",\n  \"326835.jpg\": \"Aves/Platycercus elegans/f5f7f5fbb65e263b92b191966be0f71a.jpg\",\n  \"326837.jpg\": \"Aves/Platycercus elegans/79aa9bfc7a826720b1419582d165a492.jpg\",\n  \"326840.jpg\": \"Aves/Platycercus elegans/14e743551b9c82a57afe236e1d49385b.jpg\",\n  \"326842.jpg\": \"Insecta/Erythemis plebeja/1c875eaaee4bb1feb75159b15c0782e7.jpg\",\n  \"326843.jpg\": \"Insecta/Erythemis plebeja/8faf6d831d17a097126cd8ec3e795941.jpg\",\n  \"326849.jpg\": \"Insecta/Erythemis plebeja/0ecd2605841a694a3dc4c9508ccd37be.jpg\",\n  \"326850.jpg\": \"Insecta/Erythemis plebeja/3bac788971d09ac7804dad2f4292dc58.jpg\",\n  \"326855.jpg\": \"Insecta/Erythemis plebeja/ad9a31ff7069c059c3a709c820eea391.jpg\",\n  \"326859.jpg\": \"Insecta/Erythemis plebeja/47e4b7376455f48b792b67e8ebeabc7e.jpg\",\n  \"326872.jpg\": \"Insecta/Erythemis plebeja/e6239d652b6ef98442950a3e09d3d182.jpg\",\n  \"326879.jpg\": \"Aves/Nestor notabilis/cfb1aced2a9f52fae0a145d95605f2ac.jpg\",\n  \"326880.jpg\": \"Aves/Nestor notabilis/06b4aa43bb281bb128e26226b281b517.jpg\",\n  \"326883.jpg\": \"Aves/Nestor notabilis/37f4768b8bb4b34d71b3bb4a087f9d01.jpg\",\n  \"326891.jpg\": \"Aves/Nestor notabilis/84458734528d3951cfadc1cd73936b8d.jpg\",\n  \"326899.jpg\": \"Aves/Nestor notabilis/20bb386bea1f02ecfa0b284abdb4cac3.jpg\",\n  \"32690.jpg\": \"Insecta/Papilio glaucus/1f8bdbb7d487350c7741a61cd759a54a.jpg\",\n  \"326903.jpg\": \"Aves/Nestor notabilis/67958474877d06c465c80bba3b3669b2.jpg\",\n  \"326905.jpg\": \"Aves/Nestor notabilis/6ca2f1c6652446b65d3c382c74373221.jpg\",\n  \"326908.jpg\": \"Aves/Nestor notabilis/d35fe8919c76ced18603d672885bab07.jpg\",\n  \"326915.jpg\": \"Insecta/Cetonia aurata/3d8ac4c779418119e8bdaf519740bbf1.jpg\",\n  \"326919.jpg\": \"Insecta/Cetonia aurata/b2a3d79eecc1a86e492df47f116d4d1f.jpg\",\n  \"326921.jpg\": \"Insecta/Cetonia aurata/300ae39f819675df0ebd7dbb59e9ccd6.jpg\",\n  \"326929.jpg\": \"Insecta/Cetonia aurata/f8385b58686e38aedfb3e51ada1922d1.jpg\",\n  \"326934.jpg\": \"Aves/Cardellina canadensis/2f5ed9d2c6129091218829ce0e3fc616.jpg\",\n  \"326935.jpg\": \"Aves/Cardellina canadensis/540c895e484c2a4d837408dde2601908.jpg\",\n  \"326936.jpg\": \"Aves/Cardellina canadensis/8ca7f3e2ce595dabecdb80e20dd5e387.jpg\",\n  \"326937.jpg\": \"Aves/Cardellina canadensis/f9d546c390e211f7c118128a13f96b3f.jpg\",\n  \"326938.jpg\": \"Aves/Cardellina canadensis/b2da8955eff396633a4b51299075a4c5.jpg\",\n  \"326939.jpg\": \"Aves/Cardellina canadensis/ee548926a450285c3201f1d6dcf02a56.jpg\",\n  \"326940.jpg\": \"Aves/Cardellina canadensis/473f330f498c981b88e4aeb059dc13e8.jpg\",\n  \"326941.jpg\": \"Aves/Cardellina canadensis/23d01954355f9c4afbeb406014bd8122.jpg\",\n  \"326942.jpg\": \"Aves/Cardellina canadensis/773ced5705f2143b0e7c702db3563e72.jpg\",\n  \"326956.jpg\": \"Aves/Cardellina canadensis/5343b022d6aa40d7c374e7946832ecba.jpg\",\n  \"326957.jpg\": \"Aves/Cardellina canadensis/41878f6938ba2e2a65b7d9efa2fbd798.jpg\",\n  \"326958.jpg\": \"Aves/Cardellina canadensis/72811f6d358573878b7575296d602df9.jpg\",\n  \"326959.jpg\": \"Aves/Cardellina canadensis/1e76d3f3f39929a1b142ff351f73e7d6.jpg\",\n  \"326960.jpg\": \"Aves/Cardellina canadensis/7ec3f98154192a6fd2b1f4b4da22e908.jpg\",\n  \"326961.jpg\": \"Aves/Cardellina canadensis/3703320b194a3932546b802656dfba3a.jpg\",\n  \"326968.jpg\": \"Aves/Cardellina canadensis/e0897495596a7aedf139847ede2aa918.jpg\",\n  \"326971.jpg\": \"Aves/Cardellina canadensis/b04fddb3cd008aa68f25b93caec9fbf2.jpg\",\n  \"326972.jpg\": \"Aves/Cardellina canadensis/b36698c0423b3f3bafb1ea360cbc3402.jpg\",\n  \"326973.jpg\": \"Aves/Cardellina canadensis/0978dcd3f83ce732bbeb84699566fcc4.jpg\",\n  \"326975.jpg\": \"Aves/Cardellina canadensis/94c62922cac782f7b1ff7a45eaf1a693.jpg\",\n  \"326976.jpg\": \"Aves/Cardellina canadensis/1dcd5f8e6080f71667ee5cbd6a53b066.jpg\",\n  \"326977.jpg\": \"Aves/Cardellina canadensis/7a8e8bf20ac703bbd0d9a973a267b828.jpg\",\n  \"327056.jpg\": \"Animalia/Velella velella/bd22acf5630600d10a9aa434238598fe.jpg\",\n  \"327067.jpg\": \"Animalia/Velella velella/3d2403fb0f568572b3511d565481571d.jpg\",\n  \"327068.jpg\": \"Animalia/Velella velella/800451d6e148bc33a5de68dfe6585553.jpg\",\n  \"327076.jpg\": \"Animalia/Velella velella/8545e6f8581db6fc6663b1d168d6e7a0.jpg\",\n  \"327134.jpg\": \"Animalia/Velella velella/751a5c63dc72a823e3e0d023629f4f25.jpg\",\n  \"327191.jpg\": \"Amphibia/Ambystoma gracile/38f5a51c148d71e9578d78a5216f399b.jpg\",\n  \"327199.jpg\": \"Amphibia/Ambystoma gracile/c6efb134c7e3b1b1cb9451c2b8dc6456.jpg\",\n  \"327200.jpg\": \"Amphibia/Ambystoma gracile/e6448d212a99af51325b2840accdb5f0.jpg\",\n  \"327213.jpg\": \"Amphibia/Ambystoma gracile/4dbd21d1e60655b5a9b7270f67d0a429.jpg\",\n  \"327217.jpg\": \"Amphibia/Ambystoma gracile/e44bbc3e23591255775744144f5243fd.jpg\",\n  \"327227.jpg\": \"Amphibia/Anaxyrus punctatus/363dcc705b6994d079fbe5b56352cb8f.jpg\",\n  \"327232.jpg\": \"Amphibia/Anaxyrus punctatus/3b3cda95e63634de097f72a0f958e35f.jpg\",\n  \"327244.jpg\": \"Amphibia/Anaxyrus punctatus/9031728c51e2b7218879c40028b008a1.jpg\",\n  \"327246.jpg\": \"Amphibia/Anaxyrus punctatus/d4db699bb32d3058dfccb73ef49bd78c.jpg\",\n  \"327258.jpg\": \"Amphibia/Anaxyrus punctatus/eff82d7409d92cd7747282734e4ed950.jpg\",\n  \"327259.jpg\": \"Amphibia/Anaxyrus punctatus/f6dfd58a8cfeae244832bb5f7f0704a5.jpg\",\n  \"327260.jpg\": \"Amphibia/Anaxyrus punctatus/3624058516bdbd8dc907831c120d4d1b.jpg\",\n  \"327271.jpg\": \"Amphibia/Anaxyrus punctatus/1fa64c91998dc018f70dd0ef72b64e48.jpg\",\n  \"327273.jpg\": \"Amphibia/Anaxyrus punctatus/96d657a2ddb93bbddad2d502764ce259.jpg\",\n  \"327289.jpg\": \"Amphibia/Anaxyrus punctatus/256f9c3a2ab4b9c55fcaf2a3eb2d315b.jpg\",\n  \"327301.jpg\": \"Amphibia/Anaxyrus punctatus/9170a7d534a764235a10bdfa4f625f14.jpg\",\n  \"327305.jpg\": \"Amphibia/Anaxyrus punctatus/6c242bfa87356b896ffc5e698b20ecf0.jpg\",\n  \"327314.jpg\": \"Amphibia/Anaxyrus punctatus/0047b91be125178ef776c4034761bab6.jpg\",\n  \"327326.jpg\": \"Amphibia/Anaxyrus punctatus/b184911e161555411e970416b0334f23.jpg\",\n  \"327327.jpg\": \"Amphibia/Anaxyrus punctatus/bd010141bd3423bf16586aa48a3979ea.jpg\",\n  \"327335.jpg\": \"Amphibia/Anaxyrus punctatus/f39022f9c809ff8cb69ec5fdd6aa8a6f.jpg\",\n  \"327344.jpg\": \"Amphibia/Anaxyrus punctatus/8e89b50fc73225dc8c301e2936e3296a.jpg\",\n  \"327345.jpg\": \"Amphibia/Anaxyrus punctatus/9dd7c41d35494029f42809a7db8ffa4b.jpg\",\n  \"327350.jpg\": \"Amphibia/Anaxyrus punctatus/0d932ed5a548c1aa92af1cd169ed878b.jpg\",\n  \"327355.jpg\": \"Amphibia/Anaxyrus punctatus/03ca1717380a728e94eea39697f40727.jpg\",\n  \"327356.jpg\": \"Amphibia/Anaxyrus punctatus/c14569b9306fc5298455c49ae240bcd6.jpg\",\n  \"327367.jpg\": \"Aves/Acanthis flammea/89a33e62289cde8db328c6e9f6c640a2.jpg\",\n  \"327368.jpg\": \"Aves/Acanthis flammea/806b473a775e59ca61a500e21855341f.jpg\",\n  \"327404.jpg\": \"Aves/Acanthis flammea/3230408096d9529b70f318968d3bb538.jpg\",\n  \"327405.jpg\": \"Aves/Acanthis flammea/a7b0e89dfbcc598f7a7e7f10c1032b06.jpg\",\n  \"327407.jpg\": \"Aves/Acanthis flammea/f6da270d82b3f3e2f6380abbaa5a8b32.jpg\",\n  \"327409.jpg\": \"Aves/Acanthis flammea/bcdab3e43db28d633c4d8906c0db7d17.jpg\",\n  \"327411.jpg\": \"Aves/Acanthis flammea/87c3e5d7429fdbc7a027fbeea3a26a06.jpg\",\n  \"327412.jpg\": \"Aves/Acanthis flammea/23580969a05c521ad3ad5a582a5b0fa5.jpg\",\n  \"327414.jpg\": \"Aves/Acanthis flammea/68ab8027ec63f5ee66cf2ce6b04dbe98.jpg\",\n  \"327419.jpg\": \"Aves/Acanthis flammea/8174b82d37edc14c06b63171bf302169.jpg\",\n  \"327422.jpg\": \"Aves/Acanthis flammea/2f77d54a0ca6daec34e1541857a8bb6e.jpg\",\n  \"327424.jpg\": \"Aves/Acanthis flammea/46371558975e2cae80acf82d1028db44.jpg\",\n  \"327426.jpg\": \"Aves/Acanthis flammea/5628f252794f3ed1868202193541ae9c.jpg\",\n  \"327432.jpg\": \"Aves/Acanthis flammea/68b8f7b3bc793d256499ea747bc63bfa.jpg\",\n  \"327433.jpg\": \"Aves/Acanthis flammea/2eb6d069b4b6d810f493b2d24db4d06a.jpg\",\n  \"327456.jpg\": \"Aves/Acanthis flammea/bc2f2fe629ed9eaef64a18e08082acd3.jpg\",\n  \"327460.jpg\": \"Aves/Acanthis flammea/5ce91f765b0bd685cdc5a6958a15fc91.jpg\",\n  \"327480.jpg\": \"Insecta/Eudryas unio/54cd84fd8d782407587577b7a10f400c.jpg\",\n  \"327486.jpg\": \"Insecta/Eudryas unio/c0ebc64486c7498fde6d95b8bb6112ad.jpg\",\n  \"327487.jpg\": \"Insecta/Eudryas unio/d82eb706f500c7f3d283b7203ca8c2ea.jpg\",\n  \"327488.jpg\": \"Insecta/Eudryas unio/4ce2c32ecb0bc550c8e9f1a33b79163b.jpg\",\n  \"327490.jpg\": \"Insecta/Eudryas unio/8ae666e30c83b03eb8f713b093fcb986.jpg\",\n  \"32752.jpg\": \"Insecta/Papilio glaucus/424d21cfe500c24c88982004a7f4feae.jpg\",\n  \"327522.jpg\": \"Insecta/Malacosoma disstria/0b5d83640da4591d17ecec9427d22d21.jpg\",\n  \"327525.jpg\": \"Insecta/Malacosoma disstria/49c26d234b0bb2b22dc7978d8b7b6e94.jpg\",\n  \"327529.jpg\": \"Insecta/Malacosoma disstria/e1f2784798db7c1d1a26b839af6cbfc1.jpg\",\n  \"327534.jpg\": \"Insecta/Malacosoma disstria/212f22ed43b970407175c0a48f71c966.jpg\",\n  \"327537.jpg\": \"Insecta/Malacosoma disstria/a55aec235e595e81a97ca62cd042db15.jpg\",\n  \"327547.jpg\": \"Insecta/Malacosoma disstria/ce66e81700d4f65b0c7fb41c926bfaba.jpg\",\n  \"327553.jpg\": \"Insecta/Malacosoma disstria/79c4f1a79edeb88311eb6771687b8c04.jpg\",\n  \"327554.jpg\": \"Insecta/Malacosoma disstria/7a6e425319b9c25a553aa41b13bbb6d0.jpg\",\n  \"327558.jpg\": \"Insecta/Malacosoma disstria/fdd62d6319a1a68b0557ea949b793739.jpg\",\n  \"327569.jpg\": \"Insecta/Malacosoma disstria/dce06b4fabf8c50421ed2727c23d2c15.jpg\",\n  \"327570.jpg\": \"Insecta/Malacosoma disstria/8de6f60510445a3c6bdd9f7b9b4447ce.jpg\",\n  \"327572.jpg\": \"Insecta/Malacosoma disstria/fe50d5bc6d3969cac10f5e889e3c5281.jpg\",\n  \"327581.jpg\": \"Insecta/Malacosoma disstria/35377284d3404d3cbfb1941edaf15e1b.jpg\",\n  \"327583.jpg\": \"Insecta/Malacosoma disstria/e2afa62349244c7497e6db188437a086.jpg\",\n  \"327584.jpg\": \"Insecta/Malacosoma disstria/3811e27c53c7db16d29ff946d08983c5.jpg\",\n  \"327590.jpg\": \"Insecta/Malacosoma disstria/4b19de77720a5af92204aa61991b38ec.jpg\",\n  \"327636.jpg\": \"Arachnida/Araneus trifolium/91a5ed8654cb1a16d0acfbf7fb4783ad.jpg\",\n  \"327652.jpg\": \"Arachnida/Araneus trifolium/77cfba10f642cc6120f4e82b078f427a.jpg\",\n  \"327723.jpg\": \"Aves/Larus heermanni/44c87f12a36ed9fa5edb0cfe0f284848.jpg\",\n  \"327752.jpg\": \"Aves/Larus heermanni/18446f32e5f0b962a79d8b07f57b1375.jpg\",\n  \"327764.jpg\": \"Aves/Larus heermanni/6693cb6e2699ba279e08e3cb26e48a87.jpg\",\n  \"327787.jpg\": \"Aves/Larus heermanni/6c4df7da6c8afa6d7d04e582b55b8fe2.jpg\",\n  \"327792.jpg\": \"Aves/Larus heermanni/2c57062d858011ad8e8522dea3299bdf.jpg\",\n  \"327795.jpg\": \"Aves/Larus heermanni/5f81e0ae303f41a108f54092ba7c2775.jpg\",\n  \"327800.jpg\": \"Aves/Larus heermanni/6b35b675cfd80f0f354223727089b5d5.jpg\",\n  \"327811.jpg\": \"Aves/Larus heermanni/0eb8f2483d9713adedfa40558e8e5e20.jpg\",\n  \"327824.jpg\": \"Aves/Larus heermanni/6c6db4313064e3fce0afd94e4f94873d.jpg\",\n  \"327878.jpg\": \"Aves/Larus heermanni/9c76447f1ababeb02bace9b210e1d4b3.jpg\",\n  \"327909.jpg\": \"Aves/Larus heermanni/678c326689c650c3f79460f923d24fb5.jpg\",\n  \"327916.jpg\": \"Aves/Larus heermanni/1e9de64f4825afdbd844e2184e62d1c4.jpg\",\n  \"327923.jpg\": \"Aves/Larus heermanni/225c7cad8bff0366c1aedcb679966a4f.jpg\",\n  \"327937.jpg\": \"Aves/Larus heermanni/0c33b824e58af3921e528447026d086b.jpg\",\n  \"32808.jpg\": \"Aves/Bubo virginianus/1171a989efc40f9c158dcb3eb76a6c75.jpg\",\n  \"328193.jpg\": \"Reptilia/Oxybelis aeneus/10d860285f989b2c31a2872ef54b1b5a.jpg\",\n  \"328199.jpg\": \"Reptilia/Oxybelis aeneus/6185f0a6e0520f3422a4acc3ece3f0fc.jpg\",\n  \"328203.jpg\": \"Reptilia/Oxybelis aeneus/fe4df91c19d1d604db3fdc8422ce5188.jpg\",\n  \"328204.jpg\": \"Reptilia/Oxybelis aeneus/a2d82f2b2f93e833a019d3119abb41ca.jpg\",\n  \"328205.jpg\": \"Reptilia/Oxybelis aeneus/e5efa36c8b216a47eb681a7075e8cc44.jpg\",\n  \"328210.jpg\": \"Insecta/Libellula croceipennis/29d2be8c788a613844c5ab433182e5f2.jpg\",\n  \"328225.jpg\": \"Insecta/Libellula croceipennis/ff86a4ab398e6f1636041ee559ec512f.jpg\",\n  \"328227.jpg\": \"Insecta/Libellula croceipennis/85a8e6eb96bdc2acc3ed648ce87b6327.jpg\",\n  \"328228.jpg\": \"Insecta/Libellula croceipennis/7cae65bc76f0c558fc67c6adfc0579da.jpg\",\n  \"328234.jpg\": \"Insecta/Libellula croceipennis/c199047af59769f9eca64c3f95496dd8.jpg\",\n  \"328235.jpg\": \"Insecta/Libellula croceipennis/0fba5dc870ef5541f0e244429949acaa.jpg\",\n  \"328238.jpg\": \"Insecta/Libellula croceipennis/3014d301a0f5beedcfb3277e8968375c.jpg\",\n  \"328267.jpg\": \"Insecta/Libellula croceipennis/bc12e765a2ebcc8182cca7917b9fe4ec.jpg\",\n  \"328272.jpg\": \"Insecta/Libellula croceipennis/57dfbc463dd1c625bd789d17863e5462.jpg\",\n  \"328307.jpg\": \"Insecta/Libellula croceipennis/6ce4411dc305acbc06e4c125f8e5d4a1.jpg\",\n  \"328314.jpg\": \"Insecta/Libellula croceipennis/c3fb3a45a39846125dd584c030639187.jpg\",\n  \"328317.jpg\": \"Insecta/Libellula croceipennis/0c8f66b9e1d95d1b54b8077b91177578.jpg\",\n  \"328334.jpg\": \"Insecta/Libellula croceipennis/1a6ca6d46cf8c578eb03a3e36584ec2a.jpg\",\n  \"328335.jpg\": \"Insecta/Libellula croceipennis/221b0311469a6868516489bf775ba643.jpg\",\n  \"328339.jpg\": \"Insecta/Libellula croceipennis/0a70b974a9fd2e6b96a960870014e60d.jpg\",\n  \"328341.jpg\": \"Insecta/Libellula croceipennis/0569e2fcb844f8410b63b6ab64c94398.jpg\",\n  \"328350.jpg\": \"Insecta/Libellula croceipennis/55c93c4cadf1a1a0f565127c9adc7b0f.jpg\",\n  \"328352.jpg\": \"Insecta/Libellula croceipennis/22ea08cf61f0be1de472f71d77d8cebe.jpg\",\n  \"328353.jpg\": \"Insecta/Libellula croceipennis/955caa72eb6a70fca22cdc14cc0acbac.jpg\",\n  \"328362.jpg\": \"Insecta/Libellula croceipennis/07b47677a33a64a97fe8a46e67b5fe07.jpg\",\n  \"328363.jpg\": \"Insecta/Libellula croceipennis/a64eaec7d6ae2ba2abf18d85ea994acd.jpg\",\n  \"32838.jpg\": \"Aves/Bubo virginianus/294443baf64e00b113c89de8743f6464.jpg\",\n  \"328484.jpg\": \"Insecta/Speranza pustularia/bb9cf941a63a1bbe5d66fad2f4e660bb.jpg\",\n  \"328485.jpg\": \"Insecta/Speranza pustularia/7f40b1fe0915456e032ed966d14d6adc.jpg\",\n  \"328486.jpg\": \"Insecta/Speranza pustularia/1f3ff76c4eced93f278addd352887811.jpg\",\n  \"328489.jpg\": \"Insecta/Speranza pustularia/1616f526c605417435f4b0b8f2ff5cd6.jpg\",\n  \"328500.jpg\": \"Insecta/Speranza pustularia/1b310b8bef940ee20227622c6ada5d97.jpg\",\n  \"328502.jpg\": \"Insecta/Speranza pustularia/61ed3f0942f2ac283449827119b7cd0c.jpg\",\n  \"328503.jpg\": \"Insecta/Speranza pustularia/63bf2720627b5b9c136f34aeee6bb6c4.jpg\",\n  \"328504.jpg\": \"Insecta/Speranza pustularia/e84df884e4c2d58e9f9be439bf98cf53.jpg\",\n  \"328506.jpg\": \"Insecta/Speranza pustularia/bd7799b7f92112dbb4529a58b807efd4.jpg\",\n  \"328509.jpg\": \"Insecta/Speranza pustularia/83e422061753598316385e0ec7a37bf5.jpg\",\n  \"328512.jpg\": \"Insecta/Speranza pustularia/15c6123f684d9a309d35a004f13bcc5e.jpg\",\n  \"328515.jpg\": \"Insecta/Speranza pustularia/67f62d86288640a2d4ceff548a928d83.jpg\",\n  \"328518.jpg\": \"Insecta/Speranza pustularia/86c0cf7f6e9328339b8447aaad2d9907.jpg\",\n  \"328519.jpg\": \"Insecta/Speranza pustularia/003a903448dce07c669d76ec962d2a20.jpg\",\n  \"328526.jpg\": \"Insecta/Speranza pustularia/9c3dc71fe11b56be404cd594f2bbd50d.jpg\",\n  \"328528.jpg\": \"Insecta/Speranza pustularia/eb3a293f95ad8fce2ff86d09705a6f89.jpg\",\n  \"328530.jpg\": \"Insecta/Speranza pustularia/877955ba65e971ec4b4af15ff52e7238.jpg\",\n  \"328531.jpg\": \"Insecta/Speranza pustularia/41a3bcfd809ba74b10de18c175f0d85e.jpg\",\n  \"328532.jpg\": \"Insecta/Speranza pustularia/b501adb256cce6c163e572bdf466e7a8.jpg\",\n  \"328533.jpg\": \"Insecta/Speranza pustularia/bba937ec6c8960bdc0b817839b5d3759.jpg\",\n  \"328541.jpg\": \"Insecta/Speranza pustularia/12dc007a4ce8b3be91f6b0763f6427f6.jpg\",\n  \"328543.jpg\": \"Aves/Sitta canadensis/d3777268d9bf258c4520a286c8a73cd9.jpg\",\n  \"328560.jpg\": \"Aves/Sitta canadensis/2d017ce7503cecd176bb710ab5cf5e5a.jpg\",\n  \"328568.jpg\": \"Aves/Sitta canadensis/2f39672401510d72ef86c87f7cc2a0b2.jpg\",\n  \"328570.jpg\": \"Aves/Sitta canadensis/912c948125794a8c5d2abdde3571fba1.jpg\",\n  \"328576.jpg\": \"Aves/Sitta canadensis/9ae3804a99b7ee9d71dd480f9c0f2cde.jpg\",\n  \"328585.jpg\": \"Aves/Sitta canadensis/efa91ecca2756b835d8572f8cfe3cc47.jpg\",\n  \"328586.jpg\": \"Aves/Sitta canadensis/3c12e090adb8a82b010e33574f267c7d.jpg\",\n  \"328588.jpg\": \"Aves/Sitta canadensis/09af55cb3560caa03418aea033dc5ebb.jpg\",\n  \"328599.jpg\": \"Aves/Sitta canadensis/0e7a7ebec158bab02a9512ac9228270c.jpg\",\n  \"328607.jpg\": \"Aves/Sitta canadensis/fe31cde6f3c26b577615d6085014ed98.jpg\",\n  \"328610.jpg\": \"Aves/Sitta canadensis/f2bb288332f9efd08f705f52bcbfe305.jpg\",\n  \"328621.jpg\": \"Aves/Sitta canadensis/df3ca9a11420d1eaf43f97d8f5921992.jpg\",\n  \"328641.jpg\": \"Aves/Sitta canadensis/9db3e89ca44db1cc9bbd203ba2a0a85e.jpg\",\n  \"328644.jpg\": \"Aves/Sitta canadensis/17ce6774b1515292e3d6923bc9be140b.jpg\",\n  \"328650.jpg\": \"Aves/Sitta canadensis/91be3536767225a3f2fbe3afa7a3b168.jpg\",\n  \"328652.jpg\": \"Aves/Sitta canadensis/195b048812d93ae96bdcd9120e4dcced.jpg\",\n  \"328692.jpg\": \"Aves/Sitta canadensis/7f25a1dcd0d1413b01d22cbf47b1f8ac.jpg\",\n  \"328693.jpg\": \"Aves/Sitta canadensis/30b97f3e435bc4add04e3cad2998df00.jpg\",\n  \"32871.jpg\": \"Aves/Bubo virginianus/bd4a5e9679ce1b191d66abb7f10c4c69.jpg\",\n  \"328710.jpg\": \"Aves/Sitta canadensis/51270cf74a13d804689eac44e19fa9d1.jpg\",\n  \"328715.jpg\": \"Aves/Sitta canadensis/744df32d27921f4bdf67684d74d170fe.jpg\",\n  \"328717.jpg\": \"Aves/Sitta canadensis/af95a164d7f9aa9460ddfae26888d5c0.jpg\",\n  \"328731.jpg\": \"Aves/Sitta canadensis/ee4f519bd8ae0fa5b39439588cb11827.jpg\",\n  \"328803.jpg\": \"Insecta/Euphyia intermediata/8c5aeed2ffe0db24fb876edf41d89e68.jpg\",\n  \"328804.jpg\": \"Insecta/Euphyia intermediata/b3a8531d468df661f0012e6cc98f2735.jpg\",\n  \"328806.jpg\": \"Insecta/Euphyia intermediata/3047af1769a4eba29d86f4f7a697edc7.jpg\",\n  \"328808.jpg\": \"Insecta/Euphyia intermediata/eb78b79fec607e87d58576ee230c6333.jpg\",\n  \"328813.jpg\": \"Insecta/Euphyia intermediata/318f2896c1274c6c4b0c3dad3a0c697e.jpg\",\n  \"328814.jpg\": \"Insecta/Euphyia intermediata/74b9e31096ffa8d2064acf28192c9d64.jpg\",\n  \"328819.jpg\": \"Insecta/Euphyia intermediata/edb5b157acdc176f735761441a0ecabf.jpg\",\n  \"328823.jpg\": \"Insecta/Euphyia intermediata/679cd06ad85d2283b98e930e8dc7d66b.jpg\",\n  \"328846.jpg\": \"Aves/Gavia pacifica/bb9ce47496bed12636ed5b884dfd8f87.jpg\",\n  \"328847.jpg\": \"Aves/Gavia pacifica/8d759266524f898575cba09162dc2e6c.jpg\",\n  \"328849.jpg\": \"Aves/Gavia pacifica/311a00d0f6945c2b2ad32e71062a9586.jpg\",\n  \"328850.jpg\": \"Aves/Gavia pacifica/c8bd0d1787881d19c67bed74dec85760.jpg\",\n  \"328854.jpg\": \"Aves/Gavia pacifica/33d474eae1cdfb675d99c204edf65a3d.jpg\",\n  \"328856.jpg\": \"Aves/Gavia pacifica/bb4e701e7bb4c5fc2af0302d41c90bcf.jpg\",\n  \"328857.jpg\": \"Aves/Gavia pacifica/c21121819c408650e2475c1328038654.jpg\",\n  \"328863.jpg\": \"Aves/Gavia pacifica/f32976575861018383ccf9ff216a2525.jpg\",\n  \"328864.jpg\": \"Aves/Gavia pacifica/90771fa9c854155dbf421487b9faf6a9.jpg\",\n  \"328865.jpg\": \"Aves/Gavia pacifica/7b12675eb8301cad80a1fbfde165773e.jpg\",\n  \"328870.jpg\": \"Aves/Gavia pacifica/8e94aa35f637f07228cdecd0987ea5a5.jpg\",\n  \"328872.jpg\": \"Aves/Gavia pacifica/85de2dd1a28a03b0eac682bdf04fdcf2.jpg\",\n  \"328873.jpg\": \"Aves/Gavia pacifica/bd5e369963f79ddf3e13ab5d7228a417.jpg\",\n  \"328874.jpg\": \"Aves/Gavia pacifica/f3df2ca9e2e7be338f14ec7a2d1d6048.jpg\",\n  \"328876.jpg\": \"Aves/Gavia pacifica/c1ad94c1d8d88ad572c1f07f5aad3b7a.jpg\",\n  \"328879.jpg\": \"Aves/Gavia pacifica/8ad6ea348eaa273dc5f56fb547a0d8b2.jpg\",\n  \"328881.jpg\": \"Aves/Gavia pacifica/ed86a2b546a794f226557ab99a6f854e.jpg\",\n  \"328882.jpg\": \"Aves/Gavia pacifica/009726acb5e3db79b0348e21b47660de.jpg\",\n  \"328883.jpg\": \"Aves/Gavia pacifica/01103b1a63a8ab85b8b450d32a349391.jpg\",\n  \"328889.jpg\": \"Aves/Gavia pacifica/7d698305a078dc7110edee403483a12c.jpg\",\n  \"328890.jpg\": \"Aves/Gavia pacifica/8e9e3edc8033b7b3093240d706a69af2.jpg\",\n  \"328891.jpg\": \"Aves/Gavia pacifica/00c1639aa3e4b0682b45b9f967f7c91b.jpg\",\n  \"32898.jpg\": \"Aves/Bubo virginianus/07a3ee3b39bef4af912ac54f0ea3bba0.jpg\",\n  \"329.jpg\": \"Insecta/Coleomegilla maculata/b3363b869788a56f9d89480858700926.jpg\",\n  \"329017.jpg\": \"Aves/Morus bassanus/77339e87fe48e915ab5d5ac2ff2a0da1.jpg\",\n  \"329023.jpg\": \"Aves/Morus bassanus/54975c30f689b333330796f43637ce77.jpg\",\n  \"329024.jpg\": \"Aves/Morus bassanus/1aaa62d9463c6830b0aef018e0a08cb7.jpg\",\n  \"329027.jpg\": \"Aves/Morus bassanus/c009f23d29cebdbb8cdc4463445f624d.jpg\",\n  \"329028.jpg\": \"Aves/Morus bassanus/e0c4d7d4de8698493977832ae5ab73c7.jpg\",\n  \"329033.jpg\": \"Aves/Morus bassanus/5a5004420b2cfc3fecb04fc59ddb02ec.jpg\",\n  \"329034.jpg\": \"Aves/Morus bassanus/2715f84e8e4cac18f33b384d2bf6981a.jpg\",\n  \"329039.jpg\": \"Aves/Morus bassanus/60850143f134b39d37516e16aa2d30ed.jpg\",\n  \"329043.jpg\": \"Aves/Morus bassanus/7c32d9ebc8dd74f8aa16a240f1d3d517.jpg\",\n  \"329049.jpg\": \"Aves/Morus bassanus/6a75c9d52580808f2b96d2e116b0415c.jpg\",\n  \"329052.jpg\": \"Aves/Morus bassanus/c7a1ae27c6ec8953f3b8d0b04f0e2674.jpg\",\n  \"329059.jpg\": \"Aves/Morus bassanus/10451fae72fb29e1ccee9a554a9880aa.jpg\",\n  \"329060.jpg\": \"Aves/Morus bassanus/23fdcfd7fc1f58b4e82da38760b2de2b.jpg\",\n  \"329061.jpg\": \"Aves/Morus bassanus/d9d771b35a67dc365eaffe0724b1e5c2.jpg\",\n  \"329063.jpg\": \"Aves/Morus bassanus/f6cd2f05d34899035c5d1443a9cf910c.jpg\",\n  \"329071.jpg\": \"Reptilia/Crotalus mitchellii/df393944af2e43bdf28ac5af64bbf992.jpg\",\n  \"329074.jpg\": \"Reptilia/Crotalus mitchellii/2fac744a3147e99e50fd980fd5886d83.jpg\",\n  \"329075.jpg\": \"Reptilia/Crotalus mitchellii/d223e8f574fbd5530b0e8d978fa1304b.jpg\",\n  \"329076.jpg\": \"Reptilia/Crotalus mitchellii/a515006cc8439bdaceefd03488c5e248.jpg\",\n  \"329077.jpg\": \"Reptilia/Crotalus mitchellii/9a336c138f6896433158531dcd4fc7e3.jpg\",\n  \"329087.jpg\": \"Reptilia/Crotalus mitchellii/64356c4ede3ae111c894aae46cbc638a.jpg\",\n  \"329092.jpg\": \"Reptilia/Crotalus mitchellii/6c21efd1c15e75498ac2fc2f65d20897.jpg\",\n  \"329096.jpg\": \"Reptilia/Crotalus mitchellii/9573cc30119dbd9a82c068ce5b3703e9.jpg\",\n  \"329097.jpg\": \"Reptilia/Crotalus mitchellii/6b2dbaf8edc0024f7f805a88b1bba7b0.jpg\",\n  \"329103.jpg\": \"Reptilia/Crotalus mitchellii/b5d7b097685d7ba556a9969d4b02d83a.jpg\",\n  \"32918.jpg\": \"Aves/Bubo virginianus/f7078729e1ea89bfbb5a2fe9294be0d4.jpg\",\n  \"329221.jpg\": \"Aves/Sarcoramphus papa/e8a604802a3010de5ef40cb71028c129.jpg\",\n  \"329222.jpg\": \"Aves/Sarcoramphus papa/fe0bdc7ac37a6664c8b38bb53b7ea59b.jpg\",\n  \"329223.jpg\": \"Aves/Sarcoramphus papa/796377008326a617acf0a9dc888bdaf2.jpg\",\n  \"329226.jpg\": \"Aves/Sarcoramphus papa/d21abaaa21fc1cc2c60f4f997274b89f.jpg\",\n  \"329233.jpg\": \"Aves/Thryothorus ludovicianus/3a21ce10f6d280e8920f23beae454718.jpg\",\n  \"329248.jpg\": \"Aves/Thryothorus ludovicianus/8e644a8bb8e05a06322c70175546c057.jpg\",\n  \"329250.jpg\": \"Aves/Thryothorus ludovicianus/da4508811e9699140b428e8c8f90371e.jpg\",\n  \"329260.jpg\": \"Aves/Thryothorus ludovicianus/941697aba8f68c2a7c513be11e087b46.jpg\",\n  \"329266.jpg\": \"Aves/Thryothorus ludovicianus/6cdad22318fdb5acc80ecaa6e72802c6.jpg\",\n  \"329280.jpg\": \"Aves/Thryothorus ludovicianus/41ce92780c0744a62732d83b0dcc70e7.jpg\",\n  \"329287.jpg\": \"Aves/Thryothorus ludovicianus/57d8c104011320a95245777a030330d9.jpg\",\n  \"329297.jpg\": \"Aves/Thryothorus ludovicianus/8417654e0df386be3d122b7e122b152c.jpg\",\n  \"329310.jpg\": \"Aves/Thryothorus ludovicianus/a3fe6ebbe61fd1894995cb8260096977.jpg\",\n  \"329314.jpg\": \"Aves/Thryothorus ludovicianus/58280f7e57b7bd16c6e93ba7394e3e6e.jpg\",\n  \"329315.jpg\": \"Aves/Thryothorus ludovicianus/dc046db9c90acf9ef4cdbfeee1523474.jpg\",\n  \"329355.jpg\": \"Aves/Thryothorus ludovicianus/21fb55d166ba325d5f4c89435d079e3a.jpg\",\n  \"329362.jpg\": \"Aves/Thryothorus ludovicianus/b97c12f683ffbd4ea6c8c0ff4d2c30b0.jpg\",\n  \"329381.jpg\": \"Aves/Thryothorus ludovicianus/7ad34e7837c24fd9ec9943956ec0f893.jpg\",\n  \"329384.jpg\": \"Aves/Thryothorus ludovicianus/bb7e86cd4eddbe8236e6e286a0443ef0.jpg\",\n  \"32940.jpg\": \"Aves/Bubo virginianus/7446c8ae5ed92241f917959990145271.jpg\",\n  \"329407.jpg\": \"Aves/Thryothorus ludovicianus/288e74a212b5aa2e22551fefb8047d0b.jpg\",\n  \"329413.jpg\": \"Aves/Thryothorus ludovicianus/a3eee5c477b94d149f6052087a430edf.jpg\",\n  \"329457.jpg\": \"Aves/Thryothorus ludovicianus/959bea4708ff1be9197e07742720ff4a.jpg\",\n  \"329468.jpg\": \"Aves/Thryothorus ludovicianus/bcbca6cf38c8ab513a703b59fd9c4197.jpg\",\n  \"32947.jpg\": \"Aves/Bubo virginianus/152c291c84d01650ce5d1bffbeaefa3d.jpg\",\n  \"329473.jpg\": \"Aves/Thryothorus ludovicianus/c04c81b9aa15fe9b83207e508eeb9ba6.jpg\",\n  \"329499.jpg\": \"Aves/Thryothorus ludovicianus/7c7c73096963cf553c9a7beafc2c37a3.jpg\",\n  \"329529.jpg\": \"Aves/Thryothorus ludovicianus/4b5697586a74e38ff61c2e516da21f39.jpg\",\n  \"329551.jpg\": \"Arachnida/Peucetia viridans/5f91dbb18e9aefaee43338d9126cb157.jpg\",\n  \"329566.jpg\": \"Arachnida/Peucetia viridans/b87700aba13cae31b4ec7eb4b5300e57.jpg\",\n  \"329569.jpg\": \"Arachnida/Peucetia viridans/bf23a1613f82131ce78fa48e311df82e.jpg\",\n  \"329577.jpg\": \"Arachnida/Peucetia viridans/c03502333a3e000ee11c1688501e3f21.jpg\",\n  \"329584.jpg\": \"Arachnida/Peucetia viridans/33a1224f6fe4fea1ad8c484bb17f6cbc.jpg\",\n  \"329585.jpg\": \"Arachnida/Peucetia viridans/f2ca6c057f159edd80d6fcd764c441e2.jpg\",\n  \"329589.jpg\": \"Arachnida/Peucetia viridans/7969b622913e948662ca1b69c3bacc5f.jpg\",\n  \"329617.jpg\": \"Arachnida/Peucetia viridans/e35555f588ce6fede0866a7210d6813d.jpg\",\n  \"329635.jpg\": \"Arachnida/Peucetia viridans/0c13a1491b9d1a9e2237a93b83ef6ddf.jpg\",\n  \"329648.jpg\": \"Arachnida/Peucetia viridans/8c231502a8d8dd1edfd5e575aa6ded0e.jpg\",\n  \"329652.jpg\": \"Arachnida/Peucetia viridans/517055a25d6000becebe3f7e634df9e0.jpg\",\n  \"329654.jpg\": \"Arachnida/Peucetia viridans/72562e2264735760571bc9191fec098c.jpg\",\n  \"329663.jpg\": \"Arachnida/Peucetia viridans/4dc2bdc3720132e27dae34487dbb983b.jpg\",\n  \"329676.jpg\": \"Arachnida/Peucetia viridans/1d552884d51f78ac83e97efba61f2515.jpg\",\n  \"329677.jpg\": \"Arachnida/Peucetia viridans/74462055bd3a1ad6c5082d87830a121b.jpg\",\n  \"329685.jpg\": \"Arachnida/Peucetia viridans/1ad762996a268cef6a929bd95a1157c0.jpg\",\n  \"329688.jpg\": \"Arachnida/Peucetia viridans/2043651c115a86136bbbb8ad3dcf0f15.jpg\",\n  \"329694.jpg\": \"Arachnida/Peucetia viridans/93d0130423d42f7c6228150eb95a2a93.jpg\",\n  \"329695.jpg\": \"Arachnida/Peucetia viridans/54f2bd67b150bf07710037da016ba201.jpg\",\n  \"329710.jpg\": \"Arachnida/Peucetia viridans/f11232e0fb3c3fcee5566c7d5f4c0636.jpg\",\n  \"329711.jpg\": \"Arachnida/Peucetia viridans/8ae67000e764da9cca3c76868a2ec1c7.jpg\",\n  \"329716.jpg\": \"Arachnida/Peucetia viridans/614ec9fbf3f39f449993ec2f379134b0.jpg\",\n  \"32974.jpg\": \"Aves/Bubo virginianus/40f0176a9a36d6e5c36899c8bfcf62de.jpg\",\n  \"329788.jpg\": \"Insecta/Spilosoma virginica/b280beae36fdccbbde84dadac98ae0db.jpg\",\n  \"329792.jpg\": \"Insecta/Spilosoma virginica/491f25d9c07c7c952ffcd98f2f65410b.jpg\",\n  \"329793.jpg\": \"Insecta/Spilosoma virginica/2b68ac8d684b2470e2c86169a1faeaca.jpg\",\n  \"329808.jpg\": \"Insecta/Spilosoma virginica/42bf51efcd7912c03af32a3c6f4d0985.jpg\",\n  \"329809.jpg\": \"Insecta/Spilosoma virginica/02b04e3df0f4f43498c26b39a8eb0d21.jpg\",\n  \"329814.jpg\": \"Insecta/Spilosoma virginica/c8b94547fdad15311c4c6495d91aa6e5.jpg\",\n  \"329830.jpg\": \"Insecta/Spilosoma virginica/4f488320e00db30b15cb531bb4401b4e.jpg\",\n  \"329831.jpg\": \"Insecta/Spilosoma virginica/576e2615e118bfcd8e70b91a650bfade.jpg\",\n  \"329834.jpg\": \"Insecta/Spilosoma virginica/fc356ad823b10034d4fcfd9a0df1d39e.jpg\",\n  \"329842.jpg\": \"Insecta/Spilosoma virginica/1b25f712d4cc32bc1de4932f4da54dec.jpg\",\n  \"329846.jpg\": \"Insecta/Spilosoma virginica/105f6f7bb25013275e79997b833b4abc.jpg\",\n  \"329849.jpg\": \"Insecta/Spilosoma virginica/a6857d53bf0263934b9004b7c1e51898.jpg\",\n  \"329851.jpg\": \"Insecta/Spilosoma virginica/a89666db4b5ab4924c7a75984e1650be.jpg\",\n  \"329854.jpg\": \"Insecta/Spilosoma virginica/12bb9ffdd4feea64623fcce4d59b251a.jpg\",\n  \"329855.jpg\": \"Insecta/Spilosoma virginica/59ef2175f7992aadd1d60c3de0ba805f.jpg\",\n  \"329861.jpg\": \"Insecta/Spilosoma virginica/a738588d1f86e3250ac1333cbb3c4634.jpg\",\n  \"329867.jpg\": \"Insecta/Spilosoma virginica/6a529ee326541dd7b131d254bd66122a.jpg\",\n  \"329868.jpg\": \"Insecta/Spilosoma virginica/045fe1f510f76322fdfc8905d20aa520.jpg\",\n  \"329873.jpg\": \"Reptilia/Storeria dekayi dekayi/808572d7326d9cab49941feccaaa4f1a.jpg\",\n  \"329874.jpg\": \"Reptilia/Storeria dekayi dekayi/44360545e29bf66fd6eace21071f3a61.jpg\",\n  \"329883.jpg\": \"Reptilia/Storeria dekayi dekayi/4fad9d8990bfbe012c07dffccd8c609e.jpg\",\n  \"329885.jpg\": \"Reptilia/Storeria dekayi dekayi/5aa53ee5e8594e44bcc891f0d5f00bbb.jpg\",\n  \"329893.jpg\": \"Reptilia/Storeria dekayi dekayi/588b000fcfc681e78686b004d9fa4462.jpg\",\n  \"329901.jpg\": \"Reptilia/Storeria dekayi dekayi/ace27603549ffc685d90d89c8a4f71cd.jpg\",\n  \"329902.jpg\": \"Reptilia/Storeria dekayi dekayi/69c51a761157fb89ca75f739f142b75d.jpg\",\n  \"329903.jpg\": \"Reptilia/Storeria dekayi dekayi/a9c458cfc241e991c917076ef55e3b42.jpg\",\n  \"329907.jpg\": \"Reptilia/Storeria dekayi dekayi/4b1690310fbd22e693dc210bf6fd31c9.jpg\",\n  \"329908.jpg\": \"Aves/Myiopsitta monachus/16f7a90afd96ef46c31828934cdfd42e.jpg\",\n  \"32993.jpg\": \"Aves/Bubo virginianus/4ac3c4823d7c769c809252cf981372ff.jpg\",\n  \"329938.jpg\": \"Aves/Myiopsitta monachus/1fdcba00184a5965c2f78b52d0a6079a.jpg\",\n  \"329942.jpg\": \"Aves/Myiopsitta monachus/f814d1aca7046e9893d5bacae5e899f1.jpg\",\n  \"329949.jpg\": \"Aves/Myiopsitta monachus/f4a01e4b13b9a33df6149cc0d980ee68.jpg\",\n  \"329953.jpg\": \"Aves/Myiopsitta monachus/56ee047b972cfc40437bfa2ebecdbb3e.jpg\",\n  \"329962.jpg\": \"Aves/Myiopsitta monachus/6177a2ba1648d6ba27ff510922fa4f3e.jpg\",\n  \"329963.jpg\": \"Aves/Myiopsitta monachus/a5a844f510dfac567e3a4e7b58867969.jpg\",\n  \"329971.jpg\": \"Aves/Myiopsitta monachus/1c4df9253633bcf77b6b2399b595ae9f.jpg\",\n  \"329978.jpg\": \"Aves/Myiopsitta monachus/9b8ec767e85e0dd1a6b252eb2b09bd3d.jpg\",\n  \"330.jpg\": \"Insecta/Coleomegilla maculata/366cb5110cd40a5212616c627ddf9468.jpg\",\n  \"330026.jpg\": \"Aves/Myiopsitta monachus/f196cd165653b7b8204a914be6e4b3cc.jpg\",\n  \"330034.jpg\": \"Aves/Myiopsitta monachus/cfc569019147d678fd5a4c5310e22339.jpg\",\n  \"33004.jpg\": \"Aves/Bubo virginianus/1b17b8b7a6f22ce126fe4158fa6214b1.jpg\",\n  \"330044.jpg\": \"Aves/Myiopsitta monachus/ab40b582f598990f23c8949a9c604725.jpg\",\n  \"33005.jpg\": \"Aves/Bubo virginianus/f7c12f4ef34ee5b93736a0d8358568a8.jpg\",\n  \"330085.jpg\": \"Insecta/Xylocopa virginica/e0c4bae0711a90dab44d4b47d4fe1e9d.jpg\",\n  \"330086.jpg\": \"Insecta/Xylocopa virginica/f7095030523b992848a42470ba8a711b.jpg\",\n  \"330094.jpg\": \"Insecta/Xylocopa virginica/ffc240365fce194949ce709d344c1003.jpg\",\n  \"330098.jpg\": \"Insecta/Xylocopa virginica/81cf48f1ddd23e258670afade0ec1f31.jpg\",\n  \"33010.jpg\": \"Aves/Bubo virginianus/8810452cc27a0b06012f4fdac7561dce.jpg\",\n  \"330145.jpg\": \"Insecta/Xylocopa virginica/2253ab43cf99dd218fe7df8569435eb7.jpg\",\n  \"330156.jpg\": \"Insecta/Xylocopa virginica/e418d50a811c7fc9f21716a13764088f.jpg\",\n  \"330183.jpg\": \"Insecta/Xylocopa virginica/211679e25a3ad3cb62a300d315755262.jpg\",\n  \"330248.jpg\": \"Insecta/Xylocopa virginica/5f1ce684f6792bcaa0a16b65edc8650c.jpg\",\n  \"330270.jpg\": \"Insecta/Xylocopa virginica/de2a510b021c205e3c8f7ffbed86f599.jpg\",\n  \"330282.jpg\": \"Insecta/Xylocopa virginica/ed6451af4d0e1674d8bc74139e04a8a5.jpg\",\n  \"330287.jpg\": \"Insecta/Xylocopa virginica/5b03be2c79f4258152bd4b739680e8e2.jpg\",\n  \"330309.jpg\": \"Insecta/Xylocopa virginica/af7032135dad03074ee52dcf4a93b255.jpg\",\n  \"330315.jpg\": \"Insecta/Xylocopa virginica/7d928c3de806b460be4b475d96b1f88d.jpg\",\n  \"330316.jpg\": \"Insecta/Xylocopa virginica/72979f3142ed4fb895f71881485ca5ac.jpg\",\n  \"330322.jpg\": \"Insecta/Xylocopa virginica/cd362a73fee6ab634710233c8d170050.jpg\",\n  \"330340.jpg\": \"Insecta/Xylocopa virginica/60b46afdf7a873829b51062519e699b0.jpg\",\n  \"330344.jpg\": \"Insecta/Xylocopa virginica/ab754ef914aee942c6f1c36f8671456e.jpg\",\n  \"330346.jpg\": \"Insecta/Xylocopa virginica/c232deb5dfadea26ac30fcebce5a4134.jpg\",\n  \"330352.jpg\": \"Insecta/Xylocopa virginica/e1652f023a584189b1488b81fdff4a6b.jpg\",\n  \"330394.jpg\": \"Mammalia/Microtus californicus/3302532c2eee31b2ecf106a802f41d44.jpg\",\n  \"330399.jpg\": \"Mammalia/Microtus californicus/236f79d8270770fc42f0a3f106933bfe.jpg\",\n  \"330409.jpg\": \"Mammalia/Microtus californicus/0802863dce602ccfd30b7d22427a77c5.jpg\",\n  \"330411.jpg\": \"Insecta/Harrisina americana/163535995a555a00a3bea905171dc7b2.jpg\",\n  \"330415.jpg\": \"Insecta/Harrisina americana/e3363b05f0b378a6040790a18df2e753.jpg\",\n  \"330424.jpg\": \"Insecta/Harrisina americana/cac0a1a708cd44e412eabe4b09eb015e.jpg\",\n  \"330428.jpg\": \"Insecta/Harrisina americana/8f163335d4ff344bb87ba1253e82ff95.jpg\",\n  \"330449.jpg\": \"Mammalia/Urocitellus columbianus/dd28a3ab949eec965b180de37f912bef.jpg\",\n  \"330454.jpg\": \"Mammalia/Urocitellus columbianus/7043d1e09f5c122eb38e15f3a443d135.jpg\",\n  \"330456.jpg\": \"Mammalia/Urocitellus columbianus/50f8186bd0c3e90cdfe9f1871cc86de5.jpg\",\n  \"330465.jpg\": \"Mammalia/Urocitellus columbianus/cb1c47f86789e688c056d0d2fc8c81c1.jpg\",\n  \"330477.jpg\": \"Aves/Falco tinnunculus/8256caa7598b44e037c80c0c300ea9cd.jpg\",\n  \"330482.jpg\": \"Aves/Falco tinnunculus/5e90823d290cc4734da4f6d49072ff55.jpg\",\n  \"330515.jpg\": \"Aves/Falco tinnunculus/eb6885971cb35af8e422069629fe39bc.jpg\",\n  \"330521.jpg\": \"Aves/Falco tinnunculus/25f723eb3fc9ba93a88c07c3725f5ed0.jpg\",\n  \"330527.jpg\": \"Aves/Falco tinnunculus/66a4b850c0f6dc65791600f6c24446d6.jpg\",\n  \"33053.jpg\": \"Aves/Bubo virginianus/eb219d2c77785ebe1799f843140672e6.jpg\",\n  \"330545.jpg\": \"Aves/Falco tinnunculus/e2f6394f9665aff3624103e2effdd436.jpg\",\n  \"330549.jpg\": \"Aves/Falco tinnunculus/09ad91fe31fca2fec5022f1da673fe93.jpg\",\n  \"330576.jpg\": \"Aves/Falco tinnunculus/2eed3c4a3dff24b4f91c5c3bee241493.jpg\",\n  \"330578.jpg\": \"Aves/Falco tinnunculus/62446b83376d21b89a1dd92f3be5d44d.jpg\",\n  \"330579.jpg\": \"Aves/Falco tinnunculus/06a73dbd70e2129124a27b8fe4ddd862.jpg\",\n  \"330586.jpg\": \"Aves/Falco tinnunculus/7db05f6b0a2edeb18479dba73779a364.jpg\",\n  \"330613.jpg\": \"Aves/Falco tinnunculus/67067fcc80c6134b5d0bb416b3de496d.jpg\",\n  \"330621.jpg\": \"Aves/Falco tinnunculus/0476bf1a900d78f05cac79ae531dc3af.jpg\",\n  \"330631.jpg\": \"Aves/Falco tinnunculus/3e860e706a474ceacda8fc39d66e8372.jpg\",\n  \"330636.jpg\": \"Aves/Falco tinnunculus/8a2b77d49f084c49e560eb9da340ced1.jpg\",\n  \"330638.jpg\": \"Aves/Falco tinnunculus/43e9681a60608c99bbf4473046db7411.jpg\",\n  \"33064.jpg\": \"Aves/Bubo virginianus/1a9bfe6092d401bd2aa6d959273196db.jpg\",\n  \"330650.jpg\": \"Aves/Falco tinnunculus/83213ff09b449e28d089fcfa91c3de34.jpg\",\n  \"330657.jpg\": \"Aves/Falco tinnunculus/ae4b00b5198cf7d0aaea76e7e6d65485.jpg\",\n  \"330668.jpg\": \"Aves/Falco tinnunculus/ffdf244b78d2a76b75b886dd1146b915.jpg\",\n  \"330669.jpg\": \"Aves/Falco tinnunculus/103b8b97d758cc6a36996d9d6da5ba5f.jpg\",\n  \"330699.jpg\": \"Aves/Falco tinnunculus/042495c6eed43aac7907077eb0909028.jpg\",\n  \"330717.jpg\": \"Aves/Falco tinnunculus/780746b97dc4ca00ee49ed3e1592c9ec.jpg\",\n  \"330720.jpg\": \"Aves/Falco tinnunculus/d4832f39941b1dc019ccd74b9d255b05.jpg\",\n  \"330723.jpg\": \"Aves/Falco tinnunculus/5283d490dd574a94f04fb196a9bdb713.jpg\",\n  \"330724.jpg\": \"Aves/Falco tinnunculus/2929bc19cef5ffbee24b3a98cc36b983.jpg\",\n  \"3308.jpg\": \"Insecta/Coccinella septempunctata/562c290d1219e50ab90c58bc42c37e59.jpg\",\n  \"330802.jpg\": \"Reptilia/Thamnophis elegans elegans/14db2215de7af38e2087c2b7a06eaaed.jpg\",\n  \"330805.jpg\": \"Reptilia/Thamnophis elegans elegans/cea3dafb3ee2a40f0da70232161fc309.jpg\",\n  \"330806.jpg\": \"Reptilia/Thamnophis elegans elegans/cdff13295b72122567f36b23f54acd57.jpg\",\n  \"330811.jpg\": \"Reptilia/Thamnophis elegans elegans/2d458015135c6b14e99800730af2ea51.jpg\",\n  \"330812.jpg\": \"Reptilia/Thamnophis elegans elegans/b83d4b3be9ffc2430f5d7ecb742aa47a.jpg\",\n  \"330814.jpg\": \"Reptilia/Thamnophis elegans elegans/b0e3a4037050ffd142586c176cc8f829.jpg\",\n  \"33082.jpg\": \"Aves/Bubo virginianus/c927d6ec3941c59f0392015ef47e4251.jpg\",\n  \"33085.jpg\": \"Aves/Bubo virginianus/dcf61ad7e88584e6d738e24f1bf8b2bd.jpg\",\n  \"33087.jpg\": \"Aves/Bubo virginianus/8184f858c2a782c22d67a2fcf0f5df9b.jpg\",\n  \"330920.jpg\": \"Aves/Fulica americana/24a8fc52e9f23c7b52a4da9cf9150f32.jpg\",\n  \"331065.jpg\": \"Aves/Fulica americana/3b63980e36495306d16fc5b0d1fc96ed.jpg\",\n  \"331099.jpg\": \"Aves/Fulica americana/a8533e7a30a722ce4b3bac3a3873d75b.jpg\",\n  \"331144.jpg\": \"Aves/Fulica americana/e2ec43f92cce5b237d07fc5fe96934a3.jpg\",\n  \"331152.jpg\": \"Aves/Fulica americana/f246d2914eed9bcb83f1c5e937b8fd68.jpg\",\n  \"331202.jpg\": \"Aves/Fulica americana/22d26719574f6fe79e0797e73e72b8d3.jpg\",\n  \"33133.jpg\": \"Aves/Bubo virginianus/596693ade5f0cebccbe4a039a0493556.jpg\",\n  \"331426.jpg\": \"Aves/Fulica americana/bf58d7ecdf5d814d0905b1da61d0ebc1.jpg\",\n  \"331432.jpg\": \"Aves/Fulica americana/67ed1d32c82ed9f5fcd0097ce629efbf.jpg\",\n  \"331474.jpg\": \"Aves/Fulica americana/70edfacd766515bdd488c1b4ca71abc5.jpg\",\n  \"331653.jpg\": \"Aves/Fulica americana/ae8ac433604b988f64f8214f6266fa07.jpg\",\n  \"331676.jpg\": \"Aves/Fulica americana/9ade89b7b0af3eef87a0411baac66044.jpg\",\n  \"331776.jpg\": \"Aves/Fulica americana/038d85a336b562abb410d6133ab057c0.jpg\",\n  \"331849.jpg\": \"Reptilia/Aspidoscelis tigris stejnegeri/1073f63ac293e42550678168461f403b.jpg\",\n  \"331850.jpg\": \"Reptilia/Aspidoscelis tigris stejnegeri/303e3a534ff31012e83d3d31e7850a68.jpg\",\n  \"331851.jpg\": \"Reptilia/Aspidoscelis tigris stejnegeri/54447455dbcbbbe829afa66a6a75c8a2.jpg\",\n  \"331852.jpg\": \"Reptilia/Aspidoscelis tigris stejnegeri/41b929bda6d9cba66cbfdd48ae044cf9.jpg\",\n  \"331854.jpg\": \"Reptilia/Aspidoscelis tigris stejnegeri/732182e420defb1f08b7d8f138fe613b.jpg\",\n  \"331855.jpg\": \"Reptilia/Aspidoscelis tigris stejnegeri/d3f9905472458787cacd5668ed520233.jpg\",\n  \"331861.jpg\": \"Reptilia/Aspidoscelis tigris stejnegeri/7365ed6f53ca869e119e2b1b8e7642ff.jpg\",\n  \"331867.jpg\": \"Reptilia/Aspidoscelis tigris stejnegeri/19bba49d845885d20aa664ef81e0d881.jpg\",\n  \"331868.jpg\": \"Reptilia/Aspidoscelis tigris stejnegeri/b6f8f0448dcb628298ee13ae0f2df0b8.jpg\",\n  \"331869.jpg\": \"Reptilia/Aspidoscelis tigris stejnegeri/6a715e255d47a0f492ab9e2c8e4e2ed6.jpg\",\n  \"331870.jpg\": \"Reptilia/Aspidoscelis tigris stejnegeri/482bb3f0d6c92c852f779e0f368f1824.jpg\",\n  \"331871.jpg\": \"Reptilia/Aspidoscelis tigris stejnegeri/09858b09b89faf6007afb8df34b8b002.jpg\",\n  \"331872.jpg\": \"Reptilia/Aspidoscelis tigris stejnegeri/215bba00ef2aaec6a6942b625df62b80.jpg\",\n  \"331873.jpg\": \"Reptilia/Aspidoscelis tigris stejnegeri/307d52794db1673803a92e0ce563b14b.jpg\",\n  \"331874.jpg\": \"Reptilia/Aspidoscelis tigris stejnegeri/2b462cfe173ff36ad44d1f79a24542f6.jpg\",\n  \"331875.jpg\": \"Reptilia/Aspidoscelis tigris stejnegeri/50204362bad708d2c1efee37df0e12ef.jpg\",\n  \"331876.jpg\": \"Reptilia/Aspidoscelis tigris stejnegeri/74553264a52f9d9c0eabc5934afe1eee.jpg\",\n  \"331879.jpg\": \"Reptilia/Aspidoscelis tigris stejnegeri/c10f86418e5d2f3d8fc5bef56d765084.jpg\",\n  \"331881.jpg\": \"Reptilia/Aspidoscelis tigris stejnegeri/2c522a212320cc2909caef2865510c54.jpg\",\n  \"331887.jpg\": \"Reptilia/Aspidoscelis tigris stejnegeri/0e6975f1a5d6fa42de9914651a634f50.jpg\",\n  \"331889.jpg\": \"Reptilia/Aspidoscelis tigris stejnegeri/ae2c60c3ec18d14ffe13dcf81a73e214.jpg\",\n  \"331893.jpg\": \"Reptilia/Aspidoscelis tigris stejnegeri/d527bd8e1c77fbfa065ae0468f1c2173.jpg\",\n  \"331902.jpg\": \"Reptilia/Aspidoscelis tigris stejnegeri/cedc564a8cf690447411f39e9c410e3f.jpg\",\n  \"331904.jpg\": \"Reptilia/Pituophis catenifer catenifer/b9a1fe00227550701375fc9cec74aefd.jpg\",\n  \"331912.jpg\": \"Reptilia/Pituophis catenifer catenifer/930ada6b8627173560ffd6f0371d33c7.jpg\",\n  \"331919.jpg\": \"Reptilia/Pituophis catenifer catenifer/ce19135b29bf186ea573a30b37d6705e.jpg\",\n  \"331942.jpg\": \"Reptilia/Pituophis catenifer catenifer/6dfb383a61d9dcc74842ba7856329dba.jpg\",\n  \"331947.jpg\": \"Reptilia/Pituophis catenifer catenifer/52404d2eb3ca95ac348d6d60dac62a0a.jpg\",\n  \"331948.jpg\": \"Reptilia/Pituophis catenifer catenifer/735dd754392def3b3bd7da910016d3a0.jpg\",\n  \"331954.jpg\": \"Reptilia/Pituophis catenifer catenifer/0596b75eb3350294f9c1b046e54ba0f1.jpg\",\n  \"331955.jpg\": \"Reptilia/Pituophis catenifer catenifer/168e04ff98fd3307331cfd6d1a5ffc23.jpg\",\n  \"331956.jpg\": \"Reptilia/Pituophis catenifer catenifer/abcfd1fa37baba87befbfcfb575d19b1.jpg\",\n  \"331961.jpg\": \"Reptilia/Pituophis catenifer catenifer/ff67b774c26ead23aee0d17ab8112de8.jpg\",\n  \"331965.jpg\": \"Reptilia/Pituophis catenifer catenifer/b865b2000c37ba62e3a291f1944de670.jpg\",\n  \"331975.jpg\": \"Reptilia/Pituophis catenifer catenifer/1e2bea3f9163b18dfe60f1f442655683.jpg\",\n  \"331976.jpg\": \"Reptilia/Pituophis catenifer catenifer/f92d3d2fe758293d05b2c9c6ad35f72a.jpg\",\n  \"331979.jpg\": \"Reptilia/Pituophis catenifer catenifer/3d11a88e6bef40a69cb7569c6577a0fa.jpg\",\n  \"331988.jpg\": \"Reptilia/Pituophis catenifer catenifer/5192e0fc018cd350a71365f87632caeb.jpg\",\n  \"331992.jpg\": \"Reptilia/Pituophis catenifer catenifer/fcfbdf53ed2ee6d58a8cc13a794181c0.jpg\",\n  \"331993.jpg\": \"Reptilia/Pituophis catenifer catenifer/6301aa86e954c9aac9a42be2246b396a.jpg\",\n  \"331998.jpg\": \"Reptilia/Pituophis catenifer catenifer/ee13f92c433ce908760f8490f03aa29c.jpg\",\n  \"332002.jpg\": \"Reptilia/Pituophis catenifer catenifer/b801e4fe3f196490dcecd1d39356ff98.jpg\",\n  \"332003.jpg\": \"Reptilia/Pituophis catenifer catenifer/ca80d77ff0625212b68ed0b567f4b257.jpg\",\n  \"332004.jpg\": \"Reptilia/Pituophis catenifer catenifer/9defd74df69a3cc42fcd347e5646dd78.jpg\",\n  \"332010.jpg\": \"Reptilia/Pituophis catenifer catenifer/7dd58e905d3fbf23c9a85b620baa337a.jpg\",\n  \"332012.jpg\": \"Mammalia/Macaca fascicularis/1b32c1954b1283575d8bf5c92322a4ec.jpg\",\n  \"332026.jpg\": \"Mammalia/Macaca fascicularis/cf688661f91ffc3ec8c1940599f1b724.jpg\",\n  \"332027.jpg\": \"Mammalia/Macaca fascicularis/1df945a18d5926433bdd34cd9f5690ea.jpg\",\n  \"332030.jpg\": \"Mammalia/Macaca fascicularis/f45eb79157731a4be156ba59fb15df09.jpg\",\n  \"332031.jpg\": \"Mammalia/Macaca fascicularis/284cb0867dded5a7fd339972618d7780.jpg\",\n  \"332036.jpg\": \"Mammalia/Macaca fascicularis/ce1fcaef981b64712965652b2a5cbebc.jpg\",\n  \"332037.jpg\": \"Mammalia/Macaca fascicularis/1b4a43ddeeb748b5eb99d2c77aba34cf.jpg\",\n  \"332038.jpg\": \"Mammalia/Macaca fascicularis/3a74154ed0ddc15956d5add5d9ef0b2b.jpg\",\n  \"332044.jpg\": \"Mammalia/Macaca fascicularis/fe841bef87f4fb839625f7361ca33213.jpg\",\n  \"332047.jpg\": \"Mammalia/Macaca fascicularis/6c24f16e125819707003276210381c04.jpg\",\n  \"332049.jpg\": \"Mammalia/Macaca fascicularis/b80a1a049238c692bbfa45a77d9f757f.jpg\",\n  \"332050.jpg\": \"Mammalia/Macaca fascicularis/d04fab53d1daf389509fcf1dbb86501d.jpg\",\n  \"332051.jpg\": \"Mammalia/Macaca fascicularis/bd458bdd45c02f60b394ecf0d4e949fd.jpg\",\n  \"332057.jpg\": \"Aves/Falco columbarius/ca5de58ca8e74733799d660f3a659d0c.jpg\",\n  \"332061.jpg\": \"Aves/Falco columbarius/71aececaedc751d7d769156cb68d4f62.jpg\",\n  \"332068.jpg\": \"Aves/Falco columbarius/372ff90dad182f3b097f1e6a9c7ef06c.jpg\",\n  \"332081.jpg\": \"Aves/Falco columbarius/e7eef8e451066a777f79c3c4b95e7a20.jpg\",\n  \"332094.jpg\": \"Aves/Falco columbarius/9ef06a0ed38f5068a6c41977181a8b9f.jpg\",\n  \"332110.jpg\": \"Aves/Falco columbarius/b516bfcd564b782c601e5c23818e8114.jpg\",\n  \"332123.jpg\": \"Aves/Falco columbarius/5b17e757853c5a0b7b56bb06891c2e66.jpg\",\n  \"332130.jpg\": \"Aves/Falco columbarius/97c6b268db6ca5e46885625a0555cd22.jpg\",\n  \"332131.jpg\": \"Aves/Falco columbarius/588b310ad527a491022b5edb5673ae16.jpg\",\n  \"332133.jpg\": \"Aves/Falco columbarius/c2a17ebb3116e24728ee7b80df423832.jpg\",\n  \"332163.jpg\": \"Aves/Falco columbarius/0baea56271847bf464120edfb3bf24e9.jpg\",\n  \"332169.jpg\": \"Aves/Falco columbarius/024e0ae6f5345b9f70db3e511bf5acf6.jpg\",\n  \"332172.jpg\": \"Aves/Falco columbarius/242b40761810187f83c365f99177a010.jpg\",\n  \"332191.jpg\": \"Aves/Falco columbarius/dce6fbdb64ec27abd1777ea858abdf9a.jpg\",\n  \"332192.jpg\": \"Aves/Falco columbarius/02073214f7e6254fca60c6f5d09471ad.jpg\",\n  \"332193.jpg\": \"Aves/Falco columbarius/359845f672bc9386626fdb80102045c5.jpg\",\n  \"332206.jpg\": \"Aves/Falco columbarius/6c750026c40695fd2e4d8e3af1eec422.jpg\",\n  \"332214.jpg\": \"Aves/Falco columbarius/60e36508ccb441aa0584f95e9fbcaf20.jpg\",\n  \"332217.jpg\": \"Aves/Falco columbarius/976394ef266d14865111c4afebc68651.jpg\",\n  \"332218.jpg\": \"Aves/Falco columbarius/35edd8db84f35e98bdb3b833eec4e540.jpg\",\n  \"332232.jpg\": \"Aves/Peucaea ruficauda/b9c5e6420953abeb51a8d9a6f8c7e82c.jpg\",\n  \"332234.jpg\": \"Aves/Peucaea ruficauda/5cdd6b0f04f2215f2e3bf796f73d9539.jpg\",\n  \"332236.jpg\": \"Aves/Peucaea ruficauda/a4dde22cca38430d9ff07559ff5547a2.jpg\",\n  \"332244.jpg\": \"Aves/Peucaea ruficauda/5092108a792f403318ff8829a00b3f77.jpg\",\n  \"332245.jpg\": \"Aves/Peucaea ruficauda/c579a1a8692a0f34b015547fd67fabca.jpg\",\n  \"332285.jpg\": \"Insecta/Eurema hecabe/214003c52115d03a8655383e8146926b.jpg\",\n  \"332288.jpg\": \"Insecta/Eurema hecabe/17779a29d9b88a5a91daa8f59065e334.jpg\",\n  \"332289.jpg\": \"Insecta/Eurema hecabe/b2b6f819e0d811354fa9f2b36ecbfe82.jpg\",\n  \"332290.jpg\": \"Insecta/Eurema hecabe/abd0638e7806a5a4c139b4a5e7df079a.jpg\",\n  \"332291.jpg\": \"Insecta/Eurema hecabe/74e73f81e364ec0c79e2ecb90b8af174.jpg\",\n  \"332296.jpg\": \"Aves/Gavia stellata/de07d4c307955d6192cf502ffbc4f97c.jpg\",\n  \"332308.jpg\": \"Aves/Gavia stellata/e85c8f6bc320cc5225269ba56a950fc2.jpg\",\n  \"332310.jpg\": \"Aves/Gavia stellata/fb4ca8b75e27c7b3282602ab18dc0fd2.jpg\",\n  \"332315.jpg\": \"Aves/Gavia stellata/8f71a886bd3faf22a1a1313364d537e5.jpg\",\n  \"332323.jpg\": \"Aves/Gavia stellata/cfc6a4486f81340bf7bfa5b95927d557.jpg\",\n  \"332324.jpg\": \"Aves/Gavia stellata/d1c9a8f307be23cb426a7e7bd9f5b670.jpg\",\n  \"332327.jpg\": \"Aves/Gavia stellata/1bde35f2e854c6aa322794f786949f38.jpg\",\n  \"332338.jpg\": \"Aves/Gavia stellata/e8b091e1a37b1f5bf406ec14b2a929a0.jpg\",\n  \"332340.jpg\": \"Aves/Gavia stellata/dc4c28ca22c51fba39bc0ccfabc0d9f4.jpg\",\n  \"332351.jpg\": \"Aves/Gavia stellata/dcfc6d2b86119fa04d032d5b60844dfb.jpg\",\n  \"332352.jpg\": \"Aves/Gavia stellata/40b00d3f8b8d22af51af29ba4997e4e2.jpg\",\n  \"332356.jpg\": \"Aves/Gavia stellata/a2c2d076b693140fc6c5eed1f0ed4b2c.jpg\",\n  \"332359.jpg\": \"Aves/Gavia stellata/0b4cbe4f1c602aef1cd8b38100d55aec.jpg\",\n  \"332362.jpg\": \"Aves/Gavia stellata/97fb46e247380c0e076eaaabee1fcf48.jpg\",\n  \"332373.jpg\": \"Aves/Gavia stellata/661379d984c91c459336d4abbeb16d04.jpg\",\n  \"332374.jpg\": \"Aves/Gavia stellata/0292823598e6587e9f2489c33d806608.jpg\",\n  \"33241.jpg\": \"Aves/Trogon massena/f17dad3fa28a24fdc73866aced06d1e0.jpg\",\n  \"33242.jpg\": \"Aves/Trogon massena/021bdd2c8ba498b564d7d35a3e0dff74.jpg\",\n  \"332472.jpg\": \"Insecta/Pyrausta tyralis/c395c23f16c252e9c28bda8b81fa9c6c.jpg\",\n  \"332475.jpg\": \"Insecta/Pyrausta tyralis/765d3a1c2cf97f43fec7712c83fc5824.jpg\",\n  \"332476.jpg\": \"Insecta/Pyrausta tyralis/b36b0a9987e821962ca8bd295d776ac3.jpg\",\n  \"332479.jpg\": \"Insecta/Pyrausta tyralis/31d463a349f16367ffca725dd9e7a778.jpg\",\n  \"332482.jpg\": \"Insecta/Pyrausta tyralis/cc760c567a97390fed02981577672d82.jpg\",\n  \"33252.jpg\": \"Aves/Trogon massena/62ca69690ca01b435019daa07ae219c5.jpg\",\n  \"33253.jpg\": \"Aves/Trogon massena/2ea506198a0d4a00192067a7f655f806.jpg\",\n  \"33254.jpg\": \"Aves/Trogon massena/186ef7cdcdafeb8eca234c8bbc356bd0.jpg\",\n  \"33255.jpg\": \"Insecta/Erythrodiplax umbrata/339e2a5f98b8f371d094930e288bca7a.jpg\",\n  \"332641.jpg\": \"Amphibia/Lissotriton vulgaris/31db31ccfeb243868ff2a53ea2ccbce5.jpg\",\n  \"332642.jpg\": \"Amphibia/Lissotriton vulgaris/79d38318defbda93c91ab8d8d17a2649.jpg\",\n  \"332643.jpg\": \"Amphibia/Lissotriton vulgaris/976b7047b8f28249698e8365ba27a432.jpg\",\n  \"332644.jpg\": \"Amphibia/Lissotriton vulgaris/4efb0ef606c8452d0ec1bbdb5835bcb7.jpg\",\n  \"332645.jpg\": \"Amphibia/Lissotriton vulgaris/9dbb8131b447b2e1c3ddcf9c670ca969.jpg\",\n  \"332646.jpg\": \"Amphibia/Lissotriton vulgaris/c235cea3ee7e58c63100d47f6462997e.jpg\",\n  \"332653.jpg\": \"Aves/Megascops kennicottii/fe60834fb7849f44c19c42221549cfd9.jpg\",\n  \"332654.jpg\": \"Aves/Megascops kennicottii/4fbf7506ade383cfe5bd49748f4e67e5.jpg\",\n  \"332658.jpg\": \"Aves/Megascops kennicottii/809bbce2b91a01673d94f52dd4ed3397.jpg\",\n  \"332662.jpg\": \"Aves/Megascops kennicottii/236367b9fee1822f550439caadf68bd4.jpg\",\n  \"332676.jpg\": \"Aves/Megascops kennicottii/24f8603d80c77c79a57ee0a2b3c69a26.jpg\",\n  \"33268.jpg\": \"Insecta/Erythrodiplax umbrata/076d909460c86013a7b458eb6b9ebe2f.jpg\",\n  \"332697.jpg\": \"Insecta/Megalographa biloba/995798299a489a41603e199f3550c622.jpg\",\n  \"332698.jpg\": \"Insecta/Megalographa biloba/ccac9f247e56e050cd31dc1cfd7dc5c5.jpg\",\n  \"332701.jpg\": \"Insecta/Megalographa biloba/3976abda86e6e3b745252ed29b4cf500.jpg\",\n  \"332702.jpg\": \"Insecta/Megalographa biloba/e05042a7d960e10b45f7a5bd8c75a544.jpg\",\n  \"332705.jpg\": \"Insecta/Megalographa biloba/61acfdd19fdc2337de1ac9d4ee836fa7.jpg\",\n  \"33271.jpg\": \"Insecta/Erythrodiplax umbrata/677274c7332c53359d87d137ca67bc68.jpg\",\n  \"332732.jpg\": \"Aves/Phoenicopterus ruber/1b5468ae0e834c5b6c956720057aab93.jpg\",\n  \"33274.jpg\": \"Insecta/Erythrodiplax umbrata/64542d904f10fb8a9b05649fc9efe76f.jpg\",\n  \"332784.jpg\": \"Aves/Phoenicopterus ruber/813adcf0995a0b77ab95a8042622520f.jpg\",\n  \"332786.jpg\": \"Aves/Phoenicopterus ruber/46fc6a7057125c78472d3ec1902454fd.jpg\",\n  \"332787.jpg\": \"Aves/Phoenicopterus ruber/71b4aba1f1529acadfec01cd0cff3a66.jpg\",\n  \"33279.jpg\": \"Insecta/Erythrodiplax umbrata/befda5b4c050d1b6efc2c4f5c0f6926d.jpg\",\n  \"332807.jpg\": \"Animalia/Strongylocentrotus purpuratus/d427f005fed1afc903236219b126224b.jpg\",\n  \"332812.jpg\": \"Animalia/Strongylocentrotus purpuratus/06917ccbc7ad29cc17ab2dc40d28402c.jpg\",\n  \"33283.jpg\": \"Insecta/Erythrodiplax umbrata/97f184f1f9e0469e219174f5173fc4f7.jpg\",\n  \"33285.jpg\": \"Insecta/Erythrodiplax umbrata/d3ccbcdeb13c6c70d6b53eca81a17a0d.jpg\",\n  \"332861.jpg\": \"Animalia/Strongylocentrotus purpuratus/a327e140f69af1b02c23cf2bee3fa6fa.jpg\",\n  \"332864.jpg\": \"Animalia/Strongylocentrotus purpuratus/9c6ee5b466ebf1d4af933109b3507bc4.jpg\",\n  \"332871.jpg\": \"Animalia/Strongylocentrotus purpuratus/40a8734853a4a258f80fc586f3dcb140.jpg\",\n  \"332872.jpg\": \"Animalia/Strongylocentrotus purpuratus/bb36d0854ade7b5f964c1a268756d240.jpg\",\n  \"332876.jpg\": \"Animalia/Strongylocentrotus purpuratus/6fb4a2ec7fd9416d2e799c996234bb6e.jpg\",\n  \"332879.jpg\": \"Animalia/Strongylocentrotus purpuratus/dadb40fb5949604092e23fc6ecada163.jpg\",\n  \"33292.jpg\": \"Insecta/Erythrodiplax umbrata/81000b0e1b9e7023cd459666e38d922f.jpg\",\n  \"33296.jpg\": \"Insecta/Erythrodiplax umbrata/5917fda5745153f2ff98eb9a46cd28b0.jpg\",\n  \"332978.jpg\": \"Aves/Tadorna ferruginea/22824347e429490c3026094a17462b59.jpg\",\n  \"332981.jpg\": \"Aves/Tadorna ferruginea/9be1077995769cc987c8ec81ccd2b4fa.jpg\",\n  \"332987.jpg\": \"Aves/Tadorna ferruginea/941b69995355ade84bbd5736c8ea9d35.jpg\",\n  \"33299.jpg\": \"Insecta/Erythrodiplax umbrata/ec5c16b1615c4f180ad8d53012351005.jpg\",\n  \"332990.jpg\": \"Aves/Tadorna ferruginea/64a39b7abc420a26a5d4765234e3df14.jpg\",\n  \"333.jpg\": \"Insecta/Coleomegilla maculata/fd5f7bf6a39fb5d1816d0ec0c2222fbf.jpg\",\n  \"33302.jpg\": \"Insecta/Erythrodiplax umbrata/c1d19249dffe150c3f64ad5c921546e9.jpg\",\n  \"333033.jpg\": \"Aves/Anas superciliosa/7b577d8acf95402017dd8d7385099204.jpg\",\n  \"333036.jpg\": \"Aves/Anas superciliosa/9ab7b2e8f8584aada11e7e9552353fa3.jpg\",\n  \"333037.jpg\": \"Aves/Anas superciliosa/35c6fabd2defc9f737ce7f48b6754c1c.jpg\",\n  \"333041.jpg\": \"Aves/Anas superciliosa/4c75c6c9419787dbaf88d559b046f09f.jpg\",\n  \"333042.jpg\": \"Aves/Anas superciliosa/0463303151fabf374d6ac07e89f37541.jpg\",\n  \"333045.jpg\": \"Aves/Anas superciliosa/7bd68659d9cb91cbfae7216fbdfe6d83.jpg\",\n  \"333049.jpg\": \"Aves/Anas superciliosa/527335486eec6fd44300a1054db36389.jpg\",\n  \"333055.jpg\": \"Aves/Anas superciliosa/2cd118c72df28b9a0494c64c657637ba.jpg\",\n  \"333065.jpg\": \"Aves/Anas superciliosa/339b795605e58bc855f123647953eab9.jpg\",\n  \"333070.jpg\": \"Aves/Anas superciliosa/c90e7904fd78dba41315a71cce2b09bb.jpg\",\n  \"333071.jpg\": \"Aves/Anas superciliosa/1b0b099ffbcf29e3b98508a88abaec91.jpg\",\n  \"333079.jpg\": \"Insecta/Tolype velleda/0c9e95292bef0608a9ba353887f388f3.jpg\",\n  \"333083.jpg\": \"Insecta/Tolype velleda/563fb271b1599ef54eb037e7738ac067.jpg\",\n  \"33309.jpg\": \"Insecta/Erythrodiplax umbrata/bbcaea59f5fafad0bdf24e82800726dd.jpg\",\n  \"333092.jpg\": \"Insecta/Tolype velleda/886217d8cb6805463e36219397031f30.jpg\",\n  \"333097.jpg\": \"Insecta/Tolype velleda/27d4aa41fde3918d93467af0d8f4df39.jpg\",\n  \"333102.jpg\": \"Insecta/Tolype velleda/7224eb8f461a9945bb1504e7776d8156.jpg\",\n  \"333103.jpg\": \"Insecta/Tolype velleda/a1371c1b0d67c7944cdf0cc50cdc2edc.jpg\",\n  \"333108.jpg\": \"Actinopterygii/Esox lucius/bb502fa15552bf37269003d26d44ec1e.jpg\",\n  \"333109.jpg\": \"Actinopterygii/Esox lucius/c267ce6e121d5c891999d02c0689d5d5.jpg\",\n  \"333116.jpg\": \"Actinopterygii/Esox lucius/43f3a9c750f0ab49c2a9085190af625e.jpg\",\n  \"333117.jpg\": \"Actinopterygii/Esox lucius/c4db587385b112931362632065bb746b.jpg\",\n  \"333118.jpg\": \"Actinopterygii/Esox lucius/e1534f12d1f728ebe3a7c768733f4df4.jpg\",\n  \"33312.jpg\": \"Insecta/Erythrodiplax umbrata/7b8d1248c747e72b1fd28752dfaa2d45.jpg\",\n  \"333123.jpg\": \"Actinopterygii/Esox lucius/f9602547e82f27089d6f5cbf0929d2c0.jpg\",\n  \"333128.jpg\": \"Aves/Tadorna variegata/e31cb68c9bce60207b60a6609ec671e9.jpg\",\n  \"333136.jpg\": \"Aves/Tadorna variegata/9a0eae9544bca0c890bc5d66f1481485.jpg\",\n  \"333141.jpg\": \"Aves/Tadorna variegata/d582112dea63bafee0351ee8d70dcc41.jpg\",\n  \"333142.jpg\": \"Aves/Tadorna variegata/5f99f4020c5f1e628e9921a71fd2ea2b.jpg\",\n  \"333143.jpg\": \"Aves/Tadorna variegata/0de8e9dda3c16462661bfc26b51052f9.jpg\",\n  \"333158.jpg\": \"Aves/Tadorna variegata/2f857bbd4264bf049a991120663e81c6.jpg\",\n  \"333165.jpg\": \"Aves/Tadorna variegata/0542458c79f4b395c1645c4f9742dc3a.jpg\",\n  \"33317.jpg\": \"Insecta/Erythrodiplax umbrata/88ce273a5c3c5cb61a58ef3114c35277.jpg\",\n  \"333233.jpg\": \"Aves/Aquila chrysaetos/3960b780b759d05044009f0bd1530e69.jpg\",\n  \"333250.jpg\": \"Aves/Aquila chrysaetos/0fad6d68626e9115d2a1996408c5a171.jpg\",\n  \"333251.jpg\": \"Aves/Aquila chrysaetos/751d22b9cf83a5dbcdd48ca37690bb0d.jpg\",\n  \"333255.jpg\": \"Aves/Aquila chrysaetos/5622335681e3dcc59ed6a14249e13a6c.jpg\",\n  \"333259.jpg\": \"Aves/Aquila chrysaetos/35cd82db3e7a1d2b8d2bf24cf8943cdb.jpg\",\n  \"333268.jpg\": \"Aves/Aquila chrysaetos/f37e9fa18676fdb6b3bcfc9ddc038c4e.jpg\",\n  \"333269.jpg\": \"Aves/Aquila chrysaetos/74e5233ece6d1bc94acef53c769438d7.jpg\",\n  \"333275.jpg\": \"Aves/Aquila chrysaetos/c9e1cde5e2057e2a162d9eace3219f58.jpg\",\n  \"333276.jpg\": \"Aves/Aquila chrysaetos/8f5f663248a2c800899da2172dd59100.jpg\",\n  \"333283.jpg\": \"Aves/Aquila chrysaetos/a9491845b42de045b964fa92872aa41f.jpg\",\n  \"333285.jpg\": \"Aves/Aquila chrysaetos/73392cd6b19a5e27cc424af1cdf67e89.jpg\",\n  \"33329.jpg\": \"Insecta/Erythrodiplax umbrata/042f4d7b7e3f033a2fc9737d1fc550cd.jpg\",\n  \"333306.jpg\": \"Aves/Aquila chrysaetos/52a2f35b1a0806d6e26ce7e49a1d8ef7.jpg\",\n  \"33332.jpg\": \"Insecta/Erythrodiplax umbrata/d0496a8ff89d26cdc48d19803877c70d.jpg\",\n  \"333322.jpg\": \"Aves/Aquila chrysaetos/cfbc9032ceca4dcaaaaa4725047abb93.jpg\",\n  \"333347.jpg\": \"Aves/Aquila chrysaetos/b2897606c729938bd33bbd3f8723c4d3.jpg\",\n  \"33335.jpg\": \"Insecta/Erythrodiplax umbrata/5ecdb4194fb4dea9c6b4df24f9247b82.jpg\",\n  \"333355.jpg\": \"Aves/Aquila chrysaetos/d45b2fa41f042b61eaa39de6eaf232bb.jpg\",\n  \"333356.jpg\": \"Aves/Aquila chrysaetos/aa957a03c5d9534f7e4f2711281c94f0.jpg\",\n  \"333368.jpg\": \"Aves/Aquila chrysaetos/f6d843b4b6bcf1d569a5ef6154ef4bac.jpg\",\n  \"333373.jpg\": \"Aves/Aquila chrysaetos/df4e4134a447303ef142c19e1b591600.jpg\",\n  \"333385.jpg\": \"Reptilia/Lampropeltis multifasciata/dfc42e19041cad3404c4aef50192b3cd.jpg\",\n  \"333386.jpg\": \"Reptilia/Lampropeltis multifasciata/19cded8c3195a2f6e409fb55f83ba6eb.jpg\",\n  \"333389.jpg\": \"Reptilia/Lampropeltis multifasciata/e3b33de28192b4d14ff3846020584736.jpg\",\n  \"33339.jpg\": \"Insecta/Erythrodiplax umbrata/b6a26b8d5a8b80cdf8b36f36063256de.jpg\",\n  \"333390.jpg\": \"Reptilia/Lampropeltis multifasciata/3a36377b8e05de82afe1dd6b93ce074a.jpg\",\n  \"333392.jpg\": \"Reptilia/Lampropeltis multifasciata/9754789c77653c7fd3277bed7666e1dc.jpg\",\n  \"333393.jpg\": \"Reptilia/Lampropeltis multifasciata/138afcc4f0d7aea77878aa50519ce023.jpg\",\n  \"333396.jpg\": \"Insecta/Tyria jacobaeae/ef410ef770d9e0b142b597d7d14046bc.jpg\",\n  \"333397.jpg\": \"Insecta/Tyria jacobaeae/0c4d0f81feff7f17473df44abb9e1278.jpg\",\n  \"33340.jpg\": \"Insecta/Erythrodiplax umbrata/94bb47592b8b4838cc40addc1083afc7.jpg\",\n  \"333404.jpg\": \"Insecta/Tyria jacobaeae/5a2dfd50f4abe3a95f598098ffceffee.jpg\",\n  \"333406.jpg\": \"Insecta/Tyria jacobaeae/0010344a2b132eb7b5766e7e5b2d72b9.jpg\",\n  \"333407.jpg\": \"Insecta/Tyria jacobaeae/398e4f2d6aa40d9364565d3470c17f8f.jpg\",\n  \"333419.jpg\": \"Insecta/Tyria jacobaeae/e07ae33b8d64881972588c7469109ac0.jpg\",\n  \"333420.jpg\": \"Insecta/Tyria jacobaeae/2c2f808a46bec251314c19ac89b289ca.jpg\",\n  \"333422.jpg\": \"Insecta/Tyria jacobaeae/281869111794fa986f33b3e200f79f70.jpg\",\n  \"333424.jpg\": \"Insecta/Tyria jacobaeae/6374b3ec650a746962f922750ababf23.jpg\",\n  \"333425.jpg\": \"Insecta/Tyria jacobaeae/cf508bae95eb3f0d8d1b29105890e5a1.jpg\",\n  \"333434.jpg\": \"Insecta/Tyria jacobaeae/6f06262895c8319c9f4ba675efd607f9.jpg\",\n  \"333446.jpg\": \"Insecta/Tyria jacobaeae/27ca0a1a35f096dc9761514db28bebb8.jpg\",\n  \"333454.jpg\": \"Insecta/Limenitis arthemis/85d9bed4db0d4343ad6f683710933364.jpg\",\n  \"333464.jpg\": \"Insecta/Limenitis arthemis/e2838bc44506043fa1ad23e7745c57c3.jpg\",\n  \"333474.jpg\": \"Insecta/Limenitis arthemis/8d527b98ed9c368620f9cc8c323d7615.jpg\",\n  \"333478.jpg\": \"Insecta/Limenitis arthemis/95e07d8ab63ede3e1ee32de82c21bd46.jpg\",\n  \"333481.jpg\": \"Insecta/Limenitis arthemis/3b6b445b013298f44b399bc96583762c.jpg\",\n  \"333482.jpg\": \"Insecta/Limenitis arthemis/1f8098914444a61d65336c90cf71f9f7.jpg\",\n  \"333485.jpg\": \"Insecta/Limenitis arthemis/a2c7921f89641c3331bdc5b6dec72780.jpg\",\n  \"333494.jpg\": \"Insecta/Limenitis arthemis/79537a402f31936e1848a2b92a25117e.jpg\",\n  \"333495.jpg\": \"Insecta/Limenitis arthemis/9cec323487697caa08bfcb02e6957b74.jpg\",\n  \"333498.jpg\": \"Insecta/Limenitis arthemis/baec12e07da4baa2bc59b249b437bc8a.jpg\",\n  \"333501.jpg\": \"Insecta/Limenitis arthemis/5da2217f51f6ebea12451415da404657.jpg\",\n  \"333502.jpg\": \"Insecta/Limenitis arthemis/84995cc0b82eaf7be0c1048deb99c820.jpg\",\n  \"333505.jpg\": \"Insecta/Limenitis arthemis/ee738e7dd47d9cab5e7ee3d4d59da7f4.jpg\",\n  \"333518.jpg\": \"Insecta/Limenitis arthemis/8a85b0adb44a875cb53682eb51ea267b.jpg\",\n  \"333519.jpg\": \"Insecta/Limenitis arthemis/4169151fb081f08e02fc19508c91ab72.jpg\",\n  \"333521.jpg\": \"Insecta/Limenitis arthemis/e60454cde07d15ea1aa8fe99fd384b31.jpg\",\n  \"333528.jpg\": \"Insecta/Limenitis arthemis/fec7922f467ddea031e7b21d7680fa96.jpg\",\n  \"33353.jpg\": \"Insecta/Erythrodiplax umbrata/14bed424e06ec37c0baed58eeceaaa60.jpg\",\n  \"333548.jpg\": \"Insecta/Limenitis arthemis/4efa0d8394ba42aaed0c51235c2a31b1.jpg\",\n  \"333549.jpg\": \"Insecta/Limenitis arthemis/bafb11e576d81d5a366fa646683c786d.jpg\",\n  \"333550.jpg\": \"Insecta/Limenitis arthemis/1d9f931cab3ec60b6c7684cb0cd8a5e0.jpg\",\n  \"333560.jpg\": \"Insecta/Anavitrinella pampinaria/3ae310f2c029fe175c6f2642d8d46db7.jpg\",\n  \"333561.jpg\": \"Insecta/Anavitrinella pampinaria/3e5461ed2a32438e94437c7c2aa4f080.jpg\",\n  \"333565.jpg\": \"Insecta/Anavitrinella pampinaria/0f2b3763d7a0e8a235adffa8615e7806.jpg\",\n  \"333571.jpg\": \"Insecta/Anavitrinella pampinaria/aa3c6c6cf24c2baf771d4beec898dc4b.jpg\",\n  \"333601.jpg\": \"Aves/Mergus serrator/eb78d59bb8d7d4cc297be44d2d04536b.jpg\",\n  \"333603.jpg\": \"Aves/Mergus serrator/47fa5e62c188ff4db8d1a039286d9411.jpg\",\n  \"333605.jpg\": \"Aves/Mergus serrator/18019898ba2c6de4f63eddb88bf874ef.jpg\",\n  \"333607.jpg\": \"Aves/Mergus serrator/e71bab7133030b85fbda0ee26bc75e36.jpg\",\n  \"333615.jpg\": \"Aves/Mergus serrator/c2354c68850c2d4293763ee9f236d1eb.jpg\",\n  \"333646.jpg\": \"Aves/Mergus serrator/1c2773fe3de29297ee887e7c5cab4628.jpg\",\n  \"333664.jpg\": \"Aves/Mergus serrator/0167292f2d1f2b1ef766737cd88a156d.jpg\",\n  \"333666.jpg\": \"Aves/Mergus serrator/c4f73624b5e3dcd2c2e4e3b9012edf2f.jpg\",\n  \"333682.jpg\": \"Aves/Mergus serrator/203558709501bf55e39efb7ddb6087af.jpg\",\n  \"333687.jpg\": \"Aves/Mergus serrator/c65a48e63cd5c1e248d0626ea8c1c0c5.jpg\",\n  \"333694.jpg\": \"Aves/Mergus serrator/f4e058d0095d64f88bb2baf0f71fe925.jpg\",\n  \"3337.jpg\": \"Insecta/Coccinella septempunctata/926119387778fab70ffa40b6594929c2.jpg\",\n  \"333700.jpg\": \"Aves/Mergus serrator/8983a8f21dbf546fae2870efbf7faa64.jpg\",\n  \"333705.jpg\": \"Aves/Mergus serrator/9522b05cf20b824ddd39f86e4450e6e6.jpg\",\n  \"333730.jpg\": \"Aves/Mergus serrator/0a139c41b9d2a27ab485a5b5b8a81350.jpg\",\n  \"333740.jpg\": \"Aves/Mergus serrator/7331eedca94f75089c6f4d32fda10724.jpg\",\n  \"333751.jpg\": \"Aves/Mergus serrator/1b8715d7a1c1e131c1227aab315124cc.jpg\",\n  \"333755.jpg\": \"Aves/Mergus serrator/9d2d01cb4bc6a03eae9beefa15b04439.jpg\",\n  \"333794.jpg\": \"Aves/Mergus serrator/9dd3ccca0ae5716a295fd9981c8cd231.jpg\",\n  \"334.jpg\": \"Insecta/Coleomegilla maculata/a7590ed52ced3c1476130af7984e6fbf.jpg\",\n  \"334061.jpg\": \"Insecta/Anania hortulata/2300c7cea2298f1666b8257a23264b2f.jpg\",\n  \"334063.jpg\": \"Insecta/Anania hortulata/ecd12088b02b379d06d7349d928c1187.jpg\",\n  \"334068.jpg\": \"Insecta/Anania hortulata/33608f83e582a6d5b2e5d4dfe9472603.jpg\",\n  \"334070.jpg\": \"Insecta/Anania hortulata/589c2ab0623a40cf4e6f208788c4fab3.jpg\",\n  \"334076.jpg\": \"Insecta/Anania hortulata/73becf3c9383d0455fb6bf4cc4f9bf76.jpg\",\n  \"334080.jpg\": \"Reptilia/Varanus salvator/d0a5684d509d3973fc2c1ac49e537f2d.jpg\",\n  \"334081.jpg\": \"Reptilia/Varanus salvator/6d5373791956cd66d22ffa46c966dc81.jpg\",\n  \"334082.jpg\": \"Reptilia/Varanus salvator/84641498c5dae3f842e1ce91e9302f3e.jpg\",\n  \"334089.jpg\": \"Reptilia/Varanus salvator/344c40c1e0233187033940e8da96f0c6.jpg\",\n  \"334090.jpg\": \"Reptilia/Varanus salvator/20a20651eabbb4e050e48675dd90bf83.jpg\",\n  \"334094.jpg\": \"Reptilia/Varanus salvator/93ada1d159b21cd047de708de6ead31a.jpg\",\n  \"3341.jpg\": \"Insecta/Coccinella septempunctata/427404cb54c508dcf9b9a2db4057badf.jpg\",\n  \"334113.jpg\": \"Aves/Pheucticus ludovicianus/042d99a83cdf453150cd98a8e4a09ccb.jpg\",\n  \"334122.jpg\": \"Aves/Pheucticus ludovicianus/d7fadd4162e32f073c1396006dffac84.jpg\",\n  \"334126.jpg\": \"Aves/Pheucticus ludovicianus/f80fb8c7bedc9666ad388987b613fac6.jpg\",\n  \"334139.jpg\": \"Aves/Pheucticus ludovicianus/e596341d3db965ba07d2b933e63afac2.jpg\",\n  \"334142.jpg\": \"Aves/Pheucticus ludovicianus/e5323a2eadf8412319b02dccb34c12d4.jpg\",\n  \"334152.jpg\": \"Aves/Pheucticus ludovicianus/539b278771576aee51f27a2340fb88fc.jpg\",\n  \"334166.jpg\": \"Aves/Pheucticus ludovicianus/b72231739f209b9f04c63b6e8accb17b.jpg\",\n  \"334175.jpg\": \"Aves/Pheucticus ludovicianus/a09b5f0939b48ade400127dd0340f6e5.jpg\",\n  \"334186.jpg\": \"Aves/Pheucticus ludovicianus/ac0a0825bf6dd6edd6c419b75b67770f.jpg\",\n  \"3342.jpg\": \"Insecta/Coccinella septempunctata/b8d060f657edbdf2933156250141642f.jpg\",\n  \"334207.jpg\": \"Aves/Pheucticus ludovicianus/c7c6414dc8a5ccd92879d861bf71c841.jpg\",\n  \"334220.jpg\": \"Aves/Pheucticus ludovicianus/512089451e6287cb63a8e6c3ebb481aa.jpg\",\n  \"334224.jpg\": \"Aves/Pheucticus ludovicianus/825a051e1df828a432fbc5aeee2fdb3d.jpg\",\n  \"334225.jpg\": \"Aves/Pheucticus ludovicianus/ada55adcef142f8b60b52b1b0c60c1c0.jpg\",\n  \"334231.jpg\": \"Aves/Pheucticus ludovicianus/acd4881219b6c2fda5ec6bd9c7226238.jpg\",\n  \"334237.jpg\": \"Aves/Pheucticus ludovicianus/b3e6a3b672293e0cf1e2441ab0e229f8.jpg\",\n  \"334250.jpg\": \"Aves/Pheucticus ludovicianus/09c0c96f26c50a1f0fb51675c305f30d.jpg\",\n  \"334251.jpg\": \"Aves/Pheucticus ludovicianus/6525f84d9d9b55508becab32837f9ebf.jpg\",\n  \"334280.jpg\": \"Aves/Pheucticus ludovicianus/abd5b6b41a9a720ca74dbee96cdc499d.jpg\",\n  \"334282.jpg\": \"Aves/Pheucticus ludovicianus/afb8e2dc26a53ff8bf1f973cf53447bf.jpg\",\n  \"334289.jpg\": \"Aves/Pheucticus ludovicianus/eb0cd2c4da5c66edd5c1dc0f9e68d3eb.jpg\",\n  \"334298.jpg\": \"Aves/Pheucticus ludovicianus/e9c0b5dd7298fc1d1a7d48758b50f571.jpg\",\n  \"334304.jpg\": \"Amphibia/Pelophylax/c9ba739b0f3dddf9530d1b5213372202.jpg\",\n  \"334311.jpg\": \"Amphibia/Pelophylax/541753652ee17d3e2e5970bb07e8dc00.jpg\",\n  \"334314.jpg\": \"Amphibia/Pelophylax/334b845801b3cf88fa03896e02e13524.jpg\",\n  \"334315.jpg\": \"Amphibia/Pelophylax/786349aef9c5d7bc8863e6193399459a.jpg\",\n  \"334321.jpg\": \"Amphibia/Pelophylax/5f496ebc5a5a0f1e49d7de6c869b0626.jpg\",\n  \"334324.jpg\": \"Amphibia/Pelophylax/e370bc4565c8d3905ca395bf799d5a82.jpg\",\n  \"334326.jpg\": \"Amphibia/Pelophylax/a9bec348dac09f58ca83f77ba61a04a3.jpg\",\n  \"334327.jpg\": \"Amphibia/Pelophylax/097837d5850ffdf4fe673ef9b894f047.jpg\",\n  \"334332.jpg\": \"Amphibia/Pelophylax/b56ee425f1600b1f5bc7d7adabbbc09f.jpg\",\n  \"334339.jpg\": \"Amphibia/Pelophylax/ca59aeee77e909966f8383b68aa0e4b7.jpg\",\n  \"334344.jpg\": \"Amphibia/Pelophylax/f3caa284e8f22c0036a8841364abf5ae.jpg\",\n  \"334351.jpg\": \"Amphibia/Pelophylax/ede95e90025a93ff703085e28485284c.jpg\",\n  \"334353.jpg\": \"Amphibia/Pelophylax/bff4241a7cb7a6bc0d84a17863d4eaaf.jpg\",\n  \"334354.jpg\": \"Amphibia/Pelophylax/3a2ff15a97cbaa6916ac2ab88d9ad06a.jpg\",\n  \"334355.jpg\": \"Amphibia/Pelophylax/dbf79b5ad55e5140cad9d62983961ae1.jpg\",\n  \"334358.jpg\": \"Amphibia/Pelophylax/452d04c78be1f787c2babab23a02aa49.jpg\",\n  \"334365.jpg\": \"Amphibia/Pelophylax/34e434ac934896e5b1bdc5f5dffcca51.jpg\",\n  \"334366.jpg\": \"Amphibia/Pelophylax/884911b9b43ab592a279cac8a7475f54.jpg\",\n  \"334370.jpg\": \"Amphibia/Pelophylax/e83f9a3d32eb4b726c953e8a44224c4a.jpg\",\n  \"334375.jpg\": \"Amphibia/Pelophylax/14931b368af753150ed9ced4764d0ae8.jpg\",\n  \"334376.jpg\": \"Amphibia/Pelophylax/26dc024908fbf4c466dc5dde141e0478.jpg\",\n  \"334377.jpg\": \"Amphibia/Pelophylax/bf5e0de7b10b56ce47b2b0457cf6b496.jpg\",\n  \"334416.jpg\": \"Aves/Thraupis episcopus/8dcad0f2f9d4846013709065dc200905.jpg\",\n  \"334419.jpg\": \"Aves/Thraupis episcopus/ef93329cd0226184a9b030625df49326.jpg\",\n  \"334421.jpg\": \"Aves/Thraupis episcopus/2ca5c33fb961aa045cfbedf50a4c42e6.jpg\",\n  \"334427.jpg\": \"Aves/Thraupis episcopus/87e90b5f460b2face7760e2b865ec2a5.jpg\",\n  \"334428.jpg\": \"Aves/Thraupis episcopus/404f2e1715a91a9c40cd42eac2215c06.jpg\",\n  \"334431.jpg\": \"Aves/Thraupis episcopus/2fcd8409ad181fccfba3cbaa949c8f9f.jpg\",\n  \"334437.jpg\": \"Aves/Thraupis episcopus/1c8bc2e8e53e22b3b12b21971f86b221.jpg\",\n  \"334439.jpg\": \"Aves/Thraupis episcopus/1cb55cc6d6e5167fad4bf0efc8899112.jpg\",\n  \"334443.jpg\": \"Aves/Thraupis episcopus/2323c85d2304d37980318b98af6bce2d.jpg\",\n  \"334451.jpg\": \"Aves/Thraupis episcopus/8785be942ea74167e1d0582be7fe4c79.jpg\",\n  \"334454.jpg\": \"Aves/Thraupis episcopus/f5939bc93d78598d9cdc4c233b9643ca.jpg\",\n  \"334455.jpg\": \"Aves/Thraupis episcopus/611d984829b63497c6e52e30bb704b8f.jpg\",\n  \"334463.jpg\": \"Aves/Thraupis episcopus/008de5dca10faeaece114eee68ea3edd.jpg\",\n  \"334467.jpg\": \"Aves/Thraupis episcopus/d9ac40199e2f79b610b1c95fcbb687ed.jpg\",\n  \"334471.jpg\": \"Aves/Thraupis episcopus/949b888162cf7e5e68d604801a900887.jpg\",\n  \"334472.jpg\": \"Aves/Thraupis episcopus/8fbb7d994d9232aa8a1fa2bacfb49f29.jpg\",\n  \"334473.jpg\": \"Aves/Thraupis episcopus/603833c5bcf4a6477d223e4061f9f4fc.jpg\",\n  \"334479.jpg\": \"Aves/Thraupis episcopus/232a0e06beb550a9f272051286ee585f.jpg\",\n  \"334491.jpg\": \"Aves/Thraupis episcopus/1a9bfb0cee3cb110291efe0bd7d604d9.jpg\",\n  \"334503.jpg\": \"Aves/Motacilla cinerea/449bedaa4db70fda36f3049a3556d190.jpg\",\n  \"334504.jpg\": \"Aves/Motacilla cinerea/b9f7ec091e4fff940e945f3a0af10de7.jpg\",\n  \"334505.jpg\": \"Aves/Motacilla cinerea/e04170c444cded17d65593100a4862fa.jpg\",\n  \"334507.jpg\": \"Aves/Motacilla cinerea/69704a8dbb52a90d4ebbf91ac7073384.jpg\",\n  \"334518.jpg\": \"Aves/Motacilla cinerea/23be5f1f8c7a461ade6367c3349b9c33.jpg\",\n  \"334534.jpg\": \"Aves/Motacilla cinerea/3293f1d976deab43d8dea9d0c4b136e5.jpg\",\n  \"334535.jpg\": \"Aves/Motacilla cinerea/2dcca56112a2d50252d7b9318b181e9e.jpg\",\n  \"334542.jpg\": \"Aves/Motacilla cinerea/8d4400b6ca5efc3d4f5ee281f04bbf0d.jpg\",\n  \"334543.jpg\": \"Aves/Motacilla cinerea/9e3b1dcba48b10418a2858d709b08220.jpg\",\n  \"334547.jpg\": \"Aves/Motacilla cinerea/85997129720cdcb26a4c78cb4b73f97a.jpg\",\n  \"334548.jpg\": \"Aves/Motacilla cinerea/4099d9c815194e782ceccd34d747a305.jpg\",\n  \"334553.jpg\": \"Aves/Motacilla cinerea/54557fa542fc0621c494e76a48cf2ef3.jpg\",\n  \"334554.jpg\": \"Aves/Motacilla cinerea/dde6a4e7ebc6acf24a58df02fb1985c1.jpg\",\n  \"334558.jpg\": \"Aves/Motacilla cinerea/5f4d9e2d306c3c175dc6864b859e3fb7.jpg\",\n  \"334559.jpg\": \"Aves/Motacilla cinerea/01ba643311433015735bbe12b9f39df6.jpg\",\n  \"334562.jpg\": \"Aves/Motacilla cinerea/022d9cb8e05b26746dca8a19e69b6b13.jpg\",\n  \"334567.jpg\": \"Aves/Motacilla cinerea/58cab2306cbea2a84c27dac22f89f8d7.jpg\",\n  \"334571.jpg\": \"Aves/Motacilla cinerea/32281028c5a2e6ed824d181062423b43.jpg\",\n  \"334574.jpg\": \"Aves/Motacilla cinerea/36b7b1f450d7f6f513690f26efc01331.jpg\",\n  \"334575.jpg\": \"Aves/Motacilla cinerea/a6970d3769a441863d35cb76532c43cc.jpg\",\n  \"334576.jpg\": \"Aves/Motacilla cinerea/13cb54a51355333fc1310202361cde2e.jpg\",\n  \"334578.jpg\": \"Mollusca/Limacus flavus/59b42c98d40a5d3139bdc5090dd7ac37.jpg\",\n  \"334579.jpg\": \"Mollusca/Limacus flavus/dac6d72a49cd36f0ffa6369027f9ac86.jpg\",\n  \"334580.jpg\": \"Mollusca/Limacus flavus/a86c9a2c1ce82fe263c864d34509295d.jpg\",\n  \"334581.jpg\": \"Mollusca/Limacus flavus/a8b04e7e20789b52e88e887a2cca4922.jpg\",\n  \"334583.jpg\": \"Mollusca/Limacus flavus/19c03a012ea9ceb2113e679798bb4275.jpg\",\n  \"334584.jpg\": \"Mollusca/Limacus flavus/4bc3c3430d782ba248566331b960ff41.jpg\",\n  \"334585.jpg\": \"Mollusca/Limacus flavus/4fe95d8b4fbd7ad01db1c3ae1e9613e3.jpg\",\n  \"334586.jpg\": \"Mollusca/Limacus flavus/f2214823b6b91dd69541f2a165677ee1.jpg\",\n  \"334587.jpg\": \"Mollusca/Limacus flavus/d249b10c96c45440a8d8d855ffafea1b.jpg\",\n  \"334588.jpg\": \"Mollusca/Limacus flavus/5956012a2ae2fc717917e06a2c9a1fa5.jpg\",\n  \"334593.jpg\": \"Mollusca/Limacus flavus/b79e57ae2db52ffdf1c931a23d7602b3.jpg\",\n  \"334601.jpg\": \"Mollusca/Limacus flavus/1712532925d703125ddaf3d25fccbed2.jpg\",\n  \"334602.jpg\": \"Mollusca/Limacus flavus/3f6ecb05e2ecb04d9df408d431d1c96a.jpg\",\n  \"334603.jpg\": \"Mollusca/Limacus flavus/c0cf9f92ff6b7769d0cde70e383d7264.jpg\",\n  \"334611.jpg\": \"Mollusca/Limacus flavus/7a8c21fcfa1066abb2942c643bb4d403.jpg\",\n  \"334612.jpg\": \"Mollusca/Limacus flavus/36cead55a136da65306a2ecaaf9c4dda.jpg\",\n  \"334613.jpg\": \"Mollusca/Limacus flavus/2dbe89c73738d2f883c1f5efbe6b2c92.jpg\",\n  \"334625.jpg\": \"Mammalia/Urocitellus beldingi/9167594e0b40288b41dc1c617e378415.jpg\",\n  \"334629.jpg\": \"Mammalia/Urocitellus beldingi/af0cb8338fd602d3f47fb02153915395.jpg\",\n  \"334636.jpg\": \"Mammalia/Urocitellus beldingi/4abd7c07296d49a239b9e063548aca4e.jpg\",\n  \"334640.jpg\": \"Mammalia/Urocitellus beldingi/8dd43f0a821cb92c4770451ff82b1676.jpg\",\n  \"334721.jpg\": \"Aves/Phoenicopterus roseus/c49f7ff5974bb8cdc2c25062dda0d034.jpg\",\n  \"334754.jpg\": \"Aves/Phoenicopterus roseus/342fbf8dd4330f6fa15133fd6c59f0da.jpg\",\n  \"334755.jpg\": \"Aves/Phoenicopterus roseus/711ed38bcc593951eb1f6ecc072f6197.jpg\",\n  \"334769.jpg\": \"Reptilia/Sceloporus occidentalis longipes/47d1e0e7b101745aa7b3b831637c3aac.jpg\",\n  \"334790.jpg\": \"Reptilia/Sceloporus occidentalis longipes/17a2ddbbf27294c4f0790f4d358a1635.jpg\",\n  \"334793.jpg\": \"Reptilia/Sceloporus occidentalis longipes/8cce8c48f972ff8c17104c0712aff02a.jpg\",\n  \"334798.jpg\": \"Reptilia/Sceloporus occidentalis longipes/fe4a27674c57fd3f1eb0a638a64d2cb1.jpg\",\n  \"334813.jpg\": \"Reptilia/Sceloporus occidentalis longipes/db91d07f7d9bfbafc767bf22010611fa.jpg\",\n  \"334883.jpg\": \"Reptilia/Sceloporus occidentalis longipes/0d333ffb4041bc39ad4308e1368fb0c5.jpg\",\n  \"334884.jpg\": \"Reptilia/Sceloporus occidentalis longipes/94647579ba79108f875bffbf578d8efb.jpg\",\n  \"334887.jpg\": \"Reptilia/Sceloporus occidentalis longipes/b23899a3397c6fbea9fe144f0b52c058.jpg\",\n  \"334890.jpg\": \"Reptilia/Sceloporus occidentalis longipes/0cb51e44bde2978b2d1d27c1347c9181.jpg\",\n  \"334899.jpg\": \"Reptilia/Sceloporus occidentalis longipes/0537e97a5753ebb9c7057e35060a4915.jpg\",\n  \"334928.jpg\": \"Reptilia/Sceloporus occidentalis longipes/90d10f633cce9fff1223097aa6b7e823.jpg\",\n  \"334933.jpg\": \"Reptilia/Sceloporus occidentalis longipes/f15a517bc96da18297bf249677bf4d6f.jpg\",\n  \"334956.jpg\": \"Reptilia/Sceloporus occidentalis longipes/75b89fe971fb92a5ae5c43620d99dce9.jpg\",\n  \"334963.jpg\": \"Reptilia/Sceloporus occidentalis longipes/c4e012fc0f04d748067ab42f23b48a98.jpg\",\n  \"334976.jpg\": \"Reptilia/Sceloporus occidentalis longipes/fa8c85edc44b7b1941905f332e5b2125.jpg\",\n  \"334978.jpg\": \"Reptilia/Sceloporus occidentalis longipes/0782387c5d758ee0087d497b35f0bf4f.jpg\",\n  \"335010.jpg\": \"Reptilia/Sceloporus occidentalis longipes/bbc1cf15a946183944cff0747f39119f.jpg\",\n  \"335023.jpg\": \"Reptilia/Sceloporus occidentalis longipes/fcc6ef2b642f33af8a60d6272907bcd2.jpg\",\n  \"335062.jpg\": \"Aves/Ardenna creatopus/acfc6392d342d26de9c043247e6b1df1.jpg\",\n  \"335066.jpg\": \"Aves/Ardenna creatopus/58d4d0fca7d2dc27e46580a76c8e5929.jpg\",\n  \"335067.jpg\": \"Aves/Ardenna creatopus/47db264d10f6aceaf0c70d65fb26cdaa.jpg\",\n  \"335079.jpg\": \"Aves/Ardenna creatopus/c30d44805f545f2a5b78ab2d4d7642aa.jpg\",\n  \"335089.jpg\": \"Aves/Ardenna creatopus/75b6e53c148db00da5954b3530981d20.jpg\",\n  \"335102.jpg\": \"Reptilia/Sceloporus occidentalis bocourtii/3900da925dc8ca688a17f919dce8668e.jpg\",\n  \"335103.jpg\": \"Reptilia/Sceloporus occidentalis bocourtii/a7498d4e880d9aea61eedd5bd4f2b72c.jpg\",\n  \"335104.jpg\": \"Reptilia/Sceloporus occidentalis bocourtii/aef4f56c16efd4f814e7f9e428b86644.jpg\",\n  \"335106.jpg\": \"Reptilia/Sceloporus occidentalis bocourtii/df0c71250368dcf8024e8485f215d513.jpg\",\n  \"335107.jpg\": \"Reptilia/Sceloporus occidentalis bocourtii/83fd6eca8ece77e1b4e3b7d894617a1e.jpg\",\n  \"335108.jpg\": \"Reptilia/Sceloporus occidentalis bocourtii/bd3e696f6b98fead548b1002063f07e4.jpg\",\n  \"335110.jpg\": \"Reptilia/Sceloporus occidentalis bocourtii/dc8f00e306880d9ac9719ae636b94a71.jpg\",\n  \"335111.jpg\": \"Reptilia/Sceloporus occidentalis bocourtii/f5ff5f43484a2c9aa8389b79e7d9c8a0.jpg\",\n  \"335112.jpg\": \"Reptilia/Sceloporus occidentalis bocourtii/7add639f972cc5f42af045d984502c5e.jpg\",\n  \"335113.jpg\": \"Reptilia/Sceloporus occidentalis bocourtii/731b5e8679aa407e1aba1025fef3f07e.jpg\",\n  \"335114.jpg\": \"Reptilia/Sceloporus occidentalis bocourtii/e310fe9ee997c1a6863d595d75faab6d.jpg\",\n  \"335118.jpg\": \"Reptilia/Sceloporus occidentalis bocourtii/c14801ce36439edce630da326809a13b.jpg\",\n  \"335119.jpg\": \"Reptilia/Sceloporus occidentalis bocourtii/954374995f5a87294363e03488da71fe.jpg\",\n  \"335123.jpg\": \"Reptilia/Sceloporus occidentalis bocourtii/253b8522f35b8f6a3780e3919feac811.jpg\",\n  \"335128.jpg\": \"Reptilia/Sceloporus occidentalis bocourtii/c0448ea7285f4b953a31427f1f8b7f8b.jpg\",\n  \"335129.jpg\": \"Reptilia/Sceloporus occidentalis bocourtii/9528cc774969596faae2377eba2ea2ae.jpg\",\n  \"335130.jpg\": \"Reptilia/Sceloporus occidentalis bocourtii/9e9b0dd055f29989042400157c1a0225.jpg\",\n  \"335134.jpg\": \"Reptilia/Sceloporus occidentalis bocourtii/4bb752283e7d2d3e721696a4833c385e.jpg\",\n  \"335135.jpg\": \"Reptilia/Sceloporus occidentalis bocourtii/0ccf823dbd0ef97bcf4ddb503502ed71.jpg\",\n  \"335140.jpg\": \"Reptilia/Sceloporus occidentalis bocourtii/0e29b0d1b934f57d68d1c566903c2c1e.jpg\",\n  \"335141.jpg\": \"Reptilia/Sceloporus occidentalis bocourtii/2cc8b783974dd56a3633737789d542dd.jpg\",\n  \"335157.jpg\": \"Mollusca/Anteaeolidiella oliviae/80ee44fc18bbe06b1d104d733a69eccc.jpg\",\n  \"335162.jpg\": \"Mollusca/Anteaeolidiella oliviae/78408b6dbac25de0cb5414d097ba5605.jpg\",\n  \"335169.jpg\": \"Mollusca/Anteaeolidiella oliviae/12663876c8155b851508659bc3c7e484.jpg\",\n  \"335173.jpg\": \"Mollusca/Anteaeolidiella oliviae/0864f09f5dd4edc8ef2f66eed46a92e2.jpg\",\n  \"335210.jpg\": \"Aves/Phalacrocorax varius varius/9375042edc3b4b1327cc303938e02e74.jpg\",\n  \"335220.jpg\": \"Aves/Phalacrocorax varius varius/297c65161f7fd21b8880545909b64c03.jpg\",\n  \"335247.jpg\": \"Aves/Phalacrocorax varius varius/3c454245f4d704d682de198167bfbcb2.jpg\",\n  \"335248.jpg\": \"Aves/Phalacrocorax varius varius/893d31d91638098eea67305fa12ba706.jpg\",\n  \"335251.jpg\": \"Aves/Phalacrocorax varius varius/85829efe667118f7f98a6a92d1e7ec8c.jpg\",\n  \"335267.jpg\": \"Aves/Phalacrocorax varius varius/1a288a2835d6f42b3816c9f9e972c5a5.jpg\",\n  \"335286.jpg\": \"Aves/Phalacrocorax varius varius/763ffab06948e4d7ae56a404029409b0.jpg\",\n  \"335288.jpg\": \"Aves/Phalacrocorax varius varius/65b4bfa7986cf3e5367b3e9238d3d990.jpg\",\n  \"335324.jpg\": \"Aves/Phalacrocorax varius varius/ef6b6b589a17c43c50b3b681c1eea8b7.jpg\",\n  \"335348.jpg\": \"Aves/Phalacrocorax varius varius/cfed88e9f3716ed58df0d248a7011ea4.jpg\",\n  \"335350.jpg\": \"Aves/Phalacrocorax varius varius/73aa2bf30a63a7541444d4472f1bef85.jpg\",\n  \"335355.jpg\": \"Aves/Phalacrocorax varius varius/d81b600868111607544dbe251e9aa522.jpg\",\n  \"335372.jpg\": \"Aves/Pycnonotus barbatus/8bcb5ec0aa2a29b460c5bf812266807b.jpg\",\n  \"335377.jpg\": \"Aves/Pycnonotus barbatus/3b61c1bd97e1414cef7278adb94c3c98.jpg\",\n  \"335378.jpg\": \"Aves/Pycnonotus barbatus/ca87e32f7ddb8428c3b3a5cf2e5cdc03.jpg\",\n  \"335380.jpg\": \"Aves/Pycnonotus barbatus/d4107002f943f7d2a69c73c65818f431.jpg\",\n  \"335462.jpg\": \"Reptilia/Dermochelys coriacea/b7816b980441d5cf2dceb0274dffe699.jpg\",\n  \"335463.jpg\": \"Reptilia/Dermochelys coriacea/20fa4679ebe0ae4ce0b2c6ff759342be.jpg\",\n  \"335478.jpg\": \"Reptilia/Dermochelys coriacea/c47295e12edfd214064c79f815c3c858.jpg\",\n  \"335481.jpg\": \"Reptilia/Dermochelys coriacea/f1ff8410eef28a3601243a3c4a82b8ca.jpg\",\n  \"335497.jpg\": \"Insecta/Plebejus acmon/190ef94cd658625e2e22c93f269e25ab.jpg\",\n  \"335498.jpg\": \"Insecta/Plebejus acmon/1a2d5ed7c9d3a66325154a42d32ec2c7.jpg\",\n  \"335503.jpg\": \"Insecta/Plebejus acmon/4192858180291089d8304e86c07dd94b.jpg\",\n  \"335515.jpg\": \"Insecta/Plebejus acmon/8f249a678335c01f121e7f41030851a3.jpg\",\n  \"335516.jpg\": \"Insecta/Plebejus acmon/88636b3709b3c70be63b554df377b76a.jpg\",\n  \"335526.jpg\": \"Insecta/Plebejus acmon/d83b57aa5e5fedd21a2126bf51a034c1.jpg\",\n  \"335534.jpg\": \"Insecta/Plebejus acmon/477ca6cac7d1d6f5496c5c3295e90a7c.jpg\",\n  \"335548.jpg\": \"Insecta/Plebejus acmon/b171f69b95d7f562a304049a1574ba41.jpg\",\n  \"335558.jpg\": \"Insecta/Plebejus acmon/39983c1a018f56321fc8928e6fb4f88f.jpg\",\n  \"335566.jpg\": \"Insecta/Plebejus acmon/f285666288bd9f38837387d9968d8ca4.jpg\",\n  \"335570.jpg\": \"Insecta/Plebejus acmon/8ec90e32685ccbe9dbfe019d40bd0dc3.jpg\",\n  \"335577.jpg\": \"Insecta/Plebejus acmon/002301cf7cde1f171017a61ee070b58e.jpg\",\n  \"335590.jpg\": \"Insecta/Plebejus acmon/5e412bc958234faa56ee15e1f3b2b47a.jpg\",\n  \"335591.jpg\": \"Insecta/Plebejus acmon/aff5fc37a55ccd82a87351549f5cdff2.jpg\",\n  \"3356.jpg\": \"Insecta/Coccinella septempunctata/2254ca3ef41a2a480e2160fd2aea19e2.jpg\",\n  \"335607.jpg\": \"Insecta/Plebejus acmon/eaa8a295ca5932429cb2dd52e853f83f.jpg\",\n  \"335611.jpg\": \"Insecta/Plebejus acmon/c15a1ee90bbf2de4c4a2b4a152b18946.jpg\",\n  \"335619.jpg\": \"Insecta/Plebejus acmon/f8e2f94dc716e9ae15330c65e1693713.jpg\",\n  \"335620.jpg\": \"Insecta/Plebejus acmon/d5701d61b9fdde06121b1477029757aa.jpg\",\n  \"33563.jpg\": \"Reptilia/Crocodylus acutus/639d377ae7c4a922d6302f7807b0cdf6.jpg\",\n  \"335630.jpg\": \"Insecta/Plebejus acmon/84959dca7a3f2753a9c6fb7336966fd8.jpg\",\n  \"335632.jpg\": \"Insecta/Plebejus acmon/85feb7ddd4c91e15c16491c1e1f1f39b.jpg\",\n  \"335634.jpg\": \"Insecta/Plebejus acmon/6a75fa16b24c86d2cbd6f8c777b98380.jpg\",\n  \"335638.jpg\": \"Insecta/Plebejus acmon/bd5a2c1e99f29aefa13f57372136f060.jpg\",\n  \"335658.jpg\": \"Insecta/Plebejus acmon/4878a954a500708886e1b531d64e7baa.jpg\",\n  \"335677.jpg\": \"Aves/Anas fulvigula/3659f86f8da7646d865f6d9f70f265dd.jpg\",\n  \"335683.jpg\": \"Aves/Anas fulvigula/b8af14f1e7c8cdb399b677075d8e2ffa.jpg\",\n  \"33569.jpg\": \"Reptilia/Crocodylus acutus/22d3291bed7965b87c788d7e9404962b.jpg\",\n  \"335690.jpg\": \"Aves/Anas fulvigula/49319001ab1af9b164dbf364b3b215b9.jpg\",\n  \"335694.jpg\": \"Aves/Anas fulvigula/a52b26b23c526faa6c2be3dd7c14c59d.jpg\",\n  \"335707.jpg\": \"Aves/Anas fulvigula/cd34bb3ae6be8f21648997849ddb4a1b.jpg\",\n  \"335716.jpg\": \"Insecta/Aedes albopictus/bd133a0edf419783b32606d3dc1559dd.jpg\",\n  \"335717.jpg\": \"Insecta/Aedes albopictus/77e3b355387c9a8959c39b6e44744daf.jpg\",\n  \"335718.jpg\": \"Insecta/Aedes albopictus/8e889caa55b53d6aab61b53255afbcd2.jpg\",\n  \"335723.jpg\": \"Insecta/Aedes albopictus/a3899fbadf7c260ae8d0249227c45e05.jpg\",\n  \"335726.jpg\": \"Insecta/Aedes albopictus/176fbc98426211f3203e5c75c810ee53.jpg\",\n  \"335727.jpg\": \"Insecta/Aedes albopictus/7273d62680d4975eac34bb2c05332a29.jpg\",\n  \"335731.jpg\": \"Insecta/Aedes albopictus/35cf7863d3c167a58e071f92599a3291.jpg\",\n  \"335732.jpg\": \"Insecta/Aedes albopictus/2f8c6ee24e08b255e64c02022aa3382b.jpg\",\n  \"335735.jpg\": \"Insecta/Aedes albopictus/fd3427f153bdc6c9e8abf6694c1db8e5.jpg\",\n  \"335736.jpg\": \"Insecta/Aedes albopictus/527aaad639b60b51d5dec953c62182c4.jpg\",\n  \"335737.jpg\": \"Insecta/Aedes albopictus/56b20c32eb1b3b87ca3e62edd51367c3.jpg\",\n  \"335742.jpg\": \"Insecta/Aedes albopictus/c7850e892d1df145bf7c2b487d2fa007.jpg\",\n  \"335750.jpg\": \"Insecta/Aedes albopictus/0fb597ccff0587831f2a7b3326ffa022.jpg\",\n  \"335756.jpg\": \"Insecta/Aedes albopictus/a8da2e96755acfa8224b8c84a8dd5fac.jpg\",\n  \"33576.jpg\": \"Reptilia/Crocodylus acutus/886d15a7d64e7b142931b2f6367730e6.jpg\",\n  \"335763.jpg\": \"Insecta/Aedes albopictus/b16dfb300ae79e9e034c9a00ac03f7cf.jpg\",\n  \"335773.jpg\": \"Insecta/Aedes albopictus/a92d4f54ec4bfba017d9a634a2ac37ee.jpg\",\n  \"335774.jpg\": \"Insecta/Aedes albopictus/375d12eb34ddc1741ac9e44ee3d21d6d.jpg\",\n  \"335778.jpg\": \"Insecta/Aedes albopictus/804c967f5ac03e6bf5d7e2da98ffb772.jpg\",\n  \"335779.jpg\": \"Insecta/Aedes albopictus/1745b6d0c80cdd1c1d4fb3d78b4e576f.jpg\",\n  \"33578.jpg\": \"Reptilia/Crocodylus acutus/f27cdab60b8cda7d29b22de1961da5e2.jpg\",\n  \"335780.jpg\": \"Insecta/Aedes albopictus/14263c81589782658d7f313af7a7d185.jpg\",\n  \"335782.jpg\": \"Insecta/Anthrenus verbasci/c2a3cb8d9143eeeb66338c66637558c3.jpg\",\n  \"335787.jpg\": \"Insecta/Anthrenus verbasci/f4f9e97324e5c11dbc6988ad039d71ab.jpg\",\n  \"335788.jpg\": \"Insecta/Anthrenus verbasci/a7bf3ed178605d059b5401b3857f8a9b.jpg\",\n  \"335789.jpg\": \"Insecta/Anthrenus verbasci/4309e1932add08cf256a35ba8265b584.jpg\",\n  \"335790.jpg\": \"Insecta/Anthrenus verbasci/bec1b27a31a4f5c75e27d643c5af2bde.jpg\",\n  \"335792.jpg\": \"Insecta/Anthrenus verbasci/a3c20a5df6238731ccc6a1fc68cd67ae.jpg\",\n  \"335793.jpg\": \"Insecta/Anthrenus verbasci/912ba701d5fffe706abf5cb953eb73ae.jpg\",\n  \"335794.jpg\": \"Insecta/Anthrenus verbasci/289d386187a26bc739b14ebdd671a8dd.jpg\",\n  \"335795.jpg\": \"Insecta/Anthrenus verbasci/2277bb3ea8407a21b205e28abb6c4bdb.jpg\",\n  \"335796.jpg\": \"Insecta/Anthrenus verbasci/2fb6a2b1ae3e34e96860512b67908cb5.jpg\",\n  \"335797.jpg\": \"Insecta/Anthrenus verbasci/cecb5c7b937b7fe5655f88514a4738e5.jpg\",\n  \"335798.jpg\": \"Insecta/Anthrenus verbasci/7d835e09427d5ce9ec013ad81d486910.jpg\",\n  \"335802.jpg\": \"Insecta/Anthrenus verbasci/b63749735fae2b7fa6d79cc89dfb73a9.jpg\",\n  \"335814.jpg\": \"Insecta/Anthrenus verbasci/45c5f4912c1666f44d7756db944b664a.jpg\",\n  \"335821.jpg\": \"Insecta/Anthrenus verbasci/f50550ecaf3f6c02e22355903e6a56e6.jpg\",\n  \"335822.jpg\": \"Insecta/Anthrenus verbasci/65fba3b4d1748985aab41f388bdae5f5.jpg\",\n  \"335823.jpg\": \"Insecta/Anthrenus verbasci/325dc8f723b4a0eba8e8c0fed702ef86.jpg\",\n  \"335824.jpg\": \"Insecta/Anthrenus verbasci/65079e02b3bbfd4b9d8c030015f32e8f.jpg\",\n  \"335825.jpg\": \"Insecta/Anthrenus verbasci/d3ecf80bfa87e870918b2ccf651209cf.jpg\",\n  \"33583.jpg\": \"Reptilia/Crocodylus acutus/aa2752737c02f60a272b8197deebe7f5.jpg\",\n  \"335843.jpg\": \"Mammalia/Enhydra lutris/26faf65f74fef14d6a57544dc9be469b.jpg\",\n  \"335848.jpg\": \"Mammalia/Enhydra lutris/0c13ca550c8e1e4ef2629c7a2fb5893c.jpg\",\n  \"335858.jpg\": \"Mammalia/Enhydra lutris/f4dd899165df55acb937cbf17f71bebe.jpg\",\n  \"335859.jpg\": \"Mammalia/Enhydra lutris/16291240eefb61053592d8b2e054b552.jpg\",\n  \"33586.jpg\": \"Reptilia/Crocodylus acutus/5f0df749338b04cd2c010bc149e307fd.jpg\",\n  \"335864.jpg\": \"Mammalia/Enhydra lutris/d22a4e65b086f91a3081dec72255205c.jpg\",\n  \"335865.jpg\": \"Mammalia/Enhydra lutris/39509ad74aeea1cd27dd288246057498.jpg\",\n  \"335866.jpg\": \"Mammalia/Enhydra lutris/a474c5f057c8cc0d29eee9db2add6449.jpg\",\n  \"335868.jpg\": \"Mammalia/Enhydra lutris/06878b2e30f9ce50a5ee6b36277aa244.jpg\",\n  \"335882.jpg\": \"Mammalia/Enhydra lutris/f5195849b778b943ae496a52101c7f29.jpg\",\n  \"335886.jpg\": \"Mammalia/Enhydra lutris/6980f1f6570cfd45fa8c41ce3ba71214.jpg\",\n  \"335887.jpg\": \"Mammalia/Enhydra lutris/6832a2cb95a7e06af89ab53e5e94cc5d.jpg\",\n  \"33589.jpg\": \"Reptilia/Crocodylus acutus/f8de5ca49e1dbb9aea161f00a4ab8db2.jpg\",\n  \"335895.jpg\": \"Mammalia/Enhydra lutris/bc6f7501bd83a5e2ca1245863bee2468.jpg\",\n  \"335897.jpg\": \"Mammalia/Enhydra lutris/8d47694ebd40df6e37ea567dea67618a.jpg\",\n  \"3359.jpg\": \"Insecta/Coccinella septempunctata/9f2ee7fbe85247c0c393b3219726e5c4.jpg\",\n  \"335902.jpg\": \"Mammalia/Enhydra lutris/fca2989ce73aa5b9a33fb8cfcb2971b0.jpg\",\n  \"335926.jpg\": \"Aves/Fratercula arctica/0eeaf7cd787adfb54f59571ac1c595b9.jpg\",\n  \"335927.jpg\": \"Aves/Fratercula arctica/9975a3dc636f3b33b9a9b1f3700bc864.jpg\",\n  \"335928.jpg\": \"Aves/Fratercula arctica/aa1975121c3a4238b08d312348a1a547.jpg\",\n  \"335933.jpg\": \"Aves/Fratercula arctica/fd23b963d183472d20c50d6d57ea49c9.jpg\",\n  \"335934.jpg\": \"Aves/Fratercula arctica/3cdabc0df1b4310503903782f770f842.jpg\",\n  \"335942.jpg\": \"Aves/Fratercula arctica/96ee7a45550080cd2e12cc0c44938c14.jpg\",\n  \"335950.jpg\": \"Aves/Fratercula arctica/d829be45459494e3d9e1d843955f8efb.jpg\",\n  \"335959.jpg\": \"Aves/Phalacrocorax sulcirostris/9141d33b29ff7dbfbba5fe4b56836dd9.jpg\",\n  \"335960.jpg\": \"Aves/Phalacrocorax sulcirostris/8b2d70b20a14fffd82132215017c613e.jpg\",\n  \"335961.jpg\": \"Aves/Phalacrocorax sulcirostris/8096e15b75432c01158b40542cecec6c.jpg\",\n  \"335964.jpg\": \"Aves/Phalacrocorax sulcirostris/39e25492b46691f187d3e96194b3a459.jpg\",\n  \"335965.jpg\": \"Aves/Phalacrocorax sulcirostris/a4a6d377fa6901a4a8b0619178e7d240.jpg\",\n  \"335966.jpg\": \"Aves/Phalacrocorax sulcirostris/4da70e0d2d7bf2471885aeac908271dc.jpg\",\n  \"335968.jpg\": \"Aves/Phalacrocorax sulcirostris/895659f56ac8c8405e77f9fe43be099a.jpg\",\n  \"335974.jpg\": \"Aves/Phalacrocorax sulcirostris/d12bd8d103b8da47cc5c25e070aa753d.jpg\",\n  \"335982.jpg\": \"Aves/Phalacrocorax sulcirostris/eb1168013cf0582c1f43dabf90fbf5fe.jpg\",\n  \"335988.jpg\": \"Aves/Phalacrocorax sulcirostris/cdd334cdf33c1e0988b4540304a7e9b0.jpg\",\n  \"335992.jpg\": \"Aves/Phalacrocorax sulcirostris/71af1361c2fd2c9af9b195726bf14850.jpg\",\n  \"335993.jpg\": \"Mollusca/Aplysia californica/48ef1bedba83fe5a076c6d612781d21c.jpg\",\n  \"335995.jpg\": \"Mollusca/Aplysia californica/3976c8cc09476b645c60c0e22460ca3a.jpg\",\n  \"336.jpg\": \"Insecta/Coleomegilla maculata/57ee104e285918175c49f9d96c9ca61f.jpg\",\n  \"33601.jpg\": \"Reptilia/Crocodylus acutus/004f158b4a7bb0bae5027adb1a861a74.jpg\",\n  \"336017.jpg\": \"Mollusca/Aplysia californica/9675873b210066cf4a52041db116ab10.jpg\",\n  \"336019.jpg\": \"Mollusca/Aplysia californica/6aad48333a31607e2bc62f6fc3dc285d.jpg\",\n  \"336025.jpg\": \"Mollusca/Aplysia californica/2772e46b9764a0cab10e1020a398b591.jpg\",\n  \"336026.jpg\": \"Mollusca/Aplysia californica/842737c95fe394a460723b5b9aaa6a86.jpg\",\n  \"336032.jpg\": \"Mollusca/Aplysia californica/34a8569b950c2d10ab39d7ffdf2b8ab2.jpg\",\n  \"336035.jpg\": \"Mollusca/Aplysia californica/37fa2c2125523a2367004388a5f3d310.jpg\",\n  \"336048.jpg\": \"Mollusca/Aplysia californica/6eb04ae344b2aad717a43668518a704a.jpg\",\n  \"33605.jpg\": \"Reptilia/Crocodylus acutus/6c40a4d5556acf8d9aabd38c437711e5.jpg\",\n  \"336051.jpg\": \"Mollusca/Aplysia californica/5e8bcce1f5126cbfe7d575dcd53e6157.jpg\",\n  \"336065.jpg\": \"Mollusca/Aplysia californica/81a81d078eee1ff062d2722b2c6d6cd0.jpg\",\n  \"336074.jpg\": \"Mollusca/Aplysia californica/cc3478e4500130b78534a177a1909d5c.jpg\",\n  \"336075.jpg\": \"Mollusca/Aplysia californica/f836c45f1b04b63f897f9fd9273b29a4.jpg\",\n  \"336076.jpg\": \"Mollusca/Aplysia californica/0860ec1541f47606e8480416f924534c.jpg\",\n  \"336080.jpg\": \"Mollusca/Aplysia californica/ed751aa5737d5eaee73a6011f099a351.jpg\",\n  \"33609.jpg\": \"Reptilia/Crocodylus acutus/b2d26ca001945f0ddf7a189b04491fde.jpg\",\n  \"336090.jpg\": \"Mollusca/Aplysia californica/bacfed1cf1ff58699bfe522bb2b12c00.jpg\",\n  \"33611.jpg\": \"Reptilia/Crocodylus acutus/6b6873220e7ffe45487e1da384156d80.jpg\",\n  \"33612.jpg\": \"Reptilia/Crocodylus acutus/2521207c55bc50fdaad1885a0c032ff0.jpg\",\n  \"33614.jpg\": \"Reptilia/Crocodylus acutus/e60f54c1665afebaa5dbf2e6d4d5f60d.jpg\",\n  \"33616.jpg\": \"Reptilia/Crocodylus acutus/93e3a2e29f5b623dc66762bf1baa6091.jpg\",\n  \"33622.jpg\": \"Reptilia/Crocodylus acutus/4e26df6af69b34e4f7bdb14e8029f006.jpg\",\n  \"336220.jpg\": \"Aves/Dendragapus obscurus/e510f5a8a18d671668d471897848613e.jpg\",\n  \"336221.jpg\": \"Aves/Dendragapus obscurus/8e46211ae94357cc679ed6fe6db95704.jpg\",\n  \"336222.jpg\": \"Aves/Dendragapus obscurus/d86b3159d9c7643e573ecbcc4cef9e09.jpg\",\n  \"336226.jpg\": \"Aves/Dendragapus obscurus/a052be0c93239dd1cb65e103b97723c7.jpg\",\n  \"336227.jpg\": \"Aves/Dendragapus obscurus/85c6b2a558bb8350751522d49baf46be.jpg\",\n  \"336240.jpg\": \"Aves/Dendragapus obscurus/cd62cf438afd8570bb7116c422c64dd0.jpg\",\n  \"336245.jpg\": \"Aves/Melospiza georgiana/cf324a8e1ae5a584fc847697ebb14c70.jpg\",\n  \"336249.jpg\": \"Aves/Melospiza georgiana/e709ec33f76f2ed3dd6491c87e630b3e.jpg\",\n  \"336250.jpg\": \"Aves/Melospiza georgiana/c43284ef5746e86a5c3b79fb166f41da.jpg\",\n  \"336254.jpg\": \"Aves/Melospiza georgiana/901588e9fbf9f06a60c03f96e444ad4b.jpg\",\n  \"336258.jpg\": \"Aves/Melospiza georgiana/c5de47395dc9332ab0afb3507aa50a0f.jpg\",\n  \"336270.jpg\": \"Aves/Melospiza georgiana/f39767d4ea9dbe3a5366dc0247f87dd6.jpg\",\n  \"33628.jpg\": \"Reptilia/Crocodylus acutus/a0ea1200cf76892634c4b70e42c89fc4.jpg\",\n  \"336286.jpg\": \"Aves/Melospiza georgiana/7ae73783734016b6e1c1a2ba71b0cc41.jpg\",\n  \"336288.jpg\": \"Aves/Melospiza georgiana/f9ffaca4c8d6be57b19a6e1bedaf981e.jpg\",\n  \"336291.jpg\": \"Aves/Melospiza georgiana/a779be863a9f8eba04348c28391d8f48.jpg\",\n  \"336295.jpg\": \"Aves/Melospiza georgiana/8ff0148fa3280aa5d67e37a15828836b.jpg\",\n  \"336296.jpg\": \"Aves/Melospiza georgiana/c68a991eeed5de63d50f97f4482bf469.jpg\",\n  \"336298.jpg\": \"Aves/Melospiza georgiana/9fa4fd240c3bcaba1e14c68ee737af34.jpg\",\n  \"336299.jpg\": \"Aves/Melospiza georgiana/bd9cf472fea121d398c46db2460c1cbb.jpg\",\n  \"336300.jpg\": \"Aves/Melospiza georgiana/18034a41c8ab38d4a1361a159421f40c.jpg\",\n  \"336303.jpg\": \"Aves/Melospiza georgiana/c4f63c546ceae910e05639f26d6c645f.jpg\",\n  \"336305.jpg\": \"Aves/Melospiza georgiana/fdfd536b6ddc6309d6d6c64ffc8fddb8.jpg\",\n  \"336313.jpg\": \"Aves/Melospiza georgiana/bdf5e6c2e358f4f81abbdc0af7d2c79a.jpg\",\n  \"33632.jpg\": \"Reptilia/Crocodylus acutus/5d27c3dc92c3a2717c346f4f041fbde3.jpg\",\n  \"336322.jpg\": \"Aves/Melospiza georgiana/624e09aaba06a1e897ecd6a772d8c0ff.jpg\",\n  \"336323.jpg\": \"Aves/Melospiza georgiana/7ed4899ecc39f015d2821ed64ff8019d.jpg\",\n  \"336326.jpg\": \"Aves/Melospiza georgiana/55b2d75040ef477599fd5fec12fdcf94.jpg\",\n  \"336327.jpg\": \"Aves/Melospiza georgiana/d2c30ea98f598d677401450d70aafca3.jpg\",\n  \"33633.jpg\": \"Reptilia/Crocodylus acutus/6dcd5247c04c0fb72871d79e881d932f.jpg\",\n  \"336332.jpg\": \"Aves/Melospiza georgiana/4316ebc00b86b281e7395b9b9ed26bd8.jpg\",\n  \"336341.jpg\": \"Aves/Melospiza georgiana/014b854f2ee05ac8d53d9cb29d5476c6.jpg\",\n  \"336347.jpg\": \"Insecta/Orgyia leucostigma/5fe838c30be9f57d6696184f93bbcdba.jpg\",\n  \"336349.jpg\": \"Insecta/Orgyia leucostigma/6c69b6387b4b7a65c70aff5a8829f8f3.jpg\",\n  \"336353.jpg\": \"Insecta/Orgyia leucostigma/bd5bc3ea11cb181eec3a2d5f0bd4497f.jpg\",\n  \"336354.jpg\": \"Insecta/Orgyia leucostigma/6887886a82cb4f37fb6eae28e7887cb3.jpg\",\n  \"336355.jpg\": \"Insecta/Orgyia leucostigma/d220908a9c738d5fbb269f9c67de77c2.jpg\",\n  \"336356.jpg\": \"Insecta/Orgyia leucostigma/8baf85a0950fb05491ef06b3fbac0e5b.jpg\",\n  \"336357.jpg\": \"Insecta/Orgyia leucostigma/e362af40c1e951218bfce282c1fb9f59.jpg\",\n  \"336359.jpg\": \"Insecta/Orgyia leucostigma/1f7018ec27cf579216504578e3ffc665.jpg\",\n  \"336362.jpg\": \"Insecta/Orgyia leucostigma/e1d715e53eda0bcd5ca18285400b88d7.jpg\",\n  \"336363.jpg\": \"Insecta/Orgyia leucostigma/b8b41667a0643330b15cd73c018f9102.jpg\",\n  \"336364.jpg\": \"Insecta/Orgyia leucostigma/2759e789964ab9da4fee1d6ed1ed0298.jpg\",\n  \"336370.jpg\": \"Insecta/Orgyia leucostigma/9e055592d7c313821c137ca92443aa3b.jpg\",\n  \"336371.jpg\": \"Insecta/Orgyia leucostigma/6c2032b1da0c1add8017f297168b7209.jpg\",\n  \"336372.jpg\": \"Insecta/Orgyia leucostigma/d0a0d6188fe133128b2ff3516a11b1c8.jpg\",\n  \"336375.jpg\": \"Insecta/Orgyia leucostigma/c474278c34effded4779e9f2e7dfac46.jpg\",\n  \"336377.jpg\": \"Insecta/Orgyia leucostigma/4d40cd7131333180fb198c95d9223ba8.jpg\",\n  \"336380.jpg\": \"Insecta/Orgyia leucostigma/f73e6c620bcdc71bcfa65ef6c01e6c92.jpg\",\n  \"336383.jpg\": \"Insecta/Orgyia leucostigma/d93c4d81d28df4524b300f3f4ec2a273.jpg\",\n  \"336384.jpg\": \"Insecta/Orgyia leucostigma/d086de596c257e98b363d0bf7b33a334.jpg\",\n  \"336386.jpg\": \"Insecta/Orgyia leucostigma/a07f04c10185d32650f02b1f8026c97b.jpg\",\n  \"336387.jpg\": \"Insecta/Orgyia leucostigma/1d0507fa2688b366f9bd5cb1adcb4e1e.jpg\",\n  \"336389.jpg\": \"Insecta/Orgyia leucostigma/74b51365a6c4f1e32b57f9ead7527c91.jpg\",\n  \"336392.jpg\": \"Insecta/Orgyia leucostigma/5ac515969bc0adaf52a00cbf990a1b75.jpg\",\n  \"336393.jpg\": \"Insecta/Orgyia leucostigma/cafcda42bd5bf67d509f0c996d208b82.jpg\",\n  \"336399.jpg\": \"Insecta/Tetracha carolina/83d4e64b3b65c393e23231329157e7be.jpg\",\n  \"336403.jpg\": \"Insecta/Tetracha carolina/ee91c47a18e0d579f9806ad0da14cae3.jpg\",\n  \"336406.jpg\": \"Insecta/Tetracha carolina/db1d92829df184832db49e3fa0c03e49.jpg\",\n  \"336408.jpg\": \"Insecta/Tetracha carolina/d316e7e98d73b1e6ba0e8580bc1ebddd.jpg\",\n  \"336450.jpg\": \"Insecta/Strategus aloeus/da983398a05cfa01357b67f9748f1693.jpg\",\n  \"336451.jpg\": \"Insecta/Strategus aloeus/f5d80c9242331fc9fe16ee662d00e11d.jpg\",\n  \"336456.jpg\": \"Insecta/Strategus aloeus/7f69fd6f6921fa0db617b72dad69d5ce.jpg\",\n  \"336457.jpg\": \"Insecta/Strategus aloeus/34c6cb0279b3996c47e69a2ed9334c59.jpg\",\n  \"336461.jpg\": \"Insecta/Strategus aloeus/02f072856efaeae85eea21a2603bd6f7.jpg\",\n  \"336466.jpg\": \"Insecta/Strategus aloeus/c96918d58e6ee5adc13d9ea0a646bf2b.jpg\",\n  \"336467.jpg\": \"Insecta/Strategus aloeus/e8c0dc706dcd6f9fa6f2d4e538af92cb.jpg\",\n  \"336471.jpg\": \"Insecta/Strategus aloeus/bcc9b540203988aaf472633163e5e095.jpg\",\n  \"336479.jpg\": \"Insecta/Phaneroptera nana/7075f748f00870a3f8d1092f4eca96b8.jpg\",\n  \"336482.jpg\": \"Insecta/Phaneroptera nana/bb25b3a4032dd5913df959f05802668d.jpg\",\n  \"336483.jpg\": \"Insecta/Phaneroptera nana/03823928b7cb5db194f01b4d362d53a3.jpg\",\n  \"336492.jpg\": \"Insecta/Phaneroptera nana/f656ac16e6c490742bf9ce09a4ca1dfa.jpg\",\n  \"336564.jpg\": \"Actinopterygii/Lutjanus griseus/fd51a0c47808ba05fd2a1eedf08c1b66.jpg\",\n  \"336576.jpg\": \"Actinopterygii/Lutjanus griseus/18b5d3d3553da0f2f82cee473334ff45.jpg\",\n  \"336578.jpg\": \"Reptilia/Calotes versicolor/a687bfe44b109448f0615da61767fb41.jpg\",\n  \"336579.jpg\": \"Reptilia/Calotes versicolor/c182365216f40ce1bf2ebba7ed2c61c1.jpg\",\n  \"336580.jpg\": \"Reptilia/Calotes versicolor/5925e2758fb75efd985d1f7ffe858cb4.jpg\",\n  \"336581.jpg\": \"Reptilia/Calotes versicolor/890b4e8aae558b684657a96c52512bed.jpg\",\n  \"336582.jpg\": \"Reptilia/Calotes versicolor/30a6c046d127a01f499ec9bcdcab8a4d.jpg\",\n  \"336592.jpg\": \"Reptilia/Calotes versicolor/a577842ac1bbf7711e6edbc753c18b45.jpg\",\n  \"336593.jpg\": \"Reptilia/Calotes versicolor/f15e1b262416479be4ee33b83911d183.jpg\",\n  \"336594.jpg\": \"Reptilia/Calotes versicolor/2bcbb97d381a17a29ca1956416dcf970.jpg\",\n  \"336595.jpg\": \"Reptilia/Calotes versicolor/956f790d4e41072a38262113650cd736.jpg\",\n  \"336596.jpg\": \"Reptilia/Calotes versicolor/31bee323d0385153d96d3e2c64eff465.jpg\",\n  \"336602.jpg\": \"Reptilia/Calotes versicolor/3b1b5cc240be8f5ae4f4fdcc12e35863.jpg\",\n  \"336603.jpg\": \"Reptilia/Calotes versicolor/306a64d86ca6146e3f3a5f57adee9c03.jpg\",\n  \"336604.jpg\": \"Reptilia/Calotes versicolor/8764e972cbda7d01aed6ff25579f0889.jpg\",\n  \"336606.jpg\": \"Reptilia/Calotes versicolor/754052b576f5947f231ec871298081c7.jpg\",\n  \"336609.jpg\": \"Reptilia/Calotes versicolor/63993d401aa3608c2dd92e2f65f9b9c0.jpg\",\n  \"336610.jpg\": \"Reptilia/Calotes versicolor/ec3ca84085e433b469f09ac89cde294b.jpg\",\n  \"336628.jpg\": \"Insecta/Enallagma basidens/be88d64a7b570d5be124da4bb89925e6.jpg\",\n  \"336629.jpg\": \"Insecta/Enallagma basidens/9efe596c1c63818dfee8cc564ef35adc.jpg\",\n  \"336638.jpg\": \"Insecta/Enallagma basidens/42a83ea786c1fe07f3ef82a9d7e98f61.jpg\",\n  \"336646.jpg\": \"Insecta/Enallagma basidens/60d8563e5ac10e3cb22892fe1cadaaa7.jpg\",\n  \"336650.jpg\": \"Insecta/Enallagma basidens/ab8a377faebe8383d1efe4d307b7c02a.jpg\",\n  \"336668.jpg\": \"Insecta/Enallagma basidens/eb75e134701efc2c6875696069959e2f.jpg\",\n  \"336684.jpg\": \"Insecta/Enallagma basidens/205a7726200001e8e1ba3b50b063c370.jpg\",\n  \"336685.jpg\": \"Insecta/Enallagma basidens/d5e93d0945df4f6097235460a55d9ed2.jpg\",\n  \"336692.jpg\": \"Insecta/Enallagma basidens/1607cfd1bf94c101ba048c1b326ffaab.jpg\",\n  \"336701.jpg\": \"Insecta/Enallagma basidens/0882893c8e9e810fa19770308f0a4463.jpg\",\n  \"336702.jpg\": \"Insecta/Enallagma basidens/628e5643d22aff64f7bbe914e6aae4f2.jpg\",\n  \"336705.jpg\": \"Insecta/Enallagma basidens/9989b6c82cae59a21c98a7b602d0d06b.jpg\",\n  \"336706.jpg\": \"Insecta/Enallagma basidens/77f4f8076e9a71b3ae179deef3947e0c.jpg\",\n  \"336715.jpg\": \"Insecta/Enallagma basidens/04369d27e3e8848c484f51ac7bc1f192.jpg\",\n  \"336716.jpg\": \"Insecta/Enallagma basidens/0bde4299ac006d05fe8d93350feeae9f.jpg\",\n  \"336717.jpg\": \"Insecta/Enallagma basidens/a0e8c85db34c78b266ddba13dc16ce36.jpg\",\n  \"336724.jpg\": \"Insecta/Enallagma basidens/6a3d9b88e6f3b234acf64fefc6db4931.jpg\",\n  \"336725.jpg\": \"Insecta/Enallagma basidens/49bd8ff9cab18df9362de7f3f9a4a79f.jpg\",\n  \"336727.jpg\": \"Insecta/Enallagma basidens/ef0f0f8bfacce56f8c516eeca9252ee7.jpg\",\n  \"336728.jpg\": \"Insecta/Enallagma basidens/cd161ea44984e06fa0d70a4e28a24e61.jpg\",\n  \"336729.jpg\": \"Insecta/Enallagma basidens/9d4c9437e27924af9ba09b0e70d93d06.jpg\",\n  \"33673.jpg\": \"Insecta/Plebejus melissa/bd3fe52b4cd21ce61b3f607fa0731ce2.jpg\",\n  \"336738.jpg\": \"Aves/Phalaropus lobatus/64097ee462e9649e2d31438afb9790c0.jpg\",\n  \"336748.jpg\": \"Aves/Phalaropus lobatus/f20d8c1f7b662e1d1b63712d0a790e62.jpg\",\n  \"336750.jpg\": \"Aves/Phalaropus lobatus/193e8ada2ce942cdd1ca420074a6565e.jpg\",\n  \"336751.jpg\": \"Aves/Phalaropus lobatus/e6671183a5e3149a4f3e26156880597c.jpg\",\n  \"336752.jpg\": \"Aves/Phalaropus lobatus/aa6dc0cecffd95bd776668cee59b1191.jpg\",\n  \"336759.jpg\": \"Aves/Phalaropus lobatus/f0f62baa255f1d025e1a2b3e25f62858.jpg\",\n  \"33676.jpg\": \"Insecta/Plebejus melissa/f2418c2d31f3d6bd50d311f775327b36.jpg\",\n  \"336763.jpg\": \"Aves/Phalaropus lobatus/619faa863e2292b139d38f144635b5df.jpg\",\n  \"336764.jpg\": \"Aves/Phalaropus lobatus/f2e984d11e1046a6ca8bc8e106db4d02.jpg\",\n  \"336766.jpg\": \"Aves/Phalaropus lobatus/87bc920972c06b5d9a407def97faf8e9.jpg\",\n  \"336790.jpg\": \"Aves/Phalaropus lobatus/f58f705fcf58983244ed7b97b606b0fc.jpg\",\n  \"336791.jpg\": \"Aves/Phalaropus lobatus/c85c19e346e0608a33908ef06cee7acf.jpg\",\n  \"336794.jpg\": \"Aves/Phalaropus lobatus/b3d109049ce29adf1b0e593fbf47dec1.jpg\",\n  \"336795.jpg\": \"Aves/Phalaropus lobatus/4ef318bb8af4e7e28f6eb09f53ceff46.jpg\",\n  \"33680.jpg\": \"Insecta/Plebejus melissa/0f51e4f76079d96b179d340edd1e0acf.jpg\",\n  \"33681.jpg\": \"Insecta/Plebejus melissa/0c6a452bd4145efd715209b1fcb410bf.jpg\",\n  \"336816.jpg\": \"Aves/Phalaropus lobatus/588ee3e8f8c9e57593c26e888f0518d2.jpg\",\n  \"336817.jpg\": \"Aves/Phalaropus lobatus/e3d7641b18eda56a1e79e1f4c19fce03.jpg\",\n  \"336825.jpg\": \"Mammalia/Tamias minimus/e0c9faf694be681ba0db7aaf0e798469.jpg\",\n  \"336826.jpg\": \"Mammalia/Tamias minimus/f15977b3a6892d951982e39d28dad019.jpg\",\n  \"336834.jpg\": \"Mammalia/Tamias minimus/ddb980b198f7464a80860dd0fa79f283.jpg\",\n  \"336840.jpg\": \"Mammalia/Tamias minimus/b3e3a729211c919a32a2555459292204.jpg\",\n  \"336848.jpg\": \"Mammalia/Tamias minimus/4b8d5697bc4ba5fa53fd08c52126a3c5.jpg\",\n  \"336849.jpg\": \"Mammalia/Tamias minimus/e7348cb16542f8d45999c2e60b56dce1.jpg\",\n  \"336850.jpg\": \"Mammalia/Tamias minimus/0d73aa8dfc48d4e5e80bfb0589a6cb6a.jpg\",\n  \"336851.jpg\": \"Mammalia/Tamias minimus/4c779c53c1d1196bdd682269e8653a4b.jpg\",\n  \"33687.jpg\": \"Insecta/Plebejus melissa/cd058eed1171915257dac9e17ea48e04.jpg\",\n  \"33688.jpg\": \"Insecta/Plebejus melissa/481395cccf8ad82dcdc2c6544161f729.jpg\",\n  \"33690.jpg\": \"Aves/Spizelloides arborea/7cce61b5f70bb4aeba3393b9a2a2c64a.jpg\",\n  \"336905.jpg\": \"Mollusca/Neverita duplicata/a78b055a7a749d415663dce03551611f.jpg\",\n  \"336911.jpg\": \"Mollusca/Neverita duplicata/a99722c5992e6e033f66ec5e14d61415.jpg\",\n  \"336912.jpg\": \"Mollusca/Neverita duplicata/5012bd88c6d0192af84f6a8ae2c3ade8.jpg\",\n  \"33692.jpg\": \"Aves/Spizelloides arborea/5602c2b9b1994aad528618e2c0692097.jpg\",\n  \"336921.jpg\": \"Mollusca/Neverita duplicata/bc793c08c18681995155f25ad6a4082f.jpg\",\n  \"336922.jpg\": \"Mollusca/Neverita duplicata/881c345d15907597bb000d3ef8f9e193.jpg\",\n  \"336929.jpg\": \"Insecta/Heliconius charithonia/0d70797013a948f815867b1ce7f642be.jpg\",\n  \"336949.jpg\": \"Insecta/Heliconius charithonia/594f58a96dd4eaf7afa6452b0853b245.jpg\",\n  \"336954.jpg\": \"Insecta/Heliconius charithonia/8da39021e4688ac5053a4cc10e6a82a9.jpg\",\n  \"336955.jpg\": \"Insecta/Heliconius charithonia/c5d094ab1f658c4a7229e5d1290b71ef.jpg\",\n  \"336956.jpg\": \"Insecta/Heliconius charithonia/13c1583bf53b568c345e2289759f09d8.jpg\",\n  \"336958.jpg\": \"Insecta/Heliconius charithonia/e5633eb584e90e0a7085c95b0ff0a090.jpg\",\n  \"336960.jpg\": \"Insecta/Heliconius charithonia/968a2ffcc7d2cd960ef69fede913a182.jpg\",\n  \"336961.jpg\": \"Insecta/Heliconius charithonia/7760b40d135ec9f7c588c4c24cf1e4df.jpg\",\n  \"336966.jpg\": \"Insecta/Heliconius charithonia/58b2057b540610501a71bd85ed9074ea.jpg\",\n  \"336974.jpg\": \"Insecta/Heliconius charithonia/2d0142eaa0699464cbc0e7993998e4ea.jpg\",\n  \"336991.jpg\": \"Insecta/Heliconius charithonia/923eb95eeff0d7f8a9cf40c24dc7ae82.jpg\",\n  \"336995.jpg\": \"Insecta/Heliconius charithonia/3280d06374e728a3f705bb4d22d0ddca.jpg\",\n  \"337.jpg\": \"Insecta/Coleomegilla maculata/65bcdee90e0538633682186d8c342a2c.jpg\",\n  \"337003.jpg\": \"Insecta/Heliconius charithonia/299fc03b5cb50a29eaaf60d200f2d797.jpg\",\n  \"337007.jpg\": \"Insecta/Heliconius charithonia/5d71d36aca1ab2a624620cbd294546b8.jpg\",\n  \"337015.jpg\": \"Insecta/Heliconius charithonia/83a0296594f95a7b72e9e3f048f15e13.jpg\",\n  \"337029.jpg\": \"Insecta/Heliconius charithonia/ea12c05eb72e0de4e8a96e083182f0ed.jpg\",\n  \"33704.jpg\": \"Aves/Spizelloides arborea/2c1c293f3a1c5151f100b82aeb38377c.jpg\",\n  \"337042.jpg\": \"Insecta/Heliconius charithonia/adbd009b5d9dd74188087d18866bb977.jpg\",\n  \"337046.jpg\": \"Insecta/Heliconius charithonia/5a8e40e69ffdb62f3a4443b546201c36.jpg\",\n  \"337049.jpg\": \"Insecta/Heliconius charithonia/18bb43a7349bfa61d4be9d3d7f83facc.jpg\",\n  \"33705.jpg\": \"Aves/Spizelloides arborea/7d42b72ab2ffdc6ba2fab01779c94962.jpg\",\n  \"33706.jpg\": \"Aves/Spizelloides arborea/a062b6fe69fb90c01a04371964ec34fd.jpg\",\n  \"33707.jpg\": \"Aves/Spizelloides arborea/56fae6c4c30bce7e4fd78f137396ee03.jpg\",\n  \"33709.jpg\": \"Aves/Spizelloides arborea/ce023626d231cf6eab5d2e3a62d40515.jpg\",\n  \"33711.jpg\": \"Aves/Spizelloides arborea/9aa4ed2c2d30b9766205e56aa6d1853e.jpg\",\n  \"33713.jpg\": \"Aves/Spizelloides arborea/1fc2e8d0a2fd74b696465c657a272417.jpg\",\n  \"33718.jpg\": \"Aves/Spizelloides arborea/482049d7fedcd579bbb86572c101a2ee.jpg\",\n  \"33721.jpg\": \"Aves/Spizelloides arborea/c9f11669a5606acc6a86174c9ca63366.jpg\",\n  \"337266.jpg\": \"Reptilia/Elgaria multicarinata multicarinata/1b9def24b275d009a6eb14962b9a0f21.jpg\",\n  \"337274.jpg\": \"Reptilia/Elgaria multicarinata multicarinata/ea67151cf5fb1a32c66f0057d868930e.jpg\",\n  \"337276.jpg\": \"Reptilia/Elgaria multicarinata multicarinata/29e3e5e25369ea5a52c7754c402c6bbd.jpg\",\n  \"337277.jpg\": \"Reptilia/Elgaria multicarinata multicarinata/d3a8e60caebf54131e90f1bcba4dca8a.jpg\",\n  \"337287.jpg\": \"Reptilia/Elgaria multicarinata multicarinata/5d841fbb97a616ed0e05620cd71bdfc7.jpg\",\n  \"337289.jpg\": \"Reptilia/Elgaria multicarinata multicarinata/2a32166d0e0da425dfffddae055c88ad.jpg\",\n  \"33729.jpg\": \"Aves/Spizelloides arborea/20d3d6c38297cc1bff498c36303f793b.jpg\",\n  \"337290.jpg\": \"Reptilia/Elgaria multicarinata multicarinata/2e03eebc48624d8c5dcfa7dbfcf70388.jpg\",\n  \"337291.jpg\": \"Reptilia/Elgaria multicarinata multicarinata/f1612bd0c5eee9fa35d42381a742b5a2.jpg\",\n  \"337292.jpg\": \"Reptilia/Elgaria multicarinata multicarinata/0ea5d6488c5685eb6bcdc15352bd756d.jpg\",\n  \"337296.jpg\": \"Reptilia/Elgaria multicarinata multicarinata/58dd8242976d2c69bc6c98f21e8d73c2.jpg\",\n  \"337297.jpg\": \"Reptilia/Elgaria multicarinata multicarinata/4323bb39687dc053162ea20298d720ff.jpg\",\n  \"337305.jpg\": \"Reptilia/Elgaria multicarinata multicarinata/b5d72d00868ed8c80a8baea8ff981d70.jpg\",\n  \"337306.jpg\": \"Reptilia/Elgaria multicarinata multicarinata/4669fd8dca245658732b90326204505e.jpg\",\n  \"337307.jpg\": \"Reptilia/Elgaria multicarinata multicarinata/4c810852cced5300e5ea34ef026d23fc.jpg\",\n  \"337308.jpg\": \"Reptilia/Elgaria multicarinata multicarinata/cd7da2ab2920284541b788b36bf500a2.jpg\",\n  \"337309.jpg\": \"Reptilia/Elgaria multicarinata multicarinata/5ffbe27c4401ea68e9e767498f994228.jpg\",\n  \"33731.jpg\": \"Aves/Spizelloides arborea/f0d704bceee87cefce5bdfedbea4085f.jpg\",\n  \"337315.jpg\": \"Reptilia/Elgaria multicarinata multicarinata/3f3cc7d9a8cbfa9f52040fa4af6a287f.jpg\",\n  \"337316.jpg\": \"Reptilia/Elgaria multicarinata multicarinata/82da0d7ba327046f8ad8172e1cfc627c.jpg\",\n  \"33732.jpg\": \"Aves/Spizelloides arborea/70a1295bf7b410a9ce7faa785da1a84b.jpg\",\n  \"337322.jpg\": \"Reptilia/Elgaria multicarinata multicarinata/aed4fe608945fa1f4c9304da37743d07.jpg\",\n  \"337323.jpg\": \"Reptilia/Elgaria multicarinata multicarinata/a4e336e5824b076ff376da1003f42b24.jpg\",\n  \"337324.jpg\": \"Reptilia/Elgaria multicarinata multicarinata/5c6b96d689969bcfe4784b033667679c.jpg\",\n  \"337325.jpg\": \"Reptilia/Elgaria multicarinata multicarinata/85516fc41ff3ba54275ae1d0ad18d4fc.jpg\",\n  \"337326.jpg\": \"Aves/Selasphorus sasin/600792ee904cc5cd9ca0d102b573f9e7.jpg\",\n  \"337327.jpg\": \"Aves/Selasphorus sasin/862996cc9b02ddb4e5a9e831bc3df22d.jpg\",\n  \"337337.jpg\": \"Aves/Selasphorus sasin/2644a7f034af5d37ca52ecb024100b63.jpg\",\n  \"337341.jpg\": \"Aves/Selasphorus sasin/3d99a43198258ceecdcb726608f5f658.jpg\",\n  \"337354.jpg\": \"Aves/Selasphorus sasin/946c26a968e89aba5d2b3ccc694a2cc7.jpg\",\n  \"337360.jpg\": \"Aves/Selasphorus sasin/d3b9e08ee846183ea41f2ccdc0b2d55e.jpg\",\n  \"337361.jpg\": \"Aves/Selasphorus sasin/8100416826388b4985b444ebe9719311.jpg\",\n  \"33737.jpg\": \"Aves/Spizelloides arborea/62a9914e0673a0be74f28090f6c0578a.jpg\",\n  \"337378.jpg\": \"Aves/Selasphorus sasin/662e303d3a1c599597da35310f7fc364.jpg\",\n  \"337382.jpg\": \"Aves/Selasphorus sasin/f333db4b77ccc1253615445fade7b038.jpg\",\n  \"337383.jpg\": \"Aves/Selasphorus sasin/f0cbca827db660440fd5531d52fb98f8.jpg\",\n  \"337404.jpg\": \"Aves/Selasphorus sasin/366ea169a1e21a919546a320e0fd20a9.jpg\",\n  \"337405.jpg\": \"Aves/Selasphorus sasin/6bce0502235075e72a8e387b7a880f51.jpg\",\n  \"337406.jpg\": \"Aves/Selasphorus sasin/883bea382d7a71394483bdd99189b097.jpg\",\n  \"337408.jpg\": \"Aves/Selasphorus sasin/b4d11dc5424349f6cfd5fd58b06b34a2.jpg\",\n  \"33741.jpg\": \"Aves/Spizelloides arborea/a55b11e6d5e938ba851e4371e42cdfce.jpg\",\n  \"337417.jpg\": \"Aves/Selasphorus sasin/6ee204dc1faeba87d35fe08aa0e17357.jpg\",\n  \"337421.jpg\": \"Aves/Selasphorus sasin/92f63ae561017c31e7f68f60bfc31e42.jpg\",\n  \"337422.jpg\": \"Aves/Selasphorus sasin/b472486c0707619de90a5e72ae50f703.jpg\",\n  \"337430.jpg\": \"Aves/Selasphorus sasin/2b8291e3c5a43b7e2e2678c88154b9de.jpg\",\n  \"337438.jpg\": \"Aves/Selasphorus sasin/a0fdd15ec9ad49afae6de17dad3f7663.jpg\",\n  \"337440.jpg\": \"Aves/Selasphorus sasin/40fd1b68b4b141c1725de87cf6b477f8.jpg\",\n  \"337447.jpg\": \"Aves/Selasphorus sasin/d1d75f870afc852f18cde42c29f1ad3b.jpg\",\n  \"337454.jpg\": \"Aves/Selasphorus sasin/0e6fe0225ecae0063083898b7b329ff2.jpg\",\n  \"337462.jpg\": \"Aves/Selasphorus sasin/3cc6cff085b73231d3f80e70f72a7144.jpg\",\n  \"337465.jpg\": \"Aves/Selasphorus sasin/d3a03ba510cfe671f1ea48f1da788634.jpg\",\n  \"337466.jpg\": \"Aves/Selasphorus sasin/da059d0f3bf5710757e96e485eedbbbd.jpg\",\n  \"337470.jpg\": \"Aves/Selasphorus sasin/3b484acb1194968ce203e4a3aef271c0.jpg\",\n  \"33749.jpg\": \"Aves/Spizelloides arborea/3dad0fb51783f77df4ba355abab08aa5.jpg\",\n  \"33750.jpg\": \"Aves/Spizelloides arborea/3f8d97f4939e1d5eaf089def778ac37b.jpg\",\n  \"337518.jpg\": \"Insecta/Hermeuptychia sosybius/38ab185e4ad9db44a747b31c32e2b0ad.jpg\",\n  \"337521.jpg\": \"Insecta/Hermeuptychia sosybius/c27286aa5fe9edfa329a3803483a7e8b.jpg\",\n  \"337522.jpg\": \"Insecta/Hermeuptychia sosybius/1ba1cd41fc61cc0731b4f1c22faaff8d.jpg\",\n  \"337523.jpg\": \"Insecta/Hermeuptychia sosybius/5e6b6d82e255bf29bfddc6e6b7ba3032.jpg\",\n  \"337524.jpg\": \"Insecta/Hermeuptychia sosybius/ed556f8d4ecb19ed9df7e0d68aa27121.jpg\",\n  \"337525.jpg\": \"Insecta/Hermeuptychia sosybius/782bf33c08b22bcd17b280a32d94c0be.jpg\",\n  \"337526.jpg\": \"Insecta/Hermeuptychia sosybius/8df543a4c9f1cde6d02f5bec9ba86818.jpg\",\n  \"337527.jpg\": \"Insecta/Hermeuptychia sosybius/0f03845344c4b99370a47595dfd08059.jpg\",\n  \"337528.jpg\": \"Insecta/Hermeuptychia sosybius/0f6dc45ac2f96c1c7fe997d11ce69f0c.jpg\",\n  \"337530.jpg\": \"Insecta/Hermeuptychia sosybius/473870f0ed7a696e24e602032b544032.jpg\",\n  \"337531.jpg\": \"Insecta/Hermeuptychia sosybius/4c0836a2daa14e027c1b868967dfdc75.jpg\",\n  \"337532.jpg\": \"Insecta/Hermeuptychia sosybius/cb26b309605af320b003c1b041891156.jpg\",\n  \"337534.jpg\": \"Insecta/Hermeuptychia sosybius/8b7fe4757b4a63c3a8af86c1401f12d7.jpg\",\n  \"337535.jpg\": \"Insecta/Hermeuptychia sosybius/08c3635a968fb4cd487179cba1b72c7e.jpg\",\n  \"337536.jpg\": \"Insecta/Hermeuptychia sosybius/e6e2c0073b4f7ea95e2c80b385278e3e.jpg\",\n  \"337537.jpg\": \"Insecta/Hermeuptychia sosybius/d179830abef8a5869de7da559faed61a.jpg\",\n  \"337540.jpg\": \"Insecta/Hermeuptychia sosybius/ccfe5c257846d5c9e231d4fc7e55dbf2.jpg\",\n  \"337543.jpg\": \"Insecta/Hermeuptychia sosybius/bc671f46806f816ce1605939b28be311.jpg\",\n  \"337544.jpg\": \"Insecta/Hermeuptychia sosybius/d5aea7088f9c784e554bbd449f359d76.jpg\",\n  \"337545.jpg\": \"Insecta/Hermeuptychia sosybius/ac8443e55a529de76f29f1c5e77d2e10.jpg\",\n  \"337550.jpg\": \"Insecta/Hermeuptychia sosybius/03d2ff57a5e1e6d00a61902cadf1666c.jpg\",\n  \"337551.jpg\": \"Insecta/Hermeuptychia sosybius/ff73791304f4d8d2546e39491f83631c.jpg\",\n  \"337552.jpg\": \"Insecta/Hermeuptychia sosybius/5af527ec780daeb1e49346ad59788dc5.jpg\",\n  \"337555.jpg\": \"Insecta/Hermeuptychia sosybius/56bdd3c332713a98cd27b670d1ced6cc.jpg\",\n  \"337556.jpg\": \"Insecta/Hermeuptychia sosybius/81e16d938d5f0ffee685271b4832faab.jpg\",\n  \"337557.jpg\": \"Insecta/Parapediasia teterrella/914165d689ba607135176773a0500e18.jpg\",\n  \"337558.jpg\": \"Insecta/Parapediasia teterrella/7af16ec0da551114b53ba2ea7fb41e9b.jpg\",\n  \"337564.jpg\": \"Insecta/Parapediasia teterrella/4a454b758ba443034bd2dfdadcd9997b.jpg\",\n  \"337565.jpg\": \"Insecta/Parapediasia teterrella/92498c0e1a03b508fe52c6b80f2eeedf.jpg\",\n  \"337566.jpg\": \"Insecta/Parapediasia teterrella/a8d6ca6b78a25d6bea963c0a481a94d3.jpg\",\n  \"337567.jpg\": \"Insecta/Parapediasia teterrella/07e9309642383ca5a60a313e52764a56.jpg\",\n  \"33761.jpg\": \"Aves/Spizelloides arborea/86ddbd704981e83551cc6db2157ddde8.jpg\",\n  \"337624.jpg\": \"Mammalia/Ceratotherium simum simum/93999c732e02b7d765f0134b011c5755.jpg\",\n  \"337642.jpg\": \"Mammalia/Ceratotherium simum simum/dd9c1a13a278f12a139143290d069017.jpg\",\n  \"337646.jpg\": \"Insecta/Calpodes ethlius/3a74c33befee44e119ae5044f32f3ec4.jpg\",\n  \"337648.jpg\": \"Insecta/Calpodes ethlius/3d7be39d1bc18679e25ccabc261e7239.jpg\",\n  \"337665.jpg\": \"Insecta/Calpodes ethlius/9dc0a218ee7f51e61d7b4ad9c1d7d7fb.jpg\",\n  \"337666.jpg\": \"Insecta/Calpodes ethlius/584badd680c30e43d0a65939dfec0db4.jpg\",\n  \"337667.jpg\": \"Insecta/Calpodes ethlius/ae2fd557a8c8d38901b93cd340373f31.jpg\",\n  \"33767.jpg\": \"Aves/Spizelloides arborea/737b0da2e24de93c67dd262892d0fe53.jpg\",\n  \"337670.jpg\": \"Insecta/Calpodes ethlius/59ec8a86578bac5c5e45ce78979a5383.jpg\",\n  \"33770.jpg\": \"Aves/Spizelloides arborea/8fafabbcc876a95041dd993f1feb6693.jpg\",\n  \"33771.jpg\": \"Aves/Spizelloides arborea/ac0590123082463704dede90bafa2745.jpg\",\n  \"33785.jpg\": \"Aves/Spizelloides arborea/94c4486190e584f3d26a3ffe3368606d.jpg\",\n  \"337875.jpg\": \"Insecta/Amphipyra pyramidoides/d54c82c79cfe085676578f1e553cd2a7.jpg\",\n  \"337877.jpg\": \"Insecta/Amphipyra pyramidoides/75976196319d9f10e9926efd724c6b4a.jpg\",\n  \"337879.jpg\": \"Insecta/Amphipyra pyramidoides/253dfa0212e78e44e24d306a259d4828.jpg\",\n  \"337880.jpg\": \"Insecta/Amphipyra pyramidoides/3cd774464d8a95aa1d19109546f5d43f.jpg\",\n  \"337884.jpg\": \"Insecta/Amphipyra pyramidoides/6d157d26ab1c7e5dc9587227d4b27605.jpg\",\n  \"337887.jpg\": \"Insecta/Amphipyra pyramidoides/fa03cd83778271b810b8d38b50b446d4.jpg\",\n  \"33789.jpg\": \"Mollusca/Mercenaria mercenaria/c0f899a2088da996c17d800d4f5ecbbe.jpg\",\n  \"337896.jpg\": \"Insecta/Amphipyra pyramidoides/e833dc7847893d88af47979c86b90f11.jpg\",\n  \"337899.jpg\": \"Insecta/Amphipyra pyramidoides/c1091f76b65c589427b6b6c14e9893e8.jpg\",\n  \"337900.jpg\": \"Insecta/Amphipyra pyramidoides/34455bd3f61ae9225521b77047af0a95.jpg\",\n  \"33794.jpg\": \"Mollusca/Mercenaria mercenaria/657adb6144c51f9c42f3564c404beeeb.jpg\",\n  \"33803.jpg\": \"Mollusca/Mercenaria mercenaria/6b54e2d1da241e55c0d24858aba35adb.jpg\",\n  \"33806.jpg\": \"Mollusca/Mercenaria mercenaria/1b6efb5bb392a92a14b068577f726b29.jpg\",\n  \"338079.jpg\": \"Mammalia/Ovis canadensis/0649bee74860945c1116cad4c6b2490d.jpg\",\n  \"338080.jpg\": \"Mammalia/Ovis canadensis/55658333a5daf795af8f7a7885d9a869.jpg\",\n  \"338081.jpg\": \"Mammalia/Ovis canadensis/4731a64d4aaf625a507ebfa90beb0261.jpg\",\n  \"338084.jpg\": \"Mammalia/Ovis canadensis/ab08a1cbc4c69759bacf005593cf459f.jpg\",\n  \"338085.jpg\": \"Mammalia/Ovis canadensis/f28e989dd696b06515e22dfc86e12c3f.jpg\",\n  \"338087.jpg\": \"Mammalia/Ovis canadensis/3fdea1ec26eb065c1a81fb6a5e876b2f.jpg\",\n  \"338088.jpg\": \"Mammalia/Ovis canadensis/87d2a6754773f05d959fb3f9db6856b6.jpg\",\n  \"338093.jpg\": \"Mammalia/Ovis canadensis/464254d9b501bd6b52056d258a1374d2.jpg\",\n  \"338120.jpg\": \"Mammalia/Ovis canadensis/90ffe8fac4bbc6f896ef4ab949b2a86b.jpg\",\n  \"338132.jpg\": \"Mammalia/Ovis canadensis/43655b0c359fe459d03acd1ae078ccb6.jpg\",\n  \"338145.jpg\": \"Mollusca/Navanax inermis/66457e44e0c5d2808bf017c373505f88.jpg\",\n  \"338147.jpg\": \"Mollusca/Navanax inermis/51fb2506d7bb3e6d8201c20a88b1bed9.jpg\",\n  \"338148.jpg\": \"Mollusca/Navanax inermis/31e0637dd5b594024dd090caf5696562.jpg\",\n  \"338153.jpg\": \"Mollusca/Navanax inermis/de0fb07630dc174646016965ef46d6f4.jpg\",\n  \"338161.jpg\": \"Mollusca/Navanax inermis/3a82fe83aa76ce4adad092e189389348.jpg\",\n  \"338162.jpg\": \"Mollusca/Navanax inermis/c8aa492e640f2cd6e8a831a8d50bed33.jpg\",\n  \"338165.jpg\": \"Mollusca/Mopalia muscosa/d10c06108261a4c7f2a7770ddc8271bc.jpg\",\n  \"338188.jpg\": \"Mollusca/Mopalia muscosa/0bcba9b725871bf6ca7ae05e5b236d92.jpg\",\n  \"338198.jpg\": \"Mollusca/Mopalia muscosa/6dfe7cf082621518f249880b25ed5970.jpg\",\n  \"338207.jpg\": \"Mollusca/Mopalia muscosa/c80f077dc839f966d5815bcb68efa05e.jpg\",\n  \"338212.jpg\": \"Mollusca/Mopalia muscosa/1bf3f1068732b8ba8f8ef61feff91c4f.jpg\",\n  \"338213.jpg\": \"Mollusca/Mopalia muscosa/3ca3fc0755ad82fce1fecded5c707433.jpg\",\n  \"338215.jpg\": \"Mollusca/Mopalia muscosa/69bd9916b64825d9802f53ce53882051.jpg\",\n  \"338218.jpg\": \"Mollusca/Mopalia muscosa/a74b20257e8840aa508afe0ce1bee8b0.jpg\",\n  \"338228.jpg\": \"Mollusca/Mopalia muscosa/125b73059f521ff2df7764947214d78f.jpg\",\n  \"338238.jpg\": \"Mollusca/Mopalia muscosa/fc05c715fc24b42ba4bb04e8c8370f13.jpg\",\n  \"338250.jpg\": \"Reptilia/Thamnophis ordinoides/3bb9bd50cce0997fe4c96304dd64be88.jpg\",\n  \"338251.jpg\": \"Reptilia/Thamnophis ordinoides/d9ca2c6ae83f8be20804c571f4d61e4b.jpg\",\n  \"338262.jpg\": \"Reptilia/Thamnophis ordinoides/1109974a2c5a0136df6296e3cca2621c.jpg\",\n  \"338263.jpg\": \"Reptilia/Thamnophis ordinoides/bb55fb9a6428b23a9bf7aa40a4958da1.jpg\",\n  \"338267.jpg\": \"Reptilia/Thamnophis ordinoides/128cb396ba275a6201790ab83bc1e211.jpg\",\n  \"338268.jpg\": \"Reptilia/Thamnophis ordinoides/c2a918150307b8ea3b561ef6c6ff2f77.jpg\",\n  \"338281.jpg\": \"Reptilia/Thamnophis ordinoides/dbbacf6a5772d1a75885b17be7ae45f1.jpg\",\n  \"338286.jpg\": \"Insecta/Pyrausta orphisalis/83e36d0ba0a4691b141754dbb17be79d.jpg\",\n  \"338290.jpg\": \"Insecta/Pyrausta orphisalis/271d22a435d8d00b313d94d9ca513a3d.jpg\",\n  \"338292.jpg\": \"Insecta/Pyrausta orphisalis/1615c3616f705b2c3ffb13e207ca00f9.jpg\",\n  \"338314.jpg\": \"Insecta/Oncopeltus sexmaculatus/e14779f7556f856547cded3232a8fff0.jpg\",\n  \"338316.jpg\": \"Insecta/Oncopeltus sexmaculatus/3bc7f65aee5e8d278e8fd49c79cd0687.jpg\",\n  \"338323.jpg\": \"Insecta/Oncopeltus sexmaculatus/c69f20aa50d066f0834f61c49af903f5.jpg\",\n  \"338324.jpg\": \"Insecta/Oncopeltus sexmaculatus/cca113aab7aeef81c3821655a8a20bb2.jpg\",\n  \"338325.jpg\": \"Insecta/Crambus agitatellus/02574408e6b6532c203e1df1da93b752.jpg\",\n  \"338326.jpg\": \"Insecta/Crambus agitatellus/5ccb84c3d0e3bbd4a763826b868295ba.jpg\",\n  \"338332.jpg\": \"Insecta/Crambus agitatellus/fcdae4c25e30a2f21f5e7ff6651978bc.jpg\",\n  \"338341.jpg\": \"Insecta/Crambus agitatellus/06ce6db1e0e501878a2b1596a128d210.jpg\",\n  \"338342.jpg\": \"Insecta/Crambus agitatellus/1f7cc42ad0671712a9e395b6ef6d60e0.jpg\",\n  \"338347.jpg\": \"Insecta/Crambus agitatellus/1812c9bb6455d8ac2fb14da703784ae7.jpg\",\n  \"338354.jpg\": \"Insecta/Crambus agitatellus/b14d680f341f03582acf67709d8934f8.jpg\",\n  \"338356.jpg\": \"Insecta/Crambus agitatellus/75e97bb45fce84cd0d3bf67a8bd48dd5.jpg\",\n  \"338363.jpg\": \"Insecta/Crambus agitatellus/ababbc326a03c87799eac63097ba7a26.jpg\",\n  \"338368.jpg\": \"Insecta/Bombylius major/8b805f719e695b089c8a32a7ec583091.jpg\",\n  \"338370.jpg\": \"Insecta/Bombylius major/b4de1261e3297a8ed0145d301ce01814.jpg\",\n  \"338373.jpg\": \"Insecta/Bombylius major/df6d48b095497e878914e353b7529e09.jpg\",\n  \"338376.jpg\": \"Insecta/Bombylius major/e34e965b6b014f969869f868d1658e50.jpg\",\n  \"338381.jpg\": \"Insecta/Bombylius major/d955e827d9f0890f4480452a6556799e.jpg\",\n  \"338382.jpg\": \"Insecta/Bombylius major/b8a4e9451464a3de88b53a81b5a15499.jpg\",\n  \"338392.jpg\": \"Insecta/Bombylius major/b3c5ce3f8d35f4883253dd8aaec60683.jpg\",\n  \"338393.jpg\": \"Insecta/Bombylius major/295f7e8c64320fa82beca0c9f70dd3e5.jpg\",\n  \"338394.jpg\": \"Insecta/Bombylius major/3c451c10d0cddcc3eeb56b2454318584.jpg\",\n  \"338395.jpg\": \"Insecta/Bombylius major/6aae1f7548d19df7e2b3c3d76879dd33.jpg\",\n  \"338397.jpg\": \"Insecta/Bombylius major/dc4c7b8ba224c0ba5d02664304a242df.jpg\",\n  \"338399.jpg\": \"Insecta/Bombylius major/91cf4c46fcbcaa1cb48dee847941278f.jpg\",\n  \"338400.jpg\": \"Insecta/Bombylius major/31f5caab33d8643c9cad6c97c1903d13.jpg\",\n  \"338403.jpg\": \"Insecta/Bombylius major/11545935100d348e5c71145a425eeb1e.jpg\",\n  \"338404.jpg\": \"Insecta/Bombylius major/bb5ec99ebadbd423ec9aae7f77503566.jpg\",\n  \"338405.jpg\": \"Insecta/Bombylius major/561c11134f7cd8c8df5d04d0341d6efe.jpg\",\n  \"338406.jpg\": \"Insecta/Bombylius major/7b952d7ec9b8fe6dfe17daf8555f666c.jpg\",\n  \"338409.jpg\": \"Insecta/Bombylius major/635d98e82ff3629a8e99c759632bc186.jpg\",\n  \"338410.jpg\": \"Insecta/Bombylius major/340e298db484f27a045fb4eb73c4bfb6.jpg\",\n  \"338414.jpg\": \"Insecta/Bombylius major/275b00d09251eede147d966067861dec.jpg\",\n  \"338423.jpg\": \"Reptilia/Sceloporus undulatus/9d263de2dceeead63b5ee4df1e2d35ed.jpg\",\n  \"338432.jpg\": \"Reptilia/Sceloporus undulatus/1b2927e20802184233f8e378dc3d912d.jpg\",\n  \"338434.jpg\": \"Reptilia/Sceloporus undulatus/dfd4d5b0df648e5ea9a57b5491cdeb28.jpg\",\n  \"338435.jpg\": \"Reptilia/Sceloporus undulatus/8991a60cabe67dd849149bf0d2b0b696.jpg\",\n  \"338438.jpg\": \"Reptilia/Sceloporus undulatus/1d9e2cd424bae33ba6ea9918307cc1ee.jpg\",\n  \"338440.jpg\": \"Reptilia/Sceloporus undulatus/386e7ab6a7ef0ad1077ddce93b8f64f8.jpg\",\n  \"338441.jpg\": \"Reptilia/Sceloporus undulatus/14ffdb702439f1659af2d560a26211d3.jpg\",\n  \"338443.jpg\": \"Reptilia/Sceloporus undulatus/fd379cc4069661df415a88036ba8aaaa.jpg\",\n  \"338444.jpg\": \"Reptilia/Sceloporus undulatus/b0069af4fa27fc60f4a721543749c13b.jpg\",\n  \"338450.jpg\": \"Reptilia/Sceloporus undulatus/75cc9326ba6e93a2eb71bba19ef361cb.jpg\",\n  \"338453.jpg\": \"Reptilia/Sceloporus undulatus/4aa11402c5a264d676facddbcc2cba21.jpg\",\n  \"338458.jpg\": \"Reptilia/Sceloporus undulatus/5a4429e4aec0453492bcc53820f0741e.jpg\",\n  \"338460.jpg\": \"Reptilia/Sceloporus undulatus/1f7add634da3a8b1d54679863293c879.jpg\",\n  \"338471.jpg\": \"Reptilia/Sceloporus undulatus/bf437f07c3f82181154e9cd0926ba2d2.jpg\",\n  \"338474.jpg\": \"Reptilia/Sceloporus undulatus/20d25000a01f3906838de153b7a19370.jpg\",\n  \"338477.jpg\": \"Reptilia/Sceloporus undulatus/dcc671de76b9ff53df02727323aa32b4.jpg\",\n  \"338482.jpg\": \"Reptilia/Sceloporus undulatus/5ae642b212eb098b3688e0f435d5779f.jpg\",\n  \"338483.jpg\": \"Reptilia/Sceloporus undulatus/da4165f84836214e68b2e6783a7ded44.jpg\",\n  \"338486.jpg\": \"Reptilia/Sceloporus undulatus/b5617b95aaaff26454084603743f4ce5.jpg\",\n  \"3385.jpg\": \"Insecta/Coccinella septempunctata/f687e27ec6afc9e878a2a6b0a5dbdf1d.jpg\",\n  \"338657.jpg\": \"Aves/Phalacrocorax pelagicus/a3c3e058d88ef0be26480fb6b52cf382.jpg\",\n  \"338674.jpg\": \"Aves/Phalacrocorax pelagicus/6e5d33381578c8351a28c2de3b58b6cb.jpg\",\n  \"338679.jpg\": \"Aves/Phalacrocorax pelagicus/e8a5f290690569fa1ec278929a68de04.jpg\",\n  \"338712.jpg\": \"Aves/Phalacrocorax pelagicus/6a93ae08f9f1241d1700d2efacec80ea.jpg\",\n  \"338715.jpg\": \"Aves/Phalacrocorax pelagicus/58c4ed2afdfd565430cee1395f32ec45.jpg\",\n  \"338716.jpg\": \"Aves/Phalacrocorax pelagicus/0402296fa204424e9abfe4b7aa05016d.jpg\",\n  \"338719.jpg\": \"Aves/Phalacrocorax pelagicus/d4b12e6b7259f91be18a90fda9653231.jpg\",\n  \"338723.jpg\": \"Aves/Phalacrocorax pelagicus/e37926c4fd35b90f41582dccecb991bf.jpg\",\n  \"338733.jpg\": \"Aves/Phalacrocorax pelagicus/d12b8d47d69f5b4231ad17abebe69287.jpg\",\n  \"338737.jpg\": \"Aves/Phalacrocorax pelagicus/c6cde446251a51ebd4dae18cd997b6c3.jpg\",\n  \"338738.jpg\": \"Aves/Phalacrocorax pelagicus/03f0ff0720eb8ccb600b8e89099d5049.jpg\",\n  \"338740.jpg\": \"Aves/Amazona albifrons/a0a5314d05dc4b0d5d134e8275d8bd49.jpg\",\n  \"338742.jpg\": \"Aves/Amazona albifrons/fc05313cfbfe9359b57bbe1bf6418967.jpg\",\n  \"338743.jpg\": \"Aves/Amazona albifrons/cb0adc8db6b813457204fde06c096250.jpg\",\n  \"338746.jpg\": \"Aves/Amazona albifrons/f75e39313b6bcaa89ff6cd95c1b8577c.jpg\",\n  \"338747.jpg\": \"Aves/Amazona albifrons/bd1971f41cfac655b0e0ece7c9193cb5.jpg\",\n  \"338749.jpg\": \"Aves/Amazona albifrons/e1bca54b3fce2d4d92bfc4492f9bcc3b.jpg\",\n  \"338750.jpg\": \"Aves/Amazona albifrons/88f7813b9e6dc1ce769e20a5e226f831.jpg\",\n  \"338754.jpg\": \"Aves/Amazona albifrons/df3a826504e077ce5da48e5e6207e32d.jpg\",\n  \"338757.jpg\": \"Aves/Amazona albifrons/888880eb6870f8a19ecfe12cef81cb88.jpg\",\n  \"338758.jpg\": \"Aves/Amazona albifrons/a2df3e6848309119600170d041ca1305.jpg\",\n  \"338760.jpg\": \"Aves/Amazona albifrons/d08d13131bad2e4a3af9c0026272e0cc.jpg\",\n  \"338766.jpg\": \"Aves/Amazona albifrons/42486feb8f77e83ee72d9888852966cc.jpg\",\n  \"338767.jpg\": \"Aves/Amazona albifrons/d7c2f0732ef6057534cfc35a8df26799.jpg\",\n  \"338768.jpg\": \"Aves/Amazona albifrons/0fa95930c966625323423b2d358a833c.jpg\",\n  \"338770.jpg\": \"Aves/Amazona albifrons/79b286c68529974f420d31103a4ea7d3.jpg\",\n  \"338775.jpg\": \"Aves/Amazona albifrons/86be68b9db93099b7d9f40e8f6e4f23e.jpg\",\n  \"338784.jpg\": \"Reptilia/Crotalus scutulatus scutulatus/89d954bc15c9ab72274b999d4e70bf3f.jpg\",\n  \"338785.jpg\": \"Reptilia/Crotalus scutulatus scutulatus/4c983d02163ce951ebe0595d3f2aff9c.jpg\",\n  \"338814.jpg\": \"Reptilia/Crotalus scutulatus scutulatus/f1e820accd5f579ba4cf59194cb8f2f9.jpg\",\n  \"338818.jpg\": \"Mammalia/Halichoerus grypus/c1941e3906b01ed2f201920061f82cde.jpg\",\n  \"338819.jpg\": \"Mammalia/Halichoerus grypus/e8922a8acdf06f4137a87f99e77222c7.jpg\",\n  \"338829.jpg\": \"Mammalia/Halichoerus grypus/3cad1b1cd447ccde5e23229f5c2193bc.jpg\",\n  \"338832.jpg\": \"Mammalia/Halichoerus grypus/a3f41d482301c3a77af99f204b60fd41.jpg\",\n  \"338838.jpg\": \"Mammalia/Halichoerus grypus/b404e3ba8d5e1259cf2a7db5ce4ae431.jpg\",\n  \"338844.jpg\": \"Mammalia/Halichoerus grypus/19ba13ebd8299bf343184e148e7afc64.jpg\",\n  \"338852.jpg\": \"Mammalia/Tragelaphus scriptus/634c7cb9c033ca50bab55562fa507f46.jpg\",\n  \"338858.jpg\": \"Mammalia/Tragelaphus scriptus/57baecac8697b2995dc8c40024b32ed4.jpg\",\n  \"338862.jpg\": \"Insecta/Palthis asopialis/944b3f4c361606b67e9848b816d8c7b2.jpg\",\n  \"338873.jpg\": \"Insecta/Palthis asopialis/d43ebeff6d9d401782b6c45d15972035.jpg\",\n  \"338874.jpg\": \"Insecta/Palthis asopialis/96fd51b4da3f6d15f2da4c88eee3d334.jpg\",\n  \"338876.jpg\": \"Insecta/Palthis asopialis/c46743e6537b28c78f44d6d0f015a7d1.jpg\",\n  \"338881.jpg\": \"Insecta/Palthis asopialis/60f8c042d2901082c25373c7498b2199.jpg\",\n  \"338882.jpg\": \"Insecta/Palthis asopialis/1a1d62bd3d94c9ca6b108baf887a198a.jpg\",\n  \"338890.jpg\": \"Insecta/Palthis asopialis/0cd425446220f6ca76a4f1884c1d83a2.jpg\",\n  \"338893.jpg\": \"Insecta/Palthis asopialis/b9b4f70d24bfebc98e78132f9ae550df.jpg\",\n  \"338895.jpg\": \"Insecta/Palthis asopialis/60e4678c5d314c116265a23951ce4fbf.jpg\",\n  \"339.jpg\": \"Insecta/Coleomegilla maculata/0156d123dc99ef3d20a5e4742d7e8d37.jpg\",\n  \"339056.jpg\": \"Mollusca/Doriopsilla albopunctata/293e9b7d244b3ab0801ecfbb9f2b3c0a.jpg\",\n  \"339057.jpg\": \"Mollusca/Doriopsilla albopunctata/daf99f3505b7d0d0585ccf58913676a0.jpg\",\n  \"339058.jpg\": \"Mollusca/Doriopsilla albopunctata/980e4e89f84c872c0aa072b44ebdc5f2.jpg\",\n  \"339075.jpg\": \"Mollusca/Doriopsilla albopunctata/5990816efb0e5382c3dc40275871e87b.jpg\",\n  \"339097.jpg\": \"Mollusca/Doriopsilla albopunctata/ee8e7c258fbe8e78324b0e66f2aa62a0.jpg\",\n  \"339098.jpg\": \"Mollusca/Doriopsilla albopunctata/65196048d599f1ac954d96e074f4f312.jpg\",\n  \"339112.jpg\": \"Mollusca/Doriopsilla albopunctata/0113e73be3ccb2634f881e8ba98d77a5.jpg\",\n  \"339113.jpg\": \"Mollusca/Doriopsilla albopunctata/57118a90f918a6858afa0efcc6b0e9b7.jpg\",\n  \"339117.jpg\": \"Mollusca/Doriopsilla albopunctata/cd4912bcf3f821c1b5d044049c0d4963.jpg\",\n  \"339120.jpg\": \"Mollusca/Doriopsilla albopunctata/d44eb953d5df14bbea156c42f7876808.jpg\",\n  \"339121.jpg\": \"Mollusca/Doriopsilla albopunctata/c4abf6944aa9ccb490d24f1b19971299.jpg\",\n  \"339126.jpg\": \"Mollusca/Doriopsilla albopunctata/b70d895886aebcffdaa5f557866b5820.jpg\",\n  \"339142.jpg\": \"Mollusca/Doriopsilla albopunctata/21d75ef419b2670394d3c7b45902085f.jpg\",\n  \"339145.jpg\": \"Mollusca/Doriopsilla albopunctata/ae0624ae3ed8e70d89a141fc41aa1fc6.jpg\",\n  \"339146.jpg\": \"Mollusca/Doriopsilla albopunctata/f818840243bcdfc8b283b05d17d86a42.jpg\",\n  \"339255.jpg\": \"Actinopterygii/Lepomis megalotis/f91c396a3e6f2d10b7f3baee32ed47b9.jpg\",\n  \"339256.jpg\": \"Actinopterygii/Lepomis megalotis/b51fff814826fa72797b1436317efe13.jpg\",\n  \"339263.jpg\": \"Actinopterygii/Lepomis megalotis/1f015a7563772b2fd0f8c242fcfcf586.jpg\",\n  \"339273.jpg\": \"Actinopterygii/Lepomis megalotis/45d6654c1b642b4e9c4aff9c6ba73a34.jpg\",\n  \"339277.jpg\": \"Actinopterygii/Lepomis megalotis/0fdb974c18cfcf31627cadcda2af64e6.jpg\",\n  \"3393.jpg\": \"Insecta/Coccinella septempunctata/e1721476091c2ebaeb7d4b09d408ab02.jpg\",\n  \"339481.jpg\": \"Aves/Sayornis nigricans/af94893c986751560343c76e028090db.jpg\",\n  \"339494.jpg\": \"Aves/Sayornis nigricans/1b659461c8b4d2f2b0a088d72b8f943b.jpg\",\n  \"339501.jpg\": \"Aves/Sayornis nigricans/1cecdd4ae8469b33832feeea988383fe.jpg\",\n  \"339524.jpg\": \"Aves/Sayornis nigricans/c369a589d66b97f0c28e3b0c95c36144.jpg\",\n  \"339534.jpg\": \"Aves/Sayornis nigricans/296287649e6c9eb388678426b9b5a494.jpg\",\n  \"339569.jpg\": \"Aves/Sayornis nigricans/1363d92dbbe287729e6336d613255e1e.jpg\",\n  \"339578.jpg\": \"Aves/Sayornis nigricans/ea71e2e39b0e1e9206266b11dd6950d5.jpg\",\n  \"339617.jpg\": \"Aves/Sayornis nigricans/3a0c1fb664098bfdb026b5dbdd44ccaf.jpg\",\n  \"339622.jpg\": \"Aves/Sayornis nigricans/19a5a7e8e3461407a31b6f52849aa8b0.jpg\",\n  \"339631.jpg\": \"Aves/Sayornis nigricans/445ad466507b5dfd039b006c3cc20531.jpg\",\n  \"339708.jpg\": \"Aves/Sayornis nigricans/f38271a89fd6839266347e00cec3e203.jpg\",\n  \"339712.jpg\": \"Aves/Sayornis nigricans/8b31cfc69cd76f3783218317b6197ec3.jpg\",\n  \"339726.jpg\": \"Aves/Sayornis nigricans/76d46d43ec769cc4082b3629e525e7a0.jpg\",\n  \"339735.jpg\": \"Aves/Sayornis nigricans/c42c929a003a431c3cc448456a660252.jpg\",\n  \"339736.jpg\": \"Aves/Sayornis nigricans/b5eb88d32a910124b4659818e1e68d58.jpg\",\n  \"33977.jpg\": \"Aves/Porphyrio melanotus/8884b16dbe42998982dac1c2c53fc22b.jpg\",\n  \"339805.jpg\": \"Aves/Sayornis nigricans/1adf0920e1fea3075e5a98741c3b564c.jpg\",\n  \"339821.jpg\": \"Aves/Sayornis nigricans/10d1810d6ca8ed8d6e934a38f18d4d1c.jpg\",\n  \"339823.jpg\": \"Aves/Sayornis nigricans/c74e9eca3f97e7431446d457af59c626.jpg\",\n  \"339834.jpg\": \"Aves/Sayornis nigricans/a7797069853b43fb00b4871999ebe130.jpg\",\n  \"33984.jpg\": \"Aves/Porphyrio melanotus/c0aca7b39e75aceb6c2047beb938f8f6.jpg\",\n  \"33987.jpg\": \"Aves/Porphyrio melanotus/9bd76560b48bea5c4031b22b98963dd6.jpg\",\n  \"33988.jpg\": \"Aves/Porphyrio melanotus/5e1df7dd4f92304d09e252b696beb401.jpg\",\n  \"33990.jpg\": \"Aves/Porphyrio melanotus/dcc1e671a0417966dfbbfb8f7cb51e25.jpg\",\n  \"339919.jpg\": \"Aves/Sayornis nigricans/d1dcd16f19c2570d1b26a5764fb3e5cb.jpg\",\n  \"339959.jpg\": \"Aves/Sayornis nigricans/76f6e249f777b5730059fc5be5b2889f.jpg\",\n  \"33997.jpg\": \"Aves/Porphyrio melanotus/e1e354490982d695445d9d90eace276d.jpg\",\n  \"339986.jpg\": \"Aves/Sayornis nigricans/ac72da6a6e5dda92029aa1421a3a3831.jpg\",\n  \"340.jpg\": \"Insecta/Coleomegilla maculata/10126000f508c95e50a4e7fbccc52723.jpg\",\n  \"340064.jpg\": \"Aves/Prunella modularis/48a44cfa5086a08c5f3958666dc93675.jpg\",\n  \"340066.jpg\": \"Aves/Prunella modularis/ba05f926771d0020b7013c55af5442b7.jpg\",\n  \"340073.jpg\": \"Aves/Prunella modularis/7d7144f4ca10dcf1e7a8ced4fd713f23.jpg\",\n  \"340074.jpg\": \"Aves/Prunella modularis/7da4e9ec2648b64cd8f6125acb16c666.jpg\",\n  \"340078.jpg\": \"Aves/Prunella modularis/f17ba9c566fe9d2ef68b0753eebfbaeb.jpg\",\n  \"340081.jpg\": \"Aves/Prunella modularis/386aa92bc03256362e184c7bfb005e9b.jpg\",\n  \"340083.jpg\": \"Aves/Prunella modularis/dd8e4c1aba7607db310410a5753a466d.jpg\",\n  \"340084.jpg\": \"Aves/Prunella modularis/df5941d521d760e5e9edeb3a84dab654.jpg\",\n  \"340085.jpg\": \"Aves/Prunella modularis/7758254c35f4faedab927be5853b1104.jpg\",\n  \"340095.jpg\": \"Aves/Prunella modularis/d1fb0d2e0d6ce337ce3f3d13a5d19cb7.jpg\",\n  \"340099.jpg\": \"Aves/Prunella modularis/6905e7092b0ad299376093cc8e371f2c.jpg\",\n  \"340102.jpg\": \"Aves/Prunella modularis/7ba6a8f7c857c115af615b3d9fca1a27.jpg\",\n  \"340103.jpg\": \"Aves/Prunella modularis/63cd5f2cc72fa4f510464c1548b700f5.jpg\",\n  \"340104.jpg\": \"Aves/Prunella modularis/03d82387106ffa2710b44c289fca5fd5.jpg\",\n  \"340106.jpg\": \"Aves/Prunella modularis/5145b73447a1b4d32559c681acca568d.jpg\",\n  \"340118.jpg\": \"Aves/Prunella modularis/a938c7aa5a5186148f623dcdb67e37fe.jpg\",\n  \"340119.jpg\": \"Aves/Prunella modularis/718311c169d000fceef0e4a59a6bd19e.jpg\",\n  \"340120.jpg\": \"Aves/Prunella modularis/811210c6773608506b6759f6d2b6afce.jpg\",\n  \"340122.jpg\": \"Aves/Prunella modularis/181d6ebce7867dfeebe5e78d02201570.jpg\",\n  \"340123.jpg\": \"Aves/Prunella modularis/99918ed0d15e8960d286f36c33dc8e31.jpg\",\n  \"340124.jpg\": \"Aves/Prunella modularis/59d661f16ad2b97c3606da1bd0396962.jpg\",\n  \"340141.jpg\": \"Insecta/Leptophobia aripa/f14a163c11621c5f8f58b1b2546e565f.jpg\",\n  \"340146.jpg\": \"Insecta/Leptophobia aripa/3016e3dd7ec7664bdf75a8a6ea8a2b67.jpg\",\n  \"340151.jpg\": \"Insecta/Leptophobia aripa/00aa980eb03a904b5db6b3bc31e2e950.jpg\",\n  \"340152.jpg\": \"Insecta/Leptophobia aripa/13a43fefa2b5aecb70d5308c5dbf68e0.jpg\",\n  \"340154.jpg\": \"Insecta/Leptophobia aripa/149aa6bfe9894b43c7773a52911c9e5d.jpg\",\n  \"340156.jpg\": \"Insecta/Leptophobia aripa/f9eeab7008b54056a52c5d5c3892a500.jpg\",\n  \"340224.jpg\": \"Aves/Anthus novaeseelandiae novaeseelandiae/a14a6e2ef19b261915a12ca2b51c8233.jpg\",\n  \"340225.jpg\": \"Aves/Anthus novaeseelandiae novaeseelandiae/af611d753d2ac117d0bc7aac58d7d4ab.jpg\",\n  \"340232.jpg\": \"Aves/Anthus novaeseelandiae novaeseelandiae/db30d07baa55d8515d491637f3065b73.jpg\",\n  \"340233.jpg\": \"Aves/Anthus novaeseelandiae novaeseelandiae/a999cba2488d1c7f440c214b15036e9f.jpg\",\n  \"340239.jpg\": \"Aves/Anthus novaeseelandiae novaeseelandiae/7056f93d428d01744e9c6490c772066b.jpg\",\n  \"340249.jpg\": \"Aves/Anthus novaeseelandiae novaeseelandiae/aa8f22062f2d8104f117ef2a6537fccf.jpg\",\n  \"340250.jpg\": \"Aves/Anthus novaeseelandiae novaeseelandiae/8e6f75bc5434f786cb574a5d7e25bba9.jpg\",\n  \"340319.jpg\": \"Insecta/Nymphalis californica/29b641446e52ebfd8ec5213636984db0.jpg\",\n  \"340320.jpg\": \"Insecta/Nymphalis californica/bb5b95f9d2cb229ba8ed02c90c2cbf23.jpg\",\n  \"340321.jpg\": \"Insecta/Nymphalis californica/f527377fd0f2856bd2bc48c307be00c8.jpg\",\n  \"340323.jpg\": \"Insecta/Nymphalis californica/dee0cb52effa5a49ebfdd1c4a45c85d8.jpg\",\n  \"340327.jpg\": \"Insecta/Nymphalis californica/ac0dba3b03cc2cc8b94d0ca914e4580c.jpg\",\n  \"340335.jpg\": \"Insecta/Nymphalis californica/906c5a4ddb96aa34bf6f72bd4ded5f3d.jpg\",\n  \"340336.jpg\": \"Insecta/Nymphalis californica/9e9d131b81b8a5911e1511ee866bf9d3.jpg\",\n  \"340340.jpg\": \"Insecta/Nymphalis californica/56baf7a0bf79ece083844145c3279851.jpg\",\n  \"340342.jpg\": \"Insecta/Nymphalis californica/b8cab07bf154e279839a0e1d33ae5e93.jpg\",\n  \"340350.jpg\": \"Insecta/Nymphalis californica/2daab2466d34a62c1b96cbdc44c48cc3.jpg\",\n  \"340351.jpg\": \"Insecta/Nymphalis californica/95d2751d107b8baa892ca2979096cb68.jpg\",\n  \"340352.jpg\": \"Insecta/Nymphalis californica/44392155efc0b0c91e588dd49e94fcfe.jpg\",\n  \"340353.jpg\": \"Insecta/Nymphalis californica/c4fcb3fa7c7f13a11dda0ccd1faeecff.jpg\",\n  \"340354.jpg\": \"Insecta/Nymphalis californica/d7373a6a8c76337b0d2dd24f87d0ae18.jpg\",\n  \"340355.jpg\": \"Insecta/Nymphalis californica/729f4798c7d34253725d88185ff6ad3a.jpg\",\n  \"340357.jpg\": \"Insecta/Nymphalis californica/08bb93bd7b4009a1b1a8d52ac9afb07d.jpg\",\n  \"340359.jpg\": \"Insecta/Nymphalis californica/32c4ff258a5ee044ebe2ef55e4e2e867.jpg\",\n  \"340360.jpg\": \"Insecta/Nymphalis californica/f56dae39e6047bf80df25104effa636a.jpg\",\n  \"340362.jpg\": \"Insecta/Nymphalis californica/75f83ef60cf82737e0de7c04888495a6.jpg\",\n  \"340388.jpg\": \"Insecta/Sympetrum semicinctum/9e118853e8971f60aceea8ff4e8e7e70.jpg\",\n  \"340395.jpg\": \"Insecta/Sympetrum semicinctum/e502ea99085c35a1db27a15a5f3ac2e5.jpg\",\n  \"340403.jpg\": \"Insecta/Sympetrum semicinctum/f6284e82e94ec8b481b824e5022b0b60.jpg\",\n  \"340414.jpg\": \"Insecta/Sympetrum semicinctum/a2b4c40cafab03ef263df0b3ad7f24f4.jpg\",\n  \"340415.jpg\": \"Insecta/Sympetrum semicinctum/c2fe639626d88988289fd95561a7e42d.jpg\",\n  \"340421.jpg\": \"Insecta/Sympetrum semicinctum/377ce4782c575a0b46545ac996390fed.jpg\",\n  \"340425.jpg\": \"Insecta/Sympetrum semicinctum/f6184070916f966e37ae95d7579176e9.jpg\",\n  \"340427.jpg\": \"Insecta/Sympetrum semicinctum/dd6368a3018262298517f62bda423dbe.jpg\",\n  \"340428.jpg\": \"Insecta/Sympetrum semicinctum/395ee260dad3468466ff8613fbcd684a.jpg\",\n  \"340433.jpg\": \"Insecta/Sympetrum semicinctum/cb73ae1e37af59660d4292ae206731b0.jpg\",\n  \"340434.jpg\": \"Insecta/Sympetrum semicinctum/6677fda3deed4aa48f6be9cd1af38332.jpg\",\n  \"340435.jpg\": \"Insecta/Sympetrum semicinctum/53765dcce3e49b32cd567e3118842467.jpg\",\n  \"340443.jpg\": \"Insecta/Sympetrum semicinctum/541b6a43c10e95628aefcb620c14c995.jpg\",\n  \"340451.jpg\": \"Insecta/Sympetrum semicinctum/c1bca4ccc9849839dc7588493087e441.jpg\",\n  \"340454.jpg\": \"Insecta/Sympetrum semicinctum/67762cca641b5fb1dc412d03adedbd85.jpg\",\n  \"340455.jpg\": \"Insecta/Sympetrum semicinctum/a525a7fd531b30ec71e6d70942265485.jpg\",\n  \"340456.jpg\": \"Insecta/Sympetrum semicinctum/c07e538624b09adf494dfe5c834337e7.jpg\",\n  \"340464.jpg\": \"Insecta/Sympetrum semicinctum/b067004351c1c7253ba08dda5833e68b.jpg\",\n  \"340465.jpg\": \"Insecta/Sympetrum semicinctum/50120a086bc4aeccb12d4125089e4f85.jpg\",\n  \"340476.jpg\": \"Aves/Amazona oratrix/05fdbddafedf81ad8e0d3e947f47cf26.jpg\",\n  \"340479.jpg\": \"Aves/Amazona oratrix/69874ae5e993af23b8da03e16068e585.jpg\",\n  \"340484.jpg\": \"Aves/Amazona oratrix/ad81724e94409f0903e656da10a30d10.jpg\",\n  \"340486.jpg\": \"Aves/Amazona oratrix/f1b8cd9c11c47fabe0060a1cac80d004.jpg\",\n  \"340490.jpg\": \"Aves/Sula sula/b34f0d00084507416e2cb724d5d98a23.jpg\",\n  \"340501.jpg\": \"Aves/Sula sula/ac7544751811fc06d0dbe35d0b6a248c.jpg\",\n  \"340502.jpg\": \"Aves/Sula sula/f7a695d66bd628304dbcefcdabb7127e.jpg\",\n  \"340506.jpg\": \"Aves/Sula sula/afdcb66cde5d4ea52fe7077fca9e677b.jpg\",\n  \"340520.jpg\": \"Mammalia/Rattus norvegicus/95ae8bb3fc2d3087b2d2ac8bb43956fa.jpg\",\n  \"340524.jpg\": \"Mammalia/Rattus norvegicus/c6714ec15e401967a7f269487d9a23a8.jpg\",\n  \"340526.jpg\": \"Mammalia/Rattus norvegicus/30ac4d8fdec4f06ac37e9be2bbea1a6a.jpg\",\n  \"340527.jpg\": \"Mammalia/Rattus norvegicus/6dbdbd4b26ef1c7d1556da43dd21d16f.jpg\",\n  \"340528.jpg\": \"Mammalia/Rattus norvegicus/e503e7a44c9a93ffc4cba7d8150920fc.jpg\",\n  \"340539.jpg\": \"Mammalia/Rattus norvegicus/0d0613624fb9b9b1f600a412b08455a3.jpg\",\n  \"340540.jpg\": \"Mammalia/Rattus norvegicus/1da5f24cf3f7937ff804b318b119784a.jpg\",\n  \"340542.jpg\": \"Mammalia/Rattus norvegicus/334ec659c2094e1d76fea18f87e8e902.jpg\",\n  \"340543.jpg\": \"Mammalia/Rattus norvegicus/5e0b79e4a02f495d689759af56127e7c.jpg\",\n  \"340544.jpg\": \"Mammalia/Rattus norvegicus/f74e7ac55b9c1a698359ad89eafd511a.jpg\",\n  \"340545.jpg\": \"Mammalia/Rattus norvegicus/989341c60c5d994d9f2677e641704cbd.jpg\",\n  \"340546.jpg\": \"Mammalia/Rattus norvegicus/f8b062c10def2fe03b6b0c0c11d8fe07.jpg\",\n  \"340547.jpg\": \"Mammalia/Rattus norvegicus/60465f89636cfc923e8866d19fd1479f.jpg\",\n  \"340555.jpg\": \"Mammalia/Rattus norvegicus/b2d7412a95a8cbcb6df705e79ac6eea8.jpg\",\n  \"340556.jpg\": \"Mammalia/Rattus norvegicus/8d31e5df466fe677ad1eded0fd959d8b.jpg\",\n  \"340558.jpg\": \"Mammalia/Rattus norvegicus/2de833bf6264136ebe537ef7f304d4b8.jpg\",\n  \"340565.jpg\": \"Mammalia/Rattus norvegicus/3a3302c964e42d991aed4bfde0b5e52d.jpg\",\n  \"340566.jpg\": \"Insecta/Leptotes cassius/446274f1038426b3ca7db42c607ffc95.jpg\",\n  \"340574.jpg\": \"Insecta/Leptotes cassius/ca0f06272c67dd12b964ca36868f1444.jpg\",\n  \"340575.jpg\": \"Insecta/Leptotes cassius/8fb04638adc4c210470252685d198925.jpg\",\n  \"340579.jpg\": \"Insecta/Leptotes cassius/28c782a510d5701a46d61ea26e88110f.jpg\",\n  \"340581.jpg\": \"Insecta/Leptotes cassius/e7ac9f93d9fbfd5e2a41c5e37e45bd8f.jpg\",\n  \"340593.jpg\": \"Insecta/Leptotes cassius/a0cee0c8a132109314c67781dc87f43a.jpg\",\n  \"340595.jpg\": \"Insecta/Leptotes cassius/4fc44fed43d970981b67e83403d5f152.jpg\",\n  \"340600.jpg\": \"Insecta/Leptotes cassius/89899231f4bdca337cbea42433bc5949.jpg\",\n  \"340762.jpg\": \"Aves/Junco phaeonotus/a9740a5d9c7beeb19fd21a849b0249d7.jpg\",\n  \"340766.jpg\": \"Aves/Junco phaeonotus/9058541b396a41bab2857d9d991ace4f.jpg\",\n  \"340770.jpg\": \"Aves/Junco phaeonotus/2f0b00840cb1cbcfc3a6cdc6c3f4e6ea.jpg\",\n  \"340771.jpg\": \"Aves/Junco phaeonotus/8064d45846ebacfe69c771d9a3db9cd5.jpg\",\n  \"340772.jpg\": \"Aves/Junco phaeonotus/af57363d12d1f003ff59b9a94cc18808.jpg\",\n  \"340774.jpg\": \"Aves/Junco phaeonotus/f60ef5d4d61206812519f2b6b054269e.jpg\",\n  \"340782.jpg\": \"Aves/Junco phaeonotus/ea801155238c014621054a3668113156.jpg\",\n  \"340783.jpg\": \"Aves/Junco phaeonotus/c5833de13b4cda1d23e1e5ca08eb89e8.jpg\",\n  \"340784.jpg\": \"Aves/Junco phaeonotus/98d2526ca6da8eea09462c8e741647f4.jpg\",\n  \"340786.jpg\": \"Aves/Junco phaeonotus/fd530dff2e694aa555c67b58d9fce053.jpg\",\n  \"340788.jpg\": \"Aves/Junco phaeonotus/a9a54f350358f1829034e66cd60427ea.jpg\",\n  \"340789.jpg\": \"Aves/Junco phaeonotus/b3f0c4a1007a71829dd0beaaa0b3a632.jpg\",\n  \"340790.jpg\": \"Aves/Junco phaeonotus/8d8095e9321f400a22cd19637b891aeb.jpg\",\n  \"340791.jpg\": \"Aves/Junco phaeonotus/172acbfc895f32ad2d36267aba7fd131.jpg\",\n  \"340792.jpg\": \"Aves/Junco phaeonotus/173f606dd47e8a7258911e1d3587e706.jpg\",\n  \"340793.jpg\": \"Aves/Junco phaeonotus/b1f744f5d22307a8c9221e2f02f082d0.jpg\",\n  \"340795.jpg\": \"Aves/Junco phaeonotus/2ffd73c25a1dbaab9b37cd6ed2ad8dff.jpg\",\n  \"340802.jpg\": \"Aves/Junco phaeonotus/2db9cebf52fa2a6818b3e69bd8073506.jpg\",\n  \"340803.jpg\": \"Aves/Junco phaeonotus/cfae231e320d557934b3d8e63de6f855.jpg\",\n  \"340813.jpg\": \"Aves/Junco phaeonotus/c4bba2719531e1d0f14340be531df98e.jpg\",\n  \"34090.jpg\": \"Aves/Streptopelia senegalensis/1a331127eb1203bdaa06c22832cd2732.jpg\",\n  \"34091.jpg\": \"Aves/Streptopelia senegalensis/cefa707bca799ff996e97de9cd3c9ed6.jpg\",\n  \"34095.jpg\": \"Aves/Streptopelia senegalensis/50b0872d3cebf4a729ebdc08d07772e3.jpg\",\n  \"34096.jpg\": \"Aves/Streptopelia senegalensis/78ae5cb87cde6d080b53acaba343e4a6.jpg\",\n  \"34098.jpg\": \"Aves/Streptopelia senegalensis/b010f1e20c207c39aab55069ed6bdf84.jpg\",\n  \"34100.jpg\": \"Aves/Streptopelia senegalensis/85491ef6a6644963458fa3f572fdd923.jpg\",\n  \"34105.jpg\": \"Aves/Streptopelia senegalensis/444d5637fe3bf547b3f33e247cd55ac3.jpg\",\n  \"34107.jpg\": \"Aves/Streptopelia senegalensis/266e6a4470424365f43a22c83243a08f.jpg\",\n  \"34108.jpg\": \"Aves/Streptopelia senegalensis/c97d6f6bd6f59153f96f1baf3e6288ce.jpg\",\n  \"34112.jpg\": \"Aves/Streptopelia senegalensis/60b7390de78d4fdb00ef6a4c2ac4cc96.jpg\",\n  \"34115.jpg\": \"Aves/Streptopelia senegalensis/1140ee67b36a1387dad347e5b0e23771.jpg\",\n  \"34118.jpg\": \"Aves/Streptopelia senegalensis/b3604523cd37c0e2f64c17299f13ffae.jpg\",\n  \"34123.jpg\": \"Aves/Streptopelia senegalensis/e607c1f9501f4222620f315c5d18687c.jpg\",\n  \"34124.jpg\": \"Aves/Streptopelia senegalensis/07a9db448a1c276be952a0cedfc3f7e2.jpg\",\n  \"34125.jpg\": \"Aves/Streptopelia senegalensis/cf74244f9b118fd6d5d3c3fdc8d82209.jpg\",\n  \"34131.jpg\": \"Mammalia/Equus asinus/688bb0d7745a2d8d8b58b2b965d4b740.jpg\",\n  \"34134.jpg\": \"Mammalia/Equus asinus/ea5f184a661e4015128215150ec045d4.jpg\",\n  \"34136.jpg\": \"Mammalia/Equus asinus/3a908b04997679ad7abdff794e7d178e.jpg\",\n  \"341382.jpg\": \"Mammalia/Cervus elaphus nannodes/59ba9b62a48984ccd127afd887ee6c01.jpg\",\n  \"341387.jpg\": \"Mammalia/Cervus elaphus nannodes/797d27192fa5cae1f2e0bd9161dc9dcd.jpg\",\n  \"341397.jpg\": \"Mammalia/Cervus elaphus nannodes/e5795f1fc29c24c8a82b3405c48461c3.jpg\",\n  \"341398.jpg\": \"Mammalia/Cervus elaphus nannodes/288399030291820dcac54f3e1b7fac44.jpg\",\n  \"34140.jpg\": \"Mammalia/Equus asinus/a36fee517209285aa0453979e1c9adef.jpg\",\n  \"341416.jpg\": \"Mammalia/Cervus elaphus nannodes/4a5bc6290f87cc43db00d894ff239024.jpg\",\n  \"34148.jpg\": \"Mammalia/Equus asinus/5555f8ca86ecd48c03b90468bf359294.jpg\",\n  \"34154.jpg\": \"Mammalia/Equus asinus/f33a8dc776ec560edf16f6ce12dbf948.jpg\",\n  \"34156.jpg\": \"Mammalia/Equus asinus/b4212338e6a6e4881f49ddbe3efa9612.jpg\",\n  \"341606.jpg\": \"Aves/Porphyrio hochstetteri/f9c42866241a81d7473085f4050379bc.jpg\",\n  \"341608.jpg\": \"Aves/Porphyrio hochstetteri/7af6415aedf6ba04c7a28a3bb76c7adb.jpg\",\n  \"341614.jpg\": \"Mammalia/Urocitellus armatus/03989e68fceb3049d9c4f4988f992730.jpg\",\n  \"341616.jpg\": \"Mammalia/Urocitellus armatus/20d2d9696f8bc7ef825ac69b2a8d3651.jpg\",\n  \"34162.jpg\": \"Aves/Ammodramus nelsoni/0d57d16ed3b5bae7a7a4cd5510de53b9.jpg\",\n  \"341620.jpg\": \"Mammalia/Urocitellus armatus/8de976c96dd56a1eabb209280d9836ab.jpg\",\n  \"341624.jpg\": \"Mammalia/Urocitellus armatus/25e3840ec72be428d0b0363526c4bcb0.jpg\",\n  \"341625.jpg\": \"Mammalia/Urocitellus armatus/cb3dc6d229f1111c1f432cbfc86b6207.jpg\",\n  \"341640.jpg\": \"Insecta/Limenitis lorquini/10650b51c48fdb3a2984b7a6db7eafba.jpg\",\n  \"341641.jpg\": \"Insecta/Limenitis lorquini/a40ad733609094a36f2c6784884ff94b.jpg\",\n  \"34166.jpg\": \"Aves/Ammodramus nelsoni/525ec360ec9ec17eddc76d259618b1b8.jpg\",\n  \"341679.jpg\": \"Insecta/Limenitis lorquini/d2d504c4d41e65e21c7b5431c33d8828.jpg\",\n  \"341680.jpg\": \"Insecta/Limenitis lorquini/ae3683387bd7f4cd53dadb717287161b.jpg\",\n  \"341682.jpg\": \"Insecta/Limenitis lorquini/b1ff75c2e0ad431dfe23f192a873181c.jpg\",\n  \"341687.jpg\": \"Insecta/Limenitis lorquini/0c341dfabcd2e8bd6072b34126554e7b.jpg\",\n  \"341689.jpg\": \"Insecta/Limenitis lorquini/5e960c15e0085aa45908cb4ea53ca6b0.jpg\",\n  \"341694.jpg\": \"Insecta/Limenitis lorquini/6956b7ccf340ae77d614bf5dd183636c.jpg\",\n  \"341702.jpg\": \"Insecta/Limenitis lorquini/1b7a3c60a7721dc32374c8abd0e5f0a4.jpg\",\n  \"341706.jpg\": \"Insecta/Limenitis lorquini/da70fbeb6e750d96fbd7b14f74e91d6c.jpg\",\n  \"341709.jpg\": \"Insecta/Limenitis lorquini/4209d6a9f2b954f00cb1375854d510f7.jpg\",\n  \"34171.jpg\": \"Aves/Ammodramus nelsoni/f5e97af13de6ba0b064ba23dbfac7e1d.jpg\",\n  \"341718.jpg\": \"Insecta/Limenitis lorquini/7d80b1cdb3c3d85a4850b225f491c1b6.jpg\",\n  \"341733.jpg\": \"Insecta/Limenitis lorquini/6659f2517f6e30c8d80b263fae788571.jpg\",\n  \"341734.jpg\": \"Insecta/Limenitis lorquini/f700d2315c6918f8d2618928ac4f6969.jpg\",\n  \"341743.jpg\": \"Insecta/Limenitis lorquini/2db5d5133ff606aa0dba8e27437f04d8.jpg\",\n  \"34176.jpg\": \"Aves/Ammodramus nelsoni/8220ee92ff5599cc06a5c93608dc89a1.jpg\",\n  \"341764.jpg\": \"Insecta/Limenitis lorquini/2b84c38bdd4028d67c77c61ca4d22f79.jpg\",\n  \"341774.jpg\": \"Insecta/Limenitis lorquini/b789f382e089052a0e8d0ccff257e0cb.jpg\",\n  \"341775.jpg\": \"Insecta/Limenitis lorquini/741300ba880f5555ad0917eff779780b.jpg\",\n  \"341776.jpg\": \"Insecta/Limenitis lorquini/f848427307589c0da1fd57b23be83fbf.jpg\",\n  \"341789.jpg\": \"Mammalia/Panthera onca/2b99e6c32290177fa82fd30766a141b5.jpg\",\n  \"341802.jpg\": \"Mammalia/Panthera onca/948e9de428934084fabc7462fa49765e.jpg\",\n  \"341803.jpg\": \"Mammalia/Panthera onca/f777d708de2696549f00fc947101fc2a.jpg\",\n  \"34189.jpg\": \"Mammalia/Cervus elaphus/ca1d07e925a4e861702fcb75432d3303.jpg\",\n  \"34199.jpg\": \"Mammalia/Cervus elaphus/af92ca2a2658862cb7967f3e0f32e6fb.jpg\",\n  \"342.jpg\": \"Insecta/Coleomegilla maculata/259df96a4d92082f6785ecbafd2fc5fa.jpg\",\n  \"342016.jpg\": \"Reptilia/Coluber taeniatus/4c88614c9e1caaad978d004f19496e0d.jpg\",\n  \"342020.jpg\": \"Reptilia/Coluber taeniatus/04f74a073f23970e80dccebd0401931d.jpg\",\n  \"342026.jpg\": \"Reptilia/Coluber taeniatus/11d178447b23cdb6cac6ce5d317bcfc9.jpg\",\n  \"342031.jpg\": \"Reptilia/Coluber taeniatus/e86fdfec45a07c956d9f65bfd968fe67.jpg\",\n  \"342034.jpg\": \"Reptilia/Coluber taeniatus/8c9e568999b77ef52f5cfb0ce4fc5dd8.jpg\",\n  \"342041.jpg\": \"Aves/Vireo solitarius/81c75bf930867836849b209246bdd9ee.jpg\",\n  \"342042.jpg\": \"Aves/Vireo solitarius/afb9f660eac0a18f3b59fe6329efd456.jpg\",\n  \"342043.jpg\": \"Aves/Vireo solitarius/8510f023a8f3f875b6f067d30d7f060d.jpg\",\n  \"342044.jpg\": \"Aves/Vireo solitarius/691008915d209ebfe6791fcd0a7568d1.jpg\",\n  \"342046.jpg\": \"Aves/Vireo solitarius/e1f38a939b353677fc86dd497815ba3e.jpg\",\n  \"342051.jpg\": \"Aves/Vireo solitarius/e4dcdf4a07857fde012fa44f8d69d305.jpg\",\n  \"342052.jpg\": \"Aves/Vireo solitarius/5a7814fe4692be792165cdc6f86539c0.jpg\",\n  \"34206.jpg\": \"Mammalia/Cervus elaphus/d3975d7e7b0a8f78866aba8ef0c75ad3.jpg\",\n  \"342060.jpg\": \"Aves/Vireo solitarius/22c6bd043a8712da79681148d3be221e.jpg\",\n  \"342061.jpg\": \"Aves/Vireo solitarius/9d7fde2bbb6673866ec980ee24c20ce7.jpg\",\n  \"342063.jpg\": \"Aves/Vireo solitarius/0a1e8b3f9bf10755842721a184d12334.jpg\",\n  \"342064.jpg\": \"Aves/Vireo solitarius/4a2a22822c9905be3ec6a67c2b696681.jpg\",\n  \"342065.jpg\": \"Aves/Vireo solitarius/db65c309b66862120c35a728d0002756.jpg\",\n  \"342066.jpg\": \"Aves/Vireo solitarius/beb4d9c310905d39b27b9df384488ae8.jpg\",\n  \"342067.jpg\": \"Aves/Vireo solitarius/2a1ae5d841c6a36bf1717a50098a83ec.jpg\",\n  \"342073.jpg\": \"Aves/Vireo solitarius/71d809ca2fbf9ca3f57e82b88927fd19.jpg\",\n  \"342074.jpg\": \"Aves/Vireo solitarius/80a2fa623702e9eb9f170a1bb83b6813.jpg\",\n  \"342076.jpg\": \"Aves/Vireo solitarius/0e6a9424d100fa2b4744d8b313432506.jpg\",\n  \"342077.jpg\": \"Aves/Vireo solitarius/8cdb0afbd87643aaa0366e0f520d1663.jpg\",\n  \"342082.jpg\": \"Aves/Vireo solitarius/65bbaba3ed85a0aead184c6df869f423.jpg\",\n  \"342086.jpg\": \"Aves/Vireo solitarius/ee0245a4ba100d611084a0c18e47a9e2.jpg\",\n  \"342094.jpg\": \"Aves/Vireo solitarius/0df9718b7350a8398a0cbe6f7748160f.jpg\",\n  \"342095.jpg\": \"Aves/Vireo solitarius/d0e0539ca8ae07cdf445afd50824e5f3.jpg\",\n  \"342108.jpg\": \"Aves/Vireo solitarius/a98ddb730f4e1d0f8455ee15efc4dff8.jpg\",\n  \"342113.jpg\": \"Aves/Vireo solitarius/c5844b4336be53e8a689c8a3600c81e9.jpg\",\n  \"342114.jpg\": \"Aves/Vireo solitarius/1edc863c8c9c28426cd652a0a95cb07a.jpg\",\n  \"342127.jpg\": \"Aves/Merops pusillus/c475104efbc92c5fa43a108986ce88e2.jpg\",\n  \"342128.jpg\": \"Aves/Merops pusillus/71176304bc2edb5a0e00d5c58563f31e.jpg\",\n  \"342129.jpg\": \"Aves/Merops pusillus/50cecd1c33ad679b7a2f058303a3e3e9.jpg\",\n  \"342137.jpg\": \"Aves/Merops pusillus/dcb2568539e5db8c36f102347e8bcde1.jpg\",\n  \"342138.jpg\": \"Aves/Merops pusillus/fdce133fc56e23278aeef2e8a7e141f2.jpg\",\n  \"342144.jpg\": \"Aves/Phoebastria nigripes/00ab670a1b77db90fc9abc2663a44c81.jpg\",\n  \"342145.jpg\": \"Aves/Phoebastria nigripes/9baae966689f4097d1b2e1a49b5737f5.jpg\",\n  \"342146.jpg\": \"Aves/Phoebastria nigripes/7d786d9bcf480de5258e1dee2b1ecd42.jpg\",\n  \"342147.jpg\": \"Aves/Phoebastria nigripes/a4b808ada4a86770b9cba9ed4b1534d1.jpg\",\n  \"34215.jpg\": \"Mammalia/Cervus elaphus/b950837f040c923549b26a0e5eba2b22.jpg\",\n  \"342152.jpg\": \"Aves/Phoebastria nigripes/3d089fff15566b4e544ee863ade06eea.jpg\",\n  \"342153.jpg\": \"Aves/Phoebastria nigripes/4d461f19e8271c84dc9b8f3afbcaeba9.jpg\",\n  \"342154.jpg\": \"Aves/Phoebastria nigripes/a93a3ac7713735301098e658c9837096.jpg\",\n  \"342155.jpg\": \"Aves/Phoebastria nigripes/149c9f130cf3a4ccf088628d43e53838.jpg\",\n  \"342158.jpg\": \"Aves/Phoebastria nigripes/5aa11c77515136b4eb8ff85f3903a4ed.jpg\",\n  \"342159.jpg\": \"Aves/Phoebastria nigripes/9996e2a5e95433be8ce6cc2ab22a5eb3.jpg\",\n  \"342166.jpg\": \"Aves/Phoebastria nigripes/3fbecde08193eccb862a1ae0c5017e5c.jpg\",\n  \"342167.jpg\": \"Aves/Phoebastria nigripes/9d8b63c2560b910045137ff074d8e437.jpg\",\n  \"342169.jpg\": \"Aves/Phoebastria nigripes/7bbf96ccc6a2dc5a2eeb00bf6d9c05e1.jpg\",\n  \"342170.jpg\": \"Aves/Phoebastria nigripes/b412f6431a31902dcaaf4a1232c5d38a.jpg\",\n  \"342171.jpg\": \"Aves/Phoebastria nigripes/7e72699d5266e8998aefa10a3e7750d1.jpg\",\n  \"342173.jpg\": \"Aves/Phoebastria nigripes/a369c23ee9430cd72270d3de2f7e7ec3.jpg\",\n  \"342174.jpg\": \"Aves/Phoebastria nigripes/fcdd1889878e93780a01550988a2b766.jpg\",\n  \"342175.jpg\": \"Aves/Phoebastria nigripes/341cb353ecceaf286699fcd8e3fab7ab.jpg\",\n  \"342176.jpg\": \"Aves/Phoebastria nigripes/8f76ddd23b65e305f25eefe4bf1f00d4.jpg\",\n  \"342179.jpg\": \"Aves/Phoebastria nigripes/f240b06513f80028a763f8433d448eab.jpg\",\n  \"342189.jpg\": \"Actinopterygii/Chaetodon auriga/609bad678eb4e5ddde419fc2a922f01f.jpg\",\n  \"342208.jpg\": \"Actinopterygii/Chaetodon auriga/70537c5b58fb983e250a5692897173d7.jpg\",\n  \"342218.jpg\": \"Insecta/Sympetrum ambiguum/14ae9292d56a1a2b2fb7f3b14eb55636.jpg\",\n  \"342224.jpg\": \"Insecta/Sympetrum ambiguum/9e5bc2f122bea03bd234fb15f15e57c9.jpg\",\n  \"342226.jpg\": \"Insecta/Sympetrum ambiguum/45730b4330714a94c19db826ff759c95.jpg\",\n  \"342227.jpg\": \"Insecta/Sympetrum ambiguum/e04d8f3076e4f5cfa7fc9c6cb461456d.jpg\",\n  \"342233.jpg\": \"Insecta/Sympetrum ambiguum/b868a7fad03dc52ab8619c4cab275eef.jpg\",\n  \"342234.jpg\": \"Insecta/Sympetrum ambiguum/097256068b5b6e5ef2c5bcb9005aaf1e.jpg\",\n  \"342236.jpg\": \"Insecta/Sympetrum ambiguum/3a26ce045c9f972d19d9cbd10766382b.jpg\",\n  \"342238.jpg\": \"Insecta/Sympetrum ambiguum/4d40fae447ac3c541b856c2bd0423632.jpg\",\n  \"342245.jpg\": \"Insecta/Sympetrum ambiguum/8b8104788cad936a93f9d73dfcd6e0fc.jpg\",\n  \"342247.jpg\": \"Insecta/Sympetrum ambiguum/f444f6d9619c0f0d63d962f05670f022.jpg\",\n  \"342249.jpg\": \"Insecta/Sympetrum ambiguum/afcc9561582612cd9297899630aff3a3.jpg\",\n  \"342256.jpg\": \"Insecta/Sympetrum ambiguum/519a390a45a3d81cb86fbe71cc625e19.jpg\",\n  \"342257.jpg\": \"Insecta/Sympetrum ambiguum/d7df657da514d9e46b301f975d6ebea2.jpg\",\n  \"342258.jpg\": \"Insecta/Sympetrum ambiguum/683d3b803a17992ca6b786dcd1bad38e.jpg\",\n  \"342261.jpg\": \"Insecta/Sympetrum ambiguum/fc9bd39e3d93411bdf4ad590f2354718.jpg\",\n  \"342262.jpg\": \"Insecta/Sympetrum ambiguum/baf8a12ba789f2d92c79506725a4462c.jpg\",\n  \"342263.jpg\": \"Insecta/Sympetrum ambiguum/41936081ea3cecd02507d4fc3eafa176.jpg\",\n  \"342264.jpg\": \"Insecta/Sympetrum ambiguum/9bea283a270abafe20bf4c791110ced5.jpg\",\n  \"342289.jpg\": \"Reptilia/Contia tenuis/b5c8957f8eebdec0bff323164d0cf545.jpg\",\n  \"342296.jpg\": \"Reptilia/Contia tenuis/37ae0ea8512ff3bd9fd9a98fa713aa73.jpg\",\n  \"342297.jpg\": \"Reptilia/Contia tenuis/70c444f5ae3190ae4b7235b773390700.jpg\",\n  \"342298.jpg\": \"Reptilia/Contia tenuis/4c1b106a07e86591bdb239ad6cd05283.jpg\",\n  \"342299.jpg\": \"Reptilia/Contia tenuis/470f6c846ed6fe26345679dd5f5ce979.jpg\",\n  \"342305.jpg\": \"Reptilia/Contia tenuis/942768cfc2ed6dff0ba9e5f2dcbd7d5d.jpg\",\n  \"342308.jpg\": \"Reptilia/Contia tenuis/7378ff1e4322bd630702820b6f687524.jpg\",\n  \"342309.jpg\": \"Reptilia/Contia tenuis/48db7d8918fd263ecf72c562aa3d1a03.jpg\",\n  \"342311.jpg\": \"Reptilia/Contia tenuis/854b16258763c6c114de746608dca87c.jpg\",\n  \"342317.jpg\": \"Reptilia/Contia tenuis/46e3776464e7652076dfb163bb22c8ec.jpg\",\n  \"342318.jpg\": \"Reptilia/Contia tenuis/05bb8df1d0e41ba30cdfae711c5870b0.jpg\",\n  \"342321.jpg\": \"Mammalia/Microtus pennsylvanicus/cecce216044d590c249772c840bac02b.jpg\",\n  \"342327.jpg\": \"Mammalia/Microtus pennsylvanicus/54790ec59da0361e3b9bdb082c565b6e.jpg\",\n  \"342328.jpg\": \"Mammalia/Microtus pennsylvanicus/151b7cd60e710132a0aa55f5ebd3ee90.jpg\",\n  \"342332.jpg\": \"Mammalia/Microtus pennsylvanicus/07377dce3b5c525d1b7b3df426fb0b95.jpg\",\n  \"34234.jpg\": \"Mammalia/Cervus elaphus/62b9331d5ff34e30638844551126db21.jpg\",\n  \"342416.jpg\": \"Arachnida/Misumena vatia/1b32fa7dad5a5069ee3db92f5cfb73be.jpg\",\n  \"342421.jpg\": \"Arachnida/Misumena vatia/e1c56e7c2ca54bc9c1e019d9f5ab75fe.jpg\",\n  \"342427.jpg\": \"Arachnida/Misumena vatia/97f43fcb2fe5678826f7e2473df90b28.jpg\",\n  \"342434.jpg\": \"Arachnida/Misumena vatia/227b83381d884c7b7d2331e61b386db7.jpg\",\n  \"342435.jpg\": \"Arachnida/Misumena vatia/2aafa2bf710af2fd47f796e1c082212e.jpg\",\n  \"342436.jpg\": \"Arachnida/Misumena vatia/c69c549c4cdd29dda2f486db1b1b8b54.jpg\",\n  \"342439.jpg\": \"Arachnida/Misumena vatia/a05625507bd42cc63b0dd370cc136079.jpg\",\n  \"342440.jpg\": \"Arachnida/Misumena vatia/b19a3a12e93f4f4a4e34f985f4acdb94.jpg\",\n  \"342441.jpg\": \"Arachnida/Misumena vatia/419f7da0ae932fde349bb1941043d913.jpg\",\n  \"342444.jpg\": \"Arachnida/Misumena vatia/dca61a9266163fa17e00e4e44830b38d.jpg\",\n  \"342455.jpg\": \"Arachnida/Misumena vatia/74ef0983a99f197e2576bbc2fa1b9a4b.jpg\",\n  \"342459.jpg\": \"Arachnida/Misumena vatia/a707dbeec2564dd52fe386da45f500b9.jpg\",\n  \"342467.jpg\": \"Arachnida/Misumena vatia/22b3849a439905a8cd8306abce770773.jpg\",\n  \"342472.jpg\": \"Arachnida/Misumena vatia/76f01c1170362a90700acbaf7f0d0882.jpg\",\n  \"342477.jpg\": \"Arachnida/Misumena vatia/823c5e8aa9eade8cacecc42e1c7af80d.jpg\",\n  \"342478.jpg\": \"Arachnida/Misumena vatia/d983b43ce93b5641bdeb20938491a0f7.jpg\",\n  \"342479.jpg\": \"Arachnida/Misumena vatia/28e6a1aaec345ae2b71a4f7476bd96fb.jpg\",\n  \"342480.jpg\": \"Arachnida/Misumena vatia/2814b47c17bbc7e7bbf5160babb145cf.jpg\",\n  \"342485.jpg\": \"Mollusca/Lissachatina fulica/d8c3620cbeb03daabe2ad433ad9c7172.jpg\",\n  \"342486.jpg\": \"Mollusca/Lissachatina fulica/562744900479e0e3d2a6e7f9fb885294.jpg\",\n  \"342487.jpg\": \"Mollusca/Lissachatina fulica/84ced537144c3f827c46c4f7d6c1df19.jpg\",\n  \"342488.jpg\": \"Mollusca/Lissachatina fulica/ef524a2ddffd24742fc1a25058efb206.jpg\",\n  \"342492.jpg\": \"Mollusca/Lissachatina fulica/08f44800062cbdb800348729cf988aca.jpg\",\n  \"342493.jpg\": \"Mollusca/Lissachatina fulica/cf70f13687ceec0123a6e8f1e6d902fe.jpg\",\n  \"342496.jpg\": \"Mollusca/Lissachatina fulica/66e31848803f29104aa582cd31273006.jpg\",\n  \"342500.jpg\": \"Mollusca/Lissachatina fulica/0e6fdc3f77820e0ad4d46f4f947fb5fb.jpg\",\n  \"342505.jpg\": \"Mollusca/Lissachatina fulica/c9a84eb443d6289465842c13c7f05afd.jpg\",\n  \"342506.jpg\": \"Mollusca/Lissachatina fulica/7f5c8616414a5604d34e7d8297cbc397.jpg\",\n  \"342510.jpg\": \"Mollusca/Lissachatina fulica/b88745803c7f4464527c409601bff5e0.jpg\",\n  \"342514.jpg\": \"Mollusca/Lissachatina fulica/6ea3346cf2fa1d7b2c90607c1dc76b6e.jpg\",\n  \"342515.jpg\": \"Mollusca/Lissachatina fulica/43dff4487a635346042c90e8dde25b64.jpg\",\n  \"342519.jpg\": \"Mollusca/Lissachatina fulica/7f759bbaacfb2d8c139865f7da965f24.jpg\",\n  \"342528.jpg\": \"Mollusca/Lissachatina fulica/74aa392cebf77256525efe11d5bef153.jpg\",\n  \"342529.jpg\": \"Mollusca/Lissachatina fulica/32b71c544f7ab21a1ded0e6bc4d4bfcb.jpg\",\n  \"342530.jpg\": \"Mollusca/Lissachatina fulica/cfa01e252b6f50f153693e3268fd997c.jpg\",\n  \"34265.jpg\": \"Mammalia/Cervus elaphus/45dbf36369cd9c2c1e908a0ecb063331.jpg\",\n  \"342663.jpg\": \"Aves/Pelecanus occidentalis californicus/256a9f311841f0528543c1c3b5607cd6.jpg\",\n  \"342675.jpg\": \"Aves/Pelecanus occidentalis californicus/dd46c6a60222a04fc5e3e2fde21e7440.jpg\",\n  \"342678.jpg\": \"Aves/Pelecanus occidentalis californicus/4a4a6b35ff1639d39c1a249550db95ee.jpg\",\n  \"34268.jpg\": \"Mammalia/Cervus elaphus/2fb1e04adc003eebcede9ea95afe6c2f.jpg\",\n  \"342687.jpg\": \"Aves/Pelecanus occidentalis californicus/1f93a165a7c5563bdf8d5723a9ae8dd2.jpg\",\n  \"34269.jpg\": \"Mammalia/Cervus elaphus/b7fb11c7ae835c481ada468bf684c2e1.jpg\",\n  \"34270.jpg\": \"Mammalia/Cervus elaphus/9a7b7e581d3d7b61384d60c173e9e1c3.jpg\",\n  \"34273.jpg\": \"Mammalia/Cervus elaphus/29bc0dcbc465528c500a32991303578d.jpg\",\n  \"342736.jpg\": \"Aves/Pelecanus occidentalis californicus/b9afe02773b315d86ee27ae1b696e43d.jpg\",\n  \"34274.jpg\": \"Mammalia/Cervus elaphus/bae217159fd7745abcb1a48aeaac137e.jpg\",\n  \"342742.jpg\": \"Aves/Pelecanus occidentalis californicus/b3e5979021565496062412443ad37a50.jpg\",\n  \"342749.jpg\": \"Aves/Pelecanus occidentalis californicus/107830ee9d0fe05fe33beb14df411a4a.jpg\",\n  \"342773.jpg\": \"Aves/Pelecanus occidentalis californicus/161b54fa1a90a7e49683c016a8928b50.jpg\",\n  \"342777.jpg\": \"Aves/Pelecanus occidentalis californicus/7823b65b35104e42ebe298ba291e0437.jpg\",\n  \"342779.jpg\": \"Aves/Pelecanus occidentalis californicus/b564e7820b1fa1a9a01db37a61bca2a4.jpg\",\n  \"342794.jpg\": \"Aves/Pelecanus occidentalis californicus/7bab677db42da9768936f6c2c4c4e467.jpg\",\n  \"342798.jpg\": \"Aves/Pelecanus occidentalis californicus/8cdc25e9c88f2e6002ed8ba3b6e267a0.jpg\",\n  \"342799.jpg\": \"Aves/Pelecanus occidentalis californicus/fc3a717d8dbb91a2b1a4a9a47b5efbcf.jpg\",\n  \"34286.jpg\": \"Mammalia/Cervus elaphus/2d213b7f2f299513d8552991155d0fe7.jpg\",\n  \"343.jpg\": \"Insecta/Coleomegilla maculata/7f17287173d94220da534f64e87b1862.jpg\",\n  \"34304.jpg\": \"Mammalia/Cervus elaphus/403408d83bbea1e355c667e2381a2972.jpg\",\n  \"343191.jpg\": \"Mollusca/Cepaea hortensis/b99b1c3b376df169cfc09f477e2911e5.jpg\",\n  \"343192.jpg\": \"Mollusca/Cepaea hortensis/20947329c0602585fdbea9e6e0b59023.jpg\",\n  \"343198.jpg\": \"Mollusca/Cepaea hortensis/4f915347c278c209a4a63b72707570ab.jpg\",\n  \"343207.jpg\": \"Mollusca/Cepaea hortensis/26c8ff298ee9c4d047123f4955edb819.jpg\",\n  \"343251.jpg\": \"Aves/Clangula hyemalis/cccda134ee11ef2b335c48144f71cbe3.jpg\",\n  \"343252.jpg\": \"Aves/Clangula hyemalis/7b675a45cc810f43f5e2e2b6ac6606b4.jpg\",\n  \"343256.jpg\": \"Aves/Clangula hyemalis/858540701995d995f40022c091392a2f.jpg\",\n  \"343275.jpg\": \"Aves/Clangula hyemalis/f1cd4f88770bc46b8c6a2def05036fbf.jpg\",\n  \"343277.jpg\": \"Aves/Clangula hyemalis/947db3935a7453a5d78b20955cdb5dc6.jpg\",\n  \"343278.jpg\": \"Aves/Clangula hyemalis/48b85d2af007b04f826638589f8b0fc9.jpg\",\n  \"343280.jpg\": \"Aves/Clangula hyemalis/ecfa7cecadfe9480c52c0b7fb00f8006.jpg\",\n  \"343314.jpg\": \"Aves/Clangula hyemalis/6e36ff76bd8fab36f59d5ef2ef73a648.jpg\",\n  \"343336.jpg\": \"Aves/Sicalis flaveola/d18b243464c224b90c94d4abf9698d49.jpg\",\n  \"343338.jpg\": \"Aves/Sicalis flaveola/711e5ce693eee89013508a2b3dd8e459.jpg\",\n  \"343341.jpg\": \"Aves/Sicalis flaveola/131310be1fc5a96ddd40c8fb38c1e124.jpg\",\n  \"343355.jpg\": \"Aves/Sicalis flaveola/c60f5e85f23ff42315a6c4d5cde29122.jpg\",\n  \"343364.jpg\": \"Aves/Sicalis flaveola/67aad1d649e0689b43c8b6262634d72e.jpg\",\n  \"343413.jpg\": \"Aves/Seiurus aurocapilla/f59b2a65dc12086ef445d2d4ecf409b4.jpg\",\n  \"343435.jpg\": \"Aves/Seiurus aurocapilla/6d9b7b6628bf9325b69921944019e96b.jpg\",\n  \"343462.jpg\": \"Aves/Seiurus aurocapilla/a477d3c3d5ab4179c21326d3a2ee85b8.jpg\",\n  \"343463.jpg\": \"Aves/Seiurus aurocapilla/e4c4782188136b3f3388f3b287bbc4ca.jpg\",\n  \"343474.jpg\": \"Aves/Seiurus aurocapilla/38f978aa529ffcec02f0ff8d2dd795f9.jpg\",\n  \"343477.jpg\": \"Aves/Seiurus aurocapilla/9398870b71506cc6d9000e8c4348b8a0.jpg\",\n  \"343478.jpg\": \"Aves/Seiurus aurocapilla/13b9956321ce58e11c310eca2aa5b7f2.jpg\",\n  \"343479.jpg\": \"Aves/Seiurus aurocapilla/218574e952817207e25016170653391a.jpg\",\n  \"343485.jpg\": \"Aves/Seiurus aurocapilla/ce3173f87fbfb67ad72a84feaf99cf59.jpg\",\n  \"343489.jpg\": \"Aves/Seiurus aurocapilla/7da6a8ae4c6255e9351e735c8a4df573.jpg\",\n  \"343490.jpg\": \"Aves/Seiurus aurocapilla/f891247f07289d861028a95f3be20041.jpg\",\n  \"343491.jpg\": \"Aves/Seiurus aurocapilla/89b8210980b2f4620cfec9cdbfc95e90.jpg\",\n  \"343505.jpg\": \"Aves/Seiurus aurocapilla/39e5e7ba10298b1c3f4dd51a3af1ffec.jpg\",\n  \"343507.jpg\": \"Aves/Seiurus aurocapilla/188eefbadbca2cc94a4b8f1108aec9f1.jpg\",\n  \"343509.jpg\": \"Aves/Seiurus aurocapilla/b87d795622d978516469827cc2947cad.jpg\",\n  \"343510.jpg\": \"Aves/Seiurus aurocapilla/2e2004086a7c31dda51476a3447908a7.jpg\",\n  \"343519.jpg\": \"Aves/Seiurus aurocapilla/47f848ad235cd4566bae70efff7f5c8b.jpg\",\n  \"343520.jpg\": \"Aves/Seiurus aurocapilla/b2c79e7c3f2d24eb54567f3ddf27b9fc.jpg\",\n  \"343527.jpg\": \"Aves/Seiurus aurocapilla/ad3a68ac440741299c4337d783606644.jpg\",\n  \"343529.jpg\": \"Aves/Seiurus aurocapilla/f5833b308f6595f25a355176172b3965.jpg\",\n  \"343537.jpg\": \"Aves/Calidris pusilla/e240e47a0766f7ab84262c0c0d167a0b.jpg\",\n  \"343538.jpg\": \"Aves/Calidris pusilla/1807713ff8cf04d629203c89918c5ca0.jpg\",\n  \"343543.jpg\": \"Aves/Calidris pusilla/4c6ed1cc7ca9ffd3fe924c74588e7412.jpg\",\n  \"343544.jpg\": \"Aves/Calidris pusilla/5b8d192419259bfdf29480fe2d5f0383.jpg\",\n  \"343545.jpg\": \"Aves/Calidris pusilla/d9d56c5761fdbe11d3585925b45dcd87.jpg\",\n  \"343548.jpg\": \"Aves/Calidris pusilla/2ede8e1ae6635a5657cb4c73fb133d34.jpg\",\n  \"343555.jpg\": \"Aves/Calidris pusilla/df5e2e29cb21dab45c0bb9422432c6f8.jpg\",\n  \"343556.jpg\": \"Aves/Calidris pusilla/ee87300bf0fcb059a3fddb271fc18efa.jpg\",\n  \"343573.jpg\": \"Aves/Calidris pusilla/a05ba869bf826177d5212b1ba515091e.jpg\",\n  \"343577.jpg\": \"Aves/Calidris pusilla/475d6fc1eab450ea73ef606aba6bf543.jpg\",\n  \"343580.jpg\": \"Aves/Calidris pusilla/b4a6684ee7250cb346d0502976e4787a.jpg\",\n  \"343581.jpg\": \"Aves/Calidris pusilla/ee060352eb4e54cc956911e859499e16.jpg\",\n  \"343586.jpg\": \"Aves/Calidris pusilla/005e28850cb4a04358b3598643f20ad2.jpg\",\n  \"343590.jpg\": \"Aves/Calidris pusilla/012474cb9f0fd047a71b883010a44014.jpg\",\n  \"343591.jpg\": \"Aves/Calidris pusilla/40ab0024553a875922f69a364bc44229.jpg\",\n  \"343594.jpg\": \"Aves/Calidris pusilla/488c04558bccb6095f3ebbdfa99e9ef1.jpg\",\n  \"343599.jpg\": \"Aves/Calidris pusilla/72bde4fc18da53516c5cfa2ca6105bd0.jpg\",\n  \"343608.jpg\": \"Insecta/Dorcus parallelipipedus/4d08d243cda70ad4db8548a8c447545a.jpg\",\n  \"343611.jpg\": \"Insecta/Dorcus parallelipipedus/b6aef6298b8f578a41ac43b182375736.jpg\",\n  \"343612.jpg\": \"Insecta/Dorcus parallelipipedus/8012c30aba5fafd859233352da469454.jpg\",\n  \"343613.jpg\": \"Insecta/Dorcus parallelipipedus/ff31b9d8a0b5e01b7fecfe85d1e9909d.jpg\",\n  \"343616.jpg\": \"Insecta/Dorcus parallelipipedus/ac461d09575b293dd8575c2a900b17e1.jpg\",\n  \"343617.jpg\": \"Insecta/Dorcus parallelipipedus/43004b38a34050429dc4e7de1dda53c8.jpg\",\n  \"343620.jpg\": \"Insecta/Dorcus parallelipipedus/8a9104a436b926e7c0d4701206e675e0.jpg\",\n  \"343628.jpg\": \"Insecta/Copaeodes aurantiaca/e6e68fda9f1487204fe756770725bc19.jpg\",\n  \"343630.jpg\": \"Insecta/Copaeodes aurantiaca/df9b34c5290d15230b57e5884ca3bf9e.jpg\",\n  \"343635.jpg\": \"Insecta/Copaeodes aurantiaca/ff642213580cabb6634b695ae0f33554.jpg\",\n  \"343637.jpg\": \"Insecta/Copaeodes aurantiaca/a99ab58db2abbf4f9b1d483d65afa28f.jpg\",\n  \"343638.jpg\": \"Insecta/Copaeodes aurantiaca/a99b3a73adbc98de2f259c376c8af419.jpg\",\n  \"343640.jpg\": \"Insecta/Copaeodes aurantiaca/7d63074a5a5c449cbae8321f4a89b2a2.jpg\",\n  \"343643.jpg\": \"Insecta/Copaeodes aurantiaca/3d27f13271b41681c35aeb0daf85c269.jpg\",\n  \"343648.jpg\": \"Insecta/Copaeodes aurantiaca/5236bfd0d53f48a052492436e1eadbb9.jpg\",\n  \"343661.jpg\": \"Insecta/Odontotaenius disjunctus/925f422e39f7f6f1f2f0c300603e26e7.jpg\",\n  \"343664.jpg\": \"Insecta/Odontotaenius disjunctus/e614fd74234c45ec5cc0aa8cd86ac3ab.jpg\",\n  \"343671.jpg\": \"Insecta/Odontotaenius disjunctus/45e2a3ac38e0d3e4eea8dde24fcb7b63.jpg\",\n  \"343672.jpg\": \"Insecta/Odontotaenius disjunctus/f5c6656e392a75158c82e404c367cabd.jpg\",\n  \"343676.jpg\": \"Insecta/Odontotaenius disjunctus/80aed9b1569cc7c9c243e617a57d4743.jpg\",\n  \"343679.jpg\": \"Insecta/Odontotaenius disjunctus/28df3d6b0319f59fdcc7ce0abb0c7860.jpg\",\n  \"343680.jpg\": \"Insecta/Odontotaenius disjunctus/0f953937ed9a4576dd9a5863c013d722.jpg\",\n  \"343685.jpg\": \"Insecta/Odontotaenius disjunctus/b139a8dd311e0adc3a894d70d944cd6c.jpg\",\n  \"343686.jpg\": \"Insecta/Odontotaenius disjunctus/cf36ba9a2cba0828767131d5893cc692.jpg\",\n  \"343687.jpg\": \"Insecta/Odontotaenius disjunctus/15f602761020b22129f061ac735fc07b.jpg\",\n  \"343689.jpg\": \"Insecta/Odontotaenius disjunctus/b73b3676405b7acba322a91dadf1f662.jpg\",\n  \"343690.jpg\": \"Insecta/Odontotaenius disjunctus/40957ba9a30ef705282928ad1dbeed4a.jpg\",\n  \"343701.jpg\": \"Insecta/Odontotaenius disjunctus/1c064fec29a6bb71db1434232868babc.jpg\",\n  \"343714.jpg\": \"Insecta/Odontotaenius disjunctus/3888aa3fd22d12f923454456d4cf8089.jpg\",\n  \"343721.jpg\": \"Insecta/Odontotaenius disjunctus/fbbd3a4b5d98e727bbc0f751d326ce5c.jpg\",\n  \"343722.jpg\": \"Insecta/Odontotaenius disjunctus/6fbdb080b469b7367b9aafa5e9e8be62.jpg\",\n  \"343723.jpg\": \"Insecta/Odontotaenius disjunctus/3499ccdbab949eef0ef779fa759c9917.jpg\",\n  \"343729.jpg\": \"Insecta/Odontotaenius disjunctus/d464094e55734942206c5dd8e2dc4b0c.jpg\",\n  \"343736.jpg\": \"Insecta/Odontotaenius disjunctus/1d81c8003f87ae3549d79db6df309f16.jpg\",\n  \"343738.jpg\": \"Insecta/Odontotaenius disjunctus/c6b1066bbe1083604fe251a235ccfc77.jpg\",\n  \"343741.jpg\": \"Insecta/Odontotaenius disjunctus/ac852a61a1cd181c602bff0ae80d5a0a.jpg\",\n  \"34381.jpg\": \"Animalia/Bipalium kewense/1112e15d1ba0d9f6de08d8d1304ba453.jpg\",\n  \"34383.jpg\": \"Animalia/Bipalium kewense/6ebe9c547d681c5438a6b91385d9fb60.jpg\",\n  \"34387.jpg\": \"Insecta/Megacyllene robiniae/6a4fc0499266e5066556772d23a39060.jpg\",\n  \"343906.jpg\": \"Mammalia/Sylvilagus floridanus/68ad34616ea9257e8de085d66c111119.jpg\",\n  \"343917.jpg\": \"Mammalia/Sylvilagus floridanus/527fc0f2d46d0d88b5866c32b0865027.jpg\",\n  \"343930.jpg\": \"Mammalia/Sylvilagus floridanus/b0cf0b19b95751db783c93249c72695a.jpg\",\n  \"34395.jpg\": \"Insecta/Megacyllene robiniae/d9380d6c6017987584bf446841f76c09.jpg\",\n  \"343950.jpg\": \"Mammalia/Sylvilagus floridanus/13f318edf57e852182f04cbaee768001.jpg\",\n  \"34396.jpg\": \"Insecta/Megacyllene robiniae/2de145bc4f032ad39433c927db37fa67.jpg\",\n  \"34397.jpg\": \"Insecta/Megacyllene robiniae/7e3f0ba1441d02a5f1a16dbd3ecd5707.jpg\",\n  \"343981.jpg\": \"Mammalia/Sylvilagus floridanus/dc36976a4e45daad8d232d9918a8518d.jpg\",\n  \"343985.jpg\": \"Mammalia/Sylvilagus floridanus/356e2514199eb1584d9a99a68611fbf1.jpg\",\n  \"343996.jpg\": \"Mammalia/Sylvilagus floridanus/7f9e081944547224f1a568a9d1f53ee1.jpg\",\n  \"34400.jpg\": \"Insecta/Megacyllene robiniae/728c56215bfacc9a94c779e220660cd3.jpg\",\n  \"344008.jpg\": \"Mammalia/Sylvilagus floridanus/1858f6b605785b7eafb648d9dcd1c453.jpg\",\n  \"34401.jpg\": \"Insecta/Megacyllene robiniae/c8aed18c3e79138ed3070c4b884af833.jpg\",\n  \"344011.jpg\": \"Mammalia/Sylvilagus floridanus/122eb99052d9b78234c1068f709b64b6.jpg\",\n  \"34402.jpg\": \"Insecta/Megacyllene robiniae/969873f94a2f20419be5f68f3f01cd11.jpg\",\n  \"344034.jpg\": \"Mammalia/Sylvilagus floridanus/bf5ad90589e4797fed3d9168b8feb15d.jpg\",\n  \"344035.jpg\": \"Mammalia/Sylvilagus floridanus/be836a9a977759d2cb36259a613be9b0.jpg\",\n  \"34407.jpg\": \"Insecta/Megacyllene robiniae/e84ebb5c32ea51c7b478a2e55bcbcd98.jpg\",\n  \"344087.jpg\": \"Mammalia/Sylvilagus floridanus/a194e29959a929d16e1d7e04ed8095c9.jpg\",\n  \"34409.jpg\": \"Insecta/Megacyllene robiniae/385a80cce62442a2c61a07038f865781.jpg\",\n  \"34410.jpg\": \"Insecta/Megacyllene robiniae/0a4e5c9f55d37c242a231e136fa979fc.jpg\",\n  \"34412.jpg\": \"Insecta/Megacyllene robiniae/8a9ca6fbe0e07b99ffe93275d03797cf.jpg\",\n  \"34413.jpg\": \"Insecta/Megacyllene robiniae/d96f120758ff8fed309ec4c57837947c.jpg\",\n  \"344139.jpg\": \"Mammalia/Sylvilagus floridanus/9f477c98c233024514a2326296de8ba6.jpg\",\n  \"344152.jpg\": \"Mammalia/Sylvilagus floridanus/77b80965b5cd3d47473026180f23716d.jpg\",\n  \"34416.jpg\": \"Insecta/Megacyllene robiniae/86868f90986eb112d59c2d8286866c0d.jpg\",\n  \"344162.jpg\": \"Mammalia/Sylvilagus floridanus/a998d0e25d8f1cc7907922088b83787a.jpg\",\n  \"344167.jpg\": \"Mammalia/Sylvilagus floridanus/8efe56adaf2acc55ff0df89136afc5ce.jpg\",\n  \"344183.jpg\": \"Mammalia/Sylvilagus floridanus/ec504b55b0fc4abbb39eab4255d25261.jpg\",\n  \"344207.jpg\": \"Mammalia/Sylvilagus floridanus/474756be19e6fde5f1ce07e9a2aeceba.jpg\",\n  \"344211.jpg\": \"Mammalia/Sylvilagus floridanus/33e93157718949f23ee4bb8428c600e0.jpg\",\n  \"344216.jpg\": \"Mammalia/Sylvilagus floridanus/68ecca9a0bb967ba690165373b89974c.jpg\",\n  \"344218.jpg\": \"Mammalia/Sylvilagus floridanus/4dcc6af766498ebb537e03149cace3c6.jpg\",\n  \"34422.jpg\": \"Insecta/Megacyllene robiniae/b741830b079245aaaeafb1aba5c69ad6.jpg\",\n  \"344229.jpg\": \"Mammalia/Sylvilagus floridanus/c19dce559506d834d839324f61cf40fe.jpg\",\n  \"34427.jpg\": \"Insecta/Megacyllene robiniae/c037d19e145ca14b9802e49ab4cfe74b.jpg\",\n  \"344272.jpg\": \"Mammalia/Sylvilagus floridanus/f84c8ba038afd37cf2529be0f543e05d.jpg\",\n  \"34428.jpg\": \"Insecta/Megacyllene robiniae/6cef9123c9e7950c78075af762e039d6.jpg\",\n  \"34432.jpg\": \"Insecta/Megacyllene robiniae/2db7a35a7c20cce5082527719f3cc079.jpg\",\n  \"344321.jpg\": \"Aves/Icterus galbula/886a5f6b0417eeacdb62eddb35219782.jpg\",\n  \"344325.jpg\": \"Aves/Icterus galbula/27af2fedd317325ac8415c6e61ca8f5d.jpg\",\n  \"34433.jpg\": \"Insecta/Megacyllene robiniae/f783a7a2dea1eff7fdc4b380d224074b.jpg\",\n  \"344337.jpg\": \"Aves/Icterus galbula/f1d728c4a34a58d2641e1184a922fdbe.jpg\",\n  \"344346.jpg\": \"Aves/Icterus galbula/2b3e5fce59de4e3d103c78df9c1474b6.jpg\",\n  \"344347.jpg\": \"Aves/Icterus galbula/7cc6a5bda4383068f6d5235a801cb300.jpg\",\n  \"344348.jpg\": \"Aves/Icterus galbula/a1ff39171ce22a30b0fbfa423fd5193e.jpg\",\n  \"34435.jpg\": \"Insecta/Megacyllene robiniae/912adea1eb32548f126dc9fe90e321aa.jpg\",\n  \"344356.jpg\": \"Aves/Icterus galbula/fce8b89f369daeeaed413e29fda5694d.jpg\",\n  \"34436.jpg\": \"Insecta/Megacyllene robiniae/044bc66d65e11260626a62fae48dfa4b.jpg\",\n  \"34437.jpg\": \"Insecta/Megacyllene robiniae/d042d7a598a7b74561911749b3de7192.jpg\",\n  \"344376.jpg\": \"Aves/Icterus galbula/1cef87c1202f7e670eda8bd3a4df9192.jpg\",\n  \"344377.jpg\": \"Aves/Icterus galbula/2ba1dfa9e83e4c8a65626c0430da705f.jpg\",\n  \"344383.jpg\": \"Aves/Icterus galbula/08daebd9afcde85ba1e5eab2ecb9ab8e.jpg\",\n  \"34440.jpg\": \"Insecta/Megacyllene robiniae/ddebeddd8650d22eca5c9f3c2589ad47.jpg\",\n  \"344428.jpg\": \"Aves/Icterus galbula/7181c5f9c538f4c29f47c6fe65c66cfa.jpg\",\n  \"344445.jpg\": \"Aves/Icterus galbula/351dce21c6e4239e8dd7d50e6a18ffdd.jpg\",\n  \"344450.jpg\": \"Aves/Icterus galbula/7bfe093431c097d1b21ad2debcb2f11b.jpg\",\n  \"344472.jpg\": \"Aves/Icterus galbula/4ceb31150613a2172366d1db4460ea27.jpg\",\n  \"344491.jpg\": \"Aves/Icterus galbula/80a2977e0f6ac7034261e022dc72ab9a.jpg\",\n  \"344498.jpg\": \"Aves/Icterus galbula/37353d1395b0a009f1cab021312573f6.jpg\",\n  \"34451.jpg\": \"Aves/Sporophila torqueola/2dc8a8ad079d94a87ffaa32ab2e4c5b1.jpg\",\n  \"344511.jpg\": \"Aves/Icterus galbula/adbc406d86411e0fc2eb7478de193a03.jpg\",\n  \"344518.jpg\": \"Aves/Icterus galbula/2e60d33ec216bde5dbce9608be439fd6.jpg\",\n  \"344535.jpg\": \"Aves/Icterus galbula/f29ebc1d3bc4d88ed32972cf16a73a45.jpg\",\n  \"344544.jpg\": \"Aves/Icterus galbula/550c62f2e27d598a4d2a6a1fa2dbcdc3.jpg\",\n  \"34461.jpg\": \"Aves/Sporophila torqueola/0105d222c01c9d827ad907e8990eab9b.jpg\",\n  \"34463.jpg\": \"Aves/Sporophila torqueola/55b43c7b48556ac394fb2609ad99ebc4.jpg\",\n  \"344644.jpg\": \"Insecta/Scopula limboundata/c3cf353099c18a69cca63c9f9d889bb8.jpg\",\n  \"344645.jpg\": \"Insecta/Scopula limboundata/5ffaea30f3730cd45b4f8723bbef0e12.jpg\",\n  \"344646.jpg\": \"Insecta/Scopula limboundata/6017cbfc1c11269ea026aff67a9f6d68.jpg\",\n  \"344647.jpg\": \"Insecta/Scopula limboundata/a04060d0d0a457b3ff5ef4687d81ce56.jpg\",\n  \"344650.jpg\": \"Insecta/Scopula limboundata/cf57d652daf8fc202031b26a01643638.jpg\",\n  \"344653.jpg\": \"Insecta/Scopula limboundata/bb20e6681cc95c7f0ec7fb821f7bec06.jpg\",\n  \"344656.jpg\": \"Insecta/Scopula limboundata/ddf249d6a19abd403c3631001037db4e.jpg\",\n  \"344657.jpg\": \"Insecta/Scopula limboundata/778ed9545919b477325333a7197f0548.jpg\",\n  \"344658.jpg\": \"Insecta/Scopula limboundata/9558ed34f7e5e49436a811155305e2f0.jpg\",\n  \"344660.jpg\": \"Insecta/Scopula limboundata/c3d55d6a98f205550d901b1c57c679b2.jpg\",\n  \"344666.jpg\": \"Insecta/Scopula limboundata/71f29cc68bfaf1206c0030770dbb6039.jpg\",\n  \"344668.jpg\": \"Insecta/Scopula limboundata/720d9e3c87c143c9db262896867c9f26.jpg\",\n  \"344670.jpg\": \"Insecta/Scopula limboundata/aa08022e5782731414775360789e8472.jpg\",\n  \"344676.jpg\": \"Insecta/Scopula limboundata/40fe058100afe23d4cff79b1c7517d88.jpg\",\n  \"344677.jpg\": \"Insecta/Scopula limboundata/ed668a572494ffb4dd7b3655f5bb53e6.jpg\",\n  \"344679.jpg\": \"Insecta/Scopula limboundata/5b01adfad3df1685438c694169457dda.jpg\",\n  \"344682.jpg\": \"Insecta/Scopula limboundata/93d6f1bd3274a7457ef211ed58461b28.jpg\",\n  \"344683.jpg\": \"Insecta/Scopula limboundata/6ba8096a946b2789e2b06cbed5fbf701.jpg\",\n  \"344687.jpg\": \"Insecta/Scopula limboundata/14f47b192f8fd23883a87e4878bf44ab.jpg\",\n  \"344689.jpg\": \"Insecta/Scopula limboundata/c9e4739a571fc5d63d734a6cff810c59.jpg\",\n  \"344690.jpg\": \"Insecta/Scopula limboundata/e2478ecc352a091d8f8e798b6685c168.jpg\",\n  \"344703.jpg\": \"Insecta/Scopula limboundata/531793199401f78b07e4f3fa5bc42627.jpg\",\n  \"344711.jpg\": \"Insecta/Scopula limboundata/51164b4650f166214ea5e5c176ab9c7f.jpg\",\n  \"34496.jpg\": \"Aves/Sporophila torqueola/c54de47d5e839f795ea816ccd6dce6df.jpg\",\n  \"345012.jpg\": \"Amphibia/Rhinella marina/67015b4b2fb22fd43e69b769d639a215.jpg\",\n  \"345020.jpg\": \"Amphibia/Rhinella marina/272344e5d19de0e898bbf1a630f8b778.jpg\",\n  \"34503.jpg\": \"Aves/Sporophila torqueola/64f6a202c09f5b886cf1557704a82b09.jpg\",\n  \"345036.jpg\": \"Amphibia/Rhinella marina/58ec29b1302a7289773c009c262a2a55.jpg\",\n  \"345052.jpg\": \"Amphibia/Rhinella marina/e03daacc9d7b544ec2e7ad457f7c45ed.jpg\",\n  \"345071.jpg\": \"Amphibia/Rhinella marina/7da0f43018c028d5b5797e3003741925.jpg\",\n  \"345072.jpg\": \"Amphibia/Rhinella marina/3fa493500fbcaaa1f8a27b2bb2f53046.jpg\",\n  \"345082.jpg\": \"Amphibia/Rhinella marina/a56fd882da0fbe4f096517580502e7b8.jpg\",\n  \"345083.jpg\": \"Amphibia/Rhinella marina/22a7deeaa29f3d56723f64ce676f57c5.jpg\",\n  \"345085.jpg\": \"Amphibia/Rhinella marina/038eefedd9ec9102d71365f03cf1e249.jpg\",\n  \"345108.jpg\": \"Amphibia/Rhinella marina/561a6f29c9ccecf9d52d84d3c82ae3aa.jpg\",\n  \"345109.jpg\": \"Amphibia/Rhinella marina/a59e7d0b3c7a1013caa02e9d7d29e166.jpg\",\n  \"345111.jpg\": \"Amphibia/Rhinella marina/884a9b48f95f3053dc6bfa3b194de6b6.jpg\",\n  \"345118.jpg\": \"Amphibia/Rhinella marina/b35b561f0c86fac5a260c178601fe7f9.jpg\",\n  \"345121.jpg\": \"Amphibia/Rhinella marina/4a06eefca8195b4653a09f0843dca561.jpg\",\n  \"345127.jpg\": \"Amphibia/Rhinella marina/b42cb79aa62d7e4cf40fb4d727b67780.jpg\",\n  \"345137.jpg\": \"Amphibia/Rhinella marina/81c03ed8f1d37ede421574d7fd68b2a0.jpg\",\n  \"345147.jpg\": \"Amphibia/Rhinella marina/9696b41b9162d875a96dec367c2e4f40.jpg\",\n  \"345172.jpg\": \"Amphibia/Rhinella marina/ee2422027ea2a5d49d8d73b18ae1669b.jpg\",\n  \"345175.jpg\": \"Amphibia/Rhinella marina/41356e59e287960242f567b34d0ae63e.jpg\",\n  \"345180.jpg\": \"Amphibia/Rhinella marina/54c14176aa25722e420fc4ac10cc8380.jpg\",\n  \"345181.jpg\": \"Amphibia/Rhinella marina/ca1aa5f4128aba9415dd12e0072e92a0.jpg\",\n  \"345182.jpg\": \"Reptilia/Coluber constrictor mormon/c6a7deee8f4a84605b3f131ee5b3b1b4.jpg\",\n  \"345184.jpg\": \"Reptilia/Coluber constrictor mormon/b8e9c35d4610b9f54e33cdf01ef2eaea.jpg\",\n  \"345187.jpg\": \"Reptilia/Coluber constrictor mormon/a3f7ffd1a8569f2e53288cbf3bf6b4dd.jpg\",\n  \"345188.jpg\": \"Reptilia/Coluber constrictor mormon/1f11a11bbfcb1b8408771db572fc3df8.jpg\",\n  \"345189.jpg\": \"Reptilia/Coluber constrictor mormon/bd465264a9f5d472568d6f8bc4059a8d.jpg\",\n  \"345193.jpg\": \"Reptilia/Coluber constrictor mormon/926eeb3fa6c826ee6a616f3d8dca63b9.jpg\",\n  \"345194.jpg\": \"Reptilia/Coluber constrictor mormon/e1a0cc3bb87c917b56e8e2747bc5df5c.jpg\",\n  \"345195.jpg\": \"Reptilia/Coluber constrictor mormon/27b70835c34f4463648d7425e96b9ee0.jpg\",\n  \"345196.jpg\": \"Reptilia/Coluber constrictor mormon/4d557d401e5991a60cc1f5017d47f721.jpg\",\n  \"345197.jpg\": \"Reptilia/Coluber constrictor mormon/aaff8ad04319429314318da4bbb0ad05.jpg\",\n  \"345201.jpg\": \"Reptilia/Coluber constrictor mormon/a30508f1ef645b31ee3576140fbe9246.jpg\",\n  \"345202.jpg\": \"Reptilia/Coluber constrictor mormon/e407b893968b5765daab19e1cda42221.jpg\",\n  \"345203.jpg\": \"Reptilia/Coluber constrictor mormon/4adf48e13a1360313ca6a49cf5b286fd.jpg\",\n  \"345207.jpg\": \"Reptilia/Coluber constrictor mormon/9fc5e4738a81328aa32e1779aac598f3.jpg\",\n  \"345208.jpg\": \"Reptilia/Coluber constrictor mormon/b0835f48121457dd656f9a126abdb324.jpg\",\n  \"345212.jpg\": \"Reptilia/Coluber constrictor mormon/3dec3f339b5a2b16af6adc5367b17298.jpg\",\n  \"345213.jpg\": \"Reptilia/Coluber constrictor mormon/2e7d3b206f5b85b26136630bcbb60859.jpg\",\n  \"345214.jpg\": \"Reptilia/Coluber constrictor mormon/985851294dd4b2a1fa7de249d71f484e.jpg\",\n  \"345220.jpg\": \"Reptilia/Coluber constrictor mormon/9b52832b1bffc735118d24572276b6c2.jpg\",\n  \"345221.jpg\": \"Reptilia/Coluber constrictor mormon/6dd5ddd77e1d67efeeab6c6fd9af9084.jpg\",\n  \"345222.jpg\": \"Reptilia/Coluber constrictor mormon/754c26215ba01721b274d5cee2087c1b.jpg\",\n  \"345226.jpg\": \"Reptilia/Coluber constrictor mormon/94c4748f4d1c34b4ca6643f4bfe1995b.jpg\",\n  \"345227.jpg\": \"Reptilia/Coluber constrictor mormon/6379f4e4c1a9f98d1858e0193a2685fe.jpg\",\n  \"345230.jpg\": \"Mammalia/Sylvilagus bachmani/9a27193494858f7a24ae59376bd405db.jpg\",\n  \"345234.jpg\": \"Mammalia/Sylvilagus bachmani/b99ef1fb9e28c5a3ea9f615201e51ff6.jpg\",\n  \"345235.jpg\": \"Mammalia/Sylvilagus bachmani/22653ed20cb61a4cb55297b5bf6fee55.jpg\",\n  \"345238.jpg\": \"Mammalia/Sylvilagus bachmani/cd0624abd1c63661d9ad7298e422dbf0.jpg\",\n  \"345241.jpg\": \"Mammalia/Sylvilagus bachmani/2327f60117ae0cc4c51ade349a5103d1.jpg\",\n  \"345242.jpg\": \"Mammalia/Sylvilagus bachmani/1425141eb2264659e07b21cf4ea48e39.jpg\",\n  \"345243.jpg\": \"Mammalia/Sylvilagus bachmani/f22c33681dead35375024582896d876c.jpg\",\n  \"345252.jpg\": \"Mammalia/Sylvilagus bachmani/5e1ba59f61ca6395baace7e70ffdcc2a.jpg\",\n  \"345253.jpg\": \"Mammalia/Sylvilagus bachmani/c78a547bb1885ad0e230fb52b6f843c5.jpg\",\n  \"345259.jpg\": \"Mammalia/Sylvilagus bachmani/3a0ab0b4ca5ea7fb99d6eb8420698900.jpg\",\n  \"345260.jpg\": \"Mammalia/Sylvilagus bachmani/6b60ed32982e61e566cd9ee4010bf7e5.jpg\",\n  \"345261.jpg\": \"Mammalia/Sylvilagus bachmani/3dd0508d5850d09859f09b1cedfc2407.jpg\",\n  \"345264.jpg\": \"Mammalia/Sylvilagus bachmani/100cebc4b8f27aa0a143c7ef2cc5d8a4.jpg\",\n  \"345266.jpg\": \"Mammalia/Sylvilagus bachmani/3a7f23c1b0816a94ddcfa7aa878272fe.jpg\",\n  \"345275.jpg\": \"Mammalia/Sylvilagus bachmani/e344dfce0b812025a03d6e04d5400568.jpg\",\n  \"345276.jpg\": \"Mammalia/Sylvilagus bachmani/24214a3737bca8c8ffba9d4dc7143beb.jpg\",\n  \"345283.jpg\": \"Mammalia/Sylvilagus bachmani/88789826b1f7de2b7125b277a59552e1.jpg\",\n  \"345294.jpg\": \"Mammalia/Sylvilagus bachmani/139ec85495e308126ab4dba4896db75a.jpg\",\n  \"345295.jpg\": \"Mammalia/Sylvilagus bachmani/29b589e96f7c03fa9a0835b365010c78.jpg\",\n  \"345297.jpg\": \"Mammalia/Sylvilagus bachmani/cf252d441cbb5d68b82ce03e863bb1a0.jpg\",\n  \"345298.jpg\": \"Mammalia/Sylvilagus bachmani/e656db539df79e00d2ef0e08afc582be.jpg\",\n  \"345303.jpg\": \"Mammalia/Sylvilagus bachmani/b4cf474d3ac21f3f783ebc1785c43ec5.jpg\",\n  \"345306.jpg\": \"Insecta/Diapheromera femorata/45b3441dbcdcc86321ee9ff6e78c1cf1.jpg\",\n  \"345307.jpg\": \"Insecta/Diapheromera femorata/ebb4459c05ab8bee7b2516e3fcc05c3c.jpg\",\n  \"345308.jpg\": \"Insecta/Diapheromera femorata/279e053ddcc518623a1659d5a5df8ff1.jpg\",\n  \"345309.jpg\": \"Insecta/Diapheromera femorata/b4582eea40e8cc5fe82be814741907ae.jpg\",\n  \"345310.jpg\": \"Insecta/Diapheromera femorata/13ff9abbd52979fcefbe5bf8dad4f9c4.jpg\",\n  \"345315.jpg\": \"Insecta/Diapheromera femorata/564f6a7a73b7a1f3c5eed424712ab3f4.jpg\",\n  \"345324.jpg\": \"Insecta/Diapheromera femorata/d4738c76a200dd516785a2e529f2785e.jpg\",\n  \"345327.jpg\": \"Insecta/Diapheromera femorata/78e1b3be15f7e82ffac71699b8de2313.jpg\",\n  \"345328.jpg\": \"Insecta/Diapheromera femorata/f4cffde48878bac7b052a38caeb8d54d.jpg\",\n  \"345329.jpg\": \"Insecta/Diapheromera femorata/dfad3f5d65e9a8b6c22837c285fca96c.jpg\",\n  \"345330.jpg\": \"Insecta/Diapheromera femorata/6c8ce0a2f7758d402f43881a0806cefa.jpg\",\n  \"345331.jpg\": \"Insecta/Diapheromera femorata/69419078402df2e4b87c4aebc9882eb4.jpg\",\n  \"345332.jpg\": \"Insecta/Diapheromera femorata/ed81c7dba96f87d9b87c66e26c6e492c.jpg\",\n  \"345333.jpg\": \"Insecta/Diapheromera femorata/a66d9635061ea81d3a2fc79f794c8cd9.jpg\",\n  \"345334.jpg\": \"Insecta/Diapheromera femorata/84d1d0f74b1da82d5f89d16e8a2d732a.jpg\",\n  \"345335.jpg\": \"Insecta/Diapheromera femorata/1e41227ccd1395d68e81963ebe053839.jpg\",\n  \"345337.jpg\": \"Insecta/Diapheromera femorata/d00330f821ef4403481a1da31cea86eb.jpg\",\n  \"34534.jpg\": \"Aves/Sporophila torqueola/161d314e8c479c9db9845a783be92f2c.jpg\",\n  \"345343.jpg\": \"Insecta/Diapheromera femorata/58b63bd96c0760488d364e49142562fb.jpg\",\n  \"345344.jpg\": \"Insecta/Diapheromera femorata/8c53c18b49d08614e5b5ccf0a74b3fa4.jpg\",\n  \"34548.jpg\": \"Aves/Sporophila torqueola/750eb456339d376d2d051418f71a039a.jpg\",\n  \"34552.jpg\": \"Aves/Sporophila torqueola/638537894f4b6f7d8990944b158d33e4.jpg\",\n  \"345636.jpg\": \"Aves/Haemorhous mexicanus/5766d6f157917ab737c06cde277cd6d4.jpg\",\n  \"34568.jpg\": \"Aves/Sporophila torqueola/6a8c14816ed9a464c8b8304fe6a4ebdd.jpg\",\n  \"345713.jpg\": \"Aves/Haemorhous mexicanus/3ca7c92a0d31483726e9d52611c2b740.jpg\",\n  \"34572.jpg\": \"Aves/Sporophila torqueola/96133506ce683ba4e1e3a6079bb63649.jpg\",\n  \"34575.jpg\": \"Aves/Sporophila torqueola/17b9f8dba56c00aab2afc0f06b93b457.jpg\",\n  \"34576.jpg\": \"Aves/Sporophila torqueola/e1d804d7ad6ded5babf1845cf3b2e218.jpg\",\n  \"345763.jpg\": \"Aves/Haemorhous mexicanus/6eb859d39ea4e2b2ecbc327ef1bc7dbe.jpg\",\n  \"34577.jpg\": \"Aves/Sporophila torqueola/da518389e3c3bc5dd567bfc45655db68.jpg\",\n  \"345838.jpg\": \"Aves/Haemorhous mexicanus/ad359c85b87a7870bc76a1e20debaa04.jpg\",\n  \"345898.jpg\": \"Aves/Haemorhous mexicanus/367ad53aae3cfbe821dd2a01239632de.jpg\",\n  \"34594.jpg\": \"Aves/Sporophila torqueola/ac593962478ae830e385d871f4a8b7a8.jpg\",\n  \"34599.jpg\": \"Aves/Sporophila torqueola/7e7cf0efa8debc95a11b5d0f2cebc88a.jpg\",\n  \"34612.jpg\": \"Aves/Sporophila torqueola/1584fcfc927f034f8bea2aa0918a7535.jpg\",\n  \"34617.jpg\": \"Aves/Sporophila torqueola/a21e022e3a293f7d7b2d0649b6aeb3b6.jpg\",\n  \"346288.jpg\": \"Aves/Haemorhous mexicanus/7c00e979490fedfe69b6ea313bb1d2dc.jpg\",\n  \"346304.jpg\": \"Aves/Haemorhous mexicanus/4d26c4e5d2db1d0a2f4e0a929322f639.jpg\",\n  \"346370.jpg\": \"Aves/Haemorhous mexicanus/a8cbc15ecd7ad164c5bc72e2a611e10e.jpg\",\n  \"346382.jpg\": \"Aves/Haemorhous mexicanus/d71c2ddd883b46d65bf57a64db31e326.jpg\",\n  \"34642.jpg\": \"Aves/Sporophila torqueola/4d986effe26fa3128ad6c8c3d1fd47b2.jpg\",\n  \"34647.jpg\": \"Aves/Sporophila torqueola/f3a77e6c333320e65bc5b3dcf1acc8b3.jpg\",\n  \"346603.jpg\": \"Aves/Haemorhous mexicanus/3597cfea3a8bccbf20364642f4fc1ab2.jpg\",\n  \"346616.jpg\": \"Aves/Haemorhous mexicanus/d79930d2284f58053b6d7ea3f5bb948a.jpg\",\n  \"346624.jpg\": \"Aves/Haemorhous mexicanus/ee402142147b7d1b5eda9198d0912f7f.jpg\",\n  \"346729.jpg\": \"Aves/Haemorhous mexicanus/4b8c9aa0152e2af553bd041bf86b587e.jpg\",\n  \"3468.jpg\": \"Insecta/Coccinella septempunctata/ba93ec2f27ae39031d0ed774c9f3ed43.jpg\",\n  \"346853.jpg\": \"Aves/Haemorhous mexicanus/83f9e0ab5456b371bf067040f939360d.jpg\",\n  \"347037.jpg\": \"Aves/Mimus polyglottos/e59afb9e1b01f44f93c84624a5c05aac.jpg\",\n  \"347061.jpg\": \"Aves/Mimus polyglottos/19d16d17fadea6a8f07e2d9609f7fac8.jpg\",\n  \"34715.jpg\": \"Aves/Motacilla aguimp/4a4838e1a90c6922246d8eb337e14a88.jpg\",\n  \"347160.jpg\": \"Aves/Mimus polyglottos/c01ca3d3ab9ef9a3f59787a9c867a112.jpg\",\n  \"34721.jpg\": \"Aves/Motacilla aguimp/e2c09dbe19506091fb7405a9be085e23.jpg\",\n  \"34728.jpg\": \"Aves/Motacilla aguimp/f6d330e376c924fe28bf215254df2cc9.jpg\",\n  \"34729.jpg\": \"Aves/Motacilla aguimp/23341d7d933788b3f3b59617c33cebd0.jpg\",\n  \"347294.jpg\": \"Aves/Mimus polyglottos/865bf841a86122d5a4149256d23ca591.jpg\",\n  \"347310.jpg\": \"Aves/Mimus polyglottos/df1cc83dcb96413938cb564a99c371a4.jpg\",\n  \"347332.jpg\": \"Aves/Mimus polyglottos/87395f7dedcc50486abe572dfb2f5ee2.jpg\",\n  \"3474.jpg\": \"Insecta/Coccinella septempunctata/32631d446bd25b604da6e168fa7b8213.jpg\",\n  \"347442.jpg\": \"Aves/Mimus polyglottos/f00bc19d66a93dd61f8f506544a2612d.jpg\",\n  \"347504.jpg\": \"Aves/Mimus polyglottos/bc2c3e84d8846825ef1d9c630d86303e.jpg\",\n  \"347541.jpg\": \"Aves/Mimus polyglottos/fd3c38e3dbccb06e2837abbc1e87c886.jpg\",\n  \"347551.jpg\": \"Aves/Mimus polyglottos/6e66be45a6f253af8f98663d95f8f359.jpg\",\n  \"347617.jpg\": \"Aves/Mimus polyglottos/d593be35f868e69ce2dbddb6a7a6be4f.jpg\",\n  \"347636.jpg\": \"Aves/Mimus polyglottos/feb7b9a3fa4ad186df063901c4e0a6b8.jpg\",\n  \"347674.jpg\": \"Aves/Mimus polyglottos/205a3e156b7da190e1ba5551c350ea5f.jpg\",\n  \"347732.jpg\": \"Aves/Mimus polyglottos/c3140ffbb63bdbd0d6a561a9443cfda6.jpg\",\n  \"347764.jpg\": \"Aves/Mimus polyglottos/52452fe811b19fc68801fbc8123028fb.jpg\",\n  \"347774.jpg\": \"Aves/Mimus polyglottos/1394076f2eee7b3be7a3290f44964a53.jpg\",\n  \"347790.jpg\": \"Aves/Mimus polyglottos/22a145a5ff3a6a5250a83e27e82e5b51.jpg\",\n  \"347801.jpg\": \"Aves/Mimus polyglottos/44a9331ee1b71e13ad2b22867ebacd82.jpg\",\n  \"347824.jpg\": \"Aves/Mimus polyglottos/beac35e0ac6acfb1a0dc8cae0c1a1ab3.jpg\",\n  \"347862.jpg\": \"Aves/Mimus polyglottos/50f1d69098349a50de96696100e2ae3b.jpg\",\n  \"347910.jpg\": \"Amphibia/Ecnomiohyla miotympanum/440aa83442dbff7b5aee5ee9f06cf4bd.jpg\",\n  \"347911.jpg\": \"Amphibia/Ecnomiohyla miotympanum/75175c00a76595d9c64d8b059d2ad0fb.jpg\",\n  \"347913.jpg\": \"Amphibia/Ecnomiohyla miotympanum/c0e5906398fef5550fece7160abda309.jpg\",\n  \"347914.jpg\": \"Amphibia/Ecnomiohyla miotympanum/c120400cfb5e552e0842cdff5fad6d26.jpg\",\n  \"347921.jpg\": \"Amphibia/Ecnomiohyla miotympanum/58f812b3bf52a1ec73b9156c8b4a6eaa.jpg\",\n  \"347925.jpg\": \"Amphibia/Ecnomiohyla miotympanum/8fd9c9987a1f082441c087a9151da40d.jpg\",\n  \"347945.jpg\": \"Insecta/Oncopeltus fasciatus/7766a957de83fce5151df96443e027c1.jpg\",\n  \"347948.jpg\": \"Insecta/Oncopeltus fasciatus/3d78e61d429ddc46d17b585bc7463bdf.jpg\",\n  \"347980.jpg\": \"Insecta/Oncopeltus fasciatus/badff777b92dfae37624814a3be91627.jpg\",\n  \"348024.jpg\": \"Insecta/Oncopeltus fasciatus/a94aeb3c809d9ef4c3af332d5c6d254e.jpg\",\n  \"348067.jpg\": \"Insecta/Oncopeltus fasciatus/7b5093c880305af3bb14b68212548db3.jpg\",\n  \"348109.jpg\": \"Insecta/Oncopeltus fasciatus/71472b4e62fa6d2c60efbdb9e406888d.jpg\",\n  \"348120.jpg\": \"Insecta/Oncopeltus fasciatus/504eb1053276234dd3376648fb60733f.jpg\",\n  \"348137.jpg\": \"Insecta/Oncopeltus fasciatus/334d330b6f9d04d6afbf7ea697f0e6cd.jpg\",\n  \"34816.jpg\": \"Amphibia/Acris gryllus/44b07ffdd66b9aefd95214ff6b2eceb0.jpg\",\n  \"348162.jpg\": \"Insecta/Oncopeltus fasciatus/a59c2a894b2f152aa894973e9bbd79d5.jpg\",\n  \"348176.jpg\": \"Insecta/Oncopeltus fasciatus/80b90e74a6c94494a4a75f7146725f59.jpg\",\n  \"348222.jpg\": \"Insecta/Oncopeltus fasciatus/5aff693cad7569d94c65ab6667aca2d1.jpg\",\n  \"34823.jpg\": \"Amphibia/Acris gryllus/184ca474456415a2fb3c973f3a63978b.jpg\",\n  \"348262.jpg\": \"Mammalia/Neomonachus schauinslandi/7a733c0ce07b228d48111b6f39f8debc.jpg\",\n  \"348266.jpg\": \"Mammalia/Neomonachus schauinslandi/6f7e4abacdc5909e266f2b5ce1ac4e82.jpg\",\n  \"348269.jpg\": \"Mammalia/Neomonachus schauinslandi/6fcfb502c9974408b681d76e300e2367.jpg\",\n  \"34827.jpg\": \"Amphibia/Acris gryllus/a1dde2a933bd1f2d15072ec19ff704ef.jpg\",\n  \"348270.jpg\": \"Mammalia/Neomonachus schauinslandi/46868f0e22ebc5968646c8dd2143b0dc.jpg\",\n  \"348273.jpg\": \"Mammalia/Neomonachus schauinslandi/2b850b7365d836560cb0f083fd98bd62.jpg\",\n  \"348277.jpg\": \"Mammalia/Neomonachus schauinslandi/ae56576a731d2daac91000cbf56a0357.jpg\",\n  \"348335.jpg\": \"Mollusca/Peltodoris nobilis/792879205cdb46aa530ac8e720d979d7.jpg\",\n  \"348347.jpg\": \"Mollusca/Peltodoris nobilis/6db84e0bc4febbf6e4b96b43bd1c965c.jpg\",\n  \"348356.jpg\": \"Mollusca/Peltodoris nobilis/6f9d72a90e3c589d448d4bf790290a1b.jpg\",\n  \"348357.jpg\": \"Mollusca/Peltodoris nobilis/b36538ef2e78f91e3a4f61160b2e659a.jpg\",\n  \"348366.jpg\": \"Mollusca/Peltodoris nobilis/b39e2c9c337ba26f293d37eb674b7af8.jpg\",\n  \"348380.jpg\": \"Mollusca/Peltodoris nobilis/4338030679dd773a9170330644da24ca.jpg\",\n  \"348385.jpg\": \"Mollusca/Peltodoris nobilis/64c2095e481d20c3afd0477eb1067356.jpg\",\n  \"34840.jpg\": \"Mammalia/Papio ursinus/bc0c3bf2eaf012e9145d4103ac1d8c5c.jpg\",\n  \"348402.jpg\": \"Mollusca/Peltodoris nobilis/763413a1bc12e516030ec26c71b022e5.jpg\",\n  \"348421.jpg\": \"Mollusca/Peltodoris nobilis/c4dd60e1a12e31e7d4ee6dedb486ef28.jpg\",\n  \"348429.jpg\": \"Mollusca/Peltodoris nobilis/c5ab993bf4fd017ed5f53d040e18fc59.jpg\",\n  \"34843.jpg\": \"Mammalia/Papio ursinus/e25ac74bcf045f19182e578f1bb406d4.jpg\",\n  \"348430.jpg\": \"Mollusca/Peltodoris nobilis/d9f7d8ecd779004c55e663c6c78ff574.jpg\",\n  \"348431.jpg\": \"Mollusca/Peltodoris nobilis/ba4bfe433dacc0377fd480ea08c9798f.jpg\",\n  \"348437.jpg\": \"Mollusca/Peltodoris nobilis/543d92e2d8ed800eb096521ec7ea985e.jpg\",\n  \"34846.jpg\": \"Mammalia/Papio ursinus/77770a54be77ad5640bc62fcea47196c.jpg\",\n  \"348460.jpg\": \"Mollusca/Peltodoris nobilis/9015f1b3b0be910cab90c7a44d5c2d8c.jpg\",\n  \"34849.jpg\": \"Mammalia/Papio ursinus/262da705cfc69b3dad2c2f7c2168609f.jpg\",\n  \"348526.jpg\": \"Aves/Catharus fuscescens/828215b0b3e40539ce84a2ecfe0dc272.jpg\",\n  \"348536.jpg\": \"Aves/Catharus fuscescens/23c9d56622ad4c4b6cc82add89b900bb.jpg\",\n  \"348540.jpg\": \"Aves/Catharus fuscescens/99aeabe9e735029b63e1b56367c8149e.jpg\",\n  \"348541.jpg\": \"Aves/Catharus fuscescens/f40de8f6e29875a5013ff587a37bb3f8.jpg\",\n  \"348546.jpg\": \"Aves/Catharus fuscescens/2c3ef578dcce40a0fe87867a5ef184ae.jpg\",\n  \"348547.jpg\": \"Aves/Catharus fuscescens/32f62eb4e57301b8d0816a021ccf337a.jpg\",\n  \"348548.jpg\": \"Aves/Catharus fuscescens/55d80a6365e98ae46015323219cb0473.jpg\",\n  \"348549.jpg\": \"Aves/Catharus fuscescens/c111ab8aaed91052fe806bda05d35f81.jpg\",\n  \"348552.jpg\": \"Aves/Catharus fuscescens/6d9ecafe05f37e62642ec4968bbf813c.jpg\",\n  \"348555.jpg\": \"Aves/Catharus fuscescens/71801bdb6cde1b1f0a5aa3e98de49443.jpg\",\n  \"348556.jpg\": \"Aves/Catharus fuscescens/eb66e8aee607dd70fa486ff81064a39b.jpg\",\n  \"348557.jpg\": \"Aves/Catharus fuscescens/1613458e67babda027114c574bc623b8.jpg\",\n  \"348558.jpg\": \"Aves/Catharus fuscescens/b131890336027f6de555365fe7f57e0c.jpg\",\n  \"348561.jpg\": \"Aves/Catharus fuscescens/1e6bf7d5cfbd26e13a3284832b7b7ae2.jpg\",\n  \"348563.jpg\": \"Aves/Catharus fuscescens/754edddbcb60e015d2ae0dd76c3e3d85.jpg\",\n  \"348564.jpg\": \"Aves/Catharus fuscescens/9c4392e369c014d2521f5ff6fd266a05.jpg\",\n  \"348572.jpg\": \"Aves/Catharus fuscescens/dc828e23cc9e7d4a24730a56a76e4eb4.jpg\",\n  \"348573.jpg\": \"Aves/Catharus fuscescens/149a887a63ae1452705d2750485223e7.jpg\",\n  \"348574.jpg\": \"Aves/Catharus fuscescens/86f48a975d3bbd8d70391584ea48a9fc.jpg\",\n  \"348577.jpg\": \"Aves/Catharus fuscescens/9eec9962f04724b4f3f4de50014919ee.jpg\",\n  \"348580.jpg\": \"Mammalia/Acinonyx jubatus/9a0724f238e762410aabb5453bbc0781.jpg\",\n  \"348587.jpg\": \"Mammalia/Acinonyx jubatus/0db0d7ac8177ae770bfdc157795e8d22.jpg\",\n  \"348588.jpg\": \"Mammalia/Acinonyx jubatus/b040a6e8212b94832e4be6fdbf39b16f.jpg\",\n  \"348589.jpg\": \"Mammalia/Acinonyx jubatus/fa17c9e92fedd59a58bc9e7d1be401e2.jpg\",\n  \"348598.jpg\": \"Mammalia/Acinonyx jubatus/a3dc443b546f9dfcf6574e2d1a418ce4.jpg\",\n  \"348604.jpg\": \"Mammalia/Acinonyx jubatus/3ed04dd4a532ab7dd14c071f6a92d40e.jpg\",\n  \"348605.jpg\": \"Mammalia/Acinonyx jubatus/6b19f89f55dc01f84b6329422893b803.jpg\",\n  \"348606.jpg\": \"Mammalia/Acinonyx jubatus/e8f891209eeeb1d840db06a021ffdaf6.jpg\",\n  \"348607.jpg\": \"Mammalia/Acinonyx jubatus/12d271a954ab19f8f8110760b7b4cb00.jpg\",\n  \"348616.jpg\": \"Mammalia/Acinonyx jubatus/8a322f7c49091406136114f1c44713d7.jpg\",\n  \"348618.jpg\": \"Mammalia/Acinonyx jubatus/0f0a87b5cb1c8bdb7721e66c7f2223ff.jpg\",\n  \"348620.jpg\": \"Mammalia/Acinonyx jubatus/a96143d421878787341e90d829760f9d.jpg\",\n  \"348621.jpg\": \"Mammalia/Acinonyx jubatus/d93c86c044d5a46d317dc77ea2b60bb9.jpg\",\n  \"348622.jpg\": \"Mammalia/Acinonyx jubatus/482a8b1f2e0120f833e60c9a7a607df5.jpg\",\n  \"348671.jpg\": \"Aves/Setophaga americana/1461b9085f1a9c76f7c84a457b0957f0.jpg\",\n  \"348674.jpg\": \"Aves/Setophaga americana/8045b1c041ce4961e61edab2755a63cc.jpg\",\n  \"348694.jpg\": \"Aves/Setophaga americana/57d485afc6a12055b6d9ae77c76064c6.jpg\",\n  \"348697.jpg\": \"Aves/Setophaga americana/0ba76af55be49d6b36f1d11c521726e8.jpg\",\n  \"348698.jpg\": \"Aves/Setophaga americana/7a6471a1abd3eab51dd6743261c9b331.jpg\",\n  \"348699.jpg\": \"Aves/Setophaga americana/7e31fef86ced3bf1f5b63d62d3016572.jpg\",\n  \"348718.jpg\": \"Aves/Setophaga americana/b5254e22ac8e5b6052d320a95d88e42c.jpg\",\n  \"348736.jpg\": \"Aves/Setophaga americana/5b536e74d6ffd3ab524ca90cd64f3fd0.jpg\",\n  \"348751.jpg\": \"Aves/Setophaga americana/81130a1df31d67f9e33a823013b0d12c.jpg\",\n  \"348752.jpg\": \"Aves/Setophaga americana/133ce2b76bd335c9256827a09d3a154d.jpg\",\n  \"348753.jpg\": \"Aves/Setophaga americana/d8e5740b7dc9649e907eff09537cf6a9.jpg\",\n  \"348754.jpg\": \"Aves/Setophaga americana/9a262138542d6b196e0856f9bf987b8e.jpg\",\n  \"348758.jpg\": \"Aves/Setophaga americana/bf6b36691b7f9a1be5d1299207135138.jpg\",\n  \"348774.jpg\": \"Aves/Setophaga americana/a60e836e10ee3d944e2c4475ba59774e.jpg\",\n  \"348779.jpg\": \"Aves/Setophaga americana/78f0f251497ebe8ced90394cc1764d0f.jpg\",\n  \"348780.jpg\": \"Aves/Setophaga americana/b194227de354f69a9a36bceb36a1766b.jpg\",\n  \"348781.jpg\": \"Aves/Setophaga americana/8bf0d296db7e7dfd5697c6fc029701d4.jpg\",\n  \"348787.jpg\": \"Aves/Setophaga americana/674f60be74f6146e5f2ad5e26d5ace4e.jpg\",\n  \"348788.jpg\": \"Aves/Setophaga americana/892689a774f5ccd4efc282234eb8854f.jpg\",\n  \"348817.jpg\": \"Arachnida/Argiope aurantia/6b72cf70f371377260913eb2ce91fcdd.jpg\",\n  \"348864.jpg\": \"Arachnida/Argiope aurantia/408979682793cbe3b2e9e134420ac797.jpg\",\n  \"348881.jpg\": \"Arachnida/Argiope aurantia/0b7ac42eeb24275b144a587c4bd87075.jpg\",\n  \"348909.jpg\": \"Arachnida/Argiope aurantia/731b0094d4430ca60d8029187aef73dd.jpg\",\n  \"348910.jpg\": \"Arachnida/Argiope aurantia/c3c4277ec368691398e3efd95367bba8.jpg\",\n  \"348956.jpg\": \"Arachnida/Argiope aurantia/1b413a5e6db70d6c4e229dc42427e02f.jpg\",\n  \"348957.jpg\": \"Arachnida/Argiope aurantia/4e61ce6b83b73e619984c1dc194551f7.jpg\",\n  \"348961.jpg\": \"Arachnida/Argiope aurantia/b91aa7f2bf621ec4d0aa8d5fea65b331.jpg\",\n  \"349029.jpg\": \"Arachnida/Argiope aurantia/de55555e7074c44b62a1ebc7d86d6421.jpg\",\n  \"349044.jpg\": \"Arachnida/Argiope aurantia/eaa737980b72a5ed6305e35e353548fa.jpg\",\n  \"349055.jpg\": \"Arachnida/Argiope aurantia/4cf8604a778a2014e56da328280907b2.jpg\",\n  \"349067.jpg\": \"Arachnida/Argiope aurantia/6c926194421a3ba04e4d39c1ff7106db.jpg\",\n  \"349085.jpg\": \"Arachnida/Argiope aurantia/64fc8d5b745427894b00cda4a614e075.jpg\",\n  \"349095.jpg\": \"Arachnida/Argiope aurantia/c6d74f35aea34d2978b0c355aeec21e0.jpg\",\n  \"349099.jpg\": \"Arachnida/Argiope aurantia/989218c339dfd73cf7fb3ca555c4e8d8.jpg\",\n  \"349103.jpg\": \"Arachnida/Argiope aurantia/86e953ccf4abef4a5640876a4bec22b0.jpg\",\n  \"349125.jpg\": \"Arachnida/Argiope aurantia/66bb0c77e46f9af82188a254455dbf65.jpg\",\n  \"349134.jpg\": \"Arachnida/Argiope aurantia/6ebdddd8bc6db0cb3079cdbad3da9ee7.jpg\",\n  \"349136.jpg\": \"Arachnida/Argiope aurantia/b5a01b301d4f6e0f4eeb5813dd7522da.jpg\",\n  \"349137.jpg\": \"Arachnida/Argiope aurantia/fb280fa7d7bbdae5aa9c3ecd7bff1bff.jpg\",\n  \"349144.jpg\": \"Arachnida/Argiope aurantia/849c004896eb21e91cf561de791edfaa.jpg\",\n  \"349145.jpg\": \"Arachnida/Argiope aurantia/c744baf43972e8edf450803a26d70d3e.jpg\",\n  \"349154.jpg\": \"Arachnida/Argiope aurantia/39bf5932c3c0566467b135681fe880b7.jpg\",\n  \"349193.jpg\": \"Arachnida/Argiope aurantia/393b0773a7695a87194a6a086a0fd057.jpg\",\n  \"349234.jpg\": \"Arachnida/Argiope aurantia/9b34d5fbd758dccb39ebfce640f3dbba.jpg\",\n  \"349312.jpg\": \"Aves/Podiceps grisegena/e4caa1b4872ccf1139ea7c3294273d77.jpg\",\n  \"349313.jpg\": \"Aves/Podiceps grisegena/aea992d6a7fe2257ac31411c15524533.jpg\",\n  \"349314.jpg\": \"Aves/Podiceps grisegena/ba63f47b362c2856c89c1da59851bd78.jpg\",\n  \"349317.jpg\": \"Aves/Podiceps grisegena/8350e25e97b36a9bb93353301b59fa12.jpg\",\n  \"349322.jpg\": \"Aves/Podiceps grisegena/8a749f83a55b5451898025208db43d9b.jpg\",\n  \"349323.jpg\": \"Aves/Podiceps grisegena/1e20ef3cbb171921b912bc526841a873.jpg\",\n  \"349326.jpg\": \"Aves/Podiceps grisegena/0b6539dab0a2cabfb08075f0ca6abc95.jpg\",\n  \"349328.jpg\": \"Aves/Podiceps grisegena/d439cf5ee20b0f07b02314708d28fdd8.jpg\",\n  \"349329.jpg\": \"Aves/Podiceps grisegena/73596baf0fa8a8ee526fc16b280846a5.jpg\",\n  \"349340.jpg\": \"Aves/Podiceps grisegena/c17d2204d8cea3d12294ab8bdb4150bb.jpg\",\n  \"349342.jpg\": \"Aves/Podiceps grisegena/6d60f50d165b36cb2fc9a9570f1dfc88.jpg\",\n  \"349348.jpg\": \"Aves/Podiceps grisegena/faa9fe362f46734a06e4a71057831d19.jpg\",\n  \"349360.jpg\": \"Aves/Podiceps grisegena/a4df85342d66ce883b002594bf924431.jpg\",\n  \"349361.jpg\": \"Aves/Podiceps grisegena/25d685a70698c44dfe4d841991390d8c.jpg\",\n  \"349370.jpg\": \"Aves/Podiceps grisegena/20ec9048caa5e8d7e10a358473523410.jpg\",\n  \"349371.jpg\": \"Aves/Podiceps grisegena/e5e20742161ae958e61aae0da43eb3a4.jpg\",\n  \"349372.jpg\": \"Aves/Podiceps grisegena/532a0d458d3c7a90702aabbdfef54055.jpg\",\n  \"349376.jpg\": \"Aves/Podiceps grisegena/4349ee3f782fbb22047c670c78ba9c4f.jpg\",\n  \"349400.jpg\": \"Aves/Turdus philomelos/673987635cea182ba121dd43e774c4e3.jpg\",\n  \"349403.jpg\": \"Aves/Turdus philomelos/b0c82c99ad38bebef0fb78b2e3a58c46.jpg\",\n  \"349404.jpg\": \"Aves/Turdus philomelos/9762ef2fb3e1a8115b91da84bd735914.jpg\",\n  \"349407.jpg\": \"Aves/Turdus philomelos/cdf3992f008c4d51c54ac940cfaf1b4e.jpg\",\n  \"349409.jpg\": \"Aves/Turdus philomelos/c42f94e65de8b754fa0a36ed50c35647.jpg\",\n  \"349414.jpg\": \"Aves/Turdus philomelos/dcb2a9ba827922242ef678bfc9bfcb5e.jpg\",\n  \"349427.jpg\": \"Aves/Turdus philomelos/f03bc1758cfa4164677b0c4dca64e5fd.jpg\",\n  \"349432.jpg\": \"Aves/Turdus philomelos/3f02c210ea2cec09c79cb1e0707d5169.jpg\",\n  \"349437.jpg\": \"Aves/Turdus philomelos/3b0da653f555e9bb4620652adbdf12fc.jpg\",\n  \"349438.jpg\": \"Aves/Turdus philomelos/5256c2f33517300c625bd213f031f2fd.jpg\",\n  \"349441.jpg\": \"Aves/Turdus philomelos/76a299cf365508c50b73bf06e74ef868.jpg\",\n  \"349447.jpg\": \"Aves/Turdus philomelos/d33af6a83a9ba9a6973587ec189223dd.jpg\",\n  \"349462.jpg\": \"Aves/Turdus philomelos/03c6db665263eaecd5263011378ce158.jpg\",\n  \"349471.jpg\": \"Aves/Turdus philomelos/08a13912baee1fcdb995c6111471798b.jpg\",\n  \"349483.jpg\": \"Aves/Turdus philomelos/1ab5613232917f7c14726305509b0117.jpg\",\n  \"349557.jpg\": \"Amphibia/Hypopachus variolosus/ccdb752e831bdb4c47e8b8061063955d.jpg\",\n  \"349558.jpg\": \"Amphibia/Hypopachus variolosus/35e95d89205ec67fd017d94cce9f675f.jpg\",\n  \"349561.jpg\": \"Amphibia/Hypopachus variolosus/e01c94821c460cd5a43a3e4136bc2152.jpg\",\n  \"349563.jpg\": \"Amphibia/Hypopachus variolosus/8e9033a89eb6f8b08c0316dfafc89727.jpg\",\n  \"349569.jpg\": \"Amphibia/Hypopachus variolosus/c416442237dee8cf6a60c550a3e40a9b.jpg\",\n  \"349573.jpg\": \"Amphibia/Hypopachus variolosus/45fa048c27474ba8fc482246f2cf6c0d.jpg\",\n  \"349743.jpg\": \"Aves/Pycnonotus jocosus/01871a69aa2c7ac9381d4ea71545edeb.jpg\",\n  \"349744.jpg\": \"Aves/Pycnonotus jocosus/e51c282502bf4febb5325ded3f804355.jpg\",\n  \"349745.jpg\": \"Aves/Pycnonotus jocosus/8935499e1843baa96d1f59e8c20e936b.jpg\",\n  \"349746.jpg\": \"Aves/Pycnonotus jocosus/3e61f9c2d63e01b9e86c4d4fb0158c6c.jpg\",\n  \"349748.jpg\": \"Aves/Pycnonotus jocosus/01266935a042398d85da03ca0a87c187.jpg\",\n  \"349755.jpg\": \"Aves/Pycnonotus jocosus/e2325b507e65048d4395d489d042d6c1.jpg\",\n  \"349756.jpg\": \"Aves/Pycnonotus jocosus/f323a92d9ff5ffdcb1d45b5b02e59c21.jpg\",\n  \"349757.jpg\": \"Aves/Pycnonotus jocosus/117d4e3f800f77f040e1195f89d21a87.jpg\",\n  \"349759.jpg\": \"Aves/Pycnonotus jocosus/495faf469f79e99f1e1fb1fe4c2d8934.jpg\",\n  \"349763.jpg\": \"Aves/Pycnonotus jocosus/28cc40a2f6b8102f80ebe47482b23be6.jpg\",\n  \"349765.jpg\": \"Aves/Pycnonotus jocosus/3669b5a44dd888ea2259349f3782a24f.jpg\",\n  \"349766.jpg\": \"Aves/Pycnonotus jocosus/7708c5c6f23d603b9279431850a4cce0.jpg\",\n  \"349772.jpg\": \"Aves/Pycnonotus jocosus/b8e406154f40848838d78023fb14f0bb.jpg\",\n  \"349773.jpg\": \"Aves/Pycnonotus jocosus/bb755e67e8ee4cbfb28261f919e1c430.jpg\",\n  \"349778.jpg\": \"Aves/Pycnonotus jocosus/cd0019d2409f1992a6c37ce5f3dd2e5f.jpg\",\n  \"349781.jpg\": \"Aves/Pycnonotus jocosus/d652d63ae42dda25d32826445be4cc8e.jpg\",\n  \"349846.jpg\": \"Aves/Sternula antillarum/2e896099a74f09937b1e4b31fa8a3512.jpg\",\n  \"349848.jpg\": \"Aves/Sternula antillarum/358ae0c83d799b7914b876f78084e404.jpg\",\n  \"349850.jpg\": \"Aves/Sternula antillarum/b2af613ab070e8bcd04e7bc4ff0947d7.jpg\",\n  \"349866.jpg\": \"Aves/Sternula antillarum/93467093852fe2bf45d0995fb66f8877.jpg\",\n  \"349881.jpg\": \"Aves/Sternula antillarum/4e44c833409aa3bc0d254bf0dea4b684.jpg\",\n  \"349882.jpg\": \"Aves/Sternula antillarum/85fcfd3212f078afd18c96fca4a1199c.jpg\",\n  \"349883.jpg\": \"Aves/Sternula antillarum/808e23e79f847885731145458bc81e30.jpg\",\n  \"349888.jpg\": \"Aves/Sternula antillarum/8abb950382f01b84491b62b61f40d7af.jpg\",\n  \"349896.jpg\": \"Aves/Sternula antillarum/599def8c74f4f68c1cb479a6dbff9376.jpg\",\n  \"349898.jpg\": \"Aves/Sternula antillarum/aec61ce3c45ad074f750748091ec4c1d.jpg\",\n  \"349903.jpg\": \"Aves/Sternula antillarum/8685beccaeebd362ecfac1da9222151d.jpg\",\n  \"349904.jpg\": \"Aves/Sternula antillarum/1b6fdc124c507c9d522c335761c50261.jpg\",\n  \"349907.jpg\": \"Aves/Sternula antillarum/3460168ab66b0d2b9c87ec6e128e3ffa.jpg\",\n  \"349916.jpg\": \"Aves/Sternula antillarum/3d984c5f8f451101d6b473ac52f7c5af.jpg\",\n  \"349922.jpg\": \"Aves/Sternula antillarum/c92dc9887b1c6188fbb9a30fda626519.jpg\",\n  \"349930.jpg\": \"Aves/Sternula antillarum/0b25e2ab633dd16bc0c7d8259c703658.jpg\",\n  \"349969.jpg\": \"Insecta/Eurema daira/f4010f68289711c877a4d0dcef80e45d.jpg\",\n  \"349970.jpg\": \"Insecta/Eurema daira/b0fbd6814a66d08ebb912b840aa07726.jpg\",\n  \"349973.jpg\": \"Insecta/Eurema daira/c013c07dd5163d04ee3d932f2cdf0b0a.jpg\",\n  \"349979.jpg\": \"Insecta/Eurema daira/fc431705c4524ea4dc9a228896704c5e.jpg\",\n  \"349981.jpg\": \"Insecta/Eurema daira/a3ec234fe1ba3951c0c201ff90ca3f5d.jpg\",\n  \"349982.jpg\": \"Insecta/Eurema daira/56a9f09551472ed4d132b298fa95ea97.jpg\",\n  \"349984.jpg\": \"Insecta/Maliattha synochitis/b5a1d788c526a5f196d6f2ed4f6aa8cf.jpg\",\n  \"349986.jpg\": \"Insecta/Maliattha synochitis/43cd7ebc03dc84bc24db6b380e9ec4a8.jpg\",\n  \"349987.jpg\": \"Insecta/Maliattha synochitis/5a0893774333ea857ce543ae4791cd00.jpg\",\n  \"349990.jpg\": \"Insecta/Maliattha synochitis/1c52ffb0cd3db9c83ab284ac77d871d3.jpg\",\n  \"349991.jpg\": \"Insecta/Maliattha synochitis/3f038972bcbe57eca245c66d9f19c566.jpg\",\n  \"349992.jpg\": \"Insecta/Maliattha synochitis/f7205b9380d82780ac36454e91f63c4e.jpg\",\n  \"349994.jpg\": \"Insecta/Maliattha synochitis/6db28e1d64fa0f4d3c7d0090a2f49ce5.jpg\",\n  \"349995.jpg\": \"Insecta/Maliattha synochitis/e58616eae27f6f1a9bc485e81c74efbb.jpg\",\n  \"349998.jpg\": \"Insecta/Maliattha synochitis/b518c98b8626b3bb427b444df8dfc543.jpg\",\n  \"349999.jpg\": \"Insecta/Maliattha synochitis/384addc25c19a6510ecbe4711a9511a4.jpg\",\n  \"350000.jpg\": \"Insecta/Maliattha synochitis/d56999e29b456b964ed34c3247771d84.jpg\",\n  \"350001.jpg\": \"Insecta/Maliattha synochitis/5d43197f136735f050d54c85502e54e8.jpg\",\n  \"350002.jpg\": \"Insecta/Maliattha synochitis/b940b8868a0af59b267ee2c3886ab6c8.jpg\",\n  \"350003.jpg\": \"Insecta/Maliattha synochitis/54b72f38faba997a3a141da14cd51f2d.jpg\",\n  \"350006.jpg\": \"Insecta/Maliattha synochitis/a9f9e406a3008d4f3b9fee00e0b94b24.jpg\",\n  \"350007.jpg\": \"Insecta/Maliattha synochitis/69020ed81c4b4170fac5289b61170ce4.jpg\",\n  \"350008.jpg\": \"Insecta/Maliattha synochitis/af481e3006dc704ed891abd653157e7f.jpg\",\n  \"350009.jpg\": \"Insecta/Maliattha synochitis/c1d6c8a8018cf9ce5d0972c0f21f3877.jpg\",\n  \"350010.jpg\": \"Insecta/Maliattha synochitis/9208d92dfaaaa60ab1592df9225996af.jpg\",\n  \"350011.jpg\": \"Insecta/Maliattha synochitis/a1328f733fe8b342d031f0921a0e04c7.jpg\",\n  \"350017.jpg\": \"Insecta/Maliattha synochitis/b5bcf4b5f28f37dea326b39a327811e9.jpg\",\n  \"350019.jpg\": \"Insecta/Maliattha synochitis/c373be7b2d4c6f97edbad01366f60d53.jpg\",\n  \"350024.jpg\": \"Reptilia/Agkistrodon piscivorus conanti/126732bb2e8c7474f74f19c19fb9514f.jpg\",\n  \"350025.jpg\": \"Reptilia/Agkistrodon piscivorus conanti/0ae310288355412dd061567c1bfe4a72.jpg\",\n  \"350026.jpg\": \"Reptilia/Agkistrodon piscivorus conanti/eb20d75e64d57edc3e4b721a477f1890.jpg\",\n  \"350037.jpg\": \"Reptilia/Agkistrodon piscivorus conanti/206d267062d2feb9c4111bebd139ab07.jpg\",\n  \"350047.jpg\": \"Aves/Setophaga magnolia/80b013700589ebb6fe5a71cf63e66565.jpg\",\n  \"350060.jpg\": \"Aves/Setophaga magnolia/7050218d147d4140106b9949dd7bba6e.jpg\",\n  \"350061.jpg\": \"Aves/Setophaga magnolia/d8ca65454c63b56cb801a1cd184c237d.jpg\",\n  \"350063.jpg\": \"Aves/Setophaga magnolia/f8a50fad57609469641d6849edd4a2b2.jpg\",\n  \"350064.jpg\": \"Aves/Setophaga magnolia/edfd659a832d75b7ad7d6c1cf7256134.jpg\",\n  \"350065.jpg\": \"Aves/Setophaga magnolia/fd277663c654c1424675783a47bdec48.jpg\",\n  \"350066.jpg\": \"Aves/Setophaga magnolia/a2c11d4f7a84bfb86061eb2feb58cb73.jpg\",\n  \"350068.jpg\": \"Aves/Setophaga magnolia/9df6945e1071eb58527af009938c52da.jpg\",\n  \"350074.jpg\": \"Aves/Setophaga magnolia/5741a91cae603eb31bb47cf655967b79.jpg\",\n  \"350080.jpg\": \"Aves/Setophaga magnolia/c34d783e7b52d12248fbde5bed251fbb.jpg\",\n  \"350082.jpg\": \"Aves/Setophaga magnolia/1ce4a509375297f1b3c7593201a4da90.jpg\",\n  \"350099.jpg\": \"Aves/Setophaga magnolia/f5850011995481fb8cfb006005355ca0.jpg\",\n  \"350101.jpg\": \"Aves/Setophaga magnolia/bad8ec6caa7abe2442ab9b3deec4892a.jpg\",\n  \"350107.jpg\": \"Aves/Setophaga magnolia/df7982c83bf977b458825b29ad5763d7.jpg\",\n  \"350117.jpg\": \"Aves/Setophaga magnolia/0e3baff86863d4fbd22783d36a1fcf9e.jpg\",\n  \"350144.jpg\": \"Aves/Setophaga magnolia/301a8e1fd179a1050a64c37beff05857.jpg\",\n  \"350146.jpg\": \"Aves/Setophaga magnolia/dae497401e38080993f433fc51bf313c.jpg\",\n  \"350148.jpg\": \"Aves/Setophaga magnolia/fb9dfdcaf28b998b67373e927a541103.jpg\",\n  \"350151.jpg\": \"Aves/Setophaga magnolia/8e4367d44003bf5fb55399e10a67038e.jpg\",\n  \"350164.jpg\": \"Aves/Setophaga magnolia/dc410b42730dc5985638dc246d488bf7.jpg\",\n  \"350248.jpg\": \"Actinopterygii/Oncorhynchus mykiss/ef88bb35987dab88ef3b530ac1d08411.jpg\",\n  \"350253.jpg\": \"Actinopterygii/Oncorhynchus mykiss/8dcde454c5b8f16cfa9be79e0dbabd10.jpg\",\n  \"350254.jpg\": \"Actinopterygii/Oncorhynchus mykiss/c3eaffba81560d5a730aea219b83b881.jpg\",\n  \"350255.jpg\": \"Actinopterygii/Oncorhynchus mykiss/3e3a1fb1c444db32fd6fe35316885561.jpg\",\n  \"350258.jpg\": \"Actinopterygii/Oncorhynchus mykiss/449ae7f563887d8996e81eb6b07d87fb.jpg\",\n  \"350260.jpg\": \"Actinopterygii/Oncorhynchus mykiss/a0a24f58c5588b79df7ead85d50f499f.jpg\",\n  \"350261.jpg\": \"Actinopterygii/Oncorhynchus mykiss/cdd2bcedbdbe467f65fb738862ce4248.jpg\",\n  \"350262.jpg\": \"Actinopterygii/Oncorhynchus mykiss/91e3ed4796f1b6be354929681d545e1a.jpg\",\n  \"350263.jpg\": \"Actinopterygii/Oncorhynchus mykiss/b70a567ae7a59904d62599bd3edc3995.jpg\",\n  \"350264.jpg\": \"Actinopterygii/Oncorhynchus mykiss/40bea05cec2c68fc08630b16b1bfca9c.jpg\",\n  \"350270.jpg\": \"Actinopterygii/Oncorhynchus mykiss/989c9f3da21f377632ab2726caac18db.jpg\",\n  \"350276.jpg\": \"Actinopterygii/Oncorhynchus mykiss/2a33d0c37851f76aacc7d9797c5b392e.jpg\",\n  \"350279.jpg\": \"Actinopterygii/Oncorhynchus mykiss/8ae39d297ec2d4f50cdf8e2287c9969e.jpg\",\n  \"350288.jpg\": \"Actinopterygii/Oncorhynchus mykiss/9eb35c78f85ec24a442a5338b0ea6a7c.jpg\",\n  \"350295.jpg\": \"Actinopterygii/Oncorhynchus mykiss/8ef1d9353b7fb36f350b3ea6d6c0c26e.jpg\",\n  \"350297.jpg\": \"Actinopterygii/Oncorhynchus mykiss/7e397ad86544d28b516756bc4fffee97.jpg\",\n  \"350298.jpg\": \"Actinopterygii/Oncorhynchus mykiss/28f6f2c08717259f36b656026c2597ff.jpg\",\n  \"350300.jpg\": \"Actinopterygii/Oncorhynchus mykiss/98f9bb5854098ba18aa1fac8f717a442.jpg\",\n  \"350310.jpg\": \"Actinopterygii/Oncorhynchus mykiss/198b85fcda84a424eb3730978d614227.jpg\",\n  \"350372.jpg\": \"Mammalia/Didelphis virginiana/c2372262954b07f0c4ea693a511e5f3e.jpg\",\n  \"350428.jpg\": \"Mammalia/Didelphis virginiana/cb62852426b4bb558a13dce0d6cd0180.jpg\",\n  \"350429.jpg\": \"Mammalia/Didelphis virginiana/8e3bbbed7a7ced4fbddcf3713a349f8a.jpg\",\n  \"350437.jpg\": \"Mammalia/Didelphis virginiana/df51df54ca0316d965a3c34655d55c34.jpg\",\n  \"350443.jpg\": \"Mammalia/Didelphis virginiana/1ff739f74c2e6b9709cd165b351ead6f.jpg\",\n  \"350451.jpg\": \"Mammalia/Didelphis virginiana/ea6c90ebc4ae7e5126f8cc522ab51c49.jpg\",\n  \"350459.jpg\": \"Mammalia/Didelphis virginiana/586cc6d75b3768f8843b88b09cf3b3e5.jpg\",\n  \"350472.jpg\": \"Mammalia/Didelphis virginiana/6a7749c3a1161974821c5f2615c267ee.jpg\",\n  \"350477.jpg\": \"Mammalia/Didelphis virginiana/735db478930dd4038aae38bf94021814.jpg\",\n  \"350482.jpg\": \"Mammalia/Didelphis virginiana/c7e7f1b68a7cb9c0c98e4ab6cc12f431.jpg\",\n  \"350486.jpg\": \"Mammalia/Didelphis virginiana/61d5bca0b10c308b55f8f044dff4a868.jpg\",\n  \"350489.jpg\": \"Mammalia/Didelphis virginiana/bc0aac1707f5e50f83d1e145d086d44f.jpg\",\n  \"350512.jpg\": \"Mammalia/Didelphis virginiana/a0f8fcac1bc73d4b75d5c5b62d7533c7.jpg\",\n  \"350513.jpg\": \"Mammalia/Didelphis virginiana/4c1f976b1f4bb9e977ef9c71315eccc5.jpg\",\n  \"350531.jpg\": \"Mammalia/Didelphis virginiana/41d20ab9da6fd4bbe4b0bc9583c3abf3.jpg\",\n  \"350533.jpg\": \"Mammalia/Didelphis virginiana/7097820cfd038c5e3f1931d6777ec992.jpg\",\n  \"350585.jpg\": \"Mammalia/Didelphis virginiana/1a45357896dcb35fad2bc6258db3a59c.jpg\",\n  \"350593.jpg\": \"Mammalia/Didelphis virginiana/4321a00f2ee24019be9abfc3228f227b.jpg\",\n  \"350621.jpg\": \"Mammalia/Didelphis virginiana/dec02cc2ffe50883450fa436fabd6698.jpg\",\n  \"350629.jpg\": \"Mammalia/Didelphis virginiana/de29efa52a47036aeab5b8c7ef2722a0.jpg\",\n  \"350693.jpg\": \"Aves/Ardeotis kori/50aa6a37376c7265c9f0e5c0f66a1eab.jpg\",\n  \"350697.jpg\": \"Aves/Ardeotis kori/6c421fef3020aa00d59d5b8be7c717d7.jpg\",\n  \"350699.jpg\": \"Aves/Ardeotis kori/3ffea743183dba6af8d46e8c17695661.jpg\",\n  \"350701.jpg\": \"Aves/Ardeotis kori/9b6c906ed0b441f341f58069bee55a0a.jpg\",\n  \"350715.jpg\": \"Aves/Catharus minimus/7561ecf11e2437add85ddb34053a2fe6.jpg\",\n  \"350721.jpg\": \"Aves/Catharus minimus/c27c994fc0b6c2a975090b2584dca12a.jpg\",\n  \"350722.jpg\": \"Aves/Catharus minimus/5deef6956bdc9cc8e255a9caf30915ef.jpg\",\n  \"350728.jpg\": \"Aves/Catharus minimus/ea1c410dee36e6dc53a718190a637490.jpg\",\n  \"350729.jpg\": \"Aves/Catharus minimus/0b14681a7b2483a29884541b6deef2cc.jpg\",\n  \"350733.jpg\": \"Aves/Sterna hirundo/7af62cb47fdae266b8cf54b543bfa7b3.jpg\",\n  \"350758.jpg\": \"Aves/Sterna hirundo/db62439b835213cff32271495007c423.jpg\",\n  \"350760.jpg\": \"Aves/Sterna hirundo/01cbaf702c53148f9cba030efaf530f7.jpg\",\n  \"350761.jpg\": \"Aves/Sterna hirundo/29e6bfcd26723d66142dc6ea1779e007.jpg\",\n  \"350765.jpg\": \"Aves/Sterna hirundo/b0bd536d4ba6ec47189c7a99394dff9b.jpg\",\n  \"350770.jpg\": \"Aves/Sterna hirundo/596f2893e42bd9fe695f7b31d27848a7.jpg\",\n  \"350777.jpg\": \"Aves/Sterna hirundo/5a155d8ad1ddd0cd475b9e6300310212.jpg\",\n  \"350790.jpg\": \"Aves/Sterna hirundo/d8af9096d50397608594966998460ea0.jpg\",\n  \"350792.jpg\": \"Aves/Sterna hirundo/c10cdd2779744a474e8d69076772686b.jpg\",\n  \"350793.jpg\": \"Aves/Sterna hirundo/06e584080711c42007c1cf81d287edde.jpg\",\n  \"350804.jpg\": \"Aves/Sterna hirundo/754707ce320f008256b5ae7a4ebdcbc1.jpg\",\n  \"350805.jpg\": \"Aves/Sterna hirundo/7a907a5a6f1574f810ca59fbc45745cb.jpg\",\n  \"350825.jpg\": \"Aves/Sterna hirundo/e2dedcfee95b3c54af2c1bb9dc9df722.jpg\",\n  \"350829.jpg\": \"Aves/Sterna hirundo/3969e299ef5fe557ad0743fab44e007d.jpg\",\n  \"350836.jpg\": \"Aves/Sterna hirundo/da416b22655bff7dee4f60c432c5e589.jpg\",\n  \"350837.jpg\": \"Aves/Sterna hirundo/f4976b678f067524f5b5ee773de801d1.jpg\",\n  \"350839.jpg\": \"Aves/Sterna hirundo/bfa151e326f33d306592f68ba21ddef9.jpg\",\n  \"351.jpg\": \"Insecta/Coleomegilla maculata/7004c95251ba83a035545438764589cc.jpg\",\n  \"35106.jpg\": \"Aves/Copsychus malabaricus/c2627edd3188f61bddc365e2d7981cfe.jpg\",\n  \"35115.jpg\": \"Aves/Copsychus malabaricus/8162f974d9586dd902e5ea6ee9399814.jpg\",\n  \"351193.jpg\": \"Aves/Chlorophanes spiza/8753d2b6a7e692e62bc13d4a415201a0.jpg\",\n  \"351195.jpg\": \"Aves/Chlorophanes spiza/391e974716ebb970a6472ee79227368e.jpg\",\n  \"351198.jpg\": \"Aves/Chlorophanes spiza/0c18d89b63568f03327510d333edefec.jpg\",\n  \"351199.jpg\": \"Aves/Chlorophanes spiza/d6210045c9c3e20c2dc4e8dae30d2c0d.jpg\",\n  \"3512.jpg\": \"Insecta/Coccinella septempunctata/ddecb31852a10027b9fa5fbca948968d.jpg\",\n  \"35120.jpg\": \"Aves/Copsychus malabaricus/6230dd97eb9b46c39ba181f0d3f7e8ea.jpg\",\n  \"351201.jpg\": \"Aves/Chlorophanes spiza/3fcec540ba6b34779598969208781c29.jpg\",\n  \"351205.jpg\": \"Insecta/Plagiometriona clavata/b53669bf76ec40b432e0523104d44327.jpg\",\n  \"351207.jpg\": \"Insecta/Plagiometriona clavata/6c2e2f89221f92c602962127e8fda81e.jpg\",\n  \"35121.jpg\": \"Aves/Copsychus malabaricus/294546ce7e8f70b8532b2d7bd499f36c.jpg\",\n  \"351210.jpg\": \"Insecta/Plagiometriona clavata/7105479709fc75c2a14f7cf1066c53b0.jpg\",\n  \"351213.jpg\": \"Insecta/Plagiometriona clavata/dbc3726937874e474c1b00a7ac78f682.jpg\",\n  \"351259.jpg\": \"Aves/Vireo huttoni/56e4f3960c25089e66d3fcf24545a82f.jpg\",\n  \"351263.jpg\": \"Aves/Vireo huttoni/33fe87118cd4a1aca5baa58d8bc8afcc.jpg\",\n  \"351268.jpg\": \"Aves/Vireo huttoni/173970cf602b5ee4aa885bed6f86f219.jpg\",\n  \"351273.jpg\": \"Aves/Vireo huttoni/dc1d9cee5ac377b2949d840d53843681.jpg\",\n  \"351274.jpg\": \"Aves/Vireo huttoni/28f82d2a683024f3579e2506bd19b91e.jpg\",\n  \"351277.jpg\": \"Aves/Vireo huttoni/5addc846e1fe28e45dbab297e46820e2.jpg\",\n  \"351278.jpg\": \"Aves/Vireo huttoni/98514275a25acc11a64f8550fbff0954.jpg\",\n  \"351279.jpg\": \"Aves/Vireo huttoni/44d0c9bf33b30d8193c7884cf8b526dd.jpg\",\n  \"351280.jpg\": \"Aves/Vireo huttoni/589935aa412da2dbc890cde639a14e9c.jpg\",\n  \"351281.jpg\": \"Aves/Vireo huttoni/5a1ce8e3916c21f808c23686d782f9a3.jpg\",\n  \"351291.jpg\": \"Aves/Vireo huttoni/506b885be82483a6a3f1d1abcbbc2d39.jpg\",\n  \"351296.jpg\": \"Aves/Vireo huttoni/f4be4398611c96217979f984d871b48c.jpg\",\n  \"351297.jpg\": \"Aves/Vireo huttoni/366cbe1ab0f0e39198a5164fe4864631.jpg\",\n  \"351298.jpg\": \"Aves/Vireo huttoni/7c55a50f0c45ed8abe8a5bcfd5fd88af.jpg\",\n  \"351301.jpg\": \"Aves/Vireo huttoni/bc8e0c8f35d0d7c91924ad11136a1cf7.jpg\",\n  \"351304.jpg\": \"Aves/Vireo huttoni/ed5d77c22b90be466b84f7aa8014f08a.jpg\",\n  \"351307.jpg\": \"Aves/Vireo huttoni/6cbf12460e3b99916b71445228dfd4f1.jpg\",\n  \"351308.jpg\": \"Aves/Vireo huttoni/5b53ba7c8bddddbbc985de1f041bd524.jpg\",\n  \"351309.jpg\": \"Aves/Vireo huttoni/4c5e44062e34e323d9681e5cd66ed443.jpg\",\n  \"351313.jpg\": \"Aves/Vireo huttoni/f52cbbdbbb60779d41e810d861f8ac90.jpg\",\n  \"351326.jpg\": \"Aves/Vireo huttoni/80a9443fea30e68ebca571ae5526da23.jpg\",\n  \"351328.jpg\": \"Aves/Vireo huttoni/547755a409923ef22a748f48f215ac8c.jpg\",\n  \"351329.jpg\": \"Aves/Vireo huttoni/e609b327ec62678b173c6d2c908b2e84.jpg\",\n  \"351330.jpg\": \"Aves/Vireo huttoni/20c14b29f32f8e6f19527f0f51d72d20.jpg\",\n  \"351331.jpg\": \"Aves/Vireo huttoni/604b14538f5976d88bd861861a44251a.jpg\",\n  \"35139.jpg\": \"Aves/Icterus pustulatus/9be0bc69ea8f7fb0fa7ca623f5a02045.jpg\",\n  \"35142.jpg\": \"Aves/Icterus pustulatus/918d21f2fe450687f5777ce3d60b6ce1.jpg\",\n  \"351452.jpg\": \"Aves/Poecile rufescens/13d55791169491e40f8feec101e6d158.jpg\",\n  \"351465.jpg\": \"Aves/Poecile rufescens/2b85a90163b22ebf5350589baaa22f37.jpg\",\n  \"35147.jpg\": \"Aves/Icterus pustulatus/13063722f22b5033dd01631bc9332f76.jpg\",\n  \"351470.jpg\": \"Aves/Poecile rufescens/a4efe172f8908dde3c0e8dcf55c8e9d1.jpg\",\n  \"351478.jpg\": \"Aves/Poecile rufescens/17f0f58c6c3606dabd5d29cd18b12131.jpg\",\n  \"35148.jpg\": \"Aves/Icterus pustulatus/f2f48b81353ddfcfec7cfbaa0511228d.jpg\",\n  \"35150.jpg\": \"Aves/Icterus pustulatus/5cec8e483faf12314d02ab5be232d52a.jpg\",\n  \"351501.jpg\": \"Aves/Poecile rufescens/2441821e0fc93caff516d4d86640623c.jpg\",\n  \"351504.jpg\": \"Aves/Poecile rufescens/c64188f57ff410d6c285b0f23f656b49.jpg\",\n  \"351509.jpg\": \"Aves/Poecile rufescens/8e45b9f49ec353fb87ed74e1067dafae.jpg\",\n  \"351516.jpg\": \"Aves/Poecile rufescens/5bf83199d721162ae8959c99dffaa79c.jpg\",\n  \"351522.jpg\": \"Aves/Poecile rufescens/d4e8b761520492741a5823c55dab08a3.jpg\",\n  \"35153.jpg\": \"Aves/Icterus pustulatus/36da97f8fbd37893d171fafe91c5a1ad.jpg\",\n  \"35154.jpg\": \"Aves/Icterus pustulatus/38ad56375f32182a9d9804dc0783f0c9.jpg\",\n  \"351541.jpg\": \"Aves/Poecile rufescens/a4f0f3e9ac70896c1f357830f44c9ded.jpg\",\n  \"351545.jpg\": \"Aves/Poecile rufescens/4990f341e4be3823fe05f0d824f61d37.jpg\",\n  \"35156.jpg\": \"Aves/Icterus pustulatus/b6ed7d7865a2e8d8ee6c8d914e0ba807.jpg\",\n  \"35157.jpg\": \"Aves/Icterus pustulatus/5f37d11ad90f3eda1f80d89908bb7bc5.jpg\",\n  \"351574.jpg\": \"Aves/Poecile rufescens/363b82bc5bf7a6aca13623e4d927e7d0.jpg\",\n  \"351578.jpg\": \"Aves/Poecile rufescens/35c688586a27fed96414560c1338ac8f.jpg\",\n  \"351581.jpg\": \"Aves/Poecile rufescens/f544321d5667a168c44ace115cd30713.jpg\",\n  \"35159.jpg\": \"Aves/Icterus pustulatus/65f4ebdd0fcdfa6f2ed32210f13f4587.jpg\",\n  \"351598.jpg\": \"Aves/Poecile rufescens/29c1978b65d49b9ea52b834d41b79de1.jpg\",\n  \"3516.jpg\": \"Insecta/Coccinella septempunctata/e1fe6b55e5d57ce1c87a6fbcd0f9b052.jpg\",\n  \"351611.jpg\": \"Aves/Poecile rufescens/bd6305dcfa537ba1da19674f0bbdcb50.jpg\",\n  \"351621.jpg\": \"Aves/Poecile rufescens/345195ed29c713d2c2091ecb9c230473.jpg\",\n  \"35163.jpg\": \"Aves/Icterus pustulatus/37ef8bd22e744cb24c9bc07b4ce251df.jpg\",\n  \"351631.jpg\": \"Aves/Poecile rufescens/3b76fe03af38e78ac91cabe289eae609.jpg\",\n  \"351636.jpg\": \"Aves/Poecile rufescens/a4f2a900847fa3a2b981237606159431.jpg\",\n  \"35164.jpg\": \"Aves/Icterus pustulatus/d6672b8ef0cc8449a5e93f1843b84504.jpg\",\n  \"351640.jpg\": \"Aves/Poecile rufescens/29d9549238eb28e0c128cad95470fb0d.jpg\",\n  \"351647.jpg\": \"Aves/Poecile rufescens/b17ff484ede397d8b51a355b10501f4d.jpg\",\n  \"35165.jpg\": \"Aves/Icterus pustulatus/ad50ed97feffc1ef27b71626153d4351.jpg\",\n  \"351720.jpg\": \"Aves/Anser anser domesticus/067e1f50e793df52f222f1bf9cce49fb.jpg\",\n  \"351726.jpg\": \"Aves/Anser anser domesticus/26bb092dcca63fe0aceb3ebd5b7f5e21.jpg\",\n  \"351738.jpg\": \"Aves/Anser anser domesticus/a8ac2328b5576ceaf65c25175243044f.jpg\",\n  \"351745.jpg\": \"Aves/Anser anser domesticus/8f9e521ee5b2d559e9917fdc8567ccf5.jpg\",\n  \"35175.jpg\": \"Aves/Icterus pustulatus/43d0dba917d4ea02919b5d587e9f5bee.jpg\",\n  \"351756.jpg\": \"Aves/Anser anser domesticus/e8a2cd68f9b8fb6647dbc7477899296a.jpg\",\n  \"351759.jpg\": \"Aves/Anser anser domesticus/d621f2864b06d0e00c3d5d6299de1179.jpg\",\n  \"351764.jpg\": \"Aves/Anser anser domesticus/a26b2b04220ea96a9b52ea5753b819ef.jpg\",\n  \"351782.jpg\": \"Aves/Anser anser domesticus/3a4946b234cc73d8ec51c04833fe4d91.jpg\",\n  \"35182.jpg\": \"Aves/Icterus pustulatus/8bdfbc40808b01910e8c9f2feefd9a61.jpg\",\n  \"351853.jpg\": \"Reptilia/Chelydra serpentina/2ba61b2eecf51af72eacccbea95f8337.jpg\",\n  \"351860.jpg\": \"Reptilia/Chelydra serpentina/8146275063ce1b2044b6990eb60d3005.jpg\",\n  \"351873.jpg\": \"Reptilia/Chelydra serpentina/cd5b4aae0fc0a73410ee7b6a6f43334e.jpg\",\n  \"351885.jpg\": \"Reptilia/Chelydra serpentina/542caa50ec76fe499fbf241333ae9214.jpg\",\n  \"351894.jpg\": \"Reptilia/Chelydra serpentina/4e874171c9738f7f9665db44e2b395c3.jpg\",\n  \"3519.jpg\": \"Insecta/Coccinella septempunctata/a5297fed59def39cb3db80026346ed20.jpg\",\n  \"351915.jpg\": \"Reptilia/Chelydra serpentina/84a493ede4ac5ad82ada917b9f9f0753.jpg\",\n  \"351944.jpg\": \"Reptilia/Chelydra serpentina/30397692f626235d4afda73a87db65dc.jpg\",\n  \"35195.jpg\": \"Aves/Icterus pustulatus/06ae2393005e92ef3e5261b94a35f627.jpg\",\n  \"35196.jpg\": \"Aves/Icterus pustulatus/5ba88696e51ac8ae59ccfb336c27afc0.jpg\",\n  \"351964.jpg\": \"Reptilia/Chelydra serpentina/1aaa756134603909f11beb584f1a9252.jpg\",\n  \"351966.jpg\": \"Reptilia/Chelydra serpentina/6704cc83f3c6ebaeca9dbf768c9ed474.jpg\",\n  \"35198.jpg\": \"Aves/Icterus pustulatus/eefc369f1cdc9a969032c3184a8b19a2.jpg\",\n  \"352011.jpg\": \"Reptilia/Chelydra serpentina/ac23b6db73f032bc7aef568905d728a5.jpg\",\n  \"352015.jpg\": \"Reptilia/Chelydra serpentina/bc1939b0def0b6cdcb0ab323ed74ecf2.jpg\",\n  \"352048.jpg\": \"Reptilia/Chelydra serpentina/3c90bcd573d28356505fd350d44d7367.jpg\",\n  \"352085.jpg\": \"Reptilia/Chelydra serpentina/534994b8d07544fb2bbc15523d590b0f.jpg\",\n  \"35211.jpg\": \"Reptilia/Gopherus polyphemus/d6ccf59092735f6aa5acebd891141372.jpg\",\n  \"35212.jpg\": \"Reptilia/Gopherus polyphemus/4af6ea9400f80cc28afc3ea8e6f2cdeb.jpg\",\n  \"35213.jpg\": \"Reptilia/Gopherus polyphemus/84f3e9b8cc26c78b6f195360c26b5b3c.jpg\",\n  \"352136.jpg\": \"Reptilia/Chelydra serpentina/aa83a846c4933c56fef55865e14322e3.jpg\",\n  \"352206.jpg\": \"Reptilia/Chelydra serpentina/3c86ea905d33e185446faf3867e61ec4.jpg\",\n  \"352237.jpg\": \"Reptilia/Chelydra serpentina/5353038bb7b41cb53c8efc226ef0f610.jpg\",\n  \"35225.jpg\": \"Reptilia/Gopherus polyphemus/55880ead4495e8defff6225d6b700eeb.jpg\",\n  \"352267.jpg\": \"Reptilia/Chelydra serpentina/f3b17a731a2fa65c07bc5bec2e77e698.jpg\",\n  \"35231.jpg\": \"Reptilia/Gopherus polyphemus/4e49cc395aaf6d0647922186fdc25cf1.jpg\",\n  \"35233.jpg\": \"Reptilia/Gopherus polyphemus/9be692a0cf409db8e46ae33d5ab5e6dc.jpg\",\n  \"352336.jpg\": \"Reptilia/Chelydra serpentina/c3282eef398e6a34870d050d54237c7a.jpg\",\n  \"35234.jpg\": \"Reptilia/Gopherus polyphemus/789fc28cb86540a180a96946dfca65bf.jpg\",\n  \"35235.jpg\": \"Reptilia/Gopherus polyphemus/efd3d1ff64324ae135e0d793e3d7c82e.jpg\",\n  \"35237.jpg\": \"Reptilia/Gopherus polyphemus/83dfe9c4b2e46b11f3827fd6dcc003fd.jpg\",\n  \"352375.jpg\": \"Reptilia/Chelydra serpentina/f29580e9a01b1302eb06eb319604eb66.jpg\",\n  \"35238.jpg\": \"Reptilia/Gopherus polyphemus/747e21bceecbc015d7b423d36def128f.jpg\",\n  \"352381.jpg\": \"Reptilia/Chelydra serpentina/7dc59edfdaae7929abf7cad94234defd.jpg\",\n  \"35239.jpg\": \"Reptilia/Gopherus polyphemus/ed0ce9f2d1263da35ae54cd671c0a696.jpg\",\n  \"352415.jpg\": \"Amphibia/Anaxyrus americanus/14245dcc72887e4e96d3587830a93363.jpg\",\n  \"35242.jpg\": \"Reptilia/Gopherus polyphemus/dff2c392af0f3e794b11e2d1363a0721.jpg\",\n  \"352446.jpg\": \"Amphibia/Anaxyrus americanus/d3ab894754b6175a76df530dbbf3cdba.jpg\",\n  \"352453.jpg\": \"Amphibia/Anaxyrus americanus/1f7214f58f98987e3d8f730d3de834f4.jpg\",\n  \"35250.jpg\": \"Reptilia/Gopherus polyphemus/a07616e9b73bda26c1126c3d9d2ccfee.jpg\",\n  \"352534.jpg\": \"Amphibia/Anaxyrus americanus/8aa68aef06bd3c5a6aaf05e3ffb4a098.jpg\",\n  \"352558.jpg\": \"Amphibia/Anaxyrus americanus/d5ef74b83418c53fa8e4906c2f4446b6.jpg\",\n  \"35257.jpg\": \"Reptilia/Gopherus polyphemus/742ac4351ce8142873bf7dbceef832c1.jpg\",\n  \"35261.jpg\": \"Reptilia/Gopherus polyphemus/5bbd0648a68a7d130dca9aa8af64c3df.jpg\",\n  \"352612.jpg\": \"Amphibia/Anaxyrus americanus/4426db58aa188b742a008e41fd0f0341.jpg\",\n  \"35262.jpg\": \"Reptilia/Gopherus polyphemus/1178a6f44b2fef6b91f11059f48f9024.jpg\",\n  \"352628.jpg\": \"Amphibia/Anaxyrus americanus/7db30275f347c1d2c411a9795f5767f1.jpg\",\n  \"35263.jpg\": \"Reptilia/Gopherus polyphemus/8cf9c33782a0df31457ced2613dc16c3.jpg\",\n  \"35264.jpg\": \"Reptilia/Gopherus polyphemus/6d493e6db22c408f1942985efff542c0.jpg\",\n  \"35265.jpg\": \"Reptilia/Gopherus polyphemus/5940a4c4584c39abc8a552ead8a5e260.jpg\",\n  \"352712.jpg\": \"Amphibia/Anaxyrus americanus/e5f74963510c091397d67bca68becf16.jpg\",\n  \"352723.jpg\": \"Amphibia/Anaxyrus americanus/bb094c340dd99038c51a7ba74da10b09.jpg\",\n  \"352733.jpg\": \"Amphibia/Anaxyrus americanus/67d03bf4d84e135a7f86d35daf67f7fa.jpg\",\n  \"352738.jpg\": \"Amphibia/Anaxyrus americanus/8b466adb26187d2835eeb672a6a35748.jpg\",\n  \"352741.jpg\": \"Amphibia/Anaxyrus americanus/cb90d25c96c08ec85a68add134f3113e.jpg\",\n  \"352744.jpg\": \"Amphibia/Anaxyrus americanus/6f1bcc185be2a7b7971c545f5fb982ef.jpg\",\n  \"352751.jpg\": \"Amphibia/Anaxyrus americanus/c44803c74ba30a71013ef525d581dace.jpg\",\n  \"352776.jpg\": \"Amphibia/Anaxyrus americanus/c39a0ee1e31af74ac750e3f96829d97e.jpg\",\n  \"352814.jpg\": \"Amphibia/Anaxyrus americanus/f3d28ce4c560af9123daf73d714637dc.jpg\",\n  \"352821.jpg\": \"Amphibia/Anaxyrus americanus/40ec6fd05ed42c2017c27dd42747351f.jpg\",\n  \"352840.jpg\": \"Amphibia/Anaxyrus americanus/078bf55985f89150ff425622f42fdad7.jpg\",\n  \"352850.jpg\": \"Amphibia/Anaxyrus americanus/ade451ce8bcc939d5a68f68c0bfe1e93.jpg\",\n  \"352877.jpg\": \"Amphibia/Anaxyrus americanus/563b2f45d358c26e1ae59bf28c0559f0.jpg\",\n  \"353029.jpg\": \"Aves/Anser albifrons/011a5bf10b26744547ed37fdfbcde5a3.jpg\",\n  \"353030.jpg\": \"Aves/Anser albifrons/76a4df5d4a53badfe6a3aa5a5d7a1b07.jpg\",\n  \"353031.jpg\": \"Aves/Anser albifrons/d4c463450ef57e923778a2c7e733a41a.jpg\",\n  \"353033.jpg\": \"Aves/Anser albifrons/fce11207009999014c631be2dba5ea9e.jpg\",\n  \"353048.jpg\": \"Aves/Anser albifrons/a4533b69e5552dfb6bc3224b8fac178d.jpg\",\n  \"353083.jpg\": \"Aves/Anser albifrons/ce066ddba0edfc8d9d5c0291baa1183c.jpg\",\n  \"353090.jpg\": \"Aves/Anser albifrons/cc8522ec83f768d2a42ef9efe7b1b24f.jpg\",\n  \"353098.jpg\": \"Aves/Anser albifrons/6a3d7c1d89d550ed00d6326e35cbe5a4.jpg\",\n  \"353102.jpg\": \"Aves/Anser albifrons/bf9cc1b3c02a7f4622058328c1b53645.jpg\",\n  \"353114.jpg\": \"Aves/Anser albifrons/df106c0285c57b2dbb4b0f11450913ea.jpg\",\n  \"353143.jpg\": \"Aves/Anser albifrons/a26b230a0d79b180d7204f9f6e5f6de5.jpg\",\n  \"353168.jpg\": \"Reptilia/Carphophis amoenus/4a4e426230d650b18ebe4cbdb49ad66d.jpg\",\n  \"353169.jpg\": \"Reptilia/Carphophis amoenus/2a88d09a3a99acdf25190367174013cf.jpg\",\n  \"353172.jpg\": \"Reptilia/Carphophis amoenus/756104b8b31b0682961335580d023c89.jpg\",\n  \"353175.jpg\": \"Reptilia/Carphophis amoenus/fea2f6352076e96a716e0675ca57bc20.jpg\",\n  \"353180.jpg\": \"Reptilia/Carphophis amoenus/6d1844bdd478dbaa40da2fe995be717f.jpg\",\n  \"353182.jpg\": \"Reptilia/Carphophis amoenus/ed7e5842b38e8b0c63cd47def09b5cf2.jpg\",\n  \"353192.jpg\": \"Reptilia/Carphophis amoenus/3ef1c73cf694e7f4b21955b9cf2fddf2.jpg\",\n  \"353207.jpg\": \"Aves/Numida meleagris/4d1b95b190bf358b3b4ec65339c87ce3.jpg\",\n  \"353208.jpg\": \"Aves/Numida meleagris/254fc464888181e30948c124a0fc975e.jpg\",\n  \"353209.jpg\": \"Aves/Numida meleagris/ecb0019500a256706240b4b150a0016e.jpg\",\n  \"353210.jpg\": \"Aves/Numida meleagris/23af7a9e0a7e76e1903a30c9ba6dbc1b.jpg\",\n  \"353211.jpg\": \"Aves/Numida meleagris/48a3ea89255741e6beb2c3146f75bce3.jpg\",\n  \"353212.jpg\": \"Aves/Numida meleagris/f072a6dd1324131db0e84c4fec702227.jpg\",\n  \"353222.jpg\": \"Aves/Numida meleagris/fdeb532ddc4d9543f45304ad1fc128de.jpg\",\n  \"353226.jpg\": \"Aves/Numida meleagris/281cbffe3ea20ebf211c2bdb946ed695.jpg\",\n  \"353227.jpg\": \"Aves/Numida meleagris/35c4bf82be12697fbe25453600b41374.jpg\",\n  \"353233.jpg\": \"Aves/Numida meleagris/0c18e4b2a76d54c94d26e336d3fc8ca0.jpg\",\n  \"353267.jpg\": \"Aves/Regulus ignicapilla/9d80acbc3a127b4291cc1939a30c00f0.jpg\",\n  \"353278.jpg\": \"Aves/Regulus ignicapilla/caf869fd27b1ec545bbbc9e0752169f8.jpg\",\n  \"353280.jpg\": \"Aves/Regulus ignicapilla/fbac260dae3ab0aa7dd2df67e383bfa0.jpg\",\n  \"353283.jpg\": \"Amphibia/Scaphiopus holbrookii/67a3f2f7964a6a14bd14ed2c1b63e2ad.jpg\",\n  \"353289.jpg\": \"Amphibia/Scaphiopus holbrookii/dc56f8377282bd7d11222f9a4748cb17.jpg\",\n  \"353290.jpg\": \"Amphibia/Scaphiopus holbrookii/56962fe36b86a0645a1013bd207f16f4.jpg\",\n  \"353292.jpg\": \"Amphibia/Scaphiopus holbrookii/be9c1f69aa4ec2c67c1819f2e5e10435.jpg\",\n  \"353293.jpg\": \"Amphibia/Scaphiopus holbrookii/227b78d5afdc31972308e2f531950a27.jpg\",\n  \"353324.jpg\": \"Aves/Bostrychia hagedash/9b49890e94b1ff90a00fc5c26354e72b.jpg\",\n  \"353332.jpg\": \"Aves/Bostrychia hagedash/a48f05e4218c091389fbc56022add32e.jpg\",\n  \"353333.jpg\": \"Aves/Bostrychia hagedash/10ded2d49e013244ddeff360509cbbc9.jpg\",\n  \"353335.jpg\": \"Aves/Bostrychia hagedash/369cbe8fe2c72416018442aa9ae9f3c5.jpg\",\n  \"353341.jpg\": \"Aves/Dicrurus adsimilis/a8ed2534ee5c0bd0430b76876446ccb3.jpg\",\n  \"353346.jpg\": \"Aves/Dicrurus adsimilis/ed63fc7cde4b95541b498c200c510659.jpg\",\n  \"353347.jpg\": \"Aves/Dicrurus adsimilis/28a223f6de95fa4b0791f2eb3b47c215.jpg\",\n  \"353348.jpg\": \"Aves/Dicrurus adsimilis/73b89057c455753bade013cf9cb6d17d.jpg\",\n  \"35336.jpg\": \"Insecta/Parnassius clodius/f90a46bb69dca6ba397d379ed894ff2e.jpg\",\n  \"35338.jpg\": \"Insecta/Parnassius clodius/d10dcd644e3a6e297900bacc0af94d53.jpg\",\n  \"353410.jpg\": \"Aves/Calcarius lapponicus/8c96f3139c06f3597e9f97405d385642.jpg\",\n  \"353412.jpg\": \"Aves/Calcarius lapponicus/4c29c3e8997d6bc86d0829b94172e84e.jpg\",\n  \"353414.jpg\": \"Aves/Calcarius lapponicus/e4db77c0c1d9fa48a7357c53947c76df.jpg\",\n  \"353448.jpg\": \"Aves/Phoenicurus ochruros/b4f9c70c29411af8609ab644b5f9b6f2.jpg\",\n  \"353452.jpg\": \"Aves/Phoenicurus ochruros/96861c36a3d5fddac6267217c51fe7be.jpg\",\n  \"353453.jpg\": \"Aves/Phoenicurus ochruros/f64103dc1d4bcb17afc50822984fbfd6.jpg\",\n  \"353454.jpg\": \"Aves/Phoenicurus ochruros/2dac42fbf89593957c9fd0368472b42e.jpg\",\n  \"353455.jpg\": \"Aves/Phoenicurus ochruros/86d4e0fc1acdc4516b00909e2a5c646f.jpg\",\n  \"353456.jpg\": \"Aves/Phoenicurus ochruros/1502373c322930fee67e4e810ba061d3.jpg\",\n  \"353462.jpg\": \"Aves/Phoenicurus ochruros/e4c7c9d018a1f059cba4d32e9228e35a.jpg\",\n  \"353473.jpg\": \"Aves/Phoenicurus ochruros/37b2ea4a44c2e44fa66d4346d3834a05.jpg\",\n  \"353474.jpg\": \"Aves/Phoenicurus ochruros/590d52e2346d976c48903070e07748b5.jpg\",\n  \"353478.jpg\": \"Aves/Phoenicurus ochruros/e445a62dac2f31680adf0d0ce97497ae.jpg\",\n  \"353479.jpg\": \"Aves/Phoenicurus ochruros/b655a1e0ba154c87758c46b0601765f7.jpg\",\n  \"353499.jpg\": \"Aves/Phoenicurus ochruros/d14bac4ea785014a0e0cf97a4e18e7dd.jpg\",\n  \"353501.jpg\": \"Aves/Phoenicurus ochruros/99601a4aba969e805ec8cb4e9d2849e5.jpg\",\n  \"353509.jpg\": \"Aves/Phoenicurus ochruros/fe69e1c0e3e8daeb94b240e6b37c2588.jpg\",\n  \"35351.jpg\": \"Insecta/Parnassius clodius/7dcd9c16f795bb1df4e978300b1294c3.jpg\",\n  \"353511.jpg\": \"Aves/Phoenicurus ochruros/3db22fe7b41a697ac21d7bb4f67849f0.jpg\",\n  \"35352.jpg\": \"Insecta/Parnassius clodius/704ed0e1bfd515eece5e2316c1b958e3.jpg\",\n  \"353525.jpg\": \"Aves/Phoenicurus ochruros/4012161fa2a5486e8e33793224bcbb88.jpg\",\n  \"353526.jpg\": \"Aves/Phoenicurus ochruros/07f969cdc6a3636d4dd751e20529a92c.jpg\",\n  \"353529.jpg\": \"Aves/Phoenicurus ochruros/8a8c75e8b41ffd9e3b0d1ff2050d7478.jpg\",\n  \"353541.jpg\": \"Aves/Phoenicurus ochruros/8037800aba4ac4a96c5b1e8b2d561a25.jpg\",\n  \"353543.jpg\": \"Aves/Phoenicurus ochruros/94dc0d6b6a36b784f5da2891e3512b5d.jpg\",\n  \"35356.jpg\": \"Insecta/Parnassius clodius/0103da9dec4206695739077e20354cd1.jpg\",\n  \"353560.jpg\": \"Aves/Phoenicurus ochruros/fe1a38adbd5f37bb7be448f0c10ef8f8.jpg\",\n  \"353564.jpg\": \"Aves/Phoenicurus ochruros/783b683763fff39e7182e6e4ab652973.jpg\",\n  \"35358.jpg\": \"Insecta/Parnassius clodius/cf68072ce795cfcabd15b961d78101f6.jpg\",\n  \"353589.jpg\": \"Amphibia/Eurycea longicauda/ad6069351bf396724ef81a3bb133a61c.jpg\",\n  \"35359.jpg\": \"Insecta/Parnassius clodius/e2266a6941bfbb2968e4b84ddc84d309.jpg\",\n  \"353592.jpg\": \"Amphibia/Eurycea longicauda/b60f9883e9587aedbb55e3cfdd856403.jpg\",\n  \"353596.jpg\": \"Amphibia/Eurycea longicauda/a4b88470b432dad1be7b0509981a3db3.jpg\",\n  \"353597.jpg\": \"Amphibia/Eurycea longicauda/eca9965d6e172cd702e24f521a9ea398.jpg\",\n  \"35360.jpg\": \"Mammalia/Marmota caligata/a56961f15a3340de86baae708b6f8281.jpg\",\n  \"353602.jpg\": \"Amphibia/Eurycea longicauda/645f5e268074ee45e67a65848746cffb.jpg\",\n  \"353608.jpg\": \"Insecta/Tramea carolina/81d146628b2bf0b1d4582ee8e7d1e7ba.jpg\",\n  \"353609.jpg\": \"Insecta/Tramea carolina/1bfe7f52d2fad5f6fb9e9a36a8b08b76.jpg\",\n  \"353610.jpg\": \"Insecta/Tramea carolina/ccedadab91d0e9e150eb0beef18a6248.jpg\",\n  \"353611.jpg\": \"Insecta/Tramea carolina/68286ed9c706f17ff6d16f9880ebf2fc.jpg\",\n  \"353614.jpg\": \"Insecta/Tramea carolina/f7612dc0d151b13acae2aa1dd4e1224a.jpg\",\n  \"353615.jpg\": \"Insecta/Tramea carolina/083e472f64a594ff6b5c19ebc2b94f24.jpg\",\n  \"353616.jpg\": \"Insecta/Tramea carolina/4a4ead5c47a07fb2753a560ff878cabc.jpg\",\n  \"353618.jpg\": \"Insecta/Tramea carolina/09ae17ae953b1b155003d5e0aa647c3e.jpg\",\n  \"353619.jpg\": \"Insecta/Tramea carolina/413292727f54682da6fe50d582bb2bb7.jpg\",\n  \"353620.jpg\": \"Insecta/Tramea carolina/4ae609f0034aac192af3888a93b53294.jpg\",\n  \"353628.jpg\": \"Insecta/Tramea carolina/a9098c38c6b7f654f3903f9f07987035.jpg\",\n  \"353629.jpg\": \"Insecta/Tramea carolina/c6b930afadf42482c5072962d407dbb9.jpg\",\n  \"353630.jpg\": \"Insecta/Tramea carolina/cd0187f9d596d29a5b1c34b6423e045a.jpg\",\n  \"353634.jpg\": \"Insecta/Tramea carolina/593d1fbd81624854abb174fd059def0a.jpg\",\n  \"353637.jpg\": \"Insecta/Tramea carolina/a455522ffc898b5ee05e8432983f1d39.jpg\",\n  \"353639.jpg\": \"Insecta/Tramea carolina/856b78724653664c0be380f9671760d5.jpg\",\n  \"353640.jpg\": \"Insecta/Tramea carolina/dd5288633b75c0c1a767e11a45c13826.jpg\",\n  \"353642.jpg\": \"Insecta/Tramea carolina/fdf3832fe49f5a3f8321f1938bbbc530.jpg\",\n  \"353648.jpg\": \"Insecta/Tramea carolina/482ed84ed2054123aa74842be5e84b21.jpg\",\n  \"353650.jpg\": \"Insecta/Tramea carolina/2b725b9b767c9994a99fc58019c20b5b.jpg\",\n  \"353653.jpg\": \"Aves/Vanellus spinosus/1f53eee18659b30237c8109b22e56869.jpg\",\n  \"353657.jpg\": \"Aves/Vanellus spinosus/3adcaa167dee6ed59b96a104fa949bf6.jpg\",\n  \"353660.jpg\": \"Aves/Vanellus spinosus/92048363333286f746a1c33dfa6b1054.jpg\",\n  \"353661.jpg\": \"Aves/Vanellus spinosus/1241756835e4eb27f70ded90424fe347.jpg\",\n  \"35368.jpg\": \"Mammalia/Marmota caligata/ae2f5d15177e43fd8293d96775fb819a.jpg\",\n  \"35372.jpg\": \"Mammalia/Marmota caligata/02f9e8ca1b180f7600ee234bb63439ad.jpg\",\n  \"35376.jpg\": \"Mammalia/Marmota caligata/4925ce3e248e94584b0dd5257755a6a4.jpg\",\n  \"35377.jpg\": \"Mammalia/Marmota caligata/ee002be6d424c904f5f36cf56d63db23.jpg\",\n  \"35392.jpg\": \"Mammalia/Marmota caligata/0323d6993a5e02d432c78adc1c0af3d1.jpg\",\n  \"35393.jpg\": \"Mammalia/Marmota caligata/17b24148cad56969ecd86ee4a2dc20d2.jpg\",\n  \"35403.jpg\": \"Mammalia/Marmota caligata/abf3f46912b59375006d7e9743aa8d61.jpg\",\n  \"35426.jpg\": \"Insecta/Euchaetes egle/bcd919b7ae3f19e840f0e207bce44c5e.jpg\",\n  \"35432.jpg\": \"Insecta/Euchaetes egle/1a170e97cc6d18c7f13379beaff70a15.jpg\",\n  \"35441.jpg\": \"Insecta/Euchaetes egle/5357f6907722b90d2c247ea65f4b924a.jpg\",\n  \"354549.jpg\": \"Insecta/Alaus oculatus/232d906c01549dae37a0a51c1cba67f8.jpg\",\n  \"35455.jpg\": \"Insecta/Euchaetes egle/343f3fce2781e872a9957c15ee23f4af.jpg\",\n  \"354550.jpg\": \"Insecta/Alaus oculatus/94fba44fc562ef4bc9703bb43776758a.jpg\",\n  \"35456.jpg\": \"Insecta/Euchaetes egle/a34ba59b188a8731408ef518640697b3.jpg\",\n  \"354563.jpg\": \"Insecta/Alaus oculatus/5335522aead7ad602a411f45a1d81b35.jpg\",\n  \"354564.jpg\": \"Insecta/Alaus oculatus/59a485419065096ed302efd4c6fa182a.jpg\",\n  \"354565.jpg\": \"Insecta/Alaus oculatus/edaa718ea6f83b46f6ed7ac6203f05e3.jpg\",\n  \"354570.jpg\": \"Insecta/Alaus oculatus/1f2520ea3c61c50445b71af92f273a47.jpg\",\n  \"354572.jpg\": \"Insecta/Alaus oculatus/f2fe8f5c36ed9a9c48d82aa6c68e2204.jpg\",\n  \"354577.jpg\": \"Insecta/Alaus oculatus/3f5d5a71af876f34b7046457f0b09db9.jpg\",\n  \"354578.jpg\": \"Insecta/Alaus oculatus/05e4998673724f1b331ef4ffd7ac6c08.jpg\",\n  \"35458.jpg\": \"Insecta/Euchaetes egle/e1d59aa3066c8f42b2b7ab1bf5785f2c.jpg\",\n  \"354583.jpg\": \"Insecta/Alaus oculatus/5ca20281805c2c959a72179500f779d4.jpg\",\n  \"354584.jpg\": \"Insecta/Alaus oculatus/8b1853229c018a708aa232f40b77ccb7.jpg\",\n  \"354585.jpg\": \"Insecta/Alaus oculatus/2742887db6b77ee120345862ade0235e.jpg\",\n  \"354586.jpg\": \"Insecta/Alaus oculatus/69911251ac87e17fef8d9bb8cb951940.jpg\",\n  \"354593.jpg\": \"Insecta/Alaus oculatus/17075c237e133443b2d6a75d885cf1e1.jpg\",\n  \"354598.jpg\": \"Insecta/Alaus oculatus/3cc139b1bad040aaef28902b01dd4fc5.jpg\",\n  \"354602.jpg\": \"Insecta/Alaus oculatus/77fd0693711be4a947f3707ebf11cba5.jpg\",\n  \"354603.jpg\": \"Insecta/Alaus oculatus/8f6e61dd41b099d1b9d3ef59e0fe22a0.jpg\",\n  \"35461.jpg\": \"Insecta/Euchaetes egle/81039d6763a312dd45a5695bd98c82e6.jpg\",\n  \"354610.jpg\": \"Insecta/Alaus oculatus/ab724040920552c2a6aeca3b7fa950e1.jpg\",\n  \"354611.jpg\": \"Insecta/Alaus oculatus/b7bfacbd6378437111e2ddcdf1a12c65.jpg\",\n  \"354612.jpg\": \"Insecta/Alaus oculatus/933899c57f3aa4b891032d9c6618b084.jpg\",\n  \"354619.jpg\": \"Insecta/Pontia protodice/0326578cd05e222f71f1ead112d0e10b.jpg\",\n  \"35462.jpg\": \"Insecta/Euchaetes egle/22b1c0c6a8a0a2aa559d319487b65460.jpg\",\n  \"354624.jpg\": \"Insecta/Pontia protodice/2ad1775ee98ae39e938c714e6cd8510a.jpg\",\n  \"354628.jpg\": \"Insecta/Pontia protodice/897fd2818f290d1ddfc0e251dcc3acd7.jpg\",\n  \"354629.jpg\": \"Insecta/Pontia protodice/ddfb709269b6eda107ab7900a5345a2c.jpg\",\n  \"354639.jpg\": \"Insecta/Pontia protodice/b26227de75d21e52ce26f2ba12ae498c.jpg\",\n  \"354653.jpg\": \"Insecta/Pontia protodice/bb71f09c862bf491c00983377ef396a2.jpg\",\n  \"354654.jpg\": \"Insecta/Pontia protodice/235047427189812431a51cca2e90e0b2.jpg\",\n  \"354655.jpg\": \"Insecta/Pontia protodice/4fe8950935c284dfe670c568e58ffba9.jpg\",\n  \"354666.jpg\": \"Insecta/Pontia protodice/aaaa33fe1d752037e505446e1bae1ae5.jpg\",\n  \"354669.jpg\": \"Insecta/Pontia protodice/1f3bc517df4c3c7d056d6bd1f54051d0.jpg\",\n  \"354672.jpg\": \"Insecta/Pontia protodice/b35060ddd60fafce1ddef47424b44c4c.jpg\",\n  \"354673.jpg\": \"Insecta/Pontia protodice/b704d74f622cfc9cf58ec942d8eab371.jpg\",\n  \"354692.jpg\": \"Insecta/Pontia protodice/426f5084556531664c3984b5407e877c.jpg\",\n  \"354698.jpg\": \"Insecta/Pontia protodice/47ff72110936006a66611eb3fd4c55d0.jpg\",\n  \"354720.jpg\": \"Insecta/Pontia protodice/26973f2ec22940e14191f2d06e4ed644.jpg\",\n  \"354744.jpg\": \"Insecta/Pontia protodice/f5df0f1d156b0e710263e03bc90c63c4.jpg\",\n  \"354751.jpg\": \"Insecta/Pontia protodice/c3b7ed7ac96541eaaa0639313f2423e1.jpg\",\n  \"354752.jpg\": \"Insecta/Pontia protodice/ca20df6906b47d414c4807f5c0257b56.jpg\",\n  \"354753.jpg\": \"Insecta/Pontia protodice/f1e539f13cb65a302c086c31a52a502f.jpg\",\n  \"354762.jpg\": \"Insecta/Pontia protodice/88fb3ce8ef784f1ebc6a4ea5d2d89384.jpg\",\n  \"354816.jpg\": \"Insecta/Pontia protodice/2b6f4bec35df1a99d58bdf8efaf1c98c.jpg\",\n  \"354827.jpg\": \"Insecta/Pontia protodice/4350bba842370fa24be52ab65d66bac7.jpg\",\n  \"35483.jpg\": \"Insecta/Euchaetes egle/a0b6e5aa4cc51b57952b8f59ed01c8a7.jpg\",\n  \"35484.jpg\": \"Insecta/Euchaetes egle/86b9d96a7d001986f697b014af3ccd3d.jpg\",\n  \"354840.jpg\": \"Aves/Corvus splendens/925189408618b5dc494d170d666d0719.jpg\",\n  \"354841.jpg\": \"Aves/Corvus splendens/53ee4df589fc7687ff819126eade9fa2.jpg\",\n  \"354848.jpg\": \"Aves/Corvus splendens/3c65a4e02522e19e65526ef86d548888.jpg\",\n  \"354855.jpg\": \"Aves/Corvus splendens/041592c3685ff3928c3a73bfe6863c4e.jpg\",\n  \"35492.jpg\": \"Insecta/Euchaetes egle/9e7d82df90f87c64a7810e8fd14a38c8.jpg\",\n  \"354931.jpg\": \"Aves/Mergus merganser/287629c5fd9cc5d74389ee4f6430b745.jpg\",\n  \"35494.jpg\": \"Insecta/Euchaetes egle/9abe15ec2507a2fa379b91a7f481afff.jpg\",\n  \"354967.jpg\": \"Aves/Mergus merganser/0543ff3634d51089d9593daf2bbab435.jpg\",\n  \"354991.jpg\": \"Aves/Mergus merganser/e6968d434d7a0e2ba8a96317a0b312dc.jpg\",\n  \"355014.jpg\": \"Aves/Mergus merganser/2e0f207d15224df5aef366418e990f8d.jpg\",\n  \"355029.jpg\": \"Aves/Mergus merganser/c8162477194bbcbbbb3aae288fa68f08.jpg\",\n  \"355049.jpg\": \"Aves/Mergus merganser/250c077cca1ea97194438eff1fe37843.jpg\",\n  \"355067.jpg\": \"Aves/Mergus merganser/3027339a032c26f9a5ccff526c579e5b.jpg\",\n  \"35507.jpg\": \"Insecta/Euchaetes egle/1ebf920f916a6f6dcc51d044cc743c2e.jpg\",\n  \"35510.jpg\": \"Insecta/Euchaetes egle/c1785ab7e849d7d0f506b51b4d91825a.jpg\",\n  \"355224.jpg\": \"Aves/Mergus merganser/8745c54ef032b19f9e1652e793242fcc.jpg\",\n  \"35525.jpg\": \"Insecta/Euchaetes egle/5638e5a853c23fa157f9de8288c66f84.jpg\",\n  \"355257.jpg\": \"Aves/Mergus merganser/3b6e35f61f8945ac16ed40da2832fe82.jpg\",\n  \"355274.jpg\": \"Aves/Grus americana/663350097f0ceca20f3484eef02abc44.jpg\",\n  \"355275.jpg\": \"Aves/Grus americana/30df049f59c926b059c91167504e470d.jpg\",\n  \"355323.jpg\": \"Aves/Grus americana/1138416166d1bcb37747d533864ed2e6.jpg\",\n  \"355325.jpg\": \"Aves/Grus americana/13575de17258000f3fa2d58058ef2312.jpg\",\n  \"355326.jpg\": \"Aves/Grus americana/fb57e72cb0db642eece5aa07b27154f7.jpg\",\n  \"355327.jpg\": \"Aves/Grus americana/5d6d843f970d0f561be6fd88cf97e2a4.jpg\",\n  \"355334.jpg\": \"Aves/Grus americana/c60c483b958ae6c578a7e6b4fdd71caf.jpg\",\n  \"355383.jpg\": \"Insecta/Leptophobia aripa elodia/01a0724d0c500584ed06252821ed401f.jpg\",\n  \"35539.jpg\": \"Insecta/Euchaetes egle/a927a0dd0547f56f8262ae4c9ed401d9.jpg\",\n  \"355391.jpg\": \"Insecta/Leptophobia aripa elodia/650d7eb6daf04f9c1857932f6dd35123.jpg\",\n  \"355393.jpg\": \"Reptilia/Lampropeltis triangulum/9a94b2c0057fec46937d472addb6f1f5.jpg\",\n  \"355399.jpg\": \"Reptilia/Lampropeltis triangulum/05e483f095390bcfc9a553d39b3b50db.jpg\",\n  \"355400.jpg\": \"Reptilia/Lampropeltis triangulum/cda87ff5ec10d49ae17b472b1ca65f4d.jpg\",\n  \"355401.jpg\": \"Reptilia/Lampropeltis triangulum/f78f2025ac310764a3e64fab68835c09.jpg\",\n  \"355402.jpg\": \"Reptilia/Lampropeltis triangulum/72090af577def7787735a89f3a3d8af0.jpg\",\n  \"355403.jpg\": \"Reptilia/Lampropeltis triangulum/d108eece37d11e580ff6ca6c2d1fc885.jpg\",\n  \"355404.jpg\": \"Reptilia/Lampropeltis triangulum/b246d0eaa17649c05a42ae2d04b6c8c6.jpg\",\n  \"355405.jpg\": \"Reptilia/Lampropeltis triangulum/a0a178d800ae93e7de8a9104b3dc02cf.jpg\",\n  \"355406.jpg\": \"Reptilia/Lampropeltis triangulum/261d6505a55c4dd573bdd1ee775e4817.jpg\",\n  \"355407.jpg\": \"Reptilia/Lampropeltis triangulum/17c56f9f1035023ffce13ee2f4c49c25.jpg\",\n  \"355409.jpg\": \"Reptilia/Lampropeltis triangulum/ee969f7e609bdfb351a930ff428286a9.jpg\",\n  \"355422.jpg\": \"Reptilia/Lampropeltis triangulum/9d352c5cf2bb5642dac4e1f7bdd1e0b9.jpg\",\n  \"355423.jpg\": \"Reptilia/Lampropeltis triangulum/841340b8345432d86beaeb1503362dda.jpg\",\n  \"355424.jpg\": \"Reptilia/Lampropeltis triangulum/3e497ecd9f738f41e838d955df5fff17.jpg\",\n  \"355431.jpg\": \"Aves/Parabuteo unicinctus/499b95698e6e77d3e8572644d634d8df.jpg\",\n  \"355441.jpg\": \"Aves/Parabuteo unicinctus/efb5e6cf5248d2bc759f6277d664f34e.jpg\",\n  \"355442.jpg\": \"Aves/Parabuteo unicinctus/b84692e8dfdb1af5751c828c1bde9a3b.jpg\",\n  \"355446.jpg\": \"Aves/Parabuteo unicinctus/57540c901e1ecae5edee8d03fec65b4a.jpg\",\n  \"355450.jpg\": \"Aves/Parabuteo unicinctus/8e5f430aaf8a96e0bb1f7056d7ff54df.jpg\",\n  \"355455.jpg\": \"Aves/Parabuteo unicinctus/843816257d5075852179af4da0f91f30.jpg\",\n  \"355456.jpg\": \"Aves/Parabuteo unicinctus/e11f0c6c51b0e86024ccf0ee2395e348.jpg\",\n  \"355459.jpg\": \"Aves/Parabuteo unicinctus/bf437f8edb3c42d877ee73994b52af3a.jpg\",\n  \"355466.jpg\": \"Aves/Parabuteo unicinctus/200cff6ded802026ef56eda87cc3c453.jpg\",\n  \"355477.jpg\": \"Aves/Parabuteo unicinctus/605ae46874ef6cb4d2c017e71a61420f.jpg\",\n  \"355479.jpg\": \"Aves/Parabuteo unicinctus/6542e774a4eaa6939ff2c08f696232a4.jpg\",\n  \"355483.jpg\": \"Aves/Parabuteo unicinctus/f5871f4f09bde777fcb99681d1d6d9c0.jpg\",\n  \"355484.jpg\": \"Aves/Parabuteo unicinctus/e094234e4b18a980ecda094f639ed002.jpg\",\n  \"355485.jpg\": \"Aves/Parabuteo unicinctus/fc0bd3cad37f42d37617acc8358e6953.jpg\",\n  \"355489.jpg\": \"Aves/Parabuteo unicinctus/97512324f96c3c24e112d18b5a5a9e22.jpg\",\n  \"35549.jpg\": \"Insecta/Euchaetes egle/9ea5e63eb38c3da86155e5d48a2b696c.jpg\",\n  \"355490.jpg\": \"Aves/Parabuteo unicinctus/2ab50f851c165ec308d8e0fa71bbaf42.jpg\",\n  \"355494.jpg\": \"Aves/Parabuteo unicinctus/bc6cfbeffb7fd8ac20258b23eb47195f.jpg\",\n  \"355496.jpg\": \"Aves/Parabuteo unicinctus/426742ad2fb844917d88827ac7344c5e.jpg\",\n  \"355503.jpg\": \"Aves/Parabuteo unicinctus/cbe9aaf926e4ad30cd42a939190bdfdf.jpg\",\n  \"355512.jpg\": \"Aves/Parabuteo unicinctus/74f9fb6647d2c32e41c24bdf66c862f8.jpg\",\n  \"35552.jpg\": \"Mammalia/Cervus elaphus canadensis/48002f54ab03d3069543de513cac3d46.jpg\",\n  \"355524.jpg\": \"Aves/Parabuteo unicinctus/d0415bf1b9dde24d64114a809df06f20.jpg\",\n  \"355530.jpg\": \"Aves/Parabuteo unicinctus/32d6b170e3be8c05f923f56976763967.jpg\",\n  \"355541.jpg\": \"Actinopterygii/Chaetodon capistratus/6557f525f6a0b783cea6c96d71edddea.jpg\",\n  \"355544.jpg\": \"Actinopterygii/Chaetodon capistratus/b83fb6933f4cb26dcb01d2985fe67dd8.jpg\",\n  \"35560.jpg\": \"Mammalia/Cervus elaphus canadensis/fc0563fb59cb3460a2febcfe96de3acc.jpg\",\n  \"355629.jpg\": \"Mollusca/Lottia digitalis/3065ab836d00b141d12ded1931968f71.jpg\",\n  \"355631.jpg\": \"Mollusca/Lottia digitalis/3229b678e45b9759eecfb074b2997935.jpg\",\n  \"355641.jpg\": \"Mammalia/Panthera pardus/647950391f2b3b79ec1d0fce34fbc6e9.jpg\",\n  \"355643.jpg\": \"Mammalia/Panthera pardus/fd6cc8ee2d13acd907ea1bfa45af9090.jpg\",\n  \"355648.jpg\": \"Mammalia/Panthera pardus/c96d6637dc5e7057332f9bdefb47b0f1.jpg\",\n  \"355649.jpg\": \"Mammalia/Panthera pardus/cac1be6eb0e050f216bd383354389c86.jpg\",\n  \"355663.jpg\": \"Mammalia/Panthera pardus/a6a75997a06796dfcac026ff56b25585.jpg\",\n  \"355664.jpg\": \"Insecta/Acronicta americana/8dbeceeec6102dec5e26202dbbac3380.jpg\",\n  \"355668.jpg\": \"Insecta/Acronicta americana/b843660576e499d0dc3ca03fdd0ad42c.jpg\",\n  \"355669.jpg\": \"Insecta/Acronicta americana/8a405d65504df42d51fdf66f8afce920.jpg\",\n  \"355671.jpg\": \"Insecta/Acronicta americana/1350153f77fb807c893e2edbcec0c710.jpg\",\n  \"355672.jpg\": \"Insecta/Acronicta americana/23ad2ec8d82fbd7959d4082dbd1498ef.jpg\",\n  \"355673.jpg\": \"Insecta/Acronicta americana/38b52296591d2f6cb6adfbee1361d6ff.jpg\",\n  \"355674.jpg\": \"Insecta/Acronicta americana/b3e917938212ee9ddff3145f0ebd260a.jpg\",\n  \"355676.jpg\": \"Insecta/Acronicta americana/889905235071af1293c3c7f5d80acf24.jpg\",\n  \"355677.jpg\": \"Insecta/Acronicta americana/7a4d65b09fc3347c5a33263320e822a8.jpg\",\n  \"355678.jpg\": \"Insecta/Acronicta americana/b8ee7550b88c36b1147bc77a32098295.jpg\",\n  \"355679.jpg\": \"Insecta/Acronicta americana/39d39d17fb6f0b1a3d8bfc8fb9b3b48d.jpg\",\n  \"355680.jpg\": \"Insecta/Acronicta americana/30b22724a76c2f1de35365d0a0a100fb.jpg\",\n  \"355682.jpg\": \"Insecta/Acronicta americana/2d6a3c1b49ca7e4b8ecf483a91780665.jpg\",\n  \"355683.jpg\": \"Insecta/Acronicta americana/c54e55e4044f14d4fff148d829cc2a2e.jpg\",\n  \"355684.jpg\": \"Insecta/Acronicta americana/7aec90513e49b3da264d8e6cd9fa3692.jpg\",\n  \"355688.jpg\": \"Insecta/Acronicta americana/f2a34c159aaedf81a4a042f42546476c.jpg\",\n  \"355689.jpg\": \"Insecta/Acronicta americana/1c5a58af680492df2599cab1736a499e.jpg\",\n  \"35569.jpg\": \"Reptilia/Diadophis punctatus amabilis/845271bb35b4ae053466ddfe94328080.jpg\",\n  \"35570.jpg\": \"Reptilia/Diadophis punctatus amabilis/bd06eaf1b46345c7ab6b236ce4d6fb28.jpg\",\n  \"355701.jpg\": \"Insecta/Acronicta americana/7a676a1a8bdbdd046a6872a4ee428f57.jpg\",\n  \"355703.jpg\": \"Insecta/Acronicta americana/959d0a46e3c37c040d6bedb9f9128135.jpg\",\n  \"355707.jpg\": \"Insecta/Acronicta americana/9123ed0d982953040c88b4fd94451b80.jpg\",\n  \"355708.jpg\": \"Insecta/Acronicta americana/6cc6eb44e586d0121edaffd9b24034c8.jpg\",\n  \"355743.jpg\": \"Insecta/Graphocephala coccinea/0904a37b8007e476db5884b35eec5abc.jpg\",\n  \"355744.jpg\": \"Insecta/Graphocephala coccinea/4aba7d43cc80cc6133c564665b5b96ae.jpg\",\n  \"355745.jpg\": \"Insecta/Graphocephala coccinea/0bd77a59a7d9f7f3975ee8f2754bff42.jpg\",\n  \"355748.jpg\": \"Insecta/Graphocephala coccinea/d697b625c12397f8c276b5713c206fb2.jpg\",\n  \"355761.jpg\": \"Insecta/Graphocephala coccinea/06b5d781216b4a04fc5a6f00e946b3b9.jpg\",\n  \"355763.jpg\": \"Insecta/Graphocephala coccinea/002d5ab42fee7212892f36abec447c4f.jpg\",\n  \"355764.jpg\": \"Insecta/Graphocephala coccinea/08fcb98cbb801aafd0be1f70dce31e29.jpg\",\n  \"355766.jpg\": \"Insecta/Graphocephala coccinea/0c4a4fd60a5ebc6a91cd0441f75624e0.jpg\",\n  \"355767.jpg\": \"Insecta/Graphocephala coccinea/382fe0a5f33eb42e7576fb12eb1bd79b.jpg\",\n  \"355778.jpg\": \"Insecta/Graphocephala coccinea/d68d9f95d502251ee80e7a1fcfaa9785.jpg\",\n  \"355790.jpg\": \"Insecta/Graphocephala coccinea/4f34efe7f059c07c4db49697b39c1ab7.jpg\",\n  \"355791.jpg\": \"Insecta/Graphocephala coccinea/698836865524cafb70c3e10663fec862.jpg\",\n  \"355794.jpg\": \"Insecta/Graphocephala coccinea/4ad8b95ca95b7e6598530546a8ddd6bd.jpg\",\n  \"355795.jpg\": \"Insecta/Graphocephala coccinea/328f54b8d6570b88829cbb0d0dbaa182.jpg\",\n  \"355796.jpg\": \"Insecta/Graphocephala coccinea/e202c0e31ca4cf15171288dc8e25d373.jpg\",\n  \"355797.jpg\": \"Insecta/Graphocephala coccinea/23d55ec03b7b52ea7d6f5268434518af.jpg\",\n  \"355799.jpg\": \"Insecta/Graphocephala coccinea/2b9737482483af1fa24a4637df6dc895.jpg\",\n  \"35580.jpg\": \"Reptilia/Diadophis punctatus amabilis/2554c798629f5dc4843a80d4e6eb4a89.jpg\",\n  \"355800.jpg\": \"Insecta/Graphocephala coccinea/8c4d4a7713f4b56169d2e25f1effffd0.jpg\",\n  \"355801.jpg\": \"Insecta/Graphocephala coccinea/a1563a87549ef7e41dc59a96089911ab.jpg\",\n  \"355807.jpg\": \"Aves/Opisthocomus hoazin/165aadc6f119a16f575032a97c5d75a8.jpg\",\n  \"355816.jpg\": \"Aves/Opisthocomus hoazin/26a7bcd6cdff3b501f81727cc0238bec.jpg\",\n  \"35582.jpg\": \"Reptilia/Diadophis punctatus amabilis/ce8da899db731a11d4355ce7ed360746.jpg\",\n  \"355822.jpg\": \"Aves/Opisthocomus hoazin/b81f834e367dc96245b7627e141c027e.jpg\",\n  \"35606.jpg\": \"Insecta/Diastictis fracturalis/7f7f3d8cd1b521653d09ed2785cb54bc.jpg\",\n  \"356215.jpg\": \"Aves/Dryocopus lineatus/4049d5c4e941390641cecb974ae013fe.jpg\",\n  \"356216.jpg\": \"Aves/Dryocopus lineatus/3ed50a803e79f7d010a197f5264a90b6.jpg\",\n  \"356217.jpg\": \"Aves/Dryocopus lineatus/ceca39f8fdecc3df77b6757e39f4eb31.jpg\",\n  \"356218.jpg\": \"Aves/Dryocopus lineatus/d442fb078bb0c7d9167f365d5ab63c70.jpg\",\n  \"356219.jpg\": \"Aves/Dryocopus lineatus/cee00b6e41e225ac6a63eb664cae70e2.jpg\",\n  \"35622.jpg\": \"Insecta/Diastictis fracturalis/3f4454438263c964a0e16451676e088b.jpg\",\n  \"356227.jpg\": \"Aves/Dryocopus lineatus/6cc95f64ac1c3f1aa68417d75a54994f.jpg\",\n  \"356228.jpg\": \"Aves/Dryocopus lineatus/219c16d5226029c173ae53b077e78a56.jpg\",\n  \"356229.jpg\": \"Aves/Dryocopus lineatus/8ef02b0b396adf9bf2b478b566424f3c.jpg\",\n  \"356240.jpg\": \"Aves/Dryocopus lineatus/491d1b3f229a124e18b084b9d5569db2.jpg\",\n  \"356242.jpg\": \"Aves/Dryocopus lineatus/28a60cf9194f618afe094706631b3056.jpg\",\n  \"356244.jpg\": \"Aves/Dryocopus lineatus/4d85512a52b1af0f5aa624d7f14eddf0.jpg\",\n  \"356249.jpg\": \"Aves/Dryocopus lineatus/87f0970d87e0203e2d0b78fd5c2f13f1.jpg\",\n  \"356255.jpg\": \"Aves/Dryocopus lineatus/7fd8df124b2145bacdbb7de3eb918e56.jpg\",\n  \"356256.jpg\": \"Aves/Dryocopus lineatus/1ad5e1d069b6969dec3d353b2a850cf6.jpg\",\n  \"35626.jpg\": \"Insecta/Diastictis fracturalis/dc0a282fb1804091c8c0806564a37325.jpg\",\n  \"356264.jpg\": \"Aves/Dryocopus lineatus/dae60ff9bcd52313edc3e486328091fb.jpg\",\n  \"356278.jpg\": \"Aves/Dryocopus lineatus/b477cbd76e05760d76db3993e1d118fc.jpg\",\n  \"356280.jpg\": \"Aves/Dryocopus lineatus/1b027e8828e9a23cf34d0d2181e1fd1b.jpg\",\n  \"356281.jpg\": \"Aves/Dryocopus lineatus/8bc030065cce75945b1fbf37c658618e.jpg\",\n  \"356283.jpg\": \"Aves/Dryocopus lineatus/c02bdf561efd299389095a5affacece2.jpg\",\n  \"356284.jpg\": \"Aves/Dryocopus lineatus/a46291022e5ddbc0a21d7c370f02f957.jpg\",\n  \"356287.jpg\": \"Aves/Dryocopus lineatus/f06692a9a13608163c31b23b13d47b26.jpg\",\n  \"356290.jpg\": \"Aves/Dryocopus lineatus/6db99e4981f733616a3037d83a445b7c.jpg\",\n  \"356401.jpg\": \"Mammalia/Megaptera novaeangliae/927568f16619d57e5b5f1f88e5ba452d.jpg\",\n  \"356433.jpg\": \"Mammalia/Megaptera novaeangliae/da514537b069ce42b9fb396f36fe2b59.jpg\",\n  \"356444.jpg\": \"Mammalia/Megaptera novaeangliae/54a39e72587ed3258a8e426573c926ec.jpg\",\n  \"35647.jpg\": \"Aves/Oreoscoptes montanus/2b3fd871652127a10c5fc174335bf1c9.jpg\",\n  \"356472.jpg\": \"Mammalia/Megaptera novaeangliae/c442fb3f4e27ffcb441c21f189834f63.jpg\",\n  \"35648.jpg\": \"Aves/Oreoscoptes montanus/36a8df3cc3c611b1d657db71191f6740.jpg\",\n  \"356491.jpg\": \"Mammalia/Megaptera novaeangliae/1406859b30f257f0aa7a7af3124e874f.jpg\",\n  \"356492.jpg\": \"Mammalia/Megaptera novaeangliae/467dbe7eb9f42d284470ce47d38fc1a9.jpg\",\n  \"356493.jpg\": \"Mammalia/Megaptera novaeangliae/fbc6c4b5e5f3f1fed3bf68fd7203a7f7.jpg\",\n  \"356509.jpg\": \"Mammalia/Megaptera novaeangliae/db9703f465d7db9a6fb524cdd19932b3.jpg\",\n  \"35651.jpg\": \"Aves/Oreoscoptes montanus/ef9754a0b1e49ce2b108231076c271eb.jpg\",\n  \"356523.jpg\": \"Mammalia/Megaptera novaeangliae/ada8c8e19862912e0d959e26aede23e1.jpg\",\n  \"356524.jpg\": \"Mammalia/Megaptera novaeangliae/4dfecdacdf269c6e5fe49f0abd513fec.jpg\",\n  \"356530.jpg\": \"Mammalia/Megaptera novaeangliae/405aa570cf670f8f7cf48cf37c007401.jpg\",\n  \"356548.jpg\": \"Mammalia/Megaptera novaeangliae/891d83d948f72a0b99b32ad00df6ae2f.jpg\",\n  \"356558.jpg\": \"Mammalia/Megaptera novaeangliae/fa27e74c106e54010dd2c313256c6775.jpg\",\n  \"356560.jpg\": \"Mammalia/Megaptera novaeangliae/b63bab4de761f190ec0617c156d36c62.jpg\",\n  \"356566.jpg\": \"Mammalia/Megaptera novaeangliae/e7b54d5cf7c35f0ebc214f64d76255eb.jpg\",\n  \"35657.jpg\": \"Aves/Oreoscoptes montanus/35dd62ff4bb7b2bb9ec993abde67fb4f.jpg\",\n  \"356573.jpg\": \"Mammalia/Megaptera novaeangliae/257f568aae75c34fd5f7a59d4e992b00.jpg\",\n  \"356612.jpg\": \"Mammalia/Megaptera novaeangliae/8d417203a24d7899b6ed3a08c88b3af1.jpg\",\n  \"356619.jpg\": \"Mammalia/Megaptera novaeangliae/80fd5a03e00a522a82caa4d24182ca32.jpg\",\n  \"356635.jpg\": \"Aves/Bucephala clangula/c5d883d98e44ed953068d31649bfe5a3.jpg\",\n  \"356645.jpg\": \"Aves/Bucephala clangula/16d40e59566327afb0ea2ef455e88a9c.jpg\",\n  \"35669.jpg\": \"Aves/Oreoscoptes montanus/cad7f998848c0aa164f5682432d5d280.jpg\",\n  \"356709.jpg\": \"Aves/Bucephala clangula/e0cf33bed5cfa58173c4ee916f183f4d.jpg\",\n  \"35673.jpg\": \"Aves/Oreoscoptes montanus/17c83f83023cd2fb0d02d98114ab35ae.jpg\",\n  \"356732.jpg\": \"Aves/Bucephala clangula/7a4917e640007a4eb710e86101a2cdc0.jpg\",\n  \"35674.jpg\": \"Aves/Oreoscoptes montanus/873f639c2ad6d674e1dcee5e4c83b7a6.jpg\",\n  \"356756.jpg\": \"Aves/Bucephala clangula/abeabfefc5bc1c00884540bcb54e7d64.jpg\",\n  \"35676.jpg\": \"Amphibia/Spea multiplicata/19cb4309bff3f26f34f99cd92b8daf0e.jpg\",\n  \"356770.jpg\": \"Aves/Bucephala clangula/560862a5edd0a838649eac44adc27a60.jpg\",\n  \"356772.jpg\": \"Aves/Bucephala clangula/6885ade28883202ef8975bf74b147e24.jpg\",\n  \"356780.jpg\": \"Aves/Bucephala clangula/5b8118106ba63a368862903f7a715ddc.jpg\",\n  \"356781.jpg\": \"Aves/Bucephala clangula/ef1d1997204b2aa4e6fc4011f1b2ac03.jpg\",\n  \"356782.jpg\": \"Aves/Bucephala clangula/44384621f868fd935597cbd17421d777.jpg\",\n  \"35679.jpg\": \"Amphibia/Spea multiplicata/41acd7e68630f73eed81d2d03546784a.jpg\",\n  \"35680.jpg\": \"Amphibia/Spea multiplicata/7968c115ad4ac15ba9f174b8da4c81ce.jpg\",\n  \"35681.jpg\": \"Amphibia/Spea multiplicata/40c55886da582a2431356fc029b9324b.jpg\",\n  \"356815.jpg\": \"Aves/Bucephala clangula/80bdc9ecdcf4d1a3d460778fc291e727.jpg\",\n  \"35682.jpg\": \"Amphibia/Spea multiplicata/5ddfa605cafc04964c04fe801e46b247.jpg\",\n  \"356823.jpg\": \"Aves/Bucephala clangula/1d399c819773631739f86e651ee64bea.jpg\",\n  \"35683.jpg\": \"Amphibia/Spea multiplicata/149a46d1410d24e69968c3d3662a8c18.jpg\",\n  \"356836.jpg\": \"Aves/Bucephala clangula/d9f0d462a4f031e7430a823dc2740e4f.jpg\",\n  \"35684.jpg\": \"Amphibia/Spea multiplicata/58ba72bda523e7b89a59f18fb3e11127.jpg\",\n  \"356852.jpg\": \"Aves/Bucephala clangula/0ed6c4efef5f281d3f95c696279083b9.jpg\",\n  \"356853.jpg\": \"Aves/Bucephala clangula/3796dc6f83d6f97229ded0c611d9b3f7.jpg\",\n  \"356857.jpg\": \"Aves/Bucephala clangula/a514b6736ad573e45c652eb5916b1a44.jpg\",\n  \"35686.jpg\": \"Amphibia/Spea multiplicata/1ae0c1618f7765c256780163a5ec4535.jpg\",\n  \"35687.jpg\": \"Amphibia/Spea multiplicata/c3847e224d7771bb985736e7668eab97.jpg\",\n  \"356873.jpg\": \"Aves/Bucephala clangula/3bf30a93c589d531b15fb77ea6f62ab2.jpg\",\n  \"356904.jpg\": \"Aves/Bucephala clangula/1ba585f7c252e3ee7ce4709d8d0ccf5b.jpg\",\n  \"356921.jpg\": \"Aves/Bucephala clangula/4ae84fdc4ef06e58389d3bcc8a494c59.jpg\",\n  \"356928.jpg\": \"Insecta/Nymphalis vaualbum/ed7a199dc9ec5b95f273a3599b5b30ac.jpg\",\n  \"356932.jpg\": \"Insecta/Nymphalis vaualbum/a9186bd9c1ebe5141122839a58b2a0a9.jpg\",\n  \"356946.jpg\": \"Insecta/Nymphalis vaualbum/def77bbbeddbe0f587c041a55dd4e813.jpg\",\n  \"356947.jpg\": \"Insecta/Nymphalis vaualbum/cf53a17f483fe5361edec0c7caf50429.jpg\",\n  \"356949.jpg\": \"Insecta/Nymphalis vaualbum/2eb4b3ff8369d0fbc74fb7c25a090413.jpg\",\n  \"35695.jpg\": \"Amphibia/Spea multiplicata/26e876646bec0422242b3c0410f53bb3.jpg\",\n  \"35696.jpg\": \"Amphibia/Spea multiplicata/b904a63bb844da54d044c770f7a024ae.jpg\",\n  \"35697.jpg\": \"Amphibia/Spea multiplicata/dbed19290e25d318487f13d2e8b42278.jpg\",\n  \"356971.jpg\": \"Aves/Gelochelidon nilotica/0dfd33483273d14c49155f28c4b0c398.jpg\",\n  \"356973.jpg\": \"Aves/Gelochelidon nilotica/0c797a85d5475e796918dfe33f1e9377.jpg\",\n  \"356977.jpg\": \"Aves/Gelochelidon nilotica/e37cf290c3dcb757e5842635b71b1609.jpg\",\n  \"35699.jpg\": \"Amphibia/Spea multiplicata/e0483765a1eb461cc3bab3ccb75d5577.jpg\",\n  \"356991.jpg\": \"Aves/Gelochelidon nilotica/e54e9ad95b13bc7e099e82c89f2207ad.jpg\",\n  \"35701.jpg\": \"Amphibia/Spea multiplicata/13f4317dd6e61de1253320614c7eb3b9.jpg\",\n  \"35703.jpg\": \"Amphibia/Spea multiplicata/f83d6dd18fe259532a677747b913dc0c.jpg\",\n  \"35704.jpg\": \"Amphibia/Spea multiplicata/666eafa5a14e07d458dcbf15ddc0fc8e.jpg\",\n  \"35705.jpg\": \"Amphibia/Spea multiplicata/00ac438adc63d9bd748174d1879d7353.jpg\",\n  \"35709.jpg\": \"Amphibia/Spea multiplicata/b5f4cabef5b0b0458551dcf539154168.jpg\",\n  \"35714.jpg\": \"Amphibia/Spea multiplicata/1507f3b4366e4059fe850fb787bdd2db.jpg\",\n  \"35715.jpg\": \"Amphibia/Spea multiplicata/d718e42322fbc4f0392d750a4ee7121e.jpg\",\n  \"35718.jpg\": \"Amphibia/Ambystoma macrodactylum/3a9728c3e8472bd2dcabe5479ef2735b.jpg\",\n  \"35723.jpg\": \"Amphibia/Ambystoma macrodactylum/f3377773f0ea857d26aae95d63bf6383.jpg\",\n  \"35724.jpg\": \"Amphibia/Ambystoma macrodactylum/1d70f3049ff80c75bee40233b49d4510.jpg\",\n  \"35729.jpg\": \"Amphibia/Ambystoma macrodactylum/edaf632a49fff0016cd788483f9c5930.jpg\",\n  \"35740.jpg\": \"Amphibia/Ambystoma macrodactylum/0f1171cebcc524ba35087f851255748b.jpg\",\n  \"35746.jpg\": \"Amphibia/Ambystoma macrodactylum/a5cc6641ebd30a22133e60cfd726238a.jpg\",\n  \"35747.jpg\": \"Mammalia/Sciurus griseus/0264cb29eb946bc84f8f4798d7dcd50e.jpg\",\n  \"35757.jpg\": \"Mammalia/Sciurus griseus/e151a0a060f31b859d31208815d9b3d9.jpg\",\n  \"35768.jpg\": \"Mammalia/Sciurus griseus/d532fea161d5be208d4ad8fc0f87c161.jpg\",\n  \"357688.jpg\": \"Aves/Anastomus lamelligerus/c93e68f13a1c1121e2116271353ce5d9.jpg\",\n  \"35769.jpg\": \"Mammalia/Sciurus griseus/683744851377cda23e59b7aa61b7d2c0.jpg\",\n  \"357696.jpg\": \"Aves/Anastomus lamelligerus/1838e351111c15b8af17e3de654c2bc4.jpg\",\n  \"357700.jpg\": \"Aves/Anastomus lamelligerus/6a4858b9d4c52c5fda1ec5295cf40369.jpg\",\n  \"35771.jpg\": \"Mammalia/Sciurus griseus/62aafe1af8cf72acb343ca86a1116164.jpg\",\n  \"357729.jpg\": \"Mammalia/Odocoileus hemionus columbianus/fc69b4fb2324b6d488739beb86220d08.jpg\",\n  \"357740.jpg\": \"Mammalia/Odocoileus hemionus columbianus/3b8d0e73933a6be7f49899dd4933ee20.jpg\",\n  \"357744.jpg\": \"Mammalia/Odocoileus hemionus columbianus/3ffb0f4bee963929aa79176f27711be7.jpg\",\n  \"357753.jpg\": \"Mammalia/Odocoileus hemionus columbianus/bcbe483d2d51a03867c4bf6d6de1b731.jpg\",\n  \"357756.jpg\": \"Mammalia/Odocoileus hemionus columbianus/e35ef307ab04a05bad72543c17d995b3.jpg\",\n  \"357757.jpg\": \"Mammalia/Odocoileus hemionus columbianus/39ea42bdf54330fa76f3be36939837d7.jpg\",\n  \"357759.jpg\": \"Mammalia/Odocoileus hemionus columbianus/386c861a9c95917ba256a67f6e6aef62.jpg\",\n  \"357769.jpg\": \"Mammalia/Odocoileus hemionus columbianus/3fb676eb172cf8bcf7ba670c280b6a6b.jpg\",\n  \"35777.jpg\": \"Mammalia/Sciurus griseus/699ffa63704d55e888fb11db4eb765d5.jpg\",\n  \"357776.jpg\": \"Mammalia/Odocoileus hemionus columbianus/3ff7579f85c1c77c5e63eb0e129c996f.jpg\",\n  \"35778.jpg\": \"Mammalia/Sciurus griseus/4fe883253734634d257232aea44afa8d.jpg\",\n  \"357798.jpg\": \"Mammalia/Odocoileus hemionus columbianus/1c1ad962e111c8767da6f40ba189c510.jpg\",\n  \"35783.jpg\": \"Mammalia/Sciurus griseus/1a4ae4e1cda084b220234e6afdfabbda.jpg\",\n  \"357864.jpg\": \"Mammalia/Odocoileus hemionus columbianus/6dc622ddf18ba0cd2da60c351779e8d8.jpg\",\n  \"357893.jpg\": \"Mammalia/Odocoileus hemionus columbianus/0f72bea0473a40b048dfe68c88f893c1.jpg\",\n  \"357895.jpg\": \"Mammalia/Odocoileus hemionus columbianus/53aa043a8cfccdf2e67ac64576968bc0.jpg\",\n  \"357902.jpg\": \"Mammalia/Odocoileus hemionus columbianus/c46b56bdd70903e5126ca45cb8e4bbb4.jpg\",\n  \"357909.jpg\": \"Mammalia/Odocoileus hemionus columbianus/5fe9fbf733faf379dc79211bf79acedc.jpg\",\n  \"35791.jpg\": \"Mammalia/Sciurus griseus/d8ba677d25ef7e2e7fca6f3110eecb91.jpg\",\n  \"357950.jpg\": \"Mammalia/Odocoileus hemionus columbianus/aada298b4ebc86a1db550a9a322e3340.jpg\",\n  \"357966.jpg\": \"Mammalia/Urocyon cinereoargenteus/a313b030ada1c5ef70668218b8428932.jpg\",\n  \"357967.jpg\": \"Mammalia/Urocyon cinereoargenteus/920fd0d4fa8c5fca286a350a80e2e2bc.jpg\",\n  \"357986.jpg\": \"Mammalia/Urocyon cinereoargenteus/977e9ef924b9cbe25968ca66e08d01e8.jpg\",\n  \"357987.jpg\": \"Mammalia/Urocyon cinereoargenteus/049fd09540cb4e034d73eb1ac1dfea30.jpg\",\n  \"35801.jpg\": \"Mammalia/Sciurus griseus/da20d91ecc734acfdf674f886eaca488.jpg\",\n  \"35803.jpg\": \"Mammalia/Sciurus griseus/00766facd28bfa33b798b7e58946a504.jpg\",\n  \"358035.jpg\": \"Mammalia/Urocyon cinereoargenteus/9056cb5c42c69340ac23986c9e0d8387.jpg\",\n  \"358054.jpg\": \"Mammalia/Urocyon cinereoargenteus/ca9538522535ea8bc8fe4073dee3a00d.jpg\",\n  \"358064.jpg\": \"Mammalia/Urocyon cinereoargenteus/e0e7e7bd4aad3e9e16e2dd3ac629a6d3.jpg\",\n  \"358067.jpg\": \"Mammalia/Urocyon cinereoargenteus/d168ec80dda6117b488c8d71cf7a5e6f.jpg\",\n  \"358099.jpg\": \"Mammalia/Urocyon cinereoargenteus/f85f8a5c02e67ee6fc46e81241c50224.jpg\",\n  \"358105.jpg\": \"Mammalia/Urocyon cinereoargenteus/ee5ce9071c6cb0335cdbc2fde4021f9c.jpg\",\n  \"358121.jpg\": \"Mammalia/Urocyon cinereoargenteus/d7ad9a27a7cc06619fd00ad0af6bba17.jpg\",\n  \"358131.jpg\": \"Mammalia/Urocyon cinereoargenteus/1a4bc87c054c2d887060295b2b18a555.jpg\",\n  \"358135.jpg\": \"Mammalia/Urocyon cinereoargenteus/eae7aaae88059e177097617fbed95f83.jpg\",\n  \"35814.jpg\": \"Mammalia/Sciurus griseus/70545122edbe3225a5eb0f9739773cbc.jpg\",\n  \"358146.jpg\": \"Mammalia/Urocyon cinereoargenteus/87c70dc1113c5729ea13fbce8706f669.jpg\",\n  \"358150.jpg\": \"Mammalia/Urocyon cinereoargenteus/d2b882717d977311850fc17f48ec7f8c.jpg\",\n  \"358152.jpg\": \"Mammalia/Urocyon cinereoargenteus/19839842c0f15e59e93ca0b5706fb294.jpg\",\n  \"358177.jpg\": \"Mammalia/Urocyon cinereoargenteus/d7d11f9bd6b05e30bbcce150211532ac.jpg\",\n  \"358187.jpg\": \"Mammalia/Urocyon cinereoargenteus/b345e552b309cb135d0f9c8f42bab96f.jpg\",\n  \"358191.jpg\": \"Mammalia/Urocyon cinereoargenteus/999f85a5902b3efe67d5d9aba9f2e3aa.jpg\",\n  \"35821.jpg\": \"Mammalia/Sciurus griseus/2ee6c60bec36a0c1882836fca28f8023.jpg\",\n  \"358248.jpg\": \"Aves/Leptoptilos crumenifer/6d7b953cfebc7583ba83bc4e0790ca8e.jpg\",\n  \"358252.jpg\": \"Aves/Leptoptilos crumenifer/17b51da46c6b4747428a4a178831c02d.jpg\",\n  \"358256.jpg\": \"Aves/Leptoptilos crumenifer/8c6304593937ed777660a1281bc4a6d2.jpg\",\n  \"35826.jpg\": \"Mammalia/Sciurus griseus/e4bcf25ce34e27f5f9717f1eaaec1233.jpg\",\n  \"358261.jpg\": \"Aves/Leptoptilos crumenifer/684aa6a03707765b0a239d1cde088424.jpg\",\n  \"35828.jpg\": \"Mammalia/Sciurus griseus/d54df096254adb6d221d554d8dda583c.jpg\",\n  \"35830.jpg\": \"Mammalia/Sciurus griseus/49430340c25bfda061dd7f78c8f4b567.jpg\",\n  \"358344.jpg\": \"Insecta/Coenonympha pamphilus/084be3197a636e45b455ff78e0a54ba4.jpg\",\n  \"358345.jpg\": \"Insecta/Coenonympha pamphilus/e95598afd8656c0a77e4992581192a1f.jpg\",\n  \"358347.jpg\": \"Insecta/Coenonympha pamphilus/e1b6c0568508ab93518ee806df2f9625.jpg\",\n  \"358349.jpg\": \"Insecta/Coenonympha pamphilus/2a9a4cecd70860520abb17ccbaa3c4b1.jpg\",\n  \"358359.jpg\": \"Insecta/Coenonympha pamphilus/29213be06bad9954d5ad2a829e2fb25e.jpg\",\n  \"358368.jpg\": \"Insecta/Coenonympha pamphilus/33de0188135210871214789a2fe66940.jpg\",\n  \"358369.jpg\": \"Insecta/Coenonympha pamphilus/dbaabfa41db71e4d928f6365be72e710.jpg\",\n  \"358375.jpg\": \"Insecta/Coenonympha pamphilus/0a72190a878e0a3fa919416ab21c5a1b.jpg\",\n  \"358376.jpg\": \"Insecta/Coenonympha pamphilus/3812ae9db99ea7d3ab66fd58e258ddb3.jpg\",\n  \"358380.jpg\": \"Insecta/Coenonympha pamphilus/e3f07a54a9dbfb998c46bc66b54f7c03.jpg\",\n  \"358381.jpg\": \"Insecta/Coenonympha pamphilus/d80c9d3ed95ef88119c2d5683fed3129.jpg\",\n  \"358382.jpg\": \"Insecta/Coenonympha pamphilus/8348eb18c32bde8ab5d2cd0fe75f1212.jpg\",\n  \"358383.jpg\": \"Insecta/Coenonympha pamphilus/c61735ab3c8539313d8f9705b1c805f8.jpg\",\n  \"358386.jpg\": \"Insecta/Coenonympha pamphilus/c8b0dd072291f89e7754541534914ab0.jpg\",\n  \"358387.jpg\": \"Insecta/Coenonympha pamphilus/1ae2d5832d0bf641e98469c05281aa07.jpg\",\n  \"358389.jpg\": \"Insecta/Coenonympha pamphilus/7028293267012e789851bff75131037d.jpg\",\n  \"358390.jpg\": \"Insecta/Coenonympha pamphilus/01babdafaf8ac82b4a844be8b6ffc2df.jpg\",\n  \"358391.jpg\": \"Insecta/Coenonympha pamphilus/f88bbc928fe292b9f735d95aaf9e8f1f.jpg\",\n  \"358395.jpg\": \"Insecta/Coenonympha pamphilus/eb2eb0332ac45243924866372b49473a.jpg\",\n  \"35840.jpg\": \"Mammalia/Sciurus griseus/25e82947026f731a606726f50e39d86c.jpg\",\n  \"358402.jpg\": \"Amphibia/Duttaphrynus melanostictus/f556b2a96f0d4ff3310e6e7d0a79a518.jpg\",\n  \"358404.jpg\": \"Amphibia/Duttaphrynus melanostictus/83851abffcbf145b345131715ff34338.jpg\",\n  \"358408.jpg\": \"Amphibia/Duttaphrynus melanostictus/577dbd0e486adb5737ca39bd50810f64.jpg\",\n  \"358411.jpg\": \"Amphibia/Duttaphrynus melanostictus/489ffb18fc2c8de5de7567b00b6da89e.jpg\",\n  \"358414.jpg\": \"Amphibia/Duttaphrynus melanostictus/93a44697bcce8f6dac226abb5e8e1870.jpg\",\n  \"358419.jpg\": \"Amphibia/Duttaphrynus melanostictus/833dfb55cb8eacb005821815cf2cf5de.jpg\",\n  \"358427.jpg\": \"Insecta/Celastrina echo/44734c57e131fabf58616aca7879ef4b.jpg\",\n  \"358438.jpg\": \"Insecta/Celastrina echo/ddffb5f320c64dded18797448f2f1fd3.jpg\",\n  \"358444.jpg\": \"Insecta/Celastrina echo/7c051c82d583718cd6add6cba72f5982.jpg\",\n  \"358447.jpg\": \"Insecta/Celastrina echo/24a8afa0cfcee8577fe08ced280e6d20.jpg\",\n  \"358456.jpg\": \"Insecta/Celastrina echo/d578b0e4114f6dce1a19869e48b12cd2.jpg\",\n  \"358457.jpg\": \"Insecta/Celastrina echo/c70bb49c1b41af0e136f4c94ec163dac.jpg\",\n  \"358471.jpg\": \"Insecta/Celastrina echo/3a5d660672709d7311f56604339a9560.jpg\",\n  \"358474.jpg\": \"Insecta/Celastrina echo/ffa1a74186c69d230df4ef44bcafbbf0.jpg\",\n  \"358494.jpg\": \"Insecta/Celastrina echo/eb4dfc16c9cf5823eb17e5e4a2b04ee5.jpg\",\n  \"3585.jpg\": \"Insecta/Coccinella septempunctata/687b81dd693a0121e512110f85e9ff34.jpg\",\n  \"358506.jpg\": \"Insecta/Celastrina echo/9571d1a3c6a39c3cd3db76fb82257524.jpg\",\n  \"358507.jpg\": \"Insecta/Celastrina echo/eafb44c3b1174c5c49851588a1d40bee.jpg\",\n  \"358515.jpg\": \"Insecta/Celastrina echo/c6542e551a7d0462d88a52175ef72b12.jpg\",\n  \"358516.jpg\": \"Insecta/Celastrina echo/3686a743ac3b950fa9872a9aeed9ecc9.jpg\",\n  \"358520.jpg\": \"Insecta/Celastrina echo/799aa19d42ec761b64bac1d8a26e4edd.jpg\",\n  \"358530.jpg\": \"Insecta/Celastrina echo/b205f0935fee3d9ff6397babcea125b3.jpg\",\n  \"358531.jpg\": \"Insecta/Celastrina echo/93e5bf487c1c988f18f35a1d4377336c.jpg\",\n  \"358532.jpg\": \"Insecta/Celastrina echo/c560a55cb7ef63586e653f2d696ddfc2.jpg\",\n  \"358535.jpg\": \"Insecta/Celastrina echo/24456cd900680cb2b13eb274dd8d7e5a.jpg\",\n  \"358552.jpg\": \"Insecta/Celastrina echo/a636ce87df15a58a1223077bf2738f52.jpg\",\n  \"35857.jpg\": \"Mammalia/Sciurus griseus/ac114bd56ffac51b13675f545767fa10.jpg\",\n  \"35858.jpg\": \"Mammalia/Sciurus griseus/1de6c394460274febe9f049eb3adfc3b.jpg\",\n  \"35863.jpg\": \"Mammalia/Sciurus griseus/bf0892b26d72c429b4f2d725374ed79a.jpg\",\n  \"35871.jpg\": \"Mammalia/Sciurus griseus/96a0b5fb87e9e90db61019e410e427bc.jpg\",\n  \"35874.jpg\": \"Mammalia/Sciurus griseus/13820976383eb9a2cd77914fb5d16898.jpg\",\n  \"35910.jpg\": \"Aves/Plegadis chihi/43621eef05e4bca55838bb32f7598aeb.jpg\",\n  \"35917.jpg\": \"Aves/Plegadis chihi/c0a21cf8a24fbeb6aafb025b2d413300.jpg\",\n  \"359292.jpg\": \"Amphibia/Lithobates palustris/4ac06132a0d0fb1ae8051b6eb993acb9.jpg\",\n  \"359294.jpg\": \"Amphibia/Lithobates palustris/9b32077d8a90d2f65ef0b096f37643d8.jpg\",\n  \"359303.jpg\": \"Amphibia/Lithobates palustris/b4e4b8ef81d5e5dd0d330c5acb7ad665.jpg\",\n  \"359312.jpg\": \"Amphibia/Lithobates palustris/05352928f6984eb65507cb4399c526bb.jpg\",\n  \"359322.jpg\": \"Amphibia/Lithobates palustris/5a36aaec872d92084980672f6f4b89fe.jpg\",\n  \"359323.jpg\": \"Amphibia/Lithobates palustris/f366052170cf1318616dfc46552c71d5.jpg\",\n  \"359327.jpg\": \"Amphibia/Lithobates palustris/be3b14fc3fbbfc896d7d793eb2b03628.jpg\",\n  \"359329.jpg\": \"Amphibia/Lithobates palustris/89b6011f0e6f11eaaac5b1ca4baed992.jpg\",\n  \"359332.jpg\": \"Amphibia/Lithobates palustris/cbc2cbc6d5108bc1db5a9e803c87a127.jpg\",\n  \"359336.jpg\": \"Amphibia/Lithobates palustris/8a974bd9d87a9a47518ae7471d04cb3c.jpg\",\n  \"359349.jpg\": \"Amphibia/Lithobates palustris/ae6e4dbcd26b365ff93ae964986790ba.jpg\",\n  \"359364.jpg\": \"Amphibia/Lithobates palustris/d4fe340ffc1318fafb739564f6b31b8f.jpg\",\n  \"359368.jpg\": \"Amphibia/Lithobates palustris/474750b58b44a0cf3e5e84dd3a8fd16b.jpg\",\n  \"359377.jpg\": \"Amphibia/Lithobates palustris/886dfbbcdab52e4a9c1f82e838f715cd.jpg\",\n  \"359384.jpg\": \"Amphibia/Lithobates palustris/59753cf020e23323572f50267d99031c.jpg\",\n  \"359387.jpg\": \"Amphibia/Lithobates palustris/132ccc3a22199ec8eb12881857e4380a.jpg\",\n  \"359401.jpg\": \"Amphibia/Lithobates palustris/5a74c0ccb3b528f9a2058376c0a1f08f.jpg\",\n  \"359406.jpg\": \"Amphibia/Lithobates palustris/35325adf95040013e9ddbca58ee74c24.jpg\",\n  \"359415.jpg\": \"Amphibia/Lithobates palustris/dfcdbede7c357798247461a3fe9a32fd.jpg\",\n  \"359421.jpg\": \"Aves/Anas strepera/01688f1893c3cb08bad5ebf960ac814e.jpg\",\n  \"359422.jpg\": \"Aves/Anas strepera/40b68cd2e08f67512fc2a487c84fa2d6.jpg\",\n  \"359439.jpg\": \"Aves/Anas strepera/8db495a55d46c3f437e1fef7459950da.jpg\",\n  \"359440.jpg\": \"Aves/Anas strepera/ba9f56e4a241bc273fc1387b3a61a284.jpg\",\n  \"359448.jpg\": \"Aves/Anas strepera/94f2ba52e8e9902ba609d4979b4b91e6.jpg\",\n  \"359458.jpg\": \"Aves/Anas strepera/125d8caa3979d17e132a3e9941955981.jpg\",\n  \"35949.jpg\": \"Aves/Plegadis chihi/db550b2a69df12f1534cadbc50a90dc4.jpg\",\n  \"359498.jpg\": \"Aves/Anas strepera/34b43cc1f9d46119c13690c7f297846d.jpg\",\n  \"359514.jpg\": \"Aves/Anas strepera/557decb55490aa682f58bee6249a5695.jpg\",\n  \"359520.jpg\": \"Aves/Anas strepera/7544941de462734f1d909da033e221bb.jpg\",\n  \"359560.jpg\": \"Aves/Anas strepera/29e57b309c80fc401e0f495e2aa1f3e8.jpg\",\n  \"359579.jpg\": \"Aves/Anas strepera/3d71b77400837c19a7b4cbc429d263c7.jpg\",\n  \"359641.jpg\": \"Aves/Anas strepera/411f8a2ddbe9efb0750145a89e4ccd3b.jpg\",\n  \"359689.jpg\": \"Aves/Anas strepera/7bfb4bd7a29a72e2c5a34605e4bfc341.jpg\",\n  \"359690.jpg\": \"Aves/Anas strepera/06d6e0c2aa6b218bc6c7ee2213266034.jpg\",\n  \"35972.jpg\": \"Aves/Plegadis chihi/3f4d242c5ff573722ee64f4a6d249d61.jpg\",\n  \"359721.jpg\": \"Aves/Anas strepera/83013a1d32d8a202bf3a43f34820cecd.jpg\",\n  \"359732.jpg\": \"Aves/Anas strepera/b12bcaf1c82c53f869d6beb03310a5c5.jpg\",\n  \"359793.jpg\": \"Aves/Himantopus himantopus/1fc014529ceab2c392ee6e35155a52b6.jpg\",\n  \"359803.jpg\": \"Aves/Himantopus himantopus/505af268d1cc768adc9efdba74bea886.jpg\",\n  \"359805.jpg\": \"Aves/Himantopus himantopus/de1e39d83a1fbbd9534f9c1a46a4391b.jpg\",\n  \"359807.jpg\": \"Aves/Himantopus himantopus/bb41209d454be3a7f16683cdd1d90505.jpg\",\n  \"359811.jpg\": \"Aves/Himantopus himantopus/edcaca6cf52c81760f2a058c3f22f2fb.jpg\",\n  \"359814.jpg\": \"Aves/Himantopus himantopus/862cd936a4ab9bf7cb7b741030192606.jpg\",\n  \"359816.jpg\": \"Aves/Himantopus himantopus/30e77877a788040412e9bf66f48e76ca.jpg\",\n  \"359822.jpg\": \"Aves/Himantopus himantopus/822afbb88e236128034ca75d39cf525c.jpg\",\n  \"359823.jpg\": \"Aves/Himantopus himantopus/c1a5e8b910d2563404ccb89e7eb3063a.jpg\",\n  \"359827.jpg\": \"Aves/Himantopus himantopus/a4ffa7cd31e523b1f42674eb030d4be1.jpg\",\n  \"359829.jpg\": \"Aves/Himantopus himantopus/9dcc49b99b8c180d8fcbc41eaf39a6b7.jpg\",\n  \"359831.jpg\": \"Aves/Himantopus himantopus/acc034ab5f2024fd8e875fd34736115c.jpg\",\n  \"359840.jpg\": \"Aves/Himantopus himantopus/f74281039112c0327a9eab5c9d66d179.jpg\",\n  \"359841.jpg\": \"Aves/Himantopus himantopus/c7d13706a8163e7f52c1286d464d19c2.jpg\",\n  \"359847.jpg\": \"Aves/Himantopus himantopus/3283b991ad67f3d76b3fd9139916e962.jpg\",\n  \"359873.jpg\": \"Mollusca/Acanthodoris lutea/c19449fd4a0c543f06b8ae0a5144bbb1.jpg\",\n  \"359875.jpg\": \"Mollusca/Acanthodoris lutea/89f36e9c122ddfd3cea208e6b65437dc.jpg\",\n  \"359878.jpg\": \"Mollusca/Acanthodoris lutea/5dc82875d263b45bc2ccd4af458990a9.jpg\",\n  \"359881.jpg\": \"Mollusca/Acanthodoris lutea/243b282f5bc2e66573f1c89f226bff7f.jpg\",\n  \"359883.jpg\": \"Mollusca/Acanthodoris lutea/f7331cd520c601c01f7baa94a3bce401.jpg\",\n  \"359885.jpg\": \"Mollusca/Acanthodoris lutea/0c744a1dfc2aacd9ce08d520357f6e7d.jpg\",\n  \"359886.jpg\": \"Mollusca/Acanthodoris lutea/7f3430b5652ca80585d80c43240893d8.jpg\",\n  \"359890.jpg\": \"Mollusca/Acanthodoris lutea/907a3fade0bc4f6e0119911e738e405a.jpg\",\n  \"359891.jpg\": \"Mollusca/Acanthodoris lutea/97cd380cc67c9a9deb5ce8a806d770db.jpg\",\n  \"359892.jpg\": \"Mollusca/Acanthodoris lutea/80535efe834f5c2703e943feb0ec4899.jpg\",\n  \"359897.jpg\": \"Mollusca/Acanthodoris lutea/c5c46651bdfbdaa6c217780a9e1e946c.jpg\",\n  \"359902.jpg\": \"Mollusca/Acanthodoris lutea/8d11f3872c27533ecf856fb020ebbe11.jpg\",\n  \"359910.jpg\": \"Mollusca/Acanthodoris lutea/167e943a0aff884bb774d42ae0a0a0a3.jpg\",\n  \"359915.jpg\": \"Mollusca/Acanthodoris lutea/b213dce038394365d50bb23390ee3115.jpg\",\n  \"359918.jpg\": \"Mollusca/Acanthodoris lutea/daac95f572be967e1ee7b43d00f76d29.jpg\",\n  \"359923.jpg\": \"Mollusca/Acanthodoris lutea/5a62a65ebbda656b719bf5273c1d5df6.jpg\",\n  \"359929.jpg\": \"Mollusca/Acanthodoris lutea/234f914727d11ef1701f5ded7e02d014.jpg\",\n  \"359943.jpg\": \"Aves/Luscinia svecica/a43820f1d78dee8e718e189c20328f98.jpg\",\n  \"359946.jpg\": \"Aves/Luscinia svecica/1c1e3faf686aa89606bb8c781316cbb0.jpg\",\n  \"359949.jpg\": \"Aves/Luscinia svecica/0c2738267f76fda3b2c3a2f349cb8df9.jpg\",\n  \"359950.jpg\": \"Aves/Luscinia svecica/4b59ea395f2f0a442b8c042d6e643acb.jpg\",\n  \"359970.jpg\": \"Aves/Threskiornis aethiopicus/6722d926b00a0d6b8287a218f02fffce.jpg\",\n  \"360006.jpg\": \"Aves/Archilochus alexandri/d848263c296ad69e496a9d2120aee95a.jpg\",\n  \"360008.jpg\": \"Aves/Archilochus alexandri/088cae4f0c24d1900109a77568df7ef9.jpg\",\n  \"360013.jpg\": \"Aves/Archilochus alexandri/c544dbba0c71e5f081696623c611e25c.jpg\",\n  \"360014.jpg\": \"Aves/Archilochus alexandri/aa5e529973e0a34c8b27d6113ff2f21f.jpg\",\n  \"360034.jpg\": \"Aves/Archilochus alexandri/3fef6e13c8cdef64ff225b87294d671d.jpg\",\n  \"360036.jpg\": \"Aves/Archilochus alexandri/72c22aaf411b4b27c70209bd3ddb6012.jpg\",\n  \"360047.jpg\": \"Aves/Archilochus alexandri/996188612d63eb0675d7fb828de6c88d.jpg\",\n  \"360058.jpg\": \"Aves/Archilochus alexandri/cc38f0accb70f65e0576cd6dc0ec305d.jpg\",\n  \"360060.jpg\": \"Aves/Archilochus alexandri/05796e59a8807282e7bf07b40dab9271.jpg\",\n  \"360063.jpg\": \"Aves/Archilochus alexandri/2becc0d060f82819763023eb7ec9d70d.jpg\",\n  \"36007.jpg\": \"Aves/Plegadis chihi/5ddf19cf311fdf1efbcf89cc8db629fe.jpg\",\n  \"360071.jpg\": \"Aves/Archilochus alexandri/e15431d40b969d43ffe0b17b3cbb7376.jpg\",\n  \"360072.jpg\": \"Aves/Archilochus alexandri/1d6c65a1fda8e400823a4ae8df2178a7.jpg\",\n  \"360082.jpg\": \"Aves/Archilochus alexandri/8c2b67cb085c6f943b5a81eb15ef7236.jpg\",\n  \"360083.jpg\": \"Aves/Archilochus alexandri/65b77678e4195dd046cf9b6fd1d641b5.jpg\",\n  \"360088.jpg\": \"Aves/Archilochus alexandri/394e0890c1a5b1548c59aa82faa364bc.jpg\",\n  \"360089.jpg\": \"Aves/Archilochus alexandri/013aba18cafccc61be6e078aa7a6ff52.jpg\",\n  \"360090.jpg\": \"Aves/Archilochus alexandri/8a97ffe0f10cc37b480787a9377fcaaa.jpg\",\n  \"360091.jpg\": \"Aves/Archilochus alexandri/de39ee9afe54b7cce3c2078b03234eff.jpg\",\n  \"360094.jpg\": \"Aves/Archilochus alexandri/ec53d1a142a4aa98252652199c2f7a15.jpg\",\n  \"360098.jpg\": \"Aves/Archilochus alexandri/9279947e48651452b5e44fb49bd12770.jpg\",\n  \"360101.jpg\": \"Aves/Archilochus alexandri/87ddb5e3ca2a2d0056bc5f932a03bc49.jpg\",\n  \"360109.jpg\": \"Aves/Archilochus alexandri/026d5c8b1106952a3ac16999ff368b2b.jpg\",\n  \"360111.jpg\": \"Aves/Archilochus alexandri/67c50183995847783bb56ce4550c7d20.jpg\",\n  \"360113.jpg\": \"Aves/Archilochus alexandri/b1c2e2e615eab20e7afbbca439cd2a18.jpg\",\n  \"360120.jpg\": \"Aves/Glaucidium gnoma/709612ccb083190197ebd653379d9295.jpg\",\n  \"360121.jpg\": \"Aves/Glaucidium gnoma/5dc5ad9bf2158540930a4cfb4fc78673.jpg\",\n  \"360122.jpg\": \"Aves/Glaucidium gnoma/1cb59e13ad913b45dfe645d5d3ea5fde.jpg\",\n  \"360126.jpg\": \"Aves/Glaucidium gnoma/04518ad46041e40ff63b21cef20571a1.jpg\",\n  \"360128.jpg\": \"Aves/Glaucidium gnoma/6725d14a1a08adcf433389a6c7bf76c4.jpg\",\n  \"360129.jpg\": \"Aves/Glaucidium gnoma/39ec5c87901f3e021b582f642192b030.jpg\",\n  \"360131.jpg\": \"Aves/Glaucidium gnoma/c4ceacae2815781c065040ab86590351.jpg\",\n  \"360132.jpg\": \"Aves/Glaucidium gnoma/96efc02c0f7ff13206b62aab905c16e1.jpg\",\n  \"360135.jpg\": \"Aves/Glaucidium gnoma/e6146f26edf8835a0734dd394de60bd5.jpg\",\n  \"360140.jpg\": \"Aves/Glaucidium gnoma/972a0861899eb40ab74f96848265c603.jpg\",\n  \"360203.jpg\": \"Aves/Calidris mauri/83c20eafe559b719fdba80e088ca9f89.jpg\",\n  \"360213.jpg\": \"Aves/Calidris mauri/d29b67ae74a6aaf1623b8a7a07596a54.jpg\",\n  \"360223.jpg\": \"Aves/Calidris mauri/2f111d0df1101eaffe4a638576c30918.jpg\",\n  \"360244.jpg\": \"Aves/Calidris mauri/ecfaa34331370f8e35167e92862f57f9.jpg\",\n  \"360282.jpg\": \"Aves/Calidris mauri/7ad0c7aab0a6adbf21cd866d3eb81b96.jpg\",\n  \"360297.jpg\": \"Aves/Calidris mauri/6176b9e2bff27c38a3248d37b737ec86.jpg\",\n  \"360298.jpg\": \"Aves/Calidris mauri/6a974071f949e3919dc539bfd8778fde.jpg\",\n  \"360306.jpg\": \"Aves/Calidris mauri/765be31b4b7629b834a40fbdd8c9fe5e.jpg\",\n  \"360307.jpg\": \"Aves/Calidris mauri/da3e21f03263debac0d258eb5a71b59a.jpg\",\n  \"360357.jpg\": \"Insecta/Phanaeus vindex/d5da000d18ec443193655a8aef281368.jpg\",\n  \"360358.jpg\": \"Insecta/Phanaeus vindex/593e24c378a621755d3eb0bf219bcd9f.jpg\",\n  \"36036.jpg\": \"Aves/Plegadis chihi/6af38529c0be9f7052340dcc65ebfd98.jpg\",\n  \"360362.jpg\": \"Insecta/Phanaeus vindex/92e7196acfa7e26fe2c22131db91e620.jpg\",\n  \"360366.jpg\": \"Insecta/Phanaeus vindex/8210e8b68d356a85c3c2e7d84eaa0d45.jpg\",\n  \"360375.jpg\": \"Insecta/Phanaeus vindex/2388ba68d4a024100fadcfcdc3079fb1.jpg\",\n  \"360377.jpg\": \"Insecta/Phanaeus vindex/5d0deff0f8b938cd93349736685f316c.jpg\",\n  \"360383.jpg\": \"Reptilia/Sceloporus variabilis/3b81a0f3b17f553d60ea015d1d8349eb.jpg\",\n  \"360384.jpg\": \"Reptilia/Sceloporus variabilis/4060204b510a2f5a50fff098913192a0.jpg\",\n  \"360388.jpg\": \"Reptilia/Sceloporus variabilis/3f1662cbbd139c437b4b92868f44c61f.jpg\",\n  \"360389.jpg\": \"Reptilia/Sceloporus variabilis/717fed8e2603e6f31d9d64ddafb8d1c1.jpg\",\n  \"360392.jpg\": \"Reptilia/Sceloporus variabilis/ab5a01bb5d60ef5f457aad550325de32.jpg\",\n  \"360393.jpg\": \"Reptilia/Sceloporus variabilis/22162154e87fad3f985046a2a4d9326d.jpg\",\n  \"360394.jpg\": \"Reptilia/Sceloporus variabilis/db3b2f9bb43173fa1ac7323baf53f0dc.jpg\",\n  \"360399.jpg\": \"Reptilia/Sceloporus variabilis/a1cba62bb8e18b3998828c2554692684.jpg\",\n  \"360401.jpg\": \"Reptilia/Sceloporus variabilis/9f946a1a7e597bae2b9f061df3e534e0.jpg\",\n  \"360404.jpg\": \"Reptilia/Sceloporus variabilis/a0197e57bf5d1a5d62582a162f3e60fb.jpg\",\n  \"360407.jpg\": \"Reptilia/Sceloporus variabilis/c36e7834c45abe97b8ab9a6709b88046.jpg\",\n  \"360413.jpg\": \"Reptilia/Sceloporus variabilis/1d258efb111fcbd30033d57b050b2c8c.jpg\",\n  \"360414.jpg\": \"Reptilia/Sceloporus variabilis/fd6f7b3d416b1f0b99cc589e2c531657.jpg\",\n  \"360415.jpg\": \"Reptilia/Sceloporus variabilis/c1c91130a2f0358c2f6de7dccad8a251.jpg\",\n  \"360418.jpg\": \"Reptilia/Sceloporus variabilis/cc4dd63fd645a5a1a77cdfe2895d3f43.jpg\",\n  \"360419.jpg\": \"Reptilia/Sceloporus variabilis/6cdf3787f2ac2ae8929804ed8d5994c9.jpg\",\n  \"36042.jpg\": \"Aves/Plegadis chihi/5b9e8646d862227c6a31ebbf288e6074.jpg\",\n  \"360422.jpg\": \"Reptilia/Sceloporus variabilis/30ca6ec956944adfb4a0132e0b89bb36.jpg\",\n  \"360423.jpg\": \"Reptilia/Sceloporus variabilis/69c5cb71d8864cfac3fed8413ba7adea.jpg\",\n  \"360424.jpg\": \"Reptilia/Sceloporus variabilis/d49076345376ca2959afda3204664ee5.jpg\",\n  \"360426.jpg\": \"Reptilia/Sceloporus variabilis/a75a42d34bcd2b0523812d9cb7c716a9.jpg\",\n  \"360439.jpg\": \"Reptilia/Sceloporus variabilis/8c8ffb6b38605dd6da9d1a952eb138c4.jpg\",\n  \"36062.jpg\": \"Aves/Plegadis chihi/c7f6a085db330aa5617335368c1df8f0.jpg\",\n  \"360730.jpg\": \"Reptilia/Uma scoparia/10200c868b2720b112bab6a98c40fdba.jpg\",\n  \"360735.jpg\": \"Reptilia/Uma scoparia/f90e7ffd5f3f0e97b8b5d1a0bfac6421.jpg\",\n  \"360746.jpg\": \"Reptilia/Uma scoparia/f7b3431c2b467fbefb95dea0d7dc2303.jpg\",\n  \"360947.jpg\": \"Aves/Corvus ossifragus/970a44cb27ee1bcab82475bc7b66c134.jpg\",\n  \"360952.jpg\": \"Aves/Corvus ossifragus/382ccc348cb44d9ac7778ec173a847bc.jpg\",\n  \"360956.jpg\": \"Aves/Corvus ossifragus/7cb69a0145122520a05990d5681e5774.jpg\",\n  \"360957.jpg\": \"Aves/Corvus ossifragus/7c216b34ac31b0e45bff6372dbc345e9.jpg\",\n  \"360958.jpg\": \"Aves/Corvus ossifragus/a1b298486578846990572488c01cfb2e.jpg\",\n  \"360959.jpg\": \"Aves/Corvus ossifragus/e54fd6f296cdbe126001d076bb6873e4.jpg\",\n  \"360967.jpg\": \"Aves/Corvus ossifragus/fe1f104a8a94e51171cd0f09e63979e4.jpg\",\n  \"360969.jpg\": \"Aves/Corvus ossifragus/81a86202830b11d954e877af97530375.jpg\",\n  \"360974.jpg\": \"Aves/Paroaria coronata/a6fe3aa56962f561be7b6cee94eea05a.jpg\",\n  \"360975.jpg\": \"Aves/Paroaria coronata/903d3d5636480df9651a20009d1e91c9.jpg\",\n  \"360978.jpg\": \"Aves/Paroaria coronata/4d0062b3a5ab1fb93271be6d4cd71432.jpg\",\n  \"360979.jpg\": \"Aves/Paroaria coronata/f96f3ddc8e962a7a6c76c8c2e7c43d4b.jpg\",\n  \"360981.jpg\": \"Aves/Paroaria coronata/6ba46072a2d2ee77298a26bb3b1601c4.jpg\",\n  \"360982.jpg\": \"Aves/Paroaria coronata/ace262a125772ee84c52be6225bc0b48.jpg\",\n  \"360983.jpg\": \"Aves/Paroaria coronata/1805ba661fd479c5ecee386ed2edfe82.jpg\",\n  \"360984.jpg\": \"Aves/Paroaria coronata/f5a39f29a307bde9b9f1ab360271f151.jpg\",\n  \"360986.jpg\": \"Aves/Paroaria coronata/8f70127e481d50e5e46cffa79091fac4.jpg\",\n  \"360993.jpg\": \"Aves/Paroaria coronata/a75489c4531a5925b349d848e7deeaf1.jpg\",\n  \"360994.jpg\": \"Aves/Paroaria coronata/3b4da0ff7628bc2f084803cb3ee139ca.jpg\",\n  \"360996.jpg\": \"Aves/Paroaria coronata/eac53444147f8b92f81353e8b8f91bd1.jpg\",\n  \"360997.jpg\": \"Aves/Paroaria coronata/50fd753b403963a9277ed9edea08829e.jpg\",\n  \"361002.jpg\": \"Aves/Paroaria coronata/56d293776606ad3f384bc6730c89bd16.jpg\",\n  \"361003.jpg\": \"Aves/Paroaria coronata/1a8af116e0290f9091008fade73eb282.jpg\",\n  \"361008.jpg\": \"Aves/Paroaria coronata/dbd7f56df8206e844167e87d8ae3e2dc.jpg\",\n  \"361018.jpg\": \"Aves/Paroaria coronata/6c70bdfd9233df872d2a304892d4ee69.jpg\",\n  \"36105.jpg\": \"Aves/Plegadis chihi/2bc0fe32e243d6f5ae1d076a119592a4.jpg\",\n  \"361050.jpg\": \"Reptilia/Intellagama lesueurii/f899d46cddbc8c4e0a9a5620edd6c3b5.jpg\",\n  \"361055.jpg\": \"Reptilia/Intellagama lesueurii/c04790e9c6f0566ea63aa74973c406ce.jpg\",\n  \"361056.jpg\": \"Reptilia/Intellagama lesueurii/ff8b6ca7ae2e9405256ff37296bfeddb.jpg\",\n  \"361057.jpg\": \"Reptilia/Intellagama lesueurii/0e59b55209568057884964f20ee7648b.jpg\",\n  \"361061.jpg\": \"Reptilia/Intellagama lesueurii/f540b317441aaf24adfd6709d0636afa.jpg\",\n  \"361073.jpg\": \"Reptilia/Intellagama lesueurii/6cc1bec7f133ba88fa003a6a59aaaf47.jpg\",\n  \"361074.jpg\": \"Reptilia/Intellagama lesueurii/1fb52d4d07ad0c2d8877d86d60ee81f8.jpg\",\n  \"361079.jpg\": \"Actinopterygii/Rhinecanthus rectangulus/4ef580dd9bddb96d713a57c3bea0db1a.jpg\",\n  \"361088.jpg\": \"Actinopterygii/Rhinecanthus rectangulus/6351868733d288419ce644db88cf4f4c.jpg\",\n  \"361089.jpg\": \"Actinopterygii/Rhinecanthus rectangulus/e99c2f25b589485ad54fb5cbd7a8b1ca.jpg\",\n  \"361096.jpg\": \"Aves/Myioborus pictus/dbd21eadfbf47dd846ec172d733d6551.jpg\",\n  \"361097.jpg\": \"Aves/Myioborus pictus/076168235e75492c89ff64ea6eb219d5.jpg\",\n  \"361104.jpg\": \"Aves/Myioborus pictus/3e6acd81dd9556f1d586e1e48b4fecfc.jpg\",\n  \"361107.jpg\": \"Aves/Myioborus pictus/80d6cd3b621ae76d5672168878c73e7e.jpg\",\n  \"361108.jpg\": \"Aves/Myioborus pictus/aab87aa1d4b8a0d2a3fea545db21edbd.jpg\",\n  \"361112.jpg\": \"Aves/Myioborus pictus/9ea96e1135ae84104b31df2f65531ec0.jpg\",\n  \"361115.jpg\": \"Aves/Myioborus pictus/a69a52b713f46aee5d471e9d71d0457e.jpg\",\n  \"361119.jpg\": \"Aves/Myioborus pictus/be45299aaa3a5253910839e6a9b1f9a5.jpg\",\n  \"361454.jpg\": \"Insecta/Ochlodes sylvanus/dbf9d65d74607192882fe643db3d0201.jpg\",\n  \"361462.jpg\": \"Insecta/Ochlodes sylvanus/a4068ae9cd3b436d55aaab552b1acfca.jpg\",\n  \"361463.jpg\": \"Insecta/Ochlodes sylvanus/cf35397d4e087fb6c79b531f604f05eb.jpg\",\n  \"36147.jpg\": \"Aves/Plegadis chihi/11be95ff36f8067f2fa282d6c5608308.jpg\",\n  \"361470.jpg\": \"Insecta/Ochlodes sylvanus/fcdc793a3caaf622d0436255b0456bc0.jpg\",\n  \"361477.jpg\": \"Insecta/Ochlodes sylvanus/2ebf64c76b3611c997ae6bd7bcc084ae.jpg\",\n  \"361483.jpg\": \"Insecta/Ochlodes sylvanus/2a9a4195103298da2908533cdcc30e19.jpg\",\n  \"361484.jpg\": \"Insecta/Ochlodes sylvanus/956d588ab5b48bc03262f36c824886fc.jpg\",\n  \"361489.jpg\": \"Insecta/Apoda y-inversum/1c0504e24dfe4093127f93407d4c69ca.jpg\",\n  \"361495.jpg\": \"Insecta/Apoda y-inversum/8cd8292760a13152873a062ee6745f7c.jpg\",\n  \"361497.jpg\": \"Insecta/Apoda y-inversum/0e9ec13f883fee0a3d7fd222f64bb1ca.jpg\",\n  \"361499.jpg\": \"Insecta/Apoda y-inversum/729fe0ca9dfea8959f8bbfd2d126737b.jpg\",\n  \"3615.jpg\": \"Insecta/Coccinella septempunctata/4d073f1be4ceb80e610eca0bbf7c94a8.jpg\",\n  \"36156.jpg\": \"Aves/Plegadis chihi/724690c1e25c3627d283032a7301c19c.jpg\",\n  \"36175.jpg\": \"Aves/Plegadis chihi/c05ab4ffb2d95ef26eb586a1b9e3b34f.jpg\",\n  \"361985.jpg\": \"Aves/Egretta caerulea/f28160884e09bf347b26249bbe00c09f.jpg\",\n  \"362010.jpg\": \"Aves/Egretta caerulea/6bcb1bec00bd039375617ad33a4e1c4e.jpg\",\n  \"362058.jpg\": \"Aves/Egretta caerulea/83533e92a954bc5291a2d34ed3ab8641.jpg\",\n  \"362093.jpg\": \"Aves/Egretta caerulea/4df0bb6166231caafc2838a1c0fb28f1.jpg\",\n  \"362108.jpg\": \"Aves/Egretta caerulea/6ca99ab0217959ebe3a6ebe216bff988.jpg\",\n  \"362111.jpg\": \"Aves/Egretta caerulea/ce425189c3f88852a01fa44539201856.jpg\",\n  \"362127.jpg\": \"Aves/Egretta caerulea/e52f5141279e2a18209c69787fa14568.jpg\",\n  \"362130.jpg\": \"Aves/Egretta caerulea/664d3c11d69b3afc5b42905aad1b9685.jpg\",\n  \"362144.jpg\": \"Aves/Egretta caerulea/42b702d7e1c2a74bae726b82d31fcf8c.jpg\",\n  \"362157.jpg\": \"Aves/Egretta caerulea/4752458527a9c500839bf654a7475cdc.jpg\",\n  \"362163.jpg\": \"Aves/Egretta caerulea/85e39137564e46ae83bdeb032f3ede35.jpg\",\n  \"362172.jpg\": \"Aves/Egretta caerulea/cc38fe1c20aa93ffbdfdf758838fa77f.jpg\",\n  \"362189.jpg\": \"Aves/Egretta caerulea/ffb388a889e020a959c913c48d295aa5.jpg\",\n  \"362198.jpg\": \"Aves/Egretta caerulea/01908cec364989d08ada368b2acd8b7f.jpg\",\n  \"362208.jpg\": \"Aves/Egretta caerulea/03e8848551e1eea4fc0fa591c01a2312.jpg\",\n  \"362210.jpg\": \"Aves/Egretta caerulea/373a27da0c1a4eb3e5f883e501e31fbc.jpg\",\n  \"362220.jpg\": \"Aves/Egretta caerulea/85221bffde504c1e1f5adac0964d4c72.jpg\",\n  \"362234.jpg\": \"Aves/Egretta caerulea/f375539b1d78d39514c01c67769d0992.jpg\",\n  \"362240.jpg\": \"Aves/Egretta caerulea/696f709122b2cf77943d6d70dd94fa71.jpg\",\n  \"362268.jpg\": \"Aves/Egretta caerulea/d7aa8d5120962e1e4d776070df73fae7.jpg\",\n  \"362291.jpg\": \"Aves/Egretta caerulea/590dc8aa6013835e8e051fc4534948ca.jpg\",\n  \"362312.jpg\": \"Aves/Egretta caerulea/c796c712474207160a036c0441dad29c.jpg\",\n  \"362533.jpg\": \"Reptilia/Chamaeleo dilepis/3f8ff28e8fd1836852b57c1f0ef4419f.jpg\",\n  \"362537.jpg\": \"Reptilia/Chamaeleo dilepis/b3bd2f52a4e052aae8bdbed832715c90.jpg\",\n  \"362541.jpg\": \"Reptilia/Chamaeleo dilepis/26b463deb2e352fda4047aa0428ad7da.jpg\",\n  \"362542.jpg\": \"Reptilia/Sceloporus uniformis/ecfb5a44b1699efa7a31f99536c36322.jpg\",\n  \"362543.jpg\": \"Reptilia/Sceloporus uniformis/7e4b9c2d6fd0c99c283825eb1a9076e9.jpg\",\n  \"362544.jpg\": \"Reptilia/Sceloporus uniformis/5ca93fe4ea3fc1c41ec6bf9c4d862708.jpg\",\n  \"362553.jpg\": \"Reptilia/Sceloporus uniformis/d30205e9dcf7a67ad8003e54781bf266.jpg\",\n  \"362554.jpg\": \"Reptilia/Sceloporus uniformis/41d2d1dbc86e0288971122648febb03f.jpg\",\n  \"362556.jpg\": \"Reptilia/Sceloporus uniformis/904e27f0b6c34709bb06e03be658f191.jpg\",\n  \"362557.jpg\": \"Reptilia/Sceloporus uniformis/ac458db6ff42cad9a374479c5929f0ea.jpg\",\n  \"362562.jpg\": \"Reptilia/Sceloporus uniformis/98812190af9b906e4ada4dcf64a0ea30.jpg\",\n  \"362569.jpg\": \"Reptilia/Sceloporus uniformis/2da76d18bb92db124fc72b52e42d8429.jpg\",\n  \"36257.jpg\": \"Insecta/Pseudeustrotia carneola/d5bf847c17f3fdb04c359c38afc350d7.jpg\",\n  \"362571.jpg\": \"Reptilia/Sceloporus uniformis/fca690475bfa9fc6cb6155a596dde0e3.jpg\",\n  \"362574.jpg\": \"Reptilia/Sceloporus uniformis/f0230ff392b4a63346788d48f6f9fa7b.jpg\",\n  \"362575.jpg\": \"Reptilia/Sceloporus uniformis/3d51267146d85b8f5a82bfe3f6dcebe4.jpg\",\n  \"362577.jpg\": \"Reptilia/Sceloporus uniformis/1b9b712b02e85800e6268ad79a284ae4.jpg\",\n  \"362578.jpg\": \"Reptilia/Sceloporus uniformis/f5aec04f28ae98f231e3eb4d14bc73fa.jpg\",\n  \"362579.jpg\": \"Reptilia/Sceloporus uniformis/3a18d49f552c8e873c9b793d6beee68c.jpg\",\n  \"362585.jpg\": \"Reptilia/Sceloporus uniformis/02ea3f7124979800306ed4d660ef09bc.jpg\",\n  \"362586.jpg\": \"Reptilia/Sceloporus uniformis/68a52a24cde4cc27b3826d2bbff0064c.jpg\",\n  \"362587.jpg\": \"Reptilia/Sceloporus uniformis/411ccc1b6bf47d4b7401079b8f425afe.jpg\",\n  \"362589.jpg\": \"Reptilia/Sceloporus uniformis/1415568b6f51ba6959c13916e9806bc2.jpg\",\n  \"36259.jpg\": \"Insecta/Pseudeustrotia carneola/064893335bed55fe63bdab0034bc0f77.jpg\",\n  \"362590.jpg\": \"Reptilia/Sceloporus uniformis/7603ad781a91fec623a0b6812e0bf445.jpg\",\n  \"362596.jpg\": \"Insecta/Lestes congener/fc62ff16fd1203d38865a98937adbb1c.jpg\",\n  \"36260.jpg\": \"Insecta/Pseudeustrotia carneola/00ad83106ee94c2a8b3d335211faec24.jpg\",\n  \"362600.jpg\": \"Insecta/Lestes congener/8c730e54e7943d69355b95932e1a9968.jpg\",\n  \"362601.jpg\": \"Insecta/Lestes congener/dccbd86471efa5fe6ae3a32fd8a4e527.jpg\",\n  \"362604.jpg\": \"Insecta/Lestes congener/c88490346b51f553557e383a345e80fb.jpg\",\n  \"362605.jpg\": \"Insecta/Lestes congener/8f7b354e05a70038ac69bbef7c5880ac.jpg\",\n  \"362606.jpg\": \"Insecta/Lestes congener/5b4f25cb7e10560a46aed3466ceff1ce.jpg\",\n  \"362607.jpg\": \"Insecta/Lestes congener/5482eb2435659167e2e8afbbd40b05ac.jpg\",\n  \"362608.jpg\": \"Insecta/Lestes congener/944dcff022e9d4872905129b5311d9b0.jpg\",\n  \"362609.jpg\": \"Insecta/Lestes congener/1e1158a49354758286d08328f6c279cd.jpg\",\n  \"36261.jpg\": \"Insecta/Pseudeustrotia carneola/a282b5eab2b0acf5f96d559aae424433.jpg\",\n  \"362610.jpg\": \"Insecta/Lestes congener/d8d27eda0c9114a1c38d843b1813b508.jpg\",\n  \"362611.jpg\": \"Insecta/Lestes congener/cbc07f5cf0a25e7a5e8b88b3a1aa122a.jpg\",\n  \"362612.jpg\": \"Insecta/Lestes congener/82985453a99dcf9dd49cf1289dd34bdf.jpg\",\n  \"362613.jpg\": \"Insecta/Lestes congener/c91ef92898e367568115d6c069938893.jpg\",\n  \"362614.jpg\": \"Insecta/Lestes congener/fa4dda6bd58fae93f522d0c0a84a3a64.jpg\",\n  \"362619.jpg\": \"Insecta/Lestes congener/8665e40174723ddb086b51e880ef2606.jpg\",\n  \"362620.jpg\": \"Insecta/Lestes congener/7a87cd00e38b5e1ced71607f598bfef3.jpg\",\n  \"362623.jpg\": \"Insecta/Lestes congener/85bc40b914c20128f0f5cdeddc2a0155.jpg\",\n  \"362626.jpg\": \"Insecta/Lestes congener/99e74d64dcb818bbc5cdfe25fb134293.jpg\",\n  \"362627.jpg\": \"Insecta/Lestes congener/c27112c791608a76ff8d3f7e6758aa77.jpg\",\n  \"362630.jpg\": \"Insecta/Lestes congener/b9d8e3ce5c84e826afb7b81e143bcacd.jpg\",\n  \"362631.jpg\": \"Insecta/Lestes congener/4108a86c3963cb88d69684507d759a5f.jpg\",\n  \"362632.jpg\": \"Insecta/Lestes congener/a9e65f79291d3561adceea8ab65af505.jpg\",\n  \"36264.jpg\": \"Insecta/Pseudeustrotia carneola/f33fb1875b36a724d005d635793c92c9.jpg\",\n  \"36265.jpg\": \"Insecta/Pseudeustrotia carneola/005d222f148318cdcf0eb7ee0a5ed264.jpg\",\n  \"362675.jpg\": \"Aves/Catharus guttatus/9d2c874b539038f0a0aee109a8df9914.jpg\",\n  \"362688.jpg\": \"Aves/Catharus guttatus/8be8efb2c3f52fdfa7c9ec4e2a0e04af.jpg\",\n  \"362694.jpg\": \"Aves/Catharus guttatus/cc2f2733c44528a1233ab113cfade62a.jpg\",\n  \"362699.jpg\": \"Aves/Catharus guttatus/9e40b4aad0eecb8e3c533f0afdcf2714.jpg\",\n  \"362707.jpg\": \"Aves/Catharus guttatus/96cc6b421e8375386956dc4b907037e3.jpg\",\n  \"362716.jpg\": \"Aves/Catharus guttatus/50d70747889acb2295b01c4aa54f29d3.jpg\",\n  \"362717.jpg\": \"Aves/Catharus guttatus/379902f509a915eb52c5326008f4f874.jpg\",\n  \"362718.jpg\": \"Aves/Catharus guttatus/16c93ed6a80cec72bee1b3422efa746f.jpg\",\n  \"36272.jpg\": \"Insecta/Pseudeustrotia carneola/44b20e08683b029b797f56eb11bac90c.jpg\",\n  \"36273.jpg\": \"Insecta/Pseudeustrotia carneola/e25440fa70e60b1d9ee2ceb22a1537bc.jpg\",\n  \"362739.jpg\": \"Aves/Catharus guttatus/4890c4d9e2c61a204af04ec98f57455f.jpg\",\n  \"36274.jpg\": \"Insecta/Pseudeustrotia carneola/2d1392f1dab55b9405a0dd29e37eb11c.jpg\",\n  \"362745.jpg\": \"Aves/Catharus guttatus/bfc11664d5e89c05197e7037310d174a.jpg\",\n  \"362747.jpg\": \"Aves/Catharus guttatus/ca98cdb5acccfc60cca978174a7f6a33.jpg\",\n  \"362773.jpg\": \"Aves/Catharus guttatus/305baa8b39ae9cd274520d07a927525d.jpg\",\n  \"362781.jpg\": \"Aves/Catharus guttatus/704ac89da0447e34038affde14f6925c.jpg\",\n  \"362784.jpg\": \"Aves/Catharus guttatus/c1d6f12099caec55470999833a53aac2.jpg\",\n  \"36279.jpg\": \"Insecta/Pseudeustrotia carneola/22f3e1fed171034a9bafea088ff68c6d.jpg\",\n  \"362798.jpg\": \"Aves/Catharus guttatus/59ec0e5dce1ea5a7a0f10939914aa32d.jpg\",\n  \"362856.jpg\": \"Aves/Catharus guttatus/c4a2235366e288db6c9569c150ec065d.jpg\",\n  \"362857.jpg\": \"Aves/Catharus guttatus/19f7b3e3c3afff2ff5ad930a4968c6f0.jpg\",\n  \"36288.jpg\": \"Insecta/Pseudeustrotia carneola/88fd6e67ec2893839ad087bfa7514d2a.jpg\",\n  \"36289.jpg\": \"Insecta/Pseudeustrotia carneola/20499f9d41ec404fc0fc02d41584157c.jpg\",\n  \"362891.jpg\": \"Aves/Catharus guttatus/4ec5075eb813daf85022b1d6af953473.jpg\",\n  \"36290.jpg\": \"Insecta/Pseudeustrotia carneola/a7cdd46c43db81996797a0b42fdec8e1.jpg\",\n  \"362904.jpg\": \"Aves/Catharus guttatus/dd2f9131d79628d26467583268cd8774.jpg\",\n  \"362908.jpg\": \"Aves/Catharus guttatus/1604efbe09549078e9a6f5c0b675a606.jpg\",\n  \"362911.jpg\": \"Aves/Catharus guttatus/f88733f6567e0a0faf86ad835bfca6ab.jpg\",\n  \"36292.jpg\": \"Insecta/Pseudeustrotia carneola/2d22c4f09305e691a11fc74b93ba2e32.jpg\",\n  \"362928.jpg\": \"Aves/Catharus guttatus/2e4d0c38b1c3d08af18e79ec54afecc3.jpg\",\n  \"36294.jpg\": \"Insecta/Pseudeustrotia carneola/63bff9cb25f52d5ae77e2004ede94387.jpg\",\n  \"36295.jpg\": \"Insecta/Pseudeustrotia carneola/4e3eb8dc1e97009d6e49b841a83cbb77.jpg\",\n  \"36296.jpg\": \"Insecta/Pseudeustrotia carneola/804a673323008e2cabdb1a8a6e9b420a.jpg\",\n  \"362976.jpg\": \"Insecta/Chrysochus auratus/3e57e39159976f0202b305a078636350.jpg\",\n  \"362977.jpg\": \"Insecta/Chrysochus auratus/a523a8459f42b3728efbc4c97fa69f94.jpg\",\n  \"362986.jpg\": \"Insecta/Chrysochus auratus/855e4d9dcb41a09863f0a98bbea153cb.jpg\",\n  \"362987.jpg\": \"Insecta/Chrysochus auratus/7b7ce23c7914db4a879d336cc292a128.jpg\",\n  \"362989.jpg\": \"Insecta/Chrysochus auratus/75d3361d833ed6b87c508a4075ce1d0d.jpg\",\n  \"36299.jpg\": \"Insecta/Pseudeustrotia carneola/b88eac21a8153af3188af504b8e57e32.jpg\",\n  \"362997.jpg\": \"Insecta/Chrysochus auratus/c288fc219fefa0206924c3fe36dd039b.jpg\",\n  \"362998.jpg\": \"Insecta/Chrysochus auratus/fb90cc801a113f0ece09eeb97a630343.jpg\",\n  \"362999.jpg\": \"Insecta/Chrysochus auratus/b654e0cf68e7c18e5b600d1d84418490.jpg\",\n  \"363000.jpg\": \"Insecta/Chrysochus auratus/cc2b2431990478bdfe54dbc918441414.jpg\",\n  \"363002.jpg\": \"Insecta/Chrysochus auratus/085fa0a79039668407e7307f44e27129.jpg\",\n  \"363003.jpg\": \"Insecta/Chrysochus auratus/75035ffa9485746e1ee6989c38c9851d.jpg\",\n  \"363004.jpg\": \"Insecta/Chrysochus auratus/658665ad78fa44a1f19ec61b573e77ac.jpg\",\n  \"363009.jpg\": \"Insecta/Chrysochus auratus/c78373b8cfc01b99bc4f6f4cb2506f29.jpg\",\n  \"363010.jpg\": \"Insecta/Chrysochus auratus/668d2096140b842a354169ab8ec86c22.jpg\",\n  \"363012.jpg\": \"Insecta/Chrysochus auratus/04f3bef5218ff83be44752a7d24edaf7.jpg\",\n  \"363024.jpg\": \"Insecta/Chrysochus auratus/69c60590add8f07b776dd3b7bd98a03d.jpg\",\n  \"363025.jpg\": \"Insecta/Chrysochus auratus/f2bd86ce25489bea991079a8335a5f72.jpg\",\n  \"363026.jpg\": \"Insecta/Chrysochus auratus/37ae435297db533e9f1f52a12e9c3ad2.jpg\",\n  \"363032.jpg\": \"Insecta/Chrysochus auratus/d543fe92fdd88cc38da593d62a401c13.jpg\",\n  \"363033.jpg\": \"Insecta/Chrysochus auratus/c74aac6d13490f396c5f0a7480cfabf6.jpg\",\n  \"363036.jpg\": \"Insecta/Chrysochus auratus/6dd98d838c70eab272685cd5773eb6fd.jpg\",\n  \"363049.jpg\": \"Insecta/Erythemis simplicicollis/b6fb0f26caf8cf3529c29179bfe15e01.jpg\",\n  \"363057.jpg\": \"Insecta/Erythemis simplicicollis/05ab6dd6365f8240db7574445effdcfb.jpg\",\n  \"363070.jpg\": \"Insecta/Erythemis simplicicollis/b4f2820bc05f5ba3b36698e729468764.jpg\",\n  \"363080.jpg\": \"Insecta/Erythemis simplicicollis/72eed94e3bc9b4e64fcf7c064e3e8cb6.jpg\",\n  \"36309.jpg\": \"Aves/Poecile atricapillus/fe21c0946e4a13437c3c8680871b2576.jpg\",\n  \"363148.jpg\": \"Insecta/Erythemis simplicicollis/c6d7bec398116ee6351211f0c97e13a4.jpg\",\n  \"363164.jpg\": \"Insecta/Erythemis simplicicollis/b1e564b46aa8c7c2a8e3a7e1658359c8.jpg\",\n  \"363171.jpg\": \"Insecta/Erythemis simplicicollis/e45f49ba5db0b7f6406ae59cf16d848a.jpg\",\n  \"363178.jpg\": \"Insecta/Erythemis simplicicollis/518dba69c2f9ee0342caacf535d75549.jpg\",\n  \"363193.jpg\": \"Insecta/Erythemis simplicicollis/1afbd45933846fa081d63b0d0a61625d.jpg\",\n  \"363210.jpg\": \"Insecta/Erythemis simplicicollis/78201669c16a96fd72028052a69df307.jpg\",\n  \"363239.jpg\": \"Insecta/Erythemis simplicicollis/395454f13cabf44aeac85ea086654451.jpg\",\n  \"363260.jpg\": \"Insecta/Erythemis simplicicollis/d44a45fd6dfae4b50862ccb553edb487.jpg\",\n  \"363405.jpg\": \"Insecta/Erythemis simplicicollis/225949ecc46eec51e935f16ca40ef808.jpg\",\n  \"363407.jpg\": \"Insecta/Erythemis simplicicollis/80da6d5d5b87ff06283e37530e9f6d49.jpg\",\n  \"36345.jpg\": \"Aves/Poecile atricapillus/6223c51304fd4ae8262b6c4f82e7089a.jpg\",\n  \"363472.jpg\": \"Insecta/Erythemis simplicicollis/09c8e11f4c81552aa766c9e43e229990.jpg\",\n  \"36349.jpg\": \"Aves/Poecile atricapillus/704ef5985e7ce25ccd19302cf13663b4.jpg\",\n  \"363560.jpg\": \"Insecta/Erythemis simplicicollis/2587d90acb11c1cef40330069dd59c73.jpg\",\n  \"363567.jpg\": \"Insecta/Erythemis simplicicollis/de5f90529b8a86bac1fc5c7f70faf66c.jpg\",\n  \"363579.jpg\": \"Insecta/Erythemis simplicicollis/26c254a9214c58e3ccc25401aa9634db.jpg\",\n  \"363616.jpg\": \"Insecta/Erythemis simplicicollis/5425f0cdd12472ebf6f34aaaaafdc00b.jpg\",\n  \"363707.jpg\": \"Insecta/Erythemis simplicicollis/bf7d0dbc846fa305ff62bffb73765283.jpg\",\n  \"363763.jpg\": \"Insecta/Erythemis simplicicollis/e94e895e557b34e41d6d964080255fe9.jpg\",\n  \"36383.jpg\": \"Aves/Poecile atricapillus/fbb033548005452d4692a4e4c6d5ede2.jpg\",\n  \"363885.jpg\": \"Aves/Icterus wagleri/2730cacc3ff21f69987c8ffc09e41103.jpg\",\n  \"363887.jpg\": \"Aves/Icterus wagleri/384f42b9682660265aa5e27073145cb0.jpg\",\n  \"363888.jpg\": \"Aves/Icterus wagleri/f258bf5e410a95fa864af4d28dbd7937.jpg\",\n  \"363894.jpg\": \"Aves/Icterus wagleri/4dfe815f2807ac9898c1eed00f079fb3.jpg\",\n  \"363895.jpg\": \"Aves/Icterus wagleri/c1dbeb2b5a9a68729f59b4d99b148562.jpg\",\n  \"363896.jpg\": \"Aves/Cyanocompsa parellina/9830bee06d1e73ea800916f7dce14d92.jpg\",\n  \"363901.jpg\": \"Aves/Cyanocompsa parellina/f366899382783c763137edf509533546.jpg\",\n  \"363908.jpg\": \"Aves/Cyanocompsa parellina/51e20d49998b45a77ccc8c2618208754.jpg\",\n  \"363909.jpg\": \"Aves/Cyanocompsa parellina/f01228f3bcf90794d11d3c522d810ceb.jpg\",\n  \"363910.jpg\": \"Aves/Cyanocompsa parellina/042e356726868dbd5fedbab188dda81d.jpg\",\n  \"363920.jpg\": \"Reptilia/Crotaphytus bicinctores/9742f2bfbca8bdcea352339851960be7.jpg\",\n  \"363921.jpg\": \"Reptilia/Crotaphytus bicinctores/9f940053d1ee958902e3bc37e6ca2978.jpg\",\n  \"363932.jpg\": \"Reptilia/Crotaphytus bicinctores/c8cef09b8bc76e56a544dbb45a71905b.jpg\",\n  \"363937.jpg\": \"Reptilia/Crotaphytus bicinctores/7b771173d73c3860aac22bb04dbe8f14.jpg\",\n  \"363949.jpg\": \"Reptilia/Crotaphytus bicinctores/2df3bcabfe301a0400f44439104d54fd.jpg\",\n  \"363950.jpg\": \"Reptilia/Crotaphytus bicinctores/77b7c857e3484c1d3e74cbd4ac8ff60c.jpg\",\n  \"363954.jpg\": \"Reptilia/Crotaphytus bicinctores/62e09b0d5bc3ad7b11f1565465a7c29d.jpg\",\n  \"363958.jpg\": \"Reptilia/Crotaphytus bicinctores/6cfddd71130c81acf7ab519c18999db9.jpg\",\n  \"363985.jpg\": \"Actinopterygii/Lepomis cyanellus/4bc04614a8f13d89a8f2cca11e199b03.jpg\",\n  \"363986.jpg\": \"Actinopterygii/Lepomis cyanellus/40fab83caaf086fb41f52fdf28cf445f.jpg\",\n  \"363987.jpg\": \"Actinopterygii/Lepomis cyanellus/96ddca81f9e4b3ecfca1e45c8851f2aa.jpg\",\n  \"363988.jpg\": \"Actinopterygii/Lepomis cyanellus/7d54d871c1bf8c5d2a4804d1708412a7.jpg\",\n  \"363990.jpg\": \"Actinopterygii/Lepomis cyanellus/9fe00b1e14de4e9806c0678a1fe17d92.jpg\",\n  \"363992.jpg\": \"Actinopterygii/Lepomis cyanellus/460d5c5b2e953b1d632a8cc7dc22d46c.jpg\",\n  \"363997.jpg\": \"Actinopterygii/Lepomis cyanellus/1f7245520a0455066fb5f6fd27e4ff01.jpg\",\n  \"363998.jpg\": \"Actinopterygii/Lepomis cyanellus/e4aeac2f342b691e68e81f47272dd84b.jpg\",\n  \"364.jpg\": \"Insecta/Coleomegilla maculata/ccf1168236c0bb6f9c27388b5923a50b.jpg\",\n  \"364004.jpg\": \"Actinopterygii/Lepomis cyanellus/f3c04dd16ca20ad32249762e82f72911.jpg\",\n  \"364005.jpg\": \"Actinopterygii/Lepomis cyanellus/a6ffabcf14dd8eae196eabb0f677ddf8.jpg\",\n  \"364006.jpg\": \"Actinopterygii/Lepomis cyanellus/f2f5d77434aabd4b262d689f42e9c7cd.jpg\",\n  \"364008.jpg\": \"Actinopterygii/Lepomis cyanellus/803e740f2225606801e478a1a159e1c2.jpg\",\n  \"364009.jpg\": \"Actinopterygii/Lepomis cyanellus/1aecf9e796f75ab83e0b2026635cc8d4.jpg\",\n  \"364010.jpg\": \"Actinopterygii/Lepomis cyanellus/477793b750afa93425a24af13dfeb058.jpg\",\n  \"364013.jpg\": \"Actinopterygii/Lepomis cyanellus/6485af15b7174bec7f1ea0c789c886d1.jpg\",\n  \"364014.jpg\": \"Actinopterygii/Lepomis cyanellus/8e924fdcca3d050726cd97759192dad4.jpg\",\n  \"364016.jpg\": \"Actinopterygii/Lepomis cyanellus/198e9ac1097d890e9a36e47f95e13d17.jpg\",\n  \"364019.jpg\": \"Actinopterygii/Lepomis cyanellus/ce3cec13410bcc0758f3932e37b6f35a.jpg\",\n  \"364022.jpg\": \"Actinopterygii/Lepomis cyanellus/a0d800572264bfc9de4f1402dc574ac8.jpg\",\n  \"364026.jpg\": \"Actinopterygii/Lepomis cyanellus/c10a835e668f841ca58362cbdefa78d4.jpg\",\n  \"364029.jpg\": \"Reptilia/Elgaria coerulea/b84593e11361cf046f8be2835896f3cf.jpg\",\n  \"364030.jpg\": \"Reptilia/Elgaria coerulea/edc45a5bb49b23be30dea1fdc77340e9.jpg\",\n  \"364033.jpg\": \"Reptilia/Elgaria coerulea/c6e199cf86f5d94b02f3c430f527cc46.jpg\",\n  \"364039.jpg\": \"Reptilia/Elgaria coerulea/cce29daf44fd63bca602f54c9da6cfae.jpg\",\n  \"364040.jpg\": \"Reptilia/Elgaria coerulea/755957c85b8a967e99785d80d1249bfc.jpg\",\n  \"364041.jpg\": \"Reptilia/Elgaria coerulea/4927b7c8583c5aae6ed7c518028b111a.jpg\",\n  \"364042.jpg\": \"Reptilia/Elgaria coerulea/ccc2906af7299e16c2b1cf8a67d0c7bb.jpg\",\n  \"364043.jpg\": \"Reptilia/Elgaria coerulea/c8b2e3df4ce4e4367d421be7e698e2bf.jpg\",\n  \"364045.jpg\": \"Reptilia/Elgaria coerulea/4bf83bb2df0df4a0852600bb93e7fba1.jpg\",\n  \"364046.jpg\": \"Reptilia/Elgaria coerulea/1c9e10e773c9dcffeec7db4cade5cbe2.jpg\",\n  \"364047.jpg\": \"Reptilia/Elgaria coerulea/e27c9fb715f1729699d1f5efbd4f6c04.jpg\",\n  \"364048.jpg\": \"Reptilia/Elgaria coerulea/173449ce75d9da1ae9c420bef74a3219.jpg\",\n  \"364049.jpg\": \"Reptilia/Elgaria coerulea/9b9862193278c06fd7fde5d9995fb964.jpg\",\n  \"364053.jpg\": \"Reptilia/Elgaria coerulea/a3599c7f192f3523a152d5e215f1c4e1.jpg\",\n  \"364056.jpg\": \"Reptilia/Elgaria coerulea/76ab6c2fe66e2e713906d8ac5e193db3.jpg\",\n  \"364060.jpg\": \"Reptilia/Elgaria coerulea/6761c21d25a04d527480fa9baac66497.jpg\",\n  \"364067.jpg\": \"Reptilia/Elgaria coerulea/b2fe47216ce806a2243adf3439b8334a.jpg\",\n  \"364075.jpg\": \"Reptilia/Elgaria coerulea/3a1914b59850d98a150b0aae47506d42.jpg\",\n  \"364076.jpg\": \"Aves/Bubo scandiacus/0e91cf2f026d6c454767b62d48062e79.jpg\",\n  \"364077.jpg\": \"Aves/Bubo scandiacus/2fc09cf895ad93147a2c49daebbe285c.jpg\",\n  \"364084.jpg\": \"Aves/Bubo scandiacus/2d245c35d1761d7bb57dfec6c7be3de1.jpg\",\n  \"364088.jpg\": \"Aves/Bubo scandiacus/97aff7f92658dc46e7188500c669de57.jpg\",\n  \"364090.jpg\": \"Aves/Bubo scandiacus/df9d2601cd91355ef2398519e8467f33.jpg\",\n  \"364091.jpg\": \"Aves/Bubo scandiacus/15ba348debb650cfb784f82486dc56c8.jpg\",\n  \"364114.jpg\": \"Aves/Bubo scandiacus/e2ec178d5df147b3e58cfba320e27b62.jpg\",\n  \"364127.jpg\": \"Aves/Bubo scandiacus/6096dc453663d63e973415d09d2ff1a3.jpg\",\n  \"364133.jpg\": \"Aves/Bubo scandiacus/a7b75a7231f482bfc2fccaa0f446ac32.jpg\",\n  \"364135.jpg\": \"Aves/Bubo scandiacus/79fcae1dd3ee663d4a5fcb9b327e30af.jpg\",\n  \"36414.jpg\": \"Aves/Poecile atricapillus/1537b1589ea7e9307bbe1397acd86af3.jpg\",\n  \"364141.jpg\": \"Aves/Bubo scandiacus/85b666599108a21b25bd4f34c9629374.jpg\",\n  \"364157.jpg\": \"Aves/Bubo scandiacus/97ea2c45e14f8079b9460ca5777e8075.jpg\",\n  \"364162.jpg\": \"Aves/Bubo scandiacus/0ccc67718f1885ccee3752efa4f5c1b0.jpg\",\n  \"364174.jpg\": \"Aves/Bubo scandiacus/90ef763d85a77d6a346d5995d81a4ad4.jpg\",\n  \"364176.jpg\": \"Aves/Bubo scandiacus/5e9d43816e300051dc4249f7b7993ba2.jpg\",\n  \"364177.jpg\": \"Aves/Bubo scandiacus/9ef2414eb10a918149013052ff72d802.jpg\",\n  \"364178.jpg\": \"Aves/Bubo scandiacus/55611512ca7c91bf61acc3e305780de4.jpg\",\n  \"364188.jpg\": \"Aves/Bubo scandiacus/b21bc5d3be74b5c7c7d587f3c2705b14.jpg\",\n  \"364189.jpg\": \"Aves/Bubo scandiacus/48348af6842722b0e2e4840b4c83eec8.jpg\",\n  \"36419.jpg\": \"Aves/Poecile atricapillus/7afb212e0239e02a1455109dd134ceae.jpg\",\n  \"364204.jpg\": \"Aves/Bubo scandiacus/bcf4621373f4b48d3fa10f33ae7e7a50.jpg\",\n  \"36424.jpg\": \"Aves/Poecile atricapillus/aa3be190b513c585f57827bc49364a1f.jpg\",\n  \"364247.jpg\": \"Aves/Egretta thula/6d62b1ec66d69c20c166c1d0b8c9d506.jpg\",\n  \"364367.jpg\": \"Aves/Egretta thula/b70bc7941b3bae647e07076261aee3a4.jpg\",\n  \"364372.jpg\": \"Aves/Egretta thula/04e13e8256fb8265dbd9b0c11d8647f4.jpg\",\n  \"364443.jpg\": \"Aves/Egretta thula/a536d38e30b54010b8ff6e9f3e7f7001.jpg\",\n  \"36446.jpg\": \"Aves/Poecile atricapillus/54ed05f0c478798bfe7946e228481ca4.jpg\",\n  \"364529.jpg\": \"Aves/Egretta thula/533746fb722ffaff50418602182f5366.jpg\",\n  \"364595.jpg\": \"Aves/Egretta thula/6dae977c10583e437ec78203d92f6452.jpg\",\n  \"364671.jpg\": \"Aves/Egretta thula/9085f555374cb94a36f78fb232aa78db.jpg\",\n  \"364692.jpg\": \"Aves/Egretta thula/19caabc9a8dbaf56dbbd27106cc8a3a6.jpg\",\n  \"364698.jpg\": \"Aves/Egretta thula/2c6e055e25997306b46bd7f5d370373d.jpg\",\n  \"364794.jpg\": \"Aves/Egretta thula/cd025d2aeb283b6670aeabeb542a6ecc.jpg\",\n  \"3648.jpg\": \"Insecta/Coccinella septempunctata/4925f5e259725c5fb3f14ab40996fcd7.jpg\",\n  \"364838.jpg\": \"Aves/Egretta thula/aec3f84a38c5dfad196398506fb87e12.jpg\",\n  \"364944.jpg\": \"Aves/Egretta thula/7d82d28c7bec0b089dc1fd5bff482c33.jpg\",\n  \"36495.jpg\": \"Aves/Poecile atricapillus/d030dee8260db9dc6774661a22da69d0.jpg\",\n  \"365.jpg\": \"Insecta/Coleomegilla maculata/e306bfa02063d6e765115119799e8068.jpg\",\n  \"365041.jpg\": \"Aves/Egretta thula/ef269c1f92cd41fea7cf2a0f262fdd2e.jpg\",\n  \"365065.jpg\": \"Aves/Egretta thula/1b5e7abd96cb2e559123f734e0aeed3c.jpg\",\n  \"365189.jpg\": \"Aves/Egretta thula/f6f295f562bd2c64fdc26fc6b7aab342.jpg\",\n  \"365199.jpg\": \"Aves/Egretta thula/a8b0ea0c62a8c58d40e5df22a47cbf27.jpg\",\n  \"365229.jpg\": \"Aves/Egretta thula/2025da2c84960294f0bee084eae5c67a.jpg\",\n  \"365234.jpg\": \"Aves/Egretta thula/5b0242ab87c3fb5af0e6e8d03cef7a8f.jpg\",\n  \"365245.jpg\": \"Aves/Egretta thula/2fe85c05052696d7d1046f9e8fffa9ba.jpg\",\n  \"36526.jpg\": \"Aves/Poecile atricapillus/721766689f11f8fb13f9fda00388a8c2.jpg\",\n  \"365480.jpg\": \"Aves/Calypte anna/5e570534f2b9bfcc4033c47730764dac.jpg\",\n  \"3655.jpg\": \"Insecta/Coccinella septempunctata/62632a6ef1388c3233958e7d183a66f9.jpg\",\n  \"365516.jpg\": \"Aves/Calypte anna/7c62397439740a597b79bb9baca4ca01.jpg\",\n  \"365521.jpg\": \"Aves/Calypte anna/6d15bc39e99050bcbebc2c19ba9e8895.jpg\",\n  \"365544.jpg\": \"Aves/Calypte anna/dfb08aa442e435bc251930a878636fb7.jpg\",\n  \"365563.jpg\": \"Aves/Calypte anna/8aa3e63272a49ce9b7a1cac5b9b2b06c.jpg\",\n  \"365628.jpg\": \"Aves/Calypte anna/29d0935ce01fa8ba1dc7e1549640c092.jpg\",\n  \"36564.jpg\": \"Aves/Poecile atricapillus/48cb2380dc66868dee610429493e9e52.jpg\",\n  \"36574.jpg\": \"Aves/Poecile atricapillus/0db861398c57e61c9c8fc2024d7e7495.jpg\",\n  \"365787.jpg\": \"Aves/Calypte anna/2b4c3f4c3ba9ca62f6f43e17cef1ef18.jpg\",\n  \"365802.jpg\": \"Aves/Calypte anna/bf6f35f765c0e9c13026bfc433341695.jpg\",\n  \"365864.jpg\": \"Aves/Calypte anna/9d42072326e71a90a12bf881a114ee7b.jpg\",\n  \"365908.jpg\": \"Aves/Calypte anna/95cd7bff03c60a9ef62fd70bfc7f26bf.jpg\",\n  \"365914.jpg\": \"Aves/Calypte anna/62230c7b0cac73ecfb605d22cc0b04cd.jpg\",\n  \"365978.jpg\": \"Aves/Calypte anna/fac6ab3545d929b55f25361f01832b2a.jpg\",\n  \"365984.jpg\": \"Aves/Calypte anna/d9448a37166b364ca77bd09e74e20fb9.jpg\",\n  \"365999.jpg\": \"Aves/Calypte anna/5809069362faea052fbcac5cadd544dd.jpg\",\n  \"366.jpg\": \"Insecta/Coleomegilla maculata/71bbe0a6c38656ba6ac61ede76e6ffe7.jpg\",\n  \"366001.jpg\": \"Aves/Calypte anna/0c0b80f85ef3163a435c6fb6f78b6f7d.jpg\",\n  \"366025.jpg\": \"Aves/Calypte anna/a51706a18c090f1819360abb0bef66ad.jpg\",\n  \"366029.jpg\": \"Aves/Calypte anna/af65e86fa53e441b0d55f34a06c9b43a.jpg\",\n  \"366040.jpg\": \"Aves/Calypte anna/e891f401162b3bf108e7fbbe72e5178c.jpg\",\n  \"366065.jpg\": \"Aves/Calypte anna/63fd7fffa2586b9f63481f0e18f775fe.jpg\",\n  \"366070.jpg\": \"Aves/Calypte anna/3443c728b4e1f4d61629177941141037.jpg\",\n  \"366079.jpg\": \"Aves/Calypte anna/12d6374b69572711dc5be6017023727e.jpg\",\n  \"36612.jpg\": \"Aves/Poecile atricapillus/49c20393b06263f35c6853d0353028af.jpg\",\n  \"366135.jpg\": \"Aves/Calypte anna/33fe82b168f317016fba32d3a0c112e5.jpg\",\n  \"366174.jpg\": \"Aves/Piranga olivacea/046d652fd62bc925caa3e7cc68278cb4.jpg\",\n  \"366176.jpg\": \"Aves/Piranga olivacea/434cc901ca64aff88d06ac4f355e79d0.jpg\",\n  \"366177.jpg\": \"Aves/Piranga olivacea/78804f87ed11207987d94a165aa1e969.jpg\",\n  \"366179.jpg\": \"Aves/Piranga olivacea/0c3e579755f30431e3736dbb7e2ae85c.jpg\",\n  \"366204.jpg\": \"Aves/Piranga olivacea/7cd397d3c389cd4fabceae43d89a5e18.jpg\",\n  \"366215.jpg\": \"Aves/Piranga olivacea/2694764aceec0cdab91b1e62619d7ef5.jpg\",\n  \"366218.jpg\": \"Aves/Piranga olivacea/e4d15e53f72f02b8fe1722b07908b264.jpg\",\n  \"366226.jpg\": \"Aves/Piranga olivacea/2d03c40cf102cd27a311d722326b2a12.jpg\",\n  \"366236.jpg\": \"Aves/Piranga olivacea/2583bb93c9306fe63de8a4f418807c46.jpg\",\n  \"366237.jpg\": \"Aves/Piranga olivacea/1b667ef0e6900da005d7b1f887d59fff.jpg\",\n  \"366238.jpg\": \"Aves/Piranga olivacea/d8bd5d6336af3a8167b37a8e6d24df49.jpg\",\n  \"36624.jpg\": \"Aves/Poecile atricapillus/5612d8eacc9341c5f82d2f561937712f.jpg\",\n  \"366244.jpg\": \"Aves/Piranga olivacea/f6f9fecf0ba6ba18bcc81eaad30e30f0.jpg\",\n  \"366245.jpg\": \"Aves/Piranga olivacea/9bb404fee1ddd9bf4150e505608a4f41.jpg\",\n  \"366262.jpg\": \"Aves/Piranga olivacea/30f099518d94c1562a7c09b445a2a454.jpg\",\n  \"366268.jpg\": \"Aves/Piranga olivacea/e96b7dfd6ef6a2156c6142c5c3ef9102.jpg\",\n  \"366279.jpg\": \"Aves/Piranga olivacea/a6bf0e32e95dc144bb06a55f9bb32681.jpg\",\n  \"366280.jpg\": \"Aves/Piranga olivacea/ddb0fa4351a22cd91bbd5da33f19c357.jpg\",\n  \"366282.jpg\": \"Aves/Piranga olivacea/2f9ba7da21ba8801e19fe514ca97d186.jpg\",\n  \"366283.jpg\": \"Aves/Piranga olivacea/cf03411a603b16590421138903f81d8b.jpg\",\n  \"366296.jpg\": \"Aves/Psaltriparus minimus/cbecaaa53a2631d3341fb30f1aec75a2.jpg\",\n  \"366325.jpg\": \"Aves/Psaltriparus minimus/35fac2b668054913604e09596e5b38be.jpg\",\n  \"366341.jpg\": \"Aves/Psaltriparus minimus/05ed89f3e2d5d9b9bc79d8b491f3e2c9.jpg\",\n  \"366349.jpg\": \"Aves/Psaltriparus minimus/351005779d1f129d2add9ecdf9699b0f.jpg\",\n  \"366361.jpg\": \"Aves/Psaltriparus minimus/7827ff71e9ba83dc990da346f7b2d86a.jpg\",\n  \"366373.jpg\": \"Aves/Psaltriparus minimus/ba7c290958b0d4d8a514c718a15619db.jpg\",\n  \"366378.jpg\": \"Aves/Psaltriparus minimus/f5bd16b0519b6f1e407571780ce930d5.jpg\",\n  \"366403.jpg\": \"Aves/Psaltriparus minimus/692f1d6b52199a98cc72ad853c5eb9df.jpg\",\n  \"366405.jpg\": \"Aves/Psaltriparus minimus/7112e789c5b0aba4872ad6563fd0dd08.jpg\",\n  \"366438.jpg\": \"Aves/Psaltriparus minimus/7ea7bc4f05f65e3e5a55c87bf3e68853.jpg\",\n  \"366443.jpg\": \"Aves/Psaltriparus minimus/cc2d06601fb73fecd6e62d7a3418a136.jpg\",\n  \"366447.jpg\": \"Aves/Psaltriparus minimus/b4772850e4d41b4918486b15029ec4f2.jpg\",\n  \"366468.jpg\": \"Aves/Psaltriparus minimus/45a06983373561c1aa23cd36dc884347.jpg\",\n  \"366482.jpg\": \"Aves/Psaltriparus minimus/90e0a5a006e3574245712e252225a029.jpg\",\n  \"36650.jpg\": \"Aves/Poecile atricapillus/2a7ae936250a53d6e449a1ef5c50c8a2.jpg\",\n  \"36656.jpg\": \"Aves/Poecile atricapillus/b8db7e095ea0e5c6bf9238ece4ad0eec.jpg\",\n  \"366563.jpg\": \"Aves/Psaltriparus minimus/8755c552e8ed7a84b150ffd8cb2bcf07.jpg\",\n  \"366570.jpg\": \"Aves/Psaltriparus minimus/7e6f6238900da487eaaf6669d1bebd70.jpg\",\n  \"366599.jpg\": \"Aves/Psaltriparus minimus/a8dddde18587241aba92d8dc96516bd0.jpg\",\n  \"366601.jpg\": \"Aves/Psaltriparus minimus/66e56ac0f4b3b0e05540b2f2e8c9d63c.jpg\",\n  \"366642.jpg\": \"Aves/Psaltriparus minimus/a872ecef7b0da702eb265b5cd48d737f.jpg\",\n  \"366654.jpg\": \"Aves/Psaltriparus minimus/19ca40d99701a0bb7dd24cd327f8ec05.jpg\",\n  \"366660.jpg\": \"Aves/Cistothorus platensis/e55b8bb1a36e7f604f573f07a7a7604b.jpg\",\n  \"366666.jpg\": \"Aves/Cistothorus platensis/c8885445481bf132d948d82b6b8c37fb.jpg\",\n  \"366673.jpg\": \"Aves/Cistothorus platensis/14adb309450d0428f967e1d40ffc0875.jpg\",\n  \"366674.jpg\": \"Aves/Cistothorus platensis/47623d28a1a43043f3ac82384c9eca53.jpg\",\n  \"366676.jpg\": \"Aves/Cistothorus platensis/4bb49ee7de799d56136858ae1a88dbd9.jpg\",\n  \"366688.jpg\": \"Aves/Scopus umbretta/7d4b55f0308b90188615fcc891eade01.jpg\",\n  \"366690.jpg\": \"Aves/Scopus umbretta/a25c5874d9d55512ac86c871aff4e949.jpg\",\n  \"366693.jpg\": \"Aves/Scopus umbretta/6be8b2ff4e1644655be565ca4b24cc27.jpg\",\n  \"366732.jpg\": \"Aves/Geothlypis trichas/a2b31eff5012a43ddc0f5a4d64f9d426.jpg\",\n  \"366735.jpg\": \"Aves/Geothlypis trichas/7255486946cf07a24e3c5e44cde76fe1.jpg\",\n  \"366764.jpg\": \"Aves/Geothlypis trichas/3eafdb0b10452999360116304bff5f57.jpg\",\n  \"366785.jpg\": \"Aves/Geothlypis trichas/9f4ef532aa6a60cb01a98d55c96f6eba.jpg\",\n  \"366801.jpg\": \"Aves/Geothlypis trichas/35cfcedf7560653e66191c88dc316d60.jpg\",\n  \"366821.jpg\": \"Aves/Geothlypis trichas/5de8ade0dadbc58e69f7064ac1106203.jpg\",\n  \"366861.jpg\": \"Aves/Geothlypis trichas/e626958ff84ee50567617b0ba3249e31.jpg\",\n  \"366913.jpg\": \"Aves/Geothlypis trichas/51d70d7b5fea9faf443512e6f5835282.jpg\",\n  \"366915.jpg\": \"Aves/Geothlypis trichas/fb68f8060f33c2e83b585a80e4b65e2d.jpg\",\n  \"366952.jpg\": \"Aves/Geothlypis trichas/4ff0bdc123e80619255f2decb89d97de.jpg\",\n  \"366953.jpg\": \"Aves/Geothlypis trichas/7706caa6f5b89aa7793ecae693bed817.jpg\",\n  \"366961.jpg\": \"Aves/Geothlypis trichas/aaa1ce97b2f99f1e13549ed498fba33b.jpg\",\n  \"366966.jpg\": \"Aves/Geothlypis trichas/924def78e17362b2d348c65eb2977d0d.jpg\",\n  \"366968.jpg\": \"Aves/Geothlypis trichas/11f5f67d6a4b246856b02a0ba15452fa.jpg\",\n  \"366974.jpg\": \"Aves/Geothlypis trichas/5ce5eb1cb5046575f7c209bba4298056.jpg\",\n  \"366980.jpg\": \"Aves/Geothlypis trichas/4ab88302f9e31c6d9118d3b1d8675531.jpg\",\n  \"367003.jpg\": \"Aves/Geothlypis trichas/65495f1e3283656a63cbc5fcb80d1315.jpg\",\n  \"367004.jpg\": \"Aves/Geothlypis trichas/e14982994a0e8fef59fc4a38938976dc.jpg\",\n  \"367010.jpg\": \"Aves/Geothlypis trichas/ed12385151636d75ffa555bec0075c87.jpg\",\n  \"367013.jpg\": \"Aves/Geothlypis trichas/3c7dee6049be2d0ca6b021a77f0f5486.jpg\",\n  \"367061.jpg\": \"Aves/Geothlypis trichas/1d9dd70d9b196ec0b609e39c20f46056.jpg\",\n  \"36707.jpg\": \"Aves/Poecile atricapillus/b837edd5efd9c56e8113768c30ef3005.jpg\",\n  \"367105.jpg\": \"Aves/Geothlypis trichas/904f8069b69380901a00fd4a1483582e.jpg\",\n  \"367115.jpg\": \"Aves/Geothlypis trichas/795c7a3b038adfd4cbff35cda3510956.jpg\",\n  \"367125.jpg\": \"Aves/Geothlypis trichas/ad2cceea1c5adbc638dfb15f7b8a261a.jpg\",\n  \"367126.jpg\": \"Aves/Geothlypis trichas/b1f55b362e412d9ef2a94bafa237c96e.jpg\",\n  \"367131.jpg\": \"Aves/Geothlypis trichas/2d01fa8edc01413737019abc03195b00.jpg\",\n  \"367150.jpg\": \"Aves/Anas crecca carolinensis/42cea4a819182bda846fff8ce245f3e5.jpg\",\n  \"367165.jpg\": \"Aves/Anas crecca carolinensis/0af5d84d6ec269d53234cea95621ed95.jpg\",\n  \"367167.jpg\": \"Aves/Anas crecca carolinensis/1b06bd618b49138fb04b689359273fd9.jpg\",\n  \"367175.jpg\": \"Aves/Anas crecca carolinensis/4d015de1ad7e4a76c263170edc62b299.jpg\",\n  \"367177.jpg\": \"Aves/Anas crecca carolinensis/d7ad3ce02cf4d9814cadbf92184c48f4.jpg\",\n  \"367184.jpg\": \"Aves/Anas crecca carolinensis/db58381608ab30ccb0ffda9010b4813d.jpg\",\n  \"367185.jpg\": \"Aves/Anas crecca carolinensis/62e0b4a451fa1e9ee4a026437daf4643.jpg\",\n  \"367187.jpg\": \"Aves/Anas crecca carolinensis/14cdfe0c9fc23ffaea13bb75e35d3517.jpg\",\n  \"367192.jpg\": \"Aves/Anas crecca carolinensis/282d382d5310142d0618dfbbba142c20.jpg\",\n  \"36726.jpg\": \"Aves/Poecile atricapillus/0b64c64f8f55069c6fa63c1e3451bc8d.jpg\",\n  \"36737.jpg\": \"Aves/Poecile atricapillus/7e25dbfbe3f23aa1cbf4d2ab9c57acff.jpg\",\n  \"367433.jpg\": \"Aves/Asio flammeus/34adb60c0e0e7c81a09f2d8e2cd720a9.jpg\",\n  \"367434.jpg\": \"Aves/Asio flammeus/be189edc8bf021b36e58cc6e1c8ed853.jpg\",\n  \"367435.jpg\": \"Aves/Asio flammeus/f1957b116bbecd4f972479aa45c4f4f6.jpg\",\n  \"367436.jpg\": \"Aves/Asio flammeus/3c3e2065ab16b1cf9d5081da40640875.jpg\",\n  \"367439.jpg\": \"Aves/Asio flammeus/ea093b524e939a2a0854e14955f61250.jpg\",\n  \"367440.jpg\": \"Aves/Asio flammeus/b37d11f18ea7d2f3e8f7f63c1e7e529e.jpg\",\n  \"367441.jpg\": \"Aves/Asio flammeus/d41521ef282f48065d2feb53977f4285.jpg\",\n  \"367442.jpg\": \"Aves/Asio flammeus/43154309e72d27e8052814c90b9f6eab.jpg\",\n  \"367445.jpg\": \"Aves/Asio flammeus/4da9b285dfb37033900cf1cbd60460af.jpg\",\n  \"367449.jpg\": \"Aves/Asio flammeus/e2f432dd95c088d514fd078642f46898.jpg\",\n  \"367451.jpg\": \"Aves/Asio flammeus/3646085b166e726da5a50ae44cd798ab.jpg\",\n  \"367452.jpg\": \"Aves/Asio flammeus/35240b4685d8470dc3e553ef3b03cae9.jpg\",\n  \"367456.jpg\": \"Aves/Asio flammeus/f485ac4432ee8e8eae99e5f74d83ce64.jpg\",\n  \"367458.jpg\": \"Aves/Asio flammeus/fea649f9bf7b05da0db71df713b353ea.jpg\",\n  \"367459.jpg\": \"Aves/Asio flammeus/c4b1ab5dac546d2e7038cb7ef3100a8f.jpg\",\n  \"367460.jpg\": \"Aves/Asio flammeus/84c22007a528492ebe7728084436c079.jpg\",\n  \"367461.jpg\": \"Aves/Asio flammeus/0e79da84ea411aa0c1886b4501cf35dc.jpg\",\n  \"367462.jpg\": \"Aves/Asio flammeus/b9526328cb48f7b9bde72a1c720492b3.jpg\",\n  \"367463.jpg\": \"Aves/Asio flammeus/a6aaf86dee0a327a37b65838968e8e70.jpg\",\n  \"367465.jpg\": \"Aves/Asio flammeus/b4daa2745580800ed50ba41a39087402.jpg\",\n  \"367478.jpg\": \"Aves/Asio flammeus/bff34102adaaa58cde46cb72f3cfd51d.jpg\",\n  \"367483.jpg\": \"Aves/Asio flammeus/3bebb0cdee90ffa9f9815607a250c6f4.jpg\",\n  \"367493.jpg\": \"Aves/Chroicocephalus philadelphia/319a8c70636d27c6eaa8f20df594b74d.jpg\",\n  \"367494.jpg\": \"Aves/Chroicocephalus philadelphia/12838071e751c442c6305d5a36a7e9a3.jpg\",\n  \"367502.jpg\": \"Aves/Chroicocephalus philadelphia/aef298f3bf77a54aefeec11c8c9827a6.jpg\",\n  \"367503.jpg\": \"Aves/Chroicocephalus philadelphia/0314a58871125ad97f07df269af56e3a.jpg\",\n  \"367507.jpg\": \"Aves/Chroicocephalus philadelphia/e7f750847c0766c8dee97c5e9f902bb9.jpg\",\n  \"367549.jpg\": \"Aves/Chroicocephalus philadelphia/493f213567b805842d3b9162050ae047.jpg\",\n  \"367568.jpg\": \"Aves/Chroicocephalus philadelphia/5cae947c13a8a7e7a92c790bf342ca8b.jpg\",\n  \"367573.jpg\": \"Aves/Chroicocephalus philadelphia/e1634453e7898e80d26301f147288107.jpg\",\n  \"367588.jpg\": \"Aves/Chroicocephalus philadelphia/162729f9f97b14f54cdb65f2ec711528.jpg\",\n  \"367608.jpg\": \"Aves/Chroicocephalus philadelphia/d69897a6d5edf7a2d73047a271d68430.jpg\",\n  \"367613.jpg\": \"Aves/Chroicocephalus philadelphia/47d4c7d1acbdf701285c603b1c58a148.jpg\",\n  \"36762.jpg\": \"Aves/Poecile atricapillus/448f786541f60f5c00eb01ec374e747a.jpg\",\n  \"367634.jpg\": \"Aves/Chroicocephalus philadelphia/fb9991f78b864ead8777d9b687371751.jpg\",\n  \"367636.jpg\": \"Aves/Chroicocephalus philadelphia/11cf840e2afe4e5dbbd01ee33bdc10d9.jpg\",\n  \"367639.jpg\": \"Aves/Chroicocephalus philadelphia/d59ef8cedd43cede5213526d27edbf91.jpg\",\n  \"367649.jpg\": \"Aves/Chroicocephalus philadelphia/eac97624647b5d34061a6dc7f508a531.jpg\",\n  \"367650.jpg\": \"Aves/Chroicocephalus philadelphia/5526a937ef656e17fd80c8b87a7d0239.jpg\",\n  \"36768.jpg\": \"Aves/Poecile atricapillus/44a114c97a96f8a9d9bb46e3ba2ffc4e.jpg\",\n  \"367731.jpg\": \"Mammalia/Sciurus aberti/432a2c3a3a9ac7999ef3dd1c3b78fa52.jpg\",\n  \"367732.jpg\": \"Mammalia/Sciurus aberti/b94437f46cca5077b27206a2e1f2a5b3.jpg\",\n  \"367733.jpg\": \"Mammalia/Sciurus aberti/1c8ed96bc284d8d98451a5144d6cdf1d.jpg\",\n  \"367734.jpg\": \"Mammalia/Sciurus aberti/2b63127dff8cbc657dbdc1434e9d4146.jpg\",\n  \"367743.jpg\": \"Mammalia/Sciurus aberti/18b28f5a67efecadab850137c288b887.jpg\",\n  \"367747.jpg\": \"Mammalia/Sciurus aberti/13e58625c4e7668eb72e034edf7abb1e.jpg\",\n  \"367755.jpg\": \"Mammalia/Sciurus aberti/0753c6740d65b79792ba67ca104d4ba8.jpg\",\n  \"367756.jpg\": \"Mammalia/Sciurus aberti/99740d2fbd16700a5ae6c644a2e11f6a.jpg\",\n  \"36785.jpg\": \"Aves/Poecile atricapillus/d988757472f61c078cca76f46466a467.jpg\",\n  \"367872.jpg\": \"Insecta/Asterocampa clyton/19810eb2c40e4c367a6ef8476d0a59be.jpg\",\n  \"367877.jpg\": \"Insecta/Asterocampa clyton/f116fb63142082d22c22fe1d5fb27ab5.jpg\",\n  \"367881.jpg\": \"Insecta/Asterocampa clyton/2adfb6a7cf8347bb77e0d804df35e63b.jpg\",\n  \"367889.jpg\": \"Insecta/Asterocampa clyton/9e551a567bf886068a13afc07ce986c3.jpg\",\n  \"367890.jpg\": \"Insecta/Asterocampa clyton/7da71ca6cc5429cc275d9a9d3d8ee4ca.jpg\",\n  \"367895.jpg\": \"Insecta/Asterocampa clyton/066d7c3533d8069ec199d1b758866183.jpg\",\n  \"367899.jpg\": \"Insecta/Asterocampa clyton/06607f67e0b71192cbd10af92c3c4af4.jpg\",\n  \"367906.jpg\": \"Insecta/Asterocampa clyton/dc5b7aabbbfb85bd0b6d9cfc88e419ed.jpg\",\n  \"367910.jpg\": \"Insecta/Asterocampa clyton/9c8f55500f219adb1e535cf53676df1c.jpg\",\n  \"367914.jpg\": \"Insecta/Asterocampa clyton/9fa8641fceda2ed00a3265d754372842.jpg\",\n  \"367922.jpg\": \"Insecta/Asterocampa clyton/7c6d01b0037f2e5c0f81fbeddb17cc8e.jpg\",\n  \"367926.jpg\": \"Insecta/Asterocampa clyton/41dcdd44fb5acfc6a68ef49585cb0d80.jpg\",\n  \"367933.jpg\": \"Insecta/Asterocampa clyton/e5d2fa13db71336b26f3f2ef7d203fb2.jpg\",\n  \"367937.jpg\": \"Insecta/Asterocampa clyton/69d2466819ce7385e9531b7e3a1837be.jpg\",\n  \"367946.jpg\": \"Insecta/Asterocampa clyton/17a8b9b281f3eafd0ea02098d8e684d4.jpg\",\n  \"367947.jpg\": \"Insecta/Asterocampa clyton/5288b0d6a9f36c41ce094bcf31d68183.jpg\",\n  \"36795.jpg\": \"Aves/Poecile atricapillus/db25f1fa09a805ed5f490f5dc0b9be36.jpg\",\n  \"367957.jpg\": \"Insecta/Asterocampa clyton/fc0b3d742ec3f8d43fb3630e4a10b2c4.jpg\",\n  \"36796.jpg\": \"Aves/Poecile atricapillus/8031a1db136cb44e24c8c8d58a311d36.jpg\",\n  \"367970.jpg\": \"Insecta/Asterocampa clyton/db25a2166743f3f5f9e08e026a5bacbe.jpg\",\n  \"367971.jpg\": \"Insecta/Asterocampa clyton/94596854c8dc186e63950ac9e91bd4d2.jpg\",\n  \"367983.jpg\": \"Insecta/Asterocampa clyton/34328ad62696003de8e4e8d594e6c540.jpg\",\n  \"367988.jpg\": \"Insecta/Asterocampa clyton/b1141ffda1608a937d92ad8e09257799.jpg\",\n  \"367992.jpg\": \"Insecta/Asterocampa clyton/3065316427405cab7d6f1efc45e25a47.jpg\",\n  \"368048.jpg\": \"Aves/Calidris bairdii/71a119020230ea025f7abded83650b04.jpg\",\n  \"368051.jpg\": \"Aves/Calidris bairdii/9bbbb7512024f2d739b4923b19b77588.jpg\",\n  \"368065.jpg\": \"Aves/Calidris bairdii/511b10ee3226c7188273748faa14f6ee.jpg\",\n  \"368066.jpg\": \"Aves/Calidris bairdii/3b46f068353568de25a50f852ba79630.jpg\",\n  \"368067.jpg\": \"Aves/Calidris bairdii/879dda87f19ee74754434c40bed7a72f.jpg\",\n  \"368068.jpg\": \"Aves/Calidris bairdii/db08149fc4934379091c297017f77142.jpg\",\n  \"368073.jpg\": \"Aves/Calidris bairdii/d985d21d8663b5bc3c09102221d1dffb.jpg\",\n  \"368076.jpg\": \"Aves/Calidris bairdii/41067f07360a1051254e248d11d4f8e7.jpg\",\n  \"368088.jpg\": \"Aves/Calidris bairdii/f67bdd92ce16245d6007a0a8c2380895.jpg\",\n  \"368092.jpg\": \"Aves/Calidris bairdii/4019d64a468ee55c18f3b6b6e0d0c60f.jpg\",\n  \"368097.jpg\": \"Aves/Calidris bairdii/9081f0fa267404ac365ab3635b509bfb.jpg\",\n  \"368099.jpg\": \"Aves/Calidris bairdii/0a4e200cf10bcbd5f8c8f8cc395f48cd.jpg\",\n  \"368100.jpg\": \"Aves/Calidris bairdii/282aa2d6c19e996dc300b396de941512.jpg\",\n  \"36813.jpg\": \"Aves/Euphonia affinis/e2a99f0c0195bc42fcc95945d92a0f9d.jpg\",\n  \"36814.jpg\": \"Aves/Euphonia affinis/a245402af75dde7a5cc3f3a8d189a068.jpg\",\n  \"368157.jpg\": \"Aves/Larus fuscus/6f9d0f51a233521aa2cf7c5db97ecac0.jpg\",\n  \"368158.jpg\": \"Aves/Larus fuscus/93604bcabe9f9147fdecf087cb6ad342.jpg\",\n  \"368159.jpg\": \"Aves/Larus fuscus/63a71b2bfb2e8112b8d063930848145a.jpg\",\n  \"368162.jpg\": \"Aves/Larus fuscus/5391f058b04c6059438e5f5969705feb.jpg\",\n  \"368163.jpg\": \"Aves/Larus fuscus/d655ceb8067539de2f0c0e44212b36b8.jpg\",\n  \"368164.jpg\": \"Aves/Larus fuscus/445aec61fcb07253528d62ecc91c77e6.jpg\",\n  \"368171.jpg\": \"Aves/Larus fuscus/ea700199785090ace989d6735f6eb293.jpg\",\n  \"368178.jpg\": \"Aves/Larus fuscus/60f54dc76bafc09be7f6bb82135af3d7.jpg\",\n  \"368185.jpg\": \"Aves/Larus fuscus/d387f97e8e7f46a3a4961d15b11c7514.jpg\",\n  \"368186.jpg\": \"Aves/Larus fuscus/d840090abb93502f6322a0e81422cb5d.jpg\",\n  \"36819.jpg\": \"Aves/Euphonia affinis/e7c9a2fe87ee89840bcfbe23014f6152.jpg\",\n  \"368196.jpg\": \"Aves/Larus fuscus/ceff14426d11f90adc4fe6ff628a3292.jpg\",\n  \"36820.jpg\": \"Aves/Euphonia affinis/bcd05d0bd0169b227d04d4e259b04f15.jpg\",\n  \"368201.jpg\": \"Aves/Larus fuscus/498c161f07653efa5c73e14aa58a889a.jpg\",\n  \"368207.jpg\": \"Aves/Larus fuscus/cddc34b4c95294b06b82fe9089dd2f9d.jpg\",\n  \"368208.jpg\": \"Aves/Larus fuscus/3eca03b603a057f7d1edb8732a833b6e.jpg\",\n  \"368218.jpg\": \"Aves/Larus fuscus/47b9f56c439e890a78495aee7813c4d1.jpg\",\n  \"368220.jpg\": \"Aves/Larus fuscus/469dd0df99a04bd4ac25f0f0f655e0ba.jpg\",\n  \"36823.jpg\": \"Aves/Euphonia affinis/346b61533df4ec391aa07f2d54edbb19.jpg\",\n  \"368244.jpg\": \"Aves/Garrulus glandarius/25b24367c2aa64f3eb91d44d49f627c9.jpg\",\n  \"368245.jpg\": \"Aves/Garrulus glandarius/ef6519e7e15fd39f3d3667afe395cbdc.jpg\",\n  \"368254.jpg\": \"Aves/Garrulus glandarius/449de24e10802527dda0a8c65409a240.jpg\",\n  \"368261.jpg\": \"Aves/Garrulus glandarius/240adcbd2362fb1fe40aae06436187ac.jpg\",\n  \"368262.jpg\": \"Aves/Garrulus glandarius/58df3e50f12b91e0f19e642a89f22f82.jpg\",\n  \"368265.jpg\": \"Aves/Garrulus glandarius/4f67678e43c785b1b87843cf5e9623d3.jpg\",\n  \"368267.jpg\": \"Aves/Garrulus glandarius/03c0dbeb3130cc03d983451fd0626f22.jpg\",\n  \"368274.jpg\": \"Aves/Garrulus glandarius/7acfd63a3856b16e7b1e80a9b2e2b913.jpg\",\n  \"368280.jpg\": \"Aves/Garrulus glandarius/b15801f6901a1d7e5bc35d02c973a161.jpg\",\n  \"368281.jpg\": \"Aves/Garrulus glandarius/bb0a445bd8d76d6b01bb8fa494aad31b.jpg\",\n  \"368287.jpg\": \"Aves/Garrulus glandarius/310160c421775e6f5459c0f67529c567.jpg\",\n  \"368297.jpg\": \"Aves/Garrulus glandarius/c801823bf11abb49be52798de9476526.jpg\",\n  \"368298.jpg\": \"Aves/Garrulus glandarius/38d10612ad2409447ebbd3e6f7797312.jpg\",\n  \"368304.jpg\": \"Aves/Garrulus glandarius/2267b31d9099e108eb80915717ee7fac.jpg\",\n  \"368306.jpg\": \"Aves/Garrulus glandarius/136c8f0163e0933c308f1619397f9f84.jpg\",\n  \"368323.jpg\": \"Aves/Garrulus glandarius/cd3229c1d2f5c33aa00cc677235d582d.jpg\",\n  \"368326.jpg\": \"Aves/Garrulus glandarius/6bd9564ec654724a01b6577dbc7b1c6d.jpg\",\n  \"36833.jpg\": \"Insecta/Evergestis pallidata/1740ba8bb01c30926d1c9529f5f6fe2c.jpg\",\n  \"368334.jpg\": \"Aves/Garrulus glandarius/979a0f55662a6e18ae9f6b509867cc3a.jpg\",\n  \"368336.jpg\": \"Aves/Garrulus glandarius/4f0a700d686edcbcc0d2ece576514ad7.jpg\",\n  \"368337.jpg\": \"Aves/Somateria mollissima/14df9b4fb0b81d6ba78a15a2eeeca89b.jpg\",\n  \"36835.jpg\": \"Insecta/Evergestis pallidata/2d2492da87734e58eebc289cf05bcfe8.jpg\",\n  \"368360.jpg\": \"Aves/Somateria mollissima/0fb7d99ba5bd2370cd83d7424c7847ec.jpg\",\n  \"368380.jpg\": \"Aves/Somateria mollissima/47869fa6b9c7fd2062b3ec21c5c08a28.jpg\",\n  \"368381.jpg\": \"Aves/Somateria mollissima/9114aeeada51d2267e948414cb158e24.jpg\",\n  \"368384.jpg\": \"Aves/Somateria mollissima/8c05687a1689541dbacd56ea2cf548dd.jpg\",\n  \"368390.jpg\": \"Aves/Somateria mollissima/b4ffce552ce48fe1b561921fd4eec150.jpg\",\n  \"368392.jpg\": \"Aves/Somateria mollissima/02a876cb7a1945889d45fadef8ccb7b1.jpg\",\n  \"3684.jpg\": \"Insecta/Coccinella septempunctata/fa4bb2cb0dd8b604e41d1ec33e73d99f.jpg\",\n  \"368411.jpg\": \"Aves/Somateria mollissima/0f180dde297d875d766e9b61affbe61a.jpg\",\n  \"368417.jpg\": \"Aves/Somateria mollissima/054b0863270e8a7cced0502435cdf5fb.jpg\",\n  \"368418.jpg\": \"Aves/Somateria mollissima/dd7c745130b6677723a973f1086b74a7.jpg\",\n  \"36843.jpg\": \"Amphibia/Acris blanchardi/0790dd97db34eb7ded8a82429cfb0ad4.jpg\",\n  \"368435.jpg\": \"Aves/Buteo lineatus/b570162ab539719411bd02282acb4176.jpg\",\n  \"36845.jpg\": \"Amphibia/Acris blanchardi/d652c111a977eb3d2356bfc73946dfcd.jpg\",\n  \"36846.jpg\": \"Amphibia/Acris blanchardi/49525788a6ca5b056773bffd7aff8b99.jpg\",\n  \"368498.jpg\": \"Aves/Buteo lineatus/f0693661f0edc8e6e140d1a7183ee4f6.jpg\",\n  \"368536.jpg\": \"Aves/Buteo lineatus/a35716264517ac0a8f8862e37c37b1ec.jpg\",\n  \"368556.jpg\": \"Aves/Buteo lineatus/603a4b6a8348cb76c50d784c7a343547.jpg\",\n  \"36856.jpg\": \"Amphibia/Acris blanchardi/c6c4da2f6ed2a19973e9061a74d9bcf9.jpg\",\n  \"368572.jpg\": \"Aves/Buteo lineatus/6e3470103c92711df736b00de70d4e0b.jpg\",\n  \"36859.jpg\": \"Amphibia/Acris blanchardi/03c7f7c773ae460cf0abbb233bf251f6.jpg\",\n  \"368615.jpg\": \"Aves/Buteo lineatus/09583d97a09c04e68ee6ac878c7c0f41.jpg\",\n  \"368632.jpg\": \"Aves/Buteo lineatus/0bc99b90b058fbbd57f314fe38131234.jpg\",\n  \"368633.jpg\": \"Aves/Buteo lineatus/c4750100414b357a3e08d1258e182e2d.jpg\",\n  \"36864.jpg\": \"Amphibia/Acris blanchardi/86e31cf8a5ddb93f3e00a2b385ecec3c.jpg\",\n  \"368642.jpg\": \"Aves/Buteo lineatus/7311e46ad965fdcb85bc73fb57d47287.jpg\",\n  \"368667.jpg\": \"Aves/Buteo lineatus/495f373812af1cc0ad9111cbf3dfed1c.jpg\",\n  \"368687.jpg\": \"Aves/Buteo lineatus/5d08a3be99664caa11527cdd57fec3db.jpg\",\n  \"368788.jpg\": \"Aves/Buteo lineatus/47161a9e2f05ddaec884e08d5590d1a8.jpg\",\n  \"368866.jpg\": \"Aves/Buteo lineatus/2e1d0bda70e05fd8107f82f5717396ea.jpg\",\n  \"368867.jpg\": \"Aves/Buteo lineatus/9042430ef591d4c875117700167e7f85.jpg\",\n  \"368920.jpg\": \"Aves/Buteo lineatus/ced0181d8ecf5f9005ef2113332fc8cf.jpg\",\n  \"368921.jpg\": \"Aves/Buteo lineatus/90ceb8816d303ff3a365c2d6ef2dc4b6.jpg\",\n  \"368946.jpg\": \"Aves/Buteo lineatus/657e2efce5143788000929ebb0b3e89d.jpg\",\n  \"368973.jpg\": \"Aves/Buteo lineatus/f6c2c402829e45e1ae2200ca72bf4872.jpg\",\n  \"3690.jpg\": \"Insecta/Coccinella septempunctata/3b9d706ce7c176b350e6c2270cdaa938.jpg\",\n  \"369039.jpg\": \"Aves/Buteo lineatus/165fc34585286f4d612c2fef9316a2ad.jpg\",\n  \"369044.jpg\": \"Aves/Buteo lineatus/3f5e0f16adc4e8fd4263dfb58bbb7c77.jpg\",\n  \"369099.jpg\": \"Aves/Buteo lineatus/ca27a0cf55590ddc2fb59692e18f86b4.jpg\",\n  \"369106.jpg\": \"Aves/Buteo lineatus/7b72831e1b5d75e07604672aff355e87.jpg\",\n  \"369125.jpg\": \"Reptilia/Lampropeltis triangulum triangulum/f3d742d839aae9da5fdfc3d0d9b618ab.jpg\",\n  \"369126.jpg\": \"Reptilia/Lampropeltis triangulum triangulum/1b5dc1e0186512db009457c2d44d9f3d.jpg\",\n  \"369127.jpg\": \"Reptilia/Lampropeltis triangulum triangulum/188de98402008594d981068d2d1184f7.jpg\",\n  \"369130.jpg\": \"Reptilia/Lampropeltis triangulum triangulum/31a099ad8f47695c775077254e2e869a.jpg\",\n  \"369132.jpg\": \"Reptilia/Lampropeltis triangulum triangulum/6fb4038c7a845353e6106d2e2333be1c.jpg\",\n  \"369133.jpg\": \"Reptilia/Lampropeltis triangulum triangulum/eda3c53859925fca5650943a3947b72b.jpg\",\n  \"369134.jpg\": \"Reptilia/Lampropeltis triangulum triangulum/80a52fa69270f9ebe01584538867d7c3.jpg\",\n  \"369135.jpg\": \"Reptilia/Lampropeltis triangulum triangulum/a3f4eb22fc69efa645d629ccd8f5a758.jpg\",\n  \"369136.jpg\": \"Reptilia/Lampropeltis triangulum triangulum/a71e59c0d569ca8d03bfe7a0a71269bc.jpg\",\n  \"369137.jpg\": \"Reptilia/Lampropeltis triangulum triangulum/b3fac08187b09fa444d3819cde5f39f9.jpg\",\n  \"369138.jpg\": \"Reptilia/Lampropeltis triangulum triangulum/f137bdccb549500a90bd6b52296a25d4.jpg\",\n  \"369139.jpg\": \"Reptilia/Lampropeltis triangulum triangulum/da1f3cec28236a5b513e60aab16444af.jpg\",\n  \"369143.jpg\": \"Reptilia/Lampropeltis triangulum triangulum/e96b86d2396275607d5ab8279e0b4b8a.jpg\",\n  \"369146.jpg\": \"Reptilia/Lampropeltis triangulum triangulum/5c34627e267f40aa626ed7ca8563949d.jpg\",\n  \"369147.jpg\": \"Reptilia/Lampropeltis triangulum triangulum/dfd88b6f4721010691777c7495eac4a8.jpg\",\n  \"369148.jpg\": \"Reptilia/Lampropeltis triangulum triangulum/edb72e07eceecb956d35287355555545.jpg\",\n  \"369155.jpg\": \"Reptilia/Lampropeltis triangulum triangulum/8b0f4aa395ab04be8aa04781b8f61ae1.jpg\",\n  \"369162.jpg\": \"Reptilia/Lampropeltis triangulum triangulum/8883cb502412ada0178a8f6378477fce.jpg\",\n  \"369164.jpg\": \"Reptilia/Lampropeltis triangulum triangulum/0cdc9aad153bfc8e5d6a2f4996b2f00f.jpg\",\n  \"369165.jpg\": \"Reptilia/Lampropeltis triangulum triangulum/f2926c232592c4d7924032f24b7b3d5e.jpg\",\n  \"369166.jpg\": \"Reptilia/Lampropeltis triangulum triangulum/1e646dc92f73a1d7d756107272a6d3dd.jpg\",\n  \"369169.jpg\": \"Reptilia/Lampropeltis triangulum triangulum/fcee87893497f3262fac39b4b6849310.jpg\",\n  \"36917.jpg\": \"Amphibia/Acris blanchardi/6c107b1f8ff5a978c1a6bb026e296562.jpg\",\n  \"36920.jpg\": \"Amphibia/Acris blanchardi/895f80d8a27dba0e2054a893e20c43fe.jpg\",\n  \"369201.jpg\": \"Aves/Aythya fuligula/81d1e8fc1b3c2698fc67d3eb8d3134ca.jpg\",\n  \"369202.jpg\": \"Aves/Aythya fuligula/1996c7b3bf1ccca1f2c07e181e6abc64.jpg\",\n  \"369205.jpg\": \"Aves/Aythya fuligula/9ee47d34c7907e762c9b0dc1d3961d1a.jpg\",\n  \"369215.jpg\": \"Aves/Aythya fuligula/6c8d2e2db6f1c97d274378fb70145c53.jpg\",\n  \"369230.jpg\": \"Aves/Aythya fuligula/91f46b1fe6afa1ceabbe1d427abfc554.jpg\",\n  \"369231.jpg\": \"Aves/Aythya fuligula/399c9008db37e913898ee189c3d2e240.jpg\",\n  \"369234.jpg\": \"Aves/Aythya fuligula/d58c1aab5c5408b79c27a2bf49044c27.jpg\",\n  \"369241.jpg\": \"Aves/Aythya fuligula/519594c2767239d7d3f58aa1433e12a0.jpg\",\n  \"369244.jpg\": \"Aves/Aythya fuligula/ae57feb5a4b862fb1e5e2bfa8a21cb0c.jpg\",\n  \"369253.jpg\": \"Aves/Aythya fuligula/28068d3e406e98a897131ab640cfdc7e.jpg\",\n  \"369256.jpg\": \"Aves/Aythya fuligula/2c436868137decbd7a381ec9cf031767.jpg\",\n  \"369257.jpg\": \"Aves/Aythya fuligula/eaf2d6bd94b05186d1db70c23df7994f.jpg\",\n  \"369305.jpg\": \"Insecta/Lomographa vestaliata/363195310e2b65c39ba224a679e53498.jpg\",\n  \"369308.jpg\": \"Insecta/Lomographa vestaliata/ffd246ac5b0c80533e3a624f720f4458.jpg\",\n  \"369311.jpg\": \"Insecta/Lomographa vestaliata/293cfeaff60d574830ea1058e4644388.jpg\",\n  \"369314.jpg\": \"Insecta/Lomographa vestaliata/d761a772b9f79129e62b82dcd5b202fe.jpg\",\n  \"369316.jpg\": \"Insecta/Lomographa vestaliata/c1ebd7dec1775eaf891fcb06294ff7f9.jpg\",\n  \"369317.jpg\": \"Insecta/Lomographa vestaliata/b4bdd2b48d32590229f549123018e843.jpg\",\n  \"369318.jpg\": \"Insecta/Lomographa vestaliata/576a9ce0aa160758b7facbe561364cb4.jpg\",\n  \"369320.jpg\": \"Insecta/Lomographa vestaliata/0dc3e2c64f841ae4662e4a4e518b3716.jpg\",\n  \"369326.jpg\": \"Insecta/Atteva aurea/daa2fc073f665ad7bdf222be322fbe6a.jpg\",\n  \"369343.jpg\": \"Insecta/Atteva aurea/d194620158f5696ffde29da6c8c4bebe.jpg\",\n  \"369344.jpg\": \"Insecta/Atteva aurea/53d4e0c2a766ce6ce26af9ead973f699.jpg\",\n  \"369381.jpg\": \"Insecta/Atteva aurea/f3351c8ab8fa5a5f1da283b862715c41.jpg\",\n  \"369386.jpg\": \"Insecta/Atteva aurea/2bc925432d8068b74e360dc99fd62aea.jpg\",\n  \"369394.jpg\": \"Insecta/Atteva aurea/adb2e1cb111237b3d3dbdd1e5832c4e1.jpg\",\n  \"369401.jpg\": \"Insecta/Atteva aurea/96fe6e35e633267d3c778cd55f9e9c87.jpg\",\n  \"369405.jpg\": \"Insecta/Atteva aurea/a0ca6a57a59ad57f3a02a50a00910e5e.jpg\",\n  \"369406.jpg\": \"Insecta/Atteva aurea/4575953b54ea32806a49a90fa72dd7a2.jpg\",\n  \"369415.jpg\": \"Insecta/Atteva aurea/bba2a003418755e8a7826f247e2b4d1c.jpg\",\n  \"369424.jpg\": \"Insecta/Atteva aurea/2f72200d1bb742242240c20150c4342c.jpg\",\n  \"369445.jpg\": \"Insecta/Atteva aurea/c7414883a49dff01ea6424908ba013bd.jpg\",\n  \"369449.jpg\": \"Insecta/Atteva aurea/5919fbd9ef9eb7391e3c210f760a7ca1.jpg\",\n  \"369464.jpg\": \"Insecta/Atteva aurea/75721ad7d7c977d604279691fb77c6f0.jpg\",\n  \"369466.jpg\": \"Insecta/Atteva aurea/e02db219e1cdc50804665781ec4a1424.jpg\",\n  \"369471.jpg\": \"Insecta/Atteva aurea/da010dcef3c8de63bdcb9527f8558a8e.jpg\",\n  \"369479.jpg\": \"Insecta/Atteva aurea/a62dd350590f775cbd72c130e7460fcb.jpg\",\n  \"369494.jpg\": \"Insecta/Atteva aurea/69bc2c6c6f34a12adff5da4cf9db43f0.jpg\",\n  \"369495.jpg\": \"Insecta/Atteva aurea/0455ecb6c3bfa91a111d6160b145da2f.jpg\",\n  \"369503.jpg\": \"Insecta/Atteva aurea/4a7520713905b9f420b727219af95e15.jpg\",\n  \"369505.jpg\": \"Insecta/Atteva aurea/a9c1d008d238e0d95f1ef3e247e62841.jpg\",\n  \"369536.jpg\": \"Aves/Melozone aberti/567a0e8ec95df96409e83f947d545504.jpg\",\n  \"369542.jpg\": \"Aves/Melozone aberti/414d5cd646efbf784714e8bcd30ea29b.jpg\",\n  \"369549.jpg\": \"Aves/Melozone aberti/faf2eaec40092e912fc3070eded7032a.jpg\",\n  \"369550.jpg\": \"Aves/Melozone aberti/3683bee36af395e470575405ccbbf7da.jpg\",\n  \"369551.jpg\": \"Aves/Melozone aberti/d90801d35f98daf0459cddc0a67f488f.jpg\",\n  \"369560.jpg\": \"Aves/Baeolophus bicolor/b997c011dfb83ce7cb812273f2318f92.jpg\",\n  \"369562.jpg\": \"Aves/Baeolophus bicolor/690f8163423cc2448605869899990e55.jpg\",\n  \"369563.jpg\": \"Aves/Baeolophus bicolor/8ed600a5f1a18cf20614001637dbfdd4.jpg\",\n  \"369573.jpg\": \"Aves/Baeolophus bicolor/9549b1b465161a016f890bdd7e1cf4b2.jpg\",\n  \"369588.jpg\": \"Aves/Baeolophus bicolor/62693bb1f804d84c720d23699f1d1014.jpg\",\n  \"369589.jpg\": \"Aves/Baeolophus bicolor/92c14b5eaa7f21d4376c9ce525b72aa4.jpg\",\n  \"369631.jpg\": \"Aves/Baeolophus bicolor/71dfb88571736b3db46e15eb50aaba4c.jpg\",\n  \"369632.jpg\": \"Aves/Baeolophus bicolor/230481022686890948a11c685e647f43.jpg\",\n  \"369644.jpg\": \"Aves/Baeolophus bicolor/2fa5cfffa9f96a3de90b034549ae2b91.jpg\",\n  \"369651.jpg\": \"Aves/Baeolophus bicolor/893576b20c469b4edf4fa71c653cbc2c.jpg\",\n  \"369699.jpg\": \"Aves/Baeolophus bicolor/7c96866f975fcf8d011b7bd4452a813e.jpg\",\n  \"369721.jpg\": \"Aves/Baeolophus bicolor/e66f8eb4d87cf4112be05c186612f649.jpg\",\n  \"36974.jpg\": \"Amphibia/Acris blanchardi/06a641dae2b887cca79ce6b2cc52e2dc.jpg\",\n  \"369750.jpg\": \"Aves/Baeolophus bicolor/7679da13ce4c262e8635071763d8ac76.jpg\",\n  \"369751.jpg\": \"Aves/Baeolophus bicolor/cfabbb5f6d7c247723617d932687f899.jpg\",\n  \"369767.jpg\": \"Aves/Baeolophus bicolor/ccc4e5b6bcb0c3262ee3c0b044c3b5f6.jpg\",\n  \"369776.jpg\": \"Aves/Baeolophus bicolor/a9a891923f60e83b238267bd2c7289b8.jpg\",\n  \"36978.jpg\": \"Amphibia/Acris blanchardi/4460ec3b56d157ec87ad5e91ba64ea38.jpg\",\n  \"369799.jpg\": \"Aves/Baeolophus bicolor/d1e00713330905008d2b1c8b3b739ec9.jpg\",\n  \"369821.jpg\": \"Aves/Baeolophus bicolor/9fdb77508f196be29b9c800f0da673b6.jpg\",\n  \"369827.jpg\": \"Aves/Baeolophus bicolor/2741320900ef2a286a3503bd0333a28f.jpg\",\n  \"369830.jpg\": \"Aves/Baeolophus bicolor/5e5843097c25c703b292c1c426c011e4.jpg\",\n  \"369922.jpg\": \"Aves/Thryomanes bewickii/30aa395a67f137d4b04dcb8ff5c82818.jpg\",\n  \"369923.jpg\": \"Aves/Thryomanes bewickii/ee0079294ce6b5dd9dd2327a9f5c76e1.jpg\",\n  \"369936.jpg\": \"Aves/Thryomanes bewickii/18efa85e868d188973d01811696d0768.jpg\",\n  \"369949.jpg\": \"Aves/Thryomanes bewickii/0723dccf1822f92c5df0f30d4d09743f.jpg\",\n  \"369953.jpg\": \"Aves/Thryomanes bewickii/c381c93f3fa2a7fb8387f6b53c0d9b88.jpg\",\n  \"369958.jpg\": \"Aves/Thryomanes bewickii/0454ea5b5821a70a573db99402bfff93.jpg\",\n  \"369962.jpg\": \"Aves/Thryomanes bewickii/44dcce623dd4c77926a725b13669ceaf.jpg\",\n  \"37.jpg\": \"Mammalia/Marmota flaviventris/34e4892a2cb23d54c2c90256ceeee413.jpg\",\n  \"370017.jpg\": \"Aves/Thryomanes bewickii/f56423581a46217829728223d34b74cc.jpg\",\n  \"370022.jpg\": \"Aves/Thryomanes bewickii/c1b60c9d3d6731d27118b6de97a06b7f.jpg\",\n  \"370046.jpg\": \"Aves/Thryomanes bewickii/a7fa86364eade270acc45f36262cecd7.jpg\",\n  \"370063.jpg\": \"Aves/Thryomanes bewickii/40f65b20ba9e598eb24ba78ccd975556.jpg\",\n  \"370072.jpg\": \"Aves/Thryomanes bewickii/bd231353bf13348275aca088734c1128.jpg\",\n  \"370073.jpg\": \"Aves/Thryomanes bewickii/f0efec8fab6988f9f7d216e4ef8c59c4.jpg\",\n  \"370099.jpg\": \"Aves/Thryomanes bewickii/a95f69dd7c8063fe76bc383387577028.jpg\",\n  \"370125.jpg\": \"Aves/Thryomanes bewickii/b4a38d68ac727041c03a914880cabc87.jpg\",\n  \"370148.jpg\": \"Aves/Thryomanes bewickii/30ae0e36e5c22e1a6725e2ad42ec55ed.jpg\",\n  \"37016.jpg\": \"Amphibia/Acris blanchardi/70e49c196533ea1ebf0d7fb9afe5c6a8.jpg\",\n  \"370166.jpg\": \"Aves/Thryomanes bewickii/ae836496e985674fc4f1564b9a3443c0.jpg\",\n  \"370203.jpg\": \"Aves/Thryomanes bewickii/1469d5e5383e63aa2286bcbad3a7dd84.jpg\",\n  \"370204.jpg\": \"Aves/Thryomanes bewickii/3c3e2a97f7ce65bb4d458a69775c2ba6.jpg\",\n  \"370212.jpg\": \"Aves/Thryomanes bewickii/38c654bef578a7485989d9eae4da2dd7.jpg\",\n  \"370249.jpg\": \"Aves/Thryomanes bewickii/d818143d7abe77b711b19aa8f5513292.jpg\",\n  \"370254.jpg\": \"Aves/Thryomanes bewickii/b792b0e3a908728c69d1aac30a07453f.jpg\",\n  \"370256.jpg\": \"Aves/Thryomanes bewickii/9b9aaa9e915815be51433829293a94ac.jpg\",\n  \"370278.jpg\": \"Aves/Thryomanes bewickii/4eb6ab53a677986950a74188406d8d81.jpg\",\n  \"370299.jpg\": \"Aves/Thryomanes bewickii/a6245af73a61e83365a30822ac3bcbdd.jpg\",\n  \"370303.jpg\": \"Aves/Thryomanes bewickii/5efe6e9957cc2bb89be3101ceef23c65.jpg\",\n  \"37032.jpg\": \"Amphibia/Acris blanchardi/d012bc0bdb0b348f090ca694eaae68b6.jpg\",\n  \"370341.jpg\": \"Insecta/Nyctemera annulata/4ffae5179666d059e04c44f979720bb6.jpg\",\n  \"370344.jpg\": \"Insecta/Nyctemera annulata/81bd8846c046b5b2b2a8872f60be253c.jpg\",\n  \"370345.jpg\": \"Insecta/Nyctemera annulata/065e14ec0d590a3188bd17df78477905.jpg\",\n  \"370352.jpg\": \"Insecta/Nyctemera annulata/5a76a095fb6a0d8628bef139b729dbfb.jpg\",\n  \"370354.jpg\": \"Insecta/Nyctemera annulata/d53f78936d558b7360ac0e39fd4ee63b.jpg\",\n  \"370355.jpg\": \"Insecta/Nyctemera annulata/c1998e8cc19502de0057086d890286cf.jpg\",\n  \"370366.jpg\": \"Aves/Piranga ludoviciana/5151146cbc247304b9d7aa651aa674a4.jpg\",\n  \"370370.jpg\": \"Aves/Piranga ludoviciana/0811fe7b2aad9a9b5c9d232377d24e84.jpg\",\n  \"370372.jpg\": \"Aves/Piranga ludoviciana/73c40a09eea7ba3e8b67882695c1b3c8.jpg\",\n  \"370379.jpg\": \"Aves/Piranga ludoviciana/600ba95f79c354d66c52e9833ac9ba2e.jpg\",\n  \"370387.jpg\": \"Aves/Piranga ludoviciana/c581dac1cc10e368ca0c8800bb9900e4.jpg\",\n  \"370388.jpg\": \"Aves/Piranga ludoviciana/54824d64e2b709a1ce5a3cd8e3452446.jpg\",\n  \"370394.jpg\": \"Aves/Piranga ludoviciana/c62cf544558ba9dfb0475bcc6a056a08.jpg\",\n  \"370412.jpg\": \"Aves/Piranga ludoviciana/ad73a714e833a08f6e3e71431ff6eff6.jpg\",\n  \"370414.jpg\": \"Aves/Piranga ludoviciana/3399e3e02c5248aac1bd26a0750311ec.jpg\",\n  \"370421.jpg\": \"Aves/Piranga ludoviciana/1dcb8c9bb27f1d018f256cdebd0911ed.jpg\",\n  \"370447.jpg\": \"Aves/Piranga ludoviciana/9b703578e77777c83648538413fa7e44.jpg\",\n  \"370471.jpg\": \"Aves/Piranga ludoviciana/79be1d2358279500f73542b743009a63.jpg\",\n  \"370475.jpg\": \"Aves/Piranga ludoviciana/e2cebe1096d6d99dcce828e5e8800df1.jpg\",\n  \"370491.jpg\": \"Aves/Piranga ludoviciana/8329dca6e885fc0291d7d9e153998e2e.jpg\",\n  \"370504.jpg\": \"Aves/Piranga ludoviciana/2363b8483766b7da5b9c1b1b7d6a0014.jpg\",\n  \"370521.jpg\": \"Aves/Piranga ludoviciana/bf22ce7771d92594f562d7d1dfea8fc6.jpg\",\n  \"370522.jpg\": \"Aves/Piranga ludoviciana/ee5d8c276807d43982a4f862fe3d8277.jpg\",\n  \"370525.jpg\": \"Aves/Piranga ludoviciana/c85ec80493c3059eb9c9236421f13b29.jpg\",\n  \"370529.jpg\": \"Aves/Piranga ludoviciana/4cf0df23f20c5c28190a19d1144f5d7d.jpg\",\n  \"370531.jpg\": \"Aves/Piranga ludoviciana/36a04e1a8dabcd8e317d54f5fa48137c.jpg\",\n  \"370539.jpg\": \"Aves/Piranga ludoviciana/6baa69eaa700719a7e6fdffe59dc2168.jpg\",\n  \"370540.jpg\": \"Aves/Piranga ludoviciana/88ab1974a70ecc0353e03a67cb9f8416.jpg\",\n  \"370593.jpg\": \"Aves/Corvus monedula/723ba714615373decd329d108e293405.jpg\",\n  \"370599.jpg\": \"Aves/Corvus monedula/d0076c9ca32933e5d7463538fb157ddd.jpg\",\n  \"370600.jpg\": \"Aves/Corvus monedula/650c3e20684aa557407ca56389231a9d.jpg\",\n  \"370601.jpg\": \"Aves/Corvus monedula/4b99e1529f15a1522297520403580841.jpg\",\n  \"370615.jpg\": \"Aves/Corvus monedula/49e2e6190826bcc9fdfe2cafcb7ff0d5.jpg\",\n  \"370617.jpg\": \"Aves/Corvus monedula/6cee7ee05942938ba706a289e8a63ee9.jpg\",\n  \"37062.jpg\": \"Amphibia/Acris blanchardi/64108d7895606b8f9d25e9b4d25a0c2f.jpg\",\n  \"370620.jpg\": \"Aves/Corvus monedula/1145bbed720809047fe3301cb2ab2932.jpg\",\n  \"370621.jpg\": \"Aves/Corvus monedula/684c6b92f6148f645e4cdfd5a286608c.jpg\",\n  \"370625.jpg\": \"Aves/Corvus monedula/465e9b91995fd9697e678acab341ea9e.jpg\",\n  \"370627.jpg\": \"Aves/Corvus monedula/1c9220020ad9eee2553336de732080c9.jpg\",\n  \"370636.jpg\": \"Aves/Corvus monedula/9273cbc05291baa639cc6a214af175c1.jpg\",\n  \"370641.jpg\": \"Aves/Corvus monedula/a26ea793abb86dec1ba684770b0346e7.jpg\",\n  \"370642.jpg\": \"Aves/Corvus monedula/5a9167d010c1ae76c7e59cf9bc1cbc9d.jpg\",\n  \"370652.jpg\": \"Aves/Corvus monedula/a11fe7b0e906476d68c1f1a9a8546df3.jpg\",\n  \"370654.jpg\": \"Aves/Corvus monedula/8d52be8e4a40d02ca4a2963885877a8c.jpg\",\n  \"370659.jpg\": \"Aves/Corvus monedula/fb6b5e0ded7873bfe133312d0b63313b.jpg\",\n  \"370660.jpg\": \"Aves/Corvus monedula/0eba56b707b88d2368bc8d135e445472.jpg\",\n  \"370723.jpg\": \"Insecta/Pseudosphinx tetrio/27a492543272d78e83d4614c4a91847f.jpg\",\n  \"370728.jpg\": \"Insecta/Pseudosphinx tetrio/c0f2a7d3544e066c2c7eade363553ecd.jpg\",\n  \"370729.jpg\": \"Insecta/Pseudosphinx tetrio/506afab260fafde6bf15c3d4fd37fd4c.jpg\",\n  \"370731.jpg\": \"Insecta/Pseudosphinx tetrio/8201b42f80d530f79e3da33ccd5e743e.jpg\",\n  \"370734.jpg\": \"Insecta/Pseudosphinx tetrio/1a53435eb5d7700c897f301325c3c16c.jpg\",\n  \"370736.jpg\": \"Insecta/Pseudosphinx tetrio/6418927e2319e3859342fe788e9e008d.jpg\",\n  \"370737.jpg\": \"Insecta/Pseudosphinx tetrio/4a2b65855ce06f85dc464bd299cf6825.jpg\",\n  \"370738.jpg\": \"Insecta/Pseudosphinx tetrio/9e14303b1c999ba0fb4a4d474118fa36.jpg\",\n  \"370742.jpg\": \"Insecta/Pseudosphinx tetrio/9a911a0ec7c360722f57dde1f0bf2a8d.jpg\",\n  \"370744.jpg\": \"Insecta/Pseudosphinx tetrio/f1932e807c9e397378ab5442ed716b9c.jpg\",\n  \"370747.jpg\": \"Insecta/Pseudosphinx tetrio/3ee3a7021b3ff27eff5b7128d7c07c0e.jpg\",\n  \"37075.jpg\": \"Amphibia/Acris blanchardi/1799e7c7e257546b432286d9fc57ea1f.jpg\",\n  \"370755.jpg\": \"Aves/Gymnogyps californianus/676fabd7563d0d22c8fb82c0a36e3ac3.jpg\",\n  \"370762.jpg\": \"Aves/Gymnogyps californianus/4e7ea047d4c2dc8bc45fafa0b331c093.jpg\",\n  \"370764.jpg\": \"Aves/Gymnogyps californianus/1525525d614d611ba95e1d4bf50a2a12.jpg\",\n  \"370767.jpg\": \"Aves/Gymnogyps californianus/ea2fa3a44e365674b68eb73cbf4ebf63.jpg\",\n  \"370770.jpg\": \"Aves/Gymnogyps californianus/05ef4c5c2d7a0cdad824b652fde3ac47.jpg\",\n  \"370774.jpg\": \"Aves/Gymnogyps californianus/10ebcd3d886a50c2567621d3bd460a5b.jpg\",\n  \"370775.jpg\": \"Aves/Gymnogyps californianus/4f2e91ccc72542e612d4a1714b31df6f.jpg\",\n  \"370780.jpg\": \"Aves/Gymnogyps californianus/44c7505515b98112316aaf0a12931746.jpg\",\n  \"370781.jpg\": \"Aves/Gymnogyps californianus/d339dbe7ced7b0d7a7e448f35d45e255.jpg\",\n  \"370782.jpg\": \"Aves/Gymnogyps californianus/7d935e317ac15803ef188eb8a5fabc5a.jpg\",\n  \"370783.jpg\": \"Aves/Gymnogyps californianus/5b8f5a449e0ed2cccafd8ce5919f32b3.jpg\",\n  \"370784.jpg\": \"Aves/Gymnogyps californianus/343dc2a9d1338bf1d2f6db5e3c095ddc.jpg\",\n  \"370789.jpg\": \"Aves/Gymnogyps californianus/9d42227ac47fdf4b0fe7e3bdfb66cf11.jpg\",\n  \"370790.jpg\": \"Aves/Gymnogyps californianus/adcb1842632bc039ddfc4c10f953eefb.jpg\",\n  \"370793.jpg\": \"Aves/Gymnogyps californianus/78e20fb6a0ac5bc9982dd943bf06c969.jpg\",\n  \"370794.jpg\": \"Aves/Gymnogyps californianus/1988ec30265c5920630cec4aa39af486.jpg\",\n  \"370805.jpg\": \"Aves/Gymnogyps californianus/c46056e8311835b72b5ff3a35d443064.jpg\",\n  \"370844.jpg\": \"Aves/Phasianus colchicus/572a61333b797509ada28183e8068540.jpg\",\n  \"370847.jpg\": \"Aves/Phasianus colchicus/714419f8381a23403262bb19bb85c875.jpg\",\n  \"370848.jpg\": \"Aves/Phasianus colchicus/24f1fc1adbc027e859a044cafadb027f.jpg\",\n  \"370864.jpg\": \"Aves/Phasianus colchicus/6d866bea00dbf068c532b0b0cd1517de.jpg\",\n  \"370865.jpg\": \"Aves/Phasianus colchicus/22802b3e0911b9bca0bfeecc377a9ef0.jpg\",\n  \"370867.jpg\": \"Aves/Phasianus colchicus/5a69d5ec924cc680e8f7d25146646dd6.jpg\",\n  \"370869.jpg\": \"Aves/Phasianus colchicus/2ac5ae89b6aae2384373c666107283aa.jpg\",\n  \"370880.jpg\": \"Aves/Phasianus colchicus/282500e5c6384861d1c020bb8f4f7e5e.jpg\",\n  \"370882.jpg\": \"Aves/Phasianus colchicus/b39259596703b5fa7db7ad69ae1df0dd.jpg\",\n  \"370883.jpg\": \"Aves/Phasianus colchicus/eef213675d56af498d893260de3be857.jpg\",\n  \"37089.jpg\": \"Amphibia/Acris blanchardi/f4dd5bc094d69f9c0d0adb7add620892.jpg\",\n  \"370893.jpg\": \"Aves/Phasianus colchicus/e69c2d0f8b271877bee75b2320f69206.jpg\",\n  \"370909.jpg\": \"Aves/Phasianus colchicus/9fb7e3dd4c839617ff39e3180d86ce68.jpg\",\n  \"370910.jpg\": \"Aves/Phasianus colchicus/a832a10bc616e958edd4d5a099fe76ea.jpg\",\n  \"370912.jpg\": \"Aves/Phasianus colchicus/2eb2e48ebac038f24fbc2f12eecddd2e.jpg\",\n  \"370922.jpg\": \"Aves/Phasianus colchicus/85716fb884fecfbada734e554a313985.jpg\",\n  \"37093.jpg\": \"Amphibia/Acris blanchardi/ace5b3383dad742e3f097cf965ffa7a6.jpg\",\n  \"37094.jpg\": \"Amphibia/Acris blanchardi/e1f37f8b5972083e9ec705839785a4ab.jpg\",\n  \"370948.jpg\": \"Aves/Phasianus colchicus/94aeadc8f41184a1a8d2b5e190aa129c.jpg\",\n  \"370951.jpg\": \"Aves/Phasianus colchicus/317dfb5ff9c6ef8209f83d41f091e1cc.jpg\",\n  \"370961.jpg\": \"Aves/Phasianus colchicus/af093dad0df16778353d3daa12387b3c.jpg\",\n  \"370964.jpg\": \"Aves/Phasianus colchicus/f41973c6cc3a6e77a125c8b7ed4008df.jpg\",\n  \"370992.jpg\": \"Aves/Contopus cooperi/2f1466c5b78fa75832e739681e9712d1.jpg\",\n  \"370994.jpg\": \"Aves/Contopus cooperi/0d232a3aafd46127ae81dc93c3fed26b.jpg\",\n  \"370995.jpg\": \"Aves/Contopus cooperi/f1cdd25d565b58af19354b38dc8cd91c.jpg\",\n  \"370996.jpg\": \"Aves/Contopus cooperi/2edb6a608e804215b6499bd56b6a262f.jpg\",\n  \"370998.jpg\": \"Aves/Contopus cooperi/8aba2cf044584037ce05ac0bed3ccf64.jpg\",\n  \"371000.jpg\": \"Aves/Contopus cooperi/75aecf963eb0ad335207021424ba8a51.jpg\",\n  \"371002.jpg\": \"Aves/Contopus cooperi/80c6c1bdb4e4749ff587261ea3b88127.jpg\",\n  \"371005.jpg\": \"Aves/Contopus cooperi/e1f318c2ce453cd9f15d1205e90d9598.jpg\",\n  \"371022.jpg\": \"Aves/Contopus cooperi/d6f47f582a4e6fd40e91b5dad7f3f8fa.jpg\",\n  \"371023.jpg\": \"Aves/Contopus cooperi/b34609d9e11b314e9622c0cdf50381de.jpg\",\n  \"371026.jpg\": \"Aves/Contopus cooperi/4ab266295deead813e449dbbcee955f6.jpg\",\n  \"371027.jpg\": \"Aves/Contopus cooperi/cc674abf5fbfa18c5328a16bddf4afdd.jpg\",\n  \"371032.jpg\": \"Aves/Contopus cooperi/3e71b4de969a5c62706271466fe5c0f4.jpg\",\n  \"371033.jpg\": \"Aves/Contopus cooperi/21c82689c7561e59fe94ef635b210851.jpg\",\n  \"371034.jpg\": \"Aves/Contopus cooperi/b5dd974a543892015db7380a71a038e4.jpg\",\n  \"371035.jpg\": \"Aves/Contopus cooperi/f3af7f3ff8c6857e03d4d53986ff758c.jpg\",\n  \"371036.jpg\": \"Aves/Contopus cooperi/fde87ca85608691110d6d4251c932b67.jpg\",\n  \"371056.jpg\": \"Aves/Contopus cooperi/3157253f46529db0c75232f752663313.jpg\",\n  \"371089.jpg\": \"Aves/Aythya affinis/62c55f11d09d385954e002c96e32a17d.jpg\",\n  \"371117.jpg\": \"Aves/Aythya affinis/69043135b0bd02525e394d819c2aaaaf.jpg\",\n  \"371144.jpg\": \"Aves/Aythya affinis/4f7fc7dcf071b7dae514e8c3d8068f07.jpg\",\n  \"371185.jpg\": \"Aves/Aythya affinis/00fdc84589a3f62f5ac47e7e2b84e6e0.jpg\",\n  \"37119.jpg\": \"Amphibia/Acris blanchardi/355fd9123e401c6ac274c9d3c5ee5e43.jpg\",\n  \"371203.jpg\": \"Aves/Aythya affinis/775c3ddef1147d6437976e0d5e345d01.jpg\",\n  \"371213.jpg\": \"Aves/Aythya affinis/e1037e10ac1b642cfd4943b31a25679b.jpg\",\n  \"371226.jpg\": \"Aves/Aythya affinis/3302a02b66711c3f34ab8bf8017caf1e.jpg\",\n  \"37124.jpg\": \"Amphibia/Acris blanchardi/69a6cc9907194f233eda95808b1732a7.jpg\",\n  \"371295.jpg\": \"Aves/Aythya affinis/ace6a7cbd00dd23be10ece2fd27aae47.jpg\",\n  \"371301.jpg\": \"Aves/Aythya affinis/037890b0d4cb9359674e8358bc47e6cb.jpg\",\n  \"371303.jpg\": \"Aves/Aythya affinis/a07f820900019aaf0b8e32cb42e0d37d.jpg\",\n  \"371315.jpg\": \"Aves/Aythya affinis/64f4ef23855a358024e76aaa1abb5ed7.jpg\",\n  \"371316.jpg\": \"Aves/Aythya affinis/6f1abcb17060b3702ff840f04b434bc8.jpg\",\n  \"37132.jpg\": \"Amphibia/Acris blanchardi/17e7542965be29ced6e8b1ed8e10e46e.jpg\",\n  \"371323.jpg\": \"Aves/Aythya affinis/436b75b30be9cadfb27c6c212c350dcc.jpg\",\n  \"371330.jpg\": \"Aves/Aythya affinis/4c586f0d081ddf2b9cf8a77d92560f74.jpg\",\n  \"371333.jpg\": \"Aves/Aythya affinis/17d32dc36f12acb02261887b3c54a859.jpg\",\n  \"371361.jpg\": \"Amphibia/Pseudacris maculata/0737df7f15bcc5b5414bebaaf3d96405.jpg\",\n  \"371368.jpg\": \"Amphibia/Pseudacris maculata/971de87734084542e9ac9599704a7f30.jpg\",\n  \"371370.jpg\": \"Amphibia/Pseudacris maculata/42b5206c528cd32d5d02bee143a18783.jpg\",\n  \"371371.jpg\": \"Amphibia/Pseudacris maculata/2a2f4c31098a467de9007235bd65e02a.jpg\",\n  \"371381.jpg\": \"Amphibia/Pseudacris maculata/5011eb6ea5772783dfb3480092f2161d.jpg\",\n  \"371382.jpg\": \"Amphibia/Pseudacris maculata/e2df8745197f6e619aac5c43337f96f1.jpg\",\n  \"371386.jpg\": \"Aves/Egretta novaehollandiae/2a10c19640656268d280f67142b1ba9b.jpg\",\n  \"371401.jpg\": \"Aves/Egretta novaehollandiae/063d73947f2d743b70524122eab8f913.jpg\",\n  \"371406.jpg\": \"Aves/Egretta novaehollandiae/032a1c14a5773cc3e90f4fdd6d71f8e0.jpg\",\n  \"371407.jpg\": \"Aves/Egretta novaehollandiae/8f27db152112aa533a14e193599b28a2.jpg\",\n  \"371409.jpg\": \"Aves/Egretta novaehollandiae/2c96fea8bc3050a30e49d4daf3cfa8ad.jpg\",\n  \"371411.jpg\": \"Aves/Egretta novaehollandiae/d3485066cbf67fae7309b5d5ff89aa3b.jpg\",\n  \"371412.jpg\": \"Aves/Egretta novaehollandiae/662a34220cfd002e887dbcc00c5564a6.jpg\",\n  \"371415.jpg\": \"Aves/Egretta novaehollandiae/e6d13cf4a4ad496243ec9be584483f62.jpg\",\n  \"371416.jpg\": \"Aves/Egretta novaehollandiae/f695b3a949736661df8d229421446aa8.jpg\",\n  \"371418.jpg\": \"Aves/Egretta novaehollandiae/8d1da9eb13672da17fee068e6d3da827.jpg\",\n  \"371421.jpg\": \"Aves/Egretta novaehollandiae/8b2aa55d9aa52abd096f1d5fb9c86c80.jpg\",\n  \"371424.jpg\": \"Aves/Egretta novaehollandiae/0be702701251c68b6cdc17af46d9d80f.jpg\",\n  \"37143.jpg\": \"Amphibia/Acris blanchardi/c142a4ea98cddea0f4fede09e32d6a70.jpg\",\n  \"371431.jpg\": \"Aves/Egretta novaehollandiae/cdb0a22a3083852d82039218be157b78.jpg\",\n  \"371432.jpg\": \"Aves/Egretta novaehollandiae/11898208841cbfe4f277d302056b918b.jpg\",\n  \"371435.jpg\": \"Aves/Egretta novaehollandiae/8a6ad041263b75b6e85b629ecb93b168.jpg\",\n  \"371439.jpg\": \"Aves/Egretta novaehollandiae/5d67c89eade34fda4c9f09a4573a030b.jpg\",\n  \"371448.jpg\": \"Aves/Egretta novaehollandiae/e8fa618ccfdd9937abd47af9f5cc90a9.jpg\",\n  \"371459.jpg\": \"Aves/Egretta novaehollandiae/047925da0ef9f9e61386fce288ef33eb.jpg\",\n  \"371467.jpg\": \"Aves/Egretta novaehollandiae/0b6cc1c79fcccf60e1d30ca24dce3dc6.jpg\",\n  \"371471.jpg\": \"Aves/Egretta novaehollandiae/6ad678b8dd3b3c8bba9583da82137b25.jpg\",\n  \"371472.jpg\": \"Aves/Egretta novaehollandiae/5885c85d2a28f5e0351fcfc458dbf5d1.jpg\",\n  \"371481.jpg\": \"Aves/Egretta novaehollandiae/f53dbf6f35737345063bdbf55ef8b39b.jpg\",\n  \"371488.jpg\": \"Aves/Egretta novaehollandiae/6555115735eb926e440882b927d4aea1.jpg\",\n  \"371489.jpg\": \"Aves/Egretta novaehollandiae/74c319bb043c0ee49806dc43ff3ef42a.jpg\",\n  \"371490.jpg\": \"Aves/Egretta novaehollandiae/f8fa65f865c8e75562b50ecb69cc1a90.jpg\",\n  \"371561.jpg\": \"Aves/Campylorhynchus brunneicapillus/90b2ec5acd914dd6ef829c23e8a79628.jpg\",\n  \"371562.jpg\": \"Aves/Campylorhynchus brunneicapillus/293260a4e942a772d4d71a0776ba5ad9.jpg\",\n  \"371565.jpg\": \"Aves/Campylorhynchus brunneicapillus/355c6a9a7486b5b193631148f31c4fda.jpg\",\n  \"371572.jpg\": \"Aves/Campylorhynchus brunneicapillus/6d8578ecc24587e8445ed75d2f2c999b.jpg\",\n  \"371573.jpg\": \"Aves/Campylorhynchus brunneicapillus/df6a48f7422efb06133ce8b4bae73cc1.jpg\",\n  \"37158.jpg\": \"Amphibia/Acris blanchardi/371553a8b65426f5fee36caa65bff2c9.jpg\",\n  \"371585.jpg\": \"Aves/Campylorhynchus brunneicapillus/5f5e453d2dbb6efe18cf969bdfc15c97.jpg\",\n  \"371594.jpg\": \"Aves/Campylorhynchus brunneicapillus/50aab121d924a1681b680b570606855a.jpg\",\n  \"371611.jpg\": \"Aves/Campylorhynchus brunneicapillus/1d36cb9a1839fb31d0ef59f56c9cd78a.jpg\",\n  \"371619.jpg\": \"Aves/Campylorhynchus brunneicapillus/42d26bc422e6b3c52555ee0c36169004.jpg\",\n  \"371620.jpg\": \"Aves/Campylorhynchus brunneicapillus/0ef28bddef295a1dcd31ba4965add760.jpg\",\n  \"371624.jpg\": \"Aves/Campylorhynchus brunneicapillus/9dc392875297df0e8af703d4879ab170.jpg\",\n  \"371627.jpg\": \"Aves/Campylorhynchus brunneicapillus/20e909f7dc6521d44613d3ed3633134a.jpg\",\n  \"371630.jpg\": \"Aves/Campylorhynchus brunneicapillus/c3a9a4204ae7cb7c1022bd52d27c9b1e.jpg\",\n  \"371640.jpg\": \"Aves/Campylorhynchus brunneicapillus/23b7604ce355a3611e952e0a5a73d6f7.jpg\",\n  \"371647.jpg\": \"Aves/Campylorhynchus brunneicapillus/4a6d1f139ffb686cca47c203296d3ea7.jpg\",\n  \"371648.jpg\": \"Aves/Campylorhynchus brunneicapillus/2cdd9358d6aa33753923d052263a6002.jpg\",\n  \"371660.jpg\": \"Aves/Campylorhynchus brunneicapillus/6871c752fb48dd7b2d63c3b33f6d2632.jpg\",\n  \"371687.jpg\": \"Aves/Campylorhynchus brunneicapillus/b8b6a53c42d818545aec25bedd044830.jpg\",\n  \"371689.jpg\": \"Aves/Campylorhynchus brunneicapillus/12509c6637c1cb9bbd9152a18f804239.jpg\",\n  \"371697.jpg\": \"Aves/Campylorhynchus brunneicapillus/45cd8a3f46672fc74fefe514d0572e6c.jpg\",\n  \"371729.jpg\": \"Reptilia/Opheodrys aestivus/80a4eefafdf41b89baa37f773fc9a475.jpg\",\n  \"371731.jpg\": \"Reptilia/Opheodrys aestivus/6e51c461651691ea93d0856c7b3dfa31.jpg\",\n  \"371734.jpg\": \"Reptilia/Opheodrys aestivus/54ee70acb878286f6507b6c07c5d4197.jpg\",\n  \"371749.jpg\": \"Reptilia/Opheodrys aestivus/59807b2385f3ba27f1a4c89b915e1240.jpg\",\n  \"371763.jpg\": \"Reptilia/Opheodrys aestivus/1cdf1d08fc6a041d049f272c559589bd.jpg\",\n  \"371770.jpg\": \"Reptilia/Opheodrys aestivus/438526993b8f742e3ab99b0a9220df9b.jpg\",\n  \"371775.jpg\": \"Reptilia/Opheodrys aestivus/3bc53e39a1a9c9db831945b2ff95c014.jpg\",\n  \"371779.jpg\": \"Reptilia/Opheodrys aestivus/b558941e88675a1c93b662cf33bb1e84.jpg\",\n  \"371789.jpg\": \"Reptilia/Opheodrys aestivus/aef33e6e3d33323b4c800dedd2d13d56.jpg\",\n  \"371794.jpg\": \"Reptilia/Opheodrys aestivus/3abe3306935e35bf2029df4991091662.jpg\",\n  \"371800.jpg\": \"Reptilia/Opheodrys aestivus/fd554c90d3dfb696a77a8aff5b8416b6.jpg\",\n  \"371801.jpg\": \"Reptilia/Opheodrys aestivus/2b32e27ddb85041922f1d248cb4131a7.jpg\",\n  \"371803.jpg\": \"Reptilia/Opheodrys aestivus/076d91db7b8ac4487b379c09df569ae3.jpg\",\n  \"371805.jpg\": \"Reptilia/Opheodrys aestivus/b034fbc5f977ded0fa338112bc489bfb.jpg\",\n  \"371819.jpg\": \"Reptilia/Opheodrys aestivus/915ca6ec1b3242b11b27f0208a0cb90c.jpg\",\n  \"371820.jpg\": \"Reptilia/Opheodrys aestivus/1f912df9561de81b414727e6152b1eb1.jpg\",\n  \"371830.jpg\": \"Reptilia/Opheodrys aestivus/a38e5e679cfecaaa68c0270864ead5a8.jpg\",\n  \"371831.jpg\": \"Reptilia/Opheodrys aestivus/9535fd5351a343ce7a5ba716a5693845.jpg\",\n  \"371846.jpg\": \"Reptilia/Opheodrys aestivus/7b3523463e9df801614cf4155b584dd6.jpg\",\n  \"371851.jpg\": \"Reptilia/Opheodrys aestivus/c4774516f0cc1e6a90b23228c3be8755.jpg\",\n  \"371853.jpg\": \"Reptilia/Opheodrys aestivus/0f284361188815b17e0e70dabba06c7f.jpg\",\n  \"371855.jpg\": \"Reptilia/Opheodrys aestivus/919800982b98a947e2b9fbcac6aa7b16.jpg\",\n  \"371862.jpg\": \"Reptilia/Opheodrys aestivus/1a3a576b1b6e63d17d72c98bbb754245.jpg\",\n  \"371892.jpg\": \"Aves/Pelecanus conspicillatus/5493e0834f7dd18c86014b4ed47762b1.jpg\",\n  \"371902.jpg\": \"Aves/Pelecanus conspicillatus/0ba61620230d6c2ff9903682e6ec1974.jpg\",\n  \"371904.jpg\": \"Aves/Pelecanus conspicillatus/328c8bd63beb65a8bfb118ab0eacfeb6.jpg\",\n  \"371910.jpg\": \"Aves/Pelecanus conspicillatus/eab23bd709b3ef8a156864b5bb44c952.jpg\",\n  \"371911.jpg\": \"Aves/Pelecanus conspicillatus/07351757f09834adc7f622eb1d4ae60e.jpg\",\n  \"371919.jpg\": \"Aves/Passerella iliaca/7110e029df1f1318d1dd2038c6c11896.jpg\",\n  \"371924.jpg\": \"Aves/Passerella iliaca/5d33a6f1c2c614799fdf9f6ee234b8fb.jpg\",\n  \"37194.jpg\": \"Insecta/Xylophanes tersa/6cb632bbb6e58a1c6760b631e45e108b.jpg\",\n  \"37195.jpg\": \"Insecta/Xylophanes tersa/f39ab610efbb61bacade05b32cfeb6f2.jpg\",\n  \"371951.jpg\": \"Aves/Passerella iliaca/cfe243c8239f9c719c2fae139aa97bde.jpg\",\n  \"37196.jpg\": \"Insecta/Xylophanes tersa/c6e7e23642da0b4f911729e9948fb3da.jpg\",\n  \"371960.jpg\": \"Aves/Passerella iliaca/fc9b7d71f66b889147bb3f6d9b009545.jpg\",\n  \"371967.jpg\": \"Aves/Passerella iliaca/17c35a3ab15b5af2383d07cdd0247a2d.jpg\",\n  \"371970.jpg\": \"Aves/Passerella iliaca/b767729ffca8ed3ce77654029e09aea8.jpg\",\n  \"371979.jpg\": \"Aves/Passerella iliaca/652193ee1d23ed7f7f1dc8d215040bf0.jpg\",\n  \"371981.jpg\": \"Aves/Passerella iliaca/32e634f1b36dbcc3608e524d0b394876.jpg\",\n  \"371990.jpg\": \"Aves/Passerella iliaca/808e9fbbe6febbc8bd5cbe6c2150b0e0.jpg\",\n  \"371997.jpg\": \"Aves/Passerella iliaca/e02fdf61800d4474ca036e15fbdad5ce.jpg\",\n  \"371999.jpg\": \"Aves/Passerella iliaca/dae53f0332e683a1c00fe29aec09af21.jpg\",\n  \"372001.jpg\": \"Aves/Passerella iliaca/01ab304412cb8abd8ed7e4aa62e295ac.jpg\",\n  \"372011.jpg\": \"Aves/Passerella iliaca/158c6ca22986026371972878cffe1128.jpg\",\n  \"372012.jpg\": \"Aves/Passerella iliaca/086f951f9b08ff7f3a8a10ad50b4ddb4.jpg\",\n  \"37202.jpg\": \"Insecta/Xylophanes tersa/73ccfed24ea6b551287539f3addef776.jpg\",\n  \"37203.jpg\": \"Insecta/Xylophanes tersa/86035ff7cd8a42139f032a490887d357.jpg\",\n  \"37204.jpg\": \"Insecta/Xylophanes tersa/b4d03a0ca5674545ced31ab4a0384035.jpg\",\n  \"372048.jpg\": \"Aves/Passerella iliaca/241efd4a908e8ab45d283e0c4d2c27c1.jpg\",\n  \"372055.jpg\": \"Aves/Passerella iliaca/68d317151750b40ada706ba143dc03f8.jpg\",\n  \"372056.jpg\": \"Aves/Passerella iliaca/cfa0c8270a1860c91cac93e0e5562f61.jpg\",\n  \"372061.jpg\": \"Aves/Passerella iliaca/01bf29c4aa3581af9491fec14b4f5bbb.jpg\",\n  \"372064.jpg\": \"Aves/Passerella iliaca/ddf6120e447339fc6a24f944d546de5c.jpg\",\n  \"37207.jpg\": \"Insecta/Xylophanes tersa/3025155a12a0c0d0679e1d4c55e68468.jpg\",\n  \"372075.jpg\": \"Aves/Passerella iliaca/251a8c6f267f209b776c8bf803624bdc.jpg\",\n  \"37208.jpg\": \"Insecta/Xylophanes tersa/24b301678893f683e0ce766f9255ea2d.jpg\",\n  \"372080.jpg\": \"Aves/Passerella iliaca/9884ab8ff6a3d538d537243498696299.jpg\",\n  \"372081.jpg\": \"Aves/Passerella iliaca/58ba6e9e97b033f457d4f55bd42d80f4.jpg\",\n  \"372089.jpg\": \"Aves/Passerella iliaca/9719cb89e89d7c1c88d2bc9cf757b884.jpg\",\n  \"372099.jpg\": \"Aves/Passerella iliaca/1b88301274846cc072df08aae9400f74.jpg\",\n  \"372107.jpg\": \"Aves/Passerella iliaca/37c6f3355b3d960f3de8e7f5367c07cb.jpg\",\n  \"372108.jpg\": \"Aves/Passerella iliaca/8f3b0eb9062f771246a782a79fa0a0fd.jpg\",\n  \"372117.jpg\": \"Aves/Passerella iliaca/55f53c80e7017c29bb07916175b61b38.jpg\",\n  \"37212.jpg\": \"Insecta/Xylophanes tersa/5649372fdeb82bba0bf350b173be7bca.jpg\",\n  \"372128.jpg\": \"Aves/Passerella iliaca/d9741c431f0acbe32f25e29cbc29075e.jpg\",\n  \"37215.jpg\": \"Insecta/Xylophanes tersa/7d53021cbd5630c56bfb516b979b5009.jpg\",\n  \"37216.jpg\": \"Insecta/Xylophanes tersa/4a25ede20b6f9ca7a976723a4ce0525d.jpg\",\n  \"37222.jpg\": \"Insecta/Xylophanes tersa/4b43aeec37db605ef3231dfc74eec64e.jpg\",\n  \"372229.jpg\": \"Aves/Aythya marila/5a59fc58a9eb1054101bcba35e0e224a.jpg\",\n  \"372240.jpg\": \"Aves/Aythya marila/0540005d0a31bd98ff25e2fb914798db.jpg\",\n  \"372254.jpg\": \"Aves/Aythya marila/ad8ac4d3116963312e8615dff94c8b60.jpg\",\n  \"372257.jpg\": \"Aves/Aythya marila/3e8e46f0a682d4e67ed82af49e04a1c5.jpg\",\n  \"372273.jpg\": \"Aves/Aythya marila/a8082cabcce2ee0b5634001ac681b6c7.jpg\",\n  \"37228.jpg\": \"Insecta/Xylophanes tersa/2e1ee3411b10f1bf4e1f733fa4adba64.jpg\",\n  \"37231.jpg\": \"Insecta/Xylophanes tersa/54b48b9fbc2c0080f22441097e58b3b1.jpg\",\n  \"37239.jpg\": \"Insecta/Xylophanes tersa/90ab83a727157dea36ad48d15b09d7e6.jpg\",\n  \"37243.jpg\": \"Insecta/Xylophanes tersa/eaebbbcd60155bc287c88d2c3ef28e4d.jpg\",\n  \"37249.jpg\": \"Insecta/Xylophanes tersa/4aa5a2c253a2bb4fe4a863330f9d4414.jpg\",\n  \"37254.jpg\": \"Reptilia/Crotalus viridis/2d3035e3f560dfbcc1f4b4c57d42c598.jpg\",\n  \"37255.jpg\": \"Reptilia/Crotalus viridis/68058d102a7bdfcd7a96a53698644a6a.jpg\",\n  \"37256.jpg\": \"Reptilia/Crotalus viridis/ae0d2aa72c792512df879b12008e687f.jpg\",\n  \"37259.jpg\": \"Reptilia/Crotalus viridis/27aa18db07a45f99b826dc56eb58132d.jpg\",\n  \"37260.jpg\": \"Reptilia/Crotalus viridis/62345cb94bfb459acdb1663cd5cd46fc.jpg\",\n  \"37261.jpg\": \"Reptilia/Crotalus viridis/6f54a73d04fc888dd867b67a1aa09f0b.jpg\",\n  \"37262.jpg\": \"Reptilia/Crotalus viridis/1556c7baa283873bc36a7ddba117fea0.jpg\",\n  \"372651.jpg\": \"Actinopterygii/Abudefduf saxatilis/8131c35e9a5e32bb3a6d8fa43c72ca9b.jpg\",\n  \"372656.jpg\": \"Actinopterygii/Abudefduf saxatilis/67eaed712ba72225c108250ec407757e.jpg\",\n  \"37269.jpg\": \"Reptilia/Crotalus viridis/72af8daa890d7e9b2fad202787ac45cb.jpg\",\n  \"37270.jpg\": \"Reptilia/Crotalus viridis/35831b4591466652ee047a8c74783584.jpg\",\n  \"372762.jpg\": \"Aves/Zonotrichia albicollis/a6dc5b583f28ff0a40f270ae52b3ddcc.jpg\",\n  \"372764.jpg\": \"Aves/Zonotrichia albicollis/61973efa9c9d0b120cc13369058ae4ec.jpg\",\n  \"372784.jpg\": \"Aves/Zonotrichia albicollis/38a33f1b9a9dae48ea752be963da7d12.jpg\",\n  \"372787.jpg\": \"Aves/Zonotrichia albicollis/2e4122b1656da65fa41d6cee42564055.jpg\",\n  \"372791.jpg\": \"Aves/Zonotrichia albicollis/ee7b74350945de5c31fb8165daa58244.jpg\",\n  \"37280.jpg\": \"Reptilia/Crotalus viridis/5196c371eda3d9bb8b42d153ada43866.jpg\",\n  \"372805.jpg\": \"Aves/Zonotrichia albicollis/0657d6dc7463075ecc4931542bc46c74.jpg\",\n  \"37281.jpg\": \"Reptilia/Crotalus viridis/27efdfdd90d167df166e622297ca971c.jpg\",\n  \"37282.jpg\": \"Reptilia/Crotalus viridis/7dce514428cae35ba46d11a54515cee5.jpg\",\n  \"37283.jpg\": \"Reptilia/Crotalus viridis/a8106443aa82b727772dad0d12bb9a4c.jpg\",\n  \"372830.jpg\": \"Aves/Zonotrichia albicollis/3692b8ecc967be9de4b4947cfd5e37e8.jpg\",\n  \"372831.jpg\": \"Aves/Zonotrichia albicollis/df783572ea6b17b2192620ac5be7501f.jpg\",\n  \"372847.jpg\": \"Aves/Zonotrichia albicollis/2fcba18383be21d1c0e9f44181a1a2ab.jpg\",\n  \"37286.jpg\": \"Reptilia/Crotalus viridis/663256bf2bfdb56c1968d5ea03a0b93f.jpg\",\n  \"372866.jpg\": \"Aves/Zonotrichia albicollis/f1f6c7535009c73ad13e7de210a0e4e6.jpg\",\n  \"372877.jpg\": \"Aves/Zonotrichia albicollis/b82ef00c9af5b0017850c88f6ffcf6ea.jpg\",\n  \"372903.jpg\": \"Aves/Zonotrichia albicollis/586f8eb7c91150ea36b8838a914690a0.jpg\",\n  \"372975.jpg\": \"Aves/Zonotrichia albicollis/3b4e7b664107f38aa9a18191ef2b9e50.jpg\",\n  \"372986.jpg\": \"Aves/Zonotrichia albicollis/b54845cac47fbc9a7fbfb86df6d4cd9f.jpg\",\n  \"372987.jpg\": \"Aves/Zonotrichia albicollis/5980cf952383d18156ce8d81c2f5f6dc.jpg\",\n  \"37307.jpg\": \"Reptilia/Crotalus viridis/d450aab83a06d6e447d7ae92c0f11699.jpg\",\n  \"37308.jpg\": \"Reptilia/Crotalus viridis/a053e1d0c170e4eca0b62156ac51808f.jpg\",\n  \"37309.jpg\": \"Reptilia/Crotalus viridis/703c9860d4197e163cc56ab84425afc6.jpg\",\n  \"373098.jpg\": \"Aves/Zonotrichia albicollis/e44af26d2684942567f146a7c79d8a2a.jpg\",\n  \"3731.jpg\": \"Insecta/Coccinella septempunctata/8e6ef6e531c42aff8830b1843a801e86.jpg\",\n  \"37310.jpg\": \"Reptilia/Crotalus viridis/080d2c760410b545c203a271c4725062.jpg\",\n  \"373100.jpg\": \"Aves/Zonotrichia albicollis/c8fadd321d6555f03ca57eb2f6133260.jpg\",\n  \"37312.jpg\": \"Reptilia/Crotalus viridis/8fde71802ec89b2ee1ea9a59d995273a.jpg\",\n  \"373129.jpg\": \"Aves/Zonotrichia albicollis/7b27e55ae7e9cc58d566b7b4e12342be.jpg\",\n  \"37314.jpg\": \"Reptilia/Crotalus viridis/d8a8f1c37d1ea47e851b2bf4b97943f2.jpg\",\n  \"373144.jpg\": \"Aves/Zonotrichia albicollis/81bbfdabc617020335d1aba82def8c54.jpg\",\n  \"373155.jpg\": \"Aves/Zonotrichia albicollis/3dac3515e1d67af045ca7ad0e7f9d757.jpg\",\n  \"37316.jpg\": \"Reptilia/Crotalus viridis/eb1cf40fc4aadac4713a1403459d41f8.jpg\",\n  \"373182.jpg\": \"Aves/Zonotrichia albicollis/f52cc047f11ffed0ebce703b71839299.jpg\",\n  \"373184.jpg\": \"Aves/Zonotrichia albicollis/9b1d336d874e4e0ba04d52d78f0936fb.jpg\",\n  \"373222.jpg\": \"Aves/Zonotrichia albicollis/8c8170b2273c4927cc257a8d6c6b7360.jpg\",\n  \"373225.jpg\": \"Animalia/Uca pugilator/782863fef8587febf57d90581137d156.jpg\",\n  \"373234.jpg\": \"Animalia/Uca pugilator/37f69e6f2d9603cd6daa2adda67464ee.jpg\",\n  \"373237.jpg\": \"Animalia/Uca pugilator/284cce73708e5c17ec1f6cecc086a61b.jpg\",\n  \"37325.jpg\": \"Insecta/Vanessa gonerilla/30be15097630b0dc16957ff85a9ff636.jpg\",\n  \"373274.jpg\": \"Amphibia/Taricha torosa/ab4c23631bfe066bd9b2163331ea04b8.jpg\",\n  \"37328.jpg\": \"Insecta/Vanessa gonerilla/2a234f217ad62bc5d77853f99474a5b5.jpg\",\n  \"37329.jpg\": \"Insecta/Vanessa gonerilla/1f5b2e337daf92622826d1e36c96e2c0.jpg\",\n  \"373295.jpg\": \"Amphibia/Taricha torosa/21bb3f75491a709938e0ea8f7ade878a.jpg\",\n  \"373304.jpg\": \"Amphibia/Taricha torosa/989b0fc9f3509b8b4be71b4de2d4aa1a.jpg\",\n  \"373305.jpg\": \"Amphibia/Taricha torosa/acb572122f2eb5fc484f774ac640b65d.jpg\",\n  \"373333.jpg\": \"Amphibia/Taricha torosa/1b3811dca2f7d99b8fe7278516fb2f65.jpg\",\n  \"37335.jpg\": \"Insecta/Vanessa gonerilla/5e6e2188991b23f272230ad4686a3b27.jpg\",\n  \"373352.jpg\": \"Amphibia/Taricha torosa/18cb23c6cb59797c3bbc9ecaffac7ea7.jpg\",\n  \"373356.jpg\": \"Amphibia/Taricha torosa/22dfa6ef5350790a7eb314b94bcef07f.jpg\",\n  \"373357.jpg\": \"Amphibia/Taricha torosa/e94a4f9a199094709312f2ffc3c2d489.jpg\",\n  \"373360.jpg\": \"Amphibia/Taricha torosa/302227619ef458342d115b39dbef45e1.jpg\",\n  \"373363.jpg\": \"Amphibia/Taricha torosa/0dfe76b94ba0a51a9a737e24704de727.jpg\",\n  \"373371.jpg\": \"Amphibia/Taricha torosa/1637da8fe3312353469d9975ba4da40b.jpg\",\n  \"373375.jpg\": \"Amphibia/Taricha torosa/d8dea948cc1915eec49d7a31db90d086.jpg\",\n  \"373391.jpg\": \"Amphibia/Taricha torosa/d5a707d99a8ff9577beb97e4e09edf26.jpg\",\n  \"373392.jpg\": \"Amphibia/Taricha torosa/d558a0326f222fb35b012d956f032d1e.jpg\",\n  \"373396.jpg\": \"Amphibia/Taricha torosa/de03dae6e7f2731d7fc1c8e6722ff133.jpg\",\n  \"37340.jpg\": \"Insecta/Vanessa gonerilla/5449e7adc3c4f7ad9063556c084ec7f1.jpg\",\n  \"373446.jpg\": \"Amphibia/Taricha torosa/3aa2ad2848acdbce7abc5c2b9cf2ed93.jpg\",\n  \"373464.jpg\": \"Amphibia/Taricha torosa/f9f9d240b335c16563d3d9feee649480.jpg\",\n  \"373473.jpg\": \"Amphibia/Taricha torosa/e36d4d60023ce7948371d528f62d8852.jpg\",\n  \"373475.jpg\": \"Amphibia/Taricha torosa/f8f461e3a32a3e363b7f899a2c94f5ec.jpg\",\n  \"373489.jpg\": \"Amphibia/Taricha torosa/1c32257eac48bfb68596785c9098954d.jpg\",\n  \"373498.jpg\": \"Amphibia/Taricha torosa/d0f259e1de07698ebae555e75a21b756.jpg\",\n  \"3735.jpg\": \"Insecta/Coccinella septempunctata/285a243cd848b9ddf0c9a0f1074b1c4d.jpg\",\n  \"3736.jpg\": \"Insecta/Coccinella septempunctata/1646e657c131c948627e41e414806dea.jpg\",\n  \"373741.jpg\": \"Aves/Cerorhinca monocerata/4c4cfc7b775d5cd1cf7b0062088bbfd5.jpg\",\n  \"373746.jpg\": \"Aves/Cerorhinca monocerata/07e55fa8e43320ad709f736d376930ca.jpg\",\n  \"373748.jpg\": \"Aves/Cerorhinca monocerata/013afd00ab11bdf632fc622a4f8cd47e.jpg\",\n  \"373749.jpg\": \"Aves/Cerorhinca monocerata/fcb210802301a24068d99bd16f387f29.jpg\",\n  \"373752.jpg\": \"Aves/Cerorhinca monocerata/0afa8209f67aa7da2ff45a61ad12642e.jpg\",\n  \"373754.jpg\": \"Aves/Cerorhinca monocerata/bfd27bce9d76f7dd5f112efe4e6cd1ff.jpg\",\n  \"373755.jpg\": \"Aves/Cerorhinca monocerata/0c5f7233c3741515bbb5a32bbaf6fd48.jpg\",\n  \"373756.jpg\": \"Aves/Cerorhinca monocerata/643ad48881919655552f1c67d748d79a.jpg\",\n  \"373757.jpg\": \"Aves/Cerorhinca monocerata/887cd52d12de530fa260057302bce8df.jpg\",\n  \"373758.jpg\": \"Aves/Cerorhinca monocerata/75f98a86dc93bf51d7e0602af24e6c5f.jpg\",\n  \"373761.jpg\": \"Aves/Cerorhinca monocerata/ea23a094cf2e88baea9d003c40bd9b91.jpg\",\n  \"373762.jpg\": \"Aves/Cerorhinca monocerata/2a484a74c9dee0f02198edf294ad984f.jpg\",\n  \"373766.jpg\": \"Aves/Cerorhinca monocerata/682c205de3d5102e9781738d9341b70b.jpg\",\n  \"373767.jpg\": \"Aves/Cerorhinca monocerata/722c7b3bb835671823022eef02e9d752.jpg\",\n  \"373769.jpg\": \"Aves/Cerorhinca monocerata/0d0d321e3cb2548a5dd70593a13bee00.jpg\",\n  \"373770.jpg\": \"Aves/Cerorhinca monocerata/66a221c6e6acb8f17c96a5962d84fa9c.jpg\",\n  \"373787.jpg\": \"Animalia/Grapsus grapsus/1aa976e513b85ffbefbaf071908d3194.jpg\",\n  \"373791.jpg\": \"Animalia/Grapsus grapsus/f20108b9380e0b4eb036196de26ab248.jpg\",\n  \"373797.jpg\": \"Animalia/Grapsus grapsus/58421919e199b23c7ffccd3ce74f3d2f.jpg\",\n  \"373798.jpg\": \"Animalia/Grapsus grapsus/74be149b82cbcaab41f807526b754028.jpg\",\n  \"373804.jpg\": \"Insecta/Deidamia inscriptum/719cc8e093fd8f599349f99adfab6c13.jpg\",\n  \"373805.jpg\": \"Insecta/Deidamia inscriptum/ffd2683d6771ce85eec36e44cbcc6f89.jpg\",\n  \"373806.jpg\": \"Insecta/Deidamia inscriptum/ed5e404aaee8f757e58d4a0cfa7bef25.jpg\",\n  \"373810.jpg\": \"Insecta/Deidamia inscriptum/7ce9608957dfd7a5367ee14258c4739e.jpg\",\n  \"373812.jpg\": \"Insecta/Deidamia inscriptum/05a10fe43cd590676a67d6c9600b51f9.jpg\",\n  \"373885.jpg\": \"Insecta/Toxomerus politus/939ac0cd8491dfd382e94d5511d3694b.jpg\",\n  \"373889.jpg\": \"Insecta/Toxomerus politus/35776b8f7cccc06052c7e0d9401b120e.jpg\",\n  \"373894.jpg\": \"Insecta/Toxomerus politus/2a1b0a51031dd5f2d9f0739cc11f2d59.jpg\",\n  \"373898.jpg\": \"Aves/Myiarchus cinerascens/562bb8fb28adabfa60b8dd88d332f7fe.jpg\",\n  \"373904.jpg\": \"Aves/Myiarchus cinerascens/9ddd550fb82edc9b95300363b48d87c6.jpg\",\n  \"373913.jpg\": \"Aves/Myiarchus cinerascens/5cdb67c4470c07167664ea7ff02dc042.jpg\",\n  \"373920.jpg\": \"Aves/Myiarchus cinerascens/b1877a47213dad90ce4809f8de2247c7.jpg\",\n  \"373921.jpg\": \"Aves/Myiarchus cinerascens/56169612677d860f5d00fbfffec5bab3.jpg\",\n  \"373923.jpg\": \"Aves/Myiarchus cinerascens/fd6d5eeff4de40b593b9ef7532384760.jpg\",\n  \"373933.jpg\": \"Aves/Myiarchus cinerascens/98669445a93027b941a69b28d81c8abe.jpg\",\n  \"373943.jpg\": \"Aves/Myiarchus cinerascens/f14f9c536a042681e9f146b58551e7fb.jpg\",\n  \"373954.jpg\": \"Aves/Myiarchus cinerascens/e908f538f68572f617fd4a2e5a37a188.jpg\",\n  \"373988.jpg\": \"Aves/Myiarchus cinerascens/ae6060d54b04c265cd343f8018a00f00.jpg\",\n  \"373989.jpg\": \"Aves/Myiarchus cinerascens/76be9558d2cc672bc6e0140908df1bd7.jpg\",\n  \"373995.jpg\": \"Aves/Myiarchus cinerascens/efd2806f4f0af8201eb7e219c8a1525b.jpg\",\n  \"374002.jpg\": \"Aves/Myiarchus cinerascens/7f0ae7af050132f8975b521a09dde0c2.jpg\",\n  \"374013.jpg\": \"Aves/Myiarchus cinerascens/cae3f056dbe2f41f7777d8e68f59d22e.jpg\",\n  \"374015.jpg\": \"Aves/Myiarchus cinerascens/dc49475d2012caa76549dad3f54d3331.jpg\",\n  \"374016.jpg\": \"Aves/Myiarchus cinerascens/86ef7cad41a84dc7b30bc6c723386e8a.jpg\",\n  \"374036.jpg\": \"Aves/Myiarchus cinerascens/2d2fb5018d6380e4415431b7fada5693.jpg\",\n  \"374042.jpg\": \"Aves/Myiarchus cinerascens/ce933e2671c05e4c9589414caa90b68d.jpg\",\n  \"374054.jpg\": \"Aves/Myiarchus cinerascens/8aadf3a02e92ccd1d3aeb14b4e44353a.jpg\",\n  \"374056.jpg\": \"Aves/Myiarchus cinerascens/d16d1b720c17fa14ac43737dc3533abb.jpg\",\n  \"374057.jpg\": \"Aves/Myiarchus cinerascens/3604b824611b268f576da9626df3a723.jpg\",\n  \"374521.jpg\": \"Aves/Calidris subruficollis/8110ec4ed04be954d9b2d5959a5eeb45.jpg\",\n  \"374524.jpg\": \"Aves/Calidris subruficollis/882655586f158eddf70b03690c69b7aa.jpg\",\n  \"374525.jpg\": \"Aves/Calidris subruficollis/3a233e721d1bea397010224208e533a6.jpg\",\n  \"374528.jpg\": \"Aves/Calidris subruficollis/738e5806770ff7e845215c09cc66a650.jpg\",\n  \"374529.jpg\": \"Aves/Calidris subruficollis/26ec3007e7751aecb483a7c21a18053b.jpg\",\n  \"374555.jpg\": \"Aves/Sylvia communis/a2b43d77ae5eda1d15a047dd5ef4b00e.jpg\",\n  \"374563.jpg\": \"Aves/Sylvia communis/a215ebc73374970e490afdb93c4c9d2c.jpg\",\n  \"374564.jpg\": \"Aves/Sylvia communis/5451824b58adccd5fcfc3cac5c3956bc.jpg\",\n  \"374574.jpg\": \"Aves/Sylvia communis/dd1733c2329409ce100f1ec957f400a6.jpg\",\n  \"374575.jpg\": \"Aves/Sylvia communis/c9e43c99f5d6e8e2bab22e06909ba3be.jpg\",\n  \"374580.jpg\": \"Aves/Sylvia communis/9607f45269162f3c46c9801c0da224dd.jpg\",\n  \"374587.jpg\": \"Aves/Sylvia communis/93a2023fa41975fce22d3773803771e1.jpg\",\n  \"374597.jpg\": \"Reptilia/Sceloporus magister/350fec5da61d584f70df16085f5160f6.jpg\",\n  \"374599.jpg\": \"Reptilia/Sceloporus magister/12db3fd4f202ddb29169e77ef5236ad4.jpg\",\n  \"374604.jpg\": \"Reptilia/Sceloporus magister/a892f66ac915a082fdf0088ffc685337.jpg\",\n  \"374610.jpg\": \"Reptilia/Sceloporus magister/2428cb647b232c5ffe68d341db39382c.jpg\",\n  \"374613.jpg\": \"Reptilia/Sceloporus magister/262d4381df19716b3a78ca156cee2a9a.jpg\",\n  \"374618.jpg\": \"Reptilia/Sceloporus magister/86980024325782cbd8970e207499f1e5.jpg\",\n  \"374628.jpg\": \"Reptilia/Sceloporus magister/ddfb68b16d6d011716a8062ea6174c4e.jpg\",\n  \"374629.jpg\": \"Reptilia/Sceloporus magister/24f8f1f7920bd0264cbbb92641b320ea.jpg\",\n  \"374632.jpg\": \"Reptilia/Alligator mississippiensis/24c6d31a89a35295b1f82ef97df0910d.jpg\",\n  \"374649.jpg\": \"Reptilia/Alligator mississippiensis/84dac4c454c70ee58ecb863bd7f075a6.jpg\",\n  \"374692.jpg\": \"Reptilia/Alligator mississippiensis/0f404b5d3bd9a019a74d707bfa4ab6f8.jpg\",\n  \"374720.jpg\": \"Reptilia/Alligator mississippiensis/c6031128d5b8ddd80fbc2df3e4eda928.jpg\",\n  \"374724.jpg\": \"Reptilia/Alligator mississippiensis/11ae306039e4059a0f7f1a429232613f.jpg\",\n  \"374733.jpg\": \"Reptilia/Alligator mississippiensis/29ef3f150509d55276b779f08b4610c4.jpg\",\n  \"374735.jpg\": \"Reptilia/Alligator mississippiensis/acc1a67758073af01600340601ba8da3.jpg\",\n  \"374750.jpg\": \"Reptilia/Alligator mississippiensis/32da341c84c7f1637192dad92bd2e548.jpg\",\n  \"374754.jpg\": \"Reptilia/Alligator mississippiensis/bcfe6d7b73904c2355717272f615797f.jpg\",\n  \"374823.jpg\": \"Reptilia/Alligator mississippiensis/4501a5027f43d5a3b9a904d7ba6ff8fe.jpg\",\n  \"374860.jpg\": \"Reptilia/Alligator mississippiensis/5c307fca6bcce65f8c3e375a8725afed.jpg\",\n  \"374865.jpg\": \"Reptilia/Alligator mississippiensis/5915d15b892371eb279dfd0218029d3d.jpg\",\n  \"374904.jpg\": \"Reptilia/Alligator mississippiensis/4c786311209ff568608d247006a53b56.jpg\",\n  \"374918.jpg\": \"Reptilia/Alligator mississippiensis/607d6722b045eb28d0132fe13ccc32b7.jpg\",\n  \"374921.jpg\": \"Reptilia/Alligator mississippiensis/b91f8a450573a0a064db80c6b9b4e852.jpg\",\n  \"374969.jpg\": \"Reptilia/Alligator mississippiensis/19505d5c6a75676478292a8cf1fb9961.jpg\",\n  \"374974.jpg\": \"Reptilia/Alligator mississippiensis/2426cce7ef72c78618601ba3e4552b78.jpg\",\n  \"374982.jpg\": \"Reptilia/Alligator mississippiensis/d26ea6ae4eef4857c7110767b380a421.jpg\",\n  \"375003.jpg\": \"Aves/Artemisiospiza belli/daa0ce73d3ee45a8119386b070b0d703.jpg\",\n  \"375005.jpg\": \"Aves/Artemisiospiza belli/c0824dd07ac0420f12a5924ef01f91f7.jpg\",\n  \"375006.jpg\": \"Aves/Artemisiospiza belli/2b18a50de33c2c9578b38e038f615bf7.jpg\",\n  \"375009.jpg\": \"Aves/Artemisiospiza belli/11636d33077a181dadd653b6b51877b5.jpg\",\n  \"375015.jpg\": \"Aves/Artemisiospiza belli/23368f214bdaa58b57b83ff6fbf0123f.jpg\",\n  \"375021.jpg\": \"Insecta/Allograpta obliqua/b5778a6ea22b888c5ebf963509608f3b.jpg\",\n  \"375026.jpg\": \"Insecta/Allograpta obliqua/3fe78395c031fb7c281225be0f861c90.jpg\",\n  \"375027.jpg\": \"Insecta/Allograpta obliqua/daef49687baebeb673e550868bda021f.jpg\",\n  \"375028.jpg\": \"Insecta/Allograpta obliqua/c7882c152f04c65cf6e1aacdc7fb2bf4.jpg\",\n  \"375029.jpg\": \"Insecta/Allograpta obliqua/5693671ef504bb65b3c0a0cfe81e1af2.jpg\",\n  \"375030.jpg\": \"Insecta/Allograpta obliqua/58ab98cbbc0290c7a598b3c06e1ccfe5.jpg\",\n  \"375031.jpg\": \"Insecta/Allograpta obliqua/38c0b0778dd772d02955e94bcf410524.jpg\",\n  \"375032.jpg\": \"Insecta/Allograpta obliqua/aa242c497c88cba02061af86e8ad1a5a.jpg\",\n  \"375033.jpg\": \"Insecta/Allograpta obliqua/d37908f5e556a4a8324a8d511a163989.jpg\",\n  \"375042.jpg\": \"Insecta/Allograpta obliqua/484be2f62f267cf9165a751aa468ad6c.jpg\",\n  \"375043.jpg\": \"Insecta/Allograpta obliqua/35f205d5c3b21f6496a1601a12c41bda.jpg\",\n  \"375046.jpg\": \"Insecta/Allograpta obliqua/2c049cc718edd8889374df7a1dcf6c2e.jpg\",\n  \"375050.jpg\": \"Insecta/Allograpta obliqua/72c45b901f21f829ee52844c5d4e2751.jpg\",\n  \"375051.jpg\": \"Insecta/Allograpta obliqua/2a30a3a24fc0457ed9a2121cee590a7f.jpg\",\n  \"375053.jpg\": \"Insecta/Allograpta obliqua/5836007d535418807f23eb8b4a395b85.jpg\",\n  \"375054.jpg\": \"Insecta/Allograpta obliqua/7bd83f99a6640efc5e632551ce8a681f.jpg\",\n  \"375057.jpg\": \"Insecta/Allograpta obliqua/dc924ff0db750fba5935d43c0f844d7c.jpg\",\n  \"375058.jpg\": \"Insecta/Allograpta obliqua/680bd528782c8bcfc2955f66bf9c719a.jpg\",\n  \"375059.jpg\": \"Insecta/Allograpta obliqua/b80730d0ea89b626fc777130b76062c8.jpg\",\n  \"375060.jpg\": \"Insecta/Allograpta obliqua/04c314299f89afdeca7de7b572f16678.jpg\",\n  \"375080.jpg\": \"Aves/Dryocopus pileatus/b19db259fcc9284e655d61f51cf908a8.jpg\",\n  \"375095.jpg\": \"Aves/Dryocopus pileatus/edd7508f2fa4b2c0040d07ba77ec9141.jpg\",\n  \"375100.jpg\": \"Aves/Dryocopus pileatus/d8d99dc9be04357702e7a10028fbcd7b.jpg\",\n  \"375113.jpg\": \"Aves/Dryocopus pileatus/521adc3e9fb994fb5ad0d2259c07d7a1.jpg\",\n  \"375148.jpg\": \"Aves/Dryocopus pileatus/aabf46f0dcba1127d9ab75fbb0fd0f92.jpg\",\n  \"375181.jpg\": \"Aves/Dryocopus pileatus/7f5d8c107db463071fabf3b74fb66a2a.jpg\",\n  \"375199.jpg\": \"Aves/Dryocopus pileatus/8b64a57cf29dd196c126b8382c9c1348.jpg\",\n  \"375217.jpg\": \"Aves/Dryocopus pileatus/cdbc18dcc569c39c7390602ac0511be8.jpg\",\n  \"375249.jpg\": \"Aves/Dryocopus pileatus/9d263d819dad4a901f8eac5b266acc86.jpg\",\n  \"375264.jpg\": \"Aves/Dryocopus pileatus/24518110aefa0b78251f6ec4b46d3bbf.jpg\",\n  \"375282.jpg\": \"Aves/Dryocopus pileatus/c7d93890924c23715393bb73fdfdad31.jpg\",\n  \"375324.jpg\": \"Aves/Dryocopus pileatus/027bc902c33ce3ade22adc69e78d8bc6.jpg\",\n  \"375328.jpg\": \"Aves/Dryocopus pileatus/66d3ae816cceb0dc6c8383864a42a628.jpg\",\n  \"37533.jpg\": \"Aves/Geococcyx velox/f0269310927b9a60fb5108eb8b2c4d6d.jpg\",\n  \"37535.jpg\": \"Aves/Geococcyx velox/582e82574df6f3b7a02a490bc4a58e38.jpg\",\n  \"37536.jpg\": \"Aves/Geococcyx velox/f365c03876eea0d1b4ee73677c3cf5ea.jpg\",\n  \"375366.jpg\": \"Aves/Dryocopus pileatus/698da4fdb6efba46dab966fc3983737f.jpg\",\n  \"37537.jpg\": \"Aves/Geococcyx velox/b382bdaeea2815ab5e7761dcbee066fa.jpg\",\n  \"375383.jpg\": \"Aves/Dryocopus pileatus/466849a5c6085a4e936385d3d17a30fa.jpg\",\n  \"37540.jpg\": \"Aves/Geococcyx velox/08d51c44cb7cd5ab18bb7d1ac694cf13.jpg\",\n  \"375466.jpg\": \"Mammalia/Ursus arctos/2e1a7309e0242c2dcc4d4936e677c8c3.jpg\",\n  \"375467.jpg\": \"Mammalia/Ursus arctos/9724dd2eb7ae031988e9241de35c8036.jpg\",\n  \"375479.jpg\": \"Mammalia/Ursus arctos/4979acb2dce9a37eb150bb8f2796de8c.jpg\",\n  \"375483.jpg\": \"Mammalia/Ursus arctos/d58b66fb5134478fb03e6d1743eb2f4c.jpg\",\n  \"375486.jpg\": \"Mammalia/Ursus arctos/75b5635cb4547d4b8d787b4bbea63f73.jpg\",\n  \"375494.jpg\": \"Aves/Zosterops lateralis/f2800e655217b85482160a4248dd1032.jpg\",\n  \"375495.jpg\": \"Aves/Zosterops lateralis/bccfd6aeb00ba1ba19030b8d04c150b8.jpg\",\n  \"375496.jpg\": \"Aves/Zosterops lateralis/ac4aff4c2ad982c6c73bb9ae955aba88.jpg\",\n  \"375497.jpg\": \"Aves/Zosterops lateralis/f2b0d9e7d562731dbb290718d650f845.jpg\",\n  \"375499.jpg\": \"Aves/Zosterops lateralis/a2be141779bc5d1be992729febf91613.jpg\",\n  \"375504.jpg\": \"Aves/Zosterops lateralis/1cd7c9d7d22771ce276597c86b5a05c7.jpg\",\n  \"375505.jpg\": \"Aves/Zosterops lateralis/cf5f10ff3efe097fe64ce62704b9bde2.jpg\",\n  \"375508.jpg\": \"Aves/Zosterops lateralis/388d41971d53d3796c0f1ab12fb71306.jpg\",\n  \"375509.jpg\": \"Aves/Zosterops lateralis/d3efdbc25603e71dfb999b15507efe45.jpg\",\n  \"375510.jpg\": \"Aves/Zosterops lateralis/eea48a8a62167d2370484e47ace997bf.jpg\",\n  \"375511.jpg\": \"Aves/Zosterops lateralis/56a7dd3430e74b6c7cba2869e2982e67.jpg\",\n  \"375513.jpg\": \"Aves/Zosterops lateralis/4ecef75b2cae28708637e247df95cb57.jpg\",\n  \"375523.jpg\": \"Aves/Zosterops lateralis/515321c105f55163ca72bf0c47cc5975.jpg\",\n  \"375526.jpg\": \"Aves/Zosterops lateralis/2a1bd470bed34f25b452789f7b945d5d.jpg\",\n  \"375534.jpg\": \"Aves/Zosterops lateralis/da984910155ed5bef436f1dce8ce977a.jpg\",\n  \"375535.jpg\": \"Aves/Zosterops lateralis/4559d4b85fb5f75b324776d93a04443f.jpg\",\n  \"375538.jpg\": \"Aves/Zosterops lateralis/589ac014f5cb04736912b39f41253c87.jpg\",\n  \"375540.jpg\": \"Aves/Zosterops lateralis/403d63fa5a322ec3469e0e491ae54fd4.jpg\",\n  \"375541.jpg\": \"Insecta/Arctia caja/71cfb35a5e1f29d71744781542634774.jpg\",\n  \"375542.jpg\": \"Insecta/Arctia caja/5880917d8d43ca97a8d36641e98dc013.jpg\",\n  \"375543.jpg\": \"Insecta/Arctia caja/80524864faf1786d84ff60a17d9c7cd8.jpg\",\n  \"375551.jpg\": \"Insecta/Arctia caja/6461893cd881f2188608d2a1cddae122.jpg\",\n  \"375552.jpg\": \"Insecta/Arctia caja/d0100c58ab061df46fcffe9d94e76e8a.jpg\",\n  \"375556.jpg\": \"Insecta/Arctia caja/b561cefddc1b47aa6a8407e7d4e4d985.jpg\",\n  \"375565.jpg\": \"Reptilia/Sphenodon punctatus/8e8a7ac4ae5bcf89c622e1b55b736395.jpg\",\n  \"375572.jpg\": \"Reptilia/Sphenodon punctatus/4a71d3b82e4dc50222661b5f820dd78b.jpg\",\n  \"375596.jpg\": \"Reptilia/Sphenodon punctatus/e92ee717ecbf9cebf4149d1ed9aed100.jpg\",\n  \"375600.jpg\": \"Reptilia/Sphenodon punctatus/4ae271dae958c11f090eed182058ee96.jpg\",\n  \"375613.jpg\": \"Reptilia/Sphenodon punctatus/5fa8a806190d17f5fe0f7d0ad74da14f.jpg\",\n  \"375620.jpg\": \"Reptilia/Sphenodon punctatus/b02407352940881eaac5f7524e042b8f.jpg\",\n  \"375621.jpg\": \"Reptilia/Sphenodon punctatus/a688ff3aaca954fac1e70329e5f80dc1.jpg\",\n  \"375622.jpg\": \"Reptilia/Sphenodon punctatus/336d1f4ef5917a78d327a1e977e5c539.jpg\",\n  \"375629.jpg\": \"Reptilia/Sphenodon punctatus/ef30d92c16a62cf3bcdc0720c2ae30c7.jpg\",\n  \"375664.jpg\": \"Reptilia/Sphenodon punctatus/b5e577b012ae3221cdf3cf019e3b0724.jpg\",\n  \"375666.jpg\": \"Reptilia/Sphenodon punctatus/beef56415443f17017684887628a3904.jpg\",\n  \"375677.jpg\": \"Reptilia/Sphenodon punctatus/126419dff492382dea22fe55850bb9c0.jpg\",\n  \"375679.jpg\": \"Reptilia/Sphenodon punctatus/bccf6443bccab2a42a8a174d2e7ab9b7.jpg\",\n  \"375682.jpg\": \"Reptilia/Sphenodon punctatus/8b6a3d7e2d339b9475632d373eaf9555.jpg\",\n  \"375685.jpg\": \"Reptilia/Sphenodon punctatus/0072af56cd8e24141f0de2b804ebbc11.jpg\",\n  \"375693.jpg\": \"Reptilia/Sphenodon punctatus/660f177635b0e202e723dfb41cc4a735.jpg\",\n  \"375695.jpg\": \"Reptilia/Sphenodon punctatus/a6ab3e795f3e2657e3531ed2db3f6773.jpg\",\n  \"375697.jpg\": \"Reptilia/Sphenodon punctatus/b25184c7a47ef2394d80bf2196997046.jpg\",\n  \"375698.jpg\": \"Reptilia/Sphenodon punctatus/e587ebc43724f2ae30dd1bfc12ef72d8.jpg\",\n  \"375710.jpg\": \"Reptilia/Sphenodon punctatus/4e80b040800e696f07b5f334e6fb4f34.jpg\",\n  \"375711.jpg\": \"Reptilia/Sphenodon punctatus/8fec942521e22c2ea8aac4a0c0a229a9.jpg\",\n  \"37576.jpg\": \"Insecta/Hypercompe scribonia/5e97ff026a42c463482d46f97000b1c2.jpg\",\n  \"375833.jpg\": \"Aves/Tachycineta albilinea/9fcd3436cba8d2d64f63c5f57f949796.jpg\",\n  \"375839.jpg\": \"Aves/Tachycineta albilinea/ddc22d649e36f89b9c3f2133292af44c.jpg\",\n  \"375847.jpg\": \"Aves/Tachycineta albilinea/a793601375ebe8e4db4594a50238f927.jpg\",\n  \"375853.jpg\": \"Aves/Tachycineta albilinea/fc7e86d985042a114792205991adcb26.jpg\",\n  \"375859.jpg\": \"Aves/Tachycineta albilinea/c8c7c71f2f4973a581354a95a73a6cd1.jpg\",\n  \"375866.jpg\": \"Actinopterygii/Micropterus salmoides/f1b03117fdd08bde4d947b0ed65cec5e.jpg\",\n  \"375868.jpg\": \"Actinopterygii/Micropterus salmoides/52bb4be7611d89f17ffb50872f550fae.jpg\",\n  \"375869.jpg\": \"Actinopterygii/Micropterus salmoides/e1bacddaf22b3646a2e5dcf4d2a73bb1.jpg\",\n  \"375885.jpg\": \"Actinopterygii/Micropterus salmoides/3a2875abc0a586145b8650519b88879a.jpg\",\n  \"375888.jpg\": \"Actinopterygii/Micropterus salmoides/90f0d21978d0885489021fc721b8b1ff.jpg\",\n  \"375899.jpg\": \"Actinopterygii/Micropterus salmoides/76c92ad4006dbf6ddd88e2a9cb1ac9d3.jpg\",\n  \"375900.jpg\": \"Actinopterygii/Micropterus salmoides/1d40e2df00ce3dcdb9d99b333e3a6965.jpg\",\n  \"375906.jpg\": \"Actinopterygii/Micropterus salmoides/8dd5555d5d9dd233942d26e4335fb32e.jpg\",\n  \"375916.jpg\": \"Actinopterygii/Micropterus salmoides/0dabc80dc68df872d4dd8a2c775af864.jpg\",\n  \"375918.jpg\": \"Actinopterygii/Micropterus salmoides/3e478fe89281c0c7bedeaf0ce9a63096.jpg\",\n  \"37592.jpg\": \"Insecta/Hypercompe scribonia/b74286964aead7dfc12cf62f8d18f0cd.jpg\",\n  \"375925.jpg\": \"Actinopterygii/Micropterus salmoides/eac661c9b8f6c5a4b6678980e232c9ec.jpg\",\n  \"375928.jpg\": \"Actinopterygii/Micropterus salmoides/675d9b78662e418fbabb2847ce8e311f.jpg\",\n  \"37593.jpg\": \"Insecta/Hypercompe scribonia/e5701c0d5ceaddf0da9f855a1810dc65.jpg\",\n  \"37594.jpg\": \"Insecta/Hypercompe scribonia/c47363d978f2b6fed6f13393f9fa5170.jpg\",\n  \"375942.jpg\": \"Actinopterygii/Micropterus salmoides/671c763af2cba0f8a63e470e79dfa146.jpg\",\n  \"375946.jpg\": \"Actinopterygii/Micropterus salmoides/23f34f3a59bd4d4bcc20f78c07a159f8.jpg\",\n  \"375950.jpg\": \"Actinopterygii/Micropterus salmoides/36372a4767c7e3b89fbc81b0ddde1bd6.jpg\",\n  \"375962.jpg\": \"Actinopterygii/Micropterus salmoides/b222af02ea043b38a1bcbdd33d7784bd.jpg\",\n  \"375967.jpg\": \"Actinopterygii/Micropterus salmoides/e1eda1969924ba2ce87416d30b65c26c.jpg\",\n  \"375970.jpg\": \"Actinopterygii/Micropterus salmoides/13f9484bd57609090c707716617c4f17.jpg\",\n  \"375972.jpg\": \"Actinopterygii/Micropterus salmoides/ca09940823ff3c80ca5f7fa5faf0e555.jpg\",\n  \"375982.jpg\": \"Actinopterygii/Micropterus salmoides/0141e4d29f2713b8bb765c279950edb2.jpg\",\n  \"376057.jpg\": \"Insecta/Plathemis lydia/45d7182c232499dcb5aa214e86fe62cd.jpg\",\n  \"37608.jpg\": \"Insecta/Hypercompe scribonia/77cb4ca1f765812a03876d0ba8a0cf38.jpg\",\n  \"376135.jpg\": \"Insecta/Plathemis lydia/3da711effc60e2bbdd6927c2e169d0a7.jpg\",\n  \"376156.jpg\": \"Insecta/Plathemis lydia/468b65dd75fb03cbf8fc6ac785727569.jpg\",\n  \"376157.jpg\": \"Insecta/Plathemis lydia/20958a2b17ce2ce9e28d6b454f146f4f.jpg\",\n  \"37617.jpg\": \"Insecta/Hypercompe scribonia/4bc5c35234be40ab074e105bec6b31d0.jpg\",\n  \"376172.jpg\": \"Insecta/Plathemis lydia/8c3857df561b595cd1e67370a28bf432.jpg\",\n  \"376179.jpg\": \"Insecta/Plathemis lydia/75b00d9f0cd674b848ba64a2a46103de.jpg\",\n  \"376197.jpg\": \"Insecta/Plathemis lydia/e307d0b7f6a4bdfe94ed369e708c4643.jpg\",\n  \"376209.jpg\": \"Insecta/Plathemis lydia/d79222c365a4a8e6cc1e3e5be4dd5bb3.jpg\",\n  \"37622.jpg\": \"Insecta/Hypercompe scribonia/4c56158a63ff845b3b487b9005c801a1.jpg\",\n  \"37623.jpg\": \"Insecta/Hypercompe scribonia/179d48bec45eb886392bd1186a1566cb.jpg\",\n  \"376231.jpg\": \"Insecta/Plathemis lydia/c5c639c6152f8f43abacccd9249e363a.jpg\",\n  \"37624.jpg\": \"Insecta/Hypercompe scribonia/7067abf17b0654b3d2d477e78918e96e.jpg\",\n  \"376240.jpg\": \"Insecta/Plathemis lydia/119daf88e9cea1298d0d8a01d4a8873e.jpg\",\n  \"376273.jpg\": \"Insecta/Plathemis lydia/0b433bd751ccb0c4e4a28edd36e797a0.jpg\",\n  \"37630.jpg\": \"Insecta/Hypercompe scribonia/5b51069331f6eaa038da0cccdf07aab7.jpg\",\n  \"376304.jpg\": \"Insecta/Plathemis lydia/7d725efcfa82bef73dd7bb3bafc359f4.jpg\",\n  \"376312.jpg\": \"Insecta/Plathemis lydia/e2bab0b2957533b097d21911268cf09f.jpg\",\n  \"37638.jpg\": \"Insecta/Hypercompe scribonia/abb65dc4c4faa7c82f7713498371b719.jpg\",\n  \"376401.jpg\": \"Insecta/Plathemis lydia/92e4241473a6cd29551c37e7519ef49c.jpg\",\n  \"376402.jpg\": \"Insecta/Plathemis lydia/69cdc15220853833d86d96a6f9ea4c00.jpg\",\n  \"376433.jpg\": \"Insecta/Plathemis lydia/cbe9b2a1f9f81d13503e1ac8cd6b0150.jpg\",\n  \"37644.jpg\": \"Insecta/Hypercompe scribonia/8c2545ba072b05dfa53529a94b8c435c.jpg\",\n  \"376469.jpg\": \"Insecta/Plathemis lydia/c83c5d3a7e906cf67d1bf2e01c840af5.jpg\",\n  \"376497.jpg\": \"Insecta/Plathemis lydia/8fbe4525b5d9096245e590a1e6d687a6.jpg\",\n  \"376501.jpg\": \"Insecta/Plathemis lydia/c489e78cee07b9476ee0618157f11c03.jpg\",\n  \"376526.jpg\": \"Insecta/Plathemis lydia/2ac47cae62e5be4ed02173542a85b287.jpg\",\n  \"376535.jpg\": \"Insecta/Plathemis lydia/d6a00dded7272c2026478798b39c19ed.jpg\",\n  \"37655.jpg\": \"Insecta/Hypercompe scribonia/243bf9a4ff868bcede6e420358fb0641.jpg\",\n  \"376551.jpg\": \"Insecta/Plathemis lydia/51539ddb98c2dd250f0afeeffd21265e.jpg\",\n  \"37656.jpg\": \"Insecta/Hypercompe scribonia/2b3a0fdf795298ddac3484cb513d99b6.jpg\",\n  \"37659.jpg\": \"Insecta/Hypercompe scribonia/a8073db983e75dc8f13ec9e53c9f4f44.jpg\",\n  \"37660.jpg\": \"Insecta/Hypercompe scribonia/8548cb497bffada7d7e2c10899bdfe91.jpg\",\n  \"376604.jpg\": \"Insecta/Plathemis lydia/c9de3a3d2e6122d8abfd690f130924ab.jpg\",\n  \"37664.jpg\": \"Insecta/Hypercompe scribonia/fee50f0089108867b1e0df33c8f32510.jpg\",\n  \"37666.jpg\": \"Insecta/Hypercompe scribonia/d0bb3d3f79af8aeb4476908427d0cb86.jpg\",\n  \"37668.jpg\": \"Insecta/Hypercompe scribonia/642b75eca76cc0b5665e3427583531f0.jpg\",\n  \"37676.jpg\": \"Aves/Microcarbo melanoleucos/c215bb153eab456a369643c5aaed7aba.jpg\",\n  \"37697.jpg\": \"Aves/Microcarbo melanoleucos/5276bb72f3840b79e7ae13ceeb74fda6.jpg\",\n  \"37699.jpg\": \"Aves/Microcarbo melanoleucos/852b680b460ed97320d74775fdf0d058.jpg\",\n  \"37700.jpg\": \"Aves/Microcarbo melanoleucos/f38c9fdf03a88004966e27059bb2faa4.jpg\",\n  \"377016.jpg\": \"Insecta/Panoquina ocola/b23a7d6d2d17b112fed005f94c19e8f7.jpg\",\n  \"377017.jpg\": \"Insecta/Panoquina ocola/a1dfd5b5d8c050c1e548f48e56391630.jpg\",\n  \"377018.jpg\": \"Insecta/Panoquina ocola/9fa65c0d430faf0b3aa8d6b2e9fd6c33.jpg\",\n  \"377019.jpg\": \"Insecta/Panoquina ocola/c9d7f4b14a940825a348350b0140986d.jpg\",\n  \"37702.jpg\": \"Aves/Microcarbo melanoleucos/6b471ed965f1a9a0c34f30ba00049474.jpg\",\n  \"377022.jpg\": \"Insecta/Panoquina ocola/7524453370ec423645cdf862286f8bf5.jpg\",\n  \"377025.jpg\": \"Insecta/Panoquina ocola/44ca5c342ec4c3ac87d7fdfc69855a62.jpg\",\n  \"377026.jpg\": \"Insecta/Panoquina ocola/cdbc68f3f4579e2fcd12592466d54076.jpg\",\n  \"377030.jpg\": \"Insecta/Panoquina ocola/628e8e1c502d4a6d9ac6d178d1e9e326.jpg\",\n  \"377031.jpg\": \"Insecta/Panoquina ocola/6597f6b4943d264f3fc73f4329557f86.jpg\",\n  \"377033.jpg\": \"Insecta/Panoquina ocola/8a6986b14ee98df3ecf055ecff49e7d0.jpg\",\n  \"377039.jpg\": \"Insecta/Panoquina ocola/25433461d47d45c9ee9a370e2fa49c46.jpg\",\n  \"377040.jpg\": \"Insecta/Panoquina ocola/6acf2fdb9aacc70217e9198290f45dbd.jpg\",\n  \"377044.jpg\": \"Insecta/Panoquina ocola/6e43669f86b1d00f7389cfa4ff1cb286.jpg\",\n  \"377045.jpg\": \"Insecta/Panoquina ocola/31ff136673f9a2e6889f46c57dcd35fa.jpg\",\n  \"377047.jpg\": \"Insecta/Panoquina ocola/6f9a7a850c52b1a2b6d1904eba98eee5.jpg\",\n  \"377060.jpg\": \"Insecta/Panoquina ocola/6c0fba2ffba702037655e80e88687c39.jpg\",\n  \"377064.jpg\": \"Insecta/Panoquina ocola/f808ed03419f1730b4c00b01aa27d9c0.jpg\",\n  \"377065.jpg\": \"Insecta/Panoquina ocola/69c3f2aac4be9e8ad56d38464cfa7253.jpg\",\n  \"377067.jpg\": \"Insecta/Panoquina ocola/90b241d4fb6074a5b476d824538c78ba.jpg\",\n  \"377069.jpg\": \"Insecta/Panoquina ocola/1fd36db26425d6bd262f4a712020335a.jpg\",\n  \"377074.jpg\": \"Insecta/Apodemia mormo/82c2917e76f80c4fb165f690499cbabb.jpg\",\n  \"377079.jpg\": \"Insecta/Apodemia mormo/cf174da47ff5fd46a14829b36b27e02e.jpg\",\n  \"377085.jpg\": \"Insecta/Apodemia mormo/f9d7b75e60c68eee655ebb942472bc7f.jpg\",\n  \"377090.jpg\": \"Insecta/Apodemia mormo/d92d4c3286cbd855a3a2fa49eb235c38.jpg\",\n  \"377093.jpg\": \"Insecta/Apodemia mormo/44e3999c71aa647964d3f9fa56ed12a8.jpg\",\n  \"377099.jpg\": \"Insecta/Apodemia mormo/1fb4bb0b71e4fa83d02ebd9aeda998cf.jpg\",\n  \"377100.jpg\": \"Insecta/Apodemia mormo/7e55faa61f7a8614a5cbd374ed26c0bc.jpg\",\n  \"377101.jpg\": \"Arachnida/Leucauge venusta/73fee4acb80bf4ca394089ed66fdb594.jpg\",\n  \"377104.jpg\": \"Arachnida/Leucauge venusta/0554e13a325f0a20526a65f5e924b6a0.jpg\",\n  \"377105.jpg\": \"Arachnida/Leucauge venusta/644f59f45cdc23825c16e240093711f8.jpg\",\n  \"377107.jpg\": \"Arachnida/Leucauge venusta/ee321acdb71b1b0494a0f374731532a6.jpg\",\n  \"377114.jpg\": \"Arachnida/Leucauge venusta/972f84d8aceef9a9919ecb9e108046ed.jpg\",\n  \"377116.jpg\": \"Arachnida/Leucauge venusta/6dd53952d009b9096362506b0089e98b.jpg\",\n  \"37713.jpg\": \"Aves/Strix varia/91b6bf9b5abfe89f7e9dc61ac3cd7728.jpg\",\n  \"377132.jpg\": \"Arachnida/Leucauge venusta/0ef023e79ee7471a4accfd253a7458f0.jpg\",\n  \"377133.jpg\": \"Arachnida/Leucauge venusta/a23a8137df8fdcf73297b9f8549bab26.jpg\",\n  \"377148.jpg\": \"Arachnida/Leucauge venusta/167cbb7ea3e0e4de73a0287893ab11b0.jpg\",\n  \"377149.jpg\": \"Arachnida/Leucauge venusta/f44c218fe48368fcf220014dac00df07.jpg\",\n  \"377150.jpg\": \"Arachnida/Leucauge venusta/8dc1b01123f9d39cff6d830688c9376d.jpg\",\n  \"377159.jpg\": \"Arachnida/Leucauge venusta/a6ef715424b1c4f19e235ad044a541f7.jpg\",\n  \"377165.jpg\": \"Arachnida/Leucauge venusta/33df949a0162eee5265f623ee41a1bac.jpg\",\n  \"37717.jpg\": \"Aves/Strix varia/887883e52bb5d0c0989abef443e5207d.jpg\",\n  \"377197.jpg\": \"Arachnida/Leucauge venusta/5e10d0b2bbc0fd018e7ffaf27cbc6906.jpg\",\n  \"377202.jpg\": \"Arachnida/Leucauge venusta/f07ef56849d41f39ecc0d2b5419f71fe.jpg\",\n  \"377216.jpg\": \"Arachnida/Leucauge venusta/c347da386b8a353802c23f041af73b78.jpg\",\n  \"377230.jpg\": \"Arachnida/Leucauge venusta/6e1a439e53ab1b2aeb97c1bfa68f4200.jpg\",\n  \"377249.jpg\": \"Arachnida/Leucauge venusta/4b0c8ed56f08a67a1dae22305237ffa5.jpg\",\n  \"377250.jpg\": \"Arachnida/Leucauge venusta/1ca1d3763e7f33a7dd5f946b68f8e6e4.jpg\",\n  \"377254.jpg\": \"Arachnida/Leucauge venusta/54be39d49117c7d06d691936b1c50b9a.jpg\",\n  \"377260.jpg\": \"Arachnida/Leucauge venusta/4ff098de674856ec814f140e18576f42.jpg\",\n  \"37731.jpg\": \"Aves/Strix varia/e4ce946e094f93fe0061a02c5df10f6c.jpg\",\n  \"37738.jpg\": \"Aves/Strix varia/c5a0f562323bde81eba0ff13c33aa077.jpg\",\n  \"377505.jpg\": \"Insecta/Nepytia canosaria/48370636c1d9870572e3ddcac3899ba6.jpg\",\n  \"377506.jpg\": \"Insecta/Nepytia canosaria/88c1a91e291e7d25be504d778a0ffdd1.jpg\",\n  \"377508.jpg\": \"Insecta/Nepytia canosaria/452dbd3a123bb90cf8c594141a92ba7a.jpg\",\n  \"377521.jpg\": \"Insecta/Nepytia canosaria/34e7982b337d7ed56b48731160257199.jpg\",\n  \"37753.jpg\": \"Aves/Strix varia/d3c45fff6fe67d09379d0c51aab5b84d.jpg\",\n  \"37760.jpg\": \"Aves/Strix varia/a59fe0f8a8493637b0c5a5cee47cdfb1.jpg\",\n  \"37779.jpg\": \"Aves/Strix varia/c92c4e8533b4fe3d96285ab0263d5212.jpg\",\n  \"37786.jpg\": \"Aves/Strix varia/2bd6fd5b9c4e080423898ebbcceaaa33.jpg\",\n  \"37797.jpg\": \"Aves/Strix varia/3ff7402e99af702745417111194a2bae.jpg\",\n  \"37800.jpg\": \"Aves/Strix varia/154d336f52e32e7ed3c78d0b5d44cebd.jpg\",\n  \"378010.jpg\": \"Insecta/Urbanus proteus/1499b83bc3a7fab6070875e8fba52a21.jpg\",\n  \"378011.jpg\": \"Insecta/Urbanus proteus/f696f36000acc5bbf1389c80bbc82efa.jpg\",\n  \"378016.jpg\": \"Insecta/Urbanus proteus/51b4eec8194575ae0b60819e4b459cdf.jpg\",\n  \"378021.jpg\": \"Insecta/Urbanus proteus/d9d77060927c7d23cde55d9035690203.jpg\",\n  \"37803.jpg\": \"Aves/Strix varia/a3bc57dc530efa5e0fe579b4cdcf1649.jpg\",\n  \"378032.jpg\": \"Insecta/Urbanus proteus/1240c5bd423bb3b2eb41a75b505d744a.jpg\",\n  \"378039.jpg\": \"Insecta/Urbanus proteus/14ad993ddd1fa6b19ca20ee5ca32c226.jpg\",\n  \"378045.jpg\": \"Insecta/Urbanus proteus/721d3a5756c6210cc09ad9390e973a6b.jpg\",\n  \"378056.jpg\": \"Insecta/Urbanus proteus/46035ef9c0c8f4a751a74a4c245ab309.jpg\",\n  \"378062.jpg\": \"Insecta/Urbanus proteus/5c5fcdbc63e650ddb553e1cf3bf58a3e.jpg\",\n  \"378063.jpg\": \"Insecta/Urbanus proteus/b29370d378b51a4f61b82f5d3a18a524.jpg\",\n  \"378065.jpg\": \"Insecta/Urbanus proteus/17b2296a0a0e2e2203e40a4ca9716011.jpg\",\n  \"378066.jpg\": \"Insecta/Urbanus proteus/bd600022aeb20be20ed51d3ba10b44aa.jpg\",\n  \"378068.jpg\": \"Insecta/Urbanus proteus/236c3f771f8784071fb009fb459aba30.jpg\",\n  \"37807.jpg\": \"Aves/Strix varia/13b459fd8a6cc4402961b75a9a3f97df.jpg\",\n  \"378078.jpg\": \"Insecta/Urbanus proteus/2824332f41876eb92c9b6b9ccd08f4b2.jpg\",\n  \"378082.jpg\": \"Insecta/Urbanus proteus/642d5a4a7a9d0882571db1c6b285ef97.jpg\",\n  \"378084.jpg\": \"Insecta/Urbanus proteus/8cc7b6009ed638f6cc7ceb6d1115b54a.jpg\",\n  \"378093.jpg\": \"Insecta/Urbanus proteus/a6c5e982f4748e050979ec7ff646ab57.jpg\",\n  \"378097.jpg\": \"Insecta/Urbanus proteus/531a75e2006bb9995e145dac9f61fe3b.jpg\",\n  \"378098.jpg\": \"Insecta/Urbanus proteus/9953f0881a316ffcf206fb9e1280bcdc.jpg\",\n  \"378100.jpg\": \"Insecta/Urbanus proteus/5390924d15c94df4e024d595ec076e6b.jpg\",\n  \"378103.jpg\": \"Insecta/Urbanus proteus/b5f27b11002192f69d9aacc0fe511dfe.jpg\",\n  \"378107.jpg\": \"Insecta/Urbanus proteus/e5482cc17b8323e2c7d77184c84f7f00.jpg\",\n  \"37811.jpg\": \"Aves/Strix varia/96eca24953d4dcc082e159720146465a.jpg\",\n  \"378212.jpg\": \"Aves/Rallus obsoletus/1ce74322a596d6a80ae823277468fadc.jpg\",\n  \"378214.jpg\": \"Aves/Rallus obsoletus/14aa3510eec44cc45db735cc3b25116e.jpg\",\n  \"378218.jpg\": \"Aves/Rallus obsoletus/651b2b52026f308642bba970b10d51fe.jpg\",\n  \"378228.jpg\": \"Aves/Rallus obsoletus/46252cafe57c566925f9a83b14ea030e.jpg\",\n  \"378232.jpg\": \"Aves/Rallus obsoletus/d6182c0df34ef3b3e89ab98954fd6588.jpg\",\n  \"378260.jpg\": \"Insecta/Diaphania hyalinata/cf63856ee9be69afa9206855c07e1779.jpg\",\n  \"378261.jpg\": \"Insecta/Diaphania hyalinata/200613cb9fca55259b499b92d41f79d2.jpg\",\n  \"378262.jpg\": \"Insecta/Diaphania hyalinata/b625fdc0d0e31101a95c0e53d334df9e.jpg\",\n  \"378264.jpg\": \"Insecta/Diaphania hyalinata/cd354d2d803d0b29d4e9bcb220582af0.jpg\",\n  \"378265.jpg\": \"Insecta/Diaphania hyalinata/e91aa5b4c31a9c0090b79c45741a1b8f.jpg\",\n  \"378272.jpg\": \"Insecta/Diaphania hyalinata/444e75a1c201f3b0332fdeeac7fc532b.jpg\",\n  \"378295.jpg\": \"Insecta/Dryocampa rubicunda/2266278497f491bb9cdda5dcd9708bb9.jpg\",\n  \"378296.jpg\": \"Insecta/Dryocampa rubicunda/766ab871fff557651e465b5547ce61a0.jpg\",\n  \"378303.jpg\": \"Insecta/Dryocampa rubicunda/a61b196887f9542cc94c4163005274fd.jpg\",\n  \"378311.jpg\": \"Insecta/Dryocampa rubicunda/e9677ac8acd7e08373521dd20647d4af.jpg\",\n  \"378316.jpg\": \"Insecta/Dryocampa rubicunda/a2613bbc4b1ddd2b921eb47cf9b3e460.jpg\",\n  \"378324.jpg\": \"Insecta/Dryocampa rubicunda/f53f157c8ce3df40baff8d1a7967016d.jpg\",\n  \"378338.jpg\": \"Insecta/Dryocampa rubicunda/a4a0ba7ce4ce517f40239141ce8d3364.jpg\",\n  \"378341.jpg\": \"Insecta/Dryocampa rubicunda/53466084c951c14aed843b3580fbf75b.jpg\",\n  \"378355.jpg\": \"Insecta/Dryocampa rubicunda/2b68a36e5758b3a404808a61cace37de.jpg\",\n  \"37836.jpg\": \"Aves/Strix varia/338f39705c480e6377f1f730cfc430a9.jpg\",\n  \"378360.jpg\": \"Insecta/Dryocampa rubicunda/552f143edb3d1100de1b846ae2182590.jpg\",\n  \"378364.jpg\": \"Insecta/Dryocampa rubicunda/ee59ecca3df77bb52955836c7ae27766.jpg\",\n  \"378366.jpg\": \"Insecta/Dryocampa rubicunda/2fc58a93b8608cffa78c5d721f87c0af.jpg\",\n  \"378367.jpg\": \"Insecta/Dryocampa rubicunda/cea6195da4b6c066b40121d1c14c0922.jpg\",\n  \"378377.jpg\": \"Insecta/Dryocampa rubicunda/c7583b9406223116a9460435fb96833f.jpg\",\n  \"378378.jpg\": \"Insecta/Dryocampa rubicunda/e05e634fb772ac36eaa0d31b2efd6afa.jpg\",\n  \"378386.jpg\": \"Insecta/Dryocampa rubicunda/b5e1bb93c62cd615ff3e630ad6bfc7ce.jpg\",\n  \"378390.jpg\": \"Insecta/Dryocampa rubicunda/215e41c293298c3c1ca9e815ac7b88b2.jpg\",\n  \"378391.jpg\": \"Insecta/Dryocampa rubicunda/ba700ca9a41bb213b5f6a388b6894c8c.jpg\",\n  \"378398.jpg\": \"Aves/Onychognathus morio/30e4f5e92cafbe324f37db5096c7d75d.jpg\",\n  \"378402.jpg\": \"Aves/Onychognathus morio/10d13090a43cb4558b7f5e3c1d47cf38.jpg\",\n  \"378403.jpg\": \"Aves/Onychognathus morio/5d1bc83445bacef73c4942c9a2a7a52f.jpg\",\n  \"378406.jpg\": \"Aves/Onychognathus morio/1f7c3c0ae2b3aa9ab67bba03effaba26.jpg\",\n  \"378414.jpg\": \"Aves/Sturnus vulgaris/6fe6962c60092f7b7191d8b5e54a0549.jpg\",\n  \"37844.jpg\": \"Aves/Strix varia/0482a454b5fb63c52ae6171ba8b6feb0.jpg\",\n  \"37845.jpg\": \"Aves/Strix varia/e106da566f443b4a51759d80046c680f.jpg\",\n  \"378453.jpg\": \"Aves/Sturnus vulgaris/fcc4d001be3a404b7599bc9146306d3b.jpg\",\n  \"37846.jpg\": \"Aves/Strix varia/9e6b60e9eb7872af767f8a9cccd042a9.jpg\",\n  \"378461.jpg\": \"Aves/Sturnus vulgaris/a01ad99e8f4585eff1420874e2cf78b0.jpg\",\n  \"37850.jpg\": \"Aves/Strix varia/0369d6cccb24d4b35fb224f0903aae7c.jpg\",\n  \"378506.jpg\": \"Aves/Sturnus vulgaris/1607477869c43839fb9dcaba009a8457.jpg\",\n  \"37851.jpg\": \"Aves/Strix varia/2c8e19c73c115f6f56efc76704bfa216.jpg\",\n  \"378562.jpg\": \"Aves/Sturnus vulgaris/4b81b56f5d6eff314877c30144f9995b.jpg\",\n  \"37861.jpg\": \"Aves/Strix varia/530ad41a82d5c12e0c353c34f9729c30.jpg\",\n  \"378716.jpg\": \"Aves/Sturnus vulgaris/6639cc192b27283ba088c8ea95e19be6.jpg\",\n  \"378718.jpg\": \"Aves/Sturnus vulgaris/e13a867d3d5f41f3da0e3fc29c8be7e4.jpg\",\n  \"378755.jpg\": \"Aves/Sturnus vulgaris/8c306a5177d49c288cf5d0160b5a79cb.jpg\",\n  \"378767.jpg\": \"Aves/Sturnus vulgaris/4608df6f5acfad582a3d505b46df96df.jpg\",\n  \"378808.jpg\": \"Aves/Sturnus vulgaris/4cbda82c70c8602099b6d793a84ab7f6.jpg\",\n  \"378886.jpg\": \"Aves/Sturnus vulgaris/eaed74f5664bc738e387bc96b9b3cb26.jpg\",\n  \"378954.jpg\": \"Aves/Sturnus vulgaris/7ee563774fe9f2e0b756c7a07b8d9972.jpg\",\n  \"379026.jpg\": \"Aves/Sturnus vulgaris/8c589ef11963aa212ac14543e019106d.jpg\",\n  \"37905.jpg\": \"Aves/Strix varia/7a637e3121ac36093d0d008162d0c2c0.jpg\",\n  \"379090.jpg\": \"Aves/Sturnus vulgaris/59b1ba1163457d36f5506ac10f7c8c38.jpg\",\n  \"379175.jpg\": \"Aves/Sturnus vulgaris/bc80a0e22be760c121abb548103d26c0.jpg\",\n  \"379189.jpg\": \"Aves/Sturnus vulgaris/12a7f0271c8654f3b6b5d9f66ebf1fa1.jpg\",\n  \"37924.jpg\": \"Aves/Strix varia/5f39db1432c6dcf9fcfb7af135e94daf.jpg\",\n  \"379258.jpg\": \"Aves/Alectoris chukar/939b44dd0675b64fef5028a6d1d714c5.jpg\",\n  \"379261.jpg\": \"Aves/Alectoris chukar/c52994e3fda17b140f6f69f0f74e9409.jpg\",\n  \"379265.jpg\": \"Aves/Alectoris chukar/d04b7eebe567b2b86e78f2dc512c8ed7.jpg\",\n  \"379266.jpg\": \"Aves/Alectoris chukar/99665c6c771e6c631458adc67a2d9a97.jpg\",\n  \"379267.jpg\": \"Aves/Alectoris chukar/037a8320e328e62254a4584771d68f67.jpg\",\n  \"379274.jpg\": \"Aves/Alectoris chukar/74336acb2fa99b013f3491c50ac26bb3.jpg\",\n  \"379278.jpg\": \"Aves/Alectoris chukar/3fadf9739fc67b20cbe0aec55ba4b545.jpg\",\n  \"379282.jpg\": \"Aves/Alectoris chukar/15c073d41393754fb6c7720dcaa1f28a.jpg\",\n  \"379285.jpg\": \"Aves/Alectoris chukar/62c40cc91b86fe3985fd86d8a96099f3.jpg\",\n  \"379291.jpg\": \"Aves/Alectoris chukar/4ee8bf35aebdacfe9466b2515e582e2f.jpg\",\n  \"37930.jpg\": \"Aves/Calidris canutus/e5d6a5cd21ba2b8550981b7cb9503a6e.jpg\",\n  \"379368.jpg\": \"Aves/Arenaria melanocephala/9e7b84ebe1e7b105d6e16cb9e8ea6f05.jpg\",\n  \"37937.jpg\": \"Aves/Calidris canutus/af4f932b737bc03b7cd7d77d01b6a914.jpg\",\n  \"379371.jpg\": \"Aves/Arenaria melanocephala/a0d82d8c3e514a789805badfe9cf7267.jpg\",\n  \"379374.jpg\": \"Aves/Arenaria melanocephala/8d193e8b8f25a579d433422ac61557f3.jpg\",\n  \"379375.jpg\": \"Aves/Arenaria melanocephala/a5667889535218aa6210ee1773ed057a.jpg\",\n  \"379388.jpg\": \"Aves/Arenaria melanocephala/7f0f6b5500248f1922f11563ee7253ab.jpg\",\n  \"379394.jpg\": \"Aves/Arenaria melanocephala/190dfbd31394455fb85898e037cae8b8.jpg\",\n  \"379398.jpg\": \"Aves/Arenaria melanocephala/db9619728fc2493d84a123b5e787658b.jpg\",\n  \"379425.jpg\": \"Aves/Arenaria melanocephala/2946394fc4654ff7f5ad9f99d301e0a8.jpg\",\n  \"379453.jpg\": \"Aves/Arenaria melanocephala/e65ee42514b7ab4bf38f57c68e5afddb.jpg\",\n  \"37946.jpg\": \"Aves/Calidris canutus/d78843625163951d8d512dfdd84f8e5e.jpg\",\n  \"379461.jpg\": \"Aves/Arenaria melanocephala/6ac62e6a7f85281c7f1b7f4bb65d2b52.jpg\",\n  \"37947.jpg\": \"Aves/Calidris canutus/30d198ab9f492181452f737cc4605edf.jpg\",\n  \"379471.jpg\": \"Aves/Arenaria melanocephala/0fa4d03b5eaca5be892f9516d68018a2.jpg\",\n  \"379476.jpg\": \"Aves/Arenaria melanocephala/a09f641d1d0cf900bdfd4f326d076d30.jpg\",\n  \"379492.jpg\": \"Aves/Arenaria melanocephala/59837984a510391bddb9cee837ec9845.jpg\",\n  \"379494.jpg\": \"Aves/Arenaria melanocephala/e89a77a395db8b8630a4ff4e79f3f855.jpg\",\n  \"379495.jpg\": \"Aves/Arenaria melanocephala/8a3fdbe10921682bf1beeeab97dcbc73.jpg\",\n  \"379499.jpg\": \"Aves/Arenaria melanocephala/e6c9d13b51719f759bc363780de50bf7.jpg\",\n  \"37950.jpg\": \"Aves/Calidris canutus/512d63d9d6f3c1bba05cdc280ec07d0c.jpg\",\n  \"379504.jpg\": \"Aves/Arenaria melanocephala/a0ae377bbd72b4fe358110d032765685.jpg\",\n  \"379517.jpg\": \"Mollusca/Cepaea nemoralis/bd8db4b9eca0d3fe3862aadc9bc8071d.jpg\",\n  \"379522.jpg\": \"Mollusca/Cepaea nemoralis/95fb29755d66f53f065e581a7159bcf6.jpg\",\n  \"379526.jpg\": \"Mollusca/Cepaea nemoralis/3be39af37c1bff65fa046c8f467db39b.jpg\",\n  \"37953.jpg\": \"Aves/Calidris canutus/2248f1d8881c46387ec9fb44a18255bc.jpg\",\n  \"379534.jpg\": \"Mollusca/Cepaea nemoralis/b20938e5e21dfc5ebb13c7fac44e799e.jpg\",\n  \"379535.jpg\": \"Mollusca/Cepaea nemoralis/8e6da4e4493cc3813953641e9b0e4952.jpg\",\n  \"37954.jpg\": \"Aves/Calidris canutus/e49994780e5dc7b03407919679f7f344.jpg\",\n  \"379540.jpg\": \"Mollusca/Cepaea nemoralis/1c6421e299159b23cb02f8876955ecf8.jpg\",\n  \"379543.jpg\": \"Mollusca/Cepaea nemoralis/31708ec9ddd58c5862272a2a0eceb270.jpg\",\n  \"379545.jpg\": \"Mollusca/Cepaea nemoralis/4fb78dcb26911696545a748907c9d3c9.jpg\",\n  \"37955.jpg\": \"Aves/Calidris canutus/574154fd7851c80b786b4afde42a1bb5.jpg\",\n  \"379551.jpg\": \"Mollusca/Cepaea nemoralis/a42743630dbea1f73a29d8534c32521d.jpg\",\n  \"379558.jpg\": \"Mollusca/Cepaea nemoralis/e6e4072ab8e53e6f922fc6ab10723dc4.jpg\",\n  \"379559.jpg\": \"Mollusca/Cepaea nemoralis/cbee1d99992b5918d2824b5425bb62e6.jpg\",\n  \"37956.jpg\": \"Aves/Calidris canutus/534298d35835d47fd461d46efe4d0cf0.jpg\",\n  \"379564.jpg\": \"Mollusca/Cepaea nemoralis/0e18c259e6e14225d25ff0beda096098.jpg\",\n  \"379565.jpg\": \"Mollusca/Cepaea nemoralis/cbec63c09bcd2e78a6d4f91cbde47601.jpg\",\n  \"379567.jpg\": \"Mollusca/Cepaea nemoralis/e08c2ab19bc6a0d33fbb762356df2689.jpg\",\n  \"379575.jpg\": \"Mollusca/Cepaea nemoralis/1e6f4725176e6befbcdac6d70cfccef3.jpg\",\n  \"379576.jpg\": \"Mollusca/Cepaea nemoralis/aabee9086860b02c5847d870866f4bd0.jpg\",\n  \"379578.jpg\": \"Mollusca/Cepaea nemoralis/a0398e2a28d2d61688ab3bd6f7f1b9e2.jpg\",\n  \"379585.jpg\": \"Mollusca/Cepaea nemoralis/9e7fbd866904538da77f4ce1bdaa2501.jpg\",\n  \"379586.jpg\": \"Mollusca/Cepaea nemoralis/b664f410b84a731a1112bf90298c369c.jpg\",\n  \"379592.jpg\": \"Mollusca/Cepaea nemoralis/b666e671a133c9e3dfd25751fd8a9322.jpg\",\n  \"379604.jpg\": \"Mollusca/Cepaea nemoralis/674792d86070b70a8bcdefa52c34cde9.jpg\",\n  \"37965.jpg\": \"Aves/Calidris canutus/60d59f40516177cff54fe16e86b80b0e.jpg\",\n  \"379754.jpg\": \"Aves/Motacilla alba/5afcd20710d16561a47aad958e49cf7f.jpg\",\n  \"379760.jpg\": \"Aves/Motacilla alba/6fd7f74df4cc6f216e92a38451c27d09.jpg\",\n  \"379768.jpg\": \"Aves/Motacilla alba/e758e3188e75764bc5980290540b2e16.jpg\",\n  \"379772.jpg\": \"Aves/Motacilla alba/bf468361e75915ef94c5639f7cb4e916.jpg\",\n  \"379774.jpg\": \"Aves/Motacilla alba/117f3f60a6d105504c46fdc7f142d42f.jpg\",\n  \"379792.jpg\": \"Aves/Motacilla alba/f990485fc3c252dc2fb404cfa773b839.jpg\",\n  \"379797.jpg\": \"Aves/Motacilla alba/47e78f9e7dd3b9926a612e22f2595d06.jpg\",\n  \"379805.jpg\": \"Aves/Motacilla alba/69616ba1fa1dfa00162c1e3cd54a001a.jpg\",\n  \"379806.jpg\": \"Aves/Motacilla alba/e6ea68ac847f7811ebdad164f70fcd88.jpg\",\n  \"379808.jpg\": \"Aves/Motacilla alba/99b12bc2b8d06816acf7b891e35579fd.jpg\",\n  \"379814.jpg\": \"Aves/Motacilla alba/80095335abd12629d678a034b5db7b06.jpg\",\n  \"379826.jpg\": \"Aves/Motacilla alba/b5817448aa13a24ada2ffed989a0ab1f.jpg\",\n  \"379844.jpg\": \"Aves/Motacilla alba/959958f92050d64701dc837b8c614cad.jpg\",\n  \"379896.jpg\": \"Aves/Motacilla alba/aaa0d54a953c3c83e5cdecbb98796358.jpg\",\n  \"379905.jpg\": \"Aves/Motacilla alba/5f6a213f947ed9e623f5ffc1ea83ba9b.jpg\",\n  \"379906.jpg\": \"Aves/Motacilla alba/1730f1c3fd3e0712626498c8829d959e.jpg\",\n  \"379916.jpg\": \"Aves/Motacilla alba/fc7f59aa9573b75e2103ae501adfbe16.jpg\",\n  \"379917.jpg\": \"Aves/Motacilla alba/168f753005475559b950fe63ab25377c.jpg\",\n  \"379931.jpg\": \"Aves/Motacilla alba/81069a871982e6a69add97072848a2bc.jpg\",\n  \"379942.jpg\": \"Aves/Motacilla alba/615ef59db6a08bca7b7cc02b47a3bb9a.jpg\",\n  \"379958.jpg\": \"Aves/Motacilla alba/f695d04341d432b566d047a9c7a8cf12.jpg\",\n  \"379966.jpg\": \"Aves/Motacilla alba/7e897ecf5d4d37de0b04406cd6c3def3.jpg\",\n  \"379967.jpg\": \"Aves/Motacilla alba/60bbe1284d4e72d3b743570004a35d59.jpg\",\n  \"38.jpg\": \"Mammalia/Marmota flaviventris/e7b8bc321665d110f4e0d04eb254cbe7.jpg\",\n  \"380.jpg\": \"Insecta/Coleomegilla maculata/82e93161902a2fddc147d53f36b11b16.jpg\",\n  \"380090.jpg\": \"Insecta/Pyrrhocoris apterus/70fadac4f386c737a88bca8c56b9706e.jpg\",\n  \"380091.jpg\": \"Insecta/Pyrrhocoris apterus/18b858d82bc2539351fa8661311d133e.jpg\",\n  \"380093.jpg\": \"Insecta/Pyrrhocoris apterus/b0f4b7b0e0c557b5f698e0b35c215f3d.jpg\",\n  \"380094.jpg\": \"Insecta/Pyrrhocoris apterus/fa9a110ad4b9fd6dd3bb8f444c5010b2.jpg\",\n  \"380097.jpg\": \"Insecta/Pyrrhocoris apterus/0108496fbcb7b7f61d5e6d43619c55ba.jpg\",\n  \"380098.jpg\": \"Insecta/Pyrrhocoris apterus/bc8d89ee1dc3233ada722b4203d89587.jpg\",\n  \"380101.jpg\": \"Insecta/Pyrrhocoris apterus/db8bffdb96242fdf7f53ddbadc67e1b3.jpg\",\n  \"380103.jpg\": \"Insecta/Pyrrhocoris apterus/eae65ac12c924f40b7cda0564049b596.jpg\",\n  \"380104.jpg\": \"Insecta/Pyrrhocoris apterus/8214ddc03a669b070b27f1583be96ad6.jpg\",\n  \"380110.jpg\": \"Insecta/Pyrrhocoris apterus/5eb1b49cec98dd7a0405008d4cc60bbb.jpg\",\n  \"380111.jpg\": \"Insecta/Pyrrhocoris apterus/4a3f71f6c46fef14d09f13f22b45d807.jpg\",\n  \"380121.jpg\": \"Insecta/Pyrrhocoris apterus/83c9c8bce4d11a9d9c5e795903c554a2.jpg\",\n  \"380122.jpg\": \"Insecta/Pyrrhocoris apterus/2203347ab0b65faf9be5c74fd692993b.jpg\",\n  \"380272.jpg\": \"Mollusca/Limacia cockerelli/bc4dff55e22f9aa1548553d3bd4c41f3.jpg\",\n  \"380286.jpg\": \"Mollusca/Limacia cockerelli/61b9c527acdc099e71bb3e6d77c049a8.jpg\",\n  \"380288.jpg\": \"Mollusca/Limacia cockerelli/888923460e0dfb7270501b76c4aa4ec2.jpg\",\n  \"380292.jpg\": \"Mollusca/Limacia cockerelli/1c846845d5711f520e905cc9392815ac.jpg\",\n  \"380295.jpg\": \"Mollusca/Limacia cockerelli/7dab7bb1c0c04e5d3caf24d2a990d97b.jpg\",\n  \"380297.jpg\": \"Mollusca/Limacia cockerelli/01560fdeb2af2726415eb81b5e939571.jpg\",\n  \"380298.jpg\": \"Mollusca/Limacia cockerelli/9bc5d49e8f80dd425b6060c92e4955aa.jpg\",\n  \"380300.jpg\": \"Mollusca/Limacia cockerelli/f2ca6333a1139a606728cee326a16f53.jpg\",\n  \"380302.jpg\": \"Mollusca/Limacia cockerelli/439760c4a9dcbb223b6227c27af43019.jpg\",\n  \"380303.jpg\": \"Mollusca/Limacia cockerelli/2cf2b71fec4a8338cbfab9a0117f5b36.jpg\",\n  \"380305.jpg\": \"Mollusca/Limacia cockerelli/c1d5e088168fe5a22a0723799297a590.jpg\",\n  \"380311.jpg\": \"Mollusca/Limacia cockerelli/3ea507316b4b5a9d124982a076c54b0c.jpg\",\n  \"380312.jpg\": \"Mollusca/Limacia cockerelli/a0b971655d02e99ddc4ddb362abef2fd.jpg\",\n  \"380313.jpg\": \"Mollusca/Limacia cockerelli/5484f4d26c74d8c52bfb840619aec68a.jpg\",\n  \"380317.jpg\": \"Mollusca/Limacia cockerelli/7b25d0973e48e800797173b63cf18e53.jpg\",\n  \"380323.jpg\": \"Mollusca/Limacia cockerelli/2b5c08b9be515fe5ed29a9bfd18618d3.jpg\",\n  \"380326.jpg\": \"Mollusca/Limacia cockerelli/ba510680f660133a382fa50dd155fdaf.jpg\",\n  \"380342.jpg\": \"Mollusca/Limacia cockerelli/263bdd668225362c8b80480937a433b3.jpg\",\n  \"380353.jpg\": \"Mollusca/Limacia cockerelli/966cd55f420572794d2582218332e15c.jpg\",\n  \"380354.jpg\": \"Mollusca/Limacia cockerelli/9873dbdbcebaae17581de0ae2592269f.jpg\",\n  \"380358.jpg\": \"Mollusca/Limacia cockerelli/9ed4ba533b348874599d7f742a20b65a.jpg\",\n  \"380367.jpg\": \"Mollusca/Limacia cockerelli/ecef307a6ca481299a195c49a1447c7b.jpg\",\n  \"380374.jpg\": \"Mollusca/Limacia cockerelli/ad02dd33d8608177393c484cfdab6174.jpg\",\n  \"38052.jpg\": \"Insecta/Eacles imperialis/d7480c83d5ebfe36761b9a578474cbbd.jpg\",\n  \"38053.jpg\": \"Insecta/Eacles imperialis/5a28fdbf8c2bfd47fe37f618f1cf667c.jpg\",\n  \"38054.jpg\": \"Insecta/Eacles imperialis/6454822a56a1115ab0d7eaad92252b20.jpg\",\n  \"38058.jpg\": \"Insecta/Eacles imperialis/bade6eb53df7133604ce566e02b70c37.jpg\",\n  \"38064.jpg\": \"Insecta/Eacles imperialis/b2b88d8f394fb26aa621a409affee4c7.jpg\",\n  \"38076.jpg\": \"Insecta/Eacles imperialis/a367123d83159d5ebadeff3f3087cfa1.jpg\",\n  \"38079.jpg\": \"Insecta/Eacles imperialis/f0f94001b7139800acd33ff53c2eb39f.jpg\",\n  \"38081.jpg\": \"Insecta/Eacles imperialis/2e93ce10ce416a1ce40ca88afdac1ea2.jpg\",\n  \"38083.jpg\": \"Insecta/Eacles imperialis/0a4c1a17134978c97ad71310e1e154d2.jpg\",\n  \"38090.jpg\": \"Insecta/Eacles imperialis/fe30b4a63d05f1fe8564d093ccbe28ee.jpg\",\n  \"38095.jpg\": \"Insecta/Eacles imperialis/85659fe8176ccaa0d822306aee636b89.jpg\",\n  \"380987.jpg\": \"Amphibia/Pseudacris sierra/744ea760ba1389525be7fcdfbf228fb5.jpg\",\n  \"38099.jpg\": \"Insecta/Eacles imperialis/e9699971580cdd06cde5cef724479c13.jpg\",\n  \"380994.jpg\": \"Amphibia/Pseudacris sierra/2738added520776f414a68234b7da079.jpg\",\n  \"381.jpg\": \"Insecta/Coleomegilla maculata/554b368929770056d84061f17ca0c766.jpg\",\n  \"38100.jpg\": \"Insecta/Eacles imperialis/4ffa33bc8b83303adcf80d33f0f44111.jpg\",\n  \"381008.jpg\": \"Amphibia/Pseudacris sierra/61ac5b714e5b7c1d9071caa62fd3199b.jpg\",\n  \"381013.jpg\": \"Amphibia/Pseudacris sierra/67c9ba95a0de7585c44e8bf1afcb7e0b.jpg\",\n  \"381022.jpg\": \"Amphibia/Pseudacris sierra/628ca435bd652861531851665bcaae03.jpg\",\n  \"381028.jpg\": \"Amphibia/Pseudacris sierra/a3183b66b4357b84d589c19abc4b6be3.jpg\",\n  \"381084.jpg\": \"Amphibia/Pseudacris sierra/1f8cab0da4e96b5430fdd9a1c0410ae6.jpg\",\n  \"38110.jpg\": \"Insecta/Eacles imperialis/5aed981b0865d57256d5d2e1691bd71e.jpg\",\n  \"381103.jpg\": \"Amphibia/Pseudacris sierra/e8ca991dd0a8bcfc285c93f790585573.jpg\",\n  \"38112.jpg\": \"Insecta/Eacles imperialis/45dbef70c4265f8e4af0cf1bc7440e24.jpg\",\n  \"38115.jpg\": \"Insecta/Eacles imperialis/1e3239146ba76abdd1bb318998331eb0.jpg\",\n  \"381163.jpg\": \"Amphibia/Pseudacris sierra/bbb5c8a37a72f83c117213888218a883.jpg\",\n  \"381186.jpg\": \"Amphibia/Pseudacris sierra/098b2c05eca08c4730eca02c3eb1b29e.jpg\",\n  \"381205.jpg\": \"Amphibia/Pseudacris sierra/f5055f9325ad5d515a7ac734af789b05.jpg\",\n  \"381213.jpg\": \"Amphibia/Pseudacris sierra/52b5a6e7840201fddbec1f30bae4c2e8.jpg\",\n  \"381239.jpg\": \"Amphibia/Pseudacris sierra/cbf3daf2e187da691b946bbd49270f3f.jpg\",\n  \"38125.jpg\": \"Insecta/Eacles imperialis/63e037f8e75d7f938856ea7b91f9141d.jpg\",\n  \"381278.jpg\": \"Amphibia/Pseudacris sierra/c023287030a31a962ccf1d3c9bb556b9.jpg\",\n  \"381293.jpg\": \"Amphibia/Pseudacris sierra/d74a5dc93a160562922aab088643ba4f.jpg\",\n  \"381294.jpg\": \"Amphibia/Pseudacris sierra/6a130cdef018f733a10460fd51102a38.jpg\",\n  \"381297.jpg\": \"Amphibia/Pseudacris sierra/3726c2c3c8448974a57b35bd4dc98b9e.jpg\",\n  \"38132.jpg\": \"Insecta/Eacles imperialis/8f9c86eb5a8d8865e172f096dd1ed20b.jpg\",\n  \"381349.jpg\": \"Amphibia/Pseudacris sierra/dd3b7027e6e32c5d7e38fe9ad8f85a4d.jpg\",\n  \"381355.jpg\": \"Amphibia/Pseudacris sierra/5ab00a89e491adab24c2e9aba2fa9012.jpg\",\n  \"381380.jpg\": \"Amphibia/Pseudacris sierra/17808f8c52d09e344fe556d73008678c.jpg\",\n  \"381388.jpg\": \"Insecta/Halysidota tessellaris/d1c440ddde0f772a9e123b5fd48d8bf2.jpg\",\n  \"381389.jpg\": \"Insecta/Halysidota tessellaris/60fc03d4980a757710558a741b57446d.jpg\",\n  \"381390.jpg\": \"Insecta/Halysidota tessellaris/4f6444d68902ddb7d8df100d8cfcc772.jpg\",\n  \"381391.jpg\": \"Insecta/Halysidota tessellaris/93068467e0defd298ea0df05a599f37a.jpg\",\n  \"381394.jpg\": \"Insecta/Halysidota tessellaris/e4cac35f947b899d46d85aa15cd9a642.jpg\",\n  \"381395.jpg\": \"Insecta/Halysidota tessellaris/89852150407c9e629c47c0c5b57e9ef3.jpg\",\n  \"381396.jpg\": \"Insecta/Halysidota tessellaris/77a7c89c190d70314b00755ce80cb759.jpg\",\n  \"381398.jpg\": \"Insecta/Halysidota tessellaris/e7ece6d18e3351e507f75b3cd5345d6b.jpg\",\n  \"381399.jpg\": \"Insecta/Halysidota tessellaris/85b4f0d49bc692c855573468c6b08a36.jpg\",\n  \"381400.jpg\": \"Insecta/Halysidota tessellaris/c86ef68f3c5a71804b015d5aa8321cdd.jpg\",\n  \"381404.jpg\": \"Insecta/Halysidota tessellaris/34a5dce87b808602cd0c0fb605bf33ca.jpg\",\n  \"38142.jpg\": \"Insecta/Eacles imperialis/656ab6e979c9dcdb368a86c9798ef1f6.jpg\",\n  \"381427.jpg\": \"Insecta/Halysidota tessellaris/c9cb92f64ca87b1aacb8a8aeb4c8e375.jpg\",\n  \"381430.jpg\": \"Insecta/Halysidota tessellaris/1af3d6441e170ef615aa4f725de98212.jpg\",\n  \"381433.jpg\": \"Insecta/Halysidota tessellaris/51cb4c6055a5106828046a75872a42db.jpg\",\n  \"381434.jpg\": \"Insecta/Halysidota tessellaris/790bfec0e542e20d4eb8e2d8bccc9932.jpg\",\n  \"381437.jpg\": \"Insecta/Halysidota tessellaris/aa93e159b6d0343f35be8190c333e495.jpg\",\n  \"381441.jpg\": \"Insecta/Halysidota tessellaris/541c347af6435238a8cd5129b9d31239.jpg\",\n  \"381442.jpg\": \"Insecta/Halysidota tessellaris/eef91f703a3cbe7f6e561348b1ed8bdb.jpg\",\n  \"381444.jpg\": \"Insecta/Halysidota tessellaris/0092bb37d9c6e31e200ac87957f7e1b1.jpg\",\n  \"381445.jpg\": \"Insecta/Halysidota tessellaris/db96ff5c74be5f7c0c238629056075ea.jpg\",\n  \"381449.jpg\": \"Insecta/Apamea amputatrix/7b614fb44855df1ce2958ce2b8c9824d.jpg\",\n  \"381450.jpg\": \"Insecta/Apamea amputatrix/7a53b588be6662c106fbfc7c77cd2a8b.jpg\",\n  \"381454.jpg\": \"Insecta/Apamea amputatrix/40d46d124d474782a6264801e532ea30.jpg\",\n  \"381518.jpg\": \"Insecta/Strymon melinus/a93e545ef1834cb619d59298e84c8383.jpg\",\n  \"38153.jpg\": \"Insecta/Eacles imperialis/37f1f94216f19c00ae8ed046ae998e37.jpg\",\n  \"381559.jpg\": \"Insecta/Strymon melinus/db3bf4072004bae63f5677c2ce4f223d.jpg\",\n  \"381617.jpg\": \"Insecta/Strymon melinus/6609d5b255d88f9664ea9e59d18c88cf.jpg\",\n  \"381673.jpg\": \"Insecta/Strymon melinus/81b36ec61af35114386fa4f661f828e7.jpg\",\n  \"381683.jpg\": \"Insecta/Strymon melinus/e0af959a56325edf9a21c0d00de6f3e4.jpg\",\n  \"381770.jpg\": \"Insecta/Strymon melinus/eba5de93eee9e8146864759566b337c2.jpg\",\n  \"381794.jpg\": \"Insecta/Strymon melinus/91a1d3a25a7d4cfa4632654b5a2c417c.jpg\",\n  \"381800.jpg\": \"Insecta/Strymon melinus/1f9225a1f7ed1220798f6764ff0489f1.jpg\",\n  \"381819.jpg\": \"Insecta/Strymon melinus/94293d3ab65cf7a1c89c68873939a4b3.jpg\",\n  \"381829.jpg\": \"Insecta/Strymon melinus/6b6b4d156184ab307601be83dd4d1d48.jpg\",\n  \"381838.jpg\": \"Insecta/Strymon melinus/f420caf16f0ee3ac42d35d22f8e3b2f4.jpg\",\n  \"381869.jpg\": \"Insecta/Strymon melinus/cfa2465cc2bee742e0077cf0b511a5e0.jpg\",\n  \"381872.jpg\": \"Insecta/Strymon melinus/391bffe363f44edc7bb455bb78ecfedf.jpg\",\n  \"381892.jpg\": \"Insecta/Strymon melinus/e8fd4bc843c2d9a54f4aa35516080868.jpg\",\n  \"381900.jpg\": \"Insecta/Strymon melinus/10ad47b153ac88356cd235a678f8a4e7.jpg\",\n  \"381918.jpg\": \"Insecta/Strymon melinus/6989eb384144ebcf2701dfb7dc02ea9d.jpg\",\n  \"38195.jpg\": \"Insecta/Leucorrhinia frigida/f34273059607d8351562bf5aa430316a.jpg\",\n  \"38196.jpg\": \"Insecta/Leucorrhinia frigida/7a4c5892137a7bc2d454c36efce351f9.jpg\",\n  \"381991.jpg\": \"Insecta/Strymon melinus/1e1b7798940a09a884bcd28699d7da7e.jpg\",\n  \"382008.jpg\": \"Insecta/Strymon melinus/bc4d1c177562cdb42fd0abfff948362f.jpg\",\n  \"38202.jpg\": \"Insecta/Leucorrhinia frigida/76387475d5d7f70d4b9f89cd7218ab39.jpg\",\n  \"382023.jpg\": \"Insecta/Strymon melinus/6823ec2a0ace5702491143ff57dda7b2.jpg\",\n  \"382036.jpg\": \"Insecta/Strymon melinus/0eecfc692938c17fc25c48c3f4e234f7.jpg\",\n  \"38205.jpg\": \"Insecta/Leucorrhinia frigida/ffe055ce3ee17b140d0a5148adf01a5e.jpg\",\n  \"38206.jpg\": \"Insecta/Leucorrhinia frigida/498d9cd2b1cf9f14f1710983be246ca3.jpg\",\n  \"38211.jpg\": \"Insecta/Leucorrhinia frigida/605582d48ccb13cfd480ec593d2d5b05.jpg\",\n  \"38212.jpg\": \"Aves/Baeolophus atricristatus/22acf8c1ef0c05df4b1ecb10c651525a.jpg\",\n  \"38215.jpg\": \"Aves/Baeolophus atricristatus/01884eb5e24d8eccff4e08214864974e.jpg\",\n  \"382156.jpg\": \"Aves/Phalacrocorax auritus/55bf58878e64bc0e28424efa09dbe40e.jpg\",\n  \"38223.jpg\": \"Aves/Baeolophus atricristatus/b451fdf3215069542efcc2b32a52bfe8.jpg\",\n  \"38224.jpg\": \"Aves/Baeolophus atricristatus/628eba92dec94a65f12189dba3681cd1.jpg\",\n  \"38227.jpg\": \"Aves/Baeolophus atricristatus/d9e237db71e239263fd28955677e1512.jpg\",\n  \"382351.jpg\": \"Aves/Phalacrocorax auritus/9bd6e12f521afdd5b6c3fd8fb36dd363.jpg\",\n  \"38236.jpg\": \"Aves/Baeolophus atricristatus/4aba91445405fc4d5c13145c564b18c5.jpg\",\n  \"382368.jpg\": \"Aves/Phalacrocorax auritus/59022d35c430ca308dfc8b2cd89fbb3c.jpg\",\n  \"38238.jpg\": \"Aves/Baeolophus atricristatus/7dfd8c6c5a11aa96b335b100c05ce93a.jpg\",\n  \"38239.jpg\": \"Aves/Baeolophus atricristatus/8e420006b215c742ad39426db6c7d5f6.jpg\",\n  \"38240.jpg\": \"Aves/Baeolophus atricristatus/bacbf731f98541a7cc41d30bd614c42c.jpg\",\n  \"38241.jpg\": \"Aves/Baeolophus atricristatus/6a5712b61c0925aa347df507d69d5721.jpg\",\n  \"38243.jpg\": \"Aves/Baeolophus atricristatus/12fe54dafb3952c3ab312009a0596eac.jpg\",\n  \"382443.jpg\": \"Aves/Phalacrocorax auritus/adc337d54a7ecb713424c77f346105fa.jpg\",\n  \"38245.jpg\": \"Aves/Baeolophus atricristatus/68833dc2fd8f2c35f9f3ee66f9cbb82e.jpg\",\n  \"38246.jpg\": \"Aves/Baeolophus atricristatus/2cc92dadaf0b5d1844e4fce9f38c58b8.jpg\",\n  \"38253.jpg\": \"Aves/Baeolophus atricristatus/c27525f83cebd8ba26d729c2b91d696c.jpg\",\n  \"38254.jpg\": \"Aves/Baeolophus atricristatus/41d5f266cd2a8b2608d5bbcc9911770b.jpg\",\n  \"38255.jpg\": \"Aves/Baeolophus atricristatus/2a7c7b5ac8c04a429ce374d60f413269.jpg\",\n  \"38259.jpg\": \"Aves/Baeolophus atricristatus/ad4725b5ca51e40464513e9230cf30ce.jpg\",\n  \"382600.jpg\": \"Aves/Phalacrocorax auritus/3850252926cd3f358b7650de93e5665a.jpg\",\n  \"382625.jpg\": \"Aves/Phalacrocorax auritus/e1cb5d21e1c2888120707dbedae147b6.jpg\",\n  \"38264.jpg\": \"Aves/Baeolophus atricristatus/0ac73de5247595b68c4dcb99be501180.jpg\",\n  \"38269.jpg\": \"Aves/Baeolophus atricristatus/2e00aaa05669265806226cf24f7ff166.jpg\",\n  \"38271.jpg\": \"Aves/Baeolophus atricristatus/6902307403c45b1d4364288db9010102.jpg\",\n  \"38274.jpg\": \"Aves/Baeolophus atricristatus/7e9049de6d64945a29f7ef425bb695a0.jpg\",\n  \"382744.jpg\": \"Aves/Phalacrocorax auritus/8d641044642d23a00b964c1080fdefd7.jpg\",\n  \"38275.jpg\": \"Aves/Baeolophus atricristatus/5ed4e6beb643155401262f60fb8b7975.jpg\",\n  \"38276.jpg\": \"Aves/Baeolophus atricristatus/84c65a1df603d0c801efcee96607d9da.jpg\",\n  \"38278.jpg\": \"Aves/Baeolophus atricristatus/94073c93c7264791840d50b34d02b754.jpg\",\n  \"382800.jpg\": \"Aves/Phalacrocorax auritus/a1824883a8c27a407c6171a98e321b9e.jpg\",\n  \"38281.jpg\": \"Aves/Baeolophus atricristatus/321698447db06cfc2964f3b7326833c5.jpg\",\n  \"382823.jpg\": \"Aves/Phalacrocorax auritus/0e01e75921be16584f07c42fd3cfb4cb.jpg\",\n  \"382874.jpg\": \"Aves/Phalacrocorax auritus/b4925ae43c990f68ef1b618e0718be7c.jpg\",\n  \"382886.jpg\": \"Aves/Phalacrocorax auritus/c452561507d27009a9c74f8f122a8af4.jpg\",\n  \"383013.jpg\": \"Aves/Pluvialis dominica/e04b02ac899fca8973f7efa3a93fe686.jpg\",\n  \"383014.jpg\": \"Aves/Pluvialis dominica/a9be4da17c99494b42704388404c6b81.jpg\",\n  \"383022.jpg\": \"Aves/Pluvialis dominica/02df3455e688192c4ec26b63c9ebd3f2.jpg\",\n  \"383028.jpg\": \"Aves/Pluvialis dominica/0da499a078ea6ac9216dbab6518e68c8.jpg\",\n  \"383032.jpg\": \"Aves/Pluvialis dominica/41de1d6f4745acf57c7f3ccf63e55554.jpg\",\n  \"383033.jpg\": \"Aves/Pluvialis dominica/6475bfc3f0825650f8476fe84732a4b6.jpg\",\n  \"383034.jpg\": \"Aves/Pluvialis dominica/95787d125f88ba3147705e1864a78c3c.jpg\",\n  \"383035.jpg\": \"Aves/Pluvialis dominica/11d49d914bb7b988ceab00582df34545.jpg\",\n  \"383036.jpg\": \"Aves/Pluvialis dominica/5760c0d5f09903ef1466f7b41e966b09.jpg\",\n  \"383042.jpg\": \"Aves/Pluvialis dominica/55d48465805bca01b8eca1dcc2ea9c06.jpg\",\n  \"383043.jpg\": \"Aves/Pluvialis dominica/fe44481257a9b93a141a59def2b4f65f.jpg\",\n  \"383044.jpg\": \"Aves/Pluvialis dominica/4f85c5b63e41d0c14d4d49b4a0758162.jpg\",\n  \"383046.jpg\": \"Aves/Pluvialis dominica/c1ae57df2e725929293ae124ff086be8.jpg\",\n  \"383057.jpg\": \"Aves/Pluvialis dominica/771917623b4e722c5aa4e75b205d83a4.jpg\",\n  \"383058.jpg\": \"Aves/Pluvialis dominica/801a1bcdb4ccd367cc375a1cf47743fe.jpg\",\n  \"383059.jpg\": \"Aves/Pluvialis dominica/b4d321aa24aa779faada3162731e48f9.jpg\",\n  \"383060.jpg\": \"Aves/Pluvialis dominica/32a032b7b06b6da870596c898daffd6b.jpg\",\n  \"383061.jpg\": \"Aves/Pluvialis dominica/212aa60a618f365c7f647c1374df33d4.jpg\",\n  \"383075.jpg\": \"Aves/Setophaga coronata/c54deba53e86137ddd95037e60b6d7ea.jpg\",\n  \"383097.jpg\": \"Aves/Setophaga coronata/773d8f0874c9570b742ff7ad8df5a39b.jpg\",\n  \"38312.jpg\": \"Aves/Bombycilla garrulus/9b1307909f3763ca6abd021f1d4a4581.jpg\",\n  \"38313.jpg\": \"Aves/Bombycilla garrulus/c1b7594154d7940027f7592894b22d33.jpg\",\n  \"383141.jpg\": \"Aves/Setophaga coronata/e363d67eb72030b663be19247e4bd709.jpg\",\n  \"383162.jpg\": \"Aves/Setophaga coronata/416832228df5bdd33c85622d08f498c2.jpg\",\n  \"383241.jpg\": \"Aves/Setophaga coronata/2e44f8532678e07395438e16e612d58c.jpg\",\n  \"383242.jpg\": \"Aves/Setophaga coronata/a76aba65a148559536d89ee9464d9f8d.jpg\",\n  \"38325.jpg\": \"Aves/Bombycilla garrulus/6ee21588a60e0e38ccb2d00d41c0089d.jpg\",\n  \"383256.jpg\": \"Aves/Setophaga coronata/d1251b141a09b4c6231ee79d422b1e7c.jpg\",\n  \"383273.jpg\": \"Aves/Setophaga coronata/d965e901fd14154535d39200f383e809.jpg\",\n  \"383288.jpg\": \"Aves/Setophaga coronata/a4fda1e35bf7d354696aa3cdbd70da07.jpg\",\n  \"38335.jpg\": \"Aves/Bombycilla garrulus/6eaa77a88bc63da3164f6b28164947f1.jpg\",\n  \"383350.jpg\": \"Aves/Setophaga coronata/5b651f6737a4d342f3dc27b03e8b124d.jpg\",\n  \"38336.jpg\": \"Aves/Bombycilla garrulus/33796ddfe604e6cb73e726651fae1a75.jpg\",\n  \"383366.jpg\": \"Aves/Setophaga coronata/be93eef3d46bab258debade494092918.jpg\",\n  \"383372.jpg\": \"Aves/Setophaga coronata/07a412d1cbdb9f345a933bfef217cb83.jpg\",\n  \"38338.jpg\": \"Aves/Bombycilla garrulus/225e78dd1a8abaaa00ac48580920f7c3.jpg\",\n  \"383427.jpg\": \"Aves/Setophaga coronata/9f5fd441e28af6737741f89ff92609a9.jpg\",\n  \"383462.jpg\": \"Aves/Setophaga coronata/d87541f41b75b00922410403dcafe377.jpg\",\n  \"383489.jpg\": \"Aves/Setophaga coronata/bd19892ea83701d9a003c26bc6b9854f.jpg\",\n  \"38357.jpg\": \"Aves/Bombycilla garrulus/b3fb663d3bb5f5e5558c12e6ec2e4acd.jpg\",\n  \"383597.jpg\": \"Aves/Setophaga coronata/1119244eb56c09c6eaaee6cd1e703e91.jpg\",\n  \"38362.jpg\": \"Aves/Bombycilla garrulus/85f0734230e2a4be0671b0e3ae4ca6c5.jpg\",\n  \"383634.jpg\": \"Aves/Setophaga coronata/b6f6a8b4b1f32f01bbff86ecce8fcf69.jpg\",\n  \"38366.jpg\": \"Aves/Bombycilla garrulus/9e02fb4644081f0527787f3d5475b980.jpg\",\n  \"38368.jpg\": \"Aves/Bombycilla garrulus/fa94e72d56db5f1a3fe926ed129a3f7d.jpg\",\n  \"38369.jpg\": \"Mammalia/Panthera leo/7d3115876a7f79e15fc50142b28eb67f.jpg\",\n  \"38375.jpg\": \"Mammalia/Panthera leo/056d93a2ec3b4eff578625e22d85c4b8.jpg\",\n  \"38380.jpg\": \"Mammalia/Panthera leo/4db7d09fb1f8c120e5cbedfc02918ed2.jpg\",\n  \"38381.jpg\": \"Mammalia/Panthera leo/f477fe574a92bae9e80afac5de9851e1.jpg\",\n  \"383834.jpg\": \"Aves/Setophaga coronata/d1c18ebb4865fb7e2f7c13e780c85a85.jpg\",\n  \"38386.jpg\": \"Mammalia/Panthera leo/65ecde992d586d74c5cc90858568d0a9.jpg\",\n  \"383871.jpg\": \"Aves/Setophaga coronata/0d136c360d622fce7c703165b5559d67.jpg\",\n  \"383899.jpg\": \"Aves/Setophaga coronata/663be858d3680fa7995df672b43540c5.jpg\",\n  \"383961.jpg\": \"Aves/Setophaga coronata/fbe7f6b6ec8474d7946ef46b905b39f3.jpg\",\n  \"383982.jpg\": \"Aves/Setophaga coronata/860345d0df23b0ae1bd965278dc84987.jpg\",\n  \"384.jpg\": \"Insecta/Coleomegilla maculata/91d13582d6490ccf09f08742036da225.jpg\",\n  \"384002.jpg\": \"Aves/Setophaga coronata/2b47d66a18a6799bdc1f249e0115b170.jpg\",\n  \"384102.jpg\": \"Aves/Prosthemadera novaeseelandiae/bf811f5933946c94d36d494f82d159e8.jpg\",\n  \"384103.jpg\": \"Aves/Prosthemadera novaeseelandiae/2d9581dd4ee3cb887e630e1b31ee1ee5.jpg\",\n  \"384105.jpg\": \"Aves/Prosthemadera novaeseelandiae/7374a5720b78b12c561c31826a20b212.jpg\",\n  \"384116.jpg\": \"Aves/Prosthemadera novaeseelandiae/9e89455b4f2bfcbe3bd6f5e2bc654b3f.jpg\",\n  \"384119.jpg\": \"Aves/Prosthemadera novaeseelandiae/5b51a9184856d57b80e0b7b3b8176da4.jpg\",\n  \"384133.jpg\": \"Aves/Prosthemadera novaeseelandiae/2837b5f0f2c8cd92413747fc4438f25a.jpg\",\n  \"384143.jpg\": \"Aves/Prosthemadera novaeseelandiae/26c57a10987726a6865fedc88eba1312.jpg\",\n  \"384155.jpg\": \"Aves/Prosthemadera novaeseelandiae/98cda7a9ac09ef5efa2caec389d88af3.jpg\",\n  \"384176.jpg\": \"Aves/Prosthemadera novaeseelandiae/cf5693143d6aed863fafde568a4def02.jpg\",\n  \"384186.jpg\": \"Aves/Prosthemadera novaeseelandiae/f6256619b40dfe6988e5d210cbcb779b.jpg\",\n  \"384192.jpg\": \"Aves/Prosthemadera novaeseelandiae/83149d15525c099b3c99719ddc7d0eda.jpg\",\n  \"384197.jpg\": \"Aves/Prosthemadera novaeseelandiae/bc2af44387848330d60bafa0cfd40876.jpg\",\n  \"38420.jpg\": \"Mammalia/Panthera leo/1556d5ef8546d73386738371f1681e3f.jpg\",\n  \"384201.jpg\": \"Aves/Prosthemadera novaeseelandiae/c3fd727be5943296857faa8fefd7157a.jpg\",\n  \"38421.jpg\": \"Mammalia/Panthera leo/89165b338a26bba5f05f4ea64b72569f.jpg\",\n  \"384217.jpg\": \"Aves/Prosthemadera novaeseelandiae/f049915afa7b3b07795948bf169d668d.jpg\",\n  \"384220.jpg\": \"Aves/Prosthemadera novaeseelandiae/ba966b14ef809cb940aff6ab1001f216.jpg\",\n  \"384230.jpg\": \"Aves/Prosthemadera novaeseelandiae/841cc3b328ca0bf4ded80fc11bd96862.jpg\",\n  \"384231.jpg\": \"Aves/Prosthemadera novaeseelandiae/28f253a8c25abc40553a4e18d970f048.jpg\",\n  \"384233.jpg\": \"Aves/Prosthemadera novaeseelandiae/eaa1502269f4cdf23c4e6ec72ccccd1e.jpg\",\n  \"384241.jpg\": \"Insecta/Graphosoma lineatum/55ec1ad0aa4bf523d6770fc9c799b8fb.jpg\",\n  \"384251.jpg\": \"Insecta/Graphosoma lineatum/32f39ac5c93397494d66e26dce785c88.jpg\",\n  \"384264.jpg\": \"Insecta/Graphosoma lineatum/712a747a7eb9392ae6e35f564147a65b.jpg\",\n  \"384265.jpg\": \"Insecta/Graphosoma lineatum/33a85bef51927079a0e06088efe16595.jpg\",\n  \"384268.jpg\": \"Insecta/Graphosoma lineatum/b28564a66ab13cf8197b69f3c60e3e47.jpg\",\n  \"38427.jpg\": \"Mammalia/Panthera leo/ac6993116734a7d6c9aa5108f8602b00.jpg\",\n  \"384273.jpg\": \"Insecta/Graphosoma lineatum/199c74b2087518eeb4a8727a7e550321.jpg\",\n  \"38433.jpg\": \"Amphibia/Bufotes viridis/57dcb9920d76cd381ef7dd25c0610890.jpg\",\n  \"384343.jpg\": \"Insecta/Solenopsis invicta/b78c40cc0a7aee57cbf117caf1fefbfd.jpg\",\n  \"384351.jpg\": \"Insecta/Solenopsis invicta/e6580062c1cb97b6f039bba27cbdf27c.jpg\",\n  \"384378.jpg\": \"Aves/Crotophaga ani/02d5b06404a1a2eee25e69ea3c42711b.jpg\",\n  \"384379.jpg\": \"Aves/Crotophaga ani/880737751eb9f5044c01602a2e44b008.jpg\",\n  \"384382.jpg\": \"Aves/Crotophaga ani/78adb43eab0f37e5dd655c68bdfe5544.jpg\",\n  \"384386.jpg\": \"Aves/Crotophaga ani/75155e14db20fe4bb69fbae214b38cf1.jpg\",\n  \"384387.jpg\": \"Aves/Crotophaga ani/14316f34c7bc79cb6e5c64ed5e517f0f.jpg\",\n  \"384390.jpg\": \"Aves/Crotophaga ani/fed728f9499a6292091c90f16172d98a.jpg\",\n  \"384401.jpg\": \"Aves/Crotophaga ani/be1cbe80710e257ebb40c60760bd1f8a.jpg\",\n  \"38443.jpg\": \"Amphibia/Bufotes viridis/32284b8c9511bba03e84b30f6275f93a.jpg\",\n  \"384436.jpg\": \"Insecta/Didymops transversa/7764b0aa4c921c0fa060204f9d1512c9.jpg\",\n  \"384441.jpg\": \"Insecta/Didymops transversa/78aba6ca289e757c131addc247a78d9f.jpg\",\n  \"384448.jpg\": \"Insecta/Didymops transversa/aeb0a663e8937a20f23a7ce35d8399da.jpg\",\n  \"384451.jpg\": \"Insecta/Didymops transversa/461bf1876ce4e23d13e3ce5fab6214b5.jpg\",\n  \"384452.jpg\": \"Insecta/Didymops transversa/476047f4e8144876890e3745f76904fd.jpg\",\n  \"384460.jpg\": \"Insecta/Didymops transversa/bd772cae9186d7fab1cb4be36d783f41.jpg\",\n  \"384504.jpg\": \"Insecta/Ladona julia/2e72c1c37ac8e5d5014b732670d54197.jpg\",\n  \"384508.jpg\": \"Insecta/Ladona julia/ce2f021bbceb478dc776da7915312e7f.jpg\",\n  \"384514.jpg\": \"Insecta/Ladona julia/cd7db2a7c0fe05e32dbf83f2f42b41bf.jpg\",\n  \"384515.jpg\": \"Insecta/Ladona julia/27221fe021ab276c8104c606506213f1.jpg\",\n  \"384518.jpg\": \"Insecta/Ladona julia/68277b5f4068188f649069f6190db6ef.jpg\",\n  \"384522.jpg\": \"Insecta/Ladona julia/6edcc859295ee8a455784129cbd6b2ab.jpg\",\n  \"384534.jpg\": \"Insecta/Ladona julia/0e062d501375b0a8166980c37c7cf1c4.jpg\",\n  \"384536.jpg\": \"Insecta/Ladona julia/b72ba2d6f1db77cdfaf24de181ceaeba.jpg\",\n  \"384541.jpg\": \"Insecta/Ladona julia/50e5dd1c653bfbc07d2f9e100bd1391b.jpg\",\n  \"384545.jpg\": \"Insecta/Ladona julia/1edf3f0e83673bfc7045255f86be8350.jpg\",\n  \"384547.jpg\": \"Insecta/Ladona julia/143b60ade883d7bd5f3e5b696f71f7ec.jpg\",\n  \"384549.jpg\": \"Insecta/Ladona julia/ef4875bcaa46fa5bb5624a5e9eb91a78.jpg\",\n  \"384554.jpg\": \"Insecta/Ladona julia/424e76e55282dde289126c6f6cd61eca.jpg\",\n  \"384556.jpg\": \"Insecta/Ladona julia/f93cde1dd6e07d59a23e9ded5bbdc787.jpg\",\n  \"384558.jpg\": \"Insecta/Ladona julia/53bf7d10d58fbc37eb6f46ab7f7fb73f.jpg\",\n  \"384573.jpg\": \"Insecta/Ladona julia/24cd4acc0a2a55d09f6078b39977fef6.jpg\",\n  \"384582.jpg\": \"Insecta/Ladona julia/d809723355b02a236171d837a5451720.jpg\",\n  \"384583.jpg\": \"Insecta/Ladona julia/c75e4358119c6eeff2848296e9c0b3b9.jpg\",\n  \"384589.jpg\": \"Insecta/Ladona julia/987da0ae869219d6153c292bfcebed60.jpg\",\n  \"384592.jpg\": \"Insecta/Ladona julia/c7efd310d8569317d83498cd2123cdf7.jpg\",\n  \"384593.jpg\": \"Insecta/Ladona julia/c5f4b90f1b73b62082472c22e642a3f0.jpg\",\n  \"384596.jpg\": \"Insecta/Ladona julia/06c20bab5f46adffff5c65596c93c592.jpg\",\n  \"384597.jpg\": \"Insecta/Ladona julia/0133a4c39b778e2161c24ff88e24b557.jpg\",\n  \"384599.jpg\": \"Insecta/Ladona julia/7b401c4ae301352ab552f0e9c11f0c3f.jpg\",\n  \"384642.jpg\": \"Insecta/Pieris marginalis/043e1778dba9028e1771fcef34388bba.jpg\",\n  \"384643.jpg\": \"Insecta/Pieris marginalis/412cbc7ca046b695c2bd30f77f58d5a2.jpg\",\n  \"384644.jpg\": \"Insecta/Pieris marginalis/0dc10891581dce334c51facc1dd066bf.jpg\",\n  \"384645.jpg\": \"Insecta/Pieris marginalis/5bca4d7c06a65d763f37d32832a2399c.jpg\",\n  \"384646.jpg\": \"Insecta/Pieris marginalis/9f208a918325ce57d17da5f3e2327d5b.jpg\",\n  \"384648.jpg\": \"Insecta/Pieris marginalis/5a07dcb6e4760529cf49274a22a38fb9.jpg\",\n  \"384665.jpg\": \"Insecta/Pieris marginalis/114be70715bccf767dde3d3c6f530525.jpg\",\n  \"384667.jpg\": \"Insecta/Pieris marginalis/662abfe52e5b1116b556d86c361f1896.jpg\",\n  \"384669.jpg\": \"Insecta/Pieris marginalis/61cac35462d812d311ff7123067b276b.jpg\",\n  \"384670.jpg\": \"Insecta/Pieris marginalis/0ebb06930ebf72273f7214ebf911c74c.jpg\",\n  \"384672.jpg\": \"Insecta/Pieris marginalis/f6b78869cd070ba664d3dcc8dee61969.jpg\",\n  \"384679.jpg\": \"Insecta/Pieris marginalis/96369720bae45ff525c29952aa8306e7.jpg\",\n  \"384683.jpg\": \"Insecta/Pieris marginalis/98de46c04bbc2e0e0f8861017d2eadf7.jpg\",\n  \"384688.jpg\": \"Insecta/Pieris marginalis/92946a09aa0d791b31b1777d11454283.jpg\",\n  \"384690.jpg\": \"Insecta/Pieris marginalis/f6d1255065c6fe7379fe193fd0cab877.jpg\",\n  \"384691.jpg\": \"Insecta/Pieris marginalis/261b87c35ba55960eec45f9625f831af.jpg\",\n  \"384692.jpg\": \"Insecta/Pieris marginalis/a8430c85ca88490149cbc1757cac8ab0.jpg\",\n  \"384695.jpg\": \"Insecta/Pieris marginalis/1b0a035ddca809064d1c84eba0221593.jpg\",\n  \"384696.jpg\": \"Insecta/Pieris marginalis/e7f6ba1bc7be08925938df720a5a1535.jpg\",\n  \"384697.jpg\": \"Insecta/Eubaphe mendica/a4127317b20f9895fd7438cb9e72261b.jpg\",\n  \"384698.jpg\": \"Insecta/Eubaphe mendica/d433e72261e90a7bc5aca0e6159007f1.jpg\",\n  \"384707.jpg\": \"Insecta/Eubaphe mendica/b39d1308524f0984292daa29eeffc54e.jpg\",\n  \"384709.jpg\": \"Insecta/Eubaphe mendica/5b16060a581fca922b9c3c74c5ce443b.jpg\",\n  \"384714.jpg\": \"Insecta/Eubaphe mendica/ab587ec733e3103b539a57de7b8fb688.jpg\",\n  \"384715.jpg\": \"Insecta/Eubaphe mendica/e6868764318a64aa733f3b9d3bc975e4.jpg\",\n  \"384719.jpg\": \"Insecta/Eubaphe mendica/b53554e2a5e2db0d61c7ab508ef72313.jpg\",\n  \"38473.jpg\": \"Aves/Coracias benghalensis/f624ceebec293cdffef291dcb88302cf.jpg\",\n  \"38474.jpg\": \"Aves/Coracias benghalensis/1fe5046111100a87023d2ceeff1c287b.jpg\",\n  \"384779.jpg\": \"Insecta/Telebasis salva/491f9aac74095c997bbfec3efbe05988.jpg\",\n  \"384782.jpg\": \"Insecta/Telebasis salva/4d3b23aae4bc079639149d166b42cafc.jpg\",\n  \"384788.jpg\": \"Insecta/Telebasis salva/9c8e72306747e20fa046289d29339a52.jpg\",\n  \"384789.jpg\": \"Insecta/Telebasis salva/68ba954d540b17a30ee2e400efd7a956.jpg\",\n  \"384799.jpg\": \"Insecta/Telebasis salva/35d7232b11c503d648cbb6b3c2cedab8.jpg\",\n  \"384802.jpg\": \"Insecta/Telebasis salva/16f83544ec324b8c1e43a9fa4ca7a769.jpg\",\n  \"384807.jpg\": \"Insecta/Telebasis salva/a715c6b03d0f3a541bf0e90fbae9de0b.jpg\",\n  \"384808.jpg\": \"Insecta/Telebasis salva/acbea698f73748a3b8aae8000307cbdf.jpg\",\n  \"384811.jpg\": \"Insecta/Telebasis salva/7d103a768f651c5ec4774a6aa0b0ae45.jpg\",\n  \"384816.jpg\": \"Insecta/Telebasis salva/73ed3c0882c0caae26be220405d1c5d7.jpg\",\n  \"38482.jpg\": \"Aves/Coracias benghalensis/3d904ee34717202049df940d42d88f9f.jpg\",\n  \"384820.jpg\": \"Insecta/Telebasis salva/c4d33baac8b4b6004daf290921990278.jpg\",\n  \"384821.jpg\": \"Insecta/Telebasis salva/107ee851279cffaba1f6e75912ae9c54.jpg\",\n  \"384848.jpg\": \"Insecta/Telebasis salva/75bd7ea0c12c58c474aac55db6816af9.jpg\",\n  \"384849.jpg\": \"Insecta/Telebasis salva/80a523012ce277af5b785fb481ac36b9.jpg\",\n  \"384853.jpg\": \"Insecta/Telebasis salva/4ac047264a2d1d9f8aa0ee33fc72e0e4.jpg\",\n  \"384854.jpg\": \"Insecta/Telebasis salva/e294dcfd60e9cc80ab52d8c0f9487ac2.jpg\",\n  \"384857.jpg\": \"Insecta/Telebasis salva/23b35f570548a8f061d32f1fd88752a6.jpg\",\n  \"384863.jpg\": \"Insecta/Telebasis salva/7231235385ffdee5e84c3e7b44745661.jpg\",\n  \"384897.jpg\": \"Insecta/Telebasis salva/2d9315ba8465f4a1b4eb98e282419021.jpg\",\n  \"384898.jpg\": \"Insecta/Telebasis salva/78e72b9a162eb47e1c99489c00e33d5e.jpg\",\n  \"384906.jpg\": \"Aves/Tachybaptus dominicus/5ee2dbe624d82847110398ed2850b8b6.jpg\",\n  \"384913.jpg\": \"Aves/Tachybaptus dominicus/05c93075e53b0dc50cbdbf467e37a6eb.jpg\",\n  \"384916.jpg\": \"Aves/Tachybaptus dominicus/b0e556c19edaf77dce1327e14054acf5.jpg\",\n  \"384918.jpg\": \"Aves/Tachybaptus dominicus/3341b0ddf662953c8113f334774a77ad.jpg\",\n  \"384919.jpg\": \"Aves/Tachybaptus dominicus/b1d1d9ee0c56743291f9063f79e5451e.jpg\",\n  \"384924.jpg\": \"Aves/Tachybaptus dominicus/85da459ef6249597ab53606d19147681.jpg\",\n  \"384944.jpg\": \"Aves/Tachybaptus dominicus/a9989809f379a10a0e6b085a79e491e4.jpg\",\n  \"384945.jpg\": \"Aves/Tachybaptus dominicus/e2ba2e60f833c6361e6a16dd7719514a.jpg\",\n  \"384946.jpg\": \"Aves/Tachybaptus dominicus/bd39bede6ee3fa9b5a1ac7d0880ee66a.jpg\",\n  \"384947.jpg\": \"Aves/Tachybaptus dominicus/ae8118e53ec8cdc54ec23b99d7371936.jpg\",\n  \"384952.jpg\": \"Aves/Tachybaptus dominicus/f9113b55abd11ae615a31b3e4f50bf52.jpg\",\n  \"384954.jpg\": \"Aves/Tachybaptus dominicus/3b878f9cfc3daf8053d945f18240232c.jpg\",\n  \"384964.jpg\": \"Aves/Tachybaptus dominicus/d878e9f1f10fed8b58eb703c4789b89d.jpg\",\n  \"384968.jpg\": \"Aves/Tachybaptus dominicus/37b6c7c7c156538ff59688f0391ecb2e.jpg\",\n  \"384969.jpg\": \"Aves/Tachybaptus dominicus/5b8e939bfc26021b8ec35bbd187d2137.jpg\",\n  \"384970.jpg\": \"Aves/Tachybaptus dominicus/085014a59e2a852a84f3f9965a3be707.jpg\",\n  \"384971.jpg\": \"Aves/Tachybaptus dominicus/12c0717a81c0a11eaede1b36c6c993b7.jpg\",\n  \"384972.jpg\": \"Aves/Tachybaptus dominicus/ea119e7d43ce9724fd6bbc53769aca27.jpg\",\n  \"384977.jpg\": \"Aves/Tachybaptus dominicus/cbc0c38153ed0be98b9b42efee848098.jpg\",\n  \"384980.jpg\": \"Aves/Tachybaptus dominicus/a6f7910298cc69ac6122ab08b6918e39.jpg\",\n  \"384981.jpg\": \"Aves/Tachybaptus dominicus/c52dc308dcb325be95346f6cace2094f.jpg\",\n  \"384987.jpg\": \"Aves/Tachybaptus dominicus/af3afe2f7dc28b9164764138411a1bbc.jpg\",\n  \"385000.jpg\": \"Insecta/Adelpha californica/d88a41ef9f633f38231867251c5b75aa.jpg\",\n  \"385003.jpg\": \"Insecta/Adelpha californica/f91a13eae77254448083c23cf0b59248.jpg\",\n  \"385029.jpg\": \"Insecta/Adelpha californica/ed9967a5648046aca6d57f44ae0fe4aa.jpg\",\n  \"385031.jpg\": \"Insecta/Adelpha californica/1df53b3d7b1f026a1223c256f276bc42.jpg\",\n  \"385041.jpg\": \"Insecta/Adelpha californica/dbe81eff42685cba270a254ab836ce03.jpg\",\n  \"385052.jpg\": \"Insecta/Adelpha californica/0cedf5340f488bd6102252cab4b1f433.jpg\",\n  \"385073.jpg\": \"Insecta/Adelpha californica/2c0f71e676f2f5d942405ce2456581f6.jpg\",\n  \"385085.jpg\": \"Insecta/Adelpha californica/06e07ea5fe4a26ec2678971e0b137785.jpg\",\n  \"385094.jpg\": \"Insecta/Adelpha californica/0ff5cdb6e5644d804b0148815a5e50a4.jpg\",\n  \"385099.jpg\": \"Insecta/Adelpha californica/1a0c5fdafd110d6c9feef28831419c26.jpg\",\n  \"385114.jpg\": \"Insecta/Adelpha californica/917fafe82da88c9f43b959df4b86d5bb.jpg\",\n  \"385115.jpg\": \"Insecta/Adelpha californica/e90189cd17326a97a35b20ad7d28b752.jpg\",\n  \"385116.jpg\": \"Insecta/Adelpha californica/4f0ce98541d0552a0472efc36fe72e5e.jpg\",\n  \"38514.jpg\": \"Insecta/Schizura unicornis/90bdc1a762699bbd81c7221a19708186.jpg\",\n  \"385165.jpg\": \"Insecta/Adelpha californica/664905d545ea6098c949606fee75bf1a.jpg\",\n  \"385174.jpg\": \"Insecta/Adelpha californica/783db6f3ba3ea7b533eebb2d90a5a4d7.jpg\",\n  \"38518.jpg\": \"Insecta/Schizura unicornis/1f0e34418197148945ade28fe8209a1b.jpg\",\n  \"385209.jpg\": \"Animalia/Harpaphe haydeniana/98ac424cb2bd7c6de8fd41e22c09851c.jpg\",\n  \"385211.jpg\": \"Animalia/Harpaphe haydeniana/4f965a92250d62a7ec3d3647a764f823.jpg\",\n  \"385212.jpg\": \"Animalia/Harpaphe haydeniana/9dcd071b5f490605cbde009dfcf7ead3.jpg\",\n  \"385217.jpg\": \"Animalia/Harpaphe haydeniana/7ac3c4f5ef4f9daabe5dc52417271652.jpg\",\n  \"385218.jpg\": \"Animalia/Harpaphe haydeniana/27313966897c184c2b03d90ec92340e0.jpg\",\n  \"385222.jpg\": \"Animalia/Harpaphe haydeniana/f09789898c9ba65ee56eb230cfdbeaa8.jpg\",\n  \"385237.jpg\": \"Animalia/Harpaphe haydeniana/404d355d6b7190fc314fa37ec4caf52f.jpg\",\n  \"385238.jpg\": \"Animalia/Harpaphe haydeniana/c65f9cbc3d85606a634ff9903eacecb6.jpg\",\n  \"38524.jpg\": \"Insecta/Schizura unicornis/58f674dcf1776fc3470222efc902f6e8.jpg\",\n  \"385240.jpg\": \"Animalia/Harpaphe haydeniana/a42d12dc360aabc595fa7f994d695085.jpg\",\n  \"385242.jpg\": \"Animalia/Harpaphe haydeniana/29b72fae055e3a029cb1fe8acc04b188.jpg\",\n  \"385247.jpg\": \"Animalia/Harpaphe haydeniana/aff4eb96ab4ffb7679d2385896c3e2ff.jpg\",\n  \"385254.jpg\": \"Animalia/Harpaphe haydeniana/263923dce955413a9460500a03be4bf0.jpg\",\n  \"385260.jpg\": \"Animalia/Harpaphe haydeniana/df2333f8e466b3872d809197d3b8681b.jpg\",\n  \"385262.jpg\": \"Animalia/Harpaphe haydeniana/06f866f55ec3db7b5048ab50026bc964.jpg\",\n  \"385263.jpg\": \"Animalia/Harpaphe haydeniana/8a3d051ab642275972b2a42a263219a6.jpg\",\n  \"385267.jpg\": \"Animalia/Harpaphe haydeniana/5d225edc6affa973a918802b43c6b487.jpg\",\n  \"385271.jpg\": \"Animalia/Harpaphe haydeniana/907e2b21e071325d372179ab9c93acd5.jpg\",\n  \"385272.jpg\": \"Animalia/Harpaphe haydeniana/0151a46fdb836b76c345582dc397fa97.jpg\",\n  \"385279.jpg\": \"Aves/Poecile gambeli/2766fd3375e86e7f8240516da43aa687.jpg\",\n  \"385280.jpg\": \"Aves/Poecile gambeli/8b9c69e87d217bb456bc69a5e65995db.jpg\",\n  \"385285.jpg\": \"Aves/Poecile gambeli/d41d686f8d9919805222f6d0e3ba4fea.jpg\",\n  \"385287.jpg\": \"Aves/Poecile gambeli/c6bd67a98cc0bb1edc5cb6aaf7f67b61.jpg\",\n  \"385288.jpg\": \"Aves/Poecile gambeli/a1097e6a2d1cf5526e8cdbcab0a09ba6.jpg\",\n  \"385294.jpg\": \"Aves/Poecile gambeli/a38a727e27603d794405c9f40cac7322.jpg\",\n  \"385296.jpg\": \"Aves/Poecile gambeli/31cb3411693187ffd43cf4a12d17dd2c.jpg\",\n  \"385297.jpg\": \"Aves/Poecile gambeli/f7da14bdb82d917baa0bf3256c276017.jpg\",\n  \"385300.jpg\": \"Aves/Poecile gambeli/afdd75df351fe17d567d33d86c6b7fea.jpg\",\n  \"385302.jpg\": \"Aves/Poecile gambeli/75cc69fe2cfc7f1278a1b4f0b5d63766.jpg\",\n  \"385308.jpg\": \"Aves/Poecile gambeli/25ec9df9888355da5cda86868859906a.jpg\",\n  \"385309.jpg\": \"Aves/Poecile gambeli/385742421dc4f1494aacb61487704a3a.jpg\",\n  \"38532.jpg\": \"Insecta/Schizura unicornis/ab67395e83707f65ac0d1fd45573d084.jpg\",\n  \"385323.jpg\": \"Aves/Poecile gambeli/4baecfc5cbda7b43d33fce8d80f4156e.jpg\",\n  \"385325.jpg\": \"Aves/Poecile gambeli/8ca488661768c91a83c21dff3cbee33e.jpg\",\n  \"385339.jpg\": \"Aves/Poecile gambeli/ce87aaef4b6fd3ddc0d895834f2a3432.jpg\",\n  \"385340.jpg\": \"Aves/Poecile gambeli/2f7ae32e1922d7245c6b5478b1be24ee.jpg\",\n  \"385341.jpg\": \"Aves/Poecile gambeli/78439020640ab1ed20256b720efefbcd.jpg\",\n  \"385342.jpg\": \"Aves/Poecile gambeli/5e06a6dd01bf26af4f71755876b5f5b3.jpg\",\n  \"385365.jpg\": \"Aves/Poecile gambeli/5f44d695faf5af25dd066f59ab8bc7a3.jpg\",\n  \"385372.jpg\": \"Aves/Poecile gambeli/68686ecad90a51fb3feb9d8d6e7f9f70.jpg\",\n  \"385375.jpg\": \"Insecta/Bombus ternarius/a6487d2b59401b67766182805d6444b0.jpg\",\n  \"385376.jpg\": \"Insecta/Bombus ternarius/f41a5878566a58ced711fe028bfe0274.jpg\",\n  \"385381.jpg\": \"Insecta/Bombus ternarius/fdef43201899655a2a4b6a921d289ddb.jpg\",\n  \"385386.jpg\": \"Insecta/Bombus ternarius/d33d8ec09d886ffbf60b61253cef123d.jpg\",\n  \"385387.jpg\": \"Insecta/Bombus ternarius/cfd16587af0724ed31e014f8e05b7aa3.jpg\",\n  \"385388.jpg\": \"Insecta/Bombus ternarius/7a484db8e9ba749a9f3a82ef73c6ccb4.jpg\",\n  \"38539.jpg\": \"Insecta/Schizura unicornis/0363859e276d7c9aed3a89861016c2a3.jpg\",\n  \"385407.jpg\": \"Insecta/Bombus ternarius/6c6b55b92a900ebd13657b408021b134.jpg\",\n  \"385411.jpg\": \"Insecta/Bombus ternarius/35fa4d5f43c5a325fc61e00d574f5041.jpg\",\n  \"385415.jpg\": \"Insecta/Bombus ternarius/aa6b25abbc953a4bbf5f5b47404e510f.jpg\",\n  \"385419.jpg\": \"Insecta/Bombus ternarius/c2b7b0ac4637372f6ad8b5148582b8d5.jpg\",\n  \"38542.jpg\": \"Insecta/Schizura unicornis/cbefe3f98a9a6d7dc9eae96e393916d8.jpg\",\n  \"385420.jpg\": \"Insecta/Bombus ternarius/6138bbda983ebafc1214b5e7bc4a0d1d.jpg\",\n  \"385421.jpg\": \"Insecta/Bombus ternarius/3a743bda4da9510c66ba9cf93bb97b55.jpg\",\n  \"385433.jpg\": \"Insecta/Bombus ternarius/7e3d484032578d57379e47799b59bb1f.jpg\",\n  \"385434.jpg\": \"Insecta/Bombus ternarius/73999521a75b8b39e7e76fc491b34274.jpg\",\n  \"385435.jpg\": \"Insecta/Bombus ternarius/c337191cb3e31b014f0c26bb251826a0.jpg\",\n  \"385450.jpg\": \"Insecta/Bombus ternarius/3ee54d4b654fd07b2cf7f00f5b077437.jpg\",\n  \"385451.jpg\": \"Insecta/Bombus ternarius/006938f78449330ff6b1b9572e21eede.jpg\",\n  \"385464.jpg\": \"Insecta/Bombus ternarius/ce3c89b1ee67a43b62940318b592a9d4.jpg\",\n  \"385465.jpg\": \"Insecta/Bombus ternarius/a4608c495dc640ce9c58f73c24c76c15.jpg\",\n  \"385473.jpg\": \"Insecta/Bombus ternarius/f10a52250937acdd2c9287066ef12ac1.jpg\",\n  \"385485.jpg\": \"Insecta/Bombus ternarius/fd1b1b441a44d722261c207e0f8043d3.jpg\",\n  \"385487.jpg\": \"Insecta/Bombus ternarius/3e90ca1340a01f196be92d2f1ce30bdf.jpg\",\n  \"385552.jpg\": \"Aves/Ara macao/679b38d1a671ae79c8e5320b58917f0a.jpg\",\n  \"385559.jpg\": \"Aves/Ara macao/d96625208aa443452b760b480e0fec9a.jpg\",\n  \"385560.jpg\": \"Aves/Ara macao/504b10ce70e16b8684c235684e0278d1.jpg\",\n  \"385565.jpg\": \"Aves/Ara macao/8094d3fcefc33812f0d1ef08f51f4faa.jpg\",\n  \"385567.jpg\": \"Aves/Ara macao/37d20fa728ef8c64daefc4a3abc3f890.jpg\",\n  \"385595.jpg\": \"Insecta/Araschnia levana/90f44b67c764030d3bd6388f6c3ef3be.jpg\",\n  \"385596.jpg\": \"Insecta/Araschnia levana/a52da2f4f434c4e206091abca23afcb8.jpg\",\n  \"385598.jpg\": \"Insecta/Araschnia levana/a58d810b5e78e6315f60a1c615d7136e.jpg\",\n  \"385600.jpg\": \"Insecta/Araschnia levana/879b9fabb87ef95f5fd0c648aaad01d1.jpg\",\n  \"385602.jpg\": \"Insecta/Araschnia levana/da27f3550e7119cbf77f29d298eedd87.jpg\",\n  \"385607.jpg\": \"Insecta/Araschnia levana/0fc3d94b78d8f817d58f6eeb5ca8031e.jpg\",\n  \"385609.jpg\": \"Insecta/Araschnia levana/5fd950020bb6c664446b4b7136f5b583.jpg\",\n  \"385612.jpg\": \"Insecta/Araschnia levana/05dad2c503c8a5e4e4e56f881ff949b7.jpg\",\n  \"385613.jpg\": \"Insecta/Araschnia levana/563b540e8f94654cd45c4065f509916b.jpg\",\n  \"385615.jpg\": \"Insecta/Araschnia levana/579d7f9aa03b682fd418475f1928ba2c.jpg\",\n  \"385617.jpg\": \"Insecta/Araschnia levana/28cadba7199745a364aa97d610ef4b90.jpg\",\n  \"385618.jpg\": \"Insecta/Araschnia levana/6b4f03e31f6035b64b088d8fdd2b2acb.jpg\",\n  \"385619.jpg\": \"Insecta/Araschnia levana/30f3539c64a39d17057d7a40cc01d56d.jpg\",\n  \"385622.jpg\": \"Insecta/Araschnia levana/ae2e95249c8223b7208440eaacd3d269.jpg\",\n  \"385623.jpg\": \"Insecta/Araschnia levana/e44c213bcdb4691dc6b4daf8b69100a2.jpg\",\n  \"385627.jpg\": \"Insecta/Araschnia levana/d20be982701d282ec537b845d6002921.jpg\",\n  \"385628.jpg\": \"Insecta/Araschnia levana/41b00a8a6105841e33e42519a49efa84.jpg\",\n  \"385629.jpg\": \"Insecta/Araschnia levana/cc980ecbf792bfa5be4a5ba1a5b29b92.jpg\",\n  \"385630.jpg\": \"Insecta/Araschnia levana/89273d6e1677e156eb7adb1759ec0b51.jpg\",\n  \"385644.jpg\": \"Reptilia/Crotalus oreganus oreganus/ddd8160b83bddcf4f112fddfba3b6338.jpg\",\n  \"385646.jpg\": \"Reptilia/Crotalus oreganus oreganus/711d1f598525a3fad3fd80074fe9b698.jpg\",\n  \"385655.jpg\": \"Reptilia/Crotalus oreganus oreganus/93071e90df79ae6efb5f172cde4ff5fe.jpg\",\n  \"385659.jpg\": \"Reptilia/Crotalus oreganus oreganus/8fdabcddef0cd258d488c82645b9e5bd.jpg\",\n  \"385673.jpg\": \"Reptilia/Crotalus oreganus oreganus/b3e4c87d6f1cf243c8c3027f78b91c8c.jpg\",\n  \"385679.jpg\": \"Reptilia/Crotalus oreganus oreganus/49e376f5e89a34129652b74189aad18c.jpg\",\n  \"385686.jpg\": \"Reptilia/Crotalus oreganus oreganus/1f40a00b2cfa7fff4f050b4a7e0266aa.jpg\",\n  \"385695.jpg\": \"Reptilia/Crotalus oreganus oreganus/1f51f8cd159af3a65144988d1e1989f6.jpg\",\n  \"385712.jpg\": \"Reptilia/Crotalus oreganus oreganus/89d866d7925c87a42cd2e334b5620fd6.jpg\",\n  \"385714.jpg\": \"Reptilia/Crotalus oreganus oreganus/27a1c248b73fc77df730cd263a9e59db.jpg\",\n  \"385716.jpg\": \"Reptilia/Crotalus oreganus oreganus/c885e9d4b4d3ce889b0fcf008b7b35fb.jpg\",\n  \"385723.jpg\": \"Reptilia/Crotalus oreganus oreganus/d3abfe4076dbd984c1eb87aaa3a0c81e.jpg\",\n  \"385738.jpg\": \"Reptilia/Crotalus oreganus oreganus/2078d25d793cd1ad449951010206e0cb.jpg\",\n  \"385743.jpg\": \"Reptilia/Crotalus oreganus oreganus/1c21256a43f4e2c334b8e734c18e87ac.jpg\",\n  \"385762.jpg\": \"Reptilia/Crotalus oreganus oreganus/8c302c975a3c3f46b9f0ceabbaa9cc13.jpg\",\n  \"385763.jpg\": \"Reptilia/Crotalus oreganus oreganus/53e42ba0ab1e5bc7697f46082338c7cd.jpg\",\n  \"385767.jpg\": \"Reptilia/Crotalus oreganus oreganus/70f27e44f145057b094cf457fd2e6f23.jpg\",\n  \"385768.jpg\": \"Reptilia/Crotalus oreganus oreganus/d99b3164d54bece65719ab42382e0161.jpg\",\n  \"385779.jpg\": \"Reptilia/Crotalus oreganus oreganus/3682227f3383076247374f82d1f8506a.jpg\",\n  \"385782.jpg\": \"Reptilia/Crotalus oreganus oreganus/42ca7ecbf2c9adc9a2a7ccf14a0d6996.jpg\",\n  \"385804.jpg\": \"Reptilia/Crotalus oreganus oreganus/c75daee725e8a082de1ebc071df70065.jpg\",\n  \"385805.jpg\": \"Reptilia/Crotalus oreganus oreganus/5800dfbcfdcb9021380520cae1d37453.jpg\",\n  \"385807.jpg\": \"Insecta/Arigomphus villosipes/4c220f63ebc4984f2389191c00770a83.jpg\",\n  \"385808.jpg\": \"Insecta/Arigomphus villosipes/3fd79c91d5c7767913fed29812ed6eb9.jpg\",\n  \"385810.jpg\": \"Insecta/Arigomphus villosipes/b717bb89a97d10e899f03b1469393ef7.jpg\",\n  \"385813.jpg\": \"Insecta/Arigomphus villosipes/a1ad8d0ca0e2ac9f48f2f6f250832a4f.jpg\",\n  \"385814.jpg\": \"Insecta/Arigomphus villosipes/ef2b6a99e0e35c37fec9294c0bfa3e53.jpg\",\n  \"385863.jpg\": \"Reptilia/Trachemys scripta elegans/d6b4efa338d66283accb8de790c57dd2.jpg\",\n  \"385937.jpg\": \"Reptilia/Trachemys scripta elegans/07cdc6444386103ee2dd785b9a7055eb.jpg\",\n  \"385940.jpg\": \"Reptilia/Trachemys scripta elegans/ef04fd48693c41b26db2362cf7f3df1a.jpg\",\n  \"385968.jpg\": \"Reptilia/Trachemys scripta elegans/f4b7cd4e975dfdefb3a335a3032f86a4.jpg\",\n  \"386063.jpg\": \"Reptilia/Trachemys scripta elegans/d1a2e2a8792410b03d267542ec3f4017.jpg\",\n  \"386092.jpg\": \"Reptilia/Trachemys scripta elegans/815827e20a8b3a1bf291035bed33eb54.jpg\",\n  \"386096.jpg\": \"Reptilia/Trachemys scripta elegans/18ae8818dd5d1bcb1d055198377209cc.jpg\",\n  \"386183.jpg\": \"Reptilia/Trachemys scripta elegans/e1bc73e9d1bfab99e8147883a08b3976.jpg\",\n  \"386205.jpg\": \"Reptilia/Trachemys scripta elegans/278f16d67f22c29a08ce53bbbaa548ef.jpg\",\n  \"38623.jpg\": \"Aves/Eumomota superciliosa/078327db5a4d59e0b4e86682784f4fe0.jpg\",\n  \"386290.jpg\": \"Reptilia/Trachemys scripta elegans/b257989ca67550be15f8e3ad30c7db4f.jpg\",\n  \"386294.jpg\": \"Reptilia/Trachemys scripta elegans/c75bbd0a457ac6bdf2ddb85edbebbd2c.jpg\",\n  \"386330.jpg\": \"Reptilia/Trachemys scripta elegans/547501cf96e3ad7b648f22ddc10b944c.jpg\",\n  \"386353.jpg\": \"Reptilia/Trachemys scripta elegans/48eb5c235a7d8c089f9740897e3c64c0.jpg\",\n  \"386356.jpg\": \"Reptilia/Trachemys scripta elegans/6af4941270d8d59f34f7e5ec52b9f768.jpg\",\n  \"38637.jpg\": \"Aves/Eumomota superciliosa/59e760a94ef18dab80d81abc131c5171.jpg\",\n  \"386372.jpg\": \"Reptilia/Trachemys scripta elegans/b1b352e1fde8609af5ddfb67dde7780f.jpg\",\n  \"38639.jpg\": \"Aves/Eumomota superciliosa/796dd35437126b77dba9e17a2d3b7d30.jpg\",\n  \"386397.jpg\": \"Reptilia/Trachemys scripta elegans/d4f5fe0a7241d88cd8e1f59d0aac8a4b.jpg\",\n  \"38640.jpg\": \"Aves/Eumomota superciliosa/18677d69c8a1e958bceb579fd9f46dff.jpg\",\n  \"386469.jpg\": \"Reptilia/Trachemys scripta elegans/b8befb8018fc9bcac427c8e13bfb7ea7.jpg\",\n  \"386502.jpg\": \"Reptilia/Trachemys scripta elegans/ed4085dc599fd91826c8731fcf87ae28.jpg\",\n  \"38653.jpg\": \"Aves/Eumomota superciliosa/c8b5045e384ef260550152ce406d3555.jpg\",\n  \"38655.jpg\": \"Aves/Eumomota superciliosa/6a332ad05d8746415727cf640a345c7c.jpg\",\n  \"38656.jpg\": \"Aves/Eumomota superciliosa/65b865bddb2238f54cebc13d5039380c.jpg\",\n  \"38660.jpg\": \"Aves/Eumomota superciliosa/d6f2b7af9e30ea50342dd3f1ae5834a7.jpg\",\n  \"38661.jpg\": \"Aves/Eumomota superciliosa/382c40c2875e7a103e3438d137ee672d.jpg\",\n  \"386673.jpg\": \"Insecta/Bombus pensylvanicus/f89261aa65ed3cdacaacb57de6d35514.jpg\",\n  \"386696.jpg\": \"Insecta/Bombus pensylvanicus/a5bd9468552ae99203f94caedaea67e1.jpg\",\n  \"386706.jpg\": \"Insecta/Bombus pensylvanicus/a0dcede37a488efa7f07b4a3403bb3c6.jpg\",\n  \"386717.jpg\": \"Insecta/Bombus pensylvanicus/da78c2441de11709007016baabbaf9c4.jpg\",\n  \"386739.jpg\": \"Insecta/Bombus pensylvanicus/ac4078e5e10ff9fd5a5523824ff04434.jpg\",\n  \"386745.jpg\": \"Insecta/Bombus pensylvanicus/a117326e0ebcdeae7518336c5220944a.jpg\",\n  \"386759.jpg\": \"Insecta/Bombus pensylvanicus/a6a8f2ccbdff1a89aa3e11cd48446a7b.jpg\",\n  \"386774.jpg\": \"Insecta/Bombus pensylvanicus/bcd5b7329024de20cd1f8d5b46fdd793.jpg\",\n  \"386777.jpg\": \"Insecta/Bombus pensylvanicus/d1c7eb0d5b11616d9e3690e97e738299.jpg\",\n  \"386812.jpg\": \"Insecta/Bombus pensylvanicus/2f69a9ed2f0c3da147013e44edfe987c.jpg\",\n  \"386815.jpg\": \"Insecta/Bombus pensylvanicus/8125a7eea55a5232331ee2abe99bbdd1.jpg\",\n  \"386817.jpg\": \"Insecta/Bombus pensylvanicus/ee150c2873c3d6d4ed627015f5b0c252.jpg\",\n  \"386837.jpg\": \"Insecta/Bombus pensylvanicus/de3dfdfc3514e47eaa3498a1e2aa819e.jpg\",\n  \"386838.jpg\": \"Insecta/Bombus pensylvanicus/6a96f3bacb6ca2f10647587d83255afb.jpg\",\n  \"386853.jpg\": \"Insecta/Bombus pensylvanicus/1fe724a4494a44446a3f1f99a4c89606.jpg\",\n  \"386861.jpg\": \"Insecta/Bombus pensylvanicus/23ce2ef918959c91719ca6ed7d3ff0eb.jpg\",\n  \"386869.jpg\": \"Insecta/Bombus pensylvanicus/7427f712ca7222e977b1a8908081a4d9.jpg\",\n  \"386870.jpg\": \"Insecta/Bombus pensylvanicus/ef0f4a794716f210e34e23618d1b4da4.jpg\",\n  \"386890.jpg\": \"Insecta/Bombus pensylvanicus/70d902b886a8142b510a6dd9f4a77812.jpg\",\n  \"386926.jpg\": \"Insecta/Bombus pensylvanicus/121da47a20a2cb782d6fae1aa32334c1.jpg\",\n  \"386941.jpg\": \"Insecta/Bombus pensylvanicus/b1cf87b1b26960999a3f6cf303281d3c.jpg\",\n  \"386949.jpg\": \"Insecta/Bombus pensylvanicus/5bab991f20a982ccef57265e02ec879f.jpg\",\n  \"38710.jpg\": \"Insecta/Euphydryas chalcedona/9080f6f21eb1fc1ce11bbf111c7dbb8e.jpg\",\n  \"387213.jpg\": \"Insecta/Malacosoma americanum/7c180eee4e3e9c169a846aec2faada85.jpg\",\n  \"387228.jpg\": \"Insecta/Malacosoma americanum/135ee2b9d23f655cf1fa6bb393df21c2.jpg\",\n  \"387241.jpg\": \"Insecta/Malacosoma americanum/2ca070443f2cf7b2bc271f6cae449e81.jpg\",\n  \"387263.jpg\": \"Insecta/Malacosoma americanum/8493112b63e938ed482b4bd6fb09a8dc.jpg\",\n  \"387266.jpg\": \"Insecta/Malacosoma americanum/f65fe5d3ad0a07d8ed1960fdd815819a.jpg\",\n  \"38727.jpg\": \"Insecta/Euphydryas chalcedona/a63fc59acd5e20aa6038b01fcca087e1.jpg\",\n  \"387276.jpg\": \"Insecta/Malacosoma americanum/5d3beb6275b8d9881c5d438c76f52b27.jpg\",\n  \"387287.jpg\": \"Insecta/Malacosoma americanum/4d7dd83fa7d277f13e2008511e53a862.jpg\",\n  \"387300.jpg\": \"Insecta/Malacosoma americanum/0f1912f86da33e722c8b51660ec28ba3.jpg\",\n  \"387305.jpg\": \"Insecta/Malacosoma americanum/04b76ecf44dab6649bf2862a1f28306d.jpg\",\n  \"387306.jpg\": \"Insecta/Malacosoma americanum/4ea2785d907a7987db391b42ded9f540.jpg\",\n  \"38733.jpg\": \"Insecta/Euphydryas chalcedona/00ac68fa4924fc620e18d07098a5755a.jpg\",\n  \"38736.jpg\": \"Insecta/Euphydryas chalcedona/1ba211e75a87a8df84ce6791fd8e989e.jpg\",\n  \"38737.jpg\": \"Insecta/Euphydryas chalcedona/296b4a9bdb254234eff3906afb77a052.jpg\",\n  \"387376.jpg\": \"Insecta/Malacosoma americanum/577423cfb1c0cbab01dd96f1258c8ed7.jpg\",\n  \"387377.jpg\": \"Insecta/Malacosoma americanum/5b349c3c8d202891272c14328e17067a.jpg\",\n  \"38743.jpg\": \"Insecta/Euphydryas chalcedona/11ac7bf3af28bd2f95f616eaf35b2125.jpg\",\n  \"38757.jpg\": \"Insecta/Euphydryas chalcedona/4b165841ce7983678bd47f78b23b6ac0.jpg\",\n  \"387581.jpg\": \"Insecta/Chinavia hilaris/be4d8363be02135ec7e41318ac87a52e.jpg\",\n  \"387590.jpg\": \"Insecta/Chinavia hilaris/7976955a210e7691f25dd425c357bd96.jpg\",\n  \"387593.jpg\": \"Insecta/Chinavia hilaris/fdccd89eb6f2d6897a1d63a05ee1fe79.jpg\",\n  \"387595.jpg\": \"Insecta/Chinavia hilaris/b1c7bc4e2e1016d9f50405dbbb3699f2.jpg\",\n  \"387598.jpg\": \"Insecta/Chinavia hilaris/b5a61bec5b9a8e2a9042c977bac7a857.jpg\",\n  \"387604.jpg\": \"Insecta/Chinavia hilaris/7f56dbec37d982dc67223c52470bc59b.jpg\",\n  \"387613.jpg\": \"Insecta/Chinavia hilaris/30876243a31262094b0ddf11e331d21c.jpg\",\n  \"387628.jpg\": \"Insecta/Chinavia hilaris/90563a9c25f09d04605b1d4223ad1be9.jpg\",\n  \"387629.jpg\": \"Insecta/Chinavia hilaris/c9bce1e81f83fb638e5bde2795946a35.jpg\",\n  \"387630.jpg\": \"Insecta/Chinavia hilaris/720615443b131db64ddf6640dd021720.jpg\",\n  \"387631.jpg\": \"Insecta/Chinavia hilaris/5da41e9bd35248210aff3d84a644c181.jpg\",\n  \"387633.jpg\": \"Insecta/Chinavia hilaris/eefba73800253eb0fd76a1b85a0dfb67.jpg\",\n  \"387635.jpg\": \"Insecta/Chinavia hilaris/aabc412c1843edef52977749aef4e1ee.jpg\",\n  \"387646.jpg\": \"Insecta/Chinavia hilaris/ff2896653121b63d1f096ec699f93cae.jpg\",\n  \"387647.jpg\": \"Insecta/Chinavia hilaris/72f80b9300739a5500b14ab2d2fb44b2.jpg\",\n  \"387649.jpg\": \"Insecta/Chinavia hilaris/bef58fd9292c0c498ce01071b6e488d5.jpg\",\n  \"387650.jpg\": \"Insecta/Chinavia hilaris/3609629934fb8844e703623e1efc321a.jpg\",\n  \"38766.jpg\": \"Insecta/Euphydryas chalcedona/5be0521383f39456a9fe3ea1f557306d.jpg\",\n  \"387660.jpg\": \"Insecta/Chinavia hilaris/3d2930651daa3409d1fb1122062946eb.jpg\",\n  \"387663.jpg\": \"Aves/Porphyrio melanotus melanotus/5e20bcfc1d51640d8cc4788b3a8eb4db.jpg\",\n  \"387669.jpg\": \"Aves/Porphyrio melanotus melanotus/8bbe1c336e987f5d94cd5dfd5d21a141.jpg\",\n  \"387673.jpg\": \"Aves/Porphyrio melanotus melanotus/abaa39f1b8fa367643ba6a6e3b3275e2.jpg\",\n  \"387674.jpg\": \"Aves/Porphyrio melanotus melanotus/57f0328a60c1422cb67a3269e6583881.jpg\",\n  \"387675.jpg\": \"Aves/Porphyrio melanotus melanotus/2f8afc3f144d87fc6f40c33d53794e4f.jpg\",\n  \"387676.jpg\": \"Aves/Porphyrio melanotus melanotus/4c99e96e1aa0055e4c59219478efe479.jpg\",\n  \"387677.jpg\": \"Aves/Porphyrio melanotus melanotus/8ab71ae59d830c0442ac093cf83ae68d.jpg\",\n  \"387678.jpg\": \"Aves/Porphyrio melanotus melanotus/d5525e90cc883b29d78946bcade6bb8c.jpg\",\n  \"387681.jpg\": \"Aves/Porphyrio melanotus melanotus/3ad6ae7216dbbe9b61b3b6630aca83b4.jpg\",\n  \"387682.jpg\": \"Aves/Porphyrio melanotus melanotus/3173cde2359c42399890f15744162174.jpg\",\n  \"387683.jpg\": \"Aves/Porphyrio melanotus melanotus/58109fe7516301d4146a8a2be9230eb6.jpg\",\n  \"387686.jpg\": \"Aves/Porphyrio melanotus melanotus/dac9e52b8f5f47d5ddc18bf17225fa28.jpg\",\n  \"387687.jpg\": \"Aves/Porphyrio melanotus melanotus/4ca3a8a33e279e7aefb0fd89fbbda140.jpg\",\n  \"387690.jpg\": \"Aves/Porphyrio melanotus melanotus/95da3c4d2fac9cbd25c2fc64d67380f9.jpg\",\n  \"387696.jpg\": \"Aves/Porphyrio melanotus melanotus/c7ad3a39fde09d0e6637260ef255fced.jpg\",\n  \"387718.jpg\": \"Aves/Porphyrio melanotus melanotus/035ada5a5895309746f86497cbfc6548.jpg\",\n  \"38772.jpg\": \"Insecta/Euphydryas chalcedona/77e2549a8957b62e39807bec9364c49c.jpg\",\n  \"38775.jpg\": \"Insecta/Euphydryas chalcedona/15f0f9ccbaf370a75cf25d0b60b70d9b.jpg\",\n  \"38776.jpg\": \"Insecta/Euphydryas chalcedona/448476a3bf125acbc0d1bda7d6964191.jpg\",\n  \"38777.jpg\": \"Insecta/Euphydryas chalcedona/bdd21088e7c5b230f435f4402c27d41d.jpg\",\n  \"38779.jpg\": \"Insecta/Euphydryas chalcedona/f18df3966b9e3eb3e576747a5fb2f08f.jpg\",\n  \"387806.jpg\": \"Insecta/Synchlora frondaria/8aeee3c5cbac86a84b9ddb0f31587536.jpg\",\n  \"387811.jpg\": \"Insecta/Synchlora frondaria/468ee8ab3a8940ec930c97c62a9f90e8.jpg\",\n  \"387814.jpg\": \"Insecta/Synchlora frondaria/527cc029f355e20006291751fc7ca07b.jpg\",\n  \"387821.jpg\": \"Insecta/Synchlora frondaria/78ef7656785b0b85cccaa0a6e8c9bd08.jpg\",\n  \"387827.jpg\": \"Insecta/Synchlora frondaria/74fe3c9aaad19a9c426023ede8537c3f.jpg\",\n  \"387829.jpg\": \"Insecta/Synchlora frondaria/0d837b809b29323ef4d028d0151952fa.jpg\",\n  \"387844.jpg\": \"Aves/Spinus lawrencei/10ea86dbc27163c0ff13969fc3d011f9.jpg\",\n  \"387850.jpg\": \"Aves/Spinus lawrencei/19562a6959948e9267e225b0616a8fe8.jpg\",\n  \"387852.jpg\": \"Aves/Spinus lawrencei/726afeb9ad5e341e4a2cb5ff4acbfc61.jpg\",\n  \"387867.jpg\": \"Arachnida/Gasteracantha cancriformis/df41db25b0d774411e938e13c178ca52.jpg\",\n  \"387871.jpg\": \"Arachnida/Gasteracantha cancriformis/298cf1c47a77162892839f6a0817f88c.jpg\",\n  \"387877.jpg\": \"Arachnida/Gasteracantha cancriformis/53b87ddbe8a00512ae08e1997c32df48.jpg\",\n  \"387878.jpg\": \"Arachnida/Gasteracantha cancriformis/9c5740d417aa65900edb842705d1bd11.jpg\",\n  \"387879.jpg\": \"Arachnida/Gasteracantha cancriformis/0b767cb0337f1bae227f13965c5045a3.jpg\",\n  \"387880.jpg\": \"Arachnida/Gasteracantha cancriformis/005131535b22e6bd0f30c0cb09e338d9.jpg\",\n  \"387898.jpg\": \"Arachnida/Gasteracantha cancriformis/401610a6961905204d4bbf44c2196b51.jpg\",\n  \"387904.jpg\": \"Arachnida/Gasteracantha cancriformis/accc28c750c15fb4dbf03af7346aee03.jpg\",\n  \"387929.jpg\": \"Arachnida/Gasteracantha cancriformis/a02f5be259912726e1f9935b84dc43de.jpg\",\n  \"387937.jpg\": \"Arachnida/Gasteracantha cancriformis/98259030c94776979db28e4636722ba8.jpg\",\n  \"387943.jpg\": \"Arachnida/Gasteracantha cancriformis/1a69378de07cd582978aa63334f8660d.jpg\",\n  \"38795.jpg\": \"Insecta/Euphydryas chalcedona/a9c2b19bbaf875009f10856233817ab0.jpg\",\n  \"387954.jpg\": \"Arachnida/Gasteracantha cancriformis/8fb3ebc2806041500be61b105bd1e0bc.jpg\",\n  \"387959.jpg\": \"Arachnida/Gasteracantha cancriformis/d45c2895197c6ba94f7cdef8d023ea8a.jpg\",\n  \"38796.jpg\": \"Insecta/Euphydryas chalcedona/4414464fade69f37f489d1013eac40b9.jpg\",\n  \"387961.jpg\": \"Arachnida/Gasteracantha cancriformis/6f2f6c0d1aff1aa00bc214f53e037209.jpg\",\n  \"387963.jpg\": \"Arachnida/Gasteracantha cancriformis/e7cd0386cddb6c603397076963e9ac2e.jpg\",\n  \"387968.jpg\": \"Arachnida/Gasteracantha cancriformis/258d82c323d42f81145fa0ec1b658557.jpg\",\n  \"387969.jpg\": \"Arachnida/Gasteracantha cancriformis/c00b531877500487bc0c41483fdac6c3.jpg\",\n  \"387983.jpg\": \"Arachnida/Gasteracantha cancriformis/9969f00902346a461adcc98d30313308.jpg\",\n  \"387984.jpg\": \"Arachnida/Gasteracantha cancriformis/51950567929e67fba2a4044b393e18d7.jpg\",\n  \"387990.jpg\": \"Arachnida/Gasteracantha cancriformis/326d376c018f6ddc90553d0703119093.jpg\",\n  \"38800.jpg\": \"Insecta/Euphydryas chalcedona/dcc2b81ea4a1d22614aa14cfeb8c294e.jpg\",\n  \"388006.jpg\": \"Arachnida/Gasteracantha cancriformis/dc893c78041a07626103e8988b079c51.jpg\",\n  \"38804.jpg\": \"Insecta/Euphydryas chalcedona/57efe26d3d910229766a686de72531c2.jpg\",\n  \"388058.jpg\": \"Insecta/Phragmatobia fuliginosa/cab6034d8b63a8ff3072b3d16865d68c.jpg\",\n  \"388065.jpg\": \"Insecta/Phragmatobia fuliginosa/7a06ba303cf759bf84a1810aa1785f49.jpg\",\n  \"388069.jpg\": \"Insecta/Phragmatobia fuliginosa/59790511a1e89eed81bba3bb0d20da91.jpg\",\n  \"388079.jpg\": \"Insecta/Phragmatobia fuliginosa/5a371b6c7f6f9ed9a8ccf012893bd431.jpg\",\n  \"388141.jpg\": \"Insecta/Xylocopa varipuncta/bb7b249f05612543128a60c4d352211d.jpg\",\n  \"388147.jpg\": \"Insecta/Xylocopa varipuncta/2d6385db37bd3d6ceb442fe74de0a210.jpg\",\n  \"388154.jpg\": \"Insecta/Xylocopa varipuncta/563e88f46195af7b426b4fd9c642d128.jpg\",\n  \"388155.jpg\": \"Insecta/Xylocopa varipuncta/97a0f811f2cd2a1772565f39b45dbd52.jpg\",\n  \"388159.jpg\": \"Insecta/Xylocopa varipuncta/f6a38d6d2881f850156114a4c9eca2da.jpg\",\n  \"388162.jpg\": \"Insecta/Xylocopa varipuncta/d1eed1162ef9dd0705b66645ff2340c0.jpg\",\n  \"38822.jpg\": \"Insecta/Euphydryas chalcedona/0b4ba54e3e0222831590a40220dc0dd6.jpg\",\n  \"38829.jpg\": \"Insecta/Euphydryas chalcedona/35d6809a445a81a1e66e2c4dc82d76c4.jpg\",\n  \"388318.jpg\": \"Aves/Cacatua galerita/3b19156c7e7534ebc39a91ef8e79f26b.jpg\",\n  \"388319.jpg\": \"Aves/Cacatua galerita/05270e35f9be6b028d4abede20683bde.jpg\",\n  \"388320.jpg\": \"Aves/Cacatua galerita/7ed1270af3bcd90c732f154bc35c76ce.jpg\",\n  \"388321.jpg\": \"Aves/Cacatua galerita/665ad6d7e38f4a66cd63624c7fb80bd5.jpg\",\n  \"388322.jpg\": \"Aves/Cacatua galerita/4b8e1e40bc8b8e428e8aba4618dd34d4.jpg\",\n  \"388324.jpg\": \"Aves/Cacatua galerita/c54f26be7bb3f5c0a31ba1562bd550c4.jpg\",\n  \"388326.jpg\": \"Aves/Cacatua galerita/e8d523e9e09738408cebdb7a6fdd21fb.jpg\",\n  \"388329.jpg\": \"Aves/Cacatua galerita/ce9ddf6b532500e43ee3a0687783a415.jpg\",\n  \"388330.jpg\": \"Aves/Cacatua galerita/4dd301b61746d4de09bbd5ca709b3608.jpg\",\n  \"388335.jpg\": \"Aves/Cacatua galerita/3220ed46f9a2d5224d2ed72ebbec5dc2.jpg\",\n  \"388336.jpg\": \"Aves/Cacatua galerita/3636c8da273f518740a53f56aefc0e59.jpg\",\n  \"388338.jpg\": \"Aves/Cacatua galerita/218efb72950db396ac9743ff5e8613e1.jpg\",\n  \"388342.jpg\": \"Aves/Cacatua galerita/e70ddf872362d279998b85d62a8d10d2.jpg\",\n  \"388343.jpg\": \"Aves/Cacatua galerita/d9ed3a85ab95d8d30e09de006b15f4e0.jpg\",\n  \"388347.jpg\": \"Aves/Cacatua galerita/974ebb091786f47627b91b5842adab2a.jpg\",\n  \"388348.jpg\": \"Aves/Cacatua galerita/c566352a18c62ad05b0c0b874d82b58b.jpg\",\n  \"388349.jpg\": \"Aves/Cacatua galerita/e7b74d80af85f4600f82866b3a8a093b.jpg\",\n  \"388352.jpg\": \"Aves/Cacatua galerita/d15395e037d724d3770c3dc2c91e8e94.jpg\",\n  \"388379.jpg\": \"Aves/Fringilla coelebs/fd44eec6c16c050a365668d50b875aec.jpg\",\n  \"388381.jpg\": \"Aves/Fringilla coelebs/ae47b44bea6b307b51591452215c7b19.jpg\",\n  \"388392.jpg\": \"Aves/Fringilla coelebs/5f7932b75aba058802eb05249120e98c.jpg\",\n  \"388397.jpg\": \"Aves/Fringilla coelebs/8d1018b12bdf31c397da7fc32318f422.jpg\",\n  \"388406.jpg\": \"Aves/Fringilla coelebs/80af65cd1822b1a38c7b33b7218d3fbd.jpg\",\n  \"388407.jpg\": \"Aves/Fringilla coelebs/8e1d4640b6706a6977bd30b2cd66e836.jpg\",\n  \"38843.jpg\": \"Insecta/Euphydryas chalcedona/cbd46e82fd05cfbf4340235212ce6241.jpg\",\n  \"388431.jpg\": \"Aves/Fringilla coelebs/16631d4edbf9d0b927ee491d2e262bdc.jpg\",\n  \"388439.jpg\": \"Aves/Fringilla coelebs/a215fb2536eb076639c73883e0c5f0f6.jpg\",\n  \"388446.jpg\": \"Aves/Fringilla coelebs/acd03b3863ae5f52859ab5311388d7e1.jpg\",\n  \"388449.jpg\": \"Aves/Fringilla coelebs/254e82c3e242e012c7ecc7d1a1e795b1.jpg\",\n  \"388461.jpg\": \"Aves/Fringilla coelebs/b217cd1e0c668b6e6fec6fc90c661af7.jpg\",\n  \"388469.jpg\": \"Aves/Fringilla coelebs/bcfc5ee156c3317908b2668383396212.jpg\",\n  \"38847.jpg\": \"Insecta/Euphydryas chalcedona/1a3019fdf13933929aed3d4d444a753e.jpg\",\n  \"38848.jpg\": \"Insecta/Euphydryas chalcedona/553542b9999c9e46c6e35735f4995fbd.jpg\",\n  \"388482.jpg\": \"Aves/Fringilla coelebs/bfa4c03f903012709d70a9b30fd4aed0.jpg\",\n  \"388495.jpg\": \"Aves/Fringilla coelebs/fa0c960c6a23ed681243e2f0d4399307.jpg\",\n  \"388497.jpg\": \"Aves/Fringilla coelebs/e505a3e4f804007c5e6e4e5289f5ce64.jpg\",\n  \"388503.jpg\": \"Aves/Fringilla coelebs/9a807b8d4f2a6743c102cb8969d0edcc.jpg\",\n  \"388507.jpg\": \"Aves/Fringilla coelebs/dde5c82bf93a6db0b2d9929a6ae93a5e.jpg\",\n  \"388515.jpg\": \"Aves/Fringilla coelebs/bc6c91e51079b645a1ad9b0c7af496ac.jpg\",\n  \"388517.jpg\": \"Aves/Fringilla coelebs/2e57db046a4b72d2ef07e5671c904c5e.jpg\",\n  \"388518.jpg\": \"Aves/Fringilla coelebs/c568cd57ce4a1416f007ed8025d55979.jpg\",\n  \"388519.jpg\": \"Aves/Fringilla coelebs/093dc3d7e3e2dad64879736eaf0f365a.jpg\",\n  \"388520.jpg\": \"Aves/Fringilla coelebs/d39961b858c6577de1740d5a5f639aa7.jpg\",\n  \"388522.jpg\": \"Aves/Fringilla coelebs/34adda23c82a40488226672c1614520f.jpg\",\n  \"388538.jpg\": \"Aves/Fringilla coelebs/997da3ed82e6fecff5865038e6856d41.jpg\",\n  \"388592.jpg\": \"Aves/Sphyrapicus varius/edf8d08979d745dcffa1662a1716cd50.jpg\",\n  \"388593.jpg\": \"Aves/Sphyrapicus varius/03fdb5c24955e3231320c760b6bcddbc.jpg\",\n  \"388604.jpg\": \"Aves/Sphyrapicus varius/7c3b696c193565b0084964dc2ca13037.jpg\",\n  \"388607.jpg\": \"Aves/Sphyrapicus varius/5fb7328677aa3bc398b147edbfe2ef86.jpg\",\n  \"388608.jpg\": \"Aves/Sphyrapicus varius/89ed8dd4a1f5e60e4ae63b2693b0822b.jpg\",\n  \"388611.jpg\": \"Aves/Sphyrapicus varius/201c6c43ef9eccc81e64511f68d09867.jpg\",\n  \"388635.jpg\": \"Aves/Sphyrapicus varius/3f2b4b4c4b39c411f300a4b468f7e2e2.jpg\",\n  \"388638.jpg\": \"Aves/Sphyrapicus varius/f3c15457157af1aac31540c8a8e4ae8c.jpg\",\n  \"388667.jpg\": \"Aves/Sphyrapicus varius/4c099bf7d42258ff3baf9b9716e74730.jpg\",\n  \"388675.jpg\": \"Aves/Sphyrapicus varius/c6f0635f35952b4c869bbbe42652c6c2.jpg\",\n  \"388690.jpg\": \"Aves/Sphyrapicus varius/a3e5816abe393706978b01b30266b5a8.jpg\",\n  \"388701.jpg\": \"Aves/Sphyrapicus varius/2baeaf4868f22906a78fffe9b8b664e8.jpg\",\n  \"38874.jpg\": \"Insecta/Euphydryas chalcedona/777d022a9ebcb1ab1190b03c0d093f13.jpg\",\n  \"388755.jpg\": \"Aves/Sphyrapicus varius/adda0fad364d1333feb6755945d3e32e.jpg\",\n  \"388756.jpg\": \"Aves/Sphyrapicus varius/ddc9dfcfce25387d736d872b1c21c7ac.jpg\",\n  \"38876.jpg\": \"Insecta/Euphydryas chalcedona/f37e73606d4b24abd0277fb5511b9302.jpg\",\n  \"388811.jpg\": \"Aves/Sphyrapicus varius/dab2908532ac9beb8c2e92df81f35c4b.jpg\",\n  \"388841.jpg\": \"Aves/Sphyrapicus varius/494bc631f5dd588b150578ec472e5b06.jpg\",\n  \"388866.jpg\": \"Aves/Sphyrapicus varius/07040b790c229a781bfde07f0e7910fd.jpg\",\n  \"388883.jpg\": \"Aves/Sphyrapicus varius/cb6d39bf8f5a6a5c0a6f6774dd49bbcb.jpg\",\n  \"388888.jpg\": \"Aves/Sphyrapicus varius/606d1136c6b9d98895489a517c45bd47.jpg\",\n  \"388896.jpg\": \"Aves/Falco subbuteo/5eb835ef2fe69f17d44d0bbf8f9a66e3.jpg\",\n  \"388897.jpg\": \"Aves/Falco subbuteo/e46d67fbe523ec1ab801e0c077d09dd0.jpg\",\n  \"388899.jpg\": \"Aves/Falco subbuteo/8340faf530a6246bc6281be6cb65895a.jpg\",\n  \"388901.jpg\": \"Aves/Falco subbuteo/f16d7fd65f02000039854ac90478b2d8.jpg\",\n  \"388902.jpg\": \"Aves/Falco subbuteo/d7c98259e533eb843842b8f15a0fdd63.jpg\",\n  \"388907.jpg\": \"Aves/Falco subbuteo/5a393e7de3c7877950f46dce38572335.jpg\",\n  \"38895.jpg\": \"Aves/Carduelis cannabina/308ddc5b7ed1e34b6ba2d9fdfc13d89e.jpg\",\n  \"38896.jpg\": \"Aves/Carduelis cannabina/69857f24f10e57bae7e3b53d11d8790f.jpg\",\n  \"38898.jpg\": \"Aves/Carduelis cannabina/75d06cc1c2c3852b015a321a8e211c97.jpg\",\n  \"38899.jpg\": \"Aves/Carduelis cannabina/45db03634de4e34b0fa2fefd632fa86a.jpg\",\n  \"38901.jpg\": \"Aves/Carduelis cannabina/be20720faad05aa5ed76c1a636887467.jpg\",\n  \"38902.jpg\": \"Aves/Carduelis cannabina/4a7975f5bbbfa3efa89c1f26bc4b0aed.jpg\",\n  \"389024.jpg\": \"Aves/Haematopus bachmani/1bea73a12dd58b4084fc824eba252b96.jpg\",\n  \"389027.jpg\": \"Aves/Haematopus bachmani/160d8dbef5734a46aacda588be063bd1.jpg\",\n  \"38903.jpg\": \"Aves/Carduelis cannabina/5d296548b4ff21e65b447f74d509e609.jpg\",\n  \"38904.jpg\": \"Aves/Carduelis cannabina/22550a65804d2459b1e06497c579ae0a.jpg\",\n  \"38905.jpg\": \"Aves/Carduelis cannabina/972028540bbcc0b334ed6cdb836e2f7b.jpg\",\n  \"38910.jpg\": \"Aves/Carduelis cannabina/84943c2ac1f126e113ac9925bc7a1d72.jpg\",\n  \"389109.jpg\": \"Aves/Haematopus bachmani/b00177967131a70951ad273298b3daee.jpg\",\n  \"389123.jpg\": \"Aves/Haematopus bachmani/13d200646ed877eb499a5f5cd9386773.jpg\",\n  \"389128.jpg\": \"Aves/Haematopus bachmani/6e40f06085bb4c5ad6eeedf7bee979e5.jpg\",\n  \"38915.jpg\": \"Aves/Carduelis cannabina/7789ab29863ea3c443c10ab01b477119.jpg\",\n  \"389152.jpg\": \"Aves/Haematopus bachmani/f3546b22402c7ffe6d5e053513804678.jpg\",\n  \"389156.jpg\": \"Aves/Haematopus bachmani/c0f930c8d4c000d0c9f135798ff502d6.jpg\",\n  \"389158.jpg\": \"Aves/Haematopus bachmani/f048d4bc45f94f15cfa5014ca9468f40.jpg\",\n  \"38916.jpg\": \"Aves/Carduelis cannabina/2b89a00af7b724c7dc97a8020deee53b.jpg\",\n  \"389167.jpg\": \"Aves/Haematopus bachmani/02f5069333b5bb0f947e2d5bede39987.jpg\",\n  \"389185.jpg\": \"Aves/Haematopus bachmani/ec8b77ba1edb5d4f3fa93e9ba8b1a692.jpg\",\n  \"389199.jpg\": \"Aves/Haematopus bachmani/c21c822dc341073d8bd5a37b91c4f5b1.jpg\",\n  \"389201.jpg\": \"Insecta/Costaconvexa centrostrigaria/ba2a78933a134aaf734faaaceeded81f.jpg\",\n  \"389202.jpg\": \"Insecta/Costaconvexa centrostrigaria/b374545687797712c9fa976e0c67ef64.jpg\",\n  \"389203.jpg\": \"Insecta/Costaconvexa centrostrigaria/3de322986636d909e30d312374cfa13a.jpg\",\n  \"389205.jpg\": \"Insecta/Costaconvexa centrostrigaria/65895bebc92d9ce6e540f4d16c1a2aa3.jpg\",\n  \"389207.jpg\": \"Insecta/Costaconvexa centrostrigaria/cacf32976b7713d056419ffd029b5b24.jpg\",\n  \"389209.jpg\": \"Insecta/Costaconvexa centrostrigaria/562a438504524bf999590216fbe92b84.jpg\",\n  \"38921.jpg\": \"Aves/Carduelis cannabina/a3eab6f594a5e5d5dfb77a9cab100029.jpg\",\n  \"389213.jpg\": \"Insecta/Costaconvexa centrostrigaria/7a6ef039780114cfe008f62894bf3329.jpg\",\n  \"389214.jpg\": \"Insecta/Costaconvexa centrostrigaria/f38da28e28a59ee5355e097f39028e8c.jpg\",\n  \"389217.jpg\": \"Insecta/Costaconvexa centrostrigaria/554608c83b54bd4e511d9c6d31bae7ae.jpg\",\n  \"38922.jpg\": \"Aves/Carduelis cannabina/cbd78b87563f6f1226262d217e788174.jpg\",\n  \"389220.jpg\": \"Insecta/Costaconvexa centrostrigaria/3a1f6336f389bf4b5da9b3a97fa7e4da.jpg\",\n  \"389221.jpg\": \"Insecta/Costaconvexa centrostrigaria/c223428f8a3badbc46e827358d84305e.jpg\",\n  \"389222.jpg\": \"Insecta/Costaconvexa centrostrigaria/bbda6aae96e26eb1b4a7603f1a6c2561.jpg\",\n  \"389226.jpg\": \"Insecta/Costaconvexa centrostrigaria/18b0cb774c443afd155fba10ab93674d.jpg\",\n  \"389228.jpg\": \"Insecta/Costaconvexa centrostrigaria/0066ac241efc28fa7bf30978d981bbcf.jpg\",\n  \"389229.jpg\": \"Insecta/Costaconvexa centrostrigaria/d491d35739f41df38c04179daf0a7cde.jpg\",\n  \"38923.jpg\": \"Aves/Carduelis cannabina/b4f195eb23531d529b342bb5053da434.jpg\",\n  \"389231.jpg\": \"Insecta/Costaconvexa centrostrigaria/15031f2236416a91a6c277c1e6e77e7a.jpg\",\n  \"389232.jpg\": \"Insecta/Costaconvexa centrostrigaria/2c22b5013455e3b37ba2fab55c6a4b40.jpg\",\n  \"389233.jpg\": \"Insecta/Costaconvexa centrostrigaria/9fc790b17de2c8cdf6fea6745b3b230d.jpg\",\n  \"389236.jpg\": \"Insecta/Costaconvexa centrostrigaria/faf2940f41c2471da674220803d54aff.jpg\",\n  \"389237.jpg\": \"Insecta/Costaconvexa centrostrigaria/89e6746a0384b796df476ec258dbbd87.jpg\",\n  \"38924.jpg\": \"Aves/Carduelis cannabina/5fa8f3db8784bf4a652e6b8dee48a6bb.jpg\",\n  \"389242.jpg\": \"Insecta/Costaconvexa centrostrigaria/a1e541d5c7c4ca5bfc18ca037a6e20a2.jpg\",\n  \"389243.jpg\": \"Insecta/Costaconvexa centrostrigaria/784ef462a5f16ea7d42db290d1d61ecb.jpg\",\n  \"389248.jpg\": \"Insecta/Argia fumipennis/04a87cd4fee1f928d245af7c4d7aa244.jpg\",\n  \"38925.jpg\": \"Aves/Carduelis cannabina/2502b15b8db581ed628fda65fba847cf.jpg\",\n  \"389253.jpg\": \"Insecta/Argia fumipennis/1e8a773bbf565db835117f19611a638d.jpg\",\n  \"389260.jpg\": \"Insecta/Argia fumipennis/ab700cf8789420b37437f9cafbfe1e30.jpg\",\n  \"389270.jpg\": \"Insecta/Argia fumipennis/e2eddc7941d302a69e40b297578a6b0e.jpg\",\n  \"389274.jpg\": \"Insecta/Argia fumipennis/9f218ce74746e7462bbed97449df7cc7.jpg\",\n  \"389286.jpg\": \"Insecta/Argia fumipennis/b6a49e09107816da153c08a2d75983b5.jpg\",\n  \"389287.jpg\": \"Insecta/Argia fumipennis/75193b4a9a060f3628e8608f45085dda.jpg\",\n  \"389289.jpg\": \"Insecta/Argia fumipennis/05fc8a3a53a4f0a588da39fcb7d394db.jpg\",\n  \"389290.jpg\": \"Insecta/Argia fumipennis/ebedabd65215070a49a3581e4e9c3994.jpg\",\n  \"389295.jpg\": \"Insecta/Argia fumipennis/e7341082bce2cfd6883b7a27fabafdaf.jpg\",\n  \"389299.jpg\": \"Insecta/Argia fumipennis/b3e75324ef83c85036aae28ed156f9c7.jpg\",\n  \"389300.jpg\": \"Insecta/Argia fumipennis/2f48511af942ee2a0b56156a1f59ac2b.jpg\",\n  \"389303.jpg\": \"Insecta/Argia fumipennis/951547b206c0980a6432f62250980c40.jpg\",\n  \"38931.jpg\": \"Aves/Carduelis cannabina/e9b7621eec9d4165ed88a028e349e70f.jpg\",\n  \"389310.jpg\": \"Insecta/Argia fumipennis/4cab39a01fec972ee83d57ce8ca9000d.jpg\",\n  \"389311.jpg\": \"Insecta/Argia fumipennis/6d09199c679923879d70d707328dd643.jpg\",\n  \"389313.jpg\": \"Insecta/Argia fumipennis/df74a5d02ce4f697357e91be0041f955.jpg\",\n  \"389314.jpg\": \"Insecta/Argia fumipennis/451e689727435b9fa51a653f43a49851.jpg\",\n  \"389321.jpg\": \"Insecta/Argia fumipennis/846c326b55b0f2902e725be7225bed78.jpg\",\n  \"389326.jpg\": \"Insecta/Argia fumipennis/76f8dcf3e473c06c5784e2824f5d112d.jpg\",\n  \"389331.jpg\": \"Insecta/Argia fumipennis/798cebd691d3281e9342cb651ca3f24c.jpg\",\n  \"389332.jpg\": \"Insecta/Argia fumipennis/190283345f03595ab05aa0765c653d3b.jpg\",\n  \"38934.jpg\": \"Aves/Carduelis cannabina/234f918d527e1fb39100cd41e5f62b81.jpg\",\n  \"389341.jpg\": \"Insecta/Argia fumipennis/9d33f9315e3fdcee5052aa51b6bbbba2.jpg\",\n  \"38935.jpg\": \"Aves/Carduelis cannabina/df4ae212d4bf92043628cc119d7a91a3.jpg\",\n  \"38941.jpg\": \"Aves/Carduelis cannabina/5e14be0f9cdbc1a3ed1126af9878811e.jpg\",\n  \"389416.jpg\": \"Insecta/Cosmopepla lintneriana/b49c9caae1bb2f381a82e992eb3c8907.jpg\",\n  \"389417.jpg\": \"Insecta/Cosmopepla lintneriana/ed71bae02f6f8b5ce591c9b9e5469190.jpg\",\n  \"389423.jpg\": \"Amphibia/Taricha rivularis/1c73a6251a9f201cdd3a4659f110581f.jpg\",\n  \"389424.jpg\": \"Amphibia/Taricha rivularis/aeee2d14695516ca853416e9ee56cab2.jpg\",\n  \"389430.jpg\": \"Amphibia/Taricha rivularis/6b150f725a21d2bd1e8b1b8ca85232fe.jpg\",\n  \"389449.jpg\": \"Amphibia/Taricha rivularis/054089453a91c152e35ce17defd1fc80.jpg\",\n  \"389450.jpg\": \"Amphibia/Taricha rivularis/72ef949b22a892b17613e61ba2dbd877.jpg\",\n  \"389455.jpg\": \"Amphibia/Taricha rivularis/b600bc01d47a647524773cf2e3f77a9d.jpg\",\n  \"389456.jpg\": \"Amphibia/Taricha rivularis/ef091cf8d02a725e8e37a032aef33e11.jpg\",\n  \"389457.jpg\": \"Amphibia/Taricha rivularis/cf072e1acb318e79f2b941cd2d4282af.jpg\",\n  \"38948.jpg\": \"Aves/Carduelis cannabina/23d7287aaebf9a5ad11e979d29abcd97.jpg\",\n  \"38952.jpg\": \"Aves/Carduelis cannabina/f071d1cf4770a94e8a5e7f4dd4aec2d9.jpg\",\n  \"389535.jpg\": \"Aves/Lophodytes cucullatus/492183c5d41b76c373c90f644d79ff4d.jpg\",\n  \"389602.jpg\": \"Aves/Lophodytes cucullatus/c010b906d1bcb60e2331462a32d37190.jpg\",\n  \"389629.jpg\": \"Aves/Lophodytes cucullatus/8bf8be36a100d19ffd4868e866317de0.jpg\",\n  \"389655.jpg\": \"Aves/Lophodytes cucullatus/25383255022aa1e84918cf556852054d.jpg\",\n  \"389735.jpg\": \"Aves/Lophodytes cucullatus/a810161cc3841cd25dac7c11ebb58d20.jpg\",\n  \"389785.jpg\": \"Aves/Lophodytes cucullatus/4c14ee9f81b849a47642dd2c7ce6900a.jpg\",\n  \"389786.jpg\": \"Aves/Lophodytes cucullatus/7e1f3f7b0dd2fb69310715dbcf10eb20.jpg\",\n  \"389791.jpg\": \"Aves/Lophodytes cucullatus/e75f19d0f9599b0686e000439594ff81.jpg\",\n  \"389883.jpg\": \"Aves/Gavia immer/a60a58f0a0bc242b253f96a55802a997.jpg\",\n  \"389934.jpg\": \"Aves/Gavia immer/d78970f7b2d5656161be70de84989a65.jpg\",\n  \"389940.jpg\": \"Aves/Gavia immer/facf17fdc3d155e3386d5ca6836ef260.jpg\",\n  \"389948.jpg\": \"Aves/Gavia immer/7f0d068c324fb8ea341163d0956e5ec5.jpg\",\n  \"389956.jpg\": \"Aves/Gavia immer/3d953eb864fcd1e16d83cc02bf3df91a.jpg\",\n  \"389973.jpg\": \"Aves/Gavia immer/9c72b64e95a93706306217fc0ae497bf.jpg\",\n  \"389998.jpg\": \"Aves/Gavia immer/3e9246498e83c2fb9ccdbd5f710bef4d.jpg\",\n  \"390044.jpg\": \"Aves/Gavia immer/c1c1d7841506b65fc148ce4ab7a4856a.jpg\",\n  \"390069.jpg\": \"Aves/Gavia immer/f6dfd66756fd66ade548c2736b908836.jpg\",\n  \"390082.jpg\": \"Aves/Gavia immer/c4463afb8a713462740bd95e50f7488d.jpg\",\n  \"390083.jpg\": \"Aves/Gavia immer/3233c0215d648afe0269428b5cd62a4b.jpg\",\n  \"390091.jpg\": \"Aves/Gavia immer/8a159603c6b623c45f176ae386d017bc.jpg\",\n  \"390143.jpg\": \"Aves/Gavia immer/e10eff32b2f27a0cab35f99a81b459a8.jpg\",\n  \"390151.jpg\": \"Aves/Gavia immer/194f6ab0a21549a374457fb51e2b3de6.jpg\",\n  \"390159.jpg\": \"Aves/Gavia immer/bc57b90b39ab4e0b25b79b98296e9076.jpg\",\n  \"390165.jpg\": \"Aves/Gavia immer/8ae8080e82d240e85a281edd38fbd585.jpg\",\n  \"390206.jpg\": \"Aves/Gavia immer/650367740a047e54a6c3a3d26d091541.jpg\",\n  \"390209.jpg\": \"Aves/Gavia immer/1ebb959136b8141b928b891b3094b155.jpg\",\n  \"390211.jpg\": \"Aves/Gavia immer/efd6488a808a4493748865ebef4e9fec.jpg\",\n  \"390219.jpg\": \"Aves/Gerygone igata/aa31cb55d28552e5fe8277bc64647f19.jpg\",\n  \"390222.jpg\": \"Aves/Gerygone igata/cf79a25988ae5c40afb88127716f9be4.jpg\",\n  \"390241.jpg\": \"Aves/Gerygone igata/fae376ffb40e538366184d79614325ce.jpg\",\n  \"390243.jpg\": \"Aves/Gerygone igata/c57cc269769dae5ac88ef8b1023b58a6.jpg\",\n  \"390262.jpg\": \"Aves/Platalea alba/9989d29a0a5a9a0d9edc73e113f716da.jpg\",\n  \"390264.jpg\": \"Aves/Platalea alba/be5d9fcc3a048bfc80807e5768f65564.jpg\",\n  \"390447.jpg\": \"Arachnida/Nephila clavipes/1fb7eefd7b65741b3566654a953be716.jpg\",\n  \"390452.jpg\": \"Arachnida/Nephila clavipes/ca14301e341246a9565083bd9ea8e39c.jpg\",\n  \"390460.jpg\": \"Arachnida/Nephila clavipes/1fe743fa79c06c937562d1662efc5402.jpg\",\n  \"390468.jpg\": \"Arachnida/Nephila clavipes/800f5b21d8dd97d6647781a0e1355148.jpg\",\n  \"390473.jpg\": \"Arachnida/Nephila clavipes/9824c617dd072bc31b093f22ebf0f9d7.jpg\",\n  \"390497.jpg\": \"Arachnida/Nephila clavipes/dd50c04b02a49990e894197cd72fcc71.jpg\",\n  \"390516.jpg\": \"Arachnida/Nephila clavipes/541a178ee3c4175b26b6f86cd35e302a.jpg\",\n  \"390540.jpg\": \"Arachnida/Nephila clavipes/7dfd4335a23a6094a53aa4a01017b3fc.jpg\",\n  \"390546.jpg\": \"Arachnida/Nephila clavipes/4fd4040ddb4f1ba3117e1ebf2f7f8756.jpg\",\n  \"390547.jpg\": \"Arachnida/Nephila clavipes/734b9d444b24f2fe8f0217f42b27714f.jpg\",\n  \"390551.jpg\": \"Arachnida/Nephila clavipes/6406b5731221a8f61bb620b22a9a1e60.jpg\",\n  \"390569.jpg\": \"Arachnida/Nephila clavipes/6a91c34a8f3281c2a252bf248a330620.jpg\",\n  \"390570.jpg\": \"Arachnida/Nephila clavipes/1aa22d4cdc99d9bedade70ec11b103c0.jpg\",\n  \"390573.jpg\": \"Arachnida/Nephila clavipes/f2207c10a42dec6b178332aa8f02eb92.jpg\",\n  \"390574.jpg\": \"Arachnida/Nephila clavipes/2ea42d36e98725ab81afa967f0db48f2.jpg\",\n  \"390576.jpg\": \"Arachnida/Nephila clavipes/a2aa87c8e9904f0440ecc502294c8d00.jpg\",\n  \"390592.jpg\": \"Arachnida/Nephila clavipes/863c3952ddd1d6d291bd7de34bbc8055.jpg\",\n  \"390597.jpg\": \"Arachnida/Nephila clavipes/36815996e9174b9309161f70798a5a04.jpg\",\n  \"390598.jpg\": \"Arachnida/Nephila clavipes/a473c71bd329e924fea580b16366fedf.jpg\",\n  \"390613.jpg\": \"Arachnida/Nephila clavipes/8b0db124f9518beff05b23764eeabca9.jpg\",\n  \"39073.jpg\": \"Insecta/Macrodiplax balteata/4be6835dc5cbbd09449eaf2052933ec1.jpg\",\n  \"39077.jpg\": \"Insecta/Macrodiplax balteata/872bf388ed323bdd01acf05471b3853e.jpg\",\n  \"39081.jpg\": \"Insecta/Macrodiplax balteata/9e6efd0458dbd227b0489bffd3cc905c.jpg\",\n  \"39085.jpg\": \"Insecta/Macrodiplax balteata/4fca5484c2b9f4beea8f66c55e9c7573.jpg\",\n  \"39089.jpg\": \"Insecta/Macrodiplax balteata/5d80a336ae60d944e4e678ed484ef209.jpg\",\n  \"39090.jpg\": \"Insecta/Macrodiplax balteata/13d4bfc2c6deab2df4b607ed10a51fd0.jpg\",\n  \"39095.jpg\": \"Insecta/Macrodiplax balteata/29b6d9b1377a035e238cff6705b55073.jpg\",\n  \"39098.jpg\": \"Insecta/Macrodiplax balteata/5b9fb77f7f0738284355fbdc8ca7da18.jpg\",\n  \"391108.jpg\": \"Insecta/Orgyia vetusta/8da3c0c2417b1b9674ffdc9bbfd56968.jpg\",\n  \"391112.jpg\": \"Insecta/Orgyia vetusta/f348f545e93dc6260dbf60271503d7cd.jpg\",\n  \"391116.jpg\": \"Insecta/Orgyia vetusta/4e612f8105786558001744f1631cd164.jpg\",\n  \"391117.jpg\": \"Insecta/Orgyia vetusta/bcb41456572720b9a7bae5fd1aaf08e9.jpg\",\n  \"391278.jpg\": \"Aves/Aphelocoma woodhouseii/a966f57619264206eb52946f28a470e7.jpg\",\n  \"391279.jpg\": \"Aves/Aphelocoma woodhouseii/77eeec770c60cf1ebbbd9cb9f3a22a9f.jpg\",\n  \"391280.jpg\": \"Aves/Aphelocoma woodhouseii/300db0cc8da707dee1aec7e940c2a0b1.jpg\",\n  \"391281.jpg\": \"Aves/Aphelocoma woodhouseii/eb374eba89ad0946ebc76a4c71cf7475.jpg\",\n  \"391282.jpg\": \"Aves/Aphelocoma woodhouseii/52dfb43b516156e02489f66f0d471156.jpg\",\n  \"391283.jpg\": \"Aves/Aphelocoma woodhouseii/9ec5bf3bcb8a99102a50b78fb1167561.jpg\",\n  \"391284.jpg\": \"Aves/Aphelocoma woodhouseii/eb8ca0b5e9b9f8931d0e4e6f678f2615.jpg\",\n  \"391290.jpg\": \"Aves/Aphelocoma woodhouseii/5d2a12b12783a05a957ebda8f3c89738.jpg\",\n  \"391291.jpg\": \"Aves/Aphelocoma woodhouseii/1273f7cc44990bf16644cd4281f99671.jpg\",\n  \"391295.jpg\": \"Aves/Aphelocoma woodhouseii/adf199acb27eba31cb3818d232b617fe.jpg\",\n  \"391296.jpg\": \"Aves/Aphelocoma woodhouseii/cb06a40add53ab75a8469b4710442d92.jpg\",\n  \"391297.jpg\": \"Aves/Aphelocoma woodhouseii/469919e3eeb5d5ee22e8a7f7399d8261.jpg\",\n  \"391299.jpg\": \"Aves/Aphelocoma woodhouseii/80598a0b1e8ecf1e6afd8b111ba63b24.jpg\",\n  \"391300.jpg\": \"Aves/Aphelocoma woodhouseii/66c916c9a79e0183b1a1eac00d3af239.jpg\",\n  \"391305.jpg\": \"Aves/Aphelocoma woodhouseii/c068ec97a80ec58a47932cab63f3d542.jpg\",\n  \"391310.jpg\": \"Aves/Aphelocoma woodhouseii/7382b821878869664e217af2c75b1d07.jpg\",\n  \"391311.jpg\": \"Aves/Aphelocoma woodhouseii/65bad498a817574d366b2d626e413019.jpg\",\n  \"391312.jpg\": \"Aves/Aphelocoma woodhouseii/023d69b46456f595fe4454726d7f5496.jpg\",\n  \"391314.jpg\": \"Aves/Aphelocoma woodhouseii/3b18cd659eb2b44c26632368e9ef6971.jpg\",\n  \"391315.jpg\": \"Aves/Aphelocoma woodhouseii/d955040306e9a2e7d2106eb0db4ec3a2.jpg\",\n  \"391316.jpg\": \"Aves/Aphelocoma woodhouseii/58a8cc4651869219f5ba91d4cfed88fc.jpg\",\n  \"391578.jpg\": \"Aves/Picus viridis/601d0d7ecb9f3b26e6ff2996430df9b6.jpg\",\n  \"391585.jpg\": \"Aves/Picus viridis/e7c041c799f208de9efeaed81b7fb26e.jpg\",\n  \"391588.jpg\": \"Aves/Picus viridis/daebec949c0ac0c16573e2e440639044.jpg\",\n  \"391590.jpg\": \"Aves/Picus viridis/61c445bd3789ba922cef981c41d2276b.jpg\",\n  \"391595.jpg\": \"Aves/Picus viridis/9abf7b046963542658795bf8e52b7fa6.jpg\",\n  \"391601.jpg\": \"Aves/Picus viridis/6314d4737394f6962b72e22f5437b793.jpg\",\n  \"391606.jpg\": \"Aves/Picus viridis/af743ffbc10204d66460a244a2c3634d.jpg\",\n  \"391611.jpg\": \"Aves/Picus viridis/6f7a5e8b2202c6dea7583e04e1067cc5.jpg\",\n  \"391612.jpg\": \"Aves/Picus viridis/e8fbb3a306b9a6e496d33b1f19841f5d.jpg\",\n  \"391616.jpg\": \"Reptilia/Pantherophis obsoletus lindheimeri/e2be2ed2d27574aa93d3ebca99a5d4a5.jpg\",\n  \"391619.jpg\": \"Reptilia/Pantherophis obsoletus lindheimeri/c26f75a4beff9e721aa6c9d154969ac5.jpg\",\n  \"391628.jpg\": \"Reptilia/Pantherophis obsoletus lindheimeri/3947d591d23a9eafe38e94d419511475.jpg\",\n  \"391633.jpg\": \"Reptilia/Pantherophis obsoletus lindheimeri/496729a6bf4f69d29394019c3485ac7a.jpg\",\n  \"391635.jpg\": \"Reptilia/Pantherophis obsoletus lindheimeri/81f23d7c82b00b6ae710e1db339f9963.jpg\",\n  \"391637.jpg\": \"Reptilia/Pantherophis obsoletus lindheimeri/5090727efdb35dbaa201a3d4dc183439.jpg\",\n  \"391640.jpg\": \"Reptilia/Pantherophis obsoletus lindheimeri/42b3a9fcc5885340aaa925966de135d1.jpg\",\n  \"391643.jpg\": \"Reptilia/Pantherophis obsoletus lindheimeri/0fc0dff3727d04f37f5a0cd17cffa369.jpg\",\n  \"391653.jpg\": \"Reptilia/Pantherophis obsoletus lindheimeri/4462481720e60ace1e04cf38da45a1b2.jpg\",\n  \"391656.jpg\": \"Reptilia/Pantherophis obsoletus lindheimeri/bc3d2083d8bb6aa4915d83740203fcd2.jpg\",\n  \"391659.jpg\": \"Reptilia/Pantherophis obsoletus lindheimeri/fc4f9eec003dbf26d9bc9c5d174693ec.jpg\",\n  \"391662.jpg\": \"Reptilia/Pantherophis obsoletus lindheimeri/7c4464686c4629238f24f03ed132a97c.jpg\",\n  \"391663.jpg\": \"Reptilia/Pantherophis obsoletus lindheimeri/29951d1d14a6d2097bc6a2833f68d5bb.jpg\",\n  \"391674.jpg\": \"Reptilia/Pantherophis obsoletus lindheimeri/dbc0b0225529759cc100adf00734417f.jpg\",\n  \"391678.jpg\": \"Reptilia/Pantherophis obsoletus lindheimeri/4765116026d254a8a2243e52cc6b8e29.jpg\",\n  \"391689.jpg\": \"Reptilia/Pantherophis obsoletus lindheimeri/5db227b6ce8e40ff22b235a306acbbfa.jpg\",\n  \"391835.jpg\": \"Aves/Butorides virescens/c5b9dc677d7a3a232d34e18d8f96493c.jpg\",\n  \"391849.jpg\": \"Aves/Butorides virescens/a596a72a5cdb8f52460fead94e9f0c9d.jpg\",\n  \"391858.jpg\": \"Aves/Butorides virescens/5bac384fc9f37b104d69835768966275.jpg\",\n  \"391862.jpg\": \"Aves/Butorides virescens/a3d7722a1a620da05da3df12b1348a49.jpg\",\n  \"391865.jpg\": \"Aves/Butorides virescens/0ab9e6d0092f547ea13465924d60f9c4.jpg\",\n  \"391878.jpg\": \"Aves/Butorides virescens/675b0d2fee1ff55fd5fd69e4f005ab57.jpg\",\n  \"391891.jpg\": \"Aves/Butorides virescens/febce0735a91a481d89fd0e2d495db2f.jpg\",\n  \"391932.jpg\": \"Aves/Butorides virescens/e619c3a2ddc3aa1bc607b20c56d6c633.jpg\",\n  \"391966.jpg\": \"Aves/Butorides virescens/36e1f5f73ff9e891f017e6c0195df407.jpg\",\n  \"392.jpg\": \"Insecta/Coleomegilla maculata/5ee1233431ae36ae72baf54700ac1166.jpg\",\n  \"392019.jpg\": \"Aves/Butorides virescens/15899b161bf4ff9d6765da612f078184.jpg\",\n  \"392049.jpg\": \"Aves/Butorides virescens/a65f8465eb3afc5fa099e07b64ed4f2b.jpg\",\n  \"392054.jpg\": \"Aves/Butorides virescens/64028dea9daa4d0798f9e29087edeb27.jpg\",\n  \"392111.jpg\": \"Aves/Butorides virescens/690ccba99ad77e2fb9fc2f39bbc8e42d.jpg\",\n  \"392142.jpg\": \"Aves/Butorides virescens/50ab6132ca2b4700c0e598a2133310a5.jpg\",\n  \"392156.jpg\": \"Aves/Butorides virescens/8b35e5f9b8588b129753209f9372b74d.jpg\",\n  \"392205.jpg\": \"Aves/Butorides virescens/361b17e8488a76bbc18a3a17658b03c9.jpg\",\n  \"392236.jpg\": \"Aves/Butorides virescens/39e3aa2deff8203ff64580fe4efde327.jpg\",\n  \"392251.jpg\": \"Aves/Butorides virescens/6d9c6562a434634c1defcd53bd5263f3.jpg\",\n  \"392275.jpg\": \"Aves/Butorides virescens/0e2d814441c082556e0793d85fdfdfe9.jpg\",\n  \"392291.jpg\": \"Aves/Butorides virescens/068464ac5cfb2cbe177a5f1ecf895c28.jpg\",\n  \"392315.jpg\": \"Aves/Butorides virescens/454ef2ca275aef8baa20fd4b6c40a49f.jpg\",\n  \"392349.jpg\": \"Aves/Butorides virescens/c3d7dbf9c4766f67b7b56123235cf708.jpg\",\n  \"392386.jpg\": \"Aves/Butorides virescens/c37d90d8bc732a5a549c1a6ba7b4545c.jpg\",\n  \"392396.jpg\": \"Aves/Butorides virescens/17c0086107883565acfcd993bfa2f4b5.jpg\",\n  \"39244.jpg\": \"Insecta/Heraclides cresphontes/e38aa41399fbd20a43bf511c69718759.jpg\",\n  \"39250.jpg\": \"Insecta/Heraclides cresphontes/8c960ff388b52139c04cbea6bb317c90.jpg\",\n  \"392572.jpg\": \"Aves/Agelaius tricolor/654e9efcab6d7a555b4836cea5cd0f50.jpg\",\n  \"392578.jpg\": \"Aves/Agelaius tricolor/accdcf7ea4fe9a55e7eaa578e2cfd05e.jpg\",\n  \"39258.jpg\": \"Insecta/Heraclides cresphontes/da5d3880189e24e853361d284fdbc66a.jpg\",\n  \"392587.jpg\": \"Aves/Agelaius tricolor/08708d754981ed3b87e405d00449c20f.jpg\",\n  \"392590.jpg\": \"Insecta/Caenurgina crassiuscula/3e52d2c75d6e64bd9101b0e045e54189.jpg\",\n  \"392592.jpg\": \"Insecta/Caenurgina crassiuscula/f57005d6fec00accae2dd58fbaf878cb.jpg\",\n  \"392593.jpg\": \"Insecta/Caenurgina crassiuscula/0197df60e5e568a586b52e30e4314179.jpg\",\n  \"392598.jpg\": \"Insecta/Caenurgina crassiuscula/f8386c487e50dd1fd7e102476993d61d.jpg\",\n  \"392599.jpg\": \"Insecta/Caenurgina crassiuscula/9b3cc34e2aadfb0207eae3fbc56d2d26.jpg\",\n  \"392600.jpg\": \"Insecta/Caenurgina crassiuscula/24222335fe7c2b7c4cae61a09e5d60fe.jpg\",\n  \"392607.jpg\": \"Reptilia/Phrynosoma platyrhinos/9e0161c7d3a44b3f0cf1066e86f863b3.jpg\",\n  \"392608.jpg\": \"Reptilia/Phrynosoma platyrhinos/fc8e88c9821099c565f765f3a79912ab.jpg\",\n  \"392609.jpg\": \"Reptilia/Phrynosoma platyrhinos/02d4b09bc995625e661204163aa10dfd.jpg\",\n  \"392610.jpg\": \"Reptilia/Phrynosoma platyrhinos/c9a8d0098e5e36782aa4b07eb93d4d1a.jpg\",\n  \"392615.jpg\": \"Reptilia/Phrynosoma platyrhinos/f60148529b3eeecd6140198419e1d3f4.jpg\",\n  \"392616.jpg\": \"Reptilia/Phrynosoma platyrhinos/fe66439d11eb5ca1d9956041ad1dfd64.jpg\",\n  \"392619.jpg\": \"Reptilia/Phrynosoma platyrhinos/306bef429a05b13439a8cd78a8a83b7c.jpg\",\n  \"392620.jpg\": \"Reptilia/Phrynosoma platyrhinos/f1458f4dd5c10bdfc52524cc1df3f5c2.jpg\",\n  \"392621.jpg\": \"Reptilia/Phrynosoma platyrhinos/b622383ef48afac4cc92322961c0cc60.jpg\",\n  \"392630.jpg\": \"Reptilia/Phrynosoma platyrhinos/a7a10cfc855668fe09f1034c8f54b6ed.jpg\",\n  \"392631.jpg\": \"Reptilia/Phrynosoma platyrhinos/dbe8d36a9c159e147e245bf486076dba.jpg\",\n  \"392635.jpg\": \"Reptilia/Phrynosoma platyrhinos/65b474713eb2697af5bdedca2eb71fbc.jpg\",\n  \"392636.jpg\": \"Reptilia/Phrynosoma platyrhinos/c99883e200915b884d7fc0816337d154.jpg\",\n  \"392641.jpg\": \"Reptilia/Phrynosoma platyrhinos/e1267ded3c76859d82a55c8515a2ca67.jpg\",\n  \"392642.jpg\": \"Reptilia/Phrynosoma platyrhinos/5039b9dfb4afed5a52c942d9a7226d24.jpg\",\n  \"392647.jpg\": \"Reptilia/Phrynosoma platyrhinos/f58808a16e5b5d2bf5d7deab6727a872.jpg\",\n  \"392648.jpg\": \"Reptilia/Phrynosoma platyrhinos/ed9ee492fa4eff128a0a116a6914f43d.jpg\",\n  \"392649.jpg\": \"Reptilia/Phrynosoma platyrhinos/16b72cbed5f2559b74fd37fe087d9b05.jpg\",\n  \"392650.jpg\": \"Reptilia/Phrynosoma platyrhinos/106377218f83e9473c1ed0f749841411.jpg\",\n  \"392660.jpg\": \"Insecta/Coenagrion puella/ae377d6428a26d532662edc9f5c50bc8.jpg\",\n  \"392667.jpg\": \"Insecta/Coenagrion puella/8b6c25443b9ba534b0fd7e3eb4216ae8.jpg\",\n  \"392674.jpg\": \"Mammalia/Sciurus variegatoides/b664aa915886ecbd457df45f4daf53ef.jpg\",\n  \"392678.jpg\": \"Mammalia/Sciurus variegatoides/49d0298fab5c278a1bd8a2026464c6f4.jpg\",\n  \"392679.jpg\": \"Mammalia/Sciurus variegatoides/ee3a7e2c58a58be88ae0f484ea392477.jpg\",\n  \"392680.jpg\": \"Mammalia/Sciurus variegatoides/20e3822fc69b3cf20fbee47796cb9757.jpg\",\n  \"392805.jpg\": \"Insecta/Macromia taeniolata/f7d758167fe5e4a1bfeb398df63671c5.jpg\",\n  \"39282.jpg\": \"Insecta/Heraclides cresphontes/6531132d48a9809463c8e765a9cc0fdb.jpg\",\n  \"392830.jpg\": \"Insecta/Macromia taeniolata/f88140023816321da884b01b59d5a180.jpg\",\n  \"392839.jpg\": \"Aves/Anhinga anhinga/3596ffc6396de2cd41b0cfd1b300a5fe.jpg\",\n  \"392848.jpg\": \"Aves/Anhinga anhinga/0effcc13b99275b58e9e5beece849bb9.jpg\",\n  \"392851.jpg\": \"Aves/Anhinga anhinga/23f8f3bafd60ef21989e04ae3fd8049b.jpg\",\n  \"39286.jpg\": \"Insecta/Heraclides cresphontes/6d8f1e984a4e75658253ae978506dd86.jpg\",\n  \"392874.jpg\": \"Aves/Anhinga anhinga/fda4bd486a932cc8b06389ed7aab41b0.jpg\",\n  \"392893.jpg\": \"Aves/Anhinga anhinga/c9527ec6c374545d921ed9b64a38d7c1.jpg\",\n  \"392935.jpg\": \"Aves/Anhinga anhinga/908dd92242ee8de3cc28d396e7c9888e.jpg\",\n  \"392948.jpg\": \"Aves/Anhinga anhinga/518a6f35cdd847bed2144dd12f2a2703.jpg\",\n  \"392949.jpg\": \"Aves/Anhinga anhinga/0d30fa7ab1f4b5726a713f837abfc5d3.jpg\",\n  \"392954.jpg\": \"Aves/Anhinga anhinga/b9a870d40c250c3b305c12261bd59bb6.jpg\",\n  \"392955.jpg\": \"Aves/Anhinga anhinga/b35cf3460efb807ded8efee5183c7eb3.jpg\",\n  \"392961.jpg\": \"Aves/Anhinga anhinga/fd5400e56aa7924228a47dcb8426d020.jpg\",\n  \"392962.jpg\": \"Aves/Anhinga anhinga/90ea04b9a8f6e42df2b1cb275869a529.jpg\",\n  \"392964.jpg\": \"Aves/Anhinga anhinga/abe425e39ab65a463a16fcd61b26ca3a.jpg\",\n  \"392965.jpg\": \"Aves/Anhinga anhinga/677cb841316d2178f677ce33094549a1.jpg\",\n  \"392966.jpg\": \"Aves/Anhinga anhinga/23eff109dada5ac2a5ed90662656e22c.jpg\",\n  \"393008.jpg\": \"Aves/Anhinga anhinga/df3cf037c884f0614c62ea340df41ad9.jpg\",\n  \"393012.jpg\": \"Aves/Anhinga anhinga/f34b46248452c074e343e2dc654bab68.jpg\",\n  \"393015.jpg\": \"Aves/Anhinga anhinga/74f5f5e279d34317e9fed5f0d7873dfc.jpg\",\n  \"393050.jpg\": \"Aves/Anhinga anhinga/198ca39257301efe2093ce303fd31bdc.jpg\",\n  \"393052.jpg\": \"Aves/Anhinga anhinga/afd91aee1851006c9208355ea0d7bba1.jpg\",\n  \"39309.jpg\": \"Insecta/Heraclides cresphontes/3a4dedcf7305a0726d668b00cd20b3cb.jpg\",\n  \"39323.jpg\": \"Insecta/Heraclides cresphontes/16d47a53277a25b364cc7f19955696d5.jpg\",\n  \"39324.jpg\": \"Insecta/Heraclides cresphontes/f553a2789376b504005e74c517b1950c.jpg\",\n  \"393240.jpg\": \"Animalia/Coenobita clypeatus/9a795e394334e21c76d6270d58e9ca4b.jpg\",\n  \"393247.jpg\": \"Animalia/Coenobita clypeatus/3e52f94fa478c04f2952ece0d0be8188.jpg\",\n  \"39334.jpg\": \"Insecta/Heraclides cresphontes/cabd5c22d12b2a01983767880564e3da.jpg\",\n  \"39336.jpg\": \"Insecta/Heraclides cresphontes/45835ae84572f7ef52107d728af0eed4.jpg\",\n  \"393482.jpg\": \"Insecta/Cotinis mutabilis/16d283d0d749eeda2b05e95499f758eb.jpg\",\n  \"393497.jpg\": \"Insecta/Cotinis mutabilis/c5ecc59ace4129c4126b7fe8ced861a4.jpg\",\n  \"393498.jpg\": \"Insecta/Cotinis mutabilis/b1f030a3dba5500988491088e5ce1ee9.jpg\",\n  \"393499.jpg\": \"Insecta/Cotinis mutabilis/58795ed4a7575374e3bc84d71a9ec7ab.jpg\",\n  \"393509.jpg\": \"Insecta/Cotinis mutabilis/417293d9723bd069a772b0d5eb31d762.jpg\",\n  \"393518.jpg\": \"Insecta/Cotinis mutabilis/ae135dac39d752d826eb75d0d4863ea7.jpg\",\n  \"393532.jpg\": \"Insecta/Cotinis mutabilis/a83e47aeb844720ff465039ffc45fd0a.jpg\",\n  \"393540.jpg\": \"Insecta/Cotinis mutabilis/067610bfed174f380f84866e85228541.jpg\",\n  \"393549.jpg\": \"Insecta/Cotinis mutabilis/c61006ddf9ecbec550e81395cbdcea17.jpg\",\n  \"39356.jpg\": \"Insecta/Heraclides cresphontes/d824ba9e8715377c798025bd131b9942.jpg\",\n  \"393572.jpg\": \"Insecta/Cotinis mutabilis/47f5ca51b38f3655241ff900b7f503d9.jpg\",\n  \"393577.jpg\": \"Insecta/Cotinis mutabilis/b262d55d0e07315a250c9d6e7794e731.jpg\",\n  \"39358.jpg\": \"Insecta/Heraclides cresphontes/0382502c4241b302e11f842623d1edc6.jpg\",\n  \"393580.jpg\": \"Insecta/Cotinis mutabilis/3d5351d28db4362ed28664b9dca2719f.jpg\",\n  \"393582.jpg\": \"Insecta/Cotinis mutabilis/14aa92ff3ec4b1c3dcabb8087b2f3eff.jpg\",\n  \"393593.jpg\": \"Insecta/Cotinis mutabilis/2fa6ced2c0e03c71f3b0675a8faff187.jpg\",\n  \"393594.jpg\": \"Insecta/Cotinis mutabilis/d6c380d26b8b4abbc152f155ad28522a.jpg\",\n  \"393602.jpg\": \"Insecta/Cotinis mutabilis/bdbf6880d08ed96b390bcc2f45e5e8cd.jpg\",\n  \"393605.jpg\": \"Insecta/Cotinis mutabilis/7f814340ae297ed2e41e3fbf4943b8ab.jpg\",\n  \"393617.jpg\": \"Insecta/Cotinis mutabilis/374c4a86c1b6d8a4c34a925394fee213.jpg\",\n  \"393618.jpg\": \"Insecta/Cotinis mutabilis/de55121432fb4f0d4223dcb73d4f067a.jpg\",\n  \"393621.jpg\": \"Insecta/Cotinis mutabilis/8cbe22d79f47f25df13b7a11da3c97fd.jpg\",\n  \"393625.jpg\": \"Insecta/Cotinis mutabilis/7068b5a85fcf0b85d4aad2bf901db88a.jpg\",\n  \"393754.jpg\": \"Insecta/Maniola jurtina/3d6cd6ea5e2d22cb0f78d3bd2ac70156.jpg\",\n  \"393756.jpg\": \"Insecta/Maniola jurtina/5002bf45541861948ff3097585b1c9cb.jpg\",\n  \"393757.jpg\": \"Insecta/Maniola jurtina/c565a41eeec9ba08de75566be8560e15.jpg\",\n  \"393759.jpg\": \"Insecta/Maniola jurtina/2bc0380d0a089f178966e4571a1c5ca9.jpg\",\n  \"393762.jpg\": \"Insecta/Maniola jurtina/808123d5c2871af01cf7da1483030f53.jpg\",\n  \"393765.jpg\": \"Insecta/Maniola jurtina/038b3bee3d87b327459a82bbd03fdbf8.jpg\",\n  \"393766.jpg\": \"Insecta/Maniola jurtina/87cfe9ee2f7a8134172c9e6f3eb802f5.jpg\",\n  \"393767.jpg\": \"Insecta/Maniola jurtina/54f21263ede98ac00596be884db8eb16.jpg\",\n  \"393768.jpg\": \"Insecta/Maniola jurtina/f9db3b6d7d224a8be9983445d7c9eb91.jpg\",\n  \"393769.jpg\": \"Insecta/Maniola jurtina/afe58f5acb8181a3f5643b557fdf92dd.jpg\",\n  \"393771.jpg\": \"Insecta/Maniola jurtina/4f83d38e81cd614a110b1ae12aeac506.jpg\",\n  \"393775.jpg\": \"Insecta/Maniola jurtina/1b95f9979fb153a1e0ee936ce5c031c0.jpg\",\n  \"393780.jpg\": \"Insecta/Maniola jurtina/2a5254327e1ae4344eef0f8e272c0bce.jpg\",\n  \"393785.jpg\": \"Insecta/Maniola jurtina/c6beb26cb7aa7eaf6eb83542bd164f72.jpg\",\n  \"393788.jpg\": \"Insecta/Maniola jurtina/2f212c8c56796390991e47b90280144e.jpg\",\n  \"393789.jpg\": \"Insecta/Maniola jurtina/ad5a0f3bc8d18f629367f0721402a054.jpg\",\n  \"393790.jpg\": \"Insecta/Maniola jurtina/28d0cb5bdecbf5ada87b11a89e39e907.jpg\",\n  \"393795.jpg\": \"Insecta/Maniola jurtina/595e0f57da8294006bdaf7fcacc39488.jpg\",\n  \"393799.jpg\": \"Insecta/Maniola jurtina/d43d40adb4c4ea01375d9f983c6a89ee.jpg\",\n  \"393800.jpg\": \"Insecta/Maniola jurtina/5f0d7f668b4f0a95c1e4d84fbed1da83.jpg\",\n  \"393809.jpg\": \"Aves/Gallinula galeata/45eca6d1685362ff45d13b6cdebf91b1.jpg\",\n  \"39384.jpg\": \"Insecta/Heraclides cresphontes/4cc4b950750bc7759d0e50cc6249d390.jpg\",\n  \"393849.jpg\": \"Aves/Gallinula galeata/cb91c38044037ec956fc4b8c0270b969.jpg\",\n  \"393862.jpg\": \"Aves/Gallinula galeata/a17fdd6f8190756aa23a295c10cd9cdb.jpg\",\n  \"393888.jpg\": \"Aves/Gallinula galeata/2f53d9d1246d9714ac7d3b2f7972c714.jpg\",\n  \"393915.jpg\": \"Aves/Gallinula galeata/df78fc4d3d3c55e58de45fcfa0cf751b.jpg\",\n  \"39393.jpg\": \"Insecta/Heraclides cresphontes/b3ac0fdbb169b6b49c9ba7303c08ffb3.jpg\",\n  \"393939.jpg\": \"Aves/Gallinula galeata/75bcf088605eec92731b45e7cc590470.jpg\",\n  \"39394.jpg\": \"Insecta/Heraclides cresphontes/e0a198b28796c5a98ff869ef27e8d858.jpg\",\n  \"393946.jpg\": \"Aves/Gallinula galeata/95150a59c1fb8789e88504877b7732c1.jpg\",\n  \"393947.jpg\": \"Aves/Gallinula galeata/da24dfd053acca07bf6a317f8c380017.jpg\",\n  \"393961.jpg\": \"Aves/Gallinula galeata/618e32a167f948b05be36e367b95d83d.jpg\",\n  \"393975.jpg\": \"Aves/Gallinula galeata/13937070cf29a5f7d8258e4a8f22c063.jpg\",\n  \"393982.jpg\": \"Aves/Gallinula galeata/9b6daf1956842db5db38b9d2a2fa8631.jpg\",\n  \"39402.jpg\": \"Insecta/Heraclides cresphontes/367019d49cad7fb938480acbe33a018b.jpg\",\n  \"394023.jpg\": \"Aves/Gallinula galeata/9291243a109f33efd2a327b9b88a7d6d.jpg\",\n  \"394033.jpg\": \"Aves/Gallinula galeata/6efb98d4f4e6ada88bc1684fb9f6ded9.jpg\",\n  \"394058.jpg\": \"Aves/Gallinula galeata/2c5daaf19c53597ecd9ff4133047d8e1.jpg\",\n  \"394062.jpg\": \"Aves/Gallinula galeata/669e5a090f7881255890ed9ed9d9a07d.jpg\",\n  \"394098.jpg\": \"Aves/Gallinula galeata/8383d5a3967ce2a539c094397a2eb239.jpg\",\n  \"394119.jpg\": \"Aves/Gallinula galeata/75d38ddda4225dc7c441143335c9575a.jpg\",\n  \"394123.jpg\": \"Aves/Gallinula galeata/4dc435d905bd43d48380ab313cf1784f.jpg\",\n  \"394137.jpg\": \"Insecta/Lycaena phlaeas/5c432f159eae664779de2faced5de3cb.jpg\",\n  \"394141.jpg\": \"Insecta/Lycaena phlaeas/2800c5e82f9196bb5b2d7ca9e19467d0.jpg\",\n  \"394144.jpg\": \"Insecta/Lycaena phlaeas/06b550cfd148a606e22668634c9af5ce.jpg\",\n  \"394145.jpg\": \"Insecta/Lycaena phlaeas/e96fb30a8b1874a66c1ea3c0116fed51.jpg\",\n  \"394150.jpg\": \"Insecta/Lycaena phlaeas/7040efd60e286dbb8ad2f3cca0e29a09.jpg\",\n  \"394159.jpg\": \"Insecta/Lycaena phlaeas/07eafcd2b21b5bb90a79b62ca5ca439d.jpg\",\n  \"39416.jpg\": \"Insecta/Heraclides cresphontes/057d44da127ba8929dff7ddcb30f0af5.jpg\",\n  \"394165.jpg\": \"Insecta/Lycaena phlaeas/95db72f2c46c3d88f83b7072a519bbec.jpg\",\n  \"394166.jpg\": \"Insecta/Lycaena phlaeas/ef7223c087d68834f5f9b71bf6db1f45.jpg\",\n  \"39418.jpg\": \"Insecta/Heraclides cresphontes/71caaaefbc61dcfe30e304e14aac5b78.jpg\",\n  \"394182.jpg\": \"Insecta/Lycaena phlaeas/3c6cf1f8de7fdb1441df034a834bf639.jpg\",\n  \"394193.jpg\": \"Insecta/Lycaena phlaeas/de922509d682775ab0a18871625b82c8.jpg\",\n  \"394200.jpg\": \"Insecta/Lycaena phlaeas/e04a541cd5e7fafe52b19cf057419007.jpg\",\n  \"394203.jpg\": \"Insecta/Lycaena phlaeas/bda5c38fe6cd6e258491e31a6be23056.jpg\",\n  \"394204.jpg\": \"Insecta/Lycaena phlaeas/03d7882ce965058d5d5f8abc01e0a201.jpg\",\n  \"394207.jpg\": \"Insecta/Lycaena phlaeas/831702956ad783d23b1ecf1adb7bf380.jpg\",\n  \"394208.jpg\": \"Insecta/Lycaena phlaeas/0ce53fc8d144b876c66bdf034a57feea.jpg\",\n  \"394213.jpg\": \"Insecta/Lycaena phlaeas/20c31785b06457c721f4b3d0a8ea6cd4.jpg\",\n  \"394214.jpg\": \"Insecta/Lycaena phlaeas/be570d55654a2c19e9ff543b3b4044db.jpg\",\n  \"394222.jpg\": \"Insecta/Lycaena phlaeas/2955dc842c4c09d2b62e4c49e7428231.jpg\",\n  \"394228.jpg\": \"Insecta/Lycaena phlaeas/b7dd1cff78f57fb03884ead88f83d266.jpg\",\n  \"394230.jpg\": \"Insecta/Lycaena phlaeas/93ffbd3c16a270a7bf7356eaf9ccedc1.jpg\",\n  \"394256.jpg\": \"Aves/Cygnus buccinator/2f2176d9b0feb699ac0d8efa9bf0f9d9.jpg\",\n  \"394263.jpg\": \"Aves/Cygnus buccinator/f45e2c26c36dc0523d8b20b26cbe7340.jpg\",\n  \"394292.jpg\": \"Aves/Cygnus buccinator/a0630207f934d86b0cbfde853d5d793d.jpg\",\n  \"394293.jpg\": \"Aves/Cygnus buccinator/6a76e768fa4ef46ff9c6b43704843ddc.jpg\",\n  \"394294.jpg\": \"Aves/Cygnus buccinator/4afc5191644f3f9615ac5d4efd7bb131.jpg\",\n  \"39430.jpg\": \"Insecta/Heraclides cresphontes/ed1c4cbddba8f9a0ae1d43bac8f07e63.jpg\",\n  \"394306.jpg\": \"Aves/Cygnus buccinator/37064133e982d0abe39fcb24cd08270a.jpg\",\n  \"394310.jpg\": \"Aves/Cygnus buccinator/62d3c63fb6eebec8639052e8816b5ccb.jpg\",\n  \"394313.jpg\": \"Aves/Cygnus buccinator/10601a302442169d7175bbc72785b01e.jpg\",\n  \"394463.jpg\": \"Aves/Podilymbus podiceps/02037579aa3ce99d6e3dcb27a64a0a40.jpg\",\n  \"394464.jpg\": \"Aves/Podilymbus podiceps/e647a3461b393ae707e1006664008f3c.jpg\",\n  \"394475.jpg\": \"Aves/Podilymbus podiceps/12fb4e46892c10547b5f1ea9665caf9a.jpg\",\n  \"394481.jpg\": \"Aves/Podilymbus podiceps/0a88d36d0a26ab5eda17233f5616580d.jpg\",\n  \"394487.jpg\": \"Aves/Podilymbus podiceps/cb9c3faa2fa78a314832f4a37ba9d1f5.jpg\",\n  \"394491.jpg\": \"Aves/Podilymbus podiceps/0c50d3f02cd3542fdbe02ccb6808ce18.jpg\",\n  \"394548.jpg\": \"Aves/Podilymbus podiceps/86db18366ef8c86f037a2bae9b6f503e.jpg\",\n  \"394563.jpg\": \"Aves/Podilymbus podiceps/c74e9dafb6bbbaae56e2de01699387c5.jpg\",\n  \"39460.jpg\": \"Insecta/Heraclides cresphontes/2216661b56304540a9149876ea138336.jpg\",\n  \"394605.jpg\": \"Aves/Podilymbus podiceps/58857dca1c1b8403f3b66cab788fe199.jpg\",\n  \"39462.jpg\": \"Insecta/Heraclides cresphontes/afd8919390e80c5578d07fc7857466b4.jpg\",\n  \"394630.jpg\": \"Aves/Podilymbus podiceps/4cfd4a90acc519610f54b93ceb254bc5.jpg\",\n  \"394646.jpg\": \"Aves/Podilymbus podiceps/07f55b2d7b94f59ed217b0501d6b9911.jpg\",\n  \"394686.jpg\": \"Aves/Podilymbus podiceps/21e09095a8842bb683181de4d2330e74.jpg\",\n  \"394735.jpg\": \"Aves/Podilymbus podiceps/fc0a5ac97ff6b6579942a29992622911.jpg\",\n  \"394737.jpg\": \"Aves/Podilymbus podiceps/cc74f05c29c1fa640e7a7b8b851141f1.jpg\",\n  \"394754.jpg\": \"Aves/Podilymbus podiceps/39a8978503fbd1b309d2c6596056acd0.jpg\",\n  \"394765.jpg\": \"Aves/Podilymbus podiceps/8015670d44f7de323a7323ae10cd85ed.jpg\",\n  \"394773.jpg\": \"Aves/Podilymbus podiceps/f0a84cf63c83327e71deae5a252adef0.jpg\",\n  \"394816.jpg\": \"Aves/Podilymbus podiceps/1badf6ecf71918b83843432116e0678b.jpg\",\n  \"39483.jpg\": \"Insecta/Brachymesia furcata/b16bf0575297430f59b3a8e89c987c7a.jpg\",\n  \"394834.jpg\": \"Aves/Podilymbus podiceps/792beff6315b139727c7be5602d7af45.jpg\",\n  \"39484.jpg\": \"Insecta/Brachymesia furcata/39afcbc6eef491b2b67e5cf5ab9e9ec4.jpg\",\n  \"39485.jpg\": \"Insecta/Brachymesia furcata/0ab2f2c43cdd26c2247dc805dd47cc8b.jpg\",\n  \"394856.jpg\": \"Aves/Podilymbus podiceps/a8ec5fdf94869d803279dc2f4d3654b3.jpg\",\n  \"394862.jpg\": \"Aves/Podilymbus podiceps/b86ccf573ca48abc4204ba891585295b.jpg\",\n  \"394919.jpg\": \"Aves/Podilymbus podiceps/dc05baf5e390e4424eccd11787ebaffd.jpg\",\n  \"39492.jpg\": \"Insecta/Brachymesia furcata/274d53dea2886313abad13f9c4c2ea97.jpg\",\n  \"39493.jpg\": \"Insecta/Brachymesia furcata/efc146446346e9f7cdeebb4def4e8fae.jpg\",\n  \"394957.jpg\": \"Aves/Podilymbus podiceps/85bbddb78df8a2ad132732361bafcd61.jpg\",\n  \"39498.jpg\": \"Insecta/Brachymesia furcata/5b9fbd3bdba818e2a8b66f1feaa0be54.jpg\",\n  \"39499.jpg\": \"Insecta/Brachymesia furcata/c73a1217a102259ca520759cd5bcc76a.jpg\",\n  \"39502.jpg\": \"Insecta/Brachymesia furcata/50c56f9ff1effb9bf5ffed101c4babd5.jpg\",\n  \"39504.jpg\": \"Insecta/Brachymesia furcata/4a20708f4770ebd2f1a70ae5115dc053.jpg\",\n  \"39510.jpg\": \"Insecta/Brachymesia furcata/d6c8a28422959bd99f2819c4a4e2415d.jpg\",\n  \"39513.jpg\": \"Insecta/Brachymesia furcata/4cafbd58b28f443c7da39b412f227a80.jpg\",\n  \"395149.jpg\": \"Insecta/Tetraopes tetrophthalmus/924c67acffb4491e4fb21478c71aaa84.jpg\",\n  \"395154.jpg\": \"Insecta/Tetraopes tetrophthalmus/81c6f4af3529849fa11b4f75f3d072cd.jpg\",\n  \"395168.jpg\": \"Insecta/Tetraopes tetrophthalmus/018ea755da08c529500d80c3c5d66adf.jpg\",\n  \"395172.jpg\": \"Insecta/Tetraopes tetrophthalmus/5349dbbfa17ca81728c1fcdc601fc877.jpg\",\n  \"395192.jpg\": \"Insecta/Tetraopes tetrophthalmus/79499dfad3d1c84b2f061fc4a5d6ff75.jpg\",\n  \"395193.jpg\": \"Insecta/Tetraopes tetrophthalmus/2e0bf0eb532f2ee428c5a1b726f7e7a4.jpg\",\n  \"395202.jpg\": \"Insecta/Tetraopes tetrophthalmus/ba214ae28f4499951fb714bff75f7538.jpg\",\n  \"395211.jpg\": \"Insecta/Tetraopes tetrophthalmus/742cc581970f34621163de1064204c2e.jpg\",\n  \"395231.jpg\": \"Insecta/Tetraopes tetrophthalmus/fd108799ba4eabe1bda1431741a5fc42.jpg\",\n  \"395241.jpg\": \"Insecta/Tetraopes tetrophthalmus/1029c304816e77e7fe0aa440c2a95c1f.jpg\",\n  \"395400.jpg\": \"Aves/Spinus psaltria/d7f355203dd3c03d7a920a1089f696a9.jpg\",\n  \"395468.jpg\": \"Aves/Spinus psaltria/3287926931f2242cc9f1cd074b709397.jpg\",\n  \"395474.jpg\": \"Aves/Spinus psaltria/0cb5b9c481752b720d381bf93a2b6d0b.jpg\",\n  \"395584.jpg\": \"Aves/Spinus psaltria/202167001a35f314caaa2300cffb311f.jpg\",\n  \"395640.jpg\": \"Aves/Spinus psaltria/81310a87852ee4a1e0561a29edd9c19d.jpg\",\n  \"395669.jpg\": \"Aves/Spinus psaltria/53fd98aeca334e93ac1e7a64c46909a5.jpg\",\n  \"395673.jpg\": \"Aves/Spinus psaltria/2b53a3079a1b03c55ffb5d3635859933.jpg\",\n  \"395723.jpg\": \"Aves/Spinus psaltria/d205f0d84fd438f4005ae9fa64796a94.jpg\",\n  \"395747.jpg\": \"Aves/Spinus psaltria/dc5c58f246654067faffa7729b9c6eac.jpg\",\n  \"395852.jpg\": \"Aves/Spinus psaltria/2324d7272fc1dedcfadd3376f3fc634b.jpg\",\n  \"395969.jpg\": \"Aves/Spinus psaltria/288bf81244deeba44b4b7be2fc87f729.jpg\",\n  \"395988.jpg\": \"Aves/Spinus psaltria/d7a10d7f9cb43e6f71143a5204390b85.jpg\",\n  \"396002.jpg\": \"Aves/Spinus psaltria/1ee75249eb33758938de12bfa8c1257e.jpg\",\n  \"396006.jpg\": \"Aves/Spinus psaltria/f8861774923a3e014a929d0574a93b43.jpg\",\n  \"39604.jpg\": \"Animalia/Anthopleura xanthogrammica/33d1ec97d3a39914fac4ca21d837629f.jpg\",\n  \"396065.jpg\": \"Aves/Saltator atriceps/cdc02b278e5ea8d4f179450b8d7ca2ff.jpg\",\n  \"396068.jpg\": \"Aves/Saltator atriceps/deaf020f991cce597577375ce46cc808.jpg\",\n  \"396070.jpg\": \"Aves/Saltator atriceps/263f57397dbb7079981ddbae6089fa54.jpg\",\n  \"396076.jpg\": \"Aves/Saltator atriceps/36e01ed3a0681be434b6859257eb37ca.jpg\",\n  \"396092.jpg\": \"Aves/Bucorvus leadbeateri/1cde209c74b1712096303a1efaebeda8.jpg\",\n  \"396093.jpg\": \"Aves/Bucorvus leadbeateri/e8a3174d5ebaf706ef561e44c6b60fba.jpg\",\n  \"396307.jpg\": \"Aves/Platalea ajaja/19d410bbde9d4deddc2c2f596de8cac8.jpg\",\n  \"396338.jpg\": \"Aves/Platalea ajaja/214faf131e03b3d4e17bced0ab23a6b7.jpg\",\n  \"39634.jpg\": \"Animalia/Anthopleura xanthogrammica/e6a05e61a05542939b5ffbb6a196d7d7.jpg\",\n  \"396344.jpg\": \"Aves/Platalea ajaja/345af0926ca7c378ea65429f3f5ccac9.jpg\",\n  \"396351.jpg\": \"Aves/Platalea ajaja/8db47c3aa05ce3232ff2bbb012cc9448.jpg\",\n  \"396353.jpg\": \"Aves/Platalea ajaja/f9ba8f8ef393a878e27ad93cbd5d3600.jpg\",\n  \"396360.jpg\": \"Aves/Platalea ajaja/8c654e8de3d738d2d15a3048fb194e6e.jpg\",\n  \"39638.jpg\": \"Animalia/Anthopleura xanthogrammica/d6d8226a18b636cd0af94ad06ee02f08.jpg\",\n  \"396383.jpg\": \"Aves/Platalea ajaja/5fb68e8914daf67294527ed72951ae31.jpg\",\n  \"396392.jpg\": \"Aves/Platalea ajaja/71cef4ce256cef1cb509c594727567ff.jpg\",\n  \"39640.jpg\": \"Animalia/Anthopleura xanthogrammica/4bc7b64997f5fb501cbed9fd3f829598.jpg\",\n  \"396408.jpg\": \"Aves/Platalea ajaja/f62424139794570e4b7db238cfd15a52.jpg\",\n  \"396427.jpg\": \"Aves/Platalea ajaja/ad0b13f35192f35900c3387fb6e59ae9.jpg\",\n  \"396433.jpg\": \"Aves/Platalea ajaja/03944bd88175a3c232341bbaa89f6822.jpg\",\n  \"396434.jpg\": \"Aves/Platalea ajaja/7052222cf1008935639711836a353e90.jpg\",\n  \"396453.jpg\": \"Aves/Platalea ajaja/b29ec7b571ff04891870bed06b6e4082.jpg\",\n  \"396463.jpg\": \"Aves/Platalea ajaja/204f866a0e50c13e8ff6a8748643b1b8.jpg\",\n  \"396466.jpg\": \"Aves/Platalea ajaja/1d8c3ac680d0870396bf27f784e95ba0.jpg\",\n  \"396477.jpg\": \"Aves/Platalea ajaja/65e628f594c4726fee92b6e4d94b4dcb.jpg\",\n  \"396480.jpg\": \"Aves/Platalea ajaja/8d22c29df418f44293cdb312b961d731.jpg\",\n  \"396540.jpg\": \"Reptilia/Leiocephalus carinatus/5642fe3b380918568f0a5437fb741f00.jpg\",\n  \"396542.jpg\": \"Reptilia/Leiocephalus carinatus/a480c8e5f41926e1e38b33c0edf3eb89.jpg\",\n  \"396545.jpg\": \"Reptilia/Leiocephalus carinatus/ed939ff25ca91782ad780115975fa6fe.jpg\",\n  \"396547.jpg\": \"Reptilia/Leiocephalus carinatus/ca4f74df19c40cd9c4af92fab5c435f7.jpg\",\n  \"396548.jpg\": \"Reptilia/Leiocephalus carinatus/cac8dffed65d8169cb5299f83fe3b020.jpg\",\n  \"396549.jpg\": \"Reptilia/Leiocephalus carinatus/96f07fe6db57480e45578b988629d4cd.jpg\",\n  \"396551.jpg\": \"Reptilia/Leiocephalus carinatus/8f845cdfa409d8e4d317e34c1d991792.jpg\",\n  \"396579.jpg\": \"Aves/Aramus guarauna/b8b78ae5c8fdf682214b62c44c0d291c.jpg\",\n  \"396585.jpg\": \"Aves/Aramus guarauna/e34320eed8b54903ff6dff9f242ae2a5.jpg\",\n  \"396586.jpg\": \"Aves/Aramus guarauna/84a2c0dfa2d55c28ed535befe71123d2.jpg\",\n  \"396590.jpg\": \"Aves/Aramus guarauna/68681d6806f00ac3ced344820c41564e.jpg\",\n  \"396605.jpg\": \"Aves/Aramus guarauna/9c317ef835b4e1be0232cd0a51be941c.jpg\",\n  \"396607.jpg\": \"Aves/Aramus guarauna/ae9df7fb60f56bcfaacfa51b46c32f6c.jpg\",\n  \"396611.jpg\": \"Aves/Aramus guarauna/3b2443a19f07f5672b637c4b44a837c7.jpg\",\n  \"396615.jpg\": \"Aves/Aramus guarauna/20a7e524469b6f5d3c4eee2960ea2c7b.jpg\",\n  \"396625.jpg\": \"Aves/Aramus guarauna/02855e9cebcded745eb7708bf04f25ad.jpg\",\n  \"396626.jpg\": \"Aves/Aramus guarauna/a0ef205b11903012f9bbe17c04043fdd.jpg\",\n  \"396627.jpg\": \"Aves/Aramus guarauna/fe96e95a518f11983aa6232882af7244.jpg\",\n  \"396633.jpg\": \"Aves/Aramus guarauna/d827c1f620cbdb3f0c9722c669bc8765.jpg\",\n  \"396634.jpg\": \"Aves/Aramus guarauna/5153dbdb98e5dcb4bad6523354437869.jpg\",\n  \"396635.jpg\": \"Aves/Aramus guarauna/707b7290641cca964ec27f687b3fdea7.jpg\",\n  \"396643.jpg\": \"Aves/Aramus guarauna/638217b99312c405705887c4275af3f7.jpg\",\n  \"396649.jpg\": \"Aves/Aramus guarauna/19b1df270791d005c9eddac1e49ecccb.jpg\",\n  \"396650.jpg\": \"Aves/Aramus guarauna/f92df9d2da5cf476e609eca729cb9920.jpg\",\n  \"396654.jpg\": \"Aves/Aramus guarauna/1477b832c93174d8805f85d377a50d78.jpg\",\n  \"396658.jpg\": \"Aves/Aramus guarauna/179bc5662aaef586a5232d142707bfb5.jpg\",\n  \"396660.jpg\": \"Aves/Aramus guarauna/00fc2d572df244ec72acacac7be56bc1.jpg\",\n  \"396664.jpg\": \"Aves/Aramus guarauna/7980f2b3a25cb3681b9426a8ae6262a1.jpg\",\n  \"396671.jpg\": \"Aves/Aramus guarauna/9c37f4c682b2919490866a2d15c8e6be.jpg\",\n  \"396680.jpg\": \"Aves/Melanerpes lewis/461e27621861a48998781f9d1a7fa8ca.jpg\",\n  \"396681.jpg\": \"Aves/Melanerpes lewis/d4e5c70b06c55ee4c166eb26dd58a77b.jpg\",\n  \"396682.jpg\": \"Aves/Melanerpes lewis/e555df66aa62747821bf696624f0a852.jpg\",\n  \"396687.jpg\": \"Aves/Melanerpes lewis/70ffad76f70007fc6169b30681149867.jpg\",\n  \"396688.jpg\": \"Aves/Melanerpes lewis/6e7eea78d381c179703a5e8437e99061.jpg\",\n  \"396689.jpg\": \"Aves/Melanerpes lewis/35dde3cf34d2d4a1758aa6269accb996.jpg\",\n  \"396691.jpg\": \"Aves/Melanerpes lewis/d38cf9aee96b2a1bb1a7e68ea6649da5.jpg\",\n  \"396692.jpg\": \"Aves/Melanerpes lewis/39b14e46fed648223f9195ba33499173.jpg\",\n  \"396697.jpg\": \"Aves/Melanerpes lewis/71cd3c235d0a81a13c170afd44587219.jpg\",\n  \"39670.jpg\": \"Animalia/Anthopleura xanthogrammica/0555b5e9b1a9cf44d680a44baead36d2.jpg\",\n  \"396706.jpg\": \"Aves/Melanerpes lewis/d2c51ada86ea1a0f5224df844faea79c.jpg\",\n  \"396767.jpg\": \"Aves/Stelgidopteryx serripennis/67e6b4063e28846151ebd65050a354b8.jpg\",\n  \"396770.jpg\": \"Aves/Stelgidopteryx serripennis/27e2c0433e71d9c0adbf8e76d77e21a9.jpg\",\n  \"396773.jpg\": \"Aves/Stelgidopteryx serripennis/bead96af854637b34b8126ecb66778f9.jpg\",\n  \"396787.jpg\": \"Aves/Stelgidopteryx serripennis/c91290525197f2df6e80dc75a5ea13cc.jpg\",\n  \"396801.jpg\": \"Aves/Stelgidopteryx serripennis/68da6d2a265bdf07982c7c549b8b4ef3.jpg\",\n  \"396815.jpg\": \"Aves/Stelgidopteryx serripennis/f89888198ce6af8127fa3e011ad8bca0.jpg\",\n  \"396835.jpg\": \"Aves/Stelgidopteryx serripennis/e450121835e19121b7d2a2553e3a38b8.jpg\",\n  \"396848.jpg\": \"Aves/Stelgidopteryx serripennis/6b4008310c577df85c1f2c566618ac47.jpg\",\n  \"396865.jpg\": \"Aves/Stelgidopteryx serripennis/c286168242610e84bbffdf2b62d587ee.jpg\",\n  \"396885.jpg\": \"Aves/Stelgidopteryx serripennis/7043dc9df5052100099bf2bba593227e.jpg\",\n  \"396910.jpg\": \"Aves/Stelgidopteryx serripennis/c508029f27b89971aaf7840c99f2c998.jpg\",\n  \"396917.jpg\": \"Aves/Stelgidopteryx serripennis/d3b9e07d11dcf43f2bf208372f22fd0c.jpg\",\n  \"396920.jpg\": \"Aves/Stelgidopteryx serripennis/b174876284122ea4bd2dd2dab81195be.jpg\",\n  \"396922.jpg\": \"Aves/Stelgidopteryx serripennis/ff1d2f7f37b9d07e85e8c4d3d1352d11.jpg\",\n  \"396923.jpg\": \"Aves/Stelgidopteryx serripennis/eec043d4fc3e31d0fd23b010fdbfda48.jpg\",\n  \"39696.jpg\": \"Animalia/Anthopleura xanthogrammica/50824a97848d623d1a199f81e50d4567.jpg\",\n  \"397.jpg\": \"Insecta/Coleomegilla maculata/465a1fe5c85ad5d834568327bef80088.jpg\",\n  \"397065.jpg\": \"Aves/Picoides villosus/a365ad311dae354ec331d0fe26cfc668.jpg\",\n  \"397068.jpg\": \"Aves/Picoides villosus/60899cbad47d62d352b3bc43bbde03a3.jpg\",\n  \"397086.jpg\": \"Aves/Picoides villosus/ccd0c37a35f50416d1cdc5bd748a9011.jpg\",\n  \"397089.jpg\": \"Aves/Picoides villosus/176245accb11a4e96f559c89d49e8161.jpg\",\n  \"397105.jpg\": \"Aves/Picoides villosus/f0d0406924bba6f819c698c7cfcb53fb.jpg\",\n  \"397116.jpg\": \"Aves/Picoides villosus/80df3d0f148f132d92ec3099e62214e1.jpg\",\n  \"397121.jpg\": \"Aves/Picoides villosus/c8ecf9bc307e540f51c1ee0615b2f2e0.jpg\",\n  \"397130.jpg\": \"Aves/Picoides villosus/4c2ba49a81ecbf65da840dd641152e60.jpg\",\n  \"39716.jpg\": \"Animalia/Anthopleura xanthogrammica/9242b28a33f1be273635e3a1b7707e0e.jpg\",\n  \"397164.jpg\": \"Aves/Picoides villosus/0fee5bda662acc2edf8c69a184440fe1.jpg\",\n  \"397199.jpg\": \"Aves/Picoides villosus/248e7ba0042aa8c6a7f07915ee637ba6.jpg\",\n  \"397211.jpg\": \"Aves/Picoides villosus/c8824966e50839775121361c94e0c23b.jpg\",\n  \"397215.jpg\": \"Aves/Picoides villosus/b12cdf452da2de44aff5276ace5a2c1f.jpg\",\n  \"397231.jpg\": \"Aves/Picoides villosus/22128c209e96f24349652c7f92befa63.jpg\",\n  \"397244.jpg\": \"Aves/Picoides villosus/bd165a47a892a1ccdbe0ce0f751b8377.jpg\",\n  \"397309.jpg\": \"Aves/Picoides villosus/b26becec270fef14a49a9fc2ac820283.jpg\",\n  \"397335.jpg\": \"Aves/Picoides villosus/ee7f6b315d03359e56522134f948b72f.jpg\",\n  \"397337.jpg\": \"Aves/Picoides villosus/87b0b1a9823ab7111f92019b90049b6d.jpg\",\n  \"397338.jpg\": \"Aves/Picoides villosus/71c093dfc0c5e9991e470e9f38bdca31.jpg\",\n  \"397348.jpg\": \"Aves/Picoides villosus/44eae4037ef7570f63c94134b8385cf8.jpg\",\n  \"397364.jpg\": \"Aves/Picoides villosus/5d5fc137b51a8f55f7514d122801f699.jpg\",\n  \"397366.jpg\": \"Insecta/Ischnura verticalis/657a59fde957864b2f9440ed60c6c1af.jpg\",\n  \"397368.jpg\": \"Insecta/Ischnura verticalis/b6bb7d28c8efdd966cf2f5e0f268c72c.jpg\",\n  \"397374.jpg\": \"Insecta/Ischnura verticalis/a80f04d6955339dfaf35d01d320f30e9.jpg\",\n  \"397381.jpg\": \"Insecta/Ischnura verticalis/7ec44fed760ac57c813cbd79db1288ec.jpg\",\n  \"397382.jpg\": \"Insecta/Ischnura verticalis/4a073d31fa74c7ad358588cdd2ac0206.jpg\",\n  \"397383.jpg\": \"Insecta/Ischnura verticalis/82138a8ddfcb09a695030c32aa821322.jpg\",\n  \"397393.jpg\": \"Insecta/Ischnura verticalis/c78e182e80d457d04f9eeb009f7ae68a.jpg\",\n  \"397396.jpg\": \"Insecta/Ischnura verticalis/a1f2f0f7c62e94a6bb119ed988bc3777.jpg\",\n  \"397403.jpg\": \"Insecta/Ischnura verticalis/5730038917ed07750f88c19daacdb3fc.jpg\",\n  \"397430.jpg\": \"Insecta/Ischnura verticalis/0f133ebce749b7acca0b7ca08ee86d6a.jpg\",\n  \"397436.jpg\": \"Insecta/Ischnura verticalis/702266b035029ba602e894f344fb36c5.jpg\",\n  \"397460.jpg\": \"Insecta/Ischnura verticalis/1debf7e4c39bacaf59d89d64b3549a73.jpg\",\n  \"397468.jpg\": \"Insecta/Ischnura verticalis/eb431c76b5feaf6d5956b66d45e0e37d.jpg\",\n  \"397481.jpg\": \"Insecta/Ischnura verticalis/3f40da15536643f71d22cf4ac926c5b8.jpg\",\n  \"397482.jpg\": \"Insecta/Ischnura verticalis/e1e523a7916faccaee8d5c49050c3efb.jpg\",\n  \"397485.jpg\": \"Insecta/Ischnura verticalis/95b2edef4486d9cbf28782799ee70a57.jpg\",\n  \"397499.jpg\": \"Insecta/Ischnura verticalis/202ed6c3dde40a34a65086d272f6f471.jpg\",\n  \"397515.jpg\": \"Insecta/Ischnura verticalis/47b580917886597318982dbef70a9d89.jpg\",\n  \"397525.jpg\": \"Insecta/Ischnura verticalis/5350f0cfa650ebd9d1d653dc37ac94b4.jpg\",\n  \"397529.jpg\": \"Insecta/Ischnura verticalis/9a0fe2b50088b94aa0b0d2f485c0045e.jpg\",\n  \"397531.jpg\": \"Insecta/Ischnura verticalis/071d477df19c549e4fe3f6b3edcf6c1c.jpg\",\n  \"397533.jpg\": \"Insecta/Ischnura verticalis/22593dff7bf29f0fef3b428f2c5cf26c.jpg\",\n  \"397545.jpg\": \"Insecta/Ischnura verticalis/207beba2262b67dfbe24a487638135e0.jpg\",\n  \"397551.jpg\": \"Insecta/Ischnura verticalis/1295dc73e2845e54ab7b8044fb25e8e4.jpg\",\n  \"397565.jpg\": \"Aves/Histrionicus histrionicus/746480afa5e48289323cd5476d5ce832.jpg\",\n  \"397569.jpg\": \"Aves/Histrionicus histrionicus/c3313da3390941720f3a8360eb484138.jpg\",\n  \"397571.jpg\": \"Aves/Histrionicus histrionicus/02a529a38078be0955b48d8df94f4f2a.jpg\",\n  \"397572.jpg\": \"Aves/Histrionicus histrionicus/54f08054f3d78ee436e4f26a8d0f589d.jpg\",\n  \"397573.jpg\": \"Aves/Histrionicus histrionicus/1f371a10c822fce8725f470dc0fe594d.jpg\",\n  \"397586.jpg\": \"Aves/Histrionicus histrionicus/1277fd25ab276fd9a216bc0ce0a51069.jpg\",\n  \"397587.jpg\": \"Aves/Histrionicus histrionicus/7cb2298eb119df12acc9b96215f1af14.jpg\",\n  \"397600.jpg\": \"Aves/Histrionicus histrionicus/9e585e2bf0967c94517330b93befa0e7.jpg\",\n  \"397605.jpg\": \"Aves/Histrionicus histrionicus/18b662f381141d4c2cedf7b07a39eb23.jpg\",\n  \"397606.jpg\": \"Aves/Histrionicus histrionicus/abbe191a6f2b5de202846bbbacb22d21.jpg\",\n  \"397609.jpg\": \"Aves/Histrionicus histrionicus/01e603fb24a8a5b02d6bcd3b680def0b.jpg\",\n  \"397611.jpg\": \"Aves/Histrionicus histrionicus/ba5e23af0f4cea270874d91e04c030c4.jpg\",\n  \"397619.jpg\": \"Aves/Histrionicus histrionicus/b5e3af165846add26c40c3a4bc91e5de.jpg\",\n  \"397625.jpg\": \"Aves/Histrionicus histrionicus/98c35fd077530b66351214f894488791.jpg\",\n  \"397642.jpg\": \"Aves/Histrionicus histrionicus/8127c203dc570caf48c92c02ee2881d2.jpg\",\n  \"397643.jpg\": \"Aves/Histrionicus histrionicus/e3c84f36c982564dc351ae020e4872d2.jpg\",\n  \"397846.jpg\": \"Animalia/Patiria miniata/1e21fb6ab6a625f3bd85fc65d0cdec0f.jpg\",\n  \"397852.jpg\": \"Animalia/Patiria miniata/68fa9aad8c065e82acc41f110d4071c8.jpg\",\n  \"397854.jpg\": \"Animalia/Patiria miniata/bae1f5c270aee8fbaf441e14429dccfa.jpg\",\n  \"397860.jpg\": \"Animalia/Patiria miniata/a3a989145c94f778ebbf72238461e1ba.jpg\",\n  \"397869.jpg\": \"Animalia/Patiria miniata/9849cdef644bf18de49aea5e2e95a245.jpg\",\n  \"397874.jpg\": \"Animalia/Patiria miniata/16df4cca3f7940bf20f846ca1d7e87b3.jpg\",\n  \"397875.jpg\": \"Animalia/Patiria miniata/a418a8a11365dc0ad10319d90c9ea4c3.jpg\",\n  \"397885.jpg\": \"Animalia/Patiria miniata/34ec1119ced95f32d3238c018dc72288.jpg\",\n  \"397898.jpg\": \"Animalia/Patiria miniata/b293b812f451836cc1a20508f00ef9ad.jpg\",\n  \"397906.jpg\": \"Animalia/Patiria miniata/f21fb515c7103fc1738dd7b435169955.jpg\",\n  \"397918.jpg\": \"Animalia/Patiria miniata/7f24a0555ae00d437bcbe5451bf8b5ef.jpg\",\n  \"397933.jpg\": \"Animalia/Patiria miniata/8e9cfeefe81f61581e0a1fab269dccbe.jpg\",\n  \"397937.jpg\": \"Animalia/Patiria miniata/3a40998c1f18a0eebc2ec87119016d65.jpg\",\n  \"397949.jpg\": \"Animalia/Patiria miniata/e7aec8af74047d9731239f30e92cb9b7.jpg\",\n  \"397951.jpg\": \"Animalia/Patiria miniata/b8498fe3f37e67212c6afdba83b735f4.jpg\",\n  \"397957.jpg\": \"Animalia/Patiria miniata/d1b0e303bcb2ca1aeae700e5210acf26.jpg\",\n  \"397961.jpg\": \"Animalia/Patiria miniata/89928f1370b705988516e283bdecd235.jpg\",\n  \"397970.jpg\": \"Animalia/Patiria miniata/66a34b6934c025ae63c761871f4f949e.jpg\",\n  \"397971.jpg\": \"Animalia/Patiria miniata/1390292e938d09bd2fb03954adb16659.jpg\",\n  \"398.jpg\": \"Insecta/Coleomegilla maculata/8dcd7553173897ad257c86c0c27d7b60.jpg\",\n  \"398086.jpg\": \"Insecta/Paonias excaecata/a13893917521172b3374ca95ead0aef0.jpg\",\n  \"398087.jpg\": \"Insecta/Paonias excaecata/e5c888d1b24f31477ffb96a61247fe5f.jpg\",\n  \"398092.jpg\": \"Insecta/Paonias excaecata/aac46f35cb681015208ba81fc301fd6a.jpg\",\n  \"398093.jpg\": \"Insecta/Paonias excaecata/d52bd552eff0d2b8e96c305610a8a5c9.jpg\",\n  \"398097.jpg\": \"Insecta/Paonias excaecata/0551e4fb89e831e2c1b6240bbc4b4f55.jpg\",\n  \"398104.jpg\": \"Insecta/Paonias excaecata/e11756626fda5df3755871b19b43e6dc.jpg\",\n  \"398105.jpg\": \"Insecta/Paonias excaecata/16d772ba93ad4478146f3fb593f774a5.jpg\",\n  \"398108.jpg\": \"Insecta/Paonias excaecata/637b4707fd9b3fd0d4c92c31bd9a9a4f.jpg\",\n  \"398109.jpg\": \"Insecta/Paonias excaecata/e6c53cc25c3e61bef1e922396b3c458e.jpg\",\n  \"398110.jpg\": \"Insecta/Paonias excaecata/26dc13595a523501d93966ee82224905.jpg\",\n  \"398111.jpg\": \"Insecta/Paonias excaecata/25e2582372612e9cbbefff61a29cb3fd.jpg\",\n  \"398114.jpg\": \"Insecta/Paonias excaecata/e1525835781006d0f510b264e473dffe.jpg\",\n  \"398123.jpg\": \"Insecta/Paonias excaecata/b2ec3040f6eabd87021f16fdcf87200b.jpg\",\n  \"398124.jpg\": \"Insecta/Paonias excaecata/991e15c0e9b11fe497043e65a6ee5a96.jpg\",\n  \"398125.jpg\": \"Insecta/Paonias excaecata/13c098f64b3e86a33c363fc60db12c84.jpg\",\n  \"398126.jpg\": \"Insecta/Paonias excaecata/f278beaf4277e18d8c0739c7f7da4b42.jpg\",\n  \"398127.jpg\": \"Insecta/Paonias excaecata/240d24d7be084d0117d820b4195e6812.jpg\",\n  \"398128.jpg\": \"Insecta/Paonias excaecata/327d32dc054baf45939432b9a4efb362.jpg\",\n  \"398130.jpg\": \"Aves/Empidonax minimus/3ab566c2f7edcb6467c42688517cd590.jpg\",\n  \"398146.jpg\": \"Aves/Empidonax minimus/97a170800a44e7b0ee3dcb7982d865fe.jpg\",\n  \"398151.jpg\": \"Aves/Empidonax minimus/89669927c8232450bb5f504d02d0929b.jpg\",\n  \"398152.jpg\": \"Aves/Empidonax minimus/e8abd9feeeb679a4ced0eef0841161c0.jpg\",\n  \"398157.jpg\": \"Aves/Empidonax minimus/6ffcac988f097f53a4cd7e2909c504ae.jpg\",\n  \"398159.jpg\": \"Insecta/Danaus eresimus/b3edf677cea57ad66305b39139e1b5bb.jpg\",\n  \"398160.jpg\": \"Insecta/Danaus eresimus/7a28314c57171ef29f7530dee05cbe44.jpg\",\n  \"398162.jpg\": \"Insecta/Danaus eresimus/a24596a37ce6b1df30e97476db6e95f9.jpg\",\n  \"398163.jpg\": \"Insecta/Danaus eresimus/cb6e2c7114b9e23959750b4a668e4f01.jpg\",\n  \"398164.jpg\": \"Insecta/Danaus eresimus/b124054b28e7e2fb69888498dea76369.jpg\",\n  \"398165.jpg\": \"Insecta/Danaus eresimus/6911bc92b39dd186d4013fde7c1d75ed.jpg\",\n  \"398166.jpg\": \"Insecta/Danaus eresimus/a7c06a9740fc553b55e95bb14d2f5e6d.jpg\",\n  \"398167.jpg\": \"Insecta/Danaus eresimus/e23d17a24f7fbce378f681ce767b67ff.jpg\",\n  \"398168.jpg\": \"Insecta/Danaus eresimus/f0e785040291d81b24f9887cf3468aab.jpg\",\n  \"398172.jpg\": \"Insecta/Danaus eresimus/9a3f8b00efc1830f36ab95b1522986f0.jpg\",\n  \"398173.jpg\": \"Insecta/Danaus eresimus/bf75422ead16b7cfc334bc510cde427b.jpg\",\n  \"398174.jpg\": \"Insecta/Danaus eresimus/ed213b3997161dd3d944b98635855d97.jpg\",\n  \"398175.jpg\": \"Insecta/Danaus eresimus/2da1a538bbe21a82bbcf9cad2dcf45f8.jpg\",\n  \"398176.jpg\": \"Insecta/Danaus eresimus/363a20b34a1465a326d5f947e448851e.jpg\",\n  \"398177.jpg\": \"Insecta/Danaus eresimus/6eccb6d1516972adff50aadaade9c01c.jpg\",\n  \"398178.jpg\": \"Insecta/Danaus eresimus/e53aa644b064758f051afd124aeb5bc2.jpg\",\n  \"398179.jpg\": \"Insecta/Danaus eresimus/cc0f5395f3319a1a3223ac9509361fe8.jpg\",\n  \"398181.jpg\": \"Insecta/Danaus eresimus/092e3f516c68547247db4f640b72e89f.jpg\",\n  \"398182.jpg\": \"Insecta/Danaus eresimus/c3177418b5c29f3e2ec981baa7c78b2e.jpg\",\n  \"398186.jpg\": \"Insecta/Danaus eresimus/a7fb33c9048bada764e974e465a92845.jpg\",\n  \"398191.jpg\": \"Insecta/Danaus eresimus/c85b0c45a438cb7d51be80c3f0918f1d.jpg\",\n  \"398301.jpg\": \"Insecta/Atalopedes campestris/e584ddd902f8772ba7bdc63ced560bc1.jpg\",\n  \"398309.jpg\": \"Insecta/Atalopedes campestris/dceecf51b8dd0e873ba5963498b16628.jpg\",\n  \"398310.jpg\": \"Insecta/Atalopedes campestris/316b47e856eb20f1bf4ba0dc5ebcd252.jpg\",\n  \"398319.jpg\": \"Insecta/Atalopedes campestris/e0068af4b2b5bc494568de83ee387797.jpg\",\n  \"398320.jpg\": \"Insecta/Atalopedes campestris/a0ca1871e4990c05251f6302e27ef134.jpg\",\n  \"398321.jpg\": \"Insecta/Atalopedes campestris/a93a8615bbd0cae7bcd950b8ecc0d6e6.jpg\",\n  \"398341.jpg\": \"Insecta/Atalopedes campestris/c9ace982d538e870cd9366fa3b158aa3.jpg\",\n  \"398342.jpg\": \"Insecta/Atalopedes campestris/e8707b98d49fd6e7b890581274210b9a.jpg\",\n  \"398358.jpg\": \"Insecta/Atalopedes campestris/d7545fd5602761e271d200eba568f366.jpg\",\n  \"398359.jpg\": \"Insecta/Atalopedes campestris/d9863c5a28b7e29bf71b03927a4302d9.jpg\",\n  \"398364.jpg\": \"Insecta/Atalopedes campestris/9ffe221ee1cf58fec5d2b19cad861342.jpg\",\n  \"398385.jpg\": \"Insecta/Atalopedes campestris/42191e88f94fce6b9303f06f356d5473.jpg\",\n  \"398397.jpg\": \"Insecta/Atalopedes campestris/89864af0bbf67d181b3dbb3472436fb4.jpg\",\n  \"398400.jpg\": \"Insecta/Atalopedes campestris/2360290b3f5c3915517e10e1bce3ae60.jpg\",\n  \"398406.jpg\": \"Insecta/Atalopedes campestris/c9caf6761413f5dec0a7243a4082c1d1.jpg\",\n  \"398412.jpg\": \"Insecta/Atalopedes campestris/b8cc121071a0f4a957a8ba367b5fa80c.jpg\",\n  \"398433.jpg\": \"Insecta/Atalopedes campestris/07b899df44befb5fa6e871a77e4aaadd.jpg\",\n  \"398442.jpg\": \"Insecta/Atalopedes campestris/6d1c02c7508edec8d2b598846debd5d2.jpg\",\n  \"398443.jpg\": \"Insecta/Atalopedes campestris/39d647c755c114d5e9914e077e93767c.jpg\",\n  \"398474.jpg\": \"Insecta/Atalopedes campestris/e00913a11dd776436d993205803bf6b0.jpg\",\n  \"398483.jpg\": \"Insecta/Atalopedes campestris/747dde963a1dcb7dee63fc2a619fd539.jpg\",\n  \"398489.jpg\": \"Insecta/Atalopedes campestris/2a15b389c016888de0fd06ca0450776b.jpg\",\n  \"398504.jpg\": \"Insecta/Atalopedes campestris/0787e23fe348aa121440c1370a558c65.jpg\",\n  \"398545.jpg\": \"Mammalia/Zalophus californianus/7c5ed89222d396833d09854c5db302e6.jpg\",\n  \"398549.jpg\": \"Mammalia/Zalophus californianus/4c6e358fa01d0f3309fcb33973b5803e.jpg\",\n  \"398626.jpg\": \"Mammalia/Zalophus californianus/4ab3f6085011f39253d1785b3c3c2fe3.jpg\",\n  \"398633.jpg\": \"Mammalia/Zalophus californianus/2645f5294ed026978a28f7b9e41d99f0.jpg\",\n  \"398664.jpg\": \"Mammalia/Zalophus californianus/222890abd4f920d45e04a3b953035a71.jpg\",\n  \"398693.jpg\": \"Mammalia/Zalophus californianus/11424729da1ab0e1c8f999756260ceae.jpg\",\n  \"398720.jpg\": \"Mammalia/Zalophus californianus/f8d1369302ed3d0069a55c8f4a66abc1.jpg\",\n  \"398752.jpg\": \"Mammalia/Zalophus californianus/8dc45341a20fc9c4dd6de43a155c7f2b.jpg\",\n  \"398760.jpg\": \"Mammalia/Zalophus californianus/994aa0b9021180461d3439afebf187cd.jpg\",\n  \"398804.jpg\": \"Reptilia/Lacerta bilineata/f83e45c42a5abbf85d14c0d16f5eb708.jpg\",\n  \"398806.jpg\": \"Reptilia/Lacerta bilineata/553654a943ed28cc22e7a2395b5e6230.jpg\",\n  \"398810.jpg\": \"Reptilia/Lacerta bilineata/c23724cea37df5c5cf97382094e17878.jpg\",\n  \"398813.jpg\": \"Reptilia/Lacerta bilineata/029390797c65deee9cc828b20db42e5d.jpg\",\n  \"398814.jpg\": \"Reptilia/Lacerta bilineata/0d29fee897d16df8dc43668151f712bc.jpg\",\n  \"398819.jpg\": \"Reptilia/Lacerta bilineata/4f88ab85dfd699da95a32201a4a3b589.jpg\",\n  \"398820.jpg\": \"Reptilia/Lacerta bilineata/402cb1dc2c232279aaa9e3c07db392e7.jpg\",\n  \"398821.jpg\": \"Reptilia/Lacerta bilineata/b1e70afaf0c02ee159c8d799bbb8b97c.jpg\",\n  \"398830.jpg\": \"Reptilia/Lacerta bilineata/2d7f76cd1092fb598a41dd7ca3ad1167.jpg\",\n  \"398838.jpg\": \"Reptilia/Lacerta bilineata/0f5c243a65d861fab82ba3d92f66c339.jpg\",\n  \"398839.jpg\": \"Reptilia/Lacerta bilineata/a801a0a39a58d1b07ac15531e186faba.jpg\",\n  \"398867.jpg\": \"Insecta/Enallagma signatum/74a7948837e8a7ab9d442e0ad950cccb.jpg\",\n  \"398869.jpg\": \"Insecta/Enallagma signatum/3c8cca8c185524554f513450ac343601.jpg\",\n  \"398870.jpg\": \"Insecta/Enallagma signatum/2d7951dc68adf3691a4e71051de1d50b.jpg\",\n  \"398871.jpg\": \"Insecta/Enallagma signatum/75dea72ecf50406c74b23fe8f362e53e.jpg\",\n  \"398877.jpg\": \"Insecta/Enallagma signatum/7b151cf0c21a6c00196c9fc46cddd0c4.jpg\",\n  \"398878.jpg\": \"Insecta/Enallagma signatum/a040e93f6570afc8ed782ba57f3664ed.jpg\",\n  \"398888.jpg\": \"Insecta/Enallagma signatum/1dc8f9b34ea03788f7cd72e799729547.jpg\",\n  \"398890.jpg\": \"Insecta/Enallagma signatum/93eb63c69fb237458137cd2d1a73f90e.jpg\",\n  \"398903.jpg\": \"Insecta/Enallagma signatum/c73e8fb820e218cae99c176459ecd33a.jpg\",\n  \"398905.jpg\": \"Insecta/Enallagma signatum/d875c425eff0863b5cd11ef53fabfc0e.jpg\",\n  \"398935.jpg\": \"Insecta/Enallagma signatum/b89b2bd9d16dc15afd6afa17eefa6045.jpg\",\n  \"398943.jpg\": \"Insecta/Enallagma signatum/c1e20183f7ff8b4a8c537c969b6669c3.jpg\",\n  \"398947.jpg\": \"Insecta/Enallagma signatum/c8830dd27a5b62454c6520651a08784a.jpg\",\n  \"398948.jpg\": \"Insecta/Enallagma signatum/e965744d9ae49fb42c09c028d70c9713.jpg\",\n  \"398956.jpg\": \"Insecta/Enallagma signatum/869a4805feabd3871b73aae4fdfbe361.jpg\",\n  \"399000.jpg\": \"Aves/Anas clypeata/3cf1caef3f5e2fadf7e922adc746d1a8.jpg\",\n  \"399059.jpg\": \"Aves/Anas clypeata/ff646f38ff7453f31f69f30b326f8c5f.jpg\",\n  \"399116.jpg\": \"Aves/Anas clypeata/ce6dc748a61e6383160af540c4978c8f.jpg\",\n  \"399119.jpg\": \"Aves/Anas clypeata/72ee7493c338982d61b8a539e3e95d62.jpg\",\n  \"399205.jpg\": \"Aves/Anas clypeata/52f7b3845d8898c66891b9aa1b9d3dc7.jpg\",\n  \"399239.jpg\": \"Aves/Anas clypeata/cfb1cd198fc4c2d233b94241881b1abf.jpg\",\n  \"39929.jpg\": \"Aves/Accipiter striatus/6c8f18969d534513b24f996ac20d6ef2.jpg\",\n  \"399348.jpg\": \"Aves/Anas clypeata/673da007ae318e8170e6dc5950eb4edc.jpg\",\n  \"39940.jpg\": \"Aves/Accipiter striatus/42ed7075e199cc8f09304f91505152b1.jpg\",\n  \"399401.jpg\": \"Aves/Anas clypeata/5a3fb08ef440724afde5275d0e35bf21.jpg\",\n  \"39949.jpg\": \"Aves/Accipiter striatus/df3215f650d8255d69f944310d054870.jpg\",\n  \"399628.jpg\": \"Animalia/Aurelia aurita/a010187a7762c0234a04f6951d61fcb8.jpg\",\n  \"399630.jpg\": \"Animalia/Aurelia aurita/2163586a29bb68b9dbe3d1583239e368.jpg\",\n  \"399632.jpg\": \"Animalia/Aurelia aurita/3f69650d8f0cfce863a7a372d2e13031.jpg\",\n  \"399636.jpg\": \"Animalia/Aurelia aurita/fcae558caa04662f12df4e3bef50644c.jpg\",\n  \"399638.jpg\": \"Animalia/Aurelia aurita/4bee79599ebe5f50ca6d8bb19f17e5df.jpg\",\n  \"399639.jpg\": \"Animalia/Aurelia aurita/e8f4c90725732de24850aacb907590a3.jpg\",\n  \"39964.jpg\": \"Aves/Accipiter striatus/7686ab46737ddd8ab91ee2c41288383b.jpg\",\n  \"399641.jpg\": \"Animalia/Aurelia aurita/2e74d6ccb4c650c0f689dd1dcb7ee538.jpg\",\n  \"39965.jpg\": \"Aves/Accipiter striatus/a5ba53e0e370d02c0d3cb6aca41bcdda.jpg\",\n  \"399652.jpg\": \"Animalia/Aurelia aurita/ab9c17454a9135455d5ef4b50c79ff47.jpg\",\n  \"399655.jpg\": \"Animalia/Aurelia aurita/5ffd315ec076c56cf209eaebf6869d5a.jpg\",\n  \"399656.jpg\": \"Animalia/Aurelia aurita/2ce433281ae3fcb73d836694a85e88b1.jpg\",\n  \"399658.jpg\": \"Animalia/Aurelia aurita/1eff119cf6456fd5572121b01a0e8338.jpg\",\n  \"399659.jpg\": \"Animalia/Aurelia aurita/3b2f0683a9d8fb6683dbdb2d4e2df4dc.jpg\",\n  \"399667.jpg\": \"Animalia/Aurelia aurita/1d7b3ad5376663dcf277562b2a458e98.jpg\",\n  \"399671.jpg\": \"Mammalia/Odocoileus hemionus californicus/3390a24a8df8e80237dc6636a9d4c7d5.jpg\",\n  \"399673.jpg\": \"Mammalia/Odocoileus hemionus californicus/975602bbd619bde32b371c61bafb3ed7.jpg\",\n  \"399686.jpg\": \"Mammalia/Odocoileus hemionus californicus/7bab38ea57f27991da128c9b5f548526.jpg\",\n  \"399687.jpg\": \"Mammalia/Odocoileus hemionus californicus/27c55657d7bf3b20362962c9ff022722.jpg\",\n  \"399699.jpg\": \"Mammalia/Odocoileus hemionus californicus/83386e60ca0e4cecacc2e4aa1ecba670.jpg\",\n  \"399701.jpg\": \"Mammalia/Odocoileus hemionus californicus/606489649ffccfca23a36d82b1be038a.jpg\",\n  \"399704.jpg\": \"Mammalia/Odocoileus hemionus californicus/e159ae044e4999133821d9f53c6bf608.jpg\",\n  \"399706.jpg\": \"Mammalia/Odocoileus hemionus californicus/d3f24d4c6b617008da38b7c426f7b4de.jpg\",\n  \"399723.jpg\": \"Mammalia/Odocoileus hemionus californicus/b8e0fb455878640a78e156f927d9afd8.jpg\",\n  \"399724.jpg\": \"Mammalia/Odocoileus hemionus californicus/d739fe611d7528e62760dd929f8ded05.jpg\",\n  \"399728.jpg\": \"Mammalia/Odocoileus hemionus californicus/83a0865f896d7d03199771f599de73a4.jpg\",\n  \"399737.jpg\": \"Mammalia/Odocoileus hemionus californicus/73dc05e5b10be8fa31033fee4d42dee5.jpg\",\n  \"399744.jpg\": \"Insecta/Lepturobosca chrysocoma/bc6b1649a0757973a2698925274758ab.jpg\",\n  \"39975.jpg\": \"Aves/Accipiter striatus/55f7684926f832efc69ffb5d614ddbcc.jpg\",\n  \"399759.jpg\": \"Insecta/Lepturobosca chrysocoma/4ca345af132c802acb6cc523e66b3268.jpg\",\n  \"39976.jpg\": \"Aves/Accipiter striatus/466844dce2e97d6518dcb60a65fd8d88.jpg\",\n  \"399760.jpg\": \"Insecta/Lepturobosca chrysocoma/a285f3cb98a0c21de44eebf80c381563.jpg\",\n  \"399765.jpg\": \"Insecta/Lepturobosca chrysocoma/ad3a05f85a37db22b57ee950dc2b54fd.jpg\",\n  \"39977.jpg\": \"Aves/Accipiter striatus/367b48b65efa14873af017cf374e948a.jpg\",\n  \"399770.jpg\": \"Aves/Rissa tridactyla/340bedab83ea1410e24cc4cbe9c12e2d.jpg\",\n  \"399783.jpg\": \"Aves/Rissa tridactyla/8e9145c52ab386579ee2efa2fc827715.jpg\",\n  \"399784.jpg\": \"Aves/Rissa tridactyla/9e88cca692f509e5c9ca178ede7d53ec.jpg\",\n  \"399789.jpg\": \"Aves/Rissa tridactyla/236cdb67a2f1dd0a447f3ea1c94f288f.jpg\",\n  \"399800.jpg\": \"Aves/Rissa tridactyla/d7be8425dad8bcc2f00dc68ebcaf3062.jpg\",\n  \"399801.jpg\": \"Aves/Rissa tridactyla/fe5bdfd20cc87408dcaaaf83fb813b15.jpg\",\n  \"399807.jpg\": \"Aves/Rissa tridactyla/8284b9caa1baf6f4c768585b68b7cf3d.jpg\",\n  \"39982.jpg\": \"Aves/Accipiter striatus/bb44ca36a5f31da1bfb703bd004d6f4e.jpg\",\n  \"399820.jpg\": \"Aves/Rissa tridactyla/7bb2b9ff0ab8e38b8b55cd46bbcdcb46.jpg\",\n  \"39983.jpg\": \"Aves/Accipiter striatus/ebd25d5294172889e1e7c1af9554f569.jpg\",\n  \"39986.jpg\": \"Aves/Accipiter striatus/d6a40d80936c9cd7054d284b9a5ece34.jpg\",\n  \"39988.jpg\": \"Aves/Accipiter striatus/6805d7061dff91bff44acdb59ba1241f.jpg\",\n  \"39989.jpg\": \"Aves/Accipiter striatus/d75755e588e20fe47aa01deb270565c4.jpg\",\n  \"39993.jpg\": \"Aves/Accipiter striatus/86d2d081d76b485bbb501bf7aad25205.jpg\",\n  \"399954.jpg\": \"Amphibia/Dicamptodon ensatus/3463ff36fcc13eea68fe003d66e4d6fa.jpg\",\n  \"399959.jpg\": \"Amphibia/Dicamptodon ensatus/d89073d0b21de36414dd6db817a1664f.jpg\",\n  \"39996.jpg\": \"Aves/Accipiter striatus/0787056af1cd13d79977c54c89f77ce5.jpg\",\n  \"399961.jpg\": \"Amphibia/Dicamptodon ensatus/cfd9b137cd48471e0717701bdd25e9aa.jpg\",\n  \"399966.jpg\": \"Amphibia/Dicamptodon ensatus/64b8bb3192efecba61fcc5e316b16a9f.jpg\",\n  \"399968.jpg\": \"Amphibia/Dicamptodon ensatus/4ff4b5e89e362aaf7ce51a57130cc5a4.jpg\",\n  \"399969.jpg\": \"Amphibia/Dicamptodon ensatus/1192599639ae734b5f6090fa2b229684.jpg\",\n  \"399970.jpg\": \"Amphibia/Dicamptodon ensatus/68fba7ffa44216051ee5c40356065ec3.jpg\",\n  \"399971.jpg\": \"Amphibia/Dicamptodon ensatus/f0084a1e480aa0ae0b2089676811d780.jpg\",\n  \"399978.jpg\": \"Amphibia/Dicamptodon ensatus/a88b4e451891595b9484d0d32948cff9.jpg\",\n  \"399979.jpg\": \"Amphibia/Dicamptodon ensatus/782b09dc03f8b76154643385c6c5fbf2.jpg\",\n  \"399989.jpg\": \"Amphibia/Dicamptodon ensatus/09f598a53177341f71ccfaba28bb395a.jpg\",\n  \"399994.jpg\": \"Amphibia/Dicamptodon ensatus/233d1de3e1f06e35b13b04b1adbab127.jpg\",\n  \"399995.jpg\": \"Amphibia/Dicamptodon ensatus/110fed30accf3106f26eb555bf62470f.jpg\",\n  \"400002.jpg\": \"Amphibia/Dicamptodon ensatus/1414aee7ae72b339a7bff4e515953038.jpg\",\n  \"400003.jpg\": \"Amphibia/Dicamptodon ensatus/4ccd107d02321534db532c97bfa939b1.jpg\",\n  \"400004.jpg\": \"Amphibia/Dicamptodon ensatus/1a6f600afa8771edf667355af661c98c.jpg\",\n  \"400015.jpg\": \"Amphibia/Dicamptodon ensatus/03bcc1f00268358ca6c9bf59b77aa050.jpg\",\n  \"400016.jpg\": \"Amphibia/Dicamptodon ensatus/54563c5cfd4485b1e3368a6540cdcfd4.jpg\",\n  \"400024.jpg\": \"Amphibia/Dicamptodon ensatus/fa7b06da618c7bd7ee49ad5d450b089e.jpg\",\n  \"400030.jpg\": \"Amphibia/Dicamptodon ensatus/08e2456d5f5c41031884c468ae4cb123.jpg\",\n  \"400031.jpg\": \"Amphibia/Dicamptodon ensatus/2a446d4fb5e076dc8fa7c3bc86bc8668.jpg\",\n  \"400032.jpg\": \"Amphibia/Dicamptodon ensatus/87d84d5edcd36a0ea88d77c5d013c390.jpg\",\n  \"400040.jpg\": \"Aves/Malurus cyaneus/c51aaa3606f60e194e3cea5cfae5d4b7.jpg\",\n  \"400044.jpg\": \"Aves/Malurus cyaneus/96c1138a0e731c9624fd9b7c5b30a5b5.jpg\",\n  \"400045.jpg\": \"Aves/Malurus cyaneus/c5d26a011a0344166e6c80f75917fab6.jpg\",\n  \"400046.jpg\": \"Aves/Malurus cyaneus/acfdb1368897c03ddec31aeaa1fde7eb.jpg\",\n  \"40024.jpg\": \"Aves/Accipiter striatus/c09d9e94c1e31cfd09fa7a1739de1ab5.jpg\",\n  \"40030.jpg\": \"Aves/Accipiter striatus/ee533cd4663c7be589f4c546304ce48f.jpg\",\n  \"40044.jpg\": \"Aves/Accipiter striatus/8c15c550b728b5586eb3229f9c0db7d6.jpg\",\n  \"40045.jpg\": \"Aves/Accipiter striatus/fd74f6f8071d0311a6370b0c33a3b2cd.jpg\",\n  \"400504.jpg\": \"Aves/Oreothlypis peregrina/942c3f65eb47a10900fb547a65772d4b.jpg\",\n  \"400509.jpg\": \"Aves/Oreothlypis peregrina/b4dcf3ff8f6f0532e8ea8246bbc906f0.jpg\",\n  \"400515.jpg\": \"Aves/Oreothlypis peregrina/eb0fb8299c83bff36d2dd96fd7c7391b.jpg\",\n  \"400523.jpg\": \"Aves/Oreothlypis peregrina/5500cc3360cb1eed908223692a17ba97.jpg\",\n  \"400524.jpg\": \"Aves/Oreothlypis peregrina/ac65fd36d8cbfcc0d3b396cbff3ed112.jpg\",\n  \"400525.jpg\": \"Aves/Oreothlypis peregrina/4cb260b976b2a98ca69f45c20dd88d18.jpg\",\n  \"400532.jpg\": \"Aves/Oreothlypis peregrina/629a068023caf4d5bbff55475b369bcb.jpg\",\n  \"400533.jpg\": \"Aves/Oreothlypis peregrina/e0762db3276e90c4dbc8919b62302d39.jpg\",\n  \"400539.jpg\": \"Aves/Oreothlypis peregrina/89096608658f42c3cf4d9649c62100a7.jpg\",\n  \"400540.jpg\": \"Aves/Oreothlypis peregrina/d0782f025ab324d8c220a760474d10e6.jpg\",\n  \"400541.jpg\": \"Aves/Oreothlypis peregrina/b1ef0667187269a26ff82780dc4a2cc6.jpg\",\n  \"400542.jpg\": \"Aves/Oreothlypis peregrina/cf9d616ca4329bd28d75039a8535646d.jpg\",\n  \"400553.jpg\": \"Aves/Oreothlypis peregrina/d722eb3d1c41305f5f87215765b4cd58.jpg\",\n  \"400556.jpg\": \"Aves/Oreothlypis peregrina/467d9ed391d186d87768f563f1c850d4.jpg\",\n  \"400557.jpg\": \"Aves/Oreothlypis peregrina/9e5c7f11b692bcc988cabef51d5acfc6.jpg\",\n  \"400560.jpg\": \"Aves/Oreothlypis peregrina/bdc317ac77726a2a865e14be99c440d7.jpg\",\n  \"400562.jpg\": \"Aves/Oreothlypis peregrina/2fa44af54934b45f045bcfccf8265a83.jpg\",\n  \"400563.jpg\": \"Aves/Oreothlypis peregrina/7890ef04923cec526b99bde7505577b4.jpg\",\n  \"400564.jpg\": \"Aves/Oreothlypis peregrina/9b723967b28868c5c6634c96e81eded7.jpg\",\n  \"40061.jpg\": \"Aves/Accipiter striatus/6929ec120721da2df321c52d811be664.jpg\",\n  \"40062.jpg\": \"Aves/Accipiter striatus/7aab369ed0686604a552629a9289a601.jpg\",\n  \"400720.jpg\": \"Insecta/Trithemis annulata/4656868aeb70a2cc54293a3244979768.jpg\",\n  \"400721.jpg\": \"Insecta/Trithemis annulata/613fb32e232b0518894b77bf23f20c35.jpg\",\n  \"400722.jpg\": \"Insecta/Trithemis annulata/f32158bfaeae5857acdce60f1a03bbc3.jpg\",\n  \"400724.jpg\": \"Insecta/Trithemis annulata/1fa86182138f7f645492564156c085d2.jpg\",\n  \"400748.jpg\": \"Mammalia/Rhynchonycteris naso/73a4e03b1c259cc18fb138bc6f285853.jpg\",\n  \"400749.jpg\": \"Mammalia/Rhynchonycteris naso/63c63f6e0c8281ed016b52025395e5c8.jpg\",\n  \"400759.jpg\": \"Mammalia/Rhynchonycteris naso/22e3852dd7a948aa9123174bf63fd5e9.jpg\",\n  \"400760.jpg\": \"Mammalia/Rhynchonycteris naso/36316e6c979ee1602cd653aab2037b95.jpg\",\n  \"400771.jpg\": \"Reptilia/Thamnophis radix/ee94afe892aaa5367447f4d187b030ae.jpg\",\n  \"400772.jpg\": \"Reptilia/Thamnophis radix/a320683635f0449c9f9ee26b96845ca5.jpg\",\n  \"400776.jpg\": \"Reptilia/Thamnophis radix/bf06d919b0ab01ce02d6fb91ee577346.jpg\",\n  \"400777.jpg\": \"Reptilia/Thamnophis radix/0be0a4ed342624ad6646a7e5c2d3dc40.jpg\",\n  \"400779.jpg\": \"Reptilia/Thamnophis radix/5af50253b51b320ac9ea21362145ca3c.jpg\",\n  \"400780.jpg\": \"Reptilia/Thamnophis radix/fef8ebfcbc2e177e56373509988d74f8.jpg\",\n  \"400782.jpg\": \"Reptilia/Thamnophis radix/27f23aae71ea54bded69975f24b6ff94.jpg\",\n  \"401261.jpg\": \"Mammalia/Lepus americanus/9d717dccc19d0e48d3019b04a460f026.jpg\",\n  \"401265.jpg\": \"Mammalia/Lepus americanus/951724710f997be8a4dbbd4136f929bc.jpg\",\n  \"401266.jpg\": \"Mammalia/Lepus americanus/f45ac48764f5a06413053232ac8065e7.jpg\",\n  \"401267.jpg\": \"Mammalia/Lepus americanus/72656b9eb633866da154fbeafdcaa89d.jpg\",\n  \"401269.jpg\": \"Mammalia/Lepus americanus/03adbf31423670271b68bbc3ab2cd349.jpg\",\n  \"401271.jpg\": \"Mammalia/Lepus americanus/318633ea94b9004d2d0d1239a79c5f4e.jpg\",\n  \"401272.jpg\": \"Mammalia/Lepus americanus/b2c4b866547f4aae1c582c9ec694e0bc.jpg\",\n  \"401273.jpg\": \"Mammalia/Lepus americanus/bd706af37a723cea9875961eee8f7625.jpg\",\n  \"401274.jpg\": \"Mammalia/Lepus americanus/302ce7962d7dc7c5798230e562d87190.jpg\",\n  \"401276.jpg\": \"Mammalia/Lepus americanus/634ba220ee46f16364bbc7b7b8752865.jpg\",\n  \"401282.jpg\": \"Mammalia/Lepus americanus/cc32d770e86f783df2a3828afca2c071.jpg\",\n  \"401285.jpg\": \"Mammalia/Lepus americanus/e83e60689e273078ed07cb9ab6735659.jpg\",\n  \"401286.jpg\": \"Mammalia/Lepus americanus/ff8602c74a063ba2aecfde95bda4cf03.jpg\",\n  \"401287.jpg\": \"Mammalia/Lepus americanus/8796d4a2101059560f33e92c424eaaeb.jpg\",\n  \"401293.jpg\": \"Mammalia/Lepus americanus/e3f3e9a8b9bae9424753f8520e5022ee.jpg\",\n  \"401294.jpg\": \"Mammalia/Lepus americanus/e50b717b18089085c7b00862cd82c22e.jpg\",\n  \"401302.jpg\": \"Mammalia/Lepus americanus/78f544878f4311cd2ba5be8153cadaba.jpg\",\n  \"401305.jpg\": \"Mammalia/Lepus americanus/91aaabee57e6c5fa65e4d2314a30330b.jpg\",\n  \"401312.jpg\": \"Aves/Amazona viridigenalis/0147e6236644fe48d2a9e91faef9231b.jpg\",\n  \"401314.jpg\": \"Aves/Amazona viridigenalis/7e1cd42d0e8ed41cea01e9db4375406c.jpg\",\n  \"401315.jpg\": \"Aves/Amazona viridigenalis/5542c46e2c867618d85a6aa13a676eb3.jpg\",\n  \"401316.jpg\": \"Aves/Amazona viridigenalis/8f1f524b570329e36e2a5058769bccbb.jpg\",\n  \"401317.jpg\": \"Aves/Amazona viridigenalis/6d78ee3168353442c7ca79d83619dd2a.jpg\",\n  \"401318.jpg\": \"Aves/Amazona viridigenalis/3b5245bab67db843d7415bda1de8c12e.jpg\",\n  \"401319.jpg\": \"Aves/Amazona viridigenalis/2402f1dcb6dbd8d93bd0f52920af3171.jpg\",\n  \"401324.jpg\": \"Aves/Amazona viridigenalis/be12e2333555650e15eb502c0f9ae1e0.jpg\",\n  \"401335.jpg\": \"Aves/Amazona viridigenalis/364b697bc971ac67f0a08c7375fbc264.jpg\",\n  \"401336.jpg\": \"Aves/Amazona viridigenalis/38ede802f29a5d2f2306becc5d966973.jpg\",\n  \"401337.jpg\": \"Aves/Amazona viridigenalis/595472d7914f1967c98015b9aad73d32.jpg\",\n  \"401338.jpg\": \"Aves/Amazona viridigenalis/e59a5d268ff521eb69f038f8934abfc0.jpg\",\n  \"401341.jpg\": \"Aves/Amazona viridigenalis/4f1c9b4e8c5a9da1f44f0505646a1f07.jpg\",\n  \"401342.jpg\": \"Aves/Amazona viridigenalis/49a6cdda4ff9b82178eb4f28a6cf6b9b.jpg\",\n  \"401346.jpg\": \"Aves/Amazona viridigenalis/92a2839923665ffd4544d635faa5549b.jpg\",\n  \"401351.jpg\": \"Aves/Amazona viridigenalis/91b19eb406b86b21f5b7f5370a357272.jpg\",\n  \"401352.jpg\": \"Insecta/Pyrgus communis/5b4002a7b32030cf2f8c3071d0968bb4.jpg\",\n  \"401373.jpg\": \"Insecta/Pyrgus communis/cd6c9ec73c26c8221a7a6a28ece818b3.jpg\",\n  \"401382.jpg\": \"Insecta/Pyrgus communis/0a9aae1cb9e05343cca859c5406bb2f7.jpg\",\n  \"401385.jpg\": \"Insecta/Pyrgus communis/a43b0c397eb8eec4f2a4466f9f534b1e.jpg\",\n  \"401387.jpg\": \"Insecta/Pyrgus communis/45343ea220dd749af4dbc30a8eec7973.jpg\",\n  \"401388.jpg\": \"Insecta/Pyrgus communis/26843e7cadd95613dddbbb7b23cfae1c.jpg\",\n  \"401397.jpg\": \"Insecta/Pyrgus communis/984619a438cdd0b337b474c371d6ad66.jpg\",\n  \"401421.jpg\": \"Insecta/Pyrgus communis/987a8708395b5a7f14c9ba4f0266470c.jpg\",\n  \"401429.jpg\": \"Insecta/Pyrgus communis/566a91d2e24ec076e0cd67d5646fa03b.jpg\",\n  \"401430.jpg\": \"Insecta/Pyrgus communis/ee85565199c4530173f6b15a614e70d0.jpg\",\n  \"401435.jpg\": \"Insecta/Pyrgus communis/dab2e2e3163b852fd4446640a39285bc.jpg\",\n  \"401457.jpg\": \"Insecta/Pyrgus communis/288f54b47fcc25bc017cd978b1c5b0ee.jpg\",\n  \"401460.jpg\": \"Insecta/Pyrgus communis/58b11e5ed877135d6c9765b8a0869ab5.jpg\",\n  \"401461.jpg\": \"Insecta/Pyrgus communis/be42a17c1582c37a86ccc47459f42291.jpg\",\n  \"401468.jpg\": \"Insecta/Pyrgus communis/aa95e861621fc9d5ad5d510761a93b27.jpg\",\n  \"401521.jpg\": \"Insecta/Pyrgus communis/24e0f181788b700ec60a9c169fb196ed.jpg\",\n  \"401525.jpg\": \"Insecta/Pyrgus communis/b2af103f5e86334f2be24e74b84a845c.jpg\",\n  \"401535.jpg\": \"Insecta/Pyrgus communis/1c1a315c20f1b38cedce714c5559fe4f.jpg\",\n  \"401537.jpg\": \"Insecta/Pyrgus communis/16bd1db36926e6bc4ddded92d4ff495b.jpg\",\n  \"401539.jpg\": \"Insecta/Pyrgus communis/20acd23c1850f53e35c0fc52e4753137.jpg\",\n  \"401540.jpg\": \"Insecta/Pyrgus communis/c7b7f4761ee9c8c617d5b0b1d3b0c0d1.jpg\",\n  \"401541.jpg\": \"Insecta/Pyrgus communis/a94fdbb5762c67c501fd87dbcb809c30.jpg\",\n  \"401550.jpg\": \"Insecta/Pyrgus communis/03c6cb141247a052dbf72095efb2871e.jpg\",\n  \"401561.jpg\": \"Mammalia/Phoca vitulina/64abb969e9d2297b30185e88878b1efb.jpg\",\n  \"401578.jpg\": \"Mammalia/Phoca vitulina/806dca41ee1d3a945fe718d824649d39.jpg\",\n  \"401579.jpg\": \"Mammalia/Phoca vitulina/721f97c5184325e8c8a6c5aa4af16357.jpg\",\n  \"401621.jpg\": \"Mammalia/Phoca vitulina/805e4f784537ea19e9536ecb04f3079f.jpg\",\n  \"401623.jpg\": \"Mammalia/Phoca vitulina/4fbaad2e5cd26f6da75fae0cae7bda42.jpg\",\n  \"401632.jpg\": \"Mammalia/Phoca vitulina/9f1eccc5d98b46623f639ef7dcfa57c8.jpg\",\n  \"401638.jpg\": \"Mammalia/Phoca vitulina/007ab3debf629fe9b450b0acd905deac.jpg\",\n  \"401708.jpg\": \"Mammalia/Phoca vitulina/488665e1264a2edbd0bc0d463cad6ce3.jpg\",\n  \"401764.jpg\": \"Mammalia/Phoca vitulina/7633b72b8366afd902dd33479dc267ad.jpg\",\n  \"401800.jpg\": \"Mammalia/Phoca vitulina/7108e5497b581a62836750a5ad4ed13a.jpg\",\n  \"401808.jpg\": \"Mammalia/Phoca vitulina/ffa52e08d8333b9e1e8020ad1d928d7c.jpg\",\n  \"401849.jpg\": \"Mammalia/Phoca vitulina/95b008a18725a5df45e244214c337b3d.jpg\",\n  \"401860.jpg\": \"Mammalia/Phoca vitulina/3c24d04487c9caed11e74b9cf1101a73.jpg\",\n  \"402.jpg\": \"Insecta/Coleomegilla maculata/4532ab36422dc82ef7f57590c1326f22.jpg\",\n  \"402140.jpg\": \"Reptilia/Anolis sagrei/60fda7539eea42880f7f871d86f757dd.jpg\",\n  \"402142.jpg\": \"Reptilia/Anolis sagrei/2fcb2e7f32fcdd646b02c9b8d95e51b8.jpg\",\n  \"402161.jpg\": \"Reptilia/Anolis sagrei/9aa2c40c9a41635782588377cf29c303.jpg\",\n  \"402192.jpg\": \"Reptilia/Anolis sagrei/37700811ac4a4b253a20f6b9bdadb04e.jpg\",\n  \"402228.jpg\": \"Reptilia/Anolis sagrei/4b1a776179fa1373bc4b227e05ad80e7.jpg\",\n  \"402236.jpg\": \"Reptilia/Anolis sagrei/d41b4b3de8e80d9bb58f58c56d83151f.jpg\",\n  \"402243.jpg\": \"Reptilia/Anolis sagrei/925cc5aaf0507d7a99b8f98c0e5b443c.jpg\",\n  \"402249.jpg\": \"Reptilia/Anolis sagrei/5a2b93f5a4c03c65fcf3360ecf74a140.jpg\",\n  \"402290.jpg\": \"Reptilia/Anolis sagrei/890ab6eb2475f42569359640a8a5e038.jpg\",\n  \"402298.jpg\": \"Reptilia/Anolis sagrei/2f44ad3f8277fceff735da73835240e8.jpg\",\n  \"402299.jpg\": \"Reptilia/Anolis sagrei/4110b74f79f96114f9a7e475d30fad2c.jpg\",\n  \"402312.jpg\": \"Reptilia/Anolis sagrei/6390f5a06915e2035fc62a6a79cdb123.jpg\",\n  \"402330.jpg\": \"Reptilia/Anolis sagrei/333716e32c56efce2ee8f6d538a8d2a1.jpg\",\n  \"402336.jpg\": \"Reptilia/Anolis sagrei/05a24b562614cef3e977a665831bcd3b.jpg\",\n  \"402337.jpg\": \"Reptilia/Anolis sagrei/dc3ac9752ac9b83167eacf14475e69a9.jpg\",\n  \"402348.jpg\": \"Reptilia/Anolis sagrei/82ed40112943e8e1157ba717bf201c44.jpg\",\n  \"402352.jpg\": \"Reptilia/Anolis sagrei/fc649cd6894fd939b68576f47439e658.jpg\",\n  \"402400.jpg\": \"Reptilia/Anolis sagrei/b2da76757231a67e90cf809818b062d3.jpg\",\n  \"402411.jpg\": \"Reptilia/Anolis sagrei/3833323b1d367334e0c5132ad702855f.jpg\",\n  \"402427.jpg\": \"Reptilia/Anolis sagrei/9ccbcce9a576e23cf8d9bde8cac15128.jpg\",\n  \"402443.jpg\": \"Reptilia/Anolis sagrei/45a8c823d46ddd7316b390ce2d825a5a.jpg\",\n  \"402517.jpg\": \"Aves/Psilorhinus morio/c6a0d9178ee6f8c0eb23ebf7e4c793a2.jpg\",\n  \"402518.jpg\": \"Aves/Psilorhinus morio/6c31ae05229d67aaffcb9f8f755a5fc3.jpg\",\n  \"402519.jpg\": \"Aves/Psilorhinus morio/8feb2dd34b957866a63437360f432191.jpg\",\n  \"402520.jpg\": \"Aves/Psilorhinus morio/06dc1df1323cd5cb0ab7f152b9690be0.jpg\",\n  \"402521.jpg\": \"Aves/Psilorhinus morio/be697c7df6549d33b973e60a21528a05.jpg\",\n  \"402526.jpg\": \"Aves/Psilorhinus morio/13a0d5ca1a6174c11ec1c6879e454988.jpg\",\n  \"402535.jpg\": \"Aves/Psilorhinus morio/0ddcc7633906cfdc58a9853524996e67.jpg\",\n  \"402536.jpg\": \"Aves/Psilorhinus morio/98df5b9ea975ccc33b8c7c3a32cdd71f.jpg\",\n  \"402539.jpg\": \"Aves/Psilorhinus morio/2fbdf2c786e6b5f6869642257609b5be.jpg\",\n  \"402541.jpg\": \"Aves/Psilorhinus morio/534b7ee69924077a096021c2cbbae23c.jpg\",\n  \"402546.jpg\": \"Aves/Psilorhinus morio/533af8d775e053624c86ef3eed00c91f.jpg\",\n  \"402547.jpg\": \"Aves/Psilorhinus morio/916c3d10eb0183e372bf4091037459c6.jpg\",\n  \"402550.jpg\": \"Aves/Psilorhinus morio/29ee4d9a707fbdfdcb89b825700225e4.jpg\",\n  \"402555.jpg\": \"Aves/Psilorhinus morio/337c05453bd5041e04ce3e9a7a870f7c.jpg\",\n  \"402556.jpg\": \"Aves/Psilorhinus morio/8a1d7a54c73343950a363ac142191b68.jpg\",\n  \"402558.jpg\": \"Aves/Psilorhinus morio/a49e2948028b8098b43644e4a8668f4e.jpg\",\n  \"402559.jpg\": \"Aves/Psilorhinus morio/4523410b0507919bf8a66f2a0378c3e2.jpg\",\n  \"402566.jpg\": \"Aves/Psilorhinus morio/75e720ea9e556224736b3094c727b191.jpg\",\n  \"402567.jpg\": \"Aves/Psilorhinus morio/09bb70381587fe396a5241e6beef3e81.jpg\",\n  \"402576.jpg\": \"Aves/Psilorhinus morio/3f7d80b81bacffc32655cb6a558f5bcc.jpg\",\n  \"402578.jpg\": \"Aves/Psilorhinus morio/8cb9ae0d3493741de83e155f76bbefdc.jpg\",\n  \"402584.jpg\": \"Aves/Francolinus pondicerianus/2985a155fc9baec81aa6af5b90154b9c.jpg\",\n  \"402588.jpg\": \"Aves/Francolinus pondicerianus/0ba26fc51e38fb2e619ac3f129a5c8be.jpg\",\n  \"402591.jpg\": \"Aves/Francolinus pondicerianus/3af837c81c34723062795555900eeeb1.jpg\",\n  \"402595.jpg\": \"Insecta/Aquarius remigis/9cddb9883935f69bc552857d1a6e94ab.jpg\",\n  \"402601.jpg\": \"Insecta/Aquarius remigis/1cc7f2170429ea74c88d055d0a0a0a2a.jpg\",\n  \"402602.jpg\": \"Insecta/Aquarius remigis/a8237cd96ef5703e70e7855c69600d68.jpg\",\n  \"402604.jpg\": \"Insecta/Aquarius remigis/30872a26b1ad9079ebef05fdce91cfb5.jpg\",\n  \"402607.jpg\": \"Insecta/Aquarius remigis/0ba1521ce0afa9a38de4607fe147a464.jpg\",\n  \"402617.jpg\": \"Insecta/Aquarius remigis/07bfbdb5915fa922b2323ff5e48f59df.jpg\",\n  \"402619.jpg\": \"Insecta/Aquarius remigis/6345a31857f8a9f8fc5cf0f5ad1601f3.jpg\",\n  \"402620.jpg\": \"Insecta/Aquarius remigis/3d706f37ff633d682bb01d008c16e137.jpg\",\n  \"402621.jpg\": \"Insecta/Aquarius remigis/48c66b43201c2fbc1559de1c3f10484a.jpg\",\n  \"402622.jpg\": \"Insecta/Aquarius remigis/55d5d1765b30d093bf6c15735ec8aee8.jpg\",\n  \"402768.jpg\": \"Animalia/Callinectes sapidus/2c48e0450010ebc1b3a6b11676e1bb4f.jpg\",\n  \"402776.jpg\": \"Animalia/Callinectes sapidus/6a0a3403492ec2e6524d5d98ef0576b4.jpg\",\n  \"402777.jpg\": \"Animalia/Callinectes sapidus/802d23b98fd1fa680712ef2b89428684.jpg\",\n  \"402778.jpg\": \"Animalia/Callinectes sapidus/5aa65a88a1cefdb8d129bf7a14fafb4d.jpg\",\n  \"402779.jpg\": \"Animalia/Callinectes sapidus/731be2534a6d2d31bdc1907ee315c6d1.jpg\",\n  \"402780.jpg\": \"Animalia/Callinectes sapidus/8823885d914e7a836c6fac4456b1dfbf.jpg\",\n  \"402781.jpg\": \"Animalia/Callinectes sapidus/92d3366c64720562fdafa9ef919a2486.jpg\",\n  \"402785.jpg\": \"Animalia/Callinectes sapidus/301aa6d5c013fe71d50d20cc31ee1bf7.jpg\",\n  \"402789.jpg\": \"Animalia/Callinectes sapidus/2001f002a6dac5876476489f543ead2d.jpg\",\n  \"40280.jpg\": \"Actinopterygii/Gambusia holbrooki/03e879656b2cce0f3d1628f9ddcfb5a4.jpg\",\n  \"402801.jpg\": \"Animalia/Callinectes sapidus/2d7bed574293fb1f4d2101f29cb1fb59.jpg\",\n  \"402814.jpg\": \"Animalia/Callinectes sapidus/3400b7ac4583610dd1678a077c98398f.jpg\",\n  \"402815.jpg\": \"Animalia/Callinectes sapidus/cb3c99f34a9b8b059bdc4a18df866b68.jpg\",\n  \"402816.jpg\": \"Animalia/Callinectes sapidus/6cccfb43faef69d3fd17cf2a14e11e27.jpg\",\n  \"402822.jpg\": \"Animalia/Callinectes sapidus/5ed09df842b35365d50f79383a7e5f57.jpg\",\n  \"402830.jpg\": \"Animalia/Callinectes sapidus/f98eedd8455d9cdeacdcd1d020b4e69e.jpg\",\n  \"402836.jpg\": \"Animalia/Callinectes sapidus/fa19f4faeed51ced6e97e3864a3ba744.jpg\",\n  \"402837.jpg\": \"Animalia/Callinectes sapidus/52fbf8c9e240486b8fcd5d00537121e8.jpg\",\n  \"402839.jpg\": \"Aves/Selasphorus rufus/3da150d73ae82f6d61e87c908331c855.jpg\",\n  \"402844.jpg\": \"Aves/Selasphorus rufus/9fc1ac311b2ca8dc1daa752fecba2d24.jpg\",\n  \"402849.jpg\": \"Aves/Selasphorus rufus/830efc8cbb977d3df1dc0cf46a8ae880.jpg\",\n  \"402854.jpg\": \"Aves/Selasphorus rufus/4dfd5da99c4d2097fa386752c4d80b1f.jpg\",\n  \"402857.jpg\": \"Aves/Selasphorus rufus/9a16d79e99003240b34dd8611a1a85ee.jpg\",\n  \"402861.jpg\": \"Aves/Selasphorus rufus/c2bb79bf8c59e3f8c2c5850c83399845.jpg\",\n  \"402866.jpg\": \"Aves/Selasphorus rufus/4ec2c8f40d98b80806e69e0fa31e2e88.jpg\",\n  \"402872.jpg\": \"Aves/Selasphorus rufus/d90accde0479fb0f32f21f2f37463106.jpg\",\n  \"402873.jpg\": \"Aves/Selasphorus rufus/645f38e518b984668ce9560bd702baf4.jpg\",\n  \"402876.jpg\": \"Aves/Selasphorus rufus/6b6c72ea20ddcb4367c49f4a9456bb2e.jpg\",\n  \"402884.jpg\": \"Aves/Selasphorus rufus/04e3d26abd63246570575c3de650284a.jpg\",\n  \"402892.jpg\": \"Aves/Selasphorus rufus/58843babfa15672d938013c3fed80b6e.jpg\",\n  \"40290.jpg\": \"Insecta/Arilus cristatus/09ed03dabb9e156e40d9f3c6b5147e2a.jpg\",\n  \"402902.jpg\": \"Aves/Selasphorus rufus/ee1bd5414c5e5fcc325e5c90a0a5e1d1.jpg\",\n  \"402910.jpg\": \"Aves/Selasphorus rufus/ef20ea8f6046d4f35e9ae0a206e8d1b3.jpg\",\n  \"402911.jpg\": \"Aves/Selasphorus rufus/88c60b65707a859874ff1729d97decce.jpg\",\n  \"402912.jpg\": \"Aves/Selasphorus rufus/9bbec2bfd6dc772e2fb929448b2f7147.jpg\",\n  \"402918.jpg\": \"Aves/Selasphorus rufus/dfc72ce8c1a618e9f54cf0b298e7a45a.jpg\",\n  \"402919.jpg\": \"Aves/Selasphorus rufus/1d3268108973b1f06897490e8eda2f39.jpg\",\n  \"402920.jpg\": \"Aves/Selasphorus rufus/35c423b3bf320a550ccee4bd9451c9e5.jpg\",\n  \"402924.jpg\": \"Aves/Selasphorus rufus/9e2c4b0896d1fa02cebc59ba5f50a4e5.jpg\",\n  \"402925.jpg\": \"Aves/Selasphorus rufus/6dacf4b53194d30f024ec41799778a7c.jpg\",\n  \"402926.jpg\": \"Aves/Selasphorus rufus/894208740a6d5c8a4a38eb7657f6fe58.jpg\",\n  \"402931.jpg\": \"Aves/Selasphorus rufus/570128c84e5ab44332e552f56deda07c.jpg\",\n  \"402938.jpg\": \"Insecta/Anthocharis midea/9096e065688f3ac5bf423c4b7f0ef62d.jpg\",\n  \"402940.jpg\": \"Insecta/Anthocharis midea/14e746c00c8d9bd6c4c0223e57210e8c.jpg\",\n  \"402944.jpg\": \"Insecta/Anthocharis midea/c92a4b7b8ae6f93a1bdbc37eb43e6331.jpg\",\n  \"402946.jpg\": \"Insecta/Anthocharis midea/137b3f2cd6c1cdef44a5b020a27ff7a4.jpg\",\n  \"402950.jpg\": \"Insecta/Anthocharis midea/917e16b5d3af9d4ca1673ff765281c92.jpg\",\n  \"402952.jpg\": \"Insecta/Anthocharis midea/f8226c32941fa003eeb3f7d58270ec05.jpg\",\n  \"402967.jpg\": \"Aves/Acridotheres tristis/bbb41a0d24fda99f6942133dc61703cc.jpg\",\n  \"402968.jpg\": \"Aves/Acridotheres tristis/48a3718b5789fb00d871f96e3e21e9ca.jpg\",\n  \"402974.jpg\": \"Aves/Acridotheres tristis/543dc9b8bc0d2470125ebf2a957818f0.jpg\",\n  \"402979.jpg\": \"Aves/Acridotheres tristis/c91009e3f77aa5a5859887957fa26c65.jpg\",\n  \"402989.jpg\": \"Aves/Acridotheres tristis/703d0f706db9a9bf1603ab68527739ab.jpg\",\n  \"402994.jpg\": \"Aves/Acridotheres tristis/4cddcce4a899c1ee53ed2ae97fe7da2d.jpg\",\n  \"403000.jpg\": \"Aves/Acridotheres tristis/77ba88bdbebea1508b6124c7da7feb0c.jpg\",\n  \"403001.jpg\": \"Aves/Acridotheres tristis/887ddd4b4d66dbbd19f429b4aec42f0e.jpg\",\n  \"403007.jpg\": \"Aves/Acridotheres tristis/34ec51df0db3098e1f7272a6c654f3c6.jpg\",\n  \"403011.jpg\": \"Aves/Acridotheres tristis/40691a2f5d8383fa125dd74572e53702.jpg\",\n  \"403013.jpg\": \"Aves/Acridotheres tristis/555f86dcb912d8dfd63c50088544b93f.jpg\",\n  \"403030.jpg\": \"Aves/Acridotheres tristis/e0ae2d657baa59e2a90a1386721c9d3f.jpg\",\n  \"403040.jpg\": \"Aves/Acridotheres tristis/2e7e8dc3fe6980a5891190f7b56b7c9f.jpg\",\n  \"403043.jpg\": \"Aves/Acridotheres tristis/34b5a71d83b9bc0470ab0b585b5be4ba.jpg\",\n  \"403044.jpg\": \"Aves/Acridotheres tristis/20f7068c0629c1ec85af20f28e7a1b21.jpg\",\n  \"403047.jpg\": \"Aves/Acridotheres tristis/0db989646ee573cc697e231343c5b0b7.jpg\",\n  \"403058.jpg\": \"Aves/Acridotheres tristis/07188bdf1e6f9e160134974ca1638d1b.jpg\",\n  \"403068.jpg\": \"Aves/Acridotheres tristis/df5e3788e3fee87032ef262385467ddb.jpg\",\n  \"403069.jpg\": \"Aves/Acridotheres tristis/34900f6b9310eb9a9d03f35857a0bfdb.jpg\",\n  \"403075.jpg\": \"Mammalia/Desmodus rotundus/c703271df741de3377c4e5bc6116171d.jpg\",\n  \"403076.jpg\": \"Mammalia/Desmodus rotundus/48bc93013f142c7a6a4c77faf26d4b05.jpg\",\n  \"403078.jpg\": \"Mammalia/Desmodus rotundus/07e17e34e05006c6d3477d77635497fd.jpg\",\n  \"403079.jpg\": \"Mammalia/Desmodus rotundus/f2c6f1177adc8ebdccf8112550cc8994.jpg\",\n  \"403080.jpg\": \"Mammalia/Desmodus rotundus/43a340f05ec4f74dce2c43bd702974f2.jpg\",\n  \"403087.jpg\": \"Reptilia/Crotalus scutulatus/9c213755e01ba77563c6fbd95fb63624.jpg\",\n  \"403092.jpg\": \"Reptilia/Crotalus scutulatus/02695ceb489b0a91711047f0c8294842.jpg\",\n  \"403093.jpg\": \"Reptilia/Crotalus scutulatus/4f41e069cd11aafd62c898f0f47da0fd.jpg\",\n  \"403096.jpg\": \"Reptilia/Crotalus scutulatus/1f424616b1f62cf6664f29976c48c558.jpg\",\n  \"403097.jpg\": \"Reptilia/Crotalus scutulatus/e880ef5bbb37c98d960cd31b0f5805f1.jpg\",\n  \"403101.jpg\": \"Reptilia/Crotalus scutulatus/5da68dcb3bc2fda49490aecfa36d069c.jpg\",\n  \"403102.jpg\": \"Reptilia/Crotalus scutulatus/c9d998008c8e3824e61d712c8e12b56d.jpg\",\n  \"403103.jpg\": \"Reptilia/Crotalus scutulatus/872155d5faf3ccd56e1c42f7aa69b0c3.jpg\",\n  \"403107.jpg\": \"Reptilia/Crotalus scutulatus/4de3e96549ed22a5ca1551a9028c4578.jpg\",\n  \"403108.jpg\": \"Reptilia/Crotalus scutulatus/a34aa97e01c6786df79a91e7c68e700a.jpg\",\n  \"403109.jpg\": \"Reptilia/Crotalus scutulatus/20617592c17b07971c8192821d540e70.jpg\",\n  \"403115.jpg\": \"Reptilia/Crotalus scutulatus/751bae155817ab514c138b995d43f7b0.jpg\",\n  \"403131.jpg\": \"Reptilia/Crotalus scutulatus/96feb942307eea1a71343608c01bbd30.jpg\",\n  \"403132.jpg\": \"Reptilia/Crotalus scutulatus/db05fdcf14b7802244c211184ca891d0.jpg\",\n  \"403134.jpg\": \"Reptilia/Crotalus scutulatus/0832be0b041e63df75ec7ee550f3eccd.jpg\",\n  \"403135.jpg\": \"Reptilia/Crotalus scutulatus/908950b730cde8a20104f13b75ceec4a.jpg\",\n  \"403141.jpg\": \"Reptilia/Crotalus scutulatus/935693304d14fa5c1e1b9288200f7e54.jpg\",\n  \"403185.jpg\": \"Aves/Chordeiles minor/1dd294d1adb6563901ab29bff37393a7.jpg\",\n  \"403188.jpg\": \"Aves/Chordeiles minor/a41414948225938b4583a09382b322d8.jpg\",\n  \"403189.jpg\": \"Aves/Chordeiles minor/a824574349548928c96c60a54af00b85.jpg\",\n  \"403190.jpg\": \"Aves/Chordeiles minor/04f71825fbf92ffd59f64d31b6ce6215.jpg\",\n  \"403194.jpg\": \"Aves/Chordeiles minor/d4cfe1b304f00c3e9f31b2f099de3fff.jpg\",\n  \"403195.jpg\": \"Aves/Chordeiles minor/456c7834f5b607c8b757149c987697a0.jpg\",\n  \"403196.jpg\": \"Aves/Chordeiles minor/9341b4fb52b9d02dd6259cd91ef24e65.jpg\",\n  \"403198.jpg\": \"Aves/Chordeiles minor/556aea5d0c50c6d407d47fd0aeae63df.jpg\",\n  \"40320.jpg\": \"Insecta/Arilus cristatus/2f34f467659d6bffb15b18e8f67d1c4a.jpg\",\n  \"403214.jpg\": \"Aves/Chordeiles minor/9b27772acffecd828327e981286e0886.jpg\",\n  \"403226.jpg\": \"Aves/Chordeiles minor/b227bc0b4c7099ebf03f9a1429d401c2.jpg\",\n  \"403229.jpg\": \"Aves/Chordeiles minor/b975e6b982d6f0122b952aa43dab87ef.jpg\",\n  \"40325.jpg\": \"Insecta/Arilus cristatus/bb994a3450b8ad581138d17e4070b271.jpg\",\n  \"403250.jpg\": \"Aves/Chordeiles minor/933af834b448b0ca9f773b5058650af0.jpg\",\n  \"403253.jpg\": \"Aves/Chordeiles minor/00a9552a506f420c4e8c8ec3cdf5ea31.jpg\",\n  \"403255.jpg\": \"Aves/Chordeiles minor/f270f3ee4d1d752a761d06335c0f4623.jpg\",\n  \"403256.jpg\": \"Aves/Chordeiles minor/02c248f564f518ac03acde4da8a9b403.jpg\",\n  \"403260.jpg\": \"Aves/Chordeiles minor/040e9a4f5763516626d62b59096e9756.jpg\",\n  \"403261.jpg\": \"Aves/Chordeiles minor/74a08c36e10411e4b14cab27fd872493.jpg\",\n  \"403267.jpg\": \"Aves/Chordeiles minor/6665638606ff9b0de340d86f4a079efb.jpg\",\n  \"403269.jpg\": \"Aves/Chordeiles minor/415a8d5cebb5d8a4a7342ca7525e88ae.jpg\",\n  \"403273.jpg\": \"Aves/Chordeiles minor/82740660f265cb179bfa043eaa2407d0.jpg\",\n  \"403278.jpg\": \"Aves/Chordeiles minor/6cf3401facec8bcf5fa07925e2ff801d.jpg\",\n  \"403374.jpg\": \"Amphibia/Anaxyrus boreas/71410600457673e2ca72ff5da19bcc66.jpg\",\n  \"403402.jpg\": \"Amphibia/Anaxyrus boreas/120195d97f4ae3218752fbf23aebc1c1.jpg\",\n  \"403415.jpg\": \"Amphibia/Anaxyrus boreas/ea17565adf6a9d2fec4ab216950c989a.jpg\",\n  \"403420.jpg\": \"Amphibia/Anaxyrus boreas/d7026fd9fd9725d78d4401ccf2137784.jpg\",\n  \"403440.jpg\": \"Amphibia/Anaxyrus boreas/f99c789483c9bf8749accca393c0021e.jpg\",\n  \"403442.jpg\": \"Amphibia/Anaxyrus boreas/79e38a969bd8a62488babf8cba1c23a0.jpg\",\n  \"403445.jpg\": \"Amphibia/Anaxyrus boreas/3fa7241090b23322ae2c93e8e7b32be7.jpg\",\n  \"403457.jpg\": \"Amphibia/Anaxyrus boreas/8c20adeb766a14e45687ce1b7fb5cfd9.jpg\",\n  \"403458.jpg\": \"Amphibia/Anaxyrus boreas/473ca48da54add56bb8e859d242bb497.jpg\",\n  \"403462.jpg\": \"Amphibia/Anaxyrus boreas/95b3b6844b1270a5da51dc6cd38fcf1f.jpg\",\n  \"403468.jpg\": \"Amphibia/Anaxyrus boreas/108eebc17e9259caabce2ce2f67219dc.jpg\",\n  \"403472.jpg\": \"Amphibia/Anaxyrus boreas/e76f2fa164a176849a52a9c49094851c.jpg\",\n  \"403489.jpg\": \"Amphibia/Anaxyrus boreas/c2dc40da40ac5573926b504affa86194.jpg\",\n  \"403493.jpg\": \"Amphibia/Anaxyrus boreas/559b60f344bc8b2ef86a8982dd97a6f8.jpg\",\n  \"403518.jpg\": \"Amphibia/Anaxyrus boreas/4bf142ea7deb2d1867cf6f92594ed3f9.jpg\",\n  \"40353.jpg\": \"Insecta/Arilus cristatus/4fef2beaffeb4891cf6c5703e8f8cd97.jpg\",\n  \"403540.jpg\": \"Amphibia/Anaxyrus boreas/37d1bb43c0fed6a136ccc9e03f7f2445.jpg\",\n  \"403560.jpg\": \"Amphibia/Anaxyrus boreas/94a4e98a83d0699411a5d5bb4f81cb5d.jpg\",\n  \"403566.jpg\": \"Amphibia/Anaxyrus boreas/a4ae6f7e77d753a9db0481cbd6c18619.jpg\",\n  \"40364.jpg\": \"Insecta/Arilus cristatus/f2c4abbcb1edeb9fd8077c5fac1a9824.jpg\",\n  \"403670.jpg\": \"Aves/Zonotrichia atricapilla/20813113865581e2e679d6cf7b60239b.jpg\",\n  \"403680.jpg\": \"Aves/Zonotrichia atricapilla/8d77c7a91aa51362dfc24cf6da62ed2e.jpg\",\n  \"403697.jpg\": \"Aves/Zonotrichia atricapilla/465817df1ad9eea750bb5b34f32c418e.jpg\",\n  \"403708.jpg\": \"Aves/Zonotrichia atricapilla/fbfa721f5e737caca5a811f37bb78842.jpg\",\n  \"403719.jpg\": \"Aves/Zonotrichia atricapilla/4a1537094309dfee9fd976e13eeccce3.jpg\",\n  \"403728.jpg\": \"Aves/Zonotrichia atricapilla/4c7cb977801ea4b5023697c49ffc7d29.jpg\",\n  \"403738.jpg\": \"Aves/Zonotrichia atricapilla/540ca18206a282de77b2775fb5bb0bd1.jpg\",\n  \"403763.jpg\": \"Aves/Zonotrichia atricapilla/3cd3326a4efc00c1f30d90bc3a1dd8ec.jpg\",\n  \"403780.jpg\": \"Aves/Zonotrichia atricapilla/68fc4518796a38e89ffd923e61e4d997.jpg\",\n  \"403804.jpg\": \"Aves/Zonotrichia atricapilla/10c1ab2ac3f9df61961039d3ac95bdd6.jpg\",\n  \"403822.jpg\": \"Aves/Zonotrichia atricapilla/1a445c768ab7122b90a066f579d815bf.jpg\",\n  \"403834.jpg\": \"Aves/Zonotrichia atricapilla/16ec4ef85c6c635a97f62f0db37f58a2.jpg\",\n  \"403835.jpg\": \"Aves/Zonotrichia atricapilla/77a55b70299ac6edf578241ade7b8b02.jpg\",\n  \"403836.jpg\": \"Aves/Zonotrichia atricapilla/c89af0b683ede45c26744765a97d89d2.jpg\",\n  \"403838.jpg\": \"Aves/Zonotrichia atricapilla/3512440e6f1e89b0046d78d641df5d54.jpg\",\n  \"403847.jpg\": \"Aves/Zonotrichia atricapilla/40e763847e3d2410c09b157ca5905272.jpg\",\n  \"403848.jpg\": \"Aves/Zonotrichia atricapilla/de23780ec03f7f01a07e6402bac3e88b.jpg\",\n  \"40385.jpg\": \"Insecta/Arilus cristatus/3366abb166df5683e59aaa1bdcc07a4a.jpg\",\n  \"403861.jpg\": \"Aves/Zonotrichia atricapilla/f1469aebb0342ee730b27498824ebecc.jpg\",\n  \"40387.jpg\": \"Insecta/Arilus cristatus/1f2c321e04466d6abed986be22fe91a5.jpg\",\n  \"40388.jpg\": \"Insecta/Arilus cristatus/1bb7f3b4a49cf109b6d5c58e72d88fd1.jpg\",\n  \"403887.jpg\": \"Aves/Zonotrichia atricapilla/98c7402e00666b08e9432bb3e1167b08.jpg\",\n  \"40389.jpg\": \"Insecta/Arilus cristatus/13fc795805412e8ca4f5765cc0cf1ef5.jpg\",\n  \"40390.jpg\": \"Insecta/Arilus cristatus/ed0ad450bf576035554fd9f0c618405b.jpg\",\n  \"403924.jpg\": \"Aves/Zonotrichia atricapilla/3586ab9e1da37e6e863441b076c0915c.jpg\",\n  \"403929.jpg\": \"Aves/Zonotrichia atricapilla/3f6d24e750167104f783746c4ece71ff.jpg\",\n  \"403933.jpg\": \"Aves/Zonotrichia atricapilla/35960f7afdb3024e359ecb5fe3dbf67c.jpg\",\n  \"403941.jpg\": \"Aves/Zonotrichia atricapilla/32784727bf3802c5a79b35007f0a3725.jpg\",\n  \"403942.jpg\": \"Aves/Zonotrichia atricapilla/2a9cec41bd182788a141c066649d0107.jpg\",\n  \"404066.jpg\": \"Mammalia/Sylvilagus audubonii/a0b634722071edcd62e50b9c27d9c5f9.jpg\",\n  \"40407.jpg\": \"Insecta/Arilus cristatus/f8a804859f0b84d269660f49ab684170.jpg\",\n  \"404083.jpg\": \"Mammalia/Sylvilagus audubonii/e819f486e7c82921a7254ecda38ca570.jpg\",\n  \"404084.jpg\": \"Mammalia/Sylvilagus audubonii/cc314335da4030c9ed6c2de0d043c625.jpg\",\n  \"404090.jpg\": \"Mammalia/Sylvilagus audubonii/03190f047b649a9e442685e65986e832.jpg\",\n  \"404093.jpg\": \"Mammalia/Sylvilagus audubonii/e4a80df0a414057e2a751d220074b93f.jpg\",\n  \"404099.jpg\": \"Mammalia/Sylvilagus audubonii/dcf33cbd58bf06d38300d41b6f63dc2b.jpg\",\n  \"404105.jpg\": \"Mammalia/Sylvilagus audubonii/77e273b7be2ba48c2d3a48962ecda93d.jpg\",\n  \"404126.jpg\": \"Mammalia/Sylvilagus audubonii/d0d9da541f3bf49fcee89c455199864f.jpg\",\n  \"404143.jpg\": \"Mammalia/Sylvilagus audubonii/632e9c7f5f65a68a1d4dd8fe94c20869.jpg\",\n  \"404156.jpg\": \"Mammalia/Sylvilagus audubonii/aef780a0c0407f3663db888f70dc5afe.jpg\",\n  \"404157.jpg\": \"Mammalia/Sylvilagus audubonii/60bc119c9330fa669213366ae3b125e5.jpg\",\n  \"404185.jpg\": \"Mammalia/Sylvilagus audubonii/97a82ead9f215bf898558e93b284fd36.jpg\",\n  \"404200.jpg\": \"Mammalia/Sylvilagus audubonii/fee0e96969cdcbbb9e724edce56c5558.jpg\",\n  \"404205.jpg\": \"Mammalia/Sylvilagus audubonii/df0ec758cf7921e7d670dd1cbb32705c.jpg\",\n  \"404207.jpg\": \"Mammalia/Sylvilagus audubonii/f49130e4c340bb815c45b3819b7392e9.jpg\",\n  \"404216.jpg\": \"Mammalia/Sylvilagus audubonii/59cf3361069f727f5bec072886668767.jpg\",\n  \"404220.jpg\": \"Mammalia/Sylvilagus audubonii/b69213077b0ffd4bae5d12c6e01ba018.jpg\",\n  \"404229.jpg\": \"Mammalia/Sylvilagus audubonii/9c361da791a1af59b704790da01aca50.jpg\",\n  \"404232.jpg\": \"Mammalia/Sylvilagus audubonii/6abc871e2cab45deb55dd11dfccace9d.jpg\",\n  \"404233.jpg\": \"Mammalia/Sylvilagus audubonii/1d676f94c9aa64fd493085e0b4f9b08d.jpg\",\n  \"404235.jpg\": \"Mammalia/Sylvilagus audubonii/df1fd136743b5ea4d74f5247b73b74b6.jpg\",\n  \"404258.jpg\": \"Aves/Pharomachrus mocinno/d008f2c8cdd092b1dc65146ef8f08a3d.jpg\",\n  \"404261.jpg\": \"Aves/Pharomachrus mocinno/8963a7b427ecab1a74e556692d6902a9.jpg\",\n  \"404271.jpg\": \"Aves/Pharomachrus mocinno/9767524bc16d3bb01e89efd29e03c6e5.jpg\",\n  \"404274.jpg\": \"Aves/Pharomachrus mocinno/7bf42c479d471927bd05df5cd6c507b7.jpg\",\n  \"404367.jpg\": \"Reptilia/Chrysemys picta bellii/8345ce36274a1d8a2dcd6a8c7768cfcc.jpg\",\n  \"40437.jpg\": \"Insecta/Arilus cristatus/56cc112a6b87b43005f92367324fd914.jpg\",\n  \"404371.jpg\": \"Reptilia/Chrysemys picta bellii/76e5e45f946189a3c2e25cf1ea55a069.jpg\",\n  \"404372.jpg\": \"Reptilia/Chrysemys picta bellii/5e579cfa7b661cee8bf2d7a5d3ab555c.jpg\",\n  \"404395.jpg\": \"Aves/Circaetus gallicus/daabe732957344323143893d5a870342.jpg\",\n  \"404403.jpg\": \"Aves/Circaetus gallicus/b2dbb8d3d9b67cc9cc58af8f6c3fa280.jpg\",\n  \"40441.jpg\": \"Insecta/Arilus cristatus/69b8e4b279fb14752c0e7816b5223298.jpg\",\n  \"404411.jpg\": \"Aves/Circaetus gallicus/79f582a10a762a814097dd504bfcade5.jpg\",\n  \"404488.jpg\": \"Aves/Thraupis abbas/606a9830a63c78772b0120b3ec46ba00.jpg\",\n  \"404495.jpg\": \"Aves/Thraupis abbas/0125ded95f6c4bc726c33c8648c200d6.jpg\",\n  \"404511.jpg\": \"Aves/Thraupis abbas/08c3204aa6a61cb54385a15dd136d835.jpg\",\n  \"404516.jpg\": \"Aves/Thraupis abbas/c747105c3295cef692720a63c6b81a60.jpg\",\n  \"404517.jpg\": \"Aves/Thraupis abbas/48fa41ea2172fa070426755180f84cf8.jpg\",\n  \"404580.jpg\": \"Aves/Accipiter cooperii/58f7b2b4af97012bf086b370eaaa8116.jpg\",\n  \"404587.jpg\": \"Aves/Accipiter cooperii/e859cf22be13700c442c2159dd08d2ca.jpg\",\n  \"40462.jpg\": \"Insecta/Arilus cristatus/6ce7a428cb177c180d1d95755ae5a8ec.jpg\",\n  \"40465.jpg\": \"Insecta/Arilus cristatus/6d1eebe3dc2912470289f6e394bf148f.jpg\",\n  \"404660.jpg\": \"Aves/Accipiter cooperii/f7644abb1229d4709b6f9c9f85fbde4c.jpg\",\n  \"404668.jpg\": \"Aves/Accipiter cooperii/b55ab4507c3e7e63e5e628ff13d24223.jpg\",\n  \"404678.jpg\": \"Aves/Accipiter cooperii/d16b17cad3f1c1f34d6d35e1f54f347b.jpg\",\n  \"404681.jpg\": \"Aves/Accipiter cooperii/0e2f78999cdfc007a142359ee17a22be.jpg\",\n  \"404712.jpg\": \"Aves/Accipiter cooperii/2bfeb07e0401ce3b72fbc8c4821d9932.jpg\",\n  \"40472.jpg\": \"Insecta/Arilus cristatus/87d18ecbc34c4f1f03a252a084aa1477.jpg\",\n  \"40473.jpg\": \"Insecta/Arilus cristatus/3c1abdd32b34732a515089165601cdab.jpg\",\n  \"404755.jpg\": \"Aves/Accipiter cooperii/5deae7063929b4af20e518731658309b.jpg\",\n  \"40477.jpg\": \"Insecta/Arilus cristatus/b4a1d1d3c4977c80baf4d60c53087c88.jpg\",\n  \"404805.jpg\": \"Aves/Accipiter cooperii/651346f29507eba8c4a1562704b309b8.jpg\",\n  \"404826.jpg\": \"Aves/Accipiter cooperii/431b3e36148843b300c3c35e4212df17.jpg\",\n  \"404876.jpg\": \"Aves/Accipiter cooperii/1a46a10843d89f7a874add68d0cef982.jpg\",\n  \"404921.jpg\": \"Aves/Accipiter cooperii/b4d34a0693258581e835d65ab84b0b46.jpg\",\n  \"404966.jpg\": \"Aves/Accipiter cooperii/848757b2156ed7888042a1ee1bacd564.jpg\",\n  \"405.jpg\": \"Mammalia/Ursus americanus/3916ecd2c51d536de8dbe586a0910c05.jpg\",\n  \"405033.jpg\": \"Aves/Accipiter cooperii/a2539333c62beafe65ecd6a2e087e21a.jpg\",\n  \"405053.jpg\": \"Aves/Accipiter cooperii/c0677e6213ab144250f1e8033f25b6cd.jpg\",\n  \"405086.jpg\": \"Aves/Accipiter cooperii/46699916749e27141689ee08c11e94f3.jpg\",\n  \"405179.jpg\": \"Aves/Accipiter cooperii/156562a2f3f171bd3496a7a5f1d0bfba.jpg\",\n  \"405217.jpg\": \"Aves/Accipiter cooperii/380ba213313b1dee298f11b0eaddeb5e.jpg\",\n  \"405230.jpg\": \"Aves/Accipiter cooperii/4964ddd35cca291bcfd12910790fc233.jpg\",\n  \"405253.jpg\": \"Aves/Accipiter cooperii/9f38fcdd962ea650ec6d0199028255df.jpg\",\n  \"405277.jpg\": \"Aves/Accipiter cooperii/597532036c98debc2086ba3ba26b6ac6.jpg\",\n  \"405310.jpg\": \"Reptilia/Thamnophis elegans/7f02f905e3758c7ff67894b73e06e21c.jpg\",\n  \"405314.jpg\": \"Reptilia/Thamnophis elegans/0bba34c05816b5d43fcceb63096e28da.jpg\",\n  \"405338.jpg\": \"Reptilia/Thamnophis elegans/51dd05fdb9e83ae356ee5b2158f6da5a.jpg\",\n  \"405339.jpg\": \"Reptilia/Thamnophis elegans/593e3eff8f188f94a6e77f7826595ea5.jpg\",\n  \"405340.jpg\": \"Reptilia/Thamnophis elegans/9946c58fe6d6e26e21c739903c3f834b.jpg\",\n  \"405341.jpg\": \"Reptilia/Thamnophis elegans/7bdae144cf5f51999239596cca2195e5.jpg\",\n  \"405342.jpg\": \"Reptilia/Thamnophis elegans/2bc9587fe46a2cd839355c1dbc76476f.jpg\",\n  \"405354.jpg\": \"Reptilia/Thamnophis elegans/267e7f1a47d248f07c0b1034b64b33e6.jpg\",\n  \"405356.jpg\": \"Reptilia/Thamnophis elegans/ed8540f794851b089e38ac00268f4e8e.jpg\",\n  \"405357.jpg\": \"Reptilia/Thamnophis elegans/34519222dbdd46cd68d4b17af19696ff.jpg\",\n  \"405358.jpg\": \"Reptilia/Thamnophis elegans/4b27e0c8f38ab5e97c516d690bd15b6c.jpg\",\n  \"405374.jpg\": \"Reptilia/Thamnophis elegans/f30f1b193ab5eb4b369c0203538893b5.jpg\",\n  \"405378.jpg\": \"Reptilia/Thamnophis elegans/8254b17ab7e76c44f0b3b30b535277fe.jpg\",\n  \"405379.jpg\": \"Reptilia/Thamnophis elegans/9554fab9837a931a265cda846d6587a7.jpg\",\n  \"405392.jpg\": \"Reptilia/Thamnophis elegans/bc55ab796b94e9bbdb51662cdb509e54.jpg\",\n  \"405393.jpg\": \"Reptilia/Thamnophis elegans/89e74d759e7d4489ccbea80439e0f2e3.jpg\",\n  \"405397.jpg\": \"Reptilia/Thamnophis elegans/5a3da613dff88071e50ade7cad408a25.jpg\",\n  \"405398.jpg\": \"Reptilia/Thamnophis elegans/ce8274766d58970ce57d76e457299aa2.jpg\",\n  \"405407.jpg\": \"Reptilia/Thamnophis elegans/6f0077502fa63786b6a43925638f5d70.jpg\",\n  \"405409.jpg\": \"Reptilia/Thamnophis elegans/02ba16acd994dc53365641f0dc95e23a.jpg\",\n  \"405413.jpg\": \"Reptilia/Thamnophis elegans/fb5c3b07b47b6e0e31652c66499351e2.jpg\",\n  \"405415.jpg\": \"Reptilia/Thamnophis elegans/49cbd7e5b65ab06fd13629c694c26662.jpg\",\n  \"405416.jpg\": \"Reptilia/Thamnophis elegans/cc773083b6e630e782b5774ee63e29b3.jpg\",\n  \"405436.jpg\": \"Animalia/Anthopleura sola/ca836195358b922fc0b8989fbe145f6d.jpg\",\n  \"405439.jpg\": \"Animalia/Anthopleura sola/155bad7ca0522db7ca1bbb24eb9f056f.jpg\",\n  \"405444.jpg\": \"Animalia/Anthopleura sola/011a245896417ca50ae198a322ba3f96.jpg\",\n  \"405452.jpg\": \"Animalia/Anthopleura sola/c5e181102f88038325a1f7281bfd5b1f.jpg\",\n  \"405460.jpg\": \"Animalia/Anthopleura sola/9369741c0e3aed4a4f246cdc515db7ed.jpg\",\n  \"405471.jpg\": \"Animalia/Anthopleura sola/78b2e4e793f95baf6256c1ff68bb9110.jpg\",\n  \"405498.jpg\": \"Animalia/Anthopleura sola/1a75b978561dc2d674aa18924b730619.jpg\",\n  \"405508.jpg\": \"Animalia/Anthopleura sola/bcd1302fc9a303efd367af77c84f46cf.jpg\",\n  \"405519.jpg\": \"Animalia/Anthopleura sola/3bc325db0c253d4ce3e2d51a24bbe89f.jpg\",\n  \"405527.jpg\": \"Animalia/Anthopleura sola/eba1a7e9d399fb3f7931031c0602ebd4.jpg\",\n  \"405534.jpg\": \"Animalia/Anthopleura sola/c7444d9a97764eeb926a676e176a8a55.jpg\",\n  \"405536.jpg\": \"Animalia/Anthopleura sola/80cf023ea22875a6e7d3f11d957b0b5e.jpg\",\n  \"405539.jpg\": \"Animalia/Anthopleura sola/34dc0c89fad6e2f6cce2542d26d68380.jpg\",\n  \"405549.jpg\": \"Animalia/Anthopleura sola/1fbaf92debcdd99f22be24f0f21125e2.jpg\",\n  \"405556.jpg\": \"Amphibia/Ambystoma maculatum/dcb4ef3ccce1aa16986b5485a95d22fe.jpg\",\n  \"405579.jpg\": \"Amphibia/Ambystoma maculatum/75b3d0b67e4b37b84bfb9c4b0cc4e1d4.jpg\",\n  \"405586.jpg\": \"Amphibia/Ambystoma maculatum/92435f31c7a8c33ef0f8536540426ba6.jpg\",\n  \"405587.jpg\": \"Amphibia/Ambystoma maculatum/e0eeb10ea2e1b4a95afb222b7388c3a8.jpg\",\n  \"405590.jpg\": \"Amphibia/Ambystoma maculatum/bf2461006564c722f9fc0e878a4cfdaf.jpg\",\n  \"405591.jpg\": \"Amphibia/Ambystoma maculatum/c628c501f6cc61734eb8167e7910625c.jpg\",\n  \"405593.jpg\": \"Amphibia/Ambystoma maculatum/71c82958774252e02a1cc9f22681a967.jpg\",\n  \"405595.jpg\": \"Amphibia/Ambystoma maculatum/2f1eb2c02e873068d1aa43519197eccb.jpg\",\n  \"405613.jpg\": \"Amphibia/Ambystoma maculatum/075e043ff8fb7f05dc66368ba2c9b614.jpg\",\n  \"405617.jpg\": \"Amphibia/Ambystoma maculatum/0b63429cc711354d4d44fab6a29e4a1b.jpg\",\n  \"405620.jpg\": \"Amphibia/Ambystoma maculatum/b93f504c7a0c57f8541c568c5d27da74.jpg\",\n  \"405632.jpg\": \"Amphibia/Ambystoma maculatum/e3f39695e8130505e7f1592abb59553c.jpg\",\n  \"405814.jpg\": \"Actinopterygii/Acanthurus triostegus/7fc05b0995c7cb7ca9e487aed8ff0d9d.jpg\",\n  \"405817.jpg\": \"Actinopterygii/Acanthurus triostegus/cbff0ea2a10f78f1732968fb01fbeb1f.jpg\",\n  \"405846.jpg\": \"Mammalia/Macropus giganteus/6dc5083fc59799e1088e031342b1fe53.jpg\",\n  \"405907.jpg\": \"Reptilia/Terrapene carolina/293202bd78a822da1fc121d1337d3d6b.jpg\",\n  \"405908.jpg\": \"Reptilia/Terrapene carolina/7ce3d6b46b03b56ddc49774a85d88408.jpg\",\n  \"405931.jpg\": \"Reptilia/Terrapene carolina/8abc048106d62df95c2a2c836bc3f3ac.jpg\",\n  \"405940.jpg\": \"Reptilia/Terrapene carolina/2aa583288efaaf24a37d7808f116d9ea.jpg\",\n  \"405983.jpg\": \"Reptilia/Terrapene carolina/f06748a608c1c6eb18febe5e410ef0b8.jpg\",\n  \"405991.jpg\": \"Reptilia/Terrapene carolina/f3971b0fe427cf39ce26e660740b242b.jpg\",\n  \"405996.jpg\": \"Reptilia/Terrapene carolina/d785d4c5989685a8cc7198ca5268c6a6.jpg\",\n  \"405998.jpg\": \"Reptilia/Terrapene carolina/29c14faba2256311ec6129a47b4cd67b.jpg\",\n  \"406000.jpg\": \"Reptilia/Terrapene carolina/a1c24d5070cc28d6ba918929dc8ba5bf.jpg\",\n  \"406024.jpg\": \"Reptilia/Terrapene carolina/e6aafb0af911dc9b032a5bb3ee3bf3d6.jpg\",\n  \"406032.jpg\": \"Reptilia/Terrapene carolina/9467ac7f175f4b18167a9e2e58e3ba2d.jpg\",\n  \"406049.jpg\": \"Reptilia/Terrapene carolina/6887ae4a6efadbb3f879aa1ad74140c6.jpg\",\n  \"40605.jpg\": \"Aves/Haliaeetus leucogaster/c3aa8eb49e41974329c056cfcfdc1ef5.jpg\",\n  \"406050.jpg\": \"Reptilia/Terrapene carolina/2304e15e40c74d6c6a771fd520c26ba9.jpg\",\n  \"40606.jpg\": \"Aves/Haliaeetus leucogaster/43a8432c56496157314397af140b78bc.jpg\",\n  \"406060.jpg\": \"Reptilia/Terrapene carolina/1587f1d44338305505361280717e0e14.jpg\",\n  \"406062.jpg\": \"Reptilia/Terrapene carolina/424d8cd890c8690465881476aace003a.jpg\",\n  \"406067.jpg\": \"Reptilia/Terrapene carolina/5b5c8e3193ee6560de02929a0ddb542b.jpg\",\n  \"40607.jpg\": \"Aves/Haliaeetus leucogaster/707fbfb8b83686b35ed7c7eac12ca191.jpg\",\n  \"406077.jpg\": \"Reptilia/Terrapene carolina/fcb853865f253cf70fe4a0e75d2ccc51.jpg\",\n  \"406079.jpg\": \"Reptilia/Terrapene carolina/d81ef3a0325f67f7d1a0f510ccd55caf.jpg\",\n  \"40608.jpg\": \"Aves/Haliaeetus leucogaster/71483131c65da6cb705984cb7c8820ee.jpg\",\n  \"406083.jpg\": \"Reptilia/Terrapene carolina/dacb21b471e4ad9aebab1d4c7afd1059.jpg\",\n  \"406086.jpg\": \"Reptilia/Terrapene carolina/e83d92a4a4db3f655a2e991bacb50fcd.jpg\",\n  \"406087.jpg\": \"Mollusca/Monadenia fidelis/13590880c9606b58cd4881270299493a.jpg\",\n  \"406089.jpg\": \"Mollusca/Monadenia fidelis/f7ab428f6f364dc7f6c8e2d495349c7e.jpg\",\n  \"40609.jpg\": \"Aves/Haliaeetus leucogaster/4938111adcfb71670bafe418b105a041.jpg\",\n  \"406090.jpg\": \"Mollusca/Monadenia fidelis/60b6559f3beb9ba6b8aa3bbcc6d41e50.jpg\",\n  \"406091.jpg\": \"Mollusca/Monadenia fidelis/1a35d7acf327192d14807a7e3530fd96.jpg\",\n  \"406093.jpg\": \"Mollusca/Monadenia fidelis/7c74190f9f11189b46082591ab4f6947.jpg\",\n  \"406095.jpg\": \"Mollusca/Monadenia fidelis/788c30e616037ec91e77f1e000d4b995.jpg\",\n  \"406098.jpg\": \"Mollusca/Monadenia fidelis/9d424ff0ea821a06227d4286a3426180.jpg\",\n  \"406102.jpg\": \"Mollusca/Monadenia fidelis/1071b3bf91e9bf8a358c2bc0932a72d3.jpg\",\n  \"406103.jpg\": \"Mollusca/Monadenia fidelis/2c2f7b2eedb78149691814cb0b82ebec.jpg\",\n  \"406104.jpg\": \"Mollusca/Monadenia fidelis/99e6a16ff1f00ac2ee9a7faca9ccd1b1.jpg\",\n  \"406113.jpg\": \"Mollusca/Monadenia fidelis/28adce5e674002bd2ca1ad819c2ab14d.jpg\",\n  \"406114.jpg\": \"Mollusca/Monadenia fidelis/37f2a9234af20760dc07786147c116b5.jpg\",\n  \"406123.jpg\": \"Mollusca/Monadenia fidelis/b8f3ee3af75122a7116b9be77fa779f5.jpg\",\n  \"406124.jpg\": \"Mollusca/Monadenia fidelis/b8ce64383ed7253ab93f8c34b76befb4.jpg\",\n  \"406125.jpg\": \"Mollusca/Monadenia fidelis/071efca90a3df170c5577d88b777c49b.jpg\",\n  \"406126.jpg\": \"Mollusca/Monadenia fidelis/2cf3a57fb0a42d13eeec7a67ca08fb9e.jpg\",\n  \"406130.jpg\": \"Mollusca/Monadenia fidelis/2ab6de17724cface1c45fe39b7529cbc.jpg\",\n  \"40617.jpg\": \"Aves/Haliaeetus leucogaster/b4cedc40d52ee66f14e7176b752a806b.jpg\",\n  \"406195.jpg\": \"Aves/Larus argentatus/f40043e7071f96d4fb1e04815e2c85d8.jpg\",\n  \"406205.jpg\": \"Aves/Larus argentatus/ade8638d383cd44d550b1142da6908bf.jpg\",\n  \"406211.jpg\": \"Aves/Larus argentatus/0fa8d1bcfe6a6b6988efd3e6d2bd29c8.jpg\",\n  \"406214.jpg\": \"Aves/Larus argentatus/67987068b36dfa27dbb4e493d3204142.jpg\",\n  \"406239.jpg\": \"Aves/Larus argentatus/0e3d7b47c5f5749c780ec965b3223e2e.jpg\",\n  \"406241.jpg\": \"Aves/Larus argentatus/91deb5b879d6fcd3bfff6121f6a69562.jpg\",\n  \"406284.jpg\": \"Aves/Larus argentatus/ed806127d4eec54e30207e24e70a92f8.jpg\",\n  \"406287.jpg\": \"Aves/Larus argentatus/fba1dc3f2d7167f0ba9914de6e54ee9a.jpg\",\n  \"406291.jpg\": \"Aves/Larus argentatus/2a908f340e7840eb16d7c246228c9584.jpg\",\n  \"406302.jpg\": \"Aves/Larus argentatus/ed3a4b69cbb6b4a1565773a4f2afcf91.jpg\",\n  \"406303.jpg\": \"Aves/Larus argentatus/57efd2d15f69b588283aaf55ee66ca8b.jpg\",\n  \"406318.jpg\": \"Aves/Larus argentatus/bdb1e0d4d496125b4c139c5c88ab0778.jpg\",\n  \"406320.jpg\": \"Aves/Larus argentatus/a098a6c701315965188e9c5247e51460.jpg\",\n  \"406335.jpg\": \"Aves/Larus argentatus/9b68d6b45518da5db2900eeed0b0acff.jpg\",\n  \"406341.jpg\": \"Aves/Larus argentatus/4b58eab4a6559e455463489d3970489e.jpg\",\n  \"406352.jpg\": \"Aves/Larus argentatus/d39377787f0feeb392031002d461d9c5.jpg\",\n  \"406353.jpg\": \"Aves/Larus argentatus/4818138a56282307c8baf92581889a61.jpg\",\n  \"406354.jpg\": \"Aves/Larus argentatus/bd6d31510edd65f6db628c216f205576.jpg\",\n  \"406365.jpg\": \"Aves/Larus argentatus/8b62b3d9042999e74057bd6ed92cc041.jpg\",\n  \"4064.jpg\": \"Reptilia/Coluber constrictor flaviventris/b94f9ff4f34383b68888712f01dadb77.jpg\",\n  \"406629.jpg\": \"Insecta/Papilio palamedes/afbe3d93cf5e2feb8d3753775ba933bd.jpg\",\n  \"406630.jpg\": \"Insecta/Papilio palamedes/5a5167f9ad4b715892478d121f79e4a4.jpg\",\n  \"406631.jpg\": \"Insecta/Papilio palamedes/15f5ecd67db9781f5006ad05fd41f472.jpg\",\n  \"406641.jpg\": \"Insecta/Papilio palamedes/bda5a881777873afd8f8088753f0920e.jpg\",\n  \"406642.jpg\": \"Insecta/Papilio palamedes/e246a5b2391c5a8f97b81a4eb2524c22.jpg\",\n  \"406643.jpg\": \"Insecta/Papilio palamedes/ff4c2ec249cf5ddb96de093b25120207.jpg\",\n  \"406644.jpg\": \"Insecta/Papilio palamedes/bbc3666341639bcd5a92b03c675edafa.jpg\",\n  \"406648.jpg\": \"Insecta/Papilio palamedes/29aa819ff35036b8af656ac40c633d3e.jpg\",\n  \"406649.jpg\": \"Insecta/Papilio palamedes/269bf233c519a2f588ab48e7afccdca3.jpg\",\n  \"406650.jpg\": \"Insecta/Papilio palamedes/4721f587a74500f5e627fd26a61943c0.jpg\",\n  \"406657.jpg\": \"Insecta/Papilio palamedes/81673d9897c6e776cb5921c38595f1eb.jpg\",\n  \"406659.jpg\": \"Insecta/Papilio palamedes/2c2eae15fd92e677aa82ea3f60536cc7.jpg\",\n  \"406660.jpg\": \"Insecta/Papilio palamedes/7904490174e8b4927538decdbd6548dd.jpg\",\n  \"406661.jpg\": \"Insecta/Papilio palamedes/a33fd9a8e62795988d6519f4d467ac4b.jpg\",\n  \"406663.jpg\": \"Insecta/Papilio palamedes/7c5313364e830d29f1a765feb9a182ce.jpg\",\n  \"406664.jpg\": \"Insecta/Papilio palamedes/3cdbae416a48886af16e1f67af6c0cbe.jpg\",\n  \"406665.jpg\": \"Insecta/Papilio palamedes/e1468e7c6797a5edb4a2187cc6c6e4a9.jpg\",\n  \"406669.jpg\": \"Insecta/Papilio palamedes/53c610c049c1552249082e42c82d72e1.jpg\",\n  \"406671.jpg\": \"Insecta/Papilio palamedes/ab6a9922766173b56468d53b89703d7c.jpg\",\n  \"406672.jpg\": \"Insecta/Papilio palamedes/ccd8fcd2165b951a92341a0feae57eb4.jpg\",\n  \"40673.jpg\": \"Insecta/Leptysma marginicollis/6fcaec5a21d44160fc029369d332e260.jpg\",\n  \"406755.jpg\": \"Aves/Aythya collaris/b04016b342bd63306e9b6807d8a3cd5d.jpg\",\n  \"406764.jpg\": \"Aves/Aythya collaris/315cc369949cd484783b33e56cd9b23e.jpg\",\n  \"40677.jpg\": \"Insecta/Leptysma marginicollis/5a38428f20671cf3cda7f8cfcf81e023.jpg\",\n  \"40683.jpg\": \"Insecta/Leptysma marginicollis/12458453b566f278af42089e31c2f0b6.jpg\",\n  \"406837.jpg\": \"Aves/Aythya collaris/a3230a1a3b062128b2becae397a7412e.jpg\",\n  \"40689.jpg\": \"Insecta/Anthanassa texana/c5e4c1a084bf5c8845394f51cf77fe83.jpg\",\n  \"406897.jpg\": \"Aves/Aythya collaris/a2f217c42e4e8cbabe24038f9795f418.jpg\",\n  \"406929.jpg\": \"Aves/Aythya collaris/7886e6dc56ef12962efd96a8381f89c6.jpg\",\n  \"40696.jpg\": \"Insecta/Anthanassa texana/36dce55ec98d061e8e02833be9f5c7ec.jpg\",\n  \"407014.jpg\": \"Aves/Aythya collaris/15232b4c88b7375af196b36dd7dcff70.jpg\",\n  \"407025.jpg\": \"Amphibia/Aneides lugubris/35b8a1bdb2a042b9c1fe205e7c609f79.jpg\",\n  \"407026.jpg\": \"Amphibia/Aneides lugubris/2b130489931a22fc180b237e5f57601f.jpg\",\n  \"407037.jpg\": \"Amphibia/Aneides lugubris/09d4f0979bdf6b758cdbb8704310761f.jpg\",\n  \"407039.jpg\": \"Amphibia/Aneides lugubris/a7a8d782528485313241b7559fd76cdb.jpg\",\n  \"407042.jpg\": \"Amphibia/Aneides lugubris/3d2acd951d444bd3f96f0510ea687099.jpg\",\n  \"407055.jpg\": \"Amphibia/Aneides lugubris/851b252a6760b47238f54bd0fc25add6.jpg\",\n  \"407057.jpg\": \"Amphibia/Aneides lugubris/0ff4f7b3045904610e7e41c80851a709.jpg\",\n  \"407058.jpg\": \"Amphibia/Aneides lugubris/4c6b441fdb850e62bf6ec7ee74503a17.jpg\",\n  \"407063.jpg\": \"Amphibia/Aneides lugubris/254b0bdcde64805a49d2a698e8b7bf12.jpg\",\n  \"407073.jpg\": \"Amphibia/Aneides lugubris/af2f0e030d0a714b5d7bc4d22d15051b.jpg\",\n  \"407075.jpg\": \"Amphibia/Aneides lugubris/79fa79809ce54fc3eee2f679767db9fd.jpg\",\n  \"407080.jpg\": \"Amphibia/Aneides lugubris/8ba2765bdce12e38965340eed038d633.jpg\",\n  \"407081.jpg\": \"Amphibia/Aneides lugubris/eb29e4f0e67cca631fb2a702e1c6d63b.jpg\",\n  \"407087.jpg\": \"Amphibia/Aneides lugubris/6dee01c5772f31baa42b490b09ff48af.jpg\",\n  \"407088.jpg\": \"Amphibia/Aneides lugubris/20bb9196a4cf841d831f705659f514cd.jpg\",\n  \"407090.jpg\": \"Amphibia/Aneides lugubris/2ad23d164b5db88fbd850982b79d863a.jpg\",\n  \"407091.jpg\": \"Amphibia/Aneides lugubris/6c4223f20adec410647dabe37300511c.jpg\",\n  \"407092.jpg\": \"Amphibia/Aneides lugubris/24ccc6ab7703dc9547a60497e98237c9.jpg\",\n  \"4071.jpg\": \"Reptilia/Coluber constrictor flaviventris/e750cf10f8a4ba26d9d7ff4cd2abdfd8.jpg\",\n  \"407103.jpg\": \"Amphibia/Aneides lugubris/878740be4d5f2d24238c3d6e23efb628.jpg\",\n  \"407107.jpg\": \"Amphibia/Aneides lugubris/c6be711ea0c70621615b491b48e7b58b.jpg\",\n  \"40711.jpg\": \"Insecta/Anthanassa texana/20a68c6c22f0f77fe4d0213653577158.jpg\",\n  \"407121.jpg\": \"Animalia/Megalorchestia californiana/450d2598886f5ab74ab72d8c7fc71752.jpg\",\n  \"407124.jpg\": \"Animalia/Megalorchestia californiana/41b3a1ad952ed9902baf03302f5e1774.jpg\",\n  \"407127.jpg\": \"Animalia/Megalorchestia californiana/78986621417cef056ad2eca0923915d0.jpg\",\n  \"407129.jpg\": \"Animalia/Megalorchestia californiana/2a92497d6390ee4cb011741aaef888d7.jpg\",\n  \"40713.jpg\": \"Insecta/Anthanassa texana/6efc1feec4b579cd4e94fd0e364ea018.jpg\",\n  \"407142.jpg\": \"Insecta/Chlosyne lacinia/f7482cf5d9dba362d02a0a993792e162.jpg\",\n  \"407145.jpg\": \"Insecta/Chlosyne lacinia/202689dd3c0c1968667e80d68db3c2bd.jpg\",\n  \"407150.jpg\": \"Insecta/Chlosyne lacinia/6dbeb86f463360708ef4d4b5c3c463c3.jpg\",\n  \"407154.jpg\": \"Insecta/Chlosyne lacinia/3c7c8f883a76cfd0c4b2b99ec5fd7724.jpg\",\n  \"407156.jpg\": \"Insecta/Chlosyne lacinia/6546084b01e0caba1b34dfda212faffa.jpg\",\n  \"407158.jpg\": \"Insecta/Chlosyne lacinia/c51c8cc1919554216ba723f0a58cafdc.jpg\",\n  \"407163.jpg\": \"Insecta/Chlosyne lacinia/6806b4cb55cdd773deb392b6df1038b1.jpg\",\n  \"407165.jpg\": \"Insecta/Chlosyne lacinia/a063a4c0873486d9f4fbedb732c3e306.jpg\",\n  \"407194.jpg\": \"Insecta/Chlosyne lacinia/4ec12231c015b86a2398646ee340f9d9.jpg\",\n  \"407202.jpg\": \"Insecta/Chlosyne lacinia/2b1da523e2c3c789eb58f21d70036d5f.jpg\",\n  \"407203.jpg\": \"Insecta/Chlosyne lacinia/ea605cec6101c6e5e12fc0cbc9b71964.jpg\",\n  \"407206.jpg\": \"Insecta/Chlosyne lacinia/c9dd53245f8bd1088403e6cb26976493.jpg\",\n  \"407209.jpg\": \"Insecta/Chlosyne lacinia/d480f462b7727dc1c95338e5fece2abf.jpg\",\n  \"40721.jpg\": \"Insecta/Anthanassa texana/d7f13ef4070c93a06091084bf9841fd0.jpg\",\n  \"407214.jpg\": \"Insecta/Chlosyne lacinia/10114631cdc2283d5630eddb05c4d8c1.jpg\",\n  \"407215.jpg\": \"Insecta/Chlosyne lacinia/974cff436ef4b112cd998c4a26710fa5.jpg\",\n  \"40722.jpg\": \"Insecta/Anthanassa texana/21824021aa79dabfc0949b1fb9f16735.jpg\",\n  \"407220.jpg\": \"Insecta/Chlosyne lacinia/64426577ae376eff4fe02f5f3284fba6.jpg\",\n  \"407221.jpg\": \"Insecta/Chlosyne lacinia/69e622b5fb50c9af2df70e95d0e63cab.jpg\",\n  \"407229.jpg\": \"Insecta/Chlosyne lacinia/d721b1bbb4a6cbbcc285ca5645ac7825.jpg\",\n  \"407233.jpg\": \"Insecta/Chlosyne lacinia/06466dd561789bfc993e6cd9db96ee85.jpg\",\n  \"407243.jpg\": \"Insecta/Chlosyne lacinia/9da776fce303f6f35d72841a43ff52a3.jpg\",\n  \"40725.jpg\": \"Insecta/Anthanassa texana/28eb959f6058e40adb0ed9ca80bdca11.jpg\",\n  \"407253.jpg\": \"Insecta/Chlosyne lacinia/291d16c668bd515a30b917ed4ae700c0.jpg\",\n  \"407266.jpg\": \"Insecta/Chlosyne lacinia/d0301d1e26e7b37562f3e81add2a7954.jpg\",\n  \"40727.jpg\": \"Insecta/Anthanassa texana/9b1772ea42675630ce8b0ecd0242e109.jpg\",\n  \"407270.jpg\": \"Mammalia/Connochaetes taurinus/b5aef8e446c574fe9fc6e6d089c73aa5.jpg\",\n  \"407295.jpg\": \"Mammalia/Connochaetes taurinus/319c367aa6d47dfc74657f6f3d00895c.jpg\",\n  \"40730.jpg\": \"Insecta/Anthanassa texana/6059311a8fa2d8a0ccb3150dfad6690d.jpg\",\n  \"407301.jpg\": \"Mammalia/Connochaetes taurinus/33abbc7de7400f735380a558e05462de.jpg\",\n  \"407302.jpg\": \"Mammalia/Connochaetes taurinus/e1de62df7c9f8420a7b109b0243e72d0.jpg\",\n  \"407309.jpg\": \"Aves/Amazilia rutila/ec88602a3e87a1395e497b9b596eefb2.jpg\",\n  \"407314.jpg\": \"Aves/Amazilia rutila/410fab702bd5e18b7619026e24dfd7cb.jpg\",\n  \"407316.jpg\": \"Aves/Amazilia rutila/d60bcbc068b83bddb0f2d39e5965649c.jpg\",\n  \"407319.jpg\": \"Aves/Amazilia rutila/b1fa701f88bbf32df4e83ea89e935a85.jpg\",\n  \"40732.jpg\": \"Insecta/Anthanassa texana/110daa810a1a3dec1c194c5fe2d3fb47.jpg\",\n  \"407322.jpg\": \"Aves/Amazilia rutila/636c7597c366360293425ba738f49a7e.jpg\",\n  \"407324.jpg\": \"Aves/Amazilia rutila/22d742fd8130a61e082d8e3f95659bfb.jpg\",\n  \"407325.jpg\": \"Aves/Amazilia rutila/6df9f2eb7a4380a9e1d4e1c907544a50.jpg\",\n  \"407327.jpg\": \"Aves/Amazilia rutila/d034a3d4d3cfd931dc0df5cb2668a83c.jpg\",\n  \"407328.jpg\": \"Aves/Amazilia rutila/c4edc367d35e4f41b4b0eb8b56de53f8.jpg\",\n  \"407330.jpg\": \"Aves/Amazilia rutila/0112be36214f547437fbcf61a652d17c.jpg\",\n  \"407334.jpg\": \"Aves/Amazilia rutila/e2688797399be1602a37df252718a62d.jpg\",\n  \"407339.jpg\": \"Aves/Amazilia rutila/7816233648cb5172187bd7220160e03b.jpg\",\n  \"407341.jpg\": \"Aves/Amazilia rutila/640f4870b9eeddaea136c280c132c498.jpg\",\n  \"407343.jpg\": \"Aves/Amazilia rutila/0e1a36211a46c2970e58ab42c54456fe.jpg\",\n  \"40735.jpg\": \"Insecta/Anthanassa texana/7288e196d85456ec0502d0e4e7d0fa2d.jpg\",\n  \"407351.jpg\": \"Aves/Amazilia rutila/665f73f3fede7ec8a1b1a4c18ecb5c00.jpg\",\n  \"407352.jpg\": \"Aves/Amazilia rutila/61d1258ee96494d06c5464c2cbda47a2.jpg\",\n  \"407355.jpg\": \"Aves/Amazilia rutila/a8f8fc9b4e0629bfbb4b2e90669b5b91.jpg\",\n  \"407356.jpg\": \"Aves/Amazilia rutila/29271f4f1c5dac3d16b50e7b3a781f1d.jpg\",\n  \"40736.jpg\": \"Insecta/Anthanassa texana/baf67bc7510b6b576a91b210352de21a.jpg\",\n  \"40740.jpg\": \"Insecta/Anthanassa texana/2d1915342bf32ba0dcb17748efb602c6.jpg\",\n  \"407416.jpg\": \"Insecta/Danaus plexippus/1c7e3d8a5dfeaac591c43755163bbb8c.jpg\",\n  \"407430.jpg\": \"Insecta/Danaus plexippus/970cbf6fb1fd359e2c8fdfb7df3b1ff5.jpg\",\n  \"40745.jpg\": \"Insecta/Anthanassa texana/b4a97334032d74409f304451de657961.jpg\",\n  \"40747.jpg\": \"Insecta/Anthanassa texana/55e7714efb2ae2cde2d479825a8d5350.jpg\",\n  \"407471.jpg\": \"Insecta/Danaus plexippus/ae89f759c65be81d0c72ae98521bde84.jpg\",\n  \"40748.jpg\": \"Insecta/Anthanassa texana/ac18b395f32f52fe25da9ff9f00267af.jpg\",\n  \"40749.jpg\": \"Insecta/Anthanassa texana/30c2b7f44c81b13a3b14be2ca0df9bc3.jpg\",\n  \"40753.jpg\": \"Insecta/Anthanassa texana/cc2487ad2d3cb177a3857f33284caebc.jpg\",\n  \"40754.jpg\": \"Insecta/Anthanassa texana/61650fb69f986e0edc30be2b0c6dca9f.jpg\",\n  \"40758.jpg\": \"Insecta/Anthanassa texana/84c35783db68e75baaf11a362da1291a.jpg\",\n  \"4076.jpg\": \"Reptilia/Coluber constrictor flaviventris/8b87fb39a572b2d0910801ceef3e7b42.jpg\",\n  \"40767.jpg\": \"Reptilia/Zamenis longissimus/8d85c2153e541424fd7cffb424ecde0e.jpg\",\n  \"40769.jpg\": \"Reptilia/Zamenis longissimus/d4683f3461a439e1b972088db7bca302.jpg\",\n  \"4077.jpg\": \"Reptilia/Coluber constrictor flaviventris/76a477ee965ebc372d077e6e751efb40.jpg\",\n  \"40773.jpg\": \"Reptilia/Zamenis longissimus/82507259d1935160f235d89ffc69d157.jpg\",\n  \"40775.jpg\": \"Reptilia/Zamenis longissimus/69948203a03a46584433570f4b525797.jpg\",\n  \"4078.jpg\": \"Reptilia/Coluber constrictor flaviventris/0ca1833c869fbc6d23347a8cdc89f37a.jpg\",\n  \"407838.jpg\": \"Insecta/Danaus plexippus/f8d61152dae8d5cc6b833312ab0a3fbc.jpg\",\n  \"4079.jpg\": \"Reptilia/Coluber constrictor flaviventris/0b5fc7e03c2545a761a50746bf9af2b4.jpg\",\n  \"4080.jpg\": \"Reptilia/Coluber constrictor flaviventris/6a3a4dbe5f6374666e7dda52e193dfc1.jpg\",\n  \"408037.jpg\": \"Insecta/Danaus plexippus/8231cc1eaec067c5aad2bd8eee1868cd.jpg\",\n  \"408155.jpg\": \"Insecta/Danaus plexippus/e62b96afc3e07864ebfd29c8a84ddc2c.jpg\",\n  \"408188.jpg\": \"Insecta/Danaus plexippus/84284f65e9e48cb5fd6ab23f0ab6cf46.jpg\",\n  \"4082.jpg\": \"Reptilia/Coluber constrictor flaviventris/cb063e2feb639fa2052ba71da956568f.jpg\",\n  \"40822.jpg\": \"Insecta/Callophrys niphon/85f5ff7627790efcfaeecc8abb946952.jpg\",\n  \"40827.jpg\": \"Insecta/Callophrys niphon/451d021fed535d858e9102590c900076.jpg\",\n  \"40828.jpg\": \"Insecta/Callophrys niphon/8234594e8f2ba4fedf5a470af98be4de.jpg\",\n  \"40830.jpg\": \"Insecta/Callophrys niphon/91b5e375758194541cff9b6e40929771.jpg\",\n  \"40831.jpg\": \"Insecta/Callophrys niphon/42e234f80aaa98b18a13dfc23c253b07.jpg\",\n  \"40839.jpg\": \"Insecta/Callophrys niphon/a70db8145201ae945ded213c314cbe51.jpg\",\n  \"40840.jpg\": \"Insecta/Pterophylla camellifolia/975d4afa054b365526fece46b1f72e2b.jpg\",\n  \"40844.jpg\": \"Insecta/Pterophylla camellifolia/decdcd18745969f27e095de83f9fa213.jpg\",\n  \"40845.jpg\": \"Insecta/Pterophylla camellifolia/bb30327a916d76c05ae984106f3f7b7a.jpg\",\n  \"40846.jpg\": \"Insecta/Pterophylla camellifolia/5605e206ea52824d07164a97e09e3a3f.jpg\",\n  \"4085.jpg\": \"Reptilia/Coluber constrictor flaviventris/23ad2f11ad8757f05bf7dde0d74ce2b5.jpg\",\n  \"40855.jpg\": \"Insecta/Pterophylla camellifolia/9f94f6a4173598cb2c4572594ef1a1d3.jpg\",\n  \"408557.jpg\": \"Insecta/Danaus plexippus/5d7948638489a3653a6ffa70b40a814f.jpg\",\n  \"40856.jpg\": \"Insecta/Haploa clymene/7ed4a8185dfdaa2d56617773ca5628a8.jpg\",\n  \"4086.jpg\": \"Reptilia/Coluber constrictor flaviventris/67a97ce1c95002b6561e1a260bd97c92.jpg\",\n  \"408643.jpg\": \"Insecta/Danaus plexippus/46c5a01e823c7f814ecd8f5887c5b934.jpg\",\n  \"40867.jpg\": \"Insecta/Haploa clymene/f44e89ed04ce215b871ad7cdfe04e3b5.jpg\",\n  \"40869.jpg\": \"Insecta/Haploa clymene/5e8818647028a4037561282e07c6ba94.jpg\",\n  \"408694.jpg\": \"Insecta/Danaus plexippus/1a157adf2c2c572707e0b3a5f90dd8e1.jpg\",\n  \"4087.jpg\": \"Reptilia/Coluber constrictor flaviventris/8be5ae38fe58d1f64b0bed9e8b5da53c.jpg\",\n  \"40873.jpg\": \"Insecta/Haploa clymene/6b337432d8cd3b3e93def3fb82021af6.jpg\",\n  \"40876.jpg\": \"Insecta/Haploa clymene/fc2925084132a055a389bffb172ffd5e.jpg\",\n  \"40877.jpg\": \"Insecta/Haploa clymene/7b2d8444017896370eb000f69bc5a02e.jpg\",\n  \"40878.jpg\": \"Insecta/Haploa clymene/4468927dac74491e3415d33a2a7413eb.jpg\",\n  \"40880.jpg\": \"Insecta/Haploa clymene/e241c64dae0c369bef58a8d83e8c34cc.jpg\",\n  \"40887.jpg\": \"Insecta/Haploa clymene/72056760a4a5f96c36a3ac6c0358ff51.jpg\",\n  \"4089.jpg\": \"Reptilia/Coluber constrictor flaviventris/ee961d8c4902c54bef2edd82632d352e.jpg\",\n  \"40890.jpg\": \"Insecta/Haploa clymene/adaf600dac9c5744f384224710628583.jpg\",\n  \"408918.jpg\": \"Insecta/Danaus plexippus/6492fe36d4d476f4a5754cd7777b4bfc.jpg\",\n  \"40892.jpg\": \"Insecta/Haploa clymene/fe6beec51f60db15dcac530c975a1338.jpg\",\n  \"40895.jpg\": \"Insecta/Haploa clymene/971ffffed49ab06a7c233384377753e9.jpg\",\n  \"40896.jpg\": \"Insecta/Haploa clymene/86185fe39012b976e654d37c872ddc46.jpg\",\n  \"40897.jpg\": \"Insecta/Haploa clymene/069d46c88f0ab3d3d73b7865bf485dfd.jpg\",\n  \"408970.jpg\": \"Insecta/Danaus plexippus/5d125eb04a2c8e5fdebbbd3a98decada.jpg\",\n  \"409021.jpg\": \"Insecta/Danaus plexippus/8c49b7634e40bf094fe2747580dfeda5.jpg\",\n  \"409022.jpg\": \"Insecta/Danaus plexippus/713bb3e30b85fc460830e264b246d98c.jpg\",\n  \"40903.jpg\": \"Insecta/Haploa clymene/4135275866652c8fa390127da1e15bea.jpg\",\n  \"409030.jpg\": \"Insecta/Danaus plexippus/32e76c4d3e78735387966741bd1b5b9a.jpg\",\n  \"40905.jpg\": \"Insecta/Haploa clymene/0028934e79968c75031669d6a09ebc4b.jpg\",\n  \"40907.jpg\": \"Insecta/Haploa clymene/ecaf37b1c08c5e28abee6f7471a1c200.jpg\",\n  \"40908.jpg\": \"Insecta/Haploa clymene/0d185d70e2d45057048e21af0b968451.jpg\",\n  \"4091.jpg\": \"Reptilia/Coluber constrictor flaviventris/d17f422dabd5bd569e47e0d16393b6b9.jpg\",\n  \"40910.jpg\": \"Insecta/Haploa clymene/a338217b0eb3b97f31db26ad70a68981.jpg\",\n  \"40911.jpg\": \"Insecta/Haploa clymene/aaa9796232bbe86f772023fd1bb7c252.jpg\",\n  \"409256.jpg\": \"Insecta/Danaus plexippus/1a777414a2275b8fed88c62b2c6b1d39.jpg\",\n  \"409553.jpg\": \"Insecta/Danaus plexippus/c84c5de34cdf3fd6a4ac1fb11ce85932.jpg\",\n  \"4096.jpg\": \"Reptilia/Coluber constrictor flaviventris/da7bcaced3bb5bc562b5e43b8c94841a.jpg\",\n  \"4097.jpg\": \"Reptilia/Coluber constrictor flaviventris/c5825090f374a2e1df9f3940794c097e.jpg\",\n  \"4098.jpg\": \"Reptilia/Coluber constrictor flaviventris/3ed53c8e46b55d6cbe5742d85001754f.jpg\",\n  \"409814.jpg\": \"Mollusca/Geitodoris heathi/26a0538a8317c74650651d782b78d6a6.jpg\",\n  \"409821.jpg\": \"Mollusca/Geitodoris heathi/34f8b06f76b9e0199462dfe6986abb71.jpg\",\n  \"409831.jpg\": \"Mollusca/Geitodoris heathi/9c76b6e3331436b1a586fc8236240979.jpg\",\n  \"409834.jpg\": \"Mollusca/Geitodoris heathi/c41170a50ec254074a154d7d28afafb1.jpg\",\n  \"409835.jpg\": \"Mollusca/Geitodoris heathi/a50468b469eb40444373d9caf7b8e25b.jpg\",\n  \"409840.jpg\": \"Mollusca/Geitodoris heathi/4e88fcf5c619ff259631000cff109e6f.jpg\",\n  \"409841.jpg\": \"Mollusca/Geitodoris heathi/ee1daf91adbe639ecc403a7ff534f405.jpg\",\n  \"409852.jpg\": \"Mollusca/Geitodoris heathi/b31660f0827bbc0e4b8a4e303a14bd75.jpg\",\n  \"409853.jpg\": \"Mollusca/Geitodoris heathi/de42540e4b9398c47d85f33add32ee52.jpg\",\n  \"409867.jpg\": \"Mollusca/Geitodoris heathi/54ada70c6be0da8f3114f46cd6cafe09.jpg\",\n  \"409871.jpg\": \"Mollusca/Geitodoris heathi/5dab81d1235be9c7671c51bf7da4eed5.jpg\",\n  \"409876.jpg\": \"Mollusca/Geitodoris heathi/5536e5bfafe9717b36f00fed1aa11d8b.jpg\",\n  \"409881.jpg\": \"Mollusca/Geitodoris heathi/74dd98011d6493b919991c3b1fb5d39f.jpg\",\n  \"409899.jpg\": \"Mollusca/Triopha maculata/ca97c6accdef345cbf0cf648156353fb.jpg\",\n  \"409903.jpg\": \"Mollusca/Triopha maculata/c5ceac696ab0029cfc631d0fc2fa82ae.jpg\",\n  \"409916.jpg\": \"Mollusca/Triopha maculata/0269b9c88fc4b8bdb2c2183cb1c680f6.jpg\",\n  \"40992.jpg\": \"Aves/Pyrocephalus rubinus/b36efaf93828e6ec342c4cbe75365fd4.jpg\",\n  \"409929.jpg\": \"Mollusca/Triopha maculata/083c5d59cb842c967bf3028c1dff3b34.jpg\",\n  \"409946.jpg\": \"Mollusca/Triopha maculata/5357a8e4fd016fe35aefe6d39900ae76.jpg\",\n  \"409957.jpg\": \"Mollusca/Triopha maculata/386134f34d3ecf95d7a683a63f2b5fd6.jpg\",\n  \"409959.jpg\": \"Mollusca/Triopha maculata/d6d30fb9166fceadfaef163adfcf4e52.jpg\",\n  \"409966.jpg\": \"Mollusca/Triopha maculata/f0967d4584949095a170b7f047d0c8c1.jpg\",\n  \"409972.jpg\": \"Mollusca/Triopha maculata/7da9323ffba253fa3de5227c123a73d3.jpg\",\n  \"410019.jpg\": \"Mollusca/Triopha maculata/3dd4427624b90172cfae2b975fc71c9b.jpg\",\n  \"41002.jpg\": \"Aves/Pyrocephalus rubinus/cbea2083dea49548a6dd9ce89af59837.jpg\",\n  \"410028.jpg\": \"Mollusca/Triopha maculata/230c2aebf7f4c4081c3e72b32c139547.jpg\",\n  \"41005.jpg\": \"Aves/Pyrocephalus rubinus/08e33a70237e5cfe1d8ddbca7004f767.jpg\",\n  \"410052.jpg\": \"Mollusca/Triopha maculata/d68b9ebf581ad2d02c7302af83523d31.jpg\",\n  \"410076.jpg\": \"Mollusca/Triopha maculata/f1fa47c8b0c89251fb1d2fae0f7eac35.jpg\",\n  \"410082.jpg\": \"Mollusca/Triopha maculata/c64dda6c35e486208859bad087649459.jpg\",\n  \"410306.jpg\": \"Insecta/Murgantia histrionica/a18620ac8fe865faa32df170b2184033.jpg\",\n  \"410317.jpg\": \"Insecta/Murgantia histrionica/b1789e3c1c2fc11811cd0da87a5fc20c.jpg\",\n  \"410318.jpg\": \"Insecta/Murgantia histrionica/8b33f505bf4f70edd922aa2fa345d3e6.jpg\",\n  \"41033.jpg\": \"Aves/Pyrocephalus rubinus/6ea735af3931c8a405cf098b18435610.jpg\",\n  \"410334.jpg\": \"Insecta/Murgantia histrionica/977da4d543beb5f8f03015189005c02f.jpg\",\n  \"410338.jpg\": \"Insecta/Murgantia histrionica/9f7f38c06a9c66f5780c1232f8c96101.jpg\",\n  \"410350.jpg\": \"Insecta/Murgantia histrionica/d03061c4d505a7c7e6b14b6f075cca71.jpg\",\n  \"410364.jpg\": \"Insecta/Murgantia histrionica/c7e4e326117b4faaa6e8743b52398028.jpg\",\n  \"410386.jpg\": \"Insecta/Murgantia histrionica/25bdc217220cc182d61e4572967d4084.jpg\",\n  \"410387.jpg\": \"Insecta/Murgantia histrionica/2b296b64604a6cc57ec162ef25f7a78c.jpg\",\n  \"410388.jpg\": \"Insecta/Murgantia histrionica/526463a252271c8c8d7cd99c72dc7db1.jpg\",\n  \"410399.jpg\": \"Insecta/Murgantia histrionica/9b25142bb45f30a87751d04961a515e8.jpg\",\n  \"410409.jpg\": \"Insecta/Murgantia histrionica/752ff0d748361e227e4cf0ba86a93faf.jpg\",\n  \"41041.jpg\": \"Aves/Pyrocephalus rubinus/3136a196698680452f029b124a891324.jpg\",\n  \"410485.jpg\": \"Aves/Columba livia/b3db45a893264fa76b27e69e9b0d7a28.jpg\",\n  \"410506.jpg\": \"Aves/Columba livia/d06e376c1d74e1d57d24bc6d89988876.jpg\",\n  \"410512.jpg\": \"Aves/Columba livia/087752122d4ae5c7e2e9482c4fd876d1.jpg\",\n  \"410544.jpg\": \"Aves/Columba livia/6d6d954a3c4051d58c295115dd621b86.jpg\",\n  \"410550.jpg\": \"Aves/Columba livia/80453a4d404e1964ca4f63ace23d6697.jpg\",\n  \"410613.jpg\": \"Aves/Columba livia/0530f856ca406b5d52515cb07d19e254.jpg\",\n  \"410658.jpg\": \"Aves/Columba livia/4edf8be1ba0a99befa3785c0b579cf9a.jpg\",\n  \"410749.jpg\": \"Aves/Columba livia/1ae971c7d10b22d65215c7a9b18bea67.jpg\",\n  \"410771.jpg\": \"Aves/Columba livia/32d44486dee20ee481858b5b75fbba3f.jpg\",\n  \"410785.jpg\": \"Aves/Columba livia/5f71bd1a20dd0fe0f65668e7f8fa5cde.jpg\",\n  \"41082.jpg\": \"Aves/Pyrocephalus rubinus/ec3fe2e71d36b9f332a68868d6da1695.jpg\",\n  \"4109.jpg\": \"Reptilia/Coluber constrictor flaviventris/fb0178b080123275f23280c6c023af9b.jpg\",\n  \"410974.jpg\": \"Aves/Columba livia/da42d7ae473d9871c157582498ea5091.jpg\",\n  \"4110.jpg\": \"Reptilia/Coluber constrictor flaviventris/8c508a136881b4b93f0cd2e8db15ca86.jpg\",\n  \"4111.jpg\": \"Reptilia/Coluber constrictor flaviventris/08f3e03b7ec49fa818bc38685a903406.jpg\",\n  \"411126.jpg\": \"Aves/Columba livia/bfa10c80ea26e0527fa89c54cac6fb9e.jpg\",\n  \"411236.jpg\": \"Aves/Columba livia/9164285c9c5104f785c1582b2714f6ea.jpg\",\n  \"411297.jpg\": \"Insecta/Notarctia proxima/c9631e5511dab2a5ea45518cfd05e9ea.jpg\",\n  \"411305.jpg\": \"Insecta/Notarctia proxima/baf3fb699adf63468c59fc0f1d20358b.jpg\",\n  \"411306.jpg\": \"Insecta/Notarctia proxima/873169d21123436e49b82d66f02f3ab9.jpg\",\n  \"411309.jpg\": \"Aves/Piranga bidentata/7ed91875562cf967786a79fc6470199c.jpg\",\n  \"411312.jpg\": \"Aves/Piranga bidentata/b776e8eb0e8508e60dd94d3c5f15264b.jpg\",\n  \"411316.jpg\": \"Aves/Piranga bidentata/6be256e3e982469f8988f6de1cbdb30b.jpg\",\n  \"411317.jpg\": \"Aves/Piranga bidentata/c5516984a76325097549312c339bd239.jpg\",\n  \"411318.jpg\": \"Aves/Piranga bidentata/9967fc9c28d4737f521c21f623dc9300.jpg\",\n  \"411332.jpg\": \"Mammalia/Marmota flaviventris/b32405bc1d1d87f7d7998030af2d296e.jpg\",\n  \"411333.jpg\": \"Mammalia/Marmota flaviventris/e36ffeebb603ec2818d58d8eae585ab6.jpg\",\n  \"411393.jpg\": \"Mammalia/Marmota flaviventris/811e3ab0d5552d0addfae6cea365bd3e.jpg\",\n  \"411396.jpg\": \"Mammalia/Marmota flaviventris/4fec4b51840ad1401d10f550b3b40e97.jpg\",\n  \"411415.jpg\": \"Aves/Zonotrichia capensis/143a91ed798768098e433d612c35f716.jpg\",\n  \"411416.jpg\": \"Aves/Zonotrichia capensis/827b6b4c1518a6d49ac43cce0f248bc2.jpg\",\n  \"411417.jpg\": \"Aves/Zonotrichia capensis/bc3aff85687715f943c60cdee213e20f.jpg\",\n  \"411423.jpg\": \"Aves/Zonotrichia capensis/8a871e98b570682a32933c4e24827e81.jpg\",\n  \"411428.jpg\": \"Aves/Zonotrichia capensis/4d67431fba4641d11e45a387f70ac1aa.jpg\",\n  \"411432.jpg\": \"Aves/Zonotrichia capensis/f40b418d2c0985f65707cb58cfd503c6.jpg\",\n  \"411438.jpg\": \"Aves/Zonotrichia capensis/0fcfe5182a9def76b1aa430bb090a69e.jpg\",\n  \"411489.jpg\": \"Mollusca/Olivella biplicata/af7b5a8a2a9b48e80ee05124b8446fca.jpg\",\n  \"411491.jpg\": \"Mollusca/Olivella biplicata/1f87730e320c9bed61592c7242481d8b.jpg\",\n  \"411492.jpg\": \"Mollusca/Olivella biplicata/c94810578b11ff02a3b3877699946a7c.jpg\",\n  \"411493.jpg\": \"Mollusca/Olivella biplicata/9f303a267cc1e2f082a7676e2391c968.jpg\",\n  \"411494.jpg\": \"Mollusca/Olivella biplicata/f413671baf5b8613fca8450520553461.jpg\",\n  \"411497.jpg\": \"Mollusca/Olivella biplicata/e2c2a70f44c4b3898e9d8f82bab5caa1.jpg\",\n  \"411498.jpg\": \"Mollusca/Olivella biplicata/cc09d59e8aec199b5cee311c933145e6.jpg\",\n  \"411499.jpg\": \"Mollusca/Olivella biplicata/b56b18c2c55dcaedd4b4c416c3e83dd9.jpg\",\n  \"411500.jpg\": \"Mollusca/Olivella biplicata/227b09b8796e45d5f641fcc61576423d.jpg\",\n  \"411502.jpg\": \"Mollusca/Olivella biplicata/9e8ca042b0119ba0e81ade16787d3bf1.jpg\",\n  \"411507.jpg\": \"Mollusca/Olivella biplicata/ef050de5e6aec1d037d9b5652cf2e234.jpg\",\n  \"411516.jpg\": \"Mollusca/Olivella biplicata/47e40a7ae718e4800b2e5155da11ec4f.jpg\",\n  \"411519.jpg\": \"Insecta/Coleomegilla maculata/94eb0c3bd463cffee61920f53659c0c0.jpg\",\n  \"411530.jpg\": \"Insecta/Coleomegilla maculata/4b5041cad894cc3dbfcdbc101f235eb3.jpg\",\n  \"411531.jpg\": \"Insecta/Coleomegilla maculata/ba6d724a0e1ec27ed88b7e04552b7388.jpg\",\n  \"411532.jpg\": \"Insecta/Coleomegilla maculata/ee715e3785db8eb9cf08be807df5c528.jpg\",\n  \"411533.jpg\": \"Insecta/Coleomegilla maculata/d5fe10d90322f3656f714a4a940cb04b.jpg\",\n  \"411549.jpg\": \"Insecta/Coleomegilla maculata/e4232a15808a810dbfafc4d5dabd89f7.jpg\",\n  \"411550.jpg\": \"Insecta/Coleomegilla maculata/1e074eb63a074950b72d72306590a6f8.jpg\",\n  \"411558.jpg\": \"Insecta/Coleomegilla maculata/f2656bf038f58f02a49e0b89cd0b60ab.jpg\",\n  \"411735.jpg\": \"Mammalia/Ursus americanus/30083bff2cae44b3ac9343a47d188099.jpg\",\n  \"4118.jpg\": \"Reptilia/Coluber constrictor flaviventris/5cd6f73339a88e1dc1dd79c5147f1cc7.jpg\",\n  \"411803.jpg\": \"Mammalia/Ondatra zibethicus/33b401f2a9cf0f16f7bd5f216fc07423.jpg\",\n  \"411812.jpg\": \"Mammalia/Ondatra zibethicus/d5b657e1215ff4c9a9a6c41cbd1f93d2.jpg\",\n  \"411833.jpg\": \"Mammalia/Ondatra zibethicus/8e7eea67800b56dd6909352cc5c3b204.jpg\",\n  \"411848.jpg\": \"Mammalia/Ondatra zibethicus/0a829d232ebd85adacfeee1ffa765e7e.jpg\",\n  \"411851.jpg\": \"Mammalia/Ondatra zibethicus/b93cd2ad86f376fa8e66557022fee170.jpg\",\n  \"411862.jpg\": \"Mammalia/Ondatra zibethicus/e6ee806cc74e157de7a5c8f96b88932e.jpg\",\n  \"411870.jpg\": \"Mammalia/Ondatra zibethicus/bfe7736f2c2cd6b6f4126c7a2bebd043.jpg\",\n  \"411883.jpg\": \"Arachnida/Parasteatoda tepidariorum/23b0b9ba57caae809ed045b752c05575.jpg\",\n  \"411890.jpg\": \"Reptilia/Nerodia cyclopion/b2814ada06abf8dc268ce2ff282b9344.jpg\",\n  \"4122.jpg\": \"Insecta/Texola elada/bc10d8ca775465908a34c0fc03aea029.jpg\",\n  \"412275.jpg\": \"Amphibia/Agalychnis dacnicolor/b72a9692d38ddc3bd03e55c7dc7ac96c.jpg\",\n  \"412285.jpg\": \"Amphibia/Agalychnis dacnicolor/c142c6ce39bd86fb23faaac7b545dae9.jpg\",\n  \"412316.jpg\": \"Reptilia/Sceloporus orcutti/b0be81560b3163a6325ade05e84c3808.jpg\",\n  \"412317.jpg\": \"Reptilia/Sceloporus orcutti/18ab35f70c1a191d3c18fb149b67a4c0.jpg\",\n  \"412318.jpg\": \"Reptilia/Sceloporus orcutti/0db4c441b21f7662dd293e7e014714bd.jpg\",\n  \"412322.jpg\": \"Reptilia/Sceloporus orcutti/8bfd70d2cb79921161efd033dc93de2c.jpg\",\n  \"412326.jpg\": \"Reptilia/Sceloporus orcutti/1c04b03cabc1464457b295d9ae510f9a.jpg\",\n  \"412327.jpg\": \"Reptilia/Sceloporus orcutti/2fc3020aeb69c129bc8a63970d607008.jpg\",\n  \"412330.jpg\": \"Reptilia/Sceloporus orcutti/0d4c39ac72764bde459a8d58abe272f0.jpg\",\n  \"412333.jpg\": \"Reptilia/Sceloporus orcutti/6cb83438d0b47e5e44848b9866d0e69a.jpg\",\n  \"412335.jpg\": \"Reptilia/Sceloporus orcutti/fb17907fa344e1650b90111869852bb2.jpg\",\n  \"412337.jpg\": \"Reptilia/Sceloporus orcutti/6d12183d8bcdca5e240fd72872a1ef2d.jpg\",\n  \"412343.jpg\": \"Reptilia/Sceloporus orcutti/9e110fa06f8a7ee03887e9fbab6cd7fe.jpg\",\n  \"412344.jpg\": \"Reptilia/Sceloporus orcutti/7a72492633cf562ae8f46d8e2fe4aa7a.jpg\",\n  \"412365.jpg\": \"Insecta/Trimerotropis pallidipennis/ffd2c6f5009297721f78e77092359c95.jpg\",\n  \"412368.jpg\": \"Insecta/Trimerotropis pallidipennis/1cbd924301f7a73e88bd296bbc882783.jpg\",\n  \"412369.jpg\": \"Insecta/Trimerotropis pallidipennis/648f9e4c5c7ab9d276f38f0db88f09da.jpg\",\n  \"412370.jpg\": \"Insecta/Trimerotropis pallidipennis/0d1f331d6156718d8cd210c3d8b9f46b.jpg\",\n  \"412371.jpg\": \"Insecta/Trimerotropis pallidipennis/232ce8192029747bb3571a19d880272b.jpg\",\n  \"412373.jpg\": \"Insecta/Trimerotropis pallidipennis/c8ea7e7aa53b912da7d0f6a53719516d.jpg\",\n  \"412374.jpg\": \"Insecta/Trimerotropis pallidipennis/39f5cacc0c39ea60c4c716a35660911a.jpg\",\n  \"412389.jpg\": \"Aves/Sagittarius serpentarius/5a9dd0af3e6ab069b9ddea0da462c00c.jpg\",\n  \"412413.jpg\": \"Aves/Junco hyemalis oreganus/9514f369eff9732ec3ea0511395cfdb1.jpg\",\n  \"412414.jpg\": \"Aves/Junco hyemalis oreganus/3943660d8a5262ef967bdb640776bd86.jpg\",\n  \"412423.jpg\": \"Aves/Junco hyemalis oreganus/1928c972c44375931a6f6ea344c9fe2a.jpg\",\n  \"412433.jpg\": \"Aves/Junco hyemalis oreganus/6bab66ffdab81cdcae747476412ce2cf.jpg\",\n  \"412436.jpg\": \"Aves/Junco hyemalis oreganus/7e3300d19062ce70b2ec1a2a5f68ee99.jpg\",\n  \"412438.jpg\": \"Aves/Junco hyemalis oreganus/4c325c57a1c5f5cc1682e081ac9522f6.jpg\",\n  \"41244.jpg\": \"Aves/Pyrocephalus rubinus/24b684d366dbf611bf612d83907898f3.jpg\",\n  \"412442.jpg\": \"Aves/Junco hyemalis oreganus/9aeceff0466ef69505247eaac2f0a564.jpg\",\n  \"412444.jpg\": \"Aves/Junco hyemalis oreganus/6540b6f00ee2b81b79ed1ec44c829ceb.jpg\",\n  \"412445.jpg\": \"Aves/Junco hyemalis oreganus/bd7ac92effd64f17907451b179186ddf.jpg\",\n  \"412452.jpg\": \"Aves/Junco hyemalis oreganus/f85be86edc0f6b1e5268a6b2f01c40da.jpg\",\n  \"412468.jpg\": \"Mollusca/Neobernaya spadicea/c1b4b349018342c22de2f5eab45a5651.jpg\",\n  \"412469.jpg\": \"Mollusca/Neobernaya spadicea/0fa786796f70b83e22aa0580e1065110.jpg\",\n  \"412536.jpg\": \"Aves/Loxia curvirostra/9f30eb82814fb92b9ff86d968336e1e0.jpg\",\n  \"412540.jpg\": \"Aves/Loxia curvirostra/ae0073c429aec41c8625ae90f888d973.jpg\",\n  \"412545.jpg\": \"Aves/Loxia curvirostra/4f75a490ee83112ecd28cc66edb7376c.jpg\",\n  \"412549.jpg\": \"Aves/Loxia curvirostra/2f1b979be82cd2dd258e4bba7471954f.jpg\",\n  \"412559.jpg\": \"Aves/Loxia curvirostra/a86b5c6687dbcee54e41d6c4831372e3.jpg\",\n  \"412561.jpg\": \"Aves/Loxia curvirostra/1c539000348ca4f16df85cccfb99c820.jpg\",\n  \"412566.jpg\": \"Aves/Loxia curvirostra/29c0c2410614b15e964ec3675cabc210.jpg\",\n  \"412572.jpg\": \"Aves/Loxia curvirostra/040c16c33e07a8abab4b3bc12c3f3264.jpg\",\n  \"412661.jpg\": \"Aves/Picoides pubescens/8f9e88403c6b7226fa9514c1d8dbf755.jpg\",\n  \"412738.jpg\": \"Aves/Picoides pubescens/1ca35e937c805f95981e8b59feba8928.jpg\",\n  \"41284.jpg\": \"Aves/Pyrocephalus rubinus/9e4892e1762ed8273850ccdc3e0bdf76.jpg\",\n  \"41285.jpg\": \"Aves/Pyrocephalus rubinus/7225387ef649b6750cb8055d8dde4bcd.jpg\",\n  \"41286.jpg\": \"Aves/Pyrocephalus rubinus/c804b80f8eebb1dc060233acea323c8b.jpg\",\n  \"412899.jpg\": \"Aves/Picoides pubescens/c480e9b52d7cf15a120b67715d07ac28.jpg\",\n  \"412903.jpg\": \"Aves/Picoides pubescens/660b9c81e74017cc9bfb7e5f70108858.jpg\",\n  \"41294.jpg\": \"Aves/Pyrocephalus rubinus/58e3bc90b733d8a6459ef840a59ad0e6.jpg\",\n  \"41306.jpg\": \"Aves/Pyrocephalus rubinus/99a1a28b27e4c255aa781a12f6d48206.jpg\",\n  \"41314.jpg\": \"Aves/Pyrocephalus rubinus/59eb987f27414278d21f6fb970a27c5f.jpg\",\n  \"41318.jpg\": \"Aves/Pyrocephalus rubinus/07b7c3740966782a637142d2465fadcd.jpg\",\n  \"4132.jpg\": \"Insecta/Texola elada/0bd94508eede879f38b1b8bfa00f43ee.jpg\",\n  \"413278.jpg\": \"Aves/Picoides pubescens/8d641144b0096c5b58794c6559d702af.jpg\",\n  \"413291.jpg\": \"Aves/Picoides pubescens/b515337fbd6ec63fc15c45bbd821bd29.jpg\",\n  \"4133.jpg\": \"Insecta/Texola elada/84087d2c6fe62afcb11b56103e7ffb5e.jpg\",\n  \"413310.jpg\": \"Aves/Picoides nuttallii/f088fffaacc150661f4e6f3d10ba5852.jpg\",\n  \"413379.jpg\": \"Aves/Picoides nuttallii/75a838ab3bcc6a2ceabf3d05a8ba2f52.jpg\",\n  \"413405.jpg\": \"Aves/Picoides nuttallii/5e7ff53d0e51d4c042911e21425cd156.jpg\",\n  \"413428.jpg\": \"Aves/Picoides nuttallii/d9e8fceb443605feda17ef7f01d23921.jpg\",\n  \"413441.jpg\": \"Insecta/Trichodes ornatus/07979d4dde19e98ffb42a5164906b0d8.jpg\",\n  \"413443.jpg\": \"Insecta/Trichodes ornatus/dd2f3814e4432d348abed51c27633772.jpg\",\n  \"4135.jpg\": \"Insecta/Texola elada/878617305f733f4ff4da580ff4b0d311.jpg\",\n  \"413523.jpg\": \"Insecta/Coccinella septempunctata/048481d5f83cbdea64d9a1a23eda1450.jpg\",\n  \"413624.jpg\": \"Insecta/Coccinella septempunctata/aa8b7b2e351d8a1047096c6105324ee1.jpg\",\n  \"41363.jpg\": \"Aves/Pyrocephalus rubinus/a78745542b0c2c1fc4f32c032eb888eb.jpg\",\n  \"413649.jpg\": \"Insecta/Coccinella septempunctata/220b352f46f229748bbbaa95f4300221.jpg\",\n  \"41370.jpg\": \"Aves/Pyrocephalus rubinus/980cd640f2c0a9078a9edb0f21f727c8.jpg\",\n  \"413715.jpg\": \"Insecta/Coccinella septempunctata/7c9eef570cbcca672c0a407138d227e2.jpg\",\n  \"413724.jpg\": \"Insecta/Coccinella septempunctata/75c0cdf55ac35a54b735ab2c0643448c.jpg\",\n  \"413749.jpg\": \"Insecta/Coccinella septempunctata/1f7920e649e8b69915732dce0d258df6.jpg\",\n  \"414010.jpg\": \"Reptilia/Coluber constrictor flaviventris/8374d67bf65b5fa3221b11160b2ba002.jpg\",\n  \"414012.jpg\": \"Reptilia/Coluber constrictor flaviventris/ff3f0f7d131b39796f26f1ba0f9f70fd.jpg\",\n  \"414023.jpg\": \"Reptilia/Coluber constrictor flaviventris/228452675e89a4a25256a0e57b53250b.jpg\",\n  \"414024.jpg\": \"Reptilia/Coluber constrictor flaviventris/f179ef0886f903406e38088b80ede627.jpg\",\n  \"414028.jpg\": \"Reptilia/Coluber constrictor flaviventris/2cb8dc786b0612b7e844fb483e5d4fcb.jpg\",\n  \"414033.jpg\": \"Reptilia/Coluber constrictor flaviventris/7c2d50c75ed69628daa3d680b497e9a9.jpg\",\n  \"414038.jpg\": \"Reptilia/Coluber constrictor flaviventris/48f88f5a88e46b125ef57d25d46407f1.jpg\",\n  \"414039.jpg\": \"Reptilia/Coluber constrictor flaviventris/db846d237c7c7d988217266d81cf3609.jpg\",\n  \"414040.jpg\": \"Reptilia/Coluber constrictor flaviventris/f7a7887ade24941d3821b688b18bfc2e.jpg\",\n  \"414126.jpg\": \"Aves/Setophaga petechia/ff5ebfd2733adc6e0d2991c64f2207aa.jpg\",\n  \"41420.jpg\": \"Aves/Pyrocephalus rubinus/23dbacd1afc0a2a3808177f5c8fd8e97.jpg\",\n  \"414202.jpg\": \"Aves/Setophaga petechia/5fd6d8e78f745cb6290653ae2a5dbdc7.jpg\",\n  \"414357.jpg\": \"Aves/Setophaga petechia/3e6e2bcdb1ff93b654a14e2362dda219.jpg\",\n  \"414372.jpg\": \"Aves/Spizella breweri/0a6af886cdfc3ff4c2bf4336049e5c46.jpg\",\n  \"414373.jpg\": \"Aves/Spizella breweri/5b79b8c2f50a80c06f68b6299b6561d9.jpg\",\n  \"41441.jpg\": \"Aves/Pyrocephalus rubinus/235ea155604c0fd01bc96a559d55c68f.jpg\",\n  \"414448.jpg\": \"Arachnida/Hypoblemum albovittatum/e29d43eb5aace7dad8f5953ba5182c13.jpg\",\n  \"414449.jpg\": \"Arachnida/Hypoblemum albovittatum/6f23af48da35c07a6ef3aa544594a88c.jpg\",\n  \"41447.jpg\": \"Aves/Pyrocephalus rubinus/02f99bd5864e82baba25e81151b2e08d.jpg\",\n  \"414472.jpg\": \"Aves/Cyanoramphus novaezelandiae/c072288aa2986f6a3bd80b93721ef4b6.jpg\",\n  \"414478.jpg\": \"Aves/Cyanoramphus novaezelandiae/f1e0a148acbe7e0ea74c25732e20d31f.jpg\",\n  \"414479.jpg\": \"Aves/Cyanoramphus novaezelandiae/9cbe1578f6f87aadd9f622dcf60296b1.jpg\",\n  \"414488.jpg\": \"Aves/Calocitta colliei/2eb5b0f6643914f3873c0ab37f956c5b.jpg\",\n  \"414489.jpg\": \"Aves/Calocitta colliei/0cef3aa7a7f4a420984fe02110814789.jpg\",\n  \"414490.jpg\": \"Aves/Calocitta colliei/d642f73562e87da9ae6c9bdc8473f2c3.jpg\",\n  \"414491.jpg\": \"Aves/Calocitta colliei/6f04dab483a2e9c5fa7c18492f8b031e.jpg\",\n  \"414499.jpg\": \"Aves/Calocitta colliei/f46185a46c059cb98d0d4697a03c0395.jpg\",\n  \"414503.jpg\": \"Aves/Calocitta colliei/dd3107c052fd7e6a0ea4a45fed3775b1.jpg\",\n  \"414513.jpg\": \"Aves/Calocitta colliei/627b91f97948b26991bd263f3ec02a11.jpg\",\n  \"414514.jpg\": \"Aves/Calocitta colliei/7b749709013b624316e6a7ebd85be7e4.jpg\",\n  \"414520.jpg\": \"Aves/Calocitta colliei/958cbfe72b8f433a0dca7666165123e2.jpg\",\n  \"414521.jpg\": \"Aves/Calocitta colliei/17b5df4bc10cb9a2661fc9cc84f36c7c.jpg\",\n  \"414750.jpg\": \"Aves/Tyrannus crassirostris/ab8202754a143fa98631490895faa88c.jpg\",\n  \"414751.jpg\": \"Aves/Tyrannus crassirostris/be25b4dc60cdacad0c033261b951ebab.jpg\",\n  \"414767.jpg\": \"Amphibia/Plethodon cinereus/6cf50772846f04c755c7f6dbbcbf7fec.jpg\",\n  \"414783.jpg\": \"Amphibia/Plethodon cinereus/791611b9a45c4f003df449354fb591ee.jpg\",\n  \"41481.jpg\": \"Aves/Pyrocephalus rubinus/b29d828a44a8da64c6d2e5ddb8380791.jpg\",\n  \"41482.jpg\": \"Aves/Pyrocephalus rubinus/7583ee150ecc166af99fab5df207cf70.jpg\",\n  \"414820.jpg\": \"Amphibia/Plethodon cinereus/24ce17bfb0de447349b0aa5454ab7292.jpg\",\n  \"414855.jpg\": \"Amphibia/Plethodon cinereus/3aab8267c6e7e313b9cb536bbfe9e63a.jpg\",\n  \"414874.jpg\": \"Amphibia/Plethodon cinereus/3e98b1ee2066c5fe0a8dc0015d3fba56.jpg\",\n  \"414888.jpg\": \"Amphibia/Plethodon cinereus/9931f06fa1e9f91073c50151a9a3a51f.jpg\",\n  \"414911.jpg\": \"Amphibia/Plethodon cinereus/47812b5406cd6eb86c5c1658c786127f.jpg\",\n  \"414915.jpg\": \"Amphibia/Plethodon cinereus/39591fbdd53f93b15b9935697bad0c9d.jpg\",\n  \"414939.jpg\": \"Reptilia/Eretmochelys imbricata/51859391b503933e4a44cabd57dc341b.jpg\",\n  \"414941.jpg\": \"Reptilia/Eretmochelys imbricata/1dad861384f305914a43d5d1272e9e25.jpg\",\n  \"414946.jpg\": \"Reptilia/Eretmochelys imbricata/38fccc4ad62522f93266cf09b8e25d3d.jpg\",\n  \"414954.jpg\": \"Reptilia/Eretmochelys imbricata/74c053e08b4ccbf4ad8cbd36acdc8da2.jpg\",\n  \"414955.jpg\": \"Reptilia/Eretmochelys imbricata/517dada4b99987a9a935daa8227009dd.jpg\",\n  \"414961.jpg\": \"Reptilia/Eretmochelys imbricata/4576e0cc9abc840c190052dedd463502.jpg\",\n  \"414962.jpg\": \"Reptilia/Eretmochelys imbricata/690196d94ee7c4f47849595f15e706a6.jpg\",\n  \"414970.jpg\": \"Reptilia/Eretmochelys imbricata/18be9614e01eb5434f742d1ee82138a4.jpg\",\n  \"414971.jpg\": \"Reptilia/Eretmochelys imbricata/198f41d1abc90f5f79af01359a91e434.jpg\",\n  \"41506.jpg\": \"Amphibia/Hemidactylium scutatum/5bfce96f66d71d858a6d6f242112ccd6.jpg\",\n  \"415071.jpg\": \"Aves/Myiarchus crinitus/f5403decf2d332769939eb95dc061efd.jpg\",\n  \"415081.jpg\": \"Aves/Myiarchus crinitus/8480b4f748cb5b52494802488c7aeaec.jpg\",\n  \"415083.jpg\": \"Aves/Myiarchus crinitus/840693a56cd337f8e331f51d3b5fe852.jpg\",\n  \"415088.jpg\": \"Aves/Myiarchus crinitus/74d50ac38fd4c4b3c185d0d2c24f8f6e.jpg\",\n  \"415098.jpg\": \"Aves/Myiarchus crinitus/53631a8262619ce3b0b628f26e0c45ae.jpg\",\n  \"415116.jpg\": \"Aves/Myiarchus crinitus/bb88e178a5551347959173936c560f9e.jpg\",\n  \"41513.jpg\": \"Amphibia/Hemidactylium scutatum/5b9de5eb866205fca9b7e324ac646dee.jpg\",\n  \"415130.jpg\": \"Aves/Myiarchus crinitus/84d8521b2765b2f4dc30ad99ada35ea3.jpg\",\n  \"415138.jpg\": \"Aves/Myiarchus crinitus/d798668f959a5bb8ecda83fecc0ab550.jpg\",\n  \"415143.jpg\": \"Insecta/Peridea ferruginea/2b952de85b72bfdd14e84bac74064666.jpg\",\n  \"415144.jpg\": \"Insecta/Peridea ferruginea/57746e7ec35af72ed89f42215e5eed63.jpg\",\n  \"415148.jpg\": \"Mammalia/Leopardus pardalis/61bfce4a8f56d8aa71475a5447cdeb01.jpg\",\n  \"41515.jpg\": \"Amphibia/Hemidactylium scutatum/aaa26091b1a8d8d8356de3eefa1e8c9f.jpg\",\n  \"41516.jpg\": \"Amphibia/Hemidactylium scutatum/4632c5e3bb595245ab2960eddab71e2a.jpg\",\n  \"415180.jpg\": \"Insecta/Ammophila procera/43e841711ab2119fa27d01106c2b1a8d.jpg\",\n  \"415181.jpg\": \"Insecta/Ammophila procera/21e56de3c63bc995869d1514d6c70661.jpg\",\n  \"415186.jpg\": \"Insecta/Dymasia dymas/ba2add5c1ed67ea898d91e489f556f7e.jpg\",\n  \"415187.jpg\": \"Insecta/Dymasia dymas/30616a9519048f349786584f6356c407.jpg\",\n  \"415212.jpg\": \"Insecta/Melanis pixe/4574aeafa5f6a2e17076092aa4ddc819.jpg\",\n  \"415214.jpg\": \"Insecta/Melanis pixe/6276f71c353fefa8f80a191750fdd677.jpg\",\n  \"415223.jpg\": \"Aves/Columba palumbus/0f09187ddc4809dd7ed1a3aa293402a2.jpg\",\n  \"415255.jpg\": \"Aves/Columba palumbus/dcb51a0b960cf1607e15a87c581e734b.jpg\",\n  \"415273.jpg\": \"Aves/Columba palumbus/c4dac6af48226206baa9499be73342c6.jpg\",\n  \"415280.jpg\": \"Aves/Columba palumbus/5dddc70b869aae15095bd109da04bfa9.jpg\",\n  \"415283.jpg\": \"Aves/Columba palumbus/11e6e16e3f8589a7b2f3db17319d2def.jpg\",\n  \"415285.jpg\": \"Aves/Columba palumbus/646c15177be8409f9402a88318bc174c.jpg\",\n  \"415296.jpg\": \"Aves/Trogon elegans/76ee57c17208ff4bb2380f41b40ec3f3.jpg\",\n  \"415297.jpg\": \"Aves/Trogon elegans/e972f555ef070b24e670b0706b6101bb.jpg\",\n  \"415298.jpg\": \"Aves/Trogon elegans/cb361902b453295b16cd8d5146651c1a.jpg\",\n  \"415299.jpg\": \"Aves/Trogon elegans/6ff48b90eba923e23e0db6415f06a569.jpg\",\n  \"415301.jpg\": \"Aves/Trogon elegans/fd68288d80cedd4a00f01b6cee09ddf1.jpg\",\n  \"415303.jpg\": \"Aves/Trogon elegans/5fe4d019f46f506342a521183c92306d.jpg\",\n  \"415304.jpg\": \"Aves/Trogon elegans/4d17df9be5553d646b9aab07eeb8fe4f.jpg\",\n  \"415306.jpg\": \"Aves/Trogon elegans/eeec20f674a2558d54f26391b25950ce.jpg\",\n  \"415309.jpg\": \"Aves/Trogon elegans/1c239ceabc4289e83ce7c75c43caa09b.jpg\",\n  \"415315.jpg\": \"Reptilia/Rena dulcis/52448a06a859e8bb7c721bd0a9adf4b1.jpg\",\n  \"415319.jpg\": \"Reptilia/Rena dulcis/d4143f284fac601cd864d3d5bb1d4c12.jpg\",\n  \"415326.jpg\": \"Reptilia/Rena dulcis/3647530ac28cf94c1354a62d68490e53.jpg\",\n  \"415334.jpg\": \"Reptilia/Rena dulcis/20a5346c727f3988ab2999d3cda72322.jpg\",\n  \"415335.jpg\": \"Reptilia/Rena dulcis/16fd65f5717dfe6f7033d552695a40fb.jpg\",\n  \"415336.jpg\": \"Reptilia/Rena dulcis/fddcb322fb5de420420b11aa013ef634.jpg\",\n  \"415346.jpg\": \"Reptilia/Rena dulcis/c8c04f68732272b2b7b9c86dd78db4b6.jpg\",\n  \"415434.jpg\": \"Insecta/Glaucopsyche lygdamus/534d7a510c5560e880c0ba996e173a49.jpg\",\n  \"415435.jpg\": \"Insecta/Glaucopsyche lygdamus/f001671a6690c7a676f15592b9362f15.jpg\",\n  \"415436.jpg\": \"Insecta/Glaucopsyche lygdamus/9c93c84508061b0bb98f487e036fdc32.jpg\",\n  \"415545.jpg\": \"Aves/Regulus regulus/17e711d4f669643249dc6cd4556facbe.jpg\",\n  \"415558.jpg\": \"Aves/Regulus regulus/9293e013de1377dc9a67e39bfcd34a9c.jpg\",\n  \"415565.jpg\": \"Mammalia/Eschrichtius robustus/90e3a92e65718941a0c626c36b51f251.jpg\",\n  \"415568.jpg\": \"Mammalia/Eschrichtius robustus/30f8036d8e23de528ea36bac295dcef5.jpg\",\n  \"415592.jpg\": \"Mammalia/Eschrichtius robustus/b6674ab5b3b69a3711e3a1016fb1a30a.jpg\",\n  \"415595.jpg\": \"Mammalia/Eschrichtius robustus/6838e09d30327db3501d2fa0479fdb2c.jpg\",\n  \"415601.jpg\": \"Mammalia/Eschrichtius robustus/585c3715360d54d074ff545ee0fff5e6.jpg\",\n  \"415602.jpg\": \"Mammalia/Eschrichtius robustus/9b2fda283816a9d96963fa9b3877e6e1.jpg\",\n  \"415609.jpg\": \"Aves/Pluvialis squatarola/72c21b48fad06ee102e84cbd683052c9.jpg\",\n  \"415681.jpg\": \"Aves/Pluvialis squatarola/05050ca4fad53069df7c1e44aa58b642.jpg\",\n  \"415738.jpg\": \"Aves/Pluvialis squatarola/0b2afdbcc96ba5dda46f86c685859779.jpg\",\n  \"415815.jpg\": \"Insecta/Pangrapta decoralis/391f3bbf84e55235cc7702db4003ba0f.jpg\",\n  \"41607.jpg\": \"Aves/Jacana spinosa/7d37b4b24c98d5bd558f4aee97f654c5.jpg\",\n  \"416095.jpg\": \"Insecta/Zale lunata/bc1320e00f3e6b6ddec39622a18a4fef.jpg\",\n  \"416096.jpg\": \"Insecta/Zale lunata/367658965e11ce896d9663dcf24aa0e2.jpg\",\n  \"416097.jpg\": \"Insecta/Zale lunata/4baa70cd7373e74ad14d5d1fbc32b6ca.jpg\",\n  \"416098.jpg\": \"Insecta/Zale lunata/eeab6b2aca36a7dc07dd249775ee6f17.jpg\",\n  \"416109.jpg\": \"Insecta/Zale lunata/cb6661400a7f08f446dd2f57fc60722e.jpg\",\n  \"416117.jpg\": \"Insecta/Zale lunata/2c61de1ba854a4f72475e372fefd80b6.jpg\",\n  \"41612.jpg\": \"Aves/Jacana spinosa/e1907266d242d13ab9760f710f27c035.jpg\",\n  \"416122.jpg\": \"Insecta/Zale lunata/ad8eab0afc25e5bab963771ca12a17bc.jpg\",\n  \"416123.jpg\": \"Insecta/Zale lunata/d32e982dd53b708e6b19db06d53eccc9.jpg\",\n  \"41614.jpg\": \"Aves/Jacana spinosa/750ccb722bac6d8955ab54fb76d69ad4.jpg\",\n  \"41617.jpg\": \"Aves/Jacana spinosa/9abdc94e58d863da82e94b2bfb1c4655.jpg\",\n  \"416171.jpg\": \"Insecta/Leptoglossus zonatus/69c43ee73ffcb59469183f1da0d81960.jpg\",\n  \"416173.jpg\": \"Insecta/Leptoglossus zonatus/1900108102f6a2d74b882d4ec674a3a0.jpg\",\n  \"416179.jpg\": \"Insecta/Leptoglossus zonatus/3c184668a2611df0335222b8f7be7308.jpg\",\n  \"41623.jpg\": \"Aves/Jacana spinosa/191a501d30cf3edb8451f6e70c36f7bb.jpg\",\n  \"416254.jpg\": \"Aves/Pica pica/10212a15627c0b5269607d7814dc5d8c.jpg\",\n  \"416262.jpg\": \"Aves/Pica pica/4fdbcd11ae1252fa18387627b6006944.jpg\",\n  \"416285.jpg\": \"Aves/Pica pica/78b7368d88557f3da1c0207626e19d4f.jpg\",\n  \"416317.jpg\": \"Aves/Pica pica/5c041400695e94581b2cfaa9c2185818.jpg\",\n  \"41633.jpg\": \"Aves/Jacana spinosa/3315eef8a68cfa45d6abdf67a74b316b.jpg\",\n  \"416334.jpg\": \"Aves/Pica pica/93fcf94a4e439f2caa2051812a9cd08b.jpg\",\n  \"41636.jpg\": \"Aves/Jacana spinosa/066bbfab07efb24704b18071cb1762c4.jpg\",\n  \"416367.jpg\": \"Aves/Calypte costae/3eb3912b9202b1852b03874237e38316.jpg\",\n  \"416369.jpg\": \"Aves/Calypte costae/2b3a57577d1b1baf8ebe87eee1125ec8.jpg\",\n  \"416370.jpg\": \"Aves/Calypte costae/db636e19a8a8189812d1d585aaea7365.jpg\",\n  \"416371.jpg\": \"Aves/Calypte costae/caf825b111ac4403327dcf3ee9189586.jpg\",\n  \"416372.jpg\": \"Aves/Calypte costae/814abf7b0632e62e23db27a91d43381a.jpg\",\n  \"416376.jpg\": \"Aves/Calypte costae/7b96a8fd32eba7f93df353e1f88e78da.jpg\",\n  \"416377.jpg\": \"Aves/Calypte costae/8d89ed5538040e11e51bceb94d3fa760.jpg\",\n  \"416378.jpg\": \"Aves/Calypte costae/8824589b2f1f278cb63dd3b44f77c799.jpg\",\n  \"416379.jpg\": \"Aves/Calypte costae/98aba9493469c7cfed29058d6ac6f5da.jpg\",\n  \"416382.jpg\": \"Aves/Calypte costae/8e4bc577303ad0262429643543ab5e4c.jpg\",\n  \"416386.jpg\": \"Aves/Calypte costae/1dbc4bd08ff4f1aa55f263dbd4b32fa9.jpg\",\n  \"416387.jpg\": \"Aves/Halcyon smyrnensis/7ca075ed783d3470cc706bbdea6e2bb8.jpg\",\n  \"416388.jpg\": \"Aves/Halcyon smyrnensis/f28da7d739c996f1564ccde7ae3dab8c.jpg\",\n  \"416398.jpg\": \"Insecta/Phoebis philea/c43ceb4ee312bb010c7d6cb0a8461369.jpg\",\n  \"41640.jpg\": \"Aves/Jacana spinosa/326410a1122d3bc92fe144cac401917a.jpg\",\n  \"416405.jpg\": \"Insecta/Phoebis philea/2fcfc1b878f0963c04df068e5bda5069.jpg\",\n  \"416406.jpg\": \"Insecta/Phoebis philea/3c08c62c3093693f8652a050f9c981c5.jpg\",\n  \"416417.jpg\": \"Aves/Amphispiza bilineata/8e94e44be803c95b59d487f3b8b4c39a.jpg\",\n  \"416428.jpg\": \"Aves/Amphispiza bilineata/41f14779182b495d738c4227b4287697.jpg\",\n  \"416431.jpg\": \"Aves/Amphispiza bilineata/86f8302468e3cd5ae01b9a521278c920.jpg\",\n  \"416443.jpg\": \"Aves/Amphispiza bilineata/bfd618b0ac217096cce6d528233c9274.jpg\",\n  \"416451.jpg\": \"Aves/Amphispiza bilineata/3928fafbd11e09ab8c65dcdd85342bbb.jpg\",\n  \"416455.jpg\": \"Aves/Amphispiza bilineata/e0a4675b2881de6bdf242656e6fef422.jpg\",\n  \"416471.jpg\": \"Aves/Amphispiza bilineata/f3e01d3b720864c86259be876893eede.jpg\",\n  \"41648.jpg\": \"Aves/Jacana spinosa/ee33b97024d37dd6237295eebe6cec56.jpg\",\n  \"416482.jpg\": \"Aves/Amphispiza bilineata/d2f0aa951fd094305db05b867e5649d8.jpg\",\n  \"41650.jpg\": \"Aves/Jacana spinosa/d26166b8ee0b002d8c693b90b4f859a3.jpg\",\n  \"41651.jpg\": \"Aves/Jacana spinosa/5223e7d4c1d319588c4870d9073f10c0.jpg\",\n  \"416512.jpg\": \"Insecta/Libellula needhami/7c667767e2461e32ba1f35fc090631ed.jpg\",\n  \"416516.jpg\": \"Insecta/Libellula needhami/aa1075e8fa2974c95d10e951bda7459d.jpg\",\n  \"416517.jpg\": \"Insecta/Libellula needhami/cabdf571ac3b8cebc362fecab89cc10c.jpg\",\n  \"41653.jpg\": \"Aves/Jacana spinosa/3e01338464500aa91434201d47b1ec2b.jpg\",\n  \"416545.jpg\": \"Insecta/Libellula needhami/98b122d128a265f0444660929a11fa37.jpg\",\n  \"416548.jpg\": \"Insecta/Libellula needhami/8a4d2a30957616cccefa32c3bc5c2e60.jpg\",\n  \"416550.jpg\": \"Insecta/Libellula needhami/af35f5c7c09083bc5d2181e415e6d2bc.jpg\",\n  \"416551.jpg\": \"Insecta/Libellula needhami/141fdd73f511b701871900ae372a8b6f.jpg\",\n  \"416553.jpg\": \"Insecta/Libellula needhami/83fbe96c51d2afb721d66b07a9038823.jpg\",\n  \"416556.jpg\": \"Insecta/Libellula needhami/aad6a26af1d0e89c272521f58aa11df0.jpg\",\n  \"41658.jpg\": \"Aves/Jacana spinosa/d019899971c588c3e4cf28520b629292.jpg\",\n  \"416597.jpg\": \"Animalia/Ocypode quadrata/4ebc9a823eccd7d31947f02f8a05a13a.jpg\",\n  \"416601.jpg\": \"Animalia/Ocypode quadrata/5c7dd66860d69b3874fcb8a280dbc4d4.jpg\",\n  \"416606.jpg\": \"Animalia/Ocypode quadrata/595d3995254417cc8e1698952001fe5e.jpg\",\n  \"416610.jpg\": \"Animalia/Ocypode quadrata/43bc239cd0e9bfed2b2e09f26ef4bcc2.jpg\",\n  \"41668.jpg\": \"Aves/Jacana spinosa/14e816fb469fcb76b1cf0a40cb352b17.jpg\",\n  \"41671.jpg\": \"Aves/Jacana spinosa/5ee39719bcf56331672b64cd3d1885d0.jpg\",\n  \"41674.jpg\": \"Aves/Jacana spinosa/0bd72c841eed32717da341b967df5b6e.jpg\",\n  \"416870.jpg\": \"Mammalia/Odocoileus hemionus/a85bb7f8baa1e7bc448d7c7e73b567cd.jpg\",\n  \"416901.jpg\": \"Mammalia/Odocoileus hemionus/3e79c3ff207d128dbdac52e364b88941.jpg\",\n  \"416931.jpg\": \"Mammalia/Odocoileus hemionus/519a1636e322ee7c777a7c7a87492b06.jpg\",\n  \"416975.jpg\": \"Mammalia/Odocoileus hemionus/371c8755beba58602ef06e2d3adb9fb2.jpg\",\n  \"41703.jpg\": \"Animalia/Pleuroncodes planipes/a4af22ef94f0942eb8090bf071bdbc3c.jpg\",\n  \"41705.jpg\": \"Animalia/Pleuroncodes planipes/68d7b69c6da486be54a1568c1ecbe61f.jpg\",\n  \"41706.jpg\": \"Animalia/Pleuroncodes planipes/845aecc8ce38ce7baf18a7df4cee2500.jpg\",\n  \"41707.jpg\": \"Animalia/Pleuroncodes planipes/886261d22ac1f3486598fde516240a84.jpg\",\n  \"417089.jpg\": \"Mammalia/Odocoileus hemionus/4635b0524d32722cdb78652de8e13b13.jpg\",\n  \"417140.jpg\": \"Mammalia/Odocoileus hemionus/1ff54985402cc09e325f191cde2b3102.jpg\",\n  \"417183.jpg\": \"Aves/Prosthemadera novaeseelandiae novaeseelandiae/4e5d61a2b5a6b9a4859a7b9079040193.jpg\",\n  \"417184.jpg\": \"Aves/Prosthemadera novaeseelandiae novaeseelandiae/196af03aee067fe255fc13b9c75c2b66.jpg\",\n  \"417201.jpg\": \"Aves/Prosthemadera novaeseelandiae novaeseelandiae/77d4ca049e23867583ff75c55ecc8132.jpg\",\n  \"417203.jpg\": \"Aves/Prosthemadera novaeseelandiae novaeseelandiae/e6c09ed235467d8d72edf8f34cff6bbe.jpg\",\n  \"417204.jpg\": \"Aves/Prosthemadera novaeseelandiae novaeseelandiae/439daf478f9d83f3f019b33efde0c873.jpg\",\n  \"417205.jpg\": \"Aves/Prosthemadera novaeseelandiae novaeseelandiae/93dc9302753e9c89520be36f1ba99045.jpg\",\n  \"417206.jpg\": \"Aves/Prosthemadera novaeseelandiae novaeseelandiae/298edcb7a1e499c16db421227a9f726e.jpg\",\n  \"417207.jpg\": \"Aves/Prosthemadera novaeseelandiae novaeseelandiae/a5b7d36a44e8c67d76c97b3bf7fada72.jpg\",\n  \"417208.jpg\": \"Aves/Prosthemadera novaeseelandiae novaeseelandiae/ad9956fae71a27be73cc35ca3ed8cb4e.jpg\",\n  \"417209.jpg\": \"Aves/Prosthemadera novaeseelandiae novaeseelandiae/0168cbbadbf4aa6ba3adfe92028ff6a6.jpg\",\n  \"417210.jpg\": \"Aves/Prosthemadera novaeseelandiae novaeseelandiae/2c6ca59b2d2b05a5c47efe22c976432f.jpg\",\n  \"417211.jpg\": \"Aves/Prosthemadera novaeseelandiae novaeseelandiae/aaf923d5305017f12f69ecad5a712201.jpg\",\n  \"417222.jpg\": \"Aves/Prosthemadera novaeseelandiae novaeseelandiae/af12de368c4e29346cca59d763c7efc7.jpg\",\n  \"417226.jpg\": \"Insecta/Euphyes vestris/050506a5ed36540e8376448780361d0d.jpg\",\n  \"417243.jpg\": \"Insecta/Euphyes vestris/1a0fe0a26d8524394a296319b80b9bef.jpg\",\n  \"41725.jpg\": \"Animalia/Pleuroncodes planipes/a495c62e7d57505f2926428da6eaac74.jpg\",\n  \"417257.jpg\": \"Insecta/Euphyes vestris/7af77b7de70955eda9537b0a9cb2563b.jpg\",\n  \"41726.jpg\": \"Animalia/Pleuroncodes planipes/f2ccc2eda6f5ce551ef69a546ee7e217.jpg\",\n  \"41727.jpg\": \"Animalia/Pleuroncodes planipes/c50c08ce2aea03e7c401be7d1988fab1.jpg\",\n  \"417270.jpg\": \"Insecta/Euphyes vestris/63d200f84fa7f81201bbcd4dce76c455.jpg\",\n  \"417277.jpg\": \"Insecta/Euphyes vestris/1322a958c92b6f29ca79334e3ac2dfba.jpg\",\n  \"417279.jpg\": \"Insecta/Euphyes vestris/e431ee7953b018f76dfe4c312133c041.jpg\",\n  \"417286.jpg\": \"Insecta/Euphyes vestris/9ed19ba1cb035e653ee7703e074389c6.jpg\",\n  \"41729.jpg\": \"Animalia/Pleuroncodes planipes/f4bd69e5e3bcaf3d193bee1c50a463ce.jpg\",\n  \"417292.jpg\": \"Insecta/Euphyes vestris/8122762b40d89e8a109d6febcf2201ca.jpg\",\n  \"417330.jpg\": \"Aves/Circus approximans/61844cb5b1a38548b0745a9f2f1cbf0c.jpg\",\n  \"417335.jpg\": \"Aves/Circus approximans/d4c44eecc422d0c065d90aa4ac8bb289.jpg\",\n  \"41734.jpg\": \"Animalia/Pleuroncodes planipes/47eeeba50c52755c255d5008cec09627.jpg\",\n  \"41737.jpg\": \"Animalia/Pleuroncodes planipes/09e63e40efefbc5556c8f333e76a0404.jpg\",\n  \"41743.jpg\": \"Animalia/Pleuroncodes planipes/2337f0739130fd7fdf22c50616fa4b7f.jpg\",\n  \"41745.jpg\": \"Insecta/Prochoerodes lineola/86aa6e9c3c474f1d3115e3b764f0220f.jpg\",\n  \"41749.jpg\": \"Insecta/Prochoerodes lineola/d4baaf17f463d47569ce2078732db1d1.jpg\",\n  \"41753.jpg\": \"Insecta/Prochoerodes lineola/dde594ced5ceac28e523b3b97ea20f30.jpg\",\n  \"41755.jpg\": \"Insecta/Prochoerodes lineola/7095134f6f0d168c2b8d5704f754b1bd.jpg\",\n  \"417590.jpg\": \"Aves/Melospiza melodia/c1833bb0ffbd4a0fe14dc6943cd7ea75.jpg\",\n  \"41764.jpg\": \"Insecta/Prochoerodes lineola/e8b4d20a32045704ed4f0a71a89d3262.jpg\",\n  \"41766.jpg\": \"Insecta/Prochoerodes lineola/0d6fa81f548b9f579b85e9da0a8a1927.jpg\",\n  \"417685.jpg\": \"Aves/Melospiza melodia/c70590b3d6e68109cc09141ce4006fce.jpg\",\n  \"41772.jpg\": \"Insecta/Prochoerodes lineola/549d2e2366c7e44216ea14800d432805.jpg\",\n  \"41773.jpg\": \"Insecta/Prochoerodes lineola/43dd8f12f2713849c98e74252e4739dd.jpg\",\n  \"41776.jpg\": \"Insecta/Prochoerodes lineola/23293daf00a75553fc8432d01fef637c.jpg\",\n  \"41777.jpg\": \"Insecta/Prochoerodes lineola/c2ea6bae9b51f1ee0366c6a692d55b0d.jpg\",\n  \"417817.jpg\": \"Aves/Melospiza melodia/c6e3ae1b15ce7b77033384f4423358f2.jpg\",\n  \"41782.jpg\": \"Insecta/Prochoerodes lineola/35a84b63e72e7135b6da8a5318eb7c05.jpg\",\n  \"41784.jpg\": \"Insecta/Prochoerodes lineola/e868c0189ae90805dca6c0f2a9d72e02.jpg\",\n  \"41785.jpg\": \"Insecta/Prochoerodes lineola/b71d9d92c8f7a4fcebc9778fb7a993a1.jpg\",\n  \"41786.jpg\": \"Insecta/Prochoerodes lineola/3abd06d0a9387cb85ef5abb6e470383e.jpg\",\n  \"41787.jpg\": \"Insecta/Prochoerodes lineola/034b114db15ec16407d5c13e0170920b.jpg\",\n  \"4179.jpg\": \"Aves/Setophaga petechia/bb1bfbf89c2099c833e6fd4370031f21.jpg\",\n  \"41790.jpg\": \"Insecta/Prochoerodes lineola/d3777ed8f8bf1daa9937436ffb0df1a0.jpg\",\n  \"417962.jpg\": \"Aves/Melospiza melodia/47ba23e9ab43210cd65e068d948e1af0.jpg\",\n  \"41798.jpg\": \"Insecta/Prochoerodes lineola/26d7b0df35308c63074a0d4312360e53.jpg\",\n  \"418085.jpg\": \"Aves/Melospiza melodia/e0ae37bc14b4d443c04ad9caab8a788e.jpg\",\n  \"418246.jpg\": \"Aves/Melospiza melodia/37d53d2bffe66dd3f51e7011d039d9ee.jpg\",\n  \"418266.jpg\": \"Insecta/Macroglossum stellatarum/dcdb9fc2dc6cf0a89be32a7213a480d5.jpg\",\n  \"418270.jpg\": \"Insecta/Macroglossum stellatarum/c79d5a74d41fb903daa33a6967d9e40e.jpg\",\n  \"418274.jpg\": \"Insecta/Macroglossum stellatarum/7424e6c442fce6338d8076343425bcb2.jpg\",\n  \"418280.jpg\": \"Insecta/Macroglossum stellatarum/3e53ab016046bacb787b52691f170aac.jpg\",\n  \"418281.jpg\": \"Insecta/Macroglossum stellatarum/44695a4c07113acb8ab8c576a279b3b4.jpg\",\n  \"418282.jpg\": \"Insecta/Macroglossum stellatarum/66f9f401c010ce715a757d989fc2f8b8.jpg\",\n  \"418290.jpg\": \"Insecta/Macroglossum stellatarum/f69013d9a0007be76f82f36d6c2708ee.jpg\",\n  \"418291.jpg\": \"Insecta/Macroglossum stellatarum/553b997ffc95a24c13eded09fe74de21.jpg\",\n  \"41834.jpg\": \"Insecta/Hylephila phyleus/d7788ae625bb0575ebf438671cedb959.jpg\",\n  \"418350.jpg\": \"Insecta/Gomphus vastus/03cd5c791c8e21ff77cc814ca0c28fd9.jpg\",\n  \"418410.jpg\": \"Aves/Rupornis magnirostris/fda81867838a45d867fb86c16f723fa6.jpg\",\n  \"418438.jpg\": \"Aves/Rupornis magnirostris/ed7646a658976f263d1262f2d757f666.jpg\",\n  \"418441.jpg\": \"Aves/Rupornis magnirostris/30c7289d8d0a6d10cfd8082ce81026af.jpg\",\n  \"418490.jpg\": \"Insecta/Aeshna umbrosa/83cc6fd2db2058c875212bb2d92aae82.jpg\",\n  \"418491.jpg\": \"Insecta/Aeshna umbrosa/ba256e5976a5539712c7d21aec050393.jpg\",\n  \"418499.jpg\": \"Insecta/Aeshna umbrosa/25c218a1f3aa3da9fa59ab69c607f8a2.jpg\",\n  \"418503.jpg\": \"Insecta/Aeshna umbrosa/d5532725859b0724ecd1f6ece28500de.jpg\",\n  \"418508.jpg\": \"Insecta/Aeshna umbrosa/07a3f63da2906d0072a003e7adcc409a.jpg\",\n  \"418511.jpg\": \"Insecta/Aeshna umbrosa/d839aa98a7521fe4d350ea3254020782.jpg\",\n  \"41853.jpg\": \"Insecta/Hylephila phyleus/4e16096e32d76a415a4af50019a083f1.jpg\",\n  \"418549.jpg\": \"Aves/Rhipidura fuliginosa/5df1621cc586da5d7528bf717bf343a0.jpg\",\n  \"418557.jpg\": \"Aves/Rhipidura fuliginosa/7e71728412d94219344e825efa499091.jpg\",\n  \"418559.jpg\": \"Aves/Rhipidura fuliginosa/399f061b66dc8726f8c4f5523340ea3c.jpg\",\n  \"418563.jpg\": \"Aves/Rhipidura fuliginosa/6f7ccf6fbb3003d1c5a93cadddc5f16e.jpg\",\n  \"418564.jpg\": \"Aves/Rhipidura fuliginosa/48c28ab6ef7f8f2e34a6bf1a2db6d704.jpg\",\n  \"418571.jpg\": \"Aves/Rhipidura fuliginosa/82f65058598b092a1c7f42ec5f661db7.jpg\",\n  \"418572.jpg\": \"Amphibia/Pseudacris crucifer/9effaa1e8faab3d660e194eae5937ffd.jpg\",\n  \"418575.jpg\": \"Amphibia/Pseudacris crucifer/ab490ee63024c7bb8305f915bf597301.jpg\",\n  \"418583.jpg\": \"Amphibia/Pseudacris crucifer/2f981b5d408df3270078bde9fd92a539.jpg\",\n  \"418590.jpg\": \"Amphibia/Pseudacris crucifer/af0453e52639f5b5f30e8a65562e4376.jpg\",\n  \"418597.jpg\": \"Amphibia/Pseudacris crucifer/683b2895837241e24f650a240ee44bea.jpg\",\n  \"418607.jpg\": \"Amphibia/Pseudacris crucifer/a0b1f9ca177887b41e12723ec5ace5d6.jpg\",\n  \"418628.jpg\": \"Amphibia/Pseudacris crucifer/5402639498349bbafa306bcb33706728.jpg\",\n  \"418636.jpg\": \"Amphibia/Pseudacris crucifer/d13f68b229855b4b3e1ac5337d9133ad.jpg\",\n  \"418678.jpg\": \"Aves/Trogon caligatus/0cbe58164a26f6435d000e97ab11852a.jpg\",\n  \"41868.jpg\": \"Insecta/Hylephila phyleus/f198c65105024cbc44d96efb428afdc5.jpg\",\n  \"418683.jpg\": \"Aves/Trogon caligatus/1f3f1e2ee200ae29e1e3d42e70c51996.jpg\",\n  \"418685.jpg\": \"Aves/Trogon caligatus/a10b029328bad7b07b03c6a078bc8f51.jpg\",\n  \"418686.jpg\": \"Aves/Trogon caligatus/e0f4b871c9eeff5ad6104d6868697750.jpg\",\n  \"418701.jpg\": \"Reptilia/Sonora semiannulata/0299ff5a51516058b51d2201e71e2b82.jpg\",\n  \"418702.jpg\": \"Reptilia/Sonora semiannulata/a6d164295b527d26cd6b60aefaace596.jpg\",\n  \"418706.jpg\": \"Reptilia/Sonora semiannulata/e39580b84c68177b8b0193a2eca072ff.jpg\",\n  \"418712.jpg\": \"Reptilia/Sonora semiannulata/9d506671a960b8ca8cd5d84fbbc2299f.jpg\",\n  \"418713.jpg\": \"Reptilia/Sonora semiannulata/cf8d0b454e955fc7ef3fa675d4544efe.jpg\",\n  \"418714.jpg\": \"Reptilia/Sonora semiannulata/0fea79382a1dac31f5b4c19151484264.jpg\",\n  \"418715.jpg\": \"Reptilia/Sonora semiannulata/3818e39c4328f66b7d2dc94755cb7e6a.jpg\",\n  \"418716.jpg\": \"Reptilia/Sonora semiannulata/86b56429d5d4dc6b77670c5f085f6b4a.jpg\",\n  \"418718.jpg\": \"Reptilia/Sonora semiannulata/bf3649d86479684e1afa08d188af967a.jpg\",\n  \"418722.jpg\": \"Insecta/Anax longipes/3258a7c87d06cdfd15c6622d7a5a256f.jpg\",\n  \"418729.jpg\": \"Insecta/Anax longipes/32d53aade25ac92c5c8bfe5a3ac48066.jpg\",\n  \"418732.jpg\": \"Insecta/Anax longipes/924c1078e7a71403d432c8eb0a5cb0fb.jpg\",\n  \"418839.jpg\": \"Aves/Cyanocitta cristata/54892eb753062fdbefc022a73d023b70.jpg\",\n  \"418994.jpg\": \"Aves/Cyanocitta cristata/74dbe2725acafd0a07aa024c91a4b756.jpg\",\n  \"41901.jpg\": \"Insecta/Hylephila phyleus/3a7f8d93ca28562565255a2c2a5b61e6.jpg\",\n  \"419109.jpg\": \"Aves/Cyanocitta cristata/3341a6d6c6d39252fca0f97c71419583.jpg\",\n  \"41921.jpg\": \"Insecta/Hylephila phyleus/a82d5fb488582a9ff1532c35e57c9f95.jpg\",\n  \"419245.jpg\": \"Aves/Saxicola rubicola/557d3427deac350b17476e77c4e78a7d.jpg\",\n  \"419246.jpg\": \"Aves/Saxicola rubicola/744681226ebb916bacaa27baa22a4acb.jpg\",\n  \"419248.jpg\": \"Aves/Saxicola rubicola/d3281502ed76103fd343522109d0e202.jpg\",\n  \"419251.jpg\": \"Aves/Saxicola rubicola/df18f2c4f9b9356b78bc7e9d4aaccb7b.jpg\",\n  \"419255.jpg\": \"Aves/Saxicola rubicola/b15a61e87d6b309d42f224afbe2c3071.jpg\",\n  \"419256.jpg\": \"Aves/Saxicola rubicola/e3a45af21cdb28d37b52798a06b67af8.jpg\",\n  \"419264.jpg\": \"Aves/Saxicola rubicola/60c688cd9599d5b7b7a5b5873c9f8a70.jpg\",\n  \"419268.jpg\": \"Reptilia/Crotaphytus collaris/2342e7a6b7995fc1e8756c7178cd0502.jpg\",\n  \"419287.jpg\": \"Reptilia/Crotaphytus collaris/a4de3b274573a8df89fe9abfa75d552e.jpg\",\n  \"419309.jpg\": \"Reptilia/Crotaphytus collaris/b113b6efee4669bbc3518527bc2e55c7.jpg\",\n  \"419324.jpg\": \"Reptilia/Crotaphytus collaris/ffd250ae32e9108d9ed0eab1c3774a94.jpg\",\n  \"419330.jpg\": \"Reptilia/Crotaphytus collaris/34e28cfb36bc42f47bb5dfbc0dc2e60f.jpg\",\n  \"419336.jpg\": \"Aves/Plectrophenax nivalis/8957a05345831ff42973c5cc56546e47.jpg\",\n  \"419345.jpg\": \"Aves/Plectrophenax nivalis/c8cf62c0678033d5fed135448514d75d.jpg\",\n  \"419347.jpg\": \"Aves/Plectrophenax nivalis/7d985c46294479ad01f43c45c75d8a76.jpg\",\n  \"419354.jpg\": \"Aves/Plectrophenax nivalis/b39eab72a7a05b202fae224040f26aa0.jpg\",\n  \"419364.jpg\": \"Aves/Plectrophenax nivalis/b10120d4a1f3f40ef8ed1735f2b00fc6.jpg\",\n  \"419365.jpg\": \"Aves/Plectrophenax nivalis/d0fbb8d5be8d1ae86c58bcc4087a71d8.jpg\",\n  \"419367.jpg\": \"Aves/Plectrophenax nivalis/e2efc18f20e8fb9b4aa153d981fb7cae.jpg\",\n  \"419373.jpg\": \"Insecta/Ischnura elegans/2037d11bdfa21e0b94c997e6b431a263.jpg\",\n  \"419374.jpg\": \"Insecta/Ischnura elegans/a14741fd29057d3015f8890b989e4336.jpg\",\n  \"419375.jpg\": \"Insecta/Ischnura elegans/dd735c3c3b2ee85e102744450d4a2ecc.jpg\",\n  \"419382.jpg\": \"Insecta/Ischnura elegans/dfddfc66983107ca75b2dca91239cc65.jpg\",\n  \"419386.jpg\": \"Insecta/Ischnura elegans/f26f7b8deeb1bc3ff35605c17b6ae82b.jpg\",\n  \"419390.jpg\": \"Insecta/Ischnura elegans/8ea8a54ca3b0532e5e3fd0ca97dedeec.jpg\",\n  \"419394.jpg\": \"Insecta/Ischnura elegans/781fd5c20f5ac52bb712360cf2a86321.jpg\",\n  \"419399.jpg\": \"Insecta/Ischnura elegans/25683c081bbaf3f95957463887cdb097.jpg\",\n  \"4194.jpg\": \"Aves/Setophaga petechia/8adbd78e4d0f20ffd24dcca0b4cd4b7e.jpg\",\n  \"41940.jpg\": \"Insecta/Hylephila phyleus/7d27ecf7fcb070d4b33cdd8b9bbc7788.jpg\",\n  \"419408.jpg\": \"Insecta/Chauliognathus marginatus/09670d700c53a2117ec577684c317d6f.jpg\",\n  \"419409.jpg\": \"Insecta/Chauliognathus marginatus/361b1db16078951ffa72df9471d981cf.jpg\",\n  \"419410.jpg\": \"Insecta/Chauliognathus marginatus/9ea259b7af4dbaff0165d5705ae38264.jpg\",\n  \"419412.jpg\": \"Insecta/Chauliognathus marginatus/970f97a4dae408908b6a9a617a5f0bfd.jpg\",\n  \"419413.jpg\": \"Insecta/Chauliognathus marginatus/6e2dc85a628eb43c06d18396e1ec9a40.jpg\",\n  \"419417.jpg\": \"Insecta/Chauliognathus marginatus/7b2b697e8245d996effdc7af89ed514b.jpg\",\n  \"419433.jpg\": \"Aves/Crotophaga sulcirostris/90516a58eeb04869d7ab722c0390b413.jpg\",\n  \"419472.jpg\": \"Aves/Crotophaga sulcirostris/137ca1985b017c2fabd8ec4262d521d1.jpg\",\n  \"419484.jpg\": \"Aves/Crotophaga sulcirostris/9c9250b86b29fbddf7f8a68844e8ea5c.jpg\",\n  \"419501.jpg\": \"Aves/Crotophaga sulcirostris/04e36e17763f418194b8d702f5ac5454.jpg\",\n  \"419539.jpg\": \"Aves/Crotophaga sulcirostris/945e1398ebe5153b05de2012b271d5a1.jpg\",\n  \"419547.jpg\": \"Aves/Crotophaga sulcirostris/969c3b830b189b724be92a4ec845cbbd.jpg\",\n  \"419605.jpg\": \"Insecta/Nezara viridula/a26fc8457124b7ea5cab327cd852830e.jpg\",\n  \"419622.jpg\": \"Insecta/Nezara viridula/2c4820373ce8d6f81cf3454f90cb0ed4.jpg\",\n  \"419623.jpg\": \"Insecta/Nezara viridula/36e6a3b64a158b324d58cd716012f137.jpg\",\n  \"419624.jpg\": \"Insecta/Nezara viridula/743871e38f025ffa906d8b8833099666.jpg\",\n  \"419625.jpg\": \"Insecta/Nezara viridula/5b9e9f0ca3f8a7ddcf7850f71d38cced.jpg\",\n  \"419627.jpg\": \"Insecta/Nezara viridula/344f4ac5619dd4c078c9be5cb2167b66.jpg\",\n  \"419628.jpg\": \"Insecta/Nezara viridula/6a5a0b94681b240f80a21a72bb3148cb.jpg\",\n  \"41964.jpg\": \"Insecta/Hylephila phyleus/d6739500b44789d8a0bdb71bb2a7f5f8.jpg\",\n  \"41968.jpg\": \"Insecta/Hylephila phyleus/6753bd6fe2e3fb00970d02146ed88f13.jpg\",\n  \"419697.jpg\": \"Insecta/Microcrambus elegans/24f70024dadf5885bd2a47207b5e57c3.jpg\",\n  \"419706.jpg\": \"Insecta/Microcrambus elegans/f78d06e3ce31d9bd68fc729750ec52d8.jpg\",\n  \"419714.jpg\": \"Insecta/Microcrambus elegans/10be00e037bf35ed38238b671c5593d7.jpg\",\n  \"419720.jpg\": \"Insecta/Microcrambus elegans/124d2254ec1724daf1066e528ad6984e.jpg\",\n  \"419721.jpg\": \"Insecta/Microcrambus elegans/793b244731d0a43df29fecedff367bed.jpg\",\n  \"419723.jpg\": \"Insecta/Microcrambus elegans/0e86c3525f6bc774f7b1e7ce62a78448.jpg\",\n  \"419728.jpg\": \"Insecta/Microcrambus elegans/882c6e40d3721e01087f7b53e279e54c.jpg\",\n  \"419734.jpg\": \"Insecta/Microcrambus elegans/38d481978275e54c21c53d2a7223bfe0.jpg\",\n  \"419737.jpg\": \"Aves/Turdus pilaris/a3ee7b265d0076294beabf414893629f.jpg\",\n  \"419738.jpg\": \"Aves/Turdus pilaris/678c246b1b45cd11482a161f32649a7b.jpg\",\n  \"419739.jpg\": \"Aves/Turdus pilaris/8a954944519358170f88e5adfd9a3e06.jpg\",\n  \"419740.jpg\": \"Aves/Turdus pilaris/b56c221a3b7e66320e77dcc932822609.jpg\",\n  \"419741.jpg\": \"Aves/Turdus pilaris/217a902f0fc66972b06c76c7b02d0049.jpg\",\n  \"419747.jpg\": \"Aves/Turdus pilaris/56f73d8d6309de8ba2fdcece48d631ba.jpg\",\n  \"419756.jpg\": \"Aves/Turdus pilaris/259629eb61ba19f59374e19d280075fa.jpg\",\n  \"419757.jpg\": \"Aves/Turdus pilaris/7a90b770bec70833049baa72b4aebb21.jpg\",\n  \"419759.jpg\": \"Aves/Turdus pilaris/bd2e0f13c4cbbb33ef6003eedbb876c0.jpg\",\n  \"419762.jpg\": \"Aves/Turdus pilaris/867182f0c8a159a85468afbd04b05757.jpg\",\n  \"419763.jpg\": \"Aves/Turdus pilaris/b005fbcd160dee2b4f74ce85f3c1156f.jpg\",\n  \"419774.jpg\": \"Arachnida/Frontinella communis/9a4d6973f08b3146e83546e80bbed5c6.jpg\",\n  \"419818.jpg\": \"Insecta/Hypagyrtis unipunctata/49ae32effe667f16318fb2d240c22e48.jpg\",\n  \"419823.jpg\": \"Insecta/Hypagyrtis unipunctata/17527025e98643bffa3c792a51ebe964.jpg\",\n  \"419826.jpg\": \"Mammalia/Odocoileus virginianus clavium/b01a0b84ae1ead130e4ddf9e0004c53e.jpg\",\n  \"419827.jpg\": \"Mammalia/Odocoileus virginianus clavium/0d1b0d8171558f6c2d1eb14d6b051a34.jpg\",\n  \"419876.jpg\": \"Insecta/Lithacodes fasciola/d757c81179517c0fb938faab83f24ce0.jpg\",\n  \"419880.jpg\": \"Insecta/Lithacodes fasciola/4a7ba8931bc5211429ba4ad2fc405f6f.jpg\",\n  \"419895.jpg\": \"Aves/Cygnus atratus/9b674aca48d18c80cd0e22ff02efd959.jpg\",\n  \"419896.jpg\": \"Aves/Cygnus atratus/4313835bcbe6efc55c7e0774d7a99028.jpg\",\n  \"419910.jpg\": \"Aves/Cygnus atratus/73a7e4056eda374d9b40cf3579e4c327.jpg\",\n  \"419947.jpg\": \"Aves/Cygnus atratus/0a11393f4618fd57f9ce8eb4224eef70.jpg\",\n  \"419949.jpg\": \"Reptilia/Salvadora hexalepis/fc739a276cfcf6240cdaa9c7da7a8978.jpg\",\n  \"41995.jpg\": \"Insecta/Hylephila phyleus/f0cbfaf5cce66b3860086d4c33b6ed61.jpg\",\n  \"419959.jpg\": \"Reptilia/Salvadora hexalepis/1e29db57cd5a6d0b46386e5628afb820.jpg\",\n  \"420043.jpg\": \"Aves/Circus cyaneus/01f7d19c4865a822ebfff26e7075496a.jpg\",\n  \"420151.jpg\": \"Aves/Circus cyaneus/43b5354503300f14d7b8c4c1b1a06c97.jpg\",\n  \"420572.jpg\": \"Amphibia/Acris crepitans/1ea2eef86d704062ea07d794060a4e92.jpg\",\n  \"420680.jpg\": \"Aves/Falco novaeseelandiae/92bbde35d0e952e251238942844ec928.jpg\",\n  \"420681.jpg\": \"Aves/Falco novaeseelandiae/5208c5b201dfccfdf424254198cc7ffc.jpg\",\n  \"4207.jpg\": \"Aves/Setophaga petechia/b19abd18733524d56d115b4a1d4ebb2a.jpg\",\n  \"420700.jpg\": \"Aves/Sitta carolinensis/4fa8bb8e337f6dbd5fc76320981a5aee.jpg\",\n  \"420733.jpg\": \"Aves/Sitta carolinensis/6a3dbeffb0e84080f78b60fb428ee70f.jpg\",\n  \"420818.jpg\": \"Aves/Sitta carolinensis/e4486feb1fc819fbd48f8f2643fba4a5.jpg\",\n  \"421011.jpg\": \"Aves/Sitta carolinensis/aec710ae86116ae89957a7d1a5b6f237.jpg\",\n  \"421033.jpg\": \"Aves/Sitta carolinensis/0f56e5ef716a0fa9c05d14c8c08fa6f3.jpg\",\n  \"42105.jpg\": \"Insecta/Hylephila phyleus/0bf18af3c54d85dc81c458dff8f47635.jpg\",\n  \"42119.jpg\": \"Insecta/Hylephila phyleus/1ab9a76bcfceb24d33bf2299df81d305.jpg\",\n  \"421420.jpg\": \"Aves/Myiodynastes luteiventris/3129ad8507aa17064e5644fd7760586e.jpg\",\n  \"421426.jpg\": \"Aves/Myiodynastes luteiventris/adeff10011a75334f0c1d5587d361962.jpg\",\n  \"421465.jpg\": \"Aves/Pheucticus melanocephalus/000e6652f24c0b4fb1b7c8c97333c0e4.jpg\",\n  \"421466.jpg\": \"Aves/Pheucticus melanocephalus/e3141a92e78bc8c149058c5ae90141a6.jpg\",\n  \"421488.jpg\": \"Aves/Pheucticus melanocephalus/b466328874d8730a78c20b0b5e1801dc.jpg\",\n  \"42150.jpg\": \"Insecta/Hylephila phyleus/b544a94fdae99423724e0f009509406d.jpg\",\n  \"421503.jpg\": \"Aves/Pheucticus melanocephalus/54e571a536e371fe808f4d91c0e75185.jpg\",\n  \"42151.jpg\": \"Insecta/Hylephila phyleus/4986da9831ba81d6579491548b2f19bd.jpg\",\n  \"421513.jpg\": \"Aves/Pheucticus melanocephalus/e2338998be2442c1aef79e195574490c.jpg\",\n  \"421534.jpg\": \"Aves/Pheucticus melanocephalus/eef0852e44e73094c9888deaf5c20d6f.jpg\",\n  \"421555.jpg\": \"Aves/Pheucticus melanocephalus/3ed8e1e36ffa01ec9206c2890aacbe38.jpg\",\n  \"421567.jpg\": \"Aves/Pheucticus melanocephalus/39f7910baa9328ff24f56c68ffe5d655.jpg\",\n  \"421572.jpg\": \"Aves/Pheucticus melanocephalus/107659660a971b36b6f012be09cc7825.jpg\",\n  \"42162.jpg\": \"Insecta/Hylephila phyleus/021a13e4a4e60b26303654621a3dcacb.jpg\",\n  \"42188.jpg\": \"Insecta/Hylephila phyleus/e6ed6a3ec44b9740c75e6834637a638a.jpg\",\n  \"4221.jpg\": \"Aves/Setophaga petechia/a4d5bd13832628164bfbc476905317aa.jpg\",\n  \"422136.jpg\": \"Aves/Buteo jamaicensis/5648e9760a755f9efaed28c047bc6685.jpg\",\n  \"422440.jpg\": \"Aves/Buteo jamaicensis/3c918a3b3ce3c5aca3a072111dd206b7.jpg\",\n  \"42250.jpg\": \"Insecta/Hylephila phyleus/048ffdf28fd9b948dfa6672738f69eaa.jpg\",\n  \"422656.jpg\": \"Aves/Buteo jamaicensis/17959e91aa19cdc10e20776fc726a341.jpg\",\n  \"422707.jpg\": \"Aves/Buteo jamaicensis/5499d1a57a47aabb87333b268535b5fe.jpg\",\n  \"422984.jpg\": \"Aves/Buteo jamaicensis/6bcbc322ec2789ca7cb67df0677735ea.jpg\",\n  \"423014.jpg\": \"Aves/Buteo jamaicensis/86c9d8107554e47d2fe937016b9b6aff.jpg\",\n  \"42304.jpg\": \"Insecta/Hylephila phyleus/9663fc1fb0f92785377580ee0e7ac657.jpg\",\n  \"423350.jpg\": \"Actinopterygii/Lepomis macrochirus/8c8a9d9a3cb444822b1ff0b12c3097ba.jpg\",\n  \"423354.jpg\": \"Actinopterygii/Lepomis macrochirus/dff82dc6731ed060078b17dda8d106f7.jpg\",\n  \"423376.jpg\": \"Actinopterygii/Lepomis macrochirus/b84d6d19bd88e793bfb6280bc4dbb5bd.jpg\",\n  \"42340.jpg\": \"Insecta/Hylephila phyleus/51b94f89770ae91aee292e3096fc017f.jpg\",\n  \"42341.jpg\": \"Insecta/Hylephila phyleus/196ac682c621bb7b23907bd8e1132632.jpg\",\n  \"423411.jpg\": \"Reptilia/Lampropeltis californiae/aba28f13dff186cda4b358fe23d56a8c.jpg\",\n  \"423434.jpg\": \"Reptilia/Lampropeltis californiae/702b9653727e5981ac8b04502f6fb7e0.jpg\",\n  \"423464.jpg\": \"Reptilia/Lampropeltis californiae/22fab3250475b666cc60c6325404db4f.jpg\",\n  \"423469.jpg\": \"Reptilia/Lampropeltis californiae/7e35683a98392adcac7ef82cedb4c335.jpg\",\n  \"423480.jpg\": \"Reptilia/Lampropeltis californiae/77eedae389088391152d34efa56ce0da.jpg\",\n  \"423482.jpg\": \"Reptilia/Lampropeltis californiae/f09c530d5d91bbaacd1a7c52bcf900d9.jpg\",\n  \"423497.jpg\": \"Reptilia/Lampropeltis californiae/0ead4ca6725094d44f593163437c0d73.jpg\",\n  \"423502.jpg\": \"Reptilia/Lampropeltis californiae/1641fd686649475979e3edbe3a4304e8.jpg\",\n  \"423514.jpg\": \"Insecta/Typocerus velutinus/9992560b0e1173ba5afd7eff1f9259c4.jpg\",\n  \"423515.jpg\": \"Insecta/Typocerus velutinus/0881ad582891ab4e6d85a8516ec96975.jpg\",\n  \"423534.jpg\": \"Mollusca/Lottia gigantea/d6964e984920b5a86c2f4a0f287efa54.jpg\",\n  \"42354.jpg\": \"Insecta/Hylephila phyleus/9bca07e445a70f2a584b4d2f10c088b7.jpg\",\n  \"423540.jpg\": \"Mollusca/Lottia gigantea/53f0211837d48d5ffcfbbf3e34d1248d.jpg\",\n  \"423543.jpg\": \"Mollusca/Lottia gigantea/89cb320f103c392483bb4eb24ba46423.jpg\",\n  \"423544.jpg\": \"Mollusca/Lottia gigantea/41b4b55816059d3c5206aeb2a1772bd5.jpg\",\n  \"423555.jpg\": \"Mollusca/Lottia gigantea/313d41e6241e6cb56ed30122831d189b.jpg\",\n  \"423556.jpg\": \"Mollusca/Lottia gigantea/e04679bcd76d93a3945a6bd85fead002.jpg\",\n  \"423563.jpg\": \"Mollusca/Lottia gigantea/4afc8bc95aa180f1ed3139c5fcef4b13.jpg\",\n  \"42357.jpg\": \"Insecta/Hylephila phyleus/187e89fab8bb4944c1dc0b6efd986867.jpg\",\n  \"423808.jpg\": \"Aves/Corvus corax/2473c81007fa15a059679e1db2c47b31.jpg\",\n  \"423813.jpg\": \"Aves/Corvus corax/be5d73b3c2d537a3c4b31906efafc467.jpg\",\n  \"42389.jpg\": \"Insecta/Hylephila phyleus/5eef11870b51e14dcdbb1af7cf5bc823.jpg\",\n  \"4240.jpg\": \"Aves/Setophaga petechia/b4d16d868d53796fe8495a90042f783f.jpg\",\n  \"424017.jpg\": \"Aves/Corvus corax/e1b7dc8f31ee1013ebd5d3e490e13dcc.jpg\",\n  \"424022.jpg\": \"Aves/Corvus corax/be327a3691b3e99af41d24c674ddfdcb.jpg\",\n  \"424109.jpg\": \"Aves/Corvus corax/e7a93040329de389d43412abb02f329f.jpg\",\n  \"42411.jpg\": \"Insecta/Ectropis crepuscularia/bf54621699312aa26ed46b7bd89bf3de.jpg\",\n  \"424119.jpg\": \"Aves/Corvus corax/2b0fe8d1809b22ea559be29f8f9ef66a.jpg\",\n  \"42414.jpg\": \"Insecta/Ectropis crepuscularia/4ba56e9a1f451c415147c815a97b98c0.jpg\",\n  \"424182.jpg\": \"Aves/Accipiter nisus/5ce5b793ee29da3c322505a6bd7118c8.jpg\",\n  \"424183.jpg\": \"Aves/Accipiter nisus/44d5ebe30f06bee16189246a7bbf9d1e.jpg\",\n  \"424184.jpg\": \"Aves/Accipiter nisus/706ec9b76db0d34117bcea0cd594cece.jpg\",\n  \"424185.jpg\": \"Aves/Accipiter nisus/6046566be84c240912bf3ac70e9a439c.jpg\",\n  \"424192.jpg\": \"Aves/Accipiter nisus/767859093714a52aed138ef628e6d987.jpg\",\n  \"424195.jpg\": \"Aves/Accipiter nisus/072c61efba9ec28520492b6f99e9c901.jpg\",\n  \"424200.jpg\": \"Aves/Accipiter nisus/4e42eae40fc78e5207242eab84ebee0a.jpg\",\n  \"42421.jpg\": \"Insecta/Ectropis crepuscularia/30641606f8325889d1869bb14b8307c6.jpg\",\n  \"42422.jpg\": \"Insecta/Ectropis crepuscularia/a44ae6109e99379be7aa918c5f77a59e.jpg\",\n  \"424240.jpg\": \"Aves/Chen caerulescens/9efefe8a1a90ed4c3317ddd700414b75.jpg\",\n  \"424245.jpg\": \"Aves/Chen caerulescens/1f0a2a40753faffeab220ad03b804fc1.jpg\",\n  \"424248.jpg\": \"Aves/Chen caerulescens/4116fe5b5fbd874b8065b3b70005a6c0.jpg\",\n  \"42426.jpg\": \"Insecta/Ectropis crepuscularia/31d14870f40d7ef8a5f511eca2fa480a.jpg\",\n  \"42428.jpg\": \"Insecta/Ectropis crepuscularia/4465aefc75e5c6749f25223d80650053.jpg\",\n  \"42430.jpg\": \"Aves/Euphagus cyanocephalus/4121b8fa08b4ecc92becae148057d941.jpg\",\n  \"424335.jpg\": \"Insecta/Acanthocephala declivis/2e39760e56e443d7e1bc627c7c3d9aa4.jpg\",\n  \"424339.jpg\": \"Insecta/Acanthocephala declivis/e4a0456050a6c9d38a8c7b372c6e0574.jpg\",\n  \"424340.jpg\": \"Insecta/Acanthocephala declivis/0fdf5169aad57fee11798a0bab48f22e.jpg\",\n  \"424346.jpg\": \"Insecta/Acanthocephala declivis/011ba37f4bbcecf7aaeaac24136b1b1a.jpg\",\n  \"424347.jpg\": \"Insecta/Acanthocephala declivis/3563764f45ba0ceec6cadb6d60fcf45f.jpg\",\n  \"424348.jpg\": \"Insecta/Acanthocephala declivis/326ae439e88c3da65bfff4839164cd76.jpg\",\n  \"424349.jpg\": \"Insecta/Acanthocephala declivis/d8fcd4ec6cc114421b5acdba62a0d67f.jpg\",\n  \"424351.jpg\": \"Insecta/Acanthocephala declivis/b1f2f1c9c66dc6533ec409424f5cccfe.jpg\",\n  \"424357.jpg\": \"Insecta/Panopoda rufimargo/9eef275d9171e70ab59c0be39e83cf22.jpg\",\n  \"424371.jpg\": \"Insecta/Junonia genoveva/8f32db14460b8e3708ec6e956a5297fc.jpg\",\n  \"424373.jpg\": \"Insecta/Junonia genoveva/76da475cde60a7ccc962db11c1442837.jpg\",\n  \"42441.jpg\": \"Aves/Euphagus cyanocephalus/19687a852b9ba4c40589f32cf884a205.jpg\",\n  \"424425.jpg\": \"Aves/Cyanocorax yncas/ae74601568c0ab5f132d2205fade8de3.jpg\",\n  \"424429.jpg\": \"Aves/Cyanocorax yncas/48fbb4fbef9f7d818417ae88c66e0e23.jpg\",\n  \"424435.jpg\": \"Aves/Cyanocorax yncas/a4f23f12c0c0717e05c182e6269963e8.jpg\",\n  \"424449.jpg\": \"Aves/Cyanocorax yncas/77d05ecbb2425b14e88cee6a1e4b728a.jpg\",\n  \"424460.jpg\": \"Aves/Branta hutchinsii/01270d7cdfe2eb7acd4ec2d3c3d68068.jpg\",\n  \"424462.jpg\": \"Aves/Branta hutchinsii/9787b0f32c6038916ae53ea093f924b4.jpg\",\n  \"424467.jpg\": \"Aves/Branta hutchinsii/bb54e11149e2d2968df74722507f54de.jpg\",\n  \"42449.jpg\": \"Aves/Euphagus cyanocephalus/1877bbfaf134bf89962289652b8b9afc.jpg\",\n  \"424575.jpg\": \"Reptilia/Thamnophis hammondii/359182a38870f88a67108a9953c81750.jpg\",\n  \"424577.jpg\": \"Reptilia/Thamnophis hammondii/f01530cc4f25a935cfa6bc10c78547e1.jpg\",\n  \"424578.jpg\": \"Reptilia/Thamnophis hammondii/c3f4485c0112221e2b79134473c6ceae.jpg\",\n  \"424579.jpg\": \"Reptilia/Thamnophis hammondii/80f784439b523c56202e8be38a99e50d.jpg\",\n  \"424581.jpg\": \"Reptilia/Thamnophis hammondii/d8221da8d4a062e09c59f5ab1923608e.jpg\",\n  \"424584.jpg\": \"Reptilia/Thamnophis hammondii/aa6feb0e6eabe1be4c2f65ff172b9027.jpg\",\n  \"424585.jpg\": \"Reptilia/Thamnophis hammondii/c5612baceac72d8de44a7859f6bd74ae.jpg\",\n  \"424586.jpg\": \"Reptilia/Thamnophis hammondii/57dbbade78611cc6de4c5545e70164da.jpg\",\n  \"424589.jpg\": \"Reptilia/Thamnophis hammondii/61d268fbfdeaaeaae5c6267ee584bbb5.jpg\",\n  \"424600.jpg\": \"Insecta/Polygonia c-album/842ff7675271aef6b33f9a83d838c260.jpg\",\n  \"424601.jpg\": \"Insecta/Polygonia c-album/983b19868fec4d804699208ab4c34d7f.jpg\",\n  \"424604.jpg\": \"Insecta/Polygonia c-album/1098865fd0a7083a365c7efbce4d06ce.jpg\",\n  \"424606.jpg\": \"Insecta/Polygonia c-album/f54b8a3ac11d2219bfc75c3814960f21.jpg\",\n  \"424607.jpg\": \"Insecta/Polygonia c-album/8bdcaa1ae2fabce67f86dc750d8f5c81.jpg\",\n  \"424609.jpg\": \"Insecta/Polygonia c-album/a87cf14bddbeaddd91098e88ab95cda6.jpg\",\n  \"424615.jpg\": \"Insecta/Polygonia c-album/993166a0d2e35fffa2d5cbb5b4001ce7.jpg\",\n  \"424620.jpg\": \"Insecta/Polygonia c-album/ff68b8f061d9e44a3d925d4af9705e4b.jpg\",\n  \"424625.jpg\": \"Insecta/Polygonia c-album/55e285751fde1b5d5ccd350e7f7e8908.jpg\",\n  \"4247.jpg\": \"Aves/Setophaga petechia/298c7ca09bcad7cced6db2ffa734efc7.jpg\",\n  \"42470.jpg\": \"Aves/Euphagus cyanocephalus/10073edd0641bf59b4b520ad12cb4292.jpg\",\n  \"424739.jpg\": \"Aves/Pipilo maculatus/04848bb294db993593d772b6c368ccd6.jpg\",\n  \"424752.jpg\": \"Aves/Pipilo maculatus/b2144a019d292f8e7bd5421caf157750.jpg\",\n  \"42478.jpg\": \"Aves/Euphagus cyanocephalus/55bc839491747577a29f27a4cc82cb0a.jpg\",\n  \"424798.jpg\": \"Aves/Pipilo maculatus/6d191d44c5ee9b34a3c4b4a3ef99d9c8.jpg\",\n  \"4248.jpg\": \"Aves/Setophaga petechia/3fdb65fe5be0820afdcaf08b0106b96c.jpg\",\n  \"424809.jpg\": \"Aves/Pipilo maculatus/11ea7deb5c62b628d8b95a0fa52c1484.jpg\",\n  \"424844.jpg\": \"Aves/Pipilo maculatus/a9482b3fd8fdea8c6cc509cf6244c181.jpg\",\n  \"424851.jpg\": \"Aves/Pipilo maculatus/72c17d6f935e0ae663ac550fc5b13917.jpg\",\n  \"424871.jpg\": \"Aves/Pipilo maculatus/daa78d709edd02a2b37bf7805f2195ec.jpg\",\n  \"42489.jpg\": \"Aves/Euphagus cyanocephalus/5a3d517cb403ac48f82f197309381198.jpg\",\n  \"424927.jpg\": \"Reptilia/Uta stansburiana/9a39819a2e6e49a50de3a7e7c1b65e14.jpg\",\n  \"4250.jpg\": \"Aves/Setophaga petechia/7aa7f50e4c75978a3cd3f6c0b052b65b.jpg\",\n  \"42502.jpg\": \"Aves/Euphagus cyanocephalus/ca6323263b0cf1d5dedb318b8ea09406.jpg\",\n  \"42506.jpg\": \"Aves/Euphagus cyanocephalus/c66fb1313253953898cde4da80d1f623.jpg\",\n  \"425106.jpg\": \"Reptilia/Uta stansburiana/a711856635fe5c80adf1e4821c8aaed1.jpg\",\n  \"425147.jpg\": \"Reptilia/Uta stansburiana/a4e42752e2c35b23c7f474d07b6640e7.jpg\",\n  \"425176.jpg\": \"Insecta/Hymenia perspectalis/12414cbf9ffe646ee8013be86cbc5ac7.jpg\",\n  \"425178.jpg\": \"Insecta/Hymenia perspectalis/75561be114e77d2d7b539a54cc465254.jpg\",\n  \"425179.jpg\": \"Insecta/Hymenia perspectalis/3de21024d19036313059f9380f29c1f1.jpg\",\n  \"425186.jpg\": \"Insecta/Hymenia perspectalis/e784b1d69d8c74a0d86e0aca51fb31af.jpg\",\n  \"425187.jpg\": \"Insecta/Hymenia perspectalis/fdaa874c8e2d71d32640c5f416bd9c0b.jpg\",\n  \"425188.jpg\": \"Insecta/Hymenia perspectalis/6478916853b3f54e028914c6c1c864e4.jpg\",\n  \"425197.jpg\": \"Insecta/Hymenia perspectalis/51dde5cc71a61f87504c9a661a08bc36.jpg\",\n  \"425198.jpg\": \"Insecta/Hymenia perspectalis/c6c6b2bc4fc2f2617b47dcaf9af8abc2.jpg\",\n  \"425200.jpg\": \"Insecta/Hymenia perspectalis/98ff1240291d731a67ece81dc6932de0.jpg\",\n  \"425212.jpg\": \"Mammalia/Tamias merriami/fef4e69c979e3468442b981814cf82d1.jpg\",\n  \"425213.jpg\": \"Mammalia/Tamias merriami/22590d540452ee63d470cd157520c9bc.jpg\",\n  \"425306.jpg\": \"Aves/Cistothorus palustris/c1411b5607d35df8a66c63813d5c78e7.jpg\",\n  \"425318.jpg\": \"Aves/Cistothorus palustris/c4e3c8027637666fd8f5aad1a9ad2308.jpg\",\n  \"425332.jpg\": \"Aves/Cistothorus palustris/29ead3feab878f74a3a3ff163d8ea54c.jpg\",\n  \"425334.jpg\": \"Aves/Cistothorus palustris/95ffd090fc16ce41f18303d190aca870.jpg\",\n  \"425337.jpg\": \"Aves/Cistothorus palustris/7f6e4ad44934c37908f9535be2b6b080.jpg\",\n  \"425343.jpg\": \"Aves/Cistothorus palustris/fbbe114777a137cbccb5975c7b835178.jpg\",\n  \"425366.jpg\": \"Aves/Cistothorus palustris/7c3068a485a8634a94495027d3721fe8.jpg\",\n  \"425379.jpg\": \"Reptilia/Terrapene ornata ornata/ee391a767247be7d83a6686e58bc8eca.jpg\",\n  \"425381.jpg\": \"Reptilia/Terrapene ornata ornata/66f921bfe3df519cd3a6b6e3923ca647.jpg\",\n  \"42550.jpg\": \"Aves/Euphagus cyanocephalus/88621fa5f645a2e22bfbcb9bcffb5223.jpg\",\n  \"42557.jpg\": \"Aves/Euphagus cyanocephalus/8246b86f7ab92c592e8cd4abd8bea086.jpg\",\n  \"425628.jpg\": \"Aves/Archilochus colubris/466dc406ce89339c50aeb95325e83159.jpg\",\n  \"425632.jpg\": \"Aves/Archilochus colubris/146a5649e50341d86348e277d026c2da.jpg\",\n  \"425688.jpg\": \"Aves/Archilochus colubris/473545c27b74c4da6529ff0e87881e27.jpg\",\n  \"425723.jpg\": \"Aves/Archilochus colubris/a5a0e7221775cb31d167950754904a0c.jpg\",\n  \"425733.jpg\": \"Aves/Archilochus colubris/0af74d0163dd760ef0036e45f8f38167.jpg\",\n  \"425806.jpg\": \"Aves/Archilochus colubris/b962ac76f7f7a90f96067e67512b78fb.jpg\",\n  \"425807.jpg\": \"Aves/Archilochus colubris/f68563249f74bee9080ed3ec92618e42.jpg\",\n  \"42582.jpg\": \"Aves/Euphagus cyanocephalus/295cfd2f45179133453dd9a22935db8a.jpg\",\n  \"425826.jpg\": \"Aves/Setophaga pinus/d60cb5e5a9f20fb30348337064022c2a.jpg\",\n  \"425829.jpg\": \"Aves/Setophaga pinus/8397f3f3af1476447336629ef7d5cf5c.jpg\",\n  \"42583.jpg\": \"Aves/Euphagus cyanocephalus/688790072e58007a4054cbd015d80b82.jpg\",\n  \"425835.jpg\": \"Aves/Setophaga pinus/06a06e03b9607b492632881c41e68d5a.jpg\",\n  \"425847.jpg\": \"Aves/Setophaga pinus/098dd11b2595f2748d86c766f888e833.jpg\",\n  \"42586.jpg\": \"Aves/Euphagus cyanocephalus/6431004a369fc19509776cee538b9b98.jpg\",\n  \"425860.jpg\": \"Aves/Setophaga pinus/d87106f190c98da10c29d70f2d067d6c.jpg\",\n  \"42587.jpg\": \"Aves/Euphagus cyanocephalus/6bcde316b11b0237cf797a7912690d3a.jpg\",\n  \"425879.jpg\": \"Aves/Setophaga pinus/659a8731edb6767b8076a4179d722645.jpg\",\n  \"425890.jpg\": \"Aves/Setophaga pinus/18b71d40480766b0c2a5c56a44848047.jpg\",\n  \"425891.jpg\": \"Aves/Setophaga pinus/a8273ff82fd95c8e80642c652cca8360.jpg\",\n  \"425892.jpg\": \"Amphibia/Dendrobates auratus/980039ec83ae51f45ca2536134c65aca.jpg\",\n  \"425893.jpg\": \"Amphibia/Dendrobates auratus/10626ca8cdc3ab3d645074d263de66fc.jpg\",\n  \"425903.jpg\": \"Actinopterygii/Salmo trutta/a87e872667f7868691c1f8cf775a65a8.jpg\",\n  \"425904.jpg\": \"Actinopterygii/Salmo trutta/1d1a51f44331b05bd86a6d55cf11b435.jpg\",\n  \"425907.jpg\": \"Actinopterygii/Salmo trutta/0e5a8d97ef835d8c9e3d2a50b2325940.jpg\",\n  \"425909.jpg\": \"Actinopterygii/Salmo trutta/65393489381ff8b162dc4e768f60d4c6.jpg\",\n  \"42594.jpg\": \"Aves/Euphagus cyanocephalus/f3f8b8607f6ab4c31c640e21e75d57a5.jpg\",\n  \"42595.jpg\": \"Aves/Euphagus cyanocephalus/5d0bfa085f098ef3ec7b363a0e09f7d1.jpg\",\n  \"42596.jpg\": \"Aves/Euphagus cyanocephalus/f0c4878f51f20c5f85180b3a5e2b5b86.jpg\",\n  \"42616.jpg\": \"Aves/Euphagus cyanocephalus/b119fb687e0004ad9b2e78fd41c2ff1b.jpg\",\n  \"42620.jpg\": \"Aves/Euphagus cyanocephalus/12d0534b228855128f94d5e12b1145cd.jpg\",\n  \"426284.jpg\": \"Aves/Sialia sialis/a2dbce342f088aee8944df654c88b451.jpg\",\n  \"426291.jpg\": \"Aves/Sialia sialis/b76a3a6ec929b0abb0bbe5e64ec53ed5.jpg\",\n  \"426330.jpg\": \"Aves/Sialia sialis/358829276a5cdd482bbf71ea0b37a826.jpg\",\n  \"426361.jpg\": \"Aves/Sialia sialis/298dca1d8a5df55dde24755c91acee04.jpg\",\n  \"42648.jpg\": \"Aves/Euphagus cyanocephalus/1b8a2f87f68ccdda605e3ac627fc9467.jpg\",\n  \"42649.jpg\": \"Aves/Euphagus cyanocephalus/49c3e177149af5d5ea397dd25131b709.jpg\",\n  \"426516.jpg\": \"Aves/Sialia sialis/f86158119605ade6e0b184eaec85e07a.jpg\",\n  \"426547.jpg\": \"Aves/Sialia sialis/c4d0e6cb0dd83b8639dc53c2aee214bf.jpg\",\n  \"42664.jpg\": \"Aves/Euphagus cyanocephalus/af858028c7db5c1a449b3d7580ef926e.jpg\",\n  \"42669.jpg\": \"Aves/Euphagus cyanocephalus/97a3f0e5082d6d2bfc23801b1cbf7ef4.jpg\",\n  \"426879.jpg\": \"Insecta/Syrbula admirabilis/45119d3f14eabbf27c28fe71e650b3d7.jpg\",\n  \"426884.jpg\": \"Insecta/Syrbula admirabilis/9fc09ad28d36b7f1666fa93e95e09113.jpg\",\n  \"426891.jpg\": \"Insecta/Syrbula admirabilis/58912407e296a37c058eb902cc8249ec.jpg\",\n  \"426927.jpg\": \"Amphibia/Lithobates pipiens/ec3bd4fac6f142a04c9e94e58fee16f6.jpg\",\n  \"426970.jpg\": \"Amphibia/Lithobates pipiens/aa147229036dac3e1d1d12d17cc4b926.jpg\",\n  \"426995.jpg\": \"Amphibia/Lithobates pipiens/e7debf53a3fe47af83dbce222f5bb712.jpg\",\n  \"427003.jpg\": \"Amphibia/Lithobates pipiens/68042b08e4c909f02d521c2ae6021732.jpg\",\n  \"42701.jpg\": \"Aves/Euphagus cyanocephalus/7607986df22a9fb56ba971168b9f4e4c.jpg\",\n  \"427038.jpg\": \"Insecta/Uresiphita reversalis/42420af7c9bc40491dc2f53eaae87861.jpg\",\n  \"427060.jpg\": \"Insecta/Uresiphita reversalis/4ce497f76fdedee5e92e4650db1da1f2.jpg\",\n  \"427061.jpg\": \"Insecta/Uresiphita reversalis/d2d1712e06e2f8b0029c12dee0e44071.jpg\",\n  \"427067.jpg\": \"Insecta/Uresiphita reversalis/2cbf1334b0e35c55df93befed696551f.jpg\",\n  \"427068.jpg\": \"Insecta/Uresiphita reversalis/fef2abf551efb27c49056598d878bd00.jpg\",\n  \"427069.jpg\": \"Insecta/Uresiphita reversalis/e2da243609bf7b0bb796561ce60134d3.jpg\",\n  \"427070.jpg\": \"Insecta/Uresiphita reversalis/67a457eaf2d3e354b59d3db3963ab8ff.jpg\",\n  \"427097.jpg\": \"Insecta/Basiaeschna janata/1c3a3c71cb5a6dda2c12e4b2e52f4874.jpg\",\n  \"427098.jpg\": \"Insecta/Basiaeschna janata/1fdc231374a3d48df0653a00cc76f55f.jpg\",\n  \"427192.jpg\": \"Insecta/Thaumetopoea pityocampa/9fb493de0a44993354fe640e09756a19.jpg\",\n  \"427198.jpg\": \"Mammalia/Tamias dorsalis/e938e3ad52bdc5b987992387883ee22c.jpg\",\n  \"427218.jpg\": \"Aves/Tyrannus vociferans/88ac510722fb834b4c50f21a53367a99.jpg\",\n  \"427222.jpg\": \"Aves/Tyrannus vociferans/d72029634723b41d104b0a0a03a841b4.jpg\",\n  \"427250.jpg\": \"Aves/Tyrannus vociferans/1ba68a3079a2a3cfbdb0264929f89293.jpg\",\n  \"427282.jpg\": \"Aves/Tyrannus vociferans/2143db7cd55efbc41a498391d6a8d333.jpg\",\n  \"427317.jpg\": \"Aves/Tyrannus vociferans/7da8785cfa2325c81870b93aca5c0ff7.jpg\",\n  \"427318.jpg\": \"Aves/Tyrannus vociferans/692d18452029a72937a73acfbee36db2.jpg\",\n  \"427323.jpg\": \"Aves/Tyrannus vociferans/3cbd0d3d5babbb6f6532a00d4baa6025.jpg\",\n  \"427365.jpg\": \"Insecta/Heliopetes laviana/fb8c22a27dfb7884d55f9ec98e486c3f.jpg\",\n  \"427366.jpg\": \"Insecta/Heliopetes laviana/01ad24d6dd5f5490971df961ee913700.jpg\",\n  \"427367.jpg\": \"Insecta/Heliopetes laviana/7a9b01beacaee2fa39087cb4717b39b6.jpg\",\n  \"427368.jpg\": \"Insecta/Heliopetes laviana/4f21d69dd6e38a99dd0f8e307e253ff1.jpg\",\n  \"427372.jpg\": \"Insecta/Heliopetes laviana/fcb0485dd50b7a541d7c083573fbdd3b.jpg\",\n  \"427375.jpg\": \"Insecta/Heliopetes laviana/65e2b78c81b2849d9d9ea151c558437a.jpg\",\n  \"427377.jpg\": \"Insecta/Heliopetes laviana/5211a977711e38bd93e18cf692044162.jpg\",\n  \"427378.jpg\": \"Insecta/Heliopetes laviana/c932a7dc4d12f0f12715dfb3602fb060.jpg\",\n  \"427379.jpg\": \"Insecta/Heliopetes laviana/5a3a2f9f78fcfce26daa5ec551672dde.jpg\",\n  \"427382.jpg\": \"Insecta/Heliopetes laviana/f784102cdf8e6927bd168682b7c7a88f.jpg\",\n  \"427383.jpg\": \"Insecta/Heliopetes laviana/4c024732e9f83d0b71eb56e763ab5bcb.jpg\",\n  \"427384.jpg\": \"Insecta/Heliopetes laviana/6fd53c2d1a0f948fb558d33ab98aab1c.jpg\",\n  \"427387.jpg\": \"Aves/Setophaga occidentalis/32989fca737e85eed72375ec826664e7.jpg\",\n  \"427401.jpg\": \"Aves/Setophaga occidentalis/a1517a8e6c2b9afa22b202301077ea37.jpg\",\n  \"427405.jpg\": \"Aves/Setophaga occidentalis/704a8e1cba58d95bdcd902e02f9babaa.jpg\",\n  \"427422.jpg\": \"Insecta/Bombus griseocollis/1d62b6182f63b4783fcfed3d878c5150.jpg\",\n  \"427426.jpg\": \"Insecta/Bombus griseocollis/bca243bf631116101de3f6551fbba599.jpg\",\n  \"427427.jpg\": \"Insecta/Bombus griseocollis/3c52efe9d2352cc6f2691b9135916e69.jpg\",\n  \"427431.jpg\": \"Insecta/Bombus griseocollis/317372485f09cc12da1ab5a751f0e9f6.jpg\",\n  \"427444.jpg\": \"Insecta/Bombus griseocollis/5d79f72fa94cfdcc8386aa4f6e6bf1a0.jpg\",\n  \"427447.jpg\": \"Insecta/Bombus griseocollis/7cb140ff38b73f7403416f9cb0e8eea8.jpg\",\n  \"427448.jpg\": \"Insecta/Bombus griseocollis/71bf418ee91a6bea3696b8f6ff204101.jpg\",\n  \"427452.jpg\": \"Insecta/Manduca rustica/99bb552df824b5c3af3405e9bf05db54.jpg\",\n  \"427454.jpg\": \"Insecta/Manduca rustica/67871f951e0ab7e13bddb16926c62b35.jpg\",\n  \"427455.jpg\": \"Insecta/Manduca rustica/5b4e7cc7089eae4c4fbe517f52a45e20.jpg\",\n  \"427458.jpg\": \"Insecta/Manduca rustica/d28d9c245cc4953dc9ad52eae14cc857.jpg\",\n  \"427462.jpg\": \"Arachnida/Dolomedes tenebrosus/270b08e9f5e8bfdfa6115fabcc1b52df.jpg\",\n  \"427464.jpg\": \"Arachnida/Dolomedes tenebrosus/f38dc2a7aa2941f35f1307c2e7aa97ab.jpg\",\n  \"427465.jpg\": \"Arachnida/Dolomedes tenebrosus/a45dfb49791518990d298839e3e970a5.jpg\",\n  \"427472.jpg\": \"Arachnida/Dolomedes tenebrosus/af34152fdb2a8df097925a42161ff758.jpg\",\n  \"427487.jpg\": \"Insecta/Colias philodice/4d27ea7e993ffaed559995a842b87ddb.jpg\",\n  \"427490.jpg\": \"Insecta/Colias philodice/ddb9e304904a6b3ad802f0f476cbd5df.jpg\",\n  \"427493.jpg\": \"Insecta/Colias philodice/557ba30a0e158342a1418c6c34d8db97.jpg\",\n  \"427496.jpg\": \"Insecta/Colias philodice/40077404ba998b73db388f6e1797af20.jpg\",\n  \"427499.jpg\": \"Insecta/Colias philodice/7505d5df8a76e13756747765135dc21e.jpg\",\n  \"427507.jpg\": \"Insecta/Colias philodice/3bcd0c39fdd9df9e81980cbbf84e7b21.jpg\",\n  \"427551.jpg\": \"Mammalia/Myocastor coypus/7e61d4893ca225d382ae4567face5dae.jpg\",\n  \"427571.jpg\": \"Mammalia/Myocastor coypus/4c977e35103c86250c2d32c2bfb99820.jpg\",\n  \"427584.jpg\": \"Mammalia/Myocastor coypus/442398ccc3acedad55961d8a530f34e5.jpg\",\n  \"427603.jpg\": \"Mammalia/Myocastor coypus/7a7edc4fa17a094643a2d32f1430bbe1.jpg\",\n  \"427605.jpg\": \"Mammalia/Myocastor coypus/a0540c91a48a29422905383fc427c1f3.jpg\",\n  \"427622.jpg\": \"Insecta/Dioprosopa clavata/fc5d12e37aeb35097af59decb229bda0.jpg\",\n  \"427626.jpg\": \"Insecta/Dioprosopa clavata/df562c32f67c00105f0e2e4ae01d6c33.jpg\",\n  \"427627.jpg\": \"Insecta/Dioprosopa clavata/220011ad424d3fd8e868c6a702d3f797.jpg\",\n  \"427630.jpg\": \"Insecta/Dioprosopa clavata/f14e92ac10be99ce07ee0c521fb1f9c7.jpg\",\n  \"427631.jpg\": \"Insecta/Dioprosopa clavata/8e39a099d216cf938fb97150919c0c46.jpg\",\n  \"427634.jpg\": \"Insecta/Dioprosopa clavata/150ce56eed6d8435ea41735106347835.jpg\",\n  \"427635.jpg\": \"Insecta/Dioprosopa clavata/880d447e617e1ed83916a14902850c2d.jpg\",\n  \"427636.jpg\": \"Insecta/Agriphila vulgivagella/df08eddc83a7386d72aee4ffd9619d2c.jpg\",\n  \"427638.jpg\": \"Insecta/Agriphila vulgivagella/95cfd22ebaca0e44a343a45b830975fb.jpg\",\n  \"428128.jpg\": \"Aves/Turdus viscivorus/4db57704cbecdada10f74ba74398cd81.jpg\",\n  \"428131.jpg\": \"Aves/Turdus viscivorus/4d927427455fcf7fae16e85f80bd3a0f.jpg\",\n  \"428133.jpg\": \"Aves/Turdus viscivorus/5056db969947182925daa100153a0ec1.jpg\",\n  \"428149.jpg\": \"Insecta/Gomphus exilis/e6941e6bef85c3f7d83e6bdcede15826.jpg\",\n  \"428151.jpg\": \"Insecta/Gomphus exilis/510d041182fd1e92876a42e275ec99cc.jpg\",\n  \"428173.jpg\": \"Insecta/Chrysodeixis includens/f83f0e2287459b5da05d439e7b246180.jpg\",\n  \"428175.jpg\": \"Insecta/Chrysodeixis includens/4c7a5b83e5db632abb22fa6324899550.jpg\",\n  \"428176.jpg\": \"Insecta/Chrysodeixis includens/c33a0a3d251240090d8e2815fcb2812c.jpg\",\n  \"428185.jpg\": \"Insecta/Chrysodeixis includens/67efae653223adb601c00ecc9f9b252a.jpg\",\n  \"428284.jpg\": \"Insecta/Urola nivalis/a095031e57811c7997019c75446e0e34.jpg\",\n  \"428288.jpg\": \"Insecta/Urola nivalis/e1db8ad01966fb2e63d6231f2c20d3be.jpg\",\n  \"428293.jpg\": \"Insecta/Urola nivalis/80fdaa14aaf8e974c78faa19cd839f85.jpg\",\n  \"428297.jpg\": \"Insecta/Urola nivalis/03d3fed880767ee2e83bd0866e99ea0c.jpg\",\n  \"428298.jpg\": \"Insecta/Urola nivalis/7df72c4c27ced33e06d3bf3a278e66df.jpg\",\n  \"428299.jpg\": \"Insecta/Urola nivalis/70a022890a655aae2e0518aa962047ca.jpg\",\n  \"428308.jpg\": \"Insecta/Urola nivalis/102a6ae8e5607f6b078863453f85cd43.jpg\",\n  \"428386.jpg\": \"Insecta/Cercyonis pegala/b11025d8039b8d3743fc88284876beaf.jpg\",\n  \"428392.jpg\": \"Insecta/Cercyonis pegala/920b02ff6639704764aadc258ba5a316.jpg\",\n  \"428393.jpg\": \"Insecta/Cercyonis pegala/3946bdc899ce29fc07443f42cd25e1b1.jpg\",\n  \"428419.jpg\": \"Insecta/Cercyonis pegala/462a19aa486140d955de668daedb0bd0.jpg\",\n  \"428437.jpg\": \"Insecta/Cercyonis pegala/8ea1165d129df8fbdcdabe343a94f0c6.jpg\",\n  \"428508.jpg\": \"Insecta/Cercyonis pegala/b0dcd6c117dbf7f3e96c0453cfb49a41.jpg\",\n  \"42857.jpg\": \"Mammalia/Canis lupus/3ce750eeab4279c127410363d62decd4.jpg\",\n  \"428579.jpg\": \"Aves/Calidris virgata/ec55ca334ca7773ae381a52096db7eee.jpg\",\n  \"428589.jpg\": \"Aves/Calidris virgata/651d300999a39ca21186fb6cbc52535e.jpg\",\n  \"428591.jpg\": \"Aves/Calidris virgata/80b227eb9b129939da0b4dbba837c85f.jpg\",\n  \"428593.jpg\": \"Aves/Calidris virgata/f7f6dece214470a7cf0ff40e66046f13.jpg\",\n  \"428594.jpg\": \"Aves/Calidris virgata/3c53f98d6f279e908c08530f7bfcbfb7.jpg\",\n  \"428597.jpg\": \"Aves/Calidris virgata/bb32f1bd944b8ea9f2b8c68e1a7bc172.jpg\",\n  \"428606.jpg\": \"Aves/Calidris virgata/6dd18f209d3108c659752e2495855592.jpg\",\n  \"428610.jpg\": \"Aves/Calidris virgata/806465e81dbeacca4d76f72f78a32561.jpg\",\n  \"42866.jpg\": \"Mammalia/Canis lupus/ec76ebc244dd6abd37cabb884269dbf0.jpg\",\n  \"428761.jpg\": \"Aves/Charadrius semipalmatus/0475c344d82ffbf88c8cee1d447d55f5.jpg\",\n  \"428811.jpg\": \"Aves/Charadrius semipalmatus/7dafaf54c2e821e7ec524240bbb1331f.jpg\",\n  \"428819.jpg\": \"Aves/Charadrius semipalmatus/430ffe2359ac4f853cab6eb62d589cac.jpg\",\n  \"428873.jpg\": \"Aves/Charadrius semipalmatus/c1f1693094b17f4aa9e7c266b9eee5e1.jpg\",\n  \"428928.jpg\": \"Aves/Buteo swainsoni/64fd983f67aeb3b8eae5b24f92596411.jpg\",\n  \"428959.jpg\": \"Aves/Buteo swainsoni/76c82c5f890ecdcadb077c3a29fbd271.jpg\",\n  \"428973.jpg\": \"Aves/Buteo swainsoni/ee067d9f2373cf3668be0adb392b5872.jpg\",\n  \"428994.jpg\": \"Aves/Buteo swainsoni/fc11dc34d8bc6d38aa96fe6551527026.jpg\",\n  \"42902.jpg\": \"Reptilia/Thamnophis sirtalis infernalis/1c77ca41b52afab39b09a793e5a8100b.jpg\",\n  \"42906.jpg\": \"Reptilia/Thamnophis sirtalis infernalis/0eba3f9ca5b62a3b29bc2966ae7a4379.jpg\",\n  \"42907.jpg\": \"Reptilia/Thamnophis sirtalis infernalis/2f28c5b82519e606786bbeecf0642248.jpg\",\n  \"429089.jpg\": \"Insecta/Anisomorpha buprestoides/55a18638abc05b4b55af1a7d3f00a72d.jpg\",\n  \"429090.jpg\": \"Insecta/Anisomorpha buprestoides/a48909918023dffa334f9c61de865413.jpg\",\n  \"429100.jpg\": \"Insecta/Nemoria bistriaria/9b6ebc6f4ca12bd9ce1a0bfc202976a4.jpg\",\n  \"42911.jpg\": \"Reptilia/Thamnophis sirtalis infernalis/5e0dbaee7af232295242316f8894be35.jpg\",\n  \"429128.jpg\": \"Actinopterygii/Gambusia affinis/adade7e9b336b94457664908fc01c383.jpg\",\n  \"429129.jpg\": \"Actinopterygii/Gambusia affinis/8c92fa7b99e4e90a51b3430439da2b68.jpg\",\n  \"429146.jpg\": \"Actinopterygii/Gambusia affinis/ef110f0a0bc309ae6955385fb1a914fe.jpg\",\n  \"42915.jpg\": \"Reptilia/Thamnophis sirtalis infernalis/31087972adbdca4ab769d80f28a71235.jpg\",\n  \"42918.jpg\": \"Reptilia/Thamnophis sirtalis infernalis/0acfc87be8fdd257751ebbe47194c250.jpg\",\n  \"42925.jpg\": \"Reptilia/Thamnophis sirtalis infernalis/74b5be292fa3aa1ac92c9b3619fdda52.jpg\",\n  \"429327.jpg\": \"Aves/Melanitta perspicillata/a727543ece006839457e4ab88ac9a82c.jpg\",\n  \"42933.jpg\": \"Insecta/Vespula pensylvanica/912420a06896157888636d35d1253e92.jpg\",\n  \"429367.jpg\": \"Aves/Melanitta perspicillata/196be03a88497e76c3964d786342ff8d.jpg\",\n  \"42937.jpg\": \"Insecta/Vespula pensylvanica/06ef704d1ea165f63a8cf7e58e827d65.jpg\",\n  \"42938.jpg\": \"Insecta/Vespula pensylvanica/e1b85d125e654f31168127b03248da5c.jpg\",\n  \"42939.jpg\": \"Insecta/Vespula pensylvanica/f239d4706b930b14bbbac8f8351bd092.jpg\",\n  \"429476.jpg\": \"Aves/Setophaga virens/acaad781f1eb2dc2f2a9064c6204b19d.jpg\",\n  \"429515.jpg\": \"Aves/Setophaga virens/48920395e484f10cbedb3af4854efb9e.jpg\",\n  \"42952.jpg\": \"Insecta/Vespula pensylvanica/afd88b4a8527b764398bf8e60838f1e9.jpg\",\n  \"429548.jpg\": \"Aves/Setophaga virens/ff1e85bce82133574f14a965132a500f.jpg\",\n  \"429596.jpg\": \"Mollusca/Leukoma staminea/9a09300e7906c9dfab3c6d3fa2d270c2.jpg\",\n  \"429598.jpg\": \"Mollusca/Leukoma staminea/9802871c7755964b10c3d3380511f27b.jpg\",\n  \"429599.jpg\": \"Mollusca/Leukoma staminea/e385136936d29045ed24d681709fa479.jpg\",\n  \"429600.jpg\": \"Mollusca/Leukoma staminea/fe63523813a3863f7d1ab8e7d855a337.jpg\",\n  \"429608.jpg\": \"Mollusca/Leukoma staminea/66e75874878b079c3b71380bb3cc70a1.jpg\",\n  \"429609.jpg\": \"Mollusca/Leukoma staminea/0ee54600f358c8d541fbed86d07e0b11.jpg\",\n  \"429611.jpg\": \"Mollusca/Leukoma staminea/5985c6d2328e83b86de777d5176f451a.jpg\",\n  \"42962.jpg\": \"Insecta/Limenitis arthemis arthemis/505fd534f8460e2729d36df20fc70e0a.jpg\",\n  \"429625.jpg\": \"Mollusca/Leukoma staminea/9ecd05ad6469c84f19da252d8da581ca.jpg\",\n  \"429626.jpg\": \"Mollusca/Leukoma staminea/c31273cd1607064a8ac65aa9bc55ae96.jpg\",\n  \"429630.jpg\": \"Aves/Tringa flavipes/b86bd64fe36a7cb03581a1b7f94b79a2.jpg\",\n  \"42965.jpg\": \"Insecta/Limenitis arthemis arthemis/c02d12a80a8f5caa7d69667d74bc9700.jpg\",\n  \"429652.jpg\": \"Aves/Tringa flavipes/07573406a568b6e911c32226b96bd720.jpg\",\n  \"429659.jpg\": \"Aves/Tringa flavipes/815b7e0252e446624ba374ef236f8c19.jpg\",\n  \"429669.jpg\": \"Aves/Tringa flavipes/6d4b1f984e97eb1f5337674616b6d8ed.jpg\",\n  \"42967.jpg\": \"Insecta/Limenitis arthemis arthemis/58335d1c8f47ddffc970c37bc861c14a.jpg\",\n  \"429682.jpg\": \"Aves/Tringa flavipes/a621190725afdb75aa000a1bb22607e8.jpg\",\n  \"42972.jpg\": \"Insecta/Limenitis arthemis arthemis/1042fd7f0b6f1eff27b9f33149cbca32.jpg\",\n  \"429747.jpg\": \"Reptilia/Thamnophis elegans vagrans/181ba11b88fd1afe3489b89fad3bd02c.jpg\",\n  \"429748.jpg\": \"Reptilia/Thamnophis elegans vagrans/9405b2ded796d74cda35d6e6072f5363.jpg\",\n  \"429750.jpg\": \"Reptilia/Thamnophis elegans vagrans/c40e5dc48defa63ae55a8e2eb72dfc9b.jpg\",\n  \"429759.jpg\": \"Aves/Circus cyaneus hudsonius/9fb9238f36776e55fad0265cd8686a3c.jpg\",\n  \"42980.jpg\": \"Insecta/Limenitis arthemis arthemis/f1a7f876193176d6f3c1aedef4ff3ca6.jpg\",\n  \"42986.jpg\": \"Insecta/Limenitis arthemis arthemis/c5865dda68e970b6e4018e4388605ef7.jpg\",\n  \"42987.jpg\": \"Insecta/Limenitis arthemis arthemis/e6f9b6aef3e436dc8b379d8a7d1c4f7d.jpg\",\n  \"429876.jpg\": \"Reptilia/Elgaria multicarinata webbii/a03772c6748d7feac02f8bf30107020c.jpg\",\n  \"429884.jpg\": \"Reptilia/Elgaria multicarinata webbii/52391aabedb42cdb2da0f3053aa9d288.jpg\",\n  \"42990.jpg\": \"Insecta/Limenitis arthemis arthemis/d3f63adf0ed18f62b29933d3bc61897b.jpg\",\n  \"429902.jpg\": \"Reptilia/Elgaria multicarinata webbii/6edeb794e131d0c174d61fa560cc68b4.jpg\",\n  \"429905.jpg\": \"Reptilia/Elgaria multicarinata webbii/11d52c3046e80affe649da2f329c3898.jpg\",\n  \"429906.jpg\": \"Reptilia/Elgaria multicarinata webbii/5c2a3a8e0a3bb080d4cdee3c67d0bab7.jpg\",\n  \"42991.jpg\": \"Insecta/Limenitis arthemis arthemis/39980a1f95a83ef9e9d31d42f3704857.jpg\",\n  \"429922.jpg\": \"Reptilia/Elgaria multicarinata webbii/e09ef0d4d96da63543a560fafaba4599.jpg\",\n  \"42994.jpg\": \"Insecta/Limenitis arthemis arthemis/596ee8c68677af3dedfa6c8a463ca808.jpg\",\n  \"429942.jpg\": \"Reptilia/Elgaria multicarinata webbii/8d90f24f25fee23460edd98589140b16.jpg\",\n  \"429952.jpg\": \"Reptilia/Elgaria multicarinata webbii/93334efa17b4dc3d217f06ba3b77ce4e.jpg\",\n  \"429976.jpg\": \"Insecta/Nehalennia irene/f329904b830c40c36fac08ad99ff34b9.jpg\",\n  \"429977.jpg\": \"Insecta/Nehalennia irene/42d8c7b722f2ea236da00396a400f007.jpg\",\n  \"429986.jpg\": \"Insecta/Nehalennia irene/056bb9145e73b41fa08e5586a56c791b.jpg\",\n  \"429992.jpg\": \"Insecta/Cydalima perspectalis/4f19135f684704c3a62531844e870195.jpg\",\n  \"429998.jpg\": \"Insecta/Crocothemis erythraea/6e92a010a43209674fc5226b34750fb6.jpg\",\n  \"429999.jpg\": \"Insecta/Crocothemis erythraea/4830029553f6391176be6b8fbfab74c1.jpg\",\n  \"43000.jpg\": \"Insecta/Limenitis arthemis arthemis/874c9aad049a3b4ce0a291ef41b17e1b.jpg\",\n  \"430001.jpg\": \"Insecta/Crocothemis erythraea/36e26c7bed5d8b54ab69b6b0073c31fe.jpg\",\n  \"430004.jpg\": \"Insecta/Crocothemis erythraea/51d4874d961efb4abc5c9a6946530dba.jpg\",\n  \"430005.jpg\": \"Insecta/Crocothemis erythraea/314ba5bd750bee67db3b7831b213194e.jpg\",\n  \"430011.jpg\": \"Insecta/Crocothemis erythraea/06b54c987167f9e0fbe2f94df9797853.jpg\",\n  \"430017.jpg\": \"Insecta/Crocothemis erythraea/c3ac8ddec4b894571aea8e7c75bd53be.jpg\",\n  \"430018.jpg\": \"Insecta/Crocothemis erythraea/4e341b8d332600b90acfdc5465d25652.jpg\",\n  \"430020.jpg\": \"Insecta/Crocothemis erythraea/88cbcf5fda1c1635b1589e87c335425e.jpg\",\n  \"430027.jpg\": \"Insecta/Crocothemis erythraea/07773cffe0a380fd2f78cad648b9716a.jpg\",\n  \"430034.jpg\": \"Aves/Aulacorhynchus prasinus/7a0b95078774ad82d37ed1efc982604a.jpg\",\n  \"430035.jpg\": \"Aves/Aulacorhynchus prasinus/abf6abb7970519d6860e2cb01587f47b.jpg\",\n  \"430046.jpg\": \"Mollusca/Dreissena polymorpha/6344e4d39395f613002db5d489231d47.jpg\",\n  \"43005.jpg\": \"Insecta/Limenitis arthemis arthemis/d5218edf5f81cd1f00328ec58b71fab4.jpg\",\n  \"43007.jpg\": \"Insecta/Limenitis arthemis arthemis/e42ed5bf1da04c70e8320bb8c402832a.jpg\",\n  \"43008.jpg\": \"Insecta/Limenitis arthemis arthemis/ebbb0b126753881ef60dd1f780278b63.jpg\",\n  \"430082.jpg\": \"Insecta/Popillia japonica/3803b280c9c5a6dd25e542ad729fbf4e.jpg\",\n  \"430116.jpg\": \"Insecta/Popillia japonica/660ecc6893068f4ab127b2060801fc87.jpg\",\n  \"430125.jpg\": \"Insecta/Popillia japonica/18b9c00612a4740b1826ea3f66e8c98d.jpg\",\n  \"430156.jpg\": \"Insecta/Popillia japonica/f483b2bacd635719d9d7ce9acd1e8e72.jpg\",\n  \"43022.jpg\": \"Insecta/Limenitis arthemis arthemis/2bdbd6240a227f2cf97f520cbdb1fa62.jpg\",\n  \"43025.jpg\": \"Insecta/Limenitis arthemis arthemis/679ab5fd9d33fa1f43aebeae2a5d681b.jpg\",\n  \"430266.jpg\": \"Insecta/Diabrotica undecimpunctata/9d42717588894f72c6e18c3684988f09.jpg\",\n  \"430268.jpg\": \"Insecta/Diabrotica undecimpunctata/23fbd2e843d53ab66b82e3606600bbec.jpg\",\n  \"430273.jpg\": \"Insecta/Diabrotica undecimpunctata/067399c763f70be90f90ab954515bc1c.jpg\",\n  \"430295.jpg\": \"Insecta/Diabrotica undecimpunctata/7d847957a0fc90b3d4d3d17f0f1e9d0d.jpg\",\n  \"43030.jpg\": \"Insecta/Limenitis arthemis arthemis/fed7a779af6f39be6dc8190cab471b1d.jpg\",\n  \"430304.jpg\": \"Insecta/Diabrotica undecimpunctata/26fd9b3100efab67a7084d72c918898b.jpg\",\n  \"43032.jpg\": \"Insecta/Limenitis arthemis arthemis/64c0a8c7c3334c2e9a2840ae46daad62.jpg\",\n  \"430328.jpg\": \"Insecta/Diabrotica undecimpunctata/c088592a05fe5a20a961af04978f4f23.jpg\",\n  \"430332.jpg\": \"Insecta/Diabrotica undecimpunctata/e183bc7838b8f0082790a127528abaf8.jpg\",\n  \"43035.jpg\": \"Insecta/Limenitis arthemis arthemis/f86d949c31fd7e6e820be3e7e2898dee.jpg\",\n  \"430350.jpg\": \"Aves/Amazilia beryllina/f1052dea20e1f9f04dba4a20ee478620.jpg\",\n  \"430352.jpg\": \"Aves/Amazilia beryllina/b7da78a59a248bc7520eb289eb0dadab.jpg\",\n  \"430355.jpg\": \"Aves/Amazilia beryllina/b56328195ab9dcf5fc769551d798d195.jpg\",\n  \"430369.jpg\": \"Insecta/Phalaenophana pyramusalis/6aba95e521b118eeba33aa57ec5a1fce.jpg\",\n  \"430380.jpg\": \"Insecta/Lygaeus kalmii/a222712e379335d64da675f2edffafb0.jpg\",\n  \"430392.jpg\": \"Insecta/Lygaeus kalmii/a2eaffa6d40fa1171d2b8ba57b1b91e6.jpg\",\n  \"43040.jpg\": \"Insecta/Limenitis arthemis arthemis/35c66c91b689f3dc58b8a6fc3c68e446.jpg\",\n  \"430402.jpg\": \"Insecta/Lygaeus kalmii/dd2e40d7db1e37e81b4635ff3bc91583.jpg\",\n  \"430403.jpg\": \"Insecta/Lygaeus kalmii/9c5a9ae67fbcb108699feed27b17ae2f.jpg\",\n  \"430418.jpg\": \"Insecta/Lygaeus kalmii/2a141e43fe04ef5708ca2c37923ad677.jpg\",\n  \"43043.jpg\": \"Insecta/Limenitis arthemis arthemis/60f947dd40706a7d518c7fceefbe1c61.jpg\",\n  \"43044.jpg\": \"Insecta/Limenitis arthemis arthemis/4095f1eb5f94ac52e6a8933b77501e3f.jpg\",\n  \"430441.jpg\": \"Insecta/Lygaeus kalmii/2d69b37e24d9c06cb9c2327bc90678de.jpg\",\n  \"430448.jpg\": \"Insecta/Lygaeus kalmii/0d442599365d2f6332e2f0dd8befb610.jpg\",\n  \"43045.jpg\": \"Insecta/Limenitis arthemis arthemis/012309ab951f700a8a8dcec0354cb183.jpg\",\n  \"430453.jpg\": \"Insecta/Ancyloxypha numitor/e7204837df3b297bfde509775300082c.jpg\",\n  \"430457.jpg\": \"Insecta/Ancyloxypha numitor/689e784b868b003cb3f8c57fb292082f.jpg\",\n  \"430479.jpg\": \"Insecta/Ancyloxypha numitor/b437b60a338ac84ff5a2894c407c0b58.jpg\",\n  \"43048.jpg\": \"Insecta/Limenitis arthemis arthemis/02c355439e578834f30ec1c84da9d180.jpg\",\n  \"430489.jpg\": \"Insecta/Ancyloxypha numitor/fca6fdab3c8a80b52bdde330a1aae3cc.jpg\",\n  \"430496.jpg\": \"Insecta/Ancyloxypha numitor/c36ad784aca02393307717c32feaa54b.jpg\",\n  \"430500.jpg\": \"Insecta/Ancyloxypha numitor/3329cc323dd4c0e679f81b69949ccdbd.jpg\",\n  \"43055.jpg\": \"Aves/Icterus gularis/9a3a5ed3c9ea221585723751921b233a.jpg\",\n  \"43056.jpg\": \"Aves/Icterus gularis/e858d7c2c2df1baca953b62ab82c3b11.jpg\",\n  \"43057.jpg\": \"Aves/Icterus gularis/1c3fb893780bf906f12ad18b1435be95.jpg\",\n  \"43058.jpg\": \"Aves/Icterus gularis/a5178ba8e5f984db77e52f1153c98743.jpg\",\n  \"430585.jpg\": \"Aves/Alectura lathami/d50fcd0e231018e8bb7e70410b593100.jpg\",\n  \"430588.jpg\": \"Aves/Alectura lathami/6411fcc4e94ce76de3f3d1965464c690.jpg\",\n  \"430599.jpg\": \"Aves/Himantopus leucocephalus/a539eb4e961d835e88679200b9a25526.jpg\",\n  \"430602.jpg\": \"Aves/Himantopus leucocephalus/d874995cfbe0e58b0d64a0e78433f4a7.jpg\",\n  \"430603.jpg\": \"Aves/Himantopus leucocephalus/b75dcde41d36dd39912905284b714d9e.jpg\",\n  \"430604.jpg\": \"Aves/Himantopus leucocephalus/e702ccd3af8367cc9ba7857b7989da80.jpg\",\n  \"430607.jpg\": \"Aves/Himantopus leucocephalus/f6ff90acb4da9bb691ef01ccf9d16363.jpg\",\n  \"430614.jpg\": \"Aves/Himantopus leucocephalus/6cd641c5b4c59fabdef062d91c0dbe8a.jpg\",\n  \"430616.jpg\": \"Aves/Himantopus leucocephalus/47a751debcb2cbce1969d2dfdba07975.jpg\",\n  \"430617.jpg\": \"Aves/Himantopus leucocephalus/a2f6b4060ed69354f1f1492d73267656.jpg\",\n  \"430620.jpg\": \"Aves/Himantopus leucocephalus/cee99c86aeb54c8c7d9435c6ed9de854.jpg\",\n  \"430623.jpg\": \"Aves/Himantopus leucocephalus/ec914e56cab2590559846795fc1e1c40.jpg\",\n  \"430624.jpg\": \"Reptilia/Thamnophis cyrtopsis/777966c32f500bca3996fcc392c7ecc7.jpg\",\n  \"430630.jpg\": \"Reptilia/Thamnophis cyrtopsis/222d5d32d94487d9e0a34b307938ea23.jpg\",\n  \"430631.jpg\": \"Reptilia/Thamnophis cyrtopsis/cc96b59ff1d669050deec24d8397f509.jpg\",\n  \"430632.jpg\": \"Reptilia/Thamnophis cyrtopsis/c38be701a327feaf52228c0fb4c1dba3.jpg\",\n  \"430635.jpg\": \"Reptilia/Thamnophis cyrtopsis/65e0687eb517f02c368890f083f1b0d3.jpg\",\n  \"430647.jpg\": \"Reptilia/Thamnophis cyrtopsis/bf445e209efdd0ecb40cbb7369478442.jpg\",\n  \"430649.jpg\": \"Reptilia/Thamnophis cyrtopsis/cefcbd266b2e4ca019ce78b63b231043.jpg\",\n  \"430651.jpg\": \"Reptilia/Thamnophis cyrtopsis/108a80eb84adec8cac399ff5abda88b7.jpg\",\n  \"430673.jpg\": \"Aves/Circus aeruginosus/70f8381cbc1c54a022212a5c092cec1b.jpg\",\n  \"430674.jpg\": \"Aves/Circus aeruginosus/932903b6c6bcee16c7643045466cc1d9.jpg\",\n  \"430675.jpg\": \"Aves/Circus aeruginosus/6658aefaa45be497dd8fce1c9799fdee.jpg\",\n  \"430676.jpg\": \"Aves/Circus aeruginosus/9d43369a87fda18d4e9aa28189340074.jpg\",\n  \"430682.jpg\": \"Aves/Circus aeruginosus/8602713ce1d11524005450937bd85726.jpg\",\n  \"430688.jpg\": \"Aves/Circus aeruginosus/e836163b0cfd6bf9d828bb2f19e0d780.jpg\",\n  \"430691.jpg\": \"Aves/Circus aeruginosus/febe088dcbc3d1484aeb85c02ba79749.jpg\",\n  \"430697.jpg\": \"Aves/Circus aeruginosus/f95aecc80e302bdcc6d40212cb6758ea.jpg\",\n  \"430698.jpg\": \"Aves/Circus aeruginosus/3f169a50e517bc5d54a119ec8049f4bc.jpg\",\n  \"43070.jpg\": \"Aves/Icterus gularis/17aff44e6b986bfdaa33462d4af24ab5.jpg\",\n  \"430704.jpg\": \"Insecta/Anania funebris/b7e247c3ac4834ae36407f0e773671cb.jpg\",\n  \"430706.jpg\": \"Insecta/Anania funebris/efd08c88d00f12ae07c78c2401a1b705.jpg\",\n  \"430707.jpg\": \"Insecta/Anania funebris/5b1c6aef9357d1c79bfa96dc8d4016d5.jpg\",\n  \"430712.jpg\": \"Insecta/Linepithema humile/b493417115df6e75b62eaf817336999a.jpg\",\n  \"430761.jpg\": \"Insecta/Celithemis elisa/839a78511cdc971c8ddfe4e619306487.jpg\",\n  \"430782.jpg\": \"Insecta/Celithemis elisa/0841d86676e2511429a58242d07ea1c5.jpg\",\n  \"430802.jpg\": \"Insecta/Celithemis elisa/40c8464f9b43b05caf4ef81f9527d802.jpg\",\n  \"430804.jpg\": \"Insecta/Celithemis elisa/5ec6a719f4c008f2a3adb485878dc026.jpg\",\n  \"430805.jpg\": \"Insecta/Celithemis elisa/906de196e329fbe5c3b46a1b2c5a9e98.jpg\",\n  \"430827.jpg\": \"Aves/Euphonia hirundinacea/59f98a4cbe5e4633008d188509b847cc.jpg\",\n  \"430857.jpg\": \"Mollusca/Triopha catalinae/d1c9cd21610713afe7228f717b1989eb.jpg\",\n  \"430862.jpg\": \"Mollusca/Triopha catalinae/92ad67b4737096223b808ee85a641982.jpg\",\n  \"430863.jpg\": \"Mollusca/Triopha catalinae/e853ef43c37396bc500751f817710466.jpg\",\n  \"430865.jpg\": \"Mollusca/Triopha catalinae/671bf91e6ad624f301f053c1775611f9.jpg\",\n  \"430885.jpg\": \"Mollusca/Triopha catalinae/1f095095af524933156fb0c4e9e52f5d.jpg\",\n  \"430888.jpg\": \"Mollusca/Triopha catalinae/e1e2a38addbb35272372a53a6020f137.jpg\",\n  \"430906.jpg\": \"Mollusca/Triopha catalinae/14c833f3769975fa7ce50d001f712c38.jpg\",\n  \"430907.jpg\": \"Mollusca/Triopha catalinae/bc6a9637e0d39182037404aa650b85de.jpg\",\n  \"430911.jpg\": \"Mollusca/Triopha catalinae/61a75f4e9253ac7ce85aa3104ebcebc6.jpg\",\n  \"43092.jpg\": \"Aves/Icterus gularis/3c218911e0fcf272ff862caa25e6f223.jpg\",\n  \"430925.jpg\": \"Insecta/Vanessa itea/6ae3b62326e599918a77ca5ecd02583a.jpg\",\n  \"430926.jpg\": \"Insecta/Vanessa itea/503f0b77982eae0f9ba847ac5170493f.jpg\",\n  \"430930.jpg\": \"Insecta/Vanessa itea/75d0de7079a1e70f647af57e4eca4da8.jpg\",\n  \"430946.jpg\": \"Insecta/Vanessa itea/c7c2e7a9027b9b4b3279124846da54c8.jpg\",\n  \"430956.jpg\": \"Insecta/Vanessa itea/ebe93207b453f4285c436150be280bf2.jpg\",\n  \"430957.jpg\": \"Insecta/Vanessa itea/95acb006177b2ca7e359d17096e76ab6.jpg\",\n  \"430964.jpg\": \"Insecta/Vanessa itea/f31eec570248780b1e68adfec697d4ef.jpg\",\n  \"430969.jpg\": \"Insecta/Vanessa itea/49f771272c9bd6b8f13b4a2811f6e5af.jpg\",\n  \"430971.jpg\": \"Insecta/Vanessa itea/c504776ec7d51e6f7a639dac3a4364da.jpg\",\n  \"43098.jpg\": \"Aves/Icterus gularis/2785349a1a952e768873ca3e7ee1a616.jpg\",\n  \"43100.jpg\": \"Aves/Icterus gularis/eb35f44da7a3f3ae14d2ec8c97c076ea.jpg\",\n  \"431002.jpg\": \"Aves/Ninox novaeseelandiae novaeseelandiae/d2d96c0b43740a453f103724f8a438de.jpg\",\n  \"431005.jpg\": \"Aves/Ninox novaeseelandiae novaeseelandiae/67c6c143fc32cfa8605ef9c2bdfc37c7.jpg\",\n  \"431006.jpg\": \"Aves/Ninox novaeseelandiae novaeseelandiae/93ab89a30808ee034a565d91fbe6f91a.jpg\",\n  \"43101.jpg\": \"Aves/Icterus gularis/6c60ebfdcf017e935ac2f3ece1a017b7.jpg\",\n  \"43107.jpg\": \"Aves/Icterus gularis/2bf908c8d83405fa6e2a40164d38254a.jpg\",\n  \"43108.jpg\": \"Aves/Icterus gularis/0bca275c88fcc44dd5c0343381ea2dfd.jpg\",\n  \"43111.jpg\": \"Aves/Icterus gularis/83c9f293199bf23700237aeaaa378d59.jpg\",\n  \"43112.jpg\": \"Aves/Icterus gularis/4d7c5741ed56d2b1636cb6ec38db4d7b.jpg\",\n  \"431126.jpg\": \"Insecta/Hypolimnas bolina/040d3f0b14818a3fe9d7cb3b4c8001fe.jpg\",\n  \"43115.jpg\": \"Aves/Icterus gularis/f288f13b2fa4d5e3d363fff457fafe6e.jpg\",\n  \"43116.jpg\": \"Aves/Icterus gularis/9f93e95e8a47dc587a18838a5785070f.jpg\",\n  \"431175.jpg\": \"Aves/Megaceryle alcyon/824f44124d522afd07277f800e98c88c.jpg\",\n  \"43119.jpg\": \"Aves/Icterus gularis/d57f75b80baaeeff118b3473e690089c.jpg\",\n  \"43122.jpg\": \"Aves/Icterus gularis/75cb2c2295d954c62938ee7ec7971b18.jpg\",\n  \"431220.jpg\": \"Aves/Megaceryle alcyon/385c65743d7b5191a7cc8df892035869.jpg\",\n  \"43123.jpg\": \"Aves/Icterus gularis/fcdfd27fd928cf6e7a66bfe2c8266b58.jpg\",\n  \"431254.jpg\": \"Aves/Megaceryle alcyon/9433dd04f539f61b5870f4195af2b575.jpg\",\n  \"43128.jpg\": \"Aves/Icterus gularis/412d7c17883f722c8ad5a5d9f2cf4acd.jpg\",\n  \"43129.jpg\": \"Aves/Icterus gularis/3ca6fbb0ab80e3c9d8fd0baced6c9921.jpg\",\n  \"431308.jpg\": \"Aves/Megaceryle alcyon/2a76506f583e45bf8a64fe2cbeb396dc.jpg\",\n  \"43132.jpg\": \"Aves/Icterus gularis/8a823d2b22b4f3cc7b5c6b39923f7c89.jpg\",\n  \"43136.jpg\": \"Aves/Icterus gularis/1d9e2e7f83399e01eb4b7e7b1ea2b51b.jpg\",\n  \"43137.jpg\": \"Aves/Icterus gularis/18d6556141d01f8f16bf169c39841dad.jpg\",\n  \"431419.jpg\": \"Aves/Megaceryle alcyon/ef68b228ba9b87ff2dd14b8cb108382f.jpg\",\n  \"43142.jpg\": \"Arachnida/Steatoda capensis/1aaac6668bdd0d00054df9921117b6fe.jpg\",\n  \"431434.jpg\": \"Aves/Megaceryle alcyon/d9185bba996e6596c653a7d5e809caab.jpg\",\n  \"43144.jpg\": \"Arachnida/Steatoda capensis/6a338bfdd9845bbf127b4521b2c0209b.jpg\",\n  \"431454.jpg\": \"Aves/Megaceryle alcyon/95c16da65842c1ad6f07f4709d8d820b.jpg\",\n  \"431456.jpg\": \"Aves/Megaceryle alcyon/8327735d13f58eaf0055cb0cd66eab70.jpg\",\n  \"43146.jpg\": \"Arachnida/Steatoda capensis/1e248ec9a0b45aed31d78addb4135e8c.jpg\",\n  \"431480.jpg\": \"Reptilia/Bothrops asper/a8cdf6b4143a32b1db1c0f2fd34ffb4b.jpg\",\n  \"431483.jpg\": \"Aves/Actitis hypoleucos/9f3967a3cfeccb6497edd40f604d97f1.jpg\",\n  \"431486.jpg\": \"Aves/Actitis hypoleucos/4aeaab3cc59f1fc620a80560f770aaeb.jpg\",\n  \"431490.jpg\": \"Aves/Actitis hypoleucos/9ac3e09c189d67bd6f737e1f398dbd85.jpg\",\n  \"431503.jpg\": \"Aves/Actitis hypoleucos/5eb64cb9e41766b7a2f24a2cbdfaf4b6.jpg\",\n  \"431506.jpg\": \"Aves/Actitis hypoleucos/fc142a2de00e6fe0d8c329ddb0d1cf7c.jpg\",\n  \"431507.jpg\": \"Aves/Actitis hypoleucos/4c59173f4b502657f6ad48f9756c3b87.jpg\",\n  \"431510.jpg\": \"Aves/Actitis hypoleucos/08da142b48e73d300379bccc651ac4d1.jpg\",\n  \"431512.jpg\": \"Aves/Actitis hypoleucos/0e42df7e75d3db291266afb63660204d.jpg\",\n  \"431513.jpg\": \"Aves/Actitis hypoleucos/0d6994be76c3ca9d6ecaccb9714a33d8.jpg\",\n  \"431514.jpg\": \"Aves/Actitis hypoleucos/f056385e48ceb4f671e6bdec43be1182.jpg\",\n  \"431521.jpg\": \"Aves/Geopelia striata/54150759bad92272a6026321332c3856.jpg\",\n  \"431524.jpg\": \"Aves/Geopelia striata/ada08402192665f98589e80acf3c787f.jpg\",\n  \"431525.jpg\": \"Aves/Geopelia striata/b3270a9be8b29d2a0bd7efe7daff34f5.jpg\",\n  \"431526.jpg\": \"Aves/Geopelia striata/b1f432b1f71806956824714b936020db.jpg\",\n  \"431534.jpg\": \"Aves/Geopelia striata/1aec2af2ff884d51deb630b7b5bf2c0f.jpg\",\n  \"431552.jpg\": \"Aves/Geopelia striata/749e5ff756813233179ae760d6461da7.jpg\",\n  \"431561.jpg\": \"Insecta/Argia vivida/82c0ad25be95d078caabf24704fea812.jpg\",\n  \"431621.jpg\": \"Insecta/Argia vivida/072eb309e8f96eea7e99962ba3f2156c.jpg\",\n  \"431655.jpg\": \"Insecta/Argia vivida/71f3c0d0b2d284e8eee4aba3ea6d0ff4.jpg\",\n  \"431660.jpg\": \"Insecta/Argia vivida/80d4cd13c36f626e97c52a8ebd62899b.jpg\",\n  \"431766.jpg\": \"Animalia/Linckia laevigata/4ed86cc4877688ca82fa48aa17924141.jpg\",\n  \"431791.jpg\": \"Reptilia/Crotalus molossus nigrescens/ca3465c75852449f446e09f421384722.jpg\",\n  \"431792.jpg\": \"Reptilia/Crotalus molossus nigrescens/5386681d8dedde3914e6a6fbe150dbec.jpg\",\n  \"431795.jpg\": \"Reptilia/Crotalus molossus nigrescens/961e11ae2e151b9b1c98c1e53bd9c621.jpg\",\n  \"431828.jpg\": \"Insecta/Hippodamia convergens/b61a47a91882f9e304cb9d340a7f9b79.jpg\",\n  \"431883.jpg\": \"Insecta/Hippodamia convergens/1e654425e9ddb0a9c2146c84cf068d25.jpg\",\n  \"431902.jpg\": \"Insecta/Hippodamia convergens/4ef6e0cf8e901a9fa58bacd8cf47200a.jpg\",\n  \"431904.jpg\": \"Insecta/Hippodamia convergens/73368e539bdfad3a5ced0dcf2f5d64c9.jpg\",\n  \"431926.jpg\": \"Insecta/Hippodamia convergens/526c2ede5660f33f7d31dee94ac2da1d.jpg\",\n  \"431936.jpg\": \"Insecta/Hippodamia convergens/775962b15995ae6db73eb0bd25705f2d.jpg\",\n  \"431974.jpg\": \"Insecta/Hippodamia convergens/d71b259dc7a9d6a0450036ad3ab0e1e4.jpg\",\n  \"432119.jpg\": \"Insecta/Photinus pyralis/46dd933b04315c6b2362c0ac919b4b19.jpg\",\n  \"432120.jpg\": \"Insecta/Photinus pyralis/476db6a588dd973b4c8e89c9ad268b32.jpg\",\n  \"432121.jpg\": \"Insecta/Photinus pyralis/f3968fd831587a0548636f5968c7ce30.jpg\",\n  \"432122.jpg\": \"Insecta/Photinus pyralis/b08a99efd29694098273b14708db6e1e.jpg\",\n  \"432123.jpg\": \"Insecta/Photinus pyralis/f6ca31ea8e8d38781988e03e268e114d.jpg\",\n  \"432130.jpg\": \"Insecta/Photinus pyralis/70754d49851077cc7c210070bb4b128d.jpg\",\n  \"432131.jpg\": \"Insecta/Photinus pyralis/ea71c3af1ba0bf98d79dcf5ee7effc5b.jpg\",\n  \"432135.jpg\": \"Insecta/Photinus pyralis/deaa767a3311fa45b6bfa5e057c5a19c.jpg\",\n  \"432139.jpg\": \"Insecta/Photinus pyralis/30bc89562e070fee6717b696fbf92c09.jpg\",\n  \"432140.jpg\": \"Insecta/Photinus pyralis/091813825c04ff4ec885005cc3ef5ca4.jpg\",\n  \"432160.jpg\": \"Insecta/Papilio glaucus/a59a5569f23f5ef195c40c85c715d8a9.jpg\",\n  \"432190.jpg\": \"Insecta/Papilio glaucus/0c458d4218a95ffa75c4690daf29f51b.jpg\",\n  \"432193.jpg\": \"Insecta/Papilio glaucus/1c38bacb0beac80b829cc5dc11da9e53.jpg\",\n  \"432357.jpg\": \"Insecta/Papilio glaucus/f741cac51666751fa96d1c24cf50dba3.jpg\",\n  \"432391.jpg\": \"Insecta/Papilio glaucus/79a2bac36f5a3d4cdf35ce746fe35034.jpg\",\n  \"432443.jpg\": \"Insecta/Papilio glaucus/1d02565e06292e1bd1fb7ab9f6c528cd.jpg\",\n  \"432505.jpg\": \"Insecta/Papilio glaucus/1473de2ed50fab0676d7de1f394d8383.jpg\",\n  \"432663.jpg\": \"Aves/Bubo virginianus/c9d01186207536e93077a5c5171480d0.jpg\",\n  \"432702.jpg\": \"Aves/Bubo virginianus/cc2bddaa93a89b20cdd659e7dd455269.jpg\",\n  \"432714.jpg\": \"Aves/Bubo virginianus/73de9508e16b7e76186ba183420b9fb8.jpg\",\n  \"432753.jpg\": \"Aves/Bubo virginianus/c72d9fc82cd8b78a34f75e693cf54467.jpg\",\n  \"432770.jpg\": \"Aves/Bubo virginianus/ec34db7b5380f7af7f1ad248cf3226ce.jpg\",\n  \"432802.jpg\": \"Aves/Bubo virginianus/f8e0c9357a9eaba141df490a958f6354.jpg\",\n  \"432968.jpg\": \"Aves/Trogon massena/08eb29fd0c6e0b60e5fd4cab9d0af109.jpg\",\n  \"432982.jpg\": \"Insecta/Erythrodiplax umbrata/855c835d7f953e589be91a917537a508.jpg\",\n  \"432984.jpg\": \"Insecta/Erythrodiplax umbrata/5ee88e70c6d83228d8154c20f29f4cb3.jpg\",\n  \"432999.jpg\": \"Insecta/Erythrodiplax umbrata/85f89fe2c99769543a8ef3aee928340f.jpg\",\n  \"433001.jpg\": \"Insecta/Erythrodiplax umbrata/d4be9a48511520f13e755b3908844528.jpg\",\n  \"433008.jpg\": \"Insecta/Erythrodiplax umbrata/519fd0b6831319d09e5e7b602d481102.jpg\",\n  \"433010.jpg\": \"Insecta/Erythrodiplax umbrata/08be354ebdbc3538db52b56e2ee0c03b.jpg\",\n  \"433024.jpg\": \"Insecta/Erythrodiplax umbrata/e10e601277bc7ab170c1c82738f675dd.jpg\",\n  \"433030.jpg\": \"Insecta/Erythrodiplax umbrata/bdab424e04593a75f20a05fe03080291.jpg\",\n  \"433041.jpg\": \"Insecta/Erythrodiplax umbrata/ae22747f72aee5296e78c8ba8ea41017.jpg\",\n  \"433197.jpg\": \"Reptilia/Crocodylus acutus/4b57185cbef057435c47585bc0a589a2.jpg\",\n  \"433205.jpg\": \"Reptilia/Crocodylus acutus/eb834ca5a612f2be3ba3effa3234d4ec.jpg\",\n  \"433208.jpg\": \"Reptilia/Crocodylus acutus/c6462a2a6e3cdbd4bf9a4b9a9c588021.jpg\",\n  \"433220.jpg\": \"Reptilia/Crocodylus acutus/e6f4fb9401f9ff2cba4a365acadc6b70.jpg\",\n  \"433222.jpg\": \"Reptilia/Crocodylus acutus/8e457f2f8f4fb177feea192e566f6ed9.jpg\",\n  \"433259.jpg\": \"Aves/Spizelloides arborea/341fb3584d6e050318cf3a1276ac5b87.jpg\",\n  \"433263.jpg\": \"Aves/Spizelloides arborea/8c8ba879f6a5ccc358ce6b5faaea2105.jpg\",\n  \"433264.jpg\": \"Aves/Spizelloides arborea/4529fa4756cb56d88b914c6914057ff2.jpg\",\n  \"433277.jpg\": \"Aves/Spizelloides arborea/40653206b7eb4974c5d3fcd3920bc041.jpg\",\n  \"433280.jpg\": \"Aves/Spizelloides arborea/58a1c8c0a228ce0d9a9e2c8cd801e67c.jpg\",\n  \"433288.jpg\": \"Aves/Spizelloides arborea/9da051534e5b9b025b9ede17e96f4e63.jpg\",\n  \"433334.jpg\": \"Mollusca/Mercenaria mercenaria/68b514b4657dd99e631dd6d6beb99e16.jpg\",\n  \"433439.jpg\": \"Aves/Porphyrio melanotus/0feb6a65e8a11aac574d50b688450681.jpg\",\n  \"433516.jpg\": \"Aves/Streptopelia senegalensis/708fd1c56d5de5dc72909409a0a964be.jpg\",\n  \"433518.jpg\": \"Aves/Streptopelia senegalensis/c3bbff12ceb98c348c5c83e039a4163e.jpg\",\n  \"433519.jpg\": \"Aves/Streptopelia senegalensis/d4a22229f4a7861545113f81ace0abce.jpg\",\n  \"433524.jpg\": \"Aves/Streptopelia senegalensis/9c540ad9a3f5e32e8c244805e7233ef9.jpg\",\n  \"433532.jpg\": \"Aves/Streptopelia senegalensis/b8abd37f8cb3476688f4e2e2861d5dcd.jpg\",\n  \"433543.jpg\": \"Aves/Streptopelia senegalensis/b8965668a98a96298c153f4d9fc2e8ec.jpg\",\n  \"433544.jpg\": \"Aves/Streptopelia senegalensis/dc95113d3d2ef6e0aaf7ed91fc125a68.jpg\",\n  \"433571.jpg\": \"Aves/Ammodramus nelsoni/0bd6989330f2df7b9d1c32126de1c542.jpg\",\n  \"433572.jpg\": \"Aves/Ammodramus nelsoni/361ff95dd2d8be62840312eda35577c1.jpg\",\n  \"433587.jpg\": \"Mammalia/Cervus elaphus/6156635b98b044874fc3da4e92adba45.jpg\",\n  \"433624.jpg\": \"Mammalia/Cervus elaphus/f01d76b86962b7e3a11c3fe425bcfb7d.jpg\",\n  \"433665.jpg\": \"Mammalia/Cervus elaphus/bc195cebc132995762530123432dd6b9.jpg\",\n  \"433685.jpg\": \"Animalia/Bipalium kewense/ebac0588f1375fee832ccfc5b03cd064.jpg\",\n  \"433690.jpg\": \"Animalia/Bipalium kewense/1670809d8252c2137c8261224e8c6202.jpg\",\n  \"433691.jpg\": \"Animalia/Bipalium kewense/39df4bafe2bb40a8b6f0f58f18f073f4.jpg\",\n  \"433694.jpg\": \"Insecta/Megacyllene robiniae/9d6502324c6d0b3d22236de08aa6d6b4.jpg\",\n  \"433696.jpg\": \"Insecta/Megacyllene robiniae/eeaf0ba8aaf8d6405addbcdfe6aacc84.jpg\",\n  \"433697.jpg\": \"Insecta/Megacyllene robiniae/755964a23c5e94d9d525289dfc1bd600.jpg\",\n  \"433698.jpg\": \"Insecta/Megacyllene robiniae/01a0c9f4892a7dfc15d3a7d20d2f12a9.jpg\",\n  \"433708.jpg\": \"Insecta/Megacyllene robiniae/08baa460d2209846806d8fa223bfe674.jpg\",\n  \"433710.jpg\": \"Insecta/Megacyllene robiniae/336ea131ca38cf3196e064c6c96b1137.jpg\",\n  \"433717.jpg\": \"Insecta/Megacyllene robiniae/483d849f82948f51bbfa801e700d6859.jpg\",\n  \"433741.jpg\": \"Aves/Sporophila torqueola/8b2e1cc83607733d1bbc975752548f94.jpg\",\n  \"433751.jpg\": \"Aves/Sporophila torqueola/955bcdacf90c1fad07a7bb98c667ac5a.jpg\",\n  \"433755.jpg\": \"Aves/Sporophila torqueola/04b1e80e7346f4760b4dbfee0b039ab6.jpg\",\n  \"433759.jpg\": \"Aves/Sporophila torqueola/4f635ac918f0fc69ba3beb5c87caf4fc.jpg\",\n  \"433774.jpg\": \"Aves/Sporophila torqueola/be9f771514f4a93ce5c091da6998cb34.jpg\",\n  \"433778.jpg\": \"Aves/Sporophila torqueola/d8e70cd84b03b8b8bc37870b8daf134b.jpg\",\n  \"433781.jpg\": \"Aves/Sporophila torqueola/06a0a93395a614ddab993c755f3895a8.jpg\",\n  \"433924.jpg\": \"Amphibia/Acris gryllus/70f8d0c4cb2dc65607f37280abd044ee.jpg\",\n  \"433925.jpg\": \"Amphibia/Acris gryllus/c735a14744c325b21b2543419ad23155.jpg\",\n  \"434092.jpg\": \"Aves/Copsychus malabaricus/e6fbdbc7b9a01738d7463f70c0e084a5.jpg\",\n  \"434107.jpg\": \"Aves/Icterus pustulatus/4632c4f2739c16b48213f131c206a4fb.jpg\",\n  \"434114.jpg\": \"Aves/Icterus pustulatus/9d5a268ba8d4844547b277c11eb75dd3.jpg\",\n  \"434118.jpg\": \"Aves/Icterus pustulatus/5ca0b395924932dbf431823124e9a0fd.jpg\",\n  \"434132.jpg\": \"Aves/Icterus pustulatus/6d21da2ccf686d07e426dd10844291d2.jpg\",\n  \"434133.jpg\": \"Aves/Icterus pustulatus/cad5f4f74b7d0003455c1d0b5a2abecd.jpg\",\n  \"434140.jpg\": \"Aves/Icterus pustulatus/abfb996e1eded7f63e7ca5745ef119c6.jpg\",\n  \"434144.jpg\": \"Aves/Icterus pustulatus/18db9dadd9d6459a31cb95516ebe5ab3.jpg\",\n  \"434148.jpg\": \"Aves/Icterus pustulatus/4a0643a3f0548cef02bc22bec9427c5c.jpg\",\n  \"434151.jpg\": \"Aves/Icterus pustulatus/785c3978aba67a9122f002e62e39f37b.jpg\",\n  \"434154.jpg\": \"Reptilia/Gopherus polyphemus/5ee6b35f358607153a24f1ece0e0e8bf.jpg\",\n  \"434156.jpg\": \"Reptilia/Gopherus polyphemus/eb5c0ce01092706a96fa845bcc520bdd.jpg\",\n  \"434164.jpg\": \"Reptilia/Gopherus polyphemus/40d7859fb8feb06be353e674ac347697.jpg\",\n  \"434177.jpg\": \"Reptilia/Gopherus polyphemus/ddbb84f0fafef04e25113a773a0cea20.jpg\",\n  \"434178.jpg\": \"Reptilia/Gopherus polyphemus/10f1357b49a3d4b033c67dad1620d81b.jpg\",\n  \"434182.jpg\": \"Reptilia/Gopherus polyphemus/c10aca3c935959dc0e7c6e468fe76220.jpg\",\n  \"434185.jpg\": \"Reptilia/Gopherus polyphemus/9202566efe8c5592155a5e9ffd14b211.jpg\",\n  \"434190.jpg\": \"Reptilia/Gopherus polyphemus/fb9fcf3d312b8d2b39d8dccf6a3439a2.jpg\",\n  \"434193.jpg\": \"Reptilia/Gopherus polyphemus/620782d5fe04ab276f190b1ecdaa5e26.jpg\",\n  \"434231.jpg\": \"Insecta/Parnassius clodius/ba0851c4f2b4013ffe2a9778a75eb003.jpg\",\n  \"434242.jpg\": \"Mammalia/Marmota caligata/210907628c65bf6642bd395d6f9b227c.jpg\",\n  \"434243.jpg\": \"Mammalia/Marmota caligata/90cae082ac9f940af278c578540c309c.jpg\",\n  \"434250.jpg\": \"Mammalia/Marmota caligata/f3a6ff3577421bf6bf4e6626848b4a5e.jpg\",\n  \"434280.jpg\": \"Insecta/Euchaetes egle/9864446afff81e213dda6fed9d5476f3.jpg\",\n  \"434294.jpg\": \"Insecta/Euchaetes egle/f502d052fedf09908a399832b1a6928b.jpg\",\n  \"434315.jpg\": \"Insecta/Euchaetes egle/c076e4d483d9f1ce985dd9c135e40b3a.jpg\",\n  \"434321.jpg\": \"Insecta/Euchaetes egle/6dc322cdce03fbc7ea83d82ff3b65ae9.jpg\",\n  \"434333.jpg\": \"Insecta/Euchaetes egle/d6ce046eb911e03c6f71eed2bc4fa15b.jpg\",\n  \"434338.jpg\": \"Insecta/Euchaetes egle/5872a245b773bbb6a2422b2337cb8f94.jpg\",\n  \"434339.jpg\": \"Insecta/Euchaetes egle/834de0957e330a37c1b904b2797ed130.jpg\",\n  \"434341.jpg\": \"Insecta/Euchaetes egle/5f3934f4d978adbe4bbc2130964e5258.jpg\",\n  \"434358.jpg\": \"Reptilia/Diadophis punctatus amabilis/acb9acf0d91747aee1e48a56929928cc.jpg\",\n  \"434362.jpg\": \"Reptilia/Diadophis punctatus amabilis/a3864668d20435d2723d6892c06b2d53.jpg\",\n  \"434381.jpg\": \"Insecta/Diastictis fracturalis/33d4bbc3f02186f577a74636f5ba1c35.jpg\",\n  \"434382.jpg\": \"Insecta/Diastictis fracturalis/365899d1aca2845ed37826d50e50b689.jpg\",\n  \"434398.jpg\": \"Aves/Oreoscoptes montanus/74af03acb6249d988b18c91ed10fe328.jpg\",\n  \"434399.jpg\": \"Aves/Oreoscoptes montanus/d6e5361fd863d6bb74b15757029459aa.jpg\",\n  \"434402.jpg\": \"Aves/Oreoscoptes montanus/0a50ca02aea68f1ba7f74f0d9c7ced99.jpg\",\n  \"434406.jpg\": \"Aves/Oreoscoptes montanus/7c1161fb9c9ec6873d90bba6a3d393c9.jpg\",\n  \"434418.jpg\": \"Amphibia/Spea multiplicata/357786ee97c0a4839da888a181439804.jpg\",\n  \"434421.jpg\": \"Amphibia/Spea multiplicata/df588b7fb54836b864c301dadabd82f6.jpg\",\n  \"43443.jpg\": \"Aves/Milvago chimachima/6261473e12475260d39743a9285be822.jpg\",\n  \"434431.jpg\": \"Amphibia/Spea multiplicata/ba5d09a8c96597a955cf31e26496080e.jpg\",\n  \"434432.jpg\": \"Amphibia/Spea multiplicata/55e253090e5b963a5a763e313d51c501.jpg\",\n  \"434433.jpg\": \"Amphibia/Spea multiplicata/64ee621271d61e439fd9bb2757ff4a2b.jpg\",\n  \"434434.jpg\": \"Amphibia/Spea multiplicata/f78f97810421c5c626b78072f6422870.jpg\",\n  \"434435.jpg\": \"Amphibia/Spea multiplicata/92af0dae16dc246f7375b7fb78fea8e2.jpg\",\n  \"434439.jpg\": \"Amphibia/Spea multiplicata/1d7bbf5417488cced0a079590730ae63.jpg\",\n  \"434441.jpg\": \"Amphibia/Spea multiplicata/21a2c38a48d71703e0300ebec55baeeb.jpg\",\n  \"434442.jpg\": \"Amphibia/Spea multiplicata/18be30603f349ec2549ec9f75a7c91c6.jpg\",\n  \"43445.jpg\": \"Aves/Milvago chimachima/f2201b436a44615aad25e48c22cfa2ca.jpg\",\n  \"434450.jpg\": \"Amphibia/Ambystoma macrodactylum/05c38ecadfeb48bcb6d746f477be87d3.jpg\",\n  \"434455.jpg\": \"Amphibia/Ambystoma macrodactylum/619b56b1e91c401cc11f949fe32ab85d.jpg\",\n  \"434468.jpg\": \"Amphibia/Ambystoma macrodactylum/1fd3a5b2d9215ed5f28d1283d4239699.jpg\",\n  \"434469.jpg\": \"Mammalia/Sciurus griseus/bdde0fd6c854a60748b03ef59983a912.jpg\",\n  \"43447.jpg\": \"Aves/Milvago chimachima/63700c64fb73f18e6ea581427ba6e722.jpg\",\n  \"434477.jpg\": \"Mammalia/Sciurus griseus/149c618cd21fde29156ad795b289e003.jpg\",\n  \"43449.jpg\": \"Aves/Milvago chimachima/6429695809394967234d05cf75a6bb24.jpg\",\n  \"434508.jpg\": \"Mammalia/Sciurus griseus/57ed1ebfcbbbe12b7eba6603f45675a5.jpg\",\n  \"434509.jpg\": \"Mammalia/Sciurus griseus/97c91a18a895fff0fa2caeecf3f88a11.jpg\",\n  \"43452.jpg\": \"Aves/Milvago chimachima/8a2c3b30b32a71d35eda86a6193fd556.jpg\",\n  \"434536.jpg\": \"Mammalia/Sciurus griseus/97bbc12e27fa4e189ba80d2d491744fd.jpg\",\n  \"434545.jpg\": \"Mammalia/Sciurus griseus/da412021616240477b44d08862d80f47.jpg\",\n  \"43455.jpg\": \"Aves/Milvago chimachima/52590009a3562373acc34385390cde6b.jpg\",\n  \"434615.jpg\": \"Aves/Plegadis chihi/9e3e27b1c4830c880926e37ac0ace9a7.jpg\",\n  \"434728.jpg\": \"Aves/Plegadis chihi/7847df50a236645ad86c89491e47cf54.jpg\",\n  \"434744.jpg\": \"Aves/Plegadis chihi/c1b0b5ef4a74efae8e29fbda30346ba6.jpg\",\n  \"434829.jpg\": \"Insecta/Pseudeustrotia carneola/e2a6dd1c5cf9030e728db5e3d48e6ec2.jpg\",\n  \"434830.jpg\": \"Insecta/Pseudeustrotia carneola/d15075b576cc61c27ac15f12cefe585a.jpg\",\n  \"434832.jpg\": \"Insecta/Pseudeustrotia carneola/330990a0006e27cfa0a433e5b0649864.jpg\",\n  \"434833.jpg\": \"Insecta/Pseudeustrotia carneola/def1d7639b436a4ccd5679a5a5156eb1.jpg\",\n  \"434834.jpg\": \"Insecta/Pseudeustrotia carneola/72f76c964448c776827efcaaa501bbb8.jpg\",\n  \"434837.jpg\": \"Insecta/Pseudeustrotia carneola/0d5915569dfe39bccee171eab499f539.jpg\",\n  \"434838.jpg\": \"Insecta/Pseudeustrotia carneola/c96770e8089a170adf73e91312440ab9.jpg\",\n  \"434842.jpg\": \"Insecta/Pseudeustrotia carneola/59568805a9a914b5dfce5f04c34a0a21.jpg\",\n  \"434846.jpg\": \"Insecta/Pseudeustrotia carneola/aa46d8de48cda2e985d6c331ec831180.jpg\",\n  \"434923.jpg\": \"Aves/Poecile atricapillus/449717ad731692677a5fb66fd904220e.jpg\",\n  \"435000.jpg\": \"Aves/Poecile atricapillus/968e6470895e347a1b8a95e729f9fd21.jpg\",\n  \"435070.jpg\": \"Aves/Poecile atricapillus/528be842f14d42f00d665add017c64d2.jpg\",\n  \"4351.jpg\": \"Aves/Setophaga petechia/cdd359814cf6341d430526698a681650.jpg\",\n  \"435118.jpg\": \"Aves/Poecile atricapillus/4861395b55be0c3986ca721ccb7a71de.jpg\",\n  \"435159.jpg\": \"Aves/Poecile atricapillus/f8475eb14cfee4c371c6bbb5c7b030d4.jpg\",\n  \"4352.jpg\": \"Aves/Setophaga petechia/8f3def5a712e5f63885036a2748ac7a9.jpg\",\n  \"435218.jpg\": \"Aves/Euphonia affinis/5348ca46494f3b016bc866260acb0c26.jpg\",\n  \"435229.jpg\": \"Insecta/Evergestis pallidata/c98f58ff6a932852fb0b87e7cab55088.jpg\",\n  \"435231.jpg\": \"Insecta/Evergestis pallidata/4c58568334c8129fdb436d42619572d3.jpg\",\n  \"435267.jpg\": \"Amphibia/Acris blanchardi/6f9481827c95801d4a20c9da4eb29848.jpg\",\n  \"435281.jpg\": \"Amphibia/Acris blanchardi/459937be7cb6a4186714075a4bc76da0.jpg\",\n  \"435283.jpg\": \"Amphibia/Acris blanchardi/3e8081d5526cb9a4ecc49872070b9f83.jpg\",\n  \"435357.jpg\": \"Amphibia/Acris blanchardi/700c85f7c6e7ccecf1efd0f0ab3840d0.jpg\",\n  \"435432.jpg\": \"Amphibia/Acris blanchardi/5584ff2e5b2edccd126076f7598a0aaf.jpg\",\n  \"435478.jpg\": \"Insecta/Xylophanes tersa/950f66147941e7a66fe89fcef6b447c1.jpg\",\n  \"435479.jpg\": \"Insecta/Xylophanes tersa/fd0ae952fd6dca625df36b562ae02be5.jpg\",\n  \"435485.jpg\": \"Insecta/Xylophanes tersa/7d88a3581990cc839e3207bdcf7e4d03.jpg\",\n  \"435486.jpg\": \"Insecta/Xylophanes tersa/6044ee6e752a68a04ee40eacb2f6e084.jpg\",\n  \"435488.jpg\": \"Insecta/Xylophanes tersa/2fc555fe6119c54db37fbd52d0f0a942.jpg\",\n  \"435489.jpg\": \"Insecta/Xylophanes tersa/92e12be7225502f0bc3997c4e5fa3406.jpg\",\n  \"435490.jpg\": \"Insecta/Xylophanes tersa/d8cdfe316e251c1ac5c0f0c1a99685cc.jpg\",\n  \"435494.jpg\": \"Insecta/Xylophanes tersa/d92126926b91f88ca4fe43d2fe8c021c.jpg\",\n  \"435496.jpg\": \"Insecta/Xylophanes tersa/c68c7b35a48f501c1d3711dd7eb1a724.jpg\",\n  \"435497.jpg\": \"Insecta/Xylophanes tersa/0c29cfce56a1f38b6fda91912179234d.jpg\",\n  \"435505.jpg\": \"Reptilia/Crotalus viridis/f0fe90f809f9974d4285ee8292c021dd.jpg\",\n  \"435506.jpg\": \"Reptilia/Crotalus viridis/dcff73b847d05e8d4d24e5b0ac0bda19.jpg\",\n  \"435508.jpg\": \"Reptilia/Crotalus viridis/f103e1efcb3094b9e53d7fbeb0e23f82.jpg\",\n  \"435510.jpg\": \"Reptilia/Crotalus viridis/59ff21ff3b9820881939aafe4c8ba188.jpg\",\n  \"435517.jpg\": \"Reptilia/Crotalus viridis/5b73fc6df11735c458b6b7f417a56469.jpg\",\n  \"435535.jpg\": \"Reptilia/Crotalus viridis/12c97b717a27716639ba7e315175ec6c.jpg\",\n  \"435536.jpg\": \"Reptilia/Crotalus viridis/50c387205ccd7abe28b340732c54a4d5.jpg\",\n  \"435541.jpg\": \"Insecta/Vanessa gonerilla/278c9202a2abe4194db1fb1bc774ff3c.jpg\",\n  \"435658.jpg\": \"Aves/Geococcyx velox/6942c28dd65f6981fd02efcb5851b09d.jpg\",\n  \"435701.jpg\": \"Insecta/Hypercompe scribonia/4a2979f5b98b21cdf7c0ec73f42b390a.jpg\",\n  \"435702.jpg\": \"Insecta/Hypercompe scribonia/580874a7dcf33d65bfbcca509f603cbf.jpg\",\n  \"435705.jpg\": \"Insecta/Hypercompe scribonia/0a617936c9c8749deb294e06e94f3fbd.jpg\",\n  \"435718.jpg\": \"Insecta/Hypercompe scribonia/47fcf36861dbe8296f1234d2f7dadbc6.jpg\",\n  \"435721.jpg\": \"Insecta/Hypercompe scribonia/3832be3a5bbae9d994854e483959726e.jpg\",\n  \"435722.jpg\": \"Insecta/Hypercompe scribonia/d62ea9e0cb3c2f1fcd4919495b9b4e7e.jpg\",\n  \"435728.jpg\": \"Insecta/Hypercompe scribonia/a609083fc6e3dc888ee265f29dd36702.jpg\",\n  \"435731.jpg\": \"Aves/Microcarbo melanoleucos/747118f2031a027c524ea2cad5a7c3f1.jpg\",\n  \"435735.jpg\": \"Aves/Microcarbo melanoleucos/af700e88c93a391faa2a4dc1136a6cc4.jpg\",\n  \"435767.jpg\": \"Aves/Strix varia/edd3af88e32a6cf30eca47aaa88f1bf4.jpg\",\n  \"435778.jpg\": \"Aves/Strix varia/3d2a1e0a9732a3ac01b7c9b1df0c70b4.jpg\",\n  \"435781.jpg\": \"Aves/Strix varia/29e76662af7505170e481f6fde42816c.jpg\",\n  \"435819.jpg\": \"Aves/Strix varia/a96d6eb86c619c5c48476442a0dd6441.jpg\",\n  \"435834.jpg\": \"Aves/Strix varia/be78b2c182ea52602d4726c53f37a6e1.jpg\",\n  \"435845.jpg\": \"Aves/Strix varia/c432219e6480973f8ac4bc106d0f939d.jpg\",\n  \"435896.jpg\": \"Aves/Calidris canutus/ccde3a44f1e3666483d3840a75f173f9.jpg\",\n  \"435900.jpg\": \"Aves/Calidris canutus/6db623c93e83af589ec02fa9478b1011.jpg\",\n  \"435901.jpg\": \"Aves/Calidris canutus/f68a1f22d8addca5b46b87080aa7df56.jpg\",\n  \"435907.jpg\": \"Aves/Calidris canutus/0ca22617d855326b0168ebaddcdfe2d5.jpg\",\n  \"435911.jpg\": \"Aves/Calidris canutus/1696c7b451e7bb5f12861ed278cba54b.jpg\",\n  \"435925.jpg\": \"Aves/Calidris canutus/a95be90e7153df28ab5400f3836f228b.jpg\",\n  \"435975.jpg\": \"Insecta/Eacles imperialis/af0463cd7ec7222747733acacda4af8c.jpg\",\n  \"435981.jpg\": \"Insecta/Eacles imperialis/06374ec8745ac6d21923b1246e9d99d7.jpg\",\n  \"435982.jpg\": \"Insecta/Eacles imperialis/08b9658cc96ca2550498e4281b8daa8a.jpg\",\n  \"435990.jpg\": \"Insecta/Eacles imperialis/ec62225ea9876d4087cd48d140ef3ca3.jpg\",\n  \"436000.jpg\": \"Insecta/Eacles imperialis/6a6c9951e1e3844414665f59382ada56.jpg\",\n  \"436006.jpg\": \"Insecta/Eacles imperialis/58ca2241b582f1c0af56ae12330d0ea3.jpg\",\n  \"436013.jpg\": \"Insecta/Eacles imperialis/17da108a3a4703fe511b0c8626232942.jpg\",\n  \"436046.jpg\": \"Insecta/Leucorrhinia frigida/c810443caf49b59148fb56958369b45b.jpg\",\n  \"436067.jpg\": \"Aves/Baeolophus atricristatus/5e14d36cf3fc4be75cdd33fe83c90dff.jpg\",\n  \"436084.jpg\": \"Aves/Baeolophus atricristatus/561548e43e59da86372b6f983a20e423.jpg\",\n  \"436086.jpg\": \"Aves/Baeolophus atricristatus/4bc00cb587f581ec09475c329cc48e48.jpg\",\n  \"436087.jpg\": \"Aves/Baeolophus atricristatus/8ecb39039a9c589d85c17ac4fe79df54.jpg\",\n  \"436091.jpg\": \"Aves/Baeolophus atricristatus/ba3ac3e78b85918aa40201640e0f88e4.jpg\",\n  \"436101.jpg\": \"Aves/Baeolophus atricristatus/570b83f8c974059decc54667d350e7d5.jpg\",\n  \"436126.jpg\": \"Aves/Bombycilla garrulus/052222bc89d3744dcca9e9df3368e4d8.jpg\",\n  \"436127.jpg\": \"Aves/Bombycilla garrulus/881228c36ea00f2a4440e93357211b93.jpg\",\n  \"436129.jpg\": \"Aves/Bombycilla garrulus/a51cdbcb28fe8617d9766aa55b052f9e.jpg\",\n  \"436141.jpg\": \"Aves/Bombycilla garrulus/2363776d596a69f1092c943a7b220201.jpg\",\n  \"436147.jpg\": \"Aves/Bombycilla garrulus/5434c3a49a2c06c7f6f89900c1f3aefb.jpg\",\n  \"436148.jpg\": \"Aves/Bombycilla garrulus/91dc6523719f3b3342be248d5d82c940.jpg\",\n  \"436162.jpg\": \"Mammalia/Panthera leo/8f6e4c4bd33d41c40ecb8d3e00edea4e.jpg\",\n  \"436164.jpg\": \"Mammalia/Panthera leo/fc9e67d1ab14499c970357fb99d72907.jpg\",\n  \"436165.jpg\": \"Mammalia/Panthera leo/8a8e4e78216a955a9d9fa94908915f0c.jpg\",\n  \"436166.jpg\": \"Mammalia/Panthera leo/6c5332357e04f135c724540ed73d72ad.jpg\",\n  \"436176.jpg\": \"Mammalia/Panthera leo/c76072b923f9951895496e06a4d6cc91.jpg\",\n  \"436178.jpg\": \"Mammalia/Panthera leo/b815ff05b4960531fe223a9113296375.jpg\",\n  \"436191.jpg\": \"Mammalia/Panthera leo/6a887f68f21805b2e4780c69e6d92900.jpg\",\n  \"436198.jpg\": \"Amphibia/Bufotes viridis/1d958ee312542368d39cd76fd290e8cc.jpg\",\n  \"436213.jpg\": \"Aves/Coracias benghalensis/1ce29c18be18092a3671b7be05039f53.jpg\",\n  \"436214.jpg\": \"Aves/Coracias benghalensis/a5e7fce4a91f794a58af707f6dabcf9c.jpg\",\n  \"436244.jpg\": \"Insecta/Schizura unicornis/3f239fa15628aa09f45511dc0e6b13dd.jpg\",\n  \"436304.jpg\": \"Aves/Eumomota superciliosa/aebf989ae0546a83c65e7e69c4a8605a.jpg\",\n  \"436310.jpg\": \"Aves/Eumomota superciliosa/514e1f82c6fd59b804c21c43e38cac75.jpg\",\n  \"436313.jpg\": \"Aves/Eumomota superciliosa/aed3ca6e94f2330d5332964fe4dbff23.jpg\",\n  \"436316.jpg\": \"Aves/Eumomota superciliosa/fd7ffc30fcae26fdf4b29cb7002f3cb7.jpg\",\n  \"436398.jpg\": \"Insecta/Euphydryas chalcedona/b3f58eb90697b36760530edd8c61d1d9.jpg\",\n  \"436399.jpg\": \"Insecta/Euphydryas chalcedona/7d1362462e87655f804bf564050a3e10.jpg\",\n  \"436436.jpg\": \"Insecta/Euphydryas chalcedona/d75c23730a2c7daeac678053678db1a0.jpg\",\n  \"436456.jpg\": \"Insecta/Euphydryas chalcedona/ed2c168b27dd973992ee034eff947577.jpg\",\n  \"436461.jpg\": \"Insecta/Euphydryas chalcedona/0cbe18c8a542a58dafdd1b770a2c62d3.jpg\",\n  \"436481.jpg\": \"Aves/Carduelis cannabina/fdcfb947a37bb7bd4131266a48bba42f.jpg\",\n  \"436489.jpg\": \"Aves/Carduelis cannabina/05131869099a28a8757a86cdeb6b84d2.jpg\",\n  \"436507.jpg\": \"Aves/Carduelis cannabina/f54230cd63cd191efd0954e1f6ac86b2.jpg\",\n  \"436524.jpg\": \"Aves/Carduelis cannabina/06e7b7272e5bfddcfcbcad2529d25497.jpg\",\n  \"436525.jpg\": \"Aves/Carduelis cannabina/6695e6a0e9e3d2520a4fd15c116ae537.jpg\",\n  \"436526.jpg\": \"Aves/Carduelis cannabina/59afc098a76c4c29e1b8b7b0b628c75c.jpg\",\n  \"43657.jpg\": \"Insecta/Chlorochlamys chloroleucaria/21da91bedefd0983a6abc3aa8a3cdf64.jpg\",\n  \"43662.jpg\": \"Insecta/Chlorochlamys chloroleucaria/05ae16269fda34385d1faea67cefeed6.jpg\",\n  \"43663.jpg\": \"Insecta/Chlorochlamys chloroleucaria/da62507d3a7eb14da6872d9a2bcabbc0.jpg\",\n  \"43664.jpg\": \"Insecta/Chlorochlamys chloroleucaria/27f4b42bdb82562110031163a9dc1e8e.jpg\",\n  \"43667.jpg\": \"Insecta/Chlorochlamys chloroleucaria/98d71abb928777a4bd1255cdbad8d88f.jpg\",\n  \"43669.jpg\": \"Insecta/Chlorochlamys chloroleucaria/571c2fb34f70a00ab9188e19baee3e50.jpg\",\n  \"4367.jpg\": \"Aves/Setophaga petechia/a3b43627345808e9b64c3b6430a915cb.jpg\",\n  \"43671.jpg\": \"Insecta/Chlorochlamys chloroleucaria/ce84d2fc81b9b2badf585942b05519a6.jpg\",\n  \"43672.jpg\": \"Insecta/Chlorochlamys chloroleucaria/77d9bfffa0c08fc8a4ec7a711f7d5519.jpg\",\n  \"43673.jpg\": \"Insecta/Chlorochlamys chloroleucaria/e5364f652018edf8b330631dc8ef4788.jpg\",\n  \"43674.jpg\": \"Insecta/Chlorochlamys chloroleucaria/8ccbc5e2badd64add4a7866c6abdff4e.jpg\",\n  \"436757.jpg\": \"Insecta/Heraclides cresphontes/d16dabea8d400353aca2431818db08ca.jpg\",\n  \"43676.jpg\": \"Insecta/Chlorochlamys chloroleucaria/d474b6de104579782d33bf20bc7f08e8.jpg\",\n  \"436775.jpg\": \"Insecta/Heraclides cresphontes/f5dee92e2232d636108701ee7a4c675d.jpg\",\n  \"43678.jpg\": \"Insecta/Chlorochlamys chloroleucaria/beaab32a21218dc8eab9a53d9b02c669.jpg\",\n  \"43679.jpg\": \"Insecta/Chlorochlamys chloroleucaria/4ba5e6d5bd8e285642f97a4f24f5f775.jpg\",\n  \"436792.jpg\": \"Insecta/Heraclides cresphontes/b45e12778ee73a3e25cab6bb1fb104c3.jpg\",\n  \"43680.jpg\": \"Insecta/Chlorochlamys chloroleucaria/3588ca29f69d9346031acf339746d91a.jpg\",\n  \"43681.jpg\": \"Insecta/Chlorochlamys chloroleucaria/9ef38e93a3e2f3222aacd3236953185e.jpg\",\n  \"436813.jpg\": \"Insecta/Heraclides cresphontes/0125567db169209b6d7ae06657fcc8c3.jpg\",\n  \"436814.jpg\": \"Insecta/Heraclides cresphontes/110f3e38c09713c60a9cc329ca2bc6e3.jpg\",\n  \"43682.jpg\": \"Insecta/Chlorochlamys chloroleucaria/a80665fae603b85d8ad276a1341d1a3c.jpg\",\n  \"436824.jpg\": \"Insecta/Heraclides cresphontes/ad11644a1cf27d69994ce29ce7301136.jpg\",\n  \"436833.jpg\": \"Insecta/Heraclides cresphontes/3f45de01f793967dd025dcdebed4c0ab.jpg\",\n  \"43685.jpg\": \"Insecta/Chlorochlamys chloroleucaria/eb729db336c0fff326f3924b82440fa6.jpg\",\n  \"436879.jpg\": \"Insecta/Brachymesia furcata/1c14f34e007cca66b134a937401e885d.jpg\",\n  \"43690.jpg\": \"Insecta/Chlorochlamys chloroleucaria/6046919cc2a3aa145cbdbaeef1caac94.jpg\",\n  \"43691.jpg\": \"Insecta/Chlorochlamys chloroleucaria/150ebfe9c258f332d6c173408214df5e.jpg\",\n  \"43692.jpg\": \"Insecta/Chlorochlamys chloroleucaria/350f25f690c134affa53621ad9cd9d33.jpg\",\n  \"436948.jpg\": \"Animalia/Anthopleura xanthogrammica/17e9466aedcca06ffbe34157065c83fa.jpg\",\n  \"436957.jpg\": \"Animalia/Anthopleura xanthogrammica/649d0de93ab154941e905ac52b14ecfb.jpg\",\n  \"43696.jpg\": \"Insecta/Chlorochlamys chloroleucaria/fb4aa049b646f26efef542151c016e27.jpg\",\n  \"436979.jpg\": \"Animalia/Anthopleura xanthogrammica/30e5c5b5092769b2758562d9c7eca911.jpg\",\n  \"437.jpg\": \"Mammalia/Ursus americanus/d56fde3bbcb5566000c4bcde91082167.jpg\",\n  \"43700.jpg\": \"Insecta/Chlorochlamys chloroleucaria/98ff0c452b38f14112bd75e36fea3468.jpg\",\n  \"43704.jpg\": \"Insecta/Zerene cesonia/673bfd4b5d44726291c603b30d4130f5.jpg\",\n  \"43705.jpg\": \"Insecta/Zerene cesonia/2f9af614622782d50188fa68ce7fac28.jpg\",\n  \"43706.jpg\": \"Insecta/Zerene cesonia/81a96420fbaab966319aecd8b05d9ca5.jpg\",\n  \"437112.jpg\": \"Aves/Accipiter striatus/8dc11ab88de19a67c5cd47b81100f5c5.jpg\",\n  \"437135.jpg\": \"Aves/Accipiter striatus/1825e2d9c1b602ae93619023e7bf1f74.jpg\",\n  \"437136.jpg\": \"Aves/Accipiter striatus/61b414ec80e2e54e3bf7a3f607c59266.jpg\",\n  \"437146.jpg\": \"Aves/Accipiter striatus/42d0d7e496dcf5f065609ed38eb3e625.jpg\",\n  \"437164.jpg\": \"Aves/Accipiter striatus/5e8323ee2488f21892130b0c133c571c.jpg\",\n  \"437172.jpg\": \"Aves/Accipiter striatus/aeb4bc1ba06ba59f6dcfba904f96689a.jpg\",\n  \"43724.jpg\": \"Insecta/Zerene cesonia/ee875b5c9ca6a829a05fd4326052e90b.jpg\",\n  \"43725.jpg\": \"Insecta/Zerene cesonia/a7a1d8a8dd148f5f1dbe93d35bb0f7a7.jpg\",\n  \"43726.jpg\": \"Insecta/Zerene cesonia/cd5e2ead62a93c1086d6ddfe9b166fe1.jpg\",\n  \"43727.jpg\": \"Insecta/Zerene cesonia/0d5509e642de1fe0471b407e1bfff6ff.jpg\",\n  \"43729.jpg\": \"Insecta/Zerene cesonia/9270bd5e9956b4df3a6869f97b374ff9.jpg\",\n  \"437333.jpg\": \"Actinopterygii/Gambusia holbrooki/01afe2d4f3710e18c07a41210304920f.jpg\",\n  \"437334.jpg\": \"Actinopterygii/Gambusia holbrooki/582e453f8d0e790728dd4ab0a9ec60e2.jpg\",\n  \"437348.jpg\": \"Insecta/Arilus cristatus/4d406c98d54a3fa60047775b826dc7c8.jpg\",\n  \"437368.jpg\": \"Insecta/Arilus cristatus/d113ab1e9426cf1d11f4cb1c7d8d6302.jpg\",\n  \"437369.jpg\": \"Insecta/Arilus cristatus/3be0c99c900f913bd4d825ec3f32c674.jpg\",\n  \"43738.jpg\": \"Insecta/Zerene cesonia/5d78f1468b00663d5d34dfc439e4f296.jpg\",\n  \"43739.jpg\": \"Insecta/Zerene cesonia/9f09087b5ac07522bfcfb0aa0924f74e.jpg\",\n  \"437390.jpg\": \"Insecta/Arilus cristatus/c83b943c2654857bd80893a526107745.jpg\",\n  \"437391.jpg\": \"Insecta/Arilus cristatus/b3e0362732704bd47c0174a16db76c65.jpg\",\n  \"437396.jpg\": \"Insecta/Arilus cristatus/be0d102fc34dc2edfc5875342944eed3.jpg\",\n  \"437401.jpg\": \"Insecta/Arilus cristatus/9f3bbab9c73b9bf3f67664083ff5601b.jpg\",\n  \"437406.jpg\": \"Insecta/Arilus cristatus/8ed0e7ba058da87139025efb353cd219.jpg\",\n  \"437419.jpg\": \"Insecta/Arilus cristatus/618f12cf09a06696879fb9319232b1f4.jpg\",\n  \"43742.jpg\": \"Insecta/Zerene cesonia/ad50fb52d9c93e2cb53b9da98b356db1.jpg\",\n  \"437421.jpg\": \"Insecta/Arilus cristatus/fd1f38063e5fc66d75b95de5b0e5e858.jpg\",\n  \"43743.jpg\": \"Insecta/Zerene cesonia/14608fab56d9fd49a7f195ce72c7761d.jpg\",\n  \"43744.jpg\": \"Insecta/Zerene cesonia/4918ed7de0932757d02b3baeaf516a76.jpg\",\n  \"43745.jpg\": \"Insecta/Zerene cesonia/52b4e8b3ded0173d1ad023ead595da4b.jpg\",\n  \"437453.jpg\": \"Insecta/Arilus cristatus/51f769a6407d73251be3094769b9941c.jpg\",\n  \"43746.jpg\": \"Insecta/Zerene cesonia/c1622505bdc0b9fe8bc8bd4d3d56985e.jpg\",\n  \"43748.jpg\": \"Insecta/Zerene cesonia/3b92a25aca554b9aa4005eb5d76b4497.jpg\",\n  \"43752.jpg\": \"Insecta/Zerene cesonia/1274b53954c9bf01c8b5766ccb8d9f46.jpg\",\n  \"437521.jpg\": \"Aves/Haliaeetus leucogaster/935175a83067906028ed357f8c90f2ff.jpg\",\n  \"437556.jpg\": \"Insecta/Leptysma marginicollis/3146b64bee5a45b4b176e4597ed098c2.jpg\",\n  \"437560.jpg\": \"Insecta/Anthanassa texana/d0bbed7f1c1532c50d798d24b9442035.jpg\",\n  \"437562.jpg\": \"Insecta/Anthanassa texana/655a932bbaba570c4a5f9b822dab0295.jpg\",\n  \"437569.jpg\": \"Insecta/Anthanassa texana/bc3e6ae1594678ba1bd04dcccaf2d6fb.jpg\",\n  \"437575.jpg\": \"Insecta/Anthanassa texana/d0ba8b6149dfc4948ce41739912fcca3.jpg\",\n  \"43759.jpg\": \"Insecta/Zerene cesonia/62037b4974739677b6e6aea5357407d6.jpg\",\n  \"437595.jpg\": \"Insecta/Anthanassa texana/e8aadafd8ecae200ba6b992de53eb0e7.jpg\",\n  \"437596.jpg\": \"Insecta/Anthanassa texana/73e58b962dd9de829286050cc0efd35e.jpg\",\n  \"43760.jpg\": \"Insecta/Zerene cesonia/62a910b13fc7bcf8e2e54bf7b3e93dcf.jpg\",\n  \"437602.jpg\": \"Insecta/Anthanassa texana/86cbba1c4e16269ae23c433cec171b5e.jpg\",\n  \"437603.jpg\": \"Insecta/Anthanassa texana/15cbb730b2e486102683d8139c42d070.jpg\",\n  \"437607.jpg\": \"Insecta/Anthanassa texana/3ed0cb43ae9fef2c81125d05a2aa1e44.jpg\",\n  \"437609.jpg\": \"Insecta/Anthanassa texana/786a1116e8986c2239bd060c1b81e441.jpg\",\n  \"43761.jpg\": \"Insecta/Zerene cesonia/73db695a16a57452bf250b1d623425d6.jpg\",\n  \"437619.jpg\": \"Reptilia/Zamenis longissimus/61442e957f329f0145c58e0232731996.jpg\",\n  \"437649.jpg\": \"Insecta/Callophrys niphon/e20de627e1c3f44142d45e49b0fbbbf3.jpg\",\n  \"437661.jpg\": \"Insecta/Callophrys niphon/62822de2d2faa1ad22aba895f71c5ea2.jpg\",\n  \"437668.jpg\": \"Insecta/Pterophylla camellifolia/f1fee6bf799c2d9c92bc5efb359616b4.jpg\",\n  \"437669.jpg\": \"Insecta/Pterophylla camellifolia/4c32365386305f6b5a9dd34a68d2eafb.jpg\",\n  \"437670.jpg\": \"Insecta/Pterophylla camellifolia/01d107d108edaa246d68a7fc7b4c4094.jpg\",\n  \"437674.jpg\": \"Insecta/Haploa clymene/a5df83b79b18dc76c50a0e7b54006e10.jpg\",\n  \"437675.jpg\": \"Insecta/Haploa clymene/fc878e86357d66fb25f7205b8bb710e3.jpg\",\n  \"437679.jpg\": \"Insecta/Haploa clymene/aa6202c0842c624ffffe3d066f7e3c0d.jpg\",\n  \"437682.jpg\": \"Insecta/Haploa clymene/71af8527df117cb74bed14c142ec3770.jpg\",\n  \"437685.jpg\": \"Insecta/Haploa clymene/f321f285c9c38ac71e06d71024ec3b06.jpg\",\n  \"437687.jpg\": \"Insecta/Haploa clymene/72fae74cedd1fe6e7c4b38743e226180.jpg\",\n  \"43769.jpg\": \"Insecta/Zerene cesonia/4abb7bdea6a932393c49646779275f42.jpg\",\n  \"437690.jpg\": \"Insecta/Haploa clymene/3154bbe7e3f6d2ac7a6fbf660f5c9c45.jpg\",\n  \"437694.jpg\": \"Insecta/Haploa clymene/0da647905cb5943b7e8cc9a1f7061fdd.jpg\",\n  \"43772.jpg\": \"Insecta/Zerene cesonia/84790dc5ccb466057eff314d94270895.jpg\",\n  \"437771.jpg\": \"Aves/Pyrocephalus rubinus/c88eb9a6311076f3750b88e1ee82759f.jpg\",\n  \"437808.jpg\": \"Aves/Pyrocephalus rubinus/5df026f4acdaaa1263f06ecaffdb2131.jpg\",\n  \"437827.jpg\": \"Aves/Pyrocephalus rubinus/3a95e53d16ebd55f769a0e234c3056a8.jpg\",\n  \"437833.jpg\": \"Aves/Pyrocephalus rubinus/fe9be05f2b895637b7057d5d96dc0d02.jpg\",\n  \"437853.jpg\": \"Aves/Pyrocephalus rubinus/9c19a049b0db381aa09c2fc5d3761b43.jpg\",\n  \"43800.jpg\": \"Reptilia/Thamnophis proximus/e2d56c97d639ecd4c8d47c4e09bac245.jpg\",\n  \"438014.jpg\": \"Aves/Pyrocephalus rubinus/1b2c798c3b9cbafe22b453e234891400.jpg\",\n  \"438020.jpg\": \"Aves/Pyrocephalus rubinus/d84168bdbfd5bda4607bc89e1e46292a.jpg\",\n  \"438035.jpg\": \"Aves/Pyrocephalus rubinus/9eaf77464c0fcb76b3f2e8b25d7c654b.jpg\",\n  \"438052.jpg\": \"Amphibia/Hemidactylium scutatum/f668d2eff73275588971671d59eef6e9.jpg\",\n  \"438055.jpg\": \"Amphibia/Hemidactylium scutatum/818389324ba1e8b6f096852c7ec9d564.jpg\",\n  \"43809.jpg\": \"Reptilia/Thamnophis proximus/549ed73737b612a80c22ca5c35166fcd.jpg\",\n  \"438110.jpg\": \"Aves/Jacana spinosa/2b97efe0f87741177639fad620d07e0c.jpg\",\n  \"438127.jpg\": \"Aves/Jacana spinosa/7e3d85d728da56cdeee5cfdf253f7aa1.jpg\",\n  \"43813.jpg\": \"Reptilia/Thamnophis proximus/b3806af64128f840da5c10e90e89c5a7.jpg\",\n  \"438130.jpg\": \"Aves/Jacana spinosa/6ed8c0fea36c3a3ed6d6045b9c4376d7.jpg\",\n  \"43814.jpg\": \"Reptilia/Thamnophis proximus/0195e31b028b94b906ef85ee7ac85c9c.jpg\",\n  \"438144.jpg\": \"Aves/Jacana spinosa/1bb757527e24e0ddfc22c62a1c28045b.jpg\",\n  \"438153.jpg\": \"Aves/Jacana spinosa/160255ade5b0100f0ec61650a1c276eb.jpg\",\n  \"438158.jpg\": \"Aves/Jacana spinosa/a27fccc3fdcb21e6c87b0b8b260a1418.jpg\",\n  \"438169.jpg\": \"Animalia/Pleuroncodes planipes/14e0c8262fbabcb014b8f719ec8d34fd.jpg\",\n  \"43817.jpg\": \"Reptilia/Thamnophis proximus/bcd7586bd6a95a66d83091224f7c6e68.jpg\",\n  \"438170.jpg\": \"Animalia/Pleuroncodes planipes/145808ef23c20f46950e0d4f35c9e8b8.jpg\",\n  \"438175.jpg\": \"Animalia/Pleuroncodes planipes/8e02766b7ee247538b46933c5d886d90.jpg\",\n  \"438176.jpg\": \"Animalia/Pleuroncodes planipes/d39ec5b574b1a19ec714dc0bb28ef562.jpg\",\n  \"438179.jpg\": \"Animalia/Pleuroncodes planipes/f93bafac67f7951ae37cc5e86b4e0d87.jpg\",\n  \"438180.jpg\": \"Animalia/Pleuroncodes planipes/ad985a57c40f40814d462d42c7f59e3c.jpg\",\n  \"438183.jpg\": \"Animalia/Pleuroncodes planipes/2b68ea8febc57df78d0555124d4d9cbc.jpg\",\n  \"438189.jpg\": \"Animalia/Pleuroncodes planipes/c6b5ed7a14f0cce2970534b62658ecab.jpg\",\n  \"43819.jpg\": \"Reptilia/Thamnophis proximus/3e81d341b50ca8e1d38a502c56cf0f61.jpg\",\n  \"438192.jpg\": \"Animalia/Pleuroncodes planipes/b3373cc4a97bc7840135ba8eacca806a.jpg\",\n  \"438194.jpg\": \"Insecta/Prochoerodes lineola/72f8ad9da24d547b1d85609bb722132c.jpg\",\n  \"438198.jpg\": \"Insecta/Prochoerodes lineola/d5b7662d2e7fac3b8491dff010229c7f.jpg\",\n  \"438199.jpg\": \"Insecta/Prochoerodes lineola/370628584ea61f4389e8505703b90847.jpg\",\n  \"438215.jpg\": \"Insecta/Prochoerodes lineola/8a0e2aa008b3f736f3db246e313d2c13.jpg\",\n  \"438218.jpg\": \"Insecta/Prochoerodes lineola/37be205e1e313b069cb6183b0cad004d.jpg\",\n  \"43822.jpg\": \"Reptilia/Thamnophis proximus/93704560c2f71f944f9804e000694fcd.jpg\",\n  \"438225.jpg\": \"Insecta/Prochoerodes lineola/3d18eeba8c9ef6ce3b2ed2f9c6835b95.jpg\",\n  \"438231.jpg\": \"Insecta/Prochoerodes lineola/3fca1cfdd9b464a6658e3650b45e244a.jpg\",\n  \"438232.jpg\": \"Insecta/Prochoerodes lineola/91a95da2b23ac0f6e149efdc3ff003dc.jpg\",\n  \"438238.jpg\": \"Insecta/Hylephila phyleus/295a2e066393ccfcea336bb0476f98cb.jpg\",\n  \"43840.jpg\": \"Reptilia/Thamnophis proximus/84b1082d4cc800e366aa10965df8988a.jpg\",\n  \"438414.jpg\": \"Insecta/Hylephila phyleus/348cda9f11a436d2b4cbb847b06fa6a7.jpg\",\n  \"438416.jpg\": \"Insecta/Hylephila phyleus/57bcbebc1481b9e3ac3fdd89d5052115.jpg\",\n  \"43847.jpg\": \"Reptilia/Thamnophis proximus/fc08cdac851a403d717fa0aff1154186.jpg\",\n  \"438517.jpg\": \"Insecta/Hylephila phyleus/a0b61178fcf3fdee9f2bc4da0856360c.jpg\",\n  \"438571.jpg\": \"Insecta/Hylephila phyleus/b196fbcb609f52bc1aecd3b049211386.jpg\",\n  \"438666.jpg\": \"Insecta/Hylephila phyleus/92df52a5aee958514136e568bff9c5b7.jpg\",\n  \"438729.jpg\": \"Insecta/Ectropis crepuscularia/069cd3db5e09c2a50a6e720a1005f8b6.jpg\",\n  \"43875.jpg\": \"Reptilia/Thamnophis proximus/950227c9de69b1f66de87a025fb7b6e1.jpg\",\n  \"43877.jpg\": \"Reptilia/Thamnophis proximus/075428fa94b9c00331419fe9d3196e08.jpg\",\n  \"438775.jpg\": \"Aves/Euphagus cyanocephalus/8134912d0fa53e58fdce5ce4829701ed.jpg\",\n  \"43897.jpg\": \"Reptilia/Thamnophis proximus/f04f091b97f559db5c75fe927649eb1f.jpg\",\n  \"439027.jpg\": \"Mammalia/Canis lupus/2fed0caa29ac396a06f30f12d8f79674.jpg\",\n  \"439033.jpg\": \"Mammalia/Canis lupus/e8ef3fbe3e8c077781f150f4d8aa8ab6.jpg\",\n  \"439062.jpg\": \"Reptilia/Thamnophis sirtalis infernalis/8c88fbbc8a6c094a4f14224176ee1914.jpg\",\n  \"439063.jpg\": \"Reptilia/Thamnophis sirtalis infernalis/65b8a5ca9d6262fa159686702a4d4202.jpg\",\n  \"43907.jpg\": \"Reptilia/Thamnophis proximus/d9d38656d36834c72d7623f193bcf8ad.jpg\",\n  \"43908.jpg\": \"Reptilia/Thamnophis proximus/1cceb89eaccc5e87a4ff839b339456f9.jpg\",\n  \"439082.jpg\": \"Insecta/Limenitis arthemis arthemis/e7365fba1c3e90fd59c5729be26322e0.jpg\",\n  \"43909.jpg\": \"Reptilia/Thamnophis proximus/7d9ea31971c359df53a29f85e3bce6ea.jpg\",\n  \"439095.jpg\": \"Insecta/Limenitis arthemis arthemis/da188998d2cddecaeacee2a5dbb8686f.jpg\",\n  \"439110.jpg\": \"Insecta/Limenitis arthemis arthemis/7a9d0937857e3cc6a9f8834a3068c009.jpg\",\n  \"439119.jpg\": \"Insecta/Limenitis arthemis arthemis/6e1ab2abf981854f3e6d3963a37a538a.jpg\",\n  \"439125.jpg\": \"Insecta/Limenitis arthemis arthemis/897f143ffc4fbad8b64c6b9346caca6a.jpg\",\n  \"439131.jpg\": \"Insecta/Limenitis arthemis arthemis/f9a237bae93d98772beff6a33593c01d.jpg\",\n  \"439142.jpg\": \"Aves/Icterus gularis/7f543efb5b350a66c52eebce7ad407d6.jpg\",\n  \"439158.jpg\": \"Aves/Icterus gularis/0c4e8de251aafb116ce54cac519d217f.jpg\",\n  \"439162.jpg\": \"Aves/Icterus gularis/aa4e3c4a5821b52a5a8c480ce96634b0.jpg\",\n  \"439163.jpg\": \"Aves/Icterus gularis/91e72685be63d6f48711f7573cc5c7e9.jpg\",\n  \"439171.jpg\": \"Aves/Icterus gularis/3ddb62718ac52cf7d33de9ddf34b02a2.jpg\",\n  \"439178.jpg\": \"Aves/Icterus gularis/7243281ff73a3ea83d14699be8cdc3d1.jpg\",\n  \"439179.jpg\": \"Aves/Icterus gularis/25928e33a9debb05558f25ec126c18d5.jpg\",\n  \"439180.jpg\": \"Aves/Icterus gularis/ff8aae98945b31db790fc80ec94c4c55.jpg\",\n  \"439191.jpg\": \"Arachnida/Steatoda capensis/f1a639ab4a9af84d3cb9910e4dfdcec4.jpg\",\n  \"439194.jpg\": \"Arachnida/Steatoda capensis/1659840df8eead21174f62dcaf021b92.jpg\",\n  \"43925.jpg\": \"Reptilia/Thamnophis proximus/4bf35865344808d71ea239db63c31f27.jpg\",\n  \"43935.jpg\": \"Reptilia/Thamnophis proximus/f880981d13579cfbeb2be2693c8ac252.jpg\",\n  \"43944.jpg\": \"Reptilia/Thamnophis proximus/c096a2127c022792478af931c174caa5.jpg\",\n  \"43945.jpg\": \"Reptilia/Thamnophis proximus/90e99e72eb183ecc1938ebe5f097ac2c.jpg\",\n  \"439484.jpg\": \"Aves/Milvago chimachima/d1c3e60ba16519dad491fdcc12952cae.jpg\",\n  \"439603.jpg\": \"Insecta/Chlorochlamys chloroleucaria/8152b25f26b70a61f765e4a30f589a08.jpg\",\n  \"439604.jpg\": \"Insecta/Chlorochlamys chloroleucaria/d7d96eb5bc8c9bee89557555b0c30be2.jpg\",\n  \"439608.jpg\": \"Insecta/Chlorochlamys chloroleucaria/d992184ce11c6bf79b9058d3302c4bcc.jpg\",\n  \"439609.jpg\": \"Insecta/Chlorochlamys chloroleucaria/9eba579444717f6d37382c12a78dff50.jpg\",\n  \"439610.jpg\": \"Insecta/Chlorochlamys chloroleucaria/63d8f2b9e1ba5d21324784b20e8c691d.jpg\",\n  \"439611.jpg\": \"Insecta/Chlorochlamys chloroleucaria/6a093531f7a47af3db5cc22037f65ad9.jpg\",\n  \"439612.jpg\": \"Insecta/Chlorochlamys chloroleucaria/0310c073d297934e09f21eeb6d54e08f.jpg\",\n  \"439631.jpg\": \"Insecta/Zerene cesonia/c707a73c14fe6c92048d57c65935f73b.jpg\",\n  \"439645.jpg\": \"Insecta/Zerene cesonia/afca26215b546adcf90af9ab806801be.jpg\",\n  \"439646.jpg\": \"Insecta/Zerene cesonia/806e2861c064841d3c8e45d7eb6b9604.jpg\",\n  \"439651.jpg\": \"Insecta/Zerene cesonia/ae19ff95a6aa1b8c879cecdb9a8e829d.jpg\",\n  \"439669.jpg\": \"Insecta/Zerene cesonia/e04363e36ec7ce88c46e9d72564cbaee.jpg\",\n  \"439676.jpg\": \"Mollusca/Thylacodes squamigerus/b2b7e0caaa44f6afe147f11ede3a65e6.jpg\",\n  \"439687.jpg\": \"Reptilia/Thamnophis proximus/a5986be7732542994e87f2a4ac0109e9.jpg\",\n  \"439696.jpg\": \"Reptilia/Thamnophis proximus/9562e5feab890b2f8cd1215c4c61db6a.jpg\",\n  \"439708.jpg\": \"Reptilia/Thamnophis proximus/7f66179a820a26d1fa1feb3f3c8f92c1.jpg\",\n  \"439729.jpg\": \"Reptilia/Thamnophis proximus/db303a7e3b762b353b5ee210ee15e05d.jpg\",\n  \"439756.jpg\": \"Reptilia/Thamnophis proximus/403fb782af7711722c9ad7f988e07d69.jpg\",\n  \"439789.jpg\": \"Reptilia/Thamnophis proximus/f0289120511fcfd05c4909e73e62a90a.jpg\",\n  \"4398.jpg\": \"Aves/Setophaga petechia/d6cb50c588d840801c4c8a883da752c5.jpg\",\n  \"440.jpg\": \"Mammalia/Ursus americanus/aab09120dcd40257bd50a5d76091c302.jpg\",\n  \"440070.jpg\": \"Insecta/Plodia interpunctella/add138d9b903daf2d636bfcdb0a5113f.jpg\",\n  \"440192.jpg\": \"Insecta/Noctua pronuba/bbff6f5a82c876599b75a88650e3f71a.jpg\",\n  \"440195.jpg\": \"Insecta/Noctua pronuba/69f511c5e562bcd4aff007e3535b902d.jpg\",\n  \"440225.jpg\": \"Insecta/Noctua pronuba/6b7c6eace4deeab449d2fa085e25f1ca.jpg\",\n  \"440242.jpg\": \"Insecta/Noctua pronuba/da81cf92d7bc6b92036d3c8ff4443a62.jpg\",\n  \"440252.jpg\": \"Insecta/Noctua pronuba/9806e2eb336de3eb75d4b0869d538e95.jpg\",\n  \"440263.jpg\": \"Insecta/Noctua pronuba/e79c5165995d714f42c34f228880b1bf.jpg\",\n  \"440265.jpg\": \"Insecta/Noctua pronuba/897cc18647d13e7a16744e78c92b3139.jpg\",\n  \"440270.jpg\": \"Insecta/Noctua pronuba/f1263b888e965c0f7e1fa6aacd644a64.jpg\",\n  \"440277.jpg\": \"Arachnida/Rabidosa rabida/394ab9fc6b36a002bf5eb65db7a9b8fb.jpg\",\n  \"440278.jpg\": \"Arachnida/Rabidosa rabida/7eec8063ac4bbde4c2b7e58f1fdbf858.jpg\",\n  \"440285.jpg\": \"Arachnida/Rabidosa rabida/66ff049328db77f861c3f1427d4ec204.jpg\",\n  \"440286.jpg\": \"Arachnida/Rabidosa rabida/bb9b091d1b0fefcc417fad593718f369.jpg\",\n  \"4403.jpg\": \"Aves/Setophaga petechia/6a047406fd4b32e8550bdd7f19a8534a.jpg\",\n  \"440346.jpg\": \"Aves/Phalaropus fulicarius/30546708cb45c7861091fa343ec5a2ab.jpg\",\n  \"440353.jpg\": \"Aves/Phalaropus fulicarius/438ee85edef0c0c8d1011eca38b92df2.jpg\",\n  \"440354.jpg\": \"Aves/Phalaropus fulicarius/2dac1bc4ee9bf33939911f9ed9dd0952.jpg\",\n  \"440362.jpg\": \"Reptilia/Lampropeltis getula/14b5f26bad241f423ab0b59c11d40c64.jpg\",\n  \"440363.jpg\": \"Reptilia/Lampropeltis getula/d1650a4071b4784234fbbe60beb93c61.jpg\",\n  \"440365.jpg\": \"Reptilia/Lampropeltis getula/74cc5242236308e80d00e56e82fb0194.jpg\",\n  \"440519.jpg\": \"Aves/Pica nuttalli/17b788bb642a485e3ff3b69cd6f13089.jpg\",\n  \"440526.jpg\": \"Aves/Pica nuttalli/2973fe5780573de668bfc270cd78455d.jpg\",\n  \"440536.jpg\": \"Aves/Pica nuttalli/436a9a1f6c2c637b6dc9b020d90a2783.jpg\",\n  \"440543.jpg\": \"Aves/Pica nuttalli/ea1d09fe27ba93b67686a684e702d893.jpg\",\n  \"4407.jpg\": \"Aves/Setophaga petechia/4b06eb897baf2ac77d2fc45f9af7f4aa.jpg\",\n  \"440896.jpg\": \"Aves/Ardea herodias/f48eb12e31f2e6714203e8d0b4a5ad85.jpg\",\n  \"440961.jpg\": \"Aves/Ardea herodias/a71e104ced13a3510640ab762fa75bb4.jpg\",\n  \"441.jpg\": \"Mammalia/Ursus americanus/01695b30ea2cfd2875aceb6d915b01b7.jpg\",\n  \"441129.jpg\": \"Aves/Ardea herodias/441d64a104d33364b9195e12e885c29d.jpg\",\n  \"441152.jpg\": \"Aves/Ardea herodias/b274e5acd8344d9003df9253ab9f4a0a.jpg\",\n  \"441393.jpg\": \"Aves/Ardea herodias/d0b91d59078e03b4c4ab592760b0c1c2.jpg\",\n  \"4414.jpg\": \"Aves/Setophaga petechia/c34a8fd2867ab8fa493d523fd703c005.jpg\",\n  \"4418.jpg\": \"Aves/Setophaga petechia/0bb489be47b1316708089c706f774945.jpg\",\n  \"441982.jpg\": \"Aves/Ardea herodias/65c6ca992c0994648808fb15ddfdbb3c.jpg\",\n  \"4422.jpg\": \"Aves/Setophaga petechia/4843df9f3ca5657961bab0ff6097ac56.jpg\",\n  \"442240.jpg\": \"Insecta/Dissosteira carolina/f64f7d9ba38ba4bf8e46fc41e99f5194.jpg\",\n  \"442241.jpg\": \"Insecta/Dissosteira carolina/a07d340247f91c7837858fbb53382a0b.jpg\",\n  \"442246.jpg\": \"Insecta/Dissosteira carolina/8bef2152b209eebf3bfb9db35bb71be1.jpg\",\n  \"442247.jpg\": \"Insecta/Dissosteira carolina/37ee63576d6919a907d028e140f04743.jpg\",\n  \"442253.jpg\": \"Mollusca/Cryptochiton stelleri/3efecdff4654186e29289b2f9cd01dae.jpg\",\n  \"442256.jpg\": \"Mollusca/Cryptochiton stelleri/0f47b97a49767610666d846527156f61.jpg\",\n  \"442257.jpg\": \"Mollusca/Cryptochiton stelleri/fc50fff20f898902c69392ea28891697.jpg\",\n  \"442261.jpg\": \"Mollusca/Cryptochiton stelleri/f67e94c32f37aa2b2a8c0c41c501dc0a.jpg\",\n  \"442262.jpg\": \"Mollusca/Cryptochiton stelleri/0dc5e0fd1432363728c94b98eca25f51.jpg\",\n  \"442284.jpg\": \"Mollusca/Cryptochiton stelleri/d1696ea7fca1701700a88745968d73c7.jpg\",\n  \"442287.jpg\": \"Mollusca/Cryptochiton stelleri/cf24785a91dda5a37abfed33c31d0856.jpg\",\n  \"442362.jpg\": \"Reptilia/Thamnophis proximus rubrilineatus/2e0aa288e43b7f1c0d0c10304608205f.jpg\",\n  \"442365.jpg\": \"Reptilia/Thamnophis proximus rubrilineatus/b776a70c025985a93830ff9cb768bc84.jpg\",\n  \"442378.jpg\": \"Reptilia/Thamnophis proximus rubrilineatus/5a2be346c1b3e6ec19ceeb04b2e3e09f.jpg\",\n  \"442379.jpg\": \"Reptilia/Sceloporus graciosus/4aaf33fc5afd14cc1650469efaceb40e.jpg\",\n  \"442383.jpg\": \"Reptilia/Sceloporus graciosus/25b72f565179bdf558c66865c1ae973d.jpg\",\n  \"442394.jpg\": \"Reptilia/Sceloporus graciosus/5ee3284357b35a631a33ea233e95b3c8.jpg\",\n  \"442395.jpg\": \"Reptilia/Sceloporus graciosus/de5a679c55288ce922778fb71a2e59d8.jpg\",\n  \"442396.jpg\": \"Reptilia/Sceloporus graciosus/2b9abb7009129b93d22d64d3ba428cd3.jpg\",\n  \"442427.jpg\": \"Reptilia/Sceloporus graciosus/11e0b780300ec48d2aee75581a59352b.jpg\",\n  \"442428.jpg\": \"Reptilia/Sceloporus graciosus/6d814b08ce479299121b133c9293695c.jpg\",\n  \"442429.jpg\": \"Reptilia/Sceloporus graciosus/dfe117dd0a770170a59c229622035d1b.jpg\",\n  \"442433.jpg\": \"Reptilia/Hypsiglena jani/b44ccd6c3c2b82b61609ff2b68dd1d8c.jpg\",\n  \"442434.jpg\": \"Reptilia/Hypsiglena jani/5caf9643e4c70a6ff0b66135803ba317.jpg\",\n  \"442437.jpg\": \"Reptilia/Hypsiglena jani/2139af844dc4044ec50268a06535b80a.jpg\",\n  \"442441.jpg\": \"Reptilia/Hypsiglena jani/734e120dbf64ec66c79dd785ecee8e6c.jpg\",\n  \"442453.jpg\": \"Reptilia/Hypsiglena jani/8cbac312252112e7639cba13c00d694a.jpg\",\n  \"442454.jpg\": \"Reptilia/Hypsiglena jani/4df75bb99a1ec76375e9a07666ee3877.jpg\",\n  \"442458.jpg\": \"Reptilia/Hypsiglena jani/7fd3e530cf25fa680b6db6f40c7d8fc8.jpg\",\n  \"442460.jpg\": \"Reptilia/Hypsiglena jani/bd730e01ee40228793cc3b8f40dcedfa.jpg\",\n  \"442463.jpg\": \"Reptilia/Hypsiglena jani/b1c984898d71e94f1d5e8d3bdd0fd22e.jpg\",\n  \"442500.jpg\": \"Insecta/Sympetrum obtrusum/27db4d357a1928ab46f4785f3e359f8c.jpg\",\n  \"442507.jpg\": \"Insecta/Sympetrum obtrusum/59330315c1f9cd298d9286fcc973519e.jpg\",\n  \"442509.jpg\": \"Insecta/Sympetrum obtrusum/fe6deb1524a912c347d3379ee36e363a.jpg\",\n  \"442527.jpg\": \"Insecta/Sympetrum obtrusum/9c52cdcae7419b451a1ccf86a6b89893.jpg\",\n  \"442528.jpg\": \"Insecta/Sympetrum obtrusum/63279fafe0bab98833d44123d260f36f.jpg\",\n  \"442529.jpg\": \"Insecta/Sympetrum obtrusum/754a267a575a83e737234e606022db2f.jpg\",\n  \"442535.jpg\": \"Insecta/Sympetrum obtrusum/bd1a9d0de6bd51374d7a003db7d55dd2.jpg\",\n  \"442538.jpg\": \"Insecta/Sympetrum obtrusum/41a696afa7b42dd5d7b805a103519c8e.jpg\",\n  \"442548.jpg\": \"Aves/Tadorna tadorna/018e50a1faff98ecad41b91a8bfc835a.jpg\",\n  \"442579.jpg\": \"Aves/Chroicocephalus scopulinus/d74370c868298d1aac79ec13ca507645.jpg\",\n  \"442598.jpg\": \"Aves/Chroicocephalus scopulinus/f67360dc3d9bded082dada75724ad1ee.jpg\",\n  \"4426.jpg\": \"Aves/Setophaga petechia/50993a2334fab20d54eec618ddcf255d.jpg\",\n  \"442627.jpg\": \"Aves/Nyctibius jamaicensis/3a0f01f24a1f22dca897d7a6ee85b06b.jpg\",\n  \"442628.jpg\": \"Aves/Nyctibius jamaicensis/bae2569ca1124413960931bc2497ee0c.jpg\",\n  \"442633.jpg\": \"Arachnida/Pholcus phalangioides/1a292e6fae5fe2d79b0ff8dc2327c5b8.jpg\",\n  \"442634.jpg\": \"Arachnida/Pholcus phalangioides/0b4adfd41f3627000f93d94248372ef3.jpg\",\n  \"442637.jpg\": \"Arachnida/Pholcus phalangioides/9fd34e7326b9fcf29b457be67bdc19a3.jpg\",\n  \"442638.jpg\": \"Arachnida/Pholcus phalangioides/6ecbcbcd4e8711365a3004d4f0e96170.jpg\",\n  \"442647.jpg\": \"Arachnida/Pholcus phalangioides/6987aa8e1917b1e62a5dbba9bfc8b697.jpg\",\n  \"442650.jpg\": \"Arachnida/Pholcus phalangioides/3eab1f96bd08d046008d4d6844b49f74.jpg\",\n  \"442652.jpg\": \"Arachnida/Pholcus phalangioides/39316f2d318922683e5b99bf5d8314ee.jpg\",\n  \"442656.jpg\": \"Arachnida/Pholcus phalangioides/484c31246f59a1e636b802c7bde207fb.jpg\",\n  \"442665.jpg\": \"Insecta/Pieris oleracea/04ea083b23305b6819fad78cc327f60e.jpg\",\n  \"442714.jpg\": \"Reptilia/Pituophis catenifer annectens/98b21dd0bf9d6f5ec9f053eb7deb8a16.jpg\",\n  \"442715.jpg\": \"Reptilia/Pituophis catenifer annectens/5d4b45299ad06d6377a59efa8ef8d738.jpg\",\n  \"442716.jpg\": \"Reptilia/Pituophis catenifer annectens/54e084ce9355c2bb7feac90ddbbb0fcd.jpg\",\n  \"442719.jpg\": \"Reptilia/Pituophis catenifer annectens/7ce44a4ca91f0744b094daeadfb5f14a.jpg\",\n  \"442724.jpg\": \"Reptilia/Pituophis catenifer annectens/d3c0d8cac2c7fc02c20422c8946112f1.jpg\",\n  \"442725.jpg\": \"Reptilia/Pituophis catenifer annectens/823c985c9ef613e044f1c76c49c28f59.jpg\",\n  \"442726.jpg\": \"Reptilia/Pituophis catenifer annectens/9998677e96b11c67e05ea27c8f5d4b3e.jpg\",\n  \"442738.jpg\": \"Reptilia/Pituophis catenifer annectens/2f0d3f2fcfc398ae5b2be8f597691b99.jpg\",\n  \"442742.jpg\": \"Reptilia/Pituophis catenifer annectens/6511ef3c21a21994da54043b2de1793b.jpg\",\n  \"442823.jpg\": \"Reptilia/Lampropholis delicata/19f529855095242726d9ad545b40d081.jpg\",\n  \"442826.jpg\": \"Reptilia/Lampropholis delicata/58211c7627999aac227c5fa76b645cf9.jpg\",\n  \"442828.jpg\": \"Reptilia/Lampropholis delicata/c5699a0c18112e707922ea5962475a02.jpg\",\n  \"442830.jpg\": \"Reptilia/Lampropholis delicata/8a0f0669f56f9141d08fdd15849bb22e.jpg\",\n  \"442843.jpg\": \"Insecta/Polistes carolina/72dad1042a031af079425ddc7f8a6a11.jpg\",\n  \"442863.jpg\": \"Insecta/Polistes carolina/d96ce030e1145a92769a8c013e438bc0.jpg\",\n  \"442877.jpg\": \"Insecta/Polistes carolina/25447aca72d2e97ae8119421e3fb31f8.jpg\",\n  \"442878.jpg\": \"Insecta/Polistes carolina/0d8310c33309a4500cc76f72ddc382fa.jpg\",\n  \"442884.jpg\": \"Insecta/Polistes carolina/b4065b59d26ab68106c89b937298ca6b.jpg\",\n  \"442897.jpg\": \"Insecta/Camponotus pennsylvanicus/f5fb80e7bcc5c33c7e9aafd59179e7d7.jpg\",\n  \"442903.jpg\": \"Insecta/Camponotus pennsylvanicus/0a71c491ebe8e88d2abc94320d22349e.jpg\",\n  \"442904.jpg\": \"Insecta/Camponotus pennsylvanicus/8d1a479497b3db0b5d8ae59b03ccf965.jpg\",\n  \"442907.jpg\": \"Insecta/Dione moneta/7db55258c7f1ff4ba03721ee7c340a8d.jpg\",\n  \"442908.jpg\": \"Insecta/Dione moneta/759dcd1718f8a53dc429a6a6b7137d3a.jpg\",\n  \"442912.jpg\": \"Insecta/Dione moneta/661e09eb884c9ab02de10c782850a43e.jpg\",\n  \"442923.jpg\": \"Insecta/Dione moneta/a899f0b0cd2c50d6fc48bbfb387812dc.jpg\",\n  \"442924.jpg\": \"Insecta/Dione moneta/bc9713d2349aeae30ffdfdb31c655d7d.jpg\",\n  \"442931.jpg\": \"Insecta/Dione moneta/56a430de9b6e346a4e2e946d9af1617c.jpg\",\n  \"442969.jpg\": \"Aves/Ortalis poliocephala/9c379692d29e0ead93a3e6588177f431.jpg\",\n  \"442995.jpg\": \"Insecta/Xanthorhoe lacustrata/ccb730ce91f3a5aaf37cbc5fa3596906.jpg\",\n  \"442996.jpg\": \"Insecta/Xanthorhoe lacustrata/971e985a7a51e3600fdbccf699a8faea.jpg\",\n  \"443093.jpg\": \"Aves/Anas discors/f94f4147accfc70d4abab018e9cf28b7.jpg\",\n  \"443651.jpg\": \"Aves/Buteogallus anthracinus/f9e0625e111cbfafdc4cd70323181a66.jpg\",\n  \"443652.jpg\": \"Aves/Buteogallus anthracinus/628448bd15198f898bba21a775350e70.jpg\",\n  \"443659.jpg\": \"Aves/Buteogallus anthracinus/a620c31d4340ae42705d6bab9ada0447.jpg\",\n  \"443669.jpg\": \"Aves/Buteogallus anthracinus/782962895a1c627b3c2fdb5eda5ba073.jpg\",\n  \"443671.jpg\": \"Aves/Buteogallus anthracinus/a0007fb7775be11f9690f1648321de67.jpg\",\n  \"443677.jpg\": \"Aves/Buteogallus anthracinus/1c67dce96777c7401d2cba1681efa89b.jpg\",\n  \"443678.jpg\": \"Aves/Buteogallus anthracinus/e35d3c0f2b74d154248289fb297a0a3e.jpg\",\n  \"443682.jpg\": \"Aves/Buteogallus anthracinus/000667cc1d9cb6e1aaa495dc961f434b.jpg\",\n  \"443685.jpg\": \"Aves/Buteogallus anthracinus/df63199993f11170527ee9e6a32e0a2c.jpg\",\n  \"443692.jpg\": \"Aves/Buteogallus anthracinus/c0740ad98eb0071b173739a8af46fb33.jpg\",\n  \"443717.jpg\": \"Insecta/Ischnura hastata/e7922d488a62bd015ef6adcd1fdc71de.jpg\",\n  \"443731.jpg\": \"Insecta/Ischnura hastata/94512195d8078578bc0982ab4ac5acf6.jpg\",\n  \"443735.jpg\": \"Insecta/Ischnura hastata/029a29007967176681ef633f07dfae9e.jpg\",\n  \"443769.jpg\": \"Insecta/Ischnura hastata/eb3adbd0698ba279c6f80b7ab0154e8f.jpg\",\n  \"443770.jpg\": \"Insecta/Ischnura hastata/d2dcfe702f1c846a0c6191b98ec3bb7b.jpg\",\n  \"443771.jpg\": \"Insecta/Ischnura hastata/ce2586211ba4558128478fc7aa99049b.jpg\",\n  \"443772.jpg\": \"Insecta/Ischnura hastata/4dea9ed616cb48b59cbad7448cc6d48d.jpg\",\n  \"443773.jpg\": \"Insecta/Ischnura hastata/a6102af5aa5fd756c4f4e33df3e63f60.jpg\",\n  \"443782.jpg\": \"Insecta/Ischnura hastata/05902625c5c0a37d2e38f3e043ce5b7d.jpg\",\n  \"443785.jpg\": \"Insecta/Ischnura hastata/bf7ef584285719de14fc9803ac0a3815.jpg\",\n  \"443788.jpg\": \"Aves/Momotus lessonii/bf53e5387259adf434dae022de7db85d.jpg\",\n  \"443842.jpg\": \"Aves/Oriturus superciliosus/67e9df6b0abd37c4f618da476e099e74.jpg\",\n  \"443843.jpg\": \"Aves/Oriturus superciliosus/2926c4fcd0064c8793888be006a246b5.jpg\",\n  \"443850.jpg\": \"Aves/Eudyptula minor/8800273d3b2acf3e03dbc22b640cf75e.jpg\",\n  \"443868.jpg\": \"Aves/Eudyptula minor/d0ecc6e2fea47a91535aefc6360294b9.jpg\",\n  \"443895.jpg\": \"Insecta/Ochlodes sylvanoides/6ec867d3b0034c7222745008106198af.jpg\",\n  \"443896.jpg\": \"Insecta/Ochlodes sylvanoides/4aac4a402c7d7bdea771ca4aa89ddae2.jpg\",\n  \"443897.jpg\": \"Insecta/Ochlodes sylvanoides/1be14ea1b9792505359898aa73fff275.jpg\",\n  \"443898.jpg\": \"Insecta/Ochlodes sylvanoides/a909ed8fff723e525590fbe0b1202d90.jpg\",\n  \"443904.jpg\": \"Insecta/Ochlodes sylvanoides/c5ba7dd61f0ada526b2cb98c57aae5e8.jpg\",\n  \"443930.jpg\": \"Insecta/Ochlodes sylvanoides/bc2dd8603af6c021402cf30091b646d9.jpg\",\n  \"443936.jpg\": \"Insecta/Ochlodes sylvanoides/ce02f8af43952c57fc92250cff21e877.jpg\",\n  \"443938.jpg\": \"Insecta/Ochlodes sylvanoides/b4d24b6cbe3873c46312b80bcc59066f.jpg\",\n  \"443940.jpg\": \"Insecta/Ochlodes sylvanoides/de273c71a87e424df47929d38fe10dec.jpg\",\n  \"443943.jpg\": \"Insecta/Ochlodes sylvanoides/e8cdaa166365f27676b0ebf57ed591b2.jpg\",\n  \"443973.jpg\": \"Insecta/Epimecis hortaria/60f53a6915a49cb033e78ed748e20b79.jpg\",\n  \"443976.jpg\": \"Insecta/Epimecis hortaria/e608c5d111ba32276e708ec265960827.jpg\",\n  \"443983.jpg\": \"Insecta/Epimecis hortaria/93ce7ef982ea547521fa076b453e4870.jpg\",\n  \"443987.jpg\": \"Insecta/Epimecis hortaria/134d24b76fe82f7d1cc09b4d15a0160b.jpg\",\n  \"443988.jpg\": \"Insecta/Epimecis hortaria/cbc87623955bffd67a02c4b6600d50dd.jpg\",\n  \"443990.jpg\": \"Insecta/Epimecis hortaria/54d437e765724afaae5c6906a653b73a.jpg\",\n  \"443991.jpg\": \"Insecta/Epimecis hortaria/3bb20ec55a29e120b6a885eaa8e7edd7.jpg\",\n  \"443992.jpg\": \"Insecta/Epimecis hortaria/1c644fa3a2942ec94a398bfa9f1f4126.jpg\",\n  \"443995.jpg\": \"Insecta/Epimecis hortaria/5be706cb3a2dac961dc5f9263e20500e.jpg\",\n  \"443996.jpg\": \"Insecta/Epimecis hortaria/b5b72f34f750db2786165f10203e27ee.jpg\",\n  \"444003.jpg\": \"Insecta/Pantala hymenaea/0e4d63fd21faab630f69913408cf9849.jpg\",\n  \"444011.jpg\": \"Insecta/Pantala hymenaea/32fcf53487e952e25c89fd88b4858214.jpg\",\n  \"444012.jpg\": \"Insecta/Pantala hymenaea/0d056ee7e86a80409a75e75364a8b74d.jpg\",\n  \"444018.jpg\": \"Insecta/Pantala hymenaea/f33f08a1d7b97d0b090615ae074a2fdf.jpg\",\n  \"444034.jpg\": \"Insecta/Pantala hymenaea/185fe895f68000f438a6bd5a81a4750e.jpg\",\n  \"444035.jpg\": \"Insecta/Pantala hymenaea/3ae82ec7ad7e2da5cd5264294e5b0ed5.jpg\",\n  \"444041.jpg\": \"Insecta/Pantala hymenaea/1cc10b5376b5f0a4a2f32b392972fb9d.jpg\",\n  \"444042.jpg\": \"Insecta/Pantala hymenaea/666cf45dc75a448cf46e5c6a30b96c62.jpg\",\n  \"444043.jpg\": \"Insecta/Pantala hymenaea/40b1a24e6fa811578b3386240bb8bfe1.jpg\",\n  \"444136.jpg\": \"Aves/Phalacrocorax varius/21f83917f288d1038a0770316d58b3a9.jpg\",\n  \"444137.jpg\": \"Aves/Phalacrocorax varius/d8b45a425c656684d194be4836be6e93.jpg\",\n  \"44421.jpg\": \"Insecta/Plodia interpunctella/8ce02faadcdf4ee104b564a34ddd4a7d.jpg\",\n  \"444240.jpg\": \"Insecta/Argia fumipennis violacea/1201589a921908d30bd2249571b5dc88.jpg\",\n  \"444241.jpg\": \"Insecta/Argia fumipennis violacea/7901e8aab6f7a55c5dddfe754fba549c.jpg\",\n  \"44425.jpg\": \"Insecta/Plodia interpunctella/1532bac6408514175117f20ee421cc8f.jpg\",\n  \"44427.jpg\": \"Insecta/Plodia interpunctella/f2a7f7a266919069b66e903795dc7944.jpg\",\n  \"44428.jpg\": \"Insecta/Plodia interpunctella/14a6277e339849343c65682a91cac51d.jpg\",\n  \"44429.jpg\": \"Insecta/Plodia interpunctella/17843187d937d2ac5a8fecb4dd6358e3.jpg\",\n  \"44430.jpg\": \"Insecta/Plodia interpunctella/93ac7a92df6bd3ac19e5906879dd3985.jpg\",\n  \"444328.jpg\": \"Insecta/Mallophora fautrix/4c076c00a305f8426f0fef953f5ebf6d.jpg\",\n  \"444329.jpg\": \"Insecta/Mallophora fautrix/65b0cd333057b7251d95cc23b62277ea.jpg\",\n  \"444348.jpg\": \"Aves/Troglodytes troglodytes/93744e29d09c24bc78a6cf72418e6615.jpg\",\n  \"44435.jpg\": \"Insecta/Plodia interpunctella/96632342ed0cc53d2a5d5b87b5e07334.jpg\",\n  \"444352.jpg\": \"Aves/Troglodytes troglodytes/fed5b74dd5cd4a42553292ecfddbaa31.jpg\",\n  \"444353.jpg\": \"Aves/Troglodytes troglodytes/a2bcad8a3ac7733d3b8c5baf3a64a4f1.jpg\",\n  \"444356.jpg\": \"Aves/Troglodytes troglodytes/9ab0ccbea1b9e589947c9c92aa054ebf.jpg\",\n  \"444357.jpg\": \"Aves/Troglodytes troglodytes/2e2ddfc1b328570c30a9fa26a0ed2b44.jpg\",\n  \"444358.jpg\": \"Aves/Troglodytes troglodytes/94519a25a50f073048d360f2ce86e951.jpg\",\n  \"444361.jpg\": \"Aves/Troglodytes troglodytes/06f2d974fcf201a0d047dd02f8202250.jpg\",\n  \"444362.jpg\": \"Aves/Troglodytes troglodytes/b0aab63378d69371dc2709291f5abfbe.jpg\",\n  \"444363.jpg\": \"Aves/Troglodytes troglodytes/050a7b6913b21ea77abddb836e566eec.jpg\",\n  \"444365.jpg\": \"Aves/Troglodytes troglodytes/571bfdf370b1a6eef3995a1196aa4180.jpg\",\n  \"44437.jpg\": \"Insecta/Plodia interpunctella/a392d56fa601132c22bad7fbde5c2ecb.jpg\",\n  \"444402.jpg\": \"Insecta/Eumorpha pandorus/7977996a291749510f81e29c24b9f7b4.jpg\",\n  \"444403.jpg\": \"Insecta/Eumorpha pandorus/32911f7e64637b0f2c0aac447a028d90.jpg\",\n  \"444404.jpg\": \"Insecta/Eumorpha pandorus/eaf931bc2ea8f6e82ee96ca0d8ea9f0a.jpg\",\n  \"444405.jpg\": \"Insecta/Eumorpha pandorus/5c02d19767c8dfc646c03337d472e6e7.jpg\",\n  \"444408.jpg\": \"Insecta/Eumorpha pandorus/e9bf7c3adc1e48bde701c74efb6bf188.jpg\",\n  \"444409.jpg\": \"Insecta/Eumorpha pandorus/319da79e4bfe1ad640ca666027943f4e.jpg\",\n  \"444410.jpg\": \"Insecta/Eumorpha pandorus/df652175fd9c01b5d5ecc0adb67a31b4.jpg\",\n  \"444411.jpg\": \"Insecta/Eumorpha pandorus/1bda3d93980b4142aaf4033694477615.jpg\",\n  \"444412.jpg\": \"Insecta/Eumorpha pandorus/6f6bbe58234fd3fce3a9d55f46ffbc2f.jpg\",\n  \"444413.jpg\": \"Insecta/Eumorpha pandorus/c2efaac790981729c6e5292680034cfb.jpg\",\n  \"444417.jpg\": \"Insecta/Eumorpha pandorus/2c64f749db511530f8a21ad85b198ad3.jpg\",\n  \"444418.jpg\": \"Insecta/Eumorpha pandorus/09da55abfc8275b001a22b0dc1493711.jpg\",\n  \"444419.jpg\": \"Insecta/Eumorpha pandorus/78d7540dd24db5db39e7f45bcaf08a69.jpg\",\n  \"444420.jpg\": \"Insecta/Eumorpha pandorus/98bbaca3637eb98354103e77fe4bce74.jpg\",\n  \"444424.jpg\": \"Insecta/Argia plana/d8ed9f0c6a16746191c0ff9c7f2fd70b.jpg\",\n  \"444443.jpg\": \"Insecta/Argia plana/012c99ac78f970132384c96b356526ba.jpg\",\n  \"444460.jpg\": \"Insecta/Argia plana/28f8da2eaccedadb2c0d3dd00ae2756e.jpg\",\n  \"444464.jpg\": \"Insecta/Argia plana/54296014771eacfa8ad09b7f233ca339.jpg\",\n  \"444468.jpg\": \"Insecta/Argia plana/da5965d881e9a61229a08e8e6e65f252.jpg\",\n  \"444469.jpg\": \"Insecta/Argia plana/dcbd4f2af25e17b728c3a765e09b2d73.jpg\",\n  \"444488.jpg\": \"Insecta/Callophrys gryneus/006869b544d915a20f7f37fd9f8a6779.jpg\",\n  \"444494.jpg\": \"Insecta/Callophrys gryneus/6f006524ca8d1579b5cb519bec1db87e.jpg\",\n  \"444496.jpg\": \"Insecta/Callophrys gryneus/fa78f5bb9c44e10b5c9f32b264867014.jpg\",\n  \"444506.jpg\": \"Insecta/Callophrys gryneus/f863973a027e2024ad016bf0ca64a02f.jpg\",\n  \"444509.jpg\": \"Insecta/Callophrys gryneus/4fc9501b80091d6805d16f2332e62b10.jpg\",\n  \"444516.jpg\": \"Insecta/Callophrys gryneus/0d8230351748b2358d4eea33726d50ed.jpg\",\n  \"44452.jpg\": \"Aves/Chaetura vauxi/676f7fc8b4b28ad0f4de02a636d8b1ae.jpg\",\n  \"444525.jpg\": \"Insecta/Callophrys gryneus/964c7e1355657c2f336e597d3bf8671d.jpg\",\n  \"44453.jpg\": \"Aves/Chaetura vauxi/bacd6f431894f1723b77bc288cc2dfa3.jpg\",\n  \"444530.jpg\": \"Aves/Cassiculus melanicterus/038cd26d94bed6486556bbb9dc03145e.jpg\",\n  \"444531.jpg\": \"Aves/Cassiculus melanicterus/b8587c9cee36103d7ba5230ac1e92b86.jpg\",\n  \"444541.jpg\": \"Aves/Cassiculus melanicterus/5b68ed048205e5647efcefb201a67f8e.jpg\",\n  \"444542.jpg\": \"Aves/Cassiculus melanicterus/526ed799f8da65a04277d8c66e7eeed1.jpg\",\n  \"444544.jpg\": \"Aves/Cassiculus melanicterus/01eee9bcc1928f2af19fafad6dd696ac.jpg\",\n  \"444545.jpg\": \"Aves/Cassiculus melanicterus/12a1d1c75b3e3fcb451eecabd453880a.jpg\",\n  \"444546.jpg\": \"Aves/Cassiculus melanicterus/30613829ae132fbeeaa28d72c651bc6e.jpg\",\n  \"444547.jpg\": \"Aves/Cassiculus melanicterus/8ebc1bbd5a856100db3b19b2c308ceea.jpg\",\n  \"444550.jpg\": \"Aves/Cassiculus melanicterus/bf68c25935d6680620935878f25ba9ed.jpg\",\n  \"444551.jpg\": \"Aves/Cassiculus melanicterus/a35c01f6d3499f8df39659b8e5e4d838.jpg\",\n  \"444579.jpg\": \"Mollusca/Euglandina rosea/8ce1ca2caa67a97bb67daf69f826b83e.jpg\",\n  \"444633.jpg\": \"Aves/Ixobrychus exilis/2208a7f6459595f765f05af9d5db512f.jpg\",\n  \"444641.jpg\": \"Aves/Ixobrychus exilis/ef8b0de8a2b420c768dabbf1e2fdf62b.jpg\",\n  \"444642.jpg\": \"Aves/Ixobrychus exilis/28c3e97665cb5cf6925ce0cb87994a58.jpg\",\n  \"444644.jpg\": \"Aves/Ixobrychus exilis/9812323577f3deb8c071f94dfde96518.jpg\",\n  \"444645.jpg\": \"Aves/Ixobrychus exilis/6655aaceb7c7610144045c6c208d56c6.jpg\",\n  \"444648.jpg\": \"Aves/Ixobrychus exilis/b208d1a5f12d55e6bfdf7023e7d31fec.jpg\",\n  \"444652.jpg\": \"Aves/Ixobrychus exilis/e00332cf51292191cb54b6dcca84a985.jpg\",\n  \"444653.jpg\": \"Aves/Ixobrychus exilis/9db409bdad96b002f51164149a0ef649.jpg\",\n  \"444654.jpg\": \"Aves/Ixobrychus exilis/49790a66b94915e200732fa2eb87f7bf.jpg\",\n  \"444658.jpg\": \"Insecta/Monochamus scutellatus/2f9ec6c6ce09df14debbe142f89ca754.jpg\",\n  \"444662.jpg\": \"Insecta/Monochamus scutellatus/03c317e680e116dd53cab68614a80f3c.jpg\",\n  \"444664.jpg\": \"Insecta/Monochamus scutellatus/fbfa41e6eb407a678cbc1b293e7e0cb9.jpg\",\n  \"444665.jpg\": \"Insecta/Monochamus scutellatus/0fbe93d7e734dcdba1b5e43178b17002.jpg\",\n  \"444668.jpg\": \"Insecta/Monochamus scutellatus/63ba9326770e92b552ee13d0d61bc343.jpg\",\n  \"444678.jpg\": \"Insecta/Monochamus scutellatus/b05b3886ade1a1c07a297f19e9af0b79.jpg\",\n  \"444679.jpg\": \"Insecta/Monochamus scutellatus/f3f9cf1a2739cffc0704abb635f9d1a0.jpg\",\n  \"444698.jpg\": \"Insecta/Issoria lathonia/93481d80afb66d4cf9cc6e7633e730e1.jpg\",\n  \"4447.jpg\": \"Aves/Setophaga petechia/188a2735b99faca74f8aa8676311f294.jpg\",\n  \"444741.jpg\": \"Insecta/Scudderia furcata/0a3700b6bd6e68a41cb39c9e12ed01b1.jpg\",\n  \"444742.jpg\": \"Insecta/Scudderia furcata/63d503ba82eedb8af229610346fdf7b7.jpg\",\n  \"444744.jpg\": \"Insecta/Scudderia furcata/e3daa2bb3b1ddf343aa17e7b219e7d9b.jpg\",\n  \"444747.jpg\": \"Aves/Melanerpes pucherani/49efd83590b30183d0ef00432c116a87.jpg\",\n  \"444752.jpg\": \"Aves/Melanerpes pucherani/1162e12da0b08cb5a007fe2e1dc31f58.jpg\",\n  \"444758.jpg\": \"Reptilia/Drymarchon melanurus/cb7a7d53e8c3dba47b57bb3ec29990f7.jpg\",\n  \"444792.jpg\": \"Insecta/Ischnura ramburii/46c1a09be1e56c8c3a777cb48e59c7ff.jpg\",\n  \"444793.jpg\": \"Insecta/Ischnura ramburii/fda75ce094bbfef02c22792ca86bb863.jpg\",\n  \"444848.jpg\": \"Insecta/Ischnura ramburii/e4f4794c8a064ee50062b3e9e698c1fa.jpg\",\n  \"444861.jpg\": \"Insecta/Ischnura ramburii/f8190eedc8f48d0ddcf49ceadb5a4945.jpg\",\n  \"444862.jpg\": \"Insecta/Ischnura ramburii/5a48d129c6cabd3a6ae3466cc0204293.jpg\",\n  \"444863.jpg\": \"Insecta/Ischnura ramburii/124a14f8063ddc7274865df9c7093f82.jpg\",\n  \"444897.jpg\": \"Insecta/Ischnura ramburii/f2bb341f4fa3d4ab0a5089d0235287b2.jpg\",\n  \"444935.jpg\": \"Aves/Bucephala islandica/e8aa6eeb59b269f8f826fde4d1a9b0af.jpg\",\n  \"444962.jpg\": \"Aves/Bucephala islandica/28c7d20720cc73fb90660dfbffea35f3.jpg\",\n  \"444963.jpg\": \"Aves/Bucephala islandica/1590c91eac76246370bec2011b3e392b.jpg\",\n  \"444965.jpg\": \"Aves/Bucephala islandica/5d21f6b5b4f41a02bfc17ce8d126968a.jpg\",\n  \"444967.jpg\": \"Aves/Bucephala islandica/9364a225dbfc4c61f62e7e15ae15ff81.jpg\",\n  \"445.jpg\": \"Mammalia/Ursus americanus/719cb26dffe7b1e84097544c9d2f413c.jpg\",\n  \"445063.jpg\": \"Insecta/Melanchra adjuncta/03cd99f0e383d12e06b299088689157f.jpg\",\n  \"445065.jpg\": \"Insecta/Melanchra adjuncta/b31280b4702ab697ab972096cc87be3a.jpg\",\n  \"445081.jpg\": \"Insecta/Celastrina neglecta/0b2f12b80165cfa3a1774ce74d48bb70.jpg\",\n  \"445086.jpg\": \"Insecta/Celastrina neglecta/6e7e85af77fcc712f2b1d4f004fb3345.jpg\",\n  \"445102.jpg\": \"Insecta/Celastrina neglecta/a277a7045d28cca4664fb5be62cd77dc.jpg\",\n  \"445103.jpg\": \"Insecta/Celastrina neglecta/a49ed0afd031ac86e003fde6a022c42f.jpg\",\n  \"445107.jpg\": \"Insecta/Celastrina neglecta/08d96ed924eb4e053cc4a0508f62ade2.jpg\",\n  \"445114.jpg\": \"Insecta/Celastrina neglecta/4180d14f19f250b45a8fac61d40ff935.jpg\",\n  \"445132.jpg\": \"Insecta/Choristoneura rosaceana/9a5596c71548e512fbbd991020966b3b.jpg\",\n  \"445134.jpg\": \"Insecta/Choristoneura rosaceana/3eeaae5ae44f368bf0c0d35a9fd8740d.jpg\",\n  \"445135.jpg\": \"Insecta/Choristoneura rosaceana/6e9a75773c49b2bdc3694dae5c7dcd77.jpg\",\n  \"445157.jpg\": \"Insecta/Orgyia antiqua/9be1f249b41cced48a0fbb28eb86c184.jpg\",\n  \"445161.jpg\": \"Insecta/Orgyia antiqua/400b718cd7bb08ec5afe6977e000bee0.jpg\",\n  \"445162.jpg\": \"Insecta/Orgyia antiqua/c1d20117aeb7fb674abec192c36ff7ac.jpg\",\n  \"445170.jpg\": \"Insecta/Phyciodes pulchella/b831af63700dcde9adc0306a64df2a2f.jpg\",\n  \"445176.jpg\": \"Insecta/Phyciodes pulchella/b9b1300557373fb2e138995664bd777c.jpg\",\n  \"445177.jpg\": \"Insecta/Phyciodes pulchella/72374b1f9d916d58a061fe9baa27d627.jpg\",\n  \"445187.jpg\": \"Insecta/Phyciodes pulchella/72c08c27ac9f20222bfc31c123ea3279.jpg\",\n  \"445191.jpg\": \"Insecta/Phyciodes pulchella/8e4a94a715174117d8aa144a75b41f3b.jpg\",\n  \"445192.jpg\": \"Insecta/Phyciodes pulchella/7dfcc5211cae462daab00520a278b593.jpg\",\n  \"445196.jpg\": \"Insecta/Phyciodes pulchella/0ffb569bc3795d1e52f4e834875eb59d.jpg\",\n  \"445207.jpg\": \"Insecta/Phyciodes pulchella/59143fc001c8fe7f08fca93acd282635.jpg\",\n  \"445236.jpg\": \"Aves/Coereba flaveola/f79d1e0b0620a15fcf01c16b9bd9fec2.jpg\",\n  \"445238.jpg\": \"Aves/Coereba flaveola/79acfe3e4516450236d4267c729bf7ab.jpg\",\n  \"445239.jpg\": \"Aves/Coereba flaveola/8d302c3be00451c5e38e9775301ebf56.jpg\",\n  \"445244.jpg\": \"Aves/Coereba flaveola/323739608d172af5c25bc7d37c8db6df.jpg\",\n  \"445247.jpg\": \"Aves/Coereba flaveola/b7b775610d8cefe244badca2190dfb95.jpg\",\n  \"445251.jpg\": \"Aves/Coereba flaveola/65102ad8dbb95518a016d16bc69e7d7b.jpg\",\n  \"445258.jpg\": \"Aves/Coereba flaveola/d87ab6e9168c195ffc5c82abae7b4b16.jpg\",\n  \"445262.jpg\": \"Insecta/Cicindela oregona/4a140314c59f745d9c2947d207244d96.jpg\",\n  \"445290.jpg\": \"Arachnida/Pisaurina mira/ea14ef840aed1cb0e5e82126d30718d4.jpg\",\n  \"445363.jpg\": \"Aves/Zonotrichia querula/f12713a51402d41b25db7508918f1ea8.jpg\",\n  \"445368.jpg\": \"Aves/Zonotrichia querula/d720a66b5477915789e9394da9ae315b.jpg\",\n  \"445389.jpg\": \"Aves/Zonotrichia querula/eacc669c5a18097d47903a2a895d8ebb.jpg\",\n  \"445395.jpg\": \"Aves/Zonotrichia querula/bdc31908209d867acdda472d693f0a8d.jpg\",\n  \"445402.jpg\": \"Aves/Zonotrichia querula/cc68522f053f42e9573d73553765e0bc.jpg\",\n  \"445403.jpg\": \"Aves/Zonotrichia querula/64991d37ff42e8839cf5500b0fa401a6.jpg\",\n  \"445404.jpg\": \"Aves/Zonotrichia querula/fff0f1243c91dfcccab2b46d79296146.jpg\",\n  \"445411.jpg\": \"Aves/Zonotrichia querula/896e3835c6699be9a361855b67155422.jpg\",\n  \"445471.jpg\": \"Insecta/Coccinella undecimpunctata/9f72f0e3e1f7a47c585f8468e2cd1145.jpg\",\n  \"445523.jpg\": \"Aves/Coccothraustes vespertinus/f6676ee065eaefc010b0f6ebaf5ea5ac.jpg\",\n  \"445524.jpg\": \"Aves/Coccothraustes vespertinus/056d0a4d3814912136076d4dc77d771e.jpg\",\n  \"445530.jpg\": \"Aves/Coccothraustes vespertinus/79c4304bcd1b5dd8da10d7abd831818b.jpg\",\n  \"445534.jpg\": \"Aves/Coccothraustes vespertinus/3f301f6e0a816a6c4e2731d5b6ee2109.jpg\",\n  \"445541.jpg\": \"Aves/Coccothraustes vespertinus/0bd7e0f0fd5deed6006c5146c4d1bef5.jpg\",\n  \"445544.jpg\": \"Aves/Coccothraustes vespertinus/926e6c311e8e04ea82525497e4fb3ed5.jpg\",\n  \"445545.jpg\": \"Aves/Coccothraustes vespertinus/f7eca38e8531e37a7f1c9baa371250e3.jpg\",\n  \"445566.jpg\": \"Aves/Nestor meridionalis septentrionalis/bd39442e203e9ae23cb2fb74d5cd1e0e.jpg\",\n  \"445577.jpg\": \"Aves/Nestor meridionalis septentrionalis/7322bc7466a28a79741a1508021651bf.jpg\",\n  \"445587.jpg\": \"Aves/Nestor meridionalis septentrionalis/f87ac8925c968e7bcf2a4aad19b41f95.jpg\",\n  \"445588.jpg\": \"Aves/Nestor meridionalis septentrionalis/5914511a6459f91a809095b1b7119998.jpg\",\n  \"445591.jpg\": \"Aves/Nestor meridionalis septentrionalis/d5764c2ba1d5fb2ba3666dba71def388.jpg\",\n  \"445600.jpg\": \"Aves/Nestor meridionalis septentrionalis/23de4adb1447085e31c6cb06b5587456.jpg\",\n  \"445740.jpg\": \"Insecta/Lyssa zampa/69000bc9139126bf917e9e159025f919.jpg\",\n  \"445742.jpg\": \"Insecta/Lyssa zampa/80cb7d2a50fb70079e534f1324d8f999.jpg\",\n  \"445743.jpg\": \"Insecta/Lyssa zampa/0fefa9130cc0159b5a7f94f4e21a3e4d.jpg\",\n  \"445748.jpg\": \"Insecta/Lyssa zampa/7ea3a829c7af7835def5878cab0b3a16.jpg\",\n  \"445749.jpg\": \"Insecta/Lyssa zampa/73691d5f8400390f5de18fd7a05267c3.jpg\",\n  \"445750.jpg\": \"Insecta/Lyssa zampa/282661558651e767896c79370c0c278a.jpg\",\n  \"445751.jpg\": \"Insecta/Lyssa zampa/69f8ff3b0d5af16f1389edf3cd0e1ca5.jpg\",\n  \"445752.jpg\": \"Insecta/Lyssa zampa/9bed76167671cee100378c0667dd01e8.jpg\",\n  \"445760.jpg\": \"Insecta/Lyssa zampa/47de49a1e79fc3ddf38c7bf3d9b1715e.jpg\",\n  \"445761.jpg\": \"Insecta/Lyssa zampa/f43538fa7a3d9aa6ad935c23370e9550.jpg\",\n  \"44587.jpg\": \"Insecta/Noctua pronuba/928e77e23db93284d8830a723bb610d9.jpg\",\n  \"445891.jpg\": \"Insecta/Melangyna novaezelandiae/b028427c00d07d60c9f14b51f006576e.jpg\",\n  \"446082.jpg\": \"Aves/Columbina passerina/acfe2f13f30af1353eec15807909031a.jpg\",\n  \"446084.jpg\": \"Aves/Columbina passerina/579d50ef0d93467970cdc62e57c2bb73.jpg\",\n  \"446099.jpg\": \"Aves/Columbina passerina/541834b3cfcd3e66134684b95f34e321.jpg\",\n  \"446118.jpg\": \"Aves/Columbina passerina/56a14cf1aab053183d99e72dfe2ef6cc.jpg\",\n  \"446120.jpg\": \"Aves/Columbina passerina/f0329b0a2694548f4c10dd1056d0a565.jpg\",\n  \"446121.jpg\": \"Aves/Columbina passerina/dd450c44f839beb7b3962246d23a40e9.jpg\",\n  \"446130.jpg\": \"Aves/Columbina passerina/63c196271c4695344fcb2cb67b5b2e22.jpg\",\n  \"446131.jpg\": \"Arachnida/Dysdera crocata/d9b8d26d494c59f12473bf2a9a23dace.jpg\",\n  \"446133.jpg\": \"Arachnida/Dysdera crocata/69d8bb9cb35ad07ec1cacd38d18695cf.jpg\",\n  \"446134.jpg\": \"Arachnida/Dysdera crocata/b42e9e042c92f2608fe1bb879e67c130.jpg\",\n  \"446136.jpg\": \"Arachnida/Dysdera crocata/05ddf3d3b4c58d996987af6c17127867.jpg\",\n  \"44614.jpg\": \"Insecta/Noctua pronuba/82fbb803df8a98ebcb0506e2907690a6.jpg\",\n  \"446144.jpg\": \"Arachnida/Dysdera crocata/3f0ae3bb67c4eb2b4c987bd03fa32c0d.jpg\",\n  \"446150.jpg\": \"Arachnida/Dysdera crocata/b81f22a0aca88f016a0cf31942fd33b3.jpg\",\n  \"446160.jpg\": \"Arachnida/Dysdera crocata/c8ee37a58336b084edad2b20a25a2dcb.jpg\",\n  \"446170.jpg\": \"Arachnida/Dysdera crocata/80ecb9ed23e162184caed93b33aeac46.jpg\",\n  \"446206.jpg\": \"Aves/Cinclus mexicanus/39190aa6181d289c326ffbb1239154ae.jpg\",\n  \"446209.jpg\": \"Aves/Cinclus mexicanus/d8b0572d21c5e3bff3facdea04d7e18b.jpg\",\n  \"446210.jpg\": \"Aves/Cinclus mexicanus/c1968f8c9fb1b0524ab0d6ca2615661c.jpg\",\n  \"446225.jpg\": \"Aves/Cinclus mexicanus/550e57eb763e8f752c45f945b1a7bd43.jpg\",\n  \"446233.jpg\": \"Aves/Cinclus mexicanus/3411a4942da17c25437c8b86083edc9f.jpg\",\n  \"446235.jpg\": \"Aves/Cinclus mexicanus/8f5525c1128ff7479d5f856ab8f5c660.jpg\",\n  \"446239.jpg\": \"Aves/Cinclus mexicanus/6de23d0a5cab837cce3f30b3f9482a1f.jpg\",\n  \"446240.jpg\": \"Aves/Cinclus mexicanus/e5366a12d18555da8d22341ba81e9ca8.jpg\",\n  \"446242.jpg\": \"Mammalia/Phascolarctos cinereus/3f586707e0efe4c251d57e4bfa2c46fa.jpg\",\n  \"446254.jpg\": \"Arachnida/Argiope argentata/a3119d7f91596474af086c86ed2ea2ea.jpg\",\n  \"446255.jpg\": \"Arachnida/Argiope argentata/7c15f745c5326fb4b0a76400e8a863b3.jpg\",\n  \"446266.jpg\": \"Arachnida/Argiope argentata/72150122879d682720b52b4f31572de1.jpg\",\n  \"446268.jpg\": \"Arachnida/Argiope argentata/dd1073d133fc01d0443418c31a1fb740.jpg\",\n  \"446293.jpg\": \"Arachnida/Argiope argentata/0d65d91d197de09b90118d5596cbd01a.jpg\",\n  \"446308.jpg\": \"Arachnida/Argiope argentata/a663a95d343593251b4ca1e5443c8a23.jpg\",\n  \"446321.jpg\": \"Arachnida/Argiope argentata/a6248a3dc3d3b8e9a00eeeebb611fc98.jpg\",\n  \"446348.jpg\": \"Arachnida/Argiope argentata/9cbb3d883f5065fd31c72f32406342c8.jpg\",\n  \"446354.jpg\": \"Arachnida/Argiope argentata/374adc34169e82892433a6d99647bdaf.jpg\",\n  \"446365.jpg\": \"Aves/Phalacrocorax carbo novaehollandiae/b90c6c28c078725d971cbe8a179b66c8.jpg\",\n  \"44638.jpg\": \"Insecta/Noctua pronuba/e8495f1791f9aed96f0ef043ec267760.jpg\",\n  \"44643.jpg\": \"Insecta/Noctua pronuba/425ed99257b87adb078df1b5b8a5e9e6.jpg\",\n  \"44647.jpg\": \"Insecta/Noctua pronuba/210656aaf4780cc37e80526ccf21a7f6.jpg\",\n  \"446528.jpg\": \"Mammalia/Tamiasciurus hudsonicus/601b1790f4e595280926b000d99fb741.jpg\",\n  \"44654.jpg\": \"Insecta/Noctua pronuba/b450d027e2fccb0e42b6313e10fbcc59.jpg\",\n  \"446542.jpg\": \"Mammalia/Tamiasciurus hudsonicus/8a6229f5787bf8532291767ecdde6a8d.jpg\",\n  \"44655.jpg\": \"Insecta/Noctua pronuba/5c160fa2dcecb3869cd03b11a1f438c0.jpg\",\n  \"446559.jpg\": \"Mammalia/Tamiasciurus hudsonicus/cc0bcccc830a0162b50ebda521a9099f.jpg\",\n  \"446575.jpg\": \"Mammalia/Tamiasciurus hudsonicus/12dfef1e94dd1a97772ee5cbb367a243.jpg\",\n  \"44662.jpg\": \"Insecta/Noctua pronuba/da846b49ee916ebf52f48e19eac8cf6d.jpg\",\n  \"446620.jpg\": \"Mammalia/Tamiasciurus hudsonicus/add28137e71d5e6a2d37c73e15ab2f4e.jpg\",\n  \"446628.jpg\": \"Mammalia/Tamiasciurus hudsonicus/3525d9271a02b0c487ca5bacc45c39e7.jpg\",\n  \"44663.jpg\": \"Insecta/Noctua pronuba/35701515cda552726da1f2cdf390e389.jpg\",\n  \"44665.jpg\": \"Insecta/Noctua pronuba/d3168ad58d7b6c27adfe5e17c5cf8548.jpg\",\n  \"446668.jpg\": \"Reptilia/Holbrookia maculata/8dd13c83497a25a370ce4864efe6bd15.jpg\",\n  \"44667.jpg\": \"Insecta/Noctua pronuba/0e4f88fae2364da47b599b6e83cd2038.jpg\",\n  \"446673.jpg\": \"Reptilia/Holbrookia maculata/613422e89eb19ade8d9d93556af981c3.jpg\",\n  \"44671.jpg\": \"Insecta/Noctua pronuba/c9b97e9ad54596788e3c14710a46cc59.jpg\",\n  \"446718.jpg\": \"Aves/Rhipidura fuliginosa placabilis/922cc933e3577c9368315121ae234bbf.jpg\",\n  \"446719.jpg\": \"Aves/Rhipidura fuliginosa placabilis/53986137c69de8a56c1a65d47b784068.jpg\",\n  \"446720.jpg\": \"Aves/Rhipidura fuliginosa placabilis/d8d13aaf4946e572ae27a070b0970825.jpg\",\n  \"446721.jpg\": \"Aves/Rhipidura fuliginosa placabilis/5b8178640ce458864ed0552873e32d41.jpg\",\n  \"446722.jpg\": \"Aves/Rhipidura fuliginosa placabilis/692d2be5a159350594cfbd2d4bfce28f.jpg\",\n  \"446727.jpg\": \"Aves/Rhipidura fuliginosa placabilis/4c52cfff4a9dc87b7dc94e3c56ff40af.jpg\",\n  \"446728.jpg\": \"Aves/Rhipidura fuliginosa placabilis/71ae4d75ce1230ae4f8e33ad7f7b6086.jpg\",\n  \"446729.jpg\": \"Aves/Rhipidura fuliginosa placabilis/a5426dff20904e1b2ba07b5015a3a34e.jpg\",\n  \"446730.jpg\": \"Aves/Rhipidura fuliginosa placabilis/0764809b82b2c89c89c3a8c574ff73e3.jpg\",\n  \"446735.jpg\": \"Aves/Rhipidura fuliginosa placabilis/7b82459ebf2d719dc1a0c3f665a320a2.jpg\",\n  \"446736.jpg\": \"Aves/Rhipidura fuliginosa placabilis/62f7b76b4e341b534cdfacc5178185c6.jpg\",\n  \"446737.jpg\": \"Aves/Rhipidura fuliginosa placabilis/38b3824cd2ed55b004e7aa6eb8eeae00.jpg\",\n  \"446751.jpg\": \"Insecta/Cicindela repanda/c5891ee64655100ef80acb5def927147.jpg\",\n  \"446753.jpg\": \"Insecta/Cicindela repanda/ecec8383e0db0e09404799a958eb51e7.jpg\",\n  \"44676.jpg\": \"Insecta/Noctua pronuba/5e47e988e67b2340f1b96ea0e07afddc.jpg\",\n  \"446764.jpg\": \"Aves/Cygnus cygnus/d5c564f8dbd8c975d6e71eb5f81c380d.jpg\",\n  \"44678.jpg\": \"Insecta/Noctua pronuba/1d4fca9fe3b1ee79285ccafcb842dd59.jpg\",\n  \"446781.jpg\": \"Insecta/Aenetus virescens/b27352dc9b3214ea4b0976e3f4e1cb2a.jpg\",\n  \"446906.jpg\": \"Amphibia/Hyla versicolor/4ac321f9c483f19a88614d6ad389cc0c.jpg\",\n  \"44692.jpg\": \"Insecta/Noctua pronuba/64b86df2f76d012bbb03d0bc1721d669.jpg\",\n  \"446929.jpg\": \"Amphibia/Hyla versicolor/eb9a868d42509bf0f460d2786d2ff78c.jpg\",\n  \"446933.jpg\": \"Amphibia/Hyla versicolor/e21a730af2c3cd6c1c83a3d72f1a893f.jpg\",\n  \"446938.jpg\": \"Amphibia/Hyla versicolor/f31d5a0fb1c41834fe9961216b540539.jpg\",\n  \"446947.jpg\": \"Amphibia/Hyla versicolor/ca295dd205c667d28f966d0bb12ed723.jpg\",\n  \"446954.jpg\": \"Amphibia/Hyla versicolor/f5f5b3b679bf1a6a1c4e0584f8729f11.jpg\",\n  \"44696.jpg\": \"Insecta/Noctua pronuba/395b084cfa71ea4d947797923763ee42.jpg\",\n  \"446965.jpg\": \"Amphibia/Hyla versicolor/bb5bc525bc5a170dc71f477cd6536330.jpg\",\n  \"446975.jpg\": \"Amphibia/Hyla versicolor/febc7a3cc05773f94d7efae834d0a673.jpg\",\n  \"446976.jpg\": \"Amphibia/Hyla versicolor/977d2d3a0213edf2d80f083bcbd24136.jpg\",\n  \"44698.jpg\": \"Insecta/Noctua pronuba/e0ceb39bd8a83d6d93a46c538e970cb5.jpg\",\n  \"446986.jpg\": \"Amphibia/Hyla versicolor/1163e6ecfc8ad70fe8c749be0a475a5a.jpg\",\n  \"44699.jpg\": \"Insecta/Noctua pronuba/0f6319385ded90989982b3e68b8eb337.jpg\",\n  \"446998.jpg\": \"Amphibia/Hyla versicolor/9cb989dd650ebe98435724193682896a.jpg\",\n  \"447006.jpg\": \"Arachnida/Trite planiceps/7cc729f485a3aeaf004523639bb23457.jpg\",\n  \"447008.jpg\": \"Arachnida/Trite planiceps/3e64cd582fd44a56903c2a478d636422.jpg\",\n  \"447012.jpg\": \"Insecta/Lucanus cervus/9cfef70d02ad1fc8fffe05067463569a.jpg\",\n  \"447021.jpg\": \"Insecta/Lucanus cervus/666e06275baa047abda281e0067ded37.jpg\",\n  \"447035.jpg\": \"Insecta/Miomantis caffra/22570bc992d547afb02974d5a6c19f3f.jpg\",\n  \"447036.jpg\": \"Insecta/Miomantis caffra/8bc4e9814a6fc9429439d3b123ce76f8.jpg\",\n  \"447037.jpg\": \"Insecta/Miomantis caffra/2b7d3a7e45fa6be68689382bc6431d55.jpg\",\n  \"447040.jpg\": \"Insecta/Miomantis caffra/6147561a2b561fd39633bba9c789dc34.jpg\",\n  \"447042.jpg\": \"Insecta/Miomantis caffra/f976e5054ac70f8cc589e3133f72e027.jpg\",\n  \"447049.jpg\": \"Insecta/Miomantis caffra/050a6444b300245adf4fc8be96b158be.jpg\",\n  \"447050.jpg\": \"Insecta/Miomantis caffra/2ac78d6504e4cc8d670f75bedd8a20b4.jpg\",\n  \"447051.jpg\": \"Insecta/Miomantis caffra/2bf2b319acf94a70a6c53903534abfe3.jpg\",\n  \"447052.jpg\": \"Insecta/Miomantis caffra/e742cdbbd8a05ce0866c205f92b80e7c.jpg\",\n  \"447060.jpg\": \"Insecta/Miomantis caffra/739036b7085df6c27d9d1c488b2eb2ae.jpg\",\n  \"447106.jpg\": \"Reptilia/Urosaurus ornatus/96ac2318748b49c5b444d80af481bf0c.jpg\",\n  \"44711.jpg\": \"Insecta/Noctua pronuba/c3480acd52b83669ebcfc984c417b0bf.jpg\",\n  \"447111.jpg\": \"Reptilia/Urosaurus ornatus/6f827c61be0d87cbd97ce7f7af3c6d5b.jpg\",\n  \"447139.jpg\": \"Reptilia/Urosaurus ornatus/487c1fb08fed27404d52a84347ab9260.jpg\",\n  \"447163.jpg\": \"Reptilia/Urosaurus ornatus/1a730b84bd6665fd8f014dbc547e0693.jpg\",\n  \"447166.jpg\": \"Reptilia/Urosaurus ornatus/5cf5101bb367d009253a60e0616e1991.jpg\",\n  \"44717.jpg\": \"Insecta/Noctua pronuba/4c9da4ad20300c114e8ab990756c81ef.jpg\",\n  \"44731.jpg\": \"Arachnida/Rabidosa rabida/a3edee114f941b29a68bc43c6437664e.jpg\",\n  \"44740.jpg\": \"Arachnida/Rabidosa rabida/769c642709e9d0344ef228956f8da208.jpg\",\n  \"44741.jpg\": \"Arachnida/Rabidosa rabida/f734167dcc9c38c2aa1bdbc80bb887b7.jpg\",\n  \"447425.jpg\": \"Aves/Petroica macrocephala macrocephala/ce0dd875be86d709a44bc67f8919bec1.jpg\",\n  \"447432.jpg\": \"Amphibia/Ambystoma tigrinum/e13e1b10b97eef7b358897214feb4abd.jpg\",\n  \"447433.jpg\": \"Amphibia/Ambystoma tigrinum/5d96a8958ebb75ff4aea75cc899e7b0a.jpg\",\n  \"447436.jpg\": \"Amphibia/Ambystoma tigrinum/36869b7b2dca304454f808670722afcb.jpg\",\n  \"44745.jpg\": \"Arachnida/Rabidosa rabida/803bacc0719956f96e1dff75ccac6778.jpg\",\n  \"44749.jpg\": \"Arachnida/Rabidosa rabida/3bb44e8b0c96e7b4d48f4bae44d2b71d.jpg\",\n  \"44753.jpg\": \"Arachnida/Rabidosa rabida/c4ed226de073ee3058c7ed202197b101.jpg\",\n  \"44754.jpg\": \"Arachnida/Rabidosa rabida/593294e60d0cf64974113d57e3fa0bb4.jpg\",\n  \"447585.jpg\": \"Arachnida/Eriophora pustulosa/56899e71b8373484892549e8d4960af6.jpg\",\n  \"447587.jpg\": \"Arachnida/Eriophora pustulosa/c2ef2b7c52e1a0162a916fd76a4e70b6.jpg\",\n  \"447588.jpg\": \"Arachnida/Eriophora pustulosa/cedda7b2358e95fd83e952507e914ea1.jpg\",\n  \"447589.jpg\": \"Arachnida/Eriophora pustulosa/b542434fb0609937e3876e576142ab6e.jpg\",\n  \"447597.jpg\": \"Arachnida/Eriophora pustulosa/9f34c4d8d950f92ec270972315015c91.jpg\",\n  \"447598.jpg\": \"Arachnida/Eriophora pustulosa/1f20506593a4343c50555fb987d6231b.jpg\",\n  \"447604.jpg\": \"Arachnida/Eriophora pustulosa/c68a905f9be48560ad8ce5ffa300f928.jpg\",\n  \"447605.jpg\": \"Arachnida/Eriophora pustulosa/e8f90f1ca1fbc20120cd40f95b177512.jpg\",\n  \"447614.jpg\": \"Arachnida/Eriophora pustulosa/09d15b444db8df8539ab22457c88d482.jpg\",\n  \"447615.jpg\": \"Arachnida/Eriophora pustulosa/db1fe509ea1379dd5e0e9071b6d952b6.jpg\",\n  \"447641.jpg\": \"Insecta/Lineodes integra/7831b952065c8d9cd5b6a0113cc2b8e5.jpg\",\n  \"447731.jpg\": \"Actinopterygii/Megalops atlanticus/ca60ccf45dc622ba90713bd24cd13e04.jpg\",\n  \"447733.jpg\": \"Actinopterygii/Megalops atlanticus/373e2e0bd57dd0c6632e29d4be9156fe.jpg\",\n  \"447790.jpg\": \"Insecta/Nathalis iole/09325592cb23c30f0e7163d69591b554.jpg\",\n  \"447795.jpg\": \"Insecta/Nathalis iole/7a9bb05d14fe920eae64f14b368c9ceb.jpg\",\n  \"447796.jpg\": \"Insecta/Nathalis iole/85f1487dd1dad5e923fd7c7e6761ce91.jpg\",\n  \"447827.jpg\": \"Insecta/Nathalis iole/15e62a278765e7585596f403a32e0d00.jpg\",\n  \"448104.jpg\": \"Aves/Rhipidura fuliginosa fuliginosa/ba65029b864479cda1557e7ab5328ec3.jpg\",\n  \"448105.jpg\": \"Aves/Rhipidura fuliginosa fuliginosa/e64e2520ee3b7cfccab090f3be8d330e.jpg\",\n  \"448106.jpg\": \"Aves/Rhipidura fuliginosa fuliginosa/4b664c82afebc02903dfa0177a2255b4.jpg\",\n  \"448111.jpg\": \"Aves/Rhipidura fuliginosa fuliginosa/e2be1d5e33e3a55d26a000a036f190fb.jpg\",\n  \"448117.jpg\": \"Aves/Rhipidura fuliginosa fuliginosa/cf0ff2ad22cab9cee9b2097324352c92.jpg\",\n  \"448118.jpg\": \"Aves/Rhipidura fuliginosa fuliginosa/ef51fe0eb135066b209a86ecb56bb2f0.jpg\",\n  \"448119.jpg\": \"Aves/Rhipidura fuliginosa fuliginosa/a2a7648729f687584b089d644e701870.jpg\",\n  \"448125.jpg\": \"Aves/Rhipidura fuliginosa fuliginosa/21fede93f012602d840021d4e59aff1a.jpg\",\n  \"448131.jpg\": \"Aves/Rhipidura fuliginosa fuliginosa/9c92db528100eedd8d0222b4e9a78d73.jpg\",\n  \"448183.jpg\": \"Insecta/Pseudothyatira cymatophoroides/f971d579037a405a2508aa9490ff2b9f.jpg\",\n  \"448295.jpg\": \"Animalia/Henricia leviuscula/31f663d2a3376332edf1898d2f0988b5.jpg\",\n  \"448297.jpg\": \"Animalia/Henricia leviuscula/ef4ccff66e81952c4365ddc2acbb582f.jpg\",\n  \"448298.jpg\": \"Animalia/Henricia leviuscula/9cc951af7c3c9cc83a60991e46e8337b.jpg\",\n  \"448299.jpg\": \"Animalia/Henricia leviuscula/3ba1fe6f643dad8586c2998d4aec3d0d.jpg\",\n  \"448305.jpg\": \"Mollusca/Oligyra orbiculata/04f3b9a0824b68666d1a1e5200079232.jpg\",\n  \"448306.jpg\": \"Mollusca/Oligyra orbiculata/35235c5f8fd28460706dccc1c79f5be6.jpg\",\n  \"448307.jpg\": \"Mollusca/Oligyra orbiculata/742923fc92ad3ad2ab1016cd3331b7f5.jpg\",\n  \"448314.jpg\": \"Mollusca/Oligyra orbiculata/58d3cd634636f5e3b6b40f5359435ff8.jpg\",\n  \"448370.jpg\": \"Aves/Icterus spurius/60ab11ac9f5539bab14bb8cfe498c9f3.jpg\",\n  \"448381.jpg\": \"Aves/Icterus spurius/91b600342dd9f71fb46370f6adf39e8a.jpg\",\n  \"448403.jpg\": \"Aves/Icterus spurius/92660bd32ad38d3e557276a7525d0054.jpg\",\n  \"448418.jpg\": \"Aves/Icterus spurius/653b0d49f60ee93550036957d695d36f.jpg\",\n  \"448431.jpg\": \"Aves/Icterus spurius/d87d95be2a7f8d3671ce5d3a2ab4ccc7.jpg\",\n  \"448435.jpg\": \"Aves/Icterus spurius/73205e798db9dc14bea06b5afcbfc27c.jpg\",\n  \"448497.jpg\": \"Insecta/Prionoplus reticularis/23dcbe7464018fee140ff401c32ebe28.jpg\",\n  \"44853.jpg\": \"Aves/Phalaropus fulicarius/37f3eda73b496faa82d5cd0907c048d7.jpg\",\n  \"44860.jpg\": \"Aves/Phalaropus fulicarius/dd89dae3579b345dfd9865639029aa83.jpg\",\n  \"448705.jpg\": \"Insecta/Orthodera novaezealandiae/7947591b005f23cfcc10b6cc0b289fa6.jpg\",\n  \"448706.jpg\": \"Insecta/Orthodera novaezealandiae/1ac9a9b09108c43153d707834762a92f.jpg\",\n  \"448710.jpg\": \"Insecta/Orthodera novaezealandiae/cc2df70868c9d0c8203214370bedfbb0.jpg\",\n  \"448715.jpg\": \"Insecta/Orthodera novaezealandiae/31913607f0e6ce6f68f682b10c865ce4.jpg\",\n  \"448718.jpg\": \"Insecta/Orthodera novaezealandiae/0daf3f4854bc7f7379ae378a30ed29ff.jpg\",\n  \"448719.jpg\": \"Insecta/Orthodera novaezealandiae/5f897327f0c0e5b879fa6eb4bafdd459.jpg\",\n  \"448720.jpg\": \"Insecta/Orthodera novaezealandiae/91ee0231a975c46dbfdc9989566771ed.jpg\",\n  \"448758.jpg\": \"Insecta/Austrolestes colensonis/3775b3e73d55d27faeaeb375dad001d8.jpg\",\n  \"448759.jpg\": \"Insecta/Austrolestes colensonis/25a9b184717c53b7af6484cff3ff5bb4.jpg\",\n  \"448809.jpg\": \"Reptilia/Salvadora grahamiae lineata/f119d76280398c256fa9461e7d113d2c.jpg\",\n  \"448817.jpg\": \"Reptilia/Salvadora grahamiae lineata/e04401b38b2a0c06611ced7ec9939944.jpg\",\n  \"44882.jpg\": \"Reptilia/Lampropeltis getula/16ba2b9b383f9af12bdb415aba71a85f.jpg\",\n  \"448820.jpg\": \"Reptilia/Salvadora grahamiae lineata/9ba65c01b8d34b6b2006f5745d0b2b38.jpg\",\n  \"44884.jpg\": \"Reptilia/Lampropeltis getula/80889610b7f7bd7fffa27073fa476cbd.jpg\",\n  \"448915.jpg\": \"Aves/Zosterops lateralis lateralis/1162dfc0df91387d5d66bdd2717a9e9a.jpg\",\n  \"448924.jpg\": \"Aves/Zosterops lateralis lateralis/0a8a935f934fb6292f971b30970a0e4c.jpg\",\n  \"44894.jpg\": \"Reptilia/Lampropeltis getula/70fdbd94861779524966a0eb55b7182e.jpg\",\n  \"44898.jpg\": \"Reptilia/Lampropeltis getula/18c74566a3e21924c5d3c78262a9d2c3.jpg\",\n  \"449018.jpg\": \"Insecta/Phaulacridium marginale/2438491c256f9582d555d7aeb15c3fdc.jpg\",\n  \"449020.jpg\": \"Insecta/Phaulacridium marginale/63112b196d9ce4a210e6ee8157668344.jpg\",\n  \"449091.jpg\": \"Insecta/Vanessa gonerilla gonerilla/b891fdc8d6641366ef08d414575359bc.jpg\",\n  \"449098.jpg\": \"Insecta/Vanessa gonerilla gonerilla/f3e3883ae7d068a3d56d893ebb4583e5.jpg\",\n  \"449099.jpg\": \"Insecta/Vanessa gonerilla gonerilla/147558e2e4fc64fd2f8871d12906130c.jpg\",\n  \"449100.jpg\": \"Insecta/Vanessa gonerilla gonerilla/1be7aa4e3312bea101881db481a912d0.jpg\",\n  \"449101.jpg\": \"Insecta/Vanessa gonerilla gonerilla/9fc14acbebf1376d9cd346c5da469af1.jpg\",\n  \"449102.jpg\": \"Insecta/Vanessa gonerilla gonerilla/b626a1794acc79662b6e4ffb39804285.jpg\",\n  \"449103.jpg\": \"Insecta/Vanessa gonerilla gonerilla/c22c89dc6c0f8d907f470753259a7690.jpg\",\n  \"449105.jpg\": \"Insecta/Vanessa gonerilla gonerilla/8cbdef3a77528288ee70bb49c72e74ac.jpg\",\n  \"449109.jpg\": \"Insecta/Eudryas grata/a2a2d771cfa867c8ccb66f2a49afbdb1.jpg\",\n  \"449110.jpg\": \"Insecta/Eudryas grata/733312da1b02004ff8fbd93ca1065cd5.jpg\",\n  \"449114.jpg\": \"Insecta/Eudryas grata/6f6617789711ad02be2105ca04d53b81.jpg\",\n  \"449121.jpg\": \"Insecta/Eudryas grata/ff98acadad280e128b3afb38754b77fc.jpg\",\n  \"449127.jpg\": \"Insecta/Eudryas grata/82939c9adabf98cdab8d9e118659a376.jpg\",\n  \"449128.jpg\": \"Insecta/Eudryas grata/342b503632d6158b0e0c3bec4aab32b4.jpg\",\n  \"449133.jpg\": \"Insecta/Eudryas grata/8389db6ff07b4c0cd8706ecf2869f4d9.jpg\",\n  \"449137.jpg\": \"Insecta/Eudryas grata/8dabd0c7fd556ca92d051de08d37cf1e.jpg\",\n  \"449425.jpg\": \"Animalia/Dasyatis americana/18264124a8b0079f84bd4012ef4960e5.jpg\",\n  \"449427.jpg\": \"Animalia/Dasyatis americana/b3522276b88acbafdb4108cb16a2f6d5.jpg\",\n  \"449429.jpg\": \"Animalia/Dasyatis americana/5a31589f93029d6ec3564d93a1418058.jpg\",\n  \"449431.jpg\": \"Insecta/Phloeodes diabolicus/a72a21d498ddde0998761e9336ec6019.jpg\",\n  \"449437.jpg\": \"Insecta/Phloeodes diabolicus/44a7b9c093a258580843b07c99a7a8aa.jpg\",\n  \"449438.jpg\": \"Insecta/Phloeodes diabolicus/c50c211d3a41ea9d928e6203c5ba478a.jpg\",\n  \"449564.jpg\": \"Aves/Dendrocygna autumnalis/c018ca922205e74bce94f0bb03767b22.jpg\",\n  \"449573.jpg\": \"Aves/Dendrocygna autumnalis/9de22cb00f61f45a25b75b28a2699c03.jpg\",\n  \"449581.jpg\": \"Aves/Dendrocygna autumnalis/ab1560ba0211bec1afc78f255fc793a7.jpg\",\n  \"449593.jpg\": \"Reptilia/Graptemys geographica/67f1618a2830b83360ca7037ddc3d2cd.jpg\",\n  \"449594.jpg\": \"Reptilia/Graptemys geographica/0355b159753e081dbbc358f5b563aa16.jpg\",\n  \"449595.jpg\": \"Reptilia/Graptemys geographica/800ab79cda207e9cf33064dd6a991ede.jpg\",\n  \"449600.jpg\": \"Reptilia/Graptemys geographica/20576f0b4c6dc9ebdca919270f50c093.jpg\",\n  \"449601.jpg\": \"Reptilia/Graptemys geographica/5212589ad103c01d11ffd1a9ff4d16fc.jpg\",\n  \"449602.jpg\": \"Reptilia/Graptemys geographica/6762665b15904de7a48cb81b78153653.jpg\",\n  \"449603.jpg\": \"Reptilia/Graptemys geographica/77a2a10a5804102f857b3f9651eb2ab0.jpg\",\n  \"449615.jpg\": \"Reptilia/Graptemys geographica/071f17b6fee64d13efe1fe2e8430f543.jpg\",\n  \"449616.jpg\": \"Reptilia/Graptemys geographica/9112912167f62248e654885a23f1526f.jpg\",\n  \"449617.jpg\": \"Reptilia/Graptemys geographica/146eab83d86c59282d96646a6fea197b.jpg\",\n  \"449619.jpg\": \"Insecta/Catasticta nimbice/f191b459b2db2f8f0b9bdeb77e27cb75.jpg\",\n  \"449670.jpg\": \"Insecta/Eutrapela clemataria/afb884887f53a9f7ffc02073bde923af.jpg\",\n  \"449673.jpg\": \"Insecta/Eutrapela clemataria/8ca47d2dd067520fdd623dddfc30878c.jpg\",\n  \"449694.jpg\": \"Aves/Onychoprion fuscatus/9be1f8c50358289ec335b17769c9f570.jpg\",\n  \"449697.jpg\": \"Aves/Onychoprion fuscatus/4e2ec06f1cacf4ce4f9d26a73eecb1a8.jpg\",\n  \"449709.jpg\": \"Insecta/Eumenes fraternus/8b399838dc3ac12ee631bc570f9f19e0.jpg\",\n  \"449747.jpg\": \"Actinopterygii/Gasterosteus aculeatus/0dcb7d65ef108a5feda55ab5965395ea.jpg\",\n  \"449749.jpg\": \"Actinopterygii/Gasterosteus aculeatus/2f5e3834398e32fb5bc215b8ef071c97.jpg\",\n  \"449775.jpg\": \"Reptilia/Nerodia erythrogaster transversa/fa352fc9ff630d141462e89dbfbfa927.jpg\",\n  \"449785.jpg\": \"Reptilia/Nerodia erythrogaster transversa/47192db1c892ace44bb1e8b32edea001.jpg\",\n  \"449830.jpg\": \"Reptilia/Nerodia erythrogaster transversa/16d9400235c8116dfed981c9936389f9.jpg\",\n  \"449832.jpg\": \"Reptilia/Nerodia erythrogaster transversa/fd3bc10b8c29af921bddeda9d43671ee.jpg\",\n  \"449840.jpg\": \"Reptilia/Nerodia erythrogaster transversa/fc636bd2b052538c4ddb12cfab7f8f82.jpg\",\n  \"449841.jpg\": \"Reptilia/Nerodia erythrogaster transversa/87e517c1daac949e9573ebb75f998707.jpg\",\n  \"449870.jpg\": \"Animalia/Rhincodon typus/c18c6c30f5b66edb01bcab893c8731cc.jpg\",\n  \"449985.jpg\": \"Reptilia/Coleonyx variegatus/b3f63ea5a0817d0fd362d70f5b45d237.jpg\",\n  \"449988.jpg\": \"Reptilia/Coleonyx variegatus/bae8a7dd64f436da7c6e478a81201f18.jpg\",\n  \"449992.jpg\": \"Reptilia/Coleonyx variegatus/c13148bb7386a1a4654fd82fcd1749a8.jpg\",\n  \"449998.jpg\": \"Reptilia/Coleonyx variegatus/368fb308a79d016d1efa48bd6c4b80c6.jpg\",\n  \"449999.jpg\": \"Reptilia/Coleonyx variegatus/b228b0916fba315fc125c054aee2a7e4.jpg\",\n  \"45.jpg\": \"Mammalia/Marmota flaviventris/0b172c792f0c6975906c3cb9f6ec4b5f.jpg\",\n  \"450000.jpg\": \"Reptilia/Coleonyx variegatus/eabc4f41f28e706708167d6d5e75f402.jpg\",\n  \"450012.jpg\": \"Insecta/Dythemis fugax/642518ddff27f9e579ee19143ef5a094.jpg\",\n  \"450014.jpg\": \"Insecta/Dythemis fugax/4282267b6cded2f65c09c0254daaf7db.jpg\",\n  \"450022.jpg\": \"Insecta/Dythemis fugax/2fe9c814a84cb5eeca2f1d1abec5390a.jpg\",\n  \"450023.jpg\": \"Insecta/Dythemis fugax/3dd5008934e2dd42fa149cab24178ba4.jpg\",\n  \"450024.jpg\": \"Insecta/Dythemis fugax/52c60e137235dd3e769f2cf439c54ed5.jpg\",\n  \"450030.jpg\": \"Insecta/Dythemis fugax/90b5542c041f2fc0d40e047075ad75e5.jpg\",\n  \"450033.jpg\": \"Insecta/Dythemis fugax/d4968842b5800451d1c367e46352f2f1.jpg\",\n  \"450035.jpg\": \"Insecta/Dythemis fugax/7761b1737ce4cc15c81b92c9bb92d884.jpg\",\n  \"450048.jpg\": \"Insecta/Dythemis fugax/5aa25c32bea8ed98df5b63b3fb6f884e.jpg\",\n  \"450053.jpg\": \"Insecta/Dythemis fugax/54d03b5f866a62a77525b78f7d4b493f.jpg\",\n  \"450145.jpg\": \"Amphibia/Salamandra salamandra/6720c01aa7fc820e92f13775779a79c4.jpg\",\n  \"450172.jpg\": \"Amphibia/Salamandra salamandra/dd589f4f7466eaa5ff21f1ed410008f3.jpg\",\n  \"450180.jpg\": \"Amphibia/Salamandra salamandra/9eef19c4f2b58cfc7a9c9999b87c2a46.jpg\",\n  \"450187.jpg\": \"Amphibia/Salamandra salamandra/17ecbd41b88ca67e91ea5fa4e3b811e1.jpg\",\n  \"450188.jpg\": \"Amphibia/Salamandra salamandra/b3f2328dedc17734ec0d6f2177ee688f.jpg\",\n  \"450198.jpg\": \"Amphibia/Salamandra salamandra/aa0e91ebbdef8dbc98306899d9209115.jpg\",\n  \"450243.jpg\": \"Aves/Hirundo rustica erythrogaster/9fd843f7a7fc996b02cd3717a359dcf2.jpg\",\n  \"450251.jpg\": \"Aves/Ptiliogonys cinereus/5c3870466d743b77159b1fa6d45eb38b.jpg\",\n  \"450259.jpg\": \"Aves/Ptiliogonys cinereus/1b542899b87f0eee8a74b77190e67638.jpg\",\n  \"450260.jpg\": \"Aves/Ptiliogonys cinereus/46e3d480f8e02f6bdc9dfdba86738e0c.jpg\",\n  \"450261.jpg\": \"Aves/Ptiliogonys cinereus/493451b6413028f6776bc0ddd9f9e710.jpg\",\n  \"450262.jpg\": \"Aves/Ptiliogonys cinereus/d38b8633016bca631d680c194d9aa23a.jpg\",\n  \"450268.jpg\": \"Aves/Ptiliogonys cinereus/87a0e438c6a8166fd515884ff020d188.jpg\",\n  \"450270.jpg\": \"Aves/Ptiliogonys cinereus/8d2edb2394fa33613dcf30ecd356ef6a.jpg\",\n  \"450272.jpg\": \"Aves/Ptiliogonys cinereus/5785273ab6f3f21f06ff56a13b9dfe82.jpg\",\n  \"450293.jpg\": \"Amphibia/Anaxyrus fowleri/cb4c8f172c9d8129472f221056d47521.jpg\",\n  \"450298.jpg\": \"Amphibia/Anaxyrus fowleri/10aae98f430f19574a079f64a3446e93.jpg\",\n  \"450313.jpg\": \"Amphibia/Anaxyrus fowleri/60eb22ac7d35ea5d4a6c6fcc2b9323fa.jpg\",\n  \"450349.jpg\": \"Amphibia/Anaxyrus fowleri/8b367d6471330a03be90631fc7c30809.jpg\",\n  \"450354.jpg\": \"Amphibia/Anaxyrus fowleri/2c1912de67a5684d8061949b2acbd01f.jpg\",\n  \"450364.jpg\": \"Amphibia/Anaxyrus fowleri/faae9207ec2dfd3acc7d2ea824a79d32.jpg\",\n  \"450385.jpg\": \"Amphibia/Anaxyrus fowleri/f841764167f8789b9de9f8785a66219a.jpg\",\n  \"450398.jpg\": \"Amphibia/Anaxyrus fowleri/3b76896e67fefdadf91f06ca804fb254.jpg\",\n  \"450408.jpg\": \"Amphibia/Anaxyrus fowleri/3e53f444f9a87a12c1a59fee3f1af939.jpg\",\n  \"450412.jpg\": \"Amphibia/Anaxyrus fowleri/dfec526cac35a93c6379d5f5af77eec4.jpg\",\n  \"450426.jpg\": \"Insecta/Apantesis phalerata/941fec98b7ea8050091bad41c079ccac.jpg\",\n  \"450447.jpg\": \"Insecta/Pompeius verna/b9e8c03e00d18502cfb8798ac5a0b95e.jpg\",\n  \"450453.jpg\": \"Insecta/Pompeius verna/6461b122611b7cdf450dc5ae65f71e2c.jpg\",\n  \"4505.jpg\": \"Aves/Setophaga petechia/ce69ce5859a23a6dc6c7270c9d663890.jpg\",\n  \"450591.jpg\": \"Insecta/Polygonia interrogationis/4bc64bc9bf774e7bebd99e9702e73605.jpg\",\n  \"450601.jpg\": \"Insecta/Polygonia interrogationis/9ba953df20498205d0ff47542fecdf29.jpg\",\n  \"450607.jpg\": \"Insecta/Polygonia interrogationis/4803c6553bfc0dc56d4dd0835ef888bc.jpg\",\n  \"450618.jpg\": \"Insecta/Polygonia interrogationis/9ae9c4d25e7468f23d404a0d7d1c5f13.jpg\",\n  \"450619.jpg\": \"Insecta/Polygonia interrogationis/162726b315a709f71a257c8fbe2c837e.jpg\",\n  \"450634.jpg\": \"Insecta/Polygonia interrogationis/5130ca6f2d7ab216d28babc95ba599c5.jpg\",\n  \"450690.jpg\": \"Insecta/Vanessa annabella/61b355abc35e5a6bf69410faf3c83e3b.jpg\",\n  \"450694.jpg\": \"Insecta/Vanessa annabella/4f9c21c588f5253351d6213d38412278.jpg\",\n  \"450714.jpg\": \"Insecta/Vanessa annabella/c3717c74b5d55da0e892674bbe73f903.jpg\",\n  \"450715.jpg\": \"Insecta/Vanessa annabella/f51ff62019e02264d20c0f9a522a85fe.jpg\",\n  \"450740.jpg\": \"Insecta/Vanessa annabella/eec46a29666d566cce9098ac81791d79.jpg\",\n  \"450749.jpg\": \"Insecta/Vanessa annabella/926e8d8cfa63319f6e49f6d7926e3f20.jpg\",\n  \"450763.jpg\": \"Insecta/Vanessa annabella/7e603387e3a76a6df9977e6cca4cdfc8.jpg\",\n  \"450916.jpg\": \"Amphibia/Taricha sierrae/1e639edbee0f9617cf58085f9f3cc68b.jpg\",\n  \"451032.jpg\": \"Insecta/Urbanus dorantes/a0d2a59787ff290f1a93938fbde59c6a.jpg\",\n  \"451035.jpg\": \"Insecta/Urbanus dorantes/9ee05a1a00e48efad97918cabac2f474.jpg\",\n  \"451036.jpg\": \"Insecta/Urbanus dorantes/2558e178f061755022b5ba409e0773db.jpg\",\n  \"451047.jpg\": \"Animalia/Hemigrapsus nudus/2ba6d1917e1b794a4f44b087f5a051b3.jpg\",\n  \"451048.jpg\": \"Animalia/Hemigrapsus nudus/4f52a00ef3bbe8686c37f40ff20690c1.jpg\",\n  \"451057.jpg\": \"Animalia/Hemigrapsus nudus/1d4eefb0e19f3dc815d0a581ce2a2ddf.jpg\",\n  \"451059.jpg\": \"Animalia/Hemigrapsus nudus/b1f6d2b2ea805bee0a3fbdbbdd6fd849.jpg\",\n  \"451060.jpg\": \"Animalia/Hemigrapsus nudus/46ef516e97a02b28f3c1b805a021b3ac.jpg\",\n  \"451143.jpg\": \"Insecta/Pyralis farinalis/aaae05ceb5220b3b1f38066f916626d9.jpg\",\n  \"451144.jpg\": \"Insecta/Pyralis farinalis/826d3576ad43b31bec7fba436bb6b14d.jpg\",\n  \"451146.jpg\": \"Insecta/Pyralis farinalis/bcd80f7243c71ffff7a8ab2ec4a9372f.jpg\",\n  \"451150.jpg\": \"Insecta/Pyralis farinalis/6e677da316248c297ec0bce181921824.jpg\",\n  \"451156.jpg\": \"Insecta/Pyralis farinalis/f649b693a6d7f71dd6ee3f8c7247cac3.jpg\",\n  \"451163.jpg\": \"Insecta/Pyralis farinalis/5ee732cf9c6a4ae91700a947fe78035e.jpg\",\n  \"451164.jpg\": \"Insecta/Pyralis farinalis/92a07626a2490046abc167d1e7a8c420.jpg\",\n  \"451168.jpg\": \"Insecta/Pyralis farinalis/239b3b640fe16df3131723f0bc7075ff.jpg\",\n  \"451169.jpg\": \"Insecta/Pyralis farinalis/53234dd0cbc83f9e663964dd1c339f8e.jpg\",\n  \"451190.jpg\": \"Reptilia/Regina septemvittata/523c404ec1a618ce2d57434368c76cd6.jpg\",\n  \"451191.jpg\": \"Reptilia/Regina septemvittata/f992bd47699e932e6273554d0b8f7e8e.jpg\",\n  \"451218.jpg\": \"Insecta/Catocala ultronia/a635f32585cd5d31c2e0271e760f242e.jpg\",\n  \"451377.jpg\": \"Mollusca/Doris montereyensis/7a46f07a28c2fa8238f37f64feaa4b33.jpg\",\n  \"451378.jpg\": \"Mollusca/Doris montereyensis/43683b6c97aa3a54bd10cc1077d9d337.jpg\",\n  \"451383.jpg\": \"Mollusca/Doris montereyensis/c56602f2482d51989c75d2f2297d726f.jpg\",\n  \"451389.jpg\": \"Mollusca/Doris montereyensis/1891b59f64cad90ec6f10e03ef8914eb.jpg\",\n  \"451403.jpg\": \"Mollusca/Doris montereyensis/19d3be4042ce9fa54e983c30a2b8f168.jpg\",\n  \"451414.jpg\": \"Mollusca/Doris montereyensis/f77742c8979c6d4d56add6c4f53d607e.jpg\",\n  \"451416.jpg\": \"Mollusca/Doris montereyensis/803c4fb3bada6fc576e33e3f1f6b82ac.jpg\",\n  \"451425.jpg\": \"Mollusca/Doris montereyensis/fd0d650d53b5dee1815f37abc42da028.jpg\",\n  \"451426.jpg\": \"Mollusca/Doris montereyensis/5d98d5a4c9b9615a2d7a05e337d77e9c.jpg\",\n  \"451432.jpg\": \"Mollusca/Doris montereyensis/0122ff73f7e21466c3002f4dd05c5963.jpg\",\n  \"45144.jpg\": \"Aves/Pica nuttalli/7c5a35fd8d1a6ea16a79994b03a4c171.jpg\",\n  \"45145.jpg\": \"Aves/Pica nuttalli/6220ca06e168d1a5ba58bfab5418b713.jpg\",\n  \"45147.jpg\": \"Aves/Pica nuttalli/f3711972c9ecd836864f266b6890b11e.jpg\",\n  \"451474.jpg\": \"Mammalia/Lycaon pictus/3a8a234427cd4bec8e4c287723c7b68b.jpg\",\n  \"45148.jpg\": \"Aves/Pica nuttalli/930b36fff41e788e8614e1ed788d1f46.jpg\",\n  \"45149.jpg\": \"Aves/Pica nuttalli/51fc0f1c17ddff9cf737e65949c28462.jpg\",\n  \"451493.jpg\": \"Insecta/Trachymela sloanei/4b0a0eee1673e398a3e0b93a09180c18.jpg\",\n  \"451496.jpg\": \"Insecta/Trachymela sloanei/f5078105c21760af399451e8e2e63be1.jpg\",\n  \"451497.jpg\": \"Insecta/Trachymela sloanei/15be1154250a5ffeabadcff779ed7057.jpg\",\n  \"451498.jpg\": \"Insecta/Trachymela sloanei/1c8e25a9665b862adc39058924532c26.jpg\",\n  \"45150.jpg\": \"Aves/Pica nuttalli/93b84ca17efa23044bb9225b8f9023f0.jpg\",\n  \"451500.jpg\": \"Insecta/Trachymela sloanei/e9022e1d577a62f07f0b702349a9ab97.jpg\",\n  \"45151.jpg\": \"Aves/Pica nuttalli/216c4130e1d23bc5325f6d80a14b875c.jpg\",\n  \"451524.jpg\": \"Amphibia/Plethodon glutinosus/55986a1b269c452fb184a32c9fc5a3e9.jpg\",\n  \"451526.jpg\": \"Amphibia/Plethodon glutinosus/e02ea626d52d6861e22f1accab264ef3.jpg\",\n  \"451527.jpg\": \"Amphibia/Plethodon glutinosus/73d8da01bb3cba0c29eb8c84e35fb2c0.jpg\",\n  \"451528.jpg\": \"Amphibia/Plethodon glutinosus/5979f0f3f3c39a112acdda368e0659d8.jpg\",\n  \"451536.jpg\": \"Amphibia/Plethodon glutinosus/a02f6d41c5c1c1e9e0eea3ef61c4110b.jpg\",\n  \"45154.jpg\": \"Aves/Pica nuttalli/041764e458a8a7518e98c078483e3845.jpg\",\n  \"451543.jpg\": \"Amphibia/Plethodon glutinosus/3588609d6cc74b1f25b11e251f575869.jpg\",\n  \"451544.jpg\": \"Amphibia/Plethodon glutinosus/c79a9f5d8009fe7e949f4770a53b4590.jpg\",\n  \"451545.jpg\": \"Amphibia/Plethodon glutinosus/15379dd8f03cf89e059b2626417a27ed.jpg\",\n  \"451551.jpg\": \"Insecta/Caripeta divisata/b3c06456f3356695e13598126e1123c0.jpg\",\n  \"45156.jpg\": \"Aves/Pica nuttalli/ae644c15cfd4b29dd5a6ba049ae1f2c2.jpg\",\n  \"45157.jpg\": \"Aves/Pica nuttalli/0538c081a1dcec751544c19cfbcece93.jpg\",\n  \"451612.jpg\": \"Insecta/Thymelicus lineola/93dc5b0f10d49956a48a2b03508c0d52.jpg\",\n  \"45162.jpg\": \"Aves/Pica nuttalli/f73440ba487a1bc4ab102c59924d5d85.jpg\",\n  \"451627.jpg\": \"Insecta/Thymelicus lineola/03bbc190bd24eac0b11ca65e8ed4c6be.jpg\",\n  \"451636.jpg\": \"Insecta/Thymelicus lineola/c5999e9530bc7b09e547fabfaadb340e.jpg\",\n  \"451640.jpg\": \"Insecta/Thymelicus lineola/f6a881892ab2a6119802bca92cafbd88.jpg\",\n  \"45166.jpg\": \"Aves/Pica nuttalli/697292c8847d8623fd642fb04c0d3ad3.jpg\",\n  \"45167.jpg\": \"Aves/Pica nuttalli/22d6461e5a694edb1bc4e9d2ce5ed735.jpg\",\n  \"45169.jpg\": \"Aves/Pica nuttalli/8c68210f294f9d5862a1d16e8d7862a9.jpg\",\n  \"45170.jpg\": \"Aves/Pica nuttalli/7f16baa3e33edbc63ff82fc5752c216a.jpg\",\n  \"45178.jpg\": \"Aves/Pica nuttalli/9110d9295a838457ad8788f18f5e8a0b.jpg\",\n  \"45179.jpg\": \"Aves/Pica nuttalli/9506a8a5c3fe9295e78ffbcf105e17c9.jpg\",\n  \"45187.jpg\": \"Aves/Pica nuttalli/e527f44c2a29572ed4f92122f054206f.jpg\",\n  \"45188.jpg\": \"Aves/Pica nuttalli/60e152f01802c0bdb0f53bbc278c0a2c.jpg\",\n  \"45191.jpg\": \"Aves/Pica nuttalli/49ff38d60444b225e5f3ba6424a69989.jpg\",\n  \"45192.jpg\": \"Aves/Pica nuttalli/c9e516478984c688d2d375cd66acbd93.jpg\",\n  \"452035.jpg\": \"Reptilia/Lichanura orcutti/03e161701d4b1061333050f63dc34883.jpg\",\n  \"452075.jpg\": \"Aves/Plegadis falcinellus/47d4179b40729f5434a46141a23b7b22.jpg\",\n  \"452079.jpg\": \"Aves/Plegadis falcinellus/b3aed91d2699f60a2e80f060bb7b8227.jpg\",\n  \"452080.jpg\": \"Aves/Plegadis falcinellus/e1c54b0b059da523dd61994aa412dddc.jpg\",\n  \"452123.jpg\": \"Aves/Certhia americana/f425b1032224aa3febde360fe02be0e5.jpg\",\n  \"452137.jpg\": \"Aves/Certhia americana/64cff1ce713ae589a515a97ae2790665.jpg\",\n  \"452167.jpg\": \"Aves/Certhia americana/f4c8b1cc71c0502e1184ff10a3cbd009.jpg\",\n  \"452209.jpg\": \"Aves/Certhia americana/8d3868323866948cc7bf8e24410693cd.jpg\",\n  \"452212.jpg\": \"Aves/Certhia americana/00b75ccde4895dc8f580be11553dcddb.jpg\",\n  \"452240.jpg\": \"Aves/Certhia americana/bb45a9996a368319d55a30cbc84be5ef.jpg\",\n  \"452278.jpg\": \"Aves/Certhia americana/36a1bb7320b64818a189d61899b6ed02.jpg\",\n  \"452289.jpg\": \"Insecta/Megisto rubricata/7b225b3f60d0ba0e0f1429b34557e522.jpg\",\n  \"452292.jpg\": \"Insecta/Megisto rubricata/27c839d5abae049f9fe4a51a82ff8ca1.jpg\",\n  \"452332.jpg\": \"Insecta/Hypselonotus punctiventris/1077d263499dd9a953dc8e52c93258bd.jpg\",\n  \"452339.jpg\": \"Insecta/Sympetrum costiferum/8a714a5a9cf151b950633b3a4ec9d52f.jpg\",\n  \"452340.jpg\": \"Insecta/Sympetrum costiferum/6df6b127c1c2ce86eb70de8f59140477.jpg\",\n  \"452391.jpg\": \"Aves/Passerina ciris/db2fd6abe927015a43687fbeeb91d337.jpg\",\n  \"452398.jpg\": \"Aves/Passerina ciris/510c987019dcaa6051c45244f992f62f.jpg\",\n  \"4524.jpg\": \"Aves/Setophaga petechia/692e741e3b155637cd2673f02a2acd41.jpg\",\n  \"452466.jpg\": \"Aves/Passerina ciris/c796244f8a4a67f32e9e99e1cc047d6a.jpg\",\n  \"452528.jpg\": \"Aves/Passerina ciris/01e30f965b78476679892ceb57993a7f.jpg\",\n  \"452545.jpg\": \"Aves/Empidonax fulvifrons/4d3a13a91ed91b8233753f91eb2237c9.jpg\",\n  \"452547.jpg\": \"Aves/Empidonax fulvifrons/c04ff0bcc839a50289db87d77f345e63.jpg\",\n  \"452588.jpg\": \"Insecta/Oxythyrea funesta/70d900ac9475b54455d9f934e47493b4.jpg\",\n  \"452604.jpg\": \"Aves/Alopochen aegyptiaca/4d3d6e6e8d4f428f027df782eaf5320a.jpg\",\n  \"452623.jpg\": \"Aves/Alopochen aegyptiaca/a90ea2167fceb73775714ee56aeb2ce7.jpg\",\n  \"452632.jpg\": \"Aves/Alopochen aegyptiaca/8bec6d0c2b7855d7d5e93b7b3d61191c.jpg\",\n  \"452656.jpg\": \"Aves/Alopochen aegyptiaca/a6e15cdf666bca941cf6478b25757ed2.jpg\",\n  \"452660.jpg\": \"Aves/Alopochen aegyptiaca/b1acbf4a67a9c0aa8a709753d2c5b298.jpg\",\n  \"452688.jpg\": \"Insecta/Phocides polybius/4f2a15a387b254744433aa893664463b.jpg\",\n  \"452691.jpg\": \"Insecta/Phocides polybius/75899be511ef4dd64778498dfac20fe5.jpg\",\n  \"452771.jpg\": \"Amphibia/Ensatina eschscholtzii/3e81ca8d83953d781fc888a59cc5f182.jpg\",\n  \"452787.jpg\": \"Amphibia/Ensatina eschscholtzii/436f8e6e2004b0dba2f7502db3bcc41f.jpg\",\n  \"452810.jpg\": \"Amphibia/Ensatina eschscholtzii/b30bb55af97e66be2138afb65928c005.jpg\",\n  \"452827.jpg\": \"Insecta/Necrophila americana/a049aa3360413b9c356453e431f4e962.jpg\",\n  \"452828.jpg\": \"Insecta/Necrophila americana/4adeac5bb52ad95385fc2eb97db7739a.jpg\",\n  \"452835.jpg\": \"Insecta/Necrophila americana/dcda70030f4d0952334ad1e690205429.jpg\",\n  \"452836.jpg\": \"Insecta/Necrophila americana/d8492cd95625d5b417d314e690c07464.jpg\",\n  \"452846.jpg\": \"Insecta/Necrophila americana/5e5dd714f488719cddd5de25135fe733.jpg\",\n  \"452859.jpg\": \"Insecta/Necrophila americana/0643b0d5175c88c80c69d8cc88fb26dc.jpg\",\n  \"452861.jpg\": \"Insecta/Necrophila americana/ddfcbcd2c57e7f753a6851cc2ef34d05.jpg\",\n  \"452873.jpg\": \"Aves/Charadrius hiaticula/add3c06b51a25259a61df4c4369d53b2.jpg\",\n  \"452909.jpg\": \"Insecta/Thorybes pylades/aa5af7e24b97dacd8ea33ee6ed8004e6.jpg\",\n  \"452913.jpg\": \"Insecta/Thorybes pylades/4b96df38c89a09b49b36442ddf5f5e41.jpg\",\n  \"452920.jpg\": \"Insecta/Thorybes pylades/0c8a2cd796a895ced1087505f35cc9dc.jpg\",\n  \"452947.jpg\": \"Insecta/Hyalymenus tarsatus/17e1eb3560e9cfb28e74d89b506238ab.jpg\",\n  \"452948.jpg\": \"Insecta/Hyalymenus tarsatus/b6f8a3046854f30375e0e96abd79f94d.jpg\",\n  \"452949.jpg\": \"Insecta/Hyalymenus tarsatus/abfcb5fcd92e18d97e4589f8a495b6a9.jpg\",\n  \"452966.jpg\": \"Amphibia/Anaxyrus woodhousii/ac7d6620000c8005703ddf7b0fc84604.jpg\",\n  \"452970.jpg\": \"Amphibia/Anaxyrus woodhousii/cf51e3b294f4e05f9d31c3858ddfa272.jpg\",\n  \"452971.jpg\": \"Amphibia/Anaxyrus woodhousii/0aecd4a22a7b0a21f33946d9de3e470c.jpg\",\n  \"452978.jpg\": \"Amphibia/Anaxyrus woodhousii/575f8f1b183f376150824970bc18b11e.jpg\",\n  \"452982.jpg\": \"Amphibia/Anaxyrus woodhousii/015f03767b5fd30019df9ca7720cb869.jpg\",\n  \"452992.jpg\": \"Amphibia/Anaxyrus woodhousii/22787f1b14d442845454d61a7e1a1b8b.jpg\",\n  \"453.jpg\": \"Mammalia/Ursus americanus/46f50130b90fde02b0adf5b52e890f81.jpg\",\n  \"453024.jpg\": \"Amphibia/Anaxyrus woodhousii/8ef304b206ce2b22fa56a0c0e71f8f7d.jpg\",\n  \"453042.jpg\": \"Insecta/Argia moesta/ed58c1a82cbddde754cc253264593164.jpg\",\n  \"453069.jpg\": \"Insecta/Argia moesta/218261037d1806c91c882c33583a225d.jpg\",\n  \"453086.jpg\": \"Insecta/Argia moesta/4c44faab9a58ef01779fba677834c384.jpg\",\n  \"4531.jpg\": \"Aves/Setophaga petechia/4a84b71f72a8acabd8e7aff787056ba0.jpg\",\n  \"453112.jpg\": \"Insecta/Argia moesta/da21a074076d339aecbe4bd3587814f1.jpg\",\n  \"453120.jpg\": \"Insecta/Argia moesta/b7c972d1879b478289782ed7f4c76133.jpg\",\n  \"453125.jpg\": \"Insecta/Argia moesta/4d9d3e7cc4cf0d5679a428b77e8ede61.jpg\",\n  \"453209.jpg\": \"Insecta/Chlosyne palla/db5f1939c9b6166cd0ced04be0ec57d2.jpg\",\n  \"453210.jpg\": \"Insecta/Chlosyne palla/b5c6334d8d74e1568a5b60ff415600f4.jpg\",\n  \"453231.jpg\": \"Insecta/Chlosyne palla/b2c1bb54d066990becb99274c08ab7ad.jpg\",\n  \"453232.jpg\": \"Insecta/Chlosyne palla/980aa01b7496e20faff4bf20ca4799a0.jpg\",\n  \"453237.jpg\": \"Insecta/Chlosyne palla/f82145c99f3b07ac1e3d1f045907dd34.jpg\",\n  \"453238.jpg\": \"Insecta/Chlosyne palla/8804cd7c1c6944d5667d7d49c96698d9.jpg\",\n  \"453241.jpg\": \"Insecta/Chlosyne palla/90a0c00ce088db8651a72bbe02f9cd44.jpg\",\n  \"453244.jpg\": \"Insecta/Chlosyne palla/d1febbf85d314b46c15eb84da21ac52e.jpg\",\n  \"453255.jpg\": \"Insecta/Udea rubigalis/233b93a3b8381d7e026ee5a0fea72ed1.jpg\",\n  \"453259.jpg\": \"Insecta/Udea rubigalis/c90c052bce89b0d1640612d725165f7e.jpg\",\n  \"453263.jpg\": \"Insecta/Udea rubigalis/cc975dfed44eb5a91c1439324d40feef.jpg\",\n  \"453269.jpg\": \"Insecta/Udea rubigalis/0313b3b6a741658ead612e05a8be49ca.jpg\",\n  \"453271.jpg\": \"Insecta/Udea rubigalis/3a371382a269f45071dc55facc50d509.jpg\",\n  \"453273.jpg\": \"Insecta/Udea rubigalis/4cc5ecbbfb2936533e04f31b479c1d93.jpg\",\n  \"453274.jpg\": \"Insecta/Udea rubigalis/1c638eb2e2d6215f81b6e4c1b0a72381.jpg\",\n  \"453276.jpg\": \"Insecta/Udea rubigalis/e8938b740bdd9539e570ddf8d5aaa076.jpg\",\n  \"453277.jpg\": \"Insecta/Udea rubigalis/ed58953e651f749bb658ff263488ba66.jpg\",\n  \"453340.jpg\": \"Aves/Coccothraustes coccothraustes/4adac150668ddd2cfa3b1dfe8f971b77.jpg\",\n  \"453393.jpg\": \"Insecta/Heraclides rumiko/61a0c1e5fcc14f726d1d140756585f1f.jpg\",\n  \"453394.jpg\": \"Insecta/Heraclides rumiko/b5c7063c388e7eecb37a957e9442ce37.jpg\",\n  \"453395.jpg\": \"Insecta/Heraclides rumiko/1de4a41cac13f7616adc6b1daf8b3f1c.jpg\",\n  \"453399.jpg\": \"Insecta/Heraclides rumiko/782a15cf9f5214de2f529951795aa0c1.jpg\",\n  \"453401.jpg\": \"Insecta/Heraclides rumiko/ca7dca15b0af36175095f4d10b0c9681.jpg\",\n  \"453420.jpg\": \"Insecta/Heraclides rumiko/305083c9b3f76376e4cf177c97cd2291.jpg\",\n  \"453429.jpg\": \"Insecta/Heraclides rumiko/385c87b1c77db04f5b0bfa5e047231a9.jpg\",\n  \"453435.jpg\": \"Insecta/Heraclides rumiko/ff2489b23b509fc1ab4c82a7a7cc3908.jpg\",\n  \"453445.jpg\": \"Insecta/Deloyala guttata/de6e70f59d2bb7eaa834a44685b1bfb9.jpg\",\n  \"453454.jpg\": \"Insecta/Deloyala guttata/ddf25affb8237af7fdc796ed60c2a6c1.jpg\",\n  \"453456.jpg\": \"Amphibia/Gastrophryne olivacea/048f8e9b814c22b7f5526330f6889032.jpg\",\n  \"453461.jpg\": \"Amphibia/Gastrophryne olivacea/efdfa93105a930fa63ec4949ba898145.jpg\",\n  \"453464.jpg\": \"Amphibia/Gastrophryne olivacea/4791cf48746a03d4eaf2826f7e279524.jpg\",\n  \"453467.jpg\": \"Amphibia/Gastrophryne olivacea/6cf73b6505629e6d75d279c3ee454a80.jpg\",\n  \"453470.jpg\": \"Amphibia/Gastrophryne olivacea/a044c6148075b6a662b3d58c3c9677ea.jpg\",\n  \"453471.jpg\": \"Amphibia/Gastrophryne olivacea/b52dac45340bd4c1dd6cda3575437df1.jpg\",\n  \"453473.jpg\": \"Amphibia/Gastrophryne olivacea/ae9c3e9ae9450bbb993344fa4f5c65c5.jpg\",\n  \"453480.jpg\": \"Amphibia/Gastrophryne olivacea/2892b5d0e5c7eb545046f9b58724fd46.jpg\",\n  \"453481.jpg\": \"Amphibia/Gastrophryne olivacea/e15129cbda364abbd989355fb6d2ee94.jpg\",\n  \"4535.jpg\": \"Aves/Setophaga petechia/53aac90636aeeae0bacd66bd0f36f0f6.jpg\",\n  \"453510.jpg\": \"Mammalia/Eumetopias jubatus/2997de30ce19d33037c8988149593f1a.jpg\",\n  \"453511.jpg\": \"Mammalia/Eumetopias jubatus/2d8dcb544ead72495dfc02b1584eb974.jpg\",\n  \"453516.jpg\": \"Mammalia/Eumetopias jubatus/7819180fc7cdf60c83ccdad5bc3f4575.jpg\",\n  \"453523.jpg\": \"Mammalia/Eumetopias jubatus/3fb6658f81442ce232bd56a17db61c79.jpg\",\n  \"453545.jpg\": \"Reptilia/Hemidactylus frenatus/f1e570e1baba0963f689743edf22115b.jpg\",\n  \"453548.jpg\": \"Reptilia/Hemidactylus frenatus/830443535f60cff62384d8762cbe4caf.jpg\",\n  \"453555.jpg\": \"Reptilia/Hemidactylus frenatus/93822409f93d8ee32439171b8e6f858e.jpg\",\n  \"453556.jpg\": \"Reptilia/Hemidactylus frenatus/fb7dbfb66e6bfc04542dd66fa4b6cf12.jpg\",\n  \"453559.jpg\": \"Reptilia/Hemidactylus frenatus/c99da5bbcf263a17c3be49a66a0b52cc.jpg\",\n  \"453560.jpg\": \"Reptilia/Hemidactylus frenatus/10a4f1dfe09b73c038d7e6c3f3b16007.jpg\",\n  \"453566.jpg\": \"Reptilia/Hemidactylus frenatus/d11b5880c4c91faa5ab1a320a7b64105.jpg\",\n  \"453572.jpg\": \"Aves/Pinicola enucleator/61593cd88e7d8e6fbdf927208cc7d840.jpg\",\n  \"453573.jpg\": \"Aves/Pinicola enucleator/9465078fbb7439bb3bc188696d03d5f1.jpg\",\n  \"453577.jpg\": \"Aves/Pinicola enucleator/6c2deb63564c868a9188999fcb90482d.jpg\",\n  \"453578.jpg\": \"Aves/Pinicola enucleator/c46f80fd08cc10cf9ea2dfecffc91574.jpg\",\n  \"453579.jpg\": \"Aves/Pinicola enucleator/5dd3c82228f66e4df1cf0d0aac3a3acb.jpg\",\n  \"453580.jpg\": \"Aves/Pinicola enucleator/b452a98cb5916d73e23c31da22f538c6.jpg\",\n  \"453592.jpg\": \"Aves/Pinicola enucleator/8d95a3783bcbdb63cfd2bbe3c96cc5c4.jpg\",\n  \"453600.jpg\": \"Aves/Pinicola enucleator/83b52b18df9b6977f04724c6b10e4d70.jpg\",\n  \"453601.jpg\": \"Aves/Pinicola enucleator/053e143e6794588652157ffa52f0d082.jpg\",\n  \"453602.jpg\": \"Aves/Pinicola enucleator/9658658623d3eb43c1b68cb8c90295fa.jpg\",\n  \"453603.jpg\": \"Aves/Pinicola enucleator/f92cef25588dbf81a0c00e1f94a78b3c.jpg\",\n  \"453633.jpg\": \"Insecta/Chioides albofasciatus/30530a66c688ed0d25510824ee124f6a.jpg\",\n  \"453636.jpg\": \"Insecta/Chioides albofasciatus/aa122c1ad5b512186cbef283cd4d45db.jpg\",\n  \"453646.jpg\": \"Insecta/Chioides albofasciatus/ab8ac0942a73bf5479e658d5fecd9155.jpg\",\n  \"453647.jpg\": \"Insecta/Chioides albofasciatus/3fdeb03ae4cb4a75a7669df6a477c719.jpg\",\n  \"45365.jpg\": \"Aves/Ardea herodias/002f2ca557b6350fef72ea0921414efb.jpg\",\n  \"453650.jpg\": \"Insecta/Chioides albofasciatus/5b4c62e7742930ac9a096225736b8cf4.jpg\",\n  \"453651.jpg\": \"Insecta/Chioides albofasciatus/774a3ee3e07630e0ae1c7ba0ac6ec66a.jpg\",\n  \"453674.jpg\": \"Aves/Toxostoma redivivum/b680062ea9f20ab020d0c22cec64868e.jpg\",\n  \"4537.jpg\": \"Aves/Setophaga petechia/c000a00b7f6b33ff2a4ea328d5b97dca.jpg\",\n  \"453705.jpg\": \"Aves/Toxostoma redivivum/a54cc317442407bcf059240028158b1c.jpg\",\n  \"453706.jpg\": \"Aves/Toxostoma redivivum/e49caadf68d6fd738d342b594153812c.jpg\",\n  \"453713.jpg\": \"Aves/Toxostoma redivivum/a76a0a235a9611e5bd7c044dc9c445e5.jpg\",\n  \"453724.jpg\": \"Aves/Toxostoma redivivum/ff593e6b56069b7878abd7139e2178ef.jpg\",\n  \"453737.jpg\": \"Aves/Toxostoma redivivum/dd66892681c876116abe40b4d84dd09b.jpg\",\n  \"453752.jpg\": \"Aves/Toxostoma redivivum/37cbbdd5b9e2329228fcd956db6d960e.jpg\",\n  \"453755.jpg\": \"Aves/Spizella atrogularis/806e248ac35f21b4dfcd26bc26e22d22.jpg\",\n  \"453762.jpg\": \"Aves/Spizella atrogularis/d47c99f1b67024a53dd4dfc79dca152d.jpg\",\n  \"4539.jpg\": \"Aves/Setophaga petechia/7c6647c3c9a55027fd22239edcb92ad9.jpg\",\n  \"453977.jpg\": \"Aves/Gyps fulvus/52ec743dde40f5c333242ccbc3a63011.jpg\",\n  \"453978.jpg\": \"Aves/Gyps fulvus/91c3d4d360e3466f8e145701b78ed4cd.jpg\",\n  \"453980.jpg\": \"Aves/Gyps fulvus/bad3552b2265625c38db74d89b42d3e9.jpg\",\n  \"453986.jpg\": \"Aves/Gyps fulvus/94efbd691c9b137823d6b783fe463bd6.jpg\",\n  \"453990.jpg\": \"Aves/Gyps fulvus/7e8781444359e3188c0668e2c4360eca.jpg\",\n  \"453995.jpg\": \"Aves/Gyps fulvus/c018c56121c80a414e86e0c5518f688e.jpg\",\n  \"453997.jpg\": \"Aves/Gyps fulvus/b41e4b40a80e7a1553eaacd00791433f.jpg\",\n  \"454018.jpg\": \"Mammalia/Sus scrofa/0fa1d0e02a49cdf4be3dd3df6e483106.jpg\",\n  \"454054.jpg\": \"Mammalia/Sus scrofa/bb20f789e25e8ebc7c569f33547461b2.jpg\",\n  \"454098.jpg\": \"Insecta/Toxomerus geminatus/3676c9e84d50bf9ae8fa16764fd810b8.jpg\",\n  \"454099.jpg\": \"Insecta/Toxomerus geminatus/5a684921dab9851baae3b0b5e0f667e7.jpg\",\n  \"454101.jpg\": \"Insecta/Toxomerus geminatus/8892274078ea795adc65cea20df3c136.jpg\",\n  \"454105.jpg\": \"Insecta/Toxomerus geminatus/56bec826970e5862615237b9e75510ef.jpg\",\n  \"454106.jpg\": \"Insecta/Toxomerus geminatus/e2a61a045ea9ba2686172ca273425117.jpg\",\n  \"454107.jpg\": \"Insecta/Toxomerus geminatus/52378d5077e5e8d7bc8b752d23a68472.jpg\",\n  \"454108.jpg\": \"Insecta/Toxomerus geminatus/738d2fefdc62800b0f3ed514b837eb99.jpg\",\n  \"454109.jpg\": \"Insecta/Toxomerus geminatus/26727ba1c228daf5a7eff1f14d1c8fa5.jpg\",\n  \"454115.jpg\": \"Insecta/Toxomerus geminatus/95963d01f2bc80ff48490e808bacd26c.jpg\",\n  \"454116.jpg\": \"Insecta/Toxomerus geminatus/66a3db5415af0e9fa9d27af58d58df0a.jpg\",\n  \"454118.jpg\": \"Insecta/Toxomerus geminatus/407a32b4db21b4fdb913313e6ef33f2e.jpg\",\n  \"454136.jpg\": \"Mammalia/Castor canadensis/8dc67e9c50131ead1c95e1dbbe50eb90.jpg\",\n  \"454189.jpg\": \"Mammalia/Castor canadensis/a9edb6408c7fa65bce1b32b6bac8a879.jpg\",\n  \"454231.jpg\": \"Mammalia/Castor canadensis/38ddcba392b34f5ef6a3ccf8d10b76c4.jpg\",\n  \"454234.jpg\": \"Mammalia/Castor canadensis/c38d6be9e17374ab196ed89a00d8d215.jpg\",\n  \"454328.jpg\": \"Mammalia/Castor canadensis/50621c64116ee64eed81b62862d5b326.jpg\",\n  \"454405.jpg\": \"Reptilia/Sceloporus merriami/1c5e6ebe340eaee5f8eef42d55196c3e.jpg\",\n  \"454410.jpg\": \"Reptilia/Sceloporus merriami/cd682512e92e6c2677bf6d17454dcc98.jpg\",\n  \"454413.jpg\": \"Reptilia/Sceloporus merriami/f76e27f66861b756532f06c314e0ac9d.jpg\",\n  \"454421.jpg\": \"Mammalia/Sciurus aureogaster/e496cd6c15248a04d0ea35b1f13ab3d1.jpg\",\n  \"454423.jpg\": \"Mammalia/Sciurus aureogaster/1c1b653535af4b7b3a502df1cf7a9af5.jpg\",\n  \"454432.jpg\": \"Mammalia/Sciurus aureogaster/5ad1fe46a1fc0cb43ae7f2b49c61fc86.jpg\",\n  \"454434.jpg\": \"Mammalia/Sciurus aureogaster/6a9e68dfc26e17f18653e3bdff1d22d5.jpg\",\n  \"454439.jpg\": \"Mammalia/Sciurus aureogaster/27dcac0cd0c0bf4bece5998412156585.jpg\",\n  \"454454.jpg\": \"Mammalia/Sciurus aureogaster/4d6f5dc06e0f023feeceee2fed8b3231.jpg\",\n  \"454455.jpg\": \"Mammalia/Sciurus aureogaster/dc993b1e7a421eed004ebaa5dcd6fe7e.jpg\",\n  \"454469.jpg\": \"Mammalia/Sciurus aureogaster/af2eb6455b101813ebe6e547485c26d6.jpg\",\n  \"454475.jpg\": \"Aves/Botaurus lentiginosus/e9baad87a9d78d4f977653f5783374f9.jpg\",\n  \"454476.jpg\": \"Aves/Botaurus lentiginosus/22dd7d3a830dbdc0d7f4d503c76a0509.jpg\",\n  \"454483.jpg\": \"Aves/Botaurus lentiginosus/4253037489b03a2d64c4f503a36d42a4.jpg\",\n  \"454485.jpg\": \"Aves/Botaurus lentiginosus/236590adcef18c3c1f03fee58d4796a8.jpg\",\n  \"454492.jpg\": \"Aves/Botaurus lentiginosus/eaaeef07147c89fb6d2afa0450bf94ac.jpg\",\n  \"454497.jpg\": \"Aves/Botaurus lentiginosus/0417db0ecb70fefacc69c45e980cfa12.jpg\",\n  \"454512.jpg\": \"Aves/Botaurus lentiginosus/cae1e176ace4d2f613bf0dd987d94b75.jpg\",\n  \"454539.jpg\": \"Aves/Falco mexicanus/af11210edd8e08751962c03aca802be3.jpg\",\n  \"454551.jpg\": \"Aves/Falco mexicanus/a0687192f53b8c9579670e7e11bded9c.jpg\",\n  \"454552.jpg\": \"Aves/Falco mexicanus/3c1c9e9f16f4773224d952cbb41ea53b.jpg\",\n  \"454553.jpg\": \"Aves/Falco mexicanus/3bf8ffd588586fb5a10a5a76addbb555.jpg\",\n  \"454554.jpg\": \"Aves/Falco mexicanus/4b0716989496267e7828254830b27bb0.jpg\",\n  \"454558.jpg\": \"Aves/Falco mexicanus/40ae7bf1c642ea1ec1f4ee1313a7e147.jpg\",\n  \"454559.jpg\": \"Aves/Falco mexicanus/f5e32417692e867b805bbafcabddfef3.jpg\",\n  \"454566.jpg\": \"Aves/Falco mexicanus/bba5fe6071e18261f1e0476cfc56b1df.jpg\",\n  \"454567.jpg\": \"Aves/Falco mexicanus/4d8217ca2eb3e934cd238ca29eb2eed6.jpg\",\n  \"454568.jpg\": \"Aves/Falco mexicanus/2b92ccd171ce9431a40a81d2f5adfdcb.jpg\",\n  \"454582.jpg\": \"Insecta/Pentatoma rufipes/467eb91230dfdf34e1b368c0176302ae.jpg\",\n  \"454586.jpg\": \"Insecta/Pentatoma rufipes/c03b1b4c2cfb644ed7abe547a6c92d23.jpg\",\n  \"454633.jpg\": \"Aves/Larus marinus/c80f3447caa2c3b816a86ec783926ec1.jpg\",\n  \"454662.jpg\": \"Aves/Larus marinus/7ff5066cea922d498d68b7be0272be33.jpg\",\n  \"454702.jpg\": \"Insecta/Erynnis juvenalis/a9c9c5a6de41976045cb236c61b36a02.jpg\",\n  \"454706.jpg\": \"Insecta/Erynnis juvenalis/73b4e4768ebbb762982b7126c5425e29.jpg\",\n  \"454707.jpg\": \"Insecta/Erynnis juvenalis/6e22be6f825480d111f53cf5427d554e.jpg\",\n  \"454709.jpg\": \"Insecta/Erynnis juvenalis/6ff3ec97d088874343ad37b34dcd515e.jpg\",\n  \"454712.jpg\": \"Insecta/Erynnis juvenalis/496ed26af7741dd06effb124fdb6aabc.jpg\",\n  \"454713.jpg\": \"Insecta/Erynnis juvenalis/438512e35bfeec6d9f3c90c8c4b0207c.jpg\",\n  \"454714.jpg\": \"Insecta/Erynnis juvenalis/bb05f1409a2e1a32412f06c0060f294e.jpg\",\n  \"454744.jpg\": \"Actinopterygii/Pterois volitans/3d4513329cfcddf8dadb39f5f7fbea8d.jpg\",\n  \"454750.jpg\": \"Actinopterygii/Pterois volitans/585aa0f487ba7f1f022c45e88beae442.jpg\",\n  \"454762.jpg\": \"Actinopterygii/Pterois volitans/02c22347264f200f702910258721a641.jpg\",\n  \"454763.jpg\": \"Actinopterygii/Pterois volitans/fc04a7c8689812ca3324ef4ffada4ec6.jpg\",\n  \"454765.jpg\": \"Insecta/Anthocharis cardamines/e122258c31c94465c9fe63363d851768.jpg\",\n  \"454769.jpg\": \"Insecta/Anthocharis cardamines/eb40252db7e4100a8be3597377841d1d.jpg\",\n  \"45477.jpg\": \"Aves/Ardea herodias/b2020621d7d02776e6e4a9092800dba1.jpg\",\n  \"454772.jpg\": \"Insecta/Anthocharis cardamines/a91250d6d5414f51c681b040e683f4d3.jpg\",\n  \"454774.jpg\": \"Insecta/Anthocharis cardamines/86c0609f40db9514293951a756a09f07.jpg\",\n  \"454801.jpg\": \"Aves/Coccyzus americanus/01a8a0aaaf962f2b3a2bd0282d3c9c36.jpg\",\n  \"454802.jpg\": \"Aves/Coccyzus americanus/d5f771aa7ffec84962b40b4d92aa0900.jpg\",\n  \"454822.jpg\": \"Aves/Coccyzus americanus/af68664b4f5a26f59217bfffd5c0060f.jpg\",\n  \"454834.jpg\": \"Aves/Coccyzus americanus/234ad6b7bdf2233c23e3dd6f79811408.jpg\",\n  \"454849.jpg\": \"Aves/Coccyzus americanus/50b655f4aede61228e8ec3bab7b63ac0.jpg\",\n  \"45485.jpg\": \"Aves/Ardea herodias/50b5e164eec1151d68f5f1cdd6e254c4.jpg\",\n  \"454858.jpg\": \"Aves/Coccyzus americanus/cbb623c5b2a8ae478d82fb7c19a78626.jpg\",\n  \"454859.jpg\": \"Aves/Coccyzus americanus/4ed9b2561d0a97a85e30220583cbf364.jpg\",\n  \"454862.jpg\": \"Aves/Coccyzus americanus/2532802ae4b6cf5bef06e78ce1135f17.jpg\",\n  \"454865.jpg\": \"Aves/Coccyzus americanus/c867f04aa18144bff05b380270896a0c.jpg\",\n  \"454935.jpg\": \"Insecta/Tenodera sinensis/d7cb959f166c060f239755b4105d1cd2.jpg\",\n  \"454937.jpg\": \"Insecta/Tenodera sinensis/b87f44a2d88c1d933d8c7ece080d404b.jpg\",\n  \"454942.jpg\": \"Insecta/Tenodera sinensis/826799caa6342c805b9cc6c3d366cb45.jpg\",\n  \"454948.jpg\": \"Insecta/Tenodera sinensis/e39190892f1bf42ccadde36edef0bafa.jpg\",\n  \"454949.jpg\": \"Insecta/Tenodera sinensis/bd02fca96133ec226358d2ed180e5a69.jpg\",\n  \"454950.jpg\": \"Insecta/Tenodera sinensis/8142f6a8963c669fa18f9da25c0aa69a.jpg\",\n  \"454956.jpg\": \"Insecta/Tenodera sinensis/65d4259ac1e79c4708adbf6a3f166591.jpg\",\n  \"454957.jpg\": \"Insecta/Tenodera sinensis/3f42220e9d34a52e6ef8df2104e30c05.jpg\",\n  \"454958.jpg\": \"Insecta/Tenodera sinensis/231217ec7aa9311e575f76d36f913878.jpg\",\n  \"455060.jpg\": \"Insecta/Dolichovespula maculata/f45d503f6a3e47b0cb3913761815f134.jpg\",\n  \"455077.jpg\": \"Insecta/Dolichovespula maculata/d7b01de45fc2754d818f2d8b89d9a2c3.jpg\",\n  \"455085.jpg\": \"Insecta/Dolichovespula maculata/a80fb6a4ecd0ab5567fb15b5068ae54d.jpg\",\n  \"455086.jpg\": \"Insecta/Dolichovespula maculata/15c26b1c4d528b36b5d30f2442262bb4.jpg\",\n  \"455117.jpg\": \"Mammalia/Mephitis mephitis/e1b9c7d83ae585933cfb4cfeaf8dd1f2.jpg\",\n  \"455139.jpg\": \"Mammalia/Mephitis mephitis/9575843e9222bfda18ada6599b0dea74.jpg\",\n  \"455148.jpg\": \"Mammalia/Mephitis mephitis/307df49a1c4c9a589fba3e625a7426b9.jpg\",\n  \"455175.jpg\": \"Mammalia/Mephitis mephitis/ad8acd58f60b49998fd910903fcd4dee.jpg\",\n  \"455196.jpg\": \"Mammalia/Mephitis mephitis/0808cc51064a8a6314178b40da084ae8.jpg\",\n  \"455205.jpg\": \"Mammalia/Mephitis mephitis/8f24c46a2bd4557342fbfe6a87f3f8e0.jpg\",\n  \"455208.jpg\": \"Mammalia/Mephitis mephitis/c8539a72dcb31a8d7c59ad6caa302c59.jpg\",\n  \"455214.jpg\": \"Mammalia/Mephitis mephitis/0513c4d7b5b7f7280c8415186931fb9b.jpg\",\n  \"455501.jpg\": \"Aves/Ardea purpurea/f8433e13b52e32933a6225f68ea7a4ad.jpg\",\n  \"455504.jpg\": \"Aves/Ardea purpurea/feb703f9e200dc91f16878754ded53c6.jpg\",\n  \"455505.jpg\": \"Aves/Ardea purpurea/9d8e638154e25ce248a74b26330a7a1d.jpg\",\n  \"455507.jpg\": \"Aves/Ardea purpurea/ff3e45484a445bdc5db49991e16d4189.jpg\",\n  \"455509.jpg\": \"Aves/Ardea purpurea/93c8ecab3dd70063d871913df679ee90.jpg\",\n  \"455510.jpg\": \"Aves/Ardea purpurea/0cfff4cb7fb915829fe08a4ce8a50820.jpg\",\n  \"455514.jpg\": \"Aves/Ardea purpurea/d1358ab81715fca31207cee42f52f13d.jpg\",\n  \"455515.jpg\": \"Aves/Ardea purpurea/b06cda204c9cebb34a90461a6ecf7312.jpg\",\n  \"455518.jpg\": \"Aves/Ardea purpurea/fd0b2bac0d2a932429df613db8ff3488.jpg\",\n  \"455519.jpg\": \"Aves/Ardea purpurea/bbdb056db40e3fe78ad9b4d6bf355e3f.jpg\",\n  \"455520.jpg\": \"Aves/Ardea purpurea/38f8520babd9ca2d749205932b80cf18.jpg\",\n  \"455525.jpg\": \"Aves/Ardea purpurea/fa1962d16f2d6e3f57910c6c1e350322.jpg\",\n  \"455530.jpg\": \"Aves/Trogon collaris/694a2b2a06e0f10fb78e0b16b88c3e32.jpg\",\n  \"455573.jpg\": \"Insecta/Estigmene acrea/0839edbc91815539e9dbc1206290d72b.jpg\",\n  \"455575.jpg\": \"Insecta/Estigmene acrea/cc1fa4613a65b04d65769cef66599c6c.jpg\",\n  \"455579.jpg\": \"Insecta/Estigmene acrea/82464c267173bf9116f1fb9e89531d3f.jpg\",\n  \"455580.jpg\": \"Insecta/Estigmene acrea/aa7cb961b78927b847ec84d7718c1c23.jpg\",\n  \"455588.jpg\": \"Insecta/Estigmene acrea/78334bc0fa7a163a952bf033e3075761.jpg\",\n  \"455589.jpg\": \"Insecta/Estigmene acrea/a272286ef9cc6662abecb36d6f2a2184.jpg\",\n  \"455622.jpg\": \"Aves/Pipilo erythrophthalmus/ae4695b944d1291574b3ed724f4cf954.jpg\",\n  \"455628.jpg\": \"Aves/Pipilo erythrophthalmus/e252af07c26f648ce6831a6010ce21b2.jpg\",\n  \"455633.jpg\": \"Aves/Pipilo erythrophthalmus/2b2d070e6a64a1c1920f6084345b7481.jpg\",\n  \"455644.jpg\": \"Aves/Pipilo erythrophthalmus/4b86cf984106a8ef5d5a9f9c04ab62df.jpg\",\n  \"455647.jpg\": \"Aves/Pipilo erythrophthalmus/146727499ca362a3d3c73c3a09872ced.jpg\",\n  \"455651.jpg\": \"Aves/Pipilo erythrophthalmus/86dc9c9e606baa7e1fc0a530249f1b5a.jpg\",\n  \"455652.jpg\": \"Aves/Pipilo erythrophthalmus/8aa9a62176e948a31b1d1d33458cc3c7.jpg\",\n  \"455658.jpg\": \"Aves/Pipilo erythrophthalmus/b196c40b59633380780de35e78f5e325.jpg\",\n  \"455672.jpg\": \"Aves/Pipilo erythrophthalmus/af837eda79fff0b42e10e546209a67bf.jpg\",\n  \"455680.jpg\": \"Aves/Pipilo erythrophthalmus/e7c070174c8b100f9bd1b7bbc7dcd7fc.jpg\",\n  \"455736.jpg\": \"Amphibia/Pseudacris hypochondriaca/b27ff2382e58a1da20f865ed54daca13.jpg\",\n  \"455754.jpg\": \"Amphibia/Pseudacris hypochondriaca/4efc15b709ca21bf95ddbfb11bb2513c.jpg\",\n  \"455783.jpg\": \"Amphibia/Pseudacris hypochondriaca/e17f3ba2cca3bf2774903ffc92eb43c0.jpg\",\n  \"455795.jpg\": \"Amphibia/Pseudacris hypochondriaca/d45bb5d2f89ae9b10451d59b8d525819.jpg\",\n  \"455822.jpg\": \"Animalia/Pachygrapsus crassipes/54a1dd368d694f7d2247d10ff757e85b.jpg\",\n  \"455868.jpg\": \"Animalia/Pachygrapsus crassipes/cf247bd629c06f4f74269eec8f7806f6.jpg\",\n  \"455874.jpg\": \"Animalia/Pachygrapsus crassipes/85310c7bc5f301e32fca3163548c4601.jpg\",\n  \"455880.jpg\": \"Animalia/Pachygrapsus crassipes/51598011c2c3c2bfcd605ec03d93dae6.jpg\",\n  \"455893.jpg\": \"Animalia/Pachygrapsus crassipes/3f5d7bc371446acb8a1fe203c0334c8e.jpg\",\n  \"455919.jpg\": \"Animalia/Pachygrapsus crassipes/01c83415b47b1d9f9815b0d5bac1dac9.jpg\",\n  \"455927.jpg\": \"Animalia/Pachygrapsus crassipes/bb294aff82919ae70aba4d21a44aa714.jpg\",\n  \"455935.jpg\": \"Insecta/Speyeria atlantis/3237d1d999b3ae9b33329cda7aba38af.jpg\",\n  \"455939.jpg\": \"Insecta/Speyeria atlantis/8c40fe6034151eb05fc7e3addee61f2f.jpg\",\n  \"455946.jpg\": \"Reptilia/Crotalus cerastes cerastes/1918f206cf27314af1f9e20575344d6f.jpg\",\n  \"456033.jpg\": \"Insecta/Dysphania militaris/d804107194a8924f773eff8a501d569c.jpg\",\n  \"456040.jpg\": \"Actinopterygii/Lepisosteus osseus/065112102c6090c16876e60f17bcfd1b.jpg\",\n  \"456052.jpg\": \"Animalia/Pugettia producta/0b02ad36fccd18b6bb8764c91beb3928.jpg\",\n  \"456053.jpg\": \"Animalia/Pugettia producta/0777cf19acd5ecd0dbcd78b0dcefa3dd.jpg\",\n  \"456062.jpg\": \"Animalia/Pugettia producta/a5d3fe8323fab2ae0e917bd037568ac1.jpg\",\n  \"456067.jpg\": \"Animalia/Pugettia producta/b3f24ab834e47dba07bab60366292162.jpg\",\n  \"456068.jpg\": \"Animalia/Pugettia producta/be1988d57183754901844e167d5c7d1d.jpg\",\n  \"456069.jpg\": \"Animalia/Pugettia producta/f3cee4ffce3db286b4936c74b2330951.jpg\",\n  \"456097.jpg\": \"Animalia/Pugettia producta/3066952967c6e0287fdb6f1f0ee5f848.jpg\",\n  \"456098.jpg\": \"Animalia/Pugettia producta/702e6cefc44f34e1feb93b62960b58d8.jpg\",\n  \"456099.jpg\": \"Animalia/Pugettia producta/b3f7215bf83e8768a7057c733d57bd17.jpg\",\n  \"456138.jpg\": \"Insecta/Halmus chalybeus/ec53df6978514ae60541bb3c14ce9f65.jpg\",\n  \"456139.jpg\": \"Insecta/Halmus chalybeus/026043557822c0b43f460e78d427d9b2.jpg\",\n  \"456144.jpg\": \"Insecta/Halmus chalybeus/4bc4b60c9893d823e47990382d0ed664.jpg\",\n  \"456149.jpg\": \"Insecta/Halmus chalybeus/70c01673e7c23d21928864048f97ff92.jpg\",\n  \"456153.jpg\": \"Insecta/Halmus chalybeus/371b52990bb4ffc325d4eb1a975d3cfa.jpg\",\n  \"456154.jpg\": \"Insecta/Halmus chalybeus/a9bfb573a281cbd8d247cf141544a9b1.jpg\",\n  \"456159.jpg\": \"Insecta/Halmus chalybeus/4eebfcd9bb7eac1b9feb0c3fba8415b3.jpg\",\n  \"456163.jpg\": \"Actinopterygii/Ictalurus punctatus/8145b3daab166ad16ee1ca7e1f0089ed.jpg\",\n  \"456164.jpg\": \"Actinopterygii/Ictalurus punctatus/47bc23a5ca06e818b57384381ddb4f21.jpg\",\n  \"456166.jpg\": \"Actinopterygii/Ictalurus punctatus/cefd7385614b2cc343a192e13f1997b9.jpg\",\n  \"456179.jpg\": \"Reptilia/Crotalus horridus/64b927cea1ec7b0c9cc277fdb17d56fa.jpg\",\n  \"456185.jpg\": \"Reptilia/Crotalus horridus/98c10419222c3753a4c18d8d3456c71b.jpg\",\n  \"456188.jpg\": \"Reptilia/Crotalus horridus/7ccd1a6f92bb2dc961576267f6f824e4.jpg\",\n  \"456190.jpg\": \"Reptilia/Crotalus horridus/6746c6e89f0bbe429fd1e38ec2cd4d9a.jpg\",\n  \"456191.jpg\": \"Reptilia/Crotalus horridus/d427263d3d237e5297aff91f27458174.jpg\",\n  \"456199.jpg\": \"Reptilia/Crotalus horridus/af781d42321128b804f3ae2304539a94.jpg\",\n  \"456208.jpg\": \"Reptilia/Crotalus horridus/aa5bb856ce36cc72a42b7fd94fbe473b.jpg\",\n  \"456209.jpg\": \"Reptilia/Crotalus horridus/02225ca6eeb11bf90354319fc0d52117.jpg\",\n  \"456215.jpg\": \"Reptilia/Crotalus horridus/ef39601c906e34350cbcad7d3ac4aabd.jpg\",\n  \"456236.jpg\": \"Insecta/Epirrhoe alternata/c30c5f3d4e135bc405b672103110a726.jpg\",\n  \"45628.jpg\": \"Aves/Ardea herodias/0559f8da7ef65dfbc3bd00ddd67eaf52.jpg\",\n  \"45638.jpg\": \"Aves/Ardea herodias/1781bebb08384dc317cdd0f43015b2bc.jpg\",\n  \"456459.jpg\": \"Aves/Aphelocoma coerulescens/3b266f43823fd9f6370921b0a5bedc45.jpg\",\n  \"456462.jpg\": \"Aves/Aphelocoma coerulescens/5efb98b1ecb1f7a393213e53154136f5.jpg\",\n  \"456467.jpg\": \"Insecta/Enallagma carunculatum/2c9803f4a5d0b11e6b0e0dd2a297adb0.jpg\",\n  \"456471.jpg\": \"Insecta/Enallagma carunculatum/5571a4b89e88c27e97a0f2f9a8454ea8.jpg\",\n  \"456472.jpg\": \"Insecta/Enallagma carunculatum/aa54371cc5dc2ab01d103f3038012e7c.jpg\",\n  \"456474.jpg\": \"Insecta/Enallagma carunculatum/3ebac5f8d374a162636921705771f960.jpg\",\n  \"456482.jpg\": \"Insecta/Enallagma carunculatum/863d78a965c69a71860a2014e6eceb48.jpg\",\n  \"456489.jpg\": \"Insecta/Enallagma carunculatum/528237ff0bd0a8ce82bdca0f26535e72.jpg\",\n  \"456490.jpg\": \"Insecta/Enallagma carunculatum/c7d1f77eb40d28062fb256f4b743a2ea.jpg\",\n  \"456491.jpg\": \"Insecta/Enallagma carunculatum/d26214dc292a73d298030fbfb634d92f.jpg\",\n  \"456496.jpg\": \"Insecta/Enallagma carunculatum/06597a6cfcbcac5acea27ff548d3ab7b.jpg\",\n  \"456660.jpg\": \"Actinopterygii/Thalassoma bifasciatum/3c89fc2bd3757f2cceed4cb46d418076.jpg\",\n  \"456670.jpg\": \"Insecta/Hemaris diffinis/5ba14a89177791a762d65dd4909e9c2b.jpg\",\n  \"456674.jpg\": \"Insecta/Hemaris diffinis/1035df1cf44cb489651b22d52addbc6b.jpg\",\n  \"456686.jpg\": \"Insecta/Hemaris diffinis/9a87c377165b127c6097d7d1e0c05dfb.jpg\",\n  \"456694.jpg\": \"Insecta/Hemaris diffinis/6a77b1f596ab364d22c9490854d0a324.jpg\",\n  \"456697.jpg\": \"Insecta/Hemaris diffinis/4acd259a59464d5a4ca365d4b2d5b7eb.jpg\",\n  \"456705.jpg\": \"Insecta/Hemaris diffinis/8a00756a8f148999f76e7fe30e4b8621.jpg\",\n  \"456720.jpg\": \"Insecta/Hemaris diffinis/651d9a8d2de42f6a09b541b3781bacee.jpg\",\n  \"456738.jpg\": \"Insecta/Hemaris diffinis/803d868814aaa79611956c73179dfbab.jpg\",\n  \"456766.jpg\": \"Insecta/Polyphylla decemlineata/4a0840b63ad4f5879bbd13527c50387e.jpg\",\n  \"456770.jpg\": \"Insecta/Polyphylla decemlineata/d5abdddc8d78cd340056c6309563e3e5.jpg\",\n  \"456771.jpg\": \"Insecta/Polyphylla decemlineata/eba957604f81f0c28295aad150faaea7.jpg\",\n  \"456772.jpg\": \"Insecta/Polyphylla decemlineata/92ff1d22ec5efb53d3e3a515be164689.jpg\",\n  \"456776.jpg\": \"Insecta/Polyphylla decemlineata/1f55537421f0685e27ff8ff296c788b4.jpg\",\n  \"456783.jpg\": \"Insecta/Polyphylla decemlineata/1b1371b1312182e9170e0d9c4a977900.jpg\",\n  \"456784.jpg\": \"Insecta/Polyphylla decemlineata/e149e031f3f95800ef66f6ebec72236c.jpg\",\n  \"456792.jpg\": \"Insecta/Polyphylla decemlineata/59745923a917d68da104a59eacba4a3b.jpg\",\n  \"456795.jpg\": \"Insecta/Polyphylla decemlineata/6f33a1b91e04f55badbe0eec53a45f64.jpg\",\n  \"456799.jpg\": \"Insecta/Polyphylla decemlineata/9963f1c7e95dafd0acbde38672726f08.jpg\",\n  \"456885.jpg\": \"Amphibia/Hyla chrysoscelis/7b7dc2baea5071c141a14559024fe6cf.jpg\",\n  \"456886.jpg\": \"Amphibia/Hyla chrysoscelis/c478f97543ee52ba33a4eff4b30dac79.jpg\",\n  \"456890.jpg\": \"Amphibia/Hyla chrysoscelis/c1d247c365f5f998427b0f25890151cf.jpg\",\n  \"456907.jpg\": \"Amphibia/Hyla chrysoscelis/6e1d149c06d29650b704f61ae7954d7b.jpg\",\n  \"456912.jpg\": \"Amphibia/Hyla chrysoscelis/c61828418b7bbfccfcc00526babb0943.jpg\",\n  \"456988.jpg\": \"Insecta/Chytonix palliatricula/73071e43aefede4bc6c17423bdd6b68c.jpg\",\n  \"457032.jpg\": \"Mammalia/Crocuta crocuta/1a677dfacce5d6110f1acd4409713a99.jpg\",\n  \"457038.jpg\": \"Mammalia/Crocuta crocuta/56f3ca50dc9cdcd010f5d710727024ba.jpg\",\n  \"457063.jpg\": \"Insecta/Danaus chrysippus/674037264c2056d7005a9d3bba51fcec.jpg\",\n  \"457064.jpg\": \"Insecta/Danaus chrysippus/0794792ffb20fdb9358218c45505c7e7.jpg\",\n  \"457069.jpg\": \"Insecta/Danaus chrysippus/5d34a350d7e2930d035fc1557ea2ec58.jpg\",\n  \"457070.jpg\": \"Insecta/Danaus chrysippus/337194d54df43e09d07c02d77e214cf1.jpg\",\n  \"457083.jpg\": \"Insecta/Amphion floridensis/55fe560028947ba4da57888ccfc2b0f3.jpg\",\n  \"457085.jpg\": \"Insecta/Amphion floridensis/b3ac6836378da9b98b56fff6e2c40e62.jpg\",\n  \"457087.jpg\": \"Insecta/Amphion floridensis/a616734785f0037d38b9dfb02aa0c6f7.jpg\",\n  \"457089.jpg\": \"Insecta/Amphion floridensis/7bd7b39d5dd141614c2bdfc036f479ee.jpg\",\n  \"457098.jpg\": \"Insecta/Amphion floridensis/bc296338e479144b9b663e88be0ea029.jpg\",\n  \"457099.jpg\": \"Insecta/Nadata gibbosa/86c9de6e514d5d24ea990bed12ad1452.jpg\",\n  \"457100.jpg\": \"Insecta/Nadata gibbosa/cb0aec8d860ce298a8ea27f4d4707524.jpg\",\n  \"457102.jpg\": \"Insecta/Nadata gibbosa/4a0546f8a91932f6d97c30e5a578a0ce.jpg\",\n  \"457105.jpg\": \"Insecta/Nadata gibbosa/e48fae986309b21979350fd4e8112392.jpg\",\n  \"457108.jpg\": \"Insecta/Nadata gibbosa/ce28c68a5159c87caca284d9309f0348.jpg\",\n  \"457110.jpg\": \"Insecta/Nadata gibbosa/a6d6eae3bddc2a1ec7377d3ed8f69298.jpg\",\n  \"457117.jpg\": \"Insecta/Nadata gibbosa/8c0a0a18edb88cb7314b94997146682f.jpg\",\n  \"457118.jpg\": \"Insecta/Nadata gibbosa/03fcd7aa5678b01bbf20ac82dd824389.jpg\",\n  \"457125.jpg\": \"Insecta/Nadata gibbosa/2cf10db9bfeec273f914d0e9b6e9ae00.jpg\",\n  \"457126.jpg\": \"Arachnida/Badumna longinqua/6f003bd63999883a94d53c0027ef3192.jpg\",\n  \"457141.jpg\": \"Arachnida/Badumna longinqua/e48347f6ce38bdbd990487f73a94c022.jpg\",\n  \"457144.jpg\": \"Insecta/Calopteryx virgo/28ab52576da25e6dc9cdb3794c8a41e8.jpg\",\n  \"457145.jpg\": \"Insecta/Calopteryx virgo/9e3e97116f462d2b2e9397679e23ed4c.jpg\",\n  \"457153.jpg\": \"Insecta/Calopteryx virgo/74e3c0c8d3727c93297d3f63ac78b0a5.jpg\",\n  \"457164.jpg\": \"Mammalia/Axis axis/b9d1a6e5ad82e3aafd0c40e2411ca15a.jpg\",\n  \"457168.jpg\": \"Mammalia/Axis axis/283f0df5993e9b17898b40fc38a785a4.jpg\",\n  \"457186.jpg\": \"Insecta/Colias croceus/c39601b0e81328d0b494da5111541059.jpg\",\n  \"457191.jpg\": \"Aves/Grus grus/d86d7cb2eed51c80ce5cd1c86681d2fc.jpg\",\n  \"457192.jpg\": \"Aves/Grus grus/acbd1bdb5af0c6b8edf670049152d489.jpg\",\n  \"457211.jpg\": \"Aves/Grus grus/10c6729283d68cef8f50751f798eac24.jpg\",\n  \"457217.jpg\": \"Aves/Grus grus/785a7ac087e19000fb32eb7b81f6b154.jpg\",\n  \"457354.jpg\": \"Aves/Colaptes auratus/aadab5bc5412f2f7edddededb2da8e17.jpg\",\n  \"45738.jpg\": \"Aves/Ardea herodias/2ded9751f0ee263656b897f927cbf306.jpg\",\n  \"457418.jpg\": \"Aves/Colaptes auratus/86264f3fba166d2cc9716c729d9a5a3d.jpg\",\n  \"457510.jpg\": \"Aves/Colaptes auratus/e34f687cf68a26d6995b7b92d040e859.jpg\",\n  \"457523.jpg\": \"Aves/Colaptes auratus/9bc2cefded465cb51b0115d0eae6e1e9.jpg\",\n  \"457602.jpg\": \"Aves/Colaptes auratus/c9a757ac556d2263460034b3c5a88469.jpg\",\n  \"457607.jpg\": \"Aves/Colaptes auratus/fb8d6b8c3dee0311c4a5781639d3bae5.jpg\",\n  \"45764.jpg\": \"Aves/Ardea herodias/a7be5197df4353c52de3ca3006cf8756.jpg\",\n  \"457732.jpg\": \"Aves/Colaptes auratus/f7bcd77f2da25d166b8f11b30983423d.jpg\",\n  \"4578.jpg\": \"Aves/Spizella breweri/6ca8026c6552ebee14a13add125f0616.jpg\",\n  \"457844.jpg\": \"Insecta/Bombus vagans/2746ee591eb70edd78eef93d5d9fef7a.jpg\",\n  \"457903.jpg\": \"Aves/Cygnus columbianus/c08df12d7e4fabdde547ac324a6dd609.jpg\",\n  \"457904.jpg\": \"Aves/Cygnus columbianus/4c6beb6dcbfffd0d05865c987ef3cd80.jpg\",\n  \"457905.jpg\": \"Aves/Cygnus columbianus/c56457e8548f63df2c9f813255d739c4.jpg\",\n  \"457957.jpg\": \"Aves/Larus delawarensis/0c2974f2f9d38ecd3e1d44e791b6d1cb.jpg\",\n  \"457969.jpg\": \"Aves/Larus delawarensis/b91020f8a4c278fb544f01e1aa1070dc.jpg\",\n  \"458.jpg\": \"Mammalia/Ursus americanus/1df9880334e04c140534b2a4b0bd529d.jpg\",\n  \"458030.jpg\": \"Aves/Larus delawarensis/cabc44705bd89123ea895e5623b60018.jpg\",\n  \"458134.jpg\": \"Aves/Larus delawarensis/4690119331f89a04eda8c20a9c6dee58.jpg\",\n  \"458421.jpg\": \"Aves/Larus delawarensis/2546fbd7e8dfea5200c70829c07e9626.jpg\",\n  \"458474.jpg\": \"Aves/Junco hyemalis/9047975ab27353ccacb7f30e4815e903.jpg\",\n  \"45857.jpg\": \"Aves/Ardea herodias/0bc5995da6664a3c223ac3cdb950c89b.jpg\",\n  \"458695.jpg\": \"Aves/Junco hyemalis/f02d6fd8da311f876cf94cd8c28d41c9.jpg\",\n  \"458715.jpg\": \"Aves/Junco hyemalis/fe56d1a24402cc6764be8ff7463a6a75.jpg\",\n  \"458845.jpg\": \"Aves/Junco hyemalis/777f24aa9b5823bd901ec1f43a0593ea.jpg\",\n  \"458885.jpg\": \"Aves/Junco hyemalis/e9e80d5d617367fff275897e698d93be.jpg\",\n  \"459272.jpg\": \"Reptilia/Trachemys scripta/380040f76b38f9de5222866fb58a956c.jpg\",\n  \"4593.jpg\": \"Aves/Spizella breweri/7c1d90fbd1f274abcbbc61f6c34a0921.jpg\",\n  \"459312.jpg\": \"Reptilia/Trachemys scripta/1eccaa151a26228717e9b4189fbe4d4c.jpg\",\n  \"459317.jpg\": \"Reptilia/Trachemys scripta/6488c57ac8ae993113826ee270411d25.jpg\",\n  \"459328.jpg\": \"Reptilia/Trachemys scripta/3d90ff9a7bb10fe804e82d1bf7e7c2f4.jpg\",\n  \"459338.jpg\": \"Reptilia/Trachemys scripta/17b4e8860fd13e191ecc0e013ad0850d.jpg\",\n  \"459370.jpg\": \"Reptilia/Trachemys scripta/409e4cc543b51db20d1db2f3bdf78d17.jpg\",\n  \"459380.jpg\": \"Reptilia/Trachemys scripta/c0b7a80395c3c26f780e81f59bdb512d.jpg\",\n  \"459390.jpg\": \"Reptilia/Trachemys scripta/787b57a2faf716d63e0039dacda1b71a.jpg\",\n  \"4594.jpg\": \"Aves/Spizella breweri/ece95f2d9105322fbc589f5304bf0f4b.jpg\",\n  \"459440.jpg\": \"Insecta/Pyrrhosoma nymphula/ad44a37d4a6f17fffad259734513c328.jpg\",\n  \"459443.jpg\": \"Insecta/Pyrrhosoma nymphula/1f6100e40bae9b89f12d21b34df58d74.jpg\",\n  \"459494.jpg\": \"Amphibia/Pseudacris regilla/dedac54eb3f2bdd9d0be11baa94fe0de.jpg\",\n  \"4595.jpg\": \"Aves/Spizella breweri/0903b5c18bdc55fadd840ba343f3cdf1.jpg\",\n  \"459500.jpg\": \"Amphibia/Pseudacris regilla/b0ac8bfc19eb2ee8cae99bcf86fad16c.jpg\",\n  \"459536.jpg\": \"Amphibia/Pseudacris regilla/6eedac6e080e0c440a22909acc8341f4.jpg\",\n  \"459539.jpg\": \"Amphibia/Pseudacris regilla/2c03cf6c10258953db496668f140a7ab.jpg\",\n  \"459540.jpg\": \"Amphibia/Pseudacris regilla/a30c56dbccb56dc54a5d0eb9286343e9.jpg\",\n  \"459544.jpg\": \"Reptilia/Arizona elegans/6ac8e4c6466d301974614433d439c733.jpg\",\n  \"459546.jpg\": \"Reptilia/Arizona elegans/c1dd98b0fa8ca306ec8a3118d8b56490.jpg\",\n  \"459547.jpg\": \"Reptilia/Arizona elegans/90dbc6ffdd23475fad1404640e6deddd.jpg\",\n  \"459551.jpg\": \"Reptilia/Arizona elegans/0b1be3e2e8b9ca20586fc7c96aab504a.jpg\",\n  \"459555.jpg\": \"Reptilia/Arizona elegans/73ed9fa5762974d9e60324e836eaa157.jpg\",\n  \"459556.jpg\": \"Reptilia/Arizona elegans/0e970cd9f541b73ad8da864eff1fdfdc.jpg\",\n  \"459565.jpg\": \"Reptilia/Arizona elegans/db06260181ab93363cde86d9b20262d1.jpg\",\n  \"459566.jpg\": \"Reptilia/Arizona elegans/1dadeb0db9d6640d80b1d1b092dd284b.jpg\",\n  \"459577.jpg\": \"Animalia/Epiactis prolifera/7d8aa055bf7072437ecd01ec21d15525.jpg\",\n  \"459579.jpg\": \"Animalia/Epiactis prolifera/741ef12646b46980a73a916d3a2f456e.jpg\",\n  \"459582.jpg\": \"Animalia/Epiactis prolifera/2ef760a5559d316344d92ca0258917e7.jpg\",\n  \"459587.jpg\": \"Animalia/Epiactis prolifera/1c449831873aa716b6e793770d626314.jpg\",\n  \"459589.jpg\": \"Animalia/Epiactis prolifera/a44cc89584a51b352aaff264487e84be.jpg\",\n  \"459591.jpg\": \"Animalia/Epiactis prolifera/2976ecfb1207cb2eb908768a5d4e24c8.jpg\",\n  \"4596.jpg\": \"Aves/Spizella breweri/41d9f48fcbc84a2d13ac96559c6e9147.jpg\",\n  \"459614.jpg\": \"Amphibia/Ambystoma mavortium/f898592a7eec2c4ac8d85e7b26272f53.jpg\",\n  \"459617.jpg\": \"Amphibia/Ambystoma mavortium/2e41de831e6743c6cfa4f79748d2bb42.jpg\",\n  \"459620.jpg\": \"Amphibia/Ambystoma mavortium/7e189129d1455be48a3e8310ce328add.jpg\",\n  \"459622.jpg\": \"Amphibia/Ambystoma mavortium/f0daa4afdb93ed377be03b829ec92a31.jpg\",\n  \"459623.jpg\": \"Amphibia/Ambystoma mavortium/a4e6eb36becfa8c85fd96808d4558cfb.jpg\",\n  \"459624.jpg\": \"Amphibia/Ambystoma mavortium/5803c666cca1ddce0ef891a36644feb4.jpg\",\n  \"45963.jpg\": \"Aves/Ardea herodias/011b86d48d32aa6432eb523424361aad.jpg\",\n  \"459648.jpg\": \"Mammalia/Cervus elaphus roosevelti/18127c829c7ea15fecac80425cb1ea7c.jpg\",\n  \"459657.jpg\": \"Mammalia/Cervus elaphus roosevelti/5582393311ee01f9c4592cb49dc14488.jpg\",\n  \"4597.jpg\": \"Aves/Spizella breweri/6f6dbd5dd205b7f0d7cb83fefb4cfb83.jpg\",\n  \"459778.jpg\": \"Mammalia/Vulpes vulpes/c50b19a716e02b5f2add4ff057e9522d.jpg\",\n  \"459791.jpg\": \"Mammalia/Vulpes vulpes/8d371c46c12bd4044928307b1969f226.jpg\",\n  \"459793.jpg\": \"Mammalia/Vulpes vulpes/6d77721f0a82d786d70eb8d691516cfd.jpg\",\n  \"459837.jpg\": \"Mammalia/Vulpes vulpes/77f862b9823189a39dd6037d1ce90acc.jpg\",\n  \"459863.jpg\": \"Mammalia/Vulpes vulpes/5f9309a1ccf0fd0779d8ba23b61e0e88.jpg\",\n  \"45987.jpg\": \"Aves/Ardea herodias/a532c9e4dcb0ffea3b8ce12f06b3a19c.jpg\",\n  \"4599.jpg\": \"Aves/Spizella breweri/e36a5b79bf685100c5cd5155d830eaaf.jpg\",\n  \"45994.jpg\": \"Aves/Ardea herodias/ddd677760195e0c802720ad41edd5cf6.jpg\",\n  \"459943.jpg\": \"Mammalia/Vulpes vulpes/65f8e23902dabfd19dfbe9ae7dc81aaf.jpg\",\n  \"4600.jpg\": \"Aves/Spizella breweri/2b3c53d1a3d8b2dc65bd7f7e01d1410a.jpg\",\n  \"460001.jpg\": \"Aves/Melopsittacus undulatus/a058174722d52123c9c24df02e2b2164.jpg\",\n  \"460062.jpg\": \"Aves/Trichoglossus haematodus/cd66e655ffbd6e1f53c94101ee575b3e.jpg\",\n  \"460066.jpg\": \"Aves/Trichoglossus haematodus/f9dab1be354aa2466b556a8d78ad0097.jpg\",\n  \"460067.jpg\": \"Aves/Trichoglossus haematodus/2acba8f264f1f2ae3c7efebdde4467e4.jpg\",\n  \"460069.jpg\": \"Aves/Trichoglossus haematodus/202e53d73f60c8c2d041b0eb116b78f1.jpg\",\n  \"460070.jpg\": \"Aves/Trichoglossus haematodus/9bfc18bcd8c0563cf909e6885992674b.jpg\",\n  \"460075.jpg\": \"Aves/Trichoglossus haematodus/6ac6363100c7e24050c332e308bca33d.jpg\",\n  \"460076.jpg\": \"Aves/Trichoglossus haematodus/8e25c610782adcb6206c365d6313af2f.jpg\",\n  \"460084.jpg\": \"Insecta/Schistocerca obscura/9820f9de6afb353ad5b0f7e0c124821b.jpg\",\n  \"460085.jpg\": \"Insecta/Schistocerca obscura/a3942f5ccd41ad07e43588b489058e42.jpg\",\n  \"460086.jpg\": \"Insecta/Schistocerca obscura/2dc6d1bad4de543f1a8806feb773d431.jpg\",\n  \"460087.jpg\": \"Insecta/Schistocerca obscura/5b53abc44af6dc98c48761f545ea9ff7.jpg\",\n  \"460100.jpg\": \"Reptilia/Apalone spinifera spinifera/cd099b22122ed4b441532d7b7b659115.jpg\",\n  \"460113.jpg\": \"Insecta/Plebejus icarioides/e56a56cfdd14a30e8476a36ba2b3e7f6.jpg\",\n  \"460120.jpg\": \"Insecta/Plebejus icarioides/d9b2a095eb5e271bf245f21c8314bdb4.jpg\",\n  \"4602.jpg\": \"Aves/Spizella breweri/2a90d23e8ae745993af21f25016adf92.jpg\",\n  \"460264.jpg\": \"Insecta/Pachydiplax longipennis/3e33b7c5c75319c79c2637e9a0728d0f.jpg\",\n  \"460269.jpg\": \"Insecta/Pachydiplax longipennis/0e0652056f2a96ba026d55e31004479a.jpg\",\n  \"460452.jpg\": \"Insecta/Pachydiplax longipennis/77af5d6d5efc543df8ba0d01ba809de8.jpg\",\n  \"460642.jpg\": \"Insecta/Pachydiplax longipennis/64ccf4823ca04ee69486165db7b5b005.jpg\",\n  \"460721.jpg\": \"Insecta/Pachydiplax longipennis/e3e290ae10bc1102a550b359042928bd.jpg\",\n  \"460857.jpg\": \"Aves/Cinclus cinclus/ebff649836bc6221244f81696ee22166.jpg\",\n  \"460873.jpg\": \"Aves/Vanellus miles/1a7a365fda4f4243dfc1168a9c2d6420.jpg\",\n  \"460883.jpg\": \"Aves/Vanellus miles/0a4c40f0c61b2cc856ab52cc5fd1730b.jpg\",\n  \"460886.jpg\": \"Aves/Vanellus miles/4a58a3c632c312ba94ec8f913eaa9418.jpg\",\n  \"460887.jpg\": \"Aves/Vanellus miles/6e9807989c3597578b496ea980bdc6d8.jpg\",\n  \"460895.jpg\": \"Aves/Vanellus miles/6544b45a6affd41d7096384e211befb5.jpg\",\n  \"460921.jpg\": \"Aves/Elanoides forficatus/a097ceebf5e96cbf770a7e8d013ee952.jpg\",\n  \"460922.jpg\": \"Aves/Elanoides forficatus/100a166b5b188ac2eb0e59b32cad5dee.jpg\",\n  \"460926.jpg\": \"Aves/Elanoides forficatus/3d4ab3e540a431e05a4ba66ece4cda5c.jpg\",\n  \"460928.jpg\": \"Aves/Elanoides forficatus/9ff3fd8c7deb520b2402625eafef8496.jpg\",\n  \"460935.jpg\": \"Aves/Elanoides forficatus/56264d9bc4ba839ac7ffd404a4f881d9.jpg\",\n  \"460936.jpg\": \"Aves/Elanoides forficatus/fc9c10f39ed484b805eb2922edc74c30.jpg\",\n  \"460937.jpg\": \"Aves/Elanoides forficatus/64b62d25cddc404eaabbb2d5626ec34f.jpg\",\n  \"460943.jpg\": \"Aves/Elanoides forficatus/f353c742ff66fee821ea8dcc87311925.jpg\",\n  \"460950.jpg\": \"Mollusca/Diaulula sandiegensis/5fed055467eb30f6f2062782324c33b0.jpg\",\n  \"460978.jpg\": \"Mollusca/Diaulula sandiegensis/98e6f2a805449efe1c8ea9379c650e58.jpg\",\n  \"460979.jpg\": \"Mollusca/Diaulula sandiegensis/488777ddff8d0e0ddb54d6f12abf5f60.jpg\",\n  \"460984.jpg\": \"Mollusca/Diaulula sandiegensis/d79e811cb839ee020df3c467067bd9c5.jpg\",\n  \"460989.jpg\": \"Mollusca/Diaulula sandiegensis/e1d2a3c381ec5eb74e8af4666844810e.jpg\",\n  \"461000.jpg\": \"Mollusca/Diaulula sandiegensis/02b02789b5e7e5085762d9d01fa4ffbc.jpg\",\n  \"461007.jpg\": \"Insecta/Scolia dubia/dc49eb420db6f490774b6e3aa600327d.jpg\",\n  \"461008.jpg\": \"Insecta/Scolia dubia/36940f37cffebfeabc3c1506db677249.jpg\",\n  \"461009.jpg\": \"Insecta/Scolia dubia/77e2e6bdcabec21507acb9de79ea58f2.jpg\",\n  \"461011.jpg\": \"Insecta/Scolia dubia/66a007146848480fb5593a14c8eee98f.jpg\",\n  \"461012.jpg\": \"Insecta/Scolia dubia/a7cdc6f0379b3b5645ea2d6adfdfc74c.jpg\",\n  \"461016.jpg\": \"Insecta/Scolia dubia/c1edbbbf4cda9216f5a536fcb520541d.jpg\",\n  \"461017.jpg\": \"Insecta/Scolia dubia/379f4ce6f393fc09736aa9a242367689.jpg\",\n  \"461018.jpg\": \"Insecta/Scolia dubia/4c558698482f1fa806a0eef8770ee2e7.jpg\",\n  \"461020.jpg\": \"Insecta/Scolia dubia/91b16ed9842be616e5cc64ca4041d44c.jpg\",\n  \"461031.jpg\": \"Insecta/Prionoxystus robiniae/dd526b1ddff47d10dfb0596cf6502e0d.jpg\",\n  \"461057.jpg\": \"Insecta/Zale horrida/0bbd79247469f8862931567b616527dd.jpg\",\n  \"461058.jpg\": \"Insecta/Zale horrida/499a6b095be445d897673f57e546a9bc.jpg\",\n  \"461060.jpg\": \"Aves/Chondestes grammacus/300307752932f816a8d88fbe4183897f.jpg\",\n  \"461068.jpg\": \"Aves/Chondestes grammacus/815b82d79022a60ddf19f6fd92726390.jpg\",\n  \"461202.jpg\": \"Aves/Chondestes grammacus/7d113156407b5085c74482188f70807f.jpg\",\n  \"461243.jpg\": \"Aves/Chondestes grammacus/0ccc67e68f500fce139fb214bdfe49f1.jpg\",\n  \"461264.jpg\": \"Aves/Lophura leucomelanos/5aef73d13a24c3cf93cef2b9c253ed5e.jpg\",\n  \"461269.jpg\": \"Aves/Geothlypis tolmiei/9e12b4a86079a24d47004edc80ccafb4.jpg\",\n  \"461279.jpg\": \"Aves/Geothlypis tolmiei/2403ad38b9c6e4df15cdde1632fe308f.jpg\",\n  \"461280.jpg\": \"Aves/Geothlypis tolmiei/e4c662a713b9f4cbccadd67b5b02b070.jpg\",\n  \"461287.jpg\": \"Mammalia/Rangifer tarandus/8f2513780405c225c60d14746789fff9.jpg\",\n  \"461291.jpg\": \"Mammalia/Rangifer tarandus/aa6dc1da13e4244f61b11f49652dd689.jpg\",\n  \"461351.jpg\": \"Insecta/Tetracis crocallata/84183b3c7370bd7c9d02fc8b758cea44.jpg\",\n  \"461352.jpg\": \"Insecta/Tetracis crocallata/a32698780348808d1cb9d288d8ec68ce.jpg\",\n  \"461375.jpg\": \"Insecta/Vanessa kershawi/5b5c7f0e2a545a85e3e15afc8e6043f5.jpg\",\n  \"4614.jpg\": \"Insecta/Carabus nemoralis/f8213111f89373da306e2d6f1168cefa.jpg\",\n  \"461400.jpg\": \"Aves/Monticola solitarius/c031cc0ad46afa9cf93ad916fa229e44.jpg\",\n  \"461443.jpg\": \"Insecta/Tremex columba/274e34028fa8237e9f67e613a669269b.jpg\",\n  \"461448.jpg\": \"Insecta/Tremex columba/4b15d7dcc59685f648ca273ad1f4b8d6.jpg\",\n  \"461484.jpg\": \"Reptilia/Micrurus tener tener/8c5a44811b34cefba49d6c8ce03f82be.jpg\",\n  \"461494.jpg\": \"Reptilia/Coluber lateralis/1302e62a5b95e8a593b28bc450d5b210.jpg\",\n  \"4615.jpg\": \"Insecta/Carabus nemoralis/f4abcc082272dbc9a5db163df1de3a03.jpg\",\n  \"461580.jpg\": \"Insecta/Limenitis archippus/bf349d5f54663be89158482d59e465aa.jpg\",\n  \"461591.jpg\": \"Insecta/Limenitis archippus/50f30e0e7013000307938f2880d040e3.jpg\",\n  \"4616.jpg\": \"Insecta/Carabus nemoralis/438b87019b63b711672350e006d48268.jpg\",\n  \"461613.jpg\": \"Insecta/Limenitis archippus/bcffd7819944f7cbba8f315bf19feb3a.jpg\",\n  \"461648.jpg\": \"Insecta/Limenitis archippus/968f578f80ec4c1df1f1f56bf70bbf23.jpg\",\n  \"461681.jpg\": \"Insecta/Limenitis archippus/f0273eb4d852e851cb51ddb4ddbf1fc3.jpg\",\n  \"461682.jpg\": \"Insecta/Limenitis archippus/5065e049416f0f900b8414fadb8c90a4.jpg\",\n  \"461696.jpg\": \"Insecta/Limenitis archippus/967dd7a5274e2e60c96556f02633479b.jpg\",\n  \"461699.jpg\": \"Insecta/Limenitis archippus/8b051002d6bf0714329cba0f416e654c.jpg\",\n  \"4617.jpg\": \"Insecta/Carabus nemoralis/97f9d0f530202fbeca84861ee77c10b5.jpg\",\n  \"461718.jpg\": \"Insecta/Limenitis archippus/7cd0334e522d9bde58ce75a5b05a44e5.jpg\",\n  \"461722.jpg\": \"Insecta/Limenitis archippus/6077232d2b56316b37939675b8988591.jpg\",\n  \"461730.jpg\": \"Reptilia/Drymobius margaritiferus/81a80cc500f8a8909f2395b0757121f6.jpg\",\n  \"461731.jpg\": \"Reptilia/Drymobius margaritiferus/aec53d06bf992993415337716f4eec32.jpg\",\n  \"461765.jpg\": \"Arachnida/Phidippus audax/d580515991cbe95d98651dc442cf6f1a.jpg\",\n  \"461769.jpg\": \"Arachnida/Phidippus audax/ef1da78377e5a9f8cd62ec370bbb81e7.jpg\",\n  \"4618.jpg\": \"Insecta/Carabus nemoralis/f14ae5420529f7c78fc2fb132f6de234.jpg\",\n  \"461808.jpg\": \"Arachnida/Phidippus audax/a0f2da001f6cedfda40e5db927c527a3.jpg\",\n  \"46182.jpg\": \"Aves/Ardea herodias/52d1d7e9928944187fa6e4e7b535f851.jpg\",\n  \"461830.jpg\": \"Arachnida/Phidippus audax/bf6b121bb027538f498a0f613fcf1af6.jpg\",\n  \"461836.jpg\": \"Arachnida/Phidippus audax/ddbf66b13d39b01464ac8159b77d8c67.jpg\",\n  \"461849.jpg\": \"Arachnida/Phidippus audax/23ca6f2d7504441b7a47be50ec986e30.jpg\",\n  \"462001.jpg\": \"Insecta/Hyles gallii/2c27cec17eb06dbd11829f79ace64044.jpg\",\n  \"46201.jpg\": \"Aves/Ardea herodias/9db60a4852472c8964a116f21899f4d6.jpg\",\n  \"462026.jpg\": \"Aves/Charadrius montanus/e183dd4bf582399999e2dfe15c01c89b.jpg\",\n  \"462149.jpg\": \"Aves/Contopus sordidulus/52792397fb7eb54bb31321db303b05f2.jpg\",\n  \"462155.jpg\": \"Aves/Contopus sordidulus/46c7ff348025d29a21f653145fb47edd.jpg\",\n  \"462166.jpg\": \"Aves/Contopus sordidulus/b0cae5a28d8354674d1981e74afe3f26.jpg\",\n  \"462167.jpg\": \"Aves/Contopus sordidulus/83b41de13d172c808af0807c71e373be.jpg\",\n  \"462168.jpg\": \"Aves/Contopus sordidulus/5d90094a958e39c41ff5145f3b1dd199.jpg\",\n  \"462183.jpg\": \"Aves/Contopus sordidulus/c2458342bad2bcad1cd56b8069926612.jpg\",\n  \"462188.jpg\": \"Aves/Contopus sordidulus/1bf6200396cafbe6a3ae9b22d5f7e625.jpg\",\n  \"462192.jpg\": \"Aves/Contopus sordidulus/572b02ecdb4b61189a11433159f1bfc6.jpg\",\n  \"462366.jpg\": \"Aves/Pitangus sulphuratus/eab4e20e964b333d10e88ab7f42184ae.jpg\",\n  \"462420.jpg\": \"Aves/Pitangus sulphuratus/ed648273003622ff9ca4095a9b5d1111.jpg\",\n  \"462475.jpg\": \"Aves/Pitangus sulphuratus/36bcda120ee3f7f0aadff0b6b2e66906.jpg\",\n  \"462539.jpg\": \"Aves/Pitangus sulphuratus/3da52437fef7cb0e933347bcedb3b15d.jpg\",\n  \"462553.jpg\": \"Aves/Pitangus sulphuratus/82333449ad04345760362670269080ca.jpg\",\n  \"462566.jpg\": \"Aves/Pitangus sulphuratus/9f0f2abc58013c14e13ee2ea390d7163.jpg\",\n  \"462595.jpg\": \"Insecta/Strymon istapa/7453f3bc096d4fec4df3564be151bec9.jpg\",\n  \"462597.jpg\": \"Insecta/Strymon istapa/4279d781f62846ed8075eee86def5e12.jpg\",\n  \"462599.jpg\": \"Insecta/Strymon istapa/263f155b0a06790d30ee6b5e09789567.jpg\",\n  \"462601.jpg\": \"Insecta/Strymon istapa/74581cf92da255bc0af872ecb06d97d4.jpg\",\n  \"462603.jpg\": \"Insecta/Strymon istapa/e62ab1da5523a7e803404e51482f02c8.jpg\",\n  \"462604.jpg\": \"Insecta/Strymon istapa/5754e6d5686ac30549fbbb325ea3c29d.jpg\",\n  \"462610.jpg\": \"Insecta/Strymon istapa/9bc730bc3cf270c6dd30dcfa10d97303.jpg\",\n  \"462611.jpg\": \"Insecta/Strymon istapa/014191714989e54f4e3b81c91332f2e8.jpg\",\n  \"462612.jpg\": \"Insecta/Strymon istapa/90c00d72fbefe39c0789dbbfe084943d.jpg\",\n  \"462613.jpg\": \"Insecta/Strymon istapa/0d50cae04ae24b5f46c2c095f3d1383f.jpg\",\n  \"462632.jpg\": \"Aves/Spiza americana/d3a39c19236290889974d9bc5633ac9b.jpg\",\n  \"462638.jpg\": \"Aves/Spiza americana/cbce03331416d1d0855b2b8f55bd9a90.jpg\",\n  \"462663.jpg\": \"Aves/Spiza americana/fb6d5c498ebd86b4c1bb473c154e30a4.jpg\",\n  \"462674.jpg\": \"Aves/Spiza americana/b93006e77f57f38c9037b222075cc1cf.jpg\",\n  \"462681.jpg\": \"Aves/Spiza americana/e8778e365bd6c570b7bfe876698e3905.jpg\",\n  \"462866.jpg\": \"Insecta/Apodemia virgulti/180cebc92aa9bdf6548f9e4ed444fb3f.jpg\",\n  \"462872.jpg\": \"Insecta/Apodemia virgulti/71735eb8e2e4670c0f33637854c57b16.jpg\",\n  \"462875.jpg\": \"Insecta/Apodemia virgulti/6ebc9038b194ef33e4276da2740094e7.jpg\",\n  \"462883.jpg\": \"Insecta/Apodemia virgulti/532c0fb531651f8ea3863870c77d595f.jpg\",\n  \"462916.jpg\": \"Mollusca/Katharina tunicata/c48ea318e02214da14e41bd15b31f966.jpg\",\n  \"462938.jpg\": \"Reptilia/Coluber constrictor constrictor/7d286ef2ec617c0753325b81a7df317a.jpg\",\n  \"462943.jpg\": \"Reptilia/Coluber constrictor constrictor/03e2d46384dfb23146a66caec423240e.jpg\",\n  \"462946.jpg\": \"Reptilia/Coluber constrictor constrictor/ae6924f29936ccabb6d96fc573275fbf.jpg\",\n  \"462949.jpg\": \"Reptilia/Coluber constrictor constrictor/4eaa8ff38acf330a7f8cf57d277aa68a.jpg\",\n  \"462951.jpg\": \"Aves/Cepphus columba/6b8064845cf5491fd2cd610066136ed5.jpg\",\n  \"462953.jpg\": \"Aves/Cepphus columba/5bedbc4722d07c3dd708bf4114a38ea2.jpg\",\n  \"462955.jpg\": \"Aves/Cepphus columba/f8025bd211aedb4f7bf0692e7365f914.jpg\",\n  \"462964.jpg\": \"Aves/Cepphus columba/50b80796d8e3d9b29179a9a4d367447d.jpg\",\n  \"462978.jpg\": \"Aves/Cepphus columba/4719773e2f9cc649f5eaed7d4379f3e2.jpg\",\n  \"462982.jpg\": \"Aves/Cepphus columba/264fb025bd861acd8dd7e85f3bbe7cea.jpg\",\n  \"462992.jpg\": \"Aves/Cepphus columba/90292a5c2be9004e07d5a9acc3f8c107.jpg\",\n  \"462995.jpg\": \"Aves/Cepphus columba/20ff2596b537e68b82dde1e7eb258362.jpg\",\n  \"463005.jpg\": \"Aves/Leptotila verreauxi/db33f6062f1b9b865da79dd9c5a1c550.jpg\",\n  \"463006.jpg\": \"Aves/Leptotila verreauxi/39c9ae7b52ca73c2ca81acc687f4e16b.jpg\",\n  \"463007.jpg\": \"Aves/Leptotila verreauxi/857a88d3e5070d0a95dafebf093e8f52.jpg\",\n  \"463010.jpg\": \"Aves/Leptotila verreauxi/426ca4b7a59945056110d94b38d76733.jpg\",\n  \"463013.jpg\": \"Aves/Leptotila verreauxi/3d3fb885a1079c2fed741bef8fdcc91e.jpg\",\n  \"463016.jpg\": \"Aves/Leptotila verreauxi/713d8a209db946290a3a52e685efe54a.jpg\",\n  \"463017.jpg\": \"Aves/Leptotila verreauxi/9055017a74bb1e652316bc3b46be8b03.jpg\",\n  \"463027.jpg\": \"Aves/Leptotila verreauxi/9eb0ac0ee70a72bc6098c272159ed10d.jpg\",\n  \"463030.jpg\": \"Aves/Leptotila verreauxi/65967940f9c867758af099dfd7d78d71.jpg\",\n  \"463033.jpg\": \"Aves/Leptotila verreauxi/1339763276485b55061889c7343c72c4.jpg\",\n  \"463057.jpg\": \"Reptilia/Sceloporus spinosus/cf406a2476a399ba6f7ca1babe111dc1.jpg\",\n  \"463064.jpg\": \"Reptilia/Sceloporus spinosus/8d4207de3a48260f59e6b2ffbce46c92.jpg\",\n  \"463095.jpg\": \"Mammalia/Lavia frons/2d7109075ae189ce4cc5e702ab6405d3.jpg\",\n  \"463099.jpg\": \"Mammalia/Lavia frons/14594f5ee4132253a321958ba09dbcb4.jpg\",\n  \"46324.jpg\": \"Aves/Ardea herodias/68dd50685f3078792f233f5f398b2e4a.jpg\",\n  \"463328.jpg\": \"Insecta/Besma quercivoraria/076f951ae2cb4e1e092ae26509fbbb94.jpg\",\n  \"463382.jpg\": \"Aves/Tyto alba/d3576f8f99267b0c0cb74cac880fd35f.jpg\",\n  \"463392.jpg\": \"Aves/Tyto alba/8f8b2953b2c4ea210e29115664d49e91.jpg\",\n  \"463406.jpg\": \"Aves/Tyto alba/f2c8f017f102be7fc62dd3c874c9b6b7.jpg\",\n  \"463426.jpg\": \"Aves/Tyto alba/837adbea98e94670b0e5ef4daf632e85.jpg\",\n  \"463427.jpg\": \"Aves/Tyto alba/bf8d7215b5328335657475f587ac72cc.jpg\",\n  \"463428.jpg\": \"Aves/Tyto alba/a2ec16efce810426483a55dfd044eea2.jpg\",\n  \"463435.jpg\": \"Aves/Tyto alba/b66d8f0b11dd011b3ca141f1ff6f43e2.jpg\",\n  \"463442.jpg\": \"Aves/Tyto alba/b33d6fb27d422326b1228fbcc2eb063f.jpg\",\n  \"463527.jpg\": \"Insecta/Xenox tigrinus/0df6fd48415ccc0bc9d2e780f0e91739.jpg\",\n  \"463531.jpg\": \"Insecta/Xenox tigrinus/1c227baaf0e97ca4bc47fdf883427eab.jpg\",\n  \"463532.jpg\": \"Insecta/Xenox tigrinus/f4b95ea5c19f52c68d8a5b7e8adb3d4c.jpg\",\n  \"463533.jpg\": \"Insecta/Xenox tigrinus/e08126aa1a01316f61fbbbb4fc97cb9b.jpg\",\n  \"463534.jpg\": \"Insecta/Xenox tigrinus/74412667597b9cd2852ad3f650532bbc.jpg\",\n  \"463536.jpg\": \"Insecta/Xenox tigrinus/011b35173d6deb384316876ef80e8cf2.jpg\",\n  \"463544.jpg\": \"Insecta/Xenox tigrinus/9d2b0d96b2496f68636de0e6e995927f.jpg\",\n  \"463546.jpg\": \"Insecta/Xenox tigrinus/4688c9201e06a149a411cdda1feb39a9.jpg\",\n  \"463547.jpg\": \"Insecta/Xenox tigrinus/58dc07151afe2b51820f227c088ea3bb.jpg\",\n  \"463554.jpg\": \"Mammalia/Lasiurus cinereus/42d6ca417ab696ecd7896ce4283f0881.jpg\",\n  \"463617.jpg\": \"Insecta/Lophocampa maculata/005535a360292b9152c411718ba4f113.jpg\",\n  \"463618.jpg\": \"Insecta/Lophocampa maculata/a61cf5173c166ccbef5a29cb52857161.jpg\",\n  \"463621.jpg\": \"Insecta/Lophocampa maculata/84a0735968a8a08fc290d2f9857614f3.jpg\",\n  \"463624.jpg\": \"Insecta/Lophocampa maculata/31071591d5c0f7f822263fea9855bf13.jpg\",\n  \"463631.jpg\": \"Insecta/Lophocampa maculata/8465e3c92417465ccf1659f30a9fe1c9.jpg\",\n  \"463633.jpg\": \"Insecta/Lophocampa maculata/d19f25714b8c83a09c763b953ded40d5.jpg\",\n  \"463634.jpg\": \"Insecta/Lophocampa maculata/c8693ab9ff3b77c5cfb621511e56f4ab.jpg\",\n  \"463635.jpg\": \"Insecta/Lophocampa maculata/62c47b653094b4a96222b4e274ac1629.jpg\",\n  \"463636.jpg\": \"Insecta/Lophocampa maculata/8f076c413ee5c72d44757522c32e188e.jpg\",\n  \"463655.jpg\": \"Aves/Chaetura pelagica/8b478259f7b69ba15daed9b3fff5a903.jpg\",\n  \"463670.jpg\": \"Aves/Chaetura pelagica/e11d6928f37aca56ecceaca3e1b8d1e6.jpg\",\n  \"463671.jpg\": \"Aves/Chaetura pelagica/1d15bf41fc25df713744a1dd4cb474ce.jpg\",\n  \"463672.jpg\": \"Aves/Chaetura pelagica/96d2472725a737a9dc214121af871815.jpg\",\n  \"463675.jpg\": \"Aves/Chaetura pelagica/a5afe55a5f345541ebea94479295d792.jpg\",\n  \"463680.jpg\": \"Aves/Chaetura pelagica/a3efe9a644fa6d748b23453fb4d61956.jpg\",\n  \"463683.jpg\": \"Aves/Chaetura pelagica/fdc318bc28c70a9d8213f2b6dacd58da.jpg\",\n  \"463806.jpg\": \"Insecta/Zelus luridus/95c66bae36e18ba4c33c826c02c247f9.jpg\",\n  \"463807.jpg\": \"Insecta/Zelus luridus/241b5d665acf7c58b8f1acca9f9556ae.jpg\",\n  \"463808.jpg\": \"Insecta/Zelus luridus/b9105eaa593f75ef91a08d88c8643af4.jpg\",\n  \"463809.jpg\": \"Insecta/Zelus luridus/ad9471f3ea67b996bb16015bf0d2c7ad.jpg\",\n  \"463812.jpg\": \"Insecta/Zelus luridus/7877fc8efbb9b733b1760c6194008ec9.jpg\",\n  \"463813.jpg\": \"Insecta/Zelus luridus/f9ca20aeb37a681ef6a2c4c0f4128797.jpg\",\n  \"463815.jpg\": \"Insecta/Zelus luridus/2b2f60c8ff904fea4a950858632d0dfb.jpg\",\n  \"463822.jpg\": \"Insecta/Zelus luridus/bc411e31efb46e6ec831abad1f958ec9.jpg\",\n  \"463823.jpg\": \"Insecta/Zelus luridus/093deadaa20313087499ecfe99bdc19a.jpg\",\n  \"463964.jpg\": \"Aves/Erithacus rubecula/afab3ee084b9fa09fb94c4b76187f08a.jpg\",\n  \"464015.jpg\": \"Aves/Erithacus rubecula/8ab71f30cd0e37be1b4b293f63739875.jpg\",\n  \"464025.jpg\": \"Aves/Erithacus rubecula/3ff82ad46d160e495bdfdb2700484808.jpg\",\n  \"464052.jpg\": \"Aves/Erithacus rubecula/ef32d9be8253a7614dea39028fb71362.jpg\",\n  \"464063.jpg\": \"Aves/Erithacus rubecula/97093ece20664c9c2251cc70f5ec7440.jpg\",\n  \"4641.jpg\": \"Insecta/Carabus nemoralis/3c60c7795ff3abde46df45c468e576e2.jpg\",\n  \"464114.jpg\": \"Aves/Nycticorax nycticorax/b15e77f39a17394347f3f5e2fc4e55d0.jpg\",\n  \"464158.jpg\": \"Aves/Nycticorax nycticorax/ab2800c2d7f0f85964c2d6d7b05748f1.jpg\",\n  \"464189.jpg\": \"Aves/Nycticorax nycticorax/2b83bd58cb6c9b4e710456aebffcdb5f.jpg\",\n  \"464511.jpg\": \"Aves/Nycticorax nycticorax/035a9cd2c66e9af269f73b1ec93b6108.jpg\",\n  \"464525.jpg\": \"Aves/Nycticorax nycticorax/4ff7435462698a738a4bac635c92526f.jpg\",\n  \"464590.jpg\": \"Aves/Tringa ochropus/20909d60c98ee0b60f3b4754a3b26b89.jpg\",\n  \"464598.jpg\": \"Aves/Tringa ochropus/9af6a011822accb2ec6a86d74a528f03.jpg\",\n  \"4646.jpg\": \"Insecta/Anticarsia gemmatalis/820ed660367d7a154f5564addc5c6f94.jpg\",\n  \"464758.jpg\": \"Arachnida/Argiope trifasciata/0aadf58aa17ea1c1bbc9eb0558f3607d.jpg\",\n  \"464761.jpg\": \"Arachnida/Argiope trifasciata/2a404808876996a708e6105a9de3f0bf.jpg\",\n  \"464765.jpg\": \"Arachnida/Argiope trifasciata/7159a4cbc3a095c78071bcbb17a76485.jpg\",\n  \"464775.jpg\": \"Arachnida/Argiope trifasciata/8062e03a9a02891d7137ac930e9e628e.jpg\",\n  \"464798.jpg\": \"Arachnida/Argiope trifasciata/6ace5bc35795649d9413ae3e9030dbb2.jpg\",\n  \"464865.jpg\": \"Insecta/Anteos maerula/aa095614c68f89ca480d79ff2c9f378b.jpg\",\n  \"464919.jpg\": \"Aves/Setophaga palmarum/54bb1cb0e57125acc8c4e0dcef589cba.jpg\",\n  \"464936.jpg\": \"Aves/Setophaga palmarum/faeec19ade62d02aa8ee6a61395a3e5f.jpg\",\n  \"464969.jpg\": \"Aves/Setophaga palmarum/f242eae1c32dd10cd6f0cc7c4e845931.jpg\",\n  \"4650.jpg\": \"Insecta/Anticarsia gemmatalis/8ec9f4e1f2deb2108b6624e9c82a068b.jpg\",\n  \"465002.jpg\": \"Aves/Setophaga palmarum/17645df7c484ea5c324c8593f487b3df.jpg\",\n  \"465016.jpg\": \"Aves/Setophaga palmarum/bdc31604b11a84dd7c7c26c720d0386a.jpg\",\n  \"465044.jpg\": \"Insecta/Hypena scabra/a08e603635217ae814b3844129feb671.jpg\",\n  \"465050.jpg\": \"Insecta/Hypena scabra/e06612ca7adb438e6bb5c6e0baa721af.jpg\",\n  \"465076.jpg\": \"Insecta/Hypena scabra/b814a43639974f142d1587ab446b7d16.jpg\",\n  \"465095.jpg\": \"Insecta/Hypena scabra/eefafac3e04747f7bef6278a6cacb78f.jpg\",\n  \"4651.jpg\": \"Insecta/Anticarsia gemmatalis/7d6ba55441871b723109dff335191261.jpg\",\n  \"465124.jpg\": \"Insecta/Calephelis nemesis/545678af985df0679afc966bf50391f6.jpg\",\n  \"465130.jpg\": \"Insecta/Calephelis nemesis/1c148c4f3470a9f9e3e7373a3097d8d4.jpg\",\n  \"465241.jpg\": \"Aves/Anas cyanoptera/d761216b1312562d79b73c9a464dbe92.jpg\",\n  \"4654.jpg\": \"Insecta/Anticarsia gemmatalis/42692bea61606b6d9982b7121f691dec.jpg\",\n  \"4655.jpg\": \"Insecta/Anticarsia gemmatalis/45f42746a7e83c6c5cadc6b400618bfa.jpg\",\n  \"4656.jpg\": \"Insecta/Anticarsia gemmatalis/81b1b5f33dfb143dee4ef902b56363df.jpg\",\n  \"465734.jpg\": \"Aves/Quiscalus mexicanus/9af16e80efcbfe5f97ff294c13729968.jpg\",\n  \"4658.jpg\": \"Insecta/Anticarsia gemmatalis/52086bb7f6e8482045b18bb11938da6a.jpg\",\n  \"465824.jpg\": \"Aves/Quiscalus mexicanus/ccfeae0a8c663119f07cba595156a539.jpg\",\n  \"465840.jpg\": \"Aves/Quiscalus mexicanus/fd3864c6e20f2574f28eabd38193f64c.jpg\",\n  \"466137.jpg\": \"Aves/Quiscalus mexicanus/2908d4b36edb57b7dd64609d896e52d1.jpg\",\n  \"466155.jpg\": \"Aves/Quiscalus mexicanus/3a4efb7c6897306ee4123b7644abc6e0.jpg\",\n  \"466208.jpg\": \"Aves/Quiscalus mexicanus/297fc3d4ca3689b9ff73685cf4f4236b.jpg\",\n  \"466266.jpg\": \"Aves/Colaptes auratus auratus/b6f4dbf79a5d3e592652ba18d223dac5.jpg\",\n  \"466285.jpg\": \"Insecta/Nomophila nearctica/7fd32d414c3f56cea1a56ebf5033e5dc.jpg\",\n  \"466297.jpg\": \"Insecta/Nomophila nearctica/e755a0e099dfb8319af69ede8788689b.jpg\",\n  \"466319.jpg\": \"Insecta/Nomophila nearctica/332b90dab46733dd8adcb052d9f6931d.jpg\",\n  \"466323.jpg\": \"Insecta/Nomophila nearctica/211951d27b636a59c4f53001f03fa0dd.jpg\",\n  \"466326.jpg\": \"Insecta/Nomophila nearctica/4f2cc2d62063c778640cbdb5db234b42.jpg\",\n  \"466336.jpg\": \"Reptilia/Sceloporus cowlesi/301db4e1a06246a516945147b0f38e2d.jpg\",\n  \"466337.jpg\": \"Reptilia/Sceloporus cowlesi/c082b5047f755d8621842a344e9f1d85.jpg\",\n  \"466338.jpg\": \"Reptilia/Sceloporus cowlesi/8ae31f4f76c0e30df979782daf280acd.jpg\",\n  \"466340.jpg\": \"Reptilia/Sceloporus cowlesi/f294a26a6e713c229f746d41fd139e42.jpg\",\n  \"466341.jpg\": \"Reptilia/Sceloporus cowlesi/93fb95bd1f42ed3768d6230c09a0a248.jpg\",\n  \"466342.jpg\": \"Reptilia/Sceloporus cowlesi/f3aaf784c2ac507d42d45306a27d087d.jpg\",\n  \"466344.jpg\": \"Reptilia/Sceloporus cowlesi/596753c17f5bc3a0a050be220d4857ff.jpg\",\n  \"466352.jpg\": \"Reptilia/Sceloporus cowlesi/c72439b69b7484bd1d5765ecbf8733c5.jpg\",\n  \"466353.jpg\": \"Reptilia/Sceloporus cowlesi/74109ccdb048f79ced01d806f8165dc9.jpg\",\n  \"466373.jpg\": \"Aves/Passerina amoena/94eb0a4c2597d89f5570ba95fc319e87.jpg\",\n  \"466377.jpg\": \"Aves/Passerina amoena/19df2982080e8b20d46c4844b971bd71.jpg\",\n  \"466386.jpg\": \"Aves/Passerina amoena/a028e814bde1ab1190440deaf559891f.jpg\",\n  \"466397.jpg\": \"Aves/Passerina amoena/d1168be78182178a36795c43eddd6887.jpg\",\n  \"466402.jpg\": \"Aves/Passerina amoena/db5f543b2ba291c8c342344315744d08.jpg\",\n  \"466405.jpg\": \"Aves/Passerina amoena/4b19b5ee33722f6a5c66c1886ae84525.jpg\",\n  \"466412.jpg\": \"Insecta/Halysidota harrisii/73cb6ea03163028249fcb898596fa681.jpg\",\n  \"466456.jpg\": \"Mammalia/Ursus americanus californiensis/dc288c990d626e3692a10d393cb43481.jpg\",\n  \"466494.jpg\": \"Aves/Spinus pinus/f1e51484b6c69fc76e100c5e334a8106.jpg\",\n  \"466514.jpg\": \"Aves/Spinus pinus/4c85ef75c089f1cdbabae878fdaabb31.jpg\",\n  \"466527.jpg\": \"Aves/Spinus pinus/51d172044f11efa7d6f65fd90c3f752e.jpg\",\n  \"466533.jpg\": \"Aves/Spinus pinus/7b83c2eaf66c3b7b6e81bc356451a1d9.jpg\",\n  \"466584.jpg\": \"Aves/Spinus pinus/9040637e37b350a2004dbcf80efd90b2.jpg\",\n  \"466607.jpg\": \"Aves/Spinus pinus/00a52bef4c6a11dc63f37feee08130ac.jpg\",\n  \"46661.jpg\": \"Aves/Ardea herodias/302be129f1232768dc7efeff1cc87d00.jpg\",\n  \"466625.jpg\": \"Insecta/Autographa precationis/1cbde872c3e534d1196b501a1096262d.jpg\",\n  \"466635.jpg\": \"Insecta/Autographa precationis/6fd5af7b5c497e9d4aa65a61b5ef6048.jpg\",\n  \"466636.jpg\": \"Insecta/Autographa precationis/e41a7c2d83caa4cd89d61c684591cee8.jpg\",\n  \"466644.jpg\": \"Aves/Setophaga castanea/7fcb0e659c485fa4d374080e7cc70c4a.jpg\",\n  \"466651.jpg\": \"Aves/Setophaga castanea/a2dac260d55066930267ac9938191541.jpg\",\n  \"466656.jpg\": \"Aves/Setophaga castanea/5df3213074d7b67431753baa8ed1decb.jpg\",\n  \"466657.jpg\": \"Aves/Setophaga castanea/1bc709ce70188c2b7b5188c13448aba0.jpg\",\n  \"466658.jpg\": \"Aves/Setophaga castanea/5ac2a947cee296738dc9793275cbbf16.jpg\",\n  \"4668.jpg\": \"Insecta/Anticarsia gemmatalis/4c08484fac27fa7c0362531841a3801e.jpg\",\n  \"466919.jpg\": \"Insecta/Homophoberia apicosa/d56850fe986481fadcb92c038081f91b.jpg\",\n  \"466924.jpg\": \"Insecta/Homophoberia apicosa/f1c3cec4c796170343024286eb6e443f.jpg\",\n  \"466960.jpg\": \"Insecta/Megalopyge opercularis/d6977876e738003b16e22fdd1e3ec7e7.jpg\",\n  \"466966.jpg\": \"Insecta/Megalopyge opercularis/bdf31fd903072d0e51b1263b604b06be.jpg\",\n  \"467030.jpg\": \"Insecta/Iridopsis larvaria/739be529c435a95e11557f36bfd75fe9.jpg\",\n  \"467041.jpg\": \"Insecta/Iridopsis larvaria/c76837ef9ee33bb1ef37ba6f7bf23a67.jpg\",\n  \"467070.jpg\": \"Insecta/Aeshna cyanea/cd888f15bd8e46a4189448013baea25f.jpg\",\n  \"467072.jpg\": \"Insecta/Aeshna cyanea/8093f0bd370383485f30f78730113de3.jpg\",\n  \"467073.jpg\": \"Insecta/Aeshna cyanea/bcf0ac89ccd74d565ac10747220aeb1b.jpg\",\n  \"467074.jpg\": \"Insecta/Aeshna cyanea/ea9c5c5b3980a8a8d9ef2ded4b944c24.jpg\",\n  \"467075.jpg\": \"Insecta/Aeshna cyanea/28720dcf168096ded243dbdc3f7bf9a7.jpg\",\n  \"467079.jpg\": \"Insecta/Aeshna cyanea/80d5785da2d638ffcbfce7e13ec79163.jpg\",\n  \"46711.jpg\": \"Aves/Ardea herodias/cf9966e0fede8438dd761ad7db6083a1.jpg\",\n  \"467133.jpg\": \"Insecta/Heterophleps triguttaria/f5523a1db22c4b2947b7f81e00ae552a.jpg\",\n  \"467139.jpg\": \"Insecta/Heterophleps triguttaria/9122e4d39039f3f6767d3f7dee523620.jpg\",\n  \"467145.jpg\": \"Aves/Setophaga cerulea/257d2dbf8cf3e62e915c9b90848948cd.jpg\",\n  \"467156.jpg\": \"Animalia/Philoscia muscorum/027ca87f90959dceda72184ec6a0f70f.jpg\",\n  \"467271.jpg\": \"Insecta/Deinacrida rugosa/0addb9d8a74964ba526555d43697bd96.jpg\",\n  \"467273.jpg\": \"Insecta/Deinacrida rugosa/f9b9b6968c92b0ab402cbbd4105b5e95.jpg\",\n  \"467278.jpg\": \"Aves/Thalasseus bergii/3ff7a4f1a5d695aa7d367882ec4e95f0.jpg\",\n  \"467367.jpg\": \"Insecta/Calopteryx maculata/b11daf96dc01ef33b84c344949cbbe47.jpg\",\n  \"467381.jpg\": \"Insecta/Calopteryx maculata/759c98346ba06f5da2d75209fa341fbf.jpg\",\n  \"467401.jpg\": \"Insecta/Calopteryx maculata/7d4203c12ba610a19de90d2e3e192eeb.jpg\",\n  \"467469.jpg\": \"Insecta/Calopteryx maculata/cc9727dea8acb09c8182a0e79257abf4.jpg\",\n  \"467473.jpg\": \"Insecta/Calopteryx maculata/1f8964f512b6242e435e131dc35adcdb.jpg\",\n  \"467482.jpg\": \"Insecta/Calopteryx maculata/f1983b8d4bf5f7e6ef9f15ee544bb8dc.jpg\",\n  \"467494.jpg\": \"Insecta/Calopteryx maculata/a419a6dc0814d3b2f4cff69a82ab6953.jpg\",\n  \"467786.jpg\": \"Insecta/Cicindela sexguttata/ad6682fda4925de60a2abd2e0dc49fa6.jpg\",\n  \"467787.jpg\": \"Insecta/Cicindela sexguttata/c926be809f8735716697adc13bc6a2f8.jpg\",\n  \"467800.jpg\": \"Insecta/Cicindela sexguttata/e7acec1475c2de838edbf48180982337.jpg\",\n  \"467815.jpg\": \"Insecta/Cicindela sexguttata/063c048dadf1935b8f307373f3afcef5.jpg\",\n  \"467824.jpg\": \"Insecta/Cicindela sexguttata/37ce9a298b2aff8dcde8413ce5973824.jpg\",\n  \"467848.jpg\": \"Insecta/Cicindela sexguttata/14e7b78cef982cce0c3ce080d9e1625b.jpg\",\n  \"467895.jpg\": \"Insecta/Cicindela sexguttata/72fe770f5e1e0caba870f2c33316c361.jpg\",\n  \"467915.jpg\": \"Mammalia/Trichosurus vulpecula/b73a2b57a0570e7caded558848ce8b10.jpg\",\n  \"467920.jpg\": \"Mammalia/Trichosurus vulpecula/f47b056fa87a6bc41804b945b76135bc.jpg\",\n  \"467924.jpg\": \"Mammalia/Trichosurus vulpecula/94a96cb159f86dce4275a4f2fc08c176.jpg\",\n  \"467935.jpg\": \"Mammalia/Trichosurus vulpecula/a512e3261d7c9077023e0f4fcbb3691e.jpg\",\n  \"46805.jpg\": \"Aves/Ardea herodias/0da77a6fa1874413a9eaf0317cbc6b42.jpg\",\n  \"468211.jpg\": \"Reptilia/Sceloporus occidentalis/b3bb2527df33a9b00adf1274edd67c06.jpg\",\n  \"468216.jpg\": \"Reptilia/Sceloporus occidentalis/a1e1d075f0037284a3f52c4b44487e3b.jpg\",\n  \"46836.jpg\": \"Aves/Ardea herodias/f6dea2066fb1c4d2300ad988f6f3695c.jpg\",\n  \"468511.jpg\": \"Reptilia/Sceloporus occidentalis/befcafc1bea393ea7797939f9eff7831.jpg\",\n  \"46858.jpg\": \"Aves/Ardea herodias/7dd539398bc2d1fb8c9e5d05bb2e1c2e.jpg\",\n  \"46890.jpg\": \"Aves/Ardea herodias/60641ec87826049320cc26c07fefedc6.jpg\",\n  \"468983.jpg\": \"Reptilia/Sceloporus occidentalis/7c5db8e4d0f06e80b8d306cb55793186.jpg\",\n  \"469444.jpg\": \"Insecta/Buprestis aurulenta/c8f3ea6bed7f9d9f423edb1b36247729.jpg\",\n  \"469457.jpg\": \"Insecta/Ladona depressa/91da09e9307ab5175961e18a9239a3ef.jpg\",\n  \"469465.jpg\": \"Insecta/Ladona depressa/ff730071a73e5667339a011f6f4a54da.jpg\",\n  \"469515.jpg\": \"Reptilia/Scincella lateralis/035816a6a936d291550731254a81eeb3.jpg\",\n  \"469532.jpg\": \"Reptilia/Scincella lateralis/78ec57769dfd6c703e4fccdf8e962e2d.jpg\",\n  \"469563.jpg\": \"Reptilia/Scincella lateralis/fd8a67263ddb1f5bdea3e2f13e782557.jpg\",\n  \"469578.jpg\": \"Reptilia/Scincella lateralis/b9a97ae8cff8283cebea3240ca2451e8.jpg\",\n  \"469584.jpg\": \"Reptilia/Scincella lateralis/cdaffde277ae3d016169d8ec9a2b345c.jpg\",\n  \"469659.jpg\": \"Aves/Rallus crepitans/089072f112055e4c89428fb8a6f27803.jpg\",\n  \"469662.jpg\": \"Aves/Rallus crepitans/ac74ba0233191334eee7a994e0ffcda8.jpg\",\n  \"469663.jpg\": \"Aves/Rallus crepitans/b068c4a0f1fb4849ba8719eadf310731.jpg\",\n  \"469777.jpg\": \"Mollusca/Sypharochiton pelliserpentis/9900b8f958537edb5fabefed2704c974.jpg\",\n  \"469843.jpg\": \"Insecta/Protodeltote muscosula/3e4a9d112b5d30109ccf71bfe1bb11b2.jpg\",\n  \"469898.jpg\": \"Insecta/Sitochroa palealis/a7d38c2a99529611338eee4c161544ef.jpg\",\n  \"469899.jpg\": \"Insecta/Sitochroa palealis/0b4d9710d32ce921cfc6a1462db97374.jpg\",\n  \"469917.jpg\": \"Insecta/Pyrausta acrionalis/fe596c7fe178873df3ff4a7efbb78932.jpg\",\n  \"469928.jpg\": \"Aves/Sterna paradisaea/f58fcdeca7b2f56ce2f69d81f567be7c.jpg\",\n  \"469929.jpg\": \"Aves/Sterna paradisaea/6b9e91ea47328afb0eea66e96aa6412c.jpg\",\n  \"469934.jpg\": \"Aves/Sterna paradisaea/4d5cf402a14ef141610f4802a5eded05.jpg\",\n  \"469935.jpg\": \"Aves/Sterna paradisaea/ee7c761131429142862d42da60edbb60.jpg\",\n  \"469936.jpg\": \"Aves/Sterna paradisaea/59071c79c881843571f7e1498e00a190.jpg\",\n  \"469937.jpg\": \"Aves/Sterna paradisaea/153a97548b57913b0d8117ff55d9f35a.jpg\",\n  \"469943.jpg\": \"Aves/Sterna paradisaea/49142ba08c0b926b1153094c54fe1311.jpg\",\n  \"470047.jpg\": \"Aves/Ardea cinerea/1e1592a7fa4d5aeeaae39a4e37b259b1.jpg\",\n  \"47007.jpg\": \"Aves/Ardea herodias/a311f4cb02deb2fec761483a65b5c7b6.jpg\",\n  \"470097.jpg\": \"Aves/Ardea cinerea/82d453f6f82bbbcd2467432d4e843af8.jpg\",\n  \"470101.jpg\": \"Aves/Ardea cinerea/1ccccef0df416c73bfa01aa50a41f6af.jpg\",\n  \"470128.jpg\": \"Aves/Ardea cinerea/621205e922e048f6b6f71d8bcef283f2.jpg\",\n  \"470145.jpg\": \"Aves/Ardea cinerea/27b444db1173b01581f1bdffbc0034ef.jpg\",\n  \"470161.jpg\": \"Aves/Ardea cinerea/c4b461098949d3277bf66b405cbbdb32.jpg\",\n  \"470175.jpg\": \"Insecta/Rutpela maculata/f7b7749fd04d8861c383f0d42491a7bf.jpg\",\n  \"470227.jpg\": \"Animalia/Narceus annularus/dc9c8a5ec149900d6c15598405e7e128.jpg\",\n  \"470232.jpg\": \"Animalia/Narceus annularus/1bbe128936315b6f9cf42bcf3861b956.jpg\",\n  \"470292.jpg\": \"Reptilia/Micrurus tener/cdd2271c1f86adb63a1b3f09a8b3e5d7.jpg\",\n  \"470299.jpg\": \"Reptilia/Micrurus tener/c7ba942ec1fd5868714f3d26909881d4.jpg\",\n  \"470302.jpg\": \"Reptilia/Micrurus tener/6be87843d4e5a2b87bc5224006001930.jpg\",\n  \"470304.jpg\": \"Reptilia/Micrurus tener/172edfcb8391e4dda991058a0a7b7d65.jpg\",\n  \"470316.jpg\": \"Reptilia/Micrurus tener/173b4c976d8e93a3392be91e48c91f74.jpg\",\n  \"470336.jpg\": \"Aves/Calidris ferruginea/5b8db413a26746112d0ef9b3b1cecc19.jpg\",\n  \"470354.jpg\": \"Insecta/Papilio polytes/fea4409ec71a00607d198fb0c18ba063.jpg\",\n  \"470739.jpg\": \"Aves/Charadrius vociferus/3b316a7448739f81ce57d2b176d9eb4c.jpg\",\n  \"470782.jpg\": \"Aves/Charadrius vociferus/7f7f7efe8020dd1100e6b1d64b10559e.jpg\",\n  \"470854.jpg\": \"Aves/Charadrius vociferus/dae2e919fc14e42e23e25d51292a0c17.jpg\",\n  \"470937.jpg\": \"Amphibia/Desmognathus fuscus/1477d8b387f5ad475b57930fba9c2ac1.jpg\",\n  \"470944.jpg\": \"Amphibia/Desmognathus fuscus/605a0bac10392b208615981e5de97ca5.jpg\",\n  \"470945.jpg\": \"Amphibia/Desmognathus fuscus/bbc87ac9c80dae5ebc99b448d1156b5b.jpg\",\n  \"470947.jpg\": \"Amphibia/Desmognathus fuscus/554fd1f8ebf5043ff49d63f69d1ccc8f.jpg\",\n  \"470952.jpg\": \"Amphibia/Desmognathus fuscus/68c72296affdbb57db1ce9743067c6cb.jpg\",\n  \"470953.jpg\": \"Amphibia/Desmognathus fuscus/5fae4ad080d934e60a99fbf735f9774f.jpg\",\n  \"470956.jpg\": \"Amphibia/Desmognathus fuscus/344eedc0209637664de9599cbd8f16e4.jpg\",\n  \"470966.jpg\": \"Amphibia/Desmognathus fuscus/df42a3edd522f9be8a4d87f238fa45be.jpg\",\n  \"470976.jpg\": \"Mammalia/Marmota marmota/27c9b608a7faf311e78433ac488a569d.jpg\",\n  \"470991.jpg\": \"Insecta/Bombus terrestris/6efe91c48ddfd606bbdcffa788d9e65e.jpg\",\n  \"470994.jpg\": \"Insecta/Bombus terrestris/8492ba2bd7de279a0567b7873b19f8bf.jpg\",\n  \"471000.jpg\": \"Insecta/Bombus terrestris/44d584397e6d989b6bbd7c8a057a1c10.jpg\",\n  \"471007.jpg\": \"Insecta/Bombus terrestris/2d7444d9629acf57b8b35d1ba07087ff.jpg\",\n  \"471024.jpg\": \"Insecta/Bombus terrestris/a5c2501a44a3294afc178cfc70cfbd31.jpg\",\n  \"471039.jpg\": \"Insecta/Bombus terrestris/50a7ddca51cf5fbbb96c78db22320f54.jpg\",\n  \"471048.jpg\": \"Reptilia/Agkistrodon contortrix/5efe0e1c578cab6381b836d3854cd108.jpg\",\n  \"471070.jpg\": \"Reptilia/Agkistrodon contortrix/d10c4c8f6326add5a7c69bf9f74bb190.jpg\",\n  \"471074.jpg\": \"Reptilia/Agkistrodon contortrix/afdc5e0065eed2c0d2426f9c188fa837.jpg\",\n  \"471075.jpg\": \"Reptilia/Agkistrodon contortrix/79754cd69a880c649041deaeef788f6f.jpg\",\n  \"471079.jpg\": \"Reptilia/Agkistrodon contortrix/0f5ae41ec709e36e9af5efa8ef28cfe2.jpg\",\n  \"471090.jpg\": \"Reptilia/Agkistrodon contortrix/f7d2fab0bb50b39a7489b5e4b11ff370.jpg\",\n  \"471110.jpg\": \"Reptilia/Agkistrodon contortrix/9730411f048b6fb2b04a74967769227e.jpg\",\n  \"471117.jpg\": \"Reptilia/Agkistrodon contortrix/67c4236887f150ab2ce70962bf80d5a9.jpg\",\n  \"471163.jpg\": \"Insecta/Pedicia albivitta/46cbaea65d99cbb259656368d93c9f9f.jpg\",\n  \"471202.jpg\": \"Aves/Jabiru mycteria/fd9a4f3f537c27f477ef0a552690eedc.jpg\",\n  \"47121.jpg\": \"Aves/Ardea herodias/4154579f8a1a1d823e0ef1fc1f8be33a.jpg\",\n  \"471279.jpg\": \"Aves/Saxicola rubetra/5eab6a39e1876fd9e75a988016c3890f.jpg\",\n  \"471282.jpg\": \"Aves/Saxicola rubetra/7a6d137a74482271def41dcdb330ef59.jpg\",\n  \"471292.jpg\": \"Aves/Saxicola rubetra/6a0e6fbffa4072100b322e7b5cf8a8a7.jpg\",\n  \"471297.jpg\": \"Insecta/Euplagia quadripunctaria/51ee2a3c3982a04bf4923a7e5699c0ce.jpg\",\n  \"471298.jpg\": \"Insecta/Euplagia quadripunctaria/fed8d86240bbd95aefd47535ae898c56.jpg\",\n  \"471299.jpg\": \"Insecta/Euplagia quadripunctaria/39618d66afc9e40b4fb3fdafc878e1e5.jpg\",\n  \"471305.jpg\": \"Insecta/Euplagia quadripunctaria/057cc2c5fb5731784692aaae1baf61eb.jpg\",\n  \"471310.jpg\": \"Insecta/Euplagia quadripunctaria/2012f4cd39cdc87f5a9f90c19ed3bfab.jpg\",\n  \"471349.jpg\": \"Mollusca/Neverita lewisii/f1f52ae17fabe7676055f8bb4d74b641.jpg\",\n  \"471350.jpg\": \"Mollusca/Neverita lewisii/b32860dd2b2aafa276200801351d7b96.jpg\",\n  \"471411.jpg\": \"Insecta/Pyrausta inornatalis/e2c2e64d413ff407700cdb0ab9a07b02.jpg\",\n  \"471437.jpg\": \"Aves/Cyanocitta stelleri/7361921ae9eeb60cab0e1fe0d7fc4eb2.jpg\",\n  \"471452.jpg\": \"Aves/Cyanocitta stelleri/cd0da8060c5f10c8339a8d733d72ee2d.jpg\",\n  \"471463.jpg\": \"Aves/Cyanocitta stelleri/ab77f54c1b5aab2f4b37db0d258b5e64.jpg\",\n  \"471504.jpg\": \"Aves/Cyanocitta stelleri/a9afb38223aaa144785f8b9bf76872ff.jpg\",\n  \"471552.jpg\": \"Aves/Cyanocitta stelleri/0f224ef9f7457f34de461ccad4386cc7.jpg\",\n  \"471555.jpg\": \"Aves/Cyanocitta stelleri/9263e6c1312164aaee0feeb3b6c0d1b2.jpg\",\n  \"471569.jpg\": \"Aves/Cyanocitta stelleri/59d321a2b24bc5aa934c50bdea074607.jpg\",\n  \"471583.jpg\": \"Aves/Cyanocitta stelleri/71d8b1f5c8e6aa80036d2282438cc341.jpg\",\n  \"471595.jpg\": \"Aves/Cyanocitta stelleri/79465f1e276bf594dc1ce7ed22e40647.jpg\",\n  \"471600.jpg\": \"Aves/Cyanocitta stelleri/f021646ca7e2057a4ca75f5d0c65e2c0.jpg\",\n  \"471619.jpg\": \"Aves/Cyanocitta stelleri/bb81427c3eb46e1dda1a23d03b1141a4.jpg\",\n  \"471701.jpg\": \"Insecta/Amorpha juglandis/661908d6ebb8e60231b77880efa76d37.jpg\",\n  \"471702.jpg\": \"Insecta/Amorpha juglandis/794bda78882c18210decc44ebc7b023f.jpg\",\n  \"471704.jpg\": \"Insecta/Amorpha juglandis/45a0844f1b5554599d57ed25b0dfaeac.jpg\",\n  \"471709.jpg\": \"Insecta/Amorpha juglandis/2af24dca95ad5f2788f7eb16f1c2b92d.jpg\",\n  \"471714.jpg\": \"Insecta/Amorpha juglandis/3e3173d14d4f3174a367e10461908988.jpg\",\n  \"471715.jpg\": \"Insecta/Amorpha juglandis/adf9ed064fe6b2a5d595130132a5773f.jpg\",\n  \"471716.jpg\": \"Insecta/Amorpha juglandis/dc677dda05582fad4a88b25377c3ab9e.jpg\",\n  \"471718.jpg\": \"Insecta/Amorpha juglandis/a0b4396e123db48b73df08695e8b2278.jpg\",\n  \"471737.jpg\": \"Aves/Porphyrio martinicus/659e43c5af6e46bbddf1c8170c4f7d4e.jpg\",\n  \"471738.jpg\": \"Aves/Porphyrio martinicus/f608e775a7d69f34f5735a7ce74482f9.jpg\",\n  \"471744.jpg\": \"Aves/Porphyrio martinicus/82dfbc959c8928352f476845ae07ef21.jpg\",\n  \"471748.jpg\": \"Aves/Porphyrio martinicus/81a003a2780021081d541532927ffe1c.jpg\",\n  \"471756.jpg\": \"Aves/Porphyrio martinicus/f4520b06b5d5beb3739fcf547ffa92f7.jpg\",\n  \"471771.jpg\": \"Aves/Porphyrio martinicus/6c4a07f781eff8632f95ad38295d58ad.jpg\",\n  \"471781.jpg\": \"Aves/Porphyrio martinicus/063af7242bee6c89241402e9e9b3b3e4.jpg\",\n  \"471785.jpg\": \"Insecta/Pyromorpha dimidiata/eb68ffa41ecfd8781d78037658d9b49d.jpg\",\n  \"471826.jpg\": \"Insecta/Ceratomia undulosa/4a0544477b7976c5079e9e076d478533.jpg\",\n  \"471827.jpg\": \"Insecta/Ceratomia undulosa/568678e81a0d5f9c3ead10647e593839.jpg\",\n  \"471844.jpg\": \"Insecta/Ceratomia undulosa/ff2012a07101c3faa7f074ff4e1e0ce9.jpg\",\n  \"471860.jpg\": \"Insecta/Ceratomia undulosa/f653eb469fd9951d2bd8a41882144d5c.jpg\",\n  \"471862.jpg\": \"Insecta/Ceratomia undulosa/cf1b16ab2a08fdf9a8710c9c24085c62.jpg\",\n  \"471863.jpg\": \"Insecta/Ceratomia undulosa/60299843f65d0065c6886d597232c377.jpg\",\n  \"471867.jpg\": \"Insecta/Ceratomia undulosa/af34efb1e43f38c3aec46c2cecefecae.jpg\",\n  \"471872.jpg\": \"Insecta/Ceratomia undulosa/4240723ea9468cb61f100f74eb091e01.jpg\",\n  \"471877.jpg\": \"Aves/Buteo lineatus elegans/2cacb41550f04806e114c79807bc5b91.jpg\",\n  \"471920.jpg\": \"Insecta/Mythimna unipuncta/eb568ab295dea7b6f6f8f5c511a94e85.jpg\",\n  \"471927.jpg\": \"Insecta/Mythimna unipuncta/91d8349291e4922987bfcdaa8885b555.jpg\",\n  \"471928.jpg\": \"Insecta/Mythimna unipuncta/d6ffe459a6e3dc38242fd42e48e18100.jpg\",\n  \"471931.jpg\": \"Insecta/Mythimna unipuncta/3c1d2950f2a564d4096352407cd90e40.jpg\",\n  \"471951.jpg\": \"Insecta/Mythimna unipuncta/3cd1bcffcbc409af8949d3b25bcd8f4e.jpg\",\n  \"471953.jpg\": \"Insecta/Mythimna unipuncta/d10acadf17bff487c156e7da0d7847a5.jpg\",\n  \"471973.jpg\": \"Aves/Leucophaeus pipixcan/91e45831bedd1e8526080b7651b3e717.jpg\",\n  \"471974.jpg\": \"Aves/Leucophaeus pipixcan/47754b06c27fc5345c691b977bc5a8b7.jpg\",\n  \"471984.jpg\": \"Aves/Leucophaeus pipixcan/6c63589c9c4efc2e284e2403c0dfede3.jpg\",\n  \"472007.jpg\": \"Aves/Leucophaeus pipixcan/85e8850ff66b004f1c9ac155d04b93a7.jpg\",\n  \"472078.jpg\": \"Aves/Tityra semifasciata/d3024d56be4e5ad888aa4773f3015caf.jpg\",\n  \"472082.jpg\": \"Aves/Tityra semifasciata/3059526dcb5a3e5f4c123584b5997830.jpg\",\n  \"472083.jpg\": \"Aves/Tityra semifasciata/912a11e7aab1699a702100428d6951a8.jpg\",\n  \"472084.jpg\": \"Aves/Tityra semifasciata/458f13d219e38f7eae2a8f6a1dfacc83.jpg\",\n  \"472088.jpg\": \"Aves/Tityra semifasciata/b2ac2d2ad1f5a926c2f1f3af765a1b3a.jpg\",\n  \"472089.jpg\": \"Aves/Tityra semifasciata/35b2bc2d0c5f0ace7bbea02c5be5012c.jpg\",\n  \"472091.jpg\": \"Aves/Tityra semifasciata/4161e6e012d7e420897917a8ecfbe223.jpg\",\n  \"472092.jpg\": \"Aves/Tityra semifasciata/4b143213e5a1948cf5bd47d05b089403.jpg\",\n  \"472093.jpg\": \"Aves/Tityra semifasciata/953b6a70bf8c7dcf660ada25eb06a7b7.jpg\",\n  \"472094.jpg\": \"Aves/Tityra semifasciata/9c8af58f6e9b3684fd6aa79d68262e7c.jpg\",\n  \"472095.jpg\": \"Aves/Tityra semifasciata/95c7a27ec63c0da9a1d1be162b5f16cf.jpg\",\n  \"472137.jpg\": \"Actinopterygii/Anguilla rostrata/232f7d45c55438a413a7719b3c0b1e8f.jpg\",\n  \"472212.jpg\": \"Aves/Rostrhamus sociabilis/f8a4af00bd3557e65cd3a8252bc6b772.jpg\",\n  \"472215.jpg\": \"Aves/Rostrhamus sociabilis/3423fa4e33f8c9b7e1eb167597d6d254.jpg\",\n  \"472219.jpg\": \"Aves/Rostrhamus sociabilis/008cd4a36405412d5adf852fc1ad1897.jpg\",\n  \"472224.jpg\": \"Aves/Rostrhamus sociabilis/d31829c3e98f9663e4d7509e9fd3b398.jpg\",\n  \"472225.jpg\": \"Aves/Rostrhamus sociabilis/b8c45eaedeb8237e61c4d62cb929375d.jpg\",\n  \"472227.jpg\": \"Aves/Rostrhamus sociabilis/86ce29421c63c2ecc4360f7502c886cc.jpg\",\n  \"472318.jpg\": \"Mammalia/Tamiasciurus douglasii/1c262ccad134ceccfeb1a835d2ac78db.jpg\",\n  \"472321.jpg\": \"Mammalia/Tamiasciurus douglasii/8b71bdcc0cb96e04fd66f45bc6df0ebb.jpg\",\n  \"472322.jpg\": \"Mammalia/Tamiasciurus douglasii/2331f50f4e45bcdef8f519676a14b9dd.jpg\",\n  \"472332.jpg\": \"Mammalia/Tamiasciurus douglasii/05a5dc21a076c4134bcc14ea9d6f58e0.jpg\",\n  \"472334.jpg\": \"Mammalia/Tamiasciurus douglasii/94b404e39c8f73dbacbf8e11432b7813.jpg\",\n  \"472347.jpg\": \"Mammalia/Tamiasciurus douglasii/c75a35fdc7f3b140094521eba9971f3b.jpg\",\n  \"472352.jpg\": \"Mammalia/Tamiasciurus douglasii/17093a6bf0e242b7de5fe8cdf4abb8cb.jpg\",\n  \"472353.jpg\": \"Mammalia/Tamiasciurus douglasii/d19934934816b36cd855e7cbdeab1be0.jpg\",\n  \"472358.jpg\": \"Mammalia/Tamiasciurus douglasii/90a0c00c2de2a9b2a88e746d48f2ff59.jpg\",\n  \"472406.jpg\": \"Insecta/Bombus sonorus/5ad7badd2c5ea5deb7691cdc8a0d2c09.jpg\",\n  \"472409.jpg\": \"Insecta/Bombus sonorus/54da79dd4ed62e06145227c0d0fa53e7.jpg\",\n  \"472410.jpg\": \"Insecta/Bombus sonorus/acc6900b3c16d142fc21885eaf6f55c5.jpg\",\n  \"472414.jpg\": \"Insecta/Bombus sonorus/b3d300b4acfd7e8ef7fa9c2dffefec42.jpg\",\n  \"472467.jpg\": \"Insecta/Sphex pensylvanicus/65f3dc173ed3e00604d8755d65bd9f87.jpg\",\n  \"472468.jpg\": \"Insecta/Sphex pensylvanicus/c5eb62a3c57b85429aac2f82f1360426.jpg\",\n  \"472470.jpg\": \"Insecta/Sphex pensylvanicus/984e2bb1c13faef232bb881baf49f6d0.jpg\",\n  \"472471.jpg\": \"Insecta/Sphex pensylvanicus/84c6d57fef6766ab9684b6649f8c9cbd.jpg\",\n  \"472472.jpg\": \"Insecta/Sphex pensylvanicus/8b0e669963f5d74b5055879e352668ac.jpg\",\n  \"472476.jpg\": \"Insecta/Sphex pensylvanicus/7e288dd081b05a3827021853ec185b0a.jpg\",\n  \"472479.jpg\": \"Insecta/Sphex pensylvanicus/9c036c2c4601313e4ddb5ed304eb3703.jpg\",\n  \"472481.jpg\": \"Insecta/Sphex pensylvanicus/40b7b50c00577dbecbca1c9c83e0ee92.jpg\",\n  \"472486.jpg\": \"Insecta/Sphex pensylvanicus/af006f8408639d227c2074b14c17aaa8.jpg\",\n  \"472521.jpg\": \"Reptilia/Plestiodon laticeps/ae7d8db13566767ca02a642046307418.jpg\",\n  \"472522.jpg\": \"Reptilia/Plestiodon laticeps/e5d77a37f08a3f5067ab109a216c432c.jpg\",\n  \"472527.jpg\": \"Reptilia/Plestiodon laticeps/5581c93ca6d221bdafaff91589ae331a.jpg\",\n  \"472529.jpg\": \"Reptilia/Plestiodon laticeps/fcf6d308f429426d59271aac42dd072f.jpg\",\n  \"472531.jpg\": \"Reptilia/Plestiodon laticeps/f7cf0c89f579e18d8a24bb7a256f6da7.jpg\",\n  \"472537.jpg\": \"Reptilia/Plestiodon laticeps/743734c34cf1cb1f2887f1a61b7ac77b.jpg\",\n  \"472549.jpg\": \"Reptilia/Plestiodon laticeps/9e77d696e7e3b671600b42355ff058a7.jpg\",\n  \"472664.jpg\": \"Mammalia/Sciurus niger/c10cb9b0a74ba101f42bde60ce889793.jpg\",\n  \"472767.jpg\": \"Mammalia/Sciurus niger/e4ff11eee6950637638ba5d5814f773a.jpg\",\n  \"472863.jpg\": \"Mammalia/Sciurus niger/cb951368ab97817068e97ac58898dff0.jpg\",\n  \"473151.jpg\": \"Mammalia/Sciurus niger/158032bd6ea752d9aac7f2a0b945bb8a.jpg\",\n  \"473158.jpg\": \"Mammalia/Sciurus niger/1b2b5997985873eb5861f91c8011a45e.jpg\",\n  \"473325.jpg\": \"Mammalia/Sciurus niger/f678a91d201e3ed516479a39cb3fc791.jpg\",\n  \"473520.jpg\": \"Insecta/Poecilanthrax lucifer/d825581f0b1b9a0da02af3b1f7c0df04.jpg\",\n  \"473526.jpg\": \"Insecta/Poecilanthrax lucifer/7230049ea83fb36f8aba448f7b76c2fc.jpg\",\n  \"473568.jpg\": \"Mammalia/Ochotona princeps/7af245a69fb80fcfcace9fed892083ab.jpg\",\n  \"473608.jpg\": \"Mammalia/Ochotona princeps/aa64f0d31e4e8fa10f13d1a3645ac533.jpg\",\n  \"473610.jpg\": \"Mammalia/Ochotona princeps/5f56af381f7b8681f78c663425dad14c.jpg\",\n  \"473616.jpg\": \"Mammalia/Ochotona princeps/fc9951c0b9801e469af8302b35e89036.jpg\",\n  \"473619.jpg\": \"Mammalia/Ochotona princeps/5aaebc4ec108cd4a7a3a5b605736c0e7.jpg\",\n  \"473653.jpg\": \"Insecta/Orthosoma brunneum/9166d7d2388b60a42eba49df689cfcff.jpg\",\n  \"473654.jpg\": \"Insecta/Orthosoma brunneum/dd2541a10c769e7ca023b2b720f38833.jpg\",\n  \"473727.jpg\": \"Aves/Euphonia elegantissima/50c4eec76c7c42e69b91f3810cf11080.jpg\",\n  \"473732.jpg\": \"Insecta/Cordulegaster dorsalis/7294dddac24820ac0323572a4f6c109e.jpg\",\n  \"473737.jpg\": \"Insecta/Cordulegaster dorsalis/ff012262ce103f667dd1a7af2a028a66.jpg\",\n  \"473753.jpg\": \"Mammalia/Bison bison/204221e3f8e3a32541212d3e5fd5de5c.jpg\",\n  \"473755.jpg\": \"Mammalia/Bison bison/7853834783217c27f6357247e897dde0.jpg\",\n  \"473796.jpg\": \"Mammalia/Bison bison/9500424d09d9034b0ce1074cec4d5558.jpg\",\n  \"473831.jpg\": \"Mammalia/Bison bison/9de606b737ac11fd41535f972116418b.jpg\",\n  \"473842.jpg\": \"Aves/Ceryle rudis/e0e0054f760c20a1c2d5d20e66000d68.jpg\",\n  \"473844.jpg\": \"Aves/Ceryle rudis/5ac6515d291677d2cb61b9b43eebf1db.jpg\",\n  \"473849.jpg\": \"Aves/Ceryle rudis/b275822fa628fbd1d5bc22b7b4718b1b.jpg\",\n  \"473851.jpg\": \"Aves/Thraupis palmarum/c112b9b93d50b5920a2870c30f624524.jpg\",\n  \"473855.jpg\": \"Aves/Lampornis clemenciae/e6d079d0886469637296cb1b96f85670.jpg\",\n  \"473901.jpg\": \"Insecta/Palpita magniferalis/5c5d2477b5e4fb5b04b8fb2fb309d9fb.jpg\",\n  \"473915.jpg\": \"Insecta/Satyrium liparops/aaa10bc3c5d8f09f9c6626f22c29c7d5.jpg\",\n  \"473918.jpg\": \"Insecta/Satyrium liparops/e72a6ab8a27946f56d03ac486c3264d5.jpg\",\n  \"474060.jpg\": \"Aves/Lanius excubitor/f40e104d4a4be887efc21598c2a3285b.jpg\",\n  \"474062.jpg\": \"Aves/Lanius excubitor/551456491c4bcbee63fc47caac646509.jpg\",\n  \"474063.jpg\": \"Aves/Lanius excubitor/e620c398c0fd675a860fb6fdfb91f78e.jpg\",\n  \"474068.jpg\": \"Aves/Lanius excubitor/85e6efd03c4426bed89433e95e715e80.jpg\",\n  \"474069.jpg\": \"Aves/Lanius excubitor/45bd45390271a439b99cac7748c6fec7.jpg\",\n  \"474071.jpg\": \"Aves/Lanius excubitor/6119ed5a3cf3c214c481067b794e2795.jpg\",\n  \"474074.jpg\": \"Aves/Lanius excubitor/04eb838078a29b03bdd8ffee3344d076.jpg\",\n  \"474075.jpg\": \"Aves/Lanius excubitor/61fb361e7a8af5aea02f7638d4450494.jpg\",\n  \"474077.jpg\": \"Aves/Lanius excubitor/a0859259a9e7f4c8f7946890d749c7d1.jpg\",\n  \"474078.jpg\": \"Aves/Lanius excubitor/b9f6c7eb0d1435df9b5c07c5e85c13e6.jpg\",\n  \"474107.jpg\": \"Aves/Empidonax traillii/9f6afcb383eb3a5354dcb5fa492bbc93.jpg\",\n  \"474135.jpg\": \"Reptilia/Chrysemys picta picta/aaaa5ed1faa1fc27f9081d07cb5150ed.jpg\",\n  \"474154.jpg\": \"Reptilia/Chrysemys picta picta/9e4572128ba9340840970e0cf4e5f87c.jpg\",\n  \"474158.jpg\": \"Reptilia/Chrysemys picta picta/6f3efe7cfe019f808a140b57934d2605.jpg\",\n  \"474166.jpg\": \"Reptilia/Chrysemys picta picta/054935a2e0a8705a37264294d6432500.jpg\",\n  \"47418.jpg\": \"Insecta/Dissosteira carolina/5359a4666fbec4d0176fb320a11e74b8.jpg\",\n  \"474180.jpg\": \"Reptilia/Chrysemys picta picta/376bb9daa725c2c1de74a84086874ff6.jpg\",\n  \"474189.jpg\": \"Insecta/Libellula saturata/65567527375a39c94b41dd2d868bfc8f.jpg\",\n  \"47421.jpg\": \"Insecta/Dissosteira carolina/046d07b5555f506bebc8423eb8e0a525.jpg\",\n  \"474239.jpg\": \"Insecta/Libellula saturata/cef01a0977a63da41e1c15f96b4e0d65.jpg\",\n  \"474254.jpg\": \"Insecta/Libellula saturata/61efcca98fc4e47b748daeeabc09e3b6.jpg\",\n  \"474262.jpg\": \"Insecta/Libellula saturata/30d375a2fff09c65d34eb4e5fbc407f0.jpg\",\n  \"474306.jpg\": \"Insecta/Libellula saturata/3ef075cdb42fca6058d0a67924bf37c7.jpg\",\n  \"474308.jpg\": \"Insecta/Libellula saturata/c1046d651bd05c6a5ac617c84e0ff22d.jpg\",\n  \"47431.jpg\": \"Insecta/Dissosteira carolina/1005a9fa4e85adf5e0515811bb5fe167.jpg\",\n  \"474311.jpg\": \"Insecta/Libellula saturata/2315cfd37bdb2a1739ba4e546bf89a2a.jpg\",\n  \"474314.jpg\": \"Insecta/Libellula saturata/654f1db95824e38e529e6400bd3e4cea.jpg\",\n  \"474356.jpg\": \"Insecta/Libellula saturata/2fc54cefed42c84b8e674f64b9504127.jpg\",\n  \"47437.jpg\": \"Insecta/Dissosteira carolina/a16c372dc678d352032ee6b5b9c95c29.jpg\",\n  \"47440.jpg\": \"Insecta/Dissosteira carolina/ad1374427da3b70e0dbab67e290fef7b.jpg\",\n  \"474401.jpg\": \"Insecta/Parrhasius m-album/323c3b02a11d7e9814bba2ffd4feb32a.jpg\",\n  \"47446.jpg\": \"Insecta/Dissosteira carolina/efd7a2aad81719ba0a518fe894fc539a.jpg\",\n  \"47447.jpg\": \"Insecta/Dissosteira carolina/680cef315b21b3ea3510c795a3fd1d01.jpg\",\n  \"47448.jpg\": \"Insecta/Dissosteira carolina/9fd0ba279ac4ef3722fbcb7498e99d30.jpg\",\n  \"474500.jpg\": \"Aves/Saltator coerulescens/57f379008febea9298d437cc2641b316.jpg\",\n  \"474512.jpg\": \"Aves/Saltator coerulescens/3a25e4410eb545ba4aa5047fa5cc5eca.jpg\",\n  \"474513.jpg\": \"Aves/Saltator coerulescens/6fcc795f1bb33240dd4f38eaa7e6d0fe.jpg\",\n  \"474514.jpg\": \"Aves/Saltator coerulescens/8975a89ba9306facd6c296b7c75a32fc.jpg\",\n  \"474515.jpg\": \"Aves/Saltator coerulescens/570d1e904899fe647c580c796d4863e1.jpg\",\n  \"474519.jpg\": \"Aves/Saltator coerulescens/deddac54e30a66ef37cf70d79106eb0b.jpg\",\n  \"474525.jpg\": \"Aves/Saltator coerulescens/0d5c5ff53bc65665e59d60be467a4e29.jpg\",\n  \"474526.jpg\": \"Aves/Saltator coerulescens/dc80a150607a755125bb80e3029ee065.jpg\",\n  \"474527.jpg\": \"Aves/Saltator coerulescens/4bb4cadda36c8d2658042f214d5c1c8c.jpg\",\n  \"474528.jpg\": \"Aves/Saltator coerulescens/bb85e6adcd140eb7c6cbc024a2024446.jpg\",\n  \"47459.jpg\": \"Mollusca/Cryptochiton stelleri/89ed533d8912045e2ec51b6e21baf644.jpg\",\n  \"47461.jpg\": \"Mollusca/Cryptochiton stelleri/544a6454c3f14ad7b135bfe145ebb596.jpg\",\n  \"47462.jpg\": \"Mollusca/Cryptochiton stelleri/c8f550622b0f1fbb36143b99b0588a8c.jpg\",\n  \"47467.jpg\": \"Mollusca/Cryptochiton stelleri/3ae7846961c3864bb8884bc6a6af08af.jpg\",\n  \"474720.jpg\": \"Aves/Eupsittula nana/695a5f719eae79eff7f7ea0084028696.jpg\",\n  \"474731.jpg\": \"Aves/Eupsittula nana/0f8c6e41e884d1ffe31ff065f47e303d.jpg\",\n  \"474732.jpg\": \"Aves/Eupsittula nana/4319b59153a16696912587b75aeaa12f.jpg\",\n  \"474736.jpg\": \"Aves/Eupsittula nana/fcc8af4f563c916ee3843b4f2c962da7.jpg\",\n  \"474737.jpg\": \"Aves/Eupsittula nana/7fd58511ffc36fff2158d323dc885ac7.jpg\",\n  \"474738.jpg\": \"Aves/Eupsittula nana/852f4e160491b11a140cd1b742627d28.jpg\",\n  \"474760.jpg\": \"Actinopterygii/Sparisoma viride/12eaec604c8d4421746815a9fad13bc3.jpg\",\n  \"474781.jpg\": \"Insecta/Chortophaga viridifasciata viridifasciata/2680079cc64cdd59e5ed25d6e711bcb7.jpg\",\n  \"474782.jpg\": \"Insecta/Chortophaga viridifasciata viridifasciata/e5c584efc19f3b078fff9069ec43eaf5.jpg\",\n  \"474783.jpg\": \"Insecta/Chortophaga viridifasciata viridifasciata/a45d3c18d86a1dfe7b811fb1f10153c9.jpg\",\n  \"474786.jpg\": \"Reptilia/Sceloporus graciosus vandenburgianus/5344f3ccc847cdbc0fad1983e083ac7e.jpg\",\n  \"474787.jpg\": \"Reptilia/Sceloporus graciosus vandenburgianus/f07e80fc54329a725ca4106f05b3f797.jpg\",\n  \"474788.jpg\": \"Reptilia/Sceloporus graciosus vandenburgianus/394d2179008ca84929a96149bc9516a9.jpg\",\n  \"474789.jpg\": \"Reptilia/Sceloporus graciosus vandenburgianus/7e6aa1d991229f0ead684b70e625c76d.jpg\",\n  \"474795.jpg\": \"Reptilia/Sceloporus graciosus vandenburgianus/3107d032ecfc65caaf7190f8d320116f.jpg\",\n  \"474803.jpg\": \"Reptilia/Sceloporus graciosus vandenburgianus/bb0a5ed51ab553478eba602f7ed8e3dd.jpg\",\n  \"474804.jpg\": \"Reptilia/Sceloporus graciosus vandenburgianus/1669c2ad05e83a3ce0384f3a8eb68de5.jpg\",\n  \"474808.jpg\": \"Reptilia/Sceloporus graciosus vandenburgianus/53db067233bcdc9cff97db6519ae951a.jpg\",\n  \"474809.jpg\": \"Reptilia/Sceloporus graciosus vandenburgianus/b992bb1feed530dffd57a2580558bcee.jpg\",\n  \"474810.jpg\": \"Reptilia/Sceloporus graciosus vandenburgianus/8a98bb82ac35fea43937ae45a4082ae7.jpg\",\n  \"474811.jpg\": \"Reptilia/Sceloporus graciosus vandenburgianus/38154d21df64e8b9f658557955f1cbc0.jpg\",\n  \"474812.jpg\": \"Reptilia/Sceloporus graciosus vandenburgianus/fbf38c15798cabc0fc7b89d0060a0cc0.jpg\",\n  \"474813.jpg\": \"Reptilia/Sceloporus graciosus vandenburgianus/635ce97827b92e6f9551ba049b289f29.jpg\",\n  \"474820.jpg\": \"Reptilia/Sceloporus graciosus vandenburgianus/9f11e3449d9bea404ff8674e002bc40b.jpg\",\n  \"474821.jpg\": \"Aves/Phaethon aethereus/fae9a2d40db6aa0f0ade0d26cff3256c.jpg\",\n  \"474824.jpg\": \"Aves/Phaethon aethereus/aa8f294018f9614002c26efd2bf98672.jpg\",\n  \"47483.jpg\": \"Mollusca/Cryptochiton stelleri/6f646a39a1198654cea8b27fa16c90f6.jpg\",\n  \"474855.jpg\": \"Reptilia/Varanus niloticus/6ece3dc7bf6045891a152ef28e18345b.jpg\",\n  \"474865.jpg\": \"Reptilia/Varanus niloticus/11a0e250f564f3b3dcfadb25227beb85.jpg\",\n  \"474874.jpg\": \"Amphibia/Bufo bufo/6ff8925057a9c0d649428d4423af2087.jpg\",\n  \"474890.jpg\": \"Amphibia/Bufo bufo/45f9859044073204d69a0d323c40d875.jpg\",\n  \"4749.jpg\": \"Arachnida/Hypoblemum albovittatum/bbe487d9002ab78af45c7054a2743142.jpg\",\n  \"474913.jpg\": \"Amphibia/Bufo bufo/adbe463ac4747806ab1ebe76927791fb.jpg\",\n  \"474931.jpg\": \"Amphibia/Bufo bufo/9e6bb6686084706a0e3f4726e55827ed.jpg\",\n  \"474955.jpg\": \"Amphibia/Bufo bufo/2928b849354fb10d1dfd7b23c5aa56b0.jpg\",\n  \"474993.jpg\": \"Insecta/Epitheca canis/c3f0028c26abd51b20d882f972ff7e43.jpg\",\n  \"4750.jpg\": \"Arachnida/Hypoblemum albovittatum/6c972bdaa1453891890b91fbbdf683ec.jpg\",\n  \"475004.jpg\": \"Insecta/Chlosyne ehrenbergii/8ec486a2778d036f658662ada3e1bcef.jpg\",\n  \"475009.jpg\": \"Insecta/Chlosyne ehrenbergii/cc9372e63befa68e4902757a5a4b5f68.jpg\",\n  \"475011.jpg\": \"Insecta/Chlosyne ehrenbergii/cba8c59b5979df8a10b5f3984e431db1.jpg\",\n  \"475012.jpg\": \"Insecta/Chlosyne ehrenbergii/87c764f77b7834974a2858490ba2ea61.jpg\",\n  \"475021.jpg\": \"Animalia/Stomolophus meleagris/29c2df4b5f28703075e27abdc271874b.jpg\",\n  \"4751.jpg\": \"Arachnida/Hypoblemum albovittatum/3f1da7954e9a90c4a9ee643b675b87f8.jpg\",\n  \"475110.jpg\": \"Insecta/Stylurus plagiatus/83c30038522f1c3147ae3e541d0395a7.jpg\",\n  \"475111.jpg\": \"Insecta/Stylurus plagiatus/1112d9cba1a8e53b21cce95468e3d5b3.jpg\",\n  \"475112.jpg\": \"Insecta/Stylurus plagiatus/ba2d90b013572412e135e3e42dd7cc5b.jpg\",\n  \"475113.jpg\": \"Insecta/Stylurus plagiatus/766bc47ab9754069af99471d635be36a.jpg\",\n  \"475114.jpg\": \"Insecta/Stylurus plagiatus/b1ee55bf8f7e99294b655334c16bde25.jpg\",\n  \"475115.jpg\": \"Insecta/Stylurus plagiatus/322288b51e07683566bea4e48a3bdaa9.jpg\",\n  \"475116.jpg\": \"Insecta/Stylurus plagiatus/1f4d541a7944d5571934625fb3e3f282.jpg\",\n  \"475117.jpg\": \"Insecta/Stylurus plagiatus/f9e0a73c267f0c228a1fca769a6bfb6e.jpg\",\n  \"475118.jpg\": \"Insecta/Stylurus plagiatus/cf3254f4cbaf12bb8618e39baf0b8352.jpg\",\n  \"475119.jpg\": \"Insecta/Stylurus plagiatus/895903eabfe7736f2f9a9d7d6302fd04.jpg\",\n  \"47512.jpg\": \"Mollusca/Cryptochiton stelleri/441dcf9e28eb9a05332d2ef0a44a1061.jpg\",\n  \"475120.jpg\": \"Insecta/Stylurus plagiatus/853c6effbebf6696d80d496128c2b1c6.jpg\",\n  \"475121.jpg\": \"Insecta/Stylurus plagiatus/8f4c95c8b2b44d637553680b935d0fdf.jpg\",\n  \"47514.jpg\": \"Mollusca/Cryptochiton stelleri/8cde0f1b1387ce86872d7c6a487e7786.jpg\",\n  \"475176.jpg\": \"Aves/Aegolius acadicus/6b4e8968ee8d1297a25de82b46ed3ddc.jpg\",\n  \"475177.jpg\": \"Aves/Aegolius acadicus/ade1c63558e95ce98d80b574137cb372.jpg\",\n  \"47518.jpg\": \"Mollusca/Cryptochiton stelleri/f47642f25e81899cc51e3668bc6bbf5c.jpg\",\n  \"475180.jpg\": \"Aves/Aegolius acadicus/61614d3ccdad7e64b0d2f64fa9b62f58.jpg\",\n  \"475182.jpg\": \"Aves/Aegolius acadicus/958d28dd97684881a290fd50c4b86d5b.jpg\",\n  \"475183.jpg\": \"Aves/Aegolius acadicus/563b3820318e4b750aeca630477a1493.jpg\",\n  \"475192.jpg\": \"Aves/Meleagris gallopavo intermedia/2b822a6225f005d60b849b777404d5e4.jpg\",\n  \"475193.jpg\": \"Aves/Meleagris gallopavo intermedia/2776ad18faded333bea3ba23d8d0506d.jpg\",\n  \"4752.jpg\": \"Arachnida/Hypoblemum albovittatum/36e8a5e2f1fb592c1b6f358ce74925fb.jpg\",\n  \"475224.jpg\": \"Insecta/Heliopetes ericetorum/a5dd3da83de57ce28ea3f46a8cbfde8f.jpg\",\n  \"47523.jpg\": \"Mollusca/Cryptochiton stelleri/6329b55b621ce5771a2b3c6a28b834d8.jpg\",\n  \"475275.jpg\": \"Aves/Jacana jacana/87b3ac0195b2f206258c095e35cbd177.jpg\",\n  \"475283.jpg\": \"Aves/Jacana jacana/73ef1e091234a3e8b6cdbbbd1f1c885c.jpg\",\n  \"47529.jpg\": \"Mollusca/Cryptochiton stelleri/fa6f3a6e0b96ae87a16c488573cd794b.jpg\",\n  \"47530.jpg\": \"Mollusca/Cryptochiton stelleri/f31e84cc524ad08d5092fb445f9e1905.jpg\",\n  \"475306.jpg\": \"Aves/Chlidonias niger/8b03d104fdd4da94298be6141ee7a0f0.jpg\",\n  \"475314.jpg\": \"Aves/Chlidonias niger/63dd0b7f0b15f79b97845611540f82af.jpg\",\n  \"475321.jpg\": \"Aves/Chlidonias niger/76eeeb28e320d1cfff54bd105a234c50.jpg\",\n  \"475326.jpg\": \"Aves/Chlidonias niger/5b12b8338f20fc44d4e907ff7df03fd2.jpg\",\n  \"475327.jpg\": \"Aves/Chlidonias niger/96d76a7f8ad26b2ce2e1e50621d84a7d.jpg\",\n  \"475333.jpg\": \"Aves/Chlidonias niger/e66fc7a12de2b41196f45237069db2d9.jpg\",\n  \"475338.jpg\": \"Insecta/Aglais milberti/f684f3787a03dd2e7c50484493809e5c.jpg\",\n  \"475343.jpg\": \"Insecta/Aglais milberti/084a9909a587315e5fa71675d9d7c2f7.jpg\",\n  \"475344.jpg\": \"Insecta/Aglais milberti/cd0a50cbda61e2f7903e77571cd4d163.jpg\",\n  \"475347.jpg\": \"Insecta/Aglais milberti/f4aca9f429c3641808b60ad036f0bec1.jpg\",\n  \"475350.jpg\": \"Insecta/Aglais milberti/8fd0e3479b85dd9cb08800bf05cdd504.jpg\",\n  \"475356.jpg\": \"Insecta/Aglais milberti/09ca1dc7b0a34494d824a332ee3bd211.jpg\",\n  \"475358.jpg\": \"Insecta/Aglais milberti/6b67e0c24d48d002beaf46f86f0ab700.jpg\",\n  \"475359.jpg\": \"Insecta/Aglais milberti/6afb6399456a8266cbd2f34f6cbb53aa.jpg\",\n  \"475363.jpg\": \"Insecta/Aglais milberti/26c18853a5c584cf797174a250a13b4e.jpg\",\n  \"47537.jpg\": \"Mollusca/Cryptochiton stelleri/6bf258925b676c5bea46f3c7699ebcc1.jpg\",\n  \"475380.jpg\": \"Aves/Spheniscus demersus/6eb933abae52764cc40e81495dd723b5.jpg\",\n  \"47539.jpg\": \"Mollusca/Cryptochiton stelleri/abda8a5de364e1aacd89e3d106ddf58c.jpg\",\n  \"475438.jpg\": \"Insecta/Promachus hinei/d977b688dbe32ae29a556a990ecc5df2.jpg\",\n  \"475452.jpg\": \"Insecta/Promachus hinei/24aafffb763b0a25baa79afda54b7763.jpg\",\n  \"475456.jpg\": \"Insecta/Promachus hinei/cb54b68def5afd8398a0717a9faee4fe.jpg\",\n  \"475495.jpg\": \"Aves/Eudocimus albus/826de86f415b68064ff421aa69e1e5dd.jpg\",\n  \"475497.jpg\": \"Aves/Eudocimus albus/027c412f466e4ccd307fcb4786a613e3.jpg\",\n  \"475551.jpg\": \"Aves/Eudocimus albus/c2f109d561717d86a4b4f6fc40bd307a.jpg\",\n  \"475573.jpg\": \"Aves/Eudocimus albus/6f96dcb57d6af765028e6c2038056059.jpg\",\n  \"475576.jpg\": \"Aves/Eudocimus albus/dfcea74af6466ac3e55529d1ce0f00e4.jpg\",\n  \"475598.jpg\": \"Aves/Eudocimus albus/97143cc75390ef0b1144d4f69a3462de.jpg\",\n  \"475623.jpg\": \"Aves/Eudocimus albus/40e4786384ced1fa9fcdaf1b3acb7782.jpg\",\n  \"475773.jpg\": \"Aves/Eudocimus albus/d5a942090f6c6d4ead0b6f488290169b.jpg\",\n  \"475779.jpg\": \"Reptilia/Regina grahamii/f7dbccfb260ac38430b84c8349ecca07.jpg\",\n  \"475787.jpg\": \"Reptilia/Regina grahamii/ca126464727b2b36aeccfd276c744f46.jpg\",\n  \"475825.jpg\": \"Reptilia/Drymarchon melanurus erebennus/5d3e02ff40da4f6fe1471e3f1640c003.jpg\",\n  \"475837.jpg\": \"Reptilia/Drymarchon melanurus erebennus/232f6e3133d96a6d1affc80373a6925d.jpg\",\n  \"475918.jpg\": \"Aves/Thalasseus maximus/f95fce28d559b4124769e561b0f6c663.jpg\",\n  \"475933.jpg\": \"Aves/Thalasseus maximus/0db30537de173f392d75f0f32fca6d87.jpg\",\n  \"475990.jpg\": \"Aves/Thalasseus maximus/ab89df7b489eb0ffe4fe63a6074c18cf.jpg\",\n  \"47604.jpg\": \"Aves/Picoides dorsalis/ae1e1f6e2b9b71a8d034cd616395ca87.jpg\",\n  \"47613.jpg\": \"Aves/Picoides dorsalis/0ad0faa6495929e1714d68a337c33700.jpg\",\n  \"476143.jpg\": \"Aves/Anas platyrhynchos domesticus/a24ed8530f48e8a5e98d6f45d79e4233.jpg\",\n  \"47619.jpg\": \"Reptilia/Thamnophis proximus rubrilineatus/7fc6e5d9e6530dd2d0365ecebb69ebe1.jpg\",\n  \"476221.jpg\": \"Aves/Anas platyrhynchos domesticus/ff0a750ca5413f99a110f397dc8e54ab.jpg\",\n  \"47623.jpg\": \"Reptilia/Thamnophis proximus rubrilineatus/b1ae207608bc996f41c4860225f6acbb.jpg\",\n  \"47624.jpg\": \"Reptilia/Thamnophis proximus rubrilineatus/18b3cd4be6c3e6455e898c748742901e.jpg\",\n  \"476257.jpg\": \"Aves/Anas platyrhynchos domesticus/ce9cd030eb67121161b2ccc85fd97040.jpg\",\n  \"476284.jpg\": \"Aves/Anas platyrhynchos domesticus/6930fa7a6eaaa11c1736e69c3e45a2c5.jpg\",\n  \"476324.jpg\": \"Aves/Anas platyrhynchos domesticus/bc72a8c84ffd25a3315ec57016645233.jpg\",\n  \"47635.jpg\": \"Reptilia/Thamnophis proximus rubrilineatus/1b572e38c18ffef54f1bbd15ddc6f6bc.jpg\",\n  \"47636.jpg\": \"Reptilia/Thamnophis proximus rubrilineatus/a9aecec3b1d129d1d034a6f6d84c46c1.jpg\",\n  \"476378.jpg\": \"Reptilia/Agkistrodon contortrix contortrix/7a1841dc61433910babec61ded9f771d.jpg\",\n  \"476387.jpg\": \"Reptilia/Agkistrodon contortrix contortrix/f5a21c36feaff5f569fb658d7f07c686.jpg\",\n  \"476388.jpg\": \"Reptilia/Agkistrodon contortrix contortrix/286cf64b4d54ef81d74107bf50b1bc49.jpg\",\n  \"47639.jpg\": \"Reptilia/Thamnophis proximus rubrilineatus/0b9d40c0ce37c09d466db9733ecc03b9.jpg\",\n  \"476391.jpg\": \"Mollusca/Rumina decollata/5a67bc08a98332928436e27b2adea1ca.jpg\",\n  \"47640.jpg\": \"Reptilia/Thamnophis proximus rubrilineatus/857b0980433e6344b278e5054a1993af.jpg\",\n  \"476408.jpg\": \"Mollusca/Rumina decollata/5ed3389d117e27758ab8fbfda2f4ba93.jpg\",\n  \"476414.jpg\": \"Mollusca/Rumina decollata/a81cbf7c81bd6f7bcd72ee6e061c0fcf.jpg\",\n  \"476423.jpg\": \"Mollusca/Rumina decollata/c90464d185d27fbeff397a5194149325.jpg\",\n  \"476435.jpg\": \"Mollusca/Rumina decollata/d6c64a2aabfca568337403d0aac3b531.jpg\",\n  \"476441.jpg\": \"Mollusca/Rumina decollata/661b68e97d12514e956d221d85b230c6.jpg\",\n  \"476454.jpg\": \"Mollusca/Rumina decollata/5377be8de9082b71ef444f2b35ba07af.jpg\",\n  \"476464.jpg\": \"Mollusca/Rumina decollata/34cadbd98392841064e45a55bb1da41e.jpg\",\n  \"476472.jpg\": \"Mollusca/Rumina decollata/205963567af0deea683bc83a0399189f.jpg\",\n  \"47650.jpg\": \"Reptilia/Thamnophis proximus rubrilineatus/d0bbed628b86468685058773e15c08e5.jpg\",\n  \"476532.jpg\": \"Insecta/Dorocordulia libera/4976714418cf11d0cdc9e9ec025a6b07.jpg\",\n  \"476534.jpg\": \"Insecta/Dorocordulia libera/afb81ef216d1505b2625d2c09380a98f.jpg\",\n  \"476536.jpg\": \"Insecta/Dorocordulia libera/de96a7ae5e6da43954115cca5806b724.jpg\",\n  \"476585.jpg\": \"Insecta/Neophasia menapia/bd91b4cd122cece9c3eea5ee7e25b129.jpg\",\n  \"476590.jpg\": \"Insecta/Neophasia menapia/01ab5763a3082f8f9718c40820236039.jpg\",\n  \"476594.jpg\": \"Insecta/Darapsa choerilus/8c2451ccf1d5c749a6d4db60817025f2.jpg\",\n  \"476596.jpg\": \"Aves/Pycnonotus cafer/8d855611cce5e9b9ca6afed4d573d193.jpg\",\n  \"476597.jpg\": \"Aves/Pycnonotus cafer/faeda414ab8a6c41eddc475051eec82c.jpg\",\n  \"476598.jpg\": \"Aves/Pycnonotus cafer/3ce74472e90b680a73ff8bb8d94b89f1.jpg\",\n  \"476600.jpg\": \"Aves/Pycnonotus cafer/7fca2bd37a36da6925e49a7ede0e49be.jpg\",\n  \"476602.jpg\": \"Aves/Pycnonotus cafer/39c78c40a50b2eca0a6dcdcf97806f0a.jpg\",\n  \"476603.jpg\": \"Aves/Pycnonotus cafer/c3c5a36f42fb72b51fa5a7db85436ef1.jpg\",\n  \"476608.jpg\": \"Aves/Pycnonotus cafer/bf1b5328c4a72d13f49bbe3bf36ca132.jpg\",\n  \"476609.jpg\": \"Aves/Pycnonotus cafer/c1c00ebf10abe4b9497a3879efab8aea.jpg\",\n  \"47661.jpg\": \"Reptilia/Sceloporus graciosus/3a3a299362cc54d1f1815c48102d703a.jpg\",\n  \"476611.jpg\": \"Aves/Pycnonotus cafer/b79c2c70f37a455e5443bf6a355e6b23.jpg\",\n  \"476612.jpg\": \"Aves/Pycnonotus cafer/1794551c30afe23eb335bf2d6ec3df1c.jpg\",\n  \"476617.jpg\": \"Aves/Pycnonotus cafer/a9bb934a47f1714c09bb4bb0c7269824.jpg\",\n  \"47662.jpg\": \"Reptilia/Sceloporus graciosus/bb50090560a3c6361768b981987967f4.jpg\",\n  \"476623.jpg\": \"Arachnida/Holocnemus pluchei/850c25eda59bbea0e4c8e60ac3e03567.jpg\",\n  \"476625.jpg\": \"Aves/Megaceryle torquata/9d204516abd38e192678944a236d79a3.jpg\",\n  \"476627.jpg\": \"Aves/Megaceryle torquata/f426b862bc5da74fd5a1ef66c5a73537.jpg\",\n  \"47663.jpg\": \"Reptilia/Sceloporus graciosus/4d398c7488b073698156a7e251d99af6.jpg\",\n  \"47664.jpg\": \"Reptilia/Sceloporus graciosus/3e6228fc8d04b9cf2d02a4c22093a652.jpg\",\n  \"476642.jpg\": \"Aves/Megaceryle torquata/9a271eec7b5e510752a842c31346e06b.jpg\",\n  \"476643.jpg\": \"Aves/Megaceryle torquata/65cf7694746c8b95e9021b314c2eeee6.jpg\",\n  \"476645.jpg\": \"Aves/Megaceryle torquata/5a0f1862810bcf85de7beffac7a1f63f.jpg\",\n  \"476650.jpg\": \"Aves/Megaceryle torquata/5fd443cfd0a37564cd6407e7c6253cf8.jpg\",\n  \"476654.jpg\": \"Aves/Megaceryle torquata/84532a2b5a7fe4dd944cd24995d6fa45.jpg\",\n  \"47669.jpg\": \"Reptilia/Sceloporus graciosus/db803359d1393b5156915a9f5ffe824a.jpg\",\n  \"47670.jpg\": \"Reptilia/Sceloporus graciosus/00e4390a1b479fca8f3fc0b8f50adc7d.jpg\",\n  \"47688.jpg\": \"Reptilia/Sceloporus graciosus/469a45a8676c2bd9bad6ce75961b87fd.jpg\",\n  \"47691.jpg\": \"Reptilia/Sceloporus graciosus/1bcd3c0f9ea474c07df8cd1d7c5f3f21.jpg\",\n  \"477098.jpg\": \"Aves/Spinus spinus/97bb7fa6cabf1aa5b0e8a3988d46b53f.jpg\",\n  \"477100.jpg\": \"Aves/Spinus spinus/f7f0c03293a691c8a4f913aacde105eb.jpg\",\n  \"477101.jpg\": \"Aves/Spinus spinus/12bb6b3cd811033964d29a930bda2ef0.jpg\",\n  \"477104.jpg\": \"Aves/Spinus spinus/198f61ea8246e5f1366f5e97a3338f50.jpg\",\n  \"477105.jpg\": \"Aves/Spinus spinus/4359b566d1bea499f45f241a7cb8d363.jpg\",\n  \"477106.jpg\": \"Aves/Spinus spinus/9e3e72f7a131a400353b1b98d7b7f31a.jpg\",\n  \"477107.jpg\": \"Aves/Spinus spinus/9c84d7247b8b9177fe0cdef42e29690e.jpg\",\n  \"477108.jpg\": \"Aves/Spinus spinus/0872bf3af5d272b889755ec22044390d.jpg\",\n  \"477110.jpg\": \"Aves/Spinus spinus/0e8e36b30e0d5ce134c10e09cc7ea3c1.jpg\",\n  \"477114.jpg\": \"Amphibia/Agalychnis callidryas/00814e2e7028af0fdbc49aa3eae98ac9.jpg\",\n  \"47712.jpg\": \"Reptilia/Sceloporus graciosus/648eb0df482d966b1bfeeec7b7ca9cbc.jpg\",\n  \"47716.jpg\": \"Reptilia/Sceloporus graciosus/85839f6e177d4c6b9d998f7ab58786d9.jpg\",\n  \"47724.jpg\": \"Reptilia/Sceloporus graciosus/b87edc16e7fb961f4f4722e005262d9f.jpg\",\n  \"477242.jpg\": \"Aves/Cynanthus latirostris/09154d90cb509c08755407e911f918cc.jpg\",\n  \"477243.jpg\": \"Aves/Cynanthus latirostris/baa48899ce5ad92fbc43df64af051ca6.jpg\",\n  \"477289.jpg\": \"Aves/Cynanthus latirostris/9af90a6d5cd85116cae996925d902566.jpg\",\n  \"477291.jpg\": \"Aves/Cynanthus latirostris/404ded21add51772a3f8317298be976b.jpg\",\n  \"477298.jpg\": \"Aves/Cynanthus latirostris/347b588c8524281706495635c339e699.jpg\",\n  \"477299.jpg\": \"Aves/Cynanthus latirostris/1a7b0fa304d05276a1ada9efcf6df155.jpg\",\n  \"47735.jpg\": \"Reptilia/Sceloporus graciosus/2189c6404aca78be9678b905e71df5c8.jpg\",\n  \"47736.jpg\": \"Reptilia/Sceloporus graciosus/8442e76a6205d28e3cf17d03eb40e1de.jpg\",\n  \"477364.jpg\": \"Animalia/Porcellio scaber/63f2abdb3660863fc2920be4ea8292a4.jpg\",\n  \"47737.jpg\": \"Reptilia/Sceloporus graciosus/75930c223dceb99e7f0fe627be1b9c6a.jpg\",\n  \"477374.jpg\": \"Animalia/Porcellio scaber/d270b4d1056a1d8d4f255d10558b267a.jpg\",\n  \"477375.jpg\": \"Animalia/Porcellio scaber/ca5c14cb98825339008460df47609c0f.jpg\",\n  \"477377.jpg\": \"Animalia/Porcellio scaber/b485376b91d05a443653d85b372c4865.jpg\",\n  \"477378.jpg\": \"Animalia/Porcellio scaber/36bfe7b932c30df323d557e6c53dfeff.jpg\",\n  \"47738.jpg\": \"Reptilia/Sceloporus graciosus/bd8ccacb8a58f2e8b91c2822642335de.jpg\",\n  \"477381.jpg\": \"Animalia/Porcellio scaber/b9e8a3b0d01694e0b5f1967324a9aa52.jpg\",\n  \"477384.jpg\": \"Animalia/Porcellio scaber/a5b91e76a532d25262484020ecc97532.jpg\",\n  \"47739.jpg\": \"Reptilia/Sceloporus graciosus/3492f579920f1e99f94bce31e79a4294.jpg\",\n  \"47740.jpg\": \"Reptilia/Sceloporus graciosus/4c10e9127b6adb2fe58dd7bf92b3acaa.jpg\",\n  \"477416.jpg\": \"Insecta/Mermiria bivittata/0cf0b84630e642380e73f9d7efd3fc58.jpg\",\n  \"477428.jpg\": \"Insecta/Gomphaeschna furcillata/845e5ce6351701dd61e79e9065b54306.jpg\",\n  \"477434.jpg\": \"Insecta/Gomphaeschna furcillata/1ce78d193d4ccdb939ac31f4f321e790.jpg\",\n  \"477506.jpg\": \"Aves/Ocyphaps lophotes/b812fa33993ffaaefb298cfeea1221f1.jpg\",\n  \"477511.jpg\": \"Aves/Ocyphaps lophotes/3d8a81223c0baad4bdeaebad4ff93368.jpg\",\n  \"477512.jpg\": \"Aves/Ocyphaps lophotes/f614f7d8c8b08dbacbe79e2910b96eb9.jpg\",\n  \"47752.jpg\": \"Reptilia/Sceloporus graciosus/cabd06887b0cae87a62f4e0902951349.jpg\",\n  \"477540.jpg\": \"Insecta/Sympetrum corruptum/82f5a433c43c26d1bc016be589af567e.jpg\",\n  \"47756.jpg\": \"Reptilia/Hypsiglena jani/6e8eda18c5fb601ca1993fcbcd5981af.jpg\",\n  \"47757.jpg\": \"Reptilia/Hypsiglena jani/a27b37d43ae75295609ef10f849139c6.jpg\",\n  \"47758.jpg\": \"Reptilia/Hypsiglena jani/4cdce0831566d75aaf105f5fec896760.jpg\",\n  \"47772.jpg\": \"Reptilia/Hypsiglena jani/bb85bf3686eeeb440c32750f8e70c879.jpg\",\n  \"477729.jpg\": \"Insecta/Sympetrum corruptum/ddb415203633543fb3d85d72e41d01b0.jpg\",\n  \"477757.jpg\": \"Insecta/Sympetrum corruptum/9fd9cd142288c387d4c6499f049d3e0e.jpg\",\n  \"477797.jpg\": \"Insecta/Sympetrum corruptum/a5f4411f21a6fb4e4dfe89c9fef54344.jpg\",\n  \"47782.jpg\": \"Reptilia/Hypsiglena jani/95d25c5d30be1e0cfc03e881e7872736.jpg\",\n  \"47783.jpg\": \"Reptilia/Hypsiglena jani/5f1337b2a77e58882edb83e4edd3dd0f.jpg\",\n  \"47789.jpg\": \"Reptilia/Hypsiglena jani/62c203d6d562e3b57180097ed30278bd.jpg\",\n  \"477945.jpg\": \"Reptilia/Boa constrictor/68bdc3921b4c0eb1c4fdf7fa049480bf.jpg\",\n  \"477948.jpg\": \"Reptilia/Boa constrictor/26de3d06d3c1c53d0560910d57bc8c75.jpg\",\n  \"477953.jpg\": \"Reptilia/Boa constrictor/67084dff0cdf5efba0e51441bf6c52d1.jpg\",\n  \"47796.jpg\": \"Reptilia/Hypsiglena jani/e42297fbbaeb28aa283d1f0bd78b087f.jpg\",\n  \"477988.jpg\": \"Insecta/Coenonympha tullia/83dacdbab4ee0e6d8100e8cbffc8c32a.jpg\",\n  \"47799.jpg\": \"Reptilia/Hypsiglena jani/6fce8ad9c12eb96ce626c22fa4eef8aa.jpg\",\n  \"477990.jpg\": \"Insecta/Coenonympha tullia/41972133bd383a823ef67158ebbb1be7.jpg\",\n  \"477995.jpg\": \"Insecta/Coenonympha tullia/985e78b695ed4ed3099a83f0be48985a.jpg\",\n  \"477998.jpg\": \"Insecta/Coenonympha tullia/d2a724f1ae879c6657d5475c38e17591.jpg\",\n  \"47800.jpg\": \"Reptilia/Hypsiglena jani/fb4ba89b865323eb3134ca4d8e30ba6e.jpg\",\n  \"47801.jpg\": \"Reptilia/Hypsiglena jani/1376b33340409f8c31e99fc65cfff947.jpg\",\n  \"478015.jpg\": \"Insecta/Coenonympha tullia/5a140345d40d31941a214bddb6c19d77.jpg\",\n  \"478020.jpg\": \"Insecta/Coenonympha tullia/9fa917d583126f311dd6266bd77861b9.jpg\",\n  \"478024.jpg\": \"Insecta/Coenonympha tullia/6a2df6a1c6b6b67ab5f779df6e9f86bf.jpg\",\n  \"478025.jpg\": \"Insecta/Coenonympha tullia/f5460e70f4ac03e4888650c3d8994e07.jpg\",\n  \"47803.jpg\": \"Reptilia/Hypsiglena jani/27f9a396fdd2cc7181e69e6a26266731.jpg\",\n  \"478033.jpg\": \"Insecta/Coenonympha tullia/9652184578d9430355e1a1d3d632bce5.jpg\",\n  \"478048.jpg\": \"Aves/Merops orientalis/eefe6fe578f72851e0ddaf6b0d5665b1.jpg\",\n  \"47805.jpg\": \"Reptilia/Hypsiglena jani/3ac84107990899bd74225992a3ed4de0.jpg\",\n  \"47806.jpg\": \"Reptilia/Hypsiglena jani/fd008755c349d9dba0ea89948db999a3.jpg\",\n  \"478076.jpg\": \"Insecta/Argia translata/18a56e91f9735c378ceb21dc88068075.jpg\",\n  \"478090.jpg\": \"Insecta/Argia translata/d135540d0e62fbf8581f97d0a2865232.jpg\",\n  \"478097.jpg\": \"Insecta/Argia translata/97c85a8e363638396bc432af09bb4910.jpg\",\n  \"47811.jpg\": \"Reptilia/Hypsiglena jani/9d75521b4245cc1c27864f6c52122870.jpg\",\n  \"478114.jpg\": \"Insecta/Argia translata/86ad80be6c6e89468d0bf048a4c27467.jpg\",\n  \"47812.jpg\": \"Reptilia/Hypsiglena jani/d6f4698cd2747666415fa3f3636020e0.jpg\",\n  \"478121.jpg\": \"Insecta/Argia translata/29b121105d36a66d298c561e1a33526d.jpg\",\n  \"478122.jpg\": \"Insecta/Argia translata/518d84c6dbae8d9bdff2ab1cebc43261.jpg\",\n  \"478125.jpg\": \"Insecta/Argia translata/d3c474f5d1268bd106d5f129a25832aa.jpg\",\n  \"478128.jpg\": \"Insecta/Argia translata/2d9266720aac422ae7e9182b2de4b885.jpg\",\n  \"478136.jpg\": \"Insecta/Argia translata/866eed73fa0b1cde1054d4f1417b09bb.jpg\",\n  \"47817.jpg\": \"Reptilia/Hypsiglena jani/3d5794ab3b185f46a502592858a3c66b.jpg\",\n  \"478183.jpg\": \"Insecta/Tramea onusta/2b547fb59e025c3414694b0b7f2c080f.jpg\",\n  \"47819.jpg\": \"Reptilia/Hypsiglena jani/9412e670814d30ca26516eeb993ac414.jpg\",\n  \"478196.jpg\": \"Insecta/Tramea onusta/02af3fc0ee2783548c199414ae57d079.jpg\",\n  \"478217.jpg\": \"Insecta/Tramea onusta/563073e20829e12a11b011fa9143ae2d.jpg\",\n  \"47823.jpg\": \"Reptilia/Hypsiglena jani/0b9094e56ec0478bd7c98b25c36a3001.jpg\",\n  \"47828.jpg\": \"Reptilia/Hypsiglena jani/00a0af1e934f5f57ce221f59bcce96f6.jpg\",\n  \"478284.jpg\": \"Insecta/Tramea onusta/2ea2df686748011cfd2781e73372d019.jpg\",\n  \"47829.jpg\": \"Reptilia/Hypsiglena jani/edfb11b475a71420c78613639bf49925.jpg\",\n  \"47830.jpg\": \"Reptilia/Hypsiglena jani/221fb9976e5b64ff3ff8aed90c7d4446.jpg\",\n  \"478300.jpg\": \"Insecta/Tramea onusta/39a03f005c1c2aab72b6b4f78a39e6ab.jpg\",\n  \"478303.jpg\": \"Insecta/Enallagma vesperum/872cc75b00d4d64a671060122782aec1.jpg\",\n  \"478305.jpg\": \"Insecta/Enallagma vesperum/11b62668e4def6a166b4da56d7dcd89e.jpg\",\n  \"478326.jpg\": \"Insecta/Sympetrum pallipes/f94b23ec9de244679f809953a32c8dc5.jpg\",\n  \"478330.jpg\": \"Insecta/Sympetrum pallipes/ef794c95b12d7deef10d76216d60b8b0.jpg\",\n  \"478332.jpg\": \"Insecta/Sympetrum pallipes/9566cabed6aecdb156b9a5ba96e03b5a.jpg\",\n  \"478350.jpg\": \"Insecta/Sympetrum pallipes/2877a1dbfebbe0c3235aa4fb9c99ee8b.jpg\",\n  \"478351.jpg\": \"Insecta/Sympetrum pallipes/4b818cc34aa2b73a70c21a9f5265df2a.jpg\",\n  \"478356.jpg\": \"Insecta/Sympetrum pallipes/cb5ea569278a1c557457f7e7146aca82.jpg\",\n  \"478365.jpg\": \"Insecta/Sympetrum pallipes/d61299ec842cc317895e10acde310549.jpg\",\n  \"4784.jpg\": \"Aves/Cyanoramphus novaezelandiae/edaae4c03aa2546425efb93912ee7736.jpg\",\n  \"478436.jpg\": \"Reptilia/Chrysemys picta marginata/87d8ba951a9fbab96c32a64d830f5a3c.jpg\",\n  \"478462.jpg\": \"Reptilia/Chrysemys picta marginata/b3b6580be4ad7fb42d05ace6ea7229e5.jpg\",\n  \"478473.jpg\": \"Reptilia/Chrysemys picta marginata/7f74eb0cf1f1d0765472c1b3e0def4a1.jpg\",\n  \"478476.jpg\": \"Reptilia/Chrysemys picta marginata/b9df7efe6f34c1e1ef779ea5876c16af.jpg\",\n  \"478482.jpg\": \"Reptilia/Chrysemys picta marginata/6bf07e15b920573d47e41d8682239021.jpg\",\n  \"478486.jpg\": \"Reptilia/Chrysemys picta marginata/3330385643809aba84f277a0994d6c08.jpg\",\n  \"478597.jpg\": \"Mollusca/Calliostoma ligatum/4fdd4729c6898149bcce2798e84446a8.jpg\",\n  \"478600.jpg\": \"Mollusca/Calliostoma ligatum/92c9a94a38c7abd9f1da380774c3f874.jpg\",\n  \"478605.jpg\": \"Mollusca/Calliostoma ligatum/d44eaf3c3b966b4b9aca500ac9674c68.jpg\",\n  \"478606.jpg\": \"Mollusca/Calliostoma ligatum/6923e413df64426de4b88095770edb7b.jpg\",\n  \"478607.jpg\": \"Mollusca/Calliostoma ligatum/ebf1da4edb2e71b5544975da51f63dd0.jpg\",\n  \"478608.jpg\": \"Mollusca/Calliostoma ligatum/a658939c273a995cb1003e4f4c7f80a8.jpg\",\n  \"478617.jpg\": \"Mollusca/Calliostoma ligatum/f07123f876c9a5b6f455eff7f78ce3e4.jpg\",\n  \"478618.jpg\": \"Mollusca/Calliostoma ligatum/6cdf76c619634468da1dabec1163482e.jpg\",\n  \"478619.jpg\": \"Mollusca/Calliostoma ligatum/302d429a33a10099feffef07316bb17e.jpg\",\n  \"478620.jpg\": \"Mollusca/Calliostoma ligatum/2d32f333486ba6764a1bd92cfbdad579.jpg\",\n  \"478661.jpg\": \"Animalia/Physalia physalis/4cf8cc42521f0ee391f68f7c97aa49bb.jpg\",\n  \"478662.jpg\": \"Animalia/Physalia physalis/8b14692c6e9cd6bf3fdc4c775a5472f0.jpg\",\n  \"478674.jpg\": \"Animalia/Physalia physalis/409fe00f8b874b3c4abfcea673ee55e0.jpg\",\n  \"478679.jpg\": \"Animalia/Physalia physalis/3169411da593b4791797c6a2d0579817.jpg\",\n  \"478681.jpg\": \"Animalia/Physalia physalis/cf7cd52b777c9f66f28dd91e9139cef5.jpg\",\n  \"478683.jpg\": \"Animalia/Physalia physalis/7948b0c209c0b322095f1c74ca1dae6a.jpg\",\n  \"4787.jpg\": \"Aves/Cyanoramphus novaezelandiae/b9ff64958a6ea513a233ebaf09fcaced.jpg\",\n  \"47883.jpg\": \"Insecta/Sympetrum obtrusum/7f6f9388c596b178009eb21a49c9dae5.jpg\",\n  \"47894.jpg\": \"Insecta/Sympetrum obtrusum/bcf2faf1fd4d8ff4b359d7b4a2b2d6a9.jpg\",\n  \"47895.jpg\": \"Insecta/Sympetrum obtrusum/1d5d9dd5273649d3d919791cd22e43f2.jpg\",\n  \"478954.jpg\": \"Aves/Aythya americana/f9381991ac11f129a170fe8bc4eac7ad.jpg\",\n  \"478999.jpg\": \"Aves/Aythya americana/b619a8f9061ae6a0875119605a02275e.jpg\",\n  \"479045.jpg\": \"Mammalia/Loxodonta africana/cc38e9494323a6a6f4e6958f6fc78c90.jpg\",\n  \"479049.jpg\": \"Mammalia/Loxodonta africana/2295034ff2758a7c329527759fff8da8.jpg\",\n  \"479053.jpg\": \"Mammalia/Loxodonta africana/5c8c8010e2aa23b13345cbceaa9d8e43.jpg\",\n  \"479068.jpg\": \"Mammalia/Loxodonta africana/5a22cce349f45a41037d4fbd30151898.jpg\",\n  \"47907.jpg\": \"Insecta/Sympetrum obtrusum/df1e36756b2cc01fa4b17d8da366581d.jpg\",\n  \"479081.jpg\": \"Aves/Tyrannus dominicensis/b434e9c531ce372f1534cd23e1263225.jpg\",\n  \"479087.jpg\": \"Aves/Tyrannus dominicensis/a3a42713ddd9faf8f3360b35dd1659fb.jpg\",\n  \"47909.jpg\": \"Insecta/Sympetrum obtrusum/8f502c7267d1668222e9742fd420e2ef.jpg\",\n  \"479095.jpg\": \"Aves/Tyrannus dominicensis/9a8a5c8045e911792b3955d3b1394da5.jpg\",\n  \"4791.jpg\": \"Aves/Cyanoramphus novaezelandiae/361da073725343db05492665c71ca3f3.jpg\",\n  \"47910.jpg\": \"Insecta/Sympetrum obtrusum/05787ff47da5e05738100a04e2ecd59d.jpg\",\n  \"47911.jpg\": \"Insecta/Sympetrum obtrusum/3f7d9c18a2de8ec8330b7ee928bfff6c.jpg\",\n  \"47913.jpg\": \"Insecta/Sympetrum obtrusum/2829a5047abd29767d93593717922c60.jpg\",\n  \"479150.jpg\": \"Insecta/Battus philenor hirsuta/3878896a7adc73fbfea4b60558ca6b97.jpg\",\n  \"479152.jpg\": \"Insecta/Battus philenor hirsuta/061e145c6785f5510e1585bf4445f016.jpg\",\n  \"479153.jpg\": \"Insecta/Battus philenor hirsuta/b6b471952710483d292f8fbf34bbd28b.jpg\",\n  \"479154.jpg\": \"Insecta/Battus philenor hirsuta/ea13784543e5f856a9f811db584b4832.jpg\",\n  \"479160.jpg\": \"Insecta/Battus philenor hirsuta/9f9bc2440d7bff968bf52a79e276eaae.jpg\",\n  \"479166.jpg\": \"Insecta/Battus philenor hirsuta/b5b2f33595ee90b6ed4257a79c520ad4.jpg\",\n  \"47918.jpg\": \"Insecta/Sympetrum obtrusum/72d84925d4ce26b988e9d71b3c3d5869.jpg\",\n  \"479182.jpg\": \"Insecta/Battus philenor hirsuta/e0e0e9c5a1687518eefdd7871df47020.jpg\",\n  \"479183.jpg\": \"Insecta/Battus philenor hirsuta/fe404c7f7ec751dba2da7d3c9b7bbd05.jpg\",\n  \"479188.jpg\": \"Insecta/Battus philenor hirsuta/91b9737fdcf388f145d7aa1823d5cdd0.jpg\",\n  \"47919.jpg\": \"Insecta/Sympetrum obtrusum/9a9b8c0208fc66a84440178cab69e1e2.jpg\",\n  \"47920.jpg\": \"Insecta/Sympetrum obtrusum/bb1691075596cb3f7c9a23c7a664741a.jpg\",\n  \"47921.jpg\": \"Insecta/Sympetrum obtrusum/16b448e0b8c7493a22c65109550e5a82.jpg\",\n  \"47922.jpg\": \"Insecta/Sympetrum obtrusum/0b778390a5ce0bb38fb678c2a295695b.jpg\",\n  \"47923.jpg\": \"Insecta/Sympetrum obtrusum/ccd8e0b5a28e115e934513e8dec57b2e.jpg\",\n  \"47926.jpg\": \"Insecta/Sympetrum obtrusum/96a1e06f7f85ea258f8c5e6552403318.jpg\",\n  \"479270.jpg\": \"Aves/Melanerpes chrysogenys/bf3c6ab7226edbbcc7b74f6fceb54eb5.jpg\",\n  \"479283.jpg\": \"Aves/Melanerpes chrysogenys/7de71c7c8b3c236fe4c4d688baf36881.jpg\",\n  \"479286.jpg\": \"Aves/Melanerpes chrysogenys/90b803934f601f4bfbe45e9e3692e066.jpg\",\n  \"479287.jpg\": \"Aves/Melanerpes chrysogenys/3994ae5cdfa9dd062b4fef20b0c54cda.jpg\",\n  \"479290.jpg\": \"Aves/Melanerpes chrysogenys/ddb909f4f305045289a4c651019d9fd2.jpg\",\n  \"479292.jpg\": \"Aves/Melanerpes chrysogenys/27be3ca1799ab7d189680c5d3b84d6d0.jpg\",\n  \"479298.jpg\": \"Aves/Melanerpes chrysogenys/5762e454a8b8683374b19b7b8fb9285b.jpg\",\n  \"4793.jpg\": \"Aves/Cyanoramphus novaezelandiae/918bce1318af4dcfc8d3bf9e31928a04.jpg\",\n  \"479303.jpg\": \"Mammalia/Mustela erminea/b72e3e1c2a82b020acdf8f231298c89c.jpg\",\n  \"47931.jpg\": \"Insecta/Sympetrum obtrusum/5f4877fd32302d2d07cfdaea2dab3c42.jpg\",\n  \"479310.jpg\": \"Mammalia/Mustela erminea/40b2b0ec49f7e839e65ee3582642a77b.jpg\",\n  \"47932.jpg\": \"Insecta/Sympetrum obtrusum/43660ced287d43e37f44d9a4be4071eb.jpg\",\n  \"479339.jpg\": \"Insecta/Polygonia satyrus/aa2f340607b81b5f4fe57ccc78bbd9de.jpg\",\n  \"479340.jpg\": \"Insecta/Polygonia satyrus/6256acd7d0ba0ab8f2a30cd4e9ea07c4.jpg\",\n  \"479341.jpg\": \"Insecta/Polygonia satyrus/27ea7e96c6ca7a86de759679ab4b24f1.jpg\",\n  \"479346.jpg\": \"Insecta/Polygonia satyrus/e657ad25eecb94d656e51306feff428f.jpg\",\n  \"479350.jpg\": \"Insecta/Polygonia satyrus/c6a27d32d405fa9cd0ecf719cc2d09cc.jpg\",\n  \"479353.jpg\": \"Insecta/Polygonia satyrus/b927f1282f5389081c698a32f8ae3a47.jpg\",\n  \"479354.jpg\": \"Insecta/Polygonia satyrus/25fcdfc876f1d44a4bd14683b5e1b0ad.jpg\",\n  \"479355.jpg\": \"Insecta/Polygonia satyrus/9ae9a6810ed8b68bb5c4076cf637442f.jpg\",\n  \"479357.jpg\": \"Insecta/Polygonia satyrus/b0e731e74c44cd2c0a40a150039310bb.jpg\",\n  \"479360.jpg\": \"Insecta/Polygonia satyrus/ca6436a3ee8695a6b8f95dfc5a033318.jpg\",\n  \"47938.jpg\": \"Insecta/Sympetrum obtrusum/e4fb1665f68fb48f9e82bce9d3d2d820.jpg\",\n  \"47941.jpg\": \"Insecta/Sympetrum obtrusum/02d22a937740c820221d81bb795434df.jpg\",\n  \"47945.jpg\": \"Insecta/Sympetrum obtrusum/3aa63f14b4cd25f540d2a58eb9aabcf8.jpg\",\n  \"47946.jpg\": \"Insecta/Sympetrum obtrusum/9aed4e446dc818b2e2126eec47262c08.jpg\",\n  \"479464.jpg\": \"Reptilia/Crotalus oreganus/feff17236b5852c3e225d02ceae6b2ef.jpg\",\n  \"479469.jpg\": \"Reptilia/Crotalus oreganus/0b5ed306507cf83944fde38d2c895304.jpg\",\n  \"479470.jpg\": \"Reptilia/Crotalus oreganus/5126e4b6238df4cd64d6e999bf66c470.jpg\",\n  \"479476.jpg\": \"Reptilia/Crotalus oreganus/9ed7939cfbd378356efe5852a29676c4.jpg\",\n  \"479477.jpg\": \"Reptilia/Crotalus oreganus/f5b2f4444ae821f69d5b3267c94c5fff.jpg\",\n  \"479479.jpg\": \"Reptilia/Crotalus oreganus/938637c9906db46588a6a4cb5714afdb.jpg\",\n  \"47948.jpg\": \"Insecta/Sympetrum obtrusum/2b034f2ad0df2d22107078f500e0a682.jpg\",\n  \"479485.jpg\": \"Reptilia/Crotalus oreganus/3ea60b448676f52a1ba202c6125f520a.jpg\",\n  \"479486.jpg\": \"Reptilia/Crotalus oreganus/b2b6c7193df0dd36830a301530ca3230.jpg\",\n  \"479490.jpg\": \"Reptilia/Crotalus oreganus/1bc9578e5940120931b65cd2779e1d1f.jpg\",\n  \"479491.jpg\": \"Reptilia/Crotalus oreganus/8ca790e000e2a3a0935081e19ee231f6.jpg\",\n  \"479539.jpg\": \"Aves/Patagioenas flavirostris/f5cc16c94026feca0f7a47bb812c7bca.jpg\",\n  \"479540.jpg\": \"Aves/Patagioenas flavirostris/c1d1a6e9d3988fad42ef463e72f6c3e4.jpg\",\n  \"479544.jpg\": \"Aves/Patagioenas flavirostris/bbb40c936559531f96cc47f2c82cc305.jpg\",\n  \"479545.jpg\": \"Aves/Patagioenas flavirostris/90ebcf6dd648aa805b2aedddfb847d20.jpg\",\n  \"479550.jpg\": \"Aves/Patagioenas flavirostris/0ca58385aafd1b43d6ec599637de3533.jpg\",\n  \"479551.jpg\": \"Aves/Patagioenas flavirostris/ac2e70538430f3842cdac653474caa8b.jpg\",\n  \"479558.jpg\": \"Aves/Patagioenas flavirostris/f1396fb6c8e2ef2fac886655d179bf6a.jpg\",\n  \"479559.jpg\": \"Aves/Patagioenas flavirostris/ab282de97a63fc607012c377264cf811.jpg\",\n  \"479560.jpg\": \"Aves/Patagioenas flavirostris/d173fcc8b93b12098afa7a57c683afef.jpg\",\n  \"479561.jpg\": \"Aves/Patagioenas flavirostris/6883d54ba0c580be620bad60a3378c50.jpg\",\n  \"479564.jpg\": \"Aves/Patagioenas flavirostris/20596244680ad0f34da29b041f14a7f8.jpg\",\n  \"479565.jpg\": \"Aves/Patagioenas flavirostris/0c9591de19ec6b35c0d7484047c8f565.jpg\",\n  \"479570.jpg\": \"Aves/Tyrannus savana/3c14892948519e1ee32fe908eecbf824.jpg\",\n  \"479574.jpg\": \"Aves/Tyrannus savana/700889326a2dcd0a124e86686ba7a877.jpg\",\n  \"479591.jpg\": \"Insecta/Aphomia sociella/1c5bf846a2fc0750029f1df416f1c445.jpg\",\n  \"479592.jpg\": \"Insecta/Aphomia sociella/11c133aadcf63073d38033690e680302.jpg\",\n  \"479620.jpg\": \"Insecta/Metanema inatomaria/c179f1b6586596ad6c66a1f92788ec41.jpg\",\n  \"479637.jpg\": \"Aves/Setophaga coronata coronata/f9ecdac340837345cf2dba0ffe3a5e67.jpg\",\n  \"479657.jpg\": \"Aves/Setophaga coronata coronata/e74c58765c8fdbe6aa8311e86add305e.jpg\",\n  \"479668.jpg\": \"Aves/Setophaga coronata coronata/4bac65101c123021122843c51dbbe411.jpg\",\n  \"479679.jpg\": \"Aves/Setophaga coronata coronata/12bda0e4e6d4dced0cadcbc0c932c67a.jpg\",\n  \"479686.jpg\": \"Aves/Setophaga coronata coronata/1b82c8d2e16a5e97cecc89affedfe2ae.jpg\",\n  \"479687.jpg\": \"Aves/Setophaga coronata coronata/fe186eb978a6d291e447bda21567da18.jpg\",\n  \"479696.jpg\": \"Aves/Setophaga coronata coronata/314fd9a04756ed2ed09274c239b87b44.jpg\",\n  \"479717.jpg\": \"Reptilia/Zootoca vivipara/6f2340571a500e54cd058846004e8ad8.jpg\",\n  \"479731.jpg\": \"Insecta/Phoebis agarithe/2ebe897ca03ff46a87a3b4491c3a96e8.jpg\",\n  \"479732.jpg\": \"Insecta/Phoebis agarithe/3c968552db88034f85061bb66a172630.jpg\",\n  \"479734.jpg\": \"Insecta/Phoebis agarithe/87480de7edfc0851976a27498047b795.jpg\",\n  \"479737.jpg\": \"Insecta/Phoebis agarithe/5b326826b4f846dd5c86d543d4e64cd2.jpg\",\n  \"479748.jpg\": \"Insecta/Phoebis agarithe/25515c81e45240461f439e754563260c.jpg\",\n  \"479749.jpg\": \"Insecta/Phoebis agarithe/5ad52eaf11c257914e2835714a887650.jpg\",\n  \"479750.jpg\": \"Insecta/Phoebis agarithe/6d4ce155496c5576294e650dca022437.jpg\",\n  \"479752.jpg\": \"Insecta/Phoebis agarithe/932f09264df7d25097d500846affad48.jpg\",\n  \"479756.jpg\": \"Insecta/Phoebis agarithe/8f4579cbbb72a15585b9a3629dbef32e.jpg\",\n  \"479762.jpg\": \"Insecta/Phoebis agarithe/376efcbca237f1f1ea259ac360aa0365.jpg\",\n  \"479763.jpg\": \"Insecta/Phoebis agarithe/23b4e9eaffe5cd15fa57183a99cc7126.jpg\",\n  \"479770.jpg\": \"Insecta/Lycaena phlaeas hypophlaeas/beb9afa4625741182c6b9faa45873d26.jpg\",\n  \"479771.jpg\": \"Insecta/Lycaena phlaeas hypophlaeas/ba0d3002bdeff55ea11eba08420273fa.jpg\",\n  \"479773.jpg\": \"Insecta/Lycaena phlaeas hypophlaeas/1a148d504615a0ca82f231afa1cc4614.jpg\",\n  \"479774.jpg\": \"Insecta/Lycaena phlaeas hypophlaeas/eb99c5a1fff8142d91cd961e28310bc8.jpg\",\n  \"479776.jpg\": \"Insecta/Prolimacodes badia/76235da6ffe55205a5fadb7f223b5cf1.jpg\",\n  \"47978.jpg\": \"Aves/Tadorna tadorna/5676aebe834650438f38aff2ca7939d4.jpg\",\n  \"479785.jpg\": \"Amphibia/Rana temporaria/6dae00925b5c06454f092243a5aeca1a.jpg\",\n  \"4798.jpg\": \"Aves/Cyanoramphus novaezelandiae/ad837b83e8a6271b5fce79a5fcb0b6e6.jpg\",\n  \"47981.jpg\": \"Aves/Chroicocephalus scopulinus/2d3528d17e0809578f269eeb827985c5.jpg\",\n  \"479813.jpg\": \"Amphibia/Rana temporaria/6c1bf8c8646fef34f3a590c40a5104ae.jpg\",\n  \"479816.jpg\": \"Amphibia/Rana temporaria/9f99d26aec8b4d14cddb11c1bdecade6.jpg\",\n  \"479823.jpg\": \"Amphibia/Rana temporaria/566b80a88b4ecc02eefc7701ba693190.jpg\",\n  \"479824.jpg\": \"Amphibia/Rana temporaria/38fac2b8fd8007b17e2c1301d6958fc1.jpg\",\n  \"479846.jpg\": \"Amphibia/Rana temporaria/fc507679f60f45fe3163c3c36b731bdf.jpg\",\n  \"47987.jpg\": \"Aves/Chroicocephalus scopulinus/38b5d7f3943a883d9817a274db03dca8.jpg\",\n  \"479870.jpg\": \"Mollusca/Limax maximus/0f6cfd2848c8e0a00ce6a04009ba8e38.jpg\",\n  \"479879.jpg\": \"Mollusca/Limax maximus/51ae6415aa11ee9e4c65846ab24a3ea2.jpg\",\n  \"4799.jpg\": \"Aves/Cyanoramphus novaezelandiae/404fe7b9b841057eea67f834d3fd4cf4.jpg\",\n  \"47991.jpg\": \"Aves/Chroicocephalus scopulinus/b8931ced95729a173ad60f568262dca7.jpg\",\n  \"479920.jpg\": \"Mollusca/Limax maximus/53ae0616cb3384b7bb63ba3897a1e5c2.jpg\",\n  \"47994.jpg\": \"Aves/Chroicocephalus scopulinus/fa06e6d1fbf1601012526b3ebfde79c5.jpg\",\n  \"479999.jpg\": \"Aves/Molothrus aeneus/6a15db373604f2f9aa53c2924ac93877.jpg\",\n  \"480033.jpg\": \"Aves/Molothrus aeneus/9b5a193a80534cd3aa0242525c05f31d.jpg\",\n  \"480044.jpg\": \"Aves/Molothrus aeneus/b4b52639966d005260cfe1bc5cba1601.jpg\",\n  \"480052.jpg\": \"Aves/Molothrus aeneus/63c1ad2eea64c807589adb601d9f7b29.jpg\",\n  \"480058.jpg\": \"Insecta/Polydrusus formosus/e6d887cd6c08458b792751f81a121162.jpg\",\n  \"480079.jpg\": \"Aves/Sitta pygmaea/8cd3fee216165c615116b7c24184d9c7.jpg\",\n  \"480080.jpg\": \"Aves/Sitta pygmaea/82f51967cc024ced26018d174fe686d2.jpg\",\n  \"480084.jpg\": \"Aves/Sitta pygmaea/5551410caee6654528dd87f1bb3eb401.jpg\",\n  \"480087.jpg\": \"Aves/Sitta pygmaea/08866f30d1df5da9567e08c0bfd66883.jpg\",\n  \"480100.jpg\": \"Aves/Sitta pygmaea/3143b0199520abd40caf3fb10bf41657.jpg\",\n  \"480101.jpg\": \"Aves/Sitta pygmaea/c4fce4e8b934f6686ef9a2535b71ae06.jpg\",\n  \"480113.jpg\": \"Aves/Sitta pygmaea/8f88a16207916df86e1cfac0794b8d13.jpg\",\n  \"480130.jpg\": \"Reptilia/Nerodia clarkii clarkii/44af155a92ec8c6709b202f843fd5d3a.jpg\",\n  \"480133.jpg\": \"Reptilia/Nerodia clarkii clarkii/2d66ba5d4b84cb99d908024294f7b6a4.jpg\",\n  \"480138.jpg\": \"Insecta/Clostera albosigma/ac0a1b1d5b1f87fa195eacf7ead38e51.jpg\",\n  \"480142.jpg\": \"Insecta/Clostera albosigma/10c39ef284606e1da011a30d8a2908ca.jpg\",\n  \"480160.jpg\": \"Reptilia/Thamnophis marcianus/06194772ab84c3592522884417fa4b30.jpg\",\n  \"480164.jpg\": \"Reptilia/Thamnophis marcianus/c3048e9f0068af2a8c3aea3bf539859b.jpg\",\n  \"480168.jpg\": \"Reptilia/Thamnophis marcianus/e8666790cec8e0fb6d827fee4e6f8381.jpg\",\n  \"480181.jpg\": \"Reptilia/Thamnophis marcianus/a282f51e1aabc1bbc3abd521f0eb540a.jpg\",\n  \"480185.jpg\": \"Reptilia/Thamnophis marcianus/a1d7bf067781dc9d9387450542e17e36.jpg\",\n  \"48020.jpg\": \"Aves/Chroicocephalus scopulinus/6ab4ea363fdf83cb63b48d6f0f47ee39.jpg\",\n  \"480218.jpg\": \"Reptilia/Thamnophis marcianus/fe574145b0fedbf0d7114f012d52d1ca.jpg\",\n  \"480219.jpg\": \"Reptilia/Thamnophis marcianus/7dc1919229cd9bf383eb41e4038da47a.jpg\",\n  \"480222.jpg\": \"Reptilia/Thamnophis marcianus/0191b11a65402abad8d31a3127e58ccd.jpg\",\n  \"480225.jpg\": \"Reptilia/Thamnophis marcianus/69e1d8813eec0a3d4871110f40488ebc.jpg\",\n  \"48028.jpg\": \"Aves/Chroicocephalus scopulinus/7fa78b5cdd0932792056b1d32f5b4748.jpg\",\n  \"480312.jpg\": \"Aves/Meleagris gallopavo/c0d19b1a9bca79e311a25f7eafb8a3c4.jpg\",\n  \"480317.jpg\": \"Aves/Meleagris gallopavo/deb72f3fe4be34d39e163980902f3e3a.jpg\",\n  \"48036.jpg\": \"Aves/Chroicocephalus scopulinus/52f6615ddaa4ce5f459c8c607775ec79.jpg\",\n  \"480395.jpg\": \"Aves/Meleagris gallopavo/e65ee930fd8bab9f0bcfb860a1e92d78.jpg\",\n  \"48046.jpg\": \"Aves/Chroicocephalus scopulinus/da0a2a58915f5b2d359ef098e7e2b3db.jpg\",\n  \"48050.jpg\": \"Aves/Chroicocephalus scopulinus/8a789132ce8066f67b6d9ec34e4dc604.jpg\",\n  \"48062.jpg\": \"Aves/Chroicocephalus scopulinus/84803a50351f46fe810737fc6dd25b00.jpg\",\n  \"48066.jpg\": \"Aves/Chroicocephalus scopulinus/c06e7125994cd76314a0e24fe07cdf5a.jpg\",\n  \"480769.jpg\": \"Aves/Meleagris gallopavo/26e7769c700d06d9ae03555b149faa7a.jpg\",\n  \"480794.jpg\": \"Aves/Calidris alpina/40879ab4e121e36b8347421ccf8acb2d.jpg\",\n  \"48080.jpg\": \"Aves/Chroicocephalus scopulinus/0508d3a086b41d231f3cc209382a3e9f.jpg\",\n  \"480802.jpg\": \"Aves/Calidris alpina/46858af7e5c6a0dd4e7ef0129b5f8869.jpg\",\n  \"480808.jpg\": \"Aves/Calidris alpina/d5cd44fb6fc36486cb40cd50b5a3dfd2.jpg\",\n  \"480811.jpg\": \"Aves/Calidris alpina/c0e3a3ca697ec95df1dd445db5018bc0.jpg\",\n  \"48085.jpg\": \"Aves/Chroicocephalus scopulinus/fa53d68d38d072f1a7c31e4209aef2bb.jpg\",\n  \"48086.jpg\": \"Aves/Chroicocephalus scopulinus/844230e78482d8dbc359ffd4b2426ec9.jpg\",\n  \"480876.jpg\": \"Aves/Calidris alpina/2a02b8f110516bde9abaf1261a945e82.jpg\",\n  \"4809.jpg\": \"Aves/Cyanoramphus novaezelandiae/0174a913af98b4bef7ca9dd8e667e62d.jpg\",\n  \"48091.jpg\": \"Aves/Nyctibius jamaicensis/43d438013d34da150d0f3d275a132e3c.jpg\",\n  \"48098.jpg\": \"Aves/Nyctibius jamaicensis/77337e73b280f284f3b82a24aec2acfc.jpg\",\n  \"48102.jpg\": \"Aves/Nyctibius jamaicensis/d32aa48188af61e5fabc9dec2713dfea.jpg\",\n  \"48107.jpg\": \"Aves/Nyctibius jamaicensis/8a6ceaba42ca7cac4503df5138a95e73.jpg\",\n  \"48111.jpg\": \"Arachnida/Pholcus phalangioides/9fc2832a14db9ca3cd687944ec35bcb7.jpg\",\n  \"481111.jpg\": \"Aves/Melozone fusca/53845d3c25b00bb886d6453640fd9626.jpg\",\n  \"48112.jpg\": \"Arachnida/Pholcus phalangioides/b3b88b61c505c5e8ab4d1c9b5d9a1fda.jpg\",\n  \"48113.jpg\": \"Arachnida/Pholcus phalangioides/c6b3a01dedef11dae9d84f259b5c2df5.jpg\",\n  \"48114.jpg\": \"Arachnida/Pholcus phalangioides/a7ba7fb74ab39646676f7391c28bcf1f.jpg\",\n  \"48116.jpg\": \"Arachnida/Pholcus phalangioides/481e25a375cb1a7f69ceaa3d21a3dbdd.jpg\",\n  \"481182.jpg\": \"Aves/Melozone fusca/b4a7a9483da734cd15ddb464fa4e6921.jpg\",\n  \"48119.jpg\": \"Arachnida/Pholcus phalangioides/896ebca6e352be55db5a60ed8724c751.jpg\",\n  \"48120.jpg\": \"Arachnida/Pholcus phalangioides/4d2897286660afc96ccb3ed0aa1e862c.jpg\",\n  \"481209.jpg\": \"Aves/Melozone fusca/b7b08e6af64ca4a3f69fc3fc740ef148.jpg\",\n  \"481211.jpg\": \"Aves/Melozone fusca/208866d38ecb8725cab40e65a8b50919.jpg\",\n  \"481246.jpg\": \"Insecta/Acanthocephala terminalis/9e9d3943640770a9c0c0cf7e07ecf172.jpg\",\n  \"481249.jpg\": \"Insecta/Acanthocephala terminalis/a806020048feb68b8c307bdf7ee22a6f.jpg\",\n  \"481260.jpg\": \"Insecta/Acanthocephala terminalis/10b20b8a4c3c7620ad6fa04dd3959b1f.jpg\",\n  \"48128.jpg\": \"Arachnida/Pholcus phalangioides/7c911500477273983470addc0be62c8a.jpg\",\n  \"481287.jpg\": \"Insecta/Acanthocephala terminalis/f154111c25c1ca045107fcf10b1b22dd.jpg\",\n  \"48129.jpg\": \"Arachnida/Pholcus phalangioides/3e9b35adeffbf2bc2347be85dd644d01.jpg\",\n  \"481292.jpg\": \"Insecta/Acanthocephala terminalis/bd46c87fab244b18b83f34305a4ed8e1.jpg\",\n  \"481295.jpg\": \"Insecta/Acanthocephala terminalis/167c3c77f5ef92573e3b76bc7955dbdc.jpg\",\n  \"48130.jpg\": \"Arachnida/Pholcus phalangioides/e072629495153324ca4f1d50fd14efd6.jpg\",\n  \"481305.jpg\": \"Insecta/Dythemis nigrescens/f2dc80c850987e64ff3909d3342807eb.jpg\",\n  \"481306.jpg\": \"Insecta/Dythemis nigrescens/2559808bb8ec3451d2fdfb306aff69ea.jpg\",\n  \"48131.jpg\": \"Arachnida/Pholcus phalangioides/35532e6b0d924274cc63b201a93b9e7f.jpg\",\n  \"481313.jpg\": \"Insecta/Dythemis nigrescens/d931ec1e1e69bbfdb2226abb0d83375c.jpg\",\n  \"481315.jpg\": \"Insecta/Dythemis nigrescens/70051ef937711d07f5a9941f0ebf3dcf.jpg\",\n  \"481316.jpg\": \"Insecta/Dythemis nigrescens/8a9e1d7b05cdcb23eed785a904bae8ae.jpg\",\n  \"481321.jpg\": \"Insecta/Dythemis nigrescens/06e75520b4f3591f4b3192e4480b931f.jpg\",\n  \"481323.jpg\": \"Insecta/Dythemis nigrescens/486993ef1483e2baa2f39960654144a1.jpg\",\n  \"481331.jpg\": \"Insecta/Dythemis nigrescens/21a1d253c4af7313c598b65b2df1626f.jpg\",\n  \"48135.jpg\": \"Arachnida/Pholcus phalangioides/d33c3562aa03eeccd877d5d6176a12b3.jpg\",\n  \"48136.jpg\": \"Arachnida/Pholcus phalangioides/1a66f6958e2de5a5426a0c8d18aec9f8.jpg\",\n  \"481369.jpg\": \"Insecta/Lestes australis/b2be1057ae63721d52818b13d40a9867.jpg\",\n  \"481383.jpg\": \"Insecta/Lestes australis/72ff58ad62e7ccfb01bae21f6cc7b845.jpg\",\n  \"481384.jpg\": \"Insecta/Lestes australis/3356b915f8531c52acc3a5838036ce4e.jpg\",\n  \"481385.jpg\": \"Insecta/Lestes australis/4e36c6350e7098c45828e5d4dc12ec3c.jpg\",\n  \"481388.jpg\": \"Insecta/Lestes australis/899e7040c06aaa73567a548c0377f78a.jpg\",\n  \"48139.jpg\": \"Arachnida/Pholcus phalangioides/9d4f4d3000c842002b3d9475b685ac03.jpg\",\n  \"48140.jpg\": \"Arachnida/Pholcus phalangioides/3dc778cb32c5328e2ea8c682c6fa131d.jpg\",\n  \"48141.jpg\": \"Arachnida/Pholcus phalangioides/edce20f0fb5e13395b2aeabdcb707769.jpg\",\n  \"481411.jpg\": \"Insecta/Lestes australis/32d914e6bfca137907f5776faf4714ff.jpg\",\n  \"481422.jpg\": \"Insecta/Lestes australis/289317a3aaaa51f3e7ab8421fd6a7f03.jpg\",\n  \"48143.jpg\": \"Arachnida/Pholcus phalangioides/26b73d73be35dda09e9ae451f60409b1.jpg\",\n  \"481431.jpg\": \"Insecta/Tettigonia viridissima/77e4e709757db8f6ce7a2e9122203fe2.jpg\",\n  \"481432.jpg\": \"Insecta/Tettigonia viridissima/a4af80682fbd09dfb0ff007e31301233.jpg\",\n  \"481433.jpg\": \"Aves/Falcipennis canadensis/57ef87ad87a244a54b4c2c03ae27f948.jpg\",\n  \"481441.jpg\": \"Aves/Falcipennis canadensis/a18319cd2f1bb82f2f4304296ddc7ec3.jpg\",\n  \"48145.jpg\": \"Arachnida/Pholcus phalangioides/66d888fe33207239bcd033e196a3b057.jpg\",\n  \"481450.jpg\": \"Aves/Falcipennis canadensis/3133a56fe54b4cab2ca88ab9db634cd3.jpg\",\n  \"48148.jpg\": \"Arachnida/Pholcus phalangioides/d93ddb961eb659d66722dcd56f38761b.jpg\",\n  \"48149.jpg\": \"Arachnida/Pholcus phalangioides/d3c5c12e339949bc11dc4d24785ce68e.jpg\",\n  \"481545.jpg\": \"Aves/Tachycineta bicolor/1cb85991ba4169ad3cf13f7eecd6b59c.jpg\",\n  \"48155.jpg\": \"Arachnida/Pholcus phalangioides/dc648ddce4f7476cde2adc513f817912.jpg\",\n  \"481550.jpg\": \"Aves/Tachycineta bicolor/7c92519b52b6053796c0d83ed9730021.jpg\",\n  \"481555.jpg\": \"Aves/Tachycineta bicolor/37d03213e52cb7b2dd382753bd26b6f0.jpg\",\n  \"48158.jpg\": \"Insecta/Pieris oleracea/e4dbb8240f35bd5e2889b84c0f887447.jpg\",\n  \"481669.jpg\": \"Aves/Tachycineta bicolor/1de219dbf1c6b42abb9497b118855883.jpg\",\n  \"48167.jpg\": \"Insecta/Pieris oleracea/3eadd1a627f7747675a36fc9064c9673.jpg\",\n  \"481675.jpg\": \"Aves/Tachycineta bicolor/d1eb4ba4ebd1a75180e14bfad2f960a5.jpg\",\n  \"481680.jpg\": \"Aves/Tachycineta bicolor/90d4c570407d9bd60788114ddf0bb401.jpg\",\n  \"48170.jpg\": \"Insecta/Pieris oleracea/773a2a7af8aa4b3b9dd1533e5b71e6c3.jpg\",\n  \"481700.jpg\": \"Aves/Tachycineta bicolor/b68f4d0c4d06e5eff5cdc8eaeef082b5.jpg\",\n  \"48171.jpg\": \"Insecta/Pieris oleracea/38415470ef76a561296da281a2da1948.jpg\",\n  \"48172.jpg\": \"Insecta/Pieris oleracea/ddbecae5906c8ea8aa06fc0d4037ea9e.jpg\",\n  \"48176.jpg\": \"Reptilia/Pituophis catenifer annectens/2af5e98fdf71fac62f410112e92d58a1.jpg\",\n  \"481794.jpg\": \"Insecta/Pyrausta insequalis/dfa4a6b25117e82678cbe42ed05dda69.jpg\",\n  \"481803.jpg\": \"Reptilia/Basiliscus vittatus/2818433c32e5a23f0e46c40f21581b2d.jpg\",\n  \"48182.jpg\": \"Reptilia/Pituophis catenifer annectens/d8f5f20ecf1ba661688eb7707f7e3a72.jpg\",\n  \"481821.jpg\": \"Reptilia/Basiliscus vittatus/c006150a306f111c48f5ee3fe29f50c9.jpg\",\n  \"481826.jpg\": \"Reptilia/Basiliscus vittatus/1779eb0175b0590a7818a4a4123db59c.jpg\",\n  \"481828.jpg\": \"Reptilia/Basiliscus vittatus/2d321d8ed77739dec9f32e296c677d35.jpg\",\n  \"481829.jpg\": \"Reptilia/Basiliscus vittatus/a164b2da31b4eee17682b8bdc2cd71e9.jpg\",\n  \"48185.jpg\": \"Reptilia/Pituophis catenifer annectens/1301fd1ebeba455ea32075046b7a9a23.jpg\",\n  \"48186.jpg\": \"Reptilia/Pituophis catenifer annectens/e70a9026268180da906f4f7dcbb4adc2.jpg\",\n  \"481860.jpg\": \"Reptilia/Basiliscus vittatus/b2ba134d0e72707c0150d2020d745691.jpg\",\n  \"48187.jpg\": \"Reptilia/Pituophis catenifer annectens/1e512d4841ea5d6f00fa2673ad052ac4.jpg\",\n  \"481897.jpg\": \"Aves/Parkesia noveboracensis/3463a855d1b3370337f6d7f4eca604ea.jpg\",\n  \"481903.jpg\": \"Aves/Parkesia noveboracensis/3051b44b16d413b4916a3023228ac864.jpg\",\n  \"481910.jpg\": \"Aves/Parkesia noveboracensis/7d06cb84b94dc17a223f6053d08ea8b1.jpg\",\n  \"481914.jpg\": \"Aves/Parkesia noveboracensis/38bf2f731324f560e8d1e9273a5d7289.jpg\",\n  \"481927.jpg\": \"Aves/Parkesia noveboracensis/5690468366cc5448277cea2e94b3bc97.jpg\",\n  \"481931.jpg\": \"Aves/Parkesia noveboracensis/2cc7e23a29e2a3643793ee215d52af70.jpg\",\n  \"481932.jpg\": \"Aves/Parkesia noveboracensis/0038b4ae0880112aef752b40c8c120aa.jpg\",\n  \"481938.jpg\": \"Aves/Parkesia noveboracensis/28a99daa06e926dbeb2223f578c73824.jpg\",\n  \"48195.jpg\": \"Reptilia/Pituophis catenifer annectens/9ed24539fe55edc86f3cf124c064e21b.jpg\",\n  \"481975.jpg\": \"Insecta/Daphnis nerii/204962896705ece31142506e59a744a7.jpg\",\n  \"481979.jpg\": \"Insecta/Daphnis nerii/5efc27cd54f2961e9e929e1698b6860b.jpg\",\n  \"48198.jpg\": \"Reptilia/Pituophis catenifer annectens/2dbfcf608e995593286046fe14e0391b.jpg\",\n  \"481980.jpg\": \"Insecta/Daphnis nerii/bfb2ab958a71036ecbf6287ae9e4c451.jpg\",\n  \"48199.jpg\": \"Reptilia/Pituophis catenifer annectens/0f42e0191b1b2daab0c7c25b8ff48668.jpg\",\n  \"48200.jpg\": \"Reptilia/Pituophis catenifer annectens/8b7ad80adf2dc52172311a0bbac92416.jpg\",\n  \"48201.jpg\": \"Reptilia/Pituophis catenifer annectens/b0cc530dd24fc962232c272555596efd.jpg\",\n  \"48202.jpg\": \"Reptilia/Pituophis catenifer annectens/30d66efbd19108a48ae25e2533ca213f.jpg\",\n  \"482089.jpg\": \"Aves/Hydroprogne caspia/d3408ff4cb2932211fd0a6db436c3acf.jpg\",\n  \"48209.jpg\": \"Reptilia/Pituophis catenifer annectens/f9d1b814a73849b77cd39e45e5aee771.jpg\",\n  \"482175.jpg\": \"Aves/Hydroprogne caspia/8dd363c9451addc7e15084ad4cf04999.jpg\",\n  \"48219.jpg\": \"Reptilia/Pituophis catenifer annectens/da13ea10da9aaf3dda8b5240a26a7382.jpg\",\n  \"482204.jpg\": \"Insecta/Leptinotarsa decemlineata/97834d222ce5d38951528d128b58abab.jpg\",\n  \"48221.jpg\": \"Reptilia/Pituophis catenifer annectens/f1ff135a2567f902de76b1d98e6acbde.jpg\",\n  \"482215.jpg\": \"Insecta/Synchlora aerata/3d7e7e6d80e3fb409c90690d3c9c3f09.jpg\",\n  \"482216.jpg\": \"Insecta/Synchlora aerata/f66b8ef2130f4854c021ccb530ad1e45.jpg\",\n  \"482217.jpg\": \"Insecta/Synchlora aerata/49be0aa60f3e820df7845aeb1ffcaeca.jpg\",\n  \"482220.jpg\": \"Insecta/Synchlora aerata/3006dea6fa8e1d8789987b7a6f31b8bb.jpg\",\n  \"482225.jpg\": \"Insecta/Synchlora aerata/97dd8d7a9168014727d559650b3f9215.jpg\",\n  \"482231.jpg\": \"Insecta/Synchlora aerata/81bb00222dd0a52c27dbf24fa4c3787e.jpg\",\n  \"482234.jpg\": \"Mollusca/Nucella lamellosa/443fa5f1d7f982b23a10d7229e606ad8.jpg\",\n  \"482235.jpg\": \"Mollusca/Nucella lamellosa/5db9f1e8e06dfe9584a68e0f56162edf.jpg\",\n  \"482236.jpg\": \"Mollusca/Nucella lamellosa/a677f2a615ef19c5f6d7555645978664.jpg\",\n  \"482239.jpg\": \"Mollusca/Nucella lamellosa/e37280d79f9092ea9a308db5bf3b6184.jpg\",\n  \"482240.jpg\": \"Mollusca/Nucella lamellosa/4d7936804e0cb6e039f3ed603daba48f.jpg\",\n  \"482241.jpg\": \"Mollusca/Nucella lamellosa/408b7e2edeafbe7dc622e8431d81fc9d.jpg\",\n  \"482242.jpg\": \"Mollusca/Nucella lamellosa/f79338cf41799a1b50ad747ecf190a2d.jpg\",\n  \"482244.jpg\": \"Mollusca/Nucella lamellosa/642a68fad12ef72989e276d4e15bcdda.jpg\",\n  \"482245.jpg\": \"Mollusca/Nucella lamellosa/122b1adb0a4d27d6cf8ca687533aef5f.jpg\",\n  \"482250.jpg\": \"Mollusca/Nucella lamellosa/28c2a4418ac14880f6a1406cce656052.jpg\",\n  \"482252.jpg\": \"Mollusca/Nucella lamellosa/9c1eb4675d4e820230cf446b52475caf.jpg\",\n  \"48226.jpg\": \"Reptilia/Pituophis catenifer annectens/21cd6214631e195953234d143de26b6c.jpg\",\n  \"48227.jpg\": \"Reptilia/Pituophis catenifer annectens/08d430276c4d692ec5210e053b03576a.jpg\",\n  \"48228.jpg\": \"Reptilia/Pituophis catenifer annectens/43535427ef6e7e378a92cecc1201c18d.jpg\",\n  \"482321.jpg\": \"Reptilia/Pituophis catenifer deserticola/03c94a093a4f6ee5989ed2d473e67f2f.jpg\",\n  \"482324.jpg\": \"Reptilia/Pituophis catenifer deserticola/85778a81904e31318940d813bf24b1be.jpg\",\n  \"48233.jpg\": \"Reptilia/Pituophis catenifer annectens/30bdc74c3576f21cc427d7d3424e8385.jpg\",\n  \"48234.jpg\": \"Reptilia/Pituophis catenifer annectens/b5ba36f6ef7b4fc11ba29381637264b2.jpg\",\n  \"482355.jpg\": \"Insecta/Rivula propinqualis/677f87b342658ae1610366c6b8c084c0.jpg\",\n  \"482357.jpg\": \"Insecta/Rivula propinqualis/9698ab799a95db7c30da1ca505d3b3be.jpg\",\n  \"482369.jpg\": \"Aves/Serinus serinus/e27f7cd6cd7078aa06a1c43d30f8e270.jpg\",\n  \"482436.jpg\": \"Aves/Fregata magnificens/f489b4c80b460872ad52115fbbc31685.jpg\",\n  \"482457.jpg\": \"Aves/Fregata magnificens/94d8c4301c6f5970dbbee3cb9033a60f.jpg\",\n  \"482458.jpg\": \"Aves/Fregata magnificens/44e6023f4faefb0e671af06cb1226017.jpg\",\n  \"482469.jpg\": \"Aves/Fregata magnificens/a3f7a281491bb27044c734d7fa3b8e41.jpg\",\n  \"482505.jpg\": \"Reptilia/Nerodia taxispilota/b14d92f00780f5198d76680481190c94.jpg\",\n  \"482510.jpg\": \"Animalia/Triakis semifasciata/10459f5e065fd04e36dcaec354991c72.jpg\",\n  \"482518.jpg\": \"Aves/Microcarbo melanoleucos brevirostris/ad5bbc39031225f73c79844d4303916c.jpg\",\n  \"482520.jpg\": \"Aves/Microcarbo melanoleucos brevirostris/af7e3ccfe79e204cece559df17c11270.jpg\",\n  \"482548.jpg\": \"Aves/Microcarbo melanoleucos brevirostris/7024c660116217c41dfbb1eecee1072f.jpg\",\n  \"482549.jpg\": \"Aves/Microcarbo melanoleucos brevirostris/c35c231a4696adaf7478074a6f42a35f.jpg\",\n  \"482552.jpg\": \"Aves/Microcarbo melanoleucos brevirostris/337caead2859d8b0caa494b91663b446.jpg\",\n  \"482553.jpg\": \"Aves/Microcarbo melanoleucos brevirostris/a86015162731eca8c336998c4254c8e4.jpg\",\n  \"482564.jpg\": \"Insecta/Pieris brassicae/93a782f1d5223932209c257b54196283.jpg\",\n  \"482601.jpg\": \"Insecta/Naupactus cervinus/8c2a2add14601423027bc9ca60211ae3.jpg\",\n  \"48262.jpg\": \"Reptilia/Pituophis catenifer annectens/23e9ac16616eb99708edcfd97cac3bf2.jpg\",\n  \"48263.jpg\": \"Reptilia/Pituophis catenifer annectens/ac011b22aa3ca1519eaaa81e90137862.jpg\",\n  \"482634.jpg\": \"Insecta/Actias luna/46294f206098ddb620ce1d299accca35.jpg\",\n  \"482654.jpg\": \"Insecta/Actias luna/d5525c4187cd0d3210c09ea9130a1532.jpg\",\n  \"482659.jpg\": \"Insecta/Actias luna/31ddce36fb039f6c6560562b9d5bff7b.jpg\",\n  \"482696.jpg\": \"Insecta/Actias luna/9e27b7c2e6d75f3b9a7aa5de7ff8ab10.jpg\",\n  \"482722.jpg\": \"Insecta/Actias luna/c4976772c32f78819911e3974865efda.jpg\",\n  \"482724.jpg\": \"Insecta/Actias luna/90e6d426f50daa7d474863d6fba5ef5b.jpg\",\n  \"482738.jpg\": \"Insecta/Actias luna/c06bf27195602fd0e112c8149757ca2d.jpg\",\n  \"482743.jpg\": \"Insecta/Actias luna/cdf9c5851caa0d16d5329fe160a73fa9.jpg\",\n  \"482746.jpg\": \"Insecta/Actias luna/a999ad56e3c5b95344a93340fcf5bbdf.jpg\",\n  \"482758.jpg\": \"Insecta/Actias luna/c53db942a4411d25ef7e66f1c785bc84.jpg\",\n  \"482884.jpg\": \"Mammalia/Tamias striatus/e74b86a695731e21ca05002d2273db04.jpg\",\n  \"482921.jpg\": \"Mammalia/Tamias striatus/daf2b6bc0c86ee5eee6ec0939a50bec6.jpg\",\n  \"482942.jpg\": \"Mammalia/Tamias striatus/5b26e9161a5d6fdebdb9b3ff048f8e8c.jpg\",\n  \"482959.jpg\": \"Mammalia/Tamias striatus/6ff1b1b534c035a9ede50fd9d46b2794.jpg\",\n  \"482964.jpg\": \"Mammalia/Tamias striatus/0d1b31c8db196519b72cead136866375.jpg\",\n  \"482997.jpg\": \"Mammalia/Tamias striatus/c7659665d472cbe082c6afc8b8fc5fd9.jpg\",\n  \"483121.jpg\": \"Mammalia/Tamias striatus/49ce86a492c6dd3f7e1a02b1b5ac4040.jpg\",\n  \"483126.jpg\": \"Insecta/Diphthera festiva/9c5226ae16603e69743f203cd3de6295.jpg\",\n  \"483127.jpg\": \"Insecta/Diphthera festiva/be52397b3a7687712faa485c7d0c93a9.jpg\",\n  \"483128.jpg\": \"Insecta/Diphthera festiva/fd2c6c9d2e5a8f6add2d334e730c2f17.jpg\",\n  \"483161.jpg\": \"Arachnida/Aphonopelma iodius/ba14ede16b7293e3a4c532c5f2e995bc.jpg\",\n  \"483277.jpg\": \"Aves/Mitrephanes phaeocercus/45a6c371244d8b92d89f5dc23298081f.jpg\",\n  \"483278.jpg\": \"Aves/Mitrephanes phaeocercus/d229472512eb524d3178d3bde0848d07.jpg\",\n  \"483279.jpg\": \"Arachnida/Phidippus johnsoni/9bfd0b5986f00b20abbf45c1c042eef8.jpg\",\n  \"483280.jpg\": \"Arachnida/Phidippus johnsoni/a430b22ea1d7d80589116942cf1a8653.jpg\",\n  \"483282.jpg\": \"Arachnida/Phidippus johnsoni/154f5b2662259c1b660825b14e0328e3.jpg\",\n  \"483286.jpg\": \"Arachnida/Phidippus johnsoni/863cec9f2dee11ed1eee484cb07cc1c0.jpg\",\n  \"483287.jpg\": \"Arachnida/Phidippus johnsoni/08b4369b5a9c90449fa971306ee5c88d.jpg\",\n  \"483288.jpg\": \"Arachnida/Phidippus johnsoni/57afebe90e85d329b572956562d28730.jpg\",\n  \"483290.jpg\": \"Arachnida/Phidippus johnsoni/74cdfd05d07b8422997ad737770a1d4f.jpg\",\n  \"483294.jpg\": \"Arachnida/Phidippus johnsoni/be898259bfbce7733a1d9dad22388693.jpg\",\n  \"483295.jpg\": \"Arachnida/Phidippus johnsoni/13a196b33caa7805d174ef086f58b09c.jpg\",\n  \"483298.jpg\": \"Arachnida/Phidippus johnsoni/9f40a15bb92d97649a8d1f7f83f8da5c.jpg\",\n  \"483299.jpg\": \"Arachnida/Phidippus johnsoni/ef19f8c85b8847703d00de71d49ce5ee.jpg\",\n  \"4833.jpg\": \"Aves/Calocitta colliei/9f98fe7167b37798cd9193af6db02110.jpg\",\n  \"483310.jpg\": \"Reptilia/Pseudemys concinna/bdd46682e1e78dbcd3b5f07b3588b7d2.jpg\",\n  \"483312.jpg\": \"Reptilia/Pseudemys concinna/90f7564c2344fb269341ded12e270538.jpg\",\n  \"483316.jpg\": \"Reptilia/Pseudemys concinna/bc93cbf608cc318517252e86ae1faea7.jpg\",\n  \"483351.jpg\": \"Aves/Egretta rufescens/3d437d060fe67d61f9ffcb2ff744462a.jpg\",\n  \"483393.jpg\": \"Aves/Egretta rufescens/80f7aa06258b46ec338c61a6baa54c39.jpg\",\n  \"483394.jpg\": \"Aves/Egretta rufescens/46dea28f574df6eda20e558b58b62819.jpg\",\n  \"483398.jpg\": \"Aves/Egretta rufescens/d8f9273464316f5fcca733623da0b95b.jpg\",\n  \"483434.jpg\": \"Aves/Egretta rufescens/1af9771aa68afe9e6a7be0612e8799c1.jpg\",\n  \"483454.jpg\": \"Aves/Egretta rufescens/231c6967d7c90672386fcb9b1d83b779.jpg\",\n  \"483467.jpg\": \"Aves/Penelope purpurascens/59bce5a8487b022a28c9c3ceec5d9259.jpg\",\n  \"483468.jpg\": \"Aves/Penelope purpurascens/ebe8b1075d18e2683419c661aa7c7817.jpg\",\n  \"483500.jpg\": \"Insecta/Bombus huntii/3db90f62a1349fd6f6a32250a4e4fe07.jpg\",\n  \"483501.jpg\": \"Insecta/Bombus huntii/ae4b9f9600c6458d5660a3f5dca26b6c.jpg\",\n  \"483579.jpg\": \"Mammalia/Oryctolagus cuniculus/348739925246d260a23e8b0b20db0bdb.jpg\",\n  \"483580.jpg\": \"Mammalia/Oryctolagus cuniculus/9a5fe4d9c6ba682a1b4a13a0549c58d2.jpg\",\n  \"483587.jpg\": \"Mammalia/Oryctolagus cuniculus/0af197703bb57f7c10b74deccca8ad3b.jpg\",\n  \"483591.jpg\": \"Mammalia/Oryctolagus cuniculus/3670f8a1851bc3ae2e0e7fb80ae97f02.jpg\",\n  \"483596.jpg\": \"Mammalia/Oryctolagus cuniculus/e2ba590ef1f22d29b15f52c16b620199.jpg\",\n  \"483605.jpg\": \"Mammalia/Oryctolagus cuniculus/dc21073976aa7f043667563c7dbc60d3.jpg\",\n  \"483618.jpg\": \"Mammalia/Oryctolagus cuniculus/22c2158a3df01da2ea4dc555fdcc07c8.jpg\",\n  \"483619.jpg\": \"Mammalia/Oryctolagus cuniculus/c2f66470c7b05ca6d7edc906ab8c2752.jpg\",\n  \"483640.jpg\": \"Insecta/Nymphalis antiopa/8b8e6a3dbbab8a46f3fc30f4d9d9c73e.jpg\",\n  \"483662.jpg\": \"Insecta/Nymphalis antiopa/b62f01071822c5dc48e598a229f14099.jpg\",\n  \"483668.jpg\": \"Insecta/Nymphalis antiopa/4bdb40af85e708b5e8dad5dea1f48121.jpg\",\n  \"4837.jpg\": \"Aves/Calocitta colliei/82e0df5a02bc1a32678f88455622452d.jpg\",\n  \"483769.jpg\": \"Insecta/Nymphalis antiopa/59fe9a32de91dd094b49fabbe1ac8cb4.jpg\",\n  \"4838.jpg\": \"Aves/Calocitta colliei/8a4d05889c6351e373d1cef109971431.jpg\",\n  \"483832.jpg\": \"Insecta/Nymphalis antiopa/01214091a7127be0bd18505dea49cb8b.jpg\",\n  \"483846.jpg\": \"Insecta/Nymphalis antiopa/b3e40b8075dcaaca62401d526b7bedec.jpg\",\n  \"483870.jpg\": \"Insecta/Nymphalis antiopa/8b079cd33aab8d0f0b6c762f088f913f.jpg\",\n  \"483875.jpg\": \"Aves/Grallina cyanoleuca/b63937297e7655b264947e672d23f293.jpg\",\n  \"483876.jpg\": \"Aves/Grallina cyanoleuca/719e103789db71cd24366f567c3b3a34.jpg\",\n  \"483877.jpg\": \"Aves/Grallina cyanoleuca/7aa3b9166a29426eca58db2e1f603896.jpg\",\n  \"483878.jpg\": \"Aves/Grallina cyanoleuca/d5c975afab0c0ca8e3855412049d2542.jpg\",\n  \"483879.jpg\": \"Aves/Grallina cyanoleuca/f6359b1cce93d1ceecac5f5a526c92fe.jpg\",\n  \"483881.jpg\": \"Aves/Grallina cyanoleuca/907af6ca505f8f5350696855df46abad.jpg\",\n  \"483882.jpg\": \"Aves/Grallina cyanoleuca/a0c9bc6e21aae6de602a4e8967049c56.jpg\",\n  \"483884.jpg\": \"Aves/Grallina cyanoleuca/e89595a02b4343ee74f8e2f94269fec4.jpg\",\n  \"483889.jpg\": \"Aves/Grallina cyanoleuca/328e2fbf356250032d48bf8ab7711576.jpg\",\n  \"483890.jpg\": \"Aves/Grallina cyanoleuca/f55abd2d2c2e6cfed8b5333387967d35.jpg\",\n  \"483892.jpg\": \"Aves/Threskiornis moluccus/9841bfacf924f250f7e6e11410f6c6dd.jpg\",\n  \"483894.jpg\": \"Aves/Threskiornis moluccus/28b43071c0f63a7b57736ea61efd4f5b.jpg\",\n  \"483897.jpg\": \"Aves/Threskiornis moluccus/b271cb6a21fb0ae529c9378c781b8bc0.jpg\",\n  \"483899.jpg\": \"Aves/Threskiornis moluccus/74894a466d9f695b3fbfae83b12276ab.jpg\",\n  \"4839.jpg\": \"Aves/Calocitta colliei/da31d611345dcc684907318f25cea4c9.jpg\",\n  \"483905.jpg\": \"Aves/Threskiornis moluccus/30e0bda7da2c8623ca1153b86065dfcd.jpg\",\n  \"483911.jpg\": \"Aves/Threskiornis moluccus/92bb949233e1e7f3812d414606018f2e.jpg\",\n  \"4840.jpg\": \"Aves/Calocitta colliei/9f573cd2b05c761be03b1f8110617f17.jpg\",\n  \"484034.jpg\": \"Reptilia/Pituophis catenifer sayi/ad9861095c8717beacca14b09b124c83.jpg\",\n  \"484039.jpg\": \"Reptilia/Pituophis catenifer sayi/121a4f777dcb3879e96f05a785645478.jpg\",\n  \"484044.jpg\": \"Reptilia/Pituophis catenifer sayi/ed52713f89894eb9495dbb8275625eb9.jpg\",\n  \"484052.jpg\": \"Reptilia/Pituophis catenifer sayi/7053e3442abc7e424a87a980555024c6.jpg\",\n  \"484088.jpg\": \"Reptilia/Pituophis catenifer sayi/f11734c8b0b50d2ec5dcc2b3238a5d49.jpg\",\n  \"4841.jpg\": \"Aves/Calocitta colliei/29755fdd873bf04891a9ab15c07d7626.jpg\",\n  \"484163.jpg\": \"Reptilia/Apalone ferox/620b7c4d51cfcd961eb09426494bcdfe.jpg\",\n  \"484165.jpg\": \"Reptilia/Apalone ferox/b95f9aeb8b0af02fee4377a9248e9a3e.jpg\",\n  \"484173.jpg\": \"Reptilia/Apalone ferox/139752142cdd157ad57a39607b95ab83.jpg\",\n  \"484178.jpg\": \"Reptilia/Apalone ferox/d24d544cfc2753ab634206426b5dcb8a.jpg\",\n  \"4842.jpg\": \"Aves/Calocitta colliei/5429df78247b0e5b203cbd6a59b408e1.jpg\",\n  \"48429.jpg\": \"Reptilia/Lampropholis delicata/0dce942cde104c5f052212b1f3e4e23c.jpg\",\n  \"4843.jpg\": \"Aves/Calocitta colliei/4dff9d6dfa458ae03f3c3e6bf8e27b70.jpg\",\n  \"48430.jpg\": \"Reptilia/Lampropholis delicata/32873ff66bef01182a67900abea2669c.jpg\",\n  \"48434.jpg\": \"Reptilia/Lampropholis delicata/7450207acf8e5a8b4366787f9da566f2.jpg\",\n  \"484350.jpg\": \"Reptilia/Sauromalus ater/e6526aa7e1c3b24c77ed6840cfd21d36.jpg\",\n  \"484358.jpg\": \"Reptilia/Sauromalus ater/9729e275a435e3c7e37914ca8d946195.jpg\",\n  \"484361.jpg\": \"Reptilia/Sauromalus ater/f44d32d911d240142632c8fff83f7a1e.jpg\",\n  \"484363.jpg\": \"Reptilia/Sauromalus ater/a2e759dc213d4e7204187521643a53ac.jpg\",\n  \"484365.jpg\": \"Reptilia/Sauromalus ater/b90f43445bc6f141e6076b978c748456.jpg\",\n  \"484370.jpg\": \"Reptilia/Sauromalus ater/291b6a8100fddb0a9d100146d5188606.jpg\",\n  \"484384.jpg\": \"Animalia/Cancer productus/9bf205e8fe45adcc20f3925cd77ab60b.jpg\",\n  \"484389.jpg\": \"Animalia/Cancer productus/0d931fe9d8d25fbee5e590fa4af4a415.jpg\",\n  \"484395.jpg\": \"Animalia/Cancer productus/84f3f7d042cf7f0c6165c7932635a07c.jpg\",\n  \"484399.jpg\": \"Animalia/Cancer productus/a3465cb0da3dac7e8225b0ae8ddf7a23.jpg\",\n  \"4844.jpg\": \"Aves/Calocitta colliei/695fde383dedda582a8148f0faed85a3.jpg\",\n  \"484403.jpg\": \"Animalia/Cancer productus/fdd7df6dbee6729c6d56ead938d4ca96.jpg\",\n  \"484433.jpg\": \"Animalia/Emerita analoga/390348fd07c18aa112dc05aaf5bfb7a7.jpg\",\n  \"484436.jpg\": \"Animalia/Emerita analoga/4b1983d53fc5cae99cb093070015f346.jpg\",\n  \"484441.jpg\": \"Animalia/Emerita analoga/cb379371ab204dfa4a9f9619fd8c0d92.jpg\",\n  \"484446.jpg\": \"Animalia/Emerita analoga/e3d83d267847b8593386101cbfd35d60.jpg\",\n  \"48445.jpg\": \"Reptilia/Lampropholis delicata/86b97c769cf8dd8441d8441d0e814582.jpg\",\n  \"484451.jpg\": \"Animalia/Emerita analoga/16e0a78bbb2209bfdbfb6e4e93749f4e.jpg\",\n  \"484453.jpg\": \"Animalia/Emerita analoga/8d52f16066d82bb30e800436b2457f8c.jpg\",\n  \"484455.jpg\": \"Animalia/Emerita analoga/f0c0f9b816c0e41b8322632aad5e017c.jpg\",\n  \"484456.jpg\": \"Animalia/Emerita analoga/5811da336bee88d67221d2e76c14f4f3.jpg\",\n  \"484457.jpg\": \"Animalia/Emerita analoga/3359208a1885f7969f4e3cb201114a2c.jpg\",\n  \"48446.jpg\": \"Insecta/Polistes carolina/e0dcaea457854bc3f055259ff9e1585a.jpg\",\n  \"48447.jpg\": \"Insecta/Polistes carolina/41d80116fb09094d03bd5bd9ea1a6bd9.jpg\",\n  \"48449.jpg\": \"Insecta/Polistes carolina/66a9cd318441088eb4b67b8a1de9506b.jpg\",\n  \"484492.jpg\": \"Reptilia/Aspidoscelis marmorata/c2a4748b3c35fb106d42263c445483cb.jpg\",\n  \"4845.jpg\": \"Aves/Calocitta colliei/c40dc93f43ebe9e6fca9b07d54309dae.jpg\",\n  \"48450.jpg\": \"Insecta/Polistes carolina/6e656f9d6237bf13e610c0534ff92a9c.jpg\",\n  \"48451.jpg\": \"Insecta/Polistes carolina/e01840e59d0264d60f415676f034f981.jpg\",\n  \"484528.jpg\": \"Insecta/Hemileuca eglanterina/15aa4c3a6e614826a615d72abd7dcb37.jpg\",\n  \"484529.jpg\": \"Insecta/Hemileuca eglanterina/3ce6e688dcd589d38a531511fd9fac1b.jpg\",\n  \"484542.jpg\": \"Amphibia/Trachycephalus typhonius/69fad2127bbc906b35159dda1434d916.jpg\",\n  \"484548.jpg\": \"Amphibia/Trachycephalus typhonius/7c64fe32c99118454cdc22ba46aa0193.jpg\",\n  \"48459.jpg\": \"Insecta/Polistes carolina/e1c89ac95f62b68fcb1985806c927cf4.jpg\",\n  \"4846.jpg\": \"Aves/Calocitta colliei/d9134913e2ffa2a7466bc492a7bd7242.jpg\",\n  \"48464.jpg\": \"Insecta/Polistes carolina/1c031ecafb148567a040f22d23772b79.jpg\",\n  \"48466.jpg\": \"Insecta/Polistes carolina/8d79ca2fb03318dfa16dd9d1171985d0.jpg\",\n  \"4847.jpg\": \"Aves/Calocitta colliei/16b758d6c4144bf712e73c3996760afb.jpg\",\n  \"484725.jpg\": \"Insecta/Epilachna mexicana/712917965974dfa3a0a0f56240ddece9.jpg\",\n  \"484726.jpg\": \"Insecta/Epilachna mexicana/160f87f7793c5bafa73776a4b26101cb.jpg\",\n  \"484764.jpg\": \"Amphibia/Pseudotriton ruber/737b7e93ea0a19720b6f75e36668fd3b.jpg\",\n  \"484770.jpg\": \"Amphibia/Pseudotriton ruber/ad9a17264b0e31c9642635223c7658a7.jpg\",\n  \"484771.jpg\": \"Insecta/Trichodezia albovittata/52d54ca19ff895483f9493b0487ac694.jpg\",\n  \"484775.jpg\": \"Insecta/Trichodezia albovittata/178fc3e989000b6b323c8112525041d9.jpg\",\n  \"484776.jpg\": \"Insecta/Trichodezia albovittata/4b3a4cf200afb14bc45ea516d96b4b47.jpg\",\n  \"484783.jpg\": \"Insecta/Trichodezia albovittata/814ecdbe60213f6a3f6e643c0b0ecd4a.jpg\",\n  \"484785.jpg\": \"Insecta/Trichodezia albovittata/7470609120e73010bc0e8f242c86370c.jpg\",\n  \"48481.jpg\": \"Insecta/Polistes carolina/381afc90c1b9f278dc8ff9bdb933c37e.jpg\",\n  \"48482.jpg\": \"Insecta/Polistes carolina/d9979b1e506db5294014eebb3f489128.jpg\",\n  \"484849.jpg\": \"Insecta/Enallagma geminatum/ea638b6334a9876c3d0b3faea8016f47.jpg\",\n  \"484855.jpg\": \"Insecta/Enallagma geminatum/fc86f9d43c559d1c8f3ee835aec03794.jpg\",\n  \"484856.jpg\": \"Insecta/Enallagma geminatum/7bd765c37a0414a92aeb954366f6695e.jpg\",\n  \"484860.jpg\": \"Insecta/Enallagma geminatum/685e83e18bca436883c3835193670d97.jpg\",\n  \"484861.jpg\": \"Insecta/Enallagma geminatum/ac1171861862169bcfc00f048490b24a.jpg\",\n  \"484862.jpg\": \"Insecta/Enallagma geminatum/ea876332b5df88fa7bb3cc25f42d7a33.jpg\",\n  \"484863.jpg\": \"Insecta/Enallagma geminatum/d0e5986e463c47880f8882d8ea34c0fe.jpg\",\n  \"48491.jpg\": \"Insecta/Polistes carolina/ea10c362257f48260a6a30e41d41e6c9.jpg\",\n  \"48497.jpg\": \"Insecta/Polistes carolina/03dd809065ac6fb4aba91e9e55c07538.jpg\",\n  \"484970.jpg\": \"Aves/Cyanistes caeruleus/6ebe1053cc77a5cef4c263df5cbfbc23.jpg\",\n  \"484979.jpg\": \"Aves/Cyanistes caeruleus/5dcff9ed5ad7459fdc7cdb2620a0c19f.jpg\",\n  \"484985.jpg\": \"Aves/Cyanistes caeruleus/bcf0e80a05d1702ffc479f4beba12bb2.jpg\",\n  \"485000.jpg\": \"Aves/Cyanistes caeruleus/aba3785494dc1bf5b8e007f8d9736f29.jpg\",\n  \"485027.jpg\": \"Aves/Cyanistes caeruleus/8995332f90da2948097fb798e41cf8cb.jpg\",\n  \"485029.jpg\": \"Aves/Cyanistes caeruleus/0f7d2d9232cefda636896afba82ab687.jpg\",\n  \"485030.jpg\": \"Aves/Cyanistes caeruleus/4b8fc3bde933ef5e636c2e0b6e7a5d78.jpg\",\n  \"485033.jpg\": \"Aves/Cyanistes caeruleus/62271d180014e4e36b2cdb89b286f514.jpg\",\n  \"48506.jpg\": \"Insecta/Polistes carolina/35f166702171cfc79d530bf4109a97c3.jpg\",\n  \"48507.jpg\": \"Insecta/Polistes carolina/08a89c41afd144bcec61727ce1aff261.jpg\",\n  \"48511.jpg\": \"Insecta/Polistes carolina/26b573b740e0070a19d7d95636765629.jpg\",\n  \"48512.jpg\": \"Insecta/Polistes carolina/c477576e216a822623660bf2ef4990d7.jpg\",\n  \"48513.jpg\": \"Insecta/Polistes carolina/71505bbad3b867dc64d3795e6b832fe4.jpg\",\n  \"48516.jpg\": \"Insecta/Polistes carolina/92e54e4165bc531007a97b8a8b44c2dd.jpg\",\n  \"485166.jpg\": \"Aves/Spinus tristis/6b0ada6a81d964884629b582ea024b30.jpg\",\n  \"48518.jpg\": \"Insecta/Polistes carolina/84986ffd90add22fe32dd4375d8ba85d.jpg\",\n  \"485231.jpg\": \"Aves/Spinus tristis/1a4ba5f7d7f441e277f52420a25c800c.jpg\",\n  \"48528.jpg\": \"Insecta/Polistes carolina/308ffe22f9370744b5fc15eb09196395.jpg\",\n  \"4853.jpg\": \"Aves/Calocitta colliei/dc5149d7f893a5777dc8f5a1c3c28214.jpg\",\n  \"485356.jpg\": \"Aves/Spinus tristis/0af9c7e817013954d45b6165e93ce0cd.jpg\",\n  \"4854.jpg\": \"Aves/Calocitta colliei/0cfd34ce3f5c1283a57bb240e684f68a.jpg\",\n  \"4855.jpg\": \"Aves/Calocitta colliei/48594f6a2849a6bd2d6dc27fd7ba0c05.jpg\",\n  \"48553.jpg\": \"Insecta/Camponotus pennsylvanicus/215c1dd2be593796187034018d4805c4.jpg\",\n  \"48554.jpg\": \"Insecta/Camponotus pennsylvanicus/c86ab398639a1c9d282641f9e63676f4.jpg\",\n  \"485560.jpg\": \"Aves/Spinus tristis/f9825f449e92463da31c6ec4b18f4917.jpg\",\n  \"48559.jpg\": \"Insecta/Camponotus pennsylvanicus/5730b3d8b09d31ff506ac8c29873720a.jpg\",\n  \"48560.jpg\": \"Insecta/Camponotus pennsylvanicus/f29e6254bef0bb6b41c0e3ada161bd02.jpg\",\n  \"48563.jpg\": \"Insecta/Camponotus pennsylvanicus/21ff5f6944d3afe1a754f5723457b5f6.jpg\",\n  \"485640.jpg\": \"Aves/Spinus tristis/a83e87abdc89c57e7eadbf319fac7496.jpg\",\n  \"485642.jpg\": \"Aves/Camptostoma imberbe/8a69d557aef9c080c72c087eea596d95.jpg\",\n  \"485644.jpg\": \"Aves/Camptostoma imberbe/e51f490726df38a118df50055624f64b.jpg\",\n  \"485649.jpg\": \"Actinopterygii/Naso lituratus/2869316dfa299f84580b4bfa944b0a34.jpg\",\n  \"485651.jpg\": \"Actinopterygii/Naso lituratus/32513782a921f1295129ef462572c021.jpg\",\n  \"48581.jpg\": \"Insecta/Camponotus pennsylvanicus/891fd94feacff03f03343dbf23ff50cf.jpg\",\n  \"48582.jpg\": \"Insecta/Camponotus pennsylvanicus/bacfe77cdf7209ec83d21b3820301a2a.jpg\",\n  \"48584.jpg\": \"Insecta/Dione moneta/9c2ddd55ba9198ead259cb6e9ced25eb.jpg\",\n  \"48586.jpg\": \"Insecta/Dione moneta/a147b94a13b7de8446f04d9386097b75.jpg\",\n  \"48587.jpg\": \"Insecta/Dione moneta/d08a2ca43089d665655e86f09b994f1e.jpg\",\n  \"48594.jpg\": \"Insecta/Dione moneta/e5fd1f9552abf5ad26f8a3b38cf94ace.jpg\",\n  \"485974.jpg\": \"Insecta/Polistes dominula/a0cc1ef4135cd6db8b048a24dcb8f9dc.jpg\",\n  \"485983.jpg\": \"Insecta/Polistes dominula/eeb3d06e06395f1311cc9ca482071cc5.jpg\",\n  \"485988.jpg\": \"Insecta/Polistes dominula/f7bbf65913226b08881715d4e3ecdd87.jpg\",\n  \"485997.jpg\": \"Insecta/Polistes dominula/659b89cd6de3907173d4281f1494fa0f.jpg\",\n  \"485998.jpg\": \"Insecta/Polistes dominula/64624e9c964e2ee0c527c76608f9bde3.jpg\",\n  \"48600.jpg\": \"Insecta/Dione moneta/0b32362b8d45ceb444ea29f9559864c4.jpg\",\n  \"486006.jpg\": \"Insecta/Polistes dominula/3daf743c4333aacf8890fb96db943df0.jpg\",\n  \"48601.jpg\": \"Insecta/Dione moneta/50fae85a92c4baf5d172e64ed577fb52.jpg\",\n  \"48602.jpg\": \"Insecta/Dione moneta/3303116e74ad794910eab83f9957802e.jpg\",\n  \"486030.jpg\": \"Aves/Anthornis melanura/6a3521b5a0045d9d44f2219d14b59dce.jpg\",\n  \"486031.jpg\": \"Aves/Anthornis melanura/46054f303ab424b4670a4f9e86ae4165.jpg\",\n  \"486039.jpg\": \"Aves/Anthornis melanura/880179f9dca620d191b57ca088a5893b.jpg\",\n  \"48605.jpg\": \"Insecta/Dione moneta/38890589d39fec6c421ba953738d6b56.jpg\",\n  \"486052.jpg\": \"Aves/Anthornis melanura/a8debc17e7325dd2de902dddc293f001.jpg\",\n  \"486057.jpg\": \"Aves/Anthornis melanura/5982d16b2543f3c2e98516180d8bd28b.jpg\",\n  \"486068.jpg\": \"Aves/Anthornis melanura/c8e6b0720d3e9640148a2795ad471d8a.jpg\",\n  \"48609.jpg\": \"Insecta/Dione moneta/9ce408105028bc0e09c6aab92ae7392e.jpg\",\n  \"48611.jpg\": \"Insecta/Dione moneta/eff1965dc19b311bab775d6f84c2fa1b.jpg\",\n  \"48612.jpg\": \"Insecta/Dione moneta/924d3f190ffd2435cdc749e7883853ac.jpg\",\n  \"48613.jpg\": \"Insecta/Dione moneta/9179a366b17332b4cc3456123db67f0a.jpg\",\n  \"486144.jpg\": \"Mammalia/Cervus elaphus nelsoni/68af58a953b16676419f5d63a9e93aa0.jpg\",\n  \"486145.jpg\": \"Mammalia/Cervus elaphus nelsoni/64613d0f32514ff2abaffd699e03cd0d.jpg\",\n  \"486155.jpg\": \"Mammalia/Cervus elaphus nelsoni/2e0a11107caf4dcbe23673628fcf77e8.jpg\",\n  \"486162.jpg\": \"Mammalia/Cervus elaphus nelsoni/47b1f52b5cda425ebc661b2ac1f2d3ca.jpg\",\n  \"486166.jpg\": \"Mammalia/Cervus elaphus nelsoni/66cc2a54ecdd9d2214ece49445654681.jpg\",\n  \"486178.jpg\": \"Reptilia/Pantherophis alleghaniensis/9fb9dfbb9aa1deb0b8bdfe04f228c6cd.jpg\",\n  \"486188.jpg\": \"Reptilia/Pantherophis alleghaniensis/0356542f67ecc0792e028d20cd4e311a.jpg\",\n  \"4862.jpg\": \"Aves/Calocitta colliei/cb65704512e006fce7cabda8ac4c7f3a.jpg\",\n  \"48620.jpg\": \"Insecta/Dione moneta/ff6387c28e3937788edb53a9a97a2705.jpg\",\n  \"48621.jpg\": \"Insecta/Dione moneta/476cd7bebc1bbbf641bc9b9669955905.jpg\",\n  \"486235.jpg\": \"Reptilia/Pantherophis alleghaniensis/19c0b1feafb7c173b33503f7008617b8.jpg\",\n  \"48624.jpg\": \"Insecta/Dione moneta/67f99975af269a6befd3587db581f3ad.jpg\",\n  \"486240.jpg\": \"Reptilia/Pantherophis alleghaniensis/49f44b73af56ade4facf84841871c289.jpg\",\n  \"48625.jpg\": \"Insecta/Dione moneta/d5f2bc33bfc04a6f47adee6accfb77ea.jpg\",\n  \"48626.jpg\": \"Insecta/Dione moneta/93e6e83b0cac4a58641430de1bd41f18.jpg\",\n  \"486280.jpg\": \"Insecta/Anacridium aegyptium/9ac3ea1858a539e55c3a69c7050c8a9c.jpg\",\n  \"486281.jpg\": \"Insecta/Anacridium aegyptium/8c89001d53a6439ea93188660f450f4c.jpg\",\n  \"486309.jpg\": \"Mollusca/Littorina littorea/9a8e19e738ec8ff8428fdd97305ae6a8.jpg\",\n  \"486323.jpg\": \"Mollusca/Littorina littorea/cea5b8ac30e1dba7d7c695424d543f7b.jpg\",\n  \"48633.jpg\": \"Insecta/Dione moneta/574ea5f9f8196e8d2dac43e3495acabc.jpg\",\n  \"48634.jpg\": \"Insecta/Dione moneta/58caca36f5cf6193ea14b4dcf04b1f89.jpg\",\n  \"48639.jpg\": \"Insecta/Dione moneta/ab61add69443ddedca0d4a4113789e5b.jpg\",\n  \"486393.jpg\": \"Insecta/Pelecinus polyturator/001ee514a5f2fd0795c6632eeba79b8d.jpg\",\n  \"486394.jpg\": \"Insecta/Pelecinus polyturator/2c0b1daba0807c1a0e09a6d6224a1b87.jpg\",\n  \"486395.jpg\": \"Insecta/Pelecinus polyturator/c20054d0e5a7c37c94b9eba7ea08fa26.jpg\",\n  \"486397.jpg\": \"Insecta/Pelecinus polyturator/f70913d32d9cb538f752bc34c24122a9.jpg\",\n  \"486398.jpg\": \"Insecta/Pelecinus polyturator/0dbc412782886855f9d575223416a3a6.jpg\",\n  \"486412.jpg\": \"Aves/Meleagris gallopavo silvestris/c28704f4c9fd54f27b91667838c99e8d.jpg\",\n  \"486415.jpg\": \"Aves/Meleagris gallopavo silvestris/9fea72cbbdceb872c29743b210884a23.jpg\",\n  \"486418.jpg\": \"Aves/Meleagris gallopavo silvestris/8443887b849e9dbfd0929520cb2e2e69.jpg\",\n  \"486419.jpg\": \"Aves/Meleagris gallopavo silvestris/5a97708f85ee7f63df0a18b9d61280c6.jpg\",\n  \"486420.jpg\": \"Aves/Meleagris gallopavo silvestris/abddccc6a4dbd9277b5ca8b53d0250e9.jpg\",\n  \"486434.jpg\": \"Aves/Platycercus eximius/1f8e1f92f6bd2c5268512087264c5fbb.jpg\",\n  \"486503.jpg\": \"Arachnida/Phalangium opilio/b76b7a1bdef88b9a3c1477c73bc2e724.jpg\",\n  \"486504.jpg\": \"Arachnida/Phalangium opilio/bd08c33b8d3d7e3b0692cbd5308d29c5.jpg\",\n  \"486505.jpg\": \"Arachnida/Phalangium opilio/291743e1e960de4cc2ada68bbc6a301e.jpg\",\n  \"486511.jpg\": \"Arachnida/Phalangium opilio/2158b2149fb7b604053b3bacd645684d.jpg\",\n  \"486512.jpg\": \"Reptilia/Sceloporus jarrovii/bb0c7d70a838444c02aa9af98df22c38.jpg\",\n  \"486526.jpg\": \"Reptilia/Sceloporus jarrovii/a2c5139ef62d3d633dd5d8af404f77dd.jpg\",\n  \"48654.jpg\": \"Mollusca/Cipangopaludina chinensis/4305b6d3a61a0fe5b0ee344440824357.jpg\",\n  \"48655.jpg\": \"Mollusca/Cipangopaludina chinensis/8cc64249d01b01f63ee94d5287319c59.jpg\",\n  \"486560.jpg\": \"Insecta/Monobia quadridens/ec98b1da95d77606c9ad8f8be949eb2e.jpg\",\n  \"486563.jpg\": \"Insecta/Monobia quadridens/210cf1169e7c420c601f1174fe3e59c5.jpg\",\n  \"486572.jpg\": \"Insecta/Monobia quadridens/fc7db7447cdb22ef06f2f881c2635bee.jpg\",\n  \"48664.jpg\": \"Mollusca/Cipangopaludina chinensis/9935a95c230563aae5b49edb5304fa4b.jpg\",\n  \"48666.jpg\": \"Mollusca/Aplysia vaccaria/1e7f1b47283bbb2f2243e8eb1310184e.jpg\",\n  \"48667.jpg\": \"Mollusca/Aplysia vaccaria/60aa15bf315b21698311919852e357cd.jpg\",\n  \"48670.jpg\": \"Mollusca/Aplysia vaccaria/26bf8b5447ab05925a511fac52afd900.jpg\",\n  \"48672.jpg\": \"Mollusca/Aplysia vaccaria/0cb188588b6bdf4a174c4581c9518446.jpg\",\n  \"48680.jpg\": \"Mollusca/Aplysia vaccaria/4f6c51e0fd28388ba592caff8b9b1561.jpg\",\n  \"48682.jpg\": \"Mollusca/Aplysia vaccaria/0b53aafd69ece7cf6b314c09bfeaaa8b.jpg\",\n  \"48690.jpg\": \"Aves/Ortalis poliocephala/0645b90e7ad5a6d2224b0e0fe3e902dc.jpg\",\n  \"48693.jpg\": \"Aves/Ortalis poliocephala/a4d01d9b237e6d355b0609b489bbc578.jpg\",\n  \"48702.jpg\": \"Aves/Ortalis poliocephala/a6834eb7558da06c5c8d348d206dc5cc.jpg\",\n  \"487124.jpg\": \"Aves/Melanerpes formicivorus/7bc1a3e7c4802abbb5fe43adf75d7300.jpg\",\n  \"487181.jpg\": \"Aves/Melanerpes formicivorus/582cff00bb34698a42b2f060ca328d91.jpg\",\n  \"48721.jpg\": \"Actinopterygii/Acanthurus coeruleus/199e71c82832eaf6f16bb509538fcbf6.jpg\",\n  \"487222.jpg\": \"Aves/Melanerpes formicivorus/c375700dd67be36a9ae6eedbdf18a8db.jpg\",\n  \"48727.jpg\": \"Actinopterygii/Acanthurus coeruleus/a644d2b9c2a02f314e7d5390713112f7.jpg\",\n  \"487279.jpg\": \"Aves/Melanerpes formicivorus/b325a6bcbbca3f4df370ec99ec931492.jpg\",\n  \"4873.jpg\": \"Aves/Calocitta colliei/475b7f0db925c2ad9fc9da1341df7f51.jpg\",\n  \"48730.jpg\": \"Insecta/Xanthorhoe lacustrata/65da35a5efdfe5738093fcb68aa0840d.jpg\",\n  \"48732.jpg\": \"Insecta/Xanthorhoe lacustrata/10ded1c05278c1fdc55b1df3b00d9226.jpg\",\n  \"48735.jpg\": \"Insecta/Xanthorhoe lacustrata/785533738e8cc53a455e10fa3ef5ee81.jpg\",\n  \"48738.jpg\": \"Insecta/Xanthorhoe lacustrata/eba6b25874567c6dac42edda1b1bdfb8.jpg\",\n  \"487410.jpg\": \"Insecta/Spoladea recurvalis/7a8e721d6242f648bd36c1ac8b270d86.jpg\",\n  \"487414.jpg\": \"Insecta/Spoladea recurvalis/4fe22bbdd2861dbe42a89d08fb29a4af.jpg\",\n  \"487420.jpg\": \"Insecta/Spoladea recurvalis/b5c6c1f81e7dfe4c1f1f383fd3f43d17.jpg\",\n  \"487449.jpg\": \"Insecta/Spoladea recurvalis/b4658937cbdf9c2c3629af8f1995e321.jpg\",\n  \"487455.jpg\": \"Insecta/Spoladea recurvalis/2e953edca76f31fb1ec9c9b667356bfe.jpg\",\n  \"487459.jpg\": \"Insecta/Spoladea recurvalis/fc6540e0ae059f8e8505464e5ab7fec1.jpg\",\n  \"48746.jpg\": \"Insecta/Euphydryas editha/882eeae77a204f3271aae113a4c9c6cf.jpg\",\n  \"487469.jpg\": \"Insecta/Spoladea recurvalis/afc342dfc6fb45f3f2f00c2576976465.jpg\",\n  \"487473.jpg\": \"Insecta/Spoladea recurvalis/e0da89ceb5bcc3c5a97c0be856e262e6.jpg\",\n  \"487503.jpg\": \"Amphibia/Batrachoseps nigriventris/ebcc9aaf6ead9be06039756ac8218489.jpg\",\n  \"487507.jpg\": \"Amphibia/Batrachoseps nigriventris/1b03903eed77fb462ce30ab531792ae5.jpg\",\n  \"48752.jpg\": \"Insecta/Euphydryas editha/e886046126b68fc032203b1a7c8debc7.jpg\",\n  \"487521.jpg\": \"Amphibia/Batrachoseps nigriventris/1473919062d5d4de074f1eaf04814339.jpg\",\n  \"487522.jpg\": \"Amphibia/Batrachoseps nigriventris/7c5bddd5144be0e20819d44259ff8107.jpg\",\n  \"48754.jpg\": \"Insecta/Euphydryas editha/b19c5110aeaf9979a504091b5d9c5aaf.jpg\",\n  \"48755.jpg\": \"Insecta/Euphydryas editha/f56fd8097246a08702390ced0940850e.jpg\",\n  \"48767.jpg\": \"Insecta/Euphydryas editha/4f9cbd1c720ae6a75675d54196c75033.jpg\",\n  \"48768.jpg\": \"Insecta/Euphydryas editha/4b3f645a927fb3e7a6dd1ebbd2c4a919.jpg\",\n  \"487700.jpg\": \"Insecta/Autographa gamma/04231f94faa6ab337b2a6e4e499bbfba.jpg\",\n  \"487703.jpg\": \"Insecta/Autographa gamma/35a4dc69a53d9df6e88e2c80321f9134.jpg\",\n  \"487710.jpg\": \"Insecta/Autographa gamma/d39fa5dd5796d89bced5ee14774e1af3.jpg\",\n  \"487713.jpg\": \"Insecta/Autographa gamma/3b7699db8de21af612abcc3b2e95559f.jpg\",\n  \"487717.jpg\": \"Insecta/Autographa gamma/0b54c1c51f55771b39728f3f78d2e7da.jpg\",\n  \"487724.jpg\": \"Insecta/Autographa gamma/4726a2953c49524b9e3cd6f5626564f0.jpg\",\n  \"487761.jpg\": \"Insecta/Papilio polyxenes/d6ab96fd8411b9452c05c8eb958ab794.jpg\",\n  \"487870.jpg\": \"Insecta/Papilio polyxenes/5dece7cd61fa09f2c52546c2dedb62f3.jpg\",\n  \"487961.jpg\": \"Insecta/Papilio polyxenes/9f902d366be3eee0f3fc20d3a2e6e0de.jpg\",\n  \"487973.jpg\": \"Reptilia/Plestiodon skiltonianus/9b21e8921dfbc10c547b01d772318fc5.jpg\",\n  \"487984.jpg\": \"Reptilia/Plestiodon skiltonianus/be341d86e5a8eb7f46901f1d0e58aadd.jpg\",\n  \"48800.jpg\": \"Aves/Anas discors/f19a89db649763ac104574cb849c7f34.jpg\",\n  \"488002.jpg\": \"Reptilia/Plestiodon skiltonianus/a8b4cf291dee54e287b568c44456d62d.jpg\",\n  \"488012.jpg\": \"Reptilia/Plestiodon skiltonianus/cf69b0a70e8f4d21471ef10001b67703.jpg\",\n  \"488019.jpg\": \"Reptilia/Plestiodon skiltonianus/13b55ee3f71c98400d06add159295e40.jpg\",\n  \"488023.jpg\": \"Reptilia/Plestiodon skiltonianus/a83a745f9b423032f368b721718f2a14.jpg\",\n  \"488031.jpg\": \"Reptilia/Plestiodon skiltonianus/9807faeb0d622587bb753086a2add268.jpg\",\n  \"488036.jpg\": \"Reptilia/Plestiodon skiltonianus/017b392936b40eb78ade0dfef1059490.jpg\",\n  \"488038.jpg\": \"Reptilia/Plestiodon skiltonianus/31c3c669d298f2f0fdf417e7816a8d25.jpg\",\n  \"488042.jpg\": \"Reptilia/Plestiodon skiltonianus/735c86528bbc568911457be327aeb392.jpg\",\n  \"488081.jpg\": \"Insecta/Myodocha serripes/a4f6a1265fa3a1cdafe2efc2bdbba501.jpg\",\n  \"488089.jpg\": \"Insecta/Myodocha serripes/b40bc6da4d1473b606c96ca3bb71231f.jpg\",\n  \"488231.jpg\": \"Aves/Sialia currucoides/cfa1789f2feb6eeab2d0697cc0331d49.jpg\",\n  \"488252.jpg\": \"Aves/Sialia currucoides/4eaf9aa8bb2ac35adb64d645d524a4a1.jpg\",\n  \"488253.jpg\": \"Aves/Sialia currucoides/d76cabba30ef2e068db5036f256d1c55.jpg\",\n  \"488268.jpg\": \"Aves/Sialia currucoides/59460ab546d8fc076720f3bda6d7d2e1.jpg\",\n  \"488290.jpg\": \"Aves/Sialia currucoides/d783676d6e1734e87600a872e4582da8.jpg\",\n  \"488300.jpg\": \"Aves/Sialia currucoides/7424a1638ddc9f7dd68c7d840779eb0c.jpg\",\n  \"488317.jpg\": \"Actinopterygii/Pomoxis nigromaculatus/a26e3dc7b7b5e72db1193ae20a678bb6.jpg\",\n  \"488321.jpg\": \"Actinopterygii/Pomoxis nigromaculatus/1a30f67f99918c7510125f04cea50ab4.jpg\",\n  \"488364.jpg\": \"Insecta/Chrysopilus thoracicus/5348823743d8f3ca69aaec10a1f824d6.jpg\",\n  \"488365.jpg\": \"Insecta/Chrysopilus thoracicus/a2a45d0c52b367f1c1f66124cc63ed37.jpg\",\n  \"488371.jpg\": \"Insecta/Chrysopilus thoracicus/425d9c34e8d7d34a7230ff890ef3ba0d.jpg\",\n  \"488372.jpg\": \"Insecta/Chrysopilus thoracicus/8a84df2f6704169205f024bdfcba6a3f.jpg\",\n  \"488373.jpg\": \"Insecta/Chrysopilus thoracicus/1fe903e2bdf944b774c49815b4394369.jpg\",\n  \"488374.jpg\": \"Insecta/Chrysopilus thoracicus/5f7233deafd93e335748fc0cc28b3eb7.jpg\",\n  \"488375.jpg\": \"Insecta/Chrysopilus thoracicus/8dff1e1be0f639b8c50b0bc83ff0f2f7.jpg\",\n  \"488376.jpg\": \"Insecta/Chrysopilus thoracicus/a77e3cc32dbefa7f43ad81df37704ad0.jpg\",\n  \"488378.jpg\": \"Insecta/Chrysopilus thoracicus/bb33def2072e5332cfd5a8ebaae94b2a.jpg\",\n  \"488402.jpg\": \"Mammalia/Alouatta palliata/9e23c39fdfca08b2011cf82dafd88007.jpg\",\n  \"488403.jpg\": \"Mammalia/Alouatta palliata/101e0dd06f23d9d6fb662e62396845d3.jpg\",\n  \"488405.jpg\": \"Mammalia/Alouatta palliata/dd6bdaebebd38b71dae5f7d8765e9555.jpg\",\n  \"488418.jpg\": \"Insecta/Lucilia sericata/0f3dd8f9908777bcaa52b97c070be84f.jpg\",\n  \"488419.jpg\": \"Insecta/Lucilia sericata/ad305bfa5692b45b7e2ef85a68d76c6f.jpg\",\n  \"488420.jpg\": \"Insecta/Lucilia sericata/3b1b0a1bcdff5d11b618a9ecebc4a875.jpg\",\n  \"488428.jpg\": \"Insecta/Schinia arcigera/fad123ae5f9cdbdb959a930c89844634.jpg\",\n  \"488434.jpg\": \"Insecta/Schinia arcigera/0f22ff1423d8595311679c65bf8740d8.jpg\",\n  \"488448.jpg\": \"Insecta/Melanitis leda/20b0e0dc6352ba24ee3586e84a8cc4cb.jpg\",\n  \"488501.jpg\": \"Aves/Meleagris ocellata/0cd4c35d0541a60e02b47ee4265c62b5.jpg\",\n  \"488502.jpg\": \"Aves/Meleagris ocellata/1a5912d86391d5b22aa1dcc435cb7c6c.jpg\",\n  \"488508.jpg\": \"Mammalia/Ateles geoffroyi/1ce63e7674a984c22c08308f936f2b43.jpg\",\n  \"488509.jpg\": \"Mammalia/Ateles geoffroyi/8adb53d54622f97cee189574a58deb86.jpg\",\n  \"488510.jpg\": \"Mammalia/Ateles geoffroyi/1c89780feab51a54ddf96a6bbb6667c5.jpg\",\n  \"488511.jpg\": \"Mammalia/Ateles geoffroyi/4f069ec9dee587f8a495714607fd2931.jpg\",\n  \"488517.jpg\": \"Mammalia/Ateles geoffroyi/68e4a7035f1148216598c258aa6f5507.jpg\",\n  \"488521.jpg\": \"Mammalia/Ateles geoffroyi/cb4d9c4a1642bcbf3fef367232ff5341.jpg\",\n  \"488522.jpg\": \"Mammalia/Ateles geoffroyi/6b933d0f5e3a387284cb70c553f03853.jpg\",\n  \"488531.jpg\": \"Mammalia/Ateles geoffroyi/4346ac5cc4e11c4e522c51b19404de93.jpg\",\n  \"488562.jpg\": \"Insecta/Leucorrhinia hudsonica/40c5926e35a7e2e39cbc32dad204feb9.jpg\",\n  \"488563.jpg\": \"Insecta/Leucorrhinia hudsonica/20b7b837b59345b9f24b63dd51534c60.jpg\",\n  \"488603.jpg\": \"Insecta/Charaxes jasius/c1e53eafe8cb5ea9a9b2012f08c614a0.jpg\",\n  \"488606.jpg\": \"Insecta/Charaxes jasius/fb5695a240087e668540f76a70fa23fa.jpg\",\n  \"48864.jpg\": \"Aves/Anas discors/87443bcd8cdf1fcb88a1bc5fae2a8c93.jpg\",\n  \"488645.jpg\": \"Insecta/Utetheisa ornatrix/f05b935d127f957fc8b9689bac1e998b.jpg\",\n  \"488646.jpg\": \"Insecta/Utetheisa ornatrix/b5d5cbcd1776c5240095e0dcdba29702.jpg\",\n  \"488648.jpg\": \"Insecta/Utetheisa ornatrix/c6b8fad506790e51e731c07b74cf0fd4.jpg\",\n  \"488655.jpg\": \"Insecta/Utetheisa ornatrix/c167424396731862a32e8a016612ee08.jpg\",\n  \"488656.jpg\": \"Insecta/Utetheisa ornatrix/d08cb4940ab0330a8a8e51c891b2da28.jpg\",\n  \"488659.jpg\": \"Insecta/Lethe eurydice/52ed780fc93db286aafa134e12ca836d.jpg\",\n  \"488663.jpg\": \"Insecta/Lethe eurydice/ede1a6803a815fa8c20a3c8eb765025c.jpg\",\n  \"488673.jpg\": \"Insecta/Iphiclides podalirius/a30c23d1ff334ccd48c3708b9dbd485e.jpg\",\n  \"488674.jpg\": \"Insecta/Iphiclides podalirius/b52821db40aecb329464ba58fe9fa4c0.jpg\",\n  \"488677.jpg\": \"Insecta/Iphiclides podalirius/0b315afb97e58944c0169edcd8b82cd9.jpg\",\n  \"488681.jpg\": \"Insecta/Iphiclides podalirius/3565c8e807c58dd89389f7281830a5fc.jpg\",\n  \"488687.jpg\": \"Insecta/Iphiclides podalirius/669d35a4e025bcabbbfafa172e9bfecf.jpg\",\n  \"488690.jpg\": \"Insecta/Iphiclides podalirius/27595e23e083854de54641f1b4f904ba.jpg\",\n  \"488695.jpg\": \"Insecta/Iphiclides podalirius/239914ecef3ebe53fe2b61397cd5a54d.jpg\",\n  \"488696.jpg\": \"Insecta/Iphiclides podalirius/6adf8c07ef596b0a2cc924e18e2640db.jpg\",\n  \"488852.jpg\": \"Insecta/Cupido comyntas/b49325e67dccc040c83550b25055bcb3.jpg\",\n  \"488968.jpg\": \"Insecta/Cupido comyntas/534b18f4622281a6a1fc202e3aed3602.jpg\",\n  \"489001.jpg\": \"Insecta/Cupido comyntas/250667656185c8b713902c0cc6722f29.jpg\",\n  \"489083.jpg\": \"Amphibia/Hyla arenicolor/28a8667f3253c0fe993eb3685ded724d.jpg\",\n  \"48909.jpg\": \"Aves/Anas discors/137bdf1f58deab763a3e165caccafcb7.jpg\",\n  \"489099.jpg\": \"Amphibia/Hyla arenicolor/0ee705f35b9103fb123ae86e489bbbf1.jpg\",\n  \"489100.jpg\": \"Amphibia/Hyla arenicolor/9ffe42be7db6e4dba5e0caccfab1c76e.jpg\",\n  \"489105.jpg\": \"Amphibia/Hyla arenicolor/f4c87cc17ed5f2c0effd7fc4cd20a990.jpg\",\n  \"489106.jpg\": \"Amphibia/Hyla arenicolor/a636d7c1a304f3eea3a5851f81416757.jpg\",\n  \"489108.jpg\": \"Amphibia/Hyla arenicolor/ca6b6096a0ad9268fcc9bbbcfd0942ae.jpg\",\n  \"489282.jpg\": \"Amphibia/Hyla cinerea/35582bc13d53a62619f78d3cc161231e.jpg\",\n  \"489349.jpg\": \"Amphibia/Hyla cinerea/90611de2ddf641998b45ca3d48f937a4.jpg\",\n  \"489351.jpg\": \"Amphibia/Hyla cinerea/e3efa9da8d7efee15e34fbb9200e8c8c.jpg\",\n  \"489352.jpg\": \"Amphibia/Hyla cinerea/d2223bb1141b53179fc4204ec8639629.jpg\",\n  \"489388.jpg\": \"Amphibia/Hyla cinerea/eff80ddc1ee96241dab78c77a404e71b.jpg\",\n  \"489391.jpg\": \"Amphibia/Hyla cinerea/1df28b6cf4983ffed1da2f5223bbceb9.jpg\",\n  \"489425.jpg\": \"Insecta/Peridea angulosa/361e8055484deadf3a858f7352a5dba4.jpg\",\n  \"489431.jpg\": \"Insecta/Peridea angulosa/ceedb99fc18a78869b3747d1ffd10add.jpg\",\n  \"489440.jpg\": \"Insecta/Sympetrum madidum/6be60160ebc05328f2eb365092e35ae0.jpg\",\n  \"489441.jpg\": \"Insecta/Sympetrum madidum/75995445c92e73b77c9905636412628a.jpg\",\n  \"489450.jpg\": \"Insecta/Schistocerca americana/355e057cfe1b25f55933e044e0a9d0c0.jpg\",\n  \"489460.jpg\": \"Insecta/Schistocerca americana/3715c92a3cc1724d90c46f217a6c01c7.jpg\",\n  \"489477.jpg\": \"Insecta/Hermetia illucens/975a07ef354dd5a034a3ec268758afd4.jpg\",\n  \"489479.jpg\": \"Insecta/Hermetia illucens/17788a2127816544e3b96cc8fe96d899.jpg\",\n  \"489482.jpg\": \"Insecta/Hermetia illucens/d9f1657ada412d98c1995e25e45ea168.jpg\",\n  \"489489.jpg\": \"Insecta/Otiorhynchus sulcatus/26f3fc18bc311189c5f0af57b15c9077.jpg\",\n  \"489533.jpg\": \"Insecta/Phoebis sennae/5435281dec928118d1590ee6a6da07eb.jpg\",\n  \"489536.jpg\": \"Insecta/Phoebis sennae/07d9d74f326a48fb2c0f64622a1b6a1c.jpg\",\n  \"489537.jpg\": \"Insecta/Phoebis sennae/df5b359405b297802d7d069bf7ef4ef0.jpg\",\n  \"48955.jpg\": \"Aves/Anas discors/aad5f444f3a5ab617dfeda1563a6878c.jpg\",\n  \"489594.jpg\": \"Insecta/Phoebis sennae/d03669a9c8eddc56ac301ee849301b1e.jpg\",\n  \"489605.jpg\": \"Insecta/Phoebis sennae/4f8813f0ee5e72c218828d5c41372fee.jpg\",\n  \"489641.jpg\": \"Insecta/Phoebis sennae/2fa5e6ad37fca793f67fae95e7e55ac3.jpg\",\n  \"489663.jpg\": \"Insecta/Phoebis sennae/252f9aa9d7d566c7c41d4057795c48a3.jpg\",\n  \"489755.jpg\": \"Insecta/Vanessa atalanta/e7def982d77c0b8280bdd61558ff13ed.jpg\",\n  \"489867.jpg\": \"Insecta/Vanessa atalanta/139c29cc70d9a7679b1625a728644edc.jpg\",\n  \"489887.jpg\": \"Insecta/Vanessa atalanta/cbe187ab39df58f71d196e3d656f9cd8.jpg\",\n  \"489895.jpg\": \"Insecta/Vanessa atalanta/95ff6926e3c1f757665694b31c8dec93.jpg\",\n  \"489988.jpg\": \"Insecta/Vanessa atalanta/8d8c6bbd2d7d24ee81c8cbf9be4685b9.jpg\",\n  \"49005.jpg\": \"Aves/Anas discors/f53d42b5a59f588d48c3019bb87de78e.jpg\",\n  \"490103.jpg\": \"Insecta/Vanessa atalanta/b94a08cedbd2d9bdc12d7f389319346b.jpg\",\n  \"490121.jpg\": \"Insecta/Vanessa atalanta/3f4e755f8dd404ef48dba5af428cb725.jpg\",\n  \"490192.jpg\": \"Insecta/Vanessa atalanta/c87183e6df87af0820eb7fbcda597261.jpg\",\n  \"490343.jpg\": \"Insecta/Vanessa atalanta/8b73b5e5d96a8bb4a1991749eaaa504a.jpg\",\n  \"49035.jpg\": \"Aves/Anas discors/989fa10dcf3c4e92d81838c6f06fd368.jpg\",\n  \"490365.jpg\": \"Insecta/Apiomerus californicus/41e54437f9ed64c1f8a53e6c2ab621f9.jpg\",\n  \"490366.jpg\": \"Insecta/Apiomerus californicus/160a9ff56fa48e917898766753cb8f8e.jpg\",\n  \"490367.jpg\": \"Insecta/Apiomerus californicus/31b402a5d20e7c5da9c55ff476a06977.jpg\",\n  \"490368.jpg\": \"Insecta/Apiomerus californicus/b2091beaf4314a686ca2db132d896b5b.jpg\",\n  \"490373.jpg\": \"Amphibia/Scaphiopus couchii/9d13a7c6799e776d33d7b104fa802966.jpg\",\n  \"490374.jpg\": \"Amphibia/Scaphiopus couchii/df18c4966d63f6b04ae4757c75099437.jpg\",\n  \"490385.jpg\": \"Amphibia/Scaphiopus couchii/179a71e9bd16fa8838bcf5e6800a5352.jpg\",\n  \"490401.jpg\": \"Amphibia/Scaphiopus couchii/779cd40148d5c44c0f9cdc2c491e9b39.jpg\",\n  \"490430.jpg\": \"Amphibia/Scaphiopus couchii/77508f1f18db2621e4427c0c1dc18b04.jpg\",\n  \"490460.jpg\": \"Amphibia/Scaphiopus couchii/5f446dbc61b6ca31f593f82a4c81d933.jpg\",\n  \"490486.jpg\": \"Insecta/Panthea furcilla/b7441c993b8422bde7facdc2d6573277.jpg\",\n  \"490830.jpg\": \"Mammalia/Sciurus vulgaris/0187fdce048a1c16d5ea66fd74a076dc.jpg\",\n  \"490850.jpg\": \"Mammalia/Sciurus vulgaris/1efde69818d670df0b3c5b99da607977.jpg\",\n  \"490870.jpg\": \"Mammalia/Sciurus vulgaris/26e09075352b5f24db3138dba5eaadab.jpg\",\n  \"490871.jpg\": \"Mammalia/Sciurus vulgaris/5fdf04511eb5618d9bc0346207e2d5d3.jpg\",\n  \"490894.jpg\": \"Mammalia/Sciurus vulgaris/60d8f7586ac6ffd8a4844e96481860d3.jpg\",\n  \"490895.jpg\": \"Mammalia/Sciurus vulgaris/6b6ef9b0a2c62138801987e92fc64013.jpg\",\n  \"490917.jpg\": \"Animalia/Dermasterias imbricata/b8025c06047b0b255c9f1c8f8c1317ea.jpg\",\n  \"490926.jpg\": \"Animalia/Dermasterias imbricata/c02bf105c9e1a38155893ee4b5fb3751.jpg\",\n  \"490942.jpg\": \"Animalia/Dermasterias imbricata/87941e01812b0682d6f026db9b0343ce.jpg\",\n  \"490943.jpg\": \"Animalia/Dermasterias imbricata/ab87c46a38b11c898ea3f52fa8284ea0.jpg\",\n  \"490971.jpg\": \"Animalia/Dermasterias imbricata/6bb02af3f358c6f92ee6762663d203f1.jpg\",\n  \"490979.jpg\": \"Animalia/Dermasterias imbricata/a959c3e6189f6cce3a4775fd08e30974.jpg\",\n  \"491032.jpg\": \"Insecta/Gonepteryx rhamni/5f120c2f689495c1d74511cf23b3d3de.jpg\",\n  \"491044.jpg\": \"Insecta/Gonepteryx rhamni/9036a4d1b260814967eabf840fb88f77.jpg\",\n  \"491046.jpg\": \"Insecta/Gonepteryx rhamni/276b3db50fdeedc1fb0d85f763c627b9.jpg\",\n  \"491288.jpg\": \"Aves/Bucephala albeola/6ccc639011502afd81101d7ac0fb130e.jpg\",\n  \"491290.jpg\": \"Aves/Bucephala albeola/1be53cf0c792bdc937a3dc6cb9c105fc.jpg\",\n  \"491559.jpg\": \"Mollusca/Clinocardium nuttallii/d1e03bcd12cd4de802d35155365c87ac.jpg\",\n  \"491565.jpg\": \"Mollusca/Clinocardium nuttallii/7ecce91d8dbe67aa644e034e648ebf4f.jpg\",\n  \"491566.jpg\": \"Mollusca/Clinocardium nuttallii/ad88407dc218cbcad6de003dedae1c88.jpg\",\n  \"491567.jpg\": \"Mollusca/Clinocardium nuttallii/1dcae8514ed37a5be0420df40b3199e6.jpg\",\n  \"491568.jpg\": \"Mollusca/Clinocardium nuttallii/0b5d109dce04a580e5693731ae6a16f2.jpg\",\n  \"491569.jpg\": \"Mollusca/Clinocardium nuttallii/3ce458f525d776a59e737dbaae77e012.jpg\",\n  \"491577.jpg\": \"Mollusca/Clinocardium nuttallii/adea12219278932cb2d83ea61bb01eb1.jpg\",\n  \"491578.jpg\": \"Mollusca/Clinocardium nuttallii/5e47c4dc88e02700701b2d6a22fb5858.jpg\",\n  \"491580.jpg\": \"Mollusca/Clinocardium nuttallii/cc97dfc424b57861ea23b7949ea10129.jpg\",\n  \"491581.jpg\": \"Mollusca/Clinocardium nuttallii/20f07e4f926a0b567c70496b701e6761.jpg\",\n  \"491674.jpg\": \"Aves/Charadrius nivosus/56d56da3ffe4b256186446c49b74660a.jpg\",\n  \"491681.jpg\": \"Aves/Charadrius nivosus/11828c81634508dcbcd239aa0b07f38a.jpg\",\n  \"491693.jpg\": \"Aves/Charadrius nivosus/19722ef346006e3b960b2d060e748cd1.jpg\",\n  \"491694.jpg\": \"Aves/Charadrius nivosus/25b04a6548cb4d0d763cbcd10377a54f.jpg\",\n  \"491695.jpg\": \"Aves/Charadrius nivosus/e351a644180aa78bf9cb337700d0372c.jpg\",\n  \"491696.jpg\": \"Aves/Charadrius nivosus/53691da9ba47def9633f8e8ea8a122ed.jpg\",\n  \"491710.jpg\": \"Aves/Charadrius nivosus/c7d4ab7cefea426490f795f0c9ff26f9.jpg\",\n  \"491712.jpg\": \"Amphibia/Anaxyrus quercicus/8e0d89cc6000efc0c184af331940a96a.jpg\",\n  \"491713.jpg\": \"Amphibia/Anaxyrus quercicus/95b4f48727ec81183c913e36f79363f2.jpg\",\n  \"491714.jpg\": \"Amphibia/Anaxyrus quercicus/dafb628db056e2bd26f20baa810a5fc9.jpg\",\n  \"491718.jpg\": \"Insecta/Satyrium saepium/580295b9262b525a5e23040524f65dde.jpg\",\n  \"491719.jpg\": \"Insecta/Satyrium saepium/4f36ab08eeed79d03059157bafed7445.jpg\",\n  \"491720.jpg\": \"Insecta/Satyrium saepium/56a41ecc0e607f96e020a39a676cf010.jpg\",\n  \"491721.jpg\": \"Insecta/Satyrium saepium/650335136f8212b00c59d9708e12a79c.jpg\",\n  \"491722.jpg\": \"Insecta/Satyrium saepium/12e4b13e1cdfb3699f2d7b938932d4bc.jpg\",\n  \"491723.jpg\": \"Insecta/Satyrium saepium/8f76eac7533d3d2bc25d92f7225ccf79.jpg\",\n  \"491728.jpg\": \"Insecta/Satyrium saepium/f514af7495101f46eb8b3fe3bae2c93c.jpg\",\n  \"491733.jpg\": \"Insecta/Satyrium saepium/782617a9ec08c85671741e579b3e8fae.jpg\",\n  \"491738.jpg\": \"Insecta/Satyrium saepium/6ced36004e1d927ba1d4af39bc7870f0.jpg\",\n  \"491739.jpg\": \"Insecta/Satyrium saepium/37903d7965afd3b1b1788404bff19e76.jpg\",\n  \"491743.jpg\": \"Insecta/Satyrium saepium/82048223091bc9684aa7784b7714f3fb.jpg\",\n  \"491745.jpg\": \"Insecta/Pyrausta volupialis/d540bcff56843c816d9c13f9d693e5a7.jpg\",\n  \"491756.jpg\": \"Insecta/Chalcoela iphitalis/d40697d6f22ad1768dc4056463c7b802.jpg\",\n  \"491759.jpg\": \"Insecta/Chalcoela iphitalis/f8637c0259d423ac64ca09de4e7259a8.jpg\",\n  \"491760.jpg\": \"Insecta/Chalcoela iphitalis/a7aa47ebcb4c44a5df99d10e8753628a.jpg\",\n  \"491799.jpg\": \"Mammalia/Erinaceus europaeus/e08787b17f76c13e0ecb33be9020acb5.jpg\",\n  \"491813.jpg\": \"Mammalia/Erinaceus europaeus/2228067643d0ca20139ef99eca20f4f4.jpg\",\n  \"491834.jpg\": \"Mammalia/Erinaceus europaeus/2333c5b515a1c072a53fcedbf4eb100b.jpg\",\n  \"491844.jpg\": \"Mammalia/Erinaceus europaeus/c681da12bd0e1aa40e03af4be503892b.jpg\",\n  \"491856.jpg\": \"Aves/Trogon citreolus/7f723b2fe8e14b6f7322901e8d487f14.jpg\",\n  \"491857.jpg\": \"Aves/Trogon citreolus/939fecea5f5682e0d23494cc2446d97c.jpg\",\n  \"491858.jpg\": \"Aves/Trogon citreolus/0cd0e442875600228752d8af8ebd9a2c.jpg\",\n  \"491859.jpg\": \"Aves/Trogon citreolus/f676d202c845d3cfe97112ae3c1885e4.jpg\",\n  \"491861.jpg\": \"Aves/Trogon citreolus/9c35cf1c282079a7876a130c5ff11440.jpg\",\n  \"491862.jpg\": \"Aves/Trogon citreolus/61598d223218c8215888cf8e4c5881ea.jpg\",\n  \"491863.jpg\": \"Aves/Trogon citreolus/02eb8a2cf3547149f0226d1b10c5b148.jpg\",\n  \"491864.jpg\": \"Aves/Trogon citreolus/ab45bf3c46f6eea293eaef04f8fe5afc.jpg\",\n  \"491865.jpg\": \"Aves/Trogon citreolus/d2688bd3834e2f39e384e6e0c6209b5e.jpg\",\n  \"491866.jpg\": \"Aves/Trogon citreolus/35e2f62696fb0723a45a3de3c9ea0aee.jpg\",\n  \"491867.jpg\": \"Aves/Trogon citreolus/5ffd70be948ed9162d3a8e51cff1f546.jpg\",\n  \"491868.jpg\": \"Aves/Trogon citreolus/31ece1bb687d3c029516358914e1f962.jpg\",\n  \"491869.jpg\": \"Aves/Trogon citreolus/1252664083dc59664e9ff78f08cea0a8.jpg\",\n  \"491870.jpg\": \"Aves/Trogon citreolus/9379174a129fca8bb9d750a7ba55ff11.jpg\",\n  \"491872.jpg\": \"Aves/Cuculus canorus/015fc69d492ac7f79c666cb18dcf2047.jpg\",\n  \"491875.jpg\": \"Aves/Cuculus canorus/4ae925363277a89673f1e8a6b10907a3.jpg\",\n  \"491881.jpg\": \"Aves/Cuculus canorus/7a03eddb2b98d03250b8a2487ace85fb.jpg\",\n  \"491887.jpg\": \"Aves/Anthus pratensis/3830610c34cc4941cda2713a695da1c2.jpg\",\n  \"491888.jpg\": \"Aves/Anthus pratensis/5c24138e583e95b6f24988d328054c37.jpg\",\n  \"491917.jpg\": \"Aves/Struthio camelus/4ad50b0bcbc8b0e8b100a73168a2c899.jpg\",\n  \"491925.jpg\": \"Insecta/Calopteryx splendens/df0e87607791b6bd99eeff48631f4e10.jpg\",\n  \"491935.jpg\": \"Insecta/Calopteryx splendens/9313f984277240032d3806d9743988af.jpg\",\n  \"491939.jpg\": \"Insecta/Calopteryx splendens/7f3759aa52607c2d2a6697dfbf6fa6df.jpg\",\n  \"491942.jpg\": \"Insecta/Calopteryx splendens/b85dbe1dfea969aeed346c3087284594.jpg\",\n  \"491975.jpg\": \"Insecta/Ischnura denticollis/6016cb8ba5c5e7ca8d8031867777d68d.jpg\",\n  \"492059.jpg\": \"Aves/Phainopepla nitens/df7578542707f8c1b9228b1739d5ecca.jpg\",\n  \"492068.jpg\": \"Aves/Phainopepla nitens/415d7a5cefb76aa98d77725a62545378.jpg\",\n  \"492073.jpg\": \"Aves/Phainopepla nitens/1907e89116b69a2d90ba6bb9ab7550ab.jpg\",\n  \"492083.jpg\": \"Aves/Phainopepla nitens/820c17c9edd4444a720aa19384e41177.jpg\",\n  \"492087.jpg\": \"Aves/Phainopepla nitens/eee8196ba4267f1275c45d103de60e88.jpg\",\n  \"492118.jpg\": \"Aves/Phainopepla nitens/7fb51192a92bc1b685be3822f7f62010.jpg\",\n  \"492139.jpg\": \"Insecta/Ladona deplanata/029b1c0bb61f398f681b8de8fb0f46aa.jpg\",\n  \"492140.jpg\": \"Insecta/Ladona deplanata/a67121a8350bcc4bc86b79519ea899e4.jpg\",\n  \"492141.jpg\": \"Insecta/Ladona deplanata/d3d61e1d701720437edf86cad0d12f9c.jpg\",\n  \"492143.jpg\": \"Insecta/Ladona deplanata/df0250cdad8ee84640bcb5c5d8bbc722.jpg\",\n  \"492148.jpg\": \"Insecta/Ladona deplanata/db5376609ee6b517cd9e91c19a41d082.jpg\",\n  \"492149.jpg\": \"Insecta/Ladona deplanata/31b5db56c4330327a98fd691532b85aa.jpg\",\n  \"492157.jpg\": \"Insecta/Ladona deplanata/49ad49287acf143815583c9559cebf8c.jpg\",\n  \"492161.jpg\": \"Insecta/Ladona deplanata/b4f620257bcc94b92ce930b24480e41d.jpg\",\n  \"492165.jpg\": \"Insecta/Ladona deplanata/60a16e3dcc4efcfd271ba1b247d4076e.jpg\",\n  \"492170.jpg\": \"Insecta/Achalarus lyciades/3bec4c639004702ab70e9a84e0888bcd.jpg\",\n  \"492174.jpg\": \"Insecta/Achalarus lyciades/993006b21a9bf87d0a977a319560b086.jpg\",\n  \"492316.jpg\": \"Insecta/Boloria selene/7c3e577742c056a1af189277e3a99c09.jpg\",\n  \"492410.jpg\": \"Insecta/Bombus impatiens/9bf01210e7e7abb650cf0c67137e937c.jpg\",\n  \"492412.jpg\": \"Insecta/Bombus impatiens/9e4dfed6653a13707e1ac18298eba89d.jpg\",\n  \"492413.jpg\": \"Insecta/Bombus impatiens/2927b147bb241c395dd48d9c27838efd.jpg\",\n  \"492475.jpg\": \"Insecta/Bombus impatiens/9011bd8831692bbb5a6e7d321ba6989e.jpg\",\n  \"492551.jpg\": \"Insecta/Bombus impatiens/d62b31ed97ce4dc64f97b3b626769670.jpg\",\n  \"492627.jpg\": \"Insecta/Bombus impatiens/403dc79d8498f4c6c1524441832a0610.jpg\",\n  \"492691.jpg\": \"Amphibia/Pelophylax perezi/268522d73c7fd44857d19b4b42c1a52e.jpg\",\n  \"492706.jpg\": \"Animalia/Echinometra mathaei/e3f77467f061b1b5752e08f92b3ac9db.jpg\",\n  \"492717.jpg\": \"Insecta/Arctia villica/9e122962b327e38dbefbdaa84dcb7046.jpg\",\n  \"492722.jpg\": \"Insecta/Arctia villica/43c04e3efbfb65cb80d44add03383a65.jpg\",\n  \"492887.jpg\": \"Aves/Anas americana/7297406e103a059ec0cf6026480054b8.jpg\",\n  \"492900.jpg\": \"Aves/Anas americana/2f8a70a7a46bd4b9bd1668044b818160.jpg\",\n  \"493101.jpg\": \"Aves/Campylopterus hemileucurus/a0e4f4cf7cbe15684302660096994f86.jpg\",\n  \"493104.jpg\": \"Aves/Campylopterus hemileucurus/69adcccc73b0ded33f4af7885de327e0.jpg\",\n  \"493107.jpg\": \"Aves/Picoides arcticus/be960568a5307498f0fbcc0f6b65d0a5.jpg\",\n  \"493110.jpg\": \"Aves/Picoides arcticus/dc75f19af792f452b8a8680de4ef51bc.jpg\",\n  \"493133.jpg\": \"Aves/Calidris melanotos/02ea23e9bc51827cc0f49e139f4a0878.jpg\",\n  \"493137.jpg\": \"Aves/Calidris melanotos/fb7942fd3c8fc7c2ad4ad4585231c1ad.jpg\",\n  \"493142.jpg\": \"Aves/Calidris melanotos/f8e66f3be8738ebfa316f2d53678f7ef.jpg\",\n  \"493143.jpg\": \"Aves/Calidris melanotos/1c004fec117e2d4272bf7fa3bbca7045.jpg\",\n  \"493148.jpg\": \"Aves/Calidris melanotos/9b32f642669b0dfd8976f3c55b505688.jpg\",\n  \"493161.jpg\": \"Aves/Calidris melanotos/cb667b53c8da38880da1fa606840ba15.jpg\",\n  \"493165.jpg\": \"Aves/Calidris melanotos/eb5f9b8ae787e3433188019ac76bbb33.jpg\",\n  \"493172.jpg\": \"Aves/Calidris melanotos/c34e7b5a0f261b371f83bbcc1c44fb9f.jpg\",\n  \"493208.jpg\": \"Mammalia/Meles meles/db72f9a50d26fe9807942d768230b9de.jpg\",\n  \"493283.jpg\": \"Aves/Molothrus ater/294bb2ab82191e9ead568bbe18c11c80.jpg\",\n  \"493320.jpg\": \"Aves/Molothrus ater/eb68e4d7b507628fc441ed3a27e050c6.jpg\",\n  \"493443.jpg\": \"Aves/Molothrus ater/570b3597e2475d50696de2f8ab6cf532.jpg\",\n  \"493467.jpg\": \"Aves/Molothrus ater/f580246df33f9eb4b2ac9733e51f2763.jpg\",\n  \"493486.jpg\": \"Aves/Molothrus ater/7d71930b4b218204f98e4fafbd774232.jpg\",\n  \"493562.jpg\": \"Aves/Molothrus ater/eb5d050db53d8392e29b07f3f0560fad.jpg\",\n  \"493570.jpg\": \"Mollusca/Ariolimax californicus/7ae91c60c40205c8165f91d2f78910e9.jpg\",\n  \"493571.jpg\": \"Mollusca/Ariolimax californicus/585498875b45c0a0a5c9bf81f32adf26.jpg\",\n  \"493572.jpg\": \"Mollusca/Ariolimax californicus/820b9f8ccfe80b4a5af9d1d6ac07d71f.jpg\",\n  \"493573.jpg\": \"Mollusca/Ariolimax californicus/193a0e6b01cbd93e31e8e6392abe224d.jpg\",\n  \"493609.jpg\": \"Insecta/Melanargia galathea/7bce5172cfe920b5269464c3e8ec5571.jpg\",\n  \"493612.jpg\": \"Insecta/Melanargia galathea/0e59ae328cd819961f4fc40d4292981f.jpg\",\n  \"493615.jpg\": \"Insecta/Melanargia galathea/c3c6ced47235799f55195bad015b5348.jpg\",\n  \"493617.jpg\": \"Insecta/Melanargia galathea/406f99e292117461f9a5d420871c5ea6.jpg\",\n  \"493619.jpg\": \"Insecta/Melanargia galathea/82c2dd66d3fbfb507e7ec36b43e3d716.jpg\",\n  \"493623.jpg\": \"Insecta/Melanargia galathea/85dd92c62ffda0178c08e23a832ba489.jpg\",\n  \"493625.jpg\": \"Insecta/Melanargia galathea/e35ed0d7a98b260bc484f89f08e7da84.jpg\",\n  \"493632.jpg\": \"Insecta/Melanargia galathea/264a52bf3d0ef024c498661b41ec055b.jpg\",\n  \"493639.jpg\": \"Insecta/Smerinthus cerisyi/a9cfe32c60935843e63141843a32f7d9.jpg\",\n  \"493645.jpg\": \"Insecta/Smerinthus cerisyi/e73541dddc7dabf597daed6e95c7073f.jpg\",\n  \"493649.jpg\": \"Mollusca/Crepidula fornicata/5c52293299e5ddb60aa0bec0a3077008.jpg\",\n  \"493664.jpg\": \"Actinopterygii/Carassius auratus/3be567dcf578408ce4d35101f53492c6.jpg\",\n  \"493680.jpg\": \"Reptilia/Aspidoscelis sexlineata/1fe07d111e215dd5e922ddb05a2c2e46.jpg\",\n  \"493681.jpg\": \"Reptilia/Aspidoscelis sexlineata/4d2f5bc914c7a0916c58d20b3f3e71f4.jpg\",\n  \"493698.jpg\": \"Reptilia/Aspidoscelis sexlineata/4b359467e2d8aca0e5bc3ca2cae6ff9b.jpg\",\n  \"493700.jpg\": \"Reptilia/Aspidoscelis sexlineata/bae6116f74c480f7cb02231f464a74dc.jpg\",\n  \"493708.jpg\": \"Reptilia/Aspidoscelis sexlineata/cd0e3c705f07e8deee01f24b856b2783.jpg\",\n  \"493710.jpg\": \"Reptilia/Aspidoscelis sexlineata/594d831989a73fa2e5b42e24720b810e.jpg\",\n  \"493714.jpg\": \"Reptilia/Aspidoscelis sexlineata/be1cdeeea3715e00d4fbac07fe3e6298.jpg\",\n  \"493715.jpg\": \"Reptilia/Aspidoscelis sexlineata/4713300d96643fcbd4b95eb9d29e9b09.jpg\",\n  \"493716.jpg\": \"Reptilia/Aspidoscelis sexlineata/bb930410c1b0deff98fe19f129be8cbe.jpg\",\n  \"493718.jpg\": \"Arachnida/Loxosceles reclusa/c5170a1b2adb32e0a5eca56444d5622a.jpg\",\n  \"493725.jpg\": \"Arachnida/Loxosceles reclusa/01671d8d603fdf5323923bbbb4af143a.jpg\",\n  \"493732.jpg\": \"Reptilia/Heterodon nasicus/1861bc2bdc76ec281eb1e3cd64cf5495.jpg\",\n  \"493839.jpg\": \"Animalia/Aetobatus narinari/189c64aba46cb16ec9b0c6dabb53960b.jpg\",\n  \"493889.jpg\": \"Insecta/Lophocampa caryae/0c9d247c5c39f9efe597d817f9b0e7f7.jpg\",\n  \"493901.jpg\": \"Insecta/Lophocampa caryae/488e4be7bd2996dc1a9f6f25a36007cc.jpg\",\n  \"493902.jpg\": \"Insecta/Lophocampa caryae/616280968ad65849859828c3914e3b42.jpg\",\n  \"493917.jpg\": \"Insecta/Lophocampa caryae/1d1dc893e09b4eb326fe5c340ff67e4e.jpg\",\n  \"493923.jpg\": \"Insecta/Lophocampa caryae/656cbd599e06585e4aba3ba710caebe7.jpg\",\n  \"493928.jpg\": \"Insecta/Lophocampa caryae/908c73e3a61d06d9b8211aae7ee705cc.jpg\",\n  \"493932.jpg\": \"Insecta/Lophocampa caryae/36e5aecb88afe74ff1d1009c6db96fe8.jpg\",\n  \"493959.jpg\": \"Insecta/Lophocampa caryae/7af4e103cfff5bf575a502d8d1214b83.jpg\",\n  \"493962.jpg\": \"Insecta/Metcalfa pruinosa/80e0169b3736b0dad9b0d6f94d8df037.jpg\",\n  \"493965.jpg\": \"Insecta/Metcalfa pruinosa/bbdf7e695f0ea8fb97ffdc9168af0fbb.jpg\",\n  \"493966.jpg\": \"Insecta/Metcalfa pruinosa/522d8bee22cfcd6f4c3cb5a711ce68e6.jpg\",\n  \"493984.jpg\": \"Insecta/Metcalfa pruinosa/087a4b0c20b3d4748f9848f7892b0313.jpg\",\n  \"493986.jpg\": \"Insecta/Metcalfa pruinosa/62fb40cedd8473fe28f67692733286b5.jpg\",\n  \"494184.jpg\": \"Reptilia/Storeria occipitomaculata/57733c3e4eac3bbfb266db653d13ad13.jpg\",\n  \"494189.jpg\": \"Reptilia/Storeria occipitomaculata/5a26b0d5c397ca600ae15d364eb8cb32.jpg\",\n  \"494190.jpg\": \"Reptilia/Storeria occipitomaculata/b7d25b622b4f233a92ace238e426bedc.jpg\",\n  \"494194.jpg\": \"Reptilia/Storeria occipitomaculata/5af89ea10ac3612d8cd4ded84565e055.jpg\",\n  \"494195.jpg\": \"Reptilia/Storeria occipitomaculata/3d5d7463e24259598c6b6e621eb26575.jpg\",\n  \"494199.jpg\": \"Reptilia/Storeria occipitomaculata/a843d7c45c8f37c81751c02b8942815c.jpg\",\n  \"494200.jpg\": \"Reptilia/Storeria occipitomaculata/8b8dfb0ee549c7f090b5453b38aff778.jpg\",\n  \"494202.jpg\": \"Reptilia/Storeria occipitomaculata/540b6f38461253ee0c8dd5ae8da0baa7.jpg\",\n  \"494203.jpg\": \"Reptilia/Storeria occipitomaculata/5a0c2d3d5613670f6a5acddc99192fd1.jpg\",\n  \"494204.jpg\": \"Reptilia/Storeria occipitomaculata/294fe7758e54be8d8de084429dee897e.jpg\",\n  \"494205.jpg\": \"Reptilia/Storeria occipitomaculata/392de57745be59b9da7fee978a4badcd.jpg\",\n  \"494206.jpg\": \"Reptilia/Storeria occipitomaculata/460ae010aabeeeaef8e31101137a2ecd.jpg\",\n  \"494207.jpg\": \"Reptilia/Storeria occipitomaculata/5f100924d4627c20d4aed9e3deb640b8.jpg\",\n  \"494234.jpg\": \"Aves/Haliaeetus vocifer/da1672cc52930249751940f6e4f3e00d.jpg\",\n  \"494244.jpg\": \"Aves/Haliaeetus vocifer/8ff16314ed82bf180ff19f6d84635b81.jpg\",\n  \"494250.jpg\": \"Insecta/Epicallima argenticinctella/b75878843249e6d962d8469c8829786f.jpg\",\n  \"494253.jpg\": \"Insecta/Epicallima argenticinctella/f2593ea85512c962355404914ce0282a.jpg\",\n  \"494254.jpg\": \"Reptilia/Tarentola mauritanica/0c62686676a36f42c2231c5ff31cb3d2.jpg\",\n  \"494265.jpg\": \"Reptilia/Tarentola mauritanica/ab9e94f9906fd8ffe5962a77daaf4118.jpg\",\n  \"494266.jpg\": \"Reptilia/Tarentola mauritanica/b6b63242d926c77b5eb76e7424230c82.jpg\",\n  \"494276.jpg\": \"Reptilia/Tarentola mauritanica/d5c3faa86027a78ee40cf517b50b0950.jpg\",\n  \"494280.jpg\": \"Reptilia/Tarentola mauritanica/fa6049428c3ee4704e6e601b093c21fa.jpg\",\n  \"494288.jpg\": \"Reptilia/Tarentola mauritanica/453315e30ff9ed8c27d3e9670d02ed58.jpg\",\n  \"494338.jpg\": \"Insecta/Anisota virginiensis/27c52ea51f524fcd81cc4ae47b6504f3.jpg\",\n  \"494341.jpg\": \"Insecta/Anisota virginiensis/01c68ca28f95afcc078d3ccb10a7b0dd.jpg\",\n  \"494362.jpg\": \"Insecta/Achyra rantalis/a5240c2f4be8f1663cb53559f42c38d9.jpg\",\n  \"494419.jpg\": \"Aves/Branta sandvicensis/bbf45b9c41e0ab1b30832503d9e5b6f3.jpg\",\n  \"494428.jpg\": \"Arachnida/Dolomedes triton/fc0320a20511c0220fdce080d8d78ae7.jpg\",\n  \"494430.jpg\": \"Arachnida/Dolomedes triton/d6c61be0a72fac404e57413b00d59c67.jpg\",\n  \"494435.jpg\": \"Arachnida/Dolomedes triton/08bc07d92732fba0b9c9314bff453649.jpg\",\n  \"494437.jpg\": \"Arachnida/Dolomedes triton/313fe739bd73b625441c00475813dff1.jpg\",\n  \"494441.jpg\": \"Arachnida/Dolomedes triton/e6a4c9ba5a4294ddb35242cfa3c5e46e.jpg\",\n  \"494468.jpg\": \"Insecta/Nannothemis bella/6aed82867e36b588ffa5d899471f117a.jpg\",\n  \"494489.jpg\": \"Insecta/Magicicada septendecim/e6cd47f5949d40513932191c44fd6566.jpg\",\n  \"494492.jpg\": \"Insecta/Magicicada septendecim/b08273a6a4b6597fdebac31fa58feebf.jpg\",\n  \"494497.jpg\": \"Insecta/Zelus renardii/8b88b213e8bdc14f97c124e28596cfaa.jpg\",\n  \"494498.jpg\": \"Insecta/Zelus renardii/c583ef575325c20cfe9f48a611c0a441.jpg\",\n  \"494504.jpg\": \"Insecta/Zelus renardii/5291c50b7b451259c06896b418fb8c43.jpg\",\n  \"494508.jpg\": \"Insecta/Zelus renardii/abe9f3c71c98a24e5c13e55a88ab342e.jpg\",\n  \"494509.jpg\": \"Insecta/Zelus renardii/fbb545bd5a181f535727d2d909df45ed.jpg\",\n  \"494511.jpg\": \"Insecta/Zelus renardii/4d3be9967d3abdc9d4b0a6c55581973a.jpg\",\n  \"494516.jpg\": \"Insecta/Zelus renardii/814aa08673278ce64d5a6b0a2f433ebd.jpg\",\n  \"494518.jpg\": \"Insecta/Zelus renardii/7286e3962579bac9aaa54b84badba378.jpg\",\n  \"494519.jpg\": \"Insecta/Zelus renardii/78434b27451f096ba67ecf49688622c1.jpg\",\n  \"494522.jpg\": \"Insecta/Zelus renardii/b9b65891a9cdfec82e3618e3679461d8.jpg\",\n  \"494523.jpg\": \"Insecta/Zelus renardii/4c5b0e05c17ac9bd01e73be41efa4735.jpg\",\n  \"494577.jpg\": \"Aves/Pelecanus occidentalis carolinensis/dd841429eee2f2eead5dee471ee77d5f.jpg\",\n  \"494650.jpg\": \"Actinopterygii/Lepisosteus oculatus/9ffbab49a1d974ca5694e510d66a7c5e.jpg\",\n  \"494657.jpg\": \"Actinopterygii/Lepisosteus oculatus/ddd87c6b53b48b26c511a981b00ca47f.jpg\",\n  \"494680.jpg\": \"Insecta/Zanclognatha jacchusalis/61c76c979b623ec63051fcfeb6d7460e.jpg\",\n  \"494685.jpg\": \"Insecta/Zanclognatha jacchusalis/0ab0c24355d433bead1475a545db5eb6.jpg\",\n  \"494687.jpg\": \"Aves/Auriparus flaviceps/6615f491347341bcf0cff0af7bd2641c.jpg\",\n  \"494688.jpg\": \"Aves/Auriparus flaviceps/0bbcdaec6fecdd85eb229b9dd5bd241d.jpg\",\n  \"494690.jpg\": \"Aves/Auriparus flaviceps/a1edd358d4dd8828c7b1a69ae7b9d2f8.jpg\",\n  \"494691.jpg\": \"Aves/Auriparus flaviceps/d08f761bcabf33fe616f3c7ca71bbad0.jpg\",\n  \"494692.jpg\": \"Aves/Auriparus flaviceps/d9569f014cd357d99f16f6b5606c4be9.jpg\",\n  \"494704.jpg\": \"Aves/Auriparus flaviceps/fbc170fd1d59bef42d42aa6d06ceffea.jpg\",\n  \"494721.jpg\": \"Aves/Auriparus flaviceps/e212541a54291b8cd785c033618f34d1.jpg\",\n  \"494847.jpg\": \"Aves/Mycteria americana/02d24003c96a0464beb9df84ff04439a.jpg\",\n  \"494913.jpg\": \"Aves/Mycteria americana/0e333d42bf6f09c4cc22bce071d4d226.jpg\",\n  \"495086.jpg\": \"Reptilia/Stigmochelys pardalis/be6a88143844e8725da20cdc1d68d13d.jpg\",\n  \"495088.jpg\": \"Reptilia/Stigmochelys pardalis/8c459f220fb19341c83341445e709d0b.jpg\",\n  \"495232.jpg\": \"Insecta/Drepana arcuata/e41bf1b0de3ddfdbbc8e169257909ff5.jpg\",\n  \"495235.jpg\": \"Insecta/Drepana arcuata/b636ec4b4bf8497cc2940df7a18d7aa3.jpg\",\n  \"495239.jpg\": \"Insecta/Drepana arcuata/dfa82b8699f8af438b26d1a01dfb7906.jpg\",\n  \"495241.jpg\": \"Insecta/Drepana arcuata/44d7faf0c16a0a3a674ac666be3b336e.jpg\",\n  \"495243.jpg\": \"Insecta/Drepana arcuata/33a300194db7ca18b9370fd3f660d577.jpg\",\n  \"495247.jpg\": \"Insecta/Drepana arcuata/0fccb5dc2042a2a2df930e87ba8657c0.jpg\",\n  \"495250.jpg\": \"Insecta/Drepana arcuata/0bdf9cabc1aabf2cf27febd018e879c0.jpg\",\n  \"495288.jpg\": \"Aves/Egretta tricolor/82467dc37d0b2d1e5e038aabc53ffba7.jpg\",\n  \"495341.jpg\": \"Aves/Egretta tricolor/e3038c8b98aca4ba6497e049cb01f78d.jpg\",\n  \"495366.jpg\": \"Aves/Egretta tricolor/487f688fa24ead19d801af015b1bf046.jpg\",\n  \"495376.jpg\": \"Aves/Egretta tricolor/ccaa48bd1258c90eecd8e5c968c25b28.jpg\",\n  \"495421.jpg\": \"Aves/Egretta tricolor/80f83542581674defa1c5f7133c37dfb.jpg\",\n  \"495525.jpg\": \"Aves/Icterus cucullatus/77fe18fe85618abdce6a5b3eed724011.jpg\",\n  \"495595.jpg\": \"Aves/Icterus cucullatus/877868bcb804d6c5324d9ae67aae12a3.jpg\",\n  \"495649.jpg\": \"Aves/Icterus cucullatus/b041cc29b4b59e21e8b128655a10bd8a.jpg\",\n  \"495661.jpg\": \"Aves/Icterus cucullatus/45ac4914d7edeecb8dcede2d9c6570e9.jpg\",\n  \"495678.jpg\": \"Aves/Icterus cucullatus/211ded0f6a1f58d17eb7fcb55d362382.jpg\",\n  \"495716.jpg\": \"Insecta/Euphoria basalis/4fb16cb286ad83fef71590693577319d.jpg\",\n  \"495862.jpg\": \"Insecta/Dione juno/05e11727845ae5b89a27531ef0aafb83.jpg\",\n  \"495867.jpg\": \"Insecta/Dione juno/42269d73a878a52b8d806d74134ace27.jpg\",\n  \"496051.jpg\": \"Insecta/Cenopis reticulatana/a90f90bf000e4bdca838784aab267c7a.jpg\",\n  \"496072.jpg\": \"Animalia/Pisaster ochraceus/626d1de8324125a50322ca9bc9bc8e85.jpg\",\n  \"496105.jpg\": \"Animalia/Pisaster ochraceus/ca30616b2295ac4971e896e124681780.jpg\",\n  \"496279.jpg\": \"Animalia/Pisaster ochraceus/55be3a3aac7365af195efc392d341c75.jpg\",\n  \"496296.jpg\": \"Animalia/Pisaster ochraceus/3fa5449d1cf45fb08a422b6894af0212.jpg\",\n  \"496328.jpg\": \"Animalia/Pisaster ochraceus/a7437f0a76d80a550c6ba8e7489b2014.jpg\",\n  \"496525.jpg\": \"Aves/Chloris chloris/4b3b273a690a985a440a5fec06a45033.jpg\",\n  \"496529.jpg\": \"Aves/Chloris chloris/19ca4e0d1b6d2f7664b3431c807e7cc9.jpg\",\n  \"496542.jpg\": \"Aves/Chloris chloris/15ca950edb3f4c50ae36732e70ea0b06.jpg\",\n  \"496552.jpg\": \"Aves/Chloris chloris/880fa2d0767464607ce3230f2ab8a6f2.jpg\",\n  \"496563.jpg\": \"Aves/Chloris chloris/0c56e3f13c162f50cddb65b2bfc8bde4.jpg\",\n  \"496580.jpg\": \"Aves/Chloris chloris/6798008029f9542d5027ce13e58371ac.jpg\",\n  \"496680.jpg\": \"Aves/Larus occidentalis/9a167746a67d3424a925f3e538f43423.jpg\",\n  \"496739.jpg\": \"Aves/Larus occidentalis/398417c6e04720e374c1102e0300a97c.jpg\",\n  \"496741.jpg\": \"Aves/Larus occidentalis/921ea790e5a3a78234a60ad0051a007f.jpg\",\n  \"496743.jpg\": \"Aves/Larus occidentalis/d601c3439fad1a68e2974dd3217da540.jpg\",\n  \"496754.jpg\": \"Aves/Larus occidentalis/1733864b6600e74d66a3117df7b55adf.jpg\",\n  \"496866.jpg\": \"Aves/Larus occidentalis/5765b8a8345d58eb9fbd1f70321cfd2f.jpg\",\n  \"49688.jpg\": \"Aves/Buteogallus anthracinus/4893e9aaa8261913f5350a51f14e0ada.jpg\",\n  \"49689.jpg\": \"Aves/Buteogallus anthracinus/3436cb2560c9736270f1fadde66d928c.jpg\",\n  \"49690.jpg\": \"Aves/Buteogallus anthracinus/0e5d60b6498c33c7f58108dea7f6cbd1.jpg\",\n  \"496918.jpg\": \"Aves/Larus occidentalis/96e4874d90176ba446bba032ef8b5d27.jpg\",\n  \"49694.jpg\": \"Aves/Buteogallus anthracinus/1b5ecd495423ddd8e5235845cae7e9ab.jpg\",\n  \"49695.jpg\": \"Aves/Buteogallus anthracinus/2f555cd66694f13bb0eaaa927046f786.jpg\",\n  \"496954.jpg\": \"Reptilia/Terrapene carolina bauri/f2595c48394db85f0a64e73d568f5c9a.jpg\",\n  \"496957.jpg\": \"Reptilia/Terrapene carolina bauri/966d920b5c5707653d026e3247f142a5.jpg\",\n  \"49697.jpg\": \"Aves/Buteogallus anthracinus/7067603da4d0c75f173801ed8202a9e7.jpg\",\n  \"49703.jpg\": \"Aves/Buteogallus anthracinus/a99a9c1d96b0c27e754da4d824283069.jpg\",\n  \"497035.jpg\": \"Aves/Vireo gilvus/e08e9c7ce9a4feee19b0e5bdeb94844d.jpg\",\n  \"49704.jpg\": \"Aves/Buteogallus anthracinus/951b1871ed598923c2ef4eb5304ce6fa.jpg\",\n  \"497105.jpg\": \"Aves/Vireo gilvus/57f51a019d03e2d9ad71401358d7f597.jpg\",\n  \"497106.jpg\": \"Aves/Vireo gilvus/072d365bd6513365387d90d4d7fae4b9.jpg\",\n  \"497119.jpg\": \"Insecta/Archilestes grandis/dcd56e0cf30c7b729c864cc148124aea.jpg\",\n  \"497131.jpg\": \"Insecta/Archilestes grandis/af412373afaec0f8530e501b853fa96b.jpg\",\n  \"49714.jpg\": \"Aves/Buteogallus anthracinus/d00b9bfef1948d90905b0677d644657f.jpg\",\n  \"497147.jpg\": \"Insecta/Archilestes grandis/2638b5c02cdbb04ab63438687ef84c15.jpg\",\n  \"497150.jpg\": \"Insecta/Archilestes grandis/9aa7b6eca8d1dbc570266f78a06ad6b8.jpg\",\n  \"497151.jpg\": \"Insecta/Archilestes grandis/1b8ecd4c15a7daa8f45f779bcf517caa.jpg\",\n  \"497153.jpg\": \"Insecta/Archilestes grandis/0518f725ae84ae47d2ac7a4639649738.jpg\",\n  \"497158.jpg\": \"Insecta/Archilestes grandis/1e917db04431e186e49f452df4f72208.jpg\",\n  \"497159.jpg\": \"Insecta/Archilestes grandis/3bb9a1db76dc00662c605021ee9a224c.jpg\",\n  \"497161.jpg\": \"Insecta/Junonia evarete/79ce454ca0dd5e2057dbef753881fb57.jpg\",\n  \"49722.jpg\": \"Aves/Buteogallus anthracinus/6f8a79fc7990bae0851c1f3b80cdc90b.jpg\",\n  \"49723.jpg\": \"Aves/Buteogallus anthracinus/2d7221a30e0c6694d7b6b60e901f6d43.jpg\",\n  \"49724.jpg\": \"Aves/Buteogallus anthracinus/c7fba8d000165fdb73c49501c2cea955.jpg\",\n  \"497248.jpg\": \"Aves/Hirundo rustica/e8f95fb413e27d1d3aace559448becb7.jpg\",\n  \"49725.jpg\": \"Aves/Buteogallus anthracinus/082091c86cbe4602eae91e5dfe18470a.jpg\",\n  \"497263.jpg\": \"Aves/Hirundo rustica/6ab7524ed9166d52c0e20e8e8585685d.jpg\",\n  \"49727.jpg\": \"Aves/Buteogallus anthracinus/624fd730a71d77b805964b3dd8113253.jpg\",\n  \"49730.jpg\": \"Aves/Buteogallus anthracinus/1b71e93888fa321e5d7ff4f8892345aa.jpg\",\n  \"49732.jpg\": \"Aves/Buteogallus anthracinus/92eccd290e2d8147123858ab04dbf56b.jpg\",\n  \"49733.jpg\": \"Aves/Buteogallus anthracinus/afbc0f3c8efaec93ea24dd5a1f222e55.jpg\",\n  \"49734.jpg\": \"Aves/Buteogallus anthracinus/2dfc0f1102df411d95785cd6f1165810.jpg\",\n  \"49735.jpg\": \"Aves/Buteogallus anthracinus/69c0186cdb7fb21aeae434ea801a81bf.jpg\",\n  \"49736.jpg\": \"Aves/Buteogallus anthracinus/3400c49d1ea5dbb0474bc03760544762.jpg\",\n  \"497406.jpg\": \"Aves/Hirundo rustica/0a1a535eddad07d990fb5cdefb89ce9f.jpg\",\n  \"49746.jpg\": \"Aves/Buteogallus anthracinus/294348d96debc7c8ba801bfa0a2080ee.jpg\",\n  \"497666.jpg\": \"Mammalia/Lontra canadensis/d13537244ba1d51cd291c8b7e8451377.jpg\",\n  \"497679.jpg\": \"Mammalia/Lontra canadensis/b65b6ddfa64cc554ce609239df23406d.jpg\",\n  \"497715.jpg\": \"Mammalia/Lontra canadensis/a840b3c562566b22e4332991354ce471.jpg\",\n  \"497740.jpg\": \"Mammalia/Lontra canadensis/96c2cb842cf3e139222c35f4e5b96e3b.jpg\",\n  \"497745.jpg\": \"Mammalia/Lontra canadensis/a8f7e387f766b3450dec91028dff76be.jpg\",\n  \"497752.jpg\": \"Mammalia/Lontra canadensis/475c1794ebe8e566baf6da600c6aaf11.jpg\",\n  \"497786.jpg\": \"Mammalia/Lontra canadensis/6c4a31efbcf1abb23b915a2161ddb7ea.jpg\",\n  \"497815.jpg\": \"Insecta/Lerema accius/00aebd3b6991f85139d743c233e90c66.jpg\",\n  \"497835.jpg\": \"Insecta/Lerema accius/63514eea1fb88d86339639f2cbe310f2.jpg\",\n  \"497870.jpg\": \"Insecta/Lerema accius/b17b40b8f769ece9e7f0ad4df690ebe2.jpg\",\n  \"497884.jpg\": \"Insecta/Lerema accius/88e6e322dcad4c7217940761ad210440.jpg\",\n  \"497885.jpg\": \"Insecta/Lerema accius/ed9380e279554b2238d40bf627fe3974.jpg\",\n  \"497924.jpg\": \"Aves/Corvus albus/de7d0163b8457e88ff99a7397228f121.jpg\",\n  \"49795.jpg\": \"Insecta/Ischnura hastata/4abc27599934468f1668d66a2e622d38.jpg\",\n  \"497971.jpg\": \"Amphibia/Hyla intermedia/504ce70f944385e9d078ab7870bd6e9a.jpg\",\n  \"497976.jpg\": \"Arachnida/Menemerus bivittatus/7e44a019732bece367192f9b81179200.jpg\",\n  \"497981.jpg\": \"Arachnida/Menemerus bivittatus/fd9dc335841d06fb0d23bb7f95470615.jpg\",\n  \"497983.jpg\": \"Animalia/Lumbricus terrestris/2eafb7f28165fa6b858f931d00409345.jpg\",\n  \"497987.jpg\": \"Animalia/Lumbricus terrestris/1cefd1af491cd238a4814eefcc56f8c1.jpg\",\n  \"497988.jpg\": \"Animalia/Lumbricus terrestris/aabb1f3b8f9eb290eaba699218acc30a.jpg\",\n  \"497990.jpg\": \"Animalia/Lumbricus terrestris/f6eca6f248bc78b5bba067dd2000602d.jpg\",\n  \"497991.jpg\": \"Animalia/Lumbricus terrestris/cd68df056284a6d7b5c677c5cc5c330d.jpg\",\n  \"497993.jpg\": \"Animalia/Lumbricus terrestris/715379d600446bbc400b8a95c6735c57.jpg\",\n  \"497996.jpg\": \"Aves/Fregata minor/3eeaa69298c51776657afe7e6c1c826f.jpg\",\n  \"497997.jpg\": \"Aves/Fregata minor/8ffb61c17e0e68ecf33dc76484df6564.jpg\",\n  \"49800.jpg\": \"Insecta/Ischnura hastata/090ba20dd2efcef54684f772adfbfcc7.jpg\",\n  \"498043.jpg\": \"Insecta/Euptoieta hegesia/a2e0466afc92dc0d9940e8d7866bc325.jpg\",\n  \"498046.jpg\": \"Insecta/Euptoieta hegesia/008364dcd68382b96685c7dd9df0500a.jpg\",\n  \"498052.jpg\": \"Insecta/Euptoieta hegesia/49c31017d79956c61a3989442d4c129c.jpg\",\n  \"498053.jpg\": \"Insecta/Euptoieta hegesia/1b715e16e7c1cf42e1d98031f494ce64.jpg\",\n  \"498056.jpg\": \"Aves/Zenaida auriculata/5a0d163ae3a9459b47245c3558585d7b.jpg\",\n  \"498062.jpg\": \"Aves/Zenaida auriculata/774f3ea2e12f251acb5d1c817ef0b1cc.jpg\",\n  \"49807.jpg\": \"Insecta/Ischnura hastata/5c6c10ace96f325f325e13f86580bfd1.jpg\",\n  \"498081.jpg\": \"Mollusca/Caracolus caracolla/f9c677957f2bcbb9e531c0df0dec65a5.jpg\",\n  \"498096.jpg\": \"Mollusca/Caracolus marginella/c3ae018056dca4178b9fa90ddf069b85.jpg\",\n  \"498100.jpg\": \"Mollusca/Caracolus marginella/ca3d2c25d4d562b4671836c71010efc1.jpg\",\n  \"498101.jpg\": \"Mollusca/Caracolus marginella/79a32d6e5e7170ae3de008f70d0e9a77.jpg\",\n  \"498109.jpg\": \"Insecta/Acharia stimulea/f9838502dccacb876278d5fe85e06c98.jpg\",\n  \"49811.jpg\": \"Insecta/Ischnura hastata/95d3b5e27e044f3132d01d376ae79257.jpg\",\n  \"498110.jpg\": \"Insecta/Acharia stimulea/3d4fd163cfee8ac4793927e5a9b92df6.jpg\",\n  \"498111.jpg\": \"Insecta/Acharia stimulea/c940197e0369a4bb90823b8236727b73.jpg\",\n  \"498112.jpg\": \"Insecta/Acharia stimulea/5f8f129e036b87a1357d220f2305dfc4.jpg\",\n  \"498113.jpg\": \"Insecta/Acharia stimulea/d34cc089968de139ff58bab3c82575e3.jpg\",\n  \"498114.jpg\": \"Insecta/Acharia stimulea/f7ff3998fda442518c0fe1dcbd50e051.jpg\",\n  \"498118.jpg\": \"Insecta/Acharia stimulea/424b40ae3fa5e3173f6f8d3074ad70a9.jpg\",\n  \"498119.jpg\": \"Insecta/Acharia stimulea/74234b6f152e70bdcfad6c315b2c7a35.jpg\",\n  \"498120.jpg\": \"Insecta/Acharia stimulea/89ce679f90474098e19a7d201cc01fee.jpg\",\n  \"498125.jpg\": \"Insecta/Acharia stimulea/b80191e73db70a449ecd53fcb7f036d4.jpg\",\n  \"498126.jpg\": \"Insecta/Acharia stimulea/47d1310d0b82bc2b50b2c7a96b504a37.jpg\",\n  \"498140.jpg\": \"Aves/Tringa totanus/08a2b32dcb5256299849c269d53f49f0.jpg\",\n  \"498148.jpg\": \"Aves/Tringa totanus/8536578998cc14c57ae31da554fa170b.jpg\",\n  \"498150.jpg\": \"Aves/Tringa totanus/d9b3eb5400de78fdc2d80e8feb9c013a.jpg\",\n  \"498157.jpg\": \"Aves/Tringa totanus/6258461aeae5db86bcdd39f0fb4024ff.jpg\",\n  \"498163.jpg\": \"Aves/Tringa totanus/fbbb7d44cc3d0e3fd71adc073acb7946.jpg\",\n  \"498164.jpg\": \"Aves/Tringa totanus/78f1c9157cce6a207b6000873e201fd0.jpg\",\n  \"498166.jpg\": \"Aves/Tringa totanus/0049904982374ff1a53fff254c9d0216.jpg\",\n  \"498167.jpg\": \"Amphibia/Lithobates clamitans/5d24124e28b2739f995bbfe057349ba2.jpg\",\n  \"498175.jpg\": \"Amphibia/Lithobates clamitans/33d01a5c8c46a659790683154b5597d5.jpg\",\n  \"498189.jpg\": \"Amphibia/Lithobates clamitans/ae458a9aaf2112e4e8cd2cb1a2473834.jpg\",\n  \"49820.jpg\": \"Insecta/Ischnura hastata/1e6eb4a868c70e53e740dea623197442.jpg\",\n  \"49821.jpg\": \"Insecta/Ischnura hastata/659975f975e36970892d41c0aa0d3651.jpg\",\n  \"498257.jpg\": \"Amphibia/Lithobates clamitans/0f179df000bc6c3da1e6d0154cdbaa4e.jpg\",\n  \"49831.jpg\": \"Insecta/Ischnura hastata/23d538335431782429536853b5fbaf6e.jpg\",\n  \"498331.jpg\": \"Amphibia/Lithobates clamitans/2feca1cad45092f86f4962d7f7cbcb78.jpg\",\n  \"498338.jpg\": \"Amphibia/Lithobates clamitans/76f2cb1721bf02e67d313e571a5f9b21.jpg\",\n  \"498342.jpg\": \"Amphibia/Lithobates clamitans/0ee32d20246252e27d48deef86b4160d.jpg\",\n  \"498365.jpg\": \"Amphibia/Lithobates clamitans/b8cf35fe0e92da7c5fdcdd5e098d5f1a.jpg\",\n  \"498398.jpg\": \"Amphibia/Lithobates clamitans/0c3f222d2a9f1c93d68aaa24ea029da4.jpg\",\n  \"498399.jpg\": \"Amphibia/Lithobates clamitans/f376d1ce1461cc7a3e030ae4ac7bf7a2.jpg\",\n  \"498427.jpg\": \"Amphibia/Lithobates clamitans/5b76c3ff37218dff9bb787672af9aefc.jpg\",\n  \"498442.jpg\": \"Amphibia/Lithobates clamitans/8f1820e327107cf9e7c7fe95f1df6e7a.jpg\",\n  \"49849.jpg\": \"Insecta/Ischnura hastata/d732b684047c3864affb147875b637ed.jpg\",\n  \"49851.jpg\": \"Insecta/Ischnura hastata/60fb8897fd5cd6b1ed8e4c813589adbc.jpg\",\n  \"49852.jpg\": \"Insecta/Ischnura hastata/227b82543177aafe5ca7a7a6f1feed92.jpg\",\n  \"498576.jpg\": \"Mammalia/Trichechus manatus/af08bb6b726540374b45aaa9da6f9b53.jpg\",\n  \"498580.jpg\": \"Mammalia/Trichechus manatus/931f6f28c9a6ed088db1d2b87ccfb380.jpg\",\n  \"498582.jpg\": \"Mammalia/Trichechus manatus/6218d48b7c0971a45e3dcabbcc4e7823.jpg\",\n  \"49859.jpg\": \"Insecta/Ischnura hastata/ca94cb9796f58be1e9b33c7e73ab41cf.jpg\",\n  \"498616.jpg\": \"Amphibia/Batrachoseps major/f8157526b921ecbc039e357ea2f0f9c6.jpg\",\n  \"498617.jpg\": \"Amphibia/Batrachoseps major/fe1b5423ddcff4172a3d407e17eb4229.jpg\",\n  \"498626.jpg\": \"Amphibia/Batrachoseps major/1f641bc206bc3a9f4d0c51b52104a416.jpg\",\n  \"498627.jpg\": \"Amphibia/Batrachoseps major/e46aa1f5a4dd4497f17d6517127c51a8.jpg\",\n  \"498628.jpg\": \"Amphibia/Batrachoseps major/4455d0e2a3d3ae6a23f2a379922e727f.jpg\",\n  \"49863.jpg\": \"Insecta/Ischnura hastata/a19b26c122c9dc1771409b8d63fa79e9.jpg\",\n  \"498641.jpg\": \"Amphibia/Batrachoseps major/64a4a11dace503cf62e5574b77876dc7.jpg\",\n  \"498642.jpg\": \"Amphibia/Batrachoseps major/4cb1bc038e93fa3f69b737934b4b9f5c.jpg\",\n  \"498643.jpg\": \"Amphibia/Batrachoseps major/8a1eace5cd221196a1517db5331f2a40.jpg\",\n  \"498645.jpg\": \"Amphibia/Batrachoseps major/544e249dfe776c5809e3d037da3472c0.jpg\",\n  \"498654.jpg\": \"Insecta/Conchylodes ovulalis/1dcf899127b3a6ccb168998fff0c6f99.jpg\",\n  \"498675.jpg\": \"Insecta/Polygrammate hebraeicum/da56d752c196b8d9f7ab8431cbeea46e.jpg\",\n  \"49875.jpg\": \"Insecta/Ischnura hastata/34fe7539086e65a03a134aa5bafe8817.jpg\",\n  \"49876.jpg\": \"Insecta/Ischnura hastata/29e6799a3b9b9bc5e91c1fcdf7b6abc2.jpg\",\n  \"49877.jpg\": \"Insecta/Ischnura hastata/15ae10bdce605a7037323cb338c9600e.jpg\",\n  \"498791.jpg\": \"Insecta/Lestes rectangularis/ed69f483f305bb9074e023cc44fb496f.jpg\",\n  \"498792.jpg\": \"Insecta/Lestes rectangularis/47b21f9c470bd8cff9b700d824fb5ecb.jpg\",\n  \"498793.jpg\": \"Insecta/Lestes rectangularis/29064740247b0a8df86bb4331820729e.jpg\",\n  \"498804.jpg\": \"Insecta/Lestes rectangularis/cb40bd427d8509b4a7fefc232296253f.jpg\",\n  \"498809.jpg\": \"Insecta/Lestes rectangularis/75064a8ca6072c885424a52d5533b618.jpg\",\n  \"498822.jpg\": \"Insecta/Thasus neocalifornicus/1cbaf921a183a2060cb4aa75a6c16ddc.jpg\",\n  \"498823.jpg\": \"Insecta/Thasus neocalifornicus/a42bd3fa99c04715ab1230dbef4dfd7f.jpg\",\n  \"498847.jpg\": \"Insecta/Anartia fatima fatima/653f0d6d232150314854ac198c921081.jpg\",\n  \"498911.jpg\": \"Aves/Aratinga nenday/a8b574670b55540cadccdff6060cea43.jpg\",\n  \"498914.jpg\": \"Aves/Aratinga nenday/136cde3e0e7aa8660695c033d6b117d7.jpg\",\n  \"498915.jpg\": \"Insecta/Neotibicen superbus/c04fde7e701c1fe8943af659ab4e19b6.jpg\",\n  \"498930.jpg\": \"Insecta/Neotibicen superbus/0cc8c86ad5bfba00b679122d324cb0ac.jpg\",\n  \"498931.jpg\": \"Insecta/Neotibicen superbus/ada8095025a51d659992edb88a8da14a.jpg\",\n  \"498932.jpg\": \"Insecta/Neotibicen superbus/f23bbbbdce95cc75e570ed65fabfaebf.jpg\",\n  \"498936.jpg\": \"Insecta/Neotibicen superbus/bcd9b68fe8b85134ed91caf2fe82e039.jpg\",\n  \"498941.jpg\": \"Insecta/Neotibicen superbus/c5c5b2ec7c1618bfc3b108e2bfdee83a.jpg\",\n  \"498943.jpg\": \"Insecta/Neotibicen superbus/f3820c803f0d0ed4fb1579f92aee1a1c.jpg\",\n  \"498944.jpg\": \"Insecta/Neotibicen superbus/e17981c2d683e84728dfbff403604ae2.jpg\",\n  \"498969.jpg\": \"Insecta/Melissodes bimaculata/1440ca19f177fa6891bdcac33daf8f7e.jpg\",\n  \"498993.jpg\": \"Aves/Cyrtonyx montezumae/38ef42d6834555f18e5895c7d407ede1.jpg\",\n  \"499009.jpg\": \"Aves/Sturnella magna/23cebf78b4230031148252b0bdb90736.jpg\",\n  \"499012.jpg\": \"Aves/Sturnella magna/619442430a0ec6a96d6ee1e32b6946a7.jpg\",\n  \"499028.jpg\": \"Aves/Sturnella magna/0f55dbbe4460e93e6eb47aa16794ba19.jpg\",\n  \"499033.jpg\": \"Aves/Sturnella magna/1100a65d0a3ada4c3a709739abc325d1.jpg\",\n  \"499038.jpg\": \"Aves/Sturnella magna/b91f02e4b91bc221001436d762462fe4.jpg\",\n  \"499039.jpg\": \"Aves/Sturnella magna/a1b69d87ab8b3ff95109fe79001d0528.jpg\",\n  \"49909.jpg\": \"Insecta/Ischnura hastata/1975811bcbca6eface04875a637e0990.jpg\",\n  \"499093.jpg\": \"Aves/Aramides cajaneus/6457605495447c6f5b3ff99c89074442.jpg\",\n  \"499111.jpg\": \"Insecta/Harrisimemna trisignata/2a66ba83e498b67b60682081ba614048.jpg\",\n  \"499114.jpg\": \"Reptilia/Coluber constrictor priapus/72b30d38828ead89703b35b8203cf4b5.jpg\",\n  \"499115.jpg\": \"Reptilia/Coluber constrictor priapus/7c19048d89aed96da47780483032fbe0.jpg\",\n  \"499116.jpg\": \"Reptilia/Coluber constrictor priapus/60ecce20cf9137f140ad24b28e6f80b8.jpg\",\n  \"499117.jpg\": \"Reptilia/Coluber constrictor priapus/ad2bdf04ec0c4750d204db9eb837e5dc.jpg\",\n  \"499118.jpg\": \"Reptilia/Coluber constrictor priapus/899dc6cb6072e62045589857255d1401.jpg\",\n  \"499119.jpg\": \"Reptilia/Coluber constrictor priapus/bce1eed34f7447c0c4be54be0050a87e.jpg\",\n  \"499120.jpg\": \"Reptilia/Coluber constrictor priapus/83e5704ec02d0ba525580f86db64a14d.jpg\",\n  \"499122.jpg\": \"Reptilia/Coluber constrictor priapus/45c99b9315ea1292c509178a87c60b45.jpg\",\n  \"499130.jpg\": \"Reptilia/Coluber constrictor priapus/fef01910ecb200c13203038211c73af8.jpg\",\n  \"499131.jpg\": \"Reptilia/Coluber constrictor priapus/8efbbad300c643868442526c619090c1.jpg\",\n  \"499132.jpg\": \"Reptilia/Coluber constrictor priapus/035dccc1e001f4075e1be36996bce117.jpg\",\n  \"499133.jpg\": \"Aves/Motacilla flava/3b5f1315b26f7d72caa8a9e2dcc60890.jpg\",\n  \"499141.jpg\": \"Aves/Motacilla flava/7c6f14416f89816400f25bae76f32c02.jpg\",\n  \"499142.jpg\": \"Aves/Motacilla flava/0a31364f5fda299d1fbe16f584c3849a.jpg\",\n  \"499143.jpg\": \"Aves/Motacilla flava/89742928e7c324e96336c38272840446.jpg\",\n  \"499146.jpg\": \"Aves/Motacilla flava/0510c3ce0339935cb9927d70376a5686.jpg\",\n  \"499147.jpg\": \"Aves/Motacilla flava/d943ba665147d960b855f0f89903242d.jpg\",\n  \"499150.jpg\": \"Aves/Motacilla flava/d51e21f74daed94eccddd1b5f19043ba.jpg\",\n  \"499163.jpg\": \"Aves/Motacilla flava/d63ea8b42463f242df3745f830818ada.jpg\",\n  \"499167.jpg\": \"Insecta/Enallagma aspersum/50153e5cce515368be26d7efb1979bc0.jpg\",\n  \"49917.jpg\": \"Insecta/Ischnura hastata/6d6c071829fa2b15528ff49b6e3f8d82.jpg\",\n  \"499172.jpg\": \"Insecta/Enallagma aspersum/c7c1bfb08e3cc99078d2ac6e2e789cd7.jpg\",\n  \"499176.jpg\": \"Insecta/Enallagma aspersum/cd3291c2bcbea342cc065d0de1cd6d15.jpg\",\n  \"49918.jpg\": \"Insecta/Ischnura hastata/4616b47665acb0aeee303c19b44ae0db.jpg\",\n  \"499190.jpg\": \"Reptilia/Clemmys guttata/477f4170b2652e07cae6c4cf9f885876.jpg\",\n  \"499202.jpg\": \"Reptilia/Clemmys guttata/85fb9cf9916ffa976977e1cc6739360a.jpg\",\n  \"499205.jpg\": \"Mammalia/Vulpes vulpes fulvus/213a3816036fe947b82792b0ca58bedc.jpg\",\n  \"499208.jpg\": \"Mammalia/Vulpes vulpes fulvus/1fbbd719351b65e845e1253bb33df1b2.jpg\",\n  \"49921.jpg\": \"Insecta/Ischnura hastata/002f6a63f7393b96dc6d7db3c65fac2e.jpg\",\n  \"499214.jpg\": \"Mammalia/Vulpes vulpes fulvus/0d89e06b7855b883d63bd8128ac590b9.jpg\",\n  \"499218.jpg\": \"Mammalia/Vulpes vulpes fulvus/235c9657be156eb7ce726f86c894933f.jpg\",\n  \"499228.jpg\": \"Insecta/Ascalapha odorata/5311e028b066db8b4f80870595cda1dc.jpg\",\n  \"499230.jpg\": \"Insecta/Ascalapha odorata/a471fcbddd00352973e14b7e82f99580.jpg\",\n  \"499242.jpg\": \"Insecta/Ascalapha odorata/0b71520e892b1229b9aa02f37a6646c1.jpg\",\n  \"499278.jpg\": \"Insecta/Ascalapha odorata/4678b1b03762c9d930a4c92c8d5d3bf5.jpg\",\n  \"49928.jpg\": \"Aves/Momotus lessonii/d8a32cf29138b2737dbcdee244f5f162.jpg\",\n  \"499290.jpg\": \"Insecta/Ascalapha odorata/166c73c8bd82aec41f4af2817d129d8f.jpg\",\n  \"499296.jpg\": \"Insecta/Ascalapha odorata/25871bccdab0024f33f2c900f5d00c72.jpg\",\n  \"49932.jpg\": \"Aves/Momotus lessonii/b7a5df3aa6d70f039f665dab46b91ed3.jpg\",\n  \"499328.jpg\": \"Insecta/Epiaeschna heros/f1a6b940425556bd5188aa86b0fd3872.jpg\",\n  \"499329.jpg\": \"Insecta/Epiaeschna heros/a4218a53c0ad85caa0007698dfcfa1f4.jpg\",\n  \"49933.jpg\": \"Aves/Momotus lessonii/0aaf744595aa45379ad93502bdd56937.jpg\",\n  \"499330.jpg\": \"Insecta/Epiaeschna heros/e10e835ebb204fe3c186b18784843eea.jpg\",\n  \"499331.jpg\": \"Insecta/Epiaeschna heros/294429ca19f4b8c36d84ffa795891447.jpg\",\n  \"499334.jpg\": \"Insecta/Epiaeschna heros/28704bca3d460787fef06740a1979e9c.jpg\",\n  \"499337.jpg\": \"Insecta/Epiaeschna heros/b1d83e2ef812de89906e37478c4eb2fe.jpg\",\n  \"499338.jpg\": \"Insecta/Epiaeschna heros/841849b7ad063b353b98c478e3eb6d32.jpg\",\n  \"49934.jpg\": \"Aves/Momotus lessonii/6b9cd56aae522bb19e7d504504e72b1c.jpg\",\n  \"499347.jpg\": \"Insecta/Epiaeschna heros/155122badf8697b17bd6beac95d8e252.jpg\",\n  \"499362.jpg\": \"Insecta/Gluphisia septentrionis/22d843ce14b07846957183571bc9999b.jpg\",\n  \"499365.jpg\": \"Insecta/Gluphisia septentrionis/fb417642c5f0fe0433cb8dfaae05412a.jpg\",\n  \"499377.jpg\": \"Insecta/Libellula incesta/d9bb7b61eab161b7cf873572204cd9ea.jpg\",\n  \"499387.jpg\": \"Insecta/Libellula incesta/6952cd43bf639b61843acd60f029bb11.jpg\",\n  \"499404.jpg\": \"Insecta/Libellula incesta/b862229e65cc161bc723528f52396958.jpg\",\n  \"499462.jpg\": \"Insecta/Libellula incesta/07bf2e3a5c8f8597750dd9a6b8b78c8c.jpg\",\n  \"499512.jpg\": \"Amphibia/Incilius valliceps/66ab958791636aed14d252c0064cebe8.jpg\",\n  \"499513.jpg\": \"Amphibia/Incilius valliceps/eb726a4678b18f9da2001f4246148869.jpg\",\n  \"499523.jpg\": \"Reptilia/Coleonyx elegans/0355ad403b5a7972bfe5154f5eb20634.jpg\",\n  \"499525.jpg\": \"Reptilia/Coleonyx elegans/500841b12463f761d0262ed2df6a5dd8.jpg\",\n  \"499552.jpg\": \"Insecta/Orthemis discolor/71134113ab8f776f24fe30076da3eaee.jpg\",\n  \"499555.jpg\": \"Insecta/Orthemis discolor/4b2b4a7d5fd00ce4f1d4cfb61831af16.jpg\",\n  \"499556.jpg\": \"Insecta/Orthemis discolor/4d7e076cc3078cc367dd343e1d1cb50d.jpg\",\n  \"499557.jpg\": \"Insecta/Orthemis discolor/21440c385ceef906d3d5d7c5e2c1357a.jpg\",\n  \"499565.jpg\": \"Insecta/Orthemis discolor/80e2a671026a1b9cabcb09b910bdc202.jpg\",\n  \"499567.jpg\": \"Insecta/Orthemis discolor/254588349fb32bf30c47b1a9cd1af383.jpg\",\n  \"499569.jpg\": \"Insecta/Orthemis discolor/dd5f0d9c6e2903a3ff995c5e03c70181.jpg\",\n  \"499570.jpg\": \"Insecta/Orthemis discolor/7366c6ce592b918da4c811a131fb25ee.jpg\",\n  \"499571.jpg\": \"Insecta/Orthemis discolor/f490e7534a261790630ebe6f3fce13a5.jpg\",\n  \"499572.jpg\": \"Insecta/Orthemis discolor/82076c65444a88e9c14a0d9785669cc8.jpg\",\n  \"499573.jpg\": \"Insecta/Orthemis discolor/2097e5b6a7eb803007f9381c33847673.jpg\",\n  \"499575.jpg\": \"Insecta/Orthemis discolor/e6315b835d8a3892773673dc35fbcba4.jpg\",\n  \"499599.jpg\": \"Aves/Setophaga townsendi/a6c6104f97735efaebad05973c2764da.jpg\",\n  \"499643.jpg\": \"Aves/Setophaga townsendi/20e0a1b3c7a297a3618861b4c050e334.jpg\",\n  \"499644.jpg\": \"Aves/Setophaga townsendi/969f4a39131180e44fa83660cad97c05.jpg\",\n  \"499651.jpg\": \"Aves/Setophaga townsendi/4445cf360909f8c67f95a74a0e86e27c.jpg\",\n  \"499707.jpg\": \"Aves/Setophaga townsendi/dee434d701a91023e0790781df2d43a1.jpg\",\n  \"499726.jpg\": \"Aves/Setophaga townsendi/3c5616484967786341f52ccf0867edb5.jpg\",\n  \"499738.jpg\": \"Aves/Setophaga townsendi/bf26e1c9b9cc2f217c7f7d4e2a60b145.jpg\",\n  \"499743.jpg\": \"Aves/Setophaga townsendi/52b73ceb9e45d6db7fae0cca8193ed28.jpg\",\n  \"499751.jpg\": \"Insecta/Propylea quatuordecimpunctata/6afdfca4a1556ce259326258eaccb01c.jpg\",\n  \"499753.jpg\": \"Insecta/Propylea quatuordecimpunctata/1d2579009d41db67334f0717d07e925c.jpg\",\n  \"499755.jpg\": \"Insecta/Propylea quatuordecimpunctata/fc56afa892a06ad8300b00aed653b5ba.jpg\",\n  \"499786.jpg\": \"Insecta/Sphex ichneumoneus/ecfeb7d59fe1ee143953a8f6b0ffe579.jpg\",\n  \"499788.jpg\": \"Insecta/Sphex ichneumoneus/bb4574967866e032d32be6945dc1e0d3.jpg\",\n  \"499790.jpg\": \"Insecta/Sphex ichneumoneus/9e31f3017648af847edf202b1b9f35e7.jpg\",\n  \"499793.jpg\": \"Insecta/Sphex ichneumoneus/70d19c8ed7816c6ed0bc1bc968ef8006.jpg\",\n  \"499799.jpg\": \"Insecta/Sphex ichneumoneus/3f2fe3531c6980c1924244b735eaa2f1.jpg\",\n  \"499815.jpg\": \"Insecta/Sphex ichneumoneus/aa3da6f28ef171e893db466adeb3e140.jpg\",\n  \"499821.jpg\": \"Insecta/Sphex ichneumoneus/69e0eb012a2c6dbe697833a464f45fd3.jpg\",\n  \"499822.jpg\": \"Insecta/Sphex ichneumoneus/da71d38d5afee1092375962aec984171.jpg\",\n  \"499823.jpg\": \"Aves/Ramphastos ambiguus/2698616ab1bdcc4a63e134b84e665fc9.jpg\",\n  \"499833.jpg\": \"Aves/Ramphastos ambiguus/8ed811a8ada8f193d6c753a144775b90.jpg\",\n  \"499895.jpg\": \"Amphibia/Smilisca baudinii/e49d341bc04d1d33dd82ffef745f6afe.jpg\",\n  \"499896.jpg\": \"Amphibia/Smilisca baudinii/e52e158157917dfb95faf09147e4839c.jpg\",\n  \"499908.jpg\": \"Amphibia/Smilisca baudinii/3182279ba5591ca2e9814e0cc504609e.jpg\",\n  \"499909.jpg\": \"Amphibia/Smilisca baudinii/d66fb7381915d22e1e65ebd3acbf5dc7.jpg\",\n  \"499913.jpg\": \"Amphibia/Smilisca baudinii/6cd5c931f847f1a3d806fa879d83a9ee.jpg\",\n  \"499920.jpg\": \"Amphibia/Smilisca baudinii/449b6a6171b669a21247fb11db673847.jpg\",\n  \"499921.jpg\": \"Amphibia/Smilisca baudinii/18d62fefaa06467601c94236f22ceb1d.jpg\",\n  \"499927.jpg\": \"Amphibia/Smilisca baudinii/a91a7316d5c56ab2064957ea6fbc6bbb.jpg\",\n  \"499928.jpg\": \"Amphibia/Smilisca baudinii/8126a681d6365ef52336a5f40f879c03.jpg\",\n  \"499966.jpg\": \"Animalia/Strongylocentrotus droebachiensis/488fb009949757b622b99ecd7f39404a.jpg\",\n  \"499976.jpg\": \"Animalia/Strongylocentrotus droebachiensis/827ed82cd0ebe522bbc91e82535777bf.jpg\",\n  \"499986.jpg\": \"Insecta/Galasa nigrinodis/9871035f1c091a8658f964352bb7c4bb.jpg\",\n  \"499993.jpg\": \"Insecta/Galasa nigrinodis/7ee302bca726a647096942d45f406862.jpg\",\n  \"500006.jpg\": \"Insecta/Nicrophorus tomentosus/1f672efc9b938c3f75a5919bd5862ace.jpg\",\n  \"500009.jpg\": \"Reptilia/Plestiodon inexpectatus/342f38d6c16b22f7fc40221d23aef204.jpg\",\n  \"500029.jpg\": \"Reptilia/Crotalus ruber/34f2f1e2ae2eafb75216273a6adc21c6.jpg\",\n  \"500032.jpg\": \"Reptilia/Crotalus ruber/1cca2e7d5a26bd26a8f2b30d1b0b155d.jpg\",\n  \"500033.jpg\": \"Reptilia/Crotalus ruber/0f3054a1497f4f7b2f55ccce230438de.jpg\",\n  \"500034.jpg\": \"Reptilia/Crotalus ruber/d57fecc4e8ded173d9833066df1a9384.jpg\",\n  \"500041.jpg\": \"Reptilia/Crotalus ruber/46c8f902a8a7fb70d82cd1ec55083aae.jpg\",\n  \"500046.jpg\": \"Insecta/Idaea dimidiata/d9ea1cd34e749ddf59076133662e01f3.jpg\",\n  \"500058.jpg\": \"Insecta/Idaea dimidiata/30833364364b3be592cdf537f3d64616.jpg\",\n  \"500074.jpg\": \"Insecta/Jadera haematoloma/342f056751e973883951fe478479d04e.jpg\",\n  \"500075.jpg\": \"Insecta/Jadera haematoloma/61b53047550e060172d58e0c059f5a3e.jpg\",\n  \"500080.jpg\": \"Insecta/Jadera haematoloma/0fe8d2053fe775ea11c51d9fbb223194.jpg\",\n  \"500088.jpg\": \"Insecta/Jadera haematoloma/05e32105e2fa366c0c8a98e89740b3c5.jpg\",\n  \"500089.jpg\": \"Insecta/Jadera haematoloma/0e41a459bb064d9e7dc9e442a364c95c.jpg\",\n  \"500099.jpg\": \"Insecta/Jadera haematoloma/f0823212dc76aa41d67a76d0e020a506.jpg\",\n  \"500100.jpg\": \"Insecta/Jadera haematoloma/721d92079251a478ab53be2eebd2cd34.jpg\",\n  \"500101.jpg\": \"Insecta/Jadera haematoloma/8dca1dbc0792668bcca2b091ad8d492d.jpg\",\n  \"500347.jpg\": \"Insecta/Euclea delphinii/8f3ceb2529a391bbcc53815ff0d764e1.jpg\",\n  \"500348.jpg\": \"Insecta/Euclea delphinii/eefb8fe3c8c528641eee342b72a50dca.jpg\",\n  \"500350.jpg\": \"Insecta/Euclea delphinii/8040d187f1e577e521ef36abfc14d609.jpg\",\n  \"500351.jpg\": \"Insecta/Euclea delphinii/40de94a2c33a61d5353d160d574b4f56.jpg\",\n  \"500352.jpg\": \"Insecta/Euclea delphinii/9124295d6b278580f68592c9f14c4b5c.jpg\",\n  \"500353.jpg\": \"Insecta/Euclea delphinii/0cdadcbba60c3492e2efb03d84105b00.jpg\",\n  \"500358.jpg\": \"Insecta/Euclea delphinii/8faabb5b5fae44746d1c586ebbcc1499.jpg\",\n  \"500360.jpg\": \"Insecta/Euclea delphinii/7a1d8b88b1eb053cd2a3612dfe2ba8b5.jpg\",\n  \"500361.jpg\": \"Insecta/Euclea delphinii/57662834ef39b380eb9fb7fcd35e1454.jpg\",\n  \"500363.jpg\": \"Insecta/Euclea delphinii/a5de55b494403123cd2fb26de218c30a.jpg\",\n  \"50038.jpg\": \"Aves/Oriturus superciliosus/51bf842438dc87698774518412d3b4ce.jpg\",\n  \"50041.jpg\": \"Aves/Oriturus superciliosus/31f287c475a92ddaf5e07e25acfa250b.jpg\",\n  \"500468.jpg\": \"Aves/Bombycilla cedrorum/22df9ad23c83ea699955357e92fdf6cb.jpg\",\n  \"50048.jpg\": \"Aves/Oriturus superciliosus/0f97f25e86001956bc6552aea9788442.jpg\",\n  \"50052.jpg\": \"Aves/Oriturus superciliosus/a69ec1b96d522a88917c2da563d3983d.jpg\",\n  \"50053.jpg\": \"Aves/Oriturus superciliosus/6cfdd3b2b880bb8f7d16531bf3fd6845.jpg\",\n  \"50055.jpg\": \"Aves/Oriturus superciliosus/e184f13732bfcbe15cb629fda149cc2e.jpg\",\n  \"50058.jpg\": \"Aves/Oriturus superciliosus/d9c1915d3254fdbc1f477e73dfd2b270.jpg\",\n  \"500622.jpg\": \"Aves/Bombycilla cedrorum/33531647585ba2169f8177039179d9d2.jpg\",\n  \"500639.jpg\": \"Aves/Bombycilla cedrorum/b48e399cc2c8f7150984ac182d97c198.jpg\",\n  \"50064.jpg\": \"Aves/Eudyptula minor/5283e926188d26684f84d7ec94fa62f3.jpg\",\n  \"50066.jpg\": \"Aves/Eudyptula minor/bf35365d194253aecd9ca575f4227f59.jpg\",\n  \"500667.jpg\": \"Aves/Bombycilla cedrorum/202a3bbfab078deba539edf22116bf6f.jpg\",\n  \"50067.jpg\": \"Aves/Eudyptula minor/896a291d9aa85d316a09153fb73605b5.jpg\",\n  \"50072.jpg\": \"Aves/Eudyptula minor/9731835c796f689ac4834239d39e4985.jpg\",\n  \"50078.jpg\": \"Aves/Eudyptula minor/71f58593a30f85c57cba6d5dbe63069c.jpg\",\n  \"500782.jpg\": \"Aves/Bombycilla cedrorum/d8fa9330125919e62075cdd846e2887b.jpg\",\n  \"50080.jpg\": \"Aves/Eudyptula minor/4c14bebad6038d9e48258b04657caa83.jpg\",\n  \"50081.jpg\": \"Aves/Eudyptula minor/70ed900b202e75078a78e2271e62b77f.jpg\",\n  \"50085.jpg\": \"Aves/Eudyptula minor/e814e44bc96b7af3527b066b672c5235.jpg\",\n  \"50086.jpg\": \"Aves/Eudyptula minor/784565f5d49d0f57727a5fb9584039c0.jpg\",\n  \"50088.jpg\": \"Aves/Eudyptula minor/6f54e23e999ab996ba1d55ba92f08059.jpg\",\n  \"50089.jpg\": \"Aves/Eudyptula minor/a888d2a20ef75146ceabd94678bb473e.jpg\",\n  \"500902.jpg\": \"Insecta/Rhagonycha fulva/7060c8448f1706df6028b1cd42f82e42.jpg\",\n  \"500905.jpg\": \"Insecta/Rhagonycha fulva/9490bd9f544b7abe4d65523ab5289dd7.jpg\",\n  \"500962.jpg\": \"Aves/Coragyps atratus/b8bcbc164a0bcd15631223b1f74a7a2d.jpg\",\n  \"50097.jpg\": \"Aves/Eudyptula minor/2e4a4930c2ade661783ced7d7cb6dfc7.jpg\",\n  \"50099.jpg\": \"Aves/Eudyptula minor/84b883bbaed44e3927b740e2ec25d868.jpg\",\n  \"501009.jpg\": \"Aves/Coragyps atratus/5c88602872b8cb6815d99343f1ac8e10.jpg\",\n  \"501064.jpg\": \"Aves/Coragyps atratus/0a0194fb4f3aac7f708302ba8f9f8d06.jpg\",\n  \"50110.jpg\": \"Aves/Eudyptula minor/a6db3f53a9d5a8dbea9623307d7dc375.jpg\",\n  \"50111.jpg\": \"Aves/Eudyptula minor/5b1c629bfb3390dac75574401056722d.jpg\",\n  \"501185.jpg\": \"Aves/Coragyps atratus/066d254b46412ce44946f8a6a3a19712.jpg\",\n  \"501189.jpg\": \"Aves/Coragyps atratus/de44a47cb44dcf7e9099b13741af0177.jpg\",\n  \"50121.jpg\": \"Insecta/Ochlodes sylvanoides/a720d6cb94c979b51896ea444fcda88f.jpg\",\n  \"50126.jpg\": \"Insecta/Ochlodes sylvanoides/71ce788f1c001911690bcc9c4c2f5a19.jpg\",\n  \"50134.jpg\": \"Insecta/Ochlodes sylvanoides/3a4a92516ff317c9007c0f7be6cbca68.jpg\",\n  \"50135.jpg\": \"Insecta/Ochlodes sylvanoides/e2244a20058011cc4f6c61a58d8ef1cc.jpg\",\n  \"501356.jpg\": \"Reptilia/Agkistrodon piscivorus leucostoma/f3c3838721715001e690df6fae5189c6.jpg\",\n  \"501382.jpg\": \"Reptilia/Agkistrodon piscivorus leucostoma/08482c830d89c9e1666927a83e048834.jpg\",\n  \"50139.jpg\": \"Insecta/Ochlodes sylvanoides/642c0fb59defcf37736bf9807515d81b.jpg\",\n  \"50140.jpg\": \"Insecta/Ochlodes sylvanoides/1f12f1366f18986eda77b0ce04378915.jpg\",\n  \"50145.jpg\": \"Insecta/Ochlodes sylvanoides/763e7ac72c5c8ccd049ea04b6a7e26b5.jpg\",\n  \"501492.jpg\": \"Insecta/Nematocampa resistaria/2f97354a7bad48c0fcd37ca152313e91.jpg\",\n  \"501495.jpg\": \"Insecta/Nematocampa resistaria/536839ae1c928f0b984defb9e3307330.jpg\",\n  \"501501.jpg\": \"Insecta/Nematocampa resistaria/86bb58d49730325b97bcc36f46b836e5.jpg\",\n  \"501503.jpg\": \"Insecta/Nematocampa resistaria/a9976c3468a96908d9af81a1ecacaecd.jpg\",\n  \"501506.jpg\": \"Insecta/Nematocampa resistaria/238003d92cf6cad82cbc21192a6cd405.jpg\",\n  \"501508.jpg\": \"Insecta/Nematocampa resistaria/dc1e5812ebd6b6459819cc400c4ad5f4.jpg\",\n  \"501509.jpg\": \"Insecta/Nematocampa resistaria/ca0984b80e4499aabf705645b3743006.jpg\",\n  \"501510.jpg\": \"Insecta/Nematocampa resistaria/a1ab6a91e7aa3a9d99cd6dbc07a402e2.jpg\",\n  \"501511.jpg\": \"Insecta/Nematocampa resistaria/3c934032e7c2c1afc48732d9cac6c3ab.jpg\",\n  \"501515.jpg\": \"Insecta/Nematocampa resistaria/0242fc3b082b476087979501218bcb89.jpg\",\n  \"501518.jpg\": \"Insecta/Nematocampa resistaria/5176ed7a4e5ee3f0140b1785dd1b4f27.jpg\",\n  \"501565.jpg\": \"Insecta/Limenitis arthemis astyanax/1d64f13b7f1a112b40f8ff90e09cb5ae.jpg\",\n  \"501568.jpg\": \"Insecta/Limenitis arthemis astyanax/6e0c18ff6126fa7fff81beed1319e116.jpg\",\n  \"501577.jpg\": \"Insecta/Limenitis arthemis astyanax/a53abd0aea4c2283b4ea8aa3940afda1.jpg\",\n  \"50161.jpg\": \"Insecta/Ochlodes sylvanoides/b08a82f50471c22c76b0faece457d981.jpg\",\n  \"50165.jpg\": \"Insecta/Ochlodes sylvanoides/4fc504ee332a95e6c0595b01bb907bb4.jpg\",\n  \"50166.jpg\": \"Insecta/Ochlodes sylvanoides/d27d9e08be5e10733fa7fc5254572e5b.jpg\",\n  \"50178.jpg\": \"Insecta/Ochlodes sylvanoides/1fe8ba4ad854f2dac15876f3a3b84d26.jpg\",\n  \"50180.jpg\": \"Insecta/Ochlodes sylvanoides/de6e53dd6842d87cee0c99f33b38e91b.jpg\",\n  \"50184.jpg\": \"Insecta/Ochlodes sylvanoides/8c40017e862d077c50e3ed53fe690cee.jpg\",\n  \"50189.jpg\": \"Insecta/Ochlodes sylvanoides/5e4ab0627d4352ef31818cb72dea4512.jpg\",\n  \"50193.jpg\": \"Insecta/Ochlodes sylvanoides/042e3794a75bde73b28a8b9b054f573a.jpg\",\n  \"50201.jpg\": \"Insecta/Ochlodes sylvanoides/d33b882be697f652d1e7537089fcdee5.jpg\",\n  \"502029.jpg\": \"Mammalia/Oreamnos americanus/35e622844d2e22d26f8ee1a22f2c27fe.jpg\",\n  \"502031.jpg\": \"Mammalia/Oreamnos americanus/5157f0e5f41bdb3c825692f1cb1c173a.jpg\",\n  \"502035.jpg\": \"Mammalia/Oreamnos americanus/347ece050817259824320e69dcb23057.jpg\",\n  \"502042.jpg\": \"Mammalia/Oreamnos americanus/182dcf1e6a1719b61a959b1d41ea4d4a.jpg\",\n  \"502051.jpg\": \"Mammalia/Oreamnos americanus/6b1f828a2716dba8b5a0275efb1109f5.jpg\",\n  \"502056.jpg\": \"Mammalia/Oreamnos americanus/c9b9518075383a009566b28cead83ee8.jpg\",\n  \"502057.jpg\": \"Mammalia/Oreamnos americanus/bd66735f630bf9eba11e56514b937bbd.jpg\",\n  \"502063.jpg\": \"Insecta/Citheronia regalis/f8168e1e1ce70e6feaf00657ed50acde.jpg\",\n  \"502080.jpg\": \"Mammalia/Cynomys gunnisoni/d36203451e6c4ea4d38efcdb9d9cb70f.jpg\",\n  \"502126.jpg\": \"Mammalia/Hydrochoerus hydrochaeris/c67d0a3456baaeb63c2abecb1f1e629d.jpg\",\n  \"502152.jpg\": \"Amphibia/Litoria aurea/831648cece7066fea9ee8d77d732433d.jpg\",\n  \"502154.jpg\": \"Aves/Psittacara holochlorus/1b249844564084da50cb7dd5568a387c.jpg\",\n  \"502158.jpg\": \"Aves/Psittacara holochlorus/95454c751ecb2baf42feaaade147469c.jpg\",\n  \"502168.jpg\": \"Insecta/Eumorpha achemon/32b728a88b9ee1a9628d21a11679174d.jpg\",\n  \"502173.jpg\": \"Insecta/Apatura ilia/eab02f1775bf45999c8d0cf5c78e882a.jpg\",\n  \"502177.jpg\": \"Insecta/Apatura ilia/7696b21828bd9332ec2355d24b02baf2.jpg\",\n  \"50222.jpg\": \"Insecta/Ochlodes sylvanoides/882f947289badad326f11193e9d6d334.jpg\",\n  \"502239.jpg\": \"Insecta/Manduca quinquemaculata/5b607359f8e19b59499aa32dda8e08c0.jpg\",\n  \"502246.jpg\": \"Insecta/Manduca quinquemaculata/c35d7230b80382594cea580509fccc01.jpg\",\n  \"502247.jpg\": \"Insecta/Manduca quinquemaculata/084f12785f6d61a09dee079d315b7331.jpg\",\n  \"502249.jpg\": \"Insecta/Polites themistocles/b86c8144f5e67959b2b52e10c44d02d8.jpg\",\n  \"502259.jpg\": \"Insecta/Polites themistocles/b6295185d7c8a63f34297d45345c60da.jpg\",\n  \"502273.jpg\": \"Insecta/Gryllodes sigillatus/b39f9cf64a9b3c47183f9ae481984c3e.jpg\",\n  \"502277.jpg\": \"Insecta/Gryllodes sigillatus/a517eb6270bd2986fed9bd5f56921e42.jpg\",\n  \"502280.jpg\": \"Insecta/Gryllodes sigillatus/9e59909b69d3e2f368795eb6fc2b5f9d.jpg\",\n  \"50231.jpg\": \"Insecta/Ochlodes sylvanoides/2906ab46f8881da24dbeb21062fe4340.jpg\",\n  \"502320.jpg\": \"Aves/Larus dominicanus/77143a032ba82ded85643492831a4deb.jpg\",\n  \"502324.jpg\": \"Aves/Larus dominicanus/a4b1d225edea07e945de039574d57317.jpg\",\n  \"50242.jpg\": \"Insecta/Ochlodes sylvanoides/d3acec21d35dffe573e3356d5ad81720.jpg\",\n  \"502464.jpg\": \"Aves/Cygnus olor/97845c48dc9b2f3a34abfcb6269ff132.jpg\",\n  \"50247.jpg\": \"Insecta/Ochlodes sylvanoides/83cf0cef63064eb3ba873f2e8b0caf7a.jpg\",\n  \"50248.jpg\": \"Insecta/Epimecis hortaria/8135a924c4f1149ab00b1ff41008ac56.jpg\",\n  \"50249.jpg\": \"Insecta/Epimecis hortaria/9c2e32e3b625695f14a7cea3a4a2c96d.jpg\",\n  \"50251.jpg\": \"Insecta/Epimecis hortaria/01b12b9eaf764edbe968c0e9fcb1139e.jpg\",\n  \"50252.jpg\": \"Insecta/Epimecis hortaria/6fcb20f26e32cc80c8cdb29caeb2a21d.jpg\",\n  \"502528.jpg\": \"Aves/Cygnus olor/e8be1990599a4c7fd0146e77f139958a.jpg\",\n  \"50253.jpg\": \"Insecta/Epimecis hortaria/eae14bc9b13c115a04a407a9a193be71.jpg\",\n  \"502534.jpg\": \"Aves/Cygnus olor/87d98335f1d706d63475a810f5bd4ebf.jpg\",\n  \"50254.jpg\": \"Insecta/Epimecis hortaria/c05d35dee5bb7fa48618526c4f4baefa.jpg\",\n  \"50255.jpg\": \"Insecta/Epimecis hortaria/2674a1a567e2b8c00c7cb62710a1398e.jpg\",\n  \"502561.jpg\": \"Aves/Cygnus olor/a114f5880e9970d7d57950cbf683ff6b.jpg\",\n  \"50258.jpg\": \"Insecta/Epimecis hortaria/f3001155971542171f00479db230d1b0.jpg\",\n  \"50260.jpg\": \"Insecta/Epimecis hortaria/b7682f6c331f6df05517aeffd797bc5d.jpg\",\n  \"502600.jpg\": \"Insecta/Lapara bombycoides/90b0256c5071a57c78d30fa0cb5ed7b4.jpg\",\n  \"502603.jpg\": \"Insecta/Lapara bombycoides/1dfe10b85a597d6c84ba57f0a1a85149.jpg\",\n  \"50261.jpg\": \"Insecta/Epimecis hortaria/58fe7359db4ddd58291e83f8c2c53c9d.jpg\",\n  \"502619.jpg\": \"Insecta/Paonias myops/22c5442ef51cf0f85cf31be826063dcd.jpg\",\n  \"50262.jpg\": \"Insecta/Epimecis hortaria/4d34fb5dc0b9677c03c83014c23be56c.jpg\",\n  \"502631.jpg\": \"Insecta/Paonias myops/023535523a8598b56279218a887c0c13.jpg\",\n  \"502635.jpg\": \"Insecta/Paonias myops/eb60ef47278402a1ee87a4d9423d0ae3.jpg\",\n  \"502637.jpg\": \"Insecta/Paonias myops/af284e9aee10c6e109a7a8a29c6d9161.jpg\",\n  \"50265.jpg\": \"Insecta/Epimecis hortaria/845d9cd0adc7bdc19bfe347ec6eadc3f.jpg\",\n  \"50266.jpg\": \"Insecta/Epimecis hortaria/896faf172361add0252a73de11834d2b.jpg\",\n  \"50268.jpg\": \"Insecta/Epimecis hortaria/94a8be4788892b6ff9826563b5a60792.jpg\",\n  \"50269.jpg\": \"Insecta/Epimecis hortaria/b6effc6bb44cba64aa274704b2f56679.jpg\",\n  \"50275.jpg\": \"Insecta/Epimecis hortaria/f6ef8a3cdb700653579e3845c2b67cf7.jpg\",\n  \"502770.jpg\": \"Reptilia/Apalone spinifera emoryi/84682f46e5e4a150939793b801cebfb8.jpg\",\n  \"502772.jpg\": \"Reptilia/Apalone spinifera emoryi/7d817f9539d0ee7e65bbb9defc452e5e.jpg\",\n  \"50279.jpg\": \"Insecta/Epimecis hortaria/71ddb501f5a3da91c1eaf1a9e1a10722.jpg\",\n  \"502792.jpg\": \"Reptilia/Crocodylus moreletii/0da1a662e330e397182514393b0cdb34.jpg\",\n  \"502793.jpg\": \"Reptilia/Crocodylus moreletii/3342c322e438c2a6999cb364466e613e.jpg\",\n  \"50281.jpg\": \"Insecta/Epimecis hortaria/5138d78fd455009f065199d1a6bf5c24.jpg\",\n  \"50282.jpg\": \"Insecta/Epimecis hortaria/99bacfc06b4dd9ab107bd4dd0138f21b.jpg\",\n  \"50287.jpg\": \"Insecta/Pantala hymenaea/c516724b9799b1740dd85ea7edc6ff6a.jpg\",\n  \"50288.jpg\": \"Insecta/Pantala hymenaea/6ba687e1bdaed170e196f2dff50acf30.jpg\",\n  \"50289.jpg\": \"Insecta/Pantala hymenaea/d6dd4916b78c7617f1d151364b1f9e6d.jpg\",\n  \"50291.jpg\": \"Insecta/Pantala hymenaea/68781ced73d81ceda8802582e9639282.jpg\",\n  \"502935.jpg\": \"Reptilia/Nerodia sipedon sipedon/bf4aa35c4ed9ec5eba2db13f6ae71a4e.jpg\",\n  \"502948.jpg\": \"Reptilia/Nerodia sipedon sipedon/51fe9934ce82f4f1a28ad8ec14e3b2bc.jpg\",\n  \"502952.jpg\": \"Reptilia/Nerodia sipedon sipedon/dd4c096595172cdb8a51ec7606b52bd9.jpg\",\n  \"502961.jpg\": \"Reptilia/Nerodia sipedon sipedon/d8ae3cfdfa50fb20a5f2e30122e763fb.jpg\",\n  \"50297.jpg\": \"Insecta/Pantala hymenaea/414128a372fd522f5fc11574c608c46e.jpg\",\n  \"502976.jpg\": \"Reptilia/Nerodia sipedon sipedon/40ddccf39352ea26548b8463d2da2639.jpg\",\n  \"50298.jpg\": \"Insecta/Pantala hymenaea/fa3eaa49bf30deef1de52a58e4cd66ab.jpg\",\n  \"502984.jpg\": \"Reptilia/Nerodia sipedon sipedon/2a6c14e0334de3837872ab23b07e7377.jpg\",\n  \"50299.jpg\": \"Insecta/Pantala hymenaea/25d899327a6032a1bce0ddac887746b7.jpg\",\n  \"50300.jpg\": \"Insecta/Pantala hymenaea/b18c8159520a09e17b3c4fade0b9001a.jpg\",\n  \"50301.jpg\": \"Insecta/Pantala hymenaea/88b5593c4f34d8af700741b5b7624c60.jpg\",\n  \"503052.jpg\": \"Amphibia/Notophthalmus viridescens/d77d3496cbb9cc3504aef2998f910433.jpg\",\n  \"503060.jpg\": \"Amphibia/Notophthalmus viridescens/b1f7da335a5817c1f0d70dce3a38f964.jpg\",\n  \"50307.jpg\": \"Insecta/Pantala hymenaea/cdce4700413b86599d1434801d510cec.jpg\",\n  \"503077.jpg\": \"Amphibia/Notophthalmus viridescens/d1a708341d86e0533e8979dff5412aef.jpg\",\n  \"50308.jpg\": \"Insecta/Pantala hymenaea/1bf59dad49ee5f9e0f7f73ad6dd775fa.jpg\",\n  \"503083.jpg\": \"Amphibia/Notophthalmus viridescens/ab95344820b4152d51446fe152f4a8f1.jpg\",\n  \"50311.jpg\": \"Insecta/Pantala hymenaea/533b8cf13ef963d957d44e7296867aac.jpg\",\n  \"503125.jpg\": \"Amphibia/Notophthalmus viridescens/4015e5bbfe9134014dcd2736c696aec5.jpg\",\n  \"503149.jpg\": \"Amphibia/Notophthalmus viridescens/30a09bcede92f8e253ea0e0f4d696f56.jpg\",\n  \"503178.jpg\": \"Reptilia/Sceloporus tristichus/610706fe0b08ecb1fa77384fb1330228.jpg\",\n  \"503179.jpg\": \"Reptilia/Sceloporus tristichus/625389b7a68c499956d55e96e564beb4.jpg\",\n  \"503184.jpg\": \"Reptilia/Sceloporus tristichus/a3914a171f997d2945e9179cb6e1748e.jpg\",\n  \"503208.jpg\": \"Reptilia/Pituophis deppei/4054c4cac62770c036dde6e116269bdb.jpg\",\n  \"503209.jpg\": \"Reptilia/Pituophis deppei/542a571a17249b0fdcf9464914b8ca05.jpg\",\n  \"503211.jpg\": \"Reptilia/Pituophis deppei/614a7b3e70c732c756f0005045eb10d6.jpg\",\n  \"503214.jpg\": \"Insecta/Catocala maestosa/4cf4eddaeaa89009a88178dbc7754917.jpg\",\n  \"503220.jpg\": \"Insecta/Catocala maestosa/ff4b422a6a0d3a8d9dfdef72ea99032a.jpg\",\n  \"503227.jpg\": \"Insecta/Homalodisca vitripennis/eaba47adb5e9869ec1f56a99edc503c0.jpg\",\n  \"50323.jpg\": \"Insecta/Pantala hymenaea/29ac7326a527156574004ac2ec2d8252.jpg\",\n  \"503232.jpg\": \"Insecta/Homalodisca vitripennis/da3be559518abb6c402eaa262c017b88.jpg\",\n  \"503235.jpg\": \"Insecta/Homalodisca vitripennis/9cfdcece7c8d86755c29e540842ba8c9.jpg\",\n  \"503236.jpg\": \"Insecta/Homalodisca vitripennis/d2a5457308b8432eece994ab80f962cc.jpg\",\n  \"503237.jpg\": \"Insecta/Homalodisca vitripennis/d1317b6a83a99563f62b2e1350104cdb.jpg\",\n  \"503238.jpg\": \"Insecta/Homalodisca vitripennis/6895e4679be85fb10b21ea66e77bc0a8.jpg\",\n  \"503240.jpg\": \"Insecta/Homalodisca vitripennis/0b5112e55caf4e310172f38dba7892cd.jpg\",\n  \"503241.jpg\": \"Insecta/Homalodisca vitripennis/87f40727b7e01677f52c8ebc0487ce02.jpg\",\n  \"503242.jpg\": \"Insecta/Homalodisca vitripennis/32d7bc87d2a2a10cceb11b978a6bb46f.jpg\",\n  \"503243.jpg\": \"Insecta/Homalodisca vitripennis/01dcdaa7a9e8fd5750f1f62cb2f0be53.jpg\",\n  \"503244.jpg\": \"Insecta/Homalodisca vitripennis/3ad3ff5ff7ce136800cf2b051786a7c6.jpg\",\n  \"503245.jpg\": \"Insecta/Homalodisca vitripennis/c4372b63ca774e9ac8fb43606ca795e7.jpg\",\n  \"503246.jpg\": \"Insecta/Homalodisca vitripennis/db6cd6f089796474acda69b29470c3e0.jpg\",\n  \"503247.jpg\": \"Insecta/Homalodisca vitripennis/192ef44f735650ed6c339b3ba4722a43.jpg\",\n  \"503248.jpg\": \"Insecta/Homalodisca vitripennis/e8c19c18930d3c82eaecbc7ca98effd5.jpg\",\n  \"50325.jpg\": \"Insecta/Pantala hymenaea/5f824b00231b94625100efda920bf7f2.jpg\",\n  \"503253.jpg\": \"Insecta/Homalodisca vitripennis/9cf6d9257ffceee4493b5526127992d3.jpg\",\n  \"503255.jpg\": \"Insecta/Homalodisca vitripennis/d21f5512d861b0be9158016a0a981296.jpg\",\n  \"503264.jpg\": \"Insecta/Asterocampa celtis/d9357c3782c03d4e8d0757c3bb565fbb.jpg\",\n  \"503265.jpg\": \"Insecta/Asterocampa celtis/87ab780bb770ba0119b201d4b10377bd.jpg\",\n  \"50327.jpg\": \"Insecta/Pantala hymenaea/d62d836a139c06955d0c72618f02d21f.jpg\",\n  \"50328.jpg\": \"Insecta/Pantala hymenaea/aa05e999c9fd644500d876f8467a3371.jpg\",\n  \"503319.jpg\": \"Insecta/Asterocampa celtis/c3b418739f8aedb19488f81d77aaef06.jpg\",\n  \"503350.jpg\": \"Insecta/Asterocampa celtis/980e58e2e04f653294615d680e0254c8.jpg\",\n  \"503367.jpg\": \"Insecta/Asterocampa celtis/72f9bcdfd34c5aba101c1e7e0f115b7f.jpg\",\n  \"50337.jpg\": \"Insecta/Pantala hymenaea/f2d81b7e599efad4271888dd4ae5647a.jpg\",\n  \"50338.jpg\": \"Insecta/Pantala hymenaea/a39fe305f52dcb90ba1fe66697f9fd8a.jpg\",\n  \"50339.jpg\": \"Insecta/Pantala hymenaea/d6577900ea79b7e41e9be6dcc42776cc.jpg\",\n  \"503393.jpg\": \"Insecta/Asterocampa celtis/d5a645979c51079b8cee8e7691ebe454.jpg\",\n  \"50343.jpg\": \"Insecta/Pantala hymenaea/4d32c5a0b5e3c7b88ac4c813286679b0.jpg\",\n  \"50348.jpg\": \"Insecta/Pantala hymenaea/29d964a1fa6d1b490fc27ce230e49317.jpg\",\n  \"50349.jpg\": \"Insecta/Pantala hymenaea/5fce5fdf79f3166f910da68b745fe9a7.jpg\",\n  \"503532.jpg\": \"Mollusca/Mytilus californianus/37bcb857cce7c1e4900e0b15a7eb4a88.jpg\",\n  \"503556.jpg\": \"Mollusca/Mytilus californianus/e33e1940b87e81e3377c6d90ef9200bf.jpg\",\n  \"503564.jpg\": \"Mollusca/Mytilus californianus/1ece57351fb884b9e1fb71a522467745.jpg\",\n  \"503590.jpg\": \"Mollusca/Mytilus californianus/db376ab6b8f1fce791c56f3ed49d8cdd.jpg\",\n  \"503621.jpg\": \"Insecta/Limenitis reducta/a9de804c6f99940b72e338fe3f3c3418.jpg\",\n  \"50364.jpg\": \"Insecta/Pantala hymenaea/6b9a6ee1f9467791875cba5659c47832.jpg\",\n  \"503659.jpg\": \"Mammalia/Mustela frenata/4eff5853a5eaa2922ed3a200ed5172e4.jpg\",\n  \"503660.jpg\": \"Mammalia/Mustela frenata/8ba033080fce5df5f04be7fb58591a4f.jpg\",\n  \"503661.jpg\": \"Mammalia/Mustela frenata/d0f12c3c32f4d514d6fa484488391612.jpg\",\n  \"503662.jpg\": \"Mammalia/Mustela frenata/b637bebadb1fdc100296f49fba190b1f.jpg\",\n  \"503663.jpg\": \"Mammalia/Mustela frenata/4c29ab5fd402ddde7eaa1256f07a449d.jpg\",\n  \"503681.jpg\": \"Insecta/Chrysochus cobaltinus/f37bc37edb96afcb449faa0c0561186e.jpg\",\n  \"503704.jpg\": \"Aves/Charadrius melodus/aeff2e611a4207f4c220531c60e376ee.jpg\",\n  \"503705.jpg\": \"Aves/Charadrius melodus/1bb128aa0fbd66806f78be09e384ab4c.jpg\",\n  \"503714.jpg\": \"Aves/Charadrius melodus/c88aed86b92f6486d9772879c59eb67b.jpg\",\n  \"503715.jpg\": \"Aves/Charadrius melodus/4eac96a3f9e981e4ea34601b99ec5a61.jpg\",\n  \"503720.jpg\": \"Aves/Charadrius melodus/bd5f5ad65e22503ef7376f2f6f14e533.jpg\",\n  \"503723.jpg\": \"Aves/Charadrius melodus/47f7c635fe9fcd32270d8809f61b3507.jpg\",\n  \"503751.jpg\": \"Aves/Melospiza lincolnii/ef03c3eb04570ae231bf4aac49a527c8.jpg\",\n  \"503791.jpg\": \"Aves/Melospiza lincolnii/9231b797648e237f318fa8483d6b7148.jpg\",\n  \"503846.jpg\": \"Aves/Melospiza lincolnii/da5cf5e0727dbca85be22d99acc76c3a.jpg\",\n  \"503848.jpg\": \"Aves/Melospiza lincolnii/4701fd46d07e23e55002e04495ee199e.jpg\",\n  \"503880.jpg\": \"Aves/Melospiza lincolnii/14392fa71ab02e2442dfaa686b095152.jpg\",\n  \"503926.jpg\": \"Insecta/Vespula germanica/5b4b50dad3287f7ba98c9e2ab8d51ce2.jpg\",\n  \"503928.jpg\": \"Insecta/Limenitis weidemeyerii/0fba81b808090cf28f3346a390f180dd.jpg\",\n  \"503929.jpg\": \"Insecta/Limenitis weidemeyerii/ae2bce379d78e8f846454f92a2bc23b1.jpg\",\n  \"503930.jpg\": \"Insecta/Limenitis weidemeyerii/2470f2c940b8bd5e59cdf5fd1af6124e.jpg\",\n  \"503931.jpg\": \"Insecta/Limenitis weidemeyerii/091ab6554dd5ad3798ffccf8c12d15a6.jpg\",\n  \"503932.jpg\": \"Insecta/Limenitis weidemeyerii/54c524bb3466da46f02699e724162aee.jpg\",\n  \"504053.jpg\": \"Insecta/Dyspteris abortivaria/b0527b0b3da6854df7ded0fa41964a9e.jpg\",\n  \"504056.jpg\": \"Insecta/Dyspteris abortivaria/40d9b8ff9cc278d06587400b4b8d7dda.jpg\",\n  \"504136.jpg\": \"Reptilia/Pituophis catenifer/02c890de3d3c0cf064b6d92b54057c5a.jpg\",\n  \"504146.jpg\": \"Reptilia/Pituophis catenifer/169d72b27e7da616a014e6403cb1ee58.jpg\",\n  \"504158.jpg\": \"Reptilia/Pituophis catenifer/223c36a4c733fa8a9dcd86d0f07a2a36.jpg\",\n  \"504177.jpg\": \"Reptilia/Pituophis catenifer/d7eb04a9695184e3d24a7c52ced3034f.jpg\",\n  \"504196.jpg\": \"Reptilia/Pituophis catenifer/e10494289b046e344cc6bde9049e4d58.jpg\",\n  \"504202.jpg\": \"Reptilia/Pituophis catenifer/20460eadea97392158c81a800c4a7497.jpg\",\n  \"504218.jpg\": \"Reptilia/Pituophis catenifer/95012093101209409165e5f80d3a45b4.jpg\",\n  \"504244.jpg\": \"Reptilia/Pituophis catenifer/347031455659afefe58a297083f9ce2a.jpg\",\n  \"504286.jpg\": \"Reptilia/Pituophis catenifer/d5b82e03562b483c8170cb69ec0dd800.jpg\",\n  \"504294.jpg\": \"Aves/Caracara plancus/ef8d4e3e39a7536d1482a6fa1e2e73f2.jpg\",\n  \"504296.jpg\": \"Aves/Caracara plancus/cba6428fcecffcfa7c2f9073f83eda98.jpg\",\n  \"504300.jpg\": \"Aves/Caracara plancus/f90879d8ca8600cc859f8fb64c1003fc.jpg\",\n  \"504340.jpg\": \"Insecta/Polistes metricus/c8260c2cb3cfcaf673f76bf70816214a.jpg\",\n  \"504343.jpg\": \"Insecta/Polistes metricus/0f073330a1aaaf30f980bfbdbe8c0b53.jpg\",\n  \"504345.jpg\": \"Insecta/Polistes metricus/bf9fa16071369abdab92978247caadfd.jpg\",\n  \"504353.jpg\": \"Insecta/Polistes metricus/7c44dd9857c6ad2aa74e1a1ebbeb868a.jpg\",\n  \"504354.jpg\": \"Insecta/Polistes metricus/a427ad90f882330a23addc3d1939c825.jpg\",\n  \"504357.jpg\": \"Insecta/Polistes metricus/2adc9f946ce7ad8c6ec257f09fb2bc3c.jpg\",\n  \"504358.jpg\": \"Insecta/Polistes metricus/54cd3c309d08ba9e51fc603d90a6aa21.jpg\",\n  \"504362.jpg\": \"Insecta/Polistes metricus/b2b5968beaff21d14248abf37d650048.jpg\",\n  \"504363.jpg\": \"Insecta/Polistes metricus/c7c40569316ee024a1ff163de5b97d45.jpg\",\n  \"504365.jpg\": \"Insecta/Polistes metricus/ce20d5a20fd6001b06c558526a48cf30.jpg\",\n  \"504435.jpg\": \"Insecta/Blepharomastix ranalis/4380c11037c13cdcf60b6b96c4d70b5a.jpg\",\n  \"504439.jpg\": \"Insecta/Blepharomastix ranalis/4c97e96fc251f28f4d9bf7adca4d4f2a.jpg\",\n  \"504488.jpg\": \"Insecta/Hyalophora cecropia/0d84d959cafe7aa3bb7b6c667a5d2f21.jpg\",\n  \"504497.jpg\": \"Insecta/Hyalophora cecropia/ca0a60043256add347583943e526731c.jpg\",\n  \"504498.jpg\": \"Insecta/Hyalophora cecropia/b12b9223177b95a7ae30919b49f0aa72.jpg\",\n  \"504505.jpg\": \"Insecta/Hyalophora cecropia/99220a9752522327db2e22e731b357ed.jpg\",\n  \"504516.jpg\": \"Insecta/Hyalophora cecropia/6e85b486f6a4ccf3c37b9896d7274b05.jpg\",\n  \"504529.jpg\": \"Reptilia/Charina bottae/9ee276e7e9c8cbe18211c734beeabb61.jpg\",\n  \"504530.jpg\": \"Reptilia/Charina bottae/1dd00e479959765e5eef8d2f2a277a15.jpg\",\n  \"504532.jpg\": \"Reptilia/Charina bottae/a41c8a6dc3c60c26be153489190ffbde.jpg\",\n  \"504543.jpg\": \"Reptilia/Charina bottae/ac29a551c64697ec322b7b29c90c9a51.jpg\",\n  \"504544.jpg\": \"Reptilia/Charina bottae/09d59996f897db26fd242c0867d7a59a.jpg\",\n  \"504545.jpg\": \"Reptilia/Charina bottae/76017d6b9d5ec71ef62eb809c1be14b3.jpg\",\n  \"504546.jpg\": \"Reptilia/Charina bottae/b9ac2ed2d7421e78b6273104fc9ecb45.jpg\",\n  \"504550.jpg\": \"Reptilia/Charina bottae/4a2afc9d966f22fcb3ef1f1490d18a4a.jpg\",\n  \"504551.jpg\": \"Reptilia/Charina bottae/4cb9ea48fcec2a708b72e5778302cda3.jpg\",\n  \"50483.jpg\": \"Aves/Phalacrocorax varius/86ebd80e86f0f0d0bce4daec62eee85e.jpg\",\n  \"50485.jpg\": \"Aves/Phalacrocorax varius/b804783a3a2078f1a57c0bda8ad2a258.jpg\",\n  \"50487.jpg\": \"Aves/Phalacrocorax varius/5cbfb90f8c51dcfed6744bd5040b56ca.jpg\",\n  \"50488.jpg\": \"Aves/Phalacrocorax varius/9bfe21bae31a6f400822d814f9d593b1.jpg\",\n  \"504911.jpg\": \"Aves/Tringa semipalmata/5ee61aa16610b82c6ca5842fbf750efd.jpg\",\n  \"505010.jpg\": \"Aves/Falco peregrinus/3388b154f1d18682dac487e988085882.jpg\",\n  \"505024.jpg\": \"Aves/Falco peregrinus/32191c6c8bddf7447b2e0ede6c654a05.jpg\",\n  \"505081.jpg\": \"Aves/Falco peregrinus/32ff26c1cd2b1b59b53a4f62c8dbef64.jpg\",\n  \"505084.jpg\": \"Aves/Falco peregrinus/63a6eb4f68050fbc0658286d2c2c00c1.jpg\",\n  \"505117.jpg\": \"Aves/Falco peregrinus/3c6487617fa2c7405b81f2ee11cfd9cf.jpg\",\n  \"505166.jpg\": \"Aves/Falco peregrinus/24f6a40f89d674a321a58609d8f555d6.jpg\",\n  \"505179.jpg\": \"Aves/Falco peregrinus/f63619e6967c4e2acdc36145cc0985d1.jpg\",\n  \"505221.jpg\": \"Aves/Falco peregrinus/6e760821c93f206bb8639253bd5fd130.jpg\",\n  \"505265.jpg\": \"Aves/Melanitta fusca/70fcd5ec9286525feadfdc28b025b014.jpg\",\n  \"505267.jpg\": \"Aves/Melanitta fusca/00114216e95b36beb705dde50850f269.jpg\",\n  \"505270.jpg\": \"Aves/Melanitta fusca/e498bb4118f0d13495f9ba22912575a0.jpg\",\n  \"505280.jpg\": \"Aves/Melanitta fusca/39fae2adb51d1bdf86f28e2ba34bc6b6.jpg\",\n  \"505284.jpg\": \"Aves/Melanitta fusca/3003608d90120eb8a3917a6e5d2773af.jpg\",\n  \"505290.jpg\": \"Aves/Melanitta fusca/d07d46783b11626e6231556c5cb1ca27.jpg\",\n  \"505323.jpg\": \"Aves/Regulus satrapa/965674f79699b2271af1254b47053e3c.jpg\",\n  \"505364.jpg\": \"Aves/Regulus satrapa/9dc0541aefaae756c456e640e458e5c4.jpg\",\n  \"505375.jpg\": \"Aves/Regulus satrapa/8df6fde00486b50927ec65ff3f67a25b.jpg\",\n  \"505381.jpg\": \"Aves/Regulus satrapa/5cdea1d05e36d24cac1b682fd20b74c1.jpg\",\n  \"505384.jpg\": \"Aves/Regulus satrapa/b886660f681b7f4d92e53c24da30bc35.jpg\",\n  \"505386.jpg\": \"Aves/Regulus satrapa/bf84d56a8f656dd230e3d87b138a5e41.jpg\",\n  \"505393.jpg\": \"Aves/Regulus satrapa/558843e6079179b88a8b2bead0062533.jpg\",\n  \"505402.jpg\": \"Aves/Regulus satrapa/7b274bf3582a90def58fc02cb93294c4.jpg\",\n  \"505428.jpg\": \"Reptilia/Thamnophis sirtalis/808d90cfb2fad88764a43582a68cfaa1.jpg\",\n  \"505430.jpg\": \"Reptilia/Thamnophis sirtalis/94b70a1907db82582c1d18f2d984c347.jpg\",\n  \"505487.jpg\": \"Reptilia/Thamnophis sirtalis/b9e6a584060efb6d46ab2a955bc973fe.jpg\",\n  \"505495.jpg\": \"Reptilia/Thamnophis sirtalis/3f1308dc266c3e9872d58406e1c6e1f8.jpg\",\n  \"505508.jpg\": \"Reptilia/Thamnophis sirtalis/2e46b1731123ae75117f38f1e4df83fb.jpg\",\n  \"505708.jpg\": \"Aves/Anas crecca/6f703b5001579d6db5136f63e773b20d.jpg\",\n  \"505761.jpg\": \"Aves/Pteroglossus torquatus/516f9f5c3f71a3cd048065534baeea67.jpg\",\n  \"505766.jpg\": \"Aves/Pteroglossus torquatus/09d14aff310a88c4728b2c873299efcb.jpg\",\n  \"505767.jpg\": \"Aves/Pteroglossus torquatus/f54706077ca09d0a9e14f1718288064c.jpg\",\n  \"505774.jpg\": \"Aves/Pteroglossus torquatus/0ea1ad02569157d137d472e189ba1ed1.jpg\",\n  \"505779.jpg\": \"Aves/Pteroglossus torquatus/f0ae9de04a97d9aa22a6b41118a027c0.jpg\",\n  \"505780.jpg\": \"Aves/Pteroglossus torquatus/72b44830a72939a3dfed4b6f46bdc264.jpg\",\n  \"505781.jpg\": \"Aves/Pteroglossus torquatus/727226c052367841aaded2d2a97476ce.jpg\",\n  \"505797.jpg\": \"Insecta/Vespula squamosa/ebf78957b3e85fde7d0a46867c2f66fd.jpg\",\n  \"505798.jpg\": \"Insecta/Vespula squamosa/764a7196de9dfceaec8b62763f2f9f78.jpg\",\n  \"505865.jpg\": \"Aves/Junco hyemalis caniceps/b1f7fe9175a49ea2bdcd65487986b009.jpg\",\n  \"505868.jpg\": \"Aves/Junco hyemalis caniceps/a55a9dc25b1dc4ac514836c681677f7e.jpg\",\n  \"505871.jpg\": \"Aves/Junco hyemalis caniceps/cf1a77aa4fd065c1597db9d173f0b5f9.jpg\",\n  \"505875.jpg\": \"Mammalia/Tamandua mexicana/b98b380a27cfec40835877a1bd08c4ac.jpg\",\n  \"505880.jpg\": \"Mammalia/Tamandua mexicana/46b35a9572193310dc6318f9175a12af.jpg\",\n  \"505884.jpg\": \"Mammalia/Tamandua mexicana/b725b09a92e13752d6fe970ef68ff927.jpg\",\n  \"505887.jpg\": \"Aves/Chloroceryle aenea/993709b35806b408696ed90aefe3d66d.jpg\",\n  \"505962.jpg\": \"Insecta/Pasiphila rectangulata/f864e8169372c737168aa4eb85df0ab3.jpg\",\n  \"505968.jpg\": \"Insecta/Pasiphila rectangulata/021d0f4495c82cb08a9b7b2000572c17.jpg\",\n  \"505974.jpg\": \"Insecta/Pasiphila rectangulata/3c9ad2f3cfebeae2f714d677f747c035.jpg\",\n  \"506012.jpg\": \"Insecta/Vespa crabro/926c84bb4868e315833d479b439b4a62.jpg\",\n  \"506026.jpg\": \"Insecta/Vespa crabro/596c00e35a50d86468e17b98558fe270.jpg\",\n  \"506027.jpg\": \"Insecta/Vespa crabro/7dce69701f71e2954409cd5bf96c9530.jpg\",\n  \"506032.jpg\": \"Insecta/Vespa crabro/fb28d70584c429031edbe1181c35fe1e.jpg\",\n  \"506036.jpg\": \"Insecta/Vespa crabro/d8e2357a616ec3667927e84f7cceeee3.jpg\",\n  \"506041.jpg\": \"Insecta/Vespa crabro/303b9a31f6b0101bb0f77b8570911523.jpg\",\n  \"506066.jpg\": \"Reptilia/Storeria occipitomaculata occipitomaculata/8dcf30076f7e6eb606077cc5715de855.jpg\",\n  \"506074.jpg\": \"Aves/Egretta sacra/ff3a7dfdbf629f6c630332c51a43ac68.jpg\",\n  \"506084.jpg\": \"Aves/Trogon melanocephalus/a4ce60070392bc055b0ebdb7739eec22.jpg\",\n  \"506087.jpg\": \"Aves/Trogon melanocephalus/6160f636dfbe9178fd78c755707ddded.jpg\",\n  \"506088.jpg\": \"Aves/Trogon melanocephalus/1f30ac97a10d59bbf12f851e4ac73c9e.jpg\",\n  \"506091.jpg\": \"Aves/Trogon melanocephalus/fede726d59a4423df41792df89cb5f91.jpg\",\n  \"506100.jpg\": \"Aves/Trogon melanocephalus/59cda7a0723ac5b881e7f761acc8095a.jpg\",\n  \"506101.jpg\": \"Animalia/Scolopendra heros/8aa3722c9360cc84f556083eb195feb0.jpg\",\n  \"506121.jpg\": \"Insecta/Pantographa limata/de30f9fa2b7b595b456eb9632e8c9697.jpg\",\n  \"506127.jpg\": \"Insecta/Pantographa limata/6d8910cb5a01f41f791a94e3fb02f9dd.jpg\",\n  \"506136.jpg\": \"Aves/Gallinula chloropus/388f6d712d8707bb6c578d3e3ea5af0c.jpg\",\n  \"506153.jpg\": \"Aves/Gallinula chloropus/13a187428623d424450fc8e92fdcb356.jpg\",\n  \"506161.jpg\": \"Aves/Gallinula chloropus/cbdf3e1f40a102499e38de33bb52f69f.jpg\",\n  \"506162.jpg\": \"Aves/Gallinula chloropus/9086deeb10ff9f305a84b887b8bf256c.jpg\",\n  \"506199.jpg\": \"Aves/Gallinula chloropus/13eb79540b1b5244c04e741c7738ba2d.jpg\",\n  \"506211.jpg\": \"Aves/Gallinula chloropus/e74b2485313ae59e30ff034672492ae3.jpg\",\n  \"506405.jpg\": \"Aves/Falco sparverius/1863d2dc7a5f0e07945ee3ee043ea175.jpg\",\n  \"506436.jpg\": \"Aves/Falco sparverius/1b03603b94a331b1cd85cf3c36db717f.jpg\",\n  \"506451.jpg\": \"Aves/Falco sparverius/d9172d3ffcd043e119031845f8b978d6.jpg\",\n  \"506468.jpg\": \"Aves/Falco sparverius/dd9372f06e6e0a2c672ff1cffadf9776.jpg\",\n  \"506476.jpg\": \"Aves/Falco sparverius/dbbf8371946ec997322d94638c99800a.jpg\",\n  \"50651.jpg\": \"Insecta/Argia fumipennis violacea/851167b1a6096d4cde1cbbd2d034136e.jpg\",\n  \"50652.jpg\": \"Insecta/Argia fumipennis violacea/dae9068ae7921affd91255f4dd8c9197.jpg\",\n  \"50653.jpg\": \"Insecta/Argia fumipennis violacea/c97e7367c44b98820f0806b533c5613d.jpg\",\n  \"50657.jpg\": \"Insecta/Argia fumipennis violacea/383c4f7257d3d5ed417b109d623bcfee.jpg\",\n  \"50662.jpg\": \"Insecta/Argia fumipennis violacea/ebf6a8614941f0588ea5189627301861.jpg\",\n  \"50665.jpg\": \"Insecta/Argia fumipennis violacea/3a041565511f3157525538529fbd8d8c.jpg\",\n  \"506762.jpg\": \"Aves/Falco sparverius/c7b774321a117d5eb7fffe5708bdcb8c.jpg\",\n  \"506792.jpg\": \"Aves/Picoides albolarvatus/2c485b2114e22da185bea3669aa419dc.jpg\",\n  \"506795.jpg\": \"Aves/Picoides albolarvatus/0c458024b599a4d1d0b9b73c0cb1a1f0.jpg\",\n  \"506796.jpg\": \"Aves/Picoides albolarvatus/7004aca3fa5964c78019b9f255eda149.jpg\",\n  \"506797.jpg\": \"Aves/Picoides albolarvatus/9bbc03ac962e96c3869069d0aee9932b.jpg\",\n  \"506798.jpg\": \"Aves/Picoides albolarvatus/2f77115cb310e9c4016fb88cd6b158f3.jpg\",\n  \"506801.jpg\": \"Aves/Picoides albolarvatus/08128be6f0688961e53321e4ae37a55a.jpg\",\n  \"506803.jpg\": \"Aves/Picoides albolarvatus/e2a1f4ae4aa91d347da8566db4a7ed28.jpg\",\n  \"506807.jpg\": \"Aves/Picoides albolarvatus/31891e28e7498828f00922bec0f246ee.jpg\",\n  \"506808.jpg\": \"Aves/Picoides albolarvatus/2a902d54baaddac02f295ff31374a3b8.jpg\",\n  \"506810.jpg\": \"Aves/Picoides albolarvatus/c99d8f3eb7275e0546764d2684464bd2.jpg\",\n  \"506811.jpg\": \"Aves/Picoides albolarvatus/8a38ca7d8418ec3c5f5f4f37468426de.jpg\",\n  \"506817.jpg\": \"Insecta/Anax imperator/8b2a8f42030e1d0a382e60ad8f7ed5c1.jpg\",\n  \"506819.jpg\": \"Insecta/Anax imperator/642271c191dd29fb2f7f4eaf7eeaca9c.jpg\",\n  \"506845.jpg\": \"Aves/Setophaga nigrescens/a9bdd5577ea141e7e7ac14547049efd8.jpg\",\n  \"506857.jpg\": \"Aves/Setophaga nigrescens/5cfcb8844d35b4695b33ca14df44f8eb.jpg\",\n  \"506858.jpg\": \"Aves/Setophaga nigrescens/dc3413629ca19354d6ab9888faa2dbd3.jpg\",\n  \"506868.jpg\": \"Aves/Setophaga nigrescens/f39d4b931cd46e0d78ec54fa48d46509.jpg\",\n  \"506870.jpg\": \"Aves/Setophaga nigrescens/fdf6b181ad4af69767234964784f3d94.jpg\",\n  \"506875.jpg\": \"Aves/Setophaga nigrescens/8a2d12e2010cb7e9bfd2926d8be6798b.jpg\",\n  \"506886.jpg\": \"Aves/Setophaga nigrescens/5f5d692faafd8c61d26c186f003c6ac9.jpg\",\n  \"506887.jpg\": \"Aves/Setophaga nigrescens/61b9f44bff9bbdd2ed9c899de1611817.jpg\",\n  \"506888.jpg\": \"Aves/Setophaga nigrescens/4b01cee921767225c526f7fa230904ff.jpg\",\n  \"506916.jpg\": \"Aves/Corvus corone/5dfa55f5d67c75a00572c718affde2a9.jpg\",\n  \"506935.jpg\": \"Aves/Corvus corone/a53ddcbe497bbc6fcf1ee9c5bb2b028c.jpg\",\n  \"506937.jpg\": \"Aves/Corvus corone/7fb9d185c1d73e8329c51c551577571f.jpg\",\n  \"506939.jpg\": \"Aves/Corvus corone/ed1393065df4461670874a1fd8119297.jpg\",\n  \"506940.jpg\": \"Aves/Corvus corone/efd7d3ebc2435db2072d2c4876918d8f.jpg\",\n  \"506943.jpg\": \"Aves/Corvus corone/bda00b649bed0d1ba3ca4f2a7a8a22f6.jpg\",\n  \"506944.jpg\": \"Aves/Corvus corone/b9ece06c867bd3c578f2659a28c44def.jpg\",\n  \"506965.jpg\": \"Aves/Corvus corone/28c445865e13683f169551b8b2c18751.jpg\",\n  \"506967.jpg\": \"Aves/Corvus corone/ff1c5553e3697cd9e68ce7a095d7a8aa.jpg\",\n  \"506974.jpg\": \"Aves/Corvus corone/312537f23a11ec62f0c77b1716488f7a.jpg\",\n  \"506989.jpg\": \"Aves/Troglodytes pacificus/3a4db375a70a87117e02138011438875.jpg\",\n  \"506990.jpg\": \"Aves/Troglodytes pacificus/44e2d64691354519a06130194f66864e.jpg\",\n  \"506992.jpg\": \"Aves/Troglodytes pacificus/f532474a35e5de33212f300f92d2b2fe.jpg\",\n  \"506993.jpg\": \"Aves/Troglodytes pacificus/adde3869d241311f8f308a97abffba1c.jpg\",\n  \"507007.jpg\": \"Aves/Troglodytes pacificus/00dea9358245cf9ea2abc0ba565bee3a.jpg\",\n  \"507021.jpg\": \"Aves/Troglodytes pacificus/63e5136e265354f5013824ba0d6e54bf.jpg\",\n  \"507022.jpg\": \"Aves/Troglodytes pacificus/4f73a51cff497bcc6b9a58fdd0dcbb8b.jpg\",\n  \"507023.jpg\": \"Aves/Troglodytes pacificus/824f60952769c3fbd5d018b0174549b2.jpg\",\n  \"507024.jpg\": \"Aves/Troglodytes pacificus/41363856f0bdf3ea92b8c92f74487df3.jpg\",\n  \"507045.jpg\": \"Aves/Icterus abeillei/4c91c83741337ae4d0334e120de364d0.jpg\",\n  \"507046.jpg\": \"Aves/Icterus abeillei/9cb15f8ad615b3a6c0cf064c35985a03.jpg\",\n  \"507047.jpg\": \"Aves/Icterus abeillei/e67687e065f384cf0f8748e27b7e0030.jpg\",\n  \"507048.jpg\": \"Aves/Icterus abeillei/8178fc011b42f6625a9e6357c9d53801.jpg\",\n  \"507049.jpg\": \"Aves/Icterus abeillei/2df22740479a7bfc166d0f7688577cb0.jpg\",\n  \"507050.jpg\": \"Aves/Icterus abeillei/c3fb6aa637fd06fbe38a7598f668ac80.jpg\",\n  \"507051.jpg\": \"Aves/Icterus abeillei/f9bd4626c8afec96b38b1f168e9f7605.jpg\",\n  \"507052.jpg\": \"Aves/Icterus abeillei/94bce2cf7b1f0e3fb8eb095ee3524a75.jpg\",\n  \"507053.jpg\": \"Aves/Icterus abeillei/6fc5f6af26edd098734d26be5c154081.jpg\",\n  \"507054.jpg\": \"Aves/Icterus abeillei/b547b0ccb7eea250dcbdf4379fee97af.jpg\",\n  \"507092.jpg\": \"Aves/Sphyrapicus thyroideus/c2a252723c9dc6ebe2424f4466939417.jpg\",\n  \"507093.jpg\": \"Aves/Sphyrapicus thyroideus/22be1044537417e26a518661cf90efeb.jpg\",\n  \"507148.jpg\": \"Insecta/Heliomata cycladata/56d4b1dd951cda43649a0379b4ea283b.jpg\",\n  \"507149.jpg\": \"Insecta/Heliomata cycladata/e360a0a3a6357423b576a8067d8595bb.jpg\",\n  \"507174.jpg\": \"Aves/Anser cygnoides domesticus/9b720b5849d70560d90f8a33ee7c0476.jpg\",\n  \"507176.jpg\": \"Aves/Anser cygnoides domesticus/852c2b273dbdee0536012cb01b13e7b1.jpg\",\n  \"507177.jpg\": \"Aves/Anser cygnoides domesticus/a11048fb179cf989acea218c08603f36.jpg\",\n  \"507187.jpg\": \"Aves/Anser cygnoides domesticus/20a0bd66bfe9bed9cc3c12c9fda8f138.jpg\",\n  \"507195.jpg\": \"Aves/Anser cygnoides domesticus/acc5a3f63386ecd66da2fee673c51afa.jpg\",\n  \"507212.jpg\": \"Reptilia/Lampropeltis splendida/88b10fef31f0b5a99a0ec80532283c25.jpg\",\n  \"507218.jpg\": \"Reptilia/Lampropeltis splendida/b249c8609ea690e803488a9772b13c86.jpg\",\n  \"507224.jpg\": \"Insecta/Chauliognathus pensylvanicus/e565074122fba013d23d92616a7ae185.jpg\",\n  \"507241.jpg\": \"Insecta/Chauliognathus pensylvanicus/9eabd971c282d7f70e80ccbd926ac7a3.jpg\",\n  \"507242.jpg\": \"Insecta/Chauliognathus pensylvanicus/b696cf116316052ba315dd569000b8b3.jpg\",\n  \"507262.jpg\": \"Insecta/Chauliognathus pensylvanicus/b19a634008bac0e31ed97c0e3485865d.jpg\",\n  \"507264.jpg\": \"Insecta/Chauliognathus pensylvanicus/34de741aa961fbc937b32e4e059c2ea8.jpg\",\n  \"507265.jpg\": \"Insecta/Chauliognathus pensylvanicus/285b880d081b37da642fe03ecb3239ac.jpg\",\n  \"507267.jpg\": \"Insecta/Ascia monuste/9e365075baf5c61ef464fa338b3d242b.jpg\",\n  \"507285.jpg\": \"Insecta/Ascia monuste/7edc6a7c0911cf73b604094a99a78353.jpg\",\n  \"507286.jpg\": \"Insecta/Ascia monuste/5c7a5b931c8b5584e906323b4658b67a.jpg\",\n  \"507289.jpg\": \"Insecta/Ascia monuste/5a48012fb49c8ad258ff9701f955551c.jpg\",\n  \"507290.jpg\": \"Insecta/Ascia monuste/877652476ac4423cbeb59a6d93e05ccf.jpg\",\n  \"507294.jpg\": \"Insecta/Ascia monuste/3265cfe9ec4b01be62348578ac7aa991.jpg\",\n  \"507295.jpg\": \"Insecta/Ascia monuste/12495bd949b3a8ee354509674b9010cc.jpg\",\n  \"507296.jpg\": \"Insecta/Ascia monuste/6ad8a4eb15816bc15b65ace293310a59.jpg\",\n  \"507306.jpg\": \"Insecta/Ascia monuste/9ab8de8797392f95b83263d7cf7cda22.jpg\",\n  \"507307.jpg\": \"Insecta/Ascia monuste/25d250a75eda9d55449aba1da6399fe7.jpg\",\n  \"507308.jpg\": \"Insecta/Ascia monuste/f7d46717038737e068665a784c6cb20b.jpg\",\n  \"507422.jpg\": \"Aves/Mycteria ibis/1e658f322b1e75ed959bc2116fcf9fa9.jpg\",\n  \"507440.jpg\": \"Insecta/Iridopsis defectaria/86796813c5877697be5c60e992ff674a.jpg\",\n  \"507445.jpg\": \"Insecta/Iridopsis defectaria/ddc34a9fee54cfbe435593629523ff15.jpg\",\n  \"507465.jpg\": \"Aves/Larus canus/e563dad98307e2e4847fda6388a7d190.jpg\",\n  \"507487.jpg\": \"Aves/Larus canus/a301fb097d85dc0a0f160eb47d1f67b3.jpg\",\n  \"507488.jpg\": \"Aves/Larus canus/ee3e36c4063b021be06e3a43ff33a8bf.jpg\",\n  \"507556.jpg\": \"Mollusca/Nuttallina californica/f23de14fb445eb8e901140016784101b.jpg\",\n  \"507560.jpg\": \"Aves/Merops apiaster/64717c6270b94e823a202fa9da85fe30.jpg\",\n  \"507561.jpg\": \"Aves/Merops apiaster/8a4fa428e26f5703c4eab8b60c09e0cb.jpg\",\n  \"507565.jpg\": \"Aves/Merops apiaster/527fb38e9033911f6557c850f3587d5a.jpg\",\n  \"507566.jpg\": \"Aves/Merops apiaster/c49678e88cd55cd953c4750b4a7df0c7.jpg\",\n  \"507567.jpg\": \"Aves/Merops apiaster/a1ee1aef32647b3af8d74df21eee8994.jpg\",\n  \"507571.jpg\": \"Aves/Merops apiaster/81b332100dc28f014a414dd5ce2400d1.jpg\",\n  \"507617.jpg\": \"Insecta/Haploa confusa/c3850c7572f46fc49bf49beb61e4cd52.jpg\",\n  \"507619.jpg\": \"Insecta/Haploa confusa/3fdd15c41d94eff2ad9d31b46d74ff5f.jpg\",\n  \"507623.jpg\": \"Aves/Sula granti/c3930e3ee3f99ddc1f32d24ec2ebf08a.jpg\",\n  \"50782.jpg\": \"Insecta/Mallophora fautrix/a57630e78517e461c305ef68db2d8f84.jpg\",\n  \"507833.jpg\": \"Amphibia/Eurycea lucifuga/4c26a2ea91675f0714db5f6f8ba5c9cb.jpg\",\n  \"50786.jpg\": \"Insecta/Mallophora fautrix/98355c13ae0cfbc64d346d1ebde116d4.jpg\",\n  \"507868.jpg\": \"Insecta/Chelinidea vittiger/c87a669096b068bc90c0b8793f872f6a.jpg\",\n  \"50787.jpg\": \"Insecta/Mallophora fautrix/cc66ae77c6586246efb28cb21661cbd9.jpg\",\n  \"50789.jpg\": \"Insecta/Mallophora fautrix/e924344fb44ef81286268b667c7748b6.jpg\",\n  \"50790.jpg\": \"Insecta/Mallophora fautrix/ade6e5fb7a654298425d4a575f3162c7.jpg\",\n  \"50798.jpg\": \"Insecta/Mallophora fautrix/16b3afb9daa1116a7049aa34c224bb76.jpg\",\n  \"508063.jpg\": \"Aves/Streptopelia decaocto/e3c6df3488df09946c7dfdfa958db42a.jpg\",\n  \"50807.jpg\": \"Insecta/Mallophora fautrix/8bb64cc9c51834909142ddb7e8e2db3d.jpg\",\n  \"508089.jpg\": \"Aves/Streptopelia decaocto/0aca3a40fda81fbed30c810b0fbe46eb.jpg\",\n  \"508255.jpg\": \"Aves/Streptopelia decaocto/06810c4484267ace5d0ca436ec7aeff7.jpg\",\n  \"50830.jpg\": \"Aves/Troglodytes troglodytes/8358fc1d99e45b024b691dd669d7ba73.jpg\",\n  \"50831.jpg\": \"Aves/Troglodytes troglodytes/a1a7492faa12e8353fc99a6c0f2348c9.jpg\",\n  \"50834.jpg\": \"Aves/Troglodytes troglodytes/b4ee698f7f6a1a77c008f4021c1ef306.jpg\",\n  \"50835.jpg\": \"Aves/Troglodytes troglodytes/949fea678ef8664aad8047a6c641370f.jpg\",\n  \"50836.jpg\": \"Aves/Troglodytes troglodytes/f12199f3ede0d91d4927206fe8018d31.jpg\",\n  \"50837.jpg\": \"Aves/Troglodytes troglodytes/e5c8199cb0ab92f3665dc1a5511420e8.jpg\",\n  \"50838.jpg\": \"Aves/Troglodytes troglodytes/476ac938a73e8aee722736571f221c7f.jpg\",\n  \"50842.jpg\": \"Aves/Troglodytes troglodytes/e819d23bab71ad9e69297fa8c2185875.jpg\",\n  \"508438.jpg\": \"Insecta/Polyommatus icarus/17374e30e0e1af35097afa93954ca64a.jpg\",\n  \"508440.jpg\": \"Insecta/Polyommatus icarus/9d6cc2cec1c5b8fd64ed303e57637277.jpg\",\n  \"508444.jpg\": \"Insecta/Polyommatus icarus/a9d685ec818ed04e8a9cdf6d6a2d6780.jpg\",\n  \"508445.jpg\": \"Insecta/Polyommatus icarus/383774350968a6ae2b378592cbe0d270.jpg\",\n  \"508447.jpg\": \"Insecta/Polyommatus icarus/6e31177c561ee00eb780eceb2ec205ed.jpg\",\n  \"508448.jpg\": \"Insecta/Polyommatus icarus/a520308e4b755f11b9ac552df89fe7bd.jpg\",\n  \"508453.jpg\": \"Insecta/Polyommatus icarus/1fd28b568c4f29dec8a886134d585f0e.jpg\",\n  \"508466.jpg\": \"Insecta/Polyommatus icarus/3e3512a0d45ccc2b0826dbf7ea587dd0.jpg\",\n  \"508467.jpg\": \"Insecta/Polyommatus icarus/b7419277176a8fdf693f6f513051c16e.jpg\",\n  \"50853.jpg\": \"Aves/Troglodytes troglodytes/93887a0dc1f60bcb641bc3b9209de53d.jpg\",\n  \"50854.jpg\": \"Aves/Troglodytes troglodytes/7060e262dc47d8dc82614afc422c1a40.jpg\",\n  \"50857.jpg\": \"Aves/Troglodytes troglodytes/e65403fb5974272d1cc3635dbbce535e.jpg\",\n  \"50861.jpg\": \"Aves/Troglodytes troglodytes/771190dc925fc8b0dc217899bf3f599a.jpg\",\n  \"50864.jpg\": \"Aves/Troglodytes troglodytes/80771f761c47ac39c82adfc5d8b34308.jpg\",\n  \"50865.jpg\": \"Aves/Troglodytes troglodytes/04f904a3e624d7fb51421d4ed6482195.jpg\",\n  \"50866.jpg\": \"Aves/Troglodytes troglodytes/8af861ca066c9e3181f1676047cacd85.jpg\",\n  \"50867.jpg\": \"Aves/Troglodytes troglodytes/1b58467d3050dc80c22d06f31b6f6902.jpg\",\n  \"508676.jpg\": \"Reptilia/Diadophis punctatus edwardsii/08da00c7484ffaba908b9ab3d7c73025.jpg\",\n  \"50868.jpg\": \"Aves/Troglodytes troglodytes/7a1f66553dde6422287912d4e090d74c.jpg\",\n  \"508681.jpg\": \"Reptilia/Diadophis punctatus edwardsii/468c853de08bc6d3036f7f54d7e502c2.jpg\",\n  \"50869.jpg\": \"Aves/Troglodytes troglodytes/d49da959a6ff4804c764b2a75bc94f57.jpg\",\n  \"50870.jpg\": \"Aves/Troglodytes troglodytes/96c81bf695af00ac07236259f55d24ee.jpg\",\n  \"508730.jpg\": \"Insecta/Cycloneda sanguinea/9ac4fcae76a4426631dccfd62f078852.jpg\",\n  \"508733.jpg\": \"Insecta/Cycloneda sanguinea/85bf0eafd3cf6935035378d29feb89f4.jpg\",\n  \"508738.jpg\": \"Insecta/Cycloneda sanguinea/b3aa585e44217f81476e244927f878d3.jpg\",\n  \"508748.jpg\": \"Insecta/Cycloneda sanguinea/44b0024757a2a43c5c5f013a70a4d858.jpg\",\n  \"508752.jpg\": \"Insecta/Cycloneda sanguinea/f0b8d9418878f7c8dadbf977eb57e05b.jpg\",\n  \"508759.jpg\": \"Insecta/Cycloneda sanguinea/332249602b13a2828f0aa4b4e087cb01.jpg\",\n  \"50876.jpg\": \"Aves/Troglodytes troglodytes/4f4c9594613f44aef5bba2a56eb2dd6c.jpg\",\n  \"508771.jpg\": \"Insecta/Cycloneda sanguinea/07ee6dcf6978433b2ea7a7d3ea515230.jpg\",\n  \"50878.jpg\": \"Aves/Toxostoma longirostre/918523bbc087e47a8669bf552305779c.jpg\",\n  \"50881.jpg\": \"Aves/Toxostoma longirostre/ea8a137c6717f5f6e15779ef8c8c7827.jpg\",\n  \"508827.jpg\": \"Insecta/Cycloneda sanguinea/fa64eec0a60f05bd53b3b1cadefe6407.jpg\",\n  \"50883.jpg\": \"Aves/Toxostoma longirostre/43aa41ced4601f8e69e256b5fd39295f.jpg\",\n  \"508832.jpg\": \"Reptilia/Trachemys scripta scripta/f80a61000121320ffeb4c7ed82cb6afc.jpg\",\n  \"508833.jpg\": \"Reptilia/Trachemys scripta scripta/5af2d1146ee79919bf8fb6977b6e491d.jpg\",\n  \"508835.jpg\": \"Reptilia/Trachemys scripta scripta/d1c1c3c09308026af431fa990f330cdc.jpg\",\n  \"508842.jpg\": \"Reptilia/Trachemys scripta scripta/1a19823e079c873f49544debc981d951.jpg\",\n  \"508843.jpg\": \"Reptilia/Trachemys scripta scripta/c737ca027cbdcd778df55eea9b9d8b9a.jpg\",\n  \"508844.jpg\": \"Reptilia/Trachemys scripta scripta/c93c930dc7ff6209cff9f7dfc2006cad.jpg\",\n  \"508845.jpg\": \"Reptilia/Trachemys scripta scripta/95478e6b0f38de4be4fb553375892509.jpg\",\n  \"508846.jpg\": \"Reptilia/Trachemys scripta scripta/0865fc572abb88915bfddcdfdc6c8d70.jpg\",\n  \"508875.jpg\": \"Insecta/Ecliptopera silaceata/8397962b4e613376c37263a2dfa6e214.jpg\",\n  \"508899.jpg\": \"Arachnida/Oxyopes salticus/ed7cfdf3277d5b3b06fa21dce732a247.jpg\",\n  \"50891.jpg\": \"Aves/Toxostoma longirostre/eb472b66cb3b019ecbc596951a5c7a52.jpg\",\n  \"508914.jpg\": \"Reptilia/Terrapene carolina triunguis/6908182259f40693da1ab550aac0bc98.jpg\",\n  \"508936.jpg\": \"Reptilia/Terrapene carolina triunguis/e96d612e2adbdb6d81a9b55a6c6447b8.jpg\",\n  \"50894.jpg\": \"Aves/Toxostoma longirostre/e8bd13268f2d66a9fc130eac8714df16.jpg\",\n  \"50897.jpg\": \"Aves/Toxostoma longirostre/71b678a86e76536a544b7797df1cef0e.jpg\",\n  \"508975.jpg\": \"Reptilia/Terrapene carolina triunguis/8dc49add6d49aaf240b821b79a61ae01.jpg\",\n  \"508991.jpg\": \"Reptilia/Terrapene carolina triunguis/5fccaf1d59fc590d626d757c01f77168.jpg\",\n  \"50900.jpg\": \"Aves/Toxostoma longirostre/d9bf882ce609c75a8e1ad6c59866e395.jpg\",\n  \"509000.jpg\": \"Reptilia/Terrapene carolina triunguis/7896dd651c4c9d8120b0c4bfd2c74a37.jpg\",\n  \"509011.jpg\": \"Reptilia/Terrapene carolina triunguis/da23f54fedb3509b4814f1ae09696b1f.jpg\",\n  \"509015.jpg\": \"Insecta/Ancistrocerus gazella/72824bc3c53759736c03e4ea7f396909.jpg\",\n  \"509016.jpg\": \"Insecta/Ancistrocerus gazella/947e6ac6bc0167de77b4339c0289472f.jpg\",\n  \"509017.jpg\": \"Insecta/Ancistrocerus gazella/4c42f1de8950c3ef6c66312647e3fa31.jpg\",\n  \"50911.jpg\": \"Aves/Toxostoma longirostre/ef8ef2b092240cbc73845ea5e273b3be.jpg\",\n  \"509116.jpg\": \"Insecta/Libellula quadrimaculata/03a68c7b0c3fad22a653d074619bb810.jpg\",\n  \"509119.jpg\": \"Insecta/Libellula quadrimaculata/1dbc1f38acc8329c3053d355f179bcfa.jpg\",\n  \"509123.jpg\": \"Insecta/Libellula quadrimaculata/9f8b567471f6db9f49c947b5415150cf.jpg\",\n  \"509124.jpg\": \"Insecta/Libellula quadrimaculata/238e168096840d0669b2e4d37950a010.jpg\",\n  \"509146.jpg\": \"Aves/Nucifraga columbiana/2ddb00c552157ad65522791461e22792.jpg\",\n  \"509163.jpg\": \"Aves/Nucifraga columbiana/60e77786b992666b1862a6e30105bea3.jpg\",\n  \"509164.jpg\": \"Aves/Nucifraga columbiana/e996964e961333c759c6332c2f89c648.jpg\",\n  \"509167.jpg\": \"Aves/Nucifraga columbiana/43fe26b2be3aa79a79be38d1b492f21a.jpg\",\n  \"509169.jpg\": \"Aves/Nucifraga columbiana/6c3dd1ed31c17cb63e4ffd27953cd5af.jpg\",\n  \"509173.jpg\": \"Aves/Nucifraga columbiana/c88dd55a53d9341744c613a1b32eb312.jpg\",\n  \"509182.jpg\": \"Aves/Nucifraga columbiana/db80838facbcbfce1d1acf7c84cf510c.jpg\",\n  \"509186.jpg\": \"Aves/Nucifraga columbiana/a19dc078477400d239b81f49c4531f8e.jpg\",\n  \"509195.jpg\": \"Insecta/Cactophagus spinolae/ba0fc82cd7db0292f6bb95f6e6ff21d7.jpg\",\n  \"509198.jpg\": \"Insecta/Cactophagus spinolae/f68d633f8478524fc0db13a1293f73cf.jpg\",\n  \"509234.jpg\": \"Insecta/Simyra insularis/f955de8ebd13a643b1915c9808ade7a1.jpg\",\n  \"509237.jpg\": \"Reptilia/Pantherophis guttatus/af7b3a4cd55d680cc625d8f5fddbbed4.jpg\",\n  \"509238.jpg\": \"Reptilia/Pantherophis guttatus/58eef4914610fc3ccc22e26b06860e30.jpg\",\n  \"509239.jpg\": \"Reptilia/Pantherophis guttatus/42b225b02121a4bb1d0a95395d16f27c.jpg\",\n  \"509240.jpg\": \"Reptilia/Pantherophis guttatus/3a3cd8edc8ea2e575931fcfecd762620.jpg\",\n  \"509244.jpg\": \"Reptilia/Pantherophis guttatus/d813b2b080a39b53e4a76643677da6a1.jpg\",\n  \"509362.jpg\": \"Insecta/Argynnis paphia/0cd48974c7a295a66013c5c3ae83cc11.jpg\",\n  \"509365.jpg\": \"Insecta/Argynnis paphia/6150b81052228e2d095e3fc8c5e3cfae.jpg\",\n  \"509366.jpg\": \"Insecta/Argynnis paphia/a188eb1e85347b656d53eea8dd5dca79.jpg\",\n  \"509370.jpg\": \"Insecta/Argynnis paphia/302a7df1da6c0fd000c1315f32456731.jpg\",\n  \"509371.jpg\": \"Insecta/Argynnis paphia/d8f56cb8c1ddb21b5ed6e448934d6234.jpg\",\n  \"509372.jpg\": \"Insecta/Argynnis paphia/b9f583b16b1dd2859beb48e90be367c5.jpg\",\n  \"509374.jpg\": \"Insecta/Argynnis paphia/dc8f92bc21e46196973a964133d70b86.jpg\",\n  \"509376.jpg\": \"Insecta/Argynnis paphia/8f6c37b0e463516421300f8059052b5e.jpg\",\n  \"509377.jpg\": \"Insecta/Argynnis paphia/f9fdfc637c22e2c458d3f4f8c57dcd1b.jpg\",\n  \"509380.jpg\": \"Insecta/Argynnis paphia/b174a1290815408cd080137714daf0f1.jpg\",\n  \"509414.jpg\": \"Aves/Cardellina pusilla/4f05eb66389bef3fe4118282de9ca79d.jpg\",\n  \"509431.jpg\": \"Aves/Cardellina pusilla/a0faddfeab2b5d0cb1b2cabd4a8f084a.jpg\",\n  \"50945.jpg\": \"Insecta/Eumorpha pandorus/90c07eb7e9c5c1981adf59bf842708f5.jpg\",\n  \"509455.jpg\": \"Aves/Cardellina pusilla/d067321bbb4ea9e83cc0d2cfd660cb9d.jpg\",\n  \"509463.jpg\": \"Aves/Cardellina pusilla/105f9613e7dfcba7411e06f517a58353.jpg\",\n  \"50948.jpg\": \"Insecta/Eumorpha pandorus/22e2b810a5a47f07b253e961456cc6b9.jpg\",\n  \"50949.jpg\": \"Insecta/Eumorpha pandorus/ab8f36b038ccb27a5f1bc10ca1390bf3.jpg\",\n  \"50950.jpg\": \"Insecta/Eumorpha pandorus/e52ca60471c7c06d7067bca9bfdceb97.jpg\",\n  \"50951.jpg\": \"Insecta/Eumorpha pandorus/2645c4c21c8b95030f114055f09ee746.jpg\",\n  \"509512.jpg\": \"Aves/Cardellina pusilla/a6ebac60dc4cf506101165f2e7b74169.jpg\",\n  \"509555.jpg\": \"Aves/Cardellina pusilla/7096bd14d983c9bc194457c1854c426c.jpg\",\n  \"50958.jpg\": \"Insecta/Eumorpha pandorus/ba868e144fb89d384b35fa2070362346.jpg\",\n  \"509630.jpg\": \"Aves/Larus glaucescens/2f9093154bfac26daff11b7f404fe74d.jpg\",\n  \"509646.jpg\": \"Aves/Larus glaucescens/a0458a762ea5f365954a309ad1477a10.jpg\",\n  \"50965.jpg\": \"Insecta/Eumorpha pandorus/0178fe092b521460b6278f8332ce7d1c.jpg\",\n  \"509675.jpg\": \"Aves/Larus glaucescens/b99d7d10169cdeff3c578ea1a1457731.jpg\",\n  \"509676.jpg\": \"Aves/Larus glaucescens/374c9416d8a7be6e06e9ec54c66e5306.jpg\",\n  \"50975.jpg\": \"Insecta/Eumorpha pandorus/29f30958361d2a71b783de428aec8f55.jpg\",\n  \"50976.jpg\": \"Insecta/Eumorpha pandorus/a6e436aafb7783375cd4005b8ac79017.jpg\",\n  \"509775.jpg\": \"Mollusca/Tegula funebralis/3d2c425cc84b39c80655352b565291b8.jpg\",\n  \"509785.jpg\": \"Mollusca/Tegula funebralis/bf5b73e3b7869c89e0555afdba84a3c5.jpg\",\n  \"509802.jpg\": \"Mollusca/Tegula funebralis/c3fa9018269937cff5b45d8455502c83.jpg\",\n  \"509805.jpg\": \"Mollusca/Tegula funebralis/285fdcf54e1d4167858bbd1753add3b3.jpg\",\n  \"50981.jpg\": \"Insecta/Eumorpha pandorus/558cb3db3b4d2b551e50ac90fa7ee730.jpg\",\n  \"509813.jpg\": \"Mollusca/Tegula funebralis/48f35c0aef1f5081a416fbddf88c19a7.jpg\",\n  \"509815.jpg\": \"Mollusca/Tegula funebralis/cafaaaa1572e5bde9543b8b3a03960a8.jpg\",\n  \"509822.jpg\": \"Mollusca/Tegula funebralis/9ba7d024aaa774d95e1e59593c620dba.jpg\",\n  \"509844.jpg\": \"Insecta/Acalymma vittatum/6fa26c09d5888127c702bdcbcf63f9ef.jpg\",\n  \"509845.jpg\": \"Insecta/Acalymma vittatum/80bc8e8b0c003533f197592d195dcda7.jpg\",\n  \"50985.jpg\": \"Insecta/Eumorpha pandorus/c02aa1df3e7fe276d305df7c7137364e.jpg\",\n  \"509877.jpg\": \"Reptilia/Aspidoscelis tigris/e4e031ee18eaba7ffa1cff87cdf3dfce.jpg\",\n  \"50989.jpg\": \"Insecta/Eumorpha pandorus/d71b635595e065b77553b27e8179a309.jpg\",\n  \"50993.jpg\": \"Insecta/Eumorpha pandorus/d0ee2f889af97d269e30f8e30b6a5ad7.jpg\",\n  \"509934.jpg\": \"Reptilia/Aspidoscelis tigris/cf5155d03b571a182c1ee6f3e231a4d1.jpg\",\n  \"50994.jpg\": \"Insecta/Eumorpha pandorus/5c5fed07b20e45e0513a4ba26e88020f.jpg\",\n  \"50995.jpg\": \"Insecta/Eumorpha pandorus/03cb4f15e5197e09bbb4bf7ce4e067f3.jpg\",\n  \"509951.jpg\": \"Reptilia/Aspidoscelis tigris/f90a8ea414b3c9abaa1b5006bdc45173.jpg\",\n  \"509955.jpg\": \"Reptilia/Aspidoscelis tigris/79b351e7a426fbf8afc988467efedc5f.jpg\",\n  \"509959.jpg\": \"Reptilia/Aspidoscelis tigris/06af0a23817a7e51d089388d3a36c28e.jpg\",\n  \"509963.jpg\": \"Reptilia/Aspidoscelis tigris/2458f1ba0c87862bf713f673401326f1.jpg\",\n  \"509967.jpg\": \"Insecta/Psychomorpha epimenis/d5a829b95a4be3fd7935dfec80cb6c42.jpg\",\n  \"509972.jpg\": \"Insecta/Psychomorpha epimenis/438ff5a427f40209a02e606eaa33be18.jpg\",\n  \"50998.jpg\": \"Insecta/Eumorpha pandorus/be72a05f0ef15edd639dee932e33432f.jpg\",\n  \"509981.jpg\": \"Insecta/Aeshna palmata/796a7badc61cf3c4aa56b340b67db645.jpg\",\n  \"509995.jpg\": \"Insecta/Aeshna palmata/cedb1bd96738c75bf7d459b99ce5ded4.jpg\",\n  \"51.jpg\": \"Mammalia/Marmota flaviventris/1909c2d50d862495d0b860d81b0e33fe.jpg\",\n  \"510016.jpg\": \"Aves/Limosa fedoa/3a650571363164d7a16c17e40db67d6e.jpg\",\n  \"510031.jpg\": \"Aves/Limosa fedoa/15e78cd6e54f2b45b72373a831f7b5b4.jpg\",\n  \"51005.jpg\": \"Insecta/Argia plana/f5a888cdccd7fba3495fdf20e50af89d.jpg\",\n  \"510070.jpg\": \"Aves/Limosa fedoa/c518c5a89ed3b8ea4ec66cf17b73c39d.jpg\",\n  \"510109.jpg\": \"Aves/Limosa fedoa/314a44a950dfcdaef47639be3d20b04f.jpg\",\n  \"510119.jpg\": \"Aves/Limosa fedoa/e30076e37e5f64ce89cb7be97460f202.jpg\",\n  \"51012.jpg\": \"Insecta/Argia plana/c75b8ae579b16aa83b364b9f6b20678b.jpg\",\n  \"51014.jpg\": \"Insecta/Argia plana/2a71379b2c6e7bef385a69e8845e2c96.jpg\",\n  \"510159.jpg\": \"Aves/Limosa fedoa/da81e2e58cdb9f8bc373fab645dd88a2.jpg\",\n  \"51017.jpg\": \"Insecta/Argia plana/1889e0580aed9c28bcc4db04a64a3f08.jpg\",\n  \"510172.jpg\": \"Aves/Limosa fedoa/38a779938e472a7cd33ba65970d51bd2.jpg\",\n  \"510181.jpg\": \"Aves/Limosa fedoa/596709aea50e3f5f97531410091eea98.jpg\",\n  \"510196.jpg\": \"Reptilia/Phelsuma laticauda/8ce2d2dfbf32aeebb95432367c8e62be.jpg\",\n  \"510198.jpg\": \"Reptilia/Phelsuma laticauda/5d18be2396212c452e6e8bf66cffd34f.jpg\",\n  \"510200.jpg\": \"Reptilia/Phelsuma laticauda/e5b7ba5302d9db4fc29e19989b749a31.jpg\",\n  \"510202.jpg\": \"Reptilia/Phelsuma laticauda/82e863c5e2ef14cd6f7d4c1ee595e4bd.jpg\",\n  \"510205.jpg\": \"Reptilia/Phelsuma laticauda/38c7b5d0912fd85bb0ffaf42e70ab466.jpg\",\n  \"510210.jpg\": \"Reptilia/Phelsuma laticauda/5eca3f5a9421d5cf33501fc65894000a.jpg\",\n  \"510211.jpg\": \"Reptilia/Phelsuma laticauda/d3820b6ff449439377709c7677e448f7.jpg\",\n  \"510212.jpg\": \"Reptilia/Phelsuma laticauda/d754621ac8b55c44c4bc49ba3c184ce4.jpg\",\n  \"510213.jpg\": \"Reptilia/Phelsuma laticauda/8d667553fa996128e42ca36dd8de8208.jpg\",\n  \"510214.jpg\": \"Reptilia/Phelsuma laticauda/e0f5ad3f685a4aaa98d05733f5064239.jpg\",\n  \"510215.jpg\": \"Reptilia/Phelsuma laticauda/17e1e037f1dc7231cfde3d1765440eeb.jpg\",\n  \"51022.jpg\": \"Insecta/Argia plana/c2f00007a86401036da15483984b461c.jpg\",\n  \"51023.jpg\": \"Insecta/Argia plana/084292cff309c4bfd879ff4dcd94cd5c.jpg\",\n  \"51026.jpg\": \"Insecta/Argia plana/bed44d4fea63018820096ce6fa17679c.jpg\",\n  \"51027.jpg\": \"Insecta/Argia plana/f7c87ab3045d7876befee410099ab6d9.jpg\",\n  \"510289.jpg\": \"Aves/Petrochelidon fulva/7ecb2748af1163f718df5bb1043dc72e.jpg\",\n  \"51029.jpg\": \"Insecta/Argia plana/ec1aa0d147cb53b73fd12c8300ad8547.jpg\",\n  \"51030.jpg\": \"Insecta/Argia plana/618586cd697162d4c0813d44958daccc.jpg\",\n  \"51031.jpg\": \"Insecta/Argia plana/ebba98eaa84c7dbb62e42f37c3078884.jpg\",\n  \"51032.jpg\": \"Insecta/Argia plana/6bc9315e6264e7f36a34c929239770ae.jpg\",\n  \"51033.jpg\": \"Insecta/Argia plana/cb42f696118333a8eae47a47794bd413.jpg\",\n  \"51036.jpg\": \"Insecta/Argia plana/ca1876ef2757354c19f6e7a4be4673c7.jpg\",\n  \"510419.jpg\": \"Amphibia/Taricha granulosa/0fe500ec8e3e83eca2473138834d8e46.jpg\",\n  \"510420.jpg\": \"Amphibia/Taricha granulosa/3078096671fb231cc7584fd569948e6d.jpg\",\n  \"51045.jpg\": \"Insecta/Argia plana/b9d5a62b3f0172694535fa71debc5057.jpg\",\n  \"510453.jpg\": \"Amphibia/Taricha granulosa/7a2f5d2ccd46154201e200109148cb7f.jpg\",\n  \"51046.jpg\": \"Insecta/Argia plana/bb0b9b2068d9404a39d8ab25e242437f.jpg\",\n  \"51047.jpg\": \"Insecta/Argia plana/633d50bf954000931441e2f18d29c779.jpg\",\n  \"510492.jpg\": \"Amphibia/Taricha granulosa/36d7884c14e51e648bebda521008bd8a.jpg\",\n  \"510504.jpg\": \"Amphibia/Taricha granulosa/9923f382a7f044cdad0d09d619044759.jpg\",\n  \"51053.jpg\": \"Insecta/Argia plana/4feaf95c236387e96fa5bdfef9145a4c.jpg\",\n  \"510538.jpg\": \"Reptilia/Pantherophis obsoletus/9ce48f06be41e0cf47aa83493b319c0e.jpg\",\n  \"51059.jpg\": \"Insecta/Argia plana/1dfc7f66aebec63191ebf734dad8491e.jpg\",\n  \"510592.jpg\": \"Reptilia/Pantherophis obsoletus/eb7bdd4338c85da04774e424e32722d6.jpg\",\n  \"51063.jpg\": \"Insecta/Argia plana/ae9a55fed4dad1d3874706f924e8d3eb.jpg\",\n  \"51064.jpg\": \"Insecta/Argia plana/e3dc4cf1d4afe9c2659456db4a69fb81.jpg\",\n  \"510640.jpg\": \"Reptilia/Pantherophis obsoletus/d4190fe272f77d8fa2174de756db9a92.jpg\",\n  \"510641.jpg\": \"Reptilia/Pantherophis obsoletus/a501884ecba2d40e17d05a005b371aa1.jpg\",\n  \"510642.jpg\": \"Reptilia/Pantherophis obsoletus/2edf7e6b26b07073a1aa43194f2e683c.jpg\",\n  \"51066.jpg\": \"Insecta/Argia plana/e6906099dfc158a037a652bb987c09ce.jpg\",\n  \"510661.jpg\": \"Reptilia/Pantherophis obsoletus/975af2615d76d74f1a1cafbc7d1d5c01.jpg\",\n  \"51067.jpg\": \"Insecta/Argia plana/6e6bea239601d1425ad21b030d7ff55e.jpg\",\n  \"510724.jpg\": \"Reptilia/Pantherophis obsoletus/4d3c2800ffa6ebf8e62a49a9db7d0392.jpg\",\n  \"510737.jpg\": \"Insecta/Anicla infecta/526640090bc8fead719078138944cbd2.jpg\",\n  \"510739.jpg\": \"Insecta/Anicla infecta/2570b8e51ce9efc8f382a261edfd3fcb.jpg\",\n  \"510740.jpg\": \"Insecta/Anicla infecta/78ecb558ddc99b26205385cea0809f53.jpg\",\n  \"510747.jpg\": \"Insecta/Anicla infecta/86a350780e7018c63a30e4594fc5a61d.jpg\",\n  \"510748.jpg\": \"Insecta/Anicla infecta/c2f1b0797f8da44d5a3f25daa2811058.jpg\",\n  \"510761.jpg\": \"Insecta/Anicla infecta/791481a02f31999e8d97b4da59568394.jpg\",\n  \"510763.jpg\": \"Insecta/Anicla infecta/bdc7cb0a9433ae668f9963171760d8f7.jpg\",\n  \"510764.jpg\": \"Insecta/Anicla infecta/572b009590a7fb97dbda64897cbf1644.jpg\",\n  \"510767.jpg\": \"Insecta/Anicla infecta/f21d639d31fc54c04623787b42d9591e.jpg\",\n  \"510771.jpg\": \"Insecta/Anicla infecta/7ca735012e1d29fabb22d25e37ecc2d5.jpg\",\n  \"510796.jpg\": \"Insecta/Lucanus capreolus/efec8b92145f1fb1e927cd5baabf07f3.jpg\",\n  \"510798.jpg\": \"Insecta/Lucanus capreolus/9f39ecafd74c259eef1f93cc952cba1d.jpg\",\n  \"510802.jpg\": \"Amphibia/Rana draytonii/bd70d33cc53b119cf88592e16b0a8d10.jpg\",\n  \"510812.jpg\": \"Amphibia/Rana draytonii/3b57f81ff0666e0be574adcabf836c7e.jpg\",\n  \"510813.jpg\": \"Amphibia/Rana draytonii/d9b763f3dad85bf35277e8278c1468bf.jpg\",\n  \"510817.jpg\": \"Amphibia/Rana draytonii/77b4cb3060c59d84598aa938d03639fd.jpg\",\n  \"510819.jpg\": \"Amphibia/Rana draytonii/6b3db4836719caabc811fdbfa3073f95.jpg\",\n  \"510820.jpg\": \"Amphibia/Rana draytonii/ccf1100f3b96746c252f98483522402c.jpg\",\n  \"510823.jpg\": \"Amphibia/Rana draytonii/9da257d3372cbe63d0d85c0470e86027.jpg\",\n  \"510880.jpg\": \"Insecta/Sympetrum illotum/aad28ac8aaad95818d494c0b6acf5b2f.jpg\",\n  \"510882.jpg\": \"Insecta/Sympetrum illotum/fc6bbcb6c5f3d3cca4f9a0dfa63bae20.jpg\",\n  \"510899.jpg\": \"Insecta/Sympetrum illotum/7f10eab538d5ef17822a0acb7c7a33b4.jpg\",\n  \"510938.jpg\": \"Insecta/Sympetrum illotum/a57adfe3dd88cdcc1da6f781e6090d28.jpg\",\n  \"51094.jpg\": \"Insecta/Callophrys gryneus/fdeca5b73e865ec8fd9c5a66e579e36e.jpg\",\n  \"510948.jpg\": \"Insecta/Cordulegaster diastatops/b3f27511f8b7276d9e29564fa3759bd1.jpg\",\n  \"510962.jpg\": \"Insecta/Psamatodes abydata/d718e6f2973037b6ce3af0eb3d472585.jpg\",\n  \"51097.jpg\": \"Insecta/Callophrys gryneus/2da333b0c3c08f8d58bad00fec323bab.jpg\",\n  \"510974.jpg\": \"Insecta/Psamatodes abydata/ee443dd2fa9699e78107da1404b59927.jpg\",\n  \"510978.jpg\": \"Insecta/Psamatodes abydata/a085b2af885feadbdee85aca72ab5054.jpg\",\n  \"510982.jpg\": \"Insecta/Psamatodes abydata/3338e036f3f86e6c7f26d4a608b7c2b9.jpg\",\n  \"510988.jpg\": \"Insecta/Psamatodes abydata/8374eecbd7537282f0238822015e6b56.jpg\",\n  \"510989.jpg\": \"Insecta/Psamatodes abydata/a2877206c69d686c20616f9040d0bef3.jpg\",\n  \"51100.jpg\": \"Insecta/Callophrys gryneus/3c57fec808df3d3057310b624a16757a.jpg\",\n  \"511001.jpg\": \"Insecta/Gomphus militaris/c7bc17729f311a746c631d055f9fe45e.jpg\",\n  \"511013.jpg\": \"Insecta/Gomphus militaris/6d54c4890760d1910689cc6fae6cbeca.jpg\",\n  \"511014.jpg\": \"Insecta/Gomphus militaris/5f8e13c035edc89b2630fb1c42d5ae0f.jpg\",\n  \"511016.jpg\": \"Insecta/Gomphus militaris/fa9a8226902213b0ad8d69b038e7ed44.jpg\",\n  \"511018.jpg\": \"Insecta/Gomphus militaris/c8b3cd69486eac5fbf25b26a0368dc5c.jpg\",\n  \"511019.jpg\": \"Insecta/Gomphus militaris/88cc64a9e847a637f9d74748eedeb003.jpg\",\n  \"51102.jpg\": \"Insecta/Callophrys gryneus/e4a1c070197bfbbff00948ec69dff574.jpg\",\n  \"511020.jpg\": \"Insecta/Gomphus militaris/076218bbecaf5e9b4abf05c0bda7edb3.jpg\",\n  \"511025.jpg\": \"Insecta/Gomphus militaris/dfee2d994d7400f7982279ff2c277614.jpg\",\n  \"511027.jpg\": \"Insecta/Gomphus militaris/43dd92b45e684c63bc468d4e4bf584c1.jpg\",\n  \"51106.jpg\": \"Insecta/Callophrys gryneus/7f1e20fe2a2f57025e05b449fe92cb38.jpg\",\n  \"51108.jpg\": \"Insecta/Callophrys gryneus/f4c3f3f15e87a086772d876426821bf2.jpg\",\n  \"51109.jpg\": \"Insecta/Callophrys gryneus/152444b9cd446762882819e61cd6f7b6.jpg\",\n  \"51110.jpg\": \"Insecta/Callophrys gryneus/0f471c5b61f40552f3857b6b3a58a7b7.jpg\",\n  \"511124.jpg\": \"Reptilia/Gerrhonotus infernalis/6895168373bed68be28291c60ac23001.jpg\",\n  \"511125.jpg\": \"Reptilia/Gerrhonotus infernalis/529cf7d173ea7437ccc3e2b88cffb789.jpg\",\n  \"511126.jpg\": \"Reptilia/Gerrhonotus infernalis/e1c8ef7d4c0660cf0d1564756b63c64b.jpg\",\n  \"51114.jpg\": \"Insecta/Callophrys gryneus/913d5607669a704e615153514a868b04.jpg\",\n  \"51115.jpg\": \"Insecta/Callophrys gryneus/5e4fca6b3843ca00abac05af15001a01.jpg\",\n  \"511166.jpg\": \"Aves/Passerina leclancherii/755dd00e95070601149464cd47c02c77.jpg\",\n  \"511171.jpg\": \"Aves/Passerina leclancherii/04f59ab0c18da90edc4ec4fe3e83a084.jpg\",\n  \"51118.jpg\": \"Insecta/Callophrys gryneus/cfcc205e194708c8b0e029e59d9d2e2a.jpg\",\n  \"511188.jpg\": \"Mollusca/Discus rotundatus/86bf1b23e98a83453cf6511607637f60.jpg\",\n  \"511189.jpg\": \"Mollusca/Discus rotundatus/cffcd37f5a1733507810c913a18b3101.jpg\",\n  \"511192.jpg\": \"Mollusca/Discus rotundatus/a8f51df1780e58d573252181a51b4b7c.jpg\",\n  \"51123.jpg\": \"Insecta/Callophrys gryneus/fb987559deb8da5070858b81e244f8b0.jpg\",\n  \"51125.jpg\": \"Insecta/Callophrys gryneus/86dc7dfb49627e9322043057a4dc8003.jpg\",\n  \"511294.jpg\": \"Aves/Parus major/bd483d34b85ebd23c262d6ff5b067911.jpg\",\n  \"511304.jpg\": \"Aves/Parus major/da6e218db7c849d29d72107561daa786.jpg\",\n  \"511305.jpg\": \"Aves/Parus major/597672054ff3e6c604a9840508d9d7b4.jpg\",\n  \"511309.jpg\": \"Aves/Parus major/82e5af56f5daf0e7504e0f5c46c8cebd.jpg\",\n  \"51131.jpg\": \"Insecta/Callophrys gryneus/8919e108a1110e29902b3d00b923fe99.jpg\",\n  \"511314.jpg\": \"Aves/Parus major/4c4bc5de52331f90e95035985b4dd005.jpg\",\n  \"511320.jpg\": \"Aves/Parus major/03bb3fb8bc5aa4be7f253f5382199fb9.jpg\",\n  \"511328.jpg\": \"Aves/Parus major/895101801da6653b6488f97109667064.jpg\",\n  \"511335.jpg\": \"Aves/Parus major/12732988cefbef4292e040873e556d08.jpg\",\n  \"51134.jpg\": \"Insecta/Callophrys gryneus/c5936b9c10cb0a9bebf29b75e33c7fc4.jpg\",\n  \"51135.jpg\": \"Insecta/Callophrys gryneus/cd2e5646cb707107c2ce6c3c2e177b6c.jpg\",\n  \"51136.jpg\": \"Insecta/Callophrys gryneus/f0cb1d7c2ed7f4866fdd11870843437f.jpg\",\n  \"511371.jpg\": \"Aves/Parus major/0ab26b1ae5185bea8c9143dee77def8f.jpg\",\n  \"511392.jpg\": \"Insecta/Chromagrion conditum/1419ef7f33928b361e052bb566dea8e0.jpg\",\n  \"51141.jpg\": \"Insecta/Callophrys gryneus/24519d2dd2e3959fb73be40e6766a32a.jpg\",\n  \"511430.jpg\": \"Aves/Ciconia ciconia/549c4286501ff0065e8d1cc3d1b56d57.jpg\",\n  \"511439.jpg\": \"Aves/Ciconia ciconia/0d0fbf61ea34f3bedcc842e5c28525ad.jpg\",\n  \"51145.jpg\": \"Insecta/Callophrys gryneus/039380851e0d16cf628245f20fe70ea8.jpg\",\n  \"511456.jpg\": \"Aves/Ciconia ciconia/0562de50183cca78b0835ef9c7d65085.jpg\",\n  \"511459.jpg\": \"Aves/Ciconia ciconia/41cf14b2fb09705554c535fbe19efcb5.jpg\",\n  \"51146.jpg\": \"Insecta/Callophrys gryneus/8e85f974ab60d2989b80af1d9b49a787.jpg\",\n  \"511467.jpg\": \"Aves/Ciconia ciconia/a0311fff82a4e2685d136c85581475e2.jpg\",\n  \"511484.jpg\": \"Insecta/Anaea aidea/0d2a42066e15a08a055e2728d1431754.jpg\",\n  \"51149.jpg\": \"Insecta/Callophrys gryneus/d6ae44fb4e73877a07ff1df340ca6dbb.jpg\",\n  \"511492.jpg\": \"Aves/Larus thayeri/4395d02dcc272d70089d1d8776231d57.jpg\",\n  \"511498.jpg\": \"Aves/Larus thayeri/2a64163d426237f5ce6cd85bbe38d364.jpg\",\n  \"51150.jpg\": \"Insecta/Callophrys gryneus/08cd2753c6b36efc48846797d5629eb5.jpg\",\n  \"511509.jpg\": \"Aves/Selasphorus platycercus/bebe5a14fbe14b0a2261ea21913fdb7d.jpg\",\n  \"511510.jpg\": \"Aves/Selasphorus platycercus/36682b8fd1dbcd3ba1095a1716b84b06.jpg\",\n  \"511511.jpg\": \"Aves/Selasphorus platycercus/e0cce026113e97a0c0877f820aea6439.jpg\",\n  \"511515.jpg\": \"Aves/Selasphorus platycercus/93e83924cca71c1f7329579700ffbaa3.jpg\",\n  \"511516.jpg\": \"Aves/Selasphorus platycercus/6c18d54516f7f54c265779d20d249ac6.jpg\",\n  \"511517.jpg\": \"Aves/Selasphorus platycercus/144cf0dadade32eb9946f84d37960a17.jpg\",\n  \"511518.jpg\": \"Aves/Selasphorus platycercus/fa9797db9136f275074a24f3f530a391.jpg\",\n  \"511519.jpg\": \"Aves/Selasphorus platycercus/911b34c9500ad04f24275cc07ec52acc.jpg\",\n  \"511521.jpg\": \"Aves/Selasphorus platycercus/ec7db7a98cd0bcef78bd9d19f793a0f8.jpg\",\n  \"511526.jpg\": \"Aves/Selasphorus platycercus/625ed85ec3fe8ad1b9a7fa7b2d13ca07.jpg\",\n  \"511536.jpg\": \"Aves/Anas platyrhynchos diazi/85027675e224496c7d58ac86ac6b42b9.jpg\",\n  \"511539.jpg\": \"Aves/Anas platyrhynchos diazi/3cad3d1315f162121a6957ed5e6ad0b1.jpg\",\n  \"511564.jpg\": \"Aves/Anas platyrhynchos diazi/dfcc9f250909535296470b2f69412820.jpg\",\n  \"511566.jpg\": \"Aves/Anas platyrhynchos diazi/d3c12c1a9288a8b16dd8187c34e13a83.jpg\",\n  \"51158.jpg\": \"Insecta/Callophrys gryneus/06e9a4061abb77f4d3e3d46713342a88.jpg\",\n  \"511588.jpg\": \"Mollusca/Bradybaena similaris/b7cb6fb82ee39fbde9a796a5ad9ce9e8.jpg\",\n  \"511668.jpg\": \"Aves/Pandion haliaetus/dbedaeabeabe800de6036b58cd9dfc97.jpg\",\n  \"51167.jpg\": \"Aves/Cassiculus melanicterus/43ff2f903648ae06b57beca92eb1c98d.jpg\",\n  \"51172.jpg\": \"Aves/Cassiculus melanicterus/ddf434c6a80e7aa10f26686791a04934.jpg\",\n  \"51179.jpg\": \"Aves/Cassiculus melanicterus/df236225f73f8c03be2303e92a5ef4e4.jpg\",\n  \"51181.jpg\": \"Aves/Cassiculus melanicterus/ffe349b836cbbaf2d87e6a8731182a3a.jpg\",\n  \"51182.jpg\": \"Aves/Cassiculus melanicterus/1c61c0b28b39291307cad617cebc1d50.jpg\",\n  \"51183.jpg\": \"Aves/Cassiculus melanicterus/1bc71dc677d4ebc02aca570613147939.jpg\",\n  \"51184.jpg\": \"Aves/Cassiculus melanicterus/0e8911afc3d3ffa754b5def5dce8e791.jpg\",\n  \"51185.jpg\": \"Aves/Cassiculus melanicterus/bc1a98e2cf2541b2d3392431b603272c.jpg\",\n  \"51186.jpg\": \"Aves/Cassiculus melanicterus/6ed8f842fb1cc9c8fdb009c884538cdf.jpg\",\n  \"51188.jpg\": \"Aves/Cassiculus melanicterus/b52b97693f23cb3bd5726dec7233fb4b.jpg\",\n  \"511910.jpg\": \"Aves/Pandion haliaetus/a18d139e80a5097d3654c6fcc26b49bd.jpg\",\n  \"51212.jpg\": \"Aves/Cassiculus melanicterus/1aad97885d3ed10025856fda8db8440d.jpg\",\n  \"51221.jpg\": \"Aves/Cassiculus melanicterus/2f8428eed01e0c2bbe69f5ebb264e0ff.jpg\",\n  \"51223.jpg\": \"Aves/Cassiculus melanicterus/dd18758f2b6fcddaee6afd70e4425862.jpg\",\n  \"51224.jpg\": \"Aves/Cassiculus melanicterus/6ff5c10dc7d7b0a7ecb349bdfff9fa9c.jpg\",\n  \"51225.jpg\": \"Aves/Cassiculus melanicterus/e494e222b74340e6b7f78220c646ee19.jpg\",\n  \"51229.jpg\": \"Aves/Cassiculus melanicterus/c58bade96ed6a4796319c424716bba3a.jpg\",\n  \"51236.jpg\": \"Aves/Cassiculus melanicterus/7535dd7c950e3fccb59ab6b51d94a3c0.jpg\",\n  \"51237.jpg\": \"Aves/Cassiculus melanicterus/ee67e598937c12718b84d14e8321e27f.jpg\",\n  \"512416.jpg\": \"Insecta/Ischnura cervula/f5bba46a91ada2b6514d956741ad9bb3.jpg\",\n  \"51242.jpg\": \"Mammalia/Conepatus leuconotus/c01b340f07750e36bcb330d77238be23.jpg\",\n  \"51243.jpg\": \"Mammalia/Conepatus leuconotus/bfe9be9b8381096c699932040decf6e2.jpg\",\n  \"512466.jpg\": \"Insecta/Ischnura cervula/c4129c34501a887b06a85f78813d3ac6.jpg\",\n  \"51247.jpg\": \"Mammalia/Conepatus leuconotus/607a1cc095928d86d04e6fd1213f806c.jpg\",\n  \"512472.jpg\": \"Insecta/Ischnura cervula/704eb6c68e381e88461caa056e74d18e.jpg\",\n  \"51248.jpg\": \"Mammalia/Conepatus leuconotus/15cdded03a4976b79a52aa7b009572f0.jpg\",\n  \"512485.jpg\": \"Insecta/Ischnura cervula/a6629f4a19648f6c0ef855e7d428db7d.jpg\",\n  \"512490.jpg\": \"Insecta/Ischnura cervula/d8086547863490879aef941f17244143.jpg\",\n  \"512532.jpg\": \"Aves/Buteo plagiatus/a47f7fc100c8ad8bfa1fdc0f923d02af.jpg\",\n  \"512538.jpg\": \"Aves/Buteo plagiatus/1a6897325ffabdf4094180dfce7b3b09.jpg\",\n  \"512539.jpg\": \"Aves/Buteo plagiatus/70d44ca88a772120d089b3286fb959c9.jpg\",\n  \"512542.jpg\": \"Aves/Buteo plagiatus/36b695fdc84d67e9a2ac7edbcd94aebd.jpg\",\n  \"512544.jpg\": \"Aves/Buteo plagiatus/83521c68b12bc480fc1a6865e3494a0b.jpg\",\n  \"512564.jpg\": \"Aves/Buteo plagiatus/d1713161ac939513c40e3b60b8c6820e.jpg\",\n  \"512567.jpg\": \"Aves/Buteo plagiatus/9f8471d2e104a58ab7b02480fb740e72.jpg\",\n  \"512599.jpg\": \"Insecta/Pantala flavescens/a48c900c752c42b7366bdb69dea34326.jpg\",\n  \"51260.jpg\": \"Mammalia/Conepatus leuconotus/b8fe1c4f68a7c1c3d5a6fbef5cdc810f.jpg\",\n  \"512603.jpg\": \"Insecta/Pantala flavescens/78e0634e95dbea0c99b1168d1a35064a.jpg\",\n  \"512605.jpg\": \"Insecta/Pantala flavescens/d7d4671a6145b38445396fb9271619e2.jpg\",\n  \"512609.jpg\": \"Insecta/Pantala flavescens/bfa98334e45b9646ae67bd1cd8c4065e.jpg\",\n  \"512617.jpg\": \"Insecta/Pantala flavescens/65d1795b4ef6036a31e9e43f055960e3.jpg\",\n  \"512641.jpg\": \"Insecta/Pantala flavescens/2698d06d68f12b649d7e85ff447e002c.jpg\",\n  \"512659.jpg\": \"Insecta/Pantala flavescens/115964bd51feb720c4a2b748dc7e7bc6.jpg\",\n  \"512668.jpg\": \"Insecta/Pantala flavescens/9405842c99f105c40b4de88a9fc455d2.jpg\",\n  \"512689.jpg\": \"Insecta/Pantala flavescens/d0ae9577758e6eef36e1357e6cc8a3ea.jpg\",\n  \"512695.jpg\": \"Insecta/Pantala flavescens/6ce8285b6ef9d49745402608f9849db9.jpg\",\n  \"51273.jpg\": \"Mollusca/Euglandina rosea/bf457ca516078ad36add2eb13dd0c4a7.jpg\",\n  \"512749.jpg\": \"Insecta/Hyles lineata/32d9842b1bedf0f85299a33c0cf46fa4.jpg\",\n  \"512783.jpg\": \"Insecta/Hyles lineata/c8dd245b0c6a70707df971240d9f1ae9.jpg\",\n  \"512789.jpg\": \"Insecta/Hyles lineata/d32196c0de93ce0460edc63f3f5ddf0c.jpg\",\n  \"512790.jpg\": \"Insecta/Hyles lineata/bd81f4234592a2e86c6c4a122e9f6163.jpg\",\n  \"512792.jpg\": \"Insecta/Hyles lineata/64d8345c967ce75d82724bd0f2f406d7.jpg\",\n  \"51283.jpg\": \"Mollusca/Euglandina rosea/7259f996600f7e3546f1ac5c8c0ff48a.jpg\",\n  \"512833.jpg\": \"Insecta/Hyles lineata/a6130c0632ea5aa97d334cb2ee0511f4.jpg\",\n  \"512834.jpg\": \"Insecta/Hyles lineata/5ab14edd67bf432e2faf4c1d06968c86.jpg\",\n  \"51284.jpg\": \"Mollusca/Euglandina rosea/b4d2a848198dc45dd2e4c56985e11c74.jpg\",\n  \"51285.jpg\": \"Mollusca/Euglandina rosea/ee16ac510f1ebedf9b221f0e61a4b9b6.jpg\",\n  \"51286.jpg\": \"Mollusca/Euglandina rosea/aff9396eed50d6a4781be6c09d44019c.jpg\",\n  \"512873.jpg\": \"Insecta/Hyles lineata/0653356fc8d00d0c7be647dc893e4964.jpg\",\n  \"512926.jpg\": \"Aves/Setophaga discolor/9c0a099fbce1c83ffc77a3c6ef108649.jpg\",\n  \"512928.jpg\": \"Aves/Setophaga discolor/8286a24ecd5fd2f8c2c56d5fa8c21043.jpg\",\n  \"512931.jpg\": \"Aves/Setophaga discolor/c22e277646560d5fb71e7418f1a30892.jpg\",\n  \"512943.jpg\": \"Aves/Setophaga discolor/94ec14118830c86797f8a464eef1948b.jpg\",\n  \"512946.jpg\": \"Aves/Setophaga discolor/9979436a2020af75af9bf14e58433c0b.jpg\",\n  \"512951.jpg\": \"Aves/Setophaga discolor/1850e045cdcbb2b77d4a96721df14100.jpg\",\n  \"512955.jpg\": \"Aves/Setophaga discolor/feb810f1040e74563ba159260a57e52c.jpg\",\n  \"512956.jpg\": \"Aves/Setophaga discolor/68ff7e5cb1a8485aeb7adffc4442698d.jpg\",\n  \"512957.jpg\": \"Aves/Setophaga discolor/74ca18b6a75ae6a293b0af6277bc7a10.jpg\",\n  \"513155.jpg\": \"Insecta/Parallelia bistriaris/1d2e12c0a346b9790c9c626b034a5759.jpg\",\n  \"513157.jpg\": \"Insecta/Parallelia bistriaris/995fecd724a499e5b43318b043fc5169.jpg\",\n  \"513161.jpg\": \"Insecta/Parallelia bistriaris/4597d47a3a81a02a2885fd74019e89bd.jpg\",\n  \"513196.jpg\": \"Insecta/Clogmia albipunctata/6798a859f636c2fb9ecdb98acc10cd49.jpg\",\n  \"513201.jpg\": \"Insecta/Clogmia albipunctata/252e2181ef5a78b4994e0739ad468a60.jpg\",\n  \"513207.jpg\": \"Insecta/Labidomera clivicollis/48796d15e47c63ab7908e7418ee26fcd.jpg\",\n  \"513209.jpg\": \"Insecta/Labidomera clivicollis/bd8a91edd89eca63ba03767811584d5a.jpg\",\n  \"513222.jpg\": \"Insecta/Labidomera clivicollis/c7a0a6e1efdd7a3565df08bd7c913942.jpg\",\n  \"513232.jpg\": \"Insecta/Labidomera clivicollis/e0a91508c279222b344b9273fed07cda.jpg\",\n  \"513236.jpg\": \"Insecta/Labidomera clivicollis/d9ea29e2506f178351cc3ef9cf4ac3da.jpg\",\n  \"513240.jpg\": \"Insecta/Labidomera clivicollis/862217cdce6d516e0d3f096bbef9d377.jpg\",\n  \"513247.jpg\": \"Insecta/Labidomera clivicollis/52c00acb3ae39aa5e2ca88a7734dd0f4.jpg\",\n  \"513250.jpg\": \"Insecta/Labidomera clivicollis/a43d708c42d6eda5381450be38e654b9.jpg\",\n  \"513277.jpg\": \"Insecta/Wallengrenia otho/285d7b0f55e4fceabf9674aac7a19bb6.jpg\",\n  \"513281.jpg\": \"Insecta/Wallengrenia otho/0774d4284c7fff9da624302aaef1a92c.jpg\",\n  \"513285.jpg\": \"Insecta/Wallengrenia otho/31c8c08124a9a0c772f912c9165c9625.jpg\",\n  \"513292.jpg\": \"Insecta/Romalea microptera/adcc55c0b1b11b92fa7a07068d3a97d1.jpg\",\n  \"513293.jpg\": \"Insecta/Romalea microptera/9744df4ac6d82d913f60a7ed8df188fe.jpg\",\n  \"513298.jpg\": \"Insecta/Romalea microptera/9d10e59e520d69b7d985910a735e33e9.jpg\",\n  \"513305.jpg\": \"Insecta/Romalea microptera/d9cabc923ad74ba8f2e4afaedae5ea3b.jpg\",\n  \"513319.jpg\": \"Insecta/Romalea microptera/88c7e4b1db04a3353d145ea0f3f05ac1.jpg\",\n  \"513320.jpg\": \"Insecta/Romalea microptera/0cea3540ffe9ba8d96d4923d93207083.jpg\",\n  \"513322.jpg\": \"Insecta/Romalea microptera/105ec4035ed88642e280ce21604ccf0e.jpg\",\n  \"513324.jpg\": \"Insecta/Romalea microptera/95b7d30a6179bcf15293dd824d970c25.jpg\",\n  \"513362.jpg\": \"Insecta/Adelpha eulalia/e0a26f6dc5d39445f12fddcfe75cf5f8.jpg\",\n  \"513440.jpg\": \"Insecta/Hagenius brevistylus/7aa9c54b43df8ef3d333ff4a484b2875.jpg\",\n  \"513443.jpg\": \"Insecta/Hagenius brevistylus/9a4301b1653eab05167759040dc6ab80.jpg\",\n  \"513450.jpg\": \"Insecta/Hagenius brevistylus/96eacf1c30f803f5b8e7ba1a04f7dc9a.jpg\",\n  \"513461.jpg\": \"Insecta/Hagenius brevistylus/32e0875db72817475c37a82c34cfb7e3.jpg\",\n  \"513465.jpg\": \"Insecta/Hagenius brevistylus/319f8dd292f9495933467a04e28c0cbc.jpg\",\n  \"513498.jpg\": \"Aves/Anthus rubescens/f4b33e1b29c28a5e9fac1f76480f3685.jpg\",\n  \"513511.jpg\": \"Aves/Anthus rubescens/6a546e260fbb72b7150cd79cdc59e0be.jpg\",\n  \"513539.jpg\": \"Aves/Anthus rubescens/c06ff88b27f4451b79ba933798d07045.jpg\",\n  \"513655.jpg\": \"Reptilia/Aspidoscelis exsanguis/960018b4e3953b7eb4a822aa4b22814c.jpg\",\n  \"513656.jpg\": \"Reptilia/Aspidoscelis exsanguis/d83d8262a0c529465f6a12c044a60fbe.jpg\",\n  \"513657.jpg\": \"Reptilia/Aspidoscelis exsanguis/53e5d8cd0feab6ae112316f34e8962d9.jpg\",\n  \"513661.jpg\": \"Reptilia/Aspidoscelis exsanguis/df68069a0eb536b7c7d4a29cb1af1abb.jpg\",\n  \"513671.jpg\": \"Reptilia/Aspidoscelis exsanguis/dde1c3e9d99bf20c963ced1fb5bed413.jpg\",\n  \"513676.jpg\": \"Reptilia/Aspidoscelis exsanguis/fc063e8d1bedd1f50a304fec0651bb7b.jpg\",\n  \"513679.jpg\": \"Reptilia/Aspidoscelis exsanguis/5f1247f0c7ce828ce2816098a2526507.jpg\",\n  \"513680.jpg\": \"Reptilia/Aspidoscelis exsanguis/dbf5e6aeef92ba154f8a4e16e746e56c.jpg\",\n  \"513681.jpg\": \"Reptilia/Aspidoscelis exsanguis/171caffd624b42973143bd41f30a8006.jpg\",\n  \"513682.jpg\": \"Reptilia/Aspidoscelis exsanguis/a455ded05fda7f5a4ccbb387466cc303.jpg\",\n  \"513683.jpg\": \"Reptilia/Aspidoscelis exsanguis/fa2bd63c69b7c2daa638d79b04d85155.jpg\",\n  \"513698.jpg\": \"Aves/Cardinalis sinuatus/7338ed53f213b1efa99320498093cbbb.jpg\",\n  \"513709.jpg\": \"Aves/Cardinalis sinuatus/a4a6445eca5be2aa7bb5d4fa2754079d.jpg\",\n  \"513710.jpg\": \"Aves/Cardinalis sinuatus/68040c3498eb5209705aa82933e64841.jpg\",\n  \"513721.jpg\": \"Aves/Cardinalis sinuatus/4a36ea65f2d8322e11b559c6f2aaa3a7.jpg\",\n  \"513726.jpg\": \"Aves/Cardinalis sinuatus/b1fa89371b2448576ab18f613684e7f2.jpg\",\n  \"513749.jpg\": \"Insecta/Polygonia gracilis/984b27496e76256ae4a7e7f28447b2d4.jpg\",\n  \"513751.jpg\": \"Insecta/Polygonia gracilis/3fde8d591a7ab67b56ee33163c2b16c6.jpg\",\n  \"513755.jpg\": \"Insecta/Aglais urticae/2a5916c6a3d949e7be5b21cb78dc15a3.jpg\",\n  \"513756.jpg\": \"Insecta/Aglais urticae/d69ddc5da2214f4fb8b66d0466bb269b.jpg\",\n  \"513761.jpg\": \"Insecta/Aglais urticae/054feae58368d234331494bce2effa33.jpg\",\n  \"513762.jpg\": \"Insecta/Aglais urticae/94fb8e9a4b40159e0ebea842a0be2054.jpg\",\n  \"513765.jpg\": \"Insecta/Aglais urticae/573cfbdb5e7ba73cce1a9403de061c42.jpg\",\n  \"513766.jpg\": \"Insecta/Aglais urticae/8d25c3eb3fe8a2952fb2243c4bece830.jpg\",\n  \"513767.jpg\": \"Insecta/Aglais urticae/51156d4a8c5d345aca6f227d26d79450.jpg\",\n  \"513790.jpg\": \"Insecta/Aglais urticae/93b1242ecdef07c163b6a4088f8cf4dc.jpg\",\n  \"513791.jpg\": \"Aves/Salpinctes obsoletus/f17f1f2cbdbccb334e496b3fec1c17f7.jpg\",\n  \"513800.jpg\": \"Aves/Salpinctes obsoletus/e4208869ff9370630f39af93fca364cc.jpg\",\n  \"51381.jpg\": \"Aves/Ixobrychus exilis/90353a3f0bd84cd8b151187efeb8aaa8.jpg\",\n  \"513819.jpg\": \"Aves/Salpinctes obsoletus/4447ebd8e9fd01d22b5f34e98e3692a9.jpg\",\n  \"513830.jpg\": \"Aves/Salpinctes obsoletus/d77cb0c2d0a4d4e4f49836ccfec6ec5b.jpg\",\n  \"51384.jpg\": \"Aves/Ixobrychus exilis/bdca3f9ca8fc2d0e78861fb6c360e908.jpg\",\n  \"51385.jpg\": \"Aves/Ixobrychus exilis/5bbd2826da7c88a12273d445ac643aee.jpg\",\n  \"51386.jpg\": \"Aves/Ixobrychus exilis/384deaa899431c70e7961376f20393b9.jpg\",\n  \"51387.jpg\": \"Aves/Ixobrychus exilis/5d7ca669e50a55dc882dcc7a5a8ad629.jpg\",\n  \"513874.jpg\": \"Aves/Salpinctes obsoletus/8fbd21389a933951db95df525772081e.jpg\",\n  \"51388.jpg\": \"Aves/Ixobrychus exilis/329cc450df12eabec8dcec7de79172dd.jpg\",\n  \"513888.jpg\": \"Aves/Salpinctes obsoletus/a7a68945c679c2dce1557eb530a1fc64.jpg\",\n  \"513889.jpg\": \"Aves/Salpinctes obsoletus/9ab4bf8e4f29f079e0db8b232f4f42e0.jpg\",\n  \"51389.jpg\": \"Aves/Ixobrychus exilis/8b75f07035759dfba44a9563d69894b6.jpg\",\n  \"51390.jpg\": \"Aves/Ixobrychus exilis/7d5f4d898e22681a2d34d251889cbf48.jpg\",\n  \"51391.jpg\": \"Aves/Ixobrychus exilis/1843e5c744ba7db478779105f4f14191.jpg\",\n  \"513925.jpg\": \"Aves/Limnodromus griseus/05f3145c29de24b629cbb7cdba8ef81d.jpg\",\n  \"513926.jpg\": \"Aves/Limnodromus griseus/c5bae7ca33580dafccf630801281bb82.jpg\",\n  \"513929.jpg\": \"Aves/Limnodromus griseus/c00b97f94980f6a7d1eb10b65ed6f014.jpg\",\n  \"51394.jpg\": \"Aves/Ixobrychus exilis/0510b4cf3cf50907b2b899b0a5e7978d.jpg\",\n  \"51395.jpg\": \"Aves/Ixobrychus exilis/f2efa38662eac1cec20188f358a1bfa2.jpg\",\n  \"513957.jpg\": \"Insecta/Calosoma scrutator/23d970c39858e680b59b1ad1e009ee45.jpg\",\n  \"51396.jpg\": \"Aves/Ixobrychus exilis/d40a278d3e29850fc29a0879a192f910.jpg\",\n  \"513961.jpg\": \"Insecta/Calosoma scrutator/27f930b8acff6997a0bf1d3c56c8286f.jpg\",\n  \"513965.jpg\": \"Insecta/Calosoma scrutator/29a6c8fa88bd2d5dd417d93a7ec55ab4.jpg\",\n  \"513967.jpg\": \"Insecta/Calosoma scrutator/7dcc60e721ac61da02dc35ace5ae5958.jpg\",\n  \"513968.jpg\": \"Insecta/Calosoma scrutator/5bcbc25c67e69c7b42705c48544c5077.jpg\",\n  \"513969.jpg\": \"Insecta/Calosoma scrutator/edd920b0c1660ace7100e509966647fe.jpg\",\n  \"513970.jpg\": \"Insecta/Calosoma scrutator/337813eb4f36e3fafddb5d139bee2c7a.jpg\",\n  \"513971.jpg\": \"Insecta/Calosoma scrutator/2bb9591320f183abf0027ddf10cd5b6c.jpg\",\n  \"513976.jpg\": \"Reptilia/Aspidoscelis tesselata/140f150a289bf678ba015f90208eaa12.jpg\",\n  \"513987.jpg\": \"Reptilia/Diadophis punctatus/9f8328950c8dc2f4cc537e1bddeefd03.jpg\",\n  \"513995.jpg\": \"Reptilia/Diadophis punctatus/0ecef43d1eac0a687ffb68816afd3715.jpg\",\n  \"51400.jpg\": \"Aves/Ixobrychus exilis/a2653fdd6238e4ee91e3331c370d1088.jpg\",\n  \"51401.jpg\": \"Aves/Ixobrychus exilis/5d964217da270bb30f3fcfec42d418f7.jpg\",\n  \"51402.jpg\": \"Aves/Ixobrychus exilis/96e3c6d18b038daf8d6c87d0fde96a77.jpg\",\n  \"514023.jpg\": \"Reptilia/Diadophis punctatus/36d18aa631d27db3ff5818809ce6a502.jpg\",\n  \"514035.jpg\": \"Reptilia/Diadophis punctatus/cee0a7116c381a2de629c4e8dcdc04e2.jpg\",\n  \"514048.jpg\": \"Reptilia/Diadophis punctatus/a6ed7f1a92a7527824cc06242e39c6bb.jpg\",\n  \"514057.jpg\": \"Reptilia/Diadophis punctatus/0cabb866ddb43d759622c29d078d2fbf.jpg\",\n  \"51406.jpg\": \"Aves/Ixobrychus exilis/c29779db9c01fd7c42c14c7466ea9502.jpg\",\n  \"514068.jpg\": \"Reptilia/Diadophis punctatus/300ad784a05a6cb1005122929547b8cd.jpg\",\n  \"514088.jpg\": \"Reptilia/Diadophis punctatus/5d598520d5db6b5460005c2099cd6f1e.jpg\",\n  \"514099.jpg\": \"Reptilia/Diadophis punctatus/0e3472fcbfeb79bf29e908bbf1b9a34d.jpg\",\n  \"51410.jpg\": \"Aves/Ixobrychus exilis/47bd0a282355ed6d8411d7f7934cae02.jpg\",\n  \"51413.jpg\": \"Aves/Ixobrychus exilis/8796685422296e0dce9fe7b2715be4a3.jpg\",\n  \"51414.jpg\": \"Aves/Ixobrychus exilis/d54fe64816bf089227773901ababfb71.jpg\",\n  \"51420.jpg\": \"Insecta/Monochamus scutellatus/2e1687a80afb0bc4195bafb7a03daa03.jpg\",\n  \"51421.jpg\": \"Insecta/Monochamus scutellatus/9f6142e2092bd70df2d820008176958f.jpg\",\n  \"51422.jpg\": \"Insecta/Monochamus scutellatus/dafcfd0b5b37228dd3f4bf6e410a25ae.jpg\",\n  \"51423.jpg\": \"Insecta/Monochamus scutellatus/b57919e4cbb2586288dbbac24f5ad7d1.jpg\",\n  \"51424.jpg\": \"Insecta/Monochamus scutellatus/0d6b135279cb0322d1f6e20909e09f7b.jpg\",\n  \"51427.jpg\": \"Insecta/Monochamus scutellatus/a2bf65f06498be5a5fc8226aefde0ac3.jpg\",\n  \"514317.jpg\": \"Insecta/Plagodis phlogosaria/68667542d732e209101cc30deae4837f.jpg\",\n  \"51433.jpg\": \"Insecta/Monochamus scutellatus/5231b2e650b95a3ff263be09440ba98d.jpg\",\n  \"51435.jpg\": \"Insecta/Monochamus scutellatus/dd9e819bc49a64e27abf826d93225f50.jpg\",\n  \"51436.jpg\": \"Insecta/Monochamus scutellatus/d953ad4ca2bec3a6272f493331ebde7d.jpg\",\n  \"514367.jpg\": \"Insecta/Ctenucha virginica/e9a6ec21dd669e9b81134b7c0e580ee8.jpg\",\n  \"51437.jpg\": \"Insecta/Monochamus scutellatus/938e6eeb2be081180ec6e2667e25238a.jpg\",\n  \"51438.jpg\": \"Insecta/Monochamus scutellatus/e22b12bec26499aa89a2256bdd5be643.jpg\",\n  \"514386.jpg\": \"Insecta/Ctenucha virginica/63ba1ebab9bae5ddfa02c0cd9d6c78fc.jpg\",\n  \"51439.jpg\": \"Insecta/Monochamus scutellatus/1dfa8761dd14ce77918538d4cd84902f.jpg\",\n  \"51441.jpg\": \"Insecta/Monochamus scutellatus/7d42ddf509af56d245efda2f70c6f7da.jpg\",\n  \"51442.jpg\": \"Insecta/Monochamus scutellatus/1802f27e2891ebe1bbf1a0a06a8eba10.jpg\",\n  \"51448.jpg\": \"Insecta/Monochamus scutellatus/ecc32105496a160961432883f333dd1a.jpg\",\n  \"514484.jpg\": \"Insecta/Erynnis propertius/3a6463454634d318af4f41979000f1a0.jpg\",\n  \"514485.jpg\": \"Insecta/Erynnis propertius/f8a850a3114a07e2c18cff1029649ce5.jpg\",\n  \"51449.jpg\": \"Insecta/Monochamus scutellatus/5ab527e037bf5fdede81081d5c67e87e.jpg\",\n  \"51450.jpg\": \"Insecta/Monochamus scutellatus/8282a3979d742dc7a94d11a78be40876.jpg\",\n  \"51451.jpg\": \"Insecta/Monochamus scutellatus/08d545c84839ab4566013ee318505559.jpg\",\n  \"51452.jpg\": \"Insecta/Monochamus scutellatus/cf4afce75e3fba253dd9c84fd9631ee3.jpg\",\n  \"51453.jpg\": \"Insecta/Monochamus scutellatus/51e105573cfdcc9689db8595867334fe.jpg\",\n  \"514560.jpg\": \"Aves/Notiomystis cincta/de223dce28ce176a1ae7580ba0d79a5e.jpg\",\n  \"514562.jpg\": \"Aves/Notiomystis cincta/04bb810dfc9292c5ebc5a2a042371495.jpg\",\n  \"514563.jpg\": \"Aves/Notiomystis cincta/f7613d6c528382ed6cf734b3cc25ef08.jpg\",\n  \"514607.jpg\": \"Mammalia/Antilocapra americana/3db89687d123622d702bd621e87ebde6.jpg\",\n  \"514608.jpg\": \"Mammalia/Antilocapra americana/982316b05bf09a5a051744ca8dcab81c.jpg\",\n  \"514630.jpg\": \"Mammalia/Antilocapra americana/fa6ca2c2270ef8d9c6bf41cdb38779d9.jpg\",\n  \"51465.jpg\": \"Insecta/Monochamus scutellatus/b1bbb88288318a366a85374485a82759.jpg\",\n  \"51466.jpg\": \"Insecta/Monochamus scutellatus/e13d796632713121e27247b65eee5024.jpg\",\n  \"51467.jpg\": \"Insecta/Monochamus scutellatus/95d64ee3249c1b1157cf24eda0aabf7f.jpg\",\n  \"514680.jpg\": \"Mammalia/Antilocapra americana/1c2d7aa6f1fccb12e775e21827357ca6.jpg\",\n  \"514681.jpg\": \"Mammalia/Antilocapra americana/4112bb9c28638b09af1066bf77f66f6c.jpg\",\n  \"514686.jpg\": \"Mammalia/Antilocapra americana/a0ed623e98d29506f88bccd762255969.jpg\",\n  \"514719.jpg\": \"Insecta/Calycopis cecrops/1d61ad439d58ab6b7be015bd74ac12be.jpg\",\n  \"514735.jpg\": \"Insecta/Calycopis cecrops/572ced93cdbb5e6be2d5b22efbd87b64.jpg\",\n  \"514750.jpg\": \"Insecta/Calycopis cecrops/7aba90114f0cadd3eee09e7175a80a61.jpg\",\n  \"514751.jpg\": \"Insecta/Calycopis cecrops/d356f921623773c50e963908722da8f2.jpg\",\n  \"514754.jpg\": \"Insecta/Calycopis cecrops/9a288cab51abfbfd825699b6e2289ce8.jpg\",\n  \"514794.jpg\": \"Aves/Setophaga fusca/7aa7bcaf2d5baf69a318623ac6db3a5d.jpg\",\n  \"514797.jpg\": \"Aves/Setophaga fusca/05289f4f811890d0c199826b215b884b.jpg\",\n  \"514812.jpg\": \"Aves/Setophaga fusca/6424d455240732a6500a67859788e71b.jpg\",\n  \"514813.jpg\": \"Aves/Setophaga fusca/8c7cf569586f6a73a54be7127da0dfda.jpg\",\n  \"514814.jpg\": \"Aves/Setophaga fusca/4e3bf28fda9aa87b2a975a2988f31072.jpg\",\n  \"514816.jpg\": \"Aves/Setophaga fusca/85f39b3f6901361c332306aea3cf0036.jpg\",\n  \"514822.jpg\": \"Aves/Setophaga fusca/532f3c10209a01185858fa1213d37b14.jpg\",\n  \"514828.jpg\": \"Insecta/Anartia jatrophae/ab631600d9aa8ec113459bfc2f144804.jpg\",\n  \"514831.jpg\": \"Insecta/Anartia jatrophae/1964da32f112c1a4ec5a7f67d71dab40.jpg\",\n  \"514838.jpg\": \"Insecta/Anartia jatrophae/e7d663167032470c07e316ae1b38e710.jpg\",\n  \"514842.jpg\": \"Insecta/Anartia jatrophae/580b2fa6bf96400396e4b69b1aaebe50.jpg\",\n  \"514862.jpg\": \"Insecta/Anartia jatrophae/8a5d318d8e8eba53ea467364cc0ba343.jpg\",\n  \"514868.jpg\": \"Insecta/Anartia jatrophae/0abcc672f9556055a4a0f47e416ab4cf.jpg\",\n  \"514898.jpg\": \"Insecta/Anartia jatrophae/64e66917d7c634dcd0fe3c5cfd973459.jpg\",\n  \"514900.jpg\": \"Insecta/Anartia jatrophae/1eeede63c7f4108b3ce131a8b7bfe4f2.jpg\",\n  \"514918.jpg\": \"Aves/Passerina versicolor/baec00bb2c7d2c2a466b919b1a6a301a.jpg\",\n  \"514923.jpg\": \"Aves/Passerina versicolor/648c1e7504387b18d9d0f407d541bec1.jpg\",\n  \"514924.jpg\": \"Aves/Passerina versicolor/2e5ed559c65d19fa0c178a4ad62f9b8b.jpg\",\n  \"514925.jpg\": \"Aves/Passerina versicolor/12e63fe0c2bb2766ac7f236cabab9fec.jpg\",\n  \"514926.jpg\": \"Aves/Passerina versicolor/069c7ad1db7352ac0f2d4171ad72de3a.jpg\",\n  \"514927.jpg\": \"Aves/Passerina versicolor/f58ad5610b2723261865a9b8ff864448.jpg\",\n  \"514928.jpg\": \"Aves/Passerina versicolor/0fe61d3e06d826b6ceebc21b496e20af.jpg\",\n  \"514929.jpg\": \"Aves/Passerina versicolor/2ebcffeac15a20533adac76962565bb3.jpg\",\n  \"514930.jpg\": \"Aves/Passerina versicolor/7623c1de779596f711f1f9347cb1af82.jpg\",\n  \"514936.jpg\": \"Aves/Passerina versicolor/62064dbb76e4867a788df802dcc422cb.jpg\",\n  \"514951.jpg\": \"Aves/Passerina versicolor/0063b9239233baeaba8ac048ab20a285.jpg\",\n  \"514956.jpg\": \"Insecta/Zanclognatha pedipilalis/d62e2550897da885f0a699974b87d275.jpg\",\n  \"514966.jpg\": \"Aves/Corvus cornix/a069822a8d904488193fd6557708a60a.jpg\",\n  \"514981.jpg\": \"Aves/Corvus cornix/513ba859b6ff0e215056a98f2633c5eb.jpg\",\n  \"515004.jpg\": \"Aves/Corvus cornix/ba1d00713d8a7151738c6b1939865ed2.jpg\",\n  \"515009.jpg\": \"Aves/Corvus cornix/970ddd4176a29bd4f50b64a7d665567c.jpg\",\n  \"515014.jpg\": \"Aves/Corvus cornix/6c682c8b71dcbc338e7083917aad7c35.jpg\",\n  \"515020.jpg\": \"Aves/Corvus cornix/8843d6b28aa80066272d863675a98c97.jpg\",\n  \"51505.jpg\": \"Insecta/Issoria lathonia/79c6c16c45d9dd6ce3ab0227842230cf.jpg\",\n  \"51508.jpg\": \"Insecta/Issoria lathonia/36f3833dcc781fecf8be48b54224c9ba.jpg\",\n  \"515123.jpg\": \"Aves/Agelaius phoeniceus/75c4cda2226952dfffcabc305329de65.jpg\",\n  \"51515.jpg\": \"Insecta/Issoria lathonia/6d9b837f7feac2d56998109b5a8be478.jpg\",\n  \"515169.jpg\": \"Aves/Agelaius phoeniceus/dbd5c80b7dd691b3a7c90237e6109453.jpg\",\n  \"51517.jpg\": \"Insecta/Issoria lathonia/de2547c7f1c95999bc36797115b9b5ba.jpg\",\n  \"51520.jpg\": \"Insecta/Issoria lathonia/d4668617e1a6a8ec1db2919ac5da4b96.jpg\",\n  \"51521.jpg\": \"Insecta/Issoria lathonia/868eb2721832ee1d9d1425c63085c399.jpg\",\n  \"515298.jpg\": \"Aves/Agelaius phoeniceus/ccc3191cc661344ab7a062e238ddb839.jpg\",\n  \"515300.jpg\": \"Aves/Agelaius phoeniceus/9ea24850741f3b791ba935e73069e86b.jpg\",\n  \"515405.jpg\": \"Aves/Agelaius phoeniceus/cb38de2250db8847e0749e54fef004a3.jpg\",\n  \"515467.jpg\": \"Aves/Agelaius phoeniceus/015eee7901b8b74d4eb0ba0661eae63c.jpg\",\n  \"515595.jpg\": \"Aves/Agelaius phoeniceus/aa94da7f3bc288014b822a87809839d9.jpg\",\n  \"51578.jpg\": \"Insecta/Scudderia furcata/939fadfc0a09c17a8abb587d6a09e7d7.jpg\",\n  \"51586.jpg\": \"Insecta/Scudderia furcata/fcc3674a896b4936c30e8ba6d1867e1a.jpg\",\n  \"51591.jpg\": \"Insecta/Scudderia furcata/ed79180eff15a4b6c097bb8c380e904b.jpg\",\n  \"51593.jpg\": \"Aves/Melanerpes pucherani/9194063c9859e7480a3cbe101b0bc33f.jpg\",\n  \"51595.jpg\": \"Aves/Melanerpes pucherani/2da9c9a5851ca6d899973f11970aced2.jpg\",\n  \"51598.jpg\": \"Aves/Melanerpes pucherani/b2253243c875a93aa0725ee506bb600c.jpg\",\n  \"515989.jpg\": \"Aves/Calocitta formosa/6cd9bbb367250a74c211b274fd412db7.jpg\",\n  \"515998.jpg\": \"Aves/Calocitta formosa/db410b4b9465ffcbb234f1f898e91cde.jpg\",\n  \"515999.jpg\": \"Aves/Calocitta formosa/eccf01559c3714860e20b13f18407703.jpg\",\n  \"516009.jpg\": \"Reptilia/Plestiodon tetragrammus/5b3d114cbd3a3e9b64c3b9e6e514d7ea.jpg\",\n  \"516015.jpg\": \"Reptilia/Plestiodon tetragrammus/172eb956d2fd7a53925bc2871b2d26a9.jpg\",\n  \"516017.jpg\": \"Reptilia/Plestiodon tetragrammus/e58834233ce9f29fa69908ff9d925041.jpg\",\n  \"516024.jpg\": \"Insecta/Apiomerus spissipes/508c50b67943f0b77ce47e354bd5a32e.jpg\",\n  \"51603.jpg\": \"Aves/Melanerpes pucherani/607cd0f6c6f1c9eb3c357ebb2d72b48f.jpg\",\n  \"516036.jpg\": \"Insecta/Apiomerus spissipes/9aa3d9f13eb82d22e668747c9a95e2a9.jpg\",\n  \"516045.jpg\": \"Insecta/Tabanus atratus/241b27e1609c7cee1bbee4047d207027.jpg\",\n  \"516068.jpg\": \"Aves/Tringa melanoleuca/6c24083a8c329cb2078ebeda573f84cc.jpg\",\n  \"51608.jpg\": \"Aves/Melanerpes pucherani/43af9c8184ea72ad733f3501412b1a7c.jpg\",\n  \"516142.jpg\": \"Aves/Tringa melanoleuca/71a361b3f4374aa8ed88260cace43fe4.jpg\",\n  \"51615.jpg\": \"Reptilia/Drymarchon melanurus/65cd97c90b46a816cb15dfeea1f50df0.jpg\",\n  \"516222.jpg\": \"Aves/Tringa melanoleuca/b13364330824dcf89720df28219e6126.jpg\",\n  \"51628.jpg\": \"Reptilia/Drymarchon melanurus/363e6da9ce8acec906dc81b32e59c20d.jpg\",\n  \"51629.jpg\": \"Reptilia/Drymarchon melanurus/9a43cb336c92999bef84502eb37392dd.jpg\",\n  \"51630.jpg\": \"Reptilia/Drymarchon melanurus/f15cbcbafd3935b06195738d04c54be5.jpg\",\n  \"516301.jpg\": \"Aves/Tringa melanoleuca/ebb89afc5466b70e5e67170b6dc25e68.jpg\",\n  \"516305.jpg\": \"Aves/Tringa melanoleuca/9c77647b428024249efa41b00cf3b7fa.jpg\",\n  \"51631.jpg\": \"Reptilia/Drymarchon melanurus/b2065fb81bac4faad395debfcdd8513a.jpg\",\n  \"51635.jpg\": \"Reptilia/Drymarchon melanurus/fd1b68fe76e2b401b0074f7750905284.jpg\",\n  \"516403.jpg\": \"Aves/Vanellus vanellus/053aef0ded09a27465c14e8fdbce8bc4.jpg\",\n  \"516405.jpg\": \"Aves/Vanellus vanellus/2a358a066d86a14e09412963ef3b4961.jpg\",\n  \"516411.jpg\": \"Aves/Vanellus vanellus/9f332d197a9073776efdd6987fcc2d0b.jpg\",\n  \"516412.jpg\": \"Aves/Vanellus vanellus/71c026d892de855ecbf87f3bb86a34b4.jpg\",\n  \"516415.jpg\": \"Aves/Vanellus vanellus/0397b6465ee260a2bfe69d91d4728339.jpg\",\n  \"516418.jpg\": \"Aves/Vanellus vanellus/d1b97928216915b07e98fd96d2a1d84a.jpg\",\n  \"516419.jpg\": \"Aves/Vanellus vanellus/9a37c7f5c978f6b201ed10866c22625e.jpg\",\n  \"516420.jpg\": \"Aves/Vanellus vanellus/e783786b124ab486c319fd03320bdffc.jpg\",\n  \"516428.jpg\": \"Aves/Vanellus vanellus/2af7827a106dc58f0f106821f2b1ec9f.jpg\",\n  \"516430.jpg\": \"Animalia/Dendraster excentricus/7dfc9191095befcc073678fab3f7f7a8.jpg\",\n  \"516438.jpg\": \"Animalia/Dendraster excentricus/0a72e18dc5f198a47f5a9f51b6160525.jpg\",\n  \"516459.jpg\": \"Animalia/Dendraster excentricus/73f8e98b69ba4baf435ed28bec1b7a97.jpg\",\n  \"516460.jpg\": \"Animalia/Dendraster excentricus/9cbaeb5052ad740a29e6bd631879b04e.jpg\",\n  \"516461.jpg\": \"Reptilia/Sceloporus grammicus/8b640a6d1804f7d0dbfb59809ec04bd0.jpg\",\n  \"516463.jpg\": \"Reptilia/Sceloporus grammicus/ead4a06feb02b4c43f727b5d92afa228.jpg\",\n  \"516464.jpg\": \"Reptilia/Sceloporus grammicus/9b05d8ed07e0c7b7443e7b03fc8ec7ef.jpg\",\n  \"516471.jpg\": \"Reptilia/Sceloporus grammicus/fc623f0b36651ac236856b585e399982.jpg\",\n  \"516561.jpg\": \"Aves/Piaya cayana/20d055138641e8f52ffdd3444a7b872a.jpg\",\n  \"516563.jpg\": \"Aves/Piaya cayana/2aaf179d8864eccde9fc9f2c03c47010.jpg\",\n  \"516589.jpg\": \"Aves/Piaya cayana/e2a1a3db0ea8e225328848694b0cb186.jpg\",\n  \"516597.jpg\": \"Aves/Piaya cayana/6b4f8914150411ad487ede1d0fff05e3.jpg\",\n  \"516605.jpg\": \"Aves/Piaya cayana/0394b6edbd98c71c9d1412a4809100ea.jpg\",\n  \"516610.jpg\": \"Aves/Piaya cayana/f33d3fdd310020773c606b23429bac86.jpg\",\n  \"516643.jpg\": \"Aves/Oceanites oceanicus/f24e3e1258a31b5b82a267f83f65e89b.jpg\",\n  \"516645.jpg\": \"Aves/Oceanites oceanicus/d9ae0971fbc84f13d0d0879836ca6fa9.jpg\",\n  \"516670.jpg\": \"Insecta/Poecilocapsus lineatus/3e2d0a88065706d919e4685b644ee50b.jpg\",\n  \"516674.jpg\": \"Insecta/Poecilocapsus lineatus/a00897a269a51f35c1f34335c0136607.jpg\",\n  \"516675.jpg\": \"Insecta/Poecilocapsus lineatus/e750b5d0226d63f7f7fa86aa422b84de.jpg\",\n  \"516704.jpg\": \"Mammalia/Canis latrans/b32548b7d7ef6c1802623631de30d33f.jpg\",\n  \"516716.jpg\": \"Mammalia/Canis latrans/4be5f080056f90a16cea5dfe5c1c1a19.jpg\",\n  \"51680.jpg\": \"Insecta/Ischnura ramburii/c7ddf975c48e3fa46769d7246fbba3e7.jpg\",\n  \"516826.jpg\": \"Mammalia/Canis latrans/c9f3b1b21b0b6fa7b5ff66ace7db0d18.jpg\",\n  \"51703.jpg\": \"Insecta/Ischnura ramburii/d0c5353e013f2e6ee71ce1d44eca2d3e.jpg\",\n  \"517042.jpg\": \"Mammalia/Canis latrans/da3d2718a0122673c53fd63d2e16ebcc.jpg\",\n  \"517226.jpg\": \"Aves/Upupa epops/fcca510a8b5059456827603444574349.jpg\",\n  \"517231.jpg\": \"Aves/Upupa epops/c590a314513f237d285853fc767962d5.jpg\",\n  \"517232.jpg\": \"Aves/Upupa epops/d331bad2818e1919431f1d3a8b5afb1c.jpg\",\n  \"517237.jpg\": \"Aves/Upupa epops/dde15801c1403a2dcb465b7ba965870a.jpg\",\n  \"517238.jpg\": \"Aves/Upupa epops/cd2e686f144e6ab43485d4df7a999598.jpg\",\n  \"517240.jpg\": \"Aves/Upupa epops/66c107900bb8a1ded020914a9e95453e.jpg\",\n  \"517242.jpg\": \"Aves/Upupa epops/10af70c3ad15604817ce7f4093a7b925.jpg\",\n  \"517246.jpg\": \"Aves/Upupa epops/84850aaa30fd1704a259e23027348f07.jpg\",\n  \"517247.jpg\": \"Aves/Upupa epops/c438d9afcd17b2b7d40a0aec2924de5a.jpg\",\n  \"517259.jpg\": \"Actinopterygii/Micropterus dolomieu/2e9f9909955950c86f1fe23ecec21476.jpg\",\n  \"51727.jpg\": \"Insecta/Ischnura ramburii/d4bb2f57f9a795bf56561c51b07496ca.jpg\",\n  \"517271.jpg\": \"Actinopterygii/Micropterus dolomieu/f3e24124ce5c19d2c9d964a20980fcbf.jpg\",\n  \"517277.jpg\": \"Aves/Geranoaetus albicaudatus/264a8ddbbacc2ad2ea06fd420390b16a.jpg\",\n  \"517278.jpg\": \"Aves/Geranoaetus albicaudatus/a46f87fa8790bb7c3c360276e5553577.jpg\",\n  \"517282.jpg\": \"Aves/Geranoaetus albicaudatus/936a768d476bdab75639189058899924.jpg\",\n  \"517291.jpg\": \"Aves/Geranoaetus albicaudatus/afe4a0c3cfadf1884d71f870f8ebb84b.jpg\",\n  \"517292.jpg\": \"Aves/Geranoaetus albicaudatus/cb929e5671e4010005a31dcdce59760b.jpg\",\n  \"517293.jpg\": \"Aves/Geranoaetus albicaudatus/f61c3071be545c2ef39168af2af459d0.jpg\",\n  \"517302.jpg\": \"Aves/Geranoaetus albicaudatus/3351a31985035553cd3dd4d2a853073b.jpg\",\n  \"517303.jpg\": \"Aves/Geranoaetus albicaudatus/9ee2b41017c14aca463974ede5924b99.jpg\",\n  \"517304.jpg\": \"Aves/Geranoaetus albicaudatus/0b994bd33d12aeba8953a9a91a72543e.jpg\",\n  \"517323.jpg\": \"Aves/Geranoaetus albicaudatus/347f98c3fb71a67a15c83181189d82c5.jpg\",\n  \"517324.jpg\": \"Aves/Geranoaetus albicaudatus/f96144cae1460863f1fd3cc76b050da7.jpg\",\n  \"517408.jpg\": \"Insecta/Taeniopoda eques/deadaef007816320b7670813cd7bc7fe.jpg\",\n  \"517409.jpg\": \"Insecta/Taeniopoda eques/0bb64d1f31863ba4e46d61248ee6fd53.jpg\",\n  \"517413.jpg\": \"Insecta/Taeniopoda eques/2b1b98e2bdf30f44291f15e27c273a6f.jpg\",\n  \"517421.jpg\": \"Insecta/Taeniopoda eques/465d838f6bf12a3b5eb36bd924bb4a30.jpg\",\n  \"517423.jpg\": \"Insecta/Taeniopoda eques/da687319193c46003a33565383f8735a.jpg\",\n  \"517426.jpg\": \"Insecta/Taeniopoda eques/77ae2453ec9ad432b708ef02260c8f31.jpg\",\n  \"517427.jpg\": \"Insecta/Taeniopoda eques/e2006a9947fc95c6a858303dcefb4792.jpg\",\n  \"517428.jpg\": \"Insecta/Taeniopoda eques/ab5158331ee35a14c8e8adb2c7ed973c.jpg\",\n  \"51748.jpg\": \"Insecta/Ischnura ramburii/12485e635080ea80e311a20126e2e4ec.jpg\",\n  \"517489.jpg\": \"Insecta/Pseudomops septentrionalis/c6034f372719e064e0d7ca2caba1a63b.jpg\",\n  \"517492.jpg\": \"Insecta/Pseudomops septentrionalis/550848768e59f0e1a9657c56c11ea1b0.jpg\",\n  \"51755.jpg\": \"Insecta/Ischnura ramburii/ad3bb80552e80def48e0c756b11bab9c.jpg\",\n  \"517644.jpg\": \"Reptilia/Tantilla nigriceps/cc554cdb53361071adcc08e023673a7a.jpg\",\n  \"517649.jpg\": \"Reptilia/Tantilla nigriceps/608e0fbd2f40cd0a639d6d25b478b16f.jpg\",\n  \"51766.jpg\": \"Insecta/Ischnura ramburii/b80bbbdd240e4c0ff4a592f9feed8f65.jpg\",\n  \"517669.jpg\": \"Insecta/Microtia elva/18d358222f4061ed12e6d0c34855588f.jpg\",\n  \"517671.jpg\": \"Insecta/Microtia elva/e3c0589d031a772399a7a889387bebc8.jpg\",\n  \"517678.jpg\": \"Insecta/Microtia elva/542f192d4643397b838f453114ae3c9b.jpg\",\n  \"51768.jpg\": \"Insecta/Ischnura ramburii/97a457a5a91451bed750a7e6137390d6.jpg\",\n  \"517682.jpg\": \"Insecta/Microtia elva/538197c7f538c8c97cb7e8dbef14e603.jpg\",\n  \"517692.jpg\": \"Insecta/Microtia elva/5e488d937243ef13cdf9b9e8598fb48f.jpg\",\n  \"517693.jpg\": \"Insecta/Microtia elva/48c902d74795663162bc0574e7f5e79b.jpg\",\n  \"517694.jpg\": \"Aves/Rallus limicola/ab1009a7a830c0a9fb70d74d91875555.jpg\",\n  \"517702.jpg\": \"Aves/Rallus limicola/d33fbb7c1461fd121b46bf762ac8b8d1.jpg\",\n  \"517707.jpg\": \"Aves/Rallus limicola/8d0b3c419ff87d71003699441c58bc4f.jpg\",\n  \"517712.jpg\": \"Aves/Rallus limicola/6e1133911924bd7bb726c7fee046a986.jpg\",\n  \"517714.jpg\": \"Aves/Rallus limicola/12fae0b8ba4f79f618737fde33544625.jpg\",\n  \"51772.jpg\": \"Insecta/Ischnura ramburii/05241cea7bb9938aa0cb2baeec560e9a.jpg\",\n  \"517722.jpg\": \"Aves/Rallus limicola/80ca81d7ae7a478936ab8ad53130c481.jpg\",\n  \"517725.jpg\": \"Aves/Rallus limicola/063c83de2015aec01b7359711ecd14cc.jpg\",\n  \"51775.jpg\": \"Insecta/Ischnura ramburii/ee09bdee9628fd1177895fe062d9fe77.jpg\",\n  \"51777.jpg\": \"Insecta/Ischnura ramburii/19ef7ddee155e097763613f006c96063.jpg\",\n  \"517935.jpg\": \"Animalia/Porpita porpita/3dc8d7cd79cfc80de1cb3b120feb8ab3.jpg\",\n  \"517943.jpg\": \"Reptilia/Vipera berus/c1836af893d0f8e16af2b6a1af26b453.jpg\",\n  \"517944.jpg\": \"Reptilia/Vipera berus/2c00237b5f0d05ac59fdca29a75027d5.jpg\",\n  \"517953.jpg\": \"Aves/Empidonax hammondii/243447d6d87fc2b5e4639b010ec957f6.jpg\",\n  \"517954.jpg\": \"Aves/Empidonax hammondii/172103e1848c972e839ea914a38f6872.jpg\",\n  \"517966.jpg\": \"Insecta/Chytolita morbidalis/deb79b77d3707f90cd9c82c870bca2a2.jpg\",\n  \"517967.jpg\": \"Insecta/Chytolita morbidalis/089eaaaef209e2d064de26fc2b63b3f2.jpg\",\n  \"517971.jpg\": \"Insecta/Papilio troilus/7f0954422eabad0b51a745c5b49570bf.jpg\",\n  \"517980.jpg\": \"Insecta/Papilio troilus/c113f2ede2af176f03c08da38588da30.jpg\",\n  \"517981.jpg\": \"Insecta/Papilio troilus/50f81757f4797e9596c0c724413cc0b2.jpg\",\n  \"518013.jpg\": \"Insecta/Papilio troilus/717ac2755bb3923eb8360f07dea9e885.jpg\",\n  \"518019.jpg\": \"Insecta/Papilio troilus/0a4cc1054571c3502c20d637d0f5c129.jpg\",\n  \"518028.jpg\": \"Insecta/Papilio troilus/31692c23d4d6ba5c4d4a4ae3378de5a6.jpg\",\n  \"518042.jpg\": \"Insecta/Papilio troilus/3771fa2bd8962f2790fe5d38b81276b5.jpg\",\n  \"518058.jpg\": \"Insecta/Papilio troilus/d1a8433138c73577ed510afa800ba295.jpg\",\n  \"518077.jpg\": \"Insecta/Papilio troilus/c894fb447723b54f1dbc36575c60db16.jpg\",\n  \"518113.jpg\": \"Aves/Recurvirostra avosetta/bcc2153e5cba2bfc7ced49aac900e03d.jpg\",\n  \"518119.jpg\": \"Aves/Recurvirostra avosetta/56676c098ea505fc62a0847b6a9a728e.jpg\",\n  \"51813.jpg\": \"Insecta/Ischnura ramburii/a2d573ef7f9413dc51a6c522edef9031.jpg\",\n  \"518160.jpg\": \"Aves/Ardea herodias occidentalis/5d255b084455c63df7054b4467ebfa29.jpg\",\n  \"518171.jpg\": \"Insecta/Cyclophora pendulinaria/f866d034fa0b2d2156949a5f5f568433.jpg\",\n  \"51819.jpg\": \"Insecta/Ischnura ramburii/bcf41d6db616b3d6749150c55a2c8333.jpg\",\n  \"518193.jpg\": \"Aves/Ficedula hypoleuca/1794c13e5a0d3ad0b2e5b96581626c42.jpg\",\n  \"518197.jpg\": \"Aves/Turdus grayi/c49bd17479a3b0f893101887c2db09ef.jpg\",\n  \"518198.jpg\": \"Aves/Turdus grayi/f841a4f3fa8149afe21ef9e9fbaa2ffa.jpg\",\n  \"51820.jpg\": \"Insecta/Ischnura ramburii/15de10743a17673d73bbf7437f022fcf.jpg\",\n  \"518223.jpg\": \"Aves/Turdus grayi/2c5d49115c80bbd2986cf01fb4fc0079.jpg\",\n  \"518227.jpg\": \"Aves/Turdus grayi/4f5d05f3e8501d3e15c3b1e9438104a3.jpg\",\n  \"518238.jpg\": \"Aves/Turdus grayi/fae0a319e448205cb22e32993e18b2af.jpg\",\n  \"518246.jpg\": \"Aves/Turdus grayi/051256483142e4b21b5b7460dafaedbb.jpg\",\n  \"518252.jpg\": \"Aves/Turdus grayi/d78be6d595c3f335a2f8b479262f279f.jpg\",\n  \"51828.jpg\": \"Insecta/Ischnura ramburii/bf050edf0bdfb2b6a3d6288cb21f1073.jpg\",\n  \"51836.jpg\": \"Insecta/Ischnura ramburii/60e2b8050cdbf118e1385d0bca48dad6.jpg\",\n  \"51837.jpg\": \"Insecta/Ischnura ramburii/331231b2d60471fc010a834425926095.jpg\",\n  \"51840.jpg\": \"Insecta/Ischnura ramburii/604c637633ad6ec5a0da485bd746c17d.jpg\",\n  \"51841.jpg\": \"Insecta/Ischnura ramburii/57b5c9ee61f3c7935699cb2c46c21d7b.jpg\",\n  \"518430.jpg\": \"Aves/Hirundo neoxena/a2814352b9ebabcbb6f5759ee63645f7.jpg\",\n  \"518444.jpg\": \"Reptilia/Coleonyx brevis/eca81b87b2492437208b6ab9cb57b88d.jpg\",\n  \"518448.jpg\": \"Reptilia/Coleonyx brevis/ab70af629a431c677b12caaca86428f9.jpg\",\n  \"518469.jpg\": \"Reptilia/Coleonyx brevis/e6a0a656d0a4b56c8b19176eaa69f49f.jpg\",\n  \"518471.jpg\": \"Reptilia/Coleonyx brevis/8ca8c6dd0f6b4d9627fbd02608b1dfc2.jpg\",\n  \"518475.jpg\": \"Reptilia/Coleonyx brevis/ba3e02c5ec57993338c9fadf10ec8968.jpg\",\n  \"518476.jpg\": \"Reptilia/Coleonyx brevis/8fab807e48f1adc7f31e95a10b52e687.jpg\",\n  \"518483.jpg\": \"Insecta/Cordulia shurtleffii/6f40ef4c0eaeaf0910908c26dfcbf090.jpg\",\n  \"518553.jpg\": \"Insecta/Celithemis eponina/0c5a6d11a7df04dd58bf99e89dc20c38.jpg\",\n  \"518568.jpg\": \"Insecta/Celithemis eponina/c9fe3d9e7d16a47b9bfef0fafe0e0e5b.jpg\",\n  \"518570.jpg\": \"Insecta/Celithemis eponina/e584eec84ac6d71e826d0674aad68244.jpg\",\n  \"518600.jpg\": \"Insecta/Celithemis eponina/9abc6d6a05af0f39ba390c33e5969cad.jpg\",\n  \"518663.jpg\": \"Insecta/Celithemis eponina/023f6fc1a221747b6886d59eca77492c.jpg\",\n  \"518676.jpg\": \"Insecta/Celithemis eponina/bced54bbb78cd1be444df88ce0671ca9.jpg\",\n  \"518679.jpg\": \"Aves/Cinnyris jugularis/95a583c27588fd3154b5397e16584188.jpg\",\n  \"518687.jpg\": \"Insecta/Leptoglossus oppositus/a9e2d9a377370b1b5a853880426cb577.jpg\",\n  \"518716.jpg\": \"Insecta/Hemipenthes sinuosa/ab5d1fe67ed9b2d604a2eb61968b0647.jpg\",\n  \"518727.jpg\": \"Insecta/Hemipenthes sinuosa/24b0db72d4414562a3e770cb3fd9d094.jpg\",\n  \"518732.jpg\": \"Mollusca/Aeolidia papillosa/81a877494eabadd8c5d67513a6df2579.jpg\",\n  \"518733.jpg\": \"Mollusca/Aeolidia papillosa/e1f4628230e32399e1618280cb773e76.jpg\",\n  \"518737.jpg\": \"Mollusca/Aeolidia papillosa/c0985b76517fdc7dac63177a053ffede.jpg\",\n  \"518738.jpg\": \"Mollusca/Aeolidia papillosa/6d76ec5638531e893b7956f9de2fc977.jpg\",\n  \"518739.jpg\": \"Mollusca/Aeolidia papillosa/eb8054e917da30376be97cdfc01a5488.jpg\",\n  \"518745.jpg\": \"Mollusca/Aeolidia papillosa/6a25e25ed076d765462f663b76c17c20.jpg\",\n  \"518746.jpg\": \"Mollusca/Aeolidia papillosa/b4852d7fcf52eff6855483c73eabb662.jpg\",\n  \"518748.jpg\": \"Mollusca/Aeolidia papillosa/c2b8c7950f32eb5ecd7a4ce44580c75b.jpg\",\n  \"518750.jpg\": \"Mollusca/Aeolidia papillosa/2c5c3e8780bfc03c476110eb6dbc6850.jpg\",\n  \"518752.jpg\": \"Mollusca/Aeolidia papillosa/fc65b6e7ac752af7427be958290dabf7.jpg\",\n  \"518757.jpg\": \"Mollusca/Aeolidia papillosa/e2780c3bf24e7015b8e179a1da6f6155.jpg\",\n  \"518775.jpg\": \"Insecta/Psyllobora vigintimaculata/e7ce9b4323767742e18ca05af6a261cd.jpg\",\n  \"518783.jpg\": \"Insecta/Psyllobora vigintimaculata/a2450e1c8d34bad6cd869fbfb95feb53.jpg\",\n  \"518784.jpg\": \"Insecta/Psyllobora vigintimaculata/5a673e6b86e3cd928bf98139ad76dcf1.jpg\",\n  \"518787.jpg\": \"Insecta/Palthis angulalis/e40c42a36b806df7196df19c6aa6e642.jpg\",\n  \"518793.jpg\": \"Insecta/Palthis angulalis/8958bbb09708f0755a3babf9e67ae222.jpg\",\n  \"518796.jpg\": \"Insecta/Palthis angulalis/610178ff73aef9e701e3d0b8af963e09.jpg\",\n  \"518801.jpg\": \"Insecta/Palthis angulalis/6a358802b92e5ef0fdb5889a373b7b17.jpg\",\n  \"518803.jpg\": \"Insecta/Palthis angulalis/428585ff265f87be761c42040e468400.jpg\",\n  \"518806.jpg\": \"Insecta/Palthis angulalis/1774464f17f555458f7a618cbb88a155.jpg\",\n  \"518807.jpg\": \"Insecta/Palthis angulalis/ff44723ea9877d91f19f4b515272519a.jpg\",\n  \"518811.jpg\": \"Insecta/Palthis angulalis/f0749565bc8368a9fdb52e4a2c862331.jpg\",\n  \"518812.jpg\": \"Insecta/Palthis angulalis/b57cfcf02d7931a96f8232de592a93ee.jpg\",\n  \"518948.jpg\": \"Mammalia/Otospermophilus beecheyi/3054ef167ae14fcb09de581b09fd83fe.jpg\",\n  \"518952.jpg\": \"Mammalia/Otospermophilus beecheyi/b2c9f70f62882a01f674e60fa2bc9559.jpg\",\n  \"518976.jpg\": \"Mammalia/Otospermophilus beecheyi/ba673a44fdb05d626fdf3ab55316d5f2.jpg\",\n  \"518979.jpg\": \"Mammalia/Otospermophilus beecheyi/9e10b9c6cf91d1e47f6b5e78c4fd949a.jpg\",\n  \"518986.jpg\": \"Mammalia/Otospermophilus beecheyi/9190f348ea30345eb32aa807cae4da23.jpg\",\n  \"518989.jpg\": \"Mammalia/Otospermophilus beecheyi/1e791142cb5b577ee643333769c0d82b.jpg\",\n  \"519038.jpg\": \"Mammalia/Otospermophilus beecheyi/d92d6293b0424fbc7c4c7cb8173424b8.jpg\",\n  \"51906.jpg\": \"Insecta/Ischnura ramburii/2c362667e7ee165dbcc997eb252561f4.jpg\",\n  \"519061.jpg\": \"Mammalia/Otospermophilus beecheyi/f0f0c1d021d198ca2a7b1ad47a5b0887.jpg\",\n  \"51907.jpg\": \"Insecta/Ischnura ramburii/427d1fa525d11953fc5564f951899a06.jpg\",\n  \"519231.jpg\": \"Aves/Empidonax occidentalis/baebe3efab2d17bdd7b06c1790adb7e3.jpg\",\n  \"519241.jpg\": \"Aves/Gymnorhina tibicen/a5ac10da1aa3fb9bbc97c2b994ab63d1.jpg\",\n  \"519253.jpg\": \"Aves/Gymnorhina tibicen/fcafd2d45edbe71209e25df805120c6c.jpg\",\n  \"519272.jpg\": \"Aves/Gymnorhina tibicen/2786853c01b5d5b9bc57ca7b1a438bc4.jpg\",\n  \"519279.jpg\": \"Aves/Gymnorhina tibicen/2cd146b816d6fdb929a22cdf825bc139.jpg\",\n  \"51928.jpg\": \"Aves/Bucephala islandica/35fb021fd42b057e62b9a6debc0d38c2.jpg\",\n  \"519287.jpg\": \"Insecta/Musca domestica/e3ddb44ea3ac88e11acb78e4e2923f20.jpg\",\n  \"519293.jpg\": \"Insecta/Musca domestica/c8c8ccc5c1d913626273862e35509003.jpg\",\n  \"519294.jpg\": \"Insecta/Musca domestica/71d1df7adabf4ddc068e3fc942fc9ef6.jpg\",\n  \"519296.jpg\": \"Reptilia/Xantusia vigilis/eb4a4855b7d513adb801f552fbdfe482.jpg\",\n  \"519312.jpg\": \"Reptilia/Xantusia vigilis/fb019528b5db8938a802a8bd41dcf91a.jpg\",\n  \"519318.jpg\": \"Mollusca/Lobatus gigas/5afd5fe2e25769b2333d59a46f3dabf8.jpg\",\n  \"519321.jpg\": \"Mollusca/Lobatus gigas/33c2cefd89ae66ba324ae4ac0cbedd7f.jpg\",\n  \"51935.jpg\": \"Aves/Bucephala islandica/c315d1641a5f267060489f258dd28b2c.jpg\",\n  \"519357.jpg\": \"Insecta/Hypsopygia olinalis/42c261ff5dff76b462d72140037e93c4.jpg\",\n  \"519362.jpg\": \"Insecta/Hypsopygia olinalis/ce8b14e227485a6c1c3c09ad305e6694.jpg\",\n  \"519369.jpg\": \"Insecta/Hypsopygia olinalis/0839a16824aed4b4a7741c7a4bdff8f5.jpg\",\n  \"519370.jpg\": \"Insecta/Hypsopygia olinalis/79bab86d33a9eb06794da0eb0db2f463.jpg\",\n  \"519375.jpg\": \"Insecta/Hypsopygia olinalis/b09893fb91926fbbe1e5ccfb35233824.jpg\",\n  \"519377.jpg\": \"Insecta/Hypsopygia olinalis/8c0223ffba39a551ae042c43aa10b142.jpg\",\n  \"519378.jpg\": \"Insecta/Hypsopygia olinalis/d69f4fd54bbf26f42a4345ee70a55577.jpg\",\n  \"51939.jpg\": \"Aves/Bucephala islandica/d3fc35bc3025c6478b33b7d476d2df20.jpg\",\n  \"519399.jpg\": \"Animalia/Cardisoma guanhumi/6d9994a8056b749fdcd2e2fd4c4a406c.jpg\",\n  \"51946.jpg\": \"Aves/Bucephala islandica/50861ff7a00216c78965d4e7347821f7.jpg\",\n  \"519487.jpg\": \"Mammalia/Phacochoerus africanus/6f67883546a805d740e17fcba9620078.jpg\",\n  \"51949.jpg\": \"Aves/Bucephala islandica/209c4642acf0cf21f1d80f2db2377bb6.jpg\",\n  \"519491.jpg\": \"Mammalia/Phacochoerus africanus/fc29b9587e97656c29f4ba0ef6120d9f.jpg\",\n  \"519498.jpg\": \"Mammalia/Phacochoerus africanus/5b09816c9f144526319c0cf57d731707.jpg\",\n  \"519505.jpg\": \"Mammalia/Phacochoerus africanus/1c0f7ab23c194aca96ca6aa54b51bbc7.jpg\",\n  \"519506.jpg\": \"Mammalia/Phacochoerus africanus/1527c897b11ab9cd73ea29c3e2f0a92c.jpg\",\n  \"519507.jpg\": \"Mammalia/Phacochoerus africanus/11f196ad08154ac4e7a40a3f17ff77f8.jpg\",\n  \"519510.jpg\": \"Mammalia/Phacochoerus africanus/eeb05eea6edc3404fb966df3fb1d5a0c.jpg\",\n  \"519513.jpg\": \"Mammalia/Phacochoerus africanus/159499d63c3410d9df19232c4bf8d5b7.jpg\",\n  \"51960.jpg\": \"Aves/Bucephala islandica/d50a55bff4174a580fe575a411656c2e.jpg\",\n  \"51965.jpg\": \"Aves/Bucephala islandica/16a9a069a412317ee505433229c1fbd9.jpg\",\n  \"51973.jpg\": \"Aves/Bucephala islandica/f7513f36f857bca1856fd47b984b41f5.jpg\",\n  \"51975.jpg\": \"Aves/Bucephala islandica/caefee8269435737626a0d14e39dd821.jpg\",\n  \"519768.jpg\": \"Reptilia/Thamnophis atratus/ed3d578d7398adb81b797e39992b1aff.jpg\",\n  \"519773.jpg\": \"Reptilia/Thamnophis atratus/26a28d63d624d0da02b93885faa41877.jpg\",\n  \"519777.jpg\": \"Reptilia/Thamnophis atratus/468373a6768afd13dde3c7d54f8945ba.jpg\",\n  \"519791.jpg\": \"Insecta/Lambdina fiscellaria/61cb2975a078b243d67574dfea5b4b9b.jpg\",\n  \"519823.jpg\": \"Reptilia/Gopherus berlandieri/061cabbb076ab2f11a9f5443bdd3161a.jpg\",\n  \"519826.jpg\": \"Reptilia/Gopherus berlandieri/a84bbdb4ba52a688f65cfefd5132bd3c.jpg\",\n  \"519833.jpg\": \"Reptilia/Gopherus berlandieri/5fda2395395873f71f39a63454d03999.jpg\",\n  \"519844.jpg\": \"Reptilia/Gopherus berlandieri/d1e0edeedf1466d1dac05d06d572560b.jpg\",\n  \"519847.jpg\": \"Reptilia/Gopherus berlandieri/c0760ca3d0319012b78c3e6c0f168483.jpg\",\n  \"519851.jpg\": \"Reptilia/Gopherus berlandieri/b4afde2275fcb1f5efd7d85663abdb54.jpg\",\n  \"519859.jpg\": \"Reptilia/Gopherus berlandieri/0f65ab367e648662c091a7bd669a836e.jpg\",\n  \"519958.jpg\": \"Arachnida/Micrathena sagittata/d2a1b2977ab6e2c973403cdd3f349eaa.jpg\",\n  \"519960.jpg\": \"Arachnida/Micrathena sagittata/9a176b4b7c2f86db124ccf3114869ed4.jpg\",\n  \"519996.jpg\": \"Insecta/Paltothemis lineatipes/5e7b7d3a8fc11a847b08465b91c45d20.jpg\",\n  \"519997.jpg\": \"Insecta/Paltothemis lineatipes/9dc5b137861b346e717b969ec74710fb.jpg\",\n  \"519998.jpg\": \"Insecta/Paltothemis lineatipes/57239f65ac4c775acadac736fd5d3dba.jpg\",\n  \"519999.jpg\": \"Insecta/Paltothemis lineatipes/9c0151fec20b302844bf12b15b0f1013.jpg\",\n  \"52.jpg\": \"Mammalia/Marmota flaviventris/89c5d9afb4cfc22cd03e6bf20c65a2d4.jpg\",\n  \"520000.jpg\": \"Insecta/Paltothemis lineatipes/190ff6562ed05bbdf7b68b27a4198065.jpg\",\n  \"520001.jpg\": \"Insecta/Paltothemis lineatipes/8c832ec7580acf06bf2029187bcbd75c.jpg\",\n  \"520002.jpg\": \"Insecta/Paltothemis lineatipes/d56165cb3c59e650759dfe21a51a3553.jpg\",\n  \"520026.jpg\": \"Insecta/Paltothemis lineatipes/6702892bb9ba4f64cc64b6f0e0cd5a9c.jpg\",\n  \"520046.jpg\": \"Aves/Vanellus chilensis/ba876304a63f3dadc75111fcae6434a8.jpg\",\n  \"520048.jpg\": \"Aves/Vanellus chilensis/fb626d6526c1ac9929457772512c5e0f.jpg\",\n  \"520054.jpg\": \"Aves/Vanellus chilensis/693af371a80bfe4c4d5d9d0c563b5a1f.jpg\",\n  \"520058.jpg\": \"Aves/Vanellus chilensis/cb417081f1823f1adfe69d122882fe53.jpg\",\n  \"520079.jpg\": \"Aves/Larus californicus/2838488bdfedc19dc0093ec6081a21de.jpg\",\n  \"520097.jpg\": \"Aves/Larus californicus/ab9c4591696cad3f989a9467c6ea6893.jpg\",\n  \"520217.jpg\": \"Aves/Larus californicus/60cbfb11af64210aac5f2d97c5c522e4.jpg\",\n  \"520233.jpg\": \"Aves/Larus californicus/455bcf76154e41d4f3011c07030f0775.jpg\",\n  \"520234.jpg\": \"Aves/Larus californicus/435a8c57fa1d0bcbbf32936a0ed8c97a.jpg\",\n  \"520339.jpg\": \"Reptilia/Opheodrys vernalis/85443a887adf1c9207c57a443b0912ca.jpg\",\n  \"520343.jpg\": \"Reptilia/Opheodrys vernalis/b2069009d12ab76a9856505f0c9250ec.jpg\",\n  \"520345.jpg\": \"Reptilia/Sternotherus odoratus/db1b0b3595ba2fab2adac5af3183556f.jpg\",\n  \"520346.jpg\": \"Reptilia/Sternotherus odoratus/8dcaceca51ea32eb0c130862c01b30dc.jpg\",\n  \"520347.jpg\": \"Reptilia/Sternotherus odoratus/b717ef794eb10bb720b374ca4f644b9b.jpg\",\n  \"520349.jpg\": \"Reptilia/Sternotherus odoratus/9dbe2c73959912d12422485b2c167a9a.jpg\",\n  \"520353.jpg\": \"Reptilia/Sternotherus odoratus/e95e8da2ed88fccb66411ad8fc26b0f2.jpg\",\n  \"520363.jpg\": \"Reptilia/Sternotherus odoratus/694fcc3aeff0d551f3070240c2f58cae.jpg\",\n  \"520364.jpg\": \"Reptilia/Sternotherus odoratus/212fff9dc61b532764b7c3a75aeb47a8.jpg\",\n  \"520369.jpg\": \"Reptilia/Sternotherus odoratus/e6d2dc936bff26acaf2ae9cc73bda020.jpg\",\n  \"520376.jpg\": \"Reptilia/Sternotherus odoratus/a7eb55049fea84e1b9a375062c401f52.jpg\",\n  \"520408.jpg\": \"Mammalia/Neovison vison/f6bc23996ecdc4123cbad2d1a928026d.jpg\",\n  \"520409.jpg\": \"Mammalia/Neovison vison/c69056bae242f4e065e147bdf9fabd44.jpg\",\n  \"520411.jpg\": \"Mammalia/Neovison vison/e4bc2d54bce24a36cc2f4cef16e8bfb6.jpg\",\n  \"520420.jpg\": \"Mammalia/Neovison vison/7d29b052b8be1d7973be2cb644768dc1.jpg\",\n  \"520440.jpg\": \"Mammalia/Neovison vison/4bb9c079553037045e050da12b2e3f5a.jpg\",\n  \"520447.jpg\": \"Mammalia/Neovison vison/ceef30f5defd437089123978f40f961e.jpg\",\n  \"520629.jpg\": \"Aves/Passerculus sandwichensis/56c4eb3fc8dc3b5df90b322e55d8f1de.jpg\",\n  \"520784.jpg\": \"Aves/Passerculus sandwichensis/cb931a342e2be714bd3054944087d497.jpg\",\n  \"520916.jpg\": \"Aves/Passerculus sandwichensis/e71787cd257858c5d9d53454fac4605b.jpg\",\n  \"520955.jpg\": \"Arachnida/Argiope bruennichi/bc439119efdbfc969eae7278d4eaed34.jpg\",\n  \"520957.jpg\": \"Arachnida/Argiope bruennichi/77a26969de330ec09dce9fc31ae0a276.jpg\",\n  \"520960.jpg\": \"Arachnida/Argiope bruennichi/6da2fecfb7be997060b4abe8dc7d99fb.jpg\",\n  \"520961.jpg\": \"Arachnida/Argiope bruennichi/0d1032c19972465692429a7ae4d9c99d.jpg\",\n  \"521017.jpg\": \"Aves/Dumetella carolinensis/b44a7e74601659c4521878979d3a6d7e.jpg\",\n  \"521018.jpg\": \"Aves/Dumetella carolinensis/85447e4fb0cc845346f3d036cb4600d9.jpg\",\n  \"521140.jpg\": \"Aves/Dumetella carolinensis/2e8a5f762b653883d3d22008be989aed.jpg\",\n  \"521213.jpg\": \"Aves/Dumetella carolinensis/bbf9fa54aee7ad160cf8c7225e0ce2b3.jpg\",\n  \"521218.jpg\": \"Aves/Dumetella carolinensis/7ee319e48bef6b644b5ad9c2330e8be1.jpg\",\n  \"521430.jpg\": \"Mollusca/Ariolimax columbianus/5f5a82654fc13c7b4d4424c0cf72ccbf.jpg\",\n  \"521434.jpg\": \"Mollusca/Ariolimax columbianus/fc4f117bfdb4b3af4030e302cb3486f2.jpg\",\n  \"521439.jpg\": \"Mollusca/Ariolimax columbianus/00405d076f8e1f1f5d9a3683883eaf9f.jpg\",\n  \"521445.jpg\": \"Mollusca/Ariolimax columbianus/daa2649f70afb86ac60bdef23543bdd8.jpg\",\n  \"521466.jpg\": \"Mollusca/Ariolimax columbianus/c3d7f4ce0f6d85d130799529827586b5.jpg\",\n  \"521474.jpg\": \"Mollusca/Ariolimax columbianus/4069d240432d998916d5e9a93c7189d9.jpg\",\n  \"521476.jpg\": \"Mollusca/Ariolimax columbianus/c4aabbdb3c4a77c7dd00f95c3c9c2f8d.jpg\",\n  \"521477.jpg\": \"Mollusca/Ariolimax columbianus/294f114dc49c27facb580a820ce00f4e.jpg\",\n  \"521520.jpg\": \"Mammalia/Marmota monax/ba40e85e1fe66fe455f897d1620fda8a.jpg\",\n  \"521539.jpg\": \"Mammalia/Marmota monax/a0555432397646e6989cd17f9c80a06c.jpg\",\n  \"521557.jpg\": \"Mammalia/Marmota monax/43911503c2133f552a23b2456fb5d81b.jpg\",\n  \"521578.jpg\": \"Mammalia/Marmota monax/33d21c6353fafa07d3aa41a03ea14e86.jpg\",\n  \"52158.jpg\": \"Insecta/Melanchra adjuncta/414fe7534a986dba98c481e8e177c9ca.jpg\",\n  \"52159.jpg\": \"Insecta/Melanchra adjuncta/1b32621d2b098531ddb669f3ed5fac94.jpg\",\n  \"521594.jpg\": \"Mammalia/Marmota monax/5bda809a0891783f02a74d8091c4b5ea.jpg\",\n  \"521596.jpg\": \"Mammalia/Marmota monax/ff943e5de2550f9e563c9a6e3a840410.jpg\",\n  \"521597.jpg\": \"Mammalia/Marmota monax/43dd5cfac74dd60cf6335aed20d279fe.jpg\",\n  \"521612.jpg\": \"Insecta/Biston betularia/ecc8111da15ee2ac752794faf3123fc9.jpg\",\n  \"521623.jpg\": \"Reptilia/Phrynosoma hernandesi/1066d543c63de95fcde29a8c96bf882a.jpg\",\n  \"521624.jpg\": \"Reptilia/Phrynosoma hernandesi/cf7208dbd28eb70dfe42bee8e0e603a0.jpg\",\n  \"521638.jpg\": \"Reptilia/Phrynosoma hernandesi/66d8d215744b54c01073d54c9b30c699.jpg\",\n  \"521646.jpg\": \"Reptilia/Phrynosoma hernandesi/f3b275294052580ed251fb25ad8aaa0d.jpg\",\n  \"521650.jpg\": \"Reptilia/Phrynosoma hernandesi/f410fbb898d6d58750ea647508dd8cfc.jpg\",\n  \"521661.jpg\": \"Reptilia/Phrynosoma hernandesi/a1398535bbbc11ba3ee1df839a52b297.jpg\",\n  \"521666.jpg\": \"Reptilia/Phrynosoma hernandesi/34592e581abefdfa7565cf1d1ce6969f.jpg\",\n  \"52171.jpg\": \"Insecta/Melanchra adjuncta/35c1011ac71b31f6e6c683653d1736fa.jpg\",\n  \"52175.jpg\": \"Insecta/Celastrina neglecta/7aa9428ca07e9fcca10dcb64721f952f.jpg\",\n  \"52178.jpg\": \"Insecta/Celastrina neglecta/e3b442aa44dd1a3f5009217d28e4f98b.jpg\",\n  \"52179.jpg\": \"Insecta/Celastrina neglecta/f3b0e1a3e5fa2a80c61564acd9a014d0.jpg\",\n  \"52181.jpg\": \"Insecta/Celastrina neglecta/7ec4d38664846db743a8bc69dd7175c3.jpg\",\n  \"521828.jpg\": \"Insecta/Schinia florida/069aa875f6f008683766920d1e9d995a.jpg\",\n  \"521836.jpg\": \"Insecta/Schinia florida/8f66e9e615aa71f6847032774a154955.jpg\",\n  \"52184.jpg\": \"Insecta/Celastrina neglecta/c29fb0f41a9b9681873bcb3978cf7519.jpg\",\n  \"52185.jpg\": \"Insecta/Celastrina neglecta/ca36b5c6e12b1b40f7de35289ca06eb6.jpg\",\n  \"521853.jpg\": \"Aves/Phoenicurus phoenicurus/986b23ee287ad230a6366a0a46344911.jpg\",\n  \"521866.jpg\": \"Aves/Oenanthe oenanthe/c4992090bc974539894596930b64b073.jpg\",\n  \"521878.jpg\": \"Aves/Oenanthe oenanthe/0841cfeb7475869f1ec26b887549f4e0.jpg\",\n  \"52188.jpg\": \"Insecta/Celastrina neglecta/d57f40fc71c5b7aab6584d54bfd22516.jpg\",\n  \"521880.jpg\": \"Aves/Oenanthe oenanthe/34e7d38ef959627ce0757a159f303faa.jpg\",\n  \"521881.jpg\": \"Aves/Oenanthe oenanthe/c187610a815593ae0e4f4640e3f0b4ee.jpg\",\n  \"521882.jpg\": \"Aves/Oenanthe oenanthe/d6c0296b8695a0e68bca0045b1a52a8f.jpg\",\n  \"521894.jpg\": \"Aves/Oenanthe oenanthe/156b122623506b07602772ad3ad76873.jpg\",\n  \"521895.jpg\": \"Aves/Oenanthe oenanthe/50c1b2134c0deb23237fdec83092d893.jpg\",\n  \"52190.jpg\": \"Insecta/Celastrina neglecta/c26219c62e02994d3c3ccf98b229a9d8.jpg\",\n  \"521915.jpg\": \"Aves/Terathopius ecaudatus/5256ea3c44c9847ac9891d43d02b0009.jpg\",\n  \"521916.jpg\": \"Aves/Terathopius ecaudatus/45369a535fd5e55c81f75b413facf54c.jpg\",\n  \"521922.jpg\": \"Amphibia/Anaxyrus terrestris/39cf6f96e0a127bf14c4cf172cb1243a.jpg\",\n  \"521927.jpg\": \"Amphibia/Anaxyrus terrestris/5d7f955094aaf491fbf0088c204a9200.jpg\",\n  \"521944.jpg\": \"Amphibia/Anaxyrus terrestris/a3e7f4e0b38961003da75ffe8301abf8.jpg\",\n  \"52195.jpg\": \"Insecta/Celastrina neglecta/545bc6014e98cb7b9038f045055622c8.jpg\",\n  \"52196.jpg\": \"Insecta/Celastrina neglecta/142410bc5031712a7880da08bf07fb3d.jpg\",\n  \"521989.jpg\": \"Amphibia/Batrachoseps attenuatus/b10b08b88ee74145f621c376f236bb95.jpg\",\n  \"521992.jpg\": \"Amphibia/Batrachoseps attenuatus/3cc0c56376e0bcfa4a77ee9a46c865d3.jpg\",\n  \"522031.jpg\": \"Amphibia/Batrachoseps attenuatus/d1ac48da0c8a5db04efb947676081b57.jpg\",\n  \"52209.jpg\": \"Insecta/Celastrina neglecta/d0d396826dc37424ad5ba3fd0f18df32.jpg\",\n  \"522137.jpg\": \"Amphibia/Batrachoseps attenuatus/d80301db4e1cc977b768f7e15cd846e8.jpg\",\n  \"522188.jpg\": \"Reptilia/Nerodia sipedon/10aef71872a71a7a1cc862c62c5a661b.jpg\",\n  \"522189.jpg\": \"Reptilia/Nerodia sipedon/223f8c7d2feb15b4ea0b8451a32c89a2.jpg\",\n  \"522190.jpg\": \"Reptilia/Nerodia sipedon/8f820fb7ff240d3141b4d3b1402ec3ca.jpg\",\n  \"522196.jpg\": \"Reptilia/Nerodia sipedon/6a20eeae8cde77b491a2cb6848611b8d.jpg\",\n  \"5222.jpg\": \"Aves/Tyrannus crassirostris/d4ced970288b34e0693626502057773a.jpg\",\n  \"52220.jpg\": \"Insecta/Celastrina neglecta/00c6a79f0a3b5b4903eadc01d87dc6ca.jpg\",\n  \"522210.jpg\": \"Reptilia/Nerodia sipedon/2132fa3cbe197af0a3f8d17554a49ba9.jpg\",\n  \"52222.jpg\": \"Insecta/Celastrina neglecta/0edf292f04464e05acac5b095a6aaa03.jpg\",\n  \"52223.jpg\": \"Insecta/Celastrina neglecta/14db799277bb3daa7a109d519e081c3a.jpg\",\n  \"522232.jpg\": \"Reptilia/Nerodia sipedon/6ab5ed23d3b6b5e8409d79e76c27f6d6.jpg\",\n  \"522242.jpg\": \"Reptilia/Nerodia sipedon/615b9e1790e8397f2c6b6a64b5381ac2.jpg\",\n  \"522260.jpg\": \"Reptilia/Nerodia sipedon/35df0bf467e03d499e6f89b24da0e353.jpg\",\n  \"522269.jpg\": \"Reptilia/Nerodia sipedon/665973a358e9cac382c453cb4cf23215.jpg\",\n  \"52227.jpg\": \"Insecta/Celastrina neglecta/b372a9e9ee37b27af77e12faa867d202.jpg\",\n  \"522273.jpg\": \"Insecta/Microcentrum rhombifolium/ee15962038f142777afceff2ac490b1f.jpg\",\n  \"522277.jpg\": \"Insecta/Microcentrum rhombifolium/5d6e62ec994da815e008e63881322644.jpg\",\n  \"522278.jpg\": \"Insecta/Microcentrum rhombifolium/5c671299f485916e05760178ef83e1d3.jpg\",\n  \"52229.jpg\": \"Insecta/Celastrina neglecta/85278acee0eae0b0d4033a1072dfebf6.jpg\",\n  \"522303.jpg\": \"Insecta/Lycomorpha pholus/5b61beccb507977c036045e429ddaecc.jpg\",\n  \"522308.jpg\": \"Insecta/Lycomorpha pholus/55ff34325b98e4bf8ca10223b3847dfb.jpg\",\n  \"522309.jpg\": \"Insecta/Lycomorpha pholus/57b9d42f6ccc80749d98863822db0f1a.jpg\",\n  \"522310.jpg\": \"Insecta/Lycomorpha pholus/2c5990a8dcf921198bfdcca6dfd98124.jpg\",\n  \"52234.jpg\": \"Insecta/Celastrina neglecta/d011615ce6f4a2c965ac24422050c534.jpg\",\n  \"522341.jpg\": \"Insecta/Argia sedula/fccff391607b956e16a11e9ccd541e96.jpg\",\n  \"52236.jpg\": \"Insecta/Celastrina neglecta/32371c5666445fa3cb0eab694ed34d92.jpg\",\n  \"52238.jpg\": \"Insecta/Celastrina neglecta/48796895988aeb2b5061325696996c46.jpg\",\n  \"522405.jpg\": \"Insecta/Argia sedula/0b36f223e13e1fad1cb61ff3a789e55e.jpg\",\n  \"52241.jpg\": \"Insecta/Celastrina neglecta/a7d903c30c064382b50ac635f6eb8f88.jpg\",\n  \"52242.jpg\": \"Insecta/Celastrina neglecta/a5007794ada601ca0da846721bc657bb.jpg\",\n  \"52243.jpg\": \"Insecta/Celastrina neglecta/8fd0a8dc2bb9a1daa3f7ebd087d468be.jpg\",\n  \"522430.jpg\": \"Insecta/Argia sedula/310de8a7b7ee48974d7b7d37b971add0.jpg\",\n  \"522453.jpg\": \"Insecta/Argia sedula/cd0d123de9bfda5ee8a9a661e7ff26fa.jpg\",\n  \"52250.jpg\": \"Insecta/Celastrina neglecta/d750767b46ae4375fb0bc1e4776b9f19.jpg\",\n  \"522552.jpg\": \"Reptilia/Aspidoscelis hyperythra/ca5fe7deb2ec928a43ab20b5cc2a356a.jpg\",\n  \"522652.jpg\": \"Insecta/Papilio multicaudata/56fb37c4ac3e05f019168e377b5af93d.jpg\",\n  \"522663.jpg\": \"Insecta/Papilio multicaudata/49975ca8adeb475b4e635afccebb4a35.jpg\",\n  \"522670.jpg\": \"Insecta/Papilio multicaudata/c9771601ee2295278df4f993960aa49b.jpg\",\n  \"522671.jpg\": \"Insecta/Papilio multicaudata/8ef31f6fd46076161ebcd76f09d68c1f.jpg\",\n  \"522672.jpg\": \"Insecta/Papilio multicaudata/f003a4c812c507913152c90df2aa5bf4.jpg\",\n  \"522674.jpg\": \"Insecta/Papilio multicaudata/afedb516474f31a5952a1ddcf67cf423.jpg\",\n  \"522680.jpg\": \"Insecta/Papilio multicaudata/00445b1ad131c44e11e9b5bcbeb5c282.jpg\",\n  \"522695.jpg\": \"Insecta/Campaea perlata/1a6e17af245865e2a192ff860cb01dac.jpg\",\n  \"522697.jpg\": \"Insecta/Campaea perlata/65d7d1df624dd41fcd9c64b4de218976.jpg\",\n  \"522702.jpg\": \"Insecta/Campaea perlata/ab944195b03a5cd854bff4831088fcef.jpg\",\n  \"522713.jpg\": \"Insecta/Campaea perlata/d2bcd41f6d75d20c84a2df34b4d34916.jpg\",\n  \"522714.jpg\": \"Insecta/Campaea perlata/31c3dd0d5745ce94ce160bbb332684b3.jpg\",\n  \"522719.jpg\": \"Insecta/Campaea perlata/ff9f3185eea93bd71235d6804b313af8.jpg\",\n  \"52272.jpg\": \"Insecta/Choristoneura rosaceana/8f66eafb3f0f444059b92c3908ac1b7d.jpg\",\n  \"522732.jpg\": \"Insecta/Campaea perlata/56ffff52def3432dcd430f55ab082625.jpg\",\n  \"522737.jpg\": \"Insecta/Campaea perlata/63893ca88ba2d52258a1f0ae88b98e46.jpg\",\n  \"52274.jpg\": \"Insecta/Choristoneura rosaceana/296286bd7dd6d91946809444dd0896a3.jpg\",\n  \"522752.jpg\": \"Amphibia/Anaxyrus debilis/245874f26446d5e1c80ed0c6a458eaab.jpg\",\n  \"52276.jpg\": \"Insecta/Choristoneura rosaceana/ec0c7086f863bf8335a545d7b2d8463b.jpg\",\n  \"522762.jpg\": \"Amphibia/Anaxyrus debilis/1614c006f11b212c3552845fda033fab.jpg\",\n  \"52281.jpg\": \"Insecta/Choristoneura rosaceana/11ab97d3092cb0605b02c6a00818e0d5.jpg\",\n  \"522815.jpg\": \"Insecta/Grammia ornata/ad16ccb6c94c0b8ae9fb86659d80d5e9.jpg\",\n  \"522822.jpg\": \"Insecta/Grammia ornata/176c81301f089731775c03205dc74447.jpg\",\n  \"522829.jpg\": \"Insecta/Marpesia petreus/2e90d2144df26940aa55f39bce8bbd95.jpg\",\n  \"522830.jpg\": \"Insecta/Marpesia petreus/9bf238eb29ba40e3aca96239ee169d8a.jpg\",\n  \"522831.jpg\": \"Insecta/Marpesia petreus/37f7cdb0d1914dd50a4fab6938a50470.jpg\",\n  \"522832.jpg\": \"Insecta/Marpesia petreus/8ef46755ee331b9a1d2eb7fe2766ac35.jpg\",\n  \"522838.jpg\": \"Insecta/Marpesia petreus/5a33c8561c2a754db3b05f959e9a8262.jpg\",\n  \"522840.jpg\": \"Insecta/Marpesia petreus/f873c7b5f00fcdcbe90d40049c8955b7.jpg\",\n  \"522841.jpg\": \"Insecta/Marpesia petreus/9796ad7ba0dd2edb89591265ac74e534.jpg\",\n  \"52285.jpg\": \"Insecta/Choristoneura rosaceana/913953aab73515ba4843decd332e2111.jpg\",\n  \"522860.jpg\": \"Insecta/Marpesia petreus/a06b923432937859f48818ae02232ad3.jpg\",\n  \"522864.jpg\": \"Insecta/Marpesia petreus/ac496e0599f3b936ed218dedefc69cb3.jpg\",\n  \"522889.jpg\": \"Aves/Contopus pertinax/e4af2041459db066453294eaa0a4f1f1.jpg\",\n  \"522892.jpg\": \"Aves/Contopus pertinax/4ba7364b6faa704be1244163ff89b89c.jpg\",\n  \"522893.jpg\": \"Aves/Contopus pertinax/37a97011c5421ee754386967a91f0d58.jpg\",\n  \"522896.jpg\": \"Insecta/Leptoglossus clypealis/984f2864e9d0aa7cdeda7685b0690030.jpg\",\n  \"522948.jpg\": \"Aves/Patagioenas fasciata/2ad64c303c517cf07ea002b4f79815bd.jpg\",\n  \"523.jpg\": \"Mammalia/Ursus americanus/fe6c5ba1cc5153dd01cd859ac367e851.jpg\",\n  \"5230.jpg\": \"Aves/Tyrannus crassirostris/2a6b1e6128ba63c04adc840029092e42.jpg\",\n  \"523004.jpg\": \"Aves/Patagioenas fasciata/79c0942c4caa9924de98c8c1cb81fa61.jpg\",\n  \"523005.jpg\": \"Aves/Patagioenas fasciata/a56254a2adf932e328523cdb895d7da2.jpg\",\n  \"523008.jpg\": \"Aves/Patagioenas fasciata/10f2d786d7f2c5d7ceaf71fe0131c329.jpg\",\n  \"523012.jpg\": \"Aves/Cyanocorax yucatanicus/1e7ec373c01e9f8fdf480a4f4553b990.jpg\",\n  \"523015.jpg\": \"Aves/Cyanocorax yucatanicus/6fd3cf3dc09b211314195cd16cb72318.jpg\",\n  \"523027.jpg\": \"Aves/Cyanocorax yucatanicus/2401aa1607c183f07cce162179cca9fd.jpg\",\n  \"523028.jpg\": \"Aves/Cyanocorax yucatanicus/9fc1d3639aa8ffbe2204d956f637907a.jpg\",\n  \"523079.jpg\": \"Insecta/Echinargus isola/99bacf30ce0131e5956e848a79e91862.jpg\",\n  \"523087.jpg\": \"Insecta/Echinargus isola/e8854421c8706f6b8a095e2fb8583fa7.jpg\",\n  \"523098.jpg\": \"Insecta/Echinargus isola/1b6ad9f5ad848482552b7821900dbe9d.jpg\",\n  \"5231.jpg\": \"Aves/Tyrannus crassirostris/ad51bf43d175d41a45e51e8124306cb3.jpg\",\n  \"523106.jpg\": \"Insecta/Echinargus isola/6f2615bd2d5e821cdbc3fb625719a940.jpg\",\n  \"523111.jpg\": \"Insecta/Echinargus isola/476d7271a716cfa0f791929e7362e0ad.jpg\",\n  \"523120.jpg\": \"Insecta/Echinargus isola/04a4802e6551bafcf3f93d5d05dd276c.jpg\",\n  \"523130.jpg\": \"Insecta/Echinargus isola/2c1c4a7e9bdf5c25207ebe787bc397c4.jpg\",\n  \"523167.jpg\": \"Animalia/Ligia occidentalis/ec96bd6863eea1fcbed7a5b9bb2b7997.jpg\",\n  \"523168.jpg\": \"Animalia/Ligia occidentalis/4278ad45cd399f35b60e429e60a294f8.jpg\",\n  \"523169.jpg\": \"Animalia/Ligia occidentalis/438340927d8f6d3f182864b05bd3fb95.jpg\",\n  \"523175.jpg\": \"Animalia/Ligia occidentalis/eb5e071681a8e564da728139aaa363b9.jpg\",\n  \"523176.jpg\": \"Animalia/Ligia occidentalis/ec13414024300c465ee551096eb69e51.jpg\",\n  \"523182.jpg\": \"Animalia/Ligia occidentalis/7304b1c6164820fd07202b5a2f19748c.jpg\",\n  \"523183.jpg\": \"Animalia/Ligia occidentalis/867f757716a431ccf5890b1334a3a9b0.jpg\",\n  \"523192.jpg\": \"Aves/Pica hudsonia/57d6cccdf56090ae9acd05cca65c8a0d.jpg\",\n  \"5232.jpg\": \"Aves/Tyrannus crassirostris/53440189c493acb0a554501f26ceb99d.jpg\",\n  \"523204.jpg\": \"Aves/Pica hudsonia/4ac4208992f3c59efc90255379138640.jpg\",\n  \"523224.jpg\": \"Aves/Pica hudsonia/ce1be0207a6a6c5a1ab548ac8e91d4cf.jpg\",\n  \"523232.jpg\": \"Aves/Pica hudsonia/9769e73c22f0b5196deda177760c8244.jpg\",\n  \"523235.jpg\": \"Aves/Pica hudsonia/09ad811b03af4f08565e13a3b7cade76.jpg\",\n  \"523248.jpg\": \"Aves/Pica hudsonia/77bb217f55bef747a3536453bc20a5db.jpg\",\n  \"523256.jpg\": \"Aves/Pica hudsonia/3411f03b527fe0d2243a56ccebe9cc1b.jpg\",\n  \"523277.jpg\": \"Insecta/Hamadryas februa/da362a0c1db724e6fa6c84571d73d52b.jpg\",\n  \"52328.jpg\": \"Insecta/Orgyia antiqua/de88d020795d9ef7a474239040213ddb.jpg\",\n  \"5233.jpg\": \"Aves/Tyrannus crassirostris/743b967a9e66995715b21d854a0923f2.jpg\",\n  \"52333.jpg\": \"Insecta/Orgyia antiqua/b87b658feb35ba7366b0875bd964ba2b.jpg\",\n  \"523335.jpg\": \"Amphibia/Lithobates sylvaticus/ede3461a63a894b14f03e8aa53cdaa09.jpg\",\n  \"523336.jpg\": \"Amphibia/Lithobates sylvaticus/ac3f2722f45080192c0da869f2b2ab24.jpg\",\n  \"523339.jpg\": \"Amphibia/Lithobates sylvaticus/4e4e4244024c01275f0e010abcdf4709.jpg\",\n  \"52334.jpg\": \"Insecta/Orgyia antiqua/3ff37db279161b66bbc99e51b64d242e.jpg\",\n  \"523358.jpg\": \"Amphibia/Lithobates sylvaticus/9a8d0a4d1604e6d4d1173b0e138ffb29.jpg\",\n  \"52337.jpg\": \"Insecta/Orgyia antiqua/7218b1378c95cef5210753673205c0e0.jpg\",\n  \"52341.jpg\": \"Insecta/Phyciodes pulchella/a6c0e8f055bace46449dfd6425bd7a33.jpg\",\n  \"52342.jpg\": \"Insecta/Phyciodes pulchella/37a7a48389273e22414eabe0e069e4f3.jpg\",\n  \"523444.jpg\": \"Mollusca/Corbicula fluminea/590731f24053f304cf4f8847e3a0b3b1.jpg\",\n  \"523448.jpg\": \"Mollusca/Corbicula fluminea/bd4d7544f4fa867d7cb5f4812b121e15.jpg\",\n  \"523468.jpg\": \"Mollusca/Corbicula fluminea/e10f84d1b423c0d2d22affe3fb90834e.jpg\",\n  \"523472.jpg\": \"Insecta/Euborellia annulipes/3643a3a3a699445d6a3e30d8e6663a55.jpg\",\n  \"523476.jpg\": \"Insecta/Euborellia annulipes/522e7c938008b8db0198d67f7a5bd5fc.jpg\",\n  \"523478.jpg\": \"Insecta/Euborellia annulipes/b4a8167cce48448a8b62565f9a3c3d7d.jpg\",\n  \"523481.jpg\": \"Insecta/Phyciodes mylitta/a2d8e51f082f614631aa54301262951d.jpg\",\n  \"52349.jpg\": \"Insecta/Phyciodes pulchella/9fa0c5a9b0c99bfbefb28c3876aad18d.jpg\",\n  \"523501.jpg\": \"Insecta/Phyciodes mylitta/06bc5bd1dc036819c3578d8745aaa276.jpg\",\n  \"523517.jpg\": \"Insecta/Phyciodes mylitta/8bf2a3dbe25b941ad80d1fe300508905.jpg\",\n  \"523558.jpg\": \"Insecta/Phyciodes mylitta/34bdd499f0bfe2e30c274866a3872440.jpg\",\n  \"52356.jpg\": \"Insecta/Phyciodes pulchella/6b91b8fb7e3f9934c429ae1439d71c1b.jpg\",\n  \"52357.jpg\": \"Insecta/Phyciodes pulchella/0160d29816423da11015fc14560f886b.jpg\",\n  \"523575.jpg\": \"Insecta/Phyciodes mylitta/8f0820bca1f4a2acfc515ed300ca5619.jpg\",\n  \"52358.jpg\": \"Insecta/Phyciodes pulchella/be0f72e00e78e34e4b09b774763a3181.jpg\",\n  \"523583.jpg\": \"Insecta/Phyciodes mylitta/85808111cb28aadda310b7ffc252de17.jpg\",\n  \"523586.jpg\": \"Insecta/Phyciodes mylitta/1948beaa59d773a083ef018f17ba01b0.jpg\",\n  \"523603.jpg\": \"Insecta/Anisota senatoria/16f949d5a4044d83811758f7b04da9e4.jpg\",\n  \"523607.jpg\": \"Insecta/Anisota senatoria/253d380dde46d3444a00baf482160bc8.jpg\",\n  \"523608.jpg\": \"Insecta/Anisota senatoria/62b356e0b32343e70eea5d7e5c859fbd.jpg\",\n  \"523652.jpg\": \"Mollusca/Deroceras reticulatum/d6962de2600f5418261ab5e9306e88d9.jpg\",\n  \"523655.jpg\": \"Mollusca/Deroceras reticulatum/24ad35e39e35150eb0759cf4063bdda0.jpg\",\n  \"523677.jpg\": \"Mollusca/Deroceras reticulatum/bb62ca6445707d1230fc6b2bc70c9829.jpg\",\n  \"523681.jpg\": \"Mollusca/Deroceras reticulatum/da7903eccc7eb94a818e72e7ff0baac9.jpg\",\n  \"523682.jpg\": \"Mollusca/Deroceras reticulatum/7b9811fdd411e11e119f11edb8d48c00.jpg\",\n  \"523691.jpg\": \"Insecta/Antheraea polyphemus/3b6016c7e08b74f531b568abc0223d11.jpg\",\n  \"523709.jpg\": \"Insecta/Antheraea polyphemus/c0f7e03c6b7e40c206e1b814db80f99a.jpg\",\n  \"523723.jpg\": \"Insecta/Antheraea polyphemus/27dae6b2bfe8d623a6a3ff46c6baff61.jpg\",\n  \"523733.jpg\": \"Insecta/Antheraea polyphemus/04e74330678f2816de21dcca62317a20.jpg\",\n  \"523740.jpg\": \"Insecta/Antheraea polyphemus/f8cae2d3f74cd09fb1eff5b97fa0138c.jpg\",\n  \"523754.jpg\": \"Insecta/Antheraea polyphemus/8c3cefdb82f4c49955e0b9a9d9c122b3.jpg\",\n  \"523770.jpg\": \"Insecta/Antheraea polyphemus/c2f8fbd276c21ab83c2d11ce8eaaa282.jpg\",\n  \"523772.jpg\": \"Insecta/Antheraea polyphemus/e35cfebdd65cb61052a7e5457bd3340c.jpg\",\n  \"523773.jpg\": \"Insecta/Antheraea polyphemus/536ef1c2bf45a8ddadb15fec2e5c06aa.jpg\",\n  \"523777.jpg\": \"Insecta/Antheraea polyphemus/338ef9da2e7e309c5b1bc94cc670772b.jpg\",\n  \"523782.jpg\": \"Insecta/Isa textula/bed37a6c8079e71b723d89da720b0653.jpg\",\n  \"523786.jpg\": \"Insecta/Isa textula/5c5a9ed90f5c91ce6bb8e4507e88847e.jpg\",\n  \"523795.jpg\": \"Mammalia/Cynomys ludovicianus/7f8451c3208cde98cd3ef553e40f6545.jpg\",\n  \"523807.jpg\": \"Mammalia/Cynomys ludovicianus/ceb700ef545e3f704ea2455232735007.jpg\",\n  \"52381.jpg\": \"Insecta/Phyciodes pulchella/d074b3ce72e743d087d2dfa54c40084d.jpg\",\n  \"523810.jpg\": \"Mammalia/Cynomys ludovicianus/5f413fae320a74917a6d9a62cc75ec0c.jpg\",\n  \"523811.jpg\": \"Mammalia/Cynomys ludovicianus/862a241a3d5a6e5fd59f24963b4fb974.jpg\",\n  \"523812.jpg\": \"Mammalia/Cynomys ludovicianus/82c17f5ff296b2c27862cc84ab23d9e2.jpg\",\n  \"523824.jpg\": \"Mammalia/Cynomys ludovicianus/cfc7c7a2e3fe67d3be1775510116c7b6.jpg\",\n  \"523828.jpg\": \"Mammalia/Cynomys ludovicianus/9b352c6e30d5bc86f4a314563431f468.jpg\",\n  \"523839.jpg\": \"Mammalia/Cynomys ludovicianus/25ffc20d37084a0f229dc59b07a77914.jpg\",\n  \"52385.jpg\": \"Insecta/Phyciodes pulchella/3040c36e810880bc7c6fb4ad1eb45736.jpg\",\n  \"523858.jpg\": \"Reptilia/Bothriechis schlegelii/59e3bfd05daa4325a8590d58868deff8.jpg\",\n  \"52386.jpg\": \"Insecta/Phyciodes pulchella/4dd61e07a64375815fe5f41df2080880.jpg\",\n  \"523864.jpg\": \"Reptilia/Bothriechis schlegelii/0c50284f9e6e56140cbc79254032f274.jpg\",\n  \"523867.jpg\": \"Aves/Crax rubra/1f50627ab00b9d4ba464f6ecb61ff29d.jpg\",\n  \"52387.jpg\": \"Insecta/Phyciodes pulchella/887993a82be47b2b51d723ee16fadd5e.jpg\",\n  \"523876.jpg\": \"Aves/Crax rubra/a0fac0192331819b87da64e3698f4a90.jpg\",\n  \"52388.jpg\": \"Insecta/Phyciodes pulchella/c3033b2f3eeba64c27eb8ff58221f804.jpg\",\n  \"523880.jpg\": \"Aves/Ramphocelus passerinii/2edb3e5aac2c374fee4a85439160c6fc.jpg\",\n  \"52389.jpg\": \"Insecta/Phyciodes pulchella/5ff01502e2e318941413a509605c0166.jpg\",\n  \"523905.jpg\": \"Aves/Todiramphus chloris/ecbf200022323b949acaa04dd05d8d05.jpg\",\n  \"523907.jpg\": \"Aves/Todiramphus chloris/6f695b447d3663b0305bb06549c41e37.jpg\",\n  \"52394.jpg\": \"Insecta/Phyciodes pulchella/dab2a9a356186b3a81fd98cff5573736.jpg\",\n  \"52395.jpg\": \"Insecta/Phyciodes pulchella/361de922144cb0d11842b76b6fbc91d8.jpg\",\n  \"52396.jpg\": \"Insecta/Phyciodes pulchella/d563ee47aa7c37ef3e1b7ab8967211be.jpg\",\n  \"52402.jpg\": \"Insecta/Phyciodes pulchella/5d20836b1064b84f8972688249ebd81e.jpg\",\n  \"524040.jpg\": \"Aves/Junco hyemalis hyemalis/0860554b2fd08ee386f62a84ec083e93.jpg\",\n  \"524041.jpg\": \"Aves/Junco hyemalis hyemalis/d31e6e78766c5f67ce8e203af2811edc.jpg\",\n  \"524042.jpg\": \"Aves/Junco hyemalis hyemalis/63fe59debdf9b4d72a13516e1e04ba21.jpg\",\n  \"524052.jpg\": \"Aves/Junco hyemalis hyemalis/e80dcfbb9ebe12cb56ac6789ae5d2eb1.jpg\",\n  \"524053.jpg\": \"Aves/Junco hyemalis hyemalis/0483d9ae4e8dc2372c9f9dcbbba81493.jpg\",\n  \"524055.jpg\": \"Aves/Junco hyemalis hyemalis/3d478caaac9b545f5c8b759d979102bd.jpg\",\n  \"524059.jpg\": \"Aves/Junco hyemalis hyemalis/ad5d5c432a0a98ebda3124c912c6a7e7.jpg\",\n  \"52406.jpg\": \"Insecta/Phyciodes pulchella/3aec5fb033c9fc43e82466311b6c9c95.jpg\",\n  \"524060.jpg\": \"Aves/Junco hyemalis hyemalis/2f765ceb02bab345a8197dab8c4e3142.jpg\",\n  \"524061.jpg\": \"Aves/Junco hyemalis hyemalis/cd0e1cefdb7f65431088ee0379ab8788.jpg\",\n  \"524062.jpg\": \"Aves/Junco hyemalis hyemalis/c831709dd30c7815cd41964898b551d3.jpg\",\n  \"52407.jpg\": \"Insecta/Phyciodes pulchella/966a931ea8e85d2a22f77a63951c7d09.jpg\",\n  \"524181.jpg\": \"Mollusca/Theba pisana/86846444c122b50d65488e273d22197b.jpg\",\n  \"524193.jpg\": \"Insecta/Chrysolina bankii/fdd9e64dbe1b4629a17fc7e202ba1974.jpg\",\n  \"524198.jpg\": \"Aves/Emberiza citrinella/c8912ea4aff4d53ddf0522cecd8d765a.jpg\",\n  \"52420.jpg\": \"Insecta/Phyciodes pulchella/a1653796ae47d08b7eab0ea07d3aa7d6.jpg\",\n  \"524204.jpg\": \"Aves/Emberiza citrinella/386fda134b14da3533bd8556f6ae41da.jpg\",\n  \"524205.jpg\": \"Aves/Emberiza citrinella/33b179a3abe05a542d887715f2e4c339.jpg\",\n  \"524218.jpg\": \"Aves/Emberiza citrinella/423569da21c5d52c7da608173c1cd997.jpg\",\n  \"524240.jpg\": \"Aves/Emberiza citrinella/82874c2d5252183ff348b023076edbed.jpg\",\n  \"524252.jpg\": \"Aves/Emberiza citrinella/b6cf7dc157c8e0292cb9db3ea7cd63d9.jpg\",\n  \"524253.jpg\": \"Aves/Emberiza citrinella/09c6c36ecc33c6042d1a96c3670dc63f.jpg\",\n  \"524260.jpg\": \"Insecta/Scantius aegyptius/e35fcf95f300d3151f52fc472947bcea.jpg\",\n  \"524284.jpg\": \"Insecta/Prosapia bicincta/d0e4bb44d766d58c2a3b55ffb75758a0.jpg\",\n  \"524288.jpg\": \"Insecta/Prosapia bicincta/0c0b75e11487fe51328eb17960455060.jpg\",\n  \"524291.jpg\": \"Insecta/Prosapia bicincta/0ee1afeead98416c56d459c8a6f49ad2.jpg\",\n  \"524292.jpg\": \"Insecta/Prosapia bicincta/333c6399fc3d39cc3fb763cbddcc57dd.jpg\",\n  \"524297.jpg\": \"Insecta/Prosapia bicincta/18660d1fa1c13ac9bad15c9bb57f2af1.jpg\",\n  \"524298.jpg\": \"Insecta/Prosapia bicincta/f374e29089ddf6b746d416512fbdcfe5.jpg\",\n  \"524301.jpg\": \"Insecta/Prosapia bicincta/73c73cebad6c41059f52c29f6344c4c3.jpg\",\n  \"524302.jpg\": \"Insecta/Prosapia bicincta/3dd1484112a8b9b06d3400724fe67e5f.jpg\",\n  \"524303.jpg\": \"Insecta/Desmia funeralis/00c252e389e54b5e8a6a7b58faead493.jpg\",\n  \"524309.jpg\": \"Insecta/Desmia funeralis/e3fd5eac9d5b6b6efdcc4a6a9b207efa.jpg\",\n  \"5244.jpg\": \"Amphibia/Plethodon cinereus/8ed22b29e13c97aae739bbdf573fd0e3.jpg\",\n  \"52458.jpg\": \"Aves/Coereba flaveola/bdd237d428d560c628d5bfdf00d90593.jpg\",\n  \"52459.jpg\": \"Aves/Coereba flaveola/1a49190af7869fe32e4598c1c432af78.jpg\",\n  \"52460.jpg\": \"Aves/Coereba flaveola/b7099b601e9dac9dfd44c8ce92035c19.jpg\",\n  \"52461.jpg\": \"Aves/Coereba flaveola/623c841619b5ee00c1d4f5eb69e631c2.jpg\",\n  \"524617.jpg\": \"Aves/Corvus brachyrhynchos/59f23ace035ac4bc3525beffffeadb3c.jpg\",\n  \"524643.jpg\": \"Aves/Corvus brachyrhynchos/65f8ddedd40c2d70b6cd2f29617964ed.jpg\",\n  \"524649.jpg\": \"Aves/Corvus brachyrhynchos/6ccd81d7c599699629cd145d5b35059d.jpg\",\n  \"52472.jpg\": \"Aves/Coereba flaveola/9fa74f78d42356ea3b085c0cde94365a.jpg\",\n  \"52473.jpg\": \"Aves/Coereba flaveola/06853a9902df8a0f0f96a179baecd82b.jpg\",\n  \"52474.jpg\": \"Aves/Coereba flaveola/8183668d8e14a5fb8103e32348338a50.jpg\",\n  \"52475.jpg\": \"Aves/Coereba flaveola/f308b74d9528420c94434ba4f559a59b.jpg\",\n  \"52480.jpg\": \"Aves/Coereba flaveola/4e4b7872a239940901141aabab7bf00e.jpg\",\n  \"52481.jpg\": \"Aves/Coereba flaveola/f4a0d9a6e12ce1fb6effdd02384b35d5.jpg\",\n  \"52482.jpg\": \"Aves/Coereba flaveola/f538e7594c49cfae040e11dacfedc6ed.jpg\",\n  \"524850.jpg\": \"Aves/Corvus brachyrhynchos/70dea177e9839c78c912097e9873cf5b.jpg\",\n  \"524863.jpg\": \"Aves/Corvus brachyrhynchos/7c8ba54fda55fd205273e6637463d88b.jpg\",\n  \"52487.jpg\": \"Aves/Coereba flaveola/b15e320d0af29bc11ad7bbe745dda4d3.jpg\",\n  \"52488.jpg\": \"Aves/Coereba flaveola/cc5cb95ed7fddfa055c09d51e5523918.jpg\",\n  \"5249.jpg\": \"Amphibia/Plethodon cinereus/8aa862f26b0425f284596837393acfde.jpg\",\n  \"52493.jpg\": \"Aves/Coereba flaveola/f6d8897693413a05625d28f2c586bea3.jpg\",\n  \"52494.jpg\": \"Aves/Coereba flaveola/33b87f4479b32cf992bbe7445befb46e.jpg\",\n  \"52496.jpg\": \"Aves/Coereba flaveola/d4775d58f41d4140ea84aa9643b49537.jpg\",\n  \"52498.jpg\": \"Aves/Coereba flaveola/b11e61c05649f0d00a173e7df4df8594.jpg\",\n  \"524989.jpg\": \"Aves/Charadrius wilsonia/39d046a678043ca4d22da42391beb8be.jpg\",\n  \"52499.jpg\": \"Aves/Coereba flaveola/052fc1322fd2deaabcad5ce2dc9d994e.jpg\",\n  \"524997.jpg\": \"Aves/Charadrius wilsonia/7cf24fc91c84cf574a7465b74bfaa84b.jpg\",\n  \"524999.jpg\": \"Aves/Charadrius wilsonia/a8a90846eeb7138e6e8b49ac732faf4a.jpg\",\n  \"525000.jpg\": \"Aves/Charadrius wilsonia/f5d0e6a49d01005c55e2887060979a4b.jpg\",\n  \"525001.jpg\": \"Aves/Charadrius wilsonia/f50d177b9762876414a9830563fe4875.jpg\",\n  \"525009.jpg\": \"Aves/Charadrius wilsonia/812775c33d9ad32a2e26e33cf89f5983.jpg\",\n  \"52501.jpg\": \"Insecta/Cicindela oregona/204a9f2978e038d38ae746ee80f09a8a.jpg\",\n  \"525012.jpg\": \"Aves/Charadrius wilsonia/0c65bd1c360f8a245e53ff609eb00cf2.jpg\",\n  \"525013.jpg\": \"Insecta/Euphydryas phaeton/d244b169c3922d6de429a64063f801d6.jpg\",\n  \"525014.jpg\": \"Insecta/Euphydryas phaeton/ecf3e98a94c38a42292f30328036554c.jpg\",\n  \"525017.jpg\": \"Insecta/Euphydryas phaeton/1ec52c738a4032eacb10f5561ef33c3c.jpg\",\n  \"525019.jpg\": \"Insecta/Euphydryas phaeton/8a6f6b8ee1ad2fa63c70043c6f13ea30.jpg\",\n  \"525022.jpg\": \"Insecta/Euphydryas phaeton/77e3623245f6c654ec55b7ade9753f3a.jpg\",\n  \"525023.jpg\": \"Insecta/Euphydryas phaeton/d850e8bc7fd688e03004dea5dc8701fe.jpg\",\n  \"525034.jpg\": \"Insecta/Euphydryas phaeton/57922570716088517bac4d2b1d50ac9e.jpg\",\n  \"525039.jpg\": \"Insecta/Euphydryas phaeton/87fe519883a4ab0dc17520969c9db92f.jpg\",\n  \"525045.jpg\": \"Insecta/Euphydryas phaeton/36419a0f59068e4fee06ccc834e0e6e9.jpg\",\n  \"525046.jpg\": \"Insecta/Euphydryas phaeton/e73d2c981c0496e9da24fdc76256b22c.jpg\",\n  \"525047.jpg\": \"Insecta/Euphydryas phaeton/e4f571e609a209f1770bdc834097488b.jpg\",\n  \"525048.jpg\": \"Insecta/Euphydryas phaeton/151e3325fd28658e778b54ca1d0f5f01.jpg\",\n  \"525049.jpg\": \"Insecta/Euphydryas phaeton/d6fe12daf326a48fc5c8d85827c5b76f.jpg\",\n  \"525069.jpg\": \"Insecta/Idia americalis/12b7cc48be4122013f9a2f949f7fde2d.jpg\",\n  \"525075.jpg\": \"Insecta/Idia americalis/d18f389b1ac1dcbedc349e2eaee19370.jpg\",\n  \"525078.jpg\": \"Insecta/Idia americalis/0c30a7a7a0fc57afcfd5247fe9f13f2c.jpg\",\n  \"525079.jpg\": \"Insecta/Idia americalis/67059746b5e3e29500245d5652b84f31.jpg\",\n  \"525082.jpg\": \"Insecta/Idia americalis/2ef6147cab7d962121ac4b62d10e7a20.jpg\",\n  \"525086.jpg\": \"Insecta/Idia americalis/038f9a6abd22a2b478e818a3cb0ce24a.jpg\",\n  \"52509.jpg\": \"Insecta/Cicindela oregona/815095b75cde6c0228956baf11284d3d.jpg\",\n  \"525090.jpg\": \"Insecta/Idia americalis/0eda06c7acd672618daff2e72cba610f.jpg\",\n  \"525095.jpg\": \"Insecta/Idia americalis/d8e7b5590be116a045b61cf9031afc99.jpg\",\n  \"525098.jpg\": \"Insecta/Idia americalis/7754f5f0947cbe0d9ffdf91ed546abeb.jpg\",\n  \"525105.jpg\": \"Insecta/Idia americalis/5bd32f0cd368cd777c12ff1983f1592e.jpg\",\n  \"52513.jpg\": \"Insecta/Cicindela oregona/e0cc0d862a901a4c34e34ff2a04e2134.jpg\",\n  \"52518.jpg\": \"Insecta/Cicindela oregona/fe2f68b4365b003ddae2f11b66c555d2.jpg\",\n  \"525191.jpg\": \"Aves/Gallinago delicata/33a48fdc54224dbfa36e73dca1ca3263.jpg\",\n  \"5252.jpg\": \"Amphibia/Plethodon cinereus/78a9a1c5d42720cf9418d447c18350ef.jpg\",\n  \"525203.jpg\": \"Aves/Gallinago delicata/c7eeb88c43ea97b33ca60f0384d2ea86.jpg\",\n  \"525206.jpg\": \"Aves/Gallinago delicata/0a243bd3ac25bfac23a3292573e95387.jpg\",\n  \"525222.jpg\": \"Aves/Gallinago delicata/aac5fe631ec3d3edc480e5d6ec7e72c9.jpg\",\n  \"525245.jpg\": \"Aves/Gallinago delicata/448d258afdb332581d82f9995809c488.jpg\",\n  \"525247.jpg\": \"Aves/Gallinago delicata/e4247ed3028f686fbdaf43b6a256e930.jpg\",\n  \"525249.jpg\": \"Aves/Gallinago delicata/55e98d9c7d76aa8816100eb7ef90f8c9.jpg\",\n  \"525278.jpg\": \"Reptilia/Elgaria kingii/380cf780a32911479848d62df2ad4101.jpg\",\n  \"525280.jpg\": \"Reptilia/Elgaria kingii/c2def70f60dbfb13e92056619d6b539c.jpg\",\n  \"525284.jpg\": \"Insecta/Celithemis fasciata/3688b2d794c15dafeb7147155a094eae.jpg\",\n  \"525285.jpg\": \"Insecta/Celithemis fasciata/a3fcb33feb1aa10c877bf4a3d5734df7.jpg\",\n  \"525286.jpg\": \"Insecta/Celithemis fasciata/7da3f37c8be0ebf3476d88dcd2361078.jpg\",\n  \"525287.jpg\": \"Insecta/Celithemis fasciata/1177247abdb4d54dff95671be998b5ea.jpg\",\n  \"525288.jpg\": \"Insecta/Celithemis fasciata/040dc2c86bf00b9d417fe322f377427e.jpg\",\n  \"525289.jpg\": \"Insecta/Celithemis fasciata/fa5563c18c76954e55e7c38d01c27d77.jpg\",\n  \"525294.jpg\": \"Insecta/Celithemis fasciata/c7db87ec42c312a7c127dc073f0875f9.jpg\",\n  \"525296.jpg\": \"Insecta/Celithemis fasciata/ef029b19e08932ddee0319d922a51db5.jpg\",\n  \"525299.jpg\": \"Insecta/Celithemis fasciata/9ad15aa61cbd6dc2b71469b9477b0f24.jpg\",\n  \"5253.jpg\": \"Amphibia/Plethodon cinereus/74ae54f2621c02715ffd25f633a19fbc.jpg\",\n  \"52530.jpg\": \"Aves/Dendrocygna bicolor/ffa5a22eb4f03ed392838d6b5c92f745.jpg\",\n  \"525302.jpg\": \"Insecta/Celithemis fasciata/4752365c6bfdce80e9d6c0f204938c10.jpg\",\n  \"525303.jpg\": \"Insecta/Celithemis fasciata/f9cb1cbc60858843aebe9935c98ddd5e.jpg\",\n  \"525304.jpg\": \"Insecta/Celithemis fasciata/81f7c4989a6c27237f41425b930b66d6.jpg\",\n  \"525307.jpg\": \"Insecta/Celithemis fasciata/b3e6e68ff24e5b2de27511d6e7e47d75.jpg\",\n  \"525318.jpg\": \"Aves/Petroica australis longipes/e03c78b62901f671297e4598924d5f75.jpg\",\n  \"525320.jpg\": \"Aves/Petroica australis longipes/dedcd3308388695b27748075c2b3c4d8.jpg\",\n  \"525321.jpg\": \"Aves/Petroica australis longipes/9e7b596bddac7c8e7847569e60db6b23.jpg\",\n  \"525326.jpg\": \"Aves/Petroica australis longipes/5bdc43ffb0f7dc7d6e5dd4ff9a562626.jpg\",\n  \"525328.jpg\": \"Aves/Petroica australis longipes/ea35467807893edb3ea47f6f05aeda59.jpg\",\n  \"525329.jpg\": \"Aves/Petroica australis longipes/023726bdb3ead4b8b28990f7de3cf9d2.jpg\",\n  \"525340.jpg\": \"Aves/Petroica australis longipes/571ed91d2840138114fc4362baab91ec.jpg\",\n  \"525345.jpg\": \"Aves/Petroica australis longipes/e5c8aff33aa457ee1c9f915fef92a78a.jpg\",\n  \"525352.jpg\": \"Insecta/Sympetrum internum/0e7467b691cd948393e0e4c93a06f345.jpg\",\n  \"525353.jpg\": \"Insecta/Sympetrum internum/809b7a9fb38b1ac296ce14c8db8b0302.jpg\",\n  \"525359.jpg\": \"Reptilia/Anolis equestris/15ddae6a0a3eaab63d48ffeabb7b6ba5.jpg\",\n  \"525369.jpg\": \"Insecta/Forficula auricularia/aff9b0916567f50e08d42c7d77c96454.jpg\",\n  \"525370.jpg\": \"Insecta/Forficula auricularia/ade7e0c6ecc33ba0f8486d6a63c6c404.jpg\",\n  \"525387.jpg\": \"Insecta/Forficula auricularia/f83a8719114588f3a41b301f621a27d4.jpg\",\n  \"52539.jpg\": \"Aves/Dendrocygna bicolor/d7ace2f62c26c34788c7976f6fefa866.jpg\",\n  \"525393.jpg\": \"Insecta/Forficula auricularia/08abc687a28f5969549d2a3665070a68.jpg\",\n  \"525394.jpg\": \"Insecta/Forficula auricularia/5c30e25aca0466e44c9970d3bcf14a07.jpg\",\n  \"525400.jpg\": \"Insecta/Forficula auricularia/5e1c88cd2f1bcabea2cd56f04a20ef41.jpg\",\n  \"525401.jpg\": \"Insecta/Forficula auricularia/43cd13382530faf54a191c9e9e5013b8.jpg\",\n  \"52545.jpg\": \"Aves/Dendrocygna bicolor/9fd31214c033b3928e9fb9f33ca93408.jpg\",\n  \"525458.jpg\": \"Mammalia/Rupicapra rupicapra/5ec84f20867d1038d12718fe5ad8b66a.jpg\",\n  \"525483.jpg\": \"Aves/Arenaria interpres/2d1f7e3be26e8ce0f90bbad779ce60c7.jpg\",\n  \"525507.jpg\": \"Aves/Arenaria interpres/ed13a8bc9a0a1679c76b60aa9a006395.jpg\",\n  \"525531.jpg\": \"Aves/Arenaria interpres/b02609b3fa3f2f160e858bf4b505da93.jpg\",\n  \"525533.jpg\": \"Aves/Arenaria interpres/80b5c22ab2d4e271040303872de82a3d.jpg\",\n  \"525616.jpg\": \"Aves/Arenaria interpres/56cea467158c0f2dbaed4860740f99f5.jpg\",\n  \"525657.jpg\": \"Insecta/Coccinella californica/751211280468dac3ac87133f2f31d619.jpg\",\n  \"525662.jpg\": \"Insecta/Coccinella californica/20b07cb0bad7e3b1bad8f3af7f064eac.jpg\",\n  \"525663.jpg\": \"Insecta/Coccinella californica/0d1b34488ff10f49e71254b612807628.jpg\",\n  \"525666.jpg\": \"Insecta/Coccinella californica/3132456f60244a7d7e283118ec8ed1f6.jpg\",\n  \"525672.jpg\": \"Insecta/Coccinella californica/7312f1bad135ae6af1d3486dae0961a1.jpg\",\n  \"525673.jpg\": \"Insecta/Coccinella californica/afdd7689bcbf07b7a61b528b03aaad08.jpg\",\n  \"525674.jpg\": \"Insecta/Coccinella californica/8a7b1e6f0837ffe6f9294aca5e00e070.jpg\",\n  \"525681.jpg\": \"Insecta/Coccinella californica/fea70bdd61483c720825207ee0e8dcae.jpg\",\n  \"525682.jpg\": \"Insecta/Coccinella californica/1f7616ea6d17d09a274a2a4f44100c61.jpg\",\n  \"52570.jpg\": \"Arachnida/Pisaurina mira/940ddde6e54d9aeae0df8e615e28ab97.jpg\",\n  \"52571.jpg\": \"Arachnida/Pisaurina mira/a795ac1f4dfa43e6553383fd941b7020.jpg\",\n  \"52573.jpg\": \"Arachnida/Pisaurina mira/326c45c9f513997c8d2c84e88a494abb.jpg\",\n  \"52578.jpg\": \"Arachnida/Pisaurina mira/9cb7222d2efecd057d43244880adbc8d.jpg\",\n  \"525818.jpg\": \"Insecta/Diaethria anna/c8f86fd576e068f537b605de232c2e18.jpg\",\n  \"525822.jpg\": \"Insecta/Diaethria anna/95a8168796e3ba6485a3fd89a4e7a628.jpg\",\n  \"525825.jpg\": \"Insecta/Diaethria anna/d8e68142ce28dfe4dd365b0c29820572.jpg\",\n  \"525826.jpg\": \"Insecta/Diaethria anna/0a054be445b2e005fa40f5a606fa19b8.jpg\",\n  \"52587.jpg\": \"Arachnida/Pisaurina mira/a6d15f110bb4acd30f9761c45be6dace.jpg\",\n  \"52589.jpg\": \"Arachnida/Pisaurina mira/eb47b58238c3e04ff193f0bd36ad333a.jpg\",\n  \"52590.jpg\": \"Arachnida/Pisaurina mira/79b4e3c8fbb935a67a2c310b8eefab80.jpg\",\n  \"52593.jpg\": \"Arachnida/Pisaurina mira/de202b1a58e00559616a12b03c6d60f8.jpg\",\n  \"52594.jpg\": \"Arachnida/Pisaurina mira/6a1b08899bea1fd70d20ae846077b6fb.jpg\",\n  \"52597.jpg\": \"Arachnida/Pisaurina mira/ac0fff9c5c6b7f60c3d3a114433e6825.jpg\",\n  \"52601.jpg\": \"Arachnida/Pisaurina mira/b8f8d1f83f8cd43f017c4f2687c7fa25.jpg\",\n  \"52602.jpg\": \"Insecta/Lygaeus turcicus/864b903053c29349b00f9b27bb17c121.jpg\",\n  \"52604.jpg\": \"Insecta/Lygaeus turcicus/64fff8655dc31be6aee9a7cb82be27df.jpg\",\n  \"52607.jpg\": \"Insecta/Lygaeus turcicus/2807cae2f04d83c58f247bb4368d5032.jpg\",\n  \"52608.jpg\": \"Insecta/Lygaeus turcicus/653821468ddeb7483d42b68e3bdfa3b2.jpg\",\n  \"52610.jpg\": \"Insecta/Lygaeus turcicus/70d37412d27c16ad6ea48a468c86bd96.jpg\",\n  \"52614.jpg\": \"Insecta/Lygaeus turcicus/61c8b7f326f10d0488acec24cf40bc69.jpg\",\n  \"52615.jpg\": \"Insecta/Lygaeus turcicus/5f31ab25a041a9b04211fe7db87296d9.jpg\",\n  \"52617.jpg\": \"Insecta/Lygaeus turcicus/aa662be7972bfba89b8bf4e0c08e9927.jpg\",\n  \"526354.jpg\": \"Aves/Paroaria capitata/c3029b9220f66c84da2ca3fa0a0fb4a0.jpg\",\n  \"526355.jpg\": \"Aves/Paroaria capitata/4f41805330d57b8444c1cf1f532e95a3.jpg\",\n  \"526358.jpg\": \"Aves/Paroaria capitata/e5c2342549e094db993e1180adcbef4a.jpg\",\n  \"526429.jpg\": \"Aves/Melanerpes uropygialis/3c59ff2d0ceb6d19cc83a026434c2ea5.jpg\",\n  \"526431.jpg\": \"Aves/Melanerpes uropygialis/7fa0ab1e25868e1a24e72695aa8ccca3.jpg\",\n  \"526434.jpg\": \"Aves/Melanerpes uropygialis/39957331b8dd84b1ca70a43f89f47b94.jpg\",\n  \"526435.jpg\": \"Aves/Melanerpes uropygialis/caf8bf6bd577bf6c110c35557b1886bb.jpg\",\n  \"526441.jpg\": \"Aves/Melanerpes uropygialis/267fd1dd1841824422be0b1fe16b03c2.jpg\",\n  \"526453.jpg\": \"Aves/Melanerpes uropygialis/0bc08442746a3e4ac18fb62bc141ef4c.jpg\",\n  \"526454.jpg\": \"Aves/Melanerpes uropygialis/fdb1afee236bc7d085985eb6e3b2bcf5.jpg\",\n  \"526455.jpg\": \"Aves/Melanerpes uropygialis/b28904004cc9180196623f71dd7ce9c1.jpg\",\n  \"526457.jpg\": \"Aves/Melanerpes uropygialis/09f3d642ef529e7834a9159836f1b301.jpg\",\n  \"526465.jpg\": \"Aves/Melanerpes uropygialis/513a0f6d87cb9952c20ed32968b0b07f.jpg\",\n  \"526466.jpg\": \"Aves/Melanerpes uropygialis/4fe199dfdbf60480703365b41e89f829.jpg\",\n  \"526523.jpg\": \"Aves/Passerina caerulea/b69a27f3bbef0da85ab4608ecc324989.jpg\",\n  \"526524.jpg\": \"Aves/Passerina caerulea/faa8e503255057bda5d6d6547128e068.jpg\",\n  \"526546.jpg\": \"Aves/Passerina caerulea/83e0c3e276448a4ea71b7fc88817152c.jpg\",\n  \"526551.jpg\": \"Aves/Passerina caerulea/1b7702e833177f708f1f7805f1fccba9.jpg\",\n  \"526553.jpg\": \"Aves/Passerina caerulea/688b7452fe08f079f392d0f6ceec207b.jpg\",\n  \"526565.jpg\": \"Aves/Passerina caerulea/eea6c2ee9c2c2dbc5952ff3d03a78cba.jpg\",\n  \"526642.jpg\": \"Aves/Passerina caerulea/b74d9becb3db80d35ca0eed9e3042953.jpg\",\n  \"526674.jpg\": \"Aves/Amazilia violiceps/fa15b8d4a9f1624e5cc7b135b8ec8ee3.jpg\",\n  \"526675.jpg\": \"Aves/Amazilia violiceps/caa91ea5db37d9391334df371ef4705f.jpg\",\n  \"526678.jpg\": \"Aves/Amazilia violiceps/0509e5b3d069d95cf61cdc454dfe4687.jpg\",\n  \"526679.jpg\": \"Aves/Amazilia violiceps/d53b55f4b56b44bd1978e94b3c5e5c66.jpg\",\n  \"526680.jpg\": \"Aves/Amazilia violiceps/162e5fdb566af3e996f2a6e65579c2ab.jpg\",\n  \"526682.jpg\": \"Aves/Amazilia violiceps/acddf0909d0a708d7d1d159c7007598d.jpg\",\n  \"526693.jpg\": \"Aves/Amazilia violiceps/eec49c81f7fa7cfda116ffdeb5a702ce.jpg\",\n  \"526695.jpg\": \"Aves/Amazilia violiceps/46c6156a026a7c68bee30d5d14867e76.jpg\",\n  \"526696.jpg\": \"Aves/Amazilia violiceps/157704e05c816a3df47c0feb585f598a.jpg\",\n  \"526697.jpg\": \"Aves/Amazilia violiceps/8673421c7cc79f5a768c347195a03eee.jpg\",\n  \"526718.jpg\": \"Insecta/Libellula vibrans/fed4ee01a6b30afd8ac02cb8d5c51faf.jpg\",\n  \"526719.jpg\": \"Insecta/Libellula vibrans/012185bdcca6addbeab33337bf79b91d.jpg\",\n  \"526720.jpg\": \"Insecta/Libellula vibrans/37e4d3c6ca441cce1407be681496ee73.jpg\",\n  \"526744.jpg\": \"Insecta/Libellula vibrans/144fc8beee5ae1eced78b3cf4348e000.jpg\",\n  \"526774.jpg\": \"Insecta/Libellula vibrans/8c9a6110aff9ed3dfce8cb6fa49ddae6.jpg\",\n  \"526789.jpg\": \"Insecta/Libellula vibrans/6b97002d9b2330c2259ffc83c1defe5a.jpg\",\n  \"526790.jpg\": \"Insecta/Libellula vibrans/c6baae32db3fb04f61d7ad4b9bb7a314.jpg\",\n  \"526792.jpg\": \"Insecta/Libellula vibrans/9452acd987999ff6be647d649bb904db.jpg\",\n  \"526811.jpg\": \"Insecta/Libellula vibrans/4a459d2b78ac399cfeeb7443bea520d2.jpg\",\n  \"526812.jpg\": \"Insecta/Libellula vibrans/b9c71001d044ccc77b8505af2a67818c.jpg\",\n  \"526833.jpg\": \"Insecta/Palpita quadristigmalis/f434f04b8bad2346cec9a37e4f8ab3bc.jpg\",\n  \"526856.jpg\": \"Insecta/Libellula cyanea/d46f08172cda0cacc0d72fe56e183259.jpg\",\n  \"526875.jpg\": \"Insecta/Libellula cyanea/4a6c21c21fe8aade7f01d1e31d1796bc.jpg\",\n  \"526876.jpg\": \"Insecta/Libellula cyanea/a70b1f556de74cab95c25598d3a638b0.jpg\",\n  \"526879.jpg\": \"Insecta/Libellula cyanea/c1a400d7db22285af6f17e086c2826e0.jpg\",\n  \"526880.jpg\": \"Insecta/Libellula cyanea/7d3fab0c97ffc3ed6291451e0c5ef0b7.jpg\",\n  \"526882.jpg\": \"Insecta/Libellula cyanea/bd3ad8f07be1072d75b07dcf3ca02ad9.jpg\",\n  \"526883.jpg\": \"Insecta/Libellula cyanea/c7d786f727016a82a82331a30286858f.jpg\",\n  \"526887.jpg\": \"Insecta/Libellula cyanea/486c40ea48d253ba875f9301ecce0c6c.jpg\",\n  \"526888.jpg\": \"Insecta/Libellula cyanea/a49c726834fbdc0f9f3a2161131d86a8.jpg\",\n  \"52701.jpg\": \"Aves/Zonotrichia querula/9c44ee3aa14630eaea626dd4aaabbf7c.jpg\",\n  \"527083.jpg\": \"Amphibia/Pseudacris streckeri/5b2fac8b3bc66016c46e3c9f8f2868f1.jpg\",\n  \"527084.jpg\": \"Amphibia/Pseudacris streckeri/ed2d55928e5583e1d50750bb981f6f18.jpg\",\n  \"52712.jpg\": \"Aves/Zonotrichia querula/ab33b0e0539879b5442df42872a26638.jpg\",\n  \"52714.jpg\": \"Aves/Zonotrichia querula/34b85862b65d296600b6011eabc85b81.jpg\",\n  \"527153.jpg\": \"Animalia/Myliobatis californica/185243f42b199834f6a0f2a3bd0f4efe.jpg\",\n  \"52718.jpg\": \"Aves/Zonotrichia querula/9bd8c1ac72e878fac41ac1c1110527a7.jpg\",\n  \"527222.jpg\": \"Insecta/Vanessa virginiensis/d8677d87f00a29c2dcd558eb7914a968.jpg\",\n  \"527269.jpg\": \"Insecta/Vanessa virginiensis/ee0b4ef97195e43590d50274285078fd.jpg\",\n  \"527326.jpg\": \"Insecta/Vanessa virginiensis/3526ff856ff4a2398b9433d35f11ad26.jpg\",\n  \"527330.jpg\": \"Insecta/Vanessa virginiensis/2119af2aee502aa27cc47fe8bbcc8f14.jpg\",\n  \"52734.jpg\": \"Aves/Zonotrichia querula/432e548e484328e66be9285c290471a7.jpg\",\n  \"527343.jpg\": \"Insecta/Vanessa virginiensis/098f2f890a49cda3e54d221c10476dc8.jpg\",\n  \"52735.jpg\": \"Aves/Zonotrichia querula/a6c8374c727af2458de883dc2146cb74.jpg\",\n  \"527428.jpg\": \"Insecta/Vanessa virginiensis/81ed1f384230ea273f657739a07b2df3.jpg\",\n  \"527456.jpg\": \"Insecta/Vanessa virginiensis/1d1ab00c123a35143f561dec3ede8804.jpg\",\n  \"527462.jpg\": \"Insecta/Vanessa virginiensis/36c5e10d2c2064c70d1ad54f5132df54.jpg\",\n  \"527505.jpg\": \"Insecta/Vanessa virginiensis/c44921b316c7859fa4b865f9e69943d6.jpg\",\n  \"527554.jpg\": \"Insecta/Calycopis isobeon/e327b2e87f8dc05bb619e15a94703eb0.jpg\",\n  \"527556.jpg\": \"Insecta/Calycopis isobeon/48ebbea24a13491eb2fb7f693e2a255b.jpg\",\n  \"527557.jpg\": \"Insecta/Calycopis isobeon/7dc31cf3ded8dde59d23a92ba8460519.jpg\",\n  \"527562.jpg\": \"Insecta/Calycopis isobeon/b9364c945fdc84c2ad301958e432a6bf.jpg\",\n  \"527563.jpg\": \"Insecta/Calycopis isobeon/162352420d407adcbcbc4415a5f894e0.jpg\",\n  \"527571.jpg\": \"Insecta/Calycopis isobeon/cebbda5a79d2101635c0145df0268333.jpg\",\n  \"527573.jpg\": \"Insecta/Calycopis isobeon/cf6db10f9b1187d4265b48b7ce859e83.jpg\",\n  \"527580.jpg\": \"Insecta/Calycopis isobeon/0846ac339d049e8ed054079584e1fe2c.jpg\",\n  \"527583.jpg\": \"Insecta/Calycopis isobeon/2055c0d6741cb0f7683458f87b8517e4.jpg\",\n  \"527593.jpg\": \"Insecta/Leptoglossus occidentalis/02569a18b66f1854cbde0a04d19399bf.jpg\",\n  \"527594.jpg\": \"Insecta/Leptoglossus occidentalis/2e71ee4ae71f74113e2233a6f2ee7c12.jpg\",\n  \"527598.jpg\": \"Insecta/Leptoglossus occidentalis/952662e41b13bbbac7da2af0ead65731.jpg\",\n  \"527599.jpg\": \"Insecta/Leptoglossus occidentalis/f8135cf29b64372c500a43673b8d6d85.jpg\",\n  \"527600.jpg\": \"Insecta/Leptoglossus occidentalis/46b2c7c55afbcf8dfec81949b02ccb13.jpg\",\n  \"527602.jpg\": \"Insecta/Leptoglossus occidentalis/19be0a74ba531a529c1c9dd577bb9567.jpg\",\n  \"527603.jpg\": \"Insecta/Leptoglossus occidentalis/d16fa483f4272f524c1b3387d9ce88e7.jpg\",\n  \"527604.jpg\": \"Insecta/Leptoglossus occidentalis/46d0a2e7bf35ee4f8b7d7924bd38d0b2.jpg\",\n  \"527605.jpg\": \"Insecta/Leptoglossus occidentalis/248f73e2ecf743d1c3ea59e267957534.jpg\",\n  \"527606.jpg\": \"Insecta/Leptoglossus occidentalis/e8bcefa6b2d82e98d2c283def33d018d.jpg\",\n  \"527607.jpg\": \"Insecta/Leptoglossus occidentalis/85318737742cd9b27a53394cb58a2367.jpg\",\n  \"52762.jpg\": \"Aves/Zonotrichia querula/7b980efca277c010806cc19b48e16f4f.jpg\",\n  \"527623.jpg\": \"Mollusca/Lottia scabra/14ef787a1c0a08302d0d55bbd59f42c6.jpg\",\n  \"527629.jpg\": \"Mollusca/Lottia scabra/97d821bd47bdd3f14792feef5b301f12.jpg\",\n  \"52763.jpg\": \"Aves/Zonotrichia querula/5e3492739c07320428cc2e1cb91a90a4.jpg\",\n  \"527631.jpg\": \"Mollusca/Lottia scabra/d2c304dbdf4db1bbf02423132c51297c.jpg\",\n  \"527634.jpg\": \"Mollusca/Lottia scabra/17999192583b3d8f6094961b75471f3f.jpg\",\n  \"52767.jpg\": \"Aves/Zonotrichia querula/52c9e7364b6a556fc5c259e72e255c5b.jpg\",\n  \"527724.jpg\": \"Aves/Fulica atra/2f705f497130554d759e7bac1d99d093.jpg\",\n  \"527801.jpg\": \"Aves/Fulica atra/de58c9808a995affd0db9c5d1f51859f.jpg\",\n  \"52781.jpg\": \"Aves/Zonotrichia querula/81bac6e2bbaab9a338a3c132346f160b.jpg\",\n  \"52782.jpg\": \"Aves/Zonotrichia querula/7b9d66284c4aaa6f34b850d34e468021.jpg\",\n  \"52783.jpg\": \"Aves/Zonotrichia querula/8f25b484e4d1b1e3ab6c7ad8d60ccf28.jpg\",\n  \"52784.jpg\": \"Aves/Zonotrichia querula/493bce52ed848be75b5208675a0c40b4.jpg\",\n  \"52787.jpg\": \"Aves/Zonotrichia querula/7253005a8227a83e1b73abf962ca80c7.jpg\",\n  \"527884.jpg\": \"Insecta/Callophrys augustinus/7c38502fff3e2ff7d60c557192dfdc22.jpg\",\n  \"527892.jpg\": \"Insecta/Callophrys augustinus/b7084f124f4fbae9fb903b5a4be1d6c8.jpg\",\n  \"527909.jpg\": \"Insecta/Callophrys augustinus/8100a68a0f383010db9ae286148d054f.jpg\",\n  \"527910.jpg\": \"Insecta/Callophrys augustinus/9a8fc57fdefe7ca6b754a0c74d993b9c.jpg\",\n  \"527911.jpg\": \"Insecta/Callophrys augustinus/b1eec8b1988430e129a4f0e6f183280c.jpg\",\n  \"527912.jpg\": \"Insecta/Callophrys augustinus/8c49569787ca502fabfa0d8e15b6bd0d.jpg\",\n  \"527915.jpg\": \"Insecta/Callophrys augustinus/35a1bbb318c4c2152b0143528c5895e1.jpg\",\n  \"527933.jpg\": \"Insecta/Celastrina ladon/84427c526cdd5eb5e3b7343f14ccc84d.jpg\",\n  \"527934.jpg\": \"Insecta/Celastrina ladon/d6799664e2b428259d36b437b952e2d8.jpg\",\n  \"527935.jpg\": \"Insecta/Celastrina ladon/6639a6410802dad899102689233ff0bc.jpg\",\n  \"527936.jpg\": \"Insecta/Celastrina ladon/d117dbf44b2350d431e04a4f6441801e.jpg\",\n  \"527939.jpg\": \"Insecta/Celastrina ladon/7f3fee697d452cc9f70a044438f3f91e.jpg\",\n  \"527940.jpg\": \"Insecta/Celastrina ladon/427145090334001999e7789f402bc1a9.jpg\",\n  \"527941.jpg\": \"Insecta/Celastrina ladon/1dbc61bdca5b4341f7d8f8a6c85954c7.jpg\",\n  \"527943.jpg\": \"Insecta/Celastrina ladon/594d6b87403dad530409c593ce7443bc.jpg\",\n  \"527945.jpg\": \"Insecta/Celastrina ladon/4e0141faca59cd3cfcb3baec423eda88.jpg\",\n  \"528085.jpg\": \"Mollusca/Oxychilus draparnaudi/afc257f719176b74c795ab8a32d069ac.jpg\",\n  \"528086.jpg\": \"Mollusca/Oxychilus draparnaudi/6e61cbc2cd19e5e21705e137b3a4c087.jpg\",\n  \"528087.jpg\": \"Mollusca/Oxychilus draparnaudi/e870132c2bd849bcf8f14fea2c142f46.jpg\",\n  \"528094.jpg\": \"Mollusca/Oxychilus draparnaudi/2498fd12a7536492094a84bc4f518b8c.jpg\",\n  \"528101.jpg\": \"Mollusca/Oxychilus draparnaudi/6634a7da7fcff718598efad21b0d9166.jpg\",\n  \"528105.jpg\": \"Mollusca/Oxychilus draparnaudi/79b895bc3a11347d654bcefddb2e0d0b.jpg\",\n  \"528106.jpg\": \"Mollusca/Oxychilus draparnaudi/b262582aa7ef0d626322d3caf8b07cff.jpg\",\n  \"52811.jpg\": \"Aves/Zonotrichia querula/2c7206ba16fcf3af490fa90281d4e9d5.jpg\",\n  \"528123.jpg\": \"Mollusca/Flabellina iodinea/946ed9f975d0fd822dc62d1fb3cdc483.jpg\",\n  \"528129.jpg\": \"Mollusca/Flabellina iodinea/da5db6742bed0ef14de9bc8949eb4b37.jpg\",\n  \"528130.jpg\": \"Mollusca/Flabellina iodinea/fe0cdf1edc8700792cd0d122c7551cce.jpg\",\n  \"528136.jpg\": \"Mollusca/Flabellina iodinea/4053668cd153b5e4dc47a80c0d2b5872.jpg\",\n  \"528138.jpg\": \"Mollusca/Flabellina iodinea/61f3e8fca06f2be9eb1c1b36501a3d15.jpg\",\n  \"528139.jpg\": \"Mollusca/Flabellina iodinea/2f38686c2374c923747488506ee08786.jpg\",\n  \"528188.jpg\": \"Aves/Galbula ruficauda/436bcea9479ad506f1852f7660da2de3.jpg\",\n  \"528189.jpg\": \"Aves/Galbula ruficauda/375a9520e053ae5830e08bcd49b92769.jpg\",\n  \"528193.jpg\": \"Aves/Myioborus miniatus/5ee95478fca25f9941e53e126934c803.jpg\",\n  \"528194.jpg\": \"Aves/Myioborus miniatus/b49b7d081bc906559dfa40d9015c0c5d.jpg\",\n  \"528195.jpg\": \"Aves/Myioborus miniatus/4ac555b8d9b067a1a640992fada10011.jpg\",\n  \"528197.jpg\": \"Aves/Myioborus miniatus/4a1134c982718be5ce6973d767211381.jpg\",\n  \"528297.jpg\": \"Aves/Podiceps nigricollis/0cb01df15b77c74e1cff36e0ae4aacd6.jpg\",\n  \"528410.jpg\": \"Aves/Podiceps nigricollis/487bf144053fc87fb76281fed8ead751.jpg\",\n  \"528436.jpg\": \"Insecta/Microstylum morosum/c0d68d0329c49633291599062a329b96.jpg\",\n  \"528437.jpg\": \"Insecta/Microstylum morosum/cad2390d0005bdbc2121834a1564f02c.jpg\",\n  \"528438.jpg\": \"Insecta/Microstylum morosum/283b9911a47e2d455307ff9abd9a0596.jpg\",\n  \"528462.jpg\": \"Aves/Numenius americanus/ef11f3124733394ed0982f3205cc5d88.jpg\",\n  \"528546.jpg\": \"Aves/Numenius americanus/0bbc50f3e19d8e5bb21272143080a5ef.jpg\",\n  \"528568.jpg\": \"Aves/Numenius americanus/a8966efe8be9b6845a2e7e8c3f864121.jpg\",\n  \"528580.jpg\": \"Aves/Numenius americanus/379adad2f90de8ae0c51c4e707907762.jpg\",\n  \"528619.jpg\": \"Aves/Chroicocephalus novaehollandiae/9b9bd5a591a83f6ead52816eef986c04.jpg\",\n  \"528626.jpg\": \"Aves/Chroicocephalus novaehollandiae/a7c313c98bdd7c4afb5f409b4f61e33a.jpg\",\n  \"528627.jpg\": \"Aves/Chroicocephalus novaehollandiae/0473fbcdcafc10e3c7e7d00a390f3ff0.jpg\",\n  \"528629.jpg\": \"Aves/Chroicocephalus novaehollandiae/18a9f950e7edd7ee84d96478fb70f7b5.jpg\",\n  \"528630.jpg\": \"Aves/Chroicocephalus novaehollandiae/26022a99f97af3613e2e5fd662088443.jpg\",\n  \"528632.jpg\": \"Aves/Chroicocephalus novaehollandiae/8bb1cfcb107b58bed1e7ce6701d13110.jpg\",\n  \"528633.jpg\": \"Aves/Chroicocephalus novaehollandiae/c2f9472a82b43d8b6742b798869cfefa.jpg\",\n  \"528646.jpg\": \"Insecta/Phyllopalpus pulchellus/331179ced26d7494a00293d8c73c7c53.jpg\",\n  \"528652.jpg\": \"Insecta/Phyllopalpus pulchellus/557bd6f95d84398b68e1f166db6ca659.jpg\",\n  \"528653.jpg\": \"Insecta/Phyllopalpus pulchellus/2a877dfee2c4b706ed10fa59ce02ecec.jpg\",\n  \"528654.jpg\": \"Insecta/Phyllopalpus pulchellus/50094042416e25123cfdf7f66d62c50f.jpg\",\n  \"5287.jpg\": \"Amphibia/Plethodon cinereus/2b5e7d71bb3cd3c953dd69a9c646858f.jpg\",\n  \"528750.jpg\": \"Aves/Coccyzus erythropthalmus/4ba832605c053bbbc92ec4f73e94fb6f.jpg\",\n  \"528758.jpg\": \"Aves/Coccyzus erythropthalmus/455e3e7b4306b1d04e9faa12b4808d20.jpg\",\n  \"528759.jpg\": \"Aves/Coccyzus erythropthalmus/b11483ced4326120a7268a4c3885db82.jpg\",\n  \"528760.jpg\": \"Aves/Coccyzus erythropthalmus/95ce9d3fb74df391a013b7126a20e121.jpg\",\n  \"528761.jpg\": \"Aves/Coccyzus erythropthalmus/bbf0fbc4b6dd23a1e494d7acd1811513.jpg\",\n  \"528765.jpg\": \"Mollusca/Planorbella trivolvis/9d82cde80574962834658acc08517ab2.jpg\",\n  \"528766.jpg\": \"Mollusca/Planorbella trivolvis/ff1bbdd213a46f4d5088a48369f302b3.jpg\",\n  \"528767.jpg\": \"Mollusca/Planorbella trivolvis/76d2e49e5fb8d6f5d7e43ac137206b3c.jpg\",\n  \"52877.jpg\": \"Insecta/Coccinella undecimpunctata/1c7201e9c96809989055f433af3cd80d.jpg\",\n  \"52878.jpg\": \"Insecta/Coccinella undecimpunctata/38b0dfa233def8b52ead7f12cc89ddee.jpg\",\n  \"52884.jpg\": \"Insecta/Coccinella undecimpunctata/70544a135b1e4c4858380befa759a648.jpg\",\n  \"528862.jpg\": \"Insecta/Poanes hobomok/b6a05e4b5eb066259e78860a7437309a.jpg\",\n  \"528876.jpg\": \"Insecta/Poanes hobomok/f66c3e44f2986f6a2736c43a40ae742f.jpg\",\n  \"528878.jpg\": \"Insecta/Poanes hobomok/663da5ba5c7c29d0073986a310ede157.jpg\",\n  \"528882.jpg\": \"Insecta/Poanes hobomok/fbadf2e8e6ba7db8cdbb448947b98b99.jpg\",\n  \"528884.jpg\": \"Insecta/Poanes hobomok/3efbb27644aff53f3e604baea99d47ab.jpg\",\n  \"528888.jpg\": \"Insecta/Poanes hobomok/9455d77ad9cf8cb5e39f9ce0dd14cc73.jpg\",\n  \"528893.jpg\": \"Insecta/Poanes hobomok/0e468dd8cf22909986d2011c30f51be8.jpg\",\n  \"528899.jpg\": \"Insecta/Poanes hobomok/44d6f4527eb18671f18c16601199b239.jpg\",\n  \"528901.jpg\": \"Insecta/Poanes hobomok/21f519ce69b26b28a57c2dce8495590b.jpg\",\n  \"528912.jpg\": \"Insecta/Aidemona azteca/fbe6b960a75dc2603cde44d85040bf5d.jpg\",\n  \"528913.jpg\": \"Insecta/Aidemona azteca/6dd661150c2938a87f7409fe0259697e.jpg\",\n  \"52892.jpg\": \"Insecta/Coccinella undecimpunctata/cbd12f693c954f9cee73ad5e8520104a.jpg\",\n  \"52893.jpg\": \"Insecta/Coccinella undecimpunctata/e167dc0762839f5744b75b910db9cac4.jpg\",\n  \"52895.jpg\": \"Insecta/Coccinella undecimpunctata/6395b8b5d1652b81f18c7542b00f9c90.jpg\",\n  \"52897.jpg\": \"Insecta/Coccinella undecimpunctata/2e1c0d297633a1d01efef00ebb1206d4.jpg\",\n  \"528971.jpg\": \"Mammalia/Thomomys bottae/8a7d3cf13c6a762727c372c3bc861ccc.jpg\",\n  \"528973.jpg\": \"Mammalia/Thomomys bottae/fb84535468862649721a3fd21bc4d41c.jpg\",\n  \"528974.jpg\": \"Mammalia/Thomomys bottae/fa4488cca6ba3fa27051a5f7c920d794.jpg\",\n  \"528992.jpg\": \"Mammalia/Thomomys bottae/121e4bf1750447e855728f3e5dbe12fa.jpg\",\n  \"529013.jpg\": \"Mammalia/Thomomys bottae/ce968f0b8a6dea85a57a38e63d59ab4e.jpg\",\n  \"529014.jpg\": \"Mammalia/Thomomys bottae/8225b33fb50a4ef9b54d9266e49b015c.jpg\",\n  \"529033.jpg\": \"Aves/Tringa nebularia/6a9f37d9c10122cf31d40332e3bb1242.jpg\",\n  \"529040.jpg\": \"Insecta/Sphinx kalmiae/14ba099d1a345c8aa5abd1dcfd0240c5.jpg\",\n  \"529044.jpg\": \"Insecta/Sphinx kalmiae/92b32cfa9ad815a2d6159213abe0808c.jpg\",\n  \"529048.jpg\": \"Insecta/Sphinx kalmiae/dbd941df278ca68a5a46120dd87ab574.jpg\",\n  \"529061.jpg\": \"Aves/Passer italiae/1d67c6a38c0f2fa2b12984a05ca1bc0c.jpg\",\n  \"529086.jpg\": \"Animalia/Limulus polyphemus/8c6f410f07cc5b7d0622a20e7b2242cf.jpg\",\n  \"529089.jpg\": \"Animalia/Limulus polyphemus/735e12ed85d799a8e058d6f2249d0dcd.jpg\",\n  \"529092.jpg\": \"Animalia/Limulus polyphemus/5c5cb1a00fd82493bf0061a7adc66dfc.jpg\",\n  \"529096.jpg\": \"Animalia/Limulus polyphemus/2bda92932fce5e1ec76191e1b6b69c6f.jpg\",\n  \"529101.jpg\": \"Animalia/Limulus polyphemus/90ff9a42541ce75db8c6093c545e70bd.jpg\",\n  \"529102.jpg\": \"Animalia/Limulus polyphemus/e1f4f3441919b7d4f9e653f55e2ab651.jpg\",\n  \"529106.jpg\": \"Reptilia/Anolis nebulosus/c3f8551d64bd5b95cc6dd8a411680d8c.jpg\",\n  \"529107.jpg\": \"Reptilia/Anolis nebulosus/1285753f5df317a55df527edac987c8c.jpg\",\n  \"529108.jpg\": \"Reptilia/Anolis nebulosus/387cc9f1be8a5c3737883ce50332301b.jpg\",\n  \"529109.jpg\": \"Reptilia/Anolis nebulosus/12d958378c98d51b8649ff033d630b2a.jpg\",\n  \"529110.jpg\": \"Reptilia/Anolis nebulosus/ceb6ebf712d72ab7d0f8504fb418e46d.jpg\",\n  \"529111.jpg\": \"Reptilia/Anolis nebulosus/c2c436297eb6d89663da3e4943dcc33c.jpg\",\n  \"529112.jpg\": \"Reptilia/Anolis nebulosus/7c567c930a3a7909fdd97d2a4eaded74.jpg\",\n  \"529114.jpg\": \"Reptilia/Anolis nebulosus/d2dce5604a4ba79b8a10bb0282ff71d1.jpg\",\n  \"529116.jpg\": \"Reptilia/Anolis nebulosus/023c5c8f53334c5b7c04f399e8159a27.jpg\",\n  \"529117.jpg\": \"Reptilia/Anolis nebulosus/b281763c4b7b926018f24aeecb896411.jpg\",\n  \"529118.jpg\": \"Reptilia/Anolis nebulosus/b98b85ef8ecbae1ed6396344163dbfe9.jpg\",\n  \"529119.jpg\": \"Reptilia/Anolis nebulosus/e8e40ab63fe4fc77fc427aec05260d7a.jpg\",\n  \"529120.jpg\": \"Reptilia/Anolis nebulosus/1c34ab01de863cc291a9fae85d1e192c.jpg\",\n  \"529121.jpg\": \"Reptilia/Anolis nebulosus/1db948ee586cd3410e33b64573226a66.jpg\",\n  \"529163.jpg\": \"Aves/Callipepla californica/28cd608f51177b1aff7c3dc61664286e.jpg\",\n  \"529193.jpg\": \"Aves/Callipepla californica/6131337ddd97b7e9e7aff4e2b3a8c9a9.jpg\",\n  \"529263.jpg\": \"Aves/Callipepla californica/a50588d2bfc5b4db60310a16d085cfcf.jpg\",\n  \"529267.jpg\": \"Aves/Callipepla californica/f1baaf74f0e43f4e3c4b0f345ae38a8e.jpg\",\n  \"529279.jpg\": \"Aves/Callipepla californica/3b66a7d446d5b5a86140f02d4ab08588.jpg\",\n  \"529309.jpg\": \"Aves/Callipepla californica/a4bea7a71a791c464ad1977f3f2e908f.jpg\",\n  \"529346.jpg\": \"Insecta/Ischnura perparva/58c3218187aca9d127f8f96dcb78a8d3.jpg\",\n  \"529407.jpg\": \"Mammalia/Macaca mulatta/067eeee2b24407ec2702e5a97120f1ca.jpg\",\n  \"529415.jpg\": \"Insecta/Polites vibex/9179ff67cb5874b81e94fb9fd4a255a0.jpg\",\n  \"529559.jpg\": \"Mammalia/Canis mesomelas/67769171012713b2efc051de554635ed.jpg\",\n  \"529560.jpg\": \"Mammalia/Canis mesomelas/1023fc2e99b1c599e97094b5f29be2eb.jpg\",\n  \"529571.jpg\": \"Aves/Tiaris olivaceus/d232282b7292799650a146b46eb6a20f.jpg\",\n  \"529573.jpg\": \"Aves/Tiaris olivaceus/42457d3695d95974e93e9874db4294d9.jpg\",\n  \"5296.jpg\": \"Amphibia/Plethodon cinereus/765a45587b95e8ec2d283ed1737e50aa.jpg\",\n  \"52960.jpg\": \"Aves/Coccothraustes vespertinus/373a7f5a8595fbb8d62e26bce4c697da.jpg\",\n  \"52961.jpg\": \"Aves/Coccothraustes vespertinus/f059cde4edae1e2381bad9a10951e4f1.jpg\",\n  \"529635.jpg\": \"Reptilia/Chrysemys picta/ab4f9a53d3508d32fe7f31297d7efcb0.jpg\",\n  \"529640.jpg\": \"Reptilia/Chrysemys picta/4bf99f03e5a310c837f13c70b80435fd.jpg\",\n  \"52968.jpg\": \"Aves/Coccothraustes vespertinus/546b30bc08c10e159ffa1d707190f3ab.jpg\",\n  \"529680.jpg\": \"Reptilia/Chrysemys picta/5788f4717cd49681ff70ea421353b909.jpg\",\n  \"52971.jpg\": \"Aves/Coccothraustes vespertinus/615a343d6d20e11978d047680ea8679a.jpg\",\n  \"52972.jpg\": \"Aves/Coccothraustes vespertinus/07c0ff0404f5f7ea3c2341cfd58fa6b9.jpg\",\n  \"529726.jpg\": \"Reptilia/Chrysemys picta/eddf6353d84e34418c9f2037b7cabe73.jpg\",\n  \"529733.jpg\": \"Reptilia/Chrysemys picta/63377d80dd4ba0bae29ce75671fab697.jpg\",\n  \"529734.jpg\": \"Reptilia/Chrysemys picta/9805ba2d91ddabb0d648cf01551f0e0a.jpg\",\n  \"529774.jpg\": \"Insecta/Syntomoides imaon/bf693cbe4d9002a3f613cb03f194dafc.jpg\",\n  \"529778.jpg\": \"Insecta/Syntomoides imaon/6dc0068ba1b6796adb596ea0596cfb28.jpg\",\n  \"529788.jpg\": \"Insecta/Epargyreus clarus/21213b796b767d596e97e546e6a7f913.jpg\",\n  \"52979.jpg\": \"Aves/Coccothraustes vespertinus/1bf7372c53b2d4ac3c71354a770a2013.jpg\",\n  \"52980.jpg\": \"Aves/Coccothraustes vespertinus/d28ff21f45770155002a290c9e58dc41.jpg\",\n  \"52981.jpg\": \"Aves/Coccothraustes vespertinus/3e18cbc05f1d8e9fafcb94eccc039d7f.jpg\",\n  \"52982.jpg\": \"Aves/Coccothraustes vespertinus/92539bfcf2dd78fa68bec6d2dab7724d.jpg\",\n  \"529824.jpg\": \"Insecta/Epargyreus clarus/4e1ae785b668ce3fc5f57c43dcfa49af.jpg\",\n  \"52984.jpg\": \"Aves/Coccothraustes vespertinus/493d242ad6131137ac5ce7406405e471.jpg\",\n  \"529844.jpg\": \"Insecta/Epargyreus clarus/75cc4e2fec22bc3a34533047c3a962db.jpg\",\n  \"52987.jpg\": \"Aves/Coccothraustes vespertinus/73ec8c40d985e785d534a0292bdfe7a5.jpg\",\n  \"529878.jpg\": \"Insecta/Epargyreus clarus/a511de38546601e452210517be4e5ae0.jpg\",\n  \"52990.jpg\": \"Aves/Coccothraustes vespertinus/66e4e7c6a277663690c4ec4fdad3719c.jpg\",\n  \"52992.jpg\": \"Aves/Coccothraustes vespertinus/2e4b463cfa368b65bf9c3b113731a086.jpg\",\n  \"529953.jpg\": \"Insecta/Epargyreus clarus/c7ed692e23899ae558adf632ec458083.jpg\",\n  \"529958.jpg\": \"Insecta/Epargyreus clarus/fa5b99ac6df6592a85a6037a0ad509ed.jpg\",\n  \"529994.jpg\": \"Insecta/Epargyreus clarus/0daa700ba94ccfaf47884e0cbaafb69d.jpg\",\n  \"529997.jpg\": \"Insecta/Epargyreus clarus/a4de16e195a1ac8141d5527af6cfdcaf.jpg\",\n  \"530005.jpg\": \"Insecta/Epargyreus clarus/dbc0be034e9655a083ec68ae7430bdfc.jpg\",\n  \"53001.jpg\": \"Aves/Coccothraustes vespertinus/8da15019fe5e03cc9d484bd511781013.jpg\",\n  \"53003.jpg\": \"Aves/Coccothraustes vespertinus/c577b336f38d413c55390cb0210ca10e.jpg\",\n  \"53004.jpg\": \"Aves/Coccothraustes vespertinus/e359265c8cc3babd367cdf7a1eee44fb.jpg\",\n  \"530052.jpg\": \"Mammalia/Capra hircus/832158787f81b746c09258a0fffcb1ad.jpg\",\n  \"53006.jpg\": \"Aves/Coccothraustes vespertinus/42b5defd78dbe0d82740e47513c34d56.jpg\",\n  \"53007.jpg\": \"Aves/Coccothraustes vespertinus/3bb7bad56416f3ca6254428160cf2f78.jpg\",\n  \"530075.jpg\": \"Insecta/Agrius convolvuli/9d0d43331d142513b97a74a580dbc7de.jpg\",\n  \"530082.jpg\": \"Insecta/Agrius convolvuli/3f4a06d950110475938289d4b53cc055.jpg\",\n  \"530088.jpg\": \"Insecta/Agrius convolvuli/03c193aafcdc22af0a742f1fd5663fc5.jpg\",\n  \"530090.jpg\": \"Insecta/Agrius convolvuli/337b7537fe6d8732cd7317457d6113e1.jpg\",\n  \"530095.jpg\": \"Insecta/Aeshna constricta/62b63686d18f8e36cf0da6461f4609af.jpg\",\n  \"530096.jpg\": \"Insecta/Aeshna constricta/0a41aca515ad4a0a8fdb19c0d6648bc4.jpg\",\n  \"530097.jpg\": \"Insecta/Aeshna constricta/d5b348e25efb51777338b75506491b58.jpg\",\n  \"530108.jpg\": \"Insecta/Lithacodia musta/da24af67ddb7c76a52c613a157a4ad0b.jpg\",\n  \"530114.jpg\": \"Aves/Amazona autumnalis/a954ab0ebbe5ef50e39a1b08c1f1eb34.jpg\",\n  \"530119.jpg\": \"Aves/Amazona autumnalis/0f5ba735f034a226bdadd28e80586c6a.jpg\",\n  \"53012.jpg\": \"Aves/Nestor meridionalis septentrionalis/ea908f2552f08ed951eb04ef2f16b7c5.jpg\",\n  \"530125.jpg\": \"Aves/Amazona autumnalis/f0363dd235f11bb86610881bd7daf1c7.jpg\",\n  \"53013.jpg\": \"Aves/Nestor meridionalis septentrionalis/c130b9655c62083a6a47270ee05d8980.jpg\",\n  \"530148.jpg\": \"Insecta/Poanes melane/0bb46f2dbd5f8e2d893232cc56ed1bd3.jpg\",\n  \"530149.jpg\": \"Insecta/Poanes melane/7e678e0decd9d64172e27c42d8ff60db.jpg\",\n  \"53016.jpg\": \"Aves/Nestor meridionalis septentrionalis/29a0d6083e17d51719aec93280c6c754.jpg\",\n  \"530177.jpg\": \"Insecta/Poanes melane/5cba4db77af752b46f2aa1d96dd22896.jpg\",\n  \"530180.jpg\": \"Insecta/Poanes melane/1d359d79c52fee13f2caabc2a0e3095c.jpg\",\n  \"530181.jpg\": \"Insecta/Poanes melane/22d5679654d4dea350b301115a411f82.jpg\",\n  \"5302.jpg\": \"Amphibia/Plethodon cinereus/a4f32cb844932d47240e87fdd9bda9ed.jpg\",\n  \"530201.jpg\": \"Insecta/Poanes melane/fec20bae5a50f9957b83420bf6a4bbaa.jpg\",\n  \"53022.jpg\": \"Aves/Nestor meridionalis septentrionalis/9f4da481bd87f4c3cbf8741c7dd876e1.jpg\",\n  \"530228.jpg\": \"Insecta/Poanes melane/95b4c24b4cd54334221c355e1cd6b3c6.jpg\",\n  \"53023.jpg\": \"Aves/Nestor meridionalis septentrionalis/ec15d7bb1fe8390d355b445f6ff27288.jpg\",\n  \"530251.jpg\": \"Insecta/Poanes melane/d2800defbf3e5bf60cf07fa6ce73ea77.jpg\",\n  \"530252.jpg\": \"Insecta/Poanes melane/e36f2beafb147a797dfbad7558946823.jpg\",\n  \"53029.jpg\": \"Aves/Nestor meridionalis septentrionalis/8eb535f87235cf882ad778672b335459.jpg\",\n  \"53030.jpg\": \"Aves/Nestor meridionalis septentrionalis/3b2e5cfe93606930b4c1911960735fb6.jpg\",\n  \"530311.jpg\": \"Amphibia/Eurycea bislineata/cfcf1ef0fd4d451197a6edfe188bf163.jpg\",\n  \"530315.jpg\": \"Amphibia/Eurycea bislineata/6a3033acfe937340ab2c3a6a9f118205.jpg\",\n  \"530320.jpg\": \"Amphibia/Eurycea bislineata/754092cf019f4bc4acc45c50258a286c.jpg\",\n  \"530321.jpg\": \"Amphibia/Eurycea bislineata/46e30771a8fefa2a265e7fa153fd61dc.jpg\",\n  \"53033.jpg\": \"Aves/Nestor meridionalis septentrionalis/9177392154b36ca8e56a1b95ae12c078.jpg\",\n  \"530330.jpg\": \"Amphibia/Eurycea bislineata/e4a8f5fdecbb359d00f7a7b1dab23731.jpg\",\n  \"530331.jpg\": \"Amphibia/Eurycea bislineata/91dabc792e7dcc0f60f4153f0a6ed617.jpg\",\n  \"530333.jpg\": \"Amphibia/Eurycea bislineata/ae6d25a8eb7443727bc8c20304701bcd.jpg\",\n  \"530336.jpg\": \"Amphibia/Eurycea bislineata/e3db627845ac3637f32d8252eb3047b5.jpg\",\n  \"530346.jpg\": \"Insecta/Urbanus procne/33c03114b092102b9d627594958bbce0.jpg\",\n  \"530347.jpg\": \"Insecta/Urbanus procne/74aa0de9389221f0c99cc1d3c7aa8f9b.jpg\",\n  \"530350.jpg\": \"Insecta/Urbanus procne/0be8dd05c45b7f0f4233e00f257661a0.jpg\",\n  \"53036.jpg\": \"Aves/Nestor meridionalis septentrionalis/adbad536e0cb34b6bf41cac95c644618.jpg\",\n  \"53040.jpg\": \"Aves/Nestor meridionalis septentrionalis/01418f5e0a24c9ff6f625108fbc0b37c.jpg\",\n  \"53041.jpg\": \"Aves/Nestor meridionalis septentrionalis/84a49f947d560c560eda453268693ce1.jpg\",\n  \"53044.jpg\": \"Aves/Nestor meridionalis septentrionalis/ed3e3290b84774b38ba6e6c6cd884334.jpg\",\n  \"530497.jpg\": \"Insecta/Kricogonia lyside/24ae1dfff4f2959357f67df4be328d2b.jpg\",\n  \"530508.jpg\": \"Insecta/Kricogonia lyside/647634b43761055c566f11bfdd12fbf1.jpg\",\n  \"53053.jpg\": \"Aves/Nestor meridionalis septentrionalis/88e595a333d413e689a809b7a43e2692.jpg\",\n  \"53059.jpg\": \"Aves/Nestor meridionalis septentrionalis/1b5e76be4df57a227ae0e7ec66659a2f.jpg\",\n  \"53060.jpg\": \"Aves/Nestor meridionalis septentrionalis/a6c7d8f9fd74af3581320a24fdf80d40.jpg\",\n  \"530627.jpg\": \"Insecta/Scoliopteryx libatrix/e235e205c4c21a2f52b5edce19491abd.jpg\",\n  \"530628.jpg\": \"Aves/Vireo cassinii/198de5c482579b0204cd3e7913345d63.jpg\",\n  \"530637.jpg\": \"Aves/Vireo cassinii/31ff5972ae351e822fd3f1c396119e78.jpg\",\n  \"53064.jpg\": \"Aves/Nestor meridionalis septentrionalis/739d7eaaf6fef63d9c44695936456c81.jpg\",\n  \"53069.jpg\": \"Aves/Nestor meridionalis septentrionalis/6d691dbf4203fa6f130307b3ddf734ab.jpg\",\n  \"530706.jpg\": \"Insecta/Battus philenor/e7176645272638421b0ec82965f7a82c.jpg\",\n  \"530709.jpg\": \"Insecta/Battus philenor/1b7b9cd1b68464c835239d1f31ade0cf.jpg\",\n  \"530742.jpg\": \"Insecta/Battus philenor/7c62ca77e5a022030c0e443b5c13cfae.jpg\",\n  \"530744.jpg\": \"Insecta/Battus philenor/d6210ce7f99fd1573f92a6819e1d6db6.jpg\",\n  \"5308.jpg\": \"Amphibia/Plethodon cinereus/e401fc844252d0224e9ea642bf9f3322.jpg\",\n  \"530802.jpg\": \"Insecta/Battus philenor/16b3cf0b13e706948161b36237544f94.jpg\",\n  \"530817.jpg\": \"Insecta/Battus philenor/f26b20dd2cd17eb084e2b7af7dc6cab9.jpg\",\n  \"53082.jpg\": \"Aves/Nestor meridionalis septentrionalis/c543956df6632c457250833838cd9bea.jpg\",\n  \"530831.jpg\": \"Insecta/Battus philenor/55a80859a0f905cf54bbe87fd5eeb91d.jpg\",\n  \"53087.jpg\": \"Aves/Nestor meridionalis septentrionalis/b88bc25bc54f676311a3e542d326a9a9.jpg\",\n  \"530902.jpg\": \"Insecta/Battus philenor/d390cd10c82a1a3c6032650fbb7f3806.jpg\",\n  \"53093.jpg\": \"Aves/Nestor meridionalis septentrionalis/acbc437b135bc50167449ca842b3472d.jpg\",\n  \"530937.jpg\": \"Aves/Ara militaris/830d686ca535dffe8ecd1eb8f2d894f0.jpg\",\n  \"53094.jpg\": \"Aves/Nestor meridionalis septentrionalis/2587a79615b7e16072ec1e199e3bb71e.jpg\",\n  \"530951.jpg\": \"Insecta/Anaea andria/f6a229522dbfcd49ff04394660d4f4de.jpg\",\n  \"530952.jpg\": \"Insecta/Anaea andria/ca069416b51d75e89a674a072d897e89.jpg\",\n  \"530954.jpg\": \"Insecta/Anaea andria/f3c325f6bde95eaae8411e6649f99866.jpg\",\n  \"530964.jpg\": \"Insecta/Anaea andria/71a737d1c3ba3566ed0581198b9691a5.jpg\",\n  \"530972.jpg\": \"Insecta/Anaea andria/df18251474a00a7b904e8b44fd9acbd0.jpg\",\n  \"530974.jpg\": \"Insecta/Anaea andria/75968d9ed218427195c72a5e6bf72a56.jpg\",\n  \"530975.jpg\": \"Insecta/Anaea andria/51c2cf74d31d20f73a551c46ce1ea939.jpg\",\n  \"530976.jpg\": \"Insecta/Anaea andria/009b07015b702f62db03b1fc34ace816.jpg\",\n  \"530978.jpg\": \"Insecta/Anaea andria/703880ede0541bfa5733f00fed3f0b28.jpg\",\n  \"530981.jpg\": \"Insecta/Anaea andria/670eeae9d90f63b25c2cf43fc563f0f4.jpg\",\n  \"530990.jpg\": \"Aves/Icterus parisorum/85c144df856beff9f7561d53f322c563.jpg\",\n  \"530992.jpg\": \"Aves/Icterus parisorum/3a7ae5965e2360660c48a2afd8a32a2f.jpg\",\n  \"530993.jpg\": \"Aves/Icterus parisorum/6f09425b599c8d2f1f819c6e69333d0b.jpg\",\n  \"530997.jpg\": \"Aves/Icterus parisorum/22330a7d9fefcfab78a761df6d173bce.jpg\",\n  \"531.jpg\": \"Mammalia/Ursus americanus/eb9f8f9432d0875666be7a56e43389f7.jpg\",\n  \"531002.jpg\": \"Aves/Icterus parisorum/44e2c15bdb19cd41df195a2779055488.jpg\",\n  \"531003.jpg\": \"Aves/Icterus parisorum/c2c873b418434a524379e2354905577e.jpg\",\n  \"531004.jpg\": \"Aves/Icterus parisorum/f6e6c5466ec9f16669ef4cc9fe6b5c52.jpg\",\n  \"531007.jpg\": \"Aves/Icterus parisorum/0cf8b184e66bb6f37044c37311c0a726.jpg\",\n  \"531008.jpg\": \"Aves/Icterus parisorum/2d373dae931239261cbea1fbf5771136.jpg\",\n  \"531009.jpg\": \"Aves/Icterus parisorum/70995e80fb769e0851964578d409f05a.jpg\",\n  \"531019.jpg\": \"Aves/Milvus milvus/fa4031f487dc48ef0b058cc955abb15e.jpg\",\n  \"531024.jpg\": \"Aves/Milvus milvus/bd8b772648dcfbc73acf109d74cb97b4.jpg\",\n  \"531040.jpg\": \"Aves/Milvus milvus/0d9b56fc2ab33610257728eb9812779c.jpg\",\n  \"531047.jpg\": \"Aves/Milvus milvus/646b68fcc019b02b6f1ec41c77482b60.jpg\",\n  \"531048.jpg\": \"Aves/Milvus milvus/ef9c8544d61ba83b2effecaec9805e12.jpg\",\n  \"531049.jpg\": \"Aves/Milvus milvus/1cbd494412f24e552c02288490ec92bb.jpg\",\n  \"531050.jpg\": \"Aves/Milvus milvus/f4302208756039ea4143cc07d306b631.jpg\",\n  \"531055.jpg\": \"Aves/Milvus milvus/87ec510226ec8d21576cdbce06ea3525.jpg\",\n  \"531056.jpg\": \"Aves/Milvus milvus/71c978c6a14fadf82ede35cd2dd31ad4.jpg\",\n  \"531075.jpg\": \"Aves/Thalasseus sandvicensis/8f6c413fc6aaf5a14d5fb0b5f3bc18be.jpg\",\n  \"531106.jpg\": \"Aves/Thalasseus sandvicensis/6ee425558b75ad97f9aa423d597b6319.jpg\",\n  \"531121.jpg\": \"Aves/Thalasseus sandvicensis/8f8d51edafc8e9cb0f2370abb6841f23.jpg\",\n  \"531126.jpg\": \"Aves/Thalasseus sandvicensis/11c5ee438550bda1bd0433bc92733e16.jpg\",\n  \"531132.jpg\": \"Insecta/Trichopoda pennipes/678adf931aee9a35ea99d572fc9f7bae.jpg\",\n  \"531155.jpg\": \"Insecta/Scopula rubraria/adc539b2358026f99d7ad0da2f321f88.jpg\",\n  \"531156.jpg\": \"Insecta/Scopula rubraria/0fc6c2e4f7f6c751c03bd1e3378d0d8a.jpg\",\n  \"531182.jpg\": \"Mammalia/Equus quagga/7983877086f0e609d2906459171e0dd0.jpg\",\n  \"531187.jpg\": \"Mammalia/Equus quagga/519062447b70b708b541b0771f377cf1.jpg\",\n  \"531188.jpg\": \"Mammalia/Equus quagga/dd8fb7831a4622feff4fb6120a49ef7f.jpg\",\n  \"531190.jpg\": \"Mammalia/Equus quagga/acb505c01553f4fcbf18b8cc97650baa.jpg\",\n  \"531191.jpg\": \"Mammalia/Equus quagga/ce131a5fed018888f8a039c40488ab0a.jpg\",\n  \"531213.jpg\": \"Insecta/Enallagma civile/b8c93b521dfd1da2460e8661c0a90fa1.jpg\",\n  \"531243.jpg\": \"Insecta/Enallagma civile/2327bab36829dd207ac0a99b5e5ed60e.jpg\",\n  \"531257.jpg\": \"Insecta/Enallagma civile/0b4eaf4b3b4872f1f3b027a0c8d7b141.jpg\",\n  \"531285.jpg\": \"Insecta/Enallagma civile/b3b8171ca9aed840a1b2a59adcb359bb.jpg\",\n  \"5313.jpg\": \"Amphibia/Plethodon cinereus/65dcb94eb5e2be577c6fa1118a8ef751.jpg\",\n  \"531380.jpg\": \"Aves/Actitis macularius/78b012a45143d1e1ff1102f9dc4f0699.jpg\",\n  \"531419.jpg\": \"Aves/Actitis macularius/c017d87e4864b3655d1e1de62c8d132d.jpg\",\n  \"531444.jpg\": \"Aves/Actitis macularius/42b58ff44dfbb8128368bf7d1b219846.jpg\",\n  \"531466.jpg\": \"Aves/Actitis macularius/9ad232236aa158bfdec78a5de9382dea.jpg\",\n  \"531490.jpg\": \"Aves/Actitis macularius/8aa6d3550fafe5a2d132a1113d96f389.jpg\",\n  \"531526.jpg\": \"Aves/Actitis macularius/5f1070c8e6935fe6bdc3a1039114287d.jpg\",\n  \"531563.jpg\": \"Aves/Actitis macularius/9e7548238873bf39df820fb94052af49.jpg\",\n  \"531598.jpg\": \"Aves/Actitis macularius/b1c03fb5978b91d71f7d2ffa89d2a884.jpg\",\n  \"531604.jpg\": \"Aves/Actitis macularius/f062d8e6310f67fd1f37ffae85e0c112.jpg\",\n  \"531623.jpg\": \"Aves/Actitis macularius/621bbef6b2dc5780afcbe23cbf83946b.jpg\",\n  \"531707.jpg\": \"Insecta/Crocothemis servilia/7043ae87d1483943a0c7ded55a27bb17.jpg\",\n  \"5318.jpg\": \"Amphibia/Plethodon cinereus/dad81d265ecc04e39cbd978066e584c9.jpg\",\n  \"531839.jpg\": \"Animalia/Parastichopus californicus/e7960369d7677904434ec50e0bc88a45.jpg\",\n  \"531840.jpg\": \"Animalia/Parastichopus californicus/75ac27407a673d4f7443c9527fc00565.jpg\",\n  \"531884.jpg\": \"Insecta/Lethe anthedon/0abe5fb6bee53f082377820c178badc5.jpg\",\n  \"531892.jpg\": \"Insecta/Lethe anthedon/9378b26b9c3048a9102f91708e6f29a8.jpg\",\n  \"531905.jpg\": \"Insecta/Lethe anthedon/f02711669f77ae415e8677ceab794535.jpg\",\n  \"531912.jpg\": \"Insecta/Lethe anthedon/f17a39e4e96c21fca2fbc8156d37e8ab.jpg\",\n  \"531924.jpg\": \"Insecta/Lethe anthedon/5ea3dfcbb0342d1ceff62daad2c3fe81.jpg\",\n  \"531928.jpg\": \"Insecta/Lethe anthedon/aa3ab622ccda27b9c50a59cf82782f72.jpg\",\n  \"531929.jpg\": \"Insecta/Lethe anthedon/6a19f2b6f0a181541d46cd63ca9eedbc.jpg\",\n  \"531999.jpg\": \"Aves/Sitta europaea/07a05c860bf79198e495109de28e1f7d.jpg\",\n  \"532005.jpg\": \"Aves/Sitta europaea/bdb9876782de376596cf195727afad33.jpg\",\n  \"532006.jpg\": \"Aves/Sitta europaea/ba7e466625d40c656952d9a2e7a5a720.jpg\",\n  \"532014.jpg\": \"Aves/Sitta europaea/fdf1698e5c9008eba95ac23bf713c12a.jpg\",\n  \"532015.jpg\": \"Aves/Sitta europaea/3e7d693e4e8e9a893de945155b76ac6e.jpg\",\n  \"532016.jpg\": \"Aves/Sitta europaea/7b5b955786b16ed2b00bff3150ebf7cc.jpg\",\n  \"532031.jpg\": \"Aves/Sitta europaea/86f35d9ce392b87b307969107421fc4f.jpg\",\n  \"532037.jpg\": \"Aves/Sitta europaea/fa8d2eacfc7209450ad87a1a62cfb54c.jpg\",\n  \"532039.jpg\": \"Aves/Sitta europaea/6dad182f8c00548c1501bcb8a7895695.jpg\",\n  \"532163.jpg\": \"Mammalia/Rattus rattus/b07d27a5afc26750bbcf65fa65402eb9.jpg\",\n  \"532164.jpg\": \"Mammalia/Rattus rattus/53a86e77486b2056cae876cada3cb8ac.jpg\",\n  \"532170.jpg\": \"Mammalia/Rattus rattus/320605cfbf87efb8778cc73785ef97c3.jpg\",\n  \"532181.jpg\": \"Mammalia/Rattus rattus/10277e468d397060a9de103c538ef38e.jpg\",\n  \"532182.jpg\": \"Mammalia/Rattus rattus/945db4d5dafe00f3bd8b8ecbed4ee726.jpg\",\n  \"532183.jpg\": \"Mammalia/Rattus rattus/21e96d39342ea6e21525003af98f6f8b.jpg\",\n  \"532190.jpg\": \"Mammalia/Rattus rattus/fc46ce00474dccbb1f52949c72a90816.jpg\",\n  \"532198.jpg\": \"Insecta/Chalybion californicum/4dfb41f37389f1fa315fc8d8ea1afb4a.jpg\",\n  \"532206.jpg\": \"Insecta/Eumorpha vitis/be9a43dcaa82beb1be9f1c1b6bf3c5f9.jpg\",\n  \"532207.jpg\": \"Insecta/Eumorpha vitis/6aba16d05beeed5c9c4b7e15d12365bd.jpg\",\n  \"532215.jpg\": \"Insecta/Eumorpha vitis/de86cefc25da588ee9f6c319c8ff85f5.jpg\",\n  \"532231.jpg\": \"Insecta/Pyrausta laticlavia/c7a2aeb1f7dbeefa0ee234a434fbf1ce.jpg\",\n  \"532232.jpg\": \"Insecta/Pyrausta laticlavia/9550dc2a21b99cda2e638f76d2ce2f41.jpg\",\n  \"532244.jpg\": \"Aves/Lanius collurio/8ef1575ad45cf601e9077e1431758dcc.jpg\",\n  \"532248.jpg\": \"Aves/Lanius collurio/62ace0600f9b7c805c5fb09c5b7af1f8.jpg\",\n  \"532249.jpg\": \"Aves/Lanius collurio/1af29e9dfcde1773c6322bc17a3a93f2.jpg\",\n  \"532251.jpg\": \"Aves/Lanius collurio/a18aadbeff8c110897b91d0d813ae3d9.jpg\",\n  \"532256.jpg\": \"Aves/Lanius collurio/080f26adfa85fc830ee07ed693bf3bfe.jpg\",\n  \"532261.jpg\": \"Aves/Lanius collurio/cda15d1567dcc84636851920b4a177e4.jpg\",\n  \"532262.jpg\": \"Aves/Lanius collurio/faf91da2ab5894f8a195f285867f8802.jpg\",\n  \"532263.jpg\": \"Aves/Lanius collurio/873e95e0c73ae416db52e03ff3d98e8d.jpg\",\n  \"532264.jpg\": \"Aves/Lanius collurio/016a37f2f4daf40f0d8ebb2a0ee0f720.jpg\",\n  \"532266.jpg\": \"Aves/Lanius collurio/042cb2a6ca6f7bfd84dd2b148fcd703c.jpg\",\n  \"532276.jpg\": \"Aves/Lanius collurio/c97f24fff74aaebed5792265b0584cf1.jpg\",\n  \"532277.jpg\": \"Aves/Lanius collurio/42fce2bb9d62f2687dc01fcdc1e54c02.jpg\",\n  \"532278.jpg\": \"Aves/Lanius collurio/bacce9a97b651240404fe32d2944ec80.jpg\",\n  \"532307.jpg\": \"Amphibia/Rana aurora/edf7cadc50d7787d92b879628622d869.jpg\",\n  \"532314.jpg\": \"Amphibia/Rana aurora/166fc19aac63f8e326a580cbcc1e70d9.jpg\",\n  \"532315.jpg\": \"Amphibia/Rana aurora/7aaccc88b19e683c14628a619e3f88ea.jpg\",\n  \"532316.jpg\": \"Amphibia/Rana aurora/51fc16f3e1489c58237f43eb287d40c6.jpg\",\n  \"532317.jpg\": \"Amphibia/Rana aurora/ee513b148cbc8f09ccf71d1ad02dea53.jpg\",\n  \"532319.jpg\": \"Amphibia/Rana aurora/e325685d716d63cb23efd2ab90cfb974.jpg\",\n  \"532320.jpg\": \"Insecta/Phaeoura quernaria/ad0cd210e8f6d1e8f257c0fc392de8cd.jpg\",\n  \"532333.jpg\": \"Insecta/Ischnura posita/01cdb8f7112725f8a2f2b990d5a0e052.jpg\",\n  \"532456.jpg\": \"Insecta/Ischnura posita/7641570a7af91c3ef0cb8ccd07aedf7d.jpg\",\n  \"532465.jpg\": \"Insecta/Ischnura posita/0bf1f98eaa43c81e1b2233ff3933c158.jpg\",\n  \"532468.jpg\": \"Insecta/Ischnura posita/7ad0a5618ffd6065cf1ee271c2dbced9.jpg\",\n  \"532538.jpg\": \"Reptilia/Apalone spinifera/269fc84b3a5844ffa502f51182788baa.jpg\",\n  \"532548.jpg\": \"Reptilia/Apalone spinifera/5bb57bab824cc9623f1e1b0d28357f55.jpg\",\n  \"532566.jpg\": \"Reptilia/Apalone spinifera/8e6fcd55a5f107e507609427b435b999.jpg\",\n  \"532567.jpg\": \"Reptilia/Apalone spinifera/8d46baf105749e87ad8755979888cdca.jpg\",\n  \"532570.jpg\": \"Reptilia/Apalone spinifera/edfbfd7b625724bb8028a9c20476beff.jpg\",\n  \"532571.jpg\": \"Reptilia/Apalone spinifera/ab0cdf5e5283d70522e265037e79de0c.jpg\",\n  \"532591.jpg\": \"Reptilia/Apalone spinifera/3a1695a6268806a57a5247c08266aae3.jpg\",\n  \"532608.jpg\": \"Insecta/Astraptes fulgerator/4e621c8e71511f54e0e28fbb5e8fa685.jpg\",\n  \"532626.jpg\": \"Insecta/Astraptes fulgerator/62ef69813f752fc6ad8378a3b2c67944.jpg\",\n  \"532628.jpg\": \"Insecta/Dryas iulia moderata/c6549abcb0d0c2ed037b365df1895a21.jpg\",\n  \"532629.jpg\": \"Insecta/Dryas iulia moderata/9f4f67dcffe4b855f5b2cfac373698b5.jpg\",\n  \"532649.jpg\": \"Amphibia/Hyla plicata/7f1bcf24eb86906245bf816a42dd2e55.jpg\",\n  \"532651.jpg\": \"Amphibia/Hyla plicata/9eadafecaf8535a24e2d3d21464d019e.jpg\",\n  \"532653.jpg\": \"Arachnida/Latrodectus geometricus/d2b7b58a5483da65e88844c886276194.jpg\",\n  \"532658.jpg\": \"Arachnida/Latrodectus geometricus/bdde1c3fac8b732e860d5173283c364e.jpg\",\n  \"532661.jpg\": \"Arachnida/Latrodectus geometricus/642d265d7de466505c16642730a561e0.jpg\",\n  \"532662.jpg\": \"Arachnida/Latrodectus geometricus/88588c0a82fc3c3ff50b03eaf7830937.jpg\",\n  \"532663.jpg\": \"Arachnida/Latrodectus geometricus/5de1c07e985ec8f8dddc574772dbed23.jpg\",\n  \"532679.jpg\": \"Arachnida/Latrodectus geometricus/b758788c3eeae86f2a83be88983cb4ad.jpg\",\n  \"532688.jpg\": \"Arachnida/Latrodectus geometricus/db479663f85efb5c4509634ec05fc6df.jpg\",\n  \"532737.jpg\": \"Aves/Carduelis carduelis/e245e4221fdbda9109757e8fa1dfa8fa.jpg\",\n  \"532739.jpg\": \"Aves/Carduelis carduelis/b4827144f4bfe669454de88c7498b5f7.jpg\",\n  \"53274.jpg\": \"Insecta/Lyssa zampa/71bdda9f852d6531ce4611c28b4af27d.jpg\",\n  \"532751.jpg\": \"Aves/Carduelis carduelis/ee654e23735e5843c9dae1683eeb6df9.jpg\",\n  \"532753.jpg\": \"Aves/Carduelis carduelis/f6ff98f8ded0ccb6417b25831f69da00.jpg\",\n  \"53276.jpg\": \"Insecta/Lyssa zampa/96307007e7088c8447e337e905273b4a.jpg\",\n  \"532765.jpg\": \"Aves/Carduelis carduelis/36b099cc9f463e59560e758e8711579d.jpg\",\n  \"53278.jpg\": \"Insecta/Lyssa zampa/5da90b38da4261a86d111d11b722dd1e.jpg\",\n  \"53281.jpg\": \"Insecta/Lyssa zampa/626994cba00ac331b7e402617ce00f43.jpg\",\n  \"53282.jpg\": \"Insecta/Lyssa zampa/d3613a50dbe07f9b584ecba4ba321732.jpg\",\n  \"53283.jpg\": \"Insecta/Lyssa zampa/95f0082b27bfc2831855895987d31325.jpg\",\n  \"532844.jpg\": \"Insecta/Hetaerina americana/7a57b10035b515518728b658175b0a8d.jpg\",\n  \"532846.jpg\": \"Insecta/Hetaerina americana/0327513f777e2a5d1c8bcb2a09b91d6a.jpg\",\n  \"532861.jpg\": \"Insecta/Hetaerina americana/9039693ed9bd2fc599cb75ef8373d13d.jpg\",\n  \"53287.jpg\": \"Insecta/Lyssa zampa/75f1351b22622bb4e8ff3fd62e0aa18f.jpg\",\n  \"532893.jpg\": \"Insecta/Hetaerina americana/c9db3c81854fe230a2ea3c7b9bc2c947.jpg\",\n  \"53292.jpg\": \"Insecta/Lyssa zampa/c36285719c111d076016f2bed17940a1.jpg\",\n  \"532921.jpg\": \"Insecta/Hetaerina americana/d5671281a334a46e90e5669c536b571d.jpg\",\n  \"53293.jpg\": \"Insecta/Lyssa zampa/39d8a4ff90bd30ad9f6a9bf90040f6fa.jpg\",\n  \"53294.jpg\": \"Insecta/Lyssa zampa/88c83bf79c28e5f63bbf61fd6a82f23a.jpg\",\n  \"532948.jpg\": \"Insecta/Hetaerina americana/2fd002665a34264660a81a555e38ebb8.jpg\",\n  \"53295.jpg\": \"Insecta/Lyssa zampa/85e23949bd68265b60c9381be996aaba.jpg\",\n  \"532957.jpg\": \"Insecta/Hetaerina americana/3ad309e64e9756c7e6e1f7086562ffd0.jpg\",\n  \"53296.jpg\": \"Insecta/Lyssa zampa/bd893c4360a7730158b84de24d9d77f1.jpg\",\n  \"53297.jpg\": \"Insecta/Lyssa zampa/91fa60aefb020fd495605f32823055b0.jpg\",\n  \"53298.jpg\": \"Insecta/Lyssa zampa/b8beb1c2a95b3a79ac6c5dca89b252c8.jpg\",\n  \"532983.jpg\": \"Mammalia/Sylvilagus palustris/219941fc461c3728a871b20a5e25f7a4.jpg\",\n  \"53299.jpg\": \"Insecta/Lyssa zampa/4cac830e24201120afbce24f5e4470f9.jpg\",\n  \"532992.jpg\": \"Mammalia/Erethizon dorsatum/21a551ecd02d72a0e6ce9cefb8a9687b.jpg\",\n  \"533028.jpg\": \"Mammalia/Erethizon dorsatum/6d61cd78bb426bd4abfe61057570f785.jpg\",\n  \"53304.jpg\": \"Insecta/Lyssa zampa/d3b8e6f38fee85163ab7e1189b2a6bde.jpg\",\n  \"53305.jpg\": \"Insecta/Lyssa zampa/5db7e9d10959d3559b9a53ab6062ba5c.jpg\",\n  \"533051.jpg\": \"Mammalia/Erethizon dorsatum/5c01b2581285f4c2940f46159f589a40.jpg\",\n  \"533061.jpg\": \"Mammalia/Erethizon dorsatum/9dd7543f0e286c24831a1af308d52c3f.jpg\",\n  \"533068.jpg\": \"Mammalia/Erethizon dorsatum/3b0dbec4da3419d00f74beed3030229f.jpg\",\n  \"53307.jpg\": \"Insecta/Lyssa zampa/9552bdd2d93625732fbcd198e41e4662.jpg\",\n  \"533072.jpg\": \"Mammalia/Erethizon dorsatum/bbbfd92aa7d27ae662afcc5dc1a6d18a.jpg\",\n  \"53308.jpg\": \"Insecta/Lyssa zampa/5d0903835be317aee699a241d27a2205.jpg\",\n  \"53310.jpg\": \"Insecta/Lyssa zampa/b8807dd49b694b114911a62f7b94beb6.jpg\",\n  \"533120.jpg\": \"Animalia/Romaleon antennarium/c3ae3a20bc423d6341ce60c30e0b7433.jpg\",\n  \"533123.jpg\": \"Animalia/Romaleon antennarium/84c17eb646961085a8ededafab0f4e66.jpg\",\n  \"533133.jpg\": \"Animalia/Romaleon antennarium/1b0de6681e5afe184cdbabb7e1a55e87.jpg\",\n  \"533135.jpg\": \"Mollusca/Geukensia demissa/f807a12fbe2d6be9eba9d61e87750165.jpg\",\n  \"533143.jpg\": \"Mollusca/Geukensia demissa/603e8679e53fac943d94d6f42f5dab9a.jpg\",\n  \"533146.jpg\": \"Insecta/Anartia fatima/1866637d2011ca77154db44b77df2f82.jpg\",\n  \"533149.jpg\": \"Insecta/Anartia fatima/3956d20681a759a57eaf1a8a9aa6aec3.jpg\",\n  \"533155.jpg\": \"Insecta/Anartia fatima/2590d8302994dd58e1d7d60ae8cab657.jpg\",\n  \"533156.jpg\": \"Insecta/Anartia fatima/284e2c42838309ae2a63ddb30e756fbd.jpg\",\n  \"533160.jpg\": \"Insecta/Anartia fatima/228c4cf6171c6f6e879f70aaccbf0bde.jpg\",\n  \"533161.jpg\": \"Insecta/Anartia fatima/d746c5f33ce6796857445913775ffa08.jpg\",\n  \"533162.jpg\": \"Insecta/Anartia fatima/e8720261841ce3a5aba9c5d8ad27cca8.jpg\",\n  \"533166.jpg\": \"Insecta/Anartia fatima/c1e90c2d86ebc849e1abc31863cefb8b.jpg\",\n  \"533170.jpg\": \"Insecta/Anartia fatima/63a5b582a557b4f63434adcc03af8051.jpg\",\n  \"533171.jpg\": \"Insecta/Anartia fatima/f4fe1a49c202fa18c5e152c46307a52a.jpg\",\n  \"533222.jpg\": \"Aves/Riparia riparia/70de8cb4663d4ef6f5509544d72e011e.jpg\",\n  \"533223.jpg\": \"Aves/Riparia riparia/c2562eea8af4d27ecde86126dd9dd49f.jpg\",\n  \"533235.jpg\": \"Aves/Riparia riparia/d2db251a9febd08faf46945e50c5291f.jpg\",\n  \"533247.jpg\": \"Aves/Scolopax minor/c1b34b274cc32d246375989aaf20912b.jpg\",\n  \"533255.jpg\": \"Aves/Scolopax minor/35c9d30c09e1b313daea1ef8b114cf87.jpg\",\n  \"533259.jpg\": \"Aves/Scolopax minor/7b14bd8d78435f78782f135462b4906e.jpg\",\n  \"533261.jpg\": \"Aves/Scolopax minor/6c2a657ff382c61c113ff2d530a2c4be.jpg\",\n  \"533262.jpg\": \"Aves/Scolopax minor/5e872bcc215a6e849e4ad128b25bbd17.jpg\",\n  \"533265.jpg\": \"Aves/Scolopax minor/4f58d8a53a1252c702bbb558aef6e2c4.jpg\",\n  \"533266.jpg\": \"Aves/Scolopax minor/3f5463dab6034075134845cb22a0bebe.jpg\",\n  \"533314.jpg\": \"Insecta/Haploa lecontei/c77045629db2c89d8f4bbc872822542f.jpg\",\n  \"533321.jpg\": \"Insecta/Haploa lecontei/11eca3a7c65ffb5f56fd26d7c90d7362.jpg\",\n  \"533392.jpg\": \"Aves/Lonchura oryzivora/b6c1a4eb0e584349648334ff2928fbfb.jpg\",\n  \"533404.jpg\": \"Insecta/Chlosyne janais/46db2551b94c29baa53109dc75d80624.jpg\",\n  \"533411.jpg\": \"Insecta/Chlosyne janais/95449789cf59d33f17011815b5ac0d4b.jpg\",\n  \"533412.jpg\": \"Insecta/Chlosyne janais/3349df0627769f673d878c8f3bbf5cbe.jpg\",\n  \"533413.jpg\": \"Insecta/Chlosyne janais/0845376f842d51a6be7e8da5f01e920b.jpg\",\n  \"533416.jpg\": \"Insecta/Chlosyne janais/d44722c216e547992c7c6034e9cba779.jpg\",\n  \"533422.jpg\": \"Insecta/Chlosyne janais/de30cf76c823b2b6ed1fcf1e53664d1c.jpg\",\n  \"533424.jpg\": \"Insecta/Chlosyne janais/f83fa652beb0a364c730489dd03ad52f.jpg\",\n  \"533426.jpg\": \"Insecta/Chlosyne janais/fe04006f7f05bda102ce79125ec9e86d.jpg\",\n  \"533430.jpg\": \"Mollusca/Phidiana hiltoni/150e12319d6f2427f82a13609dc67506.jpg\",\n  \"533432.jpg\": \"Mollusca/Phidiana hiltoni/9a9f5d26b11b64f76dcfdcaee71e9b88.jpg\",\n  \"533437.jpg\": \"Mollusca/Phidiana hiltoni/58dee6c0645042f56419129cffb09939.jpg\",\n  \"533454.jpg\": \"Mollusca/Phidiana hiltoni/b4925137cdb0864120e9b739dcaceb83.jpg\",\n  \"533456.jpg\": \"Mollusca/Phidiana hiltoni/3888a907fa45d0b87d2f897551db5a05.jpg\",\n  \"533459.jpg\": \"Mollusca/Phidiana hiltoni/10c3251fc76ed5a5854fb55e2277d622.jpg\",\n  \"533465.jpg\": \"Mollusca/Phidiana hiltoni/8b854c2b8560a601271728a193a87541.jpg\",\n  \"533474.jpg\": \"Mollusca/Phidiana hiltoni/b71ce0de6c6c973fd6ebb505f1d9f4c6.jpg\",\n  \"533482.jpg\": \"Mollusca/Phidiana hiltoni/f9816e831f7ff96be71cdb2d73385d85.jpg\",\n  \"533486.jpg\": \"Mollusca/Phidiana hiltoni/deb8ef78e5c85fc5ed28e511c7416f83.jpg\",\n  \"533487.jpg\": \"Mollusca/Phidiana hiltoni/91f946b581fdbab8efa4fa0b3866de91.jpg\",\n  \"533489.jpg\": \"Mollusca/Phidiana hiltoni/407a33ab7fb6bfaf1c231c6fb5c63f79.jpg\",\n  \"533491.jpg\": \"Mollusca/Phidiana hiltoni/e726c79c002b77ed6f7a991df5056806.jpg\",\n  \"533492.jpg\": \"Mollusca/Phidiana hiltoni/aa85367d08911fccad930bc4237876be.jpg\",\n  \"533494.jpg\": \"Mollusca/Phidiana hiltoni/adb27d02274e6b76bd01eaf6cc619093.jpg\",\n  \"533495.jpg\": \"Mollusca/Phidiana hiltoni/f0b55cfb31903152a6ff1e949d0a08ec.jpg\",\n  \"533497.jpg\": \"Mollusca/Phidiana hiltoni/e5ee16bb4192005e638f41d292a3e5a0.jpg\",\n  \"533499.jpg\": \"Mollusca/Phidiana hiltoni/11365b4819e01dab5f3771db6b6436db.jpg\",\n  \"533503.jpg\": \"Actinopterygii/Sphyraena barracuda/2df46c36181c6540384d8b482b86f1e8.jpg\",\n  \"533515.jpg\": \"Insecta/Delphinia picta/dc16c69039f473079306dd12983b3fd9.jpg\",\n  \"533524.jpg\": \"Insecta/Delphinia picta/8c113ebc1a769714d95080174c12878b.jpg\",\n  \"533530.jpg\": \"Insecta/Delphinia picta/964c38c8c9dc18bc2b3436b15aab4cde.jpg\",\n  \"533545.jpg\": \"Aves/Spizella pallida/7f1b82ceb932e44e0f81934824976155.jpg\",\n  \"533559.jpg\": \"Aves/Spizella pallida/82c187c7e702d9b2d4b4642dae4eb2b5.jpg\",\n  \"533568.jpg\": \"Aves/Spizella pallida/89445d1f95223d56f8de421ba148655a.jpg\",\n  \"533574.jpg\": \"Aves/Spizella pallida/503f2c83cd502676637f50f0edf23640.jpg\",\n  \"533575.jpg\": \"Aves/Spizella pallida/a64511676f00f008379c9b77724b015b.jpg\",\n  \"533577.jpg\": \"Aves/Spizella pallida/3c22bdfafb386785a127e99c056fd6b4.jpg\",\n  \"533578.jpg\": \"Aves/Spizella pallida/0d8b86578b63aa19e15217359a3bb1cb.jpg\",\n  \"533581.jpg\": \"Aves/Spizella pallida/e132e64a38dece1a2ca14d1ffe999476.jpg\",\n  \"533591.jpg\": \"Aves/Spizella pallida/04ca3b5a1723cccd5c8cd078cddeacb6.jpg\",\n  \"533651.jpg\": \"Aves/Sayornis phoebe/5d66ac01da9421753e3a1d13f318f502.jpg\",\n  \"533751.jpg\": \"Aves/Sayornis phoebe/c347ff8b589d03efa4e1659c1e4bfcc2.jpg\",\n  \"533767.jpg\": \"Aves/Sayornis phoebe/9660dcb99ff1f1f81620f8a7ff8ff03d.jpg\",\n  \"533823.jpg\": \"Aves/Sayornis phoebe/894ed74ea54f62221b070e2d9c07082e.jpg\",\n  \"533889.jpg\": \"Aves/Sayornis phoebe/34b6d1d4963875bab7415ee99c689976.jpg\",\n  \"53396.jpg\": \"Aves/Philesturnus rufusater/15f99a345ce87a70c3510af5fe56d7b9.jpg\",\n  \"533992.jpg\": \"Aves/Sayornis phoebe/d00cdaba4cc86be380bcba6a2eba4fb9.jpg\",\n  \"533999.jpg\": \"Aves/Sayornis phoebe/25788d9fa5ee0f3e107e909693c5c347.jpg\",\n  \"53400.jpg\": \"Aves/Philesturnus rufusater/9621b92a67c70fb321623755cc513735.jpg\",\n  \"534003.jpg\": \"Aves/Sayornis phoebe/dcba532346a8539f1206228dabb75278.jpg\",\n  \"53404.jpg\": \"Aves/Philesturnus rufusater/75fda91b66615dae51427eb3808fddf0.jpg\",\n  \"534059.jpg\": \"Aves/Melanotis caerulescens/c4599908a31aadb41974ebd274d99724.jpg\",\n  \"53406.jpg\": \"Aves/Philesturnus rufusater/da3dfeaa6f8ca566be4cb0b765295720.jpg\",\n  \"534061.jpg\": \"Aves/Melanotis caerulescens/fa88f3d9f83d36ee08739f39b3a88165.jpg\",\n  \"534078.jpg\": \"Reptilia/Caiman crocodilus/5c72f9c530846112250decc5f80e2739.jpg\",\n  \"534080.jpg\": \"Reptilia/Caiman crocodilus/fabe1a13dbb05a58c624ec5d9740fb7e.jpg\",\n  \"53410.jpg\": \"Aves/Philesturnus rufusater/1f259095483ddafe56093b25b7cb2b2b.jpg\",\n  \"534115.jpg\": \"Aves/Egretta gularis/64b4fecd23083f91e32bbf7856d3394d.jpg\",\n  \"534250.jpg\": \"Insecta/Diplacodes trivialis/04370995c585d40f2f8a00b12f5ebe4c.jpg\",\n  \"534305.jpg\": \"Amphibia/Oophaga pumilio/93aa4e5e3ded3db1434d84e4fde10bd8.jpg\",\n  \"534310.jpg\": \"Amphibia/Oophaga pumilio/0260b84997fed44c449a8c29b8c45f7d.jpg\",\n  \"534316.jpg\": \"Amphibia/Oophaga pumilio/b6dac7c8725e99cab7ddb5aca6e246ee.jpg\",\n  \"534317.jpg\": \"Amphibia/Oophaga pumilio/903d57ee285964f80391e4757cab47b2.jpg\",\n  \"534329.jpg\": \"Mammalia/Alouatta pigra/5d9f6ae5122be32cde04db6454376555.jpg\",\n  \"534363.jpg\": \"Insecta/Brephidium exilis/634e99ae8454bdede8103b4949d05e33.jpg\",\n  \"534379.jpg\": \"Insecta/Brephidium exilis/2c535f956f6783622157be9252b0db69.jpg\",\n  \"534432.jpg\": \"Insecta/Brephidium exilis/6767a247292b8cf1b3c73a89f9d94cbf.jpg\",\n  \"534436.jpg\": \"Insecta/Brephidium exilis/74c7b72001cf0a2d1eb25a9adb45b1a5.jpg\",\n  \"534438.jpg\": \"Insecta/Brephidium exilis/f35e1e166c442212b7b690f707512966.jpg\",\n  \"534484.jpg\": \"Insecta/Brephidium exilis/aea8767767250278e21f478980561ae3.jpg\",\n  \"534516.jpg\": \"Amphibia/Hyla eximia/4dd52e52c5a2f874378cfc1b308a1ce5.jpg\",\n  \"534517.jpg\": \"Amphibia/Hyla eximia/3975b0bf84893f9d1530a3cbb770d113.jpg\",\n  \"534527.jpg\": \"Amphibia/Hyla eximia/7a9aebb9dd381e61eb5d464747eda1e9.jpg\",\n  \"534544.jpg\": \"Arachnida/Araneus marmoreus/9719c724bca90c027f587795fa5ac987.jpg\",\n  \"534549.jpg\": \"Arachnida/Araneus marmoreus/262645801a6f3278463a05228693f8ba.jpg\",\n  \"534552.jpg\": \"Arachnida/Araneus marmoreus/6a80545f25f49767b0ab907cc76b36d0.jpg\",\n  \"534554.jpg\": \"Arachnida/Araneus marmoreus/13fb246f2b776c14247db33e9e881d61.jpg\",\n  \"534555.jpg\": \"Reptilia/Phrynosoma orbiculare/41a7271fb1e172e17b09384fdecc1fdb.jpg\",\n  \"534568.jpg\": \"Reptilia/Phrynosoma orbiculare/e0f8329349467c9758a37ae067984cba.jpg\",\n  \"534569.jpg\": \"Reptilia/Phrynosoma orbiculare/f2850e2410ac5f28a066a375a4d75d02.jpg\",\n  \"534749.jpg\": \"Insecta/Junonia almana/38771382387e82d681b700daa26be599.jpg\",\n  \"534750.jpg\": \"Insecta/Junonia almana/391b620f3c9b34fe35f8d86013ad6339.jpg\",\n  \"534755.jpg\": \"Reptilia/Hierophis viridiflavus/a7659e6bff557a28eadf4ce0ab7bdaf9.jpg\",\n  \"534769.jpg\": \"Reptilia/Hierophis viridiflavus/2a450e32622ec00afb3545fca84155b6.jpg\",\n  \"534774.jpg\": \"Reptilia/Hierophis viridiflavus/be48fd12a3e8a5f2296a1c1753cea53e.jpg\",\n  \"534789.jpg\": \"Aves/Empidonax difficilis/ac56b77d7abf7df726a4cbce36e86eff.jpg\",\n  \"534796.jpg\": \"Aves/Empidonax difficilis/d0a93e5859dcd3665131641a65173841.jpg\",\n  \"534797.jpg\": \"Aves/Empidonax difficilis/b9030c2bf91eb835ce36bd3b4a0fc1d0.jpg\",\n  \"534805.jpg\": \"Aves/Empidonax difficilis/50d99d78917dd3add499f80aedf014b7.jpg\",\n  \"534808.jpg\": \"Aves/Empidonax difficilis/d3e3008efe5774f7cb3ec962fac58753.jpg\",\n  \"534812.jpg\": \"Aves/Empidonax difficilis/ff51134d4a7cf8a0c1dc50a5235dcca8.jpg\",\n  \"534813.jpg\": \"Aves/Empidonax difficilis/db23682cdda7171eb9d830fb3741c159.jpg\",\n  \"534819.jpg\": \"Aves/Empidonax difficilis/4bd702e87f060f04ad120ba2375081b1.jpg\",\n  \"534829.jpg\": \"Aves/Empidonax difficilis/fdeb5af4dbaa31b7630ff2ad42a982d5.jpg\",\n  \"534832.jpg\": \"Aves/Basileuterus rufifrons/6d4420762447f83be39c6333ffd77953.jpg\",\n  \"534834.jpg\": \"Aves/Basileuterus rufifrons/583c1f1f793c42e1eac89403d63a2c46.jpg\",\n  \"534843.jpg\": \"Aves/Basileuterus rufifrons/cab429a30ca95a602e3f791e9b612be2.jpg\",\n  \"534847.jpg\": \"Aves/Basileuterus rufifrons/e976470076f78bc92171003343e99c5d.jpg\",\n  \"534848.jpg\": \"Aves/Basileuterus rufifrons/5a595298efb58a89cd146f48057fad2c.jpg\",\n  \"534849.jpg\": \"Aves/Basileuterus rufifrons/d6333b37c8c08f57c08a50d6edc83a57.jpg\",\n  \"534850.jpg\": \"Aves/Basileuterus rufifrons/6ba94965e8752d3a4833514d8c8e8abb.jpg\",\n  \"534854.jpg\": \"Aves/Basileuterus rufifrons/319394c18ce59fff03b6956870c94eae.jpg\",\n  \"534860.jpg\": \"Aves/Basileuterus rufifrons/5ac2550869cd4bffdb7ca63ab8f820e7.jpg\",\n  \"534912.jpg\": \"Insecta/Anatrytone logan/ac85694fefd994acc96b6f47fc2290ba.jpg\",\n  \"534914.jpg\": \"Insecta/Anatrytone logan/21ced1c85e8983301b9f9d168508f40e.jpg\",\n  \"534915.jpg\": \"Insecta/Anatrytone logan/7122c94f4993dc18eb29ebfd2ae224b6.jpg\",\n  \"534916.jpg\": \"Insecta/Anatrytone logan/25c1bacee265abe712b30a2b8dcd24cc.jpg\",\n  \"534917.jpg\": \"Insecta/Anatrytone logan/f74a6f761e22ad744916cab542c62e92.jpg\",\n  \"534927.jpg\": \"Insecta/Anatrytone logan/cf13cb62ffd321241cb1944948a8c75f.jpg\",\n  \"534929.jpg\": \"Insecta/Anatrytone logan/37afa2363b1d96865f404e6fc6457927.jpg\",\n  \"534930.jpg\": \"Insecta/Anatrytone logan/3e2c318ac0359151729853b4cdd8008a.jpg\",\n  \"534931.jpg\": \"Insecta/Anatrytone logan/ced9118c76a78ef4be8feb6a333d5c31.jpg\",\n  \"534932.jpg\": \"Insecta/Anatrytone logan/d53ca236ca43585473784c6505e65ee9.jpg\",\n  \"534935.jpg\": \"Insecta/Anatrytone logan/4cb79db677fb1cb0fc71c5283872b205.jpg\",\n  \"535060.jpg\": \"Insecta/Catocala ilia/8e66ce3178b5c4698a1d9ecb04c48a99.jpg\",\n  \"535063.jpg\": \"Insecta/Catocala ilia/4e01bc55658ce4de38bf194601c92abb.jpg\",\n  \"535069.jpg\": \"Insecta/Eusarca confusaria/f481eb2d6f962f1c3e28442ada597446.jpg\",\n  \"535071.jpg\": \"Insecta/Eusarca confusaria/f3bdc12398dba85e08619758bf8ad43b.jpg\",\n  \"535074.jpg\": \"Insecta/Eusarca confusaria/444e049ddd59d0d8d3e249a58cd68795.jpg\",\n  \"535076.jpg\": \"Insecta/Eusarca confusaria/8c02f787fad3665c7b84483b01994035.jpg\",\n  \"535077.jpg\": \"Insecta/Eusarca confusaria/0cc213939dca1b680ed8f1ae1a4752c2.jpg\",\n  \"535080.jpg\": \"Insecta/Eusarca confusaria/558ecbcf796e164d3359baddaa785085.jpg\",\n  \"535085.jpg\": \"Insecta/Eusarca confusaria/0e2a180f76e89627f1e64a0b55f770c7.jpg\",\n  \"535087.jpg\": \"Insecta/Eusarca confusaria/47dfff4e0b013d04180b2ebd02209497.jpg\",\n  \"535090.jpg\": \"Insecta/Eusarca confusaria/a8b80bd9e9df3193b4609b2151ebf3b9.jpg\",\n  \"535091.jpg\": \"Insecta/Eusarca confusaria/2ce6e4ced487157b3d3f090c698e6a41.jpg\",\n  \"535274.jpg\": \"Insecta/Coryphista meadii/d98335a650ac4defabe2bffec6be6883.jpg\",\n  \"535279.jpg\": \"Insecta/Coryphista meadii/d63716625d5e52b860322007d67f2a8f.jpg\",\n  \"535298.jpg\": \"Reptilia/Crotalus molossus/bac2fca22de960b49933a714133ad9b7.jpg\",\n  \"535301.jpg\": \"Reptilia/Crotalus molossus/559337da23ee8634c3653dca1d38e4ae.jpg\",\n  \"535306.jpg\": \"Reptilia/Crotalus molossus/7a3f32bf0ad0dd8676c13d35296a7f42.jpg\",\n  \"535310.jpg\": \"Reptilia/Crotalus molossus/50acec0c9664e47036ae3f25abeb1036.jpg\",\n  \"535313.jpg\": \"Reptilia/Crotalus molossus/f3a5151e86056b9c7bf68e697ddc6cdc.jpg\",\n  \"535320.jpg\": \"Reptilia/Crotalus molossus/57678e0382954f9a0018fce504c67f8b.jpg\",\n  \"535323.jpg\": \"Reptilia/Crotalus molossus/7b41cbf872751a375972a3b19511c0b0.jpg\",\n  \"535330.jpg\": \"Reptilia/Crotalus molossus/f15dbd76237377489cc990eab44602e4.jpg\",\n  \"535331.jpg\": \"Reptilia/Crotalus molossus/4d421d36d0ce96cec7ed905e6cff328c.jpg\",\n  \"535354.jpg\": \"Reptilia/Crotalus molossus/c70491bc487c703e3d1023fd3d42ca64.jpg\",\n  \"535385.jpg\": \"Animalia/Armadillidium vulgare/27ec59ba587c931a0d66bd2f2dd5ed80.jpg\",\n  \"535404.jpg\": \"Animalia/Armadillidium vulgare/112a9318b7ec44cbb52b73a7e72ebae9.jpg\",\n  \"535426.jpg\": \"Animalia/Armadillidium vulgare/70a3df7e00c624ee053db281a4a3573e.jpg\",\n  \"535433.jpg\": \"Animalia/Armadillidium vulgare/a6d9c33321604b0bf9e44054ecdbdced.jpg\",\n  \"535452.jpg\": \"Animalia/Armadillidium vulgare/f8e984fbee6b0a0249bf9241e2391aac.jpg\",\n  \"53547.jpg\": \"Insecta/Melangyna novaezelandiae/dc8f25fe8c982b2e7bb79b4a52ce04fe.jpg\",\n  \"535475.jpg\": \"Animalia/Armadillidium vulgare/3347b67a7218b70f8cb9f1ed61eb9802.jpg\",\n  \"53548.jpg\": \"Insecta/Melangyna novaezelandiae/bac8b3247fa3f403d7c40ce0d99b6fee.jpg\",\n  \"53549.jpg\": \"Insecta/Melangyna novaezelandiae/a38837696cbdd80c8ddc80e3c13da7c7.jpg\",\n  \"53550.jpg\": \"Insecta/Melangyna novaezelandiae/93d422609ecafe9239351206070ce6fd.jpg\",\n  \"535511.jpg\": \"Animalia/Armadillidium vulgare/d605e866fdcdbbdbefcd0706af20b6df.jpg\",\n  \"535535.jpg\": \"Insecta/Phigalia titea/4d57e968ddb3cd4a37b3026ee74b8f18.jpg\",\n  \"535542.jpg\": \"Insecta/Phigalia titea/52af7504f62d9f27c258bbb9c9d0c713.jpg\",\n  \"535551.jpg\": \"Reptilia/Crotalus triseriatus/af086eeef0b495b02666df9c722c8719.jpg\",\n  \"535552.jpg\": \"Reptilia/Crotalus triseriatus/405f211c6f7669cf384ffbbee1023212.jpg\",\n  \"535553.jpg\": \"Insecta/Schistocerca nitens/ccf9b841bde4b83c8dc6c9ce0c7b1f5e.jpg\",\n  \"535562.jpg\": \"Insecta/Schistocerca nitens/96fd9362421340e76764c7bd987ea6ea.jpg\",\n  \"535563.jpg\": \"Insecta/Schistocerca nitens/9af9af49c0a996168f036d1ecbd0c025.jpg\",\n  \"535578.jpg\": \"Insecta/Schistocerca nitens/31aedc5fe674b1cfa48462ddc6025826.jpg\",\n  \"535585.jpg\": \"Insecta/Schistocerca nitens/61b57b89cedd9bc045e0b93ea2fb4804.jpg\",\n  \"535588.jpg\": \"Insecta/Schistocerca nitens/7ea4de9341bfc9efc2b127ab4ad43511.jpg\",\n  \"535597.jpg\": \"Insecta/Schistocerca nitens/19413da548606c7d3d40fd49c3cae812.jpg\",\n  \"535605.jpg\": \"Insecta/Schistocerca nitens/61d88f48307c5161787e392a9a7c5798.jpg\",\n  \"535607.jpg\": \"Insecta/Schistocerca nitens/69ddda7a0c80d23c7f2704590cb8e65f.jpg\",\n  \"535616.jpg\": \"Amphibia/Dicamptodon tenebrosus/01f6ed921c85d4e59d185cc36b57e2f2.jpg\",\n  \"535620.jpg\": \"Mammalia/Eptesicus fuscus/e846020fb9faeeb3e8ef42067587ab5f.jpg\",\n  \"535621.jpg\": \"Mammalia/Eptesicus fuscus/8ae2eb25d3b0fc0ee53e6533ecc1718a.jpg\",\n  \"535632.jpg\": \"Mammalia/Eptesicus fuscus/ee82668895ea2a1e4ebfb8d5b142c8c4.jpg\",\n  \"535652.jpg\": \"Insecta/Megisto cymela/0c1d1885bab12b15efb84da7296cb8cc.jpg\",\n  \"535657.jpg\": \"Insecta/Megisto cymela/72af5bdb69d25b572f35bb51785e2ff0.jpg\",\n  \"535658.jpg\": \"Insecta/Megisto cymela/630c1ccbe62100542080c00469522b20.jpg\",\n  \"535661.jpg\": \"Insecta/Megisto cymela/071f327dabbd6ee555092372913907bb.jpg\",\n  \"535682.jpg\": \"Insecta/Megisto cymela/b0dee58caeb2cdd4cd9272ea2098a536.jpg\",\n  \"535688.jpg\": \"Insecta/Megisto cymela/f4592caffaa03cbb467f2a5b70d5c2da.jpg\",\n  \"535787.jpg\": \"Reptilia/Agkistrodon piscivorus/c1f49ccddd0c5191d9005014a04e4a1f.jpg\",\n  \"535788.jpg\": \"Reptilia/Agkistrodon piscivorus/5b9a7c0138b9ecb95c2b9587dd53f04c.jpg\",\n  \"535812.jpg\": \"Reptilia/Agkistrodon piscivorus/7b1d1a1ebbe27f81ae2543bbe0fd2a09.jpg\",\n  \"535813.jpg\": \"Reptilia/Agkistrodon piscivorus/0d79137ddde79d6d63bd1612eda70a58.jpg\",\n  \"535823.jpg\": \"Reptilia/Agkistrodon piscivorus/5f0bdcf2a7272cfbf4be01a53c216379.jpg\",\n  \"535827.jpg\": \"Reptilia/Agkistrodon piscivorus/0f002877aa2e40b30e40170bc2fd12fe.jpg\",\n  \"535834.jpg\": \"Reptilia/Agkistrodon piscivorus/df40fa50d869fb235df2bcae6d9ae31b.jpg\",\n  \"535853.jpg\": \"Reptilia/Agkistrodon piscivorus/7ba74930a0ece09b077730df0c58ff79.jpg\",\n  \"535874.jpg\": \"Reptilia/Agkistrodon piscivorus/89c5e2a8f3be2f1e44b171b5e1ea79ca.jpg\",\n  \"535891.jpg\": \"Reptilia/Agkistrodon piscivorus/4f0ef3afc780fe8add1eefd0596dc64e.jpg\",\n  \"535893.jpg\": \"Aves/Porphyrio poliocephalus/f17af024c4e724e85f5d141daf8f7207.jpg\",\n  \"535896.jpg\": \"Aves/Porphyrio poliocephalus/a4b1218bd1a66e5175b6110f72a8c184.jpg\",\n  \"535909.jpg\": \"Aves/Megarynchus pitangua/36a48624cd6788b9c6be18fa20b68183.jpg\",\n  \"535913.jpg\": \"Aves/Megarynchus pitangua/5ceb6fdc665a2ff06d59dafd29dad56a.jpg\",\n  \"535920.jpg\": \"Reptilia/Storeria dekayi texana/f93b37a7e5cd213bf3d666b15f30694a.jpg\",\n  \"535921.jpg\": \"Reptilia/Storeria dekayi texana/80e09e8ee31284b52dfee1e29b493507.jpg\",\n  \"535923.jpg\": \"Reptilia/Storeria dekayi texana/c04712634a556bd31e049079666bb431.jpg\",\n  \"535933.jpg\": \"Reptilia/Storeria dekayi texana/55bc5d5db3c52ab333d2b1e6e211af94.jpg\",\n  \"535934.jpg\": \"Reptilia/Storeria dekayi texana/478e94d8b34e8054980360e3dd3701a6.jpg\",\n  \"535935.jpg\": \"Reptilia/Storeria dekayi texana/a49c5286054b7b4f0e618e2ef1e72703.jpg\",\n  \"535942.jpg\": \"Reptilia/Storeria dekayi texana/62ccfbf8dbfda89fafd3f48b82550a70.jpg\",\n  \"535950.jpg\": \"Reptilia/Storeria dekayi texana/d841762a0d0b8395c84a566e4eb365c2.jpg\",\n  \"535951.jpg\": \"Reptilia/Storeria dekayi texana/03024a01e570669f2c8e505498646db9.jpg\",\n  \"535966.jpg\": \"Aves/Pluvialis fulva/1d0f20082c511487d27cb4e54ffff141.jpg\",\n  \"535967.jpg\": \"Aves/Pluvialis fulva/5c55f1b95f9ecfa77ba5830d4ad6692e.jpg\",\n  \"535969.jpg\": \"Aves/Pluvialis fulva/fa4f8e5d1c2198b4139396a0d79bdce2.jpg\",\n  \"535972.jpg\": \"Aves/Pluvialis fulva/0dc77671f626bfc5536f092c844c4a58.jpg\",\n  \"535978.jpg\": \"Aves/Pluvialis fulva/8980fd5160c086dbdca29b9c817abad9.jpg\",\n  \"535983.jpg\": \"Aves/Pluvialis fulva/cf824f78717fb27343e3353cb0f87569.jpg\",\n  \"535987.jpg\": \"Aves/Pluvialis fulva/1f1ff02e59eb0a5a8c540cbe4be60400.jpg\",\n  \"535988.jpg\": \"Aves/Pluvialis fulva/35004f52d31c1efcaed5a6b1834da620.jpg\",\n  \"535989.jpg\": \"Aves/Pluvialis fulva/50aa9e73ba72d3bde5dbed8b32f2dc06.jpg\",\n  \"535990.jpg\": \"Aves/Pluvialis fulva/31a473c0160f0f1c64e234aa2f5422c8.jpg\",\n  \"536123.jpg\": \"Aves/Anser anser/c35a3bbd328d84c3cb72325eef4c9c5f.jpg\",\n  \"536364.jpg\": \"Aves/Melanerpes aurifrons/12c022d5836d7f80327f1ad76c284984.jpg\",\n  \"536395.jpg\": \"Aves/Melanerpes aurifrons/89f41dc39e25a11cad6087efd7b899c7.jpg\",\n  \"536407.jpg\": \"Aves/Melanerpes aurifrons/5b068349ac16b9ee960d72e624769952.jpg\",\n  \"536423.jpg\": \"Aves/Melanerpes aurifrons/dca4578cc842fc63b40d5a900d72fcf7.jpg\",\n  \"536439.jpg\": \"Aves/Melanerpes aurifrons/217ed74de1826962a57ff2cd8cc7e4b2.jpg\",\n  \"536462.jpg\": \"Aves/Melanerpes aurifrons/5d525cddc56f7a4fa18b6ee7e9149628.jpg\",\n  \"536492.jpg\": \"Aves/Melanerpes aurifrons/2c3f122cd008684fe68c7210f60d4f4c.jpg\",\n  \"536505.jpg\": \"Aves/Melanerpes aurifrons/a8a44e329b4a4e91663cb76ebdd8cc62.jpg\",\n  \"536514.jpg\": \"Aves/Melanerpes aurifrons/436f1cc18906701bad51e1c37ee93c02.jpg\",\n  \"536529.jpg\": \"Reptilia/Lepidochelys olivacea/5eedfe75caa9a66250b0feec9d7848a2.jpg\",\n  \"536536.jpg\": \"Arachnida/Salticus scenicus/3c9ef5c2ac69a263163acbd3e2c2cb45.jpg\",\n  \"536538.jpg\": \"Arachnida/Salticus scenicus/14616e592cbe78b05ebddccf95e47d40.jpg\",\n  \"536543.jpg\": \"Arachnida/Salticus scenicus/35a45f3c49886eb865976d123e15b17d.jpg\",\n  \"536547.jpg\": \"Arachnida/Salticus scenicus/9d9c5ce079553d485bc18a407dfbc404.jpg\",\n  \"536548.jpg\": \"Arachnida/Salticus scenicus/cb79ca5f13ec209aad106db5c987a973.jpg\",\n  \"536549.jpg\": \"Arachnida/Salticus scenicus/ca09ff43021127bf2db6da9eb691d240.jpg\",\n  \"536557.jpg\": \"Arachnida/Salticus scenicus/7d6f5d54b5006d61efadcd15d887ca9d.jpg\",\n  \"536561.jpg\": \"Arachnida/Salticus scenicus/47e93704a7400fe7a97bcf7c3a838018.jpg\",\n  \"536562.jpg\": \"Arachnida/Salticus scenicus/2198e0d7690b408f2807fed19ad6fd03.jpg\",\n  \"536563.jpg\": \"Arachnida/Salticus scenicus/8049b93be58f22832881c47d4043bc76.jpg\",\n  \"536572.jpg\": \"Insecta/Heliconius erato/0dff74925e8f7391d74dfc942240a8cd.jpg\",\n  \"536603.jpg\": \"Reptilia/Ctenosaura pectinata/fd2fa1051e0039c24d0ff0ed30aef429.jpg\",\n  \"536607.jpg\": \"Reptilia/Ctenosaura pectinata/de8a13d860f0867c2460f86cd764cda2.jpg\",\n  \"536609.jpg\": \"Reptilia/Ctenosaura pectinata/f2d247f6622668f416aeb1113aa036c7.jpg\",\n  \"536614.jpg\": \"Reptilia/Ctenosaura pectinata/5e124eccb6c6de7c898e34c665de1247.jpg\",\n  \"536617.jpg\": \"Reptilia/Ctenosaura pectinata/2b6777264ed3cc1fbf1ebaf4d9918100.jpg\",\n  \"536620.jpg\": \"Reptilia/Ctenosaura pectinata/2d969843e18793065a255d5512157803.jpg\",\n  \"536627.jpg\": \"Reptilia/Ctenosaura pectinata/b62758cc1b8407bc00860bf72306c502.jpg\",\n  \"536752.jpg\": \"Reptilia/Podarcis sicula/93a6f2bd1e5ac6cd94ecb38c84be0e76.jpg\",\n  \"536759.jpg\": \"Reptilia/Podarcis sicula/526728bc3f55ef305938840ab8d891ee.jpg\",\n  \"536766.jpg\": \"Reptilia/Podarcis sicula/89a4d08670f01818c811b92c898c8795.jpg\",\n  \"536769.jpg\": \"Reptilia/Podarcis sicula/d7230c352ec89d3fd3343ce286f57a3a.jpg\",\n  \"536776.jpg\": \"Reptilia/Podarcis sicula/b189af3cb6dded9979320ee5ecc0d68b.jpg\",\n  \"536799.jpg\": \"Reptilia/Podarcis sicula/c1edc3eac6830f3a33513c3939cdf9e3.jpg\",\n  \"536800.jpg\": \"Reptilia/Podarcis sicula/cf34c3402f1897e9de14429652d8f6aa.jpg\",\n  \"536815.jpg\": \"Reptilia/Podarcis sicula/a79d992f31ebdc0cbb99464170a4e9db.jpg\",\n  \"536823.jpg\": \"Reptilia/Podarcis sicula/7acc4666a4a2db3d94da72c240f28286.jpg\",\n  \"536941.jpg\": \"Insecta/Papilio canadensis/9fda749d047b499a96c388358a292257.jpg\",\n  \"536947.jpg\": \"Insecta/Papilio canadensis/14b9c5661f92ecd4cabc745ed2341094.jpg\",\n  \"536948.jpg\": \"Insecta/Papilio canadensis/20ebe95e4c839f8847a9b820d864d8c9.jpg\",\n  \"536958.jpg\": \"Insecta/Papilio canadensis/334d9703ce02987cb9d5ae9f22529389.jpg\",\n  \"536968.jpg\": \"Insecta/Papilio canadensis/5791a0c6b546c1c0bd212782e456b6cf.jpg\",\n  \"536969.jpg\": \"Insecta/Papilio canadensis/33928e50c7f2ba896a02b7478bc583f2.jpg\",\n  \"536970.jpg\": \"Insecta/Papilio canadensis/4b7aff7a8b355f15ba4a50220b4c92be.jpg\",\n  \"536978.jpg\": \"Insecta/Cacyreus marshalli/f58f48f8d320ccfaeb0a5aa496a8c707.jpg\",\n  \"536986.jpg\": \"Insecta/Habrosyne scripta/3aa964a6de995ad9752c648decec3b1f.jpg\",\n  \"536995.jpg\": \"Insecta/Habrosyne scripta/40e4ea42d5a4728877c47e0423325e09.jpg\",\n  \"537.jpg\": \"Mammalia/Ursus americanus/baba7eea696ec0e8d9f0014378a2b9d2.jpg\",\n  \"537091.jpg\": \"Aves/Phalacrocorax brasilianus/814aadbd285c42d2b4fb3b3c4ead8841.jpg\",\n  \"537125.jpg\": \"Aves/Phalacrocorax brasilianus/4b0ea7288718c9f91309d0f2672612d8.jpg\",\n  \"537129.jpg\": \"Aves/Phalacrocorax brasilianus/db828f123ae508ac8fdbfaae127f9525.jpg\",\n  \"537150.jpg\": \"Aves/Phalacrocorax brasilianus/b7781c0e0706b90a88ef76dfffaa1e6f.jpg\",\n  \"537193.jpg\": \"Aves/Phalacrocorax brasilianus/d7c6ee6bda9718880d46380b0c63c1b0.jpg\",\n  \"537242.jpg\": \"Aves/Lonchura punctulata/a32e2c3f34b0fd699d9ee5ffa9c2e9f2.jpg\",\n  \"537251.jpg\": \"Aves/Lonchura punctulata/758e50de3640c8acdde8898c718a2763.jpg\",\n  \"537256.jpg\": \"Aves/Lonchura punctulata/7146c33f8f175a98a0cfd8b12d0ab686.jpg\",\n  \"537259.jpg\": \"Aves/Lonchura punctulata/65ab7909014ff39b2866bc4b3303f4f8.jpg\",\n  \"537402.jpg\": \"Mammalia/Procyon lotor/e0c8d8d03380649ea7cb02aa2cc5432f.jpg\",\n  \"537406.jpg\": \"Mammalia/Procyon lotor/863406c35189daf17ec902575cf7e242.jpg\",\n  \"537437.jpg\": \"Mammalia/Procyon lotor/5104c47adcf6f5304c0fd8224f88de87.jpg\",\n  \"5377.jpg\": \"Amphibia/Plethodon cinereus/a0016fad8a015b92dbafad3182c034e6.jpg\",\n  \"537716.jpg\": \"Mammalia/Procyon lotor/546cc7ce25fc81e2e8e1cf25bd2a2211.jpg\",\n  \"537801.jpg\": \"Mammalia/Procyon lotor/07fb32fda4858356710f8161b52adef4.jpg\",\n  \"537882.jpg\": \"Mammalia/Procyon lotor/835ccdbd1993c2168e48ec56d9baddfe.jpg\",\n  \"537906.jpg\": \"Aves/Chloroceryle americana/f37f8dae749fc7921db679d7f58636c5.jpg\",\n  \"537907.jpg\": \"Aves/Chloroceryle americana/b590ed9b8ea7308d81950b8d67baf4f7.jpg\",\n  \"537908.jpg\": \"Aves/Chloroceryle americana/20eb13af5b8060bd08d5f7b0fba2de2b.jpg\",\n  \"537909.jpg\": \"Aves/Chloroceryle americana/ccf95416b110aae9e6338e3c1b9f10b8.jpg\",\n  \"537926.jpg\": \"Aves/Chloroceryle americana/b1bda7ee33e9792ec0e8676d40882e9a.jpg\",\n  \"537930.jpg\": \"Aves/Chloroceryle americana/e2adcd3d38943cf4ee86c5b6817da835.jpg\",\n  \"537941.jpg\": \"Aves/Chloroceryle americana/e1be81d2fa2205b9e68d0a0751219b53.jpg\",\n  \"537944.jpg\": \"Aves/Chloroceryle americana/8106cf18759c6d8a9a37df6923657458.jpg\",\n  \"537951.jpg\": \"Aves/Chloroceryle americana/6b776fa8eb67ff86ec59b352279735df.jpg\",\n  \"537972.jpg\": \"Mammalia/Sigmodon hispidus/35ef60150dacd4231a4cdeeee5bce8f9.jpg\",\n  \"537973.jpg\": \"Mammalia/Sigmodon hispidus/819d0176d2ad78d43ba6aa45a35418cd.jpg\",\n  \"537982.jpg\": \"Mammalia/Sigmodon hispidus/6c57f7411d48301808242a4b22a1b739.jpg\",\n  \"537983.jpg\": \"Mammalia/Sigmodon hispidus/e035e26a9998b6a97be13907e7b9bb9d.jpg\",\n  \"537984.jpg\": \"Mammalia/Sigmodon hispidus/7d87272e2617cfaa90a1c6c57a683e25.jpg\",\n  \"537985.jpg\": \"Mammalia/Sigmodon hispidus/50ad8846a3b8a06fad4b9b017a8fa885.jpg\",\n  \"537986.jpg\": \"Mammalia/Sigmodon hispidus/79ad24932ba8bc13a0e4fd151d77dc57.jpg\",\n  \"537992.jpg\": \"Mammalia/Sigmodon hispidus/dbd86c0f6751db7276490edf9c7108e8.jpg\",\n  \"537994.jpg\": \"Mammalia/Sigmodon hispidus/a6bb40bf4e99de369b90a86aa703838f.jpg\",\n  \"537995.jpg\": \"Mammalia/Sigmodon hispidus/b5267b1b5edcac97d018d1cae445fd2f.jpg\",\n  \"538055.jpg\": \"Insecta/Speyeria cybele/011a79fa1c20f5aaee2e8f15edf7016f.jpg\",\n  \"538094.jpg\": \"Insecta/Speyeria cybele/7f2f01d15c3acff57d2f2aa802a092b3.jpg\",\n  \"538099.jpg\": \"Insecta/Speyeria cybele/1ad98b2e969a9933c1b24722d0fa62dd.jpg\",\n  \"538113.jpg\": \"Insecta/Speyeria cybele/b1e77935c44e7c7d95a519c3911e7884.jpg\",\n  \"538121.jpg\": \"Insecta/Speyeria cybele/2c1646257cffc8d183c6675b892e18c8.jpg\",\n  \"538133.jpg\": \"Insecta/Speyeria cybele/d2e30ccdbb65984f22677ec6c5764335.jpg\",\n  \"538142.jpg\": \"Insecta/Speyeria cybele/92a0c97414d5699e737016abf38370b1.jpg\",\n  \"538205.jpg\": \"Mammalia/Lasiurus borealis/b701ec20788ad4347a511fae56c3582c.jpg\",\n  \"538206.jpg\": \"Mammalia/Lasiurus borealis/f4c863f5ee99c7777d9ab4067e679cc0.jpg\",\n  \"538241.jpg\": \"Insecta/Platyprepia virginalis/1083df68717cc30cb9bdf573721111a7.jpg\",\n  \"538398.jpg\": \"Reptilia/Anolis carolinensis/8a79b58b70ae373e7a5fe47e8f01f82e.jpg\",\n  \"538401.jpg\": \"Reptilia/Anolis carolinensis/08cb9a55dd23d6a0a2f83b62c2c8e84c.jpg\",\n  \"538486.jpg\": \"Reptilia/Anolis carolinensis/df9d93220860a80a2feeb23fea989ed6.jpg\",\n  \"538524.jpg\": \"Reptilia/Anolis carolinensis/8a09c5ceed4b823dc39031bfc500f52e.jpg\",\n  \"538556.jpg\": \"Reptilia/Anolis carolinensis/62011924ac2586546ceaa56b7e4d819e.jpg\",\n  \"538562.jpg\": \"Reptilia/Anolis carolinensis/00bec9440396d54c84c0e2b6267d93d8.jpg\",\n  \"538589.jpg\": \"Reptilia/Anolis carolinensis/7eb99e833438310033f9213cae63daae.jpg\",\n  \"538671.jpg\": \"Reptilia/Anolis carolinensis/64149f0afd627e2417590460489c79c4.jpg\",\n  \"538741.jpg\": \"Reptilia/Anolis carolinensis/3d163e983f6e84e57f6a8c6c508cf65d.jpg\",\n  \"538768.jpg\": \"Reptilia/Anolis carolinensis/46292d2f9c5341ed585583ce380c79d2.jpg\",\n  \"53879.jpg\": \"Aves/Columbina passerina/bd0930cdbf618b55b8adccc4dcde8037.jpg\",\n  \"538808.jpg\": \"Insecta/Sympetrum striolatum/275c9b18bba651de18eb83f62499faa2.jpg\",\n  \"538809.jpg\": \"Insecta/Sympetrum striolatum/69a353f219982d195773a9e4da4e939d.jpg\",\n  \"538816.jpg\": \"Insecta/Sympetrum striolatum/251ebd3b5672391cfa2975ef9729c37b.jpg\",\n  \"538818.jpg\": \"Insecta/Sympetrum striolatum/d2c630b299a7e99500fb4a52e563de42.jpg\",\n  \"538821.jpg\": \"Insecta/Sympetrum striolatum/adddaee416311322055f2bd9c338e493.jpg\",\n  \"538822.jpg\": \"Insecta/Sympetrum striolatum/cec0bd777c148362cca4e1d5adf68f48.jpg\",\n  \"538823.jpg\": \"Insecta/Sympetrum striolatum/bb029cc8a79f186060ec430811e2c585.jpg\",\n  \"538824.jpg\": \"Insecta/Sympetrum striolatum/d6f0304d37fa3198558529a29d541d49.jpg\",\n  \"538830.jpg\": \"Insecta/Chlosyne nycteis/98c7355a926e0986fd5c94becc93e815.jpg\",\n  \"538838.jpg\": \"Insecta/Chlosyne nycteis/5d00df4bd6e593d23b7cf9456cf61667.jpg\",\n  \"538841.jpg\": \"Insecta/Chlosyne nycteis/9bc7199858c5fd6edc2970d6b7e4768e.jpg\",\n  \"538845.jpg\": \"Insecta/Chlosyne nycteis/b3907d53716a718c68e5e8408610b1bb.jpg\",\n  \"538850.jpg\": \"Insecta/Chlosyne nycteis/9acb3a1c3abdde031a7808f9266102a8.jpg\",\n  \"538868.jpg\": \"Aves/Buteo regalis/ae7966e9f9d895a63d62c7e8146a16f4.jpg\",\n  \"53887.jpg\": \"Aves/Columbina passerina/47871dd3d920d8b3735f858f4ca93247.jpg\",\n  \"538870.jpg\": \"Aves/Buteo regalis/aece96621a5bd19c2d4005f3e5c4659f.jpg\",\n  \"538877.jpg\": \"Aves/Buteo regalis/e65cd7b65ead292fc82ab3576ce168be.jpg\",\n  \"538890.jpg\": \"Aves/Buteo regalis/b825ba9db5c198410a35e23b79b0d3f1.jpg\",\n  \"53890.jpg\": \"Aves/Columbina passerina/1569e45e6d08320f38b9fcb8eedc6f9e.jpg\",\n  \"538902.jpg\": \"Insecta/Erpetogomphus designatus/fe5db7cfcb4512bfab9a7c9df5dc4b61.jpg\",\n  \"538903.jpg\": \"Insecta/Erpetogomphus designatus/e447184029af78d82a2816aa0db978c5.jpg\",\n  \"538906.jpg\": \"Insecta/Erpetogomphus designatus/862e48b6f9c32b2f427d1d1978274465.jpg\",\n  \"538907.jpg\": \"Insecta/Erpetogomphus designatus/46230dd0c4a0d1ad582c12229b9b04af.jpg\",\n  \"538908.jpg\": \"Insecta/Erpetogomphus designatus/8716ea375aee3e607bc7c9c4c06490ec.jpg\",\n  \"53891.jpg\": \"Aves/Columbina passerina/ae2dd9d2b68ab9aca7579619cebc4c3f.jpg\",\n  \"538920.jpg\": \"Insecta/Erpetogomphus designatus/d7efaa69ce68f1dd24df184b0cd38cca.jpg\",\n  \"538928.jpg\": \"Insecta/Erpetogomphus designatus/46f75fa65540057a73ca9d87722eed2e.jpg\",\n  \"53894.jpg\": \"Aves/Columbina passerina/f19b23cfa29a8b97c14eb68ed04d8795.jpg\",\n  \"538940.jpg\": \"Insecta/Erpetogomphus designatus/fc5d0de27ca5d758c1bdeac4531af9a2.jpg\",\n  \"53895.jpg\": \"Aves/Columbina passerina/a1a09d96ccc7baf56407cb23f41327e6.jpg\",\n  \"53896.jpg\": \"Aves/Columbina passerina/4b37cedbdbd72981d1c7e3974d259e78.jpg\",\n  \"538963.jpg\": \"Insecta/Erpetogomphus designatus/e42e3aeb93b970bfeb67f44e2d0c7397.jpg\",\n  \"53897.jpg\": \"Aves/Columbina passerina/201d757759640a8d439e96046341aa2b.jpg\",\n  \"538972.jpg\": \"Mammalia/Dasyprocta punctata/6d7eeb00f15cd37e5737061b3a1e08a7.jpg\",\n  \"538976.jpg\": \"Mammalia/Dasyprocta punctata/467e9203a728031325c6a666cb0f9150.jpg\",\n  \"538980.jpg\": \"Mammalia/Dasyprocta punctata/3bb1a00e19ff418eb45b23a8ccad231c.jpg\",\n  \"53900.jpg\": \"Aves/Columbina passerina/b1337a5a3679d51cc4a2bc6e12bd9aec.jpg\",\n  \"539012.jpg\": \"Aves/Fulmarus glacialis/148bc15ce63ac339efbbe42b1a737598.jpg\",\n  \"539014.jpg\": \"Aves/Fulmarus glacialis/2cf3ea034c8ee54104a56f318443a1a8.jpg\",\n  \"539020.jpg\": \"Aves/Fulmarus glacialis/f4c2e72ef226be9eab340d0dc909a012.jpg\",\n  \"539029.jpg\": \"Aves/Fulmarus glacialis/25faef52f2e9694875a2c70b5c04fe7f.jpg\",\n  \"53903.jpg\": \"Aves/Columbina passerina/858390ca80e8a4a4cb3e4821687ef2e7.jpg\",\n  \"539032.jpg\": \"Aves/Fulmarus glacialis/0dfb30aad1981b00644c54dc92f41914.jpg\",\n  \"539033.jpg\": \"Aves/Fulmarus glacialis/12d0b65d3c7480bf4dac126850c1cf37.jpg\",\n  \"539041.jpg\": \"Aves/Fulmarus glacialis/ef888a562c5d4bbb222ca7594eac4f6e.jpg\",\n  \"539042.jpg\": \"Aves/Fulmarus glacialis/0a8475933c60a6450be0221adb459ea4.jpg\",\n  \"53907.jpg\": \"Aves/Columbina passerina/ad570f9e4b3fc2ad3e0b5fc1d934da1e.jpg\",\n  \"53908.jpg\": \"Aves/Columbina passerina/3f4b2db37f05135310a5d600bf1b8cea.jpg\",\n  \"539229.jpg\": \"Aves/Anas acuta/5ea76081ddd30757282c2344a044bb11.jpg\",\n  \"53924.jpg\": \"Aves/Columbina passerina/f582fd45675fa3a953d5fd2ba3683dbd.jpg\",\n  \"53925.jpg\": \"Aves/Columbina passerina/82ad8c392e5e54567ded17583c28bc77.jpg\",\n  \"53927.jpg\": \"Aves/Columbina passerina/8d63b62eb4beff6f646d9cb8457b8d49.jpg\",\n  \"539287.jpg\": \"Aves/Anas acuta/3a22463d46178e39468aaf468a0358ee.jpg\",\n  \"53930.jpg\": \"Aves/Columbina passerina/7cd0128fbcea0d42aae446296164c7ee.jpg\",\n  \"539333.jpg\": \"Aves/Anas acuta/c901251e2bc668540e48cfd2d8ea7254.jpg\",\n  \"53938.jpg\": \"Aves/Columbina passerina/f50993f3798dd96317f25a807fc71b75.jpg\",\n  \"539395.jpg\": \"Aves/Anas acuta/ca66d087faf1bde69ced69cd64af1fcc.jpg\",\n  \"53944.jpg\": \"Aves/Columbina passerina/28f5526a65d469ac6b0a883c413ff4bd.jpg\",\n  \"53945.jpg\": \"Aves/Columbina passerina/9cbd6c4298ea4517b340935492d1e9ac.jpg\",\n  \"53953.jpg\": \"Aves/Columbina passerina/86f7e9b9b85d0b52eee48e7abb6bd122.jpg\",\n  \"539563.jpg\": \"Insecta/Toxomerus marginatus/2c8f65ff54ddfb673c050a55fae88b48.jpg\",\n  \"539578.jpg\": \"Insecta/Toxomerus marginatus/45a1f051696ad7226086431535a0a090.jpg\",\n  \"539582.jpg\": \"Insecta/Toxomerus marginatus/4a0d4b47bfd4c897750431b46fbfa662.jpg\",\n  \"539587.jpg\": \"Insecta/Toxomerus marginatus/3660aa98e23101076f906d7bb3014eba.jpg\",\n  \"539593.jpg\": \"Insecta/Toxomerus marginatus/23e12ce4cc396d26323e551560df4e4d.jpg\",\n  \"539594.jpg\": \"Insecta/Toxomerus marginatus/de9b7cc2ecddef11368b9656b3878948.jpg\",\n  \"539596.jpg\": \"Insecta/Toxomerus marginatus/c1486f03b0ed2253d6e54a0acb011bf4.jpg\",\n  \"539610.jpg\": \"Insecta/Toxomerus marginatus/728b9e12479dff8295833dbcc8ba6303.jpg\",\n  \"539611.jpg\": \"Insecta/Toxomerus marginatus/b9ac28c4d759e45a9525ead6a968d39f.jpg\",\n  \"53964.jpg\": \"Aves/Columbina passerina/5faf7c010216d8d7de5b5461e937713b.jpg\",\n  \"539668.jpg\": \"Mollusca/Okenia rosacea/edb708dc46449f16f158ccf971be61b5.jpg\",\n  \"539718.jpg\": \"Mollusca/Okenia rosacea/165ff997ed8caafb45facf54978b0f81.jpg\",\n  \"53975.jpg\": \"Arachnida/Dysdera crocata/e818dc0bad7431c8047bcdd55fd37080.jpg\",\n  \"539768.jpg\": \"Mollusca/Okenia rosacea/c9cc4665ac064755416c852a0da0b247.jpg\",\n  \"539776.jpg\": \"Mollusca/Okenia rosacea/89438f05d52f96d3fe48c98010d6a981.jpg\",\n  \"53980.jpg\": \"Arachnida/Dysdera crocata/a62453d6883c2124d3746ebb085d713e.jpg\",\n  \"53984.jpg\": \"Arachnida/Dysdera crocata/31e33562208c51d88756636844d2c229.jpg\",\n  \"53985.jpg\": \"Arachnida/Dysdera crocata/b1f34aa5c06d21f8fe343a158b18375e.jpg\",\n  \"539868.jpg\": \"Insecta/Aporia crataegi/79e21f96c35cb2468fbef00e13c297ef.jpg\",\n  \"539872.jpg\": \"Insecta/Clepsis peritana/d40e3f8fb8575435bb9baeded15da902.jpg\",\n  \"539874.jpg\": \"Insecta/Clepsis peritana/76cff62e955467d6205be275715476f0.jpg\",\n  \"539880.jpg\": \"Insecta/Clepsis peritana/eb1a918b52bdd73cc57bd5c1578ed8b3.jpg\",\n  \"539881.jpg\": \"Insecta/Clepsis peritana/5fc80807186c8cae6e23a1b7cb682306.jpg\",\n  \"539888.jpg\": \"Insecta/Clepsis peritana/3afbac3e4c4479bca29055abd8f17cde.jpg\",\n  \"539891.jpg\": \"Insecta/Clepsis peritana/dc7765cec20cf9df4f1948cf4c6e734f.jpg\",\n  \"539892.jpg\": \"Insecta/Clepsis peritana/fcbbc7826ea8b63962bda451114e4dfb.jpg\",\n  \"539893.jpg\": \"Insecta/Clepsis peritana/049decce741a66d3236e9dd72d36d672.jpg\",\n  \"53994.jpg\": \"Arachnida/Dysdera crocata/f42582659c064870d4fc0a0286371944.jpg\",\n  \"53999.jpg\": \"Arachnida/Dysdera crocata/c915bea86e94251bb5db81f28939688b.jpg\",\n  \"54000.jpg\": \"Arachnida/Dysdera crocata/23ecb20a29cca929a151f5620d6c4fef.jpg\",\n  \"540034.jpg\": \"Aves/Cairina moschata/c97d7f62c2705657d40f6585cd57585a.jpg\",\n  \"540037.jpg\": \"Aves/Cairina moschata/6a0ec837ff423f9e7b8c40b97b19b538.jpg\",\n  \"54009.jpg\": \"Arachnida/Dysdera crocata/e7159ac3e800e0907fff65eb15fee9a4.jpg\",\n  \"54010.jpg\": \"Arachnida/Dysdera crocata/b1ec175e0979a095b80e0153ec066856.jpg\",\n  \"54011.jpg\": \"Arachnida/Dysdera crocata/cc288dcb2a4a0fe0048c3d4d9471c1a8.jpg\",\n  \"540163.jpg\": \"Aves/Limnodromus scolopaceus/ba5c0eba1728f61d8ad004ea4d5757e4.jpg\",\n  \"54017.jpg\": \"Arachnida/Dysdera crocata/7766fe32e13b2062d4b8a968a6fad7d5.jpg\",\n  \"54018.jpg\": \"Arachnida/Dysdera crocata/6c9386ce9e54aff25df5a9b8fd300e93.jpg\",\n  \"54019.jpg\": \"Arachnida/Dysdera crocata/3a4cdc918a823a683f996d98e0ef6b28.jpg\",\n  \"54020.jpg\": \"Arachnida/Dysdera crocata/38d4348f72bd3a81dedb8aef70681c8e.jpg\",\n  \"540215.jpg\": \"Reptilia/Nerodia erythrogaster/2a392fb8ccfc3c5c39f5d87d14120acb.jpg\",\n  \"540216.jpg\": \"Reptilia/Nerodia erythrogaster/c563e43faf9befd349f7d016b7269b1b.jpg\",\n  \"540218.jpg\": \"Reptilia/Nerodia erythrogaster/6b1810d680b02b8393a15a730168f0fb.jpg\",\n  \"540219.jpg\": \"Reptilia/Nerodia erythrogaster/cc9b507b54587d31592f0306f0856d8b.jpg\",\n  \"540227.jpg\": \"Reptilia/Nerodia erythrogaster/d108cd7910db73fcf62408b1ca1b4524.jpg\",\n  \"540234.jpg\": \"Reptilia/Nerodia erythrogaster/fdd7e55c83372df3473ff53fc5c9ca65.jpg\",\n  \"540249.jpg\": \"Reptilia/Nerodia erythrogaster/6a8d9a18b1849d4e9fd2e9b7968f6847.jpg\",\n  \"540253.jpg\": \"Reptilia/Nerodia erythrogaster/0cd22fa23d0e1042d6c97bd79a6d57f5.jpg\",\n  \"540268.jpg\": \"Aves/Ardea cocoi/8c44536e0051f6bccbbd35fd2ce95eb9.jpg\",\n  \"54028.jpg\": \"Arachnida/Dysdera crocata/e47cd3728a415c4d9551d22d1ea3a362.jpg\",\n  \"54030.jpg\": \"Arachnida/Dysdera crocata/2117ede452509b2578bf84c246349ae5.jpg\",\n  \"540308.jpg\": \"Animalia/Metacarcinus magister/d78241a14f2ba87f46afe9688e6ded89.jpg\",\n  \"540309.jpg\": \"Animalia/Metacarcinus magister/7ed33901b599eea8a998a74e7e3f63ad.jpg\",\n  \"54031.jpg\": \"Arachnida/Dysdera crocata/9403692d2fb73e117507f8733a233f63.jpg\",\n  \"540313.jpg\": \"Animalia/Metacarcinus magister/c21e23099092958a1e82e0402a700519.jpg\",\n  \"540316.jpg\": \"Animalia/Metacarcinus magister/ba9df260efd87b68b2bfd0addfb17207.jpg\",\n  \"540317.jpg\": \"Animalia/Metacarcinus magister/a240b7a1142a0413d5f2871689dae767.jpg\",\n  \"540318.jpg\": \"Animalia/Metacarcinus magister/b5cb9708c78176e02c173a9639ffb6d7.jpg\",\n  \"540319.jpg\": \"Animalia/Metacarcinus magister/e8abdc093f28e27911ca6c244279f0ff.jpg\",\n  \"54032.jpg\": \"Arachnida/Dysdera crocata/48a08f7a3b79125237a74d686b912d1a.jpg\",\n  \"540499.jpg\": \"Aves/Aeronautes saxatalis/af3b25d4911f2f2af1d45b08d8a82572.jpg\",\n  \"540504.jpg\": \"Aves/Aeronautes saxatalis/c6f8f1891aa7645185012ea1e5b0b002.jpg\",\n  \"540554.jpg\": \"Insecta/Atta texana/01563936b20c483504490bcf5c3cdaa4.jpg\",\n  \"540559.jpg\": \"Insecta/Callophrys henrici/3d7a32c63754c0b5467654728f08c40e.jpg\",\n  \"540563.jpg\": \"Insecta/Callophrys henrici/c5db3c1d69a48d43b9f8c90e0bb9fb7c.jpg\",\n  \"540565.jpg\": \"Insecta/Callophrys henrici/eb3bf3273f10b82ccf8c1c00fda75d65.jpg\",\n  \"540574.jpg\": \"Insecta/Copaeodes minima/1dbfe23c55b58a024fcb34f885e3bd80.jpg\",\n  \"540583.jpg\": \"Insecta/Copaeodes minima/f30133de58d26a6d3c86f79017997d1b.jpg\",\n  \"540629.jpg\": \"Aves/Setophaga chrysoparia/3c4ca33cb9e4c8faf19c0efa000431e7.jpg\",\n  \"540630.jpg\": \"Insecta/Erynnis funeralis/08cb65ff02af410d1d57d25261cfd007.jpg\",\n  \"540634.jpg\": \"Insecta/Erynnis funeralis/b6d387ab4366299fef1f361a75305911.jpg\",\n  \"540635.jpg\": \"Insecta/Erynnis funeralis/32675f24756e3ce64097edbc6c51f7b6.jpg\",\n  \"540642.jpg\": \"Insecta/Erynnis funeralis/f6f0f306bdf8312247da4cda39499e37.jpg\",\n  \"540663.jpg\": \"Insecta/Erynnis funeralis/8662fe69bbeef541c5001284ef474244.jpg\",\n  \"540668.jpg\": \"Insecta/Erynnis funeralis/84de801ed9cf22f20b4bccf93abd950c.jpg\",\n  \"540670.jpg\": \"Insecta/Erynnis funeralis/49e62e44fb5aa013333ec1ceaeb136d9.jpg\",\n  \"540680.jpg\": \"Insecta/Erynnis funeralis/add7bd50c933914b0e8de0b664c83775.jpg\",\n  \"540689.jpg\": \"Aves/Setophaga tigrina/9e2ded3e0af72b85253510551133fef8.jpg\",\n  \"540690.jpg\": \"Aves/Setophaga tigrina/2984afc4d40e9ddf7a49f6d1ccbf0690.jpg\",\n  \"540692.jpg\": \"Aves/Setophaga tigrina/079ab0968f7ffded7963d1f79e855080.jpg\",\n  \"540693.jpg\": \"Aves/Setophaga tigrina/9a575ae45b9a16e16e7eef28601ef547.jpg\",\n  \"540694.jpg\": \"Aves/Setophaga tigrina/b8989e683db8c06897e7ca6efb6194e6.jpg\",\n  \"540695.jpg\": \"Aves/Setophaga tigrina/b8d2bc8f78f72b2711a00332aa549298.jpg\",\n  \"540696.jpg\": \"Aves/Setophaga tigrina/2fb86ec4135c9f4b30ac54dcbedef9b6.jpg\",\n  \"540706.jpg\": \"Aves/Setophaga tigrina/1ad84d79d4dbb4808f7a0bdbd72c9e98.jpg\",\n  \"540707.jpg\": \"Aves/Setophaga tigrina/dd754e0a986c5a77107a8dfe95f21bee.jpg\",\n  \"540714.jpg\": \"Insecta/Charadra deridens/6990f07d36b6c58a80bb33a411a591be.jpg\",\n  \"540717.jpg\": \"Reptilia/Natrix tessellata/f31e14423e23a9703a14b6b58ba08f14.jpg\",\n  \"540741.jpg\": \"Aves/Mimus gilvus/883e317e37582e76b4bfca74e4827deb.jpg\",\n  \"540743.jpg\": \"Aves/Mimus gilvus/e59fca465eb01317227872aed80db703.jpg\",\n  \"540762.jpg\": \"Aves/Mimus gilvus/e6a2ca4bf949e307749a5c819e28f69b.jpg\",\n  \"540768.jpg\": \"Aves/Mimus gilvus/728f82b7f78b0089aaab9d8a12e9d8d9.jpg\",\n  \"540769.jpg\": \"Aves/Mimus gilvus/7a8505df2b7aa0b7270e75b3a4ad7619.jpg\",\n  \"540771.jpg\": \"Aves/Mimus gilvus/7f441930f8e68a270be6260989e65000.jpg\",\n  \"5408.jpg\": \"Amphibia/Plethodon cinereus/4c636b1b55415859a30b9347193870a7.jpg\",\n  \"540811.jpg\": \"Reptilia/Coluber lateralis lateralis/367a339a6c12b2248f114045d0e8a14b.jpg\",\n  \"540812.jpg\": \"Reptilia/Coluber lateralis lateralis/6e1e201d7c011da131907704ebfe87e3.jpg\",\n  \"540815.jpg\": \"Reptilia/Coluber lateralis lateralis/fad8e03520411b6900c23d6da274545f.jpg\",\n  \"540817.jpg\": \"Reptilia/Coluber lateralis lateralis/0539dbdc1b1618be9e8ee8556777005c.jpg\",\n  \"540818.jpg\": \"Reptilia/Coluber lateralis lateralis/d3e7f535745428572cf327e40efd95c9.jpg\",\n  \"540820.jpg\": \"Reptilia/Coluber lateralis lateralis/42a94ac4fa9b073ab2471a4dd515a622.jpg\",\n  \"540821.jpg\": \"Reptilia/Coluber lateralis lateralis/2bb3684bf785456b4f122b9b8ccfda00.jpg\",\n  \"540822.jpg\": \"Reptilia/Coluber lateralis lateralis/af2b83d25d731a6a755b8b773cd63fb9.jpg\",\n  \"540823.jpg\": \"Reptilia/Coluber lateralis lateralis/a082db96bc2d38260122e10d44f04219.jpg\",\n  \"540830.jpg\": \"Reptilia/Coluber lateralis lateralis/5bd8dc628afbd7250c1bcc94ef3ab107.jpg\",\n  \"540831.jpg\": \"Reptilia/Coluber lateralis lateralis/dea2acce48f2da8e323e648be0ae3f24.jpg\",\n  \"540835.jpg\": \"Reptilia/Coluber lateralis lateralis/e9740ac4e0c6304953dc189c736496eb.jpg\",\n  \"540843.jpg\": \"Animalia/Cyanea capillata/24db88549691e8fdc73eb8f8c7ea026e.jpg\",\n  \"540845.jpg\": \"Animalia/Cyanea capillata/cf6028ccceb335c6ada1777a54cbc142.jpg\",\n  \"540860.jpg\": \"Mollusca/Pomacea canaliculata/9cc7b2311da898241e00e97ec96492f4.jpg\",\n  \"540877.jpg\": \"Aves/Setophaga citrina/6917555ca1afd8f0d3663bbf14f94ba1.jpg\",\n  \"540878.jpg\": \"Aves/Setophaga citrina/24c2162f821c18ab24f6721af3007c28.jpg\",\n  \"540893.jpg\": \"Aves/Setophaga citrina/6cb2814289db3d4f4e77ad52912acc60.jpg\",\n  \"540897.jpg\": \"Aves/Setophaga citrina/e752c58f0b0ea71b5b570932a3192c77.jpg\",\n  \"540899.jpg\": \"Aves/Setophaga citrina/dc828d858ecb19cdecf47b833e94de69.jpg\",\n  \"540901.jpg\": \"Aves/Setophaga citrina/893179cec0727406bb80c3e665496a9e.jpg\",\n  \"540902.jpg\": \"Aves/Setophaga citrina/1a77e373ea6deb8e842d127bdc985c2a.jpg\",\n  \"540904.jpg\": \"Aves/Setophaga citrina/a121f41bc06aa8ccc1a40a9eed518f33.jpg\",\n  \"540905.jpg\": \"Aves/Setophaga citrina/bf62b12a7664ca6d626d681d7310a10d.jpg\",\n  \"540912.jpg\": \"Aves/Setophaga citrina/42bc829e61487e58faddc64dc27ed15f.jpg\",\n  \"540930.jpg\": \"Aves/Ardea alba modesta/263ddaf464aea759a6386ec06fb4ba82.jpg\",\n  \"540933.jpg\": \"Aves/Ardea alba modesta/1eef25ee5a6ef68f3abcbc36d5f36c5b.jpg\",\n  \"540934.jpg\": \"Aves/Ardea alba modesta/e361b066523be2c365394f55902981f7.jpg\",\n  \"540938.jpg\": \"Aves/Vireo bellii/fafb3e25a9de346f5a2756d4ffe8957f.jpg\",\n  \"540940.jpg\": \"Aves/Vireo bellii/1953f5da6fcfebece17173c8e1690502.jpg\",\n  \"541032.jpg\": \"Aves/Larus livens/f5fd263b7d4b214fe40925a3390f38d7.jpg\",\n  \"541037.jpg\": \"Aves/Larus livens/52578dd3f67ec7eedff71dfdc15dab90.jpg\",\n  \"541038.jpg\": \"Aves/Larus livens/fbc3a24a51704fe527a588d78d6b9789.jpg\",\n  \"541066.jpg\": \"Insecta/Largus californicus/2a14b9ef5eb1b3f23450a760cd476d90.jpg\",\n  \"541067.jpg\": \"Insecta/Largus californicus/693c168e3d9c9ad79cb71ab52dbdbcf7.jpg\",\n  \"541080.jpg\": \"Insecta/Largus californicus/30cd154ff2f9b2dc10a1bf77db3c5ea0.jpg\",\n  \"541081.jpg\": \"Insecta/Largus californicus/8741b8f19b6a41bbc22c3f943db3be36.jpg\",\n  \"5411.jpg\": \"Amphibia/Plethodon cinereus/c1070c6e7158d3e678038708b4fb32cf.jpg\",\n  \"54116.jpg\": \"Aves/Cinclus mexicanus/330906ed43de1df2969f3aa4aa765ab4.jpg\",\n  \"54119.jpg\": \"Aves/Cinclus mexicanus/847ccc1038a30009c44ba18099ade736.jpg\",\n  \"54121.jpg\": \"Aves/Cinclus mexicanus/18134ef4c48adf520f2d41236f26c4b9.jpg\",\n  \"54134.jpg\": \"Aves/Cinclus mexicanus/50a570b30c6ac74f13ef11855476533a.jpg\",\n  \"54135.jpg\": \"Aves/Cinclus mexicanus/a9ce1e4f8a5a54728866765958370292.jpg\",\n  \"54136.jpg\": \"Aves/Cinclus mexicanus/e108e2d607439f7c4dd36403cca8dc68.jpg\",\n  \"54137.jpg\": \"Aves/Cinclus mexicanus/60a2863c93b3cfcc007e9ea22515e1b7.jpg\",\n  \"541414.jpg\": \"Aves/Zenaida macroura/bfa0a3cd0c748513e179e0d716b8c5a1.jpg\",\n  \"54143.jpg\": \"Aves/Cinclus mexicanus/f70a331200398abf023dcd3733036e11.jpg\",\n  \"54145.jpg\": \"Aves/Cinclus mexicanus/e77d5586a9da2567867c9d93954b01d8.jpg\",\n  \"54146.jpg\": \"Aves/Cinclus mexicanus/91a34faffe5a004d64184819b0c5e085.jpg\",\n  \"54147.jpg\": \"Aves/Cinclus mexicanus/1e0f4dae1d9b5bbba0a11a5e205aa079.jpg\",\n  \"54148.jpg\": \"Aves/Cinclus mexicanus/07dba04ea0485bd71bfafb66aadf1355.jpg\",\n  \"54150.jpg\": \"Aves/Cinclus mexicanus/eb439b9255054f4c6a447671a774aa14.jpg\",\n  \"54152.jpg\": \"Aves/Cinclus mexicanus/a65808bc8fa38f0c7765af0d1e46ef42.jpg\",\n  \"54153.jpg\": \"Aves/Cinclus mexicanus/2d57b93daf998915f3c2f3e761118b6f.jpg\",\n  \"54154.jpg\": \"Aves/Cinclus mexicanus/0016927abc93bc78fa8afc22256e78f1.jpg\",\n  \"541602.jpg\": \"Aves/Zenaida macroura/15a5542117150c9fe90a3eb93da7934a.jpg\",\n  \"54164.jpg\": \"Aves/Cinclus mexicanus/2408b450f7a29e210819a167e437222c.jpg\",\n  \"54170.jpg\": \"Aves/Cinclus mexicanus/a86bd2bc34de378a53d79ac46644bb28.jpg\",\n  \"54174.jpg\": \"Aves/Cinclus mexicanus/8f499808d91e72d2d42250de79d4a1c1.jpg\",\n  \"54181.jpg\": \"Aves/Cinclus mexicanus/072633c14ccb7f5415a964c8a05d0616.jpg\",\n  \"54184.jpg\": \"Mammalia/Phascolarctos cinereus/0f71c43268a4666ef371f99daaf82986.jpg\",\n  \"5419.jpg\": \"Amphibia/Plethodon cinereus/31765942909edc1bae369c018a42202d.jpg\",\n  \"54194.jpg\": \"Mammalia/Phascolarctos cinereus/6bb513e9abcc7aec57b63475986a1231.jpg\",\n  \"54195.jpg\": \"Mammalia/Phascolarctos cinereus/809bd4d3eb999bdb27efc164c44229d3.jpg\",\n  \"541975.jpg\": \"Actinopterygii/Perca flavescens/5f7a36876f37eb68d6eed6b7b1cbb70f.jpg\",\n  \"541989.jpg\": \"Insecta/Callosamia promethea/743d5d5794a1c188710fac719979c662.jpg\",\n  \"54200.jpg\": \"Mammalia/Phascolarctos cinereus/3983b531b9c2a77d55c627948f80bfb8.jpg\",\n  \"542043.jpg\": \"Mammalia/Phocarctos hookeri/993a8f52e681742d355413aed4b36792.jpg\",\n  \"542053.jpg\": \"Mammalia/Phocarctos hookeri/971090ec9d7766d0f6ab142c86cda196.jpg\",\n  \"542058.jpg\": \"Mammalia/Phocarctos hookeri/ed790ebcf2f03714972590355c0c6958.jpg\",\n  \"542061.jpg\": \"Mammalia/Phocarctos hookeri/ec26c734aa7cf655555931743729b9ca.jpg\",\n  \"542062.jpg\": \"Mammalia/Phocarctos hookeri/fd8f184ab8433954502b1097f7b970d9.jpg\",\n  \"542063.jpg\": \"Mammalia/Phocarctos hookeri/9ad832694415f34f7cbe463b568c353d.jpg\",\n  \"542069.jpg\": \"Mammalia/Phocarctos hookeri/cb97eac056dbc22b0cd972ee430d18dd.jpg\",\n  \"54207.jpg\": \"Mammalia/Phascolarctos cinereus/ca956d9c29747961af8482f7d2e6765a.jpg\",\n  \"542070.jpg\": \"Mammalia/Phocarctos hookeri/fdfd0ee96b8785721cc38b68315ad6dc.jpg\",\n  \"542072.jpg\": \"Mammalia/Phocarctos hookeri/a19311359c0a68d7c6e84fc1e50cb234.jpg\",\n  \"542073.jpg\": \"Mammalia/Phocarctos hookeri/19cf79e871dba86b11d522afb6d41752.jpg\",\n  \"542074.jpg\": \"Mammalia/Phocarctos hookeri/1369a1d5a5f7cb9f16abef0187ebd183.jpg\",\n  \"54208.jpg\": \"Arachnida/Argiope argentata/ed03925cb217583439a308cb682987ba.jpg\",\n  \"542104.jpg\": \"Aves/Zonotrichia leucophrys/aad338f434dd745ca1b70e0505666bf6.jpg\",\n  \"54211.jpg\": \"Arachnida/Argiope argentata/9884ddc0a18be73468873ba9dc2c93d3.jpg\",\n  \"54220.jpg\": \"Arachnida/Argiope argentata/5f8b02b1ba1f4e26310a27838dde25dc.jpg\",\n  \"542200.jpg\": \"Aves/Zonotrichia leucophrys/9e4cf0d9d319f0827f3be98d0be93fb9.jpg\",\n  \"54224.jpg\": \"Arachnida/Argiope argentata/8a4a636a1d0f22f0f7887d3d524d3afc.jpg\",\n  \"54225.jpg\": \"Arachnida/Argiope argentata/9c785100ff9289f7fb72b265743e482b.jpg\",\n  \"54230.jpg\": \"Arachnida/Argiope argentata/9a6765fbf045546d7773b040ee9fffb2.jpg\",\n  \"54231.jpg\": \"Arachnida/Argiope argentata/7e0f424c1d63f67f2c5ea9f031ef706e.jpg\",\n  \"542365.jpg\": \"Aves/Zonotrichia leucophrys/be5f39d65389b2fb1d6f142c01b53100.jpg\",\n  \"542416.jpg\": \"Aves/Zonotrichia leucophrys/6c1cbf4c2647938415ee42c1147a1fac.jpg\",\n  \"542443.jpg\": \"Aves/Zonotrichia leucophrys/8f9458c925075eb7c3b3e9f0402e93c7.jpg\",\n  \"54251.jpg\": \"Arachnida/Argiope argentata/79b5f85923b91d98d02f6c981200bced.jpg\",\n  \"54252.jpg\": \"Arachnida/Argiope argentata/08818a1b5400d45d5c0cf444580378db.jpg\",\n  \"54262.jpg\": \"Arachnida/Argiope argentata/5738723fcd46a3051a85db3f3b0cab09.jpg\",\n  \"542646.jpg\": \"Reptilia/Gopherus agassizii/fb149fda45688a08683111ccc8c29440.jpg\",\n  \"542647.jpg\": \"Reptilia/Gopherus agassizii/ae40bc467e65987a1235abec6ee34ddd.jpg\",\n  \"542648.jpg\": \"Reptilia/Gopherus agassizii/b4b91d546a54d55758419d4690ce8ed4.jpg\",\n  \"542649.jpg\": \"Reptilia/Gopherus agassizii/1998fd6a18d7981f1d38cc486a8d00f1.jpg\",\n  \"542650.jpg\": \"Reptilia/Gopherus agassizii/41c365c04baca4a1f9ec3e4655972c9d.jpg\",\n  \"542653.jpg\": \"Reptilia/Gopherus agassizii/88c46eef5a210d2269ecd9594686abed.jpg\",\n  \"542656.jpg\": \"Reptilia/Gopherus agassizii/6acb531f8ab7f6459b915b2bdff40046.jpg\",\n  \"542657.jpg\": \"Reptilia/Gopherus agassizii/677c8d2088040e9e5eed476de3b0782f.jpg\",\n  \"54266.jpg\": \"Arachnida/Argiope argentata/def88d31ac6447b01bee541ef1880399.jpg\",\n  \"542680.jpg\": \"Animalia/Pisaster brevispinus/e623e2ae60ac16aaf0bdca8ce27217a9.jpg\",\n  \"542682.jpg\": \"Animalia/Pisaster brevispinus/560cb7f38b5e68548e25e77fe5a088c1.jpg\",\n  \"54269.jpg\": \"Arachnida/Argiope argentata/53852d9ac1de7c5d16162fbc1296faa5.jpg\",\n  \"542759.jpg\": \"Insecta/Leucorrhinia proxima/93499593318b794ce57b096aaaa76d1d.jpg\",\n  \"542762.jpg\": \"Insecta/Heterocampa biundata/d3e9543154ff72ff63783f4d1f39d447.jpg\",\n  \"542763.jpg\": \"Insecta/Heterocampa biundata/4518f01b5d74b170f5851ea258fceabb.jpg\",\n  \"542770.jpg\": \"Aves/Amazilia yucatanensis/805551ddf5cb2c1f0896debcc886c835.jpg\",\n  \"542771.jpg\": \"Aves/Amazilia yucatanensis/233542897043111a5f8398253166ba2d.jpg\",\n  \"542775.jpg\": \"Aves/Amazilia yucatanensis/0a54d0a5568f6f77d712a3ad17ce4907.jpg\",\n  \"542779.jpg\": \"Aves/Amazilia yucatanensis/0734af46b9d79d461b9f1b6f37c130d2.jpg\",\n  \"542790.jpg\": \"Aves/Amazilia yucatanensis/c98155691016b7fdf5e666b5616965a8.jpg\",\n  \"542797.jpg\": \"Mammalia/Bassariscus astutus/df33dc31dab389fd425d00cd20a2429d.jpg\",\n  \"542798.jpg\": \"Mammalia/Bassariscus astutus/a1989f9080daebc0a5fd1d4e78847c42.jpg\",\n  \"542801.jpg\": \"Mammalia/Bassariscus astutus/06b722ae1b215e3445fce5fb1697489d.jpg\",\n  \"542805.jpg\": \"Mammalia/Bassariscus astutus/47c698c61233a37f1c903b5020f5e574.jpg\",\n  \"542806.jpg\": \"Mammalia/Bassariscus astutus/e06197d5eff8ea1543aa5ffb98cc69ca.jpg\",\n  \"542807.jpg\": \"Mammalia/Bassariscus astutus/5eb01d8f107bd788bc5ad6634d45345c.jpg\",\n  \"542808.jpg\": \"Mammalia/Bassariscus astutus/6c35b45f9f529c913404680d016b5912.jpg\",\n  \"542809.jpg\": \"Mammalia/Bassariscus astutus/e87c10fdc114193aa1b73850122a463d.jpg\",\n  \"542817.jpg\": \"Mammalia/Bassariscus astutus/4fa7431bd15ec440cedea976fb66978f.jpg\",\n  \"542820.jpg\": \"Aves/Aix galericulata/dd897cbcfd72addbb23fe6b4c77f681e.jpg\",\n  \"542821.jpg\": \"Aves/Aix galericulata/e7cec118b5d6549ad7ef6b5126ffbabd.jpg\",\n  \"542822.jpg\": \"Aves/Aix galericulata/141e76a50f4d79f0de7373a02f020764.jpg\",\n  \"542824.jpg\": \"Aves/Aix galericulata/c4b6e429c8566e583f7b10942bd75fb3.jpg\",\n  \"542826.jpg\": \"Aves/Aix galericulata/32b8a8b13e15016f00048e2d33091ecf.jpg\",\n  \"542831.jpg\": \"Aves/Aix galericulata/aa27a8f7473762916b4520f371498092.jpg\",\n  \"542833.jpg\": \"Aves/Aix galericulata/dc2357e3f8e8170a75d755143d715597.jpg\",\n  \"542835.jpg\": \"Aves/Aix galericulata/80a8fca97108a1055291db7bb9216a39.jpg\",\n  \"542836.jpg\": \"Aves/Aix galericulata/a1e135757b9da956902557aec87ed8f5.jpg\",\n  \"542837.jpg\": \"Aves/Aix galericulata/812caa1bfccff6e99c7bf8fa91cfc911.jpg\",\n  \"542840.jpg\": \"Aves/Alca torda/da5eb524bc501eb0ff53b7ce4058347f.jpg\",\n  \"542841.jpg\": \"Aves/Alca torda/ed332967f804ed926f335daaec4b6ee4.jpg\",\n  \"542910.jpg\": \"Reptilia/Ophisaurus attenuatus attenuatus/40c07b2f8c73576662500fd769c2652c.jpg\",\n  \"542923.jpg\": \"Aves/Patagioenas leucocephala/7b3d447a6f57aa17b01aeca55a9aa380.jpg\",\n  \"54296.jpg\": \"Arachnida/Argiope argentata/76094ed6796a00bb2a12c46af59628fa.jpg\",\n  \"542970.jpg\": \"Aves/Lanius ludovicianus/5f072063be16ff533e0f449b03996527.jpg\",\n  \"542991.jpg\": \"Aves/Lanius ludovicianus/72ffba6e1935c00b91fb4a28c568137f.jpg\",\n  \"543021.jpg\": \"Aves/Lanius ludovicianus/a4cdfeb15cff7abdd1293d63349f44ec.jpg\",\n  \"54304.jpg\": \"Arachnida/Argiope argentata/24fa5098f6b29a1fc1d8e2b9217a9d8c.jpg\",\n  \"543070.jpg\": \"Aves/Lanius ludovicianus/8eb6b5001fe1e422690af753c5f30f38.jpg\",\n  \"54314.jpg\": \"Arachnida/Argiope argentata/d028e32f068ae7159926e976b7a5cfb9.jpg\",\n  \"543149.jpg\": \"Mammalia/Ovis canadensis canadensis/ca41b6d2b7d9dc9e5f8cc4d93950bd96.jpg\",\n  \"543150.jpg\": \"Mammalia/Ovis canadensis canadensis/9a73201395bc3c4998efe5145885fee5.jpg\",\n  \"543152.jpg\": \"Insecta/Anagrapha falcifera/d3df3e160c66476bf2c6dd2fd295edb7.jpg\",\n  \"543157.jpg\": \"Insecta/Anagrapha falcifera/2e781e65018daafb8191e13d25b814c5.jpg\",\n  \"543158.jpg\": \"Insecta/Anagrapha falcifera/149fc6c01423f52266fca582e07758f7.jpg\",\n  \"543162.jpg\": \"Insecta/Anagrapha falcifera/ead0ce04892401cc974b093d2c46d20b.jpg\",\n  \"54317.jpg\": \"Arachnida/Argiope argentata/190c4249297951034ca652cb4b443ddf.jpg\",\n  \"543180.jpg\": \"Insecta/Bagrada hilaris/e11b0b6363c635c76b59c199d9e436cb.jpg\",\n  \"543193.jpg\": \"Insecta/Bagrada hilaris/3bfa346fc474927c85a94b39c1d33493.jpg\",\n  \"543198.jpg\": \"Insecta/Bagrada hilaris/4638f4a9f36edf25f1373f39ec20d2c8.jpg\",\n  \"543200.jpg\": \"Insecta/Bagrada hilaris/eb2f9eef9df9730074ab5576c963ca61.jpg\",\n  \"543201.jpg\": \"Insecta/Bagrada hilaris/16d189aceec1560daf209b1a248ad2bb.jpg\",\n  \"543205.jpg\": \"Insecta/Bagrada hilaris/ce39532edbb5fad73a386c4354a69061.jpg\",\n  \"54321.jpg\": \"Arachnida/Argiope argentata/9eac8f1a9c347c4510b93e00030b1568.jpg\",\n  \"543248.jpg\": \"Mammalia/Alces americanus/f11f37d3921455a0cdbe936f8fad902e.jpg\",\n  \"543256.jpg\": \"Mammalia/Alces americanus/3ddeea6c51a2b1364c451694dea086a5.jpg\",\n  \"543268.jpg\": \"Mammalia/Alces americanus/3c636e7015d6a57f40c489f54705061f.jpg\",\n  \"54329.jpg\": \"Arachnida/Argiope argentata/a2ba5c4fa7b7ca6ffb27f2709f601a2a.jpg\",\n  \"543290.jpg\": \"Mammalia/Alces americanus/527a80ffb9308233b3c7848f261f9f42.jpg\",\n  \"543295.jpg\": \"Mammalia/Alces americanus/8dd5ae64883bfdbc0b82170e98dfb1e0.jpg\",\n  \"543306.jpg\": \"Mammalia/Alces americanus/ea54ba2b956d174b0a4d684e2ffa527b.jpg\",\n  \"543318.jpg\": \"Mammalia/Alces americanus/652930e8bf4868e30ebb66bdaa978de1.jpg\",\n  \"543330.jpg\": \"Mammalia/Alces americanus/7c8fc6c48133dd16559b3a02250f8c03.jpg\",\n  \"543346.jpg\": \"Mammalia/Alces americanus/56c88334b00e5fad8e72299a59bb454b.jpg\",\n  \"543366.jpg\": \"Mammalia/Alces americanus/b9a08976363d25d360788d61afa29199.jpg\",\n  \"543367.jpg\": \"Mammalia/Alces americanus/ba69aafffed494b6aed79bb63444d93a.jpg\",\n  \"54338.jpg\": \"Arachnida/Argiope argentata/66b7ef8e6c7356d6f7a06a29abab07b6.jpg\",\n  \"54348.jpg\": \"Arachnida/Argiope argentata/d97153a3fd694126b4cd83b1631f8e1f.jpg\",\n  \"543482.jpg\": \"Insecta/Erythrodiplax minuscula/3ba882e229301890551c51735a6931f2.jpg\",\n  \"543492.jpg\": \"Insecta/Erythrodiplax minuscula/97a7bf7cda2a836737a1e849ce1ae6cd.jpg\",\n  \"543493.jpg\": \"Aves/Eugenes fulgens/fb4164c480e52952934cd09be3e00e28.jpg\",\n  \"543495.jpg\": \"Aves/Eugenes fulgens/1bfc2eb0639df3f3609cca0e7d5616b1.jpg\",\n  \"54350.jpg\": \"Aves/Phalacrocorax carbo novaehollandiae/44e03da66fab7cb38feb982462f21aab.jpg\",\n  \"543509.jpg\": \"Aves/Eugenes fulgens/f5d85f584d904dc5e048c4c311accbd6.jpg\",\n  \"543518.jpg\": \"Aves/Eugenes fulgens/dcc8f7cbb037f2215d40eb3b7af1f569.jpg\",\n  \"54352.jpg\": \"Aves/Phalacrocorax carbo novaehollandiae/5cb4768e4b924e025f3a9574e00333f8.jpg\",\n  \"543572.jpg\": \"Reptilia/Crotalus oreganus helleri/b75a78e8fd32e672f288955428de223a.jpg\",\n  \"54359.jpg\": \"Aves/Phalacrocorax carbo novaehollandiae/f89597f75b042b1c4ff41d318af7e548.jpg\",\n  \"543593.jpg\": \"Reptilia/Crotalus oreganus helleri/55ebaf431c757af4183b4c96b05b18c2.jpg\",\n  \"543602.jpg\": \"Reptilia/Crotalus oreganus helleri/a5ce0de9e4dd6e054571cc75c4cf6747.jpg\",\n  \"543606.jpg\": \"Reptilia/Crotalus oreganus helleri/9c7223f7a757cd2fe58cca46cbc578ac.jpg\",\n  \"543608.jpg\": \"Reptilia/Crotalus oreganus helleri/7c7bd988810e37cdbd528c0d04b8fa8b.jpg\",\n  \"543629.jpg\": \"Reptilia/Crotalus oreganus helleri/51fcf801d9fdbf6e0f1714de444d4cb7.jpg\",\n  \"543645.jpg\": \"Reptilia/Crotalus oreganus helleri/8c2b5019c287ae64b70c116e6df9e2fb.jpg\",\n  \"543648.jpg\": \"Reptilia/Crotalus oreganus helleri/1f9b66e311d9b8ca71934fb1c5ffe5cd.jpg\",\n  \"543681.jpg\": \"Amphibia/Lithobates catesbeianus/6d3bb7364ea917e1b88c13780b263802.jpg\",\n  \"543684.jpg\": \"Amphibia/Lithobates catesbeianus/1b258c102ad49c7fdc21c740e09edadf.jpg\",\n  \"543711.jpg\": \"Amphibia/Lithobates catesbeianus/45f3fab4706b758aed4dbc5978d8663d.jpg\",\n  \"543732.jpg\": \"Amphibia/Lithobates catesbeianus/3ac7ea3265b89572c7b92bd76227ade0.jpg\",\n  \"544037.jpg\": \"Amphibia/Lithobates catesbeianus/5dc701aa5c26c98501748645767d1ee4.jpg\",\n  \"544066.jpg\": \"Amphibia/Lithobates catesbeianus/048f562cc33e06e31fb9e88b2060257b.jpg\",\n  \"544118.jpg\": \"Amphibia/Lithobates catesbeianus/26dd64f4f04af717790e1cc24b84499f.jpg\",\n  \"544170.jpg\": \"Aves/Turdus rufopalliatus/6d4f234b05b7dc231cc01d4ab0b28992.jpg\",\n  \"544171.jpg\": \"Aves/Turdus rufopalliatus/50938eeddf877611ea0d15db64b31fe8.jpg\",\n  \"544202.jpg\": \"Aves/Turdus rufopalliatus/6ab8a10eb81aba25339dbadabed30598.jpg\",\n  \"544207.jpg\": \"Aves/Turdus rufopalliatus/6973f39bed7c8c132aade46f37f65f7d.jpg\",\n  \"544208.jpg\": \"Aves/Turdus rufopalliatus/d8940ed1703df212fd21ec33397c3e00.jpg\",\n  \"544210.jpg\": \"Aves/Turdus rufopalliatus/1c051ee245cb08be67c252b09a527502.jpg\",\n  \"544214.jpg\": \"Aves/Turdus rufopalliatus/3e3628b344cfdef66155ae20d48fc39e.jpg\",\n  \"544218.jpg\": \"Aves/Turdus rufopalliatus/6f9c784be6c456ce65856a59c1f8e5a0.jpg\",\n  \"544224.jpg\": \"Aves/Copsychus saularis/7ac973092ff74a58615b43fba88379f3.jpg\",\n  \"544226.jpg\": \"Aves/Copsychus saularis/e742987da9b706de3a6a7f6dd2d68ec4.jpg\",\n  \"544231.jpg\": \"Aves/Copsychus saularis/6c6439d7025315989b53489dc3df81f7.jpg\",\n  \"544303.jpg\": \"Aves/Ardeola ralloides/ce7f371e5aa99d7874b05a9f9ef8f1cc.jpg\",\n  \"544316.jpg\": \"Animalia/Pycnopodia helianthoides/bb73f086e20237e7211b321865fb5b66.jpg\",\n  \"544318.jpg\": \"Animalia/Pycnopodia helianthoides/f0e26a77fbf25fa17d28a568a8cf65af.jpg\",\n  \"544322.jpg\": \"Animalia/Pycnopodia helianthoides/278042005d3b8a11120c96e446a127bf.jpg\",\n  \"544349.jpg\": \"Amphibia/Lithobates sphenocephalus/f861405f8a640272b316d4f2e965847c.jpg\",\n  \"544360.jpg\": \"Amphibia/Lithobates sphenocephalus/28134f65f19a3cb43f46e89de8b42298.jpg\",\n  \"544378.jpg\": \"Amphibia/Lithobates sphenocephalus/ad76d27916d1f2183296ca12349d59f4.jpg\",\n  \"544395.jpg\": \"Amphibia/Lithobates sphenocephalus/c261cc476bc0214cbd8c87f0861f7407.jpg\",\n  \"544412.jpg\": \"Amphibia/Lithobates sphenocephalus/7531ea7e95ea62808eef92fe301a30f6.jpg\",\n  \"544427.jpg\": \"Amphibia/Lithobates sphenocephalus/3e9fd85a6e63d4a65c71df45706d6616.jpg\",\n  \"544444.jpg\": \"Amphibia/Lithobates sphenocephalus/7433fbbb11cd0ded76ad79d06db2042b.jpg\",\n  \"544456.jpg\": \"Insecta/Pseudoleon superbus/5a9356e7a12b8e7d2e3a7302018cbadb.jpg\",\n  \"544473.jpg\": \"Mollusca/Arion ater/0d7b6819cc1c9bf99c9c9ee80ad986da.jpg\",\n  \"544474.jpg\": \"Mollusca/Arion ater/fa0ca9aef3317fc88e85c3242396e86c.jpg\",\n  \"544475.jpg\": \"Mollusca/Arion ater/f97d9b91ef70d73a2e0edef525adceb1.jpg\",\n  \"544477.jpg\": \"Mollusca/Arion ater/ef25a7d5d1bc0b678ef5c484e8671f1a.jpg\",\n  \"544482.jpg\": \"Mollusca/Arion ater/bf01a53b672880bf6771d83fb82aa9bb.jpg\",\n  \"544484.jpg\": \"Mollusca/Arion ater/1251440d9921ef7d215a3a749dc7215a.jpg\",\n  \"544486.jpg\": \"Mollusca/Arion ater/b872d74f9f6e8f7a66b6952525cc9d05.jpg\",\n  \"544487.jpg\": \"Mollusca/Arion ater/bf4d8282a4ad02bdccbf22a89fd0177d.jpg\",\n  \"544509.jpg\": \"Insecta/Erythrodiplax berenice/7f228ca1214c1044a9ff241811415b48.jpg\",\n  \"544510.jpg\": \"Insecta/Erythrodiplax berenice/c0c4c04237eccf5515bb6c52298a6102.jpg\",\n  \"544519.jpg\": \"Insecta/Erythrodiplax berenice/c32d06e2ac528ef23f2ab4764f71f882.jpg\",\n  \"544520.jpg\": \"Insecta/Erythrodiplax berenice/1ae123fe7c8ac1021b4430f83b0c9db8.jpg\",\n  \"544524.jpg\": \"Insecta/Erythrodiplax berenice/b1001b3d52635d94e51bb240e33c1a54.jpg\",\n  \"544532.jpg\": \"Insecta/Erythrodiplax berenice/6017d93e41630b8b44156d579111994e.jpg\",\n  \"544533.jpg\": \"Insecta/Erythrodiplax berenice/0d05c17ecb729abfb2bf3667eb324d38.jpg\",\n  \"544535.jpg\": \"Insecta/Erythrodiplax berenice/c9b95a0b1c29b611f4a31878ccdd7a4d.jpg\",\n  \"54463.jpg\": \"Animalia/Pollicipes polymerus/6feaa3456026b5b1bb72cbb42dca3e27.jpg\",\n  \"544776.jpg\": \"Reptilia/Sceloporus clarkii/7ea9ef439e8f4beaed1a8f0d534bdb39.jpg\",\n  \"544779.jpg\": \"Reptilia/Sceloporus clarkii/ea8fe070fb7f487670c70b8d738d7867.jpg\",\n  \"544780.jpg\": \"Reptilia/Sceloporus clarkii/867bf5656f6a8bf7f77c812f0d491e46.jpg\",\n  \"544909.jpg\": \"Insecta/Pieris napi/12461bf2025f90fbc8e2b642855af07f.jpg\",\n  \"544920.jpg\": \"Insecta/Pieris napi/66a7a3160f2f71b3b6b52e838299b966.jpg\",\n  \"544922.jpg\": \"Insecta/Pieris napi/5a18960e83c55908c1666154fdb91f16.jpg\",\n  \"544929.jpg\": \"Insecta/Pieris napi/12b5a38e32dcb21439b5ed24d03b1c88.jpg\",\n  \"544933.jpg\": \"Insecta/Pieris napi/d38ee3fdff98f10b6f712c55d4958201.jpg\",\n  \"54501.jpg\": \"Animalia/Pollicipes polymerus/be03ee56ed8e8a2ce78acc2d09ffb5fe.jpg\",\n  \"545087.jpg\": \"Insecta/Agraulis vanillae/8e399ec0b12b8eb5dbfa8915e8700a14.jpg\",\n  \"54509.jpg\": \"Animalia/Pollicipes polymerus/e522a77c62105f79010528a33281047e.jpg\",\n  \"5451.jpg\": \"Amphibia/Plethodon cinereus/4d3e0aafbe614130b33ea4f8f44f8ed5.jpg\",\n  \"545114.jpg\": \"Insecta/Agraulis vanillae/219d668e51a5ef207dab729db9c36309.jpg\",\n  \"545158.jpg\": \"Insecta/Agraulis vanillae/94d2ce87ebe5a2e7c0e7dc3f963c8263.jpg\",\n  \"545321.jpg\": \"Insecta/Agraulis vanillae/026d1b9a85936d55f5d27d84eb1d8002.jpg\",\n  \"545396.jpg\": \"Insecta/Agraulis vanillae/d26caa9d1e39473203f1a8d5607065ba.jpg\",\n  \"545444.jpg\": \"Insecta/Agraulis vanillae/4a67ba9870603e756b5cf1c1f140744f.jpg\",\n  \"545448.jpg\": \"Mammalia/Ictidomys tridecemlineatus/055ab12d6729c44182c065a05ecd17e9.jpg\",\n  \"545466.jpg\": \"Mammalia/Ictidomys tridecemlineatus/431c9eb56da03af5ea45e4d4c038c227.jpg\",\n  \"545467.jpg\": \"Mammalia/Ictidomys tridecemlineatus/5b0b4aac28245f5506c31a93b98a1dd9.jpg\",\n  \"545500.jpg\": \"Arachnida/Latrodectus hesperus/90423ceaa325b6cb511ef5e38e62f95b.jpg\",\n  \"545505.jpg\": \"Arachnida/Latrodectus hesperus/c5df79a02bc97e4419773dd079acac67.jpg\",\n  \"545510.jpg\": \"Arachnida/Latrodectus hesperus/c3cafb3eae66ebd33fb970bd3b7b7d34.jpg\",\n  \"545511.jpg\": \"Arachnida/Latrodectus hesperus/258376ea9cb6a43eb19192abb93623cd.jpg\",\n  \"545516.jpg\": \"Arachnida/Latrodectus hesperus/afbd1939e74bbb5a53a1878942fc8c01.jpg\",\n  \"545525.jpg\": \"Arachnida/Latrodectus hesperus/4b9dcfb5f5ce58be6759e1a664427f69.jpg\",\n  \"545536.jpg\": \"Arachnida/Latrodectus hesperus/259a98687ecdabb72aa872bbf2ee015e.jpg\",\n  \"545539.jpg\": \"Arachnida/Latrodectus hesperus/484827f0050080ce57c5e36be3858b18.jpg\",\n  \"545567.jpg\": \"Aves/Aimophila ruficeps/b57a9c6d581993dd63a9b908c16a4f6f.jpg\",\n  \"545568.jpg\": \"Aves/Aimophila ruficeps/186b9ee0f5a8cc132a85f38b11906d41.jpg\",\n  \"545576.jpg\": \"Aves/Aimophila ruficeps/15cdadf29f84a7ef2e015e31e0724141.jpg\",\n  \"545583.jpg\": \"Aves/Aimophila ruficeps/c2365eecea425aa35bb1ac69236b2040.jpg\",\n  \"545595.jpg\": \"Aves/Aimophila ruficeps/75ac0ab22c6cbb56535df33a2d402520.jpg\",\n  \"545596.jpg\": \"Aves/Aimophila ruficeps/4a919d0b047e2e9cd1dfc0dc301c8632.jpg\",\n  \"545597.jpg\": \"Aves/Aimophila ruficeps/19197f45fd596c569e551ce504b376e1.jpg\",\n  \"545673.jpg\": \"Mammalia/Dasypus novemcinctus/e4148cda88dcad585e9c61501ae5a05d.jpg\",\n  \"5457.jpg\": \"Amphibia/Plethodon cinereus/886a17f66f59f5e26bdc7aaca9d1cc5d.jpg\",\n  \"545714.jpg\": \"Mammalia/Dasypus novemcinctus/a4ff07b6cba25398d41b5377fa32bdf3.jpg\",\n  \"545716.jpg\": \"Mammalia/Dasypus novemcinctus/149270d3a6d6d920260f0c23adec498f.jpg\",\n  \"545747.jpg\": \"Mammalia/Dasypus novemcinctus/4099570509fa745b3ba0f0a0ee547a09.jpg\",\n  \"545773.jpg\": \"Mammalia/Dasypus novemcinctus/de73b0b620e7d0095db6e2a3faf5b217.jpg\",\n  \"545782.jpg\": \"Mammalia/Dasypus novemcinctus/c346e9566b0997796ca03e2eef9d2c7b.jpg\",\n  \"54599.jpg\": \"Mammalia/Tamiasciurus hudsonicus/7f7e731ac395421724ab6f79b69fd656.jpg\",\n  \"546236.jpg\": \"Aves/Bubulcus ibis/8c8d63ba2e459359c90de3f5e5396230.jpg\",\n  \"546257.jpg\": \"Aves/Bubulcus ibis/4e617ec09aa9f62e4a0ba1017b2ca254.jpg\",\n  \"5463.jpg\": \"Amphibia/Plethodon cinereus/316bc10e685cb3bdecaf909d89fbe695.jpg\",\n  \"54636.jpg\": \"Mammalia/Tamiasciurus hudsonicus/b16da6254bfc6213a05b0def78e22081.jpg\",\n  \"54642.jpg\": \"Mammalia/Tamiasciurus hudsonicus/8119934b1aec9012630ff3fb7c61e825.jpg\",\n  \"54652.jpg\": \"Mammalia/Tamiasciurus hudsonicus/338a25bbb08c71783065815952c85217.jpg\",\n  \"546526.jpg\": \"Insecta/Amphipsalta zelandica/bf35dca73d9e08a4be391d8fab57aaae.jpg\",\n  \"54656.jpg\": \"Mammalia/Tamiasciurus hudsonicus/8b7597848896d631984fe7942f042fde.jpg\",\n  \"546641.jpg\": \"Insecta/Cicindela punctulata/3539f48e36a9a16f1e4b027143cc531e.jpg\",\n  \"546645.jpg\": \"Insecta/Cicindela punctulata/a6c61bcf75ee02ee99a5ebeb4eb4e25a.jpg\",\n  \"546646.jpg\": \"Insecta/Cicindela punctulata/e6d44dc0c399e1fdcaa229486f8de037.jpg\",\n  \"54665.jpg\": \"Mammalia/Tamiasciurus hudsonicus/ebd08405447986d35a81db34af38fabb.jpg\",\n  \"546667.jpg\": \"Animalia/Procambarus clarkii/075823571f6daeec52e060b3c41f44af.jpg\",\n  \"546680.jpg\": \"Animalia/Procambarus clarkii/d9e7c89d9aff3c29755bd20c4d421b82.jpg\",\n  \"546690.jpg\": \"Animalia/Procambarus clarkii/f6861fcaa4cf7abbee37c00056807c97.jpg\",\n  \"546695.jpg\": \"Animalia/Procambarus clarkii/bb85c22129c144bc77e199eb15513a03.jpg\",\n  \"5467.jpg\": \"Amphibia/Plethodon cinereus/3117d51471bf4c6ff8ed87edd067789c.jpg\",\n  \"546703.jpg\": \"Animalia/Procambarus clarkii/b6b0bed4f85bd152dc698f88b7c648c3.jpg\",\n  \"546708.jpg\": \"Animalia/Procambarus clarkii/e69621fde38dfeaf76f3f26f043f120a.jpg\",\n  \"54671.jpg\": \"Mammalia/Tamiasciurus hudsonicus/fa44c639a736a616371c9703bb07c9ec.jpg\",\n  \"546731.jpg\": \"Insecta/Eurema mexicana/c9341200db88a40f99d9b8522a6122f5.jpg\",\n  \"546733.jpg\": \"Insecta/Eurema mexicana/51291c6e3b703f64fbd229632a5c01c6.jpg\",\n  \"546735.jpg\": \"Insecta/Eurema mexicana/90c10c41f1c22d426ee13d746b9d6b74.jpg\",\n  \"546736.jpg\": \"Aves/Gygis alba/de9a688c1a8b4c3b28342e99edc019c8.jpg\",\n  \"546739.jpg\": \"Aves/Gygis alba/fd3583590ab2ba01f14d7f41f17f8510.jpg\",\n  \"546745.jpg\": \"Aves/Myiarchus tuberculifer/5e77f077c616daee324d554207d9e1ea.jpg\",\n  \"546746.jpg\": \"Aves/Myiarchus tuberculifer/d76e5904a4d85e1b785f3b8c9f16fd2b.jpg\",\n  \"546748.jpg\": \"Aves/Myiarchus tuberculifer/8312597038e931278e77bcb4700852ca.jpg\",\n  \"546750.jpg\": \"Aves/Myiarchus tuberculifer/bb509a3e7b042fc18ed0d8f909cba662.jpg\",\n  \"546759.jpg\": \"Insecta/Papilio zelicaon/cd55b7dbf4ce603f335ec30a78577d04.jpg\",\n  \"54679.jpg\": \"Mammalia/Tamiasciurus hudsonicus/9d15ff9a4faf9eff642a1ff7f2309959.jpg\",\n  \"546794.jpg\": \"Insecta/Papilio zelicaon/b5eb10825ed95317796185bfe17adf39.jpg\",\n  \"5468.jpg\": \"Amphibia/Plethodon cinereus/aed6f91cc02dbf0d66625d9279f02d4d.jpg\",\n  \"546808.jpg\": \"Insecta/Papilio zelicaon/8827f5894b010f68ef4e3e9a963fb203.jpg\",\n  \"546854.jpg\": \"Insecta/Papilio zelicaon/885a3e4f7eea29197ed6f25eeea792c9.jpg\",\n  \"546864.jpg\": \"Insecta/Papilio zelicaon/e8a0658a447f104d14ace132266b8e83.jpg\",\n  \"546925.jpg\": \"Mammalia/Ovis canadensis nelsoni/903203135436b54a1fdc4779413baf3e.jpg\",\n  \"546926.jpg\": \"Mammalia/Ovis canadensis nelsoni/c8106cc626ca15b81507413d3adcae91.jpg\",\n  \"546930.jpg\": \"Mammalia/Ovis canadensis nelsoni/cf43d9e796e45868898931f8a503f7b4.jpg\",\n  \"546945.jpg\": \"Insecta/Pelidnota punctata/4b6fec4a0c6e6a2347381ebce656262f.jpg\",\n  \"546949.jpg\": \"Insecta/Pelidnota punctata/0278174279450145b15f37682fdd79d1.jpg\",\n  \"546951.jpg\": \"Insecta/Pelidnota punctata/67e1728ecc49facb81db3c83ce1551ac.jpg\",\n  \"546954.jpg\": \"Insecta/Pelidnota punctata/7547e4aacef79054aeba5f95b0c27bb4.jpg\",\n  \"546955.jpg\": \"Insecta/Pelidnota punctata/72e60c049a17a02a2c95ced0d0387d16.jpg\",\n  \"546956.jpg\": \"Insecta/Pelidnota punctata/d0850bedad1a8468d4a4a2aa8a70142f.jpg\",\n  \"546957.jpg\": \"Insecta/Pelidnota punctata/ad72e3e07bec2bb6fbca49c7ece7b8a3.jpg\",\n  \"546958.jpg\": \"Insecta/Pelidnota punctata/c768b1a66c673b3ff915bd0ac4d7bd71.jpg\",\n  \"546964.jpg\": \"Insecta/Pelidnota punctata/b5e6947b6f280cda01e83d468598143a.jpg\",\n  \"54704.jpg\": \"Mammalia/Tamiasciurus hudsonicus/1bdb8a9d1f93f2ef01898b810ae4388a.jpg\",\n  \"54706.jpg\": \"Mammalia/Tamiasciurus hudsonicus/47a3633ac602678da50b5d30b5722090.jpg\",\n  \"547087.jpg\": \"Insecta/Dromogomphus spoliatus/b160034e109eaa460b421410ec7e17a4.jpg\",\n  \"547092.jpg\": \"Insecta/Dromogomphus spoliatus/16b3a1ed711a7f47843d3825530c1535.jpg\",\n  \"547093.jpg\": \"Insecta/Dromogomphus spoliatus/480f4791928cc71963f5c5194e0a6539.jpg\",\n  \"547094.jpg\": \"Insecta/Dromogomphus spoliatus/8e430452aa8cebc19d4bead43f4d340e.jpg\",\n  \"547096.jpg\": \"Insecta/Dromogomphus spoliatus/a0340dffcb62d32ea1a3d8cba2d76d01.jpg\",\n  \"547097.jpg\": \"Insecta/Dromogomphus spoliatus/2df87986a497b2c5797a39aa92bf2fa2.jpg\",\n  \"547098.jpg\": \"Insecta/Dromogomphus spoliatus/ddb85b55a6a167d4b0727c2c75be6013.jpg\",\n  \"54710.jpg\": \"Mammalia/Tamiasciurus hudsonicus/b7439ee3b880f9fed1760a7440e5a0ad.jpg\",\n  \"547101.jpg\": \"Insecta/Dromogomphus spoliatus/9e8beda1eee4bd9f8dcd4e7b452320b4.jpg\",\n  \"547110.jpg\": \"Insecta/Dromogomphus spoliatus/d348390c2d59eadf3d4419b098e2bec9.jpg\",\n  \"547113.jpg\": \"Insecta/Dromogomphus spoliatus/92387b0d577aa764e0c1348bd105fc1d.jpg\",\n  \"54715.jpg\": \"Mammalia/Tamiasciurus hudsonicus/cff852f4fd350d446fad966a3d10b27c.jpg\",\n  \"5472.jpg\": \"Amphibia/Plethodon cinereus/1a1e82668bd53164fc24137ab9eb2b08.jpg\",\n  \"547227.jpg\": \"Mammalia/Odocoileus virginianus/4594919730103769e57c13fa984a9cbc.jpg\",\n  \"547302.jpg\": \"Mammalia/Odocoileus virginianus/a03cd5e4a43b8ebe16158755f35b15ff.jpg\",\n  \"54735.jpg\": \"Mammalia/Tamiasciurus hudsonicus/7809fd2688b5278f1b3aed6ece80ad5e.jpg\",\n  \"547369.jpg\": \"Mammalia/Odocoileus virginianus/799ef1a3729a7a9115c5d897fcb97b6c.jpg\",\n  \"54739.jpg\": \"Mammalia/Tamiasciurus hudsonicus/dcf9e0e4a83528fe40382105ac963bac.jpg\",\n  \"54741.jpg\": \"Mammalia/Tamiasciurus hudsonicus/7d00f9dc2c5e345da1d3be67a8aac7cb.jpg\",\n  \"547597.jpg\": \"Mammalia/Odocoileus virginianus/822ec3d7c2ac2d452d2a0965c28380d6.jpg\",\n  \"547640.jpg\": \"Mammalia/Odocoileus virginianus/bd3a7b01e440ec1ab005f88fd2caed30.jpg\",\n  \"547662.jpg\": \"Mammalia/Odocoileus virginianus/fcfe78a45b0349eb6e1f32c7e4cabc8f.jpg\",\n  \"54777.jpg\": \"Mammalia/Tamiasciurus hudsonicus/441c4abacf6168b2885e69cc0e50176e.jpg\",\n  \"54810.jpg\": \"Mammalia/Tamiasciurus hudsonicus/5c6fb6ec3221dd86e7e2fb09f19537e2.jpg\",\n  \"548120.jpg\": \"Aves/Icterus bullockii/698aa88eb88250401dd0712be763458b.jpg\",\n  \"548127.jpg\": \"Aves/Icterus bullockii/a18a3fc6cba27c374c41b46c65aa3f09.jpg\",\n  \"54813.jpg\": \"Mammalia/Tamiasciurus hudsonicus/036b6fd1a030d0301e64850852c04452.jpg\",\n  \"548148.jpg\": \"Aves/Icterus bullockii/8487c6ed57cede26a8bb318434d82b19.jpg\",\n  \"548149.jpg\": \"Aves/Icterus bullockii/120cdfadc328ca968bc927b393c0bd9f.jpg\",\n  \"548154.jpg\": \"Aves/Icterus bullockii/6a499b8288fcf4798bdef2647a2920ac.jpg\",\n  \"548156.jpg\": \"Aves/Icterus bullockii/ec121ad2db2b354e852a570646e7c381.jpg\",\n  \"548162.jpg\": \"Aves/Icterus bullockii/a263f0f0819468b7ed816dfbd40c6961.jpg\",\n  \"548164.jpg\": \"Aves/Icterus bullockii/485903083f4a0098ff9bee017be47976.jpg\",\n  \"54817.jpg\": \"Mammalia/Tamiasciurus hudsonicus/406cc4c6465416818d3c4af367cee270.jpg\",\n  \"548176.jpg\": \"Aves/Icterus bullockii/e4ebd6eb9c5cefbab35f3468374a2f3d.jpg\",\n  \"548210.jpg\": \"Insecta/Thasus gigas/46cec7967ff311d00fbb3b46554cb9a2.jpg\",\n  \"548214.jpg\": \"Insecta/Thasus gigas/2e4c46b4bb8b4884cab4a670fbd0ca63.jpg\",\n  \"548343.jpg\": \"Insecta/Libellula comanche/1949c251ded67194b09e503d8eff4260.jpg\",\n  \"548346.jpg\": \"Insecta/Libellula comanche/5acbf35af5ce0f437472c509ab900478.jpg\",\n  \"548347.jpg\": \"Insecta/Libellula comanche/b0846e7a8c7857d4770609110f4d439b.jpg\",\n  \"548356.jpg\": \"Insecta/Libellula comanche/c462db11c5687bfd744925774b8f8520.jpg\",\n  \"548357.jpg\": \"Insecta/Libellula comanche/932aaa722803116ebe430dbe8a5f5020.jpg\",\n  \"548358.jpg\": \"Insecta/Libellula comanche/95aeb7b27bff009f3f1fd5a07e47e7fb.jpg\",\n  \"548366.jpg\": \"Insecta/Libellula comanche/e327b901c659f498e7aa35009e6eb4a2.jpg\",\n  \"548369.jpg\": \"Insecta/Libellula comanche/3440ea130bbd7fd5920488f230276fd1.jpg\",\n  \"548370.jpg\": \"Insecta/Libellula comanche/f2fc30604df8aeebeae51b732016cc88.jpg\",\n  \"548371.jpg\": \"Insecta/Libellula comanche/1e5357d2a333b32eec2a36731a0f71a7.jpg\",\n  \"548372.jpg\": \"Insecta/Libellula comanche/2dfb5e205222c8a04c13235c81c42a8d.jpg\",\n  \"548374.jpg\": \"Insecta/Libellula comanche/9827c5c1050ed3363cb4f06f4b42dd48.jpg\",\n  \"548376.jpg\": \"Insecta/Libellula comanche/0464553728592d163a401944ac12d6eb.jpg\",\n  \"548428.jpg\": \"Insecta/Panorpa nuptialis/f50fc4f3caf4344191abda038437c321.jpg\",\n  \"548437.jpg\": \"Insecta/Panorpa nuptialis/724ece4572fa26d543a64c03f8a75d85.jpg\",\n  \"548443.jpg\": \"Insecta/Boloria bellona/9ec12292b9916964aaab888f7bfbeb6c.jpg\",\n  \"548459.jpg\": \"Insecta/Boloria bellona/a261c26bf30c22927053c78f8053b372.jpg\",\n  \"548461.jpg\": \"Insecta/Boloria bellona/677be85d13b47a35ffd9bd3e5f90800d.jpg\",\n  \"548475.jpg\": \"Aves/Piranga flava/efbfc71453d0e8285210a0545d80cfe1.jpg\",\n  \"548476.jpg\": \"Aves/Piranga flava/5f903a8b57caf67d196d70e8146ca9fd.jpg\",\n  \"548477.jpg\": \"Aves/Piranga flava/30c7349841a972dd99e79dc6015bba29.jpg\",\n  \"548483.jpg\": \"Aves/Piranga flava/e6ba005b4efa07ad7116eab276836a76.jpg\",\n  \"548484.jpg\": \"Aves/Piranga flava/ec82d886069beaceafd64a45384224c0.jpg\",\n  \"548485.jpg\": \"Aves/Piranga flava/b2a89ed5dd9c5e57fa0f37cbc2b491db.jpg\",\n  \"548487.jpg\": \"Aves/Piranga flava/df561b08885887b148a75d397043c8c7.jpg\",\n  \"548492.jpg\": \"Aves/Piranga flava/107fbe55a7a8272193e39f662127ac93.jpg\",\n  \"548493.jpg\": \"Aves/Callipepla squamata/5694c0fc16942b59fc1ac7052590e08f.jpg\",\n  \"548506.jpg\": \"Aves/Callipepla squamata/a065c8b5eef0d68dd06f2185cb7dda7f.jpg\",\n  \"548507.jpg\": \"Aves/Callipepla squamata/519628e7e8a74105a6baf3f59504dc0b.jpg\",\n  \"548520.jpg\": \"Aves/Callipepla squamata/55d41f569e56bd2164f8815eebe65872.jpg\",\n  \"548531.jpg\": \"Aves/Callipepla squamata/632afedb6c1d7fbd0494a1742a83002e.jpg\",\n  \"548532.jpg\": \"Aves/Callipepla squamata/d2b38020fef7ceb8488ac8d15af026e2.jpg\",\n  \"548534.jpg\": \"Insecta/Hypena baltimoralis/316eceb4709ca1ea7460b63624e5dac1.jpg\",\n  \"548535.jpg\": \"Insecta/Hypena baltimoralis/c94a50c016976050d180a6c795a48711.jpg\",\n  \"548538.jpg\": \"Insecta/Hypena baltimoralis/f45b8a3643ba11efbde01ffb40856788.jpg\",\n  \"548550.jpg\": \"Insecta/Hypena baltimoralis/f4b0b69f10592f382f73083e87008cc8.jpg\",\n  \"548553.jpg\": \"Insecta/Hypena baltimoralis/4eb70e1d21c3f6aeb8ff923deb236dd6.jpg\",\n  \"548554.jpg\": \"Insecta/Hypena baltimoralis/5726d29b06a6f71f046602b7fb7317f6.jpg\",\n  \"548555.jpg\": \"Insecta/Hypena baltimoralis/bd966eaba733dea679606e143a329f43.jpg\",\n  \"548559.jpg\": \"Insecta/Hypena baltimoralis/6387bc1c625822ca1882ebec9a3f2192.jpg\",\n  \"548567.jpg\": \"Aves/Vireo flavifrons/442421705855f8d93e4b1b86386cb9c8.jpg\",\n  \"548568.jpg\": \"Aves/Vireo flavifrons/55c6381c9c7677c83e3053fcdec3fd96.jpg\",\n  \"548618.jpg\": \"Aves/Contopus virens/0ab0754624f4351f7dafc469f3e1bb04.jpg\",\n  \"548619.jpg\": \"Aves/Contopus virens/2aee3e702b266bd3748897c0cfb00e79.jpg\",\n  \"548640.jpg\": \"Aves/Contopus virens/fa37ec2faa539bce7e26a0f10c8dc9b6.jpg\",\n  \"548641.jpg\": \"Aves/Contopus virens/f9798f5b677e74b803495be675727873.jpg\",\n  \"548645.jpg\": \"Aves/Contopus virens/dbbbb232b797dcd089c17446a2fe6709.jpg\",\n  \"548658.jpg\": \"Reptilia/Coluber flagellum/12b9d89a0049537705edffda175ccd51.jpg\",\n  \"548663.jpg\": \"Reptilia/Coluber flagellum/c2b61001ff09bf537b423e3560d222ef.jpg\",\n  \"548690.jpg\": \"Reptilia/Coluber flagellum/50f2f518d259029c5c97fcdd9a988708.jpg\",\n  \"548701.jpg\": \"Reptilia/Coluber flagellum/5f2dfcdcf29a51a3c648769ccd2d6eb2.jpg\",\n  \"548707.jpg\": \"Reptilia/Coluber flagellum/ec8d755ac459eccc0ecb385b9ee71234.jpg\",\n  \"548708.jpg\": \"Reptilia/Coluber flagellum/3edaad8b9594fc69c83d34bc6bc9f778.jpg\",\n  \"54871.jpg\": \"Mammalia/Tamiasciurus hudsonicus/9ec6d31788ac298484da19a8fd05f1b1.jpg\",\n  \"548733.jpg\": \"Aves/Tringa incana/45df9a49e0efdba77806da74d6a40f05.jpg\",\n  \"548746.jpg\": \"Aves/Tringa incana/3775eaf93ee5a3af091de117a6ec4ca6.jpg\",\n  \"548747.jpg\": \"Aves/Tringa incana/e067125732f026833f856da864730146.jpg\",\n  \"548748.jpg\": \"Aves/Tringa incana/7467b45e6557bc66a31afd236691efb8.jpg\",\n  \"548755.jpg\": \"Aves/Tringa incana/9428772cb711c07097e1ad688755a5de.jpg\",\n  \"548757.jpg\": \"Aves/Tringa incana/97a514f9eb4f933cfffbbc71db1b9bc7.jpg\",\n  \"548758.jpg\": \"Aves/Tringa incana/ab501c45e516433471615b17e91999d5.jpg\",\n  \"54876.jpg\": \"Mammalia/Tamiasciurus hudsonicus/5fd4122af96f63a04b3c3db192eaf586.jpg\",\n  \"548766.jpg\": \"Aves/Tringa incana/7cd8f1c78608be68b64217ac9fab50b0.jpg\",\n  \"548770.jpg\": \"Aves/Tringa incana/34d5213252698fe86c48f57d34f07a03.jpg\",\n  \"548787.jpg\": \"Reptilia/Amblyrhynchus cristatus/035ae11c0ba526f82225f922b6dd89fb.jpg\",\n  \"548845.jpg\": \"Mollusca/Crassostrea virginica/061e0ec187911e7e88f384eaf7179696.jpg\",\n  \"548847.jpg\": \"Mollusca/Crassostrea virginica/2d911fc6811bfe661b947ce0e7053760.jpg\",\n  \"54886.jpg\": \"Mammalia/Tamiasciurus hudsonicus/174ad84a3de3674242ae7a036200656e.jpg\",\n  \"548867.jpg\": \"Aves/Dives dives/9950bbc209da4d8ce2a08a5a924b902b.jpg\",\n  \"548874.jpg\": \"Aves/Dives dives/11750a91ac4d3f646f1511026f1a4781.jpg\",\n  \"548954.jpg\": \"Aves/Ardea alba/d607b2aa8eb08aabcb7e6180f4f8f5ea.jpg\",\n  \"54899.jpg\": \"Reptilia/Holbrookia maculata/6ba18afbff038569ea323dea4c3fad6a.jpg\",\n  \"549032.jpg\": \"Aves/Ardea alba/be5801ef783187b2533f1207d9a7bd59.jpg\",\n  \"54904.jpg\": \"Reptilia/Holbrookia maculata/b164581017109983b442fde7f59a44bc.jpg\",\n  \"54906.jpg\": \"Reptilia/Holbrookia maculata/fbd7e9161194f39518bebae272682131.jpg\",\n  \"54914.jpg\": \"Reptilia/Holbrookia maculata/442324be55a279fc46ce2dab8b9fc5df.jpg\",\n  \"54915.jpg\": \"Reptilia/Holbrookia maculata/abbf46fb3ce28cf734fd51392e19d53c.jpg\",\n  \"54920.jpg\": \"Reptilia/Holbrookia maculata/6987e691e233b31cf2f6f734e8f174f0.jpg\",\n  \"549278.jpg\": \"Aves/Ardea alba/729dd61ae3ed924d5ddc0cce92d1460f.jpg\",\n  \"549576.jpg\": \"Aves/Ardea alba/cffa22bfcc595931bdd855c22bb4f63e.jpg\",\n  \"549618.jpg\": \"Aves/Ardea alba/4a50514eb25bc919f88dab79d8517902.jpg\",\n  \"54989.jpg\": \"Aves/Rhipidura fuliginosa placabilis/6f964744d8537927cd58b5830bc9d910.jpg\",\n  \"54990.jpg\": \"Aves/Rhipidura fuliginosa placabilis/a6825cc9e0bd2e1e0b11c8b578228e84.jpg\",\n  \"54994.jpg\": \"Aves/Rhipidura fuliginosa placabilis/96ec5069b94258f8f1672fc205c2d23a.jpg\",\n  \"54996.jpg\": \"Aves/Rhipidura fuliginosa placabilis/f496a89885a8518b7b18f0f64ba80006.jpg\",\n  \"54997.jpg\": \"Aves/Rhipidura fuliginosa placabilis/3316efd040da26fcc5a0517e75422ea9.jpg\",\n  \"549981.jpg\": \"Aves/Ardea alba/db38f26080d198ff3a5bbcd9d58f4747.jpg\",\n  \"5500.jpg\": \"Reptilia/Eretmochelys imbricata/d71ad223e2e763350682d8996459ee14.jpg\",\n  \"55002.jpg\": \"Aves/Rhipidura fuliginosa placabilis/fb9e5371a835cdfe05baebaa08321dd5.jpg\",\n  \"55008.jpg\": \"Aves/Rhipidura fuliginosa placabilis/918435c7c1cd353ac3e2411072ffe93e.jpg\",\n  \"550083.jpg\": \"Aves/Ardea alba/3eeeeb4984f55972f23b3cc02d54b8d4.jpg\",\n  \"55014.jpg\": \"Aves/Rhipidura fuliginosa placabilis/1e5f787c23b2d583755b14f968b10150.jpg\",\n  \"5502.jpg\": \"Reptilia/Eretmochelys imbricata/4644f13b068f7ddf4fc544e07e4a5f01.jpg\",\n  \"55020.jpg\": \"Aves/Rhipidura fuliginosa placabilis/a4323fa5310145e7df641b123b7747d1.jpg\",\n  \"55021.jpg\": \"Aves/Rhipidura fuliginosa placabilis/8221d69248f5b2fd5bb1fbba0a6989b9.jpg\",\n  \"55022.jpg\": \"Aves/Rhipidura fuliginosa placabilis/b898a5c6b18c8f5b1c666297b3806b2c.jpg\",\n  \"55023.jpg\": \"Aves/Rhipidura fuliginosa placabilis/3a0261276451ec0fca47820232dc4c57.jpg\",\n  \"55025.jpg\": \"Aves/Rhipidura fuliginosa placabilis/a29aabbd0fcd3fde8ea54a57b533dc69.jpg\",\n  \"55026.jpg\": \"Aves/Rhipidura fuliginosa placabilis/553b9b7c9c71164614e403b449b92b7d.jpg\",\n  \"55027.jpg\": \"Aves/Rhipidura fuliginosa placabilis/2ae0bafa27c2545aa21a29485d564c13.jpg\",\n  \"55029.jpg\": \"Aves/Rhipidura fuliginosa placabilis/8fb989b72c30ed0f9afbf7423563eb7b.jpg\",\n  \"5503.jpg\": \"Reptilia/Eretmochelys imbricata/577f0df6fdd35c4f7fae2b23b426de2c.jpg\",\n  \"55030.jpg\": \"Aves/Rhipidura fuliginosa placabilis/3a4210f5e1a7ef0db0b8476d9a554b8e.jpg\",\n  \"55031.jpg\": \"Aves/Rhipidura fuliginosa placabilis/f5717e4665021fbcdd6fadba6efa4029.jpg\",\n  \"55041.jpg\": \"Insecta/Cicindela repanda/8e4ba3edb6f22017a6861aff734623a0.jpg\",\n  \"55042.jpg\": \"Insecta/Cicindela repanda/6773c7b64f434cb6483a1c5d58f7c4be.jpg\",\n  \"55046.jpg\": \"Insecta/Cicindela repanda/95f0d70e8deca12eaaac24aa4d7abf60.jpg\",\n  \"55049.jpg\": \"Insecta/Cicindela repanda/f933d80dd0ac59e5f1da00854685831a.jpg\",\n  \"550503.jpg\": \"Mammalia/Oryctolagus cuniculus domesticus/5afa6f58e4776ae4a3d27c761fd05485.jpg\",\n  \"550509.jpg\": \"Aves/Manorina melanocephala/d251e3957c626b71c22da43b501fb550.jpg\",\n  \"550514.jpg\": \"Aves/Manorina melanocephala/e2bf0cb68496063239542a3998e36c5b.jpg\",\n  \"550527.jpg\": \"Insecta/Mestra amymone/5030a16a238cb5c9caeeb3c8600827be.jpg\",\n  \"55054.jpg\": \"Insecta/Cicindela repanda/a1113a491634a95177c696db557b8827.jpg\",\n  \"550541.jpg\": \"Insecta/Mestra amymone/db10bb1d0b445154f8d11c744a23bf7e.jpg\",\n  \"550542.jpg\": \"Insecta/Mestra amymone/79d2490662fc2ea2975c55cae86bc080.jpg\",\n  \"550551.jpg\": \"Insecta/Mestra amymone/17de0f3949d7b3a851d40ecec34d5762.jpg\",\n  \"550575.jpg\": \"Insecta/Mestra amymone/128703062f6bac8f4b9e06c63fe2b109.jpg\",\n  \"550577.jpg\": \"Insecta/Mestra amymone/ef36ae1168bc0ab3bfb4b6a0c29891ce.jpg\",\n  \"550587.jpg\": \"Insecta/Mestra amymone/268f1f68016a579ae5f88f49a3203db7.jpg\",\n  \"550600.jpg\": \"Reptilia/Agkistrodon contortrix laticinctus/4cf7ecc5e2c1b10657a1d7b080f13e08.jpg\",\n  \"550601.jpg\": \"Reptilia/Agkistrodon contortrix laticinctus/f31223990a606f73f43227b6e7f8ad8d.jpg\",\n  \"550605.jpg\": \"Reptilia/Agkistrodon contortrix laticinctus/5f29b785501d534382bcf581e4cb3ae3.jpg\",\n  \"55061.jpg\": \"Insecta/Cicindela repanda/6d63a9f0bbf6ba51e1e63d95d7cceb8c.jpg\",\n  \"550613.jpg\": \"Reptilia/Agkistrodon contortrix laticinctus/e85048b56fbb9416dfce68f09806b48b.jpg\",\n  \"550617.jpg\": \"Reptilia/Agkistrodon contortrix laticinctus/404f160f18a26b3ac6b023aaeb0748bf.jpg\",\n  \"550618.jpg\": \"Reptilia/Agkistrodon contortrix laticinctus/48a8a4161f3dc3fd9656f0bda6365c11.jpg\",\n  \"550619.jpg\": \"Reptilia/Agkistrodon contortrix laticinctus/2c4353aebca3225dcc193df852c89b35.jpg\",\n  \"550629.jpg\": \"Reptilia/Agkistrodon contortrix laticinctus/d785683358cd5d283896673f157aed80.jpg\",\n  \"55065.jpg\": \"Insecta/Cicindela repanda/f756d382017a49af3b581e681d53c7cd.jpg\",\n  \"55067.jpg\": \"Insecta/Cicindela repanda/754bc2ee0a59820fec73d9b445e9ffce.jpg\",\n  \"550677.jpg\": \"Aves/Petrochelidon pyrrhonota/8be40610714694055901b25f92ef2c7a.jpg\",\n  \"55068.jpg\": \"Insecta/Cicindela repanda/c50074f562fc148b5e469bc4bab1540a.jpg\",\n  \"550754.jpg\": \"Aves/Petrochelidon pyrrhonota/5c0c1ac5fdd558eb23b22281a79bd1c5.jpg\",\n  \"550772.jpg\": \"Aves/Petrochelidon pyrrhonota/77a7f49be90fc8ad5d3bf416332a258d.jpg\",\n  \"550789.jpg\": \"Insecta/Pararge aegeria/4648ab3ece13b8f540e708d2112f325a.jpg\",\n  \"550797.jpg\": \"Insecta/Pararge aegeria/2e969380458f4984a2a1535526499002.jpg\",\n  \"550801.jpg\": \"Insecta/Pararge aegeria/f4a29aa148f76f738e8dbad96db7036f.jpg\",\n  \"550815.jpg\": \"Insecta/Pararge aegeria/966c26764112e92c3061e0938f4100e0.jpg\",\n  \"550832.jpg\": \"Insecta/Pararge aegeria/7a98e24bc95dc88029ae3dd0dea891be.jpg\",\n  \"550838.jpg\": \"Insecta/Pararge aegeria/413c1dcee0f250587d3b2d1bf58abea4.jpg\",\n  \"550881.jpg\": \"Aves/Nyctidromus albicollis/1d42beba11a15b60ef2b8bdb7bc301a7.jpg\",\n  \"550883.jpg\": \"Aves/Nyctidromus albicollis/9cd2517e6351365c22e648714e64fa85.jpg\",\n  \"550889.jpg\": \"Aves/Nyctidromus albicollis/1ff983811e73af877a2baba1f725a58c.jpg\",\n  \"550902.jpg\": \"Mammalia/Orcinus orca/90ff75c5dc05e6c361541c0dc8a7b0c9.jpg\",\n  \"550908.jpg\": \"Mammalia/Orcinus orca/867aec9bd9870e4865b512d621f70b89.jpg\",\n  \"550917.jpg\": \"Mammalia/Orcinus orca/029d813b63896b621094c7fb4f53c5cc.jpg\",\n  \"550919.jpg\": \"Mammalia/Orcinus orca/704743513240d79b8fe1f4dd592dc2de.jpg\",\n  \"550928.jpg\": \"Mammalia/Aepyceros melampus/498a9d9e0e1de5023157b9e96fc4c91c.jpg\",\n  \"5510.jpg\": \"Reptilia/Eretmochelys imbricata/762119bd8f591557a5fd30698c8d2ade.jpg\",\n  \"55105.jpg\": \"Insecta/Aenetus virescens/a4e4eab2e2beae80172c58a57402b3bf.jpg\",\n  \"551111.jpg\": \"Aves/Netta rufina/10b77240e896b03e24af9225f4aa63a7.jpg\",\n  \"551129.jpg\": \"Mollusca/Ambigolimax valentianus/668c348a0da64e3a89deb96f83b99003.jpg\",\n  \"551130.jpg\": \"Mollusca/Ambigolimax valentianus/8bd4d2bc7cea8ccb4f71866eb97cca90.jpg\",\n  \"551132.jpg\": \"Mollusca/Ambigolimax valentianus/727ebb1ab03c58321738b5d4b72d7fe5.jpg\",\n  \"551134.jpg\": \"Mollusca/Ambigolimax valentianus/7e4a16d5083704c39e4595e28d261882.jpg\",\n  \"551135.jpg\": \"Mollusca/Ambigolimax valentianus/4037edc5a3feeccd08cf146f82fcfdee.jpg\",\n  \"55114.jpg\": \"Insecta/Aenetus virescens/411a17b50b0e1808305663b07d584ad0.jpg\",\n  \"551163.jpg\": \"Aves/Himantopus mexicanus/624173d750b8da7c602ca2908da71c9c.jpg\",\n  \"55118.jpg\": \"Insecta/Aenetus virescens/d83b476558b90b3a7da8d9f7497d6725.jpg\",\n  \"55119.jpg\": \"Insecta/Aenetus virescens/98ea09b0f7328967d96ef1536d354b3c.jpg\",\n  \"55124.jpg\": \"Insecta/Aenetus virescens/56788fd10320b06c1080788e95928e27.jpg\",\n  \"551349.jpg\": \"Aves/Himantopus mexicanus/18b7114fe8d26510c9ad4d5ff67289a3.jpg\",\n  \"551389.jpg\": \"Aves/Himantopus mexicanus/7ff05fe322c5e9ca087c13a291e8ac35.jpg\",\n  \"551423.jpg\": \"Aves/Himantopus mexicanus/10fbf04c117841e8a7da4f2a5e620fb5.jpg\",\n  \"551439.jpg\": \"Aves/Himantopus mexicanus/2e87d3de64ad0285a52b7ae3d5fc4382.jpg\",\n  \"551459.jpg\": \"Aves/Himantopus mexicanus/9d528b99f73a7aaa26a6ba600ce2171c.jpg\",\n  \"551557.jpg\": \"Mollusca/Megapallifera mutabilis/928c5df3b9e92c3b6a10d2d8ca7db61d.jpg\",\n  \"551559.jpg\": \"Mollusca/Megapallifera mutabilis/1d0c11ab58c6fec117555facf8776e99.jpg\",\n  \"551561.jpg\": \"Insecta/Euphoria kernii/9ce670ea7098d259a535d6f6449841d8.jpg\",\n  \"551570.jpg\": \"Insecta/Euphoria kernii/1ebaef91df5535d3a2bb0288e08f2378.jpg\",\n  \"551571.jpg\": \"Insecta/Euphoria kernii/a3071d870e72883b4e8bb54974302c76.jpg\",\n  \"551584.jpg\": \"Insecta/Dactylotum bicolor/32429fb5593825f6f6f7022019ee0685.jpg\",\n  \"551585.jpg\": \"Insecta/Dactylotum bicolor/f4444c4db8202d288dd21a1c1db47fa1.jpg\",\n  \"551626.jpg\": \"Aves/Lagopus lagopus/c18715f659d96365a28a5ef53d43a7b0.jpg\",\n  \"551628.jpg\": \"Insecta/Anthidium manicatum/792accca49385f6740998e7fe37aa9b5.jpg\",\n  \"551629.jpg\": \"Insecta/Anthidium manicatum/63e9d28e8e9fc1feefeb9244e95253e0.jpg\",\n  \"551630.jpg\": \"Insecta/Anthidium manicatum/3dcf74d0bc3eacbd979118a69fd7ce23.jpg\",\n  \"551631.jpg\": \"Insecta/Anthidium manicatum/baa811a5b7f9c822e45275a1a944f610.jpg\",\n  \"551634.jpg\": \"Insecta/Anthidium manicatum/95820a1b07b11bcb7b1c8381ebaa0201.jpg\",\n  \"551639.jpg\": \"Insecta/Anthidium manicatum/c25206d22b01df79990e792e41b06809.jpg\",\n  \"551641.jpg\": \"Insecta/Anthidium manicatum/58c28d5097ef1b9764cd830ffaa5ac04.jpg\",\n  \"551642.jpg\": \"Insecta/Anthidium manicatum/aec23f28c07781ca450f82fb0582a3ad.jpg\",\n  \"551646.jpg\": \"Insecta/Anthidium manicatum/7f88e63f3453d37b4e02eb51873d9fd1.jpg\",\n  \"551647.jpg\": \"Insecta/Anthidium manicatum/012b6d5644256ea8daf58470e00eb9c1.jpg\",\n  \"551651.jpg\": \"Aves/Polioptila melanura/3ebef916c3e6e2bebcbc65263ac18109.jpg\",\n  \"551652.jpg\": \"Aves/Polioptila melanura/c670fdbe7f7beaae30bb848919384105.jpg\",\n  \"551656.jpg\": \"Aves/Polioptila melanura/ce2feca1b135e055bb64da3811a84862.jpg\",\n  \"551662.jpg\": \"Aves/Polioptila melanura/706573c8c8d66ef3ecb7c411bdd569f1.jpg\",\n  \"551667.jpg\": \"Reptilia/Chelonia mydas/f218aabf36b688377e8563171d8c27d8.jpg\",\n  \"551669.jpg\": \"Reptilia/Chelonia mydas/b971623490c357722438d284db5c6ad4.jpg\",\n  \"551693.jpg\": \"Reptilia/Chelonia mydas/4777ad425f08a4d2f5ee79c2198a09a9.jpg\",\n  \"551730.jpg\": \"Reptilia/Chelonia mydas/b1bfdad828873e08070f500c0eb5cf56.jpg\",\n  \"551744.jpg\": \"Reptilia/Chelonia mydas/a1aa373c509b46a999d8d65bac4ef9a2.jpg\",\n  \"551818.jpg\": \"Aves/Selasphorus calliope/c3f977831c3e9bcdc3a59bf9e079b247.jpg\",\n  \"551819.jpg\": \"Aves/Selasphorus calliope/04c1a9f84338cea2a070bbc31c611103.jpg\",\n  \"551820.jpg\": \"Aves/Selasphorus calliope/23fa1f71915aedbfa683444e0336aefa.jpg\",\n  \"551830.jpg\": \"Insecta/Pyrgus oileus/8c6aa5b74977f53f6cbf9f6a455a039c.jpg\",\n  \"551832.jpg\": \"Insecta/Pyrgus oileus/e9ca4c960dc2d85d32050aa814ea7382.jpg\",\n  \"551850.jpg\": \"Insecta/Pyrgus oileus/8b150246e8778dbed1e7faac8b2b1057.jpg\",\n  \"551857.jpg\": \"Insecta/Pyrgus oileus/03f968b0703b653ee3ebb9f239a5e36d.jpg\",\n  \"551860.jpg\": \"Insecta/Pyrgus oileus/fd649518ea757d6ca118332d9d8f6b4a.jpg\",\n  \"551866.jpg\": \"Insecta/Pyrgus oileus/c1f4ee000bc6afc6174657a378ab5ccf.jpg\",\n  \"5519.jpg\": \"Reptilia/Eretmochelys imbricata/4cc70e7c0118affb2cdbaec11b5d0098.jpg\",\n  \"551913.jpg\": \"Aves/Setophaga coronata auduboni/39772da90bab660b33bf8c49c45c5c8a.jpg\",\n  \"551921.jpg\": \"Aves/Setophaga coronata auduboni/3144aabdfa38980118229f5758c597fa.jpg\",\n  \"551926.jpg\": \"Aves/Setophaga coronata auduboni/6f2dd0250a2c2cd1c34c1a3301fcae71.jpg\",\n  \"551978.jpg\": \"Aves/Setophaga coronata auduboni/1682c21d00273684e38e7ae643fcc30b.jpg\",\n  \"5520.jpg\": \"Reptilia/Eretmochelys imbricata/cd5a471ec821424963ab51e3c0644235.jpg\",\n  \"552021.jpg\": \"Aves/Setophaga coronata auduboni/40a69ddd640f0b3712bca59fd159730b.jpg\",\n  \"552043.jpg\": \"Insecta/Plutella xylostella/04741a1d807d00bdd0054d4a25a4a11b.jpg\",\n  \"552048.jpg\": \"Insecta/Plutella xylostella/04ba422e4fa3aa33a4bf60088b01472d.jpg\",\n  \"552051.jpg\": \"Insecta/Plutella xylostella/cfdbbeaeaa0bfc6fd5f39d6864439d74.jpg\",\n  \"552052.jpg\": \"Insecta/Plutella xylostella/51004ef1b89aace78618ddbb4c5a130c.jpg\",\n  \"552055.jpg\": \"Insecta/Plutella xylostella/f1000be17498ad98c8856e9c89f784c7.jpg\",\n  \"552056.jpg\": \"Insecta/Plutella xylostella/e74d2a712c0cbe5cdf7cce8e3e22adf8.jpg\",\n  \"552058.jpg\": \"Insecta/Plutella xylostella/c0fb2f53e0e65f824725ab978a34101f.jpg\",\n  \"552064.jpg\": \"Insecta/Plutella xylostella/73c26dd8f71c9faf84ea3285e53bcc36.jpg\",\n  \"552071.jpg\": \"Insecta/Plutella xylostella/a3f1f91d69223a9a0df945cca9b0174e.jpg\",\n  \"552076.jpg\": \"Insecta/Plutella xylostella/994192bd794e462a35e0c1de690d61d0.jpg\",\n  \"552205.jpg\": \"Insecta/Pieris rapae/16e025ffad6ab3e83dd430cac8de0187.jpg\",\n  \"552215.jpg\": \"Insecta/Pieris rapae/95f91b864801ac1f3ddba3c3803e811a.jpg\",\n  \"5523.jpg\": \"Reptilia/Eretmochelys imbricata/86e566474899ed8e38f9a9afac28f453.jpg\",\n  \"552386.jpg\": \"Insecta/Pieris rapae/f555e3962f0c58777da0958ac1fc524c.jpg\",\n  \"552397.jpg\": \"Insecta/Pieris rapae/600eb432f011a698d11aac446491ca84.jpg\",\n  \"552445.jpg\": \"Insecta/Pieris rapae/7202e818cccd679b1b1c16b515c7269e.jpg\",\n  \"552458.jpg\": \"Insecta/Pieris rapae/9914f37f2ee39f04cd3e0c4259518017.jpg\",\n  \"552515.jpg\": \"Insecta/Pieris rapae/d4780b123d3eeb4b5fa68e14fcd35990.jpg\",\n  \"552609.jpg\": \"Insecta/Pieris rapae/d260636312a2a38e04e40a1188f29432.jpg\",\n  \"552642.jpg\": \"Insecta/Pieris rapae/5e6096e79e96dc849591b5b729de03fb.jpg\",\n  \"552694.jpg\": \"Reptilia/Barisia imbricata/077ca832db98f23b59e1f412b46cf428.jpg\",\n  \"552695.jpg\": \"Reptilia/Barisia imbricata/195ca758d20bfdf1d15c12c7ba78ccf3.jpg\",\n  \"552712.jpg\": \"Reptilia/Caretta caretta/8b86180842a880dc5b8646d842da942e.jpg\",\n  \"552713.jpg\": \"Reptilia/Caretta caretta/8c50d742331436786e3df09925474102.jpg\",\n  \"552721.jpg\": \"Aves/Larus glaucoides/f4ce434d3a5711510d4b7e8860333bdd.jpg\",\n  \"5528.jpg\": \"Reptilia/Eretmochelys imbricata/8f763877faf897b816239f06bb21879c.jpg\",\n  \"552853.jpg\": \"Mollusca/Hermissenda crassicornis/6a7f7bfff6bf70d9bd65a07353e0c424.jpg\",\n  \"552888.jpg\": \"Mollusca/Hermissenda crassicornis/8fb5e154eaddc1c9c063065a2be4345d.jpg\",\n  \"552889.jpg\": \"Mollusca/Hermissenda crassicornis/37f16d6b6f1ff44ba9dab2c959187567.jpg\",\n  \"552890.jpg\": \"Mollusca/Hermissenda crassicornis/885abf5e3ce779aaf58ab9598059cc51.jpg\",\n  \"552892.jpg\": \"Mollusca/Hermissenda crassicornis/dbe01ee510b5921d122a0137a4c4f7e3.jpg\",\n  \"552893.jpg\": \"Mollusca/Hermissenda crassicornis/2f938021f1cd49b9861baf26e0a5c68f.jpg\",\n  \"552894.jpg\": \"Mollusca/Hermissenda crassicornis/fff9d5d282445d6e1f4c70048bec6289.jpg\",\n  \"552903.jpg\": \"Mollusca/Hermissenda crassicornis/07c7e387cd1ad82dc3cbb69314a7e766.jpg\",\n  \"552923.jpg\": \"Aves/Calothorax lucifer/00d3c6516629659aa23a2c502cb1043d.jpg\",\n  \"552929.jpg\": \"Aves/Calothorax lucifer/79e4918c5f48af3f364b65b39b4cce43.jpg\",\n  \"553016.jpg\": \"Aves/Hylocharis leucotis/89b6ee561698176bed8203a9c87cd722.jpg\",\n  \"553024.jpg\": \"Aves/Hylocharis leucotis/f25519184ec792807915c7374294f213.jpg\",\n  \"553031.jpg\": \"Aves/Hylocharis leucotis/6cb50cf2405fe641275cf816055751e5.jpg\",\n  \"553032.jpg\": \"Aves/Troglodytes hiemalis/357dbaf7664581bb3a9f11630e460611.jpg\",\n  \"553033.jpg\": \"Aves/Troglodytes hiemalis/57c36f95567877d6e9dfb0613dc26d8c.jpg\",\n  \"553034.jpg\": \"Aves/Troglodytes hiemalis/461584008f44abd871e4c94a2c4b0aa9.jpg\",\n  \"553046.jpg\": \"Aves/Troglodytes hiemalis/9dc44e8047a47b287233784d5d8fee0a.jpg\",\n  \"553048.jpg\": \"Aves/Troglodytes hiemalis/bef908aa9f3b155a653a2b0e3c9d8933.jpg\",\n  \"553049.jpg\": \"Aves/Troglodytes hiemalis/f4cfed8663adc0d1974efec8e377862f.jpg\",\n  \"553056.jpg\": \"Aves/Troglodytes hiemalis/2d7e7a38aedbfe615a096135f5bc16bd.jpg\",\n  \"553057.jpg\": \"Aves/Troglodytes hiemalis/4294e6c41db5a9f96cd7d8d5b04a0c6d.jpg\",\n  \"553058.jpg\": \"Aves/Troglodytes hiemalis/3d6272a6c65ab2d4ae70e570d36d20d8.jpg\",\n  \"553062.jpg\": \"Aves/Buteo albonotatus/2cc45e3785145c65efdef9431d19290f.jpg\",\n  \"553063.jpg\": \"Aves/Buteo albonotatus/4fd02ef39d5b3e2fde0d55695ca27f6b.jpg\",\n  \"553065.jpg\": \"Aves/Buteo albonotatus/197c7fd6ef1036f2b8b6c24304ccd81e.jpg\",\n  \"553069.jpg\": \"Aves/Buteo albonotatus/7c217ada7b5480084863a153ad7c6221.jpg\",\n  \"55307.jpg\": \"Amphibia/Hyla versicolor/763a41508615c79179714fc2890acc3c.jpg\",\n  \"553073.jpg\": \"Aves/Buteo albonotatus/56a8757b9088c550d1b95dcb1431df9c.jpg\",\n  \"553074.jpg\": \"Aves/Buteo albonotatus/2fb6bce9951b23e2cccfc3dbbf39ee7d.jpg\",\n  \"553076.jpg\": \"Aves/Buteo albonotatus/ccf88d06fc5281b521dfc3aa2af4452d.jpg\",\n  \"553082.jpg\": \"Aves/Buteo albonotatus/eb4c81d791d529fd71ba7c490fc8571d.jpg\",\n  \"553084.jpg\": \"Aves/Buteo albonotatus/2be90464fe4e627a17174d1709dd477e.jpg\",\n  \"5531.jpg\": \"Reptilia/Eretmochelys imbricata/b2654ed792bba760af551c546591a546.jpg\",\n  \"553124.jpg\": \"Reptilia/Tantilla gracilis/23b6bd2af3de025688bccb4ffb443da3.jpg\",\n  \"553129.jpg\": \"Reptilia/Tantilla gracilis/804d668de2751eaf7284041a736fe660.jpg\",\n  \"553133.jpg\": \"Reptilia/Tantilla gracilis/e8e1657a99351368a2fccf2a6c584cd8.jpg\",\n  \"553134.jpg\": \"Reptilia/Tantilla gracilis/a7ed472889360ed27952c8354fa3d62c.jpg\",\n  \"553138.jpg\": \"Reptilia/Tantilla gracilis/7b0b3fadf5fcfc1ead857e06737964e7.jpg\",\n  \"553156.jpg\": \"Reptilia/Tantilla gracilis/26d4ea528de9fd6358d48c3d5bae5806.jpg\",\n  \"553163.jpg\": \"Reptilia/Pantherophis emoryi/7a84670a7a5b7428c96e639743a5d49e.jpg\",\n  \"553165.jpg\": \"Reptilia/Pantherophis emoryi/05aa1d6083422e8cc8f64104aedb4b6f.jpg\",\n  \"553178.jpg\": \"Reptilia/Pantherophis emoryi/3f367f153e8a34793711ffaa16d1091a.jpg\",\n  \"55318.jpg\": \"Amphibia/Hyla versicolor/ee9cb9ebbd67e7ee5d73c2d8b080730a.jpg\",\n  \"553187.jpg\": \"Reptilia/Pantherophis emoryi/bee65c464c2b6bdf7099e8b88de7896e.jpg\",\n  \"5532.jpg\": \"Reptilia/Eretmochelys imbricata/e86cc4a953728a19dc0760851b978a5a.jpg\",\n  \"55321.jpg\": \"Amphibia/Hyla versicolor/cfb1b73da6c6b6df14c1144d8ed486db.jpg\",\n  \"553217.jpg\": \"Reptilia/Pantherophis emoryi/0a31ced5f9a9b6ebb0ff189c67e52f52.jpg\",\n  \"553222.jpg\": \"Reptilia/Pantherophis emoryi/82a4d255c2dde2c3f530580e0e2549e1.jpg\",\n  \"553239.jpg\": \"Reptilia/Pantherophis emoryi/34d8ddc2a8dff127d89ce1c179ae6fac.jpg\",\n  \"553244.jpg\": \"Reptilia/Pantherophis emoryi/9546790e959d21da416bda731fad898e.jpg\",\n  \"553248.jpg\": \"Aves/Estrilda astrild/d501a68be397f6113002a328e5796dda.jpg\",\n  \"553255.jpg\": \"Aves/Estrilda astrild/3ddde86787e283472f0459d608e563be.jpg\",\n  \"553256.jpg\": \"Aves/Estrilda astrild/5e5fa41aff50ca7bbdcba87285bdaef3.jpg\",\n  \"553264.jpg\": \"Aves/Icterus graduacauda/dcf08366f973835af1688526a0bf63b3.jpg\",\n  \"553271.jpg\": \"Aves/Icterus graduacauda/04531be699aa68094b887446dee392b7.jpg\",\n  \"553274.jpg\": \"Reptilia/Lampropeltis holbrooki/beb648f0ce94c4b2670e747854106ae1.jpg\",\n  \"553287.jpg\": \"Reptilia/Lampropeltis holbrooki/1479d00916fc590d6436e70986729c31.jpg\",\n  \"553288.jpg\": \"Reptilia/Lampropeltis holbrooki/6d2b90ec186441c4f1ac98b507fbad54.jpg\",\n  \"553293.jpg\": \"Reptilia/Lampropeltis holbrooki/1aacee6c3f8747adb53633d13114049b.jpg\",\n  \"553294.jpg\": \"Reptilia/Lampropeltis holbrooki/20c318c65f129e8a5b3e6932f6ec1062.jpg\",\n  \"553295.jpg\": \"Reptilia/Lampropeltis holbrooki/f9b1274b7a118f4c2381cb4784b8c6c1.jpg\",\n  \"553296.jpg\": \"Reptilia/Lampropeltis holbrooki/4c9ec6d8bcf15556caf28aec5824264b.jpg\",\n  \"553297.jpg\": \"Reptilia/Lampropeltis holbrooki/2b748946db89bc1e406abc68685cf29e.jpg\",\n  \"5533.jpg\": \"Reptilia/Eretmochelys imbricata/f2d586e4c641bf27576314d4a9b90452.jpg\",\n  \"553301.jpg\": \"Reptilia/Lampropeltis holbrooki/c7e627f71bce3feaf7f46c20a1340df1.jpg\",\n  \"553302.jpg\": \"Reptilia/Lampropeltis holbrooki/67eedde464f3fbbbbe3f576afec0a002.jpg\",\n  \"553309.jpg\": \"Reptilia/Lampropeltis holbrooki/12ef9cdb1b9766039252c2b7ce4f654c.jpg\",\n  \"553324.jpg\": \"Aves/Toxostoma curvirostre/0a0915d97fd2f4c3fc679ded368b90a3.jpg\",\n  \"553332.jpg\": \"Aves/Toxostoma curvirostre/788b0841f5dba336e21cafc1e7c5d5c5.jpg\",\n  \"55339.jpg\": \"Amphibia/Hyla versicolor/9491a126b7884a31887658b8ebbb4f86.jpg\",\n  \"553441.jpg\": \"Aves/Toxostoma curvirostre/301c772096fa0af528fc9059856142a1.jpg\",\n  \"55345.jpg\": \"Amphibia/Hyla versicolor/448b155e33feb95520e354a66b752cd0.jpg\",\n  \"553524.jpg\": \"Aves/Toxostoma curvirostre/797b310c3b44f77803e305efc2d4b9d9.jpg\",\n  \"553525.jpg\": \"Aves/Buteo lagopus/d1e264df608b3bf303fd947386e7951e.jpg\",\n  \"553536.jpg\": \"Aves/Buteo lagopus/dbcadf7d5a5e9752776237062f37cb62.jpg\",\n  \"553540.jpg\": \"Aves/Buteo lagopus/c8f31b7e64b845161cbc9fa17a82e777.jpg\",\n  \"553553.jpg\": \"Aves/Buteo lagopus/aeca929b5bb82714cd4ff735a9e31933.jpg\",\n  \"553554.jpg\": \"Aves/Buteo lagopus/844c7ee106c50930cd10207f3ef10782.jpg\",\n  \"553558.jpg\": \"Aves/Buteo lagopus/2ebc894eb576ac20314d5369b93a6c8d.jpg\",\n  \"553559.jpg\": \"Aves/Buteo lagopus/ebc3e0a6bc22125cb5c59228be5fbaba.jpg\",\n  \"553561.jpg\": \"Aves/Buteo lagopus/aff4b6d2a4258b7e1d596b0c7b23596f.jpg\",\n  \"553574.jpg\": \"Aves/Buteo lagopus/b2a0cd061994a8ab4120bd854a3e1067.jpg\",\n  \"553575.jpg\": \"Aves/Buteo lagopus/cff2dc6c32b756151f1211681eeb6c2b.jpg\",\n  \"5536.jpg\": \"Reptilia/Eretmochelys imbricata/135129919594f732d83aa227148d0f03.jpg\",\n  \"55360.jpg\": \"Amphibia/Hyla versicolor/4b68d7a83de67250bebb5d14e1d006ed.jpg\",\n  \"553601.jpg\": \"Reptilia/Lampropeltis calligaster calligaster/dba3b3cd2b231a78155ea1baf4914b49.jpg\",\n  \"55361.jpg\": \"Amphibia/Hyla versicolor/6a144a3281e7bef948e06273d06cc902.jpg\",\n  \"55367.jpg\": \"Amphibia/Hyla versicolor/b80c666f5de66db52b75728923001f58.jpg\",\n  \"55373.jpg\": \"Amphibia/Hyla versicolor/258b4edd795cf4522c01edc7cf213f27.jpg\",\n  \"553758.jpg\": \"Aves/Ictinia mississippiensis/018eda7ede156af9607cc737a78d7d11.jpg\",\n  \"553771.jpg\": \"Aves/Ictinia mississippiensis/fd32c7b5ecc8ae7593e19b43b131a160.jpg\",\n  \"553805.jpg\": \"Aves/Ictinia mississippiensis/a5500f92b51dcd4407d275270589c920.jpg\",\n  \"553807.jpg\": \"Aves/Ictinia mississippiensis/8ba2b291f4b28bc6bf004e116a4bf702.jpg\",\n  \"55381.jpg\": \"Amphibia/Hyla versicolor/ad1de7a567bfa444bb37290b234f7e59.jpg\",\n  \"553815.jpg\": \"Aves/Ictinia mississippiensis/9ab32581b69bd3d215eb049c0e2e6231.jpg\",\n  \"553818.jpg\": \"Aves/Ictinia mississippiensis/9b7c1deb64c0820b7ca24bddfca073e4.jpg\",\n  \"553833.jpg\": \"Reptilia/Pantherophis bairdi/580671418ed8130d8a691f8c9f78b7ce.jpg\",\n  \"553841.jpg\": \"Reptilia/Pantherophis bairdi/c7ddf9e300b1db9911b79bcf94763d72.jpg\",\n  \"553849.jpg\": \"Insecta/Callophrys dumetorum/23e6469210ec8e40f6ef359d0291ede9.jpg\",\n  \"55385.jpg\": \"Amphibia/Hyla versicolor/29592a727b39acef0ea23686d5cb8e6f.jpg\",\n  \"553861.jpg\": \"Reptilia/Sceloporus cyanogenys/50867c93f2afd8381c220fa4a23a113a.jpg\",\n  \"553863.jpg\": \"Reptilia/Sceloporus cyanogenys/7df54f4e0160338e892ea1622cca56b1.jpg\",\n  \"553876.jpg\": \"Insecta/Cyllopsis gemma/99054f8bfacca12d487b6ffb971d27f1.jpg\",\n  \"553884.jpg\": \"Insecta/Calledapteryx dryopterata/7dbc5deb322310e0920de594ee3925ed.jpg\",\n  \"553885.jpg\": \"Insecta/Calledapteryx dryopterata/359d5bda3559bc890f1663a7f7314718.jpg\",\n  \"553892.jpg\": \"Aves/Vireo philadelphicus/6de880295b5641a323fc3d1ce65833b5.jpg\",\n  \"553896.jpg\": \"Aves/Vireo philadelphicus/b239718cd4266daaee08be2a3deec31d.jpg\",\n  \"553908.jpg\": \"Amphibia/Eleutherodactylus planirostris/1b0e482d81e20e7e79c270d672dac315.jpg\",\n  \"553918.jpg\": \"Animalia/Panulirus interruptus/dd81d2aa135358afc75f0ef41194b914.jpg\",\n  \"5540.jpg\": \"Reptilia/Eretmochelys imbricata/7777f1d1162de42246b18e57bef8087b.jpg\",\n  \"554011.jpg\": \"Reptilia/Plestiodon obsoletus/1381d5f8fd9a641be93c53b8a470e62e.jpg\",\n  \"554014.jpg\": \"Reptilia/Plestiodon obsoletus/59e4b1b54c5af3a45b30aa48b31370a3.jpg\",\n  \"554018.jpg\": \"Reptilia/Plestiodon obsoletus/eb66def2bf5e05c48d451c543e6f6602.jpg\",\n  \"554026.jpg\": \"Insecta/Myscelia ethusa/ee2304f39a84f67947800ab4b116e8bb.jpg\",\n  \"55404.jpg\": \"Amphibia/Hyla versicolor/8da4a0319e94cd6a3fb11a53d599fa8d.jpg\",\n  \"554040.jpg\": \"Insecta/Myscelia ethusa/06c5ac2b2cea4e3c6a369ea19f814ddf.jpg\",\n  \"55407.jpg\": \"Amphibia/Hyla versicolor/a88d6a30be022f03f3517121d393ab74.jpg\",\n  \"5541.jpg\": \"Reptilia/Eretmochelys imbricata/63e1042459565634844b4641f5b4e3ac.jpg\",\n  \"554110.jpg\": \"Aves/Streptopelia chinensis/915d5bef7a835e4f3730348031ef330e.jpg\",\n  \"554112.jpg\": \"Aves/Streptopelia chinensis/6d5bb3f80410a6bf67f5fd98691103ab.jpg\",\n  \"55412.jpg\": \"Amphibia/Hyla versicolor/0948b267b2a3007cb5a008b8df645e20.jpg\",\n  \"554143.jpg\": \"Aves/Streptopelia chinensis/8f8b44befdb0356a35dd9b391f5f5072.jpg\",\n  \"554144.jpg\": \"Aves/Streptopelia chinensis/5ffcca94c6326f54e1507ac7711ffcaa.jpg\",\n  \"554145.jpg\": \"Aves/Streptopelia chinensis/33b9114368ee047c179b80200491c914.jpg\",\n  \"554146.jpg\": \"Aves/Streptopelia chinensis/a3328e68c451417df0899725797af8ed.jpg\",\n  \"554166.jpg\": \"Aves/Delichon urbicum/80e47df3734a80bc2157246495afaa4c.jpg\",\n  \"554167.jpg\": \"Aves/Delichon urbicum/d207554ce122d7d603754a694a2d3f5b.jpg\",\n  \"554168.jpg\": \"Aves/Delichon urbicum/5aa95acc2c0636303375cffc48a27ab5.jpg\",\n  \"554170.jpg\": \"Aves/Delichon urbicum/eb5c5f3354a740a917af5e99c659a406.jpg\",\n  \"554171.jpg\": \"Aves/Delichon urbicum/48df25503f279c0c686895682e40f5b1.jpg\",\n  \"554177.jpg\": \"Aves/Delichon urbicum/92ea68b2cf4bf813673afaee09ab280e.jpg\",\n  \"554180.jpg\": \"Aves/Delichon urbicum/c7a568b456dd200c5db3ede5571c9bea.jpg\",\n  \"554181.jpg\": \"Aves/Delichon urbicum/cb3a51b656fabd16ae0d8f87710d50da.jpg\",\n  \"554183.jpg\": \"Aves/Delichon urbicum/440c011b82344bec45e0c36748f35c42.jpg\",\n  \"554188.jpg\": \"Aves/Delichon urbicum/7ac743b3aa908b3ec916b3672050963a.jpg\",\n  \"554267.jpg\": \"Reptilia/Pseudemys gorzugi/e8bdfd01a83a62438de163e51f48daef.jpg\",\n  \"554273.jpg\": \"Reptilia/Pseudemys gorzugi/7d06064dc2267bfa4a611c12a9dae7c0.jpg\",\n  \"554280.jpg\": \"Aves/Psarocolius montezuma/65cc581a3e67e7ae6dfe0a30b5ad53d5.jpg\",\n  \"554282.jpg\": \"Aves/Psarocolius montezuma/96390d35289d537fd573941d4d70ea9b.jpg\",\n  \"554289.jpg\": \"Aves/Psarocolius montezuma/156805a4aa11296a68564e564c34e537.jpg\",\n  \"55429.jpg\": \"Amphibia/Hyla versicolor/0cb4dad40ded6e13e3d1a1d11bce4a74.jpg\",\n  \"554290.jpg\": \"Aves/Psarocolius montezuma/baab5bf9797b920c40c6af6b420192d5.jpg\",\n  \"554291.jpg\": \"Aves/Psarocolius montezuma/d64b6c15be8924dd74c20a3a8bea151b.jpg\",\n  \"554292.jpg\": \"Aves/Psarocolius montezuma/94c0e2fe36b9eb3a21f80000d67e3cb8.jpg\",\n  \"554293.jpg\": \"Aves/Psarocolius montezuma/957d874d91df2b5fb74f516f2d91f4d4.jpg\",\n  \"554294.jpg\": \"Aves/Psarocolius montezuma/0fea4a2e1ae8c9ceccae5b103307c380.jpg\",\n  \"554298.jpg\": \"Aves/Psarocolius montezuma/2124a9d80aa1051e86949301310af479.jpg\",\n  \"554375.jpg\": \"Insecta/Euptoieta claudia/45a2eb3d22d8d27683b77e60d07ed66e.jpg\",\n  \"55440.jpg\": \"Amphibia/Hyla versicolor/0cfbf2ff4f78c57888998936ce843b53.jpg\",\n  \"554410.jpg\": \"Insecta/Euptoieta claudia/cd715a6dbc1b6ec3c42658f80f0ec442.jpg\",\n  \"554411.jpg\": \"Insecta/Euptoieta claudia/6a9fffb616e49e9b17ff646b339a4a62.jpg\",\n  \"55444.jpg\": \"Amphibia/Hyla versicolor/cdf7c873ac28a971d18ae4cc107896aa.jpg\",\n  \"554443.jpg\": \"Insecta/Euptoieta claudia/fe66a58f06354bdee4cd5a40debcac00.jpg\",\n  \"554444.jpg\": \"Insecta/Euptoieta claudia/0af25f073e9588d7515c42d2d6c85f64.jpg\",\n  \"55446.jpg\": \"Amphibia/Hyla versicolor/2da40c5c617aadb48550d809e82dfe06.jpg\",\n  \"554461.jpg\": \"Insecta/Euptoieta claudia/d6f255dd0da9f008c1fa027254ca61f2.jpg\",\n  \"554469.jpg\": \"Insecta/Euptoieta claudia/5d0f6ef569252370fadb922cfa48484f.jpg\",\n  \"554524.jpg\": \"Insecta/Euptoieta claudia/1b13557808afb58efdabad58aa738b06.jpg\",\n  \"554534.jpg\": \"Insecta/Tramea lacerata/00eb9ab7fe03571008714db4ddaf0560.jpg\",\n  \"554546.jpg\": \"Insecta/Tramea lacerata/62b32937ff86edb09af31b389b91a30d.jpg\",\n  \"554557.jpg\": \"Insecta/Tramea lacerata/64c29c5d624045d7777190f8fc3f0771.jpg\",\n  \"554559.jpg\": \"Insecta/Tramea lacerata/b2cec066ad5f42d696091bc7e71507ba.jpg\",\n  \"554641.jpg\": \"Insecta/Tramea lacerata/fdd02a1964cff63c4a8a4beb5583d6e8.jpg\",\n  \"554659.jpg\": \"Amphibia/Lithobates blairi/8dc99df807d0ffee9ad126b2b6585159.jpg\",\n  \"554660.jpg\": \"Amphibia/Lithobates blairi/4facef9e1adcd9dab448272934e5b5fd.jpg\",\n  \"554667.jpg\": \"Amphibia/Lithobates blairi/baecb1e9ee346b1dc18fe307147199bf.jpg\",\n  \"554668.jpg\": \"Amphibia/Lithobates blairi/33dbbca6c9cadd3e24ab63792380d5ca.jpg\",\n  \"554669.jpg\": \"Amphibia/Lithobates blairi/bc7e12b300e46862bb41308cd6cc2557.jpg\",\n  \"554672.jpg\": \"Amphibia/Lithobates blairi/11ca9c195ce68ec52f092af31e825ecc.jpg\",\n  \"554674.jpg\": \"Amphibia/Lithobates blairi/fec9898ae7a29d254ffbec0397c55ee5.jpg\",\n  \"554675.jpg\": \"Amphibia/Lithobates blairi/5f81cd63a0ef68ea603346c1806d63e4.jpg\",\n  \"554677.jpg\": \"Amphibia/Lithobates blairi/8570a4e8c46017ab9975b04d64cd7733.jpg\",\n  \"554708.jpg\": \"Amphibia/Gastrophryne carolinensis/855aded61b380ad9fb0a75b375bf44ca.jpg\",\n  \"554709.jpg\": \"Amphibia/Gastrophryne carolinensis/f38652615ba50025177901fbe6c7247d.jpg\",\n  \"554714.jpg\": \"Amphibia/Gastrophryne carolinensis/ccc19d0f773278106db1529453c852cf.jpg\",\n  \"554716.jpg\": \"Amphibia/Gastrophryne carolinensis/33c6605bad576662b08e6f80d1fc49ea.jpg\",\n  \"554719.jpg\": \"Amphibia/Gastrophryne carolinensis/53ac6f255cec2028c9b2fc8e453be3eb.jpg\",\n  \"554728.jpg\": \"Amphibia/Gastrophryne carolinensis/2ccafbb52c55844db2ed883714c1dfd9.jpg\",\n  \"554734.jpg\": \"Amphibia/Gastrophryne carolinensis/949c1d4dfe9a5af4968d986134816fa5.jpg\",\n  \"554735.jpg\": \"Amphibia/Gastrophryne carolinensis/8d7324cad660c332c01e8f8ccb3657aa.jpg\",\n  \"554746.jpg\": \"Reptilia/Nerodia erythrogaster flavigaster/615f25122f1672f77a1a10e12023ae46.jpg\",\n  \"554754.jpg\": \"Reptilia/Nerodia erythrogaster flavigaster/2fea2b8decfeaeababa103be272cb6e2.jpg\",\n  \"554755.jpg\": \"Insecta/Orthetrum cancellatum/d2f5f52c711ab9480786128eb4b3f33d.jpg\",\n  \"554757.jpg\": \"Insecta/Orthetrum cancellatum/22fed127e0c67c8416bbe3d7c2296ca2.jpg\",\n  \"554760.jpg\": \"Insecta/Orthetrum cancellatum/cf8ce5b8586d42cc800c5c37a52f94a2.jpg\",\n  \"554761.jpg\": \"Insecta/Orthetrum cancellatum/cba79bc6f33179d7d5894541caf33f80.jpg\",\n  \"554771.jpg\": \"Insecta/Orthetrum cancellatum/44adaabdf082895b3ca56c8cf8a7f78f.jpg\",\n  \"554780.jpg\": \"Insecta/Orthetrum cancellatum/ac3a06cbe9d620db96d1ce15d73012ba.jpg\",\n  \"554781.jpg\": \"Insecta/Orthetrum cancellatum/059c6686eaf9fa330f097493bce9b1bb.jpg\",\n  \"554784.jpg\": \"Insecta/Orthetrum cancellatum/9ee73cbad50174c56a617f359ab603bf.jpg\",\n  \"554785.jpg\": \"Insecta/Orthetrum cancellatum/87144a490d612f19dd8a471a850fc45c.jpg\",\n  \"554787.jpg\": \"Insecta/Orthetrum cancellatum/2c6f009fe3236587eb67a421f321d37b.jpg\",\n  \"554788.jpg\": \"Insecta/Orthetrum cancellatum/780e30fdeab0154a6aebb52cb7eb0fb9.jpg\",\n  \"554791.jpg\": \"Aves/Volatinia jacarina/cde976621c64bbefda256a909e654ccd.jpg\",\n  \"554797.jpg\": \"Aves/Volatinia jacarina/91cf4042ac5f726613b49472ddd35a71.jpg\",\n  \"5548.jpg\": \"Reptilia/Eretmochelys imbricata/4d6eafa8f5d02fdd0bc91106f1cf080f.jpg\",\n  \"55480.jpg\": \"Arachnida/Trite planiceps/48e7f76408392350f2cc3b1e6e7479b0.jpg\",\n  \"554800.jpg\": \"Aves/Volatinia jacarina/774d2f7fef19ced3d07e6dc98ef30af9.jpg\",\n  \"554805.jpg\": \"Aves/Volatinia jacarina/0eb1ad9525d5b232e09a94295dc4c043.jpg\",\n  \"554806.jpg\": \"Aves/Volatinia jacarina/4c7554ef170211b0ad5ea35d93b4e3c6.jpg\",\n  \"554807.jpg\": \"Aves/Volatinia jacarina/19a8d1244d3b6c72e05544ebc72db191.jpg\",\n  \"55481.jpg\": \"Arachnida/Trite planiceps/b1f1f0187eb5876ab537118135e400c8.jpg\",\n  \"554817.jpg\": \"Aves/Volatinia jacarina/228f61275c27d4ce777bb0f71312e377.jpg\",\n  \"554818.jpg\": \"Aves/Volatinia jacarina/524baf948a448dc268edf7c1e345a141.jpg\",\n  \"554829.jpg\": \"Insecta/Pontania californica/c194f173d3395599480d2741072295a4.jpg\",\n  \"554838.jpg\": \"Amphibia/Anaxyrus cognatus/d126651a87cded2f1b1bd4ece67ee84e.jpg\",\n  \"554839.jpg\": \"Amphibia/Anaxyrus cognatus/90faac032379b4014667dcdb49b9c057.jpg\",\n  \"55484.jpg\": \"Arachnida/Trite planiceps/968f834d2d414dc1c842a3e957b38258.jpg\",\n  \"554843.jpg\": \"Amphibia/Anaxyrus cognatus/b2e3758e3de40a32937dcb5c42da8d3a.jpg\",\n  \"554844.jpg\": \"Amphibia/Anaxyrus cognatus/312fb85667ce036462feb86de7db244c.jpg\",\n  \"554849.jpg\": \"Amphibia/Anaxyrus cognatus/0985ed1ed000b186adb9855f7c55506e.jpg\",\n  \"554850.jpg\": \"Amphibia/Anaxyrus cognatus/54b481faa1379f8a4d828918227198f5.jpg\",\n  \"554855.jpg\": \"Amphibia/Anaxyrus cognatus/808c3063913f69a082b4df478e0ad8d8.jpg\",\n  \"554856.jpg\": \"Amphibia/Anaxyrus cognatus/60758e62c60c400ac7f46fce94e1e793.jpg\",\n  \"554860.jpg\": \"Amphibia/Anaxyrus cognatus/dfea460c30b4cd07782e90e546e6ec74.jpg\",\n  \"554862.jpg\": \"Amphibia/Craugastor augusti/fbbe52054f134aa5cac7d6789e359c07.jpg\",\n  \"554868.jpg\": \"Amphibia/Craugastor augusti/aacb3d48ff597074dc454e4be939e6d0.jpg\",\n  \"554869.jpg\": \"Aves/Helmitheros vermivorum/b40e6a8ec42a58ff8261cb584e645f38.jpg\",\n  \"55487.jpg\": \"Arachnida/Trite planiceps/262430acd5734c798815cea193ff050f.jpg\",\n  \"554878.jpg\": \"Aves/Helmitheros vermivorum/d3c6835ae84db7a66d70b38dc63757c0.jpg\",\n  \"554896.jpg\": \"Insecta/Probole amicaria/fd76c4831dd4534ee17716a469438d8b.jpg\",\n  \"5549.jpg\": \"Reptilia/Eretmochelys imbricata/95c7ca8e2f43b4cf5a2cf92abcc4a7b9.jpg\",\n  \"554904.jpg\": \"Amphibia/Eleutherodactylus cystignathoides/742660962bb347661fb7e548f54c4176.jpg\",\n  \"554905.jpg\": \"Amphibia/Eleutherodactylus cystignathoides/ebe248c8085c0be90444cdf71e394360.jpg\",\n  \"554906.jpg\": \"Amphibia/Eleutherodactylus cystignathoides/f446b124ed00461f5a908503fe3a9e5d.jpg\",\n  \"554907.jpg\": \"Amphibia/Eleutherodactylus cystignathoides/66cc204759c561982786b4417f4c8735.jpg\",\n  \"554908.jpg\": \"Amphibia/Eleutherodactylus cystignathoides/446849307800cddf6bfbfc8eec6ca7b8.jpg\",\n  \"554916.jpg\": \"Amphibia/Eleutherodactylus cystignathoides/77e709fafac7b77d51cc05d4e65e8517.jpg\",\n  \"554917.jpg\": \"Amphibia/Eleutherodactylus cystignathoides/210d037f1af5be29afe13dbc9ff53dcd.jpg\",\n  \"554921.jpg\": \"Amphibia/Eleutherodactylus cystignathoides/775ccecc8ed045cb7acd2dcbe3a5fa39.jpg\",\n  \"554942.jpg\": \"Insecta/Lampides boeticus/db84300722d098f6e8689f76bee4d23d.jpg\",\n  \"554943.jpg\": \"Insecta/Lampides boeticus/1204720f400784ffacc36f077d9c3f3f.jpg\",\n  \"554944.jpg\": \"Insecta/Lampides boeticus/b7d69ea76c3883d3e226ad498149c6c0.jpg\",\n  \"554949.jpg\": \"Insecta/Lampides boeticus/5ed81f65170f009082efd28562125650.jpg\",\n  \"554950.jpg\": \"Insecta/Lampides boeticus/bbcac473dccbc6140cc9ce5093f80c62.jpg\",\n  \"554951.jpg\": \"Insecta/Lampides boeticus/5c61c9e3c14c947fa907c44c8d855602.jpg\",\n  \"554952.jpg\": \"Insecta/Lampides boeticus/d20ecca2eff8c010bea9ead71e974cee.jpg\",\n  \"554953.jpg\": \"Insecta/Lampides boeticus/e99bcb2eb5b50353cb8d8e3538063a50.jpg\",\n  \"554960.jpg\": \"Insecta/Lampides boeticus/88c1925a1d7c6c214647bc7c4ca7d897.jpg\",\n  \"554961.jpg\": \"Insecta/Lampides boeticus/4d56c183c99f23d9b493bea1dc9ebdb6.jpg\",\n  \"554965.jpg\": \"Insecta/Lampides boeticus/4cc884caee0826011968b441f7e3de35.jpg\",\n  \"554999.jpg\": \"Insecta/Datana ministra/dcb5170d023abeaa82aecfee3660ed3f.jpg\",\n  \"555002.jpg\": \"Insecta/Datana ministra/42197b143f713eb469b60477a8e8e496.jpg\",\n  \"555045.jpg\": \"Reptilia/Crotalus atrox/9181e02d6f1ff98be507cdee62279bba.jpg\",\n  \"55508.jpg\": \"Insecta/Lucanus cervus/795e37c31031d9881dec28db028ff81a.jpg\",\n  \"55509.jpg\": \"Insecta/Lucanus cervus/2ba6f458869e944da1a633fe0c5c1c78.jpg\",\n  \"555097.jpg\": \"Reptilia/Crotalus atrox/5c02dd1d592e4556356eb88448e3b178.jpg\",\n  \"55511.jpg\": \"Insecta/Lucanus cervus/9c19df244492ef16afa06e5510a40558.jpg\",\n  \"555115.jpg\": \"Reptilia/Crotalus atrox/f3df35398d00c73635a694f07c3eb0f7.jpg\",\n  \"555120.jpg\": \"Reptilia/Crotalus atrox/df07ef03014a3d4fb4540e4185f9a352.jpg\",\n  \"555184.jpg\": \"Reptilia/Crotalus atrox/24943b33dd2350f07037994ca8576741.jpg\",\n  \"555208.jpg\": \"Reptilia/Crotalus atrox/28d056d8af93de5477a8f30b75ec0dc2.jpg\",\n  \"555256.jpg\": \"Reptilia/Crotalus atrox/ea40b3143dccacb3deb042c6c874b0b1.jpg\",\n  \"55526.jpg\": \"Insecta/Lucanus cervus/54d12a8c3817ca09781c3abca12866cc.jpg\",\n  \"55527.jpg\": \"Insecta/Lucanus cervus/4ec44d87ff1e4027633dd9af78ee65be.jpg\",\n  \"55528.jpg\": \"Insecta/Lucanus cervus/92fb6d883d96dc542c72fab48ecf2158.jpg\",\n  \"555302.jpg\": \"Reptilia/Crotalus atrox/ccb02c05ed4116c3245020d4064dbcf3.jpg\",\n  \"55531.jpg\": \"Insecta/Lucanus cervus/d97eb3d5d41d2cd89b418a75746cd1bf.jpg\",\n  \"55532.jpg\": \"Insecta/Lucanus cervus/00a07953e7cc328e7a2d02cef230ca39.jpg\",\n  \"55536.jpg\": \"Insecta/Lucanus cervus/5c2eb99404cab9b292d959ffd113b592.jpg\",\n  \"555394.jpg\": \"Aves/Dolichonyx oryzivorus/184285734f3d810bdfe0b136f94723cb.jpg\",\n  \"555408.jpg\": \"Aves/Dolichonyx oryzivorus/287d51422fe610d9e6634dbc5853ce52.jpg\",\n  \"555418.jpg\": \"Aves/Dolichonyx oryzivorus/4fbc08196bdf010e342327c21d6b63c2.jpg\",\n  \"555419.jpg\": \"Aves/Dolichonyx oryzivorus/71e930dbc66fe4b6b47dcd8ce23b4b1b.jpg\",\n  \"555430.jpg\": \"Aves/Dolichonyx oryzivorus/11c25e9c5d7cd8452227c51394403576.jpg\",\n  \"555436.jpg\": \"Aves/Dolichonyx oryzivorus/b26749281b21586ccd1aa7890da982bf.jpg\",\n  \"555445.jpg\": \"Aves/Dolichonyx oryzivorus/46799ece28b4046ee9ae3917b604e491.jpg\",\n  \"55545.jpg\": \"Insecta/Miomantis caffra/2e73c1dc516ce182f4daefcc86a99897.jpg\",\n  \"55546.jpg\": \"Insecta/Miomantis caffra/030bbe8eafdb229e2a9dbc8106450508.jpg\",\n  \"555461.jpg\": \"Insecta/Ennomos magnaria/7b5401c598fb1724af3a22270379e81a.jpg\",\n  \"555462.jpg\": \"Insecta/Ennomos magnaria/920d0a3d6ce8685f1e2318799e702b54.jpg\",\n  \"55549.jpg\": \"Insecta/Miomantis caffra/f4515db27fce20dd220737d07892a6d0.jpg\",\n  \"55550.jpg\": \"Insecta/Miomantis caffra/8f16a727e111d604576ff9ee53f2629d.jpg\",\n  \"55552.jpg\": \"Insecta/Miomantis caffra/e3ad9d5b181e8140d41bc551e449d891.jpg\",\n  \"555525.jpg\": \"Insecta/Gibbifer californicus/b3cb48fab83079675f4fef97a61a9d11.jpg\",\n  \"555527.jpg\": \"Insecta/Gibbifer californicus/51e7ddaaf553a00d853fcdc5d00cd6a2.jpg\",\n  \"555528.jpg\": \"Insecta/Gibbifer californicus/ea8be583506f312d168086c2342f89c3.jpg\",\n  \"55553.jpg\": \"Insecta/Miomantis caffra/cd20e8eea9770516567ceeb1a0f69c05.jpg\",\n  \"555534.jpg\": \"Insecta/Gibbifer californicus/fdfe29c2ecd151d338f4e303fd6efb15.jpg\",\n  \"555545.jpg\": \"Arachnida/Micrathena gracilis/aa2ab66edce469a2b8119b23c93d3f29.jpg\",\n  \"555546.jpg\": \"Arachnida/Micrathena gracilis/f3735973d50eecb5332d577c000d6979.jpg\",\n  \"555547.jpg\": \"Arachnida/Micrathena gracilis/be5989e633c18ef46d4b9ddb40db7e61.jpg\",\n  \"555548.jpg\": \"Arachnida/Micrathena gracilis/047d694090e44360540ec2413cac610d.jpg\",\n  \"555549.jpg\": \"Arachnida/Micrathena gracilis/ae05c032956a1392dc80c8dba97ed7d8.jpg\",\n  \"55555.jpg\": \"Insecta/Miomantis caffra/5a2a2f0fe3ef1704b46988666d370ab1.jpg\",\n  \"555551.jpg\": \"Arachnida/Micrathena gracilis/b789a8692e515feb533156de31d2dc96.jpg\",\n  \"555552.jpg\": \"Arachnida/Micrathena gracilis/cd68dfdfdf256bb249b0a0fffc70631f.jpg\",\n  \"555556.jpg\": \"Arachnida/Micrathena gracilis/cff15956536f397034f7dcdd474a1684.jpg\",\n  \"555559.jpg\": \"Arachnida/Micrathena gracilis/b14f0cc0f1a8d77cf9be4f6c5abd74a6.jpg\",\n  \"555560.jpg\": \"Arachnida/Micrathena gracilis/39c97f1866c390849e700b15de2c238b.jpg\",\n  \"555561.jpg\": \"Arachnida/Micrathena gracilis/3db15fbd7a535abcea2ffd7d3697c8f7.jpg\",\n  \"555583.jpg\": \"Insecta/Sunira bicolorago/407b925987acfd265455a773f4bee6fc.jpg\",\n  \"55559.jpg\": \"Insecta/Miomantis caffra/7f72f1c7df5c46bb5cfecc5a102cf0b5.jpg\",\n  \"5556.jpg\": \"Reptilia/Eretmochelys imbricata/45ad4bb96a9634d12b4dd74a87d300ab.jpg\",\n  \"55560.jpg\": \"Insecta/Miomantis caffra/3a52186af5dd16580fecc2b0226e40ab.jpg\",\n  \"55561.jpg\": \"Insecta/Miomantis caffra/98c3bc7c2e99c347f1f3f9807dacf34f.jpg\",\n  \"55563.jpg\": \"Insecta/Miomantis caffra/683e902de2cbb56c7e881ed9d6b594c6.jpg\",\n  \"55564.jpg\": \"Insecta/Miomantis caffra/118af1ef72d442881745e784ac6088b2.jpg\",\n  \"555687.jpg\": \"Reptilia/Callisaurus draconoides/416b96ae44e7949437262c761601bad3.jpg\",\n  \"555690.jpg\": \"Reptilia/Callisaurus draconoides/232e92a6f31179556f9a19a8acf7f6bb.jpg\",\n  \"555694.jpg\": \"Reptilia/Callisaurus draconoides/765e41eb82e1dda702f200f8e7bb7257.jpg\",\n  \"555695.jpg\": \"Reptilia/Callisaurus draconoides/6f09ce04e7b4736a4608e5b776588376.jpg\",\n  \"5557.jpg\": \"Reptilia/Eretmochelys imbricata/048690638a60aef1311e47faa1a370a6.jpg\",\n  \"555700.jpg\": \"Reptilia/Callisaurus draconoides/a67389e5fa2137e868c1bb924f32d0cf.jpg\",\n  \"555709.jpg\": \"Reptilia/Callisaurus draconoides/7228f2d43dfc82eb584fb1ded2d1e80a.jpg\",\n  \"555718.jpg\": \"Reptilia/Callisaurus draconoides/a253a7fcb31e708845e082322b224d76.jpg\",\n  \"55572.jpg\": \"Insecta/Miomantis caffra/f5af145f74430c11a9bbc19ea411fe88.jpg\",\n  \"555720.jpg\": \"Reptilia/Callisaurus draconoides/dc66c472c7feed6ca24c1a8504d80c3d.jpg\",\n  \"555726.jpg\": \"Reptilia/Callisaurus draconoides/fb5fb1bdd97f8ad88398b1d2a10d4654.jpg\",\n  \"55573.jpg\": \"Insecta/Miomantis caffra/fa44537bf61c29407c132f64a1f451ae.jpg\",\n  \"555739.jpg\": \"Arachnida/Aphonopelma hentzi/491042d273a42fc605c2299df7f7ae40.jpg\",\n  \"55574.jpg\": \"Insecta/Miomantis caffra/6008d97fcb7c6011b38b332b683f975b.jpg\",\n  \"555743.jpg\": \"Arachnida/Aphonopelma hentzi/a9fbb71743bff5c9f609b0cbda07c58e.jpg\",\n  \"55575.jpg\": \"Insecta/Miomantis caffra/6a2a90bf8225a691680dbf5943ed8406.jpg\",\n  \"55576.jpg\": \"Insecta/Miomantis caffra/c84c97d00e99f07dd6aa9a88a8f4adf2.jpg\",\n  \"555771.jpg\": \"Arachnida/Aphonopelma hentzi/581c1b2b73288d4f2a58880d9ed39fa6.jpg\",\n  \"555772.jpg\": \"Arachnida/Aphonopelma hentzi/f285e0fa11c1f9efbea41802ce4f99ab.jpg\",\n  \"555773.jpg\": \"Arachnida/Aphonopelma hentzi/93085dd52e8b22308209a9b575ee63ff.jpg\",\n  \"555774.jpg\": \"Arachnida/Aphonopelma hentzi/c1fd250d30f4ee45e5533a6d56f8c5a9.jpg\",\n  \"555777.jpg\": \"Arachnida/Aphonopelma hentzi/9a0eaebdc9b350ecccca52e3cc76172f.jpg\",\n  \"555778.jpg\": \"Arachnida/Aphonopelma hentzi/43bd1fd4691a7da0065e2bf11bc83aed.jpg\",\n  \"555799.jpg\": \"Aves/Oxyura jamaicensis/54ce86eb4d624c517cb475ba97857b38.jpg\",\n  \"55585.jpg\": \"Insecta/Miomantis caffra/2a06e19175823badaa0ee014ac9e4b1f.jpg\",\n  \"55586.jpg\": \"Insecta/Miomantis caffra/5a9496dc8fd99359be8251000e490d4c.jpg\",\n  \"555883.jpg\": \"Aves/Oxyura jamaicensis/1c5857ee0f7c3898255df6bc93cee6bb.jpg\",\n  \"556143.jpg\": \"Aves/Branta leucopsis/9e81f37b24d3240fd48d6760f70310f8.jpg\",\n  \"556144.jpg\": \"Aves/Branta leucopsis/078008c6f1a9510368b5ac9f51949881.jpg\",\n  \"556145.jpg\": \"Aves/Branta leucopsis/277ec4ef4dc69b2d263c444c6da067ce.jpg\",\n  \"556148.jpg\": \"Aves/Branta leucopsis/43d9886229b12588db6e811697f5a0a8.jpg\",\n  \"556203.jpg\": \"Mammalia/Callospermophilus lateralis/3fb4eab15434b1df8a8d89c5015d7171.jpg\",\n  \"556212.jpg\": \"Mammalia/Callospermophilus lateralis/89a8f56f00e6e7f41d1e13b4ab602b61.jpg\",\n  \"556215.jpg\": \"Mammalia/Callospermophilus lateralis/2eb612cef1c1aa0130efff4e5a06db2e.jpg\",\n  \"556225.jpg\": \"Mammalia/Callospermophilus lateralis/bd878574a7a626615424065f6771beb2.jpg\",\n  \"556226.jpg\": \"Mammalia/Callospermophilus lateralis/e85430ffd6966f626e88ca72bdb7f1eb.jpg\",\n  \"556229.jpg\": \"Mammalia/Callospermophilus lateralis/6e7666af047389837f1fb470f5232eba.jpg\",\n  \"556238.jpg\": \"Mammalia/Callospermophilus lateralis/441f13a5fb474c9d0ac5159b983228bd.jpg\",\n  \"556251.jpg\": \"Mammalia/Callospermophilus lateralis/dbd6697ba262a596896fea85b9325eb3.jpg\",\n  \"556258.jpg\": \"Insecta/Dasymutilla occidentalis/048309baee5755e1c5794048d56a1b1d.jpg\",\n  \"556264.jpg\": \"Insecta/Dasymutilla occidentalis/5e958c94cd9d6d1dd8270d41c234c622.jpg\",\n  \"556266.jpg\": \"Insecta/Dasymutilla occidentalis/7e5d5c624857a09f3532c77ba4ebf7fc.jpg\",\n  \"556271.jpg\": \"Insecta/Dasymutilla occidentalis/9de74aa41544b5440b2a1111371df2b5.jpg\",\n  \"556287.jpg\": \"Insecta/Scathophaga stercoraria/b03afbc184552361ab05401b5c213c9a.jpg\",\n  \"556294.jpg\": \"Actinopterygii/Zanclus cornutus/bacc16be4636b3145fb3b916f6b6ddc9.jpg\",\n  \"556296.jpg\": \"Actinopterygii/Zanclus cornutus/57e7584256fa78e455bc2e53127fe88a.jpg\",\n  \"556297.jpg\": \"Actinopterygii/Zanclus cornutus/b0501bbcd3ca015bf475d0ef8d4bcb02.jpg\",\n  \"556298.jpg\": \"Actinopterygii/Zanclus cornutus/3db5f4dd36be6b2c4b446b30bc64b5ac.jpg\",\n  \"556302.jpg\": \"Actinopterygii/Zanclus cornutus/466c3159666561ad9800f4842c626693.jpg\",\n  \"556304.jpg\": \"Actinopterygii/Zanclus cornutus/a0459ffef3b987960a17140bd8f9d15e.jpg\",\n  \"556310.jpg\": \"Actinopterygii/Zanclus cornutus/92d8839045e54e81574d5cb2f5f5c4ea.jpg\",\n  \"556315.jpg\": \"Actinopterygii/Zanclus cornutus/34dca27b1dd7f256cc2b80f7144d5df6.jpg\",\n  \"556318.jpg\": \"Actinopterygii/Zanclus cornutus/bb1dd101ab26371e6e3bf8faaa4761bc.jpg\",\n  \"556340.jpg\": \"Aves/Quiscalus niger/b0db5ddda38b9642119532e0fe8d1872.jpg\",\n  \"556342.jpg\": \"Aves/Quiscalus niger/f7a2e5100ff128075851a56b8bff1d03.jpg\",\n  \"556372.jpg\": \"Reptilia/Glyptemys insculpta/95261cb10b22f1d91d129fe85728cd09.jpg\",\n  \"556373.jpg\": \"Reptilia/Glyptemys insculpta/b83b190d3fde71668f5cd48641886caf.jpg\",\n  \"556377.jpg\": \"Insecta/Lasiommata megera/6601e90830fefbdef9ad1e98c73d94c1.jpg\",\n  \"556381.jpg\": \"Insecta/Lasiommata megera/3542e04e8ff699fe0ea21dd25470969f.jpg\",\n  \"556388.jpg\": \"Insecta/Lasiommata megera/3ba0e6f050b08084d233e3096f30efab.jpg\",\n  \"55639.jpg\": \"Reptilia/Urosaurus ornatus/d1b52d5e1b3ca24a6e95bc26a4d3653a.jpg\",\n  \"556394.jpg\": \"Insecta/Lasiommata megera/7e000a165d083706fc3d8d91a566d78d.jpg\",\n  \"556395.jpg\": \"Insecta/Lasiommata megera/d7e741866f39d5395ca26f6ffc31d0d0.jpg\",\n  \"556397.jpg\": \"Insecta/Lasiommata megera/c3776249c5b519d55f48c4da12896383.jpg\",\n  \"556398.jpg\": \"Insecta/Lasiommata megera/0fb58fcbecd77375d746e2a2061dbbac.jpg\",\n  \"55640.jpg\": \"Reptilia/Urosaurus ornatus/5313cfddcc8bb6d358cb7842b619d50f.jpg\",\n  \"556401.jpg\": \"Insecta/Lasiommata megera/3ddcb99fb6f0c5cba8e2976ebd081153.jpg\",\n  \"556407.jpg\": \"Insecta/Lasiommata megera/6a31ba6c4e9fff7070f29d9dcedeb8bf.jpg\",\n  \"55641.jpg\": \"Reptilia/Urosaurus ornatus/d8ad010fb09c5176db21a236b1d45daa.jpg\",\n  \"55642.jpg\": \"Reptilia/Urosaurus ornatus/137ed1a2e3a07c71c8035e86b0cdb0b0.jpg\",\n  \"556440.jpg\": \"Aves/Cathartes burrovianus/c01a7a27c58d44c5978e30023f56573d.jpg\",\n  \"556442.jpg\": \"Aves/Cathartes burrovianus/a25334a61813da077b00b33b84c800ec.jpg\",\n  \"55646.jpg\": \"Reptilia/Urosaurus ornatus/3204abc14c52f5419a227fe7a8583268.jpg\",\n  \"556529.jpg\": \"Aves/Picoides scalaris/5e6fc8352ab9dd7d1658ae494f3f630d.jpg\",\n  \"556531.jpg\": \"Aves/Picoides scalaris/ea15608f26e7141fd11d8c99c4823252.jpg\",\n  \"55655.jpg\": \"Reptilia/Urosaurus ornatus/caeaf1d9c732089f06bfc98e422963c9.jpg\",\n  \"556568.jpg\": \"Aves/Picoides scalaris/4929d7d4dda8cdac8ea80344fc8c9607.jpg\",\n  \"556570.jpg\": \"Aves/Picoides scalaris/7276bb06df6611347452f389111ae9a7.jpg\",\n  \"556575.jpg\": \"Aves/Picoides scalaris/a4d3ba239074e0d0966df6bed1eb160a.jpg\",\n  \"55661.jpg\": \"Reptilia/Urosaurus ornatus/ad8948a40db3fa2efe244bceffee2337.jpg\",\n  \"55662.jpg\": \"Reptilia/Urosaurus ornatus/8062d71336a542b31c790a8a2171013e.jpg\",\n  \"55663.jpg\": \"Reptilia/Urosaurus ornatus/3898f1f29fd40364ce34f15cbb6d7cfa.jpg\",\n  \"556635.jpg\": \"Aves/Picoides scalaris/2d49fc3f9bc316094281efc02531f933.jpg\",\n  \"556637.jpg\": \"Aves/Picoides scalaris/5ccd196f186111c0e76fed2a718f1151.jpg\",\n  \"556663.jpg\": \"Reptilia/Holbrookia propinqua/d0809c479132fc99775e09f288279059.jpg\",\n  \"55671.jpg\": \"Reptilia/Urosaurus ornatus/2b147829d475ea54b165948b3ca659f6.jpg\",\n  \"55674.jpg\": \"Reptilia/Urosaurus ornatus/d203bddd36004ad7c8ec00b7ca0a8389.jpg\",\n  \"55675.jpg\": \"Reptilia/Urosaurus ornatus/ec266a0da85223e94b8f8f12a54d19da.jpg\",\n  \"55678.jpg\": \"Reptilia/Urosaurus ornatus/53b8d8aec3d8a4f6455863b5bbc5b359.jpg\",\n  \"556837.jpg\": \"Reptilia/Sceloporus consobrinus/d326682decfa9296d5703e4538a5f40f.jpg\",\n  \"556848.jpg\": \"Reptilia/Sceloporus consobrinus/83a821217a07a0cdf9531868dcf37176.jpg\",\n  \"556857.jpg\": \"Reptilia/Sceloporus consobrinus/dca6c4f649ba7246a3217ca1d5bc5fc3.jpg\",\n  \"556858.jpg\": \"Reptilia/Sceloporus consobrinus/54092816688ebcf50c28be6057950c69.jpg\",\n  \"55686.jpg\": \"Reptilia/Urosaurus ornatus/2f8fe250acfb289817eeff344061e46b.jpg\",\n  \"556866.jpg\": \"Reptilia/Sceloporus consobrinus/0275846721e54f3ec73c73fc364849d9.jpg\",\n  \"556869.jpg\": \"Reptilia/Sceloporus consobrinus/61cc87b6f26bfb42b1cdaa208d7ade0c.jpg\",\n  \"55687.jpg\": \"Reptilia/Urosaurus ornatus/14fe0b67f3f121ab183a9e2c5e57ce00.jpg\",\n  \"556870.jpg\": \"Reptilia/Sceloporus consobrinus/407b734da96155fbf52eb31890a31105.jpg\",\n  \"556875.jpg\": \"Reptilia/Sceloporus consobrinus/8583838f105cdb3bfa7c7eb3f006009c.jpg\",\n  \"556876.jpg\": \"Reptilia/Sceloporus consobrinus/130b8a77086e3bf664f1af692a1d296c.jpg\",\n  \"556881.jpg\": \"Reptilia/Sceloporus consobrinus/903bfed08858bf0e466f76bae9236780.jpg\",\n  \"556884.jpg\": \"Reptilia/Sceloporus consobrinus/9787fa83e2094b2087ae9724a0e918ab.jpg\",\n  \"556896.jpg\": \"Reptilia/Nerodia fasciata confluens/aaee1e9fe93b3b52187ceaecee575a1c.jpg\",\n  \"556897.jpg\": \"Reptilia/Nerodia fasciata confluens/bdf0a41c065340d7f9531ecbb0f74337.jpg\",\n  \"556898.jpg\": \"Reptilia/Nerodia fasciata confluens/891ab8635406dd372cf06467f0b96e08.jpg\",\n  \"556900.jpg\": \"Reptilia/Nerodia fasciata confluens/66a005cca9bdf7a18c9e933c9f7df157.jpg\",\n  \"556911.jpg\": \"Reptilia/Nerodia fasciata confluens/e3bdd02b75b5ff2b4c9cbe0a50c45d76.jpg\",\n  \"55693.jpg\": \"Reptilia/Urosaurus ornatus/1508104d7d312435a565617b574c3479.jpg\",\n  \"556931.jpg\": \"Reptilia/Nerodia fasciata confluens/b5c4ca52d036b5265eada4a3ac8278d4.jpg\",\n  \"556932.jpg\": \"Reptilia/Nerodia fasciata confluens/e4c8fa3e54a92838134c73aa603cc4ae.jpg\",\n  \"556944.jpg\": \"Reptilia/Cophosaurus texanus/9ad6b59cfdcf48660834cf40c0e9c208.jpg\",\n  \"556949.jpg\": \"Reptilia/Cophosaurus texanus/80751b379fd4389e6edef3de74b0db1d.jpg\",\n  \"556953.jpg\": \"Reptilia/Cophosaurus texanus/68eeb98ac79abe90c0beda0b15845de7.jpg\",\n  \"55696.jpg\": \"Reptilia/Urosaurus ornatus/3a1b80091d1593d20b7c9eefea4876b3.jpg\",\n  \"556962.jpg\": \"Reptilia/Cophosaurus texanus/339d2e5cebed007e3651b632e5e2286d.jpg\",\n  \"556978.jpg\": \"Reptilia/Cophosaurus texanus/56d688fe32c0ba33e4a7ecda968a73a0.jpg\",\n  \"556989.jpg\": \"Reptilia/Cophosaurus texanus/bc55a8fa23414ecd878ef42b9e5a9c0b.jpg\",\n  \"55699.jpg\": \"Reptilia/Urosaurus ornatus/80afa6d51cc6b347a759fd8bbb2335f7.jpg\",\n  \"556991.jpg\": \"Reptilia/Cophosaurus texanus/484f9201a8b7189ac0d5dc5c69f8d339.jpg\",\n  \"556998.jpg\": \"Reptilia/Cophosaurus texanus/63c7aa76cf1e52bfc0891bdd1837daba.jpg\",\n  \"55701.jpg\": \"Reptilia/Urosaurus ornatus/a1a97b1a7665c936d3e0251dd2de6733.jpg\",\n  \"557017.jpg\": \"Reptilia/Crotalus lepidus/bd0cc251d94c09397338ba1cc1fc9586.jpg\",\n  \"557018.jpg\": \"Reptilia/Crotalus lepidus/f161268ea53b39b55db30bd98727f088.jpg\",\n  \"55711.jpg\": \"Reptilia/Urosaurus ornatus/ce685c0c313632cba92f863a518df4d1.jpg\",\n  \"55717.jpg\": \"Reptilia/Urosaurus ornatus/d80c5f107485a5ade13afa388e3d18ab.jpg\",\n  \"557286.jpg\": \"Reptilia/Phrynosoma cornutum/7967ecf5bd0d02e987a30ee185ca4473.jpg\",\n  \"557312.jpg\": \"Reptilia/Phrynosoma cornutum/c311f2aeb6e7973d23b13b2f175bd299.jpg\",\n  \"557331.jpg\": \"Reptilia/Phrynosoma cornutum/c9868c0bbf18949020d61b5f94c575ec.jpg\",\n  \"557360.jpg\": \"Reptilia/Phrynosoma cornutum/35a6ee9f221269019bd58bc009632897.jpg\",\n  \"55739.jpg\": \"Reptilia/Urosaurus ornatus/16dd6c9ea1222e763c0b96417b87f825.jpg\",\n  \"557417.jpg\": \"Aves/Muscicapa striata/68a9e2a860e4b8e87cebc6bcb5906df2.jpg\",\n  \"557418.jpg\": \"Aves/Muscicapa striata/16de249d268e7835147e6baba7e1f5da.jpg\",\n  \"557425.jpg\": \"Aves/Muscicapa striata/d90787d7455b6b51a14828d41de4bc79.jpg\",\n  \"557430.jpg\": \"Aves/Muscicapa striata/c8d6efed146e9a1994c94c43352a6794.jpg\",\n  \"557436.jpg\": \"Aves/Muscicapa striata/e26d7fe6a1098df7abf54a54ccc8e7c6.jpg\",\n  \"557437.jpg\": \"Aves/Muscicapa striata/58026fed777ffad9089575ad76821d25.jpg\",\n  \"557438.jpg\": \"Aves/Muscicapa striata/8da2eeee042839c0a6c0cb11bea9a959.jpg\",\n  \"557439.jpg\": \"Aves/Muscicapa striata/c3532d879d84c4b0ea0ca86213f2da7b.jpg\",\n  \"55745.jpg\": \"Reptilia/Urosaurus ornatus/41830e30249db617152a641b23afbd94.jpg\",\n  \"55746.jpg\": \"Reptilia/Urosaurus ornatus/6e979dd272971aae6ed7e07fe57956d1.jpg\",\n  \"557466.jpg\": \"Aves/Calidris himantopus/694c2ba365912ae47003c64623713484.jpg\",\n  \"557470.jpg\": \"Aves/Calidris himantopus/321439f1d37ef429da048d2d4f36af6f.jpg\",\n  \"557472.jpg\": \"Aves/Calidris himantopus/ea11538d88f5aa16c070f0e3793c5902.jpg\",\n  \"557474.jpg\": \"Aves/Calidris himantopus/f68c746b07c4121f3523070c9f8aaaa8.jpg\",\n  \"557475.jpg\": \"Aves/Calidris himantopus/2a2b5ff06eec276f9ed847fda561b4df.jpg\",\n  \"557489.jpg\": \"Aves/Calidris himantopus/eca0a28dda1524705274c12dda52f550.jpg\",\n  \"55749.jpg\": \"Reptilia/Urosaurus ornatus/34519ffc86ec3047ec4242a665ebb705.jpg\",\n  \"557491.jpg\": \"Aves/Calidris himantopus/5a92743b37a5936210977f3e16f0f1a9.jpg\",\n  \"557552.jpg\": \"Aves/Haliaeetus leucocephalus/700fd50fd20f11757e11134e28e53283.jpg\",\n  \"557928.jpg\": \"Aves/Haliaeetus leucocephalus/7867973d3c8334abeb27f3c7f93fe9e4.jpg\",\n  \"557942.jpg\": \"Aves/Haliaeetus leucocephalus/3d12ba64358a5789031bcb9347c16022.jpg\",\n  \"557965.jpg\": \"Aves/Haliaeetus leucocephalus/822ca9a55250f2508d7505405f046af1.jpg\",\n  \"557978.jpg\": \"Aves/Haliaeetus leucocephalus/42684de874360eed47bb9a74452276a4.jpg\",\n  \"558092.jpg\": \"Insecta/Apatelodes torrefacta/0f1acb24be9ac1b054c5eacd7da1f656.jpg\",\n  \"558108.jpg\": \"Aves/Corvus frugilegus/f1a9ad1fe1a859bef2ffaf043ede7ef3.jpg\",\n  \"558116.jpg\": \"Aves/Corvus frugilegus/d6d781f774d5cd348f5d71b6bbd8a54e.jpg\",\n  \"558120.jpg\": \"Aves/Corvus frugilegus/e8bb9e8080a5a95f3d18db04ae22d4c3.jpg\",\n  \"558138.jpg\": \"Aves/Porzana carolina/6701ae60c885a1bb283acfea9d1f818a.jpg\",\n  \"558155.jpg\": \"Aves/Porzana carolina/d831e141f0dda188ae76a7ce986f8972.jpg\",\n  \"558157.jpg\": \"Aves/Porzana carolina/7614d1f978eed6a8d4215ac312ce4deb.jpg\",\n  \"558159.jpg\": \"Aves/Porzana carolina/4fae3352a676e1f2f7d03e62a4ef9591.jpg\",\n  \"558165.jpg\": \"Aves/Porzana carolina/25de17bf4a6026190c29d9bbd65e7803.jpg\",\n  \"558168.jpg\": \"Aves/Porzana carolina/37b0887063e33c0b492c1f9d51dac5a3.jpg\",\n  \"558173.jpg\": \"Aves/Porzana carolina/466d26e7a01db63075c8d3d5d0217413.jpg\",\n  \"558242.jpg\": \"Animalia/Carcinus maenas/f3bc70e5e75457d39e3cf8aa60b5e48d.jpg\",\n  \"558246.jpg\": \"Animalia/Carcinus maenas/e1dc5e2f5873271ea6a5cfdb009c8a23.jpg\",\n  \"558264.jpg\": \"Aves/Melanerpes carolinus/c7f30d8f82542796f5d816edb7a433f7.jpg\",\n  \"558341.jpg\": \"Aves/Melanerpes carolinus/9d1f82bf43326837787a90f98513e8be.jpg\",\n  \"558353.jpg\": \"Aves/Melanerpes carolinus/2d1961f8d2807d5663e1969a8925bef3.jpg\",\n  \"55837.jpg\": \"Animalia/Oniscus asellus/f2042c6342ff36d8b6a7fb78b3e9e81e.jpg\",\n  \"55839.jpg\": \"Animalia/Oniscus asellus/459b79bb568785aeadb223170783b9c6.jpg\",\n  \"55840.jpg\": \"Animalia/Oniscus asellus/cb31855dc62703dc31261cab93631dd9.jpg\",\n  \"55849.jpg\": \"Animalia/Oniscus asellus/e32b8b879e265c08de593c8bb2361ab2.jpg\",\n  \"55852.jpg\": \"Animalia/Oniscus asellus/b92c59c1e16d20bf9640d0cf10a59d81.jpg\",\n  \"55853.jpg\": \"Animalia/Oniscus asellus/fe5c56ead61ff7a0f97ffe2cb5183b57.jpg\",\n  \"55854.jpg\": \"Animalia/Oniscus asellus/c7a814716379552daef26995dca607b5.jpg\",\n  \"55867.jpg\": \"Animalia/Oniscus asellus/169ec2f8311e4a1a472efaaa29dbe21e.jpg\",\n  \"558709.jpg\": \"Aves/Todiramphus sanctus/dca53164e395734e14d3c6c735008adf.jpg\",\n  \"558714.jpg\": \"Aves/Todiramphus sanctus/3f529383c5a4a7e0460fda357da700cc.jpg\",\n  \"558719.jpg\": \"Insecta/Lycaena hyllus/e38597a17b5c1251e3bbeb630a7f9f4e.jpg\",\n  \"558722.jpg\": \"Insecta/Lycaena hyllus/857b7606f0fd40be719a8cbf3d90e2c9.jpg\",\n  \"558724.jpg\": \"Insecta/Lycaena hyllus/56399c348cb2be5353ad4cb1c7e180e1.jpg\",\n  \"558727.jpg\": \"Insecta/Lycaena hyllus/521adf58152a620bf64451921a1798db.jpg\",\n  \"558728.jpg\": \"Insecta/Lycaena hyllus/448aaeb4fe04eb22023984cf6386ba36.jpg\",\n  \"558738.jpg\": \"Insecta/Lycaena hyllus/c00ba5d95cb789e62ec33543c572d66a.jpg\",\n  \"558739.jpg\": \"Insecta/Lycaena hyllus/0707bd938d3b7734837b007c2af73b08.jpg\",\n  \"558767.jpg\": \"Insecta/Papilio eurymedon/55ad2dad1866f8380ca2ab2a39729351.jpg\",\n  \"558778.jpg\": \"Insecta/Papilio eurymedon/a8809e8c3b0c3069d054a243ecaac9bf.jpg\",\n  \"558787.jpg\": \"Insecta/Papilio eurymedon/9cb7965cc79a1112e693dce60363f2d3.jpg\",\n  \"558789.jpg\": \"Insecta/Papilio eurymedon/a7923a5b0bd7269f834b74856916ed9c.jpg\",\n  \"558790.jpg\": \"Insecta/Papilio eurymedon/c3868106d867774517486a195b6303f3.jpg\",\n  \"558804.jpg\": \"Insecta/Papilio eurymedon/9f53fec8e6dd8512b610031a0e4a76d0.jpg\",\n  \"558822.jpg\": \"Insecta/Papilio eurymedon/bf287741e0b3327a0197f9f72efe242c.jpg\",\n  \"558825.jpg\": \"Insecta/Papilio eurymedon/afb3b5a67099ca3f55157fd0e6d6833e.jpg\",\n  \"558831.jpg\": \"Insecta/Papilio eurymedon/559decebc3af3b1beeddbedfbab84279.jpg\",\n  \"558858.jpg\": \"Mammalia/Mus musculus/fba4bb21bb0e7880c110314c1364f5bc.jpg\",\n  \"558863.jpg\": \"Mammalia/Mus musculus/22f47b4757113081f2ab9b484ad77462.jpg\",\n  \"558864.jpg\": \"Mammalia/Mus musculus/3874fe06986c97c4f92d57fb5e36490f.jpg\",\n  \"558865.jpg\": \"Mammalia/Mus musculus/6830a77d710e97d5a2ce54636da11a31.jpg\",\n  \"558874.jpg\": \"Mammalia/Mus musculus/3eb48d2bf2b7fd4811e56b3979894027.jpg\",\n  \"558884.jpg\": \"Mammalia/Mus musculus/dd2bf396e9c0940cdf781466cb09264a.jpg\",\n  \"558893.jpg\": \"Mammalia/Mus musculus/4cb7b1305aea8bd51f3d9eae1427c297.jpg\",\n  \"558904.jpg\": \"Amphibia/Rana dalmatina/b6e082c929d54b94e0978b8be29f1cd3.jpg\",\n  \"558905.jpg\": \"Amphibia/Rana dalmatina/7977cf1824fc0c8015e3b8bb10348d5d.jpg\",\n  \"558912.jpg\": \"Amphibia/Rana dalmatina/8b9da5bac852974ccf02c8447561270e.jpg\",\n  \"558913.jpg\": \"Amphibia/Rana dalmatina/f16d92c0e7706096c808605c25e3e2ef.jpg\",\n  \"558914.jpg\": \"Amphibia/Rana dalmatina/b11935ea416ce277fe1b89251f9c0ef4.jpg\",\n  \"558920.jpg\": \"Amphibia/Rana dalmatina/a66beef0e855ded5af00117a4162e77c.jpg\",\n  \"558922.jpg\": \"Amphibia/Rana dalmatina/6e4019fb8633d72813117ee61fc3b19e.jpg\",\n  \"558938.jpg\": \"Amphibia/Rana dalmatina/3484ebc94223239a806854da9d59cd79.jpg\",\n  \"558943.jpg\": \"Insecta/Helicoverpa zea/40a9a07f2d32bef4ce1f829caba80961.jpg\",\n  \"558954.jpg\": \"Insecta/Helicoverpa zea/8e6b8963e5dc5b8c5dfee0e1c9309023.jpg\",\n  \"558955.jpg\": \"Insecta/Helicoverpa zea/f9d1f5840d06281df2298f180b4bd3eb.jpg\",\n  \"558978.jpg\": \"Insecta/Nephelodes minians/69eaf6e374ebbfccc445d80c49c3d2a1.jpg\",\n  \"558980.jpg\": \"Insecta/Nephelodes minians/8cf709a5701e3b291696023e3b36ed2c.jpg\",\n  \"558984.jpg\": \"Insecta/Nephelodes minians/bfbce9de20db456fdaaa42181b262629.jpg\",\n  \"558996.jpg\": \"Amphibia/Anaxyrus speciosus/f349a59fe8237389d5c17b47fb3b7218.jpg\",\n  \"558997.jpg\": \"Amphibia/Anaxyrus speciosus/c6ec1f9e41c3f31350b142706862c087.jpg\",\n  \"558999.jpg\": \"Amphibia/Anaxyrus speciosus/f6cbaadf5e977494791ff434604b9c87.jpg\",\n  \"559011.jpg\": \"Amphibia/Anaxyrus speciosus/ae3fbc214786d02dcedbef0c51b4229d.jpg\",\n  \"559050.jpg\": \"Amphibia/Anaxyrus speciosus/a799174d3ee208a72ae4f0aecc9c5306.jpg\",\n  \"559059.jpg\": \"Amphibia/Anaxyrus speciosus/cc7b25654c664e4281736d425e0033a8.jpg\",\n  \"559074.jpg\": \"Amphibia/Anaxyrus speciosus/1cfa60ab1a70c60ad2f723d1bbc446ff.jpg\",\n  \"559105.jpg\": \"Reptilia/Rhinocheilus lecontei/bd238365f5da612eba2be24242783ca8.jpg\",\n  \"559110.jpg\": \"Reptilia/Rhinocheilus lecontei/4db32b8b70c3eb1cdf7620d13e806bd8.jpg\",\n  \"559112.jpg\": \"Reptilia/Rhinocheilus lecontei/19bd86d225295322f1d5fe25c09ccd82.jpg\",\n  \"559115.jpg\": \"Reptilia/Rhinocheilus lecontei/9824e91e150282edf7a637ca0cf29663.jpg\",\n  \"559147.jpg\": \"Reptilia/Rhinocheilus lecontei/2f84350b7db2ab334c662b78f421d93d.jpg\",\n  \"559153.jpg\": \"Reptilia/Rhinocheilus lecontei/8d1eebd6e87ca426b458d7ab91021e95.jpg\",\n  \"559156.jpg\": \"Reptilia/Rhinocheilus lecontei/7e66a237604b5ba3de8b02f7aabdd8fc.jpg\",\n  \"559157.jpg\": \"Reptilia/Rhinocheilus lecontei/61f37ba6d1786a3fd7326d78bf8e705f.jpg\",\n  \"559168.jpg\": \"Reptilia/Rhinocheilus lecontei/04774f22ae9eb13c0284ea92c823f84a.jpg\",\n  \"559186.jpg\": \"Aves/Baeolophus inornatus/432ffdb70d6017f31f912a4d96de1110.jpg\",\n  \"559217.jpg\": \"Aves/Baeolophus inornatus/ab3992987e0f35d2a73f5e7cc002ef54.jpg\",\n  \"559218.jpg\": \"Aves/Baeolophus inornatus/6f878f86e0e2a8f90cefe5ddbc4097c2.jpg\",\n  \"559234.jpg\": \"Aves/Baeolophus inornatus/f2aba1ba8e0314d05d1695f8d9092bcb.jpg\",\n  \"559265.jpg\": \"Aves/Baeolophus inornatus/f12280eb48f8c414ad8945aa6e7412df.jpg\",\n  \"559272.jpg\": \"Reptilia/Heterodon platirhinos/c97a1ff3f3468e58d5caf410af86f75a.jpg\",\n  \"559299.jpg\": \"Reptilia/Heterodon platirhinos/34a28cbec65c33dfa338d40fcc5e5567.jpg\",\n  \"559312.jpg\": \"Reptilia/Heterodon platirhinos/145524d8baf335e6f0553cf241e50f8b.jpg\",\n  \"559315.jpg\": \"Reptilia/Heterodon platirhinos/adb10f1d212e15a5dbe01805d6db990b.jpg\",\n  \"559321.jpg\": \"Reptilia/Heterodon platirhinos/321a2a18880f8fe09e40aba35a8a2745.jpg\",\n  \"559342.jpg\": \"Insecta/Anageshna primordialis/f0f4cb84529896d4c5b5ec75a7c27001.jpg\",\n  \"559477.jpg\": \"Aves/Sialia mexicana/297f2944efe4be19d0fe1aa2a0e50578.jpg\",\n  \"559512.jpg\": \"Aves/Sialia mexicana/630dba8927b21313c12ad18bc37cf454.jpg\",\n  \"559607.jpg\": \"Aves/Sialia mexicana/b6e61a66b39c92119fbd02fa485e9f70.jpg\",\n  \"559803.jpg\": \"Aves/Setophaga pensylvanica/cf2dd9bb9dc1931a9fdd8d0eb9136044.jpg\",\n  \"559810.jpg\": \"Aves/Setophaga pensylvanica/11fb52e05bab1f829c5fbafb414fa6cb.jpg\",\n  \"559832.jpg\": \"Aves/Setophaga pensylvanica/a159ab0f7dfca4ff6edd1154c2cdff96.jpg\",\n  \"559834.jpg\": \"Aves/Setophaga pensylvanica/f1a8d430b0057f72d1aabd07e15ca7c1.jpg\",\n  \"559849.jpg\": \"Aves/Setophaga pensylvanica/ac8fca7c91c015dfc1d912be610ace43.jpg\",\n  \"559850.jpg\": \"Aves/Setophaga pensylvanica/d78bb7f02df84f34f2cc9848cab5c526.jpg\",\n  \"559872.jpg\": \"Aves/Setophaga pensylvanica/d13c234bbb482b8184afa06fa6e6321f.jpg\",\n  \"559883.jpg\": \"Insecta/Progomphus obscurus/08e896bc1e8fdd7ecdd414f049e51d2c.jpg\",\n  \"559896.jpg\": \"Aves/Aythya novaeseelandiae/46eb8502c730b18ebb9417bbce075647.jpg\",\n  \"559899.jpg\": \"Aves/Aythya novaeseelandiae/47f4ae3d5e2b849bb33f3a738a498879.jpg\",\n  \"559906.jpg\": \"Aves/Aythya novaeseelandiae/d872f532a2a8457cabdb4aeb70f49a42.jpg\",\n  \"559910.jpg\": \"Aves/Aythya novaeseelandiae/67a263649d3acf0e13592403cd0fc761.jpg\",\n  \"559923.jpg\": \"Reptilia/Crotalus lepidus lepidus/c9240b67b39e2449b32325f7ea8c205e.jpg\",\n  \"559933.jpg\": \"Aves/Tringa glareola/eaeba18958e031ecbc9c7f61f016b3c9.jpg\",\n  \"56.jpg\": \"Mammalia/Marmota flaviventris/5fa344663f2da2e104f3d635fc617ac8.jpg\",\n  \"560013.jpg\": \"Aves/Psittacula krameri/c6603a395b3e38094956fdb2dd8e9948.jpg\",\n  \"560015.jpg\": \"Aves/Psittacula krameri/28667b19897368cf47999e315d26c33c.jpg\",\n  \"560022.jpg\": \"Aves/Psittacula krameri/b5df00222a0aa568d1d6ff2bca3eb1b9.jpg\",\n  \"560023.jpg\": \"Aves/Psittacula krameri/fcca234e86d5214ff2a77a91d4e02bb9.jpg\",\n  \"560025.jpg\": \"Aves/Psittacula krameri/6ddd998242469d047e7bda73c041c6eb.jpg\",\n  \"560038.jpg\": \"Aves/Psittacula krameri/818939ca791e4e0fb7c30f0952e0b0d7.jpg\",\n  \"560039.jpg\": \"Aves/Psittacula krameri/b12d1a39ee8be18ec6735781e611dc7d.jpg\",\n  \"560040.jpg\": \"Aves/Psittacula krameri/0ac8168a355496c2299e622e1fcc97db.jpg\",\n  \"560046.jpg\": \"Aves/Psittacula krameri/62973f85faa162a1d828d40b608b3510.jpg\",\n  \"560047.jpg\": \"Aves/Psittacula krameri/712544eefd84618dd641346ad4c45666.jpg\",\n  \"560048.jpg\": \"Aves/Psittacula krameri/14da6d326ccb094c2bc0a38d3c4ed399.jpg\",\n  \"560069.jpg\": \"Reptilia/Nerodia rhombifer/3ada8a2a6f318ecf112f34ac0e137b85.jpg\",\n  \"560094.jpg\": \"Reptilia/Nerodia rhombifer/eef6544eb7413852bd29faf6bdb18e18.jpg\",\n  \"560105.jpg\": \"Reptilia/Nerodia rhombifer/00c2e061da8267417ded5e48ff78cc7f.jpg\",\n  \"560140.jpg\": \"Reptilia/Nerodia rhombifer/4f0d1e6c87836673355c47d91ae604f4.jpg\",\n  \"560270.jpg\": \"Insecta/Pyrrharctia isabella/af525173a6d4f4fea71c13034023420d.jpg\",\n  \"560291.jpg\": \"Insecta/Pyrrharctia isabella/f22cbe7dbf55170bc25f714031263a60.jpg\",\n  \"560329.jpg\": \"Insecta/Pyrrharctia isabella/aa821019a3ccd8e2161cc50ceaaeb856.jpg\",\n  \"560356.jpg\": \"Insecta/Pyrrharctia isabella/4d436868c0b4560362b2548bf20fb530.jpg\",\n  \"560367.jpg\": \"Insecta/Pyrrharctia isabella/04df57aa964c984c93185ce6dccf6914.jpg\",\n  \"56039.jpg\": \"Actinopterygii/Caranx melampygus/96b98dd09f85f3fc3019dca375f5ba27.jpg\",\n  \"560436.jpg\": \"Mammalia/Taxidea taxus/9afa1d326d9fd3dc079d44248c69fe33.jpg\",\n  \"560438.jpg\": \"Mammalia/Taxidea taxus/0db1cc13e02ee2ebe7923996d0560b09.jpg\",\n  \"560439.jpg\": \"Mammalia/Taxidea taxus/ece47ca6e335976fb8525ab67c914d09.jpg\",\n  \"56045.jpg\": \"Actinopterygii/Caranx melampygus/9e9d6538ec6e1d64bf2bd639f36cbebc.jpg\",\n  \"560455.jpg\": \"Mammalia/Taxidea taxus/6146d11198d9aeaee5c96567446f4a74.jpg\",\n  \"560462.jpg\": \"Mammalia/Taxidea taxus/07ac80db5488a743dede287bdb115f14.jpg\",\n  \"560464.jpg\": \"Aves/Calamospiza melanocorys/a749b627258f52195a18059c6f03fc9d.jpg\",\n  \"560468.jpg\": \"Aves/Calamospiza melanocorys/82fd53bd1c928155248493dfe05674c3.jpg\",\n  \"560469.jpg\": \"Aves/Calamospiza melanocorys/788afff304597354127b8dfc6a7430a7.jpg\",\n  \"560475.jpg\": \"Aves/Calamospiza melanocorys/d1bf4f84f1a3221a5c0421f5f17d16ec.jpg\",\n  \"560478.jpg\": \"Aves/Calamospiza melanocorys/f106f6e682e0069a0123bac93fb532e6.jpg\",\n  \"560479.jpg\": \"Aves/Calamospiza melanocorys/7bbf6900aac52811f633c5f8a0624e78.jpg\",\n  \"560550.jpg\": \"Insecta/Mantis religiosa/792906854ee4a51dfb4b5b0e26abf6b7.jpg\",\n  \"560563.jpg\": \"Insecta/Mantis religiosa/319c0eb196f987098d6e99e88db00098.jpg\",\n  \"560569.jpg\": \"Insecta/Mantis religiosa/eeae5f24d2564d53f9919ff7a76f01a3.jpg\",\n  \"56057.jpg\": \"Aves/Petroica macrocephala macrocephala/61b243ba3972752b57f77dcc9ad244ee.jpg\",\n  \"560589.jpg\": \"Insecta/Mantis religiosa/47939eddfbb564ed7886ecad682d64c1.jpg\",\n  \"560603.jpg\": \"Insecta/Mantis religiosa/3c0ed54f57a2a60cacdc915a7a6c670f.jpg\",\n  \"560621.jpg\": \"Insecta/Mantis religiosa/d23a32aad3af2a7e44ea4689ce67bb01.jpg\",\n  \"560630.jpg\": \"Insecta/Mantis religiosa/69e3afda47e67d6694327b31d02d8b12.jpg\",\n  \"560634.jpg\": \"Insecta/Marimatha nigrofimbria/43c4ca12d8d684fec963dcbfcccab619.jpg\",\n  \"560638.jpg\": \"Insecta/Marimatha nigrofimbria/c490e6658ae0b95109e9241151f7bfed.jpg\",\n  \"560641.jpg\": \"Insecta/Marimatha nigrofimbria/de52c88060b9a69c5ec9fd5a3a384fc6.jpg\",\n  \"560643.jpg\": \"Insecta/Marimatha nigrofimbria/f7ea1a2f500cd1d2d944de34773916f0.jpg\",\n  \"560646.jpg\": \"Insecta/Marimatha nigrofimbria/596eca083495ee48e889cbbb910f9396.jpg\",\n  \"560649.jpg\": \"Insecta/Marimatha nigrofimbria/fcd5f648665a85df4939b078e8dc40cb.jpg\",\n  \"560657.jpg\": \"Insecta/Marimatha nigrofimbria/9f8e067e1e41d5814647f946092fe22d.jpg\",\n  \"560660.jpg\": \"Insecta/Marimatha nigrofimbria/fb14e503a4f92cb8512dc1c22572303b.jpg\",\n  \"560661.jpg\": \"Insecta/Marimatha nigrofimbria/263f1edb71965a61ddb3f654c87a93f6.jpg\",\n  \"560662.jpg\": \"Insecta/Marimatha nigrofimbria/c0b71aa25f28b418a32eb0975a599564.jpg\",\n  \"56069.jpg\": \"Aves/Petroica macrocephala macrocephala/130ccb038c750388f8683811782877a4.jpg\",\n  \"56070.jpg\": \"Aves/Petroica macrocephala macrocephala/55d91e0c1693f3099000d2b5aa0cc0ad.jpg\",\n  \"56071.jpg\": \"Aves/Petroica macrocephala macrocephala/7f859a4ea3bf772affbb998242ba17dc.jpg\",\n  \"560754.jpg\": \"Reptilia/Sceloporus poinsettii/4a42d9fe84d14e811ab92a16a2a7be15.jpg\",\n  \"560779.jpg\": \"Reptilia/Sceloporus poinsettii/9fb5629f26501b1f61eb782310be5dfe.jpg\",\n  \"56078.jpg\": \"Aves/Petroica macrocephala macrocephala/5a3e8b65c6b1ac91ef6e76ce9fe34c2e.jpg\",\n  \"560793.jpg\": \"Reptilia/Sceloporus poinsettii/a44b28e40ba9c5e4eca9fa391d4e9a23.jpg\",\n  \"560801.jpg\": \"Reptilia/Sceloporus poinsettii/1f4b45f81082d14952adb7eae6466dac.jpg\",\n  \"560810.jpg\": \"Reptilia/Sceloporus poinsettii/05e8a09aad4446e1be6b5be08ef4238c.jpg\",\n  \"560811.jpg\": \"Reptilia/Sceloporus poinsettii/ae961d3cb79b5f9f9aed61dff0dcd791.jpg\",\n  \"560852.jpg\": \"Insecta/Clemensia albata/0ec0306cc0d97677be0139137deab47e.jpg\",\n  \"560954.jpg\": \"Aves/Vanellus armatus/1e491b7a6bcf3551c656c5dcc1204509.jpg\",\n  \"56096.jpg\": \"Amphibia/Ambystoma tigrinum/187d741ba1547e54ea2f0dd9ffab0ae6.jpg\",\n  \"560964.jpg\": \"Reptilia/Aspidoscelis gularis/b58edd47c5f8ac7df0a1808eff9ef51e.jpg\",\n  \"561021.jpg\": \"Reptilia/Aspidoscelis gularis/56e727c236f473196af94c2e81ae98c6.jpg\",\n  \"561029.jpg\": \"Reptilia/Aspidoscelis gularis/23a716de700b5e601f202f772f1d7457.jpg\",\n  \"56103.jpg\": \"Amphibia/Ambystoma tigrinum/a7efaee8293944015f3fc93257e64880.jpg\",\n  \"561038.jpg\": \"Reptilia/Aspidoscelis gularis/20cc96acdd230348561e81f9d56d7144.jpg\",\n  \"56104.jpg\": \"Amphibia/Ambystoma tigrinum/4756e85b5f09a7a6e2196464bfe950d5.jpg\",\n  \"561063.jpg\": \"Reptilia/Aspidoscelis gularis/6aba8a07d00298f46cda9f2970a4aeb8.jpg\",\n  \"561080.jpg\": \"Insecta/Polygonia faunus/c73f543838c650792cb3d282a08d7325.jpg\",\n  \"561084.jpg\": \"Insecta/Phlogophora periculosa/194b90130af24c9638c8680a95ab1e58.jpg\",\n  \"561088.jpg\": \"Aves/Emberiza schoeniclus/bd702e3f309be6385deeff4cb6adc896.jpg\",\n  \"561100.jpg\": \"Aves/Emberiza schoeniclus/dca02ccf4f2c4c752c07d0104ce26af8.jpg\",\n  \"561140.jpg\": \"Aves/Buteo platypterus/206774e15542b73414ca19543f285711.jpg\",\n  \"561149.jpg\": \"Aves/Buteo platypterus/2cdd6da30dae8d08d7181333e339bcf5.jpg\",\n  \"561153.jpg\": \"Aves/Buteo platypterus/7fbed6e7e04b9134a3ee17929a909be5.jpg\",\n  \"561160.jpg\": \"Aves/Buteo platypterus/d004fff845d570aaf4243fa6330c89eb.jpg\",\n  \"561173.jpg\": \"Aves/Buteo platypterus/5d047ac1b0adcd154b787a929c37cf6c.jpg\",\n  \"561186.jpg\": \"Aves/Buteo platypterus/4de785647338d9582977946b3fc6c556.jpg\",\n  \"561188.jpg\": \"Arachnida/Verrucosa arenata/6ea0fa344029ff9dbd406e82375376c7.jpg\",\n  \"561189.jpg\": \"Arachnida/Verrucosa arenata/33d37e79eeae1733c883ba91e8f7a896.jpg\",\n  \"561191.jpg\": \"Arachnida/Verrucosa arenata/b26c2f1c01c9e7d5ecd4f74a2b7bb2ac.jpg\",\n  \"561192.jpg\": \"Arachnida/Verrucosa arenata/8f19a86ad4cc5f1e8ec702981cbecb54.jpg\",\n  \"561193.jpg\": \"Arachnida/Verrucosa arenata/be2d7cbf144b953a5a27a8880ceb84e5.jpg\",\n  \"561195.jpg\": \"Arachnida/Verrucosa arenata/1be9e3d53a695e1f146862a83d77dab3.jpg\",\n  \"561196.jpg\": \"Arachnida/Verrucosa arenata/beed917294b90c30dfe624698cbc7955.jpg\",\n  \"561200.jpg\": \"Arachnida/Verrucosa arenata/b3e4f406e15c49cf92798394a15cab68.jpg\",\n  \"561203.jpg\": \"Arachnida/Verrucosa arenata/020a440628cc00c0a51f143779f7051c.jpg\",\n  \"561205.jpg\": \"Arachnida/Verrucosa arenata/4562432db18b7b22175aa75f88dd4942.jpg\",\n  \"561321.jpg\": \"Aves/Ammodramus savannarum/c0fdc4074efbfd57d53f74f00e20cd71.jpg\",\n  \"561322.jpg\": \"Aves/Ammodramus savannarum/b8549583d9d0d25349772e9567acdd82.jpg\",\n  \"561333.jpg\": \"Aves/Ammodramus savannarum/673c858ab47cdeac2b5f3dde58c65506.jpg\",\n  \"561334.jpg\": \"Aves/Ammodramus savannarum/b053641d4fa341c307f02faa6282c855.jpg\",\n  \"561336.jpg\": \"Aves/Ammodramus savannarum/29537cb52b9ea4b97ea3bc1ec0808f55.jpg\",\n  \"561339.jpg\": \"Aves/Ammodramus savannarum/866e5d9d807d7a964f699a861742a201.jpg\",\n  \"561348.jpg\": \"Aves/Ammodramus savannarum/e338bb03af1f9da152964a4df7e2e82a.jpg\",\n  \"561351.jpg\": \"Aves/Ammodramus savannarum/59190c4e60fa9c1aea700be9c83f5814.jpg\",\n  \"561354.jpg\": \"Aves/Ammodramus savannarum/9c51702c78ee29ade5f7801ee78fbbab.jpg\",\n  \"561643.jpg\": \"Insecta/Apis mellifera/edcbbc9625dc884757b3dd2c078e6e76.jpg\",\n  \"561720.jpg\": \"Insecta/Apis mellifera/f6f2a2b4b56e1c0b1fd3ceb1cb1007cb.jpg\",\n  \"561899.jpg\": \"Insecta/Apis mellifera/d65bbb1d0e5a7969578bfdada9b06c53.jpg\",\n  \"562086.jpg\": \"Insecta/Apis mellifera/ad6b1ae365961c2f69e7f6c207c13427.jpg\",\n  \"562281.jpg\": \"Insecta/Apis mellifera/2857dac3384d89b1e1bc0e7b43a89b4f.jpg\",\n  \"562312.jpg\": \"Insecta/Apis mellifera/3f555a772473c40003df2d84cddfb6b4.jpg\",\n  \"562374.jpg\": \"Insecta/Apis mellifera/b8eb4c146043c59cb9c9cf695f839fb4.jpg\",\n  \"562430.jpg\": \"Insecta/Apis mellifera/a8ca1427357b9ed1a025d50d1538fb99.jpg\",\n  \"562562.jpg\": \"Insecta/Apis mellifera/ad8c94a01ee668dcec03817cbc0681f0.jpg\",\n  \"562628.jpg\": \"Insecta/Apis mellifera/3f8983df29323fa405665daaf0b5347a.jpg\",\n  \"562632.jpg\": \"Insecta/Apis mellifera/2db5fb8942416149a44fe1b08337fcc5.jpg\",\n  \"562640.jpg\": \"Insecta/Apis mellifera/4c794f27eacc328d9b89754b47035f1a.jpg\",\n  \"562681.jpg\": \"Insecta/Apis mellifera/b3bd939106a85231bef6013638a6d500.jpg\",\n  \"562764.jpg\": \"Insecta/Plebejus lupini/95f784588f1541add113cc68a6261772.jpg\",\n  \"562770.jpg\": \"Insecta/Plebejus lupini/a6ef804df98cf9f46dd8cf7f20f90a25.jpg\",\n  \"562771.jpg\": \"Insecta/Plebejus lupini/dd7a7de0c000d9b87181f15d436adcab.jpg\",\n  \"562808.jpg\": \"Insecta/Pleuroprucha insulsaria/515a3ccf6dbcedc31f0a982d89863f94.jpg\",\n  \"562809.jpg\": \"Insecta/Pleuroprucha insulsaria/912659a87dd8d2c9bd25d405d5f7e299.jpg\",\n  \"562811.jpg\": \"Insecta/Pleuroprucha insulsaria/db3e1502e8a29ee316aad45de77fdd16.jpg\",\n  \"562814.jpg\": \"Insecta/Pleuroprucha insulsaria/6a688dbefa126d1e01b40286e5949381.jpg\",\n  \"562815.jpg\": \"Insecta/Pleuroprucha insulsaria/e684a6ce9b101ae6fd1049806380b9eb.jpg\",\n  \"562816.jpg\": \"Insecta/Pleuroprucha insulsaria/cc8fbe375e152167f6969fb48cdf3839.jpg\",\n  \"562820.jpg\": \"Insecta/Pleuroprucha insulsaria/f548fa9a968f7f5f6cd648e63c61590e.jpg\",\n  \"562821.jpg\": \"Insecta/Pleuroprucha insulsaria/ae2311fde78424ecaf11c468baecfc6b.jpg\",\n  \"562822.jpg\": \"Insecta/Pleuroprucha insulsaria/dfaacf53d994ff8974c6d11694555d5b.jpg\",\n  \"562823.jpg\": \"Insecta/Pleuroprucha insulsaria/7934a98535d47a567338e191c0b2b4b8.jpg\",\n  \"562827.jpg\": \"Insecta/Pleuroprucha insulsaria/569e229feb1f95c967b30c2eac1b0e3f.jpg\",\n  \"562836.jpg\": \"Insecta/Aeshna mixta/afa0b32c3a89317d0265adeb2f71ba15.jpg\",\n  \"562837.jpg\": \"Insecta/Aeshna mixta/49b7d55b1f9c331a6d4f689f3c5802fc.jpg\",\n  \"562856.jpg\": \"Insecta/Battus polydamas/5b0fa3af89aaa17fca0e59bfd571ede2.jpg\",\n  \"562868.jpg\": \"Reptilia/Bogertophis subocularis/7400928cccb77c083da564a62d968eea.jpg\",\n  \"562870.jpg\": \"Reptilia/Bogertophis subocularis/5b0a4db714a67a1fe6009a447b60d7ce.jpg\",\n  \"562871.jpg\": \"Reptilia/Bogertophis subocularis/3486c596d173c67647adb5e142a02bd4.jpg\",\n  \"562872.jpg\": \"Reptilia/Bogertophis subocularis/b97d59117ce0fb9f820bf6b1c54e882d.jpg\",\n  \"562875.jpg\": \"Reptilia/Bogertophis subocularis/53c7d8cc768503de4846f780d6ecebf5.jpg\",\n  \"562876.jpg\": \"Reptilia/Bogertophis subocularis/22022534bdd5695653db66b2e275f0ef.jpg\",\n  \"562877.jpg\": \"Reptilia/Bogertophis subocularis/60b2c685df186564b747f27ebe7ab51e.jpg\",\n  \"562878.jpg\": \"Reptilia/Bogertophis subocularis/74e5118d09d88ff56b7cf869e1d2807d.jpg\",\n  \"562880.jpg\": \"Reptilia/Bogertophis subocularis/5e1e6307afad4160dcdbafbbc87db244.jpg\",\n  \"562885.jpg\": \"Reptilia/Bogertophis subocularis/46c7053a0d9d4fbb677a943a34c5f8d6.jpg\",\n  \"562892.jpg\": \"Aves/Chloroceryle amazona/187e7b8317310b5d1c46e5b8acc6929e.jpg\",\n  \"562899.jpg\": \"Aves/Chloroceryle amazona/f3ba0f0e53041e79bd41d2f1eb397de8.jpg\",\n  \"562926.jpg\": \"Reptilia/Terrapene ornata luteola/72661276386834a9425df0b0ba19b863.jpg\",\n  \"562929.jpg\": \"Reptilia/Terrapene ornata luteola/cacbdf505c577aebb715ee55330de8cd.jpg\",\n  \"563353.jpg\": \"Aves/Cardinalis cardinalis/b5084e86e44f4c1ddba9e01fa4679b82.jpg\",\n  \"563385.jpg\": \"Aves/Cardinalis cardinalis/9d391d3674ad6a88f3f37feb944871b9.jpg\",\n  \"56352.jpg\": \"Arachnida/Eriophora pustulosa/b4daa97f16f611c8291829e5b237e52f.jpg\",\n  \"56354.jpg\": \"Arachnida/Eriophora pustulosa/1a1126dc5e7fbe71fb192d9db880655f.jpg\",\n  \"56355.jpg\": \"Arachnida/Eriophora pustulosa/a4b216c16daedeee9c0217fde1a11e44.jpg\",\n  \"56359.jpg\": \"Arachnida/Eriophora pustulosa/af0fc1fab878adbc986c0490694aa7c8.jpg\",\n  \"56360.jpg\": \"Arachnida/Eriophora pustulosa/c1484b871a7fa9c9f4bf08e9bf94c4b3.jpg\",\n  \"56361.jpg\": \"Arachnida/Eriophora pustulosa/0303fc76ad07034622428cb1652bc720.jpg\",\n  \"56362.jpg\": \"Arachnida/Eriophora pustulosa/997c733cc5a8160bc9135eda56f93354.jpg\",\n  \"56363.jpg\": \"Arachnida/Eriophora pustulosa/aae973fdc8030e80d4152644b9f7e560.jpg\",\n  \"56364.jpg\": \"Arachnida/Eriophora pustulosa/0ba4294728f7234905c722984fe96aba.jpg\",\n  \"56365.jpg\": \"Arachnida/Eriophora pustulosa/b1c697d5e447b18031e4d5c8d62c9789.jpg\",\n  \"56372.jpg\": \"Arachnida/Eriophora pustulosa/363ea0c72f88cc1d9fbcaa184cc466b0.jpg\",\n  \"56376.jpg\": \"Arachnida/Eriophora pustulosa/bf7f5967cae609db5ff8d5ad7bc66196.jpg\",\n  \"56377.jpg\": \"Arachnida/Eriophora pustulosa/cfc7b27b958341593ff6710010977207.jpg\",\n  \"56378.jpg\": \"Arachnida/Eriophora pustulosa/523e6c17e4d279005c2ed18560fe0f82.jpg\",\n  \"56379.jpg\": \"Arachnida/Eriophora pustulosa/096144219729d29fac572244948c0999.jpg\",\n  \"56380.jpg\": \"Arachnida/Eriophora pustulosa/ed3fbbbcf8ff4d17e2482ccfdc8e0c10.jpg\",\n  \"56381.jpg\": \"Arachnida/Eriophora pustulosa/6fc83520d97b2754f4b3411c75088dd3.jpg\",\n  \"56385.jpg\": \"Arachnida/Eriophora pustulosa/a478365fb67e00214763de46c0b8c78e.jpg\",\n  \"56386.jpg\": \"Arachnida/Eriophora pustulosa/2c41005825670d5f73db07d00b8a8127.jpg\",\n  \"56393.jpg\": \"Insecta/Lineodes integra/b75270a8306876617e194612a7fa98cb.jpg\",\n  \"563951.jpg\": \"Reptilia/Terrapene ornata/f89a12003a32da63e5e31557b814a448.jpg\",\n  \"56397.jpg\": \"Insecta/Lineodes integra/344894651401cd64536ae3d5a2b67b68.jpg\",\n  \"563994.jpg\": \"Reptilia/Terrapene ornata/7c155e767e15f8eeb562d1f6e2f6db56.jpg\",\n  \"563997.jpg\": \"Reptilia/Terrapene ornata/09323d6d4b69f6db35ad9a51cfe32196.jpg\",\n  \"564.jpg\": \"Mammalia/Ursus americanus/cb9549f15ef4f828c1e9d05385d5d6de.jpg\",\n  \"564019.jpg\": \"Reptilia/Terrapene ornata/a0bbda772fb3bfbf6e5705f2d0f85c52.jpg\",\n  \"564020.jpg\": \"Reptilia/Terrapene ornata/d3449e48626602d84b4e61296dd7b70f.jpg\",\n  \"564021.jpg\": \"Reptilia/Terrapene ornata/cda19eabd245581a1f8653406284bc4f.jpg\",\n  \"564053.jpg\": \"Insecta/Hemideina thoracica/1d25c9e7d36ac11494de04d7a136d912.jpg\",\n  \"564056.jpg\": \"Insecta/Hemideina thoracica/c98b2c6e4f581ca294b5c97a9c86c153.jpg\",\n  \"564064.jpg\": \"Actinopterygii/Amia calva/8c6a06880de13aa4e9b7ee5a99c7831f.jpg\",\n  \"564065.jpg\": \"Insecta/Hordnia atropunctata/1e481716e7455ec2a43ae979dec5de92.jpg\",\n  \"56408.jpg\": \"Insecta/Lineodes integra/83e790cd579b849196fba14b3cdda43a.jpg\",\n  \"56411.jpg\": \"Insecta/Lineodes integra/1126fed7494c7a3baa8df2636e2779c1.jpg\",\n  \"56417.jpg\": \"Insecta/Lineodes integra/2c7ed98295fcd859c40683e2a64addeb.jpg\",\n  \"564170.jpg\": \"Insecta/Lascoria ambigualis/ad9b6e4b83507c69fd43652b4bf71728.jpg\",\n  \"564180.jpg\": \"Insecta/Lascoria ambigualis/c62c6a22418b4acc64c614f404985fd3.jpg\",\n  \"56423.jpg\": \"Insecta/Lineodes integra/b47213494456bd0ddd7a42652ecbaf30.jpg\",\n  \"564256.jpg\": \"Aves/Aix sponsa/59eab410ea3ee81dc5a72d788fa11f25.jpg\",\n  \"564607.jpg\": \"Mammalia/Equus caballus/cf73caf45fd844b705a007f39a392cce.jpg\",\n  \"564609.jpg\": \"Mammalia/Equus caballus/91837233403b408eaaee478f2e30e0e1.jpg\",\n  \"564651.jpg\": \"Insecta/Cucullia convexipennis/23df76f2cce39e545e61cbd60453c3a9.jpg\",\n  \"564652.jpg\": \"Insecta/Cucullia convexipennis/72643d1673b65fa72fda7446cca80970.jpg\",\n  \"564656.jpg\": \"Insecta/Cucullia convexipennis/10d3271e673f93612daf2ad576a17758.jpg\",\n  \"564661.jpg\": \"Insecta/Cucullia convexipennis/3768012d1741ce8fe502a7cd96eeef3c.jpg\",\n  \"564663.jpg\": \"Insecta/Herpetogramma pertextalis/1ff53a8e86caeed05a290e830da71174.jpg\",\n  \"564665.jpg\": \"Insecta/Herpetogramma pertextalis/f2726553a67a6b967e596b9b1f2f49d1.jpg\",\n  \"564666.jpg\": \"Insecta/Herpetogramma pertextalis/95eeebea1cb903ce6fa054deadf70246.jpg\",\n  \"564668.jpg\": \"Insecta/Platynota idaeusalis/0ecc465ddfb2c9f94c91b42160a5d287.jpg\",\n  \"564669.jpg\": \"Insecta/Platynota idaeusalis/6349b5ef6a305e87aefe82d6a768f859.jpg\",\n  \"564671.jpg\": \"Insecta/Platynota idaeusalis/e7042971052389d13d663f2dd17d1808.jpg\",\n  \"564776.jpg\": \"Aves/Podiceps cristatus/47ecbd5fc96679e7cd38512654c26970.jpg\",\n  \"564779.jpg\": \"Aves/Podiceps cristatus/ad2b31d70c2d8076d68e43b862c3a86c.jpg\",\n  \"564792.jpg\": \"Aves/Podiceps cristatus/67cd88be3e7e81273d83713c807919fc.jpg\",\n  \"564793.jpg\": \"Aves/Podiceps cristatus/6113f121591955d03110ae381deeb155.jpg\",\n  \"564797.jpg\": \"Aves/Podiceps cristatus/d0512ac52bc38edb6e92cc6ca75f83ad.jpg\",\n  \"564825.jpg\": \"Aves/Podiceps cristatus/5c88fece68b8b1f49894398783dc364c.jpg\",\n  \"564827.jpg\": \"Aves/Podiceps cristatus/cc624d1870babad85cef180613ca7e46.jpg\",\n  \"564838.jpg\": \"Aves/Podiceps cristatus/3418bd9896d8584f599d036310b3a23d.jpg\",\n  \"564903.jpg\": \"Insecta/Conocephalus brevipennis/7e2a0f76724c6eea20cf36151a805bc4.jpg\",\n  \"564904.jpg\": \"Insecta/Conocephalus brevipennis/595a4edb847e4887cac5242678dc771e.jpg\",\n  \"564907.jpg\": \"Aves/Coccyzus minor/33f6c6e53cdac146e2cb757a98d8a4ed.jpg\",\n  \"564912.jpg\": \"Aves/Coccyzus minor/a1635a582eb5ea455f94f4009a9e25b3.jpg\",\n  \"564913.jpg\": \"Aves/Coccyzus minor/3925e22429ee5582b7ee59144c6939cb.jpg\",\n  \"565.jpg\": \"Mammalia/Ursus americanus/818d2f2897f917a44532a499ff11aba1.jpg\",\n  \"565006.jpg\": \"Aves/Fringilla montifringilla/f4bcb78c671a87b44d45b8aec59c806d.jpg\",\n  \"565009.jpg\": \"Aves/Fringilla montifringilla/b8413ab3d514dbe7924b8b2f6bfb4666.jpg\",\n  \"565011.jpg\": \"Aves/Fringilla montifringilla/9e47c9ffdbb26fe6c2510ab5d52edeb4.jpg\",\n  \"565012.jpg\": \"Aves/Fringilla montifringilla/8679e1ba829cf4e66562662b775a24c8.jpg\",\n  \"565013.jpg\": \"Aves/Fringilla montifringilla/80d1b575b8f8db731e6c2a051ef2c63d.jpg\",\n  \"565014.jpg\": \"Aves/Fringilla montifringilla/6731fa10fc0474c1211ee1549da6bc01.jpg\",\n  \"565018.jpg\": \"Aves/Fringilla montifringilla/f51f3c24b74e55bbeb3839d7b10fcb54.jpg\",\n  \"565019.jpg\": \"Aves/Fringilla montifringilla/2e081f49656e4f97a9d3d15dc2f23b28.jpg\",\n  \"565024.jpg\": \"Aves/Geococcyx californianus/f037da6ceffb3fd7406caec9fc1ca117.jpg\",\n  \"565026.jpg\": \"Aves/Geococcyx californianus/821ca1af5c3a887f1b86da79bd77c24b.jpg\",\n  \"565057.jpg\": \"Aves/Geococcyx californianus/d3b558dbc8600dfe0e45f391ccf58d18.jpg\",\n  \"565119.jpg\": \"Aves/Geococcyx californianus/21252937e1fb413d42f23f27e33face8.jpg\",\n  \"565174.jpg\": \"Aves/Geococcyx californianus/98be4fad89e29b8ddfe92c57dca9a807.jpg\",\n  \"565287.jpg\": \"Aves/Thalasseus elegans/6ce772fcd7fd8dc6f9e05c954d156330.jpg\",\n  \"565301.jpg\": \"Aves/Thalasseus elegans/1bb8eb0bf4855c3b104d188344f330f5.jpg\",\n  \"565349.jpg\": \"Aves/Gallus gallus domesticus/25cb2c8feb8cea6175e63ec626cd0ed1.jpg\",\n  \"565351.jpg\": \"Aves/Gallus gallus domesticus/5c543eecd2c90a0ff04f691d0bd3c677.jpg\",\n  \"565358.jpg\": \"Aves/Gallus gallus domesticus/cf39257e973f40f7b3031699924d0e14.jpg\",\n  \"565365.jpg\": \"Aves/Gallus gallus domesticus/a1a9a3822474952e98ea410c6cd81026.jpg\",\n  \"565381.jpg\": \"Aves/Gallus gallus domesticus/b030e00b81cf53d4ed1b1b69f80a7449.jpg\",\n  \"565407.jpg\": \"Insecta/Hetaerina titia/447ed7aa9dd555d844c1d81e17fd5183.jpg\",\n  \"565408.jpg\": \"Insecta/Hetaerina titia/3ad5cbb62d795e091d733dec2f885770.jpg\",\n  \"565409.jpg\": \"Insecta/Hetaerina titia/4563a7ea8c738f54801117a097ffd340.jpg\",\n  \"565410.jpg\": \"Insecta/Hetaerina titia/356346d97812118a6b7d88d3c3647fe5.jpg\",\n  \"565411.jpg\": \"Insecta/Hetaerina titia/f84b0aaf07b6025c579bcd6aa9e746bf.jpg\",\n  \"565412.jpg\": \"Insecta/Hetaerina titia/3ed5ae906f15c5ed459d1987094b45b9.jpg\",\n  \"565413.jpg\": \"Insecta/Hetaerina titia/992f7b358872420606775c4bf0b8b834.jpg\",\n  \"565418.jpg\": \"Insecta/Hetaerina titia/5b398b75bdcc2a03c4597bbfc151d7e4.jpg\",\n  \"565419.jpg\": \"Insecta/Hetaerina titia/d461422f93320c1749741c2569cf1a5b.jpg\",\n  \"565420.jpg\": \"Insecta/Hetaerina titia/d11d9b1c49a35e003c6f3d83c0af0ee9.jpg\",\n  \"565422.jpg\": \"Insecta/Hetaerina titia/83eb778c416dbfa3a30a2ce20b4a5bd0.jpg\",\n  \"565426.jpg\": \"Insecta/Hetaerina titia/a8e2e23fa98f62e0b7cd5a4c649a8bc7.jpg\",\n  \"565427.jpg\": \"Insecta/Hetaerina titia/79ce4a174b755273464a14c0da9443d2.jpg\",\n  \"565428.jpg\": \"Insecta/Hetaerina titia/0fdbd30f79100c3bcd92d2adca3413c6.jpg\",\n  \"565434.jpg\": \"Insecta/Hetaerina titia/c4002a93de4092d641885b97a2a25460.jpg\",\n  \"565436.jpg\": \"Insecta/Hetaerina titia/cefce8929051331e886c3321dac830a5.jpg\",\n  \"565437.jpg\": \"Insecta/Hetaerina titia/c5be0b62acf712dfeaa0912fbd6825e6.jpg\",\n  \"565438.jpg\": \"Insecta/Hetaerina titia/d967f3d0060f5ef1aec1dc860510119b.jpg\",\n  \"565439.jpg\": \"Insecta/Hetaerina titia/ba1f8a28db72994b036c67ef56b945e9.jpg\",\n  \"565440.jpg\": \"Insecta/Hetaerina titia/1a05c96693ce657a1a18fdac2a22229e.jpg\",\n  \"565441.jpg\": \"Insecta/Hetaerina titia/3a7d79c408794c5e432e43247c8023d7.jpg\",\n  \"565449.jpg\": \"Insecta/Xylocopa micans/83784ba1c34a18ffc8576a72252d2189.jpg\",\n  \"565450.jpg\": \"Insecta/Xylocopa micans/de0d3bd5ea6083fe61edd4db6a1b8a1a.jpg\",\n  \"565451.jpg\": \"Insecta/Xylocopa micans/c401d458a109dd96770eac6d867408c6.jpg\",\n  \"565470.jpg\": \"Insecta/Xylocopa micans/81009baca4c41cd960376662f02d9ece.jpg\",\n  \"565475.jpg\": \"Insecta/Xylocopa micans/d961e216ebe41d5aab0505c27f72cabc.jpg\",\n  \"565476.jpg\": \"Insecta/Xylocopa micans/0f575b7bf71af94223874b5b92d3d18b.jpg\",\n  \"565482.jpg\": \"Insecta/Xylocopa micans/48d6c5167ea9c685562263cce0fa2a60.jpg\",\n  \"565483.jpg\": \"Insecta/Xylocopa micans/9813deaf11c345311ce48e02cc4f9578.jpg\",\n  \"565496.jpg\": \"Insecta/Xylocopa micans/f86a73563fe1c7491b39638074818313.jpg\",\n  \"565529.jpg\": \"Insecta/Brachystola magna/0478a514905634c8a6d5b5d0fd7ef697.jpg\",\n  \"56560.jpg\": \"Actinopterygii/Megalops atlanticus/304d1aebc5c96a618c483ecbdfd013ff.jpg\",\n  \"565652.jpg\": \"Insecta/Chlosyne theona/b8d896a9fc6bf77d2219eb8b36611b00.jpg\",\n  \"565658.jpg\": \"Insecta/Chlosyne theona/53d338c59a94cc106bbaa4ee3a7996d3.jpg\",\n  \"565659.jpg\": \"Insecta/Chlosyne theona/51d9186499661c3558a5e768c83d8eeb.jpg\",\n  \"56566.jpg\": \"Actinopterygii/Megalops atlanticus/1c34a0a7414b1132bfbecd802fef22b2.jpg\",\n  \"565660.jpg\": \"Insecta/Chlosyne theona/f050122156884af64e125fffd9dc4f55.jpg\",\n  \"565663.jpg\": \"Insecta/Chlosyne theona/6c1d146a1454d9a3f523955d257c2d73.jpg\",\n  \"565665.jpg\": \"Insecta/Chlosyne theona/8f166c247c0ae3e7b722a1539630ab2d.jpg\",\n  \"565693.jpg\": \"Actinopterygii/Morone saxatilis/01379f705b3b98dc8abb7ccb19cf9d74.jpg\",\n  \"565695.jpg\": \"Actinopterygii/Morone saxatilis/5c7732abaed174d80b5d2f48ef156d22.jpg\",\n  \"565707.jpg\": \"Insecta/Siphanta acuta/12e8d945e669de484f8f5c4c412a69f2.jpg\",\n  \"565712.jpg\": \"Insecta/Siphanta acuta/cc0fcba1c69cd7e93b2744104ec162b0.jpg\",\n  \"565713.jpg\": \"Insecta/Siphanta acuta/8d2ff46de0629fd883e107d12872635e.jpg\",\n  \"565717.jpg\": \"Insecta/Siphanta acuta/86dd6c7d53f1808263cf559f248326df.jpg\",\n  \"565735.jpg\": \"Insecta/Sabulodes aegrotata/51c05864f32bf209df21878adf59f8bc.jpg\",\n  \"56575.jpg\": \"Insecta/Nathalis iole/de4bc3c604ef5fa5742cf3dd5ddb976f.jpg\",\n  \"565751.jpg\": \"Aves/Sula leucogaster/a369bb082231a1c9a7addaf62c04e186.jpg\",\n  \"565763.jpg\": \"Aves/Sula leucogaster/adf1087b42c82af0c36c23c36cb693e2.jpg\",\n  \"565765.jpg\": \"Aves/Sula leucogaster/9e28558e9a6de1ec726e78145969aa3d.jpg\",\n  \"565783.jpg\": \"Aves/Sula leucogaster/a6e4e59c6eee2b3b2f51ad38973e8cf7.jpg\",\n  \"565785.jpg\": \"Aves/Sula leucogaster/01b8a0830cf410a6c21ed67aa2b40f15.jpg\",\n  \"56580.jpg\": \"Insecta/Nathalis iole/33156f146620f811706ccfabee304ca4.jpg\",\n  \"565829.jpg\": \"Insecta/Bombus bimaculatus/e63685447a56c1811bfd37bf1ab4536f.jpg\",\n  \"56583.jpg\": \"Insecta/Nathalis iole/326f60e16b41f7bbfaebe733cbdecd8b.jpg\",\n  \"565830.jpg\": \"Insecta/Bombus bimaculatus/3312101d8b130ce2c30231ab6a2f78fc.jpg\",\n  \"565831.jpg\": \"Insecta/Bombus bimaculatus/a44bb48027a7299d3d57aeab91fe4201.jpg\",\n  \"565836.jpg\": \"Insecta/Bombus bimaculatus/bde4ffa3e3bbbe6246f5cb5c5e7bf0e7.jpg\",\n  \"565842.jpg\": \"Insecta/Bombus bimaculatus/2986663727d7cacc0f535ec7ea3a97d6.jpg\",\n  \"565844.jpg\": \"Insecta/Bombus bimaculatus/82e4fe4fc0b7f9d525661e1ef873f5d7.jpg\",\n  \"565849.jpg\": \"Insecta/Bombus bimaculatus/66ff5ba53497a71cdef99759752c26db.jpg\",\n  \"565850.jpg\": \"Insecta/Bombus bimaculatus/9757f47e51b53898b6c2307965b68016.jpg\",\n  \"565852.jpg\": \"Insecta/Mallodon dasystomus/7039111740081cd47f8f58da8a52f7f0.jpg\",\n  \"565857.jpg\": \"Insecta/Mallodon dasystomus/2ccd928c7eea0a3797269b52672f6a7c.jpg\",\n  \"56586.jpg\": \"Insecta/Nathalis iole/7cf0a0822ac7e9e4bac39f7bbdd886d9.jpg\",\n  \"56588.jpg\": \"Insecta/Nathalis iole/30ebc32f82980cd72826102040eabf83.jpg\",\n  \"565962.jpg\": \"Mollusca/Mytilus edulis/f6787a71c89796d2bf4dd925db4e1602.jpg\",\n  \"565963.jpg\": \"Mollusca/Mytilus edulis/74ddf83d801ac4113693b0aca3b91d8f.jpg\",\n  \"565995.jpg\": \"Aves/Spizella pusilla/937f92230fe70b200599d5088c8c594f.jpg\",\n  \"565997.jpg\": \"Aves/Spizella pusilla/347c15f36d0deb894ca22ab133cc79d4.jpg\",\n  \"566016.jpg\": \"Aves/Spizella pusilla/07c694757723c22346693f2ce6e5b538.jpg\",\n  \"56602.jpg\": \"Insecta/Nathalis iole/832e9bad5886ed1b7ed6e230b8f1af3d.jpg\",\n  \"566021.jpg\": \"Aves/Spizella pusilla/1f2006a93bb5419909c2d1d6d917a7a5.jpg\",\n  \"56603.jpg\": \"Insecta/Nathalis iole/0a4f29601060d5ab8a75b4929d40c5c4.jpg\",\n  \"566042.jpg\": \"Aves/Spizella pusilla/f28682c6aa4d57ae57ed1e87d5e0cddc.jpg\",\n  \"566074.jpg\": \"Aves/Spizella pusilla/9e37c8bfa37fa9d97d2f9af9845262f7.jpg\",\n  \"56608.jpg\": \"Insecta/Nathalis iole/1ed6f45769385f7b9a7ee6de4349cd52.jpg\",\n  \"566089.jpg\": \"Reptilia/Gekko gecko/09433430d1704047e1c5b7385fb07de8.jpg\",\n  \"566090.jpg\": \"Reptilia/Gekko gecko/b17b3ca9473551dfa86ee49ea0b6e923.jpg\",\n  \"566094.jpg\": \"Reptilia/Gekko gecko/3d97973e1e2c4834023f6f746f59d1b1.jpg\",\n  \"56619.jpg\": \"Insecta/Nathalis iole/0192dd958ff9664ec5ccbdcd3ab4a7fb.jpg\",\n  \"566198.jpg\": \"Insecta/Hippodamia variegata/5545ab62d57e52ebd8299150a7130e3e.jpg\",\n  \"566200.jpg\": \"Insecta/Hippodamia variegata/e74cff06b988f04b9cb8d0ca4db8bf78.jpg\",\n  \"566204.jpg\": \"Aves/Chordeiles acutipennis/460edc7cda16a9988168c12708a00428.jpg\",\n  \"566213.jpg\": \"Aves/Chordeiles acutipennis/043ae33e3f8d49f069a5e8cb240fa4b4.jpg\",\n  \"566223.jpg\": \"Insecta/Hypena madefactalis/f1bd544abbf607d4ce4c3f1d39a4b586.jpg\",\n  \"566231.jpg\": \"Aves/Falco femoralis/5c43d59d3e659b4b43eb79400ea3718e.jpg\",\n  \"566232.jpg\": \"Aves/Falco femoralis/dc4ece7f5fbfbc008e2f28ccbc7ded39.jpg\",\n  \"566279.jpg\": \"Insecta/Cisseps fulvicollis/d107c70f56cf617843bc2a1c09990484.jpg\",\n  \"566280.jpg\": \"Insecta/Cisseps fulvicollis/06ae88ca64584e4ffe8d2852bc0a3c92.jpg\",\n  \"566290.jpg\": \"Insecta/Cisseps fulvicollis/9a42e4c886c60836253661acbb76f41f.jpg\",\n  \"566302.jpg\": \"Insecta/Cisseps fulvicollis/a1f40c10a5c066a49b159539d3fa0f0a.jpg\",\n  \"566343.jpg\": \"Aves/Vireo plumbeus/eaf0642c33ba9504a6367784131929bc.jpg\",\n  \"566345.jpg\": \"Aves/Vireo plumbeus/775e2cb8f859c49c55fc0165241d0927.jpg\",\n  \"56638.jpg\": \"Insecta/Nathalis iole/14479bd8634fd05cb3c5354bfb3dcd5c.jpg\",\n  \"56639.jpg\": \"Insecta/Nathalis iole/eca10c6490833e64c41bdc90fd08e7b1.jpg\",\n  \"566413.jpg\": \"Aves/Columbina inca/138b71f3c8589008113c5708a388078b.jpg\",\n  \"56642.jpg\": \"Insecta/Nathalis iole/0bdfc0a982b4ab88dedf39d28219667c.jpg\",\n  \"566423.jpg\": \"Aves/Columbina inca/2559f3cbe69d91eebe995f147e327c32.jpg\",\n  \"56643.jpg\": \"Insecta/Nathalis iole/0889c3c7a381205fe99e11076419e13c.jpg\",\n  \"566439.jpg\": \"Aves/Columbina inca/a703de5477efceaf46d630ab506c6215.jpg\",\n  \"566478.jpg\": \"Aves/Columbina inca/d07d4b6b41b0f851a546e05f1dd821de.jpg\",\n  \"566517.jpg\": \"Aves/Columbina inca/e8ea7f9c18596aef417492ad0892946d.jpg\",\n  \"566522.jpg\": \"Aves/Columbina inca/3454824fd84991795523fbe0ae9d1a66.jpg\",\n  \"56654.jpg\": \"Insecta/Nathalis iole/572e9286aa50a11c7440db4d53169367.jpg\",\n  \"56655.jpg\": \"Insecta/Nathalis iole/20e10c3e8a3d9aa0be3b51edc465d741.jpg\",\n  \"566574.jpg\": \"Aves/Columbina inca/01e68c2ad7b4b27a727cf8afd41697db.jpg\",\n  \"566622.jpg\": \"Actinopterygii/Salvelinus fontinalis/cc76de83e56b58fe2bdfbd267bf677f0.jpg\",\n  \"566627.jpg\": \"Actinopterygii/Salvelinus fontinalis/2fa72517c21aa6abc43bbcd4daf9ce94.jpg\",\n  \"566628.jpg\": \"Actinopterygii/Salvelinus fontinalis/6c95ac0c8324c50ac391e2bb36171fe4.jpg\",\n  \"566639.jpg\": \"Actinopterygii/Salvelinus fontinalis/e0ab736ae2ecf217bf6eec9853e79834.jpg\",\n  \"566646.jpg\": \"Actinopterygii/Salvelinus fontinalis/ca5b5486bf028a3c0f1cc007758a90d3.jpg\",\n  \"566647.jpg\": \"Actinopterygii/Salvelinus fontinalis/13993b3b92fa0f0c4c7907700ca92d7d.jpg\",\n  \"566652.jpg\": \"Amphibia/Lithobates berlandieri/50e79e423f79d2926156dece6984f53a.jpg\",\n  \"56666.jpg\": \"Insecta/Nathalis iole/b917a4d3731108968e7e53c37203cbed.jpg\",\n  \"566667.jpg\": \"Amphibia/Lithobates berlandieri/68da09fbdda12bb84c68ba05a65aeeb1.jpg\",\n  \"566674.jpg\": \"Amphibia/Lithobates berlandieri/9e9d7d7b74e48725143d7cb0afc743a7.jpg\",\n  \"566684.jpg\": \"Amphibia/Lithobates berlandieri/27a00dbbd00ee6a5cdf38a41c62c3cae.jpg\",\n  \"566721.jpg\": \"Amphibia/Lithobates berlandieri/cc2521670f8ea85def706e41e2f62f93.jpg\",\n  \"566733.jpg\": \"Amphibia/Lithobates berlandieri/03754f95e47a9348a5a6607870aa4ee6.jpg\",\n  \"566735.jpg\": \"Amphibia/Lithobates berlandieri/e2f153a70e9aa7f41e3769a9a6fb7f3d.jpg\",\n  \"566740.jpg\": \"Amphibia/Lithobates berlandieri/5f3775a641dc5feadb7a53c8099f854b.jpg\",\n  \"566746.jpg\": \"Amphibia/Lithobates berlandieri/4019abf35b46971b828496dcbe2e5035.jpg\",\n  \"566804.jpg\": \"Aves/Ciccaba virgata/fb740b2ede0e8b33fea879968f29eafa.jpg\",\n  \"566805.jpg\": \"Aves/Ciccaba virgata/45f174d90f0540955d22f9f57ccca516.jpg\",\n  \"566812.jpg\": \"Insecta/Idia lubricalis/a922c033db552602c905d3dd78a8c64e.jpg\",\n  \"566835.jpg\": \"Insecta/Asterocampa leilia/2c2d448b51f30b6ed13bcf11fe91cd80.jpg\",\n  \"56684.jpg\": \"Insecta/Nathalis iole/7af849614f7b34abd939e6e152419153.jpg\",\n  \"566882.jpg\": \"Aves/Columba livia domestica/10e913fbd12d63e024af121ad3cc65e8.jpg\",\n  \"566905.jpg\": \"Aves/Columba livia domestica/282c6ee778320ab259c8759bae1e2ce1.jpg\",\n  \"566922.jpg\": \"Aves/Columba livia domestica/9a90fec78484d4fb110effd9f0f0a198.jpg\",\n  \"56694.jpg\": \"Insecta/Nathalis iole/beef3d3a9530aa95b2f644c3863ff632.jpg\",\n  \"566942.jpg\": \"Aves/Columba livia domestica/b32c64e336292914a9f2e507c2678100.jpg\",\n  \"566945.jpg\": \"Aves/Columba livia domestica/2a482b8e4bca4c18298fec2b2d4e4f19.jpg\",\n  \"566951.jpg\": \"Aves/Columba livia domestica/35b5407cc64978732bf8392bf43bdb05.jpg\",\n  \"566968.jpg\": \"Insecta/Darapsa myron/fc9c1d18b19c7776091bfeb924561d01.jpg\",\n  \"566971.jpg\": \"Insecta/Darapsa myron/3258996d2729b0bdc1205fa488173d92.jpg\",\n  \"566972.jpg\": \"Insecta/Darapsa myron/096d127768b69e730592c40166c8333a.jpg\",\n  \"566973.jpg\": \"Insecta/Darapsa myron/6cfb3a1d0e87d99f499f83c24c0a8d63.jpg\",\n  \"566976.jpg\": \"Insecta/Darapsa myron/c753afeba111393f937b9723e9d4b00f.jpg\",\n  \"566977.jpg\": \"Insecta/Darapsa myron/0d649877e304aab2e728e5346e4d44b5.jpg\",\n  \"566982.jpg\": \"Insecta/Darapsa myron/bc6fc7e1c06bedbfea79ae5f9ae7ec97.jpg\",\n  \"566983.jpg\": \"Insecta/Darapsa myron/35aef63322b99b45eb62dc9dc51eda12.jpg\",\n  \"56704.jpg\": \"Insecta/Nathalis iole/c12f5ed4ac1f51fc34ee627d22b08a70.jpg\",\n  \"56707.jpg\": \"Insecta/Nathalis iole/86a1c05f97b920a9e1316bd6a0e79697.jpg\",\n  \"56708.jpg\": \"Insecta/Nathalis iole/d7cdadb71dabbf30de5b77df1dabb67a.jpg\",\n  \"56710.jpg\": \"Insecta/Nathalis iole/1a272c019f084ef8f8adb4c438e72516.jpg\",\n  \"567126.jpg\": \"Mammalia/Felis catus/58e07a5fc2703bb97e6c2d6bb1a639b4.jpg\",\n  \"567146.jpg\": \"Mammalia/Felis catus/4f39fb7c279529827bfb20b042a635c1.jpg\",\n  \"567147.jpg\": \"Mammalia/Felis catus/b1b202216812993ea0951e7480229625.jpg\",\n  \"567156.jpg\": \"Mammalia/Felis catus/a7a7eba352326edb039f1495ed301f78.jpg\",\n  \"567161.jpg\": \"Mammalia/Felis catus/971b1c283294df8aa8f602bbbd4ab56e.jpg\",\n  \"567164.jpg\": \"Mammalia/Felis catus/88a6b0ded129adef7ed9eb7e6fa6dc11.jpg\",\n  \"567168.jpg\": \"Mammalia/Felis catus/84a794feb9a55f43429930a335ee1e4a.jpg\",\n  \"56718.jpg\": \"Insecta/Nathalis iole/6b2f3328410052835592cb39b61b4777.jpg\",\n  \"567182.jpg\": \"Insecta/Polites peckius/f728cd6133bcdbd5b3ee613cf4fc324e.jpg\",\n  \"567186.jpg\": \"Insecta/Polites peckius/453fec24bc0c11b44d579b7cfcdae477.jpg\",\n  \"567187.jpg\": \"Insecta/Polites peckius/48a747c6a23b84eeb9d271e30b2e6223.jpg\",\n  \"56719.jpg\": \"Insecta/Nathalis iole/72e9e09d1db1fe535185c2ef171d5ef0.jpg\",\n  \"567193.jpg\": \"Insecta/Polites peckius/753f2d5dbbda19c5332cef0adb202056.jpg\",\n  \"567200.jpg\": \"Insecta/Polites peckius/278f07ef04dc881e12cbf9009fda34f1.jpg\",\n  \"567221.jpg\": \"Insecta/Polites peckius/0d1be435c0ce1d0234f4021b8504bc3d.jpg\",\n  \"567228.jpg\": \"Insecta/Polites peckius/23a980ad836171bf4fcdfee53dafa6fc.jpg\",\n  \"567235.jpg\": \"Insecta/Polites peckius/4e13bc68124b9afaef6e48148a13862b.jpg\",\n  \"567293.jpg\": \"Insecta/Argia immunda/424b1d0799cf5a029f40b65a87a6276e.jpg\",\n  \"567294.jpg\": \"Insecta/Argia immunda/bd50855d4669ac6d56999c1fb34006d1.jpg\",\n  \"567315.jpg\": \"Insecta/Argia immunda/37c6e3aa7fada70faf87738b57b95bef.jpg\",\n  \"567322.jpg\": \"Insecta/Argia immunda/08a1c54b04fe1e2804d79a9af0305bda.jpg\",\n  \"567333.jpg\": \"Insecta/Argia immunda/3efb221acd3dedeb97aa0fb8959c5f42.jpg\",\n  \"567340.jpg\": \"Insecta/Argia immunda/7649f2a700e7afd758d688b4fbe3c9f8.jpg\",\n  \"567344.jpg\": \"Insecta/Argia immunda/2ddece69f0e2daa60c37cd36bbf0b2fc.jpg\",\n  \"567345.jpg\": \"Insecta/Argia immunda/bc2ebe175e37fef5d3e8b1b9f6e73c64.jpg\",\n  \"567348.jpg\": \"Insecta/Argia immunda/dadd72c4f4989482203ef17f128954a0.jpg\",\n  \"567360.jpg\": \"Amphibia/Plethodon albagula/78ff234883af3c48b0c79717a730d347.jpg\",\n  \"567368.jpg\": \"Amphibia/Plethodon albagula/073a9266c26d70b5c66d445bac482da1.jpg\",\n  \"567372.jpg\": \"Amphibia/Plethodon albagula/27c96aec56f12178a6b7bb85bac2b6f9.jpg\",\n  \"567373.jpg\": \"Amphibia/Plethodon albagula/1653e7f6cd103bd3a3c85745a625cb0d.jpg\",\n  \"567374.jpg\": \"Amphibia/Plethodon albagula/59a0685bcc811763c76a2cb7f5ccb61b.jpg\",\n  \"567384.jpg\": \"Amphibia/Plethodon albagula/17c18241ed12c46cd81efe61d1293833.jpg\",\n  \"567388.jpg\": \"Amphibia/Plethodon albagula/7a3be2688a4701ac15ee4ca21d570eba.jpg\",\n  \"567395.jpg\": \"Amphibia/Plethodon albagula/f8a6a00231abef144b26067de471b1a6.jpg\",\n  \"567396.jpg\": \"Amphibia/Plethodon albagula/08c9a26c5d79a07508edcd5ebe641b84.jpg\",\n  \"567403.jpg\": \"Aves/Pyrrhocorax graculus/4971eb9ee3ff12764eae30fbf8b5bb0a.jpg\",\n  \"567404.jpg\": \"Aves/Pyrrhocorax graculus/d3bec11121c45a7bbf91cf2fabd3f531.jpg\",\n  \"567439.jpg\": \"Insecta/Bittacomorpha clavipes/df0fb3d2db0e4d9ef9ad6d6c37afe62c.jpg\",\n  \"567440.jpg\": \"Insecta/Bittacomorpha clavipes/9d3498b8a8a7fd23b0ec41a20abab0f6.jpg\",\n  \"567501.jpg\": \"Aves/Pavo cristatus/1e7869af0bbca86bdd51e4b06cab4c10.jpg\",\n  \"567503.jpg\": \"Aves/Pavo cristatus/cbc5892d638c1e7fcc98bad300eb5d0a.jpg\",\n  \"567527.jpg\": \"Aves/Pavo cristatus/73aff1d5c440677603a025f27a4fc5f1.jpg\",\n  \"567609.jpg\": \"Aves/Xema sabini/2c854f639dc06c886ff5ea6f175f6a6a.jpg\",\n  \"567611.jpg\": \"Aves/Xema sabini/141b5244c5c209bcebd88a9dcc690878.jpg\",\n  \"567653.jpg\": \"Reptilia/Coluber constrictor/688c9d129c3c5baea0f8067d0a3debbf.jpg\",\n  \"567659.jpg\": \"Reptilia/Coluber constrictor/19c53b1c2a0d38bec0e7ed3fc3a3f97c.jpg\",\n  \"567662.jpg\": \"Reptilia/Coluber constrictor/1b7cf40c43192194f72cd6c42b33c9e2.jpg\",\n  \"567664.jpg\": \"Reptilia/Coluber constrictor/22cb04312237452b3c7072a49121f567.jpg\",\n  \"567666.jpg\": \"Reptilia/Coluber constrictor/9ce8aec3186711b13596022b5eb38f37.jpg\",\n  \"567672.jpg\": \"Reptilia/Coluber constrictor/ed794889eef5bd4a9e86e605f218c25c.jpg\",\n  \"567676.jpg\": \"Reptilia/Coluber constrictor/b80470e6ff23eb6be25c571a75b0432a.jpg\",\n  \"567681.jpg\": \"Reptilia/Coluber constrictor/a249735a75fefb8bf595933b310b973e.jpg\",\n  \"567682.jpg\": \"Reptilia/Coluber constrictor/7aab8341a6baa15635a9c524bd9efa8e.jpg\",\n  \"567709.jpg\": \"Reptilia/Heloderma suspectum/70ec127ec74ba2d34fcf223f5369f16d.jpg\",\n  \"567715.jpg\": \"Reptilia/Heloderma suspectum/a74b8bee13bc553418b342bfe01bc375.jpg\",\n  \"567716.jpg\": \"Reptilia/Heloderma suspectum/0796a54db68aa0d227302c562e7a1dd7.jpg\",\n  \"567720.jpg\": \"Reptilia/Heloderma suspectum/38e993e6993e5fdeff3ffc6a1629cb1f.jpg\",\n  \"567721.jpg\": \"Reptilia/Heloderma suspectum/4bd0bfa616ebebd05909795634128d81.jpg\",\n  \"567722.jpg\": \"Reptilia/Heloderma suspectum/36f4ea0f86bb92163e6aa2425c532a41.jpg\",\n  \"567731.jpg\": \"Reptilia/Heloderma suspectum/afe66c8f884596ea6b5842a15f1cbe38.jpg\",\n  \"567738.jpg\": \"Reptilia/Heloderma suspectum/bf535ec4d96139004621f7e7258766d0.jpg\",\n  \"567739.jpg\": \"Reptilia/Heloderma suspectum/2f555d5f21eb7d93a806e61589883258.jpg\",\n  \"567780.jpg\": \"Insecta/Idia aemula/aeed97ff936947ffcd2c56c0f088efeb.jpg\",\n  \"567781.jpg\": \"Insecta/Idia aemula/4e9dda73ef2ac201eb128617136c54f3.jpg\",\n  \"567791.jpg\": \"Insecta/Idia aemula/b23696bdcfcfaa57aa38b3133043eb18.jpg\",\n  \"567805.jpg\": \"Insecta/Idia aemula/ce551baadd18d7179ecc195b324cd933.jpg\",\n  \"567807.jpg\": \"Insecta/Idia aemula/bc5717adb4820366a04f3a907de74b8b.jpg\",\n  \"567811.jpg\": \"Insecta/Idia aemula/9f124f7a7b13e21b56855297a9dfe16c.jpg\",\n  \"567812.jpg\": \"Insecta/Idia aemula/6f754703203fc0b2cccd1221fa85ee12.jpg\",\n  \"567815.jpg\": \"Insecta/Idia aemula/9f8c861e162ca7d26cda1d4d780f0f56.jpg\",\n  \"567818.jpg\": \"Insecta/Ochropleura implecta/b478f5ad566e701e3be86bdab5c5209c.jpg\",\n  \"567827.jpg\": \"Insecta/Ochropleura implecta/839040817c0224bfb62d0c1a527b64cf.jpg\",\n  \"567835.jpg\": \"Insecta/Galgula partita/c8afb72de94dd8b25af9bdcbe5deeb43.jpg\",\n  \"567840.jpg\": \"Insecta/Galgula partita/60c01f2ff77c319cf71602eb4cb553a6.jpg\",\n  \"567849.jpg\": \"Insecta/Galgula partita/d56ecd55dd8153126978f528236f39bf.jpg\",\n  \"567856.jpg\": \"Insecta/Galgula partita/c9dbd3f584677657e68f5975bee16a2e.jpg\",\n  \"567860.jpg\": \"Insecta/Galgula partita/4082bd37c6701684f781ddc1612447da.jpg\",\n  \"567861.jpg\": \"Insecta/Galgula partita/1eadfb1804f95ad4a20a777852841dda.jpg\",\n  \"567869.jpg\": \"Insecta/Galgula partita/501ace746eb2f901e5df4b2fbef318aa.jpg\",\n  \"567888.jpg\": \"Insecta/Macaria aemulataria/ce6bfa7b4938fe9f54b324670f1d075b.jpg\",\n  \"567899.jpg\": \"Insecta/Satyrium titus/213e73d04d43b237ad23b72f7deade71.jpg\",\n  \"568128.jpg\": \"Aves/Zosterops japonicus/67fe4025d82bc4eb8ff52251b496fa50.jpg\",\n  \"568129.jpg\": \"Aves/Zosterops japonicus/890e740b15b7309914184fd704fdb9a9.jpg\",\n  \"568169.jpg\": \"Aves/Surnia ulula/354c988874ffde427735bf3874624631.jpg\",\n  \"568173.jpg\": \"Aves/Surnia ulula/fdc5d626915231d1b7778b56abe73389.jpg\",\n  \"568181.jpg\": \"Insecta/Orthonama obstipata/a76dbe3b58324803b7d7eaa53ee07fa3.jpg\",\n  \"568183.jpg\": \"Insecta/Orthonama obstipata/506bdb438b5a9ee805902471fdf29eb9.jpg\",\n  \"568193.jpg\": \"Insecta/Orthonama obstipata/4bbf1308fbf4e0443ee340bb41b8bf14.jpg\",\n  \"568194.jpg\": \"Insecta/Orthonama obstipata/59bb53269032e8bf3c33ed1fec36597d.jpg\",\n  \"568195.jpg\": \"Insecta/Orthonama obstipata/46a131f415f8fafb8b9b0b69d8ea7da4.jpg\",\n  \"568197.jpg\": \"Insecta/Orthonama obstipata/8ff19eb575f4b096af4851ecd36123b6.jpg\",\n  \"568198.jpg\": \"Insecta/Orthonama obstipata/e42f8684b3296a6a4e52d224133bf919.jpg\",\n  \"568205.jpg\": \"Insecta/Orthonama obstipata/9ee7bfd80af28d670d4486ad73e7d9c9.jpg\",\n  \"568238.jpg\": \"Aves/Nestor meridionalis/5b55e456e1535560c5a6d25c50cdd09f.jpg\",\n  \"568247.jpg\": \"Aves/Nestor meridionalis/8b3f34e315e8128a303a2b1709a66396.jpg\",\n  \"568248.jpg\": \"Aves/Nestor meridionalis/d4a35310f88e6e5a82960937c6b2c1f9.jpg\",\n  \"568254.jpg\": \"Aves/Nestor meridionalis/1749676f0cdfee491c3f072cc932f906.jpg\",\n  \"568270.jpg\": \"Aves/Nestor meridionalis/38bdc8efc4aaef0f0b3af0c190ac6c9a.jpg\",\n  \"568337.jpg\": \"Insecta/Adalia bipunctata/02e6c424bf4d1879c869c95e642548a3.jpg\",\n  \"568340.jpg\": \"Insecta/Adalia bipunctata/1ac23cfdb1ee500bc7a1a198d8a4bf57.jpg\",\n  \"568344.jpg\": \"Insecta/Adalia bipunctata/42721357b760fb06674b4f3ffd3c0ac6.jpg\",\n  \"568346.jpg\": \"Insecta/Adalia bipunctata/ec5c45f630aeb1be33ace6e61743a798.jpg\",\n  \"568351.jpg\": \"Insecta/Adalia bipunctata/9073d6b4d306e876dd496b05ee167ad4.jpg\",\n  \"568353.jpg\": \"Insecta/Adalia bipunctata/76de22715b45762a164a134a43ca03cc.jpg\",\n  \"568354.jpg\": \"Insecta/Adalia bipunctata/679af9a82944d24e1c02f270ce4b872a.jpg\",\n  \"568358.jpg\": \"Insecta/Adalia bipunctata/e4f64bf3e2a76efa065d3ae823cea0bc.jpg\",\n  \"568372.jpg\": \"Aves/Setophaga caerulescens/f4ca2ebc03b88a72af5a0f8069d67dc8.jpg\",\n  \"568379.jpg\": \"Aves/Setophaga caerulescens/5776f32070705190176fca8f76fd8e8f.jpg\",\n  \"568380.jpg\": \"Aves/Setophaga caerulescens/6e263875d9d5eeea41e8bdb725955470.jpg\",\n  \"568381.jpg\": \"Aves/Setophaga caerulescens/7118be386f3624593afde25bca287d40.jpg\",\n  \"568398.jpg\": \"Aves/Setophaga caerulescens/ed6ef8c7f59b084db13b55ec1564b1fb.jpg\",\n  \"568401.jpg\": \"Aves/Setophaga caerulescens/72b977cc2f5564652a6ea3c2ee086276.jpg\",\n  \"568403.jpg\": \"Insecta/Atlides halesus/eb6407ff02d5d626ff4ca2925b7ec602.jpg\",\n  \"568404.jpg\": \"Insecta/Atlides halesus/68a844991ba93e04df2697d1b0f9ce27.jpg\",\n  \"568407.jpg\": \"Insecta/Atlides halesus/259aa5efe4b92ea4476ffd8ab3327daa.jpg\",\n  \"568410.jpg\": \"Insecta/Atlides halesus/b539c46ad273384318ad729756df1cf8.jpg\",\n  \"568413.jpg\": \"Insecta/Atlides halesus/e40e94f13d5ef9afa0608964c3ff524a.jpg\",\n  \"568415.jpg\": \"Insecta/Atlides halesus/7bd01a8ec7305400f13d755f4ee3aa86.jpg\",\n  \"568423.jpg\": \"Insecta/Atlides halesus/d07f35ece795ff3016b0ab2121e0633b.jpg\",\n  \"568424.jpg\": \"Insecta/Atlides halesus/0081eb94c053f5fa2d941fc5708736d0.jpg\",\n  \"568433.jpg\": \"Insecta/Atlides halesus/69701b26eb40167c03f69d9968b6e657.jpg\",\n  \"568436.jpg\": \"Insecta/Atlides halesus/2d46dcba319475f3c37c29eb9f2f7bbb.jpg\",\n  \"568437.jpg\": \"Insecta/Argia nahuana/0ce78b2816293d80ed273669f15a75a9.jpg\",\n  \"568438.jpg\": \"Insecta/Argia nahuana/f22dce032eef9befc403ba1be775f576.jpg\",\n  \"568442.jpg\": \"Insecta/Argia nahuana/093530055932d44ddc918e53d8bcbca0.jpg\",\n  \"568443.jpg\": \"Insecta/Argia nahuana/189f5368a428afb30be48175c63ddf0e.jpg\",\n  \"568445.jpg\": \"Insecta/Argia nahuana/1db2b4049f0c84f56af5639e90516ea7.jpg\",\n  \"568448.jpg\": \"Insecta/Argia nahuana/1bf8bc152fa81137f6fee456248f2746.jpg\",\n  \"568450.jpg\": \"Insecta/Argia nahuana/ed24b1d8237a9b039e4fb4db7c0c9725.jpg\",\n  \"568455.jpg\": \"Insecta/Argia nahuana/aed25721a9ceecc13f71860589d98331.jpg\",\n  \"568459.jpg\": \"Insecta/Argia nahuana/9bde8c46fd0db3757ccf15fd46d2bc04.jpg\",\n  \"568460.jpg\": \"Insecta/Argia nahuana/0cfbddaffa9762fdebebd887b9087192.jpg\",\n  \"568462.jpg\": \"Insecta/Argia nahuana/9e2b733c24c35afd5bff0378b91bcef7.jpg\",\n  \"568464.jpg\": \"Insecta/Argia nahuana/0f0fa4ba02fc80b440435a556c7535eb.jpg\",\n  \"568465.jpg\": \"Insecta/Argia nahuana/811587324836877a7f9f19eb1f2d8599.jpg\",\n  \"568467.jpg\": \"Insecta/Argia nahuana/77428db9bc816779ced0c66acff84c92.jpg\",\n  \"568469.jpg\": \"Insecta/Argia nahuana/fde86b283c1f0b59308e60fdf2fb9277.jpg\",\n  \"568473.jpg\": \"Insecta/Eupithecia miserulata/6137923760b474ef340967330b960b0e.jpg\",\n  \"568684.jpg\": \"Insecta/Haematopis grataria/f3bcd02c1c249e3d733c373bae904fa7.jpg\",\n  \"568694.jpg\": \"Insecta/Haematopis grataria/4f448c2072fba9f5139960963044db69.jpg\",\n  \"568708.jpg\": \"Insecta/Haematopis grataria/7d7a2f5d2d3991a92bdbc47ab008aa93.jpg\",\n  \"568710.jpg\": \"Insecta/Haematopis grataria/222252a0cf9f6d6d7574afc6aea0c0f1.jpg\",\n  \"568712.jpg\": \"Insecta/Haematopis grataria/21a37dc6ddacc4ecbe789c1565762183.jpg\",\n  \"568715.jpg\": \"Insecta/Haematopis grataria/8d884f1cd4126123c08205f530a5feb0.jpg\",\n  \"568716.jpg\": \"Insecta/Haematopis grataria/00ba5a815637a1cebc431b53ae58f547.jpg\",\n  \"568839.jpg\": \"Mammalia/Arctocephalus forsteri/9aa47013a4bc2234ef68f5e62d7a053f.jpg\",\n  \"568848.jpg\": \"Mammalia/Arctocephalus forsteri/e0a76cc8d940fd6449a463137da63271.jpg\",\n  \"568884.jpg\": \"Mammalia/Arctocephalus forsteri/8e7dd5a5cfcb3aa445853895c76ec2e8.jpg\",\n  \"568893.jpg\": \"Mammalia/Arctocephalus forsteri/88ca376fb58132e265fc14274502e0d9.jpg\",\n  \"568904.jpg\": \"Mammalia/Arctocephalus forsteri/4ac559740492caa41c658189aad8b69a.jpg\",\n  \"568905.jpg\": \"Mammalia/Arctocephalus forsteri/dac4130abf253b95a1cfa38614ad2cb7.jpg\",\n  \"568998.jpg\": \"Aves/Platalea regia/cd2289f4a1c47633d271c9e2600f13dc.jpg\",\n  \"569036.jpg\": \"Actinopterygii/Cyprinus carpio/24d6e574d54d457b756bb495d77b948a.jpg\",\n  \"569052.jpg\": \"Actinopterygii/Cyprinus carpio/7edfe2472e2ba008bb0e4eed646077f0.jpg\",\n  \"569064.jpg\": \"Actinopterygii/Cyprinus carpio/82ad95d4e9a75ccbd2d5a84de4e73c6c.jpg\",\n  \"569073.jpg\": \"Actinopterygii/Cyprinus carpio/ab608aadccc2165a34284a3272ba04cb.jpg\",\n  \"569087.jpg\": \"Insecta/Lygus lineolaris/cf912dfd12978bc2a3e0a52ff7b2be56.jpg\",\n  \"569091.jpg\": \"Insecta/Lygus lineolaris/97a1c6fb7fda1ac74ddac9fd5c93175a.jpg\",\n  \"569092.jpg\": \"Actinopterygii/Lepomis gibbosus/68ac42c904d016b47c504d3840a2a663.jpg\",\n  \"569093.jpg\": \"Actinopterygii/Lepomis gibbosus/895994b160bbd0c6a28cd511a42ddfda.jpg\",\n  \"569099.jpg\": \"Actinopterygii/Lepomis gibbosus/4f77701b774a71c73a3fb297c0a15e67.jpg\",\n  \"569101.jpg\": \"Actinopterygii/Lepomis gibbosus/744df16a02b6a9ef9651dcc846034285.jpg\",\n  \"569102.jpg\": \"Actinopterygii/Lepomis gibbosus/40c46ac27b500327bdf0a2169ab7b511.jpg\",\n  \"569104.jpg\": \"Actinopterygii/Lepomis gibbosus/645e5a2380c9ac68ff91811041859257.jpg\",\n  \"569109.jpg\": \"Actinopterygii/Lepomis gibbosus/32276a368739a73125e46b195d3a025b.jpg\",\n  \"569110.jpg\": \"Actinopterygii/Lepomis gibbosus/f132db7fb739f6786fc8b372ca3d6a15.jpg\",\n  \"569167.jpg\": \"Amphibia/Anaxyrus boreas halophilus/0496305845aa134dbf40a72e17228933.jpg\",\n  \"569168.jpg\": \"Amphibia/Anaxyrus boreas halophilus/b395aa459aff5fa1de12d49298934b8b.jpg\",\n  \"569170.jpg\": \"Amphibia/Anaxyrus boreas halophilus/8bdba8426035055c1a959ada4997dc84.jpg\",\n  \"569174.jpg\": \"Amphibia/Anaxyrus boreas halophilus/89e807d73b434da066a03910222eae91.jpg\",\n  \"569175.jpg\": \"Amphibia/Anaxyrus boreas halophilus/f39acbbdbd33005c55a2bb854ae23000.jpg\",\n  \"569176.jpg\": \"Amphibia/Anaxyrus boreas halophilus/71b032ceb483705ec3aac9cb681731bf.jpg\",\n  \"569179.jpg\": \"Amphibia/Anaxyrus boreas halophilus/a0298c8ff5afd033a48bd45db3b548ce.jpg\",\n  \"569183.jpg\": \"Amphibia/Anaxyrus boreas halophilus/589f371ffd962862fbeed0aa30a77792.jpg\",\n  \"569197.jpg\": \"Arachnida/Larinioides cornutus/8a87f1c7d80736b0d84742dd370ac687.jpg\",\n  \"569199.jpg\": \"Arachnida/Larinioides cornutus/1ea547416f95287d4c53e77547ab5aee.jpg\",\n  \"569210.jpg\": \"Amphibia/Spea hammondii/304e8e0145b404a2ee13ca0e1961ecf6.jpg\",\n  \"569218.jpg\": \"Amphibia/Eleutherodactylus marnockii/2d9762850be4ac781a42c8b0184087f7.jpg\",\n  \"569220.jpg\": \"Amphibia/Eleutherodactylus marnockii/0faa85b63aca0f4a1a24373459188308.jpg\",\n  \"569221.jpg\": \"Amphibia/Eleutherodactylus marnockii/5debc71a7c2bb308da723daa05e9f1ab.jpg\",\n  \"569225.jpg\": \"Amphibia/Eleutherodactylus marnockii/15a7fc56f3a5fcfac89cb86322730cae.jpg\",\n  \"569232.jpg\": \"Amphibia/Eleutherodactylus marnockii/2444c59594bd9cb13ed11dcf496889a0.jpg\",\n  \"569233.jpg\": \"Amphibia/Eleutherodactylus marnockii/6f45ff062ae872fd4dda88d910085ec6.jpg\",\n  \"569234.jpg\": \"Amphibia/Eleutherodactylus marnockii/318d796d63aed21642bac4a361e0c1ec.jpg\",\n  \"569238.jpg\": \"Amphibia/Eleutherodactylus marnockii/fd4a5eeee92d131e0a2dd7ea71417bc7.jpg\",\n  \"569239.jpg\": \"Amphibia/Eleutherodactylus marnockii/bf9751e3efe53bbdecccd502b04ef684.jpg\",\n  \"569291.jpg\": \"Reptilia/Agkistrodon contortrix mokasen/2e4431a65ae2b9c6291907084a2d59cc.jpg\",\n  \"569447.jpg\": \"Insecta/Aglais io/f5ba6b2ebcf6409d45c3e3306170f793.jpg\",\n  \"569465.jpg\": \"Insecta/Aglais io/7d38a7912c98daac80f0bd043cca26b6.jpg\",\n  \"569477.jpg\": \"Insecta/Aglais io/c730d8fb8fbf94596e3928852c8ede6d.jpg\",\n  \"569482.jpg\": \"Insecta/Aglais io/c3ad289e180bf4327382661220af152c.jpg\",\n  \"569487.jpg\": \"Insecta/Aglais io/42c833be767f066b185a70771dea36d1.jpg\",\n  \"569489.jpg\": \"Insecta/Aglais io/fad11c0032a1a5c1441a950925aaf72e.jpg\",\n  \"569491.jpg\": \"Insecta/Aglais io/a03931b5fb2c9b4e95033ea83a24c150.jpg\",\n  \"569520.jpg\": \"Aves/Ortalis vetula/f62c9413b9ca21d6b0dca2e892b64cac.jpg\",\n  \"569532.jpg\": \"Aves/Ortalis vetula/df4860c719bc88e2ad5d76ccb444fab2.jpg\",\n  \"569556.jpg\": \"Aves/Ortalis vetula/b7bdd0919cdff0309145fb91ec0ab3bd.jpg\",\n  \"569566.jpg\": \"Aves/Ortalis vetula/93ba7ef63887b82e3a4bff0066df83b3.jpg\",\n  \"569569.jpg\": \"Insecta/Eantis tamenund/420b7e9e136f5b8da16f39f50d0cbff8.jpg\",\n  \"569570.jpg\": \"Insecta/Eantis tamenund/d4bf9b85c13a6a85301fe214a811f221.jpg\",\n  \"569574.jpg\": \"Insecta/Eantis tamenund/8d923901f50714bb646490b751e9b691.jpg\",\n  \"569576.jpg\": \"Insecta/Eantis tamenund/079c47fb34b71ff9d5ecb9d705b7e059.jpg\",\n  \"569577.jpg\": \"Insecta/Eantis tamenund/09d3b5cd6399028e4c2df49a9df952f4.jpg\",\n  \"569578.jpg\": \"Insecta/Eantis tamenund/4f8cd39a67da9eca2cd26c6c832ada5a.jpg\",\n  \"569582.jpg\": \"Insecta/Eantis tamenund/72f0d09c7c018b7f67aa11b778b00296.jpg\",\n  \"569588.jpg\": \"Insecta/Eantis tamenund/099bf307ad0e429ce33012b7e3df4df1.jpg\",\n  \"569590.jpg\": \"Insecta/Eantis tamenund/bf0d42d27caab3ef9c641ce5deaec67b.jpg\",\n  \"569591.jpg\": \"Insecta/Eantis tamenund/80df36c66353a0c1a078545bbb914c63.jpg\",\n  \"569597.jpg\": \"Insecta/Diabrotica undecimpunctata undecimpunctata/9c062f67986ee5060d3070e8aa6ee258.jpg\",\n  \"569600.jpg\": \"Insecta/Diabrotica undecimpunctata undecimpunctata/6de5ec95d155bba56867d652a347b067.jpg\",\n  \"569603.jpg\": \"Insecta/Polistes fuscatus/1b2f96d0cfe3d73cfbf5b8337a90dec8.jpg\",\n  \"569605.jpg\": \"Insecta/Polistes fuscatus/4ffe2faf397dc7b07fbadece3f28251f.jpg\",\n  \"569608.jpg\": \"Insecta/Polistes fuscatus/33f63db99c2623e28f37befe2c00eb83.jpg\",\n  \"569613.jpg\": \"Insecta/Polistes fuscatus/c342556d36a19a453ac0cd5f38f3ae3a.jpg\",\n  \"569614.jpg\": \"Insecta/Polistes fuscatus/adc2af2050a61a56aae76e7191714bed.jpg\",\n  \"569620.jpg\": \"Insecta/Polistes fuscatus/a9e97d5f3ac8bb7a441d3b2d7bd5ad53.jpg\",\n  \"569628.jpg\": \"Insecta/Sphecius speciosus/401ea76c19de0ee6320e7ec64bb24746.jpg\",\n  \"569640.jpg\": \"Insecta/Sphecius speciosus/88246569fe27442457e250e7f21200ae.jpg\",\n  \"569641.jpg\": \"Insecta/Sphecius speciosus/30e11063f6bfca2da9067a42c1887ec0.jpg\",\n  \"569643.jpg\": \"Insecta/Sphecius speciosus/805d8444246e97728e15e85e1c5f44ea.jpg\",\n  \"569648.jpg\": \"Insecta/Sphecius speciosus/fe54b6e0784991612c2fdcca291149b4.jpg\",\n  \"569649.jpg\": \"Insecta/Sphecius speciosus/a8503d0e87ab3197ff1d1e482a262a49.jpg\",\n  \"569656.jpg\": \"Insecta/Sphecius speciosus/37b2c45d37d0b14cd05acd49df6f5031.jpg\",\n  \"569658.jpg\": \"Insecta/Sphecius speciosus/17f24f4741d333fb3c9afe1db6bbd178.jpg\",\n  \"569695.jpg\": \"Insecta/Hypoprepia fucosa/272dc665d3a64d20beade62a3301f834.jpg\",\n  \"569697.jpg\": \"Insecta/Hypoprepia fucosa/9ee914da6a1d107688e995555dcdbbe7.jpg\",\n  \"569698.jpg\": \"Insecta/Hypoprepia fucosa/3c9b7d60c161ba6cd489eea754847a2d.jpg\",\n  \"569699.jpg\": \"Insecta/Hypoprepia fucosa/f5958304af83f7df8da6a1d9ee0eef6a.jpg\",\n  \"569702.jpg\": \"Insecta/Hypoprepia fucosa/8f2e1ae66bc7be2f01b81b949e727087.jpg\",\n  \"569709.jpg\": \"Insecta/Hypoprepia fucosa/c46d2a833f9407b0f324549ecc1fc4b5.jpg\",\n  \"569726.jpg\": \"Insecta/Hypoprepia fucosa/66ed8ed2e50abf7dba339ba68def3656.jpg\",\n  \"569820.jpg\": \"Mammalia/Sciurus carolinensis/e04958da4592fce7bae34fe785ef1b18.jpg\",\n  \"569896.jpg\": \"Mammalia/Sciurus carolinensis/16e41969e4edda5d3e86a6a6342f01a5.jpg\",\n  \"569898.jpg\": \"Mammalia/Sciurus carolinensis/310af6baec253b4fb333d7d2f7989394.jpg\",\n  \"570012.jpg\": \"Mammalia/Sciurus carolinensis/771f10775d6c25f77d40cd74f96e9c16.jpg\",\n  \"570126.jpg\": \"Mammalia/Sciurus carolinensis/2dd89b795c83ed12b6f9af7b6befa240.jpg\",\n  \"570217.jpg\": \"Mammalia/Sciurus carolinensis/619db9b43b479a951a4f4fb84c9f448e.jpg\",\n  \"570251.jpg\": \"Mammalia/Sciurus carolinensis/13afdbf3a9f61924cb38544b1277d500.jpg\",\n  \"570323.jpg\": \"Mammalia/Sciurus carolinensis/c393619377cf8f74f66394686343570f.jpg\",\n  \"570391.jpg\": \"Mammalia/Sciurus carolinensis/dd35c45adbd0f4f2c0c62307216fe6e7.jpg\",\n  \"570464.jpg\": \"Aves/Busarellus nigricollis/750f3e5e9e0641cba5ac66d2e1b7d0ef.jpg\",\n  \"570467.jpg\": \"Aves/Busarellus nigricollis/464318926f7a3c42a5f65234c7233fe5.jpg\",\n  \"570741.jpg\": \"Insecta/Libellula luctuosa/d1e5c36b78cda4378a11aaf6b8387ce0.jpg\",\n  \"570764.jpg\": \"Insecta/Libellula luctuosa/8b78a7abf842892276b14dc1b69487eb.jpg\",\n  \"570838.jpg\": \"Insecta/Libellula luctuosa/74b1fd1fbb2483556a27a4080958114b.jpg\",\n  \"570865.jpg\": \"Insecta/Libellula luctuosa/5e442a639124ec49f4f56b8f5b15aeea.jpg\",\n  \"570904.jpg\": \"Mammalia/Enhydra lutris nereis/79cd3994576f6e92fd9283498b12815c.jpg\",\n  \"570920.jpg\": \"Mammalia/Enhydra lutris nereis/81e03b9d56a04473e27861f785dc2e5b.jpg\",\n  \"570960.jpg\": \"Mammalia/Enhydra lutris nereis/be1c3caa315f3bbb3a907a121539726d.jpg\",\n  \"570997.jpg\": \"Arachnida/Dermacentor variabilis/3ef2fb10d283937cbff654687df695f0.jpg\",\n  \"571000.jpg\": \"Arachnida/Dermacentor variabilis/cfc36eb21be856a99203524efa39fee1.jpg\",\n  \"571001.jpg\": \"Arachnida/Dermacentor variabilis/59dce41580770e7fa4c5be846429c6f2.jpg\",\n  \"571003.jpg\": \"Arachnida/Dermacentor variabilis/d69e5c3d0eaeb78bfa2c12a98208b2c2.jpg\",\n  \"571007.jpg\": \"Arachnida/Dermacentor variabilis/bcd3a57f9c651cf32552ed4d7c905573.jpg\",\n  \"571008.jpg\": \"Arachnida/Dermacentor variabilis/b709b80c24c945a8ece9d744a04b3d13.jpg\",\n  \"571009.jpg\": \"Arachnida/Dermacentor variabilis/32797f45b7ded54e9d594f890d63b409.jpg\",\n  \"571010.jpg\": \"Arachnida/Dermacentor variabilis/d05560696b2b15f7de5df07c5eaab9d6.jpg\",\n  \"571011.jpg\": \"Arachnida/Dermacentor variabilis/8837a378caeb805dcbe2629a91e5bbc3.jpg\",\n  \"571013.jpg\": \"Arachnida/Dermacentor variabilis/feda0cd9163c3ff942069a911f71fa8e.jpg\",\n  \"571017.jpg\": \"Arachnida/Dermacentor variabilis/04a75aea552ef3f1623c91bc294c5597.jpg\",\n  \"571025.jpg\": \"Insecta/Epitheca princeps/07abfdec63f944aa9e50f15945593566.jpg\",\n  \"571026.jpg\": \"Insecta/Epitheca princeps/02e98089ecbf29cd96f17e8ee8d02c34.jpg\",\n  \"571029.jpg\": \"Insecta/Epitheca princeps/74eeb825d32ecbba1b2a73c485e543cf.jpg\",\n  \"571030.jpg\": \"Insecta/Epitheca princeps/a20e653fc7f5c439594b59aafcbf5d52.jpg\",\n  \"571031.jpg\": \"Insecta/Epitheca princeps/f8a1b83338e2f39659c454b39cbbdd1b.jpg\",\n  \"571035.jpg\": \"Insecta/Epitheca princeps/2b4873fdbf5a7181ca991bdc3189790a.jpg\",\n  \"571036.jpg\": \"Insecta/Epitheca princeps/58e3feb010bf1cf049fdc3e6a322ce4b.jpg\",\n  \"571037.jpg\": \"Insecta/Epitheca princeps/0dfebff272a19d2518b736b654a3c3cb.jpg\",\n  \"571039.jpg\": \"Insecta/Epitheca princeps/d9da8186fe19cac42a4b090623ffdcd2.jpg\",\n  \"571046.jpg\": \"Insecta/Epitheca princeps/32ce2b2d25252ae940ddc76908eec26e.jpg\",\n  \"571048.jpg\": \"Insecta/Epitheca princeps/1cf8a258fac31f0865a8d0aa2a7b63f0.jpg\",\n  \"571058.jpg\": \"Insecta/Epitheca princeps/51ab77dababfb767a354842e099d61e3.jpg\",\n  \"571110.jpg\": \"Aves/Tyrannus forficatus/88dce2e1d13cc55dd57e59fe2d525ed4.jpg\",\n  \"571120.jpg\": \"Aves/Tyrannus forficatus/f5ed4dfa9aa4451be920277f13eeae21.jpg\",\n  \"571128.jpg\": \"Aves/Tyrannus forficatus/0802ebfa09b872f28d3b7e5ce5a11fcf.jpg\",\n  \"571142.jpg\": \"Aves/Tyrannus forficatus/727815d7245258f01009ea670ecbd297.jpg\",\n  \"571221.jpg\": \"Aves/Tyrannus forficatus/577421fa9c5c7f170aa72b946e72e1f5.jpg\",\n  \"571249.jpg\": \"Aves/Tyrannus forficatus/0ba3140bb5fceeb82883c894de58ade1.jpg\",\n  \"571262.jpg\": \"Aves/Tyrannus forficatus/faea2c6fd86ddd75b56be6d992f4332a.jpg\",\n  \"57127.jpg\": \"Aves/Rhipidura fuliginosa fuliginosa/6e7c9676b2dcf6201b46fadb30fdbe31.jpg\",\n  \"571279.jpg\": \"Amphibia/Notophthalmus viridescens viridescens/3173316bf072a77a1ba93f949ff1c8fe.jpg\",\n  \"57128.jpg\": \"Aves/Rhipidura fuliginosa fuliginosa/408d56a4509f296f9ba624f6a90eebbd.jpg\",\n  \"571280.jpg\": \"Amphibia/Notophthalmus viridescens viridescens/e3c386eee0faf2109de536647e12be65.jpg\",\n  \"571283.jpg\": \"Amphibia/Notophthalmus viridescens viridescens/bf23390fbfde4e0316268d9e9a95223b.jpg\",\n  \"57129.jpg\": \"Aves/Rhipidura fuliginosa fuliginosa/1c0d6149d4ceb08d38cdaa1b58b09e1e.jpg\",\n  \"571296.jpg\": \"Amphibia/Notophthalmus viridescens viridescens/386f5b9332f95e703db020178ac5d0c9.jpg\",\n  \"571301.jpg\": \"Amphibia/Notophthalmus viridescens viridescens/e27e8695da54ef886d24353317c8a9c2.jpg\",\n  \"571306.jpg\": \"Amphibia/Notophthalmus viridescens viridescens/e23d5f339fcfa20374e3da0650e3ac1d.jpg\",\n  \"571340.jpg\": \"Aves/Pachyramphus aglaiae/1ae6a401ceb5a14f9d17c157cfa5f45e.jpg\",\n  \"57135.jpg\": \"Aves/Rhipidura fuliginosa fuliginosa/cc41e10c3cb9a19669ecf213e02c73b0.jpg\",\n  \"57136.jpg\": \"Aves/Rhipidura fuliginosa fuliginosa/a47718fa11370564a32091dac85f0dc7.jpg\",\n  \"57138.jpg\": \"Aves/Rhipidura fuliginosa fuliginosa/e6b61e10c836d990ffd3eca0dc249380.jpg\",\n  \"57142.jpg\": \"Aves/Rhipidura fuliginosa fuliginosa/89b2d274b20df944530cd706d799cc98.jpg\",\n  \"571460.jpg\": \"Insecta/Phyciodes cocyta/8f56ab15011c0856ec8e9ced3a3a0d9a.jpg\",\n  \"571463.jpg\": \"Insecta/Phyciodes cocyta/339412c9a52099181a4420be213aa239.jpg\",\n  \"571466.jpg\": \"Insecta/Phyciodes cocyta/9a5eca192cb9232caf6e8149c47ea26a.jpg\",\n  \"571467.jpg\": \"Insecta/Phyciodes cocyta/6726869013dc807baaea6dd1b756a6fd.jpg\",\n  \"571468.jpg\": \"Insecta/Phyciodes cocyta/aa8bbd447f85d82bb1e33e103b77234b.jpg\",\n  \"571476.jpg\": \"Insecta/Phyciodes cocyta/595813fd23f08be78e79e9d7a4c6b411.jpg\",\n  \"571478.jpg\": \"Insecta/Phyciodes cocyta/4da519f6c2f6293e04f22f5a28080373.jpg\",\n  \"57149.jpg\": \"Aves/Rhipidura fuliginosa fuliginosa/db04f2a8182d7be5077b35bbc88e12c3.jpg\",\n  \"57150.jpg\": \"Aves/Rhipidura fuliginosa fuliginosa/4cd5ae67ba70b12ec6952203688a9bac.jpg\",\n  \"571501.jpg\": \"Mammalia/Lynx rufus/e11040d94f20c64c5ce7dab9a618749d.jpg\",\n  \"57153.jpg\": \"Aves/Rhipidura fuliginosa fuliginosa/abd263441ae9f28d00d49f7b4a17c4d9.jpg\",\n  \"571532.jpg\": \"Mammalia/Lynx rufus/038313bcb34854f4509122c8c26620ed.jpg\",\n  \"571540.jpg\": \"Mammalia/Lynx rufus/d5046e717db20988cfd12c5e399056d0.jpg\",\n  \"57158.jpg\": \"Aves/Rhipidura fuliginosa fuliginosa/3c28df468361e6b96f6f6f91c1444f6c.jpg\",\n  \"57164.jpg\": \"Aves/Rhipidura fuliginosa fuliginosa/541e056b47f22190e9e748fa19cb7b5f.jpg\",\n  \"57165.jpg\": \"Aves/Rhipidura fuliginosa fuliginosa/9402358985347e9c210145a914ab9077.jpg\",\n  \"57166.jpg\": \"Aves/Rhipidura fuliginosa fuliginosa/4c1c22c05bd03b99b1b7e9ecbe1d9b5a.jpg\",\n  \"57167.jpg\": \"Aves/Rhipidura fuliginosa fuliginosa/172bb9070630d0cc3ab8545e9225a0a6.jpg\",\n  \"57168.jpg\": \"Aves/Rhipidura fuliginosa fuliginosa/4d0786fdf90dd0712bfcf6194b499930.jpg\",\n  \"57173.jpg\": \"Aves/Rhipidura fuliginosa fuliginosa/fe2598fc69a24e915594f4fdaf178fcc.jpg\",\n  \"57174.jpg\": \"Aves/Rhipidura fuliginosa fuliginosa/46963ab360f3e598bd230cc05aeb335d.jpg\",\n  \"57175.jpg\": \"Aves/Rhipidura fuliginosa fuliginosa/7a00465c166fddcccba4273cc52d0963.jpg\",\n  \"571809.jpg\": \"Insecta/Agrotis ipsilon/fd72fdd3939bfaece54cf9e7a28f6b39.jpg\",\n  \"571810.jpg\": \"Insecta/Agrotis ipsilon/1b009f4c2fae7e47ca209b5377385bcc.jpg\",\n  \"571814.jpg\": \"Insecta/Agrotis ipsilon/fdd71133f91fdc2bcfee6848afe0b04a.jpg\",\n  \"571825.jpg\": \"Insecta/Agrotis ipsilon/4a4ca9dc9efcb5b471722b6345b9b179.jpg\",\n  \"571829.jpg\": \"Insecta/Agrotis ipsilon/209d708539c835d187c927d837bbd36d.jpg\",\n  \"572125.jpg\": \"Reptilia/Natrix natrix/b66e054ffd8d43450432dea0400a289d.jpg\",\n  \"572152.jpg\": \"Reptilia/Natrix natrix/68f65703756f928ef63d3866381d2fad.jpg\",\n  \"572153.jpg\": \"Reptilia/Natrix natrix/b2b30c9391016a0bb167274291583d61.jpg\",\n  \"572155.jpg\": \"Reptilia/Natrix natrix/33364cb3286dc06edd0ea5c39d13fcf8.jpg\",\n  \"572174.jpg\": \"Reptilia/Natrix natrix/465fbfe7e6193a875811100366920a19.jpg\",\n  \"572198.jpg\": \"Insecta/Oryctes nasicornis/7cdb9f0b30afde2f8e183b72c3c9af29.jpg\",\n  \"5722.jpg\": \"Aves/Myiarchus crinitus/f693a32d2efbc48a9070526c86fd6460.jpg\",\n  \"572215.jpg\": \"Arachnida/Latrodectus mactans/07de549630ad3e305c2e133f8b4e8b64.jpg\",\n  \"572218.jpg\": \"Arachnida/Latrodectus mactans/9235e91db1f7cbd8999147aa389a8390.jpg\",\n  \"572219.jpg\": \"Arachnida/Latrodectus mactans/62fb1aa45dcaaf6d77ba1f3f3cb92ec5.jpg\",\n  \"572220.jpg\": \"Arachnida/Latrodectus mactans/2137740a9c406ff9ea4043152e817e3f.jpg\",\n  \"572230.jpg\": \"Arachnida/Latrodectus mactans/4ca09781471c93b57f27419c87074bef.jpg\",\n  \"572231.jpg\": \"Arachnida/Latrodectus mactans/62b580319b79070504dfb571397ce735.jpg\",\n  \"572233.jpg\": \"Arachnida/Latrodectus mactans/2ed09ee89768cc4a5c752b614090fb3d.jpg\",\n  \"572234.jpg\": \"Arachnida/Latrodectus mactans/c2cb1798763a7702e678569f9a3b13ef.jpg\",\n  \"572243.jpg\": \"Insecta/Cerma cerintha/ce8b4ed024c0034d18b5f81cb78cf768.jpg\",\n  \"572247.jpg\": \"Animalia/Scutigera coleoptrata/de5b88a014cc2326c5a4f39f969c1bda.jpg\",\n  \"572251.jpg\": \"Animalia/Scutigera coleoptrata/a409af26b8ccc429f93336291256fc3a.jpg\",\n  \"572252.jpg\": \"Animalia/Scutigera coleoptrata/91a9bddae0ae30b5b96f8f83a4aec3f7.jpg\",\n  \"572253.jpg\": \"Animalia/Scutigera coleoptrata/2162182b9d34b51c62b84082c599747e.jpg\",\n  \"572257.jpg\": \"Animalia/Scutigera coleoptrata/81fcd77fd309251af755f48eb173ba27.jpg\",\n  \"572259.jpg\": \"Animalia/Scutigera coleoptrata/867887d6f121274082d4e59faa4e3ac3.jpg\",\n  \"572260.jpg\": \"Animalia/Scutigera coleoptrata/75da42419f501f6cda856725993ffbc4.jpg\",\n  \"572265.jpg\": \"Animalia/Scutigera coleoptrata/4fae1c84069a0e5f7e8f7f5c984613ab.jpg\",\n  \"572287.jpg\": \"Reptilia/Plestiodon fasciatus/5e966cad40c70473146385a3797a7a0d.jpg\",\n  \"572320.jpg\": \"Reptilia/Plestiodon fasciatus/0004691bb7150aef9a468ab747380599.jpg\",\n  \"572336.jpg\": \"Reptilia/Plestiodon fasciatus/4dd4d06d80f031b764e68d7c578039f3.jpg\",\n  \"572377.jpg\": \"Reptilia/Plestiodon fasciatus/0e816426ba8aaa54e7129a3c50e6fde0.jpg\",\n  \"5724.jpg\": \"Aves/Myiarchus crinitus/cdd5da08132c819fcc46e9f646f078bb.jpg\",\n  \"572404.jpg\": \"Insecta/Anax junius/b812c1ce83d00a426f4774799b20c449.jpg\",\n  \"57246.jpg\": \"Insecta/Pseudothyatira cymatophoroides/a4789a3a1d7e538ccdd2c3ed82e8ca11.jpg\",\n  \"572466.jpg\": \"Insecta/Anax junius/45f330c491755ab05e6c1c537632b7d0.jpg\",\n  \"572495.jpg\": \"Insecta/Anax junius/22d74a23a15f8750dce4e6dd387c0799.jpg\",\n  \"57250.jpg\": \"Insecta/Pseudothyatira cymatophoroides/e9b6b80a3a4e598593391a4c0e193cce.jpg\",\n  \"57251.jpg\": \"Insecta/Pseudothyatira cymatophoroides/aa1e7ed18d4ad931f180fe4220e4d601.jpg\",\n  \"57253.jpg\": \"Insecta/Pseudothyatira cymatophoroides/e9b75bcc73ab9096b847ac6305ed49a2.jpg\",\n  \"57254.jpg\": \"Insecta/Pseudothyatira cymatophoroides/caa592fb9079552558929fb25471883d.jpg\",\n  \"572544.jpg\": \"Insecta/Anax junius/f9223f426ff161b84f2ce513e0586242.jpg\",\n  \"57264.jpg\": \"Insecta/Pseudothyatira cymatophoroides/545c24df81867fa94cd722fedc414ff1.jpg\",\n  \"5727.jpg\": \"Aves/Myiarchus crinitus/477e4fb6a743b75b99bd89fdbf540fae.jpg\",\n  \"572714.jpg\": \"Reptilia/Kinosternon subrubrum/cc0bba2fb48f4a488e9fda33d87adc70.jpg\",\n  \"572715.jpg\": \"Reptilia/Kinosternon subrubrum/2acd3def2fda0a5baabb7d9bac64ac34.jpg\",\n  \"572716.jpg\": \"Reptilia/Kinosternon subrubrum/d53a7792f34cda87dbd1c61edc38c11e.jpg\",\n  \"57272.jpg\": \"Insecta/Pseudothyatira cymatophoroides/327e6ef089bbccd0e764025bc35a396b.jpg\",\n  \"57275.jpg\": \"Insecta/Pseudothyatira cymatophoroides/57d37b63bf77161a9e0850ad77a3894e.jpg\",\n  \"572790.jpg\": \"Reptilia/Pituophis catenifer affinis/8e50229e0fbd1d2364b3dca5d0ca3fc0.jpg\",\n  \"572792.jpg\": \"Reptilia/Pituophis catenifer affinis/901cbafcaaee214862ab179407080e86.jpg\",\n  \"572816.jpg\": \"Insecta/Melanoplus bivittatus/9dc427b08643c4e98cf62778ebe081a3.jpg\",\n  \"572817.jpg\": \"Insecta/Melanoplus bivittatus/0445fc44810b5d64277bc1cdf61ecf10.jpg\",\n  \"572818.jpg\": \"Insecta/Melanoplus bivittatus/403536287ac7caff17fad0b58cabdf83.jpg\",\n  \"572819.jpg\": \"Insecta/Melanoplus bivittatus/f2cd5c092f41c4fc64924cd3eb2849aa.jpg\",\n  \"572825.jpg\": \"Insecta/Melanoplus bivittatus/8f2aca78c290095d92059920b634675a.jpg\",\n  \"572836.jpg\": \"Insecta/Melanoplus bivittatus/9aded010bdc03227ccdb4e1b3516f721.jpg\",\n  \"572842.jpg\": \"Insecta/Melanoplus bivittatus/3455c684247b6c33dbda0dca60772c50.jpg\",\n  \"572843.jpg\": \"Insecta/Melanoplus bivittatus/50cea7515f76fe427197c40c2c42ef2a.jpg\",\n  \"572876.jpg\": \"Aves/Coracias garrulus/c2115a52e91a0d11852986780a20926b.jpg\",\n  \"572880.jpg\": \"Aves/Coracias garrulus/397053d2bd716317ddb5e80d998de972.jpg\",\n  \"572927.jpg\": \"Mammalia/Antrozous pallidus/2406972b195404d0e5cc5fbf8dc4afba.jpg\",\n  \"572983.jpg\": \"Aves/Haematopus unicolor/5708bc6e00dd3b2ea5fd832854eb9223.jpg\",\n  \"573004.jpg\": \"Aves/Haematopus unicolor/a95c41910d989c51269e3113154ee832.jpg\",\n  \"573044.jpg\": \"Aves/Haematopus unicolor/808b681129d43a8d79460e51d94b1455.jpg\",\n  \"573096.jpg\": \"Insecta/Celastrina argiolus/a96cb1fdc8baa1b18499e9e11da28e54.jpg\",\n  \"573097.jpg\": \"Insecta/Celastrina argiolus/98884868e628fc8485e7f6c6c6e33119.jpg\",\n  \"573099.jpg\": \"Insecta/Celastrina argiolus/f7a435a17ee97cf4e67ed22c761ce361.jpg\",\n  \"573114.jpg\": \"Reptilia/Pseudemys texana/ed2f3d8cfe82f39f55e00e08873cf2d6.jpg\",\n  \"573115.jpg\": \"Reptilia/Pseudemys texana/ef2dfc940401059903d0c245b1146811.jpg\",\n  \"573127.jpg\": \"Reptilia/Pseudemys texana/d54e2388229911ed477339a424cbc39d.jpg\",\n  \"573129.jpg\": \"Reptilia/Pseudemys texana/436447b6575abc343bf359b4d8ee5387.jpg\",\n  \"573131.jpg\": \"Reptilia/Pseudemys texana/d97a449cc869ca0a98501eca7f0b4248.jpg\",\n  \"573132.jpg\": \"Reptilia/Pseudemys texana/506c78dbf0239473349a88a1f96366dc.jpg\",\n  \"573242.jpg\": \"Aves/Alauda arvensis/8b6b73c656cd5718f370a3cb9fb49d4a.jpg\",\n  \"573249.jpg\": \"Aves/Alauda arvensis/f131f792821fb32902ef66ddd947d30f.jpg\",\n  \"573266.jpg\": \"Aves/Alauda arvensis/d79470573be016b76866ce241b69a6ed.jpg\",\n  \"573355.jpg\": \"Reptilia/Aspidoscelis tigris tigris/542c5d2c860a72c8c9318d1f4103ff67.jpg\",\n  \"573362.jpg\": \"Insecta/Eumorpha fasciatus/3aa0ca6552209c8ab421af993a9cebe5.jpg\",\n  \"573365.jpg\": \"Insecta/Eumorpha fasciatus/dcb67078e5ff4b32e605bfcfdbc8b781.jpg\",\n  \"573366.jpg\": \"Insecta/Eumorpha fasciatus/a979a3c4e941b3e0fbafaa922f746641.jpg\",\n  \"573367.jpg\": \"Insecta/Eumorpha fasciatus/1c5ba4420bc7bc4d10f4d6f0136a66ab.jpg\",\n  \"573370.jpg\": \"Insecta/Eumorpha fasciatus/c5b682bb2a8e079794ad5a16c0596a2b.jpg\",\n  \"573373.jpg\": \"Insecta/Abaeis nicippe/7465e7ea865bc2a10ebf63d3c6166cca.jpg\",\n  \"573381.jpg\": \"Insecta/Abaeis nicippe/98a69557e1c75c03ae7ba235902642f7.jpg\",\n  \"573396.jpg\": \"Insecta/Abaeis nicippe/9a6d06e3c3893b86b6b41055d9311774.jpg\",\n  \"573398.jpg\": \"Insecta/Abaeis nicippe/54c8acb7af2c144f2b91c6b8d6197b10.jpg\",\n  \"573399.jpg\": \"Insecta/Abaeis nicippe/026ed7226c0db570d68851621328e12f.jpg\",\n  \"573407.jpg\": \"Insecta/Abaeis nicippe/08706d27df07f883164ae9067269902b.jpg\",\n  \"573447.jpg\": \"Aves/Asio otus/5ce7f3e037a1991e3dab7b4d96167ea6.jpg\",\n  \"573450.jpg\": \"Aves/Asio otus/a05de938e36757de8c9df139cdcf600a.jpg\",\n  \"573454.jpg\": \"Aves/Asio otus/aa7bce337e157908ed80259fe46c749b.jpg\",\n  \"573456.jpg\": \"Aves/Asio otus/69c25f70d73f1243ccbb26d2ff800793.jpg\",\n  \"573462.jpg\": \"Aves/Asio otus/aa471a958e4f5b76080e4ed9d1f589a5.jpg\",\n  \"5735.jpg\": \"Aves/Myiarchus crinitus/252adb45f70b6b977b605fa8f890d1c0.jpg\",\n  \"573553.jpg\": \"Aves/Aphelocoma californica/202a48a825d68c006388257f53d26c3e.jpg\",\n  \"573571.jpg\": \"Aves/Aphelocoma californica/8d4ca5d81395d33532bec50a6a39fd22.jpg\",\n  \"573573.jpg\": \"Aves/Aphelocoma californica/24f6b5db5fd59287a64dd3f7563e507d.jpg\",\n  \"573780.jpg\": \"Aves/Aphelocoma californica/23f57c344de52118a908cd9cd362c013.jpg\",\n  \"573837.jpg\": \"Aves/Aphelocoma californica/40c1c39e902e02a53804921647bf230e.jpg\",\n  \"573862.jpg\": \"Aves/Aphelocoma californica/86962bedd59ed38c45260bf18e5f1d5f.jpg\",\n  \"573867.jpg\": \"Aves/Aphelocoma californica/5ca51d5970b87a486bac157e6009480f.jpg\",\n  \"573939.jpg\": \"Aves/Leucophaeus atricilla/82634cf36724a2646a8e5fb886ba9822.jpg\",\n  \"574007.jpg\": \"Aves/Leucophaeus atricilla/f6de99b7ae7b86b701125e414659b22a.jpg\",\n  \"574028.jpg\": \"Aves/Leucophaeus atricilla/7f041cd7b236caa0fb0024a514c13e0e.jpg\",\n  \"574034.jpg\": \"Aves/Leucophaeus atricilla/820ffdccb3c0350610deb0f8919bec36.jpg\",\n  \"574116.jpg\": \"Aves/Leucophaeus atricilla/a8aeaeddad2788da92fe113ed1d77273.jpg\",\n  \"574207.jpg\": \"Aves/Pelecanus occidentalis/f25f1d3dcb9a2469d961b3627353a3be.jpg\",\n  \"574214.jpg\": \"Aves/Pelecanus occidentalis/1e83b7551f8a761c40abc2297e4f71f6.jpg\",\n  \"5743.jpg\": \"Aves/Myiarchus crinitus/710bcdeb0bbf7adf4522ff6e9d9a4430.jpg\",\n  \"574302.jpg\": \"Aves/Pelecanus occidentalis/5e822c11761458af13ab25e734799038.jpg\",\n  \"5744.jpg\": \"Aves/Myiarchus crinitus/9428977cfcc0216d9b97818a10d503e6.jpg\",\n  \"574416.jpg\": \"Aves/Pelecanus occidentalis/02d506b7d9c0b2bf84c9979518cc5bf9.jpg\",\n  \"57472.jpg\": \"Animalia/Henricia leviuscula/22bf4e096de25404108af24fbfad9cae.jpg\",\n  \"574783.jpg\": \"Insecta/Prionus californicus/e8b5c35d98d086be8dba5e1da0759994.jpg\",\n  \"574784.jpg\": \"Insecta/Prionus californicus/8a7ad32a6d7622d6bdec082d88a66ec1.jpg\",\n  \"574801.jpg\": \"Insecta/Dromogomphus spinosus/c5c74e07ea85470f98ee48c174878cb7.jpg\",\n  \"574802.jpg\": \"Insecta/Dromogomphus spinosus/315a7b935c4d892b5c3f2541e476fe52.jpg\",\n  \"574803.jpg\": \"Insecta/Dromogomphus spinosus/f49d36d909364748d6256a84407d4dab.jpg\",\n  \"574804.jpg\": \"Insecta/Dromogomphus spinosus/27a6f3481bc426952f017584b872c84f.jpg\",\n  \"574812.jpg\": \"Insecta/Dromogomphus spinosus/aac3347018ac27a143815977781dfa13.jpg\",\n  \"57483.jpg\": \"Animalia/Henricia leviuscula/65488cc5bcf169fcd62e1f35fb797935.jpg\",\n  \"574848.jpg\": \"Insecta/Brechmorhoga mendax/d98f4e5dbd1ab30561c4b356efc27570.jpg\",\n  \"574852.jpg\": \"Insecta/Brechmorhoga mendax/b732a1b4a29b8c6854c9e1ca8b6c5659.jpg\",\n  \"574858.jpg\": \"Insecta/Brechmorhoga mendax/cd9f8878bfda53b072cb72321dfe33d4.jpg\",\n  \"574859.jpg\": \"Insecta/Brechmorhoga mendax/c0ee53117ea3c1d87bfb560d967b2572.jpg\",\n  \"574863.jpg\": \"Insecta/Brechmorhoga mendax/1d5b88cdbd28ca618e0ef9419d2c7a25.jpg\",\n  \"574864.jpg\": \"Insecta/Brechmorhoga mendax/31124a1a904970f325461e714f6a0089.jpg\",\n  \"574873.jpg\": \"Aves/Pipilo chlorurus/eb030926b59f623b84cbbee9f9876076.jpg\",\n  \"574874.jpg\": \"Aves/Pipilo chlorurus/876fc8e11372b7c420ba6b8c90603a3f.jpg\",\n  \"574875.jpg\": \"Aves/Pipilo chlorurus/b5387286bbc9b5ffc8f186207bb77fff.jpg\",\n  \"574877.jpg\": \"Aves/Pipilo chlorurus/01f1853b064acf42c5eaf7b2a0e342a8.jpg\",\n  \"574878.jpg\": \"Aves/Pipilo chlorurus/eae516598c96bd0c81569ad99e105c51.jpg\",\n  \"574880.jpg\": \"Aves/Pipilo chlorurus/04a8fcbcfeb247e34c8bb8d3856118d7.jpg\",\n  \"574883.jpg\": \"Aves/Pipilo chlorurus/72ece51e83cee6b20c05e92fa26ae8f4.jpg\",\n  \"574887.jpg\": \"Aves/Pipilo chlorurus/574ad9d81008e553ca8405d0fa7c2959.jpg\",\n  \"574889.jpg\": \"Aves/Pipilo chlorurus/7ad22aac7151a0898c1ad618fda4a7c2.jpg\",\n  \"57489.jpg\": \"Animalia/Henricia leviuscula/01fbd9ce1261866feb46b9446392de24.jpg\",\n  \"574899.jpg\": \"Insecta/Arigomphus submedianus/dd403e3b89cbd8b3abe9503d95d049ea.jpg\",\n  \"574927.jpg\": \"Insecta/Spodoptera ornithogalli/569b5b6c5afccaf0de812e9622f3ecd4.jpg\",\n  \"574933.jpg\": \"Insecta/Spodoptera ornithogalli/e65030751fb7968aa88ed889cb3be354.jpg\",\n  \"57494.jpg\": \"Animalia/Henricia leviuscula/9118a102d591cb58cbe885f7543b8d36.jpg\",\n  \"574944.jpg\": \"Insecta/Spodoptera ornithogalli/481b3652e54bf500f405308ddf3800e4.jpg\",\n  \"574946.jpg\": \"Insecta/Spodoptera ornithogalli/a74ee3e3bfeb2bcf2ed72b9f7775678a.jpg\",\n  \"574952.jpg\": \"Insecta/Spodoptera ornithogalli/1804f4e99d8708b08950bd80adc3d910.jpg\",\n  \"574953.jpg\": \"Insecta/Spodoptera ornithogalli/0ade990f457a353ee05ddbfce0ddc29c.jpg\",\n  \"574955.jpg\": \"Insecta/Spodoptera ornithogalli/a91a8c05c74868b93b642b7f2fbd6a6c.jpg\",\n  \"575031.jpg\": \"Insecta/Polistes exclamans/21fb8230d184ef796a36230beae0f65d.jpg\",\n  \"57505.jpg\": \"Mollusca/Oligyra orbiculata/e5ffb287183dba76384d2fad86e03b64.jpg\",\n  \"575051.jpg\": \"Insecta/Polistes exclamans/044ba50f9f1f11d13503ad24086c2f72.jpg\",\n  \"575059.jpg\": \"Insecta/Polistes exclamans/172bb7227f1595df63acea9b0224ee96.jpg\",\n  \"57506.jpg\": \"Mollusca/Oligyra orbiculata/f2b1d1ea5b1480dee959c3fb75ac9fbc.jpg\",\n  \"575096.jpg\": \"Mammalia/Mirounga leonina/e22ae26596d436b5adaeb3c31cf01179.jpg\",\n  \"575103.jpg\": \"Insecta/Cimbex americana/b683018880a7e21f2e0c8e6230b294ca.jpg\",\n  \"57511.jpg\": \"Mollusca/Oligyra orbiculata/8ecc7f654833e16752312f6a60c89863.jpg\",\n  \"575112.jpg\": \"Insecta/Hyalophora euryalus/3c401310ccdabf5dad5879e82889142e.jpg\",\n  \"575114.jpg\": \"Insecta/Hyalophora euryalus/48dcb8f93a826746affcb6666b0eb940.jpg\",\n  \"575122.jpg\": \"Aves/Anous stolidus/d1c82235a5ca9e82311455edab4cde38.jpg\",\n  \"575124.jpg\": \"Aves/Anous stolidus/a47dcb8f981124a91a713de49b4b7b1f.jpg\",\n  \"57513.jpg\": \"Mollusca/Oligyra orbiculata/3fd469fd9323857e743c0939d52c7415.jpg\",\n  \"575198.jpg\": \"Aves/Anas querquedula/ee5023a0c4859b9016206fc0f45eebae.jpg\",\n  \"575202.jpg\": \"Aves/Anas querquedula/429bf47954a4271c148609a68b747c21.jpg\",\n  \"575210.jpg\": \"Insecta/Erynnis tristis/7c9f37ad2bd5cdd0dc10a43a7d591949.jpg\",\n  \"575220.jpg\": \"Insecta/Erynnis tristis/e9cd9c3a50b22ec550c88aa242353a3c.jpg\",\n  \"575222.jpg\": \"Insecta/Erynnis tristis/f45bf7f1d3a5838144cbfefae2d522c3.jpg\",\n  \"575223.jpg\": \"Insecta/Erynnis tristis/aa5b2df4063b2f1dd405098413bb3ee5.jpg\",\n  \"575237.jpg\": \"Insecta/Erynnis tristis/1d45354952e0c11161ab25dae68b8e37.jpg\",\n  \"575240.jpg\": \"Insecta/Erynnis tristis/794e1d5e274ab2a070a3b436eb3caa72.jpg\",\n  \"575244.jpg\": \"Insecta/Erynnis tristis/c2e81c710c86a9628e18ba7bd4504012.jpg\",\n  \"575247.jpg\": \"Insecta/Erynnis tristis/83d43d80deb9b5939a4b7e1b2c9954e1.jpg\",\n  \"575258.jpg\": \"Aves/Poecile carolinensis/97ed0cc77b0cee01e593de8d8b90b693.jpg\",\n  \"57526.jpg\": \"Mollusca/Oligyra orbiculata/ac3909d694c797cb0c39c8baca6c4403.jpg\",\n  \"57527.jpg\": \"Mollusca/Oligyra orbiculata/f46c63cef9a8290af91a5e348a835e40.jpg\",\n  \"57528.jpg\": \"Mollusca/Oligyra orbiculata/8c2e0a7b429fc34f7dad32b5f6af656b.jpg\",\n  \"575288.jpg\": \"Aves/Poecile carolinensis/39b3ee4b373b6ebda59481a50a90ff48.jpg\",\n  \"57529.jpg\": \"Mollusca/Oligyra orbiculata/53e30af27ef473dd05b17c4fb74f052b.jpg\",\n  \"57530.jpg\": \"Mollusca/Oligyra orbiculata/1ceaeddf0036ea8b7ea11bebfa4f1552.jpg\",\n  \"575315.jpg\": \"Aves/Poecile carolinensis/b269b4dd4ec4f15ab298601d8a6410c5.jpg\",\n  \"575332.jpg\": \"Aves/Poecile carolinensis/8f075a50728bad23dda42ec12fbf7aa9.jpg\",\n  \"57534.jpg\": \"Mollusca/Oligyra orbiculata/f3db94d081c4d957e3d1eb254ed067a1.jpg\",\n  \"57535.jpg\": \"Mollusca/Oligyra orbiculata/b44c8409d33f2b1c063ddb3b977b525d.jpg\",\n  \"57536.jpg\": \"Mollusca/Oligyra orbiculata/e1e965935b318bb04420bed54ba61a86.jpg\",\n  \"57539.jpg\": \"Mollusca/Oligyra orbiculata/f3b8970e5e20c35366e45420e44b9550.jpg\",\n  \"5754.jpg\": \"Aves/Myiarchus crinitus/862a648092d4767f2f037793d22adc78.jpg\",\n  \"575401.jpg\": \"Aves/Poecile carolinensis/3d4f2be7fd450b940a26192528e59bdf.jpg\",\n  \"57542.jpg\": \"Mollusca/Oligyra orbiculata/88937f8df326cc3e1f7da8b1be054995.jpg\",\n  \"57543.jpg\": \"Mollusca/Oligyra orbiculata/3d55ec344d867d156934a7100b913c50.jpg\",\n  \"57544.jpg\": \"Mollusca/Oligyra orbiculata/5da63218bf393cbf76369583154d5470.jpg\",\n  \"57545.jpg\": \"Mollusca/Oligyra orbiculata/0dcecb606a68d06a50f12811eaacc54f.jpg\",\n  \"575457.jpg\": \"Mammalia/Scalopus aquaticus/45229b0451ea9845f47bfb91286c6f1d.jpg\",\n  \"575497.jpg\": \"Reptilia/Ctenosaura similis/e52ebc0f8fe6b56e93476e33433a3bb7.jpg\",\n  \"575498.jpg\": \"Reptilia/Ctenosaura similis/b16833ee2ed6db02c2b484fd1ea74633.jpg\",\n  \"575509.jpg\": \"Reptilia/Ctenosaura similis/291ea13530ee68d5b69ec1d92d4d50bd.jpg\",\n  \"575523.jpg\": \"Reptilia/Ctenosaura similis/6a9a2d09a70458893ae063cbe1d4b772.jpg\",\n  \"575527.jpg\": \"Reptilia/Ctenosaura similis/8c8f6416266e0cf179f3fbfbb74e31bd.jpg\",\n  \"575529.jpg\": \"Reptilia/Ctenosaura similis/59446b80fa9d37b8b4409d3275d7c675.jpg\",\n  \"575574.jpg\": \"Actinopterygii/Mola mola/5cd185fbf3327ee29a55073bfdcabcb6.jpg\",\n  \"575576.jpg\": \"Actinopterygii/Mola mola/85d56b91b63e8dadeeaf6a42ebd53801.jpg\",\n  \"575578.jpg\": \"Actinopterygii/Mola mola/86c345bcaef58426f69a7c7827d76cb1.jpg\",\n  \"575580.jpg\": \"Actinopterygii/Mola mola/5dcbd4d845f386f6189f7cd2657a515a.jpg\",\n  \"575585.jpg\": \"Actinopterygii/Mola mola/ff30661946703b673b1a97ae844e4249.jpg\",\n  \"575587.jpg\": \"Actinopterygii/Mola mola/cab978c90731104646935227206d8d17.jpg\",\n  \"575598.jpg\": \"Reptilia/Virginia striatula/da0a79a95e5c84df2de1f87df2cefd9b.jpg\",\n  \"575599.jpg\": \"Reptilia/Virginia striatula/6637c5824bd5db8272abf8914fff6dbc.jpg\",\n  \"575603.jpg\": \"Reptilia/Virginia striatula/4970106e2b98bd9e45d1036e990b81d4.jpg\",\n  \"575615.jpg\": \"Reptilia/Virginia striatula/df419c547786588514b761ada95d585c.jpg\",\n  \"575643.jpg\": \"Reptilia/Virginia striatula/d4fd90a567c12dbef17cb64d835f2d18.jpg\",\n  \"575650.jpg\": \"Reptilia/Virginia striatula/7221e69c5ebfc2112f4d3a650b733ada.jpg\",\n  \"575677.jpg\": \"Reptilia/Virginia striatula/77c22707860c748905314d931c3f11f7.jpg\",\n  \"575685.jpg\": \"Reptilia/Aspidoscelis velox/27858ce9fb77d167ac2ff40ed1633003.jpg\",\n  \"575686.jpg\": \"Reptilia/Aspidoscelis velox/6cabce6e535d75be502aa445bd531f98.jpg\",\n  \"575692.jpg\": \"Reptilia/Aspidoscelis velox/33f113064592daf0b72835069b97e64d.jpg\",\n  \"575745.jpg\": \"Insecta/Hemiargus ceraunus/18b6fefc1dbe82d61a9e7b315ff09ea3.jpg\",\n  \"575747.jpg\": \"Insecta/Hemiargus ceraunus/f9c49ae1b5ffc568daf7e3b6e81f3262.jpg\",\n  \"575761.jpg\": \"Insecta/Hemiargus ceraunus/c385d5ab0dc3cb8a87461f540c49a6c3.jpg\",\n  \"575772.jpg\": \"Insecta/Hemiargus ceraunus/999982f6eefc619cb3ebf11ee12c77cc.jpg\",\n  \"575778.jpg\": \"Insecta/Hemiargus ceraunus/9761a950ac051406e168ee9559c394fb.jpg\",\n  \"575786.jpg\": \"Insecta/Hemiargus ceraunus/52f1b37a6effd982a563e50d86968933.jpg\",\n  \"575795.jpg\": \"Insecta/Hemiargus ceraunus/e508763ab75e09eca6fa93cc1784d5cc.jpg\",\n  \"575797.jpg\": \"Insecta/Hemiargus ceraunus/220d9e585353e0b5b561a514ee06079f.jpg\",\n  \"575800.jpg\": \"Insecta/Hemiargus ceraunus/3b41a3d5e39409401ad84f2fc30ec08b.jpg\",\n  \"575809.jpg\": \"Insecta/Hemaris thysbe/719ad8c93be88ee09a0f510fed870b42.jpg\",\n  \"575831.jpg\": \"Insecta/Hemaris thysbe/22bd8f0c7f5aa7ef624d9c8bdf70226f.jpg\",\n  \"575844.jpg\": \"Insecta/Hemaris thysbe/e0c77c5941d5b76a47db1c6e9283a7dd.jpg\",\n  \"575845.jpg\": \"Insecta/Hemaris thysbe/e2e76032eb1d615014e8163b11ba6fba.jpg\",\n  \"575856.jpg\": \"Insecta/Hemaris thysbe/f0a7f233b89b8a793ed884fe53058123.jpg\",\n  \"575860.jpg\": \"Insecta/Hemaris thysbe/4c73fe03e479367b49e3222ed86ae36c.jpg\",\n  \"575883.jpg\": \"Insecta/Feniseca tarquinius/2d996bb3111805566be8a30ee15df266.jpg\",\n  \"575885.jpg\": \"Insecta/Feniseca tarquinius/c1022a28f50fb9f81f51e3c28b10005c.jpg\",\n  \"575898.jpg\": \"Insecta/Melittia cucurbitae/009dd89a5c916db04181e4fc12bacf32.jpg\",\n  \"576014.jpg\": \"Insecta/Macromia illinoiensis/0d13351ecd47a475892eddda2ec58c4b.jpg\",\n  \"576015.jpg\": \"Insecta/Macromia illinoiensis/a4118c194bc70daba914a38b0f32af03.jpg\",\n  \"576019.jpg\": \"Insecta/Macromia illinoiensis/d5ce6fc48c9d74c858949ad7f94932e9.jpg\",\n  \"57602.jpg\": \"Aves/Icterus spurius/7c87b5c1eb7fa8560320636d1c42681e.jpg\",\n  \"576023.jpg\": \"Insecta/Macromia illinoiensis/4b922f0e4726c84ea39c71b301466218.jpg\",\n  \"576024.jpg\": \"Insecta/Macromia illinoiensis/ebc853cc5fff54d0e7871d6cc2b31cff.jpg\",\n  \"576025.jpg\": \"Insecta/Macromia illinoiensis/9b1fa70a8aa8b4d22ac3d8cf32bcaddb.jpg\",\n  \"576026.jpg\": \"Insecta/Macromia illinoiensis/d4bc2c5a2d22c83d68d0f3d4578cb189.jpg\",\n  \"576028.jpg\": \"Insecta/Macromia illinoiensis/16340c044db0bf905a4e5ecbcfe575b4.jpg\",\n  \"57603.jpg\": \"Aves/Icterus spurius/882fb7f36256bc2051bf426e9f1b450f.jpg\",\n  \"576049.jpg\": \"Mammalia/Chlorocebus pygerythrus/5858d523513eb30b61c3f350dc5788da.jpg\",\n  \"57606.jpg\": \"Aves/Icterus spurius/0982209a6cb0a65e172f0803b3fc144b.jpg\",\n  \"576200.jpg\": \"Reptilia/Crocodylus niloticus/93100bf77fe6073911c91bd824f13f84.jpg\",\n  \"57622.jpg\": \"Aves/Icterus spurius/9f282c7d73282febff5c0e930a7635f2.jpg\",\n  \"57626.jpg\": \"Aves/Icterus spurius/2042b190dc38414ec7d4f01fa2411801.jpg\",\n  \"576326.jpg\": \"Aves/Mniotilta varia/c1b2cb877cb7e5ab6596121b05566db2.jpg\",\n  \"576347.jpg\": \"Aves/Mniotilta varia/c0a3b04cfcbf1afcc39ed1d04a66d573.jpg\",\n  \"57636.jpg\": \"Aves/Icterus spurius/352ea8c21a9d96c5ef298a61b84e7a76.jpg\",\n  \"57637.jpg\": \"Aves/Icterus spurius/19e051167add418c11de8a122c9ef977.jpg\",\n  \"576429.jpg\": \"Aves/Mniotilta varia/75c76d93ba69b0979072be7546b98a88.jpg\",\n  \"576430.jpg\": \"Aves/Mniotilta varia/770727bd13eb6a8c461d53c78567cfb7.jpg\",\n  \"576431.jpg\": \"Aves/Mniotilta varia/4f3e3466f03cde38c9859c1f024957a5.jpg\",\n  \"57653.jpg\": \"Aves/Icterus spurius/461ca0a0fb734d22aed1c515f3a9f63e.jpg\",\n  \"576531.jpg\": \"Insecta/Eristalis tenax/0194b309023e8f3e183fc6ac24c5c5a5.jpg\",\n  \"576532.jpg\": \"Insecta/Eristalis tenax/bb1861ba6c19c85ee0c187df0599ad57.jpg\",\n  \"57654.jpg\": \"Aves/Icterus spurius/b4c0602c248558328332276e75a37341.jpg\",\n  \"576540.jpg\": \"Insecta/Eristalis tenax/f55cff40909685dffed6403c6ba081f9.jpg\",\n  \"576541.jpg\": \"Insecta/Eristalis tenax/39e1a2940fc55b9bd4060e44af51f9f1.jpg\",\n  \"576542.jpg\": \"Insecta/Eristalis tenax/5ab72103f64c329574a5243f8d6e7422.jpg\",\n  \"576543.jpg\": \"Insecta/Eristalis tenax/bb3f5b02a776a20d923d46e86805bac3.jpg\",\n  \"576544.jpg\": \"Insecta/Eristalis tenax/4e9d9a8175641c4d939e1054bdbee057.jpg\",\n  \"576545.jpg\": \"Insecta/Eristalis tenax/5f9412a52a55e08006116e99e552b047.jpg\",\n  \"576547.jpg\": \"Insecta/Eristalis tenax/98f9cb633a8a82ae72c36cf1dc5a3ee2.jpg\",\n  \"576549.jpg\": \"Insecta/Eristalis tenax/03e28d47dd44e976380bd58c5eaa4652.jpg\",\n  \"576555.jpg\": \"Insecta/Eristalis tenax/9e39d6bcef4c4de4968f5dca89c82aa9.jpg\",\n  \"57661.jpg\": \"Aves/Icterus spurius/242d1846505ddc95e4f4d8d452ed6ae3.jpg\",\n  \"576658.jpg\": \"Insecta/Syritta pipiens/d7fa2b04d3339c213e4351df1043740f.jpg\",\n  \"576671.jpg\": \"Insecta/Boisea trivittata/1e42a51c83da7f0aa1d07699d23c37bb.jpg\",\n  \"576675.jpg\": \"Insecta/Boisea trivittata/7f3c6b5a3e1eca05a35c23a224df94f0.jpg\",\n  \"576678.jpg\": \"Insecta/Boisea trivittata/bd0e18950ce7dd967651943d9b96b53b.jpg\",\n  \"57668.jpg\": \"Aves/Icterus spurius/3f42a1459511ffe648dc00501a249413.jpg\",\n  \"576681.jpg\": \"Insecta/Boisea trivittata/85d7d0bf75b8b756b5e03615b9e3fad3.jpg\",\n  \"576686.jpg\": \"Insecta/Boisea trivittata/d2eb9b8ab60ac5aa4453facf2458918a.jpg\",\n  \"576687.jpg\": \"Insecta/Boisea trivittata/94a076549fcdb8c0218d6f545594aad9.jpg\",\n  \"576703.jpg\": \"Insecta/Dythemis velox/f3f062ac766cf89b26ba6a36be082d1f.jpg\",\n  \"576716.jpg\": \"Insecta/Dythemis velox/1e6e57507a6c5688b7bfd66c9164462c.jpg\",\n  \"576717.jpg\": \"Insecta/Dythemis velox/66ec850b3d92e674511b8e46e4538eb9.jpg\",\n  \"576722.jpg\": \"Insecta/Dythemis velox/180165ee48e153e3c86b6d2e4980b34c.jpg\",\n  \"576736.jpg\": \"Insecta/Dythemis velox/5702b8357fb2f59bd2949a5d0945e9a3.jpg\",\n  \"576737.jpg\": \"Insecta/Dythemis velox/d766d3eeca70e99888ccb503a1fe6c9f.jpg\",\n  \"576738.jpg\": \"Insecta/Dythemis velox/bae2502a07517c1c278a085e9c81ba3a.jpg\",\n  \"576739.jpg\": \"Insecta/Dythemis velox/969177aad5d7a94720ef9c28aa5cfc50.jpg\",\n  \"576742.jpg\": \"Insecta/Dythemis velox/0dc8db61ce0e3d13a534974187a84355.jpg\",\n  \"576743.jpg\": \"Insecta/Dythemis velox/2b1a949b8a6cbfad5fc5652ee3fbce4b.jpg\",\n  \"57684.jpg\": \"Aves/Icterus spurius/f9c18ea4fb384fcdc6400df12f131d36.jpg\",\n  \"576847.jpg\": \"Mammalia/Cebus capucinus/ca7769067744dd6b28496aacd86233e7.jpg\",\n  \"576848.jpg\": \"Mammalia/Cebus capucinus/0577f441eadb01b3da1b2458fcb7bbbd.jpg\",\n  \"576849.jpg\": \"Mammalia/Cebus capucinus/86e814ef5634a33e20f64debf0ccee2b.jpg\",\n  \"576854.jpg\": \"Mammalia/Cebus capucinus/77cd1793708ad999e4d070785ef4bb32.jpg\",\n  \"5769.jpg\": \"Aves/Myiarchus crinitus/3abcf6e64c3f0ffc238b01f3a85b98c4.jpg\",\n  \"576942.jpg\": \"Aves/Bonasa umbellus/8f3317b32ddaeac226486cfe18953ba5.jpg\",\n  \"57696.jpg\": \"Aves/Icterus spurius/b8a37017b6d06e74f6b6d53644428e86.jpg\",\n  \"576974.jpg\": \"Aves/Bonasa umbellus/70e14c0b9372082b38418eee914579a7.jpg\",\n  \"576975.jpg\": \"Aves/Bonasa umbellus/eb23d40ad76c4ee1f8f37372f105c7a1.jpg\",\n  \"576976.jpg\": \"Aves/Bonasa umbellus/e60f0499e6b845b42ac6991b4aa50afa.jpg\",\n  \"57698.jpg\": \"Aves/Icterus spurius/8122c847cf5dda5c10fa06012ba1c991.jpg\",\n  \"576980.jpg\": \"Aves/Bonasa umbellus/cf7db205679ff4fdf915e135ee045e09.jpg\",\n  \"576994.jpg\": \"Insecta/Leucorrhinia intacta/a1a74f7245fa7b706248e19db16d904b.jpg\",\n  \"576997.jpg\": \"Insecta/Leucorrhinia intacta/7878e88e99de2ac28fd7217911e4eee8.jpg\",\n  \"5770.jpg\": \"Aves/Myiarchus crinitus/a84d1b5203e2268d09a248dadae980d2.jpg\",\n  \"577010.jpg\": \"Insecta/Leucorrhinia intacta/249b1c42de4ebfecd8cdd229ff709243.jpg\",\n  \"577019.jpg\": \"Insecta/Leucorrhinia intacta/fcf819a39a1a372136359fe92879e7d5.jpg\",\n  \"577029.jpg\": \"Insecta/Leucorrhinia intacta/1983e6a54c0c22c5c86f8992b578cc8c.jpg\",\n  \"577056.jpg\": \"Insecta/Leucorrhinia intacta/2032905bd4cb5478aa956ad8a3ec968a.jpg\",\n  \"577062.jpg\": \"Insecta/Leucorrhinia intacta/eb76517a2fd9ed2b1c1fd12e9c240620.jpg\",\n  \"577066.jpg\": \"Insecta/Leucorrhinia intacta/86902deeb8ebf7ed06cbc952a837527f.jpg\",\n  \"57707.jpg\": \"Aves/Icterus spurius/4914a8b5d4dbf2f8dbbe555a358b98b3.jpg\",\n  \"57709.jpg\": \"Aves/Icterus spurius/d1ee290e217786b7a1cfc750f9fc1645.jpg\",\n  \"577116.jpg\": \"Arachnida/Amblyomma americanum/91e3046f0c8b39ed977705257b948baa.jpg\",\n  \"577119.jpg\": \"Reptilia/Aspidoscelis tigris mundus/66ac043be2f3c35692887366816dccb3.jpg\",\n  \"57712.jpg\": \"Aves/Icterus spurius/51e09c23def3369909c2a3a68daaa528.jpg\",\n  \"57713.jpg\": \"Aves/Icterus spurius/6a7e1d3b2d56cc3a487e6bdf04fd3a18.jpg\",\n  \"577132.jpg\": \"Reptilia/Aspidoscelis tigris mundus/12214a9073ae053271c59b0fc38833f3.jpg\",\n  \"577143.jpg\": \"Mollusca/Helix pomatia/28fcc27c6087d322dd813b61ba63715f.jpg\",\n  \"577144.jpg\": \"Mollusca/Helix pomatia/b7055663ffff63231bd9707eeafc9f0f.jpg\",\n  \"577145.jpg\": \"Mollusca/Helix pomatia/3c88e53791d910efca92bc0f06fc2276.jpg\",\n  \"577146.jpg\": \"Mollusca/Helix pomatia/310c376595fcc0cc850c89662bf34462.jpg\",\n  \"577147.jpg\": \"Mollusca/Helix pomatia/130d0688c2a11c46dd348a20658a0005.jpg\",\n  \"577148.jpg\": \"Mollusca/Helix pomatia/516b4a97532e2d16adef352b78c30745.jpg\",\n  \"577151.jpg\": \"Mollusca/Helix pomatia/502022166aa30091328142d4926fa7a0.jpg\",\n  \"577152.jpg\": \"Mollusca/Helix pomatia/99b621eccc0bd66965d43d7f84b3f09c.jpg\",\n  \"577162.jpg\": \"Mollusca/Helix pomatia/170e214b0b76be25717db758cc27deee.jpg\",\n  \"57726.jpg\": \"Aves/Icterus spurius/04e1a9ecde99e8ad31ce95a2d5ee0e2d.jpg\",\n  \"57737.jpg\": \"Aves/Icterus spurius/ce9453834082d66f403f5a79642586ad.jpg\",\n  \"57738.jpg\": \"Aves/Icterus spurius/efa8398918cc77476bf8ed616f926093.jpg\",\n  \"57745.jpg\": \"Aves/Icterus spurius/9bcdb9c95885d1a2029dfbabd930503a.jpg\",\n  \"57748.jpg\": \"Aves/Icterus spurius/351ad9c5ca17863deaedec6807b74f99.jpg\",\n  \"577628.jpg\": \"Insecta/Siproeta stelenes/f0163e3228aabba10c097af3e33ec4f0.jpg\",\n  \"577629.jpg\": \"Insecta/Siproeta stelenes/8bf2585a57d8688b74e8777581311547.jpg\",\n  \"577641.jpg\": \"Insecta/Siproeta stelenes/c4293ccb6965e92072eb599732638e59.jpg\",\n  \"577642.jpg\": \"Insecta/Siproeta stelenes/2d9644c3bc6ec9cd2a1f855e98b9062c.jpg\",\n  \"577645.jpg\": \"Insecta/Siproeta stelenes/2e371aba6c6cd2097ec509e49651c92c.jpg\",\n  \"577649.jpg\": \"Insecta/Siproeta stelenes/3a5aeb679d2f60f2437a2506ba01cdd4.jpg\",\n  \"577694.jpg\": \"Aves/Amazilia tzacatl/2cf21af2106fef15104fbe0deca71cf8.jpg\",\n  \"577700.jpg\": \"Aves/Amazilia tzacatl/a9153d29247bb44729e2e3e376344094.jpg\",\n  \"577701.jpg\": \"Aves/Amazilia tzacatl/bbee401ccab0681eea3d197fa4522e97.jpg\",\n  \"577726.jpg\": \"Insecta/Dynastes tityus/d8d89f53d00207223039e5cbeb18b145.jpg\",\n  \"577867.jpg\": \"Aves/Passer domesticus/15b17b2ad6b56fc777d2abbb0688c89a.jpg\",\n  \"577951.jpg\": \"Aves/Passer domesticus/135489c72ae2b159ee768153aac951dc.jpg\",\n  \"578067.jpg\": \"Aves/Passer domesticus/bf1d1de39a936cdd307d75b754460bfd.jpg\",\n  \"578353.jpg\": \"Aves/Passer domesticus/8161d6b5022586a617fb780cefbdc237.jpg\",\n  \"578372.jpg\": \"Aves/Passer domesticus/d98bf570d4f5326b092eb697409dcdd4.jpg\",\n  \"57873.jpg\": \"Insecta/Prionoplus reticularis/b5be9a7db82d88f4ac5620a2de4d752e.jpg\",\n  \"57878.jpg\": \"Insecta/Prionoplus reticularis/9e002054a8cf760226be3c2066435135.jpg\",\n  \"578812.jpg\": \"Amphibia/Osteopilus septentrionalis/4690de97cc75e546a53ceb54ed65d17c.jpg\",\n  \"578814.jpg\": \"Amphibia/Osteopilus septentrionalis/fa451466ff293f38780febd344b9924c.jpg\",\n  \"578819.jpg\": \"Amphibia/Osteopilus septentrionalis/8c8383bbc34c2d5a947299118d591c48.jpg\",\n  \"578820.jpg\": \"Amphibia/Osteopilus septentrionalis/14d669d6838f62dbcc86496f07428529.jpg\",\n  \"578821.jpg\": \"Amphibia/Osteopilus septentrionalis/39a2dc4477b5f5ec0da7d08a7bb2237e.jpg\",\n  \"578822.jpg\": \"Amphibia/Osteopilus septentrionalis/b0884450bb14fb39dd9f37466a0d2410.jpg\",\n  \"578840.jpg\": \"Amphibia/Osteopilus septentrionalis/977cd4b04e4ad2fcf8d2afe8455cb1cd.jpg\",\n  \"578849.jpg\": \"Aves/Hylocichla mustelina/1452de38b37593724001a887652675aa.jpg\",\n  \"578867.jpg\": \"Aves/Hylocichla mustelina/3d4f8480a7cd054b53dac541eccfcac2.jpg\",\n  \"578875.jpg\": \"Aves/Hylocichla mustelina/ea684d2e0f52b46e878010710e6e47cb.jpg\",\n  \"578876.jpg\": \"Aves/Hylocichla mustelina/0b8768387a4d3e9a45a68e5b33603bb3.jpg\",\n  \"578877.jpg\": \"Aves/Hylocichla mustelina/297aced125dca241d4d4ed7f3841387a.jpg\",\n  \"578880.jpg\": \"Aves/Hylocichla mustelina/45c5c696cf012a59af36e8504566490f.jpg\",\n  \"578881.jpg\": \"Aves/Hylocichla mustelina/466864f9314071f3a20a758a9a3f1385.jpg\",\n  \"578948.jpg\": \"Aves/Turdus merula/19ae492dd8cf04b1941724b9d0de64fd.jpg\",\n  \"578965.jpg\": \"Aves/Turdus merula/463223217e9211f8e34b86f3589ccc95.jpg\",\n  \"579040.jpg\": \"Aves/Turdus merula/683fc3b3678685b47e3883658496d962.jpg\",\n  \"579042.jpg\": \"Aves/Turdus merula/c9f2497e816f8f530735caeafbf27e7e.jpg\",\n  \"579060.jpg\": \"Aves/Turdus merula/457369e9b7f7b121e01f448fd0b0fe9c.jpg\",\n  \"579095.jpg\": \"Aves/Turdus merula/c49bc07292011efe3712b3289eb9453c.jpg\",\n  \"579098.jpg\": \"Aves/Turdus merula/9c7751d5891ac9201e798921b44e4435.jpg\",\n  \"579140.jpg\": \"Aves/Cepphus grylle/ac7cc14fdfb41732128471fd9dfc6e87.jpg\",\n  \"579143.jpg\": \"Aves/Cepphus grylle/9264c9c204fa4ea44d9635ff9b91b741.jpg\",\n  \"579147.jpg\": \"Insecta/Satyrium calanus/81b37d2388ea085d47abbbc591c6e72c.jpg\",\n  \"579151.jpg\": \"Insecta/Satyrium calanus/6d599f64ac77fc17c9e38c31326cba27.jpg\",\n  \"579157.jpg\": \"Insecta/Satyrium calanus/bcb1de92400e38b4da79cfab68626fc7.jpg\",\n  \"579164.jpg\": \"Insecta/Satyrium calanus/867e429c07da98949d3a37c94122f4ba.jpg\",\n  \"579169.jpg\": \"Insecta/Satyrium calanus/9f307165e63177a00d3b96832d30222b.jpg\",\n  \"5793.jpg\": \"Aves/Myiarchus crinitus/91c9069daab1fce856c69017567b7b74.jpg\",\n  \"579327.jpg\": \"Aves/Cochlearius cochlearius/31f45fd0a3e41c7fb9b2abf9411bed57.jpg\",\n  \"579331.jpg\": \"Aves/Cochlearius cochlearius/c142422eb3374f224d1f6dc712f2ea02.jpg\",\n  \"579332.jpg\": \"Aves/Cochlearius cochlearius/63800d99f004d35951a0d8bc0fbc6fd7.jpg\",\n  \"579335.jpg\": \"Aves/Cochlearius cochlearius/94d7b6173a6f7ad0bfe6a831594e1322.jpg\",\n  \"579336.jpg\": \"Aves/Cochlearius cochlearius/45823c6fc1d87c133bd4ee625b41b5f7.jpg\",\n  \"579370.jpg\": \"Aves/Todiramphus sanctus vagans/d2f58e6380f5551e9e5dc5f0eee092ba.jpg\",\n  \"579371.jpg\": \"Aves/Todiramphus sanctus vagans/e659a8e3c730a3da8950c3e0433f6f34.jpg\",\n  \"579372.jpg\": \"Aves/Todiramphus sanctus vagans/2c8f3966d09f6723b136bc6e99ef6ff2.jpg\",\n  \"579373.jpg\": \"Aves/Todiramphus sanctus vagans/91138b2b92e1a966b749dca18f32df9b.jpg\",\n  \"579383.jpg\": \"Aves/Todiramphus sanctus vagans/2aefe971282de4383b00bfac0c9aecd1.jpg\",\n  \"579384.jpg\": \"Aves/Todiramphus sanctus vagans/8520b60f1ba6b031017eeb9ac31830bd.jpg\",\n  \"579385.jpg\": \"Aves/Todiramphus sanctus vagans/2e6c562a7b30fa112f22f8595e668165.jpg\",\n  \"579409.jpg\": \"Aves/Fratercula cirrhata/b58a183c043753ca34dba12660fffe47.jpg\",\n  \"579410.jpg\": \"Aves/Fratercula cirrhata/f462d679a9915b11305d63e0699b3cee.jpg\",\n  \"579416.jpg\": \"Aves/Fratercula cirrhata/056089be11a51150289ac9a433ee6b7a.jpg\",\n  \"579424.jpg\": \"Aves/Larus argentatus smithsonianus/1f3617859fbca0d2999697fc15bd0af2.jpg\",\n  \"5796.jpg\": \"Aves/Myiarchus crinitus/db24235e43f94a597635113f39b41f4f.jpg\",\n  \"579634.jpg\": \"Aves/Cathartes aura/10d0c23cc6b5fea45635b1a7b9a2b23a.jpg\",\n  \"579845.jpg\": \"Aves/Cathartes aura/de2ad32dba8f425139f39ac6d58ba48e.jpg\",\n  \"579948.jpg\": \"Aves/Cathartes aura/a28289eb07d4aceea7e598aff79edd24.jpg\",\n  \"5800.jpg\": \"Aves/Myiarchus crinitus/01e4f61f5f27dc3fee443f99c730a37e.jpg\",\n  \"580222.jpg\": \"Aves/Cathartes aura/22c954ceb80ea530be75067bc599c062.jpg\",\n  \"580359.jpg\": \"Aves/Cathartes aura/924ce9fb3fe03a0fd851bb7809587ee9.jpg\",\n  \"580397.jpg\": \"Aves/Cathartes aura/b5b6d99904fe29c5a9e2068a91d198b0.jpg\",\n  \"580457.jpg\": \"Aves/Haliastur indus/5a28a2dc5b9ac6296a94b936e4f13d1a.jpg\",\n  \"580582.jpg\": \"Reptilia/Thamnophis sauritus/9612d57013a0740c9e4175dc6aae3751.jpg\",\n  \"580583.jpg\": \"Reptilia/Thamnophis sauritus/0e25761a008405b6cf71c0455155e58a.jpg\",\n  \"580584.jpg\": \"Reptilia/Thamnophis sauritus/738d6bba67478ab73fd8151b5386544a.jpg\",\n  \"580585.jpg\": \"Reptilia/Thamnophis sauritus/0d39fbe7b214d8176b29b450abb034f3.jpg\",\n  \"580586.jpg\": \"Reptilia/Thamnophis sauritus/5909bb032a11740f78890ff03e09c99e.jpg\",\n  \"580594.jpg\": \"Insecta/Enallagma cyathigerum/dd47100ec808e8538cbb37db081b085a.jpg\",\n  \"580607.jpg\": \"Aves/Colinus virginianus/a3f008bc5416bafc2a60043e56adec2c.jpg\",\n  \"580614.jpg\": \"Aves/Colinus virginianus/9747c4e71934ee669a838629e3edb13c.jpg\",\n  \"580615.jpg\": \"Aves/Colinus virginianus/8dbb3e77d7af241ab26d06b5a154aa7b.jpg\",\n  \"580619.jpg\": \"Aves/Colinus virginianus/e0c057299cd80c75ce29a0231a1ea955.jpg\",\n  \"580640.jpg\": \"Aves/Colinus virginianus/f68b84c232c22faff146dc864cb061f5.jpg\",\n  \"580644.jpg\": \"Aves/Colinus virginianus/7c82c2ed2f75f5b77e1ea9f84f0ba470.jpg\",\n  \"580673.jpg\": \"Insecta/Lerodea eufala/8a4cebcf7c0a52baf2027125ab5cc5f7.jpg\",\n  \"580674.jpg\": \"Insecta/Lerodea eufala/9a56f01b14d0c11a0270b22a9080791f.jpg\",\n  \"580678.jpg\": \"Insecta/Lerodea eufala/9fdb0792962c992bbbf6cecdb02613c9.jpg\",\n  \"580685.jpg\": \"Insecta/Lerodea eufala/0282a94628bf64b744a3bac657e60ee4.jpg\",\n  \"580689.jpg\": \"Insecta/Lerodea eufala/87dbeb872d69b5c0beb8a1a0ca8f27b5.jpg\",\n  \"580692.jpg\": \"Insecta/Lerodea eufala/c9a80c93953d09020da190a2b014d6d4.jpg\",\n  \"580696.jpg\": \"Insecta/Lerodea eufala/b15d68c1d4124371345753f2e3132e15.jpg\",\n  \"580697.jpg\": \"Insecta/Lerodea eufala/8342c14d3f09787d092b9ec8ab44980e.jpg\",\n  \"580703.jpg\": \"Insecta/Lerodea eufala/2e85db24a2082c9909eeb7f727e424da.jpg\",\n  \"580704.jpg\": \"Insecta/Lerodea eufala/5dd68642d37df6696086de54db76eee2.jpg\",\n  \"580712.jpg\": \"Insecta/Lerodea eufala/587ea93b113a81687f3d10d17dc7cffa.jpg\",\n  \"580750.jpg\": \"Arachnida/Araneus diadematus/064c75cf1947a479ec1f179aea36512e.jpg\",\n  \"580769.jpg\": \"Arachnida/Araneus diadematus/6bac0464b461ac148f8f3dbd82cc8e15.jpg\",\n  \"580810.jpg\": \"Arachnida/Araneus diadematus/fadadefe1514e2b6d9f09a227aed863d.jpg\",\n  \"580816.jpg\": \"Arachnida/Araneus diadematus/1c2d603f6a39464ef301180e240178fb.jpg\",\n  \"580817.jpg\": \"Arachnida/Araneus diadematus/4d3514dd1481ebe8af9cff4b987e74cc.jpg\",\n  \"580822.jpg\": \"Aves/Alcedo atthis/f9a32792ae60b256f58923c1ce8683ff.jpg\",\n  \"580825.jpg\": \"Aves/Alcedo atthis/5c155dadd64408bca8c7d3e6fc1f5121.jpg\",\n  \"580837.jpg\": \"Aves/Alcedo atthis/eb4ca262fb9ef0b99ee91f97b7c5bb36.jpg\",\n  \"580841.jpg\": \"Aves/Alcedo atthis/445c1585cf570c7c9aa26e6f3a27ba74.jpg\",\n  \"580842.jpg\": \"Aves/Alcedo atthis/0c7a28198c91b28ec54fb806e7da1193.jpg\",\n  \"580843.jpg\": \"Aves/Alcedo atthis/c2838e6f961d8449236a5c67546c35ea.jpg\",\n  \"580845.jpg\": \"Aves/Alcedo atthis/92e9c75cf0ddb1d90d5fa59cee80eeef.jpg\",\n  \"580860.jpg\": \"Aves/Alcedo atthis/6d3a0446bb767adbbdb25baee8fe33c0.jpg\",\n  \"580862.jpg\": \"Aves/Alcedo atthis/f8703aae3aa5cd4b9609d4299b967269.jpg\",\n  \"580863.jpg\": \"Aves/Alcedo atthis/7f555b994be48f13b62e3ebe3c168f53.jpg\",\n  \"580873.jpg\": \"Aves/Aythya valisineria/eecbc830150b9b26110ab8b7eadc5469.jpg\",\n  \"580923.jpg\": \"Aves/Aythya valisineria/21689c8f070c46e8ec5bb5760bc0eb67.jpg\",\n  \"580976.jpg\": \"Insecta/Pachysphinx modesta/57035cc393e41e41564b58a1e8f81191.jpg\",\n  \"580978.jpg\": \"Insecta/Pachysphinx modesta/c0dd5782d7a3faab3c82a4908b893b19.jpg\",\n  \"580979.jpg\": \"Insecta/Pachysphinx modesta/2a8cbe08a6a8db798f3b4753e7e3b88b.jpg\",\n  \"581003.jpg\": \"Aves/Haematopus palliatus/b09a5422ff8b034ad99ab868827ac570.jpg\",\n  \"581030.jpg\": \"Aves/Haematopus palliatus/818097ac2bf03df9e5751b363c364e42.jpg\",\n  \"581038.jpg\": \"Aves/Haematopus palliatus/6af7a99a5efdbaa5e570d867e88088c2.jpg\",\n  \"581065.jpg\": \"Aves/Haematopus palliatus/58de8169ce66d2c1e52db9a18271f767.jpg\",\n  \"5811.jpg\": \"Aves/Myiarchus crinitus/19d49cf9395317231e0f8e7f27afb99e.jpg\",\n  \"581168.jpg\": \"Aves/Charadrius dubius/036a35b512911997866a1c41d0aa99fd.jpg\",\n  \"581269.jpg\": \"Aves/Caracara cheriway/ab2fb05379aed47f9959da1684dfca3e.jpg\",\n  \"581300.jpg\": \"Aves/Caracara cheriway/2a839082837bfdff807474f8d46a4581.jpg\",\n  \"581323.jpg\": \"Aves/Caracara cheriway/d5bda808ae48bc9a0bf42fe985abf718.jpg\",\n  \"581340.jpg\": \"Aves/Caracara cheriway/525e77537595d3bb80d9ea842800fb71.jpg\",\n  \"581350.jpg\": \"Aves/Caracara cheriway/f97ce221b9660c6fadea669f17b5a8a9.jpg\",\n  \"581376.jpg\": \"Aves/Caracara cheriway/b95a19a30a1057e60fea335091b315d8.jpg\",\n  \"581400.jpg\": \"Aves/Alectoris rufa/78304f2396e0dab98490e7b50929ae85.jpg\",\n  \"581402.jpg\": \"Aves/Falco rufigularis/7cc12813afa97e9d2fb9bb23aef6b3b1.jpg\",\n  \"581403.jpg\": \"Aves/Falco rufigularis/6cbac66d6048012a27be645bdaed3729.jpg\",\n  \"581407.jpg\": \"Aves/Falco rufigularis/baf513e26fe88343850c9077ed1eb29c.jpg\",\n  \"581415.jpg\": \"Aves/Falco rufigularis/8ed6faa3e163ab6f5d98c2ed0629c10f.jpg\",\n  \"581433.jpg\": \"Aves/Buteo buteo/a781ccfd68cfdf52d2e963dd2713721c.jpg\",\n  \"581490.jpg\": \"Aves/Buteo buteo/04a7892654f15d17396fcd25159ea5db.jpg\",\n  \"581497.jpg\": \"Aves/Buteo buteo/b9933351455466073128a2a51320e458.jpg\",\n  \"5815.jpg\": \"Aves/Myiarchus crinitus/2f819d11076b7a57e3393cb6fbe43280.jpg\",\n  \"581510.jpg\": \"Aves/Buteo buteo/6783f95a38a0040161c5240f80c572f9.jpg\",\n  \"5816.jpg\": \"Aves/Myiarchus crinitus/2e4a67ce89d7454963d76ec32c255718.jpg\",\n  \"581622.jpg\": \"Aves/Ramphastos sulfuratus/c5e2934d0d264d89f893d8bc8143df66.jpg\",\n  \"581623.jpg\": \"Aves/Ramphastos sulfuratus/806848895f1818dcc55a1a21deda35b2.jpg\",\n  \"581624.jpg\": \"Aves/Ramphastos sulfuratus/4e2d24b03aa87980cfe9b7ccb704af7c.jpg\",\n  \"581629.jpg\": \"Aves/Ramphastos sulfuratus/fd9243bb930224309d55c54a22453756.jpg\",\n  \"581636.jpg\": \"Aves/Ramphastos sulfuratus/06f82962bb2f657da49a041443745afc.jpg\",\n  \"581637.jpg\": \"Aves/Ramphastos sulfuratus/e5c8783a2a37d0bada1eddde2b5b9575.jpg\",\n  \"581638.jpg\": \"Aves/Ramphastos sulfuratus/143b3912b861800b3153ad7a5fe66daa.jpg\",\n  \"581721.jpg\": \"Insecta/Libellula pulchella/efa52d6fb7d78ac3b8a1803481c290e4.jpg\",\n  \"581726.jpg\": \"Insecta/Libellula pulchella/589cbcaf0596d40b0f112f0b5d902059.jpg\",\n  \"581790.jpg\": \"Insecta/Libellula pulchella/e243e5674d58d84ec8edd54eeab17eb9.jpg\",\n  \"581810.jpg\": \"Insecta/Libellula pulchella/3c343cd4b5dcbc9907590da9079521df.jpg\",\n  \"581813.jpg\": \"Insecta/Libellula pulchella/51d9b1df71c7eff8d619f5ece941d00d.jpg\",\n  \"581834.jpg\": \"Insecta/Libellula pulchella/8c73eabbff8291e4c32358fb45616da2.jpg\",\n  \"581874.jpg\": \"Insecta/Nicrophorus orbicollis/b68cfdb2ae692dca5f2c5c464ce535a7.jpg\",\n  \"581876.jpg\": \"Insecta/Nicrophorus orbicollis/c3e72c92023fb97e5f25c2b177b7aa9f.jpg\",\n  \"5821.jpg\": \"Aves/Myiarchus crinitus/3d1e2a1cecca1e81e98afc0f916ae205.jpg\",\n  \"582230.jpg\": \"Amphibia/Incilius nebulifer/22c12b313ed996b27e2c6d98edc8bdd3.jpg\",\n  \"582257.jpg\": \"Insecta/Sympetrum danae/1b51a2722e4c6cfb7c2eac949e0ce710.jpg\",\n  \"582264.jpg\": \"Insecta/Sympetrum danae/b5363335738aa535a276b420e8bec4f7.jpg\",\n  \"582270.jpg\": \"Insecta/Sympetrum danae/f11d67c4d28155f7f4af7013ba0c0bac.jpg\",\n  \"582271.jpg\": \"Insecta/Sympetrum danae/937b8684508bdfeed916e7572d1e8e6d.jpg\",\n  \"582274.jpg\": \"Insecta/Sympetrum danae/8a3316cad92b44c6e43f99568027c6fc.jpg\",\n  \"582275.jpg\": \"Insecta/Sympetrum danae/26cb9452009b62e80f7e4c9bb27dd91a.jpg\",\n  \"58228.jpg\": \"Insecta/Orthodera novaezealandiae/d1ab2edddc1096692cc4b99e37a0f104.jpg\",\n  \"582311.jpg\": \"Insecta/Emmelina monodactyla/fdfc79f4482bbd4c7cc423d4e59d43fd.jpg\",\n  \"582318.jpg\": \"Insecta/Emmelina monodactyla/cb33f6ad23c7f1cc4e1f161a4005dbca.jpg\",\n  \"582323.jpg\": \"Insecta/Emmelina monodactyla/7662efa1899adf5d627e6a48dff7cceb.jpg\",\n  \"582330.jpg\": \"Insecta/Ocypus olens/765b125cc4aad2b94d0eddc02f67c28a.jpg\",\n  \"582332.jpg\": \"Insecta/Ocypus olens/481f4c8985cae3943e72957c030c9203.jpg\",\n  \"58237.jpg\": \"Insecta/Orthodera novaezealandiae/c5024a5beb9bfba16dfa660136e892bd.jpg\",\n  \"58238.jpg\": \"Insecta/Orthodera novaezealandiae/57f068b05fed0e0fec813a02f0ee27c5.jpg\",\n  \"58239.jpg\": \"Insecta/Orthodera novaezealandiae/5ee5e8f984074095e5db305ca7c9a85a.jpg\",\n  \"58242.jpg\": \"Insecta/Orthodera novaezealandiae/74f078d9b14ef793836e9b97ac0c1d18.jpg\",\n  \"582427.jpg\": \"Insecta/Platycnemis pennipes/c7ad76fe7a007f425991a5c2f53431f6.jpg\",\n  \"582429.jpg\": \"Insecta/Platycnemis pennipes/b1e54a4517878ea825f7a217f5278be6.jpg\",\n  \"58243.jpg\": \"Insecta/Orthodera novaezealandiae/7740746c316e4c0b379456ba924aa40d.jpg\",\n  \"582433.jpg\": \"Insecta/Platycnemis pennipes/99e7be030f48b894af0c375a62274162.jpg\",\n  \"582435.jpg\": \"Insecta/Platycnemis pennipes/5310484b8aeeec1cd0a67b5f9d233abb.jpg\",\n  \"582437.jpg\": \"Insecta/Platycnemis pennipes/7f3420b3e97d5c15ffa6b03987a6a84a.jpg\",\n  \"58244.jpg\": \"Insecta/Orthodera novaezealandiae/ad984dc9e400b5961ea44ee2b29931f1.jpg\",\n  \"582445.jpg\": \"Aves/Larus michahellis/d0f090d727f67e6d5e307d1a934c4d3d.jpg\",\n  \"58245.jpg\": \"Insecta/Orthodera novaezealandiae/7491b647d05cfd4ffbbb8682655cc70d.jpg\",\n  \"582451.jpg\": \"Aves/Larus michahellis/16732075070d1a08331a409b78f30f0b.jpg\",\n  \"582454.jpg\": \"Aves/Larus michahellis/0db008eb3ec503a49935834eb0ffc23e.jpg\",\n  \"582455.jpg\": \"Aves/Larus michahellis/15a417b08201211d81d59b44a641d78f.jpg\",\n  \"582457.jpg\": \"Aves/Larus michahellis/294eeb01ce0b9749e7bc14d2e17bedbd.jpg\",\n  \"58246.jpg\": \"Insecta/Orthodera novaezealandiae/02b8d1bf9e4606a02c4eb8be656d41dd.jpg\",\n  \"582461.jpg\": \"Aves/Larus michahellis/944b4bdfc58ef6c01d84e24136cf8ccf.jpg\",\n  \"582464.jpg\": \"Aves/Larus michahellis/b5de0b64d6db61b3c554519687acdb3a.jpg\",\n  \"582467.jpg\": \"Aves/Larus michahellis/8d16a457793bc48f31a599c458736522.jpg\",\n  \"582468.jpg\": \"Aves/Larus michahellis/af510c72421d27036f321b45e942ccdb.jpg\",\n  \"582469.jpg\": \"Aves/Larus michahellis/62a7a1d58efa7c8ef7ef4210a3b8eb74.jpg\",\n  \"58247.jpg\": \"Insecta/Orthodera novaezealandiae/477ac7c3c79d0cff06f813b1c8814216.jpg\",\n  \"582470.jpg\": \"Aves/Larus michahellis/db1e8f29d57b2742aa8c83c973262c38.jpg\",\n  \"58248.jpg\": \"Insecta/Orthodera novaezealandiae/af5c80ec384cdd3d19923158440abf8d.jpg\",\n  \"58252.jpg\": \"Insecta/Orthodera novaezealandiae/e0f4f86b056564be52ae2c63df630764.jpg\",\n  \"58253.jpg\": \"Insecta/Orthodera novaezealandiae/9a3ab024d6bd1d5e9576dd73732b2799.jpg\",\n  \"58254.jpg\": \"Insecta/Orthodera novaezealandiae/d8697e9a24871a20a4def0580dd0a9be.jpg\",\n  \"58255.jpg\": \"Insecta/Orthodera novaezealandiae/5fcf0ec58530a9a0844510ea9e324bfc.jpg\",\n  \"58256.jpg\": \"Insecta/Orthodera novaezealandiae/8d482fef696af91186099265ef6b5973.jpg\",\n  \"58257.jpg\": \"Insecta/Orthodera novaezealandiae/4fcd4c1d2ff7544d732f4b8c016f1583.jpg\",\n  \"58258.jpg\": \"Insecta/Orthodera novaezealandiae/cc823b161420fe30fffdfcb0cc08dd58.jpg\",\n  \"5826.jpg\": \"Aves/Myiarchus crinitus/23f67ce1c18bdf78a74f348ec965ca99.jpg\",\n  \"58260.jpg\": \"Insecta/Orthodera novaezealandiae/2d4f974849d22ad92f61f2ebd2b4b094.jpg\",\n  \"582645.jpg\": \"Mammalia/Tragelaphus strepsiceros/ec38b3ee6a53744e2793b5179865917b.jpg\",\n  \"582650.jpg\": \"Mammalia/Tragelaphus strepsiceros/67ccd63e7a98ef28301782ba11ff6de2.jpg\",\n  \"582651.jpg\": \"Aves/Hymenolaimus malacorhynchos/7c63b114ef558481e18e132af7fbda4f.jpg\",\n  \"582652.jpg\": \"Aves/Hymenolaimus malacorhynchos/f63e78150e1596f3729c47f92230c315.jpg\",\n  \"58266.jpg\": \"Insecta/Orthodera novaezealandiae/e692036718bea8816c093a2110f5da1b.jpg\",\n  \"58268.jpg\": \"Insecta/Xanthocnemis zealandica/0f96ce7788c566cc5ad1d7b7c1be161e.jpg\",\n  \"5827.jpg\": \"Aves/Myiarchus crinitus/4410afdf6c1cbb7365fccb9a331fe963.jpg\",\n  \"582752.jpg\": \"Insecta/Chortophaga viridifasciata/8ebabb5910fbd5980d4f27867a2f0eac.jpg\",\n  \"58276.jpg\": \"Insecta/Xanthocnemis zealandica/a59f14c852d050f10bf3f4007223c65d.jpg\",\n  \"582764.jpg\": \"Aves/Sterna forsteri/d9ff3bea98a7e762194f273057a50842.jpg\",\n  \"582791.jpg\": \"Aves/Sterna forsteri/eb8882a103adca3bcfa31da3f0dc2ea7.jpg\",\n  \"582876.jpg\": \"Aves/Sterna forsteri/65ac68f9f7175fcdcf05fdc8fa9d2c2f.jpg\",\n  \"582882.jpg\": \"Aves/Sterna forsteri/e5524a1b8313f2accbb6b16d0f60eb3f.jpg\",\n  \"58292.jpg\": \"Insecta/Xanthocnemis zealandica/8b2e144ea588e6e52682d9c367e64d00.jpg\",\n  \"583068.jpg\": \"Amphibia/Desmognathus ochrophaeus/58f84aac9003c4eb6872e029c20b6076.jpg\",\n  \"58307.jpg\": \"Insecta/Austrolestes colensonis/8edae4fbcb567d84a5d7f402eeb6721c.jpg\",\n  \"583083.jpg\": \"Aves/Limosa lapponica/929fe8fea8e3ce557cbbbbe8ed347cd7.jpg\",\n  \"583084.jpg\": \"Aves/Limosa lapponica/5186f35c5f957f860d337e1c16dad85f.jpg\",\n  \"5831.jpg\": \"Aves/Myiarchus crinitus/5e9b348809c660a66b6bdf6ae6f8d31e.jpg\",\n  \"58317.jpg\": \"Insecta/Austrolestes colensonis/7bbce9cb1a688e7f1765f64f4ea229c4.jpg\",\n  \"583175.jpg\": \"Aves/Coracias caudatus/60db24289d170170e309b109cb467e65.jpg\",\n  \"583176.jpg\": \"Aves/Coracias caudatus/6f588f061675df8b3d3161dea8ad1bdc.jpg\",\n  \"583183.jpg\": \"Aves/Coracias caudatus/9e0470f8d8caaf169c60e05a1076ee83.jpg\",\n  \"58322.jpg\": \"Insecta/Austrolestes colensonis/a1266a77e4fa914833f17145208e7d89.jpg\",\n  \"583220.jpg\": \"Insecta/Calopteron reticulatum/469e8160291a23fbfc3810213466d6d4.jpg\",\n  \"583221.jpg\": \"Insecta/Calopteron reticulatum/d7deba46bf4a7757ee13f7a71b619dbb.jpg\",\n  \"583222.jpg\": \"Insecta/Calopteron reticulatum/7a1397862d62695bb3c28df21d797bf3.jpg\",\n  \"583232.jpg\": \"Insecta/Calopteron reticulatum/e3c758fe77f8ce92418e93b77e160a53.jpg\",\n  \"583234.jpg\": \"Insecta/Calopteron reticulatum/6c021ff33d6beca309406b9dd722b219.jpg\",\n  \"583238.jpg\": \"Aves/Vireo griseus/4ecf501d1413fe1d7d6ef239cfdd8deb.jpg\",\n  \"583239.jpg\": \"Aves/Vireo griseus/d845e8520acba68ad8c2879dd340ab19.jpg\",\n  \"58324.jpg\": \"Insecta/Austrolestes colensonis/e169e25feb5bc4d643f63f50da466caf.jpg\",\n  \"583243.jpg\": \"Aves/Vireo griseus/aa49a67d2ab0d6930c4fcc111c5e27b0.jpg\",\n  \"583262.jpg\": \"Aves/Vireo griseus/934f48759cee6c877e530ea6d5a37a96.jpg\",\n  \"583279.jpg\": \"Aves/Vireo griseus/0664607b0a0fa4291aec129b5b7b28fe.jpg\",\n  \"583280.jpg\": \"Aves/Vireo griseus/6c99641a03106deca68bd683e9ec4177.jpg\",\n  \"583313.jpg\": \"Reptilia/Nerodia fasciata/2d1c74fd879404548044408bdd92ba4c.jpg\",\n  \"583314.jpg\": \"Reptilia/Nerodia fasciata/f79cc88de28f8e1110c680a7333bc891.jpg\",\n  \"583320.jpg\": \"Reptilia/Nerodia fasciata/b6fcb5037dbbaf044b4afdccc8bd2af5.jpg\",\n  \"583324.jpg\": \"Reptilia/Nerodia fasciata/2a69498d398cb3da60e1c468e46f442e.jpg\",\n  \"583325.jpg\": \"Reptilia/Nerodia fasciata/33f42feb8e4dac59657efb5cc5822511.jpg\",\n  \"583331.jpg\": \"Aves/Limosa limosa/3a93e0fb1ce210eef88c926fc4020490.jpg\",\n  \"583335.jpg\": \"Aves/Limosa limosa/1c6d9023d1c59222735e74263f371715.jpg\",\n  \"583417.jpg\": \"Aves/Quiscalus major/93a5af21750c7718613254df7e860b64.jpg\",\n  \"583427.jpg\": \"Aves/Quiscalus major/39e1bda130d5ccbdfe580be4d2cc307a.jpg\",\n  \"583452.jpg\": \"Aves/Quiscalus major/5fd4365c9c845ddc96de642c36e849a3.jpg\",\n  \"583454.jpg\": \"Aves/Quiscalus major/fd63eb1518125120fdad18b0a0926649.jpg\",\n  \"583477.jpg\": \"Aves/Quiscalus major/eabff0276f79d314bbf0008bf5fce059.jpg\",\n  \"583478.jpg\": \"Aves/Quiscalus major/74a2fb478668c0a5be6429ef3b16dfd0.jpg\",\n  \"583493.jpg\": \"Aves/Sula nebouxii/86e2bb4e0eecde6538cdb8d37d008b7d.jpg\",\n  \"583497.jpg\": \"Aves/Sula nebouxii/c3a297d40f8c5c4109be0fd37e60ed57.jpg\",\n  \"5835.jpg\": \"Aves/Myiarchus crinitus/516e57df4b8fa086e7a18b480506a742.jpg\",\n  \"583501.jpg\": \"Aves/Sula nebouxii/a8f7dbc085cea72342ac143a14760c42.jpg\",\n  \"583504.jpg\": \"Aves/Sula nebouxii/61de1beaa29f4f43e9586c78099206e8.jpg\",\n  \"583508.jpg\": \"Aves/Sula nebouxii/c1d116b91fea4e60d88014bb444a5680.jpg\",\n  \"583509.jpg\": \"Aves/Sula nebouxii/47149402a9165a903cd52fbd964a33c9.jpg\",\n  \"583510.jpg\": \"Aves/Sula nebouxii/331e430ffefa7cabd2087e9c03996f2b.jpg\",\n  \"583653.jpg\": \"Aves/Egretta garzetta/d3a8125575f86bac212e22cfc9ddbee3.jpg\",\n  \"583673.jpg\": \"Aves/Egretta garzetta/4d36a6354b0a4522ed7753dd72eadf2a.jpg\",\n  \"583690.jpg\": \"Aves/Egretta garzetta/bdaf02bed7befc90c80826b41f4d7277.jpg\",\n  \"583697.jpg\": \"Aves/Egretta garzetta/f16c25ef7cb55b353caf02d06e32218f.jpg\",\n  \"583701.jpg\": \"Aves/Egretta garzetta/bbbbda1e824965dcef8e98a38cc86834.jpg\",\n  \"583708.jpg\": \"Aves/Egretta garzetta/5f79c75a0de9ff1e2203a27d5795a8a3.jpg\",\n  \"583846.jpg\": \"Aves/Geothlypis formosa/04a3c654ce5940ab04bfd54d1fb1cd09.jpg\",\n  \"583849.jpg\": \"Aves/Geothlypis philadelphia/272e9f1ced74197c0e6fb3bff288bf06.jpg\",\n  \"583850.jpg\": \"Aves/Geothlypis philadelphia/926522a1be5fb52a6f19369851fbde1e.jpg\",\n  \"583851.jpg\": \"Aves/Geothlypis philadelphia/e107fb45febe962fd2871d06cdcab4ff.jpg\",\n  \"583852.jpg\": \"Aves/Geothlypis philadelphia/0122676459cea97b0c692c6ab504472e.jpg\",\n  \"583854.jpg\": \"Aves/Geothlypis philadelphia/d02106723c13a776f48758c65c9012a5.jpg\",\n  \"5839.jpg\": \"Aves/Myiarchus crinitus/36ae03345e83e84901a4bc7d88ee7dc3.jpg\",\n  \"583926.jpg\": \"Mollusca/Mopalia lignosa/a00a06e8faff7e9a50d09f00ad217179.jpg\",\n  \"584012.jpg\": \"Aves/Regulus calendula/4e1b106352750e6d454c033048c65b68.jpg\",\n  \"58408.jpg\": \"Reptilia/Salvadora grahamiae lineata/bfcbac6a9e6674a3081d69bf38c7eb44.jpg\",\n  \"584094.jpg\": \"Aves/Regulus calendula/9d63d189a4d5fe963d87f98478865838.jpg\",\n  \"584127.jpg\": \"Aves/Regulus calendula/45bbd402e6f9bcdc5f67e5c90fd49431.jpg\",\n  \"58414.jpg\": \"Reptilia/Salvadora grahamiae lineata/7d2de4a546869b8801484e5d6c5b658b.jpg\",\n  \"584152.jpg\": \"Aves/Regulus calendula/9e6f2e959485801d5ffffa56e977a874.jpg\",\n  \"58416.jpg\": \"Reptilia/Salvadora grahamiae lineata/3c8b4f059fc000eea76d9fde6f79f3a4.jpg\",\n  \"58418.jpg\": \"Reptilia/Salvadora grahamiae lineata/614d16095ceec46d739d2f04cce7f5ba.jpg\",\n  \"58420.jpg\": \"Reptilia/Salvadora grahamiae lineata/2afcea33000fd2cd503c31353fbe6d3a.jpg\",\n  \"584222.jpg\": \"Aves/Regulus calendula/3ae690d10245c230e50b239164a7a317.jpg\",\n  \"584268.jpg\": \"Aves/Regulus calendula/a52c3c43992dd5b9a57884e5ad01f5a3.jpg\",\n  \"584370.jpg\": \"Mammalia/Puma concolor/6cc077fceb249710bdf15364cd629f1f.jpg\",\n  \"584378.jpg\": \"Mammalia/Puma concolor/d8b72c695cb7fcabe9298ac373cdbe0a.jpg\",\n  \"584384.jpg\": \"Mammalia/Puma concolor/6d00fbda71f552e2ce54bc22d5700a5b.jpg\",\n  \"584385.jpg\": \"Mammalia/Puma concolor/daf7d00d761d8539f4843209bcfd825a.jpg\",\n  \"584413.jpg\": \"Insecta/Erythemis collocata/4f7976622cc394d7a5a1fd2bc855bdc8.jpg\",\n  \"584416.jpg\": \"Insecta/Erythemis collocata/8891babe8aa2e0e85e48cbe46770b455.jpg\",\n  \"584432.jpg\": \"Insecta/Erythemis collocata/b5a0bfe8ebaefa1df62495f67935e9e6.jpg\",\n  \"584433.jpg\": \"Insecta/Erythemis collocata/a51ca826b5f963e8da0be20c971ffafc.jpg\",\n  \"584437.jpg\": \"Insecta/Erythemis collocata/fc68f5b40bfb7e6c1652f05686771733.jpg\",\n  \"584441.jpg\": \"Insecta/Erythemis collocata/6ec900226bb06343d6539200e94da028.jpg\",\n  \"584444.jpg\": \"Insecta/Erythemis collocata/98ca9553a9c85b8b32e76cd207218611.jpg\",\n  \"584447.jpg\": \"Insecta/Erythemis collocata/a1796a00997ee2da5f89370d1ab8a1b1.jpg\",\n  \"584451.jpg\": \"Mollusca/Arion rufus/2bf3f504ae600b23cfd67bbe07f1ed12.jpg\",\n  \"584452.jpg\": \"Mollusca/Arion rufus/036942b86ef8b1ffe5b05994ea0296bd.jpg\",\n  \"584460.jpg\": \"Mollusca/Arion rufus/b581d5e71bb350340b8c760edec850cf.jpg\",\n  \"584463.jpg\": \"Mollusca/Arion rufus/ada6877843341e5b88e93fe74f812b6f.jpg\",\n  \"584464.jpg\": \"Mollusca/Arion rufus/6088402c16397034041d5ae2552cec6f.jpg\",\n  \"584466.jpg\": \"Mollusca/Arion rufus/c1b2291037b97783b3de3c28e52ad42d.jpg\",\n  \"584473.jpg\": \"Mollusca/Arion rufus/d57488c3b69483a38a100b326148058d.jpg\",\n  \"584520.jpg\": \"Aves/Tyrannus tyrannus/56daa3a64892dcb68eb9b1ba91f6dfcf.jpg\",\n  \"5846.jpg\": \"Aves/Myiarchus crinitus/e57d5b5efd57b5fc82a64cd867f8f58f.jpg\",\n  \"584612.jpg\": \"Aves/Tyrannus tyrannus/b773f30497c1e538e891cb4122c15f14.jpg\",\n  \"584660.jpg\": \"Aves/Tyrannus tyrannus/795c8212c3c80ca5a7b775fd8e0fc913.jpg\",\n  \"584852.jpg\": \"Aves/Tachybaptus ruficollis/0fbef979884ac2623b988171107f83e1.jpg\",\n  \"584853.jpg\": \"Aves/Tachybaptus ruficollis/cefbb8223ec41c28bb1b158ffe24129c.jpg\",\n  \"584858.jpg\": \"Aves/Tachybaptus ruficollis/00b8e3f86b797c7108616e71424ef326.jpg\",\n  \"584869.jpg\": \"Aves/Tachybaptus ruficollis/233394ee862195dc7d03c7a03217a297.jpg\",\n  \"584874.jpg\": \"Aves/Tachybaptus ruficollis/33df5aca7976b0b04c21d30edefa581f.jpg\",\n  \"584878.jpg\": \"Aves/Tachybaptus ruficollis/1e19c985fb19d6540c75ee7b08ff6224.jpg\",\n  \"584879.jpg\": \"Aves/Tachybaptus ruficollis/7cec0206ccee8828e1df88b1adb89b51.jpg\",\n  \"584880.jpg\": \"Aves/Tachybaptus ruficollis/e9f6adc2c48ebabde1e2650bfb934314.jpg\",\n  \"584978.jpg\": \"Insecta/Copestylum mexicanum/e4f724ba508e9e46d5f4aff12eee8c09.jpg\",\n  \"584990.jpg\": \"Insecta/Copestylum mexicanum/c2d99bebe4b4f268f6f59924c907156c.jpg\",\n  \"584996.jpg\": \"Aves/Dendrocopos major/97f156c0c02429ac117100cd52631e58.jpg\",\n  \"585013.jpg\": \"Aves/Dendrocopos major/507c7f077794f50060537cf07466a173.jpg\",\n  \"585028.jpg\": \"Aves/Dendrocopos major/0ad13ef0af5678c4f1f6c96aae8c55de.jpg\",\n  \"585029.jpg\": \"Aves/Dendrocopos major/ea77ccaaeaeeaa2908c2efa5780c649f.jpg\",\n  \"585033.jpg\": \"Aves/Dendrocopos major/c0bec85a8e259d71d444686a265ccaae.jpg\",\n  \"585036.jpg\": \"Aves/Dendrocopos major/c98580b7cb1e2e026c4b7568440efe3a.jpg\",\n  \"585042.jpg\": \"Aves/Dendrocopos major/fdda99bbf3bbdb28447adcd40bd834c7.jpg\",\n  \"585200.jpg\": \"Aves/Haemorhous cassinii/7f686fbbe76ab7c6b006cb2d0797d33d.jpg\",\n  \"585203.jpg\": \"Aves/Haemorhous cassinii/449e89a2e09f7fe62727965d8fca40a4.jpg\",\n  \"585204.jpg\": \"Aves/Haemorhous cassinii/5d107db241930a14ed973fd53c2e0730.jpg\",\n  \"585206.jpg\": \"Aves/Haemorhous cassinii/d9d69c1272ed7a895d8126d54d365dc4.jpg\",\n  \"585226.jpg\": \"Aves/Haemorhous cassinii/9dbc91bda87697bacaf44b41e5af0992.jpg\",\n  \"585227.jpg\": \"Aves/Haemorhous cassinii/37b1737b1fb9b6bf9b07b85cc40d0a87.jpg\",\n  \"585228.jpg\": \"Aves/Haemorhous cassinii/2be230e5784571ad61a635589e11334b.jpg\",\n  \"585230.jpg\": \"Aves/Haemorhous cassinii/ad202a4b471a62f7df6c49c9c390fe16.jpg\",\n  \"585231.jpg\": \"Aves/Haemorhous cassinii/422654ebc2e130cdafe98594b5798674.jpg\",\n  \"585232.jpg\": \"Aves/Haemorhous cassinii/88d42ed58c85994485151df773704852.jpg\",\n  \"585235.jpg\": \"Aves/Haemorhous cassinii/a14636a0d1eb51c0db4416e9bfb7dd6d.jpg\",\n  \"585242.jpg\": \"Aves/Catharus ustulatus/8debb99715a8b1c93688da36e91ea328.jpg\",\n  \"585261.jpg\": \"Aves/Catharus ustulatus/c4b7e99c4ef8920db8c4a56039f45de8.jpg\",\n  \"585262.jpg\": \"Aves/Catharus ustulatus/da830d1e22ceff362036f04846fd93a5.jpg\",\n  \"585263.jpg\": \"Aves/Catharus ustulatus/0870a6e6eecdaec51db6f651bba9ca55.jpg\",\n  \"585280.jpg\": \"Aves/Catharus ustulatus/44372fdf91669c9ad34a9a263f3f782c.jpg\",\n  \"585289.jpg\": \"Aves/Catharus ustulatus/9d25c59ab0a4da6e0f2cb17f6746aecd.jpg\",\n  \"585290.jpg\": \"Aves/Catharus ustulatus/41cfb421d10e3ac5520c1aa337fea3d3.jpg\",\n  \"585296.jpg\": \"Aves/Catharus ustulatus/473eb45be7bdbabdb509be898aa68e37.jpg\",\n  \"585373.jpg\": \"Amphibia/Eurycea cirrigera/ad2855d87140da7161e943fdce117998.jpg\",\n  \"585379.jpg\": \"Amphibia/Eurycea cirrigera/fe2166a68d9615e9aa1486097432ac2a.jpg\",\n  \"585472.jpg\": \"Reptilia/Kinosternon flavescens/71f6c43ef33de2f372302e8122ad86aa.jpg\",\n  \"585480.jpg\": \"Reptilia/Kinosternon flavescens/3e7f4653648e77d67c500ba8916fa07a.jpg\",\n  \"585482.jpg\": \"Reptilia/Kinosternon flavescens/a14320890963ac3706e376763c343332.jpg\",\n  \"585483.jpg\": \"Reptilia/Kinosternon flavescens/895c8a9dd9d09ee8300ee4c2bf840a20.jpg\",\n  \"585484.jpg\": \"Reptilia/Kinosternon flavescens/88c00af1dda41bccb937f3df9dc1e468.jpg\",\n  \"585485.jpg\": \"Reptilia/Kinosternon flavescens/8f0720957a106be796d4604e50fc0144.jpg\",\n  \"585488.jpg\": \"Reptilia/Kinosternon flavescens/8ae1cf24a8364df362bfe0c18582fc91.jpg\",\n  \"585489.jpg\": \"Reptilia/Kinosternon flavescens/6310d64a4edfa43969088ec8e5ed8890.jpg\",\n  \"5855.jpg\": \"Insecta/Peridea ferruginea/a8327e97d5b3ea9001aa334f13ae41df.jpg\",\n  \"585513.jpg\": \"Aves/Milvus migrans/c4c57dde075a7c356bde1eeaf107f828.jpg\",\n  \"585515.jpg\": \"Aves/Milvus migrans/5ded9d995c6f0e9970a527f677f11c3c.jpg\",\n  \"585516.jpg\": \"Aves/Milvus migrans/a147141d74850cfa39a1aeda724b5b9a.jpg\",\n  \"585523.jpg\": \"Aves/Milvus migrans/1a169e1de9c59cecb770090cfc9bb6fc.jpg\",\n  \"585527.jpg\": \"Aves/Milvus migrans/c010d8a42e3c99a2d2dec176796fc63b.jpg\",\n  \"585528.jpg\": \"Aves/Milvus migrans/16e19fd6500e50cb808b4ae13b61265c.jpg\",\n  \"585529.jpg\": \"Aves/Milvus migrans/9417eca97f766b44610c897435c42e4e.jpg\",\n  \"585558.jpg\": \"Reptilia/Anguis fragilis/d0ea3f088ac2140e332758267e0e793b.jpg\",\n  \"585564.jpg\": \"Reptilia/Anguis fragilis/b7280ef115cff12836c4a46b1199ead3.jpg\",\n  \"585569.jpg\": \"Reptilia/Anguis fragilis/bb76311b414aa23df9d867a45e1319fc.jpg\",\n  \"585570.jpg\": \"Reptilia/Anguis fragilis/631b76432f9cce99040c538b5f7048cb.jpg\",\n  \"585571.jpg\": \"Reptilia/Anguis fragilis/8d9a4895f0a72314101904aa4bd684d7.jpg\",\n  \"585572.jpg\": \"Reptilia/Anguis fragilis/57e3c02490def1ec3371c6b158472da5.jpg\",\n  \"585573.jpg\": \"Reptilia/Anguis fragilis/bc0b83bab048b72d639e34334a68a7c9.jpg\",\n  \"585574.jpg\": \"Reptilia/Anguis fragilis/4bc2230be2fecfa597d3af279eb318d3.jpg\",\n  \"585580.jpg\": \"Reptilia/Anguis fragilis/d694b6a1b8ec417ad932e91ce7b326a2.jpg\",\n  \"585581.jpg\": \"Reptilia/Anguis fragilis/3b320d4a8cd1b45909b3e44be17c8567.jpg\",\n  \"585582.jpg\": \"Reptilia/Anguis fragilis/56cc9cb8e3d326c4472136295cc043a4.jpg\",\n  \"585583.jpg\": \"Reptilia/Anguis fragilis/d4c46b09d67b6196e91d2b7a341bca92.jpg\",\n  \"585591.jpg\": \"Reptilia/Anguis fragilis/eaa281422c9fe2069df18426871bd33e.jpg\",\n  \"585594.jpg\": \"Reptilia/Anguis fragilis/a1abf15724812b15ce1a84b4769698fe.jpg\",\n  \"58573.jpg\": \"Aves/Zosterops lateralis lateralis/e6e2feb64919430f21fa963948ae717f.jpg\",\n  \"58574.jpg\": \"Aves/Zosterops lateralis lateralis/f2176d43af52f751dff3998d9e4bbaa6.jpg\",\n  \"585746.jpg\": \"Insecta/Junonia coenia/a6a561870b3a3ab9dfead9c7d70a7f15.jpg\",\n  \"58580.jpg\": \"Aves/Zosterops lateralis lateralis/82171af50381c0b5fb02fcf4044b7c6c.jpg\",\n  \"585884.jpg\": \"Insecta/Junonia coenia/38533ed4ddc8ee0185951ed5c4d205b5.jpg\",\n  \"58589.jpg\": \"Aves/Zosterops lateralis lateralis/7200e23c3bd427ae785e6458643efd6b.jpg\",\n  \"5859.jpg\": \"Insecta/Peridea ferruginea/f4d6b6d4bd0742d821f8cb2f09c73308.jpg\",\n  \"585923.jpg\": \"Insecta/Junonia coenia/b4eee5fc4b27b44bbc091ca0821990ca.jpg\",\n  \"585992.jpg\": \"Insecta/Junonia coenia/2ecf8e99fa9024ea9a1c2128cd741ef7.jpg\",\n  \"5860.jpg\": \"Insecta/Peridea ferruginea/9a7c91a1738bac3d80bb28eb3787f85d.jpg\",\n  \"586064.jpg\": \"Insecta/Junonia coenia/b8d9637ac53d1e22f1e1152ba7843209.jpg\",\n  \"586095.jpg\": \"Insecta/Junonia coenia/f9672da0eb8b0696c5f6b488fe2431bd.jpg\",\n  \"586214.jpg\": \"Insecta/Junonia coenia/730ce796de25c860ae52809e6d293bc5.jpg\",\n  \"586215.jpg\": \"Insecta/Junonia coenia/4c1ec12d169ced06f90275212aa17525.jpg\",\n  \"5863.jpg\": \"Insecta/Peridea ferruginea/c76c3b48cc94a6ed804a9ede987110c2.jpg\",\n  \"586385.jpg\": \"Insecta/Junonia coenia/9c31d6097bdb590cbe728b1ac9cd9594.jpg\",\n  \"586400.jpg\": \"Insecta/Perithemis intensa/6aba57d98f94815872c672b2732d9abf.jpg\",\n  \"586401.jpg\": \"Insecta/Perithemis intensa/cb21202e665a49b0b89fd53e26c633b6.jpg\",\n  \"586416.jpg\": \"Insecta/Perithemis intensa/b0e63912f6335a46892a131f18f1827e.jpg\",\n  \"586458.jpg\": \"Aves/Aechmophorus occidentalis/de097db9b61df5e01900c4ef0c15cb8e.jpg\",\n  \"586514.jpg\": \"Aves/Aechmophorus occidentalis/389bea6c179c68ac5ea41bb34cd0a5fb.jpg\",\n  \"586516.jpg\": \"Aves/Aechmophorus occidentalis/708749426cbca67db35ebb8f61354653.jpg\",\n  \"586723.jpg\": \"Insecta/Olla v-nigrum/2b677e08765ed88a5f88023648e706d6.jpg\",\n  \"586724.jpg\": \"Insecta/Olla v-nigrum/00ddc7a6312f3b1cb9463061fd498e63.jpg\",\n  \"586725.jpg\": \"Insecta/Olla v-nigrum/90fe9b06f45552809fabc72a81e7f9e6.jpg\",\n  \"586726.jpg\": \"Insecta/Olla v-nigrum/499ef0165509787dd58ef87b5c16a528.jpg\",\n  \"586728.jpg\": \"Insecta/Olla v-nigrum/de6ddf630df30794bb541980893f395a.jpg\",\n  \"586729.jpg\": \"Insecta/Olla v-nigrum/896b736d91cf502aedd34ef287d0a1ad.jpg\",\n  \"586733.jpg\": \"Insecta/Olla v-nigrum/17d2c2817c759661b49c7dbad2d312b4.jpg\",\n  \"586735.jpg\": \"Insecta/Olla v-nigrum/004a0155aa4ddf7e95a8b8ad19e225f1.jpg\",\n  \"586737.jpg\": \"Insecta/Olla v-nigrum/ab9eab8db28441d70d45c85a2572c807.jpg\",\n  \"586782.jpg\": \"Animalia/Anthopleura elegantissima/70716322021b0b194f4432592000e622.jpg\",\n  \"586843.jpg\": \"Aves/Phalacrocorax carbo/ac60efdd5061338124075e595bec0b56.jpg\",\n  \"586923.jpg\": \"Aves/Phalacrocorax carbo/20ff58ed573e38be580a17f716dbf639.jpg\",\n  \"586926.jpg\": \"Aves/Phalacrocorax carbo/636197cece9542331264c1107d5250f6.jpg\",\n  \"586954.jpg\": \"Reptilia/Gambelia wislizenii/94f1ce6b1a2e71826a636b77afa6c94e.jpg\",\n  \"586967.jpg\": \"Reptilia/Gambelia wislizenii/1daa51046ce0593dd1f4796ad8f4225a.jpg\",\n  \"587000.jpg\": \"Insecta/Lethe appalachia/ab3ba9cfa57a7113e7e741d71963fa7c.jpg\",\n  \"587006.jpg\": \"Insecta/Lethe appalachia/b138d1290f8343430c3654968d7fb7de.jpg\",\n  \"587007.jpg\": \"Insecta/Lethe appalachia/6afddbf6ebdf5d15cc6b45661c3b0894.jpg\",\n  \"587009.jpg\": \"Insecta/Lethe appalachia/de1979771af15466b0a9d81c1d5df752.jpg\",\n  \"587012.jpg\": \"Insecta/Lethe appalachia/5c784c70a2e6b2dcc13bd06cc85388c4.jpg\",\n  \"587017.jpg\": \"Insecta/Celastrina lucia/f3b0c3b110584c8265ea353f50d2c968.jpg\",\n  \"587021.jpg\": \"Insecta/Celastrina lucia/58ac9dad6646619f80003ad86a636a4b.jpg\",\n  \"587036.jpg\": \"Mollusca/Octopus rubescens/635532c7bd08e65ce170a613cca371ec.jpg\",\n  \"587038.jpg\": \"Mollusca/Octopus rubescens/5697efbea1d5752f78c1c66fad57d1f7.jpg\",\n  \"587045.jpg\": \"Mollusca/Octopus rubescens/d741b808e3f270ea4cc82b0ee7450f91.jpg\",\n  \"587049.jpg\": \"Mollusca/Octopus rubescens/f7e6a0a707bdcd0b92ff8987ace34b00.jpg\",\n  \"587050.jpg\": \"Mollusca/Octopus rubescens/19abfc293aa1b581aca60ec433b396eb.jpg\",\n  \"587054.jpg\": \"Mollusca/Octopus rubescens/f910632a6d2e518ace52459eceb9ad00.jpg\",\n  \"587056.jpg\": \"Mollusca/Octopus rubescens/32dee6680783dffbad56acce1ba241f6.jpg\",\n  \"587073.jpg\": \"Aves/Myiarchus tyrannulus/3a298be373fc5a80ec99bb4f290ba8d9.jpg\",\n  \"587091.jpg\": \"Insecta/Melanoplus differentialis/8c53cb674b664dbdb68aed3eacd1aaeb.jpg\",\n  \"587097.jpg\": \"Insecta/Melanoplus differentialis/78eeb64538f756566595c5f479fd86d1.jpg\",\n  \"587105.jpg\": \"Insecta/Melanoplus differentialis/3e2ddd20c9430a9ddc021d1bdbcdbdd1.jpg\",\n  \"587107.jpg\": \"Insecta/Melanoplus differentialis/0ff884bd27e86d9e37e43bbca83f54a7.jpg\",\n  \"587138.jpg\": \"Insecta/Melanoplus differentialis/91a581e9ed296b2b3c611ccef102db06.jpg\",\n  \"587142.jpg\": \"Insecta/Melanoplus differentialis/20771202b16bb50712c013874c93150b.jpg\",\n  \"587147.jpg\": \"Insecta/Melanoplus differentialis/a037639bbbe7735819549df41b6ca4b5.jpg\",\n  \"587151.jpg\": \"Insecta/Autochton cellus/6a1668641ff94d617aa00bdf4eb82917.jpg\",\n  \"5872.jpg\": \"Mammalia/Leopardus pardalis/7a004126fdb9e179393032f702009423.jpg\",\n  \"587215.jpg\": \"Insecta/Acanalonia conica/d7aa723a7ef5580f892f1506bbbf96ca.jpg\",\n  \"587242.jpg\": \"Insecta/Libellula forensis/31cf8cdebbb43f4bbf1c34bd1e2548bd.jpg\",\n  \"587253.jpg\": \"Insecta/Libellula forensis/9189d752622c978908939eb33c0687ac.jpg\",\n  \"587267.jpg\": \"Insecta/Libellula forensis/7a7b9289090f85a04f8d2ae6596c0cb4.jpg\",\n  \"587274.jpg\": \"Insecta/Libellula forensis/65cb61e9a37804509beadb7f7d7b0488.jpg\",\n  \"587278.jpg\": \"Insecta/Libellula forensis/56d098c12e08e0bcfb16afecc911161a.jpg\",\n  \"587283.jpg\": \"Insecta/Libellula forensis/16e45166a4393b681021f5c90cb319ee.jpg\",\n  \"587284.jpg\": \"Insecta/Libellula forensis/fcd4b70c3b36940c155304de1371d1cd.jpg\",\n  \"58734.jpg\": \"Insecta/Phaulacridium marginale/5e8c564e24e064f015c9fe7a64ef41f7.jpg\",\n  \"587367.jpg\": \"Insecta/Argia apicalis/5fd73ac14d2efbd43389ca912249f396.jpg\",\n  \"587386.jpg\": \"Insecta/Argia apicalis/28e491a15984113beee1bb75a9542982.jpg\",\n  \"587394.jpg\": \"Insecta/Argia apicalis/25e831cb998f21439dcfc2a141d2f4dc.jpg\",\n  \"587397.jpg\": \"Insecta/Argia apicalis/91e7881e03715dd0bc364a4939cfd222.jpg\",\n  \"5874.jpg\": \"Mammalia/Leopardus pardalis/33267ad2a2f4cab8a43ec79c9c97edaa.jpg\",\n  \"58741.jpg\": \"Insecta/Phaulacridium marginale/864f390c450343dc9f51c33a82716d53.jpg\",\n  \"587430.jpg\": \"Insecta/Argia apicalis/109e463128ba09e4679d67281bb964d9.jpg\",\n  \"587435.jpg\": \"Insecta/Argia apicalis/2a2539160c6d701b5501bd2c1826b34d.jpg\",\n  \"587449.jpg\": \"Insecta/Argia apicalis/2de7c704496c551f21d920ece496e3bd.jpg\",\n  \"58750.jpg\": \"Insecta/Phaulacridium marginale/67bb97c5daf0eaf28b6f5f112e300a89.jpg\",\n  \"58753.jpg\": \"Insecta/Phaulacridium marginale/a17144b79065e3cb12702227e07aa6ba.jpg\",\n  \"5878.jpg\": \"Mammalia/Leopardus pardalis/a6837965bf38ff5b0414c22368e194a1.jpg\",\n  \"587898.jpg\": \"Mammalia/Otospermophilus variegatus/87105cd560d4694d6f4d5cf424450b53.jpg\",\n  \"587900.jpg\": \"Mammalia/Otospermophilus variegatus/a951d87f1196407a3bb734bec3c6105a.jpg\",\n  \"587910.jpg\": \"Mammalia/Otospermophilus variegatus/e2dea61dbf863426f2ee13855c8201b7.jpg\",\n  \"587915.jpg\": \"Mammalia/Otospermophilus variegatus/f13b6d97f60d141ca7ace919f3758158.jpg\",\n  \"587950.jpg\": \"Mammalia/Otospermophilus variegatus/f373b48518805c06adba5001228d373b.jpg\",\n  \"587951.jpg\": \"Mammalia/Otospermophilus variegatus/2b3dc29d840c364da1d669d16ea8a566.jpg\",\n  \"588038.jpg\": \"Amphibia/Ensatina eschscholtzii oregonensis/8d89c191afdeee7d9ae11bd40b23dd60.jpg\",\n  \"588039.jpg\": \"Amphibia/Ensatina eschscholtzii oregonensis/df90b85d97c881473345978e4383d444.jpg\",\n  \"588044.jpg\": \"Amphibia/Ensatina eschscholtzii oregonensis/9a028c80085fb0c87ed9b2038d9d1072.jpg\",\n  \"588050.jpg\": \"Amphibia/Ensatina eschscholtzii oregonensis/654a349fad562d15099f2c0d4d2c9529.jpg\",\n  \"588051.jpg\": \"Amphibia/Ensatina eschscholtzii oregonensis/e5952ead285f01ae3b57d73a6ff8fdb6.jpg\",\n  \"588052.jpg\": \"Amphibia/Ensatina eschscholtzii oregonensis/554fc3d3844c8338cd820e2fde07ac6c.jpg\",\n  \"588053.jpg\": \"Amphibia/Ensatina eschscholtzii oregonensis/78dcd60ce0f180ccb46e41ad282bdf0b.jpg\",\n  \"588056.jpg\": \"Amphibia/Ensatina eschscholtzii oregonensis/bb83010f6dadbca12f081d4374977e0a.jpg\",\n  \"588057.jpg\": \"Amphibia/Ensatina eschscholtzii oregonensis/51eb90e7c320a16d4630745b5ed3015c.jpg\",\n  \"588058.jpg\": \"Amphibia/Ensatina eschscholtzii oregonensis/09b7ba7b579ffa9b3b7184dc51790e70.jpg\",\n  \"588059.jpg\": \"Amphibia/Ensatina eschscholtzii oregonensis/310fbe6c095df122de36f311676843ed.jpg\",\n  \"588060.jpg\": \"Amphibia/Ensatina eschscholtzii oregonensis/364f5d4eb7c09066331a02add619bf88.jpg\",\n  \"588061.jpg\": \"Amphibia/Ensatina eschscholtzii oregonensis/f31d7b302da541eafa6a55a3196e994f.jpg\",\n  \"588082.jpg\": \"Insecta/Pyronia tithonus/1551cd3c2949776f744bb5c75f0ac424.jpg\",\n  \"588087.jpg\": \"Insecta/Trigonopeltastes delta/ad3453d2e51dd25256e6bf5968a83549.jpg\",\n  \"588089.jpg\": \"Insecta/Trigonopeltastes delta/ca407ae11f696f7e25db2caeb711a625.jpg\",\n  \"588090.jpg\": \"Insecta/Trigonopeltastes delta/ed808efb3f56a3712ff65575bddf7b47.jpg\",\n  \"588141.jpg\": \"Aves/Vermivora chrysoptera/80c8b59e2fc2fc777b1984108df3ad27.jpg\",\n  \"588148.jpg\": \"Aves/Vermivora chrysoptera/93f68e3a5bc83f4260fa6d4ddef36be4.jpg\",\n  \"588161.jpg\": \"Aves/Dacelo novaeguineae/a5b56367c9296ba2bb9fc5e6d6cc41d6.jpg\",\n  \"588163.jpg\": \"Aves/Dacelo novaeguineae/1d7ad9521e14f3c868fc3f6f9e9a52e8.jpg\",\n  \"588165.jpg\": \"Aves/Dacelo novaeguineae/97ab0884e978a6bee5183853d4dbee30.jpg\",\n  \"588167.jpg\": \"Aves/Dacelo novaeguineae/0876d42541f9f9d8ce895f22bcf6116f.jpg\",\n  \"588173.jpg\": \"Aves/Dacelo novaeguineae/09ac1621c6979f9725f21268853163f3.jpg\",\n  \"588176.jpg\": \"Aves/Dacelo novaeguineae/03a0eb25f8be6387c12f06f022dfbdb1.jpg\",\n  \"588413.jpg\": \"Insecta/Plectrodera scalator/b4f138e38bde4550b47fb268399a3f4e.jpg\",\n  \"58856.jpg\": \"Insecta/Vanessa gonerilla gonerilla/aa3e81ff1da97a8920c8812c29edac0b.jpg\",\n  \"58872.jpg\": \"Insecta/Vanessa gonerilla gonerilla/b1dab3b3431eaffc41fa87b48c5b92e4.jpg\",\n  \"58875.jpg\": \"Insecta/Vanessa gonerilla gonerilla/39b9f28350879025ae6c404ac8688df7.jpg\",\n  \"588857.jpg\": \"Aves/Bartramia longicauda/436b37699c277976f80342d8a665a247.jpg\",\n  \"588858.jpg\": \"Aves/Bartramia longicauda/1aaafcf0e1d6cf6ae93d1c4968b0d0c7.jpg\",\n  \"588859.jpg\": \"Aves/Bartramia longicauda/24a4d4170293632f4527d0ff23253137.jpg\",\n  \"588864.jpg\": \"Aves/Bartramia longicauda/0f7cb6ea698fffa82aa5bfc6167c5189.jpg\",\n  \"588868.jpg\": \"Aves/Bartramia longicauda/777f902b288ecebc995670da15db4579.jpg\",\n  \"588872.jpg\": \"Aves/Bartramia longicauda/ea9b472e87d5dfb192deb3091bdf5ec4.jpg\",\n  \"588873.jpg\": \"Aves/Bartramia longicauda/fa939068ae55522526d34b18fc4b74f3.jpg\",\n  \"588874.jpg\": \"Aves/Bartramia longicauda/9462cb03f9c6d4368ec5483116344a92.jpg\",\n  \"58890.jpg\": \"Insecta/Vanessa gonerilla gonerilla/db765c7517d0478778a88ffc67ff487e.jpg\",\n  \"58891.jpg\": \"Insecta/Vanessa gonerilla gonerilla/c0fc0aadca38aacc6b5ee81ad767b2fe.jpg\",\n  \"588916.jpg\": \"Insecta/Poanes zabulon/a73e2f26d14ea2a0f547820628540b0b.jpg\",\n  \"58892.jpg\": \"Insecta/Vanessa gonerilla gonerilla/3708948221aab6798f76d711fc7deafe.jpg\",\n  \"588923.jpg\": \"Insecta/Poanes zabulon/03970238f2d31472be4cee55d6ed4393.jpg\",\n  \"588928.jpg\": \"Insecta/Poanes zabulon/598f7e8240afe022001b581f4c0a53f2.jpg\",\n  \"588933.jpg\": \"Insecta/Poanes zabulon/ddccf068a8342f1f04b0ee0cbca7a0eb.jpg\",\n  \"588937.jpg\": \"Insecta/Poanes zabulon/32f1416aeb0f1d05c105fa4a3c1ff716.jpg\",\n  \"588939.jpg\": \"Insecta/Poanes zabulon/fb71660450045da83ff037558381c864.jpg\",\n  \"588945.jpg\": \"Insecta/Poanes zabulon/de089b9db42254d8d84668976e6a8b29.jpg\",\n  \"588961.jpg\": \"Insecta/Poanes zabulon/f9a39346ccf8458b1108a7bd4090575d.jpg\",\n  \"588964.jpg\": \"Insecta/Poanes zabulon/669bb1084724430042a8aba815d0c20d.jpg\",\n  \"588967.jpg\": \"Insecta/Poanes zabulon/9bf63e4f47347d5a3a0fc0fb98be612a.jpg\",\n  \"58897.jpg\": \"Insecta/Vanessa gonerilla gonerilla/9db0a06d95455298192cb6ec88642a9e.jpg\",\n  \"58906.jpg\": \"Insecta/Vanessa gonerilla gonerilla/bb7927546497db938fdb927df9611f76.jpg\",\n  \"58907.jpg\": \"Insecta/Vanessa gonerilla gonerilla/ad1ec15eaac7c491011d622c4d337543.jpg\",\n  \"58908.jpg\": \"Insecta/Vanessa gonerilla gonerilla/d1ed589d69c7437b5be2f0648220a3f6.jpg\",\n  \"58909.jpg\": \"Insecta/Vanessa gonerilla gonerilla/7204057f3235169bcff838b0fef46377.jpg\",\n  \"58910.jpg\": \"Insecta/Vanessa gonerilla gonerilla/fc8529815cda9ec0620ff68a12ed1818.jpg\",\n  \"58911.jpg\": \"Insecta/Vanessa gonerilla gonerilla/4c0632a3260a152ff4f9d3979115cca3.jpg\",\n  \"58912.jpg\": \"Insecta/Vanessa gonerilla gonerilla/7cd0ad1d938ef536e5e31c312a9c1f23.jpg\",\n  \"58914.jpg\": \"Insecta/Vanessa gonerilla gonerilla/593da12c20789fcd923fe728e57f7cbd.jpg\",\n  \"58915.jpg\": \"Insecta/Vanessa gonerilla gonerilla/67f5bd2578d94d8deb44b37daf5cf56d.jpg\",\n  \"58916.jpg\": \"Insecta/Vanessa gonerilla gonerilla/c69be7d2c0713844f51f45fb468070c0.jpg\",\n  \"58925.jpg\": \"Insecta/Vanessa gonerilla gonerilla/67cbad9dc5656fd7d4337968a2423808.jpg\",\n  \"589341.jpg\": \"Aves/Uria aalge/d9fca321cf6634311c5383652fb31526.jpg\",\n  \"589346.jpg\": \"Aves/Uria aalge/1c8fa55a255cbf40816843af33631f57.jpg\",\n  \"58938.jpg\": \"Insecta/Vanessa gonerilla gonerilla/859083d60933883e0d5d417385159870.jpg\",\n  \"58940.jpg\": \"Insecta/Vanessa gonerilla gonerilla/86b5df525313bf91383d364088fe0862.jpg\",\n  \"589403.jpg\": \"Aves/Uria aalge/bf923210ca3d7ff824bf7e75a8325cc6.jpg\",\n  \"589443.jpg\": \"Mammalia/Ursus arctos horribilis/e90b59f3508ae57940817bb2994f2373.jpg\",\n  \"589445.jpg\": \"Mammalia/Ursus arctos horribilis/24cacf0d0f62a07e39a857962a09f64f.jpg\",\n  \"589469.jpg\": \"Amphibia/Ensatina eschscholtzii xanthoptica/f564617fcffce7d4510d6358f407ea38.jpg\",\n  \"589470.jpg\": \"Amphibia/Ensatina eschscholtzii xanthoptica/6fd5cac3c89165cdae331949dea2eba4.jpg\",\n  \"589471.jpg\": \"Amphibia/Ensatina eschscholtzii xanthoptica/3877e192e4bfb40d6e20adbb2025948a.jpg\",\n  \"589491.jpg\": \"Amphibia/Ensatina eschscholtzii xanthoptica/a82663f93a21759d1be0323823e18349.jpg\",\n  \"589499.jpg\": \"Amphibia/Ensatina eschscholtzii xanthoptica/f61486a4fab4bc02d07b22288b13293e.jpg\",\n  \"58950.jpg\": \"Insecta/Eudryas grata/25116d076addbecb4ea7ebfd4ea65797.jpg\",\n  \"589524.jpg\": \"Aves/Columbina talpacoti/cb3b275a9bccb85fc954220b1bbfe285.jpg\",\n  \"589527.jpg\": \"Aves/Columbina talpacoti/57712f19e36c4dcc99d794611b1be813.jpg\",\n  \"589537.jpg\": \"Aves/Columbina talpacoti/7175308650507e6c95e5d4241aee12fa.jpg\",\n  \"589545.jpg\": \"Aves/Columbina talpacoti/9269b53f1f8f930a1b2afc2d414b1b4b.jpg\",\n  \"589547.jpg\": \"Aves/Columbina talpacoti/be39ec272f4cf6b372d088a038e08b52.jpg\",\n  \"589549.jpg\": \"Aves/Columbina talpacoti/cb8fa0bf71945839df4617451354faa0.jpg\",\n  \"58956.jpg\": \"Insecta/Eudryas grata/eaf23cb88bcc74064e50c76f5e5af020.jpg\",\n  \"589561.jpg\": \"Aves/Aegithalos caudatus/6b4626e334a4c7c71aee128474cd3356.jpg\",\n  \"589563.jpg\": \"Aves/Aegithalos caudatus/e93bac8f73a03f524819b40386991142.jpg\",\n  \"589564.jpg\": \"Aves/Aegithalos caudatus/09a562b18d7a4bc3ad267489d2397fa5.jpg\",\n  \"589566.jpg\": \"Aves/Aegithalos caudatus/b9c5cdb89989bbf3c2043c6690f926f1.jpg\",\n  \"58957.jpg\": \"Insecta/Eudryas grata/e7ee1b71902063dbb5df37e29542ad22.jpg\",\n  \"589571.jpg\": \"Aves/Aegithalos caudatus/06aa8f35cb3dae7e850876c8eb7db683.jpg\",\n  \"589572.jpg\": \"Aves/Aegithalos caudatus/a6bf14abbc1341c4e0ee4d6523ccd20b.jpg\",\n  \"58958.jpg\": \"Insecta/Eudryas grata/dfe6a8d2fca739a9915468077f9cdf41.jpg\",\n  \"589583.jpg\": \"Aves/Aegithalos caudatus/2bf9722de589fa936e0c187aea0478e3.jpg\",\n  \"589589.jpg\": \"Aves/Aegithalos caudatus/c68de6326fbc4cc1f6401cc16f4ac6e1.jpg\",\n  \"58959.jpg\": \"Insecta/Eudryas grata/d4acb51bbbda34af5cac59bcdf05b6c4.jpg\",\n  \"589591.jpg\": \"Aves/Aegithalos caudatus/4ee100d347ac4497690f099693bef8ab.jpg\",\n  \"589624.jpg\": \"Mammalia/Kobus ellipsiprymnus/d3dd6659a52906de2ae54c606de98205.jpg\",\n  \"58965.jpg\": \"Insecta/Eudryas grata/12a98308603fdaa7dd0a9034819d1b8e.jpg\",\n  \"589659.jpg\": \"Aves/Anas platyrhynchos/321ecec4a16d85d104a1e812a2115996.jpg\",\n  \"58966.jpg\": \"Insecta/Eudryas grata/c9d3bda4ca54699fa1356480e3b853dc.jpg\",\n  \"58967.jpg\": \"Insecta/Eudryas grata/0178a7402c1a70553bb6f39df61d53cc.jpg\",\n  \"58968.jpg\": \"Insecta/Eudryas grata/32b4519da1f3399ed05f8d68339e7576.jpg\",\n  \"58969.jpg\": \"Insecta/Eudryas grata/ab4d76dc64d441aac72713a754286ca2.jpg\",\n  \"58977.jpg\": \"Insecta/Eudryas grata/4b56f298756252cdf92b7a9723fcbfda.jpg\",\n  \"58978.jpg\": \"Insecta/Eudryas grata/f6717ad55be2e981fca8b68da41f2228.jpg\",\n  \"58979.jpg\": \"Insecta/Eudryas grata/fde3c4a65aeaa8237e663d03391dd142.jpg\",\n  \"58980.jpg\": \"Insecta/Eudryas grata/2ed22d2c8b6ee1df85769fee2704df12.jpg\",\n  \"58981.jpg\": \"Insecta/Eudryas grata/6ef67629c74a0f7f39e18541c9871a03.jpg\",\n  \"58982.jpg\": \"Insecta/Eudryas grata/b5ace0e317262e2989484beb62939bc1.jpg\",\n  \"58983.jpg\": \"Insecta/Eudryas grata/d3e12db1eec19eb09b11eaa627b27eb3.jpg\",\n  \"58985.jpg\": \"Insecta/Eudryas grata/54958262065b0f6ad60a1719306c13b5.jpg\",\n  \"58987.jpg\": \"Insecta/Eudryas grata/9b84ed444b0cb1fa97224279da5654bc.jpg\",\n  \"58994.jpg\": \"Insecta/Eudryas grata/e33e6ed991b11f9a107a1b5fb06027d8.jpg\",\n  \"59.jpg\": \"Mammalia/Marmota flaviventris/61352c412a46400bfce880e904b4f612.jpg\",\n  \"590080.jpg\": \"Aves/Anas platyrhynchos/e77ae8d0e9f80e936bbb40e345e742f8.jpg\",\n  \"591139.jpg\": \"Aves/Anas platyrhynchos/6909052d7178ae58ff3cd5971f21cccd.jpg\",\n  \"591268.jpg\": \"Aves/Anas platyrhynchos/f4f0b6cbe729ec35ea4508ae2b26a190.jpg\",\n  \"591319.jpg\": \"Aves/Anas platyrhynchos/c4def34d6c295fa3430291a3a543b49d.jpg\",\n  \"591373.jpg\": \"Aves/Ardenna gravis/f8d6ec3fe31b6702cc41588a500e9dba.jpg\",\n  \"591374.jpg\": \"Aves/Ardenna gravis/b1a4b4a828b6bca86ef217bad94b6033.jpg\",\n  \"591458.jpg\": \"Aves/Zenaida asiatica/5b38782231fe57672049a7b66e9d2471.jpg\",\n  \"591501.jpg\": \"Aves/Zenaida asiatica/bfac991b4183eff0d40423d33b86635d.jpg\",\n  \"591574.jpg\": \"Aves/Zenaida asiatica/ed28629c49eb3b07b9c9ee72e0b188ea.jpg\",\n  \"591579.jpg\": \"Aves/Zenaida asiatica/9ee55ae56fa45cdf3d9c919af83cdec3.jpg\",\n  \"591599.jpg\": \"Aves/Zenaida asiatica/79ff1d6ba9aa0279bdea6ee8eecdd61c.jpg\",\n  \"591615.jpg\": \"Aves/Zenaida asiatica/885df43cb7703b5c33260c4469490453.jpg\",\n  \"591665.jpg\": \"Aves/Tachycineta thalassina/302e28ed3d57bf4bd8cd1b5c7d9b7080.jpg\",\n  \"591695.jpg\": \"Aves/Tachycineta thalassina/9268487b84ae47d86961568a13bb7b55.jpg\",\n  \"591706.jpg\": \"Aves/Tachycineta thalassina/43ca05e799d0019500dfa320da86160c.jpg\",\n  \"591716.jpg\": \"Aves/Tachycineta thalassina/0ea76393d2f2f7b9ae5c6b3f74eb03fe.jpg\",\n  \"591721.jpg\": \"Aves/Tachycineta thalassina/1fe648d38d197ef1b7e23b9e5bb12e6f.jpg\",\n  \"591723.jpg\": \"Aves/Tachycineta thalassina/1b7663f7d25de283139fd0e485070b4a.jpg\",\n  \"591732.jpg\": \"Aves/Tachycineta thalassina/952e77029d4961c881d0742714f88f76.jpg\",\n  \"591747.jpg\": \"Mammalia/Giraffa camelopardalis/291c1015f8356deadc133de471806b83.jpg\",\n  \"591749.jpg\": \"Mammalia/Giraffa camelopardalis/d725d9ee11316ed675e4909f6044ef51.jpg\",\n  \"591818.jpg\": \"Insecta/Sceliphron caementarium/46c85e8c2605d73dc58186bed60c9652.jpg\",\n  \"591820.jpg\": \"Insecta/Sceliphron caementarium/ad5e2a0809ffef76b723309b68bbc225.jpg\",\n  \"591821.jpg\": \"Insecta/Sceliphron caementarium/cc9b35968f68dba59ce06b9d89c8a768.jpg\",\n  \"591850.jpg\": \"Insecta/Sceliphron caementarium/51cd15e7334f21c629cd406a7c8a101d.jpg\",\n  \"591871.jpg\": \"Insecta/Sceliphron caementarium/865e11b8859d82cead265acd602b92fc.jpg\",\n  \"591897.jpg\": \"Insecta/Sceliphron caementarium/bcd84cd19ce109d0143f046b7418f4ed.jpg\",\n  \"591912.jpg\": \"Insecta/Sceliphron caementarium/702f5055e082a7cda357d414fa0caa54.jpg\",\n  \"591946.jpg\": \"Mollusca/Crepidula adunca/ca4a4ba860fcd85221ec1e5b4ba7ef2d.jpg\",\n  \"591968.jpg\": \"Aves/Podiceps auritus/e3144747d0ccbfd09b60a667e56d663e.jpg\",\n  \"591974.jpg\": \"Aves/Podiceps auritus/8872be27b609de7a598bc7b8f88ee470.jpg\",\n  \"592004.jpg\": \"Aves/Podiceps auritus/8348907f8d631eb7279ea03f4bd29048.jpg\",\n  \"592040.jpg\": \"Aves/Podiceps auritus/ba93cabc1a36ab4b1d734890b536b702.jpg\",\n  \"592111.jpg\": \"Insecta/Megaphasma denticrus/b11970eead03bfe3246592ef0c2426bd.jpg\",\n  \"592114.jpg\": \"Insecta/Megaphasma denticrus/a1a53a3765d7ff6e862ac1d6d79623b8.jpg\",\n  \"592168.jpg\": \"Aves/Anas bahamensis/7ecbacb479b16445b00b38aec9def52f.jpg\",\n  \"592172.jpg\": \"Aves/Anas bahamensis/e77380e7a193afa85b818e14ecfafc19.jpg\",\n  \"592176.jpg\": \"Mollusca/Hermissenda opalescens/8a588b116d2c8791ad2d9ae273d6a34c.jpg\",\n  \"592179.jpg\": \"Mollusca/Hermissenda opalescens/ce79c09b1289404f9b6c43aff2aa3bba.jpg\",\n  \"592184.jpg\": \"Mollusca/Hermissenda opalescens/f3da8abcfe1f105034f692c2f7816a81.jpg\",\n  \"592197.jpg\": \"Mollusca/Hermissenda opalescens/c53d61e74219c817a15561c115dfed8f.jpg\",\n  \"592203.jpg\": \"Mollusca/Hermissenda opalescens/99e40aaa8a52959441cf69784e8a1cbd.jpg\",\n  \"592209.jpg\": \"Mollusca/Hermissenda opalescens/c793c951d83f3eee64cf730e1f798c65.jpg\",\n  \"592212.jpg\": \"Mollusca/Hermissenda opalescens/659e019ed24b3e6c335922561e0ff9cd.jpg\",\n  \"592213.jpg\": \"Mollusca/Hermissenda opalescens/e9d132202dda2ac523f31ce5d5837187.jpg\",\n  \"592214.jpg\": \"Mollusca/Hermissenda opalescens/57095f51dc8d4503e8e55fa171edacaf.jpg\",\n  \"592246.jpg\": \"Insecta/Manduca sexta/ceb6d9796fb3c0d8269d2fccb61494ab.jpg\",\n  \"592254.jpg\": \"Insecta/Manduca sexta/0259e0386b7b38dcd63ee6f7bbbdcf11.jpg\",\n  \"592259.jpg\": \"Insecta/Manduca sexta/eed9291ffffd8d981e1fd2ba0fb40350.jpg\",\n  \"592266.jpg\": \"Insecta/Manduca sexta/77c881a24ff4f86fe2a07c2aa7f2bb9e.jpg\",\n  \"592270.jpg\": \"Insecta/Manduca sexta/0a6f5e82c67d71e23d0891e303a0da6b.jpg\",\n  \"592275.jpg\": \"Insecta/Manduca sexta/e403b09f76fe38ff3ba99a4b019e8fee.jpg\",\n  \"592280.jpg\": \"Insecta/Manduca sexta/33efee66d08af4b006e0dc7fe9c4917e.jpg\",\n  \"592284.jpg\": \"Insecta/Manduca sexta/2adc55c6d216d34260fdca4024120885.jpg\",\n  \"592291.jpg\": \"Insecta/Manduca sexta/8877fc3f9b22b0993a418cbe44fb365f.jpg\",\n  \"592293.jpg\": \"Insecta/Manduca sexta/efe6c24fc7b20113a9cd5843800fed07.jpg\",\n  \"592302.jpg\": \"Insecta/Tetracis cachexiata/888cc35a8522efae69c8641f096d7ce2.jpg\",\n  \"592306.jpg\": \"Mammalia/Ammospermophilus leucurus/47ae9187ce3ab1b6ef360403db770f1f.jpg\",\n  \"592307.jpg\": \"Mammalia/Ammospermophilus leucurus/79ea4fe2b00ef80b36f2e9634d5fa813.jpg\",\n  \"592308.jpg\": \"Mammalia/Ammospermophilus leucurus/b42c42b86470c0675b389dd57d2a4c14.jpg\",\n  \"592310.jpg\": \"Mammalia/Ammospermophilus leucurus/a9412af56f66e06308f150c0d03f696e.jpg\",\n  \"592315.jpg\": \"Mammalia/Ammospermophilus leucurus/4112f88285b8789375074d6362b15255.jpg\",\n  \"592325.jpg\": \"Mammalia/Ammospermophilus leucurus/39148a109c97479e9bbb048427f94ebd.jpg\",\n  \"592326.jpg\": \"Mammalia/Ammospermophilus leucurus/58087f3903e06c501421c41da5ceb657.jpg\",\n  \"592332.jpg\": \"Mammalia/Ammospermophilus leucurus/2a098fd5e5938eee6dd74b49c8b970d0.jpg\",\n  \"592334.jpg\": \"Mammalia/Ammospermophilus leucurus/cbb635b97e612096a66d7c4fa341dbe5.jpg\",\n  \"592347.jpg\": \"Insecta/Erynnis baptisiae/1aaa89ac42ac2a62eba78098bdcd87cc.jpg\",\n  \"592353.jpg\": \"Insecta/Erynnis baptisiae/644ac78349e8c888177ce5e1fe258596.jpg\",\n  \"592370.jpg\": \"Insecta/Erynnis baptisiae/8bcbd12cf0610449ed1010ce6340b598.jpg\",\n  \"592372.jpg\": \"Insecta/Erynnis baptisiae/4b05a04e96bc1391e5314929f9eb3329.jpg\",\n  \"592373.jpg\": \"Insecta/Erynnis baptisiae/8db33127be9d5f9e7ebb20d6d61dba29.jpg\",\n  \"592421.jpg\": \"Aves/Aphelocoma wollweberi/567bd454c743f0e62886931ade944990.jpg\",\n  \"592427.jpg\": \"Aves/Aphelocoma wollweberi/a52e1cd51e6961244200db7d8256d51f.jpg\",\n  \"592437.jpg\": \"Aves/Aphelocoma wollweberi/ce0408899c8d23f72b9e7dd834808f53.jpg\",\n  \"592440.jpg\": \"Aves/Aphelocoma wollweberi/63ab34ee83d52d49da2d6f18f644d5af.jpg\",\n  \"592443.jpg\": \"Aves/Aphelocoma wollweberi/35da0db6978ebe3aec3233c1637b5d66.jpg\",\n  \"592444.jpg\": \"Aves/Aphelocoma wollweberi/5f2b2c00f89debc7597b8c6cf6667514.jpg\",\n  \"592448.jpg\": \"Aves/Aphelocoma wollweberi/b7a55fe8e9899715610e02fcb832e223.jpg\",\n  \"592449.jpg\": \"Aves/Aphelocoma wollweberi/f1b61b6ca58d80c6be2b0161a58ba664.jpg\",\n  \"592461.jpg\": \"Insecta/Stenopelmatus fuscus/2bf75d10320ee888f20a33da528c7767.jpg\",\n  \"592479.jpg\": \"Mammalia/Pecari tajacu/e30f2b820ce74099a30f924690a4cbc9.jpg\",\n  \"592499.jpg\": \"Mammalia/Pecari tajacu/5c22bf1b42a6f9addf4fab9cb23265a0.jpg\",\n  \"592503.jpg\": \"Mammalia/Pecari tajacu/fe0e268b4c9c153040da1f1110d1da45.jpg\",\n  \"592506.jpg\": \"Mammalia/Pecari tajacu/47b0ddf97ed51dc0c27fdeda03eb89da.jpg\",\n  \"592513.jpg\": \"Mammalia/Pecari tajacu/c71cce12c789bfedc8a10f25aed8cb1f.jpg\",\n  \"592535.jpg\": \"Mammalia/Pecari tajacu/b67ced2e5951afb6a244cb8d766624af.jpg\",\n  \"592705.jpg\": \"Insecta/Alypia octomaculata/94775aa2f23be8a5c3958acb4db9fe75.jpg\",\n  \"592706.jpg\": \"Insecta/Alypia octomaculata/76c21ffbf7b718e267d9f2131b9ad711.jpg\",\n  \"592710.jpg\": \"Insecta/Alypia octomaculata/1a8ac3d721914cdd6dbfcff6e395022d.jpg\",\n  \"592711.jpg\": \"Insecta/Alypia octomaculata/89d5b9f061b165f9c2b6527067014abb.jpg\",\n  \"592713.jpg\": \"Insecta/Alypia octomaculata/a9e05415a995c607ff2b5eaddb4dd6d3.jpg\",\n  \"592718.jpg\": \"Insecta/Alypia octomaculata/f422cb38325e2e5b4b89c46d5cbd26b4.jpg\",\n  \"592725.jpg\": \"Insecta/Alypia octomaculata/fb81192845b187a3605bf873e2830432.jpg\",\n  \"592728.jpg\": \"Insecta/Alypia octomaculata/dbd562cdd3758480bdd431c344eae2a5.jpg\",\n  \"592729.jpg\": \"Insecta/Alypia octomaculata/64f8793b5e32e24673b5b55ec6db7792.jpg\",\n  \"592731.jpg\": \"Insecta/Alypia octomaculata/1f3e407f3d6f10f0ff88c507c1066463.jpg\",\n  \"592741.jpg\": \"Reptilia/Iguana iguana/29bd66f782bb36147540cffe229bb5b3.jpg\",\n  \"592762.jpg\": \"Reptilia/Iguana iguana/fa215b4d6cbc79f0a832b1e9880be030.jpg\",\n  \"592785.jpg\": \"Reptilia/Iguana iguana/433e07ed6a5d9aebf581624eb1ac143b.jpg\",\n  \"592797.jpg\": \"Reptilia/Iguana iguana/6910866f417588ca5771141659e9d9e2.jpg\",\n  \"592800.jpg\": \"Reptilia/Iguana iguana/dd392c956e7fc671cb6d1c5c97c2df40.jpg\",\n  \"592810.jpg\": \"Reptilia/Iguana iguana/38c623f70c350e639d131e3e630d4189.jpg\",\n  \"592825.jpg\": \"Reptilia/Iguana iguana/1b876091c35a009bd9008a841b423b67.jpg\",\n  \"592836.jpg\": \"Reptilia/Iguana iguana/c21c22935bd202d0e24e54bd4bb81809.jpg\",\n  \"592894.jpg\": \"Insecta/Spragueia leo/31f4ddcd73eae1dc664b0ec1aad0aba2.jpg\",\n  \"592916.jpg\": \"Aves/Polioptila caerulea/0eb7eceba48e97f255f0005caa8bcd5f.jpg\",\n  \"592985.jpg\": \"Aves/Polioptila caerulea/5af0b8d0c8c4cd99e596d73162c33f95.jpg\",\n  \"592986.jpg\": \"Aves/Polioptila caerulea/617de556f50f621a18ed847bfcd73a5f.jpg\",\n  \"593065.jpg\": \"Aves/Polioptila caerulea/d2fa607ae820a14d98ba345ccaeb09f4.jpg\",\n  \"593136.jpg\": \"Aves/Polioptila caerulea/c04cf7347c44f7a0320e4845e290cb51.jpg\",\n  \"593189.jpg\": \"Aves/Polioptila caerulea/04a88f8f39f0e7e3d1eca62bafd17962.jpg\",\n  \"593329.jpg\": \"Insecta/Ceratomia catalpae/ec6c0088d2073079f7b810495d21bf09.jpg\",\n  \"593333.jpg\": \"Mollusca/Megastraea undosa/265b2a11549dfc13de22b478c07bb8b8.jpg\",\n  \"593359.jpg\": \"Aves/Tyrannus verticalis/e58a60e4571e2722626db6addde26eb0.jpg\",\n  \"593386.jpg\": \"Aves/Tyrannus verticalis/9ba22594126a97ea5e6c4dbe6704ebb9.jpg\",\n  \"593427.jpg\": \"Aves/Tyrannus verticalis/9920dc7015a937c5738409d3d5c29308.jpg\",\n  \"593561.jpg\": \"Aves/Tyrannus verticalis/c10ecfdf8dbadfdda20aad181c81b39c.jpg\",\n  \"593562.jpg\": \"Aves/Tyrannus verticalis/7101892a55b148090211c09041e0f3a8.jpg\",\n  \"593589.jpg\": \"Aves/Tyrannus verticalis/d4f1972e8632ecdaacdd28945541a900.jpg\",\n  \"593617.jpg\": \"Aves/Serinus mozambicus/2241b4411643238830fc5d0a787e7305.jpg\",\n  \"593619.jpg\": \"Aves/Serinus mozambicus/7d53661950abb4e0e626d495ce732ab1.jpg\",\n  \"593709.jpg\": \"Mollusca/Arion subfuscus/3a5c42dd7d20885eeed8ad08ef7b97d5.jpg\",\n  \"593712.jpg\": \"Mollusca/Arion subfuscus/80981cfa3fd8b1332d11bf067cc036e9.jpg\",\n  \"593723.jpg\": \"Arachnida/Neoscona crucifera/379c740973b0fcec5c4830270f3bf79a.jpg\",\n  \"593724.jpg\": \"Arachnida/Neoscona crucifera/614d28e7ce05157ccc3f81cc14621c08.jpg\",\n  \"593729.jpg\": \"Arachnida/Neoscona crucifera/44c4e3ece3ce693fc556dd961fc0f4ea.jpg\",\n  \"593731.jpg\": \"Arachnida/Neoscona crucifera/c4c1ee938315cf7973560de781db8af8.jpg\",\n  \"593732.jpg\": \"Arachnida/Neoscona crucifera/1eacc7c322ff2b66205ec4e586c6ac38.jpg\",\n  \"593733.jpg\": \"Arachnida/Neoscona crucifera/1dadd114a15b5d16c4e801b8405ce858.jpg\",\n  \"593734.jpg\": \"Arachnida/Neoscona crucifera/1b52da6f2dca08201a1dc886de84481f.jpg\",\n  \"593735.jpg\": \"Arachnida/Neoscona crucifera/1bf7cd368acad76795b829d7a9f9de59.jpg\",\n  \"593740.jpg\": \"Arachnida/Neoscona crucifera/d55fde179b5cfbe95a8eee22fdce150b.jpg\",\n  \"593741.jpg\": \"Arachnida/Neoscona crucifera/3fd6bb1e58df4d57a23f924198491a07.jpg\",\n  \"593755.jpg\": \"Aves/Myiozetetes similis/267c4bc28f2e372f85d0e4223f62aad1.jpg\",\n  \"593789.jpg\": \"Aves/Myiozetetes similis/707daf4ae84304f3c079f32d35f36693.jpg\",\n  \"593810.jpg\": \"Aves/Myiozetetes similis/07c6911a4e9aff53c400b3ee1577db03.jpg\",\n  \"593814.jpg\": \"Aves/Myiozetetes similis/f6ccfc05e1de1221eaa8d25f4e67ff0c.jpg\",\n  \"593820.jpg\": \"Aves/Myiozetetes similis/51d1a04a48e74c52ffcb63dda92e1734.jpg\",\n  \"593837.jpg\": \"Aves/Myiozetetes similis/6e744707b95d87da7a4aa39b86c808b2.jpg\",\n  \"593967.jpg\": \"Insecta/Grammia virgo/b26c8528c6a24c5e01579e049fd1e47c.jpg\",\n  \"593981.jpg\": \"Insecta/Papilio machaon/d5d9c3bd06365b7c4719c38d7eac46aa.jpg\",\n  \"593984.jpg\": \"Insecta/Papilio machaon/249d6b28541a6c3bd0e3acf619c33b22.jpg\",\n  \"593985.jpg\": \"Insecta/Papilio machaon/55b1b8d894040a900313c0fb8227fb2a.jpg\",\n  \"593987.jpg\": \"Insecta/Papilio machaon/9ed9c0cbb535873e947a4a0d9d709aec.jpg\",\n  \"593993.jpg\": \"Insecta/Papilio machaon/eb8e5d4157b9428c5572bc25716b0378.jpg\",\n  \"593994.jpg\": \"Insecta/Papilio machaon/6c768fdd48f46c430191e9810b013b22.jpg\",\n  \"594000.jpg\": \"Insecta/Papilio machaon/fbce5e4d0c471438dbcfb57cae49cb6e.jpg\",\n  \"594017.jpg\": \"Insecta/Papilio machaon/340ff3cc3f3dd6b6a187ac4c255aea55.jpg\",\n  \"594020.jpg\": \"Aves/Pyrrhula pyrrhula/c5e5e2c23166977ec8f4577154379392.jpg\",\n  \"594021.jpg\": \"Aves/Pyrrhula pyrrhula/7b9c7bb89cae76549153db2d2b3812e7.jpg\",\n  \"594025.jpg\": \"Aves/Pyrrhula pyrrhula/479f494eb7b812f2a998187dd948f69a.jpg\",\n  \"594029.jpg\": \"Aves/Pyrrhula pyrrhula/4e6d576b756fe5fc5d373dd86e20291d.jpg\",\n  \"594031.jpg\": \"Aves/Pyrrhula pyrrhula/a891c20abdecafd14e3b20718e01dfed.jpg\",\n  \"594032.jpg\": \"Aves/Pyrrhula pyrrhula/ffaa0b7eccf6f15af2923c23feb0ae58.jpg\",\n  \"594034.jpg\": \"Aves/Pyrrhula pyrrhula/01d14443bae651d95673df1d62ee53f6.jpg\",\n  \"594036.jpg\": \"Insecta/Halyomorpha halys/2bade6ce047f746c2e95d8bce3f2ea2e.jpg\",\n  \"594043.jpg\": \"Insecta/Halyomorpha halys/a33532bb82383652714c83cfb06bb814.jpg\",\n  \"594044.jpg\": \"Insecta/Halyomorpha halys/031e8e0add59aa6a295ac17bcdc5644a.jpg\",\n  \"594047.jpg\": \"Insecta/Halyomorpha halys/20d46bb7c84333a852620173520528ed.jpg\",\n  \"594049.jpg\": \"Insecta/Halyomorpha halys/e706dcaa9dc93d9b4cb67e2286cb3611.jpg\",\n  \"594053.jpg\": \"Insecta/Halyomorpha halys/e45f6b1d5e8c852b0fc03013a59a19e9.jpg\",\n  \"594062.jpg\": \"Insecta/Halyomorpha halys/0ece181907736ebca9c084ccfe1fdf10.jpg\",\n  \"594063.jpg\": \"Insecta/Halyomorpha halys/67d8baa809ccff96d500a9e7c09e0dcc.jpg\",\n  \"594064.jpg\": \"Insecta/Halyomorpha halys/de8ae4b69bf8c0058744e16db67ca503.jpg\",\n  \"594073.jpg\": \"Arachnida/Paraphidippus aurantius/28063ba95dccd644fcbdcda5e7dee3a4.jpg\",\n  \"594081.jpg\": \"Arachnida/Paraphidippus aurantius/590804da84dbea39be099d2299283cec.jpg\",\n  \"594087.jpg\": \"Arachnida/Paraphidippus aurantius/605205436b838516b62b973a16c13718.jpg\",\n  \"594088.jpg\": \"Arachnida/Paraphidippus aurantius/15c2ae8a6908be578b8d7654617565db.jpg\",\n  \"594089.jpg\": \"Arachnida/Paraphidippus aurantius/759cf19087bb049ed8d3debb0dfeb739.jpg\",\n  \"594090.jpg\": \"Arachnida/Paraphidippus aurantius/ad5a029339b046279e182d870aa19adb.jpg\",\n  \"594091.jpg\": \"Arachnida/Paraphidippus aurantius/a538c7f85ce0e521d9d10a3150b7f0c5.jpg\",\n  \"594092.jpg\": \"Arachnida/Paraphidippus aurantius/acb0aed434678da1fa5b853d97c4b2cd.jpg\",\n  \"5941.jpg\": \"Insecta/Ammophila procera/907d5b3d3c2469602f9ecc1b13637485.jpg\",\n  \"594101.jpg\": \"Arachnida/Paraphidippus aurantius/399a196f990c84f727becbdd4c9ca45f.jpg\",\n  \"594102.jpg\": \"Arachnida/Paraphidippus aurantius/1bbe42fa280bc9a376bd2c85b376730a.jpg\",\n  \"594103.jpg\": \"Arachnida/Paraphidippus aurantius/8fe15bf60d9ac809650cd2fcef2a7a30.jpg\",\n  \"594104.jpg\": \"Arachnida/Paraphidippus aurantius/32cc76482e1c83acc45a3f5f80560517.jpg\",\n  \"594105.jpg\": \"Arachnida/Paraphidippus aurantius/38407a2d83426c1eb72559c4cbea180a.jpg\",\n  \"594171.jpg\": \"Insecta/Orthemis ferruginea/7fc66e9bc6e11b0d28bf55c432e1d40c.jpg\",\n  \"594266.jpg\": \"Insecta/Orthemis ferruginea/a4d85a70a6e3b0cbd55d0401215a2254.jpg\",\n  \"594272.jpg\": \"Insecta/Orthemis ferruginea/3eb903998d7f3c928f139817712c2119.jpg\",\n  \"594315.jpg\": \"Insecta/Orthemis ferruginea/cb73dd2ba2ac6736d607a802586cdaba.jpg\",\n  \"594320.jpg\": \"Aves/Strix nebulosa/2fba7a809f5e085027de9d12410574b1.jpg\",\n  \"594325.jpg\": \"Aves/Strix nebulosa/030f422445f010b6a7c6d0c23df01913.jpg\",\n  \"594330.jpg\": \"Aves/Strix nebulosa/43af6df48b1816e904dbed6696a8741d.jpg\",\n  \"594339.jpg\": \"Mammalia/Choloepus hoffmanni/7f288fa77c7976a82f79e674b90264f1.jpg\",\n  \"594340.jpg\": \"Mammalia/Choloepus hoffmanni/4cbf0be1eaaf1cb28db8ea01897dfdd8.jpg\",\n  \"594354.jpg\": \"Insecta/Papilio rutulus/e5abfce27c0a4ab593bc81770c0fced7.jpg\",\n  \"594417.jpg\": \"Insecta/Papilio rutulus/d9194fafe5e215c4a6c8427f846c749e.jpg\",\n  \"594418.jpg\": \"Insecta/Papilio rutulus/8d866ccf71992c2b06edc6c6edbf34ad.jpg\",\n  \"594424.jpg\": \"Insecta/Papilio rutulus/2445f028eb32551a17bc4923776bd49a.jpg\",\n  \"594425.jpg\": \"Insecta/Papilio rutulus/88bc8e4eb626de1c87cac7623269561d.jpg\",\n  \"594451.jpg\": \"Insecta/Papilio rutulus/5e60de1b0b307e1a0bf4578d3c8c18d6.jpg\",\n  \"594478.jpg\": \"Insecta/Papilio rutulus/6533276bc3cafa8dda6c0d9c935c644a.jpg\",\n  \"594538.jpg\": \"Reptilia/Phrynosoma blainvillii/7a414ddb565143ba6078170c345b9a4d.jpg\",\n  \"594551.jpg\": \"Reptilia/Phrynosoma blainvillii/9795ff2c1820de81b7f96cf7addd5fdc.jpg\",\n  \"594554.jpg\": \"Reptilia/Phrynosoma blainvillii/5305183bba306df5fd1bf93c885bca35.jpg\",\n  \"594563.jpg\": \"Reptilia/Phrynosoma blainvillii/fd7dc86402719bf7becbff8bbc843555.jpg\",\n  \"594580.jpg\": \"Reptilia/Phrynosoma blainvillii/e45d65c71d101989353c816406393e83.jpg\",\n  \"594584.jpg\": \"Reptilia/Phrynosoma blainvillii/d6c3b703aeaf82fa9079e2d177457906.jpg\",\n  \"594585.jpg\": \"Reptilia/Phrynosoma blainvillii/81b002296c0eb4dbf2303376910df40c.jpg\",\n  \"594586.jpg\": \"Reptilia/Phrynosoma blainvillii/93cd6610608dc2a04e7b573b0d59c1de.jpg\",\n  \"594594.jpg\": \"Reptilia/Phrynosoma blainvillii/cfcce7cf9d7ad9bfed4cfb283bdc9d5c.jpg\",\n  \"5946.jpg\": \"Insecta/Ammophila procera/a006a806526a770d6e378de71b9a0664.jpg\",\n  \"594641.jpg\": \"Aves/Elanus leucurus/a6d980a1073639e5ddd65f535f89a505.jpg\",\n  \"594711.jpg\": \"Aves/Elanus leucurus/b2409a5cfd7664fb72c82bd2cc5aa55f.jpg\",\n  \"594712.jpg\": \"Aves/Elanus leucurus/62cf3613b9d60efb85c1edd8b918a9eb.jpg\",\n  \"594713.jpg\": \"Aves/Elanus leucurus/f70b8f27abed3514105788853413a007.jpg\",\n  \"594764.jpg\": \"Aves/Elanus leucurus/45fec30097bff6f0e9a6a1747779188d.jpg\",\n  \"594769.jpg\": \"Aves/Elanus leucurus/8b55d3b1cc3e5eeee2e0aef6062d1fa4.jpg\",\n  \"59481.jpg\": \"Animalia/Dasyatis americana/3a8a65c1f5635de64777117eb3d20d2e.jpg\",\n  \"594813.jpg\": \"Aves/Oreothlypis ruficapilla/0830f2a4214401d8aaf81af217692b4e.jpg\",\n  \"594827.jpg\": \"Aves/Oreothlypis ruficapilla/80212603fa71426a702adedd2d02d255.jpg\",\n  \"594833.jpg\": \"Aves/Oreothlypis ruficapilla/71c5b95e518aa38e861094c8f9585024.jpg\",\n  \"594835.jpg\": \"Aves/Oreothlypis ruficapilla/d3403835fa6905cb588bfb5f905fd7ce.jpg\",\n  \"594847.jpg\": \"Aves/Oreothlypis ruficapilla/5859806bed306b486ffb49ecaa404c87.jpg\",\n  \"594853.jpg\": \"Aves/Oreothlypis ruficapilla/01001ca2f7d927980cae4cf247c6f29e.jpg\",\n  \"594891.jpg\": \"Aves/Oreothlypis ruficapilla/3ee84cb4f4c7703835e1948550a08671.jpg\",\n  \"594897.jpg\": \"Aves/Oreothlypis ruficapilla/f3f64a227d45b1eafba4f4914deaea77.jpg\",\n  \"594909.jpg\": \"Insecta/Leptoglossus phyllopus/5986567150984159c6f6f8fd89534a35.jpg\",\n  \"59491.jpg\": \"Animalia/Dasyatis americana/1ff3ed0a508ac26ae0e78915c96c3d5b.jpg\",\n  \"594910.jpg\": \"Insecta/Leptoglossus phyllopus/9a1ed63c681ee2c91ff71e24632be288.jpg\",\n  \"594913.jpg\": \"Insecta/Leptoglossus phyllopus/59793524f9d5429675ab1a28a3c838a0.jpg\",\n  \"594914.jpg\": \"Insecta/Leptoglossus phyllopus/1717ebdb5b282993d262e5c25a264c61.jpg\",\n  \"594915.jpg\": \"Insecta/Leptoglossus phyllopus/6b7702ceee62ed35cacbdbe8c1c2fd58.jpg\",\n  \"59492.jpg\": \"Animalia/Dasyatis americana/2f0c10e6ca99e8125ef76d0d2a6172c0.jpg\",\n  \"59493.jpg\": \"Animalia/Dasyatis americana/a5924a59235434121a30a03f93af6cc5.jpg\",\n  \"594936.jpg\": \"Insecta/Leptoglossus phyllopus/9f935e57a3507b012c7f6efe7525f6f1.jpg\",\n  \"594947.jpg\": \"Insecta/Leptoglossus phyllopus/94ae00358f62d1e471837c21a256d090.jpg\",\n  \"594955.jpg\": \"Aves/Petroica australis australis/544abc4c02b1da832b9209d7e7bcbe6c.jpg\",\n  \"594966.jpg\": \"Aves/Petroica australis australis/7b63b75ebb565303117699a31735c517.jpg\",\n  \"594967.jpg\": \"Aves/Petroica australis australis/a58c8518e8c63dcecff462398c368c54.jpg\",\n  \"594968.jpg\": \"Aves/Petroica australis australis/1fcb0269358a04eb17d42dd57b9d50fb.jpg\",\n  \"594971.jpg\": \"Aves/Sylvia atricapilla/b67dcd9f8a0e8763ae10f6e8f1414cd9.jpg\",\n  \"594972.jpg\": \"Aves/Sylvia atricapilla/c487be001c98943b96052685fc8d9c40.jpg\",\n  \"594973.jpg\": \"Aves/Sylvia atricapilla/4fe1f6e4b63e0c26e93899a903320cfc.jpg\",\n  \"594974.jpg\": \"Aves/Sylvia atricapilla/d810c124b901830abc8f78851b7b5592.jpg\",\n  \"594975.jpg\": \"Aves/Sylvia atricapilla/aaa14080237681ae8623bde726f215ca.jpg\",\n  \"594977.jpg\": \"Aves/Sylvia atricapilla/470e1bbc4927d5547e6448b22b523745.jpg\",\n  \"594989.jpg\": \"Aves/Sylvia atricapilla/879ea7fef1af8990332ea07fd23d5073.jpg\",\n  \"594993.jpg\": \"Aves/Sylvia atricapilla/9bd931bf6060c78a84fcaed1b0b91516.jpg\",\n  \"594995.jpg\": \"Aves/Sylvia atricapilla/8e04fd4959d494c7fd2864cba021bc22.jpg\",\n  \"5950.jpg\": \"Insecta/Ammophila procera/b2af76c2b6ec0e91a21e5f794205ab20.jpg\",\n  \"59500.jpg\": \"Insecta/Phloeodes diabolicus/8b95a6a0d5faa4e9b8621ab83b138a15.jpg\",\n  \"59501.jpg\": \"Insecta/Phloeodes diabolicus/cc311732aac0ce4e429a3c7f0d843ccc.jpg\",\n  \"595046.jpg\": \"Insecta/Perithemis tenera/f8608ec479a0ae51e9f5670d10cd76af.jpg\",\n  \"595057.jpg\": \"Insecta/Perithemis tenera/162b0dcb270acbaed014d7c64eb7ae0d.jpg\",\n  \"59507.jpg\": \"Insecta/Phloeodes diabolicus/e5e527b1d26eaa325ba36e6651e2e515.jpg\",\n  \"595073.jpg\": \"Insecta/Perithemis tenera/099d219c8641900b64a7ad0c4e1f9c06.jpg\",\n  \"595079.jpg\": \"Insecta/Perithemis tenera/73b3e8aa828c655e5a5ac79cfbeb1f24.jpg\",\n  \"595126.jpg\": \"Insecta/Perithemis tenera/205514edd42e560d5d46ef1050567b06.jpg\",\n  \"59513.jpg\": \"Insecta/Phloeodes diabolicus/2288cc1e74341c86b97e3e1384e26f7e.jpg\",\n  \"595151.jpg\": \"Insecta/Perithemis tenera/9527b7a413c28aadc2ea7fdf6fa0e14b.jpg\",\n  \"59516.jpg\": \"Insecta/Phloeodes diabolicus/ec63436f82355748a04acb3d14b50d82.jpg\",\n  \"595171.jpg\": \"Insecta/Perithemis tenera/48f6aa515137ae3c634ff3aea098e0ef.jpg\",\n  \"595276.jpg\": \"Arachnida/Centruroides vittatus/81dc01bdd681c8d8013b884c131e0c92.jpg\",\n  \"595277.jpg\": \"Arachnida/Centruroides vittatus/5fbc2c87700cadb29c3ffb1d8bc5966d.jpg\",\n  \"595278.jpg\": \"Arachnida/Centruroides vittatus/32ad0332d70e7609b771d570d960c7f7.jpg\",\n  \"595290.jpg\": \"Arachnida/Centruroides vittatus/3533b5ea1158926beefa3c06b94dcabe.jpg\",\n  \"595309.jpg\": \"Arachnida/Centruroides vittatus/2bdd5642f09a5c692fbe6a6dc9d45052.jpg\",\n  \"595318.jpg\": \"Arachnida/Centruroides vittatus/aa311f7dfaf59c4e548b377d0a50d538.jpg\",\n  \"595323.jpg\": \"Amphibia/Ichthyosaura alpestris/fc901e5f94b30a95f9d93732908fde17.jpg\",\n  \"595339.jpg\": \"Amphibia/Ichthyosaura alpestris/f1647e7a62dfb7eb722654f17152a175.jpg\",\n  \"595344.jpg\": \"Insecta/Protoboarmia porcelaria/5ff1775210f23aba269acea2a621f1b5.jpg\",\n  \"595346.jpg\": \"Insecta/Protoboarmia porcelaria/c1821073dee7378d53f0ac6563240e8d.jpg\",\n  \"595351.jpg\": \"Insecta/Stagmomantis carolina/b33f65ce3052b33941cda1599188f7f0.jpg\",\n  \"595352.jpg\": \"Insecta/Stagmomantis carolina/317e30006a71f93d220d57cc08d863a6.jpg\",\n  \"595369.jpg\": \"Insecta/Stagmomantis carolina/9271be43b2d694ea3f8db834752740fc.jpg\",\n  \"595374.jpg\": \"Insecta/Stagmomantis carolina/b98c5dbd8645596a35efdf390f9bdc87.jpg\",\n  \"595375.jpg\": \"Insecta/Stagmomantis carolina/631a505d834a80f0686ce48aa9d6ede1.jpg\",\n  \"595388.jpg\": \"Insecta/Stagmomantis carolina/7ca703e966e99372f08a445164a4a775.jpg\",\n  \"595395.jpg\": \"Insecta/Stagmomantis carolina/3f12877667a0fc935950172e4912db1e.jpg\",\n  \"595401.jpg\": \"Aves/Anhinga rufa/88f8cfdca3a23b511d2f7e04766c2b3a.jpg\",\n  \"595405.jpg\": \"Aves/Anhinga rufa/ed7f4c6aa00d7464fd1402c91113e8bd.jpg\",\n  \"595498.jpg\": \"Aves/Chroicocephalus ridibundus/5896518f8d1d2995b19f830c41cb4ea7.jpg\",\n  \"595522.jpg\": \"Aves/Chroicocephalus ridibundus/888f326a14be9f63ecd12c8b2084c831.jpg\",\n  \"595524.jpg\": \"Aves/Chroicocephalus ridibundus/f03913e290b695aa4be034c2667f618c.jpg\",\n  \"595529.jpg\": \"Aves/Cairina moschata domestica/0b07c96be4812ee8c280b8e987a1b236.jpg\",\n  \"595566.jpg\": \"Aves/Cairina moschata domestica/2c2eb574713ae8e955c03ceb41045de5.jpg\",\n  \"595569.jpg\": \"Aves/Cairina moschata domestica/edd1dbf0a70f7fc7e2cc81bc415590d5.jpg\",\n  \"595579.jpg\": \"Aves/Cairina moschata domestica/a35ea1e228ce08e9539cdded45067e98.jpg\",\n  \"595602.jpg\": \"Aves/Cairina moschata domestica/1b923e1c313c4938dd2097efc8ab40fd.jpg\",\n  \"595637.jpg\": \"Aves/Cairina moschata domestica/c42c860c4fe3b57d15c9f7bd80b757c1.jpg\",\n  \"595741.jpg\": \"Aves/Aechmophorus clarkii/0b0a0ab8cd1a7691a721b5a732a1a077.jpg\",\n  \"595754.jpg\": \"Aves/Aechmophorus clarkii/6379e83f663c29aad89b651eaa86d962.jpg\",\n  \"595755.jpg\": \"Aves/Aechmophorus clarkii/a140a9da07d6c8cbd9785eb481a4d9f9.jpg\",\n  \"595763.jpg\": \"Aves/Aechmophorus clarkii/b423d2895e65e4a18348a9081f8d6b52.jpg\",\n  \"595789.jpg\": \"Insecta/Caenurgina erechtea/1489901ffdc0a8e4433b1ecb13c2e191.jpg\",\n  \"595790.jpg\": \"Insecta/Caenurgina erechtea/bae720ef74a6731f8bbf8a6bfc382e9c.jpg\",\n  \"5958.jpg\": \"Insecta/Ammophila procera/d08b86f677b2ed6a858726e4f1815f16.jpg\",\n  \"595804.jpg\": \"Insecta/Caenurgina erechtea/e1524b2a0a8ca9b0f0616414ef48cb2d.jpg\",\n  \"595810.jpg\": \"Insecta/Caenurgina erechtea/f29ce2a04e9a8d488aa66e4d90a8ba23.jpg\",\n  \"595811.jpg\": \"Insecta/Caenurgina erechtea/6643510af27f76ad35a80082034a393b.jpg\",\n  \"595812.jpg\": \"Insecta/Caenurgina erechtea/650aecdafc64ba37f34db2f3cebed7ec.jpg\",\n  \"595819.jpg\": \"Aves/Phylloscopus collybita/d43c6d08184d1ce28f51886d342b5687.jpg\",\n  \"595825.jpg\": \"Aves/Phylloscopus collybita/da45e95a8703e5f3bb1af34baab88c16.jpg\",\n  \"595826.jpg\": \"Aves/Phylloscopus collybita/e6f9c8627aa2b114cd949168fa4f0138.jpg\",\n  \"595827.jpg\": \"Aves/Phylloscopus collybita/e0d32e0ad151adfe5aaec856c40ac102.jpg\",\n  \"595828.jpg\": \"Aves/Phylloscopus collybita/3672fb243c6b3f439fb2733b26abe833.jpg\",\n  \"595829.jpg\": \"Aves/Phylloscopus collybita/5a3fbc3e867caefbd463d499244cf267.jpg\",\n  \"595830.jpg\": \"Aves/Phylloscopus collybita/9cd6d8092077d5896c0efea55d871c7d.jpg\",\n  \"595831.jpg\": \"Aves/Phylloscopus collybita/e0bc4a7da4bc718cf005f9e97515ccf3.jpg\",\n  \"595832.jpg\": \"Aves/Phylloscopus collybita/c8fc551d0d0c14c84ec80c9999681a86.jpg\",\n  \"595837.jpg\": \"Aves/Phylloscopus collybita/df9e68fdfb48227d8cc6274f5b3638a7.jpg\",\n  \"595842.jpg\": \"Aves/Phylloscopus collybita/d3ce7c3ca33540a7798a1573dd82488b.jpg\",\n  \"595847.jpg\": \"Amphibia/Gyrinophilus porphyriticus/edc8cc67b750d38bc427cbe8733ec9f8.jpg\",\n  \"595850.jpg\": \"Amphibia/Gyrinophilus porphyriticus/7da04ecaa1c3af1d422927e98a4a457c.jpg\",\n  \"595873.jpg\": \"Insecta/Polistes apachus/976f96d5b2a4f5b15ee9d5c3d5639323.jpg\",\n  \"595884.jpg\": \"Aves/Momotus coeruliceps/20a7d280dc55d2d01e5ffa06d26a9c54.jpg\",\n  \"595885.jpg\": \"Aves/Momotus coeruliceps/997f55d3a19caa939b583522811f7c70.jpg\",\n  \"595899.jpg\": \"Aves/Periparus ater/46ef8ad23832c1d2c32444de85c86959.jpg\",\n  \"595935.jpg\": \"Aves/Periparus ater/118c5cd21739a7fb83bc827a9f1025ee.jpg\",\n  \"595995.jpg\": \"Arachnida/Steatoda triangulosa/dd65fabc09bc80a2625dde5e9f354405.jpg\",\n  \"595996.jpg\": \"Arachnida/Steatoda triangulosa/4faa1e5f4cd92d41dd5c815970fb9a27.jpg\",\n  \"5960.jpg\": \"Insecta/Dymasia dymas/84b1c679a651cbeb01bb7737cde2f1bd.jpg\",\n  \"596002.jpg\": \"Aves/Phoebastria immutabilis/e2b0290cae7d709a19015d7d1ba96445.jpg\",\n  \"596003.jpg\": \"Aves/Phoebastria immutabilis/ce7500f31571e5be8dc1b23e7bda8041.jpg\",\n  \"5962.jpg\": \"Insecta/Dymasia dymas/76f16f180b6671326e56a2c699ae6372.jpg\",\n  \"59625.jpg\": \"Aves/Dendrocygna autumnalis/2bb56f565edae5785d3a2d950ba47625.jpg\",\n  \"596285.jpg\": \"Aves/Xiphorhynchus flavigaster/5a8d987575de439f8be90a2570dfbc99.jpg\",\n  \"596288.jpg\": \"Aves/Xiphorhynchus flavigaster/f7f3b686b1099660c56e7104f779d12a.jpg\",\n  \"596326.jpg\": \"Reptilia/Podarcis muralis/6e813b150fac2e0317b904a7bd3aac8b.jpg\",\n  \"596334.jpg\": \"Reptilia/Podarcis muralis/704f004c31878f615b7cbff70849fbd7.jpg\",\n  \"59635.jpg\": \"Aves/Dendrocygna autumnalis/1a888fca3f29ae629bdc84f93c687fd4.jpg\",\n  \"596350.jpg\": \"Reptilia/Podarcis muralis/6e5f8139861674db0c3c36f7ebe4c35f.jpg\",\n  \"596368.jpg\": \"Reptilia/Podarcis muralis/36c6f9492015e20150088f9c08447223.jpg\",\n  \"596390.jpg\": \"Reptilia/Podarcis muralis/3d25ed4b0901d14f8786b42f9811359c.jpg\",\n  \"596417.jpg\": \"Insecta/Orgyia postica/8fa0d77796635952a1457440c966b6f9.jpg\",\n  \"596426.jpg\": \"Insecta/Orgyia postica/871acf8a94581fddea7b9c883af7d8d2.jpg\",\n  \"596427.jpg\": \"Insecta/Orgyia postica/2bf0a12398161c3e080edd6dd9b00089.jpg\",\n  \"596433.jpg\": \"Insecta/Thyris sepulchralis/281cc7981e91202207fed55210904456.jpg\",\n  \"596438.jpg\": \"Insecta/Thyris sepulchralis/251ce4a06e1be18a1d279a2e96bcfadb.jpg\",\n  \"59649.jpg\": \"Aves/Dendrocygna autumnalis/06cbf661d3b6f77f51007cd4c9d7a444.jpg\",\n  \"596556.jpg\": \"Aves/Sphyrapicus ruber/ab125aa11515060e4b312ec5af274e04.jpg\",\n  \"596584.jpg\": \"Aves/Sphyrapicus ruber/2f5a58a1c64a426688275f8fc46f9912.jpg\",\n  \"59661.jpg\": \"Aves/Dendrocygna autumnalis/55fc8e18d6b4d9ec75aa361499612422.jpg\",\n  \"596615.jpg\": \"Aves/Sphyrapicus ruber/760c8dd838cf5156360b8fcd95194245.jpg\",\n  \"59663.jpg\": \"Aves/Dendrocygna autumnalis/58c94143d7722a559a123a7e2f22c3e2.jpg\",\n  \"596631.jpg\": \"Reptilia/Coluber flagellum flagellum/6f85c30aa3c275cd106daf1036dece5f.jpg\",\n  \"596633.jpg\": \"Reptilia/Coluber flagellum flagellum/3b4c59b8d5bbd87401bf38c944b2f838.jpg\",\n  \"596647.jpg\": \"Arachnida/Uroctonus mordax/4395aff04d5bda930dcd58b485f35de6.jpg\",\n  \"596652.jpg\": \"Arachnida/Uroctonus mordax/4ac19899ae1b5d8b581675bd64183c7d.jpg\",\n  \"596657.jpg\": \"Arachnida/Uroctonus mordax/132650e511c83285060a1c794dd54176.jpg\",\n  \"596659.jpg\": \"Arachnida/Uroctonus mordax/7d87ca14f2982fa66ffccd21eb0ad244.jpg\",\n  \"596661.jpg\": \"Arachnida/Uroctonus mordax/2d9d3b4614e1991e170f7df79620dd3a.jpg\",\n  \"596662.jpg\": \"Arachnida/Uroctonus mordax/ec3c882d900bc9d77bab467fa4c29b58.jpg\",\n  \"596665.jpg\": \"Arachnida/Uroctonus mordax/fef9a82a966c0dbc8a3679c9bfcdec7b.jpg\",\n  \"596666.jpg\": \"Arachnida/Uroctonus mordax/b3463c7c1f75632a13cc96a55971be75.jpg\",\n  \"596667.jpg\": \"Arachnida/Uroctonus mordax/4b0e3616b97b977c8e4d4f3316a85329.jpg\",\n  \"596674.jpg\": \"Arachnida/Uroctonus mordax/0c3368e721481e4af84ab3a84bb99be0.jpg\",\n  \"596675.jpg\": \"Arachnida/Uroctonus mordax/a597df55af3d52e5f50dfc338d205eca.jpg\",\n  \"596680.jpg\": \"Arachnida/Uroctonus mordax/636e4b2c2ad415780e0f292a9fb7cbef.jpg\",\n  \"596685.jpg\": \"Arachnida/Ixodes scapularis/e6cebcc7483a1c01bce6fb65c83c3169.jpg\",\n  \"596691.jpg\": \"Arachnida/Ixodes scapularis/01287684b63248d4a257ff4b6dc1a047.jpg\",\n  \"596692.jpg\": \"Arachnida/Ixodes scapularis/18d0cf712d27bac753cd6f15ab21e120.jpg\",\n  \"596812.jpg\": \"Insecta/Pyrisitia proterpia/f379b7041ec4cdd1819037d93eb8697c.jpg\",\n  \"596852.jpg\": \"Aves/Glaucidium brasilianum/c0292915c55a63c936e4398a52bab1b5.jpg\",\n  \"596853.jpg\": \"Aves/Glaucidium brasilianum/1318ceeccd14d66f43546bc202d38ef7.jpg\",\n  \"596856.jpg\": \"Aves/Glaucidium brasilianum/b8d2608a39dd686325512ffa0bd88115.jpg\",\n  \"596858.jpg\": \"Aves/Glaucidium brasilianum/6ac5ff492d755a4a1f2891f6cd8ad051.jpg\",\n  \"59686.jpg\": \"Aves/Dendrocygna autumnalis/b87d7873cc479d594f17db5c5da3a164.jpg\",\n  \"596860.jpg\": \"Aves/Glaucidium brasilianum/16f64b921eb58eae342465af53219609.jpg\",\n  \"596863.jpg\": \"Aves/Glaucidium brasilianum/665596a01517ed3b617a349e2e7c2425.jpg\",\n  \"596864.jpg\": \"Aves/Glaucidium brasilianum/152a876b7e28d843c654ce65c23a4d09.jpg\",\n  \"596884.jpg\": \"Aves/Glaucidium brasilianum/e51d224fd5993ca71b447b4f7b3f181e.jpg\",\n  \"596891.jpg\": \"Aves/Platalea leucorodia/44fb3a0a7d114a7e4d6401ea3da350e6.jpg\",\n  \"596996.jpg\": \"Insecta/Marpesia chiron/189744593798104b3ac67a5de660f5de.jpg\",\n  \"596998.jpg\": \"Insecta/Marpesia chiron/e34b4df49c17ea614b301a407aa5e720.jpg\",\n  \"597002.jpg\": \"Insecta/Marpesia chiron/b16e127e9fe38c33119ffeab38868f43.jpg\",\n  \"59723.jpg\": \"Aves/Dendrocygna autumnalis/c8e253e692b2b41e2989e6b240553d50.jpg\",\n  \"59727.jpg\": \"Aves/Dendrocygna autumnalis/1142ec2de6458d231d98ce89c2d59635.jpg\",\n  \"59731.jpg\": \"Aves/Dendrocygna autumnalis/4d46b43ad3bcf72158ce61b8ed76f120.jpg\",\n  \"597507.jpg\": \"Aves/Turdus migratorius/be5fd5ff5599cb0471c40f677426274a.jpg\",\n  \"59751.jpg\": \"Aves/Dendrocygna autumnalis/6c5d23b6175a24ce1b8a477e3378e2fe.jpg\",\n  \"597592.jpg\": \"Aves/Turdus migratorius/6ed478db6800befe60cc0caab917b7f6.jpg\",\n  \"59779.jpg\": \"Reptilia/Graptemys geographica/1110773fbcdcf460860f833a4ed4d506.jpg\",\n  \"59782.jpg\": \"Reptilia/Graptemys geographica/c48da6fbccd54f28a5d44d3344b9bef1.jpg\",\n  \"59783.jpg\": \"Reptilia/Graptemys geographica/7e269c8781741026a1f282d22b70a580.jpg\",\n  \"59784.jpg\": \"Reptilia/Graptemys geographica/ab7466143c55561e0e74291181735b18.jpg\",\n  \"597846.jpg\": \"Aves/Turdus migratorius/4d0ce8a79c7babac8b89b5a788dfbd16.jpg\",\n  \"59788.jpg\": \"Reptilia/Graptemys geographica/2d2dcc945d8f36afa9e004d0e7e9894d.jpg\",\n  \"59790.jpg\": \"Reptilia/Graptemys geographica/6811fdb4940b20b97813189a34f8ec68.jpg\",\n  \"59795.jpg\": \"Reptilia/Graptemys geographica/15550e636aa6b430cb2d7d87248212db.jpg\",\n  \"59796.jpg\": \"Reptilia/Graptemys geographica/788f5294adbe0d3e160266170856f1c7.jpg\",\n  \"597962.jpg\": \"Aves/Turdus migratorius/b6f670220c617ac7142d65dd2b455ab0.jpg\",\n  \"59797.jpg\": \"Reptilia/Graptemys geographica/ad991875b8dd3d26d24bddd2961c28a7.jpg\",\n  \"598016.jpg\": \"Aves/Turdus migratorius/968fb674925a9b9bc356bd018ab134df.jpg\",\n  \"59802.jpg\": \"Reptilia/Graptemys geographica/5e6f382f59b4fd89bf7edb00d377b382.jpg\",\n  \"59806.jpg\": \"Reptilia/Graptemys geographica/86cbd2ac7516b8b47a3dc0fbb5ca266d.jpg\",\n  \"59807.jpg\": \"Insecta/Catasticta nimbice/04bdcdb47eb215dd8470328fdb5c092a.jpg\",\n  \"59814.jpg\": \"Insecta/Catasticta nimbice/60786eee0bc224fa53986f14bb73e546.jpg\",\n  \"598224.jpg\": \"Mammalia/Mirounga angustirostris/fc2c1ab2671fae51f6fc2360b7dd9b3d.jpg\",\n  \"59824.jpg\": \"Insecta/Catasticta nimbice/28ce3b70b9b6ab502491a649fb48af51.jpg\",\n  \"598250.jpg\": \"Mammalia/Mirounga angustirostris/7d11f0a5b8e4cc2bae866014ec360bba.jpg\",\n  \"598251.jpg\": \"Mammalia/Mirounga angustirostris/33544fcd9cea6b56cb3006bb398a06c9.jpg\",\n  \"598255.jpg\": \"Mammalia/Bradypus variegatus/0decceaa62342ccef71106f770addbdf.jpg\",\n  \"59826.jpg\": \"Insecta/Catasticta nimbice/4492cc59e1a833fe4803cafee6879bb2.jpg\",\n  \"598261.jpg\": \"Insecta/Boyeria vinosa/f5b1114ae9918c0aebf9ea8042ca1e69.jpg\",\n  \"598268.jpg\": \"Insecta/Boyeria vinosa/90e91615e5fad2d276a63604e676502b.jpg\",\n  \"598274.jpg\": \"Aves/Turdus plumbeus/18b741030a6ea1911f95c2864a5ba461.jpg\",\n  \"598290.jpg\": \"Aves/Zenaida aurita/f430e149aea41b67bef4af02075ef675.jpg\",\n  \"598293.jpg\": \"Aves/Zenaida aurita/9cdf9481bd07919fac201c886acb3ac2.jpg\",\n  \"59830.jpg\": \"Insecta/Catasticta nimbice/06f610d2bba8f871fec23fdcf8a1ef00.jpg\",\n  \"598333.jpg\": \"Aves/Amaurornis phoenicurus/91281945fbb70833728925b3bb819db4.jpg\",\n  \"598341.jpg\": \"Arachnida/Mastigoproctus giganteus/0e109077c1ae2ae08828b411ff2fe48f.jpg\",\n  \"598456.jpg\": \"Aves/Colaptes rubiginosus/8e753d729c53005bb71335eac198effd.jpg\",\n  \"598620.jpg\": \"Aves/Myadestes townsendi/dde79cab451d3b01c77eda3918e6a0ce.jpg\",\n  \"598625.jpg\": \"Aves/Myadestes townsendi/63d84d9be6bc3d61ca45650ed509257b.jpg\",\n  \"598627.jpg\": \"Aves/Myadestes townsendi/defe6249e5876c732a037f5e47cdaff3.jpg\",\n  \"598632.jpg\": \"Aves/Myadestes townsendi/fe4c1c5de330a57f2f8a955069c550c5.jpg\",\n  \"598633.jpg\": \"Aves/Myadestes townsendi/62548aa3f855af5c97298506802235d4.jpg\",\n  \"598653.jpg\": \"Aves/Tringa solitaria/70f63717a2d4051c7ca08ed3bbf3c536.jpg\",\n  \"598668.jpg\": \"Aves/Tringa solitaria/06e17fc83bd8fec05e8f0f98249843e6.jpg\",\n  \"598693.jpg\": \"Aves/Tringa solitaria/ee1d87d3274632768c7bf9affc32d312.jpg\",\n  \"598712.jpg\": \"Aves/Tringa solitaria/afdc57a342d6297a0e2a74c31f81efa6.jpg\",\n  \"598733.jpg\": \"Aves/Tringa solitaria/1e250a92acf9de8e410413088fbe3724.jpg\",\n  \"598748.jpg\": \"Aves/Callipepla gambelii/4f91cac0ad477830b1312ae942b5ce1e.jpg\",\n  \"59875.jpg\": \"Insecta/Eutrapela clemataria/78805e656c2b36e22706102279fd6296.jpg\",\n  \"598760.jpg\": \"Aves/Callipepla gambelii/79f5f4567796a036d2ce1f6069b9d237.jpg\",\n  \"598761.jpg\": \"Aves/Callipepla gambelii/ebcad45dfaf9e34fe5518de6143c20d9.jpg\",\n  \"598763.jpg\": \"Aves/Callipepla gambelii/21fa49aa597a1feacd450a198c6311f0.jpg\",\n  \"59877.jpg\": \"Insecta/Eutrapela clemataria/2c5f3db1e750bc99a1177af8046530a0.jpg\",\n  \"598774.jpg\": \"Aves/Callipepla gambelii/efe4ce450f1ae946f51b7b1ee1ae5887.jpg\",\n  \"598788.jpg\": \"Aves/Callipepla gambelii/bfbf4d701bfcbc110c7e2d63b326a29d.jpg\",\n  \"59879.jpg\": \"Insecta/Eutrapela clemataria/e6ad8aa57caebcd64a93751c57ebbdfa.jpg\",\n  \"598806.jpg\": \"Insecta/Enallagma exsulans/d7dd4bceb4496830dd9d8948248bbd4d.jpg\",\n  \"59881.jpg\": \"Insecta/Eutrapela clemataria/ca8a2f4036d84821e44462fd46b3fb6b.jpg\",\n  \"598813.jpg\": \"Insecta/Enallagma exsulans/bd89e6edd7aebb6d86e9b11bbfe0cf47.jpg\",\n  \"598814.jpg\": \"Insecta/Enallagma exsulans/1f2da5bc547775829e364e694fd0f748.jpg\",\n  \"598818.jpg\": \"Insecta/Enallagma exsulans/dfa3437ad00cc17d06eaa914caaad75d.jpg\",\n  \"598822.jpg\": \"Insecta/Enallagma exsulans/384b0a869c688bf34023deb45bc69abe.jpg\",\n  \"598832.jpg\": \"Insecta/Enallagma exsulans/13e3ffc0907f83b06a7c3277602d606d.jpg\",\n  \"59884.jpg\": \"Insecta/Eutrapela clemataria/1f3b61b6d3a15e94b9e3600ecb337ae2.jpg\",\n  \"598840.jpg\": \"Insecta/Enallagma exsulans/3a4e9390927545b9b22200471ad054e0.jpg\",\n  \"598841.jpg\": \"Insecta/Enallagma exsulans/0c6a6462fe415bd48b422cc7dd212c81.jpg\",\n  \"59885.jpg\": \"Insecta/Eutrapela clemataria/3252e7eef4081bd24fd2ce3dea8b8684.jpg\",\n  \"598852.jpg\": \"Insecta/Libellula semifasciata/1883db23c97d494c7abfac664972de22.jpg\",\n  \"59886.jpg\": \"Insecta/Eutrapela clemataria/caabfbab2fa09f67920c323dccca06eb.jpg\",\n  \"598861.jpg\": \"Insecta/Libellula semifasciata/b63fa5ccabbddb7184b1fad3e06a9732.jpg\",\n  \"598863.jpg\": \"Insecta/Libellula semifasciata/33d012a08ef879ad6fea24180345d2a4.jpg\",\n  \"598864.jpg\": \"Insecta/Libellula semifasciata/3b712833c24ece7897d870478cf6b644.jpg\",\n  \"59887.jpg\": \"Insecta/Eutrapela clemataria/e5220825a711177ab2de8021b8f211ae.jpg\",\n  \"598896.jpg\": \"Aves/Calidris alba/95f33ea3efacccdf579c66a4cd1d20a2.jpg\",\n  \"598918.jpg\": \"Aves/Calidris alba/a859ecadd2b563c31503d6e0f5eb1f6d.jpg\",\n  \"598927.jpg\": \"Aves/Calidris alba/ec0f87a52516fee1bdbca7eea9e51bd9.jpg\",\n  \"59897.jpg\": \"Insecta/Eutrapela clemataria/fa61a3ae765246ead7e66dbf43252c35.jpg\",\n  \"59900.jpg\": \"Insecta/Eutrapela clemataria/a66a9b1a385251864b3a7f4bbf47fad3.jpg\",\n  \"599148.jpg\": \"Aves/Anas rubripes/a2e256b51becc6d3e099319bf80e8bf8.jpg\",\n  \"599150.jpg\": \"Aves/Anas rubripes/c36f2dd3c3a225e6932552d50f70c5db.jpg\",\n  \"599221.jpg\": \"Insecta/Sympetrum vicinum/8378706a5024a2aa873501c9caa15864.jpg\",\n  \"599244.jpg\": \"Insecta/Sympetrum vicinum/b9c49a008ad04624888aa1372972c11c.jpg\",\n  \"599330.jpg\": \"Insecta/Sympetrum vicinum/0c4b50022e1e0429f868b05e878873ed.jpg\",\n  \"599363.jpg\": \"Insecta/Sympetrum vicinum/b51c98300e643f4b7f35ff34863c670a.jpg\",\n  \"599369.jpg\": \"Insecta/Sympetrum vicinum/d2d5da6f39810718f55706eabf427c5d.jpg\",\n  \"599432.jpg\": \"Insecta/Eurytides marcellus/6c163f9efc7a5726915e3562be91ecd7.jpg\",\n  \"599433.jpg\": \"Insecta/Eurytides marcellus/003f85a6afc798502dcbb338a30a735b.jpg\",\n  \"599436.jpg\": \"Insecta/Eurytides marcellus/8aae37ea5df3e3fc1fa8b28ca59c4251.jpg\",\n  \"599441.jpg\": \"Insecta/Eurytides marcellus/def2495f019be9a8ad00cccdd28d2beb.jpg\",\n  \"599455.jpg\": \"Insecta/Eurytides marcellus/57b579c286ca389e24372fafd334d83c.jpg\",\n  \"599457.jpg\": \"Insecta/Eurytides marcellus/1c52c3017606b14fd4c67b9f6d74ceb0.jpg\",\n  \"599469.jpg\": \"Insecta/Eurytides marcellus/45f2f351f0a00f4a96877e2cf83f7c64.jpg\",\n  \"599478.jpg\": \"Insecta/Eurytides marcellus/83e7e4c5b2163be71bd708b4b67aa1ec.jpg\",\n  \"599485.jpg\": \"Amphibia/Hyla squirella/ac804e82b502aff2f9be8ac34e831bc2.jpg\",\n  \"599486.jpg\": \"Amphibia/Hyla squirella/d47401eb1f9533499e644e7655692c84.jpg\",\n  \"599487.jpg\": \"Amphibia/Hyla squirella/b9c778a65a4164e3115b1a599dd4b2d6.jpg\",\n  \"599488.jpg\": \"Amphibia/Hyla squirella/4b46666363f3910b197866cbde76e1ab.jpg\",\n  \"599489.jpg\": \"Amphibia/Hyla squirella/12d156c31a01b94863bf754693306fc7.jpg\",\n  \"599490.jpg\": \"Amphibia/Hyla squirella/8cc0d96feb2b0612d3d0c0db261858aa.jpg\",\n  \"599491.jpg\": \"Amphibia/Hyla squirella/f951c0fd465c684263105e005ae1c653.jpg\",\n  \"599492.jpg\": \"Amphibia/Hyla squirella/399939a047be9b879f0d023a7cadd619.jpg\",\n  \"599494.jpg\": \"Amphibia/Hyla squirella/27651fb4922658d6dec6cf9b4c46bde5.jpg\",\n  \"599499.jpg\": \"Amphibia/Hyla squirella/a4dcfcf985803395c3e5d9b0ac41d746.jpg\",\n  \"599501.jpg\": \"Amphibia/Hyla squirella/f61f50b9a51eb2a1ab7a9c17a76660cb.jpg\",\n  \"599559.jpg\": \"Insecta/Polygonia progne/f004ab1ac4a52be5ac3856efc7cb8008.jpg\",\n  \"599567.jpg\": \"Insecta/Polygonia progne/b9612f09420b2f2009035eec5f63f2bf.jpg\",\n  \"599568.jpg\": \"Insecta/Polygonia progne/ef3fcf5dc4841997bf1cf6de879c2931.jpg\",\n  \"599569.jpg\": \"Insecta/Polygonia progne/285777845b86dfd74418f96af699a9ce.jpg\",\n  \"599581.jpg\": \"Insecta/Speyeria aphrodite/a9082a75cab34a4283418b69268bf431.jpg\",\n  \"599583.jpg\": \"Mammalia/Mustela nivalis/b6b3d37a1987f43c3c3407aa96870b09.jpg\",\n  \"59959.jpg\": \"Aves/Onychoprion fuscatus/fe1d11844192fa6801e50edb16472be3.jpg\",\n  \"599630.jpg\": \"Mammalia/Peromyscus maniculatus/95b31b4d1f9bb75bddfe2b5ed3622b14.jpg\",\n  \"599632.jpg\": \"Mammalia/Peromyscus maniculatus/e0c69fe47fb21cf0e92e00b82546360c.jpg\",\n  \"599633.jpg\": \"Mammalia/Peromyscus maniculatus/37feecaf8ef179d13c17830d42288fb4.jpg\",\n  \"599634.jpg\": \"Mammalia/Peromyscus maniculatus/0dc026dfbbbe9586d4333c6b08347a55.jpg\",\n  \"599636.jpg\": \"Mammalia/Peromyscus maniculatus/63a3401cecda1b2e2f30a3fbd3e9de1e.jpg\",\n  \"599637.jpg\": \"Insecta/Pholisora catullus/88891e2d92333ed89e0d582ac454314c.jpg\",\n  \"599638.jpg\": \"Insecta/Pholisora catullus/3c05c8d2ef4043ef5da647fa9dae6e47.jpg\",\n  \"599639.jpg\": \"Insecta/Pholisora catullus/584977ef86605881e51fec42d213e354.jpg\",\n  \"599640.jpg\": \"Insecta/Pholisora catullus/761833591427064b49db2565083afb8e.jpg\",\n  \"599641.jpg\": \"Insecta/Pholisora catullus/d7b63b487c8e5c819b03d2949535dc56.jpg\",\n  \"599642.jpg\": \"Insecta/Pholisora catullus/84917de2505ca6f48454f61b5bbc6794.jpg\",\n  \"599645.jpg\": \"Insecta/Pholisora catullus/8ad765c899b5da8502c5efaec36cb48f.jpg\",\n  \"599646.jpg\": \"Insecta/Pholisora catullus/328009fed502d17625c36c3a16c46168.jpg\",\n  \"599647.jpg\": \"Insecta/Pholisora catullus/f394edda46c247761562b8d82cdd72d5.jpg\",\n  \"599651.jpg\": \"Insecta/Pholisora catullus/239157de3e4b074248fed36e27b90a1e.jpg\",\n  \"599659.jpg\": \"Aves/Butorides striata/b6609eeb44b2580b0b4c6381efc76686.jpg\",\n  \"599667.jpg\": \"Aves/Butorides striata/411e548bf1c054094f7f393c25042bf2.jpg\",\n  \"599668.jpg\": \"Aves/Butorides striata/08a1a262aefa7fe7ea6fdbb3b0ffa516.jpg\",\n  \"599670.jpg\": \"Aves/Butorides striata/96a64a449656486c610c8f725943a8f1.jpg\",\n  \"599672.jpg\": \"Aves/Butorides striata/1edc68a1f5318976e468bb723b4a12aa.jpg\",\n  \"599679.jpg\": \"Aves/Butorides striata/1df547dd562bbfc0e06fcf07e0e4227f.jpg\",\n  \"599681.jpg\": \"Aves/Butorides striata/fab1e95d33eaa8b1e2ebf09a0e4aabf1.jpg\",\n  \"599685.jpg\": \"Aves/Butorides striata/0c22e170987ca494d086fdda554cd89b.jpg\",\n  \"599689.jpg\": \"Aves/Butorides striata/adb7103037328a834a9da47349ac987a.jpg\",\n  \"59969.jpg\": \"Aves/Onychoprion fuscatus/15bdd7d33a0e11a817b16fb2cd6b9b0d.jpg\",\n  \"599699.jpg\": \"Insecta/Papilio demodocus/4cdecfad1bd2fefb8c618dbeecd3aad0.jpg\",\n  \"59970.jpg\": \"Aves/Onychoprion fuscatus/ff8a1ef077a16fbefadf7f0b50b3f4e0.jpg\",\n  \"599707.jpg\": \"Amphibia/Ambystoma texanum/c1e3900fae94c644be7798346eab0bfe.jpg\",\n  \"59971.jpg\": \"Aves/Onychoprion fuscatus/fc210a6c8e2b416f0e6cbaacf69d24b4.jpg\",\n  \"599710.jpg\": \"Amphibia/Ambystoma texanum/883bffc3488832f1debf66a7e73a5589.jpg\",\n  \"599739.jpg\": \"Mammalia/Hippopotamus amphibius/900e00ecae6ab94fa96b459b2d408629.jpg\",\n  \"599749.jpg\": \"Aves/Herpetotheres cachinnans/ae33632fc2d29e37a74440b5b1228e56.jpg\",\n  \"599754.jpg\": \"Aves/Herpetotheres cachinnans/b7ec74722626b955fe4e13cc57896315.jpg\",\n  \"599755.jpg\": \"Aves/Herpetotheres cachinnans/e0e454e432f8810f0ae15346e25ff816.jpg\",\n  \"599760.jpg\": \"Aves/Herpetotheres cachinnans/a95a67330100a4e050644b3adcb96c8f.jpg\",\n  \"599762.jpg\": \"Aves/Herpetotheres cachinnans/36078bdafb133814d6ffcb928b3b1090.jpg\",\n  \"599763.jpg\": \"Aves/Herpetotheres cachinnans/a91d82f7aedd04083f963deaf9e77f0e.jpg\",\n  \"599764.jpg\": \"Aves/Herpetotheres cachinnans/79102ce264e67f62f5e95e03bfecc9c2.jpg\",\n  \"599770.jpg\": \"Aves/Herpetotheres cachinnans/df659c03c23cbc052ff386a09a0d72c8.jpg\",\n  \"599902.jpg\": \"Aves/Xanthocephalus xanthocephalus/2ef5dcfcc6f90511faa60494f154fb99.jpg\",\n  \"599930.jpg\": \"Aves/Xanthocephalus xanthocephalus/bde96dd2656d21608a77ba14b0ab0d09.jpg\",\n  \"599949.jpg\": \"Aves/Xanthocephalus xanthocephalus/02ffc324cd8961273d4ee33f7bc9b8d0.jpg\",\n  \"59998.jpg\": \"Insecta/Eumenes fraternus/260815e7e60d932046ea7ba643462543.jpg\",\n  \"59999.jpg\": \"Insecta/Eumenes fraternus/7c801c5c43febac6b6f0c59caa483529.jpg\",\n  \"60.jpg\": \"Mammalia/Marmota flaviventris/7c5be8ba4912976f40c1ca21d5c49af5.jpg\",\n  \"600026.jpg\": \"Aves/Passerina cyanea/a16e18295bd53463c3c3eb8ce27829ee.jpg\",\n  \"600058.jpg\": \"Aves/Passerina cyanea/8f8967d7b8f4704e40999f274650f0d6.jpg\",\n  \"600108.jpg\": \"Aves/Passerina cyanea/8149838600814b79fca5c4c1d16d86c5.jpg\",\n  \"600158.jpg\": \"Aves/Passerina cyanea/462ac03c9d244255b786cb16fda020f4.jpg\",\n  \"600162.jpg\": \"Aves/Passerina cyanea/0edc0de534365fa6631ae50ec5b830e7.jpg\",\n  \"600255.jpg\": \"Insecta/Micrathyria hagenii/aed4dc13dad5d1b48ec6084d76622a10.jpg\",\n  \"600256.jpg\": \"Insecta/Micrathyria hagenii/b5b8ac4cbac3ef34bf6d906c225f82a3.jpg\",\n  \"600257.jpg\": \"Insecta/Micrathyria hagenii/f56e00a3a7801120fb9050f0b4da976e.jpg\",\n  \"600258.jpg\": \"Insecta/Micrathyria hagenii/b041ab67d28a8dddee8bd37f07c76f05.jpg\",\n  \"600259.jpg\": \"Insecta/Micrathyria hagenii/7077db1f5b2c15e8aa256cef0fa15fd9.jpg\",\n  \"600260.jpg\": \"Insecta/Micrathyria hagenii/be97ceb484308cee1a960804a28605e2.jpg\",\n  \"600262.jpg\": \"Insecta/Micrathyria hagenii/10da15f86b0b040ac0ef7c5d7478bb72.jpg\",\n  \"600263.jpg\": \"Insecta/Micrathyria hagenii/cb3c3e62e52b7565032de89f20008991.jpg\",\n  \"600264.jpg\": \"Insecta/Micrathyria hagenii/bc8471c486d2d5f3e5f5151729c7c02a.jpg\",\n  \"600265.jpg\": \"Insecta/Micrathyria hagenii/3c93bf89844c19592f02cc07307c95c1.jpg\",\n  \"600266.jpg\": \"Insecta/Micrathyria hagenii/a4180d70f316494911ef2ba2aae70b9a.jpg\",\n  \"600269.jpg\": \"Insecta/Micrathyria hagenii/2b134180433e9f4cf3ed6096e275efdb.jpg\",\n  \"600270.jpg\": \"Insecta/Micrathyria hagenii/7c2da21c592e00acf298b8d88dfa2684.jpg\",\n  \"600277.jpg\": \"Insecta/Micrathyria hagenii/49adef025dd261ddf5b0e06f0a9ac4d1.jpg\",\n  \"600278.jpg\": \"Insecta/Micrathyria hagenii/fc7fd3c743c4beb087d271ae3ddb7903.jpg\",\n  \"600292.jpg\": \"Aves/Arremonops rufivirgatus/f2799fb9ffdd5452a3c4d45857ab0b76.jpg\",\n  \"600293.jpg\": \"Aves/Arremonops rufivirgatus/c3c602862b00b6d0b75c6eae3b4e0f1f.jpg\",\n  \"600299.jpg\": \"Aves/Arremonops rufivirgatus/bece81959908e7a8ca229cbe3f7457f6.jpg\",\n  \"60063.jpg\": \"Actinopterygii/Gasterosteus aculeatus/e4ba317ff638e22e1ba74d541ce43396.jpg\",\n  \"60066.jpg\": \"Actinopterygii/Gasterosteus aculeatus/58e559691adcb6c222e9dcc054b3df91.jpg\",\n  \"60071.jpg\": \"Actinopterygii/Gasterosteus aculeatus/b7639010d987ef871426b577ccae45ec.jpg\",\n  \"60083.jpg\": \"Actinopterygii/Gasterosteus aculeatus/99c68bc515c37a4140412c4124660183.jpg\",\n  \"600893.jpg\": \"Aves/Protonotaria citrea/921a579d3f9e66c960f8528557b439a8.jpg\",\n  \"600897.jpg\": \"Aves/Protonotaria citrea/3e4632e13afb4bafdd3d80164f1a5b35.jpg\",\n  \"600900.jpg\": \"Aves/Protonotaria citrea/0d175c87b37d7ceddc4fe01409d0428f.jpg\",\n  \"600901.jpg\": \"Aves/Protonotaria citrea/dd11140925001202d95887a2a1f9565b.jpg\",\n  \"600906.jpg\": \"Aves/Protonotaria citrea/ab46ae379074b0cfc8d723e05f1a4ac5.jpg\",\n  \"600915.jpg\": \"Aves/Protonotaria citrea/842598cc57d82d722d0dcbdf6b498929.jpg\",\n  \"600916.jpg\": \"Aves/Protonotaria citrea/a4f3d6a8404f43191d295ca57880843e.jpg\",\n  \"600926.jpg\": \"Aves/Protonotaria citrea/d9375a22d66689ff20b47b24eda58177.jpg\",\n  \"600932.jpg\": \"Aves/Protonotaria citrea/98ffff6c436875a759187f8fd344d909.jpg\",\n  \"60113.jpg\": \"Reptilia/Nerodia erythrogaster transversa/2eb43203979427292dac9cc9b085c430.jpg\",\n  \"60116.jpg\": \"Reptilia/Nerodia erythrogaster transversa/1ef091bb50cee8b90ee7574d68b89be5.jpg\",\n  \"60119.jpg\": \"Reptilia/Nerodia erythrogaster transversa/6b7130817e8d1343e78059a6e344a8bf.jpg\",\n  \"601197.jpg\": \"Mammalia/Syncerus caffer/1722fa7554981820a8e27cd5a4166683.jpg\",\n  \"60120.jpg\": \"Reptilia/Nerodia erythrogaster transversa/17d26e872776d164a86b7b3215a92fdf.jpg\",\n  \"601200.jpg\": \"Mammalia/Syncerus caffer/c4cb60211ad13ed849482f14796a7f5a.jpg\",\n  \"601202.jpg\": \"Mammalia/Syncerus caffer/008027a898169ff7659ca2206da690c4.jpg\",\n  \"601222.jpg\": \"Insecta/Anthocharis sara/ebc903bcfd86248829b8867938a8b117.jpg\",\n  \"601223.jpg\": \"Insecta/Anthocharis sara/9232674c852fae38ee944c97d102e4bc.jpg\",\n  \"601227.jpg\": \"Insecta/Anthocharis sara/f2f80fa7f94803cb0c3a15f23b6109ef.jpg\",\n  \"601230.jpg\": \"Insecta/Anthocharis sara/6c64ddfaae80648718f4c62d6e3d26d9.jpg\",\n  \"601231.jpg\": \"Insecta/Anthocharis sara/94d7d35b043e20002f6a45adc83dd27a.jpg\",\n  \"601292.jpg\": \"Insecta/Polygonia comma/bc902f94438714831ed2e17f6bd55b7a.jpg\",\n  \"601308.jpg\": \"Insecta/Polygonia comma/0d1698f19268761bd0e3a0305a28ec21.jpg\",\n  \"601327.jpg\": \"Insecta/Polygonia comma/4baa84b104a34698e7633d9ff31340e3.jpg\",\n  \"60133.jpg\": \"Reptilia/Nerodia erythrogaster transversa/3a6b1c9fffa309206f75eea39869295d.jpg\",\n  \"601332.jpg\": \"Insecta/Polygonia comma/d55f1c9886844b2aaadda05a4add542b.jpg\",\n  \"601363.jpg\": \"Insecta/Polygonia comma/1b5c4228db9583cdae5be7d219fe11b9.jpg\",\n  \"601370.jpg\": \"Insecta/Polygonia comma/33d0f05991d8e33f223739aa497a01fb.jpg\",\n  \"6014.jpg\": \"Insecta/Melanis pixe/a1226f8c1dd0e24a7eafa136b3a78bfa.jpg\",\n  \"60140.jpg\": \"Reptilia/Nerodia erythrogaster transversa/36a72a4d17a31761e09c0c0703869e96.jpg\",\n  \"60143.jpg\": \"Reptilia/Nerodia erythrogaster transversa/5408d7cf1f1a76e079f5b4e93a0730dc.jpg\",\n  \"601453.jpg\": \"Aves/Athene cunicularia/367e570be8640ff2853a7d9b4badf7dd.jpg\",\n  \"601456.jpg\": \"Aves/Athene cunicularia/f5e53008a2c6f408115f4665accadd3d.jpg\",\n  \"60147.jpg\": \"Reptilia/Nerodia erythrogaster transversa/304e0721a18c2df70784fa1c498a93df.jpg\",\n  \"601474.jpg\": \"Aves/Athene cunicularia/e2a0360a13c72e1e672f69f611548ffe.jpg\",\n  \"60148.jpg\": \"Reptilia/Nerodia erythrogaster transversa/8678aad34d354241a2dc0692a09f5961.jpg\",\n  \"601494.jpg\": \"Aves/Athene cunicularia/7af988d320dfb14adcb9b683e68affa2.jpg\",\n  \"601497.jpg\": \"Aves/Athene cunicularia/5f71555ccdadfefeb6eb7d576e9cc52a.jpg\",\n  \"601515.jpg\": \"Aves/Athene cunicularia/af8b536230587cc06bb7f3a2b5bb1a8d.jpg\",\n  \"60153.jpg\": \"Reptilia/Nerodia erythrogaster transversa/060ae9561ff57750c83172dad119e3a0.jpg\",\n  \"601544.jpg\": \"Aves/Megascops asio/813c2a5df0edaa19fce542fc41ee8a8b.jpg\",\n  \"601547.jpg\": \"Aves/Megascops asio/0a6b1b16791e34181976a8067bb074e3.jpg\",\n  \"601556.jpg\": \"Aves/Megascops asio/897e02b707087ac788ccbf73f4a4e772.jpg\",\n  \"601561.jpg\": \"Aves/Megascops asio/cfd4878a20124d132394f6e7a21c27a3.jpg\",\n  \"601571.jpg\": \"Aves/Megascops asio/e508d864af8f0852785d06a240c30ff4.jpg\",\n  \"601576.jpg\": \"Aves/Megascops asio/ad30467a7f9aeeef8e97b3754edc3746.jpg\",\n  \"601578.jpg\": \"Aves/Megascops asio/09a8c7c4505a71724579313f881abd4d.jpg\",\n  \"601590.jpg\": \"Insecta/Periplaneta americana/38c657c3bc2e2f3c27c700ee4f38c645.jpg\",\n  \"601600.jpg\": \"Insecta/Periplaneta americana/17e6d0e4b5a9aff576a30b04d1517b77.jpg\",\n  \"601603.jpg\": \"Insecta/Periplaneta americana/5c01e2b18b21c90595eed299a42e3c45.jpg\",\n  \"601609.jpg\": \"Aves/Perisoreus canadensis/5ecd67492ac74bc9bbeed09fb7208457.jpg\",\n  \"601611.jpg\": \"Aves/Perisoreus canadensis/3a7d9e4d7980ba290ed66e7ab48db280.jpg\",\n  \"601619.jpg\": \"Aves/Perisoreus canadensis/90759c9c6467f84bf808371615d88f9f.jpg\",\n  \"601632.jpg\": \"Aves/Perisoreus canadensis/1a1fc4effc76a3f47b6bb90372f69007.jpg\",\n  \"601640.jpg\": \"Aves/Perisoreus canadensis/efec2cb89cc88b992362a6a1693e7a26.jpg\",\n  \"601642.jpg\": \"Aves/Perisoreus canadensis/867a4dab92893de9d337c806990dc352.jpg\",\n  \"6017.jpg\": \"Insecta/Melanis pixe/f121e6ea2f5b77184aa1e0263abe7d0c.jpg\",\n  \"60175.jpg\": \"Reptilia/Nerodia erythrogaster transversa/03c074f96a2c3af4b3f000a56a2f2f94.jpg\",\n  \"60178.jpg\": \"Reptilia/Nerodia erythrogaster transversa/6f69fadbc822dfae4553e02c2a7ab182.jpg\",\n  \"60179.jpg\": \"Reptilia/Nerodia erythrogaster transversa/f54669bc951124effec3bfa9964d4769.jpg\",\n  \"601793.jpg\": \"Insecta/Phyciodes phaon/35cf161ed0b7222c91b93a8aec937552.jpg\",\n  \"601817.jpg\": \"Insecta/Phyciodes phaon/b0d8768e81c1b8baea3267e4301328b9.jpg\",\n  \"601823.jpg\": \"Insecta/Phyciodes phaon/4c873ee2b96d444e0c549b41f3e5c238.jpg\",\n  \"601833.jpg\": \"Insecta/Phyciodes phaon/334bee54e60b0a3dda268aedba86f71c.jpg\",\n  \"601865.jpg\": \"Insecta/Phyciodes phaon/811747f17c37d531eb3082c8fe7ee12a.jpg\",\n  \"601872.jpg\": \"Arachnida/Neoscona oaxacensis/4298ed849c49648c2245a2fb59159560.jpg\",\n  \"601873.jpg\": \"Arachnida/Neoscona oaxacensis/a8693359175c1c0b208c2cef9e68579d.jpg\",\n  \"601880.jpg\": \"Arachnida/Neoscona oaxacensis/80822f93805bb1d4154e43e996768b41.jpg\",\n  \"60189.jpg\": \"Reptilia/Nerodia erythrogaster transversa/8f01e60c321e90c92477dc866afc8fe3.jpg\",\n  \"601890.jpg\": \"Arachnida/Neoscona oaxacensis/56189f19d63decc2ac3d0b6d99858e4f.jpg\",\n  \"601892.jpg\": \"Arachnida/Neoscona oaxacensis/0ac6083850f3bb83f291c15bf7b67b55.jpg\",\n  \"60190.jpg\": \"Reptilia/Nerodia erythrogaster transversa/dd8770b92fca29baee96b37411723bb9.jpg\",\n  \"601904.jpg\": \"Arachnida/Neoscona oaxacensis/36e294b7a4793e318f1bf2da49dd1d66.jpg\",\n  \"601905.jpg\": \"Arachnida/Neoscona oaxacensis/32a3d050103b9ddafa9bebce3f28c86e.jpg\",\n  \"601909.jpg\": \"Arachnida/Neoscona oaxacensis/e6ba86578be043a29249167ff7482bb6.jpg\",\n  \"60193.jpg\": \"Reptilia/Nerodia erythrogaster transversa/5fe3860dda4e3ea7ed2d0737e82af21e.jpg\",\n  \"601980.jpg\": \"Reptilia/Coronella austriaca/c1a99614b2252bf1edcf3c9c44e34727.jpg\",\n  \"60204.jpg\": \"Reptilia/Nerodia erythrogaster transversa/04aa2213ec626094cf13894d9e299192.jpg\",\n  \"602227.jpg\": \"Insecta/Aphantopus hyperantus/0c401d930efdbf33689d596d889aa7e4.jpg\",\n  \"602228.jpg\": \"Insecta/Aphantopus hyperantus/cabca4e29b91f0b171899b5ffcfa99a7.jpg\",\n  \"602229.jpg\": \"Insecta/Aphantopus hyperantus/98459782b5fdcc94548753e18918262e.jpg\",\n  \"602230.jpg\": \"Insecta/Aphantopus hyperantus/d56464432c95f30e9028eeb40b570287.jpg\",\n  \"602231.jpg\": \"Insecta/Aphantopus hyperantus/2f3eb0458cbcb426e252c7a3301e6f92.jpg\",\n  \"602233.jpg\": \"Insecta/Aphantopus hyperantus/5ad496263345394951c22722e9e2544d.jpg\",\n  \"602234.jpg\": \"Insecta/Aphantopus hyperantus/cb0fbd8e9311b1681803e0c8beffb6bd.jpg\",\n  \"60224.jpg\": \"Reptilia/Nerodia erythrogaster transversa/3989691b41d2ef16cddafe3495391ad5.jpg\",\n  \"602242.jpg\": \"Insecta/Aphantopus hyperantus/b68e01bd25db2a4d39d9dae28784f643.jpg\",\n  \"602244.jpg\": \"Insecta/Aphantopus hyperantus/9cee9a27a1813c8b00a25cadba814877.jpg\",\n  \"602245.jpg\": \"Insecta/Aphantopus hyperantus/0fed3d7681bed7dbe30f7011f8c04474.jpg\",\n  \"602247.jpg\": \"Insecta/Aphantopus hyperantus/3e137a814cfefc0de5c14cf241d6a08b.jpg\",\n  \"602248.jpg\": \"Insecta/Aphantopus hyperantus/cf769188ea5766076a75fb31422e3394.jpg\",\n  \"602249.jpg\": \"Insecta/Aphantopus hyperantus/38e108be56bda0c85fc5be7025141e34.jpg\",\n  \"60225.jpg\": \"Reptilia/Nerodia erythrogaster transversa/6775dc800717a7f21a408fee5f17b3c5.jpg\",\n  \"602250.jpg\": \"Insecta/Aphantopus hyperantus/020c238e223d40c80317edfbf13c63b5.jpg\",\n  \"602251.jpg\": \"Insecta/Hemideina crassidens/d0f977d8870bec8d2c550cf8716eb4b1.jpg\",\n  \"602259.jpg\": \"Insecta/Hemideina crassidens/81e71b4599b183bef2e3d132316dda57.jpg\",\n  \"60226.jpg\": \"Reptilia/Nerodia erythrogaster transversa/da427a0c946ba7b6a8ebfd3b4bcd7b71.jpg\",\n  \"602260.jpg\": \"Insecta/Hemideina crassidens/b0dd4a99c797d12c68f63bd1ebbab2b0.jpg\",\n  \"602263.jpg\": \"Insecta/Hemideina crassidens/fe308d81486b80a593b965a5f8ed0962.jpg\",\n  \"602266.jpg\": \"Insecta/Hemideina crassidens/8ddf8c4c8b8e7b6fc4c0b13092f0866c.jpg\",\n  \"602272.jpg\": \"Insecta/Hemideina crassidens/80cb0c06890bfec4881c8f9bb4d64b7b.jpg\",\n  \"602275.jpg\": \"Insecta/Hemideina crassidens/e0e0a6e28dfe4e427f0926e0ce86dd70.jpg\",\n  \"60229.jpg\": \"Reptilia/Nerodia erythrogaster transversa/3ff3b75bf1647d4599e94bc7b8d9695f.jpg\",\n  \"60233.jpg\": \"Reptilia/Nerodia erythrogaster transversa/4de491c8b6a659c53a810d8f238c1486.jpg\",\n  \"602330.jpg\": \"Aves/Momotus mexicanus/379abc7d082add4a1520b41e3462f2fc.jpg\",\n  \"602336.jpg\": \"Aves/Momotus mexicanus/5bcbf750428c138b1bae5acd6195f8c1.jpg\",\n  \"60234.jpg\": \"Reptilia/Nerodia erythrogaster transversa/43cfd6a54080bedbee8a4ebbf0411255.jpg\",\n  \"602356.jpg\": \"Aves/Momotus mexicanus/45f9a31438c0332916fde1e1fb46d79e.jpg\",\n  \"602359.jpg\": \"Aves/Momotus mexicanus/9b38e8a397d313f5c39ea5f70836171e.jpg\",\n  \"602374.jpg\": \"Aves/Sturnella neglecta/bf4c12edcd797f8de94d79b9a57bb1cc.jpg\",\n  \"602413.jpg\": \"Aves/Sturnella neglecta/bfd9dd8ccebe87c21ae413b91a972e4f.jpg\",\n  \"602417.jpg\": \"Aves/Sturnella neglecta/ec9b73f02c84beb8a1f28c6a8827ddc4.jpg\",\n  \"602434.jpg\": \"Aves/Sturnella neglecta/973e6df13158a7283370f7161147827b.jpg\",\n  \"602435.jpg\": \"Aves/Sturnella neglecta/37cfccf819f1b6cbc51be4b769a5d546.jpg\",\n  \"602473.jpg\": \"Aves/Sturnella neglecta/415867a8a8aacdf989edb072f74f3eb9.jpg\",\n  \"60248.jpg\": \"Animalia/Rhincodon typus/59101e54a64b05f72a0b82a817b27102.jpg\",\n  \"602510.jpg\": \"Aves/Sturnella neglecta/21336eb8286782090d1fcda391baaead.jpg\",\n  \"602529.jpg\": \"Aves/Calidris maritima/dfaacd0e267a14d8775554582d972d18.jpg\",\n  \"602536.jpg\": \"Mollusca/Cerithideopsis californica/2aeb381a114d2ff99195fdb02dc994f2.jpg\",\n  \"60257.jpg\": \"Animalia/Rhincodon typus/cdc8084be35eb554fbf9add4674be3aa.jpg\",\n  \"60258.jpg\": \"Animalia/Rhincodon typus/7c0f18c166e01e7ed1f8333ebce4d91e.jpg\",\n  \"60262.jpg\": \"Animalia/Rhincodon typus/cc62fdd31ca17b46541c482f909ac459.jpg\",\n  \"602683.jpg\": \"Insecta/Plusiodonta compressipalpis/3d7da12a9001593f6285039fa181f662.jpg\",\n  \"602684.jpg\": \"Insecta/Plusiodonta compressipalpis/f6b1720a1d1a39e06826d055f1928b58.jpg\",\n  \"602685.jpg\": \"Insecta/Plusiodonta compressipalpis/bade4ec1593feff3b97fda9f5b6a4901.jpg\",\n  \"602686.jpg\": \"Insecta/Plusiodonta compressipalpis/4b73b0ef946b0d4c238899e326cf521e.jpg\",\n  \"602690.jpg\": \"Insecta/Plusiodonta compressipalpis/e2856201d9e26787136df99bdb211b8e.jpg\",\n  \"602691.jpg\": \"Insecta/Plusiodonta compressipalpis/1201d25a0fe78f89204e9658c1f61bf1.jpg\",\n  \"602692.jpg\": \"Insecta/Plusiodonta compressipalpis/a364a0535b81b5e90324a2060befee61.jpg\",\n  \"602697.jpg\": \"Insecta/Plusiodonta compressipalpis/659d34edfbfcebdd4e494658887b7c37.jpg\",\n  \"602698.jpg\": \"Insecta/Plusiodonta compressipalpis/e47e301c1baf433de56bf82ca6d783b9.jpg\",\n  \"602699.jpg\": \"Insecta/Plusiodonta compressipalpis/13b72581fbc686c0b2fbc653ec27d645.jpg\",\n  \"602705.jpg\": \"Insecta/Plusiodonta compressipalpis/7c872f29740170a79e2813cafba60e60.jpg\",\n  \"602707.jpg\": \"Insecta/Plusiodonta compressipalpis/fd51c9165969f4d8058311d5123edb9a.jpg\",\n  \"602713.jpg\": \"Insecta/Caedicia simplex/013f9c8345be2e0d560624a68c7631ba.jpg\",\n  \"602719.jpg\": \"Insecta/Caedicia simplex/ef4b74170bf40fd15420f85dc6dd3b38.jpg\",\n  \"602721.jpg\": \"Aves/Larus hyperboreus/0934e77074773caa68fd65f9ffc79fa3.jpg\",\n  \"602722.jpg\": \"Aves/Larus hyperboreus/a098d7b73a0bcb1dabb210f596581279.jpg\",\n  \"602726.jpg\": \"Aves/Larus hyperboreus/1f8ff174324812a366ce8e9022c35a31.jpg\",\n  \"602727.jpg\": \"Aves/Larus hyperboreus/f2dad78c56a94e45bd8fe107aac95bf8.jpg\",\n  \"602731.jpg\": \"Aves/Larus hyperboreus/b8004a7f00fb0ae8ac59235619d7c63f.jpg\",\n  \"602734.jpg\": \"Aves/Larus hyperboreus/fc938de13c4c5f9c36699169227be0a7.jpg\",\n  \"602740.jpg\": \"Aves/Larus hyperboreus/f0fa33373a37565c16bff1ac561fc35f.jpg\",\n  \"602741.jpg\": \"Aves/Larus hyperboreus/b18e7ba53e9e13ea31aacdf268c793d7.jpg\",\n  \"602744.jpg\": \"Aves/Larus hyperboreus/c05d9b150e809cb6f29ab3490207c1f4.jpg\",\n  \"602748.jpg\": \"Aves/Larus hyperboreus/9cef50d7dbd72583cd590477e3bf02d5.jpg\",\n  \"602784.jpg\": \"Aves/Pelecanus erythrorhynchos/d5f061cda97f9f493c731359cdd55c16.jpg\",\n  \"6028.jpg\": \"Insecta/Melanis pixe/72308af13cecfaacc851d0859dfd4232.jpg\",\n  \"602895.jpg\": \"Aves/Pelecanus erythrorhynchos/f264b7385f47a4a4c8afb1957573a4c7.jpg\",\n  \"6030.jpg\": \"Insecta/Melanis pixe/e8fb154a2a4c963d4c76ef5844377c7b.jpg\",\n  \"603046.jpg\": \"Aves/Pelecanus erythrorhynchos/f04613022a24d4ce99a643f9eb985fc6.jpg\",\n  \"603189.jpg\": \"Reptilia/Sceloporus torquatus/1cb38c8bc4702c5b297717cb1f7c9c55.jpg\",\n  \"603190.jpg\": \"Reptilia/Sceloporus torquatus/cb5c5d545cd7e0af90d731b861210e78.jpg\",\n  \"603193.jpg\": \"Reptilia/Sceloporus torquatus/028c44f40368a7c395966e273876899a.jpg\",\n  \"603194.jpg\": \"Reptilia/Sceloporus torquatus/3717f5ba006307a556b8f0f1554e13e9.jpg\",\n  \"603197.jpg\": \"Reptilia/Sceloporus torquatus/a55bfb04f374e75fc097e20258e51b51.jpg\",\n  \"603198.jpg\": \"Reptilia/Sceloporus torquatus/3e2d7fe44b750b39eddd08141a47f691.jpg\",\n  \"603201.jpg\": \"Reptilia/Sceloporus torquatus/2160f32599f7fa172f87cb0365968c34.jpg\",\n  \"603202.jpg\": \"Reptilia/Sceloporus torquatus/e659c7c01d230b6636b5c11a781aa099.jpg\",\n  \"603203.jpg\": \"Reptilia/Sceloporus torquatus/fc0ef4d333624f89c7ed614b74ce5722.jpg\",\n  \"603207.jpg\": \"Reptilia/Sceloporus torquatus/be9464305819d857eb40c8638f6463b5.jpg\",\n  \"603210.jpg\": \"Reptilia/Sceloporus torquatus/0ca21843354c643eb0249a7ef741fbdb.jpg\",\n  \"603238.jpg\": \"Aves/Eremophila alpestris/65f5c3e8dd77df5f35bb56c5c759512d.jpg\",\n  \"603245.jpg\": \"Aves/Eremophila alpestris/edf2610eb442e48fe61e28ec8fcc8abf.jpg\",\n  \"603276.jpg\": \"Aves/Eremophila alpestris/304c6bc85a4bc6f989a94b3197474692.jpg\",\n  \"603307.jpg\": \"Aves/Eremophila alpestris/d216cf7d5df3a4d7958e5e8eadea84e8.jpg\",\n  \"603320.jpg\": \"Aves/Eremophila alpestris/64d2e0860d7c86aa92384cd5c1baa437.jpg\",\n  \"603325.jpg\": \"Animalia/Ginglymostoma cirratum/00ff68bb542e9e31fdc7c90385f3bd64.jpg\",\n  \"603328.jpg\": \"Mammalia/Lepus europaeus/5f60f405314f5da909e39b565a628d4d.jpg\",\n  \"603346.jpg\": \"Mammalia/Lepus europaeus/7759724aa857c0394d4fb02295e46399.jpg\",\n  \"603347.jpg\": \"Mammalia/Lepus europaeus/dd7644547c25e1cc74ba8c560d2c4590.jpg\",\n  \"603348.jpg\": \"Mammalia/Lepus europaeus/5dfd3aee7438a8e9bbd16356892d7183.jpg\",\n  \"603354.jpg\": \"Mammalia/Lepus europaeus/1ff74c91a225c8bc7f9112dcb14fd4fd.jpg\",\n  \"603383.jpg\": \"Aves/Loxia leucoptera/1cc1cc9eebff82d72748dfd2812a2fa7.jpg\",\n  \"603387.jpg\": \"Aves/Loxia leucoptera/3287ccada3ece1d852d7ab24c70896b4.jpg\",\n  \"603390.jpg\": \"Aves/Loxia leucoptera/8b8ffbebf47bbc151cb375dd9c072758.jpg\",\n  \"603432.jpg\": \"Mammalia/Tadarida brasiliensis/a1bb2759ac21a9f089558febdd24ac80.jpg\",\n  \"603435.jpg\": \"Mammalia/Tadarida brasiliensis/b1a267026335722f9ec8ad7e6748ae49.jpg\",\n  \"603436.jpg\": \"Mammalia/Tadarida brasiliensis/cdf6fb1db5e73b9d8fb640d3cc40ff43.jpg\",\n  \"603457.jpg\": \"Reptilia/Salvadora grahamiae/80b4b30086f8c5423227f044063fad62.jpg\",\n  \"603472.jpg\": \"Aves/Chen rossii/265bfc0e039c2e76185677d8397daaf7.jpg\",\n  \"603504.jpg\": \"Aves/Chen rossii/efbb8530049cee901c77754a73f078ae.jpg\",\n  \"603582.jpg\": \"Reptilia/Emydoidea blandingii/71f01440334772292b94474cc9b0e58b.jpg\",\n  \"603593.jpg\": \"Reptilia/Emydoidea blandingii/c596f0b22d6a3c14b02c00ba60293962.jpg\",\n  \"603595.jpg\": \"Reptilia/Emydoidea blandingii/2d641dd7497a78ccfb090da92bdb2a5d.jpg\",\n  \"603596.jpg\": \"Reptilia/Emydoidea blandingii/b2c4bf1d9e612ed36b732db3868c1cd8.jpg\",\n  \"603597.jpg\": \"Reptilia/Emydoidea blandingii/886b65632c52b6a1c52633970f569850.jpg\",\n  \"6036.jpg\": \"Insecta/Melanis pixe/ee1fd63ec2e08ef9c15bd3055bb4c8ca.jpg\",\n  \"603614.jpg\": \"Insecta/Coenonympha arcania/2f7dd3628444fc273b7d1c98634b2845.jpg\",\n  \"603793.jpg\": \"Aves/Calidris minutilla/8447730a6dac0ea9b44f78db46a81bce.jpg\",\n  \"6038.jpg\": \"Insecta/Melanis pixe/2aef967ff61be71cdcd65c679863f839.jpg\",\n  \"603805.jpg\": \"Aves/Calidris minutilla/f2c6aa0a59f70b8c71f741b77b538526.jpg\",\n  \"603885.jpg\": \"Aves/Calidris minutilla/f8927e5334999253b3f168d7c27e1fc4.jpg\",\n  \"603990.jpg\": \"Aves/Calidris minutilla/948deb73cd40d74ca3f774809bdc8ba3.jpg\",\n  \"603993.jpg\": \"Aves/Calidris minutilla/f78681e32ea0084484dc19047f8dddd8.jpg\",\n  \"604022.jpg\": \"Aves/Calidris minutilla/b1c032b57ec5d89bc6c69e2e6b4b00d2.jpg\",\n  \"604160.jpg\": \"Reptilia/Aspidoscelis sexlineata sexlineata/1c2871bac3cf0fd9e996ace002aeecb2.jpg\",\n  \"604162.jpg\": \"Reptilia/Aspidoscelis sexlineata sexlineata/c51a8227e1660eaa6c5873251d797a57.jpg\",\n  \"6042.jpg\": \"Aves/Columba palumbus/17ce94132d8297488e24eee4f4532c7e.jpg\",\n  \"604211.jpg\": \"Aves/Phalacrocorax penicillatus/c0739e5c4de2bebd0c727e81d01d0f60.jpg\",\n  \"604236.jpg\": \"Aves/Phalacrocorax penicillatus/4ca6aa8a7747a64a2b11ef2ec0ad00ee.jpg\",\n  \"604244.jpg\": \"Aves/Phalacrocorax penicillatus/4b7763cbe21cca9bfcb6fa7eb08d1bbb.jpg\",\n  \"604262.jpg\": \"Aves/Vireo olivaceus/7d22b22dbb7e0ccaaca9b7b91b32c655.jpg\",\n  \"604272.jpg\": \"Aves/Vireo olivaceus/1e901557443b2bd20bec256c898390e7.jpg\",\n  \"604274.jpg\": \"Aves/Vireo olivaceus/f293d1b77eee1030cfa74711dbfc4d29.jpg\",\n  \"604275.jpg\": \"Aves/Vireo olivaceus/a5cb9e9ceda4e5d666c3634cc327f63a.jpg\",\n  \"604286.jpg\": \"Aves/Vireo olivaceus/6ffbd7406b10556fbbf0e310c1fd915c.jpg\",\n  \"604311.jpg\": \"Aves/Vireo olivaceus/133284e4b422861c1b0900de39e032f5.jpg\",\n  \"604315.jpg\": \"Aves/Vireo olivaceus/c1a56ad5e2891b9a9c10eb8b7dca9321.jpg\",\n  \"604316.jpg\": \"Aves/Vireo olivaceus/ddd381420342d97dd12901b20d61cca3.jpg\",\n  \"604317.jpg\": \"Aves/Vireo olivaceus/fc7dd06d6acaa153d1f9cadd9b2e12b8.jpg\",\n  \"604322.jpg\": \"Aves/Vireo olivaceus/bb83079ab0b1edc2fc70df42645e04f9.jpg\",\n  \"604419.jpg\": \"Aves/Icteria virens/8418dc8f021a6a48255cd2385d7d34db.jpg\",\n  \"604427.jpg\": \"Aves/Icteria virens/e19bd541b708c103a5e37e9ac4d64c63.jpg\",\n  \"604428.jpg\": \"Aves/Icteria virens/9407881a61069e149352516db6c30c41.jpg\",\n  \"604430.jpg\": \"Aves/Icteria virens/a706900b8547072cdad88c8fc45083d2.jpg\",\n  \"604431.jpg\": \"Aves/Icteria virens/25525bebbc8aa56915dc3a23d941c9ad.jpg\",\n  \"604438.jpg\": \"Aves/Icteria virens/d756b2b9ac9b717313edaf18c8c4f697.jpg\",\n  \"604439.jpg\": \"Aves/Icteria virens/13d021b5379de3b7d54a48871ed985ff.jpg\",\n  \"604444.jpg\": \"Aves/Icteria virens/5f4f70589da0a6aa3ad0468100474977.jpg\",\n  \"604445.jpg\": \"Aves/Icteria virens/796691b2764a5b6750b1ea0ef7b0d003.jpg\",\n  \"6045.jpg\": \"Aves/Columba palumbus/216d2b9033940d14b4b7eedbd07e07f4.jpg\",\n  \"604631.jpg\": \"Mammalia/Nasua narica/eb1fe6bd08e1ea37f30b31b8a57cc08d.jpg\",\n  \"604632.jpg\": \"Mammalia/Nasua narica/8652967b9eeadb44bbec2ce9c2550159.jpg\",\n  \"604639.jpg\": \"Mammalia/Nasua narica/7aca343755edac5340e85aa4d4f4d045.jpg\",\n  \"604661.jpg\": \"Mammalia/Nasua narica/466f16bf2bdb876a04181a9aa4de70db.jpg\",\n  \"604675.jpg\": \"Mammalia/Nasua narica/3e6b92777b35d8754ababd3aa1727394.jpg\",\n  \"60468.jpg\": \"Reptilia/Coleonyx variegatus/63dfd416b5c5cb6c6fc209ce881067ed.jpg\",\n  \"604692.jpg\": \"Mammalia/Nasua narica/4ee17cac80f06241981ec6a13b4f0604.jpg\",\n  \"604696.jpg\": \"Mammalia/Nasua narica/66b84de1f5f80fb902cc1f2ff6f35e0f.jpg\",\n  \"604697.jpg\": \"Mammalia/Nasua narica/2e1aa93f560a34b7d34c7ef1432e6207.jpg\",\n  \"604709.jpg\": \"Aves/Accipiter gentilis/3c1f23244371dd7f3ed61159b8f61936.jpg\",\n  \"604710.jpg\": \"Aves/Accipiter gentilis/3c1456cff508c58d11ef31e7bab5f17c.jpg\",\n  \"604719.jpg\": \"Aves/Vermivora cyanoptera/ddea385c319f3758a10bf5acd96e73d7.jpg\",\n  \"604721.jpg\": \"Aves/Vermivora cyanoptera/e78a777ffab54171fb428af820d41db2.jpg\",\n  \"604722.jpg\": \"Aves/Vermivora cyanoptera/89a5f27f2f97fdc273298f4a01745355.jpg\",\n  \"604726.jpg\": \"Aves/Vermivora cyanoptera/95c64b9a1ba42bf299b686f559abe3e5.jpg\",\n  \"604727.jpg\": \"Aves/Vermivora cyanoptera/3d40e9b7f49839dcf3e2d2d3b9d7468d.jpg\",\n  \"604730.jpg\": \"Aves/Vermivora cyanoptera/4e2402594d92a50695d953756e7da33e.jpg\",\n  \"604731.jpg\": \"Aves/Vermivora cyanoptera/95023d29196d22397a0e85aa42329f1b.jpg\",\n  \"604732.jpg\": \"Aves/Vermivora cyanoptera/ca73a2137d872dd4605ac5c9978e0245.jpg\",\n  \"604739.jpg\": \"Aves/Setophaga striata/95df81052eb851917fa0a63e776af0cf.jpg\",\n  \"60474.jpg\": \"Reptilia/Coleonyx variegatus/deba7c50cd5abf09c99b38aa40051e82.jpg\",\n  \"604740.jpg\": \"Aves/Setophaga striata/62f872f90ddd9f5f09a27d1ab32ae130.jpg\",\n  \"604745.jpg\": \"Aves/Setophaga striata/e5f729240489bf0416d032f821042fbd.jpg\",\n  \"604746.jpg\": \"Aves/Setophaga striata/fdbf50cf1e1ba105dceba9e1bf7b1faa.jpg\",\n  \"604751.jpg\": \"Aves/Setophaga striata/ba0422ce58b99562f291637ed8a74d03.jpg\",\n  \"604752.jpg\": \"Aves/Setophaga striata/df9b1ecf1b385cf91aa2dd19090242d4.jpg\",\n  \"604759.jpg\": \"Aves/Setophaga striata/ec66a12524ea4ddeb238476011751d0f.jpg\",\n  \"60476.jpg\": \"Reptilia/Coleonyx variegatus/32c3b7f0f4866f68b88553f58627dfc8.jpg\",\n  \"604765.jpg\": \"Aves/Setophaga striata/2cb5a175341836e70de229e71c344be9.jpg\",\n  \"604766.jpg\": \"Aves/Setophaga striata/56be12271c18bd14c462fa6ca421e94d.jpg\",\n  \"604769.jpg\": \"Aves/Setophaga striata/ad1b3b6d993c5c2d9729ceffe04162bb.jpg\",\n  \"60477.jpg\": \"Reptilia/Coleonyx variegatus/fb7dcc78ca646250e2f5471f01777ae0.jpg\",\n  \"604771.jpg\": \"Mollusca/Otala lactea/120b5fb4d106978f2c5bd0ee961ceea4.jpg\",\n  \"60478.jpg\": \"Reptilia/Coleonyx variegatus/b9ffb77aac7c3299f1f9d9df8e5e0551.jpg\",\n  \"60482.jpg\": \"Reptilia/Coleonyx variegatus/9af3c581b7faeb73023d8ea8553e78fb.jpg\",\n  \"604828.jpg\": \"Mollusca/Otala lactea/d390c56d74ec873e47b20c69f120050c.jpg\",\n  \"60483.jpg\": \"Reptilia/Coleonyx variegatus/b9a5d1fa4b715bb1cbd16125bf4e2321.jpg\",\n  \"604852.jpg\": \"Mollusca/Otala lactea/21e92da7a1901ee7c44ab95dda01002c.jpg\",\n  \"604853.jpg\": \"Mollusca/Otala lactea/0386af531c880d7c6514f96ba51d41bc.jpg\",\n  \"60486.jpg\": \"Reptilia/Coleonyx variegatus/323159d0a1c21f997cc4fb3dac7b1a74.jpg\",\n  \"60487.jpg\": \"Reptilia/Coleonyx variegatus/3e1e5daec7d2da448a203902eb2700f9.jpg\",\n  \"604875.jpg\": \"Mollusca/Otala lactea/50abe33e8936e747115e9d2a6b9ff37a.jpg\",\n  \"604878.jpg\": \"Insecta/Patalene olyzonaria/29c3f552b4dcdfb3736ee12b035021a2.jpg\",\n  \"60488.jpg\": \"Reptilia/Coleonyx variegatus/a4e87aee0328006f377204fa6f4942c4.jpg\",\n  \"604884.jpg\": \"Insecta/Patalene olyzonaria/56e7408e9e823682100f27a355aeb06b.jpg\",\n  \"60489.jpg\": \"Reptilia/Coleonyx variegatus/4c4f43933a5a7c6ff00b38e43da1c9e2.jpg\",\n  \"604897.jpg\": \"Aves/Haemorhous purpureus/8c77f8e3ec1e529b1ba399cd3810bb27.jpg\",\n  \"6049.jpg\": \"Aves/Columba palumbus/0549beb33401ab23b9c69c5d88b91cb5.jpg\",\n  \"60490.jpg\": \"Reptilia/Coleonyx variegatus/ebdc04c8c14dc94b790a6f92b9333dc3.jpg\",\n  \"604913.jpg\": \"Aves/Haemorhous purpureus/e13c9a779f664d84407a13ed3effa8d6.jpg\",\n  \"604921.jpg\": \"Aves/Haemorhous purpureus/c072ec5bd66e4c4b8ce14a990900c02a.jpg\",\n  \"60493.jpg\": \"Reptilia/Coleonyx variegatus/34897e97fe34a79d692fe6f95e2c159e.jpg\",\n  \"604935.jpg\": \"Aves/Haemorhous purpureus/85b3512368b3a8438242fda862a4ded1.jpg\",\n  \"60494.jpg\": \"Reptilia/Coleonyx variegatus/ab85d56dc1e38035871e7efd90cfea30.jpg\",\n  \"604945.jpg\": \"Aves/Haemorhous purpureus/298a3ad9d25c6a40bf0f91d6e3bf5a4a.jpg\",\n  \"60495.jpg\": \"Reptilia/Coleonyx variegatus/e390cd944f41c910572ec5b712c4ce00.jpg\",\n  \"60496.jpg\": \"Reptilia/Coleonyx variegatus/577e8f85fe85a73e72d897934812bbc0.jpg\",\n  \"60497.jpg\": \"Reptilia/Coleonyx variegatus/3e27b81ea76eb8d9d7bab8ff21ad2815.jpg\",\n  \"604975.jpg\": \"Aves/Haemorhous purpureus/0b46b8142be5f0c57d9c25de14b46339.jpg\",\n  \"60498.jpg\": \"Reptilia/Coleonyx variegatus/ded651b198d551906c97b89e298be0ce.jpg\",\n  \"6050.jpg\": \"Aves/Columba palumbus/5f6a63ba8c382282e13e35a51e233e46.jpg\",\n  \"605019.jpg\": \"Actinopterygii/Abudefduf sordidus/32121c8576111856a551999ac4e1f0dc.jpg\",\n  \"60502.jpg\": \"Reptilia/Coleonyx variegatus/be0f616ed0647d0413cdc33143eccbdf.jpg\",\n  \"60503.jpg\": \"Reptilia/Coleonyx variegatus/890c5f6fcb5ae795ea16c42cf7b9ec3f.jpg\",\n  \"60505.jpg\": \"Reptilia/Coleonyx variegatus/0286f9520fc19ed9509ee556b3fe750f.jpg\",\n  \"605056.jpg\": \"Aves/Tyrannus couchii/00f18daf99404109f71c0755a53fab0d.jpg\",\n  \"60508.jpg\": \"Reptilia/Coleonyx variegatus/9d1c7e584c936db2f2c305645d260fd9.jpg\",\n  \"60509.jpg\": \"Reptilia/Coleonyx variegatus/3891fc7f942b9d69592cc9c4b89eb414.jpg\",\n  \"60513.jpg\": \"Reptilia/Coleonyx variegatus/3e3ef0279a4f693a7c3b27ae59cf4e14.jpg\",\n  \"605136.jpg\": \"Reptilia/Emys orbicularis/4f1838fd12fd345b4c168d18046afed6.jpg\",\n  \"605137.jpg\": \"Reptilia/Emys orbicularis/4b1fe804322a402e5e7e92590462918f.jpg\",\n  \"605141.jpg\": \"Mammalia/Blarina brevicauda/271b6f60e5e3397548e9320c7a28d8cf.jpg\",\n  \"605143.jpg\": \"Mammalia/Blarina brevicauda/c06cae4a34e86eb1aac280dfa73c3707.jpg\",\n  \"605147.jpg\": \"Mammalia/Blarina brevicauda/503db0e49c74e11d3dc9208dcef5ee43.jpg\",\n  \"60515.jpg\": \"Insecta/Dythemis fugax/2cd8e6770490f18975d7e75b34566c6c.jpg\",\n  \"605153.jpg\": \"Mammalia/Blarina brevicauda/2728f2ae376331bfc5afb6f14c91c7f2.jpg\",\n  \"605178.jpg\": \"Insecta/Boisea rubrolineata/b0967f6110cf46b79e9ad56ea642c60c.jpg\",\n  \"605179.jpg\": \"Insecta/Boisea rubrolineata/1d81bf9b9a82a51d53cf2e3f96845e76.jpg\",\n  \"605181.jpg\": \"Insecta/Boisea rubrolineata/b0f05cc9b3a9599b6e61598e57aeb31f.jpg\",\n  \"605183.jpg\": \"Insecta/Boisea rubrolineata/d35712d715bf9b9be9e784b6adc8e782.jpg\",\n  \"605188.jpg\": \"Insecta/Boisea rubrolineata/dfc300c4c94b95563accf818c47de0e5.jpg\",\n  \"605192.jpg\": \"Insecta/Boisea rubrolineata/c73b267bb2a5842fdc03030c24f67ca7.jpg\",\n  \"605197.jpg\": \"Insecta/Boisea rubrolineata/e74eda52bac7c01329ad83ac41c03467.jpg\",\n  \"6052.jpg\": \"Aves/Columba palumbus/3343430e8e82befc9881869d97b44d7c.jpg\",\n  \"605203.jpg\": \"Aves/Sitta pusilla/cc692bad3ee7758020734a64d648779a.jpg\",\n  \"605208.jpg\": \"Aves/Sitta pusilla/3921314c126ac6576db587c28ef3a508.jpg\",\n  \"60521.jpg\": \"Insecta/Dythemis fugax/0a8cc23b454cfd3aae93b2bcb3ff33c9.jpg\",\n  \"605211.jpg\": \"Aves/Sitta pusilla/2d5990c5904cf5ceb22bdb053c9239f5.jpg\",\n  \"605247.jpg\": \"Reptilia/Elgaria multicarinata/194922b5536d43b0e751cd8fbfce559e.jpg\",\n  \"60526.jpg\": \"Insecta/Dythemis fugax/25b82afd6e0e6a0c6346881f494c0f7f.jpg\",\n  \"605273.jpg\": \"Reptilia/Elgaria multicarinata/d8e11e21688fa2034a52555aca79bbfb.jpg\",\n  \"605287.jpg\": \"Reptilia/Elgaria multicarinata/f78beeeae7919c78b44202551809c96c.jpg\",\n  \"60531.jpg\": \"Insecta/Dythemis fugax/ea8a14d0d83e90285b0708c900abe21b.jpg\",\n  \"605312.jpg\": \"Reptilia/Elgaria multicarinata/c4725a8ca68ef8bc3251772672834e07.jpg\",\n  \"60532.jpg\": \"Insecta/Dythemis fugax/4be551807cc22edcc9d2127dce762808.jpg\",\n  \"605327.jpg\": \"Reptilia/Elgaria multicarinata/d60ff6cb784b643a5e0e1ed7b0a7667e.jpg\",\n  \"605347.jpg\": \"Reptilia/Elgaria multicarinata/5700a0acb2a7d52444af5d2ab54b5b1d.jpg\",\n  \"60542.jpg\": \"Insecta/Dythemis fugax/b911f1556bbb4a49175dbe7ea5c5f535.jpg\",\n  \"60543.jpg\": \"Insecta/Dythemis fugax/8b63ad1794537dde4828ba6c80d0851a.jpg\",\n  \"605444.jpg\": \"Reptilia/Elgaria multicarinata/207e93b99e9d0bb2ff52bc0f8ff6b2ee.jpg\",\n  \"60547.jpg\": \"Insecta/Dythemis fugax/e66afc809290c58e9f8e6ce3f2466a1c.jpg\",\n  \"605519.jpg\": \"Mammalia/Capreolus capreolus/6a1cd66a17a038e48119a973ee394d95.jpg\",\n  \"605522.jpg\": \"Mammalia/Capreolus capreolus/6b1bb93cd7f47b311d1556178d8536cb.jpg\",\n  \"605543.jpg\": \"Mammalia/Capreolus capreolus/9eb743ae805544b76cfa55ad091a13f7.jpg\",\n  \"605563.jpg\": \"Mammalia/Capreolus capreolus/6e2833d7d5d3c6b5133ee2eb31f46b38.jpg\",\n  \"60557.jpg\": \"Insecta/Dythemis fugax/e48bf9d7c882dfa7a8eedf83c5838d73.jpg\",\n  \"605576.jpg\": \"Mammalia/Capreolus capreolus/bd458ca35cc3bf528273d195a5f74759.jpg\",\n  \"60558.jpg\": \"Insecta/Dythemis fugax/9f4a63ab86c870608ccb95bf973ce8a0.jpg\",\n  \"60562.jpg\": \"Insecta/Dythemis fugax/89931e8a4fad0d0e38a045cc3a6901f5.jpg\",\n  \"60563.jpg\": \"Insecta/Dythemis fugax/5b9a025c1391e35e2f22ab921f54263b.jpg\",\n  \"60564.jpg\": \"Insecta/Dythemis fugax/a7cefb598478aafe0bd8fdabc218b119.jpg\",\n  \"60565.jpg\": \"Insecta/Dythemis fugax/258ea346b95f1e9968bbf89768b52ad5.jpg\",\n  \"60566.jpg\": \"Insecta/Dythemis fugax/c8d70ae4d23fc03ae52337bb41f8669d.jpg\",\n  \"6057.jpg\": \"Aves/Columba palumbus/3964eebc8cfdb4bc5bb160b74e518bf2.jpg\",\n  \"605702.jpg\": \"Mammalia/Lepus californicus/6770e232d8612b81fce5cfa27fff36c0.jpg\",\n  \"60576.jpg\": \"Insecta/Dythemis fugax/cc8c2eec624566cf29d0299c73fd7d82.jpg\",\n  \"6058.jpg\": \"Aves/Columba palumbus/26dc0b8dc6b9a475391fe4a6c6dc5109.jpg\",\n  \"605801.jpg\": \"Mammalia/Lepus californicus/20d6c563c68e01daccc1719bf7f6d1cc.jpg\",\n  \"605805.jpg\": \"Mammalia/Lepus californicus/538528242871d15ed03625ba9efc3a0f.jpg\",\n  \"605809.jpg\": \"Mammalia/Lepus californicus/f18179fa2300d020b619394a936f0e0e.jpg\",\n  \"605867.jpg\": \"Insecta/Libytheana carinenta/a9c90ee59d032968fd95f9a89871759a.jpg\",\n  \"60587.jpg\": \"Insecta/Dythemis fugax/ed9e728be475be323f9d49bd7f63f923.jpg\",\n  \"605897.jpg\": \"Insecta/Libytheana carinenta/5a286d0bffa4f32c6026b9ada4d20a17.jpg\",\n  \"60592.jpg\": \"Insecta/Dythemis fugax/1dd88e9ef917308f66ebe6f3cda1e714.jpg\",\n  \"605924.jpg\": \"Insecta/Libytheana carinenta/5b1f02e6b44171990ee5c7f20dc08dd6.jpg\",\n  \"605936.jpg\": \"Insecta/Libytheana carinenta/dc71f30f99dac4fed7f325023ff156c9.jpg\",\n  \"60594.jpg\": \"Insecta/Dythemis fugax/d3c7e65e7521af775d6b602e39e3dbbe.jpg\",\n  \"605940.jpg\": \"Insecta/Libytheana carinenta/19f55107451e09d428663163bf013fe6.jpg\",\n  \"605976.jpg\": \"Insecta/Libytheana carinenta/344fb67e912ccd47e9359f610c21b9d9.jpg\",\n  \"60599.jpg\": \"Insecta/Dythemis fugax/2672706ac7e719782fa82b2b5cdd8c4c.jpg\",\n  \"60600.jpg\": \"Insecta/Dythemis fugax/5df927b52e5d27ea0ec2185c1b98b584.jpg\",\n  \"606014.jpg\": \"Insecta/Libytheana carinenta/a87c3b44927dcccbde163508d859611e.jpg\",\n  \"606095.jpg\": \"Mollusca/Crassadoma gigantea/a026739ea000bc6288cffc492d9290ca.jpg\",\n  \"606098.jpg\": \"Mollusca/Crassadoma gigantea/b32034f5bb2089434e5390271fc79472.jpg\",\n  \"606099.jpg\": \"Mollusca/Crassadoma gigantea/407683326a1485850a74ce5f19157476.jpg\",\n  \"6061.jpg\": \"Aves/Columba palumbus/e73f7d23b143abe3fdcdecde19550113.jpg\",\n  \"606101.jpg\": \"Mollusca/Crassadoma gigantea/2fe4be6ce5e098d19b5b81b86b6692c4.jpg\",\n  \"606102.jpg\": \"Mollusca/Crassadoma gigantea/e0a75d6ede7570be13a518f60c74d830.jpg\",\n  \"606103.jpg\": \"Mollusca/Crassadoma gigantea/dfd238e9624e0acc0eb49371fc1e4d62.jpg\",\n  \"606105.jpg\": \"Mollusca/Crassadoma gigantea/3f1d032b0d88a9afe38315ae731153fc.jpg\",\n  \"606108.jpg\": \"Mollusca/Crassadoma gigantea/f6e370a588b17fdb0f0796741c857f65.jpg\",\n  \"606110.jpg\": \"Mollusca/Crassadoma gigantea/4c1fda317f80affcd779961ce1f0147b.jpg\",\n  \"606111.jpg\": \"Mollusca/Crassadoma gigantea/84550b8ee76c5341820317966bca489d.jpg\",\n  \"606114.jpg\": \"Mollusca/Crassadoma gigantea/f4d389938c598b1005fe4a8e90a486c0.jpg\",\n  \"606115.jpg\": \"Mollusca/Crassadoma gigantea/aec220ee63f5f320a61b56106c444245.jpg\",\n  \"606118.jpg\": \"Mollusca/Crassadoma gigantea/814249ecf5d529ff6356b0129b10a779.jpg\",\n  \"606120.jpg\": \"Mollusca/Crassadoma gigantea/10ed5cee1ed7360ce0a6251783c9ad4f.jpg\",\n  \"606130.jpg\": \"Insecta/Ponometia erastrioides/9e81a6456980d1388dc0fa3d2a47a279.jpg\",\n  \"606148.jpg\": \"Aves/Sayornis saya/d544fa8c20552834ccd0654e38eec79a.jpg\",\n  \"606150.jpg\": \"Aves/Sayornis saya/e1519ea5c580e08d767933ddc6768ac5.jpg\",\n  \"606197.jpg\": \"Aves/Sayornis saya/d362a0f58f2c9b27d124b146cc9f4460.jpg\",\n  \"606279.jpg\": \"Aves/Sayornis saya/d29e49e3943bdf24cd8d5011475314ca.jpg\",\n  \"606298.jpg\": \"Aves/Sayornis saya/1055acbdd72f3557f5ea9ab3750f213e.jpg\",\n  \"606306.jpg\": \"Aves/Sayornis saya/4ada88bda837c5eeda61601dba909114.jpg\",\n  \"606372.jpg\": \"Insecta/Dryas iulia/91dac92e4994962768fd597875d4dfb6.jpg\",\n  \"606373.jpg\": \"Insecta/Dryas iulia/6fa16855c92a777b240a6a6e9e58d568.jpg\",\n  \"606378.jpg\": \"Insecta/Dryas iulia/abc8b5d2b93752f9f5f58d85f9b5b06d.jpg\",\n  \"606391.jpg\": \"Insecta/Dryas iulia/7d0b4487212e3dd835667f9ff1203bf3.jpg\",\n  \"606399.jpg\": \"Insecta/Dryas iulia/1a99a3c6cc0c5dc266ac8482df9e044f.jpg\",\n  \"606401.jpg\": \"Insecta/Dryas iulia/7ea1095ba286bfaa150582dbf446c327.jpg\",\n  \"606402.jpg\": \"Insecta/Dryas iulia/1cdcd5cdc467ad02354de285e5e1e5e0.jpg\",\n  \"606408.jpg\": \"Insecta/Dryas iulia/b9b326dd907dee5e044ff6bb4a8259da.jpg\",\n  \"606417.jpg\": \"Insecta/Leuconycta diphteroides/cc1b11ba35897a1be98188b1478ad174.jpg\",\n  \"606425.jpg\": \"Insecta/Leuconycta diphteroides/8e64cff4773550993bcc282700897874.jpg\",\n  \"606457.jpg\": \"Mammalia/Procavia capensis/3760fead0cfb0ab199a2d0bd7079b357.jpg\",\n  \"606461.jpg\": \"Mammalia/Procavia capensis/50be6f0c8ba4788d5093e556b4c2638f.jpg\",\n  \"606475.jpg\": \"Amphibia/Anaxyrus americanus americanus/d9d3975ba7ac48e4355b435a830b3a6b.jpg\",\n  \"606476.jpg\": \"Amphibia/Anaxyrus americanus americanus/bad0b25bb2b2508a2c7981df4c39d89c.jpg\",\n  \"606489.jpg\": \"Arachnida/Platycryptus undatus/39cc1889cbd63708b0d32fbcd1049464.jpg\",\n  \"606491.jpg\": \"Arachnida/Platycryptus undatus/48799b5e635177d5a8f92dabbf43b23e.jpg\",\n  \"6065.jpg\": \"Aves/Columba palumbus/c4f62f5cdc6916eb588e37df89165867.jpg\",\n  \"606501.jpg\": \"Arachnida/Platycryptus undatus/05d2efe0aa03a4302f28f1827176b263.jpg\",\n  \"606502.jpg\": \"Arachnida/Platycryptus undatus/0ff81a45e1f7d318e87bc7c683fefc91.jpg\",\n  \"606509.jpg\": \"Arachnida/Platycryptus undatus/cd00e5b6bc523376375b23ca9c9ad376.jpg\",\n  \"6066.jpg\": \"Aves/Columba palumbus/add75a60c6695306a8d6c94ef3f78c5f.jpg\",\n  \"606604.jpg\": \"Aves/Chamaea fasciata/061e78662c9bd683e09aaf950f93e02a.jpg\",\n  \"606605.jpg\": \"Aves/Chamaea fasciata/95c9b0b6905b837307d48c795be33f8a.jpg\",\n  \"606621.jpg\": \"Aves/Chamaea fasciata/4af1309cd719377dda88885830484bb6.jpg\",\n  \"606622.jpg\": \"Aves/Chamaea fasciata/97f70cce7fe386f647f2d88d656efb8e.jpg\",\n  \"606625.jpg\": \"Aves/Chamaea fasciata/81bdd46b1c5678f7440b0aa8cb88467c.jpg\",\n  \"606626.jpg\": \"Aves/Chamaea fasciata/9493547837c1ca2e6c00b572ce88818a.jpg\",\n  \"606634.jpg\": \"Aves/Chamaea fasciata/73f1b2c24ef7c620f7dfdb6fd289b1aa.jpg\",\n  \"606636.jpg\": \"Aves/Chamaea fasciata/8af9c615651c07dac3b4e7f536f1145c.jpg\",\n  \"606641.jpg\": \"Aves/Chamaea fasciata/29cc4d23fbdab8dd8730e95d5de78eb5.jpg\",\n  \"606644.jpg\": \"Reptilia/Terrapene carolina carolina/137550c203c67b46557e73f675785ee4.jpg\",\n  \"606645.jpg\": \"Reptilia/Terrapene carolina carolina/587b36aaafdb404f095cc579bd7c4b4c.jpg\",\n  \"606646.jpg\": \"Reptilia/Terrapene carolina carolina/bab4e167c6de11c4e6a6a30027a45227.jpg\",\n  \"606665.jpg\": \"Reptilia/Terrapene carolina carolina/876f43dd617ba5d141466faf0bd2397b.jpg\",\n  \"606690.jpg\": \"Reptilia/Terrapene carolina carolina/eb5d56baefadb0ecb2d906424811a4f8.jpg\",\n  \"606701.jpg\": \"Reptilia/Terrapene carolina carolina/f687a1a8bcaa4d9b5e3c556c2b05a3d7.jpg\",\n  \"606704.jpg\": \"Reptilia/Terrapene carolina carolina/8c9e287c6f895858312ede39bb8a172b.jpg\",\n  \"606720.jpg\": \"Reptilia/Terrapene carolina carolina/175224f3053f6439033ca003c8c28315.jpg\",\n  \"606756.jpg\": \"Reptilia/Terrapene carolina carolina/4528397d764b24eb44aae3a7594e3329.jpg\",\n  \"606984.jpg\": \"Aves/Recurvirostra americana/71706c731623d6e9caae165071f10b62.jpg\",\n  \"607055.jpg\": \"Insecta/Erythemis vesiculosa/8716b290b9455e24a9e4ea95850d0b28.jpg\",\n  \"607063.jpg\": \"Insecta/Erythemis vesiculosa/1c39d5a940119e6edeaa094c0e9344eb.jpg\",\n  \"607064.jpg\": \"Insecta/Erythemis vesiculosa/42556d345bb3d7ced9574c8bece624dd.jpg\",\n  \"607072.jpg\": \"Insecta/Erythemis vesiculosa/252f8491857f8cbc9bfd91c49d2febb2.jpg\",\n  \"607082.jpg\": \"Insecta/Erythemis vesiculosa/2687d2034c131e597e08dc01811a0d18.jpg\",\n  \"607086.jpg\": \"Insecta/Erynnis horatius/9e794ccd060e075abe8decfc5a46104d.jpg\",\n  \"607087.jpg\": \"Insecta/Erynnis horatius/865bbbf19f5f93efd90282b32535d07d.jpg\",\n  \"607099.jpg\": \"Insecta/Erynnis horatius/f56955cea8d24fcaf0122ffcd9bac03d.jpg\",\n  \"6071.jpg\": \"Aves/Columba palumbus/de5051b330f15ef55c57014b26e038eb.jpg\",\n  \"607103.jpg\": \"Insecta/Erynnis horatius/cd1e3d3dedf5a66336eaf226bff5247b.jpg\",\n  \"607104.jpg\": \"Insecta/Erynnis horatius/1d9ab17e6dfb801dfff471aefb48d34c.jpg\",\n  \"607123.jpg\": \"Insecta/Erynnis horatius/a93dcb8b3bf31733777967c810bae425.jpg\",\n  \"607151.jpg\": \"Insecta/Erynnis horatius/78917bf6f8fa5661d6c6640164519141.jpg\",\n  \"607164.jpg\": \"Aves/Setophaga dominica/fde97b7a6c2e0abf17ee4c94825f70d3.jpg\",\n  \"607166.jpg\": \"Aves/Setophaga dominica/d1513d006c62f941da76ef8d1f3886a7.jpg\",\n  \"607170.jpg\": \"Aves/Setophaga dominica/1a98ccd695b253e26a60670e608d4645.jpg\",\n  \"607171.jpg\": \"Aves/Setophaga dominica/3ca1fa3f46205219d13e6d18ac1b07c3.jpg\",\n  \"607173.jpg\": \"Aves/Setophaga dominica/fab6a6ab8382a703a2c8a20cf74c762a.jpg\",\n  \"607179.jpg\": \"Aves/Setophaga dominica/00bb26ee5ba45ccc16349408c94f08b2.jpg\",\n  \"607185.jpg\": \"Aves/Setophaga dominica/66917d3f4823084f998012cd8091637b.jpg\",\n  \"607186.jpg\": \"Aves/Setophaga dominica/44ef42ccf44985985b49981caad3cfea.jpg\",\n  \"607188.jpg\": \"Aves/Parkesia motacilla/2e08de0fc606901138c0920eeb2bf292.jpg\",\n  \"607189.jpg\": \"Aves/Parkesia motacilla/a4c339538ea8b375162b91dfbc61cc1e.jpg\",\n  \"607190.jpg\": \"Aves/Parkesia motacilla/b033e227c3af7aeabfb81d2192d459d6.jpg\",\n  \"607193.jpg\": \"Aves/Parkesia motacilla/96d3dce2960b9c66b3363fdaeddef6ed.jpg\",\n  \"607199.jpg\": \"Aves/Parkesia motacilla/c9f4b0f8eaef4c215dae24557845a78e.jpg\",\n  \"607212.jpg\": \"Aves/Parkesia motacilla/4fd820cc81469073beaa5235ba7db31e.jpg\",\n  \"607215.jpg\": \"Aves/Parkesia motacilla/8c796e40a4b7770d738a299f931407fd.jpg\",\n  \"607217.jpg\": \"Aves/Parkesia motacilla/f676bce6913ea630de87d14cefff54de.jpg\",\n  \"607219.jpg\": \"Aves/Parkesia motacilla/9c387df169f81afd5ea1c727c83c232f.jpg\",\n  \"607220.jpg\": \"Aves/Parkesia motacilla/1a748fea815dab85c6b625573fd0d6a0.jpg\",\n  \"607317.jpg\": \"Insecta/Danaus gilippus/5113b02a62839a0cc3835974c53e750c.jpg\",\n  \"607324.jpg\": \"Insecta/Danaus gilippus/6fb51d90645a40d5cc57b313fae1b33d.jpg\",\n  \"607347.jpg\": \"Insecta/Danaus gilippus/a83c0d13bb90489d13e74a0103f5c431.jpg\",\n  \"607366.jpg\": \"Insecta/Danaus gilippus/a3f5102fe08276803df1b32d3b0a1f83.jpg\",\n  \"607408.jpg\": \"Insecta/Danaus gilippus/cae4e1ee111f880935a605208611b6c8.jpg\",\n  \"6075.jpg\": \"Aves/Columba palumbus/493cf00a7579efb36ec5fdb82436e7fa.jpg\",\n  \"607546.jpg\": \"Insecta/Harmonia axyridis/486fc264cbde9bb98897404e61a49f44.jpg\",\n  \"607588.jpg\": \"Insecta/Harmonia axyridis/a6549bd80d24eb62b1ded769342be04d.jpg\",\n  \"6076.jpg\": \"Aves/Columba palumbus/fae8fd739849a47d8dfbb4076c7c7ad3.jpg\",\n  \"60771.jpg\": \"Amphibia/Salamandra salamandra/d2f2a9a383aa76f8d833b9029e1d8275.jpg\",\n  \"607711.jpg\": \"Insecta/Harmonia axyridis/4922f9583be63a54d0dff167a9c85def.jpg\",\n  \"607722.jpg\": \"Insecta/Harmonia axyridis/f30031c8988175c0ac0e9981f3b7f2fb.jpg\",\n  \"607742.jpg\": \"Insecta/Harmonia axyridis/9c96e12a54cf7760b784f8f8a5a1973a.jpg\",\n  \"60776.jpg\": \"Amphibia/Salamandra salamandra/ad2cf41e350aa709c7f766201f79f9aa.jpg\",\n  \"607784.jpg\": \"Insecta/Harmonia axyridis/84ec00231f843928bc5b0db26e106a5c.jpg\",\n  \"60781.jpg\": \"Amphibia/Salamandra salamandra/277c1eef26208ba470be235a04c11e6f.jpg\",\n  \"607810.jpg\": \"Insecta/Harmonia axyridis/5ea3da0c8c81d02af1aff8640ae7c913.jpg\",\n  \"607817.jpg\": \"Insecta/Harmonia axyridis/79da688d392349626e55a403cc187bad.jpg\",\n  \"60784.jpg\": \"Amphibia/Salamandra salamandra/9e8d32f2e94d5b3f62f94c8b2dd5183e.jpg\",\n  \"60787.jpg\": \"Amphibia/Salamandra salamandra/5ff50c6283b8458321b2529cbf9e9a72.jpg\",\n  \"607896.jpg\": \"Insecta/Harmonia axyridis/24642b36d005755b0b1fbc998d93a704.jpg\",\n  \"60790.jpg\": \"Amphibia/Salamandra salamandra/9035a377147745c8b040342b3cacd491.jpg\",\n  \"607925.jpg\": \"Insecta/Harmonia axyridis/51cbf503eebb10ec8f4038ef38b0172f.jpg\",\n  \"60793.jpg\": \"Amphibia/Salamandra salamandra/7f1a70c1a37b59eebafc3db2e03d390b.jpg\",\n  \"60797.jpg\": \"Amphibia/Salamandra salamandra/a166a55fd358cb11f7cb673770048bad.jpg\",\n  \"60798.jpg\": \"Amphibia/Salamandra salamandra/eb4177519ce7f0e647c1ebc18c5736a2.jpg\",\n  \"607980.jpg\": \"Insecta/Harmonia axyridis/96360497289e590b1bb02c89b93c3501.jpg\",\n  \"60802.jpg\": \"Amphibia/Salamandra salamandra/be56cf9293e66f63cfd5d2989cdef3df.jpg\",\n  \"60805.jpg\": \"Amphibia/Salamandra salamandra/42377dfc8ddaa6cd42894af3ff00dc6e.jpg\",\n  \"60806.jpg\": \"Amphibia/Salamandra salamandra/d68ddecdc2a7115ec3b62b3b810263a0.jpg\",\n  \"608081.jpg\": \"Insecta/Harmonia axyridis/3bca71f9b167e7ffdc61630747c58fe2.jpg\",\n  \"608093.jpg\": \"Animalia/Scolopendra polymorpha/31f702683aa360443094043b80408c86.jpg\",\n  \"60811.jpg\": \"Amphibia/Salamandra salamandra/c746736f49956e68350ec16b5731af72.jpg\",\n  \"608110.jpg\": \"Animalia/Scolopendra polymorpha/f4cbda4def47cef65fc5f186001c90e3.jpg\",\n  \"608111.jpg\": \"Animalia/Scolopendra polymorpha/a7d7f142a5bf58b497e38d3979210e20.jpg\",\n  \"608116.jpg\": \"Animalia/Scolopendra polymorpha/3b5c65ffceb9d08fa30ef1920bc286b4.jpg\",\n  \"608118.jpg\": \"Animalia/Scolopendra polymorpha/12cdd54499452070f3ba9d50d4c2645f.jpg\",\n  \"60812.jpg\": \"Amphibia/Salamandra salamandra/f1f2f0fdbe735f1800eccb2cd9e826f6.jpg\",\n  \"608124.jpg\": \"Insecta/Cycnia tenera/5f0f38388920baac22498526bc3e5451.jpg\",\n  \"608126.jpg\": \"Insecta/Cycnia tenera/3c894d9ebfc5b922a06e3a48440137eb.jpg\",\n  \"608127.jpg\": \"Insecta/Cycnia tenera/948a6dc5c9effeb57836ca60ab912b39.jpg\",\n  \"608129.jpg\": \"Insecta/Cycnia tenera/eb865f0e34524afab0cc163d99628555.jpg\",\n  \"608134.jpg\": \"Insecta/Cycnia tenera/4f42f5d30cd0dd37c798fc96b444ff88.jpg\",\n  \"60814.jpg\": \"Amphibia/Salamandra salamandra/2bab81e514b451d9698818ae10ddc191.jpg\",\n  \"60815.jpg\": \"Amphibia/Salamandra salamandra/2f34100f42302bc7d12103d8c81239df.jpg\",\n  \"60816.jpg\": \"Amphibia/Salamandra salamandra/9fed5d5fe5571dc7a9f406d31657b5b2.jpg\",\n  \"60824.jpg\": \"Amphibia/Salamandra salamandra/dc8e3bea10164fc18cf278a00ce285e2.jpg\",\n  \"608263.jpg\": \"Reptilia/Dipsosaurus dorsalis/62bcd9c8200f9e08ad939143e29743ae.jpg\",\n  \"608265.jpg\": \"Reptilia/Dipsosaurus dorsalis/db7d0a6333ee5c41216e7e5bacc887d5.jpg\",\n  \"60828.jpg\": \"Amphibia/Salamandra salamandra/30dba9c89c36e5d397226a09a5e9745c.jpg\",\n  \"608281.jpg\": \"Reptilia/Dipsosaurus dorsalis/a3128a11bd649a76a3c4a47d01f575db.jpg\",\n  \"608282.jpg\": \"Reptilia/Dipsosaurus dorsalis/32d0530cdf2fe353aa8c61193f9beef5.jpg\",\n  \"608286.jpg\": \"Reptilia/Dipsosaurus dorsalis/0104534ea700a79df3bed3ea1a3e4597.jpg\",\n  \"60829.jpg\": \"Amphibia/Salamandra salamandra/4679eb08098f21428704d461ecc40b7b.jpg\",\n  \"608291.jpg\": \"Reptilia/Dipsosaurus dorsalis/1aa6e2c098648184d1c8ac64213f59b3.jpg\",\n  \"608300.jpg\": \"Reptilia/Dipsosaurus dorsalis/4262f0300b5f181392d77bd405d0edb9.jpg\",\n  \"608301.jpg\": \"Reptilia/Dipsosaurus dorsalis/b488df70f8a48397139fd6b4f26c3ede.jpg\",\n  \"608308.jpg\": \"Aves/Piranga rubra/f01972ff43512c9933f4ec2cc1182ee4.jpg\",\n  \"608334.jpg\": \"Aves/Piranga rubra/f45fae4973619d1d751dacd216dac0e5.jpg\",\n  \"608359.jpg\": \"Aves/Piranga rubra/0a2f92a44d715883ab61e58c84941edd.jpg\",\n  \"60838.jpg\": \"Amphibia/Salamandra salamandra/4c7b1bd9f36660ccacb73eeafb818454.jpg\",\n  \"60839.jpg\": \"Amphibia/Salamandra salamandra/54c59d597dfdfa10806f2dafbd35b438.jpg\",\n  \"608400.jpg\": \"Aves/Piranga rubra/d5d24ce86e8c5db8a365be581c2455ce.jpg\",\n  \"608408.jpg\": \"Aves/Piranga rubra/a2a3f53ab224ea287909692ca1a820cd.jpg\",\n  \"608415.jpg\": \"Aves/Piranga rubra/4b84753618e15a11410be2fe1a6c2559.jpg\",\n  \"608426.jpg\": \"Aves/Piranga rubra/1080cae7f4c5d96362469742b00f01bc.jpg\",\n  \"608433.jpg\": \"Aves/Piranga rubra/6535423bccc33c5991a542b74bdf5272.jpg\",\n  \"608461.jpg\": \"Aves/Piranga rubra/1ba4e6b70ed2be3c8ad717bbd7183197.jpg\",\n  \"608471.jpg\": \"Aves/Piranga rubra/37caecc0ddd25e18dc537c64b0857e62.jpg\",\n  \"608492.jpg\": \"Insecta/Hyphantria cunea/1ef51330ed794e2d3b2ae8d5570f489d.jpg\",\n  \"608493.jpg\": \"Insecta/Hyphantria cunea/8345c389c5fa1ced2541938db35869a8.jpg\",\n  \"608495.jpg\": \"Insecta/Hyphantria cunea/89ecde1d2371e5cb1e60bd3750b73573.jpg\",\n  \"608497.jpg\": \"Insecta/Hyphantria cunea/5bc3a2be6d67b49e14bbbbb78e61a74a.jpg\",\n  \"608498.jpg\": \"Insecta/Hyphantria cunea/e72a99d072e2abb6e3b7e0af57a6d6e0.jpg\",\n  \"608499.jpg\": \"Insecta/Hyphantria cunea/e1ddb4044180626b2cef61d0eb399f84.jpg\",\n  \"608510.jpg\": \"Insecta/Hyphantria cunea/3319a3ea3396080d66497683cf6f9c12.jpg\",\n  \"608640.jpg\": \"Aves/Tyrannus melancholicus/b6b67490af56b9b17be020f20ca823f6.jpg\",\n  \"608645.jpg\": \"Aves/Tyrannus melancholicus/e40b827c15d62e9ad1bd30e4932757bd.jpg\",\n  \"608674.jpg\": \"Aves/Tyrannus melancholicus/4b8329546f4f6a8771928d1a97b3b066.jpg\",\n  \"608678.jpg\": \"Aves/Tyrannus melancholicus/e18febfcd3b1a83b3171274c735f6074.jpg\",\n  \"608683.jpg\": \"Aves/Tyrannus melancholicus/d10b2cbadab6c57fe6774006bc7c65b6.jpg\",\n  \"608696.jpg\": \"Aves/Tyrannus melancholicus/4bcf74fddcf77a4bd8010899d05a87aa.jpg\",\n  \"608698.jpg\": \"Aves/Tyrannus melancholicus/807368b118af0633c90205cc802a0f1d.jpg\",\n  \"608704.jpg\": \"Aves/Tyrannus melancholicus/b18ea98cb2aae2da0944e6b27110a431.jpg\",\n  \"608711.jpg\": \"Aves/Cyanerpes cyaneus/9d8a9ce50a5532b3f02bd9f746692620.jpg\",\n  \"608713.jpg\": \"Aves/Cyanerpes cyaneus/02bc7583575572f68c2be6631db05287.jpg\",\n  \"608728.jpg\": \"Aves/Dendragapus fuliginosus/aa377ca90e94c1a48480d5b8d3d3a67e.jpg\",\n  \"608730.jpg\": \"Aves/Dendragapus fuliginosus/b06c405b4bee46cb2c2fd0e52e354591.jpg\",\n  \"608731.jpg\": \"Aves/Dendragapus fuliginosus/a6325970ee36d8d7b7dd2d30688837db.jpg\",\n  \"608734.jpg\": \"Aves/Dendragapus fuliginosus/b09ccab2f5036b1e93fe4504bafade87.jpg\",\n  \"608752.jpg\": \"Insecta/Orthetrum sabina/c01724e6f6419ad2cd2c3b8a585b69fc.jpg\",\n  \"608763.jpg\": \"Insecta/Argia lugens/1487c15a8f6facee5a6f185a4a3f8503.jpg\",\n  \"608768.jpg\": \"Insecta/Argia lugens/01c946647ce768ecb89e6b1fbb8d6de0.jpg\",\n  \"608778.jpg\": \"Insecta/Argia lugens/b2246b6d57b7fb08213bccc7e1816372.jpg\",\n  \"60879.jpg\": \"Aves/Hirundo rustica erythrogaster/28b29b13b1f75ec1115d0f71ee0e73f2.jpg\",\n  \"608794.jpg\": \"Mammalia/Tursiops truncatus/a574ed0ea0afa632bbb86455e133c8b5.jpg\",\n  \"608817.jpg\": \"Mammalia/Tursiops truncatus/4d694f85d9dafab9f89732281c7ed652.jpg\",\n  \"608851.jpg\": \"Mammalia/Tursiops truncatus/b54c11c23a85f12984755cfc619f6080.jpg\",\n  \"608870.jpg\": \"Aves/Pooecetes gramineus/f463dd5a6374e9221ebc85c9ad7471c8.jpg\",\n  \"608920.jpg\": \"Aves/Pooecetes gramineus/522d8b4f0914bd78c74e6a3125e98844.jpg\",\n  \"608922.jpg\": \"Aves/Pooecetes gramineus/4d02940930de02e8e576300a3bcb6aff.jpg\",\n  \"60895.jpg\": \"Aves/Hirundo rustica erythrogaster/30573a68fee6f8714e48d5ba235e74b9.jpg\",\n  \"60898.jpg\": \"Aves/Hirundo rustica erythrogaster/a9f402a992edef0a577646ed042b5e00.jpg\",\n  \"6090.jpg\": \"Aves/Columba palumbus/eab64cf68f6316f70dcf898394b36683.jpg\",\n  \"609004.jpg\": \"Animalia/Pagurus samuelis/e4295df857e384ac1b38ccb3ddae6c4b.jpg\",\n  \"609012.jpg\": \"Animalia/Pagurus samuelis/db6b6032520228d74eb7dd9d1210b061.jpg\",\n  \"60905.jpg\": \"Aves/Hirundo rustica erythrogaster/94f44bbe7f2f0ae0b118faefb2994ef8.jpg\",\n  \"60910.jpg\": \"Aves/Ptiliogonys cinereus/f472093018c700a25d85e7f312be0b10.jpg\",\n  \"60911.jpg\": \"Aves/Ptiliogonys cinereus/9539bc69498402d4475d5f6e00c822dc.jpg\",\n  \"60916.jpg\": \"Aves/Ptiliogonys cinereus/6e0d41061d7e68ea864467657ff5e4a6.jpg\",\n  \"609217.jpg\": \"Amphibia/Ambystoma laterale/54530648b4c000a036c89cf382e258e8.jpg\",\n  \"609218.jpg\": \"Amphibia/Ambystoma laterale/041a79a594ea6de7dbfdb5afce3c48b6.jpg\",\n  \"609219.jpg\": \"Amphibia/Ambystoma laterale/07731e5a27f93f6fb37321c824651cfd.jpg\",\n  \"609220.jpg\": \"Amphibia/Ambystoma laterale/ea5586136f41ee00cac9336a61a6f960.jpg\",\n  \"609224.jpg\": \"Amphibia/Ambystoma laterale/55caad23eabd1de1a5d6c59ba5661839.jpg\",\n  \"609225.jpg\": \"Amphibia/Ambystoma laterale/6a6235d1c9837902c97fa8ea0ff5a2b1.jpg\",\n  \"609227.jpg\": \"Amphibia/Ambystoma laterale/d6d96a899fb411098cbc6831786ba396.jpg\",\n  \"609228.jpg\": \"Amphibia/Ambystoma laterale/ccaceb0fa301d87384fbbafdf0559659.jpg\",\n  \"60923.jpg\": \"Aves/Ptiliogonys cinereus/d9462df3c97c2651b37094e7ce357dec.jpg\",\n  \"609230.jpg\": \"Amphibia/Ambystoma laterale/8ec9b4f7314f400973de91f522f3e0a3.jpg\",\n  \"609232.jpg\": \"Amphibia/Ambystoma laterale/284cb6f6fb0686f2807388c9c269d91c.jpg\",\n  \"609233.jpg\": \"Amphibia/Ambystoma laterale/961bdaf6b483c2195ed3eafbbd742426.jpg\",\n  \"609234.jpg\": \"Amphibia/Ambystoma laterale/1ec4885c69aad80c5f4be93381964ee5.jpg\",\n  \"60924.jpg\": \"Aves/Ptiliogonys cinereus/1d7f2ac09308ce9c57842451eae22d9d.jpg\",\n  \"60925.jpg\": \"Aves/Ptiliogonys cinereus/66c74c854c18db137232ee08c4573180.jpg\",\n  \"60927.jpg\": \"Aves/Ptiliogonys cinereus/eaceb944f0780becd85a0191e2f2341c.jpg\",\n  \"60930.jpg\": \"Aves/Ptiliogonys cinereus/a938274da93322365d4fc0cd7ac2e2ae.jpg\",\n  \"60935.jpg\": \"Aves/Ptiliogonys cinereus/50ce8475df02ce29b60b06c0b73c1d91.jpg\",\n  \"60937.jpg\": \"Aves/Ptiliogonys cinereus/ee5d8097ccb9443542e5ad189309b122.jpg\",\n  \"60940.jpg\": \"Aves/Ptiliogonys cinereus/8d9f8c668cd0a60faded5753409b08ee.jpg\",\n  \"60947.jpg\": \"Aves/Ptiliogonys cinereus/de1442d1db5801bf97c238f64d4a0fac.jpg\",\n  \"60948.jpg\": \"Aves/Ptiliogonys cinereus/16230e637bf2f0f027de648dece13ac5.jpg\",\n  \"60949.jpg\": \"Aves/Ptiliogonys cinereus/155b8704a0a29470448768364111fb9e.jpg\",\n  \"609490.jpg\": \"Insecta/Phyciodes tharos/436f7e1a70f47dea0f6ea7e82ff37bc8.jpg\",\n  \"60950.jpg\": \"Aves/Ptiliogonys cinereus/a69f448510b3ee2f685a1d869640eac6.jpg\",\n  \"60954.jpg\": \"Aves/Ptiliogonys cinereus/777591361ba62c531546c32685f14271.jpg\",\n  \"60956.jpg\": \"Aves/Ptiliogonys cinereus/d1a6c332c8cdb75ba4efe34da45fe17c.jpg\",\n  \"609708.jpg\": \"Aves/Gallinago gallinago/8bd50646d20a03bcc42b79182dddd097.jpg\",\n  \"609716.jpg\": \"Insecta/Leptotes marina/66058337a5c56cc02ca2c227a6ac7734.jpg\",\n  \"609727.jpg\": \"Insecta/Leptotes marina/7bf4b3884848ec01dcb09f1235f35432.jpg\",\n  \"609735.jpg\": \"Insecta/Leptotes marina/1df43b3b97566cb6b38629110a617bf1.jpg\",\n  \"609769.jpg\": \"Insecta/Leptotes marina/b5c9bce5aecd22106dedfefa9e3e0f2f.jpg\",\n  \"609785.jpg\": \"Insecta/Leptotes marina/487d9c62407fcfafac49f094e6cfdb47.jpg\",\n  \"609787.jpg\": \"Insecta/Leptotes marina/620607c9c58a87c24a48110399f93818.jpg\",\n  \"609801.jpg\": \"Insecta/Leptotes marina/c905fbd06d340d2c8b33c7d1b57e6a95.jpg\",\n  \"609802.jpg\": \"Insecta/Leptotes marina/e0377bae7a2cbe40b3994be9689f5ac7.jpg\",\n  \"60998.jpg\": \"Amphibia/Anaxyrus fowleri/7addc0d99823ca88d7a5d9a676dc3aa9.jpg\",\n  \"61003.jpg\": \"Amphibia/Anaxyrus fowleri/f83ec6c06b1d74d523d4e86d50336834.jpg\",\n  \"61014.jpg\": \"Amphibia/Anaxyrus fowleri/b18f55247c4e0e78c4e6a20511cdfa9e.jpg\",\n  \"61023.jpg\": \"Amphibia/Anaxyrus fowleri/84e2e72b1ad0a24aaa65575d23825ae1.jpg\",\n  \"610273.jpg\": \"Aves/Oreothlypis celata/6a2cb5d02a570451c4cebdb31ed1a425.jpg\",\n  \"610323.jpg\": \"Aves/Oreothlypis celata/40a8a316ebbd92000e04cc0fdac4bd77.jpg\",\n  \"610356.jpg\": \"Aves/Oreothlypis celata/dc88bfa5de64b9fe29a89e969017aacf.jpg\",\n  \"61036.jpg\": \"Amphibia/Anaxyrus fowleri/d627d3e25cf6addb6d7e388e45d18391.jpg\",\n  \"610373.jpg\": \"Aves/Oreothlypis celata/4210f17be372bc4f783d67d9400fa4f0.jpg\",\n  \"610384.jpg\": \"Insecta/Corydalus cornutus/69320f6114de4081e8eb86bc3b835291.jpg\",\n  \"610385.jpg\": \"Insecta/Corydalus cornutus/227881d3e59fea5da04fce057e74e765.jpg\",\n  \"610391.jpg\": \"Insecta/Corydalus cornutus/21c0c326275a168d368fe82e26e8db7d.jpg\",\n  \"610399.jpg\": \"Insecta/Corydalus cornutus/9636718b86824cae2ecd51d371b59166.jpg\",\n  \"610404.jpg\": \"Insecta/Corydalus cornutus/d393acf094270beb383d164774b1749b.jpg\",\n  \"610419.jpg\": \"Aves/Buteo brachyurus/93a1adb6890e3c164565a84c8e8a0090.jpg\",\n  \"610431.jpg\": \"Insecta/Cycloneda munda/d2ee252071fcd2eaf32ae91244b823a9.jpg\",\n  \"610432.jpg\": \"Insecta/Cycloneda munda/99c0fb4fd0176593bc7549f07897e05c.jpg\",\n  \"610433.jpg\": \"Insecta/Cycloneda munda/58b84d1a4ffe2955afcd6dc21c71e33b.jpg\",\n  \"610434.jpg\": \"Insecta/Cycloneda munda/b2b6c6b338983cc646ee069c4b439a27.jpg\",\n  \"610435.jpg\": \"Insecta/Cycloneda munda/5e3311a880ff9ab0615572d89682ad26.jpg\",\n  \"610438.jpg\": \"Insecta/Cycloneda munda/00452f146c0b64209d9c3fff0ba17308.jpg\",\n  \"610439.jpg\": \"Insecta/Cycloneda munda/2bdf403e7b157ad74b502c21c539b988.jpg\",\n  \"610440.jpg\": \"Insecta/Cycloneda munda/de06faf68f51ead97de6e0d8c13bbd8f.jpg\",\n  \"610441.jpg\": \"Insecta/Cycloneda munda/7900f2df8eedc0ed8cb93948bd9a7f88.jpg\",\n  \"610442.jpg\": \"Insecta/Cycloneda munda/0736f6e8174d67a6dfac4ef483b78e9f.jpg\",\n  \"610443.jpg\": \"Insecta/Cycloneda munda/21f19c7ad056f01ed624872d5d5c6689.jpg\",\n  \"61045.jpg\": \"Amphibia/Anaxyrus fowleri/361115132464419336b9aedf243bf802.jpg\",\n  \"610507.jpg\": \"Insecta/Rhionaeschna multicolor/4e9e3c4ef65e0e57a2ba5ab94f3a36b2.jpg\",\n  \"610532.jpg\": \"Insecta/Rhionaeschna multicolor/6826c147fc761515167b447bc036a0e9.jpg\",\n  \"610539.jpg\": \"Insecta/Rhionaeschna multicolor/e156b6ddd7c667d47aba0adf7cc069af.jpg\",\n  \"610540.jpg\": \"Insecta/Rhionaeschna multicolor/8b851db303078abd9f0287224ac846b4.jpg\",\n  \"610555.jpg\": \"Insecta/Rhionaeschna multicolor/67a7ed47e25e998e3fa367be1fa97b8b.jpg\",\n  \"61057.jpg\": \"Amphibia/Anaxyrus fowleri/92d01c0fa3c76695e9870544f4f9a43b.jpg\",\n  \"610574.jpg\": \"Insecta/Rhionaeschna multicolor/bcbfb3d17237683a172d9874c0a55734.jpg\",\n  \"610583.jpg\": \"Insecta/Rhionaeschna multicolor/1b9a68144c0b0ef475194e6c44854c1e.jpg\",\n  \"610591.jpg\": \"Insecta/Rhionaeschna multicolor/e52d624e4ffd24480fcd13e29f8ead0d.jpg\",\n  \"610611.jpg\": \"Aves/Phalaropus tricolor/4d22e9e52200be1d10e838b9e1dce933.jpg\",\n  \"610627.jpg\": \"Aves/Phalaropus tricolor/a6f51621875f7e5df2e59c37226b1be3.jpg\",\n  \"610660.jpg\": \"Aves/Phalaropus tricolor/0af1e0c038fe7d2eb642e93c2219a507.jpg\",\n  \"61072.jpg\": \"Amphibia/Anaxyrus fowleri/c975d5c22ba5c97af30849b0d8b022f6.jpg\",\n  \"610733.jpg\": \"Aves/Setophaga ruticilla/8e3e1d2b694bcc83f2d768f6805663b6.jpg\",\n  \"610752.jpg\": \"Aves/Setophaga ruticilla/4a4fc7574d10806610ceccfe0f06b69d.jpg\",\n  \"610778.jpg\": \"Aves/Setophaga ruticilla/9e0575b62072563a25ddb7a63e702f1a.jpg\",\n  \"61089.jpg\": \"Amphibia/Anaxyrus fowleri/20bb00ce781480f7fa9eba37df3b7655.jpg\",\n  \"610993.jpg\": \"Aves/Antigone canadensis/544b7b6686b369091be07af3c9d2fed9.jpg\",\n  \"61100.jpg\": \"Amphibia/Anaxyrus fowleri/a515a16b597e4313021e4bfb1c8ca101.jpg\",\n  \"61103.jpg\": \"Amphibia/Anaxyrus fowleri/b5fb860883485b8cfa797bcf327c9848.jpg\",\n  \"61104.jpg\": \"Amphibia/Anaxyrus fowleri/3c1ead2d8aa924f8b8b8d55d67d31668.jpg\",\n  \"61109.jpg\": \"Amphibia/Anaxyrus fowleri/8f4fe67404d1503f32fa5948390872d2.jpg\",\n  \"61114.jpg\": \"Amphibia/Anaxyrus fowleri/c9040c529a075b5723d0eac7f2f72a01.jpg\",\n  \"61116.jpg\": \"Amphibia/Anaxyrus fowleri/19a2148dbf748fac5a955f491f8621e4.jpg\",\n  \"61117.jpg\": \"Amphibia/Anaxyrus fowleri/0fc6b339901f9c7a68d4c1022b0b48c1.jpg\",\n  \"611174.jpg\": \"Insecta/Elaphria grata/ee36bc0bf3a7c41da127d938d1f39358.jpg\",\n  \"61125.jpg\": \"Amphibia/Anaxyrus fowleri/e19abe61aebaaefba0ced1f194deeb38.jpg\",\n  \"611274.jpg\": \"Reptilia/Phrynosoma modestum/ac256ad587fc1aefbe1bbf25b2888307.jpg\",\n  \"611282.jpg\": \"Reptilia/Phrynosoma modestum/ca242c45d7438a140c0349b3dd3fad9f.jpg\",\n  \"611284.jpg\": \"Reptilia/Phrynosoma modestum/bd1e831c462d2eb7b9c7f27d9167a1dd.jpg\",\n  \"611285.jpg\": \"Reptilia/Phrynosoma modestum/221adac336f99528013c2f95ac8bb4c9.jpg\",\n  \"61129.jpg\": \"Amphibia/Anaxyrus fowleri/e46ab3fe87ddfd6c0876272d2656a6ea.jpg\",\n  \"611291.jpg\": \"Reptilia/Phrynosoma modestum/146a8c8882f3bfc75dbaa1c2443e38d6.jpg\",\n  \"611294.jpg\": \"Reptilia/Phrynosoma modestum/a643fbe9f7e6f294f75a5e2b4c6523e1.jpg\",\n  \"611295.jpg\": \"Reptilia/Phrynosoma modestum/377bdadc0e54341c4f8d54f68161901d.jpg\",\n  \"611296.jpg\": \"Reptilia/Phrynosoma modestum/696ccd85d37302105c8bef4e2f972b98.jpg\",\n  \"611297.jpg\": \"Reptilia/Phrynosoma modestum/d574c751d57d2f2ef16d4a75d95a4ff2.jpg\",\n  \"611305.jpg\": \"Reptilia/Phrynosoma modestum/782bbc5d939513f580f853cf2f886807.jpg\",\n  \"611306.jpg\": \"Reptilia/Phrynosoma modestum/c608fc071dd639c4e2d316e9ef1da49b.jpg\",\n  \"611382.jpg\": \"Insecta/Pyrgus albescens/b23f46de87dcee5316ea181f1db967e6.jpg\",\n  \"611388.jpg\": \"Aves/Campephilus guatemalensis/2d31638ee4c3250adeca0e29f78f523c.jpg\",\n  \"611390.jpg\": \"Aves/Campephilus guatemalensis/352b8669cf47d8692253ef76ab2e0c07.jpg\",\n  \"611391.jpg\": \"Aves/Campephilus guatemalensis/299f5c3aa4e8219ac9605f47df02f001.jpg\",\n  \"611392.jpg\": \"Aves/Campephilus guatemalensis/20e8b6c862f821f3d95506e042872d0b.jpg\",\n  \"611393.jpg\": \"Aves/Campephilus guatemalensis/b16573b6e0d094135f1d54e501c85716.jpg\",\n  \"6114.jpg\": \"Aves/Columba palumbus/319d48eca7a9da59d4c3d6b73c484619.jpg\",\n  \"611405.jpg\": \"Aves/Campephilus guatemalensis/f6cdce0701e4af2e95364fac8e97cacc.jpg\",\n  \"61144.jpg\": \"Amphibia/Anaxyrus fowleri/c7f901012ff7e342ec7929efb773cb51.jpg\",\n  \"611466.jpg\": \"Aves/Haematopus ostralegus/77b450943571cc009e6d09c081c2e9a8.jpg\",\n  \"611476.jpg\": \"Aves/Haematopus ostralegus/baed0d241e8d6d0e2430d92ba4e1e27a.jpg\",\n  \"611483.jpg\": \"Aves/Haematopus ostralegus/7ba591e3e1cf73bc88d2ade2f201a61f.jpg\",\n  \"611484.jpg\": \"Aves/Haematopus ostralegus/bf07e0fff13213271e078799e42c656f.jpg\",\n  \"611494.jpg\": \"Amphibia/Ambystoma opacum/daeb5e7ff0940dfd6ce4bcb49d61c27d.jpg\",\n  \"611496.jpg\": \"Amphibia/Ambystoma opacum/c9f9a19089a6689e2803deed76785b03.jpg\",\n  \"611498.jpg\": \"Amphibia/Ambystoma opacum/da47046f641e15e3e2fa04c8290c9ea8.jpg\",\n  \"611500.jpg\": \"Amphibia/Ambystoma opacum/c5037b3a7528a2d53db645a87c2f86c6.jpg\",\n  \"611509.jpg\": \"Amphibia/Ambystoma opacum/b9b869c99847a4938cb82850ce4760c9.jpg\",\n  \"61151.jpg\": \"Insecta/Apantesis phalerata/75ece896cb0a2fae14c740efc4d143c7.jpg\",\n  \"611511.jpg\": \"Amphibia/Ambystoma opacum/8b5bbe49d44f719ddbb98a0ffe732c2a.jpg\",\n  \"611519.jpg\": \"Insecta/Lacinipolia renigera/ce149c2812d482c71602be1ff2618c40.jpg\",\n  \"611521.jpg\": \"Insecta/Lacinipolia renigera/0b5f414d3e725b9a91ac28df105ffc4d.jpg\",\n  \"611523.jpg\": \"Insecta/Lacinipolia renigera/991c28e673e37f4c77fa0e988a149677.jpg\",\n  \"611529.jpg\": \"Insecta/Lacinipolia renigera/c90a32b4bea11897f3129b9192e08ead.jpg\",\n  \"611530.jpg\": \"Insecta/Lacinipolia renigera/8888d1398286e89fc28b6b2af7ef2f19.jpg\",\n  \"611531.jpg\": \"Insecta/Lacinipolia renigera/60ee130e8169f7dfccf0f5f3d3ae2888.jpg\",\n  \"611532.jpg\": \"Insecta/Lacinipolia renigera/0152fae9144364611cf45c8dff34f007.jpg\",\n  \"611533.jpg\": \"Insecta/Lacinipolia renigera/3364c79ee7372caaa42252d956ba477d.jpg\",\n  \"611534.jpg\": \"Insecta/Lacinipolia renigera/eec30ca72ae40bf901925a5120d10e34.jpg\",\n  \"611538.jpg\": \"Mollusca/Megathura crenulata/8675252b5944fac4415c25a5d3bf042b.jpg\",\n  \"611539.jpg\": \"Mollusca/Megathura crenulata/7dac0909e4733b24873e7f9337fb734c.jpg\",\n  \"61154.jpg\": \"Insecta/Apantesis phalerata/6de551c213ebd2858fdb4a79accd438e.jpg\",\n  \"611552.jpg\": \"Mollusca/Megathura crenulata/a1ae0bca9ba1308e73d60a38e9932a86.jpg\",\n  \"611563.jpg\": \"Aves/Megadyptes antipodes/799e07d198b47f3b84443a8872758662.jpg\",\n  \"611584.jpg\": \"Reptilia/Actinemys marmorata/ccd67c3f847ae56a8f3c4b2eff71e86e.jpg\",\n  \"611585.jpg\": \"Reptilia/Actinemys marmorata/f97669ab0da947711534427966ff0ccb.jpg\",\n  \"611598.jpg\": \"Reptilia/Actinemys marmorata/8260a34c13ef1cd7acefae2c19103bb6.jpg\",\n  \"61161.jpg\": \"Insecta/Apantesis phalerata/b4bb697519c3e0426d120aa1b269ca58.jpg\",\n  \"611615.jpg\": \"Reptilia/Actinemys marmorata/690646745c5522d00394f3b1ab69a3e5.jpg\",\n  \"611616.jpg\": \"Reptilia/Actinemys marmorata/b2a5d28580248096d635a69baf9f16c7.jpg\",\n  \"611637.jpg\": \"Reptilia/Actinemys marmorata/e498107fdfa70ac218aaef3d6720f7a0.jpg\",\n  \"611638.jpg\": \"Reptilia/Thamnophis cyrtopsis ocellatus/65b69e38f4bd2ed79c052a5ecb47862e.jpg\",\n  \"61164.jpg\": \"Insecta/Apantesis phalerata/891cbab526e43786ca268c5044224429.jpg\",\n  \"611645.jpg\": \"Reptilia/Thamnophis cyrtopsis ocellatus/bc01e1798047c82567e1ff0ba9431790.jpg\",\n  \"611646.jpg\": \"Reptilia/Thamnophis cyrtopsis ocellatus/312872008487cf334b762d025f42270e.jpg\",\n  \"61165.jpg\": \"Insecta/Apantesis phalerata/0eec488eca72e54a2704184157cecc6f.jpg\",\n  \"611655.jpg\": \"Insecta/Oncometopia orbona/968e18eb7d86f421a1c2046e1406455c.jpg\",\n  \"61166.jpg\": \"Animalia/Metridium senile/169048de07fc0adc8ff149c68458cc0b.jpg\",\n  \"611691.jpg\": \"Arachnida/Steatoda grossa/603dc513be3a51c05307df70e39d9f22.jpg\",\n  \"611696.jpg\": \"Arachnida/Steatoda grossa/bed4cab59f186f19f9011ed6969a5532.jpg\",\n  \"611773.jpg\": \"Aves/Anas penelope/3616571186eebdea703358ab43637bf0.jpg\",\n  \"611774.jpg\": \"Aves/Anas penelope/b30251e499db0a2f1f5a3c3adb480913.jpg\",\n  \"611777.jpg\": \"Aves/Anas penelope/719f3ec824caaaf44082d73ad4b7577a.jpg\",\n  \"611792.jpg\": \"Aves/Anas penelope/01bb9366efc184a0e8d896ac16f2f1e1.jpg\",\n  \"611793.jpg\": \"Aves/Anas penelope/4e7bd8c7290b6a67a3bee8d5915b76c0.jpg\",\n  \"611794.jpg\": \"Aves/Anas penelope/fbc5cb0f360c01327a811c4f7b31d279.jpg\",\n  \"611798.jpg\": \"Aves/Anas penelope/42366d88b2ee5633a492ec74459ee325.jpg\",\n  \"611803.jpg\": \"Aves/Anas penelope/a9349d3b0023f260560c61bff5cc9ac7.jpg\",\n  \"611816.jpg\": \"Aves/Anas penelope/6e9a5136c0adeb8ba1f82973e906b22d.jpg\",\n  \"61190.jpg\": \"Insecta/Pompeius verna/2f095d337b197be314c4bf813ff3865a.jpg\",\n  \"611942.jpg\": \"Insecta/Pyrisitia lisa/86331054a7d1c9bb74a93880aaed9c47.jpg\",\n  \"61199.jpg\": \"Insecta/Pompeius verna/a236dde07533d9eda0122fc6388a4dbb.jpg\",\n  \"6120.jpg\": \"Aves/Columba palumbus/dd0d6f681525a6e71f2dd3f3235b8fc7.jpg\",\n  \"612009.jpg\": \"Insecta/Pyrisitia lisa/3079db0444f7f7781097fb929a359776.jpg\",\n  \"61203.jpg\": \"Insecta/Pompeius verna/85221d3f96a761b66173d462b9873671.jpg\",\n  \"61204.jpg\": \"Insecta/Pompeius verna/8750a51926b0427eef05c748533b1da2.jpg\",\n  \"612042.jpg\": \"Insecta/Pyrisitia lisa/48e26c9bef4e3d105629465fc897bd11.jpg\",\n  \"612048.jpg\": \"Insecta/Pyrisitia lisa/7d98138135ee435afd6bb317f16b2426.jpg\",\n  \"612049.jpg\": \"Insecta/Pyrisitia lisa/8fbc51d5a274d16c2a890af5b18e36ab.jpg\",\n  \"61206.jpg\": \"Insecta/Pompeius verna/8721412c72614dc8aa9d055cfe3ba4c7.jpg\",\n  \"612069.jpg\": \"Insecta/Sympetrum fonscolombii/921b075078e87dd4638e42a34fc10c50.jpg\",\n  \"612070.jpg\": \"Insecta/Sympetrum fonscolombii/ddf242a4e8a88ac012fdb1ae6e231cf4.jpg\",\n  \"612072.jpg\": \"Insecta/Sympetrum fonscolombii/c373b93c23bfe0f8ad16e693a3666db0.jpg\",\n  \"6121.jpg\": \"Aves/Columba palumbus/77f50f16f47f5a8e6aaac3d65117acef.jpg\",\n  \"61210.jpg\": \"Insecta/Pompeius verna/cd716fca2bfbc5496524fecc5c1ebb77.jpg\",\n  \"612183.jpg\": \"Insecta/Vanessa cardui/771dc8eb327650b09da4756a75b63ae3.jpg\",\n  \"612268.jpg\": \"Insecta/Vanessa cardui/323f54f6bcde3564396a7e1666f230e3.jpg\",\n  \"612319.jpg\": \"Insecta/Vanessa cardui/46fa53e80998398a4d5449ed83d22726.jpg\",\n  \"612327.jpg\": \"Insecta/Vanessa cardui/89e4b2f6461a663ef577d51219ff0cec.jpg\",\n  \"612334.jpg\": \"Insecta/Vanessa cardui/fe7df680e86c482be318ba28042d59b5.jpg\",\n  \"612342.jpg\": \"Insecta/Vanessa cardui/3dfbe5ca8caa2a85f9e81e54036d369d.jpg\",\n  \"612355.jpg\": \"Insecta/Vanessa cardui/0b7a4c8e6c89f2f675be3dc7a4b188d0.jpg\",\n  \"612386.jpg\": \"Insecta/Vanessa cardui/c32139e13e326cd5937148fcc30e24b3.jpg\",\n  \"612436.jpg\": \"Insecta/Cotinis nitida/4f1b167550b098dd88480246bfed73dc.jpg\",\n  \"612437.jpg\": \"Insecta/Cotinis nitida/f78b86ddb36f13435130d3f05c6f65ee.jpg\",\n  \"612450.jpg\": \"Insecta/Cotinis nitida/0693e280bd05422bea0a53d81ecd24e2.jpg\",\n  \"612451.jpg\": \"Insecta/Cotinis nitida/fe6aa6417706b0f896bdffb0167e49d8.jpg\",\n  \"612455.jpg\": \"Insecta/Cotinis nitida/95337cdf0c605da4233a78d91693a6de.jpg\",\n  \"612460.jpg\": \"Insecta/Cotinis nitida/7131dad974b0c8b7d717d80ae606f80c.jpg\",\n  \"612462.jpg\": \"Insecta/Cotinis nitida/fb40aaf0fd9b761d33bd3d3f1e0f57a0.jpg\",\n  \"612463.jpg\": \"Insecta/Cotinis nitida/82b9202cf39c22cda11fc428631271d4.jpg\",\n  \"612465.jpg\": \"Insecta/Cotinis nitida/6ce9ce5b9c219eb2d9a4c870fcf098fa.jpg\",\n  \"612480.jpg\": \"Insecta/Episyrphus balteatus/f5c56b9af8920b27ec7947999a95ba06.jpg\",\n  \"612481.jpg\": \"Insecta/Episyrphus balteatus/86f51e8ef2e43e930536f8b2ee78ee1e.jpg\",\n  \"612486.jpg\": \"Insecta/Episyrphus balteatus/9792daf9a3b158455ecc86b80febb58b.jpg\",\n  \"612492.jpg\": \"Insecta/Episyrphus balteatus/67a50e56b229dcea71f58307a2c243f3.jpg\",\n  \"612493.jpg\": \"Insecta/Episyrphus balteatus/1de45f17208f75274d1505dad390fb2e.jpg\",\n  \"612494.jpg\": \"Insecta/Episyrphus balteatus/ddd4795ca59479ee589cb68c6588db88.jpg\",\n  \"612495.jpg\": \"Insecta/Episyrphus balteatus/f42a7515e3a7c4f26951698b49f023ae.jpg\",\n  \"612496.jpg\": \"Insecta/Episyrphus balteatus/d8c671a1e13ef4bcc3ef4f81a38b9fcf.jpg\",\n  \"612500.jpg\": \"Insecta/Episyrphus balteatus/0cc6571ada1011570ed9d17c58b753e3.jpg\",\n  \"612529.jpg\": \"Aves/Quiscalus quiscula/7658f7861a72c769c6ec1ee16e72b443.jpg\",\n  \"612536.jpg\": \"Aves/Quiscalus quiscula/08a5a5af5234cd1798e83046315538bc.jpg\",\n  \"612559.jpg\": \"Aves/Quiscalus quiscula/ee66c912daa734bc8321134a803e6726.jpg\",\n  \"6126.jpg\": \"Aves/Columba palumbus/08447556df658558c1f3694898b3400c.jpg\",\n  \"612605.jpg\": \"Aves/Quiscalus quiscula/f7674a3a163024ff74f94c197b36fc14.jpg\",\n  \"612643.jpg\": \"Aves/Quiscalus quiscula/e93b465cd38e4a438a49a58f78f7bcba.jpg\",\n  \"612684.jpg\": \"Aves/Quiscalus quiscula/41b658123cf4627ded5f1900d964e8c2.jpg\",\n  \"612692.jpg\": \"Aves/Quiscalus quiscula/fcc04c5c639cc4e7961d9a3561f0676e.jpg\",\n  \"612695.jpg\": \"Aves/Quiscalus quiscula/ec99eafe63b674dd1ea8adc5d39cc146.jpg\",\n  \"61273.jpg\": \"Insecta/Polygonia interrogationis/51bea6549252a1d69713546f18e1e050.jpg\",\n  \"61274.jpg\": \"Insecta/Polygonia interrogationis/7291e00ac7f374f4b0627d6b7a14f1d3.jpg\",\n  \"612834.jpg\": \"Animalia/Pacifastacus leniusculus/4b2043f5bec0652ac63774f3f4214ea4.jpg\",\n  \"612835.jpg\": \"Animalia/Pacifastacus leniusculus/4a0006acc1364e1cd5ca75de5f041ad8.jpg\",\n  \"612840.jpg\": \"Animalia/Pacifastacus leniusculus/d4741a3e7517e4c33f1907e090ace0a4.jpg\",\n  \"612851.jpg\": \"Aves/Tigrisoma mexicanum/7955018458578673620c58958b240853.jpg\",\n  \"61286.jpg\": \"Insecta/Polygonia interrogationis/0f425d8d1eb3c1067f5fc1223106aa8d.jpg\",\n  \"612873.jpg\": \"Aves/Tigrisoma mexicanum/1dfafc73988539e55680588a1f656f0f.jpg\",\n  \"612875.jpg\": \"Aves/Tigrisoma mexicanum/7091f5deaf889a9f1d113c3ee6146db7.jpg\",\n  \"612883.jpg\": \"Aves/Tigrisoma mexicanum/d833dbcb752c3351b82a92e38b419c38.jpg\",\n  \"612901.jpg\": \"Animalia/Anthopleura artemisia/b51209011d17d981a85414ad3ec93184.jpg\",\n  \"612904.jpg\": \"Animalia/Anthopleura artemisia/9fedb8fa778f6b7441c7a9c3117126f5.jpg\",\n  \"612926.jpg\": \"Aves/Sphyrapicus nuchalis/2c14510bd61e8a1ed1223840e91c7f77.jpg\",\n  \"612929.jpg\": \"Aves/Sphyrapicus nuchalis/e84fed8d58de45b25c60f1b09c97b6f9.jpg\",\n  \"61293.jpg\": \"Insecta/Polygonia interrogationis/723fa5280b3199ba13735ea7252352a6.jpg\",\n  \"612930.jpg\": \"Aves/Sphyrapicus nuchalis/7a1e0293e8e5e0c626e778cb64169922.jpg\",\n  \"612931.jpg\": \"Aves/Sphyrapicus nuchalis/1695e6d0be0f6f74698ffc18d31d935c.jpg\",\n  \"612932.jpg\": \"Aves/Sphyrapicus nuchalis/2a34e328ae834d014b7c24d19856d071.jpg\",\n  \"612937.jpg\": \"Aves/Sphyrapicus nuchalis/17ebe82aa6e807c3afb2bed1e0b96441.jpg\",\n  \"612938.jpg\": \"Aves/Sphyrapicus nuchalis/58e51b35f872703f6ed5c09404dd1071.jpg\",\n  \"612944.jpg\": \"Aves/Sphyrapicus nuchalis/6d36f70aaffb5c4f6d201add79847b5c.jpg\",\n  \"612945.jpg\": \"Aves/Sphyrapicus nuchalis/4fccbb8c4ba12e7e64cb27b3cf985f28.jpg\",\n  \"612951.jpg\": \"Aves/Sphyrapicus nuchalis/86ce9879d004852cdf3442eb8a6c4716.jpg\",\n  \"612998.jpg\": \"Amphibia/Pseudacris cadaverina/767eccd8a30e53a4a19c2b1c66175f13.jpg\",\n  \"6130.jpg\": \"Aves/Columba palumbus/58d3fc6707ba59d6cb6566c7ac04ca01.jpg\",\n  \"613016.jpg\": \"Amphibia/Pseudacris cadaverina/f89a60a4f89e88731dfc1ebbb7646fa7.jpg\",\n  \"613026.jpg\": \"Amphibia/Pseudacris cadaverina/bc29ac863a08078dea95978c2850efff.jpg\",\n  \"613027.jpg\": \"Amphibia/Pseudacris cadaverina/cc5b091995fce48a3e342adbfe3c83a9.jpg\",\n  \"613032.jpg\": \"Amphibia/Pseudacris cadaverina/b389e425e1822405f8d752017638f13e.jpg\",\n  \"613033.jpg\": \"Amphibia/Pseudacris cadaverina/49388c80578527c5d47f2870fc5c0b32.jpg\",\n  \"61323.jpg\": \"Insecta/Polygonia interrogationis/328aaaeec0a5814d372467f4e8b8d465.jpg\",\n  \"613245.jpg\": \"Aves/Catherpes mexicanus/cad2640e1a020571e73d9a3197513745.jpg\",\n  \"613247.jpg\": \"Aves/Catherpes mexicanus/2caa4474967074427719427cf8a389ed.jpg\",\n  \"613251.jpg\": \"Aves/Catherpes mexicanus/7802b196c769f1a85d0f801a96d9ad1c.jpg\",\n  \"613267.jpg\": \"Aves/Catherpes mexicanus/c32afc70150381f67cc0fff907fb0948.jpg\",\n  \"613272.jpg\": \"Aves/Catherpes mexicanus/796e1aa9c42ad32ecd6a48d9310b31f4.jpg\",\n  \"613275.jpg\": \"Aves/Catherpes mexicanus/34b0dd00c22ff9d72a2a2772643677e3.jpg\",\n  \"613290.jpg\": \"Aves/Catherpes mexicanus/40934e89f57340547ad36ec75c776f7c.jpg\",\n  \"613295.jpg\": \"Amphibia/Rana boylii/228b320a97dd8bf13d23dee8b1316f66.jpg\",\n  \"613296.jpg\": \"Amphibia/Rana boylii/c2a993977663f97a13f0f95bc44bcac9.jpg\",\n  \"613297.jpg\": \"Amphibia/Rana boylii/0efaab30ebfb24d09fbde3f26f459411.jpg\",\n  \"613298.jpg\": \"Amphibia/Rana boylii/2cd6c4ad209e3705d555bd2410715e3a.jpg\",\n  \"613303.jpg\": \"Amphibia/Rana boylii/936f3d8485c796862cf965013ff70913.jpg\",\n  \"613307.jpg\": \"Amphibia/Rana boylii/c877084a417dc21e8832e7c206c11c21.jpg\",\n  \"613309.jpg\": \"Amphibia/Rana boylii/7159829be8b1687d7cc3f74f7a476164.jpg\",\n  \"61331.jpg\": \"Insecta/Polygonia interrogationis/8a48baf1ac64368cf32b5aea26b6ec31.jpg\",\n  \"613310.jpg\": \"Amphibia/Rana boylii/2e6110ee326914be9cdab58580990073.jpg\",\n  \"613318.jpg\": \"Amphibia/Rana boylii/61fd5488e282fd640faf73a78b98453c.jpg\",\n  \"613319.jpg\": \"Amphibia/Rana boylii/f88ec55f2b30102f8a11d5b7a7bfce4d.jpg\",\n  \"613328.jpg\": \"Insecta/Argia tibialis/53bbf0a8e4b413b0151176df7ffe8ba4.jpg\",\n  \"613335.jpg\": \"Insecta/Argia tibialis/7cbaabc3088d914506dccd5998b4f721.jpg\",\n  \"613336.jpg\": \"Insecta/Argia tibialis/f2cb218319863757865e7f85845975ab.jpg\",\n  \"613338.jpg\": \"Insecta/Argia tibialis/7ef57697b3c9845f81b25de85218c1b7.jpg\",\n  \"613344.jpg\": \"Insecta/Argia tibialis/67f3c6a8d3a5779ca0a246d3a23dea3e.jpg\",\n  \"613345.jpg\": \"Insecta/Argia tibialis/71196953434f537924d4a44167b20cb9.jpg\",\n  \"613349.jpg\": \"Insecta/Argia tibialis/5462732cd43560bbf591c76a1480a4d2.jpg\",\n  \"613352.jpg\": \"Insecta/Argia tibialis/3669a2befd8741eddbb5473a64f1d5c5.jpg\",\n  \"613353.jpg\": \"Insecta/Argia tibialis/c9c0ad5c8219028fe6b16896edab93c6.jpg\",\n  \"613359.jpg\": \"Insecta/Automeris io/1805ad9dc7e06aed8ca3acdd8c045679.jpg\",\n  \"613371.jpg\": \"Insecta/Automeris io/364f23f452aa58e0a49eec5c46a62ce3.jpg\",\n  \"613376.jpg\": \"Insecta/Automeris io/1c52cb299625ff2dead95b64dfe34ddd.jpg\",\n  \"61338.jpg\": \"Insecta/Polygonia interrogationis/faa6e586bdba64242511b5fa5234d4e5.jpg\",\n  \"61339.jpg\": \"Insecta/Polygonia interrogationis/1ab389ce30bfda8351526391f24ec37c.jpg\",\n  \"6134.jpg\": \"Aves/Columba palumbus/c508408abaa2584998c7f93bdb02fcdc.jpg\",\n  \"613401.jpg\": \"Insecta/Automeris io/a651e7814a0bbbb8797e400611bc61f0.jpg\",\n  \"613402.jpg\": \"Insecta/Automeris io/d990f69029f6cce7e067ac961b51a9bf.jpg\",\n  \"613404.jpg\": \"Insecta/Automeris io/2662c6ba92cec46c9c269b9b67d6c286.jpg\",\n  \"613405.jpg\": \"Insecta/Automeris io/b9f4ab3e7c34c2f3523d4d56eb17246b.jpg\",\n  \"613409.jpg\": \"Insecta/Zelus longipes/8c8693dfa8e4bfca62b294b0ed92fe1e.jpg\",\n  \"613410.jpg\": \"Insecta/Zelus longipes/91e3c677956a4e925cfe16ea29027380.jpg\",\n  \"613411.jpg\": \"Insecta/Zelus longipes/debff7ff0cedf55ce799940b8a53096b.jpg\",\n  \"613412.jpg\": \"Insecta/Zelus longipes/6444a2aa018d8c42891af4d76475316c.jpg\",\n  \"613413.jpg\": \"Insecta/Zelus longipes/397fc82cb4fea4c44632f671f27eb076.jpg\",\n  \"613418.jpg\": \"Insecta/Zelus longipes/990523e88d0a0f4593c649262e7c3826.jpg\",\n  \"613420.jpg\": \"Insecta/Zelus longipes/f761cb114f82a106aa695c52703b2ed8.jpg\",\n  \"613422.jpg\": \"Insecta/Zelus longipes/df46fb84c779c098ed9cae5fcbb7e6f1.jpg\",\n  \"613424.jpg\": \"Aves/Phalaenoptilus nuttallii/f704a4b9a5b41988e4eea8decee31973.jpg\",\n  \"613430.jpg\": \"Aves/Phalaenoptilus nuttallii/eb56f5abc3ecc2a28b8cd6dcf40a77b9.jpg\",\n  \"613437.jpg\": \"Aves/Phalaenoptilus nuttallii/82133598359cfc8f7874a0a84f16e46b.jpg\",\n  \"613438.jpg\": \"Aves/Phalaenoptilus nuttallii/a2a0a1ab641c0a64ef49e2450248da12.jpg\",\n  \"613493.jpg\": \"Reptilia/Sceloporus olivaceus/3a18cbc17bd30d15c93c7f5bcf6eed84.jpg\",\n  \"613501.jpg\": \"Reptilia/Sceloporus olivaceus/1f216d363b8f3318e9c5bbaa571e3aa0.jpg\",\n  \"613507.jpg\": \"Reptilia/Sceloporus olivaceus/018cead5fc9705ca7137b3e83efac287.jpg\",\n  \"61353.jpg\": \"Insecta/Polygonia interrogationis/529b8adf3a904884b704f23cef70808b.jpg\",\n  \"613546.jpg\": \"Reptilia/Sceloporus olivaceus/8ab6534d09d1b6f34791d577288d0404.jpg\",\n  \"6136.jpg\": \"Aves/Columba palumbus/7ca3612588901f90d93bebdf55180abc.jpg\",\n  \"61362.jpg\": \"Insecta/Polygonia interrogationis/fed238a93bdbeaa90366e254793da050.jpg\",\n  \"613642.jpg\": \"Reptilia/Sceloporus olivaceus/29bb0ff3e134d71778ddb21bc62e481e.jpg\",\n  \"613659.jpg\": \"Reptilia/Sceloporus olivaceus/5a8ed1d178d9926d95eb3bbbedb23963.jpg\",\n  \"61368.jpg\": \"Insecta/Polygonia interrogationis/ef801847153f1f08f0939e300b5ce9fb.jpg\",\n  \"613697.jpg\": \"Reptilia/Sceloporus olivaceus/b67de324dccb7ff25e55c67f60e54f94.jpg\",\n  \"613721.jpg\": \"Aves/Passer montanus/9f932b6bd4b1534e37750a6aae9ba632.jpg\",\n  \"613731.jpg\": \"Aves/Passer montanus/a6d44057401ca10602604917464188aa.jpg\",\n  \"613751.jpg\": \"Aves/Passer montanus/81125d03f9d5392a1fcf4cab1eed9db2.jpg\",\n  \"613752.jpg\": \"Aves/Passer montanus/e3e5042af020e33f43fd19114edaca65.jpg\",\n  \"613763.jpg\": \"Aves/Passer montanus/cb0a02a13561bb939c546dc7ebe77d55.jpg\",\n  \"613764.jpg\": \"Aves/Passer montanus/5489bbf0670d53e9ca744c87eb6d5516.jpg\",\n  \"613773.jpg\": \"Aves/Passer montanus/408db45516bc85e20b839023c1659768.jpg\",\n  \"613785.jpg\": \"Insecta/Feltia herilis/04866657b273ccca7578386a7e344f60.jpg\",\n  \"613786.jpg\": \"Insecta/Feltia herilis/91fc712c48ec5b0f6adab12fadbe6e49.jpg\",\n  \"613800.jpg\": \"Aves/Gallirallus australis/97757c127c398844ca53f235a6157c21.jpg\",\n  \"613804.jpg\": \"Aves/Gallirallus australis/2888d94bf52942692e765def6a7f8e57.jpg\",\n  \"613805.jpg\": \"Aves/Gallirallus australis/e409b2259861faeeef7a868e2e4bb3cd.jpg\",\n  \"613809.jpg\": \"Aves/Gallirallus australis/5e75ccf01a5183aaf2824f75c7923d8a.jpg\",\n  \"613813.jpg\": \"Reptilia/Pantherophis spiloides/9f79dfe558888cef5f309061e6bbc63f.jpg\",\n  \"613816.jpg\": \"Reptilia/Pantherophis spiloides/65ee6c482bc1e35aa7545731b05ee59b.jpg\",\n  \"613822.jpg\": \"Reptilia/Pantherophis spiloides/84f548ccbf79858c937e2cdcfc1a3b45.jpg\",\n  \"613823.jpg\": \"Reptilia/Pantherophis spiloides/f78496ddc75232e2b18e8e7a1df9cb79.jpg\",\n  \"613824.jpg\": \"Reptilia/Pantherophis spiloides/b22253191cc9818f7d3bb475fdb418ba.jpg\",\n  \"613825.jpg\": \"Reptilia/Pantherophis spiloides/4372b55bebbe9ae266e5ca8a92b72594.jpg\",\n  \"613826.jpg\": \"Reptilia/Pantherophis spiloides/904ef9cf8c834cdc6467cc586083e08d.jpg\",\n  \"613834.jpg\": \"Reptilia/Pantherophis spiloides/221ce5f0f25dbb811dc39d19bd496b77.jpg\",\n  \"613835.jpg\": \"Reptilia/Pantherophis spiloides/3bd544a3909fd14d903f21e93d5a1d05.jpg\",\n  \"613841.jpg\": \"Mollusca/Haliotis rufescens/fce6f7822f8a23b3e4c4569bba4dd255.jpg\",\n  \"613848.jpg\": \"Mollusca/Haliotis rufescens/12f9714ffda7eb9e10edb97662175783.jpg\",\n  \"61386.jpg\": \"Insecta/Polygonia interrogationis/7af4082c70e854bb3066d91f14956199.jpg\",\n  \"613872.jpg\": \"Insecta/Autographa californica/7554b4a860c52ea6812197687635f7ed.jpg\",\n  \"613878.jpg\": \"Insecta/Autographa californica/bd8af70160458ec25fbcdcb65ac72e28.jpg\",\n  \"6141.jpg\": \"Aves/Trogon elegans/eda87158a1dbfaf7ecf2d84de5514cee.jpg\",\n  \"614131.jpg\": \"Aves/Larus dominicanus dominicanus/5569fbfa8e50fb456aeff5579c994de8.jpg\",\n  \"614144.jpg\": \"Aves/Larus dominicanus dominicanus/4efe4aac8588a13cfae1be84d8d4ae46.jpg\",\n  \"614147.jpg\": \"Aves/Larus dominicanus dominicanus/22d9754ee16c87022456049090c961c7.jpg\",\n  \"614149.jpg\": \"Aves/Larus dominicanus dominicanus/ff8be02b98d3c2d12c3bd470295f79cc.jpg\",\n  \"614157.jpg\": \"Aves/Larus dominicanus dominicanus/e0e6dbaa29dc5adfe26cf1484505464c.jpg\",\n  \"614229.jpg\": \"Arachnida/Dolomedes minor/b1c2bbf95f296acb0f598df82f4daf9b.jpg\",\n  \"614231.jpg\": \"Arachnida/Dolomedes minor/6a61544722027b892e9829b009c64bbd.jpg\",\n  \"614237.jpg\": \"Arachnida/Dolomedes minor/001568f1b57579be8e1c98b7cd567bce.jpg\",\n  \"614243.jpg\": \"Arachnida/Dolomedes minor/d198e158e8affecdbe00dd37e87eaf12.jpg\",\n  \"614245.jpg\": \"Arachnida/Dolomedes minor/7faa1e9e684dce2b5a29f19dd6fe8c9d.jpg\",\n  \"614246.jpg\": \"Arachnida/Dolomedes minor/746697b9166a8a2dd14ed99a35debe01.jpg\",\n  \"614247.jpg\": \"Arachnida/Dolomedes minor/a8305d432002aadca357c2457d2c1548.jpg\",\n  \"61425.jpg\": \"Insecta/Polygonia interrogationis/e77eba898c165ba6d2f7dd27fac07b27.jpg\",\n  \"614257.jpg\": \"Arachnida/Dolomedes minor/184b2b504a2a61f6a84d3567af4c4382.jpg\",\n  \"614261.jpg\": \"Arachnida/Dolomedes minor/3f9b1e8165ac7834460fe85a584d48db.jpg\",\n  \"61427.jpg\": \"Insecta/Polygonia interrogationis/d57d673b7644422e4edd1d00557c3bea.jpg\",\n  \"614288.jpg\": \"Insecta/Palomena prasina/9d9d3ed2b13a4c5aea09cc6f9476e533.jpg\",\n  \"614290.jpg\": \"Insecta/Palomena prasina/e5e7fb428809becbad5836ceb3500210.jpg\",\n  \"614293.jpg\": \"Insecta/Poanes viator/a2c507399b8728ffd311eca777b067f0.jpg\",\n  \"614294.jpg\": \"Insecta/Poanes viator/a2065c06772ba9b8b50276486aae7d46.jpg\",\n  \"614297.jpg\": \"Insecta/Poanes viator/9e61b3700ea1a0d481a7e4570a66e91f.jpg\",\n  \"614301.jpg\": \"Mollusca/Tegula brunnea/2d80b08c8b23614bf155a315bf903109.jpg\",\n  \"614309.jpg\": \"Mollusca/Tegula brunnea/01a29920858c34b929421c4a78b1b9ef.jpg\",\n  \"614319.jpg\": \"Mollusca/Tegula brunnea/2046dcc8329247a2058f551b926a60a0.jpg\",\n  \"614320.jpg\": \"Mollusca/Tegula brunnea/cd3564e1f3e56d1206fa5ebb49af67eb.jpg\",\n  \"614321.jpg\": \"Mollusca/Tegula brunnea/bdb5ec7221c3bbf62c912f88f28abace.jpg\",\n  \"614322.jpg\": \"Mollusca/Tegula brunnea/4277906bdeb8024e93180fdbc6ab33c8.jpg\",\n  \"614323.jpg\": \"Mollusca/Tegula brunnea/2c11364865c99e2a26a450da7313e348.jpg\",\n  \"614324.jpg\": \"Mollusca/Tegula brunnea/e5236f241ceff1a5a4da6c4bb35f719d.jpg\",\n  \"614325.jpg\": \"Mollusca/Tegula brunnea/d978bbd526bad4abef418fb313006f9a.jpg\",\n  \"614332.jpg\": \"Aves/Progne subis/4bf27b043cb9b04a4c39bda19d76aebf.jpg\",\n  \"614337.jpg\": \"Aves/Progne subis/ef7ace182a5a07623189cd408828681a.jpg\",\n  \"614406.jpg\": \"Aves/Progne subis/25b6401f8db3ae8696da29bc45dc3529.jpg\",\n  \"614436.jpg\": \"Insecta/Bombus vosnesenskii/e1882fd49e10e2928f8d43315f255d87.jpg\",\n  \"614437.jpg\": \"Insecta/Bombus vosnesenskii/1c2b7a6a2a38489292064bdc738421a8.jpg\",\n  \"61445.jpg\": \"Insecta/Polygonia interrogationis/0415c296d279d1e3582a6aa9bbeb48ae.jpg\",\n  \"614456.jpg\": \"Insecta/Bombus vosnesenskii/6c26150cf17a3e5dabd6dd90ea6622a7.jpg\",\n  \"61446.jpg\": \"Insecta/Polygonia interrogationis/cdaf4edf2d906eadd159b2b97b9c195f.jpg\",\n  \"614467.jpg\": \"Insecta/Bombus vosnesenskii/38ab0106bac999c6e24d87549f251eb9.jpg\",\n  \"614477.jpg\": \"Insecta/Bombus vosnesenskii/e0615cf6729eb2d9eeb638036e56b849.jpg\",\n  \"614481.jpg\": \"Insecta/Bombus vosnesenskii/b189ad8c506513e4e5ac47736c420069.jpg\",\n  \"61453.jpg\": \"Insecta/Polygonia interrogationis/3da084192ece98d93dc3e71b021638da.jpg\",\n  \"614562.jpg\": \"Insecta/Oreta rosea/b934a9302f79481f92faf668ffc5d632.jpg\",\n  \"614567.jpg\": \"Insecta/Oreta rosea/1b6cf370179a273e176375eb30ae3cb4.jpg\",\n  \"614569.jpg\": \"Actinopterygii/Fistularia commersonii/6dfcfe8bf1366200ba50287a086d5c70.jpg\",\n  \"614573.jpg\": \"Actinopterygii/Fistularia commersonii/db57e7226f3b132de06014f654ce07c9.jpg\",\n  \"614715.jpg\": \"Aves/Nyctanassa violacea/abf77097d9d875b878e92ed18e592871.jpg\",\n  \"614789.jpg\": \"Aves/Nyctanassa violacea/16f7d19115707a097a8dc3b999089fa7.jpg\",\n  \"614790.jpg\": \"Aves/Nyctanassa violacea/9675bf368f3976834552275b8b8cf757.jpg\",\n  \"6148.jpg\": \"Aves/Trogon elegans/c2b4935c401d4480ed5aa1d546b85e35.jpg\",\n  \"614804.jpg\": \"Aves/Nyctanassa violacea/25438ec72d6016ef3372e3be3433c68f.jpg\",\n  \"614833.jpg\": \"Aves/Nyctanassa violacea/abcc2348baa70217191afffd77371aec.jpg\",\n  \"614844.jpg\": \"Aves/Nyctanassa violacea/c0915a072747eb7282a08e9312883e4a.jpg\",\n  \"614882.jpg\": \"Aves/Ixoreus naevius/1fe1a515bf110f2b62e55d4964320c4c.jpg\",\n  \"614883.jpg\": \"Aves/Ixoreus naevius/951e0ca18a67446aa30a74cba878adfa.jpg\",\n  \"614888.jpg\": \"Aves/Ixoreus naevius/4d1a34e83b6841f7269274d1c4cad49a.jpg\",\n  \"6149.jpg\": \"Aves/Trogon elegans/35fb7e3b5500028a709e0c2ec8a36024.jpg\",\n  \"614906.jpg\": \"Aves/Ixoreus naevius/0b221d692f6f8b7bfd81d05ae6ca43c6.jpg\",\n  \"614911.jpg\": \"Aves/Ixoreus naevius/1f8b4a46b69c2e9914b90192f7e09c1a.jpg\",\n  \"614915.jpg\": \"Aves/Ixoreus naevius/6500342ab436e3a62db90675b189348a.jpg\",\n  \"61492.jpg\": \"Insecta/Polygonia interrogationis/6766c44d0113691c067b70fb80d099c1.jpg\",\n  \"614931.jpg\": \"Aves/Ixoreus naevius/a3653d755bfbc6c38a3af9b7d324af43.jpg\",\n  \"614940.jpg\": \"Aves/Ixoreus naevius/d675bb1339f1e9891533aa102f3aa351.jpg\",\n  \"614945.jpg\": \"Aves/Ixoreus naevius/6b1105bede59c88cfd9c62bc5b76444c.jpg\",\n  \"614968.jpg\": \"Aves/Calidris fuscicollis/9219a3377f8b29521bfdc6312ce9fa02.jpg\",\n  \"614985.jpg\": \"Insecta/Erynnis icelus/81f6a8a36b2c442fbacdf0bf01e2e0c9.jpg\",\n  \"615.jpg\": \"Mammalia/Ursus americanus/8aabcea2bc285948e3d3514c7b976999.jpg\",\n  \"6150.jpg\": \"Aves/Trogon elegans/eabd2bf50078efa74530c0cd47245f73.jpg\",\n  \"615016.jpg\": \"Insecta/Brachymesia gravida/3a9366c9c29821009a4371fc6df127d3.jpg\",\n  \"615023.jpg\": \"Insecta/Brachymesia gravida/b63288ad8669c3f564dc28a974632f33.jpg\",\n  \"615026.jpg\": \"Insecta/Brachymesia gravida/c6dd9fe83ec3ccf789756bbc90302138.jpg\",\n  \"615029.jpg\": \"Insecta/Brachymesia gravida/a5b0c2f34a7e11b4e97d998aa3e83b77.jpg\",\n  \"615036.jpg\": \"Insecta/Brachymesia gravida/4cb4b1e07161ed8b0e0bb308f32e305b.jpg\",\n  \"615038.jpg\": \"Insecta/Brachymesia gravida/2f5dbb45f39fc7ab8ab4d473cf381ab1.jpg\",\n  \"61504.jpg\": \"Insecta/Polygonia interrogationis/843cc01166f91d8e570e403cffaa46a1.jpg\",\n  \"615040.jpg\": \"Insecta/Brachymesia gravida/f37c4b89570d139b67077379ef61e7f2.jpg\",\n  \"615064.jpg\": \"Animalia/Acropora palmata/f44e707a7af9f3773ef0a411752c09ea.jpg\",\n  \"615071.jpg\": \"Insecta/Melanchroia chephise/b53bb504b67bef637fab2840816ade0b.jpg\",\n  \"615082.jpg\": \"Insecta/Melanchroia chephise/89031470311ff26f0419bbc9ed1fa703.jpg\",\n  \"615083.jpg\": \"Insecta/Melanchroia chephise/89f01ce6574cec81b551f45679ec48df.jpg\",\n  \"615085.jpg\": \"Insecta/Melanchroia chephise/d660c5298930de1996c6b9a698427a03.jpg\",\n  \"615086.jpg\": \"Insecta/Melanchroia chephise/739a2514456a4fba03baecf5e79888c0.jpg\",\n  \"61509.jpg\": \"Insecta/Vanessa annabella/dd7c4793fa122d96e2156e8ad102499d.jpg\",\n  \"61514.jpg\": \"Insecta/Vanessa annabella/8dbdff90e285235635d16d0a55ebcf95.jpg\",\n  \"61517.jpg\": \"Insecta/Vanessa annabella/e6fb7dfb3370ea5b7769e9b0b9bc0c12.jpg\",\n  \"615186.jpg\": \"Aves/Alisterus scapularis/9c0cf19e2116b67f4838cfe0eb050ad9.jpg\",\n  \"615193.jpg\": \"Reptilia/Storeria dekayi/e67ea31c8a4f6d4e556f3a90081de781.jpg\",\n  \"615211.jpg\": \"Reptilia/Storeria dekayi/cb843acfa32bbb734a79fdf3e341d238.jpg\",\n  \"615218.jpg\": \"Reptilia/Storeria dekayi/9d61db878a25b43c8dc3902df322315a.jpg\",\n  \"615224.jpg\": \"Reptilia/Storeria dekayi/503f1483b736f6fed80159fdb2df3ab9.jpg\",\n  \"615229.jpg\": \"Reptilia/Storeria dekayi/e3548efd5e39fb125931bb3d3d9a84dc.jpg\",\n  \"61523.jpg\": \"Insecta/Vanessa annabella/cab5fcef8b58d51418a22879b41b5f71.jpg\",\n  \"615230.jpg\": \"Reptilia/Storeria dekayi/fa0fd0df5eb50fd01847ba84d3bb7937.jpg\",\n  \"615233.jpg\": \"Reptilia/Storeria dekayi/04e1202445094530af11b56e9b69591d.jpg\",\n  \"61524.jpg\": \"Insecta/Vanessa annabella/fb2a74b0fd5a5842ba653ca4c51eff95.jpg\",\n  \"615240.jpg\": \"Reptilia/Storeria dekayi/89ed02300f6a6be642a758666809168d.jpg\",\n  \"615248.jpg\": \"Reptilia/Storeria dekayi/a207e58ed11e4c9637c53e96d7112264.jpg\",\n  \"61527.jpg\": \"Insecta/Vanessa annabella/0149926d78e3aa7ce1294b4b1685f00b.jpg\",\n  \"615326.jpg\": \"Mammalia/Elephas maximus/37e1fbb9ffd44fb1fd11315465d3b409.jpg\",\n  \"61540.jpg\": \"Insecta/Vanessa annabella/1e96026183ccad152598e73203008227.jpg\",\n  \"615473.jpg\": \"Insecta/Colias eurytheme/c59e389db388a760da8270e1141cd534.jpg\",\n  \"615479.jpg\": \"Insecta/Colias eurytheme/3a8b531ffd2379527a885b495f8b1e5e.jpg\",\n  \"6155.jpg\": \"Aves/Trogon elegans/4d64882e7b264c297ac0a1338112114b.jpg\",\n  \"615508.jpg\": \"Insecta/Colias eurytheme/b03b15c48a7cf072c34a880a532fcf3c.jpg\",\n  \"615512.jpg\": \"Insecta/Colias eurytheme/df6e2f50a59533cc10f1c7893793f0b7.jpg\",\n  \"615529.jpg\": \"Insecta/Colias eurytheme/6c24ec6b71a2c50fe081285a30b84133.jpg\",\n  \"615585.jpg\": \"Insecta/Colias eurytheme/5e92161d8bd4d91da036015ff55b4c06.jpg\",\n  \"615586.jpg\": \"Insecta/Colias eurytheme/1936ce21871494483972d79911baf482.jpg\",\n  \"6156.jpg\": \"Aves/Trogon elegans/7214794834724e4d9ee9953cdf4a78c5.jpg\",\n  \"61566.jpg\": \"Insecta/Vanessa annabella/70a7876f168dae153e8224fed8c9512b.jpg\",\n  \"61567.jpg\": \"Insecta/Vanessa annabella/2550a544bf56754663dd714acfa34b05.jpg\",\n  \"6157.jpg\": \"Aves/Trogon elegans/d533e6664fc85aa8754779c390bd92b5.jpg\",\n  \"615706.jpg\": \"Aves/Melanerpes erythrocephalus/c2f239d87562b207ba73d70d01747190.jpg\",\n  \"615723.jpg\": \"Aves/Melanerpes erythrocephalus/65fc9d9eb96b0b1347e60a83f68586c4.jpg\",\n  \"615762.jpg\": \"Aves/Melanerpes erythrocephalus/c07aed26947f5a67cefa7adb139abc93.jpg\",\n  \"6158.jpg\": \"Aves/Trogon elegans/7777413bdae3d1d895f5806558294e23.jpg\",\n  \"61582.jpg\": \"Insecta/Vanessa annabella/5e8b26e8f5a5bd3f5c54fdb1b5ec5680.jpg\",\n  \"615835.jpg\": \"Aves/Athene noctua/fdd6a2e3585ac214e5de5d29f6d37729.jpg\",\n  \"6159.jpg\": \"Aves/Trogon elegans/10e63d307ee0af9c4aebf1575dda355e.jpg\",\n  \"61591.jpg\": \"Insecta/Vanessa annabella/e9b1277b2dd6e269420f680e6fc61189.jpg\",\n  \"61593.jpg\": \"Insecta/Vanessa annabella/145514cbccc716e6be5bfad0272d034d.jpg\",\n  \"6160.jpg\": \"Aves/Trogon elegans/412347d01c2797fbe3440f5cae96ff89.jpg\",\n  \"61601.jpg\": \"Insecta/Vanessa annabella/7e0850a28c705bbe813509856741c41f.jpg\",\n  \"61605.jpg\": \"Insecta/Vanessa annabella/83fd1e2a4bee3a60bac16403d79f795c.jpg\",\n  \"61608.jpg\": \"Insecta/Vanessa annabella/46f4ca2a66656e33e8a4f3fdd80688f8.jpg\",\n  \"61609.jpg\": \"Insecta/Vanessa annabella/b6b76ae0e60169a343b2cb31f47b0d35.jpg\",\n  \"6161.jpg\": \"Aves/Trogon elegans/b4e77c328ea90bd84a5321d668c15b59.jpg\",\n  \"61616.jpg\": \"Insecta/Vanessa annabella/01141f93fe3231721ea49c92ea616a60.jpg\",\n  \"61618.jpg\": \"Insecta/Vanessa annabella/7a032a186da0d90d1fadcfbf140ab0f8.jpg\",\n  \"61624.jpg\": \"Insecta/Vanessa annabella/7ff4480f0dbac949cb28e47abca3acc1.jpg\",\n  \"61629.jpg\": \"Insecta/Vanessa annabella/f22f97d34565db281dd3962f422e8a40.jpg\",\n  \"61630.jpg\": \"Insecta/Vanessa annabella/2cd43b786dc9f3a994027b77add9594b.jpg\",\n  \"61631.jpg\": \"Insecta/Vanessa annabella/0030942f00f989ef5c6f038e47a917c2.jpg\",\n  \"616423.jpg\": \"Aves/Branta canadensis/17572b15713a068f86344a49a7348b0b.jpg\",\n  \"616492.jpg\": \"Aves/Branta canadensis/2adbbc4f56c3e76a2bae46869ccaf2cc.jpg\",\n  \"6165.jpg\": \"Aves/Trogon elegans/d4359c86f2bd86a001b443a1052adeda.jpg\",\n  \"617058.jpg\": \"Aves/Spizella passerina/11a898b0deaffde292522db120d6b310.jpg\",\n  \"617086.jpg\": \"Aves/Spizella passerina/c9180d751607e7b049514e9a728ad121.jpg\",\n  \"6171.jpg\": \"Aves/Trogon elegans/92d65049448f632340b62a8af5af0394.jpg\",\n  \"617128.jpg\": \"Aves/Spizella passerina/df1c0f752fe7039632ef0394aee9740c.jpg\",\n  \"617256.jpg\": \"Aves/Spizella passerina/115196e5d3c8d274e749941321927322.jpg\",\n  \"617300.jpg\": \"Aves/Spizella passerina/d6c54c9ba0bc27caf5e5f019a0902a59.jpg\",\n  \"617311.jpg\": \"Aves/Spizella passerina/ccd3dcb58b33c9109c965607e38182cd.jpg\",\n  \"617316.jpg\": \"Aves/Spizella passerina/1bb5d62b60f8eed4e4f219eb22f723ec.jpg\",\n  \"617439.jpg\": \"Amphibia/Lithobates clamitans melanota/7822ba7edf8f427a3e4ab41334b7da2b.jpg\",\n  \"617440.jpg\": \"Amphibia/Lithobates clamitans melanota/192df926cc20c1b91b9cdbb29f00f27e.jpg\",\n  \"617449.jpg\": \"Amphibia/Lithobates clamitans melanota/6e6a14af5a8f6d2f2009ff93144a8313.jpg\",\n  \"617450.jpg\": \"Amphibia/Lithobates clamitans melanota/6eeb747aa2586d554ba40a1eee89d598.jpg\",\n  \"617464.jpg\": \"Insecta/Lymantria dispar/2474e3c888093343d70186ff129724fc.jpg\",\n  \"617472.jpg\": \"Insecta/Lymantria dispar/318aaea140b073e2ead789f8e0595400.jpg\",\n  \"617476.jpg\": \"Insecta/Lymantria dispar/b9ba7d5b85ec2f91453080283695dcf8.jpg\",\n  \"617477.jpg\": \"Insecta/Lymantria dispar/7bb11dee1f61f07706b4fc14cd012841.jpg\",\n  \"617479.jpg\": \"Insecta/Lymantria dispar/0600ca039153aaab1738295ef58aee74.jpg\",\n  \"617480.jpg\": \"Insecta/Lymantria dispar/0a7915391489b904cbf369c3ffbc5bd7.jpg\",\n  \"617484.jpg\": \"Insecta/Lymantria dispar/afdbfd346bc7ab713116d688112322b1.jpg\",\n  \"617485.jpg\": \"Insecta/Lymantria dispar/027960c860e2f980afaad36399a3a68e.jpg\",\n  \"617493.jpg\": \"Insecta/Lymantria dispar/bc7883349b86c59bc43e220dc248868a.jpg\",\n  \"6175.jpg\": \"Aves/Trogon elegans/04e3fdba8ae6d39f2cc270f9d0e1e013.jpg\",\n  \"617576.jpg\": \"Aves/Melozone crissalis/038bc10d2ec4151b945a42bdef94e41b.jpg\",\n  \"617593.jpg\": \"Aves/Melozone crissalis/df6d0134a748c62385bce1e98d4b84a4.jpg\",\n  \"617682.jpg\": \"Aves/Melozone crissalis/da0b71ebd0fe61e5cfa356ce7e8f7342.jpg\",\n  \"617687.jpg\": \"Aves/Melozone crissalis/d9af33134bf0456faad76a9a263fa20b.jpg\",\n  \"617708.jpg\": \"Aves/Melozone crissalis/0531c70fa88fa3b916456150efd53bcb.jpg\",\n  \"617792.jpg\": \"Aves/Melozone crissalis/18092da98575fd0cc7d7b8873fe12cdc.jpg\",\n  \"6178.jpg\": \"Aves/Trogon elegans/1c7fc1979a6bf0f559b6d0c179a3225c.jpg\",\n  \"617815.jpg\": \"Insecta/Lestes dryas/9b521f859f13a8f88229c7b217c45fd8.jpg\",\n  \"617825.jpg\": \"Insecta/Lestes dryas/fc4871330a933f6097ae92cc31fda84f.jpg\",\n  \"617829.jpg\": \"Insecta/Lestes dryas/37acde773c1fa700fa5b0e4d8894ed51.jpg\",\n  \"617858.jpg\": \"Insecta/Anthanassa tulcis/cf88470949b94996a1381246b235a56d.jpg\",\n  \"617859.jpg\": \"Insecta/Anthanassa tulcis/1cfc993b6c36e6f50cac9a3b8d1ced32.jpg\",\n  \"617861.jpg\": \"Insecta/Anthanassa tulcis/1c6e871d6b07a64298e622e354ef2c62.jpg\",\n  \"617862.jpg\": \"Insecta/Anthanassa tulcis/ddbe5d9d1798b51a8b0ebeb4a3fae864.jpg\",\n  \"617863.jpg\": \"Insecta/Anthanassa tulcis/b695bcc8d146cb9e3a41e4af8212bf61.jpg\",\n  \"617864.jpg\": \"Insecta/Anthanassa tulcis/53c56032eead83bcd24f7d9867d3d08e.jpg\",\n  \"617865.jpg\": \"Insecta/Anthanassa tulcis/d22252c6728e9a4936fc9a90de3b43d2.jpg\",\n  \"617866.jpg\": \"Insecta/Anthanassa tulcis/34d3e6321b59c23dfcfbf5c52a814cb3.jpg\",\n  \"617870.jpg\": \"Insecta/Anthanassa tulcis/3ee7c58a6cffa7da02b2a9aeb35fae66.jpg\",\n  \"617874.jpg\": \"Insecta/Anthanassa tulcis/6bdcdb7e3b32fa2569295878b35a2942.jpg\",\n  \"617950.jpg\": \"Aves/Toxostoma rufum/49bed47bdb9f1759d669bf4f79bcbb90.jpg\",\n  \"617952.jpg\": \"Aves/Toxostoma rufum/f035def43cada2bb045c007a42ee0ee5.jpg\",\n  \"617960.jpg\": \"Aves/Toxostoma rufum/4bc1735dae69983699a8d641138a518c.jpg\",\n  \"6180.jpg\": \"Aves/Trogon elegans/8fc8a3b3c06d1e94a1dec11634abe8e2.jpg\",\n  \"618010.jpg\": \"Aves/Toxostoma rufum/ee542b562f07a0b0ef656e750345fff4.jpg\",\n  \"618058.jpg\": \"Aves/Toxostoma rufum/14b61c2694d62fd045a9150049cad947.jpg\",\n  \"6181.jpg\": \"Aves/Trogon elegans/4bf0930a6a33fa38bb03197195bd770e.jpg\",\n  \"618154.jpg\": \"Reptilia/Hemidactylus turcicus/cfd583eadeeed66fa64d10549c370656.jpg\",\n  \"618192.jpg\": \"Reptilia/Hemidactylus turcicus/26aa9997abc1cbb940f704ef90b28795.jpg\",\n  \"6182.jpg\": \"Aves/Trogon elegans/500828033b04617bc840aaf4ddf243dc.jpg\",\n  \"618226.jpg\": \"Reptilia/Hemidactylus turcicus/4193bf20db6b1a243d4fb10911afb6ab.jpg\",\n  \"618314.jpg\": \"Aves/Rynchops niger/511a24f9d16847b849f6dad9927756ce.jpg\",\n  \"618407.jpg\": \"Insecta/Megachile sculpturalis/a5b153f5884f3fbbd6dea5c52475982b.jpg\",\n  \"618409.jpg\": \"Insecta/Megachile sculpturalis/77f268314be173fd06d7d4f441151e36.jpg\",\n  \"618410.jpg\": \"Insecta/Megachile sculpturalis/db4ddc71e02b371b2c0fba84de83e001.jpg\",\n  \"618576.jpg\": \"Animalia/Narceus americanus/fec2ac5f8a2a50ab5235d87d16f6325e.jpg\",\n  \"618577.jpg\": \"Animalia/Narceus americanus/26f9bbeb56425df74aea8a684a7d1c39.jpg\",\n  \"618578.jpg\": \"Animalia/Narceus americanus/11e728e7dacbbcbd94ea6793f035bee7.jpg\",\n  \"618586.jpg\": \"Animalia/Narceus americanus/208740a5ecad756633d94c5098e9b214.jpg\",\n  \"618587.jpg\": \"Animalia/Narceus americanus/e8327f4d3608e56e6b1091709a296f48.jpg\",\n  \"618590.jpg\": \"Animalia/Narceus americanus/4f6e697bf8c9ec7cb5c87dc9e1d7062c.jpg\",\n  \"618602.jpg\": \"Reptilia/Thamnophis sirtalis sirtalis/8dc44147814b1d90ec999a933fdc243b.jpg\",\n  \"618656.jpg\": \"Reptilia/Thamnophis sirtalis sirtalis/f677c343f38e3a08803f7fdfd51ef33a.jpg\",\n  \"618660.jpg\": \"Reptilia/Thamnophis sirtalis sirtalis/4fbb49575e4c03bcc5ac2375e020cb5a.jpg\",\n  \"618714.jpg\": \"Reptilia/Thamnophis sirtalis sirtalis/65642cfe21d9104998eff8660b017dde.jpg\",\n  \"618716.jpg\": \"Reptilia/Thamnophis sirtalis sirtalis/dc45451d5a6019414db06688a6b5867d.jpg\",\n  \"618760.jpg\": \"Reptilia/Thamnophis sirtalis sirtalis/3d03550ed7e59f143de8adf52a727a4b.jpg\",\n  \"618776.jpg\": \"Reptilia/Thamnophis sirtalis sirtalis/1e578da652a0660b04a947f8c0d2e3e2.jpg\",\n  \"618792.jpg\": \"Reptilia/Thamnophis sirtalis sirtalis/c1e9892b24a44a1ece66aa9b0fe129ae.jpg\",\n  \"618824.jpg\": \"Reptilia/Thamnophis sirtalis sirtalis/3c20569972cbdac3fffee49925e6fa00.jpg\",\n  \"618831.jpg\": \"Reptilia/Thamnophis sirtalis sirtalis/abfbf13a684069468539e8ce6e44b437.jpg\",\n  \"61888.jpg\": \"Amphibia/Taricha sierrae/5d7710ad796b8ad5dfc0ddbb1819c8d4.jpg\",\n  \"6189.jpg\": \"Aves/Trogon elegans/d3fe5c9419d84266567963daeba7eed2.jpg\",\n  \"61892.jpg\": \"Amphibia/Taricha sierrae/c060bb2b6a2c69633d96bac6db71227c.jpg\",\n  \"618935.jpg\": \"Insecta/Coenonympha tullia california/fe222d8eb3da2e08a6017bea402b87c1.jpg\",\n  \"618958.jpg\": \"Insecta/Coenonympha tullia california/c28b25e024ed494be40ad3705ae36f87.jpg\",\n  \"61896.jpg\": \"Amphibia/Taricha sierrae/cfa03be6f1daa45b7b20d279b0fde130.jpg\",\n  \"618964.jpg\": \"Insecta/Coenonympha tullia california/0ff4fb80289b904a1d3e8c9cabb0bae6.jpg\",\n  \"61897.jpg\": \"Amphibia/Taricha sierrae/687ed2916fe24dd33279682489a83a49.jpg\",\n  \"618972.jpg\": \"Insecta/Coenonympha tullia california/c0119807468df450920995e4a729b24e.jpg\",\n  \"61898.jpg\": \"Amphibia/Taricha sierrae/7af03cd24a10f118361dca021d4fd664.jpg\",\n  \"618997.jpg\": \"Insecta/Diacme elealis/4ea20db94a510aed82d2f37256f78cce.jpg\",\n  \"618999.jpg\": \"Insecta/Diacme elealis/e411f1c7b4617251c6fbdde502196e81.jpg\",\n  \"619025.jpg\": \"Aves/Pelecanus onocrotalus/ec70de393c91e5f1337725e2d0800aa8.jpg\",\n  \"619085.jpg\": \"Aves/Corvus caurinus/31fca9188875aa65c1e7b8d9ee5f0e22.jpg\",\n  \"619086.jpg\": \"Aves/Corvus caurinus/29c4054012b0570cb2da4d81443ef525.jpg\",\n  \"619089.jpg\": \"Insecta/Octogomphus specularis/3c642a062be970a9da956b8a71d82ab8.jpg\",\n  \"619185.jpg\": \"Animalia/Hemigrapsus oregonensis/9e61b671be38aa388b5de0d4d16f32c7.jpg\",\n  \"619186.jpg\": \"Animalia/Hemigrapsus oregonensis/f828c45d87d226270bff3e6985000305.jpg\",\n  \"619187.jpg\": \"Animalia/Hemigrapsus oregonensis/50f9d54b5e8893a20063993836c59f26.jpg\",\n  \"619197.jpg\": \"Animalia/Hemigrapsus oregonensis/e3c61a963844abdc79de9d51c9569316.jpg\",\n  \"619198.jpg\": \"Animalia/Hemigrapsus oregonensis/aec84f66f546d0dd11f1c2f2f4290d94.jpg\",\n  \"619206.jpg\": \"Animalia/Hemigrapsus oregonensis/914aa1958413e9d1bebe4d9f285cf83a.jpg\",\n  \"619207.jpg\": \"Animalia/Hemigrapsus oregonensis/578af8b658d0de6af003182882370e97.jpg\",\n  \"619268.jpg\": \"Aves/Gallus gallus/14a1da8cdf76813760fc193f995007ab.jpg\",\n  \"619274.jpg\": \"Aves/Gallus gallus/80ac27ea1c865ac3b6b3f4beb161ea6f.jpg\",\n  \"619275.jpg\": \"Aves/Gallus gallus/fffc0ea2bb824cbfaa15790310db090d.jpg\",\n  \"619289.jpg\": \"Aves/Euphagus carolinus/5a8a8652b4f4f534e9971fc2b98625a4.jpg\",\n  \"619295.jpg\": \"Aves/Euphagus carolinus/bf6babd0245df0aaf3bdf6a31d3495b2.jpg\",\n  \"6193.jpg\": \"Aves/Trogon elegans/90772e897273bb95a0e2326b54f68129.jpg\",\n  \"619301.jpg\": \"Aves/Euphagus carolinus/3d0c11d50dd1352dc90d0e46c60d0788.jpg\",\n  \"619307.jpg\": \"Aves/Euphagus carolinus/4fe8c84f4b7e32ba91ba799d6788605e.jpg\",\n  \"619308.jpg\": \"Aves/Euphagus carolinus/fbe26efe41a0b99d9023a73e7edac2f0.jpg\",\n  \"619309.jpg\": \"Aves/Euphagus carolinus/b43694e7bda8cb696460ae60f214c35e.jpg\",\n  \"619313.jpg\": \"Aves/Euphagus carolinus/1d3d17de8d44c20df996960943bfb143.jpg\",\n  \"619316.jpg\": \"Aves/Euphagus carolinus/0922b5f4679dee5512919ca07b885587.jpg\",\n  \"619317.jpg\": \"Aves/Euphagus carolinus/62cd330ca320a9dfd57d16338610c66a.jpg\",\n  \"619323.jpg\": \"Aves/Platycercus elegans/3000586b5bfa487f91c5d152db569b55.jpg\",\n  \"619324.jpg\": \"Aves/Platycercus elegans/b4469a559d85ba48ab772ec5cde4e733.jpg\",\n  \"619340.jpg\": \"Insecta/Erythemis plebeja/1df47d4d8340dfe58b346711387d5074.jpg\",\n  \"619349.jpg\": \"Aves/Nestor notabilis/8ba2b51241e08c96797dcf4e490e9b18.jpg\",\n  \"619368.jpg\": \"Aves/Nestor notabilis/4b3bb56e918cb5cb551c1ac941c2102d.jpg\",\n  \"619376.jpg\": \"Insecta/Cetonia aurata/ca49c9f4d1a37c3a4ffc03e87440d398.jpg\",\n  \"619381.jpg\": \"Aves/Cardellina canadensis/69214449ff863ce5514e20855f62ee7c.jpg\",\n  \"619383.jpg\": \"Aves/Cardellina canadensis/4f6c553e78e3b45e9663adb9adfbd8f7.jpg\",\n  \"619384.jpg\": \"Aves/Cardellina canadensis/d19e8578737922bb5f495a587f49c36e.jpg\",\n  \"619387.jpg\": \"Aves/Cardellina canadensis/dda059d1d33b9be0961a40a219f27944.jpg\",\n  \"619388.jpg\": \"Aves/Cardellina canadensis/f218b7d29fbcf906c260ad255daaf758.jpg\",\n  \"619394.jpg\": \"Aves/Cardellina canadensis/e6a34550f72d48a852cac828c55846b2.jpg\",\n  \"619396.jpg\": \"Aves/Cardellina canadensis/8eac08137c3496e9479a28355c54d217.jpg\",\n  \"619397.jpg\": \"Aves/Cardellina canadensis/64753053cb540a98d31abadb2aa47a48.jpg\",\n  \"619459.jpg\": \"Animalia/Velella velella/b88b8d94bf270d5388101e89665467ca.jpg\",\n  \"619472.jpg\": \"Animalia/Velella velella/fd3b7d2f36b4310e65796829b286aa71.jpg\",\n  \"619518.jpg\": \"Amphibia/Ambystoma gracile/d6da1efc9b1525a4d578e1fb88457722.jpg\",\n  \"619519.jpg\": \"Amphibia/Ambystoma gracile/3cfb5b37cea1dfb361f96ef73bcf3e8c.jpg\",\n  \"619575.jpg\": \"Amphibia/Anaxyrus punctatus/b8ae99717303c79c813e98614da5079d.jpg\",\n  \"619592.jpg\": \"Amphibia/Anaxyrus punctatus/cb501afeb9f4db6aeab955d7deb8b24d.jpg\",\n  \"619597.jpg\": \"Amphibia/Anaxyrus punctatus/62bf93d895e85bff87fd52a385d38dc8.jpg\",\n  \"619617.jpg\": \"Amphibia/Anaxyrus punctatus/53af77ae2facdd4429dbadec44de944b.jpg\",\n  \"619622.jpg\": \"Amphibia/Anaxyrus punctatus/217529303f90ca274e8af3b84f24a938.jpg\",\n  \"619637.jpg\": \"Aves/Acanthis flammea/3a3a3749f1a5062190945743dd71fd44.jpg\",\n  \"619639.jpg\": \"Aves/Acanthis flammea/e1ad2b1c392d7ba5663ec636a3b7deea.jpg\",\n  \"619657.jpg\": \"Aves/Acanthis flammea/221e2843d49342c9f08e36ee0f473aae.jpg\",\n  \"619666.jpg\": \"Aves/Acanthis flammea/553784425b2cfb89d3eba9759fd3bfc7.jpg\",\n  \"619669.jpg\": \"Aves/Acanthis flammea/90390e15ecf38f639f6a54fb9c6b2f62.jpg\",\n  \"619670.jpg\": \"Aves/Acanthis flammea/4b6ced01de1db8b44013508630f4fa13.jpg\",\n  \"619671.jpg\": \"Aves/Acanthis flammea/7ce2316083fcc972d1dbceda114702d0.jpg\",\n  \"619691.jpg\": \"Insecta/Eudryas unio/ead8efa157be9c50cc87947c68646310.jpg\",\n  \"619704.jpg\": \"Insecta/Malacosoma disstria/5792879abbdbad7d6eddc85c25d51ae5.jpg\",\n  \"619719.jpg\": \"Insecta/Malacosoma disstria/b91ca017d4fb9a30c2e82f4a5e09e39b.jpg\",\n  \"619724.jpg\": \"Insecta/Malacosoma disstria/467106b139f36a4aa0734407f9d0cdc0.jpg\",\n  \"619739.jpg\": \"Insecta/Malacosoma disstria/536085577ede6de3dcb2496d25516cbd.jpg\",\n  \"619754.jpg\": \"Insecta/Malacosoma disstria/4b5df754c156fc431d5460023e1cf8ad.jpg\",\n  \"619768.jpg\": \"Insecta/Malacosoma disstria/ae34974fa6d02e6be486bc6502f0119d.jpg\",\n  \"619790.jpg\": \"Arachnida/Araneus trifolium/e7bffa3677d6bd3edb73fa95e8999d2c.jpg\",\n  \"619794.jpg\": \"Arachnida/Araneus trifolium/40409f3a4cc4d190a28cbae9d69f3d1f.jpg\",\n  \"619798.jpg\": \"Arachnida/Araneus trifolium/42358714a316ac8269ab6d55aa3800c5.jpg\",\n  \"619852.jpg\": \"Aves/Larus heermanni/d0502ac366efcf9546f268e62923b5fd.jpg\",\n  \"619871.jpg\": \"Aves/Larus heermanni/57f129ce68edb474bb0dd23e45328de2.jpg\",\n  \"619978.jpg\": \"Aves/Larus heermanni/1b303b9e7b9e1ec964832731786c4c58.jpg\",\n  \"619981.jpg\": \"Aves/Larus heermanni/abd6edee020846908776f839e08b9a45.jpg\",\n  \"619992.jpg\": \"Aves/Larus heermanni/2dc0ed5ff673bd3cd6137c2110283f0d.jpg\",\n  \"620117.jpg\": \"Reptilia/Oxybelis aeneus/69ab8715590cfc20c54a7df08c4fbe9c.jpg\",\n  \"620136.jpg\": \"Insecta/Libellula croceipennis/631fe5527156c0ed4ce9187ff6313fba.jpg\",\n  \"620185.jpg\": \"Insecta/Libellula croceipennis/d16b4917a0d890dcae390c52081a5679.jpg\",\n  \"620204.jpg\": \"Insecta/Libellula croceipennis/722808387208cfbdb4979bedd6f67ac8.jpg\",\n  \"620205.jpg\": \"Insecta/Libellula croceipennis/54d82036d73eab910365d7b4436b07fc.jpg\",\n  \"620209.jpg\": \"Insecta/Libellula croceipennis/91b2f09fe116980648df2f65ead7c585.jpg\",\n  \"620218.jpg\": \"Insecta/Libellula croceipennis/e4c24e771c188dbb4afa470524e5d4eb.jpg\",\n  \"620219.jpg\": \"Insecta/Libellula croceipennis/7d525b2619237ebea879db2594921324.jpg\",\n  \"620236.jpg\": \"Insecta/Libellula croceipennis/31fc83989115f0e47d5dfe13b3753c14.jpg\",\n  \"620332.jpg\": \"Insecta/Speranza pustularia/c55e2d26623581378dfcdd8e5f29f375.jpg\",\n  \"620334.jpg\": \"Insecta/Speranza pustularia/4b747d77074d6acad8e8103f62abd756.jpg\",\n  \"620341.jpg\": \"Insecta/Speranza pustularia/8f201f21938908473118f897155022dc.jpg\",\n  \"620342.jpg\": \"Insecta/Speranza pustularia/82ac2a07c92ca9d229db0ef5e2b5e3d6.jpg\",\n  \"620344.jpg\": \"Insecta/Speranza pustularia/e30b058cf8b68200ecc3a484f25904fd.jpg\",\n  \"620353.jpg\": \"Insecta/Speranza pustularia/05dc41ec9c5cc178e330624f1801177d.jpg\",\n  \"620386.jpg\": \"Aves/Sitta canadensis/d82e3401e2532f03d94222c600ecf967.jpg\",\n  \"620388.jpg\": \"Aves/Sitta canadensis/3d0d6664da24762bb0110e021fd689b8.jpg\",\n  \"620424.jpg\": \"Aves/Sitta canadensis/5d08500a1c64a6f850bc8121f8d33678.jpg\",\n  \"620447.jpg\": \"Aves/Sitta canadensis/0dea85f7fd34891addc076e9b55bf8b0.jpg\",\n  \"620458.jpg\": \"Aves/Sitta canadensis/4084887aec5f279e4fcfb72544413a92.jpg\",\n  \"620480.jpg\": \"Aves/Sitta canadensis/37e988ac62318a51221041c408458648.jpg\",\n  \"620519.jpg\": \"Insecta/Euphyia intermediata/d4d858cde2bacd163392f95d79b609f0.jpg\",\n  \"620523.jpg\": \"Insecta/Euphyia intermediata/d7ccc7c10febd7826bd7337e447b27d0.jpg\",\n  \"620558.jpg\": \"Aves/Gavia pacifica/5f4115e2502eecf27161a82f0bb76250.jpg\",\n  \"62057.jpg\": \"Insecta/Urbanus dorantes/cab4909688bc9522dd1f66f0ad3179bf.jpg\",\n  \"620575.jpg\": \"Aves/Gavia pacifica/4b9b28f133be506b62ba6d33a4e18257.jpg\",\n  \"620581.jpg\": \"Aves/Gavia pacifica/5090995f10d98df7d8e3c99c3e84c9ac.jpg\",\n  \"620582.jpg\": \"Aves/Gavia pacifica/563afb4d10c62a5709e25a0dc08849e9.jpg\",\n  \"620583.jpg\": \"Aves/Gavia pacifica/4f13ba40575e20899f26f3cbb844219a.jpg\",\n  \"620584.jpg\": \"Aves/Gavia pacifica/e267aad0d63f3b56ae9141040c16d65b.jpg\",\n  \"620588.jpg\": \"Aves/Gavia pacifica/3700fb141abfc54a275f34c9d39f50dd.jpg\",\n  \"62059.jpg\": \"Insecta/Urbanus dorantes/12b4f92b5858b55713dc2f1977aeaa7d.jpg\",\n  \"620590.jpg\": \"Aves/Gavia pacifica/053e21571348b508c321ec7b417d0b51.jpg\",\n  \"620591.jpg\": \"Aves/Gavia pacifica/631ef589b91006fc55c543cac9bdc87e.jpg\",\n  \"620592.jpg\": \"Aves/Gavia pacifica/c4c496f644dfbfde673ef02e7db83178.jpg\",\n  \"62063.jpg\": \"Insecta/Urbanus dorantes/6718586b66f32de607a7155d3a226758.jpg\",\n  \"620675.jpg\": \"Aves/Morus bassanus/98f2e12083e20240a58e89bd7a70f64b.jpg\",\n  \"620678.jpg\": \"Aves/Morus bassanus/a3cd8e9176250de592262bdfab92b7d3.jpg\",\n  \"620679.jpg\": \"Aves/Morus bassanus/69820470f118d9f2f928b61a5eacc97c.jpg\",\n  \"620684.jpg\": \"Aves/Morus bassanus/b772a9280ab49c0821ffde9087a065a4.jpg\",\n  \"620692.jpg\": \"Aves/Morus bassanus/3787699031e74141cb3ee64beda13728.jpg\",\n  \"620695.jpg\": \"Aves/Morus bassanus/cec2697a31c74fcb2f67dab25efc15bb.jpg\",\n  \"620697.jpg\": \"Aves/Morus bassanus/4aa38dd6aece68d54cc9e938db0a7e94.jpg\",\n  \"620704.jpg\": \"Reptilia/Crotalus mitchellii/a530e74c12c066604e84fd9c029d306d.jpg\",\n  \"620708.jpg\": \"Reptilia/Crotalus mitchellii/a21925dd1fbc09d0828e21d403edc7e0.jpg\",\n  \"620714.jpg\": \"Reptilia/Crotalus mitchellii/a01833b92884f4d946e6074b65a9789d.jpg\",\n  \"620715.jpg\": \"Reptilia/Crotalus mitchellii/68fb11cae5c582606aacdbbcdbcc9736.jpg\",\n  \"620720.jpg\": \"Reptilia/Crotalus mitchellii/eba2a8e805397a8f51de685c4b4783e2.jpg\",\n  \"62074.jpg\": \"Insecta/Urbanus dorantes/6881b8e3bc7d12a357b988772da022fe.jpg\",\n  \"62075.jpg\": \"Insecta/Urbanus dorantes/2efe216426cf7b552a3c178d9fbeac77.jpg\",\n  \"62076.jpg\": \"Insecta/Urbanus dorantes/3785dbc7a68ac6bcbd41c6abefccd3c8.jpg\",\n  \"620786.jpg\": \"Aves/Sarcoramphus papa/a40afb7eae14968d0bdc4469644ea3e0.jpg\",\n  \"62079.jpg\": \"Insecta/Urbanus dorantes/1c762894cfff43fa326c961377b38d98.jpg\",\n  \"62081.jpg\": \"Insecta/Urbanus dorantes/560dee1186c95d70adeb2a85c46c09a9.jpg\",\n  \"62082.jpg\": \"Insecta/Urbanus dorantes/c241b80b57f7928c7fd4470e685d964e.jpg\",\n  \"62085.jpg\": \"Insecta/Urbanus dorantes/0cbee21309302a61ef48d1874fbade96.jpg\",\n  \"62087.jpg\": \"Animalia/Hemigrapsus nudus/5fae84973013974f37bbcf68a13c6c3a.jpg\",\n  \"620870.jpg\": \"Aves/Thryothorus ludovicianus/aab499561162f4162ce0eefc0c29ff68.jpg\",\n  \"620926.jpg\": \"Aves/Thryothorus ludovicianus/ae139fee80ba608e157c3804e0dcd7c8.jpg\",\n  \"62093.jpg\": \"Animalia/Hemigrapsus nudus/07995425fb28ae94ce16516f5ed32f00.jpg\",\n  \"620939.jpg\": \"Aves/Thryothorus ludovicianus/cfec0389c6c197467bf19cfb610de158.jpg\",\n  \"62094.jpg\": \"Animalia/Hemigrapsus nudus/139a254ffd1596100dd525c71d5077b3.jpg\",\n  \"62095.jpg\": \"Animalia/Hemigrapsus nudus/d1832516a2249f6739cc4da399b4edc2.jpg\",\n  \"6210.jpg\": \"Reptilia/Rena dulcis/1aeafeec3c3d11c33a24779b0d5de4fa.jpg\",\n  \"621009.jpg\": \"Aves/Thryothorus ludovicianus/80b3e0d7fb2bed946c539cfe822f6458.jpg\",\n  \"621044.jpg\": \"Aves/Thryothorus ludovicianus/f59c7e292ae5f08afe2718918aded087.jpg\",\n  \"621080.jpg\": \"Arachnida/Peucetia viridans/9f87039f3d331f3ffe7ac1aef6db0bce.jpg\",\n  \"621081.jpg\": \"Arachnida/Peucetia viridans/51570e1d63a246ad103f1d70df27bcd8.jpg\",\n  \"621090.jpg\": \"Arachnida/Peucetia viridans/8a381ee8273dc2bfe0d63e1e4f33c338.jpg\",\n  \"621094.jpg\": \"Arachnida/Peucetia viridans/9213493014eda2bbf30a2a1d53c009da.jpg\",\n  \"62112.jpg\": \"Animalia/Hemigrapsus nudus/13c3064d8410d7fba99946c9816a618c.jpg\",\n  \"621134.jpg\": \"Arachnida/Peucetia viridans/525ae71f297ffa975f987dfeb01bb497.jpg\",\n  \"62116.jpg\": \"Animalia/Hemigrapsus nudus/e63e9d94057bac64ac5e2f5724bc9664.jpg\",\n  \"621166.jpg\": \"Arachnida/Peucetia viridans/b43e324801393f489d1afa9d9c269459.jpg\",\n  \"62117.jpg\": \"Animalia/Hemigrapsus nudus/8939bfb7174d236f1cd0d8912bc90d0f.jpg\",\n  \"62120.jpg\": \"Animalia/Hemigrapsus nudus/5d7f5b963d62e64cda92a517c5e1bee6.jpg\",\n  \"62122.jpg\": \"Animalia/Hemigrapsus nudus/0f8b05a15dcac0bbbe53839eadfd1915.jpg\",\n  \"621236.jpg\": \"Insecta/Spilosoma virginica/dabc317fdfc3c505389df3d04e7cc24b.jpg\",\n  \"621257.jpg\": \"Insecta/Spilosoma virginica/eb583cf1c7211ae0be1cfe7e5f30dbfa.jpg\",\n  \"621263.jpg\": \"Insecta/Spilosoma virginica/9b72fc7c72be6adbed0ee6057c59abfd.jpg\",\n  \"621273.jpg\": \"Insecta/Spilosoma virginica/bd52810919c30889a857356428f6f5b6.jpg\",\n  \"621277.jpg\": \"Insecta/Spilosoma virginica/0a836f1d5dc2cbf458d6817d1cf2cd09.jpg\",\n  \"621279.jpg\": \"Insecta/Spilosoma virginica/0621772d41fae1bee82ee47afeeb1d8c.jpg\",\n  \"621282.jpg\": \"Insecta/Spilosoma virginica/bf6f71d176812528e3e4ba2caa491350.jpg\",\n  \"621283.jpg\": \"Insecta/Spilosoma virginica/98fd823431c9a708bbf50daa065f4959.jpg\",\n  \"621291.jpg\": \"Reptilia/Storeria dekayi dekayi/db977b9e66beaa40d654fe73945c5f12.jpg\",\n  \"621292.jpg\": \"Reptilia/Storeria dekayi dekayi/62fb3f4c86ade7a33ea57956e9d72078.jpg\",\n  \"621298.jpg\": \"Reptilia/Storeria dekayi dekayi/bfb4bd866a63a04aabb9b4be79c45677.jpg\",\n  \"621299.jpg\": \"Reptilia/Storeria dekayi dekayi/f4815b88a20b0dbdbc2cffeb811e0706.jpg\",\n  \"621301.jpg\": \"Reptilia/Storeria dekayi dekayi/9eb0c6df405830e814044f3ec822a903.jpg\",\n  \"621305.jpg\": \"Aves/Myiopsitta monachus/3bc1481db2a5a5b44a6bcef34b7fcdcd.jpg\",\n  \"621312.jpg\": \"Aves/Myiopsitta monachus/c042fe48410ea35f0f369c08648bb219.jpg\",\n  \"621330.jpg\": \"Aves/Myiopsitta monachus/bed944c327436b97c28b09601aa7ccea.jpg\",\n  \"621345.jpg\": \"Aves/Myiopsitta monachus/60837c08cc1dd184ee05c3c1879328d7.jpg\",\n  \"621372.jpg\": \"Aves/Myiopsitta monachus/02a58c4fb3782ae00f71a45d6b074d58.jpg\",\n  \"621394.jpg\": \"Aves/Myiopsitta monachus/d1f325f95c1d0be766dde49c34212994.jpg\",\n  \"6214.jpg\": \"Reptilia/Rena dulcis/20f09f3fa4930aa7cd1a8d1aebe98a8d.jpg\",\n  \"621424.jpg\": \"Insecta/Xylocopa virginica/6286b0aba09455c6cf24b1365899744a.jpg\",\n  \"621487.jpg\": \"Insecta/Xylocopa virginica/18eac02f1312b524edb4ae0ff7af9c0b.jpg\",\n  \"621513.jpg\": \"Insecta/Xylocopa virginica/7782a898f8829e661cb4bb169502fc84.jpg\",\n  \"621547.jpg\": \"Insecta/Xylocopa virginica/ae595fe94498c0b68a793f047935e89f.jpg\",\n  \"621551.jpg\": \"Insecta/Xylocopa virginica/5f00efa87e069162a403ea75ebf35a93.jpg\",\n  \"621559.jpg\": \"Insecta/Xylocopa virginica/a0c8ef6be36e31e8be1312b6b16ec9bd.jpg\",\n  \"621566.jpg\": \"Insecta/Xylocopa virginica/63b968cdce460eeaae713e945a76d3c3.jpg\",\n  \"621568.jpg\": \"Insecta/Xylocopa virginica/1a60b07ada76c8b40f59c1cca190019c.jpg\",\n  \"621569.jpg\": \"Insecta/Xylocopa virginica/6def8aa07621c675a952a573f9d236ff.jpg\",\n  \"621580.jpg\": \"Insecta/Xylocopa virginica/6c374d5b9bde211f02b350c226ab9734.jpg\",\n  \"621645.jpg\": \"Mammalia/Microtus californicus/d3a9df229e6d75f8f6c8163ecbc004ab.jpg\",\n  \"621656.jpg\": \"Insecta/Harrisina americana/3119719a20fa3905183a7d931985687e.jpg\",\n  \"621657.jpg\": \"Insecta/Harrisina americana/4b8c056a4be233db87053ced6487e5e7.jpg\",\n  \"621658.jpg\": \"Insecta/Harrisina americana/b924664175665beccdb97c7033c017b5.jpg\",\n  \"621665.jpg\": \"Mammalia/Urocitellus columbianus/2605a666d4ff0ccc2559700542bb9318.jpg\",\n  \"621668.jpg\": \"Mammalia/Urocitellus columbianus/6335ffc093a84d5b50fdddd99ccb1f9d.jpg\",\n  \"621669.jpg\": \"Mammalia/Urocitellus columbianus/60e49b7ca8b85175ed6b9a7c12d2ded1.jpg\",\n  \"621727.jpg\": \"Aves/Falco tinnunculus/975ccce10e0f42ac14cf184100394f90.jpg\",\n  \"621736.jpg\": \"Aves/Falco tinnunculus/7afe869a708f387ec9f4849e612749b0.jpg\",\n  \"621745.jpg\": \"Aves/Falco tinnunculus/b00e281d528fa9e48f1fd62811a67c7f.jpg\",\n  \"621760.jpg\": \"Aves/Falco tinnunculus/6c7919dd6b07cbb57f9c5f4e0bb0878b.jpg\",\n  \"621805.jpg\": \"Reptilia/Thamnophis elegans elegans/9f87b34fdf483f88b8561827195fecf5.jpg\",\n  \"6220.jpg\": \"Reptilia/Rena dulcis/5b8aea5aa29da668ddf18683b1ea3629.jpg\",\n  \"622013.jpg\": \"Aves/Fulica americana/c85451b846909fb92e58346739c3765a.jpg\",\n  \"622078.jpg\": \"Aves/Fulica americana/3e246c322162905ae3db27d8d73eef45.jpg\",\n  \"6222.jpg\": \"Reptilia/Rena dulcis/1a124d98f41ebf1e750b719eb8f0c7c2.jpg\",\n  \"6223.jpg\": \"Reptilia/Rena dulcis/1c5ed7cbeda99c371f64e1a2e9c71dcd.jpg\",\n  \"6224.jpg\": \"Reptilia/Rena dulcis/41775e3bdb1c962f376dc4e87e7276bb.jpg\",\n  \"62244.jpg\": \"Insecta/Pyralis farinalis/a03fe4cbbd80ffd9206350aef2f6240d.jpg\",\n  \"62245.jpg\": \"Insecta/Pyralis farinalis/e220517adff15c34224ef96b96cc18ac.jpg\",\n  \"62249.jpg\": \"Insecta/Pyralis farinalis/5f72aa8f98c7b4fea25210f83c52d8bf.jpg\",\n  \"62250.jpg\": \"Insecta/Pyralis farinalis/6c34b8ed0ebc9a220ce414feb36bc3bd.jpg\",\n  \"62251.jpg\": \"Insecta/Pyralis farinalis/94cb6f1cc7a75f350f06f01751b029b1.jpg\",\n  \"62252.jpg\": \"Insecta/Pyralis farinalis/cff836c1751eb469e91e70a59c6d8fdd.jpg\",\n  \"62253.jpg\": \"Insecta/Pyralis farinalis/2afbcd3b0793544c7c5ee68d7bf6f3e1.jpg\",\n  \"62254.jpg\": \"Insecta/Pyralis farinalis/fd24b77db0373f474f34e173bd19ba95.jpg\",\n  \"62258.jpg\": \"Insecta/Pyralis farinalis/7f85d8e002ec2468ae1c962b60106b08.jpg\",\n  \"62259.jpg\": \"Insecta/Pyralis farinalis/91f3b29c644cbcc01f6c0c4da53622b8.jpg\",\n  \"62260.jpg\": \"Insecta/Pyralis farinalis/c0cad4753397868f45daf7640b7b09c1.jpg\",\n  \"62261.jpg\": \"Insecta/Pyralis farinalis/b31aaeee67ca5db378f33e4cd2aeed49.jpg\",\n  \"62264.jpg\": \"Insecta/Pyralis farinalis/66d42334dad47c1a9ca9cee8a699d0fa.jpg\",\n  \"622641.jpg\": \"Reptilia/Aspidoscelis tigris stejnegeri/cf714b9e75577d76740edf34b2034ac9.jpg\",\n  \"622643.jpg\": \"Reptilia/Aspidoscelis tigris stejnegeri/bf43ef8d0f78a9dd8f32b81ca0a17da8.jpg\",\n  \"62265.jpg\": \"Insecta/Pyralis farinalis/27a0d4f9594dffc123981e814a639f02.jpg\",\n  \"622651.jpg\": \"Reptilia/Aspidoscelis tigris stejnegeri/f34645830c1fb76084b4c22ea4ad27aa.jpg\",\n  \"622652.jpg\": \"Reptilia/Aspidoscelis tigris stejnegeri/cd8543a8c9ef0fc35c8e875d4d238a47.jpg\",\n  \"622659.jpg\": \"Reptilia/Aspidoscelis tigris stejnegeri/0483a66d44c72710f05679359b1a3e3c.jpg\",\n  \"622660.jpg\": \"Reptilia/Aspidoscelis tigris stejnegeri/c7206782af6030508dc5e7bd8c4d8d07.jpg\",\n  \"62267.jpg\": \"Insecta/Pyralis farinalis/bd233d3e9f243841ccabfe9a573f8bbc.jpg\",\n  \"622673.jpg\": \"Reptilia/Aspidoscelis tigris stejnegeri/f14eed10f5b1f58e60213c3582a01cfe.jpg\",\n  \"622685.jpg\": \"Reptilia/Pituophis catenifer catenifer/9818b07633c7cef4a5c565d2a022677b.jpg\",\n  \"62269.jpg\": \"Insecta/Pyralis farinalis/e7bf12acc32292dc82618f2017caa53b.jpg\",\n  \"622693.jpg\": \"Reptilia/Pituophis catenifer catenifer/fdaceb6f9bc57ea55aa6058dfe634a7e.jpg\",\n  \"622697.jpg\": \"Reptilia/Pituophis catenifer catenifer/e6eb04b10b3866c6e8d3c35ba2262054.jpg\",\n  \"622698.jpg\": \"Reptilia/Pituophis catenifer catenifer/04415f6121d7b4ad6e26e9c481468415.jpg\",\n  \"6227.jpg\": \"Reptilia/Rena dulcis/4cb99c0fb3827aae780fc0570140ef6c.jpg\",\n  \"62270.jpg\": \"Insecta/Pyralis farinalis/2e5738e6d1a988f1a59372d5d6095ce6.jpg\",\n  \"622705.jpg\": \"Reptilia/Pituophis catenifer catenifer/91d26abd9c8c587afb48344f1099d40f.jpg\",\n  \"622708.jpg\": \"Reptilia/Pituophis catenifer catenifer/f59c2d3f013ddf798b722856fcb524ac.jpg\",\n  \"62271.jpg\": \"Insecta/Pyralis farinalis/95525502dd8641ca749e6e85a230dccb.jpg\",\n  \"62272.jpg\": \"Insecta/Pyralis farinalis/ea00dff319effa8cffa2b6f13e2511f6.jpg\",\n  \"622722.jpg\": \"Reptilia/Pituophis catenifer catenifer/a88b5a4f1e464bea263cad5bd343f8fc.jpg\",\n  \"62273.jpg\": \"Insecta/Pyralis farinalis/77d6671456a96243d8dc0cbdeb78a8a3.jpg\",\n  \"622733.jpg\": \"Reptilia/Pituophis catenifer catenifer/de4bf793012a85c488d47f3a010d06a1.jpg\",\n  \"622747.jpg\": \"Mammalia/Macaca fascicularis/cd436a77e0d71886f4f387224e259963.jpg\",\n  \"622748.jpg\": \"Mammalia/Macaca fascicularis/63860b77d51b96e5914035b2ef0cbf28.jpg\",\n  \"622749.jpg\": \"Mammalia/Macaca fascicularis/dd0600aea5f6902eb190a7b674988919.jpg\",\n  \"622752.jpg\": \"Mammalia/Macaca fascicularis/f3ccd0637f6d1b1fe0863613d96c61e0.jpg\",\n  \"622760.jpg\": \"Mammalia/Macaca fascicularis/411e96e8c6d386b82d0af8bc699908df.jpg\",\n  \"622762.jpg\": \"Mammalia/Macaca fascicularis/7439e78b5b3a7b9e1c536e2fdd67e617.jpg\",\n  \"622763.jpg\": \"Mammalia/Macaca fascicularis/242665bea39fefcb154fa54433238051.jpg\",\n  \"622765.jpg\": \"Mammalia/Macaca fascicularis/6ddd46e605c2c70a1fc6f76ffd43de80.jpg\",\n  \"622766.jpg\": \"Mammalia/Macaca fascicularis/ba8a18aad076c2a2367f5d3949b2661a.jpg\",\n  \"622774.jpg\": \"Aves/Falco columbarius/b09bcb6524f9ac6786bf7853444de977.jpg\",\n  \"622778.jpg\": \"Aves/Falco columbarius/de41e14866f3fae64e1da50b51359f56.jpg\",\n  \"622798.jpg\": \"Aves/Falco columbarius/991e8721ff2aba4bf23bfe7c01155533.jpg\",\n  \"6228.jpg\": \"Reptilia/Rena dulcis/ae9d64178855ad227f6008998ddf1b67.jpg\",\n  \"622851.jpg\": \"Aves/Falco columbarius/6b1e2936c9fa50f269060ad5aa615474.jpg\",\n  \"622852.jpg\": \"Aves/Falco columbarius/702bb10984eb42ea8a3127814ed238fc.jpg\",\n  \"622861.jpg\": \"Aves/Falco columbarius/5ec5d3885f3260e63a747ca13cf83565.jpg\",\n  \"622879.jpg\": \"Aves/Falco columbarius/4ff33a4eae01d5848092d5470b2af6dd.jpg\",\n  \"622896.jpg\": \"Aves/Peucaea ruficauda/6a63631df5a658e55f9aebff47209fc1.jpg\",\n  \"622899.jpg\": \"Aves/Peucaea ruficauda/a306a96e64f7c1ee57940a4e642e17f4.jpg\",\n  \"622935.jpg\": \"Aves/Gavia stellata/5de382adba2b6e77bfcbc877551c7b10.jpg\",\n  \"622936.jpg\": \"Aves/Gavia stellata/6bcfb5972009de111d975de0c4b666e9.jpg\",\n  \"622937.jpg\": \"Aves/Gavia stellata/3398a77342a6e51567473d179649f387.jpg\",\n  \"622938.jpg\": \"Aves/Gavia stellata/9ee8dc9f264eeb6a4bdd8a5abdc5a17b.jpg\",\n  \"622943.jpg\": \"Aves/Gavia stellata/5880d5735df54704b9ecedae78f73b10.jpg\",\n  \"622979.jpg\": \"Aves/Gavia stellata/83efe0d9c8be0630af0f37e5c75f5b9e.jpg\",\n  \"622985.jpg\": \"Aves/Gavia stellata/5ee8912e04ab5d94eaaa79f1c3ad8e65.jpg\",\n  \"622987.jpg\": \"Aves/Gavia stellata/74f0721e0cee4174a08497511fdd3909.jpg\",\n  \"623038.jpg\": \"Insecta/Pyrausta tyralis/b222896f4e97b11a1dbf7438f0eb2ccb.jpg\",\n  \"623040.jpg\": \"Insecta/Pyrausta tyralis/b718e4be1deaba18c05758003964bf23.jpg\",\n  \"62311.jpg\": \"Reptilia/Regina septemvittata/c2d5369bfdb7cfbe7140e12493d9edb6.jpg\",\n  \"62312.jpg\": \"Reptilia/Regina septemvittata/fa72e566f3169b3f550a69885fff2670.jpg\",\n  \"62313.jpg\": \"Reptilia/Regina septemvittata/d0d4dc2ed88b4ada1b2747dcfbffdd48.jpg\",\n  \"623145.jpg\": \"Aves/Megascops kennicottii/602b3cdea7653bcb8af1f1136cbcbf86.jpg\",\n  \"623146.jpg\": \"Aves/Megascops kennicottii/d23024d2df37ffbf15ff321a044445c1.jpg\",\n  \"623148.jpg\": \"Aves/Megascops kennicottii/926fde30442ae060fbfb9539846f0f21.jpg\",\n  \"623151.jpg\": \"Aves/Megascops kennicottii/b118387d212c9c36adc90284e2bf9087.jpg\",\n  \"623153.jpg\": \"Aves/Megascops kennicottii/4598265296041e0d7fe7d8644d5e4803.jpg\",\n  \"623154.jpg\": \"Insecta/Megalographa biloba/d2c23dec58619ee61a7e0bf4c82e8ac1.jpg\",\n  \"623155.jpg\": \"Insecta/Megalographa biloba/5efc227d671373053958d41c3ce7d751.jpg\",\n  \"623164.jpg\": \"Insecta/Megalographa biloba/13d0e76c43757b4886260d48bd1968b2.jpg\",\n  \"623181.jpg\": \"Aves/Phoenicopterus ruber/422120ae785f24ff66a2fa21541bdfb1.jpg\",\n  \"623187.jpg\": \"Aves/Phoenicopterus ruber/6bd2db0c95b662a32809afaa4d2082a7.jpg\",\n  \"623193.jpg\": \"Aves/Phoenicopterus ruber/0487aca84523e0df849d123cec6ab061.jpg\",\n  \"623202.jpg\": \"Aves/Phoenicopterus ruber/ec0a15b3d552d57de824e1f344fd5d86.jpg\",\n  \"623203.jpg\": \"Aves/Phoenicopterus ruber/b518177b20838c8cf31802549dce89ae.jpg\",\n  \"623234.jpg\": \"Animalia/Strongylocentrotus purpuratus/0734e76101d50611945728caf7b066e1.jpg\",\n  \"623235.jpg\": \"Animalia/Strongylocentrotus purpuratus/64adcda1a50a2a5f3418dde9ee0aef7e.jpg\",\n  \"623250.jpg\": \"Animalia/Strongylocentrotus purpuratus/8045fed924e61dfbfa7480eee41423c2.jpg\",\n  \"623360.jpg\": \"Aves/Anas superciliosa/dadf3941f741790e34adf1851b9e1319.jpg\",\n  \"623363.jpg\": \"Aves/Anas superciliosa/3c39c8b758d1b488f744c82a78ec18ef.jpg\",\n  \"623365.jpg\": \"Aves/Anas superciliosa/85f4e0cc2145c19dbf22fcbd757198df.jpg\",\n  \"623366.jpg\": \"Aves/Anas superciliosa/c3be332d14c8ae1b4fb1a7ccc4c8d0d4.jpg\",\n  \"623367.jpg\": \"Aves/Anas superciliosa/c88f115fa8023008c50398c01516144a.jpg\",\n  \"623368.jpg\": \"Aves/Anas superciliosa/846d630b9d06faecdf1e4b5dbbb0cf21.jpg\",\n  \"623371.jpg\": \"Aves/Anas superciliosa/76d4304e2813090ea2b6ad89966e24e6.jpg\",\n  \"623373.jpg\": \"Aves/Anas superciliosa/060e144bacd105e6387387d28d108552.jpg\",\n  \"623391.jpg\": \"Actinopterygii/Esox lucius/006ef434f46ba1e6b6efe52c31563faf.jpg\",\n  \"623393.jpg\": \"Actinopterygii/Esox lucius/615e6c9f22cbc3ae7518fce8dd2460f6.jpg\",\n  \"623394.jpg\": \"Actinopterygii/Esox lucius/9f4c52e42fb73a3b5dc29c0b1bdbcd90.jpg\",\n  \"623442.jpg\": \"Aves/Tadorna variegata/8525fc706b5a24e8f1f2ac11b992358e.jpg\",\n  \"623475.jpg\": \"Aves/Aquila chrysaetos/8a0db714fa170f2806cf8ab80378a7a1.jpg\",\n  \"623484.jpg\": \"Aves/Aquila chrysaetos/2e2c69b71a4cb0c102b26c78bad3067b.jpg\",\n  \"62350.jpg\": \"Insecta/Catocala ultronia/93a95dff9cfbfb1b6a542d7ff7fe9b90.jpg\",\n  \"623504.jpg\": \"Aves/Aquila chrysaetos/284fa03d92aada910cd0fe1df9773ec4.jpg\",\n  \"62351.jpg\": \"Insecta/Catocala ultronia/0fde367b68afcea8e228c1ab7241a08e.jpg\",\n  \"623520.jpg\": \"Aves/Aquila chrysaetos/2ff75f5b8378f4f25318dae3ed0ed26a.jpg\",\n  \"623526.jpg\": \"Aves/Aquila chrysaetos/5a12d953d21c1e838109e541dcc52893.jpg\",\n  \"623529.jpg\": \"Aves/Aquila chrysaetos/46b5bd02303f8d2149f4dd37453649ec.jpg\",\n  \"62353.jpg\": \"Insecta/Catocala ultronia/1bf0e52a8b3138d6c727293ec1d05771.jpg\",\n  \"623537.jpg\": \"Aves/Aquila chrysaetos/7e904db159f275caa655a341f12f2fb1.jpg\",\n  \"623539.jpg\": \"Aves/Aquila chrysaetos/b8e8343dc0dee937ea67bb706368f59f.jpg\",\n  \"62355.jpg\": \"Insecta/Catocala ultronia/73dbe632d9246804ee5803ccabd4c340.jpg\",\n  \"623567.jpg\": \"Insecta/Tyria jacobaeae/19283d7fb8892572da9efeee656d92eb.jpg\",\n  \"623568.jpg\": \"Insecta/Tyria jacobaeae/d7f71269f2e884e3b7929ff2a26285c0.jpg\",\n  \"623569.jpg\": \"Insecta/Tyria jacobaeae/b195982f3d05a92f642f104e4654c448.jpg\",\n  \"623571.jpg\": \"Insecta/Tyria jacobaeae/ed32e164b8aa3448a70436635af1168e.jpg\",\n  \"623579.jpg\": \"Insecta/Tyria jacobaeae/5d04d1978b068a920fe517e4df9af0d7.jpg\",\n  \"62358.jpg\": \"Insecta/Catocala ultronia/f3dfdbc1ed95fbe9be5a4764cad8c6e0.jpg\",\n  \"623580.jpg\": \"Insecta/Tyria jacobaeae/beeaec7081e9e638ec321246ef72e4ea.jpg\",\n  \"623581.jpg\": \"Insecta/Tyria jacobaeae/4316ea7f5f9913a3313feb07f0f675be.jpg\",\n  \"62359.jpg\": \"Insecta/Catocala ultronia/67aea09ff918606930513ac5b506b36e.jpg\",\n  \"623595.jpg\": \"Insecta/Tyria jacobaeae/6dd6bede79df177ef3dffd6ce00fc608.jpg\",\n  \"623603.jpg\": \"Insecta/Limenitis arthemis/30d282143a5a64181e4b25048ce5633a.jpg\",\n  \"623605.jpg\": \"Insecta/Limenitis arthemis/551dd1b78f389cfc86d8432a0f325cd1.jpg\",\n  \"623630.jpg\": \"Insecta/Limenitis arthemis/d31638d066509890a60932280da4b271.jpg\",\n  \"62364.jpg\": \"Insecta/Catocala ultronia/ec06a2960c6e431bd6a67154444f6ee5.jpg\",\n  \"623649.jpg\": \"Insecta/Limenitis arthemis/19e785a1bff1580bcba95388e9892664.jpg\",\n  \"623652.jpg\": \"Insecta/Limenitis arthemis/31337a0b505f1a577272993cd7fb4549.jpg\",\n  \"623653.jpg\": \"Insecta/Limenitis arthemis/0d4b7e9c0c45cd6c6b6699e7240eabc9.jpg\",\n  \"623673.jpg\": \"Insecta/Anavitrinella pampinaria/0b4a0e42e2584832b0059a4e9dd084e2.jpg\",\n  \"623678.jpg\": \"Insecta/Anavitrinella pampinaria/8476f19d14282e7bb6842d988beb9d33.jpg\",\n  \"623713.jpg\": \"Aves/Mergus serrator/6a981fac22bfee54a736eb51329683a1.jpg\",\n  \"623718.jpg\": \"Aves/Mergus serrator/61ce872a408d824daf6e5b030daa2fa9.jpg\",\n  \"62372.jpg\": \"Insecta/Catocala ultronia/6f974cc244fe32f4257423dad45a727b.jpg\",\n  \"62377.jpg\": \"Amphibia/Scaphiopus hurterii/18d2cd7a80a848312f97118429810da1.jpg\",\n  \"623800.jpg\": \"Aves/Mergus serrator/7a42c1a8dc62a884b3247c7167647a1b.jpg\",\n  \"62381.jpg\": \"Amphibia/Scaphiopus hurterii/c499c6c6644d522dcbd12d514b99fc94.jpg\",\n  \"62383.jpg\": \"Amphibia/Scaphiopus hurterii/96d8ce287c9a91be940d9e7687d7e606.jpg\",\n  \"62384.jpg\": \"Amphibia/Scaphiopus hurterii/160351ad8a97f6658981d6e40b79f6c6.jpg\",\n  \"623862.jpg\": \"Aves/Mergus serrator/559cfadc8c1e2a827c0baa91bc3b861f.jpg\",\n  \"62387.jpg\": \"Amphibia/Scaphiopus hurterii/2aa6badb9f7de26de13ce57c8a0d81f4.jpg\",\n  \"62389.jpg\": \"Amphibia/Scaphiopus hurterii/9da53d3e682183e835b4a2b7779d0ccf.jpg\",\n  \"6239.jpg\": \"Reptilia/Rena dulcis/5074307f5c5087eb1218e0b83636b80b.jpg\",\n  \"623946.jpg\": \"Insecta/Anania hortulata/0cea500535468ae8f79e39b4405ee68a.jpg\",\n  \"623987.jpg\": \"Aves/Pheucticus ludovicianus/d7abb3e28fcd8d6ca243603cf401eefb.jpg\",\n  \"624031.jpg\": \"Aves/Pheucticus ludovicianus/1aeedc9e8f17f0a093811bde709c4b43.jpg\",\n  \"624053.jpg\": \"Aves/Pheucticus ludovicianus/d19aab6aa02b3400e8eb65e9b1052881.jpg\",\n  \"624100.jpg\": \"Aves/Pheucticus ludovicianus/404eff451e32f6ef9f5a54012a55e31e.jpg\",\n  \"624102.jpg\": \"Aves/Pheucticus ludovicianus/384230b350fe4d2d75647ed1092e70da.jpg\",\n  \"624119.jpg\": \"Aves/Pheucticus ludovicianus/84fd48009690c2e2aa1d4a69153fd3be.jpg\",\n  \"624122.jpg\": \"Aves/Pheucticus ludovicianus/00bec8d8edcdf292478077804088021b.jpg\",\n  \"624124.jpg\": \"Amphibia/Pelophylax/15b6b94f16b2885215ee6dbdb8059ea1.jpg\",\n  \"624125.jpg\": \"Amphibia/Pelophylax/e4c10a55e60d42705fb5b8931bd42597.jpg\",\n  \"624134.jpg\": \"Amphibia/Pelophylax/2506de99046bbd242b5abbbee0022f0f.jpg\",\n  \"624137.jpg\": \"Amphibia/Pelophylax/5b995a5f7c3169d80ec196648acd3be7.jpg\",\n  \"624148.jpg\": \"Amphibia/Pelophylax/99bd36d9bbe22f3318989d6c2c5c79bb.jpg\",\n  \"624171.jpg\": \"Amphibia/Pelophylax/f8de7a34050bb31e94f0ec478a3bd98b.jpg\",\n  \"624174.jpg\": \"Amphibia/Pelophylax/d293ef8e21e2a26f231625db62e764b2.jpg\",\n  \"6242.jpg\": \"Reptilia/Rena dulcis/9de435c5c5694f5ed2c614ba32f5c827.jpg\",\n  \"624208.jpg\": \"Aves/Thraupis episcopus/3358d3ca1c64e8f0ee2940a285379486.jpg\",\n  \"624209.jpg\": \"Aves/Thraupis episcopus/54344ecccb530f8933328739c72dbe18.jpg\",\n  \"624211.jpg\": \"Aves/Thraupis episcopus/8c64197a532d0ca84124dae0a8f2b7ab.jpg\",\n  \"624216.jpg\": \"Aves/Thraupis episcopus/e9b7cc72b068cc168b66f272a6e47a8e.jpg\",\n  \"624223.jpg\": \"Aves/Thraupis episcopus/854448fb064fbbaec799de4539035343.jpg\",\n  \"624240.jpg\": \"Aves/Thraupis episcopus/16a7e32f52f202584594df56753cc077.jpg\",\n  \"624255.jpg\": \"Aves/Motacilla cinerea/753fc231e07c4abff8f60438cd627ec2.jpg\",\n  \"624260.jpg\": \"Aves/Motacilla cinerea/475a04fa65787bb4ce9b2a263e10c5c1.jpg\",\n  \"624262.jpg\": \"Aves/Motacilla cinerea/56a64f4b62671abd4efff29cb10ee13b.jpg\",\n  \"624264.jpg\": \"Aves/Motacilla cinerea/11f7d4dadf582fe5244d46925dd0a38e.jpg\",\n  \"624270.jpg\": \"Aves/Motacilla cinerea/03b8766e97df21da266eb17770e3e24a.jpg\",\n  \"624273.jpg\": \"Aves/Motacilla cinerea/1b821d0c8f09df1985a7abbb03437633.jpg\",\n  \"624275.jpg\": \"Aves/Motacilla cinerea/0b8531494ffb1b3cdd8b78c03dcc71fa.jpg\",\n  \"624276.jpg\": \"Aves/Motacilla cinerea/eca0bf3ac4e15fa35e70b2590814315c.jpg\",\n  \"624282.jpg\": \"Mollusca/Limacus flavus/be1f456cf4ec20667a05ad8c9c8f6477.jpg\",\n  \"624283.jpg\": \"Mollusca/Limacus flavus/2126c86ab0e016ddef60e64606c69c50.jpg\",\n  \"624291.jpg\": \"Mollusca/Limacus flavus/f10f46aa80f95c4c3df2bea9ae8fde7d.jpg\",\n  \"624295.jpg\": \"Mollusca/Limacus flavus/f3ebea1b67adc076c27199361b001e01.jpg\",\n  \"624296.jpg\": \"Mollusca/Limacus flavus/033362a85526e7b5162fdebad9f37c3c.jpg\",\n  \"624299.jpg\": \"Mollusca/Limacus flavus/2573a6b340f4f2435b1070c40db7bd68.jpg\",\n  \"624300.jpg\": \"Mollusca/Limacus flavus/83dd134bf0e3efdc92497e0262ff271f.jpg\",\n  \"624307.jpg\": \"Mammalia/Urocitellus beldingi/2fc80d3a6c5fb5bae75a5fd1c1c5da35.jpg\",\n  \"624308.jpg\": \"Mammalia/Urocitellus beldingi/a40acfce1b685a25c592e9dd2e462542.jpg\",\n  \"624368.jpg\": \"Aves/Phoenicopterus roseus/a089942640927ccecb72c783b7a2b4f2.jpg\",\n  \"624377.jpg\": \"Aves/Phoenicopterus roseus/1bace417e09cc7037f5cf541e93c6216.jpg\",\n  \"624383.jpg\": \"Reptilia/Sceloporus occidentalis longipes/4c37af8b4505b00de202934be8e6899a.jpg\",\n  \"624384.jpg\": \"Reptilia/Sceloporus occidentalis longipes/685a0fcbd4d8e94a59bb15fa2eda893c.jpg\",\n  \"6244.jpg\": \"Reptilia/Rena dulcis/701fe353af7b1574371d2bdc75168cb7.jpg\",\n  \"624400.jpg\": \"Reptilia/Sceloporus occidentalis longipes/055826efd4f762b6188c113a1b66fe2a.jpg\",\n  \"624426.jpg\": \"Reptilia/Sceloporus occidentalis longipes/ca5ce0d7d760db1917a38529124812ff.jpg\",\n  \"624442.jpg\": \"Reptilia/Sceloporus occidentalis longipes/49fa2919783aee9ca913bf158e7bf56f.jpg\",\n  \"6245.jpg\": \"Reptilia/Rena dulcis/b5047da7c1681955bfacd735cbe6abc9.jpg\",\n  \"624501.jpg\": \"Reptilia/Sceloporus occidentalis longipes/48affe625948009dfe657e580fedabc0.jpg\",\n  \"624507.jpg\": \"Reptilia/Sceloporus occidentalis longipes/eb2fb370ad72768408004f1985c6849a.jpg\",\n  \"624538.jpg\": \"Reptilia/Sceloporus occidentalis longipes/0216a5773fe99e561089939c41d97fff.jpg\",\n  \"624548.jpg\": \"Reptilia/Sceloporus occidentalis longipes/40b0f3efaf9269582008942b3f224f81.jpg\",\n  \"624576.jpg\": \"Reptilia/Sceloporus occidentalis bocourtii/b37b4c54f2126642de28571564b0b52b.jpg\",\n  \"624577.jpg\": \"Reptilia/Sceloporus occidentalis bocourtii/9333a8cd486e175bc8176118faf59d97.jpg\",\n  \"624581.jpg\": \"Reptilia/Sceloporus occidentalis bocourtii/144bc462e0660449e6a87eafa4c588de.jpg\",\n  \"624590.jpg\": \"Reptilia/Sceloporus occidentalis bocourtii/d34e8499915ad4743ff335a1e681371f.jpg\",\n  \"624591.jpg\": \"Reptilia/Sceloporus occidentalis bocourtii/ebb0e7b2025d1b22862dc50218c234ee.jpg\",\n  \"624592.jpg\": \"Reptilia/Sceloporus occidentalis bocourtii/ff4856407948dfa58efd00d39bd39f46.jpg\",\n  \"624595.jpg\": \"Reptilia/Sceloporus occidentalis bocourtii/fac16db62b4d530188ae0b7d23a18447.jpg\",\n  \"624596.jpg\": \"Reptilia/Sceloporus occidentalis bocourtii/ce239204245288ff10ed67a9e1ca2d26.jpg\",\n  \"624597.jpg\": \"Reptilia/Sceloporus occidentalis bocourtii/f8763910243700b4611f039bc9ea5527.jpg\",\n  \"624602.jpg\": \"Reptilia/Sceloporus occidentalis bocourtii/2034e9e3333904e3e22b492c32b960c2.jpg\",\n  \"624621.jpg\": \"Mollusca/Anteaeolidiella oliviae/3da1a97c50412f7506ef704fff456d34.jpg\",\n  \"6247.jpg\": \"Reptilia/Rena dulcis/1cf9173a6e20777d5595779d205385af.jpg\",\n  \"624743.jpg\": \"Aves/Phalacrocorax varius varius/a71ca8ac5e9e62ef95b9fef1424b754e.jpg\",\n  \"624759.jpg\": \"Aves/Pycnonotus barbatus/e24a6a344adfe9f341ed26011fa54a96.jpg\",\n  \"624761.jpg\": \"Aves/Pycnonotus barbatus/e49c9e9d33d07f13513e3efabbe9a413.jpg\",\n  \"6248.jpg\": \"Reptilia/Rena dulcis/3330941df150723e9e29b21d47b9b662.jpg\",\n  \"624804.jpg\": \"Reptilia/Dermochelys coriacea/b62a161853269d8414d0fcc5fb8a0e51.jpg\",\n  \"624874.jpg\": \"Insecta/Plebejus acmon/3a0e1c9d765a12dbd23d858d5a4b28f2.jpg\",\n  \"624885.jpg\": \"Insecta/Plebejus acmon/15e7273a1ce5032e6bc21451f1c2559c.jpg\",\n  \"6249.jpg\": \"Reptilia/Rena dulcis/ef97584a5be17b9673f61310e5b44d1c.jpg\",\n  \"624954.jpg\": \"Insecta/Plebejus acmon/24b4934e35f0db82bc07c086ba7e4b87.jpg\",\n  \"624982.jpg\": \"Aves/Anas fulvigula/408123460a317d8b24148fc919d593a9.jpg\",\n  \"624986.jpg\": \"Aves/Anas fulvigula/db8cc813895cd55f3dd64acd79b0bbd4.jpg\",\n  \"624988.jpg\": \"Aves/Anas fulvigula/03a48dd339b708ec66ce14731c31d2ea.jpg\",\n  \"624996.jpg\": \"Aves/Anas fulvigula/d207c49cda6a097afb5890e70173bd5f.jpg\",\n  \"624997.jpg\": \"Aves/Anas fulvigula/c379abc6fcdd46c4f5fa9bfb004141c2.jpg\",\n  \"625007.jpg\": \"Insecta/Aedes albopictus/f1f695add6b0a3593db44127d171037b.jpg\",\n  \"625010.jpg\": \"Insecta/Aedes albopictus/7324c586ba4fa4f0a397f1bfee879f10.jpg\",\n  \"625017.jpg\": \"Insecta/Aedes albopictus/5c9335893243dc68c4eb0d07abda3642.jpg\",\n  \"625022.jpg\": \"Insecta/Aedes albopictus/b235ecdebda50a37722da706edb99330.jpg\",\n  \"625028.jpg\": \"Insecta/Aedes albopictus/822e9105df37efbf69705ab8db6fc8e2.jpg\",\n  \"625031.jpg\": \"Insecta/Aedes albopictus/f414527f0dbf73621d674b9ecbdf5c3c.jpg\",\n  \"625032.jpg\": \"Insecta/Aedes albopictus/728e3d248ad63850ecde494574e65de7.jpg\",\n  \"625042.jpg\": \"Insecta/Aedes albopictus/a7cc5aedaab97e2b91d5a7d8a0e5771d.jpg\",\n  \"625043.jpg\": \"Insecta/Aedes albopictus/a97f3349fca9f3c2dfb07f83a2583652.jpg\",\n  \"625044.jpg\": \"Insecta/Aedes albopictus/1990b13f7b4a6dd5af7e38cd3736d8ed.jpg\",\n  \"625045.jpg\": \"Insecta/Aedes albopictus/3b36fff1464db5fcdaa111c5b7bb1767.jpg\",\n  \"625049.jpg\": \"Insecta/Anthrenus verbasci/f0aa076fd7862de9955a430ff7e0037b.jpg\",\n  \"625050.jpg\": \"Insecta/Anthrenus verbasci/987620bd73a08000deb15557e63f1260.jpg\",\n  \"625051.jpg\": \"Insecta/Anthrenus verbasci/1cb16235c71ef4bf7da2b119fb3d1c95.jpg\",\n  \"625052.jpg\": \"Insecta/Anthrenus verbasci/8fcf80a6d23d6e9e8cb4095d6fc03ab4.jpg\",\n  \"625053.jpg\": \"Insecta/Anthrenus verbasci/0de0df00df85c8d9bf8ed395cb913479.jpg\",\n  \"625054.jpg\": \"Insecta/Anthrenus verbasci/f7b25f5a1370a26f5421703c052fda9e.jpg\",\n  \"625060.jpg\": \"Insecta/Anthrenus verbasci/d027a10468ff7a741fe3a21715a8a138.jpg\",\n  \"625063.jpg\": \"Insecta/Anthrenus verbasci/f172ecf2c336d9dafa13fba6dde81ea9.jpg\",\n  \"625067.jpg\": \"Insecta/Anthrenus verbasci/cd74bd9b50dd2d60861b73823a4eb696.jpg\",\n  \"625068.jpg\": \"Insecta/Anthrenus verbasci/a1520b4e770e9e51c296eb03055ceefd.jpg\",\n  \"625069.jpg\": \"Insecta/Anthrenus verbasci/4e1e2b3d2cd0e205f0cc5453cbd22b84.jpg\",\n  \"625101.jpg\": \"Mammalia/Enhydra lutris/298fd6cd383e038d07d82775f4fd2d66.jpg\",\n  \"625114.jpg\": \"Mammalia/Enhydra lutris/bb8a081faceeeb7764c03336282780d9.jpg\",\n  \"625153.jpg\": \"Aves/Phalacrocorax sulcirostris/6f4002e0aa7c5c45e08de46cb58df288.jpg\",\n  \"625167.jpg\": \"Mollusca/Aplysia californica/34162d301571464dbcda3a2463a62d95.jpg\",\n  \"625185.jpg\": \"Mollusca/Aplysia californica/fd0a1d58d0158767fb50e4c30bed582f.jpg\",\n  \"625207.jpg\": \"Mollusca/Aplysia californica/2394392c0a84621f3f2ae5bf104523a7.jpg\",\n  \"625215.jpg\": \"Mollusca/Aplysia californica/bd7cf267246a43c66bcc40d03db97203.jpg\",\n  \"625219.jpg\": \"Mollusca/Aplysia californica/a5a014faf96735ff86a250c03152fc1b.jpg\",\n  \"625322.jpg\": \"Aves/Dendragapus obscurus/01beae95aba8f3b1c38a79106c224346.jpg\",\n  \"625324.jpg\": \"Aves/Dendragapus obscurus/4487e5fe7bf909ffa6413b208e7205bc.jpg\",\n  \"625326.jpg\": \"Aves/Dendragapus obscurus/b07542937229bffc62fe60dce4399bf7.jpg\",\n  \"625342.jpg\": \"Aves/Melospiza georgiana/e22906cdd5d4ad0585e8ac2e7651aa8c.jpg\",\n  \"625349.jpg\": \"Aves/Melospiza georgiana/a0073d51b4e10e5e859beedd402588f3.jpg\",\n  \"625377.jpg\": \"Aves/Melospiza georgiana/ae2ec6ed8ae2cf05841921adfb38154f.jpg\",\n  \"625394.jpg\": \"Aves/Melospiza georgiana/09dbd461a1cc068a7bdff1a443e54ade.jpg\",\n  \"625399.jpg\": \"Aves/Melospiza georgiana/bdcca08a994133c35bfb6667475853eb.jpg\",\n  \"625400.jpg\": \"Aves/Melospiza georgiana/27f643b49d78372eb319d18b50fb5339.jpg\",\n  \"625402.jpg\": \"Aves/Melospiza georgiana/a970f61020afb7cc90908282c4fa2146.jpg\",\n  \"625408.jpg\": \"Insecta/Orgyia leucostigma/70932ff178ac9e883fd023228f0e5166.jpg\",\n  \"625413.jpg\": \"Insecta/Orgyia leucostigma/fe6e4061d8ffa07d4531fde4755810c4.jpg\",\n  \"625422.jpg\": \"Insecta/Orgyia leucostigma/34d2a9b2c509bd2bea1312d24d83478a.jpg\",\n  \"625427.jpg\": \"Insecta/Orgyia leucostigma/077588972d3ced63d8de3a7fa5bdee8a.jpg\",\n  \"625433.jpg\": \"Insecta/Orgyia leucostigma/8837daa516b53235ddcd8e094b00b5ed.jpg\",\n  \"625435.jpg\": \"Insecta/Orgyia leucostigma/acfcd6bb719bdf3a5b0546ec89caa5e6.jpg\",\n  \"625436.jpg\": \"Insecta/Tetracha carolina/a6c8f7374a82939156b25a69c8326b14.jpg\",\n  \"625437.jpg\": \"Insecta/Tetracha carolina/d7381a175d05d7222c714c042c7b5b8b.jpg\",\n  \"625484.jpg\": \"Insecta/Phaneroptera nana/c47feec8224a72d80f1bf8cc531085e2.jpg\",\n  \"625528.jpg\": \"Actinopterygii/Lutjanus griseus/3e6bc085b42bdd8c1114391286ece547.jpg\",\n  \"625534.jpg\": \"Reptilia/Calotes versicolor/abe1781c7dbddf72894a8ecf7e66c3f6.jpg\",\n  \"625539.jpg\": \"Reptilia/Calotes versicolor/2fbf383a7ba94bb3ce021b3b47802f4d.jpg\",\n  \"625540.jpg\": \"Reptilia/Calotes versicolor/33e5fab77fbe2f6e2217e9e37dfc8bb5.jpg\",\n  \"625549.jpg\": \"Reptilia/Calotes versicolor/d0be0331e21dbc4124c0c36eb895c1fd.jpg\",\n  \"625553.jpg\": \"Reptilia/Calotes versicolor/45305b5fb7f34c1774a20195713929e5.jpg\",\n  \"625554.jpg\": \"Reptilia/Calotes versicolor/986830098fea430de627ae0043a6902a.jpg\",\n  \"625555.jpg\": \"Reptilia/Calotes versicolor/199ea8b810bc1b09f843decb9f40a19f.jpg\",\n  \"625556.jpg\": \"Reptilia/Calotes versicolor/d8809754c3c74dda1458748ce5847a46.jpg\",\n  \"625557.jpg\": \"Reptilia/Calotes versicolor/2b804658b6d5ec5d2a1d9bb63dee00b9.jpg\",\n  \"625558.jpg\": \"Reptilia/Calotes versicolor/2b7ec1b296bda1c99406380432542c7c.jpg\",\n  \"625559.jpg\": \"Reptilia/Calotes versicolor/e6909af220114b885414c47ce7975f92.jpg\",\n  \"625560.jpg\": \"Reptilia/Calotes versicolor/9353f56943ed920d71f0adb5f1ac1115.jpg\",\n  \"625565.jpg\": \"Insecta/Enallagma basidens/28419384576746fd06f0483e8fce845e.jpg\",\n  \"625566.jpg\": \"Insecta/Enallagma basidens/36dd3249d9c658a7ecf3483f1fb40c24.jpg\",\n  \"625577.jpg\": \"Insecta/Enallagma basidens/7a9b2c57c8d24ec2b9f72ca30a480bc7.jpg\",\n  \"625586.jpg\": \"Insecta/Enallagma basidens/aee3143a84a3e026d3694b8525f41b2e.jpg\",\n  \"6256.jpg\": \"Reptilia/Rena dulcis/cad47f11b3077242ddfc86ef2f8e2d8f.jpg\",\n  \"625606.jpg\": \"Insecta/Enallagma basidens/374f3469acb8063e16a2c603e0a437f2.jpg\",\n  \"625608.jpg\": \"Insecta/Enallagma basidens/859bcb143f68d73e6fbd6953850a36f3.jpg\",\n  \"625610.jpg\": \"Insecta/Enallagma basidens/3d0132d9cc168b28a8638bf54c5f1003.jpg\",\n  \"625622.jpg\": \"Insecta/Enallagma basidens/c3564f2130f0c8ed04a5c3663053e9ab.jpg\",\n  \"625623.jpg\": \"Insecta/Enallagma basidens/a4d89e2cdc09d8fd0c50313f079aad5a.jpg\",\n  \"625643.jpg\": \"Aves/Phalaropus lobatus/dcab339f45110f5ae5ca95fa5d44034d.jpg\",\n  \"625645.jpg\": \"Aves/Phalaropus lobatus/96519be776bda39a4b85e044af42cb7d.jpg\",\n  \"625654.jpg\": \"Aves/Phalaropus lobatus/c14bd7c6d4eac2d775841f08e913299c.jpg\",\n  \"625659.jpg\": \"Aves/Phalaropus lobatus/974e2ed3b5c692fdf39c088304dd5cdd.jpg\",\n  \"625662.jpg\": \"Aves/Phalaropus lobatus/d4482cbdeef5f789c71b92d45ad6bc6f.jpg\",\n  \"625671.jpg\": \"Aves/Phalaropus lobatus/8cf93bf54af2b149801d2a2e75d08714.jpg\",\n  \"625684.jpg\": \"Mammalia/Tamias minimus/3f00a08b91071c836604b18b3047fdfb.jpg\",\n  \"625688.jpg\": \"Mammalia/Tamias minimus/612bcbf5b66f5151f0c05483e7ac44db.jpg\",\n  \"6257.jpg\": \"Reptilia/Rena dulcis/2b1918e8a304268bd2561abbed884529.jpg\",\n  \"625712.jpg\": \"Mollusca/Neverita duplicata/940c6f2a0bbb4fba486d2a2fd61849b2.jpg\",\n  \"625728.jpg\": \"Insecta/Heliconius charithonia/3ba230a00f5f08a10dd0b9ac6c9ab220.jpg\",\n  \"625729.jpg\": \"Insecta/Heliconius charithonia/7b558f0eed801bcc2597b715b015bc1a.jpg\",\n  \"625739.jpg\": \"Insecta/Heliconius charithonia/61c781c986847fba3507945670b78c00.jpg\",\n  \"625764.jpg\": \"Insecta/Heliconius charithonia/e67b7cca388399e40cad9b8944bf5cbb.jpg\",\n  \"625765.jpg\": \"Insecta/Heliconius charithonia/ada190a069d9b5c2b53e57334b11256d.jpg\",\n  \"625816.jpg\": \"Insecta/Heliconius charithonia/4ce9f2ea2e040217cbb5e8dce04529b9.jpg\",\n  \"625912.jpg\": \"Reptilia/Elgaria multicarinata multicarinata/810f71d7098dd2b40a262aa186d19b14.jpg\",\n  \"625913.jpg\": \"Reptilia/Elgaria multicarinata multicarinata/f904844fec74666d2874f7a5afdba58b.jpg\",\n  \"625920.jpg\": \"Reptilia/Elgaria multicarinata multicarinata/cfd6231cd563345b4ba51f646d196602.jpg\",\n  \"625923.jpg\": \"Reptilia/Elgaria multicarinata multicarinata/051ca95e21192fa410c5d99874de75bf.jpg\",\n  \"625924.jpg\": \"Reptilia/Elgaria multicarinata multicarinata/79701e38580cfc478b14b6a0ee8ee615.jpg\",\n  \"625925.jpg\": \"Reptilia/Elgaria multicarinata multicarinata/9287f4514e1f67379bc977102ab08ddc.jpg\",\n  \"625930.jpg\": \"Reptilia/Elgaria multicarinata multicarinata/6c5898aeb40e24e6fcb8a8cf904bf665.jpg\",\n  \"625948.jpg\": \"Aves/Selasphorus sasin/2bcc7b9d2e3a7540a4b6019e963ca21a.jpg\",\n  \"625953.jpg\": \"Aves/Selasphorus sasin/24d426be00e56af70e141c6bdd38924f.jpg\",\n  \"625978.jpg\": \"Aves/Selasphorus sasin/79fb4c30f2d77cb85c1421eb49d0f52e.jpg\",\n  \"6260.jpg\": \"Reptilia/Rena dulcis/6057838881887485ae428f50151da5fb.jpg\",\n  \"626015.jpg\": \"Aves/Selasphorus sasin/fa936f1b9bc6c057f907c317ee20ee79.jpg\",\n  \"626041.jpg\": \"Aves/Selasphorus sasin/bdb917adb17cc613b1ca5e581bb49d92.jpg\",\n  \"626077.jpg\": \"Insecta/Hermeuptychia sosybius/c51579f667098e2e6c796639c34297f6.jpg\",\n  \"626085.jpg\": \"Insecta/Hermeuptychia sosybius/5459d16b1c3c7d09652575b9607898a5.jpg\",\n  \"626088.jpg\": \"Insecta/Hermeuptychia sosybius/d237edd0b77e9b97cf471a4ece027049.jpg\",\n  \"626093.jpg\": \"Insecta/Hermeuptychia sosybius/c4e21e9dae775e1bdc0a8013ce536494.jpg\",\n  \"626094.jpg\": \"Insecta/Hermeuptychia sosybius/5890c9b5f235a48f9a976deae67d4d79.jpg\",\n  \"6261.jpg\": \"Reptilia/Rena dulcis/826ca459e15dd038b53b2bcc530b8016.jpg\",\n  \"626102.jpg\": \"Insecta/Parapediasia teterrella/96b77a0706f8761a087df10e7a0fc8d9.jpg\",\n  \"626104.jpg\": \"Insecta/Parapediasia teterrella/2622c8fdd340e2170580060745f2e99c.jpg\",\n  \"626131.jpg\": \"Mammalia/Ceratotherium simum simum/bca655c33df5ef1e221adff028922db0.jpg\",\n  \"626144.jpg\": \"Insecta/Calpodes ethlius/33277458035bf83070e4c761df629168.jpg\",\n  \"626152.jpg\": \"Insecta/Calpodes ethlius/e6eabfef768186238e5fcbebe0e3ba6d.jpg\",\n  \"626296.jpg\": \"Insecta/Amphipyra pyramidoides/c4538eb937f86ddecebe1f8a3952c635.jpg\",\n  \"626307.jpg\": \"Insecta/Amphipyra pyramidoides/fb0471327e0535245d2647dafd426c43.jpg\",\n  \"626308.jpg\": \"Insecta/Amphipyra pyramidoides/f5181f79d259b2c7be783bb3a2385b8b.jpg\",\n  \"626393.jpg\": \"Mammalia/Ovis canadensis/c89247d27f76bf4403575b203ba78e3d.jpg\",\n  \"626394.jpg\": \"Mammalia/Ovis canadensis/756bb007f748e06b0591068261f11c91.jpg\",\n  \"626397.jpg\": \"Mammalia/Ovis canadensis/48c7a38dd6e80c8b7a011ab47d4af08a.jpg\",\n  \"62640.jpg\": \"Mollusca/Doris montereyensis/ce572380606b5b192d7b1884e9c38545.jpg\",\n  \"626406.jpg\": \"Mammalia/Ovis canadensis/e0b5a181f91efd870f86039c6a0f2116.jpg\",\n  \"626413.jpg\": \"Mammalia/Ovis canadensis/2c0503d7723b9b2e652ecd95ad43ff38.jpg\",\n  \"626417.jpg\": \"Mammalia/Ovis canadensis/53b03030b94ae7f12564ccd73db035e4.jpg\",\n  \"626431.jpg\": \"Mammalia/Ovis canadensis/bdc7def71641a23e226fad298c58de2c.jpg\",\n  \"626438.jpg\": \"Mammalia/Ovis canadensis/df6740da1ff1c520babb4943ff17136f.jpg\",\n  \"626439.jpg\": \"Mammalia/Ovis canadensis/ba10323dccaa103f3d7357ef39266cf6.jpg\",\n  \"626457.jpg\": \"Mollusca/Navanax inermis/486c6b9ffea22f5c54c3e8573c08fd7c.jpg\",\n  \"626458.jpg\": \"Mollusca/Navanax inermis/9a7555ae16c6a24bc88984805a879576.jpg\",\n  \"62647.jpg\": \"Mollusca/Doris montereyensis/994b464c9b8bb2ae75557b4c8f9dbb43.jpg\",\n  \"626479.jpg\": \"Mollusca/Mopalia muscosa/24634143a1e4a73573cb1cabab139833.jpg\",\n  \"62648.jpg\": \"Mollusca/Doris montereyensis/911265850a306b6875eb4f1651d578de.jpg\",\n  \"626483.jpg\": \"Mollusca/Mopalia muscosa/cf27850456974ea1eefab19bfb603f9b.jpg\",\n  \"626497.jpg\": \"Mollusca/Mopalia muscosa/7e6a21adef577d41c6928e65052f3ff0.jpg\",\n  \"626521.jpg\": \"Reptilia/Thamnophis ordinoides/e108f37d6f4fc5c079fe939808f92f96.jpg\",\n  \"626522.jpg\": \"Reptilia/Thamnophis ordinoides/974bc41997b5bde345ac1ad7c894877f.jpg\",\n  \"626532.jpg\": \"Reptilia/Thamnophis ordinoides/5028126aaddd1807226c9a9b490a3b29.jpg\",\n  \"626536.jpg\": \"Insecta/Pyrausta orphisalis/e2c938f3e71ac51949a4b841bab48b0d.jpg\",\n  \"626540.jpg\": \"Insecta/Pyrausta orphisalis/4bf9662ac44ad26a6e11da804f980185.jpg\",\n  \"626559.jpg\": \"Insecta/Crambus agitatellus/a43ff4d7b48b15d38f8aaf070d756ca0.jpg\",\n  \"62656.jpg\": \"Mollusca/Doris montereyensis/5aeaddba2205104cb277f2a915089be7.jpg\",\n  \"626572.jpg\": \"Insecta/Crambus agitatellus/876906f0cbaee850160f96508c1d3d9e.jpg\",\n  \"626573.jpg\": \"Insecta/Crambus agitatellus/82880c7316a99d80b26872ba23c9f929.jpg\",\n  \"626583.jpg\": \"Insecta/Bombylius major/771b93752af220a4a707f81df9c235e3.jpg\",\n  \"626584.jpg\": \"Insecta/Bombylius major/256cd9739a38cc5127c71f5e2b9a418f.jpg\",\n  \"626585.jpg\": \"Insecta/Bombylius major/e5b09b2e55017ba8da95f7ebe20f9633.jpg\",\n  \"626589.jpg\": \"Insecta/Bombylius major/39ce5a2ba3bcfb5a08d9e900899ac42b.jpg\",\n  \"626595.jpg\": \"Insecta/Bombylius major/a23914cba8179e46f1236d748c5571ad.jpg\",\n  \"626597.jpg\": \"Insecta/Bombylius major/d47c9499ab1b4e6206c60f7cddbf5dab.jpg\",\n  \"626598.jpg\": \"Insecta/Bombylius major/2f8de219e004a0524272f6a5a40b1a76.jpg\",\n  \"626603.jpg\": \"Insecta/Bombylius major/dc356ba05dbb8f1cbb39a6a84130154c.jpg\",\n  \"626606.jpg\": \"Insecta/Bombylius major/cfeb0e25c2a061dbb04bbb8db143650d.jpg\",\n  \"626609.jpg\": \"Insecta/Bombylius major/8abd9eafa55f9d04dc5ac3a3c9a43bab.jpg\",\n  \"626615.jpg\": \"Reptilia/Sceloporus undulatus/539ffc77ca8f5c39af8e70e7a02419ab.jpg\",\n  \"626620.jpg\": \"Reptilia/Sceloporus undulatus/ab80b57bf9c07f2ebf193356739111b3.jpg\",\n  \"626629.jpg\": \"Reptilia/Sceloporus undulatus/c216de3d209200c0977f956c1a5fae58.jpg\",\n  \"626642.jpg\": \"Reptilia/Sceloporus undulatus/12455c26208c7fe04bec49c8677d5aae.jpg\",\n  \"626644.jpg\": \"Reptilia/Sceloporus undulatus/ab6bdb32e5ec01287938376ef512eccf.jpg\",\n  \"626645.jpg\": \"Reptilia/Sceloporus undulatus/82f58d64ce4ee91951b061b9e655ec32.jpg\",\n  \"626648.jpg\": \"Reptilia/Sceloporus undulatus/f256d266831c5043f4bdac0b79a57cd8.jpg\",\n  \"626649.jpg\": \"Reptilia/Sceloporus undulatus/48c3ac249df6ea4d6b1ebaf6ec8aa577.jpg\",\n  \"626654.jpg\": \"Reptilia/Sceloporus undulatus/dc7b04b2e833f0fff55caba99687cf59.jpg\",\n  \"62671.jpg\": \"Mollusca/Doris montereyensis/4165432a7e476798d6571c4c23f9c5bd.jpg\",\n  \"62675.jpg\": \"Mollusca/Doris montereyensis/d1f0346b987e4cce7b1c615563ab6683.jpg\",\n  \"626793.jpg\": \"Aves/Phalacrocorax pelagicus/51d03e2709ba887dc7cfd5778aa49c08.jpg\",\n  \"626804.jpg\": \"Aves/Phalacrocorax pelagicus/4c23232216a8aa373a8f6fa80791d292.jpg\",\n  \"626837.jpg\": \"Aves/Phalacrocorax pelagicus/6ce0847e5c1ba15e032798b5ade8534b.jpg\",\n  \"626838.jpg\": \"Aves/Phalacrocorax pelagicus/88e544ade47e989c16c6481f8390389a.jpg\",\n  \"626851.jpg\": \"Aves/Phalacrocorax pelagicus/1f5c119044d8fca999469779ce31fb6e.jpg\",\n  \"626859.jpg\": \"Aves/Amazona albifrons/9dfef6ef1be45607487c331ce8a82a79.jpg\",\n  \"626865.jpg\": \"Aves/Amazona albifrons/3e419fc29a5de4e43cb7ec48a47648d3.jpg\",\n  \"626868.jpg\": \"Aves/Amazona albifrons/e5205ea4a6f86893fd1b21bc2487b2ee.jpg\",\n  \"626878.jpg\": \"Reptilia/Crotalus scutulatus scutulatus/7a3b766ee14e884b9e27524b311425a9.jpg\",\n  \"626879.jpg\": \"Reptilia/Crotalus scutulatus scutulatus/2a4f75dda4e6668be6f5870d9fbbb4e7.jpg\",\n  \"62688.jpg\": \"Mollusca/Doris montereyensis/54d0b42c48c32746b7cf71d1538c0fb2.jpg\",\n  \"626905.jpg\": \"Mammalia/Halichoerus grypus/b2d10c9551d65ba47474e08f9557a557.jpg\",\n  \"626906.jpg\": \"Mammalia/Halichoerus grypus/c35f96ec91abc0e5874285bf356ce82b.jpg\",\n  \"626910.jpg\": \"Mammalia/Tragelaphus scriptus/fff75babbe9fc8b6d658356e27ebb25f.jpg\",\n  \"626911.jpg\": \"Mammalia/Tragelaphus scriptus/b3b6ae4d57e7bb29a6b9df62bc0da7bc.jpg\",\n  \"626921.jpg\": \"Insecta/Palthis asopialis/6d2ef11220ca6b8bc8883a8535303bd9.jpg\",\n  \"627023.jpg\": \"Mollusca/Doriopsilla albopunctata/45dfa2d6f39a8ba4cac9a14ec78e5169.jpg\",\n  \"627024.jpg\": \"Mollusca/Doriopsilla albopunctata/ac0c29a8562eddf01279b808ff4e0a9a.jpg\",\n  \"627036.jpg\": \"Mollusca/Doriopsilla albopunctata/962ef76c7cce9d63eef3e9219c76f65c.jpg\",\n  \"627038.jpg\": \"Mollusca/Doriopsilla albopunctata/58662c04a4ecc45b85038eb46c4af2d1.jpg\",\n  \"627049.jpg\": \"Mollusca/Doriopsilla albopunctata/bc171a10e3da8590b8e1df7a4d0afd03.jpg\",\n  \"627051.jpg\": \"Mollusca/Doriopsilla albopunctata/024b6e644afff6deba3b6019010fd992.jpg\",\n  \"627059.jpg\": \"Mollusca/Doriopsilla albopunctata/c122c317ec3c598e8f470e17a753e717.jpg\",\n  \"627080.jpg\": \"Mollusca/Doriopsilla albopunctata/4feba0ea3513efa0df8592a0fee39f74.jpg\",\n  \"627081.jpg\": \"Mollusca/Doriopsilla albopunctata/27f36c2e1d89a8f001b8a5ae772bfd8e.jpg\",\n  \"627082.jpg\": \"Mollusca/Doriopsilla albopunctata/fb3a29a7b3ae1903663321bc132dc793.jpg\",\n  \"627083.jpg\": \"Mollusca/Doriopsilla albopunctata/f9fa8cdbc5875bb7aa8157e00ad47b91.jpg\",\n  \"627088.jpg\": \"Mollusca/Doriopsilla albopunctata/c59a8c4d85d3ee169cb651408c0c698d.jpg\",\n  \"627089.jpg\": \"Mollusca/Doriopsilla albopunctata/dd82a8d84c0d608a47b982672ced90ac.jpg\",\n  \"627097.jpg\": \"Mollusca/Doriopsilla albopunctata/db50ae5736545ef893e3fd8e832b48ef.jpg\",\n  \"6271.jpg\": \"Reptilia/Rena dulcis/a784cc9b20855ac5f64452932e7dd42c.jpg\",\n  \"627134.jpg\": \"Actinopterygii/Lepomis megalotis/88603ad6647eb9d9fb2c38fbb973ee21.jpg\",\n  \"627136.jpg\": \"Actinopterygii/Lepomis megalotis/8a42710340146fa3f1c74cdceb6a50b3.jpg\",\n  \"627143.jpg\": \"Actinopterygii/Lepomis megalotis/dceaef1cd411187356425ebb422ed8a4.jpg\",\n  \"627145.jpg\": \"Actinopterygii/Lepomis megalotis/d17c8c205134a751cb8eb122d83a4984.jpg\",\n  \"62715.jpg\": \"Mollusca/Doris montereyensis/848f618136d995e771146e0fe4e85aa6.jpg\",\n  \"6272.jpg\": \"Reptilia/Rena dulcis/62f8647d6998acd46eb148e0b8d16ab5.jpg\",\n  \"62727.jpg\": \"Mollusca/Doris montereyensis/4abc2a4fe1616ef90d6063292647cd6e.jpg\",\n  \"627272.jpg\": \"Aves/Sayornis nigricans/2f8b0194bdef76eaa5a47350fe628661.jpg\",\n  \"6273.jpg\": \"Reptilia/Rena dulcis/9176513aa81db75cb681cbcee6d17eb3.jpg\",\n  \"627337.jpg\": \"Aves/Sayornis nigricans/28b0cbc473dfc84703dc37d447b28eda.jpg\",\n  \"627394.jpg\": \"Aves/Sayornis nigricans/f14c8665129c6697a87261235025dcff.jpg\",\n  \"62745.jpg\": \"Mollusca/Doris montereyensis/ddca6b2ed628b8d347b4f46d7c5a0fc6.jpg\",\n  \"627454.jpg\": \"Aves/Sayornis nigricans/93069c86daac7cacd12e5a90840d2bd4.jpg\",\n  \"62753.jpg\": \"Mollusca/Doris montereyensis/e0fbc6b9279d8e5f883dbcfbcc07fbe7.jpg\",\n  \"62756.jpg\": \"Mollusca/Doris montereyensis/e6192ca4441c4925548e08ad03627a48.jpg\",\n  \"627578.jpg\": \"Aves/Sayornis nigricans/1ea7120069d44abb37dca244ca399e1f.jpg\",\n  \"627618.jpg\": \"Aves/Sayornis nigricans/7d06b350e034d6d1097dacc2dacdb738.jpg\",\n  \"627640.jpg\": \"Aves/Sayornis nigricans/2d9aa9bd8c88105747743582230a8013.jpg\",\n  \"62765.jpg\": \"Mollusca/Doris montereyensis/b54cc7d7550beb7dddc0819b3e356831.jpg\",\n  \"62766.jpg\": \"Mollusca/Doris montereyensis/bfb6f49173a2e593dae1b7e79ec97890.jpg\",\n  \"62767.jpg\": \"Mollusca/Doris montereyensis/aeaba764907c31f0f54f68c645c28ef7.jpg\",\n  \"627729.jpg\": \"Aves/Prunella modularis/77fe5fff7117e03faaaad2e7e4436373.jpg\",\n  \"627730.jpg\": \"Aves/Prunella modularis/87ef6339afe19da5bf046d834a010559.jpg\",\n  \"627734.jpg\": \"Aves/Prunella modularis/caebb59212dc770cf4699a657a257f30.jpg\",\n  \"627737.jpg\": \"Aves/Prunella modularis/69ce3e6ca2071e0ac93e5d3d8d199146.jpg\",\n  \"627739.jpg\": \"Aves/Prunella modularis/f8b2994e0c142831eb294b7ef974769e.jpg\",\n  \"627744.jpg\": \"Aves/Prunella modularis/acb337f7dd18456dd42aae63ea74551d.jpg\",\n  \"627748.jpg\": \"Aves/Prunella modularis/378c7f4346ade1467a64f5eea415636f.jpg\",\n  \"627754.jpg\": \"Aves/Prunella modularis/0a3ff9e6423880e14b74771df750d6e4.jpg\",\n  \"627759.jpg\": \"Aves/Prunella modularis/c40d6baaee9a470e875128c8711b81b5.jpg\",\n  \"627760.jpg\": \"Aves/Prunella modularis/0cc0d8061f9e833701eb5b963f3c6ef9.jpg\",\n  \"627774.jpg\": \"Insecta/Leptophobia aripa/db5f5319dd7bb2c32c52648653cf96f3.jpg\",\n  \"627780.jpg\": \"Insecta/Leptophobia aripa/5b617eb879610f2ee9632cb246142cf5.jpg\",\n  \"627784.jpg\": \"Insecta/Leptophobia aripa/f57194f4e0535bf74e6ae7602838cbf0.jpg\",\n  \"627786.jpg\": \"Insecta/Leptophobia aripa/769fd49949046864a34947e8262467ee.jpg\",\n  \"627884.jpg\": \"Insecta/Nymphalis californica/6930bb5109698788f772a8a131035fa2.jpg\",\n  \"627894.jpg\": \"Insecta/Nymphalis californica/29aacc85dc1cdec235a1a977686ad294.jpg\",\n  \"627895.jpg\": \"Insecta/Nymphalis californica/4bb6d70b08f3e6adf60edf55e6060e6d.jpg\",\n  \"627896.jpg\": \"Insecta/Nymphalis californica/c9f2b714822a259ecf0585c638b7579a.jpg\",\n  \"627900.jpg\": \"Insecta/Nymphalis californica/da497490727afb0b48dbfaec400727ba.jpg\",\n  \"627902.jpg\": \"Insecta/Nymphalis californica/22255d4be3ddc845ad5005741517d2a8.jpg\",\n  \"627903.jpg\": \"Insecta/Nymphalis californica/78ef1e052e4c4192ff409b6f12f32387.jpg\",\n  \"627904.jpg\": \"Insecta/Nymphalis californica/6e22a0ed1b879981b3e2edc38ff3a8e5.jpg\",\n  \"627905.jpg\": \"Insecta/Nymphalis californica/3182910d1df3191f1e492422e98a77c8.jpg\",\n  \"627919.jpg\": \"Insecta/Nymphalis californica/ccd938cbe06640b25a5476dcdcabb1ee.jpg\",\n  \"627934.jpg\": \"Insecta/Sympetrum semicinctum/cadeba61934c23ea68da97b914152b89.jpg\",\n  \"627959.jpg\": \"Insecta/Sympetrum semicinctum/9844bff578ed4f8484d4770fc2bbe6c8.jpg\",\n  \"627960.jpg\": \"Insecta/Sympetrum semicinctum/6107d642e7df005f4fcb671a388a441d.jpg\",\n  \"627972.jpg\": \"Insecta/Sympetrum semicinctum/feb730a982ea483e56ec99ea191bc9dc.jpg\",\n  \"627973.jpg\": \"Insecta/Sympetrum semicinctum/a4e7a03c5301bda84e76529f716f903a.jpg\",\n  \"627988.jpg\": \"Insecta/Sympetrum semicinctum/d964e3bc503f17fa38a39dbf4d47f047.jpg\",\n  \"628001.jpg\": \"Aves/Amazona oratrix/a8a039f3d22a741ab0fba3c66e7ca927.jpg\",\n  \"628012.jpg\": \"Aves/Sula sula/7e000ca77866089b86edc3153be52b52.jpg\",\n  \"628026.jpg\": \"Mammalia/Rattus norvegicus/00bb30b5061cc85863796abe09a388e2.jpg\",\n  \"628027.jpg\": \"Mammalia/Rattus norvegicus/c17d80d54ae54e8b222c4b7ae3b57e06.jpg\",\n  \"628044.jpg\": \"Mammalia/Rattus norvegicus/4303760392ee476922f0180a9bdebb72.jpg\",\n  \"628045.jpg\": \"Mammalia/Rattus norvegicus/a92e4505eadf93835af6a76e9180abb5.jpg\",\n  \"628049.jpg\": \"Mammalia/Rattus norvegicus/7274d12cfbf3466d9a3d287745e72e91.jpg\",\n  \"628055.jpg\": \"Insecta/Leptotes cassius/bfce1b682ac0ec414225d38e682d278b.jpg\",\n  \"628204.jpg\": \"Aves/Junco phaeonotus/1200008cdd52baa2d5cbf6c837c76a2f.jpg\",\n  \"628206.jpg\": \"Aves/Junco phaeonotus/32a79eda33eacc760c432572084dec40.jpg\",\n  \"628207.jpg\": \"Aves/Junco phaeonotus/0b48c798773a25575d87d9a961f8ccca.jpg\",\n  \"628208.jpg\": \"Aves/Junco phaeonotus/d659e5eceac94c8dfdf5eea6de45952d.jpg\",\n  \"628211.jpg\": \"Aves/Junco phaeonotus/57bd18e1e3baf9548ae283dd29e2a3ac.jpg\",\n  \"628212.jpg\": \"Aves/Junco phaeonotus/1a2dfe04039d920d5d913320d72ce45d.jpg\",\n  \"628213.jpg\": \"Aves/Junco phaeonotus/a6f1703d062617bacca18c719364e933.jpg\",\n  \"628214.jpg\": \"Aves/Junco phaeonotus/0dce22bd2391fa6a78a1c3804ca5d6a8.jpg\",\n  \"628215.jpg\": \"Aves/Junco phaeonotus/624427709ce72f29da865a214f816379.jpg\",\n  \"628583.jpg\": \"Mammalia/Cervus elaphus nannodes/b27a327600dc300ac3b791d58117ac6c.jpg\",\n  \"628588.jpg\": \"Mammalia/Cervus elaphus nannodes/9c9c5e2d573d740d7a4eb1ee624c2087.jpg\",\n  \"628589.jpg\": \"Mammalia/Cervus elaphus nannodes/96aff02dfd43a55d932256fb4b60d01e.jpg\",\n  \"628596.jpg\": \"Mammalia/Cervus elaphus nannodes/78e9431398ab41ceea60e17da3441f6e.jpg\",\n  \"62863.jpg\": \"Mammalia/Lycaon pictus/01ad0554b9bb34c4173553582322dc20.jpg\",\n  \"628713.jpg\": \"Aves/Porphyrio hochstetteri/5e84861c8747320e0bea44ccdfda3460.jpg\",\n  \"628714.jpg\": \"Mammalia/Urocitellus armatus/a32ddf471ebf616df32241bf0d3b2946.jpg\",\n  \"628717.jpg\": \"Mammalia/Urocitellus armatus/e8b55606ccbca50312fd196e8063eb75.jpg\",\n  \"628741.jpg\": \"Insecta/Limenitis lorquini/d97dd34d7a9a8e385cdd6e6f4413b807.jpg\",\n  \"628760.jpg\": \"Insecta/Limenitis lorquini/37987399b0f9478d5b9d6853714b76c9.jpg\",\n  \"628762.jpg\": \"Insecta/Limenitis lorquini/bdd1877c173908f3b25515e6733f4582.jpg\",\n  \"62879.jpg\": \"Mammalia/Lycaon pictus/ad0f86dea899fbc0959fb47010f165da.jpg\",\n  \"628793.jpg\": \"Insecta/Limenitis lorquini/1de84b945997a92187a783ef7c28627a.jpg\",\n  \"628819.jpg\": \"Insecta/Limenitis lorquini/900e35e07fb902c69fbb99e4dbb639f0.jpg\",\n  \"628828.jpg\": \"Insecta/Limenitis lorquini/d7d49444aac715f7a22d9e73d192022f.jpg\",\n  \"628834.jpg\": \"Insecta/Limenitis lorquini/fe8173005d383813107ce8cb8b67f790.jpg\",\n  \"62884.jpg\": \"Insecta/Trachymela sloanei/9543dac3fbf760ce92ab500387687d47.jpg\",\n  \"628841.jpg\": \"Insecta/Limenitis lorquini/5d8a49cc37da5942558978708d82dc41.jpg\",\n  \"628844.jpg\": \"Mammalia/Panthera onca/2181d5d2142784fd9c0b2c98330490fe.jpg\",\n  \"628848.jpg\": \"Mammalia/Panthera onca/44f569d5237f017d5709e32796dc4fde.jpg\",\n  \"62885.jpg\": \"Insecta/Trachymela sloanei/d131c0fdd63c221154021b487c861dde.jpg\",\n  \"62887.jpg\": \"Insecta/Trachymela sloanei/356dbc5d4bdd53eb8a2867fcd696f570.jpg\",\n  \"62888.jpg\": \"Insecta/Trachymela sloanei/6bd5f6adb1cb45c718923174148d5fa7.jpg\",\n  \"62890.jpg\": \"Insecta/Trachymela sloanei/05d25142bebbc9fa2080d45c47d04651.jpg\",\n  \"62891.jpg\": \"Insecta/Trachymela sloanei/d7bb8df2c022916fcd55464b13f41489.jpg\",\n  \"62892.jpg\": \"Insecta/Trachymela sloanei/61c70f09fee08a383c6cf1f0fa65c23c.jpg\",\n  \"62893.jpg\": \"Insecta/Trachymela sloanei/95a5aa5caf511192fedd2a898756c3f0.jpg\",\n  \"628990.jpg\": \"Reptilia/Coluber taeniatus/88605142ca98bb930655c09173ca7fe7.jpg\",\n  \"628991.jpg\": \"Reptilia/Coluber taeniatus/5509a465d228038f31319a7ec9c19236.jpg\",\n  \"62900.jpg\": \"Insecta/Trachymela sloanei/a8333c51b9712174cbc454f12d2d6a12.jpg\",\n  \"62901.jpg\": \"Insecta/Trachymela sloanei/0ad3b658f47a5c5d0e61af54d399a836.jpg\",\n  \"629013.jpg\": \"Aves/Vireo solitarius/c349ac0ce8a5f99c2c07e12510d11ee9.jpg\",\n  \"62902.jpg\": \"Insecta/Trachymela sloanei/a979e5452541044f28c08a0000ce775f.jpg\",\n  \"629024.jpg\": \"Aves/Vireo solitarius/2022aae1779fe247b9d1d67d4b80bfdb.jpg\",\n  \"629026.jpg\": \"Aves/Vireo solitarius/290f605f6ece2a959372a3d8ce82afb6.jpg\",\n  \"629031.jpg\": \"Aves/Vireo solitarius/18788027c78686a6f3933557651690ec.jpg\",\n  \"629034.jpg\": \"Aves/Vireo solitarius/ab996af94433a035809b5ed3f318e3c2.jpg\",\n  \"629042.jpg\": \"Aves/Vireo solitarius/a1347a22d5bbb61feb705bed27154cba.jpg\",\n  \"629049.jpg\": \"Aves/Merops pusillus/95d67275c07a4335ee6a8ed0ed8255ce.jpg\",\n  \"629070.jpg\": \"Aves/Phoebastria nigripes/d1640bbaec562909867cd2633a9770bf.jpg\",\n  \"629078.jpg\": \"Aves/Phoebastria nigripes/a731dab2510b825625df7e9faa7b5743.jpg\",\n  \"62908.jpg\": \"Insecta/Trachymela sloanei/16dc91473ed8574addaa21dc4700cd8c.jpg\",\n  \"629082.jpg\": \"Aves/Phoebastria nigripes/93b9d3d10830c62d6f744e7d68803ee7.jpg\",\n  \"629083.jpg\": \"Aves/Phoebastria nigripes/36284d04e1c6f71998d08f167208dc35.jpg\",\n  \"629086.jpg\": \"Aves/Phoebastria nigripes/49bc14b89f359bfb4b84b83fd22d17c5.jpg\",\n  \"629089.jpg\": \"Aves/Phoebastria nigripes/aa98d6b35b957ba4be611f63d5d73860.jpg\",\n  \"62909.jpg\": \"Insecta/Trachymela sloanei/cbf2e27f9685230e536e03adcfaea696.jpg\",\n  \"629090.jpg\": \"Aves/Phoebastria nigripes/7e7ac433d53a184649f5044597fbb80b.jpg\",\n  \"629091.jpg\": \"Aves/Phoebastria nigripes/b6931505c5bd7cc04fe7f553a91740b5.jpg\",\n  \"629092.jpg\": \"Aves/Phoebastria nigripes/e5ddae65138ce51351cb3b045838ad04.jpg\",\n  \"629093.jpg\": \"Aves/Phoebastria nigripes/30f2f625ece2de9b8bdef815d960d429.jpg\",\n  \"62911.jpg\": \"Insecta/Trachymela sloanei/5d1760269712382b7221d3ae10714909.jpg\",\n  \"62912.jpg\": \"Insecta/Trachymela sloanei/d64c0b4a8d06a0aac03bc38caf9af184.jpg\",\n  \"629129.jpg\": \"Insecta/Sympetrum ambiguum/123f6eb6eb9eae90368d8f8af9df878d.jpg\",\n  \"629130.jpg\": \"Insecta/Sympetrum ambiguum/de65ae9a652fbc6a92a7a1243d97f1e5.jpg\",\n  \"629131.jpg\": \"Insecta/Sympetrum ambiguum/b4889c58c525535bfd1ee242d40b894a.jpg\",\n  \"629134.jpg\": \"Insecta/Sympetrum ambiguum/f815ba350a7306116fd14ca1efde03ad.jpg\",\n  \"629136.jpg\": \"Insecta/Sympetrum ambiguum/4456d1aca15b4262fa0105025a934023.jpg\",\n  \"629137.jpg\": \"Insecta/Sympetrum ambiguum/26b743813e22a76fab97394ad48d0932.jpg\",\n  \"629138.jpg\": \"Insecta/Sympetrum ambiguum/dc1c717c7e44cd99eec5d99f13cba6b2.jpg\",\n  \"629143.jpg\": \"Insecta/Sympetrum ambiguum/b70bcc0e0f452215203495ceca3384a8.jpg\",\n  \"629144.jpg\": \"Insecta/Sympetrum ambiguum/2e0978435d99164bfd617714b3b2f397.jpg\",\n  \"629145.jpg\": \"Insecta/Sympetrum ambiguum/3659f7cb37f004f7cb2d6422ffdfb2ed.jpg\",\n  \"629146.jpg\": \"Insecta/Sympetrum ambiguum/1689bb6eb1c27f15bb43a6e24d053044.jpg\",\n  \"629154.jpg\": \"Insecta/Sympetrum ambiguum/8e69a5fea7a0a17eb46d5da4cb76833d.jpg\",\n  \"62916.jpg\": \"Insecta/Trachymela sloanei/5348881f4e37f654263cbc321030f2d0.jpg\",\n  \"62917.jpg\": \"Insecta/Trachymela sloanei/83e33144acc44dc5dea3393e3c59eb45.jpg\",\n  \"62918.jpg\": \"Insecta/Trachymela sloanei/f5a1ca127aa4d1870120229cdbdd35b0.jpg\",\n  \"629196.jpg\": \"Mammalia/Microtus pennsylvanicus/a65fcc7a42a453559f3eb459ebf904d8.jpg\",\n  \"62920.jpg\": \"Insecta/Trachymela sloanei/13719a923fdef835e7a3a137e3e0e874.jpg\",\n  \"629236.jpg\": \"Arachnida/Misumena vatia/0329889de34ff1cafeb2bf288adc3bb3.jpg\",\n  \"629238.jpg\": \"Arachnida/Misumena vatia/b4becb7d452220387f8b0de5d9da10cc.jpg\",\n  \"629240.jpg\": \"Arachnida/Misumena vatia/7e1c19a9121d08f38a2f1cc60d6c2b97.jpg\",\n  \"629252.jpg\": \"Arachnida/Misumena vatia/7fd3c2905e49545c154a6003ca0e930e.jpg\",\n  \"629258.jpg\": \"Arachnida/Misumena vatia/e5493c771af0c4aac4bed7fc7dce37cd.jpg\",\n  \"629260.jpg\": \"Arachnida/Misumena vatia/85c9f62dad1ad192d8f641dc94a69827.jpg\",\n  \"629263.jpg\": \"Arachnida/Misumena vatia/7a6baf2033ea461d7bedb2049cd1ee06.jpg\",\n  \"629264.jpg\": \"Arachnida/Misumena vatia/b1f19cc9b4f7b81edd45976bf694195c.jpg\",\n  \"629266.jpg\": \"Arachnida/Misumena vatia/73ab92b8710fd9d34f3c75392d13c157.jpg\",\n  \"629270.jpg\": \"Mollusca/Lissachatina fulica/9e82a7835b7d04e64a47dd9c3dd8f893.jpg\",\n  \"629276.jpg\": \"Mollusca/Lissachatina fulica/d59bf10f85719b1745a199220ca3823f.jpg\",\n  \"629279.jpg\": \"Mollusca/Lissachatina fulica/008163a8dc900a460f7d2b5b06a8083d.jpg\",\n  \"629280.jpg\": \"Mollusca/Lissachatina fulica/39a7ca95473d3094b5ed8de5b6618c05.jpg\",\n  \"629281.jpg\": \"Mollusca/Lissachatina fulica/c51f2ab371270a3b3eb38d91b64c9af5.jpg\",\n  \"629283.jpg\": \"Mollusca/Lissachatina fulica/cbc9ae54bb13957bb5b79a3c165b76fd.jpg\",\n  \"629288.jpg\": \"Mollusca/Lissachatina fulica/b9deb5ae329743e8b93835a6ed272a4e.jpg\",\n  \"629289.jpg\": \"Mollusca/Lissachatina fulica/4efd9f21b3a3f2999022673f22e51a5a.jpg\",\n  \"629292.jpg\": \"Mollusca/Lissachatina fulica/01c4267ef48f119d540e746ab17f0658.jpg\",\n  \"629410.jpg\": \"Aves/Pelecanus occidentalis californicus/15ef2c50183347ad347d3f686b0d9ef2.jpg\",\n  \"62947.jpg\": \"Amphibia/Plethodon glutinosus/06640af40a99a4ad38c028f5a9c047a2.jpg\",\n  \"62949.jpg\": \"Amphibia/Plethodon glutinosus/0b27ba300141978066983c786bb435b5.jpg\",\n  \"62957.jpg\": \"Amphibia/Plethodon glutinosus/3ea0e86abd74af5d04ca0ffcb8266902.jpg\",\n  \"62962.jpg\": \"Amphibia/Plethodon glutinosus/81a74fbb42abfa1e0bd4ceb477cc26f9.jpg\",\n  \"62963.jpg\": \"Amphibia/Plethodon glutinosus/59e9d993d6cad6daaf24617e4c5046a9.jpg\",\n  \"62964.jpg\": \"Amphibia/Plethodon glutinosus/c8be98e1a35bac2c72fcc21be4a65f1c.jpg\",\n  \"62965.jpg\": \"Amphibia/Plethodon glutinosus/d6b1837c2dbc00a43e835ee77a1884b5.jpg\",\n  \"62971.jpg\": \"Amphibia/Plethodon glutinosus/d78f4e600ca7752a130ab30349d93d22.jpg\",\n  \"62972.jpg\": \"Amphibia/Plethodon glutinosus/1d4de72ea6d61de89e112275a7d8982a.jpg\",\n  \"62973.jpg\": \"Amphibia/Plethodon glutinosus/3c9eef797ad2362af1805f1a566c3e27.jpg\",\n  \"629741.jpg\": \"Aves/Clangula hyemalis/42d1f2b2d7dc1034d908e71e8728c3e6.jpg\",\n  \"629755.jpg\": \"Aves/Clangula hyemalis/92e769506730e3cf5a80a6c5045e7a3a.jpg\",\n  \"629766.jpg\": \"Aves/Clangula hyemalis/be05ef4c61c08157fa192b1a1bda2c70.jpg\",\n  \"62977.jpg\": \"Amphibia/Plethodon glutinosus/82f20fceb1d393ec4e61a1e2cead1f6f.jpg\",\n  \"629770.jpg\": \"Aves/Clangula hyemalis/8ad74d93d47dc2ad89620d20f1466697.jpg\",\n  \"629771.jpg\": \"Aves/Clangula hyemalis/1d1de8e2c63b382a18d71e329daeb4ac.jpg\",\n  \"62978.jpg\": \"Amphibia/Plethodon glutinosus/cb381b2a334a9795af078db3a9e2d8d5.jpg\",\n  \"629781.jpg\": \"Aves/Clangula hyemalis/5a8f0245b371916435431f062cd15db9.jpg\",\n  \"629793.jpg\": \"Aves/Sicalis flaveola/7462c61af2507108dc9ab0018523c8e8.jpg\",\n  \"62980.jpg\": \"Amphibia/Plethodon glutinosus/ba552fafaf00ffe897e195aeb82d772f.jpg\",\n  \"629807.jpg\": \"Aves/Sicalis flaveola/83679d4633c85d0b6c3847112143d0e9.jpg\",\n  \"62981.jpg\": \"Amphibia/Plethodon glutinosus/77933bd4ea748fb2c23863121464d725.jpg\",\n  \"62983.jpg\": \"Amphibia/Plethodon glutinosus/bfc8c4911aca084903053be62800a68a.jpg\",\n  \"629836.jpg\": \"Aves/Seiurus aurocapilla/9d6af525f3ded32696a4b53102ce4651.jpg\",\n  \"629843.jpg\": \"Aves/Seiurus aurocapilla/497a5966c216ee2e37f57b2e963f54a2.jpg\",\n  \"629859.jpg\": \"Aves/Seiurus aurocapilla/f04ede4d71332297a17d8d8b3fc0b554.jpg\",\n  \"629862.jpg\": \"Aves/Seiurus aurocapilla/2c5b4ecf7e4b9827755b9078517dab4d.jpg\",\n  \"629866.jpg\": \"Aves/Seiurus aurocapilla/0a2ab981fb9f6ab0008bd5b2ea2d48e5.jpg\",\n  \"62987.jpg\": \"Amphibia/Plethodon glutinosus/6bc675db2fe7e952c473cb813ff94ed2.jpg\",\n  \"62988.jpg\": \"Amphibia/Plethodon glutinosus/86d86508ce445a8e54514519ebab3288.jpg\",\n  \"629882.jpg\": \"Aves/Seiurus aurocapilla/da8008431ce437cd95c1ab952c7b0700.jpg\",\n  \"629883.jpg\": \"Aves/Seiurus aurocapilla/3963283e9542f23e0f57ec665699505b.jpg\",\n  \"62989.jpg\": \"Amphibia/Plethodon glutinosus/c9815ff974ecb40b57677c9c5bffafe2.jpg\",\n  \"629894.jpg\": \"Aves/Seiurus aurocapilla/bb29d3a13153e14b3fe067be14a6d37c.jpg\",\n  \"629897.jpg\": \"Aves/Calidris pusilla/75f4cb56b077802c5c1b98316d7d6315.jpg\",\n  \"62990.jpg\": \"Amphibia/Plethodon glutinosus/001feb03f8776800a3fc092e78c1171d.jpg\",\n  \"629909.jpg\": \"Aves/Calidris pusilla/f166125af1be12b9192ae86879b709d4.jpg\",\n  \"629910.jpg\": \"Aves/Calidris pusilla/4499374d5354485bd2d34cdc7541a6bb.jpg\",\n  \"629911.jpg\": \"Aves/Calidris pusilla/74643acbbe6458c12897b80addd2cc4c.jpg\",\n  \"629913.jpg\": \"Aves/Calidris pusilla/87a520d3dbb6c229b3b7d0c921001fc8.jpg\",\n  \"629914.jpg\": \"Aves/Calidris pusilla/93b7fcc77b352bb0f2a7156d9fc59e3b.jpg\",\n  \"62994.jpg\": \"Amphibia/Plethodon glutinosus/8d62901a43804794e39fcaf5fb64c37e.jpg\",\n  \"629944.jpg\": \"Aves/Calidris pusilla/6146c357fb6e0be9de5ad0d95b3bcf25.jpg\",\n  \"62995.jpg\": \"Amphibia/Plethodon glutinosus/c86e79e5f65b546a628b29dafda0a7f4.jpg\",\n  \"629969.jpg\": \"Insecta/Copaeodes aurantiaca/595770ebe40f3256023fb31638373749.jpg\",\n  \"62997.jpg\": \"Insecta/Caripeta divisata/7f06c6b37ef45eacf9ce8e6a5ea2feef.jpg\",\n  \"629984.jpg\": \"Insecta/Odontotaenius disjunctus/f4cf9487df3c26b7913f5c3bca92c7d8.jpg\",\n  \"629986.jpg\": \"Insecta/Odontotaenius disjunctus/e4348a81cdb252bd25378bd0b83e798c.jpg\",\n  \"63000.jpg\": \"Insecta/Caripeta divisata/77576a9eccf3be1385ef6c69c9446b64.jpg\",\n  \"630004.jpg\": \"Insecta/Odontotaenius disjunctus/c9dece72ebd1dd230abbb5cc860d1760.jpg\",\n  \"630018.jpg\": \"Insecta/Odontotaenius disjunctus/123c7b7812b6c0bc4949cc528d60e7ec.jpg\",\n  \"630019.jpg\": \"Insecta/Odontotaenius disjunctus/26fe902dfdb7d130a9f2c13eaf2408a9.jpg\",\n  \"63002.jpg\": \"Insecta/Caripeta divisata/149a1b08506c9c3952c9248f19d1de6b.jpg\",\n  \"630033.jpg\": \"Insecta/Odontotaenius disjunctus/011d42b110fc3c5447edba411009cbae.jpg\",\n  \"630056.jpg\": \"Insecta/Odontotaenius disjunctus/1287811e90d3f1b16628dc6dde7471a6.jpg\",\n  \"63007.jpg\": \"Insecta/Caripeta divisata/8c5689ce92edc6f012d9a27ed8dbf6bd.jpg\",\n  \"63013.jpg\": \"Insecta/Caripeta divisata/e48c1234cf262d81ee8cfb6bd296e2a0.jpg\",\n  \"630168.jpg\": \"Mammalia/Sylvilagus floridanus/5e26e045a5cd20690e2f802df00889b3.jpg\",\n  \"63019.jpg\": \"Insecta/Caripeta divisata/bf45117a916c6bac63925a6f7631d33f.jpg\",\n  \"630207.jpg\": \"Mammalia/Sylvilagus floridanus/68337989256689ee9b4fddfa8be47a8a.jpg\",\n  \"63022.jpg\": \"Insecta/Caripeta divisata/d5d6fab66d22b28908384ce7990e56ec.jpg\",\n  \"630377.jpg\": \"Mammalia/Sylvilagus floridanus/0c1165bf6bbdf7fab6a348d8b75a7cb1.jpg\",\n  \"630400.jpg\": \"Mammalia/Sylvilagus floridanus/ebc4f9bbbfebdb03c549f530086e3d9c.jpg\",\n  \"630457.jpg\": \"Aves/Icterus galbula/6586b35b052ef0be01ee4341011cdacd.jpg\",\n  \"630476.jpg\": \"Aves/Icterus galbula/461a64ee030cba65fc0b76203787d993.jpg\",\n  \"630492.jpg\": \"Aves/Icterus galbula/c2bf336f6deb7aba90f592a8be29a7ff.jpg\",\n  \"630516.jpg\": \"Aves/Icterus galbula/e8d9e718195c5ed311f96ba84865083c.jpg\",\n  \"630522.jpg\": \"Aves/Icterus galbula/a8d6a4bbe8fa39741b300749a1956b56.jpg\",\n  \"630530.jpg\": \"Aves/Icterus galbula/b1e19fefd54a8e6bd1ecea6108b37d7b.jpg\",\n  \"630540.jpg\": \"Aves/Icterus galbula/23775ba9a2addd306ad0b5bdefd2e984.jpg\",\n  \"630601.jpg\": \"Aves/Icterus galbula/92a90b8057e8a4d12ed9bd6a138093f4.jpg\",\n  \"630635.jpg\": \"Insecta/Scopula limboundata/1819b06f708e208f4e2c85d12a078306.jpg\",\n  \"630640.jpg\": \"Insecta/Scopula limboundata/6de9e1cd99fb1d4289a0ec0645defd7b.jpg\",\n  \"630651.jpg\": \"Insecta/Scopula limboundata/ad263189b06c697daa7ccaa4a6c707a2.jpg\",\n  \"630666.jpg\": \"Insecta/Scopula limboundata/46aac792cb2af0740e74504eec6319d4.jpg\",\n  \"630674.jpg\": \"Insecta/Scopula limboundata/422faafff8052549d5e06003ba2a08a8.jpg\",\n  \"630680.jpg\": \"Insecta/Scopula limboundata/cd039ea0e9529bccf91bb39a6cd764bf.jpg\",\n  \"630826.jpg\": \"Amphibia/Rhinella marina/1863ee5712721def0741c9be539131e0.jpg\",\n  \"630840.jpg\": \"Amphibia/Rhinella marina/2219694fe9e41ee02ea9f1005238ace7.jpg\",\n  \"630850.jpg\": \"Amphibia/Rhinella marina/caf28caced55237df95fbdcc8b200a31.jpg\",\n  \"630851.jpg\": \"Amphibia/Rhinella marina/89e148281a31fb1f385c50001bbf7972.jpg\",\n  \"630853.jpg\": \"Amphibia/Rhinella marina/a7871f5c50cf625c187c45ea337ec084.jpg\",\n  \"630888.jpg\": \"Amphibia/Rhinella marina/eb0d8a50f742082f8e2aabfb884c7110.jpg\",\n  \"630905.jpg\": \"Amphibia/Rhinella marina/0beaeb4c281bff6968480386492b437e.jpg\",\n  \"630909.jpg\": \"Amphibia/Rhinella marina/3e36135d4eb6ec3fdc302ebbcf856f0c.jpg\",\n  \"630917.jpg\": \"Reptilia/Coluber constrictor mormon/ad731fbf7924d41cc7087940096d2b9c.jpg\",\n  \"630918.jpg\": \"Reptilia/Coluber constrictor mormon/4428231450836078170cc660879f1675.jpg\",\n  \"630921.jpg\": \"Reptilia/Coluber constrictor mormon/9914bf8d891b1f47af789137753f548e.jpg\",\n  \"630930.jpg\": \"Reptilia/Coluber constrictor mormon/3cee85af30bb1fbf6797a424c036706f.jpg\",\n  \"630942.jpg\": \"Mammalia/Sylvilagus bachmani/264a4b243791c0548fe3514c1aa6850e.jpg\",\n  \"630954.jpg\": \"Mammalia/Sylvilagus bachmani/b6e9bfb386e1798265ebf18f62ccc1bc.jpg\",\n  \"630967.jpg\": \"Mammalia/Sylvilagus bachmani/bf5b78c215587fa85379b9ccfe24f9a1.jpg\",\n  \"630974.jpg\": \"Mammalia/Sylvilagus bachmani/2548b6129d0caa2b721aac35c66eeb38.jpg\",\n  \"630976.jpg\": \"Mammalia/Sylvilagus bachmani/da1ecd2b63c9e95032ef4de4799d17f0.jpg\",\n  \"630988.jpg\": \"Mammalia/Sylvilagus bachmani/a62ab8af22b4147a57c26e60705f1c53.jpg\",\n  \"630989.jpg\": \"Insecta/Diapheromera femorata/e1e51c1b383a2685826436cd4c892e34.jpg\",\n  \"630990.jpg\": \"Insecta/Diapheromera femorata/d3ea39a28c2f63b5eff24bdb22fc0835.jpg\",\n  \"630991.jpg\": \"Insecta/Diapheromera femorata/ee0bc1e95b52b1ff5c494ffd576ae8e5.jpg\",\n  \"630993.jpg\": \"Insecta/Diapheromera femorata/5d1b2abc512ff4094ebd0b9b74f64604.jpg\",\n  \"630994.jpg\": \"Insecta/Diapheromera femorata/09884244ef9a9105718583e2f29e273a.jpg\",\n  \"631.jpg\": \"Mammalia/Ursus americanus/77d18efdc123ab91992eacb76bb13c4d.jpg\",\n  \"631003.jpg\": \"Insecta/Diapheromera femorata/20ca3da0f8d981b106cb24a80073da1d.jpg\",\n  \"631007.jpg\": \"Insecta/Diapheromera femorata/50bb5fe3eb5979054ed66a5a6b0db49e.jpg\",\n  \"631025.jpg\": \"Aves/Haemorhous mexicanus/1ed4b6ff00c7b8b2cea3d4d3dd55b8ef.jpg\",\n  \"631094.jpg\": \"Aves/Haemorhous mexicanus/d297ccef59830181660cca82fb2f455b.jpg\",\n  \"631248.jpg\": \"Aves/Haemorhous mexicanus/ab83eb90baa367beacdb39c0c56c55cb.jpg\",\n  \"631341.jpg\": \"Aves/Haemorhous mexicanus/e5c7937666b11cd6f3e6672322910b78.jpg\",\n  \"63137.jpg\": \"Insecta/Thymelicus lineola/c036cc894e1878fef9cbfd47a6ca9429.jpg\",\n  \"63138.jpg\": \"Insecta/Thymelicus lineola/9b98c57d7071e653f122c15313071594.jpg\",\n  \"63139.jpg\": \"Insecta/Thymelicus lineola/33508b9d11793d92d495deba72aa091b.jpg\",\n  \"631418.jpg\": \"Aves/Haemorhous mexicanus/202067935654b6917c47db481749f9e7.jpg\",\n  \"63144.jpg\": \"Insecta/Thymelicus lineola/69f65f4b12f5c30e36e4175cdc3501f7.jpg\",\n  \"63147.jpg\": \"Insecta/Thymelicus lineola/483d2dc4f66c2bb790d93011b1434614.jpg\",\n  \"63149.jpg\": \"Insecta/Thymelicus lineola/89cd56edac878445899071912f87fdd4.jpg\",\n  \"63150.jpg\": \"Insecta/Thymelicus lineola/fc1c335bc161e5c6053e2fe7cca6f6fc.jpg\",\n  \"63154.jpg\": \"Insecta/Thymelicus lineola/95a79b522354d05e84077ce76e561d82.jpg\",\n  \"63155.jpg\": \"Insecta/Thymelicus lineola/523a3c2840331d8fa16fcdb4b655065e.jpg\",\n  \"63156.jpg\": \"Insecta/Thymelicus lineola/93e742b695205b7bee97f906859f6733.jpg\",\n  \"63157.jpg\": \"Insecta/Thymelicus lineola/ea7283e67d12b8d0dc68c8d905be2060.jpg\",\n  \"631752.jpg\": \"Aves/Haemorhous mexicanus/566fdee134b1928611e5055329d605b5.jpg\",\n  \"63181.jpg\": \"Insecta/Thymelicus lineola/838d70b27a9c471ab2f2eb2ddc1a689c.jpg\",\n  \"63185.jpg\": \"Insecta/Thymelicus lineola/d6677617e1ce7b00fd5a78e26afba20d.jpg\",\n  \"63186.jpg\": \"Insecta/Thymelicus lineola/5b7347a41902d19dac2767de13be4e7f.jpg\",\n  \"63192.jpg\": \"Insecta/Thymelicus lineola/0936119068c08f410905788ac3253474.jpg\",\n  \"63196.jpg\": \"Insecta/Thymelicus lineola/3f429428923f7bd7d96fcdd8c7a2038d.jpg\",\n  \"63198.jpg\": \"Insecta/Thymelicus lineola/404e8376b2546f72854eb7b8c1f30742.jpg\",\n  \"632044.jpg\": \"Aves/Haemorhous mexicanus/b7bee47948ecd03b2a9c2c8c567aa987.jpg\",\n  \"63208.jpg\": \"Insecta/Thymelicus lineola/a6d5ab05026127b1456fb6d2769a92cc.jpg\",\n  \"63209.jpg\": \"Insecta/Thymelicus lineola/c0244b8775a325547e304471cb47265a.jpg\",\n  \"632161.jpg\": \"Aves/Haemorhous mexicanus/0f60712ccbc8d9f2c7a3bf6c43d3f4b8.jpg\",\n  \"632238.jpg\": \"Aves/Mimus polyglottos/ff45ce0f400a442e91e9020b89387ac4.jpg\",\n  \"632588.jpg\": \"Aves/Mimus polyglottos/30680e3ec50e65127faacc599989372a.jpg\",\n  \"632776.jpg\": \"Aves/Mimus polyglottos/5df68f74d7787288549548b6708e3b94.jpg\",\n  \"633020.jpg\": \"Aves/Mimus polyglottos/64ad6911526c553350343268bf13617e.jpg\",\n  \"633049.jpg\": \"Aves/Mimus polyglottos/10c3507c08149f16211a4ffb1bd50a65.jpg\",\n  \"633083.jpg\": \"Aves/Mimus polyglottos/9efec46114fa6b25d810b9f11163858e.jpg\",\n  \"633119.jpg\": \"Amphibia/Ecnomiohyla miotympanum/db4c7847778d425448d84a24d85a3556.jpg\",\n  \"633120.jpg\": \"Amphibia/Ecnomiohyla miotympanum/398b50e3e78fb34dbf603fee4d572b88.jpg\",\n  \"633143.jpg\": \"Insecta/Oncopeltus fasciatus/46bbc04eb348e472ddf4e39f175924b6.jpg\",\n  \"633151.jpg\": \"Insecta/Oncopeltus fasciatus/16312b4c06cd03aae314860de602141b.jpg\",\n  \"633224.jpg\": \"Insecta/Oncopeltus fasciatus/54bee9626414351c13ca3084cb3ce0fc.jpg\",\n  \"633282.jpg\": \"Insecta/Oncopeltus fasciatus/88e15b31435b2882f862fdb348df0f15.jpg\",\n  \"633403.jpg\": \"Mollusca/Peltodoris nobilis/0539283e33b6a3b8effd17e183f1ad44.jpg\",\n  \"633415.jpg\": \"Mollusca/Peltodoris nobilis/164e161a9e1f7b92c7763a31932f7dad.jpg\",\n  \"633424.jpg\": \"Mollusca/Peltodoris nobilis/8b54470f18a9bb30db68a9fcbf621813.jpg\",\n  \"633427.jpg\": \"Mollusca/Peltodoris nobilis/e8c45e676d172da0045f8b5d71c2fb18.jpg\",\n  \"633429.jpg\": \"Mollusca/Peltodoris nobilis/bc4d094d0e032acbd862fa514d543f6b.jpg\",\n  \"633443.jpg\": \"Mollusca/Peltodoris nobilis/fc2799875e863ce45cec9bdad384eaa8.jpg\",\n  \"633498.jpg\": \"Aves/Catharus fuscescens/7dc752f89f0ac94166b22f85eb4133dc.jpg\",\n  \"633502.jpg\": \"Aves/Catharus fuscescens/7ad7dbed40d77d2f2a3f0c9436ac3307.jpg\",\n  \"633503.jpg\": \"Aves/Catharus fuscescens/983a0b5dd92fd3b50997612f8b89ca4f.jpg\",\n  \"633506.jpg\": \"Aves/Catharus fuscescens/900b2bbe7c67cc78b25193b3e42ee3b8.jpg\",\n  \"633507.jpg\": \"Aves/Catharus fuscescens/1b524dc8731407d8b73dc5122a8248a2.jpg\",\n  \"633509.jpg\": \"Aves/Catharus fuscescens/384e3d93668f96cedf68bede73693c7c.jpg\",\n  \"633513.jpg\": \"Aves/Catharus fuscescens/9ff5639842ddda4d98ca1849ba3ff34d.jpg\",\n  \"633518.jpg\": \"Aves/Catharus fuscescens/646ad1f1dc4bbd7040f89db085d572b8.jpg\",\n  \"633522.jpg\": \"Aves/Catharus fuscescens/f0f741d261f436b13da01933c9fe7b5b.jpg\",\n  \"633527.jpg\": \"Mammalia/Acinonyx jubatus/1c9405a70dc5ad6abfc96831c02d0bdd.jpg\",\n  \"633528.jpg\": \"Mammalia/Acinonyx jubatus/b609faa70f1d188b272e85e50b4a3e04.jpg\",\n  \"633529.jpg\": \"Mammalia/Acinonyx jubatus/128610adcb3978fcabc39112b3f02304.jpg\",\n  \"633530.jpg\": \"Mammalia/Acinonyx jubatus/1aac048a4b5fb9292bdcb593c75104bf.jpg\",\n  \"633531.jpg\": \"Mammalia/Acinonyx jubatus/d3ad9377884998006da7bcd460e1818d.jpg\",\n  \"633542.jpg\": \"Mammalia/Acinonyx jubatus/333d92223fa5b2276c77b85c72b84c4a.jpg\",\n  \"633547.jpg\": \"Mammalia/Acinonyx jubatus/1e3ef490f7274b4e9b6556b2f409c41b.jpg\",\n  \"633579.jpg\": \"Aves/Setophaga americana/2be1b5ec1c247a6349a977f42c5ef3eb.jpg\",\n  \"633584.jpg\": \"Aves/Setophaga americana/23e7047412c3fc14a0de8326e467d95f.jpg\",\n  \"633585.jpg\": \"Aves/Setophaga americana/c5327b82c666a47f199b512ae5a99d04.jpg\",\n  \"633591.jpg\": \"Aves/Setophaga americana/aa0c3f63bd73b118ec9f7c04f81f7b4e.jpg\",\n  \"633592.jpg\": \"Aves/Setophaga americana/350b33e960c14aa6286076a0954c25ac.jpg\",\n  \"633608.jpg\": \"Aves/Setophaga americana/84e753ed79236d7aeb516f6a1c649425.jpg\",\n  \"633612.jpg\": \"Aves/Setophaga americana/0efbf36295fd3ec15f749343adb44ba8.jpg\",\n  \"633619.jpg\": \"Aves/Setophaga americana/e5a99f6f6e8c3e2e50c1bffe44c01ef1.jpg\",\n  \"633634.jpg\": \"Aves/Setophaga americana/af2c3f0805a11063bcd4dc8ab224c5dd.jpg\",\n  \"633635.jpg\": \"Aves/Setophaga americana/41edfa4406e8e609284372869f39c53a.jpg\",\n  \"633727.jpg\": \"Arachnida/Argiope aurantia/16d98d7352c490cfc4100c07cd4b872f.jpg\",\n  \"633843.jpg\": \"Arachnida/Argiope aurantia/6e58034b2851b3c126fb53923d9d8616.jpg\",\n  \"633875.jpg\": \"Arachnida/Argiope aurantia/41a2a9fa68551f8c265e1e12bf17ed7f.jpg\",\n  \"6340.jpg\": \"Insecta/Glaucopsyche lygdamus/e5101469dd9fc3236fed124fa7b0a927.jpg\",\n  \"634007.jpg\": \"Aves/Podiceps grisegena/da23d23b48d70f9163faefa1b85dc421.jpg\",\n  \"634010.jpg\": \"Aves/Podiceps grisegena/4b5a34c46b14c3f19010474babced1e9.jpg\",\n  \"634013.jpg\": \"Aves/Podiceps grisegena/902e50725c765dadbf477fc2c7eccb6f.jpg\",\n  \"634016.jpg\": \"Aves/Podiceps grisegena/3854c4c4b5e9577197ec9b22fa81ee25.jpg\",\n  \"634021.jpg\": \"Aves/Podiceps grisegena/76cb21c438d578d9204d381b218be17d.jpg\",\n  \"634023.jpg\": \"Aves/Podiceps grisegena/2c74667f8ad6d52de5e8c58414a76ba9.jpg\",\n  \"634024.jpg\": \"Aves/Podiceps grisegena/14ab8eafd344e33802552eb8a3060b2f.jpg\",\n  \"634025.jpg\": \"Aves/Podiceps grisegena/b56ad32284f4be4b3a40bcc3c0c8abd4.jpg\",\n  \"634061.jpg\": \"Aves/Turdus philomelos/af6f1143a967340118f7247d5c3b97f3.jpg\",\n  \"634069.jpg\": \"Aves/Turdus philomelos/acfd7f87976079a7d758ff8054dc62e3.jpg\",\n  \"634083.jpg\": \"Aves/Turdus philomelos/185cd2d851aa7cc2e09b03ed5d787335.jpg\",\n  \"634084.jpg\": \"Aves/Turdus philomelos/54892bfb4f8aab923af2546eccd83048.jpg\",\n  \"634097.jpg\": \"Aves/Turdus philomelos/cdd7b35113490fd042492fc8978d6915.jpg\",\n  \"634103.jpg\": \"Aves/Turdus philomelos/2b37ec06f5abcae8b831d28adf5eff50.jpg\",\n  \"634106.jpg\": \"Aves/Turdus philomelos/c98da931073df0dc3bd68d04ea31c6ab.jpg\",\n  \"634145.jpg\": \"Amphibia/Hypopachus variolosus/1d5355b540be3ed4e617627451151051.jpg\",\n  \"634146.jpg\": \"Amphibia/Hypopachus variolosus/b1394b5effd2b00a459e26904fef7347.jpg\",\n  \"634248.jpg\": \"Aves/Pycnonotus jocosus/94ffdac16386476aebfb55c74795c891.jpg\",\n  \"634250.jpg\": \"Aves/Pycnonotus jocosus/95be0fb36c563fffdcc4f5698159b10b.jpg\",\n  \"634251.jpg\": \"Aves/Pycnonotus jocosus/61fd1286cec46b7d7b75ca973aad06e8.jpg\",\n  \"634255.jpg\": \"Aves/Pycnonotus jocosus/ca64d0fc9831e7782efeeac7193b5409.jpg\",\n  \"634256.jpg\": \"Aves/Pycnonotus jocosus/a3a7f5e7e912b0a6f6de98d60b2220db.jpg\",\n  \"634257.jpg\": \"Aves/Pycnonotus jocosus/99f063ad6a655ed6352eec1623a3b7ad.jpg\",\n  \"634258.jpg\": \"Aves/Pycnonotus jocosus/8c845586ff7fb50344334f2167859279.jpg\",\n  \"634259.jpg\": \"Aves/Pycnonotus jocosus/04cd756a23d38b2044b15817968e2717.jpg\",\n  \"634261.jpg\": \"Aves/Pycnonotus jocosus/55807d3d0c8f0b9d876e1259d4d2e04a.jpg\",\n  \"634264.jpg\": \"Aves/Pycnonotus jocosus/bae0f47b79b3c60d8d3504286b9453ca.jpg\",\n  \"634269.jpg\": \"Aves/Pycnonotus jocosus/db367e103d24b43c9d4bf308cd1f5d5d.jpg\",\n  \"6343.jpg\": \"Insecta/Glaucopsyche lygdamus/05065c1a9d01fb40b1296ffda4deda61.jpg\",\n  \"634323.jpg\": \"Aves/Sternula antillarum/be8c9c1883b96bfb795d46d1e474e655.jpg\",\n  \"634325.jpg\": \"Aves/Sternula antillarum/42b940b61b0747ac9b122723cf25c2af.jpg\",\n  \"634335.jpg\": \"Aves/Sternula antillarum/9c5198c2382b43f72089f14dfd3bf823.jpg\",\n  \"634372.jpg\": \"Insecta/Eurema daira/8d227839048cb5ddb53b057cabba8503.jpg\",\n  \"634373.jpg\": \"Insecta/Eurema daira/877e05d1b164dae6b3c9bbe132a9b0b4.jpg\",\n  \"634384.jpg\": \"Insecta/Maliattha synochitis/ad9b89454005f45dad39c865c8d6a42a.jpg\",\n  \"634386.jpg\": \"Insecta/Maliattha synochitis/67625c3ed42dcc0cc4b6c6dbb4fcc7f9.jpg\",\n  \"634390.jpg\": \"Insecta/Maliattha synochitis/1a72f3d43a162489f201897a374f9626.jpg\",\n  \"634391.jpg\": \"Insecta/Maliattha synochitis/dfc0eb7c855bba134ad904215a195b5b.jpg\",\n  \"634392.jpg\": \"Insecta/Maliattha synochitis/882ea1bb76dd86b6f13deac4d64a3647.jpg\",\n  \"634394.jpg\": \"Insecta/Maliattha synochitis/3dad733d0062c643d622f25d3d1513e3.jpg\",\n  \"6344.jpg\": \"Insecta/Glaucopsyche lygdamus/d91e2367e53737ab12ff41955a531329.jpg\",\n  \"634402.jpg\": \"Insecta/Maliattha synochitis/e8f140ff480f3a2290dc08e5bd9259f0.jpg\",\n  \"634413.jpg\": \"Reptilia/Agkistrodon piscivorus conanti/538e77eccdc796f1f097463519c2dfe3.jpg\",\n  \"634416.jpg\": \"Aves/Setophaga magnolia/c0e28373869c8634f32a30443363cb2f.jpg\",\n  \"634439.jpg\": \"Aves/Setophaga magnolia/d417dbf9ec17c2f9c2a6d8f30f892c10.jpg\",\n  \"634472.jpg\": \"Aves/Setophaga magnolia/6e62dfd967808958167d9b8963d07eed.jpg\",\n  \"634482.jpg\": \"Aves/Setophaga magnolia/3a7f1e4ce443f48b78e3fcea385e02fa.jpg\",\n  \"634483.jpg\": \"Aves/Setophaga magnolia/4033a332c644cdbada5237f99bc4d7b8.jpg\",\n  \"634494.jpg\": \"Aves/Setophaga magnolia/2ec631d9c80485f3ec0bd4e983e922d3.jpg\",\n  \"634511.jpg\": \"Aves/Setophaga magnolia/b1a45510a3fe8fc42e7938f4add4586f.jpg\",\n  \"634512.jpg\": \"Aves/Setophaga magnolia/817969361089a6caf6deed5a7bebc6ec.jpg\",\n  \"634526.jpg\": \"Aves/Setophaga magnolia/ad69336376bcb780472b6be91cb57010.jpg\",\n  \"634563.jpg\": \"Actinopterygii/Oncorhynchus mykiss/81d46f9e02dbe9aecc28554d98470d84.jpg\",\n  \"634582.jpg\": \"Actinopterygii/Oncorhynchus mykiss/89792dc3ae8844f5c57aae98099f1413.jpg\",\n  \"634583.jpg\": \"Actinopterygii/Oncorhynchus mykiss/56c17dd78105858fbd88d73683f2489a.jpg\",\n  \"634588.jpg\": \"Actinopterygii/Oncorhynchus mykiss/8b6a240ba0fbcf802d906ac6e3c6e151.jpg\",\n  \"634589.jpg\": \"Actinopterygii/Oncorhynchus mykiss/76b6e44e0d22e0b36b9b44a04e8efe40.jpg\",\n  \"634591.jpg\": \"Actinopterygii/Oncorhynchus mykiss/b81ebcd3f84f9def9bc0c98bfbb25ae6.jpg\",\n  \"634627.jpg\": \"Mammalia/Didelphis virginiana/faddf2eaa2e2f90ac36a5776923fd635.jpg\",\n  \"634701.jpg\": \"Mammalia/Didelphis virginiana/a1b32784a78f743f36839ab8cbd4c4f8.jpg\",\n  \"634787.jpg\": \"Mammalia/Didelphis virginiana/35ef98fce495de119fa2b46e64eb504e.jpg\",\n  \"634810.jpg\": \"Mammalia/Didelphis virginiana/d50dd9d666480adba738b41b9ddb949b.jpg\",\n  \"634840.jpg\": \"Aves/Ardeotis kori/80f528cd6784142e0dc36fcf4b7b1785.jpg\",\n  \"634846.jpg\": \"Aves/Ardeotis kori/986c855d0badaad656fbd5415096480d.jpg\",\n  \"634853.jpg\": \"Aves/Catharus minimus/3720d0a6aa6e5c58b64ea7f37bc9660b.jpg\",\n  \"634854.jpg\": \"Aves/Catharus minimus/f0ceb5b913f3baa2c9d4e571ebaf9d62.jpg\",\n  \"634864.jpg\": \"Aves/Sterna hirundo/7fdaed0f24ce7619927e121a2f2bedc2.jpg\",\n  \"634885.jpg\": \"Aves/Sterna hirundo/5372535a6b0687ae4d5e8448b960270b.jpg\",\n  \"634900.jpg\": \"Aves/Sterna hirundo/67efaf4a3b33428c8fee20ceac19573c.jpg\",\n  \"635153.jpg\": \"Aves/Chlorophanes spiza/6ab8c973e846c6200fe63afe813f4e89.jpg\",\n  \"635154.jpg\": \"Aves/Chlorophanes spiza/fececf104321b64a84e8492db2f44e1e.jpg\",\n  \"635201.jpg\": \"Aves/Vireo huttoni/f46093eac6dbac76e0e3b5ba6ed05def.jpg\",\n  \"635202.jpg\": \"Aves/Vireo huttoni/df88894f779ad7ec372e2bbd82bd7f88.jpg\",\n  \"635206.jpg\": \"Aves/Vireo huttoni/3b1620639a4ff03f24feeb1604105250.jpg\",\n  \"635207.jpg\": \"Aves/Vireo huttoni/9fe82fadb2ac4e07e1723d043f551aba.jpg\",\n  \"635208.jpg\": \"Aves/Vireo huttoni/4e63755e006a249bd06df33876438062.jpg\",\n  \"635250.jpg\": \"Aves/Vireo huttoni/81a186ba61364463d329891da02da79f.jpg\",\n  \"6353.jpg\": \"Insecta/Glaucopsyche lygdamus/3b0585a52ca2ec26a367967a13236f8a.jpg\",\n  \"635328.jpg\": \"Aves/Poecile rufescens/ca6b3a898ac4ee7c29690d484d86bc4a.jpg\",\n  \"635340.jpg\": \"Aves/Poecile rufescens/7e153e296f31c793b7a3ef33fb167f8e.jpg\",\n  \"635388.jpg\": \"Aves/Poecile rufescens/9fab8bf172f678c029d15fe6f912f496.jpg\",\n  \"635416.jpg\": \"Aves/Poecile rufescens/f1e2370c6409ff9ecb3c70a54727a660.jpg\",\n  \"635419.jpg\": \"Aves/Poecile rufescens/cdb95c2c9af45ce226f2329109837fc4.jpg\",\n  \"635424.jpg\": \"Aves/Poecile rufescens/2a4f5c86fcc15ca09370bb03e89e8b05.jpg\",\n  \"635431.jpg\": \"Aves/Poecile rufescens/7dc9cafa59fc3ba01382da7caf1650ee.jpg\",\n  \"635499.jpg\": \"Aves/Anser anser domesticus/daa72953e10c13735d715a92cc3135f3.jpg\",\n  \"635517.jpg\": \"Aves/Anser anser domesticus/74a4f90610742cd67d822e21c1d399f9.jpg\",\n  \"635537.jpg\": \"Reptilia/Chelydra serpentina/c963098a321be15e2d8dde736746befb.jpg\",\n  \"635619.jpg\": \"Reptilia/Chelydra serpentina/9a195ff0d84328595e2d2b852ea6af9a.jpg\",\n  \"635673.jpg\": \"Reptilia/Chelydra serpentina/543101aa799b00e59a45b66ef7ddd55d.jpg\",\n  \"635745.jpg\": \"Reptilia/Chelydra serpentina/08efa8adf4475cfef8a80f9d5c013058.jpg\",\n  \"635763.jpg\": \"Reptilia/Chelydra serpentina/45384da0d9211181b7152dfde7f1806f.jpg\",\n  \"635774.jpg\": \"Reptilia/Chelydra serpentina/ec9e9891a95061a0c1f988af5588cb13.jpg\",\n  \"6359.jpg\": \"Insecta/Glaucopsyche lygdamus/1076d41cf22c65b4c84443a33cfd81c3.jpg\",\n  \"635929.jpg\": \"Amphibia/Anaxyrus americanus/8200df3da233ec1991efaa0927693f23.jpg\",\n  \"636013.jpg\": \"Amphibia/Anaxyrus americanus/7dba4a9303f11e11e252bcabcdc2b64a.jpg\",\n  \"636058.jpg\": \"Amphibia/Anaxyrus americanus/c3b1acc1c5941bb4858dc444f01acbab.jpg\",\n  \"636061.jpg\": \"Amphibia/Anaxyrus americanus/3b4f4e501e71176cdaa047ac68011c44.jpg\",\n  \"636063.jpg\": \"Amphibia/Anaxyrus americanus/a26c900939bc561b0fe2c19160b2ac37.jpg\",\n  \"636077.jpg\": \"Amphibia/Anaxyrus americanus/def34bedd47786f7facfd8bbd6d4f942.jpg\",\n  \"636163.jpg\": \"Amphibia/Anaxyrus americanus/0bd34b3d7dcb7b4a624c47f008a8e818.jpg\",\n  \"636221.jpg\": \"Amphibia/Anaxyrus americanus/7773fb64770e4d913c69f5664f9e1eaf.jpg\",\n  \"636401.jpg\": \"Reptilia/Carphophis amoenus/7a9267dfa449e9244fff04c14fae25a7.jpg\",\n  \"636405.jpg\": \"Reptilia/Carphophis amoenus/136dc0f60e5465267a9e0a93c3c53cb6.jpg\",\n  \"636409.jpg\": \"Reptilia/Carphophis amoenus/5d66c4bedef8d3b9c873434add9d9314.jpg\",\n  \"636411.jpg\": \"Aves/Numida meleagris/3f48c002516c2546106e4f19fea984ee.jpg\",\n  \"636418.jpg\": \"Aves/Numida meleagris/a4f1ddd1629831dfc86ca64dcdedd0f7.jpg\",\n  \"636421.jpg\": \"Aves/Numida meleagris/24cd5511ddae1682490c27bc3c8f2640.jpg\",\n  \"636423.jpg\": \"Aves/Numida meleagris/410061387034ca51c3fbf34d7259378b.jpg\",\n  \"636427.jpg\": \"Aves/Numida meleagris/8e0a2dde33b575051d6639d447eade96.jpg\",\n  \"636428.jpg\": \"Aves/Numida meleagris/e9b23c9e5fa52f6ad859418579c47380.jpg\",\n  \"636444.jpg\": \"Aves/Regulus ignicapilla/64df688e124cb41f9aa066a509502fa2.jpg\",\n  \"636446.jpg\": \"Aves/Regulus ignicapilla/194cfe4641be1cb0ff2e49ee41163861.jpg\",\n  \"636478.jpg\": \"Aves/Bostrychia hagedash/a17543d969cbd895e7508c552d38336f.jpg\",\n  \"636480.jpg\": \"Aves/Dicrurus adsimilis/1883a5c241da6088612b5344e03bfa79.jpg\",\n  \"636518.jpg\": \"Aves/Calcarius lapponicus/96e1b15acd699455a633d05be719a27c.jpg\",\n  \"636523.jpg\": \"Aves/Calcarius lapponicus/62e4bf747182cf18fa0ebb01426ea8fa.jpg\",\n  \"636525.jpg\": \"Aves/Calcarius lapponicus/ae7871da1b45d64c068cade389f5a5be.jpg\",\n  \"636533.jpg\": \"Aves/Phoenicurus ochruros/4b33a082013e9d9ac0e4aa386ae46eae.jpg\",\n  \"636538.jpg\": \"Aves/Phoenicurus ochruros/646b2b3c15f0530d352af04c810a77ce.jpg\",\n  \"636543.jpg\": \"Aves/Phoenicurus ochruros/4423822094a98a5fab3198d6551c1942.jpg\",\n  \"636544.jpg\": \"Aves/Phoenicurus ochruros/3fbba568245110bc8a80e65f7377fe74.jpg\",\n  \"636549.jpg\": \"Aves/Phoenicurus ochruros/062a5161ae8b7af2381f2cad3038585b.jpg\",\n  \"636556.jpg\": \"Aves/Phoenicurus ochruros/21a5c7008abf7a1182e3cc8da99de538.jpg\",\n  \"636564.jpg\": \"Aves/Phoenicurus ochruros/b245e1bf864c016ad6c4d9fe1cbceb18.jpg\",\n  \"636569.jpg\": \"Aves/Phoenicurus ochruros/1cc95d1e1c0d3e0482b539b31b5b24e2.jpg\",\n  \"636575.jpg\": \"Aves/Phoenicurus ochruros/be3ec7bf2eb11a1c7b841e2a0805869c.jpg\",\n  \"636588.jpg\": \"Amphibia/Eurycea longicauda/d905b2b1d7805acb8a8dd4b902ed786a.jpg\",\n  \"636590.jpg\": \"Amphibia/Eurycea longicauda/ab54395c6b30322da252cb813ffe7ad8.jpg\",\n  \"636594.jpg\": \"Amphibia/Eurycea longicauda/ba2d12140a22e6e1f73998c0dab5bb02.jpg\",\n  \"636596.jpg\": \"Amphibia/Eurycea longicauda/2a2022a7c735d935382a59b4dc566833.jpg\",\n  \"636598.jpg\": \"Insecta/Tramea carolina/d5563adf8bd9806d6c40495dba53bd8c.jpg\",\n  \"636599.jpg\": \"Insecta/Tramea carolina/2d0ddaf18040203bdca76952f60f148e.jpg\",\n  \"636601.jpg\": \"Insecta/Tramea carolina/e207e1ddc35a5d8cb0cfedeb564a51c9.jpg\",\n  \"636607.jpg\": \"Insecta/Tramea carolina/faa9acefb11bd3ae2b50962632a42385.jpg\",\n  \"636609.jpg\": \"Insecta/Tramea carolina/deea1daf2f277c8c34b6325e79646e2b.jpg\",\n  \"636610.jpg\": \"Insecta/Tramea carolina/d258f50c0d926f14a1121c3ef2cded99.jpg\",\n  \"636611.jpg\": \"Insecta/Tramea carolina/e1df2844b64ef1176949655cd0da219c.jpg\",\n  \"636617.jpg\": \"Insecta/Tramea carolina/14b47fd5e48f052913f56d7c8ed931fd.jpg\",\n  \"636621.jpg\": \"Insecta/Tramea carolina/142885706c4fe78e0133c665fa7c1059.jpg\",\n  \"636629.jpg\": \"Aves/Vanellus spinosus/ae9cb0aa976c6957c58b8dc915bfac90.jpg\",\n  \"6371.jpg\": \"Insecta/Glaucopsyche lygdamus/f57c3a73337dcae8372dc6aeacb54c53.jpg\",\n  \"63714.jpg\": \"Reptilia/Lichanura orcutti/fc4f79044f625b73b15357c54c789569.jpg\",\n  \"63717.jpg\": \"Reptilia/Lichanura orcutti/c94cb416cb83a7daf4f787146e2dd20e.jpg\",\n  \"637206.jpg\": \"Insecta/Alaus oculatus/51a428cbca81a45f64622c953449445e.jpg\",\n  \"637207.jpg\": \"Insecta/Alaus oculatus/44062d913ea49f8c9944923204628110.jpg\",\n  \"637213.jpg\": \"Insecta/Alaus oculatus/cf9930f9cb49833943440e18ff5cbac9.jpg\",\n  \"637217.jpg\": \"Insecta/Alaus oculatus/cb3b122962247efcea01a1e85d4ed7ee.jpg\",\n  \"637218.jpg\": \"Insecta/Alaus oculatus/59c5935e76e6e967faf8b125cbbd3fbf.jpg\",\n  \"637219.jpg\": \"Insecta/Alaus oculatus/7ecf0ea370be561c032070aea26bf2ab.jpg\",\n  \"637220.jpg\": \"Insecta/Alaus oculatus/66c76a60002a04f846454e111bff92c4.jpg\",\n  \"637227.jpg\": \"Insecta/Alaus oculatus/a72d116c05d6eedfee6ed830267fcb5f.jpg\",\n  \"637229.jpg\": \"Insecta/Alaus oculatus/c8ffae1638a02e72d92970358abe9e94.jpg\",\n  \"63724.jpg\": \"Reptilia/Lichanura orcutti/282596c3c45be8483dcaf477ee4305ed.jpg\",\n  \"637270.jpg\": \"Insecta/Pontia protodice/729f9bf65abfc6efe3e9337b09632a5c.jpg\",\n  \"63728.jpg\": \"Reptilia/Lichanura orcutti/b621110330ea735327f07f9079291d63.jpg\",\n  \"6373.jpg\": \"Insecta/Glaucopsyche lygdamus/d80af5ec84f96f3d6b451d2f2448ad31.jpg\",\n  \"637302.jpg\": \"Insecta/Pontia protodice/1f4df0caf8afc682f5f3a876b9c2852c.jpg\",\n  \"63733.jpg\": \"Reptilia/Lichanura orcutti/42a28d03188b1f7f1cd7a714d39e03ae.jpg\",\n  \"637332.jpg\": \"Insecta/Pontia protodice/59e03c8c0b32d0fe20423c2fe9089b0a.jpg\",\n  \"637335.jpg\": \"Insecta/Pontia protodice/61da117423c60d6ea057dc4303253a83.jpg\",\n  \"63734.jpg\": \"Reptilia/Lichanura orcutti/4d379d638d96a88e071d2cc9a6e83d42.jpg\",\n  \"637366.jpg\": \"Insecta/Pontia protodice/bfb43654b1dd003cac6896e0ed628339.jpg\",\n  \"637396.jpg\": \"Insecta/Pontia protodice/f219f05258eba3a485a145a76a3263c2.jpg\",\n  \"637397.jpg\": \"Insecta/Pontia protodice/d8d704285daf3e9a68bc9745512a7d6f.jpg\",\n  \"637407.jpg\": \"Aves/Corvus splendens/7723e3de96e6aede816b046cce5be847.jpg\",\n  \"637417.jpg\": \"Aves/Corvus splendens/20c02ebe3f0b792f6358ab12fa5bed59.jpg\",\n  \"63744.jpg\": \"Aves/Plegadis falcinellus/f69e81dd7e5165c4ee62a05de46e366d.jpg\",\n  \"6375.jpg\": \"Insecta/Glaucopsyche lygdamus/11ed4440d2fae011fdd4bc8cdc9a75f1.jpg\",\n  \"6376.jpg\": \"Insecta/Glaucopsyche lygdamus/1ad720bc260c77cec5ee57c5ca27b11e.jpg\",\n  \"637602.jpg\": \"Aves/Mergus merganser/f8e9a8f6eb59482eb8905a2a6691980c.jpg\",\n  \"637621.jpg\": \"Aves/Mergus merganser/a7f4968f0070ac33fad87a413aec0bca.jpg\",\n  \"63766.jpg\": \"Aves/Plegadis falcinellus/7e14d43951788b8c605d93265039c9ab.jpg\",\n  \"637663.jpg\": \"Aves/Mergus merganser/e8bed87cfb4a101a13da4387ee157572.jpg\",\n  \"6377.jpg\": \"Insecta/Glaucopsyche lygdamus/b1741dd66de85dd83e588a45f23c630a.jpg\",\n  \"637702.jpg\": \"Aves/Grus americana/1da94bc88e35ca07a8411c3d52444058.jpg\",\n  \"637713.jpg\": \"Aves/Grus americana/cb17acf1bc050f2df7e75c2c1e7a3730.jpg\",\n  \"637736.jpg\": \"Insecta/Leptophobia aripa elodia/f46daf51bc885975d44acd331eb9693f.jpg\",\n  \"637738.jpg\": \"Insecta/Leptophobia aripa elodia/7d6e8925ed95c21668edef5fa16cc550.jpg\",\n  \"63774.jpg\": \"Aves/Plegadis falcinellus/0f3dd126a8b4505a2e6fd69638170a97.jpg\",\n  \"637743.jpg\": \"Reptilia/Lampropeltis triangulum/dba1798732ec2f111ca9191b1d4a3974.jpg\",\n  \"637748.jpg\": \"Reptilia/Lampropeltis triangulum/b873bf7b7b318e9a019198292b105458.jpg\",\n  \"637750.jpg\": \"Reptilia/Lampropeltis triangulum/57112ad610a7775a94a744760e60d36e.jpg\",\n  \"637761.jpg\": \"Reptilia/Lampropeltis triangulum/1f50846a13ad5ce8a14c5399ca7dc5ef.jpg\",\n  \"637763.jpg\": \"Aves/Parabuteo unicinctus/3f76a5adfe25aed0bb47a0de6fb4e605.jpg\",\n  \"637779.jpg\": \"Aves/Parabuteo unicinctus/046c16d78b4488db99868c5fb5885f1d.jpg\",\n  \"63778.jpg\": \"Aves/Plegadis falcinellus/6148a1217c666c967cab5eac8cf54c90.jpg\",\n  \"637797.jpg\": \"Aves/Parabuteo unicinctus/321f32bcfd94ec588746f64e7db3f17a.jpg\",\n  \"637801.jpg\": \"Aves/Parabuteo unicinctus/0da53884d237538eac4c3c95c9093c63.jpg\",\n  \"637810.jpg\": \"Aves/Parabuteo unicinctus/10f3420f6713d868adf8a08560885bef.jpg\",\n  \"63782.jpg\": \"Aves/Plegadis falcinellus/b8f177fed2ef4c4c39820762973b5ae7.jpg\",\n  \"637833.jpg\": \"Actinopterygii/Chaetodon capistratus/40fcec533564f4c81afe10c86ffb4b1f.jpg\",\n  \"637853.jpg\": \"Mollusca/Lottia digitalis/47be634957746d0678a248ca647cabce.jpg\",\n  \"637869.jpg\": \"Mammalia/Panthera pardus/1063c549379d7da14d261ec7e186c028.jpg\",\n  \"637870.jpg\": \"Mammalia/Panthera pardus/28c74e2649e23e4739f414ac5f969869.jpg\",\n  \"637872.jpg\": \"Mammalia/Panthera pardus/cf1a22e2d52619696659de370f1c5f20.jpg\",\n  \"637879.jpg\": \"Insecta/Acronicta americana/0177b027cbcef25b9760792175863aff.jpg\",\n  \"637882.jpg\": \"Insecta/Acronicta americana/759937070c8d74d7c847ec3ef6572c0c.jpg\",\n  \"637885.jpg\": \"Insecta/Acronicta americana/fb37f4ed706cfb1bd6a670b84a01ed1a.jpg\",\n  \"637890.jpg\": \"Insecta/Acronicta americana/f50f6f93c01dd9f88fcc5223c6beb17d.jpg\",\n  \"637891.jpg\": \"Insecta/Acronicta americana/c18f01ea5af6d88dafbbb62b80fe4688.jpg\",\n  \"637892.jpg\": \"Insecta/Acronicta americana/b4684cd91b431d7fece5b2e4f3073add.jpg\",\n  \"637897.jpg\": \"Insecta/Acronicta americana/ebe30ca59d275471fd15fc9095fc4c8c.jpg\",\n  \"637898.jpg\": \"Insecta/Acronicta americana/7393c881f829310316ce20a82a10c8ee.jpg\",\n  \"637899.jpg\": \"Insecta/Acronicta americana/22a930cf3225b3339758c0f8896b008a.jpg\",\n  \"637916.jpg\": \"Insecta/Graphocephala coccinea/49ae489365d655772a32d0dde09d4b7a.jpg\",\n  \"637917.jpg\": \"Insecta/Graphocephala coccinea/181f22883fc7d787dc1dd6eb53fa0fea.jpg\",\n  \"637919.jpg\": \"Insecta/Graphocephala coccinea/f809f129c61d75480c4422e1df26b8e0.jpg\",\n  \"637921.jpg\": \"Insecta/Graphocephala coccinea/3dec8a9f6d6c0490e94c52ee77dc4846.jpg\",\n  \"637922.jpg\": \"Insecta/Graphocephala coccinea/313b484aa2d641c4b2cd658fc28bf7f1.jpg\",\n  \"637926.jpg\": \"Insecta/Graphocephala coccinea/691bc6ac6bc1568a030b64919534576b.jpg\",\n  \"637927.jpg\": \"Insecta/Graphocephala coccinea/e918730c67041ced20a2ea14a6483eea.jpg\",\n  \"637930.jpg\": \"Insecta/Graphocephala coccinea/a602996bde364f83da8dddc9fc26d77f.jpg\",\n  \"637931.jpg\": \"Insecta/Graphocephala coccinea/c89cba574f9325fb951f72b8417e7f8a.jpg\",\n  \"637939.jpg\": \"Aves/Opisthocomus hoazin/647bd12b48bb20f545361d04bea61b59.jpg\",\n  \"637941.jpg\": \"Aves/Opisthocomus hoazin/27c9f45c709894a4ce27d970f7ee670e.jpg\",\n  \"637942.jpg\": \"Aves/Opisthocomus hoazin/02382a8ecc8bb07466def7e7211f1f04.jpg\",\n  \"63797.jpg\": \"Aves/Plegadis falcinellus/5e879662a31b2a8e39c270c3e8491568.jpg\",\n  \"63798.jpg\": \"Aves/Plegadis falcinellus/3312ae60a3e8acc8df580f3e06bc9d2c.jpg\",\n  \"6380.jpg\": \"Insecta/Glaucopsyche lygdamus/ccef14f5b29dcdbd1c97a62a2d4ab868.jpg\",\n  \"63803.jpg\": \"Aves/Plegadis falcinellus/8310ea0ce3d3ffbb286ad11688de4efa.jpg\",\n  \"63806.jpg\": \"Aves/Plegadis falcinellus/6822611e5a90d5f4073a692e2ae3daae.jpg\",\n  \"63809.jpg\": \"Aves/Plegadis falcinellus/a42a1661e6fdbaeaf5b08e0db3cd64e4.jpg\",\n  \"63810.jpg\": \"Aves/Plegadis falcinellus/ac532609b26ef81362794ccae9e75024.jpg\",\n  \"63817.jpg\": \"Aves/Plegadis falcinellus/a93d69ad1f34860132cfb444c72a96f0.jpg\",\n  \"638189.jpg\": \"Aves/Dryocopus lineatus/0c597a999c25c5cab21e1bbcf0c6baf8.jpg\",\n  \"638190.jpg\": \"Aves/Dryocopus lineatus/e00addb0ccd112da928f5bed7cdad260.jpg\",\n  \"638191.jpg\": \"Aves/Dryocopus lineatus/d0a0240a6a71f1ac5be29c799591fb01.jpg\",\n  \"638201.jpg\": \"Aves/Dryocopus lineatus/9d273bdef90f201cc7902a8c87cb2a6b.jpg\",\n  \"638209.jpg\": \"Aves/Dryocopus lineatus/36eed4c2aa363a5b8a9f4eee87d99fad.jpg\",\n  \"638214.jpg\": \"Aves/Dryocopus lineatus/68c4d4ed702bd2730bbcc59663c5ca63.jpg\",\n  \"638222.jpg\": \"Aves/Dryocopus lineatus/f59f293fd674ed69c78760bef64c8d9e.jpg\",\n  \"638296.jpg\": \"Mammalia/Megaptera novaeangliae/7e8be268b168ea3ec73439f2509a974e.jpg\",\n  \"638297.jpg\": \"Mammalia/Megaptera novaeangliae/965a230e15b1d4c164248a967e22cae4.jpg\",\n  \"638361.jpg\": \"Mammalia/Megaptera novaeangliae/cb6c9e68abeb813fabd0e4f1fab71504.jpg\",\n  \"638383.jpg\": \"Mammalia/Megaptera novaeangliae/461bfacc2fdcc111a1f97d4c91145751.jpg\",\n  \"638392.jpg\": \"Mammalia/Megaptera novaeangliae/25882fa8037539aa78034141413535cd.jpg\",\n  \"638424.jpg\": \"Mammalia/Megaptera novaeangliae/84d23ff8d0f19986a37b334f8279763a.jpg\",\n  \"638445.jpg\": \"Mammalia/Megaptera novaeangliae/b9a30982c8615752d4a296698cdc3d26.jpg\",\n  \"63849.jpg\": \"Aves/Certhia americana/d3cd0aed5b04f54a10aae5e5a6dd8b55.jpg\",\n  \"638519.jpg\": \"Aves/Bucephala clangula/efc4c1a927da108d9e483341b39caaab.jpg\",\n  \"638538.jpg\": \"Aves/Bucephala clangula/6af3b4e469974668ad6ba83b9745e52e.jpg\",\n  \"63862.jpg\": \"Aves/Certhia americana/29059e4aa3c15b586b11df94ac47423d.jpg\",\n  \"638629.jpg\": \"Aves/Bucephala clangula/19d82004e5d4197c89fe675090604d45.jpg\",\n  \"63867.jpg\": \"Aves/Certhia americana/5fb42b3f3f208916e9ed87a299d978d1.jpg\",\n  \"638688.jpg\": \"Insecta/Nymphalis vaualbum/85a56268d4f4cb6f763994aa99ad0602.jpg\",\n  \"638701.jpg\": \"Aves/Gelochelidon nilotica/69a472db799e47ad78b67c8d5cf3ed3c.jpg\",\n  \"638705.jpg\": \"Aves/Gelochelidon nilotica/80bbcabfac8f457cb52efde64b2ca91f.jpg\",\n  \"6390.jpg\": \"Insecta/Glaucopsyche lygdamus/10cc1d16940f946aa774a2ee4df2f784.jpg\",\n  \"63917.jpg\": \"Aves/Certhia americana/08d805cd54340f067c05c7b901be2046.jpg\",\n  \"63922.jpg\": \"Aves/Certhia americana/462fa589c737c1c7283d3660f177d9f0.jpg\",\n  \"639225.jpg\": \"Aves/Anastomus lamelligerus/a4f8ccaf82345676e6d07fb9fd04d9a3.jpg\",\n  \"63923.jpg\": \"Aves/Certhia americana/289b61bacd1ddabc73139308ab59d37b.jpg\",\n  \"639248.jpg\": \"Mammalia/Odocoileus hemionus columbianus/4397c33ba000f8aa317c7d72e659ac60.jpg\",\n  \"639267.jpg\": \"Mammalia/Odocoileus hemionus columbianus/2119df17aed5053094a86e7909c62d82.jpg\",\n  \"6393.jpg\": \"Insecta/Glaucopsyche lygdamus/e5a20079e976ef7d0ca1f2d28691ed02.jpg\",\n  \"639304.jpg\": \"Mammalia/Odocoileus hemionus columbianus/d8f805d6314af2894abb6dbed0f128dc.jpg\",\n  \"639332.jpg\": \"Mammalia/Odocoileus hemionus columbianus/5a52b220ee4da7def42c5f5ec4dc7b5d.jpg\",\n  \"63937.jpg\": \"Aves/Certhia americana/b8073a74a25638642e8462a1762e4f7f.jpg\",\n  \"639372.jpg\": \"Mammalia/Odocoileus hemionus columbianus/cb8a03a192e5caaebae567bfa9681aef.jpg\",\n  \"639456.jpg\": \"Mammalia/Urocyon cinereoargenteus/77ddbdc9c7bbe579947def0a84c0cca1.jpg\",\n  \"639465.jpg\": \"Mammalia/Urocyon cinereoargenteus/2ce8ae5474f943dc790d966dd355b0e4.jpg\",\n  \"639491.jpg\": \"Mammalia/Urocyon cinereoargenteus/99ae8dac1f4d9bc8204133a22e82eb3d.jpg\",\n  \"6395.jpg\": \"Insecta/Glaucopsyche lygdamus/7c900dea83ef8dafa3073246597cf959.jpg\",\n  \"63950.jpg\": \"Aves/Certhia americana/58028733e6489b4802b36a93779e13e0.jpg\",\n  \"639520.jpg\": \"Mammalia/Urocyon cinereoargenteus/9878a0ca7fcbce38be36b128d706c6be.jpg\",\n  \"639534.jpg\": \"Mammalia/Urocyon cinereoargenteus/dce2d9b79292088ac37f1bb5a081c7dd.jpg\",\n  \"639537.jpg\": \"Mammalia/Urocyon cinereoargenteus/a7cde96aebb84324d38ee0bd935e48da.jpg\",\n  \"639539.jpg\": \"Mammalia/Urocyon cinereoargenteus/406220a4a942bee50fd855495fae27a1.jpg\",\n  \"63955.jpg\": \"Aves/Certhia americana/2ee42965bc638b4daf7dec6c21f9f14a.jpg\",\n  \"639555.jpg\": \"Mammalia/Urocyon cinereoargenteus/bb988157776bc53b81e33acba0394515.jpg\",\n  \"6396.jpg\": \"Insecta/Glaucopsyche lygdamus/9a9e0cc47b401cfc90214f72f43edd62.jpg\",\n  \"639652.jpg\": \"Insecta/Coenonympha pamphilus/f1c2009a47a25b74a91b844e0159456b.jpg\",\n  \"639658.jpg\": \"Insecta/Coenonympha pamphilus/12a277b7665f49094aaffbf61249101d.jpg\",\n  \"639659.jpg\": \"Insecta/Coenonympha pamphilus/9570e54f9d1dd82ab4d80966f753fde7.jpg\",\n  \"639669.jpg\": \"Insecta/Coenonympha pamphilus/43e56722e2c5bd8db76cee0a8e148f1a.jpg\",\n  \"639672.jpg\": \"Insecta/Coenonympha pamphilus/c0392f5eecb54eb76ac8443f7ea1fb53.jpg\",\n  \"639673.jpg\": \"Insecta/Coenonympha pamphilus/c7130c326dbda983256dfd855f659d25.jpg\",\n  \"639674.jpg\": \"Insecta/Coenonympha pamphilus/5d1eb1b5bd5b5ac6796e73f1f68d0409.jpg\",\n  \"639678.jpg\": \"Insecta/Coenonympha pamphilus/e4c4aeabfbef07f5a182fdcda5bcd44b.jpg\",\n  \"639679.jpg\": \"Insecta/Coenonympha pamphilus/c7e9dbc0926e32073f801096750b7596.jpg\",\n  \"639681.jpg\": \"Insecta/Coenonympha pamphilus/fa7d754049e25f7a03df2c0f0153aeda.jpg\",\n  \"639686.jpg\": \"Insecta/Coenonympha pamphilus/66fd33e270e6b92f3cf036da540ea8e9.jpg\",\n  \"639694.jpg\": \"Amphibia/Duttaphrynus melanostictus/723425e1638fe11609a04a3eff6218ca.jpg\",\n  \"6397.jpg\": \"Insecta/Glaucopsyche lygdamus/561c2b016f1256403f119a22e54b9238.jpg\",\n  \"639737.jpg\": \"Insecta/Celastrina echo/490ac5613ec54415588247f40dd97fbe.jpg\",\n  \"63974.jpg\": \"Aves/Certhia americana/463a50d36447340087c9c0bb22e60836.jpg\",\n  \"639755.jpg\": \"Insecta/Celastrina echo/b9a6df906cfba0a935fff9688eaff356.jpg\",\n  \"639760.jpg\": \"Insecta/Celastrina echo/0f1358f526a559f12a276c52660edd85.jpg\",\n  \"639766.jpg\": \"Insecta/Celastrina echo/94ac96ea653210c43add964e698d54b7.jpg\",\n  \"639799.jpg\": \"Insecta/Celastrina echo/526b86d945c05c0780f2d8be483b81c9.jpg\",\n  \"639814.jpg\": \"Insecta/Celastrina echo/96839c33b051e4787a073df83bdba8ea.jpg\",\n  \"63997.jpg\": \"Aves/Certhia americana/6f1c761a802682217a51ac1bc8d299b1.jpg\",\n  \"64017.jpg\": \"Aves/Certhia americana/4b115b325bf3ff23dd44986645dafedc.jpg\",\n  \"64021.jpg\": \"Aves/Certhia americana/94f7fca817389f24589455e9705d13ce.jpg\",\n  \"64024.jpg\": \"Aves/Certhia americana/bb3128435520e54353c602f23b2c068a.jpg\",\n  \"64029.jpg\": \"Aves/Certhia americana/3291ad5b1bbddb6ed4b7e61e266a848b.jpg\",\n  \"64031.jpg\": \"Aves/Certhia americana/e072a377f45b5f4bef559432f30ae8f6.jpg\",\n  \"640325.jpg\": \"Amphibia/Lithobates palustris/7503adda9d64da8c31c0a5dab32cd473.jpg\",\n  \"640353.jpg\": \"Amphibia/Lithobates palustris/aa1c444bbed6e930af084d270ec6cdb0.jpg\",\n  \"640354.jpg\": \"Amphibia/Lithobates palustris/d237e55293945bf78dfd0fb6422188a2.jpg\",\n  \"640357.jpg\": \"Amphibia/Lithobates palustris/4533a912bcf24c3ce8ded2736892d421.jpg\",\n  \"640364.jpg\": \"Amphibia/Lithobates palustris/cd81e6fff0c6e07eb385f65a02cc0f88.jpg\",\n  \"640374.jpg\": \"Amphibia/Lithobates palustris/325e9653cd32cbd7deee4a93419a2bc1.jpg\",\n  \"640400.jpg\": \"Amphibia/Lithobates palustris/98e726bc7418518730347fdfa2fc333a.jpg\",\n  \"640403.jpg\": \"Amphibia/Lithobates palustris/1df22497027ce76ae48c147ff47101ea.jpg\",\n  \"640404.jpg\": \"Amphibia/Lithobates palustris/d7811a52561f19d2854349f4f40d1059.jpg\",\n  \"64048.jpg\": \"Aves/Certhia americana/6c1d33bd87cbfacf420491f590040185.jpg\",\n  \"64049.jpg\": \"Aves/Certhia americana/55d23969cefc0d8e5e1663edc8372cc9.jpg\",\n  \"6405.jpg\": \"Insecta/Glaucopsyche lygdamus/43b1a27c58e588bcc1de598346472bcf.jpg\",\n  \"6406.jpg\": \"Insecta/Glaucopsyche lygdamus/399f818ef7a14f693a1ff5e8688b2928.jpg\",\n  \"640604.jpg\": \"Aves/Anas strepera/40a9e023cfa846ece51327efb2fac8b0.jpg\",\n  \"640613.jpg\": \"Aves/Anas strepera/2af401a00cbf6eca722fd69cc377cb90.jpg\",\n  \"64066.jpg\": \"Aves/Certhia americana/59a764e4a43bef1f5337fac3b9aaa176.jpg\",\n  \"64067.jpg\": \"Aves/Certhia americana/c71d8b4bd7cf70a7b1fbd016088048d9.jpg\",\n  \"640733.jpg\": \"Aves/Himantopus himantopus/b5ff8fe61e1facee41059173525abba6.jpg\",\n  \"640738.jpg\": \"Aves/Himantopus himantopus/5ae2f3f158d4a64b175825612db593b5.jpg\",\n  \"640747.jpg\": \"Aves/Himantopus himantopus/a845ddf1e50161e21a749cddb73e6158.jpg\",\n  \"640751.jpg\": \"Aves/Himantopus himantopus/924b453295313fa555404678ce82ca2c.jpg\",\n  \"640753.jpg\": \"Aves/Himantopus himantopus/474ae8b1165b5b6abb7db4c3a090f7c7.jpg\",\n  \"640755.jpg\": \"Aves/Himantopus himantopus/a32208244bf44bc99648e3eb7a56e33a.jpg\",\n  \"640758.jpg\": \"Aves/Himantopus himantopus/3e36c2e0ad5f134f28735dc3f9a0e0d6.jpg\",\n  \"640768.jpg\": \"Aves/Himantopus himantopus/7397ab14f262e87c3481802294df0a36.jpg\",\n  \"64077.jpg\": \"Aves/Certhia americana/bee1d7010eba77b593ba802bc02521db.jpg\",\n  \"640784.jpg\": \"Mollusca/Acanthodoris lutea/bc22c40a3bd7299cf1abf9427b7978ec.jpg\",\n  \"640786.jpg\": \"Mollusca/Acanthodoris lutea/dbc1f3d28ec8b2677f1244fa393d3b5f.jpg\",\n  \"640789.jpg\": \"Mollusca/Acanthodoris lutea/aaa7948a9f38b2426b329fe7ffb82df4.jpg\",\n  \"64079.jpg\": \"Aves/Certhia americana/261b973ae8e5228c7edf240745228127.jpg\",\n  \"640794.jpg\": \"Mollusca/Acanthodoris lutea/be11e4c14e5d952ebf4c648a35718be8.jpg\",\n  \"640802.jpg\": \"Mollusca/Acanthodoris lutea/aac73a512727155886f9a8213daf8c3f.jpg\",\n  \"640803.jpg\": \"Mollusca/Acanthodoris lutea/2f7d26921dbbc8bb5723781d5eaa25f5.jpg\",\n  \"640804.jpg\": \"Mollusca/Acanthodoris lutea/3a0e2192b43e9f37741916571a062f0d.jpg\",\n  \"640807.jpg\": \"Mollusca/Acanthodoris lutea/9082d1fc2ee5785cbe212dabc1a5842f.jpg\",\n  \"640808.jpg\": \"Mollusca/Acanthodoris lutea/a78c12f8c29d95a04a57c2b13e180b67.jpg\",\n  \"64081.jpg\": \"Insecta/Megisto rubricata/8854e0780a097e3f3995c043d3b31f4e.jpg\",\n  \"640823.jpg\": \"Aves/Luscinia svecica/11d9ceb7bc2b937276a291f1d4d7c871.jpg\",\n  \"640828.jpg\": \"Aves/Threskiornis aethiopicus/14d30ab6902ab7a913a47f587e710861.jpg\",\n  \"64083.jpg\": \"Insecta/Megisto rubricata/e718e7542444a11997d76c66ca64b53a.jpg\",\n  \"640835.jpg\": \"Aves/Threskiornis aethiopicus/0e36fc5522a9a64b16f7135859187dd3.jpg\",\n  \"640857.jpg\": \"Aves/Archilochus alexandri/9d8cfed75e80db1e35d1eda472666b43.jpg\",\n  \"640860.jpg\": \"Aves/Archilochus alexandri/922eeb6f53046b903f661bde759ce765.jpg\",\n  \"64087.jpg\": \"Insecta/Megisto rubricata/f6be443ee2b28d9aeace06fd207c0d96.jpg\",\n  \"640891.jpg\": \"Aves/Archilochus alexandri/be6611ba57e1bb9ebe37145cfb6ad23a.jpg\",\n  \"640910.jpg\": \"Aves/Archilochus alexandri/5b2c9af1abce0d0929f7e42fad391fb5.jpg\",\n  \"640982.jpg\": \"Aves/Calidris mauri/5a28258ae3c4409973f036935833a345.jpg\",\n  \"640984.jpg\": \"Aves/Calidris mauri/7407f16e6ec120fdb4ab3718f27f0d49.jpg\",\n  \"640995.jpg\": \"Aves/Calidris mauri/997f09c253efed787df401abd3370076.jpg\",\n  \"641002.jpg\": \"Aves/Calidris mauri/6b070fe5ec162951f83f900037fbff2f.jpg\",\n  \"641009.jpg\": \"Aves/Calidris mauri/4c784c73e9d8a1329b350a785095b754.jpg\",\n  \"641016.jpg\": \"Aves/Calidris mauri/593a964fc5b3fa1f108976f0ce2d5bf3.jpg\",\n  \"641019.jpg\": \"Aves/Calidris mauri/4600a655f1d8e95ad1af42177be3b7ce.jpg\",\n  \"641058.jpg\": \"Insecta/Phanaeus vindex/ad0485714819abc7230a3559d28bb9c5.jpg\",\n  \"641065.jpg\": \"Insecta/Phanaeus vindex/7038db196f3e8b37aa80d8db0404748d.jpg\",\n  \"641073.jpg\": \"Reptilia/Sceloporus variabilis/1f8ecd98832623d384e9abe7ef7e0e85.jpg\",\n  \"641077.jpg\": \"Reptilia/Sceloporus variabilis/2ba4f6c9bdc2bd86fe507cf296d65a0d.jpg\",\n  \"641078.jpg\": \"Reptilia/Sceloporus variabilis/138372e04de2b4840b7d561f200d9a75.jpg\",\n  \"641082.jpg\": \"Reptilia/Sceloporus variabilis/13905dcc6428c940176ac90bf918f788.jpg\",\n  \"641083.jpg\": \"Reptilia/Sceloporus variabilis/8c5a428436f0406057e9c46a2fd22fae.jpg\",\n  \"641088.jpg\": \"Reptilia/Sceloporus variabilis/d72e04a5f63ea9ecd96772346d26605b.jpg\",\n  \"641091.jpg\": \"Reptilia/Sceloporus variabilis/0ee73e53aa75d2808444bf271fd6470c.jpg\",\n  \"641099.jpg\": \"Reptilia/Sceloporus variabilis/6fbab7cf98d57e82dbd106ab7aed0ae6.jpg\",\n  \"6411.jpg\": \"Insecta/Glaucopsyche lygdamus/5b44844125c89847870cfd329708579b.jpg\",\n  \"641288.jpg\": \"Reptilia/Uma scoparia/0c837fd70c58cd4a3373e82175b055ff.jpg\",\n  \"641289.jpg\": \"Reptilia/Uma scoparia/0aad161dc4d43b51428a598ebd6cec8f.jpg\",\n  \"641290.jpg\": \"Reptilia/Uma scoparia/6cd8278764ec1d1769e6a045ead3c8d7.jpg\",\n  \"6414.jpg\": \"Insecta/Glaucopsyche lygdamus/2199802c3061374bfb59ade6faedf3d9.jpg\",\n  \"641424.jpg\": \"Aves/Corvus ossifragus/ac7f02332088851bfc947522b565785b.jpg\",\n  \"64143.jpg\": \"Insecta/Hypselonotus punctiventris/4118fe68075e25a2a9ac1e3be78c6456.jpg\",\n  \"641437.jpg\": \"Aves/Paroaria coronata/128fbc61a22a89e759dc7f5573614003.jpg\",\n  \"641438.jpg\": \"Aves/Paroaria coronata/d5716e82a54841b34823d39d8c73c2c4.jpg\",\n  \"641446.jpg\": \"Aves/Paroaria coronata/f7cc67c2e83f0c5c258bca3924da5411.jpg\",\n  \"641447.jpg\": \"Aves/Paroaria coronata/85d3c1c473728ecf915d35d8493ebc1a.jpg\",\n  \"641451.jpg\": \"Aves/Paroaria coronata/dad22dadec48c402faf84e40d0cba2ce.jpg\",\n  \"641463.jpg\": \"Aves/Paroaria coronata/f2ce4e7716a9fb084b0f39d1b35f9fb9.jpg\",\n  \"641464.jpg\": \"Aves/Paroaria coronata/cc5d68b9c6b1f50c5bff67e90f9eaa5e.jpg\",\n  \"641465.jpg\": \"Aves/Paroaria coronata/6d5837f17c8309953cd2202aaecbea9c.jpg\",\n  \"641492.jpg\": \"Reptilia/Intellagama lesueurii/7fa3cebd5ab78d9f9c029811d96d275e.jpg\",\n  \"641498.jpg\": \"Reptilia/Intellagama lesueurii/03acce02cfa9e4657ec3ac13c7949b27.jpg\",\n  \"641516.jpg\": \"Aves/Myioborus pictus/368d5fdad2759e6a841750157112d633.jpg\",\n  \"641517.jpg\": \"Aves/Myioborus pictus/d8dd4ee309f39cff662c2183cf3551b3.jpg\",\n  \"64152.jpg\": \"Insecta/Hypselonotus punctiventris/d425a688eb3fb3c3e26c19b947ed7521.jpg\",\n  \"641531.jpg\": \"Aves/Myioborus pictus/4a6071a5d33d53d1e3b25654b07027e0.jpg\",\n  \"641532.jpg\": \"Aves/Myioborus pictus/fc7b834a27496e841cdd88fd7f20d086.jpg\",\n  \"64158.jpg\": \"Insecta/Hypselonotus punctiventris/22bcaeb1fb8d78de629d38f6e61a47af.jpg\",\n  \"64166.jpg\": \"Insecta/Hypselonotus punctiventris/1b4d9da981eb75e28e4ad4e0c2e2bb57.jpg\",\n  \"64170.jpg\": \"Insecta/Sympetrum costiferum/fb30f41cd265beb9e7509075e1f4d6ba.jpg\",\n  \"64171.jpg\": \"Insecta/Sympetrum costiferum/6c44ec659bf02b86f360028d6e30ba89.jpg\",\n  \"641749.jpg\": \"Insecta/Ochlodes sylvanus/08a2f4792cd3a27a5416422e8f0b89ac.jpg\",\n  \"641752.jpg\": \"Insecta/Ochlodes sylvanus/2a650b6ecc9bb4e173e14e26f7223716.jpg\",\n  \"641753.jpg\": \"Insecta/Ochlodes sylvanus/dd762c8945945201acafd03b4a2c5160.jpg\",\n  \"641760.jpg\": \"Insecta/Ochlodes sylvanus/3a98f763d26c82ac890177754a2ad764.jpg\",\n  \"641769.jpg\": \"Insecta/Apoda y-inversum/c9458d232602f75a1717e3c90cc73531.jpg\",\n  \"64177.jpg\": \"Insecta/Sympetrum costiferum/566087b956dc6866e06a5d5698a11647.jpg\",\n  \"64196.jpg\": \"Insecta/Sympetrum costiferum/27685da4b4b86dd094f11016020bf649.jpg\",\n  \"642122.jpg\": \"Aves/Egretta caerulea/a98d8521c08cdf25108020c4811f459f.jpg\",\n  \"642301.jpg\": \"Aves/Egretta caerulea/f2befd0824aaa8126f200d6d65b5ccc2.jpg\",\n  \"642316.jpg\": \"Aves/Egretta caerulea/38e74b423c746d714ce0f9b554ab4342.jpg\",\n  \"642346.jpg\": \"Aves/Egretta caerulea/5cff51a925e847484bf3031a4ac7ad4f.jpg\",\n  \"6424.jpg\": \"Insecta/Glaucopsyche lygdamus/19e8d263258a70af8f1b0f8021d8eac9.jpg\",\n  \"642448.jpg\": \"Reptilia/Chamaeleo dilepis/24cba0a8ef26328d62a424d35dc3456b.jpg\",\n  \"642457.jpg\": \"Reptilia/Sceloporus uniformis/807cf734294f88bf538aae896b7266e4.jpg\",\n  \"642458.jpg\": \"Reptilia/Sceloporus uniformis/4d4af3142d1f267c9ffbf0795f3a63e1.jpg\",\n  \"642463.jpg\": \"Reptilia/Sceloporus uniformis/7cb1ca9b2387497ce740ae9c11550328.jpg\",\n  \"642476.jpg\": \"Reptilia/Sceloporus uniformis/859e38446ed5b2edeb5ff97e16b82526.jpg\",\n  \"642477.jpg\": \"Reptilia/Sceloporus uniformis/54e549b10028973ae185825ae52710eb.jpg\",\n  \"642483.jpg\": \"Reptilia/Sceloporus uniformis/7748f082e19f5703d32d8523fabf2b6c.jpg\",\n  \"642488.jpg\": \"Reptilia/Sceloporus uniformis/53ec937638b492e6f02358e1a407cab5.jpg\",\n  \"642489.jpg\": \"Reptilia/Sceloporus uniformis/3fd7ccaa1ab7bb445a450a57e7bbc3c7.jpg\",\n  \"642490.jpg\": \"Reptilia/Sceloporus uniformis/765ef4a7d0bac38868df1f5138d89259.jpg\",\n  \"642495.jpg\": \"Insecta/Lestes congener/c864a4dd947ce73b3b765f09c3802b2c.jpg\",\n  \"642506.jpg\": \"Insecta/Lestes congener/c917f1f2876ff9bc84a1e5726987b6a2.jpg\",\n  \"642515.jpg\": \"Insecta/Lestes congener/15ad3eb915e34d69dedb502093554be5.jpg\",\n  \"642516.jpg\": \"Insecta/Lestes congener/2bef1c76dc3cdf14df159c93f8764f5e.jpg\",\n  \"642529.jpg\": \"Aves/Catharus guttatus/446f6e4875ee577d1f2b7c82516cc9b3.jpg\",\n  \"642538.jpg\": \"Aves/Catharus guttatus/386b6de4ec82a8a76152374053d5f8bb.jpg\",\n  \"64254.jpg\": \"Aves/Passerina ciris/5e10212586a73182ec6361f1d4c02ea4.jpg\",\n  \"642541.jpg\": \"Aves/Catharus guttatus/8e114323ef53efa15da4ec9a863be50e.jpg\",\n  \"64255.jpg\": \"Aves/Passerina ciris/e44431275e3dc81c331c0168dd04c45d.jpg\",\n  \"64257.jpg\": \"Aves/Passerina ciris/c97de33ab2cccb7bae28de8368d2d1c7.jpg\",\n  \"642578.jpg\": \"Aves/Catharus guttatus/1c8e20e42cc815691ebff454e837bbe5.jpg\",\n  \"642633.jpg\": \"Aves/Catharus guttatus/ecab05d384e0d4f39bab706ade1ac8d1.jpg\",\n  \"642681.jpg\": \"Aves/Catharus guttatus/3695ef06951853e33eb7a4bacc9ab20a.jpg\",\n  \"64271.jpg\": \"Aves/Passerina ciris/1bd5831bb4a4af11a8c2e6b7f8b6d8bc.jpg\",\n  \"642747.jpg\": \"Aves/Catharus guttatus/67c6724964efb38e5e238688cfee75df.jpg\",\n  \"642769.jpg\": \"Insecta/Chrysochus auratus/160d3a2e2102e74ff5f1808e617d0c30.jpg\",\n  \"642770.jpg\": \"Insecta/Chrysochus auratus/c98e54165deaaa889be63e6d9b1da714.jpg\",\n  \"642775.jpg\": \"Insecta/Chrysochus auratus/92956158109d12a6002595b683b273c7.jpg\",\n  \"642796.jpg\": \"Insecta/Chrysochus auratus/aff56fe24b2c294903d1329466ded51b.jpg\",\n  \"6428.jpg\": \"Insecta/Glaucopsyche lygdamus/f7b2bcb2243dcb2cbda55fb9620e1194.jpg\",\n  \"642800.jpg\": \"Insecta/Chrysochus auratus/87f718de79f77232d95442bee723f15f.jpg\",\n  \"642830.jpg\": \"Insecta/Erythemis simplicicollis/a810b91ea016beb9996e77a1a7e34684.jpg\",\n  \"6429.jpg\": \"Insecta/Glaucopsyche lygdamus/d618ea05a560f4a50c1e73d4d52219b5.jpg\",\n  \"64291.jpg\": \"Aves/Passerina ciris/daf25884611ac019418b5781083ece8b.jpg\",\n  \"642922.jpg\": \"Insecta/Erythemis simplicicollis/c5d1fecbb549273d24164f1a03a7890b.jpg\",\n  \"642992.jpg\": \"Insecta/Erythemis simplicicollis/ab4944fa7464bc530faa19986809b51e.jpg\",\n  \"64303.jpg\": \"Aves/Passerina ciris/a3a1a2963bbf17b6e27f225e623ac907.jpg\",\n  \"643046.jpg\": \"Insecta/Erythemis simplicicollis/1d4b72da38688d8fcaa8fb10e72d96b7.jpg\",\n  \"64308.jpg\": \"Aves/Passerina ciris/26accd9eebdb4e022e667cb7a9d1ea70.jpg\",\n  \"64309.jpg\": \"Aves/Passerina ciris/9988254adbfcc53a26a227b4e4777379.jpg\",\n  \"643122.jpg\": \"Insecta/Erythemis simplicicollis/fa3e91cf5cf945e94ddd7694c6d72a3d.jpg\",\n  \"64314.jpg\": \"Aves/Passerina ciris/fcf94d9cf89f7ea49136ffea7972cbd2.jpg\",\n  \"643236.jpg\": \"Insecta/Erythemis simplicicollis/58ad18a4c69071ed53b80d8e544fa000.jpg\",\n  \"643300.jpg\": \"Insecta/Erythemis simplicicollis/f0362a98d8b223c98a7e8a5393f1f6bd.jpg\",\n  \"643324.jpg\": \"Insecta/Erythemis simplicicollis/8eec07d62cfd75dbc36469afd2e6a406.jpg\",\n  \"643347.jpg\": \"Insecta/Erythemis simplicicollis/878279fb10dd737e389d4dde982da5ac.jpg\",\n  \"64339.jpg\": \"Aves/Passerina ciris/944dba6c4a690d56792a6a4fe79dd7c3.jpg\",\n  \"643453.jpg\": \"Aves/Icterus wagleri/235fb41659cee3576ffca5e7e0b9baa2.jpg\",\n  \"643456.jpg\": \"Aves/Icterus wagleri/6f2243226721b4f5ecfbae136f7004cc.jpg\",\n  \"64346.jpg\": \"Aves/Passerina ciris/11c08357a1e00acb4d60db66fbfaf90e.jpg\",\n  \"643462.jpg\": \"Aves/Cyanocompsa parellina/265778fa58fae127bcfa7e1ab6c4024a.jpg\",\n  \"643474.jpg\": \"Reptilia/Crotaphytus bicinctores/3e36b249b84e00caec3123dd518bd9c7.jpg\",\n  \"643476.jpg\": \"Reptilia/Crotaphytus bicinctores/f08e3cd93b6c94fda54191d73fb417c9.jpg\",\n  \"643483.jpg\": \"Reptilia/Crotaphytus bicinctores/1fe49e57bf923af7db0190c560bf182e.jpg\",\n  \"643485.jpg\": \"Reptilia/Crotaphytus bicinctores/5aae354aabde47c87b9c0ca76563ff28.jpg\",\n  \"64349.jpg\": \"Aves/Passerina ciris/90dd448213bd57524e7357403de53f8e.jpg\",\n  \"643496.jpg\": \"Actinopterygii/Lepomis cyanellus/021af01bdc61efd9a8a2db59efc6eacf.jpg\",\n  \"643497.jpg\": \"Actinopterygii/Lepomis cyanellus/ab45d9c557770a1397102c9c6572bbb3.jpg\",\n  \"643500.jpg\": \"Actinopterygii/Lepomis cyanellus/44f733b05c7eb08a158fc9ec914b72fc.jpg\",\n  \"643512.jpg\": \"Actinopterygii/Lepomis cyanellus/6fa2b829cea778d12579b696f49bc7d7.jpg\",\n  \"643517.jpg\": \"Actinopterygii/Lepomis cyanellus/2f4b00d3ed6fbed90fa7e90c4828471b.jpg\",\n  \"643519.jpg\": \"Actinopterygii/Lepomis cyanellus/da5dab83fb80e8e46bf33409046e47a4.jpg\",\n  \"643525.jpg\": \"Reptilia/Elgaria coerulea/3156ff760f5b33d774533962e09524b8.jpg\",\n  \"643526.jpg\": \"Reptilia/Elgaria coerulea/c5181b67454ac6b8dc0cf237e240241b.jpg\",\n  \"643527.jpg\": \"Reptilia/Elgaria coerulea/2b0d69ecf734ab245d7bbba3661ab5a5.jpg\",\n  \"643528.jpg\": \"Reptilia/Elgaria coerulea/97ac89e0e37f827800d486b100613daa.jpg\",\n  \"643529.jpg\": \"Reptilia/Elgaria coerulea/465df5b5e5df707763e800f1840df0de.jpg\",\n  \"643531.jpg\": \"Reptilia/Elgaria coerulea/ebad6deb409e5e9abe30bdd6db904578.jpg\",\n  \"643534.jpg\": \"Reptilia/Elgaria coerulea/596dcaa7801a4b14ca322511cd26365a.jpg\",\n  \"643535.jpg\": \"Reptilia/Elgaria coerulea/28a37ad9bd433d69dd0234cbe682152d.jpg\",\n  \"643536.jpg\": \"Reptilia/Elgaria coerulea/5c4e69327593c25eedc8a30dda327fe9.jpg\",\n  \"643537.jpg\": \"Reptilia/Elgaria coerulea/1ee8fbf628634fa71a5aa9071ac36f86.jpg\",\n  \"643539.jpg\": \"Reptilia/Elgaria coerulea/659ab98b0c6a1a6a9404a8c30701fe42.jpg\",\n  \"643540.jpg\": \"Reptilia/Elgaria coerulea/9c809a45514ef042bff9601e00dacdb9.jpg\",\n  \"643551.jpg\": \"Aves/Bubo scandiacus/622116e688ce39bcec3ba9978822fb1b.jpg\",\n  \"643556.jpg\": \"Aves/Bubo scandiacus/81b7b7fa1240ee4ffd84e6bd0ffcdd2c.jpg\",\n  \"643557.jpg\": \"Aves/Bubo scandiacus/6e3c833e3d20c9777e991cd96dc3894c.jpg\",\n  \"643591.jpg\": \"Aves/Bubo scandiacus/aa4f7e438894202a89366172f79b8cb7.jpg\",\n  \"643600.jpg\": \"Aves/Bubo scandiacus/4680eae2c8dea780275db75f438970f0.jpg\",\n  \"643613.jpg\": \"Aves/Bubo scandiacus/3398a9ef1327976bfd05220a0b8b0483.jpg\",\n  \"643617.jpg\": \"Aves/Bubo scandiacus/597599bb787330839ff443760414390b.jpg\",\n  \"643618.jpg\": \"Aves/Bubo scandiacus/5085627d21e50e928fdbf3514082c242.jpg\",\n  \"643681.jpg\": \"Aves/Egretta thula/f3cb2690ab3da6adfedfdfd8e8b97c42.jpg\",\n  \"64370.jpg\": \"Aves/Passerina ciris/6b67aa75bfc3b2327d278e5b73de0205.jpg\",\n  \"64374.jpg\": \"Aves/Passerina ciris/a2f59d846826e1e4a078df83fdc8e76b.jpg\",\n  \"64377.jpg\": \"Aves/Passerina ciris/e1f6090d748d238aa67e54716ce8ce14.jpg\",\n  \"643841.jpg\": \"Aves/Egretta thula/36d930644326709a6175adfc0ef82e5a.jpg\",\n  \"64389.jpg\": \"Aves/Passerina ciris/04797bc240ab529ea6202d1eb0e925f4.jpg\",\n  \"64390.jpg\": \"Aves/Passerina ciris/981bff8a9cd7b61b4d0fe9bd9d3bc23f.jpg\",\n  \"643999.jpg\": \"Aves/Egretta thula/adae84e9c2badcb674941988219fc892.jpg\",\n  \"644.jpg\": \"Mammalia/Ursus americanus/98c9a73399f9c607681d3852e5c67de7.jpg\",\n  \"644088.jpg\": \"Aves/Egretta thula/49e0683ba7b00c5754b83072bbc149a5.jpg\",\n  \"64413.jpg\": \"Aves/Passerina ciris/ed8dbf33e6efcf4c967b779787a5b32b.jpg\",\n  \"644174.jpg\": \"Aves/Egretta thula/fc6fc85734a45e140bb2f2acf5e74530.jpg\",\n  \"64434.jpg\": \"Aves/Passerina ciris/5fc262764948e83ec0044ac13484b626.jpg\",\n  \"64436.jpg\": \"Aves/Passerina ciris/310909f05b213346034407e4daaf31a0.jpg\",\n  \"64440.jpg\": \"Aves/Passerina ciris/9e51385e1984696ace730d2fe8a04a1c.jpg\",\n  \"64442.jpg\": \"Aves/Passerina ciris/2fa7fad56fd17592666a396f69942aec.jpg\",\n  \"644427.jpg\": \"Aves/Egretta thula/761ebc2e5e9cdd4a32f115673f6458db.jpg\",\n  \"64460.jpg\": \"Aves/Passerina ciris/fb0a766d7b694de9934924402275b4fb.jpg\",\n  \"64463.jpg\": \"Aves/Passerina ciris/8dda4e5702bd741cdb223d4583510415.jpg\",\n  \"64465.jpg\": \"Aves/Passerina ciris/db678ec9656c817038905e65c16f9031.jpg\",\n  \"644693.jpg\": \"Aves/Calypte anna/b9a6ee5175925b0a8cee030d24fb10d2.jpg\",\n  \"644739.jpg\": \"Aves/Calypte anna/ee92b731b0f91dfc36d9d1c8358f50f9.jpg\",\n  \"64490.jpg\": \"Aves/Empidonax fulvifrons/4fc6617950679f2d59bd6bbd0909032b.jpg\",\n  \"644950.jpg\": \"Aves/Calypte anna/7464c147a9f5c0ec6489bd6ab5c26e20.jpg\",\n  \"645049.jpg\": \"Aves/Calypte anna/241942aa9c740eacdc976773e0f80288.jpg\",\n  \"64509.jpg\": \"Aves/Empidonax fulvifrons/3a6c918c4e8e2ec9d58e119a5eda09c6.jpg\",\n  \"64510.jpg\": \"Aves/Empidonax fulvifrons/ee9869b50c0aa98f6423aaecfc5a3cf7.jpg\",\n  \"645133.jpg\": \"Aves/Piranga olivacea/8fb9f6ec3f98d420ba213645687d82c8.jpg\",\n  \"645138.jpg\": \"Aves/Piranga olivacea/de53662d65d489eab73cbb0217bebb45.jpg\",\n  \"645145.jpg\": \"Aves/Piranga olivacea/97a9a9671b2557fb40b77cda394f8cdf.jpg\",\n  \"645149.jpg\": \"Aves/Piranga olivacea/829d625d2777949102869636a2343411.jpg\",\n  \"645158.jpg\": \"Aves/Piranga olivacea/26ff7a3e77b04047cc8397a1edc7954e.jpg\",\n  \"645161.jpg\": \"Aves/Piranga olivacea/608bc29487552a24daa190b0e73569ee.jpg\",\n  \"645168.jpg\": \"Aves/Piranga olivacea/2b8eea02ad9372662e51a9dfe232b61c.jpg\",\n  \"645175.jpg\": \"Aves/Piranga olivacea/ad90a0db8c8280f9b52a69675a46f33a.jpg\",\n  \"645185.jpg\": \"Aves/Piranga olivacea/86b3fbade5fa567a04c5c17710351b5a.jpg\",\n  \"645204.jpg\": \"Aves/Piranga olivacea/58141d56f91c59f6515cc2fb974f7583.jpg\",\n  \"645260.jpg\": \"Aves/Psaltriparus minimus/57d5c8f9b46489a2c3e9f75b2ac69309.jpg\",\n  \"645322.jpg\": \"Aves/Psaltriparus minimus/fc4bede7ca091cc4d0a7923f46eb2ae4.jpg\",\n  \"645340.jpg\": \"Aves/Psaltriparus minimus/c7d92fe7bf4a9f2f2bc03d94388223bb.jpg\",\n  \"645378.jpg\": \"Aves/Psaltriparus minimus/29042f1d88a0ecab1f320df7f387285d.jpg\",\n  \"645379.jpg\": \"Aves/Psaltriparus minimus/500020012edcabd30e477fb193267fc3.jpg\",\n  \"645471.jpg\": \"Aves/Cistothorus platensis/40a64255acfdb7090c84739d5c120c55.jpg\",\n  \"645477.jpg\": \"Aves/Scopus umbretta/f423c8994882a7839bd681ae5f49e336.jpg\",\n  \"645482.jpg\": \"Aves/Scopus umbretta/632ac5fc3363c74f55f6e94d2e69f77b.jpg\",\n  \"645483.jpg\": \"Aves/Scopus umbretta/735674bd794f478fec138b6e457bdb7e.jpg\",\n  \"645577.jpg\": \"Aves/Geothlypis trichas/048e124dcc2bcd1f0ec877514947c053.jpg\",\n  \"645578.jpg\": \"Aves/Geothlypis trichas/4d2bc4ad15c136667aa31c9486078a11.jpg\",\n  \"64573.jpg\": \"Insecta/Oxythyrea funesta/3cc8ff42059585455b9c0a0762448c5b.jpg\",\n  \"64574.jpg\": \"Insecta/Oxythyrea funesta/346c2386f6290df3da9ffa9bf6818c62.jpg\",\n  \"64575.jpg\": \"Insecta/Oxythyrea funesta/50461d9e30201dd34a1ff839afba58b0.jpg\",\n  \"645776.jpg\": \"Aves/Geothlypis trichas/26e229e8f7380b224f8402b02c046150.jpg\",\n  \"645832.jpg\": \"Aves/Anas crecca carolinensis/f59c41e719c78e0aec075b94a3c621c8.jpg\",\n  \"645833.jpg\": \"Aves/Anas crecca carolinensis/97acc62a5747f87e9ff0aa7622239c99.jpg\",\n  \"645834.jpg\": \"Aves/Anas crecca carolinensis/e0b455caeac1460c089198760b0ef56e.jpg\",\n  \"645837.jpg\": \"Aves/Anas crecca carolinensis/c372c54b598696fd058be036f35b47a9.jpg\",\n  \"645839.jpg\": \"Aves/Anas crecca carolinensis/7d3dac1dfa7df7cc5b616790ba3e8d09.jpg\",\n  \"645843.jpg\": \"Aves/Anas crecca carolinensis/991dcce98edd5a98f881a886da425974.jpg\",\n  \"645844.jpg\": \"Aves/Anas crecca carolinensis/46ddf0acd641f4f3e7688c32b98b16c1.jpg\",\n  \"645850.jpg\": \"Aves/Anas crecca carolinensis/8ab13e777b4112e56c75732057b8a0f4.jpg\",\n  \"64590.jpg\": \"Aves/Alopochen aegyptiaca/64835fb13dd817de45e4137b7b12d385.jpg\",\n  \"645980.jpg\": \"Aves/Asio flammeus/0de6b53c4134d03f682a1a9062625d11.jpg\",\n  \"645981.jpg\": \"Aves/Asio flammeus/a7e6004053605623287c91d3b6264447.jpg\",\n  \"645983.jpg\": \"Aves/Asio flammeus/f4c0818460443a5916402429513e6173.jpg\",\n  \"645984.jpg\": \"Aves/Asio flammeus/2d6526ceeda4f701809102b7f4399b4b.jpg\",\n  \"645985.jpg\": \"Aves/Asio flammeus/16ba62f91748b5afa0fd20aac2e9b0da.jpg\",\n  \"645993.jpg\": \"Aves/Asio flammeus/5aaa77f5247cdb3fa66163088630593b.jpg\",\n  \"645995.jpg\": \"Aves/Asio flammeus/65134d6356eb218a977371d0a246c355.jpg\",\n  \"645998.jpg\": \"Aves/Asio flammeus/3d6435c52820c3b7c1e523c34da96a67.jpg\",\n  \"64600.jpg\": \"Aves/Alopochen aegyptiaca/5336b71bb55560691567ae348ae29102.jpg\",\n  \"64602.jpg\": \"Aves/Alopochen aegyptiaca/1f94936828487412b63b9398bc596a3b.jpg\",\n  \"646047.jpg\": \"Aves/Chroicocephalus philadelphia/c9613e6d5c95b14d372ec6f53cfabf27.jpg\",\n  \"646048.jpg\": \"Aves/Chroicocephalus philadelphia/0f1292a99347a051030f59b5d96bbd11.jpg\",\n  \"646078.jpg\": \"Aves/Chroicocephalus philadelphia/c1ded35d5c53c78d20cf130890dee4ff.jpg\",\n  \"646088.jpg\": \"Aves/Chroicocephalus philadelphia/74c7d5c31c2c7167bb8eccc6e5ee46c8.jpg\",\n  \"646105.jpg\": \"Aves/Chroicocephalus philadelphia/8ef37323d7b1f9d6adca1ab491d3d996.jpg\",\n  \"64612.jpg\": \"Aves/Alopochen aegyptiaca/f4854297ec6c6c237b9572f067743d31.jpg\",\n  \"64616.jpg\": \"Aves/Alopochen aegyptiaca/202f75c17ad0ccf36c9bb9c1937c7484.jpg\",\n  \"646181.jpg\": \"Mammalia/Sciurus aberti/fa895f60b2009212a6b2d4ff04eac166.jpg\",\n  \"646182.jpg\": \"Mammalia/Sciurus aberti/3cfd3c6c060ee1cf770d2deffab696d1.jpg\",\n  \"646185.jpg\": \"Mammalia/Sciurus aberti/4f8b205ccd4b5f21db6c45b068fcaadd.jpg\",\n  \"64623.jpg\": \"Aves/Alopochen aegyptiaca/cf28d7f92f4f9512691d11544798db27.jpg\",\n  \"646240.jpg\": \"Insecta/Asterocampa clyton/bad6ad755f7ed263afbb20d3884b9c19.jpg\",\n  \"646258.jpg\": \"Insecta/Asterocampa clyton/d0bb4e701e1719f03cfac1fffa21d6da.jpg\",\n  \"646293.jpg\": \"Insecta/Asterocampa clyton/fac4afac5995022ccc0bbacba956067c.jpg\",\n  \"646298.jpg\": \"Insecta/Asterocampa clyton/8767892d39a65e8da596386abee87537.jpg\",\n  \"64630.jpg\": \"Aves/Alopochen aegyptiaca/f08fc6cad68d6273d93d9005b69d2461.jpg\",\n  \"646304.jpg\": \"Insecta/Asterocampa clyton/fa93365b75baafe90baaff7b1baaa8c2.jpg\",\n  \"646316.jpg\": \"Insecta/Asterocampa clyton/6d1cb959f9c1f876218cacf1cd1da9ea.jpg\",\n  \"646356.jpg\": \"Aves/Calidris bairdii/e50a09afb1701f80f5f7e37a3f882b05.jpg\",\n  \"64636.jpg\": \"Aves/Alopochen aegyptiaca/074c3d99a210af4f01557d86b4f64dbc.jpg\",\n  \"646361.jpg\": \"Aves/Calidris bairdii/6fcbc5ced366f6a0c82ea35203b34cf0.jpg\",\n  \"646366.jpg\": \"Aves/Calidris bairdii/4c5b36685dd10461c42e3ee59b90725d.jpg\",\n  \"646368.jpg\": \"Aves/Calidris bairdii/8f83b8eb072870be179685a3a2d98dbd.jpg\",\n  \"646370.jpg\": \"Aves/Calidris bairdii/3d3fcd88887563f96c6587998744a4b7.jpg\",\n  \"646378.jpg\": \"Aves/Calidris bairdii/2d0b27d5a4036b3aeeec97b86f48c86a.jpg\",\n  \"646379.jpg\": \"Aves/Calidris bairdii/018b4158200d841ea6bdfc19db144a31.jpg\",\n  \"646380.jpg\": \"Aves/Calidris bairdii/c4059b8b5b4f1395866e9555b162a3a3.jpg\",\n  \"646417.jpg\": \"Aves/Larus fuscus/9af8b3722c210e344d36e4a22bce5774.jpg\",\n  \"646421.jpg\": \"Aves/Larus fuscus/7783bb41cb4f071c468b21273b3c57d2.jpg\",\n  \"646426.jpg\": \"Aves/Larus fuscus/84e94d221b43b0bc26a0a659afea810a.jpg\",\n  \"646432.jpg\": \"Aves/Larus fuscus/054223649d94790886f073799094e2e7.jpg\",\n  \"646452.jpg\": \"Aves/Garrulus glandarius/726b56ee64cfd114ee8a4fefbad1483d.jpg\",\n  \"646453.jpg\": \"Aves/Garrulus glandarius/26faf3039bfb0898bd8711cab8d78ba5.jpg\",\n  \"64646.jpg\": \"Aves/Alopochen aegyptiaca/c1462d97cca6049b08439be243a8de36.jpg\",\n  \"646461.jpg\": \"Aves/Garrulus glandarius/c696cd7f9e34c1888a9feb2cc7379d2c.jpg\",\n  \"646477.jpg\": \"Aves/Garrulus glandarius/ed598991d5435fe6c51ae77924bdc8ba.jpg\",\n  \"646479.jpg\": \"Aves/Garrulus glandarius/45010cd044795fc6e95eb44def7ff7cf.jpg\",\n  \"646482.jpg\": \"Aves/Garrulus glandarius/508c01e3177f333cf39db8109025c620.jpg\",\n  \"646492.jpg\": \"Aves/Garrulus glandarius/f7e14c6b4ef4130bfbe966f34ae082fa.jpg\",\n  \"646517.jpg\": \"Aves/Somateria mollissima/83efbdc8391fb7b0d781d520ae7dde1e.jpg\",\n  \"64652.jpg\": \"Aves/Alopochen aegyptiaca/3d3723d2efe70d3abea9ad6198d49319.jpg\",\n  \"646546.jpg\": \"Aves/Somateria mollissima/d286383e9ace55fe3664cff711b465c0.jpg\",\n  \"646548.jpg\": \"Aves/Somateria mollissima/ca41af815afa6a71a53606bf0988ac26.jpg\",\n  \"64658.jpg\": \"Aves/Alopochen aegyptiaca/94cad61f0cb024f73a25588c39f8dff0.jpg\",\n  \"646616.jpg\": \"Aves/Buteo lineatus/9a0d3fcb1c7d42878c1c6d311983cd53.jpg\",\n  \"646641.jpg\": \"Aves/Buteo lineatus/f052787361f5ccd434dc05983cd3f54f.jpg\",\n  \"646643.jpg\": \"Aves/Buteo lineatus/bfed614e467f8b27b27f411799d77a39.jpg\",\n  \"646736.jpg\": \"Aves/Buteo lineatus/2a7a6f8a2fcd41f1facbabcbf05c3421.jpg\",\n  \"646770.jpg\": \"Aves/Buteo lineatus/c933eb07204bf26a3635ef22afd20c9d.jpg\",\n  \"64690.jpg\": \"Aves/Alopochen aegyptiaca/9548dd1e4774659beb1c347c99624117.jpg\",\n  \"646962.jpg\": \"Aves/Buteo lineatus/67950163edb7af32d665386d36fe7cdf.jpg\",\n  \"647007.jpg\": \"Aves/Buteo lineatus/74ec4f9a854e04f4890e24e8338feea0.jpg\",\n  \"647131.jpg\": \"Reptilia/Lampropeltis triangulum triangulum/dbf6ac2d6a0e72de5cf3a4b200fdb113.jpg\",\n  \"647137.jpg\": \"Reptilia/Lampropeltis triangulum triangulum/93c2d6af248765a3cc8654c50cfd478f.jpg\",\n  \"647138.jpg\": \"Reptilia/Lampropeltis triangulum triangulum/ee0f6a1d27f9de01e9bc9de43cc51d03.jpg\",\n  \"647141.jpg\": \"Reptilia/Lampropeltis triangulum triangulum/2558861acea4c51819cca0243f694e5c.jpg\",\n  \"647143.jpg\": \"Reptilia/Lampropeltis triangulum triangulum/44e450c08a7914ac8d2979f06eb54d23.jpg\",\n  \"647151.jpg\": \"Reptilia/Lampropeltis triangulum triangulum/ab044ba82faf3390d0c70c768944cfbe.jpg\",\n  \"647178.jpg\": \"Aves/Aythya fuligula/28e0b67704ac3c84a4b45de5a1abe5c2.jpg\",\n  \"647214.jpg\": \"Aves/Aythya fuligula/72baf717d1dd7b07e83b02f3d2b278ae.jpg\",\n  \"647222.jpg\": \"Aves/Aythya fuligula/360475f617dc3b7e7c3a48f86e864a2c.jpg\",\n  \"647228.jpg\": \"Aves/Aythya fuligula/7df20432c993ffb17c5ec1c64e9f822f.jpg\",\n  \"647257.jpg\": \"Insecta/Atteva aurea/6082315862b36483e092b81d8d8b28ac.jpg\",\n  \"64731.jpg\": \"Insecta/Phocides polybius/164146a0ecc5cddbc2023ddee9b3e8ca.jpg\",\n  \"647311.jpg\": \"Insecta/Atteva aurea/d748da90385cdf3bc16211b9b9b96189.jpg\",\n  \"647340.jpg\": \"Insecta/Atteva aurea/250567b21f937e9fe23037b4cdb1ce66.jpg\",\n  \"647359.jpg\": \"Insecta/Atteva aurea/748f1996b84b7ce514b36611a1fb4c64.jpg\",\n  \"647363.jpg\": \"Insecta/Atteva aurea/523603fd8089fa82d44301d2557100ab.jpg\",\n  \"64737.jpg\": \"Insecta/Phocides polybius/7017f9d46fff5c357a1a0b6e96945a49.jpg\",\n  \"647377.jpg\": \"Insecta/Atteva aurea/fe5b2f3ab18d6346f6a8bab91ab54060.jpg\",\n  \"647395.jpg\": \"Aves/Melozone aberti/a33071ae31a765129d65177d3e6e2ab9.jpg\",\n  \"64741.jpg\": \"Insecta/Phocides polybius/d83103440afec470081176f946932e9e.jpg\",\n  \"647424.jpg\": \"Aves/Baeolophus bicolor/03b3d697c7a11254182e7831e87fc4d7.jpg\",\n  \"647431.jpg\": \"Aves/Baeolophus bicolor/45eca22792cbe9d4827a65e7a949dd46.jpg\",\n  \"64744.jpg\": \"Insecta/Phocides polybius/81d579c6b1dfaaf7daa2af24acabbc73.jpg\",\n  \"64747.jpg\": \"Insecta/Phocides polybius/ba8d6f697ce6c0e7b3921e924a41a3a8.jpg\",\n  \"64749.jpg\": \"Insecta/Phocides polybius/0b76e82ef874e61d2e8d145127b663b1.jpg\",\n  \"647500.jpg\": \"Aves/Baeolophus bicolor/09275838407c4ebb40354e1a504b48f3.jpg\",\n  \"647565.jpg\": \"Aves/Baeolophus bicolor/3159d3ecdfc82348bb35872d02745a29.jpg\",\n  \"647569.jpg\": \"Aves/Baeolophus bicolor/1415e79a1ed52f858b7cf3b99d2346f1.jpg\",\n  \"647689.jpg\": \"Aves/Thryomanes bewickii/a7d3ceb201767ebc10d472db1236fb3a.jpg\",\n  \"647745.jpg\": \"Aves/Thryomanes bewickii/dd10c42c03847850bbecb8543528486a.jpg\",\n  \"647914.jpg\": \"Aves/Thryomanes bewickii/202939ac9932060d3c1b1bca2f5fa4ba.jpg\",\n  \"647958.jpg\": \"Aves/Thryomanes bewickii/9622ab6b388f8a6aca6b0a86fb91326b.jpg\",\n  \"647998.jpg\": \"Insecta/Nyctemera annulata/777914e5cf186c0303d7d9826b0e1f5e.jpg\",\n  \"648007.jpg\": \"Aves/Piranga ludoviciana/bde380edd150ba844c3b5550c332f12d.jpg\",\n  \"648011.jpg\": \"Aves/Piranga ludoviciana/e64b6acc9b33fe288922a1a5a7891e3e.jpg\",\n  \"648030.jpg\": \"Aves/Piranga ludoviciana/73ca00081c09942097ea6547d65f947b.jpg\",\n  \"648073.jpg\": \"Aves/Piranga ludoviciana/7ab36ed47581b0b0d712971fd8df100c.jpg\",\n  \"648081.jpg\": \"Aves/Piranga ludoviciana/fb6e92d5fe9190af49f3b40b27571c90.jpg\",\n  \"648104.jpg\": \"Aves/Piranga ludoviciana/94904c52c32ecde6883ecaeaa4e504e9.jpg\",\n  \"648150.jpg\": \"Aves/Corvus monedula/276b4bea1dcdc31e7f1dca07aafffb56.jpg\",\n  \"648160.jpg\": \"Aves/Corvus monedula/f0f4903e77a91e532abbb7cc0eb9dea0.jpg\",\n  \"648161.jpg\": \"Aves/Corvus monedula/a77cf14f6bdb4c7a9414c9781b924a47.jpg\",\n  \"648162.jpg\": \"Aves/Corvus monedula/cec28455f066dbdf36ec5e167cca9b13.jpg\",\n  \"648164.jpg\": \"Aves/Corvus monedula/92c51f759a4a17fe757d7dd5d69abd10.jpg\",\n  \"648168.jpg\": \"Aves/Corvus monedula/7e07689aca35365dd104a0fa9db4d0f6.jpg\",\n  \"648175.jpg\": \"Aves/Corvus monedula/14cf01e80c2c124045d445ebc544e384.jpg\",\n  \"648232.jpg\": \"Insecta/Pseudosphinx tetrio/325ebe6c98a2fb8843034f428df2c404.jpg\",\n  \"648236.jpg\": \"Aves/Gymnogyps californianus/9c9eeee14673189d59fc57d76ac6f96c.jpg\",\n  \"648237.jpg\": \"Aves/Gymnogyps californianus/66134443d4474a03b8a47cef4e511551.jpg\",\n  \"648239.jpg\": \"Aves/Gymnogyps californianus/245b3b49b08f244835b4e69dcb56c250.jpg\",\n  \"648246.jpg\": \"Aves/Gymnogyps californianus/2e3450f4ee0171102501ae37112bba87.jpg\",\n  \"648248.jpg\": \"Aves/Gymnogyps californianus/0e14f198df0795b29604bced592de00f.jpg\",\n  \"648249.jpg\": \"Aves/Gymnogyps californianus/997aee5232db147a3d415a614654edfd.jpg\",\n  \"648256.jpg\": \"Aves/Gymnogyps californianus/5e327c41f5f215d73b31320f53660d8c.jpg\",\n  \"648257.jpg\": \"Aves/Gymnogyps californianus/b5dd78e9b33a2b04df352145c2419f02.jpg\",\n  \"648258.jpg\": \"Aves/Gymnogyps californianus/3fe806ef3c666fc625c5350eb3f7f41a.jpg\",\n  \"648265.jpg\": \"Aves/Gymnogyps californianus/067bcb614b63ad6abd2b174f138f0dd1.jpg\",\n  \"648299.jpg\": \"Aves/Phasianus colchicus/3542e2134592468df746bd2032960c0f.jpg\",\n  \"648302.jpg\": \"Aves/Phasianus colchicus/0061af21499a5fb27120110e482dc353.jpg\",\n  \"648334.jpg\": \"Aves/Phasianus colchicus/0e90d211c640302952e9728d79b6a953.jpg\",\n  \"648350.jpg\": \"Aves/Phasianus colchicus/a2d994159d321a87bf5a8c93c137839e.jpg\",\n  \"648356.jpg\": \"Aves/Phasianus colchicus/f836373c341744be19efe57e0091584e.jpg\",\n  \"648358.jpg\": \"Aves/Phasianus colchicus/ce754d4538faa1d0c0fab7e627eda687.jpg\",\n  \"648372.jpg\": \"Aves/Contopus cooperi/1e4b809e70af5441cfdc31e13ba0ffa3.jpg\",\n  \"648373.jpg\": \"Aves/Contopus cooperi/5587da919c32339823a320e15d367dd6.jpg\",\n  \"648374.jpg\": \"Aves/Contopus cooperi/5356d8e78119cf783c5d9fcffdaaf168.jpg\",\n  \"648379.jpg\": \"Aves/Contopus cooperi/928eaf0df85ed269c474686d79886a63.jpg\",\n  \"648385.jpg\": \"Aves/Contopus cooperi/83a8b5ab8db20321039bcf600b59eb59.jpg\",\n  \"648386.jpg\": \"Aves/Contopus cooperi/ee80010abb76243e4af6a204ab56ac66.jpg\",\n  \"648387.jpg\": \"Aves/Contopus cooperi/536129977a693c80e2e389f77c34dfe2.jpg\",\n  \"648396.jpg\": \"Aves/Contopus cooperi/00ba5139e646c8e89c4465b36bf9b921.jpg\",\n  \"648400.jpg\": \"Aves/Contopus cooperi/075b4255962c6e18a1f3766f7a0be7eb.jpg\",\n  \"648402.jpg\": \"Aves/Contopus cooperi/b3135f7df573dc69d57da6b4c702863c.jpg\",\n  \"648404.jpg\": \"Aves/Contopus cooperi/806fbca94179f191d61165aed201596a.jpg\",\n  \"648464.jpg\": \"Aves/Aythya affinis/829e40c4a76052cb42594b7388a0b286.jpg\",\n  \"648510.jpg\": \"Aves/Aythya affinis/cf8d275e9137240afb8a9895480b950e.jpg\",\n  \"648519.jpg\": \"Aves/Aythya affinis/30223bdffec0f69ee540cccb6d24cf56.jpg\",\n  \"648610.jpg\": \"Aves/Egretta novaehollandiae/32dadbd45bd6908398263a89352dadf4.jpg\",\n  \"648621.jpg\": \"Aves/Egretta novaehollandiae/a3fc262d329e7bf692c3488d049c8fce.jpg\",\n  \"648647.jpg\": \"Aves/Egretta novaehollandiae/fc2710645f1419f64c5b04004ed0c8b9.jpg\",\n  \"648652.jpg\": \"Aves/Egretta novaehollandiae/3861f27ce84eff01ba196c18736ccad9.jpg\",\n  \"64869.jpg\": \"Amphibia/Ensatina eschscholtzii/bb62849e9c0b5ad47ee65800f8f3ebfd.jpg\",\n  \"648697.jpg\": \"Aves/Campylorhynchus brunneicapillus/cad731fbeadbaab64bbaf3e4c230cf3e.jpg\",\n  \"64870.jpg\": \"Amphibia/Ensatina eschscholtzii/6240c3d4a5b64944c843884d82e81baa.jpg\",\n  \"648702.jpg\": \"Aves/Campylorhynchus brunneicapillus/434a64640f86c1885d31ce20d28e6f2c.jpg\",\n  \"648711.jpg\": \"Aves/Campylorhynchus brunneicapillus/538600bdcbfdd50b40ffff708faa6b11.jpg\",\n  \"648746.jpg\": \"Aves/Campylorhynchus brunneicapillus/b3613cc533b687e2013aeed368ea6183.jpg\",\n  \"648809.jpg\": \"Reptilia/Opheodrys aestivus/924274fac5f0076e69c0638a096c9e6e.jpg\",\n  \"64881.jpg\": \"Amphibia/Ensatina eschscholtzii/43070a7261a751b0ee505c5dda6b6db1.jpg\",\n  \"648830.jpg\": \"Reptilia/Opheodrys aestivus/415cff88b6b03624b30b1afee155bb47.jpg\",\n  \"648858.jpg\": \"Reptilia/Opheodrys aestivus/5f2e69954a73f7fb0f93cc80187137c6.jpg\",\n  \"648880.jpg\": \"Reptilia/Opheodrys aestivus/671d894ed2dd0cac383cec8ce4f04820.jpg\",\n  \"648889.jpg\": \"Reptilia/Opheodrys aestivus/21a94b2b648493d5b378b7427f56a9cf.jpg\",\n  \"648938.jpg\": \"Aves/Pelecanus conspicillatus/a6fc8da5184489a4f5502f0e9ac54172.jpg\",\n  \"64895.jpg\": \"Amphibia/Ensatina eschscholtzii/f822c6e64447ec73083a4754f7b736fa.jpg\",\n  \"64896.jpg\": \"Amphibia/Ensatina eschscholtzii/5a41ae407a751b7ae85432aed6bdf64e.jpg\",\n  \"64900.jpg\": \"Amphibia/Ensatina eschscholtzii/3b718ca38526e8d8ce9458c07aae5046.jpg\",\n  \"649001.jpg\": \"Aves/Passerella iliaca/ed56b2b5d98e3b04f691e6f71e1b7a10.jpg\",\n  \"649017.jpg\": \"Aves/Passerella iliaca/9cf63f595e2e354dd79c2a10fce4fe1d.jpg\",\n  \"649081.jpg\": \"Aves/Aythya marila/56060e69956db08603c8534809792c93.jpg\",\n  \"649086.jpg\": \"Aves/Aythya marila/42c2a3935c3c020fc066fea1876b71b4.jpg\",\n  \"649129.jpg\": \"Aves/Aythya marila/49da05e3b5999fedb71e6c83b80e3bb0.jpg\",\n  \"649148.jpg\": \"Aves/Aythya marila/4cefbbe84b7f882ac6886ae2ca3dd430.jpg\",\n  \"64916.jpg\": \"Amphibia/Ensatina eschscholtzii/e0f3458b44966506cd58ea0536aeda3a.jpg\",\n  \"649160.jpg\": \"Aves/Aythya marila/5b4f7e2de67b545c4cb08ab3fc69d3af.jpg\",\n  \"64919.jpg\": \"Amphibia/Ensatina eschscholtzii/515ebadcd540bfb345c73ee95a961d77.jpg\",\n  \"649190.jpg\": \"Aves/Aythya marila/bf0567713c976c8d4e1be9bc817738a7.jpg\",\n  \"64922.jpg\": \"Amphibia/Ensatina eschscholtzii/5624320023e4526a93ab8552cf7cca18.jpg\",\n  \"64929.jpg\": \"Amphibia/Ensatina eschscholtzii/ef7bbd36a4fe3a7279052344a1472108.jpg\",\n  \"64937.jpg\": \"Amphibia/Ensatina eschscholtzii/b95abc562566d6f4a695af9d924a6b23.jpg\",\n  \"64939.jpg\": \"Amphibia/Ensatina eschscholtzii/ce4f19297550dc7aa86bb164c777859b.jpg\",\n  \"649424.jpg\": \"Actinopterygii/Abudefduf saxatilis/10d4c817f42724f907bdf5f640d4d472.jpg\",\n  \"64944.jpg\": \"Amphibia/Ensatina eschscholtzii/274084f274da2d8edda941619f038c5e.jpg\",\n  \"64945.jpg\": \"Amphibia/Ensatina eschscholtzii/7a35f8afa639dc74ae31fe87be8ab3e9.jpg\",\n  \"64947.jpg\": \"Amphibia/Ensatina eschscholtzii/7542e0e0152632a246fb05c1fde14ccf.jpg\",\n  \"649495.jpg\": \"Aves/Zonotrichia albicollis/10f5465725b7a350f95838d3a2f3ac07.jpg\",\n  \"649506.jpg\": \"Aves/Zonotrichia albicollis/56563e8778566e8cefb0409846f282f1.jpg\",\n  \"64957.jpg\": \"Amphibia/Ensatina eschscholtzii/627e058f13ef67aa519efb58350c590a.jpg\",\n  \"64960.jpg\": \"Amphibia/Ensatina eschscholtzii/7b9df75596725771f0fd468979c2c1a4.jpg\",\n  \"649606.jpg\": \"Aves/Zonotrichia albicollis/ca101cd5e2926e74cf57239111cd0c86.jpg\",\n  \"64961.jpg\": \"Amphibia/Ensatina eschscholtzii/ff57f0549b5924d62957bbfdb78b4997.jpg\",\n  \"649615.jpg\": \"Aves/Zonotrichia albicollis/36e950861ca64a25c5d4dd78a5a8f1a4.jpg\",\n  \"649636.jpg\": \"Aves/Zonotrichia albicollis/a4255fc4b9b31611357c6924aa112fd2.jpg\",\n  \"64964.jpg\": \"Amphibia/Ensatina eschscholtzii/902f97bfc03a004e89ab865eaa81bda1.jpg\",\n  \"649734.jpg\": \"Aves/Zonotrichia albicollis/c10ee61c6626f91e50e3130f3397b20d.jpg\",\n  \"64981.jpg\": \"Amphibia/Ensatina eschscholtzii/445315419ae20aedc7c483b5a94fc8af.jpg\",\n  \"649864.jpg\": \"Aves/Zonotrichia albicollis/1a74f3f639cb5246f8d3946547913d2f.jpg\",\n  \"64987.jpg\": \"Insecta/Necrophila americana/b65b1f52c041a582a8889b63a9462c64.jpg\",\n  \"649879.jpg\": \"Animalia/Uca pugilator/c0f33527647c7871514da6ef9eafb56e.jpg\",\n  \"64988.jpg\": \"Insecta/Necrophila americana/6d69424cf5121a7605de2f8e4a569ac3.jpg\",\n  \"649907.jpg\": \"Amphibia/Taricha torosa/25b2f5756bf624cedaeed0ad34736fe6.jpg\",\n  \"64991.jpg\": \"Insecta/Necrophila americana/d6b9c1c9fd5f2f1842026d5a0305cca1.jpg\",\n  \"649918.jpg\": \"Amphibia/Taricha torosa/14b03650d14cc119969854dcfad52afb.jpg\",\n  \"64992.jpg\": \"Insecta/Necrophila americana/24e5a9cd1985f789abb7c356bd628c70.jpg\",\n  \"64994.jpg\": \"Insecta/Necrophila americana/8c5f6447a91aa9e88e2d6e912c64c88e.jpg\",\n  \"649943.jpg\": \"Amphibia/Taricha torosa/bd4fcf0a6b77931c76c006f41a2c76d9.jpg\",\n  \"649951.jpg\": \"Amphibia/Taricha torosa/91a9be5d7754db34b6068a828f734613.jpg\",\n  \"649955.jpg\": \"Amphibia/Taricha torosa/aa345cd13c7c42a48c09b905c890b0ea.jpg\",\n  \"649961.jpg\": \"Amphibia/Taricha torosa/1f54e27562691e2ac5915b464ff956be.jpg\",\n  \"65005.jpg\": \"Insecta/Necrophila americana/8388d8c0d044a38c2756420ed37cfc14.jpg\",\n  \"65007.jpg\": \"Insecta/Necrophila americana/2210ce8d0f5440aa6c1e12019be657f3.jpg\",\n  \"650078.jpg\": \"Amphibia/Taricha torosa/1cb7192bd3017e5f8ae42521f21a3d9d.jpg\",\n  \"65011.jpg\": \"Insecta/Necrophila americana/618ae86e03b7d997e48ff320d201b726.jpg\",\n  \"65012.jpg\": \"Insecta/Necrophila americana/ddb1b9d6b66cacdec49dec22bdfe6dc9.jpg\",\n  \"65013.jpg\": \"Insecta/Necrophila americana/13549f7d06bd98d64bd2482b781fb991.jpg\",\n  \"65014.jpg\": \"Insecta/Necrophila americana/0ecfd2cbfdeb3e1eef3ac50c56547ad8.jpg\",\n  \"65015.jpg\": \"Insecta/Necrophila americana/47848516dc8ebfebff2605a980e7615a.jpg\",\n  \"650201.jpg\": \"Aves/Cerorhinca monocerata/5f91564de9de560e9cac8c0941d73663.jpg\",\n  \"650202.jpg\": \"Aves/Cerorhinca monocerata/2f659ad957b882e3b8a9e6de36fb60e7.jpg\",\n  \"650203.jpg\": \"Aves/Cerorhinca monocerata/82be7c5e5401df04dbebb9af114e7562.jpg\",\n  \"650210.jpg\": \"Aves/Cerorhinca monocerata/31fc38dc383c712d02a92e4de0840561.jpg\",\n  \"650211.jpg\": \"Aves/Cerorhinca monocerata/cb6a4776399bdb7f610d1eb7bfeb19eb.jpg\",\n  \"650212.jpg\": \"Aves/Cerorhinca monocerata/8b2c4a4e6cff33a48b5b37762ca8a673.jpg\",\n  \"650215.jpg\": \"Aves/Cerorhinca monocerata/5c2df92caa4835dfdd721796ff6da535.jpg\",\n  \"65022.jpg\": \"Insecta/Necrophila americana/1d2f3d21eec037b6ff01d7300f83d003.jpg\",\n  \"650225.jpg\": \"Aves/Cerorhinca monocerata/88d91214f505b587271adf8ae53c7dd0.jpg\",\n  \"65023.jpg\": \"Insecta/Necrophila americana/aaeda3440d1f206a1f781548294b2334.jpg\",\n  \"65024.jpg\": \"Insecta/Necrophila americana/857e7fd19b6e147d131d47a72c254b59.jpg\",\n  \"650240.jpg\": \"Animalia/Grapsus grapsus/4ef1a514c1abe68dfee7e14b988ca226.jpg\",\n  \"650243.jpg\": \"Animalia/Grapsus grapsus/e2594e3635266f987ff304885cfb894f.jpg\",\n  \"65025.jpg\": \"Insecta/Necrophila americana/ab6afad536d7e9ac3040266111bb8451.jpg\",\n  \"650255.jpg\": \"Insecta/Deidamia inscriptum/b8811a0daa6b47e418c05ac6bd67f094.jpg\",\n  \"650285.jpg\": \"Insecta/Toxomerus politus/2e0a62525ec77db54869a203fae64e8a.jpg\",\n  \"65029.jpg\": \"Insecta/Necrophila americana/5ad66804a506da1287c05f62083c193f.jpg\",\n  \"650290.jpg\": \"Insecta/Toxomerus politus/0cf552ee0adaee97bb74ed9b8f091e6b.jpg\",\n  \"650303.jpg\": \"Aves/Myiarchus cinerascens/2e61719cc0882652b40d7abf9bfdbde5.jpg\",\n  \"65031.jpg\": \"Insecta/Necrophila americana/644543ac8d2a53dda23933c7307f8095.jpg\",\n  \"65034.jpg\": \"Insecta/Necrophila americana/b58cb990fb8e61c34efbca0fac255835.jpg\",\n  \"65035.jpg\": \"Insecta/Necrophila americana/a5c6f05a80cedb29557d47391627359b.jpg\",\n  \"650352.jpg\": \"Aves/Myiarchus cinerascens/c39d7f661446c1aa6309e5a21490755f.jpg\",\n  \"650366.jpg\": \"Aves/Myiarchus cinerascens/3e9d9a56b06d40635661fab16020ec61.jpg\",\n  \"650379.jpg\": \"Aves/Myiarchus cinerascens/742bd30c0a82bf3565745284c1b427c0.jpg\",\n  \"650403.jpg\": \"Aves/Myiarchus cinerascens/67457998bdb9c838530c9e5735dc83ca.jpg\",\n  \"65053.jpg\": \"Aves/Charadrius hiaticula/c15e47db71d5d673741437380dbf4d8a.jpg\",\n  \"65054.jpg\": \"Aves/Charadrius hiaticula/678dfe11c45fdec4b060076bf0020a85.jpg\",\n  \"65057.jpg\": \"Aves/Charadrius hiaticula/90af6f5c3f5838b96daddd2fb651becd.jpg\",\n  \"65058.jpg\": \"Insecta/Melipotis indomita/9c7f3ab7b9de806adf10076e5bb34a3f.jpg\",\n  \"65060.jpg\": \"Insecta/Melipotis indomita/aa637e77fc08aed93480fd731f5d1532.jpg\",\n  \"65063.jpg\": \"Insecta/Melipotis indomita/b8688382385975f44eee1ed91856fd47.jpg\",\n  \"65074.jpg\": \"Insecta/Melipotis indomita/642cf57c0d0a64a635eae1466dea8942.jpg\",\n  \"650748.jpg\": \"Aves/Calidris subruficollis/b558c9648dfd653f0d7a31dab8385b34.jpg\",\n  \"65076.jpg\": \"Insecta/Melipotis indomita/f151f284b73ceddf206cfbe7cebd6446.jpg\",\n  \"65077.jpg\": \"Insecta/Melipotis indomita/5602e00ecfcd56cbd6270399980921c6.jpg\",\n  \"650774.jpg\": \"Reptilia/Sceloporus magister/8d5197d6b83d4a70204f6987fa582ca3.jpg\",\n  \"650782.jpg\": \"Reptilia/Sceloporus magister/de5fd1479543f70b014b540be31e4164.jpg\",\n  \"650783.jpg\": \"Reptilia/Sceloporus magister/4a110e2f0950438e50fdfcfdbbabfef8.jpg\",\n  \"650784.jpg\": \"Reptilia/Sceloporus magister/164e717d30fcc40d61ed2dbcb6f3b714.jpg\",\n  \"650792.jpg\": \"Reptilia/Sceloporus magister/fb127ea6ec32b3709c6d85c2462002b2.jpg\",\n  \"650845.jpg\": \"Reptilia/Alligator mississippiensis/4f478095889531985c3ed645b2e08f15.jpg\",\n  \"650861.jpg\": \"Reptilia/Alligator mississippiensis/96091b58976d1485ee3ded2a4d966b04.jpg\",\n  \"650888.jpg\": \"Reptilia/Alligator mississippiensis/ae4a3c70909e4564a2d4c1a1de4cce21.jpg\",\n  \"650924.jpg\": \"Reptilia/Alligator mississippiensis/e5d9ee7c7f1c4a05d89edb5536c153a2.jpg\",\n  \"650927.jpg\": \"Reptilia/Alligator mississippiensis/d41870bd5b3ae5c8000e23fb80a7c077.jpg\",\n  \"650940.jpg\": \"Reptilia/Alligator mississippiensis/af43a7d36a7ef6fabfa0da2d5788ae0a.jpg\",\n  \"650961.jpg\": \"Reptilia/Alligator mississippiensis/bee533186d6aff97d8dc097302212943.jpg\",\n  \"65107.jpg\": \"Insecta/Thorybes pylades/289752b6e6f98ee70ac1125a5bb25f13.jpg\",\n  \"651108.jpg\": \"Insecta/Allograpta obliqua/8faaf74fb73654712f6a1e6738d36330.jpg\",\n  \"651111.jpg\": \"Insecta/Allograpta obliqua/b7c7758937d4f869a3449633c7370b04.jpg\",\n  \"651112.jpg\": \"Insecta/Allograpta obliqua/f9694c158b613ca5144ee5dd9b0c5759.jpg\",\n  \"651113.jpg\": \"Insecta/Allograpta obliqua/31c6b292fbcdff66481060d44cdf1526.jpg\",\n  \"651114.jpg\": \"Insecta/Allograpta obliqua/3e3644ab7c11f7c1ea951d8495e30733.jpg\",\n  \"651115.jpg\": \"Insecta/Allograpta obliqua/0ce5a6dec315a23f6224d6c8b497e988.jpg\",\n  \"651116.jpg\": \"Insecta/Allograpta obliqua/a8a2c713fec043cb280ed45f61ac3e8a.jpg\",\n  \"651120.jpg\": \"Insecta/Allograpta obliqua/767775a50d94a89e447f56121674d9ca.jpg\",\n  \"651121.jpg\": \"Insecta/Allograpta obliqua/859e817a8046f20aa5de810e7f258737.jpg\",\n  \"651122.jpg\": \"Insecta/Allograpta obliqua/5ada62abba5c25d79796e6bac2696d22.jpg\",\n  \"651127.jpg\": \"Insecta/Allograpta obliqua/05f254f19e1b76d655b7989c47f4e5b2.jpg\",\n  \"651136.jpg\": \"Insecta/Allograpta obliqua/9f557df5c8969d2808e47d8fc7ce3cff.jpg\",\n  \"651152.jpg\": \"Aves/Dryocopus pileatus/2c57fed72c9d23bf18a89713e098372a.jpg\",\n  \"65117.jpg\": \"Insecta/Thorybes pylades/4269d20f4e94b30ad9fe5cff4731a08a.jpg\",\n  \"65118.jpg\": \"Insecta/Thorybes pylades/6634ccf5d704fd4b92e64a3cd9101eab.jpg\",\n  \"651231.jpg\": \"Aves/Dryocopus pileatus/e0b933180cda35a861294ca9a91b7453.jpg\",\n  \"651232.jpg\": \"Aves/Dryocopus pileatus/93c814b31537bb384e011dd9ad2851f7.jpg\",\n  \"65127.jpg\": \"Insecta/Thorybes pylades/2a5ba7eb72db854ce9f6e4baf0f46245.jpg\",\n  \"651273.jpg\": \"Aves/Dryocopus pileatus/50ac9f8df7020ecc6221cb24195b4c00.jpg\",\n  \"65128.jpg\": \"Insecta/Thorybes pylades/a2e28a472555dfdde21aa7af486e63d5.jpg\",\n  \"651280.jpg\": \"Aves/Dryocopus pileatus/da3dc9cf055d1a3e836db603eae8f3a9.jpg\",\n  \"65133.jpg\": \"Insecta/Thorybes pylades/05afbea1b399ff38f196fcce6d7935e4.jpg\",\n  \"651334.jpg\": \"Aves/Dryocopus pileatus/ed3e1fdc9d729531bd17b6e104c02dc8.jpg\",\n  \"65137.jpg\": \"Insecta/Thorybes pylades/421639d011cbf35572edc52f2608f3e4.jpg\",\n  \"65138.jpg\": \"Insecta/Thorybes pylades/a35a594c9737b1d8d81a71ed13bea515.jpg\",\n  \"651383.jpg\": \"Aves/Dryocopus pileatus/a4683890b5c689cdc3f538f487de73d9.jpg\",\n  \"651388.jpg\": \"Aves/Dryocopus pileatus/f86c339fc734088af3456f4efde32d2b.jpg\",\n  \"65142.jpg\": \"Insecta/Thorybes pylades/7f1d7fc3aa5def51d225f77091c8eac0.jpg\",\n  \"651432.jpg\": \"Mammalia/Ursus arctos/258c38d3a0fd02a5fe6c690e9e12eeaf.jpg\",\n  \"651433.jpg\": \"Mammalia/Ursus arctos/28e8ec11ca63a61d714bc95ca2e325c0.jpg\",\n  \"651434.jpg\": \"Mammalia/Ursus arctos/3586464a3954e99505a5bab90b222a6b.jpg\",\n  \"651446.jpg\": \"Aves/Zosterops lateralis/3aed7bce200a1b1f55ebc1139f5cdfb3.jpg\",\n  \"651459.jpg\": \"Aves/Zosterops lateralis/ee53c19dbf0bdcfee62db4ad83b1b400.jpg\",\n  \"65146.jpg\": \"Insecta/Thorybes pylades/8c92a46f536bf67614e93d17269b4420.jpg\",\n  \"651467.jpg\": \"Insecta/Arctia caja/2d90752412c0963348d31f472d60ed9b.jpg\",\n  \"651471.jpg\": \"Insecta/Arctia caja/6cbbce74e91c1e44b6c7314a5a30207d.jpg\",\n  \"651473.jpg\": \"Insecta/Arctia caja/f76d36cd7f8b67bfa9f22c4998096e95.jpg\",\n  \"651477.jpg\": \"Reptilia/Sphenodon punctatus/7dd06a8a58e9b55203979ad51925cf09.jpg\",\n  \"651493.jpg\": \"Reptilia/Sphenodon punctatus/2a49bc99d08e0dbc3b9b10b860bef9c2.jpg\",\n  \"651509.jpg\": \"Reptilia/Sphenodon punctatus/15afc462c3a44061bfd955f29ca45d71.jpg\",\n  \"651518.jpg\": \"Reptilia/Sphenodon punctatus/454349b0ee189689e450313d3402a964.jpg\",\n  \"651559.jpg\": \"Reptilia/Sphenodon punctatus/10d3362d286da772b605d9e9b5df1c4d.jpg\",\n  \"651597.jpg\": \"Reptilia/Sphenodon punctatus/84287e7e0f7f09ae5894b1a6e7d49d97.jpg\",\n  \"65167.jpg\": \"Insecta/Hyalymenus tarsatus/5b9cd7bfa749b8cd3decd00bd42ef4fa.jpg\",\n  \"651675.jpg\": \"Aves/Tachycineta albilinea/38b5d356f2d7f4c497d932c685dd8320.jpg\",\n  \"651678.jpg\": \"Aves/Tachycineta albilinea/8d965ccdcc6bf127fb2c00f9db579f07.jpg\",\n  \"651679.jpg\": \"Aves/Tachycineta albilinea/e7943e680c5800608a01e928b6bbd618.jpg\",\n  \"651685.jpg\": \"Aves/Tachycineta albilinea/73a682087ab0fe24c2990630592ea6ea.jpg\",\n  \"651686.jpg\": \"Aves/Tachycineta albilinea/25a8cc9895b232a14c4c0eca6ea75408.jpg\",\n  \"65170.jpg\": \"Insecta/Hyalymenus tarsatus/f867810d94d9d0bb2b6a0a4c4ff2ce90.jpg\",\n  \"651731.jpg\": \"Actinopterygii/Micropterus salmoides/f2e0b48ae69d46ab53f8a08c76ba7576.jpg\",\n  \"651749.jpg\": \"Actinopterygii/Micropterus salmoides/113fc321160a580d097e73dd0fbe5049.jpg\",\n  \"651757.jpg\": \"Actinopterygii/Micropterus salmoides/65388530c37f85437c035a9b080f9a15.jpg\",\n  \"651767.jpg\": \"Actinopterygii/Micropterus salmoides/59afdf24ef9e67745be7c23f1c0b0786.jpg\",\n  \"65181.jpg\": \"Insecta/Hyalymenus tarsatus/0ce5d96cfb249c20e8e02c2ed14ec264.jpg\",\n  \"651847.jpg\": \"Insecta/Plathemis lydia/e6ab44c915f62a41d0e82957eb65596b.jpg\",\n  \"651932.jpg\": \"Insecta/Plathemis lydia/37ffd848d23be4a906b13a4747cbea9a.jpg\",\n  \"651987.jpg\": \"Insecta/Plathemis lydia/3d93bc8e42c721e6500e3cc9b57e2024.jpg\",\n  \"652074.jpg\": \"Insecta/Plathemis lydia/34c4d3ba0a018c53b8dac37e96d08a83.jpg\",\n  \"65224.jpg\": \"Amphibia/Anaxyrus woodhousii/155acb7187fbfb31e3334c0ab3593161.jpg\",\n  \"65226.jpg\": \"Amphibia/Anaxyrus woodhousii/956b1d1ce627f191407329d6690cdec9.jpg\",\n  \"652290.jpg\": \"Insecta/Plathemis lydia/a839baee272938b7cd5ad6ba413e7599.jpg\",\n  \"652322.jpg\": \"Insecta/Plathemis lydia/375b57290e1cf768ee4d3addcb6f1837.jpg\",\n  \"65236.jpg\": \"Amphibia/Anaxyrus woodhousii/7cc69278e90df58ce2b84534065a8a79.jpg\",\n  \"65239.jpg\": \"Amphibia/Anaxyrus woodhousii/4b5f415d8663ad3298aa8caf9b8a713b.jpg\",\n  \"65240.jpg\": \"Amphibia/Anaxyrus woodhousii/1362719c5617d9073e864141af011c8a.jpg\",\n  \"65251.jpg\": \"Amphibia/Anaxyrus woodhousii/628ca536a7eb1be6d36a1b2795ba896c.jpg\",\n  \"652537.jpg\": \"Insecta/Panoquina ocola/b785f2fe35210a784c6b1c1604ac281c.jpg\",\n  \"652538.jpg\": \"Insecta/Panoquina ocola/b822c5c8397821bf58a6d35cc385cd1b.jpg\",\n  \"652544.jpg\": \"Insecta/Panoquina ocola/9c30340c6498b3c81dfc77ec09c9bf54.jpg\",\n  \"652550.jpg\": \"Insecta/Panoquina ocola/09c7d973c7bcf2ca2c3f32ffc496d4c1.jpg\",\n  \"652555.jpg\": \"Insecta/Panoquina ocola/e08badbe18b8589bbd9ea1653ad23121.jpg\",\n  \"652560.jpg\": \"Insecta/Panoquina ocola/7f89d7b303cce4b21462ca45c8af04fd.jpg\",\n  \"652563.jpg\": \"Insecta/Panoquina ocola/37ff4d11d784e540a89ee738ddce4299.jpg\",\n  \"652564.jpg\": \"Insecta/Apodemia mormo/55fbc53511d239f65a3376dfc9c6ca5c.jpg\",\n  \"652573.jpg\": \"Insecta/Apodemia mormo/aac2b3314cf4d87fbe8f9861c60c19fa.jpg\",\n  \"652587.jpg\": \"Arachnida/Leucauge venusta/0ffc2244981c7694ec5a2ec457813046.jpg\",\n  \"652592.jpg\": \"Arachnida/Leucauge venusta/f97ebc5fe93bd8ddff6bc47047dc0d34.jpg\",\n  \"652609.jpg\": \"Arachnida/Leucauge venusta/f4397a1bbe264d184be9c7f06e5f2f09.jpg\",\n  \"652610.jpg\": \"Arachnida/Leucauge venusta/e2abe1047478ab747e17f786f589c9c0.jpg\",\n  \"652635.jpg\": \"Arachnida/Leucauge venusta/b358f2fbb2723abee50a8a3f5c0187f1.jpg\",\n  \"652649.jpg\": \"Arachnida/Leucauge venusta/bfe9e051cb847216205800d84d85b367.jpg\",\n  \"65266.jpg\": \"Amphibia/Anaxyrus woodhousii/953eb9ae84980a923d195774e14bcedd.jpg\",\n  \"652663.jpg\": \"Arachnida/Leucauge venusta/bb23a0f97c3ebfc04d5b17af028a3c16.jpg\",\n  \"65269.jpg\": \"Amphibia/Anaxyrus woodhousii/d5ef02dc3a387e491fe8f87d8e05574b.jpg\",\n  \"65273.jpg\": \"Amphibia/Anaxyrus woodhousii/e092fa6017db747d7cd7741b7cf9e451.jpg\",\n  \"65274.jpg\": \"Amphibia/Anaxyrus woodhousii/f0a3f42bcca705ba1ad72434a92cf785.jpg\",\n  \"65275.jpg\": \"Amphibia/Anaxyrus woodhousii/0f6a59c250a5b277e278a47b4eb2385b.jpg\",\n  \"652801.jpg\": \"Insecta/Nepytia canosaria/931a1b2a2e081cda97a0763d271c8861.jpg\",\n  \"65282.jpg\": \"Amphibia/Anaxyrus woodhousii/9cd7d231ac5073e06b73a91fc6249fef.jpg\",\n  \"65285.jpg\": \"Amphibia/Anaxyrus woodhousii/6411a3184c45718cdb28626c1ff56676.jpg\",\n  \"65297.jpg\": \"Amphibia/Anaxyrus woodhousii/a8dc484eef4e63a271615b01b2a6901f.jpg\",\n  \"65301.jpg\": \"Amphibia/Anaxyrus woodhousii/bc9d008428f0147f28a1e2a43bd86220.jpg\",\n  \"65302.jpg\": \"Amphibia/Anaxyrus woodhousii/eb7e3c0e8fd4ce10daf3e8560af9768c.jpg\",\n  \"65309.jpg\": \"Amphibia/Anaxyrus woodhousii/97266b75c11a782f39baaa1638eeaedd.jpg\",\n  \"65310.jpg\": \"Amphibia/Anaxyrus woodhousii/80225e435f7456928030ce7b26c28993.jpg\",\n  \"65311.jpg\": \"Amphibia/Anaxyrus woodhousii/aeb46e3ff8f3359e924bd8c98875a95d.jpg\",\n  \"65315.jpg\": \"Amphibia/Anaxyrus woodhousii/c21bd2c1950441c6c8bd020aa3af335f.jpg\",\n  \"653150.jpg\": \"Insecta/Urbanus proteus/f998213f92d59cd856196cb2c34ffefe.jpg\",\n  \"653151.jpg\": \"Insecta/Urbanus proteus/fcce15e60722bb650753d55145181bd9.jpg\",\n  \"653172.jpg\": \"Insecta/Urbanus proteus/370ab278533ed82aecf2c3d2504c38e6.jpg\",\n  \"653184.jpg\": \"Insecta/Urbanus proteus/0192f196504a92c86037f07f48c79c28.jpg\",\n  \"653185.jpg\": \"Insecta/Urbanus proteus/544c19e94f880b6bbe10e6b73aa78501.jpg\",\n  \"653190.jpg\": \"Insecta/Urbanus proteus/b367e445c031a29805910c6df5e08af6.jpg\",\n  \"65324.jpg\": \"Amphibia/Anaxyrus woodhousii/0a52931d29333d86021c0fc55b3f68b6.jpg\",\n  \"653269.jpg\": \"Aves/Rallus obsoletus/20cd5737089085aa4f7f464bb75f48ed.jpg\",\n  \"653318.jpg\": \"Insecta/Dryocampa rubicunda/ebd75fcf4608e86303fede8c0df05127.jpg\",\n  \"653319.jpg\": \"Insecta/Dryocampa rubicunda/192bd064964f859ecdf4f9284091f4cc.jpg\",\n  \"653323.jpg\": \"Insecta/Dryocampa rubicunda/14818cd148bd4d32ec9288be898e3dd2.jpg\",\n  \"653324.jpg\": \"Insecta/Dryocampa rubicunda/725d4d25021764639df5d673ae03dc5e.jpg\",\n  \"653327.jpg\": \"Insecta/Dryocampa rubicunda/06361edbe7705cdd8e50ed409a0e6cf8.jpg\",\n  \"65334.jpg\": \"Insecta/Argia moesta/fd8610bb8e951b5f53fdec1cbaa90dea.jpg\",\n  \"653341.jpg\": \"Insecta/Dryocampa rubicunda/4df0ca37457784aa1f9f6fcc822a48ec.jpg\",\n  \"653345.jpg\": \"Insecta/Dryocampa rubicunda/973280ff562da3dafd27a998548f96af.jpg\",\n  \"653350.jpg\": \"Insecta/Dryocampa rubicunda/957062e56d5dc0fa40ccd1b2ac05b654.jpg\",\n  \"653351.jpg\": \"Insecta/Dryocampa rubicunda/d0ac675f7b308c12b152196a8389413e.jpg\",\n  \"653370.jpg\": \"Insecta/Dryocampa rubicunda/3f111cfc220024ae6f507adc84e47c68.jpg\",\n  \"653374.jpg\": \"Insecta/Dryocampa rubicunda/754c70b7d0092d2e89b5bdb94252c5e8.jpg\",\n  \"653375.jpg\": \"Aves/Onychognathus morio/51798773e088ffca60e073e3c70f0565.jpg\",\n  \"65338.jpg\": \"Insecta/Argia moesta/629cd38d39b0f62a8c3c66fe1091e71a.jpg\",\n  \"65341.jpg\": \"Insecta/Argia moesta/253bf219109ee115fcf1578ebecff452.jpg\",\n  \"65342.jpg\": \"Insecta/Argia moesta/412054ab17059994e7c19fca590df15f.jpg\",\n  \"65354.jpg\": \"Insecta/Argia moesta/db3de63c7361ec7f5e98321f5ef8dd13.jpg\",\n  \"653671.jpg\": \"Aves/Sturnus vulgaris/e51562d18d1a4fcd86e92bbb7a01513c.jpg\",\n  \"653699.jpg\": \"Aves/Sturnus vulgaris/cca4fb783cff6abf155d2a602fc97b16.jpg\",\n  \"65371.jpg\": \"Insecta/Argia moesta/4f61680e582d04da4a8378c349bea780.jpg\",\n  \"6539.jpg\": \"Aves/Regulus regulus/e908548e3d5941da94b9569e6643554b.jpg\",\n  \"653907.jpg\": \"Aves/Sturnus vulgaris/66a658f79ba3500f17564b5a14044cb1.jpg\",\n  \"653957.jpg\": \"Aves/Sturnus vulgaris/ff7696e9024a0055c7890f4c78e5d665.jpg\",\n  \"65396.jpg\": \"Insecta/Argia moesta/0de8695827112c53867e563a015019b0.jpg\",\n  \"65397.jpg\": \"Insecta/Argia moesta/d0fcecd7cd889763d0570373d0ebb0aa.jpg\",\n  \"654.jpg\": \"Mammalia/Ursus americanus/c590ce02bbed471c4405f58e3eea78bb.jpg\",\n  \"654021.jpg\": \"Aves/Alectoris chukar/b4a9f00dd3e8e5bc68aaca7fcb6c8c6f.jpg\",\n  \"654030.jpg\": \"Aves/Alectoris chukar/5bf73ac7244c96cd7ebac4058e3b3b69.jpg\",\n  \"654035.jpg\": \"Aves/Alectoris chukar/70dfe3946626b325df928a5a3adb989a.jpg\",\n  \"654095.jpg\": \"Aves/Arenaria melanocephala/4fe6e9bf3f9b634f1378be63a47060ac.jpg\",\n  \"654118.jpg\": \"Aves/Arenaria melanocephala/8f95ef1783728b89d183cca5c3098844.jpg\",\n  \"654121.jpg\": \"Aves/Arenaria melanocephala/4967abb7fbfd583f325510cb50cbd001.jpg\",\n  \"654126.jpg\": \"Aves/Arenaria melanocephala/904ab92b4163ac7e60506250d5fa5b7b.jpg\",\n  \"654149.jpg\": \"Aves/Arenaria melanocephala/9e17df36860a616c719fc1591c722c4e.jpg\",\n  \"654188.jpg\": \"Mollusca/Cepaea nemoralis/365ceec58ac958682c5f42668d212176.jpg\",\n  \"654191.jpg\": \"Mollusca/Cepaea nemoralis/0b4ee1380c026955ab64ab1ae274725c.jpg\",\n  \"654193.jpg\": \"Mollusca/Cepaea nemoralis/dadc6ff6dba6232166bb1fa33416dce4.jpg\",\n  \"654195.jpg\": \"Mollusca/Cepaea nemoralis/b562eca2eea055ab51635f6846b8a972.jpg\",\n  \"654196.jpg\": \"Mollusca/Cepaea nemoralis/fa657ab979881a42f02200a0235a9dc3.jpg\",\n  \"654211.jpg\": \"Mollusca/Cepaea nemoralis/c732ac548771f8e009d3e0dead83987a.jpg\",\n  \"654213.jpg\": \"Mollusca/Cepaea nemoralis/57a6fd876e834cae547e8addf05bb79b.jpg\",\n  \"654217.jpg\": \"Mollusca/Cepaea nemoralis/1516cd6cd241b35872bef0430cde23d8.jpg\",\n  \"654330.jpg\": \"Aves/Motacilla alba/85c8143b1efa09b678c441613a3d30f8.jpg\",\n  \"654348.jpg\": \"Aves/Motacilla alba/a02927d5a624c4a05a600ee42d383cb8.jpg\",\n  \"65435.jpg\": \"Insecta/Argia moesta/2ceb2efa9212fe91b362bb8b20569dec.jpg\",\n  \"654375.jpg\": \"Aves/Motacilla alba/9379cb5998fcba9765417281f48172ba.jpg\",\n  \"654377.jpg\": \"Aves/Motacilla alba/313309d77bc34bfe91d5b90c28ad6b4d.jpg\",\n  \"654386.jpg\": \"Aves/Motacilla alba/a8ada9b3bb60cb35c86d954499d702d7.jpg\",\n  \"6544.jpg\": \"Aves/Regulus regulus/e576d57cd26fe8f22f01dddd9027bad2.jpg\",\n  \"65440.jpg\": \"Insecta/Argia moesta/ba54e034b33f5a1ea3ca09558349499f.jpg\",\n  \"654401.jpg\": \"Aves/Motacilla alba/947be149250938e5a8bd115e04671479.jpg\",\n  \"65447.jpg\": \"Insecta/Argia moesta/634e6527847507a199b47574e588cf6a.jpg\",\n  \"654498.jpg\": \"Insecta/Pyrrhocoris apterus/a98e1c089d11bad837fd1b93338ab2e9.jpg\",\n  \"6545.jpg\": \"Aves/Regulus regulus/3d06db38d7f07e09ef97b10d56be015d.jpg\",\n  \"654511.jpg\": \"Insecta/Pyrrhocoris apterus/920b2a087ad20b3545d43429ae9a9c07.jpg\",\n  \"654623.jpg\": \"Mollusca/Limacia cockerelli/5ab2d80be86db678beee68a29163847e.jpg\",\n  \"654624.jpg\": \"Mollusca/Limacia cockerelli/5539546743a7061c10bdae03e6148f4f.jpg\",\n  \"654625.jpg\": \"Mollusca/Limacia cockerelli/534f3341783680b0e2602925ed989b5b.jpg\",\n  \"65464.jpg\": \"Insecta/Argia moesta/7c857acdbf9bda765fb3bf4c5554b659.jpg\",\n  \"65473.jpg\": \"Insecta/Argia moesta/61ab159b42aaeae057f069b2af263f26.jpg\",\n  \"65503.jpg\": \"Insecta/Argia moesta/b341f002be2a06949fc8e04b39bf90d2.jpg\",\n  \"65504.jpg\": \"Insecta/Argia moesta/aa5611ef3181027ef3e4a3e3d703ef9f.jpg\",\n  \"655048.jpg\": \"Amphibia/Pseudacris sierra/3ccf233e9244b6601a39efb763697392.jpg\",\n  \"655064.jpg\": \"Amphibia/Pseudacris sierra/7863b103bdae2331b9e86357154fb1fc.jpg\",\n  \"65512.jpg\": \"Insecta/Argia moesta/fd3fd8d6d9f41e29ed5d6159135cd6dd.jpg\",\n  \"655168.jpg\": \"Amphibia/Pseudacris sierra/4486302be286b4c5567aaafc47876699.jpg\",\n  \"655207.jpg\": \"Amphibia/Pseudacris sierra/3f089011dd50a1b06e8316eb35d5904f.jpg\",\n  \"655248.jpg\": \"Amphibia/Pseudacris sierra/ec65c47740dd5ac53dbac96a564b8415.jpg\",\n  \"65525.jpg\": \"Insecta/Argia moesta/74b493d7b69bcbca19eb89cc831d3595.jpg\",\n  \"655295.jpg\": \"Amphibia/Pseudacris sierra/001e540d0d11ed1188a5cbe4174c5c69.jpg\",\n  \"655300.jpg\": \"Amphibia/Pseudacris sierra/061c6a055367fab693ddcaf687352d70.jpg\",\n  \"655346.jpg\": \"Insecta/Halysidota tessellaris/cf390f0019009395829991072e009980.jpg\",\n  \"655352.jpg\": \"Insecta/Halysidota tessellaris/96e6eb0a415a0c7c205234a2a97db240.jpg\",\n  \"655353.jpg\": \"Insecta/Halysidota tessellaris/e82ea9c0b97da54021138cf8d0e7bfc4.jpg\",\n  \"655354.jpg\": \"Insecta/Halysidota tessellaris/2257eacb1d25f151e9438e7e75b52546.jpg\",\n  \"655357.jpg\": \"Insecta/Halysidota tessellaris/1511fb7b4aa727345b846b503ff4833d.jpg\",\n  \"655367.jpg\": \"Insecta/Halysidota tessellaris/fe7c41d94aa0d45ad055495e4ef65877.jpg\",\n  \"655376.jpg\": \"Insecta/Halysidota tessellaris/1e727bc746201d3c6f235bd1f26e7e22.jpg\",\n  \"655378.jpg\": \"Insecta/Halysidota tessellaris/93518a6b1d86c7e5b1576496c791a1ec.jpg\",\n  \"655383.jpg\": \"Insecta/Apamea amputatrix/e36205c9dd285e4167bbeecbfd337398.jpg\",\n  \"65539.jpg\": \"Insecta/Argia moesta/26066ec87d927dbf79f2ef129aeb75ab.jpg\",\n  \"655394.jpg\": \"Insecta/Strymon melinus/ebeaf09d3909af5d3502b33478b98b75.jpg\",\n  \"655417.jpg\": \"Insecta/Strymon melinus/882dc3ae24cc93ff2cf4c01f5f45df0f.jpg\",\n  \"655487.jpg\": \"Insecta/Strymon melinus/078dab7a7b923170488601a891b1888d.jpg\",\n  \"655553.jpg\": \"Insecta/Strymon melinus/bd458f305d157b75bc842c727b2f141d.jpg\",\n  \"65557.jpg\": \"Insecta/Argia moesta/7ffd0442a960fdef8298028dd9a97aa5.jpg\",\n  \"655600.jpg\": \"Insecta/Strymon melinus/f59e93db8b93248b693fda29c0b2f5c4.jpg\",\n  \"655607.jpg\": \"Insecta/Strymon melinus/b6c55c2ae7c0ecd40bacbb0b9fd5d5be.jpg\",\n  \"65567.jpg\": \"Insecta/Argia moesta/d3d80e4cb79b13db5608d8d5063a247d.jpg\",\n  \"65568.jpg\": \"Insecta/Argia moesta/e764842a851c45d10a90dc8ce9693bdb.jpg\",\n  \"65570.jpg\": \"Insecta/Chlosyne palla/f545f46642745f55a70c54bf81bf8357.jpg\",\n  \"655740.jpg\": \"Insecta/Strymon melinus/142525ec04513609c022d280d690408f.jpg\",\n  \"655757.jpg\": \"Insecta/Strymon melinus/d2696b7e9e519bd964ef43ff74f6991d.jpg\",\n  \"65576.jpg\": \"Insecta/Chlosyne palla/ea66dca900fc4696283948d01cdd2fcb.jpg\",\n  \"655790.jpg\": \"Insecta/Strymon melinus/9a5397ab827b85b385e9da1efc2b71ba.jpg\",\n  \"65582.jpg\": \"Insecta/Chlosyne palla/a1e9ca4d703a0e8d4c5bde2542cc3dd1.jpg\",\n  \"65583.jpg\": \"Insecta/Chlosyne palla/d71d8b14332586fb7afa0f87dafe3479.jpg\",\n  \"65584.jpg\": \"Insecta/Chlosyne palla/e964881bc40aee68495dfd18d49b332c.jpg\",\n  \"65586.jpg\": \"Insecta/Chlosyne palla/ffff65e5e9c6110774ec2e1a585573e8.jpg\",\n  \"65587.jpg\": \"Insecta/Chlosyne palla/fcd336b18111b5195098266cf917e658.jpg\",\n  \"655878.jpg\": \"Aves/Phalacrocorax auritus/c5981cf18c1750f53407e11f822cc519.jpg\",\n  \"65588.jpg\": \"Insecta/Chlosyne palla/d4ebae0d11cbb06f41d8dc64b693ac3d.jpg\",\n  \"65593.jpg\": \"Insecta/Chlosyne palla/eb4387c5b5d25b46c5f3cbe84125e124.jpg\",\n  \"65594.jpg\": \"Insecta/Chlosyne palla/a6f408ebdf8d063821843678706234fc.jpg\",\n  \"65595.jpg\": \"Insecta/Chlosyne palla/a090d9b2abba3170dd3bfd2a34878ad4.jpg\",\n  \"65598.jpg\": \"Insecta/Chlosyne palla/d18d3acd87efa3c1ea8ece47710d8a3d.jpg\",\n  \"65600.jpg\": \"Insecta/Chlosyne palla/bc9ca058890dd7e985635dffa2fc230f.jpg\",\n  \"65608.jpg\": \"Insecta/Chlosyne palla/453a33ce33d08ee4db3c178c9106beca.jpg\",\n  \"65609.jpg\": \"Insecta/Chlosyne palla/32389abbe254340388e5ff95245e62d5.jpg\",\n  \"6561.jpg\": \"Aves/Regulus regulus/9043482eaf3605678390bfc8d0f137b5.jpg\",\n  \"65610.jpg\": \"Insecta/Chlosyne palla/2882759dd7a60e237425766936677e44.jpg\",\n  \"65611.jpg\": \"Insecta/Chlosyne palla/bd9d2e38c2f0f84f840b0363008afef8.jpg\",\n  \"65612.jpg\": \"Insecta/Chlosyne palla/07df9eb3339e489ae006bf54e5a05f4d.jpg\",\n  \"65620.jpg\": \"Insecta/Chlosyne palla/bf88652ef1c69eee225c2865d83cfcb1.jpg\",\n  \"656204.jpg\": \"Aves/Phalacrocorax auritus/b787977404c23e2a452005d40b36f2da.jpg\",\n  \"65626.jpg\": \"Insecta/Chlosyne palla/702699bb6299338adb1c8311a7c15069.jpg\",\n  \"65627.jpg\": \"Insecta/Chlosyne palla/db732f796d0b2b1de884309ad29a6718.jpg\",\n  \"65632.jpg\": \"Insecta/Chlosyne palla/253119c03cfd8feaf982f3cb0a25b520.jpg\",\n  \"65633.jpg\": \"Insecta/Chlosyne palla/f30af3be4df765b41984ea3da931fa75.jpg\",\n  \"65634.jpg\": \"Insecta/Chlosyne palla/5eb72103d833d15afaef2aad3beb5597.jpg\",\n  \"6564.jpg\": \"Aves/Regulus regulus/baabc1d4a9292b709109841a8f1a31e3.jpg\",\n  \"656551.jpg\": \"Aves/Phalacrocorax auritus/c8ee09d39041f282e394f84ec46183bf.jpg\",\n  \"656589.jpg\": \"Aves/Pluvialis dominica/6316c50f9b6275241c51c559fdd5042d.jpg\",\n  \"656593.jpg\": \"Aves/Pluvialis dominica/dadf346759230340ed12a69cd3abc716.jpg\",\n  \"656596.jpg\": \"Aves/Pluvialis dominica/02be34f0a3e7a0381e735ddb465c3c7f.jpg\",\n  \"656599.jpg\": \"Aves/Pluvialis dominica/243734cb1186794e363b9033a7c3028e.jpg\",\n  \"656600.jpg\": \"Aves/Pluvialis dominica/60fb007303a58d352a057452ddde9280.jpg\",\n  \"656603.jpg\": \"Aves/Pluvialis dominica/daad7f17f353d4692af9576efe4506fb.jpg\",\n  \"656604.jpg\": \"Aves/Pluvialis dominica/b1ef9205a6681e790da5a784c84ef857.jpg\",\n  \"65664.jpg\": \"Insecta/Udea rubigalis/6d3a4c19281d865b81247fc6fc3dce24.jpg\",\n  \"656644.jpg\": \"Aves/Setophaga coronata/8755e746a083f85799f239943559c927.jpg\",\n  \"65665.jpg\": \"Insecta/Udea rubigalis/01dd462af96d608b36380924bde47343.jpg\",\n  \"65669.jpg\": \"Insecta/Udea rubigalis/4ea8ebcb13d306f8ee54362397d7395f.jpg\",\n  \"6567.jpg\": \"Aves/Regulus regulus/b3c4a895959a21fb939393b243a25a22.jpg\",\n  \"656710.jpg\": \"Aves/Setophaga coronata/bca22ed68a275946574773ac85a59624.jpg\",\n  \"65672.jpg\": \"Insecta/Udea rubigalis/de43a5f9c13313049911b736a0cd7935.jpg\",\n  \"65673.jpg\": \"Insecta/Udea rubigalis/05d27f2ba33f869a7c1b48140cf5dc42.jpg\",\n  \"65678.jpg\": \"Insecta/Udea rubigalis/19d9650e376a591286d33bcac9f1b1e8.jpg\",\n  \"65679.jpg\": \"Insecta/Udea rubigalis/0116ee541f9fc1dbb8345baff8ba3a97.jpg\",\n  \"65685.jpg\": \"Insecta/Udea rubigalis/fc4829fa2f71eb1c9ab0b1666b50ea97.jpg\",\n  \"65689.jpg\": \"Insecta/Udea rubigalis/cd11edb40dc2459705430e624ee74ba0.jpg\",\n  \"65690.jpg\": \"Insecta/Udea rubigalis/c5229989e2a3063122e556853ed53679.jpg\",\n  \"65691.jpg\": \"Insecta/Udea rubigalis/0ca65cec70cc2f819f93f1d9f9c6796c.jpg\",\n  \"656927.jpg\": \"Aves/Setophaga coronata/2c94079c16b2f0e70a490f41682143a8.jpg\",\n  \"65694.jpg\": \"Insecta/Udea rubigalis/24faaf04bcd1a2fbb0585ce9ed812944.jpg\",\n  \"65698.jpg\": \"Insecta/Udea rubigalis/b7c8d22102e9a7f8b585c0993c171025.jpg\",\n  \"65699.jpg\": \"Insecta/Udea rubigalis/c372bf75a56d71d92ac20969fb7e06ed.jpg\",\n  \"656993.jpg\": \"Aves/Setophaga coronata/0b698c7cfab838d3dad960ef5d6b3999.jpg\",\n  \"65701.jpg\": \"Insecta/Udea rubigalis/f5ea1a951bc3ba73a62204fbc944a621.jpg\",\n  \"65704.jpg\": \"Insecta/Udea rubigalis/54751b19989221739bbee953fef70b89.jpg\",\n  \"65709.jpg\": \"Insecta/Udea rubigalis/ae24cad36064ba9a921705ea9a472a4d.jpg\",\n  \"65713.jpg\": \"Insecta/Udea rubigalis/67b73a234306d7e5b65309faf1b288f8.jpg\",\n  \"657165.jpg\": \"Aves/Setophaga coronata/4174523d828441a6cd1e3472d7d8ca9a.jpg\",\n  \"65720.jpg\": \"Insecta/Udea rubigalis/156614a7f597383ef2d0beaea5eb4b4d.jpg\",\n  \"65723.jpg\": \"Insecta/Udea rubigalis/d9c7376edcc0bfed733bfaf95f2c6220.jpg\",\n  \"65724.jpg\": \"Insecta/Udea rubigalis/f38eecee2b9ae297a8730033495fb594.jpg\",\n  \"657266.jpg\": \"Aves/Prosthemadera novaeseelandiae/81dbddab817e258c239fe5db84ffea38.jpg\",\n  \"657280.jpg\": \"Aves/Prosthemadera novaeseelandiae/4f5a9bf73d90c7d7686ae72775a80fee.jpg\",\n  \"657281.jpg\": \"Aves/Prosthemadera novaeseelandiae/6807b8c861ddbce9c7f1aff70509f570.jpg\",\n  \"657292.jpg\": \"Aves/Prosthemadera novaeseelandiae/eb921fc6839310c5489dfd99b61012f1.jpg\",\n  \"657301.jpg\": \"Aves/Prosthemadera novaeseelandiae/5f0963061525df9a2623d462ce4cf4a1.jpg\",\n  \"657312.jpg\": \"Aves/Prosthemadera novaeseelandiae/8865cf9c9d9a8c7490e0a979fde946fd.jpg\",\n  \"657315.jpg\": \"Aves/Prosthemadera novaeseelandiae/82e31404348da6a4b65a371327432d8c.jpg\",\n  \"657321.jpg\": \"Aves/Prosthemadera novaeseelandiae/37e4cdae45f278c1ce047fd8a13dab1c.jpg\",\n  \"657325.jpg\": \"Aves/Prosthemadera novaeseelandiae/3c2b870a888c8ae919942727685838d7.jpg\",\n  \"657364.jpg\": \"Insecta/Graphosoma lineatum/18e1e12175f9adca49a177ee1a7fca5c.jpg\",\n  \"657365.jpg\": \"Insecta/Graphosoma lineatum/2283c6c4fc02b4a0761472b1e16ef855.jpg\",\n  \"657423.jpg\": \"Aves/Crotophaga ani/4b649c70cc7f20bcd946e79626de84a7.jpg\",\n  \"657452.jpg\": \"Insecta/Didymops transversa/d1fce256d38c1ac99fc3a1c20e015e7e.jpg\",\n  \"657453.jpg\": \"Insecta/Didymops transversa/ba66ca349a96224d3f656407b7b92634.jpg\",\n  \"657457.jpg\": \"Insecta/Didymops transversa/c6d4c064d09e20f4f559c322e3283ad8.jpg\",\n  \"657486.jpg\": \"Insecta/Ladona julia/e3bc0612fbb63bd5e44da59265c3d9d2.jpg\",\n  \"657502.jpg\": \"Insecta/Ladona julia/3bd1ab96340225427806d3a1fa60b85f.jpg\",\n  \"657505.jpg\": \"Insecta/Ladona julia/1fa8b76730ec2af7283c49599d29c614.jpg\",\n  \"657521.jpg\": \"Insecta/Ladona julia/2c3ac1a2d88e6ac59dd6f57e435611c7.jpg\",\n  \"657526.jpg\": \"Insecta/Ladona julia/9e2186b60c5c0c6cf5a3e0526bebb70d.jpg\",\n  \"657564.jpg\": \"Insecta/Pieris marginalis/f6b70492bb8dc8af5b853b67cae19b29.jpg\",\n  \"657565.jpg\": \"Insecta/Pieris marginalis/468a30ad5e7abdca1dcb0f757402d7f9.jpg\",\n  \"657567.jpg\": \"Insecta/Pieris marginalis/8cc73a895812028e05acd1295e159722.jpg\",\n  \"657568.jpg\": \"Insecta/Pieris marginalis/e39813c47d6b568c38f4feb38fd9ed17.jpg\",\n  \"657569.jpg\": \"Insecta/Pieris marginalis/77583ca670f94291596c4d7300ae9c50.jpg\",\n  \"657580.jpg\": \"Insecta/Pieris marginalis/323a1a3b6beecbc5bf35fb2671cac090.jpg\",\n  \"657581.jpg\": \"Insecta/Pieris marginalis/3a1cf4f3cad34763fdcaa3ee23f93d74.jpg\",\n  \"657587.jpg\": \"Insecta/Pieris marginalis/704f8300f1d284a14de7855c45421c79.jpg\",\n  \"657594.jpg\": \"Insecta/Pieris marginalis/1e62d029f7ce2f5969e34b639ece24f2.jpg\",\n  \"657609.jpg\": \"Insecta/Eubaphe mendica/f9f6330f9036ea4c9f4ff46aef0fbfb0.jpg\",\n  \"657611.jpg\": \"Insecta/Eubaphe mendica/8a94ddf0ce7b130b5e7954c28909e216.jpg\",\n  \"657618.jpg\": \"Insecta/Eubaphe mendica/ce1565883b12fe9da91fac9bedb6b7de.jpg\",\n  \"657657.jpg\": \"Insecta/Telebasis salva/7e8e01cb1f401b052b2724669f440a7c.jpg\",\n  \"657658.jpg\": \"Insecta/Telebasis salva/1e0da146a8372c44b65cda3bbaa430be.jpg\",\n  \"657674.jpg\": \"Insecta/Telebasis salva/fed539c41ad494dc8e3ea1998ba93ad5.jpg\",\n  \"657700.jpg\": \"Insecta/Telebasis salva/81ebcf6d3a80baf809874d442108a8c7.jpg\",\n  \"657710.jpg\": \"Insecta/Telebasis salva/edbe91383f1a69f129e494a4ae79b306.jpg\",\n  \"657721.jpg\": \"Aves/Tachybaptus dominicus/1372b9b7321fadb42075a7b0f116a109.jpg\",\n  \"657745.jpg\": \"Aves/Tachybaptus dominicus/f75e71e5418389b7d6a1a0955ec9dc1f.jpg\",\n  \"657747.jpg\": \"Aves/Tachybaptus dominicus/ee649249f77093dd303585f4715e79b3.jpg\",\n  \"657761.jpg\": \"Aves/Tachybaptus dominicus/7e49be10531e4d95d4c2785aec874570.jpg\",\n  \"657789.jpg\": \"Insecta/Adelpha californica/423965a94237685e6d8b8ba2da89b85d.jpg\",\n  \"657792.jpg\": \"Insecta/Adelpha californica/a6b7b9bfc385a00e9e180afef4427e0c.jpg\",\n  \"657798.jpg\": \"Insecta/Adelpha californica/e252612b52efe3385757f35f26f6fbf1.jpg\",\n  \"657814.jpg\": \"Insecta/Adelpha californica/29d017a5994f5361d9b0c0658708fc54.jpg\",\n  \"657819.jpg\": \"Insecta/Adelpha californica/429ac12564adfbad66c7601fc976d857.jpg\",\n  \"657831.jpg\": \"Insecta/Adelpha californica/4d0e3934a7c10aec4ffd8cac11aa1a83.jpg\",\n  \"657837.jpg\": \"Insecta/Adelpha californica/779214e84e7ee5402cf47964ec429fc1.jpg\",\n  \"657838.jpg\": \"Insecta/Adelpha californica/dd18c3b6485a40824b4cebd9d93f9fea.jpg\",\n  \"657856.jpg\": \"Insecta/Adelpha californica/2dc6e02d2df724431797c014b9f71765.jpg\",\n  \"657875.jpg\": \"Insecta/Adelpha californica/55ab9af7426d45a2d24e2a6db8315c17.jpg\",\n  \"657888.jpg\": \"Insecta/Adelpha californica/c6ed186fdd44ab01e3176b952203c5e1.jpg\",\n  \"657926.jpg\": \"Animalia/Harpaphe haydeniana/7fdb6207fef02d70d5c8b7bb3dcd6021.jpg\",\n  \"657927.jpg\": \"Animalia/Harpaphe haydeniana/13cc885ea43d131f84871701b78b7ff0.jpg\",\n  \"657932.jpg\": \"Animalia/Harpaphe haydeniana/474667cfbc5330fd0f393bfb29faa8eb.jpg\",\n  \"657933.jpg\": \"Animalia/Harpaphe haydeniana/304780d9b3ed3d96c605e7b1a01d4e23.jpg\",\n  \"657934.jpg\": \"Animalia/Harpaphe haydeniana/dade7cfa6255ecb25b7fa87c19579127.jpg\",\n  \"657935.jpg\": \"Animalia/Harpaphe haydeniana/5db9602807efa4efc7b8acb1ae279a81.jpg\",\n  \"657940.jpg\": \"Animalia/Harpaphe haydeniana/a5e79066cfea7c086c5b47aa92f37145.jpg\",\n  \"657945.jpg\": \"Aves/Poecile gambeli/ff328374c30d13bda2f75f82677b4f76.jpg\",\n  \"657951.jpg\": \"Aves/Poecile gambeli/b83f57a374a1953a4feacf8ae0db43db.jpg\",\n  \"657953.jpg\": \"Aves/Poecile gambeli/683e1a841431a0d94d8f85216148a11b.jpg\",\n  \"657961.jpg\": \"Aves/Poecile gambeli/d1921cfdc2f08ea186e6e8ccedf71c68.jpg\",\n  \"657963.jpg\": \"Aves/Poecile gambeli/58f090921d6163521ea282dfde5bfc37.jpg\",\n  \"657965.jpg\": \"Aves/Poecile gambeli/7ed475aeeee83a1e478526cac7d5caad.jpg\",\n  \"657972.jpg\": \"Aves/Poecile gambeli/2f2af01ebf973e3184de2677d1e92968.jpg\",\n  \"658013.jpg\": \"Aves/Poecile gambeli/bf0058cf6abff9ea1bc8ce07e5076dc7.jpg\",\n  \"658017.jpg\": \"Insecta/Bombus ternarius/0347b8105f6d2a3f6fe6a8f5edd6bbaa.jpg\",\n  \"65803.jpg\": \"Aves/Coccothraustes coccothraustes/58016a021218b69263cab08cab4f49d1.jpg\",\n  \"658031.jpg\": \"Insecta/Bombus ternarius/24a7ad2dafb1280b06b9431e289feb9e.jpg\",\n  \"65804.jpg\": \"Aves/Coccothraustes coccothraustes/398bfb76e0a9ffb86bac2481c783aeea.jpg\",\n  \"658057.jpg\": \"Insecta/Bombus ternarius/3e4a93dd772746ce344d451df43dab0b.jpg\",\n  \"65807.jpg\": \"Aves/Coccothraustes coccothraustes/2c665e4e5b0d042ed265ffa1850d1073.jpg\",\n  \"658071.jpg\": \"Insecta/Bombus ternarius/5f8deea7190f9dd4e74cde024b5527e3.jpg\",\n  \"658086.jpg\": \"Insecta/Bombus ternarius/5a324647449666b96898fc9d86162d74.jpg\",\n  \"658087.jpg\": \"Insecta/Bombus ternarius/40abf2c5a0dfe713ce9b6fe2a40edf1c.jpg\",\n  \"658133.jpg\": \"Aves/Ara macao/dc953385235e23bd5a22a4078a074aad.jpg\",\n  \"658136.jpg\": \"Aves/Ara macao/758f2f015ac343f6640ddd9cd7f6571f.jpg\",\n  \"658137.jpg\": \"Aves/Ara macao/2f96be55dec83b1692fd5a1328fd998a.jpg\",\n  \"658151.jpg\": \"Insecta/Araschnia levana/f3cb1c8176a9c43dd87e55268b371a45.jpg\",\n  \"658152.jpg\": \"Insecta/Araschnia levana/6d7cf382c27fdc294786dbbd941894b2.jpg\",\n  \"658157.jpg\": \"Insecta/Araschnia levana/46a0bd6763579715258c4fe758e3f27b.jpg\",\n  \"65816.jpg\": \"Aves/Coccothraustes coccothraustes/4299c96716e9775aba869f11c1fef15d.jpg\",\n  \"658165.jpg\": \"Insecta/Araschnia levana/d947bc488b2e3225040b766211f083ee.jpg\",\n  \"658166.jpg\": \"Insecta/Araschnia levana/28cc0d24d9471ecf095cb74ef2edc76b.jpg\",\n  \"658167.jpg\": \"Insecta/Araschnia levana/a74c0825eed2483c0efa3a9182069ab7.jpg\",\n  \"658168.jpg\": \"Insecta/Araschnia levana/d7783b3c7fe8766054de0a42412a55b8.jpg\",\n  \"658171.jpg\": \"Insecta/Araschnia levana/0f234fa845c6aa6a0a6020c7f3949500.jpg\",\n  \"658178.jpg\": \"Insecta/Araschnia levana/0870a36f795556e7596a558550aeda22.jpg\",\n  \"658181.jpg\": \"Insecta/Araschnia levana/46e056ac51811ce3b418cbabda7bea24.jpg\",\n  \"658210.jpg\": \"Reptilia/Crotalus oreganus oreganus/eb258ca770a73edbe3efef84d8541d22.jpg\",\n  \"658211.jpg\": \"Reptilia/Crotalus oreganus oreganus/5fccfe4260a0f1e8f3ed618c0dc583ed.jpg\",\n  \"658217.jpg\": \"Reptilia/Crotalus oreganus oreganus/e5eb06ff0c6a22fc00086c3a9a45236f.jpg\",\n  \"658235.jpg\": \"Reptilia/Crotalus oreganus oreganus/5563dc5260ff87ece6dcde65cb1c0833.jpg\",\n  \"658238.jpg\": \"Reptilia/Crotalus oreganus oreganus/4874a0873e0a0998dab01bb34fd6ea4c.jpg\",\n  \"658266.jpg\": \"Insecta/Arigomphus villosipes/abeb8d3d9ed9784b3b8ebd6bf51f7548.jpg\",\n  \"65830.jpg\": \"Aves/Coccothraustes coccothraustes/1ad9c9d0c78dbe726a27c8c1312dc6f7.jpg\",\n  \"658378.jpg\": \"Reptilia/Trachemys scripta elegans/007cafef59f4e6d81d80d75f6370053d.jpg\",\n  \"658416.jpg\": \"Reptilia/Trachemys scripta elegans/8e8debf9863fec94b580375a09d4cecc.jpg\",\n  \"6586.jpg\": \"Mammalia/Eschrichtius robustus/08baa7c5f960f7db27c2e2f1b781d2d7.jpg\",\n  \"658605.jpg\": \"Reptilia/Trachemys scripta elegans/b34b8bda1ff562b2caf23a18f123a56f.jpg\",\n  \"658814.jpg\": \"Reptilia/Trachemys scripta elegans/51fa04b4a0c229bd0848ccc1beb139de.jpg\",\n  \"658879.jpg\": \"Reptilia/Trachemys scripta elegans/849e65ac23b98e54a81a52a6954ac954.jpg\",\n  \"658971.jpg\": \"Insecta/Bombus pensylvanicus/d3d3eb3c59a718067dcfa3d53b800b62.jpg\",\n  \"659025.jpg\": \"Insecta/Bombus pensylvanicus/36abb0839cb2f44d520182beb1d52bb8.jpg\",\n  \"659080.jpg\": \"Insecta/Bombus pensylvanicus/b87432e5f57222b5adfeb0dbd71b797c.jpg\",\n  \"659106.jpg\": \"Insecta/Bombus pensylvanicus/1fe21c9a08d79090f5fdb22599cb2f5e.jpg\",\n  \"65920.jpg\": \"Insecta/Heraclides rumiko/e11b29e8f2215f807c62fbfc4941fc24.jpg\",\n  \"65931.jpg\": \"Insecta/Heraclides rumiko/f2249bf93514ac716627b1becc571783.jpg\",\n  \"65932.jpg\": \"Insecta/Heraclides rumiko/4c04d7c18e596a8dc75e6a6473be49a7.jpg\",\n  \"65933.jpg\": \"Insecta/Heraclides rumiko/f43cebd135f1ee362c90c48a9714623e.jpg\",\n  \"65936.jpg\": \"Insecta/Heraclides rumiko/5ad9ea92d3b09afc3498926d12343fe0.jpg\",\n  \"659365.jpg\": \"Insecta/Malacosoma americanum/699cf09b2b57b247629982f6e3945b0e.jpg\",\n  \"659367.jpg\": \"Insecta/Malacosoma americanum/84bd2fc37455da98522e78a5c27a607c.jpg\",\n  \"659369.jpg\": \"Insecta/Malacosoma americanum/698f33a6248436f9e297af003385b4bd.jpg\",\n  \"65937.jpg\": \"Insecta/Heraclides rumiko/3052f56a42f65cdc07f6107dee0c1e91.jpg\",\n  \"659381.jpg\": \"Insecta/Malacosoma americanum/74a2db4fe4608691993c45714c415a22.jpg\",\n  \"65940.jpg\": \"Insecta/Heraclides rumiko/5408f728f1b33ea406a6acae75a4738d.jpg\",\n  \"659412.jpg\": \"Insecta/Malacosoma americanum/91294175b00e2e684883d711c8eb9e64.jpg\",\n  \"65945.jpg\": \"Insecta/Heraclides rumiko/b143cdb49085a8954c423c823584cd00.jpg\",\n  \"65951.jpg\": \"Insecta/Heraclides rumiko/039a4848fc8bcc864e2c63096f0761f2.jpg\",\n  \"65952.jpg\": \"Insecta/Heraclides rumiko/d75d153d43cfa7ec960cc8b349e38161.jpg\",\n  \"659592.jpg\": \"Insecta/Chinavia hilaris/a6b5bce056b7dffde20bb84e90d82dff.jpg\",\n  \"659597.jpg\": \"Insecta/Chinavia hilaris/89270f70a5c426be0e647c04f16cd77d.jpg\",\n  \"65960.jpg\": \"Insecta/Heraclides rumiko/0df3e2b1d9a1f2e68c2fc2f39be3b45e.jpg\",\n  \"659607.jpg\": \"Insecta/Chinavia hilaris/fa8adc20555dc9c66457b061f7c04955.jpg\",\n  \"659608.jpg\": \"Insecta/Chinavia hilaris/9036627e9a3d830f3b5dd8beebb7c805.jpg\",\n  \"659612.jpg\": \"Insecta/Chinavia hilaris/79b77875e4434946d018c8f958ac3123.jpg\",\n  \"659616.jpg\": \"Insecta/Chinavia hilaris/cdbdae5d3e11c657d5b9fb85d9c45488.jpg\",\n  \"659625.jpg\": \"Insecta/Chinavia hilaris/26b403cd94d6dc848a378ce9f0c832d5.jpg\",\n  \"659630.jpg\": \"Aves/Porphyrio melanotus melanotus/04420e2784d9281f0af9eaf1ed591020.jpg\",\n  \"659634.jpg\": \"Aves/Porphyrio melanotus melanotus/da83f2d806b38c90e688483ed4a639f9.jpg\",\n  \"659640.jpg\": \"Aves/Porphyrio melanotus melanotus/1822dc0b0f741cf5b52c9ca7cd5e1ccc.jpg\",\n  \"659659.jpg\": \"Aves/Porphyrio melanotus melanotus/71caf8474b1692c2f594b98de8e5b6a2.jpg\",\n  \"659726.jpg\": \"Insecta/Synchlora frondaria/c629ecc22c8356f1aa5cbc4e35ce4fec.jpg\",\n  \"65973.jpg\": \"Insecta/Heraclides rumiko/5b1613116bdb1e7a606ea505d46807e9.jpg\",\n  \"659761.jpg\": \"Arachnida/Gasteracantha cancriformis/b4f38fdc27bd11ba82fa12c7ad7c9db1.jpg\",\n  \"659772.jpg\": \"Arachnida/Gasteracantha cancriformis/3b8279f8f02ea5aeb6c4a5bb5cfc0822.jpg\",\n  \"659777.jpg\": \"Arachnida/Gasteracantha cancriformis/7084325e1d7a3464808ae98d5e7277bf.jpg\",\n  \"659780.jpg\": \"Arachnida/Gasteracantha cancriformis/f40bb4facc614ebf6298122f2cf4a7ca.jpg\",\n  \"659789.jpg\": \"Arachnida/Gasteracantha cancriformis/d2c007406783b227c0b2941c567d6793.jpg\",\n  \"659810.jpg\": \"Arachnida/Gasteracantha cancriformis/e9329e6d02d42bdf2788f8fb584d703a.jpg\",\n  \"659814.jpg\": \"Arachnida/Gasteracantha cancriformis/3c197200c31dd5d89913bc0ed8efada8.jpg\",\n  \"659819.jpg\": \"Arachnida/Gasteracantha cancriformis/5384ad541940f03bae5fc34712dbf9d1.jpg\",\n  \"659830.jpg\": \"Arachnida/Gasteracantha cancriformis/54b2777cf830d141357b9ce69efd2a71.jpg\",\n  \"659882.jpg\": \"Insecta/Phragmatobia fuliginosa/fc359347006a9910db80ac2d174ced0c.jpg\",\n  \"659885.jpg\": \"Insecta/Phragmatobia fuliginosa/aa28c806424c2cf4b90f7807198ec724.jpg\",\n  \"659888.jpg\": \"Insecta/Phragmatobia fuliginosa/4f50f6459fc2a163f7eb2857c0536f7a.jpg\",\n  \"659891.jpg\": \"Insecta/Phragmatobia fuliginosa/e50886871d9f9707f61f30f81d1e6a64.jpg\",\n  \"659919.jpg\": \"Insecta/Xylocopa varipuncta/4d4c5eff9b6d6ac6b7a6742ee2ca748a.jpg\",\n  \"65992.jpg\": \"Insecta/Heraclides rumiko/a8de0d912895d6258df7f9d18879a41b.jpg\",\n  \"659920.jpg\": \"Insecta/Xylocopa varipuncta/14088537ff628e89edd56620b58170c2.jpg\",\n  \"659926.jpg\": \"Insecta/Xylocopa varipuncta/c2c4cf09cc154e1207e541132d97d117.jpg\",\n  \"659930.jpg\": \"Insecta/Xylocopa varipuncta/f1818d33c24e2adc059003861467d75f.jpg\",\n  \"65996.jpg\": \"Insecta/Heraclides rumiko/872d22e724c1bff40986d0f06007ead0.jpg\",\n  \"660023.jpg\": \"Aves/Cacatua galerita/7f749cbfe4005421538621bbb9e2aa4f.jpg\",\n  \"660027.jpg\": \"Aves/Cacatua galerita/45f8a6fb6ff0efec874a492c17698d76.jpg\",\n  \"660032.jpg\": \"Aves/Cacatua galerita/f272cedc133e2fe6551551ddb246d49e.jpg\",\n  \"660033.jpg\": \"Aves/Cacatua galerita/a82a54793a01e4277c3119b942738386.jpg\",\n  \"660039.jpg\": \"Aves/Cacatua galerita/ec46f92a9ea7e87561680f9bee81d8d1.jpg\",\n  \"660044.jpg\": \"Aves/Fringilla coelebs/7bc56b96ac07489b1d77deef7325875a.jpg\",\n  \"660088.jpg\": \"Aves/Fringilla coelebs/24d48031babadcdc951026c1c35ee291.jpg\",\n  \"66011.jpg\": \"Insecta/Heraclides rumiko/e33be700964f1bf08e129dccbe58941a.jpg\",\n  \"660110.jpg\": \"Aves/Fringilla coelebs/d059d5b0834b3c2f4e44c749d8638f9b.jpg\",\n  \"66012.jpg\": \"Insecta/Heraclides rumiko/b852d053f45bf9ea2ab0734c7dfa3502.jpg\",\n  \"66013.jpg\": \"Insecta/Heraclides rumiko/f7096a2f711fea1f268cd36290d22867.jpg\",\n  \"660133.jpg\": \"Aves/Fringilla coelebs/02cbb6015c52b6aa93f21884396fa5da.jpg\",\n  \"66014.jpg\": \"Insecta/Heraclides rumiko/07be9342de0e8023cb6a0e1ca1c266a6.jpg\",\n  \"66016.jpg\": \"Insecta/Heraclides rumiko/81e82a5a2a18cebac14105ef41841d99.jpg\",\n  \"660177.jpg\": \"Aves/Fringilla coelebs/33efdc1ed9a6d92f4cfe6b2f048d3f98.jpg\",\n  \"66018.jpg\": \"Insecta/Heraclides rumiko/215a6a8e2b1af46197ab1c1e40eefd52.jpg\",\n  \"6602.jpg\": \"Mammalia/Eschrichtius robustus/2dd36bf793dfd2781d4a3e17409cafbb.jpg\",\n  \"660272.jpg\": \"Aves/Sphyrapicus varius/980534de233cd1cc78c56b5c3f149510.jpg\",\n  \"660277.jpg\": \"Aves/Sphyrapicus varius/6f644ba0bc4a456b77acbad59b40ddc8.jpg\",\n  \"6603.jpg\": \"Mammalia/Eschrichtius robustus/b59af36e7942fb88fa767f8865f25bf7.jpg\",\n  \"660307.jpg\": \"Aves/Sphyrapicus varius/528227ea8c9134e10cc56d2a5fa287de.jpg\",\n  \"66031.jpg\": \"Insecta/Deloyala guttata/9c472f460e8158d9c1b12ecdaffa54ef.jpg\",\n  \"66032.jpg\": \"Insecta/Deloyala guttata/b5c42efe3d082ae823828f9a6a2a9eb2.jpg\",\n  \"66033.jpg\": \"Insecta/Deloyala guttata/9a45f9ce5dda3dc5b1061619f313535b.jpg\",\n  \"660374.jpg\": \"Aves/Sphyrapicus varius/3ad96d6eca945839ae71a9d1fc654c2d.jpg\",\n  \"660378.jpg\": \"Aves/Sphyrapicus varius/793f8afbc03c9bf90c8d4d6fa2647575.jpg\",\n  \"66038.jpg\": \"Amphibia/Gastrophryne olivacea/1faa113e00413aac219fcb488802dad5.jpg\",\n  \"66039.jpg\": \"Amphibia/Gastrophryne olivacea/278062a96112b89f1fd47136d4abe7c8.jpg\",\n  \"660419.jpg\": \"Aves/Sphyrapicus varius/4b513c8c12d354f97fd5e10bed1bb295.jpg\",\n  \"66050.jpg\": \"Amphibia/Gastrophryne olivacea/1eb8b5d37b47dd778d78e8f3ca152dfc.jpg\",\n  \"66052.jpg\": \"Amphibia/Gastrophryne olivacea/0fb7b381f0494956601494310a61c754.jpg\",\n  \"66054.jpg\": \"Amphibia/Gastrophryne olivacea/73a52a1b59eafa0974c944863dc1e0fd.jpg\",\n  \"66055.jpg\": \"Amphibia/Gastrophryne olivacea/bbde0539beedb4fbd007fe6b6d24bed3.jpg\",\n  \"66059.jpg\": \"Amphibia/Gastrophryne olivacea/892fdb57cb4b4e387141ddf3f4a2a000.jpg\",\n  \"66060.jpg\": \"Amphibia/Gastrophryne olivacea/131f1adb07adfa36d4fd36b84c91266a.jpg\",\n  \"660601.jpg\": \"Aves/Haematopus bachmani/43c5c07d936207fd52c3d2a5e8526d1d.jpg\",\n  \"660629.jpg\": \"Aves/Haematopus bachmani/b4b08fda1047ce57feb2597529ab2675.jpg\",\n  \"660631.jpg\": \"Aves/Haematopus bachmani/68b6599855099e87c56e17ec0c8f072f.jpg\",\n  \"660632.jpg\": \"Aves/Haematopus bachmani/266a871be0b981489a7e19198baf4ec3.jpg\",\n  \"66065.jpg\": \"Amphibia/Gastrophryne olivacea/6159fb97d937482710c12a6a4b77525d.jpg\",\n  \"660652.jpg\": \"Insecta/Costaconvexa centrostrigaria/14d435b282f4b4109c7f8878a54225fc.jpg\",\n  \"660653.jpg\": \"Insecta/Costaconvexa centrostrigaria/5aad655ac7535d50cec42c54c8dc8566.jpg\",\n  \"66066.jpg\": \"Amphibia/Gastrophryne olivacea/ea7ea13706d2e6fdb54c617e6979ebcf.jpg\",\n  \"660661.jpg\": \"Insecta/Costaconvexa centrostrigaria/2f7f5724a1a4dfda9e2636973cad1a20.jpg\",\n  \"660663.jpg\": \"Insecta/Costaconvexa centrostrigaria/c7501433015b3586b2e9e36967762a85.jpg\",\n  \"660667.jpg\": \"Insecta/Costaconvexa centrostrigaria/e5b907b385401eaa73a2ce5c54ec5db5.jpg\",\n  \"660677.jpg\": \"Insecta/Costaconvexa centrostrigaria/3d68bb67367b97efc94b3cd60068c18f.jpg\",\n  \"660688.jpg\": \"Insecta/Costaconvexa centrostrigaria/a9d4090286b10f1905a33e281787c865.jpg\",\n  \"660689.jpg\": \"Insecta/Costaconvexa centrostrigaria/12e705d26ed98a71498e8138cf6fd249.jpg\",\n  \"660693.jpg\": \"Insecta/Argia fumipennis/e7fa5e9463735ddd5c02ca222d329c1c.jpg\",\n  \"660695.jpg\": \"Insecta/Argia fumipennis/d717ab6d98f8b32091a52520a7af52d7.jpg\",\n  \"660696.jpg\": \"Insecta/Argia fumipennis/8c0325f71fe2fab2d6934056f627df15.jpg\",\n  \"660713.jpg\": \"Insecta/Argia fumipennis/603f3024ac761302d2417c9e253089e5.jpg\",\n  \"660728.jpg\": \"Insecta/Argia fumipennis/b2dfe64f2bb5d41a9a518215efa67427.jpg\",\n  \"660732.jpg\": \"Insecta/Argia fumipennis/088668b601a58489a939e7ae98de3969.jpg\",\n  \"660747.jpg\": \"Insecta/Argia fumipennis/621140f8d978fa094f8c56174e0220ee.jpg\",\n  \"660755.jpg\": \"Insecta/Argia fumipennis/5093301866bf715945db3a16ae084d5e.jpg\",\n  \"660791.jpg\": \"Insecta/Cosmopepla lintneriana/71f98fa575d05872fbc5b8179e8cbbda.jpg\",\n  \"660796.jpg\": \"Insecta/Cosmopepla lintneriana/bdce8ad8ec29c162669c578a07e075ea.jpg\",\n  \"66080.jpg\": \"Amphibia/Gastrophryne olivacea/ecc286089050ca4eee6bf4cd42e4739e.jpg\",\n  \"660812.jpg\": \"Amphibia/Taricha rivularis/718e8752e55aa3ba660d600e2eb22360.jpg\",\n  \"66083.jpg\": \"Amphibia/Gastrophryne olivacea/c7127efc445454a8890e09d0bb5b1412.jpg\",\n  \"66084.jpg\": \"Amphibia/Gastrophryne olivacea/9a4458b3844090322a7ebe758bbe2820.jpg\",\n  \"660843.jpg\": \"Aves/Lophodytes cucullatus/5e55e9d1e1c23c8cfa7051995cdc4007.jpg\",\n  \"66088.jpg\": \"Amphibia/Gastrophryne olivacea/f903eafce4d6ba7f751f6ff9bd246aec.jpg\",\n  \"66089.jpg\": \"Amphibia/Gastrophryne olivacea/3f3c5ee1b2b9c1df3b10ca8f675e0397.jpg\",\n  \"660999.jpg\": \"Aves/Lophodytes cucullatus/5662d714910c92714d8f4cd0fd8966c7.jpg\",\n  \"66102.jpg\": \"Amphibia/Gastrophryne olivacea/d206ceaccdb4734cfab332d1bf1a40d6.jpg\",\n  \"661037.jpg\": \"Aves/Lophodytes cucullatus/3ab274cc8949447e301079f1f6de6647.jpg\",\n  \"66105.jpg\": \"Amphibia/Gastrophryne olivacea/f657213382eb514dae5af68149523167.jpg\",\n  \"66106.jpg\": \"Amphibia/Gastrophryne olivacea/ef4ba7eac28affb729c15a0923c70f8d.jpg\",\n  \"66107.jpg\": \"Amphibia/Gastrophryne olivacea/4ea6e31e210661b78cc12764523d55a2.jpg\",\n  \"66108.jpg\": \"Amphibia/Gastrophryne olivacea/f71c67b0f430d746caf22e9aed9d24eb.jpg\",\n  \"661141.jpg\": \"Aves/Gavia immer/032d3147c6956a521ad1c886603b35b5.jpg\",\n  \"661268.jpg\": \"Aves/Gavia immer/dad7144a9c253357d8e64b7d286252c2.jpg\",\n  \"661344.jpg\": \"Aves/Gavia immer/b1a8f123e2e4c6c7ede56562b1cca4a7.jpg\",\n  \"661362.jpg\": \"Aves/Gavia immer/14aec9e2e1194efc6d5973f213c13b6d.jpg\",\n  \"661429.jpg\": \"Aves/Gerygone igata/bb61c8bc59c264f80138c4e27c7ba2fe.jpg\",\n  \"661430.jpg\": \"Aves/Gerygone igata/3468eecaa7c761833e4579457546d040.jpg\",\n  \"661439.jpg\": \"Aves/Gerygone igata/739e6731808bc94d61bbad45fa107593.jpg\",\n  \"661440.jpg\": \"Aves/Gerygone igata/abfcbd047c9a4259b9478e64c6d1e9d3.jpg\",\n  \"661594.jpg\": \"Arachnida/Nephila clavipes/c6cb270b0081b62df991871344b4550d.jpg\",\n  \"661617.jpg\": \"Arachnida/Nephila clavipes/03ece73a723409a7bac16dd1cf62104a.jpg\",\n  \"661626.jpg\": \"Arachnida/Nephila clavipes/679446e771da029d083beb92bbf488d3.jpg\",\n  \"661636.jpg\": \"Arachnida/Nephila clavipes/fce64a2ac69d50467d15ae0da5708165.jpg\",\n  \"661640.jpg\": \"Arachnida/Nephila clavipes/ec10b8da111631274975d5921c2b485c.jpg\",\n  \"66166.jpg\": \"Mammalia/Eumetopias jubatus/d773df51571999e160e8195b6cf0536b.jpg\",\n  \"66167.jpg\": \"Mammalia/Eumetopias jubatus/6156e716928d097e5fec963a74365f91.jpg\",\n  \"66169.jpg\": \"Mammalia/Eumetopias jubatus/70e8e9bd01f61c0d14f1c90c32698f33.jpg\",\n  \"66170.jpg\": \"Mammalia/Eumetopias jubatus/ae446f942d39f523649d3aaa9f22c70b.jpg\",\n  \"6618.jpg\": \"Mammalia/Eschrichtius robustus/146db38ec0f4d902a3d1822dd253823b.jpg\",\n  \"66186.jpg\": \"Mammalia/Eumetopias jubatus/c805e82e238c6fd2d8c9660a1fb1a44c.jpg\",\n  \"661947.jpg\": \"Insecta/Orgyia vetusta/94f16b4f6129706bd8d72067a45c4bbd.jpg\",\n  \"661951.jpg\": \"Insecta/Orgyia vetusta/4cf34f5bd0b7d15ec7c7e648d21848d6.jpg\",\n  \"662027.jpg\": \"Aves/Aphelocoma woodhouseii/e8d683da1227dca11e1506f4ab513b0a.jpg\",\n  \"662028.jpg\": \"Aves/Aphelocoma woodhouseii/7620a23dac76b8d49c3a088fb0ab5db8.jpg\",\n  \"662029.jpg\": \"Aves/Aphelocoma woodhouseii/69a3e53dd84bf428afb92ae33af8acb2.jpg\",\n  \"662030.jpg\": \"Aves/Aphelocoma woodhouseii/840a819942facb3c05f03b6def6219c1.jpg\",\n  \"662031.jpg\": \"Aves/Aphelocoma woodhouseii/184fe5c0afc6228d9018f7c32c48cb67.jpg\",\n  \"662032.jpg\": \"Aves/Aphelocoma woodhouseii/3c48881f58ce145cdcea4f95a57c5b7c.jpg\",\n  \"662044.jpg\": \"Aves/Aphelocoma woodhouseii/791f792a138c861c628d142100726b85.jpg\",\n  \"66208.jpg\": \"Mammalia/Eumetopias jubatus/6ab8d4a0833658bb711f5dcc318ed621.jpg\",\n  \"66213.jpg\": \"Aves/Campylorhynchus rufinucha/bff6337955e0c988e2eeb8f5e2328b6c.jpg\",\n  \"66215.jpg\": \"Aves/Campylorhynchus rufinucha/3e2bbbf1e56ced5048be89fa726a1291.jpg\",\n  \"66216.jpg\": \"Aves/Campylorhynchus rufinucha/3ab2a56c6672e1c88d396a784c0b90d3.jpg\",\n  \"662216.jpg\": \"Aves/Picus viridis/da75c0ac92ecdf4f04ad6a5d60de4f20.jpg\",\n  \"66222.jpg\": \"Aves/Campylorhynchus rufinucha/679c8537f04055051ae4ad5853840174.jpg\",\n  \"662226.jpg\": \"Aves/Picus viridis/4addfe20437311f52055555a95744907.jpg\",\n  \"662227.jpg\": \"Aves/Picus viridis/d7c2908b20ec9305e4dc442888e41c1d.jpg\",\n  \"662232.jpg\": \"Reptilia/Pantherophis obsoletus lindheimeri/582d900d4701bfc87674340d7aff0091.jpg\",\n  \"662234.jpg\": \"Reptilia/Pantherophis obsoletus lindheimeri/46102bbd83632bc8132717100f00e4ac.jpg\",\n  \"662245.jpg\": \"Reptilia/Pantherophis obsoletus lindheimeri/fde065b186d7249adb2263220b644679.jpg\",\n  \"662246.jpg\": \"Reptilia/Pantherophis obsoletus lindheimeri/424d4a2aa13b11089bb0153a3332b251.jpg\",\n  \"662247.jpg\": \"Reptilia/Pantherophis obsoletus lindheimeri/bd24b9f577dbc7951b96d9d0658371bf.jpg\",\n  \"662257.jpg\": \"Reptilia/Pantherophis obsoletus lindheimeri/a13cb7a2c06018c770ac396efb93c3d7.jpg\",\n  \"662260.jpg\": \"Reptilia/Pantherophis obsoletus lindheimeri/3839a966c4deb4c90c6ffa67b8b04d05.jpg\",\n  \"662261.jpg\": \"Reptilia/Pantherophis obsoletus lindheimeri/e9971dc5eb3448f497860967552a8c2f.jpg\",\n  \"662267.jpg\": \"Reptilia/Pantherophis obsoletus lindheimeri/e39746dcc6af027c12325308923e287c.jpg\",\n  \"662268.jpg\": \"Reptilia/Pantherophis obsoletus lindheimeri/3c0df962e155d0cc8d20053731272d42.jpg\",\n  \"662269.jpg\": \"Reptilia/Pantherophis obsoletus lindheimeri/4712f1650e8d20e842e5d07be0576824.jpg\",\n  \"66227.jpg\": \"Aves/Campylorhynchus rufinucha/131c7a37c6970d141060599ce4a84157.jpg\",\n  \"662270.jpg\": \"Reptilia/Pantherophis obsoletus lindheimeri/54b83d1680e70b12db7310ab1b0f454b.jpg\",\n  \"66238.jpg\": \"Reptilia/Hemidactylus frenatus/8a37ea22880dd5ef2ce0e6f6e2fb67b1.jpg\",\n  \"66239.jpg\": \"Reptilia/Hemidactylus frenatus/2e0e0876e1549d1c5ff44b3aeccbb243.jpg\",\n  \"6624.jpg\": \"Mammalia/Eschrichtius robustus/f6725220964573f0ec11898f1941159e.jpg\",\n  \"662409.jpg\": \"Aves/Butorides virescens/47a942b7dc17fe0fd2cb396caec13fc7.jpg\",\n  \"66247.jpg\": \"Reptilia/Hemidactylus frenatus/dd6a4b77890ceea951a9607c1d335e40.jpg\",\n  \"66248.jpg\": \"Reptilia/Hemidactylus frenatus/2e5af50631d63b0e81f579e3cfbc2abb.jpg\",\n  \"6625.jpg\": \"Mammalia/Eschrichtius robustus/0265dff046d5a4f3786f2c5118a5be76.jpg\",\n  \"66255.jpg\": \"Reptilia/Hemidactylus frenatus/3ff2cc3a1bfb86ed821f85759b505a56.jpg\",\n  \"66256.jpg\": \"Reptilia/Hemidactylus frenatus/386bd8e8148dab048f7ede589ca70f35.jpg\",\n  \"66257.jpg\": \"Reptilia/Hemidactylus frenatus/c710c70e2485be7fa2210f01d5b98855.jpg\",\n  \"66260.jpg\": \"Reptilia/Hemidactylus frenatus/2dedf382f887ab3582c95225370cc2ff.jpg\",\n  \"66261.jpg\": \"Reptilia/Hemidactylus frenatus/aba6ac545a221fa8a5c5cdf99533b59f.jpg\",\n  \"66262.jpg\": \"Reptilia/Hemidactylus frenatus/26d42d1036f6ef7b467beee6da4dfc1d.jpg\",\n  \"662625.jpg\": \"Aves/Butorides virescens/07394e27fee58fd3c571ddf2cd7dddc8.jpg\",\n  \"662639.jpg\": \"Aves/Butorides virescens/76a2e145542b92f25819b2d247b24670.jpg\",\n  \"66266.jpg\": \"Reptilia/Hemidactylus frenatus/d8790bb76a25b51cb9ffc1d5d1630584.jpg\",\n  \"662666.jpg\": \"Aves/Butorides virescens/5d35d01af5bd4613964ff331c94ff97c.jpg\",\n  \"66273.jpg\": \"Reptilia/Hemidactylus frenatus/212dad514870b3b2b5f755845eb496e0.jpg\",\n  \"66278.jpg\": \"Reptilia/Hemidactylus frenatus/dbecb7581153210946805241700e6182.jpg\",\n  \"66279.jpg\": \"Reptilia/Hemidactylus frenatus/a20e3269211cab851c8c046c7af0182d.jpg\",\n  \"66280.jpg\": \"Reptilia/Hemidactylus frenatus/c49c1cb65aeda547a77a462c499195a2.jpg\",\n  \"66281.jpg\": \"Reptilia/Hemidactylus frenatus/1418c7a55597e2bf349f366e003da861.jpg\",\n  \"66282.jpg\": \"Reptilia/Hemidactylus frenatus/1e03fc835a3d219c111c569c9117f76a.jpg\",\n  \"662865.jpg\": \"Aves/Butorides virescens/bbbdd8f16c764a0846482b520f6164b0.jpg\",\n  \"66289.jpg\": \"Reptilia/Hemidactylus frenatus/652a18aa79d40a19eb99686a5b10a9aa.jpg\",\n  \"662929.jpg\": \"Aves/Butorides virescens/06459470b7f84a68646de5e73a5b18e0.jpg\",\n  \"66293.jpg\": \"Reptilia/Hemidactylus frenatus/df0b0a260df0af2154baed86081cb254.jpg\",\n  \"66294.jpg\": \"Reptilia/Hemidactylus frenatus/74b6e4948e36151e7695f62c8726f2ad.jpg\",\n  \"662950.jpg\": \"Aves/Agelaius tricolor/dcbd462a0fb7247af6dd61c5173f3d07.jpg\",\n  \"66296.jpg\": \"Aves/Pinicola enucleator/f82d73fe8599a4eb07681d1514887dff.jpg\",\n  \"662963.jpg\": \"Insecta/Caenurgina crassiuscula/c113769e5606f9873f725fd732ca45e2.jpg\",\n  \"662968.jpg\": \"Reptilia/Phrynosoma platyrhinos/44863d2f98a98bfb44d601dca0d04e89.jpg\",\n  \"66297.jpg\": \"Aves/Pinicola enucleator/c918bcc1c0202719e1f008dd2bbcb3b9.jpg\",\n  \"662974.jpg\": \"Reptilia/Phrynosoma platyrhinos/df4f3a3be2fb2389fb3ee4bc9ba7d1a3.jpg\",\n  \"662975.jpg\": \"Reptilia/Phrynosoma platyrhinos/9a2de509476a715830c14dcdd9cd9624.jpg\",\n  \"662976.jpg\": \"Reptilia/Phrynosoma platyrhinos/09f28fd4672de0ab8182ab5c9bb5b097.jpg\",\n  \"662977.jpg\": \"Reptilia/Phrynosoma platyrhinos/9d76065853bc28dad8765669a6e9e741.jpg\",\n  \"662978.jpg\": \"Reptilia/Phrynosoma platyrhinos/bd6ac2106377093a43d931ff05b2d77f.jpg\",\n  \"662979.jpg\": \"Reptilia/Phrynosoma platyrhinos/2083185e118e06b0d752e85e66de401d.jpg\",\n  \"66298.jpg\": \"Aves/Pinicola enucleator/fdceab8e685e9a921e1a2c0d02feab7c.jpg\",\n  \"662983.jpg\": \"Reptilia/Phrynosoma platyrhinos/75ae2860ac10172ea9a12f3bd053ec1f.jpg\",\n  \"662986.jpg\": \"Reptilia/Phrynosoma platyrhinos/1842cafc8c0888e73c5d7d17027ae7c2.jpg\",\n  \"662987.jpg\": \"Reptilia/Phrynosoma platyrhinos/91e8b1abfc80831c693d656593ca066a.jpg\",\n  \"662988.jpg\": \"Reptilia/Phrynosoma platyrhinos/11da45adae6c9d41030a3b1e09d7ce81.jpg\",\n  \"66299.jpg\": \"Aves/Pinicola enucleator/cd292e4116e26a86729fcff6928187a4.jpg\",\n  \"662995.jpg\": \"Insecta/Coenagrion puella/7b2f1ef2d3df994a8cdfce8b384773b1.jpg\",\n  \"663.jpg\": \"Mammalia/Ursus americanus/213d898b570a05b0fce4373e7b44ac35.jpg\",\n  \"6630.jpg\": \"Mammalia/Eschrichtius robustus/6687cc6fae951316fc1d8aa21db6c65e.jpg\",\n  \"663006.jpg\": \"Mammalia/Sciurus variegatoides/f9a108c20212bf8e9dceb3551ff59e14.jpg\",\n  \"663008.jpg\": \"Mammalia/Sciurus variegatoides/b7c2d2b382fe0ddc227eaf183030d99d.jpg\",\n  \"66306.jpg\": \"Aves/Pinicola enucleator/eb4e514e142f08597d4e3f60c38371a3.jpg\",\n  \"663060.jpg\": \"Insecta/Macromia taeniolata/c00549313deaf24b63f10fb858d53f5a.jpg\",\n  \"66307.jpg\": \"Aves/Pinicola enucleator/453388298338a9c4ad52053e0b274796.jpg\",\n  \"663073.jpg\": \"Aves/Anhinga anhinga/3f710911cbbffc8caa726e95f23dc0c4.jpg\",\n  \"66308.jpg\": \"Aves/Pinicola enucleator/6a3f8eb5c09c81de5c17a748431edfcf.jpg\",\n  \"663080.jpg\": \"Aves/Anhinga anhinga/7d2016b232beae4764f7538f14067c35.jpg\",\n  \"66309.jpg\": \"Aves/Pinicola enucleator/4894b7ffaa407ad187acea610934c51e.jpg\",\n  \"663131.jpg\": \"Aves/Anhinga anhinga/6fe676b737d03867b9caa1387a7606c7.jpg\",\n  \"663144.jpg\": \"Aves/Anhinga anhinga/896f8775f8420b77340f2c30c5caa30c.jpg\",\n  \"66316.jpg\": \"Aves/Pinicola enucleator/c88aaae46cd51f4360b74d4b6ba765de.jpg\",\n  \"66317.jpg\": \"Aves/Pinicola enucleator/b8cd8e74a6cdcf17f23fd522751fbe1d.jpg\",\n  \"663176.jpg\": \"Aves/Anhinga anhinga/21fbb584b04f666df6fc31e1b22b3944.jpg\",\n  \"663192.jpg\": \"Aves/Anhinga anhinga/06feee7b0307ae69da69638b3e379b6b.jpg\",\n  \"663195.jpg\": \"Aves/Anhinga anhinga/dce418ca48fbb56f9583ba2be45ffbeb.jpg\",\n  \"66320.jpg\": \"Aves/Pinicola enucleator/03b1ca6970d7fe2379e7a8a01ffaec03.jpg\",\n  \"66321.jpg\": \"Aves/Pinicola enucleator/7ca423cdd0ee6265b9df0b74ff311b4c.jpg\",\n  \"663213.jpg\": \"Aves/Anhinga anhinga/37e3fcf16ed73068cc292f2cfb2de4d8.jpg\",\n  \"66323.jpg\": \"Aves/Pinicola enucleator/560e78042afd6d4d35c69c7f4d2ba820.jpg\",\n  \"66324.jpg\": \"Aves/Pinicola enucleator/1fdacf31b5d89b4c4790319a89b46e74.jpg\",\n  \"66325.jpg\": \"Aves/Pinicola enucleator/be6968d5e9b4628b670a686cb9e31b40.jpg\",\n  \"66326.jpg\": \"Aves/Pinicola enucleator/cb634c0c22707aff3c13760dc7587924.jpg\",\n  \"6633.jpg\": \"Mammalia/Eschrichtius robustus/2bdf1db21a08756e4b75b33ee3a6d165.jpg\",\n  \"66333.jpg\": \"Aves/Pinicola enucleator/f715c5af691ff37ee114a3bb0d695e9a.jpg\",\n  \"663330.jpg\": \"Animalia/Coenobita clypeatus/e2a82cdea7a13a2418c6f6a20f35aee0.jpg\",\n  \"663333.jpg\": \"Animalia/Coenobita clypeatus/58a13263eacc7e7c805890bbdb64077a.jpg\",\n  \"66334.jpg\": \"Aves/Pinicola enucleator/4a5d0ececa5ff0c51983120706a6101f.jpg\",\n  \"6634.jpg\": \"Mammalia/Eschrichtius robustus/ee146a0415b450a9a81e89ab2dfe13b3.jpg\",\n  \"663479.jpg\": \"Insecta/Cotinis mutabilis/7713e3d9188e6f2ca4616c5b7d4d46ea.jpg\",\n  \"663504.jpg\": \"Insecta/Cotinis mutabilis/cef14ecd4be77f8213b9e6edfa5864cd.jpg\",\n  \"663515.jpg\": \"Insecta/Cotinis mutabilis/e376ffca4bd6f2fdaca53a784bdbf4a9.jpg\",\n  \"663518.jpg\": \"Insecta/Cotinis mutabilis/4385cc2f9e534150b8ef0eb15fdf0c91.jpg\",\n  \"663620.jpg\": \"Insecta/Maniola jurtina/4a912098178c5447e95b139ca1a89c27.jpg\",\n  \"663621.jpg\": \"Insecta/Maniola jurtina/4c238792b72d6a7cb6041f0725556218.jpg\",\n  \"663627.jpg\": \"Insecta/Maniola jurtina/94576b535edb747554e1990833b29c3d.jpg\",\n  \"663633.jpg\": \"Insecta/Maniola jurtina/f3215c7da11693854ce4855f49b09b73.jpg\",\n  \"663645.jpg\": \"Insecta/Maniola jurtina/3cacf14fc40f744fadba66b272bf937c.jpg\",\n  \"663655.jpg\": \"Insecta/Maniola jurtina/fd125d4355aca83d7d53fb46f76edcdc.jpg\",\n  \"663656.jpg\": \"Insecta/Maniola jurtina/d631b07d6dd2ac1d5f18021dee50fd18.jpg\",\n  \"663677.jpg\": \"Aves/Gallinula galeata/4430bd7d1d82c5d954d90c2e116f500c.jpg\",\n  \"663724.jpg\": \"Aves/Gallinula galeata/a06d4adb4e370b7a03d3060e3731f444.jpg\",\n  \"663758.jpg\": \"Aves/Gallinula galeata/678ff04776dafae8e4312ed1e47928ae.jpg\",\n  \"663807.jpg\": \"Aves/Gallinula galeata/26253ca8184c787a5ec9d353131a401d.jpg\",\n  \"66383.jpg\": \"Insecta/Chioides albofasciatus/e9d850829b6c17d8092e27fbd6cbc29e.jpg\",\n  \"66384.jpg\": \"Insecta/Chioides albofasciatus/6c1ecc4307de47044fc6630e18713fc8.jpg\",\n  \"66385.jpg\": \"Insecta/Chioides albofasciatus/93938574406e443993e72df72bc5e934.jpg\",\n  \"66386.jpg\": \"Insecta/Chioides albofasciatus/4cec4d1ac88b07bfa1d070fa0d3e67b6.jpg\",\n  \"663870.jpg\": \"Aves/Gallinula galeata/014736e9fe631c1da079d746cb9ece23.jpg\",\n  \"663898.jpg\": \"Insecta/Lycaena phlaeas/bc919fe8afe0823dfd05fa28e8cb3a00.jpg\",\n  \"663902.jpg\": \"Insecta/Lycaena phlaeas/4a6584fa22eb0e956d5e4c839878f9a8.jpg\",\n  \"66391.jpg\": \"Insecta/Chioides albofasciatus/6c3b53e06f5f790d346b61f7f93bfbf8.jpg\",\n  \"663914.jpg\": \"Insecta/Lycaena phlaeas/9e9607c53375d31e26cf72af09f4d22f.jpg\",\n  \"663930.jpg\": \"Insecta/Lycaena phlaeas/236c070d545c178b01f9a7be7931a735.jpg\",\n  \"663937.jpg\": \"Insecta/Lycaena phlaeas/fafee379404aa9c16faeaf6269540c00.jpg\",\n  \"663948.jpg\": \"Insecta/Lycaena phlaeas/57d969291201fc998a9fde2b9524ab19.jpg\",\n  \"663960.jpg\": \"Insecta/Lycaena phlaeas/17970520fedff480820e903ed648b842.jpg\",\n  \"66397.jpg\": \"Insecta/Chioides albofasciatus/4b827b81c6016702a2221ce11325dd0c.jpg\",\n  \"66399.jpg\": \"Insecta/Chioides albofasciatus/393e7ac03316c4a0d6aac18ef72d66e5.jpg\",\n  \"664.jpg\": \"Mammalia/Ursus americanus/350e08c09bbef55d732703c7d61feab4.jpg\",\n  \"66400.jpg\": \"Insecta/Chioides albofasciatus/3df63ea2c0ee7fc495040ab8983cc40e.jpg\",\n  \"66401.jpg\": \"Insecta/Chioides albofasciatus/04366236baed81a516ca66c5cc45133d.jpg\",\n  \"664011.jpg\": \"Aves/Cygnus buccinator/7be35321b7ffbd5a7425d108b46cade2.jpg\",\n  \"664014.jpg\": \"Aves/Cygnus buccinator/a6edc444ea368b1ef27a74dab80fb6fb.jpg\",\n  \"66402.jpg\": \"Insecta/Chioides albofasciatus/656b1a6820eaf9c0de70a78cf6291c25.jpg\",\n  \"66403.jpg\": \"Insecta/Chioides albofasciatus/bb3d59fa5c6b178f582e36fc8e8c2e7b.jpg\",\n  \"66404.jpg\": \"Insecta/Chioides albofasciatus/b18e3f807aeb6cc035ce48392d9381d9.jpg\",\n  \"66405.jpg\": \"Insecta/Chioides albofasciatus/b504b1e263f08077afc997de11554b11.jpg\",\n  \"66406.jpg\": \"Insecta/Chioides albofasciatus/1fb071937f372a927375ba7b19cc1452.jpg\",\n  \"664080.jpg\": \"Aves/Podilymbus podiceps/ca77f5ac17aaf517cd9fcf854a5aec1f.jpg\",\n  \"66410.jpg\": \"Insecta/Chioides albofasciatus/44beb3c09f05f7502d9d04bc3c4eef09.jpg\",\n  \"66413.jpg\": \"Insecta/Chioides albofasciatus/b74a03540a91622e8f239f6a94abe800.jpg\",\n  \"66414.jpg\": \"Insecta/Chioides albofasciatus/5feea02052a6a6c79598b9cc5964662b.jpg\",\n  \"66417.jpg\": \"Insecta/Chioides albofasciatus/b9d0156df527dddefef318e22948739f.jpg\",\n  \"6642.jpg\": \"Mammalia/Eschrichtius robustus/1dc80e7d0c685c5b4eb8140c68fb8982.jpg\",\n  \"66420.jpg\": \"Insecta/Chioides albofasciatus/134fd4d091fadd5f95d91d1fca86d104.jpg\",\n  \"664217.jpg\": \"Aves/Podilymbus podiceps/37c9eb3b89b6ea0ae0b9d174853399c8.jpg\",\n  \"66422.jpg\": \"Insecta/Chioides albofasciatus/85f79eee37bf86ac4fb64624cf7f4d2e.jpg\",\n  \"66423.jpg\": \"Insecta/Chioides albofasciatus/0e9e4fcba35d44b5a9ac9900012fc1aa.jpg\",\n  \"664267.jpg\": \"Aves/Podilymbus podiceps/a20fa35e5e236ca06bc34967e243a16f.jpg\",\n  \"66427.jpg\": \"Insecta/Chioides albofasciatus/0b1b9bec9c24899474703e9630dff24f.jpg\",\n  \"66430.jpg\": \"Insecta/Chioides albofasciatus/8bfd2e699c37eb6db91f9c6a882aa1e5.jpg\",\n  \"664324.jpg\": \"Aves/Podilymbus podiceps/88942bf7bcd7915b09fa5f218991f5ec.jpg\",\n  \"664369.jpg\": \"Aves/Podilymbus podiceps/2d29948297ca35bc710e6062b4900a14.jpg\",\n  \"664453.jpg\": \"Aves/Podilymbus podiceps/c1a2f756dce09c44f471a07591add9e5.jpg\",\n  \"66457.jpg\": \"Aves/Toxostoma redivivum/dbea58a3bead40d616370e57b0a8724c.jpg\",\n  \"66458.jpg\": \"Aves/Toxostoma redivivum/e1c819406d51e1030c4d0de46ace446e.jpg\",\n  \"66459.jpg\": \"Aves/Toxostoma redivivum/aeac90f4add3e0ee4373cf72c7a7d920.jpg\",\n  \"664592.jpg\": \"Insecta/Tetraopes tetrophthalmus/e2e19d6ad0e52264a8314da1af1ca0c5.jpg\",\n  \"664597.jpg\": \"Insecta/Tetraopes tetrophthalmus/4f22e61a41386621e7c493f854aa55d0.jpg\",\n  \"66461.jpg\": \"Aves/Toxostoma redivivum/85d4c3b8d1ab3918105888e2eac22ca4.jpg\",\n  \"664629.jpg\": \"Insecta/Tetraopes tetrophthalmus/f5bb6daeae7ceb892a9130994b4d07dc.jpg\",\n  \"664639.jpg\": \"Insecta/Tetraopes tetrophthalmus/121ef3bbce74ef9b232553ead7b1edb3.jpg\",\n  \"664643.jpg\": \"Insecta/Tetraopes tetrophthalmus/02c2b622ba710eb17507f91e55e8e454.jpg\",\n  \"664644.jpg\": \"Insecta/Tetraopes tetrophthalmus/5df5ce925ef8b980c574145f3b5bd612.jpg\",\n  \"66466.jpg\": \"Aves/Toxostoma redivivum/29e6a2b75bccc7ee97dd26514d5b5cd8.jpg\",\n  \"66467.jpg\": \"Aves/Toxostoma redivivum/8c37946427d8bacd0a6ea6943ee4d6b6.jpg\",\n  \"6648.jpg\": \"Mammalia/Eschrichtius robustus/21efd6161bc6abf9317de8fc09124061.jpg\",\n  \"664804.jpg\": \"Aves/Spinus psaltria/c49a2b9f73c9b5d6c52db42333647c86.jpg\",\n  \"66481.jpg\": \"Aves/Toxostoma redivivum/b3e0ff10641bc9b65d50cc85178ed132.jpg\",\n  \"664810.jpg\": \"Aves/Spinus psaltria/2a19c881f085706068deb7557cec1cfa.jpg\",\n  \"664811.jpg\": \"Aves/Spinus psaltria/b80fb8791b958b6e79159ef19e9bfdfb.jpg\",\n  \"66482.jpg\": \"Aves/Toxostoma redivivum/1dad493a44914f42a59f5c7965e4a1de.jpg\",\n  \"664869.jpg\": \"Aves/Spinus psaltria/6aa681baacae1ef18cf62fc634b7ea60.jpg\",\n  \"6649.jpg\": \"Mammalia/Eschrichtius robustus/5b96f5d44e79a5d38e2d0311986f2f62.jpg\",\n  \"664949.jpg\": \"Aves/Spinus psaltria/e3936949aa27c5ab768bc4c54f1f4cbb.jpg\",\n  \"66495.jpg\": \"Aves/Toxostoma redivivum/e9e97dfec28a57d34d16e66d0d13c216.jpg\",\n  \"6650.jpg\": \"Mammalia/Eschrichtius robustus/59de2a44272d1dee8f63d50a9d376804.jpg\",\n  \"66500.jpg\": \"Aves/Toxostoma redivivum/ba1b1ad963cca4ffda0981e285972b1e.jpg\",\n  \"665029.jpg\": \"Aves/Spinus psaltria/d0f31aa37674fa1557e060849f85c120.jpg\",\n  \"665055.jpg\": \"Aves/Spinus psaltria/c9bee03cf4eb4bcaa6147679488c273e.jpg\",\n  \"665081.jpg\": \"Aves/Spinus psaltria/a06ff1d383c6fd2f1fc284e7b23736f0.jpg\",\n  \"6651.jpg\": \"Mammalia/Eschrichtius robustus/ffc35ae1753330bc74e024f9fd792c8d.jpg\",\n  \"665130.jpg\": \"Aves/Saltator atriceps/f26459fd0d9ec3f0b6d381181f18fca4.jpg\",\n  \"665134.jpg\": \"Aves/Bucorvus leadbeateri/c22f253ccd32912bf39a6aa9035f024a.jpg\",\n  \"66514.jpg\": \"Aves/Toxostoma redivivum/7b5c71911bfe381c9a1c04ada60014a7.jpg\",\n  \"66515.jpg\": \"Aves/Toxostoma redivivum/dfb59a88d56e2fc63283c3ed9c80adc5.jpg\",\n  \"66527.jpg\": \"Aves/Toxostoma redivivum/c4886dd89231da89de8b234a8c21640a.jpg\",\n  \"66528.jpg\": \"Aves/Toxostoma redivivum/6bc48a86f67da4f1150cb5e7057bc10c.jpg\",\n  \"66540.jpg\": \"Aves/Toxostoma redivivum/7928e8a47c74a6b4c2ea627d9c7ceb64.jpg\",\n  \"665410.jpg\": \"Reptilia/Leiocephalus carinatus/fd8dab8130a0aa30cf8b3726c4c527a7.jpg\",\n  \"665411.jpg\": \"Reptilia/Leiocephalus carinatus/816edb0939f88d8dd2be7582bace9696.jpg\",\n  \"665444.jpg\": \"Aves/Aramus guarauna/bbf0c316a9a2d46af3b88cdad88a2fc7.jpg\",\n  \"665448.jpg\": \"Aves/Aramus guarauna/56207414682d7a7aa8bfb8954a88c826.jpg\",\n  \"665451.jpg\": \"Aves/Aramus guarauna/31a88b1e3a9255e1a3f24ce41021b124.jpg\",\n  \"665455.jpg\": \"Aves/Aramus guarauna/ba138d5872c64c655c68870dbcc3b3e3.jpg\",\n  \"665456.jpg\": \"Aves/Aramus guarauna/4bc43a3cce27184c57804715b89a68eb.jpg\",\n  \"665461.jpg\": \"Aves/Aramus guarauna/680d4f624ddf7d79cbc18986743faebe.jpg\",\n  \"665475.jpg\": \"Aves/Aramus guarauna/93722eac1241526add5594271e7f7ff9.jpg\",\n  \"665478.jpg\": \"Aves/Aramus guarauna/3be7f5f7cba1de7a5ba53dcbc8fead4d.jpg\",\n  \"665487.jpg\": \"Aves/Melanerpes lewis/11fc72fcadef1630d5d7f9b3bce06f7a.jpg\",\n  \"665491.jpg\": \"Aves/Melanerpes lewis/9f66fd035b1807db719c1a2809024879.jpg\",\n  \"665494.jpg\": \"Aves/Melanerpes lewis/1429cb034a9596fbacc6a629d659e68d.jpg\",\n  \"665499.jpg\": \"Aves/Melanerpes lewis/d3e93a036dfd9081dff0fea9ec16650c.jpg\",\n  \"665536.jpg\": \"Aves/Stelgidopteryx serripennis/6d61a19bca782a59ea3a691dbbf8eeaf.jpg\",\n  \"665573.jpg\": \"Aves/Stelgidopteryx serripennis/09f5374e7f728636baaabbacabf8b8c8.jpg\",\n  \"665594.jpg\": \"Aves/Stelgidopteryx serripennis/e8f03b707578d89740911ef01d37a045.jpg\",\n  \"665609.jpg\": \"Aves/Stelgidopteryx serripennis/71a1593d5048ac2d1336152d5d2a11fe.jpg\",\n  \"665659.jpg\": \"Aves/Stelgidopteryx serripennis/edc2e49c6acf9f59ac2b5b3bf4b876a2.jpg\",\n  \"66566.jpg\": \"Aves/Toxostoma redivivum/657d1640e54953035dce95d1e4a35fb2.jpg\",\n  \"66567.jpg\": \"Aves/Toxostoma redivivum/68005d2365712b84f077b5464039f956.jpg\",\n  \"66568.jpg\": \"Aves/Toxostoma redivivum/d1c5e2ff1729eaf81228a45bae40f49e.jpg\",\n  \"66570.jpg\": \"Aves/Toxostoma redivivum/edcf00712bfe3d5fbdbcfff7d00815c3.jpg\",\n  \"66574.jpg\": \"Aves/Toxostoma redivivum/81e66bfc1e8b0ec50a8be9512b6b1dc4.jpg\",\n  \"66575.jpg\": \"Aves/Toxostoma redivivum/7b773878bd16d2c5d0b4ae492b80789d.jpg\",\n  \"66576.jpg\": \"Aves/Toxostoma redivivum/2ca6c648966cdb2189b33b459f829a43.jpg\",\n  \"66577.jpg\": \"Aves/Toxostoma redivivum/be743b2912fee6e34af6a6ac0d4c1a4c.jpg\",\n  \"665802.jpg\": \"Aves/Picoides villosus/8cb3ab2f3a3e54e489ff9ef3fe4c9f03.jpg\",\n  \"665808.jpg\": \"Aves/Picoides villosus/ca01cfb8027ac16422a4d6195cfe471f.jpg\",\n  \"665832.jpg\": \"Aves/Picoides villosus/0278a934c505e20b56a7396468872219.jpg\",\n  \"665859.jpg\": \"Aves/Picoides villosus/c4863d09ea6d46863e5689e2c3dd9e5d.jpg\",\n  \"665879.jpg\": \"Aves/Picoides villosus/eea0958acd4f9b2192987fb66eab7bc1.jpg\",\n  \"66588.jpg\": \"Aves/Spizella atrogularis/f55ab2ba873541e3951b4746739b3b28.jpg\",\n  \"665883.jpg\": \"Aves/Picoides villosus/9f36379799be297e4ab8fe7ec2e1bf67.jpg\",\n  \"665896.jpg\": \"Aves/Picoides villosus/142b09aa408016adfe61b8ee9fbc2f24.jpg\",\n  \"665927.jpg\": \"Aves/Picoides villosus/58fced07eba97f68061b72a749bb6904.jpg\",\n  \"66596.jpg\": \"Aves/Spizella atrogularis/8932d2cff7b394093a490624755fe8f8.jpg\",\n  \"665966.jpg\": \"Insecta/Ischnura verticalis/159edc3c106173e5404c30e311cf0071.jpg\",\n  \"66597.jpg\": \"Aves/Spizella atrogularis/c6b249b5a7b734eabe78f6b8705b7f23.jpg\",\n  \"66598.jpg\": \"Aves/Spizella atrogularis/9fa0346a449e8f2b49c6ae50fb490139.jpg\",\n  \"665988.jpg\": \"Insecta/Ischnura verticalis/9bac23aa1426b63118a8c7fe9bcb7f53.jpg\",\n  \"66600.jpg\": \"Aves/Spizella atrogularis/87d31a6e76e1a5b430ce819b02c6b37e.jpg\",\n  \"66602.jpg\": \"Aves/Spizella atrogularis/7ffb013d4b7b4748b623ba4d791bd407.jpg\",\n  \"666039.jpg\": \"Insecta/Ischnura verticalis/8e8c5aaba26110ec0f5b8adf6943fbde.jpg\",\n  \"666056.jpg\": \"Insecta/Ischnura verticalis/36a91a35c645057b2ffe2ac32111bb91.jpg\",\n  \"666077.jpg\": \"Insecta/Ischnura verticalis/772a7dc36e8e795526b5595751319609.jpg\",\n  \"666146.jpg\": \"Aves/Histrionicus histrionicus/3c3e8c27eeaddd810f680fe5901712c6.jpg\",\n  \"666313.jpg\": \"Animalia/Patiria miniata/3265630eaeaf11fdca2a908a6f220d22.jpg\",\n  \"666316.jpg\": \"Animalia/Patiria miniata/82ad6837d5f3601ca42691238ec08b4a.jpg\",\n  \"666364.jpg\": \"Animalia/Patiria miniata/33d92ec18eabe736839d9593b02b0898.jpg\",\n  \"666371.jpg\": \"Animalia/Patiria miniata/4b72476249ad98a33195280e7d71115f.jpg\",\n  \"666378.jpg\": \"Animalia/Patiria miniata/762969407eb21151969f6f2f60a75bab.jpg\",\n  \"666389.jpg\": \"Animalia/Patiria miniata/01b59f489981b8a834bbd72e604afc25.jpg\",\n  \"666391.jpg\": \"Animalia/Patiria miniata/6c2108b72494af2c42a9627807498c8f.jpg\",\n  \"666439.jpg\": \"Insecta/Paonias excaecata/e98d365ba3d32de67be3bbf7ea54a66e.jpg\",\n  \"666440.jpg\": \"Insecta/Paonias excaecata/b3754491685f4bb737149c1b9e38b0b0.jpg\",\n  \"666444.jpg\": \"Insecta/Paonias excaecata/c300577c9ec29a861d894925744d5bb0.jpg\",\n  \"666445.jpg\": \"Insecta/Paonias excaecata/c684e4d9476a5cdb73d045f1d851f67e.jpg\",\n  \"666450.jpg\": \"Insecta/Paonias excaecata/4934feeedd1847c39958c6f7f7b9beb4.jpg\",\n  \"666451.jpg\": \"Insecta/Paonias excaecata/2f6380dc7d5e980d384226258a51e101.jpg\",\n  \"666453.jpg\": \"Insecta/Paonias excaecata/8a9a298b53881e3c64a700da1b4cc302.jpg\",\n  \"666455.jpg\": \"Insecta/Paonias excaecata/74fdc30b4e7e5e7b518e9867b4815c05.jpg\",\n  \"666463.jpg\": \"Insecta/Paonias excaecata/cad731f004931254cfcd8a0b403a6e00.jpg\",\n  \"666466.jpg\": \"Insecta/Paonias excaecata/5f0ba148d5585741085f26ef55ff1bf9.jpg\",\n  \"666477.jpg\": \"Aves/Empidonax minimus/49bbb16230e3e8c066b27c718a5fa950.jpg\",\n  \"666479.jpg\": \"Aves/Empidonax minimus/bfe8e07d66a62bb7b9f29fde2e40dc2a.jpg\",\n  \"666482.jpg\": \"Aves/Empidonax minimus/2f566e3de91f66181f0f65fe68ece588.jpg\",\n  \"666488.jpg\": \"Insecta/Danaus eresimus/29a6fde1fb7effa434e114fde19fe9d7.jpg\",\n  \"666493.jpg\": \"Insecta/Danaus eresimus/b0e115e016a32968559f65e0b929b0c0.jpg\",\n  \"666496.jpg\": \"Insecta/Danaus eresimus/8f1280a17159f25df33577d4087c3026.jpg\",\n  \"666505.jpg\": \"Insecta/Danaus eresimus/48471e344f221362eb2cb82dd2474321.jpg\",\n  \"666506.jpg\": \"Insecta/Danaus eresimus/007e6d5366affc5983b332d569b323a8.jpg\",\n  \"666507.jpg\": \"Insecta/Danaus eresimus/20c9b647fe3ed518062e0157a2aadff4.jpg\",\n  \"666510.jpg\": \"Insecta/Danaus eresimus/256e65440f7e5c1c0bbf7d3fe86e5498.jpg\",\n  \"666581.jpg\": \"Insecta/Atalopedes campestris/8a662ad5d40c9bf41aa5e4b8cc2b5961.jpg\",\n  \"666605.jpg\": \"Insecta/Atalopedes campestris/4989244cf07ecfeb47d8376460cc3a1d.jpg\",\n  \"666608.jpg\": \"Insecta/Atalopedes campestris/515cb090231d8a66075001added4e10b.jpg\",\n  \"666627.jpg\": \"Insecta/Atalopedes campestris/4f0b2782a195ea620a0e0dbb153bf6b0.jpg\",\n  \"666678.jpg\": \"Insecta/Atalopedes campestris/7cb064adee2e6b198c7381fbdb29a8fc.jpg\",\n  \"666816.jpg\": \"Mammalia/Zalophus californianus/fb8c923627543f8a8cb594d74513676a.jpg\",\n  \"666832.jpg\": \"Mammalia/Zalophus californianus/7c58828ad05d2135af79c8dd879a6277.jpg\",\n  \"666889.jpg\": \"Reptilia/Lacerta bilineata/5c67bba42bcfeef2fdcb9d1c9b4662cf.jpg\",\n  \"666893.jpg\": \"Reptilia/Lacerta bilineata/e1484e5086d796e646cd9540a9930c9a.jpg\",\n  \"666894.jpg\": \"Reptilia/Lacerta bilineata/2f60bd3e422338aa460d96653e16fde8.jpg\",\n  \"666895.jpg\": \"Reptilia/Lacerta bilineata/7300578f137c7a694587b5af7019f94f.jpg\",\n  \"666896.jpg\": \"Reptilia/Lacerta bilineata/72c0e715249ae0b3a157c2b5949f80bd.jpg\",\n  \"666899.jpg\": \"Reptilia/Lacerta bilineata/5d3ec68f94b0d2f06247d52e81043562.jpg\",\n  \"666900.jpg\": \"Reptilia/Lacerta bilineata/f3d5096491eb522f0dc215efa618b50a.jpg\",\n  \"666901.jpg\": \"Reptilia/Lacerta bilineata/64f00d617e53c3cb05e3b350a4f66e0e.jpg\",\n  \"666904.jpg\": \"Reptilia/Lacerta bilineata/d97fbf685a70cc5f6dac05f7a992dff9.jpg\",\n  \"666906.jpg\": \"Reptilia/Lacerta bilineata/2ca4e047c91b5800b41bde835d662e89.jpg\",\n  \"666907.jpg\": \"Reptilia/Lacerta bilineata/62d28ec649dcd3f8aa63a893cf342cb0.jpg\",\n  \"666908.jpg\": \"Reptilia/Lacerta bilineata/17d99f2d8691bf521642fd79dd33b434.jpg\",\n  \"666909.jpg\": \"Reptilia/Lacerta bilineata/ee65fabc2bfc37ee9323ee35979fe777.jpg\",\n  \"666910.jpg\": \"Reptilia/Lacerta bilineata/80630cd0d548d1341f5c4f0dd410fe13.jpg\",\n  \"666937.jpg\": \"Insecta/Enallagma signatum/9af2f644eb4b459d664f5f3e76b8ccd7.jpg\",\n  \"666953.jpg\": \"Insecta/Enallagma signatum/46342955c42cf7a890467a47a538d453.jpg\",\n  \"666954.jpg\": \"Insecta/Enallagma signatum/649efc55317f481f0dca5fff10a3f5a5.jpg\",\n  \"666962.jpg\": \"Insecta/Enallagma signatum/e02036ebe0d8a79c7e683e5c66dab3d1.jpg\",\n  \"666975.jpg\": \"Insecta/Enallagma signatum/790ce3c02f9f6de8d9140469a8b058b3.jpg\",\n  \"666983.jpg\": \"Insecta/Enallagma signatum/9892e6367af165e7f6c363e4d6532551.jpg\",\n  \"666986.jpg\": \"Insecta/Enallagma signatum/2a286878a10a8eaadb80bc2ea090a953.jpg\",\n  \"666987.jpg\": \"Insecta/Enallagma signatum/80b9192db0b0687faf55e2db4c456f58.jpg\",\n  \"667102.jpg\": \"Aves/Anas clypeata/907992d92378df4624c74d87edb2733f.jpg\",\n  \"667132.jpg\": \"Aves/Anas clypeata/2b7dabacbc95d1271779912639b01abd.jpg\",\n  \"667160.jpg\": \"Aves/Anas clypeata/dd089f53e4c287dc511abf2b105bae2f.jpg\",\n  \"667214.jpg\": \"Aves/Anas clypeata/04bbd776e4aff269848fbb55ca0c6456.jpg\",\n  \"667490.jpg\": \"Animalia/Aurelia aurita/6ec2004b842821919c6733de9802119f.jpg\",\n  \"667494.jpg\": \"Animalia/Aurelia aurita/781b07318b7e944b040b66acfb90afd3.jpg\",\n  \"667503.jpg\": \"Animalia/Aurelia aurita/e64c093fc1f4d49d204f1462802492a9.jpg\",\n  \"667504.jpg\": \"Animalia/Aurelia aurita/2d448a166ff3def53d63346e72dc42e6.jpg\",\n  \"667509.jpg\": \"Mammalia/Odocoileus hemionus californicus/b8c7b7714990408351a80ed4ef55dba5.jpg\",\n  \"667523.jpg\": \"Mammalia/Odocoileus hemionus californicus/9df4b988d20b19f0eea63412eabead6e.jpg\",\n  \"667526.jpg\": \"Mammalia/Odocoileus hemionus californicus/934bd235542425b67e33380c7963dac8.jpg\",\n  \"667530.jpg\": \"Mammalia/Odocoileus hemionus californicus/a343887170f8e91bfbb53f629d9f5fcb.jpg\",\n  \"667531.jpg\": \"Mammalia/Odocoileus hemionus californicus/a8fb023241576087ea257c7166315255.jpg\",\n  \"667535.jpg\": \"Mammalia/Odocoileus hemionus californicus/cff456d534f69cc08b871c209430064d.jpg\",\n  \"667544.jpg\": \"Mammalia/Odocoileus hemionus californicus/8f20e1d7af0fe94d0f36ad0d1b12b701.jpg\",\n  \"667559.jpg\": \"Mammalia/Odocoileus hemionus californicus/60b9d848a5e4cbfe8fc3e5268cd05937.jpg\",\n  \"667574.jpg\": \"Insecta/Lepturobosca chrysocoma/63eab9637fb06adfd1c0a5e0800725ea.jpg\",\n  \"667575.jpg\": \"Insecta/Lepturobosca chrysocoma/b0c4cb0f0c0e70f41694fa566338b963.jpg\",\n  \"667578.jpg\": \"Aves/Rissa tridactyla/2cc67b98404cda39e71dfae0ced1d2ca.jpg\",\n  \"667580.jpg\": \"Aves/Rissa tridactyla/e410978d13140669f1f561c92de3821b.jpg\",\n  \"667581.jpg\": \"Aves/Rissa tridactyla/80c9b4b14e4c42b64c97580751a82b2b.jpg\",\n  \"667585.jpg\": \"Aves/Rissa tridactyla/2a1a718288e65aed8d9e8fc6ed8c7279.jpg\",\n  \"667590.jpg\": \"Aves/Rissa tridactyla/a9a01f0126f1980aab6965fc6b38824f.jpg\",\n  \"667591.jpg\": \"Aves/Rissa tridactyla/c79dbba9077edad5b12744ef6e9346d5.jpg\",\n  \"667593.jpg\": \"Aves/Rissa tridactyla/15a3e493b00d05f635ca95ca23fa6867.jpg\",\n  \"667596.jpg\": \"Aves/Rissa tridactyla/fb9db316fe4154a2abcf6eff5a4e648f.jpg\",\n  \"667598.jpg\": \"Aves/Rissa tridactyla/f4c735f45f1858bdcb59f1e567584903.jpg\",\n  \"667681.jpg\": \"Amphibia/Dicamptodon ensatus/b0b61ffb2e9fbf580c3c29b87f11d4e2.jpg\",\n  \"667683.jpg\": \"Amphibia/Dicamptodon ensatus/40dfd8647047a1f652011932e6836083.jpg\",\n  \"667686.jpg\": \"Amphibia/Dicamptodon ensatus/0306774309928311c76b4ba7894603d4.jpg\",\n  \"667688.jpg\": \"Amphibia/Dicamptodon ensatus/a1982282cd581d2675cfcedc60110350.jpg\",\n  \"667695.jpg\": \"Amphibia/Dicamptodon ensatus/c2467d3bcdc34d9c6960f736034e8a5b.jpg\",\n  \"667733.jpg\": \"Aves/Malurus cyaneus/2a3a4d13a388acfff6ddcbbffe59336a.jpg\",\n  \"668.jpg\": \"Mammalia/Ursus americanus/6e6492fcbae98c78067aec3b78c19bc7.jpg\",\n  \"668021.jpg\": \"Aves/Oreothlypis peregrina/59d783e7c7166242fb39259368c81cf9.jpg\",\n  \"668025.jpg\": \"Aves/Oreothlypis peregrina/bf307b286e2b4a70b9c17fddaaa3a80f.jpg\",\n  \"668030.jpg\": \"Aves/Oreothlypis peregrina/9af6271174b19c52d83a4b99adb08cef.jpg\",\n  \"668047.jpg\": \"Aves/Oreothlypis peregrina/199065a6fcb65407ddcc2f969b46d5e8.jpg\",\n  \"668051.jpg\": \"Aves/Oreothlypis peregrina/d9fe1ae3ab79d14ff5b21c2d810675ce.jpg\",\n  \"668055.jpg\": \"Aves/Oreothlypis peregrina/a5981adba61249df91ab85261a7962bf.jpg\",\n  \"668161.jpg\": \"Insecta/Trithemis annulata/c9385a4570456d359fb70a12de7f910a.jpg\",\n  \"668514.jpg\": \"Mammalia/Lepus americanus/18eb82d77b359bdd4d19b47c9aeaefb5.jpg\",\n  \"668515.jpg\": \"Mammalia/Lepus americanus/0c794dbcaf7c4f8bbed81f25f2c72bfd.jpg\",\n  \"668522.jpg\": \"Mammalia/Lepus americanus/3a401b89a37c6763834317d2573e177f.jpg\",\n  \"668524.jpg\": \"Mammalia/Lepus americanus/7c08991229fec73d037d7aa9e482d525.jpg\",\n  \"668532.jpg\": \"Mammalia/Lepus americanus/7cd81e4f5334b1e42fdde2cbb0a9d819.jpg\",\n  \"668534.jpg\": \"Mammalia/Lepus americanus/94c0c7988a70f7ecdc31432e910bd6b4.jpg\",\n  \"668539.jpg\": \"Aves/Amazona viridigenalis/0c06f3a393e513ca183eccfdefc4de82.jpg\",\n  \"668540.jpg\": \"Aves/Amazona viridigenalis/8b1660c211f3775eeddf816fd87c47ee.jpg\",\n  \"668554.jpg\": \"Aves/Amazona viridigenalis/22eee7551902a85c471264d854925159.jpg\",\n  \"668566.jpg\": \"Aves/Amazona viridigenalis/43e2de3c2de987d15dae41b8084c0f8d.jpg\",\n  \"668567.jpg\": \"Aves/Amazona viridigenalis/29895c23ab03f375d07bb70d8703d3ff.jpg\",\n  \"668568.jpg\": \"Aves/Amazona viridigenalis/c79815a106e247d8bf921f8dbe2b6b50.jpg\",\n  \"668572.jpg\": \"Insecta/Pyrgus communis/9a56618dafcc9c0e1e5905fcfe44a29f.jpg\",\n  \"668579.jpg\": \"Insecta/Pyrgus communis/9f60ae53d0b129067bd0aa0e04119508.jpg\",\n  \"668594.jpg\": \"Insecta/Pyrgus communis/b9b9b341df9d5a1f4c8e5197e223460c.jpg\",\n  \"668607.jpg\": \"Insecta/Pyrgus communis/8f385e8076ff74b4fb6b29cb821010c1.jpg\",\n  \"668628.jpg\": \"Insecta/Pyrgus communis/d583150a7c14dd468357598a5611475d.jpg\",\n  \"668634.jpg\": \"Insecta/Pyrgus communis/031547c99dd78ce23ec0b94455286506.jpg\",\n  \"668638.jpg\": \"Insecta/Pyrgus communis/b235894aba8dcb17390437c4ab7ae2e9.jpg\",\n  \"668947.jpg\": \"Mammalia/Phoca vitulina/153abb597629e9b03467ba8970c99fcf.jpg\",\n  \"669130.jpg\": \"Reptilia/Anolis sagrei/377de3668fdade2d6b56c6a1bbe597f8.jpg\",\n  \"669144.jpg\": \"Reptilia/Anolis sagrei/a877f6d116962b968d634f3f20330d84.jpg\",\n  \"669168.jpg\": \"Reptilia/Anolis sagrei/63c3e59609cfb2a8e054c67c50acf78a.jpg\",\n  \"669225.jpg\": \"Reptilia/Anolis sagrei/f2f3857846afe3ee3461a0c9781b180f.jpg\",\n  \"669278.jpg\": \"Reptilia/Anolis sagrei/a1d653a98be6c08ac8e81559c01fd93a.jpg\",\n  \"669286.jpg\": \"Reptilia/Anolis sagrei/652f8a655a046953cfdee19ff6ed33af.jpg\",\n  \"669288.jpg\": \"Reptilia/Anolis sagrei/1554b9574d668f9e05d44c28d275c9a3.jpg\",\n  \"6693.jpg\": \"Aves/Pluvialis squatarola/3fd324e4728b2f3d9932f072710e0812.jpg\",\n  \"669344.jpg\": \"Aves/Psilorhinus morio/2af402c0c287582aafe2059e34963208.jpg\",\n  \"669355.jpg\": \"Aves/Psilorhinus morio/c528b67389cbd3e1006408c401cc5b95.jpg\",\n  \"669360.jpg\": \"Aves/Psilorhinus morio/552b7044bd4cb287a66e288a17c20936.jpg\",\n  \"669365.jpg\": \"Aves/Psilorhinus morio/64b3375a29119abbe372a0b6e6e153c8.jpg\",\n  \"669366.jpg\": \"Aves/Psilorhinus morio/5b81aed85e03ca4a2e0b7c00ebed4473.jpg\",\n  \"669369.jpg\": \"Aves/Psilorhinus morio/9253f9ee96c40a752f82c6b181557bc9.jpg\",\n  \"669374.jpg\": \"Aves/Psilorhinus morio/5da8bb5f40b61adada03bd287e441764.jpg\",\n  \"669378.jpg\": \"Aves/Psilorhinus morio/1548947175c4bff680f4279fbe9510fc.jpg\",\n  \"669383.jpg\": \"Aves/Francolinus pondicerianus/b4b92e46c83c80a0abba1e8a28c99c98.jpg\",\n  \"669384.jpg\": \"Aves/Francolinus pondicerianus/e800d8354c1ded9bf6ee08b7aa5919df.jpg\",\n  \"669387.jpg\": \"Insecta/Aquarius remigis/811ba3593b47278ca8b0715098de1c0b.jpg\",\n  \"669390.jpg\": \"Insecta/Aquarius remigis/93c52346c9d29678d999214a5108b9e9.jpg\",\n  \"669496.jpg\": \"Animalia/Callinectes sapidus/0690f9c7ab808d5d1238e7abca7078f5.jpg\",\n  \"669497.jpg\": \"Animalia/Callinectes sapidus/d4f01d511e00b07e7c5e6988b826f5d3.jpg\",\n  \"669498.jpg\": \"Animalia/Callinectes sapidus/626c8ed0fcb65f8a8b84e12989b3e68e.jpg\",\n  \"669499.jpg\": \"Animalia/Callinectes sapidus/c7c074c35956e486e5d9d3e26530d389.jpg\",\n  \"669516.jpg\": \"Animalia/Callinectes sapidus/73969f0245d1ff92ae07a6623d7421a0.jpg\",\n  \"669526.jpg\": \"Animalia/Callinectes sapidus/5bd1c9fcb7f2a91cd95ffe24f4b1ade7.jpg\",\n  \"669529.jpg\": \"Animalia/Callinectes sapidus/6175fc0a3bd314bf3fb19a06e8b332f8.jpg\",\n  \"669534.jpg\": \"Aves/Selasphorus rufus/a2a42cb8ade7aea2ebc787f68d365c57.jpg\",\n  \"669541.jpg\": \"Aves/Selasphorus rufus/55edacb37e3438d4b882a8a56984d135.jpg\",\n  \"669559.jpg\": \"Aves/Selasphorus rufus/61bc56983104c52c2f7526067fc3e1a2.jpg\",\n  \"669563.jpg\": \"Aves/Selasphorus rufus/a2ce08f8c1ef037f22b6a72919db9a17.jpg\",\n  \"669565.jpg\": \"Aves/Selasphorus rufus/0dae5f5f0c6dac6c0858b3695acab515.jpg\",\n  \"669566.jpg\": \"Aves/Selasphorus rufus/75d4ea4bf06133a40cd758ae64d89f71.jpg\",\n  \"669586.jpg\": \"Aves/Selasphorus rufus/e348bf20910e6ac521c05ad82536cb36.jpg\",\n  \"669600.jpg\": \"Insecta/Anthocharis midea/a92061072a28bfa8d0ee60c6382ee749.jpg\",\n  \"669613.jpg\": \"Aves/Acridotheres tristis/f6c9362d6ad225497a7e2c3e4b4242d6.jpg\",\n  \"669646.jpg\": \"Aves/Acridotheres tristis/bedd691f28f30cf147c5bccbba0f7332.jpg\",\n  \"669650.jpg\": \"Aves/Acridotheres tristis/a715fe3397baae406d95827ed6af2faa.jpg\",\n  \"669657.jpg\": \"Aves/Acridotheres tristis/71d11f7a84121008b7d72f0846ed42a2.jpg\",\n  \"669660.jpg\": \"Aves/Acridotheres tristis/21bea4ca3be817ae1425696d360b16b0.jpg\",\n  \"669681.jpg\": \"Reptilia/Crotalus scutulatus/f3611fdc23d6e337405a97db4eab2f82.jpg\",\n  \"669701.jpg\": \"Reptilia/Crotalus scutulatus/71fdd461bced8a03db1bdbec877676f4.jpg\",\n  \"669702.jpg\": \"Reptilia/Crotalus scutulatus/62771bb1b3da427566398c17ca0cf430.jpg\",\n  \"669703.jpg\": \"Reptilia/Crotalus scutulatus/b44555dd494e7fc7a08976e3d9e406da.jpg\",\n  \"669706.jpg\": \"Reptilia/Crotalus scutulatus/93bb07172f16c72b8abbfc6989dd573d.jpg\",\n  \"669707.jpg\": \"Reptilia/Crotalus scutulatus/bb327a81fe24be4ac9ea73a028093c90.jpg\",\n  \"669708.jpg\": \"Reptilia/Crotalus scutulatus/94c9949908c9d7b8a6f6dda5ef1bf58e.jpg\",\n  \"669710.jpg\": \"Reptilia/Crotalus scutulatus/746d519b02a5028bd1a4fb7d4e982bb3.jpg\",\n  \"669711.jpg\": \"Reptilia/Crotalus scutulatus/4c53b790ab4793b470d1df850d093cde.jpg\",\n  \"669712.jpg\": \"Reptilia/Crotalus scutulatus/4bd5946d79f8212b5236fa9285d1c787.jpg\",\n  \"669743.jpg\": \"Aves/Chordeiles minor/d753bda60e8e0e13f8546aefe65197db.jpg\",\n  \"669746.jpg\": \"Aves/Chordeiles minor/6a6545d01fc747631d10fb0ca6fae262.jpg\",\n  \"669750.jpg\": \"Aves/Chordeiles minor/bbacec51903a29aae985414a4d0c2ce1.jpg\",\n  \"669765.jpg\": \"Aves/Chordeiles minor/057e49bac74a02ceb8c75abbfa2d70b7.jpg\",\n  \"669780.jpg\": \"Aves/Chordeiles minor/19cefc431772836dc47d53a44eaf7c93.jpg\",\n  \"669782.jpg\": \"Aves/Chordeiles minor/194a777cf7aeffef05b6952521806fbc.jpg\",\n  \"669783.jpg\": \"Aves/Chordeiles minor/618537b6637e052c1c3c61ec56c2c5c9.jpg\",\n  \"669798.jpg\": \"Aves/Chordeiles minor/68ffc90d4603c40bc421868f437914c0.jpg\",\n  \"669807.jpg\": \"Aves/Chordeiles minor/4f2c2386bb0ce3c410bbb36061d79663.jpg\",\n  \"66982.jpg\": \"Aves/Gyps fulvus/e1d853a9ab46591e2d0246b7c927ecfa.jpg\",\n  \"66983.jpg\": \"Aves/Gyps fulvus/6b4e6b9797671a409c3eab44c15e603e.jpg\",\n  \"66984.jpg\": \"Aves/Gyps fulvus/a25659795b60ad1364e724584b0ae323.jpg\",\n  \"66989.jpg\": \"Aves/Gyps fulvus/3a31fee90575b99a96d8f54683ec855d.jpg\",\n  \"669892.jpg\": \"Amphibia/Anaxyrus boreas/38c6f9cc6b11d9047a48d099e214ceca.jpg\",\n  \"66990.jpg\": \"Aves/Gyps fulvus/02cee2a961913e41c178a0f71ee6461f.jpg\",\n  \"669900.jpg\": \"Amphibia/Anaxyrus boreas/b7baae450ee8a22503ccb02125709eaa.jpg\",\n  \"669912.jpg\": \"Amphibia/Anaxyrus boreas/c8d82adcf0101a61b30d75aef057e630.jpg\",\n  \"66992.jpg\": \"Aves/Gyps fulvus/0f001c771b909f370501c71ea7d00f78.jpg\",\n  \"669926.jpg\": \"Amphibia/Anaxyrus boreas/edcc985472bc99567afa6255906b0bbe.jpg\",\n  \"669936.jpg\": \"Amphibia/Anaxyrus boreas/060903bdea53020fcd1a6fad37b3cbfa.jpg\",\n  \"669938.jpg\": \"Amphibia/Anaxyrus boreas/48420ae953361ff69ab4ccb85a1aa1a1.jpg\",\n  \"669940.jpg\": \"Amphibia/Anaxyrus boreas/98e5ae0ac7b8d665b5ed1796365d0393.jpg\",\n  \"669964.jpg\": \"Amphibia/Anaxyrus boreas/a5e49f4d130ed96d1567ecd655214695.jpg\",\n  \"66998.jpg\": \"Aves/Gyps fulvus/1c6817c6937c59966e02f827b94960cd.jpg\",\n  \"669985.jpg\": \"Amphibia/Anaxyrus boreas/920981dfb481aaa7f263efcf5f2666da.jpg\",\n  \"67003.jpg\": \"Aves/Gyps fulvus/e87cbbdc80a0195c072aa3ed0298e5b5.jpg\",\n  \"67004.jpg\": \"Aves/Gyps fulvus/cd275a6dbbf1dd954bbb860975660540.jpg\",\n  \"67005.jpg\": \"Aves/Gyps fulvus/edc112dae5102af5dbaa0e41def03126.jpg\",\n  \"67007.jpg\": \"Aves/Gyps fulvus/dc875897958ce5511d926efcb1ec6308.jpg\",\n  \"670075.jpg\": \"Aves/Zonotrichia atricapilla/55f6611684e4b7bd9757fd65532e5e4f.jpg\",\n  \"67008.jpg\": \"Aves/Gyps fulvus/37dfdf1b9623ba4a3e04506b586cf567.jpg\",\n  \"670096.jpg\": \"Aves/Zonotrichia atricapilla/7e6964bd5fe9a435030d5f9878d50d4d.jpg\",\n  \"67013.jpg\": \"Aves/Gyps fulvus/cdbb44b587bd48535baf313ac792eea8.jpg\",\n  \"67015.jpg\": \"Aves/Gyps fulvus/02ea2a1a770815f6d6f6021787b6f48a.jpg\",\n  \"670158.jpg\": \"Aves/Zonotrichia atricapilla/0e0bfbaef8f12a7fa0ecf2bb371ad222.jpg\",\n  \"67016.jpg\": \"Aves/Gyps fulvus/7bf293d8cacfd481c995f8fc03155326.jpg\",\n  \"67017.jpg\": \"Aves/Gyps fulvus/ad2fc321a6a5f62b8c7e5d9d438d868f.jpg\",\n  \"670331.jpg\": \"Mammalia/Sylvilagus audubonii/88b5c4f9ab2f2ace3c56fca7b886aaa2.jpg\",\n  \"670332.jpg\": \"Mammalia/Sylvilagus audubonii/9f41cd2b956508179bf7ffbddf1916ab.jpg\",\n  \"670363.jpg\": \"Mammalia/Sylvilagus audubonii/9bf88f1c35c0e741c727819703a2bfa5.jpg\",\n  \"670376.jpg\": \"Mammalia/Sylvilagus audubonii/c911d669ffbb5b83a4da88b017e03593.jpg\",\n  \"670379.jpg\": \"Mammalia/Sylvilagus audubonii/8a523aa91b1d4aa9e1305b435db07b81.jpg\",\n  \"670445.jpg\": \"Mammalia/Sylvilagus audubonii/07d57a6d2c70f7f67e632a698b7dff7b.jpg\",\n  \"670457.jpg\": \"Aves/Pharomachrus mocinno/5b3ca4119fb88df46308e3b105672919.jpg\",\n  \"67046.jpg\": \"Mammalia/Sus scrofa/699eaf67b8f3b810e166f655aeb63857.jpg\",\n  \"670460.jpg\": \"Aves/Pharomachrus mocinno/39d966c4b714fccdc3e2d6cc44e5e254.jpg\",\n  \"670462.jpg\": \"Aves/Pharomachrus mocinno/a458660e8d67cbfd5312c975d20eb41e.jpg\",\n  \"670516.jpg\": \"Reptilia/Chrysemys picta bellii/b52b3287abbae2a97cac048e84a469b4.jpg\",\n  \"670523.jpg\": \"Reptilia/Chrysemys picta bellii/92e3a759ea4ca1270914b59c946106c2.jpg\",\n  \"670543.jpg\": \"Aves/Circaetus gallicus/9da2cd9461c7df8b0b304a037249501f.jpg\",\n  \"670590.jpg\": \"Aves/Thraupis abbas/84c8be37141b98f916787f9772c1ca20.jpg\",\n  \"670634.jpg\": \"Aves/Accipiter cooperii/6989f90b44760ece405b6bca69f779b3.jpg\",\n  \"67069.jpg\": \"Mammalia/Sus scrofa/6b6d773ceed46535086e66866975be74.jpg\",\n  \"670711.jpg\": \"Aves/Accipiter cooperii/f33013069e866466335154f528ccf496.jpg\",\n  \"67080.jpg\": \"Mammalia/Sus scrofa/ed1c2f72c9f55a31b6d5808c33ff4fe7.jpg\",\n  \"670803.jpg\": \"Aves/Accipiter cooperii/b1d2b1286a2ed037d5aa7997b35be1d5.jpg\",\n  \"670807.jpg\": \"Aves/Accipiter cooperii/a665f47d1167cee55b4a053fd431c3db.jpg\",\n  \"670866.jpg\": \"Aves/Accipiter cooperii/996304a5241c14f1ac46c0e36d6aa154.jpg\",\n  \"670922.jpg\": \"Aves/Accipiter cooperii/771fc2bffcd4b1a05ab354ed849a4672.jpg\",\n  \"67093.jpg\": \"Mammalia/Sus scrofa/c7dbf03a651cd7dbceb29dddbee436c2.jpg\",\n  \"67094.jpg\": \"Mammalia/Sus scrofa/409a945175b3b8fcd04f09ee2ea6dee1.jpg\",\n  \"670968.jpg\": \"Aves/Accipiter cooperii/48d3a510abe05f64747e7d4e5f704179.jpg\",\n  \"67101.jpg\": \"Mammalia/Sus scrofa/3f2543797e5db6b3d5258f3084fd8102.jpg\",\n  \"67102.jpg\": \"Mammalia/Sus scrofa/3614b32446156c3c4b5775ac27830882.jpg\",\n  \"671092.jpg\": \"Reptilia/Thamnophis elegans/cf2468b7af5a5d1ccf46196dcde68570.jpg\",\n  \"671093.jpg\": \"Reptilia/Thamnophis elegans/61e6d55d6a86cb0d9455ab7defc2fc22.jpg\",\n  \"671126.jpg\": \"Reptilia/Thamnophis elegans/66d5c862f6f7bd66b47bbc608b4e0f27.jpg\",\n  \"671149.jpg\": \"Reptilia/Thamnophis elegans/28598de48a8ee41310e61f91ec8314cf.jpg\",\n  \"671165.jpg\": \"Reptilia/Thamnophis elegans/0b87f3260a84b65c67375850a2f66bfb.jpg\",\n  \"67118.jpg\": \"Mammalia/Sus scrofa/5a7b32bedc4efa32905df07e27eddea0.jpg\",\n  \"671287.jpg\": \"Amphibia/Ambystoma maculatum/f4e5f0f16a9f1f121da498acb0ca0aed.jpg\",\n  \"671304.jpg\": \"Amphibia/Ambystoma maculatum/7bea6a7b40170f00faeb48ea6ef15c39.jpg\",\n  \"671306.jpg\": \"Amphibia/Ambystoma maculatum/dc1724f56f9df5b59a428367b5337566.jpg\",\n  \"671308.jpg\": \"Amphibia/Ambystoma maculatum/8f7f105c6d45e0d1709dca70aa417b60.jpg\",\n  \"671315.jpg\": \"Amphibia/Ambystoma maculatum/90502f0b529cec420d1a50b0d4be3f4f.jpg\",\n  \"671320.jpg\": \"Amphibia/Ambystoma maculatum/11ccd1d11f0c617a8a2b1bebaf10296a.jpg\",\n  \"671323.jpg\": \"Amphibia/Ambystoma maculatum/49b26621959edfba0b7a59a13b9b59c0.jpg\",\n  \"671324.jpg\": \"Amphibia/Ambystoma maculatum/66dec615d677d6ba013ec05c58cf39cc.jpg\",\n  \"671347.jpg\": \"Amphibia/Ambystoma maculatum/0afa7246d182e3f733868040a8f63c01.jpg\",\n  \"671402.jpg\": \"Actinopterygii/Acanthurus triostegus/ab3e9f6cc7fe9028b85e71344020d1e4.jpg\",\n  \"671404.jpg\": \"Actinopterygii/Acanthurus triostegus/59f0a1599c667b5a2b95687c2ed5d02f.jpg\",\n  \"671418.jpg\": \"Mammalia/Macropus giganteus/eb01915719f09e249b41ecd7e264f224.jpg\",\n  \"671457.jpg\": \"Reptilia/Terrapene carolina/873de843c7ac093bcc279f0ce2e1564c.jpg\",\n  \"671461.jpg\": \"Reptilia/Terrapene carolina/89e05baa49ed117a2744500769a58bf6.jpg\",\n  \"671466.jpg\": \"Reptilia/Terrapene carolina/216eb33c7b1b79a0bdfffecc3e92502d.jpg\",\n  \"671475.jpg\": \"Reptilia/Terrapene carolina/3812edf957de6955d0bc15138c02ffae.jpg\",\n  \"671481.jpg\": \"Reptilia/Terrapene carolina/41abed9a39c44b0c55b564462f69fbe1.jpg\",\n  \"671482.jpg\": \"Reptilia/Terrapene carolina/98fe3699a9212bac91372a4cae240950.jpg\",\n  \"671505.jpg\": \"Reptilia/Terrapene carolina/10b14925099bc10291018774c402f7b4.jpg\",\n  \"671512.jpg\": \"Reptilia/Terrapene carolina/b9c63c9db247d9f5cca9af51fc56cc32.jpg\",\n  \"67152.jpg\": \"Mammalia/Sus scrofa/289834b8af727e16131e703273a8f0ae.jpg\",\n  \"671522.jpg\": \"Reptilia/Terrapene carolina/0c755d6e218a05a7d02ecf79a29ad17a.jpg\",\n  \"671546.jpg\": \"Reptilia/Terrapene carolina/8cbb79f56c106672922dc8ffdc482b24.jpg\",\n  \"671552.jpg\": \"Mollusca/Monadenia fidelis/56e3987cf5f7a34a912e48cc6288cbcf.jpg\",\n  \"671553.jpg\": \"Mollusca/Monadenia fidelis/522553e38bab876a34aa4d6ef8702e1d.jpg\",\n  \"671554.jpg\": \"Mollusca/Monadenia fidelis/6b5d64ec48ebcede6777412e88ef0a89.jpg\",\n  \"671555.jpg\": \"Mollusca/Monadenia fidelis/4d2857f883c915337d05291deda4366d.jpg\",\n  \"671556.jpg\": \"Mollusca/Monadenia fidelis/d60e9dddcba785dfdf4cc235dd7fe697.jpg\",\n  \"671561.jpg\": \"Mollusca/Monadenia fidelis/6bda8d1062dfdfaaec1296302e19c06a.jpg\",\n  \"671562.jpg\": \"Mollusca/Monadenia fidelis/35e855bc777fbd230e25c5f6b2270e1b.jpg\",\n  \"671570.jpg\": \"Mollusca/Monadenia fidelis/b994428c891709a0d186ba4a3c788c58.jpg\",\n  \"671571.jpg\": \"Mollusca/Monadenia fidelis/a0b831780a24428ed0197e221f101699.jpg\",\n  \"671573.jpg\": \"Mollusca/Monadenia fidelis/7570e5e708fad534c1b9f0bdcf9488a9.jpg\",\n  \"671622.jpg\": \"Aves/Larus argentatus/0260343b8d53dcce16a09313cd265e9a.jpg\",\n  \"671718.jpg\": \"Aves/Larus argentatus/807a7dbf04b4bdb0407dc8e129e07080.jpg\",\n  \"671741.jpg\": \"Aves/Larus argentatus/970ffe5c38f52d21032b3c89163d3fb2.jpg\",\n  \"671754.jpg\": \"Aves/Larus argentatus/942aea8b7e19a011129d6a895d3675b9.jpg\",\n  \"67181.jpg\": \"Mammalia/Sus scrofa/55abb639fd5d8362c3b3707747b4f509.jpg\",\n  \"67182.jpg\": \"Mammalia/Sus scrofa/f84c61a819cc45948e6c67869b24d5df.jpg\",\n  \"67191.jpg\": \"Mammalia/Sus scrofa/cfcaf43227b38471b7613e0392f8cbb4.jpg\",\n  \"671914.jpg\": \"Insecta/Papilio palamedes/4cce59bfcdc35f3e92ecf17261ee0605.jpg\",\n  \"671916.jpg\": \"Insecta/Papilio palamedes/2eb9cad9fe29eb1541a37842bd52e7a9.jpg\",\n  \"671917.jpg\": \"Insecta/Papilio palamedes/1a72aa40883cbc6b609dae15124de790.jpg\",\n  \"671918.jpg\": \"Insecta/Papilio palamedes/97a061b29cf13e2ccd6df599ce34f1b6.jpg\",\n  \"671919.jpg\": \"Insecta/Papilio palamedes/5988073050f07fe30e51ed607e155c55.jpg\",\n  \"671920.jpg\": \"Insecta/Papilio palamedes/726a1004b81b4ae71bccdaffe416c674.jpg\",\n  \"671924.jpg\": \"Insecta/Papilio palamedes/ecc3fc641a904d61b72573c833870caf.jpg\",\n  \"671933.jpg\": \"Insecta/Papilio palamedes/7e6807a9941d485d258dcdbf510fa575.jpg\",\n  \"671942.jpg\": \"Insecta/Papilio palamedes/4378fca9a4fc0b31a06b7928fb4fac6e.jpg\",\n  \"67197.jpg\": \"Insecta/Toxomerus geminatus/9f0fae32f819cfbf5d75b885602e55e3.jpg\",\n  \"67198.jpg\": \"Insecta/Toxomerus geminatus/bb2967ce5ca4573fbaf927d6fa61e75b.jpg\",\n  \"67199.jpg\": \"Insecta/Toxomerus geminatus/975c35b05372a51fd04dff6f2efe4961.jpg\",\n  \"67200.jpg\": \"Insecta/Toxomerus geminatus/970d9e1b46e9946626d4093efbea508d.jpg\",\n  \"67202.jpg\": \"Insecta/Toxomerus geminatus/e78e1d271dc20515de6c54b15db89fa7.jpg\",\n  \"672027.jpg\": \"Aves/Aythya collaris/73120e0618e2aa43602c5fda3d62cec7.jpg\",\n  \"67203.jpg\": \"Insecta/Toxomerus geminatus/6ea4239adaad43b334414531a3b8188a.jpg\",\n  \"672032.jpg\": \"Aves/Aythya collaris/53134c654dc07a2a92b1d8dc0c462b24.jpg\",\n  \"672035.jpg\": \"Aves/Aythya collaris/cbe7f4a7484681b175c5d490860f3cd1.jpg\",\n  \"67204.jpg\": \"Insecta/Toxomerus geminatus/ab60ed7cb29f37d0723111720a92decd.jpg\",\n  \"67205.jpg\": \"Insecta/Toxomerus geminatus/27e0a33d86bdb0b65650885c1bc3db4b.jpg\",\n  \"67206.jpg\": \"Insecta/Toxomerus geminatus/557d5e59048ebf85fa207d2f1103f5a3.jpg\",\n  \"67207.jpg\": \"Insecta/Toxomerus geminatus/362613481b2541d7b65c2bcdd652a39a.jpg\",\n  \"67208.jpg\": \"Insecta/Toxomerus geminatus/44b1c458fb2933498cfa91587d3ee92c.jpg\",\n  \"67209.jpg\": \"Insecta/Toxomerus geminatus/84870d33690c3ff5f2f7223a35c3d8f5.jpg\",\n  \"67216.jpg\": \"Insecta/Toxomerus geminatus/527eaf806abf11ae9efd37bb97d1fd92.jpg\",\n  \"672178.jpg\": \"Aves/Aythya collaris/da4f53bba2739889454510ec03ac2eda.jpg\",\n  \"672230.jpg\": \"Amphibia/Aneides lugubris/c9ff1477b317c4c876c4351da7b9b2a7.jpg\",\n  \"67226.jpg\": \"Insecta/Toxomerus geminatus/9bbb1d2d060ef7cdeeef48ae5ecbe2f0.jpg\",\n  \"672261.jpg\": \"Amphibia/Aneides lugubris/71039383be2af000e58a8c6c189755d9.jpg\",\n  \"67227.jpg\": \"Insecta/Toxomerus geminatus/250f4918115949119d619cb34af36f4a.jpg\",\n  \"672271.jpg\": \"Amphibia/Aneides lugubris/f53989ad54bac0b186f4528ae96ead25.jpg\",\n  \"672274.jpg\": \"Amphibia/Aneides lugubris/41e04600e1a7e5c173fd67a40e9898b8.jpg\",\n  \"67228.jpg\": \"Insecta/Toxomerus geminatus/b76c7800e4fc0d3821b5e0ccbe92acdd.jpg\",\n  \"67229.jpg\": \"Insecta/Toxomerus geminatus/1319c4a42be5ea2bba23e6c3068c5dc2.jpg\",\n  \"672294.jpg\": \"Amphibia/Aneides lugubris/5d0727f15d4078a6b42ee496fdbca7eb.jpg\",\n  \"6723.jpg\": \"Aves/Pluvialis squatarola/c76d669a7ac875a1284a49124a885b76.jpg\",\n  \"67230.jpg\": \"Insecta/Toxomerus geminatus/303a50c772a76e7a3dc05fea4546d1d7.jpg\",\n  \"672307.jpg\": \"Animalia/Megalorchestia californiana/4d8cd4395a44b0fdc904cff0b7c80d18.jpg\",\n  \"672310.jpg\": \"Animalia/Megalorchestia californiana/773286400fae72fe2d046f76e89a397f.jpg\",\n  \"672332.jpg\": \"Insecta/Chlosyne lacinia/7d76d8e3b6221d3e0f475e78506d3317.jpg\",\n  \"672337.jpg\": \"Insecta/Chlosyne lacinia/b0eeba610a1c351cc3d8a8ab742a02fb.jpg\",\n  \"67235.jpg\": \"Insecta/Toxomerus geminatus/7ccb0aaafa6d2721ee7bfaa179350e20.jpg\",\n  \"67237.jpg\": \"Insecta/Toxomerus geminatus/daf30a958ebf39c7473ce4de9c9ca3c3.jpg\",\n  \"672374.jpg\": \"Insecta/Chlosyne lacinia/59eb07b16d3338f7d194d2efdaeb5489.jpg\",\n  \"672375.jpg\": \"Insecta/Chlosyne lacinia/d424e7c7a9153f6465b108b461aaa33b.jpg\",\n  \"672389.jpg\": \"Insecta/Chlosyne lacinia/16653ea7c8b6b599209542685aa2e0d2.jpg\",\n  \"672396.jpg\": \"Insecta/Chlosyne lacinia/6f96a56b120e3c0976b90cb0e33e95a3.jpg\",\n  \"672424.jpg\": \"Aves/Amazilia rutila/a8390ee562c4e7663a5afd341ed64700.jpg\",\n  \"672425.jpg\": \"Aves/Amazilia rutila/c2a89513f8854d33ed7af395419d6119.jpg\",\n  \"672426.jpg\": \"Aves/Amazilia rutila/a74a2dead1e76a712377afc703feb69e.jpg\",\n  \"672427.jpg\": \"Aves/Amazilia rutila/cda7a9a39fcfcfb0834d99603dd6684e.jpg\",\n  \"672429.jpg\": \"Aves/Amazilia rutila/2b69efaaa576af26e708392b1043d43f.jpg\",\n  \"672430.jpg\": \"Aves/Amazilia rutila/3dfcfd543ee3c2011b09cdd29bdef355.jpg\",\n  \"672431.jpg\": \"Aves/Amazilia rutila/428e40cb5a62597119dbf7e3b0ca1878.jpg\",\n  \"672435.jpg\": \"Aves/Amazilia rutila/605753b13f770e6be1859a4db48512b3.jpg\",\n  \"672436.jpg\": \"Aves/Amazilia rutila/acacc554eebd7375f212af2c203e29fb.jpg\",\n  \"672438.jpg\": \"Aves/Amazilia rutila/fc638094e65a13040a3e49955dc6693d.jpg\",\n  \"672439.jpg\": \"Aves/Amazilia rutila/79e451aa86d5bd683f25ec7b15e6bc09.jpg\",\n  \"672442.jpg\": \"Aves/Amazilia rutila/52a31269178190c5d7c83bfe49844cf0.jpg\",\n  \"67254.jpg\": \"Mammalia/Castor canadensis/2fb0577c9b447e8617dd2091aa828d0d.jpg\",\n  \"672646.jpg\": \"Insecta/Danaus plexippus/7ff999b5c995aa90b94d3c72eb0fb0c7.jpg\",\n  \"672669.jpg\": \"Insecta/Danaus plexippus/f53ca1c993e64d1b2f880d95941cb27b.jpg\",\n  \"6727.jpg\": \"Aves/Pluvialis squatarola/5422d647aaf68921cc4d7c5acbd6a800.jpg\",\n  \"67281.jpg\": \"Mammalia/Castor canadensis/b6dfc0f576ce7931e1d0377f310568a7.jpg\",\n  \"673178.jpg\": \"Insecta/Danaus plexippus/9e84f73ee9da59560b656f52c31cec81.jpg\",\n  \"673369.jpg\": \"Insecta/Danaus plexippus/aff1ae4b7bdf32487fdca730c0266dc2.jpg\",\n  \"6734.jpg\": \"Aves/Pluvialis squatarola/f27583e92e6f3dd385e1c250d92b8166.jpg\",\n  \"673422.jpg\": \"Insecta/Danaus plexippus/3e35ee97f04afb5d4edd15b334497b9a.jpg\",\n  \"6735.jpg\": \"Aves/Pluvialis squatarola/9a9999ede4e7b796a3ddb0f8eff75637.jpg\",\n  \"673776.jpg\": \"Insecta/Danaus plexippus/79ca564d5874ffbac82ceb0fe16f71c1.jpg\",\n  \"673887.jpg\": \"Insecta/Danaus plexippus/1971ee6dc97252d54ca25c50a125ae8b.jpg\",\n  \"673980.jpg\": \"Insecta/Danaus plexippus/abcd10d5f92f71058b6ad6bece6ccc9f.jpg\",\n  \"673992.jpg\": \"Insecta/Danaus plexippus/d4313098b5d684632d4863a61f79ef3c.jpg\",\n  \"67420.jpg\": \"Mammalia/Castor canadensis/d0c6e5c973bc6668c40884f96f111b31.jpg\",\n  \"674216.jpg\": \"Mollusca/Geitodoris heathi/c3e30a2a22b5a0447068e93a28febdf2.jpg\",\n  \"674217.jpg\": \"Mollusca/Geitodoris heathi/dec5fc0eb98d629813aa7898af5b81cb.jpg\",\n  \"674218.jpg\": \"Mollusca/Geitodoris heathi/43e1ca44adc4f8ae781af3a6576ee588.jpg\",\n  \"674219.jpg\": \"Mollusca/Geitodoris heathi/8b324d3378c918faf10eed0f4bf383fd.jpg\",\n  \"674220.jpg\": \"Mollusca/Geitodoris heathi/1bc830a7fa3a62aaa793552dbb37808c.jpg\",\n  \"674221.jpg\": \"Mollusca/Geitodoris heathi/b72c1159b5d3bf2a29c2ad14aa04462d.jpg\",\n  \"674222.jpg\": \"Mollusca/Geitodoris heathi/cc1d337f59bc195fa740c9746f6f4c4d.jpg\",\n  \"674223.jpg\": \"Mollusca/Geitodoris heathi/d07d64799aff640bfe0930d2842ffc4e.jpg\",\n  \"674224.jpg\": \"Mollusca/Geitodoris heathi/1e338aba09259866b168236812a6d1e5.jpg\",\n  \"674226.jpg\": \"Mollusca/Geitodoris heathi/9af20d1c26a8c7c1fd78a6f91f0ef4d9.jpg\",\n  \"674227.jpg\": \"Mollusca/Geitodoris heathi/a3d1c2ebc6c941c50d908e752bbd80c5.jpg\",\n  \"674228.jpg\": \"Mollusca/Geitodoris heathi/1c5098bbf49f30caeaaa41fca16eed2d.jpg\",\n  \"674229.jpg\": \"Mollusca/Geitodoris heathi/bd06937214fa4a89d0dfeb78f35164b0.jpg\",\n  \"674230.jpg\": \"Mollusca/Geitodoris heathi/f970d47bb31ac8855b83ab23d816aa86.jpg\",\n  \"674231.jpg\": \"Mollusca/Geitodoris heathi/924f5862cba03af30efc8641c4c9b27f.jpg\",\n  \"674234.jpg\": \"Mollusca/Geitodoris heathi/68da89d907ee6957920a47984785201a.jpg\",\n  \"674236.jpg\": \"Mollusca/Geitodoris heathi/fa5d5c3f0c5705beec4cf5f968c07f3a.jpg\",\n  \"674237.jpg\": \"Mollusca/Geitodoris heathi/fa8b1d816fc8c3d91efbe39998e2e2f0.jpg\",\n  \"674238.jpg\": \"Mollusca/Geitodoris heathi/60c661fa6ef4fe56e075cf8784652139.jpg\",\n  \"674240.jpg\": \"Mollusca/Geitodoris heathi/164b84095542cc8a4693ae0d04f1ab25.jpg\",\n  \"674241.jpg\": \"Mollusca/Geitodoris heathi/8fd6bba49a338713a5911ec1b9b49788.jpg\",\n  \"674243.jpg\": \"Mollusca/Geitodoris heathi/8c3db17444263e675e9f8cb03be35466.jpg\",\n  \"674244.jpg\": \"Mollusca/Geitodoris heathi/9e02a7128adfd7ffbab2a2603c8bb853.jpg\",\n  \"674270.jpg\": \"Mollusca/Triopha maculata/5db8bb65ccc4d394fa04211bb5b7be6b.jpg\",\n  \"674289.jpg\": \"Mollusca/Triopha maculata/0277865b597b183abb0a2087bd1481f6.jpg\",\n  \"674290.jpg\": \"Mollusca/Triopha maculata/7542e7700daff69745526e65558f0fe6.jpg\",\n  \"674291.jpg\": \"Mollusca/Triopha maculata/7906920be81e4845195afac91048eb99.jpg\",\n  \"6743.jpg\": \"Aves/Pluvialis squatarola/a9da1a84ab08179bc1c954c0ef0aecf6.jpg\",\n  \"674315.jpg\": \"Mollusca/Triopha maculata/47155555a328cd9bffc9d94e79c84bce.jpg\",\n  \"674336.jpg\": \"Mollusca/Triopha maculata/93a4233ad2f29b96d04f78b556211188.jpg\",\n  \"674354.jpg\": \"Mollusca/Triopha maculata/2c2db1cdff9d82c3f4cc85085c6e766d.jpg\",\n  \"674366.jpg\": \"Mollusca/Triopha maculata/4b317acdba43e48b89af1a0240f36222.jpg\",\n  \"674492.jpg\": \"Insecta/Murgantia histrionica/63952bf8f2bad220319aa691c8bc382e.jpg\",\n  \"674494.jpg\": \"Insecta/Murgantia histrionica/d7696b91675b8043a4a612a725deb65c.jpg\",\n  \"674516.jpg\": \"Insecta/Murgantia histrionica/75b5fbde5621d66109ea01faaacbce8e.jpg\",\n  \"674528.jpg\": \"Insecta/Murgantia histrionica/2ca9d0b498c229d1e0dfe8d067d26965.jpg\",\n  \"674536.jpg\": \"Insecta/Murgantia histrionica/161e3e98d03e021252b788b29b69a47d.jpg\",\n  \"674549.jpg\": \"Insecta/Murgantia histrionica/65240a73ddef963e7b7c1d07673773dc.jpg\",\n  \"674654.jpg\": \"Aves/Columba livia/17efe25d075e6b85cd9aa6384563747b.jpg\",\n  \"674752.jpg\": \"Aves/Columba livia/dd3409d5fbcd3a1856a4c0bef4ee8ce2.jpg\",\n  \"674761.jpg\": \"Aves/Columba livia/1846cdff69e21c0480134be599b48cd8.jpg\",\n  \"674791.jpg\": \"Aves/Columba livia/f2db9bc0f2a3786639168d91ac81992c.jpg\",\n  \"674807.jpg\": \"Aves/Columba livia/37201720f676e50a4ac02203b89f0b06.jpg\",\n  \"67482.jpg\": \"Mammalia/Castor canadensis/6fe7f8d01d8c70ea83f36eb2eb1ddf8d.jpg\",\n  \"674838.jpg\": \"Aves/Columba livia/410eabbdd0dddf604ce08fddd4a19f91.jpg\",\n  \"674907.jpg\": \"Aves/Columba livia/a19339a841dedaa4792401cd5564332b.jpg\",\n  \"67499.jpg\": \"Mammalia/Castor canadensis/9e788c00319a327bdec5118f278e4905.jpg\",\n  \"675028.jpg\": \"Aves/Columba livia/6b0a5bc4b8232bf06a641c98d134c0f3.jpg\",\n  \"675063.jpg\": \"Aves/Columba livia/78a62bdb5a1d0cdae63aa67ebb84cb2d.jpg\",\n  \"675110.jpg\": \"Aves/Columba livia/e37f20f5532eecf376f7e7d3e68e2412.jpg\",\n  \"675152.jpg\": \"Insecta/Notarctia proxima/8ea08202195f9eb662068e1d3fbe5da9.jpg\",\n  \"675162.jpg\": \"Aves/Piranga bidentata/181405d8547233c63ac97f3ef21caf4b.jpg\",\n  \"6753.jpg\": \"Aves/Pluvialis squatarola/c4c82e7e429220b8cfb9b4eed432cc5f.jpg\",\n  \"6755.jpg\": \"Aves/Pluvialis squatarola/c87a8c487086cec75c63997a1dba5d58.jpg\",\n  \"67599.jpg\": \"Mammalia/Castor canadensis/141a403ab9a6b28c555da2c7fb12f64c.jpg\",\n  \"67688.jpg\": \"Mollusca/Nucella ostrina/d5b30794f832fadbdf70533adc6c27ce.jpg\",\n  \"67689.jpg\": \"Mollusca/Nucella ostrina/a2915fcfa99926edcb818ba1b084d2b3.jpg\",\n  \"67699.jpg\": \"Mollusca/Nucella ostrina/2ee81e968420a6fa0a8e2b4425782ca3.jpg\",\n  \"67702.jpg\": \"Mollusca/Nucella ostrina/030a2a8a1baee3ec134e46cb7db2a6ee.jpg\",\n  \"67704.jpg\": \"Reptilia/Sceloporus merriami/6a19078f577049cef01b3778b2095066.jpg\",\n  \"67705.jpg\": \"Reptilia/Sceloporus merriami/ae93f83f3265676cbbb04606742ef6ea.jpg\",\n  \"67708.jpg\": \"Reptilia/Sceloporus merriami/eca877d267483c89d42d8403d95e11c5.jpg\",\n  \"67714.jpg\": \"Reptilia/Sceloporus merriami/93861cb0a90bd7c8fd61bf0556cb05cb.jpg\",\n  \"67717.jpg\": \"Reptilia/Sceloporus merriami/bd6da0a96071f818e3a560114a7838ef.jpg\",\n  \"67725.jpg\": \"Reptilia/Sceloporus merriami/25d8ac5d52c9dcb8cfe88b599fcfd8ee.jpg\",\n  \"67727.jpg\": \"Reptilia/Sceloporus merriami/417c5f11f1fe9b3c0689094aebcb1c7f.jpg\",\n  \"67729.jpg\": \"Mammalia/Sciurus aureogaster/b99ff1c361d597d3bc20f433f9be994f.jpg\",\n  \"67730.jpg\": \"Mammalia/Sciurus aureogaster/e90cc71eead51e22da8b7873e5796006.jpg\",\n  \"67733.jpg\": \"Mammalia/Sciurus aureogaster/487f869eabf4345a78fbb17ab1f4752d.jpg\",\n  \"67734.jpg\": \"Mammalia/Sciurus aureogaster/8bf5169b937a38a3c2c30ced412dc51e.jpg\",\n  \"67735.jpg\": \"Mammalia/Sciurus aureogaster/fcbd6196404c86ad07da040b11a97937.jpg\",\n  \"67736.jpg\": \"Mammalia/Sciurus aureogaster/ffb74f58cba2c0916628a0ff36cca904.jpg\",\n  \"67739.jpg\": \"Mammalia/Sciurus aureogaster/06c3f36fed6b6c0f682198991943510e.jpg\",\n  \"67743.jpg\": \"Mammalia/Sciurus aureogaster/a207d9ac849a000afce7284f2366e89f.jpg\",\n  \"67749.jpg\": \"Mammalia/Sciurus aureogaster/bfef5b572ef0ccc1a4bd0f07984ca715.jpg\",\n  \"67750.jpg\": \"Mammalia/Sciurus aureogaster/b7425cd117ff1291747075ff11260549.jpg\",\n  \"67752.jpg\": \"Mammalia/Sciurus aureogaster/e1a24e7df98514873b5c24a8ae4834cb.jpg\",\n  \"67756.jpg\": \"Mammalia/Sciurus aureogaster/ad6943049ac8b50522c4b6a6c136fae5.jpg\",\n  \"67761.jpg\": \"Mammalia/Sciurus aureogaster/cf6b398c68c7918247b056b2304ff6a0.jpg\",\n  \"67762.jpg\": \"Mammalia/Sciurus aureogaster/72b893813ea2c4bca7720c25fd36c701.jpg\",\n  \"67765.jpg\": \"Mammalia/Sciurus aureogaster/26e4ebb57819ead4eb66e23d78f8c381.jpg\",\n  \"67766.jpg\": \"Mammalia/Sciurus aureogaster/7f6acf52b384798815bad02811d089a2.jpg\",\n  \"67773.jpg\": \"Mammalia/Sciurus aureogaster/57d801846eb81dedff66d57911cf1817.jpg\",\n  \"67781.jpg\": \"Mammalia/Sciurus aureogaster/07ee744673e14753f2b076682d0ccc08.jpg\",\n  \"67795.jpg\": \"Mammalia/Sciurus aureogaster/2979299b5e7c7ef257e4554ced738fb5.jpg\",\n  \"67802.jpg\": \"Mammalia/Sciurus aureogaster/84171aa98203e84b78ff37ebe72ce013.jpg\",\n  \"67804.jpg\": \"Mammalia/Sciurus aureogaster/3e5465541e9a37cad50a24d4ba981a08.jpg\",\n  \"67811.jpg\": \"Mammalia/Sciurus aureogaster/b018036fba653f466172d73a6fd89d29.jpg\",\n  \"67812.jpg\": \"Aves/Botaurus lentiginosus/3abcd5a1ed99faae4ae6133def05bf54.jpg\",\n  \"67817.jpg\": \"Aves/Botaurus lentiginosus/f8aa7affd416e0c18455d655996d0e74.jpg\",\n  \"67818.jpg\": \"Aves/Botaurus lentiginosus/cd164df1d49ed6ed6806137fb58e0424.jpg\",\n  \"67819.jpg\": \"Aves/Botaurus lentiginosus/1f48de9e628ffb7b3a91ab797ce2d8c9.jpg\",\n  \"67828.jpg\": \"Aves/Botaurus lentiginosus/614fe87f493d323404ecbdfdf2237fe5.jpg\",\n  \"6783.jpg\": \"Aves/Pluvialis squatarola/15f460d7ee0fd01f7ea235bb72957cb9.jpg\",\n  \"67833.jpg\": \"Aves/Botaurus lentiginosus/a517ee8e9aa0f52e120992d8ecc48f80.jpg\",\n  \"67839.jpg\": \"Aves/Botaurus lentiginosus/90b35c8b4a4b22f223447928276edae1.jpg\",\n  \"67840.jpg\": \"Aves/Botaurus lentiginosus/72a24a4d0063f52a721e83799dceca46.jpg\",\n  \"67844.jpg\": \"Aves/Botaurus lentiginosus/95b053727407ddc6daae199bcb69f799.jpg\",\n  \"67846.jpg\": \"Aves/Botaurus lentiginosus/91eaf397b529edff290963b9dc102f54.jpg\",\n  \"67850.jpg\": \"Aves/Botaurus lentiginosus/d376083cf65d392870ced7672662c4e1.jpg\",\n  \"67855.jpg\": \"Aves/Botaurus lentiginosus/5e0371543442192984ff94e8938e034d.jpg\",\n  \"67856.jpg\": \"Aves/Botaurus lentiginosus/9a485840c55207820f88961e0bda687a.jpg\",\n  \"67872.jpg\": \"Aves/Botaurus lentiginosus/27b15fe82f45bba450ab238a93e38644.jpg\",\n  \"67875.jpg\": \"Aves/Botaurus lentiginosus/215a47f7d5bf598ba4ac74259c449530.jpg\",\n  \"67876.jpg\": \"Aves/Botaurus lentiginosus/59e4867003d0951df01cdcab3ed750b6.jpg\",\n  \"67891.jpg\": \"Aves/Botaurus lentiginosus/270610aeb5c44f1c5086fce59b238ca9.jpg\",\n  \"67892.jpg\": \"Aves/Botaurus lentiginosus/93664a84574579c269aa54c6718bf26a.jpg\",\n  \"67901.jpg\": \"Aves/Botaurus lentiginosus/a8504486a1e0db7bc45d0be01571b536.jpg\",\n  \"67913.jpg\": \"Aves/Botaurus lentiginosus/30e7993f68ffa35ac3ea1f5347cc2b22.jpg\",\n  \"67916.jpg\": \"Aves/Botaurus lentiginosus/ded971c44f387927739f5f37f46e08d6.jpg\",\n  \"67921.jpg\": \"Aves/Falco mexicanus/44ee83c6f6b1e084351fce782a5d8b41.jpg\",\n  \"67922.jpg\": \"Aves/Falco mexicanus/b5d01b77519c3b0ee604417a899c9b12.jpg\",\n  \"67923.jpg\": \"Aves/Falco mexicanus/2fc87e0eaced7821c04efc2a2dd62567.jpg\",\n  \"67928.jpg\": \"Aves/Falco mexicanus/7a6c09a4c932c540b4426cbdd4f736ea.jpg\",\n  \"67929.jpg\": \"Aves/Falco mexicanus/006be3c500e43340b7fde8a5ecde9947.jpg\",\n  \"67934.jpg\": \"Aves/Falco mexicanus/308ea29c08ba471bbd724aa79fad9122.jpg\",\n  \"67937.jpg\": \"Aves/Falco mexicanus/ed91314a46d17d41847beff910b7874d.jpg\",\n  \"67940.jpg\": \"Aves/Falco mexicanus/40911aa062b32da2d63ef0e142bd90cd.jpg\",\n  \"67941.jpg\": \"Aves/Falco mexicanus/add0b3a5d59f251cf0c7d140158316b4.jpg\",\n  \"67942.jpg\": \"Aves/Falco mexicanus/c71d49f689da044f44a4d716a74b8be8.jpg\",\n  \"67943.jpg\": \"Aves/Falco mexicanus/9e467e6d43bd7e354c658cd2f53797fe.jpg\",\n  \"67944.jpg\": \"Aves/Falco mexicanus/aa10b24ca894321c9c3195caf6291bcc.jpg\",\n  \"67945.jpg\": \"Aves/Falco mexicanus/106ffac419615943d9dc965fcdb72f87.jpg\",\n  \"67947.jpg\": \"Aves/Falco mexicanus/46a2e8c7f3ef78d231e7b80f504f467c.jpg\",\n  \"67948.jpg\": \"Aves/Falco mexicanus/c5c9d5be94c75b25b4da291b84728bdd.jpg\",\n  \"67949.jpg\": \"Aves/Falco mexicanus/393981a599fb477c5b204c5e78f9b190.jpg\",\n  \"67965.jpg\": \"Aves/Falco mexicanus/3e7ff85c4e0ca261f939aa9098e6dcf4.jpg\",\n  \"67966.jpg\": \"Aves/Falco mexicanus/e75466a0b908df06f873d6ffa1074223.jpg\",\n  \"67967.jpg\": \"Aves/Falco mexicanus/c30f4a5f60994e656e0d7d0709bace59.jpg\",\n  \"67968.jpg\": \"Aves/Falco mexicanus/0556c0b37d6c5c37ca7e94cf46ce15e7.jpg\",\n  \"68003.jpg\": \"Insecta/Pentatoma rufipes/b23318d9d8453530a5633893420d2dde.jpg\",\n  \"68008.jpg\": \"Insecta/Pentatoma rufipes/e4cd70baa42c8a3802fe5d5b98df89fe.jpg\",\n  \"68010.jpg\": \"Insecta/Pentatoma rufipes/bca6825cae478eaf82f1cc3f752b90e9.jpg\",\n  \"68026.jpg\": \"Insecta/Pentatoma rufipes/1e15c2a8cf647f3959f590b467d272fc.jpg\",\n  \"68085.jpg\": \"Aves/Larus marinus/49a0f336eba93d4b9b40b1686dae0285.jpg\",\n  \"68095.jpg\": \"Aves/Larus marinus/a2bf98e90f9fab2a458a08712977abf1.jpg\",\n  \"6810.jpg\": \"Aves/Pluvialis squatarola/bc8eb46694a3ae66d8a4148a9ae7674a.jpg\",\n  \"68106.jpg\": \"Aves/Larus marinus/60a4ebd0f9f94adf8a8d15904d81a555.jpg\",\n  \"68125.jpg\": \"Aves/Larus marinus/2bc36381c597e6617e885e2031db8962.jpg\",\n  \"68128.jpg\": \"Aves/Larus marinus/bc5284d871df06ed44761b06ebb543c0.jpg\",\n  \"68133.jpg\": \"Aves/Larus marinus/9e991bc317ea42be08a7b28eb09e8ad3.jpg\",\n  \"68152.jpg\": \"Aves/Larus marinus/9ab51844f8c53bffb16ee5dfdc35516f.jpg\",\n  \"68157.jpg\": \"Aves/Larus marinus/f9255e0d9f5c6b1303154d01a06d0460.jpg\",\n  \"68167.jpg\": \"Aves/Larus marinus/4d49a4c94bf1fd366d72e790eb2e3b48.jpg\",\n  \"68168.jpg\": \"Aves/Larus marinus/90868113fc70534e6685df541334deb8.jpg\",\n  \"68180.jpg\": \"Aves/Larus marinus/86d26b0233dcdaaf1371a00f8d635767.jpg\",\n  \"68190.jpg\": \"Aves/Larus marinus/5dc5728711984460a3906a34efbb8492.jpg\",\n  \"68194.jpg\": \"Aves/Larus marinus/66922221c44437e595d438a45105f5dd.jpg\",\n  \"68199.jpg\": \"Aves/Larus marinus/9dfcffb6e34e06bc605b40b59ab38d73.jpg\",\n  \"68207.jpg\": \"Insecta/Erynnis juvenalis/edf2a620dbfcea7dd0585b695e006c4d.jpg\",\n  \"68208.jpg\": \"Insecta/Erynnis juvenalis/cf94a31685e8528a9f6def08e98a0898.jpg\",\n  \"68210.jpg\": \"Insecta/Erynnis juvenalis/ec18c2c85edad2850ef8046f630bd1a0.jpg\",\n  \"68212.jpg\": \"Insecta/Erynnis juvenalis/c194c63da77c5a3abb089f0f155652c1.jpg\",\n  \"68213.jpg\": \"Insecta/Erynnis juvenalis/4cc546674ae0bda0d5453e2496584a32.jpg\",\n  \"68214.jpg\": \"Insecta/Erynnis juvenalis/e38045d3017f6765ca21f687ba1d2942.jpg\",\n  \"68216.jpg\": \"Insecta/Erynnis juvenalis/c321de17c3f4c418d58ed8fb96a83691.jpg\",\n  \"68217.jpg\": \"Insecta/Erynnis juvenalis/101072fa173e616edc4f2f8301c06d2e.jpg\",\n  \"68218.jpg\": \"Insecta/Erynnis juvenalis/47fcc73b26b39cee480530f8a8ad5dfb.jpg\",\n  \"68219.jpg\": \"Insecta/Erynnis juvenalis/39fc111425062ba779822e5c49751bf9.jpg\",\n  \"68220.jpg\": \"Insecta/Erynnis juvenalis/693c4131e81eecc55f72954071bae1a6.jpg\",\n  \"68221.jpg\": \"Insecta/Erynnis juvenalis/22650b3333b6fdf163acd5deaec6e545.jpg\",\n  \"68222.jpg\": \"Insecta/Erynnis juvenalis/b012b65ed9ae7f4f6aed7941775033e7.jpg\",\n  \"68223.jpg\": \"Insecta/Erynnis juvenalis/761930fcae0bf3936c13c55b3f5121f3.jpg\",\n  \"68226.jpg\": \"Insecta/Erynnis juvenalis/8a35427a0c07ca1b58375e29825490e8.jpg\",\n  \"68227.jpg\": \"Insecta/Erynnis juvenalis/a56b0dbef31a2482dd6ee81ff53d4fe1.jpg\",\n  \"68228.jpg\": \"Insecta/Erynnis juvenalis/c621ca012d7dcfc61cf0c551107e7eac.jpg\",\n  \"68229.jpg\": \"Insecta/Erynnis juvenalis/176e0f8009c075413e76761835265042.jpg\",\n  \"68232.jpg\": \"Insecta/Erynnis juvenalis/616b15ffe2e73e093e986d722975aac5.jpg\",\n  \"68233.jpg\": \"Insecta/Erynnis juvenalis/b717496ada71d20d14b8362ad094326c.jpg\",\n  \"68234.jpg\": \"Insecta/Erynnis juvenalis/eaf969df167f24835a0542dcaa94809a.jpg\",\n  \"68237.jpg\": \"Insecta/Erynnis juvenalis/c21cdc01d0cf66aa89036fc044cdcb76.jpg\",\n  \"68246.jpg\": \"Insecta/Erynnis juvenalis/9c712c22dece3ef952592f90324126c3.jpg\",\n  \"6825.jpg\": \"Aves/Pluvialis squatarola/f36323790f4095ffb8f61afa888d21c2.jpg\",\n  \"68278.jpg\": \"Actinopterygii/Pterois volitans/d48deac35b157b44c8b23bee9c927ad1.jpg\",\n  \"68280.jpg\": \"Actinopterygii/Pterois volitans/4c94f1223dac4d3cfba61c45b2a55fff.jpg\",\n  \"68283.jpg\": \"Actinopterygii/Pterois volitans/d540907539621e9a8ebd03c3c48376ab.jpg\",\n  \"68290.jpg\": \"Actinopterygii/Pterois volitans/d57fbb0febf14742594af8a00c1ddfae.jpg\",\n  \"68291.jpg\": \"Actinopterygii/Pterois volitans/13562e354d41700c7e9c705413b5bd62.jpg\",\n  \"68298.jpg\": \"Actinopterygii/Pterois volitans/b4e5e7ddbba29ff06f4fc88bcc9187f6.jpg\",\n  \"68299.jpg\": \"Actinopterygii/Pterois volitans/222eea075bba71cc2718ef7204708aa9.jpg\",\n  \"68300.jpg\": \"Actinopterygii/Pterois volitans/e2d350572c0ae1772f840f462a065aa8.jpg\",\n  \"68301.jpg\": \"Actinopterygii/Pterois volitans/1c63716345620f8dc3218934b221e130.jpg\",\n  \"68302.jpg\": \"Actinopterygii/Pterois volitans/e2a6b04c453c8c52ffec69cdcc1ad16e.jpg\",\n  \"68307.jpg\": \"Actinopterygii/Pterois volitans/e05853d06d78d6179e34624d95fe1f64.jpg\",\n  \"6833.jpg\": \"Aves/Pluvialis squatarola/806fd972ef435fdd28d928d42902feeb.jpg\",\n  \"68335.jpg\": \"Insecta/Anthocharis cardamines/3319a320547362917e8743c07a1ec0fd.jpg\",\n  \"68341.jpg\": \"Insecta/Anthocharis cardamines/c6252a926a9a8fafa78620ccc8c823ac.jpg\",\n  \"68342.jpg\": \"Insecta/Anthocharis cardamines/fb9737384aa3014febc5013898f11991.jpg\",\n  \"68343.jpg\": \"Insecta/Anthocharis cardamines/b14b89ded547ed1bde36e5a8b832d31d.jpg\",\n  \"68346.jpg\": \"Insecta/Anthocharis cardamines/f76f0795633d82546fbb3b3d755ee61b.jpg\",\n  \"68347.jpg\": \"Insecta/Anthocharis cardamines/99da498dc232232af77cd733c2db5a68.jpg\",\n  \"6839.jpg\": \"Aves/Pluvialis squatarola/0b0014419a3784da23a330f8da6a9d8e.jpg\",\n  \"68415.jpg\": \"Aves/Coccyzus americanus/085212cc380ecdd8c218cd560fc1ab7f.jpg\",\n  \"68416.jpg\": \"Aves/Coccyzus americanus/7a3a691b858fc272acb3ee6e9ceed86d.jpg\",\n  \"68420.jpg\": \"Aves/Coccyzus americanus/a52007cde6b842503aca3287a0d03cee.jpg\",\n  \"68421.jpg\": \"Aves/Coccyzus americanus/290c4c60b8b90c91daf69d347673e665.jpg\",\n  \"68423.jpg\": \"Aves/Coccyzus americanus/363631604525899d5f955aad4d6335b1.jpg\",\n  \"68439.jpg\": \"Aves/Coccyzus americanus/f06b3c67f001051bdc45f8f53b4ce43c.jpg\",\n  \"68444.jpg\": \"Aves/Coccyzus americanus/51bbf91d05485ca1a51d7e0248c7ea25.jpg\",\n  \"68445.jpg\": \"Aves/Coccyzus americanus/e19f7b337289e88755372629c107e572.jpg\",\n  \"68448.jpg\": \"Aves/Coccyzus americanus/2a12645490b2921a98ea8388992ec9d6.jpg\",\n  \"68450.jpg\": \"Aves/Coccyzus americanus/31bfab93a2b0ba5bb94f9e87854b142e.jpg\",\n  \"68453.jpg\": \"Aves/Coccyzus americanus/19bcecba2d747fdba509b95044dc0656.jpg\",\n  \"68463.jpg\": \"Aves/Coccyzus americanus/40e69d6fe627f85a19f324b859b89900.jpg\",\n  \"68466.jpg\": \"Aves/Coccyzus americanus/3944a0626950d23a547210cac39899a7.jpg\",\n  \"68471.jpg\": \"Aves/Coccyzus americanus/cf2614fcdf6aedea6ea75772279ce542.jpg\",\n  \"68475.jpg\": \"Aves/Coccyzus americanus/6f06d61e7d04b99639b4e3f915d9e64d.jpg\",\n  \"68480.jpg\": \"Aves/Coccyzus americanus/b54cd28113741687ff32dd35094c1a8e.jpg\",\n  \"68481.jpg\": \"Aves/Coccyzus americanus/4f3eb6a3e00c48348cf7735b85f58dbc.jpg\",\n  \"68486.jpg\": \"Aves/Coccyzus americanus/81f9cd72d545fd62b25e36ada0ec20e6.jpg\",\n  \"68499.jpg\": \"Aves/Coccyzus americanus/975b03c39cab24e0dc5430f2ba23ce74.jpg\",\n  \"6850.jpg\": \"Aves/Pluvialis squatarola/8e338645455d0bf8606d7fc1969a5309.jpg\",\n  \"68508.jpg\": \"Aves/Coccyzus americanus/1737a0d8e26c483d3de16c681cad8f75.jpg\",\n  \"6855.jpg\": \"Aves/Pluvialis squatarola/0ac5403d9ecb1481168503e07aaeb16c.jpg\",\n  \"68652.jpg\": \"Insecta/Tenodera sinensis/f6eec2df8473b5c3dc76b8df731c4e3e.jpg\",\n  \"68653.jpg\": \"Insecta/Tenodera sinensis/382b872aefbb167991f2e8751eed6aa9.jpg\",\n  \"68654.jpg\": \"Insecta/Tenodera sinensis/73adbe93dd528f5dc9db6685480d7d91.jpg\",\n  \"68657.jpg\": \"Insecta/Tenodera sinensis/9ed30bf872a93d08b94962a2d1e7268c.jpg\",\n  \"68663.jpg\": \"Insecta/Tenodera sinensis/73acbac8ae3163416c6eb9fa7116481e.jpg\",\n  \"68664.jpg\": \"Insecta/Tenodera sinensis/e613e93d1e316a16d5847400fd61fb68.jpg\",\n  \"68672.jpg\": \"Insecta/Tenodera sinensis/8fce00ffa457d94a7e06a6e100039430.jpg\",\n  \"68673.jpg\": \"Insecta/Tenodera sinensis/50dfcdacdc3352b033ff4bf44e8c5b22.jpg\",\n  \"68682.jpg\": \"Insecta/Tenodera sinensis/ef18d13014dc8583b389e04a88ce26d1.jpg\",\n  \"68685.jpg\": \"Insecta/Tenodera sinensis/02be78c42d064a7405171bbfaa9dd944.jpg\",\n  \"68689.jpg\": \"Insecta/Tenodera sinensis/641fb128f17cb482f139d7e6fd82c114.jpg\",\n  \"68691.jpg\": \"Insecta/Tenodera sinensis/594fb95972db157191522011d2eab5e3.jpg\",\n  \"68692.jpg\": \"Insecta/Tenodera sinensis/24a0d254b2b1d9ec553f9d3934a774f3.jpg\",\n  \"68702.jpg\": \"Insecta/Tenodera sinensis/dadba52ce21cb8a40aa6b1208f427ee1.jpg\",\n  \"68707.jpg\": \"Insecta/Tenodera sinensis/db423eca425516afab533697b12d6666.jpg\",\n  \"6871.jpg\": \"Aves/Pluvialis squatarola/6e81d6a438a3a0a3babe771f43c598f7.jpg\",\n  \"68713.jpg\": \"Insecta/Tenodera sinensis/8802a7230c37619521dfec5ef9f503ec.jpg\",\n  \"68714.jpg\": \"Insecta/Tenodera sinensis/1156ec5fcc697de3d568abee38065cc0.jpg\",\n  \"68715.jpg\": \"Insecta/Tenodera sinensis/7791bffec2288072f3dfa26b3bec460b.jpg\",\n  \"68716.jpg\": \"Insecta/Tenodera sinensis/3bd1f7e55f7d4532dc2d06ee662b47e0.jpg\",\n  \"6876.jpg\": \"Aves/Pluvialis squatarola/9e1547efb0de3e1ef8f103af61392b57.jpg\",\n  \"68862.jpg\": \"Insecta/Dolichovespula maculata/3da30c7114aba48fa68e5807a2414619.jpg\",\n  \"68864.jpg\": \"Insecta/Dolichovespula maculata/54a98b7fba98208a291e672b783b2475.jpg\",\n  \"68873.jpg\": \"Insecta/Dolichovespula maculata/5260111b5bae8954a13fe510a8e883e2.jpg\",\n  \"68879.jpg\": \"Insecta/Dolichovespula maculata/a126d6083ed3f38df1c8e0c5605caafb.jpg\",\n  \"68889.jpg\": \"Insecta/Dolichovespula maculata/ad1bd38d9f87c3db58a520bf8a6b0f5d.jpg\",\n  \"68890.jpg\": \"Insecta/Dolichovespula maculata/7458b1e8dfab6d4752f13aee0fa36788.jpg\",\n  \"68902.jpg\": \"Insecta/Dolichovespula maculata/197b11d6fb36ef535ad712079b02af3a.jpg\",\n  \"68904.jpg\": \"Insecta/Dolichovespula maculata/dfaf48ff6a4b000224b4346398f448c5.jpg\",\n  \"68907.jpg\": \"Insecta/Dolichovespula maculata/a88a0807fcf730db60c5419502c2f2dd.jpg\",\n  \"68910.jpg\": \"Insecta/Dolichovespula maculata/a6c0976d6a86a6f03ff5685c7f9e421e.jpg\",\n  \"68918.jpg\": \"Insecta/Dolichovespula maculata/eee980b0b18047a6609f490ecf325095.jpg\",\n  \"68923.jpg\": \"Insecta/Dolichovespula maculata/765d23ab3241900c3350dc64ec96063d.jpg\",\n  \"68929.jpg\": \"Insecta/Dolichovespula maculata/ed6c6cbdc6dfd93417cfce46d9dc2325.jpg\",\n  \"68942.jpg\": \"Insecta/Dolichovespula maculata/7876d8ecbea0726f101933324789dc1d.jpg\",\n  \"68954.jpg\": \"Insecta/Dolichovespula maculata/3ba12e195c9a422583ab6b72d691103a.jpg\",\n  \"68955.jpg\": \"Insecta/Dolichovespula maculata/6b3bd33b6072506ce0e96c06e06ac6cf.jpg\",\n  \"68959.jpg\": \"Insecta/Dolichovespula maculata/112f9d29cc59861fb893bf7f7869d9c0.jpg\",\n  \"68990.jpg\": \"Mammalia/Mephitis mephitis/c759c8caece64b1780ccf895792febe6.jpg\",\n  \"69001.jpg\": \"Mammalia/Mephitis mephitis/f5ad12ccbd37428bbd94efb5871a70c7.jpg\",\n  \"69010.jpg\": \"Mammalia/Mephitis mephitis/9512da9d249dbc075116f1c51a2ff019.jpg\",\n  \"6904.jpg\": \"Aves/Pluvialis squatarola/327cc384fc47b07f961923ce1836dab8.jpg\",\n  \"69048.jpg\": \"Mammalia/Mephitis mephitis/53570d6ebf7071621c0a525ba30b668c.jpg\",\n  \"69067.jpg\": \"Mammalia/Mephitis mephitis/cde2ee1086a4099b4416c5ac757db4e3.jpg\",\n  \"69072.jpg\": \"Mammalia/Mephitis mephitis/1facd0e6b3957474554ed8830cc0bdc0.jpg\",\n  \"69083.jpg\": \"Mammalia/Mephitis mephitis/8ed152003c1de01cc563c3221863f720.jpg\",\n  \"69088.jpg\": \"Mammalia/Mephitis mephitis/4d0f94019b8a1828a9d6b338009f17ac.jpg\",\n  \"69092.jpg\": \"Mammalia/Mephitis mephitis/9c9e712295a0631412f9d0c1269d3626.jpg\",\n  \"69100.jpg\": \"Mammalia/Mephitis mephitis/5ff9b1cfd166f72f02e62394b29806e0.jpg\",\n  \"69120.jpg\": \"Mammalia/Mephitis mephitis/1bdf4774545c07bb30cf9f5c787b3ea5.jpg\",\n  \"69126.jpg\": \"Mammalia/Mephitis mephitis/6a040f2cb21c2f6137d3f542031ea138.jpg\",\n  \"69128.jpg\": \"Mammalia/Mephitis mephitis/5c9066548bcea26eaef6c33cef4f7cbc.jpg\",\n  \"6914.jpg\": \"Aves/Pluvialis squatarola/53ef9b54c641d11bb4483bc5bfbda689.jpg\",\n  \"69156.jpg\": \"Mammalia/Mephitis mephitis/906014fbc2992b2a10acf12682beefc6.jpg\",\n  \"69161.jpg\": \"Mammalia/Mephitis mephitis/386334afc10f540e23cfe76accb37986.jpg\",\n  \"69174.jpg\": \"Mammalia/Mephitis mephitis/8a9025dfd711e67ddb390ed7b843c3d8.jpg\",\n  \"69191.jpg\": \"Mammalia/Mephitis mephitis/46c274b6b06ba7d9d4d1f9a393de2c50.jpg\",\n  \"69287.jpg\": \"Mollusca/Tonicella lineata/7340f6dd3c21b47cebbfa9e4252c0e13.jpg\",\n  \"69292.jpg\": \"Mollusca/Tonicella lineata/0354e9bede77c9224856d830d44c9dee.jpg\",\n  \"69297.jpg\": \"Mollusca/Tonicella lineata/a70f8d5853e11fccca92a61dd3c6d6f9.jpg\",\n  \"69303.jpg\": \"Mollusca/Tonicella lineata/313e406a7e662b44899983f523836a18.jpg\",\n  \"6936.jpg\": \"Insecta/Pangrapta decoralis/fc2b49c6b1aaae2ea312414f952b6d8b.jpg\",\n  \"6938.jpg\": \"Insecta/Pangrapta decoralis/b7648bd96ba85ca0ee78b2a8f31ee781.jpg\",\n  \"6951.jpg\": \"Insecta/Pangrapta decoralis/4fd550e58912cae3ea12f7338452bfbe.jpg\",\n  \"6952.jpg\": \"Insecta/Pangrapta decoralis/246e234c4789af0a728e1a77125464d6.jpg\",\n  \"69640.jpg\": \"Aves/Ardea purpurea/99ffb853e2bc9129002c6dab51d92ca4.jpg\",\n  \"69641.jpg\": \"Aves/Ardea purpurea/ef9432e8da685e3994b6173969b39049.jpg\",\n  \"69643.jpg\": \"Aves/Ardea purpurea/7af9d9d3dc273e97226e6882adbee1d4.jpg\",\n  \"69646.jpg\": \"Aves/Ardea purpurea/970882ab1472f8ca385fcc6d21c3195e.jpg\",\n  \"69647.jpg\": \"Aves/Ardea purpurea/c78517f2da6cae553761e324017e76be.jpg\",\n  \"69648.jpg\": \"Aves/Ardea purpurea/885920457557586a7dcf67bd37063e47.jpg\",\n  \"69649.jpg\": \"Aves/Ardea purpurea/e462421c95aa04b9edb3f6868dc1203a.jpg\",\n  \"69653.jpg\": \"Aves/Ardea purpurea/b339f76a0602464fd2e574425d642e16.jpg\",\n  \"69654.jpg\": \"Aves/Ardea purpurea/0c360ffa2d055d5e7f2b9a372228431b.jpg\",\n  \"69657.jpg\": \"Aves/Ardea purpurea/289ba18384b994d5ce9b445a61869912.jpg\",\n  \"69658.jpg\": \"Aves/Ardea purpurea/7e38630e4d5446e514ad12bbaba1e00e.jpg\",\n  \"69661.jpg\": \"Aves/Ardea purpurea/bc3291b0efd0abe59c6b94b8f9d1d802.jpg\",\n  \"69667.jpg\": \"Aves/Ardea purpurea/32d457668918a2a2782196d849956c72.jpg\",\n  \"69674.jpg\": \"Aves/Ardea purpurea/dbcedccc68d993668addfcab69c21426.jpg\",\n  \"69679.jpg\": \"Aves/Ardea purpurea/1a881a130aa955267a66f14e9ea8555e.jpg\",\n  \"69686.jpg\": \"Aves/Ardea purpurea/724516e6c0a1afd5e8f71330e73dfd99.jpg\",\n  \"69696.jpg\": \"Aves/Trogon collaris/ef103e59430aef0ae5d044a05ab77ab8.jpg\",\n  \"69698.jpg\": \"Aves/Trogon collaris/115aea8cf5ff78ac164777ebe13df9e5.jpg\",\n  \"69699.jpg\": \"Aves/Trogon collaris/1770ea3d3b9750178f8086ac211c4e6c.jpg\",\n  \"69701.jpg\": \"Aves/Trogon collaris/ef44a2d7f0f05a757aa09b2c981a9671.jpg\",\n  \"69732.jpg\": \"Insecta/Estigmene acrea/6a6427f7f4d6fc1b5a59d12c2ef25388.jpg\",\n  \"69733.jpg\": \"Insecta/Estigmene acrea/26bd42930eb7338e1ef60c9d75f8ddb9.jpg\",\n  \"69734.jpg\": \"Insecta/Estigmene acrea/0065df3de8ff2b04c92ba8f616df0794.jpg\",\n  \"69735.jpg\": \"Insecta/Estigmene acrea/d81574dd98a39a596475370041c46ba4.jpg\",\n  \"69742.jpg\": \"Insecta/Estigmene acrea/24cdab08e8a43038d73b30e42c2b3b0e.jpg\",\n  \"69745.jpg\": \"Insecta/Estigmene acrea/07d2fc7ea26f27971d5395388e89c390.jpg\",\n  \"69746.jpg\": \"Insecta/Estigmene acrea/10e30e6ee1a2b1cc9cdebcd291e3b919.jpg\",\n  \"69747.jpg\": \"Insecta/Estigmene acrea/0a7c4b511334ed4eb2862acdf6f00a1f.jpg\",\n  \"69748.jpg\": \"Insecta/Estigmene acrea/7f82c96e269974ee9fae43dda4ce43ea.jpg\",\n  \"69749.jpg\": \"Insecta/Estigmene acrea/c8f39f1ca2d53b70b1fdbedd3d4cb848.jpg\",\n  \"69752.jpg\": \"Insecta/Estigmene acrea/4f3731a07b66762bcf873550891f6866.jpg\",\n  \"69754.jpg\": \"Insecta/Estigmene acrea/edbf44dc75729abf28ec00fabd256c21.jpg\",\n  \"69756.jpg\": \"Insecta/Estigmene acrea/2d436fc76dd995888a42da5d6a3a2f7f.jpg\",\n  \"69757.jpg\": \"Insecta/Estigmene acrea/093929e2cf93e429941933edeb2aa05d.jpg\",\n  \"69758.jpg\": \"Insecta/Estigmene acrea/8fd760da08db05d7b4e6d89883f843b0.jpg\",\n  \"69761.jpg\": \"Insecta/Estigmene acrea/be3248790da5d8faf15b48b2a3c2a93c.jpg\",\n  \"69764.jpg\": \"Insecta/Estigmene acrea/e2e711defd6be167a5a0636ed810313d.jpg\",\n  \"69780.jpg\": \"Insecta/Estigmene acrea/2bc8ed17e2eb2a274632b8a8cfb57996.jpg\",\n  \"69781.jpg\": \"Insecta/Estigmene acrea/0b6fb43d3a12086b4c44b62662a787df.jpg\",\n  \"69782.jpg\": \"Insecta/Estigmene acrea/3fd9500ac54deeeec685db6561bcff5d.jpg\",\n  \"69783.jpg\": \"Insecta/Estigmene acrea/8a68f6b7704f04053178b42479a32edf.jpg\",\n  \"69786.jpg\": \"Insecta/Estigmene acrea/bfe950010f103555e6ba00df5bf5f20b.jpg\",\n  \"69837.jpg\": \"Aves/Gyps africanus/cbe8062cb07967690409751fdd263669.jpg\",\n  \"69838.jpg\": \"Aves/Gyps africanus/387262b21eb813e781411fbcc6edfa51.jpg\",\n  \"69853.jpg\": \"Aves/Pipilo erythrophthalmus/b456d809b3a4761e3058e9d2276d8bde.jpg\",\n  \"69858.jpg\": \"Aves/Pipilo erythrophthalmus/e12953f65d563ff2f23b5a33715c90ad.jpg\",\n  \"69861.jpg\": \"Aves/Pipilo erythrophthalmus/4f8e609403b42f478146e9a33212b639.jpg\",\n  \"69863.jpg\": \"Aves/Pipilo erythrophthalmus/6086d8f3434990b2e689493d6c662e8b.jpg\",\n  \"69865.jpg\": \"Aves/Pipilo erythrophthalmus/97c68bfc20feb531dda148ddeadb09e3.jpg\",\n  \"69867.jpg\": \"Aves/Pipilo erythrophthalmus/9893fc57e039c51f0d2d57c19858bea5.jpg\",\n  \"69870.jpg\": \"Aves/Pipilo erythrophthalmus/212be7ae4c76914846ef152f3b8d38a4.jpg\",\n  \"69873.jpg\": \"Aves/Pipilo erythrophthalmus/b61256c89d23b19bf57032e19d08f2af.jpg\",\n  \"69901.jpg\": \"Aves/Pipilo erythrophthalmus/51912facca55369be0b156676f5c090c.jpg\",\n  \"69906.jpg\": \"Aves/Pipilo erythrophthalmus/96bebd6c43ef6bd91c0034edb7b4ba09.jpg\",\n  \"69909.jpg\": \"Aves/Pipilo erythrophthalmus/6ec8f2364a693854331dda5145e59d01.jpg\",\n  \"69911.jpg\": \"Aves/Pipilo erythrophthalmus/b1e63fbeda114305cab02251a1c112a2.jpg\",\n  \"69916.jpg\": \"Aves/Pipilo erythrophthalmus/0d04f5c229bc99209b4ade12834a70bb.jpg\",\n  \"69944.jpg\": \"Aves/Pipilo erythrophthalmus/5a9e4435ef6f250761930d13e3a3e1f0.jpg\",\n  \"69945.jpg\": \"Aves/Pipilo erythrophthalmus/b2fdd01f51fb08c5ac2d18ecea24dbd5.jpg\",\n  \"69959.jpg\": \"Aves/Pipilo erythrophthalmus/2263d7f7691889ed660389b621aaf45b.jpg\",\n  \"69961.jpg\": \"Aves/Pipilo erythrophthalmus/fccef9982f68c70b0f12128fcaaacad7.jpg\",\n  \"69970.jpg\": \"Aves/Pipilo erythrophthalmus/678303523a01907ed87558f80d3fc0b5.jpg\",\n  \"69986.jpg\": \"Aves/Pipilo erythrophthalmus/6cd05f69fed34c7b9198738241f7a19a.jpg\",\n  \"70006.jpg\": \"Amphibia/Pseudacris hypochondriaca/57a66306fa87e5425d59537a12f028ed.jpg\",\n  \"70008.jpg\": \"Amphibia/Pseudacris hypochondriaca/732584eada6e838ce63ce3da40b4a810.jpg\",\n  \"70011.jpg\": \"Amphibia/Pseudacris hypochondriaca/8b186c07b1d74282061826fcf893dc57.jpg\",\n  \"70019.jpg\": \"Amphibia/Pseudacris hypochondriaca/d203c7bb91efded5d014a8b82674f5bd.jpg\",\n  \"70024.jpg\": \"Amphibia/Pseudacris hypochondriaca/d1ac365f8fe422e9136b9644150c6c50.jpg\",\n  \"70031.jpg\": \"Amphibia/Pseudacris hypochondriaca/474c3a86b017695125b442e3074aca8e.jpg\",\n  \"70033.jpg\": \"Amphibia/Pseudacris hypochondriaca/0440b5ffabb835418861e1d69a476aa1.jpg\",\n  \"70048.jpg\": \"Amphibia/Pseudacris hypochondriaca/d6528bb911c6125ecc233d65cd336ca2.jpg\",\n  \"70051.jpg\": \"Amphibia/Pseudacris hypochondriaca/eb9b67969aa296181001d105214c82d5.jpg\",\n  \"70052.jpg\": \"Amphibia/Pseudacris hypochondriaca/64aa4c61b81ff1799a6f347fcb1eaecb.jpg\",\n  \"70065.jpg\": \"Amphibia/Pseudacris hypochondriaca/78accd7b54732bb7585a7626f3416f98.jpg\",\n  \"70085.jpg\": \"Amphibia/Pseudacris hypochondriaca/9447635eb49c075c65f56995d5ce7e63.jpg\",\n  \"70088.jpg\": \"Amphibia/Pseudacris hypochondriaca/af7f19cccce0fc5a199d20f7cb725b9c.jpg\",\n  \"70096.jpg\": \"Amphibia/Pseudacris hypochondriaca/83549b7f169ad9c82f3fe73787b3751a.jpg\",\n  \"70100.jpg\": \"Amphibia/Pseudacris hypochondriaca/a994120535ca0245df0d7b85face234e.jpg\",\n  \"70101.jpg\": \"Amphibia/Pseudacris hypochondriaca/695003c746d302dd9ed06b9faaa72f07.jpg\",\n  \"70102.jpg\": \"Amphibia/Pseudacris hypochondriaca/a547f796ced4b9d641ea4b7a9739919d.jpg\",\n  \"70104.jpg\": \"Amphibia/Pseudacris hypochondriaca/a3ab57ddf6ae82250f4c48193e6bf24b.jpg\",\n  \"70106.jpg\": \"Amphibia/Pseudacris hypochondriaca/0e5cec5310573013f943b5e90dcbeef1.jpg\",\n  \"70107.jpg\": \"Amphibia/Pseudacris hypochondriaca/a378e5fcdef09eac321734eaa30a5846.jpg\",\n  \"70113.jpg\": \"Amphibia/Pseudacris hypochondriaca/9dcef3e68a864d26999820e6b192a763.jpg\",\n  \"70124.jpg\": \"Amphibia/Pseudacris hypochondriaca/cfef010cc6d23ea7b9dd0e3e27e213a8.jpg\",\n  \"70129.jpg\": \"Amphibia/Pseudacris hypochondriaca/4e7b6ae05344435c67bf07235e6fae9a.jpg\",\n  \"70130.jpg\": \"Amphibia/Pseudacris hypochondriaca/df0a2fd4e747b74b1ee2e73dbda4adb6.jpg\",\n  \"70131.jpg\": \"Amphibia/Pseudacris hypochondriaca/1e8a3ae56781ff6f6e0857a7ab9a73ba.jpg\",\n  \"70141.jpg\": \"Animalia/Pachygrapsus crassipes/b11e9f0943a49ea75ecc724b708e53b1.jpg\",\n  \"70147.jpg\": \"Animalia/Pachygrapsus crassipes/3e4a5af38f68f6a6ebe05e46339f162e.jpg\",\n  \"70210.jpg\": \"Animalia/Pachygrapsus crassipes/097e4eec7c784bec5e614103fe6b11eb.jpg\",\n  \"70227.jpg\": \"Animalia/Pachygrapsus crassipes/5002cc3983ce0306577d847ff8795048.jpg\",\n  \"70243.jpg\": \"Animalia/Pachygrapsus crassipes/6d6e4138b39597b234a54aec2aaa244a.jpg\",\n  \"70244.jpg\": \"Animalia/Pachygrapsus crassipes/980e574ad0e2c6ab2389a78c964ad709.jpg\",\n  \"70257.jpg\": \"Animalia/Pachygrapsus crassipes/06818ae26fad3eaa19c679dd454fe5f6.jpg\",\n  \"70260.jpg\": \"Animalia/Pachygrapsus crassipes/f4263953c7be378648982f87e932c83b.jpg\",\n  \"70261.jpg\": \"Animalia/Pachygrapsus crassipes/4182fa147a76b28bf4ca3264ede654d9.jpg\",\n  \"70268.jpg\": \"Animalia/Pachygrapsus crassipes/b19fb54d58bc75a3cbb946cf6aadbab1.jpg\",\n  \"70295.jpg\": \"Animalia/Pachygrapsus crassipes/fbee56990488287e166d8d533dca9552.jpg\",\n  \"70298.jpg\": \"Animalia/Pachygrapsus crassipes/4084f619164580ba24b8c4efb67da8d2.jpg\",\n  \"70299.jpg\": \"Animalia/Pachygrapsus crassipes/7157f5bf8de4f54fd3cd5a79b4a603d4.jpg\",\n  \"70300.jpg\": \"Animalia/Pachygrapsus crassipes/01511c5792697f9349a4ecdf88c24c81.jpg\",\n  \"70306.jpg\": \"Animalia/Pachygrapsus crassipes/6b42462160819ac23c4bbaec69ff8a36.jpg\",\n  \"70315.jpg\": \"Animalia/Pachygrapsus crassipes/5c725039995b694fd3463951fccf92c2.jpg\",\n  \"70318.jpg\": \"Animalia/Pachygrapsus crassipes/3a5544c11678160b70bab0db8f2b1a4a.jpg\",\n  \"70325.jpg\": \"Insecta/Speyeria atlantis/a2d2fcc18e635e17e2aec2970eee0702.jpg\",\n  \"70332.jpg\": \"Insecta/Speyeria atlantis/0fc83b8b883522005e67f5a77439d246.jpg\",\n  \"70343.jpg\": \"Insecta/Speyeria atlantis/88d1c088ec538370d44fbd3fa83112bb.jpg\",\n  \"70352.jpg\": \"Insecta/Speyeria atlantis/3782242309d05ce7594f7395f957ba20.jpg\",\n  \"70353.jpg\": \"Insecta/Speyeria atlantis/f6ec4c90cf88a01d089e45005257668d.jpg\",\n  \"70354.jpg\": \"Insecta/Speyeria atlantis/5d6f1fa07cf30622aa556a8817f6e7ed.jpg\",\n  \"70363.jpg\": \"Reptilia/Crotalus cerastes cerastes/0972ff45ea34458da747a45fe8512e1e.jpg\",\n  \"70366.jpg\": \"Reptilia/Crotalus cerastes cerastes/37ab082ea200e0dba63424c8f3abde3a.jpg\",\n  \"70368.jpg\": \"Reptilia/Crotalus cerastes cerastes/d90bd3c8c82e72c9d8f944424230d78d.jpg\",\n  \"70369.jpg\": \"Reptilia/Crotalus cerastes cerastes/2ff3b91c8410bd902c212c38405989de.jpg\",\n  \"704.jpg\": \"Mammalia/Ursus americanus/b71df9d40066a882b33ad0e669e5dbb4.jpg\",\n  \"70514.jpg\": \"Insecta/Dysphania militaris/5cd0077f0ea13fb1350aedae27998877.jpg\",\n  \"70528.jpg\": \"Insecta/Dysphania militaris/39cc9de2b77f91fef620fd44bd148ab9.jpg\",\n  \"70537.jpg\": \"Insecta/Dysphania militaris/f1f7effdc261ebe621a60fbc2e8bf7e9.jpg\",\n  \"70543.jpg\": \"Actinopterygii/Lepisosteus osseus/51f62add56c3f8ccbadd70de4357d948.jpg\",\n  \"70554.jpg\": \"Actinopterygii/Lepisosteus osseus/f94b70c6ab6bd7e6489dfde4fc39a425.jpg\",\n  \"70560.jpg\": \"Actinopterygii/Lepisosteus osseus/238ed278acf49fb13c3c39f723fa38de.jpg\",\n  \"70566.jpg\": \"Actinopterygii/Lepisosteus osseus/fc9724a0ec7ce7e5922d0aec745afbc4.jpg\",\n  \"70574.jpg\": \"Animalia/Pugettia producta/ac5bd156e35bc19dd87ee257cc4404fb.jpg\",\n  \"70582.jpg\": \"Animalia/Pugettia producta/de3d8c9a21ea21d9866acc4f71c3a1c6.jpg\",\n  \"70587.jpg\": \"Animalia/Pugettia producta/a9a8ef967b0702cba4332c9da91986a8.jpg\",\n  \"70595.jpg\": \"Animalia/Pugettia producta/ca0e5ee65008d381505dd08b91ef2ff8.jpg\",\n  \"70596.jpg\": \"Animalia/Pugettia producta/d3617692749b598275959a12510d5544.jpg\",\n  \"70611.jpg\": \"Animalia/Pugettia producta/bed2734ba68172d4f49c971d7b15a5e9.jpg\",\n  \"70612.jpg\": \"Animalia/Pugettia producta/d80818dbd20fc147c334b27d4ed15425.jpg\",\n  \"70621.jpg\": \"Animalia/Pugettia producta/b710622ad2bdee3d0120e8231441e3c5.jpg\",\n  \"70625.jpg\": \"Animalia/Pugettia producta/364945e581fe149eb16075073ad98cfb.jpg\",\n  \"70626.jpg\": \"Animalia/Pugettia producta/9fd8a837c7cc1ee778258ede77c6f92b.jpg\",\n  \"70627.jpg\": \"Animalia/Pugettia producta/24fb0517822f6a589c36e0518be04945.jpg\",\n  \"70635.jpg\": \"Animalia/Pugettia producta/ef4ebb0eca1f136d55716012a8201025.jpg\",\n  \"70649.jpg\": \"Animalia/Pugettia producta/4c988b1d0d8c5dbbd2b4e7d5332dfd78.jpg\",\n  \"70652.jpg\": \"Animalia/Pugettia producta/0ea234abe02c453b6ad9396b63826157.jpg\",\n  \"70713.jpg\": \"Insecta/Halmus chalybeus/568aa0b03dbbcd3cfb9364f3f20ed9ef.jpg\",\n  \"70717.jpg\": \"Insecta/Halmus chalybeus/9dfc5de33c470b7e4e1b8aed37bbe6b3.jpg\",\n  \"70718.jpg\": \"Insecta/Halmus chalybeus/d657a12815a2f5f41da82f4abc89f532.jpg\",\n  \"70721.jpg\": \"Insecta/Halmus chalybeus/52f569d1ca87165fa29ebd5c9836adb5.jpg\",\n  \"70723.jpg\": \"Insecta/Halmus chalybeus/2950624e4e9e3b53eb99969888a3c93b.jpg\",\n  \"70724.jpg\": \"Insecta/Halmus chalybeus/c40fb2d38a88f5ae6378c3cb7701692d.jpg\",\n  \"70726.jpg\": \"Insecta/Halmus chalybeus/e6bf85ec14c89829f078512c821d2743.jpg\",\n  \"70727.jpg\": \"Insecta/Halmus chalybeus/801261ca612eac6fffc5cfd0cce6d116.jpg\",\n  \"70729.jpg\": \"Insecta/Halmus chalybeus/005dbaeb758757a4cbf6e92405c6c323.jpg\",\n  \"70730.jpg\": \"Insecta/Halmus chalybeus/b25f9062d613339ffa440a773bfd0845.jpg\",\n  \"70731.jpg\": \"Insecta/Halmus chalybeus/a1d664b8cc3705a36e454947f70df039.jpg\",\n  \"70732.jpg\": \"Insecta/Halmus chalybeus/9345a18db20cb22fd3c424bd280b5722.jpg\",\n  \"70733.jpg\": \"Insecta/Halmus chalybeus/a5df6597019bc54196d9abd20b553150.jpg\",\n  \"70741.jpg\": \"Insecta/Halmus chalybeus/dba207db7258bcdf985e1d0cae0e5d13.jpg\",\n  \"70742.jpg\": \"Insecta/Halmus chalybeus/5f0e92d7e5194d99f819b7bc41994b2e.jpg\",\n  \"70746.jpg\": \"Insecta/Halmus chalybeus/e18c935fb57f4cd3589f6ac61cdb96b2.jpg\",\n  \"70747.jpg\": \"Insecta/Halmus chalybeus/01eb929a4c2ced7073caaea78fbd2e1f.jpg\",\n  \"70748.jpg\": \"Insecta/Halmus chalybeus/75e8f476e81e1b22e6483593bf50df69.jpg\",\n  \"70750.jpg\": \"Actinopterygii/Ictalurus punctatus/645642ef0aa3fc9c557f0808ba9a71d5.jpg\",\n  \"70760.jpg\": \"Actinopterygii/Ictalurus punctatus/b0be1db5908e135a2893fdb861c84e01.jpg\",\n  \"70761.jpg\": \"Actinopterygii/Ictalurus punctatus/74ae8bdf6b26423103bad8ac5496addf.jpg\",\n  \"70763.jpg\": \"Actinopterygii/Ictalurus punctatus/c95906de57e51369efb85627dcbe3b71.jpg\",\n  \"70764.jpg\": \"Actinopterygii/Ictalurus punctatus/5b2cbcc2b9d2b1aacea9636e8e379490.jpg\",\n  \"70774.jpg\": \"Reptilia/Crotalus horridus/92d364e005465ce6432089c54ca532dd.jpg\",\n  \"70782.jpg\": \"Reptilia/Crotalus horridus/7cfd55e55685966aa48e7eff6634afca.jpg\",\n  \"70785.jpg\": \"Reptilia/Crotalus horridus/519ba0717568594b2bca68bbf22fefc5.jpg\",\n  \"70791.jpg\": \"Reptilia/Crotalus horridus/81b1b32adfc916c41f47fdaff7c44219.jpg\",\n  \"70805.jpg\": \"Reptilia/Crotalus horridus/2b244ac69af400e0be753f3e95335cee.jpg\",\n  \"70815.jpg\": \"Reptilia/Crotalus horridus/a7fc9e3fb3a3d11a92892c7f3eca3b5a.jpg\",\n  \"70832.jpg\": \"Reptilia/Crotalus horridus/ba1efcba172ce50ba61998e649ec4f86.jpg\",\n  \"70839.jpg\": \"Reptilia/Crotalus horridus/03b885d0cf0c07fbbfc5285000e071bd.jpg\",\n  \"70840.jpg\": \"Reptilia/Crotalus horridus/448a7f25446c7e11f12ec1f2b2d6679c.jpg\",\n  \"70842.jpg\": \"Reptilia/Crotalus horridus/ac7e2ae262ca1f0f0c5f703249367c15.jpg\",\n  \"70843.jpg\": \"Reptilia/Crotalus horridus/330d9ec967c8995d025284fa1f483d16.jpg\",\n  \"70850.jpg\": \"Reptilia/Crotalus horridus/ea73fc2159e24bf7738b5998bca067b8.jpg\",\n  \"70852.jpg\": \"Reptilia/Crotalus horridus/e51b5aaaf61e67a0b7324d8421af1d22.jpg\",\n  \"70874.jpg\": \"Reptilia/Crotalus horridus/92e1440744adaf7ffac3285e349c984e.jpg\",\n  \"70875.jpg\": \"Reptilia/Crotalus horridus/11c54498486ac716a9df1b3df1b76bcd.jpg\",\n  \"70876.jpg\": \"Reptilia/Crotalus horridus/4e756b3436f7e0a0f856ad30e713300e.jpg\",\n  \"70877.jpg\": \"Reptilia/Crotalus horridus/c247d610a063daec9ea25817f0db15bb.jpg\",\n  \"70879.jpg\": \"Reptilia/Crotalus horridus/2dbfbde7cc35019d2e22c5b11745e9e4.jpg\",\n  \"70884.jpg\": \"Reptilia/Crotalus horridus/6421e0c579dbe54e2956c426a8480f94.jpg\",\n  \"70885.jpg\": \"Reptilia/Crotalus horridus/9d2b6db1e86950f7524ce6b606eb4dc8.jpg\",\n  \"70891.jpg\": \"Insecta/Epirrhoe alternata/24cb63827e90d1500accfbce20ed4890.jpg\",\n  \"70898.jpg\": \"Insecta/Epirrhoe alternata/90176310decb175a43ee3adb9e7c593a.jpg\",\n  \"70901.jpg\": \"Insecta/Epirrhoe alternata/eb6d35be7db775226fe7ec3bdaddd7b6.jpg\",\n  \"70906.jpg\": \"Insecta/Epirrhoe alternata/13de9cfb146e3c51a902d70fbdcc2ffd.jpg\",\n  \"70912.jpg\": \"Insecta/Epirrhoe alternata/cc281a9ed629160ddd03f21244abc1f9.jpg\",\n  \"70916.jpg\": \"Insecta/Epirrhoe alternata/597b70a44314c378c4bf68533ab94271.jpg\",\n  \"70917.jpg\": \"Insecta/Epirrhoe alternata/5bf0ad3d521cb2695b1fd75f1a9978b5.jpg\",\n  \"70918.jpg\": \"Animalia/Strongylocentrotus franciscanus/838d6decdd3383da4d1c0436a0ba7517.jpg\",\n  \"70919.jpg\": \"Animalia/Strongylocentrotus franciscanus/baa2ae57b34ca6f752e5eb370f11370f.jpg\",\n  \"70934.jpg\": \"Animalia/Strongylocentrotus franciscanus/a15a54eacc4f192e0cd182abdec672f7.jpg\",\n  \"70935.jpg\": \"Animalia/Strongylocentrotus franciscanus/984aa36eaf0b3c51ff2d7ed40fa8605b.jpg\",\n  \"70941.jpg\": \"Animalia/Strongylocentrotus franciscanus/ec0c03ff1b535c9201704b9339fe5916.jpg\",\n  \"71.jpg\": \"Mammalia/Marmota flaviventris/3a8348f4396ea3950ecf67e1566a305f.jpg\",\n  \"711.jpg\": \"Mammalia/Ursus americanus/f2761f2b4b8a7c4cdf11a4c3b9f30aea.jpg\",\n  \"71235.jpg\": \"Aves/Aphelocoma coerulescens/77794cb9ec679ae1fce66a738406aff2.jpg\",\n  \"71236.jpg\": \"Aves/Aphelocoma coerulescens/c51d72af4f5a9101039b472a23e04835.jpg\",\n  \"71237.jpg\": \"Aves/Aphelocoma coerulescens/2ac2c4806abbc10b3bfffdad1c4db68f.jpg\",\n  \"71238.jpg\": \"Aves/Aphelocoma coerulescens/6995252a0fc0dfb0a1ff97b183ddb338.jpg\",\n  \"71254.jpg\": \"Aves/Aphelocoma coerulescens/163039f4255c088b8f84309f9f24df60.jpg\",\n  \"71257.jpg\": \"Insecta/Enallagma carunculatum/fce748646398c5030364e283742f1f22.jpg\",\n  \"71259.jpg\": \"Insecta/Enallagma carunculatum/d67184be594056d72a15c9695c1edd98.jpg\",\n  \"71260.jpg\": \"Insecta/Enallagma carunculatum/ff1e2d46d91d33346ab86fc410f41cf0.jpg\",\n  \"71261.jpg\": \"Insecta/Enallagma carunculatum/0e26f3eb97e53c617d9c1ea0c502ba6a.jpg\",\n  \"71274.jpg\": \"Insecta/Enallagma carunculatum/674b569c4513b5730650bb7d3b33fc08.jpg\",\n  \"71275.jpg\": \"Insecta/Enallagma carunculatum/e443576ef882725a453de2a680ff5d24.jpg\",\n  \"71277.jpg\": \"Insecta/Enallagma carunculatum/49ee81f51b1c30b9615ecffc360b8bda.jpg\",\n  \"71278.jpg\": \"Insecta/Enallagma carunculatum/3ed928cd679170b48a7f44303da0ff2c.jpg\",\n  \"71281.jpg\": \"Insecta/Enallagma carunculatum/d6c70a9999ff3b07d40efe6329acd087.jpg\",\n  \"71282.jpg\": \"Insecta/Enallagma carunculatum/a1f5da07db7be1f6db1531eebc156d3a.jpg\",\n  \"71283.jpg\": \"Insecta/Enallagma carunculatum/6facb28c86ebaf4429aedb9d9df4d31a.jpg\",\n  \"71284.jpg\": \"Insecta/Enallagma carunculatum/a9755e67ca785e64a14cf3e76e8d5e68.jpg\",\n  \"71288.jpg\": \"Insecta/Enallagma carunculatum/3b0c56abbc3170a700a7e301645db2b7.jpg\",\n  \"71294.jpg\": \"Insecta/Enallagma carunculatum/3f34dcacf8bef4d2f742eb9ac0c35152.jpg\",\n  \"71297.jpg\": \"Insecta/Enallagma carunculatum/60ad981081d659d2310dff0cec820497.jpg\",\n  \"71306.jpg\": \"Insecta/Enallagma carunculatum/50a02d75bb3a195452ca7476295523cb.jpg\",\n  \"71308.jpg\": \"Insecta/Enallagma carunculatum/508ac1a50e86838fb16b1708f0f1d6a2.jpg\",\n  \"71566.jpg\": \"Actinopterygii/Thalassoma bifasciatum/a9ff469e551918cc90084a4182518b7b.jpg\",\n  \"71578.jpg\": \"Actinopterygii/Thalassoma bifasciatum/a5d30318267b420affbf7c4acb90353d.jpg\",\n  \"71595.jpg\": \"Insecta/Hemaris diffinis/99bd3518b36b0d4ebea6616558057816.jpg\",\n  \"71600.jpg\": \"Insecta/Hemaris diffinis/a9e23120f9c735c9598726f5db0e4261.jpg\",\n  \"71604.jpg\": \"Insecta/Hemaris diffinis/42dde793592d7cab8a916f9413208183.jpg\",\n  \"71619.jpg\": \"Insecta/Hemaris diffinis/a77a18e7ad31e120f7da60c8f0ce5a54.jpg\",\n  \"71623.jpg\": \"Insecta/Hemaris diffinis/d954ec0f3eff5639fcc8ed886654e740.jpg\",\n  \"71629.jpg\": \"Insecta/Hemaris diffinis/b1cc13738517d5dca31f64bf612e0c0f.jpg\",\n  \"71644.jpg\": \"Insecta/Hemaris diffinis/5cd17f9d7b66cbb90f60185f143f5575.jpg\",\n  \"71646.jpg\": \"Insecta/Hemaris diffinis/885c0afa8bd7e82b6c59d52717f35f5a.jpg\",\n  \"71648.jpg\": \"Insecta/Hemaris diffinis/c5e3edb47387a8fe73a5256ecbcbe36e.jpg\",\n  \"71652.jpg\": \"Insecta/Hemaris diffinis/71ac6f38f5254cc44707389e37770e9a.jpg\",\n  \"71657.jpg\": \"Insecta/Hemaris diffinis/ccc029bc61656a8f9bd01c1fe57ec22f.jpg\",\n  \"71661.jpg\": \"Insecta/Hemaris diffinis/16f3a83dbd62a5ce8017a7975a20e649.jpg\",\n  \"71666.jpg\": \"Insecta/Hemaris diffinis/f097f8155869b5ba8c93ce5b05a18d74.jpg\",\n  \"71672.jpg\": \"Insecta/Hemaris diffinis/c4e7471654be6e9c144e8892e84c2e74.jpg\",\n  \"71685.jpg\": \"Insecta/Hemaris diffinis/422e5e620e21649f2a283b244d4ae55f.jpg\",\n  \"71687.jpg\": \"Insecta/Hemaris diffinis/9000bdb0eb2be55f9badb6003c07cc3d.jpg\",\n  \"71688.jpg\": \"Insecta/Hemaris diffinis/2786630b47e4779ef695f5a14f7bef91.jpg\",\n  \"71694.jpg\": \"Insecta/Hemaris diffinis/d31d92a72c0756fe07f18c8319fdd1e7.jpg\",\n  \"71703.jpg\": \"Insecta/Hemaris diffinis/222420a8393ce5851120aca8c5ce1e3c.jpg\",\n  \"71704.jpg\": \"Insecta/Hemaris diffinis/50da347358619c1a7e04deb0a4194d92.jpg\",\n  \"71756.jpg\": \"Insecta/Polyphylla decemlineata/5065ad7bd9105c6517a431cf95da5bba.jpg\",\n  \"71757.jpg\": \"Insecta/Polyphylla decemlineata/93719aa0cad240cfa91afa956926fe83.jpg\",\n  \"71758.jpg\": \"Insecta/Polyphylla decemlineata/fec612b1d9854f6f19237bd034b4d395.jpg\",\n  \"71760.jpg\": \"Insecta/Polyphylla decemlineata/1680d9f65f88da66ffe01b737b29a7fa.jpg\",\n  \"71761.jpg\": \"Insecta/Polyphylla decemlineata/3d97bd7ffe0681df95b0562abf9e8542.jpg\",\n  \"71764.jpg\": \"Insecta/Polyphylla decemlineata/370e7ea56e9f9ea10021b115040a1612.jpg\",\n  \"71778.jpg\": \"Insecta/Polyphylla decemlineata/c7aad54613f57847a3dbaab5a9479463.jpg\",\n  \"71779.jpg\": \"Insecta/Polyphylla decemlineata/3c03ae6d41ff05858c9d56f0424ed379.jpg\",\n  \"71780.jpg\": \"Insecta/Polyphylla decemlineata/383eda8a85818c1337c000469a51aed1.jpg\",\n  \"71781.jpg\": \"Insecta/Polyphylla decemlineata/c87c594dcca08d1d1b2f2e7c1dd30107.jpg\",\n  \"71782.jpg\": \"Insecta/Polyphylla decemlineata/b8fd593937bbd45701070014557cf3ba.jpg\",\n  \"71791.jpg\": \"Insecta/Polyphylla decemlineata/472e6f4072d0f9671237973fbc3768a0.jpg\",\n  \"71792.jpg\": \"Insecta/Polyphylla decemlineata/701c7b21b2342fd2fcbe79b76f343d1e.jpg\",\n  \"71793.jpg\": \"Insecta/Polyphylla decemlineata/d2b16edb9811d84e872668c8f3eb589f.jpg\",\n  \"71794.jpg\": \"Insecta/Polyphylla decemlineata/edc7a24189b643a09980619480ce18ef.jpg\",\n  \"71801.jpg\": \"Insecta/Polyphylla decemlineata/7f872d568db8400a03f19e18376a7294.jpg\",\n  \"71802.jpg\": \"Insecta/Polyphylla decemlineata/612bffd5717cfd6dfef45edbfe261cae.jpg\",\n  \"71807.jpg\": \"Insecta/Polyphylla decemlineata/94bb8206d2be97c7a31a9982035eec2d.jpg\",\n  \"71813.jpg\": \"Insecta/Polyphylla decemlineata/6b46786a402b5da58c6ccda47673ffdd.jpg\",\n  \"71951.jpg\": \"Amphibia/Hyla chrysoscelis/dd29bff70130f47fd712d7ebcebda136.jpg\",\n  \"71953.jpg\": \"Amphibia/Hyla chrysoscelis/3189e647f16b1336383324276917c6fb.jpg\",\n  \"71956.jpg\": \"Amphibia/Hyla chrysoscelis/9e9c068cdea3d40f6bccad432ca71205.jpg\",\n  \"71959.jpg\": \"Amphibia/Hyla chrysoscelis/6e0e2b9bb879bf469153b98c9d28f115.jpg\",\n  \"71961.jpg\": \"Amphibia/Hyla chrysoscelis/c6e6bac6c38a842fdb15be43c01873a4.jpg\",\n  \"71965.jpg\": \"Amphibia/Hyla chrysoscelis/e68cafc8c38695435982e8c0dd3cac0f.jpg\",\n  \"71968.jpg\": \"Amphibia/Hyla chrysoscelis/e3009f302cb6d8b383be39ff2fa02db1.jpg\",\n  \"71969.jpg\": \"Amphibia/Hyla chrysoscelis/64adfa156d5dbfd56b47be051a4d286e.jpg\",\n  \"71973.jpg\": \"Amphibia/Hyla chrysoscelis/063153493cecd1600a64628e40c2d8ff.jpg\",\n  \"71981.jpg\": \"Amphibia/Hyla chrysoscelis/08e9f7eb10fd924d78e1d9387f942f68.jpg\",\n  \"71982.jpg\": \"Amphibia/Hyla chrysoscelis/0cc32683b37ca2f8651be1591f5cfd7f.jpg\",\n  \"71983.jpg\": \"Amphibia/Hyla chrysoscelis/151d9154ad3c7602fb58a2008d41ac68.jpg\",\n  \"71984.jpg\": \"Amphibia/Hyla chrysoscelis/8648fa1edc9d0045cd19104a4d26934b.jpg\",\n  \"71985.jpg\": \"Amphibia/Hyla chrysoscelis/31aa7e9032a3141071a410c509004311.jpg\",\n  \"71986.jpg\": \"Amphibia/Hyla chrysoscelis/133a56facbe7913d148dec2a3cfc234d.jpg\",\n  \"71988.jpg\": \"Amphibia/Hyla chrysoscelis/79d509fef0969467ea0df5a1f6d34e45.jpg\",\n  \"71990.jpg\": \"Amphibia/Hyla chrysoscelis/ad654f5d8a408cbc8e2bb6aa6790c95c.jpg\",\n  \"71991.jpg\": \"Amphibia/Hyla chrysoscelis/1671a54c5e17ad785c73b3d4f2b9bcc0.jpg\",\n  \"71992.jpg\": \"Amphibia/Hyla chrysoscelis/3b5f69496d17940fe30c722e27ffc371.jpg\",\n  \"71993.jpg\": \"Amphibia/Hyla chrysoscelis/a746b64827c83df8b0ee6370c3d8a1af.jpg\",\n  \"71994.jpg\": \"Amphibia/Hyla chrysoscelis/e2c2609a2e6f0b3c1f2805719a878b1c.jpg\",\n  \"71995.jpg\": \"Amphibia/Hyla chrysoscelis/bf0c7193ff79e3ab22058cbdbe35d6d9.jpg\",\n  \"71998.jpg\": \"Amphibia/Hyla chrysoscelis/cdd68b73d9c01afbca0fbd3441a64cb9.jpg\",\n  \"72000.jpg\": \"Amphibia/Hyla chrysoscelis/bbd53758b8c5d6af889adefcb7287a28.jpg\",\n  \"72105.jpg\": \"Insecta/Chytonix palliatricula/2f3d20b7c7d4fab6f497631446a81b70.jpg\",\n  \"72109.jpg\": \"Insecta/Chytonix palliatricula/d6316ae19281268247ffe1993aa0830b.jpg\",\n  \"72111.jpg\": \"Insecta/Chytonix palliatricula/f539981b2d7bfbaf2a9e59f35a1d4982.jpg\",\n  \"72115.jpg\": \"Insecta/Chytonix palliatricula/362aceef62c3fcfbff86596041e2bd8a.jpg\",\n  \"72118.jpg\": \"Insecta/Chytonix palliatricula/e242cf01220e6ff47575db7ff4707bce.jpg\",\n  \"72119.jpg\": \"Insecta/Chytonix palliatricula/cc4809248c76aed27bb7b41170eb304f.jpg\",\n  \"72192.jpg\": \"Mammalia/Crocuta crocuta/a32437ead938f6bb33728ff3efd964a8.jpg\",\n  \"72194.jpg\": \"Mammalia/Crocuta crocuta/3086babe2cd25292d4d9b9e7193fd9a9.jpg\",\n  \"72203.jpg\": \"Mammalia/Crocuta crocuta/fc6cef4352114ae468a68fe7443c9457.jpg\",\n  \"72212.jpg\": \"Mammalia/Crocuta crocuta/ac680daa5251a9151efd84e22ba97f45.jpg\",\n  \"72258.jpg\": \"Insecta/Danaus chrysippus/788d663d9d1249364d7fe4df4bf5eeef.jpg\",\n  \"72260.jpg\": \"Insecta/Danaus chrysippus/ff39b39d1eaab5231cbe299bc662b610.jpg\",\n  \"72261.jpg\": \"Insecta/Danaus chrysippus/b86019ac7d7c340c5b6e39891e81a1e3.jpg\",\n  \"72265.jpg\": \"Insecta/Danaus chrysippus/ff4387d49c95489c7e5407d055c00b17.jpg\",\n  \"72266.jpg\": \"Insecta/Danaus chrysippus/4b91ad213c97f351c3eb6e2d80fc2fbe.jpg\",\n  \"72267.jpg\": \"Insecta/Danaus chrysippus/fd4557b0fcd5d7403bd818499015a06f.jpg\",\n  \"72273.jpg\": \"Insecta/Danaus chrysippus/ae5bc4c42220ce43550d17135fb5149d.jpg\",\n  \"72275.jpg\": \"Insecta/Danaus chrysippus/f84f917a221069b08fffdfc40ecaebf1.jpg\",\n  \"72279.jpg\": \"Insecta/Danaus chrysippus/f99cf00e72aa8f64b4f384c1f7404aad.jpg\",\n  \"72287.jpg\": \"Insecta/Amphion floridensis/daf402b3364308fc1d276131c5a39ad2.jpg\",\n  \"72290.jpg\": \"Insecta/Amphion floridensis/ff49566d2401cacb6b3ac6d7bae12d4c.jpg\",\n  \"72291.jpg\": \"Insecta/Amphion floridensis/785cd9153d587ffd3dc75f509d9d6e31.jpg\",\n  \"72300.jpg\": \"Insecta/Amphion floridensis/a3a80b3245aca7530c116a2c567df8fa.jpg\",\n  \"72309.jpg\": \"Insecta/Amphion floridensis/3f7776ac5dbb0e5d82dde18541d8caa8.jpg\",\n  \"72310.jpg\": \"Insecta/Amphion floridensis/9c7d0363d9bf4dac015852d01086a2e6.jpg\",\n  \"72318.jpg\": \"Insecta/Amphion floridensis/01a9d3932f464e2122168506aa046d24.jpg\",\n  \"72320.jpg\": \"Insecta/Amphion floridensis/d2547e2c3ff1398a35fb8f0b5584d399.jpg\",\n  \"72321.jpg\": \"Insecta/Amphion floridensis/110565301a9f27dac368d696f43bd63b.jpg\",\n  \"72324.jpg\": \"Insecta/Nadata gibbosa/825e7fcaad247a0eee45453603a4fc41.jpg\",\n  \"72334.jpg\": \"Insecta/Nadata gibbosa/0aab86acee3b8bdaca14383821edf97d.jpg\",\n  \"72335.jpg\": \"Insecta/Nadata gibbosa/8243b8e7b0903ac80e4cf531af31ed6c.jpg\",\n  \"72337.jpg\": \"Insecta/Nadata gibbosa/cd42e30a6b7592bad2b091ac0ef263c0.jpg\",\n  \"72338.jpg\": \"Insecta/Nadata gibbosa/c50755e2d701b641582858a599fcf173.jpg\",\n  \"72342.jpg\": \"Insecta/Nadata gibbosa/fc20c129e32e6bbbc9250a6588f08f85.jpg\",\n  \"72348.jpg\": \"Insecta/Nadata gibbosa/65c1fb4d49a68ec39f59f210286b1198.jpg\",\n  \"72351.jpg\": \"Insecta/Nadata gibbosa/bfd54556dc6b2a6879ab49153e2c54c0.jpg\",\n  \"72352.jpg\": \"Insecta/Nadata gibbosa/d7e21a1896b593d8785d9013109cd1e4.jpg\",\n  \"72356.jpg\": \"Insecta/Nadata gibbosa/cfe00ff9c8a6d9fa5b1ffb6f23d4d013.jpg\",\n  \"72357.jpg\": \"Insecta/Nadata gibbosa/f7592d6e9b03de1d76ea60b7aafab316.jpg\",\n  \"72359.jpg\": \"Insecta/Nadata gibbosa/47a6c123d82e16d50533003dc9635a2f.jpg\",\n  \"72360.jpg\": \"Insecta/Nadata gibbosa/50cae74755dca558da71944a8aac8acc.jpg\",\n  \"72361.jpg\": \"Insecta/Nadata gibbosa/8a48c0ae95e9e58cfd6bb5f6e8f3c750.jpg\",\n  \"72362.jpg\": \"Insecta/Nadata gibbosa/57d6c0f27d425ceca2ab9796e6307183.jpg\",\n  \"72366.jpg\": \"Insecta/Nadata gibbosa/4f4674fd229d00e7a5ab3f0447c9f519.jpg\",\n  \"72367.jpg\": \"Insecta/Nadata gibbosa/dd77c7452353dd20b87a5102f26389c4.jpg\",\n  \"72370.jpg\": \"Insecta/Nadata gibbosa/f71c1c63e595f07fa3056c5e67d26cb0.jpg\",\n  \"72383.jpg\": \"Arachnida/Badumna longinqua/c67333dff8cb6e5f4d5a2d8cce339489.jpg\",\n  \"72384.jpg\": \"Arachnida/Badumna longinqua/c9fa367f662df87537290f795a38e2b9.jpg\",\n  \"72385.jpg\": \"Arachnida/Badumna longinqua/4de86ca1612db73b92dce77110fa4442.jpg\",\n  \"72403.jpg\": \"Arachnida/Badumna longinqua/55346e420032bf547f9200d903529298.jpg\",\n  \"72405.jpg\": \"Arachnida/Badumna longinqua/1de14b3e5fa59432acb9e9977fe918a3.jpg\",\n  \"72407.jpg\": \"Arachnida/Badumna longinqua/92e3d065c4a6c862420b498368c89317.jpg\",\n  \"72408.jpg\": \"Arachnida/Badumna longinqua/e0cf150ac3a8bc2b142977573b769e42.jpg\",\n  \"72410.jpg\": \"Insecta/Calopteryx virgo/c5c433587398036c0a1e96a4a746b956.jpg\",\n  \"72417.jpg\": \"Insecta/Calopteryx virgo/9a2229f139c6a2ece50f2484cde03343.jpg\",\n  \"72418.jpg\": \"Insecta/Calopteryx virgo/23c18ecc2b7b74e0e1a870bfb48f2471.jpg\",\n  \"72420.jpg\": \"Insecta/Calopteryx virgo/ef1dc6c7bfed837fbc1166486363c1f4.jpg\",\n  \"72423.jpg\": \"Insecta/Calopteryx virgo/1783894962561be3f5121513b67cf671.jpg\",\n  \"72429.jpg\": \"Insecta/Calopteryx virgo/110d08ad4cf4389eef3b50adf23c36c0.jpg\",\n  \"72437.jpg\": \"Insecta/Calopteryx virgo/56f9465843293ab24bd5aec1d95f3008.jpg\",\n  \"72438.jpg\": \"Insecta/Calopteryx virgo/3fae6779bf772881eafaf077a3c1a3ca.jpg\",\n  \"72443.jpg\": \"Mammalia/Axis axis/93d4519c84328e978e513dd6b3c623de.jpg\",\n  \"72452.jpg\": \"Mammalia/Axis axis/7a78de7c702d1f99c9fa3429791745f7.jpg\",\n  \"72469.jpg\": \"Insecta/Colias croceus/2097268b9857fbfd134d44f4a5b1e744.jpg\",\n  \"72470.jpg\": \"Insecta/Colias croceus/c266fc02e30914704dfbd93f99cae236.jpg\",\n  \"72472.jpg\": \"Insecta/Colias croceus/d99a6b39ee819659ad034d3c5c82c65d.jpg\",\n  \"72474.jpg\": \"Insecta/Colias croceus/3cadc983dde7379d0406c9e15123b5cc.jpg\",\n  \"72480.jpg\": \"Insecta/Colias croceus/b403b4520f7b281010fd10a4bef60bdb.jpg\",\n  \"72481.jpg\": \"Insecta/Colias croceus/b89eb1a7af64ac17d7ede7991249dcfa.jpg\",\n  \"72483.jpg\": \"Insecta/Colias croceus/b98ca2fd31ee7c2f3ee03fd564fab8c2.jpg\",\n  \"72484.jpg\": \"Insecta/Colias croceus/f12294d73a599dbb9fbfca9ab8d0e157.jpg\",\n  \"72486.jpg\": \"Insecta/Colias croceus/3841c6273b9a42b25d03ae02829e5da7.jpg\",\n  \"72499.jpg\": \"Aves/Grus grus/6b62b2458eb297bf34fed6f9b8aa3e57.jpg\",\n  \"72504.jpg\": \"Aves/Grus grus/276b4c44b75eed5b6ae474a4f302025b.jpg\",\n  \"72505.jpg\": \"Aves/Grus grus/46767a8142702677c31cdcb5d1b638ad.jpg\",\n  \"72531.jpg\": \"Aves/Grus grus/2f2f6e66cc4f9870cd7ac51beeae1236.jpg\",\n  \"72542.jpg\": \"Aves/Grus grus/f47e8780368d80df9e943396aa66af6f.jpg\",\n  \"72782.jpg\": \"Aves/Colaptes auratus/bf888d9a28897022bc47a7045d59b7b8.jpg\",\n  \"72786.jpg\": \"Aves/Colaptes auratus/04cb9c936bca11584f9c1f45cb7a9b2d.jpg\",\n  \"72787.jpg\": \"Aves/Colaptes auratus/20fed4c14139dd29c1a8306959efe530.jpg\",\n  \"72888.jpg\": \"Aves/Colaptes auratus/0be479c1f14f8e1a92b5b2bdb26a3257.jpg\",\n  \"72912.jpg\": \"Aves/Colaptes auratus/cc10a064b9fda76719d8dbdb383ad3e0.jpg\",\n  \"73024.jpg\": \"Aves/Colaptes auratus/c7270ddeaff8950fa7c17289f4109e42.jpg\",\n  \"73031.jpg\": \"Aves/Colaptes auratus/681e506cda1e5de284198384065bbd15.jpg\",\n  \"73058.jpg\": \"Aves/Colaptes auratus/8bc6ab6e9792b9a77772650701898228.jpg\",\n  \"73106.jpg\": \"Aves/Colaptes auratus/d6eb7e60256a8126057cb680dab682ba.jpg\",\n  \"73114.jpg\": \"Aves/Colaptes auratus/6597fec06f8e8f2ae0c877e86fdca7e3.jpg\",\n  \"73120.jpg\": \"Aves/Colaptes auratus/7b237e73927fba7936f7419f0b02d913.jpg\",\n  \"73148.jpg\": \"Aves/Colaptes auratus/d33bf5bf521e0b3c891a4f1351f06226.jpg\",\n  \"73179.jpg\": \"Aves/Colaptes auratus/a0966fd437f38b3759a3162489dbac37.jpg\",\n  \"73206.jpg\": \"Aves/Colaptes auratus/5933d869dce71318f1c76ceefa1b45c5.jpg\",\n  \"73232.jpg\": \"Aves/Colaptes auratus/2d275d3c22250c79caa393b01ff5f08d.jpg\",\n  \"73300.jpg\": \"Aves/Colaptes auratus/7337b00bd2df3e0e88863ab555796748.jpg\",\n  \"73309.jpg\": \"Aves/Colaptes auratus/24a48c6feefa8ea26f7a134fc7fd8e92.jpg\",\n  \"73362.jpg\": \"Aves/Stercorarius pomarinus/9efdf4a8525cc2829776bf01d2a26786.jpg\",\n  \"73369.jpg\": \"Aves/Stercorarius pomarinus/8dc8ddb72f0f73f3ef9536e73350815f.jpg\",\n  \"73370.jpg\": \"Aves/Stercorarius pomarinus/8b17d1a4d12d8412e7c7e1a2441282a2.jpg\",\n  \"73373.jpg\": \"Aves/Stercorarius pomarinus/a836d864c2f406d949f9a4efba0de26d.jpg\",\n  \"73374.jpg\": \"Aves/Stercorarius pomarinus/1a3dbd37450a22f937590f5288748f59.jpg\",\n  \"73375.jpg\": \"Aves/Stercorarius pomarinus/8af9b84b5fee5e19388cbe2c5838c68a.jpg\",\n  \"73490.jpg\": \"Insecta/Bombus vagans/d071ee0ee5784d0c9a476741719bec46.jpg\",\n  \"73496.jpg\": \"Insecta/Bombus vagans/5b7528c7563ac79cdae18ee477a32593.jpg\",\n  \"73508.jpg\": \"Insecta/Bombus vagans/5b66b4321733174a7014c03d5f6ab2f9.jpg\",\n  \"73510.jpg\": \"Insecta/Bombus vagans/e6da4ef5d56ba5f4c2fd5c4a8944ed4f.jpg\",\n  \"73511.jpg\": \"Insecta/Bombus vagans/a0f34f9b642b3b8ee85ce5ce7c3151f0.jpg\",\n  \"73606.jpg\": \"Aves/Cygnus columbianus/049c49510cbf8e7ad91b8895a8be11b0.jpg\",\n  \"73637.jpg\": \"Aves/Cygnus columbianus/67ab340c56dc578e63462de09a06daa5.jpg\",\n  \"73649.jpg\": \"Aves/Cygnus columbianus/ae85433025c1f62136787b8a6de07cd2.jpg\",\n  \"73653.jpg\": \"Aves/Cygnus columbianus/fb3408971c82c7077e7da6cc17c6fb23.jpg\",\n  \"73693.jpg\": \"Aves/Larus delawarensis/95caeb8bff8b661fd539d458fb4e535f.jpg\",\n  \"73708.jpg\": \"Aves/Larus delawarensis/7453e023266a7d7486d1b18c49ab3c9e.jpg\",\n  \"73761.jpg\": \"Aves/Larus delawarensis/05a43bc4f2401b80eb275cd2d86bb5f7.jpg\",\n  \"73769.jpg\": \"Aves/Larus delawarensis/6aa52c14688cc06ceeebe210b3eb1c18.jpg\",\n  \"73790.jpg\": \"Aves/Larus delawarensis/14d1259370e7347dc94a89ad98517b5e.jpg\",\n  \"73818.jpg\": \"Aves/Larus delawarensis/e71ad5af0f47c52d561aada2998994de.jpg\",\n  \"73831.jpg\": \"Aves/Larus delawarensis/759f88e6f2f2aa11df7975c8757ac3f1.jpg\",\n  \"73856.jpg\": \"Aves/Larus delawarensis/96b1e0c6d7e8eec4903cdb2f1864d8a9.jpg\",\n  \"73865.jpg\": \"Aves/Larus delawarensis/ac57bee93a262e8a581a8a2a43f4594a.jpg\",\n  \"73911.jpg\": \"Aves/Larus delawarensis/dafa458d7d95a53e1131cd928b624239.jpg\",\n  \"73954.jpg\": \"Aves/Larus delawarensis/e2d04396becd483a82f42c1213857b59.jpg\",\n  \"74.jpg\": \"Mammalia/Marmota flaviventris/cedff66aa4fb79707801fa6e19cc73b6.jpg\",\n  \"74016.jpg\": \"Aves/Larus delawarensis/ca4396c433c81966f27adda822f1b4d3.jpg\",\n  \"74034.jpg\": \"Aves/Larus delawarensis/46898a79b2897612cb40f27a1698a5f8.jpg\",\n  \"74042.jpg\": \"Aves/Larus delawarensis/7ec01e0186d9a9df30fc982f06e672ca.jpg\",\n  \"7421.jpg\": \"Insecta/Zale lunata/4147922c0fb4575d5758a0bda5a5dac4.jpg\",\n  \"7422.jpg\": \"Insecta/Zale lunata/8a37739bc08e1a886590285a23add5ec.jpg\",\n  \"7424.jpg\": \"Insecta/Zale lunata/57befbf7ef3fea283aa0a98216bd1091.jpg\",\n  \"74251.jpg\": \"Aves/Larus delawarensis/b13438f1d990bfd8e5a6b9a49b7761ad.jpg\",\n  \"7427.jpg\": \"Insecta/Zale lunata/60b4400b4a1a610b6a50afd79acb051c.jpg\",\n  \"7428.jpg\": \"Insecta/Zale lunata/33325f0d922df23eff4bbcf2903bce2f.jpg\",\n  \"7429.jpg\": \"Insecta/Zale lunata/ce311e365e937a0457c3e0a83782bd28.jpg\",\n  \"7432.jpg\": \"Insecta/Zale lunata/596c4afd59e39db61841898c3b0baa08.jpg\",\n  \"7434.jpg\": \"Insecta/Zale lunata/c4d4d22a6cb05679db30a3e43540edc5.jpg\",\n  \"74354.jpg\": \"Aves/Junco hyemalis/bd21bcbcfc314b9d11bb5206169a30e8.jpg\",\n  \"7437.jpg\": \"Insecta/Zale lunata/50b5fe897c0dea27999439d1d52c9008.jpg\",\n  \"7440.jpg\": \"Insecta/Zale lunata/9f62e845c33b66b187206c32c5c16204.jpg\",\n  \"7441.jpg\": \"Insecta/Zale lunata/cdcc23b828a2d854c2d41abce880f1a2.jpg\",\n  \"74418.jpg\": \"Aves/Junco hyemalis/2f5b82a3de81edca63afd0d81371576d.jpg\",\n  \"7442.jpg\": \"Insecta/Zale lunata/328a1e4a391e90865a3393f831777ef4.jpg\",\n  \"7443.jpg\": \"Insecta/Zale lunata/fdfdabb1308db7d4cfd7fe838a8a97d6.jpg\",\n  \"7444.jpg\": \"Insecta/Zale lunata/c4b68de140f8b16173ccceba75999b83.jpg\",\n  \"74452.jpg\": \"Aves/Junco hyemalis/919bd56cc0c6c0a605e57754c04f6d7b.jpg\",\n  \"74481.jpg\": \"Aves/Junco hyemalis/66037d43a76c4fcd8fd55bc4544bcc23.jpg\",\n  \"74500.jpg\": \"Aves/Junco hyemalis/033f8e0959e257ee52512a338ab9c711.jpg\",\n  \"7451.jpg\": \"Insecta/Zale lunata/50af264b5c62275e293c07ef96c491b3.jpg\",\n  \"7452.jpg\": \"Insecta/Zale lunata/e129ce23a97e9e2bdb0879fd9059b925.jpg\",\n  \"7453.jpg\": \"Insecta/Zale lunata/068c66174a8efd53fe33d87d5238b2ef.jpg\",\n  \"74549.jpg\": \"Aves/Junco hyemalis/cf47b2eff235f67df0a197102bae6923.jpg\",\n  \"74571.jpg\": \"Aves/Junco hyemalis/f254451157dd0ab01b34a5fa35e674f3.jpg\",\n  \"7458.jpg\": \"Insecta/Zale lunata/953fc6899af2f80110a2173cf1eff13a.jpg\",\n  \"7459.jpg\": \"Insecta/Zale lunata/a67a2a67b4592876f1035a461e212ca6.jpg\",\n  \"74672.jpg\": \"Aves/Junco hyemalis/21c3ac6b9c47b8618c2279049ed5961d.jpg\",\n  \"74675.jpg\": \"Aves/Junco hyemalis/156ad81860f95063ed9c9df87d2527f2.jpg\",\n  \"74710.jpg\": \"Aves/Junco hyemalis/8b137e9bf3497ae78f6b16e530944b40.jpg\",\n  \"74740.jpg\": \"Aves/Junco hyemalis/76bd6f3bc5daab2a6d1e82d6387152cb.jpg\",\n  \"74774.jpg\": \"Aves/Junco hyemalis/a633468e81bd5baaf9a91f7e67d625cb.jpg\",\n  \"74867.jpg\": \"Aves/Junco hyemalis/78faef2b133ec0d9fe9790da027236bd.jpg\",\n  \"74891.jpg\": \"Aves/Junco hyemalis/dfa4b3886fb103cf12af64ac4e27c2be.jpg\",\n  \"74897.jpg\": \"Aves/Junco hyemalis/7bc11ec18f52b992b0fb24e15fffd0f7.jpg\",\n  \"74905.jpg\": \"Aves/Junco hyemalis/0dd7c4bc4e285b7ea05649ccf601aa3c.jpg\",\n  \"74928.jpg\": \"Aves/Junco hyemalis/5dd650120d6f5e622b513ba4288eb0d9.jpg\",\n  \"74946.jpg\": \"Aves/Junco hyemalis/c6634596305e52ab0bad9b1ebea373d6.jpg\",\n  \"74951.jpg\": \"Aves/Junco hyemalis/3303383cd87505aed738348297a78491.jpg\",\n  \"74974.jpg\": \"Aves/Junco hyemalis/b7d64c253637e476a2c4fcbf741247a6.jpg\",\n  \"75329.jpg\": \"Actinopterygii/Oligocottus maculosus/c0727adb699f2f5d9ec19e51c78078c5.jpg\",\n  \"75332.jpg\": \"Actinopterygii/Oligocottus maculosus/9ddb189f5e651e5eaef5ddcd4536a8ce.jpg\",\n  \"75334.jpg\": \"Actinopterygii/Oligocottus maculosus/af8e6e948c1e100039185de78bf0ad8a.jpg\",\n  \"75471.jpg\": \"Reptilia/Trachemys scripta/e24bec2921f83e81ce1227887e3dfb92.jpg\",\n  \"75495.jpg\": \"Reptilia/Trachemys scripta/249c38c2e2ff89eb0173718480cdf89d.jpg\",\n  \"75514.jpg\": \"Reptilia/Trachemys scripta/5ea33e7ff37bbed9ac55ca1924119c76.jpg\",\n  \"75532.jpg\": \"Reptilia/Trachemys scripta/7621c2de0f2fc97ef51a64ff9067d161.jpg\",\n  \"75534.jpg\": \"Reptilia/Trachemys scripta/3e303a141848741db09557989e17d843.jpg\",\n  \"75540.jpg\": \"Reptilia/Trachemys scripta/f359303c250ae9449da7557260d126fc.jpg\",\n  \"75548.jpg\": \"Reptilia/Trachemys scripta/0c0f4f7cf61cef5bd409e70fc8f04530.jpg\",\n  \"75578.jpg\": \"Reptilia/Trachemys scripta/e345dd4ca8fe19b00fa891640ab98468.jpg\",\n  \"75593.jpg\": \"Reptilia/Trachemys scripta/5abe609dddb14d7d8b1c9845b8229554.jpg\",\n  \"75622.jpg\": \"Reptilia/Trachemys scripta/2c3549d3e9bc400c7d712a9d68a2317d.jpg\",\n  \"75630.jpg\": \"Reptilia/Trachemys scripta/f0b5f256a6d91d8542578610e0a0131d.jpg\",\n  \"75634.jpg\": \"Reptilia/Trachemys scripta/3398d2c238ba831407489561826c4475.jpg\",\n  \"75649.jpg\": \"Reptilia/Trachemys scripta/df8484b35000e2e8dcef95c97b119816.jpg\",\n  \"75650.jpg\": \"Reptilia/Trachemys scripta/3c7e75735c2ab1577793af64a49dd895.jpg\",\n  \"75665.jpg\": \"Reptilia/Trachemys scripta/3f19f2652e19cadfcafd3ad9700b16f2.jpg\",\n  \"75679.jpg\": \"Reptilia/Trachemys scripta/f30ed50ab0a3d2c89fb05f3e7b47e141.jpg\",\n  \"75707.jpg\": \"Insecta/Anartia jatrophae luteipicta/10b0ab588a3a10a8de4e817efa790b61.jpg\",\n  \"75724.jpg\": \"Insecta/Anartia jatrophae luteipicta/bb26df3575c4271ecfd2b4b52b4c651d.jpg\",\n  \"75725.jpg\": \"Insecta/Anartia jatrophae luteipicta/f2a5951cd519bd31ce0037c2e2bb31c4.jpg\",\n  \"75728.jpg\": \"Insecta/Anartia jatrophae luteipicta/1aa673dbd6ed440e41569fa5b8a4b8bc.jpg\",\n  \"75731.jpg\": \"Insecta/Anartia jatrophae luteipicta/bbf46337215324378fcc2fcc918a5990.jpg\",\n  \"7574.jpg\": \"Insecta/Leptoglossus zonatus/57dd0fe63f6aba7bdffff1636fc32759.jpg\",\n  \"75741.jpg\": \"Insecta/Pyrrhosoma nymphula/537045410e025204aa3234e515f7e83f.jpg\",\n  \"75749.jpg\": \"Insecta/Pyrrhosoma nymphula/51362f5a2193351d3a9cfab2b3e532b2.jpg\",\n  \"7575.jpg\": \"Insecta/Leptoglossus zonatus/09611f8e2354d1583812616928dc77e6.jpg\",\n  \"75751.jpg\": \"Insecta/Pyrrhosoma nymphula/f071a80f2f50d6524d3d6562908c6bd1.jpg\",\n  \"75752.jpg\": \"Insecta/Pyrrhosoma nymphula/dc9fdfd812b84aec4ec0edcfff724df7.jpg\",\n  \"7576.jpg\": \"Insecta/Leptoglossus zonatus/ee2829a6e018b38e00348c595d64b093.jpg\",\n  \"7578.jpg\": \"Insecta/Leptoglossus zonatus/7fabbe8e1cae4b3dabcfce340149a5eb.jpg\",\n  \"7581.jpg\": \"Insecta/Leptoglossus zonatus/2a3903082b969f1d7457a4ada5d34737.jpg\",\n  \"75816.jpg\": \"Amphibia/Pseudacris regilla/63174e86b425b1c64b82fc4e3d9e1045.jpg\",\n  \"75817.jpg\": \"Amphibia/Pseudacris regilla/01ec993bb8d757dd47224ecfef35ec69.jpg\",\n  \"75823.jpg\": \"Amphibia/Pseudacris regilla/14edef8e362284a3a16d61babdbcba2f.jpg\",\n  \"75831.jpg\": \"Amphibia/Pseudacris regilla/6cf5be2036436325b8d1c88fef41955c.jpg\",\n  \"75846.jpg\": \"Amphibia/Pseudacris regilla/c47875d5a848638201086dba401b1ec2.jpg\",\n  \"75847.jpg\": \"Amphibia/Pseudacris regilla/0044cbf63938e8281417e0947e2b049f.jpg\",\n  \"75848.jpg\": \"Amphibia/Pseudacris regilla/00a8ee5449138511bc45572ad1ad87b3.jpg\",\n  \"75855.jpg\": \"Amphibia/Pseudacris regilla/ca6e128177636ec7541c70463d8fad56.jpg\",\n  \"75858.jpg\": \"Amphibia/Pseudacris regilla/4f234a9ee54f0066df21c6d45039446e.jpg\",\n  \"75860.jpg\": \"Amphibia/Pseudacris regilla/f70af046ed7ddc73208011b6129f51aa.jpg\",\n  \"75861.jpg\": \"Amphibia/Pseudacris regilla/b65593935fc93747ed33a02e6dacb017.jpg\",\n  \"75866.jpg\": \"Amphibia/Pseudacris regilla/b415b748e892d5a814534ab1f9283285.jpg\",\n  \"75868.jpg\": \"Amphibia/Pseudacris regilla/ea488693b0ba622ddceca70729195835.jpg\",\n  \"75870.jpg\": \"Amphibia/Pseudacris regilla/5e867124864911623b0809f990ce8b40.jpg\",\n  \"75871.jpg\": \"Amphibia/Pseudacris regilla/cafe1fb448a9392d3d8afe0335690c4f.jpg\",\n  \"75878.jpg\": \"Amphibia/Pseudacris regilla/37ce24917da255ff35c84b71be6365af.jpg\",\n  \"75879.jpg\": \"Amphibia/Pseudacris regilla/973dc92e1229f0032ca2feedf744925f.jpg\",\n  \"75882.jpg\": \"Amphibia/Pseudacris regilla/977ebedfbbaa37c10f2d78ef061866d5.jpg\",\n  \"75883.jpg\": \"Amphibia/Pseudacris regilla/aa8f6b29c99edaeba835bcd6d2265972.jpg\",\n  \"75889.jpg\": \"Amphibia/Pseudacris regilla/ea1fb09769abe7fb2197d7a87520cf05.jpg\",\n  \"75891.jpg\": \"Amphibia/Pseudacris regilla/c13e4e5b720fc6ddacb7cf354ec424fd.jpg\",\n  \"75892.jpg\": \"Amphibia/Pseudacris regilla/730fb74dee12c962d03381431b68fd60.jpg\",\n  \"75900.jpg\": \"Amphibia/Pseudacris regilla/38ad3c7d885e268172b2700dab84c85d.jpg\",\n  \"75905.jpg\": \"Reptilia/Arizona elegans/b685781abf428ad7dbd8a5c14d65ed38.jpg\",\n  \"75906.jpg\": \"Reptilia/Arizona elegans/e5232defa3e858a59960309e59ed0647.jpg\",\n  \"75914.jpg\": \"Reptilia/Arizona elegans/843c573c580cff4fc49030bbf04486ed.jpg\",\n  \"75917.jpg\": \"Reptilia/Arizona elegans/34a48c7ce23195b4d2dcd6369dc2ad16.jpg\",\n  \"75921.jpg\": \"Reptilia/Arizona elegans/b35d721dc5527cea924d69cdd85c3132.jpg\",\n  \"75923.jpg\": \"Reptilia/Arizona elegans/c886d6cb672c9ff2fc22416037ae0b0e.jpg\",\n  \"75928.jpg\": \"Reptilia/Arizona elegans/801bb4602374299873afe26416abcbc8.jpg\",\n  \"75934.jpg\": \"Reptilia/Arizona elegans/1039008cfda17e31f2111aab382cf8a2.jpg\",\n  \"75936.jpg\": \"Reptilia/Arizona elegans/64869381a17ed62f31e1e3205a1f93d2.jpg\",\n  \"75939.jpg\": \"Reptilia/Arizona elegans/318c9cd9e834f7c13bc7bffcdabd262b.jpg\",\n  \"75940.jpg\": \"Reptilia/Arizona elegans/207bcd8d44a63883403b4d8de1f66845.jpg\",\n  \"75941.jpg\": \"Reptilia/Arizona elegans/7bcd03db2ceb599ddcfb5c650f5371f9.jpg\",\n  \"75942.jpg\": \"Reptilia/Arizona elegans/5e4d365816256f001514bd561f1b77a1.jpg\",\n  \"75945.jpg\": \"Reptilia/Arizona elegans/71b5d764b1a4e9dd19384939e67b44bf.jpg\",\n  \"75947.jpg\": \"Reptilia/Arizona elegans/ee0e4f920f37992f0c7ce967afea53e2.jpg\",\n  \"75948.jpg\": \"Reptilia/Arizona elegans/8bc96b25e1e08512a464871955f238d9.jpg\",\n  \"75951.jpg\": \"Reptilia/Arizona elegans/e9c0d0edfcb413794335a91971956a0b.jpg\",\n  \"75958.jpg\": \"Reptilia/Arizona elegans/c98f03e80f7e7c9287e4bea8bdefa961.jpg\",\n  \"75959.jpg\": \"Reptilia/Arizona elegans/bfd779dc2dd9827c20a6de86a921eb3c.jpg\",\n  \"75961.jpg\": \"Reptilia/Arizona elegans/c20e88e658dc8d6cbfc765478db7f9f0.jpg\",\n  \"75966.jpg\": \"Animalia/Epiactis prolifera/e48a51840456918ddaec42975c13f81f.jpg\",\n  \"75983.jpg\": \"Animalia/Epiactis prolifera/242765ffec229824d70a428de33a1961.jpg\",\n  \"75984.jpg\": \"Animalia/Epiactis prolifera/1bf5e263f46c4c7c771cd19c74b48cf7.jpg\",\n  \"75987.jpg\": \"Animalia/Epiactis prolifera/635aa094f5dce4ac3ae39e5ef8a3840b.jpg\",\n  \"75994.jpg\": \"Animalia/Epiactis prolifera/399b287de4ab0409a972f2ec461017c8.jpg\",\n  \"75997.jpg\": \"Animalia/Epiactis prolifera/45d1a11b24f98b442f7c71ba047a96e5.jpg\",\n  \"76.jpg\": \"Mammalia/Marmota flaviventris/028a231011841aa48ea9e97c54e91758.jpg\",\n  \"76000.jpg\": \"Animalia/Epiactis prolifera/662d1d3f7ceb1d62639423d4664541c7.jpg\",\n  \"76006.jpg\": \"Animalia/Epiactis prolifera/977ec4e5e94d461052abd4b7795fd1fd.jpg\",\n  \"76011.jpg\": \"Animalia/Epiactis prolifera/2cd749d483843052770a0ba70eb8e14e.jpg\",\n  \"76018.jpg\": \"Animalia/Epiactis prolifera/e11eeed334068e9a5fa728bd3a979b05.jpg\",\n  \"76050.jpg\": \"Amphibia/Ambystoma mavortium/dea292804294dd8b245b6e87e728149f.jpg\",\n  \"76055.jpg\": \"Amphibia/Ambystoma mavortium/ebd163dc577848901b51da0dc651da5d.jpg\",\n  \"76056.jpg\": \"Amphibia/Ambystoma mavortium/42ea0f5251e040e118fc31c963fd586e.jpg\",\n  \"76057.jpg\": \"Amphibia/Ambystoma mavortium/abe879f1ab1e3b28cc1420fd404f99f1.jpg\",\n  \"76058.jpg\": \"Amphibia/Ambystoma mavortium/ac82d500b8f9d7cf8e00f0075aca044c.jpg\",\n  \"76059.jpg\": \"Amphibia/Ambystoma mavortium/0ce460f7a62d3eaa496c4db762487e70.jpg\",\n  \"76060.jpg\": \"Amphibia/Ambystoma mavortium/baa70d8e0097196c7dadb9b4cd8262bc.jpg\",\n  \"76061.jpg\": \"Amphibia/Ambystoma mavortium/92e0f832987667ae647acab52ce2e082.jpg\",\n  \"76064.jpg\": \"Amphibia/Ambystoma mavortium/d0345aa6035b299f461d9701a18d7ca8.jpg\",\n  \"76066.jpg\": \"Amphibia/Ambystoma mavortium/61eb55123d357254e88d9f0e475c7948.jpg\",\n  \"76067.jpg\": \"Amphibia/Ambystoma mavortium/44e941293fae7a3c3846dfeb09ecdad5.jpg\",\n  \"76070.jpg\": \"Amphibia/Ambystoma mavortium/8b67931077e8c4f9465f7e51bad72bdf.jpg\",\n  \"76071.jpg\": \"Amphibia/Ambystoma mavortium/b766fc352d29d6de28959513a57faa14.jpg\",\n  \"76072.jpg\": \"Amphibia/Ambystoma mavortium/fd0523a9923996db5163c414e82936e9.jpg\",\n  \"76082.jpg\": \"Amphibia/Ambystoma mavortium/322cbb16ebd69431d3a4c3bd1a14f5d6.jpg\",\n  \"76086.jpg\": \"Amphibia/Ambystoma mavortium/222cedfc5f8ee2c451922b672686d975.jpg\",\n  \"76087.jpg\": \"Amphibia/Ambystoma mavortium/d3fb21bb00ecaaaa59a44a792c36f281.jpg\",\n  \"76088.jpg\": \"Amphibia/Ambystoma mavortium/169743cef660e8d7696343f203e4121a.jpg\",\n  \"76133.jpg\": \"Mammalia/Cervus elaphus roosevelti/0fb7cf8f9d50d2fb1bc7a34fff5d9843.jpg\",\n  \"76134.jpg\": \"Mammalia/Cervus elaphus roosevelti/f769cfedfad528243b3877cba5d1c489.jpg\",\n  \"76138.jpg\": \"Mammalia/Cervus elaphus roosevelti/e06e2998af558a0c8f97061ea6ff08da.jpg\",\n  \"76147.jpg\": \"Mammalia/Cervus elaphus roosevelti/a059f9e1af553541fe257af035cad8a0.jpg\",\n  \"76151.jpg\": \"Mammalia/Cervus elaphus roosevelti/7e842f08100e94a665d9d2e99b4ab389.jpg\",\n  \"76158.jpg\": \"Mammalia/Cervus elaphus roosevelti/8c0abfd04119634ec98d5cc49e51dff8.jpg\",\n  \"76191.jpg\": \"Mammalia/Vulpes vulpes/b8c9b310a29bb402d7f80a1cf0c6d95b.jpg\",\n  \"76197.jpg\": \"Mammalia/Vulpes vulpes/1f45cd7c0e087f39716ff737b589feb3.jpg\",\n  \"76210.jpg\": \"Mammalia/Vulpes vulpes/5d1929c32136e95860b9781ff6963d4e.jpg\",\n  \"76211.jpg\": \"Mammalia/Vulpes vulpes/23f8a1bc28743bd9f39adc53edc8f310.jpg\",\n  \"76256.jpg\": \"Mammalia/Vulpes vulpes/7e8751984902d24116664967f593bb1a.jpg\",\n  \"76262.jpg\": \"Mammalia/Vulpes vulpes/1c7c6a78cc5bca4ea307497f91aa0cd2.jpg\",\n  \"76270.jpg\": \"Mammalia/Vulpes vulpes/eede9361bde4864b3d89cb6f3e5ef86a.jpg\",\n  \"76273.jpg\": \"Mammalia/Vulpes vulpes/a359f011b17e0d56beeaa5a7b01023b9.jpg\",\n  \"76319.jpg\": \"Mammalia/Vulpes vulpes/1b5c852c438d6a5ab8726380819d63ca.jpg\",\n  \"76324.jpg\": \"Mammalia/Vulpes vulpes/625a1d2482e2905efe489fd84d1b2850.jpg\",\n  \"76338.jpg\": \"Mammalia/Vulpes vulpes/bbc5a013c447511c75c5e5c0c277b803.jpg\",\n  \"76341.jpg\": \"Mammalia/Vulpes vulpes/0d5696337188edf880e9a68f959e39f6.jpg\",\n  \"76342.jpg\": \"Mammalia/Vulpes vulpes/c48c0b9ec9d8b4353f8eb2c8d6ff03ce.jpg\",\n  \"76410.jpg\": \"Mammalia/Vulpes vulpes/7823bc6d9eeb07573c37242266a23b19.jpg\",\n  \"76417.jpg\": \"Mammalia/Vulpes vulpes/f3602b5510b647cff70c0aa7dd3cdbd6.jpg\",\n  \"76420.jpg\": \"Mammalia/Vulpes vulpes/2cfd377a179e9caa5914ce8a8e49400b.jpg\",\n  \"76478.jpg\": \"Mammalia/Vulpes vulpes/ecb0baf5ec6c34b001e9bea9f82800ee.jpg\",\n  \"76492.jpg\": \"Mammalia/Vulpes vulpes/ecfe33c3d6afcb047495ee66f29d8ed7.jpg\",\n  \"76503.jpg\": \"Mammalia/Vulpes vulpes/088795f2284e71b70dd411eed604b3bd.jpg\",\n  \"76518.jpg\": \"Mammalia/Vulpes vulpes/8555d06368dd257b3fc80afc1eed9b3b.jpg\",\n  \"76536.jpg\": \"Mammalia/Vulpes vulpes/554ac6d2faa8c49e5d73421d3acc9a5f.jpg\",\n  \"76613.jpg\": \"Aves/Melopsittacus undulatus/a35ad293a14556400524ff34bd47af2d.jpg\",\n  \"76614.jpg\": \"Aves/Melopsittacus undulatus/33b56e79c5af294b51995b51a84a4e7c.jpg\",\n  \"76615.jpg\": \"Aves/Melopsittacus undulatus/eb72a11885ffdec3177aab1c605f68fc.jpg\",\n  \"76616.jpg\": \"Aves/Melopsittacus undulatus/f4bb1bcc317f26cdd3acbbaacc514c1a.jpg\",\n  \"76693.jpg\": \"Amphibia/Bufotes balearicus/c3d5027b7c37677b47e3d32c914e5303.jpg\",\n  \"76694.jpg\": \"Amphibia/Bufotes balearicus/872fae964c2fe27b176abc835f8cb55a.jpg\",\n  \"767.jpg\": \"Mammalia/Ondatra zibethicus/fae0d43b363b8c89904d7270b3f8992e.jpg\",\n  \"76700.jpg\": \"Amphibia/Bufotes balearicus/b3232cbf5ca7cccaba05f9c21c11dc36.jpg\",\n  \"76708.jpg\": \"Amphibia/Bufotes balearicus/340feaed4a00e44d31b92cee72c93f42.jpg\",\n  \"76712.jpg\": \"Aves/Trichoglossus haematodus/12fd6b62541d69d67f44bb72171b9e25.jpg\",\n  \"76719.jpg\": \"Aves/Trichoglossus haematodus/c2f9d8c2098a2ea299a0589551be582e.jpg\",\n  \"76721.jpg\": \"Aves/Trichoglossus haematodus/6294261ec442a244b62595955fa50b80.jpg\",\n  \"76724.jpg\": \"Aves/Trichoglossus haematodus/e47cb9166f21931e7e82ccbd7974b97d.jpg\",\n  \"76725.jpg\": \"Aves/Trichoglossus haematodus/ac6225711e533976875105c023b07c7b.jpg\",\n  \"76734.jpg\": \"Aves/Trichoglossus haematodus/d154f5204d26a392740a9f42cf7c6519.jpg\",\n  \"76741.jpg\": \"Aves/Trichoglossus haematodus/fa42b3ba83e5d402d22634d98e338c6a.jpg\",\n  \"76742.jpg\": \"Aves/Trichoglossus haematodus/6ce6ef0a92b543197af2760cf37b9340.jpg\",\n  \"76745.jpg\": \"Aves/Trichoglossus haematodus/2c57e27c1bf4c28987f20f37f6e0544f.jpg\",\n  \"76753.jpg\": \"Aves/Trichoglossus haematodus/6284d80d0b0a2a03418bbf007d4a56b3.jpg\",\n  \"76754.jpg\": \"Aves/Trichoglossus haematodus/e94d0b72fa3a65d89eebd6fedc0979e2.jpg\",\n  \"76760.jpg\": \"Aves/Trichoglossus haematodus/cdd147d5f0367f6e48b1e3dfca0dd43e.jpg\",\n  \"76761.jpg\": \"Insecta/Schistocerca obscura/6bd14661c399efe635e72c9269da4aef.jpg\",\n  \"76772.jpg\": \"Insecta/Schistocerca obscura/a8968c61fc1e1831c38728ddeba3ba59.jpg\",\n  \"76775.jpg\": \"Insecta/Schistocerca obscura/8beb59d60390d8d510d78acb2cb5abb4.jpg\",\n  \"76778.jpg\": \"Insecta/Schistocerca obscura/289408aae7ba7ef940c84f2c5e76258d.jpg\",\n  \"76799.jpg\": \"Reptilia/Apalone spinifera spinifera/7860868e6470744cd0ab7cb248dd76f4.jpg\",\n  \"76800.jpg\": \"Reptilia/Apalone spinifera spinifera/0c2172d2004310be6b585ff8f2efb7a1.jpg\",\n  \"76803.jpg\": \"Reptilia/Apalone spinifera spinifera/cacc9ce80be253710b2edc2286e90a80.jpg\",\n  \"76817.jpg\": \"Reptilia/Apalone spinifera spinifera/4446e91b7d66e37426198b3c2be15e55.jpg\",\n  \"76821.jpg\": \"Insecta/Plebejus icarioides/c23ff86fbe770f63db1d7ceee38c51d4.jpg\",\n  \"76826.jpg\": \"Insecta/Plebejus icarioides/f04936f0b6e92015a3849b897dfe65ae.jpg\",\n  \"76828.jpg\": \"Insecta/Plebejus icarioides/0247d4b00eb6b1522a6fa0c5133e0a20.jpg\",\n  \"76830.jpg\": \"Insecta/Plebejus icarioides/c755898b27aaf4433987eef43327ff68.jpg\",\n  \"76843.jpg\": \"Insecta/Plebejus icarioides/9631dfd1fd421ae76296e02d150a6e3c.jpg\",\n  \"76844.jpg\": \"Insecta/Plebejus icarioides/82610ad3311add0b899b387058a066c0.jpg\",\n  \"76850.jpg\": \"Insecta/Plebejus icarioides/cfd547f7504c07bdb8f2a8d574876cf6.jpg\",\n  \"76887.jpg\": \"Insecta/Pachydiplax longipennis/ebdd14a4ab9f577b98be799e0772906f.jpg\",\n  \"76888.jpg\": \"Insecta/Pachydiplax longipennis/e9db0c574047cfaadce28cf8fb05e4e2.jpg\",\n  \"7692.jpg\": \"Aves/Pica pica/b5cb51497501c231c675b387a0432d14.jpg\",\n  \"76930.jpg\": \"Insecta/Pachydiplax longipennis/af1ca4312267cbadbff92f58d1fcb800.jpg\",\n  \"7696.jpg\": \"Aves/Pica pica/5c88b58134be9ebb20a76bb3077af459.jpg\",\n  \"76966.jpg\": \"Insecta/Pachydiplax longipennis/cecdc9ae374c8d8d6f944b6ad2456f2e.jpg\",\n  \"7697.jpg\": \"Aves/Pica pica/bb72b7412d764083b05932a709e91c64.jpg\",\n  \"7698.jpg\": \"Aves/Pica pica/7ad624cb0cffdf03f9c92c2d2ee6867b.jpg\",\n  \"77.jpg\": \"Mammalia/Marmota flaviventris/78401ee5c6547124463ceaf4fe6d1ece.jpg\",\n  \"7700.jpg\": \"Aves/Pica pica/4495838a7387883ba357879669fc5e21.jpg\",\n  \"7701.jpg\": \"Aves/Pica pica/79161d0354ca5a914c7b7200badab3d8.jpg\",\n  \"77098.jpg\": \"Insecta/Pachydiplax longipennis/591b8eab9064852dad2ccc0440187d5e.jpg\",\n  \"77099.jpg\": \"Insecta/Pachydiplax longipennis/34b93560001370d1f5623dad38bbacb4.jpg\",\n  \"7713.jpg\": \"Aves/Pica pica/c2bf73ff0baafbd3edbd340b5931c8de.jpg\",\n  \"77137.jpg\": \"Insecta/Pachydiplax longipennis/7721c865a338a819894ada5d0b9c74f5.jpg\",\n  \"77150.jpg\": \"Insecta/Pachydiplax longipennis/629205cb841317a06ac575c80d8dabd8.jpg\",\n  \"77173.jpg\": \"Insecta/Pachydiplax longipennis/b4460dddb08663b9d0f5c2ebd1e82597.jpg\",\n  \"77214.jpg\": \"Insecta/Pachydiplax longipennis/8039db45c61f8cdd29b3faa81d05532f.jpg\",\n  \"77229.jpg\": \"Insecta/Pachydiplax longipennis/d800a136ab2e7f7ae76c3b9bae8bfd2f.jpg\",\n  \"77235.jpg\": \"Insecta/Pachydiplax longipennis/89798697fb4e6cda245e420260d1c410.jpg\",\n  \"77272.jpg\": \"Insecta/Pachydiplax longipennis/7eb7e6efcbf8614cbcbf136526e132d1.jpg\",\n  \"77277.jpg\": \"Insecta/Pachydiplax longipennis/02f9503656a746b2c1d963908f5860b1.jpg\",\n  \"7729.jpg\": \"Aves/Pica pica/6e28639119de23ec5c47ddd7dae85ff0.jpg\",\n  \"77389.jpg\": \"Insecta/Pachydiplax longipennis/b3455ce30df15312373b5dab212420f8.jpg\",\n  \"774.jpg\": \"Mammalia/Ondatra zibethicus/770ffca6ddd888b306a625535a87d318.jpg\",\n  \"77406.jpg\": \"Insecta/Pachydiplax longipennis/e3c56573870a1e299e6e076bc465d618.jpg\",\n  \"77434.jpg\": \"Insecta/Pachydiplax longipennis/cef6e3ca5632279e8477168b9d7ae7c6.jpg\",\n  \"77437.jpg\": \"Insecta/Pachydiplax longipennis/502e6e1ccc9d62918f73781d6dbcb2cc.jpg\",\n  \"77443.jpg\": \"Insecta/Pachydiplax longipennis/b72e6b6844c55d8c0bbc2934da517a6f.jpg\",\n  \"77448.jpg\": \"Insecta/Pachydiplax longipennis/94805e41824edc30d093bfed52902df9.jpg\",\n  \"7751.jpg\": \"Aves/Pica pica/db1380d142323e482f9919d0af4dbad1.jpg\",\n  \"7752.jpg\": \"Aves/Pica pica/c7cce8209ab2562000977d28e92810fa.jpg\",\n  \"77524.jpg\": \"Insecta/Pachydiplax longipennis/6ac5cc95f12953050c2cb534a4d5ec18.jpg\",\n  \"77607.jpg\": \"Insecta/Pachydiplax longipennis/d9b4832ff15ef0b943bf0ab94b99367a.jpg\",\n  \"77681.jpg\": \"Insecta/Pachydiplax longipennis/87f0f74abab604edeae42310a5a765b7.jpg\",\n  \"777.jpg\": \"Mammalia/Ondatra zibethicus/7f0d8f234f90e3d25f2dd211d78158c5.jpg\",\n  \"77727.jpg\": \"Aves/Cinclus cinclus/35c3ffb3de03d687cb24622fcfdec5ba.jpg\",\n  \"77736.jpg\": \"Aves/Cinclus cinclus/bb31a426e26a435545cc956995f34e90.jpg\",\n  \"77738.jpg\": \"Aves/Cinclus cinclus/8881b825fbbc0cc74373a3faad878b55.jpg\",\n  \"77744.jpg\": \"Aves/Cinclus cinclus/fb5ade88c9bd46e1ae3b46c56ab48466.jpg\",\n  \"77745.jpg\": \"Aves/Cinclus cinclus/3ebae262cd53e5707b086e15c2089b74.jpg\",\n  \"7775.jpg\": \"Aves/Pica pica/203c4ac754607ee92a30b68ce5dcebe0.jpg\",\n  \"7776.jpg\": \"Aves/Pica pica/e6d0917657d336e6acb556d28df0f1a3.jpg\",\n  \"77782.jpg\": \"Aves/Vanellus miles/ca06776f1c7f5f0d83a0fef053edfe7a.jpg\",\n  \"77787.jpg\": \"Aves/Vanellus miles/04f777769dcb3e4e4695b1c9bde91550.jpg\",\n  \"77791.jpg\": \"Aves/Vanellus miles/7337f7d9a973c745e1ce2a9502bb80c6.jpg\",\n  \"77792.jpg\": \"Aves/Vanellus miles/fb97c515c5ec8a828ffda6e7cc69f7b3.jpg\",\n  \"77799.jpg\": \"Aves/Vanellus miles/484d7d4387a931f84b08302f6834f6c8.jpg\",\n  \"77800.jpg\": \"Aves/Vanellus miles/cebc9724dff368ae5d61193e4ab3a77c.jpg\",\n  \"77801.jpg\": \"Aves/Vanellus miles/23e86076529f109ff23cd2b9a77ab23b.jpg\",\n  \"77806.jpg\": \"Aves/Vanellus miles/5067adc20067acca70f33b6a3e5f913e.jpg\",\n  \"77809.jpg\": \"Aves/Vanellus miles/c69060971642491623023db1ed061b92.jpg\",\n  \"7781.jpg\": \"Aves/Pica pica/257a5705a03fc16916a51f496cd2c94d.jpg\",\n  \"77810.jpg\": \"Aves/Vanellus miles/5b4d3be8f17937069050d50c8eb8a5a8.jpg\",\n  \"77811.jpg\": \"Aves/Vanellus miles/e546554d97625845d87723ca508b2830.jpg\",\n  \"7782.jpg\": \"Aves/Pica pica/8adb4f98fde45b886fdf320a6dab25dc.jpg\",\n  \"7785.jpg\": \"Aves/Pica pica/c9d061c2902633829784cc07e4502154.jpg\",\n  \"7786.jpg\": \"Aves/Pica pica/49e27b709e413edc4ee7fe7dc0f54b77.jpg\",\n  \"77866.jpg\": \"Aves/Elanoides forficatus/7f42414a7fc716881cf6f1819c242c03.jpg\",\n  \"77867.jpg\": \"Aves/Elanoides forficatus/6eb41d3eac816b2bb5c15e7191e5057a.jpg\",\n  \"77868.jpg\": \"Aves/Elanoides forficatus/9fa494a77aef692be6c4ce64021c863a.jpg\",\n  \"77869.jpg\": \"Aves/Elanoides forficatus/07299379d00cc38455815ae5b332e359.jpg\",\n  \"77870.jpg\": \"Aves/Elanoides forficatus/e23327fd8f7bf7eae3dcd5fedfcf328b.jpg\",\n  \"77872.jpg\": \"Aves/Elanoides forficatus/040075f5678cc2582f81aa4de567ab3b.jpg\",\n  \"77877.jpg\": \"Aves/Elanoides forficatus/08ea556ed5ae53815a672f0a58377127.jpg\",\n  \"77883.jpg\": \"Aves/Elanoides forficatus/3508e665429be3ebc77b8cf1fc060868.jpg\",\n  \"77884.jpg\": \"Aves/Elanoides forficatus/26bce443001a5eeacf573c39962a7780.jpg\",\n  \"77887.jpg\": \"Aves/Elanoides forficatus/6bd29bc43949daa7b7be01331be9eea6.jpg\",\n  \"77891.jpg\": \"Aves/Elanoides forficatus/b727c9e08470fd9cf1638c4f465f25f5.jpg\",\n  \"77894.jpg\": \"Aves/Elanoides forficatus/5cf53bc454cf03d619f8f16f59e4f41e.jpg\",\n  \"77895.jpg\": \"Aves/Elanoides forficatus/95b11ba5c36fcaed0e2346fc34b7daef.jpg\",\n  \"77896.jpg\": \"Aves/Elanoides forficatus/7b30c85e6817a9e0728d016d04ace9e1.jpg\",\n  \"77900.jpg\": \"Aves/Elanoides forficatus/8f86d1ee67fbde2bd3178d97718c52c5.jpg\",\n  \"77901.jpg\": \"Aves/Elanoides forficatus/0b96de0b240b0df22de7effefe893e57.jpg\",\n  \"77904.jpg\": \"Aves/Elanoides forficatus/99530969a056672f63ea427a1d9c9839.jpg\",\n  \"77905.jpg\": \"Aves/Elanoides forficatus/2238ca50a5547d59b5a73ef0f92df4e9.jpg\",\n  \"77906.jpg\": \"Mollusca/Diaulula sandiegensis/4b36b510725b5fb912d22a4e71aae859.jpg\",\n  \"77912.jpg\": \"Mollusca/Diaulula sandiegensis/9d9c5060a72f7204b174defc87f9f005.jpg\",\n  \"77921.jpg\": \"Mollusca/Diaulula sandiegensis/5974879112831e758b6d62968d96fe44.jpg\",\n  \"77924.jpg\": \"Mollusca/Diaulula sandiegensis/63c35098c411b3d3db3659895673b68f.jpg\",\n  \"77927.jpg\": \"Mollusca/Diaulula sandiegensis/626da10a0c033e6785a7cf634f6094a8.jpg\",\n  \"77955.jpg\": \"Mollusca/Diaulula sandiegensis/a0c5c227d843de1dd7374b9a27fbc8b4.jpg\",\n  \"77956.jpg\": \"Mollusca/Diaulula sandiegensis/894381762bb2fbc4158d8d41082f4a79.jpg\",\n  \"77957.jpg\": \"Mollusca/Diaulula sandiegensis/6d7bb74ac3529a6438d06e5381304ceb.jpg\",\n  \"7796.jpg\": \"Aves/Pica pica/935d4bd1ef0628ac353d0be8a777eb7d.jpg\",\n  \"77972.jpg\": \"Mollusca/Diaulula sandiegensis/310de8a67a688654da4de83a06b920ab.jpg\",\n  \"77976.jpg\": \"Mollusca/Diaulula sandiegensis/72e1c9d2c6153eccb51e1f8a72bff0ae.jpg\",\n  \"77983.jpg\": \"Mollusca/Diaulula sandiegensis/9a5a3a78b992b9e9cad25b917a379425.jpg\",\n  \"77984.jpg\": \"Mollusca/Diaulula sandiegensis/edea70065376d2f8de11b05bf856cf0a.jpg\",\n  \"77988.jpg\": \"Mollusca/Diaulula sandiegensis/1df794c24c82b31144ca6561a5f36df9.jpg\",\n  \"77989.jpg\": \"Mollusca/Diaulula sandiegensis/417166ec33cc223271db94ed2a7bf449.jpg\",\n  \"77990.jpg\": \"Mollusca/Diaulula sandiegensis/79547f9c2f3dbeedb725058a4605441f.jpg\",\n  \"77994.jpg\": \"Mollusca/Diaulula sandiegensis/b436a32c14a33ed9c2316e077e38263f.jpg\",\n  \"78.jpg\": \"Mammalia/Marmota flaviventris/2ea81ac2e36becc81e15795c64c72189.jpg\",\n  \"78020.jpg\": \"Mollusca/Diaulula sandiegensis/0f84bb9d1c0b24e477e6685df5b96669.jpg\",\n  \"78022.jpg\": \"Insecta/Scolia dubia/09321f24598474c6376a2dde6d178961.jpg\",\n  \"78025.jpg\": \"Insecta/Scolia dubia/25e79d597d513556472a204966060596.jpg\",\n  \"78029.jpg\": \"Insecta/Scolia dubia/56a409fa1046e5c9ea1bb837ef064898.jpg\",\n  \"78030.jpg\": \"Insecta/Scolia dubia/1c85eb36ccc38bb437995af69b09d324.jpg\",\n  \"78031.jpg\": \"Insecta/Scolia dubia/992aca65a714aff7ec51e05ee95de3f0.jpg\",\n  \"78032.jpg\": \"Insecta/Scolia dubia/91046cb9ca028c51a9b41b609bd0d0ad.jpg\",\n  \"78033.jpg\": \"Insecta/Scolia dubia/81b8083475da4e658e4528d5c850df66.jpg\",\n  \"78037.jpg\": \"Insecta/Scolia dubia/4c65b0ee6a590b433e7231a5db548a75.jpg\",\n  \"78038.jpg\": \"Insecta/Scolia dubia/76781f7afabc0cc4df11bb31e5788671.jpg\",\n  \"78040.jpg\": \"Insecta/Scolia dubia/65c0dd5622f8024ee91d7c89e00d6596.jpg\",\n  \"78044.jpg\": \"Insecta/Scolia dubia/10f0aa29181fa2f0ea450758037ae392.jpg\",\n  \"78049.jpg\": \"Insecta/Scolia dubia/68901edce8dea9d12a08bca591c868c4.jpg\",\n  \"78055.jpg\": \"Insecta/Scolia dubia/256e29a2396d60218a0d8b72f710ccfc.jpg\",\n  \"78056.jpg\": \"Insecta/Scolia dubia/0f749116a58271b010a3adc91ba34b7d.jpg\",\n  \"78059.jpg\": \"Insecta/Scolia dubia/79761b2e79b7e0f30a18d4f9c6b6b653.jpg\",\n  \"78061.jpg\": \"Insecta/Scolia dubia/ff69abb1a9332557df962795c5413d71.jpg\",\n  \"78062.jpg\": \"Insecta/Scolia dubia/8e831ca5e23194bffcadc6d8c0e4facd.jpg\",\n  \"78064.jpg\": \"Insecta/Scolia dubia/0dc1fd6db5ae8ec00e1d365a401b510f.jpg\",\n  \"78067.jpg\": \"Insecta/Scolia dubia/37256e3cf27a0dfd1cce3feaeb3c5324.jpg\",\n  \"78068.jpg\": \"Insecta/Scolia dubia/98c28ef60bfb480ebedabddf4679d6f4.jpg\",\n  \"78075.jpg\": \"Insecta/Prionoxystus robiniae/bdd976118a75d6ab15a169ff765ab3d9.jpg\",\n  \"78076.jpg\": \"Insecta/Prionoxystus robiniae/baec7f0c82a1627e8820105aa25d41c0.jpg\",\n  \"78080.jpg\": \"Insecta/Prionoxystus robiniae/d5282713c86508063a74568315ce3723.jpg\",\n  \"78086.jpg\": \"Insecta/Prionoxystus robiniae/8c6de358f65307059192cbeedeb130f8.jpg\",\n  \"78089.jpg\": \"Insecta/Prionoxystus robiniae/763c82f27ba8a6bc52d20f4ba83ef6e4.jpg\",\n  \"7811.jpg\": \"Aves/Pica pica/e3feee44729392a4ef11656d0bed91c6.jpg\",\n  \"78125.jpg\": \"Insecta/Zale horrida/3129e7a341ca640f8b732a8a7b90cf29.jpg\",\n  \"78128.jpg\": \"Insecta/Zale horrida/37c1da40e15264d908ac26770329f1ba.jpg\",\n  \"78132.jpg\": \"Insecta/Zale horrida/020e937252bdf8b458e1651180bf9910.jpg\",\n  \"78133.jpg\": \"Insecta/Zale horrida/fa97ba2fa7a4a079ce5662eec9d2451a.jpg\",\n  \"78134.jpg\": \"Insecta/Zale horrida/b60cf90322c28d4173c2bcca82204153.jpg\",\n  \"78150.jpg\": \"Aves/Chondestes grammacus/7527adc098ff4e552c152ded391a15f5.jpg\",\n  \"78183.jpg\": \"Aves/Chondestes grammacus/7629a672be3ed5cf514772eeac50f917.jpg\",\n  \"78186.jpg\": \"Aves/Chondestes grammacus/a5a40bb7e962f13631ea11b41d10151f.jpg\",\n  \"78187.jpg\": \"Aves/Chondestes grammacus/5ec9c494f4d57eec1db31e2ea6944e22.jpg\",\n  \"782.jpg\": \"Mammalia/Ondatra zibethicus/418ee37098622ebc4f007adee0a4847a.jpg\",\n  \"78204.jpg\": \"Aves/Chondestes grammacus/f4399f1aeae4e47c96ba9cf952970b23.jpg\",\n  \"78211.jpg\": \"Aves/Chondestes grammacus/8edb3b3b601951963f7c48b348e740ec.jpg\",\n  \"78221.jpg\": \"Aves/Chondestes grammacus/583df0c6e83e648497b55d986cc391cf.jpg\",\n  \"78247.jpg\": \"Aves/Chondestes grammacus/c73ed066e696e199d8f8876b00b36e50.jpg\",\n  \"78253.jpg\": \"Aves/Chondestes grammacus/2d2c7e8220b787d405d69f016b700ea7.jpg\",\n  \"78257.jpg\": \"Aves/Chondestes grammacus/5665e41925d104c7e1650365e7fd6ed1.jpg\",\n  \"7826.jpg\": \"Aves/Pica pica/d5a68de4312dbcb748fe864351e925ff.jpg\",\n  \"78271.jpg\": \"Aves/Chondestes grammacus/33be90aad46cf85393806968c79e8127.jpg\",\n  \"78282.jpg\": \"Aves/Chondestes grammacus/00531b4076a76eb4fba94b3b025f7e26.jpg\",\n  \"78295.jpg\": \"Aves/Chondestes grammacus/b346b12d31a7c11f45e8f31dae59c1a2.jpg\",\n  \"78303.jpg\": \"Aves/Chondestes grammacus/5e95ca8a834e403f1db37e00972dfa93.jpg\",\n  \"78308.jpg\": \"Aves/Chondestes grammacus/60a31e54e8b8fd1edfd51f1cfd87ddfc.jpg\",\n  \"78326.jpg\": \"Aves/Chondestes grammacus/661df59a24430ce0a2793b83952ad0bc.jpg\",\n  \"78335.jpg\": \"Aves/Chondestes grammacus/d5517a13a7712bdf1c180ecd7b409cd9.jpg\",\n  \"78340.jpg\": \"Aves/Chondestes grammacus/00a161375b994a0bc21208de704daf74.jpg\",\n  \"78348.jpg\": \"Aves/Chondestes grammacus/24094eb3fe9eebe92e83045f01b9ab63.jpg\",\n  \"7835.jpg\": \"Aves/Pica pica/fff21bd80304372ae5faa0a61a2d32ac.jpg\",\n  \"78359.jpg\": \"Aves/Chondestes grammacus/1354d32d730748337e2a85090132660a.jpg\",\n  \"78367.jpg\": \"Aves/Chondestes grammacus/6078abd877f3d96d6534350970409c49.jpg\",\n  \"78369.jpg\": \"Aves/Chondestes grammacus/3e3dfa8e033e68fdabd44ea7c48ab400.jpg\",\n  \"78374.jpg\": \"Aves/Lophura leucomelanos/6326988b07a9caa62ee25071f8d00eca.jpg\",\n  \"7838.jpg\": \"Aves/Pica pica/e1498f3d01f2f6de4911be2d856aa911.jpg\",\n  \"78384.jpg\": \"Aves/Lophura leucomelanos/6fcadd49187a93f4f6be78a7fc2ce2c1.jpg\",\n  \"78385.jpg\": \"Aves/Lophura leucomelanos/ab3fddef993fa31b5ee0f668cb1ff3f0.jpg\",\n  \"78390.jpg\": \"Aves/Geothlypis tolmiei/9a5524a4ef8e8c9cff9f892648fbba43.jpg\",\n  \"78396.jpg\": \"Aves/Geothlypis tolmiei/d29eda82f0263d62ec0b1221f534f608.jpg\",\n  \"78397.jpg\": \"Aves/Geothlypis tolmiei/cfe510a34a069aa7291c11f7027d14c5.jpg\",\n  \"78399.jpg\": \"Aves/Geothlypis tolmiei/89c8afc9e401065671cf7e07b51b938e.jpg\",\n  \"78404.jpg\": \"Aves/Geothlypis tolmiei/6fe73a15da25559221fbbee0e9abd6aa.jpg\",\n  \"78408.jpg\": \"Aves/Geothlypis tolmiei/971269d4a24f2f0823d2b3cc298bb253.jpg\",\n  \"78409.jpg\": \"Aves/Geothlypis tolmiei/4d67daacf04969db14ff496b16878526.jpg\",\n  \"78411.jpg\": \"Aves/Geothlypis tolmiei/18cce76a9c8a1531857e18d1ca457759.jpg\",\n  \"78423.jpg\": \"Mammalia/Rangifer tarandus/41533fe43978f822483e89d40d59ecf3.jpg\",\n  \"78430.jpg\": \"Mammalia/Rangifer tarandus/65f3224454d33b0bfd30c8e219c779f1.jpg\",\n  \"78436.jpg\": \"Mammalia/Rangifer tarandus/4f325f8a1b1dd6bfcb6becf9725a443b.jpg\",\n  \"78444.jpg\": \"Mammalia/Rangifer tarandus/d7fb0c04eb7b28fff02de119bb09c5fa.jpg\",\n  \"78533.jpg\": \"Insecta/Tetracis crocallata/603523bafe578ad4d1d6083a14ce0cae.jpg\",\n  \"78535.jpg\": \"Insecta/Tetracis crocallata/574b49c14bd8e002ee8cac7d9358be7f.jpg\",\n  \"78543.jpg\": \"Insecta/Tetracis crocallata/92b131b556a0c9c95c613a5a10263530.jpg\",\n  \"78545.jpg\": \"Insecta/Tetracis crocallata/9a6206de67a989fc217eb34a02ceaf30.jpg\",\n  \"78546.jpg\": \"Insecta/Tetracis crocallata/a4f3357106f13843b73a51b53f7f31d1.jpg\",\n  \"78547.jpg\": \"Insecta/Tetracis crocallata/8cfacab291bc488781f31bd6519bedd5.jpg\",\n  \"78586.jpg\": \"Insecta/Vanessa kershawi/f089d5c689c6866aef865aef4856b4bd.jpg\",\n  \"78597.jpg\": \"Insecta/Vanessa kershawi/2a0ad23918d6431a7f8db168b667120d.jpg\",\n  \"78608.jpg\": \"Insecta/Vanessa kershawi/5063e01141fe3950a1d02c66a33eb431.jpg\",\n  \"78612.jpg\": \"Insecta/Vanessa kershawi/d03cefcd8604bc1011d013cd214479e4.jpg\",\n  \"78613.jpg\": \"Aves/Monticola solitarius/ab1c32558da9784964c52ddc62635849.jpg\",\n  \"78617.jpg\": \"Aves/Monticola solitarius/ef92ca5ea31ffe58f35a48821fd66a4e.jpg\",\n  \"78619.jpg\": \"Aves/Monticola solitarius/b44872f954334fc0a79461fc308c0e1b.jpg\",\n  \"78621.jpg\": \"Aves/Monticola solitarius/21146afbf2f3f53ff365ff927abbfcb5.jpg\",\n  \"78622.jpg\": \"Aves/Monticola solitarius/462b86d2cbc1c80e23ad404d71cd0225.jpg\",\n  \"78704.jpg\": \"Insecta/Tremex columba/602104caf1a96808a8f2e8a5d2aa5584.jpg\",\n  \"78705.jpg\": \"Insecta/Tremex columba/3296a4d374523937e54ad5d41c184cd8.jpg\",\n  \"78706.jpg\": \"Insecta/Tremex columba/04ad7ccae3f852a021aee8904f35e62e.jpg\",\n  \"78719.jpg\": \"Insecta/Tremex columba/0c642ad6452a5c81a69875ff3759b3c5.jpg\",\n  \"78772.jpg\": \"Reptilia/Micrurus tener tener/4043fbe747e72e66fb85a5dc16ca4fa6.jpg\",\n  \"78788.jpg\": \"Reptilia/Micrurus tener tener/2112ece40c5211857f17f862c6f3b4e6.jpg\",\n  \"78790.jpg\": \"Reptilia/Micrurus tener tener/dcc3e4df2385a5d210c7983026a4ce97.jpg\",\n  \"78791.jpg\": \"Reptilia/Coluber lateralis/53e040855061d59b0415cefa70ce73d7.jpg\",\n  \"78793.jpg\": \"Reptilia/Coluber lateralis/1cdf2c037e0c526167e85eb9902ed51d.jpg\",\n  \"78795.jpg\": \"Reptilia/Coluber lateralis/4188fbdda1ca084c489679485492e991.jpg\",\n  \"78807.jpg\": \"Reptilia/Coluber lateralis/8d58d3e7375e3780724d45239e431e77.jpg\",\n  \"78873.jpg\": \"Insecta/Limenitis archippus/d5a45ea516a5e0406275e6c7e252f46d.jpg\",\n  \"789.jpg\": \"Mammalia/Ondatra zibethicus/f4efe1713230e2a0ab9c212e11be1d88.jpg\",\n  \"78918.jpg\": \"Insecta/Limenitis archippus/03fe63eef91ef27692f491dbbbd49187.jpg\",\n  \"78935.jpg\": \"Insecta/Limenitis archippus/eab576e885a8c8fbc63ab5d27840633b.jpg\",\n  \"78943.jpg\": \"Insecta/Limenitis archippus/ebf739ab7170a29f586bf6ee023fcf88.jpg\",\n  \"78945.jpg\": \"Insecta/Limenitis archippus/62a2ec0ebc0b899496f9e73a009d5883.jpg\",\n  \"78954.jpg\": \"Insecta/Limenitis archippus/b8efe6eafe8ca3b8a86dc54f0dc7edc1.jpg\",\n  \"78961.jpg\": \"Insecta/Limenitis archippus/5942fea9988175e7c857c6389a7853d8.jpg\",\n  \"78965.jpg\": \"Insecta/Limenitis archippus/632912775b3e19e871a2566dcdff21cf.jpg\",\n  \"78966.jpg\": \"Insecta/Limenitis archippus/ad6e4dc081491f4ee8870d6a0c3e847f.jpg\",\n  \"78972.jpg\": \"Insecta/Limenitis archippus/538f94da7a793c9e07a6f964e165307e.jpg\",\n  \"78978.jpg\": \"Insecta/Limenitis archippus/a3c01c5d005cc67ae42a715a799f8097.jpg\",\n  \"790.jpg\": \"Mammalia/Ondatra zibethicus/c24b52f6997434662abff943e6763722.jpg\",\n  \"79008.jpg\": \"Insecta/Limenitis archippus/bec6f8b2d7e5beaa2f48aabad3748f73.jpg\",\n  \"79017.jpg\": \"Insecta/Limenitis archippus/a8df522b734b8f5728d1fb2d5c7e20a4.jpg\",\n  \"79023.jpg\": \"Insecta/Limenitis archippus/6515afaaacf88ff0673a11b4ab8a8836.jpg\",\n  \"79032.jpg\": \"Insecta/Limenitis archippus/b434998484e9742c113600976f1a4828.jpg\",\n  \"79044.jpg\": \"Insecta/Limenitis archippus/48749af616f91f9a295e608bb4ec64fe.jpg\",\n  \"79045.jpg\": \"Insecta/Limenitis archippus/a53ae43b1b431c43909d1c8a5f12f717.jpg\",\n  \"7906.jpg\": \"Aves/Calypte costae/753c363fd95a35b546129fabbc810043.jpg\",\n  \"79072.jpg\": \"Insecta/Limenitis archippus/2f570fbdc3ff92b7d0500078d6010acf.jpg\",\n  \"79104.jpg\": \"Insecta/Limenitis archippus/62b78e2932c2e473a0afaf56c026242b.jpg\",\n  \"7911.jpg\": \"Aves/Calypte costae/565d973318fa482c67a0e8d07a6f7dda.jpg\",\n  \"79128.jpg\": \"Reptilia/Drymobius margaritiferus/d07e77710daba8cb3c18fac0750b9b07.jpg\",\n  \"79129.jpg\": \"Reptilia/Drymobius margaritiferus/f1930dffe1227c5bbca8ddabc49bca11.jpg\",\n  \"79135.jpg\": \"Reptilia/Drymobius margaritiferus/921af5f274ab22d6b323292c2efae9f1.jpg\",\n  \"79139.jpg\": \"Reptilia/Drymobius margaritiferus/17d02e6907f1a24aa7ca04797a2d987d.jpg\",\n  \"79145.jpg\": \"Reptilia/Drymobius margaritiferus/76833a101a455b98dd810cde4376fddd.jpg\",\n  \"79154.jpg\": \"Reptilia/Drymobius margaritiferus/c9b3eb721ed2d5581983623cf275b9eb.jpg\",\n  \"79207.jpg\": \"Arachnida/Phidippus audax/9aa465db0036b0bebde633913f38e3a1.jpg\",\n  \"79208.jpg\": \"Arachnida/Phidippus audax/981ddbd4f2711cd3123c576044e782e4.jpg\",\n  \"79209.jpg\": \"Arachnida/Phidippus audax/d94adc80d6429ddbba6abf83874df56e.jpg\",\n  \"79222.jpg\": \"Arachnida/Phidippus audax/0d3cfad056f2e87bd90889825359f209.jpg\",\n  \"79223.jpg\": \"Arachnida/Phidippus audax/509599a772dadcc54a8513c01b2445fd.jpg\",\n  \"7923.jpg\": \"Aves/Calypte costae/ad9384efd38508c617e6dc3b2723fd1f.jpg\",\n  \"79251.jpg\": \"Arachnida/Phidippus audax/102cbd39658f76333e3ef69db6d8b4a8.jpg\",\n  \"79257.jpg\": \"Arachnida/Phidippus audax/954fce8d5dea935ac632fa0483ccdaed.jpg\",\n  \"7926.jpg\": \"Aves/Calypte costae/cd6ed03aeaa8896e126fd5c64fa68080.jpg\",\n  \"79267.jpg\": \"Arachnida/Phidippus audax/c2c70570cacf6357f1b35f38a998bbeb.jpg\",\n  \"7927.jpg\": \"Aves/Calypte costae/867dd83c26a9a925dfa5b2f39c3d64f1.jpg\",\n  \"79271.jpg\": \"Arachnida/Phidippus audax/645d66d7d8589af2a72fd34f288705a7.jpg\",\n  \"79272.jpg\": \"Arachnida/Phidippus audax/313d02de17810eb136f396f6d3da9e3f.jpg\",\n  \"79273.jpg\": \"Arachnida/Phidippus audax/9aae361c4b8382b87fa57cf66b0317f1.jpg\",\n  \"7928.jpg\": \"Aves/Calypte costae/701f77551d2b6f3aceabbbb7d81a03c0.jpg\",\n  \"79288.jpg\": \"Arachnida/Phidippus audax/92ae458e4f758f7b3db867b758f2f27e.jpg\",\n  \"7931.jpg\": \"Aves/Calypte costae/114a8e3f0cc27e458d64780a3153966b.jpg\",\n  \"79311.jpg\": \"Arachnida/Phidippus audax/07d7c41a1c4c80c9e71ea88964f723a5.jpg\",\n  \"79315.jpg\": \"Arachnida/Phidippus audax/188917e5555c9756d31c07f22aa90267.jpg\",\n  \"7932.jpg\": \"Aves/Calypte costae/87bdbc7e72968f4ffaa2a73316656f23.jpg\",\n  \"79320.jpg\": \"Arachnida/Phidippus audax/2194892ad2f71c9e43a12f5e60bbcd22.jpg\",\n  \"79325.jpg\": \"Arachnida/Phidippus audax/cbd4d9fe7547c6bcf3b5fbeff09429f1.jpg\",\n  \"79328.jpg\": \"Arachnida/Phidippus audax/9c733820ec22665e8511cfa4079b1c21.jpg\",\n  \"79329.jpg\": \"Arachnida/Phidippus audax/720a8a9775f931e356d68d4dd4a54b02.jpg\",\n  \"7934.jpg\": \"Aves/Calypte costae/342fff8336f099382e97de5ebf56301e.jpg\",\n  \"79349.jpg\": \"Arachnida/Phidippus audax/bab8c5a7e1ffcd38af21b12d45f3f021.jpg\",\n  \"79350.jpg\": \"Arachnida/Phidippus audax/cc977e49939905e9c1637fdc12d82a8c.jpg\",\n  \"7936.jpg\": \"Aves/Calypte costae/aead8fa7158a75778dd152c9dc475a4c.jpg\",\n  \"79371.jpg\": \"Arachnida/Phidippus audax/f090940ee572969c414e6fbb100eb5d0.jpg\",\n  \"79380.jpg\": \"Arachnida/Phidippus audax/c45490202c4ae27e7c598ca75e9a5ec9.jpg\",\n  \"79384.jpg\": \"Arachnida/Phidippus audax/2e5126a2afa35b463d7ffd496d18567f.jpg\",\n  \"79386.jpg\": \"Arachnida/Phidippus audax/fa418902881398ff273ce7a7a5dd5de5.jpg\",\n  \"7945.jpg\": \"Aves/Calypte costae/1a274df1391827908cdbebb175c1f656.jpg\",\n  \"7947.jpg\": \"Aves/Calypte costae/c1d6d33c388d36eee3535301f6745147.jpg\",\n  \"7948.jpg\": \"Aves/Calypte costae/789eae140b0c4dd55b2a6d1d0f5d731c.jpg\",\n  \"7949.jpg\": \"Aves/Calypte costae/868b66389c0fdd078d279161704325cf.jpg\",\n  \"7950.jpg\": \"Aves/Calypte costae/fa1520321e6df8db8f69fb18f0e2b147.jpg\",\n  \"7952.jpg\": \"Aves/Calypte costae/693c04060b9532718001ec4ebe74ed06.jpg\",\n  \"7955.jpg\": \"Aves/Calypte costae/e7a39e49acdde1c3650052af1b629e48.jpg\",\n  \"7956.jpg\": \"Aves/Calypte costae/372f9a6c1be196e3e2598668fb7a321a.jpg\",\n  \"79600.jpg\": \"Insecta/Hyles gallii/3d1e1b8461497e50aa3a843e7803ee55.jpg\",\n  \"79601.jpg\": \"Insecta/Hyles gallii/6596571b4044a514fd9f13d355206b25.jpg\",\n  \"79605.jpg\": \"Insecta/Hyles gallii/c3283f46d9b936a7f3620cb7bc18fcf5.jpg\",\n  \"7961.jpg\": \"Aves/Halcyon smyrnensis/bbcf8e9d0f773e94262a9fcec6214969.jpg\",\n  \"79611.jpg\": \"Insecta/Hyles gallii/d71b5cd0b6b82870c879c3588647efb7.jpg\",\n  \"79618.jpg\": \"Insecta/Hyles gallii/710445eaaa43a1efb0e6dec10b58f72a.jpg\",\n  \"79621.jpg\": \"Insecta/Hyles gallii/e95810d75278464e7460d940d7d89252.jpg\",\n  \"79622.jpg\": \"Insecta/Hyles gallii/0d3d869665ee8439690bcde5a52260b9.jpg\",\n  \"79623.jpg\": \"Insecta/Hyles gallii/4e229f7867e6982bba079e654ab16e69.jpg\",\n  \"79625.jpg\": \"Insecta/Hyles gallii/29c598f5915fd7645ac58dc6d26474dd.jpg\",\n  \"79679.jpg\": \"Aves/Charadrius montanus/9bdd23f318bc89b2aac8ead69dec79d2.jpg\",\n  \"7968.jpg\": \"Aves/Halcyon smyrnensis/1d8c0f90ab3f40ef293ce72e7ec22e6e.jpg\",\n  \"79684.jpg\": \"Aves/Charadrius montanus/67bd84c62a9fd4c8c47624cfd673d537.jpg\",\n  \"79685.jpg\": \"Aves/Charadrius montanus/1685a6e163efb609898408a4e63de007.jpg\",\n  \"79687.jpg\": \"Aves/Charadrius montanus/8ef98921e227a8b75af8d6b72b5b7223.jpg\",\n  \"7970.jpg\": \"Aves/Halcyon smyrnensis/f1a3e78b6b1949d410bd86b1347ba1e7.jpg\",\n  \"7971.jpg\": \"Aves/Halcyon smyrnensis/091f8870f6acebe4b8bc9e4bfcddd9fb.jpg\",\n  \"7978.jpg\": \"Aves/Halcyon smyrnensis/9c313312c37cdcb75548846962469194.jpg\",\n  \"7980.jpg\": \"Aves/Halcyon smyrnensis/dd551f5eaaaa2268142510966b9f7408.jpg\",\n  \"7981.jpg\": \"Aves/Halcyon smyrnensis/78a2042bc8e347f53d4c2a7ad9575d50.jpg\",\n  \"7984.jpg\": \"Insecta/Phoebis philea/498a824665ab5b37942f8e67b79c365e.jpg\",\n  \"7985.jpg\": \"Insecta/Phoebis philea/6f7ddd5d4022402207db16bc529fb986.jpg\",\n  \"79855.jpg\": \"Aves/Contopus sordidulus/414930de4644d03ab2186d21e0fce0c2.jpg\",\n  \"79856.jpg\": \"Aves/Contopus sordidulus/694a8ebdb18e5c20d57cd911e8994cd6.jpg\",\n  \"7986.jpg\": \"Insecta/Phoebis philea/87080a26cf6e1ae1226d7a3cc31e84cd.jpg\",\n  \"79863.jpg\": \"Aves/Contopus sordidulus/77d0db5cf30e49ae6817df060e528951.jpg\",\n  \"79864.jpg\": \"Aves/Contopus sordidulus/9a3add8770df8a7bcd6154e4db6b90c6.jpg\",\n  \"79865.jpg\": \"Aves/Contopus sordidulus/697913933113fcf0a645f1656dfa3303.jpg\",\n  \"79867.jpg\": \"Aves/Contopus sordidulus/57602abd1d8e1574e068f6d0d4842083.jpg\",\n  \"79869.jpg\": \"Aves/Contopus sordidulus/4cf0d3db07e19097485646e5ed31dab6.jpg\",\n  \"7987.jpg\": \"Insecta/Phoebis philea/56e19a151c064b12bf96d05c15b01d1e.jpg\",\n  \"79878.jpg\": \"Aves/Contopus sordidulus/664f53c7003b41980b03e085f199a713.jpg\",\n  \"79882.jpg\": \"Aves/Contopus sordidulus/0bfedef1845f618dfdcd2dbac6a49b0d.jpg\",\n  \"79883.jpg\": \"Aves/Contopus sordidulus/83f601fc891cbe0dcfe6ff58436ff55c.jpg\",\n  \"79888.jpg\": \"Aves/Contopus sordidulus/c3e6fd8ec086eb71f525bf32965f8245.jpg\",\n  \"79892.jpg\": \"Aves/Contopus sordidulus/dc9f1a3b05f64af42c8b45d6ceb9a4ce.jpg\",\n  \"7990.jpg\": \"Insecta/Phoebis philea/84d53120ceeb1453f1e23df238d032da.jpg\",\n  \"79904.jpg\": \"Aves/Contopus sordidulus/b6b309fbbc9ff6f1f2ceb7ea8fca3ff5.jpg\",\n  \"79909.jpg\": \"Aves/Contopus sordidulus/d463fa569dbd369e9fc507ed606af5f9.jpg\",\n  \"79926.jpg\": \"Aves/Contopus sordidulus/cc6e666135cffd490cd142918c893519.jpg\",\n  \"79927.jpg\": \"Aves/Contopus sordidulus/258c92c40d1249f1cc6386c1fd8b0aa2.jpg\",\n  \"79939.jpg\": \"Aves/Contopus sordidulus/78835abce0d7c29c221efb2497835e71.jpg\",\n  \"79940.jpg\": \"Aves/Contopus sordidulus/5793257d9fff95285cc57792c657c4e5.jpg\",\n  \"79947.jpg\": \"Aves/Contopus sordidulus/53c0f6ec14bf3ded02cc777d3f20b359.jpg\",\n  \"79948.jpg\": \"Aves/Contopus sordidulus/4d793130868f82ed05ba65826c4c7fb3.jpg\",\n  \"79951.jpg\": \"Aves/Contopus sordidulus/916d00eb0a7ba92ce27bd2afbaa8b215.jpg\",\n  \"79959.jpg\": \"Aves/Contopus sordidulus/25c33f55d003582aff1c17f40509a57b.jpg\",\n  \"79962.jpg\": \"Aves/Contopus sordidulus/77da48604c806446e7302f5da5718826.jpg\",\n  \"8004.jpg\": \"Aves/Amphispiza bilineata/14c0a60ca97e963f893650902d2477fe.jpg\",\n  \"8005.jpg\": \"Aves/Amphispiza bilineata/fb3b85652ea3a7bafd0081f82e5eebd9.jpg\",\n  \"8006.jpg\": \"Aves/Amphispiza bilineata/1ed93b679d0211f2fb155aa08127cead.jpg\",\n  \"80152.jpg\": \"Aves/Pitangus sulphuratus/bc90328b44e7c8735785909bfc55c1a1.jpg\",\n  \"80174.jpg\": \"Aves/Pitangus sulphuratus/587187e9fcc1b001222b19b858ff379d.jpg\",\n  \"80181.jpg\": \"Aves/Pitangus sulphuratus/4c423a97322b71e28854c138ab230c83.jpg\",\n  \"8020.jpg\": \"Aves/Amphispiza bilineata/5623ff76d13d84dc0f0735dcb9c65ee8.jpg\",\n  \"80209.jpg\": \"Aves/Pitangus sulphuratus/b6ecc90b503a789665b3e138bc3a07ab.jpg\",\n  \"8021.jpg\": \"Aves/Amphispiza bilineata/5df937ded11b7325cfe172b5905412e0.jpg\",\n  \"80217.jpg\": \"Aves/Pitangus sulphuratus/cd3e781a89916452cec2882d46073ff9.jpg\",\n  \"8022.jpg\": \"Aves/Amphispiza bilineata/04583db09517495a83b95c6fa97f8650.jpg\",\n  \"8023.jpg\": \"Aves/Amphispiza bilineata/4313468d550e12d14d1c89fe0e56ea43.jpg\",\n  \"80231.jpg\": \"Aves/Pitangus sulphuratus/978fe86025d0d62894d6f4d2426ca32f.jpg\",\n  \"80242.jpg\": \"Aves/Pitangus sulphuratus/fcfeaf6980ed0e8b41c5621f1fa9f98f.jpg\",\n  \"80256.jpg\": \"Aves/Pitangus sulphuratus/351fd7e47a9301721c08ebbde69811d2.jpg\",\n  \"80284.jpg\": \"Aves/Pitangus sulphuratus/7bf28c1bfb0e6a43714fc4e9c6d18a76.jpg\",\n  \"80294.jpg\": \"Aves/Pitangus sulphuratus/3063b3e194b29c305e271ad8310ac1c9.jpg\",\n  \"80298.jpg\": \"Aves/Pitangus sulphuratus/a6eecd5836b4e4590f5d5b1f2997c7ef.jpg\",\n  \"80299.jpg\": \"Aves/Pitangus sulphuratus/b41261698f5bad648b0541ccf5987300.jpg\",\n  \"80301.jpg\": \"Aves/Pitangus sulphuratus/eb1d8e3b826aae293fc95a8be2def685.jpg\",\n  \"80309.jpg\": \"Aves/Pitangus sulphuratus/0257c3f88cf9ba24f50d15f4092a0f62.jpg\",\n  \"80311.jpg\": \"Aves/Pitangus sulphuratus/495bce0ab208435c303205c151dde532.jpg\",\n  \"80320.jpg\": \"Aves/Pitangus sulphuratus/b0548cbce770413919e05ab98aa66d08.jpg\",\n  \"80322.jpg\": \"Aves/Pitangus sulphuratus/b65cd961ccbd9e7fdaa85212013a33a5.jpg\",\n  \"8033.jpg\": \"Aves/Amphispiza bilineata/764167c0b12c96378a8cb7b03b91be17.jpg\",\n  \"80353.jpg\": \"Aves/Pitangus sulphuratus/1c5284946ebe30b93a56e41b68f888c4.jpg\",\n  \"8039.jpg\": \"Aves/Amphispiza bilineata/0f3dbf2c07789bf1f159685aa5d17b8c.jpg\",\n  \"80390.jpg\": \"Aves/Pitangus sulphuratus/1fcd199e5ee61e7c60ad861f5b58d6bd.jpg\",\n  \"80394.jpg\": \"Aves/Pitangus sulphuratus/8351e24166180cb0bfd7eaef32a259c9.jpg\",\n  \"804.jpg\": \"Mammalia/Ondatra zibethicus/86f421648968d01731fd879106911ac6.jpg\",\n  \"80401.jpg\": \"Aves/Pitangus sulphuratus/0d392d50d9933e95e76bf3b7edf7b305.jpg\",\n  \"80411.jpg\": \"Aves/Pitangus sulphuratus/0a180b0c0a7a4abae438904b90fecd07.jpg\",\n  \"8042.jpg\": \"Aves/Amphispiza bilineata/88e6600ede27367b67b91e656f84f931.jpg\",\n  \"80441.jpg\": \"Aves/Pitangus sulphuratus/0ac9f64d3d4a04b182cab25616e85f99.jpg\",\n  \"80447.jpg\": \"Insecta/Strymon istapa/d390395755a9de37cc7ab828fbfa52db.jpg\",\n  \"80448.jpg\": \"Insecta/Strymon istapa/d47c243bbb07431da76ebf1a000d5405.jpg\",\n  \"80452.jpg\": \"Insecta/Strymon istapa/45a5b8d7f8dfa646950255e41fe5491a.jpg\",\n  \"80453.jpg\": \"Insecta/Strymon istapa/a8b44e570ecce05dfe2ba6a1506bac4a.jpg\",\n  \"80454.jpg\": \"Insecta/Strymon istapa/9b505c2828cf38de65e8c706154dcd94.jpg\",\n  \"80457.jpg\": \"Insecta/Strymon istapa/7ff01254e81522536faeec0711505f53.jpg\",\n  \"80458.jpg\": \"Insecta/Strymon istapa/4a94e8229626458e8f838ef0a2521cef.jpg\",\n  \"80459.jpg\": \"Insecta/Strymon istapa/06929767b45af9f7cb02e68dbd9aead4.jpg\",\n  \"8046.jpg\": \"Aves/Amphispiza bilineata/d9fb36f7b4351fa64bdf14cbb32a61b7.jpg\",\n  \"80461.jpg\": \"Insecta/Strymon istapa/25cb0029de5a567b4996ce9736a3fbe2.jpg\",\n  \"80462.jpg\": \"Insecta/Strymon istapa/4cb8d989f4d02714e58b0afe87430959.jpg\",\n  \"80464.jpg\": \"Insecta/Strymon istapa/cffe31eea76eaa52a5285ea1fbfe740c.jpg\",\n  \"80465.jpg\": \"Insecta/Strymon istapa/0e1e48f59f39e98425aa475afff8820e.jpg\",\n  \"80466.jpg\": \"Insecta/Strymon istapa/894cf87babe53e4ca7dfa581845dfb5b.jpg\",\n  \"80468.jpg\": \"Insecta/Strymon istapa/e49123e19f30b0136bc3f75148e8e032.jpg\",\n  \"80475.jpg\": \"Insecta/Strymon istapa/f316f863dd62679e200e0c019d86b916.jpg\",\n  \"80476.jpg\": \"Insecta/Strymon istapa/85aba59db4f3d6135f71dba3bbd92585.jpg\",\n  \"80478.jpg\": \"Insecta/Strymon istapa/4c260844f01da4383244f3c7fa3915f6.jpg\",\n  \"80484.jpg\": \"Insecta/Strymon istapa/33c3065d40630f2cef5347c4ef1de2f8.jpg\",\n  \"80514.jpg\": \"Aves/Spiza americana/bb9ba99bcdcce90bee99e8016fa61ab1.jpg\",\n  \"80518.jpg\": \"Aves/Spiza americana/0687a8cc202b8e5472c37bab560a4a89.jpg\",\n  \"80523.jpg\": \"Aves/Spiza americana/fb853e340d0447ca344e98f50f0b62b0.jpg\",\n  \"80526.jpg\": \"Aves/Spiza americana/a73aedf095a67eef44a0da24553e2774.jpg\",\n  \"80532.jpg\": \"Aves/Spiza americana/a1e2c4446ad57fb3768ae8aa13f75745.jpg\",\n  \"80534.jpg\": \"Aves/Spiza americana/0e80f8974ea38bf0b843ab787904f1b7.jpg\",\n  \"80535.jpg\": \"Aves/Spiza americana/7b4dfedf4d864c15b36da343a9d3c273.jpg\",\n  \"80544.jpg\": \"Aves/Spiza americana/08eb8a74962a826cec0f3e6afd583569.jpg\",\n  \"80559.jpg\": \"Aves/Spiza americana/85e33d197f02aa9c7fb7124ba587901e.jpg\",\n  \"80560.jpg\": \"Aves/Spiza americana/48103c542f70cc7a1ea22d4fef94deb7.jpg\",\n  \"80565.jpg\": \"Aves/Spiza americana/975e641b20b735c54584d76ececa62d6.jpg\",\n  \"80581.jpg\": \"Aves/Spiza americana/0b77ac51e667ebdc2018cec4c501b93b.jpg\",\n  \"80583.jpg\": \"Aves/Spiza americana/27d85b9dd750275c2c394bde8c44c3c3.jpg\",\n  \"80584.jpg\": \"Aves/Spiza americana/ca89de34006037932929e141068dddf0.jpg\",\n  \"80585.jpg\": \"Aves/Spiza americana/1ca3300008421ff90165b1a83c22f9b9.jpg\",\n  \"80586.jpg\": \"Aves/Spiza americana/b5fd0e8ebb66208b63e9d8306dca9b29.jpg\",\n  \"80587.jpg\": \"Aves/Spiza americana/c00b483dbfbde10196cfc75ba1043d52.jpg\",\n  \"80588.jpg\": \"Aves/Spiza americana/d0080b913d2d7520da25ea99c30fd468.jpg\",\n  \"80589.jpg\": \"Aves/Spiza americana/aac785c542d78ce8c6f5bcacf5ade481.jpg\",\n  \"8059.jpg\": \"Aves/Amphispiza bilineata/96d3204ec24a5bf45f9548a8357a6da1.jpg\",\n  \"80590.jpg\": \"Aves/Spiza americana/af412f27d048919190191e076f6e5580.jpg\",\n  \"80596.jpg\": \"Aves/Spiza americana/db67d693e17b2ee0b4a25860db9c9c18.jpg\",\n  \"80608.jpg\": \"Aves/Spiza americana/15e324b90dde49c3913821b7f98aea42.jpg\",\n  \"80611.jpg\": \"Aves/Spiza americana/156032d414e7b3bf8bb53b4d8191ca97.jpg\",\n  \"80617.jpg\": \"Aves/Spiza americana/e57088ddc6c8c838c6e3b601153a6e8a.jpg\",\n  \"8066.jpg\": \"Aves/Amphispiza bilineata/64a90bf73c2a29e1fe9f281f5ca2ff64.jpg\",\n  \"8086.jpg\": \"Aves/Amphispiza bilineata/a7fd822bd9383dad20b0cb06e6e08fe4.jpg\",\n  \"80890.jpg\": \"Insecta/Apodemia virgulti/4af27e3127912c34d0cdf3dd0e809f30.jpg\",\n  \"80891.jpg\": \"Insecta/Apodemia virgulti/f02f9eabe8898fb1181ef657e7cd91d5.jpg\",\n  \"80892.jpg\": \"Insecta/Apodemia virgulti/3b650c6baa8b6650792990ad075b1280.jpg\",\n  \"8090.jpg\": \"Aves/Amphispiza bilineata/8a30e65b7c6355f1dd11a27f4f49cb34.jpg\",\n  \"80904.jpg\": \"Insecta/Apodemia virgulti/d7e05a40bbbecf47e65c0b60d4621692.jpg\",\n  \"80905.jpg\": \"Insecta/Apodemia virgulti/bed2509f163679cdde9f46582cb379dd.jpg\",\n  \"80907.jpg\": \"Insecta/Apodemia virgulti/5db2fa49db462b553793e5e68aa8db9f.jpg\",\n  \"80911.jpg\": \"Insecta/Apodemia virgulti/670cbc8f6e943eed545bdc1678e8faf6.jpg\",\n  \"80917.jpg\": \"Insecta/Apodemia virgulti/ecc89ba0121a599cd7504fbf5ce9c62b.jpg\",\n  \"80927.jpg\": \"Insecta/Apodemia virgulti/718785dc66f18be6f127ed0ac5c1b364.jpg\",\n  \"80928.jpg\": \"Insecta/Agrius cingulata/b768eb7f9b56999a5f3f497a34721e47.jpg\",\n  \"80929.jpg\": \"Insecta/Agrius cingulata/a0e00aba1993524cb84c4ecffd4791e0.jpg\",\n  \"80930.jpg\": \"Insecta/Agrius cingulata/9a4f1aaeb03131bc087b0cb1c4c874c2.jpg\",\n  \"80936.jpg\": \"Insecta/Agrius cingulata/7696b12e61c4a2b2f6c523a7d9707f47.jpg\",\n  \"80939.jpg\": \"Insecta/Agrius cingulata/a34cbd785b053c71cff37b0b851924b2.jpg\",\n  \"8094.jpg\": \"Aves/Amphispiza bilineata/34ee314d76da8e442508c3af1c0d094b.jpg\",\n  \"80945.jpg\": \"Insecta/Agrius cingulata/1a6e2236fc5fc7ab5fce26ea8fabd9ee.jpg\",\n  \"80953.jpg\": \"Mollusca/Urosalpinx cinerea/1151bb46488c484fafafaae827df3fa6.jpg\",\n  \"80954.jpg\": \"Mollusca/Urosalpinx cinerea/3a065839e5eea916595e6436bb602967.jpg\",\n  \"80956.jpg\": \"Mollusca/Urosalpinx cinerea/843af58a6309be7b34f9a6ccd6f769e3.jpg\",\n  \"80960.jpg\": \"Mollusca/Urosalpinx cinerea/45254109fd2188f92126a1d545fc4dff.jpg\",\n  \"80963.jpg\": \"Mollusca/Urosalpinx cinerea/51c1856fdc4ea00fa286a2ec5bb5cff7.jpg\",\n  \"80965.jpg\": \"Mollusca/Urosalpinx cinerea/7c5297a0b8e132b505eafe04adeac2c0.jpg\",\n  \"80966.jpg\": \"Mollusca/Urosalpinx cinerea/ce0e6b9ffe360e9a7e1756ce9b3c2050.jpg\",\n  \"80976.jpg\": \"Mollusca/Katharina tunicata/8a2dbfb0391262f8756436ca6a5dc98d.jpg\",\n  \"80978.jpg\": \"Mollusca/Katharina tunicata/d0e69c3f777d0bc691802d102ac0552b.jpg\",\n  \"80983.jpg\": \"Mollusca/Katharina tunicata/e544198aefd47d2f9b0be3f5f5ae33c6.jpg\",\n  \"80991.jpg\": \"Mollusca/Katharina tunicata/a468447b93e6087dc28184f63350935e.jpg\",\n  \"81028.jpg\": \"Reptilia/Coluber constrictor constrictor/0821f6df4ae6d109934964112fc9c39b.jpg\",\n  \"81029.jpg\": \"Reptilia/Coluber constrictor constrictor/e4ad4d2170d0dae0aafe65c1130869d3.jpg\",\n  \"8103.jpg\": \"Aves/Amphispiza bilineata/e7e45567fec073851fac6a6bef978875.jpg\",\n  \"81030.jpg\": \"Reptilia/Coluber constrictor constrictor/3b88d4d74e0ef4ea638c083dbf1b1176.jpg\",\n  \"81031.jpg\": \"Reptilia/Coluber constrictor constrictor/9999cf19810aed9667433d5133425a1b.jpg\",\n  \"81035.jpg\": \"Reptilia/Coluber constrictor constrictor/525dfd95a5a4bc65dce4bab9992b3a01.jpg\",\n  \"81036.jpg\": \"Reptilia/Coluber constrictor constrictor/54e519102ef747daf4846ed954cdfb06.jpg\",\n  \"8104.jpg\": \"Aves/Amphispiza bilineata/45e4e94a6f45e6ffe9a462272c7f0af7.jpg\",\n  \"81045.jpg\": \"Reptilia/Coluber constrictor constrictor/a6f78fa4e72281ad5e6759517be72730.jpg\",\n  \"81055.jpg\": \"Reptilia/Coluber constrictor constrictor/47f4550fbf87967050141b34305bff10.jpg\",\n  \"81073.jpg\": \"Aves/Cepphus columba/91946d3d681b233a0ee6193ea499b3b1.jpg\",\n  \"8108.jpg\": \"Aves/Amphispiza bilineata/20eace59afcb3ce60fa6c858db7b3749.jpg\",\n  \"81085.jpg\": \"Aves/Cepphus columba/7937c833719d8c1044610c0f4cd12024.jpg\",\n  \"81086.jpg\": \"Aves/Cepphus columba/f58b5d420a441f97a3767024b8cf0528.jpg\",\n  \"81087.jpg\": \"Aves/Cepphus columba/dd13c0c212bd3535b9680dfe7576423e.jpg\",\n  \"81088.jpg\": \"Aves/Cepphus columba/744b9b26c1e2151cab986cba36fa3ee6.jpg\",\n  \"81089.jpg\": \"Aves/Cepphus columba/a73116b67e54a72ec551c281429e8575.jpg\",\n  \"8109.jpg\": \"Aves/Amphispiza bilineata/819fd57a6163222f666ddab4401c460b.jpg\",\n  \"81093.jpg\": \"Aves/Cepphus columba/8070534da604d8eb66541ba410dbdf04.jpg\",\n  \"81094.jpg\": \"Aves/Cepphus columba/e764e428d9ef4a6925d7e6fa532d1536.jpg\",\n  \"81111.jpg\": \"Aves/Cepphus columba/036a3aed6b9c1ee7b521c777b83265ab.jpg\",\n  \"81113.jpg\": \"Aves/Cepphus columba/3de9fc23eb4f622a2cedb79b0906ece0.jpg\",\n  \"81136.jpg\": \"Aves/Cepphus columba/b259e2399550546bb4bb483565c0cfff.jpg\",\n  \"81144.jpg\": \"Aves/Cepphus columba/39198946cb45bde49016db8d4b42885e.jpg\",\n  \"8116.jpg\": \"Aves/Amphispiza bilineata/6ccb9ee61deb1dd4528a39e9b5761116.jpg\",\n  \"81163.jpg\": \"Aves/Cepphus columba/b1397790f3e0486480847c4b7c63fe84.jpg\",\n  \"81170.jpg\": \"Aves/Leptotila verreauxi/cc431e6333589164b89344ea67dd33ba.jpg\",\n  \"81173.jpg\": \"Aves/Leptotila verreauxi/8b53f98447182d8eec016cd819c5b4a9.jpg\",\n  \"81174.jpg\": \"Aves/Leptotila verreauxi/9a74d8f60cb76a4ce3efeeafa36156a9.jpg\",\n  \"81175.jpg\": \"Aves/Leptotila verreauxi/5f531613475bb46cf75930569bef9a0f.jpg\",\n  \"81176.jpg\": \"Aves/Leptotila verreauxi/230eb249fa142f4cbbf11a11904e2c6f.jpg\",\n  \"81179.jpg\": \"Aves/Leptotila verreauxi/bcbcd776cc8a9c9a17c731eeeda10f9c.jpg\",\n  \"81181.jpg\": \"Aves/Leptotila verreauxi/259deaa7b64d4afec4c09ea9d3cf1523.jpg\",\n  \"81184.jpg\": \"Aves/Leptotila verreauxi/eefdbd5826d827c089f4d5a678f9e53c.jpg\",\n  \"81194.jpg\": \"Aves/Leptotila verreauxi/61cb9e90fc929e27195ff034edad7ba2.jpg\",\n  \"81210.jpg\": \"Aves/Leptotila verreauxi/72cecf48e39a18bb41b9a640b8539fc3.jpg\",\n  \"81215.jpg\": \"Aves/Leptotila verreauxi/09d372d98944ab59e39c22077801b29f.jpg\",\n  \"81224.jpg\": \"Aves/Leptotila verreauxi/8735c81ed1fa4a6b9d5c794072dd3967.jpg\",\n  \"81228.jpg\": \"Aves/Leptotila verreauxi/11d0121316af1a0a4be510b177794937.jpg\",\n  \"81230.jpg\": \"Aves/Leptotila verreauxi/d60a076939395e9d994135b1cc6918e6.jpg\",\n  \"81276.jpg\": \"Reptilia/Sceloporus spinosus/4cb67ec362debc7c1dffe93b78dc7e51.jpg\",\n  \"81287.jpg\": \"Reptilia/Sceloporus spinosus/c02ed727004d87c732bafe15b86d0645.jpg\",\n  \"81288.jpg\": \"Reptilia/Sceloporus spinosus/a945aa476c415eea5d8e3d0217398c7d.jpg\",\n  \"81300.jpg\": \"Reptilia/Sceloporus spinosus/0449239c50dfdafa04011af05752eb18.jpg\",\n  \"81305.jpg\": \"Reptilia/Sceloporus spinosus/5d4779d0672854d874a8a74edf548d1f.jpg\",\n  \"81370.jpg\": \"Mammalia/Lavia frons/aba6a1617e022cddaa0f494d53b6a5cd.jpg\",\n  \"81371.jpg\": \"Mammalia/Lavia frons/a8c9618fdc02966d20c5019b492d63cf.jpg\",\n  \"814.jpg\": \"Mammalia/Ondatra zibethicus/c78756305be5bcd2441a298c3a4704c1.jpg\",\n  \"8148.jpg\": \"Insecta/Libellula needhami/7a6b6bf79140a2b45a0704f4484d2c48.jpg\",\n  \"8149.jpg\": \"Insecta/Libellula needhami/0a887f84eb40661d498de3a1ccb23041.jpg\",\n  \"815.jpg\": \"Mammalia/Ondatra zibethicus/a1645ab74dd6353d969ac76938edb658.jpg\",\n  \"8150.jpg\": \"Insecta/Libellula needhami/e47a14623031cc2e8565bfb163350a36.jpg\",\n  \"8151.jpg\": \"Insecta/Libellula needhami/806f03095a0eec8df251e0c5e2021a5e.jpg\",\n  \"8152.jpg\": \"Insecta/Libellula needhami/b61252bd12414efab140091f87ae5729.jpg\",\n  \"8163.jpg\": \"Insecta/Libellula needhami/5b0c8ea0780c2a70c838ee0cf44d1963.jpg\",\n  \"8164.jpg\": \"Insecta/Libellula needhami/f679c8a90ef56e8cffdfa42006ab4cc2.jpg\",\n  \"8167.jpg\": \"Insecta/Libellula needhami/ae4932d6b3b169b4ac0a056f2931e112.jpg\",\n  \"81766.jpg\": \"Aves/Psittacara erythrogenys/cc5761937dcf139d8f6307f18d2d1e33.jpg\",\n  \"81770.jpg\": \"Aves/Psittacara erythrogenys/deb485eeef3abd86c0934911a3eadc64.jpg\",\n  \"81779.jpg\": \"Insecta/Besma quercivoraria/719974c179ea3567d648b6576ac2105f.jpg\",\n  \"81784.jpg\": \"Insecta/Besma quercivoraria/403b31aeeb1d97de141146baaec226bb.jpg\",\n  \"8185.jpg\": \"Insecta/Libellula needhami/4ec4f6ac2102921a169c09aeba086b4e.jpg\",\n  \"81885.jpg\": \"Aves/Tyto alba/577e1582e5fd4c0f9001897d8cf58284.jpg\",\n  \"81890.jpg\": \"Aves/Tyto alba/0d6121582d35e929aca0f5320eaea92a.jpg\",\n  \"81903.jpg\": \"Aves/Tyto alba/d7338746c33b43007699d039e0153149.jpg\",\n  \"81914.jpg\": \"Aves/Tyto alba/487546d8c5e028395c71b47c52e565c0.jpg\",\n  \"81915.jpg\": \"Aves/Tyto alba/0e1138b0113e2af2b13656d5afa14c65.jpg\",\n  \"81925.jpg\": \"Aves/Tyto alba/7e217ce560a8df5c4cd4fa33e5bdafe7.jpg\",\n  \"81927.jpg\": \"Aves/Tyto alba/995f7710b002d68e98636fbbfe287910.jpg\",\n  \"81931.jpg\": \"Aves/Tyto alba/440ba268667fbb155fed3a3893addf4a.jpg\",\n  \"81935.jpg\": \"Aves/Tyto alba/2d9f33be80a06f82ccde09ba51ddc6bc.jpg\",\n  \"81936.jpg\": \"Aves/Tyto alba/3567dc3bfd3c8e31e03ec3a400bf150e.jpg\",\n  \"81939.jpg\": \"Aves/Tyto alba/5eab1a44da8f98916d685f3727b61af9.jpg\",\n  \"81943.jpg\": \"Aves/Tyto alba/4470f9bb85dd84e20214423ec4f10327.jpg\",\n  \"81953.jpg\": \"Aves/Tyto alba/708adc5bc57cdd9f79b07fc2260c281a.jpg\",\n  \"81962.jpg\": \"Aves/Tyto alba/2e547d7d7457dae2d6d6705762de7c7d.jpg\",\n  \"81966.jpg\": \"Aves/Tyto alba/535a6f7212059829e6dc37d812d34409.jpg\",\n  \"81978.jpg\": \"Aves/Tyto alba/eddf5aa2e084412ff6a9abba606d61ae.jpg\",\n  \"8198.jpg\": \"Insecta/Libellula needhami/92f3ee0733a2bb78fbff0c276ff7c4b0.jpg\",\n  \"8207.jpg\": \"Insecta/Libellula needhami/2c2fb2434c68461c69e63d11aaf40e2e.jpg\",\n  \"8209.jpg\": \"Insecta/Libellula needhami/aafaf1c2e9f55da673a08334fb061ea5.jpg\",\n  \"8210.jpg\": \"Insecta/Libellula needhami/2031c04014bc2a16a8355a7490e88568.jpg\",\n  \"82134.jpg\": \"Insecta/Xenox tigrinus/86ecba95ca0c71385c9772fd0b8f3bfd.jpg\",\n  \"82135.jpg\": \"Insecta/Xenox tigrinus/183b43198108cbf90b4ff8a2917ea563.jpg\",\n  \"82136.jpg\": \"Insecta/Xenox tigrinus/4a1ef00d93edb402a51eed6e210289d3.jpg\",\n  \"82137.jpg\": \"Insecta/Xenox tigrinus/63e13c52e8690fb23175f71be01258d4.jpg\",\n  \"82138.jpg\": \"Insecta/Xenox tigrinus/27ba2c7b4956d73447f5d199fb0bcd38.jpg\",\n  \"82139.jpg\": \"Insecta/Xenox tigrinus/4018d86d976068846928ff1b87c306cc.jpg\",\n  \"82140.jpg\": \"Insecta/Xenox tigrinus/3d0f3753b1114a71cb5376f6c0265202.jpg\",\n  \"82148.jpg\": \"Insecta/Xenox tigrinus/ce4667a93595e2ed57a5ba694806f835.jpg\",\n  \"82154.jpg\": \"Insecta/Xenox tigrinus/ff668b9aaf921060c78f82b9872b5f32.jpg\",\n  \"82155.jpg\": \"Insecta/Xenox tigrinus/91b3309256903025c68ea1bc5fd41e8a.jpg\",\n  \"82156.jpg\": \"Insecta/Xenox tigrinus/f554c525ad3457ab74f643f9f8b2d38b.jpg\",\n  \"82157.jpg\": \"Insecta/Xenox tigrinus/96b91753f8a68c290261c34f558031e9.jpg\",\n  \"82158.jpg\": \"Insecta/Xenox tigrinus/12d5037baa01489adf864c5e49233f87.jpg\",\n  \"82159.jpg\": \"Insecta/Xenox tigrinus/d6eda05c4d82fdf71f11054829c83837.jpg\",\n  \"82163.jpg\": \"Insecta/Xenox tigrinus/355d4882a6cccd6424377525c4fa0131.jpg\",\n  \"82164.jpg\": \"Insecta/Xenox tigrinus/54d8888d59ee1420510eb0199a8bf0f6.jpg\",\n  \"82165.jpg\": \"Insecta/Xenox tigrinus/58de55f21a204199d1d463314fc2517d.jpg\",\n  \"82170.jpg\": \"Insecta/Xenox tigrinus/665b1fbaae8710b64d9f294c109b28b3.jpg\",\n  \"82171.jpg\": \"Insecta/Xenox tigrinus/fa7969e603a4b2b9205b5765fe97f9cb.jpg\",\n  \"82172.jpg\": \"Insecta/Xenox tigrinus/39d8d4d617e3ff93b0228932f92ab815.jpg\",\n  \"82182.jpg\": \"Mammalia/Lasiurus cinereus/6522dc344494eba904ab83e9a889db4d.jpg\",\n  \"82185.jpg\": \"Mammalia/Lasiurus cinereus/d72e129883f4d30d63d05157d1368f20.jpg\",\n  \"82192.jpg\": \"Mammalia/Lasiurus cinereus/cfac4a9355ed5cd2cc5b896761b729ac.jpg\",\n  \"8221.jpg\": \"Insecta/Libellula needhami/973a3be84060989c019178fa9ce596de.jpg\",\n  \"8225.jpg\": \"Insecta/Libellula needhami/a62e319c7d5d051142be605acd9d30c9.jpg\",\n  \"82286.jpg\": \"Insecta/Lophocampa maculata/bc658a9937d5db78ad7853d831218a94.jpg\",\n  \"82288.jpg\": \"Insecta/Lophocampa maculata/f6b21de73913193736c958c09897cf6c.jpg\",\n  \"82297.jpg\": \"Insecta/Lophocampa maculata/5db977e54e43801bf65d957e61ae18cd.jpg\",\n  \"82305.jpg\": \"Insecta/Lophocampa maculata/fcebcf7f2092ace7fc65e15f10a1f998.jpg\",\n  \"82310.jpg\": \"Insecta/Lophocampa maculata/3f330ca9599dc4996b54bb6fbddaac97.jpg\",\n  \"82313.jpg\": \"Insecta/Lophocampa maculata/b94620f968f04c095a139029abf827ff.jpg\",\n  \"82331.jpg\": \"Insecta/Lophocampa maculata/68cd103b708b8a6ca1c34b228709dada.jpg\",\n  \"82332.jpg\": \"Insecta/Lophocampa maculata/f6ec147c87e444e3df37b703882d8042.jpg\",\n  \"82339.jpg\": \"Insecta/Lophocampa maculata/58046dd3dd2b042e27b072bdac04b811.jpg\",\n  \"82342.jpg\": \"Insecta/Lophocampa maculata/5d656bedcd5d4ace7ee651dd562a4f11.jpg\",\n  \"82347.jpg\": \"Insecta/Lophocampa maculata/f67eed69b4214c9a0117df8412dab189.jpg\",\n  \"82348.jpg\": \"Insecta/Lophocampa maculata/4323f2624235100dad837bed4f7bc7d5.jpg\",\n  \"82349.jpg\": \"Insecta/Lophocampa maculata/cee8493a6224be90c845ed5c6e57e48c.jpg\",\n  \"82353.jpg\": \"Insecta/Lophocampa maculata/58b8a55449d234b11218f4b3c91ffbda.jpg\",\n  \"82354.jpg\": \"Insecta/Lophocampa maculata/3f1d0a69d7cb39ae7e6cfbd43edd8e1c.jpg\",\n  \"82355.jpg\": \"Insecta/Lophocampa maculata/1f57d16a8bc32a7743366afc6705cc78.jpg\",\n  \"82356.jpg\": \"Insecta/Lophocampa maculata/d095af2c7b5639ffcc3976efce3a4385.jpg\",\n  \"82358.jpg\": \"Insecta/Lophocampa maculata/d1e37d79f3cf8b518531a6c5e1aeac6b.jpg\",\n  \"8237.jpg\": \"Insecta/Libellula needhami/c732091b7f3f030a838bf1acda6b3d94.jpg\",\n  \"8238.jpg\": \"Insecta/Libellula needhami/e74d704f4dfbf25599a2034a4e5ab4d4.jpg\",\n  \"8239.jpg\": \"Insecta/Libellula needhami/ccab1ae2bd75312de7ca43c1b5c7dfe2.jpg\",\n  \"82409.jpg\": \"Aves/Chaetura pelagica/e1311112fe2c8d63e7d11bf8a9e6e212.jpg\",\n  \"82412.jpg\": \"Aves/Chaetura pelagica/ed223e2ef9a3815978b2ac9a0a2f6c47.jpg\",\n  \"82416.jpg\": \"Aves/Chaetura pelagica/b3ba00475b545c090c07574ad4dcb843.jpg\",\n  \"82419.jpg\": \"Aves/Chaetura pelagica/62acd5af11faaf37084369e6de7c306b.jpg\",\n  \"82423.jpg\": \"Aves/Chaetura pelagica/4f2ab2df24449156d0037af1bcf3f883.jpg\",\n  \"82424.jpg\": \"Aves/Chaetura pelagica/e10cbaa670458fc1e09f2ea4ed0e1e2f.jpg\",\n  \"82429.jpg\": \"Aves/Chaetura pelagica/fcccd7c3b35a45140ddfb2ef3d4272f3.jpg\",\n  \"8243.jpg\": \"Insecta/Libellula needhami/deeb8a6165a67569def7f03b44598ce6.jpg\",\n  \"82430.jpg\": \"Aves/Chaetura pelagica/0b21352b1dd334e3c7e3413e0e676cd0.jpg\",\n  \"82437.jpg\": \"Aves/Chaetura pelagica/7936b49fa8dc7326a67f4a1a1be73a5c.jpg\",\n  \"82439.jpg\": \"Aves/Chaetura pelagica/8e45742aefed24c8e167affc17aef1f8.jpg\",\n  \"82440.jpg\": \"Aves/Chaetura pelagica/02b0f38e0e0c3e2a805ef18ac8110520.jpg\",\n  \"82452.jpg\": \"Aves/Chaetura pelagica/3c04a764c3a5dc85492ca523beea1079.jpg\",\n  \"82453.jpg\": \"Aves/Chaetura pelagica/2335573b939218d45e7a7039f64ebcfe.jpg\",\n  \"82454.jpg\": \"Aves/Chaetura pelagica/84edebc678e13b52dd9820a2d18a59e7.jpg\",\n  \"8248.jpg\": \"Insecta/Libellula needhami/4e164e1b6268f6de4bccd73f4ea9ba0c.jpg\",\n  \"82522.jpg\": \"Aves/Colaptes auratus cafer/d256fd28563fc1ed2b766757181299ec.jpg\",\n  \"82526.jpg\": \"Aves/Colaptes auratus cafer/dd9e7a5ac7066c0f01bfe9cd467cf412.jpg\",\n  \"82527.jpg\": \"Aves/Colaptes auratus cafer/9f18ec5ea525713e4acc3701083f1310.jpg\",\n  \"82539.jpg\": \"Aves/Colaptes auratus cafer/c6ebbe3e8312ab6d827c71fa12c928ac.jpg\",\n  \"82548.jpg\": \"Aves/Colaptes auratus cafer/8fe2ca75511d6e3c9fd85b1af79887a1.jpg\",\n  \"8257.jpg\": \"Insecta/Libellula needhami/bf13ee49599e1abf3abac880af852677.jpg\",\n  \"8258.jpg\": \"Insecta/Libellula needhami/ea557190033340f5f4bac3a8d696adf6.jpg\",\n  \"82649.jpg\": \"Insecta/Zelus luridus/cccb39ff2bfa25b9625750af9816a248.jpg\",\n  \"82657.jpg\": \"Insecta/Zelus luridus/55695a9809a7ba8bf3748ccd4c932d2e.jpg\",\n  \"82664.jpg\": \"Insecta/Zelus luridus/a93012f11c2bdb8e9c1c6e39563e0bec.jpg\",\n  \"82665.jpg\": \"Insecta/Zelus luridus/92926786a3aafc361c96365717666638.jpg\",\n  \"82666.jpg\": \"Insecta/Zelus luridus/dc05a6c6021489df8ee9c2953a4d82ea.jpg\",\n  \"82668.jpg\": \"Insecta/Zelus luridus/a82a7ac9aacf2222f03c18c80fc380a9.jpg\",\n  \"82670.jpg\": \"Insecta/Zelus luridus/56dfca0a5dfc01f1cf09a63e9fd809af.jpg\",\n  \"82671.jpg\": \"Insecta/Zelus luridus/24d2befd66b4111b61939c14a9300bf2.jpg\",\n  \"82672.jpg\": \"Insecta/Zelus luridus/6e79e1cf9494b67cab5a371ec998b332.jpg\",\n  \"82674.jpg\": \"Insecta/Zelus luridus/6cb9a493fd179df708a2883b44302939.jpg\",\n  \"82678.jpg\": \"Insecta/Zelus luridus/816ff616dcee149b60eb93861a980cc5.jpg\",\n  \"82679.jpg\": \"Insecta/Zelus luridus/ccb132819fee0bef361bc3957a8258f4.jpg\",\n  \"82680.jpg\": \"Insecta/Zelus luridus/838384dbc3e767e7ef21c182d9a310b0.jpg\",\n  \"82681.jpg\": \"Insecta/Zelus luridus/e73ebada0459a6265cdacad0ae61a1ae.jpg\",\n  \"82682.jpg\": \"Insecta/Zelus luridus/90a44e8b65ac488bc374012ba79a657c.jpg\",\n  \"82683.jpg\": \"Insecta/Zelus luridus/b264f6951e19094cc0c2d664f38a9ccb.jpg\",\n  \"82685.jpg\": \"Insecta/Zelus luridus/1f4a78c23ef601481de22e922f46c925.jpg\",\n  \"82686.jpg\": \"Insecta/Zelus luridus/0f44b4a94fd83f33f1233a5a8f6db567.jpg\",\n  \"82687.jpg\": \"Insecta/Zelus luridus/8f00d61c7206511b73f67cd670035fc0.jpg\",\n  \"82688.jpg\": \"Insecta/Zelus luridus/7fa4cb451560ff3d7627804082afb067.jpg\",\n  \"82693.jpg\": \"Insecta/Zelus luridus/11844761e1b42b64907789326b94d521.jpg\",\n  \"82869.jpg\": \"Aves/Erithacus rubecula/4e2a9e99695fb1085b684211bf2b5393.jpg\",\n  \"82871.jpg\": \"Aves/Erithacus rubecula/c4fb494a1b2f833ff6a595740ff02bc4.jpg\",\n  \"82872.jpg\": \"Aves/Erithacus rubecula/88d47de776cc15e9dac7c240c4f9e330.jpg\",\n  \"82873.jpg\": \"Aves/Erithacus rubecula/7b3fa6edd22d9d05d8aee9bdf72b4f4c.jpg\",\n  \"82888.jpg\": \"Aves/Erithacus rubecula/b5b91e264ed86f704c3bae0c17e1b1e2.jpg\",\n  \"82894.jpg\": \"Aves/Erithacus rubecula/47fff3b2e03536a25fa10c3cdb60d24d.jpg\",\n  \"82923.jpg\": \"Aves/Erithacus rubecula/a40a4c07aa574796b4ca3f0eba8b65a2.jpg\",\n  \"82926.jpg\": \"Aves/Erithacus rubecula/9e5c46712a4005fb38ae71f21cbbd025.jpg\",\n  \"82930.jpg\": \"Aves/Erithacus rubecula/8454a26c5338e211bd9acd8a7b705aac.jpg\",\n  \"82935.jpg\": \"Aves/Erithacus rubecula/2a1bbc882d2ff6f164f9d3e93d47f4f0.jpg\",\n  \"82938.jpg\": \"Aves/Erithacus rubecula/f3c9fb2f437ab57cf65ebb4c435f0328.jpg\",\n  \"82940.jpg\": \"Aves/Erithacus rubecula/2531d621bec803640565a587dab8e781.jpg\",\n  \"82942.jpg\": \"Aves/Erithacus rubecula/a6d30276229e433774e167a130626f5f.jpg\",\n  \"82946.jpg\": \"Aves/Erithacus rubecula/e6762c2bfbfc071acd4f2aaaf7b1dfb7.jpg\",\n  \"82950.jpg\": \"Aves/Erithacus rubecula/926a953150889d124f783239a61bdc29.jpg\",\n  \"82951.jpg\": \"Aves/Erithacus rubecula/bb912ebd765b1ad4c699894291666f2c.jpg\",\n  \"82962.jpg\": \"Aves/Erithacus rubecula/6493903ff049c163f9bf0fca576bf22d.jpg\",\n  \"82979.jpg\": \"Aves/Erithacus rubecula/27476b326abafef6fff868c8d436104c.jpg\",\n  \"82991.jpg\": \"Aves/Erithacus rubecula/a708321b5be7fea413bb95e9e7f40fd6.jpg\",\n  \"82996.jpg\": \"Aves/Erithacus rubecula/c908be1aafa79431a05c0967b54e8511.jpg\",\n  \"83025.jpg\": \"Aves/Erithacus rubecula/557ad710d9681e57ee1c23d3dc4e919d.jpg\",\n  \"83029.jpg\": \"Aves/Erithacus rubecula/5eff1920348931b27eda4fb7254c7e8d.jpg\",\n  \"83031.jpg\": \"Aves/Erithacus rubecula/602b81ec49427de8539c00240dae63de.jpg\",\n  \"83037.jpg\": \"Aves/Erithacus rubecula/a8db4e71eeec36e665d34e016b8d47de.jpg\",\n  \"83059.jpg\": \"Aves/Nycticorax nycticorax/e66a1bff72792d82081dd14cd191fe99.jpg\",\n  \"83077.jpg\": \"Aves/Nycticorax nycticorax/224b8c2850ca7d694779f7e9548da642.jpg\",\n  \"83082.jpg\": \"Aves/Nycticorax nycticorax/9b8061a915af173432c790e200c7d235.jpg\",\n  \"83182.jpg\": \"Aves/Nycticorax nycticorax/1c8b89daf4d525ef26af554f82c2533b.jpg\",\n  \"83189.jpg\": \"Aves/Nycticorax nycticorax/16b00f036465ad2a5cd55edafcff39cd.jpg\",\n  \"8320.jpg\": \"Mollusca/Belocaulus angustipes/ef387c6fbd20526a84a4f926bac94444.jpg\",\n  \"83200.jpg\": \"Aves/Nycticorax nycticorax/aa7f10f3c3ebd79f3994f63ac1cbccde.jpg\",\n  \"83205.jpg\": \"Aves/Nycticorax nycticorax/241e000950e28bf873466f4586f8b718.jpg\",\n  \"8321.jpg\": \"Mollusca/Belocaulus angustipes/4a6cac3c2a2698b76a7223e9b5181446.jpg\",\n  \"8327.jpg\": \"Mollusca/Belocaulus angustipes/14c0c5505928e82dd15214ea264d7d25.jpg\",\n  \"8328.jpg\": \"Mollusca/Belocaulus angustipes/263f2824e480122027038be5d1989258.jpg\",\n  \"8329.jpg\": \"Mollusca/Belocaulus angustipes/b3ffa590694b5cacb3eb442c8e666efb.jpg\",\n  \"83295.jpg\": \"Aves/Nycticorax nycticorax/f0a576c839483c814e8e978487dbdc97.jpg\",\n  \"83297.jpg\": \"Aves/Nycticorax nycticorax/6e44c3b7f39e5629f5ee031a2c4442eb.jpg\",\n  \"8330.jpg\": \"Mollusca/Belocaulus angustipes/375c857f2e33f6ca6ebca04ded0051b5.jpg\",\n  \"83328.jpg\": \"Aves/Nycticorax nycticorax/4aa1a9d4f402bbee7cae139d50e24632.jpg\",\n  \"83364.jpg\": \"Aves/Nycticorax nycticorax/20bfc72ce4a9ce97f60598b8ba18b825.jpg\",\n  \"8339.jpg\": \"Animalia/Ocypode quadrata/6370e65ee1cbce3a4332de9c53f3a306.jpg\",\n  \"83400.jpg\": \"Aves/Nycticorax nycticorax/98ad942bc452d70827fc8a2a4c70154f.jpg\",\n  \"83459.jpg\": \"Aves/Nycticorax nycticorax/945d801ec469529178e4fbbf630fe604.jpg\",\n  \"83463.jpg\": \"Aves/Nycticorax nycticorax/ac295cf7284a7c14d1966e7621129191.jpg\",\n  \"8348.jpg\": \"Animalia/Ocypode quadrata/ccc9c59501d3af7cc9145fff53019062.jpg\",\n  \"83480.jpg\": \"Aves/Nycticorax nycticorax/b46d28b0d0d0d35037f617b89f051a26.jpg\",\n  \"83488.jpg\": \"Aves/Nycticorax nycticorax/4c762ac2c9105eb5a94b0c67ca480d6a.jpg\",\n  \"83553.jpg\": \"Aves/Nycticorax nycticorax/e112dadaff123f10d15bb738d574fa22.jpg\",\n  \"8357.jpg\": \"Animalia/Ocypode quadrata/0023df16f6c9e773360017354cea915c.jpg\",\n  \"83570.jpg\": \"Aves/Nycticorax nycticorax/f167792320e332473b0188bd465ebd7d.jpg\",\n  \"8358.jpg\": \"Animalia/Ocypode quadrata/64cc40c95b1aece727ce330e13c277c1.jpg\",\n  \"83600.jpg\": \"Aves/Nycticorax nycticorax/f990075a014854d6b2ff883dd43d7f91.jpg\",\n  \"8364.jpg\": \"Animalia/Ocypode quadrata/6d8f40a9be588bac37761f6bb3d3f426.jpg\",\n  \"8365.jpg\": \"Animalia/Ocypode quadrata/20a0a889776bcb548295078f05479ebe.jpg\",\n  \"83657.jpg\": \"Aves/Nycticorax nycticorax/5842d0b86d6b012692ea5012221dba88.jpg\",\n  \"8366.jpg\": \"Animalia/Ocypode quadrata/7705a396dc83083f252ef8749f8ad3c6.jpg\",\n  \"8367.jpg\": \"Animalia/Ocypode quadrata/b5cdceb1430516418f023832c17ae1ae.jpg\",\n  \"83687.jpg\": \"Aves/Nycticorax nycticorax/e1b079eeec833daf6b6b0680a5815a67.jpg\",\n  \"83721.jpg\": \"Aves/Nycticorax nycticorax/a45a118fba0a72c253cd0daf87a1e48d.jpg\",\n  \"83726.jpg\": \"Aves/Nycticorax nycticorax/d080ea05ad309f10cb268208e43866c7.jpg\",\n  \"83747.jpg\": \"Aves/Tringa ochropus/479d5e764f89ca4e4753ed8d140a1eed.jpg\",\n  \"83754.jpg\": \"Aves/Tringa ochropus/d76cdad398bad55606a9abc87021d11d.jpg\",\n  \"83755.jpg\": \"Aves/Tringa ochropus/7bb5b551e75112bcff975d9f87a3c280.jpg\",\n  \"83981.jpg\": \"Arachnida/Argiope trifasciata/0a7523080e3bf91226de8101a2d5e860.jpg\",\n  \"83986.jpg\": \"Arachnida/Argiope trifasciata/b8b652f0bd438d337bea02c60d798aae.jpg\",\n  \"83988.jpg\": \"Arachnida/Argiope trifasciata/c2905b3b46068bdea14ec8f3d60d2145.jpg\",\n  \"83999.jpg\": \"Arachnida/Argiope trifasciata/ae7c6b17e54c4077fb35db3036a4d66b.jpg\",\n  \"84015.jpg\": \"Arachnida/Argiope trifasciata/cfb6d47acc47a1317432a93a5592bf43.jpg\",\n  \"84029.jpg\": \"Arachnida/Argiope trifasciata/0866c2b0eb6184256a0be66c89c6f7d0.jpg\",\n  \"84045.jpg\": \"Arachnida/Argiope trifasciata/79cdb72467ea343f6e7768340eb98fae.jpg\",\n  \"84046.jpg\": \"Arachnida/Argiope trifasciata/908654d47ec85ea0fe26957691ed983b.jpg\",\n  \"84055.jpg\": \"Arachnida/Argiope trifasciata/75a62e490ffb431053c0a550aec6f878.jpg\",\n  \"84075.jpg\": \"Arachnida/Argiope trifasciata/b802987f2972c7e8da6a78f85bec1204.jpg\",\n  \"84091.jpg\": \"Arachnida/Argiope trifasciata/a79bcf880646529e575f72c0a3645252.jpg\",\n  \"84103.jpg\": \"Arachnida/Argiope trifasciata/5c7f4ae57f90c987cfba3cb05b9a1988.jpg\",\n  \"84106.jpg\": \"Arachnida/Argiope trifasciata/b99487abe61fd6cd1f55793900137d79.jpg\",\n  \"84113.jpg\": \"Arachnida/Argiope trifasciata/5d22734eddf7827be66cc737e8754104.jpg\",\n  \"84134.jpg\": \"Arachnida/Argiope trifasciata/e4d6796e3f7deddd1bf4e9542da93d7e.jpg\",\n  \"84142.jpg\": \"Arachnida/Argiope trifasciata/41a773534c137a7d93083638be7b7f76.jpg\",\n  \"84143.jpg\": \"Arachnida/Argiope trifasciata/030009d7cdf99d98d1d1fc0c63e7303d.jpg\",\n  \"84144.jpg\": \"Arachnida/Argiope trifasciata/dde3f35a12cc246a94b660092449b4bf.jpg\",\n  \"84145.jpg\": \"Arachnida/Argiope trifasciata/e992e1ed1b17959b2a6660eee9fa9e27.jpg\",\n  \"84149.jpg\": \"Arachnida/Argiope trifasciata/ee1390bf964b0a7e5368d29c00fbd1f1.jpg\",\n  \"84150.jpg\": \"Arachnida/Argiope trifasciata/1552bef7cff5a39af8274fdc08c09829.jpg\",\n  \"84151.jpg\": \"Arachnida/Argiope trifasciata/ae41344e0ec9e9c2280b7ca01dc7971f.jpg\",\n  \"84152.jpg\": \"Arachnida/Argiope trifasciata/ee162dbc9c0835bfa258f7046cf008b8.jpg\",\n  \"84158.jpg\": \"Insecta/Anteos maerula/01190b1edd10b4b6eb573111e4f282dc.jpg\",\n  \"84159.jpg\": \"Insecta/Anteos maerula/f065e15121e0e4f1cca9789945f13115.jpg\",\n  \"84162.jpg\": \"Insecta/Anteos maerula/de061a7d4d65ef7e9cdcc1a9f0bac71c.jpg\",\n  \"84163.jpg\": \"Insecta/Anteos maerula/242895896afdd0799d5ddf0354640cc0.jpg\",\n  \"84208.jpg\": \"Aves/Setophaga palmarum/425497986ce224c6298551a7c15883dd.jpg\",\n  \"84212.jpg\": \"Aves/Setophaga palmarum/c502ed777b728f5167977f72cbc90557.jpg\",\n  \"84217.jpg\": \"Aves/Setophaga palmarum/0bb4840e4a92046da83dec14d0e80308.jpg\",\n  \"84223.jpg\": \"Aves/Setophaga palmarum/683de7f69c15e5f905492ee1f15e0439.jpg\",\n  \"84227.jpg\": \"Aves/Setophaga palmarum/d3f646211880ef4af2bbde8f1162d297.jpg\",\n  \"84235.jpg\": \"Aves/Setophaga palmarum/f69a70b6ac13ab08f9181504c5b48387.jpg\",\n  \"84238.jpg\": \"Aves/Setophaga palmarum/a4b8b1a02151a7d6be43c2bdc40bc0d8.jpg\",\n  \"84246.jpg\": \"Aves/Setophaga palmarum/4c6ebdbbfcd7587b520bdad8d70f910b.jpg\",\n  \"84247.jpg\": \"Aves/Setophaga palmarum/31505a098c5701c862311f88e6f8efa7.jpg\",\n  \"84252.jpg\": \"Aves/Setophaga palmarum/b614f956a879ddb2fd817814296483af.jpg\",\n  \"84263.jpg\": \"Aves/Setophaga palmarum/f1e2c59495d8c7453c88010333800c37.jpg\",\n  \"84264.jpg\": \"Aves/Setophaga palmarum/9a7f71027fb8ed0935347727a53e034c.jpg\",\n  \"84265.jpg\": \"Aves/Setophaga palmarum/3f644d9918d64034f945cfe85596548e.jpg\",\n  \"84266.jpg\": \"Aves/Setophaga palmarum/fbf272de51e2010856400042f59a115d.jpg\",\n  \"84269.jpg\": \"Aves/Setophaga palmarum/40a996c845187792cbbe627170be7605.jpg\",\n  \"84271.jpg\": \"Aves/Setophaga palmarum/63fad9b3f49e8d795924a246d660ea4f.jpg\",\n  \"84272.jpg\": \"Aves/Setophaga palmarum/4890b183c13c816a6eadaae6d958d201.jpg\",\n  \"84275.jpg\": \"Aves/Setophaga palmarum/fe2e1c793faacae9411e8dad5f044caa.jpg\",\n  \"84294.jpg\": \"Aves/Setophaga palmarum/0b6dc62529c65acf0e844e3bb07d4477.jpg\",\n  \"84305.jpg\": \"Aves/Setophaga palmarum/16f99ca267de260d26f885f19c03cd32.jpg\",\n  \"84332.jpg\": \"Aves/Setophaga palmarum/cd84a3305839bbae039edd87b4be3226.jpg\",\n  \"84335.jpg\": \"Aves/Setophaga palmarum/477ef8d5929a8e8e4150a3c283e80a44.jpg\",\n  \"84341.jpg\": \"Aves/Setophaga palmarum/a873aa74c3bd28f6040d9fc5df147c1c.jpg\",\n  \"84349.jpg\": \"Aves/Setophaga palmarum/172061e70f12a71d16c6757884b53b1f.jpg\",\n  \"84358.jpg\": \"Aves/Setophaga palmarum/9499c94147450c75a135e92a8f57b613.jpg\",\n  \"84369.jpg\": \"Insecta/Hypena scabra/76036d2f78d60451cc222610c89c5125.jpg\",\n  \"84370.jpg\": \"Insecta/Hypena scabra/de277904d2a6c270f1be413d052bad9f.jpg\",\n  \"84372.jpg\": \"Insecta/Hypena scabra/682ae8db4b320a7821bab25485892e26.jpg\",\n  \"84375.jpg\": \"Insecta/Hypena scabra/14c4f53445c91e8f9098762017c3a63c.jpg\",\n  \"84380.jpg\": \"Insecta/Hypena scabra/8c0a91619e25bc01bc777442f12ed6f1.jpg\",\n  \"84387.jpg\": \"Insecta/Hypena scabra/54b5c91111fccdc696bd061b0689bbd4.jpg\",\n  \"84398.jpg\": \"Insecta/Hypena scabra/b997712a3d5044bebfa94cb270a05f3a.jpg\",\n  \"84402.jpg\": \"Insecta/Hypena scabra/8984b3222bfdc9247e9fb948fe4c9a3f.jpg\",\n  \"84403.jpg\": \"Insecta/Hypena scabra/21443abe0152a9bffea68f42d370e77c.jpg\",\n  \"84405.jpg\": \"Insecta/Hypena scabra/9e2546799d8f50b7e2d06ab1e385e5e4.jpg\",\n  \"84410.jpg\": \"Insecta/Hypena scabra/8b4809591ce17c70bb7fecdf99ef803a.jpg\",\n  \"84411.jpg\": \"Insecta/Hypena scabra/65a941119be267e5c78f6508dc8f60c9.jpg\",\n  \"84418.jpg\": \"Insecta/Hypena scabra/b5adfc359a6eb40ac0bb27f9ad2ee2a1.jpg\",\n  \"84425.jpg\": \"Insecta/Hypena scabra/d45c1909183fd813084af5898cd930f8.jpg\",\n  \"84430.jpg\": \"Insecta/Hypena scabra/319ef90b56aca9e0d69f8dc37c3dafbf.jpg\",\n  \"84433.jpg\": \"Insecta/Hypena scabra/80fc974d9ba9388f6f324ed1dae6216f.jpg\",\n  \"84434.jpg\": \"Insecta/Hypena scabra/d29f20ca634901d626599a88d501e881.jpg\",\n  \"84435.jpg\": \"Insecta/Hypena scabra/be5b141a3e9407069e70a5b94623556a.jpg\",\n  \"84444.jpg\": \"Insecta/Hypena scabra/138b198592776e80094047d2be31312c.jpg\",\n  \"84445.jpg\": \"Insecta/Hypena scabra/794fb07f466d98a2d7e60a64b66717d3.jpg\",\n  \"84449.jpg\": \"Insecta/Hypena scabra/d1b0008462c642753bd45c0e6be6190f.jpg\",\n  \"84452.jpg\": \"Insecta/Hypena scabra/134351534e0891bb33eccffdae0d1baa.jpg\",\n  \"84477.jpg\": \"Insecta/Hypena scabra/6706cd417994ac41be5364b1ac841237.jpg\",\n  \"84485.jpg\": \"Insecta/Hypena scabra/8ef5430294caa64926d9526bf086cf7e.jpg\",\n  \"84491.jpg\": \"Insecta/Hypena scabra/4db91ed012030df1b6a374ae821d8e8f.jpg\",\n  \"84530.jpg\": \"Insecta/Calephelis nemesis/e4d6c9f113d515cc5d3a26381b515c7b.jpg\",\n  \"84531.jpg\": \"Insecta/Calephelis nemesis/cdfd3ca634e11c4ca753af942878ca3e.jpg\",\n  \"84532.jpg\": \"Insecta/Calephelis nemesis/b675a5e8f9dcec689c754e51dfb62a78.jpg\",\n  \"84534.jpg\": \"Insecta/Calephelis nemesis/83399668a41dbb72f5271be363d85bee.jpg\",\n  \"84543.jpg\": \"Insecta/Calephelis nemesis/0d054c6c624e1ec3a1558e86f49a33cb.jpg\",\n  \"84553.jpg\": \"Insecta/Calephelis nemesis/84126d348bad6a9bbdb5520ce52c1aec.jpg\",\n  \"84705.jpg\": \"Aves/Anas cyanoptera/8b5ed83affe8a2fbaf65711aa458fcd3.jpg\",\n  \"84737.jpg\": \"Aves/Anas cyanoptera/923495d7f5beb2c0b0f5057265959788.jpg\",\n  \"84741.jpg\": \"Aves/Anas cyanoptera/e01e0900c9ec42eca96d9341b41a68fb.jpg\",\n  \"84751.jpg\": \"Aves/Anas cyanoptera/bb3ba78353807da7d714e74310243b5c.jpg\",\n  \"84766.jpg\": \"Aves/Anas cyanoptera/eba18dd79a2ee57d4f92f6d147543d7e.jpg\",\n  \"84782.jpg\": \"Aves/Anas cyanoptera/1f1e0fba71b39280585420b89b71b18c.jpg\",\n  \"84792.jpg\": \"Aves/Anas cyanoptera/e9b21bbea6d3e856ca1faf1b20a45074.jpg\",\n  \"84796.jpg\": \"Aves/Anas cyanoptera/575e2030f1a2bc6572d8bd7dacc7635e.jpg\",\n  \"84805.jpg\": \"Aves/Anas cyanoptera/b7a368a40617665ddb5f77b5ddb4e071.jpg\",\n  \"84816.jpg\": \"Aves/Anas cyanoptera/bed6d24552f845d8be73a648d39d7826.jpg\",\n  \"850.jpg\": \"Mammalia/Ondatra zibethicus/55a0a48e2582486ce4f13d24fed1d534.jpg\",\n  \"85616.jpg\": \"Aves/Quiscalus mexicanus/0650d6d52106f93d667f38bdb828dead.jpg\",\n  \"85650.jpg\": \"Aves/Quiscalus mexicanus/40b6a0f3c91a7c7def1eb982ad8a7104.jpg\",\n  \"857.jpg\": \"Mammalia/Ondatra zibethicus/2f1a0e44cc3fb176dec959df67059aa0.jpg\",\n  \"85707.jpg\": \"Aves/Quiscalus mexicanus/ed17cd325813f441561bb08eee06558a.jpg\",\n  \"85742.jpg\": \"Aves/Quiscalus mexicanus/288ab84e90b02db9f43f4e5be4bfcf52.jpg\",\n  \"85747.jpg\": \"Aves/Quiscalus mexicanus/51d21fa517646bf0241196af40bb5de8.jpg\",\n  \"85781.jpg\": \"Aves/Quiscalus mexicanus/f41470d248e54e6c655cd9049c06e886.jpg\",\n  \"85830.jpg\": \"Aves/Quiscalus mexicanus/d91c4e13552ff0fec9c1847acfe68117.jpg\",\n  \"85920.jpg\": \"Aves/Quiscalus mexicanus/660019d4079f02fd27bbf6095deb11f6.jpg\",\n  \"85930.jpg\": \"Aves/Quiscalus mexicanus/0eb0fb4c2a644cca72b8e4c630ba279a.jpg\",\n  \"86075.jpg\": \"Aves/Quiscalus mexicanus/8c7e5a6747f94525717c3b92f055faeb.jpg\",\n  \"86110.jpg\": \"Aves/Quiscalus mexicanus/ceb2e1be1039ddcea225d033835562ca.jpg\",\n  \"86135.jpg\": \"Aves/Quiscalus mexicanus/ed1eb96bbf911fad9429962c5a46432a.jpg\",\n  \"86145.jpg\": \"Aves/Quiscalus mexicanus/209ca6aa2fcd9c4cce767b85a1d2177a.jpg\",\n  \"86156.jpg\": \"Aves/Quiscalus mexicanus/7698f19dd74ba3304e74b749c988e1eb.jpg\",\n  \"86215.jpg\": \"Aves/Quiscalus mexicanus/46c5b548487e3041a16f50c4208088fd.jpg\",\n  \"86228.jpg\": \"Aves/Quiscalus mexicanus/a6a9849954678e5d2a190961dddb4e42.jpg\",\n  \"86288.jpg\": \"Aves/Quiscalus mexicanus/61df0eb5e2904964aca4d4f23b977b7a.jpg\",\n  \"86379.jpg\": \"Aves/Colaptes auratus auratus/734d16e1a7b768e64f9f0aef81fec2c7.jpg\",\n  \"86380.jpg\": \"Aves/Colaptes auratus auratus/44b5c6e6381b7ea1becaf44846439f6a.jpg\",\n  \"86382.jpg\": \"Aves/Colaptes auratus auratus/e297707f6766fff39aa5307ad0cc67eb.jpg\",\n  \"86391.jpg\": \"Aves/Colaptes auratus auratus/961697bdabb24e73d3d75dfe4e563d3d.jpg\",\n  \"86392.jpg\": \"Aves/Colaptes auratus auratus/5b30d0714ff7bb6a1328d6e408a6e9c6.jpg\",\n  \"86394.jpg\": \"Aves/Colaptes auratus auratus/3a93d06d200e5b58011df3bdff67c4c4.jpg\",\n  \"86435.jpg\": \"Insecta/Nomophila nearctica/cb06e0748faea5f84572a2770b82473e.jpg\",\n  \"86439.jpg\": \"Insecta/Nomophila nearctica/b0c25220fdaa264bba388fd83475c395.jpg\",\n  \"86441.jpg\": \"Insecta/Nomophila nearctica/48b5491076161f981426e88b34109b4e.jpg\",\n  \"86442.jpg\": \"Insecta/Nomophila nearctica/1f04b9cfe4f3f9085f2beafbe99ecc6f.jpg\",\n  \"86443.jpg\": \"Insecta/Nomophila nearctica/22a9d59c7cb44064fb3c9e06f8ff2887.jpg\",\n  \"86444.jpg\": \"Insecta/Nomophila nearctica/5e63c8286984f2071a474ea6d318d058.jpg\",\n  \"86445.jpg\": \"Insecta/Nomophila nearctica/971e0da3e8f0898a286434abd342e1b8.jpg\",\n  \"86446.jpg\": \"Insecta/Nomophila nearctica/ea762c934a6188f0d85e6bb7e6adf8dc.jpg\",\n  \"86448.jpg\": \"Insecta/Nomophila nearctica/0dfcbfb7bb0a4d9ccb0824bff5461c39.jpg\",\n  \"86450.jpg\": \"Insecta/Nomophila nearctica/32fb58f004a92925bc178fb33498e982.jpg\",\n  \"86451.jpg\": \"Insecta/Nomophila nearctica/780a3cbedfd72c268969b4b2566e5f71.jpg\",\n  \"86456.jpg\": \"Insecta/Nomophila nearctica/c0b9f64d65be2a4ceaf410ec865c9979.jpg\",\n  \"86462.jpg\": \"Insecta/Nomophila nearctica/1506b4c6028a6da5a3ff2f69c1c70aac.jpg\",\n  \"86463.jpg\": \"Insecta/Nomophila nearctica/1d6a91ce0351a54a8e2b05059376b0df.jpg\",\n  \"86468.jpg\": \"Insecta/Nomophila nearctica/ed16e139c226f1631afce96dd970d2dc.jpg\",\n  \"86475.jpg\": \"Insecta/Nomophila nearctica/8800c46c398e7b4fbe778ccbb3912179.jpg\",\n  \"86476.jpg\": \"Insecta/Nomophila nearctica/837ac0bbcb8e0b6ef343de066f005a80.jpg\",\n  \"86479.jpg\": \"Insecta/Nomophila nearctica/196c129178709eb26523094ff7248d7c.jpg\",\n  \"86483.jpg\": \"Insecta/Nomophila nearctica/99955b0cf1f6dc77b62f1950d0cbf017.jpg\",\n  \"86485.jpg\": \"Insecta/Nomophila nearctica/8cd8b23db1c3579280a923a279c2b37f.jpg\",\n  \"86501.jpg\": \"Reptilia/Sceloporus cowlesi/b32d0591e76a503731eebb2a86c9c98c.jpg\",\n  \"86502.jpg\": \"Reptilia/Sceloporus cowlesi/993f8feaaf573da480ddd770f0abb180.jpg\",\n  \"86503.jpg\": \"Reptilia/Sceloporus cowlesi/fa0ccb484c20166b2cec8d4fc8faf25f.jpg\",\n  \"86505.jpg\": \"Reptilia/Sceloporus cowlesi/a2c499e3a1c6f6692307a1dbd2ba4166.jpg\",\n  \"86509.jpg\": \"Reptilia/Sceloporus cowlesi/edd0089d02f8514bdea8b9db713e184c.jpg\",\n  \"86512.jpg\": \"Reptilia/Sceloporus cowlesi/c5f89669004e25c386813bc6e81e9654.jpg\",\n  \"86513.jpg\": \"Reptilia/Sceloporus cowlesi/d87c80ad089f4251bc61cbdee2d44c6f.jpg\",\n  \"86514.jpg\": \"Reptilia/Sceloporus cowlesi/458ca16be6a45fec3eb04d94a551cb7e.jpg\",\n  \"86515.jpg\": \"Reptilia/Sceloporus cowlesi/54dedaa12100492a572748e6426489df.jpg\",\n  \"86517.jpg\": \"Reptilia/Sceloporus cowlesi/3087ee914ad71ddac8fd7c4e5a316ab1.jpg\",\n  \"86521.jpg\": \"Reptilia/Sceloporus cowlesi/70a56f2d419e77ab2d7c128e0d9d35d6.jpg\",\n  \"86522.jpg\": \"Reptilia/Sceloporus cowlesi/daf330934bf748174d04367d1a69d9d8.jpg\",\n  \"86523.jpg\": \"Reptilia/Sceloporus cowlesi/77c883d7e5c872cb6cce2b9a6ba050c7.jpg\",\n  \"86524.jpg\": \"Reptilia/Sceloporus cowlesi/ea1cf1c10ec5542adfe9dc4fc94d64de.jpg\",\n  \"86525.jpg\": \"Reptilia/Sceloporus cowlesi/d3073dc962a8add92082f254ea24b2c3.jpg\",\n  \"86526.jpg\": \"Reptilia/Sceloporus cowlesi/72ff2db110d2dacefe9d55e7ffc50d39.jpg\",\n  \"86527.jpg\": \"Reptilia/Sceloporus cowlesi/526a19b90a07be09bf7de2fa37b35f34.jpg\",\n  \"86528.jpg\": \"Reptilia/Sceloporus cowlesi/5a6f552ae8c9b5c2f8d30df33a150316.jpg\",\n  \"86534.jpg\": \"Reptilia/Sceloporus cowlesi/fd7f341adb354fb206edf0fb33f2eed7.jpg\",\n  \"86535.jpg\": \"Reptilia/Sceloporus cowlesi/a4f9a4ebf2b0556e63e7a087bb141d11.jpg\",\n  \"86536.jpg\": \"Reptilia/Sceloporus cowlesi/b0c973838c74a85a7dd68a53d71bd321.jpg\",\n  \"86538.jpg\": \"Reptilia/Sceloporus cowlesi/f3330929c978d92c985cf37270b13e76.jpg\",\n  \"86544.jpg\": \"Aves/Passerina amoena/76035d7966017ca5e5080830d57446cd.jpg\",\n  \"86554.jpg\": \"Aves/Passerina amoena/05a10aa3ecd3cd745506c230e183edbe.jpg\",\n  \"86555.jpg\": \"Aves/Passerina amoena/68b0cf3a2e48b7466206259eb7e63bdc.jpg\",\n  \"86562.jpg\": \"Aves/Passerina amoena/f68169b53ea881e50f5a63433813b277.jpg\",\n  \"86565.jpg\": \"Aves/Passerina amoena/02631b131b5c66e61e622dc36e2cadce.jpg\",\n  \"86566.jpg\": \"Aves/Passerina amoena/1bb9f9e1ba94e33ed5c3b2dceba437f7.jpg\",\n  \"86567.jpg\": \"Aves/Passerina amoena/1ff6059b16dc24c0a6966dfee03855e8.jpg\",\n  \"86569.jpg\": \"Aves/Passerina amoena/dd522f590e4e574fc6e422f2148aa85e.jpg\",\n  \"86573.jpg\": \"Aves/Passerina amoena/a470aa18021981ea750ac49f90a1ce2c.jpg\",\n  \"86576.jpg\": \"Aves/Passerina amoena/48c44cb6bf2594edf06703e7ab4d2d5b.jpg\",\n  \"86577.jpg\": \"Aves/Passerina amoena/56a6839fed4f312cb686e40e6f56ef10.jpg\",\n  \"86580.jpg\": \"Aves/Passerina amoena/d111aebf0ad9a2809d5e354f5de80d75.jpg\",\n  \"86581.jpg\": \"Aves/Passerina amoena/bb797bd9d45165db70e1fadc12c3a46e.jpg\",\n  \"86585.jpg\": \"Aves/Passerina amoena/2fa459688e482f51550495b76582f309.jpg\",\n  \"86587.jpg\": \"Aves/Passerina amoena/7b31b136b58598d8428f92a0efa49b7e.jpg\",\n  \"86594.jpg\": \"Aves/Passerina amoena/3d4d390731ec7e499794fcc654e4f86d.jpg\",\n  \"86595.jpg\": \"Aves/Passerina amoena/b79bf9eb4369e25f2bb0ca262ee8c025.jpg\",\n  \"86598.jpg\": \"Aves/Passerina amoena/7c9ad413abc6d497db827cf71c0971ff.jpg\",\n  \"86601.jpg\": \"Aves/Passerina amoena/10354544feb4da6116235376c1880a2b.jpg\",\n  \"86602.jpg\": \"Aves/Passerina amoena/a241dcf94763f28a2b61f8ca6daa916b.jpg\",\n  \"86606.jpg\": \"Aves/Passerina amoena/ef76c730023a91342137b64d237272b4.jpg\",\n  \"86610.jpg\": \"Aves/Passerina amoena/5b2e9431ad229d2aea9c2ba552683754.jpg\",\n  \"86628.jpg\": \"Insecta/Halysidota harrisii/3fd79433eb78ff5a641f1b1be22a4a4a.jpg\",\n  \"86629.jpg\": \"Insecta/Halysidota harrisii/5320aea9321f28184bdc0a34aac0805a.jpg\",\n  \"86630.jpg\": \"Insecta/Halysidota harrisii/cf2700bbcf226dc4690a395d9d4660a8.jpg\",\n  \"86632.jpg\": \"Insecta/Halysidota harrisii/0d1e60a9f184abd219ed5002e399e88a.jpg\",\n  \"86633.jpg\": \"Insecta/Halysidota harrisii/ce8158e7a912cd8d8158c4125f48b543.jpg\",\n  \"86637.jpg\": \"Insecta/Halysidota harrisii/cfbf6c44457e846bb655cfa8a290c8c8.jpg\",\n  \"86701.jpg\": \"Mammalia/Ursus americanus californiensis/b8b5e1275897638adaaab5b555d579a7.jpg\",\n  \"86708.jpg\": \"Mammalia/Ursus americanus californiensis/97ea46d64497971d85d8f58672acd69b.jpg\",\n  \"86711.jpg\": \"Mammalia/Ursus americanus californiensis/4bf8a7c060deceb6e998edf1908d8e07.jpg\",\n  \"86725.jpg\": \"Aves/Spinus pinus/1ef504733f4de0aab2b0bf193927a417.jpg\",\n  \"86753.jpg\": \"Aves/Spinus pinus/cbe1903a27e6957671ee866cd0067245.jpg\",\n  \"86778.jpg\": \"Aves/Spinus pinus/0e6e5f32b5b2a8960f611b0bf4cf6fad.jpg\",\n  \"86779.jpg\": \"Aves/Spinus pinus/4b97187a909ed30fd289ec0202e5dafa.jpg\",\n  \"86786.jpg\": \"Aves/Spinus pinus/10152fae347ddd77531222cccd97bf99.jpg\",\n  \"86789.jpg\": \"Aves/Spinus pinus/8feee569b6331080ed3e8e4cd74482be.jpg\",\n  \"86801.jpg\": \"Aves/Spinus pinus/f4d43a48755aaaa2fbe05d38676fb55b.jpg\",\n  \"86804.jpg\": \"Aves/Spinus pinus/e9f2b5597f7188e0b7bb3ba3d07afe47.jpg\",\n  \"86815.jpg\": \"Aves/Spinus pinus/4ef7aface626db36e28f0b70a42d0ff6.jpg\",\n  \"86827.jpg\": \"Aves/Spinus pinus/9b76cd08a14c4f2c667045008a630f44.jpg\",\n  \"86857.jpg\": \"Aves/Spinus pinus/c1cb2209c026f5728f8632b836a26af5.jpg\",\n  \"86860.jpg\": \"Aves/Spinus pinus/79695e196b1f490391e44f530ebed27e.jpg\",\n  \"86887.jpg\": \"Aves/Spinus pinus/ba685fb7e4994acbf756d201d2319e0e.jpg\",\n  \"86900.jpg\": \"Aves/Spinus pinus/0f0ac1cb1fedc8d3db2d1bc095074abe.jpg\",\n  \"86919.jpg\": \"Aves/Spinus pinus/e9d7396b608cdb721c1c035ceaaa7c47.jpg\",\n  \"86938.jpg\": \"Insecta/Autographa precationis/3a3330a5ae404be76cb90ea718b7126a.jpg\",\n  \"86943.jpg\": \"Insecta/Autographa precationis/f32ee0532e8f482a706c6bc092e46da6.jpg\",\n  \"86946.jpg\": \"Insecta/Autographa precationis/2c72ceb5d7ca6d83a99e54c8681bc758.jpg\",\n  \"86948.jpg\": \"Insecta/Autographa precationis/b579ab6df815d3b140257e47d6ead4ea.jpg\",\n  \"86955.jpg\": \"Insecta/Autographa precationis/d73ccfe8ae3540b8b44dd168845edf55.jpg\",\n  \"8696.jpg\": \"Mammalia/Odocoileus hemionus/e798ba632f8f20a160d93b62f72fb115.jpg\",\n  \"86967.jpg\": \"Insecta/Autographa precationis/ab00c3b1dba7ce735c3730ed6c7f847a.jpg\",\n  \"86975.jpg\": \"Insecta/Autographa precationis/48de525309d3d07f892250aabba8987c.jpg\",\n  \"8698.jpg\": \"Mammalia/Odocoileus hemionus/57657f097915a08c3a6f0b5f279bc04f.jpg\",\n  \"86980.jpg\": \"Aves/Setophaga castanea/5dbf824f8de5b9fb294f59c385c96922.jpg\",\n  \"86981.jpg\": \"Aves/Setophaga castanea/231f958c9cac73b99be4b5dd40b763a1.jpg\",\n  \"86982.jpg\": \"Aves/Setophaga castanea/51027dde67bd89b4dc7e9af587a5496d.jpg\",\n  \"86983.jpg\": \"Aves/Setophaga castanea/131e8ce1dd2c03484e710715e1aca4a6.jpg\",\n  \"86984.jpg\": \"Aves/Setophaga castanea/14f551027fe3531749427d574d0a1b7c.jpg\",\n  \"86985.jpg\": \"Aves/Setophaga castanea/59791fce87a22d058fe2c0a72553ddf4.jpg\",\n  \"86987.jpg\": \"Aves/Setophaga castanea/3ca584726ea337b35ee17636a62e8992.jpg\",\n  \"86988.jpg\": \"Aves/Setophaga castanea/2be142fc161767eb57c5064dcaf42a9c.jpg\",\n  \"86989.jpg\": \"Aves/Setophaga castanea/2fef43d87223bd65768d9393d73c2ac5.jpg\",\n  \"86990.jpg\": \"Aves/Setophaga castanea/7411fcb30336d8896c293807910aefe5.jpg\",\n  \"86991.jpg\": \"Aves/Setophaga castanea/7af3bea04f00c0b122627d7c81bb07da.jpg\",\n  \"86992.jpg\": \"Aves/Setophaga castanea/2e69507de688b44d6a929714d72a3187.jpg\",\n  \"86995.jpg\": \"Aves/Setophaga castanea/48dbc86f05289619fb2b8bd61d0b9c55.jpg\",\n  \"86996.jpg\": \"Aves/Setophaga castanea/100ba30bf2c42d49658cbafeaf0094a9.jpg\",\n  \"86997.jpg\": \"Aves/Setophaga castanea/4605d5f8d8d5af73ee05fdd39bca62bd.jpg\",\n  \"86998.jpg\": \"Aves/Setophaga castanea/f99dd8419602dff63c92908d633e69a1.jpg\",\n  \"87001.jpg\": \"Aves/Setophaga castanea/ebe1968a1ab16f24578a9c8a5c539614.jpg\",\n  \"87002.jpg\": \"Aves/Setophaga castanea/ddc534d23882da5dad135b486c6d0fb9.jpg\",\n  \"87003.jpg\": \"Aves/Setophaga castanea/867a046e001256ceec13e29f139fa207.jpg\",\n  \"87012.jpg\": \"Aves/Setophaga castanea/20180c34de44c332b7cd716a703be4aa.jpg\",\n  \"87013.jpg\": \"Aves/Setophaga castanea/17b492275ee118c90dd952ef31aff304.jpg\",\n  \"87015.jpg\": \"Aves/Setophaga castanea/fa66effc2d89bc65e7627547339d5539.jpg\",\n  \"87016.jpg\": \"Aves/Setophaga castanea/a002707d19aab471b901220dd58af2d9.jpg\",\n  \"87017.jpg\": \"Aves/Setophaga castanea/e1932a44f9a50387a5d0210a7f2425be.jpg\",\n  \"87466.jpg\": \"Insecta/Homophoberia apicosa/6cabc847f64385e0833d5342ebb9059d.jpg\",\n  \"87471.jpg\": \"Insecta/Homophoberia apicosa/67b9344707dfeede6d32045931140c68.jpg\",\n  \"875.jpg\": \"Mammalia/Ondatra zibethicus/0f673a12e10eebcc0969be719d66e669.jpg\",\n  \"8751.jpg\": \"Mammalia/Odocoileus hemionus/22f681532b6f1bbc04cf15f1208b3a82.jpg\",\n  \"87563.jpg\": \"Insecta/Megalopyge opercularis/0ab0624011bceb628d910199d4a583f2.jpg\",\n  \"87565.jpg\": \"Insecta/Megalopyge opercularis/3b6216f8825e4d1d5ca69724ce6d1a65.jpg\",\n  \"87568.jpg\": \"Insecta/Megalopyge opercularis/f19f774b90125c0e5de090c8198808bb.jpg\",\n  \"87574.jpg\": \"Insecta/Megalopyge opercularis/2837a9d6a2df443299334eea38b0c57a.jpg\",\n  \"87578.jpg\": \"Insecta/Megalopyge opercularis/5ef9e50d3068ea8dead8ab45ed41397d.jpg\",\n  \"87653.jpg\": \"Insecta/Iridopsis larvaria/8ae135d6c9cd96a84723b5e1fbf0065e.jpg\",\n  \"87665.jpg\": \"Insecta/Iridopsis larvaria/42c1c5817d4834d7127a42366d28f090.jpg\",\n  \"87666.jpg\": \"Insecta/Iridopsis larvaria/4e11abf875169f694e2572e10a152ccf.jpg\",\n  \"87667.jpg\": \"Insecta/Iridopsis larvaria/7ec6a0b66d472d31a0b67c69987685eb.jpg\",\n  \"87668.jpg\": \"Insecta/Iridopsis larvaria/3f62f0747993b64bdbfd7c18c8dbdbd7.jpg\",\n  \"87676.jpg\": \"Insecta/Iridopsis larvaria/02e56a704e01421e8b8d8bc07a4d59e3.jpg\",\n  \"87677.jpg\": \"Insecta/Iridopsis larvaria/f2cb747438245536b7cdb498ba58106f.jpg\",\n  \"87718.jpg\": \"Insecta/Aeshna cyanea/05384ce99263687e4942cd743bcb5574.jpg\",\n  \"87719.jpg\": \"Insecta/Aeshna cyanea/c60c28d52d4206a92eca46d4d8aff1ee.jpg\",\n  \"87724.jpg\": \"Insecta/Aeshna cyanea/e174245abbda091c49c22ecb20d8809d.jpg\",\n  \"87725.jpg\": \"Insecta/Aeshna cyanea/58b5be3b95cb9fe4e91956de2432eb0f.jpg\",\n  \"87732.jpg\": \"Insecta/Aeshna cyanea/633ca7fde2abd13822bbca33bb7177dc.jpg\",\n  \"87834.jpg\": \"Insecta/Heterophleps triguttaria/7ac39d6f1eb6b26f01b9615b3c83b8de.jpg\",\n  \"87835.jpg\": \"Insecta/Heterophleps triguttaria/42a870a7b6152265dfa228a45e8d3be8.jpg\",\n  \"87837.jpg\": \"Insecta/Heterophleps triguttaria/63c16755421bbd37488dd9922d05ea68.jpg\",\n  \"87841.jpg\": \"Insecta/Heterophleps triguttaria/2034b0e6a6ac9f438c04ea15f636b1e5.jpg\",\n  \"87844.jpg\": \"Insecta/Heterophleps triguttaria/ea397f625bd01ca94e4527433964e34b.jpg\",\n  \"87849.jpg\": \"Insecta/Heterophleps triguttaria/b6694bab7ec2e21f5369a19c796c4cd8.jpg\",\n  \"87854.jpg\": \"Insecta/Heterophleps triguttaria/ea8c5170538dffa0feca6e582600ec52.jpg\",\n  \"87857.jpg\": \"Insecta/Heterophleps triguttaria/76543063e23c847b35801eff94aff48d.jpg\",\n  \"87861.jpg\": \"Aves/Setophaga cerulea/f063a66d19d7011b3220c5be5654996b.jpg\",\n  \"87872.jpg\": \"Aves/Setophaga cerulea/ad060cdfe4043ed2e82cb28cdb663c0a.jpg\",\n  \"87873.jpg\": \"Aves/Setophaga cerulea/9f1d17a703365673f9cb92886e85af52.jpg\",\n  \"87878.jpg\": \"Aves/Setophaga cerulea/b672a8ff3d44b021b1c20000d0b82b38.jpg\",\n  \"87879.jpg\": \"Aves/Setophaga cerulea/3e57bd6e6843bcf529a628b14743faa2.jpg\",\n  \"87880.jpg\": \"Aves/Setophaga cerulea/1e72e14b371311dc19899571b7ad7771.jpg\",\n  \"87884.jpg\": \"Aves/Setophaga cerulea/111d89712bd3523a6fa324086a799621.jpg\",\n  \"87889.jpg\": \"Animalia/Philoscia muscorum/0c4ebe529c87f7e41540cf459b8bd14a.jpg\",\n  \"87894.jpg\": \"Animalia/Philoscia muscorum/d195b603a184a01061e66c0a6821a8d8.jpg\",\n  \"87904.jpg\": \"Animalia/Philoscia muscorum/d5a9720ab69fbb1912f0f0dcd829a16d.jpg\",\n  \"87906.jpg\": \"Animalia/Philoscia muscorum/51b71d0a511c78b2fbdeaf86bf20d74c.jpg\",\n  \"88117.jpg\": \"Insecta/Deinacrida rugosa/7681eac65fc2922042f2543cee702bc2.jpg\",\n  \"88118.jpg\": \"Insecta/Deinacrida rugosa/97f0911871c37008687f1518ccb4a85a.jpg\",\n  \"88120.jpg\": \"Insecta/Deinacrida rugosa/c25a6cc0a75b16cc102dd0084eb8d6cb.jpg\",\n  \"88121.jpg\": \"Insecta/Deinacrida rugosa/27ce27ed93eebf1a8a0a5ff1b3033f2e.jpg\",\n  \"88267.jpg\": \"Insecta/Calopteryx maculata/c5bbb44fd173eda20623b720c551f0e1.jpg\",\n  \"883.jpg\": \"Mammalia/Ondatra zibethicus/47b1b1c51eef6122818e27243a5c5262.jpg\",\n  \"88328.jpg\": \"Insecta/Calopteryx maculata/4010f8fb53945de765d1264fdf5d57ce.jpg\",\n  \"88329.jpg\": \"Insecta/Calopteryx maculata/7424ad79cb2f57bd67e0358dd853acb1.jpg\",\n  \"88351.jpg\": \"Insecta/Calopteryx maculata/82840d77c968ae86a95677925ab8e4a5.jpg\",\n  \"88371.jpg\": \"Insecta/Calopteryx maculata/61d06659f7ac3149938c34abafc07c38.jpg\",\n  \"88378.jpg\": \"Insecta/Calopteryx maculata/951ce6851b626bf117b7bfef44bf20da.jpg\",\n  \"8838.jpg\": \"Mammalia/Odocoileus hemionus/cdc7b2cd81a8b10059e0ade4a1dcef36.jpg\",\n  \"88388.jpg\": \"Insecta/Calopteryx maculata/f3e3548aa5a186419ea72879a8a88abb.jpg\",\n  \"884.jpg\": \"Mammalia/Ondatra zibethicus/c664dbb7599c062fddbe953b13121c0a.jpg\",\n  \"88410.jpg\": \"Insecta/Calopteryx maculata/d36a3b938d42b8583be1159fcb790665.jpg\",\n  \"88429.jpg\": \"Insecta/Calopteryx maculata/b3688a701c3e4db37255cd8d4750ec43.jpg\",\n  \"88436.jpg\": \"Insecta/Calopteryx maculata/345e9a252bffc1a217aed18cb7c5df76.jpg\",\n  \"88437.jpg\": \"Insecta/Calopteryx maculata/c5ca42fbd07a14338b6890b7b2652842.jpg\",\n  \"88441.jpg\": \"Insecta/Calopteryx maculata/c4fe3bf1c761fe22497f8e175e8c0531.jpg\",\n  \"88442.jpg\": \"Insecta/Calopteryx maculata/0e88f03f45de07ff20a5e33ac0432f7c.jpg\",\n  \"88474.jpg\": \"Insecta/Calopteryx maculata/8b61d54ebfeed8b984514cc44717626e.jpg\",\n  \"88477.jpg\": \"Insecta/Calopteryx maculata/7f05f816283f876657c00a094d200dc1.jpg\",\n  \"88488.jpg\": \"Insecta/Calopteryx maculata/e7bd42f1bce5eec94b561babe3304b70.jpg\",\n  \"88508.jpg\": \"Insecta/Calopteryx maculata/7f7b9f16a9176ef58faf069c508a99a0.jpg\",\n  \"88518.jpg\": \"Insecta/Calopteryx maculata/0e77cb61a645aa880ac35a7c48d390d9.jpg\",\n  \"88531.jpg\": \"Insecta/Calopteryx maculata/ce76c928e2459d78a5a67cf84d511c89.jpg\",\n  \"88547.jpg\": \"Insecta/Calopteryx maculata/3a6de261e0161eb8e6334a2ee64267c4.jpg\",\n  \"88554.jpg\": \"Insecta/Calopteryx maculata/50ee8166771cdb45498ba92007ce20e2.jpg\",\n  \"88566.jpg\": \"Insecta/Calopteryx maculata/d253097b916ceee6621da9a1bf4d10d7.jpg\",\n  \"88567.jpg\": \"Insecta/Calopteryx maculata/30a0c07365521a80f372d8329199e08c.jpg\",\n  \"88889.jpg\": \"Insecta/Cicindela sexguttata/c3f35e6f295e4f3100b8d774b9e9a6a4.jpg\",\n  \"88905.jpg\": \"Insecta/Cicindela sexguttata/771eb8842893856ff779c4fd8030b675.jpg\",\n  \"88937.jpg\": \"Insecta/Cicindela sexguttata/e8a5447b0837215468c59daed4e8a60c.jpg\",\n  \"88943.jpg\": \"Insecta/Cicindela sexguttata/df1c015b64e2bf45f81e37313edc5b5e.jpg\",\n  \"88944.jpg\": \"Insecta/Cicindela sexguttata/badc29187b80bb6fd45342275a14779a.jpg\",\n  \"8895.jpg\": \"Mammalia/Odocoileus hemionus/d8663b7beefa503332c6f7b3fbcd10e5.jpg\",\n  \"88950.jpg\": \"Insecta/Cicindela sexguttata/7e897a4031571b202965f52ad35b95c1.jpg\",\n  \"88953.jpg\": \"Insecta/Cicindela sexguttata/c04acd7b5c93956d21fe72316904f980.jpg\",\n  \"88965.jpg\": \"Insecta/Cicindela sexguttata/d1ea201811967095ace98ef26b82f885.jpg\",\n  \"88972.jpg\": \"Insecta/Cicindela sexguttata/ffe7a69080594e570b3370b95a02eec2.jpg\",\n  \"88975.jpg\": \"Insecta/Cicindela sexguttata/d21e9d17b6a6b1ffe4c1a3a7de6285d5.jpg\",\n  \"88989.jpg\": \"Insecta/Cicindela sexguttata/de73fa191151fafb7ceabf423123b154.jpg\",\n  \"88997.jpg\": \"Insecta/Cicindela sexguttata/5115a75802f077a84c32709f4704bd91.jpg\",\n  \"89007.jpg\": \"Insecta/Cicindela sexguttata/8497af90f4fbffbd020fa6d34fd5b5c7.jpg\",\n  \"89009.jpg\": \"Insecta/Cicindela sexguttata/ec694962f8f7900f819b2b83160ae939.jpg\",\n  \"89023.jpg\": \"Insecta/Cicindela sexguttata/1dea7634fd8b217bafcc009cc9cecf33.jpg\",\n  \"89034.jpg\": \"Insecta/Cicindela sexguttata/b6e28e0c3a77053dd5c3ecf7f1dd7865.jpg\",\n  \"89038.jpg\": \"Insecta/Cicindela sexguttata/a7d7044ad108a2640c35d211a183dde9.jpg\",\n  \"89073.jpg\": \"Insecta/Cicindela sexguttata/fc68045c0b149bef001e3d387ad9c86e.jpg\",\n  \"89078.jpg\": \"Insecta/Cicindela sexguttata/93f282c27f1c623653cbfb70baa02945.jpg\",\n  \"89090.jpg\": \"Insecta/Cicindela sexguttata/241e20dbe58b62982cf39da823978afb.jpg\",\n  \"89100.jpg\": \"Insecta/Cicindela sexguttata/a86d915c338f8fff028e533ecd24e838.jpg\",\n  \"89109.jpg\": \"Mammalia/Trichosurus vulpecula/88ed3fcd4494aaeef575f42f5b821ebf.jpg\",\n  \"89110.jpg\": \"Mammalia/Trichosurus vulpecula/0e26779b7ccc65f5bbf58939b8535e38.jpg\",\n  \"89111.jpg\": \"Mammalia/Trichosurus vulpecula/16eff29cd3222fd993d2084a051f50f4.jpg\",\n  \"89121.jpg\": \"Mammalia/Trichosurus vulpecula/42b9f9f3ee95de121385d1cbe19875bc.jpg\",\n  \"89124.jpg\": \"Mammalia/Trichosurus vulpecula/0244100db4e296ba362ed1198e3a78b6.jpg\",\n  \"89127.jpg\": \"Mammalia/Trichosurus vulpecula/de0a2907ba3e4288c1cce346fcb45cfa.jpg\",\n  \"89128.jpg\": \"Mammalia/Trichosurus vulpecula/fc076aeb02d81782b6237f663622467f.jpg\",\n  \"89130.jpg\": \"Mammalia/Trichosurus vulpecula/ea4575c907c0ed53394c80d50bb91559.jpg\",\n  \"89131.jpg\": \"Mammalia/Trichosurus vulpecula/292d6de62f4bd979010dc1c7d7423395.jpg\",\n  \"89132.jpg\": \"Mammalia/Trichosurus vulpecula/2c4af8d9a8bb674db9df8057c5a7f824.jpg\",\n  \"89133.jpg\": \"Mammalia/Trichosurus vulpecula/19752ad286ac318869a54c507e339825.jpg\",\n  \"89145.jpg\": \"Mammalia/Trichosurus vulpecula/42e9ae2d072fb00f2a41f11a662a3b4f.jpg\",\n  \"89151.jpg\": \"Mammalia/Trichosurus vulpecula/b5c2d95329f91c2383217d16a543e5b3.jpg\",\n  \"89166.jpg\": \"Mammalia/Trichosurus vulpecula/f0482d4cf559217c60f39f68d788cdb4.jpg\",\n  \"89167.jpg\": \"Mammalia/Trichosurus vulpecula/64339768cfd8ba21f4bcec4ac226f085.jpg\",\n  \"89168.jpg\": \"Mammalia/Trichosurus vulpecula/790163ca89ebe3ad16fd69ab9a3126f6.jpg\",\n  \"89178.jpg\": \"Mammalia/Trichosurus vulpecula/c841ee2a5d10a75d9b06aa7cf082cf65.jpg\",\n  \"89179.jpg\": \"Mammalia/Trichosurus vulpecula/1e59d34d6b81016ce33fe1d19305ebc6.jpg\",\n  \"89180.jpg\": \"Mammalia/Trichosurus vulpecula/5d6330ec71a9c9b4626b2967ccf2c8dc.jpg\",\n  \"89189.jpg\": \"Mammalia/Trichosurus vulpecula/e80ba4bbf3af444fcee88036c5045993.jpg\",\n  \"89212.jpg\": \"Reptilia/Sceloporus occidentalis/e01e53ef0643f6f497af4a493115b472.jpg\",\n  \"89277.jpg\": \"Reptilia/Sceloporus occidentalis/c04a906b4d88dbde4db001d657cdb415.jpg\",\n  \"89428.jpg\": \"Reptilia/Sceloporus occidentalis/4d3019d9dd194e29f7b81ab940abd249.jpg\",\n  \"89492.jpg\": \"Reptilia/Sceloporus occidentalis/e7d6e8c8c0692a0145ee386f14d817dd.jpg\",\n  \"8950.jpg\": \"Mammalia/Odocoileus hemionus/d060e4727eebe1330cc55c4a46b4e483.jpg\",\n  \"89527.jpg\": \"Reptilia/Sceloporus occidentalis/852ed75b6a7a1777046b850912a893f8.jpg\",\n  \"89576.jpg\": \"Reptilia/Sceloporus occidentalis/c3d3d9193c386e13d39b612be8f2b435.jpg\",\n  \"89742.jpg\": \"Reptilia/Sceloporus occidentalis/940613063c4cbe6a3a561dcbbedadabb.jpg\",\n  \"89763.jpg\": \"Reptilia/Sceloporus occidentalis/00fa2f0c260c7b129a6f6a5a8afcd121.jpg\",\n  \"89768.jpg\": \"Reptilia/Sceloporus occidentalis/0719e97fb8b8b7ab19766124a643be55.jpg\",\n  \"89799.jpg\": \"Reptilia/Sceloporus occidentalis/65628b07d11d42ce86617f402eebd5ac.jpg\",\n  \"90030.jpg\": \"Reptilia/Sceloporus occidentalis/4eabc211f634c5fd5cc7f413faf66eb7.jpg\",\n  \"90097.jpg\": \"Reptilia/Sceloporus occidentalis/9a740da4c51c55556c625016fd2c0159.jpg\",\n  \"9010.jpg\": \"Mammalia/Odocoileus hemionus/bbd5182f42b2885bf1dc2e9dc30d7b85.jpg\",\n  \"9015.jpg\": \"Mammalia/Odocoileus hemionus/ddad7c0a65549b26625842f5d6b464d9.jpg\",\n  \"9021.jpg\": \"Mammalia/Odocoileus hemionus/9bb9a40bfde2ef620d0122d55164f491.jpg\",\n  \"90329.jpg\": \"Reptilia/Sceloporus occidentalis/dfae63ced5f3e2815818eb3746486a22.jpg\",\n  \"90340.jpg\": \"Reptilia/Sceloporus occidentalis/9931e313bfed4f2dfc9ec7ba29e6a8b3.jpg\",\n  \"90360.jpg\": \"Reptilia/Sceloporus occidentalis/9060e24995a1a904f616fd9c0b899ad9.jpg\",\n  \"9049.jpg\": \"Mammalia/Odocoileus hemionus/ec6ab50be16c5e6e71b517b5f3bd4f99.jpg\",\n  \"90518.jpg\": \"Reptilia/Sceloporus occidentalis/f292a2db77e3c863e527855d79c88ff5.jpg\",\n  \"90562.jpg\": \"Reptilia/Sceloporus occidentalis/19a4ffef03e449878080d878666826e3.jpg\",\n  \"90627.jpg\": \"Reptilia/Sceloporus occidentalis/33810eeca1541bff2c81381e9300ba05.jpg\",\n  \"90669.jpg\": \"Reptilia/Sceloporus occidentalis/fbdd8b4ac0c9617baacb43386f659244.jpg\",\n  \"9071.jpg\": \"Mammalia/Odocoileus hemionus/eaadb792952e6d9af750849e0c895da5.jpg\",\n  \"90759.jpg\": \"Reptilia/Sceloporus occidentalis/c946892d9e7e2ee887accee70a485edf.jpg\",\n  \"90766.jpg\": \"Reptilia/Sceloporus occidentalis/20d1b62950f06bc8629a7c3a3d8c6a64.jpg\",\n  \"90811.jpg\": \"Reptilia/Sceloporus occidentalis/9c9501ea832e0347ad348c98d696f5d5.jpg\",\n  \"90851.jpg\": \"Reptilia/Sceloporus occidentalis/92a21b1338c6338497dbca752276e71f.jpg\",\n  \"90976.jpg\": \"Reptilia/Sceloporus occidentalis/c03171595e3184238c3de22faa0c4a11.jpg\",\n  \"90995.jpg\": \"Reptilia/Sceloporus occidentalis/f71503ce85138b961bea46de3166d3b9.jpg\",\n  \"91104.jpg\": \"Insecta/Buprestis aurulenta/9920ff77498afc608ac2036be34061c1.jpg\",\n  \"91105.jpg\": \"Insecta/Buprestis aurulenta/35bb989435f69dc586989798074a66e3.jpg\",\n  \"91106.jpg\": \"Insecta/Buprestis aurulenta/0f07a1031751e3a23bcff5f25b020a7d.jpg\",\n  \"91109.jpg\": \"Insecta/Buprestis aurulenta/623bca836803f450264953d787222836.jpg\",\n  \"91113.jpg\": \"Insecta/Buprestis aurulenta/1852a9a505571de724b8339354f787cc.jpg\",\n  \"91121.jpg\": \"Insecta/Buprestis aurulenta/3e3287b39ef6615dce115eea4e86b601.jpg\",\n  \"91124.jpg\": \"Insecta/Buprestis aurulenta/859764232468e83dce0b29792cbb9c1c.jpg\",\n  \"91134.jpg\": \"Insecta/Ladona depressa/7d57a1e2d749b60c4f2f58fb9e08b5c3.jpg\",\n  \"91135.jpg\": \"Insecta/Ladona depressa/12f1fed4a0fb2578a5e170c2c417b2a1.jpg\",\n  \"91136.jpg\": \"Insecta/Ladona depressa/e4f448978934a0e3ce268aec5a3d5f77.jpg\",\n  \"91139.jpg\": \"Insecta/Ladona depressa/2e2a32d7122b3e3d1e353b8d715dd6d6.jpg\",\n  \"91222.jpg\": \"Reptilia/Scincella lateralis/78b8d9246495a45a1c65f7febd9f0a79.jpg\",\n  \"91229.jpg\": \"Reptilia/Scincella lateralis/a63374fac5feecdbe4ad20e892d944fb.jpg\",\n  \"91240.jpg\": \"Reptilia/Scincella lateralis/3ac8246541c7e645e3cf3d1a408ad28c.jpg\",\n  \"91263.jpg\": \"Reptilia/Scincella lateralis/e11c6b17baeeafaeb54c372938066677.jpg\",\n  \"91271.jpg\": \"Reptilia/Scincella lateralis/3d27712633334a98000df4de6d5b9ad9.jpg\",\n  \"91272.jpg\": \"Reptilia/Scincella lateralis/2603ac8b08b4d959a72500858ecf4045.jpg\",\n  \"91286.jpg\": \"Reptilia/Scincella lateralis/fbd9e1242f64804206612f608b327d79.jpg\",\n  \"91292.jpg\": \"Reptilia/Scincella lateralis/778e99515840815ce3d6edddf78c2548.jpg\",\n  \"91314.jpg\": \"Reptilia/Scincella lateralis/120440ab20e5876fb6a64741067a757c.jpg\",\n  \"91317.jpg\": \"Reptilia/Scincella lateralis/90a92a23c429aa6e434cbc11d432b57f.jpg\",\n  \"91322.jpg\": \"Reptilia/Scincella lateralis/7af1372b8127cbafd20fafec71be67e6.jpg\",\n  \"91326.jpg\": \"Reptilia/Scincella lateralis/fae07a533c8dca91ef0b27d096f2349f.jpg\",\n  \"91334.jpg\": \"Reptilia/Scincella lateralis/e63eeb343bcbece49fcc26f3fa7183fc.jpg\",\n  \"91339.jpg\": \"Reptilia/Scincella lateralis/a0cc83abc14c42f7b18a80edb9929847.jpg\",\n  \"91343.jpg\": \"Reptilia/Scincella lateralis/06a516f2e6a41560b88c3aa73f182d17.jpg\",\n  \"91352.jpg\": \"Reptilia/Scincella lateralis/cd43b20ebbb51b723029812cb56b19e1.jpg\",\n  \"91361.jpg\": \"Reptilia/Scincella lateralis/2059f301132859c71561b3bc97f1e5f3.jpg\",\n  \"91371.jpg\": \"Reptilia/Scincella lateralis/ebd456c04af84f5a0ed763987dfd1b54.jpg\",\n  \"91390.jpg\": \"Reptilia/Scincella lateralis/8442d2dc37735b6583c12ef1997eae64.jpg\",\n  \"91392.jpg\": \"Reptilia/Scincella lateralis/92e6f3bc849ec24f7223e36a2b43643a.jpg\",\n  \"91397.jpg\": \"Reptilia/Scincella lateralis/e4f645c5ea1ff4b5f503f3a7f1d30012.jpg\",\n  \"91400.jpg\": \"Reptilia/Scincella lateralis/f61843e402861cf555cb0131c0a4e213.jpg\",\n  \"91452.jpg\": \"Aves/Rallus crepitans/4ef04034273e65caad8daea278560c03.jpg\",\n  \"91454.jpg\": \"Aves/Rallus crepitans/8cca4040d1837142c896b583c57f0ad7.jpg\",\n  \"91456.jpg\": \"Aves/Rallus crepitans/56324c30b78d840343b1622c21f32a44.jpg\",\n  \"91460.jpg\": \"Aves/Rallus crepitans/e32d59575607a0e600c06bdddf3b07cf.jpg\",\n  \"91464.jpg\": \"Aves/Rallus crepitans/22ac1d6107341bbde4151aa6a025960a.jpg\",\n  \"91465.jpg\": \"Aves/Rallus crepitans/b93067e15fe4804aa863db87a5bd19f2.jpg\",\n  \"91528.jpg\": \"Reptilia/Ramphotyphlops braminus/0468c2cf569d0d0e36317467cdc8b213.jpg\",\n  \"91529.jpg\": \"Reptilia/Ramphotyphlops braminus/75152ed4229ef7c6974e58ff3ffadad4.jpg\",\n  \"91532.jpg\": \"Reptilia/Ramphotyphlops braminus/2dda04bfd44278d337dc3d93c608eb07.jpg\",\n  \"9156.jpg\": \"Mammalia/Odocoileus hemionus/f1fe27e6a6f88f5803be389384b53f8d.jpg\",\n  \"9166.jpg\": \"Mammalia/Odocoileus hemionus/e1c2b8e2246c99ba4f0041c99d227d62.jpg\",\n  \"9167.jpg\": \"Mammalia/Odocoileus hemionus/865199177d359711f9f3ba1730790046.jpg\",\n  \"91721.jpg\": \"Mollusca/Sypharochiton pelliserpentis/2a6e8fcc9d5ae162457cc7a5e72abbe2.jpg\",\n  \"91821.jpg\": \"Insecta/Protodeltote muscosula/f86b93ff21774d754afd5c4f9c3e9818.jpg\",\n  \"91822.jpg\": \"Insecta/Protodeltote muscosula/4da60abd684c139b14774b1f4e19d285.jpg\",\n  \"91833.jpg\": \"Insecta/Protodeltote muscosula/5a4c956e676df2a6c6744b1c65c9a5e1.jpg\",\n  \"91837.jpg\": \"Insecta/Protodeltote muscosula/cd0f8e284b68b987283d935d6a3860da.jpg\",\n  \"91839.jpg\": \"Insecta/Protodeltote muscosula/2b9091c065ab08e8ca983846a94986c0.jpg\",\n  \"91916.jpg\": \"Insecta/Sitochroa palealis/004b1455caf8ab6d3e4e419d276c829e.jpg\",\n  \"91917.jpg\": \"Insecta/Sitochroa palealis/8cfc050f4cedf517dea8b2a90b63bd5e.jpg\",\n  \"91921.jpg\": \"Insecta/Sitochroa palealis/32090e8120704cde606cafa2fba957c5.jpg\",\n  \"91926.jpg\": \"Insecta/Sitochroa palealis/ff724a4dc7e974e104a8cc4d3fd88997.jpg\",\n  \"91927.jpg\": \"Insecta/Sitochroa palealis/8f81cdc5ab7f16833ac4011c4e6766c1.jpg\",\n  \"91931.jpg\": \"Insecta/Sitochroa palealis/d54cf65b452448769c1f5e60d68345c4.jpg\",\n  \"91937.jpg\": \"Insecta/Pyrausta acrionalis/3621f069b1f541d4d08d8630db85efd4.jpg\",\n  \"91940.jpg\": \"Insecta/Pyrausta acrionalis/a049442ed75abe14cc00e25e149d49c2.jpg\",\n  \"91945.jpg\": \"Insecta/Pyrausta acrionalis/6a31b36bce889064fb87ef80725d2bb4.jpg\",\n  \"91952.jpg\": \"Insecta/Pyrausta acrionalis/9005e4aed84648bd8f5370c4306ac03a.jpg\",\n  \"91957.jpg\": \"Insecta/Pyrausta acrionalis/08005d472b9b8d4ae7bb976c4a7a3524.jpg\",\n  \"91960.jpg\": \"Aves/Sterna paradisaea/b6cccbcb4cc529d6990d91ca1b6316d7.jpg\",\n  \"91962.jpg\": \"Aves/Sterna paradisaea/288159696d45f0399d1db45c0a3543f8.jpg\",\n  \"91963.jpg\": \"Aves/Sterna paradisaea/7a051c90e627c653bd92df135112589f.jpg\",\n  \"91970.jpg\": \"Aves/Sterna paradisaea/78236483465b0df8f9542c6ca5c18f5a.jpg\",\n  \"91972.jpg\": \"Aves/Sterna paradisaea/141d7a4187e1e687ceb23f3307dd90e8.jpg\",\n  \"91973.jpg\": \"Aves/Sterna paradisaea/49545f7ee7f557ba912d6dc0b4c59372.jpg\",\n  \"91974.jpg\": \"Aves/Sterna paradisaea/254f6c4bbb81c4e75ae830ec55e3b80d.jpg\",\n  \"91977.jpg\": \"Aves/Sterna paradisaea/7b2e9049268555e9fc863170698605ed.jpg\",\n  \"91978.jpg\": \"Aves/Sterna paradisaea/65f2f9b28f77e6b628b3fd222ac92af7.jpg\",\n  \"91979.jpg\": \"Aves/Sterna paradisaea/812de0043cba539379ada704a2e8f27a.jpg\",\n  \"91984.jpg\": \"Aves/Sterna paradisaea/5ecf2f77b60bb14d3b4e355a6ff2ca9f.jpg\",\n  \"91988.jpg\": \"Aves/Sterna paradisaea/83d526ee139f0f2dc9665cc911fb92ed.jpg\",\n  \"91991.jpg\": \"Aves/Sterna paradisaea/f1b20b8b666ffd6c674cfe503c14c642.jpg\",\n  \"91993.jpg\": \"Aves/Sterna paradisaea/da866f3aeeeceba9695dc1b55fb93f73.jpg\",\n  \"91995.jpg\": \"Aves/Sterna paradisaea/ddd49b6be2412618b0fe1473c045dc82.jpg\",\n  \"91998.jpg\": \"Aves/Sterna paradisaea/721993c6715834dba55adc7cc4eaf603.jpg\",\n  \"91999.jpg\": \"Aves/Sterna paradisaea/3b80e8043ddde16cf4b007a7380055a8.jpg\",\n  \"92000.jpg\": \"Aves/Sterna paradisaea/cc6dd5b93ba6d1fc033a1c329bf65e54.jpg\",\n  \"92015.jpg\": \"Insecta/Amphibolips confluenta/1444909c004afb31081f864ddb8f509e.jpg\",\n  \"92027.jpg\": \"Insecta/Amphibolips confluenta/b5f71bfb696d60a6b37b4dd902dc5a95.jpg\",\n  \"9208.jpg\": \"Aves/Prosthemadera novaeseelandiae novaeseelandiae/d197fb22fc72f633100c15e91c468adb.jpg\",\n  \"9210.jpg\": \"Aves/Prosthemadera novaeseelandiae novaeseelandiae/038000bfd03988667f8f2370c0de358e.jpg\",\n  \"9211.jpg\": \"Aves/Prosthemadera novaeseelandiae novaeseelandiae/c92fe202bc14708680560470363204ee.jpg\",\n  \"92111.jpg\": \"Aves/Ardea cinerea/52abc1af4e5f36ca5166b31c17900792.jpg\",\n  \"92113.jpg\": \"Aves/Ardea cinerea/a37a82d5c192beb189990558223c2c3c.jpg\",\n  \"92116.jpg\": \"Aves/Ardea cinerea/bed9525ba14acf47c9b7cb5c6b3cac1c.jpg\",\n  \"9212.jpg\": \"Aves/Prosthemadera novaeseelandiae novaeseelandiae/7849bc9dd35de0ac30f63eff37ee1f20.jpg\",\n  \"9213.jpg\": \"Aves/Prosthemadera novaeseelandiae novaeseelandiae/97a106f330e688c8aefd3fec76671e64.jpg\",\n  \"92135.jpg\": \"Aves/Ardea cinerea/b26b391bd62bfe779ccc1fc6b58649a3.jpg\",\n  \"92154.jpg\": \"Aves/Ardea cinerea/60740d18ebe1386bef1055d1e9e1f161.jpg\",\n  \"92155.jpg\": \"Aves/Ardea cinerea/b20cb48569cac95cd94ed00c2517f159.jpg\",\n  \"92166.jpg\": \"Aves/Ardea cinerea/7a0284af2777e04e6366730ebad3750d.jpg\",\n  \"9217.jpg\": \"Aves/Prosthemadera novaeseelandiae novaeseelandiae/6b997ac345a567bc740be25ea45eb390.jpg\",\n  \"92176.jpg\": \"Aves/Ardea cinerea/0183795cef9e9f41f738f139220ca6b6.jpg\",\n  \"92180.jpg\": \"Aves/Ardea cinerea/882ac7888cd9ded32a3fad4f2b06eabc.jpg\",\n  \"9220.jpg\": \"Aves/Prosthemadera novaeseelandiae novaeseelandiae/94504c66a50ae0bf1ac4ca3c928c9991.jpg\",\n  \"92203.jpg\": \"Aves/Ardea cinerea/42b7ee9afe89426f347600c4d189dcb0.jpg\",\n  \"92212.jpg\": \"Aves/Ardea cinerea/9c7e4954344867a6fdfb4749b5e04b25.jpg\",\n  \"9222.jpg\": \"Aves/Prosthemadera novaeseelandiae novaeseelandiae/b1c9a4d84f84dc0443feaff0d15047e0.jpg\",\n  \"9223.jpg\": \"Aves/Prosthemadera novaeseelandiae novaeseelandiae/5892e51d2fd1b5ac80768a773ad97fd3.jpg\",\n  \"9224.jpg\": \"Aves/Prosthemadera novaeseelandiae novaeseelandiae/f62193bcefb40f19f609f7e330b04ce5.jpg\",\n  \"92271.jpg\": \"Aves/Ardea cinerea/fab948b75ac3633fdb2eb2ba21cc5947.jpg\",\n  \"92280.jpg\": \"Aves/Ardea cinerea/370982ce885d207c7d3b5be06e014e3d.jpg\",\n  \"92283.jpg\": \"Aves/Ardea cinerea/a915eb2db0cffd26f4cc8956ac03c06b.jpg\",\n  \"92295.jpg\": \"Aves/Ardea cinerea/2250ff83906f77e24259c7953cbfe7e7.jpg\",\n  \"92296.jpg\": \"Aves/Ardea cinerea/20ceee224e6344bb6262ecefcaec3bc7.jpg\",\n  \"923.jpg\": \"Mammalia/Ondatra zibethicus/8a4d3bf8d8c525d9798c47c299f983fc.jpg\",\n  \"92320.jpg\": \"Aves/Ardea cinerea/f8d30f8497b73b80f65fd77ce138cf19.jpg\",\n  \"92371.jpg\": \"Aves/Ardea cinerea/eb559cecce621700c46a466ece835276.jpg\",\n  \"92376.jpg\": \"Aves/Ardea cinerea/7b3214088a5e6ef6a775ff3d9be1b9f4.jpg\",\n  \"92379.jpg\": \"Aves/Ardea cinerea/3229d142a733771f657017d8f0c76391.jpg\",\n  \"92402.jpg\": \"Insecta/Rutpela maculata/51ee87df9e62c5a3b1d8a458d6deea42.jpg\",\n  \"92403.jpg\": \"Insecta/Rutpela maculata/ae7b04ed26cc9556110527ad98e03806.jpg\",\n  \"92404.jpg\": \"Insecta/Rutpela maculata/7ce75e01a0a5535e857f35f58b0d48b4.jpg\",\n  \"92406.jpg\": \"Insecta/Rutpela maculata/4a564e4f0c6f4eb554fa826faaaa63b3.jpg\",\n  \"9242.jpg\": \"Aves/Prosthemadera novaeseelandiae novaeseelandiae/4e041a06531644bcdae58b7584a93420.jpg\",\n  \"92423.jpg\": \"Insecta/Rutpela maculata/adb1bec1bfcff8c8c5d2a563cb6239f1.jpg\",\n  \"9244.jpg\": \"Aves/Prosthemadera novaeseelandiae novaeseelandiae/9aa2d51e7f4db0a2566414021d854549.jpg\",\n  \"9245.jpg\": \"Aves/Prosthemadera novaeseelandiae novaeseelandiae/0620f2638ddfcf760894e6ba6be10a29.jpg\",\n  \"9248.jpg\": \"Aves/Prosthemadera novaeseelandiae novaeseelandiae/b8d886735ba4fca33607acc2037cdf89.jpg\",\n  \"92549.jpg\": \"Animalia/Narceus annularus/bbe0731cee74afc77158f64a14325a56.jpg\",\n  \"92552.jpg\": \"Animalia/Narceus annularus/50e18bc08d6388ca800f18638bfdbdad.jpg\",\n  \"92556.jpg\": \"Animalia/Narceus annularus/1e06f1daf549cfca8a95ac8d852d708a.jpg\",\n  \"92559.jpg\": \"Animalia/Narceus annularus/22457c8c833a75758a11a6de974c280a.jpg\",\n  \"92562.jpg\": \"Animalia/Narceus annularus/e0494f9e2dd9cf4ea50777e39f99255d.jpg\",\n  \"9258.jpg\": \"Insecta/Euphyes vestris/212089e37a9ec6e4cd0d6f259ecdc222.jpg\",\n  \"926.jpg\": \"Mammalia/Ondatra zibethicus/6f2b890582ffea47b45664c6cd47f1e6.jpg\",\n  \"9262.jpg\": \"Insecta/Euphyes vestris/960e6768cda7054bc702dbf6440facb3.jpg\",\n  \"9264.jpg\": \"Insecta/Euphyes vestris/fb2180bd46909f848357b35f91163344.jpg\",\n  \"92642.jpg\": \"Insecta/Acronicta fallax/a2ebfd1ebc327478fb9da6325d1402bb.jpg\",\n  \"92643.jpg\": \"Insecta/Acronicta fallax/7e797f84fb58206a305b43fdcd683cc7.jpg\",\n  \"92644.jpg\": \"Insecta/Acronicta fallax/eebd65c0e8462579653aed3a87d1330f.jpg\",\n  \"92648.jpg\": \"Insecta/Acronicta fallax/6fc3ec48a4dad459195f31b20f9624fb.jpg\",\n  \"92651.jpg\": \"Insecta/Acronicta fallax/4f6e0f15c861484b0cb0bd19dcf43064.jpg\",\n  \"92654.jpg\": \"Reptilia/Micrurus tener/25abc6f37704fd322b2f3546d277dcce.jpg\",\n  \"92656.jpg\": \"Reptilia/Micrurus tener/afca1c3fcaab124e1ac401a8736d0a02.jpg\",\n  \"92657.jpg\": \"Reptilia/Micrurus tener/9cf1e75f6babe0f0fcdacf5252718e17.jpg\",\n  \"92658.jpg\": \"Reptilia/Micrurus tener/af537c7b61979a1f47f1d31a6f19fff2.jpg\",\n  \"92659.jpg\": \"Reptilia/Micrurus tener/5d24448059f344921ff91c3c1cc09f30.jpg\",\n  \"9266.jpg\": \"Insecta/Euphyes vestris/58d970947f2c58abd2ebf934e4d34dc5.jpg\",\n  \"92660.jpg\": \"Reptilia/Micrurus tener/90a0610ca9a43e778ed7b6869fc52745.jpg\",\n  \"92664.jpg\": \"Reptilia/Micrurus tener/a6c9091356afec3bb5b8daa985138c14.jpg\",\n  \"9267.jpg\": \"Insecta/Euphyes vestris/1fc983a1c46d8a725ad438b5ba8d2018.jpg\",\n  \"92670.jpg\": \"Reptilia/Micrurus tener/0881ea2d1357ec423abd9149b47337d5.jpg\",\n  \"92672.jpg\": \"Reptilia/Micrurus tener/162fda8b882cc9b965b8012bc777279c.jpg\",\n  \"92674.jpg\": \"Reptilia/Micrurus tener/1b2fde948fbc30c7852720eda00733bc.jpg\",\n  \"92675.jpg\": \"Reptilia/Micrurus tener/cb8b5d40237091586dcca4224b80accf.jpg\",\n  \"92684.jpg\": \"Reptilia/Micrurus tener/5c66a2f1202c10786e69dc8fffba9cff.jpg\",\n  \"92686.jpg\": \"Reptilia/Micrurus tener/8f1e48c96787d90ec7bf48510d930c3f.jpg\",\n  \"92687.jpg\": \"Reptilia/Micrurus tener/9271d712fa8a7c5f8d76fd63031bca34.jpg\",\n  \"92689.jpg\": \"Reptilia/Micrurus tener/2286e2c92f4f31500366352c127449bf.jpg\",\n  \"92691.jpg\": \"Reptilia/Micrurus tener/e75db6bf392f59886bd120bbaee0d60f.jpg\",\n  \"92698.jpg\": \"Reptilia/Micrurus tener/ad05fdfa9d6e54caf19f70eeee0aca9e.jpg\",\n  \"927.jpg\": \"Mammalia/Ondatra zibethicus/79e2e1e58013ca68436061147b3050b2.jpg\",\n  \"92702.jpg\": \"Reptilia/Micrurus tener/acd7c62aed095a083996633cfa8a1a76.jpg\",\n  \"92703.jpg\": \"Reptilia/Micrurus tener/ddbf2f28039f4248ee1a9a0bd04b23f6.jpg\",\n  \"92706.jpg\": \"Reptilia/Micrurus tener/12b079d765a6a0e410fa22a4876badc5.jpg\",\n  \"92707.jpg\": \"Reptilia/Micrurus tener/161e95b5a02ad5a2e4844c8631ac1101.jpg\",\n  \"9273.jpg\": \"Insecta/Euphyes vestris/363ecb013c1af48d2a0b8d261fc7291d.jpg\",\n  \"92736.jpg\": \"Aves/Calidris ferruginea/484ac61de00c917ac2cd18f808735c3d.jpg\",\n  \"92737.jpg\": \"Aves/Calidris ferruginea/f797a8eb0b2357af0e092f25efe2ee36.jpg\",\n  \"92738.jpg\": \"Aves/Calidris ferruginea/0b94611440b2d905423a06440293b158.jpg\",\n  \"92755.jpg\": \"Insecta/Papilio polytes/e0a101d5adc875ccc794189a313bf2cd.jpg\",\n  \"9276.jpg\": \"Insecta/Euphyes vestris/5e63811c0639a679e3163f29154fe0d0.jpg\",\n  \"92766.jpg\": \"Insecta/Papilio polytes/a1d92275d6e2610aa4d76617775c992e.jpg\",\n  \"92768.jpg\": \"Insecta/Papilio polytes/70bc65d0ae7ab49fc395e63c094a483a.jpg\",\n  \"92769.jpg\": \"Insecta/Papilio polytes/90ba04e48fed3d0c35a01af1ff500f18.jpg\",\n  \"928.jpg\": \"Mammalia/Ondatra zibethicus/de67d600ea9ec5edf1c75d66645ec47c.jpg\",\n  \"9280.jpg\": \"Insecta/Euphyes vestris/6fcd729a622753e925059a3ee069ba63.jpg\",\n  \"9284.jpg\": \"Insecta/Euphyes vestris/331dc2d58ca2735db7fbe79f61cb2349.jpg\",\n  \"92845.jpg\": \"Aves/Charadrius vociferus/f8a9191dc36aa6b96b786f6babd24f5c.jpg\",\n  \"92863.jpg\": \"Aves/Charadrius vociferus/245808d9115e99ce7e144773391f35e6.jpg\",\n  \"92865.jpg\": \"Aves/Charadrius vociferus/1b1ba2f74863929338c90efdf890e68a.jpg\",\n  \"92874.jpg\": \"Aves/Charadrius vociferus/d14797c3d250f55b35d8249491d3de59.jpg\",\n  \"9289.jpg\": \"Insecta/Euphyes vestris/04c57b232ee899a8f7e93e7dbc400ddf.jpg\",\n  \"929.jpg\": \"Mammalia/Ondatra zibethicus/39ea18ff8b4b26f1ff77fb0e81e46926.jpg\",\n  \"9291.jpg\": \"Insecta/Euphyes vestris/be5066a5cfe3d2630998edbe2992e2af.jpg\",\n  \"9295.jpg\": \"Insecta/Euphyes vestris/4c28fdd45c4003f9b4bab81693b7e21b.jpg\",\n  \"92963.jpg\": \"Aves/Charadrius vociferus/aaa0a0296b5e39d38657cbe6f5910c2e.jpg\",\n  \"92976.jpg\": \"Aves/Charadrius vociferus/40268655b40feb5ea74cfd3fe7b025e0.jpg\",\n  \"9298.jpg\": \"Insecta/Euphyes vestris/5692f0afb3ebf69880e04a7d4025163f.jpg\",\n  \"9300.jpg\": \"Insecta/Euphyes vestris/ece34ce558098752bfa54613f2ab912e.jpg\",\n  \"9303.jpg\": \"Insecta/Euphyes vestris/8dcc11cb55ed1d55765f0c8635057238.jpg\",\n  \"9305.jpg\": \"Insecta/Euphyes vestris/b240cba3b03ca906801c43383aaace61.jpg\",\n  \"9308.jpg\": \"Insecta/Euphyes vestris/969f5d286f70b0ea7986f131ba612242.jpg\",\n  \"9309.jpg\": \"Insecta/Euphyes vestris/995a8a71f92e64d93ea597ac3cf06413.jpg\",\n  \"9310.jpg\": \"Insecta/Euphyes vestris/f6439a7f25a549c53bea71dc5d411d5a.jpg\",\n  \"93102.jpg\": \"Aves/Charadrius vociferus/341e78a377bcaaec9492917915d973f7.jpg\",\n  \"93127.jpg\": \"Aves/Charadrius vociferus/751803607435533ea37bcb6a80843479.jpg\",\n  \"93139.jpg\": \"Aves/Charadrius vociferus/1f1a8e16b2a15ea30a1aab16e0189bbc.jpg\",\n  \"93186.jpg\": \"Aves/Charadrius vociferus/b7bde5895c53b0ac722600477074fea6.jpg\",\n  \"9320.jpg\": \"Insecta/Euphyes vestris/ada505f243c331782e57dc9bd7f1dc28.jpg\",\n  \"9321.jpg\": \"Insecta/Euphyes vestris/d9e71ea100c7bf24270c5568f1240c49.jpg\",\n  \"93216.jpg\": \"Aves/Charadrius vociferus/f9b0795c82e2f0d9079c57a70ce4fc46.jpg\",\n  \"93230.jpg\": \"Aves/Charadrius vociferus/15658487012186b6c2234dfb92abd14e.jpg\",\n  \"933.jpg\": \"Arachnida/Parasteatoda tepidariorum/3694e98c15816692a733137bdc2550ef.jpg\",\n  \"93315.jpg\": \"Aves/Charadrius vociferus/4a34d59bd2e85830e899147c10aba863.jpg\",\n  \"93366.jpg\": \"Aves/Charadrius vociferus/69b39d000c52c5891f4c997d2f8d2325.jpg\",\n  \"93400.jpg\": \"Aves/Charadrius vociferus/b124bda7a963bbc235cde3e35b8aa819.jpg\",\n  \"93401.jpg\": \"Aves/Charadrius vociferus/da9fe88781127d6d7e9275653783b4f4.jpg\",\n  \"93441.jpg\": \"Aves/Charadrius vociferus/f90ff43a85552c0a051611bb735c70e0.jpg\",\n  \"93459.jpg\": \"Aves/Charadrius vociferus/be348b4545349ae48c1f93fd5b7c55c5.jpg\",\n  \"93497.jpg\": \"Aves/Charadrius vociferus/184ea84d037046b1e3252f09be5fc4f4.jpg\",\n  \"93533.jpg\": \"Aves/Charadrius vociferus/36186b74a8ddd672f33b593e439196f4.jpg\",\n  \"93575.jpg\": \"Amphibia/Desmognathus fuscus/7c975c4c389491f5638b4873780c99d6.jpg\",\n  \"93576.jpg\": \"Amphibia/Desmognathus fuscus/1bc3aa15126a5c46579ba48b530f071b.jpg\",\n  \"93580.jpg\": \"Amphibia/Desmognathus fuscus/d0769b950d17dfa5ce7cc23114ed33e2.jpg\",\n  \"93581.jpg\": \"Amphibia/Desmognathus fuscus/b48ef0f002c8e1577a023b763509f0e0.jpg\",\n  \"93584.jpg\": \"Amphibia/Desmognathus fuscus/57e40c45c5ecff33a3203db363c8d7fb.jpg\",\n  \"93585.jpg\": \"Amphibia/Desmognathus fuscus/0dc1526514bbbe1b56ca911a1a83fae6.jpg\",\n  \"93590.jpg\": \"Amphibia/Desmognathus fuscus/465ebac107128d585093f11f760376cb.jpg\",\n  \"93592.jpg\": \"Amphibia/Desmognathus fuscus/b8e7f60599ebc669a73eac410612af81.jpg\",\n  \"93593.jpg\": \"Amphibia/Desmognathus fuscus/67c81e8f2f14ad917196f1ee3bf117ff.jpg\",\n  \"93596.jpg\": \"Amphibia/Desmognathus fuscus/47f4ffa25c1932700962b92935a3167d.jpg\",\n  \"93597.jpg\": \"Amphibia/Desmognathus fuscus/80159d58e1ffca4130d734c2e7399aaa.jpg\",\n  \"936.jpg\": \"Arachnida/Parasteatoda tepidariorum/21230774faaa8714ad55b99652daa68b.jpg\",\n  \"93601.jpg\": \"Amphibia/Desmognathus fuscus/06fe01ab9d7f90680d1add226354fbcb.jpg\",\n  \"93607.jpg\": \"Amphibia/Desmognathus fuscus/f55e8dcac315974c6ae5ebec079bda7a.jpg\",\n  \"93608.jpg\": \"Amphibia/Desmognathus fuscus/3b721fd81f8c242427bb133f778b0d98.jpg\",\n  \"93613.jpg\": \"Amphibia/Desmognathus fuscus/e90149f2fab518b49b2da7f0c5588794.jpg\",\n  \"93614.jpg\": \"Amphibia/Desmognathus fuscus/bf0a21edb366a782e97e015e4afd5ff0.jpg\",\n  \"93615.jpg\": \"Amphibia/Desmognathus fuscus/175bfb14c44a0f8f96a1250c3a8df398.jpg\",\n  \"93616.jpg\": \"Amphibia/Desmognathus fuscus/95c7e20b9d09ae5bf6bab88451f02709.jpg\",\n  \"93617.jpg\": \"Amphibia/Desmognathus fuscus/322ed0b38c0839a8ccadb4e8b41c331b.jpg\",\n  \"93619.jpg\": \"Amphibia/Desmognathus fuscus/0f71ed540acb6d9a6d7d6a6937e9a1d1.jpg\",\n  \"93620.jpg\": \"Amphibia/Desmognathus fuscus/c184a39b66c8807f48d8f7f9fac0b679.jpg\",\n  \"93627.jpg\": \"Mammalia/Marmota marmota/bebd636c752cc7c46eea8c16b4a9ad68.jpg\",\n  \"93628.jpg\": \"Mammalia/Marmota marmota/a238663d94c276b334d060e439e2ea48.jpg\",\n  \"93634.jpg\": \"Mammalia/Marmota marmota/71075a2e7db7066c88bb620c63db0261.jpg\",\n  \"93639.jpg\": \"Insecta/Bombus terrestris/5dc533bc202081c915596ec16d75bcf7.jpg\",\n  \"93644.jpg\": \"Insecta/Bombus terrestris/e37e105096f5fdf9d5253d2d9a3902ea.jpg\",\n  \"93664.jpg\": \"Insecta/Bombus terrestris/1e5bf2f503f7dd9d25377acd240f92be.jpg\",\n  \"93666.jpg\": \"Insecta/Bombus terrestris/dd30c7ed162857da937783ad7fed1b07.jpg\",\n  \"93674.jpg\": \"Insecta/Bombus terrestris/8346fe5b4b7ed13df3071c7b7c3acd97.jpg\",\n  \"93677.jpg\": \"Insecta/Bombus terrestris/48aff9e0453657ac04e6295990d1cf36.jpg\",\n  \"93678.jpg\": \"Insecta/Bombus terrestris/7bf6b322f4f70d77a6862a785c0e28b8.jpg\",\n  \"93698.jpg\": \"Insecta/Bombus terrestris/6d9c4e044895b3e7be2b7cc0af9f87a6.jpg\",\n  \"93706.jpg\": \"Insecta/Bombus terrestris/1903d2f22031b85d96b3f99e1855363b.jpg\",\n  \"93712.jpg\": \"Insecta/Bombus terrestris/1d534578dc11b392cbf2e19a674f42dc.jpg\",\n  \"93716.jpg\": \"Insecta/Bombus terrestris/a8a25ef64e082955d33736dd9b8d133b.jpg\",\n  \"93718.jpg\": \"Insecta/Bombus terrestris/f934caa4d8bf45f8ae95e22ed934e716.jpg\",\n  \"93719.jpg\": \"Insecta/Bombus terrestris/29dc2445667bd8f21b29cb9733fb427f.jpg\",\n  \"93729.jpg\": \"Insecta/Bombus terrestris/8eccd2ec1881efab071122caf74110fc.jpg\",\n  \"93731.jpg\": \"Insecta/Bombus terrestris/1fd97242236c17f0b96f3d728d2b2fd4.jpg\",\n  \"93735.jpg\": \"Insecta/Bombus terrestris/4400ddfabde711f4527bdd8932a2e0ad.jpg\",\n  \"93742.jpg\": \"Insecta/Bombus terrestris/b7291601f7e016f88a4d2d05f8df87a5.jpg\",\n  \"93745.jpg\": \"Insecta/Bombus terrestris/462ec0e5aa27025ed8a4588033df61c8.jpg\",\n  \"93748.jpg\": \"Insecta/Bombus terrestris/67b8fe04cad0ba4d03dbde03093ae3b0.jpg\",\n  \"93749.jpg\": \"Insecta/Bombus terrestris/8107ed4fc975bf92f948a4979c776fa7.jpg\",\n  \"93750.jpg\": \"Insecta/Bombus terrestris/bbc39fc88af0fd5f07a4f68161ab67fc.jpg\",\n  \"93751.jpg\": \"Reptilia/Agkistrodon contortrix/b28c13ba8ae0e6cc2b0a2f547940d10e.jpg\",\n  \"93763.jpg\": \"Reptilia/Agkistrodon contortrix/0f2cf7912da854ea0978ed58408eaa19.jpg\",\n  \"93773.jpg\": \"Reptilia/Agkistrodon contortrix/98e85805c234e7f01fe7768b0526a77f.jpg\",\n  \"93776.jpg\": \"Reptilia/Agkistrodon contortrix/5f9c65381398d3fd682388237144c52e.jpg\",\n  \"93780.jpg\": \"Reptilia/Agkistrodon contortrix/ff535f5998b62ac73fbf081f1d064a46.jpg\",\n  \"93795.jpg\": \"Reptilia/Agkistrodon contortrix/df53125e509189c9d7474f842a3db737.jpg\",\n  \"93852.jpg\": \"Reptilia/Agkistrodon contortrix/4944053e1daea24d6cd1ab264c4060fe.jpg\",\n  \"93854.jpg\": \"Reptilia/Agkistrodon contortrix/5aa5d05e158aa19253c166502d409ea1.jpg\",\n  \"93864.jpg\": \"Reptilia/Agkistrodon contortrix/5600c7dc2d6f882d85535ba3a6654a6a.jpg\",\n  \"93867.jpg\": \"Reptilia/Agkistrodon contortrix/9bd188fb3888f7d8c3300ba8fe7db291.jpg\",\n  \"93868.jpg\": \"Reptilia/Agkistrodon contortrix/73f67c0c4c1e201f980c703aaba02668.jpg\",\n  \"93875.jpg\": \"Reptilia/Agkistrodon contortrix/67310538358f62cbf538a944aabb3887.jpg\",\n  \"93876.jpg\": \"Reptilia/Agkistrodon contortrix/4830bb30ed4068f9e7d126828ae42c9c.jpg\",\n  \"9388.jpg\": \"Aves/Circus approximans/35f3435d2a27dafe3e3140dfba7689fe.jpg\",\n  \"93881.jpg\": \"Reptilia/Agkistrodon contortrix/b96d325cf554ba4467d3ebed9edf0536.jpg\",\n  \"93884.jpg\": \"Reptilia/Agkistrodon contortrix/ef1729771bd51a19633999789c37273d.jpg\",\n  \"93890.jpg\": \"Reptilia/Agkistrodon contortrix/2ffc2e97e4b82139e5a508f8eef14a3a.jpg\",\n  \"93901.jpg\": \"Reptilia/Agkistrodon contortrix/d2989296eb85e4b49ce924a7734f5b03.jpg\",\n  \"93907.jpg\": \"Reptilia/Agkistrodon contortrix/16242ade47db10209e610f5b627f6b33.jpg\",\n  \"9393.jpg\": \"Aves/Circus approximans/32857f797108da8bb4dc60e06a7f7432.jpg\",\n  \"93933.jpg\": \"Reptilia/Agkistrodon contortrix/d1346fbf47ef6505c8663971b3ca6a95.jpg\",\n  \"9396.jpg\": \"Aves/Circus approximans/8cbd44ca859f0409df6bc4b979231bac.jpg\",\n  \"93972.jpg\": \"Insecta/Pedicia albivitta/4eb855510894c46ce0606f588b0468db.jpg\",\n  \"93973.jpg\": \"Insecta/Pedicia albivitta/69d8bba81e5b863f81f16c372fd04328.jpg\",\n  \"93975.jpg\": \"Insecta/Pedicia albivitta/79f02c85ba5f6ec1e15f884dcb89e314.jpg\",\n  \"93978.jpg\": \"Insecta/Pedicia albivitta/1deb5a817d0af1218f19be7dc7d0793a.jpg\",\n  \"93979.jpg\": \"Insecta/Pedicia albivitta/c26fcd3e4037c09cbbb246806132ea1a.jpg\",\n  \"93980.jpg\": \"Insecta/Pedicia albivitta/bd35831af0bd4df22ee144ca35033d41.jpg\",\n  \"94026.jpg\": \"Aves/Jabiru mycteria/9142491cb7afa935d49dafaa8d91c574.jpg\",\n  \"94029.jpg\": \"Aves/Jabiru mycteria/07d08b5f32665fba601c9ad7c065e89d.jpg\",\n  \"94036.jpg\": \"Aves/Jabiru mycteria/a6e7df9dd136c99558464e9604d72e4f.jpg\",\n  \"9408.jpg\": \"Aves/Circus approximans/5d4b60d4cb9000377651c07375eef6db.jpg\",\n  \"9413.jpg\": \"Aves/Circus approximans/2b6da220568684171c77f263d5933fbd.jpg\",\n  \"9415.jpg\": \"Aves/Circus approximans/8e2179846424c3bdcc06f2d49304c1bc.jpg\",\n  \"94163.jpg\": \"Aves/Saxicola rubetra/6b2550984e1ecabc32a10b513e7f282b.jpg\",\n  \"94177.jpg\": \"Aves/Saxicola rubetra/41c4817f3fb14a3241d7b8da55f53336.jpg\",\n  \"94182.jpg\": \"Aves/Saxicola rubetra/f6c4f73647abdb57c3e2da6511742e64.jpg\",\n  \"94203.jpg\": \"Aves/Saxicola rubetra/b8b0c996b474afd49a6220fe7701ec68.jpg\",\n  \"94209.jpg\": \"Insecta/Euplagia quadripunctaria/709328226b73d59a9419b357749e1f15.jpg\",\n  \"94219.jpg\": \"Insecta/Euplagia quadripunctaria/a62dfeed83b4edfd96843861b63bb341.jpg\",\n  \"9422.jpg\": \"Aves/Circus approximans/f508161ad71103b5ee6ed04bcbf42aa7.jpg\",\n  \"94222.jpg\": \"Insecta/Euplagia quadripunctaria/3fef6adf0bef31b879e9097bc3cc85d7.jpg\",\n  \"94224.jpg\": \"Insecta/Euplagia quadripunctaria/d41394d6debf79af0ffbd8e4f367ce69.jpg\",\n  \"94225.jpg\": \"Insecta/Euplagia quadripunctaria/47de918c253b816e8983760781de6323.jpg\",\n  \"94227.jpg\": \"Insecta/Euplagia quadripunctaria/369e9add44dabb0911278c15027c34a9.jpg\",\n  \"94232.jpg\": \"Insecta/Euplagia quadripunctaria/135abb4ee58b34d2243320a9abacc3a9.jpg\",\n  \"94310.jpg\": \"Mollusca/Neverita lewisii/c888cd04e37f006c301e06b2cfabe19b.jpg\",\n  \"94311.jpg\": \"Mollusca/Neverita lewisii/173feb749c7fb2cf235370fc719c6bc8.jpg\",\n  \"94320.jpg\": \"Mollusca/Neverita lewisii/8f411942b566486cb93a5d9d3dad3cbf.jpg\",\n  \"94333.jpg\": \"Reptilia/Malaclemys terrapin/275c9405bf80c1886023c152fe1a9947.jpg\",\n  \"94336.jpg\": \"Reptilia/Malaclemys terrapin/b6dc9edc34ff4eb9dc1d0fd62d746c2d.jpg\",\n  \"94338.jpg\": \"Reptilia/Malaclemys terrapin/d2f8159b04f64e2a7d348fd2a045ca20.jpg\",\n  \"94339.jpg\": \"Reptilia/Malaclemys terrapin/4396039abe06fb8db225ff0af605afda.jpg\",\n  \"94341.jpg\": \"Reptilia/Malaclemys terrapin/c8006de026b362b2d291f5535860516d.jpg\",\n  \"94342.jpg\": \"Reptilia/Malaclemys terrapin/1338970d9e360367384de06315855efa.jpg\",\n  \"94346.jpg\": \"Mammalia/Papio anubis/7b8970f8257297c4598a9ffea5bcc72a.jpg\",\n  \"94355.jpg\": \"Mammalia/Papio anubis/a89411ae3c418536017e6e6e7a9d6dc5.jpg\",\n  \"94358.jpg\": \"Mammalia/Papio anubis/333494e655e740b198eb9707d9008c7f.jpg\",\n  \"94359.jpg\": \"Mammalia/Papio anubis/d0caa274aec9081e5d1d5ca169c31c2b.jpg\",\n  \"94426.jpg\": \"Insecta/Pyrausta inornatalis/b4e0d71013628f3dc3359d4cda3ccefc.jpg\",\n  \"94432.jpg\": \"Insecta/Pyrausta inornatalis/15abc7e4c81f0f0b9cff2fcf7dcb321b.jpg\",\n  \"94441.jpg\": \"Insecta/Pyrausta inornatalis/e7eb0206788af8dd79ff6c47fe979f4f.jpg\",\n  \"94442.jpg\": \"Insecta/Pyrausta inornatalis/49937147f27a94d5731854428c2eae4f.jpg\",\n  \"94446.jpg\": \"Insecta/Pyrausta inornatalis/22c3f7f40e48fb37518c0451db1e7498.jpg\",\n  \"94462.jpg\": \"Insecta/Pyrausta inornatalis/a8c2c3d4a53ebc874f21ea0eb6f0da3c.jpg\",\n  \"94472.jpg\": \"Aves/Cyanocitta stelleri/a5467249701189ce12a64ebf94a20a28.jpg\",\n  \"94502.jpg\": \"Aves/Cyanocitta stelleri/45216d7832b4a411c587b8aa055afa2f.jpg\",\n  \"94516.jpg\": \"Aves/Cyanocitta stelleri/266af29a11e1c2529f1912527206ba01.jpg\",\n  \"94535.jpg\": \"Aves/Cyanocitta stelleri/f96bc2d3cec44bd344300dcd913fea53.jpg\",\n  \"94556.jpg\": \"Aves/Cyanocitta stelleri/c78a7193ec967bb34c5bb51d3e502072.jpg\",\n  \"94558.jpg\": \"Aves/Cyanocitta stelleri/8993b53fca9f0bfeaefd0da474434977.jpg\",\n  \"9459.jpg\": \"Aves/Melospiza melodia/06389c9b1dce49feebc639f68fa72af2.jpg\",\n  \"946.jpg\": \"Arachnida/Parasteatoda tepidariorum/7f170954b3416043d8940466fe3d7d2a.jpg\",\n  \"94609.jpg\": \"Aves/Cyanocitta stelleri/d7cc9d6e7bab56a633c1f816c880af5b.jpg\",\n  \"94665.jpg\": \"Aves/Cyanocitta stelleri/118d38596c5e483a733a7dea83055d42.jpg\",\n  \"94681.jpg\": \"Aves/Cyanocitta stelleri/db16eb39e29f8ae539f56de0c3c9ffc2.jpg\",\n  \"94685.jpg\": \"Aves/Cyanocitta stelleri/1e824fcdfd8de58ed898f895dbc47e82.jpg\",\n  \"94688.jpg\": \"Aves/Cyanocitta stelleri/e71af9a211cc4f5482ff86c1c9cc65c0.jpg\",\n  \"947.jpg\": \"Arachnida/Parasteatoda tepidariorum/e08ea62bd35d73731ef53b08fd10d8fe.jpg\",\n  \"94710.jpg\": \"Aves/Cyanocitta stelleri/61baca94457731ccfbd1a1f8cf35d57d.jpg\",\n  \"94731.jpg\": \"Aves/Cyanocitta stelleri/3326477c48c82f33e43591db4c8a4f4a.jpg\",\n  \"94750.jpg\": \"Aves/Cyanocitta stelleri/16d3704fa60d454af02ea67f987a1061.jpg\",\n  \"94778.jpg\": \"Aves/Cyanocitta stelleri/93bf011146febbeac5ea557df79abd65.jpg\",\n  \"94937.jpg\": \"Insecta/Amorpha juglandis/1a931f5746a7b95c32de20c59f7e2d9b.jpg\",\n  \"94938.jpg\": \"Insecta/Amorpha juglandis/45992c096b22e58f1dbcb1ebacdf2098.jpg\",\n  \"94939.jpg\": \"Insecta/Amorpha juglandis/4fa3493437f66f9491fc2bca6c615900.jpg\",\n  \"94944.jpg\": \"Insecta/Amorpha juglandis/f5aed771eea64d8929cbd2e7fe092469.jpg\",\n  \"94947.jpg\": \"Insecta/Amorpha juglandis/9abcf0971efaac64ce3854d9fe3e14c7.jpg\",\n  \"94950.jpg\": \"Insecta/Amorpha juglandis/31c5f63b6afc0685e0044a3ddc08751b.jpg\",\n  \"94954.jpg\": \"Insecta/Amorpha juglandis/b0d2ad82edbbc8c6a5e642df47bc1488.jpg\",\n  \"94956.jpg\": \"Insecta/Amorpha juglandis/6f0a17af655e145511116b822aca96e6.jpg\",\n  \"94960.jpg\": \"Insecta/Amorpha juglandis/40849a77f8a3dac3a11db3bf206ab2a1.jpg\",\n  \"94962.jpg\": \"Insecta/Amorpha juglandis/deb3a489b328c547b0cc0b5df300b279.jpg\",\n  \"94963.jpg\": \"Insecta/Amorpha juglandis/bed0e467908e318757cc5b99963f2620.jpg\",\n  \"94967.jpg\": \"Insecta/Amorpha juglandis/1313209473738e2b4e59320146ea7f82.jpg\",\n  \"94968.jpg\": \"Insecta/Amorpha juglandis/ae22387948500b36fb1f592290fec35c.jpg\",\n  \"94969.jpg\": \"Insecta/Amorpha juglandis/40a69cbcf944a1b929c27d33648eebc4.jpg\",\n  \"94970.jpg\": \"Insecta/Amorpha juglandis/cbad8e2c4b8c98622996b3b20ac3a2f7.jpg\",\n  \"94971.jpg\": \"Insecta/Amorpha juglandis/91c3042dca657889d2e46fa5590221f0.jpg\",\n  \"94972.jpg\": \"Insecta/Amorpha juglandis/d6462ce5ace5b3231abaea7b27bea67f.jpg\",\n  \"94973.jpg\": \"Insecta/Amorpha juglandis/e2b6435d9510aedf8d4c1b2533b205f9.jpg\",\n  \"94975.jpg\": \"Insecta/Amorpha juglandis/0d400f4c3d82b23ca2ca0a1dea8e626f.jpg\",\n  \"94976.jpg\": \"Insecta/Amorpha juglandis/2c127033a1f4bd9ed93c9278100fcdf7.jpg\",\n  \"94977.jpg\": \"Insecta/Amorpha juglandis/52305d7e40722cb7198d4ddaec41bd64.jpg\",\n  \"94979.jpg\": \"Insecta/Amorpha juglandis/d9acd98158a54036767529e97c26af74.jpg\",\n  \"94986.jpg\": \"Aves/Porphyrio martinicus/258626c0fb42338767d42aed40709cbf.jpg\",\n  \"94992.jpg\": \"Aves/Porphyrio martinicus/2ed61623020d0148b1ea140188b01e04.jpg\",\n  \"94994.jpg\": \"Aves/Porphyrio martinicus/a21f3dc2c7bcfcf64482397976af7454.jpg\",\n  \"94995.jpg\": \"Aves/Porphyrio martinicus/a48bdce4081bd37993ab7dc9bd5ae574.jpg\",\n  \"95003.jpg\": \"Aves/Porphyrio martinicus/fe15fc80fe423db00c76df8895d4dc78.jpg\",\n  \"95005.jpg\": \"Aves/Porphyrio martinicus/6df47c446bf2fa2516f591b106012aa2.jpg\",\n  \"95007.jpg\": \"Aves/Porphyrio martinicus/56247aa096151b377f5ac4ee4c65d056.jpg\",\n  \"95010.jpg\": \"Aves/Porphyrio martinicus/214e613c690750e4f3d4e9a4b6fe037c.jpg\",\n  \"95011.jpg\": \"Aves/Porphyrio martinicus/fc11b2f89ec3e1d046ae140cd5c8f6b0.jpg\",\n  \"95018.jpg\": \"Aves/Porphyrio martinicus/06308859a8046d8b90b0c8c5f9f165eb.jpg\",\n  \"95024.jpg\": \"Aves/Porphyrio martinicus/e2276f19c05691e4345e0b8be72a75b1.jpg\",\n  \"95026.jpg\": \"Aves/Porphyrio martinicus/a08e773d3577f982832930c5cd1b05e9.jpg\",\n  \"95030.jpg\": \"Aves/Porphyrio martinicus/a619bc87a7b17a00e66e67a754963c85.jpg\",\n  \"95033.jpg\": \"Aves/Porphyrio martinicus/a487e3c96d83b70f3095dd81e70ebc35.jpg\",\n  \"95038.jpg\": \"Aves/Porphyrio martinicus/b198e45d5b8099960a1656eec7a32786.jpg\",\n  \"95039.jpg\": \"Aves/Porphyrio martinicus/7dd400cf741deb0009ed7b7f90b80521.jpg\",\n  \"95047.jpg\": \"Aves/Porphyrio martinicus/9dc6148090794678dccf943fc102a963.jpg\",\n  \"95048.jpg\": \"Aves/Porphyrio martinicus/f7f88391f9d366454e56de9575be4859.jpg\",\n  \"95050.jpg\": \"Aves/Porphyrio martinicus/1454bd6434e698321a15af68581cc297.jpg\",\n  \"95054.jpg\": \"Aves/Porphyrio martinicus/6a02f9abe66f3cd288b8798cff6a77d9.jpg\",\n  \"95063.jpg\": \"Aves/Porphyrio martinicus/4195bd476d7b0fcfaec36e8fd006919d.jpg\",\n  \"95064.jpg\": \"Aves/Porphyrio martinicus/f130473be89ad499ae76408f0119fafd.jpg\",\n  \"95066.jpg\": \"Insecta/Pyromorpha dimidiata/60c5862d9239e2866e208da985ebbcff.jpg\",\n  \"95073.jpg\": \"Insecta/Pyromorpha dimidiata/b55f9ef22637b3528de293b2826ea674.jpg\",\n  \"95076.jpg\": \"Insecta/Pyromorpha dimidiata/d7e479c7516eadf4ff9ac67ef0f5d7be.jpg\",\n  \"95077.jpg\": \"Insecta/Pyromorpha dimidiata/0d58d9e2b1283247456b26c8affbca76.jpg\",\n  \"95142.jpg\": \"Insecta/Ceratomia undulosa/37e4329670104cea62a370b80ae9eeb5.jpg\",\n  \"95151.jpg\": \"Insecta/Ceratomia undulosa/29dd428b8274ea3e2bfc9c76ae8c02f9.jpg\",\n  \"95152.jpg\": \"Insecta/Ceratomia undulosa/9865810d430706151f3437dd1eaa25e4.jpg\",\n  \"95156.jpg\": \"Insecta/Ceratomia undulosa/62868aac0a21f84f61e2981d3b6be898.jpg\",\n  \"95157.jpg\": \"Insecta/Ceratomia undulosa/59f245b0f85f4bb0ffdc9917c8408acc.jpg\",\n  \"95158.jpg\": \"Insecta/Ceratomia undulosa/4de2edc8d31c5de42a52a1dfdad8408c.jpg\",\n  \"95162.jpg\": \"Insecta/Ceratomia undulosa/441e7a9a9fe00bedd21f82dc22854861.jpg\",\n  \"95163.jpg\": \"Insecta/Ceratomia undulosa/fb9c57894c65f31ad12ac093704309d0.jpg\",\n  \"95164.jpg\": \"Insecta/Ceratomia undulosa/381cf01a467b28d382b03a17061db96c.jpg\",\n  \"95165.jpg\": \"Insecta/Ceratomia undulosa/496d0f2ac46e1240acf1a64e33a13888.jpg\",\n  \"95169.jpg\": \"Insecta/Ceratomia undulosa/cc69f1de850bdf7ec97a524f0bf07149.jpg\",\n  \"95170.jpg\": \"Insecta/Ceratomia undulosa/9d6167de1f146459671c0b9d211fcf4b.jpg\",\n  \"95175.jpg\": \"Insecta/Ceratomia undulosa/a3493854a149e7922a900359d4602bff.jpg\",\n  \"95177.jpg\": \"Insecta/Ceratomia undulosa/086d8545290f0620cec711dd5667b3a1.jpg\",\n  \"95178.jpg\": \"Insecta/Ceratomia undulosa/32b6842268d075a95889990f869109f0.jpg\",\n  \"95185.jpg\": \"Insecta/Ceratomia undulosa/3e6ff15496637effee5f620c41f5b756.jpg\",\n  \"95187.jpg\": \"Insecta/Ceratomia undulosa/90e1d7b98e9ca851dc996b351c166c4d.jpg\",\n  \"95190.jpg\": \"Insecta/Ceratomia undulosa/21bd7ee459d1b15c52b80ef085a9823d.jpg\",\n  \"95193.jpg\": \"Insecta/Ceratomia undulosa/963f2fb103a3d6a596a1737c466ad659.jpg\",\n  \"95203.jpg\": \"Insecta/Ceratomia undulosa/8e2882695bd2f01a7c383b084783e6b5.jpg\",\n  \"95205.jpg\": \"Aves/Buteo lineatus elegans/23feb2c32ff55be6c3e8b9d50c00800a.jpg\",\n  \"95208.jpg\": \"Aves/Buteo lineatus elegans/0070f93679331d1b70645c9db60cd33d.jpg\",\n  \"95209.jpg\": \"Aves/Buteo lineatus elegans/afdad3a0922008fb3d2dee46d175184d.jpg\",\n  \"95213.jpg\": \"Aves/Buteo lineatus elegans/4514ce3f255f5fd96d7769b9e3679be6.jpg\",\n  \"95217.jpg\": \"Aves/Buteo lineatus elegans/c525adf82f96af8ae191f7eacf3a9e80.jpg\",\n  \"95221.jpg\": \"Aves/Buteo lineatus elegans/d29ac4735ebe741a471166dea5267f9b.jpg\",\n  \"95275.jpg\": \"Insecta/Mythimna unipuncta/e2cd3c5cbc74bdeb98826ac6625ae020.jpg\",\n  \"95277.jpg\": \"Insecta/Mythimna unipuncta/9e1be3dfbc6122f16feaba1f2bb3318b.jpg\",\n  \"95278.jpg\": \"Insecta/Mythimna unipuncta/0d4142dc87008a7496e2e9a71575db1e.jpg\",\n  \"95281.jpg\": \"Insecta/Mythimna unipuncta/78065fd61444e9f0841865cad36393b1.jpg\",\n  \"95287.jpg\": \"Insecta/Mythimna unipuncta/ed5fe83093aeb0e95d58b37300ce72f6.jpg\",\n  \"95288.jpg\": \"Insecta/Mythimna unipuncta/7de8e6ca65d6099959fb368eaec3baa5.jpg\",\n  \"95296.jpg\": \"Insecta/Mythimna unipuncta/83cc13a8e02376eda0f5e154d4ee8459.jpg\",\n  \"95298.jpg\": \"Insecta/Mythimna unipuncta/dfb2dafdaa24639bccf5667975775c7d.jpg\",\n  \"95315.jpg\": \"Insecta/Mythimna unipuncta/4cdc245c5ffa813637e40f1c775be9f8.jpg\",\n  \"95320.jpg\": \"Insecta/Mythimna unipuncta/8d11400fe2bbeea33e07d3397edf97e0.jpg\",\n  \"95321.jpg\": \"Insecta/Mythimna unipuncta/dcd92fa7b08c6129af39ebb3657b77b2.jpg\",\n  \"95324.jpg\": \"Insecta/Mythimna unipuncta/985a03ae2abfe45dd47c926c04cbcfd2.jpg\",\n  \"95331.jpg\": \"Insecta/Mythimna unipuncta/96d93b92cbe0680144897b83d4d808c3.jpg\",\n  \"95336.jpg\": \"Insecta/Mythimna unipuncta/95ab91e88be93e3156dee5c1200d431f.jpg\",\n  \"95344.jpg\": \"Insecta/Mythimna unipuncta/bc26577c104a40b546cce44476ae3998.jpg\",\n  \"95345.jpg\": \"Insecta/Mythimna unipuncta/cc9882e02971dcffa41df91044a8ee5c.jpg\",\n  \"95347.jpg\": \"Insecta/Mythimna unipuncta/36e28d20bdd52ab801b47dcf1dd4a921.jpg\",\n  \"95349.jpg\": \"Insecta/Mythimna unipuncta/1d794207b74d8a782c54bda1d745226b.jpg\",\n  \"95350.jpg\": \"Insecta/Mythimna unipuncta/75831282bbe4102c09d466c378d9b1d7.jpg\",\n  \"95352.jpg\": \"Aves/Leucophaeus pipixcan/55c20d7ec50baea831c005b6fcd3b73f.jpg\",\n  \"95356.jpg\": \"Aves/Leucophaeus pipixcan/fd2d715ab80aa286510c8e8696d3ba44.jpg\",\n  \"95366.jpg\": \"Aves/Leucophaeus pipixcan/1bcdb765dc394e08c291032f4b426a2e.jpg\",\n  \"95371.jpg\": \"Aves/Leucophaeus pipixcan/d2ae8b9fbfecc61994ad40c760ca1c03.jpg\",\n  \"95378.jpg\": \"Aves/Leucophaeus pipixcan/078f97d7ebe995ca3c3d9c4225c47cbb.jpg\",\n  \"95382.jpg\": \"Aves/Leucophaeus pipixcan/7c4c90c5c5658900ce1be105591f6989.jpg\",\n  \"95383.jpg\": \"Aves/Leucophaeus pipixcan/4a8cb87f08a177990cbb9fa2cb3aafbf.jpg\",\n  \"95402.jpg\": \"Aves/Leucophaeus pipixcan/7d6bfe86a21444f7c0bc82a6d0b83ba9.jpg\",\n  \"95405.jpg\": \"Aves/Leucophaeus pipixcan/14157ccfa1896662087d0a805ddd2206.jpg\",\n  \"95409.jpg\": \"Aves/Leucophaeus pipixcan/d6643c552d8b42a134e93ca84d7618d8.jpg\",\n  \"95516.jpg\": \"Aves/Tityra semifasciata/cb450b629305a746726a3246087d701d.jpg\",\n  \"95517.jpg\": \"Aves/Tityra semifasciata/de3d54027bba60cff5d36c5780990176.jpg\",\n  \"95518.jpg\": \"Aves/Tityra semifasciata/ed2e8c5320d0375000d4725236a9b12c.jpg\",\n  \"95522.jpg\": \"Aves/Tityra semifasciata/06709cb5546fa55790409832b468251f.jpg\",\n  \"95523.jpg\": \"Aves/Tityra semifasciata/fb3ded438b07cb4328d2de3436e8f543.jpg\",\n  \"95524.jpg\": \"Aves/Tityra semifasciata/60a9b9c1ef331169bce2a75eda2b40be.jpg\",\n  \"95525.jpg\": \"Aves/Tityra semifasciata/6ca4a031d061548e15f54df811ef9ed1.jpg\",\n  \"95526.jpg\": \"Aves/Tityra semifasciata/468f7e7febcccc76576cc4b5ec8798d9.jpg\",\n  \"95535.jpg\": \"Aves/Tityra semifasciata/d1363457b61101a5f6d2b79e39140dc7.jpg\",\n  \"95536.jpg\": \"Aves/Tityra semifasciata/36b7224f514804bda49406f43eb8291f.jpg\",\n  \"95537.jpg\": \"Aves/Tityra semifasciata/ea9b1dea5e19920e11bda37a05bb1ae8.jpg\",\n  \"95539.jpg\": \"Aves/Tityra semifasciata/f135bc3bc2bf09963d5b054320b0e672.jpg\",\n  \"95547.jpg\": \"Aves/Tityra semifasciata/67296a2e1bd73bb074f2eecd88b1e38b.jpg\",\n  \"95567.jpg\": \"Aves/Tityra semifasciata/a80e367079fea46b13f33ca6aca3956e.jpg\",\n  \"95569.jpg\": \"Aves/Tityra semifasciata/3eb12ed0e43252ae5cd11c8b2d47a72b.jpg\",\n  \"95582.jpg\": \"Aves/Tityra semifasciata/5e2422fbbd5a04c3fd9eaf5ba9db38fe.jpg\",\n  \"95584.jpg\": \"Aves/Tityra semifasciata/247f638e9710e5b535c1bf0398d21268.jpg\",\n  \"95585.jpg\": \"Aves/Tityra semifasciata/5ce065744a179d94ed9b985a686ad5bc.jpg\",\n  \"95593.jpg\": \"Aves/Tityra semifasciata/4ad26e70ffeca34259e4ab073a443b57.jpg\",\n  \"95595.jpg\": \"Aves/Tityra semifasciata/c69354abb5f97ad3106a70a709c452da.jpg\",\n  \"956.jpg\": \"Reptilia/Nerodia cyclopion/8f9fa15ba2e03744bb943df7091023c5.jpg\",\n  \"95641.jpg\": \"Actinopterygii/Anguilla rostrata/396b64d30e845911354f98d14abde028.jpg\",\n  \"95642.jpg\": \"Actinopterygii/Anguilla rostrata/996a441811ff950850a1d6b66272e8d5.jpg\",\n  \"95643.jpg\": \"Actinopterygii/Anguilla rostrata/b4dcb8c039580492dae685ddb8f06779.jpg\",\n  \"95656.jpg\": \"Actinopterygii/Anguilla rostrata/06639afa7ba72e488981a11c79545549.jpg\",\n  \"957.jpg\": \"Reptilia/Nerodia cyclopion/8c09eb257f09b690cd8e6b81360b8a40.jpg\",\n  \"95743.jpg\": \"Aves/Rostrhamus sociabilis/c50d98d0a8a49ce2877f908c04b11d78.jpg\",\n  \"95744.jpg\": \"Aves/Rostrhamus sociabilis/16517c1ac63d8155893deb81c381ebce.jpg\",\n  \"95745.jpg\": \"Aves/Rostrhamus sociabilis/e7a476fd91f5e9a6596ba341a7f87915.jpg\",\n  \"95747.jpg\": \"Aves/Rostrhamus sociabilis/d2196e52a376ca2d98f9e7459af55463.jpg\",\n  \"95750.jpg\": \"Aves/Rostrhamus sociabilis/68162db2d2162202fa648229fb66ea24.jpg\",\n  \"95751.jpg\": \"Aves/Rostrhamus sociabilis/bfaaa48f6f6b1a8121feac03ddac4faf.jpg\",\n  \"95754.jpg\": \"Aves/Rostrhamus sociabilis/14b534fa4a9c388cdfc50b418a15f548.jpg\",\n  \"95757.jpg\": \"Aves/Rostrhamus sociabilis/15378f375b5f722df1acb684f26a7fe4.jpg\",\n  \"95759.jpg\": \"Aves/Rostrhamus sociabilis/dae57490e7962591168085c4b559fd99.jpg\",\n  \"95763.jpg\": \"Aves/Rostrhamus sociabilis/f72ac88f620218e99f86b34954d9b83d.jpg\",\n  \"95764.jpg\": \"Aves/Rostrhamus sociabilis/f0e0bdc9b41bf066bcef2a4522f71bb1.jpg\",\n  \"95765.jpg\": \"Aves/Rostrhamus sociabilis/2fbbd5c7ca4f24bb624d49d08571a883.jpg\",\n  \"95770.jpg\": \"Aves/Rostrhamus sociabilis/4d67144f2af25665b5a265e19dbc8d69.jpg\",\n  \"95772.jpg\": \"Aves/Rostrhamus sociabilis/88ed6af1113fe6c43a4a5f3961d96ee5.jpg\",\n  \"95773.jpg\": \"Aves/Rostrhamus sociabilis/e1585dd954d2e98ae8fcddac5634d25e.jpg\",\n  \"95774.jpg\": \"Aves/Rostrhamus sociabilis/c0964679477c790e87d9b38e9d15c1f5.jpg\",\n  \"95775.jpg\": \"Aves/Rostrhamus sociabilis/8420ef716ec9e032d7333624198f3741.jpg\",\n  \"95781.jpg\": \"Aves/Rostrhamus sociabilis/69246069736f3c5eef20256fd6787637.jpg\",\n  \"95787.jpg\": \"Aves/Rostrhamus sociabilis/85be23449d75959e28670f7180352b8f.jpg\",\n  \"95788.jpg\": \"Aves/Rostrhamus sociabilis/b3b763950117be24e3fbb36ec8bc991c.jpg\",\n  \"95795.jpg\": \"Aves/Rostrhamus sociabilis/c3521c00d192e0e96ecfdc2709b91fb8.jpg\",\n  \"958.jpg\": \"Reptilia/Nerodia cyclopion/a8a571ec91b9e9c54f5bb33a92e65890.jpg\",\n  \"95800.jpg\": \"Aves/Rostrhamus sociabilis/0765022efbe7a404df23d1da577a6a16.jpg\",\n  \"95802.jpg\": \"Aves/Rostrhamus sociabilis/b032904dc5026256604591f30b4b3b00.jpg\",\n  \"95803.jpg\": \"Insecta/Anteos clorinde/5a06ec792e08c67f47e54579f97ef53a.jpg\",\n  \"95804.jpg\": \"Insecta/Anteos clorinde/26466699f7cb5e3e51cf8a5c076ea9b6.jpg\",\n  \"95805.jpg\": \"Insecta/Anteos clorinde/18fd0258962d6a61c02496ae86f72420.jpg\",\n  \"9581.jpg\": \"Aves/Melospiza melodia/a57531fba0abefea3acf6892e201ab11.jpg\",\n  \"95816.jpg\": \"Insecta/Anteos clorinde/88be75996cccb12206f0645c53843ba7.jpg\",\n  \"95823.jpg\": \"Insecta/Anteos clorinde/0e22cf7cbadd25861643c15249f82209.jpg\",\n  \"95956.jpg\": \"Mammalia/Tamiasciurus douglasii/1de9076977721762e628e305a98ac665.jpg\",\n  \"95961.jpg\": \"Mammalia/Tamiasciurus douglasii/67e24fb24de02f591aa0a1072fff02f5.jpg\",\n  \"95962.jpg\": \"Mammalia/Tamiasciurus douglasii/d5adf4ad44498050f27a48093b92ee10.jpg\",\n  \"95964.jpg\": \"Mammalia/Tamiasciurus douglasii/537a5430f8cf9aa35e27abe0358c155d.jpg\",\n  \"95977.jpg\": \"Mammalia/Tamiasciurus douglasii/f1e27966e32037843ec224c584e1e3ef.jpg\",\n  \"95986.jpg\": \"Mammalia/Tamiasciurus douglasii/39ea38b8b32779fd1876f7484015606f.jpg\",\n  \"95987.jpg\": \"Mammalia/Tamiasciurus douglasii/4445b378b5a6002df55ac0457cbfdf10.jpg\",\n  \"95988.jpg\": \"Mammalia/Tamiasciurus douglasii/f341e3cb55ec797c4e83d4b2f69b43b9.jpg\",\n  \"95989.jpg\": \"Mammalia/Tamiasciurus douglasii/f20f3b1d88a9cb0dda77ee68eb27206a.jpg\",\n  \"95992.jpg\": \"Mammalia/Tamiasciurus douglasii/12679b0694ed131d732bac07397c992d.jpg\",\n  \"95994.jpg\": \"Mammalia/Tamiasciurus douglasii/e2aa77ee2ef27833820a7c06544aac4e.jpg\",\n  \"95995.jpg\": \"Mammalia/Tamiasciurus douglasii/57fc476b2dd66505b86afbb8ae543c6b.jpg\",\n  \"95996.jpg\": \"Mammalia/Tamiasciurus douglasii/a22b73940b803b76212dfc75be90f202.jpg\",\n  \"96002.jpg\": \"Mammalia/Tamiasciurus douglasii/22773351c7b4469e44ddda4e6097e83c.jpg\",\n  \"96005.jpg\": \"Mammalia/Tamiasciurus douglasii/fcc60a77de306a60cb198a8422a683b9.jpg\",\n  \"96010.jpg\": \"Mammalia/Tamiasciurus douglasii/2a9e08796408fafa4eed13e04ac8924b.jpg\",\n  \"96013.jpg\": \"Mammalia/Tamiasciurus douglasii/a611742467ed619e9f3905be575d3b92.jpg\",\n  \"96014.jpg\": \"Mammalia/Tamiasciurus douglasii/26e9fe3623975cf703ec1665c9bf417e.jpg\",\n  \"9606.jpg\": \"Aves/Melospiza melodia/af6481103c05f75980e4732d87356c15.jpg\",\n  \"96107.jpg\": \"Insecta/Bombus sonorus/b1a75a523ad54db4f4cec9b5e7be4094.jpg\",\n  \"96108.jpg\": \"Insecta/Bombus sonorus/c1a77fb4a1e82328557797322e910a87.jpg\",\n  \"96109.jpg\": \"Insecta/Bombus sonorus/d6dc88ff639186b79feadb6167097640.jpg\",\n  \"96110.jpg\": \"Insecta/Bombus sonorus/872e2d8811d9e88ec3cafc7fd3febc4c.jpg\",\n  \"96111.jpg\": \"Insecta/Bombus sonorus/ddd27e7cd3d2d1e06a01734a1a062db4.jpg\",\n  \"96113.jpg\": \"Insecta/Bombus sonorus/f837a29c50c4ef3a32c67574afe84cc4.jpg\",\n  \"96117.jpg\": \"Insecta/Bombus sonorus/b3e66c3e308e32a0f1dd63ea90fb610d.jpg\",\n  \"96125.jpg\": \"Insecta/Bombus sonorus/73f5d4a199da635fdf9fc429a9f29183.jpg\",\n  \"96126.jpg\": \"Insecta/Bombus sonorus/e0757d59c9a586ca985f0bd1870f360d.jpg\",\n  \"96127.jpg\": \"Insecta/Bombus sonorus/51d48a435e577b16fb9279f5386f5951.jpg\",\n  \"96128.jpg\": \"Insecta/Bombus sonorus/10fecc2feab4a5abb28f1f794354e28c.jpg\",\n  \"96129.jpg\": \"Insecta/Bombus sonorus/72833602da71d44bdec29638043df6a8.jpg\",\n  \"96130.jpg\": \"Insecta/Bombus sonorus/240b5e83fe7cca717f39d67f442aac7f.jpg\",\n  \"96134.jpg\": \"Insecta/Bombus sonorus/f0d9aef107bcd0046ab21028e1302ed2.jpg\",\n  \"96135.jpg\": \"Insecta/Bombus sonorus/c3b4e2fc74a7c1b38e7cc5f8eb9faa22.jpg\",\n  \"96136.jpg\": \"Insecta/Bombus sonorus/60df2fdcbb838fff12a06f04805d235e.jpg\",\n  \"96137.jpg\": \"Insecta/Bombus sonorus/872846fccea48deb02936453b3d2b7a6.jpg\",\n  \"96138.jpg\": \"Insecta/Bombus sonorus/774976aeb95676092d14a7cb1753061d.jpg\",\n  \"96139.jpg\": \"Insecta/Bombus sonorus/b4f8d9e4bdf1bcbfda405b0c93e4a7c5.jpg\",\n  \"96142.jpg\": \"Insecta/Bombus sonorus/24e8d585fecf487b4f449e58bea78f28.jpg\",\n  \"96143.jpg\": \"Insecta/Bombus sonorus/cf2ce62281a51f920509184b5a633a3a.jpg\",\n  \"96144.jpg\": \"Insecta/Bombus sonorus/ec2b5f751b2fc5e1da23147a59411f40.jpg\",\n  \"96243.jpg\": \"Insecta/Sphex pensylvanicus/6cced06ef3c3014a0bb91def8ef0f7ff.jpg\",\n  \"96244.jpg\": \"Insecta/Sphex pensylvanicus/5f4eac9d8e426555c82f4f6d502021d7.jpg\",\n  \"96248.jpg\": \"Insecta/Sphex pensylvanicus/133733a2d6bacb3e48e69f031ce6aa90.jpg\",\n  \"96251.jpg\": \"Insecta/Sphex pensylvanicus/ea4bff0c10ba23958647ca6e37598029.jpg\",\n  \"96253.jpg\": \"Insecta/Sphex pensylvanicus/833feb42d003677612ddd415c6057b19.jpg\",\n  \"96254.jpg\": \"Insecta/Sphex pensylvanicus/4eb88cf69ec1f7886be02f389c878e54.jpg\",\n  \"96256.jpg\": \"Insecta/Sphex pensylvanicus/7c4370b5ac64eefaa9fe5476106d4871.jpg\",\n  \"96257.jpg\": \"Insecta/Sphex pensylvanicus/84679cdec0da61390ec252b8549a432d.jpg\",\n  \"96265.jpg\": \"Insecta/Sphex pensylvanicus/244f50149589c65b7b8706bd242b668f.jpg\",\n  \"96266.jpg\": \"Insecta/Sphex pensylvanicus/56c376145f5c637b96339f19c0b52f3e.jpg\",\n  \"96268.jpg\": \"Insecta/Sphex pensylvanicus/fb2db3c3d90a22ff68b3a5d5f811c210.jpg\",\n  \"96269.jpg\": \"Insecta/Sphex pensylvanicus/672ed93fc7debb1461b24cc706ca0f28.jpg\",\n  \"96273.jpg\": \"Insecta/Sphex pensylvanicus/92b9b895a8767ef4cd5c50cff8f3f3f2.jpg\",\n  \"96274.jpg\": \"Insecta/Sphex pensylvanicus/c301460f4efcb793d6b54510a9168854.jpg\",\n  \"96275.jpg\": \"Insecta/Sphex pensylvanicus/56e0f2a57141d9d2462a5b9785805864.jpg\",\n  \"96276.jpg\": \"Insecta/Sphex pensylvanicus/ae27dafd2066f3816b76f7f456ae1d0d.jpg\",\n  \"96278.jpg\": \"Insecta/Sphex pensylvanicus/ea86a9173023916443675bc2d75cf2ea.jpg\",\n  \"96347.jpg\": \"Reptilia/Plestiodon laticeps/67c6c9ef10b038800160ad81a72f7f81.jpg\",\n  \"96348.jpg\": \"Reptilia/Plestiodon laticeps/bc326fdd0914aa3a53211421a20c434f.jpg\",\n  \"96350.jpg\": \"Reptilia/Plestiodon laticeps/321fc6f0a30e99b661c21af253473c8e.jpg\",\n  \"96357.jpg\": \"Reptilia/Plestiodon laticeps/7f062416eecd04768c9cb1f759fa6da8.jpg\",\n  \"96361.jpg\": \"Reptilia/Plestiodon laticeps/154a182aa4737056cd9251f57086caeb.jpg\",\n  \"96362.jpg\": \"Reptilia/Plestiodon laticeps/e821fa699719e3f06a236bb3e6802a43.jpg\",\n  \"96363.jpg\": \"Reptilia/Plestiodon laticeps/14da067eea9d52bb6a67d47785e6ef58.jpg\",\n  \"96365.jpg\": \"Reptilia/Plestiodon laticeps/9e3f7bd95547c4400419aad0daece461.jpg\",\n  \"96376.jpg\": \"Reptilia/Plestiodon laticeps/3cc3c338139989a3e029d3355f919772.jpg\",\n  \"96380.jpg\": \"Reptilia/Plestiodon laticeps/6f94a7ea9bb5297fe9f68fef266af800.jpg\",\n  \"96381.jpg\": \"Reptilia/Plestiodon laticeps/873707624b6f725444f095c12ab71fa5.jpg\",\n  \"96383.jpg\": \"Reptilia/Plestiodon laticeps/d94e97077fcb3af19fdafe06406ce683.jpg\",\n  \"96384.jpg\": \"Reptilia/Plestiodon laticeps/8ba23ca6929a13dbbdd7daeeaa61e91e.jpg\",\n  \"96385.jpg\": \"Reptilia/Plestiodon laticeps/01e1493d48fbc9b658e5f033078682e7.jpg\",\n  \"96386.jpg\": \"Reptilia/Plestiodon laticeps/0573a5e66193c481d9b09ce7cb4f4676.jpg\",\n  \"96387.jpg\": \"Reptilia/Plestiodon laticeps/5def485bbc01d4f7b621cdb8b6283ae4.jpg\",\n  \"96393.jpg\": \"Reptilia/Plestiodon laticeps/7c6e38eda43b61476573743dbf38fbed.jpg\",\n  \"96396.jpg\": \"Reptilia/Plestiodon laticeps/ed986e8a91b2b63ebf10b4788cc42a67.jpg\",\n  \"96397.jpg\": \"Reptilia/Plestiodon laticeps/782b04c7431d06683e2cfd296840ed57.jpg\",\n  \"96404.jpg\": \"Reptilia/Plestiodon laticeps/e48acdea66dad25c006b1b65b78b8d1b.jpg\",\n  \"96435.jpg\": \"Mammalia/Sciurus niger/38669d0d5186e308303d45619ca58e34.jpg\",\n  \"96473.jpg\": \"Mammalia/Sciurus niger/59e2a41b5ba92f457e56e71970029c40.jpg\",\n  \"96512.jpg\": \"Mammalia/Sciurus niger/31ecbfb30ca0dbaf87622ebd364b7284.jpg\",\n  \"96537.jpg\": \"Mammalia/Sciurus niger/48800f6ca38bcf35e7102484d1eb5a40.jpg\",\n  \"96566.jpg\": \"Mammalia/Sciurus niger/49882cb104ded28b813c42d2f5d5890c.jpg\",\n  \"96580.jpg\": \"Mammalia/Sciurus niger/6fe20d3f6a7d154a6fa6397766499d5a.jpg\",\n  \"96637.jpg\": \"Mammalia/Sciurus niger/ab615822aac0bcbbbd71040fc42632aa.jpg\",\n  \"96641.jpg\": \"Mammalia/Sciurus niger/2248ef3491bde1075fcab39aee44ee71.jpg\",\n  \"96684.jpg\": \"Mammalia/Sciurus niger/aff38fa8185fb20579245ecd0aadf87d.jpg\",\n  \"96780.jpg\": \"Mammalia/Sciurus niger/234250e2b76000ae2e9c57d0698fc72d.jpg\",\n  \"96955.jpg\": \"Mammalia/Sciurus niger/dbcac535514d03bd25352c5e225be71e.jpg\",\n  \"97.jpg\": \"Mammalia/Marmota flaviventris/fd0789a138283ece9a0a6c6f1e90fd8d.jpg\",\n  \"97001.jpg\": \"Mammalia/Sciurus niger/230eac49559ad309cde4b27e518d7d30.jpg\",\n  \"9703.jpg\": \"Aves/Melospiza melodia/2b0eb7ba4aa4784f5516bbe2a2944cdb.jpg\",\n  \"97031.jpg\": \"Mammalia/Sciurus niger/950f867af8bda1d10e95f691b2a69576.jpg\",\n  \"971.jpg\": \"Reptilia/Nerodia cyclopion/b6899e88efb7c9fab4ce631130f551a5.jpg\",\n  \"972.jpg\": \"Reptilia/Nerodia cyclopion/2ba4d9af41ddcb2631852572e482c910.jpg\",\n  \"97212.jpg\": \"Mammalia/Sciurus niger/915509944c09f13dcb89b1db15643da7.jpg\",\n  \"97228.jpg\": \"Mammalia/Sciurus niger/773e5b760277ed84a7dcf819fb331db7.jpg\",\n  \"97234.jpg\": \"Mammalia/Sciurus niger/c08e1aacbcce68ec328be2bcaea572b2.jpg\",\n  \"97251.jpg\": \"Mammalia/Sciurus niger/4cf124714b05806245fe7f4d1038eaa6.jpg\",\n  \"97296.jpg\": \"Mammalia/Sciurus niger/bc833c40ac6d92768e16083ce504dac3.jpg\",\n  \"97366.jpg\": \"Mammalia/Sciurus niger/4aa4926a2125fee0f40dcb079c18939e.jpg\",\n  \"97424.jpg\": \"Mammalia/Sciurus niger/8792b0b30fdee7254c0b79e86c7451ae.jpg\",\n  \"97440.jpg\": \"Mammalia/Sciurus niger/da2086704668def05fa55b3c7f337118.jpg\",\n  \"9751.jpg\": \"Aves/Melospiza melodia/9d5bec819d66d4b9ac8376318de05d00.jpg\",\n  \"976.jpg\": \"Reptilia/Nerodia cyclopion/6c0e65773433d66b4fcb274263b02209.jpg\",\n  \"9762.jpg\": \"Aves/Melospiza melodia/d5869b67bc4b0e97abfec03b6f1ee9ac.jpg\",\n  \"97635.jpg\": \"Insecta/Poecilanthrax lucifer/755e95c0219a9185f2529d7e9c7eaf3c.jpg\",\n  \"97637.jpg\": \"Insecta/Poecilanthrax lucifer/9513f2c11c53e74bd05fbdf8a725dda9.jpg\",\n  \"97641.jpg\": \"Insecta/Poecilanthrax lucifer/f0f651f33f4e2504cd851abf594c429b.jpg\",\n  \"97651.jpg\": \"Insecta/Poecilanthrax lucifer/5345b962e3cd5d85be045f080162420f.jpg\",\n  \"97678.jpg\": \"Mammalia/Peromyscus leucopus/9b39a8a597b7191af74ec408e07f47d2.jpg\",\n  \"97680.jpg\": \"Mammalia/Peromyscus leucopus/69f3cacb2d18a5666fc5ebb80af6ac9a.jpg\",\n  \"97682.jpg\": \"Mammalia/Peromyscus leucopus/825a362e2edd0a825d2d23ca08e9323c.jpg\",\n  \"97688.jpg\": \"Mammalia/Peromyscus leucopus/31878129658cfaf7cdff7be08ad5bbe9.jpg\",\n  \"97691.jpg\": \"Mammalia/Peromyscus leucopus/c8c114dfddba4099932912316e34667a.jpg\",\n  \"97692.jpg\": \"Mammalia/Peromyscus leucopus/d7e83b0c12107a1c35bde238030e4472.jpg\",\n  \"97693.jpg\": \"Mammalia/Ochotona princeps/abb4e459708abd539b81badc214ce315.jpg\",\n  \"97711.jpg\": \"Mammalia/Ochotona princeps/2765b5bcbe5502f96f0678dace501456.jpg\",\n  \"97726.jpg\": \"Mammalia/Ochotona princeps/5ed84ec6917df4bf0ba87bc0f3cec942.jpg\",\n  \"97737.jpg\": \"Mammalia/Ochotona princeps/b1c07dfe895c025c5801f00b6b85c29f.jpg\",\n  \"97738.jpg\": \"Mammalia/Ochotona princeps/5ac298a5722b70ae5fa958e08cc26d03.jpg\",\n  \"97741.jpg\": \"Mammalia/Ochotona princeps/cf57ea7c2e06e2b15998ba92f284e2fd.jpg\",\n  \"97744.jpg\": \"Mammalia/Ochotona princeps/f3f0bd9b747c46045698235460bfa558.jpg\",\n  \"97762.jpg\": \"Mammalia/Ochotona princeps/8344af879b9a2aff123ccceb3b708cd0.jpg\",\n  \"97771.jpg\": \"Mammalia/Ochotona princeps/573f6c613c28aa1884c3cf44c0c9de8a.jpg\",\n  \"97772.jpg\": \"Mammalia/Ochotona princeps/93d511f38f9bfa19015d44ab11cbd2ba.jpg\",\n  \"97773.jpg\": \"Mammalia/Ochotona princeps/b44381c6f863316669d169422862d22e.jpg\",\n  \"97776.jpg\": \"Mammalia/Ochotona princeps/87700cd0d2b5be323ce0ac5072042f18.jpg\",\n  \"97793.jpg\": \"Mammalia/Ochotona princeps/b055bb9adba005a7667ec1b021039f95.jpg\",\n  \"97803.jpg\": \"Mammalia/Ochotona princeps/9e68b6e49d574e7d9a3ea5059be3c608.jpg\",\n  \"97814.jpg\": \"Mammalia/Ochotona princeps/dc725b568303d8bf3dbb9468a387839c.jpg\",\n  \"97815.jpg\": \"Mammalia/Ochotona princeps/2a20e0e48a5669d2ab721d7acc03c244.jpg\",\n  \"97819.jpg\": \"Mammalia/Ochotona princeps/046957c9ee77b5819038927b1445cc41.jpg\",\n  \"97824.jpg\": \"Mammalia/Ochotona princeps/67b83e27e5d15797451be14998906147.jpg\",\n  \"97828.jpg\": \"Mammalia/Ochotona princeps/25a2bac547aff0410e1946e9677e7199.jpg\",\n  \"97834.jpg\": \"Mammalia/Ochotona princeps/e99c30a6e50ba38fc39005f39409b186.jpg\",\n  \"97836.jpg\": \"Mammalia/Ochotona princeps/26f7ece182d714a4f2561add4dee1658.jpg\",\n  \"97847.jpg\": \"Mammalia/Ochotona princeps/176ce3c622870dbb28fcb44b1d850970.jpg\",\n  \"97848.jpg\": \"Mammalia/Ochotona princeps/7cde907034585b7c4044712f7bf54bb7.jpg\",\n  \"97863.jpg\": \"Mammalia/Ochotona princeps/169eded4073b421d66894342a5f8ca84.jpg\",\n  \"97873.jpg\": \"Insecta/Orthosoma brunneum/913754840748b49e9abb873908053ae4.jpg\",\n  \"97877.jpg\": \"Insecta/Orthosoma brunneum/4a19e2772b316e457ecfdba79d3a300b.jpg\",\n  \"97880.jpg\": \"Insecta/Orthosoma brunneum/42bc84cf8b5dea1e6b576b0381f5f27d.jpg\",\n  \"97987.jpg\": \"Insecta/Callopistria mollissima/ee2a9419b041c86225c95420295a2c3c.jpg\",\n  \"97991.jpg\": \"Insecta/Callopistria mollissima/2b6c3e7a318463880d9846693f4457ed.jpg\",\n  \"97992.jpg\": \"Aves/Euphonia elegantissima/9079aed4dd6f88b6dad79598063fafcb.jpg\",\n  \"98000.jpg\": \"Aves/Euphonia elegantissima/ab81f806c18b69a3f435da3287682aca.jpg\",\n  \"98004.jpg\": \"Aves/Euphonia elegantissima/f9ce94e04bf09b6c354e5655580d2ae3.jpg\",\n  \"98013.jpg\": \"Aves/Euphonia elegantissima/f94f2fc5db869319bfe4df6b1525115d.jpg\",\n  \"98018.jpg\": \"Aves/Euphonia elegantissima/27177015cb25f63a541d6bd96da5464b.jpg\",\n  \"98019.jpg\": \"Aves/Euphonia elegantissima/4624c2dca3c6c0208f6e18c56702b180.jpg\",\n  \"9802.jpg\": \"Aves/Melospiza melodia/a11a0c12a43facface1b95b4d1f28899.jpg\",\n  \"98020.jpg\": \"Aves/Euphonia elegantissima/b18d6c8f5de7774772bac73b5134b16a.jpg\",\n  \"98027.jpg\": \"Insecta/Cordulegaster dorsalis/96f232fdcc86e009ab0346ca39a7d1b7.jpg\",\n  \"98028.jpg\": \"Insecta/Cordulegaster dorsalis/6b47a6178c4df7fe23e56a5b7e143323.jpg\",\n  \"98029.jpg\": \"Insecta/Cordulegaster dorsalis/aa9a3930c76fc6729db335a50dd899e8.jpg\",\n  \"98033.jpg\": \"Insecta/Cordulegaster dorsalis/711a6eb29f2b08ac91136cb4e1437b8c.jpg\",\n  \"98035.jpg\": \"Insecta/Cordulegaster dorsalis/05b51f5b285028a1ce8320429cf2e125.jpg\",\n  \"98038.jpg\": \"Insecta/Cordulegaster dorsalis/343b483522e0b57c0cd5d636982d6e37.jpg\",\n  \"98050.jpg\": \"Insecta/Cordulegaster dorsalis/f0f236f7769dfd7845d9fc6227d203d9.jpg\",\n  \"98079.jpg\": \"Mammalia/Bison bison/27f2ddc9af494dc13f2ef182397db5dc.jpg\",\n  \"98081.jpg\": \"Mammalia/Bison bison/6269f259308f7c89a32fc752f86446db.jpg\",\n  \"98088.jpg\": \"Mammalia/Bison bison/60f84dbd7047591eb5d42e87102d8b4d.jpg\",\n  \"98094.jpg\": \"Mammalia/Bison bison/e11fb1f632e5f703c8a07423b95e3f95.jpg\",\n  \"98100.jpg\": \"Mammalia/Bison bison/79f443ccb0dad24ffd9bbeb85d45075f.jpg\",\n  \"98102.jpg\": \"Mammalia/Bison bison/65ca7e6eddd9baeedf40806b53f2ee15.jpg\",\n  \"98112.jpg\": \"Mammalia/Bison bison/c63f528890dff58bafec783ff1bce068.jpg\",\n  \"98127.jpg\": \"Mammalia/Bison bison/f38b171338b5e4c05819978dda60cacc.jpg\",\n  \"98129.jpg\": \"Mammalia/Bison bison/2ccedec34515c8830735f90acf878735.jpg\",\n  \"98138.jpg\": \"Mammalia/Bison bison/718dc78965d04836d4296290a5db7fef.jpg\",\n  \"98139.jpg\": \"Mammalia/Bison bison/d80a16ab8422d2dc81b73554a89686bb.jpg\",\n  \"98148.jpg\": \"Mammalia/Bison bison/3c36faa4d611d3eaf79a0c5293acef18.jpg\",\n  \"98174.jpg\": \"Aves/Ceryle rudis/af8b9e2420fc8e781126016fc5797eea.jpg\",\n  \"98175.jpg\": \"Aves/Ceryle rudis/a4391fe62ff252ea6349f8ce14e25002.jpg\",\n  \"98176.jpg\": \"Aves/Ceryle rudis/0ee1393084d36fac79611124ed2949fb.jpg\",\n  \"98178.jpg\": \"Aves/Ceryle rudis/9b552de825626a548b350a20be89d70b.jpg\",\n  \"98184.jpg\": \"Aves/Ceryle rudis/10f2b106ebff643e818d1ebb27b7c396.jpg\",\n  \"98193.jpg\": \"Aves/Ceryle rudis/efc00db26a008d5090b983cb1f024dfe.jpg\",\n  \"98198.jpg\": \"Aves/Thraupis palmarum/c90cd9f46b96a7a6cc37aa71431f0a3c.jpg\",\n  \"98204.jpg\": \"Aves/Thraupis palmarum/ff9bc80fd2b0714cd31b61c153e7f442.jpg\",\n  \"9821.jpg\": \"Aves/Melospiza melodia/14d7a23627cb7b3f01f9b3fb6ad79dd8.jpg\",\n  \"98210.jpg\": \"Aves/Thraupis palmarum/b19d3fecae9e7cc7b345dc9004049498.jpg\",\n  \"98211.jpg\": \"Aves/Lampornis clemenciae/9846b698cb75e269403f085ab27675d1.jpg\",\n  \"98214.jpg\": \"Aves/Lampornis clemenciae/e863b09a74fc84d4df9d83525663e730.jpg\",\n  \"98216.jpg\": \"Aves/Lampornis clemenciae/6cc1491111c4ba13d66608589bc79998.jpg\",\n  \"98221.jpg\": \"Aves/Lampornis clemenciae/cc3cfe392d7381d6f3da4b38249936c9.jpg\",\n  \"98224.jpg\": \"Aves/Lampornis clemenciae/09194407788df0b7389025452e7f8f42.jpg\",\n  \"98226.jpg\": \"Aves/Lampornis clemenciae/cddecd7a2feefd2476498b638c8fd98d.jpg\",\n  \"98231.jpg\": \"Amphibia/Ambystoma jeffersonianum/529363f632f42195f85c334a84dd7791.jpg\",\n  \"98233.jpg\": \"Amphibia/Ambystoma jeffersonianum/b7b094e32e1c9771555c0103ffff6821.jpg\",\n  \"98234.jpg\": \"Amphibia/Ambystoma jeffersonianum/89ba610c22f57494f63b7abe51b5ce4f.jpg\",\n  \"98236.jpg\": \"Amphibia/Ambystoma jeffersonianum/c2df8f8d619983fdda4b2e10d7a6b21a.jpg\",\n  \"98239.jpg\": \"Amphibia/Ambystoma jeffersonianum/db46e16d11528f679ed11936855915c4.jpg\",\n  \"98278.jpg\": \"Insecta/Palpita magniferalis/3ab72c4c0003fcdac84c5ae85dc3a7d2.jpg\",\n  \"98281.jpg\": \"Insecta/Palpita magniferalis/2b5b503e8c1fdf695627ce34e303f646.jpg\",\n  \"98284.jpg\": \"Insecta/Palpita magniferalis/563dd8fc06b53ced5f11148cab164296.jpg\",\n  \"98288.jpg\": \"Insecta/Palpita magniferalis/50867abf6f87f2ea272c153ab28c2395.jpg\",\n  \"98290.jpg\": \"Insecta/Palpita magniferalis/ec4764d44545029a54808e3ea6678043.jpg\",\n  \"98292.jpg\": \"Insecta/Palpita magniferalis/f2109a641a500c8f325f28fc2a73e99f.jpg\",\n  \"98294.jpg\": \"Insecta/Palpita magniferalis/c5cf4e51884f18e999889f136bcc034a.jpg\",\n  \"98298.jpg\": \"Insecta/Satyrium liparops/804ee9c3f9cfdab59d01547368b4fbc5.jpg\",\n  \"98305.jpg\": \"Insecta/Satyrium liparops/d022f2524b2771fb0d4db80245c6227f.jpg\",\n  \"98311.jpg\": \"Insecta/Satyrium liparops/2752a84f301dee4991c1dbaeef43cf72.jpg\",\n  \"98312.jpg\": \"Insecta/Satyrium liparops/633ea2255f798784e08a523a4f88eeaa.jpg\",\n  \"98313.jpg\": \"Insecta/Satyrium liparops/9edb83842e0e3f1b90abce868820b297.jpg\",\n  \"98314.jpg\": \"Insecta/Satyrium liparops/15245e1eecf90b74ec952247bf1d854a.jpg\",\n  \"98315.jpg\": \"Insecta/Satyrium liparops/207eebb5ad0af566447f473b51616e3f.jpg\",\n  \"98316.jpg\": \"Insecta/Satyrium liparops/f5ae04df98ae83f6baa9820de368652a.jpg\",\n  \"98592.jpg\": \"Aves/Lanius excubitor/aba706440f2d3211785bd0465be96fa8.jpg\",\n  \"98593.jpg\": \"Aves/Lanius excubitor/d9edbb7701efee430de198de147cf479.jpg\",\n  \"98595.jpg\": \"Aves/Lanius excubitor/560a8ee9954ec24c56a92854df574186.jpg\",\n  \"98596.jpg\": \"Aves/Lanius excubitor/f85cc7de3de5d254583c30f70466f42c.jpg\",\n  \"98597.jpg\": \"Aves/Lanius excubitor/61ea170671b46820f0ab531b082b36d1.jpg\",\n  \"98599.jpg\": \"Aves/Lanius excubitor/9ba5b38a592c9441a0c3e3fef5efa60d.jpg\",\n  \"98603.jpg\": \"Aves/Lanius excubitor/2fa8d164e80f6b93b7b4d27199715100.jpg\",\n  \"98604.jpg\": \"Aves/Lanius excubitor/9a8cb519ca5df644e0f4d027eaeef3dc.jpg\",\n  \"98605.jpg\": \"Aves/Lanius excubitor/9053dddd385cf6b026ec10f09bab0d1d.jpg\",\n  \"98612.jpg\": \"Aves/Lanius excubitor/1ec2d077efaa5985733856c1be67f41e.jpg\",\n  \"98613.jpg\": \"Aves/Lanius excubitor/f2a3fd6acee2daf9dd51694ffe56966c.jpg\",\n  \"98616.jpg\": \"Aves/Lanius excubitor/d220b302b30e465882fe5fc2eb6026e1.jpg\",\n  \"98617.jpg\": \"Aves/Lanius excubitor/3691f0d4a9cdff601e78d536827ecb7a.jpg\",\n  \"98618.jpg\": \"Aves/Lanius excubitor/681da1e32c3241a67c20f0e07717e670.jpg\",\n  \"98619.jpg\": \"Aves/Lanius excubitor/86bfb35739c46ec95eda1979dff99dac.jpg\",\n  \"98626.jpg\": \"Aves/Lanius excubitor/4215e085a9b2a36e517d0ae1d1e7ea76.jpg\",\n  \"98627.jpg\": \"Aves/Lanius excubitor/27c66f2124b8ad2316bd621c9bd57833.jpg\",\n  \"98638.jpg\": \"Aves/Lanius excubitor/2096cdff2f0ec9624c89c5773cf26009.jpg\",\n  \"98639.jpg\": \"Aves/Lanius excubitor/5bd6bb362b4fb8ae654c18e5db7ddbdb.jpg\",\n  \"98640.jpg\": \"Aves/Lanius excubitor/59e28241c08190a6a98db0714a184db5.jpg\",\n  \"98641.jpg\": \"Aves/Lanius excubitor/71ccd65de5dc2ccad22296a34ede1668.jpg\",\n  \"98680.jpg\": \"Aves/Empidonax traillii/c2b66716eff1a9e53d251253b0499f55.jpg\",\n  \"98681.jpg\": \"Aves/Empidonax traillii/0e1690d75052ea3fb1ecc7714d612669.jpg\",\n  \"98683.jpg\": \"Aves/Empidonax traillii/00b04c7b33cf82bb9ff25b3376d8d614.jpg\",\n  \"98684.jpg\": \"Aves/Empidonax traillii/8f97627f7f309e2e46f0533e2ed5110f.jpg\",\n  \"98686.jpg\": \"Aves/Empidonax traillii/35c926e5689f00543e10016dba625602.jpg\",\n  \"98687.jpg\": \"Aves/Empidonax traillii/f40a7d4b24237b88f4d7e75910872110.jpg\",\n  \"98688.jpg\": \"Aves/Empidonax traillii/0d04ac3e53b00e957372602bdd94a54e.jpg\",\n  \"98698.jpg\": \"Reptilia/Chrysemys picta picta/84898a002a1e417447fbbeaa6a9a5f30.jpg\",\n  \"98700.jpg\": \"Reptilia/Chrysemys picta picta/0cc690f64fbf99c55e9778397e50bcbb.jpg\",\n  \"98701.jpg\": \"Reptilia/Chrysemys picta picta/b3634da3b710be2ed87d3f28820225e6.jpg\",\n  \"98702.jpg\": \"Reptilia/Chrysemys picta picta/c52756dc4b579314cf06cb0c7380b3fc.jpg\",\n  \"98737.jpg\": \"Reptilia/Chrysemys picta picta/e316c92ba3b5b7d74d2178c53be62f17.jpg\",\n  \"98738.jpg\": \"Reptilia/Chrysemys picta picta/f03c8a6dbc0fa6a5287d80bf74768181.jpg\",\n  \"98743.jpg\": \"Reptilia/Chrysemys picta picta/2602cdc8ca1feba486817cde7af691c5.jpg\",\n  \"98748.jpg\": \"Reptilia/Chrysemys picta picta/860c9fff8a5800689af2cd779d00adee.jpg\",\n  \"98749.jpg\": \"Reptilia/Chrysemys picta picta/792d789b65cce2afa1afe69ad2c58fff.jpg\",\n  \"98751.jpg\": \"Reptilia/Chrysemys picta picta/644e5f2a5df673b43a70994a583f05ec.jpg\",\n  \"98756.jpg\": \"Reptilia/Chrysemys picta picta/bc840612804a7963d7ea0a4c3b70a6ce.jpg\",\n  \"98757.jpg\": \"Reptilia/Chrysemys picta picta/0ee7789dd501047a9fdb821ccca5add3.jpg\",\n  \"98765.jpg\": \"Reptilia/Chrysemys picta picta/d5784224aa5eb60982e98d4ac7d4dbb9.jpg\",\n  \"98766.jpg\": \"Reptilia/Chrysemys picta picta/6e591825e1b29a01db096a17020edfab.jpg\",\n  \"98772.jpg\": \"Reptilia/Chrysemys picta picta/80e7968459d51eedb03d5c8bf5acc339.jpg\",\n  \"98775.jpg\": \"Reptilia/Chrysemys picta picta/80ba1adfb04a5ce41a69ee95781ad916.jpg\",\n  \"98796.jpg\": \"Insecta/Libellula saturata/86638d7bfce099ef6c1db9d127b7796a.jpg\",\n  \"98808.jpg\": \"Insecta/Libellula saturata/a08dc66c384351e94804ef46804e41a5.jpg\",\n  \"98834.jpg\": \"Insecta/Libellula saturata/aba1d4d2ce9911aa7a0333dd23e5e91f.jpg\",\n  \"98839.jpg\": \"Insecta/Libellula saturata/371e6b2e72c5f62cd720b99f8299a22c.jpg\",\n  \"98848.jpg\": \"Insecta/Libellula saturata/822f159b2bf624de0c6adcfa3f7ac951.jpg\",\n  \"98849.jpg\": \"Insecta/Libellula saturata/41355702f55bedb9131731a1017c331e.jpg\",\n  \"98876.jpg\": \"Insecta/Libellula saturata/6024f6f3fbff9405051fee7161312763.jpg\",\n  \"98877.jpg\": \"Insecta/Libellula saturata/1da1e2a38e3f568e67b0065ea099f576.jpg\",\n  \"98891.jpg\": \"Insecta/Libellula saturata/ba8e38ec6d620785d231f053ead2e178.jpg\",\n  \"98910.jpg\": \"Insecta/Libellula saturata/3148fb5aa9a67341c5e2c96221875dae.jpg\",\n  \"98930.jpg\": \"Insecta/Libellula saturata/e608501bfc0f95eef2ca6831e966acf9.jpg\",\n  \"98944.jpg\": \"Insecta/Libellula saturata/49531f35ccb0cede5178a00cba5263c0.jpg\",\n  \"98950.jpg\": \"Insecta/Libellula saturata/584ba72fca8f1c015b8755ae9f7ae07a.jpg\",\n  \"9896.jpg\": \"Aves/Melospiza melodia/fc04b77545701a01487153c201c73501.jpg\",\n  \"98962.jpg\": \"Insecta/Libellula saturata/30936a65e298e55226790ec094364c1c.jpg\",\n  \"99.jpg\": \"Mammalia/Marmota flaviventris/885481900bce501f00ce86fd0b243202.jpg\",\n  \"99015.jpg\": \"Insecta/Libellula saturata/233ed0d7c4543c324aa95eb2b821f333.jpg\",\n  \"99031.jpg\": \"Insecta/Libellula saturata/bb0d22583dd4d74234e33313702cf1ad.jpg\",\n  \"99041.jpg\": \"Insecta/Libellula saturata/6b7ee6957f8130b724f9e1a4bc853bcc.jpg\",\n  \"99049.jpg\": \"Insecta/Libellula saturata/962d6be3d74365dbc92b4c4645fd0bad.jpg\",\n  \"99051.jpg\": \"Insecta/Libellula saturata/1c839f4684c3cadf87527dcfacf5ab47.jpg\",\n  \"9906.jpg\": \"Aves/Melospiza melodia/1f26889a89bd9d453b33e86f57cefe51.jpg\",\n  \"99109.jpg\": \"Insecta/Parrhasius m-album/bcf8f348047b6c595d91b603cc4974ed.jpg\",\n  \"9911.jpg\": \"Aves/Melospiza melodia/20ed1a5b585ce03976260c1118acf917.jpg\",\n  \"99110.jpg\": \"Insecta/Parrhasius m-album/1ad50eac61f59acd788617cc7d056afb.jpg\",\n  \"99118.jpg\": \"Insecta/Parrhasius m-album/c82401355111a3c02d6a39c1b8d19e3e.jpg\",\n  \"99296.jpg\": \"Aves/Saltator coerulescens/cd69e4b9b633d59fdf731c5ba63bdbe1.jpg\",\n  \"99302.jpg\": \"Aves/Saltator coerulescens/f47b1b1b850fa19fe2215643104a2bd0.jpg\",\n  \"99303.jpg\": \"Aves/Saltator coerulescens/8f31c65a6a58c6d7e6b9ec978df4bc06.jpg\",\n  \"99306.jpg\": \"Aves/Saltator coerulescens/89fac29865cba29e1595bd1603a638af.jpg\",\n  \"99307.jpg\": \"Aves/Saltator coerulescens/82820096b458e530acc2fc978413254d.jpg\",\n  \"99312.jpg\": \"Aves/Saltator coerulescens/53911d8c9b4cc78352ce87163b0641f6.jpg\",\n  \"99313.jpg\": \"Aves/Saltator coerulescens/6bc5e68404c8d31b7a0933650bb7db1a.jpg\",\n  \"99318.jpg\": \"Aves/Saltator coerulescens/cca97760253cc4c868e9bfc524ddd1be.jpg\",\n  \"99322.jpg\": \"Aves/Saltator coerulescens/1759b75763c16c4e8c0ecd9ce2b091b6.jpg\",\n  \"99323.jpg\": \"Aves/Saltator coerulescens/0c4ad852ca23a63a528856ad32f61acd.jpg\",\n  \"99328.jpg\": \"Aves/Saltator coerulescens/773a8d159c42e17aebb83485c95a190d.jpg\",\n  \"99330.jpg\": \"Aves/Saltator coerulescens/2705f84bf1f08001724055813b1b4f52.jpg\",\n  \"99331.jpg\": \"Aves/Saltator coerulescens/d237f90a245a82aea88986bb0b2004f2.jpg\",\n  \"99332.jpg\": \"Aves/Saltator coerulescens/65634cb9091c57ca76eef5b56f9f55a0.jpg\",\n  \"99333.jpg\": \"Aves/Saltator coerulescens/09987e5ccfbc07c57c77199099c8c7e8.jpg\",\n  \"99334.jpg\": \"Aves/Saltator coerulescens/2cecdcf18268d1fda1efef5f0177c89a.jpg\",\n  \"99335.jpg\": \"Aves/Saltator coerulescens/da0881d9b0158a39ca0a427816119821.jpg\",\n  \"99336.jpg\": \"Aves/Saltator coerulescens/bb6ac3e24f8e9dd7f9dd98b37024eedd.jpg\",\n  \"99338.jpg\": \"Aves/Saltator coerulescens/43ff10433bcac7993e9b139316a0f829.jpg\",\n  \"99668.jpg\": \"Aves/Eupsittula nana/f00880e8cec20a4ab0fd8ea3b14dca19.jpg\",\n  \"9967.jpg\": \"Aves/Melospiza melodia/c14f6932d0422f794244e373cd5e10dc.jpg\",\n  \"99670.jpg\": \"Aves/Eupsittula nana/3059b7c9da9bb9a69b7b2393301a6c70.jpg\",\n  \"99672.jpg\": \"Aves/Eupsittula nana/168ea5c03b5d3cc7f43c4cb9b4627e8e.jpg\",\n  \"99673.jpg\": \"Aves/Eupsittula nana/fbba4d04b88612082eb1d9e513a8f11b.jpg\",\n  \"99680.jpg\": \"Aves/Eupsittula nana/068a12638d9eb5dee4851e68fbc269ec.jpg\",\n  \"99681.jpg\": \"Aves/Eupsittula nana/ba45655b8c0987b4433a31c7ff1cc7d8.jpg\",\n  \"99682.jpg\": \"Aves/Eupsittula nana/95f6b3853eecb95db4a967339e7d2c1b.jpg\",\n  \"99683.jpg\": \"Aves/Eupsittula nana/3efd46867945a5f5124a173b5874d2ba.jpg\",\n  \"99690.jpg\": \"Aves/Eupsittula nana/3134d25ca8ede48f527fb9f502b47e58.jpg\",\n  \"99705.jpg\": \"Aves/Eupsittula nana/fa392f12ae9759a58574de47a370b2b3.jpg\",\n  \"99708.jpg\": \"Aves/Eupsittula nana/8b6be0d15a3cff14b30e00cceb0111a4.jpg\",\n  \"99709.jpg\": \"Aves/Eupsittula nana/4df8473f79b3cb7d1f7681452a8389cf.jpg\",\n  \"99710.jpg\": \"Aves/Eupsittula nana/46c8c62eec89ea105d20b80a9f3335c6.jpg\",\n  \"99711.jpg\": \"Aves/Eupsittula nana/dd3a275128465e610befa127121925bc.jpg\",\n  \"99712.jpg\": \"Aves/Eupsittula nana/c9faaedf1bedf55c751439935fc482f0.jpg\",\n  \"99713.jpg\": \"Aves/Eupsittula nana/4730622a6186a14af4be209dece0a802.jpg\",\n  \"99714.jpg\": \"Aves/Eupsittula nana/5844e4a5ebdfb9fddb3ba3e9107d28e6.jpg\",\n  \"99715.jpg\": \"Aves/Eupsittula nana/9f0ed9ba902f062f4a77ff41a8d6d382.jpg\",\n  \"99716.jpg\": \"Actinopterygii/Sparisoma viride/a1bc1c396a3d57f370c99d280d79e23d.jpg\",\n  \"99727.jpg\": \"Actinopterygii/Sparisoma viride/6e28582d5e3fdb6f1fff84e49c893781.jpg\",\n  \"99732.jpg\": \"Actinopterygii/Sparisoma viride/02208bae0bb301ccd98229230f8977bc.jpg\",\n  \"99741.jpg\": \"Actinopterygii/Sparisoma viride/8c89c8dcf6d2cbf2e0ba3cab693b6303.jpg\",\n  \"99764.jpg\": \"Insecta/Chortophaga viridifasciata viridifasciata/231ac4f67a43dd56fbe32163a94bc75b.jpg\",\n  \"99767.jpg\": \"Insecta/Chortophaga viridifasciata viridifasciata/e1012862e5a44ec8fae7a3aaff8a4354.jpg\",\n  \"99778.jpg\": \"Reptilia/Sceloporus graciosus vandenburgianus/32ffd57148cf33a354ce9498ee5445dc.jpg\",\n  \"99779.jpg\": \"Reptilia/Sceloporus graciosus vandenburgianus/7506437c3e031030a502f06530a22be5.jpg\",\n  \"99780.jpg\": \"Reptilia/Sceloporus graciosus vandenburgianus/bf46e47e84723f6909b929bcf150b37f.jpg\",\n  \"99781.jpg\": \"Reptilia/Sceloporus graciosus vandenburgianus/e5c10774af6727a2ca117d01b9b72874.jpg\",\n  \"99782.jpg\": \"Reptilia/Sceloporus graciosus vandenburgianus/feab868399dc290ca1783ea7d75090b0.jpg\",\n  \"99789.jpg\": \"Reptilia/Sceloporus graciosus vandenburgianus/59565dc755b4bb4a08534e267543dad0.jpg\",\n  \"99796.jpg\": \"Reptilia/Sceloporus graciosus vandenburgianus/bb60e6346cb9d5d34649fbb1296eaa02.jpg\",\n  \"99800.jpg\": \"Reptilia/Sceloporus graciosus vandenburgianus/48797b6a5888a4c44d0320a375a4828a.jpg\",\n  \"99805.jpg\": \"Reptilia/Sceloporus graciosus vandenburgianus/a47e3d34a643e911a0953e38e1c0f72c.jpg\",\n  \"99806.jpg\": \"Reptilia/Sceloporus graciosus vandenburgianus/49f538104fa03999a87a1e8a67cc04da.jpg\",\n  \"99807.jpg\": \"Reptilia/Sceloporus graciosus vandenburgianus/77e4e0ebd97edce0b1fddd4c0b48ad42.jpg\",\n  \"99808.jpg\": \"Reptilia/Sceloporus graciosus vandenburgianus/666a14bd76eedf0c66446c93999ac5f6.jpg\",\n  \"99809.jpg\": \"Reptilia/Sceloporus graciosus vandenburgianus/2bce0a01f0cee1530f45a6ac25a8ab54.jpg\",\n  \"99812.jpg\": \"Reptilia/Sceloporus graciosus vandenburgianus/53d7f4d9e401f8a4138771b927c3e3c6.jpg\",\n  \"99815.jpg\": \"Reptilia/Sceloporus graciosus vandenburgianus/52e3f61c40f0eec537ef5a7598aaf21d.jpg\",\n  \"99817.jpg\": \"Reptilia/Sceloporus graciosus vandenburgianus/6ac9f0865472d53c6c017f538aa9794a.jpg\",\n  \"99818.jpg\": \"Reptilia/Sceloporus graciosus vandenburgianus/27f8d65901bddb0ea4f50e1d5c4ed212.jpg\",\n  \"99819.jpg\": \"Reptilia/Sceloporus graciosus vandenburgianus/305272bf440c154fd7d8134564613fba.jpg\",\n  \"99827.jpg\": \"Reptilia/Sceloporus graciosus vandenburgianus/e59f5b96288a7cb95760d01e6b346932.jpg\",\n  \"99828.jpg\": \"Reptilia/Sceloporus graciosus vandenburgianus/542a8fe4820866f887fbc2908575f820.jpg\",\n  \"9983.jpg\": \"Aves/Melospiza melodia/eafb9f59dd2923706027ca771a9db3c6.jpg\",\n  \"99830.jpg\": \"Aves/Phaethon aethereus/3e568a3e94a2096a9ce3092d2c4253db.jpg\",\n  \"99831.jpg\": \"Aves/Phaethon aethereus/34117be7c34e239d12c922634b4e0a71.jpg\",\n  \"99832.jpg\": \"Aves/Phaethon aethereus/bf50fb1334fc54dc70a5d7f11def0bc0.jpg\",\n  \"99842.jpg\": \"Aves/Phaethon aethereus/8b8535f689ea230e95aa90004089de6d.jpg\",\n  \"99891.jpg\": \"Reptilia/Varanus niloticus/263588082c7f7a2b0b6f7e026219c597.jpg\",\n  \"99892.jpg\": \"Reptilia/Varanus niloticus/06430862249e9a473c24457fba7c3a31.jpg\",\n  \"99897.jpg\": \"Reptilia/Varanus niloticus/441ba9f69a75b0667745c80cb9da599b.jpg\",\n  \"99899.jpg\": \"Reptilia/Varanus niloticus/118f601987b81e0fc98ac6bf822df8ac.jpg\",\n  \"99902.jpg\": \"Reptilia/Varanus niloticus/a2522b2a1f167909a251710b7dedc786.jpg\",\n  \"99906.jpg\": \"Reptilia/Varanus niloticus/71edd7fa1a48df4267f6a110f875a93b.jpg\",\n  \"99907.jpg\": \"Reptilia/Varanus niloticus/c1ed59ad0f3ac9bb9396f4a8b3a4ed91.jpg\",\n  \"99915.jpg\": \"Amphibia/Bufo bufo/9bd402ba329f5ae1d96fc369987cda79.jpg\",\n  \"99930.jpg\": \"Amphibia/Bufo bufo/67aacf7aa0032b5303bb5adf6287265e.jpg\",\n  \"99931.jpg\": \"Amphibia/Bufo bufo/74bab5600305beb2f1ec2a9373f81157.jpg\",\n  \"99932.jpg\": \"Amphibia/Bufo bufo/591566f4f44c56fe89fb97c152679e4e.jpg\",\n  \"99940.jpg\": \"Amphibia/Bufo bufo/f20c46d6985adb4f49b0dcef28299e6d.jpg\",\n  \"99942.jpg\": \"Amphibia/Bufo bufo/1d55432ef3fdce2d872f705609bffd26.jpg\",\n  \"99948.jpg\": \"Amphibia/Bufo bufo/158fe3bf31b4a6df1281543a534ccb07.jpg\",\n  \"99955.jpg\": \"Amphibia/Bufo bufo/985527cadd547122d6c247e5fb50a4f1.jpg\",\n  \"99978.jpg\": \"Amphibia/Bufo bufo/b5401ba7274b704d51871de455e45d0d.jpg\",\n  \"99985.jpg\": \"Amphibia/Bufo bufo/5893839c00e306a2a8722f30ebaaf15f.jpg\",\n  \"99987.jpg\": \"Amphibia/Bufo bufo/8b78a8848af37b5ea5d17e186c494da5.jpg\",\n  \"99993.jpg\": \"Amphibia/Bufo bufo/f878b040066f7a6a35e45c313e5588cf.jpg\"\n}"
  },
  {
    "path": "scripts/eval/silver/preprocess_silver_geode_bdd100k_food_rec.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\nimport argparse\nfrom multiprocessing import Pool\nfrom pathlib import Path\n\nimport pandas as pd\nimport utils\nfrom tqdm import tqdm\n\n\ndef main(args, n_workers=20):\n    raw_folder = Path(args.raw_images_folder)\n    processed_folder = Path(args.processed_images_folder)\n    utils.setup(processed_folder)\n    img_ids = utils.get_image_ids(args.annotation_file)\n    if args.dataset_name == \"geode\":\n        metadata = pd.read_csv(raw_folder / \"index.csv\")\n        metadata[\"flat_filepath\"] = metadata.file_path.apply(\n            lambda x: x.replace(\"/\", \"_\")\n        )\n        metadata[\"original_absolute_path\"] = metadata.file_path.apply(\n            lambda x: str((raw_folder / \"images\") / x)\n        )\n        metadata[\"new_absolute_path\"] = metadata.flat_filepath.apply(\n            lambda x: str(processed_folder / x)\n        )\n        metadata[\"filestem\"] = metadata.new_absolute_path.apply(lambda x: Path(x).stem)\n        img_id_mapping = metadata.set_index(\"filestem\").to_dict()\n        # print(img_id_mapping.keys())\n        paths = [\n            (\n                img_id_mapping[\"original_absolute_path\"][each],\n                img_id_mapping[\"new_absolute_path\"][each],\n            )\n            for each in img_ids\n        ]\n    elif args.dataset_name == \"bdd100k\":\n        bdd_subfolder = \"100k/train\"\n        img_filenames = utils.get_filenames(args.annotation_file)\n        raw_folder_bdd_images = raw_folder / bdd_subfolder\n        paths = [\n            (raw_folder_bdd_images / each, processed_folder / each)\n            for each in img_filenames\n        ]\n    elif args.dataset_name == \"food_rec\":\n        food_subfolder = \"public_validation_set_2.0/images\"\n        img_filenames = utils.get_filenames(args.annotation_file)\n        raw_folder_food_images = raw_folder / food_subfolder\n        paths = [\n            (\n                raw_folder_food_images\n                / f\"{Path(each).stem.split('_')[-1]}{Path(each).suffix}\",\n                processed_folder / each,\n            )\n            for each in img_filenames\n        ]\n    print(\"Preparing to copy and flatten filename for\", len(paths), \"images\")\n    with Pool(20) as p:\n        for _ in tqdm(p.imap(utils.copy_file, paths), total=len(paths)):\n            continue\n\n\nif __name__ == \"__main__\":\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"--annotation_file\", help=\"Path to annotation file\")\n    parser.add_argument(\"--raw_images_folder\", help=\"Path to downloaded images\")\n    parser.add_argument(\"--processed_images_folder\", help=\"Path to processed images\")\n    parser.add_argument(\"--dataset_name\", help=\"Path to processed images\")\n    args = parser.parse_args()\n    main(args)\n"
  },
  {
    "path": "scripts/eval/silver/utils.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\nimport json\nimport os\nimport shutil\nimport subprocess\nfrom io import BytesIO\nfrom pathlib import Path\n\nimport cv2\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport yaml\nfrom PIL import Image\nfrom pycocotools import mask as mask_utils\nfrom tqdm import tqdm\n\n\nannotation_files = {\n    \"droid\": [\n        \"silver_droid_merged_test.json\",\n    ],\n    \"sav\": [\n        \"silver_sav_merged_test.json\",\n    ],\n    \"yt1b\": [\n        \"silver_yt1b_merged_test.json\",\n    ],\n    \"ego4d\": [\n        \"silver_ego4d_merged_test.json\",\n    ],\n}\n\n\ndef load_yaml(filename):\n    with open(filename, \"r\") as f:\n        return yaml.safe_load(f)\n\n\ndef load_json(filename):\n    with open(filename, \"r\") as f:\n        return json.load(f)\n\n\ndef save_json(content, filename):\n    with open(filename, \"w\") as f:\n        json.dump(content, f)\n\n\ndef run_command(cmd):\n    \"\"\"Run a shell command and raise if it fails.\"\"\"\n    result = subprocess.run(cmd, shell=True)\n    if result.returncode != 0:\n        raise RuntimeError(f\"Command failed: {cmd}\")\n\n\nconfig = load_yaml(\"CONFIG_FRAMES.yaml\")\n\n\ndef is_valid_image(img_path):\n    try:\n        img = Image.open(img_path).convert(\"RGB\")\n        return True\n    except Exception:\n        return False\n\n\ndef get_frame_from_video(video_path, frame_id):\n    cap = cv2.VideoCapture(video_path)\n    cap.set(cv2.CAP_PROP_POS_FRAMES, frame_id)\n    ret, frame = cap.read()\n    cap.release()\n    if not ret:\n        # Some videos cannot be open with OpenCV\n        import av\n\n        container = av.open(video_path)\n        stream = container.streams.video[0]\n        for i, frame in tqdm(\n            enumerate(container.decode(stream)),\n            desc=\"Decoding with AV\",\n            total=frame_id + 1,\n        ):\n            if i == frame_id:\n                img = frame.to_ndarray(format=\"rgb24\")\n                return img\n        raise ValueError(\n            f\"Could not read frame {frame_id} from video {video_path} (out of frame)\"\n        )\n    frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n    return frame_rgb\n\n\ndef update_annotations(dataset_name, file_names_keep, key=\"original_video\"):\n    for annotation_file in annotation_files[dataset_name]:\n        path_ann = os.path.join(config[\"path_annotations\"], annotation_file)\n        path_original_ann = os.path.join(\n            config[\"path_annotations\"],\n            annotation_file.replace(\".json\", \"_original.json\"),\n        )\n        ann = load_json(path_ann)\n        shutil.copy(path_ann, path_original_ann)\n        new_images = []\n        image_ids_keep = set()\n        for image in ann[\"images\"]:\n            if image[key].replace(\".mp4\", \"\") in file_names_keep:\n                new_images.append(image)\n                image_ids_keep.add(image[\"id\"])\n        new_annotations = []\n        for annotation in ann[\"annotations\"]:\n            if annotation[\"image_id\"] in image_ids_keep:\n                new_annotations.append(annotation)\n        ann[\"images\"] = new_images\n        ann[\"annotations\"] = new_annotations\n        save_json(ann, path_ann)\n\n\ndef get_filename_size_map(annotation_path):\n    with open(annotation_path) as f:\n        annotations = json.load(f)\n    filename_size_map = {}\n    for each in annotations[\"images\"]:\n        filename_size_map[each[\"file_name\"]] = (each[\"width\"], each[\"height\"])\n    return filename_size_map\n\n\ndef get_filenames(annotation_path):\n    with open(annotation_path) as f:\n        annotations = json.load(f)\n    filenames = {Path(each[\"file_name\"]) for each in annotations[\"images\"]}\n    return filenames\n\n\ndef get_image_ids(annotation_path):\n    filenames = get_filenames(annotation_path)\n    filestems = {Path(each).stem for each in filenames}\n    return filestems\n\n\ndef setup(folder):\n    print(\"Making dir\", folder)\n    folder.mkdir(exist_ok=True)\n\n\ndef copy_file(paths):\n    old_path, new_path = paths\n    print(\"Copy from\", old_path, \"to\", new_path)\n    if not Path(new_path).exists():\n        shutil.copy2(old_path, new_path)\n"
  },
  {
    "path": "scripts/eval/standalone_cgf1.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n\n\"\"\"Simple script to run the CGF1 evaluator given a prediction file and GT file(s).\n\nUsage: python standalone_cgf1.py --pred_file <path_to_prediction_file> --gt_files <path_to_gt_file1> <path_to_gt_file2> ...\n\"\"\"\n\nimport argparse\n\nfrom sam3.eval.cgf1_eval import CGF1Evaluator\n\n\ndef main():\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\n        \"--pred_file\",\n        type=str,\n        required=True,\n        help=\"Path to the prediction file in COCO format.\",\n    )\n    parser.add_argument(\n        \"--gt_files\",\n        type=str,\n        nargs=\"+\",\n        required=True,\n        help=\"Paths to the ground truth files in COCO format.\",\n    )\n    args = parser.parse_args()\n    if len(args.gt_files) == 0:\n        raise ValueError(\"At least one GT file must be provided.\")\n\n    is_gold = args.gt_files[0].split(\"_\")[-1].startswith(\"gold_\")\n    if is_gold and len(args.gt_files) < 3:\n        print(\n            \"WARNING: based on the name, it seems you are using gold GT files. Typically, there should be 3 GT files for gold subsets (a, b, c).\"\n        )\n\n    evaluator = CGF1Evaluator(\n        gt_path=args.gt_files, verbose=True, iou_type=\"segm\"\n    )  # change to bbox if you want detection performance\n\n    results = evaluator.evaluate(args.pred_file)\n\n    print(results)\n\n\nif __name__ == \"__main__\":\n    main()\n"
  },
  {
    "path": "scripts/eval/veval/README.md",
    "content": "# SA-Co/VEval Dataset\n**License** each domain has its own License\n* SA-Co/VEval - SA-V: CC-BY-NC 4.0\n* SA-Co/VEval - YT-Temporal-1B: CC-BY-NC 4.0\n* SA-Co/VEval - SmartGlasses: CC-by-4.0\n\n**SA-Co/VEval** is an evaluation dataset comprising of 3 domains, each domain has a val and test split.\n* SA-Co/VEval - SA-V: videos are from the [SA-V dataset](https://ai.meta.com/datasets/segment-anything-video/)\n* SA-Co/VEval - YT-Temporal-1B: videos are from the [YT-Temporal-1B](https://cove.thecvf.com/datasets/704)\n* SA-Co/VEval - SmartGlasses: egocentric videos from [Smart Glasses](https://huggingface.co/datasets/facebook/SACo-VEval/blob/main/media/saco_sg.tar.gz)\n\n## Environment\nInstall the SA-Co/VEVal required environment\n```\npip install -e \".[veval]\"\n```\nThis will allow us to run:\n* `scripts/eval/veval/saco_yt1b_downloader.py` preparing frames for SA-Co/VEval - YT-Temporal-1B\n* `examples/saco_veval_eval_example.ipynb` example of running an offline evaluator\n* `examples/saco_veval_vis_example.ipynb` example of loading and visualizing the data\n\n## Download\n### The expected folder structure\nThe following folder structure is expected after finishing all the download and pre-processing steps in this section\n```\ndata/\n├── annotation/\n│   ├── saco_veval_sav_test.json\n│   ├── saco_veval_sav_val.json\n│   ├── saco_veval_smartglasses_test.json\n│   ├── saco_veval_smartglasses_val.json\n│   ├── saco_veval_yt1b_test.json\n│   ├── saco_veval_yt1b_val.json\n└── media/\n    ├── saco_sav\n    │   └── JPEGImages_24fps\n    ├── saco_sg\n    │   └── JPEGImages_6fps\n    └── saco_yt1b\n        └── JPEGImages_6fps\n```\n### Download ready-to-use data\nThe following links provide ready-to-use data, hosted on Roboflow, after completing the pre-processing steps outlined in the next section.\n\nFor each domain:\n- [SA-Co/VEval - SA-V](https://universe.roboflow.com/sa-co-veval/sa-v-test/)\n- [SA-Co/VEval - YT-Temporal-1B](https://universe.roboflow.com/sa-co-veval/yt-temporal-1b-test/)\n- [SA-Co/VEval - SmartGlasses](https://universe.roboflow.com/sa-co-veval/smartglasses-test/)\n\nFor all three domains:\n- [SA-Co/VEval](https://universe.roboflow.com/sa-co-veval)\n\nSpecial note on **SA-Co/VEval - YT-Temporal-1B**:\n* **Frame Shifting Alert!**\n* The ready-to-use data hosted on Roboflow was produced by following the preprocessing steps below. Therefore, the frame-shifting issue for YT-Temporal-1B still exists: due to the nature of Youtube videos, the re-downloaded videos may not be exactly the same as those used during annotation, which can affect eval number reproducibility.\n\n### Download via preprocessing steps\n#### Download annotations\nThe GT annotations are available at Hugging Face:\n* [SA-Co/VEval](https://huggingface.co/datasets/facebook/SACo-VEval/tree/main)\n    * SA-Co/VEval SA-V\n        * Test: `annotation/saco_veval_sav_test.json`\n        * Val: `annotation/saco_veval_sav_val.json`\n    * SA-Co/VEval YT-Temporal-1B\n        * Test: `annotation/saco_veval_yt1b_test.json`\n        * Val: `annotation/saco_veval_yt1b_val.json`\n    * SA-Co/VEval SmartGlasses\n        * Test: `annotation/saco_veval_smartglasses_test.json`\n        * Val: `annotation/saco_veval_smartglasses_val.json`\n\n#### Download videos or frames\n##### SA-Co/VEval - SAV\nFollow instructions in [SA-V dataset](https://ai.meta.com/datasets/segment-anything-video/). Only the following two datasets are needed:\n* sav_test.tar\n* sav_val.tar\n\nAfter untar:\n```\nsav_test/\n├── Annotations_6fps [ignore this is the SAM 2 annotation]\n├── JPEGImages_24fps\nsav_val/\n├── Annotations_6fps [ignore this is the SAM 2 annotation]\n└── JPEGImages_24fps\n```\nThen merge the two JPEGImages_24fps together to better match our annotation json file path e.g.\n```\nmedia/\n    └── saco_sav\n        └── JPEGImages_24fps [merged from the two JPEGImages_24fps above]\n```\nExample commands to download and merge folders\n```\ncd ../data/media/saco_sav\nwget -O sav_test.tar <sav_test.tar download link from the SA-V dataset page>\nwget -O sav_val.tar <sav_val.tar download link from the SA-V dataset page>\ntar -xf sav_test.tar\ntar -xf sav_val.tar\nmkdir JPEGImages_24fps\nchmod -R u+w sav_test/\nchmod -R u+w sav_val/\nmv sav_test/JPEGImages_24fps/* JPEGImages_24fps/\nmv sav_val/JPEGImages_24fps/* JPEGImages_24fps/\n```\n\n##### SA-Co/VEval - YT-Temporal-1B\nTwo files are needed to download the SA-Co/VEval - YT-Temporal-1B Youtube videos.\n* Download `media/yt1b_start_end_time.json` from [SA-Co/VEval](https://huggingface.co/datasets/facebook/SACo-VEval/tree/main), which contains the Youtube video ids and the start and end time used in SA-Co/VEval - YT-Temporal-1B.\n* Prepare the `cookies.txt` file. Follow instruction in yt-dlp [exporting-youtube-cookies](https://github.com/yt-dlp/yt-dlp/wiki/Extractors#exporting-youtube-cookies) and [pass-cookies-to-yt-dlp](https://github.com/yt-dlp/yt-dlp/wiki/FAQ#how-do-i-pass-cookies-to-yt-dlp) to prepare the cookies_file.\n    * Please see the full **WARNINGS** in yt-dlp regarding the risk of Youtube account ban!!\n\nThen run `scripts/eval/veval/saco_yt1b_downloader.py` to download the videos and prepare the frames e.g.\n```\npython saco_yt1b_downloader.py \\\n--data_dir ../data/media/saco_yt1b \\\n--cookies_file ../data/media/saco_yt1b/cookies.txt \\\n--yt1b_start_end_time_file ../data/media/saco_yt1b/yt1b_start_end_time.json \\\n--yt1b_frame_prep_log_file ../data/media/saco_yt1b/yt1b_frame_prep.log\n```\n* data_dir: The directoy to download the Youtube videos and store the extraced frames\n* cookies_file: the `cookies.txt` downloaded above\n* yt1b_start_end_time_file: the `yt1b_start_end_time.json` downloaded above\n* yt1b_frame_prep_log_file: a log file to track the video downloading and frame extracting status\n\nThen run `scripts/eval/veval/saco_yt1b_annot_update.py` to update the annotation based on the video availability e.g.\n```\npython saco_yt1b_annot_update.py \\\n--yt1b_media_dir ../data/media/saco_yt1b/JPEGImages_6fps \\\n--yt1b_input_annot_path ../data/annotation/saco_veval_yt1b_val.json \\\n--yt1b_output_annot_path ../data/annotation/saco_veval_yt1b_val_updated.json \\\n--yt1b_annot_update_log_path ../data/annotation/saco_veval_yt1b_val_updated.log\n```\n\n**NOTE**:\n* Not all Youtube videos might be available as Youtube videos can be deleted or become private. The script `saco_yt1b_annot_update.py` is used to remove the annotations of the unavailable videos.\n* **Frame Shifting Alert!!** Even when the videos are still available, their specifications, such as fps and duration, may differ from those used during annotation when re-downloaded from YouTube. Additionally, sometimes `ffmpeg` seems to find it hard to guarantee consistent frame extraction from the same video across different environments. This may cause the re-downloaded and re-extracted frames to have alignment issues with our annotations due to frame shifting. Please be aware of this caveat when evaluating on SA-Co/VEval - YT-Temporal-1B.\n\n##### SA-Co/VEval - SmartGlasses\nGo to [SACo-VEval](https://huggingface.co/datasets/facebook/SACo-VEval/tree/main) download `media/saco_sg.tar.gz`\n```\ncd ../data\nhf download facebook/SACo-VEval media/saco_sg.tar.gz --repo-type dataset --local-dir .\ncd ../data/media\ntar -xzf saco_sg.tar.gz\n```\n\n## Annotation Format\nThe format is similar to the [YTVIS](https://youtube-vos.org/dataset/vis/) format.\n\nIn the annotation json, e.g. `saco_veval_sav_test.json` there are 5 fields:\n* info:\n    * A dict containing the dataset info\n    * E.g. {'version': 'v1', 'date': '2025-09-24', 'description': 'SA-Co/VEval SA-V Test'}\n* videos\n    * A list of videos that are used in the current annotation json\n    * It contains {id, video_name, file_names, height, width, length}\n* annotations\n    * A list of **positive** masklets and their related info\n    * It contains {id, segmentations, bboxes, areas, iscrowd, video_id, height, width, category_id, noun_phrase}\n        * video_id should match to the `videos - id` field above\n        * category_id should match to the `categories - id` field below\n        * segmentations is a list of [RLE](https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocotools/mask.py)\n* categories\n    * A **globally** used noun phrase id map, which is true across all 3 domains.\n    * It contains {id, name}\n        * name is the noun phrase\n* video_np_pairs\n    * A list of video-np pairs, including both **positive** and **negative** used in the current annotation json\n    * It contains {id, video_id, category_id, noun_phrase, num_masklets}\n        * video_id should match the `videos - id` above\n        * category_id should match the `categories - id` above\n        * when `num_masklets > 0` it is a positive video-np pair, and the presenting masklets can be found in the annotations field\n        * when `num_masklets = 0` it is a negative video-np pair, meaning no masklet presenting at all\n```\ndata {\n    \"info\": info\n    \"videos\": [video]\n    \"annotations\": [annotation]\n    \"categories\": [category]\n    \"video_np_pairs\": [video_np_pair]\n}\nvideo {\n    \"id\": int\n    \"video_name\": str  # e.g. sav_000000\n    \"file_names\": List[str]\n    \"height\": int\n    \"width\": width\n    \"length\": length\n}\nannotation {\n    \"id\": int\n    \"segmentations\": List[RLE]\n    \"bboxes\": List[List[int, int, int, int]]\n    \"areas\": List[int]\n    \"iscrowd\": int\n    \"video_id\": str\n    \"height\": int\n    \"width\": int\n    \"category_id\": int\n    \"noun_phrase\": str\n}\ncategory {\n    \"id\": int\n    \"name\": str\n}\nvideo_np_pair {\n    \"id\": int\n    \"video_id\": str\n    \"category_id\": int\n    \"noun_phrase\": str\n    \"num_masklets\" int\n}\n```\n[sam3/examples/saco_veval_vis_example.ipynb](https://github.com/facebookresearch/sam3/blob/main/examples/saco_veval_vis_example.ipynb) shows some examples of the data format and data visualization.\n\n## Run Offline Eval\nAn example notebook and an eval script have been provided for offline evaluation.\n```\nsam3/\n├── examples/\n│   └── saco_veval_eval_example.ipynb  # this notebook will load eval res or run the eval on the fly, and print the results\n└── sam3/eval/\n    └── saco_veval_eval.py  # this script will run the offline evaluator\n```\n`saco_veval_eval.py` supports two modes, `one` and `all`.\n* `one`: will take only one pair of gt and pred files to eval\n* `all`: will eval on all 6 SACo/VEval datasets\n\nExample usage\n```\npython saco_veval_eval.py one \\\n--gt_annot_file ../sam3/assets/veval/toy_gt_and_pred/toy_saco_veval_sav_test_gt.json \\\n--pred_file ../sam3/assets/veval/toy_gt_and_pred/toy_saco_veval_sav_test_pred.json \\\n--eval_res_file ../sam3/assets/veval/toy_gt_and_pred/toy_saco_veval_sav_test_eval_res.json\n```\n* `gt_annot_file`: the location of the GT file\n* `pred_file`: the location of the Pred file\n* `eval_res_file`: the location where the eval result will be written to\n\n```\npython saco_veval_eval.py all \\\n--gt_annot_dir ../data/annotation \\\n--pred_dir ../data/pred \\\n--eval_res_dir ../data/pred\n```\n* `gt_annot_dir`: the location of the GT files\n* `pred_dir`: the location of the Pred files\n* `eval_res_dir`: the location where the eval results will be written to\n"
  },
  {
    "path": "scripts/eval/veval/__init__.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n"
  },
  {
    "path": "scripts/eval/veval/saco_yt1b_annot_update.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\nimport argparse\nimport json\nimport logging\nimport os\n\nimport pandas as pd\n\n\nlogger = logging.getLogger(__name__)\n\n\ndef get_available_saco_yt1b_ids(yt1b_meida_dir, data):\n    vdf = pd.DataFrame(data[\"videos\"])\n    expected_saco_yt1b_ids = vdf.video_name.tolist()\n\n    yt1b_media_folders = os.listdir(yt1b_meida_dir)\n\n    available_saco_yt1b_ids = []\n    for yt1b_media_folder in yt1b_media_folders:\n        if yt1b_media_folder not in expected_saco_yt1b_ids:\n            continue\n        jpeg_folder_dir = os.path.join(yt1b_meida_dir, yt1b_media_folder)\n        jpeg_count = len(os.listdir(jpeg_folder_dir))\n        if jpeg_count > 0:\n            available_saco_yt1b_ids.append(yt1b_media_folder)\n        else:\n            logger.info(\n                f\"No JPEG images found for {yt1b_media_folder}. The annotation related to this video will be removed.\"\n            )\n\n    logger.info(\n        f\"Expected {len(expected_saco_yt1b_ids)} videos for {data['info']}. Found {len(available_saco_yt1b_ids)} videos available in {yt1b_meida_dir}.\"\n    )\n    return available_saco_yt1b_ids\n\n\ndef update_yt1b_annot_per_field(data, field, id_col, available_ids):\n    field_data = data[field]\n    new_field_data = []\n    for data_entry in field_data:\n        if data_entry[id_col] not in available_ids:\n            logger.info(\n                f\"{field}: Removing {data_entry} due to the video being unavailable.\"\n            )\n            continue\n        new_field_data.append(data_entry)\n\n    data[field] = new_field_data\n    logger.info(\n        f\"Updated {field} by {id_col} - Before: {len(field_data)}, After: {len(new_field_data)}, Removed: {len(field_data) - len(new_field_data)}\"\n    )\n    return data\n\n\ndef update_yt1b_annot(yt1b_input_annot_path, yt1b_media_dir, yt1b_output_annot_path):\n    with open(yt1b_input_annot_path, \"r\") as f:\n        data = json.load(f)\n\n    available_saco_yt1b_ids = get_available_saco_yt1b_ids(yt1b_media_dir, data)\n\n    data = update_yt1b_annot_per_field(\n        data=data,\n        field=\"videos\",\n        id_col=\"video_name\",\n        available_ids=available_saco_yt1b_ids,\n    )\n\n    videos_data = data[\"videos\"]\n    available_video_incremental_ids = [data_entry[\"id\"] for data_entry in videos_data]\n\n    data = update_yt1b_annot_per_field(\n        data=data,\n        field=\"annotations\",\n        id_col=\"video_id\",\n        available_ids=available_video_incremental_ids,\n    )\n    data = update_yt1b_annot_per_field(\n        data=data,\n        field=\"video_np_pairs\",\n        id_col=\"video_id\",\n        available_ids=available_video_incremental_ids,\n    )\n\n    with open(yt1b_output_annot_path, \"w\") as f:\n        json.dump(data, f)\n\n    return data\n\n\ndef main():\n    parser = argparse.ArgumentParser(description=\"Run video grounding evaluators\")\n    parser.add_argument(\n        \"--yt1b_media_dir\",\n        type=str,\n        help=\"Path to the directory where the yt1b media is stored e.g media/saco_yt1b/JPEGImages_6fps\",\n    )\n    parser.add_argument(\n        \"--yt1b_input_annot_path\",\n        type=str,\n        help=\"Path to the saco_veval_yt1b input annotation file e.g annotation/saco_veval_yt1b_test.json or annotation/saco_veval_yt1b_val.json\",\n    )\n    parser.add_argument(\n        \"--yt1b_output_annot_path\",\n        type=str,\n        help=\"Path to the output annotation file e.g annotation/saco_veval_yt1b_test_updated.json or annotation/saco_veval_yt1b_val_updated.json\",\n    )\n    parser.add_argument(\n        \"--yt1b_annot_update_log_path\",\n        type=str,\n        help=\"Path to the yt1b annot update log file e.g annotation/yt1b_annot_update_log.log\",\n    )\n\n    args = parser.parse_args()\n\n    os.makedirs(os.path.dirname(args.yt1b_annot_update_log_path), exist_ok=True)\n    os.makedirs(os.path.dirname(args.yt1b_output_annot_path), exist_ok=True)\n\n    logging.basicConfig(\n        filename=args.yt1b_annot_update_log_path,\n        format=\"%(asctime)s [%(threadName)s] %(levelname)s: %(message)s\",\n        level=logging.INFO,\n        filemode=\"w\",\n    )\n\n    _ = update_yt1b_annot(\n        yt1b_input_annot_path=args.yt1b_input_annot_path,\n        yt1b_media_dir=args.yt1b_media_dir,\n        yt1b_output_annot_path=args.yt1b_output_annot_path,\n    )\n\n    print(\"Done!! Check the log at\", args.yt1b_annot_update_log_path)\n\n\nif __name__ == \"__main__\":\n    main()\n"
  },
  {
    "path": "scripts/eval/veval/saco_yt1b_downloader.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\nimport argparse\nimport logging\nimport multiprocessing as mp\nimport os\nfrom functools import partial\n\nimport pandas as pd\nfrom saco_yt1b_frame_prep_util import YtVideoPrep\nfrom tqdm import tqdm\n\nlogger = logging.getLogger(__name__)\n\n\ndef download_and_extract_frames(saco_yt1b_id, args):\n    video_prep = YtVideoPrep(\n        saco_yt1b_id=saco_yt1b_id,\n        data_dir=args.data_dir,\n        cookies_file=args.cookies_file,\n        yt1b_start_end_time_file=args.yt1b_start_end_time_file,\n        ffmpeg_timeout=args.ffmpeg_timeout,\n        sleep_interval=args.sleep_interval,\n        max_sleep_interval=args.max_sleep_interval,\n    )\n\n    status = video_prep.download_youtube_video()\n    logger.info(f\"[video download][{saco_yt1b_id}] download status {status}\")\n\n    if status not in [\"already exists\", \"success\"]:\n        logger.warning(\n            f\"Video download failed for {saco_yt1b_id}, skipping frame generation\"\n        )\n        return False\n\n    status = video_prep.extract_frames_in_6fps_and_width_1080()\n    logger.info(f\"[frame extracting][{saco_yt1b_id}] frame extracting status {status}\")\n    return True\n\n\ndef main():\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\n        \"--data_dir\",\n        type=str,\n        required=True,\n    )\n    parser.add_argument(\n        \"--cookies_file\",\n        type=str,\n        required=True,\n    )\n    parser.add_argument(\n        \"--yt1b_start_end_time_file\",\n        type=str,\n        required=True,\n    )\n    parser.add_argument(\n        \"--yt1b_frame_prep_log_file\",\n        type=str,\n        required=True,\n    )\n    parser.add_argument(\n        \"--ffmpeg_timeout\",\n        type=str,\n        default=7200,  # Use longer timeout in case of large videos processing timeout\n    )\n    parser.add_argument(\n        \"--sleep_interval\",\n        type=int,\n        default=10,\n    )\n    parser.add_argument(\n        \"--max_sleep_interval\",\n        type=int,\n        default=30,\n    )\n    parser.add_argument(\n        \"--num_workers\",\n        type=int,\n        default=4,\n    )\n    args = parser.parse_args()\n\n    log_dir = os.path.dirname(args.yt1b_frame_prep_log_file)\n    if log_dir:\n        os.makedirs(log_dir, exist_ok=True)\n\n    # Set up logging to both file and console\n    # Configure the ROOT logger so all child loggers inherit the configuration\n    logging.basicConfig(\n        level=logging.INFO,\n        format=\"%(asctime)s [%(processName)s/%(threadName)s] %(name)s - %(levelname)s: %(message)s\",\n        handlers=[\n            logging.FileHandler(args.yt1b_frame_prep_log_file, mode=\"w\"),\n            logging.StreamHandler(),\n        ],\n        force=True,  # Override any existing configuration\n    )\n\n    YT_DLP_WARNING_STR = \"\"\" ==========\n        NOTICE!!\n        This script uses yt-dlp to download youtube videos.\n        See the youtube account banning risk in https://github.com/yt-dlp/yt-dlp/wiki/Extractors#exporting-youtube-cookies\n        ==========\n        \"\"\"\n\n    logger.info(YT_DLP_WARNING_STR)\n\n    args = parser.parse_args()\n\n    with open(args.yt1b_start_end_time_file, \"r\") as f:\n        yt1b_start_end_time_df = pd.read_json(f)\n\n    saco_yt1b_ids = yt1b_start_end_time_df.saco_yt1b_id.unique()\n    num_workers = args.num_workers\n    logger.info(\n        f\"Starting with {num_workers} parallel worker(s) (sleep_interval={args.sleep_interval}-{args.max_sleep_interval}s)\"\n    )\n\n    with mp.Pool(num_workers) as p:\n        download_func = partial(download_and_extract_frames, args=args)\n        list(tqdm(p.imap(download_func, saco_yt1b_ids), total=len(saco_yt1b_ids)))\n\n    done_str = f\"\"\" ==========\n        All DONE!!\n        Download, frame extraction, and frame matching is all done! YT1B frames are not ready to use in {args.data_dir}/JPEGImages_6fps\n        Check video frame preparing log at {args.yt1b_frame_prep_log_file}\n        Some videos might not be available any more which will affect the eval reproducibility\n        ==========\n    \"\"\"\n    logger.info(done_str)\n\n\nif __name__ == \"__main__\":\n    main()\n"
  },
  {
    "path": "scripts/eval/veval/saco_yt1b_frame_prep_util.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\nimport argparse\nimport logging\nimport os\nimport subprocess\n\nimport pandas as pd\nimport yt_dlp\n\nlogger = logging.getLogger(__name__)\n\n\nclass YtVideoPrep:\n    def __init__(\n        self,\n        saco_yt1b_id: str,\n        data_dir: str,\n        cookies_file: str,\n        yt1b_start_end_time_file: str,\n        ffmpeg_timeout: int,\n        sleep_interval: int = 10,\n        max_sleep_interval: int = 30,\n    ):\n        self.saco_yt1b_id = saco_yt1b_id  # saco_yt1b_id is like saco_yt1b_000000\n        self.data_dir = data_dir\n        self.cookies_file = cookies_file\n        self.ffmpeg_timeout = ffmpeg_timeout\n        self.sleep_interval = sleep_interval\n        self.max_sleep_interval = max_sleep_interval\n\n        self.yt1b_start_end_time_df = pd.read_json(yt1b_start_end_time_file)\n        (\n            self.yt_video_id,\n            self.yt_video_id_w_timestamps,\n            self.start_time,\n            self.end_time,\n            self.expected_num_frames,\n        ) = self._get_yt_video_id_map_info()\n\n        self.raw_video_dir = os.path.join(self.data_dir, \"raw_videos\")\n        self.raw_video_path = os.path.join(\n            self.raw_video_dir, f\"{self.yt_video_id}.mp4\"\n        )\n\n        self.JPEGImages_6fps_dir = os.path.join(\n            self.data_dir, \"JPEGImages_6fps\", self.saco_yt1b_id\n        )\n        self.JPEGImages_6fps_pattern = os.path.join(\n            self.JPEGImages_6fps_dir, \"%05d.jpg\"\n        )\n\n        os.makedirs(self.raw_video_dir, exist_ok=True)\n        os.makedirs(self.JPEGImages_6fps_dir, exist_ok=True)\n\n    def _get_yt_video_id_map_info(self):\n        df = self.yt1b_start_end_time_df[\n            self.yt1b_start_end_time_df.saco_yt1b_id == self.saco_yt1b_id\n        ]\n        assert len(df) == 1, (\n            f\"Expected exactly 1 row for saco_yt1b_id: {self.saco_yt1b_id}, found {len(df)}\"\n        )\n        id_and_frame_map_row = df.iloc[0]\n\n        yt_video_id = (\n            id_and_frame_map_row.yt_video_id\n        )  # yt_video_id is like -06NgWyZxC0\n        yt_video_id_w_timestamps = id_and_frame_map_row.yt_video_id_w_timestamps\n        start_time = id_and_frame_map_row.start_time\n        end_time = id_and_frame_map_row.end_time\n        expected_num_frames = id_and_frame_map_row.length\n\n        return (\n            yt_video_id,\n            yt_video_id_w_timestamps,\n            start_time,\n            end_time,\n            expected_num_frames,\n        )\n\n    def download_youtube_video(self):\n        video_url = f\"https://youtube.com/watch?v={self.yt_video_id}\"\n\n        assert os.path.exists(self.cookies_file), (\n            f\"Cookies file '{self.cookies_file}' not found. Must have it to download videos.\"\n        )\n\n        outtmpl = self.raw_video_path\n\n        # Check if the output file already exists\n        if os.path.exists(outtmpl) and os.path.isfile(outtmpl):\n            return \"already exists\"\n\n        ydl_opts = {\n            \"format\": \"best[height<=720]/best\",  # 720p or lower\n            \"outtmpl\": outtmpl,\n            \"merge_output_format\": \"mp4\",\n            \"noplaylist\": True,\n            \"quiet\": True,\n            \"cookiefile\": self.cookies_file,\n            \"sleep_interval\": self.sleep_interval,  # Sleep before each download to avoid rate limiting\n            \"max_sleep_interval\": self.max_sleep_interval,  # Random sleep for more human-like behavior\n        }\n\n        if self.yt_video_id in [\"euohdDLEMRg\", \"nzfAn7n4d-0\"]:\n            # For \"euohdDLEMRg\", we have to specify the https protocol or the video sometimes can't be downloaded completely\n            # For \"nzfAn7n4d-0\", without the https protocol, the video will be downloaded as 654×480, however we need 490×360 to match the frame matching after the 1080 width resizing\n            ydl_opts[\"format\"] = (\n                \"best[height<=720][ext=mp4][protocol^=https]/best[ext=mp4][protocol^=https]/best[height<=720]/best\"\n            )\n\n        try:\n            with yt_dlp.YoutubeDL(ydl_opts) as ydl:\n                ydl.download([video_url])\n                return \"success\"\n        except Exception as e:\n            logger.warning(\n                f\"[video download][{self.saco_yt1b_id}] Error downloading video {self.yt_video_id}: {e}\"\n            )\n            return f\"error {e}\"\n\n    def extract_frames_in_6fps_and_width_1080(self):\n        \"\"\"\n        Extract target frames in 6fps and width 1080.\n        \"\"\"\n        if not os.path.exists(self.raw_video_path):\n            logger.warning(\n                f\"[frame extracting][{self.saco_yt1b_id}] Raw video file not found at {self.raw_video_path}\"\n            )\n            os.rmdir(self.JPEGImages_6fps_dir)\n            return False\n\n        if (\n            os.path.exists(self.JPEGImages_6fps_dir)\n            and len(os.listdir(self.JPEGImages_6fps_dir)) == self.expected_num_frames\n        ):\n            logger.info(\n                f\"[frame extracting][{self.saco_yt1b_id}] JPEGImages_6fps directory already exists at {self.JPEGImages_6fps_dir} and expected number of frames {self.expected_num_frames} matches\"\n            )\n            return True\n\n        # Clear the directory before extracting new frames\n        for file in os.listdir(self.JPEGImages_6fps_dir):\n            os.remove(os.path.join(self.JPEGImages_6fps_dir, file))\n\n        args = [\n            \"-nostdin\",\n            \"-y\",\n            # select video segment\n            \"-ss\",\n            str(self.start_time),\n            \"-to\",\n            str(self.end_time),\n            \"-i\",\n            self.raw_video_path,\n            # set output video resolution to be 6fps and at most 1080p\n            \"-vf\",\n            \"fps=6,scale=1080:-2\",\n            \"-vsync\",\n            \"0\",  # passthrough mode - no frame duplication/dropping\n            \"-q:v\",\n            \"2\",  # high quality JPEG output\n            \"-start_number\",\n            \"0\",  # start frame numbering from 0\n            self.JPEGImages_6fps_pattern,\n        ]\n\n        result = subprocess.run(\n            [\"ffmpeg\"] + args,\n            timeout=self.ffmpeg_timeout,\n            capture_output=True,\n            text=True,\n        )\n\n        if result.returncode != 0:\n            logger.warning(\n                f\"[frame extracting][{self.saco_yt1b_id}] Failed to extract raw frames: {result.stderr}\"\n            )\n            os.rmdir(self.JPEGImages_6fps_dir)\n            return False\n\n        if len(os.listdir(self.JPEGImages_6fps_dir)) != self.expected_num_frames:\n            logger.warning(\n                f\"[frame extracting][{self.saco_yt1b_id}] Expected {self.expected_num_frames} frames but extracted {len(os.listdir(self.JPEGImages_6fps_dir))}\"\n            )\n            # Clear the directory after failed extraction\n            for file in os.listdir(self.JPEGImages_6fps_dir):\n                os.remove(os.path.join(self.JPEGImages_6fps_dir, file))\n\n            os.rmdir(self.JPEGImages_6fps_dir)\n            return False\n\n        logger.info(\n            f\"[frame extracting][{self.saco_yt1b_id}] Successfully extracted {self.expected_num_frames} frames to {self.JPEGImages_6fps_dir}\"\n        )\n        return True\n\n\ndef main():\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"--saco_yt1b_id\", type=str, required=True)\n    parser.add_argument(\n        \"--data_dir\",\n        type=str,\n        required=True,\n    )\n    parser.add_argument(\n        \"--cookies_file\",\n        type=str,\n        required=True,\n    )\n    parser.add_argument(\n        \"--yt1b_start_end_time_file\",\n        type=str,\n        required=True,\n    )\n    parser.add_argument(\n        \"--yt1b_frame_prep_log_file\",\n        type=str,\n        required=True,\n    )\n    parser.add_argument(\n        \"--ffmpeg_timeout\",\n        type=str,\n        default=7200,  # Use longer timeout in case of large videos processing timeout\n    )\n    parser.add_argument(\n        \"--sleep_interval\",\n        type=int,\n        default=10,\n    )\n    parser.add_argument(\n        \"--max_sleep_interval\",\n        type=int,\n        default=30,\n    )\n    args = parser.parse_args()\n\n    logging.basicConfig(\n        filename=args.yt1b_frame_prep_log_file,\n        format=\"%(asctime)s [%(threadName)s] %(levelname)s: %(message)s\",\n        level=logging.INFO,\n        filemode=\"w\",\n    )\n\n    video_prep = YtVideoPrep(\n        saco_yt1b_id=args.saco_yt1b_id,\n        data_dir=args.data_dir,\n        cookies_file=args.cookies_file,\n        yt1b_start_end_time_file=args.yt1b_start_end_time_file,\n        ffmpeg_timeout=args.ffmpeg_timeout,\n        sleep_interval=args.sleep_interval,\n        max_sleep_interval=args.max_sleep_interval,\n    )\n\n    status = video_prep.download_youtube_video()\n    logger.info(f\"[video download][{args.saco_yt1b_id}] download status {status}\")\n\n    status = video_prep.extract_frames_in_6fps_and_width_1080()\n    logger.info(\n        f\"[frame extracting][{args.saco_yt1b_id}] frame extracting status {status}\"\n    )\n\n\nif __name__ == \"__main__\":\n    main()\n"
  },
  {
    "path": "scripts/extract_odinw_results.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n\n\"\"\"This script summarizes odinw results\"\"\"\n\n\"\"\"\npython3 scripts/extract_odinw_results.py --res_dir /path/to/results/directory\nExpected directory structure:\nresults_directory/\n├── AerialMaritimeDrone_large/val_stats.json\n├── Aquarium/val_stats.json\n├── CottontailRabbits/val_stats.json\n└── ...\n\"\"\"\nimport argparse\nimport json\nimport os\n\nVAL13_SET = [\n    \"AerialMaritimeDrone_large\",\n    \"Aquarium\",\n    \"CottontailRabbits\",\n    \"EgoHands_generic\",\n    \"NorthAmericaMushrooms\",\n    \"Packages\",\n    \"PascalVOC\",\n    \"Raccoon\",\n    \"ShellfishOpenImages\",\n    \"VehiclesOpenImages\",\n    \"pistols\",\n    \"pothole\",\n    \"thermalDogsAndPeople\",\n]\n\nMETRIC_NAME = \"coco_eval_bbox_AP\"\n\n\ndef parse_args():\n    parser = argparse.ArgumentParser(\"ODinW results aggregation script\")\n\n    parser.add_argument(\n        \"--res_dir\",\n        required=True,\n        type=str,\n        help=\"Parent directory containing subdirectories for each dataset with val_stats.json files\",\n    )\n\n    return parser.parse_args()\n\n\ndef main(args):\n    # Dictionary to store results for each metric type\n    metric_results = {METRIC_NAME: []}\n    subset_results = {subset: {} for subset in VAL13_SET}\n\n    # Process each subset directory\n    for subset in VAL13_SET:\n        subset_dir = os.path.join(args.res_dir, subset)\n        val_stats_path = os.path.join(subset_dir, \"val_stats.json\")\n\n        if not os.path.exists(val_stats_path):\n            print(f\"Warning: {val_stats_path} not found, skipping {subset}\")\n            continue\n\n        try:\n            res = json.load(open(val_stats_path))\n            subset_results[subset] = res\n\n            # Extract metrics for this subset and group by metric type\n            for key, value in res.items():\n                if key.endswith(METRIC_NAME):\n                    metric_results[METRIC_NAME].append(value)\n\n        except (json.JSONDecodeError, IOError) as e:\n            print(f\"Error reading {val_stats_path}: {e}\")\n            continue\n\n    # Print results\n    values = metric_results[METRIC_NAME]\n    if values:\n        avg = sum(values) / len(values)\n        print(f\"Average {METRIC_NAME}: {avg:.4f} ({len(values)} datasets)\")\n\n        # Show individual dataset results\n        for subset in VAL13_SET:\n            if subset in subset_results and subset_results[subset]:\n                for res_key, res_value in subset_results[subset].items():\n                    if res_key.endswith(METRIC_NAME):\n                        print(f\"  {subset}: {res_value:.4f}\")\n                        break\n    else:\n        print(f\"No results found for {METRIC_NAME}\")\n\n\nif __name__ == \"__main__\":\n    main(parse_args())\n"
  },
  {
    "path": "scripts/extract_roboflow_vl100_results.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved\n\n# pyre-unsafe\n\n\"\"\"\nScript to extract and analyze training results from Roboflow VL100 experiments.\n\nThis script processes training logs and configuration files to extract model performance\nmetrics and training parameters for analysis and comparison.\n\"\"\"\n\nimport argparse\nimport json\nimport os\nfrom typing import Any, Dict, List, Optional\n\nimport pandas as pd\nimport yaml\n\n\n# Constants\nCONFIG_FILENAME = \"config_resolved.yaml\"\nRESULTS_FILENAME = \"val_stats.json\"\nBBOX_AP_METRIC = \"Meters_train/val_roboflow100/detection/coco_eval_bbox_AP\"\n\n# Roboflow dataset categories organized by domain\nROBOFLOW_CATEGORIES = {\n    \"sports\": [\n        \"actions\",\n        \"aerial-pool\",\n        \"ball\",\n        \"bibdetection\",\n        \"football-player-detection\",\n        \"lacrosse-object-detection\",\n    ],\n    \"other\": [\n        \"buoy-onboarding\",\n        \"car-logo-detection\",\n        \"clashroyalechardetector\",\n        \"cod-mw-warzone\",\n        \"countingpills\",\n        \"everdaynew\",\n        \"flir-camera-objects\",\n        \"halo-infinite-angel-videogame\",\n        \"mahjong\",\n        \"new-defects-in-wood\",\n        \"orionproducts\",\n        \"pill\",\n        \"soda-bottles\",\n        \"taco-trash-annotations-in-context\",\n        \"the-dreidel-project\",\n    ],\n    \"aerial\": [\n        \"aerial-airport\",\n        \"aerial-cows\",\n        \"aerial-sheep\",\n        \"apoce-aerial-photographs-for-object-detection-of-construction-equipment\",\n        \"electric-pylon-detection-in-rsi\",\n        \"floating-waste\",\n        \"human-detection-in-floods\",\n        \"sssod\",\n        \"uavdet-small\",\n        \"wildfire-smoke\",\n        \"zebrasatasturias\",\n    ],\n    \"medical\": [\n        \"canalstenosis\",\n        \"crystal-clean-brain-tumors-mri-dataset\",\n        \"dentalai\",\n        \"inbreast\",\n        \"liver-disease\",\n        \"nih-xray\",\n        \"spinefrxnormalvindr\",\n        \"stomata-cells\",\n        \"train\",\n        \"ufba-425\",\n        \"urine-analysis1\",\n        \"x-ray-id\",\n        \"xray\",\n    ],\n    \"document\": [\n        \"activity-diagrams\",\n        \"all-elements\",\n        \"circuit-voltages\",\n        \"invoice-processing\",\n        \"label-printing-defect-version-2\",\n        \"macro-segmentation\",\n        \"paper-parts\",\n        \"signatures\",\n        \"speech-bubbles-detection\",\n        \"wine-labels\",\n    ],\n    \"industrial\": [\n        \"-grccs\",\n        \"13-lkc01\",\n        \"2024-frc\",\n        \"aircraft-turnaround-dataset\",\n        \"asphaltdistressdetection\",\n        \"cable-damage\",\n        \"conveyor-t-shirts\",\n        \"dataconvert\",\n        \"deeppcb\",\n        \"defect-detection\",\n        \"fruitjes\",\n        \"infraredimageofpowerequipment\",\n        \"ism-band-packet-detection\",\n        \"l10ul502\",\n        \"needle-base-tip-min-max\",\n        \"recode-waste\",\n        \"screwdetectclassification\",\n        \"smd-components\",\n        \"truck-movement\",\n        \"tube\",\n        \"water-meter\",\n        \"wheel-defect-detection\",\n    ],\n    \"flora_fauna\": [\n        \"aquarium-combined\",\n        \"bees\",\n        \"deepfruits\",\n        \"exploratorium-daphnia\",\n        \"grapes-5\",\n        \"grass-weeds\",\n        \"gwhd2021\",\n        \"into-the-vale\",\n        \"jellyfish\",\n        \"marine-sharks\",\n        \"orgharvest\",\n        \"peixos-fish\",\n        \"penguin-finder-seg\",\n        \"pig-detection\",\n        \"roboflow-trained-dataset\",\n        \"sea-cucumbers-new-tiles\",\n        \"thermal-cheetah\",\n        \"tomatoes-2\",\n        \"trail-camera\",\n        \"underwater-objects\",\n        \"varroa-mites-detection--test-set\",\n        \"wb-prova\",\n        \"weeds4\",\n    ],\n}\n\n\ndef load_jsonl_last_row(file_path: str, keys: List[str]) -> Optional[Dict[str, Any]]:\n    \"\"\"\n    Load the last row from a JSONL file and extract specific keys.\n\n    Args:\n        file_path: Path to the JSONL file\n        keys: List of keys to extract from the last row\n\n    Returns:\n        Dictionary with extracted key-value pairs, or None if file not found/empty\n    \"\"\"\n    if not os.path.exists(file_path):\n        print(f\"Warning: File not found: {file_path}\")\n        return None\n\n    last_row = None\n    try:\n        with open(file_path, \"r\") as file:\n            for line in file:\n                last_row = json.loads(line.strip())\n\n        if last_row is None:\n            print(f\"Warning: Empty JSONL file: {file_path}\")\n            return None\n\n        return {key: last_row.get(key) for key in keys}\n\n    except json.JSONDecodeError as e:\n        print(f\"Error: Failed to parse JSON in {file_path}: {e}\")\n        return None\n    except Exception as e:\n        print(f\"Error: Failed to read {file_path}: {e}\")\n        return None\n\n\ndef find_config_files(directory: str, filename: str = CONFIG_FILENAME) -> List[str]:\n    \"\"\"\n    Recursively find configuration files with a specific filename.\n\n    Args:\n        directory: Root directory to search\n        filename: Target filename to search for\n\n    Returns:\n        List of full paths to matching files\n    \"\"\"\n    matching_files = []\n    for root, _, files in os.walk(directory):\n        # Skip code directories\n        if \"/code/\" in root:\n            continue\n        if filename in files:\n            matching_files.append(os.path.join(root, filename))\n    return matching_files\n\n\ndef extract_config_parameters(config_path: str, keys: List[str]) -> Dict[str, Any]:\n    \"\"\"\n    Extract specific parameters from a YAML configuration file.\n\n    Args:\n        config_path: Path to the YAML configuration file\n        keys: List of keys to extract from the 'scratch' section\n\n    Returns:\n        Dictionary containing extracted parameters\n    \"\"\"\n    try:\n        with open(config_path, \"r\") as file:\n            data = yaml.safe_load(file)\n\n        # Extract parameters from scratch section\n        scratch_params = {key: data[\"scratch\"].get(key) for key in keys}\n\n        # Add computed parameters\n        launcher = data.get(\"launcher\", {})\n        scratch_params[\"batch_size\"] = int(launcher.get(\"gpus_per_node\", 1)) * int(\n            launcher.get(\"num_nodes\", 1)\n        )\n        scratch_params[\"lr_scale\"] = data[\"scratch\"].get(\"lr_scale\")\n\n        roboflow_train = data.get(\"roboflow_train\", {})\n        scratch_params[\"roboflow_num_images\"] = roboflow_train.get(\"num_images\")\n\n        return scratch_params\n\n    except Exception as e:\n        print(f\"Error: Failed to parse config file {config_path}: {e}\")\n        return {}\n\n\ndef calculate_average(values_dict: Dict[str, float]) -> float:\n    \"\"\"\n    Calculate the average of values in a dictionary.\n\n    Args:\n        values_dict: Dictionary with numeric values\n\n    Returns:\n        Average of all values, or 0 if empty\n    \"\"\"\n    if not values_dict:\n        return 0.0\n    return sum(values_dict.values()) / len(values_dict)\n\n\ndef extract_category_results(log_dir: str, categories: List[str]) -> Dict[str, float]:\n    \"\"\"\n    Extract bbox AP results for specific categories from log files.\n\n    Args:\n        log_dir: Directory containing category log subdirectories\n        categories: List of category names to extract results for\n\n    Returns:\n        Dictionary mapping category names to bbox AP scores\n    \"\"\"\n    results = {}\n    metric_keys = [BBOX_AP_METRIC]\n\n    for category in categories:\n        result_file = os.path.join(log_dir, f\"logs/{category}/{RESULTS_FILENAME}\")\n        category_result = load_jsonl_last_row(result_file, metric_keys)\n\n        if category_result is not None and category_result[BBOX_AP_METRIC] is not None:\n            results[category] = category_result[BBOX_AP_METRIC]\n\n    return results\n\n\ndef analyze_experiment_results(config_path: str) -> None:\n    \"\"\"\n    Analyze results from a single experiment configuration.\n\n    Args:\n        config_path: Path to the experiment configuration file\n    \"\"\"\n    print(\"=\" * 80)\n    print(f\"Analyzing experiment: {config_path}\")\n    print(\"=\" * 80)\n\n    # Extract configuration parameters\n    config_keys = [\n        \"lr_transformer\",\n        \"lr_vision_backbone\",\n        \"lr_language_backbone\",\n        \"max_data_epochs\",\n    ]\n\n    config_params = extract_config_parameters(config_path, config_keys)\n    print(\"Configuration Parameters:\")\n    for key, value in config_params.items():\n        print(f\"  {key}: {value}\")\n    print()\n\n    # Extract results for each category\n    experiment_dir = os.path.dirname(config_path)\n    category_results = {}\n    category_averages = {}\n    all_scores = []\n\n    for super_category, categories in ROBOFLOW_CATEGORIES.items():\n        category_results[super_category] = extract_category_results(\n            experiment_dir, categories\n        )\n\n        if category_results[super_category]:\n            category_averages[super_category] = calculate_average(\n                category_results[super_category]\n            )\n            all_scores.extend(category_results[super_category].values())\n\n    # Print results summary\n    print(\"Results by Category:\")\n    for super_category, avg_score in category_averages.items():\n        num_categories = len(category_results[super_category])\n        print(f\"  {super_category}: {avg_score:.4f} (n={num_categories})\")\n\n    print(f\"\\nOverall Results:\")\n    print(f\"  Weighted average: {calculate_average(category_averages):.4f}\")\n    print(f\"  Total categories: {len(all_scores)}\")\n    print(f\"  True average: {sum(all_scores) / len(all_scores):.4f}\")\n    print()\n\n\ndef print_results_table(results_data: List[Dict[str, Any]]) -> None:\n    \"\"\"\n    Print results in a formatted table.\n\n    Args:\n        results_data: List of dictionaries containing results data\n    \"\"\"\n    if not results_data:\n        print(\"No results data to display.\")\n        return\n\n    df = pd.DataFrame(results_data)\n    print(\"\\nResults Summary Table:\")\n    print(\"=\" * 60)\n    print(df.to_string(index=False))\n\n\ndef main() -> None:\n    \"\"\"Main function to orchestrate the results extraction and analysis.\"\"\"\n    parser = argparse.ArgumentParser(\n        description=\"Extract and analyze Roboflow VL100 training results\"\n    )\n    parser.add_argument(\n        \"-p\",\n        \"--path\",\n        type=str,\n        required=True,\n        help=\"Root directory path containing experiment results\",\n    )\n\n    args = parser.parse_args()\n\n    # Find all configuration files\n    config_files = find_config_files(args.path, CONFIG_FILENAME)\n\n    if not config_files:\n        print(f\"No configuration files found in {args.path}\")\n        return\n\n    print(f\"Found {len(config_files)} experiment configurations\")\n    print()\n\n    # Analyze each experiment\n    for config_file in config_files:\n        try:\n            analyze_experiment_results(config_file)\n        except Exception as e:\n            print(f\"Error analyzing {config_file}: {e}\")\n            continue\n\n\nif __name__ == \"__main__\":\n    main()\n"
  }
]